diff --git a/.gitignore b/.gitignore
index f1f2b400211..7a10d94bafd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,7 +27,9 @@
 *.mex*
 
 # build, distribute, and bins
-build/*
+build
+.build_debug/*
+.build_release/*
 distribute/*
 *.testbin
 *.bin
diff --git a/.travis.yml b/.travis.yml
index 3c2f5a030bd..7721de112e3 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -14,26 +14,43 @@ before_install:
   - echo $LANG
   - echo $LC_ALL
   - sudo apt-get -y update
-  - sudo apt-get -y install wget git curl python-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc
+  - sudo apt-get -y install wget git curl python-dev python-numpy libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev protobuf-compiler libatlas-dev libatlas-base-dev bc
 
 install:
   - wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz -O /tmp/glog-0.3.3.tar.gz && tar -C /tmp -xzvf /tmp/glog-0.3.3.tar.gz && rm /tmp/glog-0.3.3.tar.gz
   - cd /tmp/glog-0.3.3 && ./configure && make && sudo make install && cd -
+  - wget https://github.com/schuhschuh/gflags/archive/master.zip -O /tmp/gflags-master.zip && pushd /tmp/ && unzip gflags-master.zip && cd gflags-master && mkdir build && cd build && export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 && sudo make install && popd
   - curl http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1204/x86_64/cuda-repo-ubuntu1204_6.0-37_amd64.deb -o /tmp/cuda_install.deb && sudo dpkg -i /tmp/cuda_install.deb && rm /tmp/cuda_install.deb
-  - sudo apt-get -y update && sudo apt-get -y install cuda
+  - sudo apt-get -y update
+  # Install the minimal CUDA subpackages required to test Caffe build.
+  # For a full CUDA installation, add 'cuda' to the list of packages.
+  - sudo apt-get -y install cuda-core-6-0 cuda-extra-libs-6-0
+  # Create CUDA symlink at /usr/local/cuda
+  # (This would normally be created by the CUDA installer, but we create it
+  # manually since we did a partial installation.)
+  - sudo ln -s /usr/local/cuda-6.0 /usr/local/cuda
   - curl https://gitorious.org/mdb/mdb/archive/7f038d0f15bec57b4c07aa3f31cd5564c88a1897.tar.gz -o /tmp/mdb.tar.gz && tar -C /tmp -xzvf /tmp/mdb.tar.gz && rm /tmp/mdb.tar.gz
   - cd /tmp/mdb-mdb/libraries/liblmdb/ && make && sudo make install && cd -
 
 before_script:
   - mv Makefile.config.example Makefile.config
-  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/lib
+  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
   - export NUM_THREADS=4
 
 script:
-  - make --keep-going --jobs=$NUM_THREADS all test lint
+  # CPU-GPU: build only.
+  - export CPU_ONLY=0
+  - make --keep-going --jobs=$NUM_THREADS all
+  - make clean
+  # CPU-only: comprehensive.
+  - export CPU_ONLY=1
+  - make --keep-going --jobs=$NUM_THREADS all test warn lint
+  - make runtest
   - make --jobs=$NUM_THREADS all
   - make --jobs=$NUM_THREADS test
+  - make --jobs=$NUM_THREADS warn
   - make --jobs=$NUM_THREADS lint
+  - make --jobs=$NUM_THREADS pycaffe
 
 notifications:
 # Emails are sent to the committer's git-configured email address by default,
diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 2de2a717eff..8db66ea82c6 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -15,3 +15,5 @@ to see line-by-line credits and
 to see the change log even across renames and rewrites.
 
 Please refer to the [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements) on the Caffe site for further details.
+
+**Copyright** is held by the original contributor according to the versioning history; see LICENSE.
diff --git a/LICENSE b/LICENSE
index bac9c99fd41..efcc5c5b6b0 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,22 @@
+COPYRIGHT
+
+All contributions by the University of California:
 Copyright (c) 2014, The Regents of the University of California (Regents)
 All rights reserved.
 
+All other contributions:
+Copyright (c) 2014, the respective contributors
+All rights reserved.
+
+Caffe uses a shared copyright model: each contributor holds copyright over
+their contributions to Caffe. The project versioning records all such
+contribution and copyright details. If a contributor wants to further mark
+their specific copyright on a particular contribution, they should indicate
+their copyright solely in the commit message of the change when it is
+committed.
+
+LICENSE
+
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met: 
 
@@ -20,3 +36,9 @@ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+CONTRIBUTION AGREEMENT
+
+By contributing to the BVLC/caffe repository through pull-request, comment,
+or otherwise, the contributor releases their content to the
+license and copyright terms herein.
diff --git a/Makefile b/Makefile
index 3863fbfdcfb..84f8db26abb 100644
--- a/Makefile
+++ b/Makefile
@@ -4,7 +4,20 @@ PROJECT := caffe
 CONFIG_FILE := Makefile.config
 include $(CONFIG_FILE)
 
-# The target static library and shared library name
+BUILD_DIR_LINK := $(BUILD_DIR)
+RELEASE_BUILD_DIR := .$(BUILD_DIR)_release
+DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug
+
+DEBUG ?= 0
+ifeq ($(DEBUG), 1)
+	BUILD_DIR := $(DEBUG_BUILD_DIR)
+	OTHER_BUILD_DIR := $(RELEASE_BUILD_DIR)
+else
+	BUILD_DIR := $(RELEASE_BUILD_DIR)
+	OTHER_BUILD_DIR := $(DEBUG_BUILD_DIR)
+endif
+
+# The target shared library and static library name
 LIB_BUILD_DIR := $(BUILD_DIR)/lib
 NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
 STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a
@@ -15,16 +28,17 @@ STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a
 # CXX_SRCS are the source files excluding the test ones.
 CXX_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cpp" -name "*.cpp")
 # HXX_SRCS are the header files
-HXX_SRCS := $(shell find include/$(PROJECT) -name "*.hpp")
+HXX_SRCS := $(shell find include/$(PROJECT) ! -name "test_*.hpp" -name "*.hpp")
 # CU_SRCS are the cuda source files
-CU_SRCS := $(shell find src/$(PROJECT) -name "*.cu")
+CU_SRCS := $(shell find src/$(PROJECT) ! -name "test_*.cu" -name "*.cu")
 # TEST_SRCS are the test source files
 TEST_MAIN_SRC := src/$(PROJECT)/test/test_caffe_main.cpp
 TEST_SRCS := $(shell find src/$(PROJECT) -name "test_*.cpp")
 TEST_SRCS := $(filter-out $(TEST_MAIN_SRC), $(TEST_SRCS))
+TEST_CU_SRCS := $(shell find src/$(PROJECT) -name "test_*.cu")
 GTEST_SRC := src/gtest/gtest-all.cpp
-# TEST_HDRS are the test header files
-TEST_HDRS := $(shell find src/$(PROJECT) -name "test_*.hpp")
+# TEST_HXX_SRCS are the test header files
+TEST_HXX_SRCS := $(shell find include/$(PROJECT) -name "test_*.hpp")
 # TOOL_SRCS are the source files for the tool binaries
 TOOL_SRCS := $(shell find tools -name "*.cpp")
 # EXAMPLE_SRCS are the source files for the example binaries
@@ -48,8 +62,11 @@ NONGEN_CXX_SRCS := $(shell find \
 	examples \
 	tools \
 	-name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh")
-LINT_REPORT := $(BUILD_DIR)/cpp_lint.log
-FAILED_LINT_REPORT := $(BUILD_DIR)/cpp_lint.error_log
+LINT_OUTPUT_DIR := $(BUILD_DIR)/.lint
+LINT_EXT := lint.txt
+LINT_OUTPUTS := $(addsuffix .$(LINT_EXT), $(addprefix $(LINT_OUTPUT_DIR)/, $(NONGEN_CXX_SRCS)))
+EMPTY_LINT_REPORT := $(BUILD_DIR)/.$(LINT_EXT)
+NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
 # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
 PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
 PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
@@ -88,7 +105,9 @@ OBJS := $(PROTO_OBJS) $(CXX_OBJS) $(CU_OBJS)
 TOOL_OBJS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o})
 TOOL_BUILD_DIR := $(BUILD_DIR)/tools
 TEST_BUILD_DIR := $(BUILD_DIR)/src/$(PROJECT)/test
-TEST_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o})
+TEST_CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o})
+TEST_CU_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_CU_SRCS:.cu=.cuo})
+TEST_OBJS := $(TEST_CXX_OBJS) $(TEST_CU_OBJS)
 GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o})
 GTEST_BUILD_DIR := $(dir $(GTEST_OBJ))
 EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
@@ -98,29 +117,54 @@ EXAMPLE_BUILD_DIRS += $(foreach obj,$(EXAMPLE_OBJS),$(dir $(obj)))
 # tool, example, and test bins
 TOOL_BINS := ${TOOL_OBJS:.o=.bin}
 EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
+# symlinks to tool bins without the ".bin" extension
+TOOL_BIN_LINKS := ${TOOL_BINS:.bin=}
 # Put the test binaries in build/test for convenience.
 TEST_BIN_DIR := $(BUILD_DIR)/test
-TEST_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
-		$(foreach obj,$(TEST_OBJS),$(basename $(notdir $(obj))))))
+TEST_CU_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
+		$(foreach obj,$(TEST_CU_OBJS),$(basename $(notdir $(obj))))))
+TEST_CXX_BINS := $(addsuffix .testbin,$(addprefix $(TEST_BIN_DIR)/, \
+		$(foreach obj,$(TEST_CXX_OBJS),$(basename $(notdir $(obj))))))
+TEST_BINS := $(TEST_CXX_BINS) $(TEST_CU_BINS)
 TEST_ALL_BIN := $(TEST_BIN_DIR)/test_all.testbin
 
+##############################
+# Derive compiler warning dump locations
+##############################
+WARNS_EXT := warnings.txt
+CXX_WARNS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o.$(WARNS_EXT)})
+CU_WARNS := $(addprefix $(BUILD_DIR)/, ${CU_SRCS:.cu=.cuo.$(WARNS_EXT)})
+TOOL_WARNS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o.$(WARNS_EXT)})
+EXAMPLE_WARNS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o.$(WARNS_EXT)})
+TEST_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o.$(WARNS_EXT)})
+TEST_CU_WARNS := $(addprefix $(BUILD_DIR)/, ${TEST_CU_SRCS:.cu=.cuo.$(WARNS_EXT)})
+ALL_CXX_WARNS := $(CXX_WARNS) $(TOOL_WARNS) $(EXAMPLE_WARNS) $(TEST_WARNS)
+ALL_CU_WARNS := $(CU_WARNS) $(TEST_CU_WARNS)
+ALL_WARNS := $(ALL_CXX_WARNS) $(ALL_CU_WARNS)
+
+EMPTY_WARN_REPORT := $(BUILD_DIR)/.$(WARNS_EXT)
+NONEMPTY_WARN_REPORT := $(BUILD_DIR)/$(WARNS_EXT)
+
 ##############################
 # Derive include and lib directories
 ##############################
 CUDA_INCLUDE_DIR := $(CUDA_DIR)/include
 CUDA_LIB_DIR := $(CUDA_DIR)/lib64 $(CUDA_DIR)/lib
 
-INCLUDE_DIRS += $(BUILD_INCLUDE_DIR)
-INCLUDE_DIRS += ./src ./include $(CUDA_INCLUDE_DIR)
-LIBRARY_DIRS += $(CUDA_LIB_DIR)
-LIBRARIES := cudart cublas curand \
-	pthread \
-	glog protobuf leveldb snappy \
+INCLUDE_DIRS += $(BUILD_INCLUDE_DIR) ./src ./include
+ifneq ($(CPU_ONLY), 1)
+	INCLUDE_DIRS += $(CUDA_INCLUDE_DIR)
+	LIBRARY_DIRS += $(CUDA_LIB_DIR)
+	LIBRARIES := cudart cublas curand
+endif
+LIBRARIES += \
+	glog gflags pthread protobuf leveldb snappy \
+	lmdb \
 	boost_system \
 	hdf5_hl hdf5 \
 	opencv_core opencv_highgui opencv_imgproc
 PYTHON_LIBRARIES := boost_python python2.7
-WARNINGS := -Wall
+WARNINGS := -Wall -Wno-sign-compare
 
 ##############################
 # Set build directories
@@ -137,6 +181,7 @@ ALL_BUILD_DIRS := $(sort \
 		$(LAYER_BUILD_DIR) $(UTIL_BUILD_DIR) $(TOOL_BUILD_DIR) \
 		$(TEST_BUILD_DIR) $(TEST_BIN_DIR) $(GTEST_BUILD_DIR) \
 		$(EXAMPLE_BUILD_DIRS) \
+		$(LINT_OUTPUT_DIR) \
 		$(PROTO_BUILD_DIR) $(PROTO_BUILD_INCLUDE_DIR) $(PY_PROTO_BUILD_DIR) \
 		$(DISTRIBUTE_SUBDIRS))
 
@@ -153,7 +198,22 @@ else ifeq ($(UNAME), Darwin)
 endif
 
 ifeq ($(LINUX), 1)
-	CXX := /usr/bin/g++
+	CXX ?= /usr/bin/g++
+	GCCVERSION := $(shell $(CXX) -dumpversion | cut -f1,2 -d.)
+	# older versions of gcc are too dumb to build boost with -Wuninitalized
+	ifeq ($(shell echo $(GCCVERSION) \< 4.6 | bc), 1)
+		WARNINGS += -Wno-uninitialized
+	endif
+endif
+
+# CPU-only configuration
+ifeq ($(CPU_ONLY), 1)
+	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
+	TEST_OBJS := $(TEST_CXX_OBJS)
+	TEST_BINS := $(TEST_CXX_BINS)
+	ALL_WARNS := $(ALL_CXX_WARNS)
+	TEST_FILTER := --gtest_filter="-*GPU*"
+	COMMON_FLAGS += -DCPU_ONLY
 endif
 
 # OS X:
@@ -161,17 +221,24 @@ endif
 # libstdc++ instead of libc++ for CUDA compatibility on 10.9
 ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
+	# clang throws this warning for cuda headers
+	WARNINGS += -Wno-unneeded-internal-declaration
 	ifneq ($(findstring 10.9, $(shell sw_vers -productVersion)),)
 		CXXFLAGS += -stdlib=libstdc++
+		LINKFLAGS += -stdlib=libstdc++
 	endif
 endif
 
+# Custom compiler
+ifdef CUSTOM_CXX
+	CXX := $(CUSTOM_CXX)
+endif
+
 # Debugging
-DEBUG ?= 0
 ifeq ($(DEBUG), 1)
-	COMMON_FLAGS := -DDEBUG -g -O0
+	COMMON_FLAGS += -DDEBUG -g -O0
 else
-	COMMON_FLAGS := -DNDEBUG -O2
+	COMMON_FLAGS += -DNDEBUG -O2
 endif
 
 # BLAS configuration (default = ATLAS)
@@ -180,12 +247,12 @@ ifeq ($(BLAS), mkl)
 	# MKL
 	LIBRARIES += mkl_rt
 	COMMON_FLAGS += -DUSE_MKL
-	MKL_DIR = /opt/intel/mkl
+	MKL_DIR ?= /opt/intel/mkl
 	BLAS_INCLUDE ?= $(MKL_DIR)/include
 	BLAS_LIB ?= $(MKL_DIR)/lib $(MKL_DIR)/lib/intel64
 else ifeq ($(BLAS), open)
 	# OpenBLAS
-	LIBRARIES += openblas
+	LIBRARIES += blas
 else
 	# ATLAS
 	ifeq ($(LINUX), 1)
@@ -205,8 +272,11 @@ LIBRARY_DIRS += $(BLAS_LIB)
 
 # Complete build flags.
 COMMON_FLAGS += $(foreach includedir,$(INCLUDE_DIRS),-I$(includedir))
-CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS)
+CXXFLAGS += -pthread -fPIC $(COMMON_FLAGS) $(WARNINGS)
 NVCCFLAGS := -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
+# mex may invoke an older gcc that is too liberal with -Wuninitalized
+MATLAB_CXXFLAGS := $(CXXFLAGS) -Wno-uninitialized
+LINKFLAGS += -fPIC $(COMMON_FLAGS) $(WARNINGS)
 LDFLAGS += $(foreach librarydir,$(LIBRARY_DIRS),-L$(librarydir)) \
 		$(foreach library,$(LIBRARIES),-l$(library))
 PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
@@ -225,28 +295,44 @@ SUPERCLEAN_EXTS := .so .a .o .bin .testbin .pb.cc .pb.h _pb2.py .cuo
 ##############################
 # Define build targets
 ##############################
-.PHONY: all test clean linecount lint tools examples $(DIST_ALIASES) \
+.PHONY: all test clean linecount lint lintclean tools examples $(DIST_ALIASES) \
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
-	superclean supercleanlist supercleanfiles
+	superclean supercleanlist supercleanfiles warn everything
 
 all: $(NAME) $(STATIC_NAME) tools examples
 
-linecount: clean
+everything: all py$(PROJECT) mat$(PROJECT) test warn lint runtest
+
+linecount:
 	cloc --read-lang-def=$(PROJECT).cloc src/$(PROJECT)/
 
-lint: $(LINT_REPORT)
+lint: $(EMPTY_LINT_REPORT)
 
-$(LINT_REPORT): $(NONGEN_CXX_SRCS) | $(BUILD_DIR)
-	@ (python ./scripts/cpp_lint.py $(NONGEN_CXX_SRCS) > $(LINT_REPORT) 2>&1 \
-		&& ($(RM) $(FAILED_LINT_REPORT); echo "No lint errors!")) || ( \
-			mv $(LINT_REPORT) $(FAILED_LINT_REPORT); \
-			grep -v "^Done processing " $(FAILED_LINT_REPORT); \
-			echo "Found 1 or more lint errors; see log at $(FAILED_LINT_REPORT)"; \
-			exit 1)
+lintclean:
+	@ $(RM) -r $(LINT_OUTPUT_DIR) $(EMPTY_LINT_REPORT) $(NONEMPTY_LINT_REPORT)
+
+$(EMPTY_LINT_REPORT): $(LINT_OUTPUTS) | $(BUILD_DIR)
+	@ cat $(LINT_OUTPUTS) > $@
+	@ if [ -s "$@" ]; then \
+		cat $@; \
+		mv $@ $(NONEMPTY_LINT_REPORT); \
+		echo "Found one or more lint errors."; \
+		exit 1; \
+	  fi; \
+	  $(RM) $(NONEMPTY_LINT_REPORT); \
+	  echo "No lint errors!";
+
+$(LINT_OUTPUTS): $(LINT_OUTPUT_DIR)/%.lint.txt : % | $(LINT_OUTPUT_DIR)
+	@ mkdir -p $(dir $@)
+	@ python ./scripts/cpp_lint.py $< 2>&1 \
+		| grep -v "^Done processing " \
+		| grep -v "^Total errors found: 0" \
+		> $@ \
+		|| true
 
 test: $(TEST_ALL_BIN) $(TEST_BINS)
 
-tools: $(TOOL_BINS)
+tools: $(TOOL_BINS) $(TOOL_BIN_LINKS)
 
 examples: $(EXAMPLE_BINS)
 
@@ -256,7 +342,7 @@ py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 
 $(PY$(PROJECT)_SO): $(STATIC_NAME) $(PY$(PROJECT)_SRC)
 	$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
-		$(STATIC_NAME) $(CXXFLAGS) $(PYTHON_LDFLAGS)
+		$(STATIC_NAME) $(LINKFLAGS) $(PYTHON_LDFLAGS)
 	@ echo
 
 mat$(PROJECT): mat
@@ -269,95 +355,163 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 			"to build mat$(PROJECT)."; \
 		exit 1; \
 	fi
-	$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) $(STATIC_NAME) \
-			CXXFLAGS="\$$CXXFLAGS $(CXXFLAGS) $(WARNINGS)" \
-			CXXLIBS="\$$CXXLIBS $(LDFLAGS)" -o $@
+	$(MATLAB_DIR)/bin/mex $(MAT$(PROJECT)_SRC) \
+			CXX="$(CXX)" \
+			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
+			CXXLIBS="\$$CXXLIBS $(STATIC_NAME) $(LDFLAGS)" -output $@
 	@ echo
 
 runtest: $(TEST_ALL_BIN)
-	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle
+	$(TEST_ALL_BIN) $(TEST_GPUID) --gtest_shuffle $(TEST_FILTER)
+
+warn: $(EMPTY_WARN_REPORT)
+
+$(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
+	@ cat $(ALL_WARNS) > $@
+	@ if [ -s "$@" ]; then \
+		cat $@; \
+		mv $@ $(NONEMPTY_WARN_REPORT); \
+		echo "Compiler produced one or more warnings."; \
+		exit 1; \
+	  fi; \
+	  $(RM) $(NONEMPTY_WARN_REPORT); \
+	  echo "No compiler warnings!";
+
+$(ALL_CXX_WARNS): %.o.$(WARNS_EXT) : %.o
+
+$(ALL_CU_WARNS): %.cuo.$(WARNS_EXT) : %.cuo
+
+$(BUILD_DIR_LINK): $(BUILD_DIR)/.linked
+
+# Create a target ".linked" in this BUILD_DIR to tell Make that the "build" link
+# is currently correct, then delete the one in the OTHER_BUILD_DIR in case it
+# exists and $(DEBUG) is toggled later.
+$(BUILD_DIR)/.linked:
+	@ mkdir -p $(BUILD_DIR)
+	@ $(RM) $(OTHER_BUILD_DIR)/.linked
+	@ $(RM) -r $(BUILD_DIR_LINK)
+	@ ln -s $(BUILD_DIR) $(BUILD_DIR_LINK)
+	@ touch $@
 
-$(ALL_BUILD_DIRS):
+$(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 	@ mkdir -p $@
 
 $(NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
-	$(CXX) -shared -o $@ $(OBJS) $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
+	$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
 $(STATIC_NAME): $(PROTO_OBJS) $(OBJS) | $(LIB_BUILD_DIR)
 	ar rcs $@ $(PROTO_OBJS) $(OBJS)
 	@ echo
 
-$(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HDRS) \
+$(TEST_BUILD_DIR)/%.o: src/$(PROJECT)/test/%.cpp $(HXX_SRCS) $(TEST_HXX_SRCS) \
 		| $(TEST_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
+	@ echo
+
+$(TEST_BUILD_DIR)/%.cuo: src/$(PROJECT)/test/%.cu $(HXX_SRCS) $(TEST_HXX_SRCS) \
+		| $(TEST_BUILD_DIR)
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) $(STATIC_NAME) \
-		-o $@ $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
+		-o $@ $(LINKFLAGS) $(LDFLAGS)
+	@ echo
+
+$(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.cuo $(GTEST_OBJ) $(STATIC_NAME) \
+		| $(TEST_BIN_DIR)
+	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_NAME) \
+		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
-$(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(STATIC_NAME) \
+$(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_BUILD_DIR)/%.o $(GTEST_OBJ) $(STATIC_NAME) \
 		| $(TEST_BIN_DIR)
 	$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) $(STATIC_NAME) \
-		-o $@ $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
+		-o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
+# Target for extension-less symlinks to tool binaries with extension '*.bin'.
+$(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
+	@ $(RM) $@
+	@ ln -s $(abspath $<) $@
+
 $(TOOL_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_NAME) -o $@ $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
+	$(CXX) $< $(STATIC_NAME) -o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
 $(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
-	$(CXX) $< $(STATIC_NAME) -o $@ $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
+	$(CXX) $< $(STATIC_NAME) -o $@ $(LINKFLAGS) $(LDFLAGS)
 	@ echo
 
 $(LAYER_BUILD_DIR)/%.o: src/$(PROJECT)/layers/%.cpp $(HXX_SRCS) \
 		| $(LAYER_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(PROTO_BUILD_DIR)/%.pb.o: $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_GEN_HEADER) \
 		| $(PROTO_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(UTIL_BUILD_DIR)/%.o: src/$(PROJECT)/util/%.cpp $(HXX_SRCS) | $(UTIL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(GTEST_OBJ): $(GTEST_SRC) | $(GTEST_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(LAYER_BUILD_DIR)/%.cuo: src/$(PROJECT)/layers/%.cu $(HXX_SRCS) \
 		| $(LAYER_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(UTIL_BUILD_DIR)/%.cuo: src/$(PROJECT)/util/%.cu | $(UTIL_BUILD_DIR)
-	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@
+	$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(TOOL_BUILD_DIR)/%.o: tools/%.cpp $(PROTO_GEN_HEADER) | $(TOOL_BUILD_DIR)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ $(LDFLAGS)
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(EXAMPLE_BUILD_DIR)/%.o: examples/%.cpp $(PROTO_GEN_HEADER) \
 		| $(EXAMPLE_BUILD_DIRS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@ $(LDFLAGS)
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 $(BUILD_DIR)/src/$(PROJECT)/%.o: src/$(PROJECT)/%.cpp $(HXX_SRCS)
-	$(CXX) $< $(CXXFLAGS) -c -o $@
+	$(CXX) $< $(CXXFLAGS) -c -o $@ 2> $@.$(WARNS_EXT) \
+		|| (cat $@.$(WARNS_EXT); exit 1)
+	@ cat $@.$(WARNS_EXT)
 	@ echo
 
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
 
 $(PROTO_BUILD_DIR)/%.pb.cc $(PROTO_BUILD_DIR)/%.pb.h : \
 		$(PROTO_SRC_DIR)/%.proto | $(PROTO_BUILD_DIR)
-	protoc --proto_path=src --cpp_out=build/src $<
+	protoc --proto_path=src --cpp_out=$(BUILD_DIR)/src $<
 	@ echo
 
 $(PY_PROTO_BUILD_DIR)/%_pb2.py : $(PROTO_SRC_DIR)/%.proto \
@@ -370,6 +524,8 @@ $(PY_PROTO_INIT): | $(PY_PROTO_BUILD_DIR)
 
 clean:
 	@- $(RM) -rf $(ALL_BUILD_DIRS)
+	@- $(RM) -rf $(OTHER_BUILD_DIR)
+	@- $(RM) -rf $(BUILD_DIR_LINK)
 	@- $(RM) -rf $(DISTRIBUTE_DIR)
 	@- $(RM) $(PY$(PROJECT)_SO)
 	@- $(RM) $(MAT$(PROJECT)_SO)
diff --git a/Makefile.config.example b/Makefile.config.example
index b03f55da629..7c96d8a9356 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -1,8 +1,18 @@
 ## Refer to http://caffe.berkeleyvision.org/installation.html
 # Contributions simplifying and improving our build system are welcome!
 
+# CPU-only switch (uncomment to build without GPU support).
+# CPU_ONLY := 1
+
+# To customize your choice of compiler, uncomment and set the following.
+# N.B. the default for Linux is g++ and the default for OSX is clang++
+# CUSTOM_CXX := g++
+
 # CUDA directory contains bin/ and lib/ directories that we need.
 CUDA_DIR := /usr/local/cuda
+# On Ubuntu 14.04, if cuda tools are installed via
+# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
+# CUDA_DIR := /usr
 
 # CUDA architecture setting: going with all of them (up to CUDA 5.5 compatible).
 # For the latest architecture, you need to install CUDA >= 6.0 and uncomment
@@ -10,9 +20,9 @@ CUDA_DIR := /usr/local/cuda
 CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
 		-gencode arch=compute_20,code=sm_21 \
 		-gencode arch=compute_30,code=sm_30 \
-		-gencode arch=compute_35,code=sm_35
-		#-gencode=arch=compute_50,code=sm_50 \
-		#-gencode=arch=compute_50,code=compute_50
+		-gencode arch=compute_35,code=sm_35 \
+		#-gencode arch=compute_50,code=sm_50 \
+		#-gencode arch=compute_50,code=compute_50
 
 # BLAS choice:
 # atlas for ATLAS (default)
@@ -32,15 +42,15 @@ BLAS := atlas
 
 # NOTE: this is required only if you will compile the python interface.
 # We need to be able to find Python.h and numpy/arrayobject.h.
-PYTHON_INCLUDE := /usr/local/include/python2.7 \
-		/usr/local/lib/python2.7/dist-packages/numpy/core/include
+PYTHON_INCLUDE := /usr/include/python2.7 \
+		/usr/lib/python2.7/dist-packages/numpy/core/include
 # Anaconda Python distribution is quite popular. Include path:
 # PYTHON_INCLUDE := $(HOME)/anaconda/include \
 		# $(HOME)/anaconda/include/python2.7 \
 		# $(HOME)/anaconda/lib/python2.7/site-packages/numpy/core/include
 
 # We need to be able to find libpythonX.X.so or .dylib.
-PYTHON_LIB := /usr/local/lib
+PYTHON_LIB := /usr/lib
 # PYTHON_LIB := $(HOME)/anaconda/lib
 
 # Whatever else you find you need goes here.
@@ -50,7 +60,7 @@ LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
 BUILD_DIR := build
 DISTRIBUTE_DIR := distribute
 
-# Uncomment for debugging.
+# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
 # DEBUG := 1
 
 # The ID of the GPU that 'make runtest' will use to run unit tests.
diff --git a/docs/development.md b/docs/development.md
index ff734d17219..d586a2b1c30 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -7,6 +7,19 @@ title: Development
 Caffe is developed with active participation of the community.<br />
 The [BVLC](http://bvlc.eecs.berkeley.edu/) maintainers welcome all contributions!
 
+The exact details of contributions are recorded by versioning and cited in our [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements).
+This method is impartial and always up-to-date.
+
+## License
+
+Caffe is licensed under the terms in [LICENSE](https://github.com/BVLC/caffe/blob/master/LICENSE). By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
+
+## Copyright
+
+Caffe uses a shared copyright model: each contributor holds copyright over their contributions to Caffe. The project versioning records all such contribution and copyright details.
+
+If a contributor wants to further mark their specific copyright on a particular contribution, they should indicate their copyright solely in the commit message of the change when it is committed. Do not include copyright notices in files for this purpose.
+
 ### Documentation
 
 This website, written with [Jekyll](http://jekyllrb.com/), functions as the official Caffe documentation -- simply run `scripts/build_docs.sh` and view the website at `http://0.0.0.0:4000`.
@@ -109,12 +122,3 @@ To get a list of all options `googletest` provides, simply pass the `--help` fla
 - Wrap lines at 80 chars.
 - Remember that “a foolish consistency is the hobgoblin of little minds,” so use your best judgement to write the clearest code for your particular case.
 - **Run `make lint` to check C++ code.**
-
-### Copyright
-
-Assign copyright jointly to BVLC and contributors like so:
-
-    // Copyright 2014 BVLC and contributors.
-
-The exact details of contributions are recorded by versioning and cited in our [acknowledgements](http://caffe.berkeleyvision.org/#acknowledgements).
-This method is impartial and always up-to-date.
diff --git a/docs/getting_pretrained_models.md b/docs/getting_pretrained_models.md
index 14e6ee91262..5df2bd4dc2d 100644
--- a/docs/getting_pretrained_models.md
+++ b/docs/getting_pretrained_models.md
@@ -27,6 +27,8 @@ Soon, the community will be able to easily contribute different architectures!
   validation accuracy 57.258% and loss 1.83948.
 - This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% on the validation set, using just the center crop. (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy)
 
+**R-CNN (ILSVRC13)**: The pure Caffe instantiation of the [R-CNN](https://github.com/rbgirshick/rcnn) model for ILSVRC13 detection. Download the model (230.8MB) by running `examples/imagenet/get_caffe_rcnn_imagenet_model.sh` from the Caffe root directory. This model was made by transplanting the R-CNN SVM classifiers into a `fc-rcnn` classification layer, provided here as an off-the-shelf Caffe detector. Try the [detection example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb) to see it in action. For the full details, refer to the R-CNN site. *N.B. For research purposes, make use of the official R-CNN package and not this example.*
+
 ### Auxiliary Data
 
 Additionally, you will probably eventually need some auxiliary data (mean image, synset list, etc.): run `data/ilsvrc12/get_ilsvrc_aux.sh` from the root directory to obtain it.
diff --git a/docs/index.md b/docs/index.md
index b8e58cf52dc..0450edea6ee 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -89,9 +89,11 @@ If you'd like to contribute, please read the [developing & contributing](develop
 
 ## Contacting us
 
-All questions about installation, code, future development, and applications should be searched for and asked at [GitHub Issues](https://github.com/BVLC/caffe/issues).
+All questions about usage, installation, code, and applications should be searched for and asked on the [caffe-users mailing list](https://groups.google.com/forum/#!forum/caffe-users).
+
+All development discussion should be carried out at [GitHub Issues](https://github.com/BVLC/caffe/issues).
 
 If you have a proposal that may not be suited for public discussion *and an ability to act on it*, please email us [directly](mailto:caffe-dev@googlegroups.com).
 Requests for features, explanations, or personal help will be ignored; post such matters publicly as issues.
 
-Some developers may be able to provide [consulting services](mailto:caffe-coldpress@googlegroups.com) for appropriate projects.
+The core Caffe developers may be able to provide [consulting services](mailto:caffe-coldpress@googlegroups.com) for appropriate projects.
diff --git a/docs/installation.md b/docs/installation.md
index cc2f8486881..ff0760dd060 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -6,7 +6,7 @@ title: Caffe
 # Installation
 
 Prior to installing, it is best to read through this guide and take note of the details for your platform.
-We have successfully compiled and run Caffe on Ubuntu 12.04, OS X 10.8, and OS X 10.9.
+We have installed Caffe on Ubuntu 14.04, Ubuntu 12.04, OS X 10.9, and OS X 10.8.
 
 - [Prerequisites](#prerequisites)
 - [Compilation](#compilation)
@@ -16,20 +16,22 @@ We have successfully compiled and run Caffe on Ubuntu 12.04, OS X 10.8, and OS X
 
 Caffe depends on several software packages.
 
-* [CUDA](https://developer.nvidia.com/cuda-zone) (5.0, 5.5, or 6.0).
+* [CUDA](https://developer.nvidia.com/cuda-zone) library version 6.0, 5.5, or 5.0 and the latest driver version for CUDA 6 or 319.* for CUDA 5 (and NOT 331.*)
 * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) (provided via ATLAS, MKL, or OpenBLAS).
 * [OpenCV](http://opencv.org/).
-* [Boost](http://www.boost.org/) (we have only tested 1.55)
-* `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`
-* For the python wrapper
-    * `python`, `numpy (>= 1.7)`, Boost-provided `boost.python`
+* [Boost](http://www.boost.org/) (>= 1.55, although only 1.55 is tested)
+* `glog`, `gflags`, `protobuf`, `leveldb`, `snappy`, `hdf5`, `lmdb`
+* For the Python wrapper
+    * `Python 2.7`, `numpy (>= 1.7)`, boost-provided `boost.python`
 * For the MATLAB wrapper
     * MATLAB with the `mex` compiler.
 
+**CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
+
 ### CUDA and BLAS
 
-Caffe requires the CUDA `nvcc` compiler to compile its GPU code.
-To install CUDA, go to the [NVIDIA CUDA website](https://developer.nvidia.com/cuda-downloads) and follow installation instructions there. **Note:** you can install the CUDA libraries without a CUDA card or driver, in order to build and run Caffe on a CPU-only machine.
+Caffe requires the CUDA `nvcc` compiler to compile its GPU code and CUDA driver for GPU operation.
+To install CUDA, go to the [NVIDIA CUDA website](https://developer.nvidia.com/cuda-downloads) and follow installation instructions there. Install the library and the latest standalone driver separately; the driver bundled with the library is usually out-of-date. **Warning!** The 331.* CUDA driver series has a critical performance issue: do not use it.
 
 Caffe requires BLAS as the backend of its matrix and vector computations.
 There are several implementations of this library.
@@ -46,42 +48,73 @@ The choice is yours:
     1. Install OpenBLAS
     2. Set `BLAS := open` in `Makefile.config`
 
-### Python and/or Matlab wrappers (optional)
+### Python and/or MATLAB wrappers (optional)
 
-This is only a requirement if you'd like the Python wrapper for Caffe.
-The main required package is `numpy`, and `Boost` (in "Other dependencies" below) must be compiled with Python support.
+#### Python
 
-For **OS X**, we highly recommend using the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
-If you don't, please use Homebrew -- but beware of potential linking errors!
+The main requirements are `numpy` and `boost.python` (provided by boost). `pandas` is useful too and needed for some examples.
 
-Note that if you use the **Ubuntu** default python, you will need to `apt-get install` the `python-dev` package to have the python headers. You can install any remaining dependencies with
+You can install the dependencies with
 
     pip install -r /path/to/caffe/python/requirements.txt
 
-If you would like to have the MATLAB wrapper, install MATLAB, and make sure that its `mex` is in your `$PATH`.
+but we highly recommend first installing the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution, which provides most of the necessary packages, as well as the `hdf5` library dependency.
+
+For **Ubuntu**, if you use the default Python you will need to `apt-get install` the `python-dev` package to have the Python headers for building the wrapper.
+
+For **OS X**, Anaconda is the preferred Python. If you decide against it, please use Homebrew -- but beware of potential linking errors!
+
+To import the `caffe` Python module after completing the installation, add the module directory to your `$PYTHONPATH` by `export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH` or the like. You should not import the module in the `caffe/python/caffe` directory!
+
+*Caffe's Python interface works with Python 2.7. Python 3 or earlier Pythons are your own adventure.*
+
+#### MATLAB
+
+Install MATLAB, and make sure that its `mex` is in your `$PATH`.
+
+*Caffe's MATLAB interface works with versions 2012b, 2013a/b, and 2014a.*
 
 ### The rest of the dependencies
 
 #### Linux
 
-On **Ubuntu**, the remaining dependencies can be installed with
+On **Ubuntu**, most of the dependencies can be installed with
 
     sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
 
-And on **CentOS or RHEL**, you can install via yum using:
+And on **CentOS / RHEL**, you can install via yum with
 
     sudo yum install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel
 
-The only exception being the google logging library, which does not exist in the Ubuntu 12.04 or CentOS/RHEL repositories. To install it, do:
+and for **Ubuntu 14.04** the rest of the dependencies can be installed with
 
+    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
+
+For **Ubuntu 12.04 and CentOS / RHEL** the only exceptions to package installation are the Google flags library, Google logging library, and LMDB. To install these, do:
+
+    # glog
     wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
     tar zxvf glog-0.3.3.tar.gz
+    cd glog-0.3.3
     ./configure
     make && make install
+    # gflags
+    wget https://github.com/schuhschuh/gflags/archive/master.zip
+    unzip master.zip
+    cd gflags-master
+    mkdir build && cd build
+    export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
+    make && make install
+    # lmdb
+    git clone git://gitorious.org/mdb/mdb.git
+    cd mdb/libraries/liblmdb
+    make && make install
+
+Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
 
 #### OS X
 
-On **OS X**, we highly recommend using the [homebrew](http://brew.sh/) package manager, and ideally starting from a clean install of the OS (or from a wiped `/usr/local`) to avoid conflicts.
+On **OS X**, we highly recommend using the [Homebrew](http://brew.sh/) package manager, and ideally starting from a clean install of the OS (or from a wiped `/usr/local`) to avoid conflicts.
 In the following, we assume that you're using Anaconda Python and Homebrew.
 
 To install the OpenCV dependency, we'll need to provide an additional source for Homebrew:
@@ -99,12 +132,12 @@ In other `ENV` settings, things may not work as expected.
 
 #### 10.8-specific Instructions
 
-Simply run the following.
+Simply run the following:
 
-    brew install --build-from-source boost
-    for x in snappy leveldb protobuf gflags glog szip homebrew/science/opencv; do brew install $x; done
+    brew install --build-from-source --with-python boost
+    for x in snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv; do brew install $x; done
 
-Building boost from source is needed to link against your local python (exceptions might be raised during some OS X installs, but **ignore** these and continue).
+Building boost from source is needed to link against your local Python (exceptions might be raised during some OS X installs, but **ignore** these and continue). If you do not need the Python wrapper, simply doing `brew install boost` is fine.
 
 **Note** that the HDF5 dependency is provided by Anaconda Python in this case.
 If you're not using Anaconda, include `hdf5` in the list above.
@@ -115,12 +148,12 @@ In OS X 10.9, clang++ is the default C++ compiler and uses `libc++` as the stand
 However, NVIDIA CUDA (even version 6.0) currently links only with `libstdc++`.
 This makes it necessary to change the compilation settings for each of the dependencies.
 
-We do this by modifying the homebrew formulae before installing any packages.
-Make sure that homebrew doesn't install any software dependencies in the background; all packages must be linked to `libstdc++`.
+We do this by modifying the Homebrew formulae before installing any packages.
+Make sure that Homebrew doesn't install any software dependencies in the background; all packages must be linked to `libstdc++`.
 
-The prerequisite homebrew formulae are
+The prerequisite Homebrew formulae are
 
-    boost snappy leveldb protobuf gflags glog szip homebrew/science/opencv
+    boost snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv
 
 For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as shown:
 
@@ -135,11 +168,14 @@ For each of these formulas, `brew edit FORMULA`, and add the ENV definitions as
 
 To edit the formulae in turn, run
 
-    for x in snappy leveldb protobuf gflags glog szip boost homebrew/science/opencv; do brew edit $x; done
+    for x in snappy leveldb protobuf gflags glog szip boost lmdb homebrew/science/opencv; do brew edit $x; done
 
 After this, run
 
-    for x in snappy leveldb protobuf gflags glog szip boost homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
+    for x in snappy leveldb protobuf gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
+    brew install --build-from-source --with-python --fresh -vd boost
+
+**Note** that `brew install --build-from-source --fresh -vd boost` is fine if you do not need the Caffe Python wrapper.
 
 **Note** that the HDF5 dependency is provided by Anaconda Python in this case.
 If you're not using Anaconda, include `hdf5` in the list above.
@@ -150,7 +186,7 @@ If you're not using Anaconda, include `hdf5` in the list above.
 
 #### Windows
 
-There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng).
+There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https://github.com/niuzhiheng/caffe). Thanks [@niuzhiheng](https://github.com/niuzhiheng)!
 
 ## Compilation
 
@@ -163,11 +199,10 @@ The defaults should work, but uncomment the relevant lines if using Anaconda Pyt
     make test
     make runtest
 
-Note that if there is no GPU in your machine, building and running CPU-only works, but GPU tests will naturally fail.
+If there is no GPU in your machine, you should switch to CPU-only Caffe by uncommenting `CPU_ONLY := 1` in `Makefile.config`.
 
-To compile the python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
-Be sure to set your MATLAB and python paths in `Makefile.config` first!
-For Python support, you must add the compiled module to your `PYTHONPATH` (as `/path/to/caffe/python` or the like).
+To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
+Be sure to set your MATLAB and Python paths in `Makefile.config` first!
 
 *Distribution*: run `make distribute` to create a `distribute` directory with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.
 
diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index 237a7a0a0ed..9e7016362fc 100644
--- a/examples/cifar10/cifar10_full.prototxt
+++ b/examples/cifar10/cifar10_full.prototxt
@@ -43,6 +43,7 @@ layers {
   bottom: "pool1"
   top: "norm1"
   lrn_param {
+    norm_region: WITHIN_CHANNEL
     local_size: 3
     alpha: 5e-05
     beta: 0.75
@@ -74,6 +75,7 @@ layers {
   bottom: "conv2"
   top: "pool2"
   pooling_param {
+    norm_region: WITHIN_CHANNEL
     pool: AVE
     kernel_size: 3
     stride: 2
diff --git a/examples/cifar10/cifar10_full_solver.prototxt b/examples/cifar10/cifar10_full_solver.prototxt
index 0a0b456308d..49de3f58803 100644
--- a/examples/cifar10/cifar10_full_solver.prototxt
+++ b/examples/cifar10/cifar10_full_solver.prototxt
@@ -1,10 +1,8 @@
 # reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
 # then another factor of 10 after 10 more epochs (5000 iters)
 
-# The training protocol buffer definition
-train_net: "cifar10_full_train.prototxt"
-# The testing protocol buffer definition
-test_net: "cifar10_full_test.prototxt"
+# The train/test net protocol buffer definition
+net: "cifar10_full_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/cifar10/cifar10_full_solver_lr1.prototxt b/examples/cifar10/cifar10_full_solver_lr1.prototxt
index 4376de5493f..746f4fba15a 100644
--- a/examples/cifar10/cifar10_full_solver_lr1.prototxt
+++ b/examples/cifar10/cifar10_full_solver_lr1.prototxt
@@ -1,10 +1,8 @@
 # reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
 # then another factor of 10 after 10 more epochs (5000 iters)
 
-# The training protocol buffer definition
-train_net: "cifar10_full_train.prototxt"
-# The testing protocol buffer definition
-test_net: "cifar10_full_test.prototxt"
+# The train/test net protocol buffer definition
+net: "cifar10_full_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/cifar10/cifar10_full_solver_lr2.prototxt b/examples/cifar10/cifar10_full_solver_lr2.prototxt
index 19580c5184a..5a549ffc96d 100644
--- a/examples/cifar10/cifar10_full_solver_lr2.prototxt
+++ b/examples/cifar10/cifar10_full_solver_lr2.prototxt
@@ -1,10 +1,8 @@
 # reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
 # then another factor of 10 after 10 more epochs (5000 iters)
 
-# The training protocol buffer definition
-train_net: "cifar10_full_train.prototxt"
-# The testing protocol buffer definition
-test_net: "cifar10_full_test.prototxt"
+# The train/test net protocol buffer definition
+net: "cifar10_full_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/cifar10/cifar10_full_train.prototxt b/examples/cifar10/cifar10_full_train.prototxt
deleted file mode 100644
index 25c76060991..00000000000
--- a/examples/cifar10/cifar10_full_train.prototxt
+++ /dev/null
@@ -1,174 +0,0 @@
-name: "CIFAR10_full_train"
-layers {
-  name: "cifar"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "cifar10-leveldb/cifar-train-leveldb"
-    mean_file: "mean.binaryproto"
-    batch_size: 100
-  }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  convolution_param {
-    num_output: 32
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.0001
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "pool1"
-  top: "pool1"
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    norm_region: WITHIN_CHANNEL
-    local_size: 3
-    alpha: 5e-05
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  convolution_param {
-    num_output: 32
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: AVE
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    norm_region: WITHIN_CHANNEL
-    local_size: 3
-    alpha: 5e-05
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "pool3"
-  type: POOLING
-  bottom: "conv3"
-  top: "pool3"
-  pooling_param {
-    pool: AVE
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "ip1"
-  type: INNER_PRODUCT
-  bottom: "pool3"
-  top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 250
-  weight_decay: 0
-  inner_product_param {
-    num_output: 10
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "ip1"
-  bottom: "label"
-}
diff --git a/examples/cifar10/cifar10_full_test.prototxt b/examples/cifar10/cifar10_full_train_test.prototxt
similarity index 86%
rename from examples/cifar10/cifar10_full_test.prototxt
rename to examples/cifar10/cifar10_full_train_test.prototxt
index 0e1957a9045..4fd52fec133 100644
--- a/examples/cifar10/cifar10_full_test.prototxt
+++ b/examples/cifar10/cifar10_full_train_test.prototxt
@@ -1,4 +1,16 @@
-name: "CIFAR10_full_test"
+name: "CIFAR10_full"
+layers {
+  name: "cifar"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "cifar10-leveldb/cifar-train-leveldb"
+    mean_file: "mean.binaryproto"
+    batch_size: 100
+  }
+  include: { phase: TRAIN }
+}
 layers {
   name: "cifar"
   type: DATA
@@ -9,6 +21,7 @@ layers {
     mean_file: "mean.binaryproto"
     batch_size: 100
   }
+  include: { phase: TEST }
 }
 layers {
   name: "conv1"
@@ -166,16 +179,18 @@ layers {
     }
   }
 }
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "ip1"
-  top: "prob"
-}
 layers {
   name: "accuracy"
   type: ACCURACY
-  bottom: "prob"
+  bottom: "ip1"
   bottom: "label"
   top: "accuracy"
+  include: { phase: TEST }
+}
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "ip1"
+  bottom: "label"
+  top: "loss"
 }
diff --git a/examples/cifar10/cifar10_quick_solver.prototxt b/examples/cifar10/cifar10_quick_solver.prototxt
index 4b547cc96f4..cdd0722b3a0 100644
--- a/examples/cifar10/cifar10_quick_solver.prototxt
+++ b/examples/cifar10/cifar10_quick_solver.prototxt
@@ -1,9 +1,7 @@
 # reduce the learning rate after 8 epochs (4000 iters) by a factor of 10
 
-# The training protocol buffer definition
-train_net: "cifar10_quick_train.prototxt"
-# The testing protocol buffer definition
-test_net: "cifar10_quick_test.prototxt"
+# The train/test net protocol buffer definition
+net: "cifar10_quick_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/cifar10/cifar10_quick_solver_lr1.prototxt b/examples/cifar10/cifar10_quick_solver_lr1.prototxt
index d4ba3d525d9..2ed54ad980f 100644
--- a/examples/cifar10/cifar10_quick_solver_lr1.prototxt
+++ b/examples/cifar10/cifar10_quick_solver_lr1.prototxt
@@ -1,9 +1,7 @@
 # reduce the learning rate after 8 epochs (4000 iters) by a factor of 10
 
-# The training protocol buffer definition
-train_net: "cifar10_quick_train.prototxt"
-# The testing protocol buffer definition
-test_net: "cifar10_quick_test.prototxt"
+# The train/test net protocol buffer definition
+net: "cifar10_quick_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/cifar10/cifar10_quick_train.prototxt b/examples/cifar10/cifar10_quick_train.prototxt
deleted file mode 100644
index de5b6c32c5d..00000000000
--- a/examples/cifar10/cifar10_quick_train.prototxt
+++ /dev/null
@@ -1,168 +0,0 @@
-name: "CIFAR10_quick_train"
-layers {
-  name: "cifar"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "cifar10-leveldb/cifar-train-leveldb"
-    mean_file: "mean.binaryproto"
-    batch_size: 100
-  }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  convolution_param {
-    num_output: 32
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.0001
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "pool1"
-  top: "pool1"
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "pool1"
-  top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  convolution_param {
-    num_output: 32
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: AVE
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "pool2"
-  top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  convolution_param {
-    num_output: 64
-    pad: 2
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "gaussian"
-      std: 0.01
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "pool3"
-  type: POOLING
-  bottom: "conv3"
-  top: "pool3"
-  pooling_param {
-    pool: AVE
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "ip1"
-  type: INNER_PRODUCT
-  bottom: "pool3"
-  top: "ip1"
-  blobs_lr: 1
-  blobs_lr: 2
-  inner_product_param {
-    num_output: 64
-    weight_filler {
-      type: "gaussian"
-      std: 0.1
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "ip2"
-  type: INNER_PRODUCT
-  bottom: "ip1"
-  top: "ip2"
-  blobs_lr: 1
-  blobs_lr: 2
-  inner_product_param {
-    num_output: 10
-    weight_filler {
-      type: "gaussian"
-      std: 0.1
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "loss"
-  type: SOFTMAX_LOSS
-  bottom: "ip2"
-  bottom: "label"
-}
diff --git a/examples/cifar10/cifar10_quick_test.prototxt b/examples/cifar10/cifar10_quick_train_test.prototxt
similarity index 86%
rename from examples/cifar10/cifar10_quick_test.prototxt
rename to examples/cifar10/cifar10_quick_train_test.prototxt
index a154b9a0ea7..b34d1cd2fcb 100644
--- a/examples/cifar10/cifar10_quick_test.prototxt
+++ b/examples/cifar10/cifar10_quick_train_test.prototxt
@@ -1,4 +1,16 @@
-name: "CIFAR10_quick_test"
+name: "CIFAR10_quick"
+layers {
+  name: "cifar"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "cifar10-leveldb/cifar-train-leveldb"
+    mean_file: "mean.binaryproto"
+    batch_size: 100
+  }
+  include: { phase: TRAIN }
+}
 layers {
   name: "cifar"
   type: DATA
@@ -9,6 +21,7 @@ layers {
     mean_file: "mean.binaryproto"
     batch_size: 100
   }
+  include: { phase: TEST }
 }
 layers {
   name: "conv1"
@@ -160,16 +173,18 @@ layers {
     }
   }
 }
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "ip2"
-  top: "prob"
-}
 layers {
   name: "accuracy"
   type: ACCURACY
-  bottom: "prob"
+  bottom: "ip2"
   bottom: "label"
   top: "accuracy"
+  include: { phase: TEST }
+}
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
 }
diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index 8e223b212ad..2d5589bd30a 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 //
 // This script converts the CIFAR dataset to the leveldb format used
 // by caffe to perform classification.
@@ -7,14 +6,14 @@
 // The CIFAR dataset could be downloaded at
 //    http://www.cs.toronto.edu/~kriz/cifar.html
 
-#include <google/protobuf/text_format.h>
-#include <glog/logging.h>
-#include <leveldb/db.h>
-
-#include <stdint.h>
 #include <fstream>  // NOLINT(readability/streams)
 #include <string>
 
+#include "glog/logging.h"
+#include "google/protobuf/text_format.h"
+#include "leveldb/db.h"
+#include "stdint.h"
+
 #include "caffe/proto/caffe.pb.h"
 
 using std::string;
diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md
index 9d5bd7b2b0c..315713f7022 100644
--- a/examples/cifar10/readme.md
+++ b/examples/cifar10/readme.md
@@ -42,7 +42,7 @@ Training the model is simple after you have written the network definition proto
     cd $CAFFE_ROOT/examples/cifar10
     ./train_quick.sh
 
-`train_quick.sh` is a simple script, so have a look inside. `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `train_net.bin`, with the solver protobuf text file as its argument.
+`train_quick.sh` is a simple script, so have a look inside. `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `caffe.bin` with the `train` action, and the solver protobuf text file as its argument.
 
 When you run the code, you will see a lot of messages flying by like this:
 
diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh
index 4db7b9a98f1..9dd9ad79822 100755
--- a/examples/cifar10/train_full.sh
+++ b/examples/cifar10/train_full.sh
@@ -2,15 +2,14 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver.prototxt
+$TOOLS/caffe train --solver=cifar10_full_solver.prototxt
 
-#reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver_lr1.prototxt \
-    cifar10_full_iter_60000.solverstate
+# reduce learning rate by factor of 10
+$TOOLS/caffe train \
+    --solver=cifar10_full_solver_lr1.prototxt \
+    --snapshot=cifar10_full_iter_60000.solverstate
 
-#reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    cifar10_full_solver_lr2.prototxt \
-    cifar10_full_iter_65000.solverstate
+# reduce learning rate by factor of 10
+$TOOLS/caffe train \
+    --solver=cifar10_full_solver_lr2.prototxt \
+    --snapshot=cifar10_full_iter_65000.solverstate
diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh
index 1d954b5935e..e348e12fd94 100755
--- a/examples/cifar10/train_quick.sh
+++ b/examples/cifar10/train_quick.sh
@@ -2,7 +2,9 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_quick_solver.prototxt
+$TOOLS/caffe.bin train --solver=cifar10_quick_solver.prototxt
 
-#reduce learning rate by fctor of 10 after 8 epochs
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_quick_solver_lr1.prototxt cifar10_quick_iter_4000.solverstate
+# reduce learning rate by fctor of 10 after 8 epochs
+$TOOLS/caffe.bin train \
+  --solver=cifar10_quick_solver_lr1.prototxt \
+  --snapshot=cifar10_quick_iter_4000.solverstate
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index 5ec986f6b9b..3b0a5b2e705 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -13,214 +13,388 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "This approach follows ideas described in Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. *Rich feature hierarchies for accurate object detection and semantic segmentation*. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
+      "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n",
       "\n",
-      "First of all, we'll need a little [Python script](https://github.com/sergeyk/selective_search_ijcv_with_python) to run the Matlab Selective Search code.\n",
+      "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
       "\n",
-      "Let's run detection on an image of a couple of cats frolicking (one of the ImageNet detection challenge pictures), which we will download from the web.\n",
+      "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n",
       "\n",
-      "Before you get started with this notebook, make sure to follow [instructions](http://caffe.berkeleyvision.org/getting_pretrained_models.html) for getting the pretrained ImageNet model."
+      "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge\u2014no joke).\n",
+      "\n",
+      "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n",
+      "\n",
+      "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
+      "\n",
+      "- Follow the [model instructions](http://caffe.berkeleyvision.org/getting_pretrained_models.html) to get the Caffe R-CNN ImageNet model.\n",
+      "\n",
+      "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "!mkdir _temp\n",
-      "!curl http://farm1.static.flickr.com/220/512450093_7717fb8ce8.jpg > _temp/cat.jpg\n",
-      "!echo `pwd`/_temp/cat.jpg > _temp/cat.txt\n",
-      "!../python/detect.py --crop_mode=selective_search --pretrained_model=imagenet/caffe_reference_imagenet_model --model_def=imagenet/imagenet_deploy.prototxt _temp/cat.txt _temp/cat.h5"
+      "!mkdir -p _temp\n",
+      "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
+      "!../python/detect.py --crop_mode=selective_search --pretrained_model=imagenet/caffe_rcnn_imagenet_model --model_def=imagenet/rcnn_imagenet_deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\r\n",
-        "                                 Dload  Upload   Total   Spent    Left  Speed\r\n",
-        "\r",
-        "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\r",
-        "100  212k  100  212k    0     0   852k      0 --:--:-- --:--:-- --:--:--  858k\r\n"
-       ]
-      },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n",
-        "I0520 12:14:46.505362  2522 net.cpp:75] Creating Layer conv1\r\n",
-        "I0520 12:14:46.505406  2522 net.cpp:85] conv1 <- data\r\n",
-        "I0520 12:14:46.505462  2522 net.cpp:111] conv1 -> conv1\r\n",
-        "I0520 12:14:46.505530  2522 net.cpp:126] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0520 12:14:46.505542  2522 net.cpp:152] conv1 needs backward computation.\r\n",
-        "I0520 12:14:46.505550  2522 net.cpp:75] Creating Layer relu1\r\n",
-        "I0520 12:14:46.505556  2522 net.cpp:85] relu1 <- conv1\r\n",
-        "I0520 12:14:46.505563  2522 net.cpp:99] relu1 -> conv1 (in-place)\r\n",
-        "I0520 12:14:46.505570  2522 net.cpp:126] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0520 12:14:46.505578  2522 net.cpp:152] relu1 needs backward computation.\r\n",
-        "I0520 12:14:46.505584  2522 net.cpp:75] Creating Layer pool1\r\n",
-        "I0520 12:14:46.505590  2522 net.cpp:85] pool1 <- conv1\r\n",
-        "I0520 12:14:46.505596  2522 net.cpp:111] pool1 -> pool1\r\n",
-        "I0520 12:14:46.505606  2522 net.cpp:126] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0520 12:14:46.505612  2522 net.cpp:152] pool1 needs backward computation.\r\n",
-        "I0520 12:14:46.505620  2522 net.cpp:75] Creating Layer norm1\r\n",
-        "I0520 12:14:46.505626  2522 net.cpp:85] norm1 <- pool1\r\n",
-        "I0520 12:14:46.505632  2522 net.cpp:111] norm1 -> norm1\r\n",
-        "I0520 12:14:46.505640  2522 net.cpp:126] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0520 12:14:46.505646  2522 net.cpp:152] norm1 needs backward computation.\r\n",
-        "I0520 12:14:46.505656  2522 net.cpp:75] Creating Layer conv2\r\n",
-        "I0520 12:14:46.505661  2522 net.cpp:85] conv2 <- norm1\r\n",
-        "I0520 12:14:46.505668  2522 net.cpp:111] conv2 -> conv2\r\n",
-        "I0520 12:14:46.506363  2522 net.cpp:126] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0520 12:14:46.506383  2522 net.cpp:152] conv2 needs backward computation.\r\n",
-        "I0520 12:14:46.506392  2522 net.cpp:75] Creating Layer relu2\r\n",
-        "I0520 12:14:46.506398  2522 net.cpp:85] relu2 <- conv2\r\n",
-        "I0520 12:14:46.506409  2522 net.cpp:99] relu2 -> conv2 (in-place)\r\n",
-        "I0520 12:14:46.506417  2522 net.cpp:126] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0520 12:14:46.506422  2522 net.cpp:152] relu2 needs backward computation.\r\n",
-        "I0520 12:14:46.506429  2522 net.cpp:75] Creating Layer pool2\r\n",
-        "I0520 12:14:46.506435  2522 net.cpp:85] pool2 <- conv2\r\n",
-        "I0520 12:14:46.506441  2522 net.cpp:111] pool2 -> pool2\r\n",
-        "I0520 12:14:46.506448  2522 net.cpp:126] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0520 12:14:46.506454  2522 net.cpp:152] pool2 needs backward computation.\r\n",
-        "I0520 12:14:46.506463  2522 net.cpp:75] Creating Layer norm2\r\n",
-        "I0520 12:14:46.506469  2522 net.cpp:85] norm2 <- pool2\r\n",
-        "I0520 12:14:46.506475  2522 net.cpp:111] norm2 -> norm2\r\n",
-        "I0520 12:14:46.506482  2522 net.cpp:126] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0520 12:14:46.506489  2522 net.cpp:152] norm2 needs backward computation.\r\n",
-        "I0520 12:14:46.506496  2522 net.cpp:75] Creating Layer conv3\r\n",
-        "I0520 12:14:46.506502  2522 net.cpp:85] conv3 <- norm2\r\n",
-        "I0520 12:14:46.506508  2522 net.cpp:111] conv3 -> conv3\r\n",
-        "I0520 12:14:46.508342  2522 net.cpp:126] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0520 12:14:46.508359  2522 net.cpp:152] conv3 needs backward computation.\r\n",
-        "I0520 12:14:46.508369  2522 net.cpp:75] Creating Layer relu3\r\n",
-        "I0520 12:14:46.508375  2522 net.cpp:85] relu3 <- conv3\r\n",
-        "I0520 12:14:46.508383  2522 net.cpp:99] relu3 -> conv3 (in-place)\r\n",
-        "I0520 12:14:46.508389  2522 net.cpp:126] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0520 12:14:46.508395  2522 net.cpp:152] relu3 needs backward computation.\r\n",
-        "I0520 12:14:46.508402  2522 net.cpp:75] Creating Layer conv4\r\n",
-        "I0520 12:14:46.508409  2522 net.cpp:85] conv4 <- conv3\r\n",
-        "I0520 12:14:46.508415  2522 net.cpp:111] conv4 -> conv4\r\n",
-        "I0520 12:14:46.509848  2522 net.cpp:126] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0520 12:14:46.509870  2522 net.cpp:152] conv4 needs backward computation.\r\n",
-        "I0520 12:14:46.509877  2522 net.cpp:75] Creating Layer relu4\r\n",
-        "I0520 12:14:46.509884  2522 net.cpp:85] relu4 <- conv4\r\n",
-        "I0520 12:14:46.509891  2522 net.cpp:99] relu4 -> conv4 (in-place)\r\n",
-        "I0520 12:14:46.509897  2522 net.cpp:126] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0520 12:14:46.509903  2522 net.cpp:152] relu4 needs backward computation.\r\n",
-        "I0520 12:14:46.509912  2522 net.cpp:75] Creating Layer conv5\r\n",
-        "I0520 12:14:46.509917  2522 net.cpp:85] conv5 <- conv4\r\n",
-        "I0520 12:14:46.509923  2522 net.cpp:111] conv5 -> conv5\r\n",
-        "I0520 12:14:46.510815  2522 net.cpp:126] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0520 12:14:46.510850  2522 net.cpp:152] conv5 needs backward computation.\r\n",
-        "I0520 12:14:46.510860  2522 net.cpp:75] Creating Layer relu5\r\n",
-        "I0520 12:14:46.510867  2522 net.cpp:85] relu5 <- conv5\r\n",
-        "I0520 12:14:46.510875  2522 net.cpp:99] relu5 -> conv5 (in-place)\r\n",
-        "I0520 12:14:46.510884  2522 net.cpp:126] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0520 12:14:46.510890  2522 net.cpp:152] relu5 needs backward computation.\r\n",
-        "I0520 12:14:46.510897  2522 net.cpp:75] Creating Layer pool5\r\n",
-        "I0520 12:14:46.510903  2522 net.cpp:85] pool5 <- conv5\r\n",
-        "I0520 12:14:46.510910  2522 net.cpp:111] pool5 -> pool5\r\n",
-        "I0520 12:14:46.510920  2522 net.cpp:126] Top shape: 10 256 6 6 (92160)\r\n",
-        "I0520 12:14:46.510926  2522 net.cpp:152] pool5 needs backward computation.\r\n",
-        "I0520 12:14:46.510936  2522 net.cpp:75] Creating Layer fc6\r\n",
-        "I0520 12:14:46.510942  2522 net.cpp:85] fc6 <- pool5\r\n",
-        "I0520 12:14:46.510949  2522 net.cpp:111] fc6 -> fc6\r\n"
+        "I0610 10:12:49.299607 25530 net.cpp:36] Initializing net from parameters: \r\n",
+        "name: \"R-CNN-ilsvrc13\"\r\n",
+        "layers {\r\n",
+        "  bottom: \"data\"\r\n",
+        "  top: \"conv1\"\r\n",
+        "  name: \"conv1\"\r\n",
+        "  type: CONVOLUTION\r\n",
+        "  convolution_param {\r\n",
+        "    num_output: 96\r\n",
+        "    kernel_size: 11\r\n",
+        "    stride: 4\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv1\"\r\n",
+        "  top: \"conv1\"\r\n",
+        "  name: \"relu1\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv1\"\r\n",
+        "  top: \"pool1\"\r\n",
+        "  name: \"pool1\"\r\n",
+        "  type: POOLING\r\n",
+        "  pooling_param {\r\n",
+        "    pool: MAX\r\n",
+        "    kernel_size: 3\r\n",
+        "    stride: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"pool1\"\r\n",
+        "  top: \"norm1\"\r\n",
+        "  name: \"norm1\"\r\n",
+        "  type: LRN\r\n",
+        "  lrn_param {\r\n",
+        "    local_size: 5\r\n",
+        "    alpha: 0.0001\r\n",
+        "    beta: 0.75\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"norm1\"\r\n",
+        "  top: \"conv2\"\r\n",
+        "  name: \"conv2\"\r\n",
+        "  type: CONVOLUTION\r\n",
+        "  convolution_param {\r\n",
+        "    num_output: 256\r\n",
+        "    pad: 2\r\n",
+        "    kernel_size: 5\r\n",
+        "    group: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv2\"\r\n",
+        "  top: \"conv2\"\r\n",
+        "  name: \"relu2\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv2\"\r\n",
+        "  top: \"pool2\"\r\n",
+        "  name: \"pool2\"\r\n",
+        "  type: POOLING\r\n",
+        "  pooling_param {\r\n",
+        "    pool: MAX\r\n",
+        "    kernel_size: 3\r\n",
+        "    stride: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"pool2\"\r\n",
+        "  top: \"norm2\"\r\n",
+        "  name: \"norm2\"\r\n",
+        "  type: LRN\r\n",
+        "  lrn_param {\r\n",
+        "    local_size: 5\r\n",
+        "    alpha: 0.0001\r\n",
+        "    beta: 0.75\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"norm2\"\r\n",
+        "  top: \"conv3\"\r\n",
+        "  name: \"conv3\"\r\n",
+        "  type: CONVOLUTION\r\n",
+        "  convolution_param {\r\n",
+        "    num_output: 384\r\n",
+        "    pad: 1\r\n",
+        "    kernel_size: 3\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv3\"\r\n",
+        "  top: \"conv3\"\r\n",
+        "  name: \"relu3\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv3\"\r\n",
+        "  top: \"conv4\"\r\n",
+        "  name: \"conv4\"\r\n",
+        "  type: CONVOLUTION\r\n",
+        "  convolution_param {\r\n",
+        "    num_output: 384\r\n",
+        "    pad: 1\r\n",
+        "    kernel_size: 3\r\n",
+        "    group: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv4\"\r\n",
+        "  top: \"conv4\"\r\n",
+        "  name: \"relu4\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv4\"\r\n",
+        "  top: \"conv5\"\r\n",
+        "  name: \"conv5\"\r\n",
+        "  type: CONVOLUTION\r\n",
+        "  convolution_param {\r\n",
+        "    num_output: 256\r\n",
+        "    pad: 1\r\n",
+        "    kernel_size: 3\r\n",
+        "    group: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv5\"\r\n",
+        "  top: \"conv5\"\r\n",
+        "  name: \"relu5\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"conv5\"\r\n",
+        "  top: \"pool5\"\r\n",
+        "  name: \"pool5\"\r\n",
+        "  type: POOLING\r\n",
+        "  pooling_param {\r\n",
+        "    pool: MAX\r\n",
+        "    kernel_size: 3\r\n",
+        "    stride: 2\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"pool5\"\r\n",
+        "  top: \"fc6\"\r\n",
+        "  name: \"fc6\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 4096\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc6\"\r\n",
+        "  top: \"fc6\"\r\n",
+        "  name: \"relu6\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc6\"\r\n",
+        "  top: \"fc6\"\r\n",
+        "  name: \"drop6\"\r\n",
+        "  type: DROPOUT\r\n",
+        "  dropout_param {\r\n",
+        "    dropout_ratio: 0.5\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc6\"\r\n",
+        "  top: \"fc7\"\r\n",
+        "  name: \"fc7\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 4096\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc7\"\r\n",
+        "  top: \"fc7\"\r\n",
+        "  name: \"relu7\"\r\n",
+        "  type: RELU\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc7\"\r\n",
+        "  top: \"fc7\"\r\n",
+        "  name: \"drop7\"\r\n",
+        "  type: DROPOUT\r\n",
+        "  dropout_param {\r\n",
+        "    dropout_ratio: 0.5\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "layers {\r\n",
+        "  bottom: \"fc7\"\r\n",
+        "  top: \"fc-rcnn\"\r\n",
+        "  name: \"fc-rcnn\"\r\n",
+        "  type: INNER_PRODUCT\r\n",
+        "  inner_product_param {\r\n",
+        "    num_output: 200\r\n",
+        "  }\r\n",
+        "}\r\n",
+        "input: \"data\"\r\n",
+        "input_dim: 10\r\n",
+        "input_dim: 3\r\n",
+        "input_dim: 227\r\n",
+        "input_dim: 227\r\n",
+        "I0610 10:12:49.300204 25530 net.cpp:77] Creating Layer conv1\r\n",
+        "I0610 10:12:49.300214 25530 net.cpp:87] conv1 <- data\r\n",
+        "I0610 10:12:49.300220 25530 net.cpp:113] conv1 -> conv1\r\n",
+        "I0610 10:12:49.300283 25530 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0610 10:12:49.300294 25530 net.cpp:154] conv1 needs backward computation.\r\n",
+        "I0610 10:12:49.300302 25530 net.cpp:77] Creating Layer relu1\r\n",
+        "I0610 10:12:49.300308 25530 net.cpp:87] relu1 <- conv1\r\n",
+        "I0610 10:12:49.300314 25530 net.cpp:101] relu1 -> conv1 (in-place)\r\n",
+        "I0610 10:12:49.300323 25530 net.cpp:128] Top shape: 10 96 55 55 (2904000)\r\n",
+        "I0610 10:12:49.300328 25530 net.cpp:154] relu1 needs backward computation.\r\n",
+        "I0610 10:12:49.300335 25530 net.cpp:77] Creating Layer pool1\r\n",
+        "I0610 10:12:49.300341 25530 net.cpp:87] pool1 <- conv1\r\n",
+        "I0610 10:12:49.300348 25530 net.cpp:113] pool1 -> pool1\r\n",
+        "I0610 10:12:49.300357 25530 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0610 10:12:49.300365 25530 net.cpp:154] pool1 needs backward computation.\r\n",
+        "I0610 10:12:49.300372 25530 net.cpp:77] Creating Layer norm1\r\n",
+        "I0610 10:12:49.300379 25530 net.cpp:87] norm1 <- pool1\r\n",
+        "I0610 10:12:49.300384 25530 net.cpp:113] norm1 -> norm1\r\n",
+        "I0610 10:12:49.300393 25530 net.cpp:128] Top shape: 10 96 27 27 (699840)\r\n",
+        "I0610 10:12:49.300400 25530 net.cpp:154] norm1 needs backward computation.\r\n",
+        "I0610 10:12:49.300406 25530 net.cpp:77] Creating Layer conv2\r\n",
+        "I0610 10:12:49.300412 25530 net.cpp:87] conv2 <- norm1\r\n",
+        "I0610 10:12:49.300420 25530 net.cpp:113] conv2 -> conv2\r\n",
+        "I0610 10:12:49.300925 25530 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0610 10:12:49.300935 25530 net.cpp:154] conv2 needs backward computation.\r\n",
+        "I0610 10:12:49.300941 25530 net.cpp:77] Creating Layer relu2\r\n",
+        "I0610 10:12:49.300947 25530 net.cpp:87] relu2 <- conv2\r\n",
+        "I0610 10:12:49.300954 25530 net.cpp:101] relu2 -> conv2 (in-place)\r\n",
+        "I0610 10:12:49.300961 25530 net.cpp:128] Top shape: 10 256 27 27 (1866240)\r\n",
+        "I0610 10:12:49.300967 25530 net.cpp:154] relu2 needs backward computation.\r\n",
+        "I0610 10:12:49.300974 25530 net.cpp:77] Creating Layer pool2\r\n",
+        "I0610 10:12:49.300981 25530 net.cpp:87] pool2 <- conv2\r\n",
+        "I0610 10:12:49.300987 25530 net.cpp:113] pool2 -> pool2\r\n",
+        "I0610 10:12:49.300994 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0610 10:12:49.301000 25530 net.cpp:154] pool2 needs backward computation.\r\n",
+        "I0610 10:12:49.301007 25530 net.cpp:77] Creating Layer norm2\r\n",
+        "I0610 10:12:49.301013 25530 net.cpp:87] norm2 <- pool2\r\n",
+        "I0610 10:12:49.301019 25530 net.cpp:113] norm2 -> norm2\r\n",
+        "I0610 10:12:49.301026 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0610 10:12:49.301033 25530 net.cpp:154] norm2 needs backward computation.\r\n",
+        "I0610 10:12:49.301041 25530 net.cpp:77] Creating Layer conv3\r\n",
+        "I0610 10:12:49.301048 25530 net.cpp:87] conv3 <- norm2\r\n",
+        "I0610 10:12:49.301054 25530 net.cpp:113] conv3 -> conv3\r\n",
+        "I0610 10:12:49.302455 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0610 10:12:49.302467 25530 net.cpp:154] conv3 needs backward computation.\r\n",
+        "I0610 10:12:49.302477 25530 net.cpp:77] Creating Layer relu3\r\n",
+        "I0610 10:12:49.302484 25530 net.cpp:87] relu3 <- conv3\r\n",
+        "I0610 10:12:49.302490 25530 net.cpp:101] relu3 -> conv3 (in-place)\r\n",
+        "I0610 10:12:49.302496 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0610 10:12:49.302503 25530 net.cpp:154] relu3 needs backward computation.\r\n",
+        "I0610 10:12:49.302510 25530 net.cpp:77] Creating Layer conv4\r\n",
+        "I0610 10:12:49.302515 25530 net.cpp:87] conv4 <- conv3\r\n",
+        "I0610 10:12:49.302521 25530 net.cpp:113] conv4 -> conv4\r\n",
+        "I0610 10:12:49.303639 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0610 10:12:49.303650 25530 net.cpp:154] conv4 needs backward computation.\r\n",
+        "I0610 10:12:49.303658 25530 net.cpp:77] Creating Layer relu4\r\n",
+        "I0610 10:12:49.303663 25530 net.cpp:87] relu4 <- conv4\r\n",
+        "I0610 10:12:49.303670 25530 net.cpp:101] relu4 -> conv4 (in-place)\r\n",
+        "I0610 10:12:49.303676 25530 net.cpp:128] Top shape: 10 384 13 13 (648960)\r\n",
+        "I0610 10:12:49.303683 25530 net.cpp:154] relu4 needs backward computation.\r\n",
+        "I0610 10:12:49.303691 25530 net.cpp:77] Creating Layer conv5\r\n",
+        "I0610 10:12:49.303697 25530 net.cpp:87] conv5 <- conv4\r\n",
+        "I0610 10:12:49.303704 25530 net.cpp:113] conv5 -> conv5\r\n",
+        "I0610 10:12:49.304410 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0610 10:12:49.304420 25530 net.cpp:154] conv5 needs backward computation.\r\n",
+        "I0610 10:12:49.304427 25530 net.cpp:77] Creating Layer relu5\r\n",
+        "I0610 10:12:49.304433 25530 net.cpp:87] relu5 <- conv5\r\n",
+        "I0610 10:12:49.304440 25530 net.cpp:101] relu5 -> conv5 (in-place)\r\n",
+        "I0610 10:12:49.304446 25530 net.cpp:128] Top shape: 10 256 13 13 (432640)\r\n",
+        "I0610 10:12:49.304471 25530 net.cpp:154] relu5 needs backward computation.\r\n",
+        "I0610 10:12:49.304478 25530 net.cpp:77] Creating Layer pool5\r\n",
+        "I0610 10:12:49.304484 25530 net.cpp:87] pool5 <- conv5\r\n",
+        "I0610 10:12:49.304491 25530 net.cpp:113] pool5 -> pool5\r\n",
+        "I0610 10:12:49.304498 25530 net.cpp:128] Top shape: 10 256 6 6 (92160)\r\n",
+        "I0610 10:12:49.304504 25530 net.cpp:154] pool5 needs backward computation.\r\n",
+        "I0610 10:12:49.304512 25530 net.cpp:77] Creating Layer fc6\r\n",
+        "I0610 10:12:49.304517 25530 net.cpp:87] fc6 <- pool5\r\n",
+        "I0610 10:12:49.304523 25530 net.cpp:113] fc6 -> fc6\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0520 12:14:46.566017  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.566061  2522 net.cpp:152] fc6 needs backward computation.\r\n",
-        "I0520 12:14:46.566076  2522 net.cpp:75] Creating Layer relu6\r\n",
-        "I0520 12:14:46.566084  2522 net.cpp:85] relu6 <- fc6\r\n",
-        "I0520 12:14:46.566092  2522 net.cpp:99] relu6 -> fc6 (in-place)\r\n",
-        "I0520 12:14:46.566100  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.566140  2522 net.cpp:152] relu6 needs backward computation.\r\n",
-        "I0520 12:14:46.566149  2522 net.cpp:75] Creating Layer drop6\r\n",
-        "I0520 12:14:46.566155  2522 net.cpp:85] drop6 <- fc6\r\n",
-        "I0520 12:14:46.566161  2522 net.cpp:99] drop6 -> fc6 (in-place)\r\n",
-        "I0520 12:14:46.566174  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.566179  2522 net.cpp:152] drop6 needs backward computation.\r\n",
-        "I0520 12:14:46.566187  2522 net.cpp:75] Creating Layer fc7\r\n",
-        "I0520 12:14:46.566193  2522 net.cpp:85] fc7 <- fc6\r\n",
-        "I0520 12:14:46.566200  2522 net.cpp:111] fc7 -> fc7\r\n"
+        "I0610 10:12:49.364333 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.364372 25530 net.cpp:154] fc6 needs backward computation.\r\n",
+        "I0610 10:12:49.364387 25530 net.cpp:77] Creating Layer relu6\r\n",
+        "I0610 10:12:49.364420 25530 net.cpp:87] relu6 <- fc6\r\n",
+        "I0610 10:12:49.364429 25530 net.cpp:101] relu6 -> fc6 (in-place)\r\n",
+        "I0610 10:12:49.364437 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.364444 25530 net.cpp:154] relu6 needs backward computation.\r\n",
+        "I0610 10:12:49.364455 25530 net.cpp:77] Creating Layer drop6\r\n",
+        "I0610 10:12:49.364461 25530 net.cpp:87] drop6 <- fc6\r\n",
+        "I0610 10:12:49.364467 25530 net.cpp:101] drop6 -> fc6 (in-place)\r\n",
+        "I0610 10:12:49.364480 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.364487 25530 net.cpp:154] drop6 needs backward computation.\r\n",
+        "I0610 10:12:49.364495 25530 net.cpp:77] Creating Layer fc7\r\n",
+        "I0610 10:12:49.364501 25530 net.cpp:87] fc7 <- fc6\r\n",
+        "I0610 10:12:49.364507 25530 net.cpp:113] fc7 -> fc7\r\n",
+        "I0610 10:12:49.391316 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.391350 25530 net.cpp:154] fc7 needs backward computation.\r\n",
+        "I0610 10:12:49.391361 25530 net.cpp:77] Creating Layer relu7\r\n",
+        "I0610 10:12:49.391369 25530 net.cpp:87] relu7 <- fc7\r\n",
+        "I0610 10:12:49.391377 25530 net.cpp:101] relu7 -> fc7 (in-place)\r\n",
+        "I0610 10:12:49.391384 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.391391 25530 net.cpp:154] relu7 needs backward computation.\r\n",
+        "I0610 10:12:49.391398 25530 net.cpp:77] Creating Layer drop7\r\n",
+        "I0610 10:12:49.391427 25530 net.cpp:87] drop7 <- fc7\r\n",
+        "I0610 10:12:49.391433 25530 net.cpp:101] drop7 -> fc7 (in-place)\r\n",
+        "I0610 10:12:49.391440 25530 net.cpp:128] Top shape: 10 4096 1 1 (40960)\r\n",
+        "I0610 10:12:49.391446 25530 net.cpp:154] drop7 needs backward computation.\r\n",
+        "I0610 10:12:49.391454 25530 net.cpp:77] Creating Layer fc-rcnn\r\n",
+        "I0610 10:12:49.391459 25530 net.cpp:87] fc-rcnn <- fc7\r\n",
+        "I0610 10:12:49.391466 25530 net.cpp:113] fc-rcnn -> fc-rcnn\r\n",
+        "I0610 10:12:49.392812 25530 net.cpp:128] Top shape: 10 200 1 1 (2000)\r\n",
+        "I0610 10:12:49.392823 25530 net.cpp:154] fc-rcnn needs backward computation.\r\n",
+        "I0610 10:12:49.392829 25530 net.cpp:165] This network produces output fc-rcnn\r\n",
+        "I0610 10:12:49.392850 25530 net.cpp:183] Collecting Learning Rate and Weight Decay.\r\n",
+        "I0610 10:12:49.392868 25530 net.cpp:176] Network initialization done.\r\n",
+        "I0610 10:12:49.392875 25530 net.cpp:177] Memory required for Data 41950840\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "I0520 12:14:46.600733  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.600765  2522 net.cpp:152] fc7 needs backward computation.\r\n",
-        "I0520 12:14:46.600777  2522 net.cpp:75] Creating Layer relu7\r\n",
-        "I0520 12:14:46.600785  2522 net.cpp:85] relu7 <- fc7\r\n",
-        "I0520 12:14:46.600793  2522 net.cpp:99] relu7 -> fc7 (in-place)\r\n",
-        "I0520 12:14:46.600802  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.600808  2522 net.cpp:152] relu7 needs backward computation.\r\n",
-        "I0520 12:14:46.600816  2522 net.cpp:75] Creating Layer drop7\r\n",
-        "I0520 12:14:46.600823  2522 net.cpp:85] drop7 <- fc7\r\n",
-        "I0520 12:14:46.600829  2522 net.cpp:99] drop7 -> fc7 (in-place)\r\n",
-        "I0520 12:14:46.600836  2522 net.cpp:126] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0520 12:14:46.600843  2522 net.cpp:152] drop7 needs backward computation.\r\n",
-        "I0520 12:14:46.600850  2522 net.cpp:75] Creating Layer fc8\r\n",
-        "I0520 12:14:46.600857  2522 net.cpp:85] fc8 <- fc7\r\n",
-        "I0520 12:14:46.600864  2522 net.cpp:111] fc8 -> fc8\r\n",
-        "I0520 12:14:46.615557  2522 net.cpp:126] Top shape: 10 1000 1 1 (10000)\r\n",
-        "I0520 12:14:46.615602  2522 net.cpp:152] fc8 needs backward computation.\r\n",
-        "I0520 12:14:46.615614  2522 net.cpp:75] Creating Layer prob\r\n",
-        "I0520 12:14:46.615623  2522 net.cpp:85] prob <- fc8\r\n",
-        "I0520 12:14:46.615631  2522 net.cpp:111] prob -> prob\r\n",
-        "I0520 12:14:46.615649  2522 net.cpp:126] Top shape: 10 1000 1 1 (10000)\r\n",
-        "I0520 12:14:46.615656  2522 net.cpp:152] prob needs backward computation.\r\n",
-        "I0520 12:14:46.615664  2522 net.cpp:163] This network produces output prob\r\n",
-        "I0520 12:14:46.615682  2522 net.cpp:181] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0520 12:14:46.615696  2522 net.cpp:174] Network initialization done.\r\n",
-        "I0520 12:14:46.615702  2522 net.cpp:175] Memory required for Data 42022840\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Loading input...\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "selective_search({'/home/shelhamer/caffe/examples/_temp/cat.jpg'}, '/tmp/tmplkH92s.mat')\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Processed 223 windows in 16.525 s.\r\n"
+        "GPU mode\r\n",
+        "Loading input...\r\n",
+        "selective_search_rcnn({'/home/shelhamer/caffe/examples/images/fish-bike.jpg'}, '/tmp/tmpo7yOum.mat')\r\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
+        "Processed 1570 windows in 35.012 s.\r\n",
         "/home/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2446: PerformanceWarning: \r\n",
         "your performance may suffer as PyTables will pickle object types that it cannot\r\n",
         "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n",
         "\r\n",
         "  warnings.warn(ws, PerformanceWarning)\r\n",
-        "Saved to _temp/cat.h5 in 0.353 s.\r\n"
+        "Saved to _temp/det_output.h5 in 0.035 s.\r\n"
        ]
       }
      ],
@@ -230,7 +404,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Running this outputs a DataFrame with the filenames, selected windows, and their ImageNet scores to an HDF5 file.\n",
+      "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n",
+      "\n",
+      "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n",
       "(We only ran on one image, so the filenames will all be the same.)"
      ]
     },
@@ -240,7 +416,7 @@
      "input": [
       "import pandas as pd\n",
       "\n",
-      "df = pd.read_hdf('_temp/cat.h5', 'df')\n",
+      "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
       "print(df.shape)\n",
       "print(df.iloc[0])"
      ],
@@ -251,13 +427,13 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "(223, 5)\n",
-        "prediction    [6.67012e-06, 1.26349e-06, 1.86075e-06, 1.0960...\n",
-        "ymin                                                          0\n",
-        "xmin                                                          0\n",
-        "ymax                                                        500\n",
-        "xmax                                                        496\n",
-        "Name: /home/shelhamer/caffe/examples/_temp/cat.jpg, dtype: object\n"
+        "(1570, 5)\n",
+        "prediction    [-2.64547, -2.88455, -2.85903, -3.17038, -1.92...\n",
+        "ymin                                                     79.846\n",
+        "xmin                                                       9.62\n",
+        "ymax                                                     246.31\n",
+        "xmax                                                    339.624\n",
+        "Name: /home/shelhamer/caffe/examples/images/fish-bike.jpg, dtype: object\n"
        ]
       }
      ],
@@ -267,19 +443,21 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n",
+      "\n",
       "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
       "Simply list an image per line in the `images_file`, and it will process all of them.\n",
       "\n",
-      "Although this guide gives an example of ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models\u2019 input dimensions, batch size, and output categories. Refer to `python detect.py --help` for the parameters to describe your data set. No need for hardcoding.\n",
+      "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models\u2019 input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n",
       "\n",
-      "Anyway, let's now load ImageNet class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
+      "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "with open('../data/ilsvrc12/synset_words.txt') as f:\n",
+      "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n",
       "    labels_df = pd.DataFrame([\n",
       "        {\n",
       "            'synset_id': l.strip().split(' ')[0],\n",
@@ -299,38 +477,38 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "tench                0.000007\n",
-        "goldfish             0.000001\n",
-        "great white shark    0.000002\n",
-        "tiger shark          0.000001\n",
-        "hammerhead           0.000007\n",
-        "electric ray         0.000004\n",
-        "stingray             0.000007\n",
-        "cock                 0.000057\n",
-        "hen                  0.002985\n",
-        "ostrich              0.000010\n",
-        "brambling            0.000004\n",
-        "goldfinch            0.000001\n",
-        "house finch          0.000004\n",
-        "junco                0.000002\n",
-        "indigo bunting       0.000001\n",
+        "accordion      -2.645470\n",
+        "airplane       -2.884554\n",
+        "ant            -2.859026\n",
+        "antelope       -3.170383\n",
+        "apple          -1.924201\n",
+        "armadillo      -2.493925\n",
+        "artichoke      -2.235427\n",
+        "axe            -2.378177\n",
+        "baby bed       -2.757855\n",
+        "backpack       -2.160120\n",
+        "bagel          -2.715738\n",
+        "balance beam   -2.716172\n",
+        "banana         -2.418939\n",
+        "band aid       -1.604563\n",
+        "banjo          -2.329196\n",
         "...\n",
-        "daisy                    0.000002\n",
-        "yellow lady's slipper    0.000002\n",
-        "corn                     0.000019\n",
-        "acorn                    0.000011\n",
-        "hip                      0.000003\n",
-        "buckeye                  0.000010\n",
-        "coral fungus             0.000005\n",
-        "agaric                   0.000019\n",
-        "gyromitra                0.000039\n",
-        "stinkhorn                0.000002\n",
-        "earthstar                0.000025\n",
-        "hen-of-the-woods         0.000035\n",
-        "bolete                   0.000036\n",
-        "ear                      0.000008\n",
-        "toilet tissue            0.000019\n",
-        "Name: 0, Length: 1000, dtype: float32\n"
+        "trombone        -2.531519\n",
+        "trumpet         -2.382109\n",
+        "turtle          -2.378510\n",
+        "tv or monitor   -2.777433\n",
+        "unicycle        -2.263807\n",
+        "vacuum          -1.894700\n",
+        "violin          -2.797967\n",
+        "volleyball      -2.807812\n",
+        "waffle iron     -2.418155\n",
+        "washer          -2.429423\n",
+        "water bottle    -2.163465\n",
+        "watercraft      -2.803971\n",
+        "whale           -3.094172\n",
+        "wine bottle     -2.830827\n",
+        "zebra           -2.791829\n",
+        "Name: 0, Length: 200, dtype: float32\n"
        ]
       }
      ],
@@ -360,22 +538,22 @@
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
-        "<matplotlib.text.Text at 0x4798650>"
+        "<matplotlib.text.Text at 0x4e2c090>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
        "text": [
-        "<matplotlib.figure.Figure at 0x4668990>"
+        "<matplotlib.figure.Figure at 0x4d008d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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qByysJNMVSOElCKKlKRQKGB4eNkNMOQ1IS5YswbFjx6IWjyAIgiAIggiR1Htp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\ny9ksXpNGv8/Hp8dW0TRNaxg4nRqam9dIFYOO345s7dq1rhNeUaakmLP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lkkT/l19+GblcTuwRHA6HpDmDwUBYeYTfOp2O5NapVEq6sSz2AEiwaLfbiMVi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+9rdtp/vggw90d3env/zLv9Rv/uZv/oc/A89ggmDAc0EHgOSoAdEJXl/fpzoBRWFFRVpU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\nZp1t5rPVM+s/AIIOk5coA2g1AAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x4668ad0>"
+        "<matplotlib.figure.Figure at 0x4d00a10>"
        ]
       }
      ],
@@ -404,16 +582,16 @@
        "stream": "stdout",
        "text": [
         "name\n",
-        "proboscis monkey       0.920136\n",
-        "tiger cat              0.916973\n",
-        "milk can               0.791307\n",
-        "American black bear    0.625850\n",
-        "broccoli               0.609467\n",
-        "dhole                  0.513998\n",
-        "platypus               0.507829\n",
-        "tiger                  0.497029\n",
-        "lion                   0.481180\n",
-        "dingo                  0.474689\n",
+        "person          1.883164\n",
+        "bicycle         0.936994\n",
+        "unicycle        0.016907\n",
+        "banjo           0.013019\n",
+        "motorcycle     -0.024704\n",
+        "electric fan   -0.193420\n",
+        "turtle         -0.243857\n",
+        "cart           -0.289637\n",
+        "lizard         -0.307945\n",
+        "baby bed       -0.582180\n",
         "dtype: float32\n"
        ]
       }
@@ -424,19 +602,17 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Okay, there are indeed cats in there (and some nonsense).\n",
-      "Picking good localizations is work in progress; manually, we see that the third and thirteenth top detections correspond to the two cats."
+      "The top detections are in fact a person and bicycle.\n",
+      "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Find, print, and display max detection.\n",
-      "window_order = pd.Series(predictions_df.values.max(1)).order(ascending=False)\n",
-      "\n",
-      "i = window_order.index[3]\n",
-      "j = window_order.index[13]\n",
+      "# Find, print, and display the top detections: person and bicycle.\n",
+      "i = predictions_df['person'].argmax()\n",
+      "j = predictions_df['bicycle'].argmax()\n",
       "\n",
       "# Show top predictions for top detection.\n",
       "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
@@ -444,13 +620,13 @@
       "print(f.order(ascending=False)[:5])\n",
       "print('')\n",
       "\n",
-      "# Show top predictions for 10th top detection.\n",
+      "# Show top predictions for second-best detection.\n",
       "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n",
-      "print('10th detection:')\n",
+      "print('Second-best detection:')\n",
       "print(f.order(ascending=False)[:5])\n",
       "\n",
-      "# Show top detection in red, 10th top detection in blue.\n",
-      "im = imread('_temp/cat.jpg')\n",
+      "# Show top detection in red, second-best top detection in blue.\n",
+      "im = imread('images/fish-bike.jpg')\n",
       "imshow(im)\n",
       "currentAxis = plt.gca()\n",
       "\n",
@@ -471,20 +647,20 @@
        "text": [
         "Top detection:\n",
         "name\n",
-        "tiger cat       0.882972\n",
-        "tiger           0.073158\n",
-        "tabby           0.025290\n",
-        "lynx            0.012881\n",
-        "Egyptian cat    0.004481\n",
+        "person             1.883164\n",
+        "swimming trunks   -1.136701\n",
+        "rubber eraser     -1.251888\n",
+        "plastic bag       -1.286928\n",
+        "snowmobile        -1.304962\n",
         "dtype: float32\n",
         "\n",
-        "10th detection:\n",
+        "Second-best detection:\n",
         "name\n",
-        "tiger cat           0.677493\n",
-        "Pembroke            0.064214\n",
-        "dingo               0.050635\n",
-        "golden retriever    0.028331\n",
-        "tabby               0.021945\n",
+        "bicycle     0.936994\n",
+        "unicycle   -0.372841\n",
+        "scorpion   -0.812350\n",
+        "lobster    -1.041506\n",
+        "lamp       -1.118889\n",
         "dtype: float32\n"
        ]
       },
@@ -493,15 +669,15 @@
        "output_type": "pyout",
        "prompt_number": 6,
        "text": [
-        "<matplotlib.patches.Rectangle at 0x4ab3510>"
+        "<matplotlib.patches.Rectangle at 0x4f59f10>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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sYubFjL2Ita0Ko+mE7UsN3ERHyBxsp0+jusro3RE5ASdnCXlVYa6kYWg5Eztl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dup0uk+mM3f3LLFdTPvqR55CFOq+8eoPV0kfRIxodm7qt4M5nSKnKeLRkq/WAq3td\nbt25i92q4xYJ7tJBM/T3pbfP6R/ISzRDYzFdsdHrIAgCk8mEoiWyXM25cHABz1sxHA6ZTufoqk5V\nlu+DKTUuX7rG8fERmqoyHEwIAxvT1Oj3O+QV9Dp7tOw+88WCKMtoNNoEfkCFhFZfP20bdZvJcMKr\nr7zKdDDkv/tv/yWOG1JWFXXTwjANprMpURQxGU24fv0lNntPU5NF0vh1yjxh6swwN1qslj7NTY3Z\nQmN65nH1wvnwkbt3X+U8J3Drzg/Isxxd16hbNrpmMTyLME0TQRDwgpCaZXLr6C5pnNJq1JEkFQF5\nTf0pc6pqfX/yPEMSRYqyWCtI+x6qIhM5SxRDR1c00jQlSiPCKCV/X4NA0QRmZwvMmkzgrpA1g0qV\nqDXrIBUs7w8oCEGVSKWKUqhIwxCz3QJdoJAyksxFU2SKMsV1HaS0QFA0RMUEw0RDIYsyOnadPIuJ\npRSjZTFMFmCYCLKGl4Qoskgeh4hlRqfd4fjomK2DHSgBwaBSM2TDICoT9i9e5mQ0oJJVVkGMpqkE\nQYlcJGTEOJ5HnhY0bJM8iyjLgpqqMR0ec+mSSZXLhE7MKpgQpRGxIFJlEbpao2lbbGz3GB4NuLZ1\n8cOW+Ic7gbIseeaZZ7h79y6/8Ru/waOPPsp4PKb/Preu3+8zHo8BODs7+wcLfnd3l8FgcO5521aT\ncehj6CaFnTNbLriw2+HGW+/QbtsUaUGz1UGWVXb6u0jIZI4PsU9OxdPPP8Nbr79FKcrU2w0KBCoq\njo/vEvoZL7zwLAvfxY8clsGSohTQTRUqiSeeeoSje0dslltcunLIZD4jiXyqssK0TDz3/ABZv7eN\nVKgoikGeVpwOztja3GY4PEVV1iWm66i8g+eHUAhUwHg8ZmNrgzhKiOOMhmVg2xbO0mW5KHEdn90L\nu0hSTqjF9HobREmBqBhs7HYpcoHlbEmUZlx/8ftErosb+fzqv/hFqApc1+N0OELX1yyEyWhOHCXY\ndZu9vSuoiojrrNC1bc7cO3hVhSxb1DUJMQzxJxWKJPLOg/Nzyn/54r8B/psPjH/nla9Rr9WRsbCt\nDRTFZn/rgPlqHSOI0ogr9hPUTIG6WaMqCvIiRRRKqop1eq8oSJJ1W25VZZTlOooviTK5qhMnPoZp\nUFWQZSlpmpLnJUkeEftL0ixFNcBdpMSRj91ViOMMJIGcCqtVX+fgyxxZlkllGV3QCLIc09BJPQdN\nk/AiB1E26bZbiKaFrKiIeg0/qwiiFEXXELIEo64hGBKOEFNr1niwXCGWAkq/YK/RxNAUNNHAdefI\nis6Du6fINQVRsMnLMRgS9XaL1954G9WQ6W1u4zoLapqGVRMp4jVFaGtrh8HpA6xmA7WQWUYONdtE\noE1SwHK1QLNU0lKj3pJYOSta3Q2CIGLpLel3+ly+dpXR9MOzAx/qBERR5I033sBxHH7u536Ob33r\nW//gc0EQEITzA0d///l5FvsRnU4DJ/Qp8hxR1db8eUPCbrQ4eXBCGMTs9Td598YNunYDsQABCVEW\nWK4cfvJTn+H1119BkmVOx3fRFdAqi+1+k/v3b5GWJTv7O+RixtCZI0rQtSxEWebRp57AcRyGZxN2\ntnYIwpL9K4e8d/Q2wvkPRCy5Sylq2HWJLC6o19vcuHmbtlUnCDxqlsG7925i6BaKplFmJVVZIcsy\nge9j1UxM3eDozj0+/emfIUp8kizH81zSqMArVmz0tskLEUnVKKqSvKiIw5zBYIrnucxGM/Ik5Kln\nn6Rm1/HijNF0SqNhk2UpSZyu++uSmI2eiaoKvPvebVxvhaAFbG7vsaoGZEWOrBdUmYalZXi+SKNr\nAR+sGnTL8/UEUsnjrTs36LU7CPMKRVW4cbeiKkVESadr79Gt9+m22yR5QSVUpFmybvJDRBDWepJl\nWVIV1TpaX5QIlGRCSJD4SILIbJIgihJVVVCUBVmSkaQRcbhkWcB4dkpTbeASkS4X6FaNNMmpqgqz\nYRMFDkkaIGNQqzcI/YTZZETD3Meq22SRiySqCKVCFPqYRgNJr+HlGYWo4cxXIJYYpkmlVchbJlKV\nE4c5dW2tgSkBlmUxnc7Y7TaZxGMyWcCwVRbLiGI4RtNEbr33Nr6X0em1GU8myIqOH0VQFOiVzIXt\nawzOBkSz+9TtBpcvPcd3/vLPMDoNskWO1WlSVDmmqtLb6vDgwYQUjYbZgdQkiVPanR5JLpG7PnL+\nnxBD1mg0+NznPsdrr71Gv99nNBqxubnJcDhkY2MDgJ2dHU5O/p/+5dPTU3Z2zq9Ce+X7C0wzxfV8\n2n0N24owWk2CIuFockalSFiayWw4YqfZZjVf0FRMCkkmEwp0VWM0OyPNIrZbG/jOBFk2ESWQdJU7\nt45obrS5dfs2gljQ32ozno1ZRg5CS+LdeyMOD64g6QJhEqIqJnGco+sGOefXW3ebuwwGc5LER9NE\nRKmgvdFELHJmiynvHjlomooTL5EEhTRJ1wEzYHB2ymo5R9dNup1N3r1xiwsHu2xu9tnb3yPP83X+\nOStJy5xoOSNNU5aLJb4bEEUho+GYhiXy/EefwTDqOEHIdDqj1WwRhCFFUTKfLxCqgo9+5Hkm4xlB\nlOF6Iffu3MK0bHpbTZRCJUkS6l2bwI/ptnt0NjTunZwvsBrE5wdK54sR3V6LIndIk5im2SD0l4ii\nxlZ3h/fu/w2SKnFl/wm8VUhRCFy58hiKrJFlGaIo4vs+VVWR5zmiKCHIUFYJYeCtcexFgSbJUInI\nssRyNaMoM3TNBBUko49l7SKKEbXCIkkisjyiYu0whEokLwpUVaWkIAyXUClM5lMuXr5MIQtYbY2q\nyClLqIDV6BZ14SHkeofFeIFcSAh5QVGTiW1ASLG2WrQ0g92aRk1vEKUrbFEi9hJKNeXqxTZHpwsk\nSeLio9f42+++zONPXGKjf8DN+0fMFwtMTaGtimwdXqFMIuJSwk9CSrWgyGQWc5exfYeHrj5Jpop0\nOj3y3GN0NkKxmkzPprizlGefO0REZxn4NOwaRZGQpzm1uM7bL98C/tU/urb/UScwm82QZZlms0kU\nRfzVX/0Vv/u7v8vnP/95vva1r/E7v/M7fO1rX+OXfumXAPj85z/PF7/4RX7rt36LwWDA7du3ef75\nD+4lAT71zw9ZhT5pWCNZ+UhpRVZNsZsWs3lA394ijSsMQaZIMi5v71F4IU5WkiQes/mE1uYmaZUw\nODlGV1TiOEFBIoojLjy0y2Ll0+t0KfOYwI/pWW2SNCElwd5okBQxsq5SSgWoCUnsk2UhdqN57jWb\nso1ULbAMg0SRMUKJMBM4Ghxj6AqyLlMV1ZokW5goYkXgBQgi1C2DMIjodUTIBRAE8lzAXSWIYkYl\nVXjzJUvXo1azGU+mCKJAr9ujLGKK1OeJJ65x+bCPJmtQKZyenGLWTbI8I44j5sslTavF5tYGSRbh\nOh7zZcRyseQzn/0cDbtBqY0IxiNKTSQKA8Igxmxs8GA6oGHLwAfzyvXa+cGlIPAoshRFFlAVk/Fk\nTiFKUBXMHgyYrJZMb/wlx6s77Go9VK3J2QhcN0EzdDRVRaFOyRr9HadL4tQnSgNkpHWpcVmRSgJl\nuc5WKYoECBRlRlIm5IJOZVhEgUuQhNQsC8+dIisyaRwjSwqSKpNnBUVVIkkSQZJSFDmyYZKlAV6W\nYtVrZGFCs71BS9FYeSFq5RJ6IbIqc3BhF79KsK/aSDWZsT9nsRyTOT4bnT5JXNDt7VDVavQfeYS/\n+8Y3COKC7kYXpwjoHdgImslg6uB6PkGQrFkRYsbC9RgNHPobFoVmoSgq8fuU45PTUwzJwitzgtzl\nyvYeVBk13aBd32d/U0IWNE4mZzTbdYLMo900mT+YceW5A7Z2JN586V+9/4v96//vTmA4HPKlL32J\n8v3g1q//+q/zsz/7szz99NN84Qtf4A//8A//Y4oQ4JFHHuELX/gCjzzyCLIs8wd/8Ac/cjuwjBwk\nGeq6SM1uIlciOS4rt6Db6qEUIpIqopYahSgQuD6RG1BkGduXLxC4CxbHAx7ZPWQ+miBIMlqWUBYh\nQuYRuSWXLl9hMB6j1iTETCJPK4RKwfdSNFWhkGWipEDQFeqCzej4FF2ts/DP76s/OrtOiUIYe5RC\nwVPPPMzr77yKl2ooasnZoKDXMslUkThY73uVusTDTz9MnhYMTs4YTiZs9jo8fPAcXhhRSjKL5QTf\nd1jOpuxs7yDLOVevrQU+irzgwsFlTHMtrFIKGaP5nCyt2NjcJo0TFNlgMl4glBIXLhygqALz1Yq5\n73M2PKNVt+k1u+i2SikekjjXKcQzfETarR3izMVWKzY3TeCDisPLpXPu/cjzAtWAvIjxypiMgna9\nSymK3B8N0WoqeZHz9p03cLp9NMHAy6Y0DAvHr8irAlM2ESSdIM2ohBxJLsjTeN0rKEpAhSTIa8S4\nKOAECZVQIsUglBlJGqK2TeKqR61WI42WiHWJnHU8LiFHUVWCOMRQVLwqpTJV7p/c5XB4xHa9QcPU\n0ciphIy0zCgLBaPRxAsTBsM72G0IFjGlVlGIMXIkI5YlqmkSxBnDpUu73eVkMsLQFYbHc4q0ome2\ncecxlZijW21ORyO2NvbxvZgkKwmyGRda11guFtitBkKqIKkZmiQTSBWNZh25giLJadgGLcsgWM3Z\n7HQ4Pjtlc3MbQ9e5f3ZCmIQobokgVMRBTKfTIyl8lv8EUZF/1Ak8/vjjvP766x8Yb7fbfPOb3zz3\nmC9/+ct8+ctf/tAvHi4mbLQbKLpEUZrs7FxjMH2XDd1cM+KQcGYeCCW2ZqErdcLCpdnvkapQIrO7\n0cdbOWxt7KLIEqcnx8hKl3i+4tFnHmf+PtGnJuvMgxWqrNGw6oh5ShguQCkRRRFRzhHydW1/a6PH\nwwePwr/5Dx+45kqEuFhgtiT8yGMwv46gBsiWi+OmXHqiwXIaYfcFZE9GFg1mU4dCWfHcJx7l1v98\nB1Mz8TyHH/zgJTqdLtvqFleuXsbUJAS5QBIhTXLSpMQw6sxmc/I8Q5YthsMhFawbY0wVWZbo7+1z\nfHyCJElcunQZVVWJopjpdMl8MiKMXJ57+gVEqaBIUlZBRJUYRLGO3VeJowxRlGh1LYLo/Oqyfmfz\n3PEr1y6SxDGOuyAJIlBkgjLEkHWeffwq1996A1HS6G81iDMPL3aITz12Wl1W0yWSIpDnPrJaYxUm\nbG9dZDg5RVFkapbFhf0dZrMzyFNcL0SRdPb3LxCGEZPJjHanR6vTIY+iNQY8jkklBVGykSSVPA7R\nFJ0syVFsC1EAWSopk5JylhAEEZVZJw4DxHQdjNQlFSoZQVLWuoVJhKprjPIAZdMiIKFKMtIsRm/W\niWYZcRQhCQYU61qSt66/xk5HI3ITdrf7OGWy7g5UFaqiwNRUnnrqcVxnyGK0QBEkJAV2Nlq8cus2\n+7sd4mGCCHS6G0iCQmWoaJqON57T7/cwTYWFNyNJM4SsoNeso1omnuOTBQWyDvOZj2LUPnQt/hiB\npF3yQmNVJrTrdU5nt5DUmPF0jFVvsPIimnUbQ64T5wJ+6dI77FJD4c5yyuT0lDhf4IY+7XqDZBHR\n2uhw8cI1qrLg9OyYbr9NfWefs8UcUVIwjBpXr13j9v0HSEWCkKU0VJssScliGQGB5WTJ9bfPV2iV\n1Iws9HEDF6OuMxjfR1B1etstdi6KpInPpceaBIHH7XdifD9n87LJpcf7vPTS6+iqBpVDVSo8/5Hn\nEESJIHS5/+AOuizT7veI45ROpw1ihh84ZNm6am21WqEoClkuoqoS2zs9ihTu3LlDGCSoqs5iMefk\n9D6KXGMwOMVu6BxcfAxVkwgjlwfHx0ShiKq2+bUv/gJ/8qf/C1cfvsTb776KlED1I2Ihunw+oPVs\nOkSqCvr9LUY3b7G320NVDFajGWKW8dGnn+bt6+9BmFOrN+lZCr67ZB4vWeRL9rf3WEwc4tQlrhTS\n4YonHnuU0WRIo9lgOB2wdAfkUcYjDz+G54W8e/s6n/vFX+D7L36PJI/QLBXPm9JodzkbuVRGRU6J\n0TCw9Sak0FbquFnMcDqgEEq6jSYrZ8zZcMC2VWP/wkPomkycpHiJgGXoiIWMoUokiUdJjtnQCGop\nu1cuki8qju/fRJAFrJbGI5evMJ0uCPyIspTot5o0DZGZE3JwcZ+3712nZtYZrRbU+hq7Wz0Sd0WZ\npHihTxqmiLmEIahcvryJpqg8/cyT+G6AUglUVYofrVAFmeHZKaKc47oBiZQiCSobjQZu4HB/MuBj\nH3uBzIlYrcZIio3V2PjQtfhj6x0wDRVVVpAKlUpIyChwgwpBVPCDAEWRCIMYRy6YuzM2+n3uHp/y\n0ttvUFUR9pYNskytZlKvq6g1EbGsCCOH++PbJGXIeDHmZDKg2bHIxQrRUgg8l+cffRg3iulZTeLA\nRzfXQo0L1+PK5UMu75+vtffyuy8iGBGCnnAyv08lCjQbW8TBkqxcIdQjXr0x5I3rPiuvQtMlHn76\nMdzJKaXrUGuFKJZBWCWcjgdsb22zt3dIr3OBurWNOw/JI5gNF5yenhInCXGakhQJQZYxXYYsnAV+\nlPHW9Ttcf+82lVBRs+pYDZuyEkFQiJOAXq/NxYMrvPBTP40fJzh+hevLLBYeH3/uEzTUfX7+p7/A\ndD6HlspoWXH52qVz5z2YnO8UTVMnEysW7piLvT66UENMVURprYf3zvEtmns9dg52sGsiCBlJntHr\n99m/fMjEC7AbDeSqwlZF+jsNbt69SVWFzJcnpHlAt7ODoKpUVYWKzO7mBf76Wy+SigWFlDNfTchl\ngZP5EYkc0NzfptZpU2Q5tZpFo9PDz3NqdYM0iriwc5EyltCkOpNxjmzuEksZKRKiVsdUVQRJJNNk\nRk7AYuqiiAKRKRFRcePOXQbxgEjOOJ09QEkkQr/A8QIM1SAKfH7qySfY3tll+7DLfLngP//pX2az\nscnh3h6aKmHpKs5iiRuUyEIDXW2zc+kKyzgmWKaUpcTJ2Zgg8pmtHGbzGfEyJF0tefqZZxmOx0RZ\nBHmJZRoEYcFTTzzPo1euUpcltnptfDelKgSmgw8XGv3xcQcWDq3tPVbvA0aKIkIzBPwUDKVGmATI\neUHXrjGfOty48S6PP/sk711/F6NuUFXJGvC0wWk7AAAgAElEQVQ5W9JsqFx5+ArxTGS+9JEMFVFT\nCKMAXZdx5kO2NmySNGAyOsVzZxRFwdzz1rSacIFck9nc6+PEDqJ+vm8UzYKZP8SPXUpFIMsKBrdf\nxaorHN1PuProJfzZPXZ7DfzSoUoK4mDJD747RrEUFFkldkNMXeP09AxLv4GmGKRZRrfTRRZV/MBH\nVhSKXML3UopcBlQEUcINFqRFgZfFRGGEIkISRthWhyDwEUQBQawwTYV2u0VZprz33g3OzoacnAyo\nGXU++tFnObxygVKoOHlnil5ukgVDDq9tc/9kfu68e/3zuyoFATRNfL9eX2e+8CiIQChI0pS0hMHx\nCZfaHWQxJfTgc5/7Ai99929J84SHDw9ZODPGM5cqKWg0m0wmxySJiKarZFlOUfoYlokXR8RhTJVX\naKqC4yywm02SJGCxWPDcR57l7XfeZjKZQFFi63Vc1yPPV5SUZGHFzv4WvV6PaipgqhpFEdOsyVRZ\nAbKIrKhrPLukI6gqYegj1AXO0hmuFJEjUmQFwTShFFyKSmIwCsjDFa3NXR66dJmjm7e4d3aEl0c0\nGjVGszP+z2/8KbVml6KokIHhdEmFhJQKqDWdKPV4cPuUi5f3OZsOyPM6YlkQxTmO63PpoV1iN6ZM\nKt586wdg6Ah5jqZppGlJu93gu6/8DZcuHXD31juYdYu9/W3KskQ6J9D7gf/1P3XR/qe2SIFJsEKz\nanihw9ydE6YeolTRanZQayZGq8kyCNm9us/uvs3OoQ1WghcuQZTJqDi4eoVV4PD2e2+wSs8Iswlh\nsiBIHdrdBpKkIKNRlRKabiGpNWSxxsee/ymSosKPQzx/RZJF6A0dUYVW7/zX30qtKHVYxi5BnpCJ\nBaWeE1UlitrGblzg8PE+qRwi2yr6tsqqWPDEJx8nEQqSQqQqVYociiLDdV081yELYzRFQxJlBEEh\njjKqUuZsMMf3Ujw3YTZdkGcJy+GM6y+/zvjoAaPjAUEQMDg7RlYEJKmiXjPRNJ0kiSnLgtFozJtv\nvkGe5zz+xGM89sSjhGHAn//Fn7GYZdT1Ay50riJnJlJ6/t+hKs8fj9OQra0+SZqz8lfodYNedw/b\n2ERI6tRpsm2baGRQiMiCSsfqstXdYGdnE1NTiMKYTq9Pe7NLmga0m3Wa7SaiJNJotMnTjDCKifKU\njb1t0qrE7jTIqxxVU9B1Dc0QefnVVxEECVGU6HU31k1AeUaU+jjxCt1WySSXk+NbCEmIVmV0GjWS\n2CWLszU7sMoRWRd4FWJFrW7ipytoqNDUECSRmqGjGSJOoNDqbOOsDJrdGlG64s2bb/NgPqW9v4HR\n1lBqMrIuIWkSpmWSlSlB6KPV69TqTS5dvIiCAqkAWYmsaARBRJEXSBVURc6lhy6SFSWpLHDbmxGI\nGqIkoukyFy8fsr29xWI1ptXuoEs1NMVmufRwvDn3ju7Qbn+4stCP7U2g12pDXiAWBYIoUZYaxv/N\n3pvEypJn532/mOecM2/mne+7b6x6r+bqYrNJtkyyOWqiScmQKMADvNDCG8P2xvDCKwOGIcP2yhvD\nsiFI8sIWaMGQ4KbIpkh2kz0Vq+rVqzff+969N2/OmTHPEV5cyqDB12hTlvQki98qEEBknsiI82XE\nOef/fZaFZIoE4YpaFlEtjel8imHbaNsjwqwgrjOqoqStjbD6A5I4AM1iPZvT60os1xPe/eBDnj5/\nxmaZoyo2kipyev6E3miLPErYabcQyoTRzh6WZTKbzZBEET9Y4wgdDo/3gft/LGZPKAm9FUajweXF\nBsu2ODg4ZDoZk5YbyjplGV4ShRVqKfOjHx7w7W+dUGQr7GaNJlTkpsTyIiJMS2qhpKwyDKPFbL7k\n5OUphqEjSRK9fo8sy1Hkko+/8z3yoiQMPeqypiwq8rgCUcJxPESpRHFF8qzG1DOy/GpmfzafEwcR\nQRDwo1/+Mh98+C7Pnj3jN77+6ziGyQcffUQh1DjVOwStJwQWwB9/GlDVV6sv13WFqqmgiIRxgpK4\nCHlC4MUc7Qw5vXiMLts0Wh3KMqc12iFan1ORM19PKTKfInApBIX33v+QxWRBaenolkhVSyzmc4Zb\nPdZJxHK+wrIbvPn2XS7PLjjq3WA9n6B0RAzLYXt/wHKxQVFrAt8nTzO293d5fLLEbjZJ8pLrh+8z\n/uQlrdJAMEFSaqzWEL2MKPIEsRYoahlFsynLmtjzKR2FtZSRlBWqquJuXIo4RBEljkdvMGn8Okmd\nIYkCcRpiN00uFpc8Pz3hYG+HvKyQZI0gcen1Wly8fImlmEymM6hzGmaHLaFHVGacnJ3SGw149vyE\nd995g5cnJyhCyXLtgaWzf7z3f9u2b/e2CXwPQzOoUxHVNvn0wVO63RZNp41pqdh24+rYH4LXRgKp\nn7C/PSLKCtxFxJc/+pDPH9zH1jVKMSeva4Is5fqtW4xnFxRik0ZuIeo6PafHbOOx3+/x6OEDFKHi\n8GCPMIjp7e3xB5/cp9/bQagM8qwmExM0RWE6mdB2GswWJ+hawHwypVYaqJpC6EfYlkEUB5RC+sqY\nt1sDnjx9wmCrg2nqWKbO48fP2Bp2GZky8+kL7l67zf3wAbYBBQatLQt3lTAdl+wOZUxHRtiREPKa\nIHN58/g9zs8uefrsczqdDqdnY2RB4jvf/S7D4RajkUAYBeR5haZoRGWMoElkdYkmwGQyodlxSJMZ\nhu7w4sX4aiJPEgg9nxs3b7B3uMdf+It/nr/1P/8tdEnhR770I9y8foysawiqSJKl3J8/Jyhe3Rod\nDQ9fuV+sJKbnGxRRY7i7Q17UkGb0jjpErs/2/j5FUbAJPUIv5Z2f+nGC2CUXC2RTJaNg2G2QFxUn\nTz4nimMMRyOKamoE2u02bhBg6haFErGZr9hu7xAHOcPdXbIsRNcMBFvB9TZkcYBuWpSKgG03Gc8n\nDPZ28f0IUx2wvvTp6i3UKEe1Zaq6RlEMDENBEEVUUacSRFRdoRJkDNvEaLa5DKdUooqgqIiSxjpz\naQ57PFtNMQZNfG9Ju98nLTJ8f42mtrl+7ZjJ4kpvstNpcvL8kjzOeOutt/nke59i2AaaZZCkCXWW\nM9rbwR9ndDpt2i0HP4iwWx1OL6a0mw3WoY8qitSKQqnqHB7f5nvf/D6jvo6ht7lxcIeG1kTRK+I4\nYj6eYpomrvvqa/pH8dpIQBag3WhhlxK+F7CeegiCQZJXJHmAqlr0t3rMVwvcaE2tVDx4lFDVEpOl\ny/61a7iej2la5IHLejLH0m0Wqzl337rH8yen3L1zjOuGVFXK07Mv6Gz1WG9chp0u4xcJjcaQde6R\n5TGiKJBlKYZlstz8gFZZY4vyIOfl5TlUoEgiO9sjPN8jTVIs2+CD4xuIdUheRTx9+pLAj6hE2NnS\nqfICWTWJExfLMDBbAqVYc+v2bX7sx36crAiI44jNZo2m/QRf//pvcHE+pqoEEEpqUUXUVARZpM4B\nCvIsR5UkojgiTUsUVYZKQkRklSzY3d/n4PiYv/23/xfCKKVWKh49+oLZZMzKdWn1e3T7PUrVwdC6\nwB93sd2sX+1WvLO9CxTUcofxxRLfj9nuNVitF1iKTpCmtJot+lsDphOPk5fP0BRAzBmMenz/ex9z\nOOwh2iqr2Yp2t81ivuTu3XucvzinqAt2Dvap4pi97RHPnj5je6/Ps6df8OLlAwa9LeIyYTaeg1iz\nO9rn4uI5FQKIMgUViVhwcHhEV2mzfLzELGUcRyPPE2y7Q1WmoGo0u20EZHzXJwpCJMNif/+Q5bc8\nGsM2K3/J6dklkgztdpcsyYmkDaWQIks1G29BnBWYlklWpGRhQVlLxHnBJ/c/oeUMcRyD+/fvY7ea\nhHFITkWR1wz6PRrtDo1wibve4HkekqFw+vIMQ9MYDvsMWhbBxsewbPqtIeMvXjDotkgqj8NrAxbL\nS4SqRhNltnZ3WYynV+fVaPzQXHxtNYFGo8v5xRhFV+i2OzT0Bv1uH9OwrlRYuz1Cz0OWRRRVIStS\n9reH5HFIXiYE3pyW0yCLA6RKoK5kNkGIrlp4WcL28SG+GPDZ8+/x4uUTPnznfQ6297l78w2KUmTq\nucy9DetlyHLmUpUi3W4HUbqalHwVZpsTFLNG1CRkXSVLMrRKpKlbUKtUlch0fkGapNS5iFAHHO5v\nU8QKuVYhmBIvZhN277S4/U4bUZFYLCdcjM/57LOPWUwusRSNbrNFw3H42te+hqGb1FWJYlcIVkEu\nZchKSVXlqIZCKUiEaUJj4KA1dXKppNFrcf3Wda7fvMGdN+/S6414+vAls4s5rWaTu2/f4pd++ef4\n9/79v8qv/KVf4mtf+xpv3fgSSvHqWsiNm6/uGqwWiysp8DTi3Q/fotUzkSWZshIpJZmD/WvYVosg\nSOn2O8iGRJKklHlCFUYcbG8hVipVpRGUFWlc0m10WM6XOG0Ho+GwXi0QBYVPPvuYMNnwYnaK2XbY\nHQ05+eICU7dRZJGGaXIxHSMoChs/5PqdW0RxQrT0COYesZ9CnKAKgFijICBXIpezAF/sMos00lJl\n467Jooi6yJFLGVUyWYVrVq5LLUkIikxGzai7SxWmmHYDP4zJ0oy6KtG0mrSKSCkoq5LlYg3o5HXC\nxnOpqUjLGFnRqcoERSlRDIEgdjk+PmCzcdnfu06QFXR6HXa2h5xdjhFTAVvvkqYZy8kZRwcDVCVF\nVEs03WCymmF1G8RpwWrh0TcclEqgzF6tCvVH8dpIICOnlmumkzGiDGkWIZUVeZyiViZ1BbbdIY8q\nGo0BYqUxn89xbJ0088iLmMB3Cb2rybVSkOhsb3Pz7lsoDYexu+brv/uPufA2zKIFQeDirZZMLy/Y\nGfX4qa/9LNdvvUe7vU2jNaA16NDsddAMiyp6tRehJEskWUqr6RAXBbNlgGrAi+czmo7AoDdE1jTC\nLMYNfXr9AWG4ZrSn0LBkitTng/eu0W/2KeoKY5CxvW/y0Y+8i61rnJ2d8fTpE779+9/h9OkjsjTg\n3fffpVJz9m+McPoGx2+MEM0StZGjNxTSKmHhBkyma9IqZrjbRdJKkjzmJ3/m52m0Bnzj69+g023z\nC3/2F/jlX/mL3L17h08//ZjZxTkUKc+fPuQ7v/sZo/a9V5636726a5BlOYgilQhf/81/xMG1a2Rl\nwWg4Yrlc0u0OkESZRqODXAuYso4q1PTtLmWYUcU1Tq9HkhZstfrUVQ2iQBRGqJrOejnG91a0R1t0\nt4ag6ORFhWWbRNT0D7qs04gij5i4E86Xl0iaRZTWPPj8Ia3m1UIuTdNxZ0v2+lsYmo4sSpRFTmnI\njI7fRencJk8kVosVpdRD0kwEQQVFoWF1EUQNXTHpdDvYdvvKAk0QmM6muJsVaSWQVuA0ukiqiRsl\nVKJEq93GNA103SItKsI8Yf/6EW4YU4sKSVZTiAWXi5eUYsT/8Q/+IaplMdus6NodilTC0gRG3RZ5\n4nG0v8X+1i5BWnGxWmO1BswmG1abNYHv4voriiLBsQ1CzyPyPBTth6f4ayOB/rBLJeTkZYpp6mRZ\nSuQH9Fo99oZHbA8PWS03NJtt2q0tmnabPEvYGuxy/fguZQGL5YpbN+/RtPoc7+3jjVc8+eQB3/7G\nNzl/8RxvFdM0NcpcIJFz5I6BOWhSGiJPz5+zCVa0t9qM9ncRFYV2t0MpCSzyV9cENhsXQzUQKjjs\nbfHmwRGOqHK402FrYHL9aJvpbE5a5SRZhKLA7u4QsQ453Gty/XqXg8M2w5GGrEg8nZ7j+XPOz56y\nu7fNoL9FVV+tM0iDiG6zgWgIqJaN5wfsHw1ZeytUS74SpeiaICoIok4UlXhBhBstWYVn1EaAoCXE\nmcdsNaXRtHjv3XcQpZo09Wk5Bmno8/1vf4unDx/wZ776Hsd7x68878/v/8Er9yd5jCrJtGWdr7zx\nDvNn50iCjCCBrKj4bgyojMcTQjek5TRYrdc8fXmOn1dIpsXZ5ZyN56OKJpZhcXp6znBrj7oqafV0\ntvdHbLyQwdY+iqpT5CW7+wf4Rco82JBLOecXHm29g1XbKBS0mxpVnSFKIpZjsVwsUSsBuQJdlBHy\ngm6jg5gUiKTU1YZk9YjLJ7+NTEgpSfhpQVFXDPtbWLqJZbfI0piqSkjShDRNEWSZWtQQZR3TbqEY\nBrP1mqwQkFUdEYGyKKgp8EKfRrfLyfkZht0kzSpsp4uXzMmFmFLOePOdW9gti0a7iaaYdBsWdZkh\nIaCpEu5yxsnTZ1w7vkFW56SFzKB3wPn4gsFWlywPcb0V49mE3FLZVDnPL8Y/NBdfX4swihAFha2t\nEZKo0u/30VQVqoIkCXFXC1RRgLxEFyUi18fSGtw6foM0KfA3Pk3dotdvIRgagVBDU8cZNLl5+zpK\nWXNtZ0DPavDO23cxDI0iS1FUhcl6iZ+FeKmPoIhotkaYhKzXHkJZQfZq34Feb0C0CbA1nTBw8dwp\n/ibh1ht7DHYc3HSGIJYYikqr2UXVFXT9Sl49zSOyOqGUPEpxyTS6xFA0/HrJdPqIl+MTPvnkUy4n\nM6IgI4kjYn/G7jWVN780pFA8MH3e/XCPrb2C0aFKQczuPnz4pRGm7THoCgTRGMlKmARP+cb3/3ee\nTT/h2p0+771/i81ySR7XpGHF2dkZH3//Y1RF5Y1bt2l3h9jKqxdOSfqrR0/jZUSwiZhfrjCwaNKk\nqwyos5xrx0e8vLxk9+CQWqjotpucfPEJeeYiywWKLNBu95AkmfV6Qxy7CFLJ4dE2k+kFy8USQTB4\nfnrOcLtLEATohsFyvubJ40dUUkVR52wuN9y78y5ZphKnKfPpBlFQkCX1D2XxEoSipOc0qPKMssqx\n2jaVLFLVFacPvkfmzunaLXaO38OQZZA0nEaftFbZP3wXsbaZrF6gGiobb4OqGSwWc0RZBkXBaWmU\nVcX5eE5aidw8ehM5Slgtx1Bn2IbKoL3FdLxBlFSabRNJySnLkiiqyMuK+w8eEmc+gpQxmZ1QVx6q\nIoHQQG/02AQVfpAiFQnp4oLE80iCEEm+Mr3VLANRlSmQuJgukZ0mmSSxf/vmD83F10YCsgg7wxGX\nl2PWmwV+4GIYMjUFjqNTlAlv3n2TWihQNQnD1PHWLg8ffoYk5ty8eZ3t/R3CbE2r18J2bPZ3t7Es\ng1bDYHu3z/sf3CEvUt5+7xZZnpJEMb4X0Gt1CcKQoMqZhivc2KM37PHo6RNqQUAw1FfGbJoWYZJC\nVaKJGl/96s9z5+49wsxDUhzcMESocw72h9RkxNmGovZoNGRsQ0dTJMIgIIoyjEYTVRMZpx5iS2cV\nevyZn/oJjo/2ODzcY//WDlKvYO6+hKqkrmWaTpPJfIrrVjhdi0wKsHoGfrHB7GjUasW9e29gqDqH\n2wdoUsXzx39AsD5jMb/k1/7+r/F//qOvk1Ul3a0u/8ZP/jir9YLvfOdb/P2/9/f4/JM/3hYFaLdf\nJT8Ko0aTwk84vnGNuAwJiw22ZGDmJuPzM3a3ekSuh+suWLlLHjw5Zff6m2SIVKJELdbIgkjXdJAl\njaJSWM4DfC9EkU0Cf00YJ1yu58RVjdMf0hqMeH62YrFJGB4cEFHybPICzWzQb/eRZJ1mu4uu20ii\ngiJr2KqFVEmooowoXKkY1UKFY2jUssXtL/08B1/9y+ze+1mU9h6O1qE5Oubw9tf4ya/+2yRzjTu3\nbzA9n9EyWixnK/pbW+i6SRBGFHlOjUC33+LG7i7TySW1oSKqCmlZkNVX5mk3b93GMHVc/5Ial1qK\naLUtxmMPxzbJspSz8xMaDYOFt8aLXMyGTZSkWOYVqX30/o8SuwFbzR4qsNnMGO5uMVvPidKYXABR\nk6+Id9BnvXm1w/YfxWsjAU3QWUynyAooqkjTsWi1m6RphOetSOKAukqZzMesF3O2t0fYjsV48oKy\nTnB9l7PLlxiOQSZkIJVYtka375DmPq2OgSDl3L63z29/8zf58IMP6HS7yJJEXhXYtk3XdGiJOmUQ\nkbs+7995i/evf8i9vbdeGbMkSgjUNO0G/+Yv/hXWi4BpeIIoFyAbLDYuaZ5wfn6O03Coy5qtwQjD\ncNjuDnEMC0XSCSLI6pJaqen1GlAmvHXzNq2Gid20yYqQz5+c4IkBgsyVIaeoohgyTsdh/9ouklZj\nNEt2b/VQmjXNHY2k2pCXEUWWo8qg6wVhfE5hztF2alb5ivsPH+K024RhyOX0goODPXb2d7l+tP0D\nB0varVfvr02duM45m11SijX93Q65FjN/uKInbNHvNHn+/AtuHR1SAv3RiI0XoVkGcZ7j5ymSqqBl\nFUWaUJclsqyyt7fPYjVD1nSyrKZttrmxf4xay6zPx8jJmv2OQ7heMmp12e50CHwXy2qyO9rB2/gU\neUWS1EiCRhKkWKqNKuvIkoJQCxiKgqJJbB3eRDG6KMYOjdE9du79DP0bH9HuXsMwFGxR5m/8J/89\n64ciFAVxGNPUHZ6/eEav18G0DYq6YuOuCf2Q6WRJmZecX16yCSJa7RZnZ0sUVSQKM8I44vx8RlWD\nooiURYFhCyRZThCssW2DLIsR1Zq0SnETF89d02n3cJoO49Wc47ffIlY0Ckq2el1QK9wipZLFK53B\nbhOjbZKTILx6xOP/gddGAoIk0h60CLII19tQFjmPnj1hHYasg5CL6YTzi5eMtrdxNysiz0XXVTrt\nHq1mm36/j2UaqIqIINXkFKBKLN01jVYL09SpqpSiTjg8PuDj+99HU0UOt3cYiBqyH9O2LPzQQ5Vk\nOg2LYHyKmWb89Ft/7pUxR8mCN27eYKd3k4sXz+n1NATRpPArHNmhSgX8MkOSNfpOi+vXjlktPcIg\nRTVEDAvsnsDB9TZ6GTPsaLQsiYyMWkxYLsdEccjt23f46a/9JM1GC1VpUucid+/uICoynV6TZktn\na9RBVWWWF+dc3+ty1O0x7CjceeMYTa7pdVqkWc1mHbCz36XQVmTyhltvHfP13/gmqC0uZjNOnz1m\nOn5BUaQcHuy98rwN89WvA4pucHT7DqUIyBKT2Yr1ZsaHP3Ob8ycPGH9yjqxbREXMzmCHWjGZbFY0\nWwOaTpfML5BrjcTQkBURVRFYrl3Wyw2yLBGkOTv7gytruDzA0FTS5ZpBewtdb1JVJrJkEkcxh0f7\ndEd9REGm2+oSBQE7WweEXsaou41SCxRZjmVaSIKIWNfUQs3h8TFiBWJdIkkSum6jGi1qQURAQBQV\nBs6Q//hX/1sG1ttYdoM4jBAFuJhc0O628TcRDUchyxM8P6LbahFEBY3uFgvXZTAUuHX8DptgzHq9\n5OBgj2a7S1ZJZLVAd3vIZONSaAqZoJFkNZVS0xi2aPY7CKLAyYsTwjjkxfkzVssV82DKKjnjfP2Y\n08szFMNE0k0kXcXsWLhliKDUKNWrX23/KF7bnIBuZmSpyMHBAWEQkggVlaHQbreYL+aMrh3w8PkJ\nH779IeOXE/q3tlhvlpR1RafTAyDOfLz1hLal4McunhThBx5pomNb9pUwZeSTJgFFVuOFCY2+QxgF\nvPf+O3z22ec4skYuVjw+O6EhGXTDBfGz3+TnXhGzKsFmvuTwjbuUxYydrRGX0wlC7xoXL8aQ5fRb\nBtf2dlAlAd8Pqeqc5XpzpfZS5iRpjYjGqN0liGJaTZVMKDlzPyHPK0xzj9/6zje5dmNAY1Sz8hd8\n9Wd+gkenv8dqNYGihLqm1Wnj6wbDnS0SqSYh4I17NyiVgOt3B+xe22Hmb3jrg/cYnz/gjYM+thmw\n3DwjciuanUN6nRvotYDTbpEXFWcXJ6+8VlL6ar21v/NfPnvl/v/xn2z8b3/Cm+IP8d1/usP+GeFV\n/4sS8CHwe3+iT3r6R7a/8U8f0D93CHVd//BG4j/rLxUE/vp/t4PjHOJ6EbUiojomk8kleVHQb3co\nRBFLtthuDsgTKOuY+fKUwXCbJLsSpJzMTtE0meVyyvXDe6RRyf7+Hit/RZbGpGWAIsnEXkqUJHTb\nQ2ytRRlHRHGIJMvUtUCepchiRq+5xXy6QjIM/sZ/+Ce74P9/xn/9N/8t/qN/5+++7jD+FP+fIfCq\ndH993YFKZR2HDA+3EaQKRchpOApmWXHY7HC7vcee7ZAGc9JiflXI832SNMQL1sRJwt7OdZK4ZDTa\nZz6/pD9ocHF+hlgIaIpFnYo4apvMh/3+EXIlUJcRcRFT1gVyDUKaYooaVW5wcnlJoZb40avVhv91\nxWYzRZJe3Tb9U/yrj9f2OtBtHoEgsZquoKip0pSmaVFaKZsowDEgLzPWK5e0XmGaIxynC6VIt9Fm\nsZjzfLGg2WiSxhWG2eLxp0+RFYOiLCmyDaZh4a3WNBsOZZkTJ2vCJMHQDVRJIMsrWt0uYRiSVS69\nfoOzizF1rZLLAkrxL/wh6V865JJIUihcf/M+jz59/3WH86f454DXRgKKIrKaLTEMGVvTsByd8XTM\n9taIJKnx8iVJVKHYJvHGpSoqRDmkLASCTU6Z5twa7rI9PKBpD7lcX3Dt7hDJ6vJbH3+dwdYusqRd\nKfKoAlla0m52cf05s9kKq9EgjXzqGtYLj91rR9x/+tmVzFaU8MmhzAdPXz05+K8TxncOuTG4wV/6\na/8Nf/d/+g84efQOZfH/ouT8p/hXBq+tJvCf/s2fpWE0WPtLongDYsbG3WA2bFTLQpVF4iBFkTUC\ndwNChWO2UbQms/klolxTRCFvHO4z6h7wvScPcVeX6IaF4ajMFz4CMo1Wk0bLJg4WyJJAmdcMt/Y4\nOz+lomA1X3H9+m0ePT9j5k7YO+jTbTTR1gl/9X8Yc/ckRyn+Rf9Crx+FLDF785Df/+t/gaUUEFcr\nMjXFi12WmylHx/ts1i6BH6JoGk6jy3SyQjVlirDkjVu3OV09JggDyrxGlnWiIKPX7FMUGStvTaXI\nWLrOZHxBEidsdfo0nRYb18N2mpRCTV4lFGTIpUyZ1BRpTqvVwm40eH7yHMOQqOocy3AgFxCpqVUd\nvywJ1msOD444tLu0CpWGZlBEIbaqkxjd9U8AACAASURBVCUptmaRFwVxmiCrCrliMVluSAWD4fF7\ntNs7nL+8zzxckykBT6cP0doqi80S92KKoNS0Ow5hkaPoOnmRkZX51VRmkuGuE37ko4+YnL9AF1U2\nQYDd72DaAl1rxO/99u+yddimqHI81+X28Zs4TgvXXTA+O6Fpm/y5n/tl/sGv/0PKOsM0ZZabC/JS\n5nyxYdTpIgP97W0upxO8Iub24U1mp4+4cf02H9+/T7frEHgJjYbJ//qfTV5ZE3htJPBf/J1fQBU1\ncjIm05c4DQ1N1QmzlLXno0oi/d4WvhcRJiEIKaEf0+/vE0UhtZBS1AIHnQF5WCA3DcLYJU9TirzE\nsUxEQUOQFOaLKU5TpSxTZPFKXLMqQZU1ijIn8GOyrMRPlzR7Ojt7QxbrFf4qwFRUGrpJGsq02m2W\niwXIEo6qcdgd8Y1v/w77x9d5dHrBaGub2fkFo4MBlZhTS1eKO81Wn0cPH9LvtZDynM3lHFEyePvu\nu9y/fIyuK0TuCqc5QJYa5HmJ77uUZY5lWQThBsexuRxPsWyLXnebjXtJw5GpBAWj0WI5uaRMUqRc\nRm81UXSL9XTJaHuXlbek1+tgaSanX/wBH334VV56AX60RqollEpFylSESmO4dUWyhuyQejmqJGPZ\nCqopM/PHNAddnr94TrvXIQg21HWJJKvs7h3x8IvHHOzvsVpOMC2Ty9UCwzCIowhBFBFQQZDI4xzf\n9Wi0m7RaTV48O6HfbmFKJpSgqDKSIHGxuMTqO0zdFZZsICUVqqqhGjqaouL5Ee1Og8dPHmBbDfIo\nx7QMtnb2SXOBLI+pypKb/W26tU0ZBTiGiS5KqIpKVf4TJWyBoqpJqwpJgi8mY4TBiKwUmC1OqRsK\n4/k5hiMji1eCs9FYoHIELpcT3v/gHtPxBNuxWQUeuqmzWW2QKokqzxm0e6RFSqmAaphkeYBYGjQM\niUrOKcqSF6envP3WB1xOJtR1hiYJzGZj7h6/xXRxQaOrc3p6BoqMKMNiGVIVIlv9Bvvbuzx+/AC5\nJZNmBUKaoRoNDE2nKmH8cs4v/uIv8J//0q/9S1YYDAKKJCHYbGjaOtPzF7jTJW3FpG836VkNqiDm\nzuExDdNBlMByVEZbXUxVoWFYVFVGlstUms5kekbgrymqHMfSUBUZSQJJrJFkCLwNqiKSZjG1WNKw\ndYrIx1JkHFVDznIGTgt/6SFLJllcY+smugx5EiBRkIQBiiigCzLRPCCY+/z4hz9GmWZ8+b13aDsO\nu1v7FFGOJilogsr0tCBeiRy0b9DShjSaPQqtZhl5vJydoCglq9WVeGSah5SVjyiltDstTNNE1VSa\nzQ7L5YrBsIGqVWRZxHC4y3Q6RVHAdZfkVDjdLuswIKsKoGZwfZuTxQtuv3ObWTDndDOldTjgfPOU\nwD/HdRcUeYCqZ2h2hZ/NcYs1eZ3y/MUJd967x0q6ZCW4zKIZWRmxXm+QRYubx3cJ/Jgw8jBMk0eP\nHtHtdrGULqKo8+DZQxynQxKXVLVMUQCCjKYYqIqGqel0NIvCTxm1B5iVBmXNcrMgTWPWqzmiCE9f\nPKVj2SiImIqOqMiEWczp5CXr9YLI96Gs2N/ZwW7YiKLIejFnt9Nh8vIljqKRxRl1liMKMpZhkuVX\nakWVLIImgyxjGjqmWqHUGW1H4fTsYy7dTwnqS9buOXoDyiqj1RigpRpWT0cRK95+8wbf/9Z30GWF\nzXyNJmqUYXklzqpIGI7NOnSxuk0Mw8RdrcmzP5RrVwXKLKXlNDna32c2v0SWa8oqIU0LHNvh7Pw5\nHadB6AoEuUVYaFQodBwNP0pY+AFZrONPKoJ5ThrllLJCWFw5UYlI3HvrDZ4+efwDc/G1kYDnumTE\nCGWKgcaN4Rsc9o/JvBq1kFGRUASBp4++wNIEdFFGrES8zQzTkKnrBKXKQXLxggmiLCIKYIkiTd3B\nViyKJMbRFIJgwTo8Y+6e0GxqyDXkUUyn1aIp65Shy6Bv8fbxB+xuX8cUu0iZAElIlQoYWpvR7jaZ\nm5CHEenCRSlE1m5IGubkccBqeU5ZbPixL9/j1u4ODjqZF/DGdZvpycdIcsBy9gxVLilJee+jj1Bs\nGyH12O73KIqaWpNZeAvOzp/x5NEnUMboIrQ7Dr3eHrUogxjRaIkszp5w/eA6gqCR5AmW3aB0K778\n/o9iyjbjl5dMHj3ggzt3ODt9gWXYKCKUgsmj8zGbaEG3a+FtAqoqohR9esOr1ZutZo/dw338IqG1\nvc13P/0W6+gC1TLJ05h+u4u3DtFNG7s34GTygkIqmU+nTOcvCGOfsoaIjFzIGHTbqLqEotbEmYcX\nzdFMlSCKycuUOI/R2i0uvRmxsCISPZ4tzplvNvy7f/5XCc4uGD85pchjBMkiimtk22Rdhlws59ia\nweTF9Mp4Jk04mz7nuw9+h6pw8RczsjgnrhIcWyPJs6vFPaJMmVdURYmiKGRVhaI6mHafpmLRszSC\nYEGQZHRaNkohYggG4WZNZ7tHHOeMtkZsZlOuH99EqAQM1cDSnT9cCekhJBEyGY6lQ1mRJQldp0lL\ndRByn6IqcfOMmJxaklltQlRFwbZsSrHANNs4rRHXjt/GEDVMxeDuB19BUEeEmFxe1qRpycuLEyRH\n5Nb1bWzBYHwSE8UCURKSRCmXzx7Rab96XQi8RhK4uX+HhtJHo4UutZlfemR5haKp1LWAoTeQRB1F\nUMiiHE2xsZQm1CJFVaCIErZtIsoCiirimAZ5njPdrInLhEzI6Q46zGcLjnZv0JCvEU+b1LnFJryk\nFD1KMSKsIgY7WywmC7744j51WYKQ02xoiKKKqeuUec5muqBtNFFEC6vboW6o5GbN6fwUUReZLZec\nTc/57c9+n2f+hEUZIjWbTNyQTZYi6gp60+H0/ILeYB/P3XA5OcePC84mczS7SZVXpEGCrmvcOn6T\noqqYbk4JkiWKmVFUKaPta4RRjGaZ9DojDg+O2Nk5wnYsSiEjLVKC2OXt92+wc7xPnAeomkCSJQgy\nJHnI7nDEaLBP5KkMtw6YjFN8L0ISVLIwYXwxpddt4rtTTp48ZWe0gy46hOuay6nPi4sL7j/7lLm/\nYOVtqApI4hjZkHDjDYVQMeiNEIIKKawQC4EojimKmsnlEt3qICo6eV1RVTWyqOH5Ea3uDprZYeMn\n2I027V6PX/+t3+KnfvJX6PX3MPUReZZTlxWxnzHs7aAqFnku4dgdZpsNSZHjezEUImGU4schF9ML\ndMshKhVEzaQWZSRFoSgTBOFKal3XNKoiR5JF2p0uVSWQFAUZFWezS4oiJ/TWlEXGi2ePsMyKIFgg\nlAL+eoEo1n94H2qUUUjL0oljH0US2Rp2OXl6n2HPwm7I1EKI03bwXBdD08mjlDjwefzFCxAEVusl\naVbx9rvv0Oq0ePjFU5pGk2D+ksK94PGnnyIGGn/lZ3+cr977Mvu3b7J36w6bVEfrGexf75G5GVUt\nsloG3Lj3YzhK7wfm4uvTE9gsqCMXSShIvICP3n0fVTIQUXDsNmlSIks6qmYTujlCqkAqkgQJAFGS\nkKYRfuBSVQXeakW/1cFpNVAtjZOLp2yiDY2Wzmo6x1IcdBpEbord6uJnETkxolJwfv6Mhmbh6Aa9\nRpvVZsFyvSArIoIooPBCgpnLqigY3rpOpEoYgwZy2yARwWq1iYscxbBYhx6yIgAFSl3hqDqDVpek\nLHCDEFGV6LT7zOcLijKhFiHLYTDcRkag222y2xvRtiSalkJva8gyWBPWU6y2TV0K1GXC1rCHqVvM\nLxckUYogCLSbFqJQsL2zhR9vCKINTsOiLAqSJCUrIooypyxE3LWPqcLFs3MORz3aRgdDNmm3+gxH\nfaaLMxATBr0G/WaHzWwDaYJhCnR2bMabM/Ruk8nKR1FtqMDQBUohp6hhsvKpZZlVErNY+/TaIwQU\nNNVCUZukxZU7UI5IJsikZU1eS4haE6sxoDfcpxIUkgLuf/oQW3fIijU6Ch2rgyYZiIJCq9nGD1Ke\nnJ6gWwalWNHu9giiBFU12XgbwjSgqkUURUGUVWpRRlQN2p0uiqoiCDJFUaFqOmUBLbtLWYAgqNRS\nRVqVCIrM7HKMVBeoYs1qOqZpmZRxSZh4GKYCdU4RePQsg4amIAk1URIwW1ximjIvnz/CVGs8b0a/\n12I0GrGeL4n9ALGCr3z5Lb744gG6rhMkPk9OnxOkOWPvlAvvCU7L4uNvfo/rh/tUicvpyWO+/d3f\n4+mj73J++jl1fk7uxZgV3N7vEYYJpaKhmwIff++zH5iLr48EypyyKlkFLidnD1l556z9KZvNgjBw\nKauMIFhSxgm2ZaNoKmkWE3geZVlSiDVZHlPmOZIoUckCmyRCMwyenzxlb+eI+XzN2l2iKCJpMmN0\noBAl58jV1Zz4Jg6Zex5Ht95Baw/YlCmlKOBFEZrZR0InSzPcMiPWUwJ9yefnn1A74LOh0nwUU2Dj\n+9iNBkWeM+o1ydINcRpQFSl57aM2SsJoSp4HNJtN3HVOlqUUhUCzOaLb6/PoySMkWcSwDdaxyzzw\nCYqErApRlAKhFJCqnKTIkTWDs/mU6XpJ03FoOhZ5kmL0m0yDGWZDp9vqI0iwjn3cfIlipCgyNCwN\nQcvRLRVBq7n9pUPEnsWz8Qv8eEOWxqyna3rNHkJRY2smVqNNLZvcvPMmRzeuIcoC26MtHFlCEwuy\nPCCjZLba0B8N8IOAptOmrDS29q8xj1yCyKWqM2xHR1FFgtSnEGrKGFqKg+nYeIGHpCrkpYgbrBFl\nWK5n6Fs25+tLvLIkKOeUakiSbJDyksj1kRWZMAsRhJosSEnjEqFUkUsVSbiqA+mGRXtwgGn1MBQT\nMS1YjycIWYqQxYhiQSnkZGmEu1pxc3CAFlf0dAs5Kbk8O6fTG3D/yQM2xQJRFVkuF/R22vSbTYLA\n53xywXj2kqTwqYUUQavIEp/UdbEVHdO0WHlLcqHg5OyEjetiWzb+2qcsa4qy5qMvfQVZsFhNI/Y7\nI/qmzfHRAcs4QbJMdKVmd6tFb2uHhVQyrTMsU2ZnW6Fjaww7JmIWE8dzLEGBMONkMkWzf3Bb9/Wt\nItSb3LrxJYbtbW69+TZ+VlDJNe1+hySL8AKPJHeRNZlWs0WSBBwfb4NUcXF6yqDZRxGu2NbQKxqW\nTeCuyaOA4WCLh48e4TgWm8QlEDZkVkpQegxGA2pJQNEVTNPC0A0+f/AHrOJzmkOZgoCyKq4eD6sS\no9XEaFgs/AhR0pBEWCzmPHl5wSbzcNOAyWJDp9NC1UUWqxUINY1Gm4nncuGt2MQ+snTlHfDk8Rmd\nrkW706LX79J0THzPR6pFkjwjzTI6/Q5u5DNbu2h2CzfM0LQGRVxRpimqpjLsdxDUgqcvHxB4YwY9\nB0WScCyN5XxGnicYtYa/9HA0B1NUaelNLMNCruUr67FOmzSLWa2n7B8f8ejZEz558CnbB1ucnJ+w\n9ldkWYq7WrO7s0deXOk4vHx5hpTK3N69zVFnj4Ygkacx5laHj59/jFf7+MUaSRHJk5jBcEAURLiu\nS6fTYbUco+sqoqgTZwnrYI0oi3iRx2blopoqbuiTZDFOu4UfbHj3S+9i2Ba1JLJerxAQOTo+wmo4\ntAddnE6HKM1QdJPZbEm71cLs7fP2+3+ZX/1r/xXbN76C2d5BcbZojY6xd67T2L5B5G14+vu/DYsx\nVZEiUhGnAZogYiEyOXnJdrNFv9li1Oow6g5wpzFSLZAnKVWSIgkSy8sptmngRwmSpvFyPKYuJQ72\nr6FJOkUpoGsWm41P22mShynz2ZSearA97NNo2PTbDpcvXkJRce1wh6ODY9brFVUCYqFjSBary5LA\nLVhvYt44bvHhbZ3DLRXHsBgObuEYLUzTQSk6aKKKpUtcHx3S7XR/YC6+NhJ4efY59x/9DqUcExc+\nLzcvyeqE6fqSXMgopJS4iEGtKaqKLA158OR7pKWPqsgEsw1ipjM/X7O6XFFmPi3HRFdFjo4O2R7t\nUxY1nc4AVW7QaW2jaDbrICSra6I0Y71cUlY17V4HyRCJ8pjZes6wv8PZ2YJKEJEUGd00MS2bPBRo\nNDr0+232tvsoZZN7N3+ED957h6q8UgOWFAnHdtBVja1eFwWRMq+JooRWs8Ng0GN8cUFWRMSRTxbE\n7HT7vHnjFjICaZJg6joVJds7+zx5OiavdWpMSFUMWUamoCwTijqjpMLRTZaXY4rEp9NpoGoCcRRQ\nBDlfffsrOIJFz+kRexmL6YbVakOe5ghIaKpJXYiMFzO2dnfZO9rjs0efk5NTiSVxnGA7Nufn58iq\ngqRcuQFtwjEPX3zMIrigqEN+/qe/ipbHaKXK8c4R/UYfL5gSpy5RuKaoU46OjplMLglDl8V8jmk4\n6JZBu9fk8clD/i/m3uTZtu2q0/tWXe61dr33Ke6p7j23fIWuJIQgbQwiBcaZyAS2cdAg1CHsMD16\n+gtAEdgN3KBtBR3hCIeRQiEqJ8iZ6IGeJPT0ylufe6pdl6uulxuXJBt+L51hO0K5umuuEbEaY8Qc\nc47f92s4FlsvoNvr02g2aQ36KKZOJmZcz89Z+nNW/ooHDx6gaxovLy5IywLdtigFgfZwn7QWuXX3\ndeJA4Fc//0X+s+MTjoQAN91iCwKWriPqGqKqUYkaudMgXq74wZ99i3w9JxMKdE1CIUajAlkkFSVU\nQ0PKS5RcYq+1SxllFFGCXAusZnOG3T5lkiMqKh89P2O4d4ghGcwuZ6R+Sstpg6AgolBlFZokYskq\nO6aNlRVkwZaXL57ScV2qPKPX7/Fn3/xzTNOk23LZ322hihX/7X/9BYpswdF+g2IDenaCJt7EbewR\nxzFOs8lgKPLgQYfdwZA3T++Tbbb0ex/vLA0/SQeiumA2foHb6VBIBZpcE8Y+WZFjmg5RliLUCoIo\nMLk+B6kkiWtyAcgqQs+nrjWkxCYRRdIqQNcFyrLg2bMz/GBBw7FI0xTTtOjaLpvtilIRkDDRFANN\n0wm2OYOdHudXj4jjiGbLJfDWuG2Jpu2SZwUr3ycqU1p6AbXE9OoCVSxQbZdgc/mKJGTaiBXEVQi1\niFKXGIaOrQwospy1H+IvtzSbbaoqQ5dMbhwdcvniJYIkslguUVQDxbSYhzFeVpL7Id1WFy/2mM9H\nHPWG5EQI5avrS6GS2RscEkc+LadLXmZEUUZZQ+ylyJLGdHVJVUW09DbtnRZ5UeP5a4yGibddIOkK\nSDktS8XSDcaXM+xmA8s2WS5XtKwGvh/Q6rS5XozI2dBt68R1xfVywa3TN0m3K/7m//hbbFXFFF2y\nqOLl1YQsiTg5PeG1O/f43g9+wGg8wfN80iSh1999ZRNeJEh5jWFLWIZC7/SQxeyKvW6PzXaJocvU\ndYVumGhxheO0EWWFIAwY7N4gSjJazRaPHj+iKvrs9weoWo/X33gdqyhIL97jyvcxTAfJtChkg0rV\nkBUDuQyIxj6hZOH5ExYvLmnfM6glFdceQKZh2DppHSKWNobVpF1KeJ5Pr71LFG9ZL+doik4UBAh5\nSpbXqIhk4atW1dIsBKUCwDQN/CQgCCMcp4Hn+bxcjIiLFMU20ROJzXxG023RNNqcVY8xG7t88Py7\n1OIGy9R5OX1Oc8emyHUG/T1sq0EUJBRVgL/d0LRsZFHi2dNzksrl4cNbBJs5i+X/nSL9b5+f2E6g\n2epgmg51BXlaUJcVsgT9doeO3eKN1z6N3RxyPl3jJa9MFjRJw1ANeoMeqqWiCwKO5nDj9BTTbSGL\nKg2jiVRr7A1uQK6gSzpimRKHHmVeoNU2ciFjyBaGanL79JAsD6mqGrGqMTUTb53gqF3a1gA5lzga\n3GDgujRtBales9t3SKMKVTKJwpgoCpFl6RWAsoS23WF3Z8jaX5MkGYoqoysaDdvCNnUMU0dWdSRZ\nZXdnh3Dt0dddTro7aBlYlYyjtdjrHJBschqKQb/dIs4CVE0mTTOqHIS8IA08eu0eQqXRauwT+jK6\naCILOpJmMV6OQBXwgpBVMEHWUxy7SxUrtJtd8ugVeONwf5+iyHFbDbp9F1WV6PTbaJZGnCUEiU8t\nZdR1BXWNUEj0LAc1FdksPHZ2dtlEKZahsZxNuHWwy8nxkMvL53z3+//6le4+imm2Wmw3Cb4XImkS\nSZUQpjFus0WaJER+iKYoLJZjKiHFjxdswgWz9ZhKjpj7S7wootPvIikCzWYDVZZpORbz8QRJkumY\nNg1dRzcaSFYDwVIIwwXnH/0Di2fvkSwmjM+fMR2PsXWFRrPPxhe5Opvgr8YEgU+cROz39zCkFpOL\nBXeOTpnM5/i+h21rFEWObhiEQYiuaNiGhano3Gg5HLX7lOuIpuEQhhFZmfN3b/0Dvu+hywoCr3iV\nWRSTljlZliHWApouUdYxUegjCyX7Bzd49NFTJNWmLFREbIrcJQ51EAzCKEaWdRbXE9I4YjqacXJw\nm9l4Q2/Yodlt4qcJumnQH7Q/MRd/coakukOaVAiCgCzJSILMoNsjXvtEVzO8l1eIkc8b9+6TFgWX\nV9d4iw3JdsNmMcds2Fgdlf5Ao9/WkNOYIoxoGBpSWbHT3cUQXOpApfIVkkgh8qBvd7Brh3AREoQ+\nq+0EWao4PDhAVmTyKqckoEpi9Fyga7RoGS1u37iHWdj44y1aLXF4eMDB0Ql1JWAYxivfN0HhoNnn\nRv+QZ8+vGHYGWKaJ6dgokkan08E0TQqhwm42WARrPhidsc4iFv6K6XLB4fERpm2wu+PiuiKnp/s4\npvqPk4sWeVgg5CKUIg3TottsE2xWiFWNIahIZQJphVBW6K5OUmaUQo7halRKzWQyZ9jpYCsGuqCx\n19/HbTQ5Px/jeUtEqWY8HpHlCYvFBN1WkTWJVsd+5YuQhgi1gDfz6Td6yCHcP/kUy5UHRkkpFDx8\n7T57zRZKrVLVMlqjyTaMcW2by4sLTu/cpdHrcHn9gryIMJo2s7WHKMtIqowsq6R5xdVkzibMKQUd\npzWglmUQBc6nY6x2iziLEYScxXyEZRvs7+9R1tDrd2i5LrrWptE7otG9iWb30TWHPAU/yNHdJnVe\nM72av1KeihqXL0fImY4tG/irLUWakwYB/Y7Fi6c/RpYKTEvB6baIkzV5VmLobW4eP4BaRlUNZEGj\n1ehyY2ePPI2RFRmn2+X4dIihqOilxG57QBZEHB8cUpUlvV6XsirJKDFsFSFPuHzyA7J0jd7RqAoF\nS2tDqZAnAkVWsPZGRPGWs+dn7O/vIRYVD2494Mdvv49j9tgEGc1BD8O0ePTkKVbjP8KDwThJKAWB\nbRwRphmG1eC73/8+H7x8ztnogsvrS2S5YLH9gOENh72be7R3D7CaXdJ/hIWIjkJhx0z9EV7lI7Vq\ngszjYP8G3sLjeHhM2+iSbDPKpMJQGkxGHl7sI6oSSZyhK22EwqbOakxdpmmZnO7eQa5UIj9DEhU0\nU+N8dIYolty5/QaUOsEmx1/FOK7BarUkL2KshkpUyczCDREhF7MxqiRx0NxFlEPS1KffGxB4AduV\nz2w0od8ZIIgyeyen2JbL+fkVSVqjiBLPnjxiu12iaypxmOE0Wmi4mGqb05uvocgm680aRZWwLJXp\ndMHhwSlpETLoDjke3CRLY4q8IK8rBFHF7vR4cX1GKedUQs1sdIWj6TSNNpbYxtZUDg6HJGXOnTv3\n2XgeSRVjWjLHe3uIMsy8LY22C7WMaatcXD5D1VTu3L1LWZc8fvyEi9ElpqzTtZs0VQchirHdBr2d\nXZyeSybVNFyHe3cfsJ6vsXUDWZLodruMRyOOj2+hSRpJWLCZJESLjGyVI0Q5jqyxmixouU38yGcR\nLegdDEnLDE2E88fPqdKEIl0TBwHLxYY4zcjCnFzRKS0LbxswmUzYbhZMX87ZeDWRqPP2O+/z/vN3\nuB4/pZIyyrpgHURsoy1BmmG32iRRSqdrE4cbfH/Bd/7Vt1nOJgi1SBIVrDZbzs8nHO3fBKFktjin\nKCUMWyWVM8aLKVs/RFAkdNPm7OKSWhJpNptsgxVTb4bSspmuX0LpI6sVaVlRqjJZrWDpHcRE4+zi\nBaIF8zRhs9E52X+DBw9u44Upu+1jnr71EdnSp9PpEmxmn5iLPznzkVYDvWUTZglBGJIXNXuHRzQP\n+tCUaR3ucb3xWIdrbMNgu1xDLeC2dnHdXaTaIFpFuFYLoZBxnS6a3KDj9pjP57gNg8vzZ1DVuI0e\nzUaLTrdLb9hDFOHgcI9ev4VtG8hqRcPs4Eqn+BMDqZbZHQ7RNJnQCxhdXmJpr0aRV7M1umBx5/Ae\n4TZBAjTNJAoziqyk199h662RJZGTg1skSYof+zitBpqus1gsONg/oOm2iAIPXZXpdFq8OHtKmka4\nDZvVckpVV1imjVDDdrVFFQQ0UUU3FCzb4NnZU86unlKKNZVcU4oliikSZXPsto5AymJ2jVy5dO2b\nFLFAuIkog5Ret89qsSJLEnRVZDaaIFQF3Y5LVaZsvBWKqZIWIetogtOR2CQLfvTR+9SaQlEJZOTM\n1ltSYBlfESRrNquYooBSFFBlnRePHjEwHWo/pdmwMQyDnIyr8WOCzTWb+YwP3nsfx7IJgy2GY/PW\nD95mE8VstwGSqDLoDbj35uv093Yx7SbDnX1006DhWoxmU/rDPdIiJylzsjylRqDnGlTrS/LJM1aX\nj0mjFXldkBsaV9cXpGFIo9Vhk+Y8ny5ZAqEjMldlXqYBP1xc8Dwfs6zWKLrEdBUiaQo5EQvvmrl/\nzsvFEl+p2L9/itO26A7b5HXMJtzSaNrYrsX51TMGuy3EOuEzn7rDZHFOUoWoOty7f0ia+pwc72Nb\nJkWaMJnPKOQKtWeSCzretsJxDPI8ptnsECUZvcEOSVygKjJ3795jfD2jbzj81Bu3efnsH7h8/oL9\nXp8yL3j48C6UIfOrEQSfrIL7iR0M1kWCY+vIyoCiyOh1e5ihi5WtkJyIKFvRdhvMz9foOzG7Ozvk\nqYyfBdi2hqaKOIYLgYCrqyR5SvpTLQAAIABJREFUQaulo0kaoiwSBiGDHRdJlKmXORfXTzi98wBF\nMXEcF8/bIogVy80FlDV6rdLtulSlSCHF2LpCLMgsV2tcUaeqIQkKTvaPGM+29DuHzKdLDFUkz0WS\nJKQsa/Z7fa6vn9C0G+x0dsjDiHW4IElTXMshCONXwhZRwW22CEMfXVMwdZU0CJnMxyiWwWKxoGFb\nhFGCKinUeUkexVRlgWpopEmCoGkkRUaVVGyWIVUho1cScbwlLSJa3R0aDQcxr2hoLUzJ4vL8itP9\n+0RuhGaoCGXNcjvC1CxEAQRJYDafs3viEKQBJRVZXqKaCrGWYRttdvdvsV1vGQyHLLczjKGCIwyp\nS5UsKzk+POXqxSWKriHqCuvlmo5mksRbNtstpi7yc5/9OV6OFsxWE+IoRtV0lqsVneGQ6XSKpGjo\nuolpNMjygoW3xDI07FaXv3vrb/mFX/w8q82WII1o9TvkRYEgiOia9mrWYTKjoMLfrKgqiRwFURFJ\n0wJ/tkS1m7z77AXbxRrJVZH7Nu89Hr3iTUoCu5pOaUTkhYdpyNiGwyLbYigwX2w5PL5HlMZklYyg\ngNmw8IuQ7m6X+WaJLIKsVsxmL+nvtoizMZKSIUgOliGxWE3p9nqcPXvM3qDNfLVCCgr0tk6MzMnJ\nLXRV5Xo0RhQksuyVUCtaeURpxN7eHnmU8fD0NV48ecyLMkRVdVqdDkGW49oa6+01a8+j2e+wDsNP\nzMWf2E5g4a+ohQyBFLuh8OjRO2zDEG8b4gU1VZHjz9bc2b9Dui7YLnwUpSSOFxRVQhwGhEGCrJl8\n8P4j9vv7rGdbshBM1WU2XZNkGdPFGLdrc3r7hNDf4tg2tu2y2qzYhCuyPKWoK+LaIykn+PE5VCmC\nIHM+fo4kF0iCQJEK9Jp75JWEZhg8v36XQJgRJimO5ZLFCQISF9dnmKqFLTc5vzhn6a/wvIg0lFgu\npgxbNxHqCj8YURYRhVCzyWP8OkZtaeiuxs2TEzrtPlGSohsaru0w6Oww2Nmjriq8YEGlVEiKTFLV\nbPwYu9lhcLCPIjs42i6BVzNdjKiSLa6jIqsyhubQMJu8/9EHlJJElKQ8H11TKgpPx0+Yba9Y+xug\nYjGbMV9s0fQe621KlFRUgopltaiyAlGu2YpbUnNLmiZcjyZsNjMcp8HFxTlVnSOZFpM8ptlvIxoK\nT8+e8rnP/BQHR7d5/vKCMk446PWR5JrVdoWiysTbiJ/51M8gVhJpUrFabukN27RbDq6tM5+NePPT\nrzOeTnDtBlJdY6oWBwfHmK6FaNTMkjWxLBBEOUUK7z15wfsvH3M5vgYx5dnZI+Ks5ioIOU9zLqIM\nT8/YPdW5edLHMFQa7S5zf0taiBwMLLI4Yf/4iOX2lRFKDbh2F8+b0t9t8+zyJYop0+k0KKoASS/I\nyDBNi+VoSp5H1JuAW/sDonjLvdfuUicRtiqhiTXDlsvN4y4PX/80aq1yefk+eexhSUNKr+b+8T1M\n0yEoYgbDAZZqQl7zwQc/xssjasWh2etRiAkiFetoTaQKhEqN3DB4dPbiE3PxJ1YE3H6bZbKlkCty\ncjRN5uagj5XX7Dgd+maHfqfPfDkiLwW+8PO/zniyodvos9sdYMkq3U6bx2dPGe4OWS+WmIqJpTiU\nWYWuSkwnEwzdQhV11vM1TbNJHmakSYilWbTMAYP2CW57n01Wsg5iJF0jjkOuRmfIao1iqmx8H1lW\nsU2L7WpJVeQ0tAYN3aUudWpJZLC3T6PVZOatWYY+y43PxeUFjYaDrGhYtkkcynz6/i8hmRk5CbVU\nUNUJqpIgqinnk3MqRcALPY6Pjum6Lco0IcsSZqspHzz+ABCJw4y8qmk6He6e3CZYh6yXK8QyI1xu\nkAWZ5qCDWMkQ52yDLYv1GM3W8bY+dV2z9rakacxP/exP0x50EFyTrVDx9jtn7PfvYMk9FNUhTTN6\n/QGz+ZROq8VmtWIbrjh57S5PJ2fkSAycPi23iakojEZLXl5uyEWJZqeFqctIqkiQJByeHFACT58/\nZ+2vUKScxz/6EXJa4kgSx7tDvO2CzXaLYWj0+z16/S7eYoEk1OimhigLDIc9ri6v8bwVcbDFUG0k\nQWaxWpLVFZs45ioKKe02K1Hiqgz50J/y/uaaR/mW97ZjzjbnBKqHvicwvGlj6QaNhs3B0RDdMVis\n53zmp9/kxeWWk/1beKuAMNjSbBps/Bl5HjFfXJBmY6qqoum0qZKU3A+wBImh49DSDfpmi5bYAK9C\nw2J6tqRORa5eTsjCitXC4/z5JSd7NyllkaePrrh/9DpC6fL46RijofK5n3nAn/3v36RMKzqDLk3X\nBREUzeTG3g1sy6BhO4hUCCl4XkyZyRSRyC//zK8zMAbcu/fxGH34DywCZVny8OFDfvVXX6G4V6sV\nX/ziF7l9+za/9Eu/xGbz7+yPf//3f5/T01Pu3r3LX/7lX35izKTIyPKcNM8J0pTW7g4Xk3PclkNN\njlTpSKLM8GgHq2Wz9hLMpskmWHN+fk4tyGyCNa1+E0mXCcOQYXeH9WxNFieQiyioqIKKt/JwzCZS\nDZvVAqmCLE5RBInZaEyy8ug32pRxSdfpY1sueeFjNxrUsojddNh4S9IiZOMt0RyRJI25s/8Ghuqy\nDrfUiogXh4xWc2pNJhNLirKECsIoJstqkrTig6d/zXSRUNYVimmSxyWaaFKn0FRt9LSiXHr8/d/8\na8grJESKKkc0RXRHR9NtNpsAUdC5enHN/GzGQfcGnUYLsZbYbjxkRWK7WbPZehSiQkoFaslkfsVi\nPufmrZNXLUlV8Nf/6i/QVBlLsWnZbf7lL3+ByfUIFYlOs4XrmKRZyIO7p1RpTE1FZ6/Jdn3NUXuX\nnuIQTwOgYrIdc/PmATfvHOHuNNFsnY5pIesKhQiT+YzL8TV7BzsM+z3yKuWNT79GXPr0D4ZcTS74\n+V/4OZpNmyyPcBwL09S4fXiT01u3GC/mmJZJnuccHR+z9TZ0Om3abo/v/u3f8/BTbxL7IYc391AO\nVb794v/kukpwDm/g7A1ZqRXv+x5Zt8G6nOMOLT71c7fY1luCZIGo1JRVSBJ6dJpdRlcz/uV/+Qaz\n+QRVtYj8LVk85+SgDXHJXmeHtj1gvV5gCCKuqtOyHPrNDhfPXuJar6Yz94e3qAKZo9NPU6YSrtql\noXWpEwVTdrm5f5vJ2RxLzxm4BjebN9BTgf/qn//nGHlGMA64fXyLtulwcX5OGISEXkxZZ9huxsN7\nA6rcZ71dso5XhJ4PCZz2j7ECYLsg8s//vxWBP/zDP+T+/fsIwisIw1e/+lW++MUv8uTJE37xF3+R\nr371qwB8+OGH/Mmf/Akffvghf/7nf87v/M7vUFXVx8YMvCVuw0SQKwxTI88ydNMkSCMkXUHR4Gj/\nkNiP2R30iMuATqPJ8e4Jx4cnyA2NIIvQLQXXaWGYDqPpFFWXuX75jGFvwM7gBg27hSzqCJmMKlqE\nkcd6dUWr65KRkmUeZZXimjayqJCmNdPlhhwF1XChkAjCkKM7Jzw5f4psykxmIwQBOp0uq2BL8Y+J\nvvU8gk1E6Ef4UURr0OX51UuyPCGKTWRgtn7KJlgioFDHNa1GB9tok3gSci0TewmKqrO73ydNIrIw\npeU6GLKN56fEccSgN0BBwI98CqVgsRnjRz7T+YqO2+Pph0+AmtbugOezOfPVhjyvGY1fcPONY77x\n539HIedMwgW6pnJ9do2YZ8hCzuXojP3DIaYjEPpbAs9HkkQ2voeiqmiajKEahNs16SZgMZ5gNhyy\nNGUbicy2cwQtJ80SXrx8TpzFvLh+iWbbGI02eZ6jVhXJZgF5xMSbI5gysqXhxQFpkaPZMrPFOfPF\nJbJS8/LiCT9+94d4ScBsNWEyHxNnEaZpMZuP2AYjPvvGPaooZbGaUlCwWE/oHPZ47l2AXtDf2WG8\n9On3hiimjCfk1IrGdOGzezRk50YHu6Wx9tfoDZXXH7xBXec8/+gplm3SdAyEqsDAJvMFup0m/jKk\n8CrqUmI9H6FVEsurMa5poZoqgqQyW68QDRFFkxhdnnHY3SGYLHhjeMhnT99kz27jjWNcy8FfSGxX\nEavVC0J/jr/ecLN/EzGX6baP0OsW/eY++/0DTvYOObmxTxoFTC7PuXnYo9fWabsmn/r8a8hmymx2\nRZzGbFMPyf54bPx/UBG4urri29/+Nr/927/9T1SSb37zm3z5y18G4Mtf/jJ/+qd/CsA3vvENfvM3\nfxNFUTg6OuLWrVu8/fbbHxtXEyqSMAARRKFGLEoQSkzLIssK4jhlMl7jGC5lkrNdn0GZUQQx2TYi\njBIESWGxmCMhs7e3i2RIVGrB4HBAXNT4WcVouSKpc2RDZuVPkWQN2z1mMg6Ji5xEEgiyhKcvnrFz\nY5/ldo0gytw8eZNgk2KqDq7b5HoyQjZtsiqjLHJ0U+b9p99HM0Rabpsih7qS0SQdVVZRVIVN4HOy\nf5d265THZ8+Zrz0iocLu74Bs0mi0SMLklTZfNVAUl0ZzCHKTSgDNVmi0bCaTC0xNo+u61GKJrupU\nWYHp6lyMr0mrhOHOgNDbogkqnU6fy+mUNCtpOhZFGmMqLkVlMl5O+flfvsfuwR5JmVDUFbIoUYkl\nlVCiGDV+NiItl2hazY3hAENRWa9XlFnKajZDBcgFTo9vk5cxlmsjaxI3DnaRRJnQW1HECU2zxXzj\noaoyZZSy0+6hCTXBaom3WCEpIoamYzd0losplmGyXW14/913KaucrbemKDOenT+lBlrNJnmRsQnW\nKNo/aj80h/Figi4bZCE8fP2zZFFJXWrIssBnfu5NJMMgSzI+//BzxEHMwcERRtPijdfvkYcpSRQS\npR5etKIWCu7dvc8/fP8dTEuHFGYvZ6S+RxzE9LtNut02P/7huywmL5DrhDpXuHN4l9KrGbQ6zM5H\nGKZJIUgMdvcZTxfcufdpdjs9FqsZmizzg7fe5sMPHpOXKq/df8hiseazd34WWzQQipobg0MMtcd8\n5qHqGicnB7i2SsfRuRqfMVuPWK6mCJnI/t4p89mIKPLIi4xn5+9TKjGz1Zbr6SV+sCSPP5kW/f9Y\nBH73d3+XP/iDP0AU/93S6XTKYDAAYDAYMJ1OARiNRuzv7//Tuv39fa6vrz82ruU20XUbpVRJvIiq\nCLl1uEffdTgcntBx97hz+pCHD/8ZN4b3uXz5AqEqsSwLxdDxvA3t1oDDwyO80Ofl6AlJkfL05RMm\nwZZIysDRSJSauT/j+ewjkCJ2YpW9scBnekeIfopcv6rYuq7z4fvvoakyrmGx0xqyPzx5hcPKBYRa\nYdAZYukuum6z3iyRpAzLSLHNiMX0OTv9Fmkeo6gyqqoiIPMrP/MLxJHP7V2HXkNDIKUMA2zJJA9T\nvNUWf73mxs6QuqioKrBNizArSOscwcy59fobXI4WZEFJHmdEccxyveKgN2R8sabZ6fHOo/fp7XUR\nbZFCybn/+gPqJKWvD+lbTYJoSsNRkASJs+djzh9f0HX7tDpNaqOgEkWyqkbVNBqOBbxiKnjLOXGw\nRshiTEXCNhT8xZqjzhHjqymqpbMJl6zXMwxRYOg06RpNwvkShQBLr7hz0ifaLrl69oT55QXDbhPE\nCllvUgs1QbDBMFVUUaTKSnY6PRRJRtcNthuP4WCPNC5wnTYNp0XDaqBrBggifhCR+Cmj1RVPLz9A\nqgRM1ULTBMIwI/I2iBY4HYUdQ2e7SHj8dEZZV3Qdk91+m1s3DyiKFNPSmY03FKFIVqScTS4JM4lV\nmlIqMr/53/z3WPoxH/zoGkuzEeucycWSX//Sf4EsSNRCxiaNWEcxpSyw29tl4LZxFJ0iEmjbLsc7\nN9BFgzIXuH/nAbfunuJHzzHVnHgccMPZ4/pihpQXhMsZWb2iUDIkXcFbr0kmGxzRZDXfoBsOhuWQ\n5wL9/pCG5aBbBklaso0y1EbFKJq+kp8vP5ki+O+9IvzWt75Fv9/n4cOHfOc73/nYNYIg/FOb8Env\nP+75V//riDzPqaqK+292Obml8eLpU3SrzdXkIyyjwXh5xfzv1uiayGfe+CliP+by6glJXdIwWyxX\nc4rAo9dqvho9bbRp7B/i9nbR7B4eNUW8IqtzentdFvMlTm2weTmmW3bZ0QZETk6piFAm7OwNUQTQ\nFIn51QViLdNv7ZDkMWG4RdcMZssRjqGRZQWloPLo5TmI0BkMuZ5e8+nPfJqr0UuytKLrdvg33/0O\nLa1CaZqsNhmupqFXIlWUI8kWd47ucn1xxvVkhqIDRUUppjQaDRarGW6jwdnTS4rYZHCyx/cevc1r\nd+4R/uiCXBY4uttDsQVczea9D59i2S1ETcURBQRFAlPgxXSBIFfs9PokWUQlxCRZipIUGHKDGgFB\ng5U/Y9jtMhlN2d07QJRVXj5/yb17d9FaGgJQlwWNQQMhknBtk3QT41i3cO4MoC4JwojJfIpha9RC\nQkOv8aaXKKQUqsSgb7MOfVSngSQLxInA0D0g3G4RFJc4jVBVC1GMqRGoqoJ15HN8fMxqE5KlGXla\n4ugOlq5j9HRU3SJIFxh2zWIzwlKalFGOaQrIao0mFuyYfRgnHPdsnN0WslgzWo3otw3Or8+QVJtg\nvWbQ7/LO29/j7sObOPtH5Nuc8XzE4eGQ/+VP/mdOT0w0Aw729/HCc6JI5O//9i+olgVWu02uSwzu\nOAiyzfWjd+l1egycJmW85nK+wrRM+nt76KrNNgnQaoHQKynihFn0GFmzON45YrlZcvvuAc/PPiT2\nSzbzFd3uAFmqkYqUf/Gf/iKzYM6HH11j6gqevyGvCvzI4/DGHqPRhL3WPf7iW2+zWb06k/p/VQTe\neustvvnNb/Ltb3+bJEnwPI/f+q3fYjAYMJlMGA6HjMdj+v0+AHt7e1xeXv7T91dXV+zt7X1s7Nf/\nuYMsKRiKhiWJFElIWRQoWkyjYWMaLmVW4bg9smxD4IeMLs9oNky2QcjhwU3ee/4BB/0BaZSQhhle\nscC2YXw95fb9/wQpqnEMh9aOhW0oFKGM2tmnbPdI1JyL9x5z3D3EsnSuvBlFlFK6EvP1mKbbQ9ds\nkrhks50jKTKzVUgu13h+SMttEvsxmqrTaLWQKwVbN5iMxtiaRkNRqbMCwdGIK5nJdoFmWERVTSXI\niKqCLMn46y2funWPsb/ETz2iJEYQYDwe4zg2WVaQ5hV3X79FnIUcDnfIwhhzv0FQZhjdLts0QKpy\nmk0FWYX94yHBysdxHT5454rWgYCiSIyvr7DsBrIiYJga643HNNrQa3exuy55rRFvIpKkQBZ0nj19\nzv6NfWRETNXhB+/9mJOTPYLApxQEqjKF1CFb+dy5c4sf/PB7qLZNnoVE25IHd+5S1VuKqmA1q+gf\ntxjuWqw2W44PbyCrJt7TZwiKhiqbaIbBfDF/dXVaZyDLCFKBmVWU0ZaWLDMOffb2914BWWiQZwWT\n6SV2y+JytiJ2FO6f9CjigLOLJ6i2QafdJxfbjLYTjl4/xui1qY2E995/inrzBnM/5Hj/JoVu8Pq9\nN3kr+x5lXVFnAlsvoHe0y2yzREUlCTW26xBh36TfPWS7DakLcNoSmSyi2yWT6RSrGdDUXLwoIM48\nhoMdRPGVqa1u6Bi6Tku1+Oj9H1OXKnUlIBsAJVkYI+YFo6trGk6H3F8zmT7n9PYp4+kKXaj5xjf+\nN/ZPD1E1mSLP6XWH+EFCw+wjlgU9a4Dszvn8v7AR60MUVeF/+p1/+Nhc/Pe2A7/3e7/H5eUlZ2dn\nfP3rX+cLX/gCf/zHf8yXvvQlvva1rwHwta99jV/7tV8D4Etf+hJf//rXybKMs7Mznj59yuc+97mP\njd01+miVRpnUiJKBKOmYukGr2YYaJElgtpywWM7Jy5zNdoPtOIRRCYLA+0/eoS4TVrMZYg2iKBIE\nIXkpoKoa69kEV6rJ/TkKCePzK453TygpSFWf95cfwECkqCOKvEIqZXbaOyiCSnewixfHrLwtWZbQ\nbjlcXTzHNkTaZgtSEVIRXQFdrug0bLp2DxKFzCtQRYlWy8FuOnQGfZIkxJR1JEQ2my2KpiFpKoUk\nYThdXl7NXrnmagZWwyVJElRVRZZlVEVFN3QqOcFLNrRaPbaRh6CVJEnM7b0jWnqL2BO5efMExzVR\nNYH5fEaaZpyeDmk2HIrklXR4d/cIAYPYF9DVDrdObwElQpbz+s3X0GsbV2sipAJHwyN0UUXIS8LN\nmtfvnyLKKnGSsSq3eMmWYf8GbqvBaHaBF25QRYmD/gE3j26Qlh5hukEQUzpdA0ksubh8weHRHovl\nmCje0HUdiizH7bhUdc6DB3eQpQpJlGnYKkW5Jc7XbKMVUbbFsiy8jUccF3j+EsSMvARJbPDw9c+h\naRIbz6eoQTYl0BWKsuCH7/yQg7u3yMSM2fwKDYFBs8VHH1xxdHifxXxGJcgUlUCj72K0mkwnV2TR\nmsVkTiVo9IcDVLnBZx++hm2KBKsApbKpVAl7aGN3FVqtFpraJg4TdNsklzUmQcSL8QhFUdlOl7iq\nSrQZcfn8GVUGB4cntPo7SB2XRbAlzzKWFzMaskFV1fT3hiiKwnw2o9cZUqQSw+EeaVoxHvtUlcR4\nNEFXRCJvie95FOWKMN8iKDolBaL8/9Ow0L/d2n/lK1/hr/7qr7h9+zZ//dd/zVe+8hUA7t+/z2/8\nxm9w//59fuVXfoU/+qM/+sR2YGB16Ro9eo0BncYASW5wPpkzHi1I44zVakEUbVA0gfV2xWQ2Yr3a\nMOic0GofYze7qKpGnlZ4XsTezg12h0fIVR9TPMAxXeooRqVGqgQGVpdoE7BaTYm8NW6t0neGSGaT\nyWxF27CJywjbMclSaNhdmm6XVCipNYM7d18n9xP0Ana6A/IiA1QatovnrcmyiJZt8OaDT1PkCV4y\nxh022HghQq3S6+7iai5FlCNLCpZh0O80ycQExTF4+vwpRZFi6/Kr/8pzfM9ntZxTU7H2pyy8FY8u\nnyOYBloFQqhweXlBnSd0Ow7rdYbdcLm+GrM7HCJLIk4jo2U3ubl/F0OxyZOIXsthf3dAw7bJsgRL\nMwi3GyYXU8qkRqDA2yz58bs/pD9sE8Y5eVUQpjFFWaELNqPREqEGVUuZ+2M+OnuGaltIsoZQJ5RF\nRCnF2A0TQahRtRCJmKZtE4UhuqHjbTcIeU2YJKxTyGWVx+cvCMsQp9MnSDMKoDZFtuGWl4srFEHC\nMU2icI4XLqDO6Zl7xJMEu5CJZlssvWI6n7JYgSQ3kSSLg6Nj3vre2zSdBgYSwXhBcL2kLQuUccbu\nzhGNRo+qqugMexTriHCe4s8DpEKgSjOSKqKqRIJtyGo+I/IjbgyP2YQBXu7jRWsmswU3b96m3zwk\nymvOpxPsTgtJ0zgfX6A3bApBYTxfUUiwyTMSucId9jn/4DknB8cEScxP/+zP428C8jBls4wwdJs8\nLjnYucWbDz7P3ZufwtIcGrpCw7YRaoHQ97EsnThYvYLsrGPiTYZQwGo9/eS8/kn5DvzBH/8SVSay\n8WZohoxsK6RRiBgWSKqKl6QM+j0uL+bIaoFtNGg1HLTK4vHLRwhGhSyKNOwGoiDj2C3KomC92qIo\nFZ1uk7a9x2g2xrY1HKPBs+cv6e928YMVi+WS/Z1DTMWiSFKy2kcyNc4mL18JOzQLTTWIgwjXtlEk\nhTjNWE3n7O3tMvdmiKpIFEeohoZaGLh2i22wIRNXVLrCOvb4zI3PkG41now+xHVcqlpA1V9ND1q2\nwbCzy3Yd0W06PH78hPawRV5nTMZT3JZN02mQU1CUNapqkqQCTdsiDOacnpxw9vKCqs6oRJmqFhCq\nmiDZ0La6FElGVSXYbpc8KREVyMsVdVVjal0WcYRh2UyenGM2DQatPcpaBLEmi1Oy2KPIJbLCoxI3\nSJZMis782Zxf+83/jkfffw9DKRmvHyNIObbRJUtqFEXk/HzErddvsNqsECWRht6m1+3y7o/fpTv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X6wwQ/X1AikYcnLN+8J2wi5D29Xv0LQJd5efk1aBRRiznK3YLHb0h8NEI0GSRVA0yjqmqKt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\nvXXszy6G+l9bWFjgiSeeYOfOnezatYv33nuvoXNXKhXGx8cZHR1lZ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+/ud/Dmsds/mMqqz5a1/4HPNyDgiUiuHtwzuPePz4MW+99RZt2/LBnTt84xt/\nwuc/c4t33nmXt958nRs3rlE1x8zmE+7e+4BelrO3t4cxnRcRmSnRkmZxoOiwz2VoIxPzJCTljRDo\n0OJT8gpiLq87JnrkdvHCKu9wc4dzkA9yZKZwwtFfH5IPe9y9+yFX8iuoQlO1NaPNdap5SQies9kY\nIxUqz9jb3QWt6a+PUEqxNlrj2dEho/V1ennBeDJma3eXtrV4a1F5xu6VSwSt+PDD99jY2uG9D96n\nMIaHDx/ggDc/93k213q8duszfOOb3yYz/ajgN9c52z/g5UuXMULjAow2NmmnNcoLBv0NxvWYZycH\n3LhxGbO2hbMBrw3T4LA2oHSORCC9Z3s0oj/I0SpHEPDO4mxNmzwUkRJkSkqk0QgReDo7IlctlYdx\n2MQMPL51kVI62CTb2XvhPCp6hlQNTaY1Ugh6eUGuNME6JtUk4o5aLRw36xrm1QwhFdYFhBbUrmJW\nnuF8RsgMMQYPqJ7idHzM5s6rHB2VFIMe07KklymkELjE99faoFWs9kXEPhoq8ddDwtGF1tH79J6m\nboDYm2Y6HYMQ+GCRQaCANlha5whCkBUGKUlKNypeJwR53uPDD+/xxms3CK5AKclsNiMzBds7O2xt\n7fHh3fsM+mu88fotfuqnfholNd/97ncZDg1SxgpK68DaZumNpzzRx4m3drF2lrAFtO3CPi0ghpgb\n6crUA0XH90886YguLPHw9MvxPOGj3mesll8qvuhJx4Rlt84777iDOEIXlQePcLGFQOiuQeR/CwJK\nisiOWTlHCDFJHxONUScptawYDcQEq/ceS5d7iQnT+CaX43GOUbOi+FeVuJB6obQ7qu0Pk0+PhaI1\n3jmGgwHOOfq9gjzPca2LnO+6RmlN6x1Xr15la2uL4XCID5Yie4vXX3+VjY1NPnP7Ns5WjM+O2Nvb\nY39/n/l8zrDXp6qqBHXEx5SJXihEV53YvRSVoJuOKw1K6iVJ3yWvJ2l6n5IdMSmauKzJy/eyK4mP\ni01KifURg7fWkuUxoemBzZ1txmdn2OGAd999lyLPaUPDpZcu4VrLrC7ZubTHo4ePKKuKV197HaXi\ndM97BQHY2t2laltO53Pe+/ADdvb26I1G7Fy+ihSKz9++xdnJCc+ODnj25AHHBwfsXblC2zS0TcCF\nwAdPHiLLOWtNiapmjLbXeHZ6zPRkyuXRJlpLvvC5L8I7hrVeIBcZbSMIRY+mLMm0wbhA4Ry+KkGB\nzgxB5TFTvBYYAAAgAElEQVSKsQKnIQRH09QEEUPQxltCa6OxzHtU3uEHa9x5OqWZH1J4S9E2DHsF\nwb0YC7Qqlj57AUiFl2B1IAiHx2GMWhRltG0bvSytMXkW+echLspMaG68cpPabvBwf0LwDlMYziZj\nNguYNi1vv3eHvPcyIi+SJxdi0VRqchIbhHVsCEfrGgIBJaM3a5SKRVEhtgJTSrO7u8PP/uzf4s6d\n92mammo8RspUi+BdrA5MHn50HPyCTdPMZgyGQ57uP4tJzEGP9bU1rl67xvHJKccnZ1y/9jI/uHOH\n27c/y6XLLzGdTNjY2EjOCgsqW9eASimF0pG9VVf1C8cc4RcUPFIic6HQkzJWMrFDnD+nuGZVGesM\ntI4sE+dAdD1/YtWm1Cqu048o8LDgmSNWWkOFjyb7BOC6uoEUeUeDI841Iuv+FiIm4Rc9vAIgovOh\npIxJd++SAhcxkd0paBl7x0ixwnDy/px33p1SJwMmvMdz3tsOKbJaJHjDeVjoRfKpKXCTZXGStw1C\nQJEbBLE3B0GiJQTfYvI8FpBohfOWTCmGwyGbm5vYpuXNN19nPp9jjAEzYmMj9mkgeStZltE0ybsI\nKyFWWIY3biVh08Et3i85prFZjVx4HEpLMiWxNqCNWGCVIQRswt6jCKx3GK8Y9fu0zrGxvo7SKnlt\nOnK2TcaN69fJ85zjoyPaJja8apuWpmmoq5osy7n52uvs7+/z/p33uHrtGlVVYXoFwmg2dza5fO0l\nLl2+Qq/fY3J8BCrjn/7BH/HTn/8Ma8M1KhcLMKbjCVubm0xnDUJJdvZ2+e7jb/PSzVfYevkK33v7\nB0zmNW/euk0dYP/ZYyZ2ytMnD8muDJm3giwf0TrPwdERl69eZ1AU9J0Do5BZDDtdEw2jVwbnmlim\njMRkBmSX8Y/0yjPrKfrrjF2Gc4I6CFzjcC0gBNWz6QvnUdlWNLZFZiYpcUntXaTFtRacS55WXKRK\nZ3GuxE9QUmDbNjVm2mQyc3jXIkSND47cZHg54PGzklkjcbrFGDg6mSGQaGPITIF1NbP5PLVuiIks\nnZg5wnu89QtsW6lIw7TeUbcNX/ziFymKgsePH+M3t/HeUXuHI7YYiPSN5OkFaL2ncY48y7l25Qqj\nYZ/t7S1wlvHZKd995x3Gkykb65s8Oz7ijTffIisK6trS6w+SYxG9SkTkTxeZwXbFKVIQcOjsxUaz\nw5YJIVULurQGll5wxNW7IhhSYleAkbTOxo6bSuL90hBEIwBCReWvknqK+Hb8l0oY9QLBTpc04jl+\neMKb41oPizyoECwqILt5Ef936TOR5krUF1rE/kUkZpIPKXLwXT/OpZ5QybCKxJ5Jg5SSqUteeAhR\nkXdR/5L3HmE+JZd01L9IPjUF7oNLnUWXdBmlBFoLmqZevKgmZaKVjoPWJqvW1g0Bx2w+jdza0OLa\nZVtImf7umCixoGRp2ZAvtmzncLE0uBYBruvURge7xZ4NYtlJLdZ7xWOllLFCUkf6mQ8BYxQhNZTy\nRPrWYHdvwakWQjDs9Rc4fV3VSCm5cvkyARgORwzXRmxsbTAajbj/4AFaa6z3bAnBeHKGygwQIY2T\n08eYrMeHdx+wtb3NK6++zu61G3znnfcgSH7qC58nMzmHx/vk/YKj2Zj7D+7TlxLnYf/RQyrfEmzD\ng/ffZrPI+c6TdxlkQ/7mz/zLfO/Oh7RCcPBsn0s33+Tpe+9x9PQhl65eYt7M2WCd7e0tirwg6w8o\nyxm9jS1GayOkjuX0J8enjKdTdp2mGAz5vT/7AfZMQK4wON689gpb23v84N6Dj74sIEsrM1hPSB6d\nlDlt7XCtQyHpD/psbGyk5klxXt2/f4/5bA4hUBQF45Mz9o9raqvxrsTkDbPZGTuXXseVgSf7FabY\niQlhOaPo9ZEythiYtzXBO4SCspnTNA0mM4sk+iDLCM5hsgwpNWVVUbcNznmKvBdpqHkeDbIyeEg0\nvdiPpisj9z5CKiiNBUbra0ymY8bTM/JeRq/IebT/JPa6kYLB+hr7Tw/4N/6tf5tZVaMmUzY31qKT\nQcSGJS41VnJIufRSwaaE5kfFtTUR4lSEhccYo5El/CgWSbsOb5dCILWIfX2URGFiR8iOHRLASwHB\ngQeXiHuRyhlZJL6tEwwa16IUAuc9WpgVTDnE4jEpkzFdMlhiZ8ZVOCOtZUGCZmCBw0OCwKKSF4kC\nK0JA6dRasrM6AYJzIGJ7gRU1AcSWXEJKtIrQWwOJl+6XXPk4qvH6Cabxf4ES/9QU+FIbpp+Cw/mu\nS98yYSBFZ9nP91yGEPtjLxlG50KP1efuoIwO3xJCLEpnu9/r/r3wLlgqcKXMuZdxTsmnCb8IkrwD\nn1qfrhyvUhMfIcSivDhi8hFnVzJSp7qozFrFcDCMdMTNzZjokZKd3R3e4A28d7z+xuuUZYVMkMCf\nf/8d8l6fpmp4VD/k9mc/w+z0JBYRa0XjA+ubG2xvbzEra8YnRxQmYysv8FXDMM/IQkBZx8bmBpUU\nNFrx6itv8vYfTZjuP8PmNbOjMfc/fB8jJcG2nB4+42Gmefu7f8rGoODtd454uP+Qv/3X/xWGvo3t\nR1WP2rb40jOrSobDIRvbW/RHa+SDAX014vD4lPe/8w7N+lXMZkYWWvYGQ15+6RW+/WfvvXAWfeH1\nN9k/eMa0aZjVNbroc3h0Sr8YoIXheDphuLHDzt41JpMJQghefuVldncvc3ZyxJ333mN8cowSgsu7\nO8xmLboYEPSYfu8l+mYX6bcpy1OUNGRmgFMt1TzOTaUlQQVaV2NyjVEK2UQ83KgMYyTSA6jY6yZX\n1G0svFI6QmGTyYTf+do/AWB6NkdlGc7HyDSTEtla1vMeTVmRDYfMRcCsjdjff8Jar08InrsffMDW\nzmZUcgJ6g36skgyWz37uszw7OKG2guPjY0KI7V4DltFohHOWum1RqaGZtdG58qF94ZiLlGAlJW1b\n18RwH5M6YupE3/V4B3rZQpCgJcJ13PtkqETXW6SrwI1FP9bHYpc8z7CNix50UqgdZONFhMojxLSk\nA3cKYBE5LxT1eT0i07qLHvCy93f3KwsnLemjRSS0wNVTS+BU57CINFbW/mqyUohYIKeERITY0ji2\nxY5ObG7Uwqn1K4nQj5NPTYEvlK0g9WBfNoRJWjt5tF0GmOjfrmSbY9lsp0z9IsxaNQ7d4IXQpSqi\nhOe+7yQ8p9jjZ35xXSlEDPFYWtjV46Vggd11iGC8o7A4VwcdumR5l2MSG/DI1Lfah9ibuGxriqJY\nhFghxK5ufd1jOBpS1RVBKG7deoODg0N2tzYptCRTmoMnRUzsDXr0N9YYjEb8zO4OIkSj0cwq3v/B\n+1xa3+CtV26QIdH9EVnW47Sco3oGd3ZCOz2jlyuGV65Qn5UcPH3C9kvX2RkOsULibc21q5cZ9AoO\nz4740ms/Q2s0h+2cXGgmZUPAU0hB2zRMZo4jW9E0LcoYtnqeOnisCMyqM9ZDTpEJ7rzzbY7PptQf\n0wdlqz9i79Yu87blaDrlez+4g2tA5BlFr09fKHTWZzKrqJqIZ47PJjx6cJc3X3uVyeYRbjalX2R4\nA6GsGfWH6GKEEIZyNuP4+Ii7d9/DWU0xGJH1WuomVtCNRgXrG31m81MIDZk2DAZDNta3GPQHnJ3V\nlPOafn8Qe013fV+EQGnNg0cP+Z2v/k6ERpqG4dqI1raoYMBZVNuwYXJuX3uZPMt478E9xtMxta8I\nXlAdn2AyzfRUcnTwFKUVg8EQ51ru37tHkfc4PjyKdQ8eyrqhsU1skdxaTk6fxWIak6O8QSpNr5ej\nM/WxykOnsnNIkKMwiwRrdFsimyvCB1172LTfh5ULTnVXLdvljyK2HT3PqBB1gjRTq6kQECEmCbvW\nscYYuj73MQpIbWQFC5ZNSP/F3JfAtZ1vD8LZzv1eruUUVUOkK3f4fFzmKXErRbp/GRt0+VhlutTd\nXQ+mwKpjGWGl+J3zsfOgNgaZvotoQSqo8izG6OPk08PAV9ghMXBIuE/6JKSEkEhtRjuIolOkfjEw\n3cvtWpGmqidWMOyUUOnOGy/7Yo5r562f8/Z9AFw0FiJO3g6/gjh1FlZckMJCcU6xq+escvd5WFkk\nPoRlQsx7HI6qjQyXejpFaRW9meCRTiy55qm3cZHnrI2GHB4esre3xeuvvcbx4TFN08bmVc4ijOL6\nlUsREnIOJTW9tTVGm0N+6rOfoWkb1jc3OTkbcxXB6dEhjx98SNbT/PQXfpqgFQ/v3KUpa65f3eO4\nrtm6fIkHd++RZXGDjI2dLXavvcRs2kTFG+B0PEMQyExG8J7cOtq02Uae54znJceHp7g8cPtzr2H6\nOUfvv81r125wevQMZ18cSj7e32dtcwPynD/78+/z4eNn9PrrzO2Y6XSfxwcPee21VyN1U8emZNV0\nwt7WBm1dcvPGKxhX8dnbb/E///Z/S09vsdPbQmSSe3efsLV5nVNbUc+PUXqd+ank4f2nOKnY29vE\nGAdnU87O9tHKU1cltvV88Qv/Ar/0C7+AUQX7T495dnBIVVVMy4rJbE5mMsq65vf/4P/ibDqFEAt1\nynoePXspEL5hbzTi89dfwY1nbA/7DN94HfngA+6eHjHsjXC1o9CaXr/AOsvO1jb37j9ga3uXpmrJ\npOH9937AjTc+g0NgMo33Lnax1GDQICAvCua1pa5LXHD4eXsuGj0nnUOUvOaOBrhaGRlCOMcF7/jx\nPkhkDJwTH19SVfWyaZNIvUMALQIST9u26G79J2jEGLlI6IYFLbGrCCU6hKn3yOruRFpqnG/pKms6\nHRGCT3z7pbceo291br12P8de+GqhyIUQCNe1RF42pGKFj766K1OEp1Ljr1TkJUJXHfpReuHHyafX\njXBlcgiRFKxPr0jIheXuwIuYJZYLxZseH4gZ4NXsrZQSufL8q2FTh3/Fc533tFePXfXclUi7ZXQw\nToJtQlgeg48RgBcyNc8J55Rz97ydIZHd1mJySWfERxw3RgoCB2RFvnJ/ISZTfCy9bRqLVir1ZVCA\nZ2trk+GgR24kR8cHaG0odIHqZTTexU5sKhCcRakAwrH7ylWuv3QJ0bQEDXMVWLu2i5tV7G2tcXl7\nxCuvXaGyNZc2dhG1Y3NtnfW9XfZyg8gydtbWkK1nXpUczyb0Nte4tJXHyUpMyHrbIkJndBTT6YSg\nDArNzLcM14d89rNv0WaSujrlpe0B2z3D+mCXCdUL59HjowOOm5JgMsSgD3nOSVMzm02YTecoBM8O\nDplNJ4yGQ+aTKbvrI/7ml77Ile3XOHjyiG9944852n/M7OwhP/Plz3N6WvP44SMe3n3Exuf3wM6R\nVPSyDdpaE2ogD/R6BXhLXVYMepqXXtpmd3uLo4MTgqs4fPqIQX+dra1t1tbW2btyhSf7z/jz73+f\n9z74kHfffY+nT5/GJLsXC29SK0EvU2zvrHN1uIZoG67vbLP/dJ/Na5fJhWeQK4RzFEqz1uuTFbFq\nM5OKzdGIyekJvd6Qcj7jH//2P+Lv/f03aJ2greYURUZbTanaGkLsHWRtC4k62zQV5xHc89LRZiNu\nK9B5nKPW2kVNRYxko9KyzmJM3OEJlwJsBME5nG9RUpDJPEalziYFp9BaxrYWQqRdrNJWZYuNFEgM\nI0vWW/aisdYS5FKRdjvsIGJRVNwCMPVTIZbRd5Cm6OAcHyN2n+CcBafb2eSNp60THQulrdIGJ89H\n9V0ODpbOW5cYjdHG0gmMx/glhLMCM79IPr1KTLviheIiwL9QcqB15wXn0Zte8YpXYY4lDWfpUUea\n1XmPdzmeUQF3Gwc8T9ORckVZpsRM3E8yTemkgF3o2k+e9+zl6gsUy89VakbfNeVxKxns7h1JocCn\nl9tBRe6j0EEHOWmdRUvuli1dvW0p8jyOodALb0ECuRAEG6gn5WKcpHSI9ox5x3tv4lj7po1N7pVk\nsLfH6PJlIKCVZvf6azR1vRgPBDBIGF4zYJOd2OgreT6dB7RKO+siqpBYANY7pNTcfHnGvK7xDsp5\nxXQ2padnvOHGL5xHp9Mpfe8RwFaWMby6y6ycs37zClJrRKUJUvDo8SO8FBwbyUtpk+157bn/+IiZ\nzXn//imN3cCpIa0Yc3TaYEPO02fPODmtgE2CzJnZCQ0BoUusL5FlgLphcjZht93h6MGE09M5LpSc\nXp/Tu7rNZBZ4/95jfnbjTbb2tuDuIWF4yJ39e7heoK1a+mqIagucEdhQMhCKlwc9LucKWbecTI/I\ndtdohznDvW3c+0cU/T7aGy5vbvDSxhqElqPxCZsvbfHdD+5yVo4JMqc5OOTb3/9Tfvqvf4GD/cfM\nZ4fkucZ6cDqjUVBNzhj1CjIDta1otYAXQ+CccEYmDaKNPb6diiyvum7o9wa0dYN3gaoqUUqR93qE\nEPeMdNojnUSjUVKljUUCXtSpTzgIJ8E6moTVa61j06jcIJXBhlil2DQWpRX90YCmGaNCdAYykcU+\nLMpR+TleggqauvJoMmxoIpYePAmnQMvU6AqBTAlYFQRGZyAVzsd9cxGpgti1CJ8i/2RYmp5Iyc64\nfZuzEVLxTfTkpZIxTRYChRVRgYuA9JLYvTHu2BNbBEQvccloe7F8agq8244Llgp5YTHFShlsp7hX\nwrTnk4+rv/f8Md1xq/KiCqjn5dzviKUdXC3PPZ8MOR9uvuiaL7p2hHvOH7usYhMrVvmjnNBlyHr+\nTzzH0kB2/V2e9wJWr/fciRe8WpcazK+K1nrRuKnbtaT7vLvfzBiclB+5Vnfe5w2w8T5us6YNGyH1\ns/CBumnJ8myxe8vzcvPmzUV/6g62ap2lqipm5Zx26rn28nU2t2/jQ+Dg+JCNzU2UVNy7/wGnZ8ds\nbI4Y9Ae4oHl6cEA1bzAmj0wDESuEjTFx30UR/bI1M0LWksGwx8lpCRju3n9EXc7o5316gx5HB4/Z\n3lnjB3fv81tf/V3M5jpvffYLPNs/5Zt/9GdMj2rmB1O2+iPacUmRLYs4hr0eW6M1ckAieXb4hPJ0\nzJXREG8t68MRs3lFv5/TyzVagRGG9X6fw9mUz92+xfuP9jk8m1HNT/nDf/p/cHT4iEs721Eh6gzb\nVEgR33G/l0EI1I2NFYvBL1hbz4sKkVVhnUPpDO/j/rBKCZq2IuCROvbxDyGOn0xFMs62sXDIKaRQ\nSAnCQUitDUTasEUiQMZdn4SMnrh1bexL71zc11LGPVvLqsXaikzEjUwiNBmwocVhoyMlBEWWk6kc\nZ9u0a0/CuhXobj662CIBYpRv/XLPXYh5uM45W01whhD3yfV+SUeM+7Dq2G7BxW6E3ZruNmSXIrak\n7RAEIbrNpiP88iObxOyKazo6T6cMup+7MGxBAVyBIJ5XZquVSy9ilKwqxO6756+/8JQ7ov1HPPyl\nPG8ofti9nGsrugqjiPNcz+cz16vHLeGcj95H99lqVny1kfzqOVdLe1cNyhKrPH/d1bF5/v5W+8W8\naCy6jo7Pj9nzxmZhcFJYvWj9RiyS6joYFvmQF8n2zlbci1MbmraltW2ke/noWck2shyqpo6dF3sw\nHk+Yty0Kwdp6j+Ar5rMzLl/dpm0dp+MzTs6OOTx8RlXNMJnGyIDCs7ezwfbWRlzkPtDOLNWsoZ7P\nGfRyrIf++oBiWLA/ecaTP37KBwdTJtUJ/WGfb37rT/m93/2/Maxz88bnCZVncvSMtWGPcnaGVobM\naDb7AwqTEeqatnUMRmsUJqOfF2wN1xmPp6ie4NL2iLVBgSKgBfSzjDVfcHJywutXr/CFz22xtrFD\nluesb23S6w2YTOd4HyuOtXPgW9rSYtHorA/Wo+WyAOl50c4jVCySsq5FCUFZzhFCYkzsCWJtu4jO\nYoXpssOhkOBdi3cWnaC/RV0GDic0PmiCa1OiL4pROU1TLXJFtqljj3mtMZkmWE/TVEihUUrQhhon\n476dwTVk0tH6BoRP3UVBp775PsQoDgFCpV4zKGoXveiuEtb5uEmMlDolO2MeLoSACQIhY0fC2Ioj\nIJ2LmycnnF7IuCuRMjp57p2uS7k32RmLZbuOHyafajOrhQe3sri77zp5sXfpFwrx474PISy2J1pV\nJJ2iWY0AnlfCH+f5Pq8UVw1DpwBfpKiev/bzyvL5c79I+a7ez+r5n++3sJqA/WeJMpYJmedpmufv\n5XzOQpwrtV41Iqv3tVodt3ovq4Zs8X23ozgSrSKX3WSKXBpsaz82lFxfX4+hZ+vQRpHlJiajSDRO\nZVE69jQRUnJ5sMelS7sopbBNSzUvqcuSqqxAB4zKGAxGTKdzdna2WN8YMpmMmc3n7Gxv8PrNN5hO\nS+49eYBtPPVkzq2bN9kYrfGtb36LtmnRxTo3P/dZ8rUeTw6ecvW1Ta6+epM/+MM/4MO7h8zmjsOj\nEzY2CtZ3rxOM5Nn+fUabBe5sxt5wm77JsE2LAiyBN966xcNnh5RlRTmZUQhF3i/YHOYYLMKBkTlG\n5zjjaV1Ae8fZ0yf83X/tK9z54IPIU5/NaRtAKQga4Vu0tRR5RuMMQQ1wbY1y7mN3g8mBqv5/mHvz\nINuO+77v0322u86+vXk78ABiIwASgLiIFCmRICVTosX8IZUsJ1JVLCvlpRynUlYqVbGqnNimSxWX\n7TiJU7JUoeQsFmXtkiVKpkhxByEQ+/Y2vHVm3qx3P2t3/uhz7vQ9c+48yIoLatTDnXvPOX16/fZv\n/yUoR+HX6ujkMPxyFJlgX3nOYRMYShiHNpWa2OEmWJYJL6Cy1ABcniokz82DIEWSxx7PLUBG4eDQ\n0UgbvZfrSFxHMRiOcKWHFIbijaIUJTK8wHACWZahs5gsjfFrAQpl/C+SFFIAjczFIUKIccpAJYyn\nrud5xoIlK9zsc/wYK9sUvnTzta/JpOEGyKl1E6Aq3+sopDDOfEkaj/UFtt7BEFbOdEVyXt5xCrws\nKoDJzW5TxGVqvbjXBrFynTZA2ddssLEPkeL3sv1mUZf93X6uaEsVJQ0QFDG0rX9vp4wtXSyQtPsx\n7XAp2lhVXxlwy1Y39sFQ1FNct8G+PD72IQLV3My0fiXG5dIYjmbZmFXV2WGC2erxyfLFLnFyp6ks\n3+QKnYeZVShh2HqVt09rDZ7D7Gwbb2GewWCADDySOGFhYZn7LtxHGI7wXGMiOQyHSOFQC5rsHXQ5\nc2GRbm/AsBPiizrX39rgoSe+i/4wpjMc8IWvvkDQqqEcwfLqPFtbV+l1Unx/ASFc5mbm6Hf3EAjq\nXpOzp89xZ/sWJxYXmW+0kBqG4QjhucjAY/uggwx8RnHMsD+i7gf4Acw2arhK4wkHqYzMdqHu02i0\nGKYJjz/6btJhj5majxs06G51uXpzmzhVnD+1wplza4SDPRzX58rNPXZHMTLLWG0JqNcrx9xRTh4L\nXjAcDJjJk1s4roPreSRZShwnJmm2pYuSovCGBhw5FtFkOjHGBYLxPyFMVFedKVKVmYNAgutI0iQl\nzaKccpaM4hC35qMzRaYiY68fOCRZNo4Q6eSUr+9J4+0NFLlcC52MMSDIuU5lAk8pKQy3ok3i8yzN\nP3OPayM6MlhmTA5l7p2au83nJpdCFvvFKCldz0U6Ild1HebWjNMEpQVK5bb6d4GJd9CRZ5LtLjKd\nFMBYdKgsAzoOuMogVvXdpiiPA5ZyO21gt3+zKWIb5KcB67SDqvy+qu82OBalKuB7AXhVIpfyeFVx\nCVU6hDIlXu6HfQCWOaRp7574u7iux9w0UkjiMDX2wFPsYY3XrovQEOeZawovW+k6qMyMme8H+IFP\nnCRjGWOz0SSJIhwhmZ2dRblGbBMPIxqNBvN6liQdMuwpavUZVAa+X6fZaBI0YrSQJHgkNLj3KcVe\nL+Hn//UvMRiE6EQTXz8ApXiNN3Fch3CYMtvq0T0Y8NQTT6GbLmkSIplhf/8O7oxm1hfUfJc0yxim\nMVnmGBGBDEmUYhgmLC0tEUZDHJmi4wSECb6llMZzXXzPY6buk/mCx971EL1kxOLcLFHmkmU9VtdO\nI72A0+tLLK/UiXoutza2uLOzz0C38YVk+fwpgvnZyjGv19ooP0UJk/jj3NIyvV6Pg26HJE3Q2oS9\ncFwvtxQxDjmO5+bx2CXaMVYpQmh84SByCjwTAi1ckJIsjcaZjjJVuLJrHM8Ze4kak0KXOPcD0UKR\nZhEqVSaAmANaOSbSYpbiublllOOgEGOlopHVp8ZTRB/GTUq1SUQ8NgjIHXYK+XucJQiVixGjdJzB\nHiBTcpzUW0h56KCTAULmoK5IktSIc1zXiGucw/g6Ysq6L8o7CuAFUINpfFnUUSV/LYsSit9sCtC+\nXgUa0+SwcEiVTiv2M+V7y8FrbM7Czmht11VFJU8TZVT9bh9wR5SjFaKYqvfcDejv1rYxRQsT82nP\nRXlejrRhHD7i0GQTbUK/cowxlR/kclqliwrGTlNKpYjMGTtMJMmIgqvVWpOEqXHTFrlLtjCUf71R\nBzRSKfygycJcO6eIVJ4tXEIWkWnQXo1LNzb5w69+m1cvvcUwTGnWmvT6ezipwhcObtjAdSRtoRjt\n7dJ2XZ79+h+yuLSMdB0WlhfpJ4qZpVMMtq7iqJhm4FL3PTJHMEzicYquVGfoVBHUAnzhIrUg8AM8\n7VJ36qAlizPztObmmD+5zMFuh8hRaMehn8Cbl6/QS32iNOP29QbtjzxB0/N49dU32dwZoeuCeDji\nUpAxs7ZWOeauX2dn4xavX7+OAm61WqysrLC8uoIWgp29fa5cvsTMzIw5CGfnSOKYOFE0aj5xkpu9\nSgcpFDJToDPCKEJ4PplICeoN0iwkiqOcElUEvgnXGgQBXuAaRWWa4HsuMk0RQpssU55nQlGkKcJ1\n0ZlmZWGB2UaL7t4e6XCA4wii3IrFxE3ReI5jXNvTw3hGQinjXSpMfJtROsLLfRlMWjeTyUdKiRN4\nY2s5g10ZSEgzRc2r5TGZjJw7jAb5etVkeTYeotxIS5iE4n+hlZhl4JpGjZblpTZI29fLisoqKt0G\nn9o5RAoAACAASURBVEI5YB8IhdK0qhwnH57WfptyH7vzWyKdIltQVZ+rKNcqwC23t+rgKve5fK2K\ny5k2H3Z7ymIm+55ppSpUARSAbf7SaIQlfz22PlFY2OTst9a5hYH5LLIp2e+0zTu11iblmxDEoYnx\nUchCpQCtUzrdEbVaDc8LQPomiH/7FCrT/NEX/ohvfes59vY7xPs9sihmc2RsrcM4ZPncWVYbZ7jx\n1jXCpIvfDuhFHeZOztAZdjh16h46o5DW4gqjUYjyPLSjidE4WhvFbJLhIDDpEPL4JdrkWxRaEsUJ\n9WYDMN+Xllaot5voDJJEMVAhiQC/PceTT7wXEcwyjGJmmuZwTJVmGMVGpKEV954/w4MP3cOLb7xZ\nPeiuy62bG/huDS00/d6I++9f5GC/x2tvvM7eQZcTJ05w8sQZ9nf3efWlNxgNR9QCn729O3i1Bh/6\n2Me4ePkKuztbtDyPc6fXWVtZYRCF3NzeZmZeMNeu0+l0aDSahMMRtTyVohCCvQMTtdFxHKTnGlk2\nJsbKYNgjTRVuUGM4GBGNElq1JmdOrHP21En+5JlvAZpazZgIIgSD/gjHddGOcSAsOD4nt/JK05RM\nZQSeR5bEuXmjf+gk5HmkwliwiJx6l46xynJ8I87TDmQqNUnJHdcS/x6KgV3fy6NAGo1AkvwFVWJO\nE2+UlXTl61ByipnGlnOoEqgCmGnxjqdRrdPaUgaHSWXf+MrE75PWGZPvL6jDKnAsf7ffVz60qgDY\n84rpPgqIZdn82xHvFO2pEsGAbVPPWJZnc0qHdWmUEuO/y8qc40qW5V5s5CZhgrG8UQhAVRzgRV/H\noyFAQGDSS4CWuaw2A+FQ95oI4TCKM4TI2D8Y8I1vX2Ljxm1e/tafku11cUcJT548TbvdINQRI5kS\nuhleO6Axf5L3Pno/X/nmH3Ft6zLNWZ+D/h1qTo2rV69w78l7qGlJO2jSC5ps3nmL9ZUFkA5pFON6\nHkprXFeaaIbSQzqaOI5NUg2jj0QI8B2PK9cusbZ+CtmoEyzO4SnPZBHyfJLOAZ3dPaSUrM6dZn9n\nh9Goz/LKMqnuolDsbd3gZXosLlbHYP/d3/89ao0m8wvLbGxssLK2wsrKCv/+93+fOElxpMPm7S1c\n4bO9eQdXSk6trKPSDM916A5HXLl6jWE4QmtzcF56/U0unD3Hd154kds7O5y9D1RcZ2FhgSiKqNXb\njKKMnZ0darU6vh8QpwkLc/OMwpCZoEm/f0CWJQS+B2RkmUYKj9u3r7PQnuUb3/gW66tLNJt1dnf3\nQEjCKMHza2RKoQWozIhylKNwhMSRGVGU4DpGNOJ6HiqXfxu+0M2zPQmkw2GyakeCUGgK88LDPe0H\nHmkUjx39TKgnY2aYpibKYWGC6ExJJViUPxeAnzt3jpmZmbEw/5lnnmFvb48f/dEf5dq1a5w7d45f\n+ZVfYW5u7sizVdSxzYpXsfF3A1dbsZn/cQTkis9Ca15lWleu/4hrPdODrU+rxxa5TCpEqwGqrOic\nRhVXiXDKvxcly6qBsepgPA7AjzvAbKXyZDsn21iu38GlCM8JMrcLtt47JXrkuM2asey7ZHmeHx56\nfDiORVelNshC9unkmcx1ztK6DnGSUWvN8Pf//j/izJl7GcoZXvnO8ywFDe5/7DzeMCQ66PDouXNk\nIuPS7evsjbqkachcS9O9s82HHn6EL+7dorvfYbY9g4oFMpPsbWzx1GPv4frly5w6uU63f4c00ya4\nlHBQUYr0PTwpkdrFc0x2IymNzFc4ApwM6XlkKubWjdvs9zroep3v/uQnWVhdJpMa4UhOrq+jlWI0\nGhFGIf5ck6Rdx3VdTp44RTSI0I6AQBrbzoqytDxPt9uns7XFqYVFWq0GGxu3EEKTZQkC48Qz6PUI\nh0OWF5aYbc7S6RwQjobs7e3jtJqkaBr1GjOBz5zvsb66Qu/ggIW5WW7fvIF78hSLCyucOb3OYBQy\nGA5ZWHS4ees26+snEXikyuHdjz3JPUuL7GxvsLl1gzs7dwgcF78xwyuvXWJ754DGEzOcP3OKvZ0t\nkIIkSajVGwiZEkURfi2gVqvT63VRaYZ0oFarmexEGDPCMIyIotwnIA8VHHgeWkMURQSeb9wJc+OG\nQrfjB7WxyW0URXksJY1w8ngwooibQm777pBlmiSNMNY208ufC8CFEHzpS19iYWFh/NtnP/tZnn76\naf7e3/t7/JN/8k/47Gc/y2c/+9mpz1eJN/5j2iGEOOIkUmzUqsNiYiNTLVuvorptADsO9Kv6Uz5g\n7AOrqk/2Z7neae+dJtawr5X7acvyy4fhcaXc/wLAyxY7Ve0ot8FkqRc5iBs5eKFs1MKOr1wutkhm\n8vDRGqQogpyNW52LsZKjY4rJZ6hUbudrjIIZDke0ZuZIleSv/1d/g69+7Rm+/m9/m1NrJzizMo9S\nQ1QNPvrpj5MNQgIc/tLHPsnO5ja3rt1geHOElgHDwZAHn/gEz1x8ieevXkY3WnTjCC8I+L1vfZEP\nfOC72N/dZP3ESUadPVzh4gZ1Yj3KwxQrmrUAk6pNmaQPaBxfMoyGxNGQulPj5OlVHnrkca7f2aPR\nboIjSVWKjmOkSgmEptnyyVo+odZIP2BtbQ1fOgRCsN/rcJAltLxG5YgHdY9TjVXmGrOEoxDtCPqD\nHufOn2Vnd49+f0i9VqNWC5hptU1Qta07dDoHKDdlcWkeITSNep10OCBo1phpt0jCkPNnTrHZ7TJf\nn6Pb7fPCCy9x6vQZwjhieXmV1bWT7B/0+c4LLxGGJhJntx/SeOzdNJs1lheXuXj5IlGSUW9rXn75\nVdCCq1ffou6eJ4pCHrj/YTKl6Q8G1GsNojjh4KBDr9en0ajTnmkbZ6EswdG5NYnWLC4u5kpPRb1W\nJ01TkjglThJqtRpxNMwToZuojqKgAKSJ2hjU6ibhtuOS+ApwUCod7z/XdUmyBCFNjl2lhOECjyl/\nbhFKeZP+1m/9Fl/+8pcB+Imf+Ak++tGPTgXwMohCBWWeE6ki/8+60bAuwvyqlR7v8eOUedOo+jLl\nPwkEk6BuU6xVlHJVP6v6WPW9yoLlOIq4XEdxb1nBepx8v+r3Ktm8/a7yOFS1uUoEZLdnQsmpDoHb\nCKkd8unFQOuUIkFrlcdWPoxnbeSQxvmCkreqiVhnt8FQQFHQQycObhrgZR4uPkooajWPMMl45Y3X\n+Bf/y79ie7fLDzzyYe69517q7QY3tjZYOrHGcGmJjtzj3ecu8PWXX8Tb79DKFPefbtCcm2F/MMDH\n4wG3iRqmfGWwQTbrwyhiXvk8/+KrSBKeevBB4sGIZr1BPNgnjAeIwCfRUHNcyIyVj1+v40kX8IhV\nxsr58+x1u3z4kz/A0toJTksXHBdkngUoD5qkVRHiwESHVEqjaooszZAI2s4cXhQdxkMulQ89+SSO\n41IPTFaoRKUMhyMylXH+1DL7e/vGS1M6JPEC4Sik2+0iA4dbmyGnVs+wuLLMxUuX8D2X/W4HL/B5\n7uKbqEad9dkZFhYX89gqCuG4rDSWSTPNxtYGw+HIRNPEgwiuvnaF2288y8LCIucv3EukXLb3OoQb\n+6AypNDsHdxhtzPD9WtXeeqhdzOjXXb3h+yPQi7duM0wSZhbXGammdKqZywvLrK2ehan1WF1ZZ2N\n65sMuxHJKGFuZpaHH3yIU6fWybTiT597nheefwGphybmv+saRScCrQSLiyeYnVshSyXbdzrMzy8h\ng4wwGuE4gu07t43OJOyjB12kznA9CR4k8ZR4Bnn5c1PgH//4x3Ech5/+6Z/mp37qp9ja2mJ1dRWA\n1dVVtra2Kp91LDaj2NhVFiBVVHoVYIAJSlNVqqhmG6CqZLnlusu/VVHzVdeK9x+XYWMa93EcNX1c\nKU7z4w6i8nvKfx/HAUwoH4UYBzea1v+i2GKnMtCb2GLTubBpbXdycQfOZNuK3JEFeNuesXZ95n7z\n6crABEFyJCJTZCRoZeI939rY5F/+8/+VcKh47IFHOXXmNJ1uh1EyIotj6rUat27d5vSp06yfO4cb\nhji7u9Drcae/SzYa4TcaLNUbPHji3Xy0qXnhC79JJ4zQqYvrBnhKoIRkFMW02m1jm56mtNpNtBSk\nQjAajXCEIHA9klQRNFyk4xFFEfe+6wE+8cCDDOKYQZoYD8g8NaAx5TskmEzCFKfaWzYnlaYd4gvz\n84eEgtYEjk+r2RiP8emT6yRJMjEXYRgSRTGjgbH39gOfZt1nMBxy0O3SajfJVMbW5iaDwYD5hXlW\nV5dZXV3LY2XHNIIGURhysLvNXKuBN+vjCJcwDHFkgzSTbG916XdTrl3bNiFpqTEa9Ni6tctce552\na4ntzS22Njd5+aUXGSqBqDdxpMuoP2B5cYmZ2VkeePBB5tttvMU+InO4kWW89sYbzDVm6ex3WFpc\nxPM9avUajUaLldUTjJJ9er0e9Xabudk5ksREE818j14Sc+vGFufO3cfVqze4sXGd9z7xXqLhiKUT\n5xBC0VIJnidAp2xt3Wam3aTbq44BVJQ/F4B/7Wtf48SJE2xvb/P000/zwAMPTFyvEjEU5XP/5vPj\na48/+jCPP/ZwpYeeHY+hDJK2ss9QXNXyojJYCCGOyss5Kt+1f7ffWf4sA1pV3wuzujKAVSkB/yxl\nGsVvW73c7RCYFm/F7kN5rG0ALIuCjvu7fACU+1A1fsetoyKIV7mdVeIpmzOp4qJUaFzSHaGRbgak\nKCGo11p89h//HE7m8fR3fwiUw2DUYzTsc+fKJsuLS1x58SVOnj3H1ltv8auvvMZ84HPv6jL33n+e\nkXeKg36XLM2Im3M0W3P86KOf4a3Nm/zRM99k6CgGMkYkDp5nvA5d10VkgiCooUmN40seE8RxXdNv\nBGGaUa9L/HqD2fl5hONQq9eMAjTVFHbETq7gPTzcVC5KyibGAg5toKeVNdu8UJi41kopE68kt3Qq\nTPmK9R4EgTG7mwGEIIpjZi7cg3BMCjKNseO/cP40aZKiBcYt3pHGOkdpRlFI0h9y5sQSruPS63ZZ\nmJ/l8Uc/SK0WENTqvPjSq2zd3iUcjBAIhqMh7WabYW/E1u1t7rv3HpZPrNKPI+4fDkkdj94oIkFw\nZ3uP7Y1b3Ll1najfYWdnm3MPzjI3s8DuVofAdUyYg9l5vv3tZ/ja177C6TNnqNUaXLlyhbkT88ws\nrdIdDLhz/RbD4YizZ88R6oxubw9dh9qCx7Kap68H+K06wnf49X//e2it+cj3fA8vPP8dhErZun2L\n2dlZRqPh1HmAPyeAnzhxAoDl5WU+85nP8Mwzz7C6usrm5iZra2tGQ71Srcn+L3/yx6ZSeUURwnhu\n3Y2yK8rd3E6rAGnyerWZW0HNTqvj7YCuvWkKECvs16vaOA0Eq/pU9VtZbHNcmTauZWAte2eWAbFq\n/or2H9eH8gFZ/q1437RS5SVbPFt4/MLReDHltgXpLELGaDlCiRGJTsi0RxrH/Ozf/5/4g9/8Q+qq\nTsML2Nx7i62NWzRcj/Bgh3Z7HicMEdLh4Ycf4PIbbzBwBM9du0zSkDz4wP28+6FHePX117i8ucn6\nQYOfeP9HePaP/4iwHpD4PouNNrdu32JjpsZjF87S2xqSJLGJX60dwjTF9QKE4xvRBZokjnB0k7/1\nd/9rRnFClCZI1wUtcBwBuXM6eZIQe2zLa8wei+wYjnEwGk4o5AvK2/d96vX6BIFkrxmlFPEwJFMZ\ncRQb1/skzhV8hmNo1zxqsy283IEnyVKiyNhraw1hGGLIOoFKtbFAygaoLGbj9i3euvISYdhlZbGR\nKxY9lBpyYm2WXmeDdutetvf3eP3iG4zCmLnFFYJ6jdOnz7F55w7n772Hfr+H40gOVueI1R10EjNT\nrzNkwLPPfJP3PvYedra3GQz7JGnCQw8/SrvdpuW2+MLv/CGf+sEfZHZ9lkajYTgmR7LR22Cm5vOn\n3/wSDz/8EOfOrnL79hUuvnmR97/vvdTrTaT0mJ1bwPcDzl94hGazhZCS3/31fzd17f9HA/hwOCTL\nMto5q/eFL3yBn/3Zn+XTn/40n/vc5/iZn/kZPve5z/HDP/zDlc9XnfxFsTd+kd26fK18fyH7rCpV\nyrsqsUr53mniA7vecj32tXIpu5bbFIpdt31gHOljRd+qSpWn6ds5aKY9Y4N02Wmq3L7jxrp8f/Gv\nYNfLzxSf09j5wkFqWh/SimiKdl/sdrl4CKFQjjSBsKSL0jU8Z443XnuVutsm3uvQ795mkO3QdGG2\n4ZMMQ+JBh41b15DNFi++8QrNVos7e3do1XzCfo/NV67w1gsX+ej3fy86HMGtDo1On3/4k3+bv/6L\n/5TYrTP0AuoNI4qZDxzarmK21WYU9nFqAdFgiHYkaZ7Sq96oUWvWac7Ps9/rETSaoDITkhTj/TjO\nYGWcxCfGwdaLlOenypO3KLVabSyfBiYIkyRJxtxfeb6llLieiZrYatVJEiPfLaJb2rFXTMx7U1/s\nxZj8oxnteo3RaITv+4RhSLPRpNfrE6f7zM15fOz7niLLNL1en/5gwO7uLuFoSBgO8fwF5mZdtAtn\n7z1HEqcMhxHb27t88Q9+l7WTJ9m4cYVUZ9x74R5qjTrnT15g2I84SPucP3OasydOkyQxg0GPXr/D\n6sqKSUKcZdx88xLnVtZ54/mXSeKUubl5PM9lb3+H+cVZZudaPHz+HKszTRzVYf3COo/df4Z+b8jM\n7DyZgosv95iba9Pr7nLrrSv/6Rx5tra2+MxnPgMYB5Ef//Ef5xOf+ARPPvkkP/IjP8Iv/MIvcO6c\nMSOsKrbFyDTRBUxSSDZAlMEQGAcxKpcqC5MqxV35XfZGP05+bV8rFm554G3gK1PIZeCv5ESmjM+0\nCb6bVU25/vL1qsOr6vkqh6zyfNn6jqpxLIBkUi799kRLNoAfx42U+1JF0TuORrsaLRSJFiTaQzot\nvvH1l9G6TuDW2d6/QtrbwQlSap6LTGMcMuJkyJkT9zMULptvXeED77qPmUaD02trfOP/+U0W77mX\nP/3tL/Dis9/ifR98kkfn13nu1Zd58v0f4C899UF++8rLjLKI1uwMmzeuMQoj1teXUdmQ//a/+xlW\n1k9yY2ODq29d5+rFS8a6JRri1Os89uSTzC0tc9DtIB3XxFnXhV4pMx6p0hkn97XH0ngHTreqqirF\nfJVjAVWt+bFddH5fmItZ6jVjWue6Lmkcj/eDK/N9LfWYQCyeD4Igl+dnDEd9pJwjThLqDZ9MNegc\n9BCOII4TmrUaZClJo8E9Z04TBD6u6+IHLol2ac/OgoZ1z+ddF+5Fqafo9ru4NZ8ojtjvdpht11ld\nWEQseHzt2je4dPEaS4sr3HPPPSDWqNVNiOFu54BWs0Gr5rK2eoLZ2Xl6/RGzcwvs7Oyy3FvG9eCF\nF57j4qXXidMIJ00YDkYEQUCt0aTTHdBuz+F4Pjf7B3R7A777uz9MEAT8n8fMhdB/VqHr/w9FCMF/\n+Pf/tnLBlL+70juyocsgOd6Q8iilXbB4xwFZFWjb91ZRmnadVaBVLlXXp1HuVWMy7RB5O+KVMgdR\nbm8VmNliifIBWxZX3O1wK3M1/7H9ePSpjx259vy3/vBtiWfKlH1ln1OHTCYEDZdRmuDV5vnKV1/i\nxlv7LLdXuP7qi+xffx2ZdpkJTBabwHfpRyNay8ssnD5DfWUNt9ZEaMH64jJXX3+DR9wZbrzxBo88\n8SgvXXuDG9s3ePjcBd6zfg9f/sIXyRbn+N3XX+TV3g7tmXl2bm1wZnWeR+49zQP3neMHf+hTxCiU\ndBDCwZcuvuMQpREJCikFaWbs3Z1cJ1BkiwIjcMhtbY6M9d3W63d9+FNHrn37q7931+fL7yF/f5Z7\nyaK1EYQIYUQ7Oje5s9aWIg8ljcids8iVo+owUl8u1/eEb0ImaJOEPE1T4ig2BzzaRAPExD5PXE2W\npMaFPslyW29jwjocDclQOJ7LYDQg8ASeEyBwSWMTQ3xmpsX5e8+DEOzuHfDyy6/RH4Sk7ojeYMjc\nzCKu66O1Q6PeojfoE0Uj2jNNmq06UTiiHhtHob2DffYPTAyZwSji2o0btGdn6fX7JvFDrcGvff7X\npq7xd9SV3mbFp57+enKR2KxZVWCnsrleQQUU38vsfxXreKQJUwDHpiyqnH3K90+jEO222u8rA7D9\nzNvZOOWDwXZemuaEVK6j6qCy33+32DHlequ+279VteG4/hZJJGy5drmU48UUgdPKJRUhruPQ6Y6Q\nssZv/cbv4Mg5XOXy1qVLpOGAxaVZ9rZ2GQ4ktZrHMIyot5osra1w+dY1Hj9zmkzA1sYWbgrbW9v8\n6cFlVpcWefnam6yfO8WZh+/lmeee5anv+wh/5bG/wz//uX/G6blFeoFkexBxYv0UKh2yvHqCT3z/\nD+D4HkJlaGGyl0dZaoJGCY2WGJGJKJTyApBoWxxRJAjIl9Hb0Y1MO2iLMSyLEsul0jQYgUmfmD8j\n8nSC+f8MfhuPSLRGuCZImTl8QCgT7U8IE3Nc58GtNJq0yEsmwFMaMEGuigBR5Gaj+D6zrZqJGKgU\nOlVkqQlYpbRiNmsTpzFpltJsmNSEaaJwnBpZavZNvV6j09lDAeEoZHFhjnY7I6bDqfU1pPRIU00U\nJbTbdYTOiBxBlihatRaNoElTBGxvbzO7uM7pex5kGI3o9vpEwtiQu802tUadg07n2Hl6R13py+BV\nxcrp7Hi59wTI6aOiCptSt2V1VZYXQoiJGCk2tV117zSX/re7oG1ALG+Iqg1U9Y7jREFVwFn0qxxC\n165fiKNmlvbYHScOqSrlcasqx8n63i44HwfyBXALYXJPlusH0I4mTTKW59d47tsv06RGPWjyypuv\n0z84oOlrgobHwvIKmzf3caRxcT977jx7/T4ukp2tO9z30GPc2bhDvdmgPjPD7u5thp2IlbU1hmnM\nyfYKf+1v/E02D/a5s32bT/3VH0PWa/z43/2b0J7Hr9fo9w544cWX+Vt/86fpDTrGu09KE2NDaaQC\npMil2kfBtOzV/HZA1xpIDGFcPZ7OWDGcc2jqaF32+w7nyAQOK9Lojf07pMiDdTHOcgOg83ag87Db\nUuZKaRMVUGHs/wEckpyQ12RZXDTPrAnHmK9q8wODbmL8a6Q01jmORLoCV4N06jSFiaDoOCYjk4nW\nYCJVFvb0cRKTqRSlUmZnGsRxCpnxrEySjHqryUiGOFoxv7RAp9vF9+uIFPr9EWquiV9rcdA5YL+3\ngRYQJiELSys4rkOmNZ7vc/LUGT4/fabeOQAvW2TAJPhWBXSyN7IdybB41nf9SnB0x2zl0QVcBsUq\nK5Qq0UH597sBlF1sICy7r1dRuXZ7y9/vBnx2mRZ6ttzmQgll96cssnq7fa269zgKvOq342zo7Yzj\nVXUUnIddZ/nwGbfRcWl5bb74B3/M6y9chMSh2dijlg1w65DEI7KkhhQt3EWHg9GQhy5coNsbkSUp\nbVlD9SPatTrLC4uEWUptdoYdEpLRkN7VHpvXb7B78w4rqye5/6GH+OLWV1lbnKPp1/nJv/Kf83/9\n3hcYDEcEtTpaw1e+9jUefuwhk78SjRaZyeiSKbQSFLlTTf8P489MjpcmjxZTKUIrF6X1ONFvVRlT\n9IWDVIXuqbyex/s2fy5HV9O6PLmw1noMyEAegtWkdhuvciXzw0Uj0YxjeavCPNgA/7h6IYwCNBcg\nCSFxkjyonDIBqgwVD1opk+cTSRanoEG5Cs8LyDKN63o4rgn1G3geUnoIDa5TI0kyCGM8zyOKQpOQ\nOTCha4XjUnNnyTJNOEpMbtq4y0xDUvPahFGE43lId4Fef0Cr3UK6Hr1ez6SmO6a8owAOkwBSlq3C\nIQU+dj6ooByLeqpibVfJt48Do+NEGcVvd6M+pwHJ2733OKucsvhomihkGvVe1Y5pfS63rUzVHdcH\nuxwHFkUpKzHtZ48ToZRDEJeLzU2UzQ3Lc6+F5K0rN/jal7/B2eUzzM/NcvnSRQbDLotL87iuYDSK\nqfkN/IUGS42ThGmMSFIabkCcKtL+iLg74OaNm8jAZxiFdDu7qMGAht8gDjWjm3tsvrXBX/sH/wPf\n/fGPc3NrkzdfeZ3HHnmUL3z9OfajASLMOHv+Xg46XTwvIExGJpE2hlqV0jggUazHPGQuAjKdWf00\nYhYpJI446rRTGc4YTFCvu8zV+B132QuyoJy1CRaVXzicN8eylLHrFaqIoG3MiQGUSQeuDyVCZEIj\nqJNkysQi0XqCd1AyD9OQdynIk30IKVHCHASpUiZ6IBJXCIQXIBGMVIQUzjiWSZalKJ2QpiYhsk4V\nUoSgJa7TIM0y8DwyBxw/INMmRHF7dpYshTnpkmYamfQOxy0PqjUKI5ZbCyYPbM1jzp8jreBu7PIO\nplQrFoIN3DnrJg6T8sp84lOtyJLDzBVjFi9fQI4UZCIYD4oQh/dopcdKEIkYv8fcVwYGWzRTbPCi\n2sLd265DMFmF7dWZUxoUz2vrHlNvlk2mUyruk/KwHWXAmaYHKAPfNKAat3QM6od9LNpi3mv+Np9m\nx1TFlCo8944UOenhetwhonUZpPV4DVBde2WfjhYX8uzjBeVm2GCjkDKgLkFIaqLN137/85yYWaVW\nb0BDolSXJgNkJJGNGQ7ChDl8zrzrXSzPNrn47DdpexIdDUmHIf3dHa48+yyNMCY+6LIQePQywVy9\nTcPxaK/OkvRD4s4u/8c//kf8F//9f0MzCBhubbAVDlk5sch8Os+tm5d45PFHeM8T7yZTGT6OiY2R\n98GMaz5fOeWqC+ASh2OsNehMo4UmE9WmmNM4vWljKzR5ECYzT1pUg0wVBT4xw3nbx8Qak2t9/Lf9\nbgqZeBFRUiPzT12IVHSuYxtvctCYUK9Ka+I8DZou1LrCJGrO1ZxkKn+PEHh5th3Hc8bvEyLI94sy\nCCoEAkmcJOPWxXEKuTxfShPyV2tzMAoh8DAhDZxcJOT6DrP1GbSGpoUfd9NXvKMAnmWHWW6K4IMm\nrgAAIABJREFUBWnK4YZPJ9KhmUtK5yeflIj8OaUlKrcrteXcQhgZG9oAQlakQ3JdhBRjlnOyHUVD\nCjGFDcimfQVHULxv3PIpYFkVhe/wnUfHJ7MyWJuDTlFkvC47R1QBs/3PftfkHBSHZiEumbx2eHBZ\nnBFHuSXG9g2TxYb142LBgElHVd6qxQF5XJl2gBVFjTHLpKkqMr84noOUDmhJmmZ4rsev/Jt/y/bG\nDqfXz9EfDrl09XXmAhcnE+hoiKjVjRt7q8m5lRNcfu0l9m7dQjkZvopNtEflmABNnk+apgSuj5IO\nvU6PRrtN4Ls89sRjiE7C9YM9vvSrv8EHPvZxagd9nEDQDjy6Wcj6iVV+6Zc/x1/+9P/Nzs4dfM9H\nawPgCI0y6GfmlUmK84iY6BgirsoRayxKnEZZlwgF+732vNj1VXFXBcUgintL3N/4GfvVh2/L/2/6\n7zrG2/SI6KjAAq3R0qRJG6/aon6lj65JYah0aYmdwHipGjm7rRMycb4Fh3H+fc8d96EQ89n7JVWY\niJN5QuQkSY4kQXk75R1VYtqnbVmmPS5j4GA8GFLKsSJFZRk6U5ApEwCGYmoNpSURSHFYt5b6MG60\nNWnl9Gi2mKbs7GCbMdoAWfxWtainTUgVNV2mpI0zylGTveNk7+XDaJoYYhoBO60tjjjal2l9s+WZ\nd7NWkZKcoqw60O9epopZNBg5scIEXNGgjOItiiJ8v04SZdy8fo3tWzssrp+kE43wFLiDhGHaZ36u\nTpokxAdd3v/4h2isnOLmyy+x+eYrOIO+SZzsOdT8AKWgF3ZZWT3H3p0BC4szzKwumrUbp+ze3uTS\nIOI9Zx7gZH2GVHhsv/oaC27Ar/+/v8rB/BzXewfcd98ZnnziPYTRiGazTprmYiDhoIWJ8yKFHLu8\nTwPJ8m/l+6aFQbjbfB2XBMQWW9l1ThNzFtfsz2mlSjRYvLuov8AJ+3pZJl+1Xqo4huL3ci7YKuyo\nEu3a7Z30AzGeq8V1P882VG7L3co7BuBlxdIhUIkJxw8p5Ri8i4Etci6OBzOXoUXjcI4eUkiT9kib\nwPeu547j+Jrs0kc9/4q22KBrvxMOvcayLKNWq01MOkyRKVqTXQWoxfNFUChb3n94XRUEy0SZBqZC\nHN24VcWM8dFNUyV+EUKAVkcW8dSFpifd1ct1lttxnKx7Wrkbi+m4EoPi+X3C/KkVzLTbdA56rK6c\n4Hd++/dpN+c4c9/9pErztd/8XZpRgu86HPS6NGt1/CQj7h4QLC7R690hSfok6ZBYK9q1JlmW4Dse\n8aCHpzVhr0c4GhHUayyfPkt4a4PN7Q10GPP12/v4rRlOrj7Jk5/8XmQ/4lc//3lIErI45PbN63zu\nl36e7Tu38XxjlWD4cZELqW2O8CjRUbXmqkrZU/XtUID2Xq0KB1EFsva18vVibxX1FM5Zx4U0vlv/\nCoefMlgXh0t5vR1yuvrIeh1zJKU+lA+tQzHoZP9sM9eir1l2eHjaxJ/drrcT0vkdFKEcletWZppX\neqz1nvgnZU5c5QtIg+scatqVSscg7Xk+kKdFyjNGa2XL06sdaop2FZ/lhVJ4sdmWM1WAOu20L05u\n27QPzIJOkmSql1x54RXFSBwsKWFps1QtdMO5Vsmkj1LwAJLDhXs3sJ226Kuey7I0v6d85c9GkR15\nWpixGDPf2sTRcF2X4SBitj3P88++wN6dfR57+H2EaPb291lcWkTu7KHUgCTKGGQjslHGxTffoD4a\nMLfUIozn2Mu6KJUxUimtepM01ogkI+oOqEmP4W6PWnOWSy+/xpNnzvPkB9+PQuOOMvx6k+35Gq/2\ndki6PT7ymU9zcWuTV//gd6jVFHE0wvMd4iRC5vFPtM45iEKUqI7Oa3nu7HE6Mp8V6/puIO55XqX1\n1NudlyqAm7b/yqXc1mlth+qIp8X7yhyJHauoqFdKOWHhVEWETK7rSee34t4ysSiEsLJjHbZ7WnrE\n48o7CuA2eBXFBkQAR7rGHVgXuSXzGA9SGiWEEGQYBtnVGYULsSMkvu/nIJ0rS0XOSAuRJ7o9fKe9\nsMugV7j82jksbZbHXgRlkK06zYu/C7GRfW3c75wamLaoy20sDrUyG3c3qlYIxmM2rVRxGHa/ppnw\npRalU66vuh1VIH/8Ir6bbL3IOViuJ4oSmvU2WaJ57tvPszS/zLXtLWZmZ7n51nVklrG4vEgWuTix\nw/buDkK61Fs+rWYNT0CrUWczShA6w1XgOCna8dnvdWknMbW5RWrtGebOruCmmu3tA8TWLvc/dD9z\nXpMoTNALbS686wFGB112vv4aO5ubJHHI3/nbP8Ogd4DjO0jHMS7nGfm6zYEgN6Yur5Oy+KusfzmO\nej2OSypKmqbj9ZkV6cVKADftYLDbaq/7Mni+3VJ+n71/j9t35UPHFosWbazCJnvdl+8vnKfKeDKO\n9ZJLDorfC3GwvY/uFvukXN5RGXjZBMweuOKeIpmo1gXFnOuKs8zYxUqQrkecpiAkSmX4vk8SJbi6\nsGbJlaU5gBfvFhxl78ptLP6VvfeKwbfbW1XKis4q4LVPf631OIJeFfVQ3ih2VDjbfttWBBf5+OBo\n6FjD+UwPB1uUww1W1F9stkJNUbXpTCadwprEWAYdPbAmWcjJw69s7lkuQhzPZoqShYTWGoSDIyFJ\nUp795nPs7uzyyIOPsh+49O/skXY6NDyXXhYyPzMLfcHqqQZDCc3lNXZ2d5n1JGtLS2zXZxkc7NOL\nYhyvgRt4LJ0+zamH3oVoz3H74AB3aRlncZfhbpftToeNr3+Tk3PLPPXwe6k7LeR+zHIwy0Mn7+Xf\n/fZv8ImnP8aTTzxJt78LGGJBI3CFRGgHpTMQ2liDUA3G5fUmxNGQEgXHU/xkni/AdTqQeFaiaFvx\nBkcP+KPzUS3uqHrmbmuyXMrUbxUAw2FkUPu5she31kZM5HneRB3FPrCBuzjAbOw9HH9DLBRj6+S5\nNYv22fVWjcXd+v2OAXgx8ceddsV9IhMonSFE7qggQCnT2TevXOXC/Q/g1RuEgwPqQQ3H9RA4ZHFM\no1YnjiJjfiUETsEOZZlRZsKE7L38/vLvdilkbMWnTUGUB75YCNMWsE0VlKnvYrJt2+9iAdnhUu2F\nVR7X4jAsAN+mIMpcQ9V8jDe/mqSiimtV/SpsicvjYo/P4cEwGdP77Ra77qo2OMLJg5wZsBLC2FJr\nIXGQfOfZ59CJorN3QPvCSbZ3t1lq1El1ykgpdnodAi1IXZd7H383J8+f48WvfpvR3jYH/SHN+SWi\nROOi8Jtt5hYXiFyHURjSnHcZxSmDMCZ1HYZa4WSaDMXNzi6LGzd54vRpFoIW/WjIzOoK3/f0x3n8\ne99HWqTpyhKyLCOoNQynpMkzpmfIPKxqFXCVqU8zNpMs/sRcTRALmuMIwTLHacty7Xm267b/Lq9T\nOzKpPY9VhNG0tWG/s0wplyn7Ko/sKmKpXJcdPbSqbZM6q+pD7W7F3kvTQl7Y5R135Jk20UVJUwPc\nRRJRKQXScUzOOgTf+va3+d/+9S/y4e/5CE9/30fQSUamJaSKml8jihMzKDJfaKKwNXfHIgwbPKsi\nqpUBzQZau91VwFRFedvfiwPCXmxVGu84NoF5HOlQWNAUoCxyBw4DTEeDNVVtNrsNZQ/N8mYYAy/g\n5naxZRCvWpzSmXQ2suuqal/VGE+ruyhVm3Hyem67O2ZMBGgT2e7NVy4y7A9YXz3Nwe4+lzevwMGA\nRb+O6zu4boOD6IA407QXlmkvrtEbJqyfOcfS+x5n4/ZtTjx4Py8/+x1knJIOe3iOi8gU4c4uyysn\nCcKUWqqJfJ+e5+JLgfI1sSt4c+8Ws9cuIa8ssvDAOcR9q5zpPsD8/CJSapIUavUGnu/RH4Sm+WZA\nAI2QhwkbyvNnz+MhZX1UzmyP9+E1s8+mjftxhJf9Xrst5XbZXGZ5rqv2iv3OqlIGX5tQKBM1tvXH\nUY5kEo/KxFnR3yqv5ipiyH7H21nP9mFWhUfl8o6KUMpyq/IAQKGIAIQxfNfabMhUKbx6Ay+o8ehj\nj+MGAb/4uX/DyZPrfP/HnmZ+pk2WJBYI5gOC8e5yOPT8cxxnTJ1WUeHFSWifpEqpcUyNqgkqn+42\n5VtmwYpP+31lADdONLntMhYYMrlRit8KkCzaUQSysttjL8qi3XfbnGWTqWmLGQoZ7aScsopTMd/F\nOK+pNg+BKCzJp8vxbQ6kqmiloHgPhoMQKFSW8cd//CUW5xfZ29llYW6RwaXr+BoOHJd6o452PZr1\nJgMlWDl/D1FoUl216nWaOiN1JWtnz3L50lvs37yJpxSDbocg8FGjAW3X4cT8DHXPR9RrdH0JwyH9\n0ZCOqxlmLt/6431wBPe3PeRsg8yFtRPr7He2yYQJtJSpBN8PjBJH6NyxXKKURumj4FW1j6rEV/ZB\nWp77Ki5yPK8Wl2avicrxr6D0CyKlTCGXjQru5q1drrf8OQ30px0E5XeUwdx+rixHt9tbftc0orCq\n2Jm7qix8jtx/7NX/hCXJwbUAl2kssJHdAaKgOiRRmjA3N0d3NKLVajNKDlhYWmZxaYmrV67wT//F\nv+AHP/FJ3v/Ek7jSRWcpaEOFVclEywBj3iuO/G5PhJRyHJDeXnhlNso+JKoA3r7P5giKUt4cxUFT\nbIB6vW6NVb54pcBx5MT9xYFTRRWUuYEClMuHlmnQ0Q03bVNplR3ZUPbhYffNdTyUIS/tGo4ATbmU\nD8ojbTA+eGMvPaU1rnB58aWXuH3zFveduw+n5dLvdFnMBCEpOILe9g4aCQuLeOsn8WZm0LEgHWYs\nnDvB3u2rvPSd55l16pxaPcFo6w6OSun1DmjNrOI1XCId0k9H9G73iKIBO9u3qe91UIEgrEObFq1h\nRvzSRdQ9Z5BnV/nAd72P559/nlOnT5hs5/WA3rCP5wW5LtZ4lUoykC5CHuUCq6henespqoCxDCiG\nQ60cbjNXOciUQdP+rcoO3AamIopkmdq0/9l9sa3Bpq2DKsq9eMa+bsu77cPuz3qAldd9Wd9lj3P5\nMJhW7sZplMs7BuCeV2hmjYLyaHAq8+kKRaI0WZYi0gzhClzfJ+708TyHhTOrXNvdYtnxiLXmvfc/\nyGP3vYuXX3mFi5cv8QOf/CTzczO4UiKyDM910VmGTBMTa1ypwzgITrEwc9vhEpjb8i9b9GKXIpph\n8VwVNVHOj1mesCrxUvG9eLdN/dv/bHZROg6Ok1PhQk6IZgpwLDZ10cdyNMYy0OtM556tjDPB573N\n+2e39yiVX6bsx4s9O6qsNNyEifMxbT2X21seNylMUoNiDlwnYDRSfPE/fIO1tbP0O33m6nX29jZo\n1uqEww5CJzhuSqoEne42p86fYtA9IOyOOHNqFRGP2H3zMsEw5Lkvf4lPfeoH2Z1vkw0EiQthmNIJ\nt9HBG3TjhKgbEngeTibRjk/d9UjiEVqHRAh2d7YY7uwj5xs0GnNs3bzN2vmTyFRCnOLVA1IBfgau\ncvLYHsayqvDYtQ++o+BazPfh+JZlv0fX2/GsexmUymv1boSMTYSUwaogJOw6beuNae2BoxyiXcr7\npPit6v12H6cBehnAq2TdNgFiH2BVVH5Ve/7CilCyLC0BxyQ1W/zL4oTYNex44LggzWZ3YxCOw8nz\nZ/jDP/kSQZRQb9Q56HbJHIfVtTXa83P8/C//Eh/8wAf4/qc/zrDTxUPiOg5SZ7gSUl3EaRN5PAnz\nLq0y4+VpnYhObs5VUA+FnTZUK0GqJrIAkgJMy5poW1ZdOPNIKckyRZqkE3WB4WSKsTqMjX24sW2O\nI80S0jTNQU/jOAW1fzjexbvL7LFZSGL8DltmPWlOaYGyPmpDO21Bpmk68X4hbJv+470CizZUiQ68\nwCEZxSwuLtDpDEjijK2tfbLMAxnQagQM9u4Q9fZItKReq4GKGKiYfhwzu3qCulbcunyRXn9I04ft\nzdskG3dYnZuhH4e88tJztFo1Nnd36B/0yZJ9XM+jFjSYW5hlMxzh+D5Bs0WaQnOuxawLaTxk2I9I\n65J6s0nm1/HdGm6ikNLBlx5kEdqDBBOF0FUOGkGWW2Q51hjYn+U1aMb/qNiuWhwBx4mtinIci3+U\nqj8UnUy7v8xtVnF3ZdPdKi7AJnSqSvkwqKKO7QPHNhqw31eu3+Zk7T5XpfUrE3F222xc/AsrQrEp\n7rKSAUxH0jRFZpIUcB0nl2UqMpWRKUGWSRYXFkijmFES0XbnWV2fJWg0WM8yBlHI937047z6ysv8\n4z/9Of6zH/oh7r9wgeGgT81xifJMHMKRJFliNPyuiUgmHAcsKsIG5zhPAWWLOgozw6Jf5Qn2fR84\nqiEvStVkTrCTiLH82y52bOvDcRzXOgbUYjHU63WLCirEJJNUkVImw3hRDkGVsbdoAZZVqe3KfSjX\nY/et+F4VVdA+PI6rv3yI2Idpt9un0ahx/fotGvU2Qa3Jc9/5Eo+8+zG2rt9CpDE3N27jIvAAFCRp\nRqM5i2woTp4+z15/yLAzYHVxkZ0bN7h++SLNLCVVCW6jxq2bN1mYn6fX64LKiEchUkr6nQOWVpdw\n2jXkTI1GOk8vjdECVleXGO7vIzKXpNfnha9/i/ec+EFUmrF94xbXn3uZC4/cz0gJvEQjPAflmsh6\njpbo3PRTlPprj18Z3KqVvEcPVDM31UBr13PcgVzmWo8TDRRtLwiDMgDah5C9fuxni1IcEsVn1fuq\nYo5UHYA20VGAflWy5moO5vCaLXKq8va021DloX5ceUeVmDAp4y3Lw7XWuHh4eZ+E0djgCg+hNY6U\n+EjmZ2a5vnmbh5bOsHcwQPZGjOKYRquJ5wQ89cT70VrxK7/263zgfe/jI9/zYeIkRgY+QmtAIaUJ\nRamUOSBAgDRKwyxLkeIwXksBmoVDQ6FIK07bApxsd+M4jicUNDbFe9z4FAtIZWoMzOVFVlZWFr/b\ni9v0K52ggqR0cJzJWOnFM7ZycEIUlE3K7+02loHEpl7sOa0Sq9jgPu7zMexwUfr9/ri9ZbESQFCv\n0+32WFhcIU00X/2Tb7CzvUt9fYZz58+xefUKrZkFwu4+WTQkUikKTa87YGZ5Bc9v4EYj5lqSQGk2\nbt3Aj2McKfCkpB4ExCozLvOBj8o0sl4jCHyiUWjEdUqRRiGuI4njyCQgFhqymNHeAXqUcStJ2fwP\nAR/8wIf4/o9/gn/4P/4Dfu6X/3cO7vRpCgcnhaEHsdD4mTI+EGjQRw/P8lxWXSuvs8lS7VVsP2NT\nzMdlsbLXPFTHpC/fb7erChzL7SqAv/jbFtOU94HdBntt2RyJ/c8myMrjWrVv7LVnr/syx2oTPmUO\n4G5iE7u84wBeli+VOyOlA05+4klMlnotEFITqwwnlXzofR/gyvOv0On3MHF5XWYaAY1ag1Qr9g/2\niNOID3/oIzz3/LPc2trg0z/0l6n5PkKleMI4Sag4xMuTPyBchOOCkHkQ+pIsWOuxUrCgvrMsG3ts\n2hSq67pjWa1NiUxj4WwN9KG3JibtkzrMLmSX4ntBIZfZVziM41LcU4C/zS4Wi6zgMsptlGKSUi4v\nevu9aTYpQimUvnYZi8pKXptFXXdjIQslbln+WGyuMAmpN5qMhhFxmPGtbz7LmTMXUJkiimM27tyh\n3moRuJJ6Umdvf59hrBC1Jq3FNbb2+vQGQ86sryPjEBGFBDpDKMGw36MfD5GBh1aaU6trDNQBKToP\ng5jRPzhAxhlpqo1ZZarQGWzevsPTn/wY1y5ehkwSNgKClUVudnY5e9+7uf+e+2jUGtQaDWSS4klJ\n4mhSYZTUXm466jAd4Mpc3t1EIpP3Tb+3WGu2B2a5TLVMOoYCL9pcBd7l9Vyux14nNsdRRVmX13QV\nN1z8s8Ufdl/L1jv2b2XlZdX42ARK1fW7Ud5FeUftwG0xSplVKj5TnZHlAXrtcJGO6yBR1JCcXT/F\nN770FZ5eO0Gn26XuB+b5JCEJQ5qeTyPwCVXI448/zubWJp/9n/8ZP/zpT/PUex9j2NnFUSmtekAS\nRwZ43SKTh9kwAnGkrWXgKmvWC9bLlrmVJ6aYSDjcEIWd6iQlSq5XnZzwshNEVRvBLK5iMRaedLYY\nw67TXoDlTaoyNUHBVHmNjvuvJzebzX6WWdDi0LOTL9gUy3Gsul3HEQrKd8gShe/UuPjWNeZmF1ha\nWERlcP3KFYbDAUIKZpot3BRmfRdGMfW5BZzmDLu3N5htt9E64+rli0iVEAQuUZyisoyD/S6J0Pi+\nz/L8Ao1mk16UEIURbj1g0O1Rm19mq9dlYX2NmbUTuP2QhuOwdvIcZ8/ciyt9Xrl5g3e95wlubNwm\n9iXvfuoJRNBgFEbU2m3SXh+hQPkQIqkpgdCQVeBsmYr9s4K4Gbu732uvk3KxvTWr9vbd3n+c+KCK\nW6uyIJvGfVQpG6tM/WxCa4ILzddplQjYHo9p41esbZuCtwkPuw93K+8gBe5g2mqLTyZlxwU4ucg8\nl17eUa2JtcYNfESqObG4jK65CB1DFpPGGgn4ns/S2iqdfg/pS7xggV44YGVpiQsPPs7v/s5vc7C/\ny8c+/EF8oRkN+8bLTZms1U6u2DTB3gUZagIcgiDIe2MBapqZwyanvG1q4CgoV2u1wzAcy9OBCfar\nTJW6FXLxgoLKsixnscG4XhvPvSI0iCNzbkFnlSaGxQE7uUAVjutNaNXLsTDGC1kddYcvgLYIiFTc\nPw4TbMXYKAcBqirFuGRZNjZNnWiL0uhU4Em4evEqJ1dOEPYHLCws0OvsIXXGaBjiC43nZERCUl9Y\n4MIjjzGIU+ZThacVne4BcTxCxzGe7+J5PqM0zg/GmJrnkagMMvCDANd16fZ77O0qnvqu99Pdus2J\nhx5E1xr0Lt9CRSkvvPgqZ0+eZGl2gYcvvIvl1iLLT57k6rWrvOuDT3Hl9TfwA4+N/T1mHReZgBYQ\nuxm11GTY0e5RRZrNYd0NTKrEHyaZyHRosL2oodpqyrbGsuepvMaqqNRizVS1/W4Ua/GOKiVhFeVt\n13kcF2PvubKosri3kL1Pa5tdEit3gf15N46zXN4xAIejsbDLi0wIkwTVQRhzMAFCCySCVBgvTV84\nxCrDazXYvrNJu9UmiSKCWoPhcECn0yGo12h5LToHHfrDPkhB4tT4vo98lDffeI1/+S//FX/1x36E\ntZUlHAGjQZ+gViOMQgM2rpfLoCdP2KqFK4QwZnZMUp9JkkxYmNjP2RSHLXqZpuW2FXdlBx075Vzx\nTFF/prIj9Womw1zaIFs1J/ZGLK4Vm7WKWrEB1W53MR5l0Lap9eLvaRsDmLAEKo+D67okKqZeryOV\ny5U3L3P+3H2MhkN6eztIHbO80EaHPqNen76OiJGcPn2exvwCvd097n/oAer/H3NvHmRZdtd3fs65\n+1vz5Z6VVdVVXVW9L1W9qUGrbQlkFoFgRrLkAEaYAYNNhGE8hhk7iBjP2BIzEx4HYTDMGIwAG5DZ\nJCMJLEC0JCSrpZbUa3V37Uvu29vvfu/8cfPkO+/my5Zw2NGciIzMvO++e8/yO7/z/X1/v/M7huDZ\nz3+WLE+xPYcwivFsSRbnmK6DEeXUphqEaUK/O6BmObiOQ9M0sBwHKSTHjh0nBFLboT0MyLKca7dX\n6HS7PPbweVxTEl+7xvEL95NYJmu9Pe47eSc7u5uEjlecypNkZHFCLCBNsuIosNLRZ6p/y3JZVnB6\n35aLEPspK44oZX/HpORQR1lMZeAyiRqZZDmU21EuR+UfLyPZ8vv0RU5Refp3y+2YtOBMUrpH1V1X\n1JOskqP+Pqq87gc6qAFVZsWhzjYlZIVA5YbAzAQyF/vn4AmcTJKaBrPHj7G+vs78Q/OEUcR2e48w\niKjWaqTkbO3sEYQh1VqNWs1jzx+yvdfh7rPnMOVd/Pwv/r/8vR/7UVzLZGlpnl57j2Zziijwiy3k\n2or+Wp2cZhl5engnpa5kJilqXVkqJFk24VT/lFG9eu5RpuOozmJiznFl7pbRRHm3XLk+qv0KTU9S\nDnq79bEuR52UKTXlBFbvPCofjU4X6E7TMAyLyWdkZGHO5z/9aVrNFi89+zwnTxzn1SsXsYwco+Iy\nXW1Qn25xq7/LwuwiZ++5j+3+kO7QZ35uhu7GbeIoYG5+lv5ehyBKSf0ADJNh6FOZmmJmYZH25jaW\n4+BV6kw1mliWxU57j/WLV5i75yzbez08w8JxbIwkJSFhSMSN7VWOeSeZn64yPT3F1KADYczWzRUq\n9Sq7/S5W1SY3JIaQOFIiTUiTw1n8ykpnkhI4KmZ+XGaOVh66LByFiHUZ1b83aR4dhX4nWQ1/GUtC\nPaustCf1j6qTDq6UT2tSvfSFQZf58r1HXZtkBXy9708qr6sCn0QJ6MIhpSTJQeSF1a9Oj87yDGGa\nWEIi/CIM7vidd/DCR/6Uk6dOEaUp1akpWo6HNEwMwyRNMlr7ccXSECzPVVmYbrG9vUuYJDzy6Bv4\nvY9+nEcvnKdar2M5LmEYEEcBruUVZ+9pnGxZoaliyCJni1Ik5fAjhSwnIeWy17w84GWkq/elHk6o\nKJ3RUWn7aF2aY3XPsqw4hJVxwVVKuTxW5fFTReVZL1/XJ4uqYxnpqzaWJ6CO7CbRTaqUI1mU3Bxw\n82aKiC3++I/+iEcfeILZqRaD9h5GGuMPupihzXB7i7pXJXU95heP4XpVdq7fplavEAU+N69dJYtD\nTMvGq9SIoozA75CbKcI0WDi2RJDEZIbEcR229vZI4oSlpSWyPKe3us7pc2cIHZNmZYpkfoZgZ4eM\nFD8ecvnGKzx/8WvctXqF75qbpW7YZAJWbl1lulKlOdMgyiQDIoQAK0wJFMWQfX30V1ZiOlqdrBCL\nmPGjlGXZGV2mR4QQB/6WcgjopLBSXXEeZR2UraxymcRrq++VQeGkCJtJkSZlZD3pHZPol3LRLZZy\nvcr1+3qLb7l8XQX+gz/4g3z84x9nfn6e559/HoDd3V3e+973cuPGDU6dOsVHPvIRpqadZLylAAAg\nAElEQVSmAPjgBz/Ir/zKr2AYBj/3cz/Ht3zLt0x8rsqnrZDWeMTFSIFLaWClOYnISNh3HmSQiKQ4\nIDQFYUruvOscH7/5q/R8H69SR9ouRqWCadqs3Fpj0OsjhaDmVZhqNGnWbIRlsHDvPex2h8wtLrNw\n7AR//tlP41Uczt5xHJIQ15bESUE+6lZDlmUHsd2gKxIQ+wpEHxz1uTqooewAKm88UMKvwhYn0Qnl\nvhsJSE7xZ4ae/yIMw7HJlqbFOaGuZx9C+XrmNX1i6u/R+cGjkE75PEA9ckdfrFSETBl96VbKpFLm\n3/WQSiEEUTJkdXWVeqWKZZiYnsflSzeQMsHIE2zTJgpDht2YCAspDVZWVvFcl7mZWbZXrtPd3YU4\nIswyWq0ZvEqTtmmw3W3TnJpCGEW+8EalRqXW4Oalawz6A6qNBgiJ40lSkWI4NjEJO51tBptrGDIn\ny3ysJMHI4MXPPMWVKyv8yI/9AxZmZ6ifvYtP/f7H+Pbv/k7aZsJeHlLJwQhi+jLFsEzMNJ+oWPQF\nsmyZfSNmuhBHf/ZaTu/yc8vjqfPz6jPHcSb6eCY996jQ26OiXtT39faX01ro95StxEkcuvpe2a+l\nj8NRfV1eiMr10EOSv5HydRX4Bz7wAX78x3+c7//+7z+49qEPfYh3vOMd/KN/9I/42Z/9WT70oQ/x\noQ99iJdeeonf/u3f5qWXXmJlZYW3v/3tvPrqqxMnn65Mys46vWRZSrYfAeIaJqaZQ0aBEIDMKg4U\nXXJqTN2xyF6vg2U7DPwBq2vrVKwKqTSYmp/DSFJcIWl39tjZ7SKkJE4zDMulUq+TZzlvfONb+MJ/\n/jJJknDfvefIDUE+DCHLSCXE+7sLDdMgz/JiJ5wouOTcEJBn6Id/6wOjlLRCt/ogT0K4SvnpDo+y\ncEwSCCnVJCg266h7bMfeP8AVhDQwLQF5XmQLKU0q3drQ0a2OvnQlKxi5csuUi46udCtDv6+MUiZx\nmpNKlucHp6SLfe5WGoI0jcmyHNuu8NKzLxEHCbVmg2cvPo2VRthGTq1RhyjCzGCYJtQdpzhVfnWN\n5dOniAc23d0tHCmxvToizfCHIdK0aB07hmzWaczN0g2GOFaFilNhY20dyzXJSdhtb3Ps+DJRHPHi\nyiWGWyYVwyPa2aOaSvI0QUoLBwNERi8N2fZ7fPjDv8o9J0/y1779W3no8QtYQULFsbFyi5SMTIJp\nMMrykqbkKRiGSZKDYZtkosjGbgDGftKrhHGL8SgUqETxqG4f7fIFEPv55Mt3KcWo5KGQTwV69MX7\nG40yKlvqepH7lu/+t7Q2TKZjJlm4xXPGd0br9+n36zI9escIxKEdBD5aMPKD30dZDPq1/yoI/M1v\nfjPXr18fu/axj32Mp556CoAf+IEf4G1vexsf+tCH+OhHP8r73vc+LMvi1KlTnD17lqeffponn3zy\n0HN1pKcPZtnUsO3CnCTLyZKEfL/hxv6A+maKQDDdjXnL97yDi3/+Je4+fYauHzJdqVGTHruhz8bu\nBk3TolFtsLg0jds4SRIVyq3f77OxuUW/3ycVOXNLx1nd7fHUr3+EH/nRH6Yet7EFhLLY8m+5DkYK\nRAkkRZKkmIxYgG0ZWKXIECnlwZZ3xTmrAdIdm0qZRVGEZVmFEy6OD1Z7mExnqAkwQjD6ST+jjRb6\nTtHie6NTQnTqQX9HGWXo7ywr+7KAT4ok0BcJ/beShfIEUp8fZc5neeETwZBF9klTEsUxSRrhujbk\nFV594Sp3n72fvU4Hv99j3hSwH+duppALE7NiM1urEvfamOmQuL/NxWdvcOvKNebqU1TcGkmaEMUx\naRAR1iwWzpxh+dhJXnzhRWqeQzQY0u90ybMYYRn4YZdO1yHuBfSzBHdmBrc5Q6tRox13SHKBmUAl\ntzCzlCTPGNoW59/wGK986Ut0M5/5B8+x9uoKRlalMdVgK9rDtC0qSU4WxwjHRiRFfkLbdvFsl0EU\nkJFBniLSFJmm5EIc7CyeZP0p+VD/jyvE8ZKmhy2zctEXa/X/JOriGyn6Qq/L4/i79GujxWP093gY\n4CS5VP2iRzapz3SLZhJFVTxLB5+HfXtFKgN1tq0YW/S+HjI/qvwXceAbGxssLCwAsLCwwMbGBgCr\nq6tjyvr48eOsrKxMfIa+cUM3y8t0gNrBqDpORReUhSFJEh5/+GG++PE/Yau9SRKDIyrk1QrzrTkW\nqi6eadBZ3yDo9Vjd3CoGPcuZmZnljhOnkIZEGJJ+MMCPAra3t/mNX/0N3vcd72Cq6iERWHkKQYIw\nJKZrQ150op0X+bKjNJk4CLp5pq7pik9XUio8UffuT1KAqr8UBVFWpDrvnuf5WGiXclIKIQ4OZ9Yd\nrOWNNXp7VJkk/KpORyGqcly5TgeVfQDluO7JpZiMSZJg2TZRFJAmCdVKlTTN+MOPfpLFO89ippJw\nt42R5wR5gityzCQlNwxwDE4sLTFz7DivXr1MqzUDWcbO2hqEIWHewa7VsKVFmKb0gpDclNSnW3SD\nIYbnMbcwx+XnnydJEsxcINKcZBiyt77NdGozb5q4aXGuZTZbZ6u/g5NJ3DRDmDa1xOJsbZYb3ZQX\nv/wlHnzTk9QaLbBd2ia4kU8QpzBTYbizw/HaFGHoE+c5oQGGaxHmKZnfw8rFwdmlqSEJjCIM1uQw\natSjoyZZRpOKrmzUnNSvq890pa0sSSkPW5JKdsvPKKNitchPkj0dcY+UMyjUq7dHD+/V31NW6opC\nKVMjZfn9ev00+Z7CZi3TjmVf1H8TBV6u5FHoSH3+WtfVoJQjENRnumLXV1ClYHTFY6YSt+YSi4S5\n6TlM4TDwI/prbQzLAgmNWpWKXWFx+QQbG1v0en06nS6b61vYrkOtXqVaq4Fh88gD59lt7/Hrv/c7\n/PAHfgAnBQuJa1oMo5CQnFwKDARmLrDSDMc0ye3x9LO6wE/Kg6zapwf4R1F0IGiKStFD+3SqQSk7\nwzAOdlqqZ6r36uhFoXpdSer1UEUX0skIJD9AEeU6jU+k0RgeFeKmK/ZJMnOUIFtq70DO/lFjAsv2\nGAYpt27eZnd7QGVmihPzx3jhyqc5uXSM3mAb/C55GNMVEVmlwjHTYc9PiIXB/MIimxsrDPf2qBoG\njsjobG1Sb7YwHQeHjNbyEnatwo0bt6k0qhi2heGYuJUKcbcD+wt6nMZsNUw8I6fl+8wzTavSoNmM\n8Ve3sKTD/B1LREnE3NJx8ueu8MCb34rzxoe4sr7DmeU600sLyJ02tm3wqc8/xVsfe5Ir19dYmJvB\nTwMSU5KLCFMaWAaYcYqRCxKRk4icQBZUn7nfhbrDWM+FX6a4jipj1FmJkpk0XkoxKYVaVlq6zKrv\n6nI2OrIs0541rhxN86j83Idluzz39O+UgY7ePv39Xy90UAjG5sP+HWP1OUrOv1HlDf+FCnxhYYH1\n9XUWFxdZW1tjfn4egOXlZW7dunVw3+3bt1leXp74jH/7a791UMnzD93P+YcfOBAcHYWXHQ66o0sp\nuANFNfQ5e+4sl69fxT3pQG7iTM9yYn4OmQhwTQahT7/bp9u/jR8E2LZDqzGF47hUq1U6nTbtdodu\nr4NpGcS+zyNveAO/8R8+wv/4vu8jGoSEWYxj20QyJxM5aZ5jZClZlhOFyRjXWF5VdWWrt0t3+Akh\nDlC4cpbqQqsLled5B1EgCsWq56vv6+/WkbGaODrSLYc16spbf79+feIGmgkL2GtZEjrS0ZFdmZop\nFyFyhFDflQjhEAwTms1Znv3Kpzi+fI6doMsXvvCfmdq3VKbnZ4n3DIJulyAJ8ZqzyEqTyxsbeE6F\n1bVNtm7domHZ1C2TNAgI/CFBFJI7DnMn7uDcXXfTHw4RUlJt1NheXSNOE2qNOsI0ycOI0A/JowQx\nZ2BZDnGWIpOcumWzePIUndzl8vVrCMvi/JOPYedQw+SLn3uKU02Xu+69AJnB9OlTXLz1pzR3Qh7q\nmXz69z7Jo9/2TlZFjiMsyBOMqMgT3ut1qXkeEYIEiDNJJgWmNMg4HM1j2/bYnBsh2q9/Ik/ZijrK\n7NfnrWGMsuzpllb52brslt85WfmOEliNZG3E55cXGV2mywuPDnjKdVL1mozAdbpmnLr5y5QvP/Ms\nX3rma9/Qvf9FCvxd73oXH/7wh/mpn/opPvzhD/Pd3/3dB9ff//7385M/+ZOsrKxw6dIlnnjiiYnP\n+KEP/O0xISlzW6rhk05uLq/ASgisNOPkiVM8/YWv8Ni5R/DDlK4/xO+HmEFON4tILEkjN3A9E9d1\nSJKUIAqI45C99g6ObTM302K61SBNYgbDPt0o4Nzd9/Erv/br/MB730/QH+AJQZ6m5Psn/OTkZIbA\nNm0cbSKotpU3nOim4KQMhoZhEMfxQfpafVKpZ+toSadLVCn3pe4sKi8Iql7qmeWddOq+SV5y/fvq\n2WUnjT7Ok5R4eUKU23pUyfIEFK+IhWl41GtVvvaVF6lWW4XPJIzpbe/iCoGZR4gsR3o10tym7rk0\nFo+zOozpBCGu57Hb3mbYG1AlgTzFyDOkY9OLA+I8oxEFtLe26Q2GnF4+DnnOWuCj9r5U6nVSJyKl\n2G08ZzcRScowDNjw2+xZKSCpH6vR79hs9jpEX3yGvDNkeXkWM8/YuniFWnUev9Nn4dgMtm0Rv3SV\nN3gtNsWAf/2bv8m9D97LX7twgTm3gdEfYCYxVqVKKjJikZFSnPvp5BJSiLP4kGKK4/iAltT7/rWU\njj6W+rjrY6isrcMoOjmInlKflfcRqL+Vw7wst2U5URTa4YUjQ8Wzlz/T31derBSYkFKOgSodFE1a\nRMaPoTscvaJbIEcdGi2E4InHL/DE4xcOrv3i//drE++Fb0CBv+997+Opp55ie3ubEydO8E//6T/l\np3/6p3nPe97DL//yL3PqVBFGCHDffffxnve8h/vuuw/TNPmFX/iFIwVBrch6GJyOwHUkYNv2gRJT\npRyLmuc5ru1y99m7WV3bZOgHeLVphOvCMCEM+mzsbGM0q2S5ybTl4LgO080WhmHS6/YY9HtsBwFp\nHFOpuszNznBsYYFZK+f2+ioPnH+Uf/Vvf4Uf/5EfJvIDXNNGZmkxKJYkTGOyJIJoZBoqpVc2AcsI\ntbztXs/zrSuyskJTE1D1qa5MdeeUCt/TI2D0sSnH3OrPUTnEC6pLHDxPxXWXQ/dGZuYod7h6vk5/\nqXFTyEwItQNwvF7qwI9JpYg3zkFKRJ4hhEHFqfGHH/sEj154AscxSfb2aFkm/rCHZwqiro+o1dk1\nXI6fuId77j/PF59/idOtBmG3zebaOjUErmlhmwLhWHT9gJScqekW1XqNl7/2HIZpcWx6jtW1Nfx2\nB1cIiGOwDIRt47YspOXhVlxWN9dwKh63tjd58uydzB1b4uKVy5iLLba32hgZSAlBEvCON76J33/u\nBU7Ygmi3w3Btm/OP3s3ndj5Hfc7loZNnGcwt8J++9DS3L1/jR9//PmwEluXgVTyCxCfLMiwhkalE\nRClxmpLKdKIcqjQESt4KZZu/hpLR07qO88c6ANAd9kqOFDLVKUNdtvVka0rGdH5cp3t0+UnT4v44\njsdCVYu0AOMnZcXxSCnryrvs0Pf9ENu2D2TXNMd5+1F/jOabAj4qHa+Sf/WZOikrz8dDX1XdyvP3\nqPDZg3fn3wjR8l+5CCH4sz/6nUOoTKdO9FW3HDtc5oLVIIRRSl6r8gcf/wTB7pB7H7pAKCy83KQm\nXKxGHW+mSdIZEEY7JGlMGBaHHBjSwHNdKq6LlIIkjgj8AWmSIk0L6ViEIiXKY/x+j7/xpjch+kNk\nHCFETiRTIpFhYWDkh8+51AVWtywm56KQB23V0UiWZWMOn0ncnY5e1T06fVJWtDo6L1tA+nip3wpB\nqWfrVoDevuLHOFRXvYyjnmSsv0bvGyXcuvf8Ww4944Wv/CnIYkJU3Qa9js/Tn3+WSy9fx5QO9Zk6\nvavXSXpdcIEgRmYmN30fFo9x7s77SWJBR2ScP1HnyksvsnXtCnlvl3zQpeaaZCIjyiEyTO686z5i\nBFubm9TqdUzDYmtzE9cycE1R5AOPQzJpEiRw90MPc2xpnhcvXSTOE7wk5/47zpLZkt0s5MUXX6KR\nmniZSZynGOmAx+56gFtrbYzpBe6+9z7Czi6Lx6c5eWKJ5//4cyw0l7hlGlyqJryydYvuzhbv/d7v\nZrrqYZNhZFkReZLkRW6fPEeYBmEWHwJUhZIelxeFFPM858KTh/dxfOlznzig6YpxH6H3o9SJem+a\njucAgcOZBHUrTZfLMngZr3Mxj/Ssn0UI7XhIbPHDWP3V/bplq4rjOERRdLAxScm6slh0+daVdXmh\nUqhbSgXsjDEFrc873fo1DIMLb3jHkf36uu3EHCWCOhzFoJtdMM65qgarFV6PRmnUmgzThG97+7fw\nr37+l7jHABfB5toGoVujc/smzdlZXNel3jQwTYN6cwbXcQnCkG63y9ZuGwFUKw6t1hzVaoXdrT1i\nitSkO70ec7PT/E8//dP84r/4F3Q3NsjiCGEbGKYxFgOu2qLqP4lb0zn9SU5HPT/6pET1k7IRqnth\n3ON+tPALTcgOh/aNFgEOJoR6no5s1Lio/w3DOmSelp2VIyH+y2VhOyiyOPTaMCz6vSFxmHHj6g3S\nKMayTfrbq2zduoqIQmqzDbI4g9ym1pqmcfIOhmHAzsYup88/yJWXv8rayk3qtonRaDBMYvw8IUwz\ngjRjfnmBxvQ0K6vrVCsVbCnZ3tygs7FJbJnU5qexTEm1VmP+jtMMMsk3vfWtLMxMc/9jj9Cam+bT\nH/2PrK+tc9f5B+j2drBqHmk3RBoSI4f1wOd3P/UJ/uaJR9j66tMkx+apP3SKThzQswyWT5+h88wV\nzj76MFEtZmlpgZW9Lf7JP/sgH/zg/07LqeDvtpmv1kizAD/wqbYaBFF46BDuQn4mxViPO//KxXEc\nkiQhiiLStKApdABmGAaO4xygbj2iSQc0qujzQ6dX1Nwugx+9jBRf4cBXiLl4poE6hUhZGYXesA7p\nnPKzy9SMbp2o58G4vOsWb56nY0AHimMkDUMeLCL66VYKmEIReed53ti1o8rreKRaOqZs8jwfM0uU\nIJS3aeuTXxX1t+8PsEyL2WYDu2ozDAYQ9JmbaVKrT9GYatDZ69DPIwbDbL/zdsmFQAhJtVLBq7ao\nei5C5AyDkG5/lzSISbIMwzE5c+wUcR7zHe96N//yl36Jv/W976biVsjiCJlJTCkxStygapcSUBV1\nM0lZKaSg7ld5ufXPdYU96RAD9ZnO3+n9XUa6uhNSr5OUcmwBKK5nY2Om7lPf052z+t/qczVxyvH/\nlnX4DMWj6De9SGkipInIBdPT03zqk39KHEUcW1wgjVJuXrmEYWY0PJeg1yFKMyLpUZ9fYL5WY2tn\njyeffIiBP+D66g1EEpIJgW1a2LUae702wzTH9CpYtQY3bq+SJynN6RrDXo9Br0PNtXHynO7GBouL\nC9xx/ARTC4sYUzPU6g2eu3aVE8tLtDd2sDFIwoi1azepTlV56+Nv4ItP/QVbu7t0dtvYlmCq2kRW\nTPaCLVZuXuL0nS1cu0Kn78NMjc2aIN9cRYYue1shtmfxdz/ww3ziE59ieXGRNz56ns2hj5NnOFWP\nOImI4hDbcg4hX30x1vv7tRZRIQSe52lyMR5RlmUZg8FgTDZGyu1wtInuOFfXy45snfKYVMqyWMif\nhb4QqXvSdARO9M/KCdbUe/XFRg8W0GV0ksyq7424dPWTkaaj9yv6SjENKnR60lFs5fK6H+gAk8+d\nUx2ittzrdIK+cuqDarkmmR/R39vl3ofu5fbaLS7ceR87ux38OESGKfeeu4eeTDBFlShKWF9fZ3t3\nF0MaDPrFBpqZVos0jQqe27EwEaRJQhbFhEOf2EhIgZnjy/yfv/jzfPBnfgaGQ/KBP5YbrswlK9ML\nRocr6CabGjD9JB+dJ1TP1JWczg+Wkbj6nuIA9WgTHVmXn6HqUEb3xViNx5mrZ8P4xCub1GUrAsqh\nk+mhtultOkqhRHGCACzTYX1tk7/47F9w7o672NpYZ3Z6lrzfIyXCj3MalokvJf0soVZxuP3yS2Su\nReBPEaxtksc+rmES+T6W4yIcF8+YIYl9qo0GcS7odzo0vArdQY8wHFJsosywpcTCREQxW7du4yc5\nT977ILZhgjQIewFf/YsvMNdq8E1veQt/8Ae/xzd90xsI99oIKQiigEpuYPQiao0qz29d4/H3vp3P\nff4L3HHuNFMnzrC32mbh/nNMf6tDqxvjRDntnW2kdOh1Qs7f9SDtcMAv/Ma/5/vf/x6kJTGSGCuO\nqToucTaeFlWn1vQ+L/5+DcexRmup8ddTL0gpcV33kGVXzOeRE1yX/fI4l61BJQvl6K3RwpCPpa8Y\nIexJETJy4rN1WkS3Csp1KwcAqDoEQaDRKCP+OwzD/ecWNIoQAtt2DuaDGgM9IiyKorFjDY8qr5sC\n100HhfR0haMaVY4pLq98egfHgU/VcoiyhDvvvIPr1z7D3Ow0ruuRCAMZprzyykVC18IfpFimjVep\n8OAD9yOkQZbl7O3uEIQ+3W4P0hQha6RSUql4mFKCyAnSAM+tsnTqOMKU/JsPf5j3fue7aNo2JClx\nlJBLQIAhJNn+IcIYBrkAspwkKlZdYYwjj/KipStzGD+FfVJO4rKFovdn4TBWiFcJ+2QHpp6hUL9e\nbFEfn3j6GOh1VY6ao3wb6v5iHCn66OAEdf0IuaNzXSRRAlISRCG/+5Hfh9Rie7dLr9/HQOL3fWqz\nTfKgR5pmRGlMvdXCtSXbazuYzSovfuVLhJubeI6JbQssz8XEJApCpOUwMz/L8TtOsXF7hdwYYrg1\n+sMttm+vMO141CseMkupSJcsTLByg87OHq+8fBGj0WRuYZHrL7xE06vw8qVXWL7vFN/zA+/jU5/4\nBDPT09iWQRaEtKSF7XnkSUqfmNXeDkG3xwuff5raO2dZPH4Hu9t7nL7wAGvPXqS/sUfVtugFPlXP\no7PbIxcpjz78KP/Pz/8Cf/u9/z1n5mZxnSrx0MdybNI8RxiiWDCzHLJsPx2EAVLsW6MC+ZpOTLXz\nMdsfq8n+HB0sjOb64fSrR/1fzseTZdnBHgb9HYVit5BSHNA5I+V7mKIp7h3NO112lVJV8tZoNMmy\nlCQuHPlJOpp/WZZBrhy5OdVqtZDJJCHLVOI5tau1sD5Urn/LisboUdU+ZXEra2A8Sd3h8ropcF0x\nqDKJo9X/102K8iBKKbFESiRjMCVztTpGknJr9TYyMTCFzczsAubxCsJxiJMBw8GAIPC5fvUFHMeh\nUZ+i6pq06g3mZup0Oz0GwwF+kuJnAY7tMFVr0KhUgRzRy3n03AWei77CMy+/zMOPPEwjSbFNi2Ee\nY7lF2lBbSKRhEEoYJjFSCFyK/BFlZagrYN2E1IWsHFZV5vNUn+iCUTyvCF8b70OBYZiH+luvjz5O\nI8R8OHPi4WRUI6RT5jD1cS8moIoyUly7PPS+ScXGwnBr/KennubKq9vMeE1qzSVWdttsX71KNhSE\nWwNyGbFnZAiRs1yvQejjmDlTlsn27i7dzg62MMhnWniNJgwynMxiEOcYlSbG1CxeL8aymgyJGa7d\npB4KqlGAWUuZPb1MHmZE611MXJZPnKLj94l6bYL2NrdXbhJEAVdvX+MDp3+IKA5ZPrXM7sYm040K\n7bpJP4xoRQZJkiIcm1cuXub07Al6V1dZ29nAeOwcwncIr+5gTi2SBhF22MepSFIX7CSjkZv4uyHv\nePBNvPTsZT7ff4b3vOvdTFcqJNEAYUuiLAKRYYoMgxwDCbkgxSDBKM7azGImiMT+mJhjIXWGMXnj\n2uh+pSzzAwpFt6bL1J8qkw4UKTvxR5FZCWk67icbAYJRHRQC1wGieq+u1A/ql6ZkeYY0JI7hYOf2\nwVmk6X6kSZ7lJKk6IHx8J7lhgJTpActgmvY+Hx+NUUdpmh4gbmXR6FbIUeV1PdChvOGkvBKWzb2y\n0tcRvJQSS7qESYpTdZlqOJw8foytrXUeP/8Y6ysbvHrpBSzPI8xTpptTOLbD3LElKpUKQ9/HMk06\nnS6rt2+Q5TnVSoWZVp1KY6pAeUOfKIjY3dqm1+/RmmniDh2++Q1v5F//m19gamqK+0/ege/71CoV\nhsMh0hDE0iAOQ4QU2Oybgfu7NSUcEsgy56+bhToVUnbC6H2j949StGry6M8VQpLtxwfr/Vvexq+E\nTTczRwvD4djh4ro4pOj1RUWnS8rcORzOST6p1FsNoljwF5/6U0ynip9HTE/VsaIYw7FJHRtp5oQp\nJFFMvTXFMEwZBG1OnzpFGAzZXr+NKzOSPGfY7xElCY1KC1yTqudw+tQdbG5uEgU+D91/H3Ea0ybB\nXBqQ+312u9vEvYDZ2hThtElSsRAzNTrrG0xh8eVnn+fk2dN85i8+wwd+6Afx/QBpwBvf9CZ+97c/\nglupcs9993HplUuExEhLEvgBSIFxbIGKaWILQQOLtGKzttnmznP34GYhfSG4vraKZQucqSaeVSEJ\nI1oyw+jucufpU/yv//NP8aN/9+9w59kTWFFETUqyMMaUOZiSRAhyBHmWFagcyMVrc+C67Ol0yohn\nPuy4hMKy030zOpVRnt/jERz5gRWp36crXJ1S0Te3lflpZV3qekQPiFBhs7ps6gyBDp4OInH2w6HV\n81QfGYZxcG5rlmWaczdGiMN8v6KBdFr5tcrrng9cb0DZlFKf6SE/ZeSpT2wpLCAkDmMSkbO0uMDz\nz75Au7PDzFyDuaVpTMclTGOGuz5RGLFy4zrVWg1DSizbwhSS6UYFz/MwTZNOp8P2uo9p20hhUHUr\nTB8/gWEI+sMBg6DHxuomP/j9P8hnP/85mpUK880GoR/RqtToBUP8PEGaEjsrDqNNcwhFjhRgZOPJ\nhcppaBWyVUXnpdX/5T496A9tYQAOztosj4NCymUaRv8ZhSAW3ysjpzJNUjhNRzK08GAAACAASURB\nVEhNH9MyLaPXUbW3/NlRCjzLMn77N36LY40mmVMlFYJrly8yZRgIr4ZZdRCmoOv3qbYa1BpTtNt9\nTCTd4ZDN29eoWhKSEGG6pHFIHCf4QYzdaHDqjjM4pgFRwMMXHsSVBns31vEsk8bsDE1vCeeWTZrE\ndPba5JbLPefv53pnB1sabLx0mTvuPM4rl17FrVR445veSJCEZBIMy+ad3/EdfPbP/pyYnLnlY9y4\ndg0jzXAdm0ajgWzWuPuhs1x75TLTd9xBMD3FMIfhc8/xxoce4IXbt5nCYm+vR8+S9K0hMi4SrDUr\nHv1Oj//tH/8MH/3jj7GTDnjs7N3YdoVhZ4Bd9YiyjMzISWURcijT/Zjoib1dFH1Phr4Yly023eIa\nKdHJud91C04pUxXzrcugLmO6HIVhePBOfVNb+UAG/UfNN/VsfVOTUtijqBGdulULSWExFtx2SpJk\nh94TRVEB5LQoFikljmOPLRSWZR1E5pXn4WuV102B64Oor2zliauERT/MAMbzEqh799ptvFq16BBD\ncs/dd3HxhefZ7WzTG1j0ej0s28XyXKbcWU6ePHnw/MGgR7fbpd3ZwzAMwiijWpvm3MJpotQgCCLa\nu212Njfw/WKjxMzsNNMzTYRlsLm2xZve8M185kuf5T3f9V3IYUR/MMSwLaI8xnYs7DjDyCDNM+I0\nw7EsLC3SRhcsXRHri1t5QMtn/+kKXTcJ9SiUo4r+XkVXlWNlDeMwBaLqrWdOFEIcTIgyMp+0QKgN\nXboCUJs4XguFbG9scv3Vyzx07kHs+XliU3Lj6a8x36iRWjlbO5skqcBttrjnwuN0BwHrncs0qzUw\nimiJeNBnqmKSOgaEOVkGm702liNZzAIq/pApz2V2pomHwdUvb7O5cRvf83Dn5gtrKwq5srLC4ukz\nfOWlF7E8h87tDe5bXODa3h5fefar/POf/SCpAMMySfIEy3VYWF7mybe8mS9/8Uv4YYzRqpH7ARXD\nxbZMNru7nK4Y5FHE4MY6XqtJVPEwfJ+Lzz5HOPCxgpgZt4JZtwgMQTaIWJyZpZ+nLFQdOjs7fM+7\n3s2v/t5vsvrqdb7r7W9ncX6RJByQJTFGTnHuq8jJjX35Ojq99hgPXQ6tK1Nkk5SmHoE1Dg5GuXrU\naTi6hV1G47pMua57MIeUwtXBng4W1WYa9Zle3zIHLuU4I6BHipTbW6vVx2hEHZiq36rtvj8Ys3T1\nsF+d7pm0I1ovr+up9GWErU/gEYqTYw3UBaNsolmWVcTTZhlJnGBKyezsLFEUs3zsBEtLJ5DSpO/7\npH7O7ZXV4h1C4FVcTNNiemZmX6BywjBidW2NOM6wLId6vUK95iHygnrwgyF7O7sIE0zLYGttnTtP\nneGD/9f/zT/80R9DZjlWEmOakjiIyJIMS0oyKTGkIEtSwiwZoyYmOfj0duqcsFKQupDo/VtGKhND\nxUY04UHRBW1c4ecFP6shg0mW1Giijh9+O4k7VxNIR0u6Ga5CHI8qX/j0Z8iDkG6vjcgTrIpLlviY\nrgFhRGpIogymWguEucNGt8vi6bsx85Qrz36ZgR/S9CoIYqQwkBSbLFzPwWnU2NnaZOf6Lc5fuICd\nprzwtWe4Y2aaZL5JuLdHv9fD8RwGSUDrnjM4rRk6tzchiKk7LlnV5plnvsqP/fjf58y5s0RxVOTP\nyTKwBFme0pieJkpzatMz3Ls0y8bKbUR7gEgFcRTy6af+nHc++ha8JKN7a5VLQZepwKRmSL75ice5\n8fRz+GFEO+oSuCZObrBy6zaxCbEpkRn0+32+7c3vYK/f5iOf/CTf++7vpOm6mKHAyjNknpHkGYlI\ngSIl8VELp45q1XiVwddR41z4YRijN9Q9Sub01BFhGB4oVLXIKz2hv08HD2UHpW7dFdQEY7KrZFOf\nX6MY7Ri5v9eg4KlHSH+0GaeIN1eOR/29CnyWQxaLnccjkKLTiDq78FqyD38FFLiqYDnaBIqGqtAc\nfVDUfWqVPTCdbIEUGaZj4wgBUvLYI0/wiU/+MaZdRQqHeqVOo9HEm/MwzcLDmyQJ/nBIGIWkcYLr\nOdRqNRzHodvtkvbadLu7bG+vY0qTRqNJo9Fkbm6apWMLDP0+27s77O3tkLsW3/d9/wO//8lP8u5v\n/zYs04YgQOayOIjZLt7pyCKDXi5G6BVGlokeqqX3i8pSqCPrSear6if1WYEcxnOojDg7Dr6nC56a\nVKNS7K4s7tXNZLW92kA/S7HMVyohnUSNSFnsxizaq8ZVYlkqRG2yKXn9pUvUbI9Ov4OXJaxe2cLv\n9ahMZdSFSW45NOpNWvPHuHp7g53OkPvvO0F7c41UGJiuRzgMcT2HPC14eyjQVHWqhZnB4sISlQyu\nv/ACHjnS9wmyCK/i0Rn02OsHxFWbex56gBvXbhKFEdHAx7QkH/mTp/kH/8tPcf/DDxc5uin6yrQM\n4iQhTFNsx2N6YZ7d7R1OLx/HtkxuXrxEsDfEM02cisXu3jZB2qXT3uL2zgZxbYZ0tsV2Z5fW/Bx5\nZ4+GCYZjINKcqueSWxa5bRD6ITWnwmDoszy3hFnx+D/+5c/xE3//R5mxHUDgGRbEPgYgpCDNjlYe\nZUtOD4VTP+W88mq8kyQ7cDYWERoKZIj9+Gy1hdzcV2gKcYNlHa7TSGGOOziVjOvZOsvRUeW5UqZn\nhRAkSUyeRwf3mqZ5oGiLthY7PgsFrW+vH7cQ9IR0hdynE9893iZxaLEsl9eVQlGdVBYIfWJP4oL1\n1deyrAMlk6RFCE6exoAgN0xs22boB7Sm54nDnNWVTdZu72JVrILzNgws28YwJKZp4LgeGCbt3gDR\nH5IkCbZjMT1zjIpXA6Df6+MPh3S7bVQGI9syOXHiJMMwZNDpkRsWL1+7zvkz56gIA0MKpCWIDcji\nBCvKChRmjnu9lQe6XPS+0vtnkpNQR7zjpuN4fLj+TDXxypEk+rP19+rXRiaxQD/NXD99RX1nUqRC\n8f7DedQhP1Dcutmrl2Z1CsersNbbJdnZZri2TmTA7TBkWtjsVTwu3H0/cZoT+RHTrRYrt26xev0S\nZhLRmGoRi5TuoE+aD3ENizCOWTp1Eq/WYPXadf7a2x4j6PW4ePFlmtUKjl2lNdWgn0ZsJj5+EnHX\n/N1ceeEVBsOIWr3OznDIqzcu8/d/6id44MLDxGlanJ9TrA/7DS/kotcf0Bv4LC6fYHNtm0qthtts\nkPQCqrZHagguXnqFNz/wGJ3tHdLb6wzmMuyKST/0OTHbwqy62MQ8d/MKqZQkMiQTkjQX5AKC/pBa\nrUrQ6RMNBvz4j/w9/uzPn+Ktb/4mWp5HLkHmgrrlEMVxEfJ6BP+q7z7U5WzSrl997BUFoctPWb71\n76gEcCNr8HDEVlkp6/OhHOmi5HsEQiYf0KDXzfOqY5+N6KAi787oXrkv/3IMhOl0jP6uOC449HJd\ny33yV9qJqYqqZHly64OqKyj9R/cO25aDyAANrTXrNaZnpnnx4kucWD7NiRPHmW7OMkh8Ot02Ozs7\npN0Ux3GoVqvkCBqOQxJGBMGQKIrJkgF77Q5SGlQqFRzHwa641KemsG0b3/cZDIYEfoRjOfQDn/MP\nn+e3fuvfMfPe93N6dg7bMojThOL8ILAExIIiLldrr+6hLzuBVD+o+3TqRb+njLB1tFAWkuLekflb\nzmpYpkDK13TroTxmulO6/Hx9vEembTbGY+ql7OBWpR8lTM01MXpt/E6XuVod38zohxGdXh93tsXe\n7i57bZ/5Yyep1etcv3YJKw/J04gUcGtNEsOl3+uwNRgyPT+P6VbY2dhmqtqkNdVidzDEzaC7vkFi\nWdjbHl0Hhi5Mzy9x6fmLWFj0woj7H3+Eqys3+Hv/8Cc4/8TDhHG6f9weRdbKrPgdxzHN5hSXXrnC\nwPdpphnb27vIPcFdd93D7SCjvbJOfziAHJ69+BxveeSb2N3e5pWrl3Fn6ly/eYMNY5WHzt3D6elF\nNjfX2QgGxBKa1TrBMCCRAstxCJIIMzOZsapsXrrBO9/2LfzG7/57vu3bvxVncREPGHSHOJaFMI/e\n9VjenVuOKFLjrY+d+r+s/HXao2xJjuRzfLONrpTLzxFCjNEtOlApo2JdpnSLd/z94w718gKh3l8s\nMiM+uzxvyn1jmgZZdnifRFnZH7WIHozFa37637BMqmBZYJSy0jkpdV0pA93czzNJnqaQZiCK4Hos\ni3vuO8sffvJPePzxR9m4ucHmxgq5adJoNjlz5jSVSoUgCPF9n16vz3A4ZDgc4DgujUYDx2wggCAK\nCcOM3Z1tfN8vFLltIw2J57pUqzVs26IiJL1uj5/56X/CH/3HjzH31rdQyQXCEMVOv8AnBQzTPEDw\nqk/Kf5cHsGyJlBPhqz7Uw6JGi+Bog8w4kj6sHMsTRF9Iy5znUfka9DEuL0R6fYt2jEfTlE9NOQqJ\nbA59ZrKc3dUtamlKSgRRjuPYhB4szkyTxwF+e4eBYbB++WUGgzY1T2J4BoE/JBE20q7h1iWiMUXi\nuXTDmDTOsasOt1ZX2L55AxHF2Aj6QZ80CMGu02y0WL98nVnh0W/3mT91HGGZZAY89sSjtP0O0nSL\n9grI0ow0V+FpJsOhz82bN2k0mqRJztKJk6zcuEHYG2JVK+yGA2wDRJqxvrXG1toq9504yUp3m+e+\n+hWmlhY58fBDZKZEtvvM5BY9w6BvGsRRhJHmCCGJk6igB2t1TGGSpykb12/zHX/9b/LsM1+j8oTF\nlGMxN1VnMPSRR2ycOko+ynOzHKWip8uYpJR0xTjJylM/esK2MpDTfSb6YSiqfuq6EiWdZinL2Ui2\nx+VQHRyht03RfeUAAr3+Zd9WmsYH4YwKpesWr2EY2Lb9VzcXSpkDzw+E2kB3oKlVsdwhk5R9lhem\njCEFUkBKRpqGnDl3iuQTQ4K4y+KxJjJr0RnEhHHM+toKlm3j2A627TA/N4NpmAyGQ4IgoNPeQwgL\ny7KpVipUqx6VWhMpJH7g02m3Gfb6+G5CmpoIy8c2JdJP+PyffIbW9DQvXL3E+QfvR4Yhwo9whIGw\nBHGeUWzZHCECnbdT18tHyOmDWkYjEx2VqIgUdRL9eDpNHTHrSEPVqdz3Uo7yJE9CLapMonv0MEnd\nwVpOiqUL/WtFz9z7xGNceuZZPGHi2QZkEXG3S7fXx1qYxSSj296hbhuY0ZBwZ5VgsIdZd2g2m7jV\nCkEEaWpi15osnj5OY2GO21eukvYCKpU6r1y5zN7NW8xaJv6wT+xKasfn2Rv06F6+gbk3JJUxb3/n\nt3LiiQtUji/wZ5/8OMQpmVn4OSTFuZVIiSmU3yfjheeex3E8bNPBdVz6wZDTp07z/Be+yNKpZezp\nJuHuLmaa0qzVuPjqizx874Mszc9w+vgJ3vGOd5BWbTZfuMzLX3ieEydP0qi4dFIfQ9pY0sTPc1zL\nBlMy8IeYpoUQkqgzIOn7vPXhN/DFz3+Rex6+B1F18eoO1fBoBX7UDuBJcqjAVxlslRF6WXbVc/Q8\nQuUcQroi1/OJqPt1PaEjW5VkTRU9gKCMsMvAYZI1qtdZDzJQi03ZOT9Jf6k+LCv8v7IUSnkH1FHp\nHcskvhDFLr2ikYqv3c8fYrhIITEMMMgRIiEVOVXX5fHHL/DMl7/IXafuJAliGs1jTNVrVOarGIbJ\nYDhgOAxY39nZV5oWrVaL5YVFbK/OYDCk1++zvb1Df9DHsW3qjTr33fcAju3QbrfZ29ul7/foBQE1\ny8KRJnfedRe/+bH/gFfzuHD6LLZIi8N3pSRLs+JQiBIqdRxnRKdo5iCM704rm5cqxElHKkWM6qjP\nFac8zrkddirqaEinOvSoIeM1eNJJk1XfYaZzhVJK4jgsUUZFbvDCstqP2phQvHqNIAxpmg4iC4kB\nhEG9VsOcmSPwfUgzluYWuHX1CrnfY77pkROztXGL6bllLLuKIxzmlo7RWF6gEw9ZPr7M3Jl7uHX5\nMiu3V2DQx7VMhsMhTm2KXp6DNLDjjEW3ztu++W1sJjk319e4s16n6TQY7PWJmymmsW9WqwVrv429\nXp/d3V163SHTUzOEYZ/6XIv2zds8+OB5Pvvlz3HXfWdZjUKSTp/OoEfi52zv7eBUKvz1t/8NZmZn\nubq7ztr6Gg1pIoch0zMtupkgHsY07Aa5zEnIMAwTPIMEgWd7OIaFZxisv3qN/+47vpuPfuaPiOo2\nx+fmqIisHJykTcJi+3ieZqDFdSuAoEBHIRsKPXNI7nSFVcgFB4ejQH4ojXSZX9ZBThm5j4CH2gGq\nO/tH80d3upqmeThiJBvpHA7yqkCWFnUsqlDMMb1uqq1qLhT14qAfdPSug6QDNqFkGRxVXtedmKqj\nddSnh/MUiigau1dlPtMHrOg4gRAJiCKENQXyTCCFRe7D2598G7/6ax9m+vFZkjSlu5Ex6A1omz6W\nU+SBMAyJW62QJCmmNAmGEb32Gqa9hmUVJs2pE7Pk+Qx5njPo99hZv3ZQB8/Kma63iOOYIE4wqXD9\n5hrf8tZv4/ataxyfW2amUUGaGVmWYMpiI7NqzwFa2M+NbewfhYXIyfKMPJ98pqQqo88y1FYM07T3\nOeoigsQwDtMhutJWP2X+Xb8+cgYdjnzR61ZW4Lp5qOgf/ZnFGJv7z1PHyVnEWYwpJ4tqeOk2d80s\nIERCe9Cm5xvsxILm9HFIGqwPNnAtj1ev38b0u8xXc7Kkx24QEWIQkGLEbaJ+j51+G778FP2tm2QV\nm4FhIfGYsVzuWJplL+jAVAOnMoU1bNGcqmO4KYuRxZUXX+ZaLrh99RIrv/47nHaPYRt1hN0miSMs\nLPL9HC9ZWijy9vYOt26uMDd/jDCRuF6Vtc0us0snSTtt7l48RbbW4fjJk1y9dZ3BrR0MPD5/6xbv\n/+f/GLm4yI3NdW5euUbSj2hNT2NFKc7OgCUzZTNL2DNCNoYx09NTEA2JsxjDgH40wDMM/CCn0fB4\n8dkXOHfiHp575grRmRRjeZqmMTkPR5jHiExg5hKZQ5QGY3N6FIY4UpoKcClAMYZahUQgECJHMFLy\nSTYCAcoK1X1EuuxJ6YxZdKNoFCXf6kzOcaCiA0Q9YuVAXuX+nEK9E0b5T9ifVwczUM2Asfmpz41R\nHcb3aejzQ9Vfp6KOKq9rFEqZT1UncOgJXKIoOhiYJBkPgRspdXVtPMe0YRjkQmDaFvEgpVKpcPXq\nVZpTTe6++xEqbpU4SWh327S7bZI0Jk0TarUqrUYLyzDpdnvstXfodApKxTAMarUalYpLa6pFtVIh\nz3N836ff77PXbhe7qlyPmakmURKz12lzfOk4n/3s5/hb3/NuhkmMQBQ5qy15wIGlaUKWZ/tJcQp0\ng5BIBJChYyLVxqS0hTfZT5xlmiOnSNm8LXOMZVSkVn/duVJGFer7oJL3HF58x/n3w7sudYSiEnip\n+9RGjrW1NZaXl9nY2JgoR51BQn+nzfx8E0tIbENw4aEHsLwpbtzcoHaiQrjaZrB1m7kZi+3hHvEg\nJ40sMlMShDnVSoNmbQHjXIObX9rBq8zSH+4ys+ix1d0mri1xtd2n1mrhOhkvv/xFbHMRryaxjSFD\nr0nUk2ynJms7Q56/+BXe+He+D7+yxzACNzdIswxDCgwtvM71XGq1Krt7eywt1ZFS0vIq1A2TKBdc\nfeVl5msVZmSLc/PLrMeSGxev857v/D7uqs/Qaff53X/3W5yZP8aZpRNE7Q4r3T3m/YwTC3Nsbawg\nKh533XUnW7dWmPOqxCRklsC0TCxTItIMx/Hwk5TEMjhz+hQvX3yBY1OPkOSTMXg48DGkAdLCEBJj\nX9EXiLGwXvVoptF4G6T7cfYGIyfiSA9Atj/PlY4o88eTZEnfpKZTjXrRLVWlP8bffRhs5Hl+cOqU\nTg0VtNDhowv1UNdyBNikUqZNlXWrip5q5Kjyuinw8pZxKDoiDEPCMDz4HziklPWiKyT1/5hjTwjy\nwMerVbn7rru5fvMG9957Lxur10jSlByJ63nUKi6WbZOmGcPhkHZ7B0FOHEfUahUWFuaRUtDr9QjD\ngCRJWFtdpUheb2M7NkE4xPUq5AjCKCDeTQmCAMu2sU0Tz6nw55/9DI+cfwjDtDCkCVmOYUqkIbAw\nyfOMKAoP6ANpGOSySHylo5lJPJkQ6jBkFeYkxnhDRT0djQ5Gwjsp+X/ZxCsvBur+o7hrHZnD+AQV\notgYoe5TCP3YsWOEYcj8/MJEOYqjPm6twk6vR25KnKkmp+45y8r6NuefeBA/2ePm2gtMTy3QC3fp\nRykN18WQDlZzitqZU/Q6PbxIsHnrFTr+HlXHI82aBDsxZ2pLzDeWSOtNbrZ7JD2DE/ZDDFhjb6tN\nY3qaTdsmchKGwYDexm3uvfMYj775UfKajRlKzMzAkPvLbz5qv23ZtNsdpqfn8VyHNI0xHJO9qEcv\nbHPm8Ye59rWv0r3Vw2nVYKHBuTPfzJ3f/BDdrQ0uv3yJ6QiqnYCk0mEgQvbyAe2r6yx1djl+fJHr\nsc+tm5dpWB5x4BOLnDjOyfaRZBgEeK6LNA2CLMGsuDz60MP8zu9+lG9/57dO7HMjzyCDMN2nAFBA\nYD+HPwlCgPn/M/feQZZld53n55xz/bPps7JcV3V1VZvqaquWYGQYWTSIlgRCjERgJEwsTDATK2Ij\ndmEhxE6sNBGYQWIXEKZnVsMs0sghCYQsciAJqZ26ukttypusykrz/Lv+nP3jvpt5MysbEcRGiPtP\nvnzvPnPP/Z3f+Z3v7/v7/pRdwC150RYPIbBUIeeQa02aFYJZSlY440JMeFrFYD0f3l7a4E6nXbXX\nnQnJKs5dJUaU/8P2uooS8tv53VV44x8LUnYGMdVj51wo50s1+Hk+eLJ6fM8ceNXJVjnQsLWqZlmG\n53nbVtedjqK65SifL1/bXDEtxdrKdU6cOMGHPvJhXvCCFzA9E1APGqSZptPpE41HKKmwLYu5mRk8\nzyHNYnq9HhsbfQaDAVoXUfz8/DytRpM0i4nCiGsr1xiOBoXTkxD4Hq7rY1sug36/iMwHA2anZxmM\ne1xeWWP/gb2gEyxZqgSWN8xg25OkhywMWm/CRtsTPtUbvAU5FeNqWbISkXODoVfHaieTpYyIq864\nNM7yHlUX32r0sttEKs+pfk55lP8nSbotWSulxHVdhsMhUiqydLyrHUkzxq3VQUuu99aYm5ljY7RB\nZka4XkJ36FP3DYuWxfLYw5o9hkrBz3KsqTZT+w9xKjqH7TrcsuATpDb5RoJbs3jbT76Fs08+hmVJ\nXvFjb+SZlXUunV3G6qRM+/Dhj3+KTmrAn+XSpWew8hF72zP85rvejW416Pc0ddcGtuAmuZlvEHiu\nR61WY2Z6CqMzXMdHC41rO4wtxdTSIisX2ojBiLAzoDU/jTvbYmHfHs586u/ZOH2Bq8+d4Z5XvgpM\nRpYnJDJjGG7QjBycVXAaHgePHKIzGpFbDrk2ICRCCzzHoeHW0FmG47v4OgVbMh4MeP0bf5Q//pP3\n8bpdxtwRCiw1EcEC22xRT8uFdxyOtmG7UkqEFBidIYXCkhJhKbQQEzx5Cx/ehF7MlvZJaVPV3WDV\nxnYreCnzRTuL42C72FXVr+xsvlKsKduDkKLW4UbxOCFulMLeeU55PB+Lpwxed17f8x3fcy2UqiM2\nZnt3DsuySJOtXnTVFa0c6CpVTqrtK6EUAiUEUZIwNTVFZgwvfMEDPPboY+xfnMGxPTy3juvWadRq\nuK7PaByRpWN6vS7apLiuzZ7FBVzXR+ucKIzobKyxev0ajuPg+x7z83P4nk+cRERpRqpz+qvXydMc\n3w3wHY96rUaURoRJzONPPsXUwjxOnpFmKUqCmrBEyqa+enNswJAjKbTFq9FBFUOrRhdlsqU6zsAN\nuHVpICWdqTx2TphqFFFyvqvnV+Gw6lGdHDtZJjujqDJhVUx2gOJ6XNfFsh2Gkw4vO490OIIwJpeG\nKdfhxNFjrHY3CgchBdeuXkHpIT0zRNXqOKaObWlM2MX3XTztsuC1OTLb4HT/Al7us2dqjpv3L/LN\nr3waYaf40zOcW13h3NoqJ158K92Lp1m40ua33/mf+NaF5zi1vsxGb5WWNc2//ZE3ENSmiYXPYt0l\n7F7DeGrzfpY7KSZywklUtPJrNNt4foBOUkaDIcO1LtMH9nDk9jv4wgc/StNxiMx5XnjTLXzi//kA\nL2ruR/VDfMdiebSBK2tcXVmm01slTHpEKyPun7mLKQzXL5zDXZxjddjBtX0Cu0ae5EgEyhR5pWwc\nYXuKLElQxtDtDHn5a/4NPPT/3jDmwiiSVJMogxGFJIQQArKUsuuMwRTa8WnRB9K2bSxhIbTBSINB\noo1AG02apBhRUPY2ITpzo1PeaWM7o92qbVd3p7s5/N0KbqpB4lZEvr15evUoA81y7lnWdjruzqBm\n52s7zxNCbDbCKH/Xd3Pi39NCniqeWsWMyghsi+kARYRavncHi2KzQKKCNeki228wBL5fVD5J+JE3\nvpE/et/7eOmL7i+YJf2QwWCNZnMWiUc9qE1IGZpMJ/QHXUKd4Loxjm1jOxazs0USs9fr0u12ELoQ\nc/c8D7/mI22bwPEY9IboLCXNU6LIoDyb6elZglaDD37ow/zcT7wFlcabRi9EoT8spUAg2YIgd+dD\nV7Gz8kZvacrcqBW+G5VrNwil+v8/hglWf0s1wi7PLYoVCly0hHOM2S7YX0Y4UloVrrAs1OVkju24\n5FrTak3takdWmBANV9FK0FzcQz42ZLpGMDNHX3v4V75CHGasiRq5VpD3MVbMOI/xmi0u9DbY6F7m\nwHQDfywJc81auMbqk5eYqrvcsv8gcQfaqs3Df/8p/vB3f5dX3H0XL1+8HYIUKxC86MhxPv/pD/O6\nH34zt957H6kLab6Onbp4UhFLhZiIIqGL6lKdF+L9YTjm8qVLzC+keK5LomA97BMqOHn5Mnka4x25\nibWzl4nOLRM8/AT333EXqWPRicYMyfj28jmEhPHqGnkeE4uIoe9ydbjG//OupwAAIABJREFU4enD\n5EnKE088wYYEx/bx/QaWcmk2p/DdAG1yWs06o0GHPM9IETSm54ifR1I21QYjBcq2QUqU2MpNFTkn\nB8tSJGm82V6wYK7kKFPMzSQpNE4QE5kMoVBCIycMF53npKaMkMtIurDroiCtnBdbjRu2onWDEFs7\n8cI2q7a8PZFf/VvljRffuVVNvBXIFInYKvRSPOaGeVH1Z1XopVqJXv3+6qLwLzoC337hW4+rzqF8\nrVpyWt2i7HRA5SHMVgwqKbg7eZbhBT6WY3PvPffwzUceYWnPfqbas0zPBGAUnU6HwWBATtH6qdmq\nYdk2tbo/gRSKSZdMtl/NZoO56WmCWoDWmm63y+rqKqMwRBpJq9FgZrpwPJbtMAhHjOKYIKjhOi6n\nnnmWY4cOooTCCzzC8RB7U0SoVM6QIEDsWODKMXIcp+IcS4fOpmOsGlB17HeOJ2ydX26Dy+eq7ymf\n2/ne3SMjvS3K2cL39LZJVPzurSgpTVMMRW4ACr60el5NiAynFtCPU/qJ5pGnnqWXaeb2Z3QHQ+bC\nFQ4ELVydk0jNVW1zPgyIbJel1gyB6OLeNE124C6Wn/wbao7g4KEl5hfn6K91OX/mCnOWw+q3vsy7\n3vajpD/5On73//wtVlvXOfPYM4j5+5g3FsoRvOqNr+bCWhfpg0lHpDokF9PkZqLzQuHAjQGBIPB8\nhsMBnY0e165d48mTJ3HmpxgOh0zXGsiaTyoMSyeOszSzl97FKwyWu6xPrfPRM4+w/+A+5mpzDMkQ\ntqB/fQUdhUg7Z5DkfPHkP5AJw9GFg9weNHmms0bdrePVfKjVuLB6lUvXV6nX66RRTM22Obx3H1K5\nnD1zAW3t7kD8xhSjOCLLcnKTF5Wm5NiWjbBscgkGSYoAaVGyMzKd4yGL3aRlFXz0SZd2o3OSKC4K\ncaQsckphMpFjKHJRWbaVjC853yXHvPAX+SRg2LLz0vkXjnOLRljaWtXZVm26nEt5nm6z8/IzS/ut\nQpPlubvtNnc65Oo83gkFPV/UvtvxL6KlWhU3rf5fvF5VuCv4mDudThlxlvdCsmOrJSW2ZSERxGHE\nsaNH+R8f/jb3veD7uXr5Kq6T0Ki1mZpqMr8wSxTGxGlMrnOGgxFROKZWC7AsiyiKSJKYLMsZDvqo\nCVbreR5B4LOvUccgSJOU4WDIcDREKglxVLBLpGAUhzxwz/387Rc+x9HDR8h0zjhKkNLCsi3yLMNo\nDaYQ2geBzjW6UvK+SbXapTS5iHJ3Rg03ShWU41+lT5WfvTOnUF0Ins9x77yPeb4Fje2Gk1cXY8ey\nSbJkonlStPZK0xTpWEglGQ53x8CfxWIQZfRHGUt1zVTaI+tfJstPUYs6nJVNTLhBs3ed9TTk0Xya\nk6ODNN2ARv4Yt0yvcGUt4elzLu1ayvXrXRprkvWV8wg5YnpxGhHM8+g6cCVBDJ/j3/38K/D3nGa1\nf4K//VqTxx5Z51f+93dzobtCbvvoXCII0EqQezW0GSGFhPJeTLZWzz33HMLAiRMnEFJRr9Xpx0Ma\nh25m3/weZvbswZ9qUXMDWjh85cOf4Asf/wRpd0xrZob7XvNKpvbvASmJTcJzzz3N3/yPv8DLU/zA\nYUzK3598mMGlaxy/+VbunpmjG8fEvQ6HbjnE3S+8h74wGOVgcoOVGhrKQwiLkYFUP4+UqV9Ucw56\nXVwvIJNpgb9HhSJfnIQANBo13CCYBBcaaVuYXKM373tOnuUFjVBKHM/C8bzCEecaz/M2W4xVNVSq\nuZtqxFy1s23QaiUxWKWsVm1wp81v4ffb7bv4/kKQa9O/bNr07kVt1aT+TumL6lyAG6mM3+34nrJQ\ngG3Y005IoIjGksp2aSuSq+K25fm2XQiiK7GdX5knKa7nok0Rms9Oz3Dinnv52te/wQte8ABZkhHG\nI1avnaNeb+J5Hu32NM321GSbOyBJE4bDAWmaEgQBrZZHs16fYMKa4XDImTNXSDKN67g0Wy3a7Tau\n6xYVnf0e3f4GYRiiLAfbc/mBH3g5v/rr7+Q/ves/4kgBOiWKQmxV8GKVkEWPQgRaaIzeak5cGkop\nZF+94YWBZdvwv2I7uL0CrRzjavedKu2wNO6qklsV165ihzudcnGIbfe3PL8KoZS/LY5icvLJRDUF\nnz/PSbK02FE8j6l+a2yTaQ/HDwj7Ibc7mmMzhnZ0Fses8IlnF3lazHHi2MuIZURvbZklSyLGPU6d\nPktwq+TonIMTfpOOn3E2GzByFtnbnGE6jjmwNM9fntJ8jX389gdWeZG6yu++2CFuXmHKP4KvJa61\nyBf/4Vl+4Zd+hKunn8XOcjJVo5dGWK7GNhoz4bIrpRCqaGH26GOPYlkWF86fZ3Z+jtXVFfbdvA+/\n4RCZELIME6d04x7rWc4LH3w1l9aW6Scp/+s7f53Es9nodlma20M/HbNw+ACWNHzuL/6CeDgmNim2\nsnhm+TxaZxw8fJBjR49xaW2N73z9q+zt3kbr0CHsVpso0yjpkaNAg6OszeYOO4+xFowjjee2EIDl\n+DhSTCSBi5Zstq3Y2FjjmWcv0u/3mZ+fZ2q6RWB5ZHmGFAYpLRzXQ8lC9S/JUozJsYSF5QiSJCOY\nLABlw99qkrFsiLCTwlp1xuV55f9F27Ibg8Cdzn8n660arZfP3whn7t6kuRrklL+5mvOpBmQ7v++7\n8cCF+afG6v8/HkII/v6Ln9xWMl91ANXsdZZFmwNW3dpUb9oWPusXANdmNrsYANu2idMEqRRSKYSU\n5J7DQ3/2EEePHGHPwgLuJMkSR0lRTYYgCOpEaUYQeDjOFtfVGIPJi752xhQwRokNC1TR0SUMidIE\n27GRSuF5Hq7rkk8i5tFozMpGl/bcPNevXuHlL/1XmCzGxCFKiAmNcLJdo3DKupKVL8drZ4eeYjy3\nCmG2sz92V2GrGmYZZeyUqd1p8DvhnPKztvPEzaY2RXWR2G1HII2FsMpFIUWLQoN9HMbUa00Go4T7\nH/hXN9jSkdf+B8ZhQjoaY8UDZqwhR6cNh6ZSpuyI9ZljnL+muTRs0EnAyTsccBJqSvH0ygbtmYAX\nz4x5gPNcwCZkipA2OsvY42pq2IxrRzgbHGIjDLkjX+H1fo/mfavU21OM87187kyDb11r8qK77+OF\nezLa1iphPeC68DC2j2USpDFIJMKISeGKZGOjw9PfeYbFxT1kWaFWqGVMKlLCUYyIwJM+lwddNvKE\nNE042J5h2gtQriLXFr5TIxdQb9fxAou6K/nchz7A+ulnaTkuvXDEWBqwDK3A5baDN3N0/2HCfkiY\ngTc7T/vAIepLBzBujf4oRloWjmORRyl3njh2w5h/+fFTqNxgZwYlFNpxkarkV5fOSyOVQEwS8wLo\n93vkWcR4HLK4MIfvOVy5dAHHtvA9ByVB5xl6YtO+tbXo76ZtUrX53eocqs6+9BUFrdbedu7OYKU8\nr/iu7VBt8TlFArrqrwq73t2VVufoziDm+SLw6ly7875//byQyvc0ifl80VsV7I/j8AYnUm5FYDvl\nME0Lx2spVeBjUoGyEFLQqNWJkrhw3jonSVNe/LKX8jd/9Ve86Q1vwJnAI1PTTeq1FlmmCccx692r\nXLp0HsdxCIKAWq1Gq9kkqPl43hRRFNHtdtnY2MCyLJqNFp7r0Ww0MKpo+rDR6XDx4kpRpKQsWo0G\n87NztGYWWB0MWNvosLx8lYWZNoEfkKcJUhQTXQMm12Qm34SToEpz2i2bXTj8KqZdGnl5lJ+jVNGz\nb2vHUwjje55XwQG3JDFhaxHbjZu/tc00KLWDRlaJSoqs/XYNimgYISTUagFxFmNZipmZGbJMs7S0\nZ1c7mtKCpuUQOhb9xKVnZjnZ1zzZGVOvKVoXP48FRJ2ckXWMvruXdJjx4hOH2OPu4eTTlxk+26N5\nS5sXvPYY68+u8Nxjp8lai4xuPszljTPMXPogb1iEaSfl0NE76Qws4o1Z5rwV7NpZjh17MeneV/LU\nN69y2+0xS0tXWBtrlu58KVc3BnhOQJZmmNwUMIq0sJTF3NwcC/OLhGGI47hobZB6hJQGZTnQz6jh\nkdYDVj1D5iisUYw9jhiOBkSjjHOXVzhz7TIf+8uPouMxrmO4/eZ9zGuo5Qosj76XcSXZoBYN6D49\nYHB9jUW7yXR9htnGHONLKxi3TW3/NHnNwQ0CLpw7zc17D+w65tJxUalGxzFhFGJcge04GMqmvhDU\nAoQQpGlEkuQ0Gg2aLYc0G+PXM5TrgqW4+djtBJ7H8pULXL54ASk0c3Nz1OsB2Wi4rbCvhAurtlT6\ngpJ+Vw0A0zTdZE1Vg8DRhNG0m18pnXiZW1Jqa6eZpukk4t9qbFJdSCxre+PuKl5fpceW0ftuu9Zy\nTvxTE5nfNQJ/+9vfzl//9V8zPz/PyZMnAXjnO9/Jn/7pnzI3NwfAu971Ll772tcC8O53v5uHHnoI\npRTvfe97efWrX33jlwrB1770ic3Jbtv2phhNdeWB3TVTysGpVgmWA1IVkt9KXtzIPw60w0ho/uTD\nf4HbqHPrgcM0tUvN83EaNQZJgqUsapaLciwsx97E3DqdzqbztCyLWq2GlJIkSciyZPM3VG+a7/tk\nWUaSJERRVPxuoxiMhview9/93Rd56799E41agCUMeZrhuh55WjS1cF0PCsLNVuQwcdKWXfw2JttY\nnWVINAUqZyYUNrCtIvKQgNBFclcbyHKJMRmFIkGOELrg7BpDloGQNko5mFwj0wFCOhjLIbdsUi3I\ndYalDMokOCqBPEahMdrBQFFBawzCdrAtl0xLQCGlgzFFAU/q5Hi2xfLly5w7fYZrK2v0BjFRLjl3\n8RKNRov3//F7b7ClEw/+B3qjYUFFywwi1Vha0Aqa9De6xH5IlmtQDrbtkmcJKh+z0JAsNQymd4mD\n0w5TNcVy0oFxwr0HjtDojdHXN9C+zXU3YNyYxq/VuGna5WAb9rY2aCwMqdsKa9hCeNMs9yUOszQ8\neOTyCqveHRy995Uoa4SXaJRKGQRjIuEREEDWQ8gMV1sYDV3LQWqbunQwWqPzHGtSkl7SQzVFEY5S\nClsE2FKCHnDm9LP8lz/5cy6cucbMdJs7Txyi11/GdlyeeuocifQQgcv+dpN528KOxrQCj+N33U19\nboknzlzBas2D22D/oZs4fvcxkjTllqO33jDmjz7+DGma4Ps+g+GIWOcEfh0hBEmcIIQ1KYlXZHlK\nvV4wtJIsRUdFtyvf90nSlEynuG7R6b3erGNMTqfTZW1tlWY9J44jtMmp132kFCgM6BQLEDoDrVFS\nohMbISVZluJ4DmEU4rp2UdlpJrtlCsqsJ2zykg0kZPHYFIur1jlKgJyomRrkNt9SJk1LLfDqUTZp\nqEIy1Qi//LtTOrb0aTsVCcvjjnte9s+PwN/2trfxy7/8y/zUT/3U5nNCCN7xjnfwjne8Y9u5p06d\n4oMf/CCnTp3iypUrvPKVr+TZZ5/dtkptXez2SHJn0qx8XJZYl99bXdGq0pKls65yysuVzLa3nG95\nruvaCAFvffOP89vv+T3uPXoHlpZISxWyssMBSZwWXbqVxPOKogvHKZrNuq5LGIb0ej1GoxGe59Fq\ntZibm0UINp8fjUabi02tVmNmZoZ6vT4pdkgIgnlWri9z//0P8IlPfooff/ObyNE0603yLEMqPWk0\n4aAxZHlGnptJJzSJpSwQoOytTLxUEiUURhSMHEWRN8uzfJJJkAglJ4q7GiEjhNYwMUopLNI0R+cG\nS1lYskisaq3Bb9Ko+XTWV/GVg8oSLMdBI0mlQ6Ia4LkE9RZxOOnSY1KSJCZOQrI8RUhNpnNGvVXW\n1tcZj0f0Bi7ReMTjDz/MeDwmSQ2OV8Pymgh3nuVetNOECqPPbITxi6o/mRPGPWxH0pgSHL3tGI7y\nWF3f4PTZSyQxSOFhUPTGOWmcINM2q/0Rc1MuK8ktDPs5K2GDVx9t0ZDX6HfXSZM97PMF0/Ish3VM\ns+Pix4sIL6fvjiE2eLWcqJYQiDXUeMDe2u0sX6tz+fQTHDl6O9rVDKM+DTxMEqGZxcqmsKzzhFZK\nnu+nFgFqRKaLJLbR+WYxi5STSW40mAxjchITMRhFBJ7k0JGj/Phb38Jvvet3WF27zsULNguLLdbX\n13Etm9jAMIxoHjrEYH2Flq1ILUliCWYPLPHG7/t+3v3eP+Lhk89w66238bVvzXL33fdyyy5jbkyK\nUoI4HtNs+KRas7RnDysr13GkWzRFMYay404YjhgPIzCglINtW6RZSK1WY3VjgNYJ2miyPEJIQa3m\nEQT7mGm7KCUZh2Oee+4ZkiRmfmYKS9nkSUwWZ7QaDXrdHs1GQBRGZDpBaoGQkE2qkC1hI5yJxo4U\nmLyMfivMks1rkxMHDqARcmuXmOc5YRgCAs/zN31U6bSTZAvureapqsFc6duqvquKvZe/55+KgX9X\nB/6Sl7yE8+fP73ITb1wRPv7xj/OWt7wF27a56aabOHLkCN/85jd50YtedMO55dao3LKXK1v186uZ\n4/K56mq40+lXBW/KRGc1UoZK5V+WkxuDQvPKl76UZ575Di950YtZu3adLErZt3cvlmNjpERrw3A4\nZDgcsra2im07OI4zcepFB/syoXjhwgWgjLoDZmeDzeROFEVcunSZPC+ia8t2cGyYn1tgbX2V6dkF\nnj17gaNHDtMPI3Sa4E06Dg3HQ4RdGqHEVlX6VIHzl9suJVWhFKeLZKDGYLTBsu1tOYIixtAImRXM\nAAxSWAgUgV8jTQqOuiDHsQUoi462EGmK41nYJsa3NaNxl5VOxFpk8cxyjzPLPXrjjDAcbuGLTAzV\naIQslOuULLaolrLAqhONx4jmYRpTNmGUkBlFbIr35m64q30q6RF4LlEckcRD6o0GWdJhdl+LTA+w\nohQdjSCPEMZCC5s4hRDJ2PFRwmZq6iDhTJujfp/VUc7pCxeYve7x4vm9HHA7LK+GrFzdoOe3CDsd\nDtnrZI1ppB3i16ERxaBj3JYGexk1NaQ/OkFj7xv45qmPsjBzCquxj6B2M2q4yrTK6IsxlpQEWQNP\nJ0Qmwc0zEpETq8l2XDigzSRYSWFS6afKvIjU1FpBATMiufX4cf7Vy76fL3z2c4zCCFggiSVJkhGm\nEXajSW8w4KY9S/RXLlN3bLx2k69/+2Ge+OhHePTp03TimKdOn+LqlTr79x/cdcwPHNyPUhJLSbIk\nQQjJaDhgaX6aNEk3Of2DQR9L2DR8G8/3iuYnabaZWFxbX2Zqaor+oEd7qsV4HOLYLkkSMjM7Q783\nKGiEwuLWoyfI8pSLF85DnmArhes0GYYavzbHOOngeDbK+EXQNoGkjCmYa3bpFDMNqpCb1qYgCiAm\nzCdjikhnEoljKHZsE4ds2/aETlx8ZnXnD9s7UFVhxwJKSjcddjlvyyi7bCBehVrgRrGr3Y5/Ngb+\n+7//+7z//e/n/vvv53d+53dot9ssLy9vc9b79u3jypUru76/xLPK6LTEscujuo2oJjVLJ14mx6o4\nbBnJVwdhexJva3tjOQ4y09SUwwN33c1DT7yfZ849y80HDiHilDwKERKubqwy1Z4mCHyazQZaF53M\nh8Mh4/GQ4bAQnwqCAAClrAlzJSSKks1rabfb+L7PwsLi5rWOhyOE5TAajZmdW8T2fD7z+c/TaLfZ\nMz9Pq1Fn3OviORY6U1DJUCdJgjYGOVnBs03qoSGXCj2h8KHKQokiyjCiKHSYjHIxTloiJrAGUqEN\nhGmIbUksS6BNirAM0rJxtMc4HDLXbvHUY48gpeDwkdtoug4f/dCnWQ8tIlOjOb2E5a8WCWFjIbEw\nWpIlGqO3AgCJQGuI4ghlT2G0ZhSnjBOB7XgoxykaYOTPQ2nLImzlEWY5lrSRaA4fvoXz5y8gZUbN\nBKxudBglI4xyQXnUWjVybbBsB2Ur1qKI9WvrSOsae/c0OXDrDMtnr3De1JmeMhxeinikF/K17n7i\nwTQvnOqydzXCiTIOLU5xVAWkaZ8kbePWbZy9NiMcvvzkNa7EFvXx4+TjDifXr3HPieNYmUUj0IxM\nh4gWymSg1hiYACEdhEjJsxw9kXTNdSFQZrQp6gEQaAxRPMJ2HSzLI45zfMfibb/wM0zNNPmbT36W\n7186wuragFozIer3iYdj0t4A0SxogAvzi2gN/f6Q7zzzNPVaGztokSWaW2+9jcU9S7sOuRCGK1cu\n0qgFuI7D1StXWF1d4/bb78APaniey2AwZGlpHp3ryQJjSOMQ23VwnYAoiZmbu5lOt8uBfUuMw5Bm\nPQAhCGyPNImp+XVGo5BavUaaJGQ57F06ROD75GmCQnBl+QqDcULQcMkoGmYgFZZlI3OzGVnLyZgh\nCudc0JGLOaBNUR4vpUQYhTH5JvsLnW466i34RGwKblUdbJalm76m+lq1pqKcuzsJGFXUYWdh3j92\n/LMc+C/+4i/yG7/xGwD8+q//Or/yK7/Cn/3Zn+167k6cqDwKbZGJqM9Ed6PqjKs4UZXutlMbAbbz\nmXeubmX0XZ4jRNFgdJDFOEiSUYRl2bzhDa/nve/7I370wTeQbgw4sLiEEi5Hjt5Mb6P4rZ1OZzOh\nMj09vUlx0rqgEXY6HRzHpVar0263qdfrRFHEeDwmjmPW19c3sf49e/bg2DZJHDE3O8fl5avUp6d5\n9Wtfxwc+9BF+/u1vIxwPqLkWSZoU3VwyjRQFO0UpUEZMJGkn0UApYmUEbilGVdDIAUM8Dgu4RcnJ\nFq+IQFRem1T+gFCSzOQgIJWaOAkRQuMomyyMQAuWL1/lO8+Mufve7+fRJ5/mP//HPyRM4NDNt5PE\nOVNtl7VLZ/CnG1jKRkkHJSxAgld8T5JERFmEZSksW6GHBW6qhcCShppnF+3njMFVEunuLm0ajq4x\nNbVI05fkWtHthjz6zcdZXJxlOBpi1xSZcHHbPkJK0ixHWaAQ5HlEEuYoZaG14eTwKPHFDV522Ke9\nN6eXrLGMZD6PWKr3uZpv8M1em49e3kM9iVHrHrecG/AjSxm37DEImshxje76k7SnDF/9xp9z5IFD\nuGmMpZ8kz4/wWx8c8pY3P8hs8izGTujYGqMMyljkSmFnBrvcJOUaZSmyNENZVoGxasOETITr2FhK\nEI0zWo0ZorBHblLe8GM/ynBk8BqzaKuG34Y7lvaQDSIWplrk/SH75hbJo4zxIOTC2Yu4wifXigP7\nD/PmH/8J7rv7Xi4vX911zM+cOcNUu4ARCyjScM89dxX3F0MYhdh2AT3EcThhXmlm5ufoDoasr19n\nfn6eJA6Znp4ijmLa7aJJSn8wwHNctNE4uHgzAVEUFQleYWHbNuNxoR2vHJdbjt2B1obHT/4tnluU\noVtSkmSmgAWNQWLAFBG1lAJpqUJIjqJ2RDGRndUaIQuKpzClL9LbdvtbWkPONqdbPLcd+tiphbIz\nmt7pp6rOe2cA+nzHP8uBz8/Pbz7+uZ/7OX74h38YgL1793Lp0qXN1y5fvszevXt3/YwPfOgTm87v\nnruPc/eJO7YxHqpZ2ZLMX17obtuNcsDK6qyy2XF1QMsbEUURwiuwMZUXTm5pYYHXv+H1PPPMM7zu\nZa8k7g9J04SN5ct4KsC1PYQxoA1ZlpKnGdE4xPd9XNeh1WjSajSI4pTxaMwgjguMVUosKWlNzzA/\nO0ev1yMMQ9I4IYtjsiyjs7FBq9WiO6kCvfW2O3j62Wc5ccetxFmM47voNMdVdpHc0sUM31y0pAQN\ntrJBTooc0qQoINo0AkMt8EmzBJ3n5HmhL46W2MYvGC5WjlAU5dRKIyyL9U7CcBgyNT1P4DVRSczS\nvlv49le+wV//wQdozu1lz/GXIJGkoyGKHjUxojFnsY7cFOYSUheVe2iUYyMdiUgF0lNI26ItfcAw\nHo4xeV4U9MitoodSF37nEY+u08lHCGnj2D51V9Dau4/XvOo1fPGLX2LdOEiZksQRyoAtQeagsxRp\nBLZyMSkYFBuNgDPJmMala7zoiIPpZTx5WXD7zB3sUxv8gLuMqQ/4un0PT6VLOPZe8t5ZVltD9g7G\nSNFhnObYDYtnn/kSrfo0er1FnDbZu3+dE23Nk8NjvP+rF3jb6xdpJmdwTYcsldQti1xfJkw8hDtL\nvdEijmOM0ZCDQRR5i5KyBlhSk8cpngxIwoQ8M2ghyIXkF3753/PFL3yDbpyQ6TFZOmLB9YnHfepu\noS0zs7hIpz9mY2PEK17+Wu5+4Yvx61MgJGfPXiKM4l3HvNlsMTczh+87nHrqKfYu7SOOM7wgINWa\nQufFMBj3cG0HlCIOU/IMXMdjasoiy4qmwOPhuEjwT6QuGrU6tmWTZhlkBlsJ3EYNU/MZR/EmRJpl\nGcPxmOF4DAIOHb4TKWA4HNDd2EDnCe1GgzyLQReyzVrnSGGIUo1lFWhinmmMLppdCFE0WdmCUTRC\nbZeELZyw2BYQlr6orMQsg82yVmUnm6RazLPTYSul+NbDj/OtRx7/J7FQ/lkO/OrVq+zZU9C6Pvax\nj3HnnXcC8OCDD/LWt76Vd7zjHVy5coXnnnuOBx54YNfP+Pm3/8QmCb8q67iTVlg672qyoIzM0zTF\ncRwsy2JtbY25uTlGoxElHW59fR0pJY7jkOf5Jm5tWRbnz5xhtjnFjN/ED3ziLOfO229j9eo1vvXo\nwxzcs5dmq8Xc9Ay9jRGFQkRxY+tBbbO58Wg0ot/rkyTJpDFyg+bCQsH5znNGoyFRFHPp4kUajTq2\n7TA/N1dE72nC1WtXybOUURyRa4PfqFELajx58kmWFubYt7TAOInQcULTs4ijeJLYLYwkTVOUo7CU\ntbniF7z0ApoajUabieBi4kscx8YISLMM13ERqcAoGMURylb04xFPnz6HFh659hmP4NyVDYaDyzTs\nkG6Ucun6kMUDx8EJIMlIRj0c10XFiuvXLjLTrNGaDtCkZJnBtgK0tElyQRQbjHKR1Oh1Y5Tr4sV9\njM4xwtBoNck1JHkBlSFtbBHsakc//G9ey0ZnlfWNdbq9AZ2NIeTzqSzxAAAgAElEQVQeT596AnSO\nbQRhlOFJizxLMTonzbNiV5YbstSgbBdhwMvWSOyEk+EQ+0LIPQvTBE2byyt9HBL2+X3uDzRp+zK3\nhxdozxwl78xwPlJcW3bR44R7j3jMDhpcPHuOWdZpDfej5BTWeI1jTsiDhxb46/UX8p6PnOaX3nQT\n8+EKc5aPNmNC2eQLX3uYL335G9TrNU6cuJPbb7+dAwcOonVOmmXbdk/xcMzc7Cwb6yGgsVyPeqvN\nV/7u7/nLj/8eP/RDbyKTEqM8Ll07R+q7TDdbjBKLWq3O1598ikO33sFP/9K/54GX/GvOXVwhSQED\n3W6HO08c33XMm/UWKyurrFxb5tixoyChPTNFtz/ADwK0LvTxHS9gOBzSajZpugGdTh+35hMEDU4/\nd5pbbjnM6soalrDwPK9ohBLGaDsnqNVI9BjPlcRxShRFWMLC9Rx8z2EURkVkzqQiM5VI22ZmqsHB\nvYe5dOk8Fy6cod0IqNUdtEmK4MGSWLibO2Hf90mTpIAcKRaVIrs/IUxMIMpqr00w2yoxS18lxHaU\noOq3dvLWS3pjiYmXi4DWmvvuPcE9dx/f9HV//Gd//ry++LvSCN/ylrfw5S9/mbW1NRYWFvjN3/xN\nvvSlL/H4448jhODQoUO8733vY2Gh0Gt+17vexUMPPYRlWbznPe/hNa+5UVNYCMFXv/CxbatX9QKr\nq9HO50tHVDr/8ihX5hKKKV+3bXsz6i5pQHmeE5ucpl9DTppEZBIyS7CyusZnPvVpXvbilzDVajEa\nj3HsOjpnG3xTRv/VxqRVkn7JT3VdFyHEZkVmmcwAsJRCG43tuijLZhxHxGlKb9BDSsGTT57kVa98\neUHDEgLiQhsiSYq/WZ5hWTZZniGk3Pyu8todp+jGY9s2STJJoii52RgiN5osyRgNQvyGT5QlpMJQ\na08jrIC//MvPMhhAPLZIYoXj+hi7T5zl7N1/iNX1HrVGkzhOECYjcEAnQ5JwQBwO0OmYOEqp15tF\nhOzWsJwGWD4pDlEiyYRCC0UqxmRJTBpFuLZNFCUoxyfThY5NlkR85g9/+QZbeuAHf4406xPHA1zX\nYeXadTy3jhAWlnJJElk0U5hsbwuoSICwyDQYoZC2S5pqAm0ROxrLCplK17ln3udgG6acMY3RNWT3\nGsZxGc8sckvSp88CF8U+Tg3qfG05Jawl3NwccWuWs9fpobOzTAWL/Mgr7sKfyRjVQ4RzlP/+9RN8\n8upRmOoyHX+bg6qLbyvaU7fTu36Gq5efQkjBzOxssdvTOXEc0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FhGAe9973uI4pI0bcji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pNGwkyzBgeP0INMnickpWVJiWRafb5ehgnyLLiOOYe/dOKWso6wbb8Rnt7tM0FWkaE4YnLGYL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/iex9mNTcIw5Pe/+Ht89KMf1fS7wRIHd/bwPJ84zvEDn/FojB8GONh0goAkiamq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\njRz4yEY2spG9Q+3/AULZeneOI2OUAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x4a20f50>"
+        "<matplotlib.figure.Figure at 0x3f5b2d0>"
        ]
       }
      ],
@@ -511,14 +687,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "That's cool. Both of these detections are tiger cats. Let's take all 'tiger cat' detections and NMS them to get rid of overlapping windows."
+      "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def nms_detections(dets, overlap=0.5):\n",
+      "def nms_detections(dets, overlap=0.3):\n",
       "    \"\"\"\n",
       "    Non-maximum suppression: Greedily select high-scoring detections and\n",
       "    skip detections that are significantly covered by a previously\n",
@@ -532,49 +708,41 @@
       "    dets: ndarray\n",
       "        each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n",
       "    overlap: float\n",
-      "        minimum overlap ratio (0.5 default)\n",
+      "        minimum overlap ratio (0.3 default)\n",
       "\n",
       "    Output\n",
       "    ------\n",
       "    dets: ndarray\n",
       "        remaining after suppression.\n",
       "    \"\"\"\n",
-      "    if np.shape(dets)[0] < 1:\n",
-      "        return dets\n",
-      "\n",
       "    x1 = dets[:, 0]\n",
       "    y1 = dets[:, 1]\n",
       "    x2 = dets[:, 2]\n",
       "    y2 = dets[:, 3]\n",
+      "    ind = np.argsort(dets[:, 4])\n",
       "\n",
       "    w = x2 - x1\n",
       "    h = y2 - y1\n",
-      "    area = w * h\n",
-      "\n",
-      "    s = dets[:, 4]\n",
-      "    ind = np.argsort(s)\n",
+      "    area = (w * h).astype(float)\n",
       "\n",
       "    pick = []\n",
-      "    counter = 0\n",
       "    while len(ind) > 0:\n",
-      "        last = len(ind) - 1\n",
-      "        i = ind[last]\n",
+      "        i = ind[-1]\n",
       "        pick.append(i)\n",
-      "        counter += 1\n",
+      "        ind = ind[:-1]\n",
       "\n",
-      "        xx1 = np.maximum(x1[i], x1[ind[:last]])\n",
-      "        yy1 = np.maximum(y1[i], y1[ind[:last]])\n",
-      "        xx2 = np.minimum(x2[i], x2[ind[:last]])\n",
-      "        yy2 = np.minimum(y2[i], y2[ind[:last]])\n",
+      "        xx1 = np.maximum(x1[i], x1[ind])\n",
+      "        yy1 = np.maximum(y1[i], y1[ind])\n",
+      "        xx2 = np.minimum(x2[i], x2[ind])\n",
+      "        yy2 = np.minimum(y2[i], y2[ind])\n",
       "\n",
-      "        w = np.maximum(0., xx2 - xx1 + 1)\n",
-      "        h = np.maximum(0., yy2 - yy1 + 1)\n",
+      "        w = np.maximum(0., xx2 - xx1)\n",
+      "        h = np.maximum(0., yy2 - yy1)\n",
       "\n",
-      "        o = w * h / area[ind[:last]]\n",
+      "        wh = w * h\n",
+      "        o = wh / (area[i] + area[ind] - wh)\n",
       "\n",
-      "        to_delete = np.concatenate(\n",
-      "            (np.nonzero(o > overlap)[0], np.array([last])))\n",
-      "        ind = np.delete(ind, to_delete)\n",
+      "        ind = ind[np.nonzero(o <= overlap)[0]]\n",
       "\n",
       "    return dets[pick, :]"
      ],
@@ -587,7 +755,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "scores = predictions_df['tiger cat']\n",
+      "scores = predictions_df['bicycle']\n",
       "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n",
       "dets = np.hstack((windows, scores[:, np.newaxis]))\n",
       "nms_dets = nms_detections(dets)"
@@ -601,7 +769,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Show top 3 NMS'd detections for 'tiger cat' in the image."
+      "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes."
      ]
     },
     {
@@ -613,37 +781,53 @@
       "colors = ['r', 'b', 'y']\n",
       "for c, det in zip(colors, nms_dets[:3]):\n",
       "    currentAxis.add_patch(\n",
-      "        Rectangle((det[0], det[1]), det[2], det[3],\n",
+      "        Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
       "        fill=False, edgecolor=c, linewidth=5)\n",
-      "    )"
+      "    )\n",
+      "print 'scores:', nms_dets[:3, 4]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "scores: [ 0.93699419 -0.65612102 -1.32907355]\n"
+       ]
+      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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YMakNXlvhtNmhaGjMrTYYTyw6zTahH7I0v0r/dMzGhRV2jh6xtrGEkNjYwx5L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SAAAg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8/f8WD0EZbSJZhn5KnA/dNPWF2m3N+/jy306bX2qDSJq8mMRhN4dXHOZHnL0yePWcQTFLvB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\nVcMl27kAAAAASUVORK5CYII=\n",
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3uHXnNs/fvI1d1szOS8x0QuVbgm1YPHnMVpHTtseMsjF/+xf/Dn/53ofMhaCY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Qmmqk1kxWQ32OTm9QucvL2Fa1foDnyYrrFRE6iNFWTosrsZYnsW//CHfozHH/84RxcWeN1D59gY\n+Tgqw6l6xElEFIfYlnMA+eqLsf689YW8aIPBYF9Re56nycVYPgqZGw6HE7IxVm4Ho010x3nxedmR\nXfTvMJnQ+6krYsOw+GZRKPrGonKCteK6+mKjBwvoMnqYzBa/G3PpxSsjTceLQEFfFUxDETp9WCm2\ncvtbocD1JEQ6YlUqz7OsI1JdwcGkQrNck8yPGOzucM+Ze1hevcH52+9le6eLH4fIMOWe03fTlwmm\nqBJFCWtra2zt7GBIg+Eg30Az3WqRplHOczsWJoI0SciimHDkExsJKTB97Cj/+6/9Mh/4+Z+H0Qg1\n9Cdyw5W55ML0gnFxBd1kKwZMr+Sj84TFOXUlp/ODZSRe/K7gAPVoEx1Zl89R9KGM7vOxmowzL84N\nkxOvbFKXrQgoh06mB+5Nv6eCIoK9BV+AUhL2wgkt02FtdYPPf/bznL7tTjbX15hpz6AGfVIi/FjR\nsEx8KRlkCbWKw/JLL5C5FoE/RbC6gYp9XMMk8n0sx0U4Lp4xTRL7VBsNYiUYdLs0vAq9YZ8wHJFv\nosywpcTCREQxmzeW8RPFY/c8gG2YIA3CfsDXPv9FZlsNvuUNb+CP/ugP+JZveQ3hbgchBUEUUFEG\nRj+i1qjy7OYVHnnPm/jcF77IbadPMrV0it2VDvP3nab9FodWL8aJFJ3tLaR06HdDzt35AJ1wyK98\n+D/w99/3bqQlMZIYK46pOi5xNpkWVafW9Gee/z+JHsupDnRaqwhf1R2NrusesOzy+Tx2guuyPwkS\nJi0xXRbK0VvjhUFNpK8YW/Wv7MTUz63TIrpVUO5bOQCg6EPhz8ufzZj/LipI5U7Owq/n7M+HYgz0\niLAoiibKGt6qveoKvBAEfXNO8UDLSK04vvy++D8OfKqWQ5Ql3H77bVy98hlmZ9q4rkciDGSY8vLL\nLxK6Fv4wxTJtvEqFB+6/DyENskyxu7NNEPr0en1IU4SskUpJpeJhSglCEaQBnltl8cQxhCn5tx/6\nEO/5nnfQtG1IUuIoQUlAgCEk2d4ONgwDJYBMkUT5qiuMSeRRXrR0ZQ5jk03nz8uKsowMdKVcOHUs\nqxD2wx2Y5UolYwUcH5h4+hjofS0cNbfybRTH5+NI/oz2K6jrJeR05KNwDIuIDAWkUQJSEkQhH/29\nP4TUYmunR38wwEDiD3xqM01U0CdNM6I0pt5q4dqSrdVtzGaV57/6ZcKNDTzHxLYFludiYhIFIdJy\nmJ6b4dhtJ1hfvokyRhhujcFok63lm7Qdj3rFQ2YpFemShQmWMuhu7/LySy9iNJrMzi9w9bkXaHoV\nXrrwMkfvPcH3/eB7+fjjjzPdbmNbBlkQ0pIWtuehkpQBMSv9bYJen+e+8CVqb51h4dht7GztcvL8\n/aw+/SKD9V2qtkU/8Kl6Ht2dPkqkPHT2If7PX/4V/t57/i6nZmdwnSrxyMdybFKlEIbIF8xMQZbt\npYMwclpqb5zlnhNTT8YmRL7Q5oqtUPIKpQ735+hgYQzCDqZfvdX7cj6eLMsmeHhdvg3DQkqxT+eM\nle8r03J6niEdZBT33Wg0ybKUJM4d+Uk6nn9ZloEqHLmKarUKsOdoLRLPFbtac+ujyPVvWdEEPVrc\nX2FxF9bAZJK6g+1VV+C6iXAYR6u/102K8iBKKbFESiRjMCWztTpGknJjZRmZGJjCZnpmHvNYBeE4\nxMmQ0XBIEPhcvfwcjuPQqE9RdU1a9Qaz03V63T7D0RA/SfGzAMd2mKo1aFSqgEL0FQ+dPs8z0Vd5\n6qWXOPvgWRpJim1ajFSM5eZpQ20hkYZBKGGUxEghcMnzR5SVoa6AdRNSF7JyWNUYbUw+L10w8vPl\n4WuTz1BgGOahAl6+bnE9nbfT+3wwGdXYcihzmPq45xOwiDIquHZ54HoFWqkkNspMSVWGkUgMt8Z/\n/PSXuPSNLaa9JrXmIjd3Omxdvkw2EoSbQ5SM2DUyhFAcrdcg9HFMxZRlsrWzQ6+7jS0M1HQLr9GE\nYYaTWQxjhVFpYkzN4PVjLKvJiJjR6nXqoaAaBZi1lJmTR1FhRrTWw8Tl6NIJuv6AqN8h6GyxfPM6\nQRRwefkKP3TyR4nikKMnjrKzvkG7UaFTNxmEEa3IIElShGPz8osXOTmzRP/yCqvb6xgPn0b4DuHl\nbcypBdIgwg4HOBVJ6oKdZDSUib8T8uYHXs8LT1/kC4OnePc73kW7UiGJhghbEmURiAxTZBgoDCQo\nQYpBgpHX2sxilMq55VxW8kW9iALRQ+oM4/CNa0Ub0yNqn0LRreky9Ve0wwqKlJ3448ishDSd9JON\nAcGtCyXo9KxO4+z3L03JVIY0JI7hYCt7vxZpuhdpojJFkhYFwid3khsGSJnuswymae9F/UQT1FGa\npvuIu7BoXmnhKdqrrsD1MKPySlg29yaVw6SzT0qJJV3CJMWpukw1HI4fO8Lm5hqPnHuYtZvrfOPC\nc1ieR6hS2s0pHNth9sgilUqFke9jmSbdbo+V5WtkSlGtVJhu1ak0pnKUN/KJgoidzS36gz6t6Sbu\nyOG1r3kdv/pvf4WpqSnuO34bvu9Tq1QYjUZIQxBLgzgMEVJgs2cG7u3WlHBAIMucv24W6lSIrrR1\nGqRoZdO4mDz6eXNUFR94vuVt/IWw6WbmeGE4GDucfy4OKHp9UdHpkjJ3DgdzkhfjHKYpjjBIhcJu\nNYhiwec//glMp4qvItpTdawoxnBsUsdGmoowhSSKqbemGIUpw6DDyRMnCIMRW2vLuDIjUYrRoE+U\nJDQqLXBNqp7DyRO3sbGxQRT4nLnvXuI0pkOCuThE+QN2elvE/YCZ2hRh2ySpWIjpGt21daaw+MrT\nz3L8jpN85vOf4Yd+9Ifx/QBpwOte/3o++ru/h1upcve993Lh5QuExEhLEvgBSIFxZJ6KaWILQQOL\ntGKzutHh9tN342YhAyG4urqCZQucqSaeVSEJI1oyw+jtcPvJE/yP//3P8uP/8Ee4/Y4lrCiiJiVZ\nGGNKBaYkEQKFQGVZjsoBpW1E0ZVM8ZkuezqdUozhYY5LyBcB3Tej0yHl+T0ZwTFOmaAfpytcnVLR\nN7fp5z6sFQtScY4ibFaXTZ0h0MHTfiTOXlbQ4nzFMzIMA8/z9j8fO3djhDjI9xc0kE4rv1J71RW4\nfgPlVbL4Tg9ZKiNPfWCksICQOIxJhGJxYZ5nn36OTneb6dkGs4ttTMclTGNGOz5RGHHz2lWqtRqG\nlFi2hSkk7UYFz/MwTZNut8vWmo9p20hhUHUrtI8tYRiCwWjIMOizvrLBD//9H+azX/gczUqFuWaD\n0I9oVWr0gxG+SpCmxM7yYrSpglAopAAjm0wuVE5DWyDboum8dPG+aGUlWg5RKmpt6k1HymUaRn+N\nQxDz35WRU5kmyZ2m5i3N6nJ/y/db/k7/PrAyqkmGzDIyK+N3P/w7HGk0yZwqqRBcufgiU4aB8GqY\nVQdhCnr+gGqrQa0xRaczwETSG43YWL5C1ZKQhAjTJY1D4jjBD2LsRoMTt53CMQ2IAs6efwBXGuxe\nW8OzTBoz0zS9RZwbNmkS093toCyXu8/dx9XuNrY0WH/hIrfdfoyXL3wDt1Lhda9/HUESkkkwLJu3\nvv3tfPavPkWMYvboEa5duYKRZriOTaPRQDZr3HXmDq68fJH2bbcRtKcYKRg98wyvO3M/zy0vM4XF\n7m6fviUZWCNknCdYa1Y8Bt0+//yf/jwf+4s/Zjsd8vAdd2HbFUbdIXbVI8oyMkORyjzkUO6VOCyr\nDT1lQxltlxfp4nPd4hqDgcNzv+sWXKFM9cIdui4oI3chBGEY7l9T39SmB0Yc1vQCE/qmpkJhj6NG\ndOq2WEhyizHntlOSZOw3KF5RFOVATotikVLiOPbEQmFZ1v7u6/I8fMX+v+K3/z81fWUrT9xiA49e\nzAAm8xIUx+52Oni1av5ADMndd93Ji889y053i/7Qot/vY9kulucy5c5w/Pjx/fMPh316vR6d7i6G\nYRBGGdVam9PzJ4lSgyCI6Ox02N5Yx/fzjRLTM23a002EZbCxusnrX/NaPvPlz/Lu7/1e5ChiMBxh\n2BaRirEdCzvOMDJIVUacZjiWhaVF2uiIV1fE+uJWHtBy7T9doesmoR6FcqumX7egq8r5vw3jIAVS\n9FvPnCiE2J8QZWR+2AJR5DXXFYBeuEFvyhKQZARDn9FgyNVvXOTM6Qew5+aITcm1L32duUaN1FJs\nbm+QpAK32eLu84/QGwasdS/SrNbAyKMl4uGAqYpJ6hgQKrIMNvodLEeykAVU/BFTnsvMdBMPg8tf\n2WJjfRnf83Bn53JrKwq5dPMmCydP8dUXnsfyHLrL69y7MM+V3V2++vTX+MX/7QOkAgzLJFEJlusw\nf/Qoj73hW/nKk1/GD2OMVg3lB1QMF9sy2ejtcLJioKKI4bU1vFaTqOJh+D4vPv0M4dDHCmKm3Qpm\n3SIwBNkwYmF6hoFKma86dLe3+b53vIvf/IPfZuUbV/neN72JhbkFknBIlsQYirzuq1AoY0++0vF4\nFa2IltAX1HJoXZkiK4+zLlMHwcE4V09RDUe3vMpoXJcp13X351ChcHWwdyu5L1uuZQ5cyklGQI8U\nKd9vrVY/4Ogt+l/8Le7d94cTC6Ie9qsHIBy2I1pvr7oCLya5vjkH0FCcnLhBXTDKJpplWXk8bZaR\nxAmmlMzMzBBFMUePLLG4uISUJgPfJ/UVyzdX8msIgVdxMU2L9vT0nkApwjBiZXWVOM6wLId6vUK9\n5iFUTj34wYjd7R2ECaZlsLm6xu0nTvGB/+Nf8o9//CeQmcJKYkxTEgcRWZJhSUkmJYYUZElKmCUT\n1MRhDj79PnVOuHh2upAUTTfjdIEqK16NJtxvuqBNCr7K+VkNGegKuUzZFNy6PtnLaK2YQDpa0s3w\nIsRRfx6JkRKqjEajzif+6E9QQUiv30GoBKvikiU+pmtAGJEakiiDqdY8oXJY7/VYOHkXpkq59PRX\nGPohTa+CIEYKA0m+ycL1HJxGje3NDbav3uDc+fPYacpzX3+K26bbJHNNwt1dBv0+jucwTAJad5/C\naU3TXd6AIKbuuGRVm6ee+ho/8VM/yanTdxDFUZ4/J8vAEmQqpdFuE6WKWnuaexZnWL+5jOgMEakg\njkI++elP8daH3oCXZPRurHAh6DEVmNQMyWsffYRrX3oGP4zoRD0C18RRBjdvLBObEJsSmeUhgG/7\n1jezO+jwe088wfe/63toui5mKLBUhlQZicpIRJ7zW8rJYgYFMj4oE0wo4TK9WR7n3A/DBL1RHFOc\nX08dEYbhvkItFvlCT+jX08GDDly+GYrV6Rp9fo1jtGOkNBB7jvQoGiP98WacPN68cDzq1y3AZzm/\nfb7zeAxSdBpRXyRfCXDB3wIFXnSwHG0Ce+byXmiOPijFccUqu2862QIpMkzHxhECpOThBx/l8Sf+\nAtOuIoVDvVKn0WjizXqYZs6/J0mCPxoRRiFpnOB6DrVaDcdx6PV6pP0Ovd4OW1trmNKk0WjSaDSZ\nnW2zeGSekT9ga2eb3d1tlGvx/vf/1/zhE0/wru9+G5ZpQxAglcwLMdv5NR2ZZ9BTYoxeYbK4RcEt\n6s+lyFKoI+vDzNfiORXf5chhMofKmLMbj4UueMWk0kaLwqklhG4m5+hN7kXyFK3MV+pKQO8f5Egn\n21vM8hwWeTUhyypC1NTe5pEYsDA8j34n4OoLF6jZHt1BFy9LWLm0id/vU5nKqAsTZTk06k1ac0e4\nvLzOdnfEffcu0dlYJRUGpusRjkJcz0GlOW8POZqqTrUwM1iYX6SSwdXnnsNDIX2fIIvwKh7dYZ/d\nQUBctbn7zP1cu3KdKIyIhj6mJfm9v/wS/+0/+VnuO3s2z9FN/qxMyyBOEsI0xXY82vNz7Gxtc/Lo\nMWzL5PqLFwh2R3imiVOx2NndIkh7dDubLG+vE9emSWdabHV3aM3Norq7NEwwHAORKqqei7IslG0Q\n+iE1p8Jw5HN0dhGz4vE//6v/i5/+yR9n2nYAgWdYEPsYgJCCNJuci3qqi7IS10ysruEAACAASURB\nVEPhilc5r3wx3kmS7Tsb8wiNAmTkFaKUKraQm3vXLBA3WNZBhTZWmJMOzkLGdernsFbOzZ/L4njO\nJUmMUtG+DJumiU4jZVm+4zNX0Pr2+kkLQU9Il8v9ZDqJsk9Bv94rtVddgZeRNxzkPIu/hzn3LMva\nH5wkzUNwVBoDAmWY2LbNyA9oteeIQ8XKzQ1Wl3ewKlbOeRsGlm1jGBLTNHBcDwyTTn+IGIxIkgTb\nsWhPH6Hi1QAY9Af4oxG9Xocig5FtmSwtHWcUhgy7fZRh8dKVq5w7dZqKMDCkQFqC2IAsTrCiLEdh\n5qTXu/BAl1shUGUT9jAnoY54dYVsGJPx4fo5i4lXjiTRz30Yf60rZaUERfJ/mNwkUfzmsEiF/PoH\n86ijVYPJx8fJwxulwk9SBr0BzeoUjldhtb9Dsr3FaHWNyIDlMKQtbHYrHufvuo84VUR+RLvV4uaN\nG6xcvYCZRDSmWsQipTcckKoRrmERxjGLJ47j1RqsXLnK3/m2hwn6fV588SWa1QqOXaU11WCQRmwk\nPn4ScefcXVx67mWGo4havc72aMQ3rl3kJ3/2p7n//FniNM3r5+Trw96N53LRHwzpD30Wji6xsbpF\npVbDbTZI+gFV2yM1BC9eeJlvvf9hulvbpMtrDGcz7IrJIPRZmmlhVl1sYp65folUShIZkglJqgRK\nQDAYUatVCboDouGQn/oH/4i/+tSneeO3fgstz0NJkEpQtxyiOM5DXjWKQ0exuuLR5eywXb/62BcU\nhC4/ZfnWf1MkgBsj/4MRW+WQVX0+3CrSRW+Hfaf3zfOqE9+N6aA87874WLkn/3IChOl0jD5/4jjn\n0Mt9LT+Tv/VOTBgPdnly64OqKyj9pXuHbctBZICGApv1Gu3pNs+/+AJLR0+ytHSMdnOGYeLT7XXY\n3t4m7aU4jkO1WkUhaDgOSRgRBCOiKCZLhux2ukhpUKlUcBwHu+JSn5rCtm1832c4HBH4EY7lMAh8\nzp09x+/8zr9n+j3v4+TMLLZlEKcJef0gsATEgjwuV7tf3UNfdgIVz6E4Tqde9GPKCFtHC2UhyY8d\nm7/lrIZlCqT8mW49lMdMd0qXz6+Pd7EI6AtIWXB1WsdMBWatxie/9ikGUcLUbBOj38Hv9pit1fHN\njEEY0e0PcGda7O7ssNvxmTtynFq9ztUrF7BUiEojUsCtNUkMl0G/y+ZwRHtuDtOtsL2+xVS1SWuq\nxc5whJtBb22dxLKwtzx6DoxcaM8tcuHZF7Gw6IcR9z3yIJdvXuMf/eOf5tyjZwnjdK/cHnnWyiz/\nG8cxzeYUF16+xND3aaYZW1s7yF3BnXfezXKQ0bm5xmA0BAVPv/gMb3jwW9jZ2uLlyxdxp+tcvX6N\ndWOFM6fv5mR7gY2NNdaDIbGEZrVOMApIpMByHIIkwsxMpq0qGxeu8dZv+04+/NH/wNu++y04Cwt4\nwLA3wrEshDkJBooxKYeDlqkzXU71sdunv7St58VfHVQcLp+Tm210pVw+jxBigm75ZlSEjtj1aJrx\n9Scd6uUForh+LptjPrs8b8rPxjQNsuzgPomysn8l+gf+Fijwwzhf/X1xg+WqHYUy0M19lUlUmkKa\n54oGBZbF3ffewZ8+8Zc88shDrF9fZ2P9Jso0aTSbnDp1kkqlQhCE+L5Pvz9gNBoxGg1xHJdGo4Fj\nNhBAEIWEYcbO9ha+7+eK3LaRhsRzXarVGrZtURGSfq/Pz//cP+PP/+SPmX3jG6gogTBEvtMv8EkB\nwzT3ETwcjG0vf1a81y2RciL84hnqYVHjRXC8QWYSSR+MkS1PEH0hLXOetzJPdSEsL0R6f/P7mIym\n0dFc0Z+CS3SFjb874OtPPw8BTGeKnZVNamlKSgSRwnFsQg8WptuoOMDvbDM0DNYuvsRw2KHmSQzP\nIPBHJMJG2jXcukQ0pkg8l14Yk8YKu+pwY+UmW9evIaIYG8EgGJAGIdh1mo0WaxevMiM8Bp0BcyeO\nISyTzICHH32Ijt9Fmm5+vwKyNCNVRXiayWjkc/36dRqNJmmiWFw6zs1r1wj7I6xqhZ1wiG2ASDPW\nNlfZXF3h3qXj3Oxt8czXvsrU4gJLZ8+QmRLZGTCtLPqGwcA0iKMII1UIIYmTKKcHa3VMYaLSlPWr\ny7z927+Lp5/6OpVHLaYci9mpOsORjyz5nAqZKiuy8uJejGc5SkVPl3GYUtIV42FWXvG6VQnGYqdo\n4TPRi6Ho/Ss3HSEX/dCvbRiTclgUjtDvraD7ygEEev/Lei5N4/3NckUfdIvXMAxs2/4vIxcKjCe7\nHjJYPKCy4+KwFVUIQaZyU8aQAikgJSNNQ06dPkHy+Igg7rFwpInMWnSHMWEcs7Z6E8u2cWwH23aY\nm53GNEyGoxFBENDt7CKEhWXZVCsVqlWPSq2JFBI/8Ol2Ooz6A3w3IU1NhOVjmxLpJ3zhLz9Dq93m\nucsXOPfAfcgwRPgRjjAQliBWGfmWzbGJqfN2xeflEnL6oJbRyKGOSoqIlKISfTqhgHXErE/Qok/l\nZ1/EBN8KtRTtMLpHD5PUzddyUixd6HV6yTAMHOHy5Ke+QL3a5NiZ01x46mk8YeLZBmQRca9Hrz/A\nmp/BJKPX2aZuG5jRiHB7hWC4i1l3aDabuNUKQQRpamLXmiycPEZjfpblS5dJ+wGVSp2XL11k9/oN\nZiwTfzQgdiW1Y3PsDvv0Ll7D3B2Rypg3vfUtLD16nsqxef7qiT+DOCUzcz+HJK9biZSYovD7ZDz3\nzLM4jodtOriOyyAYcfLESZ794pMsnjiK3W4S7uxgpinNWo0Xv/E8Z+95gMW5aU4eW+LNb34zadVm\n47mLvPTFZ1k6fpxGxaWb+hjSxpImvlK4lg2mZOiPME0LISRRd0gy8Hnj2dfw5Bee5O6zdyOqLl7d\noRoenvxJl5fyAn+YHBbgqwy2ygi9LLvFefQ8QuUcQroi1/OJFMfreuJWyayKkMGxlTqJsA9EQB1i\njep91oMMisWm7Jy/lUVwmML/W0+hFApAV9xwcACLJkS+Sy+/yYKv3csfYrhIITEMMFAIkZAKRdV1\neeSR8zz1lSe588TtJEFMo3mEqXqNylwVwzAZjoaMRgFr29t7StOi1WpxdH4B26szHI7oDwZsbW0z\nGA5wbJt6o869996PYzt0Oh12d3cY+H36QUDNsnCkye133slv//Hv49U8zp+8A1ukefFdKcnSLC8K\nUUKljuOM6RTNHITJ3Wll87IIcdKRSh6jmp+7cAbCJEIoSNmySVoWpuL/Yqz0zTfldthk1XeY6Vyh\nlJI4DkumeZ4bPLes9qI29pLhB2HIjavXmGm18eo1gjCkaTqILCQGEAb1Wg1zepbA9yHNWJyd58bl\nSyi/z1zTQxGzuX6D9uxRLLuKIxxmF4/QODpPNx5x9NhRZk/dzY2LF7m5fBOGA1zLZDQa4dSm6CsF\n0sCOMxbcOt/22m9jI1FcX1vl9nqdptNguDsgbqaYxp5ZXSxYe/fY7w/Y2dmh3xvRnpomDAfUZ1t0\nri/zwAPn+OxXPsed997BShSSdAd0h30SX7G1u41TqfDtb/oOpmdmuLyzxuraKg1pIkch7ekWvUwQ\nj2IadgMlFQkZhmGCZ5Ag8GwPx7DwDIO1b1zhv3r7O/nYZ/6cqG5zbHaWisgLlYhxdxF7udrVnm9C\npRlocd0FQChARy4bBXrmgNzpCiuXC/aLo4A6kEa6zC/rIKeM3MfAo9gBergC19M0m6Z5MGIkG+sc\n9vOqQJYqil3DkM8xvW/FvRZzIe8X+89BR+86SNpnE9St+6y3Vz0f+Oc/+acTg1lGe0XYkf5d8b6M\n3ooIiaIppVAid+KEScxv/taH+PbvfDNJmtJbz50OhimxnDwPhGHIveD6FEuaWIZNHMWYNlhWbtJU\nq9V9RTcc9FF7g1UoM891ieOYIM5Lq/lxRL3ZYPnGFR558CzTjQoyiRBZAnsbmYv72UcLe7mxURkC\nBUKRKVDqICd+GG9YVL+WUmLbthaKdzDmtay0i1eZ59QRTxn96OfU+3ZYXH+BpAv6Z4zo473Fabxg\nZ1m233/J2GF1ebfLU5/7Mm4sCTYH+N0uQiR0hh16fsB2z6c5fQzHbTAaruNaLvFggDncpGVFZFnI\nThDRw6A1v4BBRjTok9qz0N1ksHmdrGIzNCwkHtOWy22LM+wGXbAtGpU2plqkMpVgRKsciSysUY0r\nSrDcMLm5usFJp8X/8E9+nN12BxUbWMrKFbi28C4vr/L5L3yJ2bkj2HYN16vSDUbMtKZQ3Q7bL79M\nFg4xGjaXb1zFv7FNK/OIqxXe94v/lOmFBcLugCuXrhBcXeOUcqlFGXLKY91M2chiArfKehjTbk9B\nNCLO4nx7d5biGQYyUVi2QyItRKPGM5cvceLUcU4fbdM0LCw/xEiTvPqRLUlMSSYMRCowM4kEIjVO\nuqTTanqEVAG4Cif3BGoVe4mDUAjG8zzJJuVbt/wOkz3dojvMX3PP2ddSbi98/fMH5Hby70GKV6nJ\nQhZj2Z/cyFP+nd6+mdrVFfyDj33nLY9/1RE4jCd6UYFDT+ASRdH+wCTJZAjcmHYoPpvMMW0YBkoI\nTNsiHqZUKhUuX75Mc6rJXXc9SMWtEicJnV6HTq9DksakaUKtVqXVaGEZJr1en93ONt1uTqkYhkGt\nVqNScWlNtahWKiil8H2fwWDAbqeT76pyPaanmkRJzG63w7HFY3z2s5/jB77vXYySGIHIc1Zbcp8D\nS9OETGV7SXFydIOQSASQ7RVvy1txjzo6L8w2yIu8FuilbN6WOcYyKipWf925UkYVxe+hSN6TlcZk\nkvsuK3L9XEqNE3gVxxUbOVZXVzl69Cjr6+u0221UmmE3j2PUbmD4Kd1hh8F2h7m5JpaQ2Ibg/Jn7\nsbwprl1fp7ZUIVzpMNxcZnbaYmu0SzxUpJFFZkqCUFGtNGjW5jFON7j+5W28ygyD0Q7TCx6bvS3i\n2iKXOwNqrRauk/HSS09imwt4NYltjBh5TaK+ZCs1Wd0e8eyLX+V1P/J+/MouowhcZZBmGYYUGFp4\nneu51GpVdnZ3WVysI6Wk5VWoGyaRElx++SXmahWmZYvTc0dZiyXXXrzKu7/n/dxZn6bbGfDRf/87\nnJo7wqnFJaJOl5u9Xeb8jKX5WTbXbyIqHnfeeTubN24y61WJScgsgWmZWKZEpBmO4+EnKYllcOrk\nCV568TmOTD1IogRTjo29V8UuDkOSUKCExJAGSAtDSPK0rQVNkVuvejTTeLwN0r04e0NbkMcKHrK9\neQ5M0C56K8uSvkmtvB+iaLdSgGX6pgxaiqpTOjWU00IHSxfqIbTlCLDDWpk2LazbohVW7iu1V12B\n6xSJUoowDAnDcP89cEAp66282hX0wf57IVCBj1erctedd3H1+jXuuece1leukKQpConredQqLpZt\nk6YZo9GITmcbgSKOI2q1CvPzc0gp6Pf7hGFAkiSsrqzknKxjYzs2QTjC9SooBGEUEO+kBEGAZdvY\nponnVPjUZz/Dg+fOYJgWhjQhUximRBoCCxOlMqIo3KcPpGGgZJ74qoyAD4veybfjFmFOYoI3LKin\nW6EDXXgPS/5fNvHKi0FxfJm71sdG7+8kLyoZJ04a5704cuQIYRgyNzePPxphmCbGYB3T38GTFeJo\ngFursN3vo0yJM9XkxN13cHNti3OPPoCf7HJ99TnaU/P0wx0GUUrDdTGkg9WconbqBP1uHy8SbNx4\nma6/S9XxSLMmwXbMqdoic41F0nqT650+Sd9gyT7DkFV2Nzs02m02bJvISRgFQ/rry9xz+xEe+taH\nUDUbM5SYmYEh95ZfNb5/27LpdLq023N4rpNbIY7JbtSnH3Y49chZrnz9a/Ru9HFaNZhvcPrUa7n9\ntWfoba5z8aULtCOodgOSSpehCNlVQzqX11js7nDs2AJXY58b1y/SsDziwCcWijhWZHtGcBgEeK6L\nNA2CLMGsuDx05iwf+ejH+O63vgXbtRnEERXLwDQtzD1USAZhukcBUACBvRz+JAgBpmGBgCzNy+Ih\nBKaRp3NIs4w4yRNmGVKLGRdiL04rf1i3yrhZyGBZaevyWnZIHtaK6KfD9lXoVoR+bZ3eeCWQUgYx\neivPhWK+6ODnb0KOvOoKXN9CrQ9WEQrkuu7E6lpWFMX7sslUrJpZliFNg631Dc6cOcPvf/QjPPLI\nI7SnK9QqdeIkY3e3RzAaYkgDyzSZnZ7GdW3iJKTb7bKz06Pf75NlOYqfm5ujWW8QJyGBH7C2vsZg\n2M+VnoSK5+I4Hpbp0O/1cmTe7zPTnqE/6rK8vsXS8aOQRZiyyBJYDJjCsvacHjIX6EwVAjKJGPQB\nLj4vFi/TlBoi54Cg68+qHMlSIGJdGeumcfH+MKrksIlUHKOfp2jF+yiKJ5y1Ukocx2EwGCClQRKP\n9tJw2lz5yqcwej3WdwOkGuFUa5BJNrpbzE7PsjPcIVFDHDeiM/CoeYoF02Rl5GLO3IURg5ekmK0p\nWksneSG4guXYnJ73qMQW6U6EUzX5ofe/l8vPfQ3TlHzH330XL69vc+PyCuZuTNuDj3zscXZjBd4M\nN268jJkOOTo1zT//xQ+QNev0uhk1xwLGoWhy398gcB2XarXKdLuFyhIc2yMTGY5lMzINWkcWWL82\nhegP8Xf7NOfaODNN5o8tcunxz7Nz8RqrFy5x/k1vBpWQpBGRTBj4OzQCG3sT7LrLbXecZHc4JDVt\n0kyBkIhM4No2dadKliTYnoOXxWBJRv0+3/uu7+f//jcf5Md/4seoVlyCJMHOMuxMYAoDTGMvCRZY\nahx6Wiy8I384we1KKRFSoLIEKQxMKRGmQSbEHp885ocL35ZQ49wnhUzp1qAuY4dteCn8Ra+EYssp\nHMrFV/I1ZRKE5DTQweRxQhxMhV0+Rp8PupIu9FcBXsv3d6v2qivwwiFXTH49xaJpmsTRuBadvqIV\nD1oPlZPG5EoohcAQgiCKaLVaJErxmkce5Wtf/RpLC9PYlovr1HCcGvVqFcfxGI4CknhEt9shUzGO\nY7G4MI/jeGRZSuAH7O5ssbmxhm3beJ7L3NwsnusRRgFBnBBnKb3NDdI4xXMqeLZLrVoliAP8KOTr\nzz1Pa34OO02IkxhDgrEXJVIU9c0oFilQpEjy3OI6OiieE4wXtCybLIelC+9hSaKA/XCmopUnjI4i\niphv/Xg9MkBvOq1SjjIpo6jCYZVPdoD8fhzHwbRsBsMhteYUf/3XT3L58jaOMLl88ToN1wM/JJWK\nlmNz5s672Ozs5ApCCtZWb2JkA7pqgFGtYasalpmh/A6e5+BmDvPuFHfM1LnYu4abeiy2Zjm1tMCX\nPvPnCCvGa09zZXOdK1ubnHn93XSuX2T+5hT/8hf+V7587QIvbK+w092kabb5ge97J5Vqm1B4LNQc\n/M4ayjX2x7OwpNhLJxwFeSm/emMK16uQRTHD/oDBVof28UXuuPc+PvG7f0DDtgnUVV5z4jR//KHf\n4bHGEkbPx7NNVoY7OLLK6voKu91N/KhLsD7k4emztFBsXLuCszDL5mAXx/KoWFXSKEUiMFTuC0pG\nAZZrkEQRhlJ0dgd8+1vexp9/8lN893e9hUbFQfohEoXIMqI4IzJyH1O2l16BJKaoOqNQee74OK8D\naVkWpjARmUJJhUKSKUGmMuIoRok8ZG+folMHlXJZxspoV5dt3Tp9pUiOYtu+7lwv/uZzJr7lbwug\nWcw905wMVSyDmvJ35eOEEPuFMIq+fzMl/qorcF0R64paj0zJ72HsyS6Q6P6A7W+Q0AYqy93nCkXF\n8/KdTxK+713v4tc++EHe8NjDeWRJz6ff36LRmEHiUqtU94IyMpIsotfv4GcRjhNiWxaWbTIzM41S\nim63Q6ezi8jyZO6u6+JVPaRlUbFd+t0BWRITpzFBoDBci3Z7hkqzzu/+/kf40b/3Xow43Bd6IfL8\nw1IKBBK1P8aT8dBF07mzYqDHO1sP5go/LJTrMApFf/9KnKDeFx1hF8fmmxVyXrSgc5SaTNhfIBwp\nxxVfTFPm2eVkimU7pFlGs9niC1/4ax7/syc4f+q1dDbWcDMb048IBptkhqCxsEg6UiRZlcr0LL3M\nxbv5GUI/YUtUSTMD0h7KDBmlIW6jybXuDjudZY6363gjiZ9mbPlbbD53g1bN4fTSbYS7MGVM8ZXP\nP86v/tIv8R3nzvLtC/dCJcasCB67437+8s8/wtu/593c/eBDxA7E6TZW7OBKg1AaiL2kSGR5BEeW\n5sn7fX/E8o0bzM3HuI5DZMC238M34NnlZdI4xL3jBFuXlwmurFD5yjM8fN9ZYttkNxgxIOHplSsI\nCaPNLdI0JBQBA89hdbDF7e3bSaOYZ555hh0JtuXheXVMw6HRaOE5FTKV0mzUGPZ3SdOEGEG9PUso\nFF5tiic+/pe85+3fQ5iGSJXfg5ICw7JASgwx9k3lPicb0zSI4nC/vCAiR9aGyudmFOU5TtircC+F\ngSEypGDv+aTEqsixXSDpXK7zDWnFvBgXbhijdYUQY0s8l83DKZQyStbjxvNrjncTj4FMHnWiUy/5\n/xyYF7o+06kXPTWBfn19UfgvAoGXH4KuHIrv9C2nuolyK25LqDEGleSxO2mS4FY8TNviwfPn+dJT\nT3FkcYnW1Azt6Qoog93dXfr9Pil56adGs4ppWVRr3h6lkE+6aC/UqNGoM9tuU6lWyLKMTqfD5uYm\nQ99HKknz/2XuzYMsO88yz9/3fWc/d8vMyqUyqyRVqVTaymUt3saWbGNkuT14azAwNtE0NjQRdEd3\nEBATMUMPhD0dY4gGmjZEdMOABUMDNpsXaIPxvoAXLZbskspSqapUa1bletezn/N988e5N/NmSoIO\nojvc5x+Vbp48N8/2fO/7vM/7vM0mc7MzAFi2wzCJiLKMIAhxHZfTz5zh1iM3ooTCCzySeIS9YyI0\ncc6QIEDsW+Am18hxnClwnAA6O8A4/QBNtv1db9MP0TT/PPls+ncmn+3/3ReOjPYa/+/ye3Uz0d6H\ndjctLooCQ10bgFovrSyLDz30u7RbHTquIBM5jipBVzhhwCArGOSax546Q7/UzB8u6Q1HzCdr3BC0\ncXVFLjXXtM2FJCC1XZbbcwSih3vTLOUNL2X1yb8mdAQ3HllmYWmewWaPC+euMm85bDzyJT7wnh+g\n+Gdv4T/8P7/MRnudc48/g1i4lwVjoRzBG//pg1zc7CF9MEVEoRMqMUtlxj4vjMHPgEAQeD6j0ZDu\ndp/r16/z5KlTOAszjEYjZsMmMvQphGH55AmW51boX7rKcLXH1swWHz33GIdvPMR8OM+IEmELButr\n6DRB2hXDvOILp75BKQzHF2/kjqDFM91NGm4DL/QhDLm4cY3L6xs0Gg2KNCO0bY6uHEIql/PnLqIt\ng0LR8Ns899wVDrbaNIIQR0miLKUsKypT1Z2mVNiWjbBsKgkGSYEAaTFRZ5S6wqNWrijLqvXo4ynt\nRlfkaVYrpqSsa0pJPrZjqGtRZblbjJ9ovica8xovqnHAsPucT8D/xSR5+xtspqPmWlhQ7HnOJ8ec\nPL/T1ORk3xfKNvcD8vR7vJ8KerGo/YW27zqAT5/A/hOqb9a0w12tx9wPOpOIc3IvJPtSLSmxLQuJ\nIEtSbj1+nD/5s29x78tfzbUr13CdnGbYYWamxcLiAdIkIysyKl0xGkakSUwYBliWRZqm5HlGWVaM\nhgPUmKv1PI8g8DnUbGAQFHnBaDhiFI2QSkKW1uoSKYiyhFfc/TI+/7nPcPzoMUpdEac5UlpYtkVV\nlrU80dRG+yDQlUZPtbzvSK1eoDW5jnL3Rw3PtyqYvAzThj+TY++vKUwvBC8G3PvvY1XtUmMvxJNP\nL8aOZZOXOUrVkjIjxs0ZjoVUktEo5vWvfwOnvn2KXtllNe3S92wGsWCYlgyikuWGZqboUw6uUFan\nCdMu52ULk2zT6q+zVSR8s5rlVHQjLTegWT3OLbNrXN3Mefo5l05YsL7eo7kp2Vq7gJARs0uziGCB\nb24BV3PE6Fn+1b/4XvyDZ9kYnOTzX23x+GNb/Oz/9Ytc7K1R2T66kggCtBJUXog2US2Vm9yLcWr1\n7LPPIgycPHkSIRWNsMEgG9E8cjOHFg4yd/Ag/kyb0A1o4/DlP/sLPveJv6DoxbTn5rj3TQ8wc/gg\nSElmcp599mn++k8+jFcV+IFDTMHfnXqU4eXrnLj5Nu6am6eXZWT9LkduOcJdr7ybgTAY5WAqg1UY\nmspDCIvIQKFLKlNPimkEHr600UUGloUlLIb9Hq4XUMqi5t/T2pEvyxMAms0QNwjGwYVG2ham0uid\n+15RlRVSyBq0PQvH82ogrjSe5+2MGJv2UJmu3UxHzNPP2R5q9UWUKTv48ALP/C5/v/f5rr+/NuSa\n/v36+C/c1DZd1N9vfTH9LsBe3/X/lu27DuCTCHK/nGZyckWRT6VLu5HcNG872d+2a0N0JeSeiLPK\nC1zPRZs6ND8wO8fJu+/hq1/7Oi9/+Sso85Iki9i4/hyNRgvP8+h0Zml1ZsZp7pC8yBmNhhRFQRAE\ntNserUZjzAlrRqMR585dJS81ruPSarfpdDq4rlt3dA769AbbJEmCshxsz+X1r38DP/fz7+OXPvDv\ncKQAXZCmCbaSCETNeYu6Kq+FxujdbsTJgzIxsp++4fUDVu7h/+p00N7zcE2u8fT0nWnZ4QRgJ983\nff2nCzUvBMr1titjnNQ6JpIv2JuuZmlGRTV+UQ2IsX1nWdQZBRavevWrOffcRR7rFTy9kbM5TCml\nT6k9HD8gGSTc4WhunTN00vM4Zo2/OLPE02Kek7e+jkym9DdXWbYkIu5z+ux5gtskx+cdnORhun7J\n+XJI5Cyx0ppjNsu4YXmBj5/WfJVD/MpHNniVusZ/uM8ha11lxj+GryWutcQXvnGGn/yX38+1s2ew\ny4pShfSLFMvV2EZjdIE1dtgTqh5h9s3Hv4llWVy8cIEDC/NsbKxx6OZDczLHDgAAIABJREFU+E2H\n1CRQlpisoJf12SorXvm2B7m8ucogL/g/3vfz5J7Ndq/H8vxBBkXM4tEbsKThMx/+MNkoJjMFtrJ4\nZvUCWpfcePRGbj1+K5c3N/nO177CSu922keOYLc7pKVGSY8KBRocZdXDHYzEEhrLWMRZjrIcSm2I\nU43nthGA5fg4UowtgeuRbLat2N7e5JkzlxgMBiwsLDAz2yawPMqqRAqDlBaO66Fk7fqXlwXGVFjC\nwnIEeV4SjBeAycDf6SLjZCDCfgnrNBhP9pvGg+lt4nu/H/z3q96mqZHJ58+nM194SPP04jH5m6dr\nPtMB2f7v+4c6Mb/rjTzf+MqnngcA09Xrskz36JGnQX4/b27bfl3K3qlm1xfAtm2yIkcqhVQKISWV\n5/DQhx7i+LFjHFxcxB0XWbI0r7vJEARBg7QoCQIPx9nVuhpjMFU9186YmsaYcMMCVU90SRLSIsd2\nbKRSeJ6H67pU44g5imLWtnt05hdYv3aVN7z2NZgyw2QJSoixjHCcrlGDsp6qyk+u1/4JPfUDwTgj\n2e/etldBMr0Awl5qZb9N7f6sZz+dMznWXp242Wkiml4kXigjkMZCWJNFoUCL2oM9TjIaYYthlBOG\nTT70oYf42Kmo5kkR2E5AnOQUUYyVDZmzRhyfNRyZKZixU7bmbuXCdc3lUZNuDk7V5QYnJ1SKp9e2\n6cwF3DcX8woucBGbhBkSOuiy5KCrCbGJw2OcD46wnSTcWa3xdr9P694NGp0Z4mqFz5xr8sj1Fq+6\n615eebCkY22QNALWhYexfSyTI41BIhFG1BEnku3tLk9/5xmWlg5SlrVboZYZhShIogyRgid9rgx7\nbFc5RZFzY2eOWS9AuYpKW/hOSCWg0WngBRYNV/KZP/0IW2fP0HZc+klELA1YhnbgcvuNN3P88FGS\nQUJSgndggc4NR2gs34BxQwZRhrQsHMeiSgukBttxyDBUtiLKshpMK4NdGpRQaMdFqonNwuT91kgl\nEOPCvAAGgz5VmRLHCUuL8/iew9XLF3FsC99zUBJ0VaLHz7Rv7S76f5+3ySSY2P8sToO9lJI7737t\n87Do1GNffl6wMvme+rv2UrU15tQF6Gm8qp/rF4bS6Xd0fxDzYhH49Lv2knu/50UziO96BL7fvnQa\npAGyLHkeiExSEdgrOSyKGngtpWp+TCpQFkIKmmGDNM9q8NYVeVFw3+tey1//1//KO9/xDpwxPTIz\n26IRtilLTRJnbPWucfnyBRzHIQgCwjCk3WoRhD6eN0OapvR6Pba3t7Esi1azjed6tJpNjKqHPmx3\nu1y6tFY3KSmLdrPJwoF52nOLbAyHbG53WV29xuJch8APqIocKeoXXQOm0pSm2qGTYFrm9ELV7Brw\npzntyUM+2aaLnr7vT2U8tTG+53lTPOBew5/p1ub9Mq3dNNOg1D4Z2VRUUlft93pQpKMUISEMA7Iy\nw7IUc3NzlKVmefkgcZxx9OhR7MfPgi5ohgFUFS3LIXEsBrlL3xzg1EDzZDemESralz6LBaTdisi6\nlYG7QjEque/kEQ66Bzn19BVGZ/q0bunw8jffytaZNZ59/Cxle4no5qNc2T7H3OU/5h1LMOsUHDn+\nErpDi2z7APPeGnZ4nltvvY9i5QGeevgat9+Rsbx8lc1Ys/yS13Jte4jnBJRFialMTaNIC0tZzM/P\ns7iwRJIkOI6L1gapI6Q0KMuBQUmIR9EI2PAMpaOwogw7ThlFQ9Ko5Lkra5y7foWPffyj6CzGdQx3\n3HyIBQ1hpcDyGHglV/NtwnRI7+khw/VNluwWs405DjTniS+vYdwO4eFZqtDBDQIuPneWm1duYNQd\n4FkuaZrQT0YI10E6LqrQ6CwjSROMK7AdB8PEFgOCMEAIQVGk5HlFs9mk1XYoyhi/UaJcFyzFzbfe\nQeB5rF69yJVLF5FCMz8/T6MRUEajPY19E7pw+lmaYMFEfjcdABZFsaOaerEIfGJZuz8KnjADSu1m\nmkVRjCP+3cEm08e1rL1OjdN8/bQ8dhK9v1DWOnkn/lsLmf9gBP7e976XT37ykywsLHDq1CkA3ve+\n9/E7v/M7zM/PA/CBD3yAN7/5zQD84i/+Ig899BBKKX7913+dBx988PlfOhWBf/WLf4lt2ztmNNMr\nDzx/avR0UWy6S3ByQaaN5KeLF3tTewi0QyQ0v/1nH8ZtNrjthqO0tEvo+TjNkGGeYymL0HJRjoXl\n2DucW7fb3QFPy7IIwxApJXmeU5b5zt8wfdN83x8PJMhJ07T+u41iGI3wPYe//dsv8O7/7Z00wwBL\nGKqixHU9qqIeauG6HtSCm50bO+HErfG0cMZprC5LJJqalTNjCRvYVk07SUDourirDZSVxJgSpQBR\nIYSuNbvGUJYgpI1SDqbSyGKIkA7Gcqgsm0ILKl1iKYMyOY7KocpQaIx2MFB30BqDsB1sy6XUElBI\n6VDbAygKp8KzLVavXOG5s+e4vrZJf5iRVpLnLl2m2WwjZN0NeOqqotQVtueAlPSjUS1FKw2i0Fha\n0A5aDLZ7ZH5CWWlQDrbtUpU5qopZbEqWmwbTv8yNsw4zoWI170Kcc88Nx2j2Y/T6Ntq3WXcD4uYs\nfhhy06zLjR1YaW/TXBzRsBXWqI3wZlkdSBwO0PTgsStrbHh3cvyeB1BWhJdrlCoYBjGp8AgIoOwj\nZImrLYyGnuUgtU1DOnXLfVVhScXEfAxMfT/HigtbBNhSgh5y7uwZfve3/4CL564zN9vhJSeP0B+s\nYjsuTz31HLn0EIHL4U6LBdvCTmPagceJl95FY36Zb5+7itVeALfJ4SM3ceKuW8mLgv4gYnZ+iX5v\nQFFVhEGDLM0oihzf9xmOIjJdEfgNhBDkWY4QFoK687KsChqNWqGVlwU6radd+b5PXhSUusB160nv\njVYDYyq63R6bmxu0GhVZlqJNRaPhI6VAYUAXWFDbUWiNkhKd2wgpKcsCx3NI0gTXtevOTlNnyy97\n5Zufh0XfevRLVNqM6dW63V8JkGM3U4Pcgy2TounEC3x6mwxpmKZkpqmZyX/3W8dOMG2/I+Fku/Pu\n170okP+DAP6Vr3yFRqPBj/7oj+4A+Pvf/36azSY/8zM/s2ff06dP8+53v5tHHnmEq1ev8sADD3Dm\nzJnnrX7TAP63n//E83TI0yT/pMV68nv7+fLpQkVZlnuquJN9bHsXfCf7tt0mmTCMhOZXPvgf+YG3\nvANPS3zLo8DQHQ3Js6Ke0q0knlc3XUzoEtd1SZKEfr9PURR4nke73abTaSME9Pt9oigiiqKdxSYM\nQ+bm5nYKMHGSo5TF2voqQhie/s6T/PAPvRNBRSto1MXMSpNnOUEYojGUVVlzbePrZymrBvZpjk7U\n+vex4qu+lgKqskIAinGkYKh1uCYDvWtQL4WgKOpCkqUsLKvuUK20Rro2zdCnu7WB7zlURY7lOGgk\nhVAY5YLtEjTapEk9pacyBXmekeUJZVUgpKbUFdGwz+bWFnEc0R+6pHHEE48+ShzH5IXB8UIsr4VQ\nFmmSMhr2GA174N5IqTWW62GEJEpTtC7BVCTDPoEjWZmf4+DSAo7y2Nja5uz5y+RaooUEU+CrikDl\nyKKHW0bMz4Ss5U1Gg4qj800ePK5pjh6n3xsylAdZPHSIWes6R1sZLeHSCpawb+hRuRlkDby5Jmt5\nzqzwacRDrkZ38Mj1O2nfHHDs+B0IpYnTDZotl1GRIjiEVbpY1gUyq6AqDuNUgIowgrFvSoW1L8Op\njKYyZc2RYZMlKYEncZTiqW99h1/+wK9SFSXHbl5hcanN1tYWmxt9BkYRYXjVHbdTbK3RtgRN3+XO\nl76U4y+9l/bKTfzir/8mj556httuu52Vgwe46657ePV995NkBY7jEcUJURTVKi9TZ3me51FozfLB\nFdbW1tGVqYeiGMNk4k6SRLUZmQGlHGy77jgOgpCN7U0sJdFG47i1KmWS/c11XJSSxEnMs88+Q55n\nLMzN1PvnGWWW0W6G9Ht9Ws0WaZJSVjmO5+7w3kopDILKGE7e/brn4du3Hv3SWCgwwR49BnAAjZC7\nWeIkCgeB5/k7GDXBpTzfpXun60aTIG46ot6PXfsz6enM9OTL3vCPp1Duv/9+Lly48LzPX+iAn/jE\nJ3jXu96FbdvcdNNNHDt2jIcffphXvepVL3r8ScffZGWbPv608mHy2fRquF8pMflswjHt8NJit/Fn\n52KXFZUxKDQPvPa1PPPMd7j/VfexeX2dMi04tLKC5dgYKdHaMBqNGI1GbG5uYNsOjuOMQb2eYD8p\nKF68eBGYRN0BBw4EO8WdNE25fPkKVVVH15bt4NiwML/I5tYGswcWOXP+IsePHWWQpOgixxtPHBrF\nI8S4Q1NIia2m5VM1zz95KJRUtVOcrouBGoPRBsu299QI6hhDI2RZKwMwSGEhUAR+SJHXGnVBhWML\nUBZdbSGKAsezsE2Gb2uiuMdaN2UztXhmtc+51T79uCRJRrv8IuMIxGiErJ3rlKxTVEtZYDVI4xjR\nOkpzxiZJc0qjyEz9u5Wb4IgAq6q7Mx3Xx7FdSq0IPJc0S8mzEY1mkzLvcuBQm1IPsdICnUZQpQhj\noYVNVkCCJHZ8lLCZmbmRZK7DcX/ARlRx9uJFDqx73Lewwg1ul9WNhLVr2/T9Nkm3yxF7i7I5i7QT\n/AY00wx0htvWYK+iZkYMopM0V97Bw6c/yuLcaazmIYLwZtRog1lVMhAxlpQEZRNP56Qmx61KclGR\nqXE6LhzQZhycFDDu9FOTuojUhO2gphmR3HbiBK953av53Kc/Q5SkwCJ5JsnzkqRIsZst+sMhNx1c\nZrB2hYZj43VafO1bj/Ltj/4533z6LN0s46mzp7l2tcHhwzcyGAxotDoIITh4cJGyGLe/K4mlJGWe\nI4QkGg1ZXpilyIsdTf9wOMASNk3fxvO9evhJUe4UFje3VpmZmWEw7NOZaRPHCY7tkucJcwfmGPSH\ntYxQWNx2/CRlVXDp4gWocmylcJ0Wo0Tjh/PEeRfHs1HGr4O2MSVlTK1cs1+EQlFKwthMyxhTRzrj\nSBxDnbGNAdm27TFe1ceczvwnWDbBo2nasaaSih3Anry3E9CeFFKnqRbYO5T8xbZ/NAf+G7/xG/z+\n7/8+L3vZy/jVX/1VOp0Oq6ure8D60KFDXL169e89ziQ6nfDYk206jZguak5AfFIcm+ZhJ8WK6Yuw\nP3qfHMNyHGSpCZXDK156Fw99+/d55rkz3HzDEURWUKUJQsK17Q1mOrMEgU+r1UTrepL5aDQijkeM\nRrX5VBAEAChljZUrCWma75xLp9PB930WF5d2zjUeRQjLIYpiDswvYXs+f/PZz9LsdDi4sEC72SDu\n9/AcC10qmKpQ53mONgY5Ts3KHemhoZIKPZbwoSaNErX+1kxlP0za97VEjGkNpEIbSIoE25JYlkCb\nAmEZpGXjaI84GTHfafPU448hpeDosdtpuQ4f/dNPsZVYpCakNbuM5W/UBWFjIbEwWlLmGqN3AwCJ\nQGtIsxRlz2C0JsoK4lxgOx7KceoBGFVJVSTYyiHwUwajTaoqx/VnsJVHUlZY0kaiOXr0Fi5cuIiU\nJaEJ2NjuEuVRnR0oj7AdUmmDZTsoW7GZpmxd30Ja11k52OKG2+ZYPX+VC6bB7Izh6HLKY/2Er/YO\nkw1neeVMj5WNFCctObI0w3EVUBQD8qKD27BxVmwiHL705HWuZhaN+AmquMuprevcffIEVmnRDDSR\n6ZLSRpkS1CZDEyCkgxAFVVmhx5aula4Nyow2dT8AAo0hzSJs18GyPLKswncs3vOTP8bMXIu//stP\n8+rlY2xsDglbOelgQDaKKfpDRKse6rC4sITWMBiM+M4zT9MIO9hBmzLX3Hbb7SwdXKbZaCGVYmtr\nm4uXLtHptFlcWODq1Us0wwDXcbh29SobG5vccced+EGI57kMhyOWlxfQlR4vMIYiS7BdB9cJSPOM\n+fmb6fZ63HBomThJaDUCEILA9ijyjNBvEEUJYSOkyHPKClaWjxD4PlWRoxBcXb3KMM4Jmi4l9cAM\nZO3bIivDBP4ke+mOySao6ZO6C1oijMKYakf9hS52gHqXPhE7hlvTAFuWxQ7WTP9suqdi8u7uF2BM\nsw77G/P+vu0fBeA/9VM/xS/8wi8A8PM///P87M/+LB/60Ide+AKJF75wk63b7e5E4dNgPM0TTcvd\n9nsjwF498/7VbRJ9T/YRoh4wOiwzHCR5lGJZNu94x9v59d/6TX7gbe+g2B5yw9IySrgcO34z/e0h\nWmu63e5OQWV2dnZH4qR1LSOsz8UlDBt0Oh0ajQZpmhLHMVmWsbW1tcP1Hzx4EMe2ybOU+QPzXFm9\nRmN2lgff/BY+8qd/zr9473tI4iGha5EXeU2HlBopanWKUqCMwJJqh6fTExMrI3AnZlS1jBwwZHFC\n7d8sxyleLVVUVTju/AGhJKWpQEAhNVmeIITGUTZlkoIWrF65xneeibnrnlfzzSef5tf+3X8myeHI\nzXeQZxUzHZfNy+fwZ5tYykZJByUsQIJXf0+ep6RlimUpLFuhRzVvqoXAkobQs+vxc8bgKol0bYRw\niYsI16kLtVk+JIkyZmaWaPmSSit6vYRvPvwES0sHGEUj7FBRChe34yOkpCgrlAUKQVWl5EmFUhZa\nG06NjpNd2uZ1R306KxX9fJNVJAtVynJjwLVqm4f7HT565SCNPENtedzy3JDvXy655aBB0ELGIb2t\nJ+nMGL7y9T/g2CuO4BYZln6SqjrGL//xiHf90Ns4kJ/B2DldW2OUQRmLSins0mBPkqRKoyxFWZQo\ny6o5Vm2YuCa7jo2lBGlc0m7OkSZ9KlPwjh/8AUaRwWseQFshfgfuXD5IOUxZnGlTDUYcml+iSkvi\nYcLF85dwhU+lFTccPsoP/fCPcO9d93Bl9Rqrq+s02x2EsFhYWARjOHfuHDOdFq7r4rr1sOy7735p\nfX8xJGkypkkMWZaMlVeauYV5esMRW1vrLCwskGcJs7MzZGlGp1MPSRkMh3iOW1MquHhzAWma1gVe\nYWHbNnFce8crx+WWW+9Ea8MTpz6P59Zt6JaU5GXd8o8xSAyYF27kUUqhqLs7jdYIWUs8hZlgkd6T\n7e96DTl7QLf+bFeLPqFcpjFwfzS9H6emwXt/APpi2z8KwBcWFnb+/RM/8RO89a1vBWBlZYXLly/v\n/OzKlSusrKy8yFHeB8AffuQUd991grtO3rlH8TBdlZ2I+Scn+kLpxuSCTbqzJsOOpy/o5EakaYrw\nFJawUVUNcsuLi7z9HW/nmWee4S2ve4BsMKIocrZXr+CpANf2EMaANpRlQVWUpHGC7/u4rkO72aLd\nbJJmBXEUM8wyiqxeaS0pac/OsXBgnn6/T5IkFFlOOZZkdbe3abfb9MZdoLfdfidPnznDyTtvIysz\nHN9FFxWussd+0vUbvrNoSQkabGWDHDc5FHndQMTkITCEgU9R5uiqoqo0xkjQEtv4tcLFqhCKup1a\naYRlsdXNGY0SZmYXCLwWKs9YPnQL3/ry1/nkf/oIrfkVDp64H4mkiEYo+oQiojlvsYXcMeYSUted\ne2iUYyMdiSgE0lNI26IjfcAQj2JMVdWprdxteijLHCVKGoHNKFf4tqLMEuKoR7eKENLGsX0arqC9\ncog3vfFNfOELX2TLOEhZkGcpyoAtQVagywJpBLZyMQUYFNvNgHN5TPPydV51zMH0S568Irhj7k4O\nqW1e765iGkO+Zt/NU8Uyjr1C1T/PRnvEyjBGii5xUWE3Lc4880XajVn0VpusaLFyeIuTHc2To1v5\n/a9c5D1vX6KVn8M1XcpC0rAsKn2FJPcQ7gEazTZZlmGMhoraKVvVDS/G1KytJTVVVuDJgDzJqUqD\nFoJKSH7yX/8bvvC5r9PLckodUxYRi65PFg9ouLW3zNzSEt1BzPZ2xPe+4c3c9cr78BszICTnz18m\nSTPCZoM0zTGAZUOWJLRabebn5vF9h9NPPcXK8iGyrMQLAgqtqX1eDMO4j2s7oBRZUlCV4DoeMzMW\nZVnXXOJRXBf4x1YXzbCBbdkUZQmlwVYCtxliQp84zXYo0rIsGcUxozgGAUeOvgQpYDQa0tveRlc5\nnWaTqsxA17bNL7RleY7R9bALIeohK7s0ikaovZawNQiLPQHhBIsmnZiTYHNS29uvJplu5tkP2Eop\nHnn0CR557In/PioUgAsXLvDWt751p4h57do1Dh48CMCv/dqv8cgjj/BHf/RHO0XMhx9+eKeIefbs\n2edF4dNFzC9++s93gHWyIk2D9QS89xP+E1B2HAfLstjc3GR+fp4oinai5CRJkLIealBV1Q5vbVkW\nZy6f50Brhjm/hR/4ZBJGlHz+c1/AN5IbD67Qardx2y362xG2susmHKVwXXdnuHEURQwGA/I8Hw9G\nbu5qvquKKBqRpnX03Ww26tFsY8qlKnKuXb+GEZK0qii1wW+GrK2vc/7sMzz4wPdwaHmRMk/RWU7L\nC0nTdKewq5SiKIo97fQw4eLqn0dRtLO/tNROlmIEFGWJa7mIwsaoiiiLUIGiF0c8ffY5tPCotE8c\ngdYuo2FM007opQWX14e0D94ITkCcl+RRH1XGVIMt8uEWc60QMXsQjaIsDbYToLVFXgnS0mCUTWEE\nUZqhXBcvG2DGLnS+61NpyKuaKkMXmCKBIkJUOaaEsOHTnmkhRMF2d4Ot7S16/SHd7RFKehw9cpxr\n1zYYmIAkjrEkVGWB0BW6KsczFGuwUbYLBnpOjqVy/OQy97QS7l6cpUxt0u0BR9nikD9gzWnyNecY\ncVLRmTtO1R1ysOrh2Sm6k3PPMZcDbPO1Z57j0fWQdvgGfuaHZrh55izCCXms9z18cuuVPLV1kX/5\nzptYSNaYkT7axCQm41OffZQvfunrNBohJ0++hDvuuIMbbrgRrSfNLLvZUx51mT8wx/ZWArgoFxpt\njy//7d/x8U98mu/7vnfyx3/yYYxIGFx/jiXfZbbVxlUWYdhgmBUcue1O7n7V/bzi/u/huUtr5EX9\navZ6XV5y8gRr61u0Wi3KKmNra5sbblhCV4bhoM/a9VVuvfU4QkIQtugNhvhBgNa1P75lWUSjEe1W\nC0tK0iTFDX2CIODss2e55ZajbKxtEgS1SqUoCvI8x7FtgjAkz2LC0CfLCtI0RQoL16/ni0ZJWkfm\n1IIASW2Y5ToOjcDj8uULXLx4jk4zIGw4YHJuP3H/87DtzFNfp8hzdDXJbsfpzXjKvbTZWTCma2nT\nnZgTrBJi1/52OqLexbzdfacHgcMuTTyhQ6e16Xe94oF/vArlXe96F1/60pfY3NxkcXGR97///Xzx\ni1/kiSeeQAjBkSNH+K3f+i0WFxeBWlL40EMPYVkWH/zgB3nTm970/C/dJyPcT9ZPr0b7P5+sWJOT\nnL4A0/rkyc9t296JuicyoKqqyExFyw+R4yERpYTSEqxtbPI3f/UpXnff/cy020RxjGM30NXeoa6T\n6H96MOm0SH+iT3VdFyHETkfmpJgBYCmFNhrbdVGWTZylZEVBf9hHSsGTT57ijQ+8oZZh1ZwBjuPU\nD7njUFYllmVTViVCyp3vmpz7ZJqNbdvk+biIouTOYIjKaMq8JBom+E2ftMwphCHszCKsgI9//NMM\nh5DFFnmmcFwfYw/IyoqVw0fY2OoTNltkWY4wJYEDOh+RJ0OyZIguYrK0oNFo1RGyG2I5TbB8ChzS\nXFIKhRaKQsSUeUaRpri2TZrmKMen1LWPTZmnVFlEVaZYqUucDKmqCK0jinJAlg1xXYe16+t4bgMh\nLCzlkueyHqYwTm9rqkiAsOqJL0IhbZei0ATaInM0lpUwU2xx94LPjR2YcWKa0XVk7zrGcYnnlrgl\nHzBgkUviEKeHDb66WpCEOTe3Im4rK1acPro8z0ywxPd/70vx50qiRoJwjvOHXzvJX147DjM9ZrNv\ncaPq4duKzswd9NfPce3KUwgpmDtwoM72dEWWZbXKqd0h8D0C36fTcGk3Ghw5cpxvPPIYl65e5tRT\n32ar12N9vcfJk3ezunoVbXIcmRI6mjRKmO3MUmlozhzgvT/1r/BbswTNOQwK3/PZ3ujS8ALKSuO4\nLlmWIS1Jp92g1+sR+jbXr6+xfHARpSSNZpPN7R6tdpvtXp9Gq12/B8KQRAme46DGQZczDnoC30fr\ngqKo3z/fc4mimKWledaub9LrbVGUMUEYMDc7X0t1hUApm6wokLIeLJEXBVIqsrQiTRKUlJRFQasd\noKuKfm+D/mCDPIt45ave+Dws+tajX0AKQTVWsMmdJp3JOzKlZtnJ5id2FXuDSaWeryjZT4VMcGqa\nBp4OWPdH9v9QI893vRPzK5/7OLB7MaYBcALG0xruyQlPwAkgDENOnTrFS1/6UtI03Wm71VrXAxXG\ndMr076dFjhSSqqjbjS3LwvI8hOew0d3mD//gD3jvj/woo+0eRVWD3uxMbVwF9Q25cuXKTlW5jr5D\n2u364V1fX6ff75PnOVmW0Ww2x5N8AixlUelq5+8vinqcmO3ZeJ4HUrK2vk5/NOLsuXP8wA/9IB4g\n0qRelCyLvKpwXAepJMMoot3pkI0pGct2yPOqjmryHNuqr5VlOUhLceXqKr1hXeHXBuaXDwOS61ub\nbPVGnP7OGba6I/q9hKXFw1QZBH4DjaSrh0ghaQZNQi8kHo3wHZc8z6lMgZaaLE9J0hi3e4VRNEQp\nTRyPkEqQlwWduQVm5pdwgjaFhiwv2WAB20AWDZlptzCVwfZCtHQox5F4nqcUeU7RK8EUCJMTx1tk\nSRdjEqKojzPu3ms2W4ziBDMaYITEsm2GcYyRivnFZSzXZ7s3IM0K0rSgMmBsXatW4gwlBbaqWPEL\n7pnT3DaT44g+cZzT75f07FnaXhvLbtFTh7msFzmfDDlz6Tu0UsWDN1rcwmOcWHFwD93DlYN3sjV3\nHK9QHD98F7/6p8+x2b6TNHsaa/1h5OoVbj/QYDS6CKqirCqa7RZ36Cv7AAAgAElEQVQYwzCO6ylR\n7TYAo8GQPMtwLYGgIisyikoThm02t7qkaYrnuliWoCwKlLAodYwbCKLhiDTJ+Lmf+7esrm/ymS99\nif/9//y3rK13OXb0OKbUONLgez7Xrm/QbDZZ39yg02nTCJokyYBzz55hZWWZQyvL+J5LWUGUZBgh\ncFyXotKMRlGdiVqKYb+P73qEgU9elvi+TxzHbG5ucPjQobFCqeKjH/tz/uZv/oZOu013uwuq4Pr1\n61RFydGjN/O2t72dN77xjYSNBlprtnt9Op0Z8rLElrXU1egaBNMsRUqDbUuErIebd1oHnodFl859\ni0G/h22NqdVxl+yOHlvt99m3kFIxAfidngxduxG+ENZNz92cKOP2W1JMAH66xX7yvf9TA/g3vvJX\ne05k0jY7iaKnee7JNi0bnBxvskJONNlQpz6e5+0A20TDnWUZlBqtJJXROJaDzoo6inYtIqn5zGc/\ng68lty3fiN+eRTm7g4YnXgau6+5kCJObUJYFruuhlBxTGfWszbrJpySKol0TeamwbLsebYWhKut9\nEIICGCU5l1ZX6Q1GPPC6+1mZ7WDZFkVR1i5uxlBRT7ORSqFFrb3t9QfkmabMC2ZmZ9lY28T3Q4SQ\nhM0mvcEIaVu4nk+claxu5ly8dJXt3oiyAqMFtqXI4gFJ1KUqRizMtUiyhCKcx5I2odvEVT5lZqhK\njbRshCXoRUNs366N/qMueTZiY+MqSuRAQVGktZJR2rRnF0lzzeyBRXRjBQtNOujT9FyKvCArDLmx\nKI0irwxFOZZmCU08GiGNQZmSKs8QpkBS4TqKdqdJmkZkeYqVlyRZRlYUOL5H0GhijCGKExASz3HQ\nZW3hu1X1aRmBjySuDJvDmBlp0Y57LAYVpRmCNAy2B3xp1ODEYsBd8w26vZRrmxFrvSEjx0eGNnc2\nDUetiLmOz2D+Rr65GrBR3o4WhnZjjdd/7/fx1SdyaPgstwuuP/opOtkTaBRathkMh7WxGXWwkqYJ\nllJIAUoq1LijeBT3idM+R4/ehO+2yRMo8wJTRYS+RKGoMonfDOklPT74wf/Ik9/6NnecOEGuK5zQ\n57cfeojbb72dE7ffydLcPP3hkGES4fsBaZrh+X7doKasevg0htlOCyHrnoKyEOPGKo/S1L78UVx7\n+pRZiaMUuqrfmUJrOp0W/X4f1/Uoi5zTp5/iqaeewrEtwiAADI5rk2QJaZywvr7O1UuX2d7eBiQz\nMzO85e1v48E3vYli/C7qqraMNVqgLEWS1FlonCa4noMRmtlm+DwseuqJv8OzLaJ4WLspirr9vzaS\nq2nGaXza7WpmT7Rcf1aD/HRjDrCDZdMDySdihv0t9dNMwmT//6kB/JG/+xTw/PZY2K3S7vf0mLaQ\nnIDhZEWbAPlkYC6wY0BVVXUqqpTCNopKCUoJnu2g8gqBIBGayIGtXpc/+93/wve//kFmlg7hhuHO\n4tLr9UjTlCzLsG17p8U+CAKM0aRpwtbW1g7lMunWnEwXqotTdYdkkmS4roVr1XaKVVWSlxUbvS7K\nbWCUzXOXrrJx7TLvfMs/YTAYMHtgbty6DNJWDKOINM+5eOki3V6XtbVNhoMMKSRvevCfMD+/SFUZ\nWs023f6AOMmwfZ/zFy5w/tJ1huUBqgpcp0Ge67qyrRM8u0AX6yTJKkL3SdOI7XKeA7OLCOOzOHuY\nPBM4bsgoTknLDK/hkeQx/cEAx3ZYmJ9ByYrAFeRJhNEVreYsVWVj8IkTTZZqtL1O03PIhn2KeDS2\nQfBIK0VSCZJck+YlZVWSqBHxKEVpCYVB5xWWgdBzOXRoieGoi+1I4mSIq336wyFFWVLpCtuzaTeb\nFEVGleWUWYIpC5SAljjKUK9TeAOwchZnW3Q8yWxnlsbMcYZFwNLSLI7cREYxT37tr+glPbrNA/jN\nNktlyIUnn2YUP0PbjOi0b2a7dYiCESvDWWaqNqvNnNVGE12EtK2ASLuEcy79ja8z76fISOJbIVGa\n4HreWFlkSJIY3/MwuvbhEUBhoB9vs7DUREmBKRxCZxad5nhOTuBWtP0m8UBzzyv+F+593cvxLZdf\n+fe/wk//7E9TCU3rwAynTz/Fb/3mf+KuO1/CO9/2dioBmVIkaYoQUFQlnuuxfHCZP/nTj/DGN7we\nozXtVpPedpfQa+EGIXGaomyHJKuLjRhwLQthIE8TPNfF2Barq1c5eHCJsqx44vHHOHv2WW666Uaa\njRAhBK1Wrd5Sto2pNIN+n+fOnefyxSu1MdxwwHMXLnDfa+/nR//5P2dhcQFh9LhL2aIqDVIpEFBW\nkBcaxwX/BQqZTz3+d6DLWi479qc39ShxtKkHqkw3B9bvs82kkLnXm2V3AtZ+GeC0RHpaJri/vmdZ\n1n9fDvx/xLafA9/PLU/++Am/PC21mfx7jxHSTvOOtedEp4854agn/+9hoYFKQikNpa4oigxH2VSV\nxg0D/vxjHwNpcd8rXoupNKNhD9dR2Ba0Ww2gNrZP0oI0K4jiFF1m+J6LZdnYjkuSpLQ7s6RZwWA4\nwrKcWrpk2eiqLqgZTB0tOjbF2OjeGENepASuj+f7XFvb4tNf+DL/7N3vZtDv87KX3YsuKvKipCgN\naVbwmc99kfbsAV72slfSaB3gP//2b+IGFvfcdYJXnjzB+oXLLB1YYpRLyrDDFx8/zVaUUJUZWpcI\nNK5jUeQ5lpBUhUFoSZ4ZLOkw6G1T5c9RVAVLy4fxgjZF5aBLn6qqGzuStItSGUUR8ZJjt1OUZe3z\nYTTd3gCpbEqtyfOSOM1otpo4jkM8jCh1SbMV4NjQ765hK02eJhSlRkuP7WEGlkeRSAx1kdp1bLzA\nJcszgsBlFEc4rk2cxBitqZIBvu/juw6mLOh1u3iuQ56luF5Q66rHLoFpUtYeNBhuOnyIdquJKfPx\n8wiDwRAh667cmw7dxOrqJdbXVlGqIM2GmKrAcV1WV9cJgg5S+XheSKIrHM+ri2QGTFUijamvs1Ro\nXVKktavk/IKHUpqtjU2qXOM4Pko6SOlRYZHkGtsNMdJG6gGBU2v9l5YP4fgBaZ4jAdeWGJ3zT9/2\nfQSujeMIBv0R291t/vIv/pIfe8+PEcUxCwsHcBwXx5H83u/9F3q9PneeOEF7dgbPc3BsC8sSJFHE\n5z/3We5/zWtYWFjiwOwcVVmSZTkaQaPVJE4y0jTHC0LyPK/nl6pdg7JmENLv9wj8ACkE16+t8pWv\nfJk7T9xRZ9GWVTs1IuthEXnNS0/qOL1ej/Pnz7O2tsZgMODxxx/nzW9+Mz/+4z+O47k7wd5u1l5r\naCcg+UJKlDNnnyWJh3WGIE1NO+U1hVZ7uO+ax8VpShCEVGO54Q74ylo+KY3eE1VP+xVNsvfdaHzX\nMGuCYQDGPJ8b//um0n/XAfzhv/1rYFcGuDuAV+zRcE8uxHRTz7Qcp74wezWTkxs5Ae49UsRcg5QI\nW6KlGFs0a6SpqQmUpDcc8ju/9//xw2/7QVzLQeuCJIkQaPr9HpZt4fo+jUYbjdiR95VlWRdrRhF+\n2KiLHgiKUjMcjkiz2mQnSwss6YCAIPRJ0gRt6nTeVorRaESaJHTabU7eczetuTk+/rGPksUx99xz\nN77rcfLkSQySjY0tuv0Rt9x6nI3NLo7f4f/90IeI84giizh++DCBkLz2Nfdj+W0ubo/49oWrRIUm\nzQrarSZZlpDnKaHvjwuTIIUiTyt0ZcjSBJFeA1kxiIbce+89RHFJmhqyWOP7AXme0Gp6tNshOs/H\nXXF191qvPyBsNOkPRyAk7c4MaZaiTa3dRUrW1lYJfAuhc8osotMM6Q2GtOeWWNscYKRNHmkqXTIz\nO8Pq6iqOV3fIZUVKs9lEKUW3t11bm5a1OlkgaDdDBr0uAhgOh/XzM+Y7W51ZyjKtrYRbLUaDHo6S\ntFtN0iQe9w/Uhbb63x6eayOpCAIbrTN6vS22t3ukaU4U57heg1Z7Fq08oijGthUzMzPEwwEznTa9\n7S2qqsRxLKg0YRigTYzjSIa9AYPegFajhWXZHJhfotsfkRUG5QUgJDobEjqSwSji2PHjdHt9bKdW\nYpRlxvGbb0JJiIY9mo0GrVaHZ589w7dPfYu3vvWtNBoNLMtiYWGOsqxwXZdut8fZc+d45uxZXNdh\nabE2lup3u/ze7z3Er/z7X8bzfBpBWLevZzlpXhtJxUlOpQ3zC/OkY/XYaDTEc10cx6YsC2xpYXQd\n6X74D/+QO+68A9etVWMTABdCoayxne14m1AKVVVx/fp1tra2OH36NGtra5w4cYJ/89P/mtq+eL8f\neD0rACaj+vZueZGTphHra2tEUR90ie+6RKMhtmPjqNooy/XceqrXuC5XjDN8baY6xavdua7THPf+\n4Sj1tteIbiJPzLJ8z2dCiL+3lf67DuBf//In95hS7Y7VsnZkctP0ygSQJ2APu5SL43g7+0yD9fRs\nvcnCUGW1csPIWo1Rt78LbGmT5BnKdVC+x//9S7/Ej/yv76jtZi0LlMR2HNywQZ4XdHt9Bv0BE4cy\ny/KptOHK1VVczwMEjlsb01uOi2XbSKGI0wTPaWK0qk3tLUk5fpmlECRxRDMM8VyHW48fY6O3ze0v\nuYNvfOPrXL54kUsXzjM3M4tSive858c4ffppGq0WjuPhuAFa+Tz2xOOcOv0kQsJoexMdxbz8rns5\nfPQYF9Y2udIb4TZnkCpka2sL17OZnZmj19saRywOVVUyGAwxprb6bCtDWUXE8SZ5NuDITUdoNTuY\nUiGFi+P4OwurpSqEFCRphmXZDKOIShuUcpCWRa/XR2PGvjIZZVURBH494NcCnSfkSYTve6xt9ahQ\nOF7IwQOLbG1ukRc5ldEcXDpIlqU4js1mt8v6+jo33HCYfn+AIMD3PDY31llZXuLc2bNgNEeOHGU4\nGNagIesidFJG5GnC8tISzWZI6Lm4tsPq1au0my1Wr11jZnaOYlwoHgz7JPEQx7HQ4yymzhpBo5DK\notnsUBESxxmWJXHsetSYqUqkqt02oUKXde3GcSykAduSBL7PsD+grOpeAWXZzC8toRHEeYYnBDON\nkOFoxMqhQ8RxhNYlWZYx02kRBj62XQM0SHq9IRcvnEdrzdr6Ovff9xqSJOHAgTkCz+XixYusrKyw\n3e0RhCHdbpdz588ShgEXzp9naWmBu+66i5XlQ7SaTZRlce3qdQCyoqDV7nBgfpbN7T6WbZOlCbat\ncF2HwPdIkoTeVo+ZdofvPP0kwtQ9FWYseZUTn/uxTYQyNfi12m2i0Wjn3XUch+FoxOrVqzz99NMM\nh0P6/z97bxZra3rW+f3ebx7XtPfa49nnnKpyja7ygLGhmwYMpoC2kqCk00FCNKGVNFcRl1aUvkly\nEYwUBYmopUgoIKUF3RDSrZZQRMd0JMBgsHHRHqpcruHMZ49r/ubxzcX7rXXWqXLZiaKkiFLv1dba\ne6+99re+9bzP+3/+Q7Tgc5/7HCcnJxRFgeN47yp6uv7uCp4VOYamIWn4xte/Ttsqmb5lKShD64zE\n2rbFtKwu8lDbhIa3TRc6s1VfHg01tccYcbqub/mPPz7rW9czXTcfq18AH/3kZ96zgL/vdrLbePW6\n2K6L+bp4b681TrT+3W0/gXcmsG9Pc7cLuqapvMlWPCLZ64DeKjms5zgkVYlpmIz394jiJUdPfIhF\nFCOlRlbAJJpTN5KibIkzoIWmgaRIFebmjzEtiyRNmUxidke7xEVBkxbdh1wnL0vaRlM+KUnCtWvX\nsG2TwHPxHAdNgGUY9Ec7lJrJvdMp82VOf3TAUatRlwVpGvMv/9Uf8LGPfBTfdbFsmzjJqGTDzZs3\nma1iLqdX9EcGsZzwV1//Bq3lMFmu2Bvvk5QFrdSxRIutG5RZTlmoUNqqKciyRA2BZAtSI0tqZKvj\neSGmXnB1fpts6eFaAc8/+1EaaVDmDZphKA5t2yhVZdviWjq26xHHCciWDz1xQpbnpFnKcBhS1Q2r\nZYzruHi2QSUkRbREly2HOwOmq4iqiJidxyRxzPXrN1guVzgiY7w/wHNdjvcGiOeeYrlc0Ld1skJF\nXV0/HlLmK/bHKsBaEwXHh0MWiwWmZWKa4Ic96tpFygramvkswtB1eqGHrgv298bUdUNdFlxNL9nf\n30PTWnzPoyxKykpx9NfD6zhJqeuCogBdGBhdvFev1yNJY0xTx3ItVB6qipCri4amlZR5ySqeES3m\npGlMrx9SNgV1W2BYJmUc4wUhSZ4CDacPb2ObBo5tMeq5+K7BcBiSpClv37rF7u4+t+/epShVsfCD\nkFe/9S1OHz7k+eeeUyc6z2MymQAa0+mCJEm5eeNJPM/h9u277B0cc3E1w7J9vvX6m1w7uY6Ukl7Y\nIzQtriZTJssVRVHgug5HR4ckSUQ8nZG6Dmmasr+jOnrTtFgt5uiG8gtfR6FJKTeJWrppgSaI4ugx\nP/CsyDFMg6Nrx8Rpwq1bt2iWNa/89Vc5PDzoPl/NVu14bzW4wrNbmkry0kc+QhStOD8/Zz6f4bgu\njgHUj3BwrYNLyq5TVvYVanNoBY/N3r6TivJR7eKxpnUbCv6/st73Ar4+bqy/hm256uOGTduqpu0i\nvR5ewrbyUK137mbr3VG3zM3XQoDWtohGYps2aZohDYPJfIbj+1xeXpJFCbYXotkBV4uYSuqkWYFm\nmugIbMtmuVhRVDp7+0fM5nN8oeP39ji5+Ryz5QK9bcmyjLjrJmxHxw1d+v0+mqapkOSoYuGYZFGM\naQhOjo64c/9t9g6O6O8esEpK4tWS0XDAJDpHYjKdLrl7/z7Xj49ZLuYcHJ0Q9gMu7tyjKCpkq5MW\nDUWr8dSzz3ExvWJ374D7D+/ieQENGePhkOUyxrYMAtukaiW2Y5NlCb0wZBktKIoSxwko05w4rbA1\nC13WRPMF0m3I4jlSmvjhECEMWiGhaTAtgyhOaaqSWmg4pkHdtpw+vIfrupi6zuTyFClgb3xMWzfM\nLi452d9ht+czGvRYrBb0eh5hf0Aez7HM6wih8bGXniUMe7z66mvUouHtN9/GsmyGwxHX9nbo7/Q3\nntLL1Yz5dM7Z2SkgCDwHzw2J44g8WwIh/V6PXhCQxBG2bZAnCQ/v3+Vg75CqqonjmCwtGB/vUBYJ\no+GAtmlwjIDR6DpFqfzhJ/MJ10+u8fD0AaauKctdAdEqxXHt7uiujL0cx1FGSbaFbQhWiyVnZ+cI\nWo7297hx4xrDYZ/JdELd1PTDkGi1JPA8HNuiH/j4jomQDXkW8/Zbb/PSR17izq23CPpDjq+fAAaa\nYWFIgYkkiiJmsyV/62//bW5ev8Ht27exuw5TCEFTg+cFSFnz5pu3+MIX/oinn3mGqqp5/oWXyIuG\nola2sGdnE5bRisPjI9Jlyt7+mFa2fPuNN6jriqos2RkO+NCHnmR6MeVb3/oWoe8T6Utg3a0qZods\nW3SVgExdqgbO932qSrHE1rbMpmkSRRF7e3ssFguqOuPLX/5LBoM+P/bpHyOKIwK/t/7Ud43euwu5\nrumADoYgileEvSFBOCDLcr797ddpdRXSbNs2eZqhGQZ1WXZui+o5hVBBFrphPgbrwtaJv8O711h4\n01QbVtwaGlLIw6PgGJVxUL3rNW+v9x1C+cs//V83R4s1bWb9z24PN9drezq77Xuihp6WwqF5hItv\nT49hy+VQU4Mw2bToQmJ0YatNLdEsi7St0UKP3/if/il7hk3oehh2QJQ3CMsnKVvKGgzTxrVddF10\n2J5LVSlq33wxJwiCzeu0bKszJgK74+kKQ2y467ZlE/gei/kM2daYGhwf7nNy7RjN9JivCt544w3C\nMMBxHBazK7I0RsiGnu+xvzvixRdfYBXH5I3gy1/9Oqu0VAMvoSFkzajv47oWZZHRSKnofqbGgwfn\nBMGIvf1r3Ll/RllLiqbFD33QUf7KjktVgaxzPF1iyZI6XaK3BVkS8eKHX8RxfWqpjKLWYocsyxFd\nWLNhWSRJRt3U9HoDVqsleV6gO5K6FeR5haGZDIMQz9QZBDaCmjSNsV2XtCzI0xWaUHjybDYnimL6\nvQF5XhKGqmDP5wscxyEt59i2RZYlDPp9PN+nqkukFOR5QRwleI5HmmVEuTIwUsd0A9MwuiOyJI0z\n0iRjOBphGTZCL0mSmLppMTQ1sM7zYgMXlHVOliXUdYUubJIkxwt8dNOkrBuk0Du6W4Fl2aqDLyvK\nIsF1bXaGQxzLpKnUc7br06UmiFYxhmUqIVZd0g9c8jTCFGDbOl7njqmZFvcenhMOhlxczMiKip3h\nCNdzCIMA27KoihzXtRkNho/iC6WgqsA0DeIk4l//6z+kkQ0///M/z737D/CDENOyiKKY4WCIJkUH\nKxkqQ7MqGQz6DAY9aCWChjzLmc1muJbL8889zauvfpM0WWGZaoi7LnxCe8Sz1g2dpml5/fVvkSQp\nTdPw5JNP8uSTT6gZTWejfHl5yVdf+TJXV1c888wz/NzP/Rzj3bG6/8R2cs+7IZSqrrshpJqTtOqX\naNtW+Rdd3GW1WuHaDlWVKw1GrmAXU9dUKlerBqRZ9Si6bZtksTbee1zJ+bjx1XqV5SPh0Lp2/d+y\nk/1/eq2xoXfKTreHAduWsGvZ+Pqxd9J21p36+rnWIptHfM1uuNl5XzdUW126wHJs0HQ806LSTc7O\nTjl86jk026WSAqEr8YuUMBwOkULZxBZVQ9s0mLqG5wUYuobvHRB0ooNVrHI1szSm1+sTrRYMR0OQ\n4DkeSZLSNpLz00ssy8A2bDQhGe/u09QQRXMup0uGgz79wZA0TdFNl6PjHaaTCy4uJiznC1768Evk\neUGU5egCfviHfoi798+YzGYEgU8SzTi/OmdnOKQqczTRglkTOpLx0KFMZ/gWUNf4vkeSpozGuziG\nxXQ6wRv06A13WU1mrFYNA2+H6cUDkAYIk6ptaNqalpqmMoiiCM/zSNKVUrMaulJG6gbxcollWeia\nTiNjTENDM5XMe393hzqNKfKc6eQUyzJpZYsX+lw7fJrpdEpZlt2HQ+P84hxDVyKJvb0Djo+P8D2f\nvAhV4o2mM53MOH14tgngCIM+N05usJgvcW2BZmjMZ+rovFrMcF0XOpGJZVnUlcbs8pzRaMTBwYD9\nnT5+EFLmNY2U3L17lzRZkqcryjzB8x1sXXBybY+ryYT5YkEcNUhNww9CRGvRD1zqvGaxXHF0eATC\nRdclVZkwj3Jsw4TGxjJUIpSum7iGj227CAvKJmU1n+O6LnkcEccrpnJCK2E6X3HzqQ/x6quvcnzt\nJpblUjcVVaVzdTXh8GAfz/UwDI1XX32Vmzdv0DQtSZwipYbnu5RlyTe+8XX+/s/+R5yenhKEIZqm\nE62UdW8cp5R5iR94XEwuKcqCk5NjDMPgwYMH7O7s8vDBAw7393nqqad5cO8Bt+/c58b1a9y9e4cs\njTAMBUtIqQyl6rrBNC0WyyVf/OIXmUwmCCGYzWa88tev8Nxzz/EzP/Mz1HWt7G7DkJ2dHYqi4C/+\n4i/44b/zwwRBgGN73XzrvU31dE1fZ0wDoKFSsDRNx3U9Do9OCMKIe/fuoQmJrqmmTaOlli0SgWUr\nHvq2WHCbiLFOvFp31EqZbW0eU4EdXf3pjP3eaVX7Xut978C/9Md/sOmU39lRr4vwNsSyzVLRNG0j\nTV8PPLcpOeu/tS0GEig/7aZuFGVJAEJgCl1ZsVYtju+TVBWlJvj8r/13/PSP/DiWYWJZLmleM5kv\nsZ2QWgr8oIdE4LoejZSIVhCvou6o1CkQNZXI47puJ+83yPIMTRisogTH9ojTDM/11JvWStq64Mb1\nE8Y7Q5bLGbt7e1RNQ1k1OJ7Hg7MLirIijiJEq6TmtqlT5SmjnQFPPvsUb7x5h15/n7KCJFdxZXVd\nMByETGdTdAS9wCWLz4njjOUyYb5MOb72BF44YrZYYdkuCINlFLOzO2JVzIiXCT17QGD5NEXO/m4f\ny2gxrZY0XeGFrjLdqg1FuRKC1WrF7u6Y1WqFpulKiNSqsAp1JC4xLAfHDdQGWTcEjklVZIwGAXXT\nUDaNCndIk647afH9gKpSlgICjbZpqKuGJEkoq4qe71M3NbZjbz7MSsVbk6Y5Ao00zTB0E80U1E3N\nZHrFeG/MvXt3GY/HzGcz9vb2sA2L0WiXIsuJV1OaVgVYR2mCYZgMh8MOf22xTIOyyJW50mJOXuSM\nDw4Y7OyS5xWabrFYrJANTK5mDPtDhr0BhtMgtBZD1zZJ9nXdUpet4lOXDUmckWUF/tDD69m0dY2h\nC5AtTVXR1BVlpcye2lYZNj04PeOHf+TTTGdTXNthb2+MrulkSYLj2Az6fVarZQdn6JimBUheeeUV\nXn31m3zm5c/Q66kNK8tyDFN14GEQ0Av7XE0muJ5LS8NqtcR11QBYCMHJ0TGz2Yy6qmkayeXlGTdO\njlgtp5gGtO0jQkLTSKQUCE3nL7/yZS4vLzedaBRFCCHIsoyXX36Z5557bgNJPHhwl1u33ubu3XvY\ntsOv/De/Qq+3hlA6OvF3KOLr6vdeRbDp6LWr1YrZdEJZ5MoITXSiPcuk7WxkFfLzKEbtnSyTdeyb\n+t7jTobrn62q5rH6pmkaL3zsR/7mduDvDHHY/sfXHNDtFHZ490XaFves1zZksp78gnqj2kbZcirm\njqIN1ip4DN1S6jcQfPWvvsrezh5CExR5jgBuHB9z7WCMEBqz+ZIki8mKkqRc0UqJazkMQxvbspRP\nSR0SRSuiOCaP480N6ToOfjDgxtEBhumwWiXkRUFZ1iAkWVXw7Ve/Tnx8iGObJLbO1XSCFDq9wS5V\nmRMnGZquWBS6lOiaIBiNuXXnTUqZ4ftDtLYiWyWUVY0X+tRIzi4uCPyApmo4Pb3CM8F1ezhun5ZL\n2rZkOj1jMFTFxnFtDCPE0FpCo+Lw5ADR2jiGiyF6lEVE1VZUWUVVlZS5gh7KKlcmX46NbmgsV/NO\n0m+gS6PzazGwTJ1h4KOZNqbtYrsOSRwxvTqn53s8OHuI716MtlUAACAASURBVAfolocQ4Hkhy8WS\nOI6QrerMPNdF03QC38exbXx3hKYJ0qSiLGJOTy8Z7QwJwlAlCMmCayf7lHlNHKfcvXMHz1Pileee\neYY333oT17HJ0ggpa/qhTxInXJw/JPR9jo/UALaoSmrZkhU5q3hJmiSYpsq83BmM8NyAVZQitYYH\nD89569Y95sslZVnheT47wx2G4YA6X1FZGqtVhGYIXM/Dse3NkGs0GpPEKbam4XkeWZZjuSbLeA4I\ncgFxlFCWBa7rcrA3xvFKNA1u377F8eEBdZESuDZCQFXkmJ5H2AuwLZskzdB1k9Goz3K5IE1j6rrk\njTe+Ra8X0AtDHNukyFMEAkvX2R0OSdOM+/fu4fgu8/mEMPQxDR0BFEXBznDIndt3lVeSJZjN54Rh\nj6JUgrq6LUE2aFIVb2Uba3J5cdHdpz55UZAlCUfHx9y7fx9N13n9jTd4+pln1Cm8aXAct0uxVzOr\nqq4pq3VCF5sT9nstAd+xSAp0mhYGg11s20G2NZeXFxR5hm0YavOxbcq8QBePGsZtZt2GKKHrW9bW\njxJ5Hs3ixAYteGfz+l7rfS/g2zDIuli/0/jlkV3jI+K74zibr9dRR2sXwvVFWa/vFHEkqgY0jUao\nbMkuIlKxWYoC1+vxpS/+GR//1A+wOxohaEmWK6p0Tuj5yKbhZNejalx0xwXNpGxqsrSgLArqOkE0\nJgYw6tk8cW0P3TAoy5ubwcx8OefyckIaKz8Ox/E5HI/UdRA9dnYHVEVGnqe01Qrf0jBdj0YqKCYI\netRVh/cLgWloLJYLXNdjNp8QLWN2h0eYmoFmG+hC0u+H9OixWCyJlimH+9exdakCE2TDtesBVV3z\n7LVj7ty5S1YkGJZOFKf0Ap/QALPJ6A8CHFtlTLa+kiAXect4tEealLiuzySZEPYU3OM4DpqukSQN\nUja0KC531XH3l1NJOBiQxAlpkRP2PA4O9kiTFTdu3CAvapZxTplVOKLF0HT6vT79sM/x4SGybSmL\nTIVpJCuSJEFKyc7OPuO9IePDEUmaIIyW84uHaLrG/Yf3qIqKumw4PDqi77sYlgrB/tQnP0GSxZRV\nxYN799jfHzM3dQZhn8lkwnyVMJ3NkLJluDvA9j0s22C0t6usG/Ka+6fntK2kFQZSuPhhn3Ck0x/t\nUdUFDx88QNNbej2bk8MnSOMY0xspbLuqqBoV5GDoJqtoTlnW1FUnWAPaouJw/4CH5xe4bsB8EfPp\nH/8pvvSlL4Fu43kqvuzZp57m9PyUNI6oa2X+JOtaDWodh8APKYqSMAxZRRF102CYGmXZYJg6H/no\niwihFMau4yGERppEaJqaExwc7JPlCbYVsoqWneJYFeS6rjENk3gZEycJUhOEYQ9koYqWJh6zRm5b\nSYPg4vyCqqpIO1dDy1YU3PVJ++HDh+hdEDlCeeT3ej1Wq5jZbMHXvvZ1Pv2jn0YgkFLZQ3zXJeU7\nyrtCCRoEmmZQljWuG9A0FQeHR9y9c4soyXEcBZ8Iw0A2jwaO7yy8a6j4UcP5KEbyUZF/FLX2zs78\nvdb7XsDX9L9t+uA2hmTb9qaz3pbUg3rTLcvaDEG3c/DWhX29m2134QCWVEPLVkjVcXdDat00CMKQ\n1994m8ViQej51HWBrGtMHaYXZ3iHB13oQI9SNGT5kkYzQTPwXR3f8dA758M4ilFqsIJ4tcRzXbIk\nQzQqAPjZp/eJkxSJKpJlscC1beJ4TlG06JpkOrvL/GqJZfUJh8opMPR9lmmKZXkkSYahC+Ikw3F9\ndLNhlcT0+gNkW5OlGa4fogklrJjMl9y8+RSB32BoBm2rkVeJol7pJk1Vcuv224z39/ACX3F8NR9d\nwH4/JIpytLogq3L8nkNRpNiOSVWqI2UWVeRpiWnq5EWK56s80bIscJwhyh/mUTK353m0iwjTsamF\nwIiXpGlCmizo90KiJKZpdYJen6KSkKwoq5K2rSiynDRWOOp4dwddqLwaIRoM3WC+OMfxXHTTRGiw\nipYEfYcoTnA8jZPr19HRKMuK1WqFbujkRY7lGHi+i2HonJwcc3r6gF4Ycnr6kBvXr5MVJrbjkRUp\nURYhZYNeCizToipr9sYHmIZLnhWcXsyYLSJs18bxHHRTMN4/5PDogJ1Bj2g65fLqIaamU7QNLRq6\nobx0pMYmdcmwG+WjLZVne5GX3L9/H9Nyee75F9Atl3sPz9k7OFY+102JSBOi+Rzbsri8OKPfHxAM\nAyzTwrYdZQZV1QwGA7Iso2layjLHNFVKku977O8r/38hBKalvHhGwwHRKka2LVdXF4zHY5bRAtMy\n0PQOy9Xazo3QQdd1ekFIlGecnZ3x3LNPUmRx91lXghpk5zHSKKHb2q55zdLK83wzUzo8PCTLsi0h\nn858vtzUkW984xvcuHGT3Z0xvu+9d7KNBIRkE/S5/ThKyNZKiWEa1HXT1ReTmzef5P6DO0ymV9i2\niS40jC3V5RohWM/31jVtncazTbZ4vNk0Hive76RRv3O97wV87de97pCrqnpsMLn2L1n/7DqwYX0k\nUewTZRmrqSx0VeilRDOszSBUdvjUeird6EJJdVtJo2nkhtqljaomSxP+6M/+nMObTyDziqR1iJMS\nU5dkUQlMuXa0z9VsRtgJJtA0srLsxBSC1XJFVdb0+yq5RwB7O7uUTYVuGkymE0QruTqbKUaKqbPT\n6+GOPUBgXjthMV8RRQm+vUfvxh6a3rJYRuz0PRarC0amhdAqNKOkbiSNBo5rMXT22B316ff7JEmC\nbmvMF5e4Ukmci8WMbBZg2S6Ty6ka+LguaVqyiDJcyyIMe7RxycC2QINayyiKgqT0OL75BEIzSNOM\nLK1oG4tlUjG9mmMdWDiOwHU1FqlLRYUmdZJVutmAm6YhzwvSNFPvRdNwfNCnzVqkhPF4l/3dHUzT\nVI6PJkyuzrEsVXSEreF6Nrbp0zYVRgtlkXJ6ltDIFoGO3+vheT4HN66T5wWNFFxNZlxezfA8QeD1\ncQKLaLEgz2L6QcDu4c7GAjhLU1arJfPpkrosefbZZ1nM5/iew9tvvwloOK7L3njMdWeEZVksFgss\n2+bt27eYX52xWC5ZLpeEYcCNo6A7CWrUZUVy8ZCmaTHrI/YPjhSrI1FeMVWpFL9Jooq1EIJ+L6TK\nS3Qh8D0fXdMojJy3z84YXbvG5P5bBHpN2xTItiQMbNpKUhQ5MoBGaLz0ie8DIXAcD920EegI3cD3\ndPIswzRtijzG0C2yrGAyneP7LpahYgR3R2PapmI1n5HFKxxX5U4OBwGz6QWGYeAYNmVeYjoGlayo\nqgLf98hLxbQyLY1nnvkQ09mUogEdHaHplGWFaenUdU7T1mhGxWinx3R2gZkpG9l4tSAMQ3zX5fq1\na8imoSwqFRTSNYHn51eYtsdw94C0aDifzPCzgjAIAftd9UeCiqrruvjNo2KtD1Ep9QBatwdohonU\nDW7eeAbfG6oQG9OkFQqGNXUNWddoUoWttHUDSOqyQqCp04B4RKFe12/FvGs2DSl87w78fR9ifuXP\n/vAx9eX65WzvWvBIFv9O74BtG9q2ZZOEIdaY+Racsu7CdV3Hblo0x2aeRPiuh4ZGVjdgW/zxn/wZ\nr/z5X/LyT7yM4TnYRkBT1ZRFSrKaUWQx++MRu+ORyu4LQ1opqdoG3w/VmyShKhuEVLhm3bYYpqFS\ncnRB0zbYpk1TNd3GVRLHauBZVzVuEDCbKse2vb0DqqpQBzpNp67VMLMoGxarCNvxSHNl2BUEIcvl\nAtc21U2ia0gkum5gWhbz+RLdtBDCxLZsmkaiGRpxkhAEPdIk6/zDCwLXQaPGtU2auqCuKgxXqSaF\n0HC9AE0op0XLMCjSmJ7vEsdL4nhJMDxSnRVq2q9pKs9Rwha1Sm3KQuY0TbsRQqhOBHxfHdnXfurL\n5RLDsZCywbFMaBr6fR/ZKNqXpusqAKCsKKsSTZM4nRMjmsm9+w+5du0EgcA0uy2/bZCypawfUTyF\nEMqvvakpsgzLstCE6N4jQVnVCKGRJMnm5x3H6bpUi8lsxnK5Yjgc4rrqccs0VdC0VM1EnuediyS4\nnt9BgFuWESgKmmkYlEWxsSWtq4oiL8iTBNdxeOqZp7m4vMK0bcWXRtJUJdDguw5Stkzmc/aOTrBt\nl7Kq0Q0LTVPOnIZuKF+TPFevDzVgPn14h9n8gh/8ge+jzAs8x0PJ3C3KsgDRdj+rb15znhcKNmqV\nH73rup2ls2qmGtmwnM85Pj6iqiuKNCYvMmUoZWpkSQLA/fv3yOuGxWLBbD6jzMtNgf3wh1/i+eef\n78zkLJCCi4szbt+5zSuv/FvG+wf8x7/4Dzk6OiaOY05OTijzgieuH7yrFlXVWvDzONV4zVj7TuPN\nd+pKkiTh9PSUZXSB77nK36iuaesKyzSQzRoq6mpYC0JTtvRr7Hvdqa8h4+3HP/zxH33PIeb7XsC/\n9Md/sBlCbr+U7VCEqqo2hPZ1p/7OJGdQzn7bw8xtKuG2vFUIga/pzKsMox/iYGA3ghzJn37tFf7J\nf/9P+OV/8I8IvQB6LtEyo0hSDg730WkJApery0vKPOH6jWtkWcb+/ljlX+a5wvpaZbV5fHyCaVro\nukFRVeRlwXy5IIpjLMPEtVwQEIQ+rut2gQ21ojF5Pq9/+w2KvFTChDDg9PSU0WiXnd09DMumKCpM\ny2axiAiCEM1QuKRp6FRVSZanREmiZMyLBZ4Xcnh0RJaVj1g7hobZCZh0zVD4cdMiREtTFRgaVGVG\nVRV85OMfx3Ec8rxiGaWq4NaNclEsMnaGPQxdJww98lonDEOm0ynT6XSzyeZ5rgQ8pko56vV6OLZi\ncaxdG9dujvfv3yfPShzHwXVdwjDEdm2yLGVydUGaxBi6xrWjIxaLhVL7HR4wGo26rg6mkxmzZURV\nNQyH441Hu+jgliDwCEKfIOyxWq2Ioog4jja4aC/wkW1LEPhMp4rStre3RxAEBEGgYvEWC779x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UUnmRd4ZNdVWoOL4sQzctLicTojjj2edVuHSel3iujaDF0ARNpWh7bSuVa6RuECcpjmXw+//8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\no47LNOoEXkcdddRxmcb/AwKQdm5yYaZEAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x38c5110>"
+        "<matplotlib.figure.Figure at 0x5a3a290>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\n",
+      "\n",
+      "You should try out detection on an image of your own next!"
+     ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Remove the temp directory to clean up."
+      "(Remove the temp directory to clean up, and we're done.)"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import shutil\n",
-      "shutil.rmtree('_temp')"
+      "!rm -rf _temp"
      ],
      "language": "python",
      "metadata": {},
diff --git a/examples/feature_extraction/imagenet_val.prototxt b/examples/feature_extraction/imagenet_val.prototxt
index 14bfe770ef8..b0451a1a114 100644
--- a/examples/feature_extraction/imagenet_val.prototxt
+++ b/examples/feature_extraction/imagenet_val.prototxt
@@ -227,3 +227,10 @@ layers {
   bottom: "label"
   top: "accuracy"
 }
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "fc8"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
index 22d7fc21a56..bbc919ba68b 100644
--- a/examples/filter_visualization.ipynb
+++ b/examples/filter_visualization.ipynb
@@ -1,8 +1,8 @@
 {
  "metadata": {
-  "name": "Filter visualization",
   "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
-  "include_in_docs": true
+  "include_in_docs": true,
+  "name": ""
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -65,9 +65,9 @@
       "net.set_phase_test()\n",
       "net.set_mode_cpu()\n",
       "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
-      "net.set_mean('data', caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')  # ImageNet mean\n",
-      "net.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB\n",
-      "net.set_input_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]"
+      "net.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'))  # ImageNet mean\n",
+      "net.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
+      "net.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
      ],
      "language": "python",
      "metadata": {},
@@ -178,12 +178,6 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# our network takes BGR images, so we need to switch color channels\n",
-      "def showimage(im):\n",
-      "    if im.ndim == 3:\n",
-      "        im = im[:, :, ::-1]\n",
-      "    plt.imshow(im)\n",
-      "    \n",
       "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
       "#  and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
       "def vis_square(data, padsize=1, padval=0):\n",
@@ -199,7 +193,7 @@
       "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
       "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
       "    \n",
-      "    showimage(data)"
+      "    imshow(data)"
      ],
      "language": "python",
      "metadata": {},
@@ -218,10 +212,7 @@
      "collapsed": false,
      "input": [
       "# index four is the center crop\n",
-      "image = net.blobs['data'].data[4].copy()\n",
-      "image -= image.min()\n",
-      "image /= image.max()\n",
-      "showimage(image.transpose(1, 2, 0))"
+      "imshow(net.deprocess('data', net.blobs['data'].data[4]))"
      ],
      "language": "python",
      "metadata": {},
@@ -229,9 +220,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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RmTD/h556AXIpjitBH7mezTJ2KT8g3eQzP1ASIcpYwXpKY0nYtggRZhMUpUJfa2aDkqif\nSPyADF7KOkWUyqPPEkRD6YT9HMYkg4gUkeoYZKHSQVwTIu+Qr7EvG/x841xXT4Or56ieKxxeqJcC\nyKLWXUJh/Cc+7zIdk0ekmtQSIpUsWbJkyZIlS3ZLSy9SyZIlS5YsWbJkt7S7c+3No81ChMtIIowI\n1hRtEtjviLJ50NtZ6iIpKZyQrpepUBjfn1fJpiCiSZ0oaaK6I1UBEu8YIOsJBGlVkWWi0al0+ixj\npGcEyFJVnIlsR3LrOC6LnTuvGn8b6dig8hPdkwJ7HsZlIucaiTcLIWCfVs5VOYorqkfCx6EL12J3\nUvfJ1QUuA7kpJGXOR9ytVPTOlDA4OPdoLlok+QC4uVbhz2WC3HUZw9NmYTz5ZNByv+iqm0QLaYA+\nkwZKEFkX/mNwqSgpGuM9V1iaKtLz0hVmIG/OoqOTU4NMKkACaCH6iPWZO1NzJjpSqGiUNLdkGxEc\noUEEXgk+jFNPylU3Qo56RmR63B/tT34Usm+4F0u3C12mk/hW6ZVQdwvHeh65+5b3k24UVVZmv3vV\nNRkbfi7OR8aVuLtGuAx1/O1Hl/hX506B346R2jS0taTuE/WL4AKcdxqwQM2mMCY20IoqVG9pggZY\nF9akEi6iQyZuKYyJ/WXIar8/XLm/e2hB9UFHKsNcUO/sTG0tJeWXrn/KMly/2WI+d6GeHdpWCNnd\nk829a1f1/uCelZnix7OsE9SZmuU+kdg+9kKUZuyQegDp2UN/qbI53WyDrH8jSMyVuOCo/TYdIWVH\nyuYIvKhLUQDHfOqN+nzL+z+rD/wYEdrP9VBUoWHliaydoKA0qrdIqohqK2Fs155aI5fCXJxWkgx7\n69ZJFeidzZVNUne/nmhAF5knco9rBCj45PKKAZGBcCTbhz7jX11rzMyqehnQQCpRqWtBcu0lS5Ys\nWbJkyZL9cOzOECnLsohESKKY5tfh2+oxGQBFnwbsmOMw8XinbxaI7ESrotx03MHK2yoVsIty+aYb\nKQeQeKyq3J4nqTsn9+bOt2oNV/eEQmk/0aRow3GEFE8oRL8aQJAtGwkn586Bl82Wb+sa/s3o17IN\nZWcPuMMQYit2eLttaP9+DxKtoFT15H47iLKzz3FHFWuVtWBovHQrd9CHXAirGAutLcmRCkmu1w5N\nU9SL7SY6p/dk3zv0azuEHTkVuFWlgx0/6/0n2VXCaksGTchvZ/yTHVHdtorkfLkWpBjYFrOAsDx+\nIjnR7Jk7XxXQhxUIqjsJnnhib5qZ2bdffuzOFSnxe+xWKnxEMZn5uhQlPDJMvcSBkF399OBGNwrr\nZmDDUuoiCiFnrkkhTLMbo7xa/tMydyHboOc4utUn+qhtwGDod4HYX2H86XiaQKIupJ8oD5LVoY0k\nm+c516llUs68DWVUnR41hyU+Hg4agIHf7EPZfufm0fbq0pd1GO9EpMYs5NUksXwSlCbDY2SQXGsZ\nZB0OsnZXG6ydgyLC6BMh4BNt8et1hFYQrtA1GUV6HNcTJZb730h/Hhnifu0mh1sCi0rcp6KRsY55\ntVoH+QmiLqpSwnVS1w4iq/udKMXfuM9Dx3yVdsTUm4KmaKwV6yF9MvmsGLImA+2Nclf6eafPAndc\nfiSvIb0D404QJCCxGrwz4XkaqUn46uk8dedRbwpRR/9OMC8D1TTXHieq1nPEe0IlCHeQvQljovQB\nJRp48oP1DxIilSxZsmTJkiVLdktLL1LJkiVLlixZsmS3tLtTNs8LrytiZh6LjKBYD+eFw+gC0t/m\ncAFFquAelRPyaiRSE0Om/K1C8TmOHyMFckCb2nP+RKpjQaKuJleOdWnUjUjScynwZAsS+/4gyrbk\nVc56XsKtqo8CDZhx2ccB4hUoFminauHUSGRJNWOzoIXTtppk0tXl5ib8dnvtynY3wQVHN4OqOM97\nuMWo+xEl3qVmyJKcOCkW7zWrlsEDbRtInAXcEVHSXMqyUIld+qSESyXX/cZAfR65PP6OouzLs1RH\n1Os1UMIr6rJM3dhsq4z/Gros6lqoa9c/ayE7Pzp7x333KEDrh51z0Xzn4w992diBvM8+lPFPsr+6\nx73eW6VuYe9ICFWnjpQmDfYZCJZzkvck0lNCWydRQg/zaekCj3aFOdcO0cDy405dsEV03nnZ/dG6\nQt0nFewnYTjTZKwMYlhJ3+H2qAYOXYqZ9OeAZNGe6y6aRSXJwaeSyDZn4tfQAwfUsz6oLtiNmZnt\nr4Lez27v1pbd1YUvo7u5rF0F+jy49ma41EdxD5PjndVST/RFI3fl0C9dOwUCVeaXJr+FmwX+Jp2v\nM2kcRxIPx5kqZpxC9YYwdjNdY2J9IrOg3k/XdiHJa1drt56Um3D9VbukBXBcq94V9fMOksm5OzAD\ng4XjcirqY1yriD36IiZs28KO6UjxmTSKu3dCeypxwfG+qwYe18UMy2mkxI7nk65/fLZpwnm6+7pO\nnmd87k8/oBEWqCqcp6rtxuCp4RCeNbyWeJGt4fwUCgYzQKiyObtMk2V30zJoQC0hUsmSJUuWLFmy\nZLe0O0Okhny0wpR0ulS29m95GhJNUqruaolcyG8ZulkcyTXnzy/x6iQ46pu2f+uVUOsRv9GUcBWJ\nqoqwcZckO3K+1VM9fBjCm3mZM1+TkOPxBn0iSIMndguxNVxXFeBtUc/QXu6+w1t2j91KLTuTvHA/\nboWIV0Pte7UOdSoQ/lwJsb1t3bkvBJEp0Mad5I6iFMDULXeGXp14Wg7TY+iTtoffDgI+1Jkjg+ay\nI5lzElrRUcUyYCAfl4iIhh935nZCXXbjy5rcEb9Huf8Ndzi6m8T1e4yNKDcWiZDS/NUGO0hBBBiK\n/ujBqS/78l/4opmZfe3bfxTq2btzv3n/NV/29T9xpPT+AMLmJEgjydGqxDwRJRCyLQnQyjCndIVm\nKiCaGxFVCYVytkfy0O53ue6+gXRpSs4jW3KPzhzJ1zUreZsqDfhX1empLD1JuHyJH4yCHE4N2iqD\nLcOcLAtBBIiSSah5AXkOjVNg+D932hLV74nPZSNthhTLMIUdeXdwP7oQwm5/cNe/vghSB/21Q0f6\nXmQSGiDmJeZEpYsd0DIh8Wct1o5MQvgzrgkinXGKzhbUu8eUKTtZd/fIKEEJAyXszyQihyplIT2C\nXN/1TyPjj0vmQRbFInfr3SiICIMCWqDvmlexwLVaSSPQYgxrFoeipgK9oC+4/61KAmDed1cB9cvG\nVwKlZGxwndL8fx6RU5UcXCuPvCRYuwSR6zCOdexM0wZ/lYEee110vHjPioz1YaaKu8iEzESVZJ2e\nlsjdxHmkNxm/CSR+edb36GM5B+dnrvMPfaY5BCkJUkfZJsqovmZmpQYoHLGESCVLlixZsmTJkt3S\n0otUsmTJkiVLlizZLe3udKQsaMKYxXpLocz9VeSehDklsVLFdhJioVdIVriT2hIkpwlkX4O82Clh\nDTBuL1pIE9x8qlhMGFX1pmav2aTJQN15OpB+a0mKSPJ4Frkflzo6Ffw4mlDyauvw8a5TzRhqG0md\nXvkbJ4+kPKy6IuAeFdciyc6luPs2J07RdiW/3bauLiS4ahs1uTBdKeMRNVlCsN1e3Zjzol17khcz\ndQGAqFkGbZcBsHgm96So2E+4XzKGJu8ykfuP83biHhhxHInA7nyuTpnA6BPV21Uxl14JuupmPQcU\nk8XdWrRMKBrO8Rj9nwkU/o+/8cdmZvaLX/5lX/bdp//AzMz+r/e/4cuqzKlYGzSzJoGwOT5jiB/1\nEMi8rGLCtpnoPOVKwKYukmgLUb/M66gpsRhQvChGk9gae+zgljym9n9k/OtRDCTgmBwGOf7Az5pk\nm9kBZO0apug7M7MJLpsxknFyZZW49pj5IAqUYZ0bEmHl/sOlVkqwAfn0o1AQ9nuQwofgMtpD5+3m\nZZiTGcneoxyH+dRu6FoTErl3KYvLDmOmbEUzjW65G3HjMwBG+riGQnsWpNps6KjKjflyJGAoV3V4\n/0HdOFSRD+fluNOYI9IiRiGvMwk6leLVFTRGOmNxnUpVQMdvjx2/l/XMMGaadVAF51n63rldJ/Ht\n8XxT1IalZhJVzPfigqswZkoh73thcdWbgltWXVsd3GfTEYVvui+nflyUqRYU3e3xo55uzGWglh7H\n7/0cl7lGpr4mfp+PzHa67PR+TkeCwvhZ3YJlllx7yZIlS5YsWbJkPxS7M0RqnuZop8EdbCxhsHxb\n9G/dUV49IDfCyu2xSxg01BFEcu7ciizarizK+GacazcBidjPYUdGcGoWNCsD+jRLmGY/dFFbj6Fw\n8a4aOx1BrrjTI+nbzCzHTmi7DTudDjuB+SC7GSr1Uh1WSb+Tu8b2JuxMV+enOE6QPuymFX0hOteU\nYVfVYHequ89ucurJ1UFChzdAU0BU7qN8fUAawobcOqj97rdLYr/mvyN5cS+htiToloIIZMw7dgQt\nud65bfJ2H8i5B+RTHASR0HxuvsyoTq2oIxAWGbtZFaNUQy+kVyp2q/wByjYiP/Hk/j0zM/vC+WdC\n2evvmpnZl9/6ki/70hs/bmZm767/0Jd9+w//czMzO3vw0MzM/vHFM//djH6/6UTZumJQRKgTA0Wy\nKIec+6sk3hJh5LGyPMieVAIXEv3AXHPCOfVzWHeQ3PXLebkTVmVlf03ZkU8ekZn5w3CtYrlb5qTR\nLAoELCOZDiK2Ok/RF7OoeGcl0QTJ08b1gWR/DWLBbZ8KRS7d8UpspvD/9bVIHdwgUOWlKLCPJOWG\na7QM7Z+o+q7BFu7EpQSA1KVbJ9ZVmP+DAeFcheMGrEV5Gfq4ghj/JNIpBWPWAZZOAitRQkCfCUQx\ni0gVH9cURNDfHmHvs+9UTqafYjmR4ojafi/jhP3DNdTVOZbaMYvzQ9L4zBrr0McdyPZETm5uBK5D\nw+LMGkv5mwEIa/TsYHs0UIC3WhVeMKAHgVN3mCe18Ryy/tMjJOOacywKBHlFasYsIILaX1525UgO\nWT83BCUPKJVeC+T0KE/m8vpUNFeUimj7IEisymgcs4RIJUuWLFmyZMmS3dLSi1SyZMmSJUuWLNkt\n7e5ce/1oufLFjsB4ROwUdmXi1yE6nIRRgRZBrFN3A/VgvBtRoFZPbFfSG92Ckdo0CdABbqULphT4\nrxtIwAtuEU9kxvnW66C7kjXMhirK4qX7PlLJISlb9K7OQEatxLW5RRLMugruRhJq976f1I1KiDW8\nW9N9dnoeYE/qCDVCrCwrV8/NKiTN9RB8L5olWyoLB3cDYVYqlvcSAODdeDuFbBEAUFz5It7XXsiO\nFaDnQQiI+45EVVHbpfsM/w8RxAyNnVldRiTCigsI/R5JltkS2vYuEjlw8mRL1EeCGFim7mbqo4yq\nDtwB7rbgWvnC+V8wM7PrpyEZ7fnmxMzMvvTjv+DL/sXvOUXrf/gn/6eZmR3EFfv0BZLXihsjq+Bu\nUi0mW2pL+dzGMnfo+srE3UbVfJKuNXsq9dl0rlNvS6c/57OSTQs//sQFizkzSNlkVMqHy0SU9Y/p\nU9FlEmVWQF9ks7QfI6ovlWxLd6PqveG65dItUfF84lqjC7AUEv9AsrHUadi5sXtzEdap7hN3Xk2Q\nO2KerGQ+M1k7XarTrLpLbq6vm5A0+97JAzMzayWRNpXad4fgFr/M3FjcVkJ2h4t8loAa6hINcLvP\neeR3cvWIMvRy8VZ5cKwTEdkbmnERUR2uup0GlIBmsIEW1kbc0xjjs/jCeL45kgI31F114TDWI/1C\nPJOUKF668x0qBieI7hT0wYZZSOTV0n011y0uKXMCa4sm6M0wtvQ56SdvpuP0lYCSXuc1iiSwgW6x\naE2ktpXca3rPVAPQUyVUAxD8joHEcnnWkqoxiguYx2nwTgOdw7YVpXrMRaXUMFAgU/L68IMxp4RI\nJUuWLFmyZMmS3dLuDJEqizxSRy0Yzn9EiVgt8/GamteHBDxRNgVpWVWEuY0diUwp0uTJ5qGMxO6s\nVEQCeag0hJLIiaAEJMpNEUctj+rWlWGnV2G3rJIIWb5sv88rGO3I3Kv7ShAu8wrs4Xx9jx0R2r/f\ni5ou+1DOe33ldo4nZ9KvD7AzEJRi5YnVEqYNRGCzCb99+NARmusm7JIHsEHzgmhB6JOuA6owhx1s\nt2P4u5D0NaMjAAAgAElEQVQz0f+Dqv3mQE6EvL3FDuNE6rnz4c8kAiuqCAkHGSckmeel7gKJyCx3\ncFpGEmMuu1mCoh4ljXJOHVH7xn1657U3QxuQu+tB+yjUHYTip0+/5ctePvuOq0cdxsk//9f/LTMz\n+4UrRzL/D3/t3/bf3Wzd+CgePQznuHmO+oYaeTRXQCovNaLtx05YpTPYPx5BFHVwkoyHiDGM3X8U\ngJJF5zIzm32YeCQLjTLZaTJCgeNe87XhvmoIO4MRdK0J92wpJ5KLnEQQWz8Suq8KEx5twrzSwBoj\nIif9xA28zB0urQfJq8acgIqwe5BckGju7InwlfKYyDM3d85Pw5i4d+bG3fokIFJE2PaHE182AE28\nKMK6PwKJn4Vs3WMNLolC6L32a72SuRmUI+rUKyIYIp0BQns2KPrG/Jeq6A+vA5D7VhZxjuc81+cE\nkJ5SZRLQRkFJmClDSedU8R41ewLz9PF3qpiOfi0EkfSPLBm7zCFXrYREjWUv1znZLJ9xRIQGQZgM\nSBhzl2q+TMr6qKxNyIknKvZ47qiKP7+P0Gzebw08YY5LFOn8Y6CI1okonQag8TcMbDEza4GO66rr\nsyfIgtZpXxyxhEglS5YsWbJkyZLd0u4MkeoPfYSq8NN0ZLemO02+wSpHwr+Qy28L+MhVEJPfZxT8\nEj4MNzhzIbmB8DFX8U/UeZDzskx3JD130ZL/h2gLcyl1EsJfIV65Vq5CxTBY8ZH7Hbm0Fd3D0Hyz\n8FatYp7Mzt3iHGUrchHDUmqiR4jzxUU4b3sPeagk1979jeNGZeKPzrH9aeUaZ+fYTbbhuN12F103\nyiG2Bx9iJxnUq6V0BMfEQXeaszsvM6ibhZRZ/S60sV35Uea+0xBl8EEyyb/H3V8pXJocIezMOWgW\nEKlojGcM/5YybohfyQNpFhC+Se4rEYm8C334uHHo1OtFQKT+0R/9H2ZmdtgGNO8EvJZJZDr+5//y\nPzYzs7/y1X/FzMz+xr/+7/jv/pP/7D8wM7OxDjn8bvoL1END0o8hp0BfFfVFu09OA5eOSOwMgUfd\nVZKHFmWw5/iXa/H+H6uHyiTMmngxlL5SXxUVzfhByjBPlF9JpEvWifmIdML8Ch/OzGyaYo4gGoLj\nKCor6ENG+YV5UaZGFG/qBP0AwlJMiqYDJTkiyXFAbr5xH6519tCNhU1z35etmjMzM3twGsYfEY6L\n7BNfdv/UXWt7Ffrpk93H7honMp4gwTLvgchpv+KjogojJBnWKmoJJHxVBPR1dwb0+zK0Z+eGsxdQ\nNTPz+qfINRrNP6znucpf4B4eRCaEY1Hvvx9G0tcjSELR/SdKS6kFlesAglTrUCbPTlFS9H8tIql5\nTaRFfsuxJpen6PBs6s3gOopnh4zJIAiqXE4gjZOO0/g7s4AwjoM+z8hDm6TM/R2OCJzOvCc6hnF/\nVqswJmoIrTZtQE5bfJ6jnLiQThCE7c+yhEglS5YsWbJkyZLd0tKLVLJkyZIlS5Ys2S3tzlx7hcUq\n5lPnXDGzks4A2Wler8wYwq5hyiDHKQQ8Um3Z5Lj4+ExYb5RiyCO1dRwnMLrP19PJtXzktiqQ4/r5\nMiSaKqmZwJl7kKizQt1YgHibABk3k3OLTOUSMlUSKes8Spg+PRUD35+zZVs15rQAZN5JaPDNC1eX\n7kxgXIa/CrGbzagkT9/6BPmnhChKGNmHk6trh0rgZQihJrQ8mbhg6TIZAgSfU51Z7mfbus9tQHvN\nqlegdYHC2XVZIST6kqRHkalYtdF3OKG9an78SRvpIfSnk1OQ7NnCdWJmNh5cCHkrxNrP3XvPzMye\nf/e7vqy/cH1WCLE+zwBpq4rzwTXy9//2f21mZj/9y7/qv/urP/VzZmb2P/3D3wttwPhXtWt/szX/\n3MgABOl/qgi36gL1MfZmFnJemgUCLnMOmgV5gkwI6HQzqvow3QyTqE17970S5fnbI14/T6KXNgxU\n2xci6mB+oIS6cy7KOjEeUD/Jk0gycqGUAmNOtAJ1lLXGSAQXlx0I1TLVbSKxvBe3INynVB03k7D2\nTsYr3Osk8ZbiMmkLN4ZWp4FE/uCRc+k1IolAr9DGwnEHSMGcrEOwye7E1amagiRKBk/y7uCOz69V\nQsSN51kaW62R//OekI1Bim42QkA/uPq169B3q/suF+fNlawdkJ2o4Nof5iXRWIMIdp1rjyqGz0dc\nYHzcVnm4/ohEiSr/k3Ftx7jLZLzkfIbIWl8fUWDnsC8boSDg60HmLknrhWoRURJCAm+Y0YKSAKO4\nO3m8UmWYM3WStX7MKAkjixxoE5kEO4SMJks3P9tYyCTuMZ6zLKx1DF7ZiNTKCj7bQoJ9/DzWZyFc\noCtZz7o+kc2TJUuWLFmyZMl+KHZ38gdmNgqZay6OvNXWR/IqwbTMkzKVbIedc9/r7sv9LQqSGEVC\nAYiIilpWNXe14bpEIlYSVso36E65acjxo+HHJUJyb3q3g5lFaqHHDjKTbOnFkXxFM4Q+M9n9NOgn\n3SWxnrOSp9mQednXDNeOQr39ri+cdwty+PY69OsOaFopufaoJZgJwrhqmONPwlpH99sezH7dwe0P\n3EEL0kFESknMsNOzcP0pRwZ7yfVVN+43jRLaa5IdKcgoBPzpiNAjiZCygyRRsa7DjjxEJCuaChKj\nhGRnDBMmShSJL7od8STie49PH5uZ2fZZ2NW/8VlX9s1v/JEvW0HioBWUgHIjtaCE5+fnZmb2/PlH\nZmb2jb/zP/jvfuVf+5tmZvbRf/Q3fdmfQv6gzpaIlIqfTgWRFgk/pr6kIDwk5fKnEekWFgWWcOwq\n0lwsf+MzuJc6J4hm6j2JScEKJDKfWn5EhiQOIliSyM0HtEjdB9RFQuKD/MAy/JyBBfHVsSOXPfAB\n66jmOuO8mmTumhc9jRqC4yXXHnbplEvI5DHRNA4R35wElPT83OV6ZGi8mVmPPG2KEm5WIKqvg0js\nDSRYFDnc4PofH16YmdleSNwD89DJLW9P3LwvJFCn4fosx50/cNc/7MO6TymC1YmsO1uSzF0f5oII\nEeFXRGgAEj5n4n2gl0Rz8uF7lT+hJEI0dLgu43RRcAoQuVxEUhkBk8naUTUM/5fxDzRLkSsGcs2R\ncO6xcZdFx2XiJaLswSjPum6PZ4JMVF52VlkDjLtS2thR6kDnKa7nhUPlvAS41EtAT4gKHG9OHPq4\nWoVxyjrpuk9P1SQepurIGqCWEKlkyZIlS5YsWbJbWnqRSpYsWbJkyZIlu6XdmWvvcNhGWiDmCaMC\n+1EJVWWUvbJvgBFzKkYreZ0K5NFPoUBNIqCo6VbU/ckVMqWO01LHJdfrwx0R8/VIYgu/JfS5XtPt\nJy6rPVS0g8fGDPCtQpw93FOrdeg7asWoW2wAGVAh2xEw9kgSYaTZs3TtVYTKxY1BbZnLy1DRi5dQ\nQF8FbRnyvmvRAGNOpKYJ0Opq5eDWYefgfs2DRUXfcQx9TWKvCovnyIW1XoebTTXy9Unop3rjKiUo\nrmUlcy3RPSXuYa/PslSbL+UkdCnkQnYk8TR2AS1VuYcMmjkgT1ZaOUDxwxTcvRPcAp9/98u+7Oq7\nTqtn1YgLpl2jTqpVBvK+6JIdoCn1xpM3zMzs4tlT/931n7r8e3/1537Zl/3ef/Xvu9/l4kalPozw\nT2fmxBNibQXNllIDKqiLxP4XwjT7uhPdq4m5AAVp91o9S29v7G6DNQLtc7zTnTJrf5HQLyeZGIAS\n5d+ktpOSotFGVXGGqyyXoAw7MP+gKjDHbkYdQ9SvmrKlC151rBhsEuXwg5snGyKugvsjx1HTJ5Dd\nZWHDulaLG7+p3efVSpTNQUHQbA8t5npZhvHHtSCXPIUdOu3xE5fD7/kU8mrefOjWiVrypWUgNNfi\nWmSgzmoT6pnhvpebcK3DAfWrxH2P/Gte20i6qwAtYBJtucLYT+oz5fey/nOCzEvXmt7PAg8tT3cQ\neoLBfZvJmqQ6izS6I/PyGFayXPdVv42ZIiIvOzMweGX38NUAysB0ZFwpBafDcbNqQTFTgPQnVevj\nFItwS8JlNyoFCHqHk7ShwVg4OV37MlIaNNcgy6I0iXh4leK+1IwjxywhUsmSJUuWLFmyZLe0u5M/\nyAvrZadJZMBELoAxyVFuKo8ICTkORE3NScQQ70y2E2XJnRbrIGhB6XYmmkmbytJRJ3kVcwmJ7olc\n6c6Ru2QJiSahln/lJffATPeSmZ0kvsONKHbvsSPthCiPXUehKA3QqWnSnStRJ1t8R7QqFyIgQ3O1\nD3P85vImKGbXF27H+Og8hDDPBgJ8HnakJPTPQnYmUZh1mST/Yn/g+STTN7qiERLh6myDa4Y+2axc\n2HUTNiRWYsfcDwFNY2sL7Dh6VaLOufuT3br/TpA2j2ZKmH4OSQTNa0UYbQr3uMG2q/T9tFTuPRf0\np6EUg5BNeyqAy66egQWzoBk1B76EjlPtf4dd6GYTVMe/850/NDOzn/6Zf8mX/fj/+mNmZvb3X37H\nl3EqCNfZE+RLmRME2zINHpipYsxMBKH/D5DdyGWn6T9OS1QrIqD7eoTzNUAxHj44D3UHOvfixUsz\nM7u6vJBrIQxcdr/FzPBvvU8YwxEkBrK9oA8k+yoBPZ8Y7CCoZ0EpFOy+Bx3/CECQ61MyotM1EetJ\ntZK5W3FMKrEa860PY2JHFAFz4sn6Qbg++rAW9Ikq2hrqzhsQ5T8dGdATkKMNEKle1eMzjG1IR2xO\nZL9/6ubCsAvrSo6xM8icJMA3CtK0AXKsc7fAgVklHoZdh/oCVRFyOsdVhHQiZ2p5JNdmLxI3XrpD\n82lWCADJpT+BsFS1+05lCGb8VgNQmJsxjxJgLgOleJxmAPENOgjqCnjmIOtEgWwUFfPwHTRQC8+u\nfagnCfi1oDpjx+e5ynkQ4ZVnJwPP5iXqR6RPc+JORNql/cy80bbhAUDvRyOSEAzammXdYc7Gwy4E\nOUzHoG2xhEglS5YsWbJkyZLd0tKLVLJkyZIlS5Ys2S3tzlx72TRFECMJuJnCfnSpqD4N/0YJCnG8\nYOZBPyNS6IiuUQgUTfV0kZ3xbq76CJlPYcemrVAmbrGcRHnRFgFpfYZORTEqwdTBw724YnoQUXfb\nAOOW3mWnLqgl2TWvoYESqZcz4S/cCWOAZ+laU7dDXSz1ScjKIyHRzOzFc6ct9Mn94AK4TxKlJEjN\nanfyxgLcSi0rJnQ+yHmPESF5T07OwzlakEML6eu8hhJutVTRVWifiDIJ/aoZ5MnhUYJYHK/Divo8\norbv9X7E38r7o5olJa7HomlWHTFXz3UezvGgcS7L0yq4Ag4XzlVZq2YTfqvziWWq99TARbMHjF1J\nAEgBTbNn3/hHvuxv/PV/08zMvv5f/Hu+7JKup06CN0qSkoUAX3rf3sI4d9SNETxaek/YmOUeUE/L\nmmigyOmZ67uTk+C+ZNLSE5CSP/kkuJ2ePnPjupLxMvd0mS4Tr+qgoFZP5BBgjIcQ6kcSz0UBncE1\nuT+tzPXsSNJ2CmHLMrXeuPu4KoKyuPVuHCl5v3zh+ufmKow7Bvx4FXsh2q5Xm6h9ZjJ2GlWlx7yW\n6IH9Hvp5EqjDRLKq3zVj7tT34AoLjAEbLh3ZfBYNQk+tkD4soAu1Xod1gmrnueqOoR11G8bdrnX9\ns9u6+o5y/IA1U8daRn0oOc67Y48ERUQSbFCKL+XmIaGBT8IcabH5NU6zQ3ANDX3ttZ1krB1AB9Fg\nl8yrjctxUJQvJMiAdRgQ+KNrTY81e5R7zcAvTRTutQ01KIIxGVkYk+yeWAOO4x4BY9JfjGfZSGDB\nCuO/Xoe1s25xnzR4zD8TleYyRdcyi8fxMUuIVLJkyZIlS5Ys2S3tzhApF24sO/2JxM6w0yhAVJ2P\nqYrKWz1VobNcECG8nWsOq6Ik6oRQc8lXR1JsVUoOJ8rn5su30Vy6jm/zg5BC+VsNne+p4goyYSHs\nXIbf686EiFnXhffdFdTRTfKPHXCeTpRlbUdERAmQuB62REpO5E5L1YGZzqwRrQEfuioEXAYNfFdC\n5++duZ1gNwjZHDsc3f10CO3nbkZ3y0S9Krl+ByJuuxako+J9lVxrDNOV8F/uemSIeRJhQKJ0twSk\nT+5/AXRIibU+x6OCX9xpCXLouzvTcccdMYjFInVQlkSkhLCJ3epJHpSlqxKFsiMl0qZ50vZQka4k\n2WCHnSvHwsVFIFu/8fpnzczs8ntf92Wfe+0XzczsSR2kLrYDlapDAALlH9aS64qoby518ptjdMkQ\n7WCPJMALX/qPPq+erie4ASshz58iFLoV+YN1Q6IugiOKh/475np78TIocRNViNqAyypKM1JZ/QgB\nWPOK8XaXraBuINITMdYAGK+EHfHaMYZEVqSpoUDehHu9Lt2YmYXY+2Lj2vb0aWhjh0G2OgfBWLI4\nnEPRPELaGGwgKBFD3W+uA5y03W3xXRgnRFhKmafM2ck53J4EVOnUiajb2AtMRaTlRvIv4rhZVcSJ\nust8YsCRridEvXOgRfsyzMmB3oH4BrhrahnOm0tfE6Vr63BPcsP8EFI6+dFEzjWvrEdrolyz7hr1\naSgbcK39LvTTJZBrzbYxYJFXKSKut6pK30HGokbGhJ3IWjDwYRayOeXGNXcmx4kGeXlgS70+/jmq\nnhCgRBPvTfhq0y7lN07P3Oc6TAkjsF8I0sXncyRcgeA1fZ4fy66ilhCpZMmSJUuWLFmyW1p6kUqW\nLFmyZMmSJbul3ZlrLy8mnyjWLMDdioRPJPYJPDtTC0Ngv9InHlXIjqQ00ScBtOtViUXjglChwn5Z\nQXheEz/imgLtE+6vhJRZUKm5V70rd/0WsOxQiRbHUkTcRpAy750F9wQhWG0XUcdrSSRM5fHDNpTl\nRgV2qkmL6q5PaCllqHtfiLsVUKnCnjzf7nDty/bwWTS5KLCDZTsKA3OGn22/I+wsZHOfSFaUaFeA\n+8WNQUJ5Le6BHG4+JcrvqQAdaRBBF4XuAXGjcptRiO4NIXMTUvQIl6Wq83qvlCajtXFxHN18VMIu\nFfbHeDk5DYTht5rPubZchvtaktcqxPYSdRqGpavQclH2hqJ8SYKzEDG/9+HXzMzszTff9GX7jxzx\n/Oe//LO+7Dd/73fMzOwi3H5rMI40oMOT9yPBJ6iXwxUwaLQD7mEk9k7NpijxNbVlJIgAfbyWZLQr\nkI1bmeN0m3Fe5eJiuIArcLcLumPbIwlVqR6vc5cK6Kp27RPzShNLdI/qQtFVvKJ/WLItsH6zjKsa\nAR1lGVwbm1PngntwHgJA2hzK4qLAf+/EaWrdexjUw5nAukH2hAcPHvvvKhB6a8kaPIHkPZRh7l5f\nOPfdyxcvfNmLF06B/zCEgdJWbmwroZqK0h3oC3kRxmu9hita9IGGjmtd6EOS5+d1aNdZ4do6F+qC\nYpCJzHtoT2UIyihkDecc0vWPD4VjiawL6ae8YsJh1RYD3UGCMir4oLhe6HON67MGO9BU7XvGs64R\n1/q9+65OW3km3OyY0UF+yyTM4oLjM4br+qwJskEpmMQ9WWQcE+FaDDapJQCAeoAaFETdNm3hq27+\nTNrfYA4zmMTM7OzM3etWFPALXF/vE13K6pUdJibc1ufzEXqRWEKkkiVLlixZsmTJbml3hkiNw2iZ\nhoszD57sVkfm1YuIfZQwWL4DRmHC+NsLAdfnv/KhmYKqNExspWgBPy/zr2lopFcs1vbxfVpQL6IE\n8p4f6gaSt+6qmROsFpSCO2jNq1djJ3rvfmjrs+duJ/jxR898WQ+SoVc2V8VmbPq0v7gznIUcSwXm\nXIj6Pl+RoARUD68bJRZCgXkMuxTmkxpQKVVxDjmZFDlEXUqpO/PaqUoFEAHNYUVitSIiIz6T46xy\nFTn6J5cwYJLHdQc3zMOiLPeh4BIogHNPilJBqTyDmnOWyQ5uRWXrgCq9/VmXE2/4+4GwW0EKQcck\nEQ6VvyDCd39z6stedO56HukQpHWzcTvH978TVMxPzxzS8c99+a/5sv/m9/9HMzO7FkkGyg5opLnP\nK6bMToZfQ5W+kHFVYNxNilJREkDQbJJXpyMogY6nhrn+BDmuQTYnmlyJFP6jJ45Q//LypS/bIdR/\nGhXVXMo6FBkV21W65ftLF+h65mMXgIhU5XKZ1l01EeFGdt/n5+4+3bt3LmUOnSqmcL4rEOk1117e\nYC6icmshexO4yY4Q669eBOTu5sohQdurQGI/7B3xeXcIY3du3HlaCYCgB6KjYr8iI1hjlNhNxOaw\nk/v/AvIvbXjGNDWI1RK8QoRbZU9Ith4I9ba6rgE5FdXvoDaugSUcu/o84fqvmgjLvHKsSo56aHAA\nvSOK0nvpmiPq2xoAQPRNZQKIAA9bQT0t7n93bvzFQnkQZXkiUZKwwWqgeiqhQDmNTNaYGsEQm1VY\nk+jEGGd5TiAnIqdaIYFlDRD21TqsP0QONf8i53opiHTfU8U+VJ4K/P0ggU99qMsxS4hUsmTJkiVL\nlizZLS29SCVLlixZsmTJkt3S7pBsnkcJSsm/HY7p+MjvxmmZ0DDg/XIciepRDmRAkF7OWpXVeYyo\ns+L7WXBXr1gsUCBh9ogozhNGkC11iagmq7o3IFiqZhFIzqtWyXkO92yEbM1EpqrBw3YrZPwC7r7d\npYMsM6kcyflKumT1xGNlpdGNJy4TJIFcC2S8Ozg9orMHIuQBqHxUvRmMAartjqqF9Or9MoG2xWfk\nUW6Be4mPKwHzANLoGOmTwN1GdWiBnQlLj6qsj+pp3/FW96LKfjDqMymJFG4kDVSAu8e7+8TFM+Be\nqLL51VMHMZ/LeakBpvcuECuX13r+PLh7NxvnAqIrep5CG7ZQdt6sJZHx+87N98U33vNln3vwtpmZ\nvRANKqq3axJszlPNS063RHDfaHYC93evKv68d+qeHakjpwly3XG7XWjPgGuVOp9a6kchyWwX3FMc\n109eC9pS1NYZREWbQ0y1ZjgU1X0+c7mVucP2jKIKXzTx2K3VtTyT2L6kGzSSDHwFovjZWdAbW507\n10crCXJzuMVP7wWi7ocfc465Op0LOfiMrmjRIuK6+snLMK62W+e+++STj33ZrrvE3ys5bo96Bl2y\n0lM+4NqSgIUR42SQ9Z8upWGQOfEC970NLplTuDstNN8m9LvwuUVbDuu0BLFw2M1RsAPVzpUyQNdW\nsNKfN5RxGdPgISbQHZjQVx4sXsdMMxZ4de5lnQpZJ+mymyXh8ckJ1mxZO6/ptlP5RCZwnpZBSUyH\noWsd65frMyZzz4KqlETCLbINrINrj0mDvWS5mc2n+1faqC7TAecKz5oW87rZBHc31wfNGc2hpZSW\nfqS7b5n54/tZQqSSJUuWLFmyZMluaXeXa8+qKIRxZIivksh9mHp4GyzwedC3dPzNFX6aSSjXMF2U\nldzCyE63X6JPDKHWcHUS0fQF1SNSEXbGXFtCQJyo7Oz+r2vZQQ28VniDLrElJ+nXzKwtkUNrHXaQ\n3JGonAR3TgKwCbLikIOdqN6S2Bi3gZLtUkbUS5AGXr8VYudYu73YVhRwa6N6fagUJRYC0KCogvvb\nCUpQgZSvJEqfB0rQlCx3nxWlo8TBIGMs94R6/NWNDvaTpYy/YSSxWc5BsXnZ6lBlPhdSaJlRJkFU\nnElex8543Yb7SjmJ1T4c/5k3P2NmZleffDdcH12sO1LuHKOQfOwcFct9ee3GwgpDsdGwchyo4dJr\nEJpffu99X/Zv/Mu/amZmf/yf/ru+bIvxpJkFOJxUYoBq/xkDAUrZheL6mi5xmJjtIJTlI0jsMnYp\nu3Aj0gUXN64/nzwIKA2DNqhwMsiSyICKchUudnrf3Z9dJ8xahmsLIkUUVXMHFmhQLxIbROVV7TnL\nSUCucXoZ66yoKrvHMTTuOOarOw/rydk9d28rkRNgAEgl7X4B5Og+glgy6cMzhL1fS/t7LDKNBFE8\nR068cRvQJ47nwxjWhBnr/nQdfnvaOnSK82nYC6qI7iylDTv/7JB1GvNz9zLUc3uNOdmo1wOSLEVA\n08saqE+F+yByKQXWNeuls/kcE5S0mJmpItSTCLsi4l6NX0LtmT2BAQj9oDPWWa7h/7iH6k0pOPEV\nKuGlSpGkwTq1bgPqPOFHu61IxzDgBe0eO5mUHk0N94ljdxbF/rp0fXHv5J4vW1Wu3zNB3TmQdZy2\nyPFIlKqflfzt2l1KEEGFvHqZeBOqfLnGdwODnIRYjzLtOg3aOWYJkUqWLFmyZMmSJbulpRepZMmS\nJUuWLFmyW9qdufacg+GIZtN8BDI9YtkxzHIcF2XqRqBrZ6yo2B2OLqh7ITo2OWDBURIEe5KnMgaP\nJK0kEqjSHoR0CRNGqutH4PmqXupenD9wsLcm8iU5TzxmHjxWQn+H9ve9g2kL0dNRUrY/hye2hrKJ\n2i4Cd6+aDf4Gt1TbQher0PsJN5bAqHskuiQ5coqSIbO+QuxnIk/pkz00RmZTVXTntpxFM2ek6y0a\nEzwvXDziHqarRt14vP+VKIAPRr2beXGcWg9/sObAzuEOoTrvJPTUtnzLzMw+//nP+rKbC9fWaQxt\nneHa0uS2oR5SJwyulWj2rEA2vwRRnIl6zQJReXst7hkkPv7wG3/qy7747jvueLl+CSi+t+A+Lkq6\nKmSOGQM14EaTMTngOFWnH3CcBgB4Su8R9F3dnS+gaXSYn/iyrHP1o/t+1GCXGslgW1FHP3Mui0qI\nzeziKXKtujHbiQtkGt1venGL7aHkP2eaSNb9piI9QRcR77HRzAaou8wdJpeldpZZuO/U0zELAQXf\n+vqf+LL3GkeuP9m5494ogjr6/Z07RyeK3dd7d47nWRgnTzGuL2Q8+UChQe8nNJCKME5aJFzOsE5o\nANIE98xh3kkZ1MbVBQz/zXQd+vXiY1e/ZhNcS3QLVZKYmOOJU1h1zKg3pYEdJdeCUcc1dNkkUIKu\nJ01kzvVU712GRWnCWsR12yzoR4nslx2gcaRUkbpYrj9cd2edO3jG6m/XIPnbGMZOt3P9eMA5JiFn\nG9D9jCsAACAASURBVNy9clutwH3NJeF1CXdfZqGMn3XqVqC3aKaKEFCGNUHGfwmXYSXBFt7LLs94\n9rHSbTjXVKmedJBR3yfm5NpLlixZsmTJkiX7odidIVKzDbE6OHcQk+40j8A0R9XOUWbLstGWMM1w\nwC5YXuu7g/uyqZf5hfIjSJPuII6p2HKXGgd/U8UZiJgw1j1yJu3npodh2GZmNVjBa1GCJWFUia3c\nsWtY53oNZeE9CMVZUBhmW+N+hTSAqVwBwmoFuSkKd75GCJtr5HhSUebZ3K5vGMIulTuBsVvudMaO\nSuxK9kbVJdcTv88kJxnHTC9h6vw2JgDXUT12sjNhzisNTQ+BDRIay2EqDGgv3ZEt0ay8UOSISJjr\nw2wOu0DmQfvoo5Cb7LWaBFjd6bpzaD8pYubriTHRiUxDNzgkqgLZtBXF+EsQhivZ3fK8D+4HcurV\nhx+amdnPfOkv+7L/7f0/RFtDPbrB3YFSkKvhEOdELGUXylybY6Y7aCLNqqzt/urunxz/Qgbgbu/G\n+wfPn/qyt15H2D3uuyomMwp/dRLIuQyKmWUPWkguzldtkHF6wHjuBaXaYaffj4HQ3fnTERFe5nCL\nsjjMy/WPx+mY8BIP+zB2v/DIoYlPPgrXeGdy9/YU7b74+Hm4VuvOdyrK7qvMzftC5m4FpPlhEYIX\nPtq69acTYjWDewZBrq4LpyRfYC7oGjbBjdDLut6h/b0GxbB+0v6X33PnXd8L97M5hZyM3Pd5Qk48\n3P9Zc64xN6Y8O4hYz4JceZkACTbhbSyi3K2UwpHzUWqA15XxfwCCutV16kBV/nDeNVBHyuWYmQ2U\nWhGPQA2JHUV4mUdxEomdXYVxundrkaJ/XFgVwGHuTBNO+G7nrksvhJkZl6lSZVLgFdKMGsxdyWe2\n1pfrn84r9mcnz65jmNIegQz7bUAkuz0DlcI1YgR8aQmRSpYsWbJkyZIlu6XdHSI1Ta8IjTHXnb75\nUSRQd/Xur4Z/ehRLN67M6i3IEd9Xp7HB7yRcHuft5W2Zm17N9D6CS6EyAUTTMkXYJmYuV0FEXAv1\nLYSrY15oVIq4g5F8dcy71azCrauw+8j3svveMdRXwrkRulthl1KPuoNaIgIHIGbTFBCEAmUbkV8g\n2WwYBGkZgLBUgghhdz7JLp3bGMoFHETo70BugIY1o1OKUfcXWdQGM7Oh585ddvOGnaYiXD5yGruw\ngyCYBbOaS14/5otT7pEXxBPhUPwtBRkiIpBLqG9dIyQdvJBKwqW3CAl/WAWOygQpiGEMO00PJwpK\nxvDrIhM5DdSzj/JKsQPcjuwwh++YQzJXoUlwyT54+sKXvY5d97/w0yH/3t/78A9cPZUrhl3lOIVt\n6sFce3r0nYb6Z8yDKLtBft5rnsyJoeYyT1r0tcydBvfieheEQy+uIdwH1LeQ8Z9DhqBuQh+ucmam\nF+QYfawh+SWOUz5gNi85L1eXboe/FcJm17u6HMwhaNO4RHAy4Q1RTFLzv43g2axkrDGse2OhPdUf\nO97Qmy9DP91cOwTqkpxSGS/TCwoyhn56+Niheg9OA0r+duP4dSrSaeC/ZUJHotyJCixvezcmCubh\nFAR5f428eqPwbDz3M5yD4pcHeXaMW9fG7WVY9wuE5+eCOlP2pvQiucIlJaem00YAaVYxY/zV9Z/r\nhPIwKYRcyLyfIbcx3+AaIkhKr0cp689+cCgVPS1mQcbjfqtrjbvGLFXnkqlcyhL1y0W4swISVKA/\nhxt5UA3MISieCzyf5yvh0hUQ+G1CBUquyZo7kq8C8ozn2s3nhXKfiRZpvkTm0LNZPCdEP2Xt6PZE\n2kIZf7qXZ8GgOQuPWEKkkiVLlixZsmTJbmnpRSpZsmTJkiVLluyWdmeuvaIobBSXQUD2juTQ0xIP\nBR85aURKxV8NyQTM6vMQictmBLFMo/Unn5tNXHAzw19DUX7EBWie7CnHeV8hidCSV6ykEvrS1AU4\nzSS7qiQA3H3igqjqJQGWbiaeL8rD5FUdQhs2yF3UD0uIs64DKZohuSbuNpKINXcdVeY7ISB2e4Sa\n4l4o6Y/5zIpKzssQ3lnJhnB35Dpe0J8aakz30UHy2XmoHgRPcSNQRV3DhYNiuEDRUNudRQE9w9RS\nxdwK90flJBgS3MDFV2YB4q9BSn/84LEvO3kJxe6tSD2EBIDeKBmRN+JawG1aNSEowHCNR4/cNQ5C\n+h1G93l/FcLaq6rGNcNxe0giSGose1S7EPqDhd+OcIfMUfDGKwElOoZ28b0xM4NyhxWydHEWa040\nSnsUteZJ8zoFvozEe7qWaumvqiKJN7hH1sgTpq5d75YSUnJZuflRr0Jf5xh/K1nicrjqK3HL07V3\ntYN7RNYaVa/215/o7gl9R5mK7z0NrrXVwV3j9UsZpx85AvbldZAT8NETVJYX0vEBUiO5DLZnHzpX\n4CN7w5e9e+89MzP7g6d/7Mt6zDV1gfv5ofEXM3P9uX81rVu5gmtvt8yNNstJJqzno67T6KerqxC8\ncfqkRZ1kjNGNg46fZBH3QVFyYubknI+FyAuxnPdac8LVCLxQdx/ZKDWyCOy34d5cQSZjliwKLcjx\ns7jC/LNG3IINpAhmka7gM2hUugXz5OVLdxvlSRpxGe6v0F9R8yk1EO7T9Y1bC+qX4pbNSKwPz5Oh\nZ6CUrMVr5MkkVUTc3S3c17n0NTMFRFIjI9dpIe/vQWnYL9ezQX47HJbzTi0hUsmSJUuWLFmyZLe0\nO0OkpnmKclN5ErnmnCKJONr9gQAsb+QZ3+b1OH/u5Zs2Q05n2f1S/FEzyA9H+GUMBY8FLElsDiWe\neDgfQdg8qiVIE3bJmZQxrDOWJAAiI2/6zEw9aKZ5lBXFsu8okllKaDh3U7lAcjnD1KPQXHeNtgk7\nCGYC183y3DPTtpDXDyDg7hS5Qhk2BJPmcMIOYpQxEWISJDTWa01oPXkK2f36fHoipsldx0QEUVBK\nEPaLKK/jMiS9AsKXVUpAZ38eCY2PgixAlAZRualCuDjJkUQBzMx2l+7zKOOqYF43Qb8o4looTMRx\nmglRG9v+i+tLHB9Qzd6LpYZ2Xbx0u8r1Sbj/V0Csbp4FAvrPfPFnzMzsb/3BfxfqhPtzI7u7rmew\nAdFCyY1IQdworRbKFGlmrkORLjEivLImMHeeyT1+8cLVmfksBxkvNZEo6a/1yRmuKRIOQFOPoeQq\nEsgxo0j0+X13vkZQ0pub+D7tRcIjIFJLUVO1FcLaX7cgU/Hmh+43tSCMJTrvZC2ISEZS7lJqoybC\nJ4vjAIj54uVLX/bkPSd6OpmiJERplWyNOS7jmULIc41gFwmi8AiP5GQsgb7fXKr6Mc4R5e7kmhgQ\nof1+x4b5Mv6kxFo4mc4XrsmyhgHBU/1o5jrUMdEAiWo2ijo3uKYgpzPJ067s9DysCWcHR+i/uAx9\nvUUbBkFEV0BE8+LI+K9qKXNtO9yENcYT0PW36Iu85LPjyJoo47DEvVZJGo7/reRf5Hm6Lqwnh4Pr\nE0WHV737HtqbVksOPwZejKOutZTEkec55VSUbN5R6kC8Lvg87iQYq0uIVLJkyZIlS5Ys2Q/F0otU\nsmTJkiVLlizZLe3udKRme0VHyqss+bLpCFbOIv0tYTwlFptXAA5Fnls9k8QqmlFwhYzDEs6M0qaB\nHK0EyIFuOVUgRlklkDHhYObcUxapRyBzbTMJ8EIOLanEHSDTPHPwpJLzOvhDOsnJNoKg3kEoI1ON\npYIupiWJtxT31ExypOqj0AUomi0dyKCZ6HhsQSTfbQMGvrtxdSLBT3MDsr/mIV+UZUKYJBFwVnjc\nX0KgfbpIJMjAcG6qJ3eiokwF4l7dDiBvqto8r1CoyxDnyUvRAOqhzyLq4QdomfXo7bWofm9y5DXb\nihsF82MQsvmIHF6DKCtTM0oJ+LxP+4NoptxAU+kT5+JarwM52tdSFPjP8H0prt3TldMRunh26cv+\n0k/+RTMz+9t/92/5so8OdK2L+w4uYIOacCeuvakj7K8q9nTZiGuTrmVxd1FHppbxPBs1iNTdj7xm\nNxyvof0ZFo+V3JMc6tnFvCQR34gLtqgY7CDaRnCpjIUqMLtrtFnoT+8O5sC6FrIzxksn563M/XZl\noe791v3mwbMw/s7hqtGcaB1c25kocM9wgbS1O98krpAa6+Qg/V+Wzt20G0L7T0bSF2T87VzdR3GV\nlcxrJ/1JhfBuphaXuFiwhmWaQxPrrqSLMy+Hpv2EtVjT0GXojFl0gqi3d4CmWSNaXAyKmYTH4Oe9\nUgtwj+syuPGYC7OQ8cR8qtORccJ1N3Jjg2xfbEJhe+OOVw0+5lPMhBTPdWwlLjtetpWceNdYu2Zp\nY+7dd/itPKdKarbJw5bK70UhendeKT7cz4sL56L8RIJXqpX7zb37ISfiGTI/rGasdZPqSC2DrVi/\nXFz71AM87IWADrJ5twvXn6mfJs+C/f775/01S4hUsmTJkiVLlizZre3OECmzOQoDPypn8AMs09xI\n/sfLnGjHUS98J58pxZBFWbj9ScI5iLpE4fcMvxTyOgmosps3j3Dhbf3Ia2yEtOHzsazyij55Eqsg\nLYeDU0Xe7QJKsNshTxKY3bMokXuOufD1fA7BKCjA1eVmG0KI12dni+NYl7yXsG5sWJlx3iwoy3Kn\np4gg263oH0ORD9LWDLs/laQIm9ilxMUkqAe70auiT0pORFi5plonIhpKbMRJZiEsUgFc9SxG7Hp7\nCSg4IOv87sbdr00V1KEnEEHrSdGC/pVWmc3YOQ0Spt0hnDmTsdNQqVx22I8fv25mYbd8cRFUv6dh\niyaE817egJTehZDs8cTtFh/cO/Nl2wuXz+7HXv+yL/uTr/0vZma2V6VqEtpxT1RF3qNPUWux+5US\nBgrMQjadOXcEzZiLJVGYgQdEJHshMW9Wjqit5HD/uZLdKvPlydjtkbszD1xvL7+hkiBEgAddm/CZ\nedKKs7Azp+zJcB121SvkSSzXAdX66cxlHng3k/GE+un8axpkINDQ8ZJq065upUipTED6ViLrwPPp\n/D9fI4ehJjHAPcmE0O9Xbg0oeSV3YiT/ckwmh94HDYpBH+o8pZyJSgJkJdcYaT/uifdIKKoNlCbO\noYm/GihEFXe5PsdYI+gjA2WatcrJ4Bz5UqamBqxTihI+pT420v8z1i4N/yf6l8v4byCFsr8JiAzl\nZibpJwJL9RpeFUEQqbauSuTMyalpKPkTneMMGqszQX0LeglCnXYIUCr8sqpZOZiTVXIIQp5Bn7E+\nr6usk/ysY7fDfO4UuZfPxywhUsmSJUuWLFmyZLe09CKVLFmyZMmSJUt2S7sz116Wz68oHPOvqlgT\nblMYld9Hzo3l+UnUVrIp4d58WhxPfaRJlZXp5tNEwiAKV4XoyPQkmwvZjnCz+JsIH1JjRF9jWbcI\n4Qe0rbDjAZotmbhnOlx/v+/kOCa3DfBoD5ceXUDqYrDsyFBgfymJnklbNeEx3W3iMsvhRlB9DiY/\nVVIkXZQkmY/T8l4OouGReXV60XahPpTmgGYeXw1e6OjuCdegu439rgRDD6nLcPFq16oBBph5Us0w\nQutCQJ6zJbReoGyLBLW7UlymK3dcNQXXTr+He1aIoH1HaF30ju653xzEjVPRHSBj8gZky/sb5wp6\n/OZb/ruLl46APksi4Z6y9AK7X0Mp+t75uS+bdu66P/+VX/Blf/jhn5qZ2R88/8CXZUxCjfmUdVv5\nzl2XbhIzs8HP66UrbFYdNer4iMuISbhVRyaHHtsBkulKmGVibiZ7NQtjcexFHR3n26kGWsZ5IiTi\ntkN7NJEs2iNT0bu00YZS3B7r9RnqGdr1oHVlbw7Bjfcjp5/FucJ5D5h/p6fhPg1wATetuJZmukAN\nf0Wdn7pYMv5auPl6mf+bypWtRLNpyJZunD2CYVTZO7heed+PqLkrtQMuuF7q6dcEuf9F487XboTS\nwOwFUdJcfGbnybWoxK5rcmFLYj0fGsf0oTTbAXMvZ7lmpYBrFb8dhZztA4Dk+BO0q++Du51rsma4\nyEhbkLFLrTqqqJuZ5Zi7yhOfMSZIVC8bOa93gYZ6NhgnSsGhG20chAKD9V+DN/hY6iQxdNlBgwzJ\n2kt9hGfUrJK1fiK1ZnnvomcSjusl2IAu+k4SGaum1DFLiFSyZMmSJUuWLNkt7e7kD7I5Usz1hEGB\nf2rsRGUD6XdJ0xHkIkJOMiqQKwGYuZPwvyrW+rxKyk5D+GmxJLvuRFaA11WlcEbERqq4eGMuG6po\nS1gx3vC1/SN2WPsh7P4pFDtnYbtQ4I18L3nSOuxOOklsNyA8mUrB+rY+M9eSkuhBbNRcXyTnDnN4\ng7+4/NjMzNp1YNbO2LmosDbV2BW58SqyRAtk92XcuWgItVf9FZSCpGQhBM4T819JG3Fc3+sON971\nKrGUaIKWMYS3k3tXkrAq+cKIDs3SVobkK0zAUOft3pG4n1/Krha37kuvvxlqS0TkpSJyB5xfSOGX\nTj34yeMnvuz5sw/NzKyVPIlN68jG3/3W183M7OEbn/HffeHHf8J99533fdnuxqFP0z4EMeQYs5dC\nWL3XubFwIn333oPPm5nZ//6tb/myNe7TNcYkFbnNzA4zSfy+yF9L81oyxFlXBH6rJFGSYTsh4DK4\nZPLjLrThwSNIWHSCKmAsDoLSHfbuuJvrsCZ8jDlx735AiVZrN0A2J0GpegWFeG331McIzK4P562B\nDJy2D3zZfSA8v/jul8I5AOx1kkMsx1jvBOGpVsgdKOseQRSv8K1EdBaKJMmI9akSCYXt9TXaGtpf\nbV3ev50gp1QPt0i9HH3MvJomX/k8oUJOz5mxQNXRgTQ1sv5TkqKVNQGnqeR8RMfpdZgj/Rt3LwqV\ni/DyN+GokgibLIBThTVO8qRSxaYTRIQ5I3mNXJ4rXn5A1l8icUqYLyEJ0OeC5rN7NNsCxr+iTy0k\nTjSjwsz1jmrjlQRloN2FLIDzvFx/y+lIVhCimEJA529LQZ0ztINjsxJ19tAcyX+YUVZCnic+h6Qq\nliNPo3iijEErKmcj/X3MEiKVLFmyZMmSJUt2S0svUsmSJUuWLFmyZLe0u9ORmudIZCPzxaqZRCVk\ntSL+wfKAf3JT7RL+nZQwTHX0cAGfwPWI3pKSzakUrCquTCRLsnUhJNoJLoi2FcgYEHw/qBIrqiYu\nSxIPD+Lao85GIRpIVOMeQE4eRDHau9ty7X8QsU0Jk9TiCNffHUByVBcgzqOk6HFyUPFBSIQk+dGN\npu5GulvnaXmfOiH20h2qyXVn/1nVzpfJZek+9e4huV8hgfMykW7bBlcME+Mq2bEsmfhVIHgm6xW9\no4vnTrdptUI/hK6xFUjOVfDOWT+SxK+EUSqbS7ABSOTPnz0NbYVLaygDBH8D8vqDR4/ducQ/8fu/\n+3tmFtxpZmYtFJNVbrnA9+dtOO8nL5BUNQ/99LNf/mkzM/vv/+Dv+LIMasTXl47YXlaS0LaAC0xc\nDGz3GLn78Y/cV/a1QvskUYtXxnhv6R5QdzuJ2LOo2DMARknkdO3ruH6JBM7dLpDnT0+dy+TiRXCL\nblB2714IKGAQSAd3QiZujwr3biPk7F968y+Zmdm6C/W82rn+L8vgAqnh0i00UAJ6Z5r4eNWu0Fa4\nkSVghZpOGlexgnvwIAEIzz92brxPJJF1hXVqiP397hqS3JYK9T7xu6yrA+dpEcZ6Qb2tRs4LkjXJ\n5GZm9TrHeSUxNebYpGs364T1Ypx0ncQx0v+kdORHiOWqbE53n+oo8RHcH8R9i580rdPi0iwSs9f2\n0+tz/QvnGOBuriMNMJLdZYzTfaoTCh1QCaF8YgACfiveRis2GC/qMqdmmIUxGVzWoT1tizVeCPD8\nTSVJyGdoBfK5NkeJpHkNaRddlhIURorKpM8EWyaG73but9sbSa4u6/IxS4hUsmTJkiVLlizZLe3u\nEKksi8KVMxDLJkE6GDqqud5IFFay8TGyH8nLukugeaVcQWsmIUD685LXp6RsqrhqrD35l4qmoR1U\niUWt3GWBTKkMA9/+56geIKUrqnFk55wfIY9z16Gh46t1g+/ccYdJ3+Dd515evbkTypScCMZiJTs9\nqv3u9yF0f0Sdpki+Hjm05O2fWzzmpitUhoGK1QI1EKXMBdVh7kJVRef2Q/vJhyRrnShU78Pqo0Rc\n7o/K8xoRuXBPGubLEsKsl2cQWXYO90nkH5gycD84VK+cAoLw6J4j6hayWyM3U9W5mR9Qd841UBRt\na1FDxboIZOfz+460POB8H33wHf/dGeQM1pLDqgLS9vIioA8EDK9vwv3frNxYqyWv3Nuvv21mZl96\n53O+7Hf/+I/MzGyF+94L+tcA/Ro1/B3zQ28J0Um9/0RbK5EaaKh2LLHTA3LXcYgrWtn3JPEr0ZQo\ntZ4DY1JIyQUQu+469NMVFfAF9e22UCWX4BEqmndAS9dyr99o3T35ifPXfdk9rEWabaBCnjwlSnN4\nlLpLR4MrIbsTiStKyiCI/AzarxIO3M2rin4PKZbzkyC18IIK/ZKTbyTJV6bdDutEVjIoR/Lg0SGh\nASgErmQ9H0FObjeS65T8byFlczzP+izw6/lSEqcCwqoBMzkgzkL6mjIRpaxnOR63iv5cAzkcrxX1\nckhUWy1RlZoEeEFuM6BOGgDRIWii34tMyujWmHFaPuuifIZAB/VZxCpMRrkSWacp9i9SF+weJdHz\nWazzaTriJfD58WTt4rOLRbHngHIR4RxErDrNlIEFOEq12mOM9brGc9zpc1+f40tLiFSyZMmSJUuW\nLNktLb1IJUuWLFmyZMmS3dLuzLWX22yjJDkkdJbH+JwzgSKpPKxKtHwfzCJ1WnwQEiFdVZ67HCUx\nZkLhcH1qBSmMTBg3E6iPStlDRNiDa01IgUw+20DGpxVy7voEkK2q2JL0qMruPd0I4VI5rqXJPcsy\nYtTigtRRAolSyIl2oHaLujHgHhKXoSePa1+jz5TsPAJa7aU9/heqdov7w0SpkpXUu8K0r4sBbjTR\nGJmh95XJb5msMxNmMUmeKgszew0oumwFiocfYcpCu0gsLyNlZZBYpU+6zF2/0iGG/pm74G7oAB9n\ncO1up0BObgnfq2YM2hW5FuCeyJWUn5HsL/pAIGW+/lZwC33vI0cKfu3xO2Zm9pnPnPjvrqGKvhcs\nfAf31InoA/UIcri+vPFl5UwYP/y2vXIk61/8ws/4sq89c5pSz3ZU/Q+uiAmE4lra0MDHcLUXlwHa\n2k3hPq04T+twP9drKltLe9CfORMaSx/SP9GJu3HCdaP5z2AHUZamO3LSKYaAkkKI3fvete1sHQ7c\nF1Bgzlx/3q/u++++8q5zi77WBDfKFoR2ddlkaA9Vss3MMhCfdZmiO1LJuw00eka44EqdQ0zuLn1S\n4xrtJriMJyREP12Fus9IzL1ehfPtD+7zXlz1RUaSNyePkp5BARAS/YHaYjInC9zr6lRI2Vh31VW2\nRZ/Vha7xDHLhOhm+a5k0uJDEw56yEcYpA0XyRtcTBBFEgSLuuEYCQOaDa9vVpZtXD5vQr3xOlmVI\nGp2j3VFyebSH2llmZhXUy7Noncb5en0WYzxXMngzrqexFqOZ2Ui1c3l2Dh1J8UIBgatclc0nJLUv\nNZE3XJW53E9PQC/5XTh+f3Djqm3CPSn4/FPXHj/2+uyA+1YzFTBTiVyjKH9wRFtCpJIlS5YsWbJk\nyW5pd0c2txhB8sHqkTo5cqPJ7puoy3DkBTEiFvvzLMlmVn7/90clbGf59yeYTVFer7i+ZgElUAIs\n8/QRTtJQb4YcN7KDOSAkNia78pqR3i/KVJ3Yfa5kR8LaMa2W5gvLpqWy98idSdSvaIuUcec2KZpG\nUnAkMYE3fal6jZ0zu11JvMyhpcrWvL6GaxMciBQxjuS1yubloCHqROmKTFDSybj7beUHIAxrTsiJ\nqIaGxJOULj8loVUCFUho3O0cmjGIOnmDezF2ilJi9yc72IHjQ1ESjM9KUM8KiMSH333uy55ANf3k\n1CFR2+2V/26DHGo7Uccm0nnx8mNfdrp2/dPJmGROxOeUQTCz6tyhE1/4y/+sL/vZP/m7Zmb23z51\n6umt3KMc3d4IsfUFUK+uDxICE2C/RtAk5vqqWgm1PnHfn56F3fwKfTYg8ENzE06z+7ztQ39Zx129\n5loEifVI/kklahMJaUSVeYc5fvHsE19WERFBOP9XfvIv+u8eUeriRiQhPBKxRM6jCBx830pePYa/\na5h8D/X80ofaizo41l+VxCABXXOYrU6cnMPzy4DSrTaOWH8YQ90rIMG9zHufT7RaeimIUnRCQOdP\nFUFo1m7MrM9CW6sjQSE1SPZxQIkzBm/kEjBSoq2V9GsLxEjzFXpUS4KNKGOjSvGGLBcqiUBPAVG9\n/rAkbGtgCddCzawxjJRaEPSL0hWqlA5oqRCEcxiZf1TlfEAy53oZWmDTQEkCQZM57gVN59oWPRMR\nFKLkea/oLh4W3lpK0mgOQa7hkUzEzOA1lcThB609vA6qeoRAsVmeBbXkZzxmCZFKlixZsmTJkiW7\npaUXqWTJkiVLlixZslvanbn2lBhnpkqocgzVrqWMrjdNWkp3R3bELZgJiY1lw0TlXLk+9WEiJPyI\ne5A6UuqWpI6V/HSA26xSGJX6UYAxS3FZEJatBOJsoN9zcxNIvNQMyuRqJNapZg6rPGnSXmrG1CP+\nisYRYFd1LXn3oUCcTIwbaTHxHOKDo6KwKsVTD2zOFVrGb3P2q+pTub+dMBu9OrDojRlI/Hmkio42\nqAYYFZNtWXdfnypA0azvaOraxGe5dyR2FlU8pt05dPQiaaz0cQENFP5SIfvxAmTP83Ctnm4+uRRd\nG+ruC0rtev/d3yHSkXF1//a3nWttuwuuvQpu2a24u+7fd+65gyTDXoPEOgu0/vS5I7G/Ubzmyy4/\ncu7A1Tvv+bJzuE1//IE77rAPrqAOLqW1jIl+httnHcjuz3rnPtxLW9u1q/vn3g0Jnz/ZfdfMa+3Z\nhwAAIABJREFUzD777tu+7OWFc9s927u/s7rs6BaI9MHgMu7CPZlzzh1JBlyTsG3htxiL50KUvkHf\nVkJoLg6u7Od/8q+YmdlPPA71Za7YnVwrx5pRC7GcJF+9fl0t982cx5EuHZMAY2BFbuSR2mqy1npt\nodB39x49dPV9EX57hvpddOEezzdwLWmmBugR9WDqq+p4qGO4Vo11bC7CORqspyIsbvUqx18hgNP1\nJu2Z/HfuuGYSHSesP+squIc3cG2rijddv4MottPb2ou7iS5KXTkYNEN1/v02uEK9+1DGOrWaNFNG\nA1ed6ljtMXeUbN1nJKrLGIeLVtd4JhpmoES0rmBc74WwPuHzdJAykve78NsSrupRKR18nst4CmRv\nPutlracb9cizrpM6UdtMl7/9lsFbojfGoCChalTrH/yqlBCpZMmSJUuWLFmyW9qdIVJZnPDK76Cj\nHFbYJUxHSOSF7BwLn9fNFscde0s1v+OSXQhRKjkFVYFHiWH26sVH5BfGaOO0JKDW9QmOJnFPEAR8\nVmJlgXqu1yH8NQPJdC+5mXwOIXlLL7FLiwnWaGNGhWfp1xK7HgW1DiRsh+P22P0oUZ7AlfY1ZR9m\nCbX1XafSATMJnWSxq6yC+7sSYqHPMTeqhgEV4ENRjoZMEfoCEmWt/e7qRyRMFXtnqCNnQhj1iu6Z\nhgbjWtp+jAkBxHzIfHWEgG+Qddisw3cPX3coTSkI3mrjxtD1RSBx77HDr4TETALo5nTjyzbI51aK\nijUDCk7uOwXqh2+ExH7ba4dO3Xz7W77s8plDmh4L0vPBB99w52/D9c9OXD13omI9MHdkd+HLPguV\n8+ff/JqZma1PzsI5kM/wkxchX1t3DRVt2ek/euLaeHkTULKnnav7y+GZLztHTrhhFxCRd9507Z2e\nOfL6C7mHh53rwyiEe+eu30kesIoojSCXnJOqlJ9hrJ+ePPBlL18g16Jc4x7QhF9478fMzKyWgX2N\nfHbVJiAie7RbJQxq3IuyCgRoot6y0fa5+wZBk7NXEClF5D2xWdAtL4kgKNGucyrrrz15z5d9sHWE\nes0TyPibIvqt6+MZ8ypXEj/6KRcEj/IweSvtZ35ECSyiR6BX8jy+PmjyQDxIVrh+JQj2KdE/zTXH\n68sjjYEqFknHQG1bgnKo2j7Kc89KZCoAcjQPYV4NV1h3REWcy8MsOQyLhjkUZe3sKKshRP0jiWq9\nPMekZcg80Lvrj/Kw7YhgHYRsvnf1O+w1/x+e0xI8U65IHg8d2q4pdaCIGGQqKq7rWjl6JFS6hB4R\nUXbHmni40awECPYZRBKiXEpseCj4+1hCpJIlS5YsWbJkyW5p6UUqWbJkyZIlS5bslnZnrr15nl8h\ns4HErPpA/tMPThj4gzRH1d0UTrMkkWdHSHS0PFd/15LY7t2C6u6D61JPx8SQZblenCOQsjVBMN1N\n4kaoqLar6rjUjFLCHP1t0pBX1OO1vl4fRhIp5yD4KWGcblElm3oiorglvWaKwLPes2rL63otKDnH\neKRfSWwcI82SZQAAGx5B10zCrKMePylqnENcFiSbZqJqW3k3n5CSwZjPVMeEpGRR8fVuy0l+S60a\nFK3a4No6hStK9VG8tpqS+AFBq+4KifL3Hz7yZVdQwL58Gtxdn/3sZ91v4fbYi2YU3T6nJ0HtfIfE\nuC+eB22lEe3qZEze4FqqrcN5Mss1njxxpOQf3T3HucI57uG3b2wCsXwPov7VTSDFPwEBfjoE+P07\nnzhi+9AHovzmnnNfvvkwKLuP5txI99bO3TZ1wY24x6Dsd2HAPBucC1C17bqcQTGh7OGpu4+XN+H6\nPdp22IeyNx+69j8RVfB/9ef+mrv+5SV+57+yG5Dx18Fj6/u4UFI4JeteCeoxi5X9vYuulzHmlbJR\nIKeom1j13EyCWGT9oWaUdeG853Atj5fhPvUHrkWh7i2I3OPogmx60aeiLlKvSXYxx5Rs7xMTy/Sj\n8nupa5fP5ByROqK/6nYKlA7VfVoS8DkXVQF+xnGDJm0e6D7W83E9c+3Z7sLxJegYrfSJwX2l6ug0\n1Qyj53cWsjeJ34M8dqibFWkKcj3j30jHClpMsk4d0MZBSeykeygrAwtfuxKiPikoEqjDJNDU4lNX\nMO9rLgs7+zVymfp7p3pTeE5J3X0SZOlODVo4ZgmRSpYsWbJkyZIlu6XdnbJ5lsW5gRYfJAxSEQkP\n8WjZUtmX51ayp3+/PRLySyJmL7nmspyq2+E4Sgz0Gn5MqYVcr8W6h7KdD2PdRMdofRXBYrM01Nc8\nciPh0niDjl71s+VO1L83E0GKQuh5fakTdgSDkFjJxe5FnTjcn1BPIkd9r2T7MmqDmZL3WUPpQ9ZJ\ndlBewkDQl8yPo+VxkfwFdhhZIfezwY64xvHCL8woZ6A7KHRarmRTv9ERAu683CX1IO9roMSIcXR6\n6ip3dvYwnHhw59tLsAN3/Tp2ORZrqWi1cjtR5pIzM2tXbty99jhIEvDeffihkwZ4+TKQ2B/dcyiN\n5rC6uHTfq9TFvQeP3bmknleXDtl58kQUi4l6irLx2UNXlwfvf92dQ5T9S+zgc0E6Pv/EkcM/+EhI\n+Y9c2U5kGjjWVprXDWP23ii5y8ydu6tdv9+7H3LDfefiAzMze+etf8aXvb9yqNb3PvwwnBf9f1/y\nDzLGorsKdVqt3Pf3hOz/hTffNTOzL70RyPsP2d+QKdlJ+HuL+6pr4gH3uBRUZw2ES9dYH8giyCHH\nTqbh52MMRSn67dcdWSc4Fssi3KeqdWOtPgvQWTk6NK3dhfYfClfPolCEg+xpXFPmS+6DU1RFf0k2\nzpuldIlXs4ieMUCEegmKocKIX2MU1UCJIn0ZlcAVpUeuRb0+FMsVaRnxvBlEqZ3ZGPadGzs3u2v/\n3Q7BBufZPV/WUnVb88EBuTlZBTQ5XwORF/hpxhqzFTSXiFQpZOtZAjTMLMpJyaUoktpArkENFMu8\nR0SQQ0hWlOJ1KSuu3SKdgPtOVLsXsjtRLQ1smscsOt4skNy7TiQRgJwpEl8ioOhEYN+6PfY8DZYQ\nqWTJkiVLlixZslvanSFS4zBEof5zxpw3stPwaIUKYuJtddZwRMoUiCAkfOQakk70I8M1pnyJdGm+\nIiIMKslA32t+JDeT0pEmO4KSYGO5RbiyZkHnBqcXQkSON2O9Enf1jeZ1405I/cHok0yuX2AXUQAl\nGfPwBk+f8ziEN3Pu4DSHH9Epz0Ew2R1HaBr5Tcsw6UIzvTf8LR344fgadbrRcN2K8heS/XsPfp2U\nZf5+yvaTcg8inDlz9wNEKgrrZjVlSEzYCUeIlL9oGH9jRo5E2H1x59zvwtg9OQUP6DXHZWoErhsg\n19DUged0ODhu0Ph/s/cmsbZk15XYjj5u85r/3u+z+z+ZmUx2IkWx5FJJLlfBpAYeCOLAkmEZIFAu\nlWzDhmcSDBjWlJq4xh7IAAXIBgQYVgl2yZCryqpGtooliqTEJDPJbH82v3/tbaP14Kx99op/ryjU\nI+RvCGdP7nvnxo04XZyIs/baa/M80bGm8V+du+MeNYaIXIecQt0YInAMGYUcSMdPfuqn/Hff/fPX\nXD1qQkSAdNSVlb34yisiIvL6a9/xZZd2HUdovbLjcvAcIq47uCmXR+CD0E4zLRxXZrGwOfk88vXd\nJOSowm46KW33/cKem9trmhMlBAtZYFLHKT9217+3uO+/+/zVV0VE5JTWlfae4169smvXVzHBHZIa\neHjk+E3jwxu+bI4t+wv7Vs/PPucQqU9eMd5WVTlu0GoFRI7WSd1VM2+zAIeMBX4VJeedfqSoC0nP\n6DrKSLBHx5XTxsiAjjtvwXGJ5cqEg6+jLpOJ7ep1zjZzK1ukyCdKYpqdSiwAuegj4hRpDrWa125I\nYkQmCSGdK8syQx9V4iGm9UfFgVvKZ6p82TWgqRWhKiOEwXfCqAZyzaU8r93nmpCuNQQxFyRI2jbu\n75qkAxSB92KZK+MDrjp3fFowp8f1Z0beFwHPrCKZHPXO8BpnuVAJJcc92BAPTf+Oe83hR2OCvqtp\nTCKcL6LnaZ4o98v6SZ9nUWr9n+A+6iNCs6IhEsqIrMrJVISqdUCkKhIOVi5n0zJyhZyktCYkeBb0\n8ZrKfjhJKiBSwYIFCxYsWLBgF7TwIhUsWLBgwYIFC3ZBe2quPecaY1/QtqM0DHdTnXtAWFYomGBE\nH1Y+SKi3KXuwcTz50Xp/KYIsY1Ux31JhJhuqYm+yef2zM+duSUkdOc6UHEnwsOZwo0t4KDSj62te\nLSYgg8Q4cJ/2w9DVeMi6FBGR0YigcBDwBnIF+My35AkcEFAV7mYXbL9JqG8A96srlomt3j1BOaT0\neCHCpsLo7Nnst4R962yPBkTV4WeSsXsE56AwWB0TlolIMEI1uQd6+AUjIpvHPpzaYOIrlxyh+2DX\nkUd3S1O9nowdPN9VpM7baag1uWfQnvnMXCsKt5+eWJ0eQ/agGJsLJEE4+96uu+53ibC9WsM9Qu4h\nzQ3HrvXvf9e5ADmv5Bd+/LMiIjIjmYKzU6fiPSY5hQIuih791JN7dFlBRZnU6fd3XD2TgbI++qLn\ncXXnPaE+KfPN7AHeaweS/0Ssvw6mzn33gw/et7JXPyUiIh99+JEve+bGMyIicrhngQLfk7dEROSI\nQtevgRT88uELvuwzzzj5ifXcSP61BoPgXl8szBVUaBw2ucw9ZYHapVIAPa0JestwQIlKlrQUPKNu\nHPPYcyaCbcE+7pNDzXNV1D/zRRJjfsQ0dhmyFsQkk6BulqqKnriS/T1wY4JYHlWDKBa0hfIfFpvS\nIY3Pv0bHaeAR+qQiasE51rAJSyLgWrO1kcLbpfvtorIyLwnACvhY7zoKwNBbq8G1qoGLzV1rVFrH\nqop9ntMY1ppFgBTrofbOOVk1J98ysTlm+Rc5/5371K7rOF+gBrRwUADuv4QoIKm6+8i1l6Yq8cMB\nVTr/6bnrnwVwLfIY9puBTS36kwMA1FWdU/ubSq9lly9AuSnYfUoE+W0WEKlgwYIFCxYsWLAL2lND\npLq+GwotbkGJtoI+PiSWzqWh7vwG228KRw5y9olIQkRk3dUNwmU1hLXbfDPmV1DdCXJePT1ft+Wt\nfnauqAIhaPjc3eP4e3xX2Bu07pYGgqD4ZOE6rWC/JUzZ/27QwZuhvppziXfwoxTif5yZO1ESL7UH\n1+KwXhWiY9TJS99pDkXeaeKyzBdXUnzDwqFKcqTdh4ofcp0a5KKK6IQKGCnqxd/VSk7lSGclmzPS\n2KqYKxEWkQurWdEcQ50ODwx1unnNkYwn2BlOUs6Nhmz1NP9mGLP9y3aOc5UkGMg/YP5TSLon29LO\n7YN7d0RE5OWXyo12ff7znxcRkW9+49/Ik3Z6avnyMkAdf/eLX/Rlr33z6yIicvv2LV+2mLnf9Osr\nvmwFgc8WKN11kgH4weuvu+NpXuuOdLU0Eun+nuuLs1NDn1RoryJEbK90pP2W0Ic95C4sM4cI7hBa\nefOZ50VEZEzh4h3Qjz0isU8w//dJpqG67Nr4HCn5nZ+7cbp1yQjofQUkqmNSsGvjfOHas79voe6r\nuTuuLE1qodbd+QCRbtAuWk9AIs6IbLxCaD3LH7RdjXpoABBLI6hILyHCuFHT2OZuBZI1o59LIJyM\nUmiwQUb5NHcQdt6CnM1x9U2/iTT7tYhuSUX1+naTxC2EpvaY733DyDlOhGutKa9jhvVxSShpEzmU\nlu8/lT/hvHZtpEEx9IzxQ8eIlIpfRht1U0T66MgQzCkQXkWXREQKBCU0CZG49ZqM0vigHEaENPCI\ng6ww7m07+J0Irdl8XlHxaVsTU19Gazwuy3NSn23sTIq9xAHWX/rSq8/Q89/Xl3JYCta/jBDRfAxE\nkB6FCWQkErp3Bv2zxQIiFSxYsGDBggULdkELL1LBggULFixYsGAXtKfm2kvi2EhqYnDfQIlXYVfC\n3dTdxE4shUXzAQETTWMC8hOwILu2lIDdkctMXXYJKcb28ClGLR2nnGTO9aMuQs4nqKq8gI7Pz4yI\nKl47xa5VeBa1wf4F4MaC3Wi9wq2sjgvCXGlwr5KhlYidEjlQU021BJ2rVknGZHv8yVCskvfynpMT\nuY8Z5fDSoe2J0KvQrtflYpcl/h64faMKbbV6FqUqgNthHVTEK1KqV30QhpZ7WeETBNeaiL1QmG6o\nvqpt1hDs7YnnREjs12gPEdBTKFrvT+y4S1B+TjGuN66b6vjsFGT/pV3r0uEBvjOyaQutlHZs1xqP\n9Lw2Juu1a5tqRomIfPoLPykiIh+8/4GIiKwWNtf+8J/+UxERObh21ZdN4SDIxjavXgRh/l/9y3/m\ny27fcL9547Xv+rKXXnpZRETmZ5Snr4ELCPU8IZfFEkRgHi/lRDOJ9gwuw+Mzy5N3hjx1o5G5luZz\n58bKST19sXZ9t0RuwFFBul+4h6ellR2j3z/5rKmdr6DtldH68/JN5xackQbWscB9ebjny7yLpKLx\nhKbQCO7O+bm5J4ux0+dizaYYCtRjcqP1WFtXTACHy6wjYnkBBfLlzEjRunZWlRKxye2Fxa4sLChF\nb8/pngUR9BO4tud0/6nrJ7fzFWO4bDqbp7Ol68edsXNfJqSZ1a7c/IiJCLxeaK5DJgcrO5pU3JvN\nACTWHlRLVauqUx0p0hGDy6xglyncdzHp07XQtmojdpk2G3VKkhLHMwUEbnkNLGF9PJCjVzQnTh+7\neb+7Y3k6l8jx2NM6VYEAn5O7rcL8ZL03fRbxs1iJ9xlcXAMai+ZLJQqCz7vKOo7aPlrPNcci6zLa\n9xwo5cMMUDerrS6/EfWr0iLYtasuW85eko/0XYDXcw1eoedEF1x7wYIFCxYsWLBgfyX2FMnm/TDk\nEcZkV92tMbHN+Gybb78sk6DKs5xrrd/GXn/ibMNQ/3hwLhHKTs9kS0VOmDyvyBUjXHqc3/HYORbY\nQfCuMkf/tPSmLyMQ8agpSiJfV6xU7nZMqyVlnwcBVdGcbb3BY5LpEVm28T2TwpW8l8kmOS/KGDlz\n52tavjLKGiUi/vB3ex8Gywr0qWsXVUlGQExWK+sTNZYp0OM8WkltaCpVdmaZCCVxE/qIXQ9LLqh0\nAhMgewQ+lBNDczRP1giq3Iu1IQ2KhNTUhix3IfmsOlwgn9Y9qG6LiDx65FCfhIInYszjwyuGML07\nd0hIWjqE4YBI7Hu1O+/nfvILvuw73/4zEREZE/qoCvw7O4ZIPDhyu+QptVXj7+8/eGDtwXjfuOrI\n2eu1jc187vpiQjvtM6BKEe00j++d4Le2q97fd/2khG0RkRV23SWhafceuJx5StjdKQ1pOTtzu/79\nS0b21iz0fO8+vO8QsevXTJ08A/pzPjeEc4Tx5DgF3enzLllXI48CEIlY25hRUEKk5GBC6VLsqmMC\nk1JF7nOb46dnp6iTVUrvj22IVAIkuqK1xoek031SASaoSAFfERmWM9G1oyXUbzx2bTufObQkomgP\nDd1PSpawQN8RqtHp+svrZNMNvnO/2SRbK9lb15qO7rUWY7Na27oaQ8aG8/UpiJZQnygSxtkm9D5m\nqYGuGcoP8NSItF2UL+4REKnRxO6/A6DEEWelQJDJsqO6o2k8ng3awcidD8ZB+xPqaw0e4DgAbTZf\nX7NRtFukabotZYzmqddDn3XxFlkhlv9ogcjytfycoHcCrV6ZbSqXDwK6tsnpkAVEKliwYMGCBQsW\n7IIWXqSCBQsWLFiwYMEuaE/NtRdJNJCsVQXwmoiQCtWxFpSHXQeEQcCzLDzh3XisdzLUomByWhRv\nks4UAY6IlJ2o2jolVFRyHMOdqsa8zbXn60P/K3l2TQltl1mjB9K1HOzKrh2VPmpJb8eTF4eXdOfo\n1cW4mTxyoBkDFfGM3Fh6HMO+BWDRPDF3gybDZLfYtHRuy9mKkzU7CDZKNt2dqooeb3ndZ+dgCndL\nQdouNRLt5uRaVKh4EGSgujjtJmSdQj2+IX0UVSBmDbIo23QBJPFQsd21A31Lv02hn5OBPJsT2bmd\nubHemZq7TaH1Z599xpednjgXVDm2ZLArKJCvV6S3BLXpux+aUneOYISXPvFpERF5n767euDcbX/y\nr//YlzVIzPoyubHuP/zQtY+Iu5/+3I+5epwZKTaCu5n1zGbnzrV0FST6x4+NiK4upiRjLSbXrmJs\n46oBFbwmfAj34ZRU1JeA+2NSO1f19qtXHcl/Sa7wUxx3gxIKJ3ALEP9ZduBSachl3OE8Hd2TL95y\nKua8DpydOLcM6+3UmMc5gh0WlHh2OnVEdSZge4Ug9lhBPyejvuuxts7XNifSLQrgDdymOk5pwvcr\nAlsos4AGAyUUbHEXAQWrlfVngxChhnxV3s1F7rYYyW3L0rVxVW9dxKhOuNc4i4BOMTqvvxbzy/X+\nJL0hvwZo4vWBGwnnoCenrrXJIIhFXUvsWsfxrMpda7YJahr+brSQyOa9/9vOu5o59/FDZC5w1YMb\nbUtAF2vl6RpfkVu8aTYzCpirblOfUecJ00K2MHC8tmOW2Nz17tMtrjM+n6fl+GcdJSjWcxBlRPu4\np3pql8VEH9E+4eeJp/n8W8BMAZEKFixYsGDBggW7oD01RKpv2yfeON1nmnHIIXYEkR2nStnbiG3M\nwO5bzSdH53uCvM4vq6p2Sy/h0nebxDblDvfR5jso77T9xoEukjwRVtox6RBEwNXcykqECWccQquq\n1BNrl6aYaihfk6JI8RYSqe4we9qt6V9M9lZ5hAERUvMVUa4pJeCOtuw0eiKKRiDIrmrb/XSQeNCu\n60nhufOI1CaJNKa619hBJ1SnHPmSqjX1cYf8Y5z/r8oG9W04HLp236Up7SBVCoEmispDJDyf/G6e\nSMHIhdXFtkvPIMkQjfEdSVgkrUOQdghpGk3c+WbnpOKNYISDy0Yi/+j9d0VE5Lnnnvdlr0Mp/Jkb\nhrAUIPa+++YPRERk98BC82cLh5YktK3823/np0VE5K3XX/Nl4113ju7c+unNt1yuuSKnMHmoaGe0\nIz44dPnp7mI3zeRwzcm2WFlo/q1bt0RE5NFj231f3XFk8PsrklXADZjwTh/I4bqysSuBAFZAhAsi\nm8/v3nXXonl986ZDrj788ENf5vMe0jw9B4n78Irl39Od/opQwhwSB6uF3RPnINRPp65do9LGv6p0\nXg1SK7i2EIk8wX2VU+5MJRRHNMcVxeYdvhLa9fiYyO6KhJckIdECTWOpiffOXRtOTk3OQlFqDqjo\nsJ43hEgkcTT4HJV2D61Sd/0zymHnA4RqO4eqvSeUqbTT9ZfusSJyfTZ4FOh/aFdP8yUCsZz7S4nY\nfJ90uC4HL3U+1yitiV00+BSxNaNv3DoxEGiAJyShYA8922pmc+i4QL/T80efrUnDXhK3/iyW1p8e\nkaJ+auA5iPHKEBPSrhkruA1ewICe3X49ZzRTVJKAA3VU9oZlN9xHDtmJhoJN7FHA8kcIChm8KGyS\nzbMnSOw4wJVZicQ/JFBteOVgwYIFCxYsWLBg/1b2IyFSt27dkt3dXUmSRLIsk69//etydHQkv/iL\nvyjvvfee3Lp1S37nd35nkCsqWLBgwYIFCxbsr4v9SC9SURTJH/7hH8oBJWH96le/Kl/60pfkV3/1\nV+U3fuM35Ktf/ap89atf3fhtkqUD0q+659ptiX+ZAK0YH5O49bt+k2w+TNQbDX46JJhFg08+70B1\n9gnC+PBIKvH5MZkBOqwbo4U5XHFrUkJeFXBjcULFTBNfErSpKq5EivaINhVpP6rLdACPYir0gzGJ\nBp9sTKxUV+E2Eh/riMxBwJ1MzN0wA1FSXbEdKcZrsuiG3GgJSNkRufYK756x+mkC6y5iEiNIsaxA\nXyvc639p36GfaiJs5iXcojSttHqDPMZQNi7I3aLu0Lolsm+uLgBnizNz++2sHIk53rUTn4CcvJzb\nOSpIurPLaDJ2ffz4gWlL7e86t931Z8zd9/DIfZ9Dv+wKuaJ0nFpyxf7Zt74hIiK3P/OKL7v3Hade\nfjo398DegdNxun37RV/27a870vrO1FxFEVjBqtlUk+6QXr+me+jo2Lks9nF+EZHTR64sIqrA9Rsu\nMTAHINw/cu7AVz/+qi+bwQWn2la7u6TOjfmyt2/uzjt3oABPfa3ugXNSB1fCckaTIlcNKlqT1iso\nkBPZd2fX6WZV0NTKSHepgCuQubkaMMD3mq6TTCL3+lHEU19hjmfkAopWSEKs+kB2uFex5jVB1wIm\n+8+gAfb4gblbPR0gYne7ayPfp61PjKv0DLtWCTdfZ8MkJwunhdaRa0913gZrV7tJS1B3HLvlNAjJ\nJ8ump6SusUNiNXS/qK97uODifvP50w4SJOs8sXWiXqsLEIEN1K96fSasq5uNn3XrpevXeWpuVHUH\nZxQUoME+THeooSNVN7ag+udCrM9J0vZCfyYdPzv1mUSq7PhJ12662zh4ymuL8XMfXavrRTvg4Oip\nOAAi5q/c934uWKl3d2/R+5Ktz/rt9iO79p4Uufy93/s9+cpXviIiIl/5ylfkd3/3d3/USwQLFixY\nsGDBgv3/0n5kROqLX/yiJEkiv/IrvyK//Mu/LPfv35dr1xwh89q1a3L//v2tv237fphzx7/wblE2\n5zdD3eFxXjX/5kovdfqWOmCUD99Ihy+BqmydPFk0zNeXaKgvIydbwj9R55ilC3wIJ76j+mpOJEbp\nFucIoV3T7g/k5GpNhDnsdFPKf6ch9tx1pqyub/OkDqxq67Rb8JsJOkf/BKolYgT0iNjWqujNoa7j\nyCEM65p2HyBgt9gFdRWr/iKEN7bdehHpbpl3elpPan+iYbpEFMUuJea0Uk8gdxGjVSqNQOetsHNK\nCmqrhsSnRAv1ZEwr0+5piVB/hjxtO7HLKzbqDK372I0XRERkSUjP/Q/d/RTHVs+ZojkUbKAw2Ypy\naOmYff/7r/uyVz7pkKUVkKD53OQKskSJzbzTdGUf/uBNuxZ2yTdJkkHr9M0/+RP7baTh1HiKAAAg\nAElEQVREUZrjJw5NUNK5kuRdXdxuejQ1asAMiF1S0L2m0iWECJTIJzhhlARh/w8eGkqnoGwCVInX\nhFc/8QkREfng/bt2PHb9x8dGoj6EGnzGZG+QbJdzy6E3B9qc0j2h47O3Z6jXGdr9pFyLiKFkvHTp\n+sfIjQbIcE40XZ8mE1OK19/Mzi1PoaLJupwxWqAK09GWYBvO6/nGt13wwqMTQ6QmUze385yka1T2\nJN4MMtF1ZSiJgvWXJFkUpapo/FXqYhBRFOvHJko4SF6g8jiyKU0gmh2Cj1dUixD5VrYFO8lG3bNu\ngmuwAjeyckC6gonYnsS9BQIpKChAgA4tSVlfkc1FY2V5rjlOKaAGbWRPkEdsvHeGpXMQ2MEOIUWf\nGj6HPru3yBQMJGk2ZW+0v9U7MeivRo/f7OvBK4GOK53YvC6bSumDzCvtgPK/YT/Si9Qf/dEfyY0b\nN+Thw4fypS99SV599dXB91EUbXULBQsWLFiwYMGC/XWwH+lF6gZ4CFeuXJEvf/nL8vWvf12uXbsm\n9+7dk+vXr8vdu3fl6tWrW397eu/I/11MSxlNx1uPCxYsWLBgwYIF+//SHr9/LEfvA3n+S3LtXfhF\narFYSNu2srOzI/P5XP7gD/5Afv3Xf11+7ud+Tr72ta/Jr/3ar8nXvvY1+fmf//mtv7/0zOGQRL7N\nFbcpeuvhOSbWbUO9tIjVTv1h+BwkKAa01xLpTklnrIXh60JQpEK13NWJJ8pT5aPBByOc/nxMRF3C\ntReRZosm6+xYs6PfTHwZwfXR9Uw2dTC2QfV0ji0E/L5RIjb1Ez4bYnZHcKMMgwJQXxq8DC6DjLTC\nPHzq3Y52/XaNepLqcyPOFRKPSYMM7jOGZz2xnmD8VFS9fXOOsf6x/0vHhHD8CPBxTwr8HTDtlKDl\nPNrU8fIQOKsi4zyr3LmsdnvbeGRw1bbkHlBi8+mxueBm0OppW+uTGmM8UJFGI6+QG6mCfkwM2P90\nYW7EvnMupoqVtaFplS6s/X/3P/yyiIj8i//1f/NlKfrx6MQSFF+97FyVBWkbqTtWFc4XKyPH7sCl\nt1haWy9fvSwiIgkxpletmxM9we93378jIiIv3brtyyYTV/cFEbvVla0u+PdIH6pDd1579oYve+N7\njliv95KIyPnM1S9NzbX33PO3RETk6NhcW1ModT8mbaXR1NVptjAXoPqbVZW8HgRgQB2c/U01CODk\nWqwwZ9mNuITeTz+nZLCYk6MtelOrld5XTMTF+egW2kW/xoXpPb39zjsiIvJoZRvm87GbW+WEyM4I\nnulTm6djrCd1t0nE73vVh6P1R7asieqWo0CRBJ6vhNb4HKs2q7erC7zv3H3XROR29Ney9vvzVbxO\nxPql/RZaZapxJCJSe/1CXnlxvjZF3ex4HXYme+u8y4lErkEO7O5VWgaTsmtoZA1U+WXzeaIrpGag\nYA59hICdJOV6brpANak2l3lNxWbzXYCzQiilwOgp1K9PtM8dp9/x8xf3hGyu/7IlKOnw5kQOb7q5\nncSxvPmv78hfZBd+kbp//758+ctuAW2aRn7pl35JfvZnf1a+8IUvyC/8wi/Ib/7mb3r5g2DBggUL\nFixYsL+OduEXqdu3b8u3vvWtjfKDgwP5J//kn/ylv+/adrBbVwXSlmEar0BOOZQ0/170w6G2becz\nAvgmU0/JccNwSSACHGrqCXOb1+Lwa0+kZ6Kovk13m9/JE9GPIhYmS/xrU0cfkCPdm/5oZGTDNbYM\naWYHaji1zw1F7MBWd7CcL0p3C9yG1nYudl63YxyVpmxsuZm4ngg/JkSqwC6mSmp80pwAOtewsnCh\ncbA0AH6TzLuaHu2hsOJKSZRcKVXR1XNYjb08BCNdQMmm1Nc+TJt2jroTHOx0I93pMqEe8g+d24Xv\nl1fseOzSpmNDC85BrI1jQ2nWmjutZ2XrzdyJisBqaLqIyPXnbqLMIUK8g28wFxjVWlfuWvPKZBr+\n8dd+W0REFpTD7XOf+ow7X2xlN3GthiQOFlAybyAnUFCuwfv3IUlAUgc7O44ofX5OCM4WwurVK64f\nHz4yYnmEHS7vnHenrj8V4Z5RHr4MKspctsYcygl9XEKp+3CyQ8e5Nq7XdL8A4aupP6sjR/KOc7t3\nPIqq84VuotnM9XueG/rTArk5Ojn1ZZf20WcUfq6I8HJtY3eCXHhT6ndFp1ThPN6ijl0RgpFgPR2N\nrU5zyJocn1CuRcjMFxPr/2wK+Y+S888BAfKyLny/uOuOiVi9WOs6YXPNey5I2j5GRw5I+aLrhJjh\ncioA33K4fK3q8DQoKZ5TlK9P1+6YUMKo0zylm49dHuMI60jc6lpPqLZmBegYJdxEn/R7fnZp4A17\nePx6RxWoa0WpyDuDdVnXWJaQ0XMMlIZEAyVoTcZSyDlhPQGc2qgeIJbzUdRRA4CGOWzR5IH3afN5\n6vPEcn+qnEPMvx1+ivzlefeCsnmwYMGCBQsWLNgFLbxIBQsWLFiwYMGCXdCeWtLiOI4HOlIK1WXk\nCmH9En8cPlnjwdwXTMBWLYhNXSpzD9KJFQpmuQh12Q0wPsV9qW56jQFTGhAoq9J6EiNOwYQ5/Xvg\nsVTIlODp1kHaDSXjVSi0ov6sQd7MO+snrUoMtjMJAXutoI60iLS+3H51lbYEo+rf7AJSTROGRFUx\nmHVc1N2kSGwyEGjBtQhiVrg9YRI5jvMJhUUkikBYJVK8uig4oEC1pbw+FmkmJVsU28uJtouTlmY4\nnuF29zlQ6ocPshnA6M4Fsp45t9CVq5YlYAlScrpjMs6KSjNhX/tpuTR9mFYTObMWio4n3Seq1TSG\ny7BtWHfGtXUyNrfT0WOnYzUiYvESbfjEpz/jy977wCmAtxTs0GOeHh0ZAVnrrgm1GyKd6vUPL122\n+qoWE/liNFnu/NxcW+o+G5CSQcZvI5snRw+d2vmlS84Vtn9gyu6Ltas7ySPJGgTsVW2uxX1kdljM\nzWWWI/mxEtxFSPmcPfq6xqVWOCpd285B/B9NzbXZVgh2oHt3jXFP6WZr0Z/Vwvq6REPYVTbBuLMG\n0Ompa1uNQIjphNTetwj05OpmI9f+6287Yu6sNzdunMGNtrB7YlxDbXtC9ziCRyaqMk9Lgronc6Fk\n4PB8V6Wd42gGtXPq60zJ5qTsnuYgm8e08GO909+yPJsuBf1AbbzauJa6ilgXL4o0iwG5xTTwaXA+\nXDfdpDF4svkWLSR+XkbqAhxED20GQCktRRX2ReyeTdjd5tcsWk98fTefE/ps7+g5qa63dksEHM/d\nbc9zrYre94OMJRqUNtA2gxs/YRekM84UoAE9g0TSnpbC9fzhMk4BkQoWLFiwYMGCBbugPTVEquu6\n4Rss/qw5/1u8ifSoUm9EIaQ+TJ3Or2/43RayuVcCHrwF939xGSENsgWlUZCqZ/kBn65nU9m834y5\ntzB82pGn2MFE9LZcg7wad7wzQLvoJT3NlVhJR2lELkY9TTfRP0bQolhzOLG0uyNP9kR27LwCOCFX\nivp0m7sKRgkVMdHrRtQp2u/cLt3VVYTINfGm2m7bul26qkOL0K6DiIW201JiJ6muI69fRiraMZCD\nLGWkb3M/kunWlciuet2WESmot6uaQ5YYgpLtOpRqRerYiuDlnMMPkgSzpYXVa3tSan/VYufMFcWY\nLZYOmYoIrTsDIvYTX/hxX/b48QMcbzIJFYjib/3gB75MSf6f//HP+bL379wTEZHTE6vnlUMgcOgT\n3hnr2O3uGol7CXL6nNC3EZSyRywTgjE5J6X2m884svu6sf6/cvkK2uVkCg4vmTTE+bn77dtvv+PL\n6krRH7pPcUMlIxuTyY6r89FjI7sraX5NZP85+qLnuYsJf/XmdRExdNUdiHuSYJKuVpSK1Z5xXxHp\ndg2FfCblthi7nHbpxyCtT8aO2K+q7yIipycO4bp+3VDCCOR9Ut+QClILDEmrnAqnRO0qV+eGsgIk\nGJ8a8zWjgBENiY8aItFHOv7WTwUCH1adzacV1s5pR8FLPb7vDXaM0WdJ1qEtLDWhz59BBNCGKeov\ndH2VJ8jonlQ5lQFKjd+kma7/dt6+3+Kl8ZIA5LnoNmUC+i0EdFMgp+ckBijifKpKaJfhJ1+XFRwU\n6Rci4Nsc3yTKC0kMyZPPSap7U2ugFL+6qDo5y4RgDEkmpUOg1OnMUNp93O+8htvwUJ9sGWO2gEgF\nCxYsWLBgwYJd0J4eRypJBqHZ22IOLa9Uv1E2yMMD5KLnXdoW1GnTz7lNmMuKFC3p2H/qG7ClTVv8\nqENVg/6JD3rj1c+I38I1rHtwERERaYngpP3TMb8LQ8soWTl2OyKVARhwv3RXMcihNETwREwQks+r\nO9yBqBq+bwcDhZrR632OnYUhLbaD0Czty9h2dT6rN12/WamT3C6laFtEO73eI3LMW8PxuFaTEtIA\nrkpKiJQiTRwuqxITKeUQS8FR6Ol8eYG20W81EljlH2YzQ1B2Uoc0jUfGPVHAdkl8nA4Nn0ztuDnQ\nlBWFhB9eBiJCvLHVyiEsNULic8phqPSqb3zjG77spZdcbr633viuL5tACoLBt1deftkd9+Zbvuxj\nH/s42mzjmYFfswIfp6a5VqC/HjyyXHc15BnKsfGGGi/cSGgacv3FhH7OwFGqCM2dAbF6FnkC17Vx\nehJM2N2JyU8sdLdO80pB730SOn187GQNDi5ZnsCHjxz6lND4TxV1JJhmBIRxCamDtrXx6oEI87p2\n5aq7LqfabNHHPeV6bCvX/iKxeaJ51WZnhhKWhbt+BkmGnvgoyuUcE29urfkHH9g4dQB4UgZkce8w\nl7BWDhvx0BQB1zyEjFbtjJAbjxCCRqVeEkYkgdyRJIc+H+qa0bxNJFoX2QL8KRouo7IxqSzZXPcV\nOSegz3tY2Jug+UlZzDcBEt6DG7Vm+Rcc15IgsHjPCaHPkN+I6EHVbMnnqnOLkS7lIcUDGAaemH4T\n/VE5h5rP4fPVkdSFclTJxaDzLyKUTiUpGGH1Eg9bdQhULsFK1BNTrVhqBYgsdV0zhSRHyZ6LLdzg\ndhMJZAuIVLBgwYIFCxYs2AUtvEgFCxYsWLBgwYJd0J6aa69tmkEuHU9D20LOjre4hypytyU4jkO9\nFYrb5lrz5LyB6qsS25n0qa41Do0EZNlv1jNKNsu6wTVkYBGRHiPAnf0gNFTl1rvNIjGosX2CMC1C\n8gg5KZsj7FhdTN2a6kYuNX8pn4fMKl4r2S/ltmrfkbtDYWQKyVW3YNQTtI0pOIGack6KuR0g7UVG\nhNFFP6ibnkVEJCaycY3rZ9T+BJIB7CpORuq+BNme/CNJjnBt6holm/cUkhwB5s/ItZcVqFNmRFkl\nz0dEVFZphxXCxJdLg6KvXHGh+CcP7/synR4Nyypod3JgAebppUOTU+jhvmAVZ+/lxPi3NckFwGWW\n0A++/73viIjI7dsf82WP77n8dFcuGwH5e9/9njsf1XOx/FMRESlIvT1GArQ9uMASknVYLp1ri8np\nKmsynlq7FjNX58mYcn3hullJRG3cgE1rfax5zBaLJa5pRPAEfXjvo498mbpir1677stKyAOcnBkB\nvyic6+sh5BVERPZBQD+d2XFd4ubHlNyH6nrNS3fesjSXYQMi9vGJBSCco386yjrQwKXRdTSeyBmX\nUk42n8eO8vntXXJ9qwrj1YIkMTDZInJt1eiUf/R//mNf1uNeLDj/XaQZAJjEq24kCvJRRXHc4x1J\nEywq54rdj8y1qK6onu7dVLNSsMYLqjI/JfkFuK9G5L/LEw1eUtI3B9Hgk12BoGMMJUlcnTlPXoZr\npYRftFuCnDSMv/H3H+XQBGE+pf4y+Rl2Aav8gbV/vWo3jtPwf3aBR08+qAb1U7kADiKK6ZuhsdRA\n79dYUntXoji59lQKheuhrlK9Fq//GmzGsi6xUjto7DRAh+LUfCBDMghKgio9Xb/vfzjbPCBSwYIF\nCxYsWLBgF7SnRzZPk2EON/3kMn3730Y2H4ifDUUVXeGmIJkKMmpoZLcFrRrUMVIBRzqtaFgpX0oR\nLjqbf0UlYp8nOSqZznY1HtUivQLfHL6WFx+jnZ4eR2HdtUe4qD0jkOdbzQ1Ib/C+KoOGueMHsJ6+\n/W+Gn3ISO0MiaOeEwxKqe/IEibAgYnUOImqW2Y64wq6WZSJ8pC1Vs9TdB+1mtU68+9Pdnob/siSE\nl7AY7KCBSBIRU9EnJpsnEB8kgM3nGmOO/xx53Pb2HfqkudRERE7yU9SXBTQdmqFEfBGR8UQzlNMu\nUbPaU1mM668Whogs5w7NiHVXz2KlkfaN1TcBmvHBB+/7shuXHTpzRkjL9es3RMRIryIiJUjply7d\ntOsj11uhYo60M1wCrVtTbjgVdWXC+nTPEc8zGmu/w6Yb4PFjF/Z85cYNX3aCsve9WKa1/8qBQ9ha\nyit34+YLIiIy2TGUSLYgwioS2tY2d+/dc/IPBeVOXCBPX0vjNJk4RKhWQdreEIQKCO9oRMgtRC3P\nSJJCNUNiup8bCIxy7jjN08ioV6XEf0gY9BSwUO64dmnAiohIDjTxzXff9mUxWOYp7eR1HWH0o4sU\nYea1SPOkQhiS+qYC6saKENEK6C9LUmB9SgbXd31C8QSyxn2f09rZou5ZDwQ9sramKuC4RVYn2vL4\nkQFygzHj9UfzmdKzIOqfQH/oBvRrAa11ivQzsVrzkw7yeqKMc91p/cYTW3f9tWicNO9e6vNVMtKE\nT/bSaFvoGTNA7PQaGhS15XkyEOTuhs9dJsJr8MIg/54iXYSm7+3v4LfkkQAiGtH5fC5EWqfjbvP9\nYNiOYMGCBQsWLFiwYBey8CIVLFiwYMGCBQt2QXtqrr1eZOjj6NW1YkwwJZ0NaMWA4JKBsuqmjpD+\nPcjJNyAoD0S3t2bS0dMOdZyiQd1EiEQtm+3hssgT9dS1SCrO/lrkxmyVHE9QONDOgYqrEs/ZjZmo\n3hK5FgFjdyqGwm6/eFMdt/daIHQcPlMqTLw+CEGm/Sah376kvyOtL7RA6PCidC6rcmSuhdW5c4Xw\n2Kk3KCFioZalGc8nB2lvC17IlPQ6UMzdDFjwOlY8d3EtdsvG2WadekDKLUHWBdxy3Uxdu3baBRSw\nG3LZLJbaFzQn0GlNbf2k7gh2H2tddvfMtaTuzsbrDpGKfgfCMiHsSqJOUxP+OT137sFXPv4JX/bR\nhy7X3qUDI6CvoKJ9emwE7NkSulDQCiuIdJ0iX93BZSOWV1A2XxMpfwd58tYza7+6IFhvTO/Z01PL\nyRcjAVu0dCTm8a7pPqkrkF3bNe6h45NjX3btxvOubqS2fgICeLUityRuclVRFxE5uOLcosdHlCcQ\nLhjVB9vZNVeo3owduXu8zhNpC6lLZzUIHtB1wvpkBV2uvLQ2TuC+e3zi2sNunBJuyZQIwztXXfBA\nQ8TiUa4K5BQUJJtrjAZgsLvlSfIwu3h6cBDmtemtpZ0j5XeU168E2T0ld5KKrUe0eMzn6rIiZWs0\nbQ3VddV14nqyG1HpCbwm6rrHJOau36SgmPYauRY7dW1iHgzWUO0nXsN0PecgFjfu7LLy6zQv/Fin\nOwpe8gkg2s0Hqq4P3RZXFyuwRxrYQmuiz13KmlFP0G3c3yr4x/QVUDq8jiQR0bXv2I0aDZ+1rkzn\nBKuob2o16m85o0fX/XDMKSBSwYIFCxYsWLBgF7SnRzaP4+FbrecrM9l1i0wBIwFP2FBtVX/L5PWh\n/EG05bfdlmsxqqW/yQg56z0plELSY0VYCBHyb79eHtd/p+2OtqBEMe2gGqPl2/Xxm3bLLoHzatWQ\nO0iBVqUkYaCE6phJ7FsSDCXJJtKmyGJPZT5Mn1SEFfUoSGohEg0/VVkBO61uJrmvdSPc8A42VRIr\nyUnojpEzmMe60yBJBpzbz42Wzyv4jtulodFmECX3ZG4Ry9PHKs6iebXEkMjjhUMnSiA9p2tDOlK0\nqyakQxGWngjIE90xNyS1UGq7DaXQ/h8CwSDWArnjPry0/5yIiOS5EVFXyIXXNCYTUGBQ3nnnHV+2\nv+/Iy0lsY3f50OVuWxF5fA6ycwU0bRRP/XcvvviiiIg8uP/Al5UjxzJ+eNfI7geVhutbvx4dOURo\nd2oIVwZC+2hsufvev3PH1RP9Obl06L975dVPiojIG98zpGs0cW0Y79h5333PkchZaWEMYj3PNSWx\nXjo0lG4N0vaotH6fAeHbP7zm6saK1UBnSso/9/77Dv1rOU8n7sWKQt1jrDEcPOPVywmRqPEblaLI\ndyd2Xg1KoLD+Oe6JncuG5u22ro/rlaGpA/IwrFfknDM6YH9fI1Ch4zyEmeuvZWPnnaBOXUb3Nao8\ntaGTeavrPpHXVyDg71h7mtpDzO6DkJkIQSQZoyUq10Dt03yiA5kS7eOWn0lbAqVEvR7uxFW7GUQx\nIJFjvDiHXoY+aVkAXeUHEn7ubgZvZFif+VmoqKTO54ieXTonWsorqBIXLJOTpBrsww85VJADWnz2\nANIp6PW6m89plSkYPuvRFtau2Xj+io8KGzy7MEzxAGf6i9873LHBggULFixYsGDBLmThRSpYsGDB\nggULFuyC9tRce1EcScfJMDtVJ6d3u0j1QTjJ7A/Tc9iE3waJgWPVm1B11C1Q8+CfTWVvJZ7yedUd\nNnQ7bpLovAaT18eyb+J+WDcRUgWPN8/L8GQaqQYQHYaTk7fRJ/pUJdyEvlNYmjWLvBIxu7bw50B1\nFt8PVIR7JTGzq1Zdi6yfhfOqPghBxuq+4jqNxs5l4hMVi0irpHCCexUyHyScRp1Zsbaphwmc2Y2g\nCYpjTrLabGpBJY2S7TnhM1xlJHimartCxMU+cW6uR2cPRUTkaO+q/+5StK8/9GVjqNI3jRFrte5l\nbq6VDslIu94w8zV0uWrSNqohqpOk6tqzdi1XjgBd1aT2jXblmfmxDkD2/uij93zZIdxXy5WN9TnI\n8+fnRqyeTJ2rrFl2qI+5ojRB8c1nnvdl7739moiIFIWR3TvcV0wOHYMUvVra+S5fc6RoHrsJNMCe\nu+3ceB27rFDfcmTuRp0fq7W5MT72oqvf7NRI9Cdwz0W0ThweOGL5g0f3rAKoTEXk8cMDp3O19m5x\nu1YJV9wpKZuXCFg4PTIF+EYbSfekajClhbkFlYA/2ZnQcQ3a7ca4pO920e/jfdOdeu1DN+4VqX1P\n8NsqYRcUXFWVtUcT3TKhXz3knpS8JZH9nFzLZYykzazZpvpIpLeluZ/X5ALN4Y9tO17jdY3Ffc0k\nZn1OkYp64/kYmxQAGRDlN9dTzRQhPQcKuHY36orl55Q+a2hd0yAqXuuNI8EUEA1y2nTZDTSTvLYf\nB0rpcw8BAFQnJaAPYnhwfV471aXIwUvqFhRKDL9GVo6+ZwrIMCtFRkFE4rXl2LWabRzXYl7z07RV\nfSoq0+cDJ5LeFjPFFhCpYMGCBQsWLFiwC9pTQ6S6vh+EiyceVeGQQyU2kylKxbmG8Ca6DasaKoWr\nTEK/8Z08QUTnE/Jxkd8tEaqAAwfokz+OdlMtUBJ9vd1CMBwS8EFYJ3KeIja8S2lwnkG+Ks0XxGgS\ndi4qbJvWFBrsdxUcmgqCH231lDDLRDzd/Q3AN1UnJpRAd0SxEAFW6+x3BtyHSuy06689UZ7quWVX\noWgjyx/oTizqBlsn1E3bR2HI9RPjJUbYHPQT8vmRALSsobacFxySOySRuuu6ttXx5s5U5Q9GpGK+\nAil7Uhgi5MmexCxV3jUrSzf4vuusT1TOwMtFEDlzDbmCS5d2fdnentv9L86N7DubO4Tp5jPP+rIs\nc+ftCKXQ/GwdbZ1T9MnZwlV4n5TtdSxOjk0uQJGmGSESFVSXxxMjkX//zT939R1f8mW6Tty9e8eX\nlWjv3p4jmZ9Rrr31zKE+lw6MgB6D5L1Y2Ry+f98R3/uWcp1h3u9RrsNTIFYZBSWsgNhFlP9ujlx7\nFZTIy9IQsRqk4BGhZDFIvAUR0E9Xrh3LpQUv9Jonj+T2I6yFKk0hIrJzyfWjqo6nFDDQANU6pzXp\ntYdO0XwR2Tn60v2mJORmrSgeKdBHmKhrki5QFFcR9Cjl/f6mF2HdOISV87rlWCd3J1R3yBmsZlZ3\nXTvbnpArlWTBYj8gVquEDQXRKPoyRDogP0Lol187KXhIA4D6fnNNqnq9X/k50W6cVzNasEyJSm1E\n7E3xEgKDUJmNskbXIKqn3rtJ6tYdzlepi0e0Re08zqyfcmR+yAseT3cNzv+YYj53HDzjvUmbfa3o\nWFna8Yqm9oNHPKROBl4PlVOgTBV4ZiiCLyKyItX4bRYQqWDBggULFixYsAtaeJEKFixYsGDBggW7\noD09ZfN+4MTxCszsMtM/+4EHTN1om+yvocSUuuoYxhuSkgeqs5rIl7WoVLOD3C1eiXVALNxyPoVR\nB4qp+rmFRN/qOcgV2G86K+MUkDG5bNonVF/dP5pxl3U89G8QBomIqr9MWfdINWOG0vJP/ILJjqzj\npTpa1sYV3CGtcU2lBNlTXYUVkY3VzcndoDpHcVzTcdrX5EaCG5VJ6fp1W2/2q/ZdKptj0rN7GD8d\nzAnUqZnYcetVN/jOXRiurYqSlqpWjdY7MnjaE0vJZbu7B7dLtdl+HpMMSZvrFRPLkSmA3MLqKqvh\n702ovtOpO8eK3Fh57iDuy5eNFH/5siOWf/C+kc3feustERH5O1/6ki97+x3nApuMjbw8Ozsf1CMv\n7btMoXpqf7127rZHD4xYnRfu+gkljS6hGTUnBXTtp/ncdKwm15xquLo985xcDDgHqz5rUul7D0zb\nagpXxXjX3IgdXJXHx1bPGgmKmb4wHTu3ISfrbjEHDw/ddxzEsLfv3Kx1ZWVHd11dDqAwLiKSVq6e\nPbnFVFtoSW48Ta6ck1vs/NTVee/ABS/s7Fu7Sqy/bzy29n+4hOt1ZPO/6Fw/rlnZPdMk2O1GWUqu\ntShRAjrWOlpXVJcsp/s0hvs4JjdiFqnav/XTaOq+P1+xW15pIayAD3dTonQTVt/zHxMAACAASURB\nVNiGK4iS4aZwszXkWtc1JqJ61nBBR5xcHm5+kvvygSSacH3wnMC63jQUbITPjnSc1C3XckQTLpvS\nPd5vIcSoWzIlvb8Y4xQn6kam/sfpmEQ+grZcObLj4G2TPGe6gzuOk1aXaFHXWD3Vbav3MK9hoxTZ\nFoiC4rUdhV2rm8rq+kxMWCkdfyfsKh5ZcMs2C4hUsGDBggULFizYBe3pIlIDddLhG6ceI/KEirYi\nAiw/AJIbh3pvJ4oPd/hM2NY30jjht39Vx7bTahjoQJBAQ+e3wE8D5Aw/2oamPfGzwd+DvELpFkQO\nzeBcgpq7jvn0SuKMUyUnElpTK9mcpgR2S5xXK9qyg9FxGiJ8iggy2RGnZVYkiOdKOm8IkapqVdEm\ndWAcx9eqkLMwiRgRRD9RnyjxnSLNxSOXse6W6fhICfOEvnWbyGWF0N0l5XqTdDMkuNbdUc+oF0jp\niUNkDndN9XoSA6WoDFXxIcxbFPgZwdR+5Plv6J+Z9qcfd54vmuttamTzqlJZBTvufAZS/MjI3kcg\niN+5Y8TuK1ddWP9H71rovuYTy7H7PZsZ2fvgCojarfXr7dtO7fwqJAJERB6dOBL3fGboT4ocetOJ\nIT1KQFX0TcTyOSpasHdgYf3H566ei4Whes+/4K7P68967canKAxNOzq6785H0gFSujqprII7ziFy\no6ldY7LjkKAzXH80sv5/ACSsJVRjByrrE1IgX5278zbU1jXmUV3ZtUYjd29XFWVAAIo33XPni0lq\nYj535PWWUjCcLlywwSArBT4Z4axw/SHqD6Jyy2RrVbHWMHRCcDU3H92nGRCjnNTRFR1JCf1I0MY4\nYw+DJurcoqyukiy0XOlVWUVem8NrUk2ZB9QazDEmqqtkTFXxGufuBZ2T8aBdKqtCofka1r9FJogJ\n+G2n6wj3p6I0tE6MNZCFZCI0d2Gi52WERh+2VqLE7yimNRHniyh4QbNstBGvsREfjnPrOo0WRJtz\nghF5T04f5DocnkNEpFApEKq7vhdkRIrPt2T5YAuIVLBgwYIFCxYs2AUtvEgFCxYsWLBgwYJd0J6a\na0+6octOUTx2j1nSYobsVIvGftt4yJAIcCos25G2i8Jz+I6hWDP2rW1xLfqTkRaKakBRnXzCYYJM\nK0CGnlDe8XkH/iacBES4nIvgiilJ28gnYySypWp2lFQn6HOowngcmRaRJk9lKDSLtqjY9krAZAV6\nTZDJquAOvmWyZYLztaSjVDVDfZCaEs+qtk1EboRe/XKsowWofOAw1S5mHatq092qyruqVdM1DPGr\n3he5h/tN1d8KujRrIjurKjrxWiWFu69IrN+9ppJXoqf+gop1tkUxOWGyp+YsXlPSWvQnu5E8oZ4I\n1dOxcxtV9RaCKS67pKTJt178GK5v0L4Slo9J7+n6s7dFROSdd9/xZedwfRZE1K0xjqu1u8alyzf9\nd0py57vk+HiG+prL4FyT/O6Ya3EMovi9+x/ateDmunrtGTtux+kxTfedO60iN96ycn3Hbq87dxyh\nnhMJ7x864v2kJA0stJFdm9ruOLG+O7zmyji5s05PvRXXNWks6ZJNivmXLjt35PmZKcafo59qYjF3\ncPdOJzb/KiXjUxtHcOmdgSj/yX3T0RIQ4P/vb/++lfkcv0QBiDWzAbu4NoNsGhCk45TXTk2CC1cc\nu3FUs4na3ymJm9auCGrwPWvWqfuesx3gWhW5lnzfwlWZsi+oGbodRcy1tC2RbtfQAqAUDFonvWQT\nK6CjLvqZsD5TvxkUI3CfRhzX4qka1q+ZroUDdob7PiMNJnWzZxk/d7F2ZQiK2qJOnqasBaXjZMep\nmzHihPMRqB30KqIu4qajizz5eKT3hArrdJlxoI77aFnbTSkoVPkWtBAmoKtYZEvXiLe8ArAFRCpY\nsGDBggULFuyC9hTJ5vETRGwfV+6LfAg9IxKeMMY5hLaow+or6Talck8EZ7K5bquoTMne/Abb6o6E\nFKNVHXWwd94M3dc8ghpWH2/ZBRWl7Ux7fVvnfG14Ne6JAK0b/NXK+qmAejLnGlJkS5vNOa8ESExE\niEyUq/wDkSOV5MokTiUAJpvTid/+SyAXldgOezFf47yqME5hyEDJkpRRIs2rRFuUtZKtt6j4Ekqo\nYzfYfaiyOca9yAh9gqL4mpAeVaJuaQ+SArGqZ1a2UJSUcDKPLBZW910QtK+lDlW4uX/N2gBybjfI\na+Xqe7hv6Ms5iMWcf05VyVNCfzSQIqOdmyJRsQ/AoHsSiNjBVVLnnmOHR0rUJfLZzWemdv7KSw6R\nun79eV/27e98x502oTBxj9KWWkn/nSJnrGx/eupQl+efu+XLVpB4ePjgvi8r8NvPfPrHfdnjx0fu\n+rkhRzpPtN0cWFGU+6gb1RdoxnhsyuJrzLFzQv8ePXD59KZju9ZspogdhfrHjii+JuRcSclKmI8i\nu08VQdvbs/Gfnx/j+LEvOz12BHzOALCYuzlRkSq+5rNMWPbD5zMD0kEIZrzrrl/RWqMk9obQZJMx\nYQVw98lyDnZ/UKBEP5QTyFsaE+SaTCgPm4qs8+rToj8HwTuxIhJ0INbgOGGUROvmyjg4RtGUAarV\nbKLv+nXD6BMyCnSc1w+IHGcU0MsVijTRcyXyoBKR0/WZQP2qiKwGh4iIlFgfBkFJWDJGNE9LoEkZ\nyUmoFEOaamYJeq6gQ1k5JwKaFDHShL7oKdonUjkbQiQTPFvXhNI12kaVNaA1McpUaoZQetSppWtp\n8Bgry+tYNIO8u3oN8rBkPxxzCohUsGDBggULFizYBS28SAULFixYsGDBgl3Qnl7S4q4buIw8BEtY\nbL8lkXC/RbPCVMz/YjceFZkW0ED3yX2kBA9muSZ0JGgVX69J90PPxyRKPWHCulTq7lEOMxHWCxDx\nOPGiJ97HDC1vkvgUHa8okagir0VJBMxUld1d2WppUHwMjJfhzBSuskG/ihLWCVoGLNvTdNKxyzm5\nMM5TkPsEOVhltnRuoZZcduoC7dgFqtA6637ApRBRMl69PvMqY2iwcKFCxHmp5Hz7LiuUHEnEUpBT\n5zNzbTVQQl7PGcZH3QlazieAm9dWgZ3Ukb13kXC3J2/rCsmA+96I1TmShi4W5ka7csVpT9398CNf\ntsVT7lW7BzouII2vQSjntsYaMEC+kJs3HRn84UdG4n74yLnUTh/f82X33ntbRESee+kzvuyF27dE\nROT3//f/xZd99jPO9ZYXzi11iRTT33jDJR5+4RkjoKur8tEjI7Y/+8xzIiLy3ltv+rLRFXee6WTf\nlx0fOxeYJl4WETmEKrvC/gm7wjNMhsjcIyN8n6XmRlOtrg/umGuxhIt4NjPNLKUSXLpkdZqdu3k0\nHjN5X/WmnCp4RG6n8cSply+WNv57O055/Ozc5skESvFHR4+sParZRPezErVHlJi6gN7WwXXXh0u6\nJ8do18GhqajPPviWiJiLT0RkuXR1aUkXTvXgWNndu9Tofl7pb7DGRGtyGeK4UW795YNRaE1UcnBM\n/iZdn9ndk3hCM/ulsLYpYZxcZo0GIA2WJM22QGu9UhU4GTIuyxQEvd9i0ilSTcMSbkx2YxWlKsZv\nkqjZtZ+nm3qHWr+YSOGdf2ZYH6dIOM5ZNnwQig+2oVcHz82nbAu90jKIWoHnmD5XhWpIjy6ig7Da\nvPaBu1hFbmRNlcF0D6W0dLR2Ks2DA5p0znKgGtMhrImb+olsAZEKFixYsGDBggW7oD09+QOJhgq3\nW17pPKoQDwrxaUWeDE6kwF6lE9pN6MZvujmmEaGeOeWLyvA2zSrCMXbznH/OSOab6NcgX56GugOl\niomwngERSTNrQw7i+WpJRMwIb91Miscw8s61U5Qu3aKAjXx9MSFDNcJlmURY6y69sTd4lZBIKTTW\nt5q2aYp+5LQjHI0cUbWhzmtRz9kSSsyciE+J3xldH1IHKecQVKI06x+oTAYNsXLWed5FIBGOxq5s\nskuhxujP8Y6hD0f33U4wnVo/LZSAvSbCIs4b13Zce47d/6Gd7+auQyd2oYq9bknFXHMyUl5F3cFy\nDsNH9x66MuqAxhPrbYwVRYloTFRtOsYObnAOoB7zxur0zTvviojIg7uWa03RoRnltcvQ/99/15Cr\nn/qpnxYRkZdeNQL4e3dd3Z971hHaH1AI/2c+9TkRETkhpEsJyAUR0N9/30ksXL1uKIm246133vZl\nVy475IZzzY3HDglcrl1bzxfW149OXF0iCqHeB2K2s2tk7/WZkwl49rqhaR+8830REalIRfzms454\nz0r9Ozuaz5AUoHEf5YhOiHObL+fnlrvOyty9c/Tg2Jcp0rB/aOibAB1hEu/BNYfIrSq7/s7IzckS\nSuxRc+6/i2OnKP/aa9/zZcu5a+N8TnkdgSI1K5sT/raLGc6B1AEFoKRALDSepmVpANF5Smi6KErC\nUisqE2GXUldAzHnTlChNEjv6zNCw+qQypKMEEsbrv8azcP67CPWMBgFVymJn9Ml9MilcERH1SHCw\nU4W6sGJ5Bxg7Ygl2eBhKkr/RayWEXNUg5VekL9BqEBKtEykkW0bIjsAomZckoueZzyJBz78cCC8H\nJaicRUNrzAo59mpaO0/m7v7U02k2ATaWetA+ZKSpwXMsHcjUwBNEfZJi7WbyfPyX6B8ERCpYsGDB\nggULFuyC9lRz7fEOQvkDA7Rgy0tg5AU5WfwMcgb01qi53obXGEoSsISCipBRZLCFdVIvrcErygtC\nGpALjPOaeaHFLTpvKp3QUlhzPgZHxzafkuXwBxPSpjmmOFxXEZ6UZQI8jEchnLmGlUJ8k1GdRPlg\nZilC0Qd8LN25cA49/IizipfY9eWEiIzAvUhpN1UAAVuv3I7j8TH5r9cI4acxaWLsKgilycbgKNCm\nvgZywRw1nVAscCcInR0doP+ntPss3WC0azvHeIJd/crqWbTufIw0jLH7ykm4UoAmXt41gcO8zfBb\nV49le06Hu93iJGOhO3d95aC4+rm/OSec5tXTT3eNzZyEY4Q9V0AQWurX+cLt+nramp2u3GAfE3Lz\nre87Hk5Ls2e2csjJp18y+YN/8S+/LiIif+Nv/ju+7MOPvisiIvnYIR0tIain9xzn6IXnrvuyHvwZ\nDuvf23eoy9nJkS979qbjTZ2dG5coBep2sGMozQp9t3fg0KTvvvYD/51Joti8TtD/C9oRa/8fPzTk\nTHlGe/uXfNlipXWn8cQ9Ga2Zy4Hwa835tjYUKgMSUJL45+zUtXsytbHeP3DXvfPue75MEYGioPmP\n+2PvktWzhLRDozItJLUikEsYTWyhWt918255bvOvA+pQUz8p55AlWXw1aDwVJdEQf/ZIKDeGc5+1\n7SaX069nA94MxE8T4pyqRjM9CzTUX9dzvl880ssiuQa1WZnKFNBzyvKP/nCZGOP3DKVJRGx9bglq\nV7SEz1HAw8FcrixR8U0rayHnwmNswpVcTzxPe5UOsmutcQ7mktW1iolSPTF3ysKutYboLaOOi9pd\n9+TMxu5kDoFVTvIJS4G+DaQ+wBdcEtKnz+S+J68P0KcJidRq/j2ek9t4U2wBkQoWLFiwYMGCBbug\nhRepYMGCBQsWLFiwC9pTc+1FUTQkrG1Le+fh0y2hhwyZwn3FZOMELqO+ZgayqqO6spxcTDlUrMfj\n5ImjfXol9zegZVbMVjdfx2rjIIo2DUsMuOOU2B2R20mhxWJEJHLIFdSUr6mda14pahX+ThmyVwHy\ngsJvO1W7VmK7naRdgdg8kAIWtNWKvMIrxatG8aa7UWUSSiJ2jsZwN+VWp8nYkawrKPzWFP//6LFz\nCyScLxASBjxfWvR7QqrwLcieQn2sORk5AKAcuePAr5WdPXNZ5JGr+4Lg5KLQvIIkawD3zGRqLpvJ\nyLUrojDhFiT/ew8tdP/RXUfGfuWZZ111O3PjXB1fRX2t/eruZJfJEmTj8djq3sIdXFFIurq2C3Kf\nVXALZIUS0c1lJIC23/vwoS96C7njyrGFy3/ib3xBREQWM3NLvnffSTG8/oG52zIQumcUuv+3/vbP\nuN+CYDqhfHmTeAfHm9ur1FyDY8rXB9g/ITf+o4fO3fj88+Za1Ntzf2rX0LHTId7bNRfX6bFzLXIA\nxiW4wI7PTcV8b8+5whZnJOuBCdqKle3guNXC2jNfuvGeTEwpvYML5OzcfeYpu4zcb89nNk/GmGsF\n3WunGIvnblpewSPcTy2FhHcgKO/s2XiqS0NnfUb9Wjfut4+OLNhgdrZEuygnoHYo5x8FpSGmMg2K\nGcTkeCkKpWyQa8vTJ0hqRN1OlJQ0RjBGxorhcIflhR2nVWGahbp29Lki7ebzh91oSna3EH1bkwYK\nO1ioU/ptXasCO1NalIKQbhyv9zCrk2sZUwtaVUxnegxclD2p6KvcT91tPieHpqRwuAIjImfjubam\nZ12N4IkmsbJI1eZp7HRutxQ8oP00kC7CeGqWB6bR1Mg80RKJXeUkpqW58U/g+mfpiKZQSQR67ibu\n3hqNKHdk8sMxp4BIBQsWLFiwYMGCXdCeKiI1zE2EN00itnrkYJDCTnPtkfhjpyJpFCaLN/aGypQ8\n6oXGKIdfWrjdlO7MRQxhiQgRiirsYOkNVvO0RYOs0iCWEurVPxFC2ZA0Q5zjTZ9yvcWQGEhLynUH\n0ceUM42nmyiR7s55l5CPEU7tM35vkiM7Er/TxO3ZiMihQFUSIkB3QJ/Wle2SC/Qjh+lmQKIK7KBF\njMR30Lhd7WxtAoaLyCE3Ce8+0ScZ5frrapWuoF0NtmJVzSihkvytORGaNtpRwj6NawdpACYaKtBF\nyGUcg0Q5MqHF0dS1P6d5mgExq8RIuTf2HCn6CnLuCRGRNYS4JkFIRWKrmmQSkFdxtTaURIn/i8bK\nVksXzj9JSLhP8wkuXL9Pdk38cv/yi+789+1akwNH/C4nttPb2XVoxk/8pIlv3v3d33Vt2DWR0Hbl\n0K6375nEwY8heCMG0nfzxi3/3ds/eM2V7RtKNpliV89Ih8Z10C795Vdfdtfk43D/7ewY+tODtF0D\npCn2iPT6AGjhC7d92cMj1597h4Zc9Qhs2LlhxxVYx1SaQERkB3Pi3uIDXzZF/smM5xh2+3nh+ma9\nsPsqBlqU55wvz11rROK7Y+zEHz009K8Ya14xm2MHeyp1YGuBonSa164mRGz92Ek9nMxsXjVAH9o5\nCTJG2teG/u1jnjBKX6WubQ2tnSusJxXWqYjW6QrrbhPZnMh92D3JH2h3MnJjSfSsTINXqI2KsHs0\nhwOWKg3rJwkX/ZOQ69ijRCzmqx4RQoSyjSp5RFJ6IGPUBiVKc77QCgK/dWQouQavsJxPokg0rUlx\npyih/bZT5VDOp9eqJICrU0oEfL3/+Fmr87kmSYgaxO88t7IE60/LAQCQG4lZCFWDMZBjsaegmCRu\nN8vUc0HepMt7TupjUdh6drZw92dVUZ3Q7M6WaUmSTVSSLSBSwYIFCxYsWLBgF7TwIhUsWLBgwYIF\nC3ZBe4q59irpSQlVvTf9gEUNEuEW7lu/JU8e6z4otBkNdKQA2SaqZ0JKqKrUnZHuCKBtQh1luQQ5\njVxgJUhprf1Ukg7KqkzefpKwFjNhclOfSXPzMWG8LAAtNwRZoy+4PXrZnJVtgVXHOG9RbsLZTcQa\nL7g+kcM92ZwESura1b0mFec0Bol8av10ALVzzrWUQttlilxzly4d+O9OK6fL09WksaN5Dcm1J4B0\nR1NSpceYrEgDqgZEnlJ7PLGzcC6QlPq6AWSejc0VUqDfWyLW9uiTjPp6DE2f0UCYDLpgK3O3zBvk\n04udu6kmFeW+VPcAqQ63GuxA7l7cABXNyRSTdrE48WUK2bfkAhgjyGEXekcH15+zusXQZyJ16l3k\n9Xv7zTu+7OVdpyg+PTCX7T2oka/n5tp6+bY79733bDzfesu5z77whZ9w7RKzK1ehFF7bOWZwKbVE\not8Hifvwk5/yZepSG++aC/LKZVfPdGyuvYcn7nz7++677sT66/bHPi4iIicnphi+RF+wErXeHxFr\nC8XQEcvtWpOJ658Xbn/Ml6n2Fefkq1WBGe5B1sKrQbxn194Y905G7vY8d66vODOqwkcfOfL8TmZu\nsXKsKtqcJ81dryhd3ce75rK+832naH5yZH2Stmg/5bBTBewisXrmcClPybV/hjyVS/LBdEio12u7\nieytbvaC1vpItfVoeU2h1ZQ0vCZqTjpb49SjE7OOlP8JAhtoXJVG0VEQk89AQA+qBi7ImDXrLN2F\nXQt17sgtqHpXfvx5qVOyOY2XUjXaml17UHtPrK26xncdZYrAM7hjvgNI+R3RIiodW9AYOqLgFKmb\nTyN2T6OeFSmW6zrG2SvWla5JtJ7i+uwqVFtCK4/J7ru4r4rc5rVqIGZMisdz/6A0Hb/+srvW3Ud3\nfdmic2sCv3Yk26PhvAVEKliwYMGCBQsW7IL21BCp/YNdOT0yEmUHwmQ0yMKtb+SUQ67bpliuYe32\nUyXb1iSxoMRm/S0TyDIlMReE4OjLPJ03wY5kPLKdntYpGtRzc5eiOxevsE7vsUrKVoV1dy3N60cq\nsiVCjWe2q1cSY5ZxXj1Vp2VEyn3mmstpCxEz7pjEDwQn43NoaKov8m/6Cwo/nc3cDmO9TwrQqpQ8\nIHa6zwI72Iza75G+hMJVsdNLaKeVIz9hMSLkDIrdRWO7dEUThAjwiWZnzyC1QHmYWk9spd0nkJ6O\nwnrLwl0rJ/kJRaQ4dFnDtPdKCr9XgA9oYUFEcEWdqtraX4NkzjnMclUvT6yei6WbJ4q0iIi0QDoi\nIoBqMMRk36E/8dQQQZlBYZvuocNdt/u7//C+L/vmn/2OiIj8o9//Z76sgRp3TXPsEZCd/Ss3rD0g\nsSo4O58ZWqf5Gj+6Y4rhH3/RhfPPTq1OmqcuGRn6cfOmI813RIrVfHpJbX2yAzRNVcdZLuBP/p8/\nFhGRd996y5edHrmda0rrVIWd8/XrpsB+62MviYjI3p6hOXPsphO6eTIgltHC2r2z40jZqvbPSuCx\nD8qh9c8j8hQA4nnFdtwLLzopiNWZEcVrnK8gpXS9B5XQzBIyH37gkMibRLYvd9yceeOt133ZCIhZ\nQRIrSt4uCZGuYnfdBaHZJdbWJUjHKa2hKs/SNnZPlJm7Z2j6izo7GFVQ5et4q9QAIw64x0Eo52eS\nLvGDDK66rvd8XyHXHh0ZefiJz4d1l+UUNPxfPQe0JjZAcCjZhc8oUJP8gZK8WdWgRT2bQVJSN041\nrd31wl2jJymeHUURdYnl7sLpMpKfaFN3vpIQ/kqRRuon9cS0HSuQ/8X93gB1S8lzorIaRWxlE6DO\nnFlDJRFSqnyHvn7lxou+7OHSSXs8OLd1J0sZK9+0gEgFCxYsWLBgwYJd0MKLVLBgwYIFCxYs2AXt\nqbn28iyW6Z65MRYLJdGyFpJCcESsBcbH0LpCoaztoW6UhF1roskoAfES7BxnmqCYtKWg7dSTjlEM\nV1lN7p4EUHVK7kbvbuDEj5GquEK5l7LxWhm553CtTgz2TFDPniB7JY9LbmRDhUUZWi4TTRrs2tWO\niWwPF8syosy/cIsx0U7dguyeaJSU2FhbT08deXa+b66iJUjDo4Y1cPRTie1U38LBs3NK5LuT7qBd\nBBkDRp5OKeMzqpwRKVcTGMfkPqtALFSNq5jcWDn6v+qICJkqOZ0Io5gn6h4WEUl0nrKKMwIF2C1R\nnbm/V5FrK+vZdICqG9KMSjGePY1J5FWkKWko3CJHj40U3IBQ+uyNy77sxnWnqL53DfpRmfXNybkj\nXmcjc/u88/03cS5yI+jwn5liu7rb/94/+M982X/5n/89ERH57/6b/9aX3Xv7OyIikmSaZJUSiYN0\nGxOsPkqd22tyzWD848fuuo+prRHmTrcml9Fl1+50ZATwau36aTF3x/2bf/4n/rv3PwK035Ir9pJz\nS56cmNr7eOTq/uAeaaDNnQbWJz5txPJ9kNdZFf/40elG2Rpq5+p22iXC/DJRdWyb/wVceqsV6bhB\ns6wobDw7EIZHO0QAxzixq2gBzZ4bLzhXYNdYuya77h6LyWVycuz6YndEyaARDLC7a8dlcMcXTIqH\nonhBlIIOun0tjs/IZ6dE6b4ndXJV3W5tTqj0HQcbmYua1m7cO1m06YLziXkHXh240fiZAFdd1NGa\njHPEsbnMEhC1454CenQdpWsoKVsT6saJrckaRFKRe36tQSSkBZUhkXpNZHuVcR8kt8ec4KTFLYjq\nizklvMfwFKB55KQxpS7IXohuoGshudtqXJ9VzNX1mNJ6rmr3aULPM1We1zWut/s6i8e4PmWxQPsv\nTUyxP0bWhoY0IFXvraf3iYPErRMJjd2yMu27bRYQqWDBggULFixYsAvaU0OksiIZqH4Lwk+XC3sL\nVLiCkSYlike00++x+2dymhHaKHQ2VmIhyNm2qfGkbCZWC5CglMI6S+w+6aXa0Aza1SQICWVindZd\ndxwREWH1EioHgAY9+YckUBbnaMwkUvI4k+dBqGeEK8Zv9RK0W9GI4CgiwnashGnb1WqYMCOH2p56\nbW/681PXdycnhiYdnANNGBsiE6NODbbEwyhT9924ICV0qDJTpLXsI09ZQcTGbKS7VDvOQo2J5A+U\nRnekPW0tulhRQtppYpdSjggRyhUloF1tqjmhSEVYUTJq5OHIheL26MOqt11d1ZaorzV2tXB9x4rR\nqhjcdZv3SUzj+eKtWyIi0ta2mxvvODJ0koOwS3NdCaCcV0xFqU+ODaXwOczoftZcf//wv/+Hvuy/\n+Af/Ka5hiMgHHzli5xq7xMN9IzF3ratnRgETH951xx8eErEU4z8iwvTunjvP0qopBweOUJ9OrO+i\nzLXxKmQC/pzUkR+cuv48XVq+wOdecufNrhmq9xh98f67hsh9/hVHPH/vLQurHu+4sR6Xdj9p/rOC\n8iTq8OgYLleGSGhQBiNYs5nbLe/vG7Fd5V+2gR8tzSc/trTsXdpzKHICUmXQ9QAAIABJREFUwu7x\nibX/9bcd0vbRfWuXhqJPS8r1CJVzDt3X+BACMyRH+/OG8mT2ro3rHsgcr2toEIfQxyBj832lBPWS\n1vMGyFZM95ionAEtFHHyxDOG0Hftu5bzqiqJndYOzSxRliS1oigW3acqd5LElONSg6FUYZ3QL/XI\nsPyMD+Ihsrd6IlqSycniTQK6SqJw3RXF4yAnVU9vsNa1TBiHdyZh6SJ8zXkqd1I3P1pCrmKgTh3J\nJOhznPMEjnHdOdbVEa21PbwojCo1WOMr8ubsZO450pPUg05/lonokUmizSgDQkuemi0WEKlgwYIF\nCxYsWLALWniRChYsWLBgwYIFu6A9PbJ5ng+0oLyXizQ2VisH3zLZue41aSQr0W6RPoexmHgO/9Xu\njoP4shHBiYAne4a9lYxOGGOWq8vMzqvQLhNAk07Jhqx35eo8hWeBodM4ng3OJcJQpdVJ3Yyse5LB\nfbRcWnt2Rkh8SgT0GvBp1KlmlcHJBQjT7FrMAXf2LZUVrj2rNWmAqY5XRSROwP3rNWtLuTaWpRH3\nxhMH965BTq1ag2JVz6arSDMKJNeIyK66H8gIRp4gget8RUR9wMj9YO4A2lYonIIIomhzn6FaUTHN\niVYJwOwf0Z+SYrLqlg1c0HAtjEAYLogwGyuLm4ioqjNWrck9ARsRKVwJtUy2Pzp2ZOxLe1amRO7D\nK87tNaPEs7t7bqKuVuYKPDy8jPOaeypaAZ7vuO9AlB7b3Pnt3/qauya5IO4/eCQiImOQQo9JMfvZ\nZ9y19vcp8e34Ms5vWkhrkKzV7SVimmHsWm3gys4Ixl9C+f34nrvubEk6amPnxiuuWLsen7v+eee9\nH/iyq5fdenLr85/zZW+85xTb9/Ys2OLk2M377LIpK+uawWrT5didT13K7MaLeg2YIfd0vEniLeEO\n7+k4nfcc0NHAtZOTBtcE92QEP/c3//RP/Xd3Z859OaG+Hsdu7DpehpXQzILZnQbvcCJx1ImCHFaN\nc6Oo7k9CJ05wP+Xkbi5x7/Bam4O3ETOxHeO+Tu3eOVm6NSmmgA7VVNLnDnnHvKucZad0nWDNPmsX\n6Wihzk1FZGvc423NuoCubzWIiJ9hPdrPQSm56ozRXO8FwU5cUdAS2paeMZoEmZ9dieot2hhXa1eX\nRe7OMSY3urIHeF1V7bOKNKu8zlNhQQlJ7qgfHUU71HgGZ7TGFtB33IE+X95bfcdTd948ZTeqs/nK\n3NJKkJ+URkD3iaxZ0xHtL2iNq1tyx2+xgEgFCxYsWLBgwYJd0J4aIpVm3SA3Tgkl6jQxhd/TU/e2\nulySOrkSAJkUjJ1wNFCsVbKtvSWXyC1XjFyZqk+LGOlNcwmJ2Bv2QAG9wA6OUqilULnOcya7KbGP\n8pqBbKeo0orUfKc77i09iinUVXekSyabY7dAGw0l/pUTyiuU6yftPpDXKsFOr6ddQIpdakZ56PT7\nnpTFlSDP6sA1wvlXtHXzCuFrO2526nZ9k8lmG5dAuFatIV26E00pX1gCleWe0Kckdu2OWW63V+SO\n0CcoRXfEwC3yCdoFhIOCCLSvK+qnNnFtyAlVibUuJP/QA+mKmKiOHSsDqFUFoiRCc0e0M0xxYF9Z\nWQ3CbE/k2KZydT8/scrvID9aTyjR7i7Cz3Or++mR27E9/zL6iVAN7aX7H1mo/8dffVVERG49Zyre\n56+9467VM/zg2v/SLVMxv3/3fRER+fM/+7Yve/H5W7gsSKRTCyw4PnHE8sn0qi/b33X9dA+kcxGR\nEiT3iOZEjrD/hBCB8tChQx3lJNM8YW+88S0REfnnf/rnVrfPfkZERH71v/qvfdlv/U//h4iI/NG3\nTSbh9TecJMQX/72/5ctufcIpm//p90zt+/bLru+mO0aoX5yDbL82VrzmWOsaVfGm+gKRbGmnrzIh\nHYXENwjiGNFYt5gLrCJdADkoCWEqEMgwxxx7cG7E8qh2c+3KZSLbI0MFo5QZ7oVFZwhjBsKuqtmL\niEjs7qcxLWgLIEcTXdczm5Nt7a6V9rQmQOW6pLVLEeMpIV01SP5xZgjnpbFbdx+dcj5FqPI3bn1O\n6YbVMP2MAit6oO+j0uZfiudE2rN0ARAmDp4S99vZGT93kL1BNAMGeySANNKttuOlWEhCQJ9ZJBOg\nATCDSJ0Oyvqx9ZMP2uisn1KPOrm6zyizwnQMFfcRBVahfgUNda2BYrRMZzhxW9o8bRC0FOdElAdp\nP0fgRU6ekxQBVUlGkUV4/rZENv/gyP32+UNTMS8yBPT0FACF85WUp68iFG+bBUQqWLBgwYIFCxbs\nghZepIIFCxYsWLBgwS5oT821Nx6NpCA9lRhVKTNOMgjXVmeEseVClU03E08yOVjdfRm5tlKfmLgd\n/O/+dp8JQbZdr+Rsg5aL0kGrNctKqLeR3Ej6Z8dEScCtCgAnmbk2FTrNCYpXNxor0S5aJUzT9QG3\nst5PWapAhkGwBeBRbWtPBDqGStXaRl0mFBQA2FlJqiJG7GvItRDB9dCQ2m4DMvp6YXVSfmbVurKW\nyOaR1/YiGBdw92ptsK8SH1nHpIU+i5BmlLq5YoLKtSOL1PVFTePVelEpg4y1j5mImOC3DbkM1S2c\nEgFVofWOdGFG0A/KO+hjrSg4AHVhF2wNN09P7qka7s7dAyMxJyB29lR3Fclar8m1U7i//9X/5VxW\nn/yxn/HfHUxdv//MT3zWl/3gvlP7vnndEv/quN99YEk+n3nmtoiIvPKSweh37rwnIiInj8yN8vHn\nr7n64h7OybU5KZxr4cHM3F7rShPZUuLpLff/BIl/VzMLbEhK6MKQC7aBinMXOYj/uY+96r977523\nRUTkP/pP/r4vO0Xi4aPHdt4ILrDvfu97viwbu/F88TNf8GWjS84duqQxTnA/sXxdCzKyBr4kxNje\nmbo2PHps95DSB1hvr2n1PuHE6FhbWO9OmcyUGDlBIucM1IMrV2ys33/g3LOUs10OQJFY0ZpwgECF\nqKJ7XUnelD1BvXEcgOG54CBKZ9Q5mjS8iChQJlMFdF7/XFtzcvdlkStLiVKSwEWVj+x+Pj5x8+3s\n3M1TTRQsIlKm6kYkNyYoGzn5sfISLnh2CcHdxOtUjaARVntXDbAoUXoIZdsABWWc2ABkU5DYaVlr\nsMbkmRXmeI7UNE5dr7p4pEuHJ1RM6vHjMVy/vc41zmzg/l4ticYCnb1RTu4+rN0DDcR0+CkiUqOf\nehIhwyNbdnbhiiT/YIZrePVzEclAB1lxgmh00NnS1p+rpaMN8HradHDpMtm95OCmTQuIVLBgwYIF\nCxYs2AXtqSFS5SgfKIZ7RICUuOWSIwLWhAi1jSOgr5akbJtobiR7S1aUqhzZTms8QZh6hrf1kkJT\n8fZZd4w+RDg/IS140+dQ2wZyzxGTHUFyZ1JoFCt5HeelncmTaJG7lsoUUF5BkNFjCnX1fHra1aYg\n2TIpUdEXVXiPKQ9S36naO/9A/7Y+6VSBm3KYad41lXcQsaj/8dTUYXdGDiVIKfx8vYQqd7yJdKkq\nMHWh37n2PeWwwnYmjelALzVgpruziOcJUCQfaj3YLoFYTyR2DWsuc2uXDgXvkttGUU/aq0CVnuHE\nJRCOQtwOqiRpdT8XCWlYYwfLUgslFKhTluqHQnfGIdFeqdkOe/DwI9dUhDqfnD3y31XI+fbpl5/1\nZX/8vW+IiMjLL1oOuQZz5+ZNC/W/ds0hTWdn1tb37zjEqiysAp/77Cfctdbuvl7MTdbgDER4Dis/\nhoTDhBCBBkEEB9csrFrzbq0IfVudI5CB8rq9/bYjyv8P/6OTZvjMT/+7/rv5zEkj/OC9O74swY48\nIaTzt/7n3xYRkb//lV/yZZ9SxeqxjcmDU4di7V0xsvnRkZNJqAg5UwQyAvoyndrxJ6fuunt71tYZ\nEDtekzT/Hq+x+n1CJNoIi8d41xCJHgrlNeXuU7v9nMu/d3puqJLes9PU7olHWB/imtYpIAZcT4HK\nNUuX6NpZqqwMIQMJsixEnOsu0gwUvJ5qXj/KCQckalUZsT4tXD+RSosPbikhCbE4t37INCcoyQrE\n+HtCwT5x4trfdZTrE5/n55btIcNv1/Q8q1btoN1ZQaRrXGJE2SZWmIsjUlFPc4dOr1uau+iziFCi\nFZDLgp5dHQI/EgrUKpApIheMMb05PD5zAROUREFW9WamisK3hw5MNpH7Dj3VL6zdZeH6sUAHsHSQ\nItJJYn0dQR1fKCdoOoJ0DnlONCtARpIgKonDqFs+eC5uWkCkggULFixYsGDBLmjhRSpYsGDBggUL\nFuyC9vRce1kqBYkxaXLDJCI3UqvQtrlMVlCdZWJlV4OULuzac591TfoQiWolqZ5Ov3F8R/iket40\n2a+IJZLMRuRGWjuYsSXNkA4kQ9a2UlJoom1NmPTp6lkQPK2JL9PI4Ok5PAWL1FwgqsHDLjMl70UE\n4wvIg6rP0Q0SWgI6Jci4B9m+IiXeRhNOkmZHpOTEmMvcb5hEOwJUnhABMwa5H0MoMUG2KTRW+t6g\n9VZVhImA33au35vO6l56TNmOq+DmyMilWlUaFQCCY8uuMNQpYsIkxoSg/RLn7YkAqfOIE/mqGjUL\npjfaj5jXs9rGRBO5ZuSeihtNPE2EaUQ+rJjEDpVz1vtSknlFkHmG+fbibaf39OiDt/x3j5FA/NVP\nmBvpP/4P/n0REfn+m+/6sjtL5wLbpYTD777ulL/nZ6YL95OfdAT0z3z6476sXrj2zM6cu0M1sUTE\nJzWv11a2D3d/SlpIV648JyIiLfV/jPXh+vNGdm/hWjh/ZOe7/9C5xfJLThepKOgegrZOQeJiFRKe\nJqRP9MrLzs2ZUTLmFRTiWxq785mbx9OXzFV69IG7J5dz6yfVEVJ9uHVtY3jJE4F9kbSYOxVpiyVY\n2uvaXIZTJGsup+bG8wnUmZQLdf9YFabn1oYOCuAVxlxE5PreTRERebS0fo0xr3YoGfMaY9aTCz6J\nNFMBufQ9ads1clQQOVw2E7Sn6ipmtXEsPBkptqcIFCrI3YqukwUFr2gG37bD8USBiOF2VTqHiEiJ\nAKGCKRiqAUj1FKzjRWXHzWcx6kEaYJpwdwk3Ys5UBLjdiAKjiv7T8TVflmAM1+3zviwdwX1emfu+\nyxBQRWvcBFpucUd6e3i25CPQLYhaoS7NE9ZCg/u8pvtU46iIbeDv8Six86Vw1Y4ogbgmodaAoURs\nTtR4T0jIZdzWGkRErtVeKS3EygdVJuMExb0mZmb9xODaCxYsWLBgwYIF+yuxp6dsnsYDYrNGOja0\nW1EyeL7mUEf3OZnarmJ+htB5Toqkb5D0pq1SCPo2z/IHSsBeLijnkap4045Ew0VZMXkNcmDMRGFf\nF3pLhlJuWSKEl/IVpeiLlEiXBeQRYkLadrBb4Pxns8rtdBMKF1XiZUwkPgXxevRJTEiXklJjYgea\nAjshUrqDJfQpQ/45zUMnYn2xd8nQjClyHHYUp5tgN9kgTJrD2r2iMKMvGhpMOwSVTEgIkdPAAyaq\nptjpdbSb0vYa/zva+C6htrY4b84q2okS+wnNAsJXEgG0A7JWNaQ27FFJkE4JkVLieUpEUAWdFgsi\nwAIdiUixOC4hCcAi0mjj2cx+W2DOvPueC/WfELH5yq7bpb379pu+7PC6Qx8+9uy+L3vh6t9EY4jE\n+nGHBE1J7bmFintU2HFrRU6gFB8ziRgo4YiQaw1xrwhBWCz1vIQIguy8oPyTBzfd/aTol/sR8q+t\nXd/NF5sEaza9nTiG4I03HIr3wgu3rJ4gts7ODWn6xA2H+uVjq+eVq66sWVhI9gLk+Xq1GVhy/4Ej\n9j77zDPWBMydgki8NVDKhO7xVom3tLlWBXhpN8PEBQjTwaGN9eyxQx1qIlafrVz+vYqCTSLMqwmT\neEG87il7gyIBHGQzbl3faU6+gu4hnRMD+QvkkOsI/VCkeefA5k6JMWl7QqmAxHURy0mA7B9B6oaC\nOHr0TdNQG1LNtWfrhM9iELPnxNUzJU9EpkExFBXUYQ1okWWDydkJAqUmO5YBIJ04WY1IKIgAa1zU\n2zMmxm+TlNbzymUtWDeGJu0gxySjlAmkWxQ5TCgnqdapYJS0A3LZ2JhMNKMGS7JorlPuu06fjxwM\nBdmXTvvLxitD3lVSv/DBC2taJ0aY6xyUps/MprXxVKmDnlDCPiBSwYIFCxYsWLBgfzX21BCpOB6G\nwapfvijttbJf6ZsphXXC901aYTLdcTuM85m9pfYIU2aERcs0r1xPAl4KiXGotX1F2yW8OffE0VLB\nyKqyN90e/t2WwvkFO6YO+Y/KEQmItsrfohxS2ImWLAaGXV21sp1ujd2XyjqIMCJFdfp/2XuzYEuy\n6zps53jH915Nr6qn6q5udDdmEBBJkLQIcxCBD0uiaNlCBBgOIkj7w/7gDz9IBiz/MKwgqJBI+8P8\ncfADdtgSGeEQCYWCIESapjmBGAiAABpodDcaPVR1zVVvuFOO/jhr5V4Xr0g4ykaULZ39U6/y3puZ\n5+TJk3nWXnst8I9YuZ2Kv1IhnA9Ggvds+nuFjeBXSe6/w/G1hPT82VB+u7PrK6cC/dTpdYf/FJ3R\nM/WwI6i45aHIv72tdQM0Kxd+F1aziaz+KD9hunJOuMIx7MOP36H96kKe9ye5b4OMhXJ04OFUyjm1\nFAJN/Ri312HVv1kElORC6c7kFKntRBCQgqDqjTbeCyvIVBDJFuO+XYuHV8d7TDwZ2U9A01bHvjJ7\n05kgVjfO/fgHN66F38nYmcIR/uw5FwQ9hKzDfO6I5O1bAU3pNiImCm7QqgpcnuMjKVfHuZ07K7IG\nuHZHwr06t38pbJP9ZoAYz5533kizDH29EtSpxHXcJX9Prus73/UuMzN75Y1rw7ZrV7GC33g//Sf/\n6T8wM7Pv+1suXLqzF5CWQsbzFGNh5yFHeLLjgEgt7rqYaQdwoEZNfi3Xnz6JdS2oAvRB9D4txxSz\n1HsX6IvoiVBYVlESAw9qfRSuiVSL2zgDqpzfGrZtwBeajPxez+jrJ4+Y1RpyBiKSPB5T4NKvcZcH\n/hWlPiwTpJXjWrhnCflCIlxJwcZOOJIZeI2TsZ8nkchESt2H7AhlbcaCTKDvUpVLoKyEyuRgzlBU\no0P/r6X/yWEcyfy74jkDrRn6wcwm8LVrGkdVR0UYQ720oaAgr/BGUyBR8rXh2ZrVwjkC2jaRZ1EP\nvlADHp4kaQbx507g7w3GkHKfkmFCl2fi8Az2+242CfdOJWKyBk7yICckc7hRCFk4VQY0Tblc5FCp\nwHGRhna3jXKZweUTmZpORZzvERGRihEjRowYMWLEuM+IL1IxYsSIESNGjBj3GQ8stdd13VYqIkHa\nbaOq04BHFXYeExYWEhuzPNOJN6eqqEDrX+NuUpQzp8nJ9NxYCHtUmE3ke8wBpVJ/TJHlVjy8qMau\nyuZUD+8BgW5EmqHAuWfCuiyKk7DjDCZX1Y6TghOkWerGS51LquEKUZryC2WC9JF4w+VIN63URDA9\nmUYiY7kRwng/D8fYbT2NcfZcgOpHSkAnBK4pKMDxSQ91cv0+07ji15UAYtV+bbsAc+eS2uhROjzR\ntACuoyqlE1rv7yEPT0+sVIygCAuPhW1M8qLKWeT4TSEYeIJUbSXE3r0E8DlkKnpFvY1jwiH2ltdQ\nfBqZZpzPPD2yAVG5l3QvVX413VSDlH1wEFJlp055ajFDWnZPUkvIWNn8lKenEihBz2Z+nk+fCym1\nm9duDNvu3Ax/952Xya+WYRvHxmblKYsKvnpn5Zw2i5CWG0l6KKPUhJQrL6CKrgrUiSEtvvLvnT4d\n9v2edwaPvRev+PnOdsI1ecuzT3ob7iC1t/b75Hve8w4zM5vMPGXEApXL8OszMxu98xkzM+tkTIxO\nhT7bO+39yeFRgGawWsv4x2eHhy4/MIevYCps276l1IbIuWB8NqL2TJZ3L9dudTOkGTPMBadmPtcc\nr8Ic89DO+WFbmYWx08jxj5DaORSmxA7uz3TqY3dahHMfjfz4OXzvDpbhGtaNp2ITpHQ2UljRUi5C\n0ngd0jJJ6tekY0GDSMyMsa0zLwApQdvYbcJ5rCRjVK3pmODXv8X8JNOaZSCKd5mPtdZQFKPXH3+W\nohMzondfSakV8TWFPFBv3ie1hes1KS8O25gC33Y2oEyNpFahGF6JdsQaciNdJ3MMzrljik9I3DzP\n+UieExhjKnVRIy1YyvdKzB2JyB6lKebzQvKnwHyygWwuziKYJ5SUQ+kkdSDoQLNJhYIxpP4EUmrx\nm1TmjqaWe+YeERGpGDFixIgRI0aM+4wHhki1/dqS1EWwapAHSVIzs0EYrTdBbkYkTEqpc0tSuhCA\ns5Pih0NZK96Sk63XSJbVKzmxOPE9frrtYA2iuqx0vGJfyM5466eY6HYZfjhWK6vFDkTx8dj7qYUg\n387cfc1GICjXrZAD07ByVpkGSgdwFZCaWLiDAFiYlEsTLRLCOqUjtqQOsHKei5jhdAbyrqymCLYl\n0qGrBVZ2QIuU7M0VtH6/x3XqxEG+Bhu2lfLndoNrnQtymVHMVdzPIcBJjz0tzaV3YCfXZIJV9RZK\nidPT8ltKR2RCLM+SgBitW7/uZ4Am7OwFQch8I4RVXK92IVIXBgKwFDH0lNoQonQOmHQpch5reOcp\nUXcfpe01xPd6Wem99tprZmY2m/r4O3sqjLujAy/Xf+xxjj/f7xuvvoxzk2tSh5VuL9cpxbjb2w19\nszzyMuzz5wPqMZUihoO7hyfaukK71isnoA/ooJBSD26He2J14HPM6fNBnPIDPxiI4oef/P3hs0ke\n+ubik1rs8N1mZlaI0OrZswF9u/TEE8O246Nwzb7nb7sg4sNP7of2yyp9UYV+LARh61A8QQ9JFQYc\noYhhU4mHIAjTO3uCXAIxVrL5MCa1nD/hHCcl+RC47bFtV9DPsyUkAVKd68I+bh/f9uNT1kTmRHpc\nTgRNn5Sn0H4RvYSMCUnhV2+5XxzRKZWOWaGgoi1FamAo3Rf5kYQikUpeh0/d1DMRNRAmetxpAcwS\niPhqKUhfEc5JMxzs/zzRQhHIGch5Ulg4E0TqWwuedF7JUJyj81QLWRUi82ZmOXzndE4cZCfkuUNB\nzCz1/qTvHoWuzby4qutZ2CP3BLCgUuRniDouJcNBMeNCClVKnKei+ZZiHHeeYUkhXExEvhWZGD5X\ntIiCXZZpPRk2qsAyi3daEbOlQW0pfp4qd3OviIhUjBgxYsSIESPGfUZ8kYoRI0aMGDFixLjPeGCp\nvdGotOXSoUgqYVMRNfwNzQqBNunX09a+keK8jeh4FPiiKuCWZYA5Sd5LM4X4oKKeKBQNtW+BbDvA\nfpIBGbIMmWCw/LsXvSP+KAERUiFbQrW1pDbHo5B20HQf4XklcQ+osEDQXULNKlFqht5H0qFvMk/Z\nNNC2yiU9d7y6juMrjIoCgEah6PCbmSjwlujjciTEZqQsTNICC5CLp9MA8apmVzoo1vq2GpB5L/Bs\njTHTCWRPzSj1sKJSei1eVwkga8LTiarDl1S2l35FWq6R1AqvdVbo9Q9tVKJ+Clj+aOnneXwc0k0l\nUiD1WiB7EBxTUewvAamPxC8txXWv1WsNfdaI3leKtKkMu0Ehndop6caJ3ck67OPoyMnhVy6/YWZm\nu6JFded60FlaS59c2A+pSqWLjkHevK1tRN9duRH6YSw3xWIR0gNvXPX0wBwaM4mMq+s4filppIY6\nclIo8PrLr4Rtoo+TJuEYO+jXf/RD3zt89uqd0O5Pf9mv11NPPm1mnmIxM+txP92946nFM7jvL5zx\n8zzzeOiTY9HAypGqPFx4H7MHqAWWyPhvkG5vRDPJkIq6es21qPbR/8ulp4Upnj2VVN1oB+NIvOMa\npFfXy7DfzcrnkB2cykruib5GakvGRIcil0L0d/qCtAy/x8rB99TTfdQWynfCdVoK2f7uOoy/TtKd\npBlvZFzVKDaqhSTcgAKg0lq0NOikoIWeoXRv0FQbtYgKGWs1iP1d531Iz85OipIGDwNJS1NLcCOa\nghX48T2fT+ri0OA5mfl1bdfQTJJxQg0kfU70Ka6j6CeWnO+lUMjasK0S39Me1zEvoCOmVTGkxagr\nAX0Si5OvGOrhx8ddsaOuHLtoqxSKZKG4YqCbSPaTc70mRBNeM1Fnp/Sj3jr8Wl7qHI/vy3OvvIfO\n4lab/sZPY8SIESNGjBgxYvy18cAQqSRJrBEiHFeQVe0rs0EJWk6zADmvHwlyhKVWIaX+OUtdxc+P\nRPUEqEeWC7F9QJV8UzOgU+o/F0ixTeek2KJESa6WKQPZyMV/jYBJhrf0VPabdCSbCxEX7Wk6X33M\nx4GAu6XOOthqi4o2VrPLzWXfH6QAygxeSiL1kHX8/k35PlAdITbTfb0UYyUSAJXYmpUooU38TX8N\nPydF2IhAZkDH1NeQ10L3u1jRr0oQOSAtvSCHaU4Fer/+dU2fLJVEoMcg0MpMSn7ZRPGmKnKiBKI2\nDdQpUaI6kKtV5av5tGBJtO/vrgXUb3EUPpumvjI/IwRQRgXfwy1EBITORNZk63U47kaI5VTAnkhZ\nb4VxRMXuZ5/2MnzqRFByw8zs4YcfxX59rLPYYk9WxLchdXD2rLfnxt1ARlYkugDCenOBEvKZt7ko\nw9+7cz+nJY471etP5FBKqBOo7d++dt3bD5Jt2fgc060C2TvfDarsFy895PsoXjczs/3vuzRse/1q\nuD8uX/f7//GLoexc+Le2fy4gPe/4nrcO21IohW8uvzBsu/o6iNSKTqMfCyih55Vf63NnA2H9ha+7\n/2EBpH0u/mtUhd8izI5xHUWmwbjS3ghKhm1ES+rK+yuFhMRavMnm49DWtSC9kwxjUgSoiTavE99f\njpL9mbg30KeSqGounbO4E65XK6jKqGSxiSDNeLZsxNdyloexWMt9WgDtrQR96FAgkbThPKtaUHKU\nxpeC5mcohlBEtm5DfyaFImc4bub3UwG/16n4LzZA+NYoPOkE/WdRZ71+AAAgAElEQVThQd0IQjKQ\nxwV9hPVH2YuyOf5WlJa1SPTQMzNrMSdSRd3MrG2wPz5rZF5db46wD0HaijAmVuKsMBQeCQE/4Xyq\nViU9UT9FYsNckBF9kmySUaZBpWbwbOtqn3+7hH59UiiBOaYTRC5tT8oepQpj3SMiIhUjRowYMWLE\niHGfEV+kYsSIESNGjBgx7jMeaGpPTQGbNkD2vZAIe0KA4rJI+FDVaUmAVhi7G7QgVFsKRF1AhqkQ\n5lIq4UoqinoXSmKjPlUvOkY9oFXVgqmpWi6mkQXOcwxS5mjsbRh0NORgJPipGfLSglnoqNwftiUg\nO46nDhl3ULTtTdIY6NsUbdS2khwoPHBLU2o7SXoMqbhEVISNKZVC1GkBvbaNmCsjRaiGq1QlTtId\n/E70PBJCvN4ukiPXkm7g+anaPFN225pRSFUoUR7nzu+Pt1KGuHYCD1NPpBTdk019F5+JQSb0fhqp\nSshBvU7uoWMyn4FEXWnaDYaqQthtkMZTg1KmsddrIaAiZXdXFLBXq/C5ptsmGLMXkLK7e9fTbrea\nkJ576KFHh22f/8LnzMzsPX/LSdm3boZjPPyIK6tPoY91JLpQOWD8TtLXhyBej6FsrSbPu5Nwnq2k\nx3mP9YlS5kMf37nj5sJrFDFoGnm+G8ZYI+nO+YRG3tD4kRTTU88GJfLXvvbVYdveM0Ez6q1vchXp\nFvPKmX1v/wTqzbevvTJsO7oR/m6FvM/06VII3Y89+ayZmd1EWvLcWVcRXxyH/tw/7zpyt6GPpfMp\n0+Fa7EIV68lpnzsM16k7cl2wApNAy/SNqjrjGErOnYI8vidG7pwnJYtmY1zbkaTKVtD+2pn4mOQ9\nMZ2EtEvSeiryzU+/x8zMrlzza3L3mErcfvymC+3pm0eGbfWKRSmSRspY0CRzYR6Ot4J5by73cE+9\nOUlj9i36NXfT7r6iQa6Y9iJVlKkGE0nhEyE2gzyel2G8LiU9Vm9oGi8FQINmmlBVcE+o3tgIz8K2\n8nNiMUwi9xPPrxenjgzjqalZxOF9UhbzE9sapEdZMBV2Eq5TLlSNFA881cUjvNNI31UoBuC8lqVS\nxENKi5DY6doxm/nxmYLs5Fjrddg2Gfn466hfKeO5FSrNvSIiUjFixIgRI0aMGPcZDwyRMnNVWTOz\nrsZbsBDBsnusqsj/zqUkNQVKkctbNVXEMyUljimJgN8JYY5vxlqamxORklrLfkAJhOxO/z0hAJMn\n1wv6kQIlYln9ljfbQKzzt+XlOqzWRoUjMpsqrEgnowtyniDPS580IDsrUXtT0WuOiunig4RVeiok\nanISlWZXw3dNr4k3Q/YH9EV93Vh2m0jpLtXgSaifpE4sHrjeonY+goSDkkOpsp7mviLn6ryT8dTi\nGtfyW147Vvp2ooTNGBd+Th19vwpHEIhqdYkWSkBWQMaTtSeV1Rkkb++UvoIagQCZCjl+PQrHWh+q\nijd8sGRFvoYX3EjGboHV5ykhb/eQpFhuwvmWIheytxfO5eDAUS32z5/++R8N2x5/PHjR3b7u57kD\nZeNm4tduhbG9u3tu2NZ2Yd8VUI/9xxz9qlkwIiT2pGNRih+LKFUn9zNX2Gmqq8rw76h0QvsSCu1P\nveeSmZmtN1LCvhv66czjl4ZtHbwJ795xT779XRaAOCJwdAs+cWtH+I6vB2L58lh83fYDsrRZ+Ir3\n+mvBn6/B2CwUuQZa0Mi8sgOkTZHeCn/viGL3zmmsunccOetBLE9F9oVoR4Uxmck8zYKGQlb1uxl8\nIsVFoR5kRfycNiCbqyr6ah0Q9i71+YxEbs67s6nck324Jg+fecewKcecdbR0X8M+DX28qV1tva5D\nP/W5tz8vwm87QZjqDee4MP4qQXVS+pNuTRPoE5nrZtNwjFoI8JsmIIz9lnQDsiOCxE9noT8bKIun\nqUuSNJCpSGQfFebkUSGuCMjmSE3IUMKvUxynjDzXMQZF/Vafp2N8HyR69ZrNw/WSXQwk9l6ekxUl\ndlLdL85DvAMryLgomsfnGcdEIq8u9CnspWGULmhF/mVwU5U5gR56G8k6jJLQnl4dNU5O2VsREakY\nMWLEiBEjRoz7jPgiFSNGjBgxYsSIcZ/xwFJ7aZpuEdxqELY7gd1GgBj7ViF76EiJZkVGzSZpDolv\neS6EOehYENrrVMeph3lhp5AlCHZi8kjNJNXRKGgumimxEMeVVFkJs9AJ0m2lmLGmNOM1jx46JpKd\nGghwi42nFmZl0L7pRcV7hHRn3Xr7+yTAlwN5Xa5+B3NfVTEfUkZCYk1TQqz+W+ozda3jyB30mHJp\n42weSJzrjaTbABU33QL7V2IrNZvENHi0hzZ4Gq3B+MjEcJm6ZJ30aN+HBicq2pOxrcztyeEBCyvp\nMMG1VrXpDPC9GokypdFICiohPixK5dM+9MnpeSCqlo2TKIlFq2J+gfRwqwUA0ABaryUtvAjXerXx\nooj9Cw+bmdmtW04szmyDfcCodc/THq++HtIumto4dzak5R561FNwj4Konoq5+Ln9QGg+vOtk8wU0\nm9YrT211SWjvdI70dCrG2+im2Y6nNjYwYZ6NvJ/W66DPNN7zlGF9IxyjnHkas0C+oTr29p89Fe6d\nBBD/8W3XndpFqj5Z+b22A1L8kegoXb/xkpmZrZaeHlhCqXxn5u0592hIgR69+DVvP7S1Tu/KeaK4\n4hAE9KMjP1bDIoJjMXRF4cFZuXYLpGNPnXZSeptQ2V7usaNwjfvKj9GSAA2l/kryWC2KaEYjTxke\nY+zkorszQ1qs2dJWQ2GJmCaXbZgnDu++7t/bCddkPgopllIMitOeaUdPbY+hip5mjw3bNl1QeU9E\n2b2dh99kUtBQd+QvKM2DxThQka98DCcbfC/xvk7hIpGKFlNh0JmSwoq2n+CYkj42UkV8PqHae4b0\nXNJ7+7uKhQDm2/B86nNNmbFQR5wloK2WFn6eK6SepyMvQKhJL+l8TFjNYizMP1KAkINuUUrKdoOi\nmLr2/qf5fCPXjibIk8Tb2Br1E2Xe7anVRyV8nxMyPGObekvbHMf0LTkI6rWkm2lgvKn8fiompAr5\nHNNEHakYMWLEiBEjRozvTDwwRCqUJQuCArXXTlXMQZ5uhFhHklsqiJSxrFRWREWh5dGG32xLDHSC\n1rh5npSfVyQHyz6GUnv/Lc9J1cZJkG7FbCyjd9a3/D/sA2Wlij4R1egOZGM4xnLtq+qBjN75OZFQ\nrv5zPL0Wqr+VeLMROWvlzXsMxeL22Fdk4wI+SFJWTsV4JVFyJZQIYa8swxt+IWTfpg0rFpINa5FL\nKMuA1vRb7nDhbyWgDzIZ0lZKKvey0mQxQrelgIuVFoeGnG/TcFwJIsrSWBkU3NZJn9QghXZCQE6B\nnLWN/5ZSAEsoIetYK9DWRvwXOxDPSSYP28Kx1FctwdjShdTV6wFZOX/ey+nZF+kkjJ1KUOIdlMlf\nvuzq+D3HtXn/50AORmPv18s3wpg9d9pLws+PAzpSzn08feYLXw6/hZzI8ugL3n6s3O9ec/TroSeC\n1126pWIfxtVy49epHQWEZ9L7eBrlQD/EV21ZkzwekKjyrKNaJNY3R96vtw/DuS/uOon5LPqzb3z8\nP/v0d5mZ2Ze/5O258vrL4RiljD+M01rGRNMucb7h/8XYCxCo3l9ooQ7GvY5TIkY7c1fgziehn/qN\no7kJlPcT2R8R5hJIqN4TLGhoRbF/QnV0GbtELJJUFauhGC6DPDWgNIJSHGWBlH9qByht78gAzyXL\nHFWidMYokyIKKOWvGr9ODdT+01zU+9GfucwJDebFpmURiX+9wX1qqUidQEW7EF+5tCRyJm3tw3m2\njY9/zmcqXUHpmMkozH8HjbgjoP16/3Wcx0V+p8T1T3rJSAARp19f2LbGflVOBv2tJf947uTJSV/P\nzSLcH6NdH2skhW8VlA3nojJBmH8FTe+RlegEOSJyVeQ7W20Jf2OvOoaBtGsBCvehMjk9HDCUvL+q\ng4zKuNT77mQRkkZEpGLEiBEjRowYMe4z4otUjBgxYsSIESPGfcYDS+01TbUF+2XUWLoHiTwtFVpG\numvLDTPAqKORwJjAmVWrierVTPsI59fuRSWjGXKSKIkdBFRJ7ZEAWo4kZVVRgV0UYwGVU9spE/NI\nKux2psbHSA+1QkCGMWUvRsaHRyEtsbfr6qz1QDwXzYyW0C5gTNVdugeZrobu1GgsBGAS+kSzhAT1\nRhXoAXf3epkGETBvYwmdoRo6Rr20v8fwbBtRrL3Hu3+W09xTlOrx21QRWWD0uRDFbRhv1L0RKJjN\nkTQSD9/qJqYRJbfRI6erkH1d40eJtgGaMT21sIRYi/NNRJ+nxTXRsdNCM4gqxXqircDoWXnydl/j\nnIqSWlSSYoIJ9/4jnlrLQPy99NRTw7bT+yG1RT0bM7PxPBz3cCXbprvYh8Po1Db6xnOBgP3IKU8P\nlEiPrI6c7P38X3zTzMxGY//e7j7Uq0UBP5sjLS259TH0xh5/8klvI0jWu/PQrn7iKtorpIeUHE0T\n8kK+d3Y/kKNv3vaUzTdeCsbEIu1mY5xfKgUw1M+SLP/gwFBD06o6diPxDtdVNaMmUADPJI0xhcuB\nFvR0TPcsRe9sAfVuGfecC46RCtOUlUEVvJeikBbXPZebbYLUTiM6djPMSRNhSt/GvXi9lzZCgfwm\nCOjNWOeVk5QB0iw6dREAobwQ4+8WhRd15u3PkMZqUp3PwvFIutdrs2nDNa7FgWBWoIimFhI9COgm\n/cT5QfUG+QhWmgdpC3SsKGX81SXSuGJQzzTueCRm1LiG2k/+HJNCFcyZycafJylTur1fO5qwNxiT\nqmK+WYIqMvFzGvSelO3d0wRbrycI6JIqrED3UGN4FmDw+1rYM0K7G9kvWTuVFFGQlK86UizyysWV\nw3Dtapk7OE7+uoiIVIwYMWLEiBEjxn3GA0OksjzZKkN3BW4ttYf/lZR/EzhppfyY6gibTtRJ8Zae\n2kmSGBEUVWelUmqvqxoQEFthGyb4u91CWqDALOWvVBnvu5MIA1EyLevnuWytICirICuDBu3uWyEW\nox/b3lcEXDmuZaWR4fxYXlrL2zqBMy31zwuWnArSxz97vyZtG0poc/HuI5rXNEpex+pHiKLsg2wc\n2r+SdhERSkzKf0EKVLI51csruZ5ZQmV7UXvH5dnylQLqp7ULw/fR12Wq6A8RId9HM5yokDg5UKU/\nG6iIV1ISnOP8uMIk+dHMrKiBsORyTeDJtxFvtoSrw43/dgH5g50dX6VuUDyxWPmK/NS5QCg/PrqL\ntnhb1yBA74ydnPvu736nmZnt7XlZ/cFxOBa97MzM9ibh3M+cEZSU/pfX/Po/fSlsu30jrPT/8lN/\nMXz2xEOhrY+Kh98M6ItJ/x8dh3Pvc1cRz4AmTKdy/SFff2os9wkQ0UO0YXrW9zvfD8jVUmoYrjz/\nFTMzOy++erduhVL7s6cFkV4CkRNfszX+7sXrj2hDI2jSGEUWBcaCjjUWOeSCdHdAmOnDaGaWoCgm\nkQIIAyk4KR25Sy2Qp6uFF7SUIDtnQPA24vWWUTpG5skU6PRY7pM7GJ+76gCB+7QWRG4EdLpovO+I\n+hzcCYhU6x9ZCVXyWtBP1FpYJ1mKBOM4FV8/InwmPqmrEdTDE7nIaBulS1bijZgY1d79WOsm9Gsq\niKj1VIyXZwIzMGt9eGDeF1lwEqSJBBYTQXX7cI9thOyfQDGeXq5hJyw20ecp5HzEk47n1IpMQ8Mi\nq058WmtcBHwvk+ca1fYPDl06ZApF/br18yQi1HSiFA/UKZGsA7MJKgnB4jKi/6k4llAKJ028/+lP\n2m/JbyBzIZmTnm4bCikN3SgyFYncR/eIiEjFiBEjRowYMWLcZ8QXqRgxYsSIESNGjPuMB6dsbskW\nw3vQdhDdCyJsvegDJUmADLNUTBuB7QrabS30MQpJI6U0ISZhUwjbRM9bVUelGe0WYTmcNA0gw+dM\n7Ygqe0GitKQvoZROGFFTgSTACcI/kFJb1XsyEptFnTUPbVV11lHJdIz/1vmM4SCVpB0yGDqbpgcH\n+F5UfwfCvsLDUEWX61lj3yNJozBVliSq4gtdIqTHNmtvQ13TeFZ0l1BskIiyN002S10WIPW42Yje\nCAZcKuk+EnvznAUIDmdXSG3VnaeMcuiTJUK2t3todqUguSaS2utZvKDtoSpxGfpmtRDF/DL0Xalm\nvPiXBtxmZssVxuTE+3UNEvPNQ0njIR33lmfeNmx78ulnzczszH7Q7BlNncRdoa8Pb/s1uXEzaEpt\nrrs+zxNPPGFmZnePHMZvkW9//ar/9mGQ1k8/5kT10V5ILT734jfNzOziU28ZPstgOLuRNE4Osu2N\nq07sfuW1oM799ve8Z9j25qfCsc7PfVCcPRug/4XoonW4duceeSb8P/OUnUGXaLbv5PQ3Q7Po2ot/\nNWy7ejOQ4deqt3Yq9PWF807Uf3w3kO2vXr06bGuQb+5yIfai4ICuDJOx6iiFfj11ytOtd66H/V29\n4urgsx0YtIu2D03DNS2WQKl8KWrzNVK/U5CHtWCnH9y97UQoLaDAPJHo3A1NtayVlE0StpWSW2nx\n96oPqaKs8bF+vAp9nLSiI4eUYV76thQmu0nvaTymg1pxRWhxTuqAwf21uDZN431T0AzbdKLGfnu/\n/i0erYXcpy2eE+OJp6Bokl0JUZqp3DInBcXn6WJEwTvVhyLdQApFBodenWuare+bmRVot6YAqxoK\n8EJor5EWS6ltJ+rsJagnmSihbzCu9Fgp6BhjuU6DtpXMsR2pNDLGip4uE0zPqeo57iHR7KuQ5t5S\ngKdSvDwnmD7cprTA3FnI5l0XU3sxYsSIESNGjBjfkXhgiFSepJZKaWzNt2p5C6Y6c6tvg1jN5/Lb\nEYl6SiLDW6oq4BYgdlLNORFyOhV7FVViGewWYa+nsrUgPVTM1lLzQSpbVWTxL9+4ZbXQEFUTQIiv\n5Foay/VdIr8lAZVq2uE31cn2NFRPB8FSV5pAPbSr23uoyKc4lyKTlRZWGiyNNTOrB382WTnkKNPP\nfJU8BnJWFqGE/nr16vBZ1YDELiXMPL6qiLMvsi21ebRHSblo3JbsBon/KFOuRPWZyvddr0TUsNKt\nal2St1vnYeYk+zJXNA1l1aKA3Vpo4+3DoHp9cc8RGb903oa7B2HV++Sltw/bnngslN9/6a+eG7Yt\n1mF/YzmnFsTOF775lWHblZsBxfg7/9HfNTOzG9dcMf/rXw0l/IX067PPBPQpU8I+lOrXd0SBGX38\n1rc5+nX1Zihxf+2al7pzzz/8Iz9sZmZ/uPBV7fGV0DetFjaAlPrIm7yfvvdHQ1/oPNFl4VzOP3ph\n2NZQlb6WMYHVdDMKCFI3ds+xFMUWsz0pzQaqceqJZ4Zt1xfhPAuVZFkFAvwrzzv69CrGpEoX7AKl\nKsQTsK5BfAdy0UoZOOekQ0FaKnxfVT06lHAXY/ckW63CcTMpvyfZWKUzxoN7Q/h3sxJ7hnvEoHYu\n7SdikwqamhkRXj/3Me6jqS7pKdOB+We58fFi8KZsloLcAsHrBWkeJAQSLeEPfdJJsUfWEbkW+Yee\nhULh/+rikBDVFiX0MeRhslRL7QM60pvMHQNR2ue/tuFv5FGMa7JGMZBKeNB5goVAZoLgbM31mGtk\nmqK0S6bom52MGufUmCLs4TcNsxQC9WxAyp8ISkaivvZTmpEUL0VJ6JOucyS4r1E8kvh8wjmWR9Vs\nyhSSLKrY3qHYoBViOx0wus7vf0o8aEHHkIJSpxBBJe8VEZGKESNGjBgxYsS4z3hwiFSRDuiCmVnb\nkD8ib/AU1ZQ3aJa/q0jaBPnwVeV5Voo/5sI9KAH3DI7fgnT0ttg6DzOzrqOrt3AUIAgn1aqW5sgH\nS57bKzdllTb4RIFnI8uFnmWokqst4YjdiahbCu+qSla1TUNURT3cgBKpr9tQfop8s3R1BS5DJ6gW\nvetGI8/pN23oJ/XVG8TUeuV8heOvavfQovHgaOwl+fTky7FK2509Mnx2/fbrOJa0f/BVUq835uMV\nEaKrupoXQupABs8gU5FQGNHHS4s2uriprwgTVVq0kyJ5BPG2V0QUs9My7dCOEivMG4fOPXrv24LU\nQHXo/bpA6fSNm450vPh84Ot0a181jYHE7e65d9wTTwWfui9//svDthpcht/9V78dzkcd5HG/PPKw\nX5OD43D9ZyJ+e3AntOGRR/x7FP/bu+CIUDpC6bZ4h7H8f439ftf3vnf47BP/a/Cpy3tfwa+ICAlH\npQL6t66uDdueufi4mZllgsiM9sLxZ+f8t4vD0Gd5ET7rpiINkFN80pGGO3cCOvLn//YPhm1veSqg\nqa++9sKwbToJ+xtPXBJiBBHPQoQLF5An4arazGwD4cjlYeCXNbWPoRlQqvVCeD7gw128eGnYtgsO\nlXI7JlOs3EUS5fgg3J8q8UFOUAePx00lHCHegFv+kz326xwVzrsqyJvX4V6c6uRdhLE9k20LTEFN\nEdpdCky/WlP+RCRRhoyBINJAompBk5qaXFaZu4BEqcco5wf60HXCm+V0muk+MBdkE+E5jfDbzuef\nyTRc/1r4aAm4tIn4vo5HEAm9BzLkQscqIEnxX/XfC9s28pzIIbqrHNEeD6ouUZ9OzIUiZ5MBFR/k\nB2S8pDj31cbvkwklgSTrQRFrzXC4FILPu7kRYRTknjxUoPkCalkL9K9VqQ0K50rmZNBr3uLc9lv/\n6vcKkbPounthdx4RkYoRI0aMGDFixLjPiC9SMWLEiBEjRowY9xkPTv4gyU3L6vOhDPMkxJqoNxGg\nUoWiSVirey2rh7J1Juk7EOUy5OV68bCrBwKww5MlSLSdKJuPy/CbrNBySRLQhcScAx/c8n/als9u\nWyW2g1jfSp8AHs2EMJnCay8V+Ye6poqrR9OEtECnegqASpuOxHJJMfX8newE8HEi/ndMS65bTy0U\nkASoNI01tEtIofRTFLJ5msy2fpFJGjU1UQoevk94Wr2xqArv58ny4FZzCwOxUdKXIE1TxV5h9P4e\nf1EBWMdEzvG0dQXCta0ljZWjnLkcSUVBHfri+97zo2Zm9tUvvjJ89MpLQWogk5RpibGWyFgb7wSi\n9CrzYz31eEizPQcPOzOzz3/2c2iDlISXIS317Fu/y8zM3vlulxD4+L/5V2ZmdrR02P9H3xW+96k/\n+eNh22aBMvVvOoy+uxdSSzeOXP4AVo8DwdrMrGHqAffC6UceGz57948EAvzzn/rTYdtsHFKVxcyV\n1XMMoUsX/bfz3bDxypU3hm1T+P4dL72f9s4GcnmJVFQmOQN6PXYyr8zh6zWd+ve+/nIokDh15qFh\n222kXneEAFxh7By+4WXye/AEvKsK6EjR7u4GAu5s5qlwqk5rwcR6EygNi2O/d/Yff3Nog6RArAxj\nLZP2pDthf4XQJ47eCAUHaUUahX9WpKRHeB8yjT6ScU2Cdi7zND0BjyW1OJTuy60zTpBmRaFQZUL0\nBQFZussmUGrX+ZRMcabCwnkGEnNnek+CRC0FILy3k55FRBpIBbZ+T+YjFDG1UhRUw/9u6iea0AGi\n8Hnt9F64Jssj7ydy4ZPBWcLnNZLYTZS2KeGg/qst5slexh+zvNset0zZCc2mn6ANUijEgh6m6tTt\nA6nCrQIkzMmZPM+bDQrFhBbB+byqxWMV87gWT1kGWgioF1WlYyj0a6KDHUUJev1JI+n1dYLzdK2F\nWuEYq6WqstvfGBGRihEjRowYMWLEuM94cIhUmlshopYV3lyVMNwNQmNCDuZbpYi6cTWhRtOD07WU\nOmYgyPZw1VawpuGbs/rv4fORrNb41k0UKnwxvB3P5iJghpLkXjyM+FbPVZiW1db00Cq9XXzD1hJe\nCkcqOTIZyup9lbQ2HleInXjTJ2HeZBVSQaRvS/4AJEsluxYkLIqDdgckJpGVTgMhzEYV0bA6Ol54\nOXNZAJ1ikYGstOgnpmPiXjGUKas30tDHunLiv+mJbRS/3GytwugXKO0HYqHoU9uxrFmIrbjuOk4o\nLPmud3zPsO25r3zRzMy++KXPmJnZfOzk8LIK5zQvvTR4sQjkYPWh2mBFOpr6955/KRCfVysvwFgd\nB3ToH/+3//2w7Z/+6q+Ymdmn/+ITZmb21a+5190PvC8gQi++6CTqP/iDPzQzs/HIj/+mNwcpgokQ\ntXus0e4eOXk+Y7FHLeX8KFRYHEDqQFb15TyIhF5f+DV507sDiXz/ossU7O0DwVj7CpIkekUOb1wD\nOqXl17fDcesqrP7PC8G2m2NVO/VzSlEA8rbv/u5hW7MM+3jt9deGbWceDUKnuSCnGxRe7M8cketx\nzx4fKxIe+onzg4plzkE2VwHJMb6fSxGH5RyTgrDdCe0/uOqkfJbWJzNH7nd2gj9iW6M44PjW8FlV\nByS6GGlZ+2DU6YfH+a0FaWB7tHiowIQzlbloAbLxBnNH1aj/Jr3+FP0mci6FEgk99MR/DWLDSaJo\nPnzlBDnuWgpywmvVPAqgc5lcV/pa6nw6L1lYpI9YzL+5Frvgmo192yahOHO4dhsRCW7gQ6gPL867\n6iHaYa5XLecGCFsmRVGjlAVdJ0WX1bsvxf08SOcosR+i073gMkTJVuLTOM3CPaaZGyJmqaBUa/gI\nJkpKh2RJQwRNCsUaXDuS9M3M6jUladTrEAizShIBkUqUgM9MjLwL3MuzVyMiUjFixIgRI0aMGPcZ\n8UUqRowYMWLEiBHjPuOBpfYsTSwVjadkUEIVii9wyVzg0RapOpLJw67gzVQLiRAwnkKr1BTKBoKh\nQnz4W2DnFqTMVAhrHcjjpSir03eInndmrqw+HntaYoN0T7MJ+1DYtca2TI7VAgIuhZtcAAKtBGnc\nYEe9pDEqamsIAZxEwSyj7pN/n6nFbR0twL7yvZZq6+I1NxCGe71O+I2qUiNVuBEF9B4pyPWKWije\nLiqg95Kea9sA89eqwZVQ9VagWLSx708O8V79B6nLgnarPNdNV4MAACAASURBVEvaE+KWcZqR7Ov7\nzb4lZRu+F9r1I//hfzxs++M/+d/NzOzFl7/obYQ/XoHzyOSeqPrQ1qONeGMhPd2Ih+BsHtIXjXio\nbQCF/8N/9JPDtn/5L/6FmZn92j/7J36eUE9eAQp/9u2Xhs/+5E//LY6pfmFB2fxHfuzHhm0pxsnr\nr7u2Fcmmc0k3MkV/59Yt+V5o2507Ie2YSCqYGki5eJMdrcP3X3z5+rBtfj3s76lLTva+/Hr4fHdX\niL1nQkotFbIvmepsYy0pmxx9k2gqCvu4uOfK8rdvXTEzs7vHoo/FVLGkGyfQw2ok3bUAGX+1cr21\nFKn3foQUoHz/qD1J7N45Ffp4d0989UAUVy2e9VFoz0RU1JsmpI3WQqytQXYnKbiY+/enUEA/uu39\nX6Z0VvBjMTKhL+ygj1eteKJV4XoqBWCG1OuCBGchtvc1izg83bUYUjvefnr8FTKfzkchVbxcS0EJ\n0zyZkLcxCVToa9WiqpCymk49jVpv6PUq4wRpscnY7+cJCpXWlRYZhT7LMk/3kt2x2cBFInctsuU6\njPVe+qsYYa4TX1eS4pUWQUK/qo0nRlqIFhSFPulaTfeFv6fUW1MNSKYUGy3ACsfYVHeHbatVKGKo\nW3/uliX1I8U7lrpkwjMhVWdczk60IcVzopJ50vUAhSqAYguS3tGwcCypduiH663pvOi1FyNGjBgx\nYsSI8R2JB4ZIdX2x5QNE4rkSm0meU5QiA8lsyxsHK7giF7VvqNjWiRKlgT7g7VL3MRAVVe4b39PS\nYP6m2yr/BIlPlK17KNu2otSerfGmj93VlSq2g+DYSrkumqOkPyXID9vwhp/I5aT/YJ84Ka9uw4qg\nH3ai3kThYqgLeYeOT2VlYiDv5UIErLFya1tdkZLsr752J0tdb90J6uWTEaQmVP4hpzqur8jqOse/\n4laPFWuaywqmJ4lRFeCx+pECBJJXW6OyuV7r8G/R++pzZxfXJ9Ny6UA2Xq/8Wj/80CUzM/s//vi3\nh21TICGJrBxJEG+xSl02Ts4umrCCLjJHUAqozO/OfdvtW4G8/+STTw/bVqsw/j75h67AffpU6GMd\nJ//Vf/3fmJnZP/mlXzIzs69+6S+Hz2angfB0Pib/sw9/yMzM/pf/+X8atu3shOtzatfRpxFUzJtG\n5STCeLp6zcnOXPStVmFMnN1z9OP4IFzX1UJK7aksLUUZRJNfv+pI13Qcth3ItvR6QH8ee8L76fTp\n0LbRGKX2giCVeUAJurWPV5KYKy0KycNKd3buiWFbC6+99S25nriPSyG7T0HQX8q28dTHu5nZ4aHL\nJZQ47lgKdRLcz+O5/24Dkm+q8x+ukxLg0yXI7geOHAyIGW6nUmwcOiATe6fODtsqzLXCobe7d7A/\necK0QJ+XK0GYcO7q50jEZgz18kUt3nTwwtzU3idVd8PMzEaFyGpg7iqFFJ/3YXxOhdjdtOFc2tYL\nYJKcxUsBJWwErSHSof6bCeQC6o23YQOEd7NyAnQOyRr9bUspFpHJYSFJCSkYKn2bmVVATmpzT8wW\nUjeFFLZkyASUhSiQG5XF/XsD+qNSOAlRIlVPp3r4At/xc8pySsj4PDk8s0WRYIU+Xi793Ds8T7Jc\nER9K4fgx6GdIpfhtb7wwPspS2oB9qGwBSeSazWrW9F8VlJBpiUTPaVsE41sjIlIxYsSIESNGjBj3\nGd/2RepnfuZn7MKFC/bOd75z2Hb79m17//vfb88++6x94AMfsLt3fTXzy7/8y/bMM8/YW97yFvvk\nJz/5nTnrGDFixIgRI0aM/w/Et03t/fRP/7T97M/+rP3UT/3UsO2jH/2ovf/977ef//mft1/5lV+x\nj370o/bRj37UnnvuOfvN3/xNe+655+zy5cv2Yz/2Y/b1r399C0pk9Ek+pITMzArqPqnx4JBuE9Ib\nSIGqd1RAnybPHEZdrQPcmaUOy9Y1UkWAkZXMxvSAKqv30CVqhGxICFBTe+mgC9LItnAuqbCXc0CP\ng46GENyowL2R1Brhy0L6KbvHFSsAgbeCYzLzRu0UM7PWQpqD+hxqxEiNK0tOku40jdKhTyoxQ6YJ\nZS3s+aoK/Z5JGq0A2TIVWHgJpeIMcHIpxMoCRGAlUfZUGxbCIjMRSSGmqSXTt37uHFuJKOoz9eTk\ncbmu2DafiDo9oPDR2DHrTR1SBn3t4++Na98IexOy/YR9m4vhKdSIE+jJJJ2fbzHBWBTdpaNFWLQ0\nI09ZPvWmkKp66aWXhm1MGRViGnoDbrDvesdbh22//qv/LJwbYfyZ9D/STf/Ff/5fDtt+7Z9/1MzM\nLl1807Dtvd///WZmdv2Kk81v3gwpreMjJ1EfH4e/d3f8GK+/FtTbmcZ95Vi+fyPsoxTF5NUSZN89\n78MUqZBc9aHgaFCouS7miYMjTy0tFyHNOD8V9ru3J4baGNeqMVNA0byRag+mux576ilv10tfNzOz\n/YedAN9B0Xu59DYOfSH34gZaYUZDbZNgWkjmqekojL+sFLL1qdAONbJOYSrbtD52Frg+1drJ2yX1\n2zqmp0QxGyl1ndE7ugKIPhL1+KZCVE/w27JVHbHQRtWbyvEsKJFSTrfyMzCoP1YSMQjrkkYukNJS\nxe4Uqa/KL//gpJBK+mwD3SoamhelFNE0JMfLXI8U0FoU2zPM9UcHfu4lHCCYCjQzqyDRnil/BfqB\n6ZCeEsoGiNVdL+4EWfhbjYxLpH7zxOekFubr6hnNDJlqFTKNleV+TjXGDq/NbCzpZ1ynrFeTXxxT\nzJgrUB+yUlLFLdN8+oynpqQfIs/47OY9KelJ0CwaIbuz7qffms/Dxk0lBRjLkCJuJN2aYez0mtrT\nKqR7xLdFpN73vvfZ6dOnt7Z9/OMftw9/+MNmZvbhD3/Yfvu3Aw/kd37nd+xDH/qQFUVhly5dsqef\nfto+/elPf7tDxIgRI0aMGDFi/P8y7otsfu3aNbtw4YKZmV24cMGugTx65coV+36sTs3MHnvsMbt8\n+fI999FVtZkQvJqekgRKcIOydOWrlT2UXzZSLpmA5D0WBeglSlKXG19ppUCASpBD9Y1zQJ/UbA4S\nAknvK0iWwneCyCRYkbVC7KSgaimKsS2I9HzTzqXUnaufRkpjKc9Qd378fkBiBOkCAbnd6Bt5+FsW\n6ZYBMUlSrtb8s3o4N20Xly6y35YKtyd/q9dudUyZAG/jlCtNIYAXILIeg1B8eu6E0QzFA62oMw/E\nfkFaGlw7LT+g2nq+Raykn6F8j6tdjI1MlLW5mFUV6aIkidfbNc5D/1f5DT9WF1adKomwhjxHob5W\nWB6XadjHSMYEEcFWvMHe9My7zMzs7jUnMb/40nOhLbXvd4YS96sL95o7ez7cs69fvuLniVXy7pmg\nqL6pvb8+8P5/aGZm/+Nv/A/Dtp//xX9sZma/+k//u2Hbx/+3fx32JSvYD7z/A+FYr/nxj47D6u/L\nX/6yn9PZQFpmAciNKz5fnDsV/PQef/Mzw7YV0JGbV738/rHHHjWz7fvpzt2ASJ8+7av/c+dC+zNB\nuAqoIQ+reSnsyEjeFUSkWaKIRGRF5kDxUlnVznbD4nOz9GuyXIS5qFr4nHR4GPZ3RpDABmObRRlT\nKaHf2Q3X6ZEnHBEsxmE+O7rjY8IOwjXuGyEbA34ohVi8BAJYCKF8dRSKJ2YzFif4GGZBDR0mzBw5\n35IVwdx9vHJS+Abo2ChR9AH3iZbzY38bul3IZ20T+qtWJ0xIYjQj79cargAjkbpIB1VsQZhQINOq\no4GRbE1nDZPv47gKIEGxvGkc/embMI+p1+DBQejrXJDzBsUzm8rn+BHRVEpzCBG9BvpSdt4u+p72\n4rXJAoRcxjMzO4lkU/jcU6eMLGPWRYp3Un5/ifP1vt6F7+VIMg1U1F8snfYzQ0boaCVSE+jjcakI\nH9TG5f1gU4frPoZ6v3oIuv+qZETak/N0A+mKjXj4NXQFkYdChgdjKs/C/tu8Kv0/JpsnSbIFfd/r\n8xgxYsSIESNGjH8X474QqQsXLtjVq1ftoYcesjfeeMPOnw8O8o8++qi99pr7Tb3++uv26KOP3nMf\nn/2jrwy8nEcu7dvjzzx+P6cSI0aMGDFixIjx/2q8/uKBXX6RiOrf7LV3Xy9SP/7jP24f+9jH7Bd+\n4RfsYx/7mP3ET/zEsP0nf/In7ed+7ufs8uXL9sILL9h73/vee+7jPT/87KBqbGa2Rgouk1QMzU37\nxKHQVQ0laNFRIXw3HflvqypAq0cCmTbQG2IGYsvQEcdSAroRvi8dClytQGIXg9YG5LVSTR77k6Q4\nQuCD7oaoiJPgabKtaZFGEHIoEb50K48FFd9UFdgBi8sAyDOacPLcZBe4FqnsmHonvRCmh/aoyWNH\npXYhUaNz1TS1A/Ldq1kyUqRjpFHWraRRO6biTqrujuScxkWAlNeiduyQtsKzMCgVSRDC9hXIlKqs\nXeH2GEkRQ5KEPi5lrK02IR2VmnMJB9NqISkyZdG3fp4FFf1BCu2E9Dqmoan04QsvBAPhUtpVothg\nZ8+P/7Xnnjczs73TnirlpW3lHmORRYUx9Ld/8O8Mn33y9/9NOMeR9/Wv/fNfNTOz973vPxi27e+H\nhdQnPvGJYdvnPve58FuR5efCqRXy/CEUzff3Ayn70YuPDZ91IOKq7tUYadZM+vX5r4W2XnrSF2MN\n0ky9EKDTMoytU6ckfYz9kJxcSC6c5PhSKjxyqHMfHTnZt9uEVNh4JGmcW4F4f+Omp9u+772hz77y\nhS8M28rDsPB89dVXh21ndpluxH3VyT0EF4HF2o9//pGQ5htLvzYHQZ8tzU6mxe4eiir4IjwoSnGU\nmEyhi4T+V2X7BmmRfsv4m6T4k4bfk8T7hGn0VDgFx9Db2nTeng3Eh9Y0nhYdpxzHGMmYSEEYPzz2\n783n4bqvRReMaclE7rGOxsgbMdzNSangXCtpx4JtES0spPFH+kxiSq/14oXpBLpkkqqvuqCttKld\nWymhywFNfpXzzJogIVFznk5lG+fJQottqAuYC1WjZ/pSioJQlEGD6tAeXmOay2vBTGjPfHzezwnP\nk7xQqgi1/fyUaCovDIiBKqK0jL4J99M0DfNELtp6fI1oZQwNZvUy/tjWROZOmjUrBaVCIcnDl/bt\n4Uv7OF5qn/m9e9OUzP5vvEh96EMfsj/6oz+ymzdv2sWLF+2XfumX7Bd/8Rftgx/8oP3Gb/yGXbp0\nyX7rt37LzMze9ra32Qc/+EF729veZnme26//+q/H1F6MGDFixIgR49/Z+LYvUv8C/lzfGr//+79/\nz+0f+chH7CMf+ci3PXDdr6wREuew5hG0gmT0tajY5pAHmE5dRZgkSi11nYyBSG1cRZlq5212Eq1h\n6aSSgwlOrYWwXoJFLmLHlqOsX0s9+faf5foiybdklub790sgMlouTw8/LVcusMJOMkGE8EpeCIl2\niVJcRf16rDr7Qf5ACHvD27+uKsN5NkKsz/DmXpbivwbFXpW5oFJ0sxGyPfazEfX6DKu+HuX3JASa\nmaXwOpzIsUgApvqvmdkIZO9WVppUDO6kPUV5kqhOm0CeuqIP6+EiOyL15BPPmpnZq5e/MmxLLBw/\nzbRQgcRWKR7ASlSlG1gU0CdhNVUmB94Gtl8QsYMmoB/T8Rk/FvZ3WUjk9AJThIUgQiooTZ6F6/Ts\noxfNzOzK698cPqMPViUeaqdPB3L4Zz77uWEbvS4vXbo0bOMK9+WXXx621ejsxx7dH7Zt1igUgAPA\nwxedCvDKS0FC4onHLgzbqPLdNjpOw783b7o69TnQDWqR31isME4LRz1Xy3BvP/PMs/i/owUEaU6J\n2noFpPXMeV9933w97O/Fl54ftu1Mwlh8fN/Rr0//2e+Zmdm7f/BHh22TJrT3U//nnwzbCiiAZ7iu\nZ/a9/bvzQDY/c9r7sIVS+FpIvJNZQCcPbvv8V2wgZ7D0+Ww+wdwl13hAPe6xCOacsU3EPZn2IHm3\nEzmV8TyMyUbIvntZOM/S/L5fYp6oQbo+VLV5oEplLhIKtA4VpGsNxE4lXrIU0g3CDKbv51pQ7z6B\n/AEkVLTYYAB4ZF5N8cwQS07LcY+pivmmooq3/5ZOEvqcoGtD3wU0cwt9aU+2ix5/Ta/IPZ4Tcg1z\nok6CPmbYdyewVw90OpfniYHc3axBYlf/TdwTlaBqWUZ0Ts4T/VNIQQ99+rRQqcLzLhc5oRGedwcH\nAZmajB3ps5SEcVGb785jX74tRx/rNeFg72Q8U9poq6BM1NDvFVHZPEaMGDFixIgR4z4jvkjFiBEj\nRowYMWLcZzww02LrGuslZVUDTkyzk7ofmRAba0CbSeK/HWcBPm8k3TUCATqXtEwLg8qmI4lbVI8B\ndyaSdhmgPdGnaHDcTkmcNCZuhFgL5LNX8jqgyr4j2VsNcqEjJcbLPBMldlaAoMeFEKChBbQWyJ5p\nQSX78aQSnIeqnhP2bRJNd0LZVzRmBkhf4Hlu20oFcNuWBhXVy4U8C0i/rgi3SiomC1CxmryWhjSL\n6IhlgHYTwex57ltZB1yLTDMWJIWiOKDR6w8l4LvHDll/+fk/C/soPd2QgxTe9n6eecFCCTV3JitS\noGWkL7I87K+uPD35zDNvMTOzF7/m6bFzewGylgzooI78tre7YvmXv/QlM9vWYmFKb1y4ZtHtVVC7\nP0BK6/p112f6u3//H5iZ2e/+7u8O2w7uhnTDubOe2tpswskoYZqE5cnUIfgRxoxaSl28GFKKR0ch\nZffG5Vd8H7PQn/Md75PBQFfIsbundvBbT23euROu2WOnXVtu/1z4e29XyOYYM0wFzeauDk7NuGO5\nr6Yg9N+94tXJe1AnP5S2Lu8ETbFOUmbndsLxv/Znfyjtf8TMzB59/NKw7e7tkKI8twsz6NPe19N5\nSOkW53ybVVAsl7mjxz1Wpk52f+21kHrMJVU+ojG4pIrW0DYbIz2vKROmjFK5iVhEoFQFuidoAQp1\niVSrLqO5+MYHdActvxHm/5kotm/w/UXpx9+0LNiQIhbQMVKZO6nzl4iTLikAvegH1iQtY14vpLCE\neUQtIukwJ/dCwO76cPxGDM/HyAv2nV8nUg+2+gRaUW2DNLYUwKy7Bc5RjZT5XPHTrHFOqbhSZLjv\nlb7C4qEsldRuRwN3pY+AAA+9J03P8ll8cOxjbW8vpKMbSdm2djIt2UMrcXueDNexklR1TjpGHtLo\ni2Mp2EF62jqfJwwFCqn5M846jE/RgKM+lTp6NHCXaFsfJ/36O6wjFSNGjBgxYsSI8e9rPDBEKs06\ntZobJAx6JYIlLM0Urzes6itBs3ahCpz0/qZZVycJ3TUQpsHfKD9Zmqsq1iTH9kKOoyfPceKE1TFK\n11UVu4EnWDkVv6KKirFoc+pv3CRR51rWytJcUXYvZ3yr1vLXDufrK40aK6y8EGIf+o5E8aT3lV7W\noz+VLw+l2kTOs6Pau5K4gXothIFPQrX66vX0kRPkhv5TBrXzjfpV4bhH/ZF//xTU2YXYnaDsO5M+\nGZClXlZkQJ9UgZkkT5Z669IirXHuvaBfQK4y8b8i6KMk0hQIZycKvFSjz0Rt130fw8rorW9xuZAv\nfPmzZmY2M0dQDu8EsuV85FIHD5172MzMnv/yc8O2U/OAfnTSHo5EVSx+4olLZmb2yitEgny8/OuP\nB/mDv/f3/v6wjUUmd+7eOrHfqRBASYBdidfbCJ5lTz510b41eJueFQmHJVCyUry5chQ71FLqPz8b\nxnGWOEpz5yCQ9guRJKkhUzIV6ZLJOIwnOhoshWw+nYTvbeT+m2Glu7PvZO/bV140M7NLz7552Hbl\nBbSrlbJ+oCTTydlh28GdgM498pjLPgwK3CUQhJGf7+hCuNZL8QubTgJKm9d+rCMca7br5/nQ2UBs\nv3bDUT/6PqYjR7hHI1nFm1km9eqcOwqZJyucSyVFJLz+/db4Z6m5eteF/k6EKD7BJNShYGNHikhu\n97g+Kr+C+zMrpFBozfvZx0mPwoOqFrM9FA8VMk92g3sCZWVkXsHkrbaCQ7FN5mM9IfokmZPFEm4P\nIufDm0clFrjvCkVWnTzX6Mlat+p2sUIbBGkbGPiCEmLe7dWTNWXfyrOz5Ryvvrfws8T3O1HMb4Hm\ndK336527QX5D50TOcfTSC3+HY2waLcCBx6xmLpDNYLtylTVBFys5PMc1pG+imRPqe2krz0W9E4kO\npqkfP/1OK5vHiBEjRowYMWL8+xrxRSpGjBgxYsSIEeM+44Gl9tq22VLWZgpOU3ttQ8K0Q7ubdYDn\n6lpSWzStTVX3AgTALX0IptYA3YkZJPV+esmFkGyaiLJwAxg1FbJdxSySI+s2AWTZr4VYiON2INYn\nAnFTWVU1TrohfSFq2xuksYREyDZqW/18RUcD8HUBonpVK3Qa/i3knCwlsd/3uwHsuqVBRQKqpABI\nPFcP6EKFVnhOUOU2tn/sKQaSmEX2xFbrsMPpXBWrQyoiVRgXqb1OrmeLfqSKtZkSOUN7NmIa/e53\nfbeZmb30yhf9fEmsFRIpdb7UtJbFCypLzPGupHymTZtlSON85vO/N3x2eh5UfBMhpyYgTCbSr9eu\nhnTf2dOqrB72u5IU0N4pJ14zbt8OqaV9pKouPfH08NkXvviXZmb2u5/wc6JW2Ll9T08dIo3WScp0\nAbPaXO7JGzeCptHiwPWeTuOc+6E4wRu2OA77vXvgJFaqHV8458dfY5ycO+sp0PMPB70l8WC2s2dh\nzCzE5h2Qyzn/6P3CdNOpU96v1jIV5dd/ciqk1g6vu0Hz6UdCCu74uqshUyA+kfRpXoa03dGhj92d\n3UDUTZAKPX3xyeGzesn0pKfle6QbN0ee7tl7KKQ5GzF3LnfCb8pD/22HvAgV082cSD0e49ptpdHC\nv42YvPK+VnVshn5vmH+VvJ5zPJ+8d5iBWshnHfpuNvVrkoMULE0YFLgXC9dlm01hkC33aQVifT5S\nsjf2i3mik/svSzhOtzgAZma2afxYPe7TXNJYpGNMJ1IUA/K4uj2wu/OCaS9RQsfDphPNqKKkCb0W\nu8CEXu4nPkd0Pu9wfbbSrTxlTQHSSJjnKbutqHslz5+iAI1Ba60SFll5WwdXDM0A41y0yKxtWIxW\nbP3OzGxNLbrEnx1sq56Tk+ylAApzVqsFAMMjQYnyMbUXI0aMGDFixIjxHYkHhkg17XLwPjLz0vGx\noE9c/jSiGJ6RgJ7622eHN9dEyyoJscjbd4tS3BRK1HUqb5wd3+qlNBTyANtlrVglyBssV139ShS4\nJ2FbKcukgeMLwnKrZaiUKRDSYQ/YYav99LqS71HRtpcVYdLA1838+FTIzbrwbyEIUpNS4VhWXyVJ\nn97+MVAtXUGwhLQs/dpVaxL2RL09DcedFk6epbLychNW072So8lAFkmC9TIgKJOZq01PqHYuiNcC\niGBXSQnruEYbRToiwxjAKnAqhM2vPf+1cN5jIcfn8L8SSChLwjkpSpeTZCmQSAEF9tQczeDwfPs7\n3m5mZq+9/IIfC/IYqajqbnAp3gklbjOzr30t/GYjK/0a13E29bauVmHluF4pShXQCSqGf/Zznxo+\ne8973mNmZl+ClIKZyxocHbmEQY/O6xWlYEGDwInjETwBZ36dDg/u4jMUbMg13D8TEISJeNgRwbp9\nx1f/Dz98AW32vp7tBfQtyX1/JVBcJVMvQSQvgAwVY//+bB7+7uT+66liv/YCiL1zQcLgxrGjSsfL\n8Pf4zCPDts3hDfzl+5uMsT8hzxZQhR7hnmwWgj7sUtFeSMyQpJhPfL9HX/2rsP+9c8M2qk1PJtL+\nIyBsipImVIDG74SITURAPRQHEnmm5GSo4m8RpcO/jazfuS2Xa0JZnONlmBNU6iUD+laKEniL+dTG\ngoisQ58sK08TLNcB/elknDbIDtgW2Tjcuy2KdwopdrgH6D+AzrnMvy0KZNqNH5/q3eroUQztEPV2\njEVmXdYLH1dtjXld0b+EXqdCoue90B/L98J+cxl/fCZtBPXjPJKNhJQNsnmCZ2gj82qCSVtRomFC\nVbJ/wueef6sDwtzLHM/sTKLq9TWyEwmfax6scajkmTybkoEuxSYd3D5SleSAx+7m5PFV7bw1vwb3\niohIxYgRI0aMGDFi3Gc8MESqt3arDJt8hFYkBFKsNLp2K4Eafi+QSEnpBFn9Mm+8/ZZMHhbQCnkL\nJjqmAnouHObdVAD9aKWs1PhWq4hAhVVaJ2//HXPUQBpUmgDt6QSSYUl2JTyXEkSL1ca3DS7hpqsq\noGTSdSxh7XtypQQZ4W/lDZ5lusp9SrE6S2RZUQ+6peo+jlLjtTqNs0xWPPkgEsc2rNaygqJIaS0r\nA0pYrL2tRUnuk8oKnMzHpxBsSzLN/YfPR/BLvHvoSMekCEiHek4lGAsqErqBwBs9qszM8pSrdOkT\n8GpGhfM7qnX47UsvvhSOKSvtquWq1ve7Mwncmy99xb3+xkD9VktHSehZORXxzfEUAovC5bh1K8gY\n/NAP/ZCZmX3+858fPvsKjrEvpf63bofvt8p9uYcgK8fuRAUuiQ5J+flp8I9471SCIND/Lc28DTdu\nBM6PypT04DxMRRBzNsPfsnLnti3/sZzyE2FMTndEVBcITr/x8+U1rEpBs4FE7T96adi2vh34Ur2M\nnQ7zWSkobQEUp5h4G6kFQe3RZiWIBJCwZOrfn+8G1Glx5XXfBeaYN776mWHb/n7g3B0c+PVvIDcy\nmTiaTGS1AUczlfuKsiqZlL8T4VPPvWHOkgmIwr25EEGJGK4raSM+HgP1qmSun1EkVZAuAxKh9xql\nAEYjv/7rwX9UhB5ztlWkE8CXTQbup3CKwK9SkWLK4yjSRvHhvhYPOfQJ/f3MzHZ3QnvGhfKmcB8n\nof0qqrw8JvdXZF2a0Fblg/I50Yt0S2cQsdRsjjHro1xe/CsqDRmyNwn218tzqsb8zDk0fB+oksj5\ntIOYqWrsgKN1D750K+8HZc57m1xm8SukTJDMky1kbm5pvQAAIABJREFUMjIZExRJVp9IAnF6Tutl\ni/ZIduwefpIaEZGKESNGjBgxYsS4z4gvUjFixIgRI0aMGPcZDyy1NxqVZr2UQfZUmHbIjiTDTjBG\nwvKppKCqVfi83SKWB3huKpD5GgS5QSVVID6KTatfUwGospFzok+ephYyEEUbgaArkNe6jZTV5tu/\n7ZR1R2u4/mQbWiECVjhsmSo5juXCDoUOZZ1yjIF/j+NnmnaAN10jKs5ZSc8lOaeB0Kcq3mpeZ2gj\n04fSnwVTi3LtKqZldtFWVYIHZL/loRdSP720iyrrqsA8Anm5r4REyyIDKAabmZWAfpliOlyq6jF8\nwKSEn5IVmh5KAcEnAv9OS7RHq6TRjjzxFNTb3/UuMzN7+RtBlbyXlCWlFgo7M2xbHQB2lmu9Qgl3\nL2NyjlL/o0Nv6xJpxMnEjz9H6u3P//zPzczs3L6Tk0fwWqMPnZmPT02jsi/WIivAtm4kLT2herak\nIJjSZ3PWooS+NwvnlguJucN40vuvQQpwR9JypAOMRE6D56ypHSqkz3ZC8UKvvppwR2hMU0HhGIV4\ngraLk8TW8TykQ5eHTso/e/GZ8P0jvybVKqRx1lLksYN2M8VWnPPr392BnEHh47RBv893/LoeLQLZ\n+sxpl4m4djVIMahMwRzSDbUWxTBtguZof6kX4dAGsH23KBgJfTU13Q5StM5n6OPdud9jHEcpU1py\nT96E76UWClF5fC3UijFI/Op1uloF2sBm7WOyoiy2FBSVIOM7pUKoAJgv6l7lIkALkZudPq6tpPbY\nPYX0P9Nxg+uD+XyfDNoB6k4Rvl8L3YH3kxb7OFFenkm4Tq26hwweo6JTwGesFIOxaImUEZVQGKRu\nJD1IwnYpaUTO3VtPi57pU5njoaxeiCsJJXYoXdHL84fPOHX2yPOwrZWijD5Fuk8dSJrQZ3L7DWrw\nLA7QNv51ERGpGDFixIgRI0aM+4wHhkjlo9lQom9m1lbhbwVpWK6Yi9feBiung8Ud31dJUT3fX92Q\nAKweRqEkusGbaWr+ttxihdEJ6YzebLmaAuLdMxcPr4F4l6ivD76dOiLW1hQODeeZbImlYWXeK9IB\nobnKna4TrJhnp/zcWQrcCtl5g1V0JyXhGxB5p2VYhZYieNaD7J3kTvbm7jIpQ+3ptC4EyAyCpWni\nyAUBg0JIlBu89s8TIVtvIFI3hoeVIA2DnIH4qrF0+WjhxGqS98vSBRnHSbjG9RYpNJynejcmWPXc\nOQ4r/Wkp4qsVjiuIG8m2SsAl0qWk/Bw+huo/R1J2JuPuG18PvmeUBmhFwG+Mfu2EWDrnankh3lgd\nkSsh1h7CfV5AgjmkCy5eenzYRmmDRx4OJPbHHvXPXnnlZbRLvO4yyoTI6pvISS4ehim9FtXVPfTd\nWOQvGpJNIU2wEb+2BUrHx62jIA0nCPFabHFPrlv/7QzkVL3WREK2ycYUnQTpVhCEBvsbjxTpgoSG\noOkpDyHSKe0knPNIVrU10MFEEMEJ7udDQQ5XkKeYzMN4Xh07qjU6F4Q2Ny9/1dsApO/GVRf/3M3D\nqn6dClGbhSqCsAwCpIL6EXUhCqA1/0dH4b4rRJJjZzecZ2OKftPXTIUT4bWmZHOMo05QKkrgrCB/\nIFqZdmoc5vB14/P/7eYYbRFP0iKc03ws4xRCmJr1OFjxGePHXxOlnIV+rQXVaFFYoj6tOe771UrQ\nZCCWnTwTKHHRCHK5XIC8L/3P4pmE7el9vqDAbSVIe4N0SitoajJcQ0Xz8UxMBSXEMyaR7w0FL4mg\nvrie9HVNVLoHz6xEnl0jZJMUTab/rUmxRZZQkkCKHTAmiQjrOXfoO0UaWeSjGYEa88hE/BcH4rvc\nuw0I6p28ixClW6d+3ScTn7PuFRGRihEjRowYMWLEuM+IL1IxYsSIESNGjBj3GQ8stZf1mWWZQNxQ\nuK5VHqQDsVjoaRmg6qpy2G1THeF7qjfFfahmEDyhoPCaiFop1WZHI/WDC8egIrWeX6ameFRs7UQJ\nNiEEKccfSMvwARKCoZvsKYkbacRcL9PJNpIIqGLDQPatEW2nvqWfXIPv+2dlSdVjvyYbpBi20iNI\n47SSRqmQslTCatJTs0vSHUjttVI80EPbab0O2/JS9IEAsSqJvAEsuxLCKIXH9ZrQV2sladEUJEPq\nA4X2IFWBtIeq46dGdW7VwgKMLbcO012qWZYmoV1d66nCOdI9qmOygqJ73hZbbTEzWxyG/tqdOuw9\n6UNqozPvw3rCKgIhsWKN9O53vmvY9unPftbMtvvpB37gB8zM7IUXgjr6F7/4heGzMcjmSg6mtsuW\nZhfGVa4EbKb7tq4n9F4ktVbic2YKqfRtZjbCMZTEmiFVnI+msg1paSGnkig8mSjcfzK1VyKVu16E\n61ALYXnvbEhBb5bq4RjmpGIqUP+QZpC0PBWgtSiEmkUyJnuqTYu21+J68CSc7IbxUkhquYOOVHru\nou/j+tfNzCzLva1r+I5Vd18bthVjpMzuSroJ51eMTiqVM42tc8KYelNyTivcu6mSqLGPTPqkxPG3\n9AMxT1Yy8a9WoAicNHswMgVa8dWkergWFjBlqwUQTClPRbH7CLp1lejy0ZN0g0Khznys89wbmetW\nHVOW8pwagRS9ZXXH+cz7usTcmid+jHpQQ6+3fhd+G/5tZP5lCi6RscZip1TSc2lCr1dJd9FjdttS\nIpxTrul70jcw17dO42D2fj73QhXOiTp3j6G8r3qLa/Q/vflC2/CvFB6tN0gpDuNJNCDRh6ojlkJR\nPS/0eYrntOpjJXOck6rXn6TZdO1Jn1iNiEjFiBEjRowYMWLcZzw4RMpKK8StmQTcYuRLci5MlESY\n4ZQ78To7QkmsrsgyKVlnkJRMEvWpPSeCb1D2X/cH8oPw9p2XWuqNVbKsNIjm6Jt2z/LQWsvvsXLg\nUktWARkkDNSFmlEUqo68wb/+pk2iYiIrohLO5bWU09cbEAZBFO+EMM8y2Vy6rd7wfBVpAdmx9RWh\nr2pOkk1rIYqz2YdSEl4ClSRK0ihhEf1ai9r1BHIWd0XFu8ZqYS7XvMTKORfUbXWENkp/1iAXs++k\nWtsy+ksJCkjF3rrythLV0H5KiGaZH2s8DWXsm7WPsQQSE+sV5Sqc7D/uQ1ubYz/+CoUSvchUkByb\nCImzx0rwU59y7zyiDgcHTmx+EYrqVKXeP+cq5rxOWsShyMFwLCrWC5rHa5flej2BEqx9jNPHMEFx\nRClk/wwXoxiJAj+u03S+N2wjYtBIpUo5CtdisXD0Zz5nUcBJtWVHuEXqA7IqlaAaExQF6Eq7wmSg\n6GsPiC0VNHlzjIIWkZMozz8a2jhT/8nQd5trV8M+ZP7JlziXsfjlofCiEDmTV64FBfrZRhDsdfD6\nO7XnPpWLZeifLfcEXAMi4Yl6yGF3OtcRVZoImp9T/kCuJ5GQVBCe9bGPRUaBuWsN5LyTwoYlVPGX\nK/8dP16LAvw0C2RzLeGnVHe/hdLzD0Vu4F5RU1ZFVfRDe1Q6h8rjqSDXDedHQZrokNB3gtwBsb5z\n28fYbLaN+iVaFMRzVEkaXJSkUQI6HSBU7Z0E9E62oT0iZ9ANhVcm2yCdgOteywNwVLIASwjruE/q\ne3jNdnKeRMy69GQBiM6xFf8efqqetJi7ZZwy26KkdCL249KLndY11ONlnhqU/aUYTNXt7xURkYoR\nI0aMGDFixLjPiC9SMWLEiBEjRowY9xkPLLVXV72NC4ddM8CXSsSzIsD3h+3tYVPfB8hUCdDHq5Aq\nac0h89MgvpVizDumQS5TNaLEOwECnTaiZ5EE+DgrJN1GErmQ06iKWieqtot0g2xrkRZjykhNhql2\nm0pqb8geCGQ8BlRcCBTKtIhtkQjH/GPYVjdIX7bQWOq8rYWFfipyUZuf8jx9mDAVkgpknfZr/uHH\nB1Su5p5Ji3SjpAUspeEoCMP+iWXURZIxUWDMnJZt1C9JcyVxhvbkkj6usys4dz9GA7i/RJo1zeT6\nUwtMUhE5Cg8aSW1SgygXc90cOkYjIWCvFku0QWBkwNwl4PZuI2RrpLbe9TYnjH/jpZCKawTGtiz0\n4WIl4w8E7ImYtqZILVx9441hGzWACMFrYUMKwmhd+349PSduA4PhuI/J+SiQOFdrT60xRTifez+R\nvFtSW0pSFgnSg52cE1NFIs9kBv0cTS1ucH5FoekBttH7ZLOhkWz4/t6eE2apWaeGvhu05/VXrw3b\nHjkdzl2NZ1vo3dRavADtn7X0Z3s9GA3PRG9sfRDG1nQP+lkj0fNZIt0kLObdc4+Zmdmdb35t2Pb2\nx0IaOcm8PS99KRgYF5IWbEGyV+VmXkemOPLc036sZyilYKMEHUNdCZiyzVSVu2HKSPTGcG1TSUuu\nmY7FuKoSTaOHa3Fq5ortN6pA7aiOVUeKBTBiQk9dINEHGmFMbkTbifMoz62tZQ6jab0ooafAIxJR\nwE97tF8mm4SE5d77bgXR9l4oAEX+LURt0YfitlK0oGqce2qaWsa/qT67oA4uhSottBoTlVGiVlon\n4w73QovP+kwKizAWklR0tPKT5GwW47SirUV2y1jGX4L7cy3k8QpGz3SR0DQqtcoyKTbJUqjtS2Z7\nPIJ+Yu6pbd5GmTzjqJmVyXmOypNUIY2ISMWIESNGjBgxYtxnPDBEarE+sknuStQd3tJTLbnEv6Ug\nVx2Y4pm8kXejsMJdCAGXpOym1bfUCfYHFenWPZyynKrXgsjgDb8zJzamefitllrXDVca3r6BWKn7\n4+sv0BzlwfHVPJf9juH1p/5jJMqqYjSVh5UQNxDQEyXMhX9ZTt7LqqrFCko9lHoqNfd6rPBPkakq\nPFEFaRAPJr5GBpX1LVI6fOKMyIksIVr0f1E4IkDy9nzmqAb3pyjZqAwrfJWkaICcVeLdlZf0KQyf\nTSY+JmuorndC2B0QOSk13mC1VGRKrGeps/hfARFUYm+P0ulqTWTMEak1yu6/+JefG7aNpyjXldU/\n1bZ7QfqqdWhjPvbjU8X31Glfka1Qak1EQhW2idKYkKh3dkCAFzSXK2iVSSDJm98PuwnnPBZEhL8h\noXYjRGyianqvV2jDWq7rQw8FVfa7d72IYQeecPRc1Pb0vajXN/QQC/9fb5zsP52xYMH3cetKQDXf\n/Na3DNtWN69h/3KfYolfy7Vm2xq5n6eU7jj7yLCtOwxq9+0aKPXcr1cLRCIXRHAFwraW/9+6FpT6\nVaakBAH49m1H+AeET64dpS0Gsrl58Bq2gqZTJqDYmqchUyPHH/aXKrE7nPOR9EmCbAMRLGU950av\nOUdEnGTu9+TxGmNBCnoaXJ8+FTSphIq5oH6cY9mGJBHPueTkvJpg7iik2IUotqLZRqK6ZDPoSdc2\nfvz1EsULyDqMxurNh2PKRSHJu5cHEAsPciGWJ0CiOsl6rFHIowVNlC6p10po355jytLbMBpTfsDn\nf86nWpRVDzI54slJDz1Bn0ZApEYCppH4T3eQVLV+8FxtxcZhvWbf+T0xKjB3SgHYZBzmp1qeHQ2k\nNRpBAtWh414REakYMWLEiBEjRoz7jPgiFSNGjBgxYsSIcZ/xwFJ7Vbu0Te0pFqYHkkZ0J4C350Ii\nzmlQKPDoDnRX6l5SVS3NIB2eY0qFqb1EIH5i+/2WoSfTDEr2A4wtysp1S9NGSUsOKSD/LY1Ou260\n9R0zs7rOcL4KcYKwnellAsTZKOBOHRshjCL1odpS1K8i7FrXDqcTukxUHTvhfv1Ik2lImW0Enp0B\nFu4aMRIuqPYt0DLVbjWlCY2w1EAsVhVjaHsUcv135kHnqJR0H1M2iZjGUjPMhNia4jw3okGTQUeK\nekc09jQzy5FakuyMNdBsSiWN19TUxxG1fQuprVHmhrs9SKGJEKqZDaAS9PrY93FqHPROupUQsPWy\nc7/UdlFS+D3InhwLmm67exgKNUbQmFqt/J4keVsNanl8NVmlpsx87mnRW7duntjGvlNlZRJESTpX\nja/5nGlMv4aHh0f4zMnZvK/HAuPTQFrT4hwnZ886UZlp4bt3Qz+UooV061a4T85LYUOP+enV578y\nbNvH/jRlnTDNLqny00hBrm6K3tBB6O/nvvCXw7a3PhW0papVSDNmh35OZcZiF0k3I1U2ErX5DBdq\nPJEUPO73iZpwI1Wi14Rpa6b7tACBbdQikrSg8bDcf7x3RQOL2XB1oOgHRwk/T67vWXizo24L0BPS\nvmasJS18fBRSe5Oxjz+mhZpeyM4Z3RuEgoB7cUxNQ9WdwriqxcWAv1XT3gJjRlXRN0jVqpE8n1Od\nGM6zKIj6YYmk8dNB707HGuZfSTeWA2FdUpB4jjVCAei6MBf2Js8z3KfL5UkF+mKYgKS/oLe4ZRrc\nHGIf4kpRQ9tKCgAKFDR1qc87pMCMpI02pwNB+G9by2ck0cuYoEF0JmnZBM+YXAol8oTXTtuDzwrf\nliUxtRcjRowYMWLEiPEdiQeHSNWVrRURScMbcVbKaq1n+bUgV2lYpXa9EIaBLJ0enRm23V5cDp/J\nG3lNBAirqU4Ii61x9e0riAI+PI34OiVlhX3JigwQyzj3VdWSStWCkrR40z7esAzbv1/yTV8VVFHi\nqaulBggKCZ5mZnlLZXH/Kb0DV+KJVOZY4QPBqkQuIseqqxT0J8WruVYGU226FGiEquSipmB1Fc5p\nPvb21xsqi/uwo+xDQgRN0Dd6EW5E2fw0jj8bO9IzLsPYWVaONHGBPRIV2wz+d2XuiMyiDvsuUc5b\nS8lrBrK/2htu1ihA0LradI1tPibGKP9eVk5eJok3kdUn951jtTiauWJ3vwCJX9pqUO/uFLmlr52U\nJDewBSgEYWzxdyGq3EQfiNaMhMRJr61WqiKIDs+mojYNFKuUFdxsSl81VRuHKr4UhUzBtu46riq9\nXylXoOgL1YvHgmCQZH76lJCy25O+ejOgM63c98dHYYk7BhKmnmvHi3Ce4/SqHx/HzWVQXHkjELsf\nPifXDuh4IqT4m0C91tdcfoJo89nO0dzL18PxzuK6T9RXDdIRzRs3h03jWRhXt274Ps6fCvfEnTu+\nbWcnIDtrkaToMO6yTH3qth8LWX6y9LsT9D1FQc9YpDY6zHV1pWMHKLWgmVzpJ3JNSqAJY1zry4fX\nh88uL8Lf61ZkNTD/q9tDhyKX47XPCcw2JJK5KDDfdomS4kM/0YlAtTYqFMe0Cg0D9RDw0boGKubi\ntZkD9WhSn89qICs6TknQHrFvckHVcU+O1cOSf4jUChH2Vu6dDMUAqYzJ1Zoq3l6A0NT8rRK6cXzs\nY6soqwl90nQ+17VV+LyqTkrn6Dn1KB4ZyTyV0ZNSnp3jETI8XWjjSuZa+u9p9qXHPLlVKDDsy/uJ\nZPdO7mf6OI5KecZlEZGKESNGjBgxYsT4jkR8kYoRI0aMGDFixLjPeGCpva6rbVXdGv4/hgJ5IYq9\nGXQ3etHnoD7UFrQIslshkPTuNKR0KkkfNiDtLdbhuKXAdQXgwVbSLjRQznPRYiLpTrRwrGMKUnUn\nSEpUFd/wbwtTyDzxXFgPgl9WKLGTxGbZtqE6ucDY1BaRtCCJvbmQ5JjGcQkkJbuDxK9GytSiEiJe\nAl2S3FR3JfxdlqJ2C62WjWiRZNh3IuayDfqiByyrklXsu0zaNZ3SNFb0wfBnZ54Ca1qo0ouybw4T\n4LFoqzC1UXc3cFAxOYZ2ihqabpBuagSKT1OSQ0VFHXB3JUTxGbStCtHbmoDQTpPnRo5/dh5IzJu1\nX5MpCiuSxFPbFbSPSjl+14TvKSWXxONr11yVm2TfIZ2jGQsMlJGMSabqVHeIyt9K9qUqdCkEaKbN\nC03t0lx7MLfOTnx299j1oeYzEI8ltXLmDFIwaiR7D22rYfzLfX/6dJgnDkG6H4uCMYfpWJTQed/1\nkkZ46tlnwj6uvzZs60D8Xx95aq1tMd/J2N3F9TxeeWplbw6iMO6N+uaV4bP+Rji/4vFHfb83wvU8\nd/7hYdvNN141M7PlkRtkD8bord8TTDdvar92gy4b59hE006hP8fShzRtX210v6ENW0r5NPdW8jrW\n8sXI58IK15tj86wYVB8iLXddUuarKszxG9X9QaqyljRWXcPcWZ56OQopVO+PRPqUKdXk5FyXSaES\n9fs6KfYYRqJMaNQWMyGb83mmzx1DSotpP6m/GDTYVO+vA1VDKRNUlFe9O87tatpsIMNv1pI+RtpM\nzZVZ0MTbWZ81GZ7P68qftS0M71crKUqhCbSkipOaWoU+J892aC7ufdKDhjIBFSAVJfjjo/BZqs86\nPFtTUWcfDEASH+t8jOp8nqTsf3UZiam9GDFixPi/2HuTWEu2rEpwW292u9f4c/fv3/+P+NEBGSnI\nColCDIpJiSkMkYIBEiEmDJgEsxjBiBEjJCSmTBAjhGqAhFQSqFI5ICtLUQ0BRAQR/O/fv3evu631\nZjk4a9teN55nhPSkKK9KnT3x53bvteacY8fsrL32Wj58+PDxU4l3hkjJGB75lZU9VtWdlbqm8Isb\nyS9tu3fIwaIw9CHEZUS0nE7hiVYTsbUH8Vo97iou1wV5L2HVXajD9g2R4/DmPFANvyrLBkc+eSAW\n0yXrIlq9hAJ6C05wrexNVennRygR/O+IbBxgVdET+jYCzQlo9aGSCCkIzQ0R1pPC/X2MKmAFOxAp\nHZ5oEaGEI84vibkkHQgXlfrriiCg9/cW11EBzQtp+RVhlRoT6VBXCVwtHUxIm6Efhy2UvZeEOmIc\ndUI+efC/asCUD4gc3eC6WYm5bLRcmfouwwqm43bS/ic0AwRMRj101adyDllsq+8S19A1XNauhE1e\nranqr527yhkwsbcB6lM1hmblkBFQtKpr76KvBaujd4qSsjmX+4dJ6fO3IEcJCgWqikjxIPJOUgd0\nTxQ4p8POiMW6wp7NjDBaQPbgWJ1bnQoY/bhbMh/hXjwBUZ1LuOdKLCdvxKZXqQvbdvPKkcOz2LZt\ndxgLtEqPS4dIPfjKL03bDh//k4iItCS7cPPC7e/xE6d2PlC5+CgOYSqv7Dw3L11hzfnS5sS6dG12\ndm7jabd22+qeZDKg6J0kb5HLwL/cJ4pWdewXqL8lv7wxuKtirkjcSPe/IvFVbfuLcIw5kM6R9vE5\n9OdIJOJMHPG+ubGigAZSAB0VhajX6kjXH4+KnJFPnM5Zoz4vbLLRdtJ7WUSkBfrN4te9tg+jOjiV\ngZAzlT0Yx7ttrHM9O1ao1EdEaueTFyshOBkKmXpGvyaJEbueEYUHfW/9rxmIgQqqtFAkwHPvWIbF\n/acmV4DmgOdJTYUt+pxqOEsBmQJydIggU5CT7ErTun2Pms1JGbmGryUbcGKeiCjroxBzO5DUAuRE\nNEvkAmR3Qm5DKjh7W3hEyocPHz58+PDh457hX6R8+PDhw4cPHz7uGe8stRdJdERibGoHt9WUdolg\nRpyQEmkGEiGbiyYgr40Bw31u25xgTFVUrRWeJzg1Ht33QoKn9dOQiKUd0jcBwX4SaBrLIPgc0GvS\n2fHL2sGog6bbQoMzR0CRAaVMwsH9lhW7+0FV3Mn4EemogLQ1GsCcqmciYhpAIWBcJicrZMoaP4H2\nDx2/QWozZsKqwASaiH1JASPpLaUboYcSkpGxKrTDY1JSTpkCHu8ptfL85b+KiMhHT/+DXT/SVz21\nSYXxFLEJNlIUDaW2QpBBR5hmMzqsar4daeEo7MtpRJmgeEqLKQTOmilIM/SUFljOkIKDyXBAyuqa\nZStIpbxH/3Bhhaa511uCrAP3vZiUnQOkGeIoufPbCuN6Qekh1WdhJWRVGU9Ix0aNb1mxWE2VT1Z2\nfM0VNW1057caq5VpQU0mt4EVpRTF7M7vND13ZIaNe3uxtHssC1VFnVS8g+PzoOEiA9JIHRGbp0wp\n9dN+C4N0mk21nqCmFMgGqfdP/uP/Mm373IeOqL7MLQXXj45sPcA0OWus/ZXQG65vpm3vPXQpwMPe\nthUr99vbG9v29OlHIiJyc2nFBtqfMemHqaPEVIhAc5JeTkAmr8oTjkmDT+eWgdJoA9qMtcWmY9Fc\nXEG4Tu+xgEyGZ0j3pRvSbMO557GlcfaVu+6BclA5qAdJYdeqhR+cAms03SY/quYt0uOpMJC43qhF\nUWxkLNAbpGsdMZ8fp0rdvwOllIMESuF4nvWsxZaDxN1wuhv3NelNVWouTEbSAea4gNw2lI7RtfSc\nhLlzRrpwC9wfmoGm+gtRRsFIzx9NX470PNVhHFIB0AzP+CKj74FyEicr+p47YFmheIJScXGmZsyc\nntY5wbY0rc5dtk0Lupi8r3QU9qAP2X35LeERKR8+fPjw4cOHj3vGO0OkgiiQgEoY1a+ro9LwflBl\nb5JEQIlnVbE6NN5+CSUI9NKIbDrL3dt3XwKF6BjVAWGZPOxUKTWg/bYg+8W5nXsNP6+YlFAF6Agr\npQehW3XusVpQ2QZ3rSB2H734uv1FvPob1WuPSj3Vr41ev3XBPJDUQKQrHFxjQKv6ACiVIhluH6pE\nzh5KbiUUsYccyOZMwG5B5A8JJewGIEzsiaQ+fZPXoV1Xp2gOqQ4Pg8o/EHIGhKvuSBIA5MT9la2+\nC6x+aipyCHFtWmQQDDyGBOd7V51ZRvveEqv/UZjsD1I8oWkxrjElhHOQGrsbcQ12rDx3aMpIiISu\n9Lk0V0nWTBge3uJFpgr0KUl3lCguiNK7ayoltr5NEuH0xBCU129cAQiT0lWSIM1IKT9UxWQ7dyWD\nq/9fmt5VU14sTIlekaM8ZwV2VdZmhFWRw/DO93jbROgF1NTVVjCgLgOMdKkq80j99NlzJ3vwpaeP\np2115UrtR1IlnxdOaqFd2PHXWzdOv/zYFPhf75zyeY2xW14aqpSv3BwW07x2+cYdf7W0FXwCku1y\nyUUJQEmpAEGHIt+7ipyqUnhLJfyKOsXhXVSxJUJu1auH3d1HzFGfYHwkNHYUde2wDyaMl0q2JgRn\nhnNazQr6ntvHgUjx2sdxZN9TP9MxtmNsDtfOb7VaAAAgAElEQVQ4LrIP7A6AuS5i/0WQ0jvq6wgN\n2xBK02JsRUdjEmrnZOgZpyrdAccEKgBJeqjTkwJ/hjmhoWKXYFREip8/kEmg/swjdx+35D/Yot8Z\nzVJpgRgFSAnNay3mp6ZmdXD3b12TewnafTzy2lTvSJ4TgRyR80cIhKuqXEEBo6QZipI4SaSuGNsN\nFVYUOBapmEdTvzNyqE4RLHvxFpNTCo9I+fDhw4cPHz583DP8i5QPHz58+PDhw8c9452l9kIJJ6hR\nxHxhG9J4yEYHe0ejQfsKswaUbyhh+FkERiwdWiWq2jE1G5JFDh4dSLFWSWcDKXGrjs5IMLaa9rYE\nrQ4gtrE6rClwG7SqV7taOkLt7YbSCICFQzZojZVEaNeVIwXDek8tTH1DUocdAd8GdI2q7B4G0PgZ\nyDQ5c58diHTY45yYnFhBqTtIS/ot1L5Jb0uPxanKDumuKLjbxqoOzxmpDKmaKGCyNzRDyMg6Amm/\nJ2i9ah0833SWFukCR+KtRyMPxzDBjKAxFRGLctKnimwQZdCd4evS9AVrm/TQ8elG66cM44iE1SWq\noZWm36N7osMYW+Q2/g87N9YZatbUSkg6RpoyObDaPlIA7AqQQ4MpwrnvttY2qgWlGlP8+UtSR1fN\nKk3PiZiOVkg3oN6zCbE9M5BMVUeK3QFUl4rTeKotFbxFsZ9TgC1SOpRFkQD3xG5vRN353B03wX3K\nKdEI536keo0xedgQYbZw17C5ej5tS5Hmv3pj5sLnj50aeZZbe2agCHz2mWkgPblwKcIfvHQpu/fn\nlvbb49yb2I6v7b7fmkFveXDjZF7Yue82G5ybtaemhVlbSFX29VicxtNbl5WgtxiTnNpNJxVz0hFD\nvwchk5JVb4rGLo7XgcV8Njd9Ou3QV0R23hzcdV1vTQG/wf0UZrRf0EKylDQI8TzJKAU9nzuF+Otb\nN8Z3t5Syw/7ilG5i0BhqoaIMUccAm//rSonqlEaFk0FDaakEY6IX1/48/g5TqszasIQqPTFAJMK5\n7EsbEwmI3zE9JwMUQzFNu0YnD/QsSpDmG0CLGIlsr64EYciFI0iB0tw5OWVQ/5el219e0DnhXmT1\n+LGD3l7snp1jYPOU6kLGpJiuPvcdUWC0Fqynd4w0+RFnBzFXhpHoG1Hw41+VPCLlw4cPHz58+PBx\nz3h3iFQgEtLSPJoIe/a2WKJMncmB4fRWS2gSFFgbWhFE8GSKRi7/1DdMIAiEVoUDVgFUQt9sQOKd\n2z4iSAgwObGp3Vt/fETAc/8mM1J7njkyaCju3+XCvLFubtzKVT3i3HW7lWgUkCQECHiMUtVKRia/\nJi3nT+lNOui0rBbXQNc/AC3Iab+Hg/YFkR2xvwORCCV06M/Y0zmVkJqgstEA6sFjTIhUpCtXbGME\nQdUXCNVTdIIRwVDLdInY2WGVVg3mNVYf3IqV+0kXVgmUxVMilupKp6bVd5QoiZnIzlhhsbLxCEQi\nIMXmTnTs8PITytIo3S5JxTuFYvFuZ/fEXH3HqE3iSFE62+8GHm/L1QWdu1uJzxaGPjx74VbdBdSb\n53NDdR49cr99+crQktnc9XHAiuVAn/i3e1yHFniImJ9YMbNt6nWnkgRFQYR5lYsgFmkORXNWMVeE\nhT38lDzKpNR+UsUnpXb07Rr3HyNYupg+ObHr2q2dFENELgYPH4CwS1IfV6/c/pjDP+7WuOYH07YS\nN2E62nh++cahqKeYL15Tvy4mdXC7h6qDG+tLIpsPaJ/N2sa/oo/E9Z2QmDSzttsAuVIkhNt/IuIS\nsVvHAo8JdW9gtfMB9xYT0FW6Rbh4JDxGCcbOrjXHtosTQ+luMNes9jZ3rksg0aQsHuAei0hOZ1a4\nvpsTIpLFrr8v5h+5XXxoDfbDZ//stoV2n04eomLttIWKPXuX6hwzEJw9qHcfofQ9kKscUiQDodp6\nTzCq02H+bwn9CXIoi3fm9SiBQ3MqQu5HIOwje9JNhVpU0ILnQybqgGCfDUB9alIs1+EZHz1jWnyf\nvodz7oRQP2SgGpo7W3jtqfyPxNwmkNWJSbEeCHtL6uwdCO3haGO3Htw9y2NSXTtinuMjmwPeFh6R\n8uHDhw8fPnz4uGe8M0Rqls6kordQ5UNEJAnQwRuvbOytWr1+hD3cwJfgklAtPw2odD0N5/h30guY\nPtPySkY6ekgHdAdyup8BQcgJfUFJfhrR6hurjoHEJ5ezBc7J/TaObAW5+sCtUl++/lfbB0rjWSxM\n/e8K4lnswRFII+I54NoSyhv34IH1kAZoSRBUS3J5BR+r12DH5erYv1i77rASjEg4rwL60FGZuPrO\nxYRIBVj1pIlbGc4SEoTUFQRxtFT+gJFLRUR4RbyYu/bc3X46bdNV90Cr/0jZAYG6sN8t6x5pZdZA\nVLHISZJCZTR4SCqaMpJIJ9QeBxI4LeF1peX0RcJ8IDee5ktafd+4le6jJ4Y0qfzHbm+8gdMTt/rs\nCWGLcXzlJYiYZEAGVGd1YmPygO8dySrguh4+fDhtq7FyZD7I2TnQVOIehJNMBnPpXPsob4u5Ctr+\nXEKtv81p/Nv53eWNJXROA47B5fch9hNgvFYlcS/Qd5eEyBVowxcvbFw9+uBDERF5+co4UkWq9xYh\nRzpOSkMzzh45Mc3LF8+mbSrieoL+H8/tWqO1O7/1zfW0TXlmQmNtvkCpOaEPRaErcUIudO4kzqX2\ngc5hXIY+ibP2XEJOhCWEirPOM5tjQiCGIyG8KrHQE8KhwIpKxwzE/QyAMPD0nycqiUGcT4yJipDL\nAShiTRIXKgC7nNt4fnj+BOfpTmR7MKTrwyfuui5vrb9KcC/7web/AVIQWc7aIbhuFk5GX7DsjkpB\nKxJHdEDJE71frV3jOSSBDoY+TrwtRhPRxirM7Da6fxiJnc9ce6bE+dR7V2WKxo7vYcyrJGGg3EhG\nzky6h8Q0gQRt6dwT8EQDesZ047HHKnOkJ+SSxU9xLilx5PpWOWokO6TvGCT7E0cP8FtDroaj/rkb\nHpHy4cOHDx8+fPi4Z/gXKR8+fPjw4cOHj3vGO0vtjUNyVK6vars9KdHKiJReS5IAePfrjjJ7gOzp\naiqkAwbyxOoBNy5PnOpyHBsU2wDS33QG7be9ql5TeqCDT11gKbOzhSrLkicXWHas9trk7u8lyLY5\nlYH32O/F2eenbTc7lyroSNl7FJfSaghpVJJtQKXbOQjYyZEpktvP7ZVTTj59ZMfSkteMUlYFfNr2\nB1JMR2orJLXzDkTRmuB5eUtZs8otDAQ3a5pl7NF5xNgOJ280GxNj4K4/D4nsLpAwIMLgydJd2+3h\n9bSthSQC49hdoORx7I/GVSqaWrDUchirYRQroEOSY+QycQcjd9QnO5QiB3SNy9yl6FSdOSHIvAV5\nsyTF9kePHokIqY6LKSYztK3+WxGlChOVzthyasP1saYPmMSdYAztiAD/5S9/2X2f0qhzpBY6UmA+\nPXWpWitYsDTv4/csLTlJN4RaLk3jFZfTUG24wvj8PT0GSy2oEnNDqXr182JSfhb+SLqRqAAR+q4k\n/8nx4NoiIZmK//yf/qOIiHzpPZJ/UFV0VvbW1GplbVJ98jGuy879BOnVA1K1ywvzH9xduxSIykWI\niOS4Z1mV/QpSAHMm1iOlwj5xU2qVTnPygkP7t5SK0TQqq70zReJHo6XUYjBAToVzVRC+4bRwr558\n6Pee0oia5uM0XoF8z9BxsQ089Giu0ZQ+p8DPTiEJQ3OCptRPUNixKi3d3fVwTDiY/6NKrbB36dmJ\nk7DYHd5M27QmJqBnx5jinKkoSltTfQpTapsEaTl+JqVwmYhJ6qJCoVbU273bghbDkgSqN8OFGrFW\nSLCjBO7tDjlVHkPqsZqRhIYoyZ6OdWjc2GVaSIu+Hmn8q+OHqvO70zweY0wPCDCGWDF9DCAJwZ6o\nRzI62ATqwxhYO2mfpbHdz1k6lx8XHpHy4cOHDx8+fPi4Z7w7snmYSEWyBlugOb3cJZsPtIIZ8JbK\nbuGCMvGR3zhBUO7IV0fdxFOUvxeFkXhjECo3ta1WGpTOp4QgBL1bEcRi554r2a+xVUXT6xuxrZIu\nL90qZvHUkdnCxN54Q6y0q5oIfgqxkdegkmK51DPCNeaJnVOigmi8WsT+YpR87m9NVHEBcnBKK6NV\n5t7CZ7Qiui4dwjMeic+BbExgQoCy2rBnYqv7m/TopGuBnGVAGjvuQxXws+tSkdLqYCjRauFQmr6z\n4ayl+P3Zz0/bfvjm793xSWhNL6OP1NeRUVJ3LqvcfOW2e7daaSsbJ6munHi/aIuKfaXQThm5xB/g\nUq9lvTkVACzVC5FKeKsGfbez/j9ZuWutaiYMK7Hd+r/GCv/xe4+mbeud298M5d+8j1cQ3Xzvscl0\nlAe3gixYJBOCicUp+aUB1RjoPi1mGX5rq7uqUuFM+ErOSBBWVBKBiiLgjRfRnKCyCkwsVzFJJsrr\nUv+I1quClCBqX10bgtA3bh85zSv7LYoDqGDidI6ScEITB/RxSOeZgGR8trLxdHmD86RjqBBigv3W\nG5I/ARJXE3I/7l2fzEg4dUBBizAPHOcSEin99IG779e3Jlw7oD2VbH4g+Y0UqF4xJ7KzygkQSqiy\nJm1l55lgzMQJI7cqcGzH0HlPC2BCEhBt9+63G0KpQxCvsxmVqF+7AgGdh0VEEszPEQmMXl7/m4iI\nXKxsjCvCdnLiruEhSS2UQKKuSitAiCoIiFIBjD5/0sTaaQQ6xrI/BVQ0UyqoaVugfkA1B7FriOBP\nGzRcROL2l4ndVx32t6dzyjDHBFQApfdTmhIihVtmIHi+hhDz5PtJRSwhMgJhn9A2ZIl4TCADs+tM\npHYI3DzORWEB5lPS5pUa2RR9nieZnVt8t9ZEKihycpamh9QIo1k5xHRHbuPEHWtff0bHIIL+W8Ij\nUj58+PDhw4cPH/cM/yLlw4cPHz58+PBxz3hnqb2+b2QkIrKS19iHSKAyzvDcoGRnIuwpzE+IrcRQ\nlu5JRVY1KNSTR4m2IiJN67a9viUSHxTAayLRFkslQDMUi++nltpoKvjKEd4YhW7bq2uXMjk/sRMu\n4KcWkECKkoeHgQiboaag7B04gz4Sq5KH0N0ISTOjOiBFAALgemfaHYvTU5wjQbyR219KKcMKBMhm\nvEsYZd0P9acLKS2qfdwTtNzA2zCEpljAsH+kRFTeB2BZUnvfliDPL784bVMy6mp5Pm2brx18f3P4\n2E59Iii74xbkFzch23StWeLGDBOW1U+RFaNVgZ/Twrs9+k5Ml0YCVVuGOvqMvBlLpFgaHmsjrs9g\n/FeXLs3A2k6bjYPM687a6Ss/+1URMX0oESPZqsbQemNjQr32WNtphbTUngi7eo8dE9XddSyJFF1W\nXCDgQptff1s3rLoMHR1Kj6l+GmsBacr4+tq0lZiMrKG/aYm8XhTZ0fFPya/vzUuX7toTOTyY0rJ2\nXz9+7IjFXW2E1RPoeO12loJuG9Wqsu8l2N/JytTOJ5I9zjfsWfXatWFDuk/zE9fGnJbV+ySktfL5\nhbsX1pdGlD5AFVxTPCKmvD3q3MEOEIGm4MlFAukpVed3G0EjyKwfYhRUjHQ/q97RQDlIPXdNLTU0\n/2r6kIstShDa9y35j47qSkB+pjv1ZLTxXDUoMjqYBtju8B7+dSm9c5oSNM39cmu0iFef/cCdL6VM\nldisBQ4i5v/GaWnV+4s4VYY5Y8S43jc2XgRFPglpBo7QvgtC0tEaoWM12jOha7WwhrxDc6RRiewd\nRO5EI+oTJchr+pILQFSrMJtZUYR6GAZkAKh8/sXMrr8NMRaPCpB0jqdtg2pqIcU43n1O8kDVwqOm\ntn0EUCoPqMit0zR6QhQgFeUn9fry8In8uPCIlA8fPnz48OHDxz3jnSFScRJKTQqvI/5mBElRn364\nq7qbUqnlKEoE4/3hTTQiOQWUOGopIzt+a/nxYmFE0GtFbCI7foc386amZcpbVFwjoFlHlb5QT30D\nkndLhMlZsTj6nfstfIhaWxH36p1EK5izM7cSUCdvEZGgd3/3jaEfyvtr1f2aPKc28BA7R9kudujO\nbU5ka5zyQMRCgaJvQO2k5fc9qRgH2k+jDTtd/Ta1Sk3YbiOsSAeCGkO0Yd0YutHvHXJwcUZFARgL\nivSJiFws/72IiJS0wqvRnrrCZqRxWtWTq3kwjTFSEYZP40DlwiHIyAmpAz/KXB83O1Zqdr+dz+DX\ntidYCwTMnNShW6hjM7rz/vsficgxIpOhUOD8oSFya/iuMQF0C8QkThRppXPD6u+EyNG6EmUPN0WE\n+HuqgM2K2Scn7vPXr02S4sEDFF6oDAYdPwM5mSUMtPydEZECJGuukFYk6MEDk1roG0Vf7PqVXF7g\nWoXKwC+ANN28NLQiBUp7tTZS+hIIx2Jh986+dNcdpzZ2KiBb1bWNvxlK5te3Rmw9O3VtMqI0fHuw\n768wZwVk4nd97e5dUy63uS0nlHxz69CXKGNCP8q/CXXVuajBfZemLNPixiT7FeoxuABFif8Dzcla\nNBQGLAkBhHXOE+Ux6hCnNiZ67G9Feg3LWsvvDf1cAbFtOiPR15Cx6DpCqXD9P/j0O9OWDPdbBjmB\nNL5Ljj/JjICeo732N0ZAHxOtYrHzzIHcjGRyqmO2o+eeyngMQGYK6sP92p17espIl/v+SHPNSQxZ\nk9a+t4mB+gujj5C9oTExTirmNu4GZDj03EdCSTOgz21j7Vpk7vhRQKr8ocr/fDhtq/CcOrSG8Ok5\n9ZSJ0UeAIt1ZThISmBMYkdWiJX6fUDX0kRCpBAVFecGZGCirk+xG29s4elt4RMqHDx8+fPjw4eOe\n4V+kfPjw4cOHDx8+7hnvLLVX9SIBpZYSVZiODM7roLvDRLgRKaWOiGWqXsuk2AjaGhFx1/elg9YD\nwMk9kaNTaDrNZkZA30CXKKB0w76COjXpbvSVpgzst0rAY3i065WA595fr9f/ZifXu7QAw9OaxhhJ\n2b0H7LqYG7ScJe7vviTFYpDsWiJ2VyA0ZzBcDkgevu/UtNdSRkoU7EnbKQNk2+xJbRq4ayCkYzMp\nFjMs39457gDoWYmabWz7TVW7I6R0l2ga1bbUnesTNr6cnSKlQwNAVW4vlj8zbfv08v9y+2jdPjR1\n5n7g+qIjIuKUvhisrzVFyvowIYoGCjKNTtA+M7FUnXRId0ArarUwwmaM9q93NE7Rd5yy7CfdH5b7\nd+OzrO16UsD3JamNL5cuBbLbIQVMaYeHD5yhbkPE+h4Q/+mZnaemkVgxej53bbeYWzstkTZ/9cpS\new8fuX7SVF1F56umteuNpadPl4uj74uItCAjM4l2pqT0vd1/er+nNCZWS9ee+1unBH5JabwM47ql\ne0Kge/b0gy9Mmw5IvY00n0TQPqp2ZJCN4fH0I/vti89cOmio7HubW5eiffjYtf/mEzNI3iDNeb6y\neUKXwy21SV9BxZrmRFXe5+9poUpHRto7aIupgfuB7qsVXBxammtC5KeKyNLIA+asmEx7Q2XRU7pF\nKzRGomoEen4oWBkpZV5Xd/WBcqR2lysrtngDU+GRKBCDuOvizGIQaWrTxvgnn/2LiIicP/icu4bw\nh9NnDxZuvHJqr4B+VUPZn1M8p0ZSO1dieUf3qWavAuqnHvQCdVYII1LVztw1XG/tYIsUhUI0TfZ4\ntC8S+200uhThnrQaQ8yxIZHCE1AZuobmbp0XoV/FKcMepPCR9PF6FBEsTmyuS0GQPzIc7x15PyCX\nj0Pl0ubdSCR7tMmIoqSQyPZVqcbXpC3XaVvfLYpKM54n9Tlpm3RoxaTBeOTQ8ZbwiJQPHz58+PDh\nw8c9450hUoeyldmC31YVaaJSY7znsbLrgDfSnpRd9U2zT+21Mkm1XNK+NYYok+0cOTOtbFUXQll7\nRirKcemOMdDragcCaB2Sii3KTyNSVs6w7OlJ2VvPuAXpNSB1ZEVkOirX1W0jqbOfzJ+KiMjjRx/Y\nOWH1t6GV80SephfpfOZWJNvOrb5zIuz1WGnfbGylc3aKVXXHJG6QQwmRq3X1Se/lFVAMVrZW0nZE\niMAI/6sRrdNSCXOPdg0HIkcqOkWE9QaSGCWp0leNKlszUd01BpNnT1eu1PnN2qEeZWn9WohbaY4H\nakT8mZIq/9hBnT2y8RRAnkFRFRGRRBmTVJQwlvCEO7jrroT6sHVIwNjY9+eJQ3XYE/HFC0fUPDuz\nEvrTU4cO7EpDeLZ7dwwustBzUdmDjz5PEhL4XkiIcLN358fq5DVQJCY7K2k/IwV0/R5LIijxe0Jf\nCf1ViYmYyOGbLRBhurG1FP707OTOtphN5IDc7Ul+QJefS/STSj6IiKQYLyEhiCOQnstLU2de4jcV\njZ1TFBYMhLQcMD9cXZn8wIMzh9jtNiaTMOI+2u3dtp5Q0lOQ8/d0rBT38dmZoX+3N7e4ZEMJzj9w\nc8f+jRHltU/Ya62vjyUOErpfJlSFkEMlSjeEXBcqI0F9lwARZ59GgSq5kAL/AOmaEdIMDameJ+q1\nSLvQM09oW5RjTs4ZuoZ0ApXEq6NBRvOZIqCffvbPIiJysfqfps8OpfutulmIiHzx0c+KiMj12pDW\nfeWKBx4SIquK7exdWaEYiM9pUopXhIfQwgxyHQPd1ypJMyf0P8E8XRD6owUlIcmpjOoUwIVfmLMj\nIqUrwtNAfqUhtGrE+ExpDI29G7tDbGP9FA4ULSFyCfxuz2g+ifHsXPf/ZteoyCUyMvs9ocSDPjtI\n6gDzwxGxfEK9SLpmQiTtPonhZxgSIsUWoG8Lj0j58OHDhw8fPnzcM/yLlA8fPnz48OHDxz3jnaX2\nyjaS+WiE2RjQXUDK2iGgxU1J5DjAwl3HsBug6JrzeNgfpZZU5fnVrSOUzlIzbx2g6TQKK7a6/VVM\ntgU78JaUbeezC2wzUuyDGATAwCDLsnSQeqLQNn2mwq45paImJVqx9MiH77nUS05k4xk0g75f/uu0\nbY00xqExCFiVgpOF+35fGcScLxwE3Y/2/RKw86G260pDJXsTsRr7DagAIAZpuSMYX2HUIDJYNlVo\nFXpKR5kYpLlGgrYHQNAxkbhTPT79dlu71MvD2eftPHGscORUmSOo1jnI9qOlEaoaqTW6Lk1VJpSe\njAOXUuo7MvKduf0mMSl1B5rutP2VUHZ/+ugjERG5eUVmyICiI0o7qD7K97//L9M2TV/erk1H6rMX\nbowvlpbuWq0ArVP6TLWV3n/iiM3nZ0YYVq22F69M42iAPlueW7pLTXYZWl8gfceaTaqKfQtit4jI\nk/ffP7rWjlSsd1vXFqzPpibHbEasqdqBdF9Uv4xVpDtca05q07tbmFCDiCtEbK6gzxORYnScufN7\neGHE5ufPnmG/ZO79yqnnL05tjimQ5ulJAX23dqn0xcraXQ2X+8615+rU+lCJ4gFd/yn6bH1rpPAI\n90dMadzr564fOQWjek9dT1pdaEfVFEvpusZBU/GkmYc+ns1sPtM01kCplUmpnri+XenmlojucU3L\nBCCK5yQud7N2Y6cicrCOiRml0VdQ2d6V1iZVdIVzsjlOzdVJKF1S3E+ffPqPIiLy3sOn02cfXrg0\nXkq5xfNT9/kXHv7CtO3ZtWpb2TytavezzOa/6xHFDaGlwPYwRp5Bq4vJ2TstFKE2OYAW8KDgoiAl\nkZMrQKFjgYoiZig8ojZpQdAeeyq86TDvg4IxsD5gqDp6ZC4Nikq1tXt9wPP8ARHwc+jnLUgXLQQf\npSNdqH50RRlRpM9/1pus8S8VMeA8k4zeJ4K7dB/VmeJs84DrD0i/cAx+PObkESkfPnz48OHDh497\nxjtDpJqmk6pkby6ovrbk1xSirLu118VW3wwHe1tsQfYN6U27rd3fARHwFE24WbuV2XsPvjJ9VkFZ\nO2QfKKzIiJsnJZYuXc9LIreCOJ3ZyqUHQTim1dw8c0TRXe284WKSf1A4ZRioXB7ncnpqJOIU6MT5\nia1gtfz2Ymdv8M9fu1VySyq+SpBue7cKjInsrx5uISEth9qtJiIiUfcg2zPZU9/cWdlbIwrtGmdY\n/RDAMBFUlUSe0EprOgSRE3XEpKwijxVkW9tKL5q5Njs0ttLLQYbeX9s2RTbOQOLfbIyIW/ZuFcQq\n+jFWNSOpo0dAZ5LAVLTT0K3O89TOfdc6xGgMbaWnyOItCKgsF6BIX5aZ1EAAEndC5biHxhE7WRKh\nAzrExG6VSWhaQl0adwwlqo9i56tSECwrkIIcrH0pYkhkEltfZxnGGqFUKZAgAiSkAWKqyEVKCIoS\n/7k4YLeFEnHEisluLBz2hiaqKn5Bas86xrS8X4TIq/ChJHsvSeFrNlLBSI0BuPvMvLeSWFfJLN3i\njru9NgLyHPfxgYji5/DJY0mKjK5XROSMEKn9xo0T9prTMnUuHgGYJeu1jdMH5+74DTkliN5/5J3W\nwDWgB6pQloRqzIB0BbzSv+trqj5xUcTnBP9PIu6qTAIXxei8H6MYJ6D7T4fdq0vLCHQYpwF5w8Wx\njlN7nkTwUeWy/gn9CN/i8Ypr+MHH/zx9dLp0MjWr2PokBxL26OK9aZvKuJRba+sQcioPH9p8PoPc\nzbMX/+e0TaUd6tbd12NnjZMUbl4JSe1/TN3f170VLBSt+15K7a8yIRH5ucZa/k/3eED37HQMIHcB\nMjZZat9fLdyzKIqpAAVzzO3B+unNjZPxmOV2/Rdzt5+2tj7RApFZZvPZoXZz9jg5UNi5KfrcUFGG\nFspw5krQxyzJE6HITChz1WKeTGKaYzwi5cOHDx8+fPjw8dMJ/yLlw4cPHz58+PBxz3hnqb26GqQl\n2D1HyoAVXhVik4FUb5XESLwy5TP2xBjroQHRUPowhDGpGhq/uTbF2iWMJ3tSEd/XN9iXQYZq2hhS\naqkDjp5ltm1E6lFVV0VEUqjM9nv3vZRIxClg0TBk41FVQrf0YAy4MYktZdMg3zBQCixXRdu5waiq\ndq6/ZXJegbQAmwwroa/vjQAtE+xpm3HWLVAAACAASURBVDKk45rR0oijuOMnhOMrHFsUBtmG0I3S\ndFMYcsoWqQA7lMygzs16P4fKEUpvyfh1NXdk4Ko3aFvR65bSGDkImNrWJ6sn02c79F1dW7pvvnDf\nHykXcUBKMU05LezGc0qKvamaa46WWhwDXD/MTUOCmOPeHauuTNurqVxfqJq/iMjDx05T7LCnNG7i\nxhETsD975aD184dGlFaisKY4y0N557OcDE2VxFqWd7/HmlmT4SxVD2iqhB0I1JA4xvGjkDVmkqPf\niRiJnUnpmuTI2YQcP7kkzaT5TI28KS2G8Vfg3Nm0WVW0OzLIXoBQXZakWZfcNS3PocBek+FwuXfn\n8sFTM21d37j+PD2z8azaWzfQqgpInVvbdbm0e0jV4GPSpSugh7dc2fd2O3esPaURU9xvEadv0d8/\nqvElIlLDmJYJ4MrUDemeiKGiPoi1tfLJY5r3E4zTkVN7qjelEtNHRtbu3C6oiGJ34+6ngExzU5Dt\ni9xS5SdLd93lzubzHhqBEWu7KSke5/Hs2T9Nn50htfelj37ejgUtppO5tXUbujR/M6NxijR2TAVV\nOXKV5+eWFpTAzWMRzq1sWB8QlBV6/um464mAvQP1YKQ5Kelwj1Ghgu47IEpLhRRZR3NMCC5HCvJ2\nUthzZbnAeCa1+Qipzds96aOhkOf1G3MPeAij8yi0cwomdwsqKEEqtdNnDD2nVLMuJWqB3jM81+g0\nMtIcqwUdCV2/zoUJ6UJyccHbwiNSPnz48OHDhw8f94x3h0g1O1mvrTRSV0FFwSRG94a5WNqq4ura\nvdXGRMoeoI7dkxLpMGr5LRPn8CYKwvDmYKvVAL5+YWorqHZ0b9NMLNeyX14lqtr3Zmfl56dYOQcj\nEcpxzlm6wvXZSms+c95NaWIrrW5wq4qBeJAbKCAvC1ulbHeuTVpapQegZc8IJetQpprAB2qgZY2q\n3g6k8HqA/xcThlusUhIqa29BzhyorjmIseobmag+4hqt7wIgMhPJnLiB46AoAa0WRkgCtLZaWOSO\n7BhQCXECxeIwsHbS8ZFnjFJiRTxAMZlWi3NIGGyIxKzfywu7rhqKydRNE/G1IwX+YAQBlFb/XYSV\nIzq5bWm1jJX4jEiPOQiYqxNDMJqpUIBI0aXb9vKlkT2zAt5dtPpe4DoUdWJPKUWzDoRgfPyxK+t/\ncGEr0rPTs6N9iFgfM3KlCtgprYgVAVIi8pzI8YycTN/HqreqDH1QdDKOra1r/DY4rmsWkWOlcP14\nQO8dKUGjPbeECBygip6l5JeI1WxPyMkBZd9ZZG0dY0xcXdm8k6G4oCP3ggHorK7S94Q0KsLXHfnl\nuXNeb0xtfbWATybNXdo/86W18e2VQzs35JMokEKYARkrqQw9gwJ1TDeqruADkpUYFE0kUnCAcx/J\nlUCV7JmorvOzktgbulZFc7j8PoNfXN+ZTIu6LOSp3Sfnp65oY0Mebp1WEhFyWwcgJYP0r6ixiMg/\nffcf3CnaEJYvPPl3IiKSUPn/PIV3K9UN3KDIpSE5n0DbKbLxFCbqv4nvkNRMgLmQ5ylV7+Z79zX8\nEU9onj5pXf8TSDPdf31tbVwDsW2pT7QIq0UR1YIKYFYr93dHVVl57vb75vaF7QPE96Kw81wf3Oes\nrJ/gOdmJHV+Ry6FVBxAujmiwX8vm6NhhFXW9VkZf1ROVCyDUJzAgUj4XyLwtPCLlw4cPHz58+PBx\nz/AvUj58+PDhw4cPH/eMd5ba6w6dDAsifYGIly3PbBvSciekBdLuoPtC0H4oDrKsBzb8dXB8Rtoq\nLYh6MfSUBgLsVOy6o/TgZN5I6YE4VLVjwwIzaEVVrUHLkrjfjqyKjl2rKWIUkHIsjptQKiAKHex/\nezB4VDVg+pFSRkjV9QOlMQPVZyIFWiikx9BpGUfDp1UrKIuprZEKCYRTMe6clQguIhKC5BoRAVVJ\nqQHtLxjc8ZuONEMSNZd0fVh1ZEYJraCxsz6cTG2JsNoDlg8JAt9Cq6sYjFitRMWBNFgimGQn8TnO\nw/o/xPFXcyOCauozTGycJmjrPakoJxhjEenNjGhj1sxJkaprkW7IKGWcQ78raiiPkKnCMGmrtVrs\nYKmFNSD1nnTEDleurVdLS8slgK9vbl2Kp8ipv3AqFZlBK8uVU0sqFcz6SKYZZeNPla/ZBHdKM+Jy\n9pRiUrg/pVyEkkMzUrYPkNroaEyq20FLpPA2hj4RpRGU3F+BFNtQKkDJ1j2llnukvZI5pey0QITm\nhB7qzTW1yRwFDSSsP6ktszGzTkvLpWsv1ozSWpiGzL0F476YGQVCdZxqapMl2iyi9bO2/5zHztrt\ne425ZrFgHTWkzCgFGiC1tjgxHTWdJ3oa7GmKY1A71ZW732Oa90IMvB5m5CO1/7Zy53Rb2dz5+tql\nNHc7K8qokdofychelcLDM+vjcgMHCBonoWoVoTiHC4CUHvDxD/5x2lYk7n56cmZFBEPS4hrs+rUY\n4vWNUVpC3M/5ggqKShQ+wdkhHa3/E2hKNUSYHnqMMSJbJ6rY3tp8utPiEXrGxSiKGslwekqfk95c\nlKqBtzvG/MTG2mzuznfHKdMefUc5sTnSmBkRyw8obolIby8BBSSm65Ep9e0+GzpW1nf9w6bxGX7b\nkN5fg8KHkSgtLfQjAzYoxkmzKmKQerK5Dx8+fPjw4cPHTyXeGSI1n6WSpVwGjbL6hNSRgXQcGlvV\nzZZQ223pDRrK5qw+GuAdkX2l8vT47Ttj0qtum9mbZzMosZNWK0B4QvLfUkJrTmS3q40rNc9CIxEm\nifs7TqDiHTGJeI592TXouTe3tqrcwE/t2XOTbjhZOtSFSYmjqEwBSULgHVuJmkHE79FYwdMqRH/K\n6vCTdxotq5VEuC2NbK8E1GE0hEEXx8FI5fQgQCuCNjZEmB/1WojEixVWUvGqWpWV7Wo2G7fq60P2\nukvxfVt91FixmKK8nVsD1W+hFelqCaQpIJmKyJ1ztTey++3WrZKrltSGUVAwMgE3df+Joc4+kPzG\ngPOMqIRZS3xvb0mxPXMrsf3GjqX3Qk/I1aNHrnS7qW01H8ZaKODGWk8IxmzuxutmY0hrDeLx++8b\niVfR2ZCQW0U6GLnNQV5msrsSQLXkn6UpFHXrCNUtCkgY0P2vZeVMQJ0BiY7Ju/LqtVMZn1NBywA1\n8jnKujtawfeoyc+o/fvWfa+uWRIlxLUQ+oG5ICQCvqIvgXB/apEFI+EoXgFRnwnwEwH82hCpDTwJ\nHz4wXz8tEDgj78TNNRAbItvqsVjiQNXl9b7a7WxcLclPT2OOcRLQfaXFFj0j8kATWOoghnRAT/NO\nkOpc5L7fUQGMni/LFehck1K5+gB/0IEIw5l6vB35+rnzq3tDLsZO1dbdb/uGj+9O/vLS7r9/jv8P\ndx4/YyjpDEUcMTlF6HHZKaCEo0GW23mewbVii2bvqQClA9KnxTQiIjEKOw4Hvq/1L0IuMf8FA/c/\njkHPOEVkBlLqjzItlEJRDhW26N8J3Wv7Sj0ZWc4EZHf6nvoaNr2hdKpGHidM8R6OPpOREWnM/xWp\ns8+BvqVGiu+RMSDQWVoQ5KOBszOQneFKiZ9AN/eIlA8fPnz48OHDxz3jnSFSsyKUcmdoRRiAU1DZ\n2//qBPnYwlZVFUqRi5m9Qe63eCNmRAooCfMr9E24Va8pKnlVkTh2y04Dx1+pWzrPt7x66ls9fxQk\nbnUwkv9TPnPXM5WcHskq3OUUDBCTHIkPtcdKi/2yqhu3bbYwNG+ZOfShOrBIKfLWuMaRfIj0ull8\nVNGElMrKB6AEIXF5NKd+dmJ8oBochiQlXyvwSwJCfdpKERlwxKiEuMFKsD8SBEQ7BXb9Ra7cC1o1\ngH/Gpdsj8vtFarl0QRvsS1eSHgrJNaiDeGRjTSuCY0JTtSR9ObPVz+u14ygdKhs7eemuf7kw4T4V\ngF1gbKxWNtYb3AtVbavlPdCBpiSvP9RiMx9qMXdt3PZU/oyV7r4ir7cHbluHVfh6bTwveQstQEuM\nGSVSFFNFIEVEXr9ysgsXJJPQAG16cG7XOJVaY38siKmoymrJUhOuX09PrK1fvXYChiw/EBQQmGXe\nFMbs7Y2hCUugLwdtu56RJjc2WPw1xmqVFvCSKtJN92mv/BpC2HP0bUvtr9fNMg3qLaicOqH7b39w\n7XVyahy9cuP6jCUhYqAzby5NauFs4XgtDSF3igCycKvKUyiacErHCgaVFaF5CtuqkvltQB+I+xXg\nOsKcuC/K2yI5G8FYzCBW2e2JewP+0iwlqQ2dz2g+Vd6cNDQnYI4ZQnvGdDEyDPwkDNU7EaXxtIth\nUATPrv+HH39PRETywua/n/3cV91viaPagP85kKzAMM1n1k4RPFgLoFptZ/ttwJuqaQx1GLPsa6qS\nPIwcTTxYysS0QI5U8kLEnmM5IUcd2jPJIRwcMarlrocRSRV7ZkRyC3mg04Q8QZEVCWmO1blbeVZu\nh+CXTnIp1mE6nzOq2dc4v4QQfvA1O+LoDg2kFjoak3jIs+hvn/74VyWPSPnw4cOHDx8+fNwz/IuU\nDx8+fPjw4cPHPeOdpfayJJOOSlMjEKVvbgwensFPaqD3PSX5BpFBhoulgwUPG1LAzUDsJsgwgMq1\nlku2RCIcciWMkq8VyiRHIqJVJVJWTMAGZJ1w3g8f51TWeTi4FJySOVuCLm8h6zDLqDQ9Uf81229T\nux3vyMNrloEATfIPS0hGvP/oP0zbvv8D5xn1cuuI6kPLZd1IRRBkmwEWZQ+z81OXnmAV+R4Xywrk\nCs/GI6csQGgmyFTTlz2IjR1JHVSQc+iJ9CiA5eutjZNl4Po6JwVwLX8fqJ/aDuzNlmFcNwbV862s\nrF1TjKEusNSSEvVjUmfWdCd7PZ0gHf3Z7ctpW1Nrm1k7LZFuUQL+QKfW1yDgU7pVswIZKTaHuMaI\nxm6I1E6R27mXpbv+mkqCP/7Bd0VEZHHqStfPLqyE/fbKQfEdEcBnC3dcTsFFsRZlWD8pAXx/MKKy\ntu1RGks9/pCWisnzSosMeiJxN0gVJAkVRejxiZR7c6PK4tYmqxOQouk+qTHGIpUTGGy/mmJjcXQt\nPAlp6uxAIg+prDtFWoavVdspI6cGJaC3PaXg1c8Oaa+B2l/TyIsTS7dF+F5NRQFzpIpDKiipcI8H\nJEmRY+5KZyx/4PajmUVOT56cufmpY1qE5oCJWK8fJ5SCUzkT4XlC06KhjWdNVYWla4c+IiI40k23\nV0ZOrpu780Qmrk2qgO9/zOdM9xBNWRFVYYv7rtdiJ5LVgZxBzN5wmJ//9YcmiRAm7ljnc5YaUQkB\n8mSEdMCRKwcI8CNkT4rAxkuAohxijEg7EbBt/DdaAEFuE6inOJIusfHJHrdaFWTH6JD6HlqV2rHU\nYo003kDPqSJx53xOckaXG1fs0ZCzgOSg5bBSu9JMjooMjh1F8ohU9HG6TItp0CYz9voEzSGma9Vs\ncN3YONFbMaJnhxBF4m3hESkfPnz48OHDh497xjtDpMLAVkMiItutI+fOC3v73ty6N9f5yt7+Z4Uj\nme4rQrPwtpgs6K0ab+esc6ero4nYOlKpM0TNoqOySvVasjdjXTl3sa1SAri/j4GdUzE5qDP6ofbT\nKNckEvXt1pFzo4BWcChJZ0kEFadTwUe3DcdMTThyWTwVEZE4MALwz33pF0VE5PK/uDLoRgwt6LH6\nKklAsM/g9VYQERTHmuW2gqxB8iNAZPJ9mtHl9CDIR1SSXB50NanoA5XGY7nQ0kozjPA3tfUBKGGU\nM2ETfUKyaupt2NKKaCIgYkXY0Oq/ASJEwKl0EBDtOkPETrHqKkhqYwapi1VmK7L11iE8u70hB+rZ\nppZ8w9z6K8KqdlNbnyzhcbVb27Ysd/IXJ2dGwL5duz6OyWuywgqfhWgvHj7Eublz4tJ8Xa3O5ndL\n3tl/UhGhtrF2XcAzj5ErJZYvaH9KkNaFMXvzKVrFK2hFdRIizOrx9ztCZCB/UB6sn2IYn603RqhP\nofFxis+qiuQXsPqlS5jmjiMBzVDJ2ezhGB79K2LCmvERUReICInZRvBdK/fuPJdLI+eqTMv6mgjz\nQKcCIrs3QPHShErty7v+g2+uLvE9+62ijkr2r0u6X+ZABDMW5ARyR9c6AgnjovHJ15AbVPdLkgAR\nkOABPzhNrDhB/VnDlK/VtTtZvUkNRGIkpKPBWAvnLLoLNDmlZ8zM7eha/ecIfW6BXLYkkqlIjD5D\nRESeffJv7nsP7Tlxce7Q3pwQvggZkxkJ4e4hIxOqr+DRnIh7ghCUroFMDm0rgerNWOoDcHYn/DzB\ns4jmU/W4SzKSk8DEX0Kcd9nbdTWQeMmoiAd6pEdFYaeQAhpG+20kmO+OClsgO0Gop0qh6H3f07je\n7dyc0THShWdmTM/TEMVTPMfr1DKQvrAWWTDqOgwsz3k3PCLlw4cPHz58+PBxz/AvUj58+PDhw4cP\nH/eMd5baK7tWciKCL6AZ0g+UbgKxrWuJxJk7mLsoSPcEKsdDZPjcHrBsLwajzlP3233vtFUOjcHD\nda16IqTOq3+Hd6HgQ0MaF/hzlVhaRjWieoYMdT+AgveURhsCB5keaoOxR6QWYyLWKVG+O/BvcSyC\ntue5g5HDI8Kg+/fJw8+LiMgnr787fZarX2BJHnogbI6k8TKCiZen1q4KmbakwTOJDJOyeJon2C+p\nEkeuT65QZHAE52ougK4hgFIwpwcU5h5YHwX6QREpu2smuaUUTAedGdUg6imN1CIVw3pflSqRZ6yY\njBQYjbUClxjT9ah+SU/tVDUuBffhk6f4jNMIIAKnpiOzgSfc2ZmRWA9IWWwPlrLSNFOxMq/BAenb\nrLDU2ps3LrWj/bm+ZXV6t+38ke3jAHL0amUpeE29cVpuj2KIszPyGkQbD2zAhdC+5n6tNBW4IA8t\nEMD3pEGnKagZ6RONYKCyZs7NjWvrE9I7KzcuRXZ1Ca/BmaXREiV7v8V/L2bCcqqpHfYEhLYOHV/9\n6Zq6vLOtpPTZHD5+Edp/JI2hCoUCC0oFbd44WsBqZdd1s4cGF6XlHz5xqf+bN5YWPL9waujPn5lT\ngqZtw0lPh7SAkJbOaGJLLlybUQZMIk13EolaNN3O0ubQ24pJ70tz6WGvuj92rAIFHY/OLY1dosjg\neke0BL3vI5t/KlXK7+wZM4f2WSD227xA+nLrLmhzS8T2Uj1Rrf0HnHu+pDkRROnb/eW0bbFwx+J0\nb470Xb4g8vTgxsQVCmrCgPWhoEXIGktKR6GmDpGKC2js6D3W0/OsR5ENuy30StDnZwfoG6pF1VIa\n83btCmouzp/a9zv1uuX+d21WdfZbbQmmYIxv0eoakXtTn0hOTye4/9hDM0ARESur62+i0Npa04Gc\nWdR7+0grS3xqz4cPHz58+PDh46cS7wyRirNRwsAOv0L566ay1cIi1xJiQ3pU7bkgFVcFbMbA3tLb\n1m1M5rZKS1EeH+XOpfvQft/2C/Jg0BLSAI+1JRErDym82UZbaarD/Y4QgQ7l6X1Db8n6RpyghJ5W\ntRXIkSWtlgIQ8dLA3qDnIGKWlR2rhjr4m0tzP/93X3T/9lRWPQbHZcIRlZqHE1pBKyMgTQH7EGHh\n2BxsBTlqWS8hDQosjER2VvXeISDkCuTp0zOnxF6+fjF9psrCAasD46cp+TCNvSI9RPYF0hTRUivI\nsDpLydcLyrb6rYBWQfrbgJiIWq7e0Z1zu3YI55JKyAOsnFNa/SSAxMaIITaHJu32DlU4z39m+mg+\nd6hPTbBigGssMrsn0sg1yosXhjSsoPxdkDdaVat6uF2PqnKrYng6Iw9ByF40B7uvZvC6q0hZOQOa\nWdBvJ4I0lV8rssBE8QkBxNfyzPZx2EOuhIotlGzNS+hIJTEIEUvQZg2TzYG6HbaGMLRAMxaQoQip\n/yuo8+fkdZekWgAybZqQ4JBLrfE9JlGrKrgQStCB7LsghG8PJe/3Hjm06M2b19Nn5ycOHdQxJyJy\nduHI5tWBVPxBjr25tX66hiTEowvz5FsDbSkWJD+g54n7ijSnJ/Vw9msL9L4nmQpR4jshwiMgq5GK\nTRRpCGuWGIACOsZCOLO2iQD/9zub/5Ygu39IrgCfokDjJrDrSnH8hhCuOHTjdLWiggo8l0ogh1fX\ndg/3eu8MNta0YGRBhVJaSDUQ0rZFQcdqYUVBKbIyDSGSWnWfQx2+rGy86rkNVKgVYJ5M6JkYAmPJ\naY4PwrtODZpEKanvAkVgBy6ecMeN1ZOVUFWdPa9uP6Hrwn1M/q96HyVi42+EsnxMBPwa+2Z5Ii3Q\nUJ/chObQJtSsgp3R5NnL8iN4jrbDhr7ofsQOKBEyW3nG6v1e/sCHDx8+fPjw4eOnEv5FyocPHz58\n+PDh457xzlJ7TdtLQrCvIO1ytiCT005VxEmzA7oQI0GRKWC8uqY0AlIKs5hScPhYU2pMIi13IJ2F\nBsWq4W5GxM5HDx0UfH1FhoZ6Lr29l7YHt28Su55SaZqpYiJcjDTidktmtPMN9mFpHFV+Tik90iAt\nw2Tf7/3wv4iIyHuPvjxtUxPWAKm1eUHaIWi7jFImyonuWuunAwRC+sqOpcruQirCwaSoTHo/SH0U\nM+vjeHQpqDxz0HpZ2zntXnzs9kAkQsW9BzJeBf9YGjKXVgX8gSBg7aaI0k1jDBVhEDaD2I4/hxJz\nRho7La7hwORYkEdvt0bU1pRaS2agSjLPAtIAAx795soRNmdPDPYPQXZmR9UdiNURwdOaquVx+qUv\nfkVERP7lu1ZQoHovDx58OG07h4Hw65cuLRiRPpKZEdt+lSjL6bkDxl1K8Px2444Vkz7SlCqsLY0x\n6S1FqvFi7VWgAIWJ5eoKkIWUHkPKMiIYfweV74YMn88xx+xIAV21j9S0N6NrVWJ7yxUjSGOkmaV7\nppQepbY75KC5nUacXyB0n4gSsIlSgGu8vHR9siCT69u1S/OtSDF6e7vD+dK9pro7c1a2d/3EavPn\n524/bWf3s/aBpmIHuq8buBLM5tbWk7YWOQtM8/OR2r4aw5MGIO6TkSgQITSKeqSPj1TEcY1pfJRw\nFBGR+dz2cQFl7f3axk6Ndp9lVLwQub+5eESnwG3u2un0gY0hTf1GYpSRRw9dqiolFfcYRTEjPbva\nxl3P7e1n07bk1N3v/OwalciNtH/b2/G1oCegPonRrgHNayG0FQNKrenfPJ/q/JOTBp+mFuvW0q3T\nPgJ1AGEtMDcmDo2NqypQzTQ7p1mi2mpcPObGX08iYDGeBbvOqCpt79KbbytiUG3BgZTQI5136D1B\n7z+eJ9LJAcLmriSGK0div+Ws9dvCI1I+fPjw4cOHDx/3jHeGSHVjKCGV0AdY9c+PVI/dKqnu7I28\nUQXywN5qtSSyo7LOEquqOb1Khurh02u5Nq1MlfRHq+U8RwlzRorNWGFOiqwiUpdQfT1wqbv7lyUB\nRM850LJeKs0E609L6UWs/JpXGmmG0mgituaQhNjfWjt98tyhOQ2tprtpNQPPLULf1E4vIHJiB3Sm\nJWJlCZ86rmoelESc00oT0hHqAyZiKuuR2IqwyM9xbm4fFyfvT58pOXN7IHLg5LlEK1icZ0QkXvVJ\nI6uxCdgJQybKK6FdFavt+znQtGVhY63GijTLCWlQsXVCs25vHRm2Il+zGOMzJKV8VTbusJOr/bPp\nsyZxEharyFa/cyARDROLsbunHxjS9L3vOSQqItTzIZSVQyJWfv/7ruDi0YVbGX/6zI7/1X//P4iI\nyPW1IW3vf84dg+/TLRAjLutWVGVf2ipVV7GMeikBVT8baaWr6uHsAKAoyYwKQCIctyFE9nTlxtiz\nj42UHZ2431yQAvybS1fcoCMioZWp3sRHJdS6gg8ZfXGfM0o1YDUfMuqspHkaZCqtoQrvIiJporIn\n7lzKAyN4brzs94a0KBK1WxsBO8FKm50KVKZkX9pvtax9taQ2eePabD5zbVgzsViJvR2RkxVpi0jt\nPEbbEXKiKFVICO84zW02ngLIrcQgO/fkdReBgJ3M7J5ob9x119TWavuWUluPkC6ISKamSBw6XtA9\nnmDcteeu7brAjl9ooUpr+3hw4c4lIrL3MCrSzZkL1+7XV0bKDjHykiOJA5XucZ+x6rpe4uSHJ4aW\njEfFPjguoX8jHvd8Tlo8wfL9XXUXJdXvRYMq1nO/ujYhAGdS7A+OlP2BkmZUPDKieIFQIpXdyVOS\n/RkUidK0Ao0/FJuUtbVJq4rtdE7BoNIVNv+pP27OzgJwlIjogkgB5K3hESkfPnz48OHDh497hn+R\n8uHDhw8fPnz4uGe8s9SeDOFkjigiomhbPzDpzMG4b25NH0ca9+43Jyi6BrTYUxpF1NSQIMMWBOUO\nSuj9QOkhfK+tCeJESovPKYHuTkFkx0hJ6XQsVcCtqIV7sMwVbWUz4hRpLyaRzxQCJc2qKHLHOpub\nZskhVC0Wg9bXWwd3d91zu0boYyxO3XnOiWCoJqcLItsKUoZNTWRzmLoOpE+lhPKeTGtDQMvz2CD7\nFsre+4ORCGc5yNVjhnO09OTJzKWi9iVB+4BsByYMRlCxpnRbCV2aWW6k3GjStCICenDAv0hZUHp4\nSguSYeUsc6nIltTeZ9Bs2pOKcTcca3aJiGSqwEspCDUSHTE2rm5eTp/NHy/wmaUxQpiQJqOlAlRn\n7NNPLS2nOYDVwn77/KVLKZyuTIF4hjbTVASnzN68cqTYR08s3ZorEfqNpcy+8NEX3PGfG4lWU38h\nTTFV6dqCTXhVtX8AETagvtF0V0z3SYf2TAobuwk66kApqAHFGxdnpi2kRs5LIm/nOe47pBHG1uYk\nvT9jSnfrmKip2KIosL8jAixUyUmXqmnuKsBrO9UNq527c9GsSEvadgnOpa24iAFaVKTYLuLap9tZ\najVW8jzRF9TcvaOiiItH7r67he7UkYq7Uhsy0sxCKowV60NU2QSkwK0UhYZSawH6LKG0aK8m5Kp3\nR/f6AH2ojObf+crdf6/IjHYNKCQ2+AAAIABJREFUcvJNz0Ux7pwSMrLN0P8jPTqy2PXnvHBpv1X9\navpM9eva2ugJIfSGWJ9J99e25IoAuklEBTBqZJ5QVVILQ/QCpOuQlcgxXwREbWmhPcgE7F7nM8pJ\npRiLLRUAhVBRZ6qGoH3ShNLn6igR3SW7T/7BNE57FOOUTJXJoIDfUao0wHOMCPCdulLYkJC+A6Vj\nUB0r20cvOAbNE22l7USpdaSeUyqKGPDc4/Gv2lIjGVOPw49/VfKIlA8fPnz48OHDxz3jnSFSUZRJ\nf7C39cW5ezNl1Vt9wWxKUpaGh9SMEBldaR0DUvCza8h/DAzEGsgJ+3DpW3BCRNhy57YtCyICqqIv\nrT5UAXWkN+IM18E8wQ4EzRqKyTW9rceRWxmdrqz8XQmDYWyrKl3prlakxAqS8wkpS7fP3W9KIrHK\nRAp1/02prH62dL+NSZJCib28IlUl7IpW/wEanhaEkmKlF4x2/AWI0vs9oTTJFvtQ0jH7mrn2XBJK\n1oHsG7OybwRfp4wRRvQFkS17lbsImJR5XP57tbfzbbGPjhBBVcDNU0NEY/hfJQtDPxogFhUhDTEI\n8qyerURJVQAOaaXXYTVdipGDlYw90jVUO0fGz0mVXomiVzdvaJsWKtj3AnHXewNC+Re/aMrqV9du\nVd9Sx+qYOFr9gtHJFnqK8CrpXMSI5eydlgGJWEOuoGBkolfHAFInV8VoIscqmpRzAYCq5wfskYXV\nLB2fVctFjlfhKkoe05wwwz3G11BCkoKRNvWri4hsG2vpPhF1605ROpvPNijZf//JE/f/jRVbbEDs\nXxRU7IJ5ZDzyMERf032iiukxVWAosjYSoX8PzzqVs1AvQRGRYubah0vYtSQ9eIskQRfavavIVUII\n7xDxb46/N+g+2FcU/X4gpFcRjouF9V3TuuPmg82xdaAFDXasplZCNxGgMccXQOTj0No6L+CAEdlz\nJcBAYZR+xD6OPNpQ5FPMGH1x11qyU8SoHoeuv9iBIkCbtFSo1LV3ffUmOHO03zbItoQhZYLUIYKe\nXYricFGIDpkZSP4DFTGN6Au+rwYgyxXJ2dStyywVM4Katu7znCQpJkY9zd0JFOhDnMjJ/GL67MXL\nT0VEJCMSu14+P2O1QGugtouRMWlafiYgm7Qn9fqY0d674REpHz58+PDhw4ePe4Z/kfLhw4cPHz58\n+LhnvLPUXpaEckJKyBEg0CwziHuA8WWRGcTWlYCnCYpU8daAXAtV+TgkuLkH2TlGSivi90hVbCVy\n3gxkv64iGDsBVJ0Q2RrHDcnIU4nlrBkzTn8ixUBK6GrgnKYGcZ4uHXz55sbMlQXtlOfWdRl0Z6KR\njDfFKVv/6zP77b5xbdfBFLSpDM4s5jDUpbSTpjRqEnZWKFgNQEVMD2uk9ux6VbGlVCl2NBCJ7+Xl\nM1yrUwdWQrKIoc0pkTNVl4qNjBPV3uKUGf7l1FIFleuezDhbpHRVW0vV7EVEahQnhKxtBLX9MDLI\nWMnOATFWc6Rgt0zKx24i0sDS1M8IuD0iIuR641Ss8wtTgs/m7vOc0PEKKeMDaQapKje306MHH4jI\nMQG+rFyq6POf/5KIiLx4YabRJ6cuVbklwnIBovaTJ6z35T5fkPGtZplqKkDQQgrWoFLNItWbYd0f\nVWDmPtRt+/3ddA9ntqb0OZHClysHzx/2VtCQ4D6eQUWdCyYaEFqzjO4rkF3ZcF0Vozm1odfK+jgl\nzqWgdKKqwrdEctfRq33BBSgx7vWOUqtFAcJ6zQR4d367Lan9o21vrqwoIoWmU7mzNsly6HIhBRcn\nlrJUbb/Tx5ZaGZHuPtKgQ4FIRFpAAlNvJQ6LUPqI+i7ANQaDm2tTmkPUZLmmAqAa6bma5u6sgGJ5\nYte1r9W9wo6lxtQRpRg1Las6Xqul3X+3MPCOEjJZ7txYDGnHmnof2aAdukQhj3Hc90Nv46mGjpMW\n52SUYlNNs56cFZpGz5dU5NO7KdMR81MonCrUNKLNcWZqbmNXDdQDaOAl1K89il3qyuYJPfeKFMsL\nUC860iDrYtc/LbmM6D2TxvQuAOqHoJ3KvV3/PHeG90KZzevGURXikOakXl0Z6LVnvPvsKHd3qTr9\n0f15Nzwi5cOHDx8+fPjwcc94Z4hUkYxHitExPM+y1E5ps3NvgSkpkQYx1LGJRKekVF4RjtiWEoms\nyN0qBfy+CY0QETmgNJlRpQBvyUFvK+K+QfkvvZHX8HiLaPWfgvgXsLI5Vlaq7Jq1LHXg/uaydi0d\n1zJcEZHbww9wLGuTJfwJo9FIkX0HUnRm5fRd6EirSthuOjt+hbYOe7uuHCuI9IhEjLJqIsAnouXa\nTNgEYZQQmR1I0e1ISskBSNmt+zeglUGIa+DS4BBIWBjxqharlZGJ6vAL623lqF5/vCRV8nIFX69Z\nQSuTA4il7KF2cO15SsTOEGheQ2W9EmvpPBUlpFCvj6n8G4rq9ajeXKREjGutyFcvTV1fV1yAoSrH\ntEpfXzli5/mDx9O2HqXYN7dGQD9/4JDAz1446YKzs0d0LNevxcJWhkqKPhAB/MUr1ya/8Au/MG37\n9Jnbdn5mJE2VETkQeXgqgMC/MRHhVYmflcUVnGTF+hL909KqWnsnEFJHVtSXCeUYsxMRm4tDoADd\nkzzygJuBid0pCOg9IeIl0BFGU8fp+m2crFauAGNHqN8MCFMDdIx2OyFt87mNoUHnGkIzN1ixr1ZE\nYodTwMmJFUrcrh3Ck9P+WiWgA5loyAEinimaTZIkMxCBCcGIUZTCfTeiLzoq3olRqDLe2vVrbUeg\npGBC9U4eQrrjxiRxbjHv1yH5HwK5SCjrEap3HM1n5cH1e0aSGDvMBSH2F9I+FB3tD4bqNlBeZ/md\nHnNrQM8pvf6BTkD9ZpMFIXeKbwDVacgbUp9rioyKiMSYMxtKHagqeEfnFMnd52QwuGvtSRW87HDd\nNMZXAHYUwWVZgaZz2zqSelAVodHSMNIDki8SKorBfioigIdQMWeZitPCFWGdrtz8tNvbeFlv3HxW\n1iars4TUQhcxcufGtSKNIiI12qyl9qwa93mU2L0TcIXCW8IjUj58+PDhw4cPH/cM/yLlw4cPHz58\n+PBxz3hnqb3Ts4UUZNSo6buMiXhIcxDXcNIKqlvSEQGkX5Lug0A/hA0qFdocoCeSZWRG2wMeZy0O\nwI68TdMt5BkpUeag/T2UgEVEWijBhnMyMh2PdWwGMo/cHhxU/fDcrktJeV1nsKJq+hRkbpzOANla\ntkVOTl1K5b1HRgp+deXap4E6cN8Q6Rrw9YEhTFxjRumWIcT5EbFatY+OzF0DJa/b7mSEGSmZ9vYg\ntDYgPbPxcKQpOIKMk0nvhYoIWsDNlFrtBtVWIQI4Usmcgkmh5K36QA2RI2Vw8HFPavcNyOabvaXH\nOqjeBsLfQ2qRlXWVAG5HkEhUFdn9Gwoz+90/N7evp03ZmUtPZ3zrIj3F90Sau+thFe0IyvazuRU0\nXF874rGqbue5wdkZ9tE3tl8lln/4gY2rNyAvf/aZKZtfXDgIfr/jtORdI+kG4zkH2VuGu+n5nFIB\nPdIcDZHIK6RnHj16OG27eu2KGJjYvoPRb0L5O22fBIRuJt2muK8bGn9BqKk126/qQzWUHtE47O36\nNR3HBR2vXjnV7AWl1jTNl4GUziRiVZa/ubE0xm6rGlyWClHaQjRa26nO1Q2p0s8xFjZrm7uU3K7d\n1HMuLFLjd1LM1tQb62jt9X62nw6dFsqQMTSOUlG/F/tjDbCSigMCnX/o+znG/4pIxGsQix8+tDGx\n/QRG4jRRDqoztrHrV9XucVTHBppXKt1GRSwogOJ0lzYZT3+aIp6RtlZWqHuEtWeIdJS2F+9Xtc2E\n7skMc0hKxrsDJm/WpctAN0joBhwwF5YNF9m4e2KR2ZhUncUYOm9tT2bkeI61lEYcMD7ynDTIci3K\nmTZJDA2yMbDrTwLXJkVqBQ0fvv9lERHZQ+cvoTTu+amba15fWQpUqQJ9b/eJzv+so6bREgVFHQ3K\ng7UJFxK8LTwi5cOHDx8+fPjwcc94d2TzIjRGmhhRjVdGChwk9KatfGEm7O2xIr0hRChHmX5P74pn\nKlkwVdwSOVXlEtK7ZNeyoRURiM05EYYjoDkjlSlrmXDHBGiU7ldAS4aRURX3pt32dqxDibf/zrY1\nrZKobb8v3zgkYJl+ftrWAnWak9r3OVaMB5xnXRnpVxVg+87ezBfwnGJ15hyE1paI1eOopGBbpenK\nPiY0ocgcUTqMbOXag+xYHlzf0aEmOYEkptLc3n0/Sm313YOc2NKqTleVPUGHSvyOiTyq/Gwl7y8K\nuiUAzpREtsVCW4LIkJsKqEpP42lQxI5QzwBoa0TFE0q2VQ+5IaTiBJAtO0LQtvApjCMrQFAPsdnc\niN0dyMBBSOR9jPEo5NWXG0cPHrj9nZ6ShyXGxOMntqofsY/12pCW09Mz7MtOXdGXloiyqmIe9Iz6\nAXVK764WJ/Vm6sME5foB+2SiNJp9xfS6O1JgVjR3uVjQ91zbVewAMF0syqCJxK+IECubtzhGEhNy\nO+j4t/7fAjlaLc3/8QRkcFZqV7X1HVTMWRrhFH56KtcgInIDVDGkpb56spVUFDCDGvqcZCqurlyZ\neEaSDNP4mFAfRlrc33Vtc0cKmYiA5pMQ9wLLNKhBwkjfa9DHBfXJuEU/QiahOLH22m3dXLgpqdQe\nqHY+I+kQ9esLDbkJ4RAQpdaeVYtnRk2SJHClSGIgkoRMDFBMbw9U7AH/0YbkJ7QYIaSbQh0F8sQQ\nqQqeiUlix1DAWId/VVIBCv5lZ4cRKHnARVmqCcROIXoehBwdoDy+ozlulj/Bv4Y6P37o5odN6RDU\n3dZQzUEgK0Opo0zRn5jaP9LiIfZuRIap4syBG5/zzJ4Ty8KhU+HgjrWl4oR96/7u6JkUJSprQ8R6\neBay/I4+C9KMEX53fJY/GMa7aDOHR6R8+PDhw4cPHz7uGe8MkRqj8Eg0DJpmstmYWFymqoODvekW\nusKnssoaHKLtgfzHYqzIelv9BIX7XhS4FUEYEx9lVJSIc9W6+rS39Zud46ssCisrXyT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T58NKqsizwd2ItQNMeYyKHd3UgCwgkmLHkmRU7hLVVMmoDg2ycWb58Efi9pamOMouShlu/C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\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/+U/+2oYmIR4QKRzLI02sjSBSw1WNe4mIrCZurFdXbU++fNkFKIxGi5kgWNsJ2kNVGrum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\nCf7X//oXERFp1G0+d3Q+zwlN7uveViX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677sZausTsgLUkjKCiPQkQVnziTnp+Sw1UCw93swJ5bPOU/SdS6jIeRmAIGsn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zz12uoZORjd9bOyd5k6RErREhFTry5yfn93K41a5SKoDVfwWBxnO8/9yV1DbQsi0Wr+nhCR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\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7N24EL67Fm0KZgwKrrCQUce72vsMi6EOZi5ncQd+jpy5fKz47PAptc3ri44pzx2Dg/Y+U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\nAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10d0961d0>"
+        "<matplotlib.figure.Figure at 0x7fd6306e1cd0>"
        ]
       }
      ],
@@ -260,7 +251,7 @@
        "output_type": "display_data",
        "png": 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HsnuPT6BhpP/ARhMFuOtruPHh1MmwX3/7sWOQp1rDDRtjzpC3VCAbERyjo0ToTZ5v1Jly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\nRK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x13612df90>"
+        "<matplotlib.figure.Figure at 0x7fd63064b6d0>"
        ]
       }
      ],
@@ -286,9 +277,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+aB+3BWsBGl82mzUZH7AZvHbwGViUZrOp7ANMHZxRGUin09oWsEFWButsNuuZ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FhY+MWH36TKaSyDVqtlL7/8crie2bHrxr8cThPxZ6bbrVwdvmgx59fKRU3gJafy1vBDgb/ZDQH0\n+/2QpZ3h3XMKrdZJJmX10GBTwosIf69EhopK5gXAbH4M0LfRaNSYi2oDx7mMuDQEwHPVj5Uq1aKK\nvZo1heUMnOP69etzhY7NjjeLcOnxptgv5rzAqzIUvlgvo9PphGfOZ7Dna4zH42DYcADE5z//eTMz\ne/PNNxvnxnVVdv9Wq9XoG7t2GPzblLhZZU3neaWeOTXv/OZFuXRUmSTVdw4OwPV57qSyrXMb+Lwl\nLxEVPcWBKAzVN/9yLXWb8D1KbXCV4D62oSrdYJyVmNtsPkiIBfDeGFJtjm3C+LiS4A21Rvs2ppDK\nR8f3VT0DMMjwmarQwJUwWEagcumVlnHzUG5LfuZ5c+o/40jYFAHjUV17FRUVFRUVFRVL4twYqVjx\nXLOTbOcptkqJpqfTabKo7TKA9QeGYHNzM+xQUZB1d3dX5tpR1jrAqQywAy518ymw+FKxMsqaYYsB\n1gS7Dbyoen19PdwTZpxwHlVMlaGEp17kzq4T/MspIoCY5eUtKs5HpASUSpjNTBPn5zLTjFO73ZaM\nmr+fV65csffff9/M5lkFXI8/U/S3x+bmpt29e9fM5msa+nQBMWZSuUbxWS7LeypdhgrPZ/zcz/2c\nmWlGitvqx7Tb7YZ7mcshp1gHxeTw9VRhZQVv1fOYK7ca+qHuIQd1sEvbH8vjrJ6vVF09FjKX9IcR\nC3NXrJc/H6ce4X74OcPzRImvU1DniwmLS13iy4LdQhwIg7+Z3VauMdU+HpcSkby6/3wef30PL/BW\nrq7Y79W8xPoKD4tiapm1S1USiKUwAHKpDEr6wW1ZhBGrjFRFRUVFRUVFxZI4N0bq6dOntrq6GgTb\n2K1vb2/L7McesRpvpWI6WIkqHJiBdrHIFZogzjS+SAZgsxPWbWdnJ2iVoL166623km3P9VGxCJxA\nEX+nBPHD4XCu1p3ZvKXOWjBv6fO9YVbBW2Ycqg+LhUXkKYH0ZDKR45DyZ6skkszkpcSNuEePHz9u\nCKhns9lc2ogYmGFV9dmuXbtmZvOMFFg+1a/79+9LkSagrDvcq93d3cA68X31qUIUlJ6DwQyLYo6+\n+c1vmpnZl770JTMze/311xvH9Pv9xrnH43FIeIuAixhyQntltadYrhQzGBO7K5Y6lQ6AWQovqlap\nLFiXpDJGs/5DCcF9gIw/tz+v6iP/Lqbt8fAC6lwGdL5/i6ZTUEilRkmJ43NQDNBsNms8h8yS51gP\nHqMSjRQzUiqVBFc4SOkmc8+K+k5VTyh5l8d0eB65+6CS0qbWKvXdbDYLbVHPRwzntpEaj8c2Ho/n\nsv6aHb/cMfhYNNfW1oLLDt+pzvHGDOf9+OOPwwsytfHa2NgILxTO64IXPCLTBoNBUojLSL3UefFA\nnqHr16+bmdmXv/xl+9GPfmRm8y5MbL4wAXjDhPFrtVqhH5zHR+WN4c8wXvjN5uZm6Du7v3z2Wqau\ngZhLx7s619bWQh9w39g1m3MVeled2lwPBoPQD7UQ8SKH66mNFI+VF4IrKtnMGhuuhw8fNjZr/DLE\ncSwsTi2eT58+DRsjVUJHvfxVyRkVsYIx44hE9TJWQBu63a4cS3yGe33z5k27devW3DGcyZ9dbjBi\nYhsbT8tzyRyVhZ3vh3/Bs4ic57jP58bPBeevUhGuvtySEulzwXPO1K7E8B4qYk65S1dWVqL52fg3\nMYPVgyUFqY1RrM2pTVjKOIhlWffIbf7PCr4tPH6cfyy12eH2KWH0om2IbbgBdV42CP0mTAVXqLyD\nubmTmscKpXMxtkFKjYvCIgFj1bVXUVFRUVFRUbEkzjWzuVkzn1Kn0wkh0xcuXDCzY0bC50uKsRQs\nUDY7ZrU8E7K7uxtofGarlDXhGZjd3d2ilA253bNKGfDGG2+Y2XGRW7BPuO6jR48a1+V8M2xdKkuY\nx0CxRd6VcPfu3cC8KHqWxc0+BQO7ydhF5e8Z/18xFylrod1uN6zTGOOkGDiAx8Kzozym7PaBy5Np\nd1VrT6UwUEJbzHcwkzdu3JCMlBdDz2azhjCaxxmMGBepZhckxk/VqmP4+aTcSL4NON5nN19dXQ3z\n6Sc/+YmZHbs0vcuOmQb0kZm60rw/MfEq+sSFqhUDokL5Vc47lb/Kz3fOh4f72u/3wzXQFg6j9+3w\nbVF52Px612q1wnG4NyqthoJaw7rdrmSGvOCeBeNqzvDY+jQj/FvF2qiAlVzuqOclMk+5MjkQRdWv\nTKUbYBbqNG3PPSvKJany3HE/fQUEVdB4MBg05g/PnVTWdBW8wK5dBV6nfJtVf3je8dz1zFoJC1YZ\nqYqKioqKioqKJXGujBQL6Hi3DguaLWlY4xya7jNW7+zsSPZEaUFwPkBZaAcHB420ADs7O8ns0Ghf\nr9cL1nMuy7nHvXv3AgN39erV0HaMB+snlI5ItQ/nU6HdXAmerWykP/jxj39sZse6KWi2mB3hsG1c\n31sOipFiq1bVt+PvS6wDxaawzk0hpykCFEvB51CpLqABSqXBMGtajLdv3w5/c9u9OJwZHx+2zP1Q\n7CiH2KdE9pxRm9ubEiPzd77P6ln46KOPbHNzs/E5+oZ27e7uhvmO73xgRUntuV6vJ5OlYu3g9qcS\nWTITkhsHs/n7nwr4iDEIXvwaSwHix5wTHipBfU4crvrhn28eAyWQ5n9TYnhAJU1lnQtfy4uc1fli\njM5pxOVKvOzF2ty+1Bqi0j0w27ZIuzxK603G5rH/jPV/rAnEOoL5l0tRotpXmsKAP1Osk28zr4Gp\n4CRe7xbR0p3bRmo4HNrFixfDQoJNQmwR8RuAVqsVblwu5w1uLG7+aDQKg5rLGYPjcG7VPi7fAvHv\nYDAI11WLJRcehguTN0i4LibjeDxuRJ1NJpNiQRxe6pwLCuBFS/0GUAVqzU42VbyJ9YtCLKNzKo+T\nEgfzA6wWArXY+BcyA9fo9/thLqjyJ7gf7E5VbjcODvAbwuFw2Ahe4ChVL073yEVjms0vXjjPhQsX\nGq5k5SaJvZhV/i2/8HF/gZWVFVlUeWtry8wsZIjvdrvSAMK5scl68uRJeJZeeOEFM2uOiXcpc7u8\nC80sPea8+ee+KXca/wbX8GsFC995nNE/tEGJjdklwhsq9UJWmxJAbeZj4lyz+ZccGwSpl3ouL5V3\n33AEmWqXElzz5sR/pjYOahNz1qJzHnt2RykXpXJH+s94Q8jEAbCystJY25SLmtd3nrslRodC7H2h\nSifhengu9/b2pGGWMmJzKIm+TM1xM+1KLJUPmFXXXkVFRUVFRUXF0jg3Rmpvb88ePXokxZwAdqmb\nm5uNvDGz2UnW6VSm336/33DtcCFWtZsFIzEYDMLuld10ODe7GTmcHUgVF8Vxg8EgsBPYvd+5cycw\nCKkdOgt3/edm8yyPD/eOtYfpURb5ms2zHSokma0dL0Dn37JbENdl91JJvSfuU0qQGbOePFRdKLN5\nNsTs2NUKVyeut7GxEb7ntiOlA7KPqzqBZifjh/NxWghmAbxQeTabNeb2cDhszAmV24qzTqNdubkB\n5ASj7LZSbhcVJMDMIH6LOQP3NrN9MWtRsY5+XnD7FROFc/O5+LlI1bJLufGZkeIQcVWlwbeZ3Wmp\n+c6fMesJpH6bY3JyWafV3ynGTAnGc1Ah7L5PKt9U7FzLslIqHUEujYN/FhjKHdlqteZYTx+4wfdK\nsfgMn05DuedY4qHey4q1USk2gH6/H67HKWiUqzD1nlOpR3LMlXen5t4DivVcZG5URqqioqKioqKi\nYkmcq9g8p0/CrjOXxRhQicdWVlYCAwL90s7Ojty5wxrGcSsrK+E4WJKz2WxOSGg2nyYB12WhvAJC\n3q9fvx6ugbD6+/fvyzBaaEsQKn7jxo0wNrCsr169Gq79zjvvJNsAKPEti/NgZbMloVIXcOVujI1P\nGGl2ct9XVlaCXgvHmTWtqph2x1snSh8ym83C94qpY3jhfq7mGdqstFxmJ5nKwUhxWgiVfBPfbW9v\nS8F9SjAO7OzsNGokbm9vNwTlKkkji9f5up4J4+z4fH01Z5XewI8/jylrknA+lXgWgnyv/1Lh0z79\nBV8P/Z1MJnLuKPGr/yymy1NCe59KYjKZNNiT2HVzrIMHj1tOxOu/A2azWdBwcjJUP+9iQmDPXCgm\nRyVLVM+y+i0HPrAWSTHYvt9qDE6bIiGVvFaxIzlWkdvl78/W1tZcehyPHFOvdKcpz5AX0vtze+ab\n3xHMpqnUGSqYSOn11DOs+ldSaUQxbKmAgBTObSO1tbUVxKYMpr/54WdXndm8aw8dHo1G4XtsMPjF\nghfps2fPwsBhk9VqtcLNxkIfE0gDuJncZmAwGCQ3imjT1atX7Yc//KGZnRRBNjuZlHBrXLt2LTws\n+Hd7ezu0H/9yO3xkotnxuPgM3lw4l118qoirKvwK8AOCFx4L230xat744nws5gb4ZYOX/v7+flFO\nkaOjo0a5mpKyBWbHcxSCe/xW3aOdnR25aKmH2S82a2trMjeSf4h5wwWwcBsLzPb29tym1Ox4rJRb\nAddAm/hFz/B5VWJuNZ8FvNfrhWcK95QF98DBwUFD9L26uho2ArxO4Hw4R+xeqkgegJ+R1GbIbL7Q\nLH6r3F/+RaAi9NRn7GLlzQmuwceVutZiL05GLFJKbXL8S7q0HJbaSKWCE/xv1UZPPWclmzB1vdSG\nKoeYWzC10Utt1pSL0ovS/e9YGsEbN/WcpvqlNi/8O79+KjekP4/ZseQBc5afeb+pjxnKqaAJdX28\n11g+oOabit4DuC2pyGSP6tqrqKioqKioqFgS58ZIra+v22AwCNYmdrudTqeRK2Z/fz/saJmtgJiS\nUxR4gXe32w0uDi7CC7YFYmKuPVUagonrc64iWJAxNgouPRx3586d0GYwCd1uN/Qd1sX7778fmBxm\nivAZj4VybzA9rzKRK6tZiSK9xRLL4I52sUvJiyWn02nDko/lHlGuJLRVMWfMOOI4JaZm9xzGBf3m\n9A9g2J48eSJdhPiM5x/YK3aX+bFS2dMvXbrUcGezVcTXx/Pz6U9/OlwfrkQGxhljwa6nlDXObCva\nrtgxbiPXUvRziPvLYmjP/Kh5eHR0NMdYYQxUP1VOGZU6Q81dtrw908wWK58vld9MMRfKPcL3wbPG\nyg3h22V2PG4YV4x1rsgwM1Mp5qKUtVHHMfPn6w3ydZV4mVkXlR/IX5dzMuVSMSzKRPG1lKuIgwhw\nXt83JWiO5XDivpeklGEpw6JQ7mizpmeDj1GpOtDOWBoXz1JypneeJ+q5UMBagPU7JjdQ5/PrBXs/\nfDtTqIxURUVFRUVFRcWSODdG6sGDB3O7Tv4XO2BY8gg392DrOoaYrgC7eeyae71eIxSfE8+xtQCt\nD9il2WwWdsU4X0y7AW3UjRs3zMzsww8/DJYjWI/Nzc3AbEA4zgyA2unjHKwtUf0101a7stD9eZSu\nbTabNYTMbNmwxegF+L1er8ECdTodaWlhfJVeRvWN01b4ZKkMlXAR1tF4PA7X5RB1MEfMkuE8PBfx\nG2YL0FalIQPARjBYx4a5xZnmc6kBvIicAwIU88i12fz9iGXUB/DZ1tbWnKZM9QnwDBcnoFUWdky/\nqNgXP7fZ+lfWLp/bawK5LUq0zusF/ub77/UrHOIOcP1KNc58DnUNPxaKoeH+pkLYWUtVwlbx3+12\nW+p+UkLs1HHM5Kjj+PqKRcilQlgEMY2UarMfP5XxnRG7Dyohp2JNYhosPrda72KsakpTyn1S2c6V\nDs+3hd8XKtgld29K2heDWgd4/pqVeajObSN1cHBg6+vrjZfh9vZ2WDRU+Q5ErHE5mNIoFoBvMBdL\nxssNAt5utxs2KHiBdzqdkFUZA8zidUySmJj++vXrc9f44Q9/GPrBEwL0PFAa4RgrYcPRDrFCpB43\nb940M7Nbt26Zmcn+HB0dhXHDPez3+w23EYvI+YHzi7gSO3O5HX9tnAe/xd9ceBhCZhTJNWvm2Flb\nWwubGp6T6nwqahP9hQuVz4M5xPMOxoIqX4Nnw2x+o+UXhcuXL4cNVK5EUEq4yR65axEAACAASURB\nVIuYfxEodyhvchi+RITKEG+m3bS+iPh4PA6BFnzffJu9i0VlkfYvJuVa4c0L/zZVUkW5mbkdaBff\nY1UQ1b+A+P6pCFLeRHDGaMAbQCwY9m00i5e9wXeLiG65fWrjwy9w5SJKba7UZkIZ44vgNFF6KVdR\nLjKw9Lps7Cghu2+LWTPKTj0DTFhg7qhz8BzntVnNJ95AoX3+PJxbSpWm8u8Nbhc/60x6lJaiAVTJ\nIYaPNFZrXeOcC7WgoqKioqKioqIiILvV+uM//mP7p3/6J7t69ap9//vfN7NjF80f/MEf2Pvvv283\nb960v//7vw8My1/8xV/Y3/zN31i73ba//Mu/tN/+7d+W57106ZJ1u925HDtmxztM7JThwrp+/XoQ\n08LKf+ONN6QLC4xAqmCwsqyePHkSGCHk/1ldXQ27XYjSNzY2Ql/B1Hz44YeyjwpwB+K3Dx48CP3F\nDn02mwUmArv1jY2N4GZAn1RGZLYg2EXE1qy3MHu9njwXBM9sOShhn7fG2Z3GTJIPP+X8VWgrW9YY\nl8PDw8Y1mH1SdDBYqL29PXvxxRfNzOx//ud/wvfeNcTzhN2SnJbBA9bMYDAIc4fHMcUC8dj6tjx4\n8CDMY7ZmvRX48ccfF7MFKSsQfez3++E8OD4los5dI5blG/dG5VrjduF8qZQbqTEGVG0vBeXi8OM7\nGo3msjT7NqhUApxmQrFZ3pJXVrJKjdDtduX98S7b2D2M5Y3ybSgR/cZYLRX672sU8nE5N55qZyqL\n9bJicg+VFiB1TIqVU0gxSur6Zsf3H88a1lm1TvAY8bhwPj/Az/d2u93wGqm5zZ/xXMC89bnIzObn\npfeIKLZSBS/wmuDTrzC4TWrep+5RiWsvy0j90R/9kX3jG9+Y++xrX/ua/dZv/Za99dZb9hu/8Rv2\nta99zcyONzd/93d/Z2+88YZ94xvfsD/5kz8ppoMrKioqKioqKn7WkGWkfv3Xfz2wJ8A//uM/2re+\n9S0zM/vDP/xD+8pXvmJf+9rX7B/+4R/sq1/9qnW7Xbt586Z95jOfse9+97v2q7/6q43zHh4e2rNn\nzxrW2Gg0CjtA7Erv3r0bdsNgaljgDTaDkzSmqr/HgOuCYbp7927YSaNu2sbGRtgFM9tSwgxA82E2\n75dGP3C+mHgODJ0SIyvEdDNoo9c2xX6f2qXHQkPB4HEKATAL6LtKjMh+cJz78PCwMb79fj+Ml9LI\nYZ602+3GeL366qsh67uvN8f9VbWnGKybUqwY5gl/p1I1AJyUEpYb9Hh37twJ44H+cNDBSy+9ZGZm\n7777blJ7gPui+ru/vx/mIsZbtVNlGOawa+gY+d4z1G8B1gvdu3fPzE6s2fF4HLRtsdBqgMc8pZHy\nOjsP/1seD9Z/KDGvnzN8DVW/kq+FcWA2wOswuf4eo1QzpBizFANZyurgONZA8jWV8F212TNrMb1b\n6u9FRee5PqVC4fmeK0YqJsj311DPg9KOMlPP1/BtVTUUzU7mFqdGUcFVQC4hptcvzWaz8LwyE6We\ne7WG59YHj5QOsN1uN/pWGkhRgqXE5h999FFwf127ds0++ugjMzsWhfKm6eWXX466vbrdrvX7/bCw\no0Pb29uNxXtzczNEu7E7B4OK65s1F/6rV6+Gm6TS6KcwnU6D+/CVV14xs+OXGMSv7JooYd6uXLli\n3/3ud81sMXegR6owagz84PpCkgrD4bBRGubGjRuhNAegomuGw6F8ieJ6/KLnzRKQyvQMsACQHy5s\n1jAPXn75ZfvP//zPud++8847YRFhd6PfDHEkCsZidXU19IPHJ7XA8kPKwQ24BoDNAUexYGPD58Ez\nwy9hzpHmixCrTRODhe3YxGNTt7293XjxqQWIM5bjHsQocRb447qAWgz5uVWCcIVYHir8xi/6LKrm\n7PkenU6nkelZldYoNdr4dxyhh8+VMaPEvOpFlBLpquzUsU3UohuolFGknhP+zIvxY8ep6DNe40ry\nesX6lfpebfRwD3q9nnQvKTejz67NwQnKDao25rGNW8qVzfffExZm1shfxuPL7kEfcKMkI2Y6KtnP\nRd5sclZxlY0fv0U7efOnorhT7+XcO7u0QLLZGYjNY6Gm/H1FRUVFRUVFxf9FLMVIXbt2ze7evWvX\nr1+3O3fuBJfVSy+9NMdYfPDBB8Hl4LG9vR123O12O1inFy5caGQsb7fbgQXiHa7feQ+Hw2ARwPJv\ntVoLM1EMWKW4/mQykZmjS/DBBx9IUfdPA9i1KzH6wcFBY8O7t7fX+GxzczPcXzAlrVYrMAzKbcTX\nwP3C951OpyGWNJvPaO/Bbi1lOYJdwW85Ky+E/g8fPpTsk3c5qc9ieYS824XBImz0nS0vb8mxpaQE\n2dxfsD+cusFncmerTbnE0A/OWA4mRIUXKwuN3Tg+E7pHzp1mFndHqKz8Ofh7w4wUf+fZTLNmIAP3\nncW67A72x/l+mc0/I0qO4NmHbrfbGLdOpxPaxQwnF1j37Uu5Hk8DJQRWUK4uzjSfEgIz26JqRubE\n2SWIuQpT7BTXV1T3vVTcnLpGLFRfHZ86LueeKw3OUlIL3/dYpvTUeVPf8Wect0/NnZL5pPrPbCb3\n58/+7M+ibTVbkpH63d/9Xfv6179uZmZf//rX7fd+7/fC53/7t39r4/HY3nvvPfvRj35kv/IrvyLP\nsbq6GooMl+RpqKioqKioqKj4aaLVamU3UtkdzFe/+lX71re+Zffv37cbN27Yn//5n9uf/umf2u//\n/u/bX//1X4f0B2Zmr732mv3+7/++vfbaa9bpdOyv/uqvopYBNB3eOllZWQnpBcDesAaK4XebSvMT\n+20psLPFeRZN/sk4KzcnrPEXXnihkRJhf38/7LQ5maLyZTOUf9mHsd+8edN+8IMfzH128eLFwEgx\nK+KtbKVbaLfbDU3bYDCQVhHYJE55kQqLBpgBBLvDob/cFs9cTCaTMNb4jNuGsVdCfgVlAY3H4zDO\nYGKZhVJzhoMRwBb5BKP8d84yRJ+uXr3aYKTW19eTCe9YJ4S/VTZzTi3A4nazefYJx7VazdpjKpOz\nSh/hfwOkLNajo6PGnGi1Wo0q8jlLmRlMr/VTz0BO+8IaKG9Jc12wlI5Dhaufho2KtRlQ7JRaf9A3\nJbhmKM0Q692UYNh/tqjOy7c5lf4A11IsY+x8Z4GYCNu/U2Pz3msZj46OiqPs/XHsceBgCID77uvq\ntVqt8Nmi+t/YupZaO9S4qTYDJferNTtLfrcQn1TdlC/fYHbygsLLq91uz0URmemM3woXLlwIriZM\nYs6ojNxC7L7kxcZP/FRqfCAVbcJuC+WSwGdwp81ms0YOndFoFNqhon/4xaiyP/sJv7W1FcaDN8bs\nlgOUeweRdPiONxa5yEoVUedLdKg280aFc/fA1cnRlt7tolxxLARFBNyDBw8aUUwxtxZHE6Y+4/ab\nHY+xNzzW19cbglF+keL5GI/Hob8sfAcwD7rd7lw+GrP5AAhgbW0t3LtUSSMuKcQvvNKIH+XuUxGY\nOVFtKVJuCPXSV8ensqJzBvQUOIoJUIEjOUE259fyn8WE5T6PVMy4S7VPubzUWndWr7ez3gSlrsH/\nV33yQm8zvZFSwQacr8mvI7GKCL78kZpfal3kaiF4ltX4xape4Lrs0sYazYE+6pwcrYc28/vJ7Hhc\nYn3h62J9SrnDa2bzioqKioqKiool8X9OnASrnnfrJSHTbCkxsGOFRf3iiy+Gv1MFWc2aoaSPHj0K\nwnekj1hfXw87YBx/4cKFxu6+1+uF9uWuq6DYFlUYkvN5gb1g5svXkuLweC++NGvWmzM7EYQrRu3Z\ns2eN+m69Xq8huu73+w0amDP9KlE/WxgqzNqnJuBixICaO5PJRDJ6noFjV6HKw6VcVClxaIx9SNVS\nVIxUKov5wcFBI7M4twNzdnt7OwQgwPpU+bXYImb3le8HF/hVDBy7U5jNgCxAscT8e+X24GvjGMUS\nqVxGKr2AgndhKgaY28rHq2AI35/YXEzV88tBMXl+LsZcnilXnQquOA2eJ2v003DcpNjA2BixYD93\nXjPNsqrjeE3365hiZlT7ptOpTH/gsb+/n8x9x0jVnOV3EwcAqHbFfqu+L5mflZGqqKioqKioqFgS\n58ZIxcKbc4A+aWNjo2F5cQJNZZEqqwLnm81m8jfY0SJB4ZMnT4rTKcA6hiXPLAm0KPfu3TsTaweW\n/mAwCNeFf9psXsSn9EZe7DeZTBo7816vF8YjF7YLKJYFyVXZWgFjxhmc1Tmgw1GM0+XLlxtsHYf0\nA8qyVvWmzE7SPIA9W1lZkbWsFMME9gf3gRMtqr4p4H4wI4Hrq1qKsXN6ZpWFyhgDrpGI+zwej8O9\nUZYyxufBgwehv6iL+eabbzYsQpX8T4nZx+Nxg7kw0/ol/t4/wzGdiZ+zSrQe1UO4umW5YAPWbXmd\nCbOjSjcF8P3F35wENXYsUMpAKX1NyW/VM5ULxT9rlid1vljQgYdKz6B0WM8TpdeIvUNjHhf/r7+v\npYEZfH48j6xB4muqtnjdabvdDs9eKq1JjPnNaUZV/3wfUzUrS+7HuW2klqVzsYkp3cy0Wq1Q3gU3\ncDKZhNxXyFj+b//2b/L3WJSwQcu9+BhwYcGNt7OzE0qTACqq4+LFi2FC4cUwGAzmXshmxyJmTA52\n3Sg6lTPC+8W31+s13J+xSC1FP/uJxpsXNUGVGBn3Znd3N4w5l0zx/TBrCqhVGg1+efID7H/LBZQ5\nq6/ahC1SRJfBean852Y6cziuv7q62hDpqvxfseviPNgUra2thfNhvqgC1GYnc4vzJvnM22YnYwnj\nRIFdT/xbFXWoCpn6/vpIOOWuSC2IKq8TRxWhDbzZYRdxCv6FwaJqdoMrdyD+hstjMpmEz7jUkpcy\ncJb1XM4odd2Ui5LH0fedoyzVpiP1XKgN7lnnucq1IdeWkveVehaf58ZLRcDl2pnKd6fudY7w4PXC\nGzn8OyWX4PdxKhCE5xXeCbjG3t5e0njxonPfZhynqmmkArU8qmuvoqKioqKiomJJfKLF5ouKB9vt\ndrDQYHlzeDTcM0wHgh3JZSvHbns4HBZlJ7948eJcODvOoVwTAIdnIhUCdujj8biRlXo4HDbqJbXb\n7Tm61YPDygGu2QWo3/JYsqvTQwkCuV3MmL344otmphlGsEaxArUYVxzHbcG1WNCOzw4ODgJ7AquI\nLSHFAjAl7oXTKseP7zuu6z9TjATPT3wWS3WhQqa91cnHsKAeTCnux9bWVigUDKytrTUE95ztnFkj\nzCvcS5WN20yPEYvWcYwSuaogDGZYU+JmRqrIMM/9VF4b7pNiKdVvvRwhxnaoYtoAXwNtxnFra2th\n3FSdPjVPFfObYrPY/QXE7m8pE+Vx1kxOjFlb9jiF3HFnzVixpEAh5f6KZSv3LAyvT/x+UnmXcG71\nLKj8avx8qLFRaTQwRxWjhnWbx0WtP4xUHjaVDimGykhVVFRUVFRUVCyJTwwjhV3scDgMO8AU87Oy\nshJ0TqxFwC4XFtqzZ8+kVYfaeQyItIHHjx8Hq/2zn/2smZn97//+b1F/Wq2WvfHGG43PS8rheFZA\nndtsnpHCbnxra2suM7eHYsU42zTv/n3KBNYWLVMz0GuKOp1OELxzW6FpU1ov3KPt7e2kVgXjPBgM\nwnmYicP3PkGm2YmFfv/+/aSwGKL9jz/+OGlZYm6Px+NGVXXWTbEFqTJCKzbTa1oUw9Xv9xsWGYu5\ngZxwla1AzDcWvHv2TAlQzZopNCaTSTKsmdvi2dbxeDx3/0uSZaoM46w3Yd0U7h2nW1B6Ds7S7q/P\nod3+/sfaid+AbeM2c4oHb5l7thlQekKv/8TnuJ5ZvP6fX1tKRemMs2CdmB3LacIWve6i7YsFM6Qy\njZ8Go9GoURdOPf98b1SGcXym1hWed7z+KKE6fqO0ozxnOVgCbfc1I2ezWcPjMJudJIRW9zqlX+52\nu+G6Kq0E1rGVlRVZVzOHc9tItdttG41Gc5FlgBoQvFwhSjU7ecHjZby7u7vww8zAb7/0pS+ZmdmN\nGzfCRuqb3/ymmc0Lpb/4xS+a2fGL17uI7t27F9xzjEVujtmJYH1tbS30FxuDhw8fhomHhffx48fJ\nXDH37t0LkW8KnAGXCz/jfCpKyFOgKqrj8PCw8YCz8Bj9fPbsWbgev1zRZo7Kw0uOI+oA/K1eLKoY\nMeel4t+khIxKNJ8SIPNCy1FbJdcyay7svMilBNcsSufF7v333zezk8i72WzWoOV3dnYaGwzOTq4M\ng1RJmVarJZ9vzCt2I6hoHD+nObu3mR5DlUNJuW38Ah/rSyrCR2Ug5/7COME5Dg4OZHZyPx4MBKC0\n2+2G+1CJg9fW1hpjrkS1/Lkqzs1GhV/HfhrRbLFnQUXe+mcgtnlJZZXPtcUfG/vt84r4U8aHij5t\ntVqNCFken1T5KJ7rfJwfNy5DxMD6gDnLGy5+l2DdwTn4u5SRxcaV6hOupfq4srISfsPPkQ/gKikL\nV117FRUVFRUVFRVL4hNTa4+tSuwEsZtdXV0NO0u4lBYtbrgMrl+/HnbDMcGz2fFuGiHfsGY//PDD\nM2kjh4hiXLj2HYekm83XacMxZidjfu3atZDDiq1yzxbFctQAJdmTzebD2pUIEWwI067eohoOh5I2\n9iJZZpVSmWrNtAvGh6ubnYyRL+DMyLmKuGaUd+2ox4/dLqr2FIvdlUA61Z/UfRsMBkX1G7l9N27c\nMDOz27dvZ38Xa4MaP0aqJmS325VMU8rFx/OEz63GC2PDlrfPvM/Z1Zl9SOXEYaTy2/Ax3t3LOc24\nv951OpvN5tguHOfd2ywE5vmRGkv0t9PpSEZ6UaQE2Urk7r+PnS+WC8ozyIrpiuVl+mm/Oj3ryXn9\nUnNNPV/MXLHrWRXxVSlAVHCST8/DQSm5fuHdwDX01PPox4LvuZqfXCfQr5GL5LJEGpXYPa+MVEVF\nRUVFRUXFkjg3RuocLltRUVFRUVFRsTBS+5ZzE5srag7/L6HbOp1Og4bmiAWIOg8PDyU16KOxYtRv\nae4PtCWV/4Wvm+tjqVsgBR7LVM6WXLRJ7rOS7xg89qfpn7q+ynlTsmlftmRRDJxRW0G52Big23Gc\nElx2Op1kMWI+btEgBwbmospIDoxGo/DMwT2o2hS7H6nxYFd2KjInN+bspjvLe22Wnvsqa/tZgKUH\nHK1cMt9XV1fD3IFrXI3JxsZGUtaQc6stazCr3+aeUV4zMZ98NYBlkHM9K6SCRHLjEivj5F177BIu\nzWGl1nIO4EI/eb1RY+ld6Ozqi5VvKgHOMxqNkvMu9R6NuahPg+w77UyuUlFRUVFRUVHx/0N8YsTm\npcelChSyiDR1vna7HXaYLLRTFncJA8OI5d3x36dYLe6bstRzzI8S4JWOObfBt6fUwiw97ubNmyEs\nVhWMXjSzcOlvF7F2/XlK+5ZjR/i6qRpVCGKYTCYyrxZEmmiTSnXQ6/VCGo9FROEeLERVIcEQm6o6\nfAzPRjMbnEK/35d5vwA15vx/FrSehpHiPG5m6fBss5PULffv35fnWpR5QSqQo6OjMCc4sCHFTgJr\na2vhfqqccDjf5uZmuAanLQFSjJTvE5+DkXumSln8RcLVY+C2pK67DEu1SBvM8ukUFmEBca8xRkoE\nPhgMQp9L8rp5YJ6DkVbPf6wwtx/jdrsdnleMs8pfVeox4lQHKg2NAr//qti8oqKioqKiouI54ROT\n2RxotVoNFoZ3gSkrQO2A+/1+w5LiXShbbzh3qZYixVJwaDof77Nxq/pGfByHiHMIdgoqQV0peHxL\nGJrU9XO4fft2yA6eqkQf+17Bt09Z9xw2nso2fFpLuURjdnR0FOYtkpKOx+PwGerWvfTSS4GN4Xnu\nM2qr/o7H48BE/eZv/qaZmf3Lv/xLsp38HT5XST9VuLhKFcHMr2dlO53OXDbxGDjpX+kcU2uHuh+d\nTkcyGT6D+7Nnz8LvU21V51Batdh1FbiuIdriMRgMihip6XSafJZUdYTTMDCl6wWzAOrZTN1/jCNn\n2+fnvCRVQKydPH/9b1WbTqNxVW0YDAbJVAKsO0VbObGxT9yrGDU+P9hWTqCrMqUzwLji+pz4GFDe\nEmaLeG3w6TT4mqybxOfQaLZarYa+SiXKbbfbyfu0yL07d9eeEn2fhnZHXiJQk2rR5NwhfJNS1DAv\nuEAsOzCO9xuZ0hvDmyseHy5qm/u9v2apaw/gF+iibrJYm05zXxfNyVO6+OaOL5kT7FLihcCPebvd\nbmykeSEAut1uaBc2I51OJ8xttGVvb0/Ou1TOK7Tps5/9rL311luN7/w8j20WUuUlIF4dDAYNd9bl\ny5eliwtt9hmJGWxMKBf5Iu5Ufx/6/b50Z1y5csXMTsYyJYCNAde4cuVKKI7OAn5/n5YJDoBB0mq1\nQqWH0yztvBlJySV4nSpZLxT4pc6/hWuHS1mp57XEyFJ5uLiNKXG4P0/sWtyP1HHclkUNRD42N9cx\nxzY2NsLmmjdLJa7QbrcbzQpuNv8+9GtF7Lc+HyJv/HkccP9x3JMnT4rnlJo7vkLDbDaTZI3v49HR\nUXXtVVRUVFRUVFQ8L5y7ay+1E2cBpWdjWAzN1mkqMzOOi1l72L3yjtXv+mPMhN+h8+8WzXDOY1LK\nwDCdq+jnRaFcNrnjTmPVeeqcz8esTSpMPsZ6pQIUcoV6U8zAomLpWEFhnAeWmZpjk8mkET7PNe+4\nLfi9yiSfu5f4HtR+t9uVzxTaD8bmwYMHjSK44/E4MCVcD1OlA8BvYX22Wq2Gi2o6nQZWriQDewzq\n+Yo9K/7emOls5yng3LgfZvMZxj16vd7CjBSCEj7++GO7dOmSmc2L21NZooGVlZUGix473md3V6ys\ngnIpTafTBiNpNr/W+88YHJBhpsXzSjLAwUkpFz+3Ge73J0+eyOLvqXaq71LrVUzmAOS8AWjPo0eP\nwjOHSgRPnz5trBN7e3uNeRdjq7iIN7fZ/9a/P3l94uvjOH534ZnDvxsbG+G5z73blBvU963X6zWK\nec9mJxUpFmF0KyNVUVFRUVFRUbEkzl0jtSxSoZP8vfoupkHw4ZG5iuG8c/XCuJh4OXfuEiyiQSgN\nT1bwY1iqm4rBs0mqUvlp4euM5WqGlfYjd5w/d6leh9sMtsVscS0OzgF/fg7dbjfUKnzw4IGZaQv3\nxRdfDG2BNaiCALa2tmTYs2IQ0U9Yjcq67Ha7weJn7RKuh3Ow4HqRMfe4cOFCEPYzkDYCY8TCWE4E\nXIK1tbXAvOC3e3t7Dau/VCPVarXCGH3qU58yM7Nbt24FFvDevXvhWG95p9podnKPY8wfmEhco3Tc\nlVaJ9VC52myqLiH6lmPi/VxUWjn1W647ynXnvCCf6w0uE6jjk1zGNGGsS/PvrEV0uGApeQ0u+T0H\nUnEAhxrDkuAfsxPGFPNge3s7muLI7IQR3dnZaeis+XnCPe/3++F8PMdyiZGBnEbq3F17yyIXZaVU\n+hjI2KApASufG995WlOBH3iesKmIOlV4lEWJiuouFSsuA3/ORTZNatHgBcesPOpJnY/pdr6/fkPL\nL6WcGw/gMS3ZaHEgwDL9QT+wYRmNRtIVk7rXatFR7lKM1eHh4ZyrCW3x1/jJT34SzoMX9OHhYSMb\n9tOnT8M4w/3x7Nkz+az5l3O73W64+w4PD+cKE+MzXI/zJ50mezH6trq62thIcbFs5cLOFVr2G6Tt\n7W179dVXzexkY6buZczI83OQox35t+q5Kikei+vE2sDX9UVr+beLRuhNJpNG+3LBKWzQYZOjNnO8\nNnghOIM3d97YVVm7V1ZWGtmzJ5NJ0mVbGtUXCxBSbjIAfSrNb3V0dBStPODh14SYu6/kurENF9Y5\n/DsajRqRvNPptHEcR97xv5hPfG/QNw5IQxuQm20wGIR1IOW29KiuvYqKioqKioqKJfEzw0gpStdD\nWY2xnBcl4fTtdltaaKmwUWYSlFDVWyzsjuBr+P6pdjITUprhNYdUaHCpoDT3fcraVfWjSsXc6rq5\nfC8KypW5yDVLkKK6Y6yCusepPuEczI7kmDP1Pc4DFuXKlSsN0fdkMplju0raB0yn08Bwgc16+vRp\nw23R6XQauaDOqpaWElWzZcsouWbMisVawAEtChBQI+O/Ou7w8DAwanzf1LpUMkd5fqX6qFJ2lF7j\ntEqSlNcAYzGbzRq5j8yaQSextc6f+9mzZ401fzQaBdf4T37yk3AsngsWvCsmWWXoVkExOcG5Hw8l\neWFXnHpfsDvSu8mQ1dtsPju6d6PGmDD8FjmemPVmeJZ6d3e3aO04OjoK94QDbzivntnxfE65q3G/\nnjx5Eu6hyqUWQ2WkKioqKioqKiqWxCeSkYJloXbmvDtV7I4Hsyjs0/bWrvotp1iAoHE6ncrEY2Ci\neLerrARlifjkbJ1Op0g/pFIUnDbxZcrqiX2/KNhCg28aFoHS6Zg1WUAWYqZYRfbJL6NjyunlUseV\n4OjoqCG0jbEZuXuTuoY6/sMPPzQzs1/4hV8wM7P//u//Dt/xeHv29uOPP07qBriel38GuC0s9PTi\nVc7kzAxLStO2DPCc7e3thWcYFihbxaybKbFQVZBDq9UK7AXXy0v9Pnd/cRzGqtfrLZ32ZDAYzKWc\nQdv9WlS6PjEUq50ax2XWMNZC+me00+mE94pK1gz0ej3JTnqWb2dnR+rElP5Pies9Yp6TUjBT65+H\nw8PD0Fa+BjN4Zib7ozwE0+k0rFkY84ODA8lm+fZxKiPMsclkEhhYtOHx48fJRKE+WSaD/7/MPFom\ntconciNV+pD6iccPqcrxw2Ln1EMMGnJ/f79BA08mkzAB8e9oNJqLkPEoEU3ycbH+q5cRZ2k3Wy53\n1Gk2Av48ZvnFnzPa+oeXxZ78mX+o2F2Vu66/h6X95QUZ4Pm0TORiClw0O+U+zgUY+I0PzwmVh+m9\n994zM7Pr16+HlwhE37FC4CnBO9rHv00dz24BtOvSpUuNkk6cPTvXlmWAc+ldnAAAIABJREFUMVf9\n5UW/BMPhMIyhCq7IlYVR5V88OCs6NlJra2vJgrNoy2g0kpGhPgJOuTZXVlYaOc1yOI0xlnppxp5l\nv4Hn+wb3MYuNgYODg7D+YxzVNWJj4OeHkhb4PnF71XeAX+sVOBiKz6nmm39eS13yqq2qvTxuXiTO\n11hbWwsub3xfKr/ItRUoDUpR5WpK1pfq2quoqKioqKioWBKf6DxSaqfJrEKqdpICW+o+A7mqm3da\nN5lHKRPCOT5SeV/YQldjxOxJStx6GkYql1tKUfqw7nPMm+rbopnNuX2nKSS6KPvE/S0F6PLxeNxg\nQFqtlkwHoOAzb7fb7caxKocXizQxzltbW4GV8OxHDPjtxsZGEEsv8xxtbW2Z2bzb14MZTC50WwqM\nJecAUowUywhK+vLCCy/YnTt3ot+fRYZ2dn8imzlnolesFvfXC4b7/X4jP5R6RpkN9s++x1mwts8r\nxUu/32/IQ1RgkFprLl68GMaGGUCfMiG2Tvm1rSTHoE+3EGNoMV5YT2azWVEh61htvBJ0u90wj7hq\nw6JVPRRycwjjwikgvHux0+mEua8y3+eA9TzWhspIVVRUVFRUVFQsiU8cI1Uq8D0rcBKvlDZrmeuX\n/kYJWgH2QftxK02Apn67LBbtEx/nE0Tm/NaKeeHMwiVZiWOZlEus3BzbFvsNzrvsmLOfHn1aW1tr\naDKYkchBjVFqfJVegs+hBKr+Xg6Hw6C5Qa099XzHGGVOe+DbxFCZoEufU+6bF7yvrKyE9sOyVuJr\nhVdeecXefffd6PXwLLBIF3OR76uad1xfE/frhRdeMLPjQAAglTpBsSKcGDVltfM9XIaRWpQZLk2/\nwmO7LBOS825g7D/1qU+F9iOAoLQ/LEBnj4F6LlJpfnLrC8a51+sl2daS9prNp04ozdyuNLyp6hMp\ncCBFLB2Q2fFY+u/5WYa3h5MI+3P4fuQYqU+c2FwtGOyiWhQ8+Dx5fcQXn5+jqPxDr9xHvAjzzVRi\naCygnLXVP1RM86oICECVODhtxEIOi7i2PPyLkNuHyc15QXhSp17wPDbeZasivmILQaoPz2tzrx5O\ntQnb2dlpZP9WL2EGj4XahKj5ic/Upkm5FDiAwxee3dvbm3se0M7UvVRjzu08C7G5epGpxfzo6CgI\nj9GG0kU/1qYS6UHsGBV9fPnyZTM7Ke3y1ltvhfHlzVpJ+1qtVpHIfZH5ro6NrWXcrpgRk7o2n8PP\nxdjvODrR7HisUms+jr9161aYG4vmncvlBASWMdr5d1zJA2sBR++pqGeswyrvHK8n3pgYjUbhM7V2\ncF/UJsff/06n03j+x+PxXBktMx0lz5s1fvfDBYvfqCLdSjxfxeYVFRUVFRUVFc8RnzhGiqHqH6Uw\nmzVzBsVoeO/uOTo6auzGzZo5alionipayp9hpzwajYJ1yO1SfUvtghU1rurvlYTLniVKxeaAym/E\nFj9bOIo9USJyFmeXtO80yIUGl56jpF18nLL4AE4lgLHY2tpKCra9iBXnTrVFpVjwVvt4PA5WINff\nU+fzmYjV/E/lY+NzlaB0LixbHzJXdJrH2jOrsecH1jjWEM6Hx4JnPCsY85g7B/cJmM1mz4XFXgTq\nvqQYH/5MuX14rUauIowVZ/JGYMNHH33UGAP+P4+lSjNR6gbzOE0OKQ8/hpynLfceRf9YhO/Htd1u\nN57T3d1dWbvTrwnM7vG/nukdj8cNNpvvPVzfvN4x053Kss7Mr7rX6Ltfk1KojFRFRUVFRUVFxZL4\nRDNSjFJLs2Rn3+l0wq6ULQyuKG02r3PCZ0ogrTQXqvYQWzCcBTaVmV2BLQPsnr1VhnP/NKEsafU9\nC5VZd4PvlCA3hZQgk8P8cyzEaYT0p2G7UswWnxdsTordmU6njYSC29vbgW1V4nTMmeFw2AiTVvN9\nNps1MiUzQ4hrMXvD58UzxVatTw6oMJ1OGyyK2eLzXCV2jAFt9Vmxc8hpjdiKVtpBlQCUE7YCGC/V\nLqWL4xQkihk4D8RS2XgoEbHS1Jqd9IXHD+OG+dnr9cL3H330UeM8HDoPqFp6DJ/8N6Zd9P09LSOl\n1iKlm1KCbOXhUIwpv2P8e2c2myUDX7AW9Pt9mXZFrSdqDH26EL7nvK4o7bAS+Cut9DL1O89tI+Vp\n2pQrxqz5sokdr8qtKPGyfzkripiRmugqWojT6OO3vLDlFi3vnptMJtLNeJaUcAkW3TCoDQ1wdHQU\n+oeHkHOP8D3yDzvnClEiQ+XazbVZbV78POMNA28ElHvMIxedltpQ8SZHvaT5HHipqoKdChAqT6fT\nRmQlFyPmuYbvlYgc7eMFjcfWb6j5MxabqrFcNs8NY5HNLtqz6HXVhnBjY2Ou/EwKak7geeC2wF11\n+/btxjliOYzMFls7fIBMLpdWLiI29X3Jc8TY3NwMzwU/634em6XHnI/348JtwUaq3+8nq1mk5hg/\nU7not2UiHFXWdL9ZZjE3sL6+3ojuW1lZCZscvM92d3eLo4U9lFHM7eAIXYyDL6HFUBnO+W/eD/hx\nOUvDobr2KioqKioqKiqWxLkxUt6qZyu1VHCq4K0nlcIgZvm//PLLZnZCHz569CjQwPxbWPCwvNlC\ng1V+dHTU2EF3u925MFuzebcgt8XvmlutdFFLL0TmseDxOM0ufJG8Sv5zJdI9OjpquKkODg6Kcs+w\na4Jdd6m6eikrMXZcSszNY1liQc9mMxn67+llJaodj8f24osvmtlJ3hp2C7GrmIu3AlwM2GzeumMG\nST1fuEecyRntY2G5b7MKJmCXQk5cD6SyRCsX+lkC864kLQCD6+Bh/K5evWpvv/323HGK6bp06VLI\nAcVQ2anhxkWeLgYHXqTY9hTa7XZYR3A+FgIrqLUG6yinF1hU0qDWLmY4+NnCXL148aKZHY9dKrs3\njh+NRkkROWf8v3//frRdOagUACl2SkGtE7PZLCm05v97t+KzZ88az3+r1QrjwYEj3pPAKVsw35kt\nfPDgQThOwQd18DqhmCZ+L6sUF56Rys179a7EZ5hDKVRGqqKioqKioqJiSZy7RsozHLzLVgk5c7t/\nxXwogSJ2vth17u3t2QcffGBmJ1mCzbRPF0JDZlNUAkiPWAVuZXWUsGiKzeJzqRpQpUnjFFJMDV9b\nCUBjmb5j4fAMldV7NBo1rHDVvljGYHWcnydqrFjwrFilnOZBZWNXok+v8ZhOp8H6AyOqkgeapRkc\nFs76tquafGYnGbI3NzfNbF6vxWkBvEBaZV4vZUSU8HV1dbUhNp1OpyEp5fPAss+K0ublzgXGcWtr\nK1jwKXA6FZ7HZ6n9mM1moS+lesMcO+tZD2ZWU79V15xMJo3f8Bgopi6FGGuF+fjjH//YzI7fEV6P\ny+8p3x6z+bVBabgAXi9SzzJncF80AGYymYTnFWsIJ4cG1Fzi9x2Luf07+vbt28l5gvfnyspKeKeq\nfqo6uJz5H+OL/jArx+sjVzHBbznZJ39ndpLCJJfKxMzOt0TMIg99alKwO81H/8Qmt4KPduLCo+ph\neOmll8zM7M6dO0k3JFLT80uAs8T66JBWqyXF8D61PveXN6Iqh0npmPM4qz4v6yJUETW8UUkJCtV1\n+QFKZUpWmyu1kVIuolgRZP9bBSVUV9cYDAYyGMH3g9vBD7uKdioRAn/uc5+zN998s/Gd+q26N+yq\nMTve5JRsmi5fvhxcIgqpiESzk2cJi6vPXI/vlxXDmqU3ombpEiepdSrmNgJ+6Zd+ycyO3SC3bt2a\n+05F9124cCEUK/YuwxzOqqAsb+BSxcjZ+PDrrAJH1PGcVHMxlV8ttWG5ePFicqO1qNA7VzSd4ecY\nrw18XfX88zpWuh6n+gIBfbvdLto0qEAQ1c+LFy+G5xnBECpTOkef476exl2vouh9+wF/nJKTtNvt\nIDmJrfnVtVdRUVFRUVFRsSQ+cUWLGSpEFL/lXWVptmFgOByG37BF5XfFCoPBIPwW7bp582Zgrphq\n9S5KDtkvbTPn10jRxjH3gWJhSrFsbiTVd84inLKeer2eFA96K1blPPLXi0FZLMraKLUw1XGz2SyI\nFB89ehRti5kl60fl2BFvcbNgPOUmuXjxYhhTuLTNTu45Ai8+/PDD5BjwvILrD65AlfJibW0t/Bb9\nVPdyY2MjWMfKPcxzk+c+2qAs69xYprDMs7Dob1599VUzM3vnnXca362vrzdYuvX19eDmxfoTg3dH\n8/ieBszoYr3OMatceByfeayuroY5oRgpTkvhx5eL9IKhNDthOTktzRe+8AUzO7lXH3zwQXCr+sza\n3GbGWTBXfD3Au+lU2hA+p9l8KhbVZq7ekZMh5Prg/130PQy26sqVK/bhhx/OXb/T6cxloI/hrJjV\nHOBhqIxURUVFRUVFRcUZ49wYqZWVlWxI/6LAOc105e5FodIVjEajcO7UTrjVOqkPmNOspBLTqRp6\nuWy4bJ2chpECUlZKbJeufqN0ASx0VO33UEJvoNQ3rti9RfqhjvHjzDWb0Ob9/f0Go8J9RL2vnZ2d\nhrXO14CVzSwOj22ptq1E8Praa6/ZG2+8MTcGSvzP7WL9EgI3mDGBVYzzsVCd72tKS8Mh+XzPVUbw\n1Bioc6rxSH1n1qxrORgMQht4ndjY2DCzecasJDhgdXW1kU17Y2Mj9InTJahxA3PF4+tZ+WVwmvWF\nPQ5giXhO+/vFzzczF/6esN6IzwEGBOfY2dkJcxbn63a74W/co1brJPM2M2HLpnFgqOSbLNrOaaRK\nz70oWLhdyuCqFDw+sICZphxKmb6crhLAHPMBGiXIMVLntpFKvbhyLy2z+U1TLL+M2WKiNVDryNOT\nyjuCNpgdL1J46LwI97TwL16zvNtKFUw+zUaK23Ka6eKFoteuXZsrzWCmI7PUddnVoV5EqjRASiiu\nrpHbXKnNEGdmVm4of5yan/1+P7zwVLFcXtRTbjf18ufr4uXKmyJkymZ3ZGrzyuf1G72dnZ25jYDZ\nccSrfwGxUDnlVlc0Ps93JfAvBbv71T0pGQNGbLMJwAWpSo2srq428j6p9eTixYsN98d4PA5jzRnu\nsbnCmlYaCa0MPX5Rcq6qs5ACMPyYqwATFRlaeo1Y+aiSe61yGy1isCs3XunGbNE1nZ/NRd15OSIC\na/pwOGy4Qnd2dhqllQ4PDxtFgWNrW44wUG2N9ZGBd/RkMikOGKmuvYqKioqKioqK54RzFZsrC0OF\nyefAVnaJVVRqxbA4HJb6xx9/XNQmFa5s1nTPtVqtOasO/3oWJWYteDrYF5tUbMxZgNuVc/2ZHY+H\ncovAUuYMueoaAFvb+DzFApZmvmZro5TNTLlkOAeMcjMrcai6HrNK3mXD2d1TbVXznefnF7/4RTMz\n+8EPfhC+R5v6/b6k4pV7EeDMwFxD0UwX3OX8VTmrMnWvT8NIra6uNmoU8nqScrEo5FIdpITlly5d\nCteBy05da3NzM7C3KjcO5g67iPh+pUTJKTD7pGpf8mclrBfO6fuZSv3B7j4Viq9So3hX+97eXrKG\nJ65x8eLFZMoOXtP9GObWH7WGxMZCBciotqixVkWBFVJ56UqrbCiAuZrNZo1ndzAYhDmbSmWj5kGv\n12v0idMCpdBqtebYKfyrUu2AIauMVEVFRUVFRUXFGeMTnf6AoVIhlIShs/6Cd8I+cR8zCClxXbfb\nbYTvxurDlWoGSnzyrAlTVh6+40R2LKpedMxL9VA5bRGzbH7cJpNJSCiYyuSsdFPMqKjx43tZau2W\nfGdWlpCRw7xxP3h+qsSCOUEzwL/l+26m52K/358b8xhi6QrAhKlq90pbwuPmx0jpnDqdTkNL4b/3\nbeexUgwS/zaV+RptZitbBUDgepyJPoUYIwVdGgILOPUEMBgMwvoEnRXPbVjRnU4nPBeYE8z8YpzX\n1tbC33weZqzM4gJ9lVqF55vZ6dZ01jGib6p2o5lmVnwovtmJqB+M3mkSVjLzy883PuNUPGquAcuw\nckCn02mwz4uMOY7FPOHzqHQzajxSYzQYDBrr+/7+frFXCb9F+3Z3d5cORFtdXQ3PAM93tJ/Tw6DP\nrNtS6+8nVmxuVh5Rl4tsY8qz5AXJQstUuY+NjY05kZzZ/EPDv+EoEv+dP4b/jvVrWSGo/36Zhw7H\nl4j+1TH8Qk7dDy4UieM5v01qg6ny6sTcqarNSuyp2lritlTf8zWUOwrfDYfDxgs35w5IRbPF8qqk\nXAhcYkG5vPGcYZHzm1qcyy9K7FrmYtOAule4hipXoQTc3W53LgO2H3Oz5su+0+k0nmeOcFVuIX5J\nKGNNCbJTWaTVGDJgYGAjwOdCSZwnT5403HiDwSD0k0tZ4XoqahPnjrXXv5Ta7face8zseBxTcza1\nDvBYsfHxvF9Na2trYVxi65hZec6/mGSk1MhOrTU+qIKPN1t83NiwRT8PDw+TwRx8rVjUtJnOHI55\nPxgMwjOsSg+hTd1uN/SDx9T3rdPpSNF6KiAohyo2r6ioqKioqKj4KeJnzrXHTI3PW6HcOCosd5Eu\nK6bJ58bgneqiOau4fafJNMvC0dL0Bzm2JcXCpCwpZbEwSwW24OjoqHEcW/cpQbtynbBguNTSy7kA\nlw3pjjFS/nwq9xVnJ1cuDHaDlKbZ8LS2sih7vV5wPXENMs+AsbssRffHrMVUugo+jxr7FIPMrj24\ndnZ3dxvultizkroGt2nZJTPHmDJSucKuXbtmZhayQZud9Gk4HIbj+H5x2gNuj9nJfVDyhn6/H37D\nLLN3f/b7fVkbz7ePmT+An/lS8TWzqak5zUETKqBFHe9ZfJXuQYGFzwD/P1UnkL0HQGy+8Drhx7Lb\n7Yb2ltab5H6mqntgvnAVkBSzGmPoSoXvpeCgFbPjeVDqcQB4f8GMutnxul0ZqYqKioqKioqK54RP\nDCOlNAhKS1XaXC98M9Phkylr5jRQIs2YcFMhJXxnn7Zis1RtKKXPUlqQlO4nhlImh9uN77zlHRsj\nb00oEWdpVfJSKNF86bmUDkuJuWNIpQtQ2c4B1tcBSvPF1m6OqfMJ9FiHkWItmW1Vz1luTEv6y+Ax\nV9Z/rH+A13icVVLdVHh8DOr+X7161cxOxkulYlldXW0k3zw6OpJ6OsUCKl1QKiM0r21Yc5m58tY/\nP6M8T1RakBRU7T72Gqh108+NXMJlhmLvUsgxrKVsqwJ+MxgMGnOU10/vxTErz8KeYhDVcbF5zWk5\nzJarcwmU6naZGeRx9mtWTEfttZK9Xs92d3eT1+/IT39K4MU8RWHGckvgweEB4vIUKXhakSMzFnXT\nqQHmGwfkzsdRT37CcdQGL3L+Gvzy9i5I31Y1rjlXoJpIarOhjvML6Gw2awiB1WZ3RlE9qQ3mZDJp\nPNjcjkWL1i6yAVMvAC8sjpV8UAsoxoU3ml64qWj16XTaeAnzfeN7n+ofj5Ear9RGhZ8p/zyORiPp\nZgK47f55URtvXAfwLqfYZtiLUWezWdEmh91auUzvqaK8ObAbxex4nJX41qPVajXGSG3elSuJNzSM\nkvbPZrPGdXJBG+qZT8k+lNut1TrJm5Ubbw5uAUpciRycUIrUOhmDzzHIxyvR98HBQaP93PfSdU6V\ngykNUuLjlGHu80NyCR7elJastTGjzV+Xx0Xl2eNny4+RygpQsuGurr2KioqKioqKiiVx7q49JeZW\ngvHTNNNbIspCY3ZMCRmZEVE5eUopaSAV5s3IZfdlq8msyTR4azw2lqWh/6X3Rv3WM2S5UGiGZx9j\n7EEuLYPZ8ZhziK7ZfEZbZm+WHYPZ7KR2FucxWzQsl90R3jLKCUtzDJzPv5OzYFU29pzQFwVFuahu\nCXKZwZXrazabzbFTZvE++eNiqUx8v7a2tsI1F3ENLYJer9dwzx0cHISx9HmiGP1+PxSKRh1Llc18\nZWWl0Tclbua5rdw4isFWzB+vWX7Oq9QZMddcaq3hd4hief1zy+s7EAs6KcEi7yl/DXb759xlaszV\nO4Gfb+86LWVgzU7GlaUZfp7kUvb49Zb7we1Q76FcLi526QFoX+7d6guox9jeKjavqKioqKioqHhO\nOHdGyqPdbjeYAQ5rVjtMlYyMBZKqi95iWabGn8KiKQW4thN21Ht7e9JPXyp05Oulau2l2Bb+uzRB\nZQolyUNxXk5n4QFL/eDgoKguEzNc/J0S3Cu2qKS/SqCvxOYqHDgWIqx0TqlnQImE2Qr0mrHcuKSE\n/kp3pDQ3Zk09V7fbDckm79692zg+1yffN7asmZHKsWtc+8tMJ0tVddwuXbqUzMJ/GqBNo9EoXBfJ\naVutlqzuoBKB3rhxY+63Dx8+DPeQtW2LrnMqICilqVSaJoZa21LMfkyH89NA6bqnAmpKEGPiVR1Z\nZpf8+XPJMoGjo6PQVtZFefbJTCf+fV5QuthlsLm5aWbzbfdeDa4MweuYyk6P8Y/dz3PbSLVaLet0\nOg06MFeWgx/IkggtRU2bWeOzRcSEeJjxsstR/Irq9u0ogaKmFWWbcu3lULqhzLm8Ui/fXO6hVKQi\nv1QhquVoohI3Iy9aiwrQecxT/VCCZs5izht55DzCPOK5CHeOz+KO7/znsfFTm0Tffr5HqUze6rxq\n48UbGxx3cHBgL774opmdLHLK7af6MRwOw9jECkWreV6SP6a0FAYL6M966bx48WK4LrI/L1qMemVl\nxV566SUzO+nHrVu3wvcovv7o0SN5ztTzENu84lo+oMEsb1gCJWO5iOsMzw3LMPyG/LSSET6Pmc7D\nZaYjjlPGkNqEqcj1Xq+3sAier6Hei7nNnNnxvFMGyLLgLObL5JZSeaSA1PxTa0y73Q7vFaxPcJdW\n115FRUVFRUVFxXPAJ861Z3aya2YBtdr5ptwzKdcEQ+V4UcyKsra4HZ52LxUR8znRb6Y1+XopK0aF\nv7Jrz/fDw1sdMaspJQZc1CJliyAXhqzOk7KeVZg8sIiwswSKlo+xI2AdkDmcRdX+O0bMBYgs1xAW\nm6XHZdH+lmYxjwFzA0wIu8V+8Rd/0czM/uu//ku6KDwODw9D5nXcVy98Vq490PyoPaeu0+12w33I\n1VgDgwjXmUKO7WCGEGMEN+h4PE6y3MyI++NWVlbs+vXrZnbyDLzzzjvhe3aNq3xZqb6rgsLMeKfW\nWQ459ylPStckzg+khNQq1L3kvP6zVECIYuUY6tkrYfk4XxuuG2ObmEnC/UrVyPR9NTu+Hz5dBOcC\n4zmhAjuAXJAVpz1AO0rY/1gexlRgDFjIo6OjRmoYzrnF840rApjZXC1PHyBRGamKioqKioqKiueA\nc2OkUGOMWRiz8krbp4ESQZayKIxlsvB6lOoEWGzMO2VvaXiNU6lGSoX0K3Gj70NORJ7qH+/wU2OU\nywjObU9ZT7HfmGlWKTZPvEUbY6S2trbMTDMhKVy4cCFYVNzv1BiltFSlurOYZsDrGGP3PMc+op1e\nn3Pz5s3AVKn2w5ptt9vB8o6xIz4Lt1mzVqBqV7/flwlOVZ/AirH16pF7LjCH2u12I5XE/v5+klVR\nCRT5utCg4btHjx41nod+vx/Giuenn9s8J9Q48nqR0tyxiBhjugwLXMKc5xjnmJ40do3Y81OaXHfR\n2nJgb58+fSozo7PuFEwKvxNSCW8VStfMFHI6KyDG6iyaPiiVeX1tbU0y1j7JrepvrDpGjpE6t40U\nMnWncooAGxsbYdHC8apgK0fAKbGfEtqmaMZFxIiLTgS+hqKQsViz4C32ew9e3FTUnto0+Q2Xilha\nBrnFJrX48m/9Sz8WhVgqKE5BuTdKAwbYzYzz8IOrxsNT/5cvX7b79++bmd5EpDAcDsMiwu7NEvf2\nIgZB6fn8piMWVQR3mX/O/THKncZj7l9auKbZ/MvNu3553BjqtyUb0NzagTnZ6/XmonX5vB6Yd+y+\nVOME1x7my97enjRUsdHHGjMejxtrA6+zXgxtdnKflGA4tpnEvcam+TRrjTKycpvYFM5KgK7OV7rZ\n4PWAnyXOk4Vz83XM5uUogIpEU3O22+2GTZUqnQRDQ40vv8f4dyow4zQBVwreJbq6uhrc5Fgv2EDj\n+6CiT71UpdVqhfxi1bVXUVFRUVFRUXHG+MSIzdmS9KkO2ALiPChK5KwKNS4qggZKQ91zv2WkXIls\n/abcYPxZilnjzMwqB01OzK3E/Ko/uTFMXTd2rdT1/G/53qSYEqawUwL5XF4l1UdVFw6/ZQYDn6Et\nzDJxP5Cd+s6dO+F7iNFhXXEVgNQ8iLFA6rrLIud+hYW4u7trly9fNjNddJeZIuVyAMOFceP+sjXO\na0hJ32F1qn6ZzYekl6TM4AoIqdD/4XAYzpcSr3NbPYPlj4FrCNd/9OiRdMuBkeLs6aqdiiH2a8LW\n1la4Jzg+Vtwa6xzW8u3t7aS7B8ix8hgX1Y+Yi/osM5srxJ69VEqE0szmPMcU+FlBwAV++/Dhw2SR\nZJUmAZ/1er2G60x5Nfh6QK6oeg6LrlVYT7hWJQPPHs4bC7yo6Q8qKioqKioqKp4TPjGM1KLgHTwz\nOimruJQd4e/8Lvush2sRsbZihvAZ76zZUi0Vm/vrcabqUg1NKcOV0mb53wBKS5WzQPl4M63NUmkm\nUohlIPZQWipOoKmsQTAE4/E4MAfMwALM2ig9lz93jpECcsk8Uxad0iTy8czA+WznsdQO/nxKQ8iC\n79lsFkT3OB8zUqofnMFZrR0lrEhMM1Iyp1ZXV0O7cuJ1z0gpcXy32w1jABbq/v374TMI/Y/+H3vv\nEmPZdZWPr/u+9erq6u7qdzuOY+LEJol5JSGAIAoBMSAgRSBlwICECRkhmFkChQEhMxQQSEiAlBHJ\nCGUAOIKQRD+IQxSRl+08TBLb7Uc/XP3uet57z39Q/7Xru+t8e+19blW7HLI/yeryuefss99nr7W+\ntdZkMkXYF8mX8ofDYbhXn22327X5jporRjBHl3imubJQbq1IfgBlDJPAiPkHncXCC0SMsPvT4uJi\nGP9YCAXLN9rPd3RhYWFq/Ynsjgdz9sghozNukUi+tin1HY791gTafE3iAAAgAElEQVQ631hkeG3j\nzs4O3QdSGqnkQeqDH/yg/PM//7OcPHlSvvnNb4qIyEc+8hH5u7/7O1ldXRURkY9+9KPya7/2ayIi\n8ud//ufyD//wD9LpdOQv//Iv5Vd+5VfqL221ZDAYTKkN0URhPaWQtIYLkm2wOao/3JRSCzLH4y5m\nZmJ1yZkUg8FgKmy/QtWzWhc26S30PV7qjCb9wZ61m2CTA5IFfvRTBzic/PY+3IxyzF8xj0TbNnbN\nvk9/Yx5kOfMTNzl7XYR/QPW30WgUTWZr62LR6/VCX3kCSezAxWBTU6QSlLKDo5oCb9++Heqv/d3r\n9aa8lPSApWUy8xczo7Tb9SS+TTCL6V9k77Aj4h+k0LtP/2Vt63a7wbSnZd+5cyf0px7QU2uReYFq\nn6+srIQDGR5Svf0QD/dNTcg5zgIi005F6JQkwk3ozOMLf8+tJ96fikHmlcE8sNm3DWPv6XzXvtna\n2qr1ea/XC+3HfvASO3tCKqYwOggve3ZA6Xa7tcM3HsxxD7bUGI0IgNc2NzdDnWfxUty3ae93f/d3\n5fHHH5+61mq15A//8A/lq1/9qnz1q18Nh6inn35aPvWpT8nTTz8tjz/+uHz4wx/e14m/oKCgoKCg\noOC1jG7qhl/4hV+YytWkYCezT3/60/KBD3xAer2e3H///fLggw/Kl7/8ZXnnO99Zu3dra2vK5VhP\nmkwqZAkb8XRoo3sjWq1WOJXq6RnDLrCcYigpewRAvM+6/mJdWdwSzEFmT/WxqLIa70Xrsry8HO7F\ntjGpCDV6jOznkds98jW65ep9+F6myVEwzUCn06EaJgVqfrTtKIky1b/WGZ9lEeGZRM36iqnUbdJV\nkT2NgN7PCI/M3Hf37t2aZqbT6Uy5H2v5qsVALdX58+dFROSFF14I1zzzFt6jv3v3YRRmTzOF5mHt\n74WFhUCqRrdlHUtt75EjR0K/aCgIkT3zp+bnO3r06JRWQp+3EdAR/X4/3Kf1i2mQY+3Seuv91t06\nBUbIZmBzzdOctVqtoE1SDIdDmmjZjjFqQjGyvDVvxqJTe2YZ770psH0Ax5XFddOydZxxr0nFh2Lw\n6ozfJF3zOgas39l7x+Nx0LJhpHacY3ZejsfjsEaQ9mHNrrF26DxisdR0PfZ6vdAm/f6sr6/X9nwW\n3gZNtkzDhZpB278xxw+Wh1D/Zu1lWif8bqgDj+4nuXubxcxk87/6q7+St73tbfKhD30oVOKll14K\nm7jI7ob+4osvzvqKgoKCgoKCgoLXNJIaKYbf//3flz/5kz8REZE//uM/lj/6oz+Sv//7v6f3eoQ4\nlCq8AHrMtRpPpB5JdDKZ1KLcos0YpTskJuq/lquEmhrL5dK66nu9LPH4XtU6Ya4t5TLo6Xlubi68\nR3+7efMmlQiQZGyBLuIKppHytDIW9uTOyMMsiCNqszyXbvYu5BSgROi5GmO7Y4RCW2dPS8X6Hv9W\nHgnmgPK0jzhutv8wz5iWcefOnSn+kJanmih0ec8hQ7OgesxZIBZA0fZHVVU0eCVzxbf3xTQ7KrTp\nmnnllVfC3yJ7axKvWTB+Ja4VprFgWmrVIGxtbTWOCm01JhZsrVi+GUOn06nlbIxF1tf9Bvk1ClYv\nrROLEB+D9h/OFzb+NqgiWgi8UCY7Ozs17e1kMqm9g5UR0zhYJ6aY8wxDzjzAQJuMhI+5CNHBgLWB\n9ZH9fiJnCC0i+juOkfabrsOdnZ0wf3Qf29nZCX2NUfa1bMtxjfWLF+qGccZifCzlBOI812s6/hiU\nWOeayN4awe+Q1p/xNWOY6SB18uTJ8Pfv/d7vya//+q+LiMi5c+fk4sWL4bcXXnhBzp07R8uwGzma\nsHI70COyKnAzRDU+A5uU7OPA6sHUqTjJROILVyeZtwi3trbox9AzH8YSACtYDBO8zy5IHDNmJtO2\nx2KxsNgpbKPNAZp7FbFNTq+nvE7Y857JM+WZo9B5inMDr9n+Gw6Htc0ID5j4oVSzlzoijEajMBfx\nQ2XHg41LVVW1+YRzw7bblmvnEJr2dL5gnXSzu379uktaZaRYrAMb15R5DonYIrsbqCXxxw7ICiS5\n7ie9Bqublq11QgEo1Vc29hW2Cx0gbKLz0WhE56o1iaXWqucdWVXVlHAowonAOzs7NS8rHFOtX1VV\noX34Djb+jKhsry0tLYVDvOcow8j1VVXVBMGUly/+Zp+tqukUQFYARuAascIke//Ozs7UgUJkd1x1\nfeoh+8iRI+Gwob/hHPNSWKX40SxCO9ZZ66fzZDwe00O8TTklUjepdjqdmtc7cwLDdqDn70c+8hG3\nLTOZ9jBI4D/90z/JW97yFhERed/73ief/OQnZXt7W37wgx/IM888I29/+9tpGTnSVUFBQUFBQUHB\nqw09LHc6neRBKqmR+sAHPiBf+MIX5JVXXpELFy7In/7pn8rnP/95+drXviatVkte//rXy9/+7d+K\niMjDDz8sv/3bvy0PP/ywdLtd+Zu/+Zuoac9KNSjR6UmWkb6UbDoYDOTKlSsiMu3mqc+ixOKRx9Bc\nwU7SOSYRVNWi2SfHnRrzAzLTE4smjH1qSfgxkxzTRNln7X1WskWTjicpxfrD9j+TWPr9fk1SRqAU\n6GmLFCy/VcxU7KnvvVAMqWfVHIXqdCZd6zy+ffs2de9WaV0lxNXV1RAdXNXaCwsL4R2M9M/gxVVj\nUjDGIEIzhNV2IYFb86tp3UX2JEnsF9Zu/Q3zvqViZKmmCTUC6NBgxxG1Nix0BgvtYSkDs4DN2aWl\npZo2CbXPHhYXF2taTNS84Br0TBZo0mQaIQ8s9AnSHPQ6M+MjEVj3etUQMa0n7kk4362GEF32Y5pN\nfBe2A60aSGy3+zYzybO9OmUeZN8rjDOF9+meoX3Z6/Vq0bo3NzdrIRHQ+YeZ2HXe3b17t1af8Xg8\nFWpA65KribKI9Ye+j4XgiGXyENndA23bUFOv96lDisj0fsEcqlJIHqT+8R//sXbtgx/8YPT+xx57\nTB577LHkiwsKCgoKCgoKfthxaJHNY7m5mIQZC1DouSt7Ntxut9s4AnoumhDUFCxK86xAaRtDCbCo\ns/Y5kfy+bOrCHNMWpQKniqTHIaVxZJq3nPqlkNJS2ejpKNmqRmcymdTm++LiYk1Do1otBBIjbYgP\nLUdkel5544YaH+RIeTnjFCyQLuZ6VMzPz4drTJOEXA/lYqrmWd8jMj1PMVChdcvudDq1tTgcDkM7\nsU2qNVMwwjuOIYaK8LToXk4znHd637lz54IUjlyPnPAMb33rWwMp+IknnhCRaQ0i8j/YeFpeJ77P\n4xAuLS25xG5cKzovda7G9mLU9Ijs9q39DnjfERHe514/oqMM5m31wuAoMOwE8mtsSBEMSuoFh8T1\niGAaPw+52RgYKd3+LjJtbWH7idf3Meg3BnPe5Wpg2bxr+n3CcWDcW9Wkxso7tIOUHVzrtSEyHdH0\n7NmzU2VcuXKlZkpgHb+0tETJt/ZwgCo9rJeW3TRKebfbrRGzNfmhyLTJxCN648TSj6qWd+fOnVqb\nbb/aRRdbVPZQEIuxYu+LlccOX/Yjwz5Ks0xHL02J5zgQg/csmhSbHqRE6u3EWGroaWTLxvsQSjJX\n0x4S1XVcUDiZJWqz9i9u6rnjxUx1es16Ddn3YvoMfb+3OVdVFTZEL/r3kSNH6CHJi5TPgKYdS2TF\nZ3OjXeuH+OjRo8EEygRID7/4i78oly5dEhGR73znO+G69rnuK6l4V6k6s7h+Ck+wwXFlH1zMwJC7\nFzDhVeujv21vb2fN+dxk0xh5G2kYbM7ggUxkd555pHQUYrQvsW24p1uvZ62byPQYa7/qd3R9fT0I\nWFiX3HVt0wthXTARuEL7BQ8q3vrqdruB8I6Hf2ae9eqO+4Ed19FoVKsD895Vr3bvIFWSFhcUFBQU\nFBQUzIhDTVqcSqbqJZzMVVeK7Lly6ntzTWi50kkuEZQh1QeeCy4SMjGXFiNkY129tsTepe+z7YxJ\nYWomQanIk1RztUW5sV3wXbnaxJy8imysGdES68fMGqk5qM+oJuHu3btT+fREpjVEp0+fFhGRS5cu\nBSncOl6ITEuSnvoe2+FpsVj+P2y3ZypAjYQ3lhomYXt7O4wri42EdWW5I/XZbrdL43RZ0j8+782X\nubk5N4ddSguo79O50el0AhE/d49TzeTP/MzPyNe//nUR2YsIj3ViGsL9gJmZvLFk+09sD/HKYe3w\naBX4DpbNgu0rnmNLr9errVHMtYfJoXP6IPY9wzVitZ6Li4uh/V7/aRtseQrPsQX3mOPHj4sIj9Ye\na5OuOeuggfXb7xHElsNoBrg3qOZ/Y2ODfov024V1v3btWtFIFRQUFBQUFBTcCxyqRooBCbTMlspO\nsUiC01N7KviiPd1jOU2zk4v42pYUbE624XAY/tZ2LC4uTtnYRXa1GqrxUWkBpfYTJ07IU089NVW2\nSN2lHSN4K9BlGvsfg+jZcj1+AIsmHYMdh5jEahEjlueOTc74M3Iwy+uHfapSKubB8kimx44dCxwZ\nnBvoCq+wy/fHf/zH5cknn0y2B7WtjKiOUrt1nWeSGdOsYjR2DEGgf3sahBjRVqGBfm/dujXl4mzJ\n0kgU9nD06NFa1PR2u+1qbrR/l5aWas4Auc4L/X6/xi3Z2tpK8qks7r//fhHZ3Sd0/F8N4J7E8qBZ\nMM3L/Px8GEO2Vr21EoO3d+mYxojqOfxOBDpFeEE/U98x5Ifpb7jWrZaVaQEXFhZqjhTtdrvWTsYT\nXFpaomuR8f5sME+M+M+gmuvJZFJbU+12O7Rd687mSbvdru3vw+Gwxv/D7wByx2wfLC0tTfGNRXY1\n+rGsF69JsrmS9XQz1ckR83bD1CsiuwOjni2MJK6TdzgchoHzSG5onsF62sHsdruhLl6qhE6nU4sS\nPBgMgilO23vjxo2ZzYI58Dw8vEMpxklJmb+8WDuIXNJt7saT6zGSY7JDgj+L5I73M6Ktvc9ugrE6\n9/v98KHAeCmnTp0SEZHLly/XytDFP5lMpswKItMxYzC+ke1zrLPW6ejRo7UowTFyMEvzodBxxoSn\n2jZc37q54hrV8ubn52sfjslkUjuYDQaDqbhElgTLnDr0OSwbPVx1TxqNRq4zxH4EL8Xx48fDx2M/\nXrt6sLx27dqBme1ygHOIZZrwzDd6/+nTp2t9MBgMaubbmMOFh1zhzdub1MtcRKYOi00/nUwAmsW8\npfcuLi6GvUCFrJiwaFMmbW1thW+R1oFFCMffdY69+OKLtfrOzc3V4pfFKB+WMB4zsereoevrILMH\nNEUhmxcUFBQUFBQU3CO8Zkx7GCvERgze2dnJOrGjaU/fgRFtbXwQkb3TfZOYF4qmIRGawOYZQm1F\nE9jwDblmUoxK3bR9sdAOHsmUkT2xDblaLwumvsf3osRq1fdM5WyfiQFV+rlzQd3fcawxHx0jdts6\noYlN6zcYDGjOPot+v18LG8DCYDDiLls/aJ7T+5aXlwMJGmEjL8cIw3Y+Y92qqprKJYftEkm7qSuY\no8RBgJkrVldXwzWM+p67j+h9Z86cERGRl156KasuMW2rHYednZ2aIwWaSzDuWI65l9V9dXU1vO/F\nF18UkV1tpY3k3+/3ayEHUv2j7VlZWQnmVxaBm2WQwD0CTUkiu/PTW4/YRt1Xmn5jWFR+ken9yWq2\nBoNBGB80W2l/qZan1+vVNH5LS0uhTfjt1XWgaxK/s162kKj2ZsY4jTFTLIPuparhnkVrjLHotre3\ni0aqoKCgoKCgoOBe4FA1UsPhkLpo2/tOnjwZNDOaMBkJ2Xoq3tjYaHzy1JP33NxcjaPQ6/WmiH8i\nXEJnkibjSInMRpwU2eXMqNspSlR6alZJwmqubPBIjFSdAtPQeOEPZiHcz6rBi0nU1okAIxUrGJGZ\nSc9IqPSCfeKz+H7VJiEROSfQ5okTJ4K0yLhe6JbLpFwm8dmggDG+mEqzmENP4Y0RI5brdbyGebpw\nTdkxiuUMjL1b62wJu6822BpQLcZ4PK6N19GjR8MzqqlLaXIQWraGv3j++eeniM5aJ6uFYVGsB4NB\nLa9eSmvgcTCbgGmBbDsQyBNUNNVw5DqxxOrJwpvYwNLM3T+2B+c62WCfs0DWWh/91szNzQX+E9bn\nIDh+Csa5xP34oNbjrN+L4XAY2olrS7+ber6YTCZh79O+Uieh1yTZXKGboHqdYcRVLwI3kv3YhJhF\nfehNLE/1i23ySJWdTqf2OxJoU7DxTebn56eSLYrsTgxVxWKKDs/MxAjUMS8sa3bFD6PdhO077KGO\n9SWL8Js7hmzjwYOv52WXIrHbtuDvjJCtqmCRXS88kV0TBUbp1rbpNVS1a/8qIfTmzZu1xKl40GMH\nc2xvzqYZi2KeOw7aDjUfYGqXFLSvdENbX1+vtbPX69WIrGhGxiTDBw2tCx42dC2wAyv2s47h7du3\na/2/sLAQzBCagHqWD9sDDzwgIiLPPfdcTcBE2MM4tiOWzcADO6x55veUNyarqx3jGPSjuL297cb/\nUhw/fjwIOcyrlMEThHEfxf2UCXze4TA1Bgd1eG0KljJnlu+sjS2VckpgUMGh2+2GsVBhbGdnp0Zv\nYA5TTepdyOYFBQUFBQUFBfcIh6qRwvghCCuJ4Am96cl1aWlpKs+PyLRky5Drpu+RsRFe2AURnoNO\nJXOMq6FAIrCWHTMFMHUrk2g8IrgiFjHWI2d66my8j+WZ81T7+rzWy/YB0z5hVHErmW9vb9fasbi4\nWDP3MqI6hslA056dC0w7hloUrR8jOQ+HQ6oByQ3toNB5Fcs95uUt87StWBbm/7MmmMlkEtaXlru1\ntVVbAzHzlie1x0JOeMB1Zcew3++HemOYhFyCLcbOEeFE/36/LydOnBCRPROwF1bF1l3rrBqpq1ev\nBvJz03htMVgNAmqfUEONc0sknaTZXsd34H0Y1ws1ubHyjhw5EigOTBPBrqGTg9U64dpTR4QbN27U\n8qHmJqrHOrM4e4rY+OGzs5omjx49GuYJatfQxKXvst/h3Hh8i4uLoRyknFhNM6NNYB10PHLXBULH\nCEn4us5Y5oXYt6ZopAoKCgoKCgoK7hEOTSOl0pSNkIqnUyTSWdL33NxcTZuxsbFRi+oaez+LbM5g\nJRYW7fj06dO1TNox2zhmI9dyVZLREzPLSj0Ljh07FgjqLAqupymLaYuYFJETmmAwGIRnUnwEm4+O\nadGqqqJ8BUsARQ4X8ias1pGFK1hYWKhppBYWFqi7s+0X1I40dQeOkdw95PL20DXaEs+ZZLq1tVXT\nwKWCESrnp9/v07xcFixMws7OTmPnhSYaKUvSnSX8ia5lzAWKXDnVXnhcsV6vJ6urqyKSH7oAcw/q\nXFTOyObmZtBosnWNmQmsY8EsOe8wsKztw16vVyO5H9TnBnmnLKL6rE49zHmG8SdxTiJHVPsUMz9Y\nzQrWic3xlKZQ6zM/P19bN6PRiGr+Pa09Zk/Q8nIDnzblTzKHMF1HWN5gMAjz2PtGa7kiexqrWb6d\naF1ABzQt79atW69Nsnm325XBYODG4EAwTymturfRzs/Ph8mqk4NtmsePH8/a9EX2lw6GAVPcaP3Y\n5qWkVYwWbb307GDnEhO9zRIPHfpxQNW5d5BiBziMyWLV4bmqfxHfQwoP1CwKd86BEM1LqWTD7GDG\nYncpGOEeVejMNKnvwHlv+zQ3mXe32w1tUlU3pklRMHJwt9sN/eeZMxYWFkI7tN9ia4bF0LJgbUOv\nx9yDVCw+GAPGTBKZHi+cQ3ZPwdhdOO9smpL5+Xm5//9P7/L0008n6y4i8uCDD4rI7ripp1/uByUn\n9pEI905jJjEdD0Y2R3jxxubm5sIzeADJ/TRhOiOts0LHCM20TUxwItPt0bF69tlns8qYBbG1YONR\n7ZdsnnPYRM9APLzadw+Hw1AvdJpp6phl3y2y991bX18PY8z2EaynrjOMpaXfAf3t5s2b2dkEimmv\noKCgoKCgoOAe4dDDH+So2BnpN1YuS8DatF76L0ZZb1oGSll6LZcsNz8/n51fyFMDIzGWkZtT2icr\nvabiSClibtSM7MkiVNv7mVScG+WWkcPn5uZCm5gJC8GkNqad0nms79ja2ppymRfhBG/sK6YlYyT8\nlOraSv/6/3gNgZG8mTTszRftv/n5eVejy0IxYL1US2Bz5dn3Wo2OyHQcKU9KT/WDLQ9zCqKG05qP\nYzHwvK1Vx/r06dNy3333iYjIF7/4xej9iDe84Q0ispuHUecgajWbasqZ4wiagmw+x9FoNKU91zL0\nGWb+SmmU2T42a5wj1Fyq48PGxkZtnJizE8Ybwvh5tg4sZAwmAtffWGiUVqtVCyMzGo1q84XF+sK/\nDzr8AWqaUDu+n1hbB3GfAhN843rcDw3GzvfRaBTmjPbFxsaGbGxsFI1UQUFBQUFBQcG9wKFrpCyW\nl5fDqU/JZowbgXmXPK1Nu92ecp8VmdZwIFExR5JDUmWTII4i08H8GGcgRXjV07hqEMbjsRu8FOuT\nK714fAhm50YbOgb28/JZobRr+UvIyWGSsiIWGNVrkyXX2newSO7WLRYJtNindu7E+Do5xHwGtgZi\n2kD7jtFo5PIhlFc2Go3CeKjGDt2ykYelZbOQDTjOlseIGjhP4xAL/qpgminW53be6H22z+fm5mrh\nDzAkxkFHZtYAxCdOnAjaOI930+l0Ql302atXr9JwJLlABxqR6bx6CJ0fLAtFan9hjhRNHSiwDEsE\nx32AudErv+bGjRu1tq2srISI3x5ivEPUXIrE+55xNLFskTytm9Xez8/Ph3bqOsBxyA1rwe7DUBce\nHwm5hhp4WOsUc7LIyavJ3iXic6Ny5xW+X59BzV/MqeI1STYX2SU+aqNwI7Zmku3tbXrw0I+DAj3l\nUmRYVh564YjsHlgwKrHIXlJNbMfc3FxYJOpR0+12AxF0li7GOohIjQTsQSf0rVu3somJ7KDixflI\nkXXt5oHEcoxVxGJ2edHOsXwtO3czwgODjgmSZbWdGteHJddN/Y7qfj1k5DpUYBnMS9G2E71AmZcN\nfih1nHQ+9Xo9aorTZ/Sgkpu4N/VRwjG1h7qUgwESm72xbuK1h+Z7RU407IOC9kGv18smvHpAxwbm\njcvaZPdA7Av06LTrkZmZ0KyVEnByPnxNyOazAk29aJLHOuh99oMrMh2DTCTfBNlu15P+okkRM0Sw\nvsK9QdepXtva2qIHUDsn0FyZm1S5qan1da97XaAKKAH98uXLYa8/deqUiOwm3MY0byK7e6buPfuJ\nH8WyP3iCNypecC+5e/duMe0VFBQUFBQUFNwLHJpG6hBeW1BQUFBQUFDQGEUjVVBQUFBQUFBwD9BN\n33JvcBCumzk221i03nsFjx/QbrdDu5Hnkuv26iHGO/DIoLnB0tRmHCOjsmzeFq1WK3CG0LXeI0Gn\nuBbIURPZtaVj5G69lhvuQYFZzi2RXoTz6ywvAYOqenXPHd9ZtLgekf6gtMLYxpxAqrH3HsRabsKR\nejVxEJyrJvvYQXK8GD8Rx9ALvBvj9TH+n6KpS/xBITWPbViO3GCS6CjT1HEhtlb0GjrDMBxWXx4E\nMBiyN09wzXtR+e3zWob3DJabGu9DO0gdBJjHil24uTF3+v1+Y1Iwg/e+3GSzKTIfmxyzeBXhu1n8\nHu9+PIAwDz17QOr1erV0QEtLS24kay8iOB4OkIyo5EYk8HrEbUawxBQ2OR9mjF+Wm5Ki6Ucu5SHq\nxdzCtDYpom/Tjxv7kOL9tp248elvqWjs3kb5WgNbm7npqLAM1m/efdinB3GQsgJfqi4x2DFj8woT\nfDOhDsH6ks3P3MTztl6tVouWZ9c1S3aNwLqz/dGOEX7UY96+9npqHLBNjGxur+3HKSp2IMkpk83j\nWHYPBb7PrjP0vPMUCazOuBc1EcqKaa+goKCgoKCgYEa8pjVSnlpubm6uFm9oPB5T6YCdmq15ZjQa\nydvf/nYREfnyl7/cuJ7eyduqCu3f9lo0VkWmpIlRdRlYf3ixUFgIBWbq0vFg+dlarVZwj9eI6bdv\n367lLcPYPSg9WampqqqaNml5eTm8A0Nj6N8YsdhKLAiMtp+jYaqqqpbjL4b9aAtsv+C8YxKaAjWt\nLK4S1ikl/eUgdX+upuZehSHYr1nTq9esZssUmmiackys9xLMjIt94IU8UYzH42xTjL3vkUcekaee\neiqrrrZfYmuAvTPH+sAyMLB2sPVrv3+54+rNN0+7i4nlc/sDE9vbMAW5c42F00g9z7JkYL/ZDCe5\ndcE+a7LvHXpAzldzgbM6zPJeLwEnoinv4170hV2UqLrO3di99p46dUouX748dY0FccOEuCxAHcaW\nwqzbItOHE3YI1H7udru1JLn4Po1jxTYHVH8zXhTCJknFuDC4cF8NjlROOalnFbkfa1YeXmNj5Jke\nmekht214H5adW9dUuSxRbO6YNeUHpZAyo9jyUua+Weagvd8zncTAgqkqUocAr//0Wr/frwk0KY5Z\n7mGXBYf09qRUecjHZKY977Ce2+cx03luHXPmzsLCQhBYr127JiK7BzN7kImtnxw+Fwv0nLu+m+wD\nMY6Ut/aLaa+goKCgoKCgYEYcukYqBzFpQr3AVDIYj8f7ik786KOPishuRFYRkU9/+tPJeomkVYB6\nUmdJPDudTk16apJGgYERHfGU7Z3Cta4YER7Nb1ZLMzc3FyRLLxJ6v98PZXraCawLRu32oghjGgib\naHl+fj70L5LIrRQW85Rk9bOJfdl4oRbgsLSuCqyf9v1gMKAagabYD9k0JXk3BdMC7lcLlENajiXz\nnsVLq2n9vGcPwqSYQko74pGqFbnOBrF16c1BTIPCTIUeKZkR4z0HmFarVYvujuVged68So3bLFpA\n771oDfDAyNxMo5bK/MAsIkxLnas1tql6ck1yud7xrVYrfKuKRqqgoKCgoKCg4IBx6BqpplIY8lOa\nkmDxXZb3k3sCXl5eDpK83r+zs0MT3uZyn5aXl0VEkgmIFRaTHykAACAASURBVEwKiCFXetHfMSaT\nfV+v1wtSi0oBo9GoVodWqzWV+wv/jbWFaYtQetI+0pxNCNQQWcmRkT1jGilL3GYSS6fTCXX2tBSv\nFkfKK89q50TS0ud+tCJWU5cqrylvJ9UH++1zxhnL0TrjfML5nEP6zeU0pThonobjoGPpMeeP1J6O\n2lmbeHg0Grl8spR2Zz88QdzHRDivD8u2JGYE9gs+y/ZHuw6xX3As9X2o1WRcqqbrJsZLtPu2V2bs\nN8X9998vzz33XO0+bRPOoRQ3Sp/TclLaMwaPy2b3fosUR+rQD1IHUQ4Oam5zWOZrLefkyZMisjt5\nY4lrY8BJYjfQY8eOhY+M9Y6wyA2WiR5mIruHOpwcTWNi4IZmk+565i0EBlNr2qZerxfqgIvFC9yp\nyS8vX77sBsvU92ICZSyfxYqxY4imTO++2Ef9IAi++FtuOewAxa7lmIjYe7H/DvqQaO+NlZ06SKXe\nN6spDMcf+9QTrrx4XuwQgXsb64MLFy6IiMjFixdr5e3nIMXMX6mDVE6Z+Kz9W8vLIZZ3Oh3ah9ah\nBc3bqUNdrjOR9dTN3ctT9AomtKHAehB9zg42qfZiPdl4aV3xW5T77KwEdHwftpEdXq05st1u1+rI\n9nJN8FxMewUFBQUFBQUF9wCv6ThSDCg1stN1DlqtVpBOWHkqocfIuCqJaBn9fj9IPt6pHqVUpjHB\n9jCSuFUbYzsQs5BLmWnA61+m2vYkmxQpXdFqtbK0Rf1+X44cOSIiEsIvMAmDaZ/m5+enVOoi033G\n6oeSIdZLJD+aOUNO2hOLXBMVahC0vWjus/3CiKAqjaXeu729na2Jsvc10cp65q39IkcDkmobc7RQ\noHOFR0BnpOTRaORqsVVDzOq63z6y72sUX4f0G9Nw6d9NI++PRiP6jM5z3ZdxfeP7mXnLmvts/RXe\neGA9GcUDNWpali0nFhcxF6wODIzekprv2uda/62trZpDy8LCgjz00EMiIvK1r31NRNIhIlJ11m+v\n1hm/2x55Hftc78OI+rjv5Zr7EEUjVVBQUFBQUFAwI16THKkcwjC7j70DCaMpoiIj5zLYk3S/3w8n\nZSWMp06xuQl590PSRS2W1+ftdrsWhDLW516IA6bdSZFvbf69Xq8XNFdsPJT7gPwvrSezYTONFOae\nY+/I1T6wwIKocTwoLmAOZtUGiXDJMDU/c/hEKQk3hyuTU398dj99PivRvtWqB+4UkRpROMYD8fYK\nrVO323W13ffff7+IiDz77LNJrU4TpPg8OfuLyL0NxcDmquVIxUJU5IRiwLYd1OfSkrpn0crud67n\nap/wGb3P7tux0D52Dvb7/RqxH/lVMStFTv1SISqaBr623GuPI/WaNO3Zjoh5R+WaRHLu63a7tQPU\nyspKOCDpgtzc3AymHIxmnZMcE9PaxKI+s/rrs/o7e1fsw5ILWyZL38DiSOHvbBGcO3dORESef/75\ncPDQw2a/3w9qdL02Go3CJsgOtPoxWVxcDOlg1MR369atcB8jrOuHDcvFv61pMkYs1zoz06/WPYVZ\nPtreYSR1cNe2af02NjbCWNpI7Qh2EE3F/UnFCVOgdyQjeOakA8khqs9qqpuFBK9IkbTRlJ6TFHpn\nZ8e9D+OsvZoH+Fx4fb/f7A7aL/ivTVfCot4zp5fxeFzr55SjBzPZeWuUpWmKIUVQ94COSPYQnrtm\n2DOt1l68LBxDmypsOByGcfC+Xe12O3w7GD3EI6KjEwbWz/Y5+7Z2u91aO7DtjeZg9p0FBQUFBQUF\nBQVTeM2Z9vDkja7EnmqYnWJz1bHq4o+52VRzMh6PXU1T05NrTMqe1aSQ0gxgmbNIqZYQOZlMsuuv\nz6jpDLVFqBWxbWZSOxvXyWQiKysrIiJBM4VA1T4j0ts5k5KKtYyqqidL1uex3Ui+3g9S0m4urLnn\n2LFjISeW/tZut0Mf4bpgbtnM/HEvieAxWFd8dM6w17x+y1lLWq4tJxYh2Zp+WZwhpplGKTu3fm94\nwxtEROR73/vegZj2ck1xuL/kavmsQ0NuOICU6zyaUplGX+e2vrfX64V9QrUoOzs7tM1eKIsUZtV+\nprSyuL8clIY7BzFNDoPG/2POWBg+gmXUsPsnmhSbzm00v2vdmZYy1p6Uaa9opAoKCgoKCgoKZsRr\nTiO133JzCXQ5ObRi78AccFpWKheXyGz5yBC57vaWJIfXcsGk51xJORX92+M0eS6sItN59dgYMsmR\nuYM31Zqwd2EZtv5NiM8HyeFBzQDjV2D/YF+yNun9OdLfLBwO+7zI/si8MY0Ue1eu9O89m4q4nZvD\nTIFjkzM/W61W0MIg/y8n8G1KY4/z2dMMNd1fYnPW1mUWLZWGgLhz5w7VSOl+7YVpYRkOctqEdUIc\ntOYKf8/tc9wDmUMJlueFarDvxzLm5uZCf6oVQkRqAaj1XhHOMWX8StzPdE3pPjzLXu45TMXWTEoj\n9UNxkNpPZF62SFlcIkaqFclXcbMPuN1wY2XkRI6OIaX+PgjTHn6EbXTgqqrcNC84QXWBaT+Px+Pa\nppZLbo4dXu2B561vfat84xvfyGqr1+doKvQ2PPQ+sX2+n3GNXWtahgL7GQ/oORGQWQy3/RxO9tMO\nEb/P2fO5H+nUu/E37wCKe4PdJ7AuuI48oQk/MGreXltbq713P23zsJ+D1CzvaHpQYeM7GAxq3mJN\nzOV6H/POnAUsxhh7J+tfPOwwz1fFLJ/2nCjs+xWaFHooqqoqK6sES77e5JA6a531gFlMewUFBQUF\nBQUF9wCvyfAHFk1iLdlr7ASJ+ejQdT/XrMFUw/Y+pnLs9XrhGp7Arft5VH3YqkdhtffeSwVjLPmq\nHZ9YzCgbfqDX69X6jUl64/G4FhdG3y2y12YtH8HmDhvXWL9pmSyJM2oVUlpH7x259+Y8z6RYjOeC\nYSG8uYjXPBMVy1npxQ5LSbielkeEu5I3jU2UOw7tdtsN7YFgMYoYWDn23n6/X5PQWb9NJpNaeSjJ\npzRwFqn7DsL8mouY1ov1gYLtORgnTrXVLMo5agA97RP+bctjtAR8hyIV04qZ1VhMJrb37he2vNg+\nhPUSmdYGK3UDQwUhnUPnO+4nNq4WW9Oj0UhOnz4tIiKXLl0K160JEP9OhTfwzJZN5nvRSBUUFBQU\nFBQUzIgfCo4UgpHRVJrA4HaeBBnjbthIxFjXU6dOiYjIyy+/XLu/0+mEkzcjeiPsKRe5PgfBY7Bt\nm4UM2qQOWH9PC5UbmuLIkSOBhM7c8hXMXt7tdsMzqkE6fvx44I8wSYS1CaVT26Zc3k8TsrmHe6UF\nSLnTexw4EZ8weq/qnNJmNenzXN7UrPxF1DIxDTeuC5sXDrVsOp+rqqLjZblU+yE3p3hJr0aUbXwX\nsyrkZqlQMG0qjqn9vdWqO2vE0DTXZoxUn9MOBOvzplyvWe7Da1YTzcpbXFwM+zbjs+q8397eppYI\ny3dNfSvvZfT8FNn8h8K01263QyexiKe4SHLiHE0mkzARMPIpG0zd8PAAZVW+qSisLG4SRkX3no0R\nLLHuCDQppsxVWG/vwKD3b29v1+7DiMvMzKOeNBqzyJan0EWDEZq1327duhXikdy8ebPWNow7ZTe1\n2GbI1PbM09AzJbwamIVYnpPyZTwe17wnmYkyZt7G2Eha7qyesLF25Jjp90MgxedjKViyEpbCWmHE\nfftOkWlhzUbwx3FDcw/7eCjZXE0dWF/PO82Wk4NZ5n7Oh5v9hoIyzjHrSYxzFvtUD0jeAa/dbtfM\n1U28VD3za67Qxg6x7PASm59NhRbve9Lr9dwI6Oxv770Ym1HrOT8/H+YleguzdmAyZRG+L+O6sMnV\nY+1gpkBsjzUz5ggJxbRXUFBQUFBQUDAjXnMaqVx3VpE9LYbev7i4GE65KMWwZ5nUYc0Vq6ur4W9U\nKTLSnyUoopkRSXiqUfFivKSQkgxTv1vtnk1AqfewPEVW64RuxSihMU0U0wKpBH/y5EkREbl48WL4\nDftZ+w01U9pvOg/W19drWjQWKyQ3WjO23Ruj4XA4FdekKWZJGqyw9UKzEGokWL1VE6Uq9q2trdBf\nuBZY3BV2jWka92NK8MwfuRGOZzHFIZgzhzc2WB/7LEr8sXkkwjXsaOpCaCwwJN/G3h9DSoL3QjZ4\n5ez3/Z7Wg2ne0Gw6ayibmPbOy7+XWqueliplRtRnY9qo3D5GTb8I13oi0T5VrtX+4P26dywtLcmV\nK1emfmcJ6NfX17O0zXg2wLFka9QDowJgO2zbsrTSyTsKCgoKCgoKCgooDk0jZe2xqL3R06aeCJnU\n2el0alwMtLmiJGdPlL1ebyrfjt6n106cOCEiIlevXq29t9Xay9mD7q96ykWtjOVDqVbFIleqsJIh\n2vORn+JJFUxaY1o7lNBRYtQ6aDu73W6tvH6/HyLa2veI7PE6rl27FuqtmiskFGK5Oib6/ps3b4Z5\nwsieb3nLW0RE5H/+539qbRqPx7U5xQJ84hzVejKpLSYB2/fG+Hssb52Xk80D3sfcvBEPPPCAiIh8\n//vfD+1QqCZ2eXmZzlsm1ds6IqHdm5NNJGtFbq6t2PtS3BmRZtpFy9Ng+878/HzQwHr5ITc3N7Nz\nuz3//PPRujTtoxgPy2rrmpCgvXH1CM0xsjmDnYusn86dOycvvvhitAzsb92f9HtSVVUtLEjKWYNp\nZxG5bVPovGFlWDAnAsYfsvlBMYo9u9/TGmGQa907Njc35d3vfreIiHzhC18QkV2tqzpV6L+4/vEc\nYPfA8Xjs8jC9aPwIZq3Sf1n4EKvNYzi0g5RVVdoBFOHqdqZuQzMEI5YrGGEdy9XBUfXjwsJC+Fuf\nxY8mlqNJXtF7RoGHClXf42B5Gx62x8YCwkjJ6DnnfQjwcID3MfW9AttpNw8kFFpTpoWOEybL1Wuq\nAhaZjv1igYdbW2dMA/HKK69E78OFyzY8m4ZAhJs6tF8wxhSDFyU4pdLOVVMjmLOB16cKNp/xEIWJ\njO17W61W6Hvs76ZkY8/EFwOutdzUKrZMFgeLxYJLmbW8ut66dSt4/7KDlK6LVqslDz/8sIhIiMrf\n7/fpPsHmXk70bWwHfoDY3PG8cm2ZMTBva2/cmdNE7DBp34tx57TueIhiZuFjx46JyO6+oQcoFJ6s\nV3ZKSEmlurJg2SDQvIkCnAIPL7j32gNtrM8ZdcMi91C8tbUV+lz/vXPnjnzuc5+bui9m9mdpwxTa\nL7HsI/Y+loED6+/NU88z1kMx7RUUFBQUFBQUzIhDJZujJJkyFXjRlTGWDVNr5pCM8Tctbzgc1qSX\n8Xgsb3rTm6bqf/PmzSDxeFLbYDAIUiL7XZ+tqoqSGlldrZQ6Ho/dUzXe7xE2mZSysrISJGk2HlrX\nWD9bbUir1aqZjfr9fk3KfuCBB+Sll14SkT2t1p07d2pqXnyvaq6YOzNKNoyMrsA+QEmK9XkOmpqv\n8JkmSXWtO3i/36eaKO1ThM4T1TQeO3YsaEo8zSmTdO11rd+sGh32LJoAsBwPzJwaWxfM5M3ySFr1\nP2vH9vZ2cKZALbpdS/Pz82FuK1Ka5qZoQsL3ok2z+2Lvs/eltB2sn215MY2+7iHat6PRqBYOAvtA\nNeInTpwI+7+nvWX1Yn0Vi1iOe73e7zlQsHmNlAaPysLGOmY6Ze9h5dnsDu12u7Zvs281tk3LeOCB\nB2rmaKSo6HixWGD9fj9c13bEcm5atNvtqTht+C5EyuIgUjRSBQUFBQUFBQUz49Ajm3vSBt7nEVmb\nuEKLTOd4Q5u3lRImk0kyqF0OvPrFAsDZZxi3yePtKPQelXBZO3Jzis3Pz9ckPZQSWKRfNr5oD0fe\njX2HPnvkyJEpRwKFjpclwOOzTPvQBDmu38x2H3OtZThIHk7uuuj3+64rfk499X0iaa1cU7I5rotc\nEimGJvA0eXjN0/KwZ2MZ6JuGsMC5oWtT5xBzfGD7RGye5OxZKY5Uai4qsIxcTZk3Z2Yhqqt2T9vL\n8qbG3mv7an5+PoxDbntwndn35jpcMPK6vWb5wb1er8b7YbxEnMdeXVNoGqYHNT46n7vdLg1Gnfte\nG/KGORo1cVRgmQsU2H+690QJ/od1kNLGNn09fsBzkpoeFHTgqqqqRVzFDRwXECO8K3AANRaMHhbY\nomKeDbmpSrCuDKlovl4qBEZuxDFF85s9+Gxvb7tk6dXVVRERuXz5cu0eTCXD0m2k1PIK7yOS+/GK\nRYG3fd7EO83bDFMbmv044HxSs/S3v/1t+iwbazs/2Ye+CWy/NNkDvJQt7PBqTX8WOOZev+H9OdkT\nbNki08KQ3s/itbF5ktoT7L0i+WltPI/f1L6Ts7/EwMaIfTRT84Pdx1IY6bfDi70l4gsgHmm+2+3S\nlCk5wgZLKYRelLP0Ofs+2Xfgu/F73NRhRGSvnSiU2wwiTUj4B50pwY5DyvMSofMxKgzPXMOCgoKC\ngoKCgh9xHBrZnBHYRNKnXi+vHmopmIoO3ZrtSf/MmTNBWlTX+eXl5SC9PPfcc6E8lVz1tL2xsVGr\nSyw2EzPBWZdPrB+739PAxaQYBXP9Z/G80PUbtRP2Gcxvx7Qn2AfMvZfh9OnTIrJHhkbJQWO8XL9+\nPZSjkubOzk4yWbW2TYGR2m1/MYIq0z6iacczp6Tc/GP32jqnyrFzZn5+PrjdoyZK3ZQVGxsbNH8Y\niwlmwcybzA05pYX2XONF6lJ9qv9S+4mnrWK/MU1ETEtl58mRI0dCAm2dszFHGebkYstl7WOmbCaN\n29+98jzNegq5oQ4YPE0Y07aoBmNjY0POnj0rIiLf+973wjM6P5kmCjVYNo4h7g2sr5B4zUI2sD63\neyDrW7tWWDigpuOKGie7NlkYnFhEfaTJiOz2vdIzsC66B50/f15EdvdttRbos2jlwXbFsg0gWH5A\nBPv+sBAWnskzpx5FI1VQUFBQUFBQMCMOjSNlT5L7yTeGWoOU9OI9m4PBYBDKxlx1lhhbVVWWBNfE\nTus9G7MnM22cYhY+Gesv+24MjInvsu85fvx4kNAVS0tLtYCn7XabanxY/Rk3woJxmkajURbBO6XR\nUyDHIwXvHbbeIr6rM84Jry9Qe8ue1bYxTR2rc67bNUOKZ5cqDyVqpu2cNbSCdy+WF3sWtaciImfP\nng1aVuTyMSeXJnWPAfdUb67uJ5I704Q37efYOvHKQQ2C9+1QiwLm/MTgydofmhuUaV+xzszJxuPK\nitT33lyyuf1Gen2u8Dh6tq52vFLaHexnTxOmWSg2NzenAjbrOywfDq0IjKOJ/ZerpWRIWWAscI6l\nOFKHHtkck9BasMmIKkr9GzuGNVSvYSwlLU8//isrK+E+XSSbm5th4WiHs5gSqA70PFFY3ZgHCTM9\notpVy2Eq1BhwMrJF4KngsS5s07Pvxg83I7JqO9bW1uhGoOWhdx87MOgmiYcx72PAFqlnXsDIwfZ+\n+679EGNzPpLsoBI7bOm4sgMUEse1PzC2mZ0bKfOwRwhNHWZS5GV2uGJz0kOuw0C3280WprzNHN9h\ny0PPUxY133NKYfHOUp6GqcNwDik9Rja35o6YQMjWN9tDGA3CEya8yOLYB6zd+nFfWloK/aoHKMxm\nwfo5Nc9tX1VVVesXRkpHj1TFzs5O7WNuy7HfgpQpFvvPlhdTbGi99FmWnk1EavvJ1tZWbX9gB0P0\nPsR57u2pjJ7BYlQhsTwm8CJmNWUX015BQUFBQUFBwYw49DhSOaY15hrKyut0OjXpc35+fipaqvcO\nq3JkUipKLOwkrM/2+/2gzWLaNnzWCy+gYNoRRqjXtijwRC4Sj42VE2NF62Hr6sWeyc3PxSQv1r9I\nLLdlDofDcK8XHZiRebFs1Uhin2M7ctyjY3nLcpDSDDCg5Gfrh7G58H4WR8qLg5ML1Jx60alZ2/Zr\nZrIR+lmoBtaXqXhTilicrlwzCe5VIlwzkGtmTIWh8PYnRkBn+0lsLVtNWcrM5GkXcqP2p+Yio1Vg\nnDokN2PdEbjmdT+YTCZZLvip+EVNY5bF+sVqavHvmHnRjg2GnEDk9NEsYCbRexWqKLW+2Txh1BeF\nWr9K+IOCgoKCgoKCgnuAQ9NIdTodGQwGQVLGU6I91TNtDHIfUBpXCURP4Dmu2x5UqtPyUjyKpiQ4\n1rYmwRctrJRqpZdUBm0v4JyIH3HdtktkT3OF9cJyrQ09Fb7Bk2JYX8bA3Hw97YMCifSelirGBckl\nKjPkzIXYulCwgKWpcj1iuefo0STCcM57U31VVVVNI8Uk0ZhDSw6BvomGzpL9WZRr5h6fGw17FjK/\nF4CWjVfqWaxnU8cdph1LaWBsXbAOqXewvrQZH9hvo9HI1Zp4mslWKy/Xawy5QVBT0eJzSdopLaYH\nj7DP6oLQ7wUG8/R4mgyp+edxqRj3GvGaJZuPx+Mpc4NHVGSsfpa4t93eS5zodX5sc2WRrS0pUA+B\nItNRrG2akpgpzk5o5p0QI1JaswBbXCkVdGqTwzJtGP52u11T9SKJT+MSra+v1yYcO9zh4cojVTLy\nKPvI5aYIqqq9tBK66LEsz9SaIksz5KZRsfXE9+V6tmEfMGInxnBhHw9vDuIY2VhVjNQdc05g7Y1t\nXrH/j/U9q4MH5hEUe79Iegyx/zCukcjufGferLZMPJgrYkTapp5yqbhZzARkCc2xcljGB885AOuc\nszbYPsAOYbEDATtMeJ50uaTj1Dty2oaHZ1a/WCwjZp61awPb5pn7cD/WvbyqqjBnsQ7MizpHiI2Z\ne5mihHkB23KwTt7+H8vesZ/0YYhi2isoKCgoKCgomBGHSjZXjY7IdKRsPRUfBBktRsjEUAMi3KQ4\nC1C96KkaY0Tx2H2pOsXuY2pgq3GJqZ+9+rPfGKEQJabc9+bkWMI4SFoGasy8fuv3++E+FleLzQPU\ntuXk88s17c2CpusiV5uVigXlaepQosb77DzJrUusHfZvq+Gw2okUkZnVwdMgpvJSIuw8ZnG6Uhpi\nvC/HHI2YJdaOZ970Qnewud6kr7wMDfpvTHvA1u1+1pnt55Sp1VuPsWf3823LNe2l6sU0bqx9dhyw\nTZ7zF5ZjNawi044K+jvus1p2bkJ0/H80EcbaxjIExMy+qq0tZPOCgoKCgoKCggPGoWmk1P1aX88i\n/XonUXTpRilUT5nHjx8XEZErV66EZ5pGNkUgiZXxQzwyOmbAzpFAlpeXad4i2y8smzzyA6qqqmlo\nckMTIMcL+41pnTzCJvYBk05Z9G0vvAAL4JpDirfv1Htj2jMLnCce4V61MZubmzT8Qc58S5GIU2XY\n+jENHCNNx95r+zf1bIrgy+qfcg3X37z7UNL3COMpeAT0/WQiQOC8t5qrWI5Ci5TGx9Mu5M5J7L9c\n4nOutr3pPNgPcTu1pliYBM+SkOpTb/9h5bH64TVcS7iGde54+xjLNHFQwLHeD6le0bTf8HuniD2b\nctzQ3+z4K4/RnUOVg+eff776pV/6perhhx+uHnnkkerjH/94VVVVtba2Vv3yL/9y9WM/9mPVe9/7\n3ur69evhmY9+9KPVgw8+WD300EPVZz7zGVquiFStVqsSkWppaalaWlqqRCT6X7/fr3q9XtXr9arh\ncFgNh8PovSsrK9XKysrUtU6nU3U6nalrrVYr1EH/03fo/y8uLrr1snXs9/vhXe12O/zH3ot10meP\nHTtWHTt2LFp+bl3wP+xzEZnqu263W3W7XdoH2F+sr9h/2CZWtu2P1LjjexcWFqqFhYWp32zfp8Zc\n/5ufn6/VZX5+nt5r50TsPttG7HM2D1g7vGuvxn+sju122+1L7B8dL2yvnTt2TjT5j/UL1hn7HOdB\nznilyt7Pf6z/cH3YPY3VGedg0/ql5hPWRccLx81b/7i/sHlurw0Gg+x2aL/l7j/6H+4FTZ9l48X6\nPqfPW63W1H6BfWH7xfa5V6bd0/G/ubk5Oob2P1YvNg6457J91hvr/d6XO0/Yf8vLy9Xy8jL9Ds3y\nn/Z3DK5pr9fryV/8xV/IU089JV/60pfkr//6r+Vb3/qWfOxjH5P3vve98t3vflfe8573yMc+9jER\nEXn66aflU5/6lDz99NPy+OOPy4c//OEDD7ZVUFBQUFBQUPCagaeRsviN3/iN6t/+7d+qhx56qLp0\n6VJVVVX18ssvVw899FDQRn3sYx8L9//qr/5q9cQTT1CNVOx0iad/lAzm5+enTtFWq6P32/KYBISn\ndJR69JpKiL1er6YJYf+lJB7vBD43N1fTeohIdd9991X33XffVBv1b09DYP+z0gs+p6f1VqsV6oDt\nPX36dHX69OmpsbFSDkooKLHY+1BCQimB9a9tH5MoYlIGatRifcTGk11j7zh+/Hh0HOfm5moSjO0L\nOxdimr8cSTp3DuAYYT2t9tS23ZPk7O/4PNNw5K4LT5JOlROT0u19uRqKWTQ+qXHztMC570j1R9M6\n41pB7URMe4L/Me0I03Cw9ZjSYHt9iZp9e43t+ey/mLaraV3Ys6idze17+66YllKB2l/7fcT/UlYI\nVq/BYFANBoOq0+kEqxHu357WS9/R7Xbp3NY662+sXxcXF2t7amo8tLzl5eXa/Wx9xb4T9r2qBcS+\nb6SRQjz77LPy1a9+Vd7xjnfI5cuX5dSpUyIicurUKbl8+bKIiLz00kty/vz58Mz58+flxRdfzH1F\nQUFBQUFBQcEPFbICct65c0fe//73y8c//vEQvVTRatVzLNnfGSaTCSVuYoRkJZEdOXJErl+/PnVf\nVVWuS6oCXbUZUZoR4/B3G2xyMplMuR+LpINgeuZNJAEqcfDYsWPy/PPPT903GAzCeyogeLI2e2RJ\nDApYAZFR/1aX/uPHj8ulS5dqz9v3YdtZcEZGJkfifizQHGI8HtfaFHNr1b89wiNzxWUu/fgOHZu1\ntTXq0u+RObHPvCjbeJ/+7ZFvY/PKPoPzAOvpEcZTJGb9nZFDY0EXvfp7azhWB72f9YMXoTk3qnOK\nBJ0KL4BhObRc1ufMYYS1x67n3L5Kgc1PRSxzgYfU4R2Z6AAAIABJREFUXNU9QbGxsVHbU1mg0hYh\nsbNrW1tbWaTl2B5hn2lB1HYWygJhCdf6ndO/tTytKwtXoO/a3Nx0Q8G0Wnv58mwuTcTm5mZtv2GR\n4XEe4H5oI5WPRiO3TbZcBAuqqtcRd+7cCWPoOTl0u93ad/jmzZthjmk7cA6jc5SWo33e6XTCHtkC\nR4AUkgepnZ0def/73y+/8zu/I7/5m78pIrtaqEuXLsnp06fl5ZdflpMnT4qIyLlz5+TixYvh2Rde\neEHOnTtHy+31enSSLy4uBo81BR6ijh49KiIiN27coBuPek1hqg4dEDwgoZeGSHxh6ITxJirep2i3\n22HwUuH77US4dOlS7WOfirKtB9yNjQ1348MPKS56b8M5ceKEiIi88sorNPo79rW2SaETdGNjY+pv\nkd3DqS5SHEvbT/1+342ayw4gHtbX12lyXtsH+BHBRewdVHEesPlpD3qpGDVenBa8D73VrJcVRp/3\n6s7KSx122LzJrTP+FrseeyY1zuyAnirHJuJFeIeDmGcg82KydcADEr7f7hmx9dk0aj6rR06cLQvb\nv7F5bGOP4TrDOegdYtnhFL1j2X1sLHNjd9m5PxwO3Y8rHnbsgScWO4z1q17DOHuekJ7yYkXvbQU7\nwHn7FHqka18uLy/XFBu2fV6dves4r1i/WQETxxfTuVnBFj2X0bvceuCL1LMdVFUlH/nIR6JtEknE\nkaqqSj70oQ/Jww8/LH/wB38Qrr/vfe+TT3ziEyIi8olPfCIcsN73vvfJJz/5Sdne3pYf/OAH8swz\nz8jb3/52Wna/38/SRhQUFBQUFBQUvJpAJUfqIOWSzf/f//t/VavVqt72trdVjz76aPXoo49W//qv\n/1qtra1V73nPe2j4gz/7sz+r3vCGN1QPPfRQ9fjjj9NyhRDPkODFiGQnT56sTp48OUVo07+VGIf3\nY/n6NyMosrogqc7eMzc3F0jVej+S71IESqy//mYJeU1CHaRcSRXYp4y0zsph/eqFiGD9q32F44lj\nbp9lbWvivprj7mrrb8NcaBls7rB3MCKjnedYhm1njvMA+z1GcrbvGA6HtfFldY6RYD1iaaousXvt\n7znvSJWN8xyJ9LnrJmccmrhgM3dxW387Tja0S2p9H4TLudenqf7DPd06L6QI9EqQxnd4LvZIrsYx\nyyHI5xDFY/2B5aT624bBYe/FPdWbc9gP2E4FC8/T7XbdMnFPZ3sl2w9Z2/U+JaKzPZe1PRa6KJfM\nz57Db0xsj8a+9ojqsffoHI/BNe39/M//fFQV9+///u/0+mOPPSaPPfaYV2xBQUFBQUFBwf8JHGqu\nPeTXMFuv8piqqgok6BSJkNl7LTEOf0NSmv7Ocop5tugYRyKHv8DInCnOCCPNIpRUPxqNQtnaToy4\njc8zUqOXU46Vge31xkH7YzgcTnEctFwvkjK7D8HI7RbYv8hVsJwWHBuNlL+2thb6RX/b3t6u8QMq\nIL6nSMQsirXtq9icwDbps8iX0n9ZtHOMpC0Sj7zvcbKaxolrEhlc+wDJtwo2X6uqCnMfeSa5/ZaD\ndrs91a+xd8Tmp60fPjfL3pZbf+++VER/th7tHtRqtdx8b/obyxkYqwPuWbbumJON1dlyEVPzAN9v\n5zbbo2M5XFldPAcTBGuTR7Rme1ds/WrZXn7Q2L5o35tqe26U+BRYm7w1onsC9m+u40uKw5nKtXdo\nB6kjR47InTt3aqSxEydOyNramohMN4olvLWLGQdYO7UFXjG5A6JgEya2MLQuHmGVETJtO/W3nGsi\n9X45ffr0lLedXRgp7z+cvNbLMVYH9lFT8ruSyTUlEL4j9UHNTReAYIdgu0jYxogph/S9vV4vHMiw\njSxNzZEjR0REplL75BykUsBnbTlNPuDWeUGJlLZeXl3Zh9Q78OPv7CPCymCetQjvY4R9lAtvHbL7\n2KE0dh+rs/6Nv+MHW++b9UCLc0LriXNxP44A+FG0a7jT6dB90Rsvtm8gbzZHUGpyULFzI1dgyU3Z\ng/tK7uFUsbKyEpKwx+pvvy14eNX6YLkq8G1vb095VGsdcg6Ctt72HXpA29zcdBOe2+8jtjPWl15i\n5Ny5beuD8IR8kT3Httu3b8toNHIPUoXtXVBQUFBQUFAwIw7VtLe4uBhUjawaqtXY3t4Op3A9MbJT\n/fz8fDhZYuwLK0Wg9gHvsSEMmBtyLlCblTJ5KWxIhhi0jLm5OTcsA/6uzxw9elRu3LhRu9dLxIua\nFdT+ifD4Jsx8s7y8HDQ4KFUyKYFpHz0pEWPPaHnMnRWlZ6sJjbVNxwRV4tZllmkuY6Y9O/4xt3F7\nX0zTlItcc7VnBp9F4vfexcyRsyYY1nrZOqRMiWgG9bZCrF+Oy3nKbKXINe2nTFMMqH3wEorngpm/\nmIbd03o0aYfXb7l7JQOOuW0HrmVtE3PJZ+uWxb5iJvTRaBT2QBYXj7W72+3WNDRMI4VhhVLfLN0T\nUDNkn1lYWKjRJGJrNKWt039xX1c0sTrYd+o5QbXYTMPZbrepVhFpDXqNhdbQPblopAoKCgoKCgoK\nDhiHppGyUvwsPBK1z+qzTGuAUhELiObxTRiQbKrA/0+R6zBomMiu9KHP4zModeh7rSSA70UtCvJM\nrI36/Pnz8sILL0yVw0jfg8EgSA4Y2A01glpP1mZLqmWRmXEOIKfKSvwxbYzVijGSKcPKykqQTmzU\nXtsH2jaVfFGjhxKVSoZa962tLTfo3iyapqa8JOxHqyk5cuRI4GSw8ZtVQ2TBOCi2nozXo5wEvA+f\nZ9eQu6VACd17X4xob9GC2DK5vB9c61YqZloMva712i9Qe5LSKjbVwGOfopOL/pszt3ANsP7DTAjs\nvWy+s/Z42lYFarrx/XbvSkV895xeYt+a3DHHucu4UVqO1gHntqetWlhYCM/ivohcK33W40PlIuVc\ngzxnkek9NTdAcur9ts7I9cN3pTRSh2raE5Ha4huPx6ED0QyhBF999tatWzSdgTaHqUpxkWo5el8q\n5H/OBxrvT6WSmAWsHLvJWHOK/YB1Op1a22PwzBRsASM02v2VK1dqz2If5qjocaP1vHViZkuLY8eO\nybVr12rX7SaOhw1sg50DuKmi2c96z8XADvUK78A1i7mPHVRww0Azhl5j9W3q/cWeTa2LnIMj/sbI\n5szLTqS++abIrVieNQtjfVgUZnaQQmI5q583z70PEGsbe9a+j5WdA2ZSwrJsObhWUOCzay92ULEm\nahT4mPnativWNm8usjU/NzdHD0laB30HRkXH9lizYVXxVEesDfZDPwtSe4fuyyxauz5v65Lr+LIf\n5OwJbI9B031T5Yk+U0x7BQUFBQUFBQX3AIemkVKiXCxMgN4nMu2GrkBJhGF1dVVEdnPyeSo/JsV4\nkjWTTmLxkDwtlr4XJWEm4Xik7oWFBTcuiEhdy5Fy0VV18Hg8zpJElpaWghoYpWgWg8q6tqPEwgjo\nXqgFlPjxXV4MGH3/ZDKhBNAcjRRrh8jefLt69Wqou9XuxCRh5jThaSKwvKbaTq+9Wh8sj7kws/em\nSN2zmO69uc/QJPyBXffMpBNrO9P4pIjJ+P8ivikby0lpwnM04CltwX405kyLysrT/t7Z2ckOV6Bg\n2jmcGzkE/1mSLzOwtZ8Tu84iNxwIA+tzpBbYMDMs7A+Wo8/G1pnOVbUaIEk7ljxeJN+Rgu2B3W63\nZpnK0drlgPWzvmsymdB9IBVHqmikCgoKCgoKCgpmxKFppJQol2t79oiEislkIseOHRORPemARYlF\nIhu+y2rCUEpFF9GmEhyTkr1uHwwGNIioQqWi7e1tepJnpEzG1/E0DExCP3PmjLz88su1+rBI2h7p\n1uMC6btFOIdLg6TduHGDShZW04BS0QMPPCAiu1ojRjJHV1l9v9U+oObF06LlakdUOyvCuQ/ePGES\nUm7EZZF6PzfhDHhjhNJ206CquWEGFFhn7PNUOchb0v+30jNqlZFX2LReDB4HytZB65mjUcH32iz2\nsbIZRwvns6cpsfsLXsM9BOfEqVOnRESmeIpMS2612jGtEgtvYr8rbE3F7rPtTDkYMG0lcnXtXGPB\nejudTi3sQsxiw/ZPVh/cg3P5S8zioNo31vc6Ruw7G7PoMLI/aizx/bE6675y5MiR8IzOE3ZGiGmz\nPF4d4jVLNtdOsRGwGRgRtNvtUvKtAicgm9wK7UgsCycMOxCwhav36Tu2traoR4A+g957HsnVI5gz\nLzlrYrSLbn5+PrSfecZgXTRaNzNvMejBYnt7u7aI0ASoYIcIraMI/8iwqOL48fRI8D/90z8tIiJf\n+cpXaiR3JMumIq/b+rENGdXfXuwzZj7q9XpZZlWGXFI625RmjeUikv4oHbTDBfv4xw6v3rv1t36/\n76YrUszinYSwe0fsg2Y3+FzibsoDzhOecmEPr1iu1kHBzHjWFIY0DRY9PRZ7zILtgd7cxoPZfubn\nQTkY5ZjaRaZNZzbaOcZLwgOj9oP+m6KEeOnBMA6fd7i3ig4RnoIH64Xt0LlgM3GkEHMIsWtvbm5u\nyhNRJC7EFrJ5QUFBQUFBQcE9wqGHP2DIlZC9vFx6mh0MBqE8dY1vt9tBq8Dc7vVZVInPIqk0dQef\nRTOQirmE0ouW7Zl0GHFSzaXXrl2rJYZE842+dzKZ0HI8UmZKxYqaA5HdMbcqc4yXgnXW9+qzN2/e\nrEm2aIZgGj/VnG5sbNTGB81pqum6ffu2O89TxFL2u/cMzgPWNpvXKqYNzInhxaI6MzNEKqRIitCu\nwPHYT669VP/ZdrIo1/1+3zUDNB3XWMgOu7cxU3sMVhMQIzTnak1yTHutVitoMVCL5pmZmdbD04Sg\nc432z3g8ploRj6zfVFsUC6uj8BIeI9CcZ39fWlqaInPbslHbbjNOePWxWFlZCeXpOOl+jOu1aSYP\njIDelPISi6VmgeOgbex0OqEdaDL0NNdsjNhZAud90UgVFBQUFBQUFNwjdNO33Ft4p2emodGT487O\nTjg9njhxIpSh0qsGgkRpRUmOly9frmmi0K7v2X1TwQhRAmI51DDiusi0PbwpIRcJdKjRwUjkCvyb\naV60TNQWIeleobypV155JVxTjQ9yoFQzg+VZzSGGjYjl7BOZDtKq96P0pO1BDQK+9x3veIeIiHz+\n858Xkd1goRgoFNsqwsefERkVqJVjGk4m0SN3iJXpaZ2QX2M1pmx+snmFUrGncYi1V2E5GvZZKxmi\ntgQlSM8lGefGfgj57D7Uytk64HzCa55GCjU0jLTsjQ2+n+Woy4XX3tiekFMe03oq2u12kncjMr3m\nMQ+oaqJw/SBnUGR3Dd53330iIvL888/XymZaQ9y7sK62HUzziw4GrP/Z98nTxnj9zTjC9913X2hn\nroYoFt5G/75+/Xr02dg7VIuFz6rTj/Jn7969W+sj3KMZb077g2kLmYYb92XkUjEeI7tm34/QOckc\nC7Kchg7LtBdLHcHUfEeOHAkbCzPjMeBisWajTqcTFmduecz05Hm2MFV8jARnid4s0SUezPD9dvhi\nXnu4KdmDAouDhRuQErzX1tZCP+i/t27dmjKj2efZB5ap75k5TefI/Px8baNhSYu73S49kDGCOkPT\nKNEKtpirqpoyOeK99lpunXLjzLB3YR+JpGMzee9KmeIQltSPKZGY44jnNYrXcD3iYa7JgcPW37aF\nxapD81JTMjT2W9M4SLFrOe1gaGIqZO9QeB5k6MDBDty4t1kKAnrAefGZ0BHJO0DmAg/6Ma8z/c3r\nZ90Tt7a2QptV4FxfX6f7tj1goPktt89tHS28fRnJ1+wQqQqLW7du1dYFmt3sWkYMBoNwHUnxOtb6\nrtj+1HQP9MrAswb2C/PeLqa9goKCgoKCgoJ7hNck2fwgMRwOa2pePWHG6uV1SSqiem55HlEegaRJ\nRnzPIcnhNQSTTvCa1RxhxG2t12AwCBKjF/G5qnhsqZz4IRhbDMnuqMHTcq0GZHl5OWiiUpKapwFB\n7QM6I8TKQ82Apy2IEbI97Riat2Ju3SLTEp8d61hMHpTW7Ps9Ui0jZiOhHTXPnjYrFQICI9vrs2gW\nYrHlPC0gc8vXtRQz93iRuVNaI7sOcT6l4kQdVAgJrcusWz8zl/b7/VC/mNZeJB5bCmPjicTb6Dms\n7CdaOIu8jmDkZTZu586dm6rftWvXpojR2EZETigGrSOaofQaCxGA5Wi/6e9IAdE9E013HjFeZM+q\nwAjms8xTtj8pUqEJbBiXfr8fykEtX05ssdi+WDRSBQUFBQUFBQX3CIcakHNlZUXW1tbCNZFpuyWe\nbK20sby8HIhuKqWK7ElDTBJpelLODYKHkiZK4Fb6ZFoZlSjstZRkJsK1GSmOlH1eZLrPtT4bGxu1\n/rpw4YJcvHgxWh+E1ZT1+/2a2y4LyIZ8CQwIihKKSDpoqoK5lzNJBKUNpuXDOYYZ6m1dUGr0NK+o\nabTB7WJaT4/fgJwhJN3qNS//GXsX06KxccP7kVuk5bI627qk8vRhGTpn9Rq+A8MjpALYKnDusKC0\nsTrHrqU0IB4fikV8xnKbbtXY7v1wS+yzuC+m9hfk2igOMvglBkNGLVWuli/nPuT0eu3AIJK6pvr9\nfhhzDDCMTlMiXIOJmvjhcBjeg+ElbNBKVq97ASSPi0xrcpqG04it/4PgQ+Ui1WcpjdShm/bYRLab\nDZKh0TvOHriYih1TK7D4HPiMjYeEprFZ2si8dtQDAonZuWAEfUvStB91VAOLTJu6UnGrrNdeLF4L\nbiQi3Bum0+mEgxGqkJlHCNZfZHfj8CLgo7qfbVBMLc/I15aUjipiRa4KGw8gqUNMLsk9RWS2v+Uk\nFk0hV5iIkZftIZd9bDA5dMrk7R2asc+x/grPZMP2k9gBiZk/7YeFzRPcT+z9tjw7PszkwA7cOA6p\nvsw1haXmOT4Xu7+pWR1TiaAXsr1vOBzSwwgTYlnbcikWdj0yB51er1d7x/LyctizdKyGw2HoIzxc\n2T6KJS/3Dq/4DOtzz/My1kcHeQhPISe1GALXBRLW2TffEwhSKKa9goKCgoKCgoJ7hEPVSKEkmoKn\nPUHtAotRYTUSKcIolhsj/IlMm0RYxG97AmaE3KqqgqZG1bhM8lpcXAwE6txEi/hudcf14ohYeNIa\n1sHmGcxJroplaDkxLC0thbK1D7A/cG6srq6KyG5iYoUlzWPePx0HjFviheJImaEUTDsSQ07SVZH8\nRMFN3hUrFyXDHFMBWyupqOgpMAlSwcyCMY2UF2WakUxThHGPLI99aX/HZ5lmgM1Fb3teWVlJanK1\nPGu6j5ldmZkxFU1e627LYznZMCQCtk21ynofZmBAJxvUcOu/OUm6YxrTnOwYaBpl5m2tC3O8YWFr\nRKS2T+VoHK1loNVqhX7TtsUcSLw5e1BgGi4PSnLf3NzMDsWhfaCZSW7dukW1WNr/Ojabm5vufFdg\nTEBFCX9QUFBQUFBQUHAPcWiRzVutVrY2CjNaM6h0EJNMmFTMXNctRwoDd6LUa8nGk8nEddXWKLAY\nDVzfdeLECbl8+XLtvRZ37txx829h3VnQT9Z/KQ2H5aWNx+NaeAER3u+Wg1RVVU1aOnPmjDz77LNT\n1xgRfDKZBG0Si4CM9bdzCvPMKfBZfcdoNArSuo7Dzs5OkHwwCrOtK+OqsTbFeCdMk8OkWHsfjj8G\nlmsaaJHNg6YBO7FtKd6j1XBhkEt8lnEMbb9gQEaGFOkbNSC2bQw4NqxsrAsGYLT3o+bH9iFGrPeQ\nG6SVBa20dVWw8cwFK8+uecb563a7NJyBDciJwVz1XRhEkkHHYDgcBu0dahw87TmGI2B7qkLrsr6+\nnm0tsO/tdrs1Zxy717B5yfrNW5uo0bPawlx4IQJSdcG9EvdhL8en/ra4uBj2d9znbZ5TLJd9K3FP\nZ3O26f4p8hogmytYRNkUGdp+5GIT3ttsMJYGSx7qwSOl9nq9WsyTlJkE26P9oYcnjJCLpif9PZae\nQdusqmQ8zHlgEZwZWXYymdTGKWbCVGiEXKyL3jccDmubw8LCQugvlmAT8fDDD4uIyNNPPx2u2XHA\nTQkPr0po13kQ856zkdyZyjdm2rNmEvxoppwhvDhHCHuQiRGuczzRsJ9ZSiEkBLNI1Ezt7iWm9UxP\nOCdZPKGUORX7NBVDzcIzAeu7Ee12u5bWCOug7WRkYkbIj2E/HlrMXHUQZHMsnx2GvbRC2i/4LHqp\n5bSz0+mEcvQdOP+OHz8uIhI8xkWmD1dWOO31emGe4VqyWQLQkYdB+widgHT/brVatX0P91Gth32f\nzh+P8hLrM3voGwwGoY7opJR7RGDOSXatM0E05tRl066h1y7SIKzXJh6uPSEL24Ye7CWOVEFBQUFB\nQUHBq4hDzbWHJjum+sP7rekkFp08B6hpSMURUe0JRsfWOrN8ebEQASI8ts1wOKQu/QzMjd8C1d9V\ntZfUGDVlrI6MnGeBLr8swjjCix+k79jZ2XEjbquGqNPpBC2RF9lYxCdxspx7qgXc2NjImk8oFXtx\neph2hM1tlDpxnjBNDtNSWXMvmq3x/daVvElIBCYF2vKYlgrXGUtArW0bDoc17QPTOGJ5bCyxz5m2\nKBXWIDW3tL2oecV2YDvRxI7aR32H3ocmCqyf0gFSWvLcPveQcpNnsKbRY8eOUeI7I6/nhCFgJuqF\nhYXwPqRX2P3s2LFjNLyM51TB3OUVrVYr7EU4XtaUxOrSbrfDHsNMWah59OgQy8vL4XmmEUVNmfd9\nwDmbE94m9qxFrO2eBjHlrOHNQRw37UPtZ1wzqe+UxWAwCM/gd7lopAoKCgoKCgoK7hEOlSOFJEi9\nhuEKWG4vdK21J8x+v187NaN7LEpFVvrEZ5ETkkuI3w+0nUiuttoMzD3Gcihpna204AXMQ/uwF8QP\nbemMFGjHptVq1ez5Mc2Lh0cffVRERL72ta/VfkMJCKUYq6nI5SBhFGFFt9sN/YbvshwK5urONFIY\nsgHnaa5GAIOqikxLXjimloPCJKmUtIj18zhwClyPlrsosjeH5ufna5oLrAuTUnH+3X///SIiU04K\nGj7k5s2bU1w7Ea5ZZdoONmdTSGl87F7U6/VckjuOOeMRMo2u1YTjusgNrugF0o1Bx5hxX7Dc/UTX\ntvNueXk5vC+XEKyaPZwbqE216zvFCcIx9YIEezzAWMR3/F3rgnxYXQe4t9l9IlervLCwENYGfoPt\n3jEYDKacbxRNQ7HYbw7+jd9e7S/cP5XTdvfuXVdb7CE2rrnzPaWROjSvPRHusaTX8V/8GzcOS5bb\n3t7O2gRxEuHCwMOXvsP76KMZwf6Oqlg8sFhvQRYhd2FhoUaSjLWLffyROIf1EdldhJYkORqNQr9i\nXRjhlW2YdjGhZyNbcMwMgf2sH0Z2gEJPEyTii+z2pSV7MiIj+5Ay9TJGmMbDov1I4yGMRaxWME9H\n9ETDDQhT9dg6pjY0ZhLRd+v4zc3N1Tb5wWAQ6oCHIPsO3CCZic0DSz2EQBOKnX/Ly8s1Z4kYwdeL\nYh7zVrNCnbfx2nK8dEE41jqeSG5msYnYx5l59dpo7Ph+e9jBNuEhx+4HWF7sIKQCnIKZdnIjm7OD\nObt3Y2ODJjy2Bxr8MKOwYYnF7BAwNzdX++jfvXs39CV+L+ze2263wwEKhS2b+Hx+fj7UlZmT9Rp+\nk3BfR3gHY9yzbF+yA15V1eMXbm9v03WQk8Vgbm4urF1G5tdyY99trb+ueW9vTQHbgIdcva71a7Va\ntXmM8Q5jKKa9goKCgoKCgoIZcWimvUN4bUFBQUFBQUFBYxSyeUFBQUFBQUHBPcChRjZ/LeH/qpbM\ncnxigfPU/p6bddtDLDjjrGi1WlOB67Se1k7/ute9LvALXn75ZRHZ5fpYjgJy1TzSKuYPw8ziNuIu\nuimjG7XWFXMRNo0ijHXROiiB9vr161nldTqdQNi8cuVKuK7cEgXyctABwvJker2evPnNbxaR3ej0\nIiKf+cxn3Dro+O3s7ARelYaq+M53vlO7f25uTn7iJ35CRPZczp988snaff1+P4z5jRs3soKgIjEe\nf2MBKi1XCOcEi9KNPJzcnII5YBwkfEcqN6KXZwx5VnZup8jiGJbGriUsDx0QbA497Cd0SlCeHoaq\nsaFRbt++HcrW8jY2NsI15Vuura3RaOLMcURx7ty58F6bgeHEiRMhoKfed+XKlRrPFjNXKObn50NQ\nX63z1atXw7O6vnu93lSwaV2f6EBk52wqTyPj1yF0nWq/4X4xC3K+IbFQFw899JCI7HFRn3nmGXe/\ny/1e6Rzr9XpTvFVWL72PBc6dutf99YcEXnyLXGBi3Ny4TrMgN7bLqwGMW8IOFNbjr9/vh3rj/Ure\n08U3mUymogbPCiQo6mbKCKy68Vy7do2OnfV2YodF9IpU4MGHbVSK1GEGnSdyPan00HTq1CkREXnh\nhRdC/dTrrd/vh43Pm7MXLlwImwdujF5KIkZeZiTd733ve9H3IjCZtz0UMwwGgzCvWEwgxc7Ojtx3\n331Z78aDLyOes2v2IMHmBNu4x+Nx+MC++OKLofxZvdjYoQ2BH3Av7YUXwZl5VCGa7l3dbneKeCwy\nLdhoHKbjx4/Lc889JyLTjgU2tRYj8t++fTvsO/qujY0NOX36tIhMH6QUGD/JHjrQcUTnnR78ReoE\naKzLwsJCqIMe9FhkehZPENuGGRi0fufPn6dr3BLoW61WaLO2c319vRbpG0n1SDzXQ4vW//z58/LC\nCy/U3puLHE+5brcb5oe28e7du3Lp0iURkeCpe/r0abl48WLyXQsLCzQtk0LHZH5+vrZGEU2+0cW0\nV1BQUFBQUFAwI/5PaKT2k7BVgaEODkLDFYOqnO+l1ssDhgNg5i1tO+ZdQglYT/OqLWi320FDEsv3\np1DzkkphV65cCdoJVGFbNXss9ISOl/ZlKmk1+x3NCzk5Gbe2thprE1k4D4RKkCqVovnj7NmzIrIr\npapUjWZE7VOEdbt/5JFHqPlMx0vNDAjWRh3pWhuwAAAgAElEQVTzVqslzz//vIhMu5f/1E/9lIhI\nkBpR+4WaP/t+hm63OyWdxjAcDkPMpRQwxheLI2dDdqArdMrUZefMcDgMUu673/1uERH53Oc+R5+1\n8y5FM2AmMWY6038xpAkLocHmfUzLJsKTKrP5gusNI7rrvVqHU6dOBY2U4ubNm8H0+/rXv15ERP73\nf/+3lgtORIL26Zvf/Ga49v3vf19ERH7rt35LREReeumlMB91z0F3egxHo3MC/9V98Y1vfKOITJuZ\n9dlz586FvVD3SdW6IcbjcZhHuu+hphvDZaCWxQK/d17C8QsXLoT+0owPGxsb8sgjj4iIBI0ThhHR\n+44dO0bjwilUc7W6uhr6PAU7dzY3N4NGDdel0jNYvkQPd+/eDfXSdjOrwfXr18NeyWKCaT1zzhRF\nI1VQUFBQUFBQMCP+T2mkZgHyJvQEqifwe6GRuhdlNkGKiMcCgKoWodfr1Z5n+bW63W5NqzQajShv\nSqU+lQh2dnZqkegZkD/gaZBWV1fDO1iQPJVw79y54+amYvnrGPR+kTqnJEZUtrnb1tfXQ5217x98\n8EHaf5aDMRwOw5zOCSRn66LQ8UApFbklti5zc3Pykz/5kyIiNe2CPqN1ygmwt7GxIT/4wQ9ExNdI\nra6uTnHpPGAkZesEgeOKEigGZ42Vp2Xis6gxiWmiWL1EpvOgxfKQWaAWw64L1CBhO73yvFyZbF0i\n74z9rpL+6upq4L7o3L127Zq86U1vEhGRb3/72+E3rQOOlWp4cN7bjBSIr3/96yKyq0myGqlerxfm\nlmp8Op0O1cBo/yoBGjVSWsapU6dCXbTOyK9S7OzsBE0ukvHVqUL7cXNz09UG9Xq9WlYJ1vcXL14M\nWlvlE964cSPs3e94xztEROQ//uM/anPB4yeK7I3r+vq6vO51rxMRvv5T0PHEPUvnrI7b0aNH3YwF\nrF4pYIDiJs9Z/NAcpGb1IEtBFyt66OR6VqXqZE2F4/H4VTlIeap6NJ2x5JhsU7UeaSJ7E7/VaoVF\nqs/evXt3KrWByDS5EYmP1uyGG6Quqn6/PxW9XiR+yNK+1oPAjRs3pgiWIrsbqd2YhsNhaJ9uqnNz\nc+E+6zFjoeY5NF0pUG2tfY4mMc/EpgcZ3aQsrNdJu90OfaCbztraWtiMbKJVbJvI9AEU3y/CP156\n3507d0I5Nvo4PnvmzBl6+La4e/cuPUBpH+lvS0tL4cOcC1yDahrFgyFmTGDrwR4AJ5PJ1EFG77HZ\nGBYXF0PbWVJlLQMj/iNYSixLIsa0Icx8h4d6Wx6mMMJ22495jDSv800PEbhWdB6hp6iuGSSHs/LU\nYw7rrM++8sor7sf+u9/9roiI/OzP/qz853/+p4jsrccLFy6EdaEf5oWFhTD+2t7Tp0+HOcYONNrO\np556St71rneJiMjnP/95ERG5dOlSLZE6K6fT6YS1t7KyEu7Xslny6vF4XEvYu7m5GeYO9r+3Jr/1\nrW+JyO7a0vGPHdz0PuuJOB6PwztSUezRUUBhBVuWsuvGjRthvSoBXcdXROStb32riExnxMD1yOqj\n7cilB8RQTHsFBQUFBQUFBTPih0YjZU+5B6WZQgKqNd/E8njZZ5l0KVLXmrTb7caJUWeB1zeTyYQS\nFLUNKtmgilWl/+3t7amYSAqVCFHrxdrnqeCRnMncy73+0roePXq0pqJFqUbLYGOKxE79PTb2+j6V\nYubm5kJfouRoYxX1er3wN85n7XPm7q+SMtNQMGCuQnQfV0nTxo6y0DowkqzWZXNzM2gQNNbTZz/7\nWfnsZz8bLVf7YmlpqWY2mpubC33vqeyHw2EgHr/00ksisqsJm8WUoFBNFIb2YHGLEHZds1xxGEdM\ny0BNXCwps/6L+dZEpvM+4h6iGjqdM+PxeMr5RmS3n208uXa7XdO23blzh+Z+s/fF8hjauGlszmKo\nDW3H0tIS1fzZa4PBoBb3SUSCCRjfYU1szzzzTLiGoQIUOPZWe/boo4/K448/LiIi//3f/z3VZpE9\nsvuTTz5JNRuqWUNYcvPdu3eD2U3H9OrVqzXiO4Ll3FxaWqp9K1FjitDxUVI3JkZWrdhoNJoyNYrs\nzhNLcWi1WuE7oc8yzfP29rZcuHBBRPbmPWrLcO9/8MEHRWTXyUCh61X/PX36dNAqarvvv//+oMVk\njhmYa886XL397W8PjjSqhWREf4uikSooKCgoKCgomBE/NBqpWcMapKAnUiSbN426HtMAMQloP5Gt\ntZyUO30KVvPG3K07nU6oq7ZjeXk5PGu1UKx8kT0uQ7fbrQVJY89ubm7WIpEj7+fkyZMisivlqeSo\n3ANrt1fkRGlGDUJK26mSHAvixqDvHY/HYQyRxK7RwT1yeG5QPIz0rVLvpUuXsl3sta6qwTp+/Hj4\nGzUMOkaqTRuNRlMckBg6nc4UB0RkOoCegvFwWq1WLZu8F5k4BowIre/Y2dmhpG82d2xforSPQQYt\nJ5BprkSk1qbRaBT6FzlBSkbGPUSvMTDNL1sDyIGymo/hcFjTEsY4Ulpn1TThfNF5z4LsjkajmsPA\nkSNHasTfyWQSylbtx+rqam3eMWeCL37xi/LOd75TRCRoHC5evFgjLzOCOWqUUFusfaDr98knn6Tz\nkfWVFwRX65KKjo+aepx3uo9oOYPBIOybqmXB76lqrjc3N2vfBgwirJqZhYWFoDVDrY2OiTcne71e\n0FQ98MADoc7a7/rs9vZ2CPZrNbaIy5cvh7aro8Lc3FxN64jtZdxLHddvfetbIWuDjrtnlVL80Byk\n7hXwo24/8Ds7OzS+RA4Gg8HUQUBk+jA0Sz0xurat86xl4r8iexsJIxuORqOkt4TCxnuJmeasKQEn\nvI21IrJ3WOp0OrW6sM2XpTjB+mHb7SEHx0sXX1VV4cOjdU55jeIH18bmQvIlI83qppN7YBgOh7UN\nB00dSKRlwom2U+uyurpKzYu6+epHqd/vZwkJeIDTvkXVub6D9enGxkbYXDUi8SyCSWrd4FzNEVbY\nwWIymdSuzc/PU6KtjRiPQozi1q1b9FDH+okdmu3BcTKZ1BwPcL6j96GNqxfz2kMiu22bPoPCjr53\ne3u79rGqqqr2QR6NRmG/0PvPnj1bO0j1er2wNtWMdPHiRTl//ryI7HmVbW5u1g5SOzs7NdPUE088\nUWsvAg+B3prCflQKAI6Vjah+5syZbAFK58udO3fCuGrbrl+/PhUbTaF9qXOyqqrQr3gws/vsZDIJ\n3ota59u3b9dMupjmB+tp97nhcCgPP/ywiOzNl//6r/8KdfBoDbiOtZ/ZQenMmTPBhOnh9u3b8uUv\nfznUH9vooZj2CgoKCgoKCgpmxI+8RgqlPOsiXFVViAOCanqVOpg0jNIJSxQ8q0YK34cak1yNFDNX\n5j6r6vRcbRSW7ZHEY6R0DywUA5oSWD44fJ/CasIw3xO6j6MmSmRXesK8cfqsNeMwjEajmup8ZWWF\n5u9S5OYsVK3O5uZm0HaoinphYSGMnY13ZGHNxpcuXapJeD/3cz8X6q/Sfe5cGo1GteSsN27cCFKx\n5hZUMrmFda1vMicRVsvC4iAx0wpbc5PJpGZ+YFqvmzdvhmtqDnr55ZdpAmVWTzt3GB2Bmahj91lt\nQVVVNTd07CN81u5j2FZcAzovtV9ipi+rdUCNnWpWJpNJTTvAzOHMiUZE5F/+5V9EZG+/OH36dIj4\nj2ZV1bZ86UtfEpE4ZcDWtdVqUU2+ap9Uw3H8+PGgHUEHAvtdmcXasLW1FZ5Ta0pVVW4MP63fxsZG\njXzNtK3b29tTYQf0vTqGOmfPnDnj5uJEzZSud92D94OYCd3GKmPA+dJkbykaqYKCgoKCgoKCGVE0\nUhAZViUBlVhQwkBtkEpB6N7skeGR0G5Jl7OQ6PW07UWGtvCkm3a7HUjhmF9I65breq+IcQZsPiUm\nnS4tLQVJSsdhfX2dEplVqlfJ6tatWzWSYVVVtQzr/X4/9J1KzEhQRY2TlqNjvrGxUevLXM0a8maQ\nH2Rd0xliBF8MrSCy21daZw1e981vfnMqj5/Ci0Ss92G7dFzOnTsXIkbr3MiV3m7cuFFzwlhbWwta\nBw20d/fuXUrytHkEWaDCGFDjZLXJrVYr9K/eh84BXtiVTqdDwx8odD3Mzc0F7YVqJObn50PbGUcO\n17rlNOKcYFoqBuSnWU1Yt9utBXNE6R7L9SK9M2K+hghgc+3u3btudHrkqngR6fF+nb9YF72m64JZ\nCjDMRC5Uk3zhwoXaGGIIDX3/m9/85hpfBx1qMMwIggXstcCgpdqmt73tbSESOxs3L3NAjCNoQxss\nLy8HbpY64aytrU1pXnPg8ZGY4wNC96elpaUwtrina+gUBdNMsbHPmQ8/sgcpFkuEecPpQOCGZcmy\nbMNFgieLZXEQXoizeu3Zek0mkxqxkyUmtb9rGTZ+DHon6odvMBhENwiRvVQKOzs74YClH5hWqxVi\nrOimOhwOg8odCZK2TxYWFmoE71arle08oOPKojVrXXLHAftKn8EEm15qosFgQDcZa44ejUbBG0Z/\nQ8KqxrlZWFioHcJ2dnbCB43VQT9ATzzxROhzfTa3D65fvx7mCY6Bzgl9/5kzZ+hBSn+fJZWD1pGl\nCGL3xaIhx+7H8nCeeO/a2NiomYOOHj0aDohsnaXWJTtcsb3I7lnj8bhm8mTzgLUD90VmPlQCN8P6\n+notVg8ehpD4rPAE0fvvvz8cbjTGEB4ImReq/n3ixImpuEUi05HmtX/a7XaYs+q5dvTo0Vp9WP3W\n1taCwKDxjra2tsJa0vba9e7RQlhMLBU+z549K295y1tEZG8vWFtbyz4w2jhY3W63JgTfvHkzjLHu\nYxiJXPfvmzdvhjmdk+EA0el0wv7FaBA6vrdu3arFa7x27Zo89dRTIrIXAf3OnTuUzG/7OWcPKKa9\ngoKCgoKCgoIZ8SOrkVIwSZpplVDit9LdaDSaCkmA/4pMk87vVTysJmCSo73GCJvD4bBm/kBzFSMA\na7+wJL2YIFQlHDz9o5bMmtjG43EoU9/RbrfDNa1fr9cLUomX5DgGJo1Yrc0sY6p9tra2Fv5mTgR6\nDV26Edb8Oj8/L2fPnhUR7gyhEt3a2lqQWLHPVDOgbcN3qhR4586doEFEjQXL42exubnpEvNVSmWk\n01arRcNkNAU+29RZI0bmVqCmhDk0KHD92H5D8yfOWRv+BOcm7mPWlI1mPLZusZ654SRsvzGyPtZB\n59rKykpNE7G1tVXTWOm+ILI3J44ePUrjUVmow4IIn2MYQZ61S4nZ2i8sc8Xm5mZYS1rOs88+G9z4\nPe3h1atXg8kLoVpWjP/EfmdAbbvdj7/zne/IG9/4RhHZc/CYTCY0dhbum9g2kWkTsMalUo0fRjbX\n3II3btyoJYoejUbhfWqqRAoNmrL1GYz+rvX7xV/8RRER+cpXvhLei9HpLQVAZC9Ui7ZpaWmplkey\n1WrV+r2EPygoKCgoKCgouIf4kdVIWUIeArkUSDwV4Tn5GJ8IpUDU9jQlbt9LsCjXHtluc3Nz5gCg\nW1tbNGhhDtcFOS2MX4XtsCEnGBm51WoFaccLMjgYDKYiXytm1YYwLeXm5mbgeqmW58iRI0FCUm1F\nv993IwZj9GGtM+srlEKtVD8YDMI19qz2VafToTyOHJ4U9h1qSfRZ5LRYoOYUeUeediKGHL4R/sb+\n9vhQ3W63Jsleu3atpqVCLRsGh8Ro6CK7fc80UQoMv2DHAZ0m8Dc7hjGHBgvUjiHsNSxPtQY4x3Au\n2PF+5ZVXgnbn6aefFpHdNWi5L7pmECwEAUIda9h6wv1I+wXrhmP9rne9S0REvvGNb4Rndd2opuPu\n3bs13tn6+nqNlN7pdGqaOjsWnrYQLQU2BMzGxoZcvHhxqu0ie+OlGqSrV6+GcnA+2b2g1WoFRxUF\n8uH0G9fpdALPUctYXV0NZeucmEwmNYvOzs5O0I6ro8KlS5fC+OicOHPmTOC04bfc+85q32MoI9aO\nJviRPUgpdDJhHA+bWFZkWjWJz4hMp5dQ4EFKNw6WuuC1gCaHI28zR084S8ivqnri4Saxh+w7er1e\n7aCaG5Oqqqra5tDr9aaIpCK74zprSh/v3RY2gvvCwsKUN6HWj8ES/fEDzsicelBpt9tBza+b3fb2\nNvVOYx8vrSseaFhfsaS62r9oKsDI0vivyN5hEj0mURCa5SDFxsEz2eE99ncWbwr/H/vA7jHj8ZjG\nadPf0dRh+xfT7ajZFQny3lqNOc0weGlyYm2O4e7du+GQgQcZz5FCP7yYSkbrwj6YL7/8cs2RAvs2\ntpZEdvvbmnu2t7eDKUvXymg0Coc4TPD9/e9/X0Rk6hDIYm7pgUC/K1VV1fpva2tr6tCZ6/jA6Bcs\nsbxNXo+EfB2bTqcTytG6bGxshDbhodTz/tO5u7a2FsYVx073Ly3vxo0bwdTNYoWpqRizRWh5mDaI\nOQfYWFkHgWLaKygoKCgoKCiYET/yGilFq9WqaSTG4/GUmU9kWkvFVIko1dpwCp4k9MMAlJq0nZgk\nU6WOjY2NGiEbtUUoKenfKt1tbm4GycHT3uSaSNGMh+VaLUZVVVNJVHOA2iItL1fLxqRLfT8m3dQ5\nE6sTc3JQaVefRfdtlbLPnj1b60PUGqpU3u/3a5ImxsZRnD59OriBI5iWymqkYrFh0Kyp91sSfLfb\npdqRWeBpqdA0ZjU0qFXCqNRab9UGLC4uUo2bbT+OF9bNmsSqqgqaKEaGxznGyOEKT7sc03DYPkcN\nl5ckfGtrq6ZlHY1G1J1d4xGpluL/Y+/bYu26qrPHvu+z97nZxz7HsWPHTpzEsdPECYFQKYWEhEQI\nKFSlVCCqPsBL3yqqtipS2/SlSZ9QqVRUlfalSC39W1GoSgm0QGhAkBJyITFJ7MSJY8d3+9zPvu//\nYesb51tjjrX2PiehJmh+Lz7ee+215n3N8Y0xv7GwsKD3xPj0FPBPnz6tv2FWFr9BMLmX8Fhkfdyx\niwfB4Ryojn4Ae8MhCJA0EQnZtmKxqP1vXb0WWLtWVlY2HFbhtT/aY2pqKkgAPTExkWDcUFbMU7By\nnU7HneujIG3dRhnQRyx/keVpYDkFtM/q6qrWDWN7bGws0JbajIRKGiIjFRERERERERGxSfxCMVJe\nrMIw4HrevXs7eWaTrAhhsVgMjpz2ej29zgasX0lsJD9flmXJQYEe62AZFFaEx/WTk5OJo7IigyPg\nmw1oZ3B8hRV7ZNX2UYKOUX5bDz6OjrYapjZvA0/5NxzYbhXQ02K1bEzTysqKxj6B5avVagl5BJGB\nNQaLDBZ4sVjUfoAFjiBVRrPZDNpt+/btrnox6ukFr2YxIVx39BvHVwGjBEeP2se2DMVi0VX1tiw1\n51AEyuVyUL/l5WW59dZbRURUGd4DP8tjmrgc3njKCgTne9vYKA4O98RLuY62H7yYOo7xQTn5kAPG\n9NzcnJvPzh64yOfzyiZA0NJjM9vtdsB8TE9PB0HEMzMzLiOFuiBOkdkgrie+ByM1Pj6u88cTk+X3\ngWWzmK1kOQwugxcrtFksLCwoa4ex4+Xj43aEeOiePXuUcUMcEzNIWGNuv/12rfOTTz4pIsMzEeAe\nzOh5sZ4seWPn3srKijJ5aFOvPzxMTU3p+ISYKAfop+EXaiNVLBY3vFnxTo5kqfnySxN0JGvGeC8C\nYLNK5G8mhr1MeENgN0OFQiFwYaSdIMIE5FN0mHR8ChDBz1moVCr6YsdEW15e1mdwGWygKC+03mYk\n6yVbLpe1vujL1dXVTJ2kYfWwpwVF1ul7TkdkA4atujBgdZ9WVlY0OSenzgGwQPGL69ChQyIyWGyw\n0NkAc8bS0lLwsvZO2fEpVSxGzWZT68n9xgsj6msDs5eXl92g7mHB5l7fZhkJ7LLjFxnKwppyIoO6\n27nS7Xb1OrwY2+22bqD4ZWnXiVarJbt37xaR5EbW1iOXy2n/8Lz0xrTXBrYt2fgD2u120FZpSWHt\nWGU9H3aXYa3EC9IbOyLrath4gfMcQJ9782JyclJdTwjGv/HGGzUJMcB9tn//fhEZpLDB5gov0pWV\nFV2neC7h2Zgze/fuTRiEqJs9GdxutxMJu9E++A3PLZ6vb/aJbzzPG2tZOHHihJaLjUqsN5jfx44d\n03pmhbUcPHhQT+EBw9xunho/j+esVDI8Fu3ay+sT/o06UhERERERERERP0P8QjFSb2THns/nE7pQ\nIulUNic6BmzCY74fLLWs46Fpz/1ZIsty9RTcq9Vqws0iMrC42O0lMrDabEBpq9UaybXCVC1+OzEx\noVYdM1icDNbWg4H2t+5X/syWAWUeVVIBSGOORAbWsR2jaTkN0fawzCYnJwMpAbZ2WeMJlj4+63a7\n2kZe+ZCb79SpU8pUeRbhddddJyIiL730UqA87rlIGLh+cXHRZTGti7LX62lbocze7yYnJxOfe8Hh\ngOeK4zlnx0m329U56wVw4zPWePIAy392dlbHL7OjuCfXE+wAu99s/TmgHX3Nytb8nWWS7b0BfI+2\n4oMFVpKBwYli+RneGmPZCXYpgU19/vnn1TWNddZjHDyXzbZt25SR2rVrV6ItGLweg5XZtWuXPPro\no4l6FotF1/VoXWFbt25V9gxubl67Rg3+57nH+UbfjJAHhtXIu/POO+WHP/zhSL9FGdM0tkQGjE7W\nOw3jaXp6Wu69914REfnWt77l3isLG12j2aNgn8NM1kbkESIjFRERERERERGxSfxCMVKjIm13jx0o\n4iL4SDx27WmMA1vSuB7XMjvjBW4CLPqZtcveaPAsno1723iUfD6fEFsTGVhPNt/b6upq8Mx2u633\nY2FHwLaLSBjHxOAAWpRpaWnJZRtt7jyWnGBYS97r/1wup6xJFrPJBwu8/GZZvx0fH3fr7AWo435g\nMAqFggbmg5EqFovB/er1ujJSHM+B/GNswWO8wUJrtVoaFwK1YDxbROSOO+4QkQEjhb5BjIQXEMpt\njGd5R/sZzMpwDFLW9RzDYFXHGXyk2x4yYSFL9AcfVOCj/XaM8W+BXq8XjPOLFy9qzrMXX3xRy2IZ\nWLaK+b7eOGHWBHW0zBCzSp48i6c0zgyhPV4+MTERMJZjY2M6Pp977rmgnHxvL+4Q4MBeLycf4lvA\nBvFY8qQEjh8/LiKDcWJzGvLhCMRIMVvBsV6Wzd6/f7+8+uqrifJxX+Hv6enpIMD62muvVeFOIG0t\nRzuPjY1tSnF7FIC9S2Mus8BjF8w26ra4uKhrEKQTGGij119/XeuJsbh9+/ZMYeE3C5xhQMT3zgw7\nQCTyFtpIWUVbThexUaQtfAAWskKhoAsAb0TQ6KCevSSJnluwWCwG+haFQiEIms7lcplqw1muoLTr\n2G25UUVX3hh5Ljurv+SdYut0OsHpxVKppAsnqPV+vy9Hjx5NlHmYy3bUAwZo07GxscTLEs/FBgV9\nNzU1FZTBS5NTLpdHOpnZ7/eDBSqXyyXSSeAeNliyUCgEn3kLa6lU0pcBB/xjMeJxgrI+8cQTIjJw\niXhjA5swrjfqcfXVV4uIBC8VCzzrqquuUveHB7gvz507p2XlzZ9d8C5duuQudJzGCdfyhsGOT8/t\nwgmWefNsXYC9Xi9xKlFk0F/2BNLS0pK6iLzk4HjRHzx4MHCxjI2NuUGvNh2It2niPuf2syEK/Nss\n97d32IA3iQzcDy/SS5cuqXGANltdXdWgcGx8cK0Fyuwd+ECZWZMKc+pHP/qR3HXXXSIi8thjjwW/\nxf14jrH72upctVqtIFTj4sWLepoQY3xtbS0YG7t37w42UjwOOCEwNmGsJj4qONPEKJpJ3N6cNDpr\nA8fq6Pj78OHDIiLy1FNP6XXYlM7NzWn/o7+89eDs2bM6Jkbd3NkAfgtO2M7Pt39bjBJqE117ERER\nERERERGbxFuGkbJHodN2iaO4vTiJp3cddr6VSiXIR8SBkpyTzwsiBvgZlllj9sZLCjsqPMuay+Ad\nt7d14++KxaJaJbDSWq1WIP3godvtqhXmBaVzW8E64MBNz9UA6/CWW24RkQFL8fzzz6eWgX+H57JF\nzwrFKKdNRr2yspJwB6EeNr8dJ7/MOua7trYWMD7FYlGZTS/nHcNTfwY8rR2g1+vpvWHtHjp0SE6c\nOCEi6+rQ4+PjbtA4+vzZZ5/Vz9BuYJA8q5LdeLBC9+3bl1oHkSQbzFplgNUg6na7mW5wj3lhbSSA\nWWD+bpQcmh6L1+l0dCyADWDlaMtSiKy7mhYXFwMmjJkSlhSxmQba7XYwnjx2vNfrub8F2K1lXRwe\nq93tdl21a+suRf1E1kMoRMR1KYNZQFuVSiX9rbd+c6JnsKgcJI7PPPzrv/6riIi8613v0s8Q8P/2\nt789mBfnzp1LsDYiIidPnpRf+7VfE5H1+XD58uVgLp86dSpw+zIbxOOZXcuc009kuG4iM6b2oIoH\nZiRRrmq1GhxoYM0o9OvMzIyuO7jH1NSUjkGM84sXL+qhFe5rgHP3sRzMKMC4X15educk1k8cQLh4\n8WKmTMJGEBmpiIiIiIiIiIhN4i3DSAHD4mFGZXK8zOc2bqZYLGp8A2JMOp2OfobrmQHgHHSWJWJ1\nYnzn+Z95Nz1qYLnXLh5rV6lUEmJmgGXKvNgnkSQLh3LZsvEz2OIeVZ7CqyusIcRPeZaVF0fSbrfV\nomLRVI6DwTM95WgbmM9BuhzMneXHx32Xl5fdGCm0CywqjxXyVIf53l7euqwAykqlojEIYEyOHj2q\nn8FqO3XqlJw8eTJ4rlUdHzaOs9STGbAap6amAiYpl8u57eeBP7dMIx+44DlgWW8+cJF1fT6fDwRo\ne72erhMYq41GQ+M0mF3kPJMiAybUskpezFIa643fcI5Jy7Zx+3iB9CgLsxReTBhQLBZdZXs8B/3O\nMWvMatu4FmY9LAPMZa5Wq1pWlG/79u0uQ4rcfdy2LGEhkgxAZykYi16vp3GBCFQ/duxYMHeZ+UN/\nHTt2THbs2CEiyXmDPkJb2LFtY6RGjbXjG14AACAASURBVA1dWVlRNgvMHwsaA+12OwjYbzQagXRK\nqVQK5gPX21u/cN3VV1/tMlEYT7x+oW2yhLJvvPFGeeGFF0Rk/R09OzurY8uTx8A4GB8fV4ae8+9h\n3nrCwWl4y22k3ix4waZ2EfGCNEXWOxQDjE9UML2MAeAtfBwIOsrm783SEUnbzHibPqtHU6lU3KTB\nNph3GF2KdpudnU240UQGmxNbxu3bt+uE4A2UTSSddhJz1PQAAMpUKpW0PbLU7jkJqQe7aWO0Wi1d\neLzTaeiXRqMRuBL43qx3xAueSFKXBotErVYL3JD9fl8OHjyYKCvcf4xqtaqBnVkq7xt1TzIWFha0\nfHzKy57kS1vkeANkNapEQiMjbX7ZF0sulws0qrxgc5Fws9/v97W9+OWL8Ym+aTabQXBwLpcL5mia\ny87bVOM3KGen0wlce57hxady+ZCInaOdTifhqsOzrDHW7/eDNt26dauOC88FhbnXbDYDpflSqRTM\nq5mZGXXL8fiAsvmnPvUpERH5whe+EPR5vV7XMsBQ8jaI7XZby4XrqtWqBrLz2PX6C3XnQ0yeThjW\nuGazOVSHMAtwcSJDxMTERFDPXq+n5eLNCz6D221lZcUdKzAScH29XleX/rB5j/UT91heXh7JtffC\nCy8Exsn09HTCHWzvwYYt5iPGZLlcDjSyRtlIRddeRERERERERMQmccUZqc1oIr0ZsHQ/A2VpNpvB\nbpQTcXLAuFX85mBZdgHAcmSX0bAj86MgK/BVZH13zZatx8Z56t+snu0pR3vBqgBYFNbkgsXCR+Zh\nHXGQLtxMi4uLASPELgfUuVKpjKSkm9be1k02qiuy3W4HCaoZ9jCBSDIhKixqj7HifoCV5VmmrIGG\n5/Gxe4ApbI9lA5uF9vPGU7PZVNfEqAyTB4yNUqnksoa2D2u1mlrPrHHG7lzbxzw+AXZHZ2kolUql\nwFXMv2Vr185Tdk0BzDrAvcGaTNyvcMWgjxqNhrYHs18eE2ZdSRwcztd5vwW4bl5QurduWoZz69at\nyubwnLNtyvcC68GMFOdDG0Vb7MKFC7qeeAHwWThz5oy2M3SRoPklIm4QO1iXm2++WQ9kYG3avn27\njkmeh1bDi12AQL1edw/IeMB9eBxyv+Fz9EehUFBWGetsrVbT0An2uticnZOTk1o/Lh/WAgSTd7td\nuf3220VE1P02PT2tz8V4P336tLYH2pJd+6hbPp9PTVLNOHHihD4D/7K70Xunoh4czrORnKqZjNRr\nr70m99xzjxw6dEhuvvlm+dznPiciIg8++KBcffXVctttt8ltt90m//mf/6m/eeihh+T666+XAwcO\nyDe+8Y2RCxIRERERERER8VZDJiNVKpXks5/9rBw+fFiWl5flbW97m7z3ve+VXC4nn/70p+XTn/50\n4vojR47Il770JTly5IicOnVK7rvvPnnxxRczlUG9I8dXAswg4V8OQOfgWpttnpXBYZWxYrEnjMn3\nGzVoMAvD2o/ZB8uocZCpF4TKsG3kXcfMW5aYW6lU0rKwBYr24Lx6Fh771W63M8XT2EKzcSF8/J1j\nn6zlXS6XE+MD8I7q20B1ZkfsGEoDt62ND2k2m8HhhU6no79BYCkf94fltbCwEGR7Hxsb08ByME5X\nXXVVcF2/31fmwBPdG/VIMRiRcrkcMFJerkoeS2kMILNJIunB/1mBxIAnu8CMKb7nnIccfM8xlCKD\nMc5CtmgDMBUYixMTE2r9X3PNNSIyYG85XgbA3zwObEyOd5CC45x4PbDjuFAoBOsYfwbk83llJOyh\nHbSHyID9sErVzMR5opNg51ZXV4PA9x07dgTxZOfOncucX5A62LZtW8ConjhxIsHQWICt8HLvLS8v\nq7o7GJBut6vzlssJRo0Vve38npubyxSvZXkBjONaraYB1JjLnU4nmJvdbjc4+LFlyxZl3MDkra2t\nBcHey8vL+gyMZz5489JLL4nIILAcwqOo07Fjx7Q/s9ZqXg/Yy5N1LbOF+AyM1NatW3XtYxYN5cdY\nXV5e1rZCX48SY5u5mu/YsUMX1PHxcbnppps04t17yX7lK1+Rj33sY1IqlWTv3r2yf/9+efzxx+Wd\n73xn6jP+rxL0Dns+v/w5rYl1ZbEbj79Dh/CL2UthYk8BFYvFN5RseaPgNDVe+hbWqPE2SKP0V6/X\ny7yOXQT2RcebUs99w/fF6RtMzPPnz4+UwJL7AfWt1Wr6DH65YhHB9V6anDRgEnvK3JuBTa3B5eKX\nrHWxVKvVQLmZF2gsHJOTk0Hgvjc2Z2ZmXCVz9MOoGykskEtLS4FLrlaraZlZMwrgU2UM3CdtE4S6\nWRcwj23+zjsxxDo5IoP+9U7F2YW7Xq8HJwMvXLigayxeXryxgMYXP5cDsm27eUYZJzIG2E3vpTzy\nUs4ArMMHzM7Oavl37twpIsmXK+AZlTxe8PJi8H2sjqC3UV5bW9N24LQw6C9s5BCkbIExxUHm1s3k\nYWFhIeGuRtm9DYC9T6fTSQRz419c561rfKIOY2x1dVXLjfXn4sWLQbov735pqVjstb1eT/sabcrG\nBHD69GldW1g3ayMuMxH/cIUHDjbHxp3nJTZL7Cq2B8dE1jfB2FBb/S4PIwebv/LKK/Lkk0/qpuiv\n/uqv5NZbb5VPfvKTelzz9ddf15QRIoMdKTZeERERERERERG/aBjJv7C8vCwf+chH5C//8i9lfHxc\nfud3fkf+5E/+RERE/viP/1h+7/d+T/7u7/7O/e0wC/xnxUhlBbF7rqxer5dgY3Cd/azX67n5qCzS\nvrMM1xthKDaCrMBSBveHF6Rr2TjvWCnrNGH33+/3XSvIy3nIue5wPxvMPzY2FgRQDhtLzAJaF0Ga\nlZTFcDGDmZX/kBkuPA8WEwfNA5VKxbXWURZ2X9mxyLIbnPAYFpfnyoIlPDU1pdcxtW/R7XZdRgrt\nm2X1MtDmnpXPVjzaYHJyMnFUWyR5+ANlE0nOf08fig+K4DPrssvn80EdWE7Bcz+wC8W6U1qtViKR\nMIC29tYizFseI2AO0g6qeLkALZhx9lx2o6q7e9egvvPz80EbMNsGRogDgdkdaNmWer0ezJXTp08H\na5un9TU2NhZIQAw7KMHuOzBlWWsMzxW4+M6dO6efc1t44S5Wm2t+fj6RL88eMmk2m/qcrKwN/DeH\noNi25Pk1KjAH6vV6kJe02+1qmVGPbdu2BZpr3W53ZJYq6zAR+nd+fl7XINZUQ1lY6gC/8Q7wbCRZ\n8lBGqt1uy6//+q/LJz7xCfnwhz8sIuv6P7lcTj71qU/J448/LiIDET+Opzh58qQK+0VERERERERE\nvNXw4IMPZn6fyUj1+3355Cc/KQcPHpTf/d3f1c9Pnz6twl5f/vKX5Zd+6ZdERORXf/VX5eMf/7h8\n+tOfllOnTsnRo0flHe94xxuswuZgc3IxvNgnz3fvBSCzJcFBmPbIMVt8Wc/YaEbvzYKtEytkyce3\nWfka9WMfNUs5ALC0uO54Bnb6nU4nsMLSrDt8nqWCzbFKWWyQdxw4Lbg/S7yN2QL4+2EFerkURbLz\nETLr4ZUdZWFGCs+Dddzv97WN0Ga1Wi0Rz4VnwNpllpGV3kUGjAmeZ5kfRpr1yGwh7pcFvrcNEuc+\nwviqVquuxcwxDJYB4UB7r9953nKAtYgvPcF14jHGwqn4zjJt3PbeGPNERNHWHIPC19vfenkEPQae\n6+bJgnjXefk8AWZvEOrR6/WCGJS1tTW9N4KrmZFiFsCyodu2bdOYMZb28NZefIb+4DhLMLBp7Lz3\nzsC4zJrTjUZD9u7dKyLrbHCn01F2hOeyFWtl4U6A4x2npqbcZ6PtbKA6w4v/m5ycDBi5paUl9z4Y\n+2i3drsdKNEzM+yNHTz/8uXLibyg+BfsJL837CGCbrebmWuX47A8Jhm/Rd1YWBjfLSwsuDGeDz74\noPzZn/1Z8DmQuZH63ve+J1/84hfllltukdtuu01ERP78z/9c/vEf/1GeeuopyeVysm/fPvmbv/kb\nERE5ePCgfPSjH5WDBw9KsViUv/7rv/4/c11ZDDsJZzdGnop5sVgMUqLwiS/eYFiXAgdpslvKBjlz\nB3OC31GCpjcCT4U768SSt8iUy+Ug4TG7+zAA05RoR3XjetfZzd8wF+VmdMlsuUulUrCZFAlPIhYK\nBW2XtKTRafBOQDWbTV1sPA0o6NvgdIyIJDYLWHx5U2FV6vP5fBDYu7S0lEgRJOL35djYWLDI8W+s\ni8LCuv48fS3erOEl7D3TBlJjDHKQMeBtlL0+4rmM73m8eS8b24f9fj9wa7RaLXfTYsdqt9sNyu9t\naIYlS/ZOJnrpYNjlhXp6Rh/X0UvOjetYPd0zdjDuvLEFN+fs7Kx7ahe/wUvd1g9ls5sr79TyRmBV\ntj2dMBHRpNw8NzEPUQbv5OLMzIyrecUnQy2uuuoqDWvAWOM+BLz1dGVlJThh2O+vJ0bmU2y4H67D\nYQdGp9MZ6eR9t9sNTsFVq1WdU3gW66vxM9CW3tjJOiHe7XZ13PEhEKwXvFm0GymEmGQhc0Tddddd\nbie8733vS/3NZz7zGfnMZz4z9MEREREREREREW91XHFl81HgBTcOw0aZMGaabEJWBuey4sBM+zwO\nGAWGuQ/ZMvR0id4IvGPhDJQVlqbH+LRarZGZMstKNJvNQFGdXaKwuFhbitsI5cmyOkRCt0xa4mYb\nGOkF7npt5Y3Fbrer5fL6GGC3C7e3fU5agDSeazWhbL2txhLnS8Nn1Wo1sJQ7nY7+jXtvhBlFG3hH\n2BnQe7H6VCI++8EyCB5Tw6wT6onPPHcasx0ey8IB/Na1zwG0XGbr9vJy/DHTzKycl4zWrj2FQiFg\nkvkZ7E6xufZYq85jpFg6YxQV+FwuTB4tsu5a4aBeTn4LoO7e+mqPrTN4XOG+niQLr8eeKyjr3cBB\n6V658Ntt27a5axFYHrDGL7zwgspboO+9urGrzNMEu3z5cjCver1eMB+GMfUoQ7VaVYYJ7N7a2pp+\nxu3FLjNc52VN2CjQDrVaTZ+Hfk1T7c9ivbLcm+12W92pHguNZ1y4cCHI3TeKnEvMtRcRERERERER\nsUm8JRgp72jyKL8R8YUlmX0CPPXftCByC97hcoC5J7FgVYLZmgWWlpY0CA47/lEZuTTRTE9kEjt+\nlnnwjnx6ytxcZq+NPMvIqsRzPAdb1FatudFouH2YpbzObJa9rtlsKiuCsnNgu2cRAmnsqFcWaxnN\nzs5qsCzHKnmAFcT3tQGvzGZ5zBX3sx2/rNqMezD75Fnb3gEOnpcYY/gsLdYPVrvHSOG5fNgB46FU\nKrnt7AWtoz1KpVIQB3Hu3LnAyubYQR7v1gKuVqt6P45By5IDQFmYfWIldM/Ktlaw145eXKe3trHY\nI4L1PYV2W26RZKwKx1zZGKl8Pq9zCmrWO3fu1Fghbm9Y+p4gJudVswwDx0Ux+2lZEV7LvfnlvUM4\nkNqun9xHaAMWcGSA0bnvvvtEZMBIoe/sWsflazQaqsEIRXIOrl5bW1OBSODChQs6l1Ce8+fPB3ka\nt27dGsRfcbvwAQ68d/D9pUuXgrG9trYWKIKnydsA6H/ODIFYqeXl5eBQQlo/YM55B15Qzo985CPy\nL//yL6ll8d6PzOxhfGL9HGXP8ZbYSIm8eS4uEX+Tw4sDnyDyTvdlbWrYxWLdFPl83j0R5L0cMLgx\ncLwEyrZO/Kw0cAB9Fi07PT2t5eLrvPQo1jXpnQzjEzzc9jZQfViQeFZyZk/BOQ1eige7APV6PV3M\nAe80Sz6fd/WWrIo9b16wOeBxx4HNXn/aQPBt27ZpsCk+m5ycDDSoeEOIhZIDgXmznjXGuN+s64QT\n2dqDARZZwb5ol5mZmeDU5vj4uEvbe8/hE5r2JF+hUNAXsd1gcD29jWCz2dQ2hDuGX+aehhbamTe+\nw5KMc9/hHlZDh1/wPE4wFvmkHOqHQGEv6JdPi/LY8AL37TipVqtBv/Ic4wwC1g3O7cf9ZteCZ555\nJqHqLuK7cTh1ig1iFhmeFsg+l9t52CnrI0eOiMh6ah+RZBYBlM9umvBsEUm4lrBmsKECcB8iyL1Y\nLCZOSosM2sqOt9XV1URSeNzPjhOR9bbjzbA14Or1uo4TGOO8HmNM5nI5V+0c48NbRwEOPfDSUeHv\nkydPyrve9S4REfnud78b3McG2Yusj212+28krCG69iIiIiIiIiIiNom3DCMFeMeHs5DmCrD3Yaud\nv/NUgr0cdJZpStMH8hTNPXeh3cl7OiO2DFyfNKQdU4UlimSUly9fTrg9uEwi6200Pj4eBMGWSiW1\nzFH+NEsPv2WGw7KFnlIx19lLDj0qwEjk8/lAb6hQKAQ5/vL5fIIlxPVW1ZthNW0Y9Xpdj9eeOHFC\nP2cLDkD5YL17x3LHx8fVWkdZVldX1fpjpWyPPckaP+yO9I6VW3dHWn/ACkdALlxBw37rWeUi2cHD\nlUolaHdmqZiJQvlZidyuE/1+X5kolqjwWBuA1w5P58qyvDwH8K93rJ3/77npMIYKhYJa4ezisa4z\nds8w82Lr5LkjV1dXg8+Wl5eVXcGY5DXkhRdeEJHBMX7Ljs/Pz2vSbaxDy8vLyuTgfp58yOTkpDI1\n3jrMbmuMadTROyTEcwL9nKYAjrH49a9/XURE7r77bvnOd76TuObcuXNyww03iEiSkQKDxy5PtMvY\n2JjLhqGPwYSx3hTab2lpSfMfgi1aWFhwwziwPmA87d27V8uFdpuZmdFnYLywnAL+FUmyPrjOugXX\n1tZG9iSMwhL94Ac/kE996lMist5PnFwdbeolMmbX6EZyAkZGKiIiIiIiIiJik3jLMVLD4oRGZSW8\neB6bf8veGxgloJmPDXO8ixdzxQJ2+MweVx4WIzYqQ1epVNQKg1XBQfCcZBqWA/7t9XqBPIKNsxAZ\nWA1ZCsA24JYxqmXCfcSsoY1L8hhEhmdZcqyHLU8+n3cDsdH+7OO3Ac1snXEuNRuvxXEkrBJu8/QV\nCoVARXhtbU3HESzDxcVFvQ5WKMdAeErEDDAXHE/oyUugvjZI3AIB92AXdu/eHQSeLy8v6zjFfa2a\nMsBMiJ27zWbTFby1LBXHRjC7bAP3OaYNFuv4+Lj+zSypzRWH8qSVHfAUxrmdOU7IW6s8pglMFEuQ\n4HuP5cffc3NzOj5ZCNRjetAGfDQeDBzjrrvuEhGRxx57TEQGbY9YPw7wxfjlWDk7Rm+44QZ57rnn\nEp9xjrq0MSMyaFuUlZ9h+4TXXtQnLb4U8XNgQLZu3app0rC2cs5Fjl1CPyDYmd8rvV4viB/iWDCA\n/48xUyqVtA9RloWFBY2R8tTkUc+FhQUtIx/QAWsGRq1QKCRU+FEnjyllNhb/jhIDzYHqw67/whe+\nICLrY23btm2BnEGj0dDycVzaRpgo4C23kcrCZlw7WS4x73RFv993X/b22XwNawd5KUfsPbzTYhup\nm6edwvXEi9g77cTAYMW/lUpFrwP92Ww2N3wQwHtWVjJKhpcew9sUD3ODYrLjefV6PXCdiaxvIvEd\nL3hZWlUiSdeaSPIlintcvHhR/8aGmjdq7EbMOqXI/7cK05VKResEV+b09LRu7LLSKU1MTATuj7Qk\nzTblzDBYFx+D3e+84R42Tmy/c4C3F1gO98e5c+dcSh/uU35BoR9Z/dnTvLEvPi9o2VPC7/V6mcHo\ncGtxIltOz2ONGO/gS1r7WW2xs2fPuomTvd9jI4rNhC2ryGBuHT9+3P2dSNKI8caYPS3obSRXVlZ0\nw+CdDAU4nRK3fdYaNCyEwr6EX3zxRdWRYmCDx5sO3NMaYCJ+eqSFhYXgWj4g4aXYOnbsmIgMTlTC\noMEGSWTdvei5/XhdRIo4azzx351Oxw27sEZMPp93TzRanTO+36jAZv3qq6/W+c2nntFWfPhko4mb\nRaJrLyIiIiIiIiJi0/iFYqQ2Aw429wLF7a6Y6VaPHbH35XvwM/geNpnuRlTcPTVX7O7ZkvdkF2BN\ndLtdtYw4GJZdfyJJa9vLhcZqw/a55XI54Wqw4ABzTzPKC/q3GJW16/f7gbuq3++7dL3H0nj9wzIP\ngOf2tLkbW62Wsh6cyBTlgqXEef+8PHi4np/JebrQd+hfL9DfA+tmeTnbGGhTZm9HgT0ejudaWQh+\nBr6zyV894FpYmqVSKcEIiiTdc8DExIQrEQCGhi1r66b2QgG4PbwxlKW+zddjrnquU2bBeC57DJhl\nvUqlUiCnUC6XdZzwZ17fWkXo6elpZV6uu+46ERmwGnBxgalhlzffF4yJtyYBZ86cCdxHLFuTBWbY\neX3JcnV7zCMDDNjNN98sIiLPPvusyyratZDZyj179ojIgKnD+N6yZYu7nuD3aMszZ84E74S5uTkd\n+xgfr7/+us4rsFClUilgYL33ncg6OwWW59y5c4kQEJQti1nF83ktsuw8gw9cZKmN33DDDXL06FER\nEXVfnj59OpiHvK4My5gxDJGRioiIiIiIiIjYJK4oIzUsOHyjUgdv5LksTcAslf2MhfF4h2stoDQF\nbE/+ANioeruIL0iH3TrHEwzLlo2dvsfKeMwbxwrAyoH1UalUAjbLez4LADJrZ5k5jsnZqI/cQ7/f\nd+MHPKC+iCNgdoctdFiObKl6YxvBrRwcDiaE+9/mAhRJBgrjOvssHrOIQbrqqqsScW4iSTXuLJFB\nzls2rO0RG7PRYM1qtRpYhMViMaGQLjJglCDPgfqkMVIc94Xys7WN37NSsl1vlpaWgriqfr8fWMNp\ngpz2fmmxT/Y6T7hzbGxMn8HslxXQ5PtyOTnwGO1in8+fIXZsfn5ef+sJGTLAOoGR4nGA9uN1EQHw\n27dv13vyWER9mZ2w6925c+dk//79IrIe/9Pr9UbKAbe2tpYQyQU484KFFzzPwHxlCQC79uVyuWCO\nvO1tb5MnnnhCRNbH6crKio7vVqvlSqtg7eU8fLaNzp49q8wM2pL7lctlWS8+nOTFDnkZEtBGLE2B\nzzj4n4V7reTNyspKkPPWk7rwsLCwoPfJYqyLxaLceOONIrIuxSEyOPwikh1fF9xr5Ct/BhhG/b8Z\nG6i0E3Xes2wKE174+H6efhFvBHA/S4kylcibE0yGN3vD6Ll+8Ewuz8TERLD54onOLyW7oSmXy/o9\nXoYckI2J22q1goVpbGwsEcSdhaxN6aguJKtfIrL+sul0OoEi99zcnC6MNsWCyLqbaXFxMZH8GLCu\nDk57wS4Ym8hWJNy0cAJY3iB7KWxsOgvWS8F9FxcXXa0qC17AswJtK5VKIvh6IyiXyzp2OPGtt4Cj\nrJ4yvcj6iTZesG1Kikqlom3OauecNFxk8FK0GQbq9bpuwlGWpaWloK9FwtRJHEDPiv5eEmyLpaWl\nYLyzO5Lvi/7n8eSdkLX6VblcTl3UaD92+2OzU6lUXMMIdfI2XPibT2hiHF+4cEHLj+dyPbjs6C9+\nBk5/YiPVbrdHChjmjdSwgzd2w5oG+xKemZkJysJzFODND9qRX+Svv/56oBvH/Y+AfS98RCQMHvd0\nyVqtls4fzMdGo6FtjrotLy/r/bCebdmyRT/D9VNTU4kTcha87tkT8xw0z6EFntFnjYOzZ8/qfETb\n8zrGp4Bx74MHD2q9UfdbbrlFRAaK+sMQXXsREREREREREZvEWy7Y/M1QNk8DdsVZLg8OfGYL3TJS\n+Xw+yC2Xdr83w101DMyEwLq2lrpI0pUEywjXcfJb1K3VaunfrE6O33hH+dkS3qhkBcsWWKuuVCrp\nMzgw27oK0xgT2z+sNO6xX5blsX/DDQWWgl2UHJhvg8jZGrPyCyLr/eLlpWJXFtDtdgOtMtalQpt5\nAZf9ft+Vy7Co1Wojyx4AcEesrKwErCY+R1lFBv0DqzdN4dhS+eya4HxudtzxeoI5MDk5mbDM8S/y\nqL366qsiMnBpoo89iQWG5+pG+bzDJugvTpaMMcFWPq9ZnmvCHpNnFzofwbdjgJ/Lh0q8tcwe6RdZ\nd4mDQeS6cXA43MJgriYmJgLNs4sXL2r7HT58WEREvvnNb2bqugEeA4O68PXsVmWADRrGnN9xxx0i\nIvK1r31Nyw4Gk/W1IB8A/aznn39e78FhB1njaceOHcrCMdA3eN7ly5eD/m82m/oZ/r1w4YLOHyih\nT0xMqNsL4+nWW2/Vvsb109PTut6hjZrNZuI9kYZWq6X9j3oye8tMXNqaJ7IepH/ixIkEMyySdB+D\nzVxYWNA5jDF71VVXJXIUiiTlIdIQGamIiIiIiIiIiE3iLcdIjcpEbSZwG2AGyWNMrOXdarWCeAMO\n+uVj3Naq/79go0SSLAwsM5S5Wq0GsQIcsOkFsnoY9To8fyNK9JYd8xiadrs9cn+jb+BL73Q62kbe\nPbIkG9JgLeB+vx8Ec7KAojdmveBkZrOsmGa/39cjyRiLly9fTgQ84zr0uSftAXhtwYwZwOKGLDrp\nzVdYmqiHp5TNTAjQ7Xa1rcCira2tuUelmT1BP9iDDXwfL4aD88chkPqll15SKxZsATMiPM9sLGUu\nlwuC/pnRZdjfcvkwdtOkObyDFCgjmGkuc9bzG42G/s25+7x+hYwFByDbtWBxcTFQ+t6yZUsQHM5l\n4nXUk+wAY8KsS9b6z21r5+iuXbuUJfLYeY8RYfChFHs9mJClpSVl2fAskfV5wdklwNDUarWgrJzL\nkCUnMFYwxsbHx7W9WCYBY4DFXNFfkJ7gcoPRefrpp4Oge3638XfD3gWAPRDCaxaLumJtw1rD8wLX\n7dmzJ6gH9wPmR7/f1zbHunLy5EkNzMdvOZA/DW+5jRQast1up2p5vFGwDD27q0QGA8OqYvd6veCU\nTbFYDE7oeYHqKysrm1JkF0mnoT2wa8oO7m63m6nLAUxMTGib83MxYe2g5O/a7XbwDB7Inoq8dZfi\nNyKDhYoXe8BuCPlFwCkucJ+0E0ijAPdjLTCGp/vC7gyRZFuxJgwWWF4A8BvWBEN/8EvEbvAuXLig\nCzdeUPV63V0M0Q9WlZ1Rr9eDn+YlmgAAIABJREFUly+PRS+AmzGKMeQlG+Z7Y0MgkkyWbNXJ2aXD\nY9amP2o0GkEy6k6no4voSy+9JCLJ9D3sbrEvNC99Bysp2xRQIsmwBTv2OZUUt4u3YcBvPIVsjDc+\npcwGFe7Np/zsGpPWfzblTK1WCzZ1nU5H+wg4d+6cjk+sTSsrK4H7a//+/erKYjV09DUHSqPf8HLl\nMvNhIluXHTt2aB/y+2XUdRabbLQjv4R5PHinyTCO0V8cwN3r9YL5cObMmeAlv7S0pHMXB2R27Nih\nG1Xv0AzX0zsZ6K2RKAvG2tLSko4ZzAXW8+LsHt57G89FG/AJQvTRyspKsO7U63W9H8bB8vKyGjnY\nmLGBw+1o27TRaOi8Rlm8ddwiuvYiIiIiIiIiIjaJtxwjNerRak8nZaNaRGzxY1dcLBaDoNBer5dg\nJ/CdDVovlUoB+/RGWLWNuC3x3FKpFJSV8zzxkXkErQLHjx93mYgsRXZ2q1g3lZfYmZWUWTYC986q\nc61WC1SY8Xt+LgNWBzN1LO0AywbMECcF9sYRy2V4WjZoP5v3SWQ9f9Xc3FyC8gfQBrCy8vm8WubM\nOsDK8vqDg81hzfJxebAeWawRa6nh+mKxmGgjrusosGPSGy8MloLgOWXXB55fLGvA0gUiAyXqZ599\nVkTWDwmcO3fOHe9emcFisBvEstmtViuoE+fL43p4OQWt9cxsG64vlUruGmnZJ34Wvms0GsoMMCvv\nlcU7WIAxDYv+qquuSijziyTlUhhWFqLdbmtQP+YCP58PgnCgOIB+w/zluZjlbqpWq5nzJuu399xz\njwZks84Z5hnfF3XisQZGFNfx2E2TZLA6UsysYlydOXNGg7nf/e53i8iAOQM7xQwy2hiSEtCis/Dm\nJv5GmWZmZrQscJddunRJ11w+wAHWB5/VajX9DbPjrPEmMmhnuCtxj7W1NR2DYMfwL/+2UCi4azTa\njdmxYYiMVERERERERETEJvGWY6Q2A6tOPuw6L2YJu20WimNGysb6sGq3FyNlg9M3Cy+eyGNKvHgJ\nD6jHpUuXXD++DaBn0VK2mGEJoixpTKJlCT2Lz4tl8LCRfElejkILFqMbJv5p47BEfMYKrEjWd2lq\nvFbRnI+gc/lGsagXFxe1j2D5jo+P6zhhix/ggH8rM1GtVoO8lB7S4qY8sVSUxTv6zc9l1sjmPGSr\nntlRO86effZZbU+Ov7GMFMdLoM05tojjSby8bPa5HAPn5dpjpsnWg+/L9bWxmb1eLxi3HPuE72q1\nmtYX5WQhWCBtjeF7iyQDqfGs2dlZd2xh7jLT+PLLLyeuYVFJnreeyC3WGzArPIayYlI9gVSR9bbM\n8iDccccd8vnPf15EkrF6VtCW3yHcjmA1ea1G3Jc3B+r1ujIqHAeIuDT+LeQKnnzySRFZZ4i4XFxv\njqX01kg7jllwGc9vNpv6HDBmHDuMNeTixYsJYWQRPy6J243LDmYNc59jy9CmHFOL9h0fH89UwMd4\nGqZmL/IW30h5lLMHNP6whJMAv5TsM5ie54XIbghYb4pfePYEzBsFu6Fwf4929fScMKBarZYuPKxy\nDBcSfsvuoLTgPZEk5cxuOvuCTdsg2c2a5yLie3HSVSxgKJOX0qNYLGZuoDwMOxBgtaBEfDck6uRN\nYKa3UT8sht7Lvd/v6+IB6t5zCTJ4/GHDhnkxNjbmun4A63oQWV8ga7WaLlr80rfodrvu+LSuokaj\nEQS+t1qtYD7azbNN38Pl8TScWHMJdWFVdO/kHf7G6TPeGKCsxWIxodkkkjxcwUaYl/SbQwm4XigD\nvstyg2e5Cj0dK25LzP1utxsYEWnrLdYEdlfZNbdUKunYhyt7dXVVxz7SvRw/flzHPNqHxxXa/uWX\nXw42Obz5Q514A28PJDDYJQvk8/lE1oY0LC4uBm2wvLysmyAYLtwv2HTwZjxLhZ6xvLysbYK67dix\nQzcWfCoW/YCy7N69W8ct7r1//3515aHt4V4VWd+8egm+O51OcAJ2eXlZr8N42rZtW5DqiI0nzI96\nvZ5Iy4RnWHBYDb731l1PHZ/rh0MCHkY5iBVdexERERERERERm0Suv9mz92/koSMEb/1flcGrPjNI\nVsXc03PhY+N8bBj3hkWVz+d1983HkLGDZhrfugW9fH6cRNhLLJzL5dQ64USSsFS8uuO3k5OTIx37\nZPCxZ/sMZjE8JvGNJKjmoF5rtWxEq+rNhJfIWiRbtwis0vz8vFrNCKQ8duyYujDAnJw5c0b27t2r\nv+F/LXA/tPPS0lIif5zIwKJDn3usA5gLDsIHq1mtVtWKRd08d0ShUNA5wAwI7s2yELgObcA5vrzy\ncZujbpVKReuEz9rtdjDOWPkYLNDExIS2J48hqD5DZ+bw4cPy05/+VER8ttvLteclnubr7BxhxoLn\nir2OXTHMxKG+3vjjdcyqpnNCZj6IgPvgu7m5Oe3/LMZnbm4uyJP4wAMPyCOPPCIiInfeeaeIDDSp\ncB3G+IULF/Se0Gs6f/58oF+0ZcuWgJmdnJwM8ibyYSIeG1iv8SyWfQGzwfIWGA/j4+M6JrxsAKw7\nZtcpZpIYzNTaeVOtVnUtABtcqVS0/Vl6Ap9x9o4DBw6IyEAXCvfDeOT5AZYQ36WxN16+SYtdu3ap\nuw99xDnvmMEEY2lz+F0JYG1JDe34Py5PRERERERERMQvDN7SMVKjBAyLhOwT7/LZ725jCwqFQkL1\nGZ/Z+AUGfwYrmxmuLKE99vVi983Pt9YnB5F6dWN4ljKsmUKhEATkD2Oj0G61Wi2IV8kK4OPyo178\n2bBgbY/NylI758/QzlNTUwkmRWTABtmg+Xa7rfeGFdXtdtWy5RgYWJueyjaD47nwW/QNt7k9+iuy\nPt75GTZIl3/DsSM2dqPZbCYkMUR84b1KpaLXwRqfmJhI5IATGfQHnucxUei3qampRO5GkYHFyYG4\nuB7l4vxgVpm73+8n5rBlVBqNRiBkycww6nH+/PmAreHgZqBYLCrzgkDmp556Sr8Hm9FsNrUuHJ9h\npUKY2WAGk2OyUCaOFbPXASsrK8rggZHguDS26m1sVi6XCwRPl5aWEkypiB+sz/FfzORwnI6IBGwU\nygyAzdi9e7dei75ihgvfsUCqPRjC4DGC6+644w750Y9+FLQLK/OjDdAeaHsuC3K8/fjHPw7WX2bC\n+JAIr+FoA7tm2aB+K5bJazrHs6K9wCSdOnVKy8usGJgobhfUBWO/UCi4+fw8eEyU9TScOnVK64R2\n8fJciqwzVj8PHqxheMttpHijYhebarUavJh5gefrs4LD0bGdTifYSHEwJ9DpdALKnpWD+R6YTOzO\ns5shb+DwS51fXlZzxNOJYnDdh214soDyp2247CazVCoFWjEi6wsSt5EXiM8bD5HkhOOXsD11lM/n\ndQHCYpjL5fSlwC9cgDe+doPKL24+vTnKZOdFnz8DeCGF6wIuI/6eKXv0A7cHaHyvHfmlg0WVlcEB\ntME111wjL774oogkjQkAbXrx4kWX9madKZHBmPM2TVZRu1arJZSKbbnQpzbDgQ00LRQKgdaSlxoE\nbcL1ZCOHTz2izgjMrdfrWm5Pid4LHsYzFhYW3EMwWXpTXD67uZqentZDBGiLsbExV0uN7y2S3NDg\n70qlEiQj9g5wXLp0STf/eKFycmMvUBjfHTlyJKg3n9jz1hjrXmXw/PAO/ADlclmuvfbaxPMqlUrg\n3qpWq4Gxzu8A1JfHsQc+BWY3wKzlx4dteEx448i6HPv9vrYJxtXVV1+tyvxpybQB70TlZsEJw7mc\n2CBh7Wu328E45hN6nvo/6//he+/UNq8XaF9uRxzmAanQbrcDPaxR9Bqjay8iIiIiIiIiYpP4uWSk\nYD2xNpN1/XDQKl9nGYROpxPsbJk9YDeD1TIaFqicljRUZLCjtjQvs15esCdb6J4Li2l+/B9Wr03q\nK+IfQ99I4DWYLzAXfAQfVDKzgKhbs9lMMEIiAyvAaoVwGbMsuXq9rvdmiwp1ZkvD5qNrNBpqgfBR\nYUvfT01NaT2YVcAz2JXkBcSP0q4eC9Lr9bRcOIp/6dIlvR9bwsyUAmBrOE8c6svH6T33SJZavJc3\nEX3e7/fdwHcv0J0D1FF2K1FQrVa1fXFd2niwc9gygWDy4PphtjBLJmV6elrbC+VjtxvGASeZzkqW\nXavV3JADb17bceGxT570g5fYeXV1Vfuf2QBbdw76Znbcuvs8rapOpxO0e7fbVSYUDI2XjHr37t2q\n/o3xx+MGbcpaavie+4Pb2+qhcZlnZmaCz4CLFy8Gueq8Ayte8LrIunwDkCZRYD0dxWIxyJFYLBb1\n93ZdA7Ikc7xno85go94scNJytH2a1ltW1guwQZyDEixVs9l0DyDhb4yrrVu36mcIdl9aWgry9K2t\nrem98e/8/LyOszTtvlERGamIiIiIiIiIiE3i54aRgsXMsUVe4DF28J1OJ2Cf0rKmA17sEN/D7upH\njV9i5oq/x98oM+eZ4jgnG+vFcgq8A7dHdbldmBXiI66AtTQZ9Xo9cVxcZBBnY33K1157rd4HVtvq\n6qqWC/fmTOBZ8FSTPfXafr8fWDwc9I+2r9fraoVzoDDKstGYsGKxGFiyzIQy0+kpc3vxK7Cu0d5s\njTF7A6ud4wjAUnD72Jg7fh6XA9bfqVOn9HMb68XAPc6cOaOxLxzbBkYKLNXS0pIbw4HP2PK2OSg5\ntsEymRY21oWZZJF1JgrlWlhYCGILG42GMhWIp1lcXNQ55LF3HANl2TCutxfDx9/Z+cpjB23UaDRc\nAU20Pz5jNgo5/trttpYnK9ehxx5OTEwE8UgsvsgHJTwW02Pg0JY2dpG/43Ub/cfzm2UfLCsvst5P\nCLLnuCmwZF6c1YULF/TAANp7dXU1M94RQqCnTp1SluqBBx4QkYFEiWVovHXQuz/PAY7vzco3yQre\nowJrzL59+/SZWGtE1scR2jcrX6hIdswVj1lPPZ3XUVa0F0myrVh/6vW6xiXiuYuLi9rHfCAI7ymw\nVPxuxzidnZ3VebCRvKAefm42Ury42k2Q507J5XKuZpDdhPFpNw4YtRskpr+x4HoBat4Czy47Hjh2\nEjQajUCllxdcL5EoB2FjEngn3LyTa9xmvGHByS0OhuXTIyJJld43AmwEuPwoNweFAt6C4QURelTx\nyspKsJgXCoUgESvXNwueC5I3zZjAPBa9+2KBn56edjf/qN/zzz+vv8GChoX5zJkzIyeptovC1NSU\nu1CgXbx+AAqFggb2YhM2Pj6uJ7mw0F66dMkNALYbipmZGR3T3vjC4jk+Ph4ECvNJQ56PPG/sCTNe\nkPlEGurMSv2eAZKlieT1B89NW/40jTQ7Zvg6XkM89w36EPVtt9tume2a2mw25eabbxYR0WTNbARg\nvPM9sl7q4+Pj7qlP+yJmFxnuNz09rRsouAW94HCe29z2WWrt+E2z2QxOZV68eFE3Uvv27RORpBvM\nc8O+/e1vF5GkQQIjRSTb1YW1nzXcPN0m7mf+jT3A0+12dfMw6oYK8+aFF17QU32s02UPfYyKyclJ\n17WK9w4ML+6jV155Rb+zm382uFC3Xbt26fjwNtJe6Mgw9zr+5oNIeC76NSYtjoiIiIiIiIj4GeKK\nM1L2ODPnt/Pcbp7bLysZZLfb1V2w1e7gZ3iJT8vlciJ/k70OKJfLwTFv1loB48BsBupdqVSCe3KQ\nHuv1eEe7rQI6uwXTcp5x0PibCbABfMyfKW7UySsXmIFGozEy8wKgzYvFoo4j1igaJa/hzMyM/hYW\nTqVSCQJi2Vocdl9ce9NNN4nIwIpF3TyLn2HVuNMSxQ5LpiwyGEOWdTpw4IBad1m5J7dv365tadXM\nRUSlEUTWqXXvSDrAVrVn6THrgTnC6wAf8/aA8eYd6gBWV1cTGkF4LuYaMzpe/3ghAOgby4iJ+EzU\nDTfcICIDZsAG0JbL5aDcXgA6r088p1Bmdi1zxgWUE0wUtIP46Ls3B7OSSF977bXyzDPPBJ97QNtj\n/nh6fN1uN5PJ46TKGBNgxCqVirpueSzyGgNgXef+spIN3BZgbxhHjx7Vv60ri+csnr+2tqZjzGOk\n+Pn8bNtOw6QMhmFUfSjrHZmamgrCFrZu3aoMDnTVuJ09Zgh19urhaQLm83mVq2DJDHs/1gnEb9fW\n1rSP4Z5dXl7WMcgHc/BsZh2HITJSERERERERERGbxBVnpKzQomdp8hFmtrz4aD3+zVLDZjbIMlxe\nAG+5XA4y3nuWGssz8GdWxsE7is0BhRwnZpmrUqkUxPr0er2EKKRIMiaE2xLWEFs+zNpZQVEvLomD\nltEua2tratUPy/fmiV9yMDX/K7LutxZZZ03AinAwPNrg8uXLmVYarM92ux0E/XpxQl6g5djYWCCc\nNz4+ru3LBwFgFbMlCmRlKuexwznP0Cf8m1FkF7husMZuueUW+da3vjX0t3Nzc8o08ZhFTAviHETW\nxxb61BtDjUbDFWm0sR7dblcDxjG/WdkcFq4VN8ya/3iul2uv3+8HucKYBQI8UVUWgmU5Co8NA/vz\nwgsv6GfWum80GkE8EufV4zgnG6jOweG437Zt27ROuI7jmbyj32BMVlZWgvhLL9icxz3AAeO8BmJ8\nYH4tLCxk5qD0FN2ZkbLM8NTUlMaBMiOF8YS6l8tlufHGG0VE5Ac/+IFeZ8cQtw+vSQCzI56IJAC2\n8ty5c1pmL+8kB2GjHxqNxs9c4btYLGrdOUbNrs2egvmFCxe0rFhjXn/9dZe1Rz1w3enTpzNzrKJN\nW62Wrv+QnuCYO7T5wsJCcLigUqnofThXoD1g0mg0dJxg3I2Sc/aKb6SsW4GVyL1TdlbrieHpSHk6\nTJ5EP9PzmMy8abNqpwxvg8QuStYP8ZIMA/w8DGS+ny0zJ2llTRsvIB4vhOnpaa0Ln6jxBgsmBgZv\np9MJFME9eCfvPMzMzAQBipVKRQOt8ULgjRk2SvyZPTiQBk8fKu00jMhg0cTCifbxEos2m013I4YX\nM7/ARwnmZKVsTmuyUZenB9RtdXU14aJLw/79+3XsoEzlctnV1cG4w4Y1bSNlXdnValXHGKemsK5x\nzwXU6/Vcd7t3ShXt57nYu91uYAB47ghv3PNLwKbOEVnfWHa73UA5mo0mhneQxhszmKNot06no89D\nf1y4cEENIGwKeEPI7kuUmz/zgtete94zYDhgmN2Xnrs0y73svWTxfA7cZkPN29jZ527ZskW12xhZ\nc9Mb0/isUCi488K7r00VxeBxzn/bd8/hw4e1TliXGo2G9j8bIHaMcRgEyj8+Pq79wG0+SviAyHr7\nwljYt2+f64KzKaK2bt0aGJbdbjeoLx/G2SjSxhefEhQZvOsQ+oLNODbAWYiuvYiIiIiIiIiITeKK\nM1I25x0zQ+xCsbti3h2zdWnpz1KplGBw7HWszWJdNsvLyy67Y9muVquVoOVFBjtc7HJh0XksFQfS\n87NwP653VvJl3C/NzQH30vz8fKbFBSulVqvpPbOChz2MwkaJJF1OzGbgeVxOtCH/a1mlYcGTHLzM\nrgGRZAAlmAhollhYVwPnN2RLDpYhj78sVskLVAayGECLrCBd1Knf7ycC2bmcjHa7rf3E483rY7Sb\nx9qyhIF1R1UqleDofKfTCRiEUqnkskQe+8DBo7ZeLE3AZYDVymuITXjMgeDM1Nh8ecvLyzpOOB+m\nFypg+4ldijzGUS64jC9cuBAce2ftM06SCyaK9bUs2BXHXoFRpB+8ce2xAKwMD6QxhGi/rPHEqvKc\n/QBrATNxlk3O5XLy3HPPBfe2fcnl48MVFp7uHOoskswfiOt4Hnm6ahyQb+fcU089pYcW0K+FQiE4\n0MLJ7VlOB8/DOF5aWsp0sY0KsFDvete75H3ve5+IiBw/flxEBm5VrCf47N3vfnfwXl9aWtIxy+ED\nm0WhUAhykPJ7m7X8sG6DmULbZiEyUhERERERERERm8QVZ6Sy8sHZ48/8dxpTZL9ngU8OMLZieV6e\nNs4jx77+tDxf/Fu27jhI3GPU7PFnzr/G9bYWyerqqlq4zKyhnuxf9yxQZuVgxbAS9Uby8jFYjJRj\n2lAnWGgsgoh/PTamVCqpVTyqSCjHCdi+5hxg9virBX6D2KJGoxGUIY1lstYp54/ioE70wzDldTuO\nOQaFLc4sxhGxLF7cIQPt9/TTT2s9ONDWGxu4d5aAKjM/wPLysiu4i2dkMRNe7IfIuqXPhzmY6fYC\nmAHuI8sE8v95DcIzUDfORwZs375d6+C1H5iQarXqqtjDMmbRShvM7c2PbrcbxEhxwDjnJ7RihBxo\nnSU2mRUbJLLeVmkCiVYahQ/AePDyr6H9+v2+thHWGg/lcln+67/+S0SS48bO50qlov2Oo/0MjtGy\neUdZxsPLkcjxs/bgkC2XxdzcnDJkzORZRnpsbCx4L66trW1YdHOj+O53v5v5PZ7/7W9/Wz/DOjsx\nMaFzNAulUsn1Vtn26na7OjcwxlhIFcwus1TAKMHmuf5m35ZvAMOSAUdERERERERE/Lwga98SXXsR\nEREREREREZvEFXPtWbcYayllsVWg5bZv3+4GQYNmhT7I008/7T4b+c/gSlpbW3NzhVkJhmHH0G+9\n9dbguax5kUXVWo0cfn6lUlHKdlRX0PT0tFKW7I70JCIsPeq5LdMSQLOGlciA4rauE3YzpklX2Gcw\nsgKi2eXg6YLYNucAZBtYys9ilVtIMnj5xLzyewr9/DyM436/77o7cDiAE9lajI2NBWOG81JuVC6B\n9av4M1tmkdAFu3Xr1oT2kMigP+xBDxG/D620QbFYDILh+/2+9hO7hFntHP3Aucw8rR7v+RhHnq4S\n8pKtra2NpHi8Z8+eQOpAZF0SA/25vLwchDBwImN2M/zWb/2WiKwnmf3Od76TWheRpIsSQcnoGy+A\n99ChQ3LXXXeJiMgXv/jFxPWMubk57WsEdReLxWDucj9jnA6Tj0Ag/fT0tPYDB9Sjj+CWOXnypLue\nvO1tbxMRkSeeeEI/s4ccGFivOp2Olpvdm/gtjvZzTj6sDd567KmoDwsjwHitVCral41GQ++ftUZ7\n4MNab5Y7z8sF67nJR5VOyALP/1Huw4dJgDS9vlHLNey6yEhFRERERERERGwSVzzYHGCrAjtQWB9s\n/cJK8NRVRdYtOGaEYD3Bmq3VahpgyUGSNgcdB8aiTLyz5dxYsFj4uciuzlaFl6Ubu2ePkeJgeK89\nPHjqu0DaLtwe3/fYJ/4c9WUmh9vGC/bD8X6vDbwysTWTZRF4x9XZIrFK2mlWmWWu+L5ekDHfx5M/\n8GAFSKvVqit7gDEAlnRpaSkIemTLFmVttVpaBoz7G2+8Ue/HonYei+YxSLYv0caMS5cuuUyOBxsA\nnMvlArao0+lsSPLBgoX9rKWcz+eV+QDD2Ol0lG245ZZbRCTJ+IB5OXDggBuIjbYE4/ThD39YPve5\nzwXlQrvyOLESMCLrYxblXFtbk//+7/8WEVFV+enpaS0LH61Hf7E4KIJ4s9YObiscNvAYqXq9HhwJ\n7/V6CSYXdbCSIgyvHXE0ftu2bYlgYJHBuoHngu1Lm8toNwhunj9/XscTxi+rxXuHl1CuvXv3yn33\n3SciIl/4wheC65ix9QR77SEmfh4fHMF90FbNZjM4xDAMnGuV13J7gIslEYbdz7I73W5Xy+0xPXj/\nZDHAuDdgD2Hl83kdO9xGKAuLBLPMA8oHeILc7A2w13nvx1GYvyvq2uMC86KOSe9RpVhEPJVjkXBi\nbd++XRsOLjuPnt++fXsidYBIcvCyzpVt6G63Gwz0sbExXci8DQSfYMO9vWTC+G779u1u4lyrVdRu\nt/V5ngsiTWfK6sd4g4zTT8B1wifR0gYw7udtMjyXWZYbj6/B97zBwGIJ1+3Kyorbn1wu728LPllp\nT+GI+KkeRqG1G42GlgubpkKhoH2Hjf6uXbt0gUJbtdvtzMTJ6Ct2b9x5550iIvLss88mFJlRzlG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bk5bTCm1j03idXkYXjfcQoIe3IkzV2DFxq/gOxm6eqrrw5OfDF27typmj7D2txupPL5fLA5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\nFu2DxVhs1WkEwITLGBoa8gKf+60Zx0BbSqWSyiXaZcUEsGUFzOr79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ms2YoOpgSpqZhCsFvW62WPg9sBU7MjEwmo30ERsrKK5fP5z2z0cjIiMlIueZ5\nK6+We2+UHSd4tOnCwoKZ5NV6nmUSxToxPz+vZXDD7l2gHdysAv0AzAfAOT5xv5mZGe+ehUJBn4vT\n8auvvmqaUaCQjX5i0xzaatu2bR4jVSqVlOXtd7yvRc7DWms4F5+IyKZNm3Ru4rNt27bpeo3vEomE\nfo96b9++3RzLeB7WlYGBgYiemki73i67try8rGOHTcZ4LsyICwsLHrPKcwJtC1YLvxGJZlHgHJOu\nCXB5eVnZx0ql0reG2WrhsjL9AO3L4w2MJZzvp6amvITczHChTYvFovY1v0fdNaZYLHosEb/j4hTw\nLetHNpv1+pDzq7Kjfy8ERiogICAgICAgYI142zBSAIdCW7Z2ZkKwg8d37C+BHebi4qKXMX5ubs47\nEWQyGf03dtScf4nL5zIXLJyGzzj0k3fN7q7fytDNz7BU0fl6V1E9mUwqS4HTjAsrD53lZBh3Hdep\nW+46kU578IkL2LRpk7ImEC3s5iDvOsFWKhVtL7Qzs1FgM4rFoqnObPVDHBvHDAcYJMu3gOGGag8N\nDXnOsqVSSdsKJ7TBwUG9DnUbGhrSExr8KrZv364nXvy2Xq9rzjiLpbBC8nF9Mpn0fLIsBfFkMqlz\niaUHXHDoPK4rl8vmyZH9oESifnG4vh+RzjhmFewEC5u6ucC6Ac7mTz/9tH7GvkoWXEXzj370o/KN\nb3zDu44FMUXajuMQAMX8Zj8iZiaRGw85+SzfQYtF6+Vkz0C7YV2JC0EXiTql89osYvv5zMzMqJM5\n6vbmm2+qPx/GO7fBU089JSJthgh9yCw4+gTz/NJLL9WxzBIgGCfXXHONiLSDBNBuYLCY/ea+coNi\nNmzY4LGyc3Nzni9vo9GIONWLRP0iu/WX5fAeh36tDLyOdROwRrkB957sA/uzn/1MRNrq9JbavRtI\nUyqVvDke51u3FlgCybVazfR/WstzLvpGyopUsPRtXPpxYGBAXwpxjuPJZFIbC3+tSJ5isaiaMdhA\njY+Pe4tcMpmMmAhEujsBWpsOdsh24ZoMGey8zguV+1mr1dIFo1sajX7QyxmXkzS7g5FNGECtVvMm\n5NTUlDqNIurIpYIBON/it+fOndN24mdhEcILxUrPYTkldotEc6M66vW6FxnYjRJ3TQRDQ0ORMSjS\n3jS55p58Pq/1wIsjnU57Ts7FYlHrx5swfMZmMGjy4LparaaLN5TQT506pYshXuQMlLlcLuuLAOYG\nOGUzRkZx6FrkAAAgAElEQVRGdA7wixL3gSmAzX18gEB74AVizdvt27dHFmuME1cPie8t0nlx9TKX\nQDUb43R6elo3UK7yNoNfvqjvpz/9aXMjBSDJ9Mc//nH50pe+JCKdsc16SVjoR0ZG5JJLLhERkX//\n93/Xz1zzdiaT0aTL/KLvJwovm82q7lOc0+369et142utbXHOxOVyWfvrs5/9rIiI/Md//Ic33tk8\nibHz8ssv69hGvdFXIp1x97Of/Uyuv/56EREzRQ02op/61Kfkm9/8poh0xsvIyIhu8HjDjfcFNlzW\n+yebzeqc4nUdGyhsUufn573NVTqd1r7evXv3qhXy+01aYrmgcMBNPya/SqXibXxPnDhhJi12x511\n0OVIaF4/sbZxlGW/9VxtEhccxuPSAgHBtBcQEBAQEBAQsEZcNEYKNK3lWI6TKGvQuMxVpVLRHaOV\nywhoNpsmtQpgtzszM6O73FtuuUVEoqY9VmPGSZ9ZIGvXjvuxRoZrjuRTAMw009PT3q69Wq161PrY\n2JiWBdclEomeDrRxsJz+gUwm46n+sgI62tk64bZarQirI9IeA6xhJNI9LxROo+94xztEpH2yxkkB\n7EI3R2aXObLYNmvssKaI1b+9Tmr4Hv2Wz+c9FpNP6mzOw+fo34mJCe/kffLkSe1jjLHh4WFlUXs5\nZt91110i0mGrzp07Z6qhYyxeddVVItI+0bvOtBbjwLnvwBLk83mdr7jv8ePHdTyBObEkJVhzibWl\nmBG68cYbRUTk2LFjWgaA6+Y68zOgfI1EwCIiv/jFL0SkPbZhCo1Lqs7tgXFw+PDhWBbzc5/7nIiI\n3Hrrrd53lrlh8+bNWgYwTfl83nvG3Nyc6hphvOzevVuZD/xllh+o1Wr6jDhn85mZGU0izqZHi/XC\nmLDm4U9+8hMRaTOL6EMLYHk4mTfuu7S0pGWFttS1116r6uRgpHh9AyN+33336WeXXnqpiHTvN5dJ\nOXfunLdGcx3xPK4bW1OwNrDeFObAsWPHZM+ePV3bg8EJeN0yAJYOI6PX2uYmtOe1jYE5x+8IsFQc\nyIJ5DysK51zlAJK4OvUqZz/SCXxN3Pz2ft/3lQEBAQEBAQEBARFcNEZqdnY24kfAIow4veB0VCqV\n9DpmHFwlcsvPhX+DXXEul/OYhmw2q45uYKLYL4nlF6y8S8iaDlu/lSGdVZ2tEzxOViIdxofZNJxy\nsMs+f/68d+JrtVp66mRfIEvhlU+YcerqQLcwadwnLoyafdUAi/1gpWrUadOmTeqH8Mwzz+i1cfmZ\nWOS0Hxt/IpEw25LLhev6FTq1gibcHHXlcll9bvCMYrGoYxFlWrdunZYLjBT7wHF4ueuUncvltK3x\n3NHRUT2tw/didnbWCxEXEbnnnntEpHNSfvLJJyNq8i7AFE9MTGh92Z8RJ3kwCHySRT2svGrZbNYL\n4FhYWIi0L3xn3NxtIlF/KMw/tFU2m9XvLSVjHkNg/Czn9ziH7D/7sz+L9QVEe1h9cN1110Uc3UXa\n7QbfNJYHgZ8Y1qdjx47JTTfdJCKddcqSRKjVaqbwMdY0ME7WOsuq+HwPjFmwKUeOHNG2t8YO2MXl\n5WUd+2gXi2Fnp3msL5VKRcfs9773PRERefbZZz1LRz6f1zkE9vG2226TRx55REQ6bNaOHTuUZeNx\n4DIchUJBRVgff/xxrYer7p/NZj1hT2YDLWavWq16chrd0M96x3OG/V3de1h5+kR8qwPPBczBa665\nRqUp0HfValWZKA5AYEZVpD12XCtUt3ysrtgnB0pxOTHeUDdmqS409+BFdTavVCrexF1ZWfFMcUyx\n8XduhF4ymYyNDuHnWsD98EKrVquedP3S0pJOXnaGw2LDL2N3Q5BKpXTAQSdmeXnZo9N5M8kvcCwU\n+M5y9Ma1ItHBYX3Gk2m1iXdZuytuELKeF/oRE61er2sbwbzEm1OAKXQeC9CSYSdM1CmuXbjenJjS\nHReZTKav9DfdIhwBTh/jfp/P57UMGBvT09O68KBt5+bmYk21KOfc3FxfQQatVktTcABwFmXs3r1b\n2wUOuSKdDQPGgRXNKtJ5eaAPl5aWtJ7WRgQv97m5Od3Q4G8ymfRMoqlUKvI8lJH7Eu2PlxcDbTAx\nMaHOvtYGHy/DgYEBLwCFgXm0bt063SBy+fBicVMeiYj87d/+rYiIvPOd7/Tua23uVlZWNHoNa8Ls\n7KyapHguuS+q5eVlbyNjHRDYBITxl0qlzGhN19zLdcMcvfnmm+XgwYMiEu1/V1+NI/QwLtmVAQ7w\nvOlEfRYXF+XQoUNafpFoEA4/w00V9Mgjj8j+/ftFpBMZyOPOCoTitRAbZWyk+HncFldffbWIdPqI\nDxPYQLEqukjvwIhu4M1Gt3Ek0n2N44AckWjdORsE5hna0op6F+n0Ccbi9u3bdU7x2mW5/bhreFxa\nNRfWeuOaq63oct5gdkMw7QUEBAQEBAQErBGJ1mpjAt+Kh/5vaGOj0YjVunApUQbTy+ycZ+Wys4DT\nCytv45SFHbV7IhBpsyk4YbLiLpcBz7d2y26IaDdzpItcLqdtZTFq3fLixUkq9Arfx4nBPWm4sE4q\nbnkSiYSXq4tDgxk40eK509PTesqAU+LCwoLJ0KAPURarbvl83vs+nU577JOVsDORSHgaT1wHfMbM\nKvo8nU5H5AdQVzzX1b7B80TabArazZJ7YFX5OMDss3fvXj35stQB5hwcrqempkzFcpiI0Fc4vTOs\nwIFewPOTyeSqtI5E2mMNZhF2ZMU4wneW6Sybzcq2bdsiv2X2E2Hqo6OjptSDi6uvvlpeeeUVEbHX\nr3vvvVdERO6//37vu9HRUXWax6necnzdtm2bmpIwFx5//HFl1jAW33jjDXWQx3gulUo6juH43K29\nXQtBKpVSBpmTArMmn4jNwBaLRW1nduYHMI4nJyc9U1apVPLWXs4ZCHCePjYpgqmDNeCxxx6LOJQD\nrlUjlUppAI8VwHHnnXeKSMdRXkS8RMouXLY1l8tp3ThvIlgUrqOb5UPkwkxTnJTazWXJQN9wrtq4\n5w4MDChbx/XoJ3ddL3D7oczswtOvWnucYz6bNMGGddsuBUYqICAgICAgIGCNuGg+UmCjXMFD7P5E\nOrtw62TbTVnbdaRmxoeZE8te6jJD8/Pz6giM6/nUxv5QLhPApwUuO04bOOHOzMyYzJD7mcVCsN8U\n/85ywgZWs1u3mC+c1lhKwm03tsNzWVxnwVqtpv0En5uFhYWIb4UL9AOf0JnpsuzbrtMy++axU7qL\nZrPp5cFrtVqmAKzLSPK9+bmuZMPKyoq2s9Uf+O38/LyZ86mbCKBIp11mZ2fVPwjjbmVlxRNuTKVS\nct1110Xu4fpRiURD7ONChFfLRonY7I2Fbky2dWJEu2FcWSfLer2uTFO3cHGR9ny1+tq63pVnYUBI\n0xL9nZiYkDvuuENEuvuZiLQZmtdff11EOvNn//79Wg+Mq9HRUWVCUZbTp09r0AzGRDdGyu1HVgTn\nz9ycpxxMxN+599u4caOyo5DJePPNNz2BSnakx/XLy8uen9Pw8LD+G+2zY8cOHct/8Ad/ICLtIBZ3\nfO/atUvLjHqUy+XYQBowUQcOHJCf//znIhL1K3IlY7hd8FmtVtM2ZcV89I01hlbDQmG+8rsDayX6\ng9+J1vzC83iNZdV7sMlgsHm9Z0d7MMO4N7PHLNIKcICJm2uP5yCLiFpw86E2Gg1vLPK7azXM2UV1\nNu+mSuouVIlEQl/gVkSVpU7NukSW6QnAJKxUKvobNr91S/8g0lmgWbGaX4qWHpKbsFOk08GcrJJT\nvqBubhoakeiGDM+1oiyArtSk85tUKqXlQp1Y3ygussmqe7FYNKMuUH5Q5oODg9qWWFCq1arWz6LW\n40xAVp8zNQ3wy5XV5y1TmRsZ2Gg0zEnnUs5LS0uefpGlvdINcQsnmxuZgkc98Bw4tzabTa8/eLML\nU5G1mc7lcrpoHj9+vO/yu+AUP9ZBBGOM5757AHIPWfie29mt5+TkpC72aKNCoaBtZLUzm5L6WWBZ\n1dlyToeWkfXd66+/rmVAuio2GwGZTEbNhzBhXX755bqmPfzww/od2oDNmqgHTF0nT540646+5nlm\nXeeuCZlMRscPxtPw8LBGLLKqN0yF3Fd4wWI9XlhYUDMkXsbnzp3z+uPkyZNqWsO4WlhY0Dny4x//\nWK91NffYbMtK6G6gjLXmPPvss/pvBBVMTExo+2HuVSoVz7THaz7GISdh5whygJObc8RaXHox63Bj\nHQys9wTmSiaT0XmGdblarXptkk6ntc15I+0mXx8dHfXmNb/LeZ3AZ+w87x5EmaBhssNd8y2Cxprb\n1rvORTDtBQQEBAQEBASsEReVkbJ2mCKdXaGl8MrO5K68AIfTW+Yry4SGXTQny+VQV+RTglNlMpn0\nQnUtzRM+YTPb4+6Ua7Wad+rn0ziewTtlvh9OBFaiUHYw57Z0zam4F/+GWSVOQOzS1JZDdqPR8EJG\nuyWcRf1waisUCnotU77cNiLtU6p7guOk1RZNzWY83IfVkAGWMnCd9JlF5VMeym8loUZZWAF/Naq5\ngEV7o77us0Si4x3XWQ7yABgCkY4T+cLCgsfK7ty5U69lR/U45jeuPhbrZVHsXLdu5mlXa23btm2e\nUzMnngZyuZwyMyg/s5+4LxgMhnWyPXfuXNcE3CKiJiALKysrytqAeWFApbxSqWjboKzJZFLZGAQA\nlMtlM3SefyNis0z5fF4d1cGyiPgm5bGxMWXXWPsM4xz9VSgUlP3hUHdXFoZ1szhDAOoLVmPPnj1m\nrjs4ecNUOD8/r/XDenHgwAFtI4tpAmuYy+X0e84k4I7bmZkZue2220REVIvq9OnT2odswYBFgtd1\n1/G91Wpp+S3m18qo0Y21dtcJ/h2/b+MYGXzHQWL4y0mV0Zf1et1b5/h9wZItcWx7XAANu0uw+4Wr\nI9XtPcWO9rjONRv2w0AHRiogICAgICAgYI24aPIHLvpx4GSwn0uc3TqVSpnhopbzIIftitjCjfl8\nXne5uEepVDIZBtdhz9oVc6gul9n1kYpzgOVnuCcl1+8H14hEw3sBZqFWm9eoF1zGotsJaK2w2hef\n819LNVck6ogpYo9FFtDsprSLZ+B03Y9ApgtXZX1wcFDvB6auVqvpvEGZGo2Gngzxl/1wUF9mjcA+\nTU5O6ukZbfCb3/xGw9Dhx3L55Zerrwr7lICtwVx4q8ZNv2AGEWvC7/zO78hPf/pT71qUlRlfOIdD\n2oEFSIFCoeD5ZPQax+ib0dFRz8G/G9773veKSGeMQcRSROSzn/2siLR9fcDGgHG6/fbbNSfir371\nKxFpZ1sAgxMnSthqtTypAxGfKWFgPF122WX6PfsdWUrucZI37J9q/dayKkCYF6LIzCpgfrA0Bufw\nA7OKPl1eXvbeRdy/cOovl8ux4xss3tTUlCd8Wi6XPSmYer2uc56FbdGWhUJB/frQbplMxsueYAVS\nWIwp//atmKf83mFha/c9m81m9Xm8BuE3GMfMoqPt6/W6+T7kwDKRKCPlqp7zdYlEIjaIgJ3cwcJ1\n2y5dNNMeNkKsIi4S1bLgDQg+YzMTgI0Ib66slCdoVDYLcmeyTopIVBsF11kvT95E8UvbihYEmBJ1\nHR7ZNMYOd1YnuoOoUqmYqsNANps1o3Dc8nP0JNO4Li3L/dXLpNOPY5+FdDptOlBiMeLvOHkz6uMu\nGKwcj3br5lhuRfy54PQy/L11P1cpv1qtev1q9R87S2JhbrVaXiqhoaEhU93dOhxg3OHlUK/X9YUL\n0wM7huKzX//616bTvxuRGJddYC3otmGxohnR18ePHzcT5/IcF2m3FeYx2sOac0tLS7qhRRv0Gsfu\n2tUPsInjAxBMergP9wE2hAsLC9r+iHBbWlqKTS/CdbReLGgjN4qOwWsg67thc8ObEzzPykzAaYPc\nMZbJZPRFy0Ea+D2+s9xERDrmMUQrTk1NmQdgtz/5/7y5ihvfODyxFiFrKrmHtsXFRf2M3zFxQR/W\n+8D6zHIw537G/LDWIgvWwXtlZcVbK633geWiwuudRYbwHHCTlvOab61xVrnj3lN8GF/NIT+Y9gIC\nAgICAgIC1oiLxkjVarVIokbA2hU3m02PYUomkx4FW6vVzNOnC07cy1Sx64jXjamJUwTHPer1urkD\nxumO8wTi1MGaRe69W62Wp/RuUZiDg4MmfW+1C05AKAfuib/uCcNKEMknJYt2tZyGuSxxbQlmgHWf\n2OyG04tlKmBnZCs/k5tclvuI1dE5pFYkmlwU/VGtVs0To6X9gjbg8efWnZlagEN6cbKtVCp62sXz\n+TTLY8PNI5lMJpWRQh9xYmyYA/lUhjFisVEoI9+v24mdy8D35e9E/DFhsSWlUsl0pMZ93nzzzYjZ\nG+XCvWBK4jkDNmF0dNSrazKZVKanWzuI2BkL4nL0uYCDv5XYF+WzTtaLi4uahw7zY25uzmTtLFhu\nEhgTcc7zJ06cME2Aro5UtVrV/rLYfbTRFVdcEQlkEGk7r8M0yqyg6+RcKBQ8S4JIZz7gHuPj49qH\nlpnWzXQhEmW6LVxzzTUiIvLcc89p/THGwOiuW7dO+xftPTY2ps9lawTm0sDAgPdOY2dptGmlUvHG\nRTKZ9HKt8lrZKxsC3hMol7WudZMyctcxy0K0vLzsBcNYefXYuZ7XYMsc7TrDN5tNz12G7426WWbO\nfpjkwEgFBAQEBAQEBKwRF1X+gGUDWGiTnbwA1zen2Wx6zm18HxYltE4nADtXW6wIdqOWaiozMG49\nLNFHLgMzHHEq0fwM7MLZvu7KR5TLZZOhYZ8gtG+ckq3FPnF9+NTkKniXSiXtGz7tuHIVIuIxPlxm\n68QKMPPGob1u+1rMWr1e9+7N7ByXD23FJyFX/oJhnXYY7oknl8t5DCKzngCXGc+wfFVarZaORWbg\ncGJlIUCclPm3qBvaoJtshQueZ3HsR6FQ0FO45XhtjZE4lnllZcX0ZQBLMTMzo+OWmSu0Lxzt2RcM\nYf74jjEwMBDLzAAs7QKsxqkXOSWZ9YKTNPrQWmPOnz+v7btr1y4RafsGocy47+LiopdBIJFIaLsx\nCwMhSQjkdgOYKDjrwz9KJLr2oh8wflnZHDhx4oTeB/5dp06d8mRLWBwSqNVqsX4wmBfHjh2LCDLj\nfhjHqC+3BcbgyMiImeGA6ywS9SFlILABa34ymdR6wBft1KlTOrZLpZLJUqOsvJ7hOouN4d9a/3Z9\nB1utlrYN19PK8WlZF6x3KuYz+0fH5fhjuM/g9RhjotFoeBYWFiq2mPK44LM4h3Tgom6kRPxCcsQF\nJnW9XtfPeDC5m4h0Oq0UMhaJXrQlbwgsp3DXzMcRet2i3dx6Wfe19I6wcExPT+vvUX52WrTu3Suq\nD2i1WroBYK0nd7JbEyCTycSaY1BWK5kwJ1C20MvJkZP3irQXDtcU242Cdb/vZk5zwWPRipAE8vm8\nXhdXD97EsvI2R6WItNvZGkeWqj/fW6S9YLhKvM1mU18YuG5gYMAzXXD/9BtdBtTrdc8Z3mrnTZs2\nqVmVTV2u0jPrnXG/uubjbgsv5j9vSnnji5ckIrk4Qg/3thxf8/l8X8mU+92AMlDOSy65RPuYD1eY\n/y+++KKItPvQfc65c+fUbMTO5rj3n/zJn4iIyA9/+ENvI9VqtWLNlbiv5ZIhImpSxGZiZGRExyy/\n6PEMjspD8mWY82ZnZ3Uji7bYt2+fRvAhgm1iYsI7UPDYYf0qa5zjM1bexhzBBm5sbMxrl0aj0dea\nu7KyohF8eIEvLS3pv9FHx44d0/kIzUKu7/T0tDrJW8/jNSEuNRMTAmwyAzhiEHDNy7VaTb/nd0jc\nczkinjeHIr03T/wM1wS4vLxsltlFr2ew6wPq0c8GSn/f95UBAQEBAQEBAQERXHRGinPmiERPEzht\nJRIJz9E6k8l4lH+9XtdTFlOnLh1cLpfNEM04xgQ76oWFhUi4KMrnKq+L+ExUIpHwqMl8Pq//xkmO\ntarw3Pn5edPEgXvj9DYzMxNLp1q/FemcVHAyq9VqegK22BEgmUyaVKlrTms0GrFaYXGsHfe1lZuO\n6xanu4V2Yfqe6w0mjZlOi951xwnX31V0Z7CqsxXqzOV06Xk2ebMJDfXkhJyuOXppaclT/M5kMur8\nijmzsLDQl/mJZQF4PGNugv1i6Qng+PHjnjp4Npv18k2Oj49rW2EdGBwc1PbtlaOQ6wH2iRkwsBgY\n75dffrkqoPN64fbPzMyM5rV7q4E5vH79elPDyk3mjrIzyuWy1vPWW2/Vz9EP0ALbuXOn9jszOmh/\nmH35Ozh4W+vA3r17PWf6bdu2yaFDh0SkrSIuElV0hwbZpk2blInCeN+6davH/FnrM5sjmdlFW6G+\nZ8+ejWhUibTlP9wxxnUHC2Wxb91cQRAI8Pd///ciIvKlL31JGSYORMI6xZpWyKKBz1566aWIsjlM\nrIDFTLP5Gs+rVCqRRL2Aq91UqVQikkO4Po5dZRbddauw5EN4TeD3C5fBrRu/Wy0mmt+lfA+uRyaT\nMa1TnGGE/64WgZEKCAgICAgICFgjLhojBbFLSzzQ2sW6dlX2HWDZAOxe8T07JXO4vMVcuP5GHDaK\nnWoikfAc2pvNpse2cPgzsxoWm+EKcnLILDvUu2KTpVJJP8Pp0lJKF7EdqDnPEH7DedXcOmUyGU/1\n22IwLCdYdr612CcWLXQlHZrNpp5e2AmSxeAA9AkLLbrh9olEwiuDm08OcOthqdgnEgltqzifPPYd\n4/uCMcHJ18opx0C75PP5SB4/kbZkhOuztrS0FHFQxncoA9q2l58a2iyTyZgnXFdl3bpfpVLxfLgS\niYTWA/fL5XIe09ltbPP9oFTNavLWKRNM0yuvvCIiIr/3e7+njBTGhHUSbzab+r3Lkl8oMD7Pnj3b\nlx8WmD8XWAvYwRw+RX/5l38pIiL33nuv3HLLLSIi8r3vfU9/i7XI8jfcuXOniLQdwd05MDU1pWPi\n+uuvF5F2rjr49YCJuvbaa+XZZ58VkU6/jI+PK2uDMXPmzBntf5YUgC8VcumdOnXKE7ccHx9Xdgys\nq0hnnMNHb2VlxZN7yOVyykThvbK8vOytF9Vq1RNmZXzlK18RkbYv2gc/+MFI3YrFojJNzDyCifrQ\nhz4kIiLf//73VUSUmTcgnU577zHOtcqwHMHjfAFXq3Zeq9U8/zD2w+pVJswhFrS23gNxsCwjzGBh\nneiV5xaI8/lycVE3UqwFhYWNNyDuBkOks0Fat26dDmB+ebnOd+Vy2dx8uSYRa5NjdZzlmJdKpTyF\n6VqtppMPCwY7a3OZUT8sOm+88YZnushkMl5EFV9jvUAsZ/NMJuOZ1qwXweTkpPYJJ6uMS8FimQDZ\nuRq/hUni/PnzXt9YiurdAgFcMJVsUb9WdB8DCxXaeW5uznuh8Bjijaa1gXLLbG3CSqWSZ1IUiUYR\n4a+bvJfHIm+4rdQ/ACfidKP7uoEd43FfKyoT85Wvs9rZHdt8DytCiFX7rc2NZaJGdNrJkydjTRN4\n9tNPPx35jUh3h3FE9V1IeiOOjkIfY5y+9tprsSYGV9HfBdoL5XznO98p3/nOdyLPeOCBB+QTn/iE\niLTNmiIiL7zwgm7CLHMKxqm1rvC4tvoDv3n22WdVoR2bCGguiUjElAWzHDvFwwS4bds2EZGIYjva\nLJ/P69jmlybGJZu53TmysrLivXesg2G5XI5VNsczvvnNb8rVV18tIqJmztnZWR07CHaYm5vTdfj7\n3/++iLQ3pEic3Gq1vGhRJg4YLiHAawyriWPs8zjCeszZKjBm0H4rKyseSVCtVr33HT/PMvdZ47fX\nZsmtG7sysEK6awbnLBA8b9GmlsnQdZGIQzDtBQQEBAQEBASsEReNkQLFD1qRHcWws2Qmyg3fZlMM\nzGCsMA3afWVlJWLmE4meNK0wftZPck9flgMtJ1jE/XjHDzSbTTNfGj4Dvb1jxw7PsdCiThnsmG/p\n+FhsEefLQ7uBrgaljO9F7FxlnJiSQ2JBnzO1jueBSSwUCp6UBOcZ5H6ICzW2lG/5GvcExGrnrBzM\nZRWxld9FosmPAcuchTHtspWMpaWl2FMY09Fx8gcAj208n8OVUfZisRg7nhiuCalarZpsjGtS7AVr\nPgKW0/7CwoIZ7s3tB/aCHcJ5LXCBzw4dOmQ6kcPsiueeO3fOlPdw0S2BNoBxun79emUBwCb3cniN\nS9LOjvvM5Nx1110iIvLggw+KSLvNn376aRHpyK688MILpio2M4Ioe1w+T55Hlq4bkiqDkbruuuu0\nLLzugBmCySuRSKhpD789cOCAmg1x/ZtvvqnsPpI6s2M5wG4abB7Eddb4ZPN13LhFve+77z75whe+\nECnL9PS0p4G3vLwsN910k4iIPP744yISZeqKxaLnftBtHXDZorm5OZ0DmKMsH8NWFEvLEGAdO06c\nLNJeE6xx684BdpexEi2zc7gLKyvHysqKzqW1SI7EsYpWhpNuCIxUQEBAQEBAQMAakWj1k+75rX5o\nzEl4bGxMT2bY/U9OTkYyhIvYonClUkk/45MVhOJwIrDAJ0h24HOZBg5Nj9up8i6bTzb9+Pqk02kN\nhYW/hoViseiFZ7vt4vobDQwMeA7j9XrdPOnDfo+TEF/HSvP9DCFmyfBbi7Xr9RuGVeZ+EfdbyzeP\ny+SWxcqr1mq1tN+5P7gf8FvXPm/Z8K2TEwsFMkOIsrAvGgCmcGJiQn2V3HxjDJYqsAQNGfAxQlmm\npqbMa9Gv8IGpVCpeW6dSKY8JbTQanmMxB6ww04hyFwoFdTyO8yfJZDJm6DX6ECrhzz//vBdGfyHY\ns2ePyh7Ah6bXyRrK1yxrAWSzWbn33ntFpOP0/cMf/lDXkwceeEBE2uwC2Gf4G8EJvBsgLFkqlZTt\nwtpwxRVXqLO+BYzFlZUVnQ8f//jHRUTk29/+tubpw9rPsgtgBZmRQdnL5bLOJfx2cHDQW6eq1ao3\nvonXbbkAACAASURBVHn9YfbYYu9dX05LpJNhvSNuvPFGEWn742Esct1Q5t27d4tIx8dNpM1cos3d\nNf1CYeURZUXwfpiZbpk8VlsG7jdXEoV9s5i5ci0O2WxW1zmsWUtLS7qesCwElx+/xTMwNrCmxr3r\nLpppD53mDjgenKjIq6++6jkXLi8vq4M1GotpSTT0yMiIt4FiapI7zorkczuOZfnjJORbrZZJwXN0\nGl/Lz63X67qBwgS2EmcuLi56mlaVSkXvbdGj3SKMrAHipm3gDYMVsWKZZ9E23L5WGhBLR4xflu7G\nh/sQyOVykee54P5yqeSBgQH9LTtmuqmJMpmM1tNyEufNXzf9Gf4NRw7xBsodW9YkXllZiWwEcL0V\nUQdgPLGWVtyCzOaPuM2/SKf/2VHVAspnpbgBWq2Wzm+0wfT0tB6KcI9jx45F2gUbBiyS09PTXjlY\nAwgv+C1btkRMKQA2Naz34/ZrN02hfoH+7/dFhLpbGmQcgYty1mo1ddKGM/fhw4e1Xdx5LtKZo5de\neqma0eB6cPPNN+umD3jhhRdk7969ItKJqGNtLjfZrEh7AyXSdmXgRMd4xsGDB0Ukuq5bh2I4r8Mx\nm1N2sbnZinDD2GfzJUfF4jveQHF9ugG6WT/96U91s/TEE09EniViJ1VGO3OEo+t2INLeXOH3nAbN\nrVM+n/fG1uLiopdGhTfwHPVswT3gsesJz0e3zdn1hNvQVVnn5NHdkpqL2H3YaDS0vTjrCW+MXFg6\nUquJyg2mvYCAgICAgICANeKiMVLLy8uRZJWg4hYXF5U65902mCjsEqvVaiTUH99hR4ud8vnz5yPq\ntiJRtsJyWu3l0GqFebomO9a8YYbFZQuYXsY9WJcIJ6FMJqPf8w6ZzWSoN/7NJwxmx9CG7ICM3T/v\nwl1WhE9PYKKYpWKq1NW8YtrYMgv20hGzkla7obW9nJxRj1QqpaYfPimjDEwB4zesWWUxB64ybzfg\nOpR9fn7eNOW4Gl6tVsszM7Nkh6WsD8ZsYGDAy1HFUgJxTNPy8nJPJkqk3VZWgMFqARaKgzAwb7kN\nXN0hAIwbTEUPP/yw94xSqeSpunMovBWebWmA8ck6zgQbh4WFBWWW4jIIcN0sphlIJBL6PZiQyy67\nTH70ox+JiM1iYQ1mFgB9bjmVDw0NaVkY6H+McTb1YR3bt2+fziWYrrhMcBI/ePCgF9Y+OjrqWRe2\nbNniJQpmrTL0R7PZNGVw8BnPQZeJSKVSkYS4Ir3dCbjuMEnChMvzA/d7//vfL//93/8tIh3rA9e1\nXq978gdursRu6GWCZtkXl2Xn4AVev9GunOMTn7FOl5uTj+UUAA5osNwbALYGsSUBZe2X0cU9rrji\nCmWwkdNwaWnJdA/qhcBIBQQEBAQEBASsERfV2dwSqLSc1ro5JeP0ihMN/w7f1et1PYHgZNPvrrhb\nCLNr9+2WDR0niF5h63HhzJZjK042zWbTc3js5WzeL6y684k1Dt36y/JBc1mvXC4X6wwY5/+TTqdX\nHYIPdHMkhA8NQsQPHTrktWmhUNATLUs2oO8w/jZu3Kj1YMaUyyAS9XPAmE0mkx5z1W9/bN68WccE\nZ03vx1k6m81GfPe6YXx8XB2y4dx99OjRvnyHxsbGvPZbi9gly5X8+Z//uYiIfO1rX/Ou27Rpk7YH\nswOWz6M1ZsH0oD3m5uaU2VjtaXZoaEgZczAX3Rg9rGlgEKzgmaGhIfnsZz8rIqLh9C+88IJ897vf\nFRGJ+CK5rOfWrVsjvmDdsHv3bp1nOMnz+sLCwmAG8Vx2mnaFORnbt2/3mCaRTkADHN+feuopvQ/a\ngy0VaDN2cmcm0WXcWKLGcnLHbzdt2mS2v+vze+mll3r+XyKd4AXkG8xkMqocj7F25MgR+eIXvygi\nIv/4j/+ov41b05PJZMS3S6Q9ni3xzdUCbVUoFHT9Ytkdt3yW72AqlYqscy5QdhYWjnsHJxIJ7Se2\nxKC+6PNcLucJo/Yrl1AqlWR+fv7t6WwuEl2wrA0D0+V4oWGRSaVSOmGwAHIUG75Lp9O68cAETiQS\nnvmu2WxG0ruIRDsa1/OGizvLovZdmp87AhM8kUjoyxRtkEwmtZ6cGgWDhynd1UaujYyM6L1Rj1wu\np4PKdYYW8RdckajKLTv54TP8hpXr3QmRz+e1jXqZRtzNdTKZ9Ewh9Xo99gWGZ4yMjGh7oS1444W6\nDQ4OalvjurGxMR1jqA+bU3kT626uWbsFm2ZuE0515JqoLfS72UilUpF0MXiGG8XCDrm8gbPMQS5a\nrZZHsffaRGFOLy4uXpBKuIU48+LU1JS+7HFdLpfT8rATPEzA/HLGixWbBNaqWy1qtVpkrRKxX0oi\nnfaMM2G0Wi15xzveISIdx/t/+Zd/MaN/0ebYlPAmyl0zGW4EtUj08AQNvFQq5b2sOHqKN1Csri7S\n3oRD1wtRio8++qjWA5snvg8OOzzP0Lb5fN402brpvsbGxryIRPxepNM3U1NT5tr7rne9S0Q6KXEO\nHz6s6wnq+OSTT3p9WKvV9ACC74aHh3UDtWnTJjVnc5nQh6wZ1+8GgTMQiLTbzX2PpNNpT0fMOqRa\n7x82eQPc9nxIsQ7PeB/iO06qzoFIbrJsC+VyWYMXYLrtN9Kwn7kdTHsBAQEBAQEBAWvERWWk2BQD\n5oVZG+xOE4mEnhyZuXD1fmq1mtJ82DVzmCfAu+e4BLp4NqPRaHhh461Wy2MOisWi7p45Nxrq6zrK\ncxuI+CxQIpHwTu3sWMpO0xa7g93/7Oysp89j1Z1PcEyjovx86nGpWnYKZFbGpb0rlYpn1rQ0mdw6\n4zq3PdatW6f34bqjLTEOZmdnvf6CeUWk49zMwD0sTabFxUXTOdetRy9mxzrpWadePINZSj41oU1x\nv5mZGe+UmkqlvCSerVbL026xxqlFcTO72O+JOI416qUM3gu//vWvY793ldPr9bqeVBluGXl8grm4\n9dZb5Wc/+9mayrm8vOzlVdy1a5eaClnbCeWLY/oajYYqh+P6Q4cO6dqCOcjJ3PGsqakp7xn9Sjtc\ne+21qsgN/bmZmRkNEmKLA5IlP/bYYyLSZs7ARGEeplIpdUbH37vvvlt1sH784x+LSNQEyHIPYLHw\nbrDkZhice9WyjqCPcN9cLqcMEb9fwETBBFmr1XQ9YROfq5u2bt06T7n8yiuv1OtmZ2d1veEyYRyz\nic1lmlkWhoNX+P0q0raSoK/Z6dxSp3fBek5WzlALlqsNfxbneoDrs9ms7gPwt1arKXMZp7bebDb7\ndr/phcBIBQQEBAQEBASsEReNkYLTITMgIlFfJSCfz+tpg53RsGtmQUPs6nHKGhwc1OvYl4flArqh\n12ksbofO7AOey74qfA+cRGGzrlQqXrlqtZqyBPDlOHPmjLYVvms0Gl5oqkhU9RVlwH2Wlpb0FIa6\nzM3NaXmszO5s37ZUva12Q3sxM+U62HdTWQesPHe43j3R4VloozhFemahcL9yueypwDO4j618hHGC\nrRYwZrdv365MCMYu9wH6LZFI6LzgNsN9LJ819gN0/eFqtZrnrG/1AX+GOg4PD+tv3wrF5Z07d6rv\nA9c9jqFjIGebpUCdzWY9po1zPMKX5ejRo+b4BNuAE7MVJLIauBIg27ZtU1aHGSnLn8/F2NiYsvK/\n/OUvRSQqiYExwX5E7AeFUz3Yj2az2VduQThNi3TkFPbu3avinPDDmp+fVyYKOHPmjGzdujVSluXl\nZbn77rtFpKPG/sADD3jO60ePHlWRUTh/J5NJT+2c5VKsNQQol8s6ji1mH/5zyWRSxTktPzIwIhs2\nbPCEStlPFWvs9PS055d2/PjxiNUF1zKstdcdH/1KcvA84TmF+zFjy/6hItE1jv1nraAul7my2OdE\nIqHtzwE6rhh2Pp/X9QF9Xa/XY3NBcvt0C1ri8vWDi7aRwmBx9T4Ylno2U+0Y8NZChsblFwZrgLgv\niFQqFYm44utduOkHMplMbDQZ7seO2VjsTp8+rQtoLwod1+EFs379em0b60XPAwFtUK1WdRByW1qb\nELQhJtD+/ft1YWfzpmuKbbVaEb0vXG8NTEvB200e3Ww2dZywJhc+YwrYnQSc0gcbbk6C3MuEYekU\nue1SqVTMl1vcBsqarFg4zp8/bzoHu4q8CwsLOvZ5I4f2sBw3WafFUoFfbeJP1npyTagW+jUVLS8v\nm6Y9y6RoJTjGM6zvNm/ebDqQor3wUu+WUopfFCLteYRxiXbuZ/PRDevWrfPMOPy8ONx8883qdI3o\nvbNnz+rmD5vsl156SduS11ccDq655hoRieqIxW0Yz54967lacPshui+VSsnv//7vi4jI/fffr9/D\n0R39NTY25qWsKZVK8sorr0Q+27dvn94ba+r8/Lw6vCPdyuLiYqS/ROwUW3Nzc9r2UFE/duxYJGhG\npL3hdDdQk5OTkaTLaBeMDbQ9HzQuueQSEWknKEa5sDE8fvy4BiVZSZLT6XTEFAbgM/xdWlrSzdxt\nt90mIiIvvvhirDmaxxrWOWttYE1CbFBxXT6fj0QJi9gajrxesKaiq9eYzWb13+yWYm3W8BnavNVq\naXuwCRBj1MpmAlhriItg2gsICAgICAgIWCMumo4U9B+wq7dUZ4FkMinXXnutiLQTPopET7Yc+onP\nrDw5OEnW63VPxZrzufGJYTUh5iIdhi2dTusJl9kb1zHb0gJKJpOewzDnMmK9KzyPQ3utfzPL4zoZ\nM52NHf/g4KCert2w227Afev1uscmsInFyqsHcA44pnHBXHECW8CioS3AXHLq1CnPyZCTc/KJxGKO\n4kyE6Dc2nTBQd0ubyTIP8jNduppPiHHyEYlEQs2BlgNnv3niMH9yuZwnFTI5OanlhvljLZIAVk5L\nIJ/PR+YrrmNJB7Q5nILPnDnjsVgbN27U8luJZ+NC/xmo+549e7z8dv2qTlv48Ic/rG0JRfJ+cd99\n98mHP/xhEemY5+r1uq6vcPSGinY3gJFKp9N6akfdmG1jTSNrroABeeSRR0QkGnLOSYvd9Z/NX3F6\nUyISazqDSXFlZUUtHGC/kslkxIwPWPMbbBfKefToUV2TeP1GG8BpntdOPH92dlYZQjBYlrM5z9ty\nuazPcSUAeqFbAI/rVD84OOjJQTBbBO2rubk504LRL1xtNtaTZGf4OHAWAqzrccnJuwH9yeZei3mF\nRl23cgVGKiAgICAgICBgjbhoPlK5XC5yasNJhMNysbtPp9PKRAFDQ0PqXMancDevHmcgt1RY2U8E\nu1LOQO0inU57+ZY4hx4zYNbOG89jxsxiz9zT/MDAgD7XYslwYlpaWtJncLswU+LmM8rlcpEy4j6u\n/002m9VysWyBe5osFoueXACf/K28euz47LJZrVbLUyVmuBnQRaIMDcrKJ2k3E/jKyor+m0+kbr4v\ny1FZpMPW9GIwXbaNwYyU64fTaDRifRTigiey2aznJ8bh/mjbRqMRy+hxFgK0L06DpVJJ+2G1TFQ2\nm43k2BOJ+h3xs1xmzfW944ANvh/XvVqtmkwUgHVpx44d6mtjAc/dtGmTp/BtnV57SXvAd2N2dlbv\nt1p88IMfVOdsnlPoE8xpzgVq4bnnnhOR9riCzw6UoQ8fPuwxbtxWrNqN+wCpVErXJ7BUuVxOy/ee\n97xHRCTivwNmjVmbu+66S0REHnzwQWWigImJCWWkwAhls1kdMwgmgMI12gPt4/oElUolfS7uUSgU\nTDkTrF1WLlJmlMBEoW13794tDz30UORerVYrwqzF+d3BnyuRSOj6hP7lMceSLaiLK6EjYvs5ckAB\n7sMZCdzcp3w/9jNyZXXY2sLAmob+mJ+f9wIGarWaMuCc/QLPYz9bvKdY+BbtizYaGRnR+4BB7Mdo\nd9E2UljosHhgAS+Xy56Zol6vey+CmZkZpUfRkOl0WjdQUMXl5JEc2cbO14CVGNd92fCgtMwQVqQW\nL2ju/cbGxrSeMClwJB/uU6vVdPDiZcJaG+ycjO+xuRKJvtxQbjcljkh00LjU78rKippM0OaJRMJ7\ncS4uLup1XF93sqRSKXMT5Jq/WPMIE9eKRMK1ItENBSYQoon27dvnOa1aaX6SyaSOS1cZXCS6+Pbr\npG1toDiBMWAFIKBfeaPMUT8A2oAXEyuFEdoU881ycLfAbY96Ly0txUYH7du3T0TaYxKHHYy1oaEh\nT8Npfn5e64bysR4W5v7ExISuISKd8WaZHtB3tVrNW2Ms5ejt27d7GynLQblcLmvboczj4+MRhXSR\n3iYHvCiPHDnStzkd2L9/v4i0+xQbFMxBaDmJdA6YO3fu1Bcj2uCyyy7T9ZIDZNwUXJOTk54DLh8u\ncN+bb75Z1aSBarXqRd598YtfVAVvbKC2bdum5cY9hoeH5dOf/rSIiHz9618XETv4w9og1mo1rRM7\n1GOjx/MXfYjrRkdHPYd7V7EbwIEA44+T6lobL2yojh8/ru8sKMdzUJQ1N9lE7aqiu8D7BGXYvHmz\nlgubPusQNTQ0pO8BbJ4rlYpXFyYJsD5w3+BZqVTKTA7ublY4NRVrTLrpb1jDj9dK60DbTwAIB2Bh\n/sZFAALBtBcQEBAQEBAQsEZcNGfz8fFxmZ2djTWF4MQ8MDDg7Sb3798vTz31lPcb11Fwx44dykRY\nJ2awFeVy2czFZNGLrmO2WzeRNtOEU0mc9k2hUIg1M1wo8EzWssIO3govZ0d0Kx+cBeT0wqmoW4h7\nnD6U5aiKU5SI39aspM3mFLes3RJK9wsraS3AjBTGBBKPvvLKKxekp4RTO8b91NSUOs7iZFir1Uyz\nMJhIDh/m/FIiUYV7MJ1WDrVMJqPfo08LhYLWFyzq4uKijndmJ+DcjJD8YrGop3/8rdfrygJwzi2M\nRXZi59+ItMcB6nTw4EGVLnDD0F3glI12K5VK3jx897vf7ZlbeDxhbBQKBY+V3bNnjypux8lBHDhw\nQPvhwQcfjC1zHD72sY+JSNs09s///M8i0jlRz83NeW4NV1xxhZ6+0W9jY2NaN8x9zqjAquguyzIw\nMKBtyrnPYHJiKQT0Na4/deqU51B+7bXXqvyBu76IdBzMZ2dnvVxrpVJJxzu+s9aBG2+8UV588UUR\n6TD2zKKAITpy5IiOX7Rpr7yecYnoLxRxSYvXr1+v44kZnX7KMTo6qn3DmkxuDkjW+uuV/9Eysblg\n+aC4hMYMzL3169dHtCVF2uOUdaZE2v2BMQi2N5VKaflw/fnz5z15I5HgbB4QEBAQEBAQ8FvDRWOk\n3H9j59hoNHRXzPZK7PA5bx0Ap7B0Oq32VPhNWJncWUARO1Erl51V5m7N5YoRcl493mVbOb4AnO6X\nl5fNE4TFPljg66zTC06C8Ll58803tc3ZV8B17E6lUno6RLsWi8WIwJ1Im0HoV83bfS6fEizByDiJ\nAK47fstsB8bJ/Py8l3tqaGgo4ujsgscOgDKMjY3pCQ73wAmG0St/HMb4xMSEXHbZZSLScYg9ffq0\nSoCAQZidnVV/IzADLBHAOSh5fuF+eB6+A3Mr0hkbhUJBP8d9R0dH9bcYD/Pz8+p3xuMTjASHiqMN\n0fY85pj9Wu1pvtVqaZ45hLjPzc3FSqsAzESA1dq8ebPmjwM4GAbzI5fLeXNyaGhIv7fYHeDAgQPy\n7ne/W0REvvzlL+vnvca5i8985jMi0mZ0nnzyych3vRzL3XKLdNalbr5/qBvLULAjs0jUnww+XE8/\n/bS3Jn3iE5+Qb33rWyLSaftjx46pjxfGy4YNGzTvHmPbtm0iEvUFA9D3KysrujawdeOKK64QEZHf\n/OY3Wi+XbRkfH/dYL5G2tUNE1I/OUtHn68CScoYLtOOGDRu0rTmgh8cO5iSeYbV5N+A6tkwAeGf2\nu2Zb2LhxoyePkUgkvHHC6ztLz+A69HUymTQtP3HZIuLej7lcLrL+i7TnJdY+fldbbdmLkbqoSYuT\nyaS+5DhyCA3CgwQDCoOSK4uBxRpJvIFCx1nOZqyuGrdZiktXwmlS+DpX06obHYxO5EgYTBr8dmpq\nKuKgDuDFjPbo5ljMzrUYSPg7Ojqqv2dK2n3pN5tNT+2ZNx24ByeSxDOKxWJkoyXS7jcsHljAWYHY\nVfIWsV8w3A9u3fnFF+cQPj8/r/1lbSpRj1arFYn+Qj3QXzwuMekx/oaHh7VN0YfFYtGLgEulUvoC\nwKZp9+7datqD6W5mZkYXXZT97Nmz+gx2wsXCwxs+1zmUgfm2uLgYSRAq0p4rHFGLtrDGdjd1cL4f\nJ6B2nftduJGV7oYVcwgbzLm5OXUAZpO2e3/eZKF92dEchxyktsK9RaILt5WNASYiEfE2AidOnDA3\n1/1uoKDcjUg0OJozum2iLLM1qz6jHHF6aQz3Jb24uKhtDzeMm2++WQ8HmF/YRInYzr7A3NyceRCN\n0zRCn3NSXYZbN+vlPzc3p/Oby4exbZkv+d3lBiwkk0ntN5jTua7oy1deeUUPKuVy2YwWZqV13Btl\n5PriOtSNN7msfYV1hzUQMZbjNlp8CGOdQLQhtynr0Ym011aUgcckxiXWx2w2G0mwjO/QLqwr6Tq5\nr6ysqEkPawOrxfMGzu1rtEkcgmkvICAgICAgIGCNuCDT3o4dO2RoaEidu5544gk5f/68fPSjH5Wj\nR4/Kjh075P7779cdnj70f3eVrOqNzwYHB/UEhV1ntVrV63hX7CZitcCK1Xz66GWqc8EaU66irUX5\nWWacyclJ3YXj9FKtVns6cwNxitqAq1Xj0uiW6acboHGCuh87dswLU282m9onOKVyGD+zOy4tm81m\n9bTBv7FOytZJP84RnNWp3RPG0NCQ59y4vLysz8DppFQq6b/BEPSi0IFWq6V1wzi+7LLLdMziM54D\nOB298MILkdyDIu2+x7PBiPTKBYm+n5iY0BMfn5pRX7RjNzVu9BvnCoNTPZ7x61//uu8sAKsFxtXQ\n0JCXc5GzGUxNTennzFyi3Gi/XC6n94xjM9jcB2zZskXvh7HIZh9WzwcOHDigz2LtIlwPhe/vfve7\nItIxS64G//qv/yoiIg899JB885vfXPXvXbBeD7e1C/Q/m7d7AY7i6Ldz5855a1E3zS13DeQ+ck1t\njOHhYW8ui3TYQtzj9OnTWnew5JxEHuZ1zgOIzw4fPqxO8+jnfD7vyZYsLy+befUsWGs+2nx8fDyS\nbB0A4wI2S6RjulwLXI28er2urC3mQCaT8ea/9e7tN9emSIeJwlrd7/rCSuloq8HBQdNlA8/Ae212\ndlaZf6w1qVRKjhw58ttzNk8kEvKLX/xCnnnmGXniiSdEROSrX/2q3HnnnfLyyy/Le97zHvnqV796\nIY8ICAgICAgICHjb4oIYqZ07d8qTTz4ZEX689NJL5aGHHpKJiQk5deqU3H777Sq8pg/9X2GtbgJ+\ncYyEdXrGabvRaHhK2alUytzJuo5piUTCC8G22AeLaep2eurHYdT6baFQUBYAJ9zDhw97rIx1YhaJ\n+kOhPVyfJZGOL0gymdR79mK7YPvnbOjoExay61egMi5MmIUlUQ/05eDgYOSE5P6GT0BAvyehOFiC\njIODgzomMBYXFhZ0jMEZdnx8PDLeRNosFE6V6EsO/e/3FMY+K5wZXcTO4zU2NuY56VqOsgMDA57T\n7/DwsDoFQ3zxtddeM8c7518UiTr1oy3YXwvjnn1aWFE9LizacvDvBjd32tLSkne6TyQSnmJ8MplU\npoLHJMqPucdj5NZbbxWRtjyDOwZHR0flk5/8pIh0/NfYZygOo6Oj+pu/+Iu/EJG2zMRf/dVfiYjN\nOGM+JJPJSLuuFWiDQqHQ95x3cdtttykjw2ySm6XCgpVztdt7BZIi8FPjscTyCyy7I9Jex8E6Infg\nY489pnOey2mJTQLMkruipOl0Wq9lp3Ks2yxUzQKVGItoA2sdLRaLOq/4t6gfz8d+10i8BzB/5ubm\nIuLRLli82nU2F+m8O9AG3Ic8p10x6dHRUW/tGBoa0vUOn507dy6WWe2FXs7mF7SR2rVrlwwPD0sq\nlZIvfOEL8vnPfz4yuVutlkn5srkCDWdpLLE+kOtEPDw87JnYrISsFuIahLF161YtOy+MHAkiEnWa\nR1m6mYAwEJBGgZ1P+UUUF53AcPWXCoWCtlU6ndYyclu6UXHu9yLRBcByvu4FTHr8LRaLuliy7od1\nzzgTZr9lsTSeOHoy7hm8iXEX52KxGDFNi7QXEVyH8p0+fVpNDVD1PnPmjOcYubS0pC88a9HldETu\nIs0v8NW+DG+77Tb97euvvy4i7Y2cO8+uu+46pbqBoaEhLReS6i4sLJgHIE6WjXphbvSr79XLDM+b\nnX43UjiU4CU4MzNjRmbh3piPlUolUhdcg4Wbx6VljsbL64YbbhCRtmI1NKCwFtx333191eFd73qX\nPPzwwyLS2SR88IMfVD0qbJD5sMVtaWm3xcG6Hn2ydetWnd+cqsNa3612sQ4OGE+ckQJjFuMqn8/r\n5gEv98nJSe/wzug30g3Ys2ePOirjWcPDw95GlfW1sBnitrfSFnFZ2L2FnwW4azO/P91r3OcAvd4r\nrIOI8mHMsoq623b8XsE9+P3D6yw2fyhLrwTIvQgJfp5Iu11Yj06k3RaYrxyA0A+BgN//1qL2Hn30\nUdm0aZOcPXtW7rzzTp3MQCKRuCBRwoCAgICAgICAtzMuaCOFk+qGDRvknnvukSeeeEJNehs3bpSp\nqamIwxsDpj1strDT66VEjd1pMpk0nWPdkHkOmcSuk3VfsDvmXTHYhampKd29shaNZU5z6eRu2iM4\nQeD0uX79ei0LTglbt271HE4thXarrfgUY5mF2Izifs5/2akcp2zOpwSUSiX9jENYwQ6irLOzsxGn\nYRGJ5BNjUyv6GM+1HLIHBwc9E1E2m9UTHtph27Zt6mSIMlntNjQ0pOOEtV1cc1e3Uyw+5+9hEoMZ\naXp62lPuXVxc9E6V4+Pj2kZArVbT8XQhSu0wyeVyOVWRRptaOaU44THmz+zsrGeOFLGd/vEbp7zx\nhgAAIABJREFU1MdKBC7SOcGjnwcGBvS5bNpx2ale+m8WBgcHdZxgfFpzFeXgv41GwwvLTiaTXlh+\nsVjUtY/HEPoOdRsZGZHbb79dRER++ctf9lV+OKdv3bpVGSk2EeGkjzpyHyFI4LXXXtMxCPNRr+CT\nuKCYs2fPxppOmFXghL4i7bmAtrRyn1pmHmZqMaYwbw8fPqzaXEh2v7CwoMwwJ4RGImY4h5dKJU+K\ng60GkC0YHR3VesC8zWwkjyf0w4033igiIo8//riuDRwAgfoyw4YysOQItw+r6+M6913EZnKrb9B+\nrDAO5txii9xyiETfK2CzZmZmTAbHtRxZrgfJZNJTaO8Gt27d2DZrvwBGmjM+gBFMpVJSqVSkXq9H\nNN4srNnZvFwuRzKK//jHP5arrrpKPvShD2lCya9//evy4Q9/2Pw9FqZUKmXq2AQEBAQEBAQEXAwM\nDAzI6OiolEqlnhupNTNSp0+flnvuuUdE2ieKT37yk/K+971Prr/+ern33nvl3/7t31T+wALn8hHp\n7Iq7nbZxgmPBPteunk6nvVORdb+VlRXdvGEnzA7NlrOi5cTHPiHuqf3MmTNaPpxMjx8/7tnnz507\n5/mWHDt2TE9oqK8V0mvZdbds2WKKILLjpBUGyqJsImJmn0+lUp7/2vLycqyjPfqmVCpFFGXdcnE/\n4WSHU5YVWlsul72TEYvHAax2zKHp+C3s5hyuDrBvH/pt/fr1OmZQ5uHhYWVUuG5oZ7CLr776qtn2\nAAc7uD4Zq4GV3wr9hTadmprSsYW27RaUgTZkts0VSxTp+ILheu5Tno84/WHO7Nq1S8sMf61Wq+Wp\nxbv/tv7fL3AItEL2mT3B/TF26/W6d1Lm9QTjKZFIxPoegRW5+uqr5frrrxcRW1HfAoIEXN81kXa/\noK+5bq6AKoN9YFymntkDFl+17uH6sORyOc+Hj1ltrF8scoo6TU1NeeKQrVbLtBDg31xWzBvMy1Qq\nFWGiAKw16Dfuc/Z7xXMhoLljxw4d55whAGsD+2hi3kAlf3x83Jw/eB7y+h05ckQ2b94sIu33o/UO\ncMV3RToCoRhPrOCNsTE8PKztHyfmnM1mzTx4mLtgn0ZHR3UN5TUY7Y/2sxzamalnJ33MKTCmHNTD\nvstxewf068DAgPYJynLu3Dllu3nOY0x0k4OxcNFSxGzcuNF8eVlRcblczlS7dhWXu1H8bmTYrl27\ndMHGdyMjI6aWR5xWURwGBgYiCy3gKrhmMhnTqdB1cmYKG53ODr4u5Q1YKWLweyxaZ8+e9crA0TCY\nzCdPnjTr6jrfp1KpVTsSA5lMRusJZ22YoHohk8lo++L5cdGh3YAFLZ1Oa1lYPb8ftFot3Vi45lwX\nrlPtasvLGBoa8gIf+ICB+VOpVGKTZaMdb7rpJnnmmWci5erWBtdcc42IdMbJ3NycjjWUqdVqeYlO\nU6lUrAYRO5PGBV+sJmrPxeDgoOcMzHMOqFarnplyenpaN4eYK0tLS1rPuHb+3Oc+J3fffbeIiHzk\nIx8Rkd6BA4gc27lzp6cZtXHjRi/VCLcL1hWen6hHq9XSvmXzOsYCNlz80kZb3XDDDbqeY5NQq9XM\n9Dyuw3MqlTJfhu513aKjAT6kosyf+tSnRKStc4bUOdb7AqTAD3/4Q+1fTnXivlu6rSuuQ/7IyIi3\nQbYc1a333o4dO3TzlE6nddOC9t2wYYOWwQo6wcFxaWlJ298KGMDYYPeGuE09vxus9yPm+rZt23Qj\ng+ueeeYZLQtHkLruISMjI3rdagNprHHSTb8K9QWBwMr22JDOz8/LSy+9FJIWBwQEBAQEBAT8NnBR\nkxazcxvAuk+8e3ZPEfl83tth1mo1b4fMzBefWKyTEr5HaPLp06c1ESvLC4BpwMlgenpaT1JWqCaf\nrPqRNbCcoXslvAU4t5xIVAVXpL27dnf42WxWmTkwA8ViUU87qHsul9Py9xs6jH4rlUqmQ6F7Aq7V\namY90V/4m0ql9IQMlXA3wSw/R6RDM5fLZa0bnjsyMqJlBaXLfcCmUQCnttHRUR2LnJMPCXQxrp5/\n/vm+282qg6vqXSwWIxpqIu124cTZIu25gjbFaTGTyWg9mcJGW91xxx0i0u57nIpRdtaMAXK5nJok\nwOxaCbAzmcwF6Ra5yOVyeu9yubxmRoo12axTNpsewOpgPJ05c0ZP1xgfxWIx4pwvEp3zGMcHDhxQ\nduKhhx5aVZknJye1rWH+SKfTej8+lWPssP5bPzpNGC8itvM3M95slkNZXDmA+fl5LxktJ+dlk2Ic\ne4J1aGBgoKeTvEjboRm/QX35/YHybd68WZkf9Jul9caMFNYfZlU5wMR1X2CWymL7+V1nSTVYVgY2\n3fezxvRi9wAex+zO4W4bRkZGtD/j3BFGRkbMZOVxwBgsFov6WzfHqEinLcvlsseoZbNZNYljvWPJ\nCU5A7jrDb9iwQc6cORMYqYCAgICAgICA3wYuGiN1ER4bEBAQEBAQELBq/NYEOS8ELqXWy2zVz3VD\nQ0NKK74V6UAuBIVCQZ39jh49KiL9KwiLdMya7HS+mt8DLg3MIqnclm77dlPIRXksVXSUr5vjrlu3\noaEhdcRl9XHWiHHLx/ezzLigfnHfbgmlXed81s2Kc5TfuHGjasSwwrDl5B9nZmL63lLuhhnCdZ5n\nrFu3zjTjuCYRJBUXsR3Z40woXA+0N9cVZo1EIqFlWW1SUitzAZcBiV27JfNF312IaW816Dfh+WoT\no78V+L86pLprCJtYOQAmriycAsj9Let18XXsoIxnuOOTIyaxHljz0VpfuCyW6Yyf5aatqtVqXn15\n7gG1Ws0zjXOgDNBsNiPrN8oFs5U11t+uJMVq50IqlfLS7Fh161bftc69bk7pve4TTHsBAQEBAQEB\nAWvERWOkRKIOtNi1FwoFj1UQ6S/sfH5+3judsG7JaiUMupXZ1VKynObuuOMOdZKDQ+hqGCU3RNz6\nLUsFcHhurxBxVwCVlcOBbs7tODWB2WCnSz61WcwWAEdMS0vFCizo5hjpli+fz3uSGtlsVn+LNmXd\nL74ujoniE7j73LWwn3wPV9WZc4rBIbher3v9Oj09bQZNWIAi82/+H3tv8mtZclUP79u/e1+bfVOV\nVemqAhurbAa2EBJCsmTBBAkx8k+MEAz5CxgiJnjMgBlIngEzkJCMkEH2wCCQBTbVuai+yaysyqzK\nfPm623+DpxVv3X1WxIl738t6Zb5Yk8q675w40Z04EWvvvfbLL1f+Vsfi4DTmHdvNTk7Ha2trwVEY\nbWMnYMUoq5yP6rl1p0E/lstAnVx5Hvs65DJePGeXzWm3CjxjYqYZl9w1sO5En8M0qeuYQeK1GvVj\nxgnzjOebl/Hg9qo1xzPZdW1oNBoLCe9xnX/HW62WZJAwxmocuN1+DY6xS/zvnLl3GjYqN6DJ32N2\n3HYVaLUqM1THCtUFhPg+VxYMVb9V9wjntpGCbD0+HvgYs9DmKgsQ7kUUy+Hh4ZlECXGHo85KkR2b\nhLfffjtsoFZ5PtqRuvfq1avhZcemsy5ig01ngJrkMYrTT7ROpxPGDnXmqA3VDkT33Lt3T26afH2m\n02nyJUDEoTL9PP3000FEj+GfMZvNkhtQToybApsIU+DnczSP2aKGC8b1ueeek4KC6mPtx206nYaN\nFrS5WNzPR1GZ6QUQY8Rzg9P0sPih2eJGCptFPnSg7nWZDTgJt1oLlMhkLtQGKrWBy/0gbG9vh3fx\nLKMUGWqjF6vfsh+IZT986uPl/526zoNNZ2xqU6Y9tM0fPs0WdfuUqYjrhb+pjU/K3QAkgNrwsRsB\n/82bD/m62AZvVbN1rrkv5eoRA9o+GAykFlzK7Ja6Rl3HSZqVua/ufU21hdu7yuavmPYKCgoKCgoK\nClbEuTJSo9GoskscjUYVM9OVK1cqTs7z+TwwH9hBPn78OJz+YklIl4VP/cL/5t98yhmV7HEZ5Jxi\n+/1+6KtV9ImU8zCbsFTb0f8qIaqidnl8cXr57//+70p5wObmZuVkM5/PA+uEdvKpjlNOeIYL9Yy1\nnU2V6gSi0oGkACVf1BH1xzO9aYL7VrFd+K3T6UhNGWWOVCwg2LpvfOMbZqZTDjHgRM7zWDn9si6R\nT8vBABN14cKFML6s5ZXSV+P2quuWzdUZO6F7EyEzoaylkzqpYp168OBB0K3BXGSz82kc0VPmyFar\n9URNiP55ADNNfI2va4wF8s7rygGdf1Pl8TvqddO63a5k7/zcYXV3nu+e9ZzPT9LVcJ29yZHnlBov\n/v9UUMxpgihyGZ+Y+SsFn+DZTDPEHJC0CtuJe4GUyZafV/csxWYpJrQOhZEqKCgoKCgoKFgR5yp/\nkKuuysq7vDuEAi0cct95553KqZ7V01dxCs69J2cH/+KLL4Z/w39qNBplK7x6DIfDbOZN7dBxWuK6\n8w6d822hDDACHNKLU59qBzMHYDlQZ+XcqE4Q6oTTarVCmxRLBGaIGRXuA58rSjEhrBKeqgufJnm+\n+LbwiY8duFk53kwzU6l8bR6oI/qFc1Ui39gv//Iv2+uvv25m2gcRrJJiVrlP4HP1+uuvB0VgzAPl\nZ7e1tRV8pzD2zEDzKRD38vgqFWnFqOY6QzNUP6RkLVQ5PJ85YbbZ4nxX7yGzlKl1Rz1fndqfJNTc\nVo75KUZF9V+d9IBiELzfDPcd+qPb7UqJF88+MOvFUPVXY+mv57JS34hYYAPff9ayBqn5WydHo9h2\n3Is+50CvFGIsb6q9uX0Z+3uqfM+O5uDcNlKgzWGuYOovFT3Fjccij8X1qaeeCgOH8o6OjoL5KbVh\niUUseHNVzAk7B5PJJLQX2jiTySR8ONHujz/+OOulqctO/dxzz4V/L+tUCxMFYz6fh75WaUXqFnGf\nAFY59qlkmRsbG5XfVeQdp9bBs5CuwkxHiamIL3YexZxAedxGOJYfHR2FslMmVt5IqcUJgQoq5cV4\nPK6kXlDRlmZWCYZQ8xoRdr5NAA4vV69eTW7W+V3wqTDMqg70jx49CgcgzCHlRLq+vh7eOa4f/s36\naqnN6yrAWMc2NMqEhd+479EWzKfLly8vzEdAuQqkUKf/pkxEdR8e1a5lr2MTF65TprNU2SmoAx+3\nTfUf5stkMgnjwBszv9FrtVrSod07ss9ms4rWWyxi0m+q2EGe12DvDM8RwqeJTGWoMUT91foUm5O4\nH++KcvDP2UT5utTVPfXu5TqMp76Fy9SHUUx7BQUFBQUFBQUr4twYKSQY9sk02+12xbF3bW0t7P7Z\nufmpp54ys5PTNTNOKqSbodRmPdrtdrgOu90rV66E07VPbliHt99+eyFprNkxg4FTLH7L3RHzyR8M\nF2QfzE4So9ZBPW9zc7OiyWR2soNnc0tKEoC1WJTWEf6ea5JQDtfAhQsXQp+ArYzpQ6VCsJna9U7p\nzABhzh4eHiZNhAyvM8On7GXp6tg8QR3AHjKbhzr/9Kc/DeZPxWxibnPSWswtZszAVq2trUmzpx/X\nvb09GQCAdxh13d/fD6wTl4E5hHqtr6+fSRYDxSrlgs19alwxHnfv3rVbt26Z2aJUh2dKcp/PzAbr\niaEclFtXXso0tcpJnVmWlNQAl+fHkM1ainHk+qW09nDvZDKpMEcxc6Mvp9E4UUrHWjKdTgMDyxIL\nyglaOZbzuqja78s4DdMa60vPZrLLQy7QL1y/lFN4zJybY0ZTZvWYvIEKcljWfLgMCiNVUFBQUFBQ\nULAiztVHajqdhh0hTk+sRI3d7OXLl8N1OH1sb2+HXHZgRHZ3d8PpAOzUw4cPw+4UrM14PA73pBSh\nJ5NJhfl47733wkn+W9/6lpkd+1H90z/9U22bh8NhMnz+NI6icF6/c+dOYA7ghO+Rw2zEdureTyd2\nrQ8DZmkCRkr9GdfzGDLr4U8xXD7KWV9fD6dIrieu5dOikm/Ab8oHgOeOUv1W8HVmZ3OwQMr3aTgc\nVk5Z4/E46QuAune73dAmfj+++tWvmpnZj370o2h92T8KTBT7TWE+NJvNhXfYTEuZzOfzCoM5mUxC\nHzCzkmL3MKYbGxvZvhgpcP+h30ajUbbTqj8hq0CaK1euBCaKZRxy6h/L54g1kufiqsyaL3sZpJyx\nzU7Wcvb1Uc7aKV8vbi/3vfevUxiPx9KnSTEc/lszn8/D+8Nrg/dzZPkDxcSpf6ecyT3TeZrMHIrx\n83XMHXNmkDyTHLtOoU7aQX0TcuuogphyhUBXwblG7TGViIVxOBxWBh2bBNxndtxRd+7cMbOTCJ7x\neFyJNGOknM0bjUYwhdRFSMEUggioVTSccoFN22effVbpF6aSWdMEC6hyWjZLRyXA7MIvBkcTeXNm\nu92W9VKmVWzw4Mi8ubkZPgBqA8JK2vjAKnPqV77yFTMze+211yo6Qzw2/AxfF7Oq/lKz2QwffeXs\nyY7o6NNlP+qz2SyYqdDng8Gg8hE8PDyUZtCUY7FPHO3rh/mhNpBcP7+gKVMp1wlz6NNPP5UbKYwD\nl4txgrl+Pp/XOu7j3rPYSDFylcjrNlleK+yTTz4Jm9dXXnmlco9K9wSoyESzapTVMhGM6sOSaxLJ\nKS/2bOWA7k12/Fz+4PsNSG7dptNpJQij1WpVymEHb3YxUWOiHJ9VxJ8yQykHdH8vO00v01b/jLqD\ncm40Hl+Pb2Xq8B/bRPl5znMW83gwGNSmvfL38DdHfePOsv88immvoKCgoKCgoGBFnCsjxWAnXOxY\n/YmE72MmgbGsJhPCszc2NqJlxpCT144R29niZA7nxY2NjbDj5/BxmFO4r1R+I+zMlf5WnQMjfuOc\nh3wa86GmfJr0z/fP8JpEzGxwud6xu9frSeqYmSiz45OOPwXFTiSexdjY2KiYLWezWdIcCbRaLTlX\nczCbzQLblpL9GI1GFaVn9Szl5MzjByr+wYMH9tJLL5nZsanObFEqAkry+/v7lfbySZHrAgaMpRVU\n/VBHOJOziR+M4/Xr1+2NN96IlpOafzlYVsE5FyzjgnpxX3omilkIHqdcNkE5EavrFHJM+rmMVIyN\n8u3gseZ7lH6Uv46ZbhXqngKb55gRxVqjTI4Yj5iJVzFNKUZK/caK+R7e7Lesgj/3n3Juz/kmqPdj\nPj/JKoI+YkZNmWzRz+12WwYn+Xtz2Si0hf/Lz65j9HISGeegMFIFBQUFBQUFBSvi3Bgps/jpA8KE\nYGWazWY4qcIvaplnpHaW7HvDoptmZteuXQuMT4otiAG+LyhvbW0t7LS5PJzMcXJdX18PO334Y21s\nbARldDBh6sTOatwxNkbt1r2sQbvdln4B8EHyeaYYjUajwvi88MIL9uabby78xkwHxoH9EVJK32ZV\nfyl1Or1y5YpkGjEOOJGovI9mJ2OjfLNYQXzZ0yIDcwzjr+rBzsuKKVSnKIzV2tpa6EPuc5zg4BPI\nz+BnpZyWeT6g3jit1t3LZeA6jMvVq1eTUhfAqozSsvflMlhqrfFSK75cz/ixEj1fp5yDU4KcfJ2a\nH/5e5Q8Te7+XZbNiDuX8X19n/1uMeUqF1qvrsDZBgsdskRHzbFGsXH8dM9Ncd+XPlXKQX4URqYPy\nZVqWaeTAMF8e+z4C3W63Ih+kmKbBYBCeg3WK81yehjVWbVSBCnX31OHcNlLKi95s0aEZDsEbGxuh\nwcoZGuj3+3KToDZBMGdgkT46OgobOJjYut1u2MxxtBg+8Jzqwk+iXq8XJhva8Ru/8Rvhg/Gf//mf\nZnZsTkEd8AF6+PBhRUl3c3PTfumXfmnhGdyHqJNahJcBmyYUvGlPmRTG43FlMt67dy84EnPwADYF\neMH4o8OpBnyb1tfXw6aaaWtf7y996UtyI4V6Y7N7eHiYXMQZ3rE39kLmAnOCFYb9My5fvhx0vdgJ\nP7XYI1CB+5vT/eAZvHn2v/F4pD7GZicmPd4YclJjXI++wtxWjvz37t2r/KawiobUKtkJVklJgfHE\nWsXXsYM/+hWbdrVeqUMRO1/7v6X+HWtTnSJ0qiz1d/V8H4lmVp9Ghe9NpZRa5QOYE/GXW4Yyl6nE\nw7E21pkoc0yYCssEC/hr1b3j8biiBacOOypqdzAYhPceB0f1La+LUlQBMrnm/rPcoIZnn3mJBQUF\nBQUFBQX/P8G5mvbU6YRVZGFyGI/H4QTJWiBeb4pPtilzAO/swQINBoPARGGHPB6PwykRO2B+DnbU\nBwcHlfpdvnw5lI3cYsxcYbfdbrcX9ErMtCnr8ePHFbPmwcFB5Z6UyriHYhZYqVadTnHKrsuHqEJ5\nvbP55cuXK6ranOiSnQi9mUedRJV5jR3IlSaLcoyso37Vc1c90fIpEGxRr9erMH6s08RMTopZwdzl\ntvH4+vyBk8kkzG3/LAaPEfq31WoFRheM1P7+vt28edPMTsaNc5SlGKm9vb0FxWjAj9sqp/Q6NmrZ\nkHO+Bqzc7u5uhR29dOlS6Bs+SatnqLyF6rm4jnOM+rlT596g9LDqrssBZwZQzrzK2byOSfD5/Jap\nz5OCqgvPT+/oze1QTB3gzbRPup2x8lW9vKtAbC3yzDoHgzET5SVWYnV59tlnzexk3WG3ipSZVLWD\n53vKDJ6T57AwUgUFBQUFBQUFK+JcfaT4vwA77MEH6ejoKOwKWcQLLBF2sbnCmP1+v5LRfjQaSVst\nTo7wc+p0OsF/AyfrTqcT6owTItcHp8of//jHC+Khqv2A931aW1sL7cRufFUhwpTonmJZ+FQBFkPJ\nTPDJwN/bbDYr8gJcBtiTGNCXqDvfi3HgUz5kI15++eWggA9GjJkCFb7LfeHnFDtQ8ylqVQdRddIc\nDocVB+Xd3d3gr4e5qxy5manF+7O1tRX8xPhZOCWy8zo7zuK/3g+LT2g8j5m1BcAqs08DxppzpKlT\npWecFXv7JE7pMXYyBcwF+HeqcX3w4EHlNw4EwDrR6XTCv5XQJgPPYwZLKaCnkNuHKd/BGIunxinl\nPJ7qbyVEGiunrl5nASWqqXx0PUs1nU4rjJS61/vAnQfzpurF6zvA/mFcZzVvU++6AtanVqsVsnW8\n++674e+5/lA5/aesCzn+lOdq2lNA6hgGfyCZJsXihQ/MwcFBRb12bW0tDCw+LMtqTfG9DCzqvV4v\nOC1zCg6f+uPw8DDL9Nbv98NGAHV//Pix3OitAhVZon4DeDFnKhe/Abx58vooKp0KjwOuOzw8lFFp\n2MjiHt5E4t9scmL9rZQ+GC+GfsEzS+uqqEVu2ei9mIMvm2rMtOK/Ai9ImHcqmTDK5GfkRtkdHR1V\nEh6r6DNca3Zi3uaoTF6Y8W9lUkS/jEYj+fdVnXBzkPOumFU/BLHoOY+jo6OK2ZqVmdm0q8xaPsE7\ngz9YytSeQl3E1FmYAJWpi5+rNha+/rFNb8oJn69fdXPCquiqfqnNZGxTmdLSytXLUlDmrdi4Yq1Q\n/auuU1kWeNzUPMK/8d0ej8fhkM3PUw7lyCbC7VFuEMsetFLX5/R7Me0VFBQUFBQUFKyILxwjVQc+\nlWMXy4lT/c4yxj6pnfKXv/xlMzN77rnnzMzs5z//ub311lu1dWLzD6sYAziN93o9e/31180s7RTe\nbDYrelN1bAQYmIsXLwZNJuUkF2Of/Aml2WyGZ/KpA/WGeUYlMeZTAu7d2toKDAmX55kNsxNNKZji\nms2mDA0HO+HD6c1OElS/8847UpqC8+mZacdyZrhYmsCblOtkCBjqJKdy6AE4DW1vb1cc89V16vlq\njBhwkO71esFsyO+RelcUU5fL9KLvmXlEeYqB4f5Rp8PP0+ShAjTqFOa5/pyU2ey4z7F+sCZXiiHm\nsvFeqPVEsQBcXooxWyV4ImXu499yGKtWqyXr7O9Vjtt1ZfM1KcmTVBksa6Dq559jlmbRYvdxO3Pn\nueojXxf1zPm8Kh+j2mGmGVjPdvHagXeg1+sFBta71/i6e1Y+1n5v/VB9pfYGsW+h/y3LJFh7RUFB\nQUFBQUFBgcQvHCOVwjKnqGeeecbMTkK1NzY2QmZ27HBjKuqcod5fF2NozI5ZlxwfqToGgQHfLLBe\ng8Eg+ATV+ZHgXmYSwJjwvRzKi5MI7mXgGVtbW8HJmMPplV+VclDGiQWM1M7OTkU6wSwdKsvlepZQ\nMSvqFMP/74UqzU4YCeXPE4OXHDCrqsWzwzizbTdu3DCzxZx4vi4q3xTXD473EPfkZzBYLA99iXru\n7e1V5nGr1UoGeyjfMcWych4+PANzbXd3t1LOYDDIzjqQctytc+pNsTbMnuDvzGZiLPv9fuhLDt9G\n2angEe4rZlHRR8sGO6jrYjIevmw+3ftr+N8xRipH6sDsZC3i8hQTuiojGfORWpb5UUgx3Sq4h/8d\n87PM8dVZxo+qjnFDef495fqzzyLWSKwxd+7cWWCYAe/r2263pTBqzve8zh9O+YSl3pVV59Iv9EYK\niz0W0mU2UnAe5//+y7/8y8JvCpcuXbLf+Z3fMbMTavLf/u3fkkrM2GipNCOnBTZBdSZITHh2JoZZ\ngDdSKTPT5uZmJUqI2+Q/EnwdR4QBOzs79uGHHy789vzzz1dMkv1+P2zMuJ54IbiuWHy5XF8evyy4\n/ujoKPQR61f5DVSdaUIlOV4WaoPx6NEju337tpmdbKSU5hZDmYXVRgpzguvOCx/afuvWLTM7Tk3k\nN/tbW1uVjZQyoTJSixZvWFl53bfz4OAgOK3WQY27Msmy+TXno68+5rwpYu01ZFRImWnNFvWo1PO4\nXNTVLF/TTH3MY6YubwJU4M0VR+oqh3EVNaw+pLjOb6j4utwPpOqXVT6aKYdw/j1VF3Yc5+t8m5rN\nZnJDpnCaNvH93hzpgTHmeY45iO/d9va2/eqv/qqZWUgP9uDBg7DeQGPOzCoBQeq5Tz/9dPg2wzRe\nZ55TZrw686ZHTgBRMe0VFBQUFBQUFKyIX2hGahkTmAdMeowUEwV87WtfC9f967/+a7gP7A6H8WMn\n+ySYqGWhdtUqZFo5yXptIbMTNtDshO1SZkvWxvFQ2lFvvvlmcMgFrl27VmGu2HTHjBTjQgJJAAAg\nAElEQVR+f+2118zsmGXxiTL5VMwnKtzLzArMSihjfX294hxsZhWFaYVWqyWZPs73Z3Z8KlJh/t45\nkxNUKyiWCnXf3NwMbcI8YKkQJZmgxgvXX7hwoWJ+5dMe3tVLly6F69SpUyUqZrkEjBG3u87JfVnd\nmtTfFJQJOBYcAv0bdhLH/dx29JcKTuDnKi2wZevMUMy1v6fu1M4aZEovya9Fy5ij/L1KhkAxDnX5\n/FZBronIs08qAbW6jln3XEaqzkTN13lTYqNRTTavsExdfvSjH0X/rlxnvPyC2cm35tGjR5Vk83V5\nC+tkDVJ9lHr3PAojVVBQUFBQUFCwIn6hGSkFzwzU7Sax2+31ehU/HLOTkx7suf/1X/8lmSvv9Gl2\ndiefswCzRWBA2H9F1ZWV280WWTycmGOnGPQDGARmDeD/wX46GIfhcBhOHRjLl19+uVJ+q9WqME1m\nJ8KdqB+frFn92fugtNvtCkPHEhAA/z+324tb4n6zRZ+HXL+qFJvEz/cnrzophjfeeMPMzG7cuFHp\nP+VLxSyYciZP5aHqdrth3mE8rl+/XmGu2JcK4zUYDMKcYZZEMWU8bin/Gx4P1JsZE+VUvay/kRo3\nrEHdbjdcx+sFfkP/7uzsBIdcvKu8jjH7hDrnMlG+3mYn7W232xWx1NiJXY0DM1H4ry9H+TSp9yIm\ntaH8ulKsAv5W9x1AnevGkqGc6lXASkoWgq/3zBpLLHDGjxRymRfVp9xHdePv2SweL/zGTuXM1Kp7\nECyFbzAziGx5SjG+XGe1DvA89+1V5SzDTP+f20gpJ0SPRqMRBg50eqfTCfdgMZnNZgsmLLO4+U+p\nsObCbzpikX2pRLLLwCuL86Tl9B1qoUAd4Vy/sbGR/YHFb8rUiTrt7+9X6PvhcFiZ/F/60pfsZz/7\n2UL95vO5jCYE+GPjn6Femn6/XzEf8/jyeCiq3n+sx+OxjFKE8zjSH3zyySfS3IF5oRYT/s1/5FTb\nOKEwKHZ+Z7DZ4eg5bKrYyR3z5bPPPqssXpzWBptwHgO1YOHv/BFm+Lnmk6WmPhjcPrUQe8folJk7\n9gxAfSzb7XZ459SHCo65P/3pT8NvfABRa4z6YHjNNZVQVn1cU/3o4eeUMrHxv1MmKjb3pe5dxgS4\nLPj9zYlc5AwcShOOr1fO0DlK8948qNqv0lqpuvu5zdexDpOK/vVtU9ks+NDBfek3pdy/XBdPYrBD\nvoog5Hv9dzE2hkDdBmklh/2l7ygoKCgoKCgoKDCzXxBGanNzM5zGQBfGdpWeEWq320H1G6fdg4OD\nwIoofSIGGI533nmn8jfslC9evFgbzhy7t9vtBnVvsGMfffSRzKuHHTc7Y3NouNnxKTTlMGxWNQPE\ntDtUHq/UKRZgbR/W+PGnHT4po+2PHj1aaAuAUwfMGmCjfJ0UY6hC2JUpxFPO+/v7FSdeNgtxecpJ\nWjGHKXMB5iSzowCbUNlECShHas436DEcDgMDBhwcHITwfDj3X758OTBS+G+3263ILgyHQ3mSQ13B\nSN27dy/0Myeb9qddbhubxvy4KUmEHKTYXT69ezmQunBr/k1lC+C/++e9+uqrlbrgnmazWVnb1Om+\n1+tVdLVYxiNl9uXfFPvF75G/n0P1uTxvAlLMBecCVH2UYqSYqeH3V5nTcsLf60y5bKLE3FFsGvdV\niklT84Cfwf2hnPSX1VpSba8LIuBkyzFwPbBmzud5SulswlT58oAYY5pSQOe5o2Q8Uu9ynXmTURip\ngoKCgoKCgoIV8QvBSK2trQUnYvgsPX78OPgPYKc6m80qO3R2ZFXOyXVQDugAnhVjo9SpF7v1a9eu\nLdTR7IRB6vf7kkXwImi8s1aq2Hx65xOpOiXgtAOfoG63m3ScBUajkTztqhOrP/1Pp9PgzwE/nX6/\nXzlR9/v9wMKx87N/7s7OTkXYzexknPiUHRPK8+UqR2WFHL+1ZrO5IJyI8rzo59raWiiPGTHFRAHK\nQRr/VUzOcDgMDBgzV97ZeGdnJ8xvxVAC7NfHJzmMq1LR53FJ9R87wyqH8FWQG+atxgtIObeqecJs\npfK/UiypEj5UzrLeid3Xry4UHtcpBX9V51gZDOXQbJYWtUyxRfwbMz7KSdvXuc5pWjEXKczn84Xv\nDn7z7VA+XwqqLzwj5e9Xfarmp6pPbI1WfoLLMDNmi/PYz1X+rqjvFxALNsiti++P2FxMlbuMr9Qv\nxEZqf38/dD42UlevXg2dj83O0dFRZUCW0ZpCpNILL7xgZscL30svvWRmeqNSB3z8sSmazWZhksHU\n0Wg0wobLO4F7sOK2B8pQ0Qn+HjW5Fd3O5gyzRUfRVOTD4eFhZROpkpDy/Zj4vElAGp/33nsvbKQB\ndjwELl++XNmAxqJw1IfCO4dz/XyCZA+lNp3a+PCBwJfJH0P+GzaCaAdHsfmUMma24BDuP9KtViu8\nN0jw/PDhw9B/KFdFFCqn/slkEvoIY8nJiAHe6KF+vV4v9JV6X3lR9Au9nwPKITZF+XOb1PVqDFOL\nLo+HN0nx2Dz77LNmdqwrpdqA304TWMJmSf9h4YAB7lOf0iM38pg/+uojrFCndq82IwD3rTIBLusw\nXLeBSx280GftdruykWYnfP6bb1ssQnSVDcCyY+fbk4McjaVOpxP+jvV7OBwmI0y5r7x5ue5AEAss\nycEqDuaMYtorKCgoKCgoKFgR585IgbVhbSPsQLGbHY/HgaXBKXptbW2BhTFbpOKVrgV2uLPZTDIS\nOA3jVP7+++9Xdty5bFSn00nSkHjW1tZWODnC9DgajYK5hTWc1LPRJjAc7XY7ae7odDqVtk+n00od\nVZ4xdWpSodVmVT2v8Xgs1Zc5Ia0Hm/HQX+q0hTKUWY/VxLn/VDkwM9axmGgbm2qU+nvKLMRaO16d\nejweL5izAMxLlklIPYPNDJ6pYZmEVJ7Izz77TI4bv5seeFa73Q5/x/u7vr5e6V9lkldOrhcuXAhM\nLp/oeb7nyBPU6UNx2V4fTL3LPIYq7yOb4lFXKJz3+/2K3Alr7aAM7vtcswbXxbPLMSd7pX2mAh98\nHWJh/r7OyplXmb9S7Itvq3JyV7ILqrwUo5NqG9/D75QyPfrrYybAlFkw1oYUU3ZW8FI2k8kkue4A\nPMc4CCjVLi5HaTPmIsUGA0phPuZwX4fCSBUUFBQUFBQUrIhzZaTY8Qw71l6vV/G14NBv4OjoKCpc\n6YFdpwrF550qTs1Q0j6N3XRzczOUlzpJHB4eBiYEzMpgMAgsAH5rNBrhJM8MhncObTab4R61C9/c\n3JQO9Ow7YbYoMskicylGivvU+wVtbm5WxBR7vZ4U8wQwvhcuXAh9qdoEcdUPPvggWhZDnbLZGV45\n3/IYqjorQU6Uh8ACZn7ALrFzPViHTqcjGSmMjQpEYMdtzAX0X8w/zrMofC/+tre3l2RFYiduPMu/\nt4rt41Byrqf34Ynli+QTc45obd2JXflZpXx3lFoz+wSqPgdGo1F419H3PA5cp5SPpGof5y1TJ++U\nwGKOPETd7zxeygdJvTNnAWZ8Un5TMUdwxbalwH6lPjglVpecstW9p4ViwOqgLCF1OSU9VG5JQNXl\nNLkRua9iTFROucv4mp3bRqrRaFi32618lI6OjrL0Q04LjmgyO55guY6dqUkETaher5eMEkSbODUJ\nRxh4h9zJZBKeC9PSo0ePKnUejUbBTKaUvFVUh9oMqboqcym/ZCkH6sePH1cit3JV4FUQAUNpR6HO\n3G/s7Kmcx9Ff/NJzOSjXv/QcgKDmRGpeqXaNx+PgnMltw7hjfFl1HKij2FHGZDKptGM4HFb6JZaW\nRX1oVz14TKfTYKJmM6w3jcUWbd5IeW0fdoKuWxC9A22uCZAdt7lNHt1ut+J6MJ/Pk863dXpnvm0q\nuEKZTtgRGKhzck/pSPFz6jYKKsJRmXtSZjLevPh2xp6pVMBP62TM5fGGum4DpDSccsyldYhtkHI0\no9S4xUy7qQ0UO4mjTBVNqlTUU6Y4vkd9iwB1L/eLujdVXg6Kaa+goKCgoKCgYEWcq2lvPB4naXQ+\n8XHiUrN8uq/f71fMJHwSy6XLgbW1tcC8cJ4+nyT54cOHSSYCrMfGxkZQV+c64O/on/39/XBixo5/\nbW0tMAzol5i5E+WwWQj9opLv8klEsU98IgR7wg7jXrF8b2+vcgpSJgx20kcZdSbcVNLkWFizYi4w\nx9TpiR2uof4NZ3hlOuOyYUpVp8XxeJw0ozA7hv5FnzabzUrfjMdjqbIO1AVLoA7MzijmAvViqQ7P\nDHS7XSn9oODngWKIh8NhUo4AdeO6xORKFJOT0rJRUI7bqZybnL8SYNYBdeJ7weKORqNQP5iy79+/\nX6ljLFzet5frzO+bWo9zQt1jJt4cJoR/V1Ir+Bszj7xuez0nZbLL1XNahaFKsXMsdaCu5/op7aOU\n+ZOd5ZVeUkozKhYw4O+N6aYBPDc4UwHgMwPw/anAkFgQk3onU4EqXK6vv3JVUb/loDBSBQUFBQUF\nBQUr4twYKZxovYSBOvVwbi/cN5lMpP8Ass17pW4zvQNeNk8XMwCXLl0KdWefEjMtwshQuegY3k7b\nbrdDX0FputvtBvYh5RBolm4nn07Qv9y3zMaoEwYYOvjz8ImAM9B79oT9l1R2cGZqvO8BCx4qxkrZ\nvFOMz3A4lI70qB8/wwumdjqd0F9gJMxOmCMwSTEHX6VejWco/6RcJ13l06LmgfJpwPiqkyGzAArM\nsGxtbZnZSR/wu5w6/a+vr1cYtcPDwzDXuB+5Lt7XT70XfJLnOqsTt2cLe71eGGtVfz6N417UYT6f\nh/FMMTTc55h3s9ksvIecSSFXkDHnlK18X1TOM4WYY3RK3DLGnuE+5dOC/kv5YeX61NY5JedCtUc5\nufN/U76oXK7yffPPrauXesYq96rxZX9CH6zDeTDxLphVrQGKMWMpiWVZwjqpA/V+pFhXL/+gcG4b\nqa2tLTs4OMhK28KbISxi29vbYWHBgttqtSrq4LmRfbloNBpBcRuT4969e8lEwKtAqZv7QWbNLbR7\nbW0t9IuiZRnKoRnlcL+hbeqj2u12ZRSZN88pMO3tTbf8Gye15M2GV1JmejnlLMv9gv4bDAaVucha\nS8DOzk5F/VtFfqq2DwaDyrgqHRyzqmM5A2Vcu3Yt1Jk3u3gfsEkcj8cV8wdDffjYEVRFpKnnAhjT\n0WgUzFAcAPGVr3zFzMxee+21Sl3wLHb05wUcawHaFkt5lIL66CsTm4om5ChBNqcoU6FK5ZQyQ/uF\nnn9jMzibN9VHJqX7pDbyPNaqz3MOm/yhUhsgIPaBTgUW+TnJ/1Ybqph5S+EsnM0BjtrjoAL/Ic6t\ns78utSkF2JSt2l7XXuXgX2eWNVsMGFBrOc/tVLAWr6k535BYG3xduTx+R1PBRMs8v5j2CgoKCgoK\nCgpWxLkxUjHzQKPRCCaRFFulQt6n06lUtz4NsLuGSWFnZyfsaJGQdW9vL+xeFaOSUn9eW1sLO+BY\njr0cpMLGzXSoKZshcMJnc4p3qt7c3Axjwrt71JsdoxUUfepPBHzyxn+VGnuz2YzOH4bS8+Exwrgy\n68nmF3a+N1scIzZ/KSdjr5l09epVe+edd8xskVXAPOH+Qx+mKOcLFy4EbSq0SUkxKMVldixWJnKl\n3g4wja9OqSo8n/GlL33JzDQjxayGP9myeaCOaVZsiBojrr+X54iFkvt5xyaMuiAHXx4HqqRO6lxn\nmMvrzBVAnTk1VT/VDs4WACgWjU33XIZ/R3me8N9ywtDrzLSxtqj/Py0Ue8NZCpjd9uydMgGaLTJv\nOSbLmNxHjkRAXZv4t9i1ZovrCcutmMWlYPy7WSc9opB691Q7zKoscExSpA6FkSooKCgoKCgoWBHn\nxkjt7u7a+vp6ELDEbn1vb68215lZPDwy10HN+xHF/CxwHeeyQ74vsDOxe1M7WbBu+/v7wSH31q1b\nZmb285//PFn3OoFBxSIwO6b8oHx/9fv9hVx3Zscndd+/k8kknCaYRUGfsA+N+g1tZ5FO376YYJvy\n5/AnHnZeV06D/Azl7wOgng8fPqz4linmSoEZVp4zKOf69etmZvbRRx9VnNfVHLt//36FseI5ocQy\nmWlCezngQTmlp0LsFZidUczRD37wAzMz+/rXv25mZj/72c8q13S73cr7MxqNwnrBDtcKdX4o6tTp\nxz0W5u2fEXNoX5YBUQEIqUAJRur0zlkKuF+Uj5//mwpbV+2N+TmlWABmSdW77MtQPlx17IgqL4Uc\nJisG5Wc3n1dlPFqtVjL4Q9WH+ygFJW6K37ledWXl+iUpBpGZ7hz5k1arVbE4nMbHuI7dU1IRKjDD\nB4ukcG4bqdFoZKPRKHQgR4thI4WouPX19aC15NOkMHhjhkXx448/Dh2S+jhsbW1Vym61WhXdKu78\nug5OKRZz2hXoDN24ccPMzL75zW/aG2+8YWaLuk/4mKMuvGGCWaLZbIbfmUrml1lF3rEZzezYhImX\ngE0ePpmq0sZhB2XVZvTL+vp6cELmRLxedZ77kRdS3w5l7ltbW1sw1Xhw2di0qHHjvvKO4DEK3Ues\nPXjwoEJh80uPDRU7pacWvEePHlVMsryQsqlIfQS9bhZ/CNh8BKh3IPWhZNV+BvoX7/Tt27eDyRPg\nSE02v+JdiZk8vXmM1d9V0mUeD5WIVUVNcmCH2XHfq8ONMiX68pSek1l1bBTqPjbKVIg6m+mIYR/t\nqFTbfV3xDLSNMwnkbCbZbFW3AfZ1UTpNCjFT65NGTC3eb2h8hB6gHKPr4DePvC6mgk1iJsU6kyOQ\n+h6ehYJ4jDwB6qIUVVR26pCYm+nErJj2CgoKCgoKCgpWxrkqm5udmHRYNwdMFDShjo6OKnpJsTxt\n+B1sxqVLlypO3/v7+xXZAGZRWBWZmSPUM5VoF4gpKgMqcTCSJV+9etU2NzcXnvvZZ59VQuH5tM1O\n0MqxXGky+fry8+7evVtxvuW/oy87nU6oA5+AcxKxcv+gDHYyTCnbqv5VJzZWz08pXJvpXHsA1x0O\n3urEl2KuFObzeZjvYGhu3boV+pzr58djPp9XAhS4n8GI8VzjfmZneZTn+5yZP1azT52OMfaTyST8\nG9jY2Ajvz4cffmhmxzIOvg+YaQBTzExdjIVQ5kAFr8IcO3krEwz6jdcB5bTq5zuPDQcY+IwLSrE+\nBsU6+fWu0WiE68CSKckSBXUqVyzVbDarzKdGo6rWbVZlmhTzx/cqVoHLSEkncHln7Vyu4J/RarXC\nO5AK/lBlMAu1St1zzYf8PPXt8Ca72Wy2YL739VNsMLtXKHZMmXa9XEFMW8oz8CrIISb34Zltrt8y\niZkLI1VQUFBQUFBQsCLOlZFiBzo+0eNUiv+anZzGVS4zZpqUo7q33c9ms1AedrZKamE4HIbTHU6f\n+/v7SXVoXN/tdlcWBf3444+DX8i1a9dCW8EssL+L8iNSdnAwegcHB1LETTndw2/p3XffNbNjhgPs\nCrMjGENm9/zpcG1trXJCV9nBue7qFMGsiEdMlZYFRT2Ug2/MmTb222w2k1IXcIhOyWCotrz//vvh\n38w4geHCbyx1ofyS+GSlQvaV879qYw5LxeCx8n2p3oV79+4F9ozh5Q8ODg5CXb0YL+Dro4ISer1e\nZSxYSZ2vZ3babHFuKP+xlN+Skl04OjqqzMtYGf4d6HQ68lqlMI3fVP8rnxz1XA7pV2WknMgVW8Tt\nUQKKgPK54mehnFR5dbIPddcqKNkFzwJx/dR1XJa6rs5xX9XJX8csP89txRamrADMKqmclvgGwvcu\n97sXy42XWnMVM8V/8+3gfuFn+fWJZWGWYQHPbSM1GAzs4sWLYXHBJiFGo3kTRqPRCIup0kBicIoG\ns+NFE52ZEyHI18XUWLHo42O3traWdDbHpOt2u0H9mTdIqB/6h5PbotxlFJ3xUefNEBAz1XhNLn4x\neDJiU8V96RcF3jTxBMWGkcdObTyUA7Wig9UCpZR2AU79gTmGMWQonSvuM7SDTSbeDNrv98MiA9Pt\n48ePQ9uV0jxDzW82o5ktOhCjnIsXL1ZMyWoBipmjlS6ZT8Xi/43/9w7Nk8nEdnZ2zOykrzqdjtRQ\nw1jz9RjDmzdvmlm1T9AfqUWcNzQ8DgDGjU0T3F8pyh9zR0XE8geD3x+OCMV1ftHnezGf2cTCdfem\nMx632GY5Bt4g5SQv9m1Tz/L1q0tDoxyueXPnf4tpEOVE8p3G/Kc+1rHAG7XB9L/xhpA3PkCz2Uyu\nlTzXvJl0Op0uHY0LqHmvkpujjmaLpnRldlvWhKn0oXjzWmfmxb1qbJZVUjcrpr2CgoKCgoKCgpVx\nboyUdxpN5d3Z3t5eMPPhPq+AzWDnSpXfLKX0C4ZrMBiEcnDyZZ0j3s2qRIwpp3Rc1+/3w6kYv925\ncyewCakQTHbcZaA85QzrnX99fXgc0G9gW5TekJlVTGfT6bTizM8ndA5nR72UxAJDMSA59H3sROjB\nc4hPap49uXbtmr333nsLz9ve3l5gEwCYRu/evWtmWsbBbNE0bbY4rnziVDm0/Nzu9/sVljXWnz5Z\nMiuHp+5nZ2PV5xhzPn2m8tc1m82FoATci/mGfmS2L3ZyVWuBv5brr0z6yhlWJUYGmIlI6eYMBoMw\nrri+2+0GXTpmC/yJmllj7w4Ra6ti21ImLmaf+PqUXhaXsSzDoxzG66DMVp7NyNWbWua5Hqq9dTIO\n6rsBKHMkm6jm83nl/VTmz1z3gVarVWGuWKFfmcGUKVsB13GCb2bx6+ZtCjkO4Mr1IMY++XawidWP\nWwqFkSooKCgoKCgoWBHn6mxel1sOJyDPRsWgQuebzWZgO8A07e/vy9MV/o7/NhqNCus1m80qPgPs\nfIe/KXkDBvyibty4EXbt8ElSqs2tViuwIxAdffrpp0PfwO/p6tWr4YT75ptvJusAdLvdyg6fHZSV\nIzif/HEK4jBf9jMxW2THwJiwbwwLBXo/Dvbd4ZNLjs/GfD4Pf1d59RheQFHlFOOTJq7nUyD3EXIV\ngpFiWQiM1/r6eqgPO1B7/yrcz1A+Tfv7+wvipijPO5Rzv/C4qT71zsusjs/X+dNdzCco5Zeo5Ah4\n7sAHCazghQsXwnxHu8wWA0uUcCvAYr2KjckRElSn6U6nU5mzSrV/NBplh1mnciMqMBOlnpHD2pid\nyNBgTeO2ASocnZ31UznjlI9mzN9R3euZnpiUhcdp5QUUvDyDYqZUAARD+S+y9ANw4cKF0Gb1vUk9\nIxZko5imXJV9z5gxO6sCH9QzUvNTBTTExi3lNA8o64Zycs/xmTq3jdTOzo50zO10OtJUhH9j0WfT\nHho+GAzCxw0bjL29vfBhwYf08ePHoXOwyWKnZEyAmFYVgOdynVn1OLVRxGboypUr9uqrr5rZyQeX\n2466X79+PXww8NIgzQ634+joaEHHw+P+/fuVDcPR0VGoN5v41MYjlZhYJavkxdybGnhiozx28GWT\nAv7N6WhSiy9HEKI/MHdiH3Jf3sWLF8NmBPciUbXZyRip1Dl4tod3tOSNlNLaAVhDCWDHbdY28s7y\nR0dHydQQ6KuDg4NkGp1UpB6Xw2YrH8nDDvfAcDisqMBzFB2vE/4djY0lf9B8fVX0XKxN7Nhttphu\nRV2HjwR/LFKbNbPqmKh5HHPmBVKbMWVOiSU8VvPYf6RZKR+IbaSU07yqn4rG8nWJjZHacCmoflZ1\nzkHMLJja6KU2d8pEyc9QG0vlatFsNivPq1N/r9u8xNrm4TdIFy5cCO8nJy32c1VlpOA6xP7fA2sN\nuw+k1rPYprHoSBUUFBQUFBQUfI44N0ZqY2PDer1eOIHi1NbpdCqaTEdHRwtJbc2Od6dgOPg07p2v\nO51OYBU4nB8MDkLO8Ryz/Bw7OEW32+0KixJzOgVNjrbdvXs3MDRge9rtdmBAcCJ99913Q9tYTwi/\nQSeKd8+sucWh9YrxUyHu6vTof4vlP0KbmP3y1K8yJcUSKadMToo5Q502NjZCXZSjPfpyNBotqH6b\nLc4XjA2zgPw8JeMA9orZCt9XPE/w/EuXLlXM2Upu4uDgILA1t2/fDs9nZhNA/2Hs2eE6dRrvdrvh\nHmZxFdvKrI3Zcd/7EyG3F3N2b28vOwcdM1ZmVUYqZXLkcVAO1J71nEwmFVaa+43LS60ZdRpTyqSI\neYn+4rxgSu6D32WsS5x/MfVMJbGQOo2nHI1RL64n38OOwMoU5x2g+TrFLnICYGZvchgmxXrlIqbX\n5FXAuc7KvJly9Oe/8/2Asgqw60lOG1BHM61YP5/Pw/vAeRi9Iza3E21nkzvDz6nZbCbzUuaODdZD\n1s9TARmqPKVPqAKv6lAYqYKCgoKCgoKCFXFujNSDBw8WbOgcuorTPxgEKGt7eCfn1DUe2L3ihMtl\nsMhhSkAP+cGazWZgxyBbEPPdgFL5s88+a2bHKtY4WWBnvbW1FU6T8E9gBkC1CSdX9rlhKEVm5XDI\npxwfGu4dewHvH8InG2Yk0JaUPxwrkTOU4CWH2fvfgIODg4W8Zh6eSeQyRqNRYE1YxBT3cLt9DjW+\nh9k+tE35kAE4nfl6si+T2THLiPnG/aJOpJ6RYn8YxTyyfIgfD35H1akYfbGzsyPZMW4T4BkuZjCZ\nOcM6EXO4Vn5EynHfn6jZj4Tr5fMb8jvDJ1vUVfmlsL+eElD049XtdsP7rE7qKIPbyu+Z9+tSId38\nTMUCLStKqHxpOMReOfMqR+zUdYppUs7BuVIMuU7puVB+Ysw0KT8xVc+YunzKGlDH3qigCcDPF4+U\nry8/37OYsfr561jkmOd7SqqD/6bqV+fEH/tt2WCmcG3tFU8Io9HINjc3K8lDOaEwDywaBSftvb29\nsGHIjWIBeEJiwWq32+FjjQ0Sm+xYhfnGjRsLz93d3a0szLFNBzZSTL+jHcpUxKbC1MuOwY5FJLIG\nUUqzi/Hcc8+Zmdlbb71lZpqqnU6nYROJPuIUHBy9501n/DFHnZSWkdIC43u47z8SOMUAACAASURB\nVNFONo2gfvxRZ7OS2bEjvDczj0ajUB5/UDn5qNliJApMt1wOm2dwj9LmAo6OjirO19xO4PLly2Ej\nxe9AjuMmX8MbYP/x6vV6lc2eMtNye/0YxK7jzQTmBkcX4l354IMPKm2IHZD4I26mTR1qY8EbxtQi\nzdS/MjMrcCSiMgv6DwuXxRt0/M5mMu/Mb1aNcuboOYb/uCozEuuw8XV1JjNcl3JeVubb1OZKbSbU\nYTwXp9k8qU2T2WLCZv935UReVwc+7PhNV2xT7+sQM/fhMKS+typtVCpogr8rPHf8u97v90OZnNge\n9/C74jf4ygTc6/WWTsFWZ07FM3wS7hSKaa+goKCgoKCgYEXUMlJ/9Ed/ZP/4j/9oV69etf/5n/8x\ns2MTzf/7f//P3n33Xbt9+7b93d/9XQjD/vM//3P767/+a2u1WvYXf/EX9tu//duy3MuXL1u73baP\nPvrIzE52191uN7AFOG3fuHHDnnnmGTM72RW/+uqrUtUbjIB3TmeoXeijR4/CPTgJX7p0Kex2wWps\nb28HtuCdd94xs8WQ+DpAPwr3fvLJJ4Ht4PBNsACo/+bmZuUEoXKy8SmZTUSsRK2oVVUW9KyYbgWT\ngjpMJpPKaZzNaSr/Ho8157Az02aVyWRSYR2Z3VEULMbo8PDQnnrqKTMze/nll8PffXn8XDZLsixD\nDL1eLzyb+9HPMz5Bct/6Ofrpp58uMFtmmkn85JNPsh0jUwmKuY24zrNuOfAyEzGmGO+S0r7hezAO\nKTOimWYxlCmb2U/fLk4ArQIuWIEf7E9KY4qZK5UrTumDpcLL1fWxpMV1Ughm2nE7Jt2RE4auTIXM\noiizIbMLfoxiZjxfh/l8Ls2WnuE6renOj03K7MR/X8bxHfAyIur5ZsfvGd41fLsU+8R9BLRarco8\nZvMcr4E+V6ma29zn/HyU4zNExNqMe9mhPdV/LIPDzJFKtO7/nTsnzkT+4A//8A/t+9///sJv3/3u\nd+23fuu37PXXX7dvf/vb9t3vftfMzF555RX727/9W3vllVfs+9//vv3xH/9xdgRBQUFBQUFBQcEv\nGmoZqd/8zd8M7AnwD//wD/bDH/7QzMz+4A/+wL71rW/Zd7/7Xfv7v/97+/3f/33rdDp2+/Zte+GF\nF+w//uM/7Nd//dcr5Y5GI9vd3a3YVQeDQUXU7u7du+E0yv4aXrGchQf5lJ9ytGPgefDJuHPnTmAG\nIIy5sbER6oJTgLLnKly9ejXUBaeB+Xwe2BOwN8oB+fDwMPjLKGdkhVj+OC/YWeeQr3bz7HOldvaQ\nW2CVdu/PweyDCulGn45Go8oYst+U8glDH7bb7Qqb9MILL9gbb7xhZmm16Ha7XQk5Z7AflvL78Q7o\n4/G44pvF5bJfFPzR4I939+7d0AdeHd3sWOXe7FjNXjFCqBfGRfX94eFhYBzZ586DfX34tIh74GPI\n8hEMlTMQYDbl3r17ZnbCMo9GowVRXV9/LpPZpZSPFDNvKoeev5d9ppR6Opfh71USFoeHh5JV5ICM\nWP1YxoGRygWo+oolFJZ1MlfgNUL5Pvl3Oebn5IMhFIsWE4xM+Vydpk2qv5VopmKkYg75/hnqfVB+\nOqPRKLzHqg4ppffZbLYgOWR2vF759Y7FV5VkBs8hJYngg4SazaYsL+XnzOUqWQPFQgMsR6Gc/lPZ\nGJbBSs7m9+7dC+ava9euhQXvzp07C5ump59+2j788ENZRqfTsbW1tbCwo/KPHz+uOODu7OyEdBus\nCYPJheebVZ1Dr169Ghb9mK5FDLPZLJSHj1y/37e3337bzBZNEzkv6ZUrV+zf//3fzcyi/ZKDOsV1\nBX5xlWaTv46db9HnzzzzTEjNAShNjn6/L9PcYFz5Q8+bJUCZWPzkZmdn/ohh04Rxu3Xrlv3kJz9Z\nuPeNN94I9/OL6TcgrVarEhm4vr5e2QiqFBcM7iNsoFAu38cRpCgbCxH3Ad4ZHj9+B3CvivhS85QV\n57Fx4mAI/+Fj53qUx9pS2GCqOcB9gE0lb4rUh5zfW//hiJmdUs7fMedrzCMVDcrX4Bk811KbFwVO\nH6NMov7jwPOfN+be7OLrEGujUidXyX79v1PwH0h1KEq9J6gD15PB9yoHeV7jUmZNX99loDZ6nHWD\n9aP8ddy3PjE2bzD4v2z2zY065ChRf52qA78reHextrBjvNLGUuZZ7nvvfqPM4CqVlNrQsEkRaxt/\nQ3zgGpdXp6WmflvGLHxqZ/NYqKmvTEFBQUFBQUHB/zWsxEhdu3bNPvroI7t+/brdvXs3sEVPPfWU\nvf/+++G6Dz74IDj6euzt7dlsNgt59HAqvnjxYtgh8+kEDt18evUOdIPBYCH/Ga5ZloligH1A6Pxk\nMklq46TwwQcfSKfuzwOK/mSTqN/wKrZqZ2cnMFIsz4BTBxwK+XSszK6cl9A7S5pVZQMYOIk8evRI\nnhjApHhtLrMTk9ODBw8qprhms1kxV7GcgqKUGalcYiiDQ9i5XG8W4lORcsjmv/uk0Ep5mxkONgcC\naBtrkOFU1+12K3NBsT08r1hhWAH1T5mRYhQ7J5SuK0OVaaa1h5rNZphbPBc9S6lO2zzmKvCB56nX\n8+K28G/eVYATi3OQBSvVA54tZLajTuk5l6VR7E6uE693LGfNrZS6N7MeXIYyv5/mAK/uVW3yfToe\nj6V5SbE3uX3PfZrj9G9WNXHFnqH62q+5sTx4ytXCs6LMrCsotqjORQa/8/qk7kkFgqRkKBQzaGb2\np3/6p9F2mK3ISP3u7/6ufe973zMzs+9973v2e7/3e+H3v/mbv7HRaGRvv/22/e///q/92q/9mixj\nfX09JBlWGkYFBQUFBQUFBeeNuo1U7Q7m93//9+2HP/yh3b9/327dumV/9md/Zn/yJ39i3/nOd+yv\n/uqvgvyBmdlXv/pV+853vmNf/epXrd1u21/+5V9GTwZwRPX211arFSQCwCSx/wcj5QgKQF5hVXg/\nrGXFPxlnZeZEnW7evFmRDWD7MDul885b7daVr4X3sbl9+7b97Gc/W/jt4sWL4WTODt54Huql/Bba\n7XZlzGICa5gTSvKC2+FPacwAQqiw2WxWnsvOiBwC7H2ZuG5gg1IOxnUYj8eB9YDvE7NQas5w3VEf\n78TO/1Y5tBgYy6tXr1YYqc3NzaTgHasiY76kcv2xbw4LtLLvo9lxu1OSF+iXulMvs0WpEyv7QwJc\nh7rcc6gPM43e16/RaFTYApa1UI7R/F7607MKM1eIzclVna9j/jqKGVC+TLgX/aMcrlU9lW+R8tfx\nf4+VG4PyJ0r5V+FZdd+Gs5Ji4PI4UMBs0c+tjqnB+oW/j8fjbNbLzym2OCi/V36ulzpotVoyswa3\nM1aXWJ+roITU2sxiwquMT2N+VqO6zEO/oH5TfrFsNBqVD1S73Q5RRCk9J4ULFy5UIg3b7XYoBx9Q\nNr+wA6z/cKtNhUcq2oTNW8okgd/4hfMTnZ2MleMkq+z6xV6Zb3Z2dkJ/MM3MZjm+n59rduIsiedy\nH9VFb/p0IGZV046qM3/M8dzJZBJMnZgfSjOKNznYiEyn0zAOUPK/f/++1ENS8Alv+Td2IgUwzpcu\nXaocWra2tirmQF7A8X6Mx+NgVlXRengGpwVSzuvA5ubmwsbcTGtDsXmLP3hq8VXjrz7wdQlUc6OA\nPWJRQrkfL4DXKb9mtdvtvJQW7XYlYppNbCkoJ+fBYFD5cMci3LyOFEc9MlS/qE2kipSLlbEqznoT\nlHoG/79qk9p4p+Ykl8MHQ29mjpkPfRYDtdlQm+tOpxPWHV5DfF9y1gt+1/2GazabBZcSXsdUm3l9\nMFvcJPI3KZVSiq9HBGps/IuyeUFBQUFBQUHBivg/5ZzUaDQqYe2NRqOSmDa2q1RqqOwobHZsTgNL\nUSdh4JXIP/vss2CuhHzExsZGRR/o2WefrdSl2+2Gnfcqzu7YybMukEriit06nxKY1cHfUee9vb3K\nSUqZDJkdAYuhNIp2d3cruk+9Xq+SPyzG7oBNYAVdXy82EfHpBEwUn4o8dazmTozR8/fyKcs7sZvV\nq/B7h/YY+6CYqpQDf0rF/OjoqKIszn3Azu6YY5gvSl+LT7c85zwjxTnoFLPB5hQ234EtVgEm6uSq\nxpPzfSnTkNLQAXjdSelI8TxgrRuz4/H1c0GNNScFBiaTidQ088wVm1jrFNCV7pNHLJQ8ZapjpiGH\nRavDk2SNPg/DjTIpAjH2U5nvUmZI7ueUUzqXkTIbq3uB6XQqg1v8tWqNUfUzq34z+N3n9QJzWpn+\nUtIniuHKYZ4LI1VQUFBQUFBQsCLOzUeqTrAuBvidbG9vV4THptNp8KtQjESqvNg9eAb8P2azWXbZ\n169fN7OTXXbufYw6fwwvRtbr9UKbBoOBvfbaawvtmE6n0t/I+4UwIwV0u91KaDXXgU9UHE7q6//c\nc8+Zmdlbb70VfmPbd0ptXOVsAm7cuFFh6zikX0HZy9kvyjN5uWJ/7IAMPwFWDlb3qveB+8U/t9fr\nLTiKpsqF/xf6lFWn8dx+vy9Pclx/tA1ADsz33nsvzHeMEeYeo91uVxyplYMp+xNxPVN5/JRTsHKC\nVj40sbntwSH4QK4EgwqtjrGKilXyYP/Es4DqlzqlZz+W/JvyuVH3fh6IBR2kruV5t4rT+pMC93ku\nk+d9B5m1AWJ9pAItUA4zqzl+U2bVbw2/A6mgDl57lfN6XV/k+pj668FSpfYs52baW3VC4gOauylp\nNpth44ABnEwmQfvq+eefNzOzH//4x/J+DA42aDk0JwATFhyGL126ZG+++WayrmbH5glMKJgoOGkx\nPjy7u7sVNebDw0OZYobVk/3i2+12F8wZ/Az+zSzPoZM3YWrRUs7I+EDu7++HuiKKjZ35+WXxzuF1\nMhrcf74vOWEvq2f7vppOp9mK4R6xj5LSMsF12MCtr6+HuuC64XAodat8nThKEX22sbER2gtT3HQ6\nlRsUjA0nOfV6LmYn48CHE4/JZBLmLN/L6uoAxpP1x/xGyfdpKmIJUA7oSll/Pp9XHF4nk0klWTaD\nx9LPMd5cc1CCOmxgnUHfs7MsK6rj32wuy9Hp8nX1/aL6jz9yft6x3pAqr25z6q9b9aCdQu47quqS\nY955Ek7uKbCrgO/7GHxksvobg6OZ1caSv4d+XVQBLewuwa4vdfpraKNPcXV4eJhMDePN5lw/3048\nIydoovK82isKCgoKCgoKCgokvtDO5ss6D7ZarXCCw8m70+mEHSgnikXZYEdiWlUAl5sjd3Dx4sWF\nky3qkqLsORwc9cJvjx49qjy33++H3TjKa7fbC5oYHuvr6zL/kT9d871cf5wEYOpUfaEcmlutVqgX\nsw43b940M80w4sTCz+DTAfpGhdjiWWzWY1MSM1Fmi6d3jPVkMqmcSljtPJdSVrnb1OmenW89xZ0y\nT3L9+PTsqWyzE9ao0+kEUxwSOO/s7NjHH3+8UK7SkWK1czbpYF6BRe10OlKtXTEWOM1ibjCzwm1U\n5k2ebyk2hNcT37/MbPF4plidFDtqVjVZNhqNBadWs8UTv7/WTJswuVyvYbS1tZVUlldO5HiX2UFf\nsaMxZ2QzvV74f3uklObPmtFRrGWd03yK1VSoq7NiNk7bzlT4Pj/Da5rFTGf+/VHrEwc5qOCalOM4\nvwMcBKbqo95bfAuYOfTuLY1GY8GFwdeTofID+sCrHNN9YaQKCgoKCgoKClbEF4aRAqvQ6/XCDlD5\n+gCNRiP4ObFtFqcx7Eh3d3cl+6MkBGB/xY50d3c3yBR8+ctfNjOzV199Nas9zWZTOtt6cTOFOnaM\nnZix00YfIPzbTJ9mO51O5XSihA7NqpIJR0dH4bdlcwayDAXXBQ7vvOuHT5kaf/jfPH78uNJ2FV6+\ntrYWylH+N8rRGfPgk08+kT4tAOafZ3E8WH0+xXDxyc+L7rEwnprPfLLyJ0jO0wZw/j0g5k/EdUWd\nwNqxer7365pOp1G/Cy5vMplkOU3zyRp9NhwOF/wfUixhysF7NptVpFNGo1FgmtCOo6MjOQ6ov2K9\n+PSe6xzsMxYwM8QirZ59UOHmZlWRVu4DZoNRZxUIwGPp51OMVUhhWUFThVgwwVk4uS/LFsV8pHL8\n9lbBYDAIfY6xjPn4qDVDzXcPlXVAlcXMqlpTWZYEdcW3ZDgcVmSGmJEGw9VoNML8VWOdUpZnx3wl\nK4H3jefxMv7Q5xa11263bTAYVDSDzE46hF9wLB5w4DY7WbzQufv7+6eapHB4/frXv25mxxFJMH/8\n8z//s5mZvfTSS+H6F198MdQJCxU6/9NPP7VXXnll5boAoN03NzfDBwobAlY2RgoV/ghPJpNwLb/k\n3ok7pkrrk/hGVV2d46nSkWJdIF5w0W+YB48fPw6bpfv371fqzKYfvIjsBI2XBf22u7ubdOZmWjhF\nTTNSlC8vYvg3bwxZRRhlpT5AeNZsNqvQ0LF7lzU5YlPU7/cr75TZyRhydB8OLIjA5END6vl1UWBs\n7lGbVx+1xx9S/oioduZuYpTmWh1SSVL5N6xf+O3g4GBhjFFfr3bPTu5cfkrVGX/b3t4ObclRco6B\nzZJeCypmojxL1AW9pJyCY3VTiWxz63JeEXyq7YBKmdRqtSruBas4xvM8SWmo8W9+reR5okz7GI/D\nw8Pkd4Xh03jxe1G3GUrVj9dytDn6DUw+paCgoKCgoKCgIIovTK493n3i1Imd5vr6etglgrpWpomz\nxo0bN8JJLkaZmx3XF9QlGJY7d+6cSYJj7NRHo1HoD/w2n88rJhG/a/enl+vXr4dEznxy9Tvzfr8v\nncaBVP4zrgOHtasccDiho5zZbFbpN9zn7/Uh83waY5NcKjyW66o0ilAO/qZCZxUzwGYopqtztIrY\n7MKMnlIJRzmqjao9KdXrtbW1LBaG63fr1i0zM3v//ffltcrxNeXozfAhzEq1m5lTPikr5XA2FXhn\n+bp8k8BkMlnIiRi7Xul01SGlGcd9pJy0+V7PIHIdWc4B18WCIPxviklgc2lK+XxZxNinlMO2+ltK\nC0ppgqm2qfacByPlTXa9Xi+827msK78fnvVkORVmeTFPOHDEJ5weDofBDI53ixPQ1wUd+LWK52ed\nRp6qH4Bvx2QyqVhYcmUt8JzCSBUUFBQUFBQUPAGcGyN1XvblgoKCgoKCgoJl8IVUNvc07LJ0W7vd\nrjjfcsQSaMbJZBJNQcHPZefglK6LmaYpQXGm0ptwObmOnUqWf5lNaIr2VvXy98WuO81GWKVlOQuo\niZ67aa9z+l4WbGZSqEsB4s3bKqqt3W6H61JRb7E0JLnw6vnKvLm+vh6c9GF+VXWKjUfKNMGmbPVe\nqXc41Y75vJqY9LRIvRcwX3v9ttPi+vXroY8xJmpsFNbX18PcQdCJ6pOtra2kW0Pu+pJC7ntbF6jA\ncyhljlwWp1kbVlmTYsEs/nunVNhjdVDX4XcO4ML7p4JNOOrNR/KyCZzXtGWd+FHOYDBIzruUGZy1\nqs7q+1JX/2LaKygoKCgoKChYEV8YZ/O66/w9aieqQj9VeRziiFMZh06r0Oplk83y39WJIPdUvgoz\nxc9bJl+Qf56vT+wk5euTywLdvn07nDo+/fTTrPp9EZAT+lvHSAHNZrPCTvFcQxADS1kwfK614XBY\nqUu32w2aVx988EFWGxVY/0kxaXDsVHn4GP40i/DiGNjh38tHMFSf8/9z4MZZMFJKzVyBdZ88VmFe\nIAUym83CnMAcarVaUdkOxvr6eni/lSYcytvc3AxMmlJ+r3P0VurUqesU6hK3+zqPx+OVmXOuS+q5\np2V5+Xlm9TICXK/cPmek9MGAtbW1cN0y0h8A5jnuVe9/LC+m7+N2u13RBxyPx9lK7h7NZlN+j1Pl\n8fjXJS0ujFRBQUFBQUFBwYr4wiib827RszCxE6vfuSs2am1trZKPinehamee60uRYmBUZnguz6s7\nM/hEykJhQJ293p+olgEratfl6vLPi/1/DB988EFgXOpOnbmnUnWfv2cZsVGP2Ik1Z36q32azWTi5\nQc5hNBqF35D/7Kmnngrzl096rHwda+9oNApM1Le//W0zM/vBD36QrKdqbx0rin+rsHoOsfblsb+j\nkofw4o/LgMtJ5YzjcHCGV3BnVjBX3gRlKF8bFoKtg8+KwHVBm/r9fhYjNZ/Pk2KFHP4OLOsnlFoj\nYmDfJjXuqXUA7el2u6H+nDUAf08J6qq5gTLN9DckR4gyhhiz6sFSAgpsbfGK5QcHBwt+xGaaUePy\nwS43m83wHnOfq/4H46pUwlVd1W885rhXSbyw3ySuA0PcaDTCu6HmEPszq3FS610dzt20pz72p6Hd\nU6rEnrLj35TejEKn06m8pKxRwlApInLQbDZDebyB4qS2dfcDPtVILurMC0CuGe+0kZq5OinLKhXX\nbaRytUxyTHucFJTrqT6u+DsWsXa7HeY26nJ4eCjNQmojw383O1YkR7Ji9Xf0d+wj79WEuX+wIex2\nuyEFEHDlypWQkJuBD1VKZ0u9C/5jnWtO9QeabrcrddOuXLliZid9mXKAjQF9denSpZD+iR34/Tit\nYjZCZoNGoxE236dZR9lknHJgf1LO5maLplg8y6/RdUFAQK7D+GnMjPyMXE2w3IPcKu4amGNbW1th\nbvNmqS7gxSw+F9E+3rSpFGA5hxNex/i95rRxZscm6NxvCOYvJ2n2a5bZ4jc81saibF5QUFBQUFBQ\n8ARx7qa9nHDWbrdb2TlynjneJaac5FKUXUw52DMcMWbCn+R597qswjn3Sa5SLecsVMmIl8VpmZzU\nPeoZdacjr8wca5tnGlQS39x6zufz7D7M6Qees6xOjOfh1Kj6YDKZVMLn2SzEQRG4X+UMrOsD/B2n\nwU6nI98pvENwYn/w4EHoK1aaB1OCYIKDgwMpB+ATUDcajQpDNJvNsh28c9rI7agzKXNd/LpT16do\nG7+jrDDuwetdLmAi//jjj0Ofs3M7m1ZjaDablbbFrgerwMhZExQzNJ/PJSOpWGWVQQBzEH2mAlfU\nms8K9ylTHNcZOTwfPnxYMffxvak5wX9T7iFqvVrFSR/1+eyzz8I798wzz5jZMbvj14nDw8PKvIuZ\nMH1AiWLH2Omf24H+8rlNcQ/qjncO/93a2lpIiJ6CMoP6tnW73coaOZ/PK245Od+NwkgVFBQUFBQU\nFKyIc2ekUsAOMmajTZ0m1a4eu+NOp7OQ2y12HTu8qbKxc+XTky/XI8cJse6kwScDXJfKi7cKlD9C\nrA655Xk2ie3vuSfw1HXqVDedTitMY90JQznJ1jnZ5/aHH/fDw8MFXwaUoXxx/L3K+dvspH0p59T3\n33/fLl26ZGYW/JiYlUVI/M2bN0PZOA3yvIO/04ULF8Lf8dzRaFSRbNjf3w++Xvwu+Dp3Op2Kv+N8\nPg/zHCdsJQmxCgaDgWRflJSEd8TOZTofPHgQmCPcq97bXAa70WhUHIsPDw8Da8fICTxhxq+OkYIE\nw2mQ8jE00/3g2zGbzQKzyeyseg89662EIxV6vV5gQPEs9mPluqV8M1NrBK+3SvYn6psj+kMB9eb6\ng6XE+1Xnc8XvgP/exb4XXj5IMZJHR0eBGeL8qf663d3dcB3Whr29vUq/8bjim7O2tlZ55zh/Lffv\nKpacL/RGKoXYxz3lpY+OrFuo6qLVfBSgQqfTqWhVDYfDiukp9jH2arKsfaUSip6lKjeQE7EYg1oA\nvHnuNOUph2HWAuMIndQHr87JMHeDtKwjPZeL+mHzsr6+Lk0xqY1gzERktjhPOckodF+4DP+MO3fu\nhLmIjddoNAobPVz38OHD0A5eDJWZ0psKm81muAcLPWvG8AcQz8MG6rR6Puij9fX14KQN9Pt9+WHM\nCR5R9drb27Pnn3/ezE42r2rcYto2fo5x1BHfqzY/OZF8eE6sDkC73a481yzvHeD2Yh0Yj8eVOtcd\nJvmAg01OXfQcR/D567g89fEHUgmtOZmv6j9+Xsp8x87karOpwJuJnG/BbDaTm3j1DF/X2HcvZ32d\nzWbym4X5if8OBoOFZOX4r7+O3wH+L/6Oe/f29kLbsAk7ODgIdcbBoN/vB9Mw2plzCCmmvYKCgoKC\ngoKCFfELw0jlUpgA61KpHXpOOH2n05EaFinHaMUCpBxV+aTB9fS7/hgDp+QN/IlqFdSF6J6GoVGn\nOqWenmKDuH4pU9wquQrZFHgWCtgKilkDRqNRUm5B/abahDnEYchKL4WR0ueB8/LVq1fDeIENYEf6\nOo0d9UwwXGCmdnd3K0rE7XY7qS1zGigWp9VqyVN7zjNj7IHX/YrNL5gAU3nwJpNJWG+4Tmru5EqU\n5DBSUHpeBblrCLtu1JUD8NqrAotyctWp9/Hx48eVdX19fT2Y4u/evRuuBdvBavGKgfHPYOXt2HW+\nzWxi98/ie/k7lnrnmS3iv+EeVkf3DvZ1ayWbjBXD41nqumASfn8w39m0y7p6ZsfzGe3gZ+E3jNej\nR4/CGHpGLFmf2isKCgoKCgoKCgokvpCMFOe/AxT74EOIlVwB/4Yd9Xg8rjA5aqc+Ho/DPXBym81m\nFbao3W6Hv6tQcWWTV6wHi5ylwpRTDNFZsSjLsE/Lgk8TOKmwc3Uui6H+DSh/BPU3xiqSDqfFbDar\nhOC2Wq1sVmFZvxTGnTt3zMzsxRdfNDOzl156KfyNmUJ/ov7444+lj4y/l+un/M6YwfIMAoem1zkF\nr6LgD+BdPjw8DH5pzLZ5Vmw6nWb5oCgmtNFo2Icffmhmi4EFqfvr3mXv99ntdlf2l+Rca2Ddm81m\nZb1bRo0d4Hcr56Qfa0OqP3hN9/3a6XRCm8B2xPwKFWvo2zsajWQf+PU/VwiU678KlPo7MB6PpRUF\n31n0lXpvFbM6nU6DPAqzQV7wejqdVt5rnp/sBwwGFnV4+PBhUiiU66mYOmCVb+EqeQa/kBup3JdU\n6ZEAbN7yTnwxxVUAH7bRaBSegUX26Ogo/BsTcTAYBMViBaVbo/7mtTk8W6LZwAAAIABJREFUlDnA\nf9BWfRmVMvdpy4iBtZRSDpR1UT258BvVmPlQOUb6D+mTMPWx6crsePxT5uM6s5Df+PCcwMaV1YTf\nf/99MzO7du3awsYCz1Lvo5pn/nDS6/Vkegn/PvLHBovYpUuXKhGdKlIq1Q/LgAM81PuHv+W+X4PB\nIDjOc7AJUGcuyFFQZ1V0fPw3NjaSZhHMscFgIJ/Bax+Xy2g2mxVNszqc5uO2bLStWh9Z0yilqXV0\ndBQ+6gg+UOuF2UnAQ0p3LuZakFpvY+1NHV4A/t7VRUByIJNZWuGcwRs1RRKodwR9rYJnNjY2Kk7k\nue4XZnnfndygFO5jNZ9iKKa9goKCgoKCgoIV8YVkpICULgU7IzJrkGJUeOftT5iz2SzQlcrBlE95\nfnedq2WTqzPSaDQWGDBcrxwUlclzFfbmLMxZuRIG3A52ykQZqzi3m9WbNZVJIddp/kma+zDf2Hzs\ntcrMTuqNuZsbcMGmGMxjPqFhDHZ3dytOmpcvXw7sA96VOkdQ/H1rayuccnMlG4AHDx7Yzs7OQv1i\nARd1+jcpcF/myH3UheUDm5ubod8U85EqI1dHjseQfwMDlpI8UCZbdjZO5TxkKYHPA7nvXoolNauy\nMr1eb0GCAdeAiUpJFGxsbFQSGPM3SQUzsZk7tf6flmFFOfiezedz+U3zGop1lhq+z2dUWFtbqwS0\ndLvdpHUJz42xr6lMGAy/ZrVarYrjPucq9d8cxqoWncJIFRQUFBQUFBSsiMb88/SsxUNXOEE+KUdg\nnMAajUatarZ/fl2dcuuc8sNhvx5fHtvDY0g5DX7eUH4/KUFRdSJkm7xqm+8P5cwfE4XzyJVi8Pfg\nGav2uQqF3tjYqPilsEN2rBzURfW9+k2dpL0Plxo/9f6sra2FUyAEKBXbkho/My0potrJeRVz1wk+\nzXrH2GazGXzK2AE5x4fzueees7feemvhN24n96Wf5zyuqm9wfbfbDeXcvHnTzE6U5s2OHXb5Pt9u\nz7a2Wq0gP4FTe4w58fcuM9dPk/tyVX+YXCZRMcCqLs8//3z4O3wMc9mMVqtV6T81Ho1GIxlIU9fn\naAuLQ6eCmFL1NVsUCo2xw1w/rgO/W5yP1izfN6vT6SSDMJS1Cmg2m4Ghg7Vnd3dXzhPAW0RSc+gL\nbdqrW2BzwJNImcGUzggGmhfMVMQUvwS8SKh7OCkrnuHrxZsmpS3C9fXPZTwpDaRVoSIlmQbGNV4r\nyExvuFJKxkoPhxeCnKjNOtPiWWzu1cupNmEq2e9wOExuCNm0pxYr9RvK4cUT9VNq8Tx+fsNzdHRU\nod1jm7ocPazYQrbsPOcsAf6/vlwsvvgA5Zq01HikIklRL3+duofNb1Cbxwbo9ddfD/2rnNy5bR6N\nRiOYWVJz+rSHWfVsv7nKDQhhsLK1Xz85HQiPIfoIzz86Oqo4dfPGB337xhtvhE12av6pOZsbkbhM\nP6v3h82+aDv+y1kg+Ln4PimTPG+A/GFiMBhU3g0eL1Uvrrs/APF3m1P6YA3EdUoTijXteOMFlwPc\n02w2K++h0hbL2SAX015BQUFBQUFBwYr4QjNSqROcOrXP5/PKqa6ONuSTEP6tcpOlQmt5x6p2r7iu\n1+sFpz9lIuT/T1GXSkFamb9ywmXPEnUMTY6jvTqNM4OkyuNxyDk9LOvEnlPOquXlmiuU/pJqr2KL\nNjc3k6aaOkVoVRfPKo1Go4V5bnbMhLDjuZl2LJ3P55K58uA1IFa/3P7PZRNVvr8c1AWgKNOpYmK5\nnt7MyKwCn8xRR7BUMXMO2gSogJbPCylzlWKpADbdK7kPBszMWN9Ho1EYh2vXrpnZokq5si5wX6qg\nC2Z8lsFZ5UpVVhzWaeO/q75G+1ij0fcrMzno+4ODg/DeA5yMHHONsxNwwmPffu5nldwcf+f1jt/R\nnO+Fmu+z2axielS5YT0KI1VQUFBQUFBQsCK+0IwUwAxNnd9Uzs6+3W6HXSY762KHzCdq70vDjqAc\nWunr12w2K+GgfILhE7g/idY5eKKenIVdMQmft4N5zKnVTPuMsVMo28iVOr0H90dqzFWI+Gl9PM46\n8KHO/87suP/AcoBpUKzHdDoNzAXm297eXkVOg4ETunJeV5ISPEb+2WZW8Sviupgt+lrhPu+bpTCd\nTissitny83yZ0z/qCtauTvoBqBOs5FO0Z7mY8WPmyvv/8W+qXsqfi/1Ac/zEPg/kBnUoRkpZLWLt\nYGd+/BffAWai/PxoNBph/HFvLGQ/Z61R34vTMlKKxVTfSuWQrSwrPHd8vzJrw32N9121HWvMbDar\nzMtms1mRdOHAEa6Xn+e8DuE94jVLsU+8tvpnzOdVUdUcnNtGyr8UatC5E3KiRHhy8IKBDmbnOv/B\n4PqkdF+Ojo4qE2UymVQGZDqdho+XpzJRB/6vagv3wWQyqURATCaTz1XPZRl4R0alKTKbzcICBZNn\nrpaJ+pizeRbPXUYzytedzW5chlr0c5Jgxw4BqUUX7RgMBmERURsoLgPXqYSdCjB5qP6Ltc3ryMzn\n8zCW2EQonTN+z5TJhg8nqi9zTWtnBe/wmgsVZcu6WsrEr6A+RtwvUOFG5Bgjlfg61wxuVg2QqdPc\nWlZ5n8dfuS1wub7M7e3tYNasi0jFe6M2DqnI0Pl8Hn6HCXAwGNhHH30k24d7YuDvRV30W050sb9X\nbUC8+bPdblfWz62trfB3fAObzWZ4/7GJPDg4SEYL17Xdg6OoeY3klGlcJ38voMyWHLHtfzvLg0Mx\n7RUUFBQUFBQUrIhzY6S86YIZJ7WjBpilUrpKOD2hjMPDw6xd/Xw+t2eeeSbcY3asyeI1NObzeXCc\nZcVn71Q3m80qO+hOpxPKY10aRVf7XbPS6VHodDqhPHb+O2tzVK5jOaBOerPZrOIAGjv5e1ZkMBiE\nPuSTRc4po05TJuWMrjR0uF4pzOdzeVL2813Vbzgc2lNPPWVmFhLfMnvHemOoC/cFJwM2WzTx4USv\nwsbn83kYI5Sxt7dXCQdXDp5KEVqZshX4bym2b5mksKsA72tu9gKA8+Bhvbhy5Yq9+eabC9epNl26\ndCloQDGUOjXMuJ9++mnlb2xmWiaUm8FmF5WgVsGvmWaL7hLKnAKk5oSqO9eF64m6grHjwIdYOWbH\n7CzeB24jykbbtre3g2bXKvMvlaUiFzFTZ530Bt9vdlL/3d3diqtIo9EI/Yb/bmxshHmHOc6mOLge\nbG1thfdGzWcGxgvveixzCcDWHqXCryxEKYCR5Hvx28WLF5P3mhVGqqCgoKCgoKBgZZyrj5TZIuNi\npjNLq5N1DMqOmlLNhmPswcGBvffee2ZmgZmK3YtQcs4SnqPSyiclgMPaeUftWbSY06KvH1+nxMpy\nFX7rsIq/kRpP5LWK3WOm/aa2trakg62/N9ZeX5fc7ODsC6RkMlLCmMw0Msvi/fqYWeUTHxgJnPiO\njo4kW+N/4z5IhfJygAQD8x257/jUziyA93fr9/sVFiX39K4cX/m5fN3ly5ezylwFq74r3I+of13b\nwTju7OwEJfgUmJVlxu80wo4KLBeQU6aaQ2ptYxbCB+YoqGdyQBCXj9/U+pKCWjPNTtr+zjvvmNmx\nr5R/b9l/kqHWd7+GxBg+nxPW/035KqaeC7DPLdclxSoDe3t78nvtrSgfffRRxT+Z6wKmu91uhzUm\n1y+Slf+9LxU7jPO6jH9zoBezrKgLgICCWGAB41xTxOTSmXWRejzZVDqQXHizx/Xr15MOhVeuXDGz\n4/QX6jkYOJTLLykPmP+4NhqNyoaQ1Wn5Pv8yc3kq2nGZjVRdBE3sbzH4MVEaILljePXq1bBIphSy\nY9oz3jynovti7ctpu3JUV5u1tbW10AdqwVB9wJFtyvSY+hjh3l/5lV+xl156qXJv6v3i+vl3ZX19\nveKAyjpHKPfKlSsLaUw8UhGJZicHH9TFBxPg7yln2DrUBQ7kbpo91MaS8c1vftPMzO7fvx8+2IA6\nTFy4cCGYHbzJsA69Xm+ldCEevIHDPFFmfD4Y4APqN8WMTqezoDMEpBLLcySZ1yVTddra2kp+JOs2\nSOp6v1blvlO8NvD8SyVOVg7UMaTmLMaj1WplbRrUOqbafunSpTBecEfgdZG1pVhZ3iw/g4ACK9Gr\nfuENlw+GURv+Vqtlw+Ew+e0spr2CgoKCgoKCghXxhU5arPLesLoz/r7syWptba3ikMvl4fSiukYx\nCE8//bTdu3dv4TeVLLfdbof659a57nSc6/S9iqZULuuUYoFUGUpNnmUSlPOgP4nGZBLUCc7XQZ1Y\nFAuVe8KMmVOQB40dgVMUN6CYy9j4exMBa6OkTnXb29uB/fnggw8qf3/66afN7Jie91pfMdVxOFXD\nsVSN0cbGRmg72qmYmu3t7UD3q/kCMFswn8+D+RH3MpYNJWecdbCGwvPPP29mZm+99VblOSpp9ebm\nZsg9xjpICp4BqWNjcpHLSKlciz4kn7G+vh7mBM83rAO4Rz2LGRNcv7a2FhhsOEpPp1P72te+ZmYn\nDOj7778fXAaYxU+9U/yOrjpPclhwz5ooqQCWEgC4fzmXXp30Qk59U/n36oC5e/nyZbtz585Cea1W\nKxkcAJwVs1oHMGmFkSooKCgoKCgoOGOcGyMF++6yfgYp8E6cVVuXPYFyCCbKxOmo1+tlO4/m+GnF\n/MTU6TmVbzDmZHgaRsqX55+Tqn8dEwUsyxKwn1hKuVnVifNgKd8ioM5hM4eBY/VfdsL2obxcT7Ap\n+/v7C1nL/TO8cjm3LeZXodqRIyL64osv2ssvv7xwr2JHuF4sHHnz5k0zs3DiNDs5FaO80WgkHfhV\nOwFcP5lMFvwclCI4UPc+pkQZU38zqyqy93q9irih2ckpnPsv9Q5gjAaDQYVB2traCuPKDJzqNzyX\nGcIU854LnvfLrtvsgwSfO2YmVUCI92lhR3W+z8vRTCaT8AyUu7+/H/6O37a3t8N4cZ+C2cK8Go/H\nWRkY6qDEN5ktV3Mjd00/DYuqHLdzwb5FagxzswPk+jmn8ngyvP/kMv1Sx0id20YqVqk6Z2h2VMe/\nV1k0FbDowxm2Ts0Yz9/c3AzUMBbcs6Ibub0pBzrAbxzPYiPFdWEqv64uHv5jtLOzU9EXYZNOrA5m\nx1Q9m/nMtDYT17FOTVhtrlIRf758s0VlZqXJBKQ2Mb1er6Krwh9wdqpN6dH4yD8P9VHnSNScugJs\nLsWCtb+/H/oDZsSHDx9WdIbYLJRyElc0Ps935eCfi8FgsBAh6bFs0mJWoldQ/Qysr6+HvkSfqw3c\nxYsXwzPQl6PRKJiKWeHeJzzOjYRWByXeNHKAxrKRY/wMtRlR885vXnq9XlZggWoHmwB5fHMPd6zd\nFru+bg3hduduzJZd0xXB4OsYe15dQBje9X6/X4nGPDw8DGsQ1urxeLxAVJgtzm02++Z873xdc65X\nh7A6FNNeQUFBQUFBQcETwrk6m7MTXyqMvw51Zg2PujBkgE17OL3n6pLEdIm8/gYrUfNzvRN07LTg\n6WAuS8kfrIK6k2bK1FV3uvOKwXWnSyWTkGJelGm3jvXMPZGmWAplZooxeqnnMYvnTTadTkc67Prx\nYCZHvR8vvviimdmCHALq1O125buSCkNnZWCMpxojDlHPcWiPlQOchpFaX1+v5Chk+YZUGLqCSgDN\neO6558zs2LHc4+LFi6Hs1Hqzvb0d2ERO5uwTLXO/8HilnJJTYPaJXSi8Q3ZdGDpD9a/6zbNUnMOz\n7r31itUxHTZup9kxc54aB17LU5pWCqrOpwkgin072K0hVbaqjw8OYNmAXMCsalZd4zkbR651idcn\n3yb+NgAxiwPWEzxX7SHwHS2MVEFBQUFBQUHBE8AXWv6A4ZkXdqQGYuHq2HXyThg7ZJxs5/N58pQN\ndDqdsLvH84+Ojlb2D0CZZmn/Cw5rV9mr8SzeoU8mkwoDkuuXtoxwp68DP4fZxxyWjeuK6+vC49Up\nqi4kOeWQXSdyV+drhb95Rornp8p5V+fThLJ5PvO4+3tZ+E7l3/NQLMr6+npgwlJCmniOf4Y/jSs/\np3a7Heqv/IoUa8BMnWKQ+N4U44s6M5uA+cS+G8v6VfR6PenMjb5EYAE74QP9fr8i4st1wfh3Op3A\nSOF6VrtGP29sbEihQ7QJ7YmxAcpnx8+306zp7A+DcsfjsVwfctjidrsd+igl+pkLJTOg1hqzah8y\ne1c3b1LrIrPPSjg4tw3MDPF3wtevbk1V9VP593J9ClE2+1Qty3oB6+vrMncrO8Hjb+hrXgeUP+IX\n1tncLF/ZPHZdymFvWY0M9YyLFy+GScGJXRVdjefyIPmJzhS7eiEBjk5ZxrFctcknfsxF3dikNo78\nu7qOX1KvQDyf66SbHsqZN6Ytpeq87OYqtgEF/N/5GWqDhL/1+/1KO+rS1dRFs/mFoNFoyKgogHXO\nUhpAvIFLbbw5FYOn7LvdbvhNjRXqORqNKvNPRQtymiSOYuWE3f5jz5sNb/Lg9irzBh9OAPUext5N\ntYH2aDabwRldpc5ARoWHDx9WzHhra2uVSL6tra3Qb9ynuCeV2FV9lFqtVugPzCeVropRF5nonb5V\nPzP4XfWaRiroRK1l6l2pO+ykEDPn5q4rqbWm1WpVvgm8/iiXhxSazWboL+5zf8iJBRukxhqEBNcV\nv/V6vTAX8Sye25wNxGuFKfKEVdF5LHMPxQpqHIqzeUFBQUFBQUHBE8IvrGmPVY7VrlPpm7Az5LL6\nRWx+4xM8nrGq3EGK1VgGMYfHlGPisjojp6krs0U8lqnw/RSUiSimb1R30jPTeeHq7k2BGSk+2fry\n+KTJZljPiii2VfVBnUk2ZT5kkxI71/rcaHXBBPy+KTOZuielm8bzISV1wWMIbZmDg4MKu1cXDl7n\ngLzsXFBO9XVQbAjqde3aNTM7yV9mdtKmfr8frgNj2W63A3PE64FvJzsl47derxfuYfV8P66cLSLV\np0rDjRmOHKkNRmxN8vpQZvmSNChTmR5TY9/tditmN6UqruqhWOgYM83rhK9Pp9PJDtwBuL3of77X\n9wdfp9TngRhzxfItsXuXgWIkcy0Ovk7z+YmeF8oYDoeFkSooKCgoKCgoeFL4wjBSKRuvOrXXla2E\nEVN+LjEWYhXhSTPNrMQcNxVSJzN2fAViTtNeVqLOT2tVBiYHvi9ZVFWdXFJ9r2Qy6mz3dW1bdp6k\n/q5C8ZfJC5WSC+D+U++Kn2NqbitFaF+O2fG4sM8TfsuZx8z8KGZq2aCDOjYoxgIqqPcLdcTfTiOq\nq6QuzPLWkUajIYMHwESh3I8//rhyLwcHsIO58o1TLKBScFeBOQC30fso1YlgqkCU3PWW64464946\nJiblYxgDf59y6resv6VZvXo+gPv7/X6lrdznPiiK/53bv8zGKdS1yTODn0dePM4qwPXza1bsu8Lf\nE7PjcTk4OEiuP2356+cENvekFvXYR9ZPlPn8RM6eF8i6xRdQC0uOwzUvBKrcXIc3jnpSNK+n2GN1\n8hslX5/URDrNBqrupfJOkuwoqsBmCPw7tcHkjz6/SD4yI9Z/ufMEUC8pwyuHs3N9nbkKCw5HkuIe\nb2pjTKfTyiaM+wD/rTNp89+VM3LKTMGHGa/MzXpNqs+4XPVOqcha1lDyGzZuuwo24N9yzPxs1uLx\nUibbnEjJGNhUZ3a8OUA5qY1Co9Go9FEsGk/pq6m65tR/Pp9X1vC69SV1kON/c58qp342Ofoy+Hk+\nSpFRt3ahbbluKeq6OpcBX3//bfJtVzpY3LZc86jSIOMDeM66GDss8KYaz/Lfw9x3T6HRqGpRDofD\nShCJWm+VqV25B2VpTq5U+4KCgoKCgoKCgvM37XFIslk9rbkKcBIBYiHeKqzUO7k3GlUlctxvlm+G\nAF0+Ho9rJQy4XF8nT+N6Z05/Go/VK1dZ2J+WYs7hKZOpYqQU+8RQwQYKqfKY7fBs5jLO5jnmT3Za\nxNweDodLh+WyqcqfjFjTLGW6idHzygyeC6/JEnunkGMvlT9RYX19PakBpBix+Xy+cLo2i7cptSak\nzJ87OzvhmamT6ipO6UCv16uwSsPhMPQlnq/6vNfr2Y0bN8zM7N69e5V6spnWt03Jh9QFFqhgFiVN\nwHNRrbM5jHNdnzKzmyNdwGbwlIvBk4BfW1UwTqwuqs/V+s+WCc8cLcMCoV/Z9KjmSaye3Ca+VuXI\nVCxlnWkc93IydNQv9T1uNpuVBOqx+VKczQsKCgoKCgoKnhDOnZHyYIEtVI0dxtWOVDmqs2+BbyKH\n/rINN9cRXLUn557YrtjngFInXXbSzK0f+zzkyh+k/L5QX7N63zHV58pXRYFPr/46pWidUuRVzq1c\nHp9icxgzVQ77pXGdfZ8rhfZY3kfl55R6BxRDw4rLPh8V+1zUBVek/M5SecbMqv5c3W7XLl68aGZm\nH330UeV6Rsr5VqlJMyNVx65hfUA7lVgq+zkBly5dsgcPHiTLXhVgUQaDQehLMHkqs4H/N+p869Yt\nMzPb3d01M7NPP/003MtMzbK+W2qdVewIwGw1swq+7jwX6wSVV2FPc8CWEe9zxVI7dX3Gyuxm+Wt1\nTOpAsXvcp778GKvo+382m1WClqbTaUVhfD6fL+WUf1osG5gRw/b2tpnZguxHKiMJM2IqEwIsFrHx\nPLeNFJw2vfNr3cd1WedQRU3zc1ahdNlxzqw+EkG9DLmmHUbKbBWbgKmFLrWhWSZSElDtTNU1tlFT\nG1r/gZzP57UpVQBfF160ltWtUVEdau4oh2ZWMedNAl56/I0XQugh4aPI2NraqvxetyDzfPdtV475\nuUrJ6lm8scFzh8OhPfXUU2Z2srl6+PBhpQzVjsFgED5yMX2d2AfdTJuo+W9+nVAm0Tr1/NMAG8zZ\nbBYCFNTYAEpJu9ls2s2bN83sZG6/88474e9QTI8l4U29D/wh8us1Z2NY5qAH5NxTpzHH6zsCPdCO\n8XicdBtJ6Vzl1MtsMS2USk3Cm8UUIaDmF9+D+nW73aXnoloz63TVfP1V1oDTgAOpVjGtel3H3Mjg\nWPACojqxPsGFppj2CgoKCgoKCgqeAL5wpj2zk9M6nxLUztfnaZtMJtlO0wCfspQjs6JEVXuWZZjU\naQztZidyNs2lTDoqQaoy7cV21SrnWMpBWZm6lKNgirVZxlFd/S1VdsrZlE/ydexYzniqk02MHQHr\n8Omnn5rZolP15cuXzczs/v37lftiJkBoC8Gx2CyPVcg9eeeaXRT4vQATwmaxb3zjG2Zm9pOf/ETW\n08+DyWQSmAb0hZ+vyrSHBMGK+cI71+l0wjjUJRFPsYRA7txhkx3MoKPRKOnIzmY6b3ZpNpt2/fp1\nMzsxX7755pvh75xYVullpcypWGvG47E07aXeR/Rpq9VKvnspM3On05EMO8Y61+ynshmwq0dqni/L\n3nDb1Xzid9XrtcXmH7OAKDulRK6sN51OpyIN0Gw2w/jnJlOvWxN8UIqS9lGoc4NRZSAYYzqdVt6L\ntbU1qYOGeuG9ePz4ceVvCJAojFRBQUFBQUFBwRPAuTFS8MFhFsbseBeey4SsitzTYi7qnM1TJ7Vc\nNisW6qqcTfm0qE5wuYxPrpJ2jpp4jFHzUE6psRxlqh2pEzVf7+9V7Yg5qvvxjDFSYEKWDf2/cOFC\nOFGllM0ZOI3xiUq1g+H7KuYz4JnaVUL7MWc3Nzcr/jm3b98OTJWqPytX46TMTCz71HGQCaCUrP2p\nvdfrSbkFNce80KpC3RrDfh2eiWDxVQWwEPP5XDrGwgcNjMbDhw8rzOza2lroQ56fqWAN1Y8pRirm\nbH4aqYEcf6NutxvmNNo4mUxWZlZzHMH52WaL649n+1OBK2bHAQ1mx35sKVHTRuNEfJXZE4x1bvty\npCLqUJelog6+H3L94ZQf48bGhmSsc1TW1Vjj2/WFdDaHc6LvLFXZ7e3tsMDi+pijpaL+lAnQ062n\nTS+yKmIbKdD8aGOsbin9jljUXo4jeCwKa1mkTK3cl2rDp5zNmY5OLVCMZR3K1WKYE71ntmhe8Kkr\nhsOh7A9fv8uXLwfzHtrNiWdTGAwG4UPHWkSpyLvcBN5c97rDgdlxv/hNR8xZG+YyXKcW0e3tbbkp\n5T5XJmplWvE6TTHTqZoL/rfY4ptaK9CXvV6vYp6JOUWjXzkyTPUTTHso7/DwMNSFy8ZGH/NFRcLy\nOsvP9R9NNrupOcZAcAVMoyo6Mhe8aQJOExBwVodstY7lrq0qPRP/2x/uUv/Gc33/qj5qt9thU6VS\nJ6mk5fxM/1xuZ6p+uVp6des7rltfXw/fT+wb2AzPgWv+26GiYxuNRlAOKKa9goKCgoKCgoIzxhfG\n2ZxPkl7qIHaS9MwGm10U26GYg9SJ2t/j/1534lxW8ZuZCbXjTzmJxxyLvVpuzLGzLkzULN5XqT6q\nM1uq3T+Qa3oE6pgSdppUcwfIZWi4Tr6u3KfMYOA3nPyU6vR0Og0h7Hfu3Al/h6M6TlnMDHhdNN/u\nVJjysu1VUMwAg5mpK1eumJnZJ598UrmO32/l5Ipy8Bu3l99/Pv2n2AmlR+Xbxc+Zz+dZDGe73U4y\npSyJgfJSzutmi0lUzeJq9nDsx/M/++wzaZYDI8Xq6aqeSrbEB5aw4jveZZ6fioHFWO/t7SXXHRUE\n9P+x92UxtmbXWevMp8ZbVXe+PfimY7uhnbgbMHFEAgkKAfGAEykKUh7yQMILeULwZonIeSDxWxSi\nICEBUp5IJCTEA6IZMxEbkBNb2N3BduKhp9u37zzUfKoOD8W36zvr//bwn6rqaif7k1q3+vzDnve/\n11rfWouhLA7KVOS1aJ1OM75SLMPBvIhpK32/sEWkNLI5zzEFrJWdnZ2gBQTu3buXJMHzvEd5WI+D\nwaBhOlPzhKklAFNPfHtK0NYsm4onifqY6UTl3D/Yz6tGqqKioqKioqLilPGB0Ui1hcqNNxwOk5L3\nvPnkYr/lcBLNlX8H11WFF+CTdYwMmirD34dorv7ZVFtShGz1jpjka0iCAAAgAElEQVSt3dclRnxH\nGbEAdny/mY6KrjRSOe1cieaG64z3rKysBG2SkgahIdjf3w98BEUEzWltPIcnpy0CYtySEndwlRuL\nJVKus492rviOufIV4Xs6nTby0Zk1M9CrOWEWzxfIKA2TUUrIX15eDvNNEe25DCbGm2my+2AwCH2A\nPr93717goCHsxnQ6bcyTUo3keDwO9+JZxZHhNaCIyBxZW2l0AX5vaYgDr6k1a+4TMRJ5KdQ+oTTs\nao/ze/ny8nIYT6XxVJktTvId5bArmCeDwUBqRUvI6N1ut8FPVETwGFJ7bkrD1QbYc5UDBM9FdYbI\naaSyB6mf+Zmfsf/wH/6DXblyxb785S+bmdlnPvMZ+5f/8l8GFf0v/uIv2t/+23/bzMx+6Zd+yf71\nv/7X1uv17J/9s39mf/Nv/s1moZ2Ojcdj6aHH6ko2D3mPC467URq3iMs/qyTJvAhKD2n+2mg0Coue\n2wGVPRZUauMFUE4qdQbI/2bx2CUx5Ly62i4C5ekT+3j5Q0QsrUmJ+UtBHf5KPeCm02nYgFQU4dT8\njCXsTXmL4XAS2whKosD3er1sDBuzdh8gjrtjpg9cPG6eBG52HF/ryZMnof6KhM/kdrxTmb9UdPpu\n9ziJ7zxCU1vzKMeOQl1SSZo5dRbmsUrd0e/3gwmYTWfoB+6rFJQXKMpdX18PnpfcZ6rf8AyvvbYm\nZCV0KFOl8mZMefGeFnjs5y1PCWj+gO4PX/1+P4wTytvd3W0kSx8OhzOOB4A6sKEMtGN3d7cxV5aW\nlsJYnEa/qgMKx5Hjg5kSgP3+znsOO3KgzqUZMRgnNu39vb/39+zVV1+d+a3T6dg/+kf/yL74xS/a\nF7/4xXCIev311+03f/M37fXXX7dXX33Vfu7nfu5UwwxUVFRUVFRUVHyQ0M/d8Ff/6l+dydUEqJPZ\nv//3/95+6qd+ygaDgd28edM+/OEP2//+3//bvv/7v79x787OjowwnTvhqphG3jWe0el0Zk7XqDtO\np7koxikoKZRP917qZVUnS2roA1yLRZWFFMiSoSeKsiaE35PK2cUSXK6dXuo8ODiYiVpsNtuXJeEX\n+FlGKhL9ZDIJbWfnBKX6R3lcP5+wM5Yf0Nch1k9q3kJqxnsV4VGZ+zY3Nxtj2Ov1giYK7VhYWAi/\nsTYDSWvffPPN8FtqfrPkV2JmVuFDYvdjbaC/l5aWgvkA7zA7nr8Y09XV1dAvHOndRylfW1ubkbLR\nXz4COmM0GjXIsir3YAxKywaNYI4wDmAexOLmAWqt5PZItB0Yj8cy0bJ/N2tCeWwUITuW6zBWBv/b\nVuOn9nXWRHlNM48PJyD2c5vzAyoyPDsLpLTjKmwNTKkqS4FKGH54eBg0iKrcyWTSKPvg4GAmqTV+\n82bXmKbT5xsdj8dhP+GQF9jHsOY2NzcbOTR5LFWeTrW2+Lvo36Pm12g0khk8eC3xv9wOBvaabrcb\ntLfox/39/bnOBHOTzX/1V3/VXn75ZfvZn/3Z0MHvvPOOPfvss+GeZ5991t5+++15i6ioqKioqKio\n+EAjq5FS+Af/4B/Yz//8z5uZ2T/5J//E/vE//sf2r/7Vv5L3pghxfFrk07Y/ETJZVrnT5giyOIGy\n7Rhl87Pe/poLFKdcPzlSu+I5AXjvZDIJWid27faRrRcWFkL9IGE8ePBAhnvwAfRQR9yXIoAq5Dhe\nvv9VcENFQDdrZu7O2a2VZo3HIRWBmCVrJdV7CVlJSoycZA0NIngMh4eHjXIVKZrnJzAajUI78czT\np09n+EO4Bk0Uk7q9NMsSuiLLKpdz1qaWkNIVF3EwGEh+i+eWxDQ7ENqwZu7cuTOTjV5lqPdQ/Mrh\ncCh5GphP3DY8Aw3C7u5uK76F2XFfKg4Kl6ecIVJgjhSI5bHI+pgfGEtug9LkYb4rblYMSkvgnQ1Q\nby6DtRlKC8T8Q08iPjg4aHC81Dtie7vP3deG04W+4T3Wj93BwYHcg3GfiorO65/h90Cz5vfTZ7vA\nPbiO9j59+nQm3yP+xd/o0/39/bA/MffWa4a4fal92dcfdfa/xdYYuMM8z7EGMMc4XBI7IGCN4LfB\nYBDq2oZLNddB6sqVK+Hvv//3/779nb/zd8zM7JlnnpkxJ7z11lshXYGHn2Bcad+BPLEUkVFNJr4H\nHcNqfAVvhlLxSGLAfSoCMt4bW7gYqNSA7e7uyo+hJ+FzeTnVJD+rPOW8OlaRdA8ODhoTjk2AAJMC\nuf5437wfIrzHLB9bJJVupdTLKlaHFNBXPDf4N3+IgCMG15M/Xkghce/evWA6gMmLU2GkyMsxz1Vv\nclAbGh8wgel02lg/bNpjsyWADfDBgwdJc5VStXP91dzJffx8klJOWqzKSMWEGwwGrecvoObQ4uJi\neDfqxAJQrq+82ZrbxQcQ7wXGB0wmkftxzfWt2jNZwMRHmGP8oC6oX8z8DjCVAYe+nOeVghdEl5aW\nwpxQ5iNuo1oDvlzeW/3zZrN9qcxQLHB7EyvvWbxG/L6vxotNWPwb5goOVCsrK+GwgT2I5xjPxbZ7\naur7xKZ2rNXJZCIP8T7llFnTpMqONGo+8Z7lU8h0Oh37zGc+E62r2ZymvVu3boW//92/+3f2vd/7\nvWZm9qlPfcp+4zd+w/b29uyb3/ymff3rX7fv+77v0wVTgLSKioqKioqKig8KcFju9XrZg1RWI/VT\nP/VT9ju/8zt29+5de+655+wXfuEX7Ld/+7ftS1/6knU6Hfuu7/ou+xf/4l+YmdlLL71kf/fv/l17\n6aWXrN/v2z//5/88elhKqe1SBDVIsaPRyG7fvm1ms67QXj16eHiYJI+pJLnKnJGCihnU6/WKXEM5\ntw/ayyYgld8MYHI1u4iqurOU6CXMmCaHE36iTaw+5zYw9vb2ZBwpr5GLkclTKn0l7aYkIFbjsnbE\nzwXWmClJNBczJqW5hDmKTdTKNA0p78mTJzIMAMYDxOErV67Ye++9N1PG4uJi6OdS1+SYZBZrz+Hh\nYUMLyX3PGmL0P8zWLD3ibx4P1W52W/YRzWOxryBZq4jRSsumzEzKuYLbycme54WasysrKw1tknJ8\nUFhZWWloMT1pGVAaEG+KYY1KqYlL7TsqyrsyH7LzkU/6rRIP837G893P/YWFhcYeyQRu1IvDPeSi\njvu1Esv/6vfbXCgY9d1jpxSg1+uFbxv6cjAYNJLz7uzszJiuUBfUgccBexDmHRPLmarC31z86/v3\nJDGfzI7bz2b+XCYPsyNtGsrGGuC5w5ouH8ONNVfqmxpD9iD1b/7Nv2n89jM/8zPR+z/96U/bpz/9\n6WzBFRUVFRUVFRXf6Ti3yOY+2jIH1fISZixAYYoMmLLh5jgNbfP5MEqiwHqkAi22hQ8L4TVbseEu\n6ctSV21VjpK8S6OE55Cqu+KOcV1PMv1T71BaSubrQaNzeHjYmO+rq6th/njXYwa7A6sAsyqoYqrO\nrPFRHKlUXymXbp5/wOLioiQgq6CpV69eNTMLmmeUY6aD8E4mk/A8+r7X6zXW4ng8nuHYAHBZBxTh\nnceQNRIpLXpKI64cVW7cuBHGjMMWlIRnePnll8N+8vnPf97MjuYY9qVUKA6uK2sXUxoG/La6uhr2\n6FgAXVxj0jLqooC6ol/6/X7jOxCL2p8KjJkaD3aUUftZaq+5cOFCkvPJHDfcl6pnrG1eS55D6T6r\nSOn+ulk+eG1pGBEGj7GZ5korrKyshO/mPLxZgDXsivuWC8h5bgcpP7g+2ajZrBcGSOtoJCdz5fv9\nwK6uroYBVV5CKjIvq2KVWhZIDdZgMGjEuWIPHfba8B+g6XTaiM20t7cXzB747enTp42yfb/6RReL\nSu3bwiYT5fGn1N4M5fHgN7B5UjSkiKzqYB4zb7Yto+34M1mf+8e/Z3FxMcxL9mbz7+b7GEhGCvMH\nE9XZI82bodpE4EY7cv2roDz0MI+91xCD5wZ7s6YIqtPpNMw7tFOZj1ZXV+UhKUVuVkAfsHlemQNK\no13jgLG2thYIvimHAYUf+qEfsnfffdfMzL761a+G3zkNiFk+3lWuzmqvBFJzjPcLdaBRXmA5KHMw\n6oNre3t7MnmwEu6Ux6p/LxO1c+PqnaJGo1FjLXO/sJexN93xe9grluujkmDjOr6jm5ub4QCi+i3X\n91hnLKh7IYa/5eh7ThifWl/s3csmSrwnJpT4uqOeyvSoYnMxFYQpJrmDVE1aXFFRUVFRUVExJ841\naXGMKOrvU+a+NmYhuHJCUiqNPlxKfDab30SU08qkJIRutxvI95Aqtra2JKmRpedSdacqu1SjATNJ\nKVEwJwm11TCxtOvLjUkWiiDv7yuNkMzkdZbASs24eAYS9ebmZnhWxf25fv26mR151MKMo0i4LEmW\n9EHsN0DFBFIxdGJaW7QnJaViju/t7YWxVLGRptPjWGUqyj7n4vKmF9basXSfMmHwGHkNQ5s1g/Iw\nvt1uN5hyS/c4aCb/8l/+y/alL33JzI5j5HCfKg3hSaDMTG3NKrE9KfWelKYzl1+RNcXKmcTHeGLz\nJjAYDMK4Yr1xrj04UsTapfaQFIGawx9womO0NdV//B1Qe3DKsYX3CcSsU9HaY9o974gxj1Y7B/8e\nRTPgvQFzZ3t7W/Y51hKHxrh//37VSFVUVFRUVFRUnAXOVSOlflNkzhxJmPlVPpiWAsewKpW8czjJ\ns5AIUPfxeNwI8Lm8vBz6ARLQkydPgsYHQRr39/dDXS5dumSvvfaamc32eUmwTMWH4rpyfsAUeZP7\npURS5YBobfuSw0GwpFFKfiy5T0m2Kq8f8/WYe+PHUBFtNzY2gjYBGh+zWVd4wC/fj3/84/Z//s//\nMbP0nOR1pvgwXGe/VpRkpoixo9EozAnFqUv1ARPfFZCK6tGjRzMuzimydApra2uNqOndbldqNwDM\n09XVVekMUILhcNjIWr+7u1scugK4efOmmR3tE1/5ylfmqss8YN4Pxjr1SVHaGOb/qbk4jwOP+k54\nzUuMzK34nam9gZ0i1D6q1mEpSZu1Y3jGa0n576WlpYYjRbfbbbRT8QRXV1flfFe8P+YSoazUfo19\n7PDwsFEGc8E4C4l/H3+3eR/FuGLu8DdE5fMEVlZWQjtgIbh165b8jn1gyeY+aikGOrZYfJRwjkAL\n8EBj8g6HwzBwqQ85x6DiD6UyKWKhpdTjvV6vkfZkOByG0PV478OHD0/Fcy2GlIfHPLGRTkLiLk10\nW3qALt2MUvVTBH+OKl5iclALTB1OVV2Gw2H4UPDGdu3aNTOzQBxmcFJQbCKoy/b2dpj7THL1GynX\nGXVaX18PBzggRg5OmYiY4OmT+fL6hilrPB6H9zDx3h+KOSErp4JB/be3txsk2Nh88vfxWsd4TCYT\nmSKG+4brMg8uXrw4k/JnXoBEfP/+/eTh77TBc0glc07tubj/xo0boQ8wT8bjceNwGnO4SKF0jHLp\nQDgdkNl8Ht0Ar715PMTx7MrKSmgfhKyYEOhTJu3u7oY4XaiDX/seSIb+1ltvNfa7hYWFhoOH6nN2\n/mKnGOXUhT0G19ocpE8blWxeUVFRUVFRUXFG+MCY9tiN08c+ysUv4vfiVIwylpeXw8kXJ2XWPgFt\nVelcv5NIJzFAmwBJyGvfSuEln5xGhyW4lKo51eZY7CZ1n5JEVMLWeTVhKjZSTAPn28tkybahFVil\nXzo/FEGfE9AqYrcvfzgcNsxpbD5KSfTD4bAhVbLbsCJzprSM3Pe478KFC5KsyhpkLp+RM6tOp9OG\n6ZnfyftKakzUOJwGlLniypUroQ2sEWgbqgPOBiosTOw5/27eP3lcvSPFcDiciUdlpnPKKWcipQm9\ncuVKKO+tt94ys6Mx4MTUvlyMZa5/0B42l6tYSakQL1xn1lyVOI6w9SWX+1TVnSOqow6pHKmj0SjM\nd/TpkydPQn9ByzMYDBoOG6urq2GP4Uj4WAds2VGR8rnNZvGxKaV4lNAHYsAaZrN/W3DGEcSXqhqp\nioqKioqKiopTxrlqpBYWFpKRYHHf1atXw0n67bffDs/iOgfcbMtXYJdIlcvI86ZyUhY/q7QZOVt8\nDFevXg2SPNeJNW9mTc2V10j1er3icAueX6AkeR9J3ayd5KWkE0XYVM+pugC4xpGKASXZqHHt9/sN\ngqeqM7+P+Thw22euR0mgzUuXLgVpUZWLflZR0bkOStOYCzYIadZr8fz7PIbDYYO/xHXFs4oQ3u/3\nG/yHpaWlmSB+KXCdfaDd9xtKU8bu8b5ea2tr4Rms71xYGAbeDU7dG2+8MaMVRZ28VklFsR6Px40x\nzGlTUxzMNvBzlrUtqTAd7Nrf9lOW0jDEoHLtsXOMz3PHcxf3dzrNvInKASoWEoH7HHNHhZ/At2Zh\nYSHsQVyf0+D4AUrTyG2aRyMUK8es/ViPx2MZDgYaOJwvJpNJI5/n4eFhcLr5wJHNAWx8iFGxtbUV\nDgOlKT9SJiheaDmUpBo5PDyUql9AlcWT3W84pWZLX47Z0SSAWhl1X1lZCYuGD02pjU4RctVmzkk5\nua9KIkLHCJYpNXrpQs85EaBe6qCXOshFF427rsrY29sL97F5jqN0o22IaM2qdrSdE7eyyRHlAuqw\nwe0t6UvlIGFWbsJGO7ApcWqXHFi4MjvaB3w7h8NhOHTyfOHN+qQf9BjYo9YLf7mYURjDJ0+eNPp/\naWkp9BsSUM9DFXjhhRfMzOzb3/52+C21j/HcYcJ92w8eO034w5f68KjEvjGobBcpwQEfRY6AnVrL\nly5dCnslHzBTh9iUB6Ha39X3J/ZBLt3vTuvw2hZ8eEV/zUNv8R5/vG5Lv4UQHPr9fsNRbX9/v5Ec\nXplx1YE2hko2r6ioqKioqKg4I5yrRiqWjJjNUGY6uWlptVdWVsK9MPGoXEcMZR6ImZfM8lHPU+8w\na2rU+v1+w92WXYvRjsFgMGMyUXXwWjOlGeI2sFrWS0YqH5TSKrHZjeut4oKwictsVhOhpAkFpZFU\n8WiYROzNZJysEuWp+ami5nI/s8u+nwNKc8V9wuZZJcl7KcssPQdTccD29/flnFESd2rNKSma8/+x\nGRL3YWw4h5bXFsbMW6l4PrGQEymo+cmEa9QbdYmZxlUfcewcM02gHw6HQRsP7Uipiz/PRWik3nvv\nvYbWLvW8WV4D4nPFsamQCfxYw+grRQxWJqCc6R5zstvtzmhyY7hw4UIgSHNZSvvt94Ht7e3G3sH5\nK7FGHz16JPeQEnDbVLgZFa6DwRr7ttpLlLe2thb2Nq43xpAdTDyxPKe1xLzi2IdMOfGaZt4reU6w\nttNMr58cMDYcZgZR57n/ct+aqpGqqKioqKioqDgjnJtGCtKUIqMCnE3a828WFxfDqRmS39bWlpTa\nY+X78hRwGmcp2kuMV69ebQQUPDw8lLZxzycaDodB+uccUL7+bXILAhcvXgwEViWpp/hBrGVht/u2\nAS8BRUaOtQmcEUgxMUlAcZ5QFx5fFWjTk6oVuX5lZaVB3l9eXpbuzr5fWDuS4xH46zGSewqlXC+0\nW2U5Zy0Ua0S99JzjHWL8RqORDHXgocIkqByJObTRSHmS7jzkdNaUeGl9OBwGDSi4T7F6XLlyxczK\nQxdw7kFoFcAZ2dnZCdoYNd84PIRfA7GwEKn1zVoD34esMfchNE4KDnOj3q000qXv9c4zKiI5z0nW\nuqJcXktes8J1UnM8pynE+xYXFxvrZjKZSM0/yuGy8Qy0UJ1OJ7yvVPtTqtWEJm97e7txL9YR6mp2\ntCdgHucipuMZ7MvzzDG8o9vthjZhrRwcHNjjx48/mGTzwWBgo9GoOJqv8pTC36mNdnl5uSj668WL\nF+3evXtFdWm7wSvw5sQHRrP44gdpGRP/yZMnMnkrwxO3Yx/z1GbJnoYgzqY261KPuphptxRqEftI\n+ZPJREbhTh0I+f24zoe71AGFo3Cr2F2+LP748+FPbbrY+NRmrlKwpMBRxzGHOE0KoFK19Pv9GVNI\nDIuLi+E+9H3swOIPz7E6+zXHXo+lBymVkiIGjAPApmyef75do9Eo3JdKrLu4uGg3/396l9dffz1b\ndzOzD3/4w2Z2ZArEQRVj2el0kpGlU7HIGMrkBLC5hz3MUuaRlEcvf0jniV6tzMcA1vloNEqaWBXU\nOsdYfetb32pdz1LE4lP5eFQnJZuXpN7hQxgfNrwpjs2f/E2ax5nH3weqwObmphxjAPOg1+uFtvG3\nH98BrMHHjx8Xx2espr2KioqKioqKijPCuYc/KNHGxMwp/n3T6XRulS6/B/+ya2Xbd7DpUUmmKSwt\nLRVLTznCuw8R0SbnlJdelbZD/abiKrHmjV2TlYlVxWfxUkxpPC9Fruc8jZBO9vb2ZN+o+cRkau4H\n/nd3dzf8jT5VBG/WsigtGWvTSl2NvfSfMxUyCV9phlLlon6Li4tJMx63DX/j38PDwxlir9mRlKwk\nV6VdZBOVktK9xq/T6SRNeZyHD+VBalfxejjUBZObU1sr55l7/vnnzczs93//96P3M777u7/bzI7C\nS2Bf5Da21ZRz3ZWjD5s48Jt3BOK1zPGQUtpYXvtKuzxv5gjWiGOtbm1tNcZcxXAbjUYNCoJyvOF2\nMAnfmxlHo1FjfZuZNMmpvQvga2cV/qDX681YDmL1yqF03NrGsRoMBtJ0epI4WH7f3t/fD3MG7dje\n3ratra2qkaqoqKioqKioOAucu0bKY21tLZxkwcNhqR3PMnchxXNgPodyt+TopSUn2+Fw2CC+l4Y6\nGI/HM2RU1F1FQFenejwDG/rh4WGSq8T1KJVelJSgNAMpLWAs6J5yF/Z8gFg+pZLI3DGkwmkwvEZI\ncb04Yj0HyPTvjPF1lOaCn4ndl4sqz23wz04mk2REfVw7ODgIGhhoIQ8ODhpzgkMGsDs41wv3eScS\nnz+M3+vrxCRos9kxh+Zqd3d3JrK173MvYeM+vz4XFxdnQhyYzWqaTiP6s9nxuCLkwcWLF8NaSfFu\nWFtw8eJFMzsisatclaXwUbg5rx6DI7PjPkDNY6XhSgX6jcFrrg4PD6Xjg8plh+vglT548KAxhuvr\n68EVPlePFHeUeZEKau0prVxOk+Pbubi4GN7DGlPFoUy9L+U4xOOlrAscgBhOEGhHzMnChz8o5SlO\np1M5d9qGy2EHMowZny9U/+c4Uud2kOp0Ora4uBg6kzdi5VGjyIpMUjSbVSXniNtqEmHC49rq6mpY\niCDhIkUN3zcajcKHB2Ts4XAYTB3zRCr2RDtPAk4BG+2TJ09kqg8F9VFTfV5iiu10OmFsONGljwuz\ns7OT3GRSZjw+rJV6k2FBMmEY9ecPNz5yMVMVRyoH/CY4nU5nvEnbQJkjDw8PZcoeper27WVvHJjx\neH4yMOZMyCxB7qPEY+oJ1zHwgc0snkgbaOO1x+Z7gL06zc4mGTmAeT8YDIodblJgMymQizemknT7\nGEbcp7EPDN7l46tFzSAF/RvzIDxNDIfDxjpT+xDHE8tREEqgUo/xR53jNqlDItcZa4k9b725iuvF\nz7KjgG9bacy4FD70oQ+F7xf2htu3b4c99+rVq2Z2lHAbZWNP2N7eDmeCeRyS/EHv4OCgiKbT6/XC\nvskHtM3NzWraq6ioqKioqKg4C5ybRuociq2oqKioqKioaI2qkaqoqKioqKioOAP087ecDU7DdbMk\nMOb7YWv35ZnFA1WWkFfniWIe64sU2bxtNOzJZCLb5XNxKaK62WwuOcAHhYvxg1Jgl3gfuTsXNkCF\nN2BiuyKoK+4YB6vDtdN2Tz4JVJ6x09QKn3SdlYx1rs7z5Np7PzAPEdzj/d7HgNxelHNmUSFPSsJC\nnJTc37bPY04fAIfEKHkvl+/DQrTNCuGRI/gDZ8X1ez8sSuxgptpRyotNjWtuTXG5ufae20HqNKAO\nUH4h5iaRIoyXQk3e1IeqNLprbhNRUcLnibKuPAKVV4Qim/IBxCd+NGt6qgwGg3AAAVR8MNWXqi54\np9ksmRtxkHgslWeT8pQEmIRZ8mFGpH6z8sSlJ0Fu0/dQnpBtNsKSzf4kG2vMU0ZF1D4NvB8fAi6j\nNB1VCrF4TP6jGvUqmvNgEduLSg+sqb1MRU/PxWsqTVHF3mQlYOFN7Xcqur8SHP37Op1Otk0oN5WK\nib3xUvWP1cG3qdvtNpIRl4LLOsnBV+1jPA9SczaVpJ37jfs39T3md/jvWclcr6a9ioqKioqKioo5\n8YHWSKVOpJywkc0pJad/s6Z55vDw0P7KX/krZmb2uc99rqh+qRg/bGZQKth51LwpN2QFdu1mKCmC\nk+16qNxOLOl5F3yVn63b7YZwAXDZffr0qYzx5FW5sf710uba2lqjDNSH25uL1svhN5REm1L9n6VG\nSkmxJWrtmCYx9l6PEi1GKgZWrn6nNd9LcRJtXKk2i++JaZNK6sL3pZ45i/5PoeQZpZU7ODjImtNx\nny+r1Lz50ksvNfIWxsbN90HpXOOYVikMh8PGnqDMpSpshdfYlPY5P+N/5zXl689R3fma1+5wX+J+\npfXOjVduHvN7Yu1R66zT6TQScrPWM9WPpfd5nHtAztPgD7yfUIMUQ4nak+OvMNqq4GMbvVeZdjqd\n1nFPUly0a9eu2bvvvjvzm9ooOCGuSpzKXCkfG4X7WdnLOcAbxoZ5WD7Nh9qUWP2tMqUzfJJUfgZ1\nPQu+TslaaWO2Om1eRcoUlzsQnMY+cNp9zpngWZgo/aCdhkmv9CA1T5+exvtyHCkFH0eMny3dm1L1\nU4eX3Ee9NBE96scmu5Okt1E0jdyzbftc7ce5+dl23q2srIR9kQNp+1hxav0o06OCSloee1+KD5Wb\n22ofw++xtVRNexUVFRUVFRUVc+LcNVIliHmOgFiMU+pkMilOP6DwiU98wszMbt68aWZm//bf/tvk\n/aWeA6zB8GrKTqeZQDXW3nkIo/4UHpPMcB0mKo6UnZL0FhYWgtShpDq0dzQazUQ5N4tLHygPkuvW\n1paMcg0gmvzDhw8bSXeXlpZCW1iCVMmXS1MM+LQ2SlI6iZ7LRPIAACAASURBVHbktMnQ3F7UaTwe\nZyOLnwZKpFl1nfugVLov7fNY/6bSMqVIy2pNDQaDpLk89h5f/km0dzmnhNPSAvqyUmWotafWT66e\n3FcpArqqK7/Dj1usz1LWBd6bfHTymIk3Na9i7VEmtrZQ5apE4Apsfk2txcuXL5uZ2Z07d+TYlWr/\nSjXw3qOyVJteuufjO1o1UhUVFRUVFRUVZ4Bz10i15WRw7py2PA7lbovm56QiYG1traHhyBGkc1wp\naFSgRcnxAzgHUG74SqUXpQUClFYpFVuq0+k0pI5YPf19PA4sPSFnE+dkBFgL5cdVkSDVWPf7/Ybb\na0x6Rhkp7ef7zZFS15T0qWJglZaRu99r6uapc9v9wEvqJ9FIzct14VyQfH/q2ZO0nZ/18ctiIWFO\nK+kyym+rHVGaHp6Lvu+5DOZ3lval4o768s2O1wjKVWM/nU4bGnHVz7kQBly3VC5SXrdqn22rkWrD\nS8J+rMj+/vnYNeCjH/2off3rX2/c5/uc/05xYA8PDwO/NpefslST63NUpnhpKY3UuR+kTgKf2b0N\n454PZHgW77t+/bqZHRGHY4lrY+BJ4jevq1evhuSNufgmqSBkauJzEl5exG1iYZjNfkzwYQQpnPvU\nm9AYsQzvJUEhh8NhuI/Ni94rjp+9cuWKmR1lG1cfQ/8bJzzmcv2YqA8QmzJT953FQeokUAco1Vcl\nH/iYKcuPeRti9mkQqM/iIFUCbjs7TaikwCmCsqpfqbnvueeeMzOzN998U7Zr3oNUjsw7j5kpZ/Yq\nqTPK6/V68lDj9wtFNuZyeaxKzLlmltwfGSXrR93X7/dnDsjzHqTUu/n5XMytUhObjxMYS2hfYmIt\nLZfv4/OAItezeRn/luz5SPBcTXsVFRUVFRUVFWeAD3QcKYBPgiw5qFNsqYINEiSkD7PjEzTMRzHz\nB6ROnFxHo1FQNaakmMePH0vXeqVZ8ypiVmtzfCgljc3juq7iIHmpk6HamTIbsSYnZWI1s4a2SMWl\nGgwGtrq6amZHmiizWZdkvs/XZ3FxcSYljS9fkSHVb+9H7CiFtm7t7NDAdVYSv3c/z8W8ASaTSaPc\neTRJbd3t51Gox55JvTtXfyCWHslstt9K0q6YHY/X/v5+0ky+vr5uZkcaKa99OE2zXqz8HLj//PzJ\naepSrviTyaSh4WIzM8cRUvVX5i2V5UG1OaWJUu1VWiHeM/17ThI2wyP1rcRersypyjRpdrwfov7b\n29uNb8JoNLLv+Z7vMTOzP/zDP5wp0yxvSlflpr5TqbXHZnA243pt1sHBQbG5j1E1UhUVFRUVFRUV\nc+IDyZHyUkeMc9PWhpq7D0Q2TybPYTgchpMypJRcHp8csXQe0q1Zs40l9vROpxlkVHGGzNIhDlBG\nTJpMaXqA4XAYiO6KvIwxOjw8DO9JBUtUfKjFxcVQhgoOWqrx8YRRbs95Ji329W+TnLMUJc+UBsFj\nnETbwRrOk2Aewr3imZxGImaea6lwFS+++KKZmX31q189tcS/JSjl6+TqdBqhGLi/fTL0WJ1UGJS2\noWfm+Q55UndMK6v6bR6OVArzODkwAdzXC2jTv6mQCKX9m2pHbi9S8Fq51Fr9QJr2fGVjJrbSSV26\nOH3S4rW1tRBjA+a+3d3dGQ8zs6PNsyQ55nA4DG3JEXz9ZBiPxw1V7DxtVOh0Og3zlPog9Pt92c6U\nxxBIsG+88UY4bHJWbxyWEPV8MpnMpJDxZWBj3NjYsDt37piZSY8+RVjHeLFHIo+5X8zqENbr9UI7\nVNwVzJfzhJ8LHGuHVdicnNlMj7kyl84TJThlEmuz4XqUEtrboO372GwAsBeoQupDr8jcpTF3/N8l\nmOcQM28ZqryYSakU3nORvYvVfYCaX7lkuW2hiO3sMcdQ5SkvxlJwqqu28RVVvbn+qu98xPoLFy6E\nlF1ArH9TjgCp7zxf53f4MtSBr9fryetzzcHiOysqKioqKioqKmbwgTTtATjh7uzsSFIgkIuNU1IG\nSzAw90wmk1MlErdx8y5BidR+EjWwl/RUosucKzk0TqwtQp8zgZaf85oSpb07PDy0ixcvmpnZvXv3\nGuVztF7MDybr+jmT07LA9DmdTpOhHdDuvb29czEzqWf5b7SRtW3cP8qpw9chlrvrrLeSnOs0a8Da\n4iQhAmKSbSrcB7vYpzQ0gHK4YJy2aa/0HW33l06nmevzJPuiKrff7zfWqKIqjEajMA7Y/5nknDKx\nlSZQztU5NeaxMk7DtHeS9domCwTv9WazmlWOlK6sGilTZ2n5/C6/56v9IuWIUsMfVFRUVFRUVFSc\nAT7QGqmTICdRKU1UKfyzLO2UutPPgxICpce80ktplN4Yv4U1Mx5w1UZwUjMdhVuRB5kPpZ5JBeRU\ndvDSQHBK66nIl4o8+n4ixzfh39QaUEEBuU0x5CT0tiEbYvekrr/fGil2nT4NYjfPIT/fYlpA3Act\n+oMHD07FCecsNVIl2ieltVH3dbvd8De4iwcHB41I88zrSbW72+1Kp5lUO3LvO03NFf9d6rTFnCHW\n5Pho7SrkQCmXazweN4LSjkajwEFlbeo83zH/LN7XhrgPqMwg6n4f2Dq1/3xHHKROMhnxvNnxJGI1\nOeoyGAzkYag0VlDOYyB2zexkauPcRD9NDw9OrZI6RMS8E7HZo5/39/eTh1JAmTVipg5/uHrllVfs\nS1/6UrZtuY80mwpLFimbxE4Lbc18qXhdHNeLxy9Vhjq45g5rp9GOHNp6SrYxa8zrrcVrJbX+Ywc4\n/7GJfbgvXbpkZhYyMMRSXZVGSC+5xjjN/cWXX3JQibW3NN1PaTsxx3BfzlmoFLk0Qqp/U6TqUsTW\nQEn2iZMeDr15W2UBUW1nk+08nsapuuRQTXsVFRUVFRUVFWeED2T4A4/YaV2ZZ1KulQATPPkdKieS\n0nooU10qcS+bg1KJZDnvn4Kq81kqFH1fxuKr+PrGEraiP6DuVW7KStLb3d1thDhQfeRjUpmVJ3GN\n9SPGhkMdsNbJl32a0Yg9SsY6llPMY3t7W5owVNuAVOiLmDYrFQcnh5S2i/+/bZ/nVP9AqZnHP+Pr\nmdPWeDPe0tJSIxRLrJ4q+bYyxZZI7ixxn6ZTzEmRImTHNE6cdcIsblFgBwr/Pl7Tao6ltFQp8BpN\nrVWep7GcgvOORew5P0/Ufby/q8jsHBmenVvMZs15qZBByqqyv79vzzzzjJmZvf3222Y2a9r14S3M\nyjMIKO29ui+GqpGqqKioqKioqJgT3xEcKYY6OfIJOBUckssvJVjiN2hEOMBYyg6POqqyzWzm9J4i\nqreVAr1tuUQ7kaqfWdoezfVPaaFKNUNra2shOCekHdYMslbOt20wGDSCbl66dCnwR7htqTax9FQS\nwVfhLMnmJ5kTQI5EnsuHmJL0T0tz0fY9bfq8lNyectVPrdecdoexsrJiZmZPnjwxM81BifFSmLtn\n1o40r4jbqRAgas2cBkdKzc8c2VxdU/XL8fpOEjpn3lybbQLaKszT5/PsGan7S6woS0tLM2ElPNB/\ne3t7Rd9jznCixvo0wn7EzgY5jtR3hGmv1+uFgeUPMhrFE7lEhc0EafZiwADwwQwfZh+h1UwPWGxC\nmR19mL3nCC/w2AB65Ih2pYuAN48UYZz7yt/Hh1e8l8cDiYW5/1T56CPuUxxonjx5YhsbGzPv4QMr\ne+14T5AYKVQdkPCbIvjmPrjvpzySM7v4w6syZU0mk8Z9qr2xD7M/2E6nU/nRagt+31maktTB0h/2\neU8oeYeZzaSKKfnQdbvdhnk7R2XgctfW1szs+CDF9U3FqorVP4WTEHsZvi6xfc+bSZnKwDHu8BvX\nD04suC+2z5bur2p/9GuA35fao2MHUdUfOdNeifl7Om16W/M9vH+rda1QcvD05mmUob7bCtzXvkym\n33jTqjLxKYFQ0YPUt0G9z6Oa9ioqKioqKioq5sQHTiNVSpY1m402bXak1YBZKKe1UZKmV5NfvHhR\nnqp9xO/JZBJOsRwp15sZFxYWQv64UtOZQuq+EikTdcUJX5G0Y9FrfcyO0WgU3sMSBswVOU0UJEdo\nnN58881wjSUwvAcS+MOHDxum3a2trRk1sH9H235WkpwC51CcR2ovNTOVEIBZcmUpOqXtTCWTZq0s\nX1dj2VYjldOSpMwfKvJ6m/JS/ZYCS6xKe6s05oAikXO2gFQYithcTOV2bEvwj5nTTjOqd6wuam2m\nylDaImAwGMj4gH5/N9OUgxQlQ8Gbf83yWmPlOKTAGuTU9dxvvo+4rvj74OCgdQJjVX/08+rqqr37\n7rszz6WsIDHg3UwjST2b60v+f9b44X3ealRiKqwaqYqKioqKioqKOXFuGinPSWB7eOr0zM97yRfa\nKDNNoAQGg8EMf8Ts6EQKKffatWtmZo3TNN6HkyryyHU6naBpYkkI7wbXh/PNsXRSKt15LQCT70q1\nAf1+v8EjUmEUer1eeDdLY54Hpd43HA4DcZaB9125csXMzN57771wDX3DJGjWcF29etXMtFZC2dq/\n93u/18zM/vAP/zD8xu3wEp4KBMr9ywR0ry3I5UHLIaeJyt3DUJJmDJcvXzaz43nOaxLtXlxcnJHg\nAcWRSOVNzGmfUu1Uv6U0PzGouZPKm5jjIPL/l0jyq6uroT9UVGdoZ7GX5NDtdu2NN95o/D5vKI5Y\nP3qNSy54pEKOVO3fqTTiOaI/oOq3sbERtNo5TQjWxZ07d8Jvik+YAvbj3d3daGiDWP1j75uX0N/p\nNKO6mzXzg8Yim6e0a0pbhP1ia2vLPvGJT5iZ2R/8wR+E5/CM6n/FfUXf7+3tJaOi5xxk1H1q3nkn\nHGWx8Ti3g5Rf5Mrbzh+ozNKqRBV1WqmoVQTkTqdjjx8/NrPjQVIEVD5g8MENpiw2l6GOfIDCROBN\nKfXB40HndBFmR33lD1C9Xm8mtL3HcDhsbAaHh4fJjYI/Nr4/eNNXMXQYIJTjANXv94Op7tatW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wGNgv//Iv22uvvWb/83/+T/u1X/s1+6M/+iP77Gc/az/6oz9qX/va1+xHfuRH7LOf/ayZmb3+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dxu119/vW6kvD73MssD3mkyjxIf9gQk3w+IS9I8DIrForYvK3Czmw/PQNtw0l+0KasFY8OD\nz/h0bBL279+vbQm38fLycuJGFa6g2dnZIHB3O0A90efNZjOihi4SXRs2NzeDNmdXAv7u3btXjh49\nKiL9VC4i0Y0NByh7wPcoX6lUUtc+XDvf+MY39Pfvfe97RaR/Sg3l43HJqTxE/Lmcy+V0LmHda7fb\nEQ2jOOzevVvbDe4hHtcf+chHRKT/ov/BD34QXI9nAPws1ixKypQAo259fX2oDdnCwkJwKpqNZ+4b\nnI7EZm3fvn3qBgWOHj2qh3qSUpDl83l1v95zzz0iIvIP//AP2tcY214dcrmcprrBMzY2NrQNOLga\nv0tK8Ds6Ohr0m0jyBiMOXjLvncLLAhEHGBGoR7Va1fUV42h9fT0SJiHSr6M1vLeTxNkDb25F+v0x\n7P2wnsfVO3XtpUiRIkWKFClS7BA/da49LxltJpPRnS129XE0p8dEIfAP9C2D1ZgRuMi7dzBgsAg2\nNzeVBeAElaBgPZaMrXdosYD+7HQ6bpA5LBrsypk9AFvVbrdd6xXW07CaOyJbLAy7CmygqCetUCgU\ntH4eg4i6cVlgOW5sbGhbctA9yoDfccAmgv5PnDih1iGsax47cLt61uewMgSDkl9690gKfB4WTH/j\n3hxsjr+s68VSEB6T4gW/2iDibDar1j3G1RtvvBG4oXK5nLIn6KPNzU29Bvfj9mOL0yovM3vHbgar\nTsw6bNuBtdxPnz4duJxmZmZ0jMJNF5ezEu2L9q9UKrpOeDk0MT84Vxw+W1hY0OeydYx5g3WOFaZh\nWddqtaGkLrLZbGKgMuQP4qRTML84GNr7rRdYjjqhjvv37x+Kkep2uxEmX6Tffh4TirphXWQ2CgH1\nzz//vFtm60Fot9sa+gHXJ6vnc9nhEv1//+//aV0xvzAHyuWyy1ajHt5BD6whuVxuaCmTJOTzeXfO\ncXniwIr1DMt6e3IFrOsIxI1DjBnO9erNdctccc5ALrPn1hwm4TGXYTvMX8pIpUiRIkWKFClS7BBX\nlJHyfI5xfnZYRTZGwv4OO1swOSyI5llMuN/a2ppap9htnzt3LhKPYO+H2BEW/WPgPrwLR8A7rHeP\nQWCxNy+GilWs7bM4R94geEHQw+QV4t8xk+QFKiLAstPpaKwFP8OK0ImIG1QPsEWKdkNb8e/xLBZf\n5YBsL2bAMmo78ccnqQ97GBRvgPp6ljhnuffagDOle2rUNui72+0GTOjIyEigxt/r9QJpAs9q43HN\ngny4j3ct2rxUKg08vj0MOp1OxMJHPRGbgmdPTU0FTJ+nRM4HEFiA0LYbA7E5nL0e82J9fT1gnxiY\n15lMRsuCZ3gxfKVSSccg5lGxWAzmJgsQI/aNmUagXq9rnzBDaH/HMYZerCSuPX/+/FBxXW+88Yay\nyoDHZszMzOjnrESPGCS0PccTcowm2pzZWbwTONME+o3Fkz1mjTNqiPTfDd6aYL0aDLT3ysrKUCKc\ng8BrwiB2xZM1sOtwNpsdiilrNBpuHjxvXUedk9Zcj32Kk51Juk/SoShWd0fZvTltccU2UvYF4lUO\nn3FCYUxIDHyRqDsKAx4LjOdqE9kKAOfJhwnGWkx2U7KysqInb5DiIJvNugkl4U7Bon3bbbfJww8/\nHPzOTrRsNpsYzIvfLy8vB+4ZXL9TJG3C+Dv0E5eTv+eFBMBk4knobaTsAsWnteAi4JQu3osWffir\nv/qr8o//+I+R57darUA3y9vQxG2y7MlQzx3N1wBJiZctvE0/XkD4bHNzU3+HF1uxWNQ2H5RWBuAX\nud28cNsmLU7DnvJhg4MD3+1G0Bv/rGa/HeAab2xjfdi/f7+6NfFsG5yKe9hg88nJSQ1a5+TbAF70\nu3fvDjZLFy5c0HbgoG6sX3gWa9+hT2644YYgwXGj0VD3N8b2xYsXNfSAN00WnU5Hy88B8Cg/q14P\nc/KS04bwyT+sDUmhABsbG7rZ5I2XvV82m5WFhQUR2VqPuX5J69nu3bsDV5zIVptzWhMPOMnH18F4\nwLrMRjbq1u129TPPwGHgJf5WMh0wBs3TYeYXnwzmccQq5yL9+eFls/AO0vA1Iv1xZzNXcD94+n+8\n3ib1u7dGc8JtmyZrGKSuvRQpUqRIkSJFih3ipy7YnMGB1jYnDrsNsFsvFouJwdewAubn59Xig5U3\nPT3tUrV2Z3v06NHACmSLmnMQ2d19XLCjtVK93XuhUNAdMizNSqXisk+DAnKtJcg7b/7O1n2Q289T\nuWYkuSthwVWr1SD3YJyOiP08n88rMwPrFNoytvzWvcBWiscGJTFNDO47a/15ulRx8H6HsqKt8vm8\n9jUzf7B8Ud9ut6tua7R9t9sNNFaazWbEBYf6eP0FVoFVrFFftio9t7VnYQ6DbDarQd1gj4bJLQl4\n1jjKsrS0pGMC94xjC1Bn9MfCwkIgEcB9jXZmloWZZA4bQFls+TwG6ZprrgnWIr4G4JyRzDR6jBDn\nIRPps/1ga3DtyMjI0My1daGzAjbGSbfbDQ6dbGxsRBLJikS9EFgzL1y44HodrBbhgQMHtF3xjpia\nmgrqsbCwEKw/9Xo9eDewFAzGJM8TlI/dr/CWxKmxe2DF/+2M9Tjw+8JqLXW7XVeTybr7+Hdxeoki\n8QfCPLbbXsPXot6FQkF/NyhxvGWaOOCe64Hyb3ctskgZqRQpUqRIkSJFih3iisdI2UBQFt3juChr\nFXuKpoVCIaIELtLf2cJygAgfW3E4Mru8vBzc75ZbbtGcXLifZwFWq9UgRmpmZiawUldWVvQzWCn1\net0VlISVgOdeuHBB4xe2I2vgwQZTcl5Az9LkQHZPrNDGkrC1y4AV4QV6498c2Ie2arVaykAgXsML\nWpyamlKrHnmq/uu//ku/Zws8iVXy4iu4zJbZKBQKWrekwMS4gxT4nC0/1M07EDAoPgVjC/cbGxvT\nPudxirHPBw3ACMAijLOCkwQ2cb/V1dWA3fPYLQ6GxvM5HoqZNbBEcTkZLcbGxoLAWC/WamlpSe8D\n8chyuezW04oyvvjii/Lxj39cRLZirvL5fBDjwTIpEAf+l3/5F22jpJg2j3WJi3fx2Cvcm+cAngsG\ns9VqBUrP9Xo96LNcLqfrEsNjuLCGcztivcCcGRsb02B9zO9qtapj0GsXG6cqsiUtwzlIf+VXfkVE\n+u1nWSovV+mpU6ciuVZF+syjl1kB8ggYB8z22FyuIslMVLFY1PaD8Ora2ppev7KyMhSbPejwincP\nDq5Gf3HMIvrJKqFboP44JNBqtbRfeT2znolut+sKcrIUiv2MD3rYucx7g0GMk1cX3BvzfJj4tCu2\nkUKHYnPACwU60zsZwC8qmx5hfX1dT2vgZbK+vq5B394mCJsSfmEgfQReyvxvz/3HHYmXU6VSkRdf\nfDHyu3vuuUf+7u/+TkR8ipWBTuQUMsNsoHiRi3sJ2kA9LzkrT0g+hYNJAr0cpvm9vmFgYWRa1iZb\nHhkZ0bqjzU+fPq16K1hoPe2hlZWV4KQPHxzgl3rSosTBhmgDdnXZhapUKmmdkk6Y8YbBS67slQm/\nP3DgQJBgm0984uU0NTWlLwjrgmJce+21+iJg4Br0wejoqL70cWjiueeei7SHSLRPPcoeyOfz+plH\n0/O1SUmQGUkvjrm5uaD+rAsEZDIZfWFjjL3//e+Xr371q8E97Yt9Y2ND/ud//kdEomMcQdCs0I12\nu/HGG0VE5JlnntHPuD+svlCtVgsOljz22GNune2J2maz6eqp8QtKJJo2CNfaJOv4HW9CROLd1t7a\nZpW+l5aW3MTI3mYJmwy0y+TkpK75nPoLwFyJS8iMsqC9FxcXdRNk1xwRUdXzI0eOBJpho6OjOjb4\ngIOX7cIexhkfH1fjAO+YXC6n5ffcZMOeeh8Wg5TDvc2Id1DCS37Oa683j73PUBcmGgatBagH4G3u\n2WC1+lV8EGA7mnWpay9FihQpUqRIkWKHuKLB5l6gdKVScXffVtNhY2MjYFwWFhZ0NzxsUlPkU3r0\n0Uf1s3vvvVdERP76r/9aP/Nyrh0+fFhE+laFVfD12KPnnnsuUCdmdwp22ZOTk4muE9R7ZGQksLZn\nZ2f1GUwlJwWRe66AarUaHLcW2bIIYY2Nj4+rvg0Hg3oWA8rNO33cD8zR3XffrdYj3wMWHLv4wDTC\nYmw2mwFbwOOALSpL17J1xznqgCQ1dB5fSVYbq8Wz5ACuYbqag25FolYejreLbDF/YKZqtZpey4Gq\nYEcwJpj9gEV/+vRpLR8zA9/85jcj9alWq8HxfO5TlCmTyejnaL/tBHVaizAu9yEYZAbGC+cttKri\ntk5oQzDXR44cCVzKIuF6MjIyouOXmR8wKsxIgYEHa7x3717X8n3Xu94lIltrzHe+8x0dM/gbJ+2C\nOQB2PI6VBTDPS6WSsieevhEwPz8frDt79uyJSMmI9Mez/R23M9Dr9XR8oo9OnjwZYaJE+vk1rcYc\ns8GecrUnYcG49dZb9d4iUeYqiVl58skn9TOMEY+Rq1QqgRbZ+Ph4JBhdJOqqRn94B5H+N8DeA7Sl\nF7jNTL2dz+ypwXtgc3MzYJ/jgGv48IqVSWD3J2ezwGcYu5xBAH9ZT473HUnrEp4/jEs1kZGq1+ty\n5513yrFjx+TIkSPye7/3eyLSXyzuu+8+uf766+UDH/hAZJH54he/KNddd50cPnzY1UxKkSJFihQp\nUqT4WUEiI1Uul+WRRx5R3/ndd98tjz32mHzrW9+S++67T37nd35H/uRP/kQefPBBefDBB+WFF16Q\nr371q/LCCy/ImTNn5N5775WTJ0/GCkRWq9XAqtrc3Az8vCx0xhYprBgEFjebTTd+xRPLhBXBTNSH\nP/xhEdliolj9ly2rI0eOiMiWJfz1r389uP/U1JTu0m+++WYR6Vs7aAtYfBzngh1zrVYLrN69e/dq\nXAKsGC8Is9PpuJYqBw9ay81j7DjQNslXzJtoZra8MnjxEtbqnJycdAPfwRIwm4AYBn4W/v1zP/dz\nIiJuNvtSqRTEjHgBsnEip7CQUBa2Zti6syKybD1jHO/atUvLwgxcEnNjc0KKROPhbH91u11loDB2\nbrvtNnn66adFZEuUNpPJuM9FrBrm0fr6emDdTUxMaP9iXHa7XbXqMN8mJycj7JlIvx1RfsyL5eXl\ngG3b2NgIssmvra0FsYhcrkwmE7Cj8/PzEZYGv7fW/6uvvqqWMsZ5nHAr8hHiHuVyWWPKONYH4/ip\np54SkX5sic1lyNdwvj8EqvMBFA8Yl2Cz4gSGbXD92bNntV2x1njxky+99FIgb1Iul5Vtw3geHx8P\nmPn19XUdE8yOocxYy7mdMWZPnToVjG0WB+W6oa0eeeQREemPO7QHYqkWFxflmmuuERFR0V4G5Ap4\nnWV2EcBcjssNB1YM7dxqtfSefNgKcUZ2jRDpB3BbL8V24qG8d7BlJzkGyXsO3hOZTCbCOgP4ftBh\nGPQn6ru4uKhjDGOoXq/reuN5NzhLBTDouduVONhORoWBrj1UutlsahLfb33rW7oB+Y3f+A35+Z//\neXnwwQflm9/8pnzyk5+UQqEgCwsLcujQIXniiSc0WNXC0xrq9Xq66KIhC4VC8PKfmJgIBhbrf7Di\ns6XyOeAV2LNnT/BSbzQaumhhU1Qul+XOO+8UkWhyTACThgOvUc5KpaLtyYk4k1yPXlJLHjBY6FGf\nuMXVG0Sc9NeeHIwbdCgHKxADgyY2qFeU33PVfPe731Wam8eH546x7odcLqdjxnMBAbOzs7p5sAcW\ndgJeTHjB4rljn4G2On/+vG5U+LQbwHpJNnFqp9OJaPGI9McdXJ4chM8Ut0h//GFeYszw+Ecy75df\nflnbFAtfsVjUF6SnXI9+7na7urFAmXlc8ZzGv9FWc3NzugHgcWXnfDabddcRtGGcZhleqtioeK74\n5eXlwAjj+Yr5Mz4+HmyG6vW6ur/RzqOjo3oKGKrYt9xyi3sYAC97DurG+MGYGAT01+7du92gcbQr\np1OxL6NsNhu4Rjm1CvDyyy9rv2PcLy8v6xzlpMC4D1zU9XpdN1DAkSNHtE0xrrw5vbq6GoQmLCws\nBBvltbU1ee973ysiogcDZmZm5Gtf+1pwT8wbtMubb77pHjKya8fc3Jyu/9hwXbx4UTdrKBOPYYx7\nTrnj1bNWqyUG7gNeAHWn03HdU0kuK3Zr2fcTp4gaBNQLZW82m1o/TvuGeT+sxpanfeYlj/cCz1lj\ninUfB/0+CQODzbvdrhw7dkzm5ubk+PHjctNNN8ni4qKmWOF8VGfPntXBJ9IfiPZ0R4oUKVKkSJEi\nxc8KBjJS2WxWnnnmGVlZWZH7779fqVLAU5i233vYt2+fGxwoEg26FfF36MViUXevrG3kqTV7dbJu\nsbNnzyqjhWsPHDgQWErvf//71ZpkBgwaVc8//7yIbFllIlsMTCaTCdSSPTaKVWz5GQgehQXb6XRc\n95K3QwfYoubjz5b6jaM14a5A8mUOWmYLx6rJe3Xd2NgI3Eb8G1w7PT3tHt+2fVMqlXSs8NFke9SY\n3WBsdWC8eQwB60R5DCc+4zaAFQvmot1u6/dMTaPuME527dqlLJGXqJX7l/P9ifT7LW5eMc6cOaNG\nDu7HCbnRr8ePH9f7IcB2c3MzYIFarVYQpD87O6vzAPdbXV1VS95jA9H/Z8+eVdcK5vfq6qrW1wbU\nx8FjaJeXl9V1BlbEs4TPnz8f1LPdbgfyIpyclcuDOcUufjBSGGNra2v6bJ5T3/rWtyLPwHNEkrXK\nOAQA97jhhhtcRgqAa3RhYUH7nw+aeArYHpsBloKZHIwdtMvk5KSOaazBV111lX4Gl9zrr7+uZcC6\nx8C6Ua/Xdc7v2bNH72vXs3a7LX/2Z38W+YyNfuADH/iA/NIv/ZKIiHzuc58Tkai7ng8f2JyMi4uL\n8v73v1+/x192zwJ33HGHiGzNqXq9Hqwr8/PzOjaWl5dd7a6k8T9obthg7m63q/2V5E4rlUpuJg+r\n5L+5uekyV55LEcwVvms2m+77y5MzsDn+PBbNuzbuHeep8Q/C0PIHExMT8qEPfUieeuopmZub0w5+\n88039eTV3r179U99RbsAACAASURBVEUr0o+9gEiaxcrKSqL7JUWKFClSpEiR4krjgQceSPw+00vY\nbl28eFHy+bwGiN5///3yh3/4h/Kd73xHZmZm5Hd/93flwQcflOXlZQ02/9SnPiVPPPGEBpu//PLL\nASuVyWQiAeSMcrkcxHMwwC6USiWNa/CO9NpcarhGJGoBI2B9aWlJj8L+53/+Z/BcBJhfvnw5sF4P\nHz4cfMZMAmJWBmX6BkZGRoI2YPXsJLAVIxLm04s7UmslGCYmJlw2BMDmmS1dDs70GClYQOwvR3Ah\nrABmENCvs7Oz6j72AkD5954lai2MOIbLgscot58dt+Pj41oejLF6vZ7I1KKc2WzWtbzQfrhfNpvV\nscwBquhL3I9jqfD8uHi3JObSA6zx9fX/3963xUhWXWevqq6ue1+qp6d7Lk3TZgZmmGFuhjDEMcEI\ngxNZwo6ILCyFoMTJQ6Q8RIqiJJal+CUXR0okEiUviSNZeQiOH2xsyWCMhAE7MjgGQoDMBDEDzEzP\nrW/VPV3dVV1V53+o/1v1nb1Xna5pE9pO9vfSM1Wnztl77bX32evb67Ka6Ihp3RcG1ezsrGfhcY1E\n9M0KENkMnKSVGSt3HSmVSvoc6LHLbop09BVy5TXDrTqwsbERCxQRifcd/ljHjx+Xxx9/PPaMgwcP\n6voAmb744ov6PWdyRlvBpPRKMunCyu5uIZ/P6zhAfvPz8yo/MEPsT8brC+SCOVqr1bw1a2ZmRhk6\na4zxjMXFRS8DdiaTUXbXSnmwmb8jxgF/v/GNb3jX3HPPPVoNgduO9w7+8hp7+PBhEemeRoj4jJML\nTs8h0tFTVx7lcjnmT4W+4zOW+WZsocsgXY8jtQt+LtCL/QLDCAaY/eFApGyWXiApqSYnOeZqDG6m\n9F4MG+RvsVmcjb3dbidmjU882rt48aI8+uijOjiPPPKI3HfffXLixAn5zGc+I1/+8pdlZmZG/vVf\n/1VEOpuNz3zmM3Lo0CHJZDLy93//9z1fJmtra2ZECL8cLKDDlnMoH1vxwgFhYYKvrq7qM+AUWCgU\nzA0UJgRevL2OALDpwMaAYS1ikMvQ0JC+hNE+LoKcFDnHLwTcg9tnUcG5XE77zk6kGAcrGsP9Pfrs\ngvMwWe21lBmLBxwyr169GnN0FolPUt5IuRvodrut3/Oi5E7yhYUFb9G1nP6z2azKyp2sDF4IrH67\nRT/dNrvOvFzigDOX4wgBR8WtVsuTARcetnLBQD/5GPzee+8Vkc7CgaMnLETnz5/Xe/PG2i2fwBsH\nXoDQBstXEi+TWq3mOZFbGdBF/AjCXuBII3djt7q66kUYisTL04h0dM0yJtAerEETExM6hjjK5P5i\nA7S2tubp9qlTp+Q3fuM3RCS5DMz09LRuMt56663EvrtgR2VEjl26dMl7gVnH8KVSSWXEpYeszPCQ\nG2TGR2LQuytXrqiRi7nEGx/oQbvd1usw1u12W3XG0ifch4+osWm6cOGCHrHxHMacwt8LFy6Yczhp\nk4bAlX379mnAxmZO065uW/p87dq1GCFgRaX1m6epn4zgFlKplFcaBoFnIt25UigUdA5AVhxBbuk2\nroNOikisQDZXscCzXOOQN9IwAprNprdOc1AKl6tKCjJyC3gnIXEjdeTIEQ2RZoyNjckzzzxj/ubz\nn/+8fP7zn9/0wQEBAQEBAQEBP+vY1szmVuZqtkzZ2maHU5E4fYfdZzabjWVzxn2x6+RdsRvaWK/X\nTasDNDScZcfHx/WIznK4Bk2/sLDghYNb4e/NZtPLJt3r2BPWOBiuWq3mUY2cl4hDdq2+QeaLi4t6\nH7S1H4dl3NetH1csFhMLsDLcjPDog0jXamc9YUvYtRiazabpIJgUHpsELqaJNlj0rnV8KOLr9ODg\noJeuYnl5OXZ04bYTWFlZ8ej0drvtHTNxODhbkmATLCb3W9/6Vk8ZjI2NKYuC1BeXL1/2rOOBgQGv\nNh7rGpjCgYEB1Us+osARAHSbLUp28HVTMTQaDdNiZJ1xWaxms2mydmAi+JiUa+IBrmXLIe7MbAAI\n4280Gh5TMTQ0pH11i5wz1tfXVUZ89GfpHcD6h35iHFKplMfqtNtt7zq+Bp8tLCyYIfGQh3UEyKws\n2GzIamBgwKwmgPFi1wjObyXSGSN3rdrY2FDHczBEO3bsUN3CKcTRo0fltddeE5HuerewsCAf/vCH\n9XkinfHD2FjuGVwvDwBLxVHtrrxE7GAS6NfExEQsW7xVv9TSY+saa45AhlZBdl5HkxzPcR0HXGDc\n2+22fgbWaWlpyUsHY+Xm6tVXd23k9ZjXDOgTnl+v1687S/z15J0KtfYCAgICAgICAraIbWOkXAdI\na9fLSQZhMbA/ieuXUKvVYqkQRDpWoBum2mq1dFcMS6PVanlRhHfddZda4VZSODBRg4ODasWwtQgL\n0rI6OFwd/YTlVavVTAsTfcL17DgOWfRyaIdVzr5Nlh8UZ+m1WCyXXavX615GYw7PtlgWWPxRFKk1\nAZaiXC57Vdyr1apaaWzRW3WSkiwvvo5rNYnE/T64fa5est8UV7F3s3CL+Mkj+dwfKJVKsWSFaG/S\nfOAwZDfhpUjXSmRfD1jL+IytM4zf0tKSJ7+FhQV56aWXPBm4fiStVstjBjKZjPaJGUo37LpWqylb\nDDmWSiWVAWRWLpe9rOgsD5FuzTROf4E2wCo+d+5cot8DvltcXNQ57K4raKNIRz8x1kinwI7CsLgv\nXryobQGDWKvVdB254YYbRMR2Dk+lUqabBe6TxJQwMO6WH1A2m9V5bWXCZt8xi9lMckrH+BYKBTOt\nDcbdkjP6xnMU16XTaU/vOGEoaqlaFQ54LjKz48q5XC7L9PS0iNjsCbPqbjqNy5cvq9zQx1qt5rW5\nVCp5jtH1el11bHV11fPn6eVjDP2FnC0nbfbDZEbc+g3WfHwGx2v+bbvdNqPw8Qwwa9lsNvYeFum8\nv7E+Id1PvV7X9SEpSTSn7sG7htftftMCcZCK69Paz+nMtm2k0Fg3+7OIP5k2NjZi0RzuPXjBcBWL\nnW/xwrhw4UKsZAo/S6S7ELz88svaLkwkzmCMRWxycjK2gQI4J4YLznPllhJhhUQ7R0ZGvEiz9fX1\n2IIi0hl8dvYFeGJA5pzfysq7hSMVpqbdBXZkZMRTNL4G/+ZFkDPvuhvGer2u48lHE7xpSYLlZNgP\nRWsVsrSO//j+3Ca3gLZIdwHixcal4HuVNeAC0HgW7gN5W1R2r74mHbVCryqVih5Xcx4rPg7C9W60\nEGdPdqNeXLhH2QzeoLvoVcibZWoVqcUREb/Q3Ptz5CUwMDBgZld3c1mJdDcN+Cyfz3sRiHz8gQ3a\nxMSE3tsKrgBqtZq5eeGM2P0Ac5mrAuAeQ0NDOu5WVCteckNDQ6a+A1bAC9bttbU1fZ41l1gn3Gjn\ngYEBM/t/Ui4wbIosfXrvvff02BqbKmtDevfdd2s29CTUajV9d2B8r169qrJGH60Nx/r6upcF/urV\nqzr3SqWSN196OY67R+zW3LR0zJqvVukk7osVtYl1IpPJeJnDG42Gt4avrKx4hgNvCJOiEPnYku/L\n0a64B9oA3eX2WdUn3tfM5gEBAQEBAQEBATa2jZGCBYDQYHaqc3eszMZYIeQMDrMV6ew6saNkx0k3\nvFyku3u1QnCtwqKwEKzMtePj47qTx7P4SAR/BwYGdBfMfXOtMW4nLBfePbO1gn6wtcq/h8WCfvZy\nVLSse9cqKhQKXugutwv95IzGjKRss2wJWfSqyypafUmn095nfCRqUdlu29xnWg7t1lGIa8lls9nY\nsaxIxzp19XhjY0P7tFlWXZcZKBaLseNKPIMZS7TXZfkWFxe1mC73F/3gwIzrBebl8vJy378HK8dH\ns1aaBB5/OA9bwBpi6bXFSDUaDT0OtNgwyJ5zt2HdaTab2mcOmnHn3sTEhM5nZrVc1sJ6Poem95sT\nDLpWq9US84yBCYmiyGsLOyUDe/bs0bWB1wOr7pvFcLjh9CI+O5TP501mFXMJ7Fe1Wk1MK8Cywv0s\ndhQO6//xH/8RqzDRC8ViUXWMj5vcNrCDNDO67nXMalr95pMEzju4WUZzwD1y5DxsWBN66ZMVbNCv\nc7abmZ/XP06d0O864a6f/M7nvYTLjm3GmPeT9kB/0/eVAQEBAQEBAQEBMWxr+oOxsbFE5z1YGtVq\n1bOoC4WCxxjceOON6hwOWD41bH2yMx8csmH9NZtNM5mmlR3WdcyOoshjUay2tFotPeNnJsy1xorF\not4P13ECOK6yDauXLRx+tuVQ6lqYqVTKPINP8lFip3PXz82qD8ZWjZWJfjMLB/dMSqppoVgselYx\n18uD/CwrtVgsahvZJ8CyXlz/kEaj4TES+D0jiiK1mtlvAuMBOa+srHg6xvf6SbIXA5aFOzg4qOPL\nNQbxPKtyPPvcoE9uHTaRuH+kJX83pQQzPyL91cWyfN9arZbWOoQfUb1e1zFGYkdm7KBDExMTytpw\nslTIAz6J6+vrusbgunPnzqmesB+Ji5GRES8JLvcV/oybVU9gHzQ3GSWnh8G6uLGx4TFSnAgYmJ2d\njdUXFenoAfrJjBT6i7bUajXvGRMTE9pf/HZ8fNzTxdXVVX2HWCHzFjDOzWZTn8GyhP4msVC9HL2x\njvFv3WzxrVZL5QI5Dw8P64kJz4GkkP0oijxd6bXOWswgZLlZqTZ3rDlIyPWLEoknCcWYcLJb18nd\nOoXYjA3iUx7IHPfpJTP3nuxYzuzX9TBRwLZupPiFi4Go1WpmTilQzTgeajabniJbOTfq9bo6AELp\nlpeXVeF404ZFiAtiuhsk66U5PDzsLRjtdjtxEqDfXOzVou+x6Fh5lrCJEukuDu+++675XJ5wmAT8\nmSvLXC7nvaTdkgW4DkiiYjd7qXOmdzdDsgUuo8NtwaLA9Kz7ck2KiBOxIzTdY19GFEWmzLGBYuda\nyJz1ynr5o424R6FQ0Bctv1DdiMnr2Tyxw6ZIvNwCl1hwF1rOhO861Lv/xvd40V+6dKmn07hId3NV\nqVR0bvKxBufkwrN4k8Y5pwCrSKqLKIq86N61tTW9N+YcA2NerVbVuRht5jI6HO3GhptIPEIXY229\n2HrNLc583wvu8Sf3WaRrxLAc2XGbHafx15IH+muVhwLYeEvKlM7AGnjmzJnEe/d7FIS+tVotL5Bi\neHg4Vi+2F6z1Z2VlxRwHvE/QPtZnGGVsQELe7E6yY8eOvpyeuU0cxebmfWLHfWBgYEDXJXbgtnTH\nLTnDecT4vknBIxaSjtWsgCB2Xkcfx8fHtc3svuIeZW8WuOSuj4nt3vSKgICAgICAgIAAE9vKSKXT\naWWfYO1MTk6aDoLurnhjY8Oroce0NpxEr1y54oUN5/N53dFa+XTYMnN30hYFPDY25jkDMnPFzr9u\nugV2CGeWAv+GLNhRnXfmYNv4SBNt5PBsBvrEx2mupddsNvsq7NsrxwY+5wzUgGVZwHLI5XKmVec6\nVTKDADAF7RY8ZVjWEWeTZ+vYzRxtOZin0+lEJshiq9CGqakptcyT5M1h48xg4jdsPeF7yJELXkPO\nVj6sVqsVc4IX2Zzh4r5ZbAG+Bwvw0Y9+VNsAx3DL+uQ0JxaY/bB0i2sy4v6QB+fuApaWlsys/m71\nBAaYkoWFBdVzrDvnz5+PORLjenducj68XkfJvZ4v0l0Xk9IgPPTQQ/LVr37V+xz3xPrIQRjMGros\nZbPZ9MaM69sBfDwHFAoFz3UjiqJYjiWReE0+Xtcx1mD28vm8mRbCXS9EujJHP3K5nH4G9wrUmmSk\n02nt72aOyBazduedd4qIyHPPPRe7J/9tNpvq3A4mqlKp6DyYn583M9+jL6xPVg4lFyxzzgxuzXfc\nm3NHJaVR6Bc8DphzSewX1810Hce5H5sdbwMc/MM1Zq1AtM0QGKmAgICAgICAgC1iWxNyjo+Pe34w\nVl2i22+/XZ08Ofkadqe8A4UFZzmxJ9Xzi6KoLwZGpHvmDb8kzqIMq5f7BUvCCmFlK4CZEteZt9Fo\nmNnJcU9mY9gRzwJ27nv37hWRTvoGi5EC2NpxP+O2gB1bXV31wrHZouaM9W7StV5WgMswbWxsxDJ8\nc9vd690UC0nV3fk6K0twsVhUqxlttULnGRj/sbEx1Qv8lv36GJAvWNKrV696rESlUlFdxpizn1iS\njx7XeORUHFYINr7vt8I82KB2ux0LtxcR+f73v6/Xs1O62+9UKuUxh/v371fmGu10xxIW/OHDh0Wk\nM9exFuB5KysrXiDA+vq6ri08d10nXfbnwWccls8O3GBccD07KEMe7LthAc/PZDKeLjabTe2Tm8CV\nceXKFfnUpz4lIiJPPPGE9z2309WZm266KVZLFLCybLuIokjvDdnyunzo0CERETl9+rTeD/1IpVJe\nWwqFgsoXa/Xq6qrKjxlAl1nJ5/PeOtxut3Ws+b3Dfq78F/cR6Z0cFvMalS5efvllrWWJ98/4+Lhe\nBxYyk8l46/Xi4qKu0RcvXjSz3SeF8qO/HKjEfXfX2oGBATPx8Wa+RICVAd1NL8OfWW3n9AcW65XE\nskE3crmc14/19XWPVeSam70SI4v05yO1bRupVqtlbppGR0dVST/2sY+JiMgzzzyj37MTJC9QInbO\nExHxonFE/CObqampni81F9gkYAPFR1Fu9m4RO8cQUCgUvM3kwYMHvdxUmUzGe1nfcMMNGulhTXZr\n49NoNGIFbkU68rOOu3hjhN8CVlFY7udmC45IR1bucSpvDhhWhAxk7ZYr4M82A57PeoXJbEVs7t69\nO+bkj7YA1lhbDqVANptVB2n8XVxc1A0D2jU6OqovFi4LYzkvW4VM8RnGKpVKeZsmi6a3sk/z8SF/\nb0XhAFa5oSNHjmjbkdUbOlYsFtXpFu3ENSLxiEgrgz+Oum+99VZ9eXPBaBQZ5iNEXMcvUuglDLSp\nqSm9Nwy4AwcOaAZ09G9lZUXXHW4b5hy/xJI2vHxEBd3qVfxcxHYsf/bZZ+WP/uiPRKSb6ZsdqtGW\nvXv3ekeqCwsLXtFnbhewtLTkRR9fvXpV9cQybIHh4WHtE7/QOHIQ7YROsNGLzQYi5fiIko943QLk\nHH0GcOkURr8Rga4Rc/z4cS14zdHWMEDR36GhIZURlzzjQvbuyz6bzapO8AYEbUhadwqFgr4zIPte\nOajcjSpHhrOLzPUWBQbYYN3MKd0tVM/vPY7aS8oLaDnFA3yM209BaP3dplcEBAQEBAQEBASYSEX9\nbLfe74f+/50e1w8CM1CtVpWSZKbm4x//uIh02amRkRHdAVu7WNyjXq971CRn2YaD3+zsrBYNhbVm\nFWfFs/m5g4ODPXf9Il2ryGLgJicnvdB1PgJwGQeRrmUwPT3tWXqFQiGWR4prIQHIJg+riC04foZL\ncbdaLS/fUyqV0s+Yqk06PsN1rVZL8/OAgePafZCBZSVajBSrclKYNKdxYOuP+45+u8zl4cOH5Y03\n3ujZN86RYh05us6qVt03ke5RCMad9csdF5EO8yLSkSPnWhOJ09oMK3+Vi3Q67dVhtK7L5/M6rlZu\nFsDKcD86Oqrt4zxMAGfqd+cZH7Vax2R79uxRpgJzfXh4WPWNGQsXnIEaDNjw8LAyUjiqSafT+j30\nan5+XqampmLXDQ8P65xLYmgGBwe9Y+t2u20eP+zbt09EurLuxarjPr/9278tIiL/+I//6M35PXv2\n6BoF5ufSpUtmIWGA1xekRHDTOTB27dqlhaWff/55Eekw627KgYGBAWX0sA5wniu8LzKZjMqU3SEw\nry12G7Iol8uqixYTwylAcG9eJ9yjrMHBQe95v/7rvy7//M//HLtOpFtcG2s/u6fgPTQ4OBg7VkWf\n4VzfL+uez+dVP9GPd9991yvSznrHdfpchtNas3iuQC6NRiMxjUFSVQl+/1gMN+5RKBRijBru5xZG\n73V0586zXulosLb02i4FRiogICAgICAgYIvY1vQHvLvDzjyfz3sW6y/90i/JU089JSJdNqhXLR73\nPJ8d+BCafPHiRb2OM9C6/lWNRsNkEFz/kJWVlUS/FDcE2IW74x8bG1PLgS1X9AXXv/POO1446Nra\nWmLYZqFQ8NpjWQ233HKLWu3spO/6RgwNDXnWfLlcVvaCGT83vNyqL1WtVr20Fnx/lyESiVuEzGL1\nAvcBY57L5WIy5L6gXSK9k4TComHLB20FuzQ/P+9ZV6zD8PXYuXOnnD59WkS6Y81Wu2U9v/nmm/pv\nsJicdRxgPe2HjG6327HM8Xi+65vBMkObU6mUjiGYJEt+S0tLqu9uygCRrkVaLpe9eebOWU5+KhKf\n39Y8BJvKyVKxTlSrVb0/2BXoA8tjY2ND74N+zM/P65qG3xQKBa82ooVKpeKlZ+nFPrhZnXsBY/i9\n731PRER+/ud/Xp599tnYNdeuXfNY3sHBwb7DwNlnTMRmg6vVqjJRgJVEcmBgQFkl9o2DfliBQVw/\n0w0s4tMP6E69XjfnkptsspeDM55n+a4BzzzzjLLFPEcxl3luYiyZneM5108wVCaT0TnASSvhw2cB\n1/E4sJ8Q3pWQm8Xesm+e5SvF8zYpaAXXcfogZpc4MbZIZ/5btVTxDrHaivFi31Zu8/X4RgHbupHi\nRRA0OFPTeAFhEyXSpT1ff/312MZIxHbIq1QqSlfzd+4LfHR01MtHwo5nwPj4uD6Pv7NKrHDBXpHO\nYuxuzObm5ryjMesIkCMcWcndbM2cDwkvMRG/nAqD8wxZiwf/2/29VUiUFZTz22DR4mdBluxUy5S6\nSFy2lnJbGWhxD4uOrVarsehP9Av0N7843N9y4Wt+Ph9/ArgP388ty8EbC9zbesa5c+f0OADy4002\nvrOie3qhH4qdwX2DrDkTOX6PPkVRpPMMz9qzZ48eLaMfb775pt6HqXY3ympxcTHxyFbE1m/3pVqt\nVnVOsqxxHV5Yu3bt8kpO8RrBmzls2Pbv36/fY95w8WX3qL2XPqN/1hwA0um0bjaSqgAwXn/99Vhf\nGYODgypzzMtsNuutsxY4WAfX8XrL8w19v+eee0Sks7m78cYbRaQbJNBsNnUDhbnC1SzQ7yiKvHVl\naGgoFjwiYgdFWA7kqVTKM9bS6XRiGZWkjcHs7KzK+hOf+ISIdDZX1uYU7UKQz/z8vLaBj7CBUqnk\nGYy1Wi0xwAeyHBgY0DZYJc8AnsMMNyK13W57LgDuffqBm4Gd/221z4o0ZMd3fFcqlbyM5tb9CoWC\ntx72U4w5HO0FBAQEBAQEBGwR28pIiXRZE7Z2YHW6VoVI16JyfyPSORKDhQkrkNkdK9cJnCo3q78G\n62Vubq6vooZRFKnVa+VQAsrlsrnjt/LcYGds5cOCY2Ymk1EZMFOEtrDDJlgbthytYyBuv2ttWLLg\nHTwseM7SCwwNDWnf+UgOz+X+uZmKrfpcvbKnu2i3295RCTs3MmAFsszdsWEnfCtHGay38fFxPf4A\n68HZ/RGSz+kAmIHhfGUiHesJv+Xv4JTK2YI55JvlwBgYGPDqUeFz9BPAv6FD1WrVux+zgbjv7Oys\nVwx2fHxcr4O+1Ot1k13iMH+RjqVupT/gtsMKh37UajW5+eabRSTO5LiWr3UUyH20aqZxxnowNHwE\nyOkdRHo7wfKxDLeNMTw8rLposdhJsObHxsaGpqT4t3/7N30+ZJXESEVRpEfTWH94jnLlAsgIuQHH\nxsY8B/5ms6nrDsZ6bW1NZYn1eHFxUWZmZkSky9BagRluW7lNInG3CVeHMpmMOX8ArE38Ww6iwRr3\nne98x/stA/3F6QyznzfddFNs7RaJpzphvXQdrfkIk5kwl4lKp9Me08RzEDprFVNOp9Padw6QsJh6\nwM01KNKVr1X9IpVKmYEv7juJ07Ogb9YRH6eeQT+td3E/CIxUQEBAQEBAQMAWsW2MFOowwWLA7nlg\nYEAtBfbhwLkxh4m6zsiXL1/W+7Glh10s7/DdVAe4P4MtqiQWin2pLOs9KckY74ARPlwoFDwLc3p6\nWp0V2WEVfeNK9Baw62dWBBgaGvL8ahYXF5XZ4BBh60zeBVttaA/7awHr6+uezwufZTMs9sQN3+Va\nTOwgbTnzu9b1zp07EzMHQz+uXbum/2aGDYwg39fN6s3V3Pka6C/X+bJYR+gsO27DwoTfUSqVMtkJ\nNyghk8l4fk4ivj+AlXqCnfqtrMSQPetBUk0u1ke2UjGX7rjjDhHpWMQ/+tGPRERiLFOSFbl3715t\nD4dlW755rt9KKpXy0mOwJY77sQ8NmMaZmRkdO/xtt9uJzAbA7LjlfA8MDQ2Z9df6Ac9bWOVLS0s6\nhjwOkK/LxDIuXLigTuYAtxky4HQUPN/ctCoMjAvXRkMbstlsoiyxhq2srHgyjKLI8xNMpVLe6URS\n2LtIfLwgI9bJJLYQ8t65c6fOA3Y2h8/d66+/7mU+twJGmD3bLADBrTPXbDa99ZxZanzHfknMZrlO\n61aABMuS1w6XneqVgsBlnwYHB7130tramteGYrHosb8bGxs6n5MqU/SqEMLYto2Umy+GIy4Afnm6\nk6BYLGrn0VErM+uOHTu8AqwjIyOqrIhIsApf8sYBL7bFxcUYleu2mZ+PhTapACsrIDYs6XTaiyDi\niA+0eWlpSU6cOCEiIi+++KJ+Pz09LSLxYzx+KcF5H8+1NhHz8/N67MnjYDmb4964Rz6f1xc76HZr\n81ev13WDxQ6j1hGsu2liyt5yjMffbDbrTdLx8XEvAqbVaiVmOeZJ6L5wd+3a5R1X4XORrn5cvnxZ\n+4Z+82YS911fX1edxeZ6YWFBdRb9OXDggLz11lsiEj+GwgsN13MRbCCpJALDMiB6ZS5PMhhuv/12\nEem8UBGRCMzMzOjY4Dgsn8+r3Fi3AbyYBwYG9GVpYXl5WTdD7Lye5JBvbRiweW40GvpbrFlciQB6\ncOjQIS8X2LVr10z9dcG5bJIMuGKxmPjSsoANfyqVUtcJ1oV///d/F5HuXJmZmdGNwGZOt1hvOHLS\nlWU6nfaMvaZTKwAAIABJREFUp1Qq5Y0Hb7jw3JGREW9+NxoNc5PJ74QkuEfZ/eais1CpVHR8sVG3\nCjeLdI7qRLqyOnPmjEY4c+4oLv3TTzss1wzWDUufkuYtvwNZHq5cLd3gNZ/Lbrkybzab3u/z+bw+\n1zLWk3LV5XI5Xe+wR1hdXVVZ4r327rvvJm6ggH7kHo72AgICAgICAgK2iG1lpEZGRkxa3rUERkdH\n1aKBRc+7bIsud7PAinQpzF27dulzredz+Cl2vriuWCx67Bm3Bb9dWlpSCy3JKmq321rgEv39r//6\nL/2ej4p++Zd/WUREnnzySf3MdUAulUr6XGYh0AZkQu4F9Hd0dNRzbuRcIbj3xsaG17/h4WE9LuB2\nuRncRboWAxia1dXVxLpWlpNsEu3eaDS8I2CLql1cXIzVTMR1YInYYnbTE2QyGTOjtBsswblsWC/x\nGfrNLCUfe7FeinSKvYL1wLjMzc0p48OOo1adLnwPuTBbYB2RA1bm/WKx6DkWb2xs6NjAsVhEvHB6\nTuMA9qjRaPQVduzmInMdnqMoijk6o5+Qr5V2BddxPyHzXbt26Tig3adOndI+4Vk8bliz1tbWtH9J\ndclY16wjXmDnzp2x48V+AHaJdR19tIpvX716VS14MCucmoKB+Q35TU5Oeu2+cOGCN1/37t3rzZ9L\nly6pnKEHPM4PPPCAiIg8/fTT+luMwdWrV/U3m+Vecuu17t69W4/gmYlwWW12rsbfcrnsvXeuXLmi\n98bae+rUKdUDDp5y2ZXx8fHYcb+LbDar8w9tbTQaOnfR1l6sJtrA7hD4Nxf4Tko/AFipgvh6Pgp0\nr7NkuVndvqR3qhWokk6ndS3DXz665+NQyz1nMwRGKiAgICAgICBgi9jW9AfM7mAHaSUe4/9jB760\ntBSrwSXS2T3DImBLxE2CyUn2ePdpJRTj+ncinV2+u6PmjMWwRIrFojIhYNOs89yDBw+qBcRWrOvk\nWqlUtM4gcOONN3phw6lUSnfc8K/hNnCWYPSdZYVduMUGsCXJrJHloAjfCPdZDK7xx/WSks6k2bpP\nuh/DtdbZSubxd8e1VCp5df+4/qJbX68X2Dkd4wo9rdVq2vfN/JagW3BAPX/+vMd6MYuSlK230Wh4\nSVjZUdX6LdgqtjRdK68fcLJH/MWYWP4QbrZyRiqVisnfZTOr1aoyFWhjqVRS1sfKNI552Gw2Vd+w\n7ly6dElZQAbaz+sA+sf1+lwn3YWFBW99YmANGRwc9Hwtr127pgyS5VfIgG8UWCVr/lhzlBli6Ozc\n3FxsbRGJB7FYqWfw2+XlZWWz0B+LDazX657fKmf3R4Z21neAx62fVDUi3fXWCoqxTk44ASUHXrgM\nUqFQ0O/h85VKpWJMFJ4BFhVrJ6+Dlg9is9lM9L9lYEyga6lUSnUAcy6VSplMjqsXnGQZa0O9XvfW\nWU6WmpRBnhPQAta48nPZyR3sKpjToaEh7Rt0a3V1VZ/Hmd+xpkBPrOCafrCtGylr4Hgh5QXw6NGj\nItJdCJaWllSYWLx6OfYhzwic+CzKkTdSliMql70AeAHEgobvp6en1QE1CePj47oZghO5RaGWSiWd\nNLju3XffNYtzYjFyC4GKSKxNaCtvSq2IDywuqVTKy/vkHoeJdF5e7rj2OsKFIvNEcl+YHKHHZWbc\nNlsbMKvEBR8lcVRUEnWNiZbJZPQ3aOfa2ppZXBry5RcC9NvaMPBmB/+GfpbLZR1rbJ7y+bxu0rAI\nN5tN77jKijriUhI8BpApL4pcUkOkI1PoIGQbRZGXZTuKIt104O/Vq1dVjyEDK/9XPp/3IuWGh4e1\nDVZkoIhdDBjXuptike6mtNFoeM7NfC3yAv3whz8089u50WScdRz927lzp1deKpvNqixh0PBCjvtW\nKhXvGDyTyejx+2YbWWxqrE0zUCwWzRcz2oUNUKlUMstCuTnSeHPFhqj1DAQjYM2y1vFKpaL95DbB\njYD7k+R8n1Tk3NrY8trFwSL4Hmvvjh07vGhgPrZKcmyuVqteJDkb49ZvrdxR/G8uFdWPU7XlImFt\nRFnX+MjzekurJG2yeCzxjuFyVZwnKilXFAPtstZogPuLtbef/oSjvYCAgICAgICALWLbGKl0Oi2z\ns7O6s+S/2OHDKjl+/LiG5TLcAsVsSfJ3sESZfXJ/K9K1kK2jBOzumfnBTrnVaqlFg/ueOnXKcxS1\nmIHR0VHdkXNb3FpYvGu32syA9e/WCXP7xLWpOHO7SJzmh/x27tzpMUssD9R7O3PmjEddcwZpy+EW\nloFlKbPVkHSE0W63vXw06XRarRh21sY9rUKdQLVa9VivnTt3xrLNo2/u8ZFI15KBTuTzeW0LjkYy\nmYzqOaxsDvPG/ZaWlnoW/GVEUaRMFI5T6vW63odrKbr52oaGhlSfksLvNzY2VAaQY6VSUZlz2DXY\nG2ZxrDHE/Oe5ZznSu+Asxr3ai8+QkoODBdDWo0ePmiksMK8hK8shW8RnhK5du6bjDoZhcnJS2RP8\n3bVrl8lcuvfNZrO6JnAYN36TVAyZj7whi1KppG3AeOzYscOs8whAN6x0Ke+8804s87VIXDc575Ob\n2qVUKqnLAa8vrtPva6+95gWKpFIprwbp4OCgypRr/CWxxmiTG2ADuDnSeKwQ4GDVL6zVanqa8p//\n+Z+xdorEcyFCpjiiWlpail3LR1sicVnyOoZ/87sBawFOZ4rFour02bNnRSSeVwv6sr6+HqvPh8/c\nNBTMvAGc6mCzmpGug7xI/NhQpCM3qxYsdB+y4mNX6Eu73fZqX2YyGdVVtI/zDia12evDplcEBAQE\nBAQEBASY2DZGiuvniHQttEuXLumuE39//OMfq/WCHXypVJKXX345dk/elcOymZmZ8fwm8vm8fo/d\n9traWuzfIh0rBFaldWYPX5Rz587pDh4WzdjYmPYJO/+xsTG1ZJDK4NVXXzV9e7iWmEjHQddNXsmW\nFayYq1evmkyUG+rK2LFjh8c0cRgo5MrsEzstA7B6/vu//9vz5+AQfFh3Vg3CwcFBtRi4VhwzVtxG\nFxx4IBL39UHNsPn5ebPWmZsYc2Njw3NertfrJgtoWbLweYDfBMvYYpeg45yxHM/fsWOHytpK4wC5\nTE9P6/O4TS6zxoDseyWpxHPwjFwuF0seKtI7zBwMIeTMliGHHrs6VqlUEpmofpHL5VTWlh8RmO7J\nyUmPzWTfPCQ+3bFjh+piUkZ1toqBM2fOqNM3vpuamtL5arFpWJOYBUD7Wq2W6qKlT0AvJ3I3YePy\n8rKG6L/99tvebyCLWq3mBaMMDQ3p2HHmatfvR6S7JkDebgoXkc64uSz/+fPnvQCJSqXi+Sqx7y0n\ntrVSSIAZ7sVEidhBLBzuD5k9//zz+j2S4p4+fVr9Uq3xZXYTY4gx39jY0Dki4q/dvd4bVqAQ5udm\n6SAsWIEJrjO/5edkfTY4OOix3VyTj9Gvz5YbDNNut02mDrA+g47V6/W+0q642FZn88nJSVVQpv4h\nGFClXNIBf0GnithHcW4hS5FOlJtIfNKwklglIly6nRdXN3W+SHcz0Ww2YxlqReJ08N133y0inZxQ\n7mLDLyp22naPFG644QY9ruA8PRaw8KytrXk09erqqucAurGxoX2BvJaXl/X4jhc/LIyvvvqqfobF\nAG1mR1Euu+PS9/V6XRc365hss4gK6AB0hycjRwe5L9VeRyOQEfp46dIl79peWb0tZ39slrAgXL16\n1cuGLNJd4LGJ4GMn9zhCpDsfXnvtNf0Mi4NIt79ou5XJXaRLe2Mxqdfr+hwueYM2Y2NQrVbNfDPu\n5owXemtDCrA+QvZ33323HkMg038URYlBAoODg/p7nvdYdDEXTp06JQ8++KCIiHzzm98UkY6M4IyO\njcWVK1fkIx/5iIiIfP/73xeReBFsyJRzaHFAiHusPTAwkHgsx8D6hbafOXNG176kzeb6+rq3RvLc\nwwv8ypUr6lSPv7Ozs966Yx1tcrkSPmrhIuMiHZni91hfOCru/vvvFxGR7373uzqnrJxVx48fFxGJ\nGdNWeRasoxyhheuazaZ5nArddstviXTn5cjIiOoElxfDPOTs/dbagGfg6LHVamlAAL+HOKO+lasO\nsuENN2RubYDYKML3uAfnWsJ4NRqNvqsb9GPsbla2httpVbNwy9Dw/Od3gzuGHMCD/g4PD+u8YZ3B\nmnDrrbeKSJekSEI42gsICAgICAgI2CJSUb+xiu/nQ1MpL08ELDW20GDNMtX20Y9+VEQ61iDqFcFB\ncvfu3V5tt2azqTtM7LLZosYOl8P48bxeu2dYJdjdz87OmvmrAD52A6MDK39gYEAtOS54iqMEMASp\nVEq//7mf+zkREXnllVfUqsDOe2JiIsboIUs6hx9DDhz671odXNSWj8tgqXIahcOHD4uIyBtvvKHP\nAgvAebXYMVGkd/bapJBfq7ipFXbLRxTusRZT9fxb92iXYdVfgxwzmYxndUZRpKwYWJuVlZXEIwRg\nx44dnqV88OBBZTj5eMMdtxtvvFHbbx1Ho7+5XC4x3Qdf388SUSgUTLldLyBTrr9nHVXfe++9ItKp\nHQl9+cIXvuDp7Pr6uh6x4358xMMFe++77z4R6Wabvnz5stx1110i0plrIh12Ab/BGjM3N2cenWKd\nwNrw9ttv62dog5XJn2sjQva7d+/Wccf8P336tH5vHY8B+Xxe9aTfunEua90LeH4+n4/lWhOJF3MG\nUqmUzk2s2+vr64k6hnnUbDZjRXxF4gwnxrxSqXhpCPg6rMHFYjHGogNuBQE+hWBYa+FPgqR8YgzO\nS5Z0pAvk83mvQPn4+LiOE2TVi6F2j6OZbcNnuVxO5WsFEeH93m63YycSIp1xdWtQsvM615PEGo31\nOJ/P63vCWu/eL4DR6qWngZEKCAgICAgICNgito2RGhgYkGw2q9ada0G418NKwN/Tp0/L9PS0iHSZ\ni6WlJbN21rFjx0RENOssWxhgOLgmFz5rNBqeRbZnzx5tM/w0OAwVuOmmm5RBYBblC1/4goiIPPbY\nYyISdxj9nwCGF8xWr2e5vkqcYf6OO+4QkY5jrsW8WTUAXQs9m816dY1qtZpaLGyFcfZdbpML14Lj\n0HRUUp+dnVXmEuNRLpdNx9MkRsrNNM/tE/GtuSiKVKbcN1cGlUpFP2N/PviAoM0WO1cqlZTtxD2g\nk/0AjAr6XSwWY9moXfCYuoEUzHRC9gcOHFD2BPP7a1/7ml4HH4RMJuNluU6lUqqzeG69Xlc5W+1k\nPwhY2+x4DFnlcjnT3+Thhx8WEZHHH39cP4PTMHw46/W6WujMlMFCdp2cRbpr1vLyssoGsmen7kOH\nDolIR2ddlnD37t36mw996EMiIvLee++prlqpGxiunwczo6ynrh4zM2AFieA7ZtY57Qcn/RXpjCUY\nJmZ3sZajykMmk9HrOHErM4i9wD6L1rzFfT/84Q/HHMRxPfoHefPaznVgoQeWLiLBKNdcxTwrl8vK\nnmH8R0ZGlOHC/dg3OIoi9YeF3vVKOuqmFOJ5k4ShoSEvDYG1Tg4PD3v+S9a78nrASTfR5usF1okD\nBw7oeKN9zMBjfLlvnOATuoU+gjFNYqS2zdm81WrJ2tqal+NpcHBQBxGUfbvdVqcwOFyKdKPmXnrp\nJf0tFJ2z/4J6teh3XrDw26Sjjmw2672srCKu7GjOL+a//uu/FpFkR1uR7uILJ7hekQ2uE2mvIxb+\nzKLt3c1KuVzW53H+EmtiYWLzfa3ClFBq3lzh3pCHVdSSN0i8eXKVmqPY2HnQdfq3sqJns1lv8vIG\nmRct6Bba3Gw2zRw11ni5496LjraOHFysrq56TvP79+83M6BDzvhseXnZiwJbWVkxjxfcbOdcYoX1\nBv+Gk/25c+e8skbZbFaf++KLL+rnWEjhIH/p0qXEqLjNjkEwbuz4ihdaOp3WtQPjf/nyZfPYFX3h\ncePoMJHOSw76yflo8G8+puWyGC44sha/xcuz1Wrpixt6Xq1WTSdzyBDjNjc3573M+T44brQ2Y+zo\nzXmEXLTbbTWyoJPr6+uew/jKyoqZ2Zyj00Tikbo8B90N1ODgoG5iMZfq9boafzBOarWa5xbwwx/+\n0DMgR0dHVQ7WMSg2cnNzc/pvbHx4zDF/d+3apRs33Jc3XtDPcrksP/rRj1RuInHdTqVS3lrBfeci\nzVaOJ+gEnNtHR0e9414OLEhyCt8sgzhvzN3IULj1iMTfF1xmB4CuchSjWzx6fX1d5wXu9+6773qb\nf56D0AOrRB3fG3+tYAcX4WgvICAgICAgIGCL2DZGqlwux6xZMCacIRm77aGhId0FI8z/pptu0my4\nvIt12YdecMMjOfeE5Vhs0dqw5NbW1mK5U0Q6LIAbDs7h6Ax3lz05OenRxeyAzCwULDlYPXNzczHH\nfYDZG9eKt1I6MNPBcrBybGAc2ILjMHuRjpwxrpBHLpfzauhZlhDrCZiflZUV7RMXm8a4WjmDkjJg\ns+M2ZGBl2eX7cI4qi2Z3c3dtZslBPz772c+qFYagg9XVVc8hc3l5WR38IVsuSv2TgNMDuDWqmC3A\ndTt27FDWA3IbGRnRcHDOeu3q8fj4uDJlOHLbu3evsrKQAbOq/RajZQaMf4sjk1/8xV8UkY4OuQVn\n8bkLNxDAKrA6OTmp8rCy51vtx7FWLpdTdgfrDgdw4K/L4gBYCzgjNfSDx8FNa2EdXVSrVS8djdVf\nka5+WMcySSz/0aNHte9AqVTSdkGH9u7dq8wg0mBsbGx42c5FuvnBwDxabWg0GqqDOC61irrz+gNW\naGpqyqyE4R4lcqZ4jEc2m42dNIjYebsYURSZNUhdlorzoWGMqtWqMnn9OKczisWil8qGK4NANry2\n4bNcLufpGH+Pv70CIDDf8TeVSulzeSytY3q3IgGDGXgX+Xw+lv3dbXsvBEYqICAgICAgIGCL2DZn\nc5HODh0+ALD8Zmdn33fna9cfZnR0VHftnDQR58fYtbMFwI6FSKoIpuHcuXNqlTAz5P52cXFRd998\nPoz2sROm5QPCWd1FOjt5DtEV6bAAcAQfHh5WyyepGjpbDpY6JDl49sqk69YZzGQyKiO2EpjVQd+S\nrFcLXOUc/2b/GsifK9Fbfh6cpsIFO6K7VcGz2ax3vyiKPJ3I5XKaXZ0ZO8ijn3qSInGmkXVBpHdK\nCResD+y/kJTwFM9NpVJmrULMC+j2e++91zdzlATIft++fV7yytHRUW3XM888o0wO9CqbzarVzuPm\nMir5fF7uvPNOEemGs7O1byX9BVge6O/hw4eVSWOLGwwJ2mKldrjtttv0Phj3SqUiJ06cEJFuzbbL\nly9rP60aeVjjxsbGVPfBGrEvJRifxcXFWJoUkXimZ4wD+3Dhfknri0hXJ4aHh+UTn/hErM3PPfec\n9hdzde/everfhPWsWCzqnOPM/xYDhiABpIWIosicSydPnhSRbiJbK2GxSHe8kA7jBz/4gX7HKXnc\ndU+ky44wE5OU4oV/h/5yzVBOOeHWhesFrOHoh5WIdDPwewoyshJHu4yTiM3qWIwfA3Mdz+2VXgUn\nSOzzh88wz1dXV3Xc8XezEwLGT62zeS6Xk9tvv12zA28GjpbohVKppIMCRU2n054D3fLysklNc4FL\nF3zUhkFip3M3UkKkq7x4FredFY8dGNEP0Ip4qXOpG0sB0G+OpttMUbgAZFLRSCtjLJd0sTZS7ma4\n2Wx6OXQY7Pho5YVyFzc+ioPMV1dXzT7jBY8x7HVf9yXNz0hyzO/1EsHnGJMoihKjqxC51G63la62\nHK75+W5/C4WCbiZ4oeLjVJGOXmFT0K8ttdlibUXeYqPHRgpelmgnzwu80Pj4CAuoWwjbAu6NagFn\nz571Iuos/VtfX9f5jJchL5xJfees3sDy8rK3VpVKJfMYwkUmk/GOrdvtto4djoFWVlbkgQceEBF7\nI4V5mcvltE/Y+FgVHVjfOdDE/U0mk/Gc5dmgwneVSkX1lzcx3/nOd0SkO+67du3SMcEctSLhLAOb\nC4EDt912m6krMJQ4sAVHiiwPaz1zqzww4G7C5WoYrh4UCgVv3nLxbXZ2BtGwurrqFUXmfnMhXrdy\nQBRFZnAI5iQbuG5h32q1qmOD+WgFC1jIZrNe2a0oirxoXHbJ4ChADuZhOXH78vm8tg/Xz8/Pb6nM\ni/sMN1N/EsLRXkBAQEBAQEDAFrFtjNTw8HCMjbLCxwH3+AefIT8Qdubnz5/X3XVSltNyuexZBCdP\nnjTpTpd+3Ldvnzq0Y6d64MABzSDO1q5bhFLEZ70mJia0rUwLu7XR3MLLLrC7Z+dYdrRk+W5GqeI+\nbmFKEfGOtaIoUquDM9H2W5/JcnzH/Th0mnMJicTHkC0+lxk4fvy4Z1Ewg8Rtcpklps7xDCtnGMuR\n5Z90zMbH2/g9O9xCF5DuY2VlRfUTMmNnfVica2trJjsB8PjByoajLddrRE6btbU11UWwCqlUynNq\nB9so0mWPy+WyHq2wMy36gesqlYoyEJjr+Xz+uo948TsRiRW+5gzK6LsFMMLQMS4y3m+NMDDJzDx9\n+tOfFpGOnnzrW9/q6x7umletVnW9ZLm4WdEZXFjanWeVSkWZBTCme/fu9QoAr6yseK4RnIkaYAYA\nOjY3NxcLjBDpyADtx/yoVqse2wI54DcinXFz83BZx69WaolCoaBtxrNarZbJorrr4szMjK7bYKQs\nxnR0dFT1mF0B3Lqfq6uryjRhHbh8+bJZl9A6HeH+4p7QWU49w3ru9imfzyfeG++uXC6n7xEr1xK/\nr9Fu9DeTyZjv1K3MawB9gn5auaB6sVFYo/D80dFRZat5/eQ8h0n3YwRGKiAgICAgICBgi9g2Rmps\nbCx23mwxUUAqlVI/Ily3urqqVi7vvF2HtNHRUbUOLMdM+FRMT0/HEgSKxH0B2PLHDh1WOULQXbg1\nm4aHhz3Lq1qtmk7YYATQ1kajoTWqYN2x1cu+HLBOuP4WfEZOnTqlfUnawbMDtVWHkENDXUdbtlbB\nOqyvr3tn66Ojo9pWjFuz2TQZH9yHq5cDPOZu+LFVx8tikDY2NjxGiutCAalUSi0bMBjcXr7HL/zC\nL4hI3JcOMsJ9LX8dbpelW5wg060ZODIyovK36j5yfyB7ZqJwb+gO+8BxAIWr21ZiSJGuXwonu3WZ\nRPaHgXyWlpb0N/AdYwsXzES5XI6lPOEElnge7pnEEHIyX1in8LO8Hlh+PFi78HcznDx5UhluME75\nfN6sp8d110Rs/5WFhQVPz4rFoufQvrCw4MmAGXOwfOVy2dQtAEwnJ+Rk5/8k303094YbbtC+4B6N\nRsNk5rFGw7neSmOxtrbmye/ee+9VnzxmTjBOGEsOMEH7LIby4sWLnq/P+Pi4rknMOFsnJlhneS3n\nUwq8q4B+A0symYwXym+tO5VKRfsJxqler/fl1yfSXTuYecM6jbGx9HNoaEjfgRw0A1mDMc9kMtou\n9ofCb6z3GPxjBwYGlH3E2nD58mXPF8+qX8oJqXth2zZS/TisYSGbnp42nQfdRYSzf2MhWFpaMqPN\noLR4AX3ta1/zrtmzZ4+X3+RDH/qQ5jABekWuYePDRYmxiHAeDig1Jt/evXu9ws0idvkPt2Bws9nU\nScMbGuuozd1Q4fcinYXCjZDhe3L5BCtqxo3+EfFfMpbcrGCCWq2mfcKEvHbtmhmAcPToURHpZC0W\n6URUutmkeTOB+y4uLno0tFVaoV6v68sa4Dxc3B9E9kDXOBjCWsjQlmw2mzg/MJaW8WHpejqd1vHi\nuYKXARboZrOpY43vepVqgKxwvzvvvFOPCvDCWltbSyzSjP7efPPNKje81Ofm5vRFYeXG6nVflJ3B\nPIuiSOXERboBK8oKuJ6oniRg3t51110aJeYabdy+iYkJrxoDOyMzOHN3L1y7ds3T/Vqtpi8IyJwr\nTQC8bnDEHxtXLqAHY2NjOsbWEQ/usXPnTr03jBRrzNlI4f5gTWCdwLoIveJCuygZdvnyZa9dxWLR\nm0PWC7dcLmsbID9+CWMTYBkYN954o75X0BZuP4zndDodW0/cNhw8eFBlbc17GDutVsv7fmBgQI0c\ntHl+ft5098C7yi03JhLXCbTVGmtez6B3HOjj5lxsNpu69lgbY/RtYGBA78O57zAm1tEtSAWR7thB\nF1utlvZjs0hURjjaCwgICAgICAjYIraNkXKpTexSK5WKV/jTYqPGx8e9zKdMdXINI1id2Cmvra15\nhSktMBv1yU9+Uu/nWpO9nNFgjYEGz+fzHvVbrVa92m3nzp2L5bcS6ey8QTnjvrlcTpkodtK1rFNY\nLsViUeXLjJTLBDUaDe+YjJkN7OR7ZVfG55zHyirsm2RJs9WJ9sEayuVyXq0o9IX/tlotM9eNlRcI\n/94s1QYsQ4wbW1Rswe/fv19Euta1lQdlZGTEy3xuySSbzWr7kwp6TkxMKGOG57333ns6B6yw8s36\ne9ttt4lId0wbjUas1pWIaEg7Y2pqShkGWKTr6+tqHeN+b7/9tscI5fN5tTo5UzOsdViQQ0NDsbUE\ncxa/yeVy3vgXCgVlepjVc49remXwdtErlxGAWqBXrlzRnE1W3U/ozksvvRRzxMd1+C0zL1xfrhfS\n6bTHNPQbwm4xKtYx+OHDh5Ud47pvFjCHcQR45coV1aOk/Ea9nKcxb3hOo384FuZqEZgXfNT3kY98\nREQ67hJgMfi43GVAucg90G63Ve+471i3Mc937dql+cOsmpqc1R5tsQIQwFqKSKzmHk4xkmTZy5Ed\neocx4txykLPF1PJnWC/y+bye3nCBdOgH5nK/6Vc40MfqG+ZAL2d2rDfuu9VtP9YstLmfo73ASAUE\nBPyPoF8fjoDN0e/GJ2BzvN8JnwMCti2zOSyEpKzZfIYK51LsROv1uu4UOVwZO1ZMFuvMd2xszLOW\n2Krk2lLIWot2/su//Eti31B1fGlpSe9n7YAB9q8B+KwdVjlbGpa/E2fvxtl3s9lUSwqWEg83rPGh\noSGcRkuHAAAHZklEQVRTXq4DYLvd9liqoaGhmKM44Dojl0olz6dnenpaLV5e3Di5nEg8KSWH1sMi\nZCYFdZLgINlqtTyLhzMGgxFAdmpOfFiv11UXWBaQEde+c5kLZMJlDA0NeX5nmyW5tIAxKBaLno+P\nxSAws4J0Cvv371eLFKzm6OhozIlbpJNFu1dGYfcZkFsvB/r/KXBiv9/93d8VkS6L/eabb3psQiqV\n8lJsiPg+dMyicuh8ku+UxXQm+WFt1ieM1+LiopdYNGldEYnXhExKZMxJdt30JtZaw6kTcF0vfxK3\n7tv6+roXrHE9wLiBkSwWi54fzPj4uN4bOrlv3z7TxxRAtvXTp097zJWFmZmZWIJnkc4axnXmRDrj\niHUv6VU7Ojqqz4PMNzY2zAoXuM/o6KiZsNcF67GV7Ph6USqV9D5ra2uaQBa608964QJsK3R8bm5O\n1yfMKc6oz36v0AXMs0ajYe4DIEP8ln1W+RkYL+h4s9ncNLO5RNsAEYn+5E/+ZDse/b8SQZbvH4Is\n3z8EWb5/CLJ8/xBk+f7h/5Isk7ZL4WgvICAgICAgIGCL2JajvY997GPy3HPPfdCPDQgICAgICAi4\nbtxzzz3yve99z/xuWzZSAQEBAQEBAQH/GxCO9gICAgICAgICtoiwkQoICAgICAgI2CI+8I3UU089\nJQcPHpSbb75ZvvSlL33Qj/+Zx8zMjBw9elROnDghd955p4h0kr/df//9csstt8gDDzxgppIIEPnN\n3/xNmZyclCNHjuhnSbL78z//c7n55pvl4MGD8vTTT29Hk39qYcnyi1/8okxNTcmJEyfkxIkT8uST\nT+p3QZa9ce7cObn33nvl8OHDctttt8nf/M3fiEjQza2glyyDbl4/1tfX5eTJk3L8+HE5dOiQ/PEf\n/7GIBL008QFFDkZRFEXNZjPat29fdPbs2ajRaETHjh2L3nzzzQ+yCT/zmJmZiebn52Of/cEf/EH0\npS99KYqiKPqLv/iL6A//8A+3o2k/9Xj++eejl19+Obrtttv0s16ye+ONN6Jjx45FjUYjOnv2bLRv\n376o1WptS7t/GmHJ8otf/GL0V3/1V961QZbJuHjxYvTKK69EURRFKysr0S233BK9+eabQTe3gF6y\nDLq5NayurkZRFEUbGxvRyZMnoxdeeCHopYEPlJF66aWXZP/+/TIzMyODg4Py8MMPyxNPPPFBNuF/\nBSInPuCb3/ymPProoyIi8uijj8o3vvGN7WjWTz3uvvtuTfgG9JLdE088IZ/97GdlcHBQZmZmZP/+\n/VrqI8CWpYid6C/IMhm7du2S48ePi0gnqeCtt94qFy5cCLq5BfSSpUjQza2Ay0K1Wi2pVCpBLw18\noBupCxcuxCovT01NqZIH9IdUKiUf//jH5Y477pB/+Id/EJFO/TRkNueaUgGbo5fsZmdnZWpqSq8L\nutof/vZv/1aOHTsmn/vc55TyD7LsH++884688sorcvLkyaCbPyEgy7vuuktEgm5uBe12W44fPy6T\nk5N6ZBr00scHupHqVUYgoH/84Ac/kFdeeUWefPJJ+bu/+zt54YUXYt+j1EnA9WMz2QW5JuN3fud3\n5OzZs/Lqq6/K7t275fd///d7Xhtk6ePatWvy0EMPyWOPPaalVYCgm9eHa9euya/+6q/KY489JuVy\nOejmFpFOp+XVV1+V8+fPy/PPPy/PPvts7Puglx18oBupvXv3xupDnTt3LraDDdgcu3fvFhGRnTt3\nyq/8yq/ISy+9JJOTk1qX6OLFizIxMbGdTfyZQi/Zubp6/vx5rQcVYGNiYkIX1t/6rd9SWj/IcnNs\nbGzIQw89JI888oh8+tOfFpGgm1sFZPlrv/ZrKsugmz8ZRkZG5JOf/KT8+Mc/Dnpp4APdSN1xxx3y\n1ltvyTvvvCONRkO++tWvyoMPPvhBNuFnGrVaTQsXr66uytNPPy1HjhyRBx98UL7yla+IiMhXvvIV\nXTwCNkcv2T344IPy+OOPS6PRkLNnz8pbb72lUZIBNrh47Ne//nWN6AuyTEYURfK5z31ODh06JL/3\ne7+nnwfdvH70kmXQzevH3NycHoGura3Jd7/7XTlx4kTQSwsftHf7t7/97eiWW26J9u3bF/3Zn/3Z\nB/34n2mcOXMmOnbsWHTs2LHo8OHDKr/5+fnovvvui26++ebo/vvvjxYXF7e5pT+dePjhh6Pdu3dH\ng4OD0dTUVPRP//RPibL70z/902jfvn3RgQMHoqeeemobW/7TB1eWX/7yl6NHHnkkOnLkSHT06NHo\nU5/6VHTp0iW9PsiyN1544YUolUpFx44di44fPx4dP348evLJJ4NubgGWLL/97W8H3dwCXnvttejE\niRPRsWPHoiNHjkR/+Zd/GUVR8vvm/6osQ4mYgICAgICAgIAtImQ2DwgICAgICAjYIsJGKiAgICAg\nICBgiwgbqYCAgICAgICALSJspAICAgICAgICtoiwkQoICAgICAgI2CLCRiogICAgICAgYIsIG6mA\ngICAgICAgC0ibKQCAgICAgICAraI/wdvk9M+hXkCqAAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1024fe090>"
+        "<matplotlib.figure.Figure at 0x7fd621d91150>"
        ]
       }
      ],
@@ -300,7 +291,7 @@
      "source": [
       "The second layer filters, `conv2`\n",
       "\n",
-      "There are 128 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
+      "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
      ]
     },
     {
@@ -318,7 +309,7 @@
        "output_type": "display_data",
        "png": 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0vzuvmqNWAfPO3EFL6ws5BltgaNfyy/LKGnOdQ8BnrH9o3PTtiEPGRv98b9eu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\nZsxo8/gdQWq+jUUreVymxRalLOrMsuefbf+Ots2sAAB+YKnms61ChvM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NAAAg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\n1k/7BsAeRj8v6JfrgWVZePb8OqPQWYG7du0ayyKM6HsB22sjRnPK9dhaT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dZbb+3kdrwB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\nxcV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c3/Gb9+s973lPRIy/53ekP5xi5p9yhg+A9wtOIb9/t9fMvG9TU1O92CV+gyy07dgO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7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\ni8T/A2aklexSCttuAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1024db950>"
+        "<matplotlib.figure.Figure at 0x7fd630658d90>"
        ]
       }
      ],
@@ -344,9 +335,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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1mv45OXPmTNGHU3mMRAEAAATgJgoAACAA6bwcDAwMlPK6M2fOLOV1UR9mzJjh\n4vPnz6d6jH9NnTt3btSvq2k2v1v4O++842KdUq+LApuZvfjiiy4+evToiK/jp9F1oVdNJV28eDHN\nYUcWRzYzW7p0qYt37tyZ6jnqSdbtWKZMmZLp8yUJSZGlTd/5QlLDei1WMZ2n17qmsbVTvVk0BUrp\nwW8E30R1dXXZzJkzbdy4cTZhwgTbunWrDQ0N2ac+9Snr6emxrq4u+8EPfmCzZ8/O8ngBAAAqITid\n19TUZM8995y99tprtnXrVjMze/LJJ+2RRx6xvXv32kMPPWRPPvlkZgcKAABQJcELEC9ZssS2bdsW\nGarv7u62TZs2WVtbmx07dsw+/OEP2549e6IvyMKJmRk/PjqQuGrVKhfv2LHDxZzzYmS9AHFSOiTt\n7DAdfk+bvtDh/KRO6Tr7Sjutm5nt3bs31XPUikWfi5d2MdzQ2W9Vp7PXikpp5b0AsabSdUWAiRMn\nRvbTtJ9+rht1RnquCxA3NTXZww8/bOvWrbPvfOc7ZmY2ODhobW1tZmbW1tZmg4ODoU8PAABQacE1\nUS+88IJ1dHTYiRMn7JFHHrHu7u7Iz5uamvifIQAAaFjBN1HDs2fmzZtnn/jEJ2zr1q0ujdfe3m4D\nAwORGQkAAAD14oknnrjlPkE1UZcuXbIbN27YjBkz7OLFi/boo4/aV7/6VXvmmWds7ty59uUvf9me\nfPJJO3PmzHuKyxmdys/dd9/t4tdee83FnPN36YjpkSNHXOyvUn/9+vVUz6d1H1oX4Hd/Xr9+vYsv\nXbrkYn+6tE7lr6KQGqus6bnVmhSu82KE1udo3Y22tqi6ZcuWRbZv3Ljh4r6+vhH/PWtJ51xrE/2O\n4vV0nqsoTU1U0EjU4OCgfeITnzCz3/yx+cM//EN79NFHbd26dfbYY4/Zd7/7XdfiAAAAoBEF3UQt\nWbLEtm/f/p5/b25utmeeeabmgwIAAKg6OpY3kN7e3rIPoXL8jrv33Xefi9esWeNiTe2ZRbtia5rO\nHy5fvHjxiK+r04TNzH7yk5+kPOJq0fNlZrZly5aSjuRdWXfWRjGqkFrS7wM/ha+6urpc7C9svW3b\nthGfT9OVZtEFsLOmKcZJkya5OM9FnjEy1s4DAAAIwE0UAABAgFLTeX431EbteloUP9WUhr4H06ZN\nc3GeXaaL5M8gO3DggIv1evMbw+qweGdnp4v9GRr+LLxhPT09qY7PT/vlmQIIkXf6Tq+5tAsDY+zQ\nmWdZpKpHdvKsAAAgAElEQVSSUnhKP5f+DFpNS+pMW3/B6jzp9xjKxUgUAABAAG6iAAAAAnATBQAA\nEKDUmii/nkTz1WVN1Uy7QrdObZ8yZUrkZ3v27Mn+wFKYOXPmiP+eNK13ePkeM7P29nYX+7U62iFX\na4n86b9aY9Xf3+9iv69YUbU/999/f2R7yZIlLj506JCLV69eHdlPz+XSpUtHfIxZ9HpRSfV+WjsR\neh70/ZgxY4aL/WnWcXVyDzzwQGRbj+nZZ5+NfV1tC6HXlV9TprVo48e/+zXjT3PXVhJpadd0lE+/\nQ7SGyX+f9u3b5+Kk+je9nv0WJXH0utyxY0eqx/h/f/Rvjn5e/WtWj+/8+fMuzrNjeVXo91qRNcz6\n/bR8+XIX63eLWfS6Onz4cO7HZcZIFAAAQBBuogAAAAIELUBc0wsmLOQHAABQJUn3LYxEAQAABOAm\nCgAAIEAps/N0ZhGys3DhQhfrYsRZnG9dkFO7cadNzf7jP/5jZHvjxo0jxllra2uLbPuzyEZr1qxZ\nke2zZ8+6WM9F3te4HofOjko7M3Tq1KmRbe28nJbOvvI7w2dNZx3qbKkiz/ns2bNdrL/vWOi0rp3l\n/RmlOkNK3yc9X2bRhXKvX7/uYn9Wsa4QoI85duxYZD99Dp291draGtnv9ttvd7HOTvYXbNffS5/P\nn9GsM5eTrjk9Pv18+TMO9Wc6Q/Cee+5x8X/+539GHsPf0GKk+fvGSBQAAEAAbqIAAAACcBMFAAAQ\noNSO5ciWdu1NS+sHtPvu5cuXI/vV2v31z//8zyPbn/zkJ1M9Lqnbehp+DZTWdiV1yI/rxqs1UGbv\nrZEa5nfS1fqIEH7diB6Hf0xp+J3Wtc4trbzroJTfNboM2in5jTfeKPFIipdU96WdujVOWvEhqfN1\n3IoIfg1T3KoW2lHcLFqbFVfX5m/rd4Pf9V9/x76+Phf7qwVoPZd+t/qrQejragd/fz9UEyNRAAAA\nAbiJAgAACEA6r4Hceeedo37M8ePHcziSW/uv//qvVPvpsLqmK3fv3h3ZT9N+SamHuLSkP+1YF5hO\nSnXFpdJqTd/5/OM7d+5cTc8Xkr4ba/xzrumZIhdfrTptl5G2VYaWEXR3d0d+dvr0aRdfvXrVxWnT\nx5o6MzN76623XPxbv/VbLvZbJhw4cMDFuqi8vu9mZsuWLXOxfj/5rR/0Gjl16tSIsVm0fYSmOePS\nlVnwSz/SLtqM92IkCgAAIAA3UQAAAAFI5zUQHYKuJ3/6p3/q4m9961ux+yUNOWsKT7v5pu2o7s/8\n03RXrTMEQy1dutTFOiMIxfBTsn4aptHpdZ/0OdIZqvr58GdU6udSn3vJkiWR/TS9pZ/D5ubmyH56\nTH4qTelMSk2X+V2/447d/911Rp5+RvW4zcxefvnl2GNS+t2l58VPI2bJnz1c1OoDWa8gkYX169e7\nOO3fC8VIFAAAQABuogAAAAJwEwUAABCAmqgG4q+aniWtt/K7mdfqe9/7nov/4R/+IfIzneL8N3/z\nNy5OqhHSvPbatWsjPwvpNB1SB6U1Bmbp6wy0S7HWShRZO6C1Ivfdd1/kZ9u3b3dx2vPS3t7uYu1A\nbWa2c+fOUR9TSN1CCL8mqsh6uCpI+/tq7Y6+136rAf3MahsDv82KPl9LS4uL/Y7g+hnbv39/qmPd\ntm2bi7U1g1m0TkjbE/j7af2VHkMWNal6zkOez1/ZQK9hbT+hn+MiFfk9pteR/h3xvfTSSzW9DiNR\nAAAAAbiJAgAACEA6r4HEDb9nMX016xSe0q7ff/EXfxG7n6YK/G7DccpaKDYkfWcWnSadNPSt08pD\nFiBOoukyTX+Ype/Ergs9axohbfou6ZjKUoVFkKtI3xvtRK7T/326qG9Sp2+d8u+3JIi77tOm0tN2\nV/dTZPo76veiv+CytlPQqf3+8cR1Jt+zZ0+q41Mf/ehHY3/21FNPjfr50lq4cGFkW8/LyZMnc3td\nn6ZAtZTjkUceiez3d3/3d5m9JiNRAAAAAbiJAgAACEA6r4HEde3V4eNx48ZFflZUJ2x/MdeQmU5p\nU3hV4KcedOaPngs/PZZ29krWKbw4oQspxy307NMZpTr7Ks/FV0P19vaWfQiV5F/rw/zPvKa0NI3t\nf671+2r58uUu9ksK/PTZSI/Pwpw5c2K39Xf0j0/TeZp+982YMcPF2qE95LP361//OrIdsih9CO3i\nXiZ9D7Zs2eLiPGcFMhIFAAAQgJsoAACAANxEAQAABKAmqoFo/UCcLGqg7r77bhen7Xw71ro9z5s3\nL7Kt03z9Ts4htMYqtG4phNayxNXgJfFr8rR+RTu0o1ri6p7MzBYvXuxibS/g1xLNnz/fxdrKQ9sd\nmEU7mGunae1ebpa+I3Uc/3eKa6Ph/x5a66S/r1/3pM+v33951nb6dXtjrY5P6yq1JUnaGs2g18zt\nmQEAABoYN1EAAAABSOc1EH9x17xoCk+n55qlT1Xp1OAiU33a0VZTYqEpNn8a9zB/UdWsFZnCUyEp\nPOWnk0nh1YekjvGaQtHPg/+Z2r17t4u13YGmA82incQ11aefV7Pod09IOi9tF3z/s6a/o6ank75D\nNNV38ODByM/8dOYwTXlWRVy6rCrKOCZGogAAAAJwEwUAABCAdF4DOX369Ij/nnZGlXaPjhti9oWm\nwdKm8HS4PIuZhVkvpKwzhLKg6cY8F332F1XV8xx3HQEj0QWD4xbnNYumsbQjvT+TVb+HdD9/ptm5\nc+cCj3h0/O84/YzqTD3/c6Pfofo7+d8Z/ozVYVVM51UxhVc2RqIAAAACcBMFAAAQgJsoAACAANRE\nNZC4Wpa009LT1kEVSWsO8qwR8lsVpK3ZOnv27Ij/7td5xK0479Oakv7+fhdn3dKgqHqSrNTanRr5\n0VpF/Q7xPwPaouDo0aMjPt4s2gVcH1PkNTt16lQX+zVRWqfldylX2o1fO2b7NVDaqkG/42ptJ2IW\n/f7U2rVGoe+TWbQ9RlEYiQIAAAjATRQAAEAA0nkNRIduG0WeKTyVddf0tOk7n6ZDyupKXkWk8KpL\nr9OBgQEXa2sVs2jKXNN0/oK8mgYr6zOgKUWNzaJd9jXFqN28/cdp2t9vcaBpTz0vWbQ4eOihh1z8\n8ssvu1gXRK9n/iodpPMAAADqBDdRAAAAAUjn1TF/+LjIhXzLUPXFL7MQN9sPqCpNuWk6pampKbKf\nzmTTrt/+rDFN3fb09GR2nKORlH6LW3RYfyczs46ODhfrOfJT/VqyoDMV/ZlnIX7605/W/BxprFmz\nJrKt3eXznFXZ19eX23OnxUgUAABAAG6iAAAAAnATBQAAEICaqDrm1wXdvHmzpCMpRhZ1UFq3UFT7\nhHrmT7PW6d0h/DYcjdhFeazRFgXHjx93cXt7e2Q/bXmgdUVVb1/hr+SgtUr6M3/FiObmZhd3dna6\nWNsYmEV/f31ufxWFKtuxY0fZh1AaRqIAAAACcBMFAAAQgHReA6l1+FcXwvQX3WwUmsJbt26di7dt\n21bG4VRSV1eXi3WqchZI3zU2Tbn7088XL17sYv2u0g7lZtHPaK3p4yz412zaa1jTe5rO81shaMsD\nPX+NWJ4xf/78yLa2rTly5EjRh5MJRqIAAAACcBMFAAAQgHReHdMZLmbvXYxxtBo1hRdHU3j+uTt1\n6lSq5/C7MqehC46GLlSclh5f2vSA7teoneGRHe2yrY4ePRrZ1i7gOuvTT29puqyMBWWzMjg46GIt\nlWhpaYnsp583/X31e6KeLV++3MV+93KdzVmvGIkCAAAIwE0UAABAAG6iAAAAAlATVcf8WoShoaGS\njqT+pa2B8oVMQ867DkqFHF9PT08OR4JGFXeN+a0LtLv3hQsXXOx3xddp71mIa/3i/7vfmbxWWo+o\nXd39WjG/TmhY2nqwWbNmuVjrzsqk3em11jbvGqgi602HMRIFAAAQgJsoAACAAKTzGggL6hZvzpw5\nZR8CUKqQruLXr193cdYpqKR0oLbsmDBhQqav69N0pqa04lpC+NJ+n1clhaf0mtDUbd6KLJUYxkgU\nAABAAG6iAAAAApDOayB+B3Pkj47eozNp0qTI9r333uvikydPunjPnj2FHVPV+B2tdZZWFTt4F7VQ\nrp9+a29vd7GmtPzZbleuXBnx+Yr87KZN4amsZwsWqREXT47DSBQAAEAAbqIAAAACcBMFAAAQgJqo\nBnL16tWyD2HMqeL04irzu1Pv27fPxbrq/VjmT9GvYh2Uiqs5CqW1nVpLNGPGjMh+EydOdHHId5//\nmJkzZ7o4ros4Gs/69etdHPJZYyQKAAAgADdRAAAAAUjn1TF/2F+Ht5Gdrq4uF/sLFWsn4ixs2LDB\nxTt27Ih93TxpG4KsU8Qskn1reS/SWnVx7QD8zujaETxpSr1ez7ro8LVr1yL76feptlPwF1IuSj2V\nZ3R0dES2NS1W9ZKHl156qabHMxIFAAAQgJsoAACAAKTz6pjfcbe3t7ekI2lshw8fdvEf/dEfRX7W\n19eX6Wtt2rQp0+cLoYsqHzt2rMQjAd7lp7d0W1Nxfmpp3rx5Lp46daqL/VmFunitzhD004iabgzp\nRJ5WFh3VZ82a5WL9fbNOFQ4MDES2Fy9e7OK77rrLxZs3b870dauAkSgAAIAA3EQBAAAE4CYKAAAg\nADVRDUSn76o8p6xnze9o7dcj1Gru3LkuPn36tIv9OgqdZq5TnL/3ve9F9mttbc30+KqgrA7ZWr+R\nNC26Ec85aqP1Q0ntDnS/RYsWRX524cIFF2srDr/uKc86qLS0e3tSm5Wy2gv09PSMGDciRqIAAAAC\ncBMFAAAQgHReA1mwYMGI/97e3u5if5HXtIuHNjU1uThpuLxWWafvfHGdv/v7+4OeL6S7dJ7dkPW5\nQ5+/rMVX06Ye9HpGMXTKf9X5bTm0nEGvbb9juaaxL1++nNPRZSPrlRLGsilTprg45H1nJAoAACAA\nN1EAAAABSOc1EB2WVCGzI3T2h1m+w8fLly938f79+3N7narIOoWnMxqzTof6i1xn0UW5VmlT0MjO\n9OnTyz6EYI0+Oyyt2bNnu9j/Dsq7jKLKak3dMhIFAAAQgJsoAACAANxEAQAABKAmqkATJ06MbOsU\n25kzZ7rY7xh9/fr1EZ9vzZo1ke2FCxfWeohOkVNo86yD8rtbh7QkyJp2Su7r64v8LK59RFLrAq1n\nyLqGqQo1UD7/nCF/cd9BqB9nzpwp+xAaEiNRAAAAAbiJAgAACFBKOu/BBx80M7P169dH/l1TL9q9\n2F8cVrtnJ6UbNH2mi0b6HZl1mFNf69ChQ5H9Tp486WJtAZC0aK6mV3TxW7PoYsD6u/tpP6VpHf91\nH3744djH5UXTkGbR32Pq1Kkunj9/fmQ/Xfz31VdfdbH/u3d3d7v4lVdeiT0OfT/0+vDTdytXrnSx\nvjd79uyJfe4sOowPX/Nm0bTrwYMHI/u9+eabLtbPQNrXTZt+0/NqFn0ft27dGvu4Bx54wMWbN29O\n9Vpxr2MWnXa9du1aF7/88suR/bTTftW7STci2kpUi/7t1I7smzZtKuNw7CMf+Uhke8WKFS7+1re+\nFfu4xx57zMVvvfWWi19//fUMjy5ZZ2eni48ePTrqxzMSBQAAEICbKAAAgABNN/NcTXakF2xqynUB\nWwAAgKwk3bcwEgUAABCAmygAAIAA3EQBAAAEKKXFgU5BRz40f9vc3Oxiv/P1iRMnRnxMEp1SO3ny\n5MjP5syZ42LtzO1PHY3rUu63bdAp8Dq13d9Pt8eNG+div/P6wMCAi7VtgD7Gfz49Br89hrZJOHXq\nlIurfo1Pnz49sq3Xxfjx734taGsQs2g7kCy6metx6DT6tB2y9Zqt+jmvCr3W/fc3zX7+9wTnPX/+\nOW9paXGxfu8gW2n+JjISBQAAEICbKAAAgAAsQDwGaBdwPw0WsiCvdlrX1J5ZtCO1vm7aRYa12/tI\n28P8tFp7e7uLL1y44OK0i276aQ19fv+1lN+Bu17oOSpTnsfR1tbmYk0z+923Dx8+XNPr+NdA0vWi\ntOu0dmvWbv5mZkuXLnWx3+E+hH5mdbFzTeOi2vyyDJSHkSgAAIAA3EQBAAAEKGX8dnh4uqenJ/Lv\nSTNF0vCH1TWdpIsHj2X+rCydXRYy28qfRTU0NORiTftlzX9dnaESukhwiLSpGxRPFy3W63LevHmZ\nvk7aa8B/3ZdeeinV4+JSeLqYtpnZ3r17Uz2fpvBU2hmRKJ/+rdMZyHHlD8gPI1EAAAABuIkCAAAI\nUEo6b3gWyNy5cyP/HjJTrLu728UrVqyI/Kyvr8/FYzmdp2k1/zzojJxr166N+rmTZvuFPF9a+r6b\nmS1fvtzFOhNwz549uR0D6oc2zTt27Fiqx3R1dUW2Nd3d29s76mPQxrY+vZ7TXrNarmAWbXzrz0BE\nY9HZeVqiQTqveIxEAQAABOAmCgAAIAA3UQAAAAFKbVGrC+OaRaepp213oPUDfnfqtLUPjaK1tXXE\nf9epy/45qXURWf99yrMOSnV0dES2V61a5eJ9+/aleg6t5wqtJdDFiZW2jjDLZrFehAuZvn/27NnI\ndlxrAJ92Ik/bxiCkdm/Hjh2p9ps1a1Zk2/+9UH+mTJniYu3G7y+krguup11gHqPDSBQAAEAAbqIA\nAAAClJLOG56K63cYv/POO12sQ4+/+MUvIvv5C3QOK7JTddY0teSnHtJ2/o5rEaHPl3VaSTtBF2nh\nwoWRbV3IVhdzTeKn3LK0bt26yPbu3btdrB2GQ+m05qosJtxo4r5nfP61WIWFfBcvXjxibGa2efPm\nUT/f1KlTaz4mZEevMY3990nLDdJez7WuYjHWMBIFAAAQgJsoAACAAKWMOw/PcvFndmkHc519tXbt\n2sh+mzZtGvF5/Q7DOiyZ1C24KP6MMp2dOGPGDBdfvnw5sp92Ge/v7x/162qa0081lLXoaNp0lHZl\nXr16tYv9WShbtmwZ9TFkkVaLO3adPWNm1tTUVPNrpXld1Kazs9PFfqra/1wO05URRtrOi5+m0wXd\ndVaWv9B7iLQzE1EMTbPpd7g/Qz3ke4IU3ugwEgUAABCAmygAAIAA3EQBAAAEKKUmarhmwJ+6v3Pn\nThdPmjRpxDjJK6+8ksHRZUtrd9ra2iI/0w7tWrPld/3289yjpdNe/WPQurG9e/fW9DpJ/FosXXE+\nKW+v3Zb1Onjttdci+23fvj3T40tbKxa338GDByPb586dCzswFEpr9ULqD30bNmxwcVwtZ6ikWqcs\nVg7QmqsjR47U/HzITtz3zoQJE2K367kFUJUxEgUAABCAmygAAIAApaTz0nTg1n3SduyuIm3jUGvK\nKYtj8FNnWU+9j+MPP2vbhiSa5tTYH7aOk3Yh4KxbPRQ1zR2jd9ddd0W2NVWVttt9kocfftjFuhJB\nki984Qsu/u53v1vzMYTwF9Nes2aNi48ePVr04SCBlnhoqYDftqWe/3bWC0aiAAAAAnATBQAAEKDp\npq70W8QLFpQ+Guv0beWcF4NzXryQc65d8H1ZzGpLS1N4mkr75je/merx/sLHRaWQ/T8ZXOv588+5\nzrKOW3getRs+701NTe95D4YxEgUAABCAmygAAIAA3EQBAAAEoCaqQenbqu0A/Cn/RdaAZEk7wfu0\npUORqIkqXhXOuXY5N0vuwN8IqIkqnn/OdSUHVkPIDzVRAAAAOeEmCgAAIEApHctrNWfOHBefPn26\nxCOpD1l3405LU4dxncJDlZWyA3x++m7p0qUu1oWo/e7lFy9ezPfA0LAKrsJBAkaiAAAAAnATBQAA\nEIDZeRWxYMECF+tCu2bpF5HUWUK6EGUVznlLS0tkW4/J/31rNW/evNyeO0kVZoqNNUnnXD8PSTPm\nyrpe6hWz84rnn/MZM2a4uNFng5aJ2XkAAAA54SYKAAAgADdRAAAAAeqyxUEjOnLkiIvnz58f+Zm2\nCjhz5oyLL126FNnv7bffzunoaqc1WmZmXV1dLu7s7HTx/v37I/uF5Puznjo+fvz4EWOzap/zsW7i\nxImp9hsaGsr5SIBsldW2Bu/FSBQAAEAAbqIAAAAClJLO6+joMDOzgYGBMl6+8o4ePRr0uLStEMow\nadKkyPaKFStcrOlKP33X29vr4rSLJftpzlrp0DnD6PUj7XtF93vUm6xXgEA4RqIAAAACcBMFAAAQ\noJR0XpXTTsiHP4tNr4HBwUEX64KtZgxbI9xYW+BXZ46GpJ21c7sZ3durLG1pA/KXOBL1+c9/3tra\n2uzOO+90/zY0NGSPPPKIrVy50h599NHIlPuvf/3rtmLFCuvu7raf/exn+R01AABAyRJvoj73uc/Z\nxo0bI//25JNP2iOPPGJ79+61hx56yJ588kkzM9u1a5c99dRTtmvXLtu4caN98YtfZBQBAAA0rMSb\nqA996EM2Z86cyL/9+Mc/ts9+9rNmZvbZz37WfvSjH5mZ2dNPP22PP/64TZgwwbq6umz58uW2devW\nnA4bAACgXKOuiRocHLS2tjYzM2tra3P1LEePHrX169e7/RYsWGD9/f0jPgfTxMeey5cvR7Z3797t\nYk0J+6OXCxYsGPFnoW0gMHZo64KpU6eOGJuZnTx5srBjypN+r7a2trr4+PHjqR5PDRQwejXNzmtq\narKmpqbEnwMAADSiUY9EtbW12bFjx6y9vd0GBgbc/3g6Ozutr6/P7XfkyJHImmjqypUrgYcLAACQ\nvyeeeOKW+zTdvHnzZtIOhw8fto997GP25ptvmpnZX/3VX9ncuXPty1/+sj355JN25swZe/LJJ23X\nrl32mc98xrZu3Wr9/f328MMP2/79+98zGtXU1GSzZs0yM7OzZ88G/mq4FX1bqzgiOHv2bBfr1Gw/\ntaKdzvV3quIU36qf80aU9pyPGzfOxXQor43/J6MK1/qECRNc3IiLglfxnI8Fw+e9qanpPe/BsMSR\nqMcff9w2bdpkJ0+etIULF9rf/u3f2l//9V/bY489Zt/97netq6vLfvCDH5iZ2erVq+2xxx6z1atX\n2/jx4+3b3/42bzQAAGhYtxyJyvwFGYkqRNVHRRiJQhYYiSpeFUdFGIlCHmoeicpLGTdPusgt/avK\nN2PGDBfPnTvXxXpzZWbW09Pj4iK/HPmjW38WLVoU2dbFq3kPG9vwf8zNGme2JeoDa+cBAAAE4CYK\nAAAgADdRAAAAAUqpiSpDPdVBaT2OWXw9h18D4q/CXmVxHcenTJkS2Z4+fbqL9T28ePFiZL9au+D7\ntVjNzc0uPnjwYE3PjWJoDRTS0YJs/3umnr4zx1odlBaWFzw3DB5GogAAAAJwEwUAABCglD5Rw+0G\nyhou1t5DZtH02aVLlwo7Dp3ar0vkaArLzGz//v0uTlpMVPstaTuAqvcU0dSZtj4wi743AwMDLvYX\nNA5pSaDn2W+fcPXq1VTPoegTVTzOefH8PxktLS0uPnXqVNGHMybQJ6ocafpEMRIFAAAQgJsoAACA\nAKWk8+6//34zMzty5EjkZzrjSrua+6mWiRMnulg71SalusaaekpzaHo1JI1mFv0ddaadXh9m0ZSn\nXi/nzp0Lel1VT+e8Ueg592d2XrlypejDGRP8PxnLli1zMTNZ80E6rxyk8wAAAHLCTRQAAEAAbqIA\nAAAClNKxfLizdkdHR+TfdZr6G2+84eJdu3ZF9tO6Geqg6l/aVhczZ850sV/DpLVP06ZNc7HfCmFw\ncDDkEFEHli5dGtn2vzfiaA3dmTNnMj2mPPkrG2jNRpHtY/r7+wt7LaBqGIkCAAAIwE0UAABAgFLS\neYcOHTIzs9dffz3y7yykOHZ0d3e7WKei++lZbUmgCy77C45ql3JNL3BNjR3aFmU06imFp3SVA7Py\nFmAObUsCNAJGogAAAAJwEwUAABCglI7lyF9c92y/q7N2g79+/Xr+B/Z/dPHfCxcupHqMzr4aGhqK\n/KwKKRk6lhdvLJ/zOXPmRLZPnz5dyOvSPbt4nPNy0LEcAAAgJ9xEAQAABOAmCgAAIEApLQ5Qnrlz\n50a2p06d6mLt5h06XTyttHVQ6sSJEy4+f/58locTbOLEiWUfAipmeEUGM7PFixe7eNu2bTU/t3ZX\nL6oGCkA8RqIAAAACcBMFAAAQoJR03oQJE8wsOr1+NDSFcu3atVE//rbboveOupCnLmSrXbB9+hh/\n6qMujhv6O8bRxXVbWlpij0m1tbW5eMWKFZGf6e+7cuVKF/stAzR1oGm1IheAjvv98qCd0mfMmOFi\nf2FXTd0AZtHPR9Zp8SJbeej3Bgt3AyNjJAoAACAAN1EAAAABSulYzqKwAACgHtCxHAAAIGPcRAEA\nAATgJgoAACBAKS0ORrsC9eTJkyPbV65cqen1tU2Amdny5ctdPH36dBcPDAxE9jt69GhNx3D//fdH\ntnXKfl9fn4v9afOvvPKKi69fv+7i5ubmyH66vW/fPhez4ncxNGfOOS9GFudcu/hfvHjRxbV+z/h0\ndQAzs0uXLqV6nH7/TZo0ycVp2yf4XfW1LYx2VJ8yZUpkvz179oz4fH5tCNd6/vxzrm1X0q7+oH/b\nQlaMGAv8z0CazygjUQAAAAG4iQIAAAhQFwsQZz2srkP2Zmavv/76qJ9j2bJlLj5w4ECqxzz//POp\n9vO7Yg93eDeLpvOGhoYi+6VNDyB/WaegcWszZ86MbOvKAUluv/12F+sKA1u2bMnmwP6P//nUz3XS\nygaa9tdj9buXa/pN0xKXL1+OfW59Xb+MQMseSNlVi6Z106bmskjh6TXh/x1tBEmflTiMRAEAAATg\nJgoAACBAXaTzqihtCi/E4cOHgx6XJmXkzz6YM2eOi3X2YVo6s9HMrL+/38UhQ6NF0jRJ0mLTITTd\naxZNh+j7yyyZ7KxevTqyrbORjh075uIdO3ZE9nvhhRdcrIvupuWnutKuyLBhwwYX792718W9vb2R\n/fQzqwt+Jy0KnDZ9rJ95//O/fv16Fxe5+Dduzf8eL8pDDz3k4h//+MeFva5+tnft2pXb69x22+jH\nlQ6vlkQAACAASURBVBiJAgAACMBNFAAAQABuogAAAAJQE+XROooFCxZEfhbXwTetRYsWRbb92oe8\naKdk7XRrFlYHpfU+J0+ejPys1jqo2bNnR7a1ZkjbO2Qhizoo/3wO0+nrZtGWB1p349ea6O/YiFOI\n8+R/PrWO4vd///ddrNPDzaIrAmjtVFppa6B8WkuVdC3q8x88eLDmY1q4cKGLdaUEn34/ZV0ziNpk\n/V2Ylt/9vih33HGHi/OsifLbC6XBSBQAAEAAbqIAAAACNFQ6T1NxZtHUUtrhaE0f+ekBHcrUFNn5\n8+cj+8W9VlHpO592OU6bkvSnbbe0tLhYU3hpF0FNy+/CnNbSpUtdrC0E/M7h+nuFpMv85/Pf+2Hj\nx0c/WtpNW1N4fjpPr1ldKDapozV+w792XnzxRRdrCroq3bd//vOfp9pP25CkXSB51apVLvZTy2vW\nrHHxtm3bXLx///7IfiGpfhSjrHReSLo7hN+uRFt75MlP9afBSBQAAEAAbqIAAAACNFQ6L+/uz5pe\n0bjqdFheUwNm0fSRpjmam5sj+504cSKnowvT3d0d2Y5LU6ZdlNlPv8UNl/sp47jU7ZEjRyLbep41\nJXP69OnIfpreI4WXHV1M2H8P9b0vK02SRK/htNeEppmvXr0a+Zmm4JNm56G69BpOeq+VduMOmYVm\n9t7vtbysXbs2sq1p5zwlnb84jEQBAAAE4CYKAAAgADdRAAAAARqqJmrevHmRbZ2Kn9TBV6c1huRE\ny6LT5s3MlixZMuJ+OvXb7yiutUVaD5J1fZnf/b3W3Hqt3eN9/urdcfUDfof2OP5U4KKmBmNke/fu\njf3ZunXrXLx9+3YXJ9VHzZo1y8VZt/nwhbQaGBwcdLFft3fq1KmajwnVkfZvVmgdlNL2MXnyV66o\ncn0oI1EAAAABuIkCAAAIUKl0nnYBTzs1XfmLwaadll9rCs9flLGo9gfnzp2LbL/++usj7pe0KHDW\nabE4RU2NNYt2L/dbCPjbw+qpZQWypZ2607Y4yDuFV6uQBYOT2nz4Xc9Rrvb2dhdri4NGSdX6qwpU\neTF2RqIAAAACcBMFAAAQoFLpPE3haWdtP90Wl+o7ePBgPgd2C1mkgrRTdchQ/FjjD/fq7EtNtVSx\nAzWqJXTR66IUNRMw6bNS5dlRY9HcuXNdvG/fvhKPJB9DQ0OR7bSzosvASBQAAEAAbqIAAAACcBMF\nAAAQoFI1UUqnomv+1yys/UFa2vU8bYuELFAHNTpJHeiLnOY7f/78wl4LY1Pa74Zly5a5uK2tzcUv\nvvhi5seEcrW0tLi4EevV6mnlEEaiAAAAAnATBQAAEKCy6TxVZHqmyBReUTQd6nd+vXLlSk3PrdOv\nzarfyTmEdsLX7sBJmpubI9v+lF0Uq6ury8V9fX2Rn1U9la6LgS9cuNDF/u+hn+Uqd3hG7VpbW13s\nr1zRCPxru8oYiQIAAAjATRQAAECAukjnoTZp06E60yypS7ymqvr7+2s8uuzpgtCh3eQ1BZp0/o4e\nPTriv69cuTKyreepnoaq86YL2+r75i/qHbdwdFq6AHbV03dJdJF2n15jVfxcIjtJs5MbQdz3ahUx\nEgUAABCAmygAAIAA3EQBAAAEaKiaKL+OQru61lOOtSxpz1HW9RbaafngwYORn82cOdPFadsnJNW8\nNDU1uTjPrucdHR2R7dtue/f/K2fOnBnxeMyi17DWAdVzHU8S7bY8efJkF/urFGhtW8j0/evXrwcc\nXb60PUjaa/vAgQN5HQ7qSNrrOaklRpV1dnZGtgcGBko6kltjJAoAACAAN1EAAAABKpXOW7dunYu3\nbds26sf709lDUnhTpkxx8eXLl0f9+NHQVJUee61dxJP4U6SzXsxZU6jaSdd/3XvuucfFmtLxUzXH\njh1L9bpp3zdN4WmrBj8VnPZ148yePTuyrek4fd/981/FtFNR3nnnHRf7LTYmTZrk4nrtxq2d783M\nPvCBD7j49ddfd3FS6qJR07oYnbQrJ2i3+3qiqf2qYyQKAAAgADdRAAAAASqVzgtJ4SXRFICfHoiT\ndwpPFbVw5KJFi1zc29ub62tpSmb58uUu9s//s88+m+nrjh8/+ku5yEWBx40b52JNPfqz8zStq/vV\n67D8aOi58K+X0M7zVaKfQzOzN99808VpZx9pOlrTwmaNuRAtRuaXC8SptdN/WbTEo+oYiQIAAAjA\nTRQAAEAAbqIAAAACVKomKq3W1lYXHz9+PHa/tHVQjS7vOiildUYhNUfa2dssWmM1ffp0F2uNkVn6\njs9F8X8PbVuh16V2rTaLtjjQthB+7ZTWDOTZEqNIWvflt5xohM+y3zaj1q74WjNnRk3UWNLoK3DQ\n4gAAAKDBcRMFAAAQoC7TeUkpvLFM011FmTBhQmS71qmpmr4zM1u7dq2LV65c6eJnnnmmpte5lTlz\n5rg4aZqwn7Yb5v8emn6cNm2ai/33TNtyKE1hm0U7ne/YsSP2+OqV37m9nqY8x6k1fefzO6APDg6O\nuJ+fCk5aeBv1wS9naDS6mkTVMRIFAAAQgJsoAACAAHWZzktLu1hresCf+dMI3ZDNyulqPXfu3Mh2\n3MK9ftpLZ54lHbfOutOO9mfOnBnVcd6Kf02k7fTrp+2G+Qsua+pFU3j+ftoxX1N2SWlTPbdxx1Mm\nTY36aTn9vXRGjqY8zaK/11jo3h5Hz5f/2du/f/+Ijykrfdeo37NVsHTp0rIPIVf1NPuQkSgAAIAA\n3EQBAAAE4CYKAAAgQEPXRPnTpIeRmx+Z1qT4HWO1BklrcOJqoHx+rU7aupaenp5U++mU3xs3bqR6\njMr6mvC7ScfV/vjnWY9dz5H/O2mdi7ZF0Jqqqkiq2Yp7r7Se0awxWhxkQc/D+fPnSzySW+N7Nj+z\nZ88u+xBytXfv3rIPITVGogAAAAJwEwUAABCgLtN5OkV8LE93zpouZJu0qG3INHq/w25cGsdPg6VN\nT4Wk8JLo9HHt+Nze3h7ZLy5lvGjRosi2pmE0teenqTRtqm0W/POir1v17sW6eHDS+6ntHvzFdBth\nAeIszJs3z8UDAwMlHgnKdOLEibIPIUja7/e0LWaqgJEoAACAANxEAQAABKjLdB4pvN/wZ2jEzYZJ\nSh8VJW26rcjZZTozzk9fxi0We/Lkyci235V52MyZM2NfS/kzrA4dOjTiz1paWiL7aepLZ+dV/bPh\nd67XVGTVO6/Xqru7O7I9a9YsF2/ZsiXVc2iaec+ePdkcGOrOr371q7IPIUiRf380dZjn3xVGogAA\nAAJwEwUAABCAmygAAIAAdVkTFULrRsqaLq2tGcyiK3H7U+fVSy+95GKd+t3W1hbZz6+bGVbFbs9J\n9Uiq1k7kSbRWzL8mtCN4krg6NL92Sn/HS5cuudividLj0GvW7+CtOf6q10ElHZ++p/W0cnsIv4Yp\nrp4uSdJnpSxxNZd+/VutdW5+XWEVz0VR6rUeLq4lTB707y01UQAAABXDTRQAAECApptp8xZZvaB0\nf06iU7g1/WEWnearQ8T11OU0b/q2pj3n9aSKQ/t6zv/sz/4s8jNtXbB161YX+ykO7USsv6PfzuLi\nxYsu1nRZwR/n0jX6dZ43bbNw9uzZVI/xrzFNNWeRctdrXY9prF3byv/dudZvLYvWPsPnvampKfb6\nYyQKAAAgADdRAAAAASo7O89P4am4btJjnS5OWhS/M/ecOXNc3NPTk+o5li9f7uL9+/fH7tfV1eVi\nPw3W29ub6rWK4s+eOXjwoIsHBwdTPYemKP0FefVnYznNgdqkTeElyXrWrC7CXc80zampeZ11axad\nsTZt2jQXl/F9XhX+TPaQGchFzUpnJAoAACAAN1EAAAABuIkCAAAIUNkWB/XE7yZdZFdWpfVJWuvQ\niOe8ivSjpHVeZmYHDhwo+nDGhCq2OPBrXoaFrpQwY8YMF/sd7svAdPvR01qnKVOmRH6mfy+0i72e\n5+PHj0ceU4VzHtIeI633v//9kW1tC1MkWhwAAADkhJsoAACAAJVtcVBPykrf+fxp8I1AFzGtdQHT\nIvlD9tr6QTvr+y0idFun6PrTyDWFrEP7x44diz0mXaDav1ZCUk2aovA7qsd1C9bH+I9LWpBXp73v\n2LFjxH10lQOz6Dnv7++Pfe6s6blcv369i3Uh8ST33XdfZLteF5sda5qbmyPb+pnVlj1Ji+FqKwT/\ns6L081+Vvz9ZqqfvekaiAAAAAnATBQAAEIB0HipF03dm9TWsqxYuXBjZ1lSTptI6Ozsj+2lK69q1\nay72z4Omy/ScLV26NPaY9DF+V2hdBSCpG7qmGxcvXuxiP92ox66pBz/Nqek8TWX4s4/0nMUtNu2v\ncpC06kFR0nZaHjdunIvnz58f+dmWLVsyPaYq0PfXT+PqNZKUtqpaZ3P/MxAyY01Tff53odKO3mWd\nhyVLlrh4+/btsfvpLD6/jCBuxps/4z1rev5CuqErRqIAAAACcBMFAAAQgJsoAACAAKV0LGfVeQAA\nUA/oWA4AAJAxbqIAAAAClNLioIzFE7Wb7Jo1ayI/27x5s4s7OjpcPDAwEPRaugDpXXfd5eKkRRTj\nOlqbRTtNnzx5MvY5Vq9e7eKdO3e6WKeRL1u2LPIYnZZ78OBBF4cudKrnb9GiRS7u6emJ7JfUWVtt\n2LDBxW+99Vbs43Wa9IIFC1zsL9yZdjqrdgu+ePFi7H76vg0NDbm4rAVC29vbI9ttbW0ufv3112Mf\np9ds2u7l+jvq65ilf3+VLtq8f//+2P30mnjuuedGPB7kx09r6GdA2xP4U/71c6nXmP98u3btyuQ4\nh2m7ET1Wf1HwpM95nLlz54743GbR6fz+99BopV302e/aH9cqQFcR8PfT85C2xYy/6LZ+H+gC2v55\n0HMUukB3ntKUHjESBQAAEICbKAAAgABjpmO5pvD27dsXu19oCk995CMfcXFS11nlp/CUpomSfPCD\nHxzx3zXV5Q/vanowNIWn9PxlcS43bdqUaj/tkK1pyVBph/aT3rcyaMrELNoRPSmdFzKUrt3LQ9J3\nvqQUnkpKaaN4mtLSFJ7/vTU4OOjiPLts+6ku/QxoV3wtDwilv4f/unGd9fNUVpd+//ujt7e3lOMo\nAyNRAAAAAbiJAgAACFCX6byQmUQ6zBm64GDc6+qsIt8Pf/jDoNdSaWdIfOc733Hx//t//8/FOmPG\nT4WcOHGixqNDEXSGi1l86tVPk+hsyayFLLCaBZ15ivzowrFJC8JqClnTZf39/ZH9NPUdMhMurdbW\n1si2ljPoQs9ZNH3W9KXO6PXVusgtqouRKAAAgADcRAEAAATgJgoAACBAXdZEhUzH1px+2qn8mks3\ni3Z51Wm9X/rSlyL7ffGLX0z1/L/927/t4hdffDHVY0Lo1Ft/2vHly5dze92xzG9tkbauTSV1f47j\n13kcOnRo1K+L4mmtTtr3Om8TJkxwsd+RWmmXbN1PW2CYZdNCJQ3tgm1mtmfPHhfr3w5ti5IFrSEz\ni9aH+R3CVVm1hXivkFUPGIkCAAAIwE0UAABAgEql83QoLYvpp7oIorYheOmll1I9Pmm49+GHH3bx\nL3/5y4Cjyz6FFzcNWX+PpGHlsUbPl59CqfX6C0nf+dKmWjV1qItNm5m98sorNR+H0kWuQxaK9YfL\n055nbfHgL3acF39BWV3E3F+8tlZVSeEpbYfid8JXmqbT697vnl1UN23/c1NUyYJfKqHngpRdfQj5\n3mckCgAAIAA3UQAAAAGabmaRNxvNCwZUv4eaPn26i3VWhp/S0mH6tIv9Vp2+rZq28mccMjsvO3rO\ni7zONdXlp3RrXSD5gQceiGxv3ry5pufLgqbZ9POa9zmv4gy6POnsvKVLl7pYZ7uZmS1atMjF2rXb\n/57VmXuagj569Ghkv3r9DtYZ22bR61E/hyHXjv9nOu5a99P5OlMxixKDsWb4vDc1NcWm+hiJAgAA\nCMBNFAAAQABuogAAAAJUqsVB1jSHmTS1v15z8GklrTSuefyiVlkfi7Ju36Gy7gSt9S9+9+ei+Nep\nXo+11nmFGgt1UErra/xu3Kqvr8/FWjfmdyzX7Xnz5rlYa1LN6vf7WGsTzaK1XkVdO9evX49sUweV\nP0aiAAAAAnATBQAAEKAu0nldXV2R7cOHD4+4nz+cun79ehdrx9itW7emet1ly5ZFttN2KdbjyDrV\nouci7jwk0S7EZmYtLS0uXrJkiYt37Ngx6ufGu/xWA+vWrXNx2o75ZdF03jPPPJPqMf7U6jNnztR0\nDFVMJ2s6qqw0ZxbSppZPnDjh4qSO29quQNNH/jR8/Vl/f7+Li+pkngftnt/e3h75mV4jev7yTO1d\nuHAht+ceC/zUchqMRAEAAATgJgoAACBAKem84VSHP5MgTtrUgJ86+/nPfz66A7PowsKhKa2sU3gq\nJIWXRNN7fqoP4TQlZvbexWyrLG0KT/kL1Naazquiek7hqZDZoXGLm5vFzwDzU4C6ELqmnbSTeb3R\nVKbOTDSL796e598H1CZkZigjUQAAAAG4iQIAAAjATRQAAECAxJqoz3/+8/aTn/zEWltb7c033zQz\nsyeeeML+9V//1XWc/fu//3v76Ec/amZmX//61+3f/u3fbNy4cfZP//RP9uijj474vBMnTjSz7Gui\nsnDo0CEXHzt2LHY/Xbm8t7c312NKa/LkyWUfAv6P35bjjTfeKOdAcrR27VoXJ31WUP8WLlwY+7Mp\nU6a4WGt/9N/NovVDqp47wevve+XKlcjPtP2BtnugJqqxJI5Efe5zn7ONGzdG/q2pqcm+9KUv2Wuv\nvWavvfaau4HatWuXPfXUU7Zr1y7buHGjffGLX6TlPAAAaFiJN1Ef+tCHRpxVNNLsjqefftoef/xx\nmzBhgnV1ddny5ctTN7UEAACoN0EtDr71rW/Z9773PVu3bp1985vftNmzZ9vRo0cjHcIXLFgQ6Uqr\nqtah9sEHH3Txs88+G7tfa2uri5NSeDqMffny5RqPLj1/OBnl8a/xuM9CPdM2Do2YrsS7klZr0E7u\nOkV86tSpkf0mTJjgYm1rUM/tMK5evepiv6WDLu7unws0jlEXlv/Jn/yJHTp0yLZv324dHR32l3/5\nl7H7+m3/AQAAGsWoR6J0NOaP//iP7WMf+5iZmXV2dlpfX5/72ZEjR6yzszODQwQAACjWE088cct9\nRn0TNTAwYB0dHWZm9sMf/tDuvPNOMzP7+Mc/bp/5zGfsS1/6kvX399u+ffvs/e9//2ifvhD+TJNX\nXnllxP38RVWPHz8+4n7+LJQiU3iopv3795d9CLm44447XMz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44sWLqd6jk/OahTXn6zYWLlwYeU2rRmu6rL29PbLeq6++6uJDhw4Nux//99rc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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x13061bad0>"
+        "<matplotlib.figure.Figure at 0x7fd621d73990>"
        ]
       }
      ],
@@ -372,9 +363,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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XuF5UztrYKEBBYg5ASeP4KErcl9/HmjVrBs4PzOF9ZbG1jfWZFF33iqvFlU3a\n3eMifc/DWrADXofdo64tXmuvK9x3KlJJkiRJkiQTZptQpBz23vvGN74haW5ti66ZINsq7GcFfWdA\nePwBihRKCgoJXiEZRPQLMTuuSNGPVHUm3uLSSy+VJL361a/u7ybGQFR1ugR10ti3DCWQdi0pUYCX\ndc8990hq1AlXoGqzC4HMllro10ngMRyjOn5XUAuXLl068P9aqBAd4coDGbHDXrfHRWLjvEbKBoqI\nK3jMKV7TbPfddx/4OzZcymqMlKhVq1YNnN/jPcfNsDFhbYkydLtWlHfGncFLPbOuoPLz29SXgpmK\nVJIkSZIkSUcmpkgtWbJkZnXJc3i8DZ7f8ne8HpQNMmd4HoxXh+cdxTQRQ8Iq3VfT0T5NxAUMu8/P\nqHAvjftAIfLaHyXFiswO2t8VjFtuuUWSdOONN0pqnjPzOW/HKJ4DpaOt4jEs2AmKWm3GVAm8/pKC\nRPsA7c11YP/YZ1SB3jOyiL+gf/m7f/+tb32rJOn888+X1Hj/xO2gKO29996Smjgb+pl+xT6wB/rx\nxBNPlNQobCVQGxhn2BFqBtfHfXh/Easnjd5DRhHqutsBY9H31hs1w7aLZxfi0fP/qKYffUff8gpu\nI6i67IGG7RFnyVx/2WWXbfV6eSrB+VDJ21aSHxZXgxkbH/rQhyQ1MW+AXVFTz3d74LeNmDTal7Ff\nO+b6wuuqobihqnNdjN22NRiZC/i+z6k+Z/jc6WsBfitRovg+czJ/5748CzIiFakkSZIkSZKOTD08\ngTSHqakpTU9Pj/u0SZIkSZIkrZmeng5jqlKRSpIkSZIk6cjEYqT+4R/+YSZDg+fgPE+mxsgdd9wh\nqXmOTKYLz1+J1fBMncWLF0uS9ttvP0mqVr+GrUXCeT7/+c9LamJOuD7iInh+z/URb8D1EzPDc3Du\nk3YhVuyoo46SJJ1zzjmS4tgw39eK5+pUF66tdsz98cpz6VHtaM55zjrrLElNBXTiMchWJDMJiC/w\n5/LEDnG92B2fe93rXjdw3gjuG3slpujrX/+6pPpqv35//j3iDYiP4O/EBHmVbbJY+bzHxXj/ATFW\nJ5988sBxP/CBDwx8jjpfjA9i5JwjjjhCkvT85z9fknTmmWdKauJsiIPBDjkfGVW33XbbwPGIs4jq\nxzG+Tj/99JlzYBu+2z0xJNjAkUceKamZM/g82WQ+NukT9uwal7Ie9V0t0X6YUTZcyTb7hvN94hOf\nkDQ3jpDWKk/8AAAgAElEQVTYNOY4YqiOPfZYSU2m6lVXXSWpsRnum5gXxvqb3vSmgfOOGs5zwQUX\nSGriCrlO5izslvhDYni+9rWvSZo7x3vNQNrttNNOkyT94z/+o6RmjqcdiHdkD0bmsptvvnng+Mxt\n/J04YeyB3zDGetf2rM22nT3WJencc8+V1Nz/05/+9IHjMFfy237ddddJatqVOFDu54YbbpDUzFH8\nhr/kJS/Z6nWlIpUkSZIkSdKRiSlS3/72t2e8Ayows3pklY5igueKN0imCF4hq0lW2SgNbekrXCzy\n1AHl4vLLL+90fLwzFKlS5XXapa8qrngneEN+XPqVDJRStiPeAsf1zB76F+UE7zPKPsQ+vCqzXz8K\nCN5hLagUvLqC0pbI249qDLl3zmtb1QBvFKXyhS984cDxUCtQQF/0ohdJatpr06ZNkpr+YjzivaGM\nec0ar7OFYrh8+fJ5r5PMGW93mN2/9913nyRpw4YNA5/Fs/QMUWyINrj22msHzslcxDme85znSJKe\n9axnzXutfYEi0VcWYlRPqFStftRKlGe10d4oC9iGZ5aiYqLy0+9cL3MTSiOKDDXbJgW/aYDKihLC\nXMicWapJ53/3jHN+ayKYu6LfTOZSjutZerWVzelXniagFHL93Dft4UoyCpGr8J79ynXyG0R7054c\nj7mVudRrADLn1WYZpiKVJEmSJEnSkYkpUrPVHzxXvEBWratXr5bUrIpZPeJdoWB5bFHbytKA98Jq\nlFWrx+CUwEPnOr12xbB74eGVwYEHHihJuummmwbO43TdX8lxBdDh+LV1t7he+jE6bq1yhHcawfWP\nu35V33gF9K7fZ2887JRXYhKJz/j7v/97SU0sFqAUE5/RtnI6EL902GGHSWpUCOYF+hUFez68Bh22\nxdhmzHEMFApXKrinZzzjGZKaucD3LRwVzHnMaV1B8SG2pFQzDWUgguthjA+r4nsdJWyAuR4FhLkU\nm0DBieA3hbmS/h52n1bsCjtrW5keFZgxxtilX4gDJXaHOGHGKGoxShD3R3uh5LXFn2pgL9yv11dq\nC+3OcbAj7h/FCntyu/LYxRLMRXyP/nJlluvw86Fk1e7SkIpUkiRJkiRJRyamSD3mMY+ZWS16ZWZW\n2WRm4E3wPJdVpa8mOV4pZiiCeApWs6yW2ypSZDLwfNp3eic2x6vA8jwWRYtVNN/Da/HntnhxJe+w\nr/2VRg2KZMSwih6Me5+oCBSXthXWa71DVzCBcUTcgdsd9o9duRIFfB6vk8wjj38pQewb45q4DT+u\nM7taNef0tvTYmRIoANw78XZ4qsxNxAF6dt+wY425oTbLLoLrR2VnLotipkpzSPS9rng7cfyuTxWw\nRZTErsqRgxKJUsRvTNvjEtMTZUpjn9gRcYf0m6vtPgdip8BvTSnWhzmXOcD3VIzmWtq5FuzLVXTW\nABD1v88pfM9j6FDUOA7tiOLK+bE3ny+izOeIVKSSJEmSJEk6MjFFapdddpnxFoip2LJli6Rm9c1q\nEC8w2jeH7/uqsy08f0UBa7svELAqZhXvXhfPs7lu/zv3gbfLcSLFYlhvqyvuDfSF71TPPlt4ScRQ\ntVWkyAqjfxfK3olRv3K/jIOu9boilQHvDnvzvexqlV28O7xsvucZNn5e90p9jz3GN/aAUuxxEnvs\nscfMv7ENPHdUO+aUkq3iweNpE4sSZQzi0XaNHYmgDegb8JiVElxf7d5+bVXEYXHlgb6kn7wGXAnm\nUu9nlJauTyuIx+U4Xccic7/jvxlcv7cDn+M+StfBdUe/ZZ7BzG+JZ0E6vodeCRRW7Nb73eNi/beF\n63QFzGMIPZMZGJ/EkKH8lbIaa+0uFakkSZIkSZKOTEyR2nHHHed4CayaXamK4g1YnfN8l9U0q+W2\ncB68QTzsyIOOIKajFCcReRO0A6+eKeR1liZF3/ES4M+lUeZKz+sd35GeGB/6BTur9arGTa3q0PU4\nKEa0B17ol7/8ZUn1NWJoPxQ+VwM4D0oSyhL9TP8wHhhvXJfvWIAdoJxhH1JjG579U1u9n3Ny78Sc\nYOuu2HSN5SnBffj5hq3rxG4SKHSuVtZmXLpSFilypar0xAcC9bz4POchc7QWlAaUFhSIrkoS9YmG\nnfOwVcaIZ3ID/Y4yw//bKp8l+8SeyFqttWf6x5+GEDvoCihjl7Feyqz2OYR28npXvM9TKH4rozmP\n8eyxZMOyMH9BkiRJkiRJtgEmpkg99NBDcyL18WxZZeLherVWwGv048zO4umC17ao9WbBvUhWy7zi\nJREPwOrdq94Cq3zfh2lbg1gXlKLoub17I7feequkud4Y3qq/v88++wwcBy8oikXaVrIZuxKpGJ55\nhF2xx2MtjA+8VPf6GY+cj37j1SugR+fHm8d+6PfZ/Y8nylzCZ/H4GWP8HQ+X+Eze5x76to22GZre\nd20VCbIPgbFBn/t11CpStdfBmI/iKX1uRfHxGJeu+6ASQ4MddI2LJK5yazXMaoh+S8i8Jo6TsXj3\n3Xdv9Xslan8L+1JWGWeMI68X1VURxC786YHXM8Pe2Hmgba1A5gPfm7FEKlJJkiRJkiQdmZgi9YMf\n/GAmgt4VJf6PFxJ5byhS7kV1zSLDQyYmozbWyWF17LFVvE+MCN4i3kakSPHcl9VylA01btq2CzFt\npcwg91oi7zeqRYJdeCwV3oorJI9WvJYK/YJXXLuHIOOG+BbsG3znga7jCu+QDCCU6tlxGsQPMmZQ\nInjlfdRQxh5jkzguKhr73DJs3Nqw2X21bYbyRd0j6kjRDtdcc8283+t7THC9jD2fm/1+aB/GLrZJ\nP9S2H2OcjF+48847q699EtD+ZLFFvwm1EBNHDBTHb1tDL6rd5zFuwFzCXI7KTH9iD9F1uALJ3F6q\nP8VxuyrJzB+clzmtRCpSSZIkSZIkHZmYIiU1niyKE688HyaWCA8Wb9EVKlafw+71hbfjWUNtYZXu\nsSCscj1rLKo8zf3gnaC0uMfflrZZiBG1GSx4Cdx/KT6k9rj+Oa+94rCPFRk92MlCyYIcN3hbqDT0\nC7VaGIel/a0Yp1E2JTFQjK+u1b/5PP3H8WbHzXiGK/fEZ7hW3ufeUMuiuE0YtmbasDFXfn0RPrew\nb6LHoZagXXys1cYs8TniTfl/ZCsoGMxNtHdbRYo5Dlvz40FfuyT0BcqNt6vXr6qNsfN9XmlH2qX2\nt4D2QenjOnwXisi++X6tEub9R79Rh+vII4+UFNeORKX2ulsR3BeZ3Si4uddekiRJkiTJiJmoIsVq\nkdUkq1Dex4vBc472xWE1yfNgVpNdYZVNJkPXHc6JacJL9towrKZRALzmCt4nsSCsysnw6Apeed+1\nNCLwgrtWnG9LlCHk7YZXNeyO8Nsqvg8Z6gx2W1IGsWu8XOwXxRTvlP5g3LZVZTg+43BrqgR9yWfx\nyL3aOqqxq8PcA2OTOEbmghUrVrS69r6pVcS8jfHsqR9Vm6WFYoOqCLVzIu3uyhRzoStsZGYSB+fq\nZW32GraJrXDc2j3dJoWPGa6/a7Yh9s+48Axyz6qL8N1D+J7HSEXZeW1jsqIYKq+Ajj0QC4jdlPY0\ndGhv2otxQjbv7N0T5iMVqSRJkiRJko5MVJFi9YeihBKEAsWqtFTfhl3jOQ6r5NqIewdFDO+grXLD\nahgvgFU11VyJ1cEriDz06LzD1jIZlxLl94d30LWWiBPFb7jX7juB+/uPNPCuaPfVq1fP+zn+7soc\n7eLxBb5jAPt4Md58RwL6weMT+DznLSlfUVyQZxlKzZinjgwePXMENsA1ulpMG6Cq0QZcq1dWbovH\nKLVVu2tjhFD/qMFGn37lK19pdT4yq12RagvtR4VrbM/jE1ECUEldva+FdmbO4dXnhmHjREcF7UW/\ndZ0zsd9ly5ZJatqReFvs3etN8dSC9vLvcT1uFyW1uZStB5Gd+5zNeKbGos8tJbtBqYzmoFolLRWp\nJEmSJEmSjkxUkfrqV78qqVGc8E7wQIl1YtXI51h9AqvPW265RVKzKp7tqUqxguGQGdB1X6XSjtIo\na1StpabNIy1WB2+bVX3b5+Qlavvn4IMPliRdeeWVA++P6rraghKLfaPgeG2jWg488EBJTXzLunXr\n5v0c4wpvHRWDzBW8QrxV4otQklF5br755oHj4gUfcMABA/cBtDeqD+eN7J/4B6C9iIc46KCDZv6G\nwuSxUigU1OWJPGru7ZJLLpHUzBn0CQrGscceO++1liALiD4tKT2egez7TpbgPMxpruqXFALPZqMd\nUapq1XGyBhmLKE+eFcn1do0JApQsVPFhj9eVtWvXdvqeq8Vd4TgoS16jkfbnfRRX7M4r4DOuGGce\ngxQpUl7hvjTnRnP77H01Z98HsVMeQ8V9+L6rQKX/qGbeddddJ0k6/vjjt3q9qUglSZIkSZJ0ZKKK\nlFc95RVFh0h5VrOlPcBYxboixPPhF7zgBZIaRQtvy1fteJ2+A7d7g3g5betW8X0UOc9CJPYEhYLV\nsu8jNipcCfRV/rihfdt6Z2Rc9RWT1RYUVuwSby7ai5H+JhbJ7c+VWAclyOMdUFNQgBhP7EMVHde9\nOa6HcRJlxFx44YWSGkWK86JwMb585/gIVCWuE6+S12uvvVbSL2vLMFYYW0CsBG1Dm6OU0EfED3Kt\nfI+28MxPVDrmEmyNtvIsNN9rzz1kvoen7HWxsAWPB+X6/XjeVg6qvWc5AvdPhXDPjqJiNnGhHm/n\ntk5lcdoRWz/kkEMkNXMPx8dGsLna+kmuhnMfHmPDebED2pXvoZBxvdwfcyK2zG/MqlWrJDX95nOP\nn7+rGl77dIXrdwWUdmQf1zVr1khqfnO9EjrtTwwicxWqN6C4MhdxHleiiN2LYgSxA/+ez22lGEPa\nP/oNuPHGG7f6/VpSkUqSJEmSJOnI1MNdiyQNc9KpKU1PT4/7tEmSJEmSJK2Znp4OFbBUpJIkSZIk\nSToysRipcShSnGNc6tdCPR9xAGTKeEwMNUN43s9zbZ6/swo//fTTJUlnnXXWwOdLRBkTEFUi577O\nOeccSfF+SRyfeAvusy1RexIPwPV5HEkJYoR4Tk/czBve8IZ5zzcqOE/Uf7QjNZhoR+KFiMchxo9s\nU4f7Pe200yRJ733veyU1cQ5eI4a4B2IDqeQf9SOxZ56JNT09PXOPfIbYJWyY94nd4V6IaSEGxOtO\nOZznIx/5iKTha7uV4Hx/8Rd/IamxpVKMTW0sDTFgvL7tbW+TJH384x+X1NTAGxXc35lnnimpiRHj\neji/97nvY0q70M8rV66U1Mwd/P1lL3vZwHlHzaR+Gxjr1O/C7skYh+c973mSmnbfsGGDpGYM8n3G\nKv3A+Dr11FMHzjtq2v72cf2XX365pGY8EBPGfTL3EIdM/HTpPKlIJUmSJEmSdGSiWXvjhtonZNqQ\nWUCmBR76Jz7xCUmNN7fffvtJajxyvCRW5xdccMHIr30YPLPCIRPnqquuqjpe22rApVo5pf3DSt50\nKTOpa9YfcH2oE3jBtVWm8drwcjzzpJaTTjpJkvTZz3620/ch6j/aj0yW2n3dHLw6KNVHQ+EjKzdS\numBr+2fRN9i818WhD8hioi9QMz1DEaUJ2/Fze7X3UdN2/8/aWmtRJehRK1EOCgC1zEpjNhqDKCYo\nDShSbTOsI7V8W4GxTkXyqL0uvfRSSc3TChRZPu/9wN8j+2L88RvKeGKXBd/rDyWYfmLO9PO0tUcy\netmH12vyMR/4nNV2P9BUpJIkSZIkSTqyTSpSbRUBoEYMr3ifrLapXcJzU1bNvoM2z99Z5Xfl2c9+\ntqSm5o3vlN5XQiVeLDEoxFWgtLXdP+wZz3iGpO6xSG0p7YmHqoC3iTeKF0L/1u4x6Ps14U3hNfG8\nHeXEawtRUwbvDq+QOJy2ihR2iTeHl4Z9HHfccZKk973vfZKGr03D/ffthZdi5UpKFGzNW2ROwDZp\nazxk2gZPlHpA0TH5PmPeFSn2zSztZtAV+gTazgnYbq2K3HU3hxK1qjD94vGEW1Mh54Oxhi2jZPA0\nohavCM5c1PZ6Jg12VFLY3I75HnGPHksX1fXyivjUlwLakbkNu2PnAR+vw7a3z9HUqaqtZVciFakk\nSZIkSZKObJOKVFslKgLlh9U5nr4/R6dqL5WyUXjw8kqKSQTPZVkdo7SxKu8LnhPjjdB+nKftzu7j\n9sb8+bWDYoh3RBYi1CpRQPugUqBwuTfn6gD9iFfNq1dJRpmpBa/p85//vKTGHg877LCB66tVotzL\n93gFVAy8974q23fd78xj/NzbnQ1jCPWQa+c79CHZXHjEtOmWLVskxZmrrmiUqs0PS1eFiPtEhfa9\nxKhozX2DZynWxgjtv//+khq1ljkRhYO+L81trgxGc01JaeM+PMO2bYwUcP/DKnbDxmt2pXZvRocY\nQDKXmcNqK8wD981vDf2GXXgleP7OeX3Py7ZwHxwHu2gbCxWRilSSJEmSJElHFrQiRSwOq1mvfdEW\n4hlQBHjuyyqVrCHOx2oVDx7vjFUyChXHaQuZKV7LhuOVlJhaWN179lTXuI6uXsGoIR6CbMy22YVA\nf+NVc1wUJ+JlOA/9RPvidfN9FBW8II97qQVlDbvctGmTpGaH8lrcy/caPHiJ1JNi3BB31FbhA1SE\ntt447cp+aPx/Pm8SNRElyust0Qd33HGHpGbMcWzmAJQt7jVSUjjOqPAsutr4SRQU7sfrSdEXpTpT\ntft6Et/Gnm3szUf8Z1eVPVKeUBx9H0bHbb3r0wPoGn8Ik9r3s6sShl0QR0t7+56REa4E0R/0A+OU\nOZW5lM/1FbOHAotCy3Evu+wySeXM4hKpSCVJkiRJknRkQStSeMbUf0IR4DltreLA91gd8z08Wl55\nn1Wxe214Z6ymo/iB2tW/P7fG+zzggAMkSZdccknN7Q1NFC+xreHec9caPyiWnqHi/e/ZjtQcwkvz\nSt6oH12vi/NTrRcVAG+qLwUTu8XLd29x2OPSDm3jNhgfNXENperznsHLHAOojcR04MH6mO4rXjPC\nj8+c5MpIpNwwV3rcHqA2bt68ed7z1/YRGcznnXde1edrieZ4j+eLcPW8bZweyhc2R394fKxnDI/a\nLtrSVZFibmEu4GkJ2awlRYpxRD+4csh10c5k16G8EsPInNEVYgR55frb7lIRkYpUkiRJkiRJRxa0\nIkWmDd4XGSGsUj0TJQLliFUuXgxKkseG8HyW1TSrcvdKWdV61lvXTACeOxOvUaq7U0sUV+HK2rYO\n/Uk/ds0SQ/nw/sYLxYsilgpoR4+dwmsrec8l8OZ4xT6oL8W4IEtzWMgW7KvWCpRUDlQS4nu4L2/v\nGlz9YowxdonRIIYHxYY5gL6rjRUaNYxlB4/ea94xdzIn8X1sHFUVJWBbqeDNGGtbV6ttLAz9v2LF\nCknNnECcLHPBlVdeKam9EsUcPGzsVQRqMnNF11gg4jGPOeYYSY2CG2X0MmYhqhHH3Op1nuhX2qXv\n9ulLiYKhZvYlS5boCU94grbbbjvtsMMO2rhxox566CGdcsopuu+++7RkyRJ95jOfmTH6JEmSJEmS\nRxJDLaSmpqZ0+eWXD2StrV+/Xsccc4ze8Y536Oyzz9b69eu1fv364S7y/3vynoFRW+MEL4Ln6sSo\nsBpmtYzX5rVG3OvhvChmrhh1zRYjS4rrGFaJAu7br4tVPvWztnXwtrnPrkpK5P1wXFe+iA9BgfJs\nPdQM7Kpr1p7D/Y7Ka5sUZAfSTnjVtKfXtNka3tbMBfQhcwt9g3LlbdlXvZlh4d5R68DVdAcb5H5d\nueL+h1VNh4W+LqnJt9xyS9XxiI9FcWwbn+h1jrALatXx/66ZzD4nR7FMnkle+xvDXISCxP20/W1B\neeI+GYv+W4n9YKfcB+Ow7RzlWX+jgv6s3V3BGVqv9kXGhRdeqFe84hWSpFe84hVDb7CaJEmSJEmy\nUBlakXr+85+v7bbbTm94wxv0ute9Tg8++OCMF7Bo0aJeqnSzCvdYpVrwvnhFUcJL4/+sejmfr055\nzow329cqmUefPFfuu1py5L3gVeBtDbt34ELBa+90xSucY3d4VbQrygheHt4Y8TjYHWpH1zgU7I/j\n0n/c77C1UBYKeLE+TlFhapQoIMYFz5Y+iDzlqK5S31Xeu0Jsl2cdlXYbIBaKMU4bYjueoTwp+lbE\n+spkpX25PmyR49cqLZ4BjJ0xlrFLV6T4jWirmHgFb54eIYC0VdLI7vT4YmBucpgTa3/baGfsFmXN\nldi+6KpEwVBWe9VVV+lpT3uavve97+mYY46Z2awVpqamwuDIJEmSJEmShQ6FOyOGWkixWtx11111\n8skna+PGjVq0aJG++93v6qlPfaq+853vzNn3rAtRdeJaWD2jjrEKx0vFayXmCS/Bs4v4PjEcXM+w\nXg/PrVltj8vrxSt6pChR2AmxUdxf2/gW9xaB49DvfA4FFkUKO8Kr8lorXWvMcFyUJ7w07Jgq0o8U\nyEQjbqaLuo3CgqKEEuNV6r1qvJ+rrwrLw+LZTCWwQdQ97peYGRSKyFbHTd/7eHo7DRvLhKLErhBt\nbTKq44TgEFVejxQTj33z49OfzBml80Qwjlzt930wXU3m74wrxl9JlXfljv93jT8eluc+97nasGFD\n+PfOMVI//vGPZ4z+Rz/6kS6++GKtXLlSJ5xwwkxRtvPOO08nnXRS11MkSZIkSZIsaDorUg8++KBO\nPvlkSb9cJf7u7/6ujj32WK1du1YvfelL9dGPfnSm/MGwDJuNxKrcva3SDuKOe0te+6IrPP9m1T6u\nWi4Ldc+8YUE9QIVo+/ybdkGRxLvCDokDwMvDfmqz6Lo+7ua+2IPSVZW+6z1NGrxf2rvLuPC4NsYw\nNelQzFGF77rrrnmP01cGbQmv2O2lY/bee29Jc/f/BBQB1EnPNPV9RrF1VFLPwho3fdfr4qkJav8e\ne+zR6TjsqeiZwW1tMtpHlacsbSt48/lSfCTXi720zWymHT27zWPavPK713GrrfvFbzX70WLvC0UZ\ndjovpJYuXaqbb755zvtPetKTxra1SZIkSZIkySRZ0JXNu4K3WYLnxHhBpX2IUADIriNzZtgYI46L\nlxJ5xcnWYWdvvGyUDOyhNmOE5/h4T3hhKIZ4WXhN7LFX620N63XvtttuA+cd1b5eqBNRBf9RQ3/h\njbbdm09qPGP32IlxwbPuK7trWPC4efUYHOLhmHOYw+gjFAdshP+jYmKzKCvYOufpO2suyoKMOOKI\nIwY+T1wg14fNl1i1apWkpjYfca1di0PXxuWyywCKoKvPkZ1FT01K1GbqclzP4quFWCePU47mHvqN\n8cfcUfv0hyw/FLxJx+6VWBj7HiRJkiRJkmyDTFSRwoti1Un8AooCCgPxC6yG8Sr8fZ7/49UAsTK7\n7767pMZL4BUvAY/XFSb2/kLh8JiYqHZGLWTpdfUGlyxZIqm5fryN2uw/FBbfU452Hvf+W22rD9P+\n3IfXcar1YuHEE0+U1NgTdur7bOGl3X333ZLm7sFIO+JN46URb9AW7A01hf4dVVxLVNNmXKBmEMfR\npU4W6jHKjFfMHlaJQi3rO9ss4qqrrpI0d46irbwCNzaK7aGakrWHooWt9pUVRdwec1NtJXKuk+si\nTo7jECPmcxztwdjac889JTXtgA3fe++9kqS1a9dKan5jyBBlrqNfiYNljDuoy5T+QfljLud+INr3\nlKcS3FcpdolYL9qJz0fKH+ftmhFO+/ouDqXPc30oSrVKGNdZul6vXM848MrrXAe/kVw/32dOZxzz\n/+qnDFWfSpIkSZIkSeYw9XDb7bP7OOnUlKanp8d92iRJkiRJktZMT0+HClUqUkmSJEmSJB2ZWIzU\nOBQpzvGFL3xBknTDDTdIap5fH3bYYZKkr33tawPfe97zniepyby45557JDXPXYnh8pitN7/5zQPn\n5Tkrz9uJ8Yie+xLPUXo+TibKS1/6UklN/AHXwXN9jkPVZuINDj/88IH3qbHC971iNtdLcdUvf/nL\nkqTbbrtt4Lr4Hs/BiUshvoH3eQ7Nc3TPVCK+4rWvfa2k0dsK13366aeP5XzAeXj1mDue55MJRCYW\n9gzEZ2DX3i/0+x/90R9Jks4991xJTWwicSFksHHeKEvOY+c8U4w4pNe85jUD9zdqpqenhz4XsTLY\nYjQWve9GDeehtAx9TZvDpz/9aUmNTb/+9a+X1Iw9/z42RzwgdXte/epXD5zXiWLDsN3aLCu3zX/+\n53+W1MxN3AdzKTZJLBhzFDbJHOvxnh4j9nu/93uSpA9+8IOS4jnZK3RzPbUZwMRQnXHGGZLa2wvt\n3DZDvNY+aUf6rbSPJf1FO9LOnOdv//ZvJTX9x28adlIbb0s78xu77777SpLuvPPOgfMx1xAHzSvt\nduaZZ0pq4krf+MY3SpL22WcfSc1cyThYunSppGZc8Vs0X6mn2aQilSRJkiRJ0pFHZB0px3eMxktB\noXLP+9prr5U0t3J55J1G3herWbzBUh2ekhLlmR2AQsb3r7zySkmN9+C1PqJq8ytWrJDUZLJQe4XV\nOIoUXp9nQEV4lVsyZyLaZu0Ny7CV84cF74l+cntEPVi+fLmkJgsR75FMJvoL7zWq9YId4t22zcr0\nivh8H69/IW9UjodM1hNzAMrDmjVrJM1VdRcKqLtkpaHUoJozN2BDN910k6QmmwnFCRWTTGaUuNr7\njbIU29b7cduM9hvluvrea62UbYltR2PEx5rTtl6Tc8ghh0hqxnrf+6LyVIb2LilSpZp12BkwriK7\nQEn0OcV3keC3D0UK+C1hTqMCPePCswv/7u/+TlIzD2BvjKcjjzxy4HoYFyVSkUqSJEmSJOnIglak\nWI1fd911kuprOjhRzYsoBgSFgOfbJa/C4xSA58es4lEW2u4XhBe6cuVKSdKWLVsG/o4C5cpK14rX\nrOp5dbh+30cponavO9p7oe6nBNSq4Xo9XgIlslaxw06wO/cK6W+8O+IAvOI4akOpCjNe+LDessPx\n+ougoqgAACAASURBVN4vrU+8jhLqq9cpIj5yoeH1lfi/75WHAnDFFVfMexzmImJForpHBx54oCTp\nxhtvHHjfq+t3xWuqoQgS98lcgDLArg8lFRVVFIUkUrJcQYlwdZ/rZtcElM2u+J6AQDzqqGDu2W+/\n/SQ1Tx98P8/a+0Ph5DeBdovmmmi/V5Qqnk5E39+0aZOk5ikJT1Xob+JA+Q3ifW9n/k57cDyUUN/T\n0lm4M16SJEmSJMkCZ0ErUjznZNWPp+uxSyV8b7QSnsVXIlpVs/ol4yOqzFxSdngOjNeIEgEcH+8t\nup4SeB9kOKBwecwL3pPHxvD5UiwYx8N7xsvjviZQ2qwVKJx4O0D/tI054v4jBRFlC2XW4wdQJVxN\nAPem2Duw73gL7h9FbiHgWWR4umTmvvzlL5fU9OUXv/hFSXNVxiiWoy3YfNdq8aiOXiWfytu1cxwK\nj++F5mowY5F25Pp5bZul53vveUwWChdjjLkCRQIbc4WIuct3ieD6on4rqekeo0MFbcYc2WkRPlc7\nZEu6PRDDxm+dXye/iaV41VLl/fvuu09SY0cHHXSQpCYzvK0yi73QL9xX7T6Z9De/iRwn+k3htw87\n4nPMebU7FzCnonBxXBTZVKSSJEmSJElGxIJWpK6//npJ9TE2EdFeeHgDrJa7xhRF3g6rY7ypyGtj\n9c/qGa/TIT6ADAPAe2FVvXHjxq1eb7QjO8+NeT4eZWFt3rxZUuPNtMVrkABeU9d+GBd4K8TTcB9t\ndziHqD8AZZY4Bs8owzuP8Ni5Ue3NxzjDC14I+JhDLUSNQ2lyxcUZVokCbMPr5LQFW0GRaDtmsDle\nuW/USiDrD2jP2vg/x23cx8rtt98+7/eYG6NMZJQiP8+wcwmKEgol/VX7VARlJSJSWvjeMcccI6m5\nH2rI8ZtViifl+slS898WjnP55ZdLahQ97jeqlxWNccYXdlmK13T4bfDxFmWTsg+u7xXJ+K5dO9CO\nfhzmhxKpSCVJkiRJknRkQStSpVibWtwrINbKd3zuSuQV4A2VVsW33nrrwOdLeGwL3mEUIwMoWdyv\nr/Kp0bFq1SpJjdfs7dNViXKiWKiu8SPjAnvC2yOzpFSDJaLkVXJcYvdoH+y4FFPmWXRRrN6w4N0y\nrhYixOCcc845khobf85zniNprgffdx0p1GleSzEsDmooMRsoJPRx7fGYW1GgUKRQm0vQbtBVWYue\nFkQqbVuFyTOvSzFLDopK1/srxdZEEKvjiiDU/jaW4iDJsmMOQXEsxXlGT1eYG10lb9tv3u8le2bu\n8cr2taBAcR7msNp43VSkkiRJkiRJOrKgFamu2VtkbIBnDOCdRNVy22aiRF5ObYZO29W6twvPsUux\nOb6XnnvbeBNkqPA83b2xKE6hL6LK2MPGlfQFqkZXZa5r5e/ICy3FIXh7dfWSS+BFTrp/amBs83rh\nhRdKko444ghJjXo76srmqL/UgiuBUsCcRl8yJ/D/kgdPXSDiM6mjVVvDrWsfe+axZ3ii7npdq7Y1\nz1AUGWuo9rxfi2fItq3c3rUSu++3OSrI3qT+Er8BZG6jWPpTlWgu5vu8oiy2/a3wTO7ILrET/+3i\n+75HHvYVqfLE2rHzgSuvEalIJUmSJEmSdGRBK1IOtV5YndZmjkQee7Tar/W2oufBMKoK3Z4xgZdU\nio8gFiu6P7LCWOXz6goG3pJ7DW0zNCIixWah1Jfy6tFtaRun0TfYSW1l+rbUenELmVHvF4gCQD2g\nWpXXxzYeP9dbqzbS52TJcVz2axwVKDReSw48Vsfhe9EuCLzP53x3ibZjlusjNo3j1CpkXeNva8dk\nSWEpQQV57oeYKtqVCvZk83kGusdfejZoyZ5pV4/v5bcHBZG/e+wWT1mYy5jbeJ/MfNT8UjtxXu4T\ney1VwE9FKkmSJEmSpCMTU6Qe+9jHht5BtFcXq+9SNdlaJYhVpis0rEK9mq3j3o7Tt4KCd8T+WIA3\ny/PsqJ5RpEThFbMKp4I2e7p5LQ36x/vJ38c7RqmK2oN+5XteMRz63vm9K9gt11lbPRfGvZegZ61i\nL3hvbRWpUuzfqLICRwn1aFDT+soYdqggzb6Z1COqjSFhDNCH9B3X7TYZqcSMOVcPRz3GUGMjtZux\nFSk+xFQx16E80X7cL/fh9YhQVmqhfWkvfjO4vkmPhWHVZOI9mZuxT+Z8fhvIGPZ+8f/zlIh+LM11\nkX1GT10crpNXfuM4L+OhNJ7pV37zsU+ur1SrLxWpJEmSJEmSjkxMkXriE58YrjJZ5eKtsOrHy8A7\niKoNo6QAz1n5PDE+vOLV+PVEVY75Htfp5wNfxXL9rgxFe885eD3+fVbPVCRnFY1yx/fwPlG08DLw\nRvCefPXu7eL7IHlcAvdZUqKA/uQ5+Kiyyvqma7XrrvE3fA97rq3a6ypDZEe1lLJQ23r9kwTbxROn\nbe+5556RnI8+w+bbZgXieXuGJHMQHjhjk/vwOeXggw+WJB166KGSGkWurbraluXLl0tqlAsf66XY\nI88Kox1RZpi7ovpUXdVg5jDqCxFLVJrbRl1TrW02YwR2uWbNGknN3E0Nu9q5wud++iGqCxb1B9mk\nKGYomf5b5Bn6Hjdd+1uCAsY4Wrt2raRGoSrdfypSSZIkSZIkHZmYIrXnnnvOROCzenWvCe8QpQVQ\nUiLcy6MmDFV78V54zk5mArUzXGlBKWJVjfeGIkENFofVMM/1WdX6/kWsqmvjJKiNcfzxxw98j/um\nPVGgXIHw9kOJwsvDq41iw4B2oZ08QwVvLcrMAPqD12H3VhwXXZWlrnENeLf0D+2E/aEmeDu718r3\nRqU+uJe4kEHRwJPGpu+9996RnM/39CrtxebQ1/QxHjqev49BPHmfW5iDiNHymBDoO7OTOZPsQK8j\nVco85vsoiFw3vyXM0bW2XVvrD2XGnz6UlApijLrCb0ip0viwcB9f+tKXJDW/iW1VayqloyR5f5Qy\nu3mKw3GAOW/Dhg0D73sdKeZIxoXPRZE9Y1e0N0ou94+9RaQilSRJkiRJ0pGJuY533HFH0WsgxofV\n8Xe+8x1J7TNqokwUvE6P+QFW1f4c97bbbhv4f/ScmuvkNaof1HbV79eJl3TjjTfO+3mUE3+ezyqb\nVTyxLSUlir/XXnfJC3EFZdRxGn1Be6IGPP3pT5fU2FXf2Xn0E+2J10bM28UXXzzv99z+R5WZhbow\n6TpZNRD7gLqM4hDtbTYsjNF169ZJapSXtkoDqrrX+8GT5j5Ke6yhvqNiL126VJJ01113SZKOPvpo\nSf3XGPO9/DzmpXZPNVR1xgBjru0ea7UxRnyurVoexWrVMmolysGevC4ZSlGkXAJ/52kPv608VWKu\n999QoJbhXnvtJalRjKkHtWXLloHPo2zS//w2cv08DaGCO3+P7JpxwzzAb3cqUkmSJEmSJCNiYorU\nTjvtNLPqxNP2WCGvwAx4B3yPVf/dd98tqb5+E94MMT4oXnireO5et8r3XSp5f+CxV3iBePKukLEa\n9tW/P6fn/yhKxDwRI8VzalblnoHCnnrcD+dnVe/nb6ugjbtu0rignbDbSPnrC+yQfsNbpDr1pKGf\n26oCw8J4bQO2jvpJzFBUwy6CvkAJwoP2GmxkAbGXH541sR943OzxhRLhau0VV1zR6vpKcN28bivQ\nPsR4dbW5F7zgBZLmZm5zfLLA7rzzTknN3MeczStzLgoI/R7VxBsXVNBnTvK5it88lDY+5zGCHMfr\nlPl+oyid/HZhV15/C3vnfMQo0e7UMmRcshbgeuGOO+6Q1PQ/CiufRx3nt49+op+9Lhv9yfejuF4n\nFakkSZIkSZKOTD08gQ3MpqamND09Pe7TJkmSJEmStGZ6ejp82pCKVJIkSZIkSUcmFiM1PT09U2OD\n561td+aO4Lno29/+9plzjQPO86lPfUqS9OxnP1tSk/3nMS28z/NY32HcK57zfJnYqsMPP1yS9Kd/\n+qeSRheLRLzHm970Jknjb89Rn4/2fde73iVJ+tCHPiSpiRtYtmyZpOb5v0NcC/EUpYrnxFa94x3v\nkCSdddZZkpr+JvuP5/2eYcLzfjLOrr/+eklNnIDvvUhmC+14ySWXSGp2fvd4gec+97mSGnultgwQ\n70O8CLFb/J84h3e+850D5x0109PTOvvssyXNnUuIXSJ2hbEHvE/8I7ERtIlXqH79618/c86tQcwF\nY5YacG3xsYAN0edcJzZEllEU3xntsuDnoz3p08gjf+UrXylJOvXUUyVJb3vb2yRJV111lSRp7733\nltTEpTK2aA/O9773vW/g+ojJ4RWbBWLO+Ptll10maW6tQGJ2uI9xzS3Aed7znvdIavZaZM4gzpbY\nIa8f5XGz/J+/E4vEXPSqV71KknT++edLmlsJn/Yh5oj2Y24hJozv8RuA/XA+frve+MY3DtznqKA/\nsbP169dLKmc3MsdTyZ85kfhg1iK0r8+5pftKRSpJkiRJkqQjEy1B7BW++6JUt2jUeIYEXineItmF\neF14H3hpKBp8f/Xq1ZIarxivAfrabyliW9k7rbZKseOf9x3bIyUKPHMlgqrUniGFl453RCaS74AO\neIfUV8PrBxRNvC6HLLeoZg+ZMFwv6gZKF14z36f9fJ+rWsiUQxUapo6Y9yXH3nfffSU1HrYrUr5H\nGK/cKx5727pKtAn1o1DGNm3aNHDe2l0NANXdPXFUaur2oI6TNUV9KDzvq6++euD7vr8oc1kplPbc\nc8+VJJ1yyimSmrmK62BvP+4zyhKkfVFO6Acfk3DllVdu9bo8y2vS0I633nrrwP+hNgM8qqXox2MO\nKGWfMeaisRf9BvRdZ6yE71pSW2eLeYEaiK7UMi90DRlPRSpJkiRJkqQj286mWCOgq4JRAq8ChQFv\nilevLB15Z3wOxQnvtkvdnEcDKH/utUTwvN9rB/W9vxjgZbsSRGwcoDawAzrqBd48SmVUZRmFE4XH\nvVz2kYrg83ihjBNe8er4f1clCrBvqhqjAjFeqIbtXuR8eJwgSgxtEanVkeJBX2AjXW0ClY/4MhSy\nG264QdLc2J8SkY272s39YzP0nVeIBp8L21bWfve73y2p8eyJ9+N+mROj2meRuu5KWVe6Vho/7rjj\nJDXqLDFErujVQnu42o2Sh72iTtfW7vPYQI+7bat89g39SA1J7KHWzoiRAub82jkoerqwatUqSc04\n+eIXv1h1PEhFKkmSJEmSpCOPSkWKVSfeJrFLeCt49LXPqx2Og1fBarvtHmcoT3gVfN93FK99rrvn\nnntKarxrvPCu97nQoJ3w1onxIT7An/9HWaKlGB3sBTuq9RajmED6AyWGDK9dd91VUuOFEaOEfaJs\n4cW6V8bnHDJ7PB7I91DE3nhFjWFvv0jF6VrNmevH+0dN8f3Z2uAqYNvK5XyftintQxlBZiX3gq16\nrNawuEqJQscrMTNuK8xZUVxeLShsZLoSx4eSgyIQjb0o83hY1RO6ZoYTa8UY5LUrUb8TU4Z98NsR\nzR3E3DEWXdEjsxelse1egX3DdbCnY9unQR6r1ZddEHfqu6bUVvxPRSpJkiRJkqQjj0pFCg/bMx9Q\nGFjldwUlCW/M99hj5/ISXAeeOXvncZ1tIRvQ99IrUdr5eqGA8oR3ccghh0hq4jEipQklCNyrw0tH\n1eC5PF4Lig7eUlvlkevCHlF0UD/wIlGSUA/4P/2K98T5S94n3jlZoPyf64iy6FByXT3ALj3mqxZ2\nhKcdUVCjDKU2cA9tlYSuCpSDrXj9J9RkrxsU4bvYo0SgnHF/Hgvj9YkcbMnnFlc8amFMcL/Mfcxl\nxMigTJSIshTHBfd/zTXXSGo/xqPjObRTbUyYH8f/z9OGSStRTte45FHVSkQh5Pht955MRSpJkiRJ\nkqQjj0pFykGZwcvoK27Bs6xQqIh5qVUM+D5eCjFAXk8qAi8QZQXvHIWrBF5lV4jpYrU/qpgszzLj\nvonX4O9RDZEIvDyO6941Xn7kpWJfUSybZ4JxfR7PQb0qFCgytMjsKR0XyDjie54V6hX1HVQEj9Xj\n/tvWgfLsWeyDVxSuYXY+oC362j2hFtqS8zOWeEVJqq0Fh7KFUua2y3G8D4i9QZlC5cO2UXqwaUB9\nbatIMYd+9atfldTEKzIHRcqYw1yJSsucVTuHcD7a29XnWhhztDcKWdtYGoiUPvqNsVAaS/QX1+Vz\nUE2m67bEsL9FEWTVkr3XllSkkiRJkiRJOrIgFKnouf6oQVng/HgJtXWIIvAiWOXiLeBZ1ypBKCgo\nAJ4lVqtIcX68H2J9amNZho1LIOOkbcZULRwXb5x4D/rTY4icUtVflCS8RNQFjkutHLLs3H5KWZXe\nvsTY3XvvvQPv0++1leYjJczjE7gf92Yjrx87Qq3wGLK247gUL4Gdlvqphtqx1xe0vdevcputBSWH\n76Ny0yeRgoEyhDqLzXF+bM0Vm65ZUdw38aCMDXZpQCEjLg5QeLg/+h4FqG2sD+clbrJrPSra3WPR\nGAO0e20GNfGHjD3mdmzcx34E52fseXxvbRysKz0oXdxn7ZzDcfx+aiEukvpSpevsC+6P+24bG5iK\nVJIkSZIkSUcWhCI1qWqreGOc3+MDuoLXgjeHx47nXZuxwOeo/YIXg3cFeNm19Yy8HlHJa+gr46Ov\nPQHdy6LfiNkhHoMYIJSoWjuL2hMvC4WP9qOfIiWTWi60o1c2937g/2RLtt0vDG+euBL3JlEyUTrd\nuy6pEMTXoDT6uKn1gkvQn9j7MLGLtO24FSnAlojR6pq1hOLh2V140KWYGsag224093Xde8zB1lCY\nPI4RUMSwSTJU+XzbfVRpd5Qfjynj+KXYOWwdW/Q6T7XtxNhgDNL+2CW/Fdxnac7y2C9XlWsVONqH\nyvtQ2mfUwb6i9vQ5hvsmNgk1njnGM3Y967Y227X2umlv+jcVqSRJkiRJkhGzIBSpSeOZNX0dD1jt\nsmpm1V0bi7X33ntLknbffXdJc2Oj8DpKihReLF6Y7zUXKUa1GTbjwr0/7htvE6+c/sSLXL58uaS5\ndbxWrFgx7/EcvD3v3yhrDi+Lfvf4D/AYIM6Dl4mCVesdta03xueIJ+F8JQUxin0btjo2XjYV1PvI\ntKPtfK+uccO9dVXhsXG+jy3WKlz0rStz7P3mfYmqyXm67tJAu6NA3XTTTZLijFnmJpQ2xnzJJrFl\nzzzlvt2WUEh8DokUJs+mi8Z+BMflvmhf3nf7RBGK4h15nxiyvp7ucJ622Zr0T9RP2A/tzm8bczBP\nD6K5yxXXvtRv8PFVSypSSZIkSZIkHUlFSs3qua94ABQQvE9W2Sg7bRUevLPIC+R4pfgB7g9FCyWE\n59BRrFRf7dIVrzMUQW2dww8/fN6/4w27IuUZMu4N45Xhzbp3TCYU+4wRo0W/3HLLLVu9bvrV+5mY\nOBSeWu+Q68K79ew79+pQvlAjsNvS+bhOz/4cVtklDgK7HLaK9Gy6xib1xbBV2n2vN1RO2ojYk6jN\nUF6iDFaPNeF4bZUJh0rm69atk9TUQHO4bleWgLjBSAni+6ir2LbvIwmuApfg88NmkHqsGhnAtDOZ\nx9xnNKZQopgjPSaq7VjEPruqyqXxhdJDe2/ZskVSM9fSrtHTGleq+pwbpKYf2h43FakkSZIkSZKO\nPKoUKVbZ/jyceIG2q3ee8+IVAF4GWVqsuvGO2io8VLRG8UBpOPXUUyU1Xlqpui7ZWygOeLUl72pY\nb7QtHqdRqyLQn9QgIcaGfrjsssvm/Z7fH8qTVzH2+AqyyFAgUQfof2r3lMDLInaO/vT4Dc/eQ1l0\nxYnjLF26dN77Q+lCAcMe8MJQ7kqxc3jNtE9fas+wXvHW8Cy1Ue3dVQtt/6xnPUvS3OrwnuG5efNm\nSY3nTB0pQB2MsvewKc/kZD9KpzYTuMTKlSslNXGK++23n6S5daSwTa+LRTvV7oPKnMbcicLhsWFt\n5/xhFRHmBlfZPZuT2nTYBXO/QzYkyh2xbtH1RtAu44qH5bpQPLFr2tPjeXna4r8NfT0twa6YD7D7\n2vZLRSpJkiRJkqQjjwpFCiWI1S0KRZSFVQurafdq77nnHklzFaLa6rCOPy/2uk61VXD9OvryNvuG\n6rZdwXvBm+26v1Zt3a+bb75ZUlP3CEUpigNxUBg5D145oJ7gxeKlooC5IsX+WnhrZNcBda08uxHl\nib/7HpTEevmegyhz2DvX1RWv7dO1uvZ8eByYKz5toS04HuokSkFJ+aLNXS3HE3aPGCUKIqXC4Tra\nZpn1FYPC/V900UUDr6520m7DquDE4vjc6XMeqjxzhI9Favih9KHoYZv0x/HHHy9JOuSQQyQ1qrjH\nrRKDhlrtczmxXMwBqL6o0T6WvX/YRxNqY7nICMfe+A0bFZ4V53sCooxx/yimbhd92Sfjg35rmymc\nilSSJEmSJElHHhWKFKtfPFu8IDxfj11iFV9bRddXyVGsku9J1heeZYWXxXNvvz4UCPcKaA++zyq9\nr4rvtZSqM0cQV+CZMMSRkDmE1zss1D6hfdlHjPPhPfL8PwJFEa8MhYvjEPOEEkXtFe9v+pP7d+8V\nPve5z0mKvVXswGsVAd56FANWWx+N+8IrZ/zhDUZeITFZjM821cr7jvdDwaAtie/yfQiJwaBviN2h\njZkzUCx47WN/QalRhNrGhLliU9rlgLkI5Y/7pR4SSkekMmJ7pbi+UrV/arhRpwlb8ZgoFCTGFP3E\nbwT3y5xEf6BEoj5jB6izXB/HYW9BKLWjx7OShehj2sd+16cMzEHDxgwyB2P33p/cN+3IWGaOQRFb\ns2aNpEaRalt7kd8sjs/5eJ/jMZcRb4oimIpUkiRJkiTJmJh6eAJFgqampjQ9PT3u0yZJkiRJkrRm\neno6zBJMRSpJkiRJkqQjE4uRaqNIeUxE23PUnsvr+fBcm+e6RPR3PZ8/z66FOAOvf8V5PvShD0ma\nu48Xz39L9aUiiC8g7uNP/uRPBs5LlhrPw4k/8Mwg2vWwww6TJN14442SmjgKjsNzaWLITjvtNEnS\nn/3Znw3cDxC3wf12rahNjM3pp58+cH+jJrIX2v3oo4+WJF188cWS5sbLlDLCaF8ycl75ylfOez7s\nC2/Ld0Avxelg11w3ry95yUvmPV+0b1gtft/c5xlnnKHPfOYzkubuA0hNLfZ48/c9PpIYJ2JeaBNi\nKX7/939fUpNNRMwPbcFcRVt4JiLxb8TrXXfddZLm1gOiCv8f/MEfSIpts+/4S86zfv16SXMrnvcF\nffnud7974Lyjgvb1uaz0eWK0ulam5zxf+tKXJDX97WA/L33pSyVJ559/vqS57V+K92z72zcsj5bz\nRaQilSRJkiRJ0pFtImuv7/10HLwOvCO8RzzyUoZFCZQiFC6Of+utt0oqZ0qgKKC44CUDGR14y2SK\nUFG9LQcffLCkxiu/9NJL5/1clBVGNhtVi+m/K664Yt7Pk9ETEdVxGnbfMlho9bRQ2sjiixShkt1g\nB6U6Y+7totDh9aK68D51vqiThaKFylOqPj1sWKbf9+xaRNgaig73ThvyPmMQlZn6T9g8tbBQelB1\nyeoB6vaQpcX5yV7i8yhfjGHajrb3PdKoDVZSk5m7UO2Za/w6u1JSooatEN/2ewcccICkps/JcK2l\nre3x+b6UPp9rGDu0s+9Fd/LJJ0tqVGn2EcV+UaRQ/SddqX+hwpza12+Gk4pUkiRJkiRJR7YJRYoa\nHyggfa8q8eaIW8ALpE6OVxNuC14u3ijeLrFYXtUV8DKIs4iUGZQn4inwrrtWUifeA2+31suh/dhX\na+3atZKkc889d+BzKHN43b7z96PNq3JvCbWC/nOvNQJVA/vy6su14DXj9S9btkxSEyfCdVEbxmv+\njGu/rvmgPhNqMm2CbWF7xDDxPnu/ebwdqhzHodI10FYoCIxRxi7V4FGusG2Ox95z9C1zD2O4hCsm\nKFJ9UYpnQ2mjPUv1nYaFfnEFD3wPuxKl/SSBduX8jAlimoj7LIH9bdq0SVI816FAERtHXSV+Sy64\n4IKBz4/qqQ123Nfx6Z9of9tRFREYVYwfpCKVJEmSJEnSkW1CkeJ5f60StWTJklbHR/E54ogjJDVe\nJt4VMSJ44rWrW7wVwJtlFc5zbsDbQBHi7yg3UayLP3ePvLVaUMgipSyC9kIhe+1rXytJOvTQQweu\n65/+6Z8kzVUythUlqtaLrSWKv0ANiGK4vJ/x8oZVBVDIsFPUBvoL1YbYPOwVFWDU3t/W4NqiPeiI\nI3RlCSXL98+84447Bv7voL7iudMHfB4P2/uQytvEm5HFxZxBReeoerzjVeH7oqQQEGPW11goQWV0\nFESnVolqi8cpEqfa9nw8VaFdUeP9tw1l8sMf/rAk6cQTT5TUKJXEnwLv93X/xL71rXRxfShsKMeM\nu40bN/Z6PmBcoNajrjMPDNtuqUglSZIkSZJ0ZGKK1K677jrz3BnlI9rPqXZ/HaiNLwD2S8LDxhv0\nWKnI0472okNRQFGCKBPH959CqUABicAbZr8nlK22ihLgfeEtlM4PeOF4qagCv/VbvyWpiQ/46Ec/\nKqnxwnyH+4UK7cCed+4VdsW9PrxUvKcoxsl3pCe2qsT+++8/cHyyR1FAURe4X15RwIjtw74Zr3jp\nqD6TgDkFj5O5g7gtVF0UAcYuY422R0HCU0Wdo60BT9dr3EUqI9fl8Zi0LW1PNlak9HgdLL9uVPRa\nvM5VLeNSogDFkbmY9sSWa8cAdL3+rnGz/htSesqCunzttddKavbzZOz5HnVdVX2+T7Yp44jYvr5j\nlxgvnAc7LilStb9FDkqxZ+b3lbGdilSSJEmSJElHJiYFPO5xj5vxEofN8mFVjqLUdpXpNWWoTcLq\nleN79WDej+rm4L3iRbnXWnudeDHET3hsFYoGx0PZcqWsrbfC/bX12vBeuF9UABQL7gdvhPYjBqdr\nhfJRQzvwWptN15bScel/lB+PxQNXOQAvGEXN/4735vE62D/2ReyiV1d2ZTVi2BpEMHs87LPPDBsx\nEQAAIABJREFUPpIam8LTJjYlOhf3ijqNEsU1ck+unm7evFlS0xZkAEcqNUrG9ddfP3CdnJ++IGYr\ngutEHeT7nm2IwkTfoXB4Hw0bCzNstfq2eEYqcwlPNaIM4GHjRx1i3Eq7XkCtooIdYdvcD3ZG/2Kn\nzP1dM9rpf+wRtbtvGBf8FjB3kOFdou1vEeOCpzXYS19PEyAVqSRJkiRJko5MTJH64Q9/OKNAoPB0\nrR6LF4QH7xk5JYhh4pVK2yhceBF4XXjs/nza4X2+j/fAc30yfgBvgr3quB4ULb7nCh7eC14Jn6dd\nOH9bBaX2c2Q9PvTQQwPnw8sh84msR7xCvCo+z/USs7NQIW4Ar432pn2HzZyiH12xRIF65jOfKamx\nv6heGHE49AsQ01RbO8fHVSmb0PeSdLXCjzsss8cDYwiliGuJlChia1AyUGw8LtN3FwD+71XdPSPV\noS1RlOhbjlP6Pp413/f6VVwH72OrfH7Dhg0Dx2ur4uPpM5b5fts+dXW9BLbjNo/iRsY2dZ1GrUhx\n/Np9VLGzEqj5HI/75fsoLNw3ilXXGCIgto7j960w0l7MkdwfNRDbZkSXxhu/1cOuMUqkIpUkSZIk\nSdKRiSlSpT2kulDapT6CukesXlkV+/P26Pju8QNeLatwXj0rymOv8LDxcvAq8ey95gXHwTvGa+N+\nfP+mWiLvBi8S79Njv/AOiBvA++Y+yd7j+mgnvK7SXm2Thn505a+vGj7Er3jsE+fFTlAlogwilCe/\nLq438qJRF/g+9sYrShcqB9mhroACKgn2i333FQs3266je+acjA2uhX0huXaP9wJUYofvEQ/pFc4j\nvI4QihTXTdtEbcSYY+7hvjzTkuthrKE00Ie+b6fD91yxItYF1Z7raatItR0z3A9zCTaIsui1zpyS\n0tcW2qGkREHbyvM+Z2PfnJd+5v9RbF5buv6W1uL2RL/Qj20z9cGfFmGXo47dS0UqSZIkSZKkI9tG\nAZ8Rg6ePl4O3hvfmiovjMVJ4CXhLrr7htVGll7gFYoncu2Q1TfyHe/x4wyhdrMbxZmq9JYfrd6+R\nWC28Zc7LeXjlvtkrkWrEqAP8He8D5WrU3lBf9KWouCIJ3s+0E+2D90nchx8n8r75XKkWDt4v/cX3\nOC797t6eqxKML9QXYvo4/7C1iGbbJ8dyJYVrQk0lro8x2LWejPcZcwH3GGVRMYaYY2gb6vgwJ1xx\nxRXzfh/bcCWIV67D62RFc0gEx/H2wfYZ6+OqJ+WxYNQfwsb4exRr43XAukJ8Z1u8f7DTWmWOMcPT\nEsZi33scjnp3Au8X7Ivfrlp8jsPeaddRVbp3UpFKkiRJkiTpyKNakWL1ipeCl8grXgNeLEoR3gix\nPu4NeL2h6PkssTAoYF5HKVKSfDWP98v14rW0Xd07kaKBt4IX7VVjfb8yMp5oV7xjMjWoHN93Rs22\nQpT1ST97LJPHvWC/2ClZpxEct7Z+E3aPisN1RPFEHt/A+VDS+lYxZseRcK2REoRtUv8HtY26TVFM\nEEqRjwls2pWOUj0f7zPaBBWwVO2ftotiSTx7z+st1dYb4vOumDBWmdvGXeGcekoHHXSQpOY+yfjl\nur2/Sk8XakE9bxvPSUwbNlsbt4pdogoTlxhl7G6r+K4mbXFldlykIpUkSZIkSdKRR4Qi5c/Dfa8v\njyFBScJr433+j9eKMoTXRTwDmQXE+ESVnEuxSXgTZM50jdPgvlnND7uqh8h747kzShjeNfeD9+TP\nqVEkaC/qMbX1zrZ1PBsvUgewH1cnUJLwtn2ftRIlNYJ+o399HJS88JI60TVmr4ZS1pIrOQceeKCk\nZo+2qA2jfSs91oU2K1V35/zMPShjKC3DqrOMJcYwcyJ9X1tPh6w8QJliDqS9J6WM0P6+b2ukpvc1\nx3gcai1dY5mwy1HXQ9rWwQ5Qeku19voiFakkSZIkSZKOPCIUKWKMUGKIZwC8xUhBevKTnyxJWrZs\nmaS5sSB4OTzf5v+ljAC8NbxOntvixaDklGJa8HJRMrxuFattf7+22m5b8HJ5jWrR4C3SLygaeIt9\n1V1aqNC/a9askdRUDWY/uFpKXi8V8GvBHnlF0UUlwV6oEu21l7DjUe012JbZGWi1nid1oZgTusbO\nECvDGEBJ4pW5Cdtnzz/ajNgszzprq3SUaoIxZ7WdC7hOzzxm7Hqtu3HzL//yL5IapbGvOkolusbi\n+NMCzzIrHc+VKH67GKu1ldMfqfhTkq4Z4IzfUqwipCKVJEmSJEnSkYkpUvvss8+MV4PXRAYM3l2p\nCi3Zc6w+WdWzMzq4t+nenu+1Nyx46r5Dt3uNriCx+uV7HIe4Bt+7DziO1+mhXVAaajN1aD++X4r3\nABQLzo+XTX0sjsPza7wo+tkraY8L92IPP/xwSY09eEwYMUO0J/aLEkqcC/Wz+H4U90K/Ypce9xF5\nVdgTMX+oBqV+pp4Xx8U+vGo0cN2MS9qFuBvuD9Wjr1o9Efvuu6+kxo62Fk/k+/w5w2ZxucITKQre\npigopViXUltiAyhf3C9Zeih09LXv01ni3nvvnfd939903Hh1fGyApwpkAo87m7AEY5r2Y87Alpnb\nsSti9yKFkn7lc+zDOSmwP+zR94LkvvitZk7xTPNDDz1U0tz9WD3GzPfa45W5mjUF7c7cXHoagt1w\nnBKpSCVJkiRJknRk6uFRb0Iz30mnpjQ9PT3u0yZJkiRJkrRmeno6VLZTkUqSJEmSJOlIMUbq1a9+\ntb74xS/qKU95ykzV2IceekinnHKK7rvvPi1ZskSf+cxnZp5F/vmf/7k+9rGPabvtttP73/9+HXvs\nsfMe9/3vf/+cGKG2+M7XxKqQdXfaaadJkt73vvdJimN9eL7ue9t5/IPXiuF8PP89/fTTJSlU2/ie\nx2UQKwRRXIJf77ve9S5J0nvf+15J0uLFiyU1z5t5ru4ZN9SG4Tkwf+f+yQRhrz1iYt72trdJamKy\neC5P/AfPw6+55hpJTYzQcccdJ6mJgeL6aDcyl3huzf0tX75cknTRRRdJap6PEx9BXAHXw/1wfOIn\neD5O7BG26vXHXvnKV0qK+8854ogjJEk33HCDpCZepDZbkvPwSqwT7UB8C5k4XK/3J7FKxBd4DB7f\nO+OMM1rd37D4/UX7nzn777+/pCbegcw4jx+Z73wf+chHJDVtSAwFcX/0EW2LbWBzxCxh03yOMUBM\ny+tf/3pJ0tlnny2piU3hGonF4BWI4aEm3Te+8Y2B66CvuUfa4OUvf7kk6ZZbbpEkbd68WVITh/js\nZz9bUpMZyth661vfKqmxyS984QuSml0FyBhmzFNXizi6cdvKJz/5SUlNuzGH0Z/0C3vs0W70D3M3\n3yOGiHbk75zvwx/+sKRmbvE50LPsmHM8xsazuzg/cyj9cM4550hqxiq/BfQXdtG2FiBzOvb7zne+\nU1LTLtwf7ce4uPbaayU1dvDiF79YUjN3X3311ZKa3z7meO6L9mXuKtkLdsgc7OOD+Ed+A2kXV4I4\nz8c//nFJzW8IdG1HZ4899pAkvepVr9rq54qK1Kte9aqZHzJYv369jjnmGG3evFnPe97ztH79ekm/\nHMSf/vSndfvtt+uiiy7SqaeeuuCC/ZIkSZIkSfqiqEgdeeSRcxSSCy+8UBs2bJAkveIVr9BznvMc\nrV+/Xp/73Of0O7/zO9phhx20ZMkS7bXXXtq4caMOOeSQOccdVo2S5u5pFx27lHWG91Ba9KFAoXhE\n4B34PljUEyJTAU8bpSqqgeOePHvXAV4C53UvzsHr9evjfbwllD6vxI03zCvg9W/cuFFSU7kcxeTS\nSy+V1HgXL3zhCyU1Xjnf4zgoUrQP3hTqAddJTSDAm+R72ANqBMod3/d24vulmkR4dWeddZakJlsO\nL+i8886TNDcL0fsPrxp1we0XhRVvGEUKL5i6VChw3i+TdmZWr14tSVqxYoWkxk7xlv/1X/9VUtPu\nL3rRiyQ13i7zD3/HHrED1BVpbk0z2or3aWO+i8JEW/qed24bnhHJ31E8mCPc0wY8/6h2HIoVyoJn\nazEmuQ+UASqNuzrqKvAnPvEJSY3NMbdgO13r7vRNlM3I+2TlYVvgygT9jO34nOfqru8S4URzaimr\nDkq/HV0VlGjPxc9+9rOS5make6YwoAhGf3eYY9/+9rdXXSfjy5/KYL/M8bX1zqJx1tfuHm5PEZ1i\npB588MGZH+9FixbN3My3v/3tmbRH6ZcpkBSgS5IkSZIkeaQxdB2pqampGc8++vtCh9V3qX4Rik2p\nTg/P791LYHXPKp7X2R71bFis4n2ikHgtGN8fjNV8dD94aX4fxKaglN1zzz3zHofrYAGNd+H1rVAC\nP/CBDwwchzpNKFWuwPh1oUjQnnjheNUOCg1eOddJP7O4x7vHG4KothDnw3sn7oD36U/iWKL2pz+B\n4/G+K0i0I0oOz+29ujL91reqQHwEXn3b/cK4P5RD7od+8Hgi7Je/0x7E7RBfwSv3vTW8zQF1C0Wp\nVEvOa1b5voM+R2Bz2Eap7VAsuF5XZjge53WV1mNJiKPEpvk7Y2jt2rWSmj529XlUcD6fu2pr3QFj\nLcqmYgxigw5jt23tutpab6jfEV4HqUTb2n7Rvpg8JcIeiXeF2sr6Pq5Kux0wh6Cycx3ECW/ZskWS\ntGnTpnmP7yDc3HXXXVXXOyo6KVKLFi0aCGIjsGvx4sUDk9r9999fNKQkSZIkSZKFymWXXbbVv3dS\npE444QSdd955+sM//EOdd955Oumkk2bef9nLXqa3vvWteuCBB7RlyxYddNBB8x5j5513Dr2EcVPy\nRljVr1y5UpL0la98RVLsBa1bt27guMRn4G1xPFbhKDwcD2+NeAn36CPwevHgI+8S7wfFi4wLr+Ia\nxdYQ64JiEFVqB29flJXIG3SvH68Er49XlBmHTBMHbw7vHK8LLwmiOAi8LN87kWQMFC5UhbZeNzFQ\nXFekRNKvZNZ4ptioQG1pq0i5gugZaU7kXXLfjAcUqS77q3l2V60i4H3J97Ap+tDntj333FNSk91H\nn3qsFLYTefS0DSotqi7xiB5bEt0X10OMEGOK47WtkB1lJEfQTt6epRgip7YMYrRnWul8jC1sj+vm\n/yUFze/PqbW72qch/4+9cw3SrKrO/zNVmsQqrUryIV4hAwyXGYa5wMhNYBwVQyJgLOMYrChRiUhF\nCPESDaJpNAVGRRNTEi2vECuoiSAowRIERgGH6wwDDJcpMrkomtJvmpgyGv8f/P/69Pt0r957n/e8\n/Tayni9d3f2+5+yz99r77PXsZ63l8FMC2s9a6FUKIkaWOczzsEbyd/+c26+vbTBq2DFrCu8C2lfS\nUi8VE7Vly5ZZXfhCKG6kTjvtNG3btk3f//73tc8+++hd73qX3va2t2nr1q36xCc+MZv+QJLWrFmj\nrVu3as2aNXrCE56gSy655DFxtJdIJBKJRCLRB8WN1OWXX77g36+//voF/37eeefNnssvhqVko1zb\nAtits4uOvBN2yTARaHDwAh3kcuF6eB0wCmhyPB+Se+quGWGX79og/o53htcQeUN4wzBeePZEOnA9\nmDPvF/oLLxQdR613iBdOP/g5uNclgwHDO+H+9FsJeDcwgHhBeOOuOfMcKcDrozH+MI4cYx911FEj\n7bvuuutGruN2yOfwChkPj5b1iDTGpbV+WiuwI/QLraB9OFUlLz2C1+Oiv+ayDdyLv7nHz9+xVVjV\ncWvGcb1oXdu5c+fI767LA6V2oFvEdvHs+2p8sGVszRmGVnhUYYRIU8Tcb2WmHDA4zNm+NodsBaam\nVKPN0cogDX2dKMqUNRj7R7NEHjFfIxkvPuc5G4H3s+dmZFxZk5kvjJPXDcU++47fUiEzmycSiUQi\nkUj0xNhRe48FRLoFdtMwFeRgceC9efQU57poYcADDzwgaf55MwwTjAPnxWifaB9MD+3DS0V/4PcD\neIOe4drB/9EGwTxxXZ4Tr9K9Q89NQ/vQXZS8J2ekYBbwjtzrc20S3+N5XWuFV+3eOs+Dt4qX5f1Z\nisRxMC7kb0IXiM6Es3Xa4Ywiz0u/1npf9D9MmGfzbY3wKYF2kX04YmT9OB8vk/YQIdSaSw42hkgd\n5slcRo57ey40+hwP16vDY3vMdWzCbbE2mqkEz19UC9cPMia1WhuAzpPcdjxva84xtELkfhtXs1Ji\nskrw8Xc2vxZe7SLSO5ZyzmFfkwb38bU6yi/FPGAuObsbvTuYD8wDZ0L9HYs9ef/7754DkdyC4zJR\njBvjWavha0UyUolEIpFIJBI98bhgpNgVe4Zwdque38mZH/cq2E2za3dtEH/HK2I3zH08ksJ38dwf\nb9oZFffauC7toH3Reb4/P9467YX54f8epcXz4I26F18LmBKyS3Nd94bwqmgHz48XxfPyXM500A/0\nM14kqTpce9T6HA4YN6I8Dz/8cEmdTsajBEErQ0M/wEa4Zmxc3U8E7IOoSc/+64wUDCH2gl3XauoA\n84Dnda2eFNu852OCQcEj9+i9yHPty0j5nMfG+Hut9ibqs9agHvrSmbHWMfF6o+MiymjugEVH58na\nAOtfO5eivEc8D6cK2C73Za0iZyD9wJrGmsjaBmo1ZA7YZuzFTzuY+84+Yxc+rjwf7aRdMIoRw8l1\nuK4zmLUZyXkO1nZYdNaWVi1aBObr+vXrJc2P3qT9VNXoi2SkEolEIpFIJHriccFIoXXxDObsutHM\n4KV6tXm8AbxIzm/ZfbuX6ufreDPs4jnHLukR8M68tpx7F85MlLwd2oUX4JmhYW7QaLkuAwbLz9Nb\ndRp4gTAKaJ+8P/FavA4W/VCr6+B7znx5/iVn5PA2a3PlwDzhFaOfcGZxKHile1DrHbYChtazIQMf\nD7x47Jd+RBfRqhfiOthhlOF+MfgcZkxLY9u3biHfw/aoI4ktkKG7hKE0WoA1hjnfGh3Gc5Vy3DkY\ns77PAxsNK4outfV6JdaW54MhYe1El8jvbjdRO2DAuC6/e78zR9AVYjfMJWekeKf4u8H1ooDxZs3m\n+s7OO7y/+s4H+tPZbMYTJm/cEnP0B/3MmlObB6wWyUglEolEIpFI9MTjgpHy/ErAtU7s/j2Chb/j\nffj5uzMM7O7ZBfOT7+M9uBfjjBM/iXri837Oy+7atU8R8ELJC4S3QpQZ5+0wHK5/8HNynhcvZ82a\nNZK6fFoR8Op4PqKy3KuKsu22ehP0u9uB349x8pxDpcgPz+cE6+EYqpA3XhuMalSzcSiQCb+VvYDh\nc1YGpteze5eA14rWrE99ONqCtqaWyXAtUmtGb+YS92/VNkWf72tTjAVjxBhPGvRDX0YKLQ9sb99o\nLOY47eBUwpk6xgtGA1YWRifSlhHJG90XZo412PMpwSgxTrD25Hni/jBVzkJjn1w/imbtG9nbl5GK\nwNo89HUZH4+IH+p0IBmpRCKRSCQSiZ54XDBSMAUeHcdu1CM32NWzO3ZGpDYail0/3gJeCPdl1+1R\nV95Oz/fk4HrOHEW5TwBeGM+P7oBd+1133bXg9/Bi+BxeHN4sz1FipMhhg3fgzB1w74Tf+0YKlbxX\nmDiPHCkxMESAYT+uPQPjRgUCZxMmDbRkkR1G4HkZXyoD8Dt2EuXicRCpRf439DEtwNa9Vl0pWghG\nAsAQYIvMNeY+topHjG07k+CIosmcYfA5AMtby1CR+43nItI0YliidrViKK3XuBnQfW1xhgm2k3Hl\n+RlfjyR2uD2xNrq+j/Hk/vQv7KvrDP1+Ub4o163yXM6IRd93eOR7H33iYqAfhs735P3NeLbWDY2Q\njFQikUgkEolETzwuGKmo/hW76kcffVTS/HNqEGVwjoDX6JEeHinh+aIirwZvgcgYr28EuG4tU0O7\nuB7tpj2liukwLzBmnOu3Ru8RsTRUva8IXt8Mr2T16tUjf/ecLV7xHG/aNT/0I/0PO8B9+fxQ2Y7R\ncbTmn+oLGMe+jBr2jZ1hNzB5JUYKu3CNH4zxqlWrZtuG5++6QsbCbd7nLPDM1c4gwWjh6RMZGkVM\nYgPM6Yi1jBif4447TlLHONEX2KQzDNFagA1i40Rr0X9kr69t12MVvtZ7HivmtjMvMEO1ea8A48Ea\nGmXQZy1k7WFt9SjCEphbfjrges5aBsj7qxTl1wrXMPEuon193w2sFf5Ou/nmm3tdz5GMVCKRSCQS\niURPPC4YqRKGyiUBvNK155WC+eJz7u3gzeI9o2Mg54Z7D+Th8eg5vh/l6aF9XquuFDFx/fXXS5rv\nTd93332S6r3Wq6++WlLnXfPcrklDv+Hn+Dwv7S3pLrwfYC3cy4Gh4nqwEnyec3W8MfIp8RwwL1u2\nbJHUMTlojGAxxkXfem2tIGoV7xZvuRZ4w3jxngXa83hFwO537NghqWN/aM9xxx03q2khohF21HWG\njB1ti2zeWTKfq7QJVjBiopj72DKf889z/YhFZ8xvuukmSfO1V/wszQXm/o033jjy9+3bt0uSZmZm\nFv3+YwVEtKI1uvfee5u+71UUsCPGJxon4Mye53qLxom1mzUHpqY1mi1iv52x7Ks39edn/rFWMOc9\n0jdi1Piev5P4fl9tHWsEP4fWlSYjlUgkEolEItETyUg1gHNbzlnxRl1745oqNFh4ix4RE52z8/c7\n77xT0vx6YABGBeYKLwbGBkYE78rP9/HS8XZgDLiO14Zr1XVEiPIscS4OaL8zUuPW98IruvXWWyVJ\nz3nOc0baRQ4YvEH6ifEsaZOuuOIKSV1UI+2nP1/96leP1f6lAsxRFMXp8PH74he/KGn8SBzs173L\nuWBuwFa5BgXbRm/Y6uE7e42tcH3YO5gmrwMZsW8wHmQ853u7du0a+Ry/+xrC9YfOv8OahwePVgfm\nANuApSWrv4O1iahA1kJfM5y9pT/p39Y5z9yFmYr0pRGwOdaCKC9UhNaM7wDmkZ/kzWoFa1uEvvmj\nIqxdu1ZSd3pSG4kLUwtjRz+36m1r0aptKyEZqUQikUgkEomeWPGz1nLfQ9x0xYpfmDP4RCKRSCQS\nv9iYmZkJ86slI5VIJBKJRCLRE1PTSM3MzMyeW3OuH53XkvMCLZJn7eX83rUZ5557rqQuEoX78HnO\nvzdv3iypi75C07F+/XpJ0qZNmyR1kS5ooObmr5G68+GIbUNf4BojtDdokmqzzHKf973vfZI63UHp\n/JeICO6HbsQjSgCfe+tb3zpy30mD+3zsYx+TVJ+tGZ0FepaSrgK7OvvssyVJn/70pyV1ug/sBY1a\nlE05ygfGuNAu8kudfPLJkuL+POWUU0aui12CM888U1Kn07nuuusWvA56ljPOOGPR+w0N7tN6P/QS\n6GNK+hB0L+ecc868e3kttb7wseU+F198saRuDrnm56yzzpLU5UpzzQr6OdYS93jR073hDW+QJF1w\nwQWS4ioI2NZ+++0naX6eHOY+UY30LVniiWY8//zzR55z0nBb8fxdQ+G0006T1EXTLfe5MOn7lapf\nlMDa+KY3vUmS9OEPf1hSNyfRtpW0Yuh5+cmajcYNe2Z+bd26VdLS92eEZKQSiUQikUgkemKqUXuH\nH364pC6KDKbGo97YhUaMBIwSu2siBsBXvvKVRdtBJAyRIkQXwdDgLcKYeXbUWq8pinYjMqFvhAJM\nGF7F3r17JcX5sWpzoABndIaqt1UL2IDa+9bmJQKejwnWgZ+14xtlpocJ9bxiJUSV5T2T+pe+9KVF\nrzN0hEpflPKagec973mSuv6MGCky0M9lm/CQeWby2kSMFAwN9yLazpksWGNnzUt9S3uibPAwSFHN\nL7c9bCGau6yR0VrJ97xPW3ODRYB1ZU1n7WSsiKYsoTbbPWC8GG/WcNpR6rfHK/oyUcDtn98ZD7dr\nr9UHonxWzD/seeio1KGQjFQikUgkEolET0yVkfra174mab7X0ArO9WFg8IJOPfXUqu9HWYZhQPAm\n+T8aJrLl0v5pgV073i2/R4xUdC6+7777Suq8b7I+O9PiWrRJAwaKdkRaJP97lBGdz8EW+LhPyvtp\nzYUD++F5vPC6v/rVr0rq8lpF6DuvvP/Q5dTW+XI4ExWNzwMPPCApZiMOOuggSZ3XO7eOmHvIpdxV\nsHuue3MGK6o7WcLtt98uab6Ncd/WuRR59LXAdqIM7H2B1gsGgrElxx3trkWtThQwXj5urHVe464V\nricl3xf5jvrOiV800E+src5IMXcffPDBquthP7yLx9U6TgrJSCUSiUQikUj0xLLIbD5uKisYqdbz\ndCICoozjnOfjNfruGm0V3kmEiEFpReSN4l3CQLmGCEYDxorf0VIR9YUmp6QjGDdDdStqdRXODEZe\nMOMQeaewBZPSgNXaAZoiZy1gXWozjUfRmBGYH+iHTjjhhJF2eH22voj0GZ7N24H9L5RZHhuvjfCM\nPFyfa1y3tb4h0XpohIhExGOvHUNw/PHHS+oYEK83CKixxvNhc0MzJ9gG0VrUQcRGPvWpT0mKbRCG\nzNFqsxF4fu7TugYz7gcccICkbo2BMUsmahS8g5g/zmLzzq3VzLFGwyy2ZqZfKiQjlUgkEolEItET\ny4KRGhetuUbwCvG88X4ibwUGBh0A32O3XMp/xedhelr1DezG8Y7c+yRK7b777lvw+9yP82kYF/fa\nXU8AIq9xucHZhdboPYAXNSlGKorgcmB349YURK/iYB64/cJEwXwRRYs9l1CrhxmXoQVzvVTXTzGG\n/ORZsWl+8mz0OXOGv5N3yNnYkm6MPjziiCMkdUwUa0drH2DTsHERm4/NwEzRvqidPEd0PfqY68Je\ne86vV77ylZK6uQgj5cBGqDHoqI1sLYG55qcPtSC/Fs8Ni993bflFB/0bMbf8nXHHTqK6pa5NjHS/\ntYBR5P5RtGwrkpFKJBKJRCKR6ImpMlJkBO9b2dqB9sd3sXhT7GY9s3lJE8TulWg2Ps9uOoro4Xt+\nPsz5ei0zReQC2V19t1+KcGG3f/3110vqGAdnpKIIoqEjfFo1YxFzUgvPLVTCUN5whFpPUaj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FrI3Pc1n5+1dtCq/YEhOuywwyR1z43d8Bw+ToAI4BKcUYtyNALWrL5zMrIP2PPf+Z3fkSTdcMMN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Okec+WKymeN9jDGirKHE81xm3v7CvTg1LbQRziVSkkiRJkiRJOrJDKFLOM57x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\nbZbwMQj4m+t2jYZrq6B4Xi+i19yv0P0ugbY/LCiZbdsu9UM7od5ro+1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tmZrYJHWYfAy0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C/MCPAMUmVb+sXzmRIFU/rJTol8H1sc5YD/w/vo4ptQh6HecjifX9cv6R+FZQLQDlAn83LFp8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\nVOIHKWOMMcaYSv4PwNFhvU7XHUcAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1024c1750>"
+        "<matplotlib.figure.Figure at 0x7fd63064ffd0>"
        ]
       }
      ],
@@ -400,9 +391,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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DikZxoXypnBtE4XnEB8oKEQ+8eoSJR0Ckct/k8kgR4YFaQD95bhKsXdqVSBTq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\nntJ3vvMdvelNb5IkvelNb9K3v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/uRcjLUk+c69t5770HHmTZ9Y8gcFDvPJqsFFYD+jrxr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\nRUoIIYQQIpLSFKks1mhe676E95LWf2Cm4IqwoDXW09MDoK5UxSonrQrrN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3h8fOeeE1eY0XPP+LLrpop+0BXo57YShweMucH/93qLvk/U8WFooX3rJnfXn2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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1024f9490>"
+        "<matplotlib.figure.Figure at 0x7fd621d25190>"
        ]
       }
      ],
@@ -428,9 +419,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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yT6dF9SKz11Nvb++Ir9nrUCrP1b/m2traJJU31JbKm/IeOHAggRk3D3vtppnO\n84hIAQAAxFQ6l0NVY6lUUk9PT9anBQAAqFlPT895F4EQkQIAAIiJCykAAICYcis2zzO158+dd4ox\nzlys2NAXwhrfydWMVTRqj/fZz3625rmkpdF/R2lhLtHs/HnPw8+hSHP53ve+F8ZOnjwpSTp06FAY\ny6LCo4jPy09/+tMw1t7eLqlyRwsrvn/xxRfDmPUW833HrKje9x2zYn4rvJekzs5OSZWLBd71rndV\nzClPNoevfe1rYezYsWOZnd8/V7feemvFnPI01hyISAEAAMRE+4MGFBWJMnH2E3r55Zdjz+XCCy+U\nVF4ujuyMFpmcMmVKOLYIRFL80vxmMH/+fEnS0NBQGGu2zuJ+ybxFSprtZ4xj27Zt4XjDhg01/Vsf\nuRptr1ZrUSBJfX19kiqfe4tIFclof2PSlPVOAkkhIgUAABATF1IAAAAxkdpDXSF+Unr5GS38nnQ6\nz4vq2NzILLV3+eWXh7Ff/vKXeU0nFbt37w7HcdL/zerIkSOpPfa4ceMkVZZONEo6NSpVmYWoxVKN\noDFnDQAAUABEpBDunIBWNDg4OGIsKprQyBo9CjV37lxJlXvoWTTJt3HIm1++b6+hIs2vWtUuVvH7\nF544caLiv3HY4qVGQ0QKAAAgJi6kAAAAYiK1h4owbjPwqUorXvQF0hZ+P3r0aLYTy5n1D5KkVatW\nSarsWrx169bM51QE1j/K95FCsVhqb/HixWHM+jM9/vjjYazWFKbvTp5EgbVPg1m380bU1tYWjkdL\n7Z05cyYcJ9EDqlFT0ESkAAAAYiIihabjC4SjioVbNSLlu1vPmjVLUuUdJeBZFMi/boaHhyVl3/Zk\ny5YtkqSDBw+GMYuA1BPFqLaDt49cWQTfR3Pt+fCPV0/R9Wh8xD2txRDVfi6M1T6iVCpJqoyGjxa5\n8nsQNhIiUgAAADFxIQUAABATqT20XHfyVkvpGR9e37dvnyRpx44deU0HBTdnzhxJlekjS2fl9Zmx\nf//+RB+v2i79fqNuW8AyVpfytLqYW1peKqfWkt5tIM5npKU8fS8oS+01Yi+tWhCRAgAAiImIFFqu\ns3mWEaksCkOr5QtD7Q4xr72//NLwJJZNNwuLfPjXTV7Pj0V/siigLjpfBG2/j7z2nPRF7kVqF2B7\nVnp+cUAzIyIFAAAQExdSAAAAMZHaQ0N34G0kVoSZV6Fu3qlFj9ReNEsX5ZU28qxnFCpT4NW+f61f\nnRVcS+VouOFjAAAgAElEQVT3YD0p0ix6v/neYdX22rKfzffcatRNiGtFRAoAACAmIlIoxN1vsxqr\ny3qrKnpHdVviXqRi3rz4vTjtNVz031/S4izKsAUDjbj0P05EytplLFmyJIy98MILkpJvW1E0RKQA\nAABi4kIKAAAgJlJ7qOhLAmShiAXmvg+Obdjb398fxg4fPpz5nIrAF1dbd/xWS+3F0YgpPeML5KsV\nVVhexPd5GohIAQAAxERECkBiqu3knldH9dFcfPHF4dgKZ320tlUjUr7YuNrCYzQ2W2xRCyss37lz\nZxgbGhpKbE5FRkQKAAAgJi6kAAAAYiK1B6TAinJ9l18r0D116lTNjzdz5sxkJpayjo6OcLxnz57Y\nj5NHUbNPSdjx4OBgZucfi38txXkNJcGKkOtJzfr0r/Ur8r9n27jZp4aT6OfleyNhdH7nAesz6NO6\n9nX/u7RFCY1cZB8XESkAAICYSudyqPoslUrq6enJ+rQAAAA16+npOW8klogUAABATFxIAQAAxJRb\n9V2eqT1/7iznYUWUUrkL7Kc+9alE51JtH58o/vz/+Z//KamywHX27NmSKjel/PnPfy5J2r17dxi7\n6aabJEkrV64MYz/5yU8kSXv37g1j06ZNkyRdeeWVYczOd9lll42Y17x588LYvn37Rsw/6QLlBQsW\nSJI+8pGPjJhLXvJ67UYp4lzynoefQz1z8X18LJ0Qpwqj2Z6XpDCXaMwl2lhzICIFAAAQU24RKYse\nmLSWOc+YMSMc57Vk2Ngy0lcfJ6nWKNT5nDhxQpLU1tYWxmzOfu+tvr6+Ef/2/vvvlyQ9+uijYezg\nwYMjvq+9vV1S5WvB9jbzESkTFYXykn4NJbH03UfR9u/fL6mYXb2bxdKlS8Ox7fPll22fPHlSknT0\n6NFEzmdR2uHh4UQez1i0VipHp1pxWXkRTZkyRVL5tQQQkQIAAIiJCykAAICYckvtZdWx+MiRI5mc\np9ns2rWr4r9SOcXgO0CPlqaKSud5tgmsbXYpSdu2bZMkrVmzpqb5FtVY6UgkyxdpWzds3xU76U13\nfQfoJPlO9pZSzzq1N3Xq1Ir/SpWLRbKUVgo1Dl/agLLRFvvY4iqpOZ8/IlIAAAAxsfkQItkdhL97\nsDv7sSJN1bLi/wMHDow4BxCHjxBZdMrvsWavr6QKhadPn57o45mBgYFEHy+O48ePV/zXi4r8Je2q\nq64Kx9ZKxUfAh4aGJEnPPPNMGEvqs2k0SUc1m0VUJMr2Zpw7d24Yq2cPzqIiIgUAABATF1IAAAAx\nkdqrknUM9yFK6/Vi/YGk5ilutxSJhWalcuj22LFjiZzDHseH6333d6BWPsVmvaJ8Giqp/lHGXsNJ\nF9AWPX3kn1OTdIrPfxZY8X1HR0cYmzVrlqTKXnZZpPZQPftsb/aeW0SkAAAAYiIiVSWLSPm956zD\nre/MbXdqjV40bfP3P69Fp6xjdFLn8Etjk+rMjtbkC8vt9ep3NEg6cmSP14xLukczf/78cGw/e9Kt\nPp577rlwbBFyH3GyjMDChQvD2Pbt2xOdQ7Xs74PfCSLp6Gcjs1Y3zYqIFAAAQExcSAEAAMREaq9K\nFr72PVWsINSPNcuGtPbzWshaKoetffg6ieJO/5xl0fHe0gRJpShRHD7NbmnpNAu37X1S9OLwevgC\nb+ty7hehpFXG4N+fjz/+uCRp2bJlYezyyy+XVLkxuBWgZ90F3soTfB8z+yzLahcP5IeIFAAAQExE\npGrko08nTpyQVFnM2iysaN5Hi+zu2/a9ksp34r7tw2h7LkXxBe1JtVYYTTNHDxrFjBkzwrHdzfvl\n7hbxqKfDt72GffQk6WhkWq8lXzRvUeG8IhsLFiwIxxaN9pHoLBbWRLVesWJuW/QjlQvQs45IRf0N\nsHkRkWp+RKQAAABi4kIKAAAgJlJ7NXrppZfCcVR332bhUwvGUplWcCqVUyU+5F5tKNv+bWdnZxjL\nIk1Aai9/vseO9STyCxvipmZ8ys561/j3bNTruh7+sZPk05x5lw7497alZH3KP63nwLPPnK6urjBm\nn79+0/Ms+xX5lLE9H3n/rpCP5r0SAAAASBkRqRr5uxC7O7NIjdQ4dyS2TFiKLry1wnIfIbJj//0W\nRYgTSbK7bisQlSr3MkTz8hGN4eFhSckU5frO+Bb18lEoK5b2i0Zq5T8D7H00NDQU+/GiFOlzxEek\n7Pn10cMsiqmjOofbIhW/ICHLbuJjtbop0u8Q6SIiBQAAEBMXUgAAADGR2quRL1S2lEQjbVBsqbPu\n7u4wFlXYa4XgPo1hvaJ8cWlU4bb1BfL/Nir8b6HvTZs2jfp4rcaeN5+SyqKgNy9Jpob8AhBLB/lu\n55ZGrqcjf9aF1nnzPeIsdZZ1OYN9xvrPBysNaJbdJNC4iEgBAADERESqDvVEovzyZlNrR/A4LIrm\n94SKigLZzxZ1xz1Woa4Vqo/FzltP9+q8+GJ9O/Z7ftky7C1btlT1eL//+78fju1xfMfmLItoG5l/\n70TtdXby5MlEz2fvpygWmZWqf0/UaunSpeHYIpg+ApcE/1o3vtjcF/inxfb7861X7P3hu+QDY/FR\n66SySUSkAAAAYuJCCgAAIKaGSO1FdZBtdFEpM0u3+a8lXchuoXFfIBp1jnr67DSzVatWSapMn1jh\nre+vZWlLn+6z9Jz//drvY//+/WHM0oL+dW9pote+9rUJ/BTNa3BwcNSvZ1kcbh3bpXJxu3+vWU8k\n35m7VjNnzgzHUYs8krB3797I4zz4z38rCXjhhRfymg4aUBqLw4hIAQAAxFQ6l0OIp1QqqaenJ+vT\nAgAA1Kynp+e8GTEiUgAAADFxIQUAABBTbsXmeab2/LnzTjFWO5dqe1/U0yOjEZ+XLFQ7F1/4a0XN\nvgN0PayQ+Lbbbgtj3/jGNyRVFuBanyTruyOVi5l9Mbz1MfObRI9VqG0WLlwoSfrwhz8cxoryO7rz\nzjvDmD0HO3fuDGO2SMAXTdv7xHeSt+/z7yHrWxTVFd0/3x/72Mcq5pQnm0O1c/GfH/a6Sqr6w+bw\nX//1X2HMnl97TUnSnj17JFXueGCv1xtuuCGM2aKMrVu3hjHreeV7eC1evFiS9PTTT4exj370oxVz\nylOtv6M0MZdoY81hzIjUBz7wAXV0dOiKK64IY8PDw1qzZo0uvfRSvfWtbw0vaEn68pe/rGXLlmnF\nihV68MEH488cAACg4MaMSL3//e/Xxz/+cb3vfe8LY7fffrvWrFmjv/3bv9VXvvIV3X777br99tu1\nadMm3XPPPdq0aZMGBgZ04403auvWrRV3OXEsX748HEctdbW7j7S6BxdBtdGlvPb9y6KLc9H5G4qk\nRT2n1sIiau83/56Lev/Z91UbhfIsYlBEvo2ERQNH6z7u+Q7/Ud3+R9tTbmhoqNopFpr//JgzZ46k\n+vYljLJ58+ZwbF3RffQpir1ef/GLX4z6fVH7hlokNu/WDc3Out1n0em+aMa8wnnTm940YouA+++/\nX7fccosk6ZZbbtGPf/xjSdJ9992nm2++WRMmTFB3d7eWLl2q9evXpzBtAACA/MUKFQ0NDYWagI6O\njnA3tmfPHnV2dobv6+zsbMh91AAAAKpRd7F5qVQatZNuEl1229vbw7FdwPnC2f7+fknFTjk0u2uv\nvTYcd3V1SaostP7Nb34jqb4uzo0ki3SzhdCj0lA+nZX0Rr1J8JtmW2G33wi32nTcq/nPBX+M2iWd\n0stCVHrp2LFjeU2npVgJjv/ct9eQ/xxsxtKPWBGpjo6OkG8eHBwM22AsWrRIfX194fv6+/u1aNGi\nBKYJAACQvUceeWTUr8eKSN10002666679OlPf1p33XWX3vGOd4Tx9773vbr11ls1MDCgbdu2VUQq\n4vJRjAULFkiSpk+fHsb83S3ysWPHjnBsS5V9cW6rRKJM3gWXRd8r0d9gXXTRRZIq2w9s2bJFkipu\nzKoxY8aMcHzkyJF6pthUfLQv79emL/+wG/IzZ84k8ti2sML/jFY0z+sheT7jZJEmH5GyRQJ5v+bi\nsH1QJen666/XunXrzvu9Y15I3XzzzVq3bp0OHDigrq4uffGLX9RnPvMZrV27Vnfeeae6u7t17733\nSpJWrlyptWvXauXKlRo/frzuuOOOxDfQBAAAKIoxL6TuvvvuyPGHH344cvy2226raBoIAADQrHLr\nbF6LXbt2heOjR49KkiZPnhzGLHyYBZ9StCJZXzzXquFj348oTm+iZpNFKDuqyDwJUVHkpPc29wtD\n7D3tU3v79u2L9bh+npbmyau3WpH4z8u8076+m769hv3CAJtfnHSfpTB9KtPKQVr1szlN/v22e/du\nSc1TTO5fQ2Nhrz0AAICYGiIi5e+mbFm5v4NJ6848iq1QlMrRKd9NmbseNDp/l9nW1iapMsKWRGGw\nX4gwWsfwWvnPBSJRZXlHoTz/WrLWHP4zvJZIwKtFtbyw4mc+m9M1e/ZsSeUIs1T+/do+lX4sKfZ3\n2J83CbVkuohIAQAAxMSFFAAAQEwNkdrzIVkLt/leFUkXwo7Gb0xrc6C4emzWO8a60KMx+PdZI6Dd\nSvH5VK51HU+zPMMWNjRjp3tLa/leinEXatTL0neW4pPK/QOT6hNm72//Nz+t4vZaUpBEpAAAAGJq\niIiULxq1okm/VNqWOWex7LLVOnTXw9+Z+C6xafPFqrZPo4+ssPdW82rGAnN7PdvnnJRty5ek+WLz\nLBYK2U4LzcgiPUkXWsdh2RprNyGVn/s0o2RFiDQSkQIAAIiJCykAAICYGiK151lBm9+c1FJ6Bw8e\nzGVOWbD+WVK5sND3hiliSsMvEhgeHs7svNOmTQvHVozZyKmQsfjUhf2cPvVtqaGkCj6LzKd1s/id\n2+dR0s+tT4VbJ3Db4Fdq7Nez/ywb7es+BVjrTgHz588Px5deeqkkaWBgoKbHGItPteb1+Vuk97R9\nxvvO9UkvBMtyYVktiEgBAADE1HARqahOqVnsa5Y3H3WIugsu0p2Jyev34pdXW5F5oy3jr4YVdfrn\n2Yo6/R25RTB964laCzR9FKGod4WS1N3dHY4tIurbk9j7yBYhSOXFB2MtQpgzZ46k6M7NSZs5c2Y4\ntuhUEaPOcfj9SpNmUSJfxG47TyRd2G5d/6XyZ06Wu2wUlY+G++NmRkQKAAAgJi6kAAAAYmrYuFur\n9XPyqalmTFMlyac5LV1ThF4jSbN0W29v74iv+TSepfnqeQ6y6NGWBP9zRxVk25h1u5aq3yQ3y8Us\nfgcFm3Oj/A7G4ovmo9Tzc1r60/99SOtvhS+3sBSWTw+3appvy5Yt4bhZ0tFjISIFAAAQU8NGpDwr\nps0r6jBx4sRwXMSi70biFxEkoQgdf9NSbYRkrAhAM/EtQapVxMUq/rOskSMb/rPR7Nq1K/uJpCAq\nkumLqxv591aPRo9CWaTf78wxFiJSAAAAMXEhBQAAEFNuqb22traKokIrqPQFfBYatdSdVA6dlkql\nMGa9Q6zPi1Qu+vPh1VmzZiU2f38+f45WSO359FsSfXR8zxzfFReji5PGykNHR8eIMf+escLqqBSb\nf59fcsklkipfI83ap8Y/P0mkiPzzmGUfsKQ+D223Ap8qtM+etHp5jcX6UzUjn9ay158vk7Dfg/97\nncXn0dKlS0eMWZ81v4Ck2k2S7ed47WtfG8YuvvhiSbXtHkBECgAAIKbSuRzaFJdKJfX09GR9WgAA\ngJr19PScN5pLRAoAACAmLqQAAABiyq1SM8/Unj933inGRp+LFfAfOnQo97mYa665Jhxv2rRJUnRB\nqhUVStKLL76YylySxlyi2fnznoefQ7Vz8Ytpku6F18jPS5pqnUuaxfp5Py++H9bnPvc5SdIXvvCF\nMJbXJuV5Py/eWHMgIgUAABBTc64dbhFXX311OH7mmWckVS6VtjYFfll50u0ZrAVEe3t7GNu6deuI\n7+vu7paUTVdjey6k6CX11i6jluWtQFr8kv5m3BMyLnuf+uO8uoUvW7YsHNvvaPfu3SO+z/a1lBpn\nR4Goz8i8olD18K0RbD/awcHBTM5NRAoAACAmIlI1yquxXZQnn3xy1K9bXZCfc9L6+/slRe+vtHDh\nwnBsTRSziEj5uUTdbVmkzu/UjvwsXrxYkjQ8PBzGWul3c+TIkbynUBeLFiW9x5p/vLz3b4uKsnu2\nP5v9Vyo3qyTyXdm4M63nw9fpWgTR/w2yv4O+kWpvb28i5yYiBQAAEBMXUgAAADGR2quShSY/9KEP\nhTELG/ql9Xfeeaek6vf6yUKaKcjRimP93kf+OG1jFaRaaNkv+0W2/F5er3vd6yRVpvN+8YtfZD4n\nxJN32q0IbN/YqAJ0ZJPenDx5cjiO2qfXykrS+FtERAoAACCmhohI+d3jZ8yYIamysOzgwYOS0r0z\nsujTZZddFsYsOuXnYo0d77nnntTmMpqZM2eG48OHD+cyh6KzFhBJt4LA2Oyu8dJLLw1jVhi6ZcuW\nXOZkxe7Tpk0LY319fZLyKwRva2sLx7aUG8D5+feMRaTsekEqt6bw76ek/kYSkQIAAIiJCykAAICY\nGiK151N2Fn73hWVWNJxmJ1nrN+GLYC2lePz48TA2MDCQ2hxGY53FfXiT1B6K5tSpU5Iq36uWRstr\ngYb1l7Hu+15eqT0rXpbK88u7b12eeA7oSzUWX2JjnyU7d+4MY9bLsLOzM4yR2gMAAMhZQ0SkfFGw\n3cn6fZj8lWjafvazn4XjBx54QFJ09+wsXHTRReHY7tSK1HYBOB/f4T6LbvejsfP7yPKBAwdyms0r\nso46FD3iU9R5ZYlI1OjG+ttn0WXb2SJJRKQAAABi4kIKAAAgpoZI7R09ejTyOG95pfSMD3db2NJ3\nWQdQvbzTeXkidYZWkcbfSCJSAAAAMTVERArRhoeHwzGFiACA0YwfX/6TP9aepKgeESkAAICYuJAC\nAACIidReA/NFc7Y544UXXjji+3x/nLwUvU8NisVex34Hg7y6jAONzt5PflN7eg4mh4gUAABATESk\nGpiP7tg+TPPnzx/xdd852neJzxKRKHgrV64Mx7aX5oIFC8LYokWLJEnPPfdcGNu4cWNGs0PRzJo1\nS1JlgfSxY8dymYsVbE+dOjWMzZ49W1LlZ63fIzaN80vVF4zbXKOiupMmTUpmYgVnz0EaGRoiUgAA\nADFxIQUAABBTU6T2rJDu7NmzOc8kP7aJsw/T2vNCWg1Fs3r16nBs6eilS5eGsVOnTkmStm3blu3E\nUEhz5syRVFm6YH30+vr6wlgW6b729nZJUkdHRxizNF5a6TypnNKztLckHT58WNLYCzGs9COqtGP6\n9OlJTTFX06ZNC8dWJmB/F6Xy38EdO3aEsaR6aRGRAgAAiKlhI1K+4M7uEAYGBvKaTu6i2gtYe4RW\njtRFmTJliiSpq6srjL3wwgt5TaclPf744+HYfg/+d7B3715J0oYNG7KdGArp9OnTkqTdu3eHMdt3\nNeui88HBQUnSxIkTw1gS+7eNVURuY729vWGss7NTUmXkxZ6rtra2MDZaxKpZ2iD43T0s8ubbp9jr\nJY2O7kSkAAAAYuJCCgAAIKbSuRwqkUulknp6erI+LQAAQM16enrOu3CLiBQAAEBMuRWbVxOR+rM/\n+zNJ5cJTSXrggQdGfN+KFSskVRbh2fLpsc6dd2SMuURjLtGYSzQ7f97z8HNgLpWaeS62gKXaonPr\n1C5Jn/jEJxKdSz0a5Xdkz7eUTKF/FL8v4Sc/+clRv5eIFAAAQExcSAEAAMRU6D5S1l/mox/96Iix\nTZs2hbG3v/3tkqTNmzeHse9///uJzuXNb36zpMoND0frcTNu3Lhw/PLLLyc6lyjWL8P3yPB9NQAA\n6ag1vXTo0KGUZtIaop7vq6++Ohz/wR/8gaRy53ep3GfywIEDYeyZZ56RFN1byv/bsRCRAgAAiKnQ\nEal77rlHkvTYY4+FsS1btkgq76UjlYvC0uzQ+qY3vUlSOTIllbvs3n///WFs69atkiq7rNdyZRvX\naMX1QFFUuy+mdY323ee3b9+e3sQwgu1v5x08eDCHmRTTWJ3IkQ3r4P7e9743jL3rXe+SVJkZsn0Q\n77vvvjD2pS99SVL91w5EpAAAAGLiQgoAACCmQqf2LE1m//X6+vrCsRV9+zBe0ux8PgQ4ffp0SZUh\ncJuDfU3KJrWXpRkzZoTjJUuWSKrcTHR4eDjzOeH8pk6dKqncb00qv06feuqpMJbF5tYWhh/rXLZQ\ngpRJtvx7+/Of/7wk6Y/+6I/C2IsvvihJ+upXvxrGHnrooVTmYmlgqbwpu21GWwTVbgqMdJ04cUKS\ntGvXrjD28MMPS5ImTZoUxmyDZ//3af/+/YnMgYgUAABATIWOSFXLisjsv2n4j//4D0nSv/3bv1X1\n/RYFaEb+rtAiUUShimvu3LmSpDe+8Y1hbNq0aZLKd3OS9Nxzz6U+l2qjxvZe9u1GkD6/l9js2bMl\nSRdffHEYmzdvnqTyayqruWQRLa0VUahi+fa3vz1izHdAt+hUGn+riEgBAADExIUUAABATE2R2stC\nrV3Cmzkl4Tu1k9IrvsHBQUmV/disqNhvzJkF31W4GqRPsnX06NFw/IMf/EBS5evGiv9977y0sDMD\n6uU7oKe1ubFERAoAACA2IlJVsuLcY8eOVfX9tlxXqiyabAbcKTYWWxywfv36nGdSuyz2qUS0n/3s\nZ3lPAWgIRKQAAABi4kIKAAAgJlJ7VbrggtquOSdMmBCOi9gDJSmTJ0+WxKbJSIf1MpLKKcpq0+sA\nkAUiUgAAADERkapSrcuwmzkK5Vm3WCJSSMPChQvDse1ZSUQKQJEQkQIAAIiJCykAAICYSO2hLtbp\n2BfX02cKSbnooovCcaukyxtNrT320mSLgtLcwB54NSJSAAAAMRGRQl0sSkAHaiRpwYIFkipfVwMD\nA3lNB6+ydOnScGyfAVlEpCZOnBiOrR1Gd3d3GNu7d68k6fTp06nPBTBEpAAAAGLiQgoAACCmhkjt\nXXjhheG4ra1NUrmnjCRNmTJFknTixIlsJwbNmDFjxNi+fftymEljmzlzZji217bvpt9sxbPjxo0L\nx1FpYfvZh4eHw1gRiplbiX2urlixIoxZXy8rMJekn/zkJ6nPZdasWZKka6+9dsT8/Pvkhz/8Yexz\n2N+Wsf6O2Gt36tSpYcw2qfd/l0ZjPw+aAxEpAACAmBoiIuWXPdtyaLt7kMp3tHEiUtXehdTKR2pq\n7YreSKyjeat0Nrc2D2O1eLA9CLu6usLY4sWLJUnnzp0b8f2Dg4Ph2O5qmy0K5Y21OMEKy6u9w0fy\nTp48KUl68sknw9imTZtGfF8Whd2HDh2SJL3wwgthbN68eZIqI+BR761qWebDfm5Jmj59uqTKv0EW\nPfZROXuv+kirzcV/NtqxRdOKyhf12+eWfy/WmnXwf68twvnUU0+N+L5G/cwjIgUAABATF1IAAAAx\nlc7VEwuNe9JSST09PVmfFgAAoGY9PT3nTR0TkQIAAIgpt2LzPCNS/tzVzmP8+FeeKttbLs+52FLb\npIOJceaSFuYSLc5crAA26e7zRXxe8p6Hn0O1c1m1alU4tmLkHTt2jPi+qK7eSc8lTUnMxdovSOXi\nZ18cnvRcLr30UknSe97znjBmBe/33HNPGLN2BpdcckkY27BhQ6JzyUIjz2XOnDnh2LrdP/HEE4nO\n5XyISAEAAMTEhRQAAEBMDdFHqlpR3aGTYmH1pFN7cVi/E9+LxPqseJbS8c/LwYMHU54dioYNpYvJ\nNmZeuXJlGLOec76ju/Wk8520N2/eLKn1frd79uzJ9HwHDhyQJD366KNhrLe3d8T3WR9CdnXIj099\nr127VlK515gk/fznP0/t3ESkAAAAYmq4iJR1k/UdUK3YMM0uyFGdz22Pp6y7sVqh6VgFp3a3ShQK\nKB7rZv/rX/86jEVFXCza7HdLaLVIVF4sMvjLX/4yjPnu5cY6n+/evTubif3/rGO437uvv78/0zkU\nhX/vbNy4UVK5M33aiEgBAADExIUUAABATIVO7c2dO1dSeZNDqbxprA/Z/c///I+kygLNpFmBt9+8\nctKkSZLi9TFB8uw18ZGPfCSMLV++XJL04x//OIz99Kc/zWxOPh1jKeBjx45ldn4UlxXHVrvhd7Ns\nfu5TY6Qoa7do0aJwfMMNN0iq7ClomwE///zz2U6sQH74wx9KGntz+aQQkQIAAIip0BEp6066evXq\nMGYdxnfu3BnG0oxEGR+JMkSiiqWrq0tSZffjK664QlI5kilJjz32mKTy0uY02RJ3qRzB7OvrC2NZ\nLgTo7OwMx9YSY+/evWEsi+cDZRZF8K/N0cyePTscZ/GZl5aoNjWNFJnKe67+7469f/1iqNOnT2c+\np6KptvN/UohIAQAAxMSFFAAAQEyFTu09/fTTkqSjR4+GMesjZV9rRfYcWJpTKhcwW28rKTodmRZf\n/G/zGxgYyOz8Urm40heTb926VVK5AFPKJoU1efJkSZXdqK3nS9bPi/GF7xb+9/OzsePHj2c7sRZl\n789q07uNnM7z/OdS3mmyRuR3sXjooYdGfD2qzxXSRUQKAAAgpkJHpGzpokUV8Aq7i/N77VkkKsso\nlNfe3h6OV61aJUm68sorw1ia+xy9mu9C7I+zZHsy+rtHK6zdv39/LnOy/dmkcisGlqLnr9We92Zs\n/9HR0SFJWrJkSRizQvBdu3aldt6o6FOrvZ6KgIgUAABATFxIAQAAxFTo1B6iWR+RIvWx8gXctjig\n2o7NzcjS0jt27Mh5JmVRm2uTBgDiscUjUrmH3aWXXhrGLO2WZmrPdv2YN29eGLPUqV/UYhtkIx1E\npAAAAGIiIoVE+H3Ann32WUmV7Rns7szfxfm2FgDQSGxBiT/2WYIsumtbl3i/H62xNjQSEam0EZEC\nAACIacwLqQ984APq6OgIe5ZJUk9Pjzo7O7V69WqtXr26Ymn7l7/8ZS1btkwrVqzQgw8+mM6sAQAA\nCtWBgG8AACAASURBVGDM1N773/9+ffzjH9f73ve+MFYqlXTrrbfq1ltvrfjeTZs26Z577tGmTZs0\nMDCgG2+8UVu3bq3oto3mVyqVJFV2zZ44cWLF1yRSe0ArapbeZT51Z4ttbCNqqXIj4bTn4M9rKb1W\nXuyTtTGvcN70pjdp1qxZI8b9L87cd999uvnmmzVhwgR1d3dr6dKlWr9+fTIzBQAAKJjYxebf+ta3\n9O///u+65ppr9LWvfU0zZ87Unj179IY3vCF8T2dnZ+L7ill0yyIcHlfgxWBL/+fOnRvGrAjTuv0C\nGMmKhz1btOHbVzTyvnt+qX6WRdA+Gp4027XAL6aJCjYkzT5Xh4aGwphFx5544onUz49XxMq5fexj\nH9POnTu1ceNGLViwQH/913993u9N88ULAACQp1gXUvPmzVOpVFKpVNKHPvShkL5btGiR+vr6wvf1\n9/dr0aJFycwUAAAgY4888sioX4+V2hscHNSCBQskST/60Y/Cir6bbrpJ733ve3XrrbdqYGBA27Zt\n07XXXhvnFBV8Pww714QJE8KYFfVt2LCh7nMhnvnz54djS7vaa0SSli1bJknatm1bGPvRj36U0exQ\nNP79a8XHF154YRhrpYUIPtV1ww03SKp8fqxHkX/vNHJqz6e8LGORRRoszXPY79AXz2dRxrBp06aK\n/2Zl0qRJkqTTp08n8nhZvg7iuP7667Vu3brzfn3MC6mbb75Z69at04EDB9TV1aUvfOEL+tWvfqWN\nGzeqVCppyZIl+u53vytJWrlypdauXauVK1dq/PjxuuOOO0jtAQCApjXmhdTdd989YuwDH/jAeb//\ntttu02233VbfrF5lxowZ4bi3t1dS5R1ZPUXmCxculFQukJak/fv3j/i+pK/Am41P4drd2erVq8PY\nVVddJam8D1SraG9vD8dRr6tW5QunZ8+eLUk6ePBgXtPJ1b59+8Lx448/LqkyImWfb83y+vHdv0eL\nQPibcPu+yZMnhzH7zPYdxkczffr0muZZC8uK+N0csmh/kCX/+0i6pVFRI1HVosETAABATFxIAQAA\nxFQ6l0NMrVQqqaenJ+vTAgAA1Kynp+e8KUgiUgAAADHF7mxerzwjUv7ceUfG8pqLFc9L5aLNz33u\nc4nOpZ49tfgdRWMu0ez8ec/Dz6ER5+I/F0ZbWOM7eFdbVN3Iz4sXtVTf2r9YV3Gp+iL4Rn5eVqxY\nEY63bNky4uv2XF122WVhrNpWDdXOxdojpbmQaaw5EJECAACIiQspAACAmHJL7dXC9w5hY+Jk+LB0\nrWm3aqX1uLWwfie+b1Gr8r1furq6JEnHjx8PY63axwllY/XJs8/iKVOmhDHrC5X0uiW/Mf2sWbMk\nRXcL92nGM2fOSKo+rVYt33XfNpb2/b+OHDmSynmLyn7/e/bsGfX7lixZIkm69NJLw5h16Pe9G0fj\ne3NFPb9F6E1IRAoAACCmhohI+atQ6xTtO/9ax99qr3CTYnf1fq8su9ou+l5hZ8+ezXsKmSASVdbR\n0RGOX//610uqfJ0++eSTkirvtJEtK5z13b+LENk1lhHIIjPgP8+XL18uSbrmmmvCmEXA+vr6wlhU\nwXMSfFfvqJ+91TIl/vU5GovU+d+RRRqr/XudVJTPdlBIY59KIlIAAAAxcSEFAAAQU0Ok9sbaUNiK\n/w4fPpzZnKTyhsdXX311GLPi3aKn9hp9k0jUbnBwMBzfe++9I77u0xfIji9TWLx4saTKDXYt1bp9\n+/ZsJ5YzvxG6fdZ2d3eHMStC7u/vD2NplSz4guciFDc3ClvA4v/e2IKArFmvL//e2rVrVyKPTUQK\nAAAgpoaISI0ly0iUX0JuxWv1dPBGcuyusVWWICfN7hppN5ItH3G3SLb/TMkium2/cz+XIr2P7HW4\nefPmMDZ37lxJ5QL9NBU1WjtnzhxJlS0grJjatzbJWxoF3sZaMfjXgWWu/GKjajuqx0FECgAAICYu\npAAAAGJqitRelnyo8JlnnpEk9fb2hrFWKwgtklpTEdaTTIpexNBqrL+LpQukykLetERtAtuqdu/e\nXfHfrBQxdeV7D1nfIp/mtAL0qVOnhrG0NrAtUprMs7IWX96SZXmJ7z5vfRX953BSxdyjsdeGL0mw\nwvKxOq+PxnezHwsRKQAAgJiISNVhYGCg4r+NxBfN+7uKJPkr+iJ2UicKVek1r3mNpMrlyVlEpIhE\n5a/aTtV5idoH0jIBK1asCGMWZW6VFgV5L26K+qwYa7/GtCS9V2gtf7OISAEAAMTEhRQAAEBMpPZa\nlC+aT2uz5yKm8/AK67llBbtSuWh3aGgolzkBtbCO8NZPSip/5rz44ou5zKmV5ZXSs8+tPBcEEJEC\nAACIiYhUAflCcB85SkuWHYzpAl8M9jv3++/Z74OIFBqBtTrwn19ZfF5aJCytSD5qYxEpv4dePW0P\n4iAiBQAAEBMXUgAAADGR2iugrFN7WbBw+IIFC8JY1t2bMZJPTzRiPzS0Blsc4XvT2abxvhN/Fmlp\nUnrFsnfvXklSR0dHbnMgIgUAABATEakETJo0KRxPmTJFkjQ8PBz78bIs/s7K8uXLJUkHDhxI/Vzz\n5s0Lx/v27Uv9fEiGfx/ltZQaxTRr1ixJ0kUXXRTG/LGp53MXjS3PRTJEpAAAAGLiQgoAACAmUnsJ\n8GkIUhLRDh8+LKlcGJiGSy65RFI5DSC1bmqvVCqF4yQ2Bfb9v9ra2up+PM/6vySx0eyMGTPC8ZEj\nR+p+vKTYxuA+bW9ztYUYUvm59f29Tp06NeLxonoZ2e+onv5sfi5W4B11/qxZIfHq1avDmC3K+fWv\nfx3G6GjeuvJcpEVECgAAICYiUshEf39/6ufYsWOHJKm7uzv1cxWBFdVb5EAqF2z7gnuL9Dz//PNV\nPW5nZ2c4njlzpqTKSE8SES7v6NGjiT1WkaJQnkWafDH0mTNnJJU7M0vlJf32vEvlLs0+mmW/Z/++\nSmKnAB/h8lHNvD333HMV/5WkyZMnSypGxAz584sPdu3aVffj+QjXmN9b99kAAABaFBdSAAAAMZXO\nJR2nr+akpZJ6enqyPi0AAEDNenp6zlvWQEQKAAAgptyKzeuNSM2fPz8cHzx4UFLl0t2zZ89Kiu4S\n7s+dd2SMuUQr6ly++MUvSkp+ea0VzkqjF8/GeV6sHcShQ4dize3VrAjz85//fM1zSYudP+95+DkU\naS7f+c53wtj+/ftHfF89hdv2+vLF7vZ4vv3I3//931fMKU9F/B0xl0rVzsUWXvgFMdZuxxZ0RH2/\nVH27orHmQEQKAAAgJi6kAAAAYmrYPlJRHbJ9DxQgDWl1zE2zF47N+cILLwxjlvr2FixYIKmyq/Zo\nj1cPH4a31NDu3bsTPUejsI3OJenkyZOpnGOszcLref1FpYyT7A0GjMY+y6ZNmxbGVqxYIUnq7e0N\nY9ZbKo3dR4hIAQAAxNSwESngfFatWhWObSHCWFGWZlZtt+8sn6PXvOY14fjiiy+WJA0MDISxxx57\nTFJrRDbSikJ5OXS5QQaWL18uqfweksrvo2eeeSaXOWXNotc++mTR7YULF4Yx2/HC/iZI5ehUvdks\nIlIAAAAxcSEFAAAQE6k91MU2Y/UbPNpmp1mnZaw/SFdXVxizDXh9ODeq0LoVjBs3LhzbpsZ5pTyH\nhobCsRWb+/C6L4xvRX7DYNJyxWL9Ci+77LIwZouffN+sLFifREvxSeUei62S2jNR6Tm/ebF9/vl+\nZ/Y3g9QeAABATohIoS4nTpyQVHkH7SMfWbLCweeffz6M2Z3aFVdcEcaeeOKJbCeWMyuyXLt2bRhb\nvHixJOmzn/1sGEuq83k1tmzZEnnc6qxo2Ld/8HfVyJ9FL4oQ8dmxY4ck6Rvf+EbOMyk+H4kyUTuf\nxEFECgAAICYupAAAAGIitVclK6b2RbBpdEhtVL4gNqlwaVy+Q7ZtXukL0FvN7NmzJUmvfe1rw5j1\nV1myZEkYyzK1h2gvvvhi3lMAWsb48a9cAtX7N4uIFAAAQExEpKpkxZ9EoRqLtWDwBeitZvPmzZKk\nf/mXfwljFn168sknc5kTADQLIlIAAAAxcSEFAAAQE6k9oMmdOnVKkvTwww/nPBM0Eltg43taoVis\nMzclJ/GcOXMmkcchIgUAABATESkAwAhEpIqPSFSZtTKQqm9nkNQ+lkSkAAAAYuJCCgAAICZSewCA\nEWbMmCGpMrVnfdn8mH2f7SKQBtKMGIvt4CBJ+/bty/TcRKQAAABiaoqIVKlUkiS1tbWFsePHj+c1\nHeRs2rRp4diKCXk9ALWx99GKFSvC2PTp0yVV7gn43HPPpT4XIlKj83vAnj17NseZ5CfrKJRHRAoA\nACAmLqQAAABiaojUXnt7ezhetGiRpHJHV29gYCAck8ppXT60PWfOHEmVr5djx45JSq6rLYpp8uTJ\nksqd3etlqa6pU6eGMXvsNAutkxAn9TNz5kxJUnd3dxizY99/54knnqh/gmOoti9Qq2rVdF5REJEC\nAACIKbeIVKlUqrqrqEUVJKmjo0NS5RW4FZn19fUlOEM0Kh9pOnnypKTygoRXf72VWIRGKr9/Xn75\n5bymkwq/4CRp9hoaHh4OY43yWooTsbAI//r168PYzp07JUn9/f1hLKnu0BidtZnwjhw5UtW/nTt3\n7ogxoljJISIFAAAQExdSAAAAMZXO5RCXLZVK6unpyfq0AAAANevp6TlvGpuIFAAAQEy5FZvnEZGy\nQvWPfexjuc7D8+evZy5dXV2SKouqd+/enctckpD0XPzzUmsQtpmfl3oUcS7VzsM6ZUvShAkTJCVX\nOF7rXNLEXKI121x8iwqza9euXOaSlCzmYotTTpw4UdVczoeIFAAAQExcSAEAAMRUuM7m48aNC8fX\nXHONJOn06dNhzHpG+U63Fp4bK5RZbc+NRmGdhyXpxhtvlCQ9+eSTYazW1F4zo9dNY7HecX4D6jip\nivMZP7780VdtSq8VNs6dOHFiOC5Sj6yFCxdKKu9K8OrjVuc71/vXdpZmz54tqbLPWhHZPKXyJtxj\npfbGQkQKAAAgpsJFpHyn5cHBQUmV+6RZN1YfYbDu1WPxka1m88gjj0hK9q69UVnxsFR+7XD3Wnx/\n8zd/E44vvvhiSdIdd9yRyrmq7eq8ePHicGyvq+3bt6cyJ2/NmjXh+OjRo5Kk//u//0v9vLNmzQrH\ne/fuTf181bL3r70upPLn+cGDB8PYgQMHsp1YQWzdujUc2+/Qd0JPKxvjI5ive93rJElDQ0NhzDJH\nfteRvDNDPmKWVKaCiBQAAEBMXEgBAADEVLjUnmfF0qtWrQpjl112maTKoupqU3vN5vDhw5HHre6l\nl16KPEZ18io4nj9/fji29MBzzz2X2fm9pUuXSpJuuOGGMGbpiSxSew899FDq5/Bskc+pU6cyPW+1\nLLX3wgsvhDFLG/mFR5AOHTqU2ble85rXhGNL7a1cuTKM2e/rzjvvDGNppfb8huWjFY/770vq2oGI\nFAAAQEyFjkgZf8dhxZ9PPPFEXtNBBB81tLv5devWhbEs75JaRdLL8a07si8WzZIvNs+b3dH6iJwv\nam42tsgni9YOPuJp0YFql8w384KhRjR16tRw/Hu/93uSpNWrV4cxa1vhM0j9/f2pzMUvDOns7JRU\nmZGw9/Szzz4bxpKKuBORAgAAiIkLKQAAgJgaIrXnewD19vZKKm5RZKvyHemtd1N7e3sYI7WXvCTS\nML7XjKV3fC+3VmU97O66664w5rtHN6spU6aEY3t91dv1eTSNWCjud5QwrbrYx/4eS+XFGFbaIZV3\nKPApwLT4z61FixZJqvx8s4UrfsPypBCRAgAAiKkhIlIDAwORxyiOp59+Ohxbl12ihsXnlyJbB232\nJYxWbTf0pFm0N4tIoS/6LpVKqZzDF/gWaT+/as2dO1dSZWRqw4YNeU0nVz4i9fDDD0uqzD7YbgC+\nS75FPZNuW+SvDR599FFJlfsObt68WVI6n29EpAAAAGLiQgoAACCmhkjtobGQ0mtMrZTS82mrov/c\nWRb/F3UnAEsRFWF+e/bskdS6Beae/3386le/kiRt2bIljFkfKf99F1100YjvS8Lx48fDsd/EOQtE\npAAAAGIiIgWg5RQ9CoVKRfp9WZF0q+7xej4HDhyQVFlsbscLFiwIY9Yep5kQkQIAAIiJCykAAICY\nSO0BQBWy7OfUSKyvkqV20mA9m6zoXCr3QCPFViz+/WEF+b7D+P79+zOfU9qISAEAAMRERAoAqmCR\nl6GhoZxnUiy2j1qaESk7h/0OpPLSeutYLRGdKgK/r968efMkSf39/WHMdlBoJkSkAAAAYuJCCgAA\nICZSewViGyz+9re/zXkmaGW+MNRvatysqu2abSkLUnvS9OnTw3EWOxns2rVLUmWKyDa/zWsz6Xo0\nYy8l418P1m28GdN5HhEpAACAmHKLSE2aNEmnT58eMX7BBeVrO7vr8cspjx07lv7kckIkqvjsLrgI\nRa1JL8dva2uTVHm3XG1Eyvaui+pA7Zes22Mn9T72e+bFZZHgsSJSO3bsqPtczWLWrFnhuLe3N7Pz\n+s/IRoxy2N80e681I/95tG/fvrofz18T/O53v6v7cep5jPM+duKPCAAA0CK4kAIAAIipdC6H3SBL\npZJ6enqyPi0AAEDNenp6zrt5NhEpAACAmHIrNv/CF75w3qu7tPloWN6RMeYSLc5cJk+eLCn55dhp\nPi/WaqDaou5G/x2lxc6f9zz8HJhLJeYSrda5+OLrRYsWSapcvGH722UxlzQVcS7nQ0QKAAAgJi6k\nAAAAYsottTdlyhSdOHEir9OjCa1evVqStHv37jDmOyEb64Fz6NChbCY2ilboHB7Fb2xq3Y/z1tHR\nEY7pXo6i8n2Qzpw5I6m8ObBUX2qvVlF9H7M8f1EQkQIAAIgpt4hUEh2JAW/Dhg2Syh2/z6cROyI3\nmyJGpPzddV5WrVolSXr7298exp588klJ0sMPP5zLnJLqLN0sLPJy4YUXhrEDBw7kMhfrHJ5EB/E4\n/Oth7ty5klSxY0nU7iXNaNRPjr6+Pl1//fW6/PLLtWrVKv3zP/+zJGl4eFhr1qzRpZdeqre+9a0V\nobwvf/nLWrZsmVasWKEHH3ww3dkDAADkaNQLqQkTJujrX/+6nn/+eT322GP6zne+o82bN+v222/X\nmjVrtHXrVr3lLW/R7bffLknatGmT7rnnHm3atEkPPPCA/uIv/oI7GAAA0LRGTe3Nnz9f8+fPl/RK\nKP6yyy7TwMCA7r//fq1bt06SdMstt+jNb36zbr/9dt13332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QkQIAAIjEhRQAAEAkUntoq8MtKb3izJYeyDJE3m1/c+sivW7dujC2d+/eoqaT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\nxEaKiIiISKf/AMt+wa3UEUvkAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x13062c050>"
+        "<matplotlib.figure.Figure at 0x7fd621d1b3d0>"
        ]
       }
      ],
@@ -456,9 +447,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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trc2I3XXXXUYsOzvbMbvo046Bn/z+ftC2Nz4+bvXaJUuWGLGhoSEjZtvkvnr1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\n794t9fX1cu+993I+QlBbWyt79+6V4eFhicfjsnv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iIzY5OWnELr77L27m+rOnqzAameOkt7c35dsYGRmxWi4d9712I4LG9++WlZVltZzW9Ay/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\nIqDoAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x13063e810>"
+        "<matplotlib.figure.Figure at 0x7fd621d7cd90>"
        ]
       }
      ],
@@ -489,9 +480,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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4GFM5nE/dUhNXsN5+e+CtgLT7YJWT9x3EZgfhSnWYmDavKq3/sOsn6BAD5WK4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\nw0iwAAAADCPBAgAAMIwECwAAwDASLAAAAMNIsADENPCVLdn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SpqSP+cc+lmx+eVKtbu+Xeh+HP9Nzm7kk7cAZ5jhz+tx/v7RsWdqlSMfWrft+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\ni4iIiEgxDlhEREREinHAIiIiIlLsO5hP7FaK+KtUAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1024f8d90>"
+        "<matplotlib.figure.Figure at 0x7fd63064b890>"
        ]
       }
      ],
@@ -520,9 +511,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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TLgXgz65de37mYgcEk7YB7Zzr6ZPa4KqmRnr99eDrX3ihVPBwY7u0bUFrT3HVuG/y3zJV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\nwCCCKwAAAIMIrgAAAAwiuAIAADCI4AoAAMAggis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oo7xIJvWCXOztt6XLL+/92bBhuT4cTjIZ6c//XFq/PtzYomC6nm64IdzOy6tX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\nGERyBQAAYND/B6hFJTD2mK/NAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10b0e8910>"
+        "<matplotlib.figure.Figure at 0x7fd6306fb450>"
        ]
       }
      ],
@@ -550,15 +541,15 @@
        "output_type": "pyout",
        "prompt_number": 18,
        "text": [
-        "[<matplotlib.lines.Line2D at 0x12b260710>]"
+        "[<matplotlib.lines.Line2D at 0x7fd63060d990>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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lyH37zIQBkC8RRs2wHAlASkQYNaNUqixB9r860nIkAHkSYRTa0HPCIixHApAG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\nbGyMxsbGmDVrVkREfOYzn4n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+acS6dRH/+q8RU6akPhoAJpqx7Ja6I2ENDQ2xcuXKWLRoUVSr1Vi2bFl0dHTEqlWrIiJi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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x13119c350>"
+        "<matplotlib.figure.Figure at 0x7fd630627290>"
        ]
       }
      ],
@@ -575,22 +566,15 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "# load labels\n",
       "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
       "try:\n",
       "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
       "except:\n",
       "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
-      "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
+      "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
+      "\n",
+      "# sort top k predictions from softmax output\n",
       "top_k = net.blobs['prob'].data[4].flatten().argsort()[-1:-6:-1]\n",
       "print labels[top_k]"
      ],
@@ -601,13 +585,13 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "['n02115913 dhole, Cuon alpinus' 'n02119022 red fox, Vulpes vulpes'\n",
-        " 'n02119789 kit fox, Vulpes macrotis' 'n02123159 tiger cat'\n",
-        " 'n02123045 tabby, tabby cat']\n"
+        "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
+        " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
+        " 'n02127052 lynx, catamount']\n"
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 19
     }
    ],
    "metadata": {}
diff --git a/examples/imagenet/alexnet_solver.prototxt b/examples/imagenet/alexnet_solver.prototxt
index 75d0d5dffa7..8581e99c6a7 100644
--- a/examples/imagenet/alexnet_solver.prototxt
+++ b/examples/imagenet/alexnet_solver.prototxt
@@ -1,5 +1,4 @@
-train_net: "alexnet_train.prototxt"
-test_net: "alexnet_val.prototxt"
+net: "alexnet_train_val.prototxt"
 test_iter: 1000
 test_interval: 1000
 base_lr: 0.01
diff --git a/examples/imagenet/alexnet_train.prototxt b/examples/imagenet/alexnet_train_val.prototxt
similarity index 91%
rename from examples/imagenet/alexnet_train.prototxt
rename to examples/imagenet/alexnet_train_val.prototxt
index 32a96cfd4d9..f65f3e7f87e 100644
--- a/examples/imagenet/alexnet_train.prototxt
+++ b/examples/imagenet/alexnet_train_val.prototxt
@@ -2,6 +2,8 @@ name: "AlexNet"
 layers {
   name: "data"
   type: DATA
+  top: "data"
+  top: "label"
   data_param {
     source: "ilsvrc12_train_leveldb"
     mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
@@ -9,8 +11,21 @@ layers {
     crop_size: 227
     mirror: true
   }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "data"
+  type: DATA
   top: "data"
   top: "label"
+  data_param {
+    source: "ilsvrc12_val_leveldb"
+    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+    batch_size: 50
+    crop_size: 227
+    mirror: false
+  }
+  include: { phase: TEST }
 }
 layers {
   name: "conv1"
@@ -308,6 +323,14 @@ layers {
   bottom: "fc7"
   top: "fc8"
 }
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}
 layers {
   name: "loss"
   type: SOFTMAX_LOSS
diff --git a/examples/imagenet/create_imagenet.sh b/examples/imagenet/create_imagenet.sh
index 11fc0999059..24f961bee4c 100755
--- a/examples/imagenet/create_imagenet.sh
+++ b/examples/imagenet/create_imagenet.sh
@@ -5,16 +5,48 @@
 TOOLS=../../build/tools
 DATA=../../data/ilsvrc12
 
-echo "Creating leveldb..."
+TRAIN_DATA_ROOT=/path/to/imagenet/train/
+VAL_DATA_ROOT=/path/to/imagenet/val/
+
+# Set RESIZE=true to resize the images to 256x256. Leave as false if images have
+# already been resized using another tool.
+RESIZE=false
+if $RESIZE; then
+  RESIZE_HEIGHT=256
+  RESIZE_WIDTH=256
+else
+  RESIZE_HEIGHT=0
+  RESIZE_WIDTH=0
+fi
+
+if [ ! -d "$TRAIN_DATA_ROOT" ]; then
+  echo "Error: TRAIN_DATA_ROOT is not a path to a directory: $TRAIN_DATA_ROOT"
+  echo "Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path" \
+       "where the ImageNet training data is stored."
+  exit 1
+fi
+
+if [ ! -d "$VAL_DATA_ROOT" ]; then
+  echo "Error: VAL_DATA_ROOT is not a path to a directory: $VAL_DATA_ROOT"
+  echo "Set the VAL_DATA_ROOT variable in create_imagenet.sh to the path" \
+       "where the ImageNet validation data is stored."
+  exit 1
+fi
+
+echo "Creating train leveldb..."
 
 GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
-    /path/to/imagenet/train/ \
+    $TRAIN_DATA_ROOT \
     $DATA/train.txt \
     ilsvrc12_train_leveldb 1
+    $RESIZE_HEIGHT $RESIZE_WIDTH
+
+echo "Creating val leveldb..."
 
 GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \
-    /path/to/imagenet/val/ \
+    $VAL_DATA_ROOT \
     $DATA/val.txt \
     ilsvrc12_val_leveldb 1
+    $RESIZE_HEIGHT $RESIZE_WIDTH
 
 echo "Done."
diff --git a/examples/imagenet/get_caffe_rcnn_imagenet_model.sh b/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
new file mode 100755
index 00000000000..c7f36edcbf0
--- /dev/null
+++ b/examples/imagenet/get_caffe_rcnn_imagenet_model.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env sh
+# This scripts downloads the Caffe R-CNN ImageNet
+# for ILSVRC13 detection.
+
+MODEL=caffe_rcnn_imagenet_model
+CHECKSUM=42c1556d2d47a9128c4a90e0a9c5341c
+
+if [ -f $MODEL ]; then
+  echo "Model already exists. Checking md5..."
+  os=`uname -s`
+  if [ "$os" = "Linux" ]; then
+    checksum=`md5sum $MODEL | awk '{ print $1 }'`
+  elif [ "$os" = "Darwin" ]; then
+    checksum=`cat $MODEL | md5`
+  fi
+  if [ "$checksum" = "$CHECKSUM" ]; then
+    echo "Model checksum is correct. No need to download."
+    exit 0
+  else
+    echo "Model checksum is incorrect. Need to download again."
+  fi
+fi
+
+echo "Downloading..."
+
+wget http://dl.caffe.berkeleyvision.org/$MODEL
+echo "Done. Please run this command again to verify that checksum = $CHECKSUM."
diff --git a/examples/imagenet/imagenet_deploy.prototxt b/examples/imagenet/imagenet_deploy.prototxt
index fbff3adbe18..4e494f420b5 100644
--- a/examples/imagenet/imagenet_deploy.prototxt
+++ b/examples/imagenet/imagenet_deploy.prototxt
@@ -9,10 +9,6 @@ layers {
   type: CONVOLUTION
   bottom: "data"
   top: "conv1"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   convolution_param {
     num_output: 96
     kernel_size: 11
@@ -52,10 +48,6 @@ layers {
   type: CONVOLUTION
   bottom: "norm1"
   top: "conv2"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   convolution_param {
     num_output: 256
     pad: 2
@@ -96,10 +88,6 @@ layers {
   type: CONVOLUTION
   bottom: "norm2"
   top: "conv3"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   convolution_param {
     num_output: 384
     pad: 1
@@ -117,10 +105,6 @@ layers {
   type: CONVOLUTION
   bottom: "conv3"
   top: "conv4"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   convolution_param {
     num_output: 384
     pad: 1
@@ -139,10 +123,6 @@ layers {
   type: CONVOLUTION
   bottom: "conv4"
   top: "conv5"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   convolution_param {
     num_output: 256
     pad: 1
@@ -172,10 +152,6 @@ layers {
   type: INNER_PRODUCT
   bottom: "pool5"
   top: "fc6"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   inner_product_param {
     num_output: 4096
   }
@@ -200,10 +176,6 @@ layers {
   type: INNER_PRODUCT
   bottom: "fc6"
   top: "fc7"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   inner_product_param {
     num_output: 4096
   }
@@ -228,10 +200,6 @@ layers {
   type: INNER_PRODUCT
   bottom: "fc7"
   top: "fc8"
-  blobs_lr: 1
-  blobs_lr: 2
-  weight_decay: 1
-  weight_decay: 0
   inner_product_param {
     num_output: 1000
   }
diff --git a/examples/imagenet/imagenet_solver.prototxt b/examples/imagenet/imagenet_solver.prototxt
index e543ba66cad..aedec4104a6 100644
--- a/examples/imagenet/imagenet_solver.prototxt
+++ b/examples/imagenet/imagenet_solver.prototxt
@@ -1,5 +1,4 @@
-train_net: "imagenet_train.prototxt"
-test_net: "imagenet_val.prototxt"
+net: "imagenet_train_val.prototxt"
 test_iter: 1000
 test_interval: 1000
 base_lr: 0.01
diff --git a/examples/imagenet/imagenet_train.prototxt b/examples/imagenet/imagenet_train_val.prototxt
similarity index 91%
rename from examples/imagenet/imagenet_train.prototxt
rename to examples/imagenet/imagenet_train_val.prototxt
index 519d4509be9..af28c1495e5 100644
--- a/examples/imagenet/imagenet_train.prototxt
+++ b/examples/imagenet/imagenet_train_val.prototxt
@@ -11,6 +11,21 @@ layers {
     crop_size: 227
     mirror: true
   }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "data"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "ilsvrc12_val_leveldb"
+    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
+    batch_size: 50
+    crop_size: 227
+    mirror: false
+  }
+  include: { phase: TEST }
 }
 layers {
   name: "conv1"
@@ -308,6 +323,14 @@ layers {
     }
   }
 }
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "fc8"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}
 layers {
   name: "loss"
   type: SOFTMAX_LOSS
diff --git a/examples/imagenet/imagenet_val.prototxt b/examples/imagenet/imagenet_val.prototxt
deleted file mode 100644
index dd26f40ea14..00000000000
--- a/examples/imagenet/imagenet_val.prototxt
+++ /dev/null
@@ -1,227 +0,0 @@
-name: "CaffeNet"
-layers {
-  name: "data"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "ilsvrc12_val_leveldb"
-    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
-    batch_size: 50
-    crop_size: 227
-    mirror: false
-  }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  convolution_param {
-    num_output: 96
-    kernel_size: 11
-    stride: 4
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "conv1"
-  top: "conv1"
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm1"
-  type: LRN
-  bottom: "pool1"
-  top: "norm1"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "norm1"
-  top: "conv2"
-  convolution_param {
-    num_output: 256
-    pad: 2
-    kernel_size: 5
-    group: 2
-  }
-}
-layers {
-  name: "relu2"
-  type: RELU
-  bottom: "conv2"
-  top: "conv2"
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "norm2"
-  type: LRN
-  bottom: "pool2"
-  top: "norm2"
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-}
-layers {
-  name: "conv3"
-  type: CONVOLUTION
-  bottom: "norm2"
-  top: "conv3"
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-  }
-}
-layers {
-  name: "relu3"
-  type: RELU
-  bottom: "conv3"
-  top: "conv3"
-}
-layers {
-  name: "conv4"
-  type: CONVOLUTION
-  bottom: "conv3"
-  top: "conv4"
-  convolution_param {
-    num_output: 384
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layers {
-  name: "relu4"
-  type: RELU
-  bottom: "conv4"
-  top: "conv4"
-}
-layers {
-  name: "conv5"
-  type: CONVOLUTION
-  bottom: "conv4"
-  top: "conv5"
-  convolution_param {
-    num_output: 256
-    pad: 1
-    kernel_size: 3
-    group: 2
-  }
-}
-layers {
-  name: "relu5"
-  type: RELU
-  bottom: "conv5"
-  top: "conv5"
-}
-layers {
-  name: "pool5"
-  type: POOLING
-  bottom: "conv5"
-  top: "pool5"
-  pooling_param {
-    pool: MAX
-    kernel_size: 3
-    stride: 2
-  }
-}
-layers {
-  name: "fc6"
-  type: INNER_PRODUCT
-  bottom: "pool5"
-  top: "fc6"
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layers {
-  name: "relu6"
-  type: RELU
-  bottom: "fc6"
-  top: "fc6"
-}
-layers {
-  name: "drop6"
-  type: DROPOUT
-  bottom: "fc6"
-  top: "fc6"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc7"
-  type: INNER_PRODUCT
-  bottom: "fc6"
-  top: "fc7"
-  inner_product_param {
-    num_output: 4096
-  }
-}
-layers {
-  name: "relu7"
-  type: RELU
-  bottom: "fc7"
-  top: "fc7"
-}
-layers {
-  name: "drop7"
-  type: DROPOUT
-  bottom: "fc7"
-  top: "fc7"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layers {
-  name: "fc8"
-  type: INNER_PRODUCT
-  bottom: "fc7"
-  top: "fc8"
-  inner_product_param {
-    num_output: 1000
-  }
-}
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "fc8"
-  top: "prob"
-}
-layers {
-  name: "accuracy"
-  type: ACCURACY
-  bottom: "prob"
-  bottom: "label"
-  top: "accuracy"
-}
diff --git a/examples/imagenet/alexnet_val.prototxt b/examples/imagenet/rcnn_imagenet_deploy.prototxt
similarity index 84%
rename from examples/imagenet/alexnet_val.prototxt
rename to examples/imagenet/rcnn_imagenet_deploy.prototxt
index 3fd6296ef9d..ef75a0a5e95 100644
--- a/examples/imagenet/alexnet_val.prototxt
+++ b/examples/imagenet/rcnn_imagenet_deploy.prototxt
@@ -1,27 +1,19 @@
-name: "AlexNet"
-layers {
-  name: "data"
-  type: DATA
-  data_param {
-    source: "ilsvrc12_val_leveldb"
-    mean_file: "../../data/ilsvrc12/imagenet_mean.binaryproto"
-    batch_size: 50
-    crop_size: 227
-    mirror: false
-  }
-  top: "data"
-  top: "label"
-}
+name: "R-CNN-ilsvrc13"
+input: "data"
+input_dim: 10
+input_dim: 3
+input_dim: 227
+input_dim: 227
 layers {
   name: "conv1"
   type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
   convolution_param {
     num_output: 96
     kernel_size: 11
     stride: 4
   }
-  bottom: "data"
-  top: "conv1"
 }
 layers {
   name: "relu1"
@@ -29,39 +21,39 @@ layers {
   bottom: "conv1"
   top: "conv1"
 }
-layers {
-  name: "norm1"
-  type: LRN
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-  bottom: "conv1"
-  top: "norm1"
-}
 layers {
   name: "pool1"
   type: POOLING
+  bottom: "conv1"
+  top: "pool1"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm1"
-  top: "pool1"
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
 }
 layers {
   name: "conv2"
   type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
   convolution_param {
     num_output: 256
     pad: 2
     kernel_size: 5
     group: 2
   }
-  bottom: "pool1"
-  top: "conv2"
 }
 layers {
   name: "relu2"
@@ -69,38 +61,38 @@ layers {
   bottom: "conv2"
   top: "conv2"
 }
-layers {
-  name: "norm2"
-  type: LRN
-  lrn_param {
-    local_size: 5
-    alpha: 0.0001
-    beta: 0.75
-  }
-  bottom: "conv2"
-  top: "norm2"
-}
 layers {
   name: "pool2"
   type: POOLING
+  bottom: "conv2"
+  top: "pool2"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "norm2"
-  top: "pool2"
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
 }
 layers {
   name: "conv3"
   type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
   convolution_param {
     num_output: 384
     pad: 1
     kernel_size: 3
   }
-  bottom: "pool2"
-  top: "conv3"
 }
 layers {
   name: "relu3"
@@ -111,14 +103,14 @@ layers {
 layers {
   name: "conv4"
   type: CONVOLUTION
+  bottom: "conv3"
+  top: "conv4"
   convolution_param {
     num_output: 384
     pad: 1
     kernel_size: 3
     group: 2
   }
-  bottom: "conv3"
-  top: "conv4"
 }
 layers {
   name: "relu4"
@@ -129,14 +121,14 @@ layers {
 layers {
   name: "conv5"
   type: CONVOLUTION
+  bottom: "conv4"
+  top: "conv5"
   convolution_param {
     num_output: 256
     pad: 1
     kernel_size: 3
     group: 2
   }
-  bottom: "conv4"
-  top: "conv5"
 }
 layers {
   name: "relu5"
@@ -147,22 +139,22 @@ layers {
 layers {
   name: "pool5"
   type: POOLING
+  bottom: "conv5"
+  top: "pool5"
   pooling_param {
     pool: MAX
     kernel_size: 3
     stride: 2
   }
-  bottom: "conv5"
-  top: "pool5"
 }
 layers {
   name: "fc6"
   type: INNER_PRODUCT
+  bottom: "pool5"
+  top: "fc6"
   inner_product_param {
     num_output: 4096
   }
-  bottom: "pool5"
-  top: "fc6"
 }
 layers {
   name: "relu6"
@@ -173,20 +165,20 @@ layers {
 layers {
   name: "drop6"
   type: DROPOUT
+  bottom: "fc6"
+  top: "fc6"
   dropout_param {
     dropout_ratio: 0.5
   }
-  bottom: "fc6"
-  top: "fc6"
 }
 layers {
   name: "fc7"
   type: INNER_PRODUCT
+  bottom: "fc6"
+  top: "fc7"
   inner_product_param {
     num_output: 4096
   }
-  bottom: "fc6"
-  top: "fc7"
 }
 layers {
   name: "relu7"
@@ -197,31 +189,19 @@ layers {
 layers {
   name: "drop7"
   type: DROPOUT
+  bottom: "fc7"
+  top: "fc7"
   dropout_param {
     dropout_ratio: 0.5
   }
-  bottom: "fc7"
-  top: "fc7"
 }
+# R-CNN classification layer made from R-CNN ILSVRC13 SVMs.
 layers {
-  name: "fc8"
+  name: "fc-rcnn"
   type: INNER_PRODUCT
+  bottom: "fc7"
+  top: "fc-rcnn"
   inner_product_param {
-    num_output: 1000
+    num_output: 200
   }
-  bottom: "fc7"
-  top: "fc8"
-}
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "fc8"
-  top: "prob"
-}
-layers {
-  top: "accuracy"
-  name: "accuracy"
-  type: ACCURACY
-  bottom: "prob"
-  bottom: "label"
 }
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index 734ce74d92f..883e09272f1 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -12,7 +12,7 @@ Yangqing's Recipe on Brewing ImageNet
     "All your braincells are belong to us."
         - Caffeine
 
-We are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their [NIPS 2012 paper](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf). Since training the whole model takes some time and energy, we provide a model, trained in the same way as we describe here, to help fight global warming. If you would like to simply use the pretrained model, check out the [Pretrained ImageNet](getting_pretrained_models.html) page. *Note that the pretrained model is for academic research / non-commercial use only*.
+We are going to describe a reference implementation for the approach first proposed by Krizhevsky, Sutskever, and Hinton in their [NIPS 2012 paper](http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf). Since training the whole model takes some time and energy, we provide a model, trained in the same way as we describe here, to help fight global warming. If you would like to simply use the pretrained model, check out the [Pretrained ImageNet](../../getting_pretrained_models.html) page. *Note that the pretrained model is for academic research / non-commercial use only*.
 
 To clarify, by ImageNet we actually mean the ILSVRC12 challenge, but you can easily train on the whole of ImageNet as well, just with more disk space, and a little longer training time.
 
@@ -33,7 +33,7 @@ You will first need to prepare some auxiliary data for training. This data can b
 
 The training and validation input are described in `train.txt` and `val.txt` as text listing all the files and their labels. Note that we use a different indexing for labels than the ILSVRC devkit: we sort the synset names in their ASCII order, and then label them from 0 to 999. See `synset_words.txt` for the synset/name mapping.
 
-You will also need to resize the images to 256x256: we do not explicitly do this because in a cluster environment, one may benefit from resizing images in a parallel fashion, using mapreduce. For example, Yangqing used his lightedweighted [mincepie](https://github.com/Yangqing/mincepie) package to do mapreduce on the Berkeley cluster. If you would things to be rather simple and straightforward, you can also use shell commands, something like:
+You may want to resize the images to 256x256 in advance. By default, we do not explicitly do this because in a cluster environment, one may benefit from resizing images in a parallel fashion, using mapreduce. For example, Yangqing used his lightedweighted [mincepie](https://github.com/Yangqing/mincepie) package to do mapreduce on the Berkeley cluster. If you would things to be rather simple and straightforward, you can also use shell commands, something like:
 
     for name in /path/to/imagenet/val/*.JPEG; do
         convert -resize 256x256\! $name $name
@@ -41,7 +41,8 @@ You will also need to resize the images to 256x256: we do not explicitly do this
 
 Go to `$CAFFE_ROOT/examples/imagenet/` for the rest of this guide.
 
-Take a look at `create_imagenet.sh`. Set the paths to the train and val dirs as needed. Now simply create the leveldbs with `./create_imagenet.sh`. Note that `ilsvrc12_train_leveldb` and `ilsvrc12_val_leveldb` should not exist before this execution. It will be created by the script. `GLOG_logtostderr=1` simply dumps more information for you to inspect, and you can safely ignore it.
+Take a look at `create_imagenet.sh`. Set the paths to the train and val dirs as needed, and set "RESIZE=true" to resize all images to 256x256 if you haven't resized the images in advance.
+Now simply create the leveldbs with `./create_imagenet.sh`. Note that `ilsvrc12_train_leveldb` and `ilsvrc12_val_leveldb` should not exist before this execution. It will be created by the script. `GLOG_logtostderr=1` simply dumps more information for you to inspect, and you can safely ignore it.
 
 Compute Image Mean
 ------------------
@@ -72,12 +73,11 @@ We will also lay out a protocol buffer for running the solver. Let's make a few
 * The network will be trained with momentum 0.9 and a weight decay of 0.0005.
 * For every 10,000 iterations, we will take a snapshot of the current status.
 
-Sound good? This is implemented in `examples/imagenet/imagenet_solver.prototxt`. Again, you will need to change the first two lines:
+Sound good? This is implemented in `examples/imagenet/imagenet_solver.prototxt`. Again, you will need to change the first line:
 
-    train_net: "imagenet_train.prototxt"
-    test_net: "imagenet_val.prototxt"
+    net: "imagenet_train_val.prototxt"
 
-to point to the actual path if you have changed them.
+to point to the actual path if you have changed it.
 
 Training ImageNet
 -----------------
@@ -102,4 +102,4 @@ Parting Words
 
 Hope you liked this recipe! Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or finetuning the various parameters in the network. The recent ILSVRC 2013 challenge suggests that there are quite some room for improvement. **Caffe allows one to explore different network choices  more easily, by simply writing different prototxt files** - isn't that exciting?
 
-And since now you have a trained network, check out how to use it: [Running Pretrained ImageNet](getting_pretrained_models.html). This time we will use Python, but if you have wrappers for other languages, please kindly send a pull request!
+And since now you have a trained network, check out how to use it: [Running Pretrained ImageNet](../../getting_pretrained_models.html). This time we will use Python, but if you have wrappers for other languages, please kindly send a pull request!
diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh
index 2b3b4031b5d..9e00d93c469 100755
--- a/examples/imagenet/resume_training.sh
+++ b/examples/imagenet/resume_training.sh
@@ -2,7 +2,8 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin \
-    imagenet_solver.prototxt caffe_imagenet_train_10000.solverstate
+$TOOLS/caffe train\
+    --solver=imagenet_solver.prototxt \
+    --snapshot=caffe_imagenet_train_10000.solverstate
 
 echo "Done."
diff --git a/examples/imagenet/time_imagenet.sh b/examples/imagenet/time_imagenet.sh
new file mode 100755
index 00000000000..c448b4977ce
--- /dev/null
+++ b/examples/imagenet/time_imagenet.sh
@@ -0,0 +1,17 @@
+#!/usr/bin/env sh
+
+TOOLS=../../build/tools
+
+if [ -z "$1" ]; then
+  echo "Using CPU! To time GPU mode, use:"
+  echo "    ./time_imagenet.sh <device ID>"
+  echo "(Try ./time_imagenet.sh 0 if you have just one GPU.)"
+  sleep 3  # Let the user read
+  GPU=""
+else
+  GPU="--gpu=$1"
+fi
+
+$TOOLS/caffe time --model=imagenet_train_val.prototxt ${GPU}
+
+echo "Done."
diff --git a/examples/imagenet/train_alexnet.sh b/examples/imagenet/train_alexnet.sh
index c91e9108ae5..6a7c0577413 100755
--- a/examples/imagenet/train_alexnet.sh
+++ b/examples/imagenet/train_alexnet.sh
@@ -2,6 +2,6 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin alexnet_solver.prototxt
+$TOOLS/caffe train --solver=alexnet_solver.prototxt
 
 echo "Done."
diff --git a/examples/imagenet/train_imagenet.sh b/examples/imagenet/train_imagenet.sh
index 0d2563b0c9d..008b96c01a1 100755
--- a/examples/imagenet/train_imagenet.sh
+++ b/examples/imagenet/train_imagenet.sh
@@ -2,6 +2,6 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin imagenet_solver.prototxt
+$TOOLS/caffe train --solver=imagenet_solver.prototxt
 
 echo "Done."
diff --git a/examples/imagenet_classification.ipynb b/examples/imagenet_classification.ipynb
index 7ac140d9e79..fc08c743093 100644
--- a/examples/imagenet_classification.ipynb
+++ b/examples/imagenet_classification.ipynb
@@ -31,9 +31,12 @@
       "import matplotlib.pyplot as plt\n",
       "%matplotlib inline\n",
       "\n",
-      "import caffe\n",
-      "\n",
+      "# Make sure that caffe is on the python path:\n",
       "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+      "import sys\n",
+      "sys.path.insert(0, caffe_root + 'python')\n",
+      "\n",
+      "import caffe\n",
       "\n",
       "# Set the right path to your model definition file, pretrained model weights,\n",
       "# and the image you would like to classify.\n",
@@ -50,7 +53,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean file, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and input scaling multiplies the feature scale from the input [0,1] to [0,255]."
+      "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255]."
      ]
     },
     {
@@ -58,9 +61,10 @@
      "collapsed": false,
      "input": [
       "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
-      "                       mean_file=caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy',\n",
+      "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'),\n",
       "                       channel_swap=(2,1,0),\n",
-      "                       input_scale=255)"
+      "                       raw_scale=255,\n",
+      "                       image_dims=(256, 256))"
      ],
      "language": "python",
      "metadata": {},
@@ -108,15 +112,15 @@
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
-        "<matplotlib.image.AxesImage at 0xa7ebfd0>"
+        "<matplotlib.image.AxesImage at 0x7fda204c0e10>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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CGz2I6GEUheHG9DIS4f13V9KSycT3Vw14CDfMB3pdN3EgRSW4HXoeBGUQx4Ef+3XPCanO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\nMZX4O/OUkKSsrXFeYrzgdQ5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VTTG3Y79uagVhAFurUb6yDlvunjzimUtLnBJrsdEKnGbQ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K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\n5+crK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hg4za+43F6waRuscignebT5NK3ZoJstsnc0jSWXiRAn5ilymkfe+94zZGN49OQB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/+dP/NH/oX/73OL56j5/7E/8pt+v71GJ4AZGaz0b0iV+8dpUsa+F4WLBiBDui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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt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\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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\nMcdrwP8F7/c7xJLyUh8AAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x3fb41d0>"
+        "<matplotlib.figure.Figure at 0x7fda517da210>"
        ]
       }
      ],
@@ -135,7 +139,8 @@
      "input": [
       "prediction = net.predict([input_image])  # predict takes any number of images, and formats them for the Caffe net automatically\n",
       "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])"
+      "plt.plot(prediction[0])\n",
+      "print 'predicted class:', prediction[0].argmax()"
      ],
      "language": "python",
      "metadata": {},
@@ -144,23 +149,16 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "prediction shape: (1000,)\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0xc8b1bd0>]"
+        "prediction shape: (1000,)\n",
+        "predicted class: 281\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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j8dTduHFj/HlFRYUqKiom/LpIjxEBcHkKhUIKhUKTsu+0QeByuRSJROLLkUhEbrd7TDse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\nBABgOYIAACxHEACA5QgCALDc/wN1ylfVql6MNwAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xa7f9290>"
+        "<matplotlib.figure.Figure at 0x7fda20473950>"
        ]
       }
      ],
@@ -170,6 +168,10 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n",
+      "\n",
+      "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n",
+      "\n",
       "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!"
      ]
     },
@@ -179,7 +181,8 @@
      "input": [
       "prediction = net.predict([input_image], oversample=False)\n",
       "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])"
+      "plt.plot(prediction[0])\n",
+      "print 'predicted class:', prediction[0].argmax()"
      ],
      "language": "python",
      "metadata": {},
@@ -188,23 +191,16 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "prediction shape: (1000,)\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0xc8e7090>]"
+        "prediction shape: (1000,)\n",
+        "predicted class: 281\n"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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iLd0g8Pv9sFqtsFgsMBqNqK6uRmtra1yZrKwsOJ1OGI3GKdelmccgICIt3SAIhULIycmJ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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xc8b9550>"
+        "<matplotlib.figure.Figure at 0x7fda1abd8e10>"
        ]
       }
      ],
@@ -214,9 +210,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n",
       "\n",
-      "Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are foxes, cats, and other cute mammals. Not unreasonable predictions, right?\n",
       "\n",
       "Now, why don't we see how long it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences."
      ]
@@ -234,7 +228,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1 loops, best of 3: 492 ms per loop\n"
+        "1 loops, best of 3: 355 ms per loop\n"
        ]
       }
      ],
@@ -267,7 +261,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1 loops, best of 3: 327 ms per loop\n"
+        "1 loops, best of 3: 210 ms per loop\n"
        ]
       }
      ],
@@ -321,15 +315,15 @@
        "output_type": "pyout",
        "prompt_number": 10,
        "text": [
-        "[<matplotlib.lines.Line2D at 0xcbba250>]"
+        "[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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wjsdTd+PGjfHnFRUVqqiomPDrIj1GBMDlKRQKKRQKTcq+0waBy+VSJBKJL0ciEbnd7jHt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\nIAAAyxEEAGA5ggAALEcQAIDl/h+B3FfVQOwSzQAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xa686ed0>"
+        "<matplotlib.figure.Figure at 0x7fda1ab53650>"
        ]
       }
      ],
@@ -339,7 +333,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a K20 GPU:"
+      "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:"
      ]
     },
     {
@@ -356,7 +350,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "10 loops, best of 3: 192 ms per loop\n"
+        "10 loops, best of 3: 174 ms per loop\n"
        ]
       }
      ],
@@ -376,7 +370,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "10 loops, best of 3: 25.2 ms per loop\n"
+        "10 loops, best of 3: 34.2 ms per loop\n"
        ]
       }
      ],
@@ -386,7 +380,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual convnet computation itself!\n",
+      "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n",
       "\n",
       "To fully utilize the power of GPUs, you really want to:\n",
       "\n",
diff --git a/examples/images/cat.jpg b/examples/images/cat.jpg
index 5303803b1c1..b4efc6c98b7 100644
Binary files a/examples/images/cat.jpg and b/examples/images/cat.jpg differ
diff --git a/examples/images/fish-bike.jpg b/examples/images/fish-bike.jpg
new file mode 100644
index 00000000000..39d9bd432e2
Binary files /dev/null and b/examples/images/fish-bike.jpg differ
diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 593a2d23313..a97a9285a19 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 //
 // This script converts the MNIST dataset to the leveldb format used
 // by caffe to perform classification.
@@ -7,14 +6,14 @@
 // The MNIST dataset could be downloaded at
 //    http://yann.lecun.com/exdb/mnist/
 
-#include <google/protobuf/text_format.h>
-#include <glog/logging.h>
-#include <leveldb/db.h>
-
-#include <stdint.h>
 #include <fstream>  // NOLINT(readability/streams)
 #include <string>
 
+#include "glog/logging.h"
+#include "google/protobuf/text_format.h"
+#include "leveldb/db.h"
+#include "stdint.h"
+
 #include "caffe/proto/caffe.pb.h"
 
 uint32_t swap_endian(uint32_t val) {
diff --git a/examples/mnist/lenet_consolidated_solver.prototxt b/examples/mnist/lenet_consolidated_solver.prototxt
new file mode 100644
index 00000000000..980f9382066
--- /dev/null
+++ b/examples/mnist/lenet_consolidated_solver.prototxt
@@ -0,0 +1,255 @@
+# lenet_consolidated_solver.prototxt consolidates the lenet_solver, lenet_train,
+# and lenet_test prototxts into a single file.  It also adds an additional test
+# net which runs on the training set, e.g., for the purpose of comparing
+# train/test accuracy (accuracy is computed only on the test set in the included
+# LeNet example).  This is mainly included as an example of using these features
+# (specify NetParameters directly in the solver, specify multiple test nets)
+# if desired.
+#
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "lenet"
+# Set a random_seed for repeatable results.
+# (For results that vary due to random initialization, comment out the below
+# line, or set to a negative integer -- e.g. "random_seed: -1")
+random_seed: 1701
+# solver mode: CPU or GPU
+solver_mode: GPU
+
+# We test on both the test and train set using "stages".  The TEST DATA layers
+# each have a stage, either 'test-on-train-set' or 'test-on-test-set'.
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+test_state: { stage: "test-on-test-set" }
+# The train set has 60K images, so we run 600 test iters (600 * 100 = 60K).
+test_iter: 600
+test_state: { stage: "test-on-train-set" }
+
+# The net protocol buffer definition
+net_param {
+  name: "LeNet"
+  layers {
+    name: "mnist"
+    type: DATA
+    top: "data"
+    top: "label"
+    data_param {
+      source: "mnist-train-leveldb"
+      scale: 0.00390625
+      batch_size: 64
+    }
+    include: { phase: TRAIN }
+  }
+  layers {
+    name: "mnist"
+    type: DATA
+    top: "data"
+    top: "label"
+    data_param {
+      source: "mnist-test-leveldb"
+      scale: 0.00390625
+      batch_size: 100
+    }
+    include: {
+      phase: TEST
+      stage: "test-on-test-set"
+    }
+  }
+  layers {
+    name: "mnist"
+    type: DATA
+    top: "data"
+    top: "label"
+    data_param {
+      source: "mnist-train-leveldb"
+      scale: 0.00390625
+      batch_size: 100
+    }
+    include: {
+      phase: TEST
+      stage: "test-on-train-set"
+    }
+  }
+  layers {
+    name: "conv1"
+    type: CONVOLUTION
+    bottom: "data"
+    top: "conv1"
+    blobs_lr: 1
+    blobs_lr: 2
+    convolution_param {
+      num_output: 20
+      kernel_size: 5
+      stride: 1
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "pool1"
+    type: POOLING
+    bottom: "conv1"
+    top: "pool1"
+    pooling_param {
+      pool: MAX
+      kernel_size: 2
+      stride: 2
+    }
+  }
+  layers {
+    name: "conv2"
+    type: CONVOLUTION
+    bottom: "pool1"
+    top: "conv2"
+    blobs_lr: 1
+    blobs_lr: 2
+    convolution_param {
+      num_output: 50
+      kernel_size: 5
+      stride: 1
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "pool2"
+    type: POOLING
+    bottom: "conv2"
+    top: "pool2"
+    pooling_param {
+      pool: MAX
+      kernel_size: 2
+      stride: 2
+    }
+  }
+  layers {
+    name: "ip1"
+    type: INNER_PRODUCT
+    bottom: "pool2"
+    top: "ip1"
+    blobs_lr: 1
+    blobs_lr: 2
+    inner_product_param {
+      num_output: 500
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "relu1"
+    type: RELU
+    bottom: "ip1"
+    top: "ip1"
+  }
+  layers {
+    name: "ip2"
+    type: INNER_PRODUCT
+    bottom: "ip1"
+    top: "ip2"
+    blobs_lr: 1
+    blobs_lr: 2
+    inner_product_param {
+      num_output: 10
+      weight_filler {
+        type: "xavier"
+      }
+      bias_filler {
+        type: "constant"
+      }
+    }
+  }
+  layers {
+    name: "accuracy"
+    type: ACCURACY
+    bottom: "ip2"
+    bottom: "label"
+    top: "accuracy"
+    include: { phase: TEST }
+  }
+  layers {
+    name: "loss"
+    type: SOFTMAX_LOSS
+    bottom: "ip2"
+    bottom: "label"
+    top: "loss"
+  }
+}
+
+# Expected results for first and last 500 iterations:
+# (with portions of log omitted for brevity)
+#
+# Iteration 0, Testing net (#0)
+# Test score #0: 0.067
+# Test score #1: 2.30256
+# Iteration 0, Testing net (#1)
+# Test score #0: 0.0670334
+# Test score #1: 2.30258
+# Iteration 100, lr = 0.00992565
+# Iteration 100, loss = 0.280585
+# Iteration 200, lr = 0.00985258
+# Iteration 200, loss = 0.345601
+# Iteration 300, lr = 0.00978075
+# Iteration 300, loss = 0.172217
+# Iteration 400, lr = 0.00971013
+# Iteration 400, loss = 0.261836
+# Iteration 500, lr = 0.00964069
+# Iteration 500, loss = 0.157803
+# Iteration 500, Testing net (#0)
+# Test score #0: 0.968
+# Test score #1: 0.0993772
+# Iteration 500, Testing net (#1)
+# Test score #0: 0.965883
+# Test score #1: 0.109374
+#
+# [...]
+#
+# Iteration 9500, Testing net (#0)
+# Test score #0: 0.9899
+# Test score #1: 0.0308299
+# Iteration 9500, Testing net (#1)
+# Test score #0: 0.996816
+# Test score #1: 0.0118238
+# Iteration 9600, lr = 0.00603682
+# Iteration 9600, loss = 0.0126215
+# Iteration 9700, lr = 0.00601382
+# Iteration 9700, loss = 0.00579304
+# Iteration 9800, lr = 0.00599102
+# Iteration 9800, loss = 0.00500633
+# Iteration 9900, lr = 0.00596843
+# Iteration 9900, loss = 0.00796607
+# Iteration 10000, lr = 0.00594604
+# Iteration 10000, loss = 0.00271736
+# Iteration 10000, Testing net (#0)
+# Test score #0: 0.9914
+# Test score #1: 0.0276671
+# Iteration 10000, Testing net (#1)
+# Test score #0: 0.997782
+# Test score #1: 0.00908085
diff --git a/examples/mnist/lenet_solver.prototxt b/examples/mnist/lenet_solver.prototxt
index 7947f2d6a73..a3b33090472 100644
--- a/examples/mnist/lenet_solver.prototxt
+++ b/examples/mnist/lenet_solver.prototxt
@@ -1,7 +1,5 @@
-# The training protocol buffer definition
-train_net: "lenet_train.prototxt"
-# The testing protocol buffer definition
-test_net: "lenet_test.prototxt"
+# The train/test net protocol buffer definition
+net: "lenet_train_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
diff --git a/examples/mnist/lenet_test.prototxt b/examples/mnist/lenet_test.prototxt
deleted file mode 100644
index 3b59b75513d..00000000000
--- a/examples/mnist/lenet_test.prototxt
+++ /dev/null
@@ -1,117 +0,0 @@
-name: "LeNet-test"
-layers {
-  name: "mnist"
-  type: DATA
-  top: "data"
-  top: "label"
-  data_param {
-    source: "mnist-test-leveldb"
-    scale: 0.00390625
-    batch_size: 100
-  }
-}
-layers {
-  name: "conv1"
-  type: CONVOLUTION
-  bottom: "data"
-  top: "conv1"
-  convolution_param {
-    num_output: 20
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "pool1"
-  type: POOLING
-  bottom: "conv1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-  }
-}
-layers {
-  name: "conv2"
-  type: CONVOLUTION
-  bottom: "pool1"
-  top: "conv2"
-  convolution_param {
-    num_output: 50
-    kernel_size: 5
-    stride: 1
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "pool2"
-  type: POOLING
-  bottom: "conv2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-  }
-}
-layers {
-  name: "ip1"
-  type: INNER_PRODUCT
-  bottom: "pool2"
-  top: "ip1"
-  inner_product_param {
-    num_output: 500
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "relu1"
-  type: RELU
-  bottom: "ip1"
-  top: "ip1"
-}
-layers {
-  name: "ip2"
-  type: INNER_PRODUCT
-  bottom: "ip1"
-  top: "ip2"
-  inner_product_param {
-    num_output: 10
-    weight_filler {
-      type: "xavier"
-    }
-    bias_filler {
-      type: "constant"
-    }
-  }
-}
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "ip2"
-  top: "prob"
-}
-layers {
-  name: "accuracy"
-  type: ACCURACY
-  bottom: "prob"
-  bottom: "label"
-  top: "accuracy"
-}
diff --git a/examples/mnist/lenet_train.prototxt b/examples/mnist/lenet_train_test.prototxt
similarity index 82%
rename from examples/mnist/lenet_train.prototxt
rename to examples/mnist/lenet_train_test.prototxt
index e8a1e74e40b..3c77452130c 100644
--- a/examples/mnist/lenet_train.prototxt
+++ b/examples/mnist/lenet_train_test.prototxt
@@ -9,7 +9,21 @@ layers {
     scale: 0.00390625
     batch_size: 64
   }
+  include: { phase: TRAIN }
 }
+layers {
+  name: "mnist"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "mnist-test-leveldb"
+    scale: 0.00390625
+    batch_size: 100
+  }
+  include: { phase: TEST }
+}
+
 layers {
   name: "conv1"
   type: CONVOLUTION
@@ -110,9 +124,18 @@ layers {
     }
   }
 }
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}
 layers {
   name: "loss"
   type: SOFTMAX_LOSS
   bottom: "ip2"
   bottom: "label"
+  top: "loss"
 }
diff --git a/examples/mnist/mnist_autoencoder_train.prototxt b/examples/mnist/mnist_autoencoder.prototxt
similarity index 88%
rename from examples/mnist/mnist_autoencoder_train.prototxt
rename to examples/mnist/mnist_autoencoder.prototxt
index 90d2cff99b8..ad1e7665bf2 100644
--- a/examples/mnist/mnist_autoencoder_train.prototxt
+++ b/examples/mnist/mnist_autoencoder.prototxt
@@ -8,6 +8,18 @@ layers {
     scale: 0.0039215684
     batch_size: 100
   }
+  include: { phase: TRAIN }
+}
+layers {
+  top: "data"
+  name: "data"
+  type: DATA
+  data_param {
+    source: "mnist-test-leveldb"
+    scale: 0.0039215684
+    batch_size: 100
+  }
+  include: { phase: TEST }
 }
 layers {
   bottom: "data"
@@ -232,4 +244,20 @@ layers {
   bottom: "flatdata"
   name: "loss"
   type: SIGMOID_CROSS_ENTROPY_LOSS
+  include: { phase: TRAIN }
+}
+layers {
+  bottom: "decode1"
+  top: "decode1neuron"
+  name: "decode1neuron"
+  type: SIGMOID
+  include: { phase: TEST }
+}
+layers {
+  bottom: "decode1neuron"
+  bottom: "flatdata"
+  name: "loss"
+  type: EUCLIDEAN_LOSS
+  top: "loss"
+  include: { phase: TEST }
 }
diff --git a/examples/mnist/mnist_autoencoder_solver.prototxt b/examples/mnist/mnist_autoencoder_solver.prototxt
index 06e057d53a4..ae1ddebccd2 100644
--- a/examples/mnist/mnist_autoencoder_solver.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver.prototxt
@@ -1,5 +1,4 @@
-train_net: "mnist_autoencoder_train.prototxt"
-test_net: "mnist_autoencoder_test.prototxt"
+net: "mnist_autoencoder.prototxt"
 test_iter: 50
 test_interval: 100
 test_compute_loss: true
diff --git a/examples/mnist/mnist_autoencoder_test.prototxt b/examples/mnist/mnist_autoencoder_test.prototxt
deleted file mode 100644
index 5090e82fe0a..00000000000
--- a/examples/mnist/mnist_autoencoder_test.prototxt
+++ /dev/null
@@ -1,145 +0,0 @@
-name: "MNISTAutoencoder"
-layers {
-  top: "data"
-  name: "data"
-  type: DATA
-  data_param {
-    source: "mnist-test-leveldb"
-    scale: 0.0039215684
-    batch_size: 100
-  }
-}
-layers {
-  bottom: "data"
-  top: "flatdata"
-  name: "flatdata"
-  type: FLATTEN
-}
-layers {
-  bottom: "data"
-  top: "encode1"
-  name: "encode1"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 1000
-  }
-}
-layers {
-  bottom: "encode1"
-  top: "encode1neuron"
-  name: "encode1neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "encode1neuron"
-  top: "encode2"
-  name: "encode2"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 500
-  }
-}
-layers {
-  bottom: "encode2"
-  top: "encode2neuron"
-  name: "encode2neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "encode2neuron"
-  top: "encode3"
-  name: "encode3"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 250
-  }
-}
-layers {
-  bottom: "encode3"
-  top: "encode3neuron"
-  name: "encode3neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "encode3neuron"
-  top: "encode4"
-  name: "encode4"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 30
-  }
-}
-layers {
-  bottom: "encode4"
-  top: "decode4"
-  name: "decode4"
-  type: INNER_PRODUCT
-  blobs_lr: 1
-  blobs_lr: 1
-  weight_decay: 1
-  weight_decay: 0
-  inner_product_param {
-    num_output: 250
-  }
-}
-layers {
-  bottom: "decode4"
-  top: "decode4neuron"
-  name: "decode4neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "decode4neuron"
-  top: "decode3"
-  name: "decode3"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 500
-  }
-}
-layers {
-  bottom: "decode3"
-  top: "decode3neuron"
-  name: "decode3neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "decode3neuron"
-  top: "decode2"
-  name: "decode2"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 1000
-  }
-}
-layers {
-  bottom: "decode2"
-  top: "decode2neuron"
-  name: "decode2neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "decode2neuron"
-  top: "decode1"
-  name: "decode1"
-  type: INNER_PRODUCT
-  inner_product_param {
-    num_output: 784
-  }
-}
-layers {
-  bottom: "decode1"
-  top: "decode1neuron"
-  name: "decode1neuron"
-  type: SIGMOID
-}
-layers {
-  bottom: "decode1neuron"
-  bottom: "flatdata"
-  name: "loss"
-  type: EUCLIDEAN_LOSS
-}
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index d609cfff9aa..65a780714ae 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -177,10 +177,8 @@ The `softmax_loss` layer implements both the softmax and the multinomial logisti
 
 Check out the comments explaining each line in the prototxt:
 
-    # The training protocol buffer definition
-    train_net: "lenet_train.prototxt"
-    # The testing protocol buffer definition
-    test_net: "lenet_test.prototxt"
+    # The train/test net protocol buffer definition
+    net: "lenet_train_test.prototxt"
     # test_iter specifies how many forward passes the test should carry out.
     # In the case of MNIST, we have test batch size 100 and 100 test iterations,
     # covering the full 10,000 testing images.
@@ -212,7 +210,7 @@ Training the model is simple after you have written the network definition proto
     cd $CAFFE_ROOT/examples/mnist
     ./train_lenet.sh
 
-`train_lenet.sh` is a simple script, but here are a few explanations: `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `train_net.bin`, with the solver protobuf text file as its argument.
+`train_lenet.sh` is a simple script, but here are a few explanations: `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `caffe.bin` with action `train`, with the solver protobuf text file as its argument.
 
 When you run the code, you will see a lot of messages flying by like this:
 
diff --git a/examples/mnist/train_lenet.sh b/examples/mnist/train_lenet.sh
index c30fc3e02e1..b93e48fb629 100755
--- a/examples/mnist/train_lenet.sh
+++ b/examples/mnist/train_lenet.sh
@@ -2,4 +2,4 @@
 
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin lenet_solver.prototxt
+$TOOLS/caffe train --solver=lenet_solver.prototxt
diff --git a/examples/mnist/train_lenet_consolidated.sh b/examples/mnist/train_lenet_consolidated.sh
new file mode 100755
index 00000000000..83fe895ba0b
--- /dev/null
+++ b/examples/mnist/train_lenet_consolidated.sh
@@ -0,0 +1,5 @@
+#!/usr/bin/env sh
+
+TOOLS=../../build/tools
+
+$TOOLS/caffe train --solver=lenet_consolidated_solver.prototxt
diff --git a/examples/mnist/train_mnist_autoencoder.sh b/examples/mnist/train_mnist_autoencoder.sh
index af2245e07f0..628c74b969a 100755
--- a/examples/mnist/train_mnist_autoencoder.sh
+++ b/examples/mnist/train_mnist_autoencoder.sh
@@ -1,4 +1,4 @@
 #!/bin/bash
 TOOLS=../../build/tools
 
-GLOG_logtostderr=1 $TOOLS/train_net.bin mnist_autoencoder_solver.prototxt
+$TOOLS/caffe.bin train --solver=mnist_autoencoder_solver.prototxt
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 0a6e208f6d7..5f151e4be3a 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -135,6 +135,11 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "# Make sure that caffe is on the python path:\n",
+      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+      "import sys\n",
+      "sys.path.insert(0, caffe_root + 'python')\n",
+      "\n",
       "import caffe\n",
       "\n",
       "# Load the original network and extract the fully-connected layers' parameters.\n",
@@ -270,9 +275,9 @@
       "# load input and configure preprocessing\n",
       "im = caffe.io.load_image('images/cat.jpg')\n",
       "plt.imshow(im)\n",
-      "net_full_conv.set_mean('data', '../python/caffe/imagenet/ilsvrc_2012_mean.npy')\n",
+      "net_full_conv.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy'))\n",
       "net_full_conv.set_channel_swap('data', (2,1,0))\n",
-      "net_full_conv.set_input_scale('data', 255.0)\n",
+      "net_full_conv.set_raw_scale('data', 255.0)\n",
       "# make classification map by forward pass and show top prediction index per location\n",
       "out = net_full_conv.forward_all(data=np.asarray([net_full_conv.preprocess('data', im)]))\n",
       "out['prob'][0].argmax(axis=0)"
@@ -285,14 +290,14 @@
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
-        "array([[278, 151, 259, 281, 282, 259, 282, 282],\n",
-        "       [283, 259, 283, 282, 283, 281, 259, 277],\n",
-        "       [283, 283, 283, 287, 287, 287, 287, 282],\n",
-        "       [283, 283, 283, 281, 281, 259, 259, 333],\n",
-        "       [283, 283, 283, 283, 283, 283, 283, 283],\n",
-        "       [283, 283, 283, 283, 283, 259, 283, 333],\n",
-        "       [283, 356, 359, 371, 368, 368, 259, 852],\n",
-        "       [356, 335, 358, 151, 283, 263, 277, 744]])"
+        "array([[282, 281, 281, 281, 281, 282, 282, 278],\n",
+        "       [281, 283, 281, 283, 281, 287, 281, 282],\n",
+        "       [283, 283, 283, 281, 283, 283, 283, 259],\n",
+        "       [283, 283, 281, 281, 283, 283, 287, 259],\n",
+        "       [283, 283, 285, 283, 283, 283, 283, 283],\n",
+        "       [283, 283, 333, 283, 283, 283, 283, 282],\n",
+        "       [905, 358, 259, 283, 371, 259, 259, 852],\n",
+        "       [335, 335, 371, 185, 186, 263, 185, 356]])"
        ]
       },
       {
@@ -300,7 +305,7 @@
        "output_type": "display_data",
        "png": 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IKqyrUEsKkeG59FMkhNGdvjnnh86+C5dLZ+zbU4KSKciGT6otrpz5YIggA9gbUTM5aVFq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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt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\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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\nMcdrwP8F7/c7xJLyUh8AAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1193b2c10>"
+        "<matplotlib.figure.Figure at 0x7f85901260d0>"
        ]
       }
      ],
@@ -310,7 +315,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The classifications include various cats -- 282 = tabby, 283 = tiger, 281 = persian -- and foxes and other mammals.\n",
+      "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
       "\n",
       "In this way the fully-connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself."
      ]
diff --git a/examples/pascal-finetuning/pascal_finetune_val.prototxt b/examples/pascal-finetuning/pascal_finetune_val.prototxt
index ff898fe7376..91ded585d85 100644
--- a/examples/pascal-finetuning/pascal_finetune_val.prototxt
+++ b/examples/pascal-finetuning/pascal_finetune_val.prototxt
@@ -313,16 +313,18 @@ layers {
     }
   }
 }
-layers {
-  name: "prob"
-  type: SOFTMAX
-  bottom: "fc8_pascal"
-  top: "prob"
-}
 layers {
   name: "accuracy"
   type: ACCURACY
-  bottom: "prob"
+  bottom: "fc8_pascal"
   bottom: "label"
   top: "accuracy"
 }
+layers {
+  name: "prob"
+  type: SOFTMAX_LOSS
+  bottom: "fc8_pascal"
+  bottom: "label"
+  top: "loss"
+}
+
diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 75101462faf..eb0c25ba394 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -1,11 +1,10 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_BLOB_HPP_
 #define CAFFE_BLOB_HPP_
 
 #include "caffe/common.hpp"
-#include "caffe/syncedmem.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/syncedmem.hpp"
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
@@ -13,8 +12,8 @@ template <typename Dtype>
 class Blob {
  public:
   Blob()
-       : num_(0), channels_(0), height_(0), width_(0), count_(0), data_(),
-       diff_() {}
+       : data_(), diff_(), num_(0), channels_(0), height_(0), width_(0),
+       count_(0) {}
   explicit Blob(const int num, const int channels, const int height,
     const int width);
   void Reshape(const int num, const int channels, const int height,
@@ -75,6 +74,10 @@ class Blob {
   void FromProto(const BlobProto& proto);
   void ToProto(BlobProto* proto, bool write_diff = false) const;
 
+  // Compute the sum of absolute values (L1 norm) of the data or diff.
+  Dtype asum_data() const;
+  Dtype asum_diff() const;
+
   // Set the data_/diff_ shared_ptr to point to the SyncedMemory holding the
   // data_/diff_ of Blob other -- useful in layers which simply perform a copy
   // in their forward or backward pass.
diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp
index ada0695472a..0af9ef04c43 100644
--- a/include/caffe/caffe.hpp
+++ b/include/caffe/caffe.hpp
@@ -1,19 +1,18 @@
-// Copyright 2014 BVLC and contributors.
 // caffe.hpp is the header file that you need to include in your code. It wraps
 // all the internal caffe header files into one for simpler inclusion.
 
 #ifndef CAFFE_CAFFE_HPP_
 #define CAFFE_CAFFE_HPP_
 
-#include "caffe/common.hpp"
 #include "caffe/blob.hpp"
+#include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
+#include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-#include "caffe/proto/caffe.pb.h"
-
 #endif  // CAFFE_CAFFE_HPP_
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 7bfa5d402bd..97a34962c63 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -1,15 +1,16 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_COMMON_HPP_
 #define CAFFE_COMMON_HPP_
 
 #include <boost/shared_ptr.hpp>
-#include <cublas_v2.h>
-#include <cuda.h>
-#include <curand.h>
-#include <driver_types.h>  // cuda driver types
 #include <glog/logging.h>
 
+#include <cmath>
+#include <map>
+#include <set>
+#include <string>
+
+#include "caffe/util/device_alternate.hpp"
+
 // Disable the copy and assignment operator for a class.
 #define DISABLE_COPY_AND_ASSIGN(classname) \
 private:\
@@ -25,6 +26,8 @@ private:\
 // is executed we will see a fatal log.
 #define NOT_IMPLEMENTED LOG(FATAL) << "Not Implemented Yet"
 
+#ifndef CPU_ONLY
+
 // CUDA: various checks for different function calls.
 #define CUDA_CHECK(condition) \
   /* Code block avoids redefinition of cudaError_t error */ \
@@ -56,10 +59,7 @@ private:\
 // CUDA: check for error after kernel execution and exit loudly if there is one.
 #define CUDA_POST_KERNEL_CHECK CUDA_CHECK(cudaPeekAtLastError())
 
-// Define not supported status for pre-6.0 compatibility.
-#if CUDA_VERSION < 6000
-#define CUBLAS_STATUS_NOT_SUPPORTED 831486
-#endif
+#endif  // CPU_ONLY
 
 namespace caffe {
 
@@ -67,6 +67,22 @@ namespace caffe {
 // because cuda does not work (at least now) well with C++11 features.
 using boost::shared_ptr;
 
+// Common functions and classes from std that caffe often uses.
+using std::fstream;
+using std::ios;
+using std::isnan;
+using std::iterator;
+using std::make_pair;
+using std::map;
+using std::ostringstream;
+using std::pair;
+using std::set;
+using std::string;
+using std::vector;
+
+// A global initialization function that you should call in your main function.
+// Currently it initializes google flags and google logging.
+void GlobalInit(int* pargc, char*** pargv);
 
 // A singleton class to hold common caffe stuff, such as the handler that
 // caffe is going to use for cublas, curand, etc.
@@ -104,10 +120,12 @@ class Caffe {
     }
     return *(Get().random_generator_);
   }
+#ifndef CPU_ONLY
   inline static cublasHandle_t cublas_handle() { return Get().cublas_handle_; }
   inline static curandGenerator_t curand_generator() {
     return Get().curand_generator_;
   }
+#endif
 
   // Returns the mode: running on CPU or GPU.
   inline static Brew mode() { return Get().mode_; }
@@ -130,8 +148,10 @@ class Caffe {
   static void DeviceQuery();
 
  protected:
+#ifndef CPU_ONLY
   cublasHandle_t cublas_handle_;
   curandGenerator_t curand_generator_;
+#endif
   shared_ptr<RNG> random_generator_;
 
   Brew mode_;
@@ -145,6 +165,8 @@ class Caffe {
   DISABLE_COPY_AND_ASSIGN(Caffe);
 };
 
+#ifndef CPU_ONLY
+
 // NVIDIA_CUDA-5.5_Samples/common/inc/helper_cuda.h
 const char* cublasGetErrorString(cublasStatus_t error);
 const char* curandGetErrorString(curandStatus_t error);
@@ -163,6 +185,7 @@ inline int CAFFE_GET_BLOCKS(const int N) {
   return (N + CAFFE_CUDA_NUM_THREADS - 1) / CAFFE_CUDA_NUM_THREADS;
 }
 
+#endif  // CPU_ONLY
 
 }  // namespace caffe
 
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
new file mode 100644
index 00000000000..1e282d33d7a
--- /dev/null
+++ b/include/caffe/common_layers.hpp
@@ -0,0 +1,219 @@
+#ifndef CAFFE_COMMON_LAYERS_HPP_
+#define CAFFE_COMMON_LAYERS_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/data_layers.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/loss_layers.hpp"
+#include "caffe/neuron_layers.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/* ArgmaxLayer
+  Compute the index of the max value across all (channels x height x width).
+  [In the future, can take specific dimension.]
+  Intended for use after a classification layer to produce prediction.
+  If parameter out_max_val is set to true, then output is a vector of pairs
+  (max_ind, max_val) for each image.
+
+  NOTE: does not implement Backwards operation.
+*/
+template <typename Dtype>
+class ArgMaxLayer : public Layer<Dtype> {
+ public:
+  explicit ArgMaxLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_ARGMAX;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    NOT_IMPLEMENTED;
+  }
+  bool out_max_val_;
+  size_t top_k_;
+};
+
+/* ConcatLayer
+  Takes at least two blobs and concatenates them along either num or
+  channel dim, outputting the result.
+*/
+template <typename Dtype>
+class ConcatLayer : public Layer<Dtype> {
+ public:
+  explicit ConcatLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_CONCAT;
+  }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  Blob<Dtype> col_bob_;
+  int count_;
+  int num_;
+  int channels_;
+  int height_;
+  int width_;
+  int concat_dim_;
+};
+
+/* FlattenLayer
+*/
+template <typename Dtype>
+class FlattenLayer : public Layer<Dtype> {
+ public:
+  explicit FlattenLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_FLATTEN;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  int count_;
+};
+
+/* SoftmaxLayer
+*/
+template <typename Dtype>
+class SoftmaxLayer : public Layer<Dtype> {
+ public:
+  explicit SoftmaxLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SOFTMAX;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  // sum_multiplier is just used to carry out sum using blas
+  Blob<Dtype> sum_multiplier_;
+  // scale is an intermediate blob to hold temporary results.
+  Blob<Dtype> scale_;
+};
+
+/* SplitLayer
+*/
+template <typename Dtype>
+class SplitLayer : public Layer<Dtype> {
+ public:
+  explicit SplitLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SPLIT;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  int count_;
+};
+
+/* SliceLayer
+  Takes one blobs and slices it along either num or channel dim,
+  outputting the result into as many top blobs as needed.
+*/
+template <typename Dtype>
+class SliceLayer : public Layer<Dtype> {
+ public:
+  explicit SliceLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SLICE;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 2; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  Blob<Dtype> col_bob_;
+  int count_;
+  int num_;
+  int channels_;
+  int height_;
+  int width_;
+  int slice_dim_;
+  vector<int> slice_point_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_COMMON_LAYERS_HPP_
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
index d9865ce485b..31774828a69 100644
--- a/include/caffe/data_layers.hpp
+++ b/include/caffe/data_layers.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_DATA_LAYERS_HPP_
 #define CAFFE_DATA_LAYERS_HPP_
 
@@ -7,13 +5,15 @@
 #include <utility>
 #include <vector>
 
-#include "leveldb/db.h"
-#include "pthread.h"
-#include "hdf5.h"
 #include "boost/scoped_ptr.hpp"
+#include "hdf5.h"
+#include "leveldb/db.h"
+#include "lmdb.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/internal_thread.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 
@@ -22,14 +22,24 @@ namespace caffe {
 #define HDF5_DATA_DATASET_NAME "data"
 #define HDF5_DATA_LABEL_NAME "label"
 
+// TODO: DataLayer, ImageDataLayer, and WindowDataLayer all have the
+// same basic structure and a lot of duplicated code.
+
 template <typename Dtype>
-class HDF5OutputLayer : public Layer<Dtype> {
+class DataLayer : public Layer<Dtype>, public InternalThread {
  public:
-  explicit HDF5OutputLayer(const LayerParameter& param);
-  virtual ~HDF5OutputLayer();
+  explicit DataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual ~DataLayer();
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
-  inline std::string file_name() const { return file_name_; }
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlobs() const { return 2; }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -37,17 +47,64 @@ class HDF5OutputLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void SaveBlobs();
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
 
-  std::string file_name_;
-  hid_t file_id_;
-  Blob<Dtype> data_blob_;
-  Blob<Dtype> label_blob_;
+  virtual void CreatePrefetchThread();
+  virtual void JoinPrefetchThread();
+  virtual unsigned int PrefetchRand();
+  // The thread's function
+  virtual void InternalThreadEntry();
+
+  shared_ptr<Caffe::RNG> prefetch_rng_;
+
+  // LEVELDB
+  shared_ptr<leveldb::DB> db_;
+  shared_ptr<leveldb::Iterator> iter_;
+  // LMDB
+  MDB_env* mdb_env_;
+  MDB_dbi mdb_dbi_;
+  MDB_txn* mdb_txn_;
+  MDB_cursor* mdb_cursor_;
+  MDB_val mdb_key_, mdb_value_;
+
+  int datum_channels_;
+  int datum_height_;
+  int datum_width_;
+  int datum_size_;
+  Blob<Dtype> prefetch_data_;
+  Blob<Dtype> prefetch_label_;
+  Blob<Dtype> data_mean_;
+  bool output_labels_;
+  Caffe::Phase phase_;
 };
 
+template <typename Dtype>
+class DummyDataLayer : public Layer<Dtype> {
+ public:
+  explicit DummyDataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_DUMMY_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+
+  vector<shared_ptr<Filler<Dtype> > > fillers_;
+  vector<bool> refill_;
+};
 
 template <typename Dtype>
 class HDF5DataLayer : public Layer<Dtype> {
@@ -58,15 +115,21 @@ class HDF5DataLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_HDF5_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void LoadHDF5FileData(const char* filename);
 
   std::vector<std::string> hdf_filenames_;
@@ -77,24 +140,22 @@ class HDF5DataLayer : public Layer<Dtype> {
   Blob<Dtype> label_blob_;
 };
 
-// TODO: DataLayer, ImageDataLayer, and WindowDataLayer all have the
-// same basic structure and a lot of duplicated code.
-
-// This function is used to create a pthread that prefetches the data.
-template <typename Dtype>
-void* DataLayerPrefetch(void* layer_pointer);
-
 template <typename Dtype>
-class DataLayer : public Layer<Dtype> {
-  // The function used to perform prefetching.
-  friend void* DataLayerPrefetch<Dtype>(void* layer_pointer);
-
+class HDF5OutputLayer : public Layer<Dtype> {
  public:
-  explicit DataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual ~DataLayer();
+  explicit HDF5OutputLayer(const LayerParameter& param);
+  virtual ~HDF5OutputLayer();
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+      vector<Blob<Dtype>*>* top) {}
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_HDF5_OUTPUT;
+  }
+  // TODO: no limit on the number of blobs
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 0; }
+
+  inline std::string file_name() const { return file_name_; }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -102,38 +163,19 @@ class DataLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
-
-  virtual void CreatePrefetchThread();
-  virtual void JoinPrefetchThread();
-  virtual unsigned int PrefetchRand();
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void SaveBlobs();
 
-  shared_ptr<Caffe::RNG> prefetch_rng_;
-  shared_ptr<leveldb::DB> db_;
-  shared_ptr<leveldb::Iterator> iter_;
-  int datum_channels_;
-  int datum_height_;
-  int datum_width_;
-  int datum_size_;
-  pthread_t thread_;
-  shared_ptr<Blob<Dtype> > prefetch_data_;
-  shared_ptr<Blob<Dtype> > prefetch_label_;
-  Blob<Dtype> data_mean_;
-  bool output_labels_;
-  Caffe::Phase phase_;
+  std::string file_name_;
+  hid_t file_id_;
+  Blob<Dtype> data_blob_;
+  Blob<Dtype> label_blob_;
 };
 
-// This function is used to create a pthread that prefetches the data.
 template <typename Dtype>
-void* ImageDataLayerPrefetch(void* layer_pointer);
-
-template <typename Dtype>
-class ImageDataLayer : public Layer<Dtype> {
-  // The function used to perform prefetching.
-  friend void* ImageDataLayerPrefetch<Dtype>(void* layer_pointer);
-
+class ImageDataLayer : public Layer<Dtype>, public InternalThread {
  public:
   explicit ImageDataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
@@ -141,21 +183,28 @@ class ImageDataLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_IMAGE_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
 
   virtual void ShuffleImages();
 
   virtual void CreatePrefetchThread();
   virtual void JoinPrefetchThread();
   virtual unsigned int PrefetchRand();
+  virtual void InternalThreadEntry();
 
   shared_ptr<Caffe::RNG> prefetch_rng_;
   vector<std::pair<std::string, int> > lines_;
@@ -164,23 +213,57 @@ class ImageDataLayer : public Layer<Dtype> {
   int datum_height_;
   int datum_width_;
   int datum_size_;
-  pthread_t thread_;
-  shared_ptr<Blob<Dtype> > prefetch_data_;
-  shared_ptr<Blob<Dtype> > prefetch_label_;
+  Blob<Dtype> prefetch_data_;
+  Blob<Dtype> prefetch_label_;
   Blob<Dtype> data_mean_;
   Caffe::Phase phase_;
 };
 
-
-// This function is used to create a pthread that prefetches the window data.
+/* MemoryDataLayer
+*/
 template <typename Dtype>
-void* WindowDataLayerPrefetch(void* layer_pointer);
+class MemoryDataLayer : public Layer<Dtype> {
+ public:
+  explicit MemoryDataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
 
-template <typename Dtype>
-class WindowDataLayer : public Layer<Dtype> {
-  // The function used to perform prefetching.
-  friend void* WindowDataLayerPrefetch<Dtype>(void* layer_pointer);
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_MEMORY_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
+  // Reset should accept const pointers, but can't, because the memory
+  //  will be given to Blob, which is mutable
+  void Reset(Dtype* data, Dtype* label, int n);
+  int datum_channels() { return datum_channels_; }
+  int datum_height() { return datum_height_; }
+  int datum_width() { return datum_width_; }
+  int batch_size() { return batch_size_; }
 
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
+
+  Dtype* data_;
+  Dtype* labels_;
+  int datum_channels_;
+  int datum_height_;
+  int datum_width_;
+  int datum_size_;
+  int batch_size_;
+  int n_;
+  int pos_;
+};
+
+template <typename Dtype>
+class WindowDataLayer : public Layer<Dtype>, public InternalThread {
  public:
   explicit WindowDataLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
@@ -188,24 +271,30 @@ class WindowDataLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_WINDOW_DATA;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {}
 
   virtual void CreatePrefetchThread();
   virtual void JoinPrefetchThread();
   virtual unsigned int PrefetchRand();
+  virtual void InternalThreadEntry();
 
   shared_ptr<Caffe::RNG> prefetch_rng_;
-  pthread_t thread_;
-  shared_ptr<Blob<Dtype> > prefetch_data_;
-  shared_ptr<Blob<Dtype> > prefetch_label_;
+  Blob<Dtype> prefetch_data_;
+  Blob<Dtype> prefetch_label_;
   Blob<Dtype> data_mean_;
   vector<std::pair<std::string, vector<int> > > image_database_;
   enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index 242f11a3513..a8366801132 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 // Fillers are random number generators that fills a blob using the specified
 // algorithm. The expectation is that they are only going to be used during
 // initialization time and will not involve any GPUs.
@@ -9,11 +7,11 @@
 
 #include <string>
 
-#include "caffe/common.hpp"
 #include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
 
diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
new file mode 100644
index 00000000000..8520899ada6
--- /dev/null
+++ b/include/caffe/internal_thread.hpp
@@ -0,0 +1,44 @@
+#ifndef CAFFE_INTERNAL_THREAD_HPP_
+#define CAFFE_INTERNAL_THREAD_HPP_
+
+#include <pthread.h>
+
+namespace caffe {
+
+/**
+ * Virutal class encapsulate pthread for use in base class
+ * The child class will acquire the ability to run a single pthread,
+ * by reimplementing the virutal function InternalThreadEntry.
+ */
+class InternalThread {
+ public:
+  InternalThread() {}
+  virtual ~InternalThread() {}
+
+  /** Returns true if the thread was successfully started **/
+  bool StartInternalThread() {
+    return pthread_create(&_thread, NULL, InternalThreadEntryFunc, this);
+  }
+
+  /** Will not return until the internal thread has exited. */
+  bool WaitForInternalThreadToExit() {
+    return pthread_join(_thread, NULL);
+  }
+
+ protected:
+  /* Implement this method in your subclass
+      with the code you want your thread to run. */
+  virtual void InternalThreadEntry() = 0;
+
+ private:
+  static void * InternalThreadEntryFunc(void * This) {
+    reinterpret_cast<InternalThread *>(This)->InternalThreadEntry();
+    return NULL;
+  }
+
+  pthread_t _thread;
+};
+
+}  // namespace caffe
+
+#endif
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 14bba63594b..dca5f8709af 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -1,14 +1,13 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_LAYER_H_
 #define CAFFE_LAYER_H_
 
+#include <string>
 #include <vector>
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
-
-using std::vector;
+#include "caffe/util/device_alternate.hpp"
 
 namespace caffe {
 
@@ -30,9 +29,12 @@ class Layer {
       }
     }
   virtual ~Layer() {}
-  // SetUp: your function should implement this.
+  // SetUp: your function should implement this, and call Layer::SetUp for
+  // common SetUp functionality.
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) = 0;
+      vector<Blob<Dtype>*>* top) {
+    CheckBlobCounts(bottom, *top);
+  }
 
   // Forward and backward wrappers. You should implement the cpu and
   // gpu specific implementations instead, and should not change these
@@ -40,7 +42,7 @@ class Layer {
   inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   inline void Backward(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down,
+      const vector<bool>& propagate_down,
       vector<Blob<Dtype>*>* bottom);
 
   // Returns the vector of blobs.
@@ -53,11 +55,65 @@ class Layer {
   // Writes the layer parameter to a protocol buffer
   virtual void ToProto(LayerParameter* param, bool write_diff = false);
 
+  // Returns the layer type as an enum value.
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_NONE;
+  }
+
+  // Returns the layer type name.
+  virtual inline const string& type_name() const {
+    return LayerParameter_LayerType_Name(type());
+  }
+
+  // These methods can be overwritten to declare that this layer type expects
+  // a certain number of blobs as input and output.
+  //
+  // ExactNum{Bottom,Top}Blobs return a non-negative number to require an exact
+  // number of bottom/top blobs; the Min/Max versions return a non-negative
+  // number to require a minimum and/or maximum number of blobs.
+  // If Exact is specified, neither Min nor Max should be specified, and vice
+  // versa.  These methods may not rely on SetUp having been called.
+  virtual inline int ExactNumBottomBlobs() const { return -1; }
+  virtual inline int MinBottomBlobs() const { return -1; }
+  virtual inline int MaxBottomBlobs() const { return -1; }
+  virtual inline int ExactNumTopBlobs() const { return -1; }
+  virtual inline int MinTopBlobs() const { return -1; }
+  virtual inline int MaxTopBlobs() const { return -1; }
+
+  // EqualNumBottomTopBlobs should return true for layers requiring an equal
+  // number of bottom and top blobs.
+  virtual inline bool EqualNumBottomTopBlobs() const { return false; }
+
+  // Declare for each bottom blob whether to allow force_backward -- that is,
+  // if AllowForceBackward(i) == false, we will ignore the force_backward
+  // setting and backpropagate to blob i only if it needs gradient information
+  // (as is done when force_backward == false).
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return true;
+  }
+
+  // param_propagate_down specifies whether the layer should compute gradients
+  // in Backward.  You can safely ignore false and always compute gradients
+  // for all parameters, but possibly with wasteful computation.
+  inline bool param_propagate_down(const int param_id) {
+    return (param_propagate_down_.size() > param_id) ?
+        param_propagate_down_[param_id] : false;
+  }
+  inline void set_param_propagate_down(const int param_id, const bool value) {
+    if (param_propagate_down_.size() <= param_id) {
+      param_propagate_down_.resize(param_id + 1, true);
+    }
+    param_propagate_down_[param_id] = value;
+  }
+
+
  protected:
   // The protobuf that stores the layer parameters
   LayerParameter layer_param_;
   // The vector that stores the parameters as a set of blobs.
   vector<shared_ptr<Blob<Dtype> > > blobs_;
+  // Vector indicating whether to compute the diff of each param blob.
+  vector<bool> param_propagate_down_;
 
   // Forward functions: compute the layer output
   // (and loss layers return the loss; other layers return the dummy value 0.)
@@ -73,15 +129,57 @@ class Layer {
   // Backward functions: compute the gradients for any parameters and
   // for the bottom blobs if propagate_down is true.
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down,
+      const vector<bool>& propagate_down,
       vector<Blob<Dtype>*>* bottom) = 0;
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down,
+      const vector<bool>& propagate_down,
       vector<Blob<Dtype>*>* bottom) {
     // LOG(WARNING) << "Using CPU code as backup.";
     Backward_cpu(top, propagate_down, bottom);
   }
 
+  // CheckBlobCounts: called by the parent Layer's SetUp to check that the
+  // number of bottom and top Blobs provided as input match the expected
+  // numbers specified by the {ExactNum,Min,Max}{Bottom,Top}Blobs() functions.
+  virtual void CheckBlobCounts(const vector<Blob<Dtype>*>& bottom,
+                               const vector<Blob<Dtype>*>& top) {
+    if (ExactNumBottomBlobs() >= 0) {
+      CHECK_EQ(ExactNumBottomBlobs(), bottom.size())
+          << type_name() << " Layer takes " << ExactNumBottomBlobs()
+          << " bottom blob(s) as input.";
+    }
+    if (MinBottomBlobs() >= 0) {
+      CHECK_LE(MinBottomBlobs(), bottom.size())
+          << type_name() << " Layer takes at least " << MinBottomBlobs()
+          << " bottom blob(s) as input.";
+    }
+    if (MaxBottomBlobs() >= 0) {
+      CHECK_GE(MaxBottomBlobs(), bottom.size())
+          << type_name() << " Layer takes at most " << MaxBottomBlobs()
+          << " bottom blob(s) as input.";
+    }
+    if (ExactNumTopBlobs() >= 0) {
+      CHECK_EQ(ExactNumTopBlobs(), top.size())
+          << type_name() << " Layer produces " << ExactNumTopBlobs()
+          << " top blob(s) as output.";
+    }
+    if (MinTopBlobs() >= 0) {
+      CHECK_LE(MinTopBlobs(), top.size())
+          << type_name() << " Layer produces at least " << MinTopBlobs()
+          << " top blob(s) as output.";
+    }
+    if (MaxTopBlobs() >= 0) {
+      CHECK_GE(MaxTopBlobs(), top.size())
+          << type_name() << " Layer produces at most " << MaxTopBlobs()
+          << " top blob(s) as output.";
+    }
+    if (EqualNumBottomTopBlobs()) {
+      CHECK_EQ(bottom.size(), top.size())
+          << type_name() << " Layer produces one top blob as output for each "
+          << "bottom blob input.";
+    }
+  }
+
   DISABLE_COPY_AND_ASSIGN(Layer);
 };  // class Layer
 
@@ -104,7 +202,7 @@ inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 inline void Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   switch (Caffe::mode()) {
   case Caffe::CPU:
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
index cc798c499d3..bd2618d6d36 100644
--- a/include/caffe/loss_layers.hpp
+++ b/include/caffe/loss_layers.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_LOSS_LAYERS_HPP_
 #define CAFFE_LOSS_LAYERS_HPP_
 
@@ -7,10 +5,10 @@
 #include <utility>
 #include <vector>
 
-#include "leveldb/db.h"
-#include "pthread.h"
 #include "boost/scoped_ptr.hpp"
 #include "hdf5.h"
+#include "leveldb/db.h"
+#include "pthread.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -22,6 +20,36 @@ namespace caffe {
 
 const float kLOG_THRESHOLD = 1e-20;
 
+/* AccuracyLayer
+  Note: not an actual loss layer! Does not implement backwards step.
+  Computes the accuracy of argmax(a) with respect to b.
+*/
+template <typename Dtype>
+class AccuracyLayer : public Layer<Dtype> {
+ public:
+  explicit AccuracyLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_ACCURACY;
+  }
+
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    NOT_IMPLEMENTED;
+  }
+
+  int top_k_;
+};
+
 /* LossLayer
   Takes two inputs of same num (a and b), and has no output.
   The gradient is propagated to a.
@@ -35,59 +63,69 @@ class LossLayer : public Layer<Dtype> {
       const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top);
   virtual void FurtherSetUp(
       const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {}
+
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int MaxTopBlobs() const { return 1; }
+  // We usually cannot backpropagate to the labels; ignore force_backward for
+  // these inputs.
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return bottom_index != 1;
+  }
 };
 
-/* SigmoidCrossEntropyLossLayer
+/* EuclideanLossLayer
+  Compute the L_2 distance between the two inputs.
+
+  loss = (1/2 \sum_i (a_i - b_i)^2)
+  a' = 1/I (a - b)
 */
 template <typename Dtype>
-class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
+class EuclideanLossLayer : public LossLayer<Dtype> {
  public:
-  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param),
-          sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
-          sigmoid_output_(new Blob<Dtype>()) {}
+  explicit EuclideanLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param), diff_() {}
   virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_EUCLIDEAN_LOSS;
+  }
+  // Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
+  // to both inputs.
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return true;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
-  // sigmoid_output stores the output of the sigmoid layer.
-  shared_ptr<Blob<Dtype> > sigmoid_output_;
-  // Vector holders to call the underlying sigmoid layer forward and backward.
-  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
-  vector<Blob<Dtype>*> sigmoid_top_vec_;
+  Blob<Dtype> diff_;
 };
 
-/* EuclideanLossLayer
-  Compute the L_2 distance between the two inputs.
-
-  loss = (1/2 \sum_i (a_i - b_i)^2)
-  a' = 1/I (a - b)
+/* HingeLossLayer
 */
 template <typename Dtype>
-class EuclideanLossLayer : public LossLayer<Dtype> {
+class HingeLossLayer : public LossLayer<Dtype> {
  public:
-  explicit EuclideanLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param), diff_() {}
-  virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+  explicit HingeLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param) {}
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_HINGE_LOSS;
+  }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  Blob<Dtype> diff_;
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
 /* InfogainLossLayer
@@ -100,71 +138,120 @@ class InfogainLossLayer : public LossLayer<Dtype> {
   virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_INFOGAIN_LOSS;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   Blob<Dtype> infogain_;
 };
 
-/* HingeLossLayer
+/* MultinomialLogisticLossLayer
 */
 template <typename Dtype>
-class HingeLossLayer : public LossLayer<Dtype> {
+class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
  public:
-  explicit HingeLossLayer(const LayerParameter& param)
+  explicit MultinomialLogisticLossLayer(const LayerParameter& param)
       : LossLayer<Dtype>(param) {}
+  virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
+  }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
-/* MultinomialLogisticLossLayer
+/* SigmoidCrossEntropyLossLayer
 */
 template <typename Dtype>
-class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
+class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
  public:
-  explicit MultinomialLogisticLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param) {}
+  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param),
+          sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
+          sigmoid_output_(new Blob<Dtype>()) {}
   virtual void FurtherSetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+
+  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
+  // sigmoid_output stores the output of the sigmoid layer.
+  shared_ptr<Blob<Dtype> > sigmoid_output_;
+  // Vector holders to call the underlying sigmoid layer forward and backward.
+  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
+  vector<Blob<Dtype>*> sigmoid_top_vec_;
 };
 
-/* AccuracyLayer
-  Note: not an actual loss layer! Does not implement backwards step.
-  Computes the accuracy and logprob of a with respect to b.
+// Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
+template <typename Dtype> class SoftmaxLayer;
+
+/* SoftmaxWithLossLayer
+  Implements softmax and computes the loss.
+
+  It is preferred over separate softmax + multinomiallogisticloss
+  layers due to more numerically stable gradients.
+
+  In test, this layer could be replaced by simple softmax layer.
 */
 template <typename Dtype>
-class AccuracyLayer : public Layer<Dtype> {
+class SoftmaxWithLossLayer : public Layer<Dtype> {
  public:
-  explicit AccuracyLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
+  explicit SoftmaxWithLossLayer(const LayerParameter& param)
+      : Layer<Dtype>(param), softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SOFTMAX_LOSS;
+  }
+  virtual inline int MaxTopBlobs() const { return 2; }
+  // We cannot backpropagate to the labels; ignore force_backward for these
+  // inputs.
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return bottom_index != 1;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-    NOT_IMPLEMENTED;
-  }
-};
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-/* Also see
-- SoftmaxWithLossLayer in vision_layers.hpp
-*/
+  shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
+  // prob stores the output probability of the layer.
+  Blob<Dtype> prob_;
+  // Vector holders to call the underlying softmax layer forward and backward.
+  vector<Blob<Dtype>*> softmax_bottom_vec_;
+  vector<Blob<Dtype>*> softmax_top_vec_;
+};
 
 }  // namespace caffe
 
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 46d72a6b0a1..8c22137e514 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -1,10 +1,10 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_NET_HPP_
 #define CAFFE_NET_HPP_
 
 #include <map>
+#include <set>
 #include <string>
+#include <utility>
 #include <vector>
 
 #include "caffe/blob.hpp"
@@ -12,13 +12,8 @@
 #include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 
-using std::map;
-using std::vector;
-using std::string;
-
 namespace caffe {
 
-
 template <typename Dtype>
 class Net {
  public:
@@ -32,6 +27,16 @@ class Net {
   // Run forward with the input blobs already fed separately. You can get the
   // input blobs using input_blobs().
   const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL);
+
+  // The From and To variants of Forward and Backward operate on the
+  // (topological) ordering by which the net is specified. For general DAG
+  // networks, note that (1) computing from one layer to another might entail
+  // extra computation on unrelated branches, and (2) computation starting in
+  // the middle may be incorrect if all of the layers of a fan-in are not
+  // included.
+  Dtype ForwardFromTo(int start, int end);
+  Dtype ForwardFrom(int start);
+  Dtype ForwardTo(int end);
   // Run forward using a set of bottom blobs, and return the result.
   const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
       Dtype* loss = NULL);
@@ -43,6 +48,9 @@ class Net {
   // computes the gradient w.r.t the parameters, and the data has already
   // been provided during the forward pass.
   void Backward();
+  void BackwardFromTo(int start, int end);
+  void BackwardFrom(int start);
+  void BackwardTo(int end);
 
   Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom) {
     Dtype loss;
@@ -79,11 +87,15 @@ class Net {
   // this unless you do per-layer checks such as gradients.
   inline vector<vector<Blob<Dtype>*> >& bottom_vecs() { return bottom_vecs_; }
   inline vector<vector<Blob<Dtype>*> >& top_vecs() { return top_vecs_; }
+  inline vector<vector<bool> >& bottom_need_backward() {
+    return bottom_need_backward_;
+  }
   // returns the parameters
   inline vector<shared_ptr<Blob<Dtype> > >& params() { return params_; }
   // returns the parameter learning rate multipliers
-  inline vector<float>& params_lr() {return params_lr_; }
+  inline vector<float>& params_lr() { return params_lr_; }
   inline vector<float>& params_weight_decay() { return params_weight_decay_; }
+  const map<string, int>& param_names_index() { return param_names_index_; }
   // Input and output blob numbers
   inline int num_inputs() { return net_input_blobs_.size(); }
   inline int num_outputs() { return net_output_blobs_.size(); }
@@ -99,7 +111,34 @@ class Net {
   bool has_layer(const string& layer_name);
   const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name);
 
+  void set_debug_info(const bool value) { debug_info_ = value; }
+
+  // Helpers for Init.
+  // Remove layers that the user specified should be excluded given the current
+  // phase, level, and stage.
+  static void FilterNet(const NetParameter& param,
+      NetParameter* param_filtered);
+  static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
+      const string& layer_name);
+
  protected:
+  // Helpers for Init.
+  // Append a new input or top blob to the net.
+  void AppendTop(const NetParameter& param, const int layer_id,
+                 const int top_id, set<string>* available_blobs,
+                 map<string, int>* blob_name_to_idx);
+  // Append a new bottom blob to the net.
+  int AppendBottom(const NetParameter& param, const int layer_id,
+                   const int bottom_id, set<string>* available_blobs,
+                   map<string, int>* blob_name_to_idx);
+  void AppendParam(const NetParameter& param, const int layer_id,
+                   const int param_id);
+
+  // Helpers for displaying debug info.
+  void ForwardDebugInfo(const int layer_id);
+  void BackwardDebugInfo(const int layer_id);
+  void UpdateDebugInfo(const int param_id);
+
   // Function to get misc parameters, e.g. the learning rate multiplier and
   // weight decay.
   void GetLearningRateAndWeightDecay();
@@ -120,9 +159,14 @@ class Net {
   // pointers.
   vector<vector<Blob<Dtype>*> > bottom_vecs_;
   vector<vector<int> > bottom_id_vecs_;
+  vector<vector<bool> > bottom_need_backward_;
   // top_vecs stores the vectors containing the output for each layer
   vector<vector<Blob<Dtype>*> > top_vecs_;
   vector<vector<int> > top_id_vecs_;
+  vector<int> param_owners_;
+  vector<string> param_display_names_;
+  vector<pair<int, int> > param_layer_indices_;
+  map<string, int> param_names_index_;
   // blob indices for the input and the output of the net
   vector<int> net_input_blob_indices_;
   vector<int> net_output_blob_indices_;
@@ -135,6 +179,11 @@ class Net {
   vector<float> params_lr_;
   // the weight decay multipliers
   vector<float> params_weight_decay_;
+  // The bytes of memory used by this net
+  size_t memory_used_;
+  // Whether to compute and display debug info for the net.
+  bool debug_info_;
+
   DISABLE_COPY_AND_ASSIGN(Net);
 };
 
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
index fb2347da436..6fe425229e8 100644
--- a/include/caffe/neuron_layers.hpp
+++ b/include/caffe/neuron_layers.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_NEURON_LAYERS_HPP_
 #define CAFFE_NEURON_LAYERS_HPP_
 
@@ -7,10 +5,9 @@
 #include <utility>
 #include <vector>
 
-#include "leveldb/db.h"
-#include "pthread.h"
 #include "boost/scoped_ptr.hpp"
 #include "hdf5.h"
+#include "pthread.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -33,6 +30,12 @@ class NeuronLayer : public Layer<Dtype> {
      : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_NONE;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
 };
 
 /* BNLLLayer
@@ -48,15 +51,19 @@ class BNLLLayer : public NeuronLayer<Dtype> {
   explicit BNLLLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_BNLL;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
 /* DropoutLayer
@@ -77,17 +84,21 @@ class DropoutLayer : public NeuronLayer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_DROPOUT;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  shared_ptr<SyncedMemory> rand_vec_;
+  Blob<unsigned int> rand_vec_;
   Dtype threshold_;
   Dtype scale_;
   unsigned int uint_thres_;
@@ -107,15 +118,19 @@ class PowerLayer : public NeuronLayer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_POWER;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   Dtype power_;
   Dtype scale_;
@@ -138,6 +153,10 @@ class ReLULayer : public NeuronLayer<Dtype> {
   explicit ReLULayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_RELU;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -145,9 +164,9 @@ class ReLULayer : public NeuronLayer<Dtype> {
       vector<Blob<Dtype>*>* top);
 
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
 /* SigmoidLayer
@@ -167,15 +186,19 @@ class SigmoidLayer : public NeuronLayer<Dtype> {
   explicit SigmoidLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_SIGMOID;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 };
 
 /* TanHLayer
@@ -191,15 +214,54 @@ class TanHLayer : public NeuronLayer<Dtype> {
   explicit TanHLayer(const LayerParameter& param)
       : NeuronLayer<Dtype>(param) {}
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_TANH;
+  }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
+};
+
+/* ThresholdLayer
+  Outputs 1 if value in input is above threshold, 0 otherwise.
+  The defult threshold = 0, which means positive values would become 1 and
+  negative or 0, would become 0
+
+  y = 1 if x > threshold
+  y = 0 if x <= threshold
+
+  y' = don't differenciable
+*/
+template <typename Dtype>
+class ThresholdLayer : public NeuronLayer<Dtype> {
+ public:
+  explicit ThresholdLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_THRESHOLD;
+  }
+
+ protected:
+  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+    NOT_IMPLEMENTED;
+  }
+
+  Dtype threshold_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index aef9b22c44d..cdf583a1429 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -1,11 +1,11 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_
 #define CAFFE_OPTIMIZATION_SOLVER_HPP_
 
 #include <string>
 #include <vector>
 
+#include "caffe/net.hpp"
+
 namespace caffe {
 
 template <typename Dtype>
@@ -14,12 +14,17 @@ class Solver {
   explicit Solver(const SolverParameter& param);
   explicit Solver(const string& param_file);
   void Init(const SolverParameter& param);
+  void InitTrainNet();
+  void InitTestNets();
   // The main entry of the solver function. In default, iter will be zero. Pass
   // in a non-zero iter number to resume training for a pre-trained net.
   virtual void Solve(const char* resume_file = NULL);
   inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
   virtual ~Solver() {}
   inline shared_ptr<Net<Dtype> > net() { return net_; }
+  inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
+    return test_nets_;
+  }
 
  protected:
   // PreSolve is run before any solving iteration starts, allowing one to
@@ -33,7 +38,8 @@ class Solver {
   // written to disk together with the learned net.
   void Snapshot();
   // The test routine
-  void Test();
+  void TestAll();
+  void Test(const int test_net_id = 0);
   virtual void SnapshotSolverState(SolverState* state) = 0;
   // The Restore function implements how one should restore the solver to a
   // previously snapshotted state. You should implement the RestoreSolverState()
@@ -44,7 +50,7 @@ class Solver {
   SolverParameter param_;
   int iter_;
   shared_ptr<Net<Dtype> > net_;
-  shared_ptr<Net<Dtype> > test_net_;
+  vector<shared_ptr<Net<Dtype> > > test_nets_;
 
   DISABLE_COPY_AND_ASSIGN(Solver);
 };
diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index bed55c3806e..b2fc287b23e 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -1,11 +1,10 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_SYNCEDMEM_HPP_
 #define CAFFE_SYNCEDMEM_HPP_
 
 #include <cstdlib>
 
 #include "caffe/common.hpp"
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
new file mode 100644
index 00000000000..40b9effc5f1
--- /dev/null
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -0,0 +1,67 @@
+// The main caffe test code. Your test cpp code should include this hpp
+// to allow a main function to be compiled into the binary.
+#ifndef CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
+#define CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
+
+#include <glog/logging.h>
+#include <gtest/gtest.h>
+
+#include <cstdio>
+#include <cstdlib>
+
+#include "caffe/common.hpp"
+
+using std::cout;
+using std::endl;
+
+int main(int argc, char** argv);
+
+namespace caffe {
+
+template <typename TypeParam>
+class MultiDeviceTest : public ::testing::Test {
+ public:
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  MultiDeviceTest() {
+    Caffe::set_mode(TypeParam::device);
+  }
+  virtual ~MultiDeviceTest() {}
+};
+
+typedef ::testing::Types<float, double> TestDtypes;
+
+struct FloatCPU {
+  typedef float Dtype;
+  static const Caffe::Brew device = Caffe::CPU;
+};
+
+struct DoubleCPU {
+  typedef double Dtype;
+  static const Caffe::Brew device = Caffe::CPU;
+};
+
+#ifdef CPU_ONLY
+
+typedef ::testing::Types<FloatCPU, DoubleCPU> TestDtypesAndDevices;
+
+#else
+
+struct FloatGPU {
+  typedef float Dtype;
+  static const Caffe::Brew device = Caffe::GPU;
+};
+
+struct DoubleGPU {
+  typedef double Dtype;
+  static const Caffe::Brew device = Caffe::GPU;
+};
+
+typedef ::testing::Types<FloatCPU, DoubleCPU, FloatGPU, DoubleGPU>
+    TestDtypesAndDevices;
+
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
diff --git a/src/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp
similarity index 95%
rename from src/caffe/test/test_gradient_check_util.hpp
rename to include/caffe/test/test_gradient_check_util.hpp
index bcf03973dd3..4cf2cbc9dc8 100644
--- a/src/caffe/test/test_gradient_check_util.hpp
+++ b/include/caffe/test/test_gradient_check_util.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_TEST_GRADIENT_CHECK_UTIL_H_
 #define CAFFE_TEST_GRADIENT_CHECK_UTIL_H_
 
@@ -13,8 +11,6 @@
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
 
-using std::max;
-
 namespace caffe {
 
 // The gradient checker adds a L2 normalization loss function on top of the
@@ -86,6 +82,7 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
   }
   // First, figure out what blobs we need to check against.
   vector<Blob<Dtype>*> blobs_to_check;
+  vector<bool> propagate_down(bottom->size(), check_bottom < 0);
   for (int i = 0; i < layer->blobs().size(); ++i) {
     blobs_to_check.push_back(layer->blobs()[i].get());
   }
@@ -94,8 +91,9 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
       blobs_to_check.push_back((*bottom)[i]);
     }
   } else {
-    CHECK(check_bottom < bottom->size());
+    CHECK_LT(check_bottom, bottom->size());
     blobs_to_check.push_back((*bottom)[check_bottom]);
+    propagate_down[check_bottom] = true;
   }
   // Compute the gradient analytically using Backward
   Caffe::set_random_seed(seed_);
@@ -103,7 +101,7 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
   Dtype computed_objective = layer->Forward(*bottom, top);
   // Get additional loss from the objective
   computed_objective += GetObjAndGradient(top, top_id, top_data_id);
-  layer->Backward(*top, true, bottom);
+  layer->Backward(*top, propagate_down, bottom);
   // Store computed gradients for all checked blobs
   vector<shared_ptr<Blob<Dtype> > >
       computed_gradient_blobs(blobs_to_check.size());
@@ -158,8 +156,8 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
           || fabs(feature) > kink_ + kink_range_) {
         // We check relative accuracy, but for too small values, we threshold
         // the scale factor by 1.
-        Dtype scale = max(
-            max(fabs(computed_gradient), fabs(estimated_gradient)), 1.);
+        Dtype scale = std::max(
+            std::max(fabs(computed_gradient), fabs(estimated_gradient)), 1.);
         EXPECT_NEAR(computed_gradient, estimated_gradient, threshold_ * scale)
           << "debug: (top_id, top_data_id, blob_id, feat_id)="
           << top_id << "," << top_data_id << "," << blob_id << "," << feat_id;
@@ -228,7 +226,7 @@ Dtype GradientChecker<Dtype>::GetObjAndGradient(vector<Blob<Dtype>*>* top,
         loss += top_blob_data[j] * top_blob_data[j];
       }
       // set the diff: simply the data.
-      memcpy(top_blob_diff, top_blob_data, sizeof(Dtype) * top_blob->count());
+      caffe_copy(top_blob->count(), top_blob_data, top_blob_diff);
     }
     loss /= 2.;
   } else {
@@ -236,7 +234,7 @@ Dtype GradientChecker<Dtype>::GetObjAndGradient(vector<Blob<Dtype>*>* top,
     for (int i = 0; i < top->size(); ++i) {
       Blob<Dtype>* top_blob = (*top)[i];
       Dtype* top_blob_diff = top_blob->mutable_cpu_diff();
-      memset(top_blob_diff, 0, sizeof(Dtype) * top_blob->count());
+      caffe_set(top_blob->count(), Dtype(0), top_blob_diff);
     }
     loss = (*top)[top_id]->cpu_data()[top_data_id];
     (*top)[top_id]->mutable_cpu_diff()[top_data_id] = 1.;
diff --git a/include/caffe/util/benchmark.hpp b/include/caffe/util/benchmark.hpp
index 1d26314c62f..f7ef8eaf3ee 100644
--- a/include/caffe/util/benchmark.hpp
+++ b/include/caffe/util/benchmark.hpp
@@ -1,10 +1,9 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_UTIL_BENCHMARK_H_
 #define CAFFE_UTIL_BENCHMARK_H_
 
 #include <boost/date_time/posix_time/posix_time.hpp>
-#include <cuda_runtime.h>
+
+#include "caffe/util/device_alternate.hpp"
 
 namespace caffe {
 
@@ -27,8 +26,10 @@ class Timer {
   bool initted_;
   bool running_;
   bool has_run_at_least_once_;
+#ifndef CPU_ONLY
   cudaEvent_t start_gpu_;
   cudaEvent_t stop_gpu_;
+#endif
   boost::posix_time::ptime start_cpu_;
   boost::posix_time::ptime stop_cpu_;
   float elapsed_milliseconds_;
diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
new file mode 100644
index 00000000000..cf1aef759cc
--- /dev/null
+++ b/include/caffe/util/device_alternate.hpp
@@ -0,0 +1,42 @@
+#ifndef CAFFE_UTIL_DEVICE_ALTERNATE_H_
+#define CAFFE_UTIL_DEVICE_ALTERNATE_H_
+
+#ifdef CPU_ONLY  // CPU-only Caffe.
+
+#include <vector>
+
+// Stub out GPU calls as unavailable.
+
+#define NO_GPU LOG(FATAL) << "CPU-only Mode"
+
+#define STUB_GPU(classname) \
+template <typename Dtype> \
+Dtype classname<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom, \
+    vector<Blob<Dtype>*>* top) { NO_GPU; } \
+template <typename Dtype> \
+void classname<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, \
+    const vector<bool>& propagate_down, \
+    vector<Blob<Dtype>*>* bottom) { NO_GPU; } \
+
+#define STUB_GPU_FORWARD(classname, funcname) \
+template <typename Dtype> \
+Dtype classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& bottom, \
+    vector<Blob<Dtype>*>* top) { NO_GPU; } \
+
+#define STUB_GPU_BACKWARD(classname, funcname) \
+template <typename Dtype> \
+void classname<Dtype>::funcname##_##gpu(const vector<Blob<Dtype>*>& top, \
+    const vector<bool>& propagate_down, \
+    vector<Blob<Dtype>*>* bottom) { NO_GPU; } \
+
+#else  // Normal GPU + CPU Caffe.
+
+#include <cublas_v2.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <curand.h>
+#include <driver_types.h>  // cuda driver types
+
+#endif  // CPU_ONLY
+
+#endif  // CAFFE_UTIL_DEVICE_ALTERNATE_H_
diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp
index a649d8cc4e8..0051e2fa067 100644
--- a/include/caffe/util/im2col.hpp
+++ b/include/caffe/util/im2col.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef _CAFFE_UTIL_IM2COL_HPP_
 #define _CAFFE_UTIL_IM2COL_HPP_
 
@@ -7,23 +5,27 @@ namespace caffe {
 
 template <typename Dtype>
 void im2col_cpu(const Dtype* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, Dtype* data_col);
 
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, Dtype* data_im);
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, Dtype* data_im);
 
 template <typename Dtype>
 void im2col_gpu(const Dtype* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, Dtype* data_col);
 
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, Dtype* data_im);
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, Dtype* data_im);
 
 }  // namespace caffe
 
diff --git a/include/caffe/util/insert_splits.hpp b/include/caffe/util/insert_splits.hpp
index e25cdd7faf1..4ca933b9203 100644
--- a/include/caffe/util/insert_splits.hpp
+++ b/include/caffe/util/insert_splits.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef _CAFFE_UTIL_INSERT_SPLITS_HPP_
 #define _CAFFE_UTIL_INSERT_SPLITS_HPP_
 
@@ -7,9 +5,6 @@
 
 #include "caffe/proto/caffe.pb.h"
 
-using std::pair;
-using std::string;
-
 namespace caffe {
 
 // Copy NetParameters with SplitLayers added to replace any shared bottom
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 056b573db4c..cdf4d9e724e 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_UTIL_IO_H_
 #define CAFFE_UTIL_IO_H_
 
@@ -8,17 +6,16 @@
 #include "google/protobuf/message.h"
 #include "hdf5.h"
 #include "hdf5_hl.h"
-#include "caffe/proto/caffe.pb.h"
 
 #include "caffe/blob.hpp"
-
-using std::string;
-using ::google::protobuf::Message;
+#include "caffe/proto/caffe.pb.h"
 
 #define HDF5_NUM_DIMS 4
 
 namespace caffe {
 
+using ::google::protobuf::Message;
+
 bool ReadProtoFromTextFile(const char* filename, Message* proto);
 
 inline bool ReadProtoFromTextFile(const string& filename, Message* proto) {
@@ -61,13 +58,19 @@ inline void WriteProtoToBinaryFile(
 }
 
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, Datum* datum);
+    const int height, const int width, const bool is_color, Datum* datum);
+
+inline bool ReadImageToDatum(const string& filename, const int label,
+    const int height, const int width, Datum* datum) {
+  return ReadImageToDatum(filename, label, height, width, true, datum);
+}
 
 inline bool ReadImageToDatum(const string& filename, const int label,
     Datum* datum) {
   return ReadImageToDatum(filename, label, 0, 0, datum);
 }
 
+
 template <typename Dtype>
 void hdf5_load_nd_dataset_helper(
   hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index d9c78355b6f..744dc849b94 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -1,14 +1,12 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_UTIL_MATH_FUNCTIONS_H_
 #define CAFFE_UTIL_MATH_FUNCTIONS_H_
 
-#include <cublas_v2.h>
 #include <stdint.h>
 #include <cmath>  // for std::fabs and std::signbit
 
 #include "glog/logging.h"
 
+#include "caffe/util/device_alternate.hpp"
 #include "caffe/util/mkl_alternate.hpp"
 
 namespace caffe {
@@ -21,65 +19,31 @@ void caffe_cpu_gemm(const CBLAS_TRANSPOSE TransA,
     const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
     Dtype* C);
 
-// Decaf gpu gemm provides an interface that is almost the same as the cpu
-// gemm function - following the c convention and calling the fortran-order
-// gpu code under the hood.
-template <typename Dtype>
-void caffe_gpu_gemm(const CBLAS_TRANSPOSE TransA,
-    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
-    const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
-    Dtype* C);
-
 template <typename Dtype>
 void caffe_cpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
     const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
     Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
-    const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
-    Dtype* y);
-
 template <typename Dtype>
 void caffe_axpy(const int N, const Dtype alpha, const Dtype* X,
     Dtype* Y);
 
-template <typename Dtype>
-void caffe_gpu_axpy(const int N, const Dtype alpha, const Dtype* X,
-    Dtype* Y);
-
 template <typename Dtype>
 void caffe_cpu_axpby(const int N, const Dtype alpha, const Dtype* X,
     const Dtype beta, Dtype* Y);
 
-template <typename Dtype>
-void caffe_gpu_axpby(const int N, const Dtype alpha, const Dtype* X,
-    const Dtype beta, Dtype* Y);
-
 template <typename Dtype>
 void caffe_copy(const int N, const Dtype *X, Dtype *Y);
 
 template <typename Dtype>
 void caffe_set(const int N, const Dtype alpha, Dtype *X);
 
-template <typename Dtype>
-void caffe_gpu_set(const int N, const Dtype alpha, Dtype *X);
-
-template <typename Dtype>
-void caffe_gpu_copy(const int N, const Dtype *X, Dtype *Y);
-
 template <typename Dtype>
 void caffe_add_scalar(const int N, const Dtype alpha, Dtype *X);
 
-template <typename Dtype>
-void caffe_gpu_add_scalar(const int N, const Dtype alpha, Dtype *X);
-
 template <typename Dtype>
 void caffe_scal(const int N, const Dtype alpha, Dtype *X);
 
-template <typename Dtype>
-void caffe_gpu_scal(const int N, const Dtype alpha, Dtype *X);
-
 template <typename Dtype>
 void caffe_sqr(const int N, const Dtype* a, Dtype* y);
 
@@ -92,21 +56,12 @@ void caffe_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 template <typename Dtype>
 void caffe_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
-
 template <typename Dtype>
 void caffe_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
-
 template <typename Dtype>
 void caffe_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
-
 unsigned int caffe_rng_rand();
 
 template <typename Dtype>
@@ -115,31 +70,15 @@ Dtype caffe_nextafter(const Dtype b);
 template <typename Dtype>
 void caffe_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
 
-// caffe_gpu_rng_uniform with two arguments generates integers in the range
-// [0, UINT_MAX].
-void caffe_gpu_rng_uniform(const int n, unsigned int* r);
-
-// caffe_gpu_rng_uniform with four arguments generates floats in the range
-// (a, b] (strictly greater than a, less than or equal to b) due to the
-// specification of curandGenerateUniform.  With a = 0, b = 1, just calls
-// curandGenerateUniform; with other limits will shift and scale the outputs
-// appropriately after calling curandGenerateUniform.
-template <typename Dtype>
-void caffe_gpu_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
-
 template <typename Dtype>
 void caffe_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
                         Dtype* r);
 
-template <typename Dtype>
-void caffe_gpu_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
-                            Dtype* r);
-
 template <typename Dtype>
 void caffe_rng_bernoulli(const int n, const Dtype p, int* r);
 
 template <typename Dtype>
-void caffe_gpu_rng_bernoulli(const int n, const Dtype p, int* r);
+void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r);
 
 template <typename Dtype>
 void caffe_exp(const int n, const Dtype* a, Dtype* y);
@@ -147,23 +86,13 @@ void caffe_exp(const int n, const Dtype* a, Dtype* y);
 template <typename Dtype>
 Dtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y);
 
-template <typename Dtype>
-void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out);
-
 template <typename Dtype>
 int caffe_cpu_hamming_distance(const int n, const Dtype* x, const Dtype* y);
 
-template <typename Dtype>
-uint32_t caffe_gpu_hamming_distance(const int n, const Dtype* x,
-                                    const Dtype* y);
-
 // Returns the sum of the absolute values of the elements of vector x
 template <typename Dtype>
 Dtype caffe_cpu_asum(const int n, const Dtype* x);
 
-template <typename Dtype>
-void caffe_gpu_asum(const int n, const Dtype* x, Dtype* y);
-
 // the branchless, type-safe version from
 // http://stackoverflow.com/questions/1903954/is-there-a-standard-sign-function-signum-sgn-in-c-c
 template<typename Dtype>
@@ -192,6 +121,111 @@ inline char caffe_sign(Dtype val) {
   template <> \
   void caffe_cpu_##name<double>(const int n, const double* x, double* y)
 
+// output is 1 for the positives, 0 for zero, and -1 for the negatives
+DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
+
+// This returns a nonzero value if the input has its sign bit set.
+// The name sngbit is meant to avoid conflicts with std::signbit in the macro
+bool caffe_signbit(float arg);
+bool caffe_signbit(double arg);
+bool caffe_signbit(long double arg);
+DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = caffe_signbit(x[i]));
+
+DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
+
+template <typename Dtype>
+void caffe_cpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
+
+#ifndef CPU_ONLY  // GPU
+
+// Decaf gpu gemm provides an interface that is almost the same as the cpu
+// gemm function - following the c convention and calling the fortran-order
+// gpu code under the hood.
+template <typename Dtype>
+void caffe_gpu_gemm(const CBLAS_TRANSPOSE TransA,
+    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
+    const Dtype alpha, const Dtype* A, const Dtype* B, const Dtype beta,
+    Dtype* C);
+
+template <typename Dtype>
+void caffe_gpu_gemv(const CBLAS_TRANSPOSE TransA, const int M, const int N,
+    const Dtype alpha, const Dtype* A, const Dtype* x, const Dtype beta,
+    Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_axpy(const int N, const Dtype alpha, const Dtype* X,
+    Dtype* Y);
+
+template <typename Dtype>
+void caffe_gpu_axpby(const int N, const Dtype alpha, const Dtype* X,
+    const Dtype beta, Dtype* Y);
+
+void caffe_gpu_memcpy(const size_t N, const void *X, void *Y);
+
+template <typename Dtype>
+void caffe_gpu_set(const int N, const Dtype alpha, Dtype *X);
+
+template <typename Dtype>
+void caffe_gpu_add_scalar(const int N, const Dtype alpha, Dtype *X);
+
+template <typename Dtype>
+void caffe_gpu_scal(const int N, const Dtype alpha, Dtype *X);
+
+template <typename Dtype>
+void caffe_gpu_add(const int N, const Dtype* a, const Dtype* b, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_sub(const int N, const Dtype* a, const Dtype* b, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_mul(const int N, const Dtype* a, const Dtype* b, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_div(const int N, const Dtype* a, const Dtype* b, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
+
+// caffe_gpu_rng_uniform with two arguments generates integers in the range
+// [0, UINT_MAX].
+void caffe_gpu_rng_uniform(const int n, unsigned int* r);
+
+// caffe_gpu_rng_uniform with four arguments generates floats in the range
+// (a, b] (strictly greater than a, less than or equal to b) due to the
+// specification of curandGenerateUniform.  With a = 0, b = 1, just calls
+// curandGenerateUniform; with other limits will shift and scale the outputs
+// appropriately after calling curandGenerateUniform.
+template <typename Dtype>
+void caffe_gpu_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r);
+
+template <typename Dtype>
+void caffe_gpu_rng_gaussian(const int n, const Dtype mu, const Dtype sigma,
+                            Dtype* r);
+
+template <typename Dtype>
+void caffe_gpu_rng_bernoulli(const int n, const Dtype p, int* r);
+
+template <typename Dtype>
+void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out);
+
+template <typename Dtype>
+uint32_t caffe_gpu_hamming_distance(const int n, const Dtype* x,
+                                    const Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_asum(const int n, const Dtype* x, Dtype* y);
+
+template<typename Dtype>
+void caffe_gpu_sign(const int n, const Dtype* x, Dtype* y);
+
+template<typename Dtype>
+void caffe_gpu_sgnbit(const int n, const Dtype* x, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_fabs(const int n, const Dtype* x, Dtype* y);
+
+template <typename Dtype>
+void caffe_gpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
 
 #define DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(name, operation) \
 template<typename Dtype> \
@@ -213,32 +247,8 @@ void caffe_gpu_##name<double>(const int n, const double* x, double* y) { \
       n, x, y); \
 }
 
-// output is 1 for the positives, 0 for zero, and -1 for the negatives
-DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign<Dtype>(x[i]));
-
-template<typename Dtype>
-void caffe_gpu_sign(const int n, const Dtype* x, Dtype* y);
-
-// This returns a nonzero value if the input has its sign bit set.
-// The name sngbit is meant to avoid conflicts with std::signbit in the macro
-using std::signbit;
-DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = signbit(x[i]));
-
-template<typename Dtype>
-void caffe_gpu_sgnbit(const int n, const Dtype* x, Dtype* y);
-
-DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]));
-
-template <typename Dtype>
-void caffe_gpu_fabs(const int n, const Dtype* x, Dtype* y);
-
-template <typename Dtype>
-void caffe_cpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
-
-template <typename Dtype>
-void caffe_gpu_scale(const int n, const Dtype alpha, const Dtype *x, Dtype* y);
+#endif  // CPU_ONLY
 
 }  // namespace caffe
 
-
 #endif  // CAFFE_UTIL_MATH_FUNCTIONS_H_
diff --git a/include/caffe/util/mkl_alternate.hpp b/include/caffe/util/mkl_alternate.hpp
index c30eab8d3d4..d72bcd2814d 100644
--- a/include/caffe/util/mkl_alternate.hpp
+++ b/include/caffe/util/mkl_alternate.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_UTIL_MKL_ALTERNATE_H_
 #define CAFFE_UTIL_MKL_ALTERNATE_H_
 
diff --git a/include/caffe/util/rng.hpp b/include/caffe/util/rng.hpp
index 5909d1715ed..8f1cf0d17c2 100644
--- a/include/caffe/util/rng.hpp
+++ b/include/caffe/util/rng.hpp
@@ -1,19 +1,43 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_RNG_CPP_HPP_
 #define CAFFE_RNG_CPP_HPP_
 
-#include <boost/random/mersenne_twister.hpp>
+#include <algorithm>
+#include <iterator>
+
+#include "boost/random/mersenne_twister.hpp"
+#include "boost/random/uniform_int.hpp"
+
 #include "caffe/common.hpp"
 
 namespace caffe {
 
-  typedef boost::mt19937 rng_t;
+typedef boost::mt19937 rng_t;
+
+inline rng_t* caffe_rng() {
+  return static_cast<caffe::rng_t*>(Caffe::rng_stream().generator());
+}
+
+// Fisher–Yates algorithm
+template <class RandomAccessIterator, class RandomGenerator>
+inline void shuffle(RandomAccessIterator begin, RandomAccessIterator end,
+                    RandomGenerator* gen) {
+  typedef typename std::iterator_traits<RandomAccessIterator>::difference_type
+      difference_type;
+  typedef typename boost::uniform_int<difference_type> dist_type;
+
+  difference_type length = std::distance(begin, end);
+  if (length <= 0) return;
 
-  inline rng_t* caffe_rng() {
-    return static_cast<caffe::rng_t*>(Caffe::rng_stream().generator());
+  for (difference_type i = length - 1; i > 0; --i) {
+    dist_type dist(0, i);
+    std::iter_swap(begin + i, begin + dist(*gen));
   }
+}
 
+template <class RandomAccessIterator>
+inline void shuffle(RandomAccessIterator begin, RandomAccessIterator end) {
+  shuffle(begin, end, caffe_rng());
+}
 }  // namespace caffe
 
 #endif  // CAFFE_RNG_HPP_
diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index a1ac060970f..1ee184a34e9 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_UTIL_UPGRADE_PROTO_H_
 #define CAFFE_UTIL_UPGRADE_PROTO_H_
 
@@ -8,8 +6,6 @@
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/proto/caffe_pretty_print.pb.h"
 
-using std::string;
-
 namespace caffe {
 
 // Return true iff any layer contains parameters specified using
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
index de99bc3033f..0797065081f 100644
--- a/include/caffe/vision_layers.hpp
+++ b/include/caffe/vision_layers.hpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_VISION_LAYERS_HPP_
 #define CAFFE_VISION_LAYERS_HPP_
 
@@ -9,46 +7,15 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/common_layers.hpp"
+#include "caffe/data_layers.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/neuron_layers.hpp"
 #include "caffe/loss_layers.hpp"
-#include "caffe/data_layers.hpp"
+#include "caffe/neuron_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
 
-/*
-ConcatLayer
-  Takes at least two blobs and concatenates them along either num or
-  channel dim, outputting the result.
-*/
-template <typename Dtype>
-class ConcatLayer : public Layer<Dtype> {
- public:
-  explicit ConcatLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  Blob<Dtype> col_bob_;
-  int count_;
-  int num_;
-  int channels_;
-  int height_;
-  int width_;
-  int concat_dim_;
-};
-
 /* ConvolutionLayer
 */
 template <typename Dtype>
@@ -59,61 +26,56 @@ class ConvolutionLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_CONVOLUTION;
+  }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  int kernel_size_;
-  int stride_;
+  int kernel_h_, kernel_w_;
+  int stride_h_, stride_w_;
   int num_;
   int channels_;
-  int pad_;
+  int pad_h_, pad_w_;
   int height_;
   int width_;
   int num_output_;
   int group_;
   Blob<Dtype> col_buffer_;
-  shared_ptr<SyncedMemory> bias_multiplier_;
+  Blob<Dtype> bias_multiplier_;
   bool bias_term_;
   int M_;
   int K_;
   int N_;
 };
 
-/* EltwiseProductLayer
+/* EltwiseLayer
+  Compute elementwise operations like product or sum.
 */
 template <typename Dtype>
-class EltwiseProductLayer : public Layer<Dtype> {
+class EltwiseLayer : public Layer<Dtype> {
  public:
-  explicit EltwiseProductLayer(const LayerParameter& param)
+  explicit EltwiseLayer(const LayerParameter& param)
       : Layer<Dtype>(param) {}
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-};
-
-template <typename Dtype>
-class FlattenLayer : public Layer<Dtype> {
- public:
-  explicit FlattenLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_ELTWISE;
+  }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
 
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
@@ -121,11 +83,12 @@ class FlattenLayer : public Layer<Dtype> {
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  int count_;
+  EltwiseParameter_EltwiseOp op_;
+  vector<Dtype> coeffs_;
 };
 
 /* Im2colLayer
@@ -138,22 +101,28 @@ class Im2colLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_IM2COL;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  int kernel_size_;
-  int stride_;
+  int kernel_h_, kernel_w_;
+  int stride_h_, stride_w_;
   int channels_;
   int height_;
   int width_;
-  int pad_;
+  int pad_h_, pad_w_;
 };
 
 /* InnerProductLayer
@@ -166,21 +135,27 @@ class InnerProductLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_INNER_PRODUCT;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int M_;
   int K_;
   int N_;
   bool bias_term_;
-  shared_ptr<SyncedMemory> bias_multiplier_;
+  Blob<Dtype> bias_multiplier_;
 };
 
 // Forward declare PoolingLayer and SplitLayer for use in LRNLayer.
@@ -198,15 +173,21 @@ class LRNLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_LRN;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   virtual Dtype CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
@@ -215,11 +196,11 @@ class LRNLayer : public Layer<Dtype> {
   virtual Dtype WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
   int size_;
   int pre_pad_;
@@ -248,47 +229,13 @@ class LRNLayer : public Layer<Dtype> {
   shared_ptr<PowerLayer<Dtype> > power_layer_;
   Blob<Dtype> power_output_;
   vector<Blob<Dtype>*> power_top_vec_;
-  shared_ptr<EltwiseProductLayer<Dtype> > product_layer_;
+  shared_ptr<EltwiseLayer<Dtype> > product_layer_;
   Blob<Dtype> product_data_input_;
   vector<Blob<Dtype>*> product_bottom_vec_;
 };
 
 /* PoolingLayer
 */
-template <typename Dtype>
-class MemoryDataLayer : public Layer<Dtype> {
- public:
-  explicit MemoryDataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  // Reset should accept const pointers, but can't, because the memory
-  //  will be given to Blob, which is mutable
-  void Reset(Dtype* data, Dtype* label, int n);
-  int datum_channels() { return datum_channels_; }
-  int datum_height() { return datum_height_; }
-  int datum_width() { return datum_width_; }
-  int batch_size() { return batch_size_; }
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { return; }
-
-  Dtype* data_;
-  Dtype* labels_;
-  int datum_channels_;
-  int datum_height_;
-  int datum_width_;
-  int datum_size_;
-  int batch_size_;
-  int n_;
-  int pos_;
-};
-
 template <typename Dtype>
 class PoolingLayer : public Layer<Dtype> {
  public:
@@ -297,108 +244,34 @@ class PoolingLayer : public Layer<Dtype> {
   virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
 
+  virtual inline LayerParameter_LayerType type() const {
+    return LayerParameter_LayerType_POOLING;
+  }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlobs() const { return max_top_blobs_; }
+
  protected:
   virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top);
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom);
 
-  int kernel_size_;
-  int stride_;
-  int pad_;
+  int max_top_blobs_;
+  int kernel_h_, kernel_w_;
+  int stride_h_, stride_w_;
+  int pad_h_, pad_w_;
   int channels_;
   int height_;
   int width_;
   int pooled_height_;
   int pooled_width_;
   Blob<Dtype> rand_idx_;
-};
-
-/* SoftmaxLayer
-*/
-template <typename Dtype>
-class SoftmaxLayer : public Layer<Dtype> {
- public:
-  explicit SoftmaxLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  // sum_multiplier is just used to carry out sum using blas
-  Blob<Dtype> sum_multiplier_;
-  // scale is an intermediate blob to hold temporary results.
-  Blob<Dtype> scale_;
-};
-
-/* SoftmaxWithLossLayer
-  Implements softmax and computes the loss.
-
-  It is preferred over separate softmax + multinomiallogisticloss
-  layers due to more numerically stable gradients.
-
-  In test, this layer could be replaced by simple softmax layer.
-*/
-template <typename Dtype>
-class SoftmaxWithLossLayer : public Layer<Dtype> {
- public:
-  explicit SoftmaxWithLossLayer(const LayerParameter& param)
-      : Layer<Dtype>(param), softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
-  // prob stores the output probability of the layer.
-  Blob<Dtype> prob_;
-  // Vector holders to call the underlying softmax layer forward and backward.
-  vector<Blob<Dtype>*> softmax_bottom_vec_;
-  vector<Blob<Dtype>*> softmax_top_vec_;
-};
-
-/* SplitLayer
-*/
-template <typename Dtype>
-class SplitLayer : public Layer<Dtype> {
- public:
-  explicit SplitLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-
- protected:
-  virtual Dtype Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual Dtype Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom);
-
-  int count_;
+  Blob<int> max_idx_;
 };
 
 }  // namespace caffe
diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
index 21f51e83994..5888676c744 100644
--- a/matlab/caffe/matcaffe.cpp
+++ b/matlab/caffe/matcaffe.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 //
 // matcaffe.cpp provides a wrapper of the caffe::Net class as well as some
 // caffe::Caffe functions so that one could easily call it from matlab.
@@ -8,6 +7,7 @@
 #include <vector>
 
 #include "mex.h"
+
 #include "caffe/caffe.hpp"
 
 #define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
@@ -54,12 +54,12 @@ static mxArray* do_forward(const mxArray* const bottom) {
         reinterpret_cast<const float* const>(mxGetPr(elem));
     switch (Caffe::mode()) {
     case Caffe::CPU:
-      memcpy(input_blobs[i]->mutable_cpu_data(), data_ptr,
-          sizeof(float) * input_blobs[i]->count());
+      caffe_copy(input_blobs[i]->count(), data_ptr,
+          input_blobs[i]->mutable_cpu_data());
       break;
     case Caffe::GPU:
-      cudaMemcpy(input_blobs[i]->mutable_gpu_data(), data_ptr,
-          sizeof(float) * input_blobs[i]->count(), cudaMemcpyHostToDevice);
+      caffe_copy(input_blobs[i]->count(), data_ptr,
+          input_blobs[i]->mutable_gpu_data());
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -77,12 +77,12 @@ static mxArray* do_forward(const mxArray* const bottom) {
     float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
     switch (Caffe::mode()) {
     case Caffe::CPU:
-      memcpy(data_ptr, output_blobs[i]->cpu_data(),
-          sizeof(float) * output_blobs[i]->count());
+      caffe_copy(output_blobs[i]->count(), output_blobs[i]->cpu_data(),
+          data_ptr);
       break;
     case Caffe::GPU:
-      cudaMemcpy(data_ptr, output_blobs[i]->gpu_data(),
-          sizeof(float) * output_blobs[i]->count(), cudaMemcpyDeviceToHost);
+      caffe_copy(output_blobs[i]->count(), output_blobs[i]->gpu_data(),
+          data_ptr);
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -104,12 +104,12 @@ static mxArray* do_backward(const mxArray* const top_diff) {
         reinterpret_cast<const float* const>(mxGetPr(elem));
     switch (Caffe::mode()) {
     case Caffe::CPU:
-      memcpy(output_blobs[i]->mutable_cpu_diff(), data_ptr,
-        sizeof(float) * output_blobs[i]->count());
+      caffe_copy(output_blobs[i]->count(), data_ptr,
+          output_blobs[i]->mutable_cpu_diff());
       break;
     case Caffe::GPU:
-      cudaMemcpy(output_blobs[i]->mutable_gpu_diff(), data_ptr,
-        sizeof(float) * output_blobs[i]->count(), cudaMemcpyHostToDevice);
+      caffe_copy(output_blobs[i]->count(), data_ptr,
+          output_blobs[i]->mutable_gpu_diff());
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -129,12 +129,10 @@ static mxArray* do_backward(const mxArray* const top_diff) {
     float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
     switch (Caffe::mode()) {
     case Caffe::CPU:
-      memcpy(data_ptr, input_blobs[i]->cpu_diff(),
-          sizeof(float) * input_blobs[i]->count());
+      caffe_copy(input_blobs[i]->count(), input_blobs[i]->cpu_diff(), data_ptr);
       break;
     case Caffe::GPU:
-      cudaMemcpy(data_ptr, input_blobs[i]->gpu_diff(),
-          sizeof(float) * input_blobs[i]->count(), cudaMemcpyDeviceToHost);
+      caffe_copy(input_blobs[i]->count(), input_blobs[i]->gpu_diff(), data_ptr);
       break;
     default:
       LOG(FATAL) << "Unknown Caffe mode.";
@@ -185,7 +183,7 @@ static mxArray* do_get_weights() {
       mxArray* mx_layer_cells = NULL;
       if (layer_names[i] != prev_layer_name) {
         prev_layer_name = layer_names[i];
-        const mwSize dims[2] = {layer_blobs.size(), 1};
+        const mwSize dims[2] = {static_cast<mwSize>(layer_blobs.size()), 1};
         mx_layer_cells = mxCreateCellArray(2, dims);
         mxSetField(mx_layers, mx_layer_index, "weights", mx_layer_cells);
         mxSetField(mx_layers, mx_layer_index, "layer_names",
@@ -206,12 +204,12 @@ static mxArray* do_get_weights() {
 
         switch (Caffe::mode()) {
         case Caffe::CPU:
-          memcpy(weights_ptr, layer_blobs[j]->cpu_data(),
-              sizeof(float) * layer_blobs[j]->count());
+          caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->cpu_data(),
+              weights_ptr);
           break;
         case Caffe::GPU:
-          CUDA_CHECK(cudaMemcpy(weights_ptr, layer_blobs[j]->gpu_data(),
-              sizeof(float) * layer_blobs[j]->count(), cudaMemcpyDeviceToHost));
+          caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->gpu_data(),
+              weights_ptr);
           break;
         default:
           LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
@@ -313,6 +311,31 @@ static void is_initialized(MEX_ARGS) {
   }
 }
 
+static void read_mean(MEX_ARGS) {
+    if (nrhs != 1) {
+        mexErrMsgTxt("Usage: caffe('read_mean', 'path_to_binary_mean_file'");
+        return;
+    }
+    const string& mean_file = mxArrayToString(prhs[0]);
+    Blob<float> data_mean;
+    LOG(INFO) << "Loading mean file from" << mean_file;
+    BlobProto blob_proto;
+    bool result = ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto);
+    if (!result) {
+        mexErrMsgTxt("Couldn't read the file");
+        return;
+    }
+    data_mean.FromProto(blob_proto);
+    mwSize dims[4] = {data_mean.width(), data_mean.height(),
+                      data_mean.channels(), data_mean.num() };
+    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
+    float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
+    caffe_copy(data_mean.count(), data_mean.cpu_data(), data_ptr);
+    mexWarnMsgTxt("Remember that Caffe saves in [width, height, channels]"
+                  " format and channels are also BGR!");
+    plhs[0] = mx_blob;
+}
+
 /** -----------------------------------------------------------------
  ** Available commands.
  **/
@@ -335,6 +358,7 @@ static handler_registry handlers[] = {
   { "get_weights",        get_weights     },
   { "get_init_key",       get_init_key    },
   { "reset",              reset           },
+  { "read_mean",          read_mean       },
   // The end.
   { "END",                NULL            },
 };
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index e9fe5cd3b05..3103d0267be 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 // pycaffe provides a wrapper of the caffe::Net class as well as some
 // caffe::Caffe functions so that one could easily call it from Python.
 // Note that for Python, we will simply use float as the data type.
@@ -25,6 +24,7 @@
 
 
 using namespace caffe;  // NOLINT(build/namespaces)
+using boost::python::dict;
 using boost::python::extract;
 using boost::python::len;
 using boost::python::list;
@@ -181,12 +181,12 @@ struct CaffeNet {
     }
   }
 
-  void Forward() {
-    net_->ForwardPrefilled();
+  void Forward(int start, int end) {
+    net_->ForwardFromTo(start, end);
   }
 
-  void Backward() {
-    net_->Backward();
+  void Backward(int start, int end) {
+    net_->BackwardFromTo(start, end);
   }
 
   void set_input_arrays(object data_obj, object labels_obj) {
@@ -274,6 +274,11 @@ struct CaffeNet {
 
   // The pointer to the internal caffe::Net instant.
   shared_ptr<Net<float> > net_;
+  // Input preprocessing configuration attributes.
+  dict mean_;
+  dict input_scale_;
+  dict raw_scale_;
+  dict channel_swap_;
   // if taking input from an ndarray, we need to hold references
   object input_data_;
   object input_labels_;
@@ -311,19 +316,23 @@ BOOST_PYTHON_MODULE(_caffe) {
   boost::python::class_<CaffeNet, shared_ptr<CaffeNet> >(
       "Net", boost::python::init<string, string>())
       .def(boost::python::init<string>())
-      .def("_forward",          &CaffeNet::Forward)
-      .def("_backward",         &CaffeNet::Backward)
-      .def("set_mode_cpu",      &CaffeNet::set_mode_cpu)
-      .def("set_mode_gpu",      &CaffeNet::set_mode_gpu)
-      .def("set_phase_train",   &CaffeNet::set_phase_train)
-      .def("set_phase_test",    &CaffeNet::set_phase_test)
-      .def("set_device",        &CaffeNet::set_device)
-      .add_property("_blobs",   &CaffeNet::blobs)
-      .add_property("layers",   &CaffeNet::layers)
-      .add_property("inputs",   &CaffeNet::inputs)
-      .add_property("outputs",  &CaffeNet::outputs)
-      .def("_set_input_arrays", &CaffeNet::set_input_arrays)
-      .def("save",              &CaffeNet::save);
+      .def("_forward",              &CaffeNet::Forward)
+      .def("_backward",             &CaffeNet::Backward)
+      .def("set_mode_cpu",          &CaffeNet::set_mode_cpu)
+      .def("set_mode_gpu",          &CaffeNet::set_mode_gpu)
+      .def("set_phase_train",       &CaffeNet::set_phase_train)
+      .def("set_phase_test",        &CaffeNet::set_phase_test)
+      .def("set_device",            &CaffeNet::set_device)
+      .add_property("_blobs",       &CaffeNet::blobs)
+      .add_property("layers",       &CaffeNet::layers)
+      .add_property("inputs",       &CaffeNet::inputs)
+      .add_property("outputs",      &CaffeNet::outputs)
+      .add_property("mean",         &CaffeNet::mean_)
+      .add_property("input_scale",  &CaffeNet::input_scale_)
+      .add_property("raw_scale",    &CaffeNet::raw_scale_)
+      .add_property("channel_swap", &CaffeNet::channel_swap_)
+      .def("_set_input_arrays",     &CaffeNet::set_input_arrays)
+      .def("save",                  &CaffeNet::save);
 
   boost::python::class_<CaffeBlob, CaffeBlobWrap>(
       "Blob", boost::python::no_init)
diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index f347be42a93..fe471ca13b1 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -14,13 +14,14 @@ class Classifier(caffe.Net):
     by scaling, center cropping, or oversampling.
     """
     def __init__(self, model_file, pretrained_file, image_dims=None,
-                 gpu=False, mean_file=None, input_scale=None, channel_swap=None):
+                 gpu=False, mean=None, input_scale=None, raw_scale=None,
+                 channel_swap=None):
         """
         Take
         image_dims: dimensions to scale input for cropping/sampling.
-                    Default is to scale to net input size for whole-image crop.
-        gpu, mean_file, input_scale, channel_swap: convenience params for
-            setting mode, mean, input scale, and channel order.
+            Default is to scale to net input size for whole-image crop.
+        gpu, mean, input_scale, raw_scale, channel_swap: params for
+            preprocessing options.
         """
         caffe.Net.__init__(self, model_file, pretrained_file)
         self.set_phase_test()
@@ -30,11 +31,13 @@ def __init__(self, model_file, pretrained_file, image_dims=None,
         else:
             self.set_mode_cpu()
 
-        if mean_file:
-            self.set_mean(self.inputs[0], mean_file)
-        if input_scale:
+        if mean is not None:
+            self.set_mean(self.inputs[0], mean)
+        if input_scale is not None:
             self.set_input_scale(self.inputs[0], input_scale)
-        if channel_swap:
+        if raw_scale is not None:
+            self.set_raw_scale(self.inputs[0], raw_scale)
+        if channel_swap is not None:
             self.set_channel_swap(self.inputs[0], channel_swap)
 
         self.crop_dims = np.array(self.blobs[self.inputs[0]].data.shape[2:])
@@ -57,12 +60,15 @@ def predict(self, inputs, oversample=True):
                      for N images and C classes.
         """
         # Scale to standardize input dimensions.
-        inputs = np.asarray([caffe.io.resize_image(im, self.image_dims)
-                             for im in inputs])
+        input_ = np.zeros((len(inputs),
+            self.image_dims[0], self.image_dims[1], inputs[0].shape[2]),
+            dtype=np.float32)
+        for ix, in_ in enumerate(inputs):
+            input_[ix] = caffe.io.resize_image(in_, self.image_dims)
 
         if oversample:
             # Generate center, corner, and mirrored crops.
-            inputs = caffe.io.oversample(inputs, self.crop_dims)
+            input_ = caffe.io.oversample(input_, self.crop_dims)
         else:
             # Take center crop.
             center = np.array(self.image_dims) / 2.0
@@ -70,11 +76,13 @@ def predict(self, inputs, oversample=True):
                 -self.crop_dims / 2.0,
                 self.crop_dims / 2.0
             ])
-            inputs = inputs[:, crop[0]:crop[2], crop[1]:crop[3], :]
+            input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
 
         # Classify
-        caffe_in = np.asarray([self.preprocess(self.inputs[0], in_)
-                    for in_ in inputs])
+        caffe_in = np.zeros(np.array(input_.shape)[[0,3,1,2]],
+                            dtype=np.float32)
+        for ix, in_ in enumerate(input_):
+            caffe_in[ix] = self.preprocess(self.inputs[0], in_)
         out = self.forward_all(**{self.inputs[0]: caffe_in})
         predictions = out[self.outputs[0]].squeeze(axis=(2,3))
 
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index 5a30dab92f5..f219b6105e1 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -12,10 +12,6 @@
 The selective_search_ijcv_with_python code required for the selective search
 proposal mode is available at
     https://github.com/sergeyk/selective_search_ijcv_with_python
-
-TODO
-- R-CNN crop mode / crop with context.
-- Bundle with R-CNN model for example.
 """
 import numpy as np
 import os
@@ -28,12 +24,16 @@ class Detector(caffe.Net):
     Detector extends Net for windowed detection by a list of crops or
     selective search proposals.
     """
-    def __init__(self, model_file, pretrained_file, gpu=False, mean_file=None,
-                 input_scale=None, channel_swap=None):
+    def __init__(self, model_file, pretrained_file, gpu=False, mean=None,
+                 input_scale=None, raw_scale=None, channel_swap=None,
+                 context_pad=None):
         """
         Take
-        gpu, mean_file, input_scale, channel_swap: convenience params for
-            setting mode, mean, input scale, and channel order.
+        gpu, mean, input_scale, raw_scale, channel_swap: params for
+            preprocessing options.
+        context_pad: amount of surrounding context to take s.t. a `context_pad`
+            sized border of pixels in the network input image is context, as in
+            R-CNN feature extraction.
         """
         caffe.Net.__init__(self, model_file, pretrained_file)
         self.set_phase_test()
@@ -43,13 +43,17 @@ def __init__(self, model_file, pretrained_file, gpu=False, mean_file=None,
         else:
             self.set_mode_cpu()
 
-        if mean_file:
-            self.set_mean(self.inputs[0], mean_file)
-        if input_scale:
+        if mean is not None:
+            self.set_mean(self.inputs[0], mean)
+        if input_scale is not None:
             self.set_input_scale(self.inputs[0], input_scale)
-        if channel_swap:
+        if raw_scale is not None:
+            self.set_raw_scale(self.inputs[0], raw_scale)
+        if channel_swap is not None:
             self.set_channel_swap(self.inputs[0], channel_swap)
 
+        self.configure_crop(context_pad)
+
 
     def detect_windows(self, images_windows):
         """
@@ -58,6 +62,7 @@ def detect_windows(self, images_windows):
 
         Take
         images_windows: (image filename, window list) iterable.
+        context_crop: size of context border to crop in pixels.
 
         Give
         detections: list of {filename: image filename, window: crop coordinates,
@@ -68,12 +73,14 @@ def detect_windows(self, images_windows):
         for image_fname, windows in images_windows:
             image = caffe.io.load_image(image_fname).astype(np.float32)
             for window in windows:
-                window_inputs.append(image[window[0]:window[2],
-                                           window[1]:window[3]])
+                window_inputs.append(self.crop(image, window))
 
         # Run through the net (warping windows to input dimensions).
-        caffe_in = np.asarray([self.preprocess(self.inputs[0], window_in)
-                    for window_in in window_inputs])
+        caffe_in = np.zeros((len(window_inputs), window_inputs[0].shape[2])
+                            + self.blobs[self.inputs[0]].data.shape[2:],
+                            dtype=np.float32)
+        for ix, window_in in enumerate(window_inputs):
+            caffe_in[ix] = self.preprocess(self.inputs[0], window_in)
         out = self.forward_all(**{self.inputs[0]: caffe_in})
         predictions = out[self.outputs[0]].squeeze(axis=(2,3))
 
@@ -106,6 +113,92 @@ def detect_selective_search(self, image_fnames):
         import selective_search_ijcv_with_python as selective_search
         # Make absolute paths so MATLAB can find the files.
         image_fnames = [os.path.abspath(f) for f in image_fnames]
-        windows_list = selective_search.get_windows(image_fnames)
+        windows_list = selective_search.get_windows(
+            image_fnames,
+            cmd='selective_search_rcnn'
+        )
         # Run windowed detection on the selective search list.
         return self.detect_windows(zip(image_fnames, windows_list))
+
+
+    def crop(self, im, window):
+        """
+        Crop a window from the image for detection. Include surrounding context
+        according to the `context_pad` configuration.
+
+        Take
+        im: H x W x K image ndarray to crop.
+        window: bounding box coordinates as ymin, xmin, ymax, xmax.
+
+        Give
+        crop: cropped window.
+        """
+        # Crop window from the image.
+        crop = im[window[0]:window[2], window[1]:window[3]]
+
+        if self.context_pad:
+            box = window.copy()
+            crop_size = self.blobs[self.inputs[0]].width  # assumes square
+            scale = crop_size / (1. * crop_size - self.context_pad * 2)
+            # Crop a box + surrounding context.
+            half_h = (box[2] - box[0] + 1) / 2.
+            half_w = (box[3] - box[1] + 1) / 2.
+            center = (box[0] + half_h, box[1] + half_w)
+            scaled_dims = scale * np.array((-half_h, -half_w, half_h, half_w))
+            box = np.round(np.tile(center, 2) + scaled_dims)
+            full_h = box[2] - box[0] + 1
+            full_w = box[3] - box[1] + 1
+            scale_h = crop_size / full_h
+            scale_w = crop_size / full_w
+            pad_y = round(max(0, -box[0]) * scale_h)  # amount out-of-bounds
+            pad_x = round(max(0, -box[1]) * scale_w)
+
+            # Clip box to image dimensions.
+            im_h, im_w = im.shape[:2]
+            box = np.clip(box, 0., [im_h, im_w, im_h, im_w])
+            clip_h = box[2] - box[0] + 1
+            clip_w = box[3] - box[1] + 1
+            assert(clip_h > 0 and clip_w > 0)
+            crop_h = round(clip_h * scale_h)
+            crop_w = round(clip_w * scale_w)
+            if pad_y + crop_h > crop_size:
+                crop_h = crop_size - pad_y
+            if pad_x + crop_w > crop_size:
+                crop_w = crop_size - pad_x
+
+            # collect with context padding and place in input
+            # with mean padding
+            context_crop = im[box[0]:box[2], box[1]:box[3]]
+            context_crop = caffe.io.resize_image(context_crop, (crop_h, crop_w))
+            crop = self.crop_mean.copy()
+            crop[pad_y:(pad_y + crop_h), pad_x:(pad_x + crop_w)] = context_crop
+
+        return crop
+
+
+    def configure_crop(self, context_pad):
+        """
+        Configure amount of context for cropping.
+        If context is included, make the special input mean for context padding.
+
+        Take
+        context_pad: amount of context for cropping.
+        """
+        self.context_pad = context_pad
+        if self.context_pad:
+            raw_scale = self.raw_scale.get(self.inputs[0])
+            channel_order = self.channel_swap.get(self.inputs[0])
+            # Padding context crops needs the mean in unprocessed input space.
+            mean = self.mean.get(self.inputs[0])
+            if mean is not None:
+                crop_mean = mean.copy().transpose((1,2,0))
+                if channel_order is not None:
+                    channel_order_inverse = [channel_order.index(i)
+                                            for i in range(crop_mean.shape[2])]
+                    crop_mean = crop_mean[:,:, channel_order_inverse]
+                if raw_scale is not None:
+                    crop_mean /= raw_scale
+                self.crop_mean = crop_mean
+            else:
+                self.crop_mean = np.zeros(self.blobs[self.inputs[0]].data.shape,
+                                          dtype=np.float32)
diff --git a/python/caffe/io.py b/python/caffe/io.py
index 0bd2f812bec..aabcfddbbeb 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -1,23 +1,30 @@
 import numpy as np
 import skimage.io
-import skimage.transform
+from scipy.ndimage import zoom
+from skimage.transform import resize
 
 from caffe.proto import caffe_pb2
 
 
-def load_image(filename):
+def load_image(filename, color=True):
     """
     Load an image converting from grayscale or alpha as needed.
 
     Take
     filename: string
+    color: flag for color format. True (default) loads as RGB while False
+        loads as intensity (if image is already grayscale).
 
     Give
-    image: an image of size (H x W x 3) with RGB channels of type uint8.
+    image: an image with type np.float32 in range [0, 1]
+        of size (H x W x 3) in RGB or
+        of size (H x W x 1) in grayscale.
     """
     img = skimage.img_as_float(skimage.io.imread(filename)).astype(np.float32)
     if img.ndim == 2:
-        img = np.tile(img[:, :, np.newaxis], (1, 1, 3))
+        img = img[:, :, np.newaxis]
+        if color:
+            img = np.tile(img, (1, 1, 3))
     elif img.shape[2] == 4:
         img = img[:, :, :3]
     return img
@@ -35,7 +42,17 @@ def resize_image(im, new_dims, interp_order=1):
     Give
     im: resized ndarray with shape (new_dims[0], new_dims[1], K)
     """
-    return skimage.transform.resize(im, new_dims, order=interp_order)
+    if im.shape[-1] == 1 or im.shape[-1] == 3:
+        # skimage is fast but only understands {1,3} channel images in [0, 1].
+        im_min, im_max = im.min(), im.max()
+        im_std = (im - im_min) / (im_max - im_min)
+        resized_std = resize(im_std, new_dims, order=interp_order)
+        resized_im = resized_std * (im_max - im_min) + im_min
+    else:
+        # ndimage interpolates anything but more slowly.
+        scale = tuple(np.array(new_dims) / np.array(im.shape[:2]))
+        resized_im = zoom(im, scale + (1,), order=interp_order)
+    return resized_im.astype(np.float32)
 
 
 def oversample(images, crop_dims):
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 5c1512cd8b9..9a230a72b3c 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -34,8 +34,7 @@ def _Net_params(self):
     return OrderedDict([(lr.name, lr.blobs) for lr in self.layers
                         if len(lr.blobs) > 0])
 
-
-def _Net_forward(self, blobs=None, **kwargs):
+def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
     """
     Forward pass: prepare inputs and run the net forward.
 
@@ -44,6 +43,8 @@ def _Net_forward(self, blobs=None, **kwargs):
     kwargs: Keys are input blob names and values are blob ndarrays.
             For formatting inputs for Caffe, see Net.preprocess().
             If None, input is taken from data layers.
+    start: optional name of layer at which to begin the forward pass
+    end: optional name of layer at which to finish the forward pass (inclusive)
 
     Give
     outs: {blob name: blob ndarray} dict.
@@ -51,26 +52,37 @@ def _Net_forward(self, blobs=None, **kwargs):
     if blobs is None:
         blobs = []
 
+    if start is not None:
+        start_ind = [lr.name for lr in self.layers].index(start)
+    else:
+        start_ind = 0
+
+    if end is not None:
+        end_ind = [lr.name for lr in self.layers].index(end)
+        outputs = set([end] + blobs)
+    else:
+        end_ind = len(self.layers) - 1
+        outputs = set(self.outputs + blobs)
+
     if kwargs:
         if set(kwargs.keys()) != set(self.inputs):
             raise Exception('Input blob arguments do not match net inputs.')
         # Set input according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for in_, blob in kwargs.iteritems():
-            if blob.shape[0] != self.blobs[in_].num:
-                raise Exception('Input is not batch sized')
             if blob.ndim != 4:
                 raise Exception('{} blob is not 4-d'.format(in_))
+            if blob.shape[0] != self.blobs[in_].num:
+                raise Exception('Input is not batch sized')
             self.blobs[in_].data[...] = blob
 
-    self._forward()
+    self._forward(start_ind, end_ind)
 
     # Unpack blobs to extract
-    outs = {out: self.blobs[out].data for out in set(self.outputs + blobs)}
-    return outs
+    return {out: self.blobs[out].data for out in outputs}
 
 
-def _Net_backward(self, diffs=None, **kwargs):
+def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
     """
     Backward pass: prepare diffs and run the net backward.
 
@@ -78,6 +90,8 @@ def _Net_backward(self, diffs=None, **kwargs):
     diffs: list of diffs to return in addition to bottom diffs.
     kwargs: Keys are output blob names and values are diff ndarrays.
             If None, top diffs are taken from forward loss.
+    start: optional name of layer at which to begin the backward pass
+    end: optional name of layer at which to finish the backward pass (inclusive)
 
     Give
     outs: {blob name: diff ndarray} dict.
@@ -85,23 +99,34 @@ def _Net_backward(self, diffs=None, **kwargs):
     if diffs is None:
         diffs = []
 
+    if start is not None:
+        start_ind = [lr.name for lr in self.layers].index(start)
+    else:
+        start_ind = len(self.layers) - 1
+
+    if end is not None:
+        end_ind = [lr.name for lr in self.layers].index(end)
+        outputs = set([end] + diffs)
+    else:
+        end_ind = 0
+        outputs = set(self.inputs + diffs)
+
     if kwargs:
         if set(kwargs.keys()) != set(self.outputs):
             raise Exception('Top diff arguments do not match net outputs.')
         # Set top diffs according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
         for top, diff in kwargs.iteritems():
-            if diff.shape[0] != self.blobs[top].num:
-                raise Exception('Diff is not batch sized')
             if diff.ndim != 4:
                 raise Exception('{} diff is not 4-d'.format(top))
+            if diff.shape[0] != self.blobs[top].num:
+                raise Exception('Diff is not batch sized')
             self.blobs[top].diff[...] = diff
 
-    self._backward()
+    self._backward(start_ind, end_ind)
 
     # Unpack diffs to extract
-    outs = {out: self.blobs[out].diff for out in set(self.inputs + diffs)}
-    return outs
+    return {out: self.blobs[out].diff for out in outputs}
 
 
 def _Net_forward_all(self, blobs=None, **kwargs):
@@ -176,29 +201,24 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
     return all_outs, all_diffs
 
 
-def _Net_set_mean(self, input_, mean_f, mode='elementwise'):
+def _Net_set_mean(self, input_, mean, mode='elementwise'):
     """
     Set the mean to subtract for data centering.
 
     Take
     input_: which input to assign this mean.
-    mean_f: path to mean .npy with ndarray (input dimensional or broadcastable)
+    mean: mean K x H x W ndarray (input dimensional or broadcastable)
     mode: elementwise = use the whole mean (and check dimensions)
           channel = channel constant (e.g. mean pixel instead of mean image)
     """
-    if not hasattr(self, 'mean'):
-        self.mean = {}
     if input_ not in self.inputs:
         raise Exception('Input not in {}'.format(self.inputs))
     in_shape = self.blobs[input_].data.shape
-    mean = np.load(mean_f)
     if mode == 'elementwise':
-        if mean.shape != in_shape[1:]:
-            # Resize mean (which requires H x W x K input in range [0,1]).
-            m_min, m_max = mean.min(), mean.max()
-            normal_mean = (mean - m_min) / (m_max - m_min)
-            mean = caffe.io.resize_image(normal_mean.transpose((1,2,0)),
-                    in_shape[2:]).transpose((2,0,1)) * (m_max - m_min) + m_min
+        if mean.shape[1:] != in_shape[2:]:
+            # Resize mean (which requires H x W x K input).
+            mean = caffe.io.resize_image(mean.transpose((1,2,0)),
+                                         in_shape[2:]).transpose((2,0,1))
         self.mean[input_] = mean
     elif mode == 'channel':
         self.mean[input_] = mean.mean(1).mean(1).reshape((in_shape[1], 1, 1))
@@ -206,22 +226,37 @@ def _Net_set_mean(self, input_, mean_f, mode='elementwise'):
         raise Exception('Mode not in {}'.format(['elementwise', 'channel']))
 
 
-
 def _Net_set_input_scale(self, input_, scale):
     """
-    Set the input feature scaling factor s.t. input blob = input * scale.
+    Set the scale of preprocessed inputs s.t. the blob = blob * scale.
+    N.B. input_scale is done AFTER mean subtraction and other preprocessing
+    while raw_scale is done BEFORE.
 
     Take
     input_: which input to assign this scale factor
     scale: scale coefficient
     """
-    if not hasattr(self, 'input_scale'):
-        self.input_scale = {}
     if input_ not in self.inputs:
         raise Exception('Input not in {}'.format(self.inputs))
     self.input_scale[input_] = scale
 
 
+def _Net_set_raw_scale(self, input_, scale):
+    """
+    Set the scale of raw features s.t. the input blob = input * scale.
+    While Python represents images in [0, 1], certain Caffe models
+    like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
+    of these models must be 255.
+
+    Take
+    input_: which input to assign this scale factor
+    scale: scale coefficient
+    """
+    if input_ not in self.inputs:
+        raise Exception('Input not in {}'.format(self.inputs))
+    self.raw_scale[input_] = scale
+
+
 def _Net_set_channel_swap(self, input_, order):
     """
     Set the input channel order for e.g. RGB to BGR conversion
@@ -232,8 +267,6 @@ def _Net_set_channel_swap(self, input_, order):
     order: the order to take the channels.
            (2,1,0) maps RGB to BGR for example.
     """
-    if not hasattr(self, 'channel_swap'):
-        self.channel_swap = {}
     if input_ not in self.inputs:
         raise Exception('Input not in {}'.format(self.inputs))
     self.channel_swap[input_] = order
@@ -244,10 +277,11 @@ def _Net_preprocess(self, input_name, input_):
     Format input for Caffe:
     - convert to single
     - resize to input dimensions (preserving number of channels)
-    - scale feature
     - reorder channels (for instance color to BGR)
-    - subtract mean
+    - scale raw input (e.g. from [0, 1] to [0, 255] for ImageNet models)
     - transpose dimensions to K x H x W
+    - subtract mean
+    - scale feature
 
     Take
     input_name: name of input blob to preprocess for
@@ -256,20 +290,23 @@ def _Net_preprocess(self, input_name, input_):
     Give
     caffe_inputs: (K x H x W) ndarray
     """
-    caffe_in = input_.astype(np.float32)
+    caffe_in = input_.astype(np.float32, copy=False)
+    mean = self.mean.get(input_name)
     input_scale = self.input_scale.get(input_name)
+    raw_scale = self.raw_scale.get(input_name)
     channel_order = self.channel_swap.get(input_name)
-    mean = self.mean.get(input_name)
     in_size = self.blobs[input_name].data.shape[2:]
     if caffe_in.shape[:2] != in_size:
         caffe_in = caffe.io.resize_image(caffe_in, in_size)
-    if input_scale:
-        caffe_in *= input_scale
-    if channel_order:
+    if channel_order is not None:
         caffe_in = caffe_in[:, :, channel_order]
     caffe_in = caffe_in.transpose((2, 0, 1))
+    if raw_scale is not None:
+        caffe_in *= raw_scale
     if mean is not None:
         caffe_in -= mean
+    if input_scale is not None:
+        caffe_in *= input_scale
     return caffe_in
 
 
@@ -278,18 +315,21 @@ def _Net_deprocess(self, input_name, input_):
     Invert Caffe formatting; see Net.preprocess().
     """
     decaf_in = input_.copy().squeeze()
+    mean = self.mean.get(input_name)
     input_scale = self.input_scale.get(input_name)
+    raw_scale = self.raw_scale.get(input_name)
     channel_order = self.channel_swap.get(input_name)
-    mean = self.mean.get(input_name)
+    if input_scale is not None:
+        decaf_in /= input_scale
     if mean is not None:
         decaf_in += mean
+    if raw_scale is not None:
+        decaf_in /= raw_scale
     decaf_in = decaf_in.transpose((1,2,0))
-    if channel_order:
+    if channel_order is not None:
         channel_order_inverse = [channel_order.index(i)
                                  for i in range(decaf_in.shape[2])]
         decaf_in = decaf_in[:, :, channel_order_inverse]
-    if input_scale:
-        decaf_in /= input_scale
     return decaf_in
 
 
@@ -345,6 +385,7 @@ def _Net_batch(self, blobs):
 Net.forward_backward_all = _Net_forward_backward_all
 Net.set_mean = _Net_set_mean
 Net.set_input_scale = _Net_set_input_scale
+Net.set_raw_scale = _Net_set_raw_scale
 Net.set_channel_swap = _Net_set_channel_swap
 Net.preprocess = _Net_preprocess
 Net.deprocess = _Net_deprocess
diff --git a/python/classify.py b/python/classify.py
index fdaeeb01be4..d3d239cbb7d 100755
--- a/python/classify.py
+++ b/python/classify.py
@@ -66,15 +66,18 @@ def main(argv):
     parser.add_argument(
         "--input_scale",
         type=float,
-        default=255,
-        help="Multiply input features by this scale before input to net"
+        help="Multiply input features by this scale to finish input preprocessing."
+    )
+    parser.add_argument(
+        "--raw_scale",
+        type=float,
+        help="Multiply raw input by this scale before preprocessing."
     )
     parser.add_argument(
         "--channel_swap",
         default='2,1,0',
         help="Order to permute input channels. The default converts " +
              "RGB -> BGR since BGR is the Caffe default by way of OpenCV."
-
     )
     parser.add_argument(
         "--ext",
@@ -85,12 +88,18 @@ def main(argv):
     args = parser.parse_args()
 
     image_dims = [int(s) for s in args.images_dim.split(',')]
-    channel_swap = [int(s) for s in args.channel_swap.split(',')]
+
+    mean, channel_swap = None, None
+    if args.mean_file:
+        mean = np.load(args.mean_file)
+    if args.channel_swap:
+        channel_swap = [int(s) for s in args.channel_swap.split(',')]
 
     # Make classifier.
     classifier = caffe.Classifier(args.model_def, args.pretrained_model,
-            image_dims=image_dims, gpu=args.gpu, mean_file=args.mean_file,
-            input_scale=args.input_scale, channel_swap=channel_swap)
+            image_dims=image_dims, gpu=args.gpu, mean=mean,
+            input_scale=args.input_scale, raw_scale=args.raw_scale,
+            channel_swap=channel_swap)
 
     if args.gpu:
         print 'GPU mode'
diff --git a/python/detect.py b/python/detect.py
index 15418bba5c2..77f4676aec1 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -57,9 +57,9 @@ def main(argv):
     )
     parser.add_argument(
         "--crop_mode",
-        default="center_only",
+        default="selective_search",
         choices=CROP_MODES,
-        help="Image crop mode"
+        help="How to generate windows for detection."
     )
     parser.add_argument(
         "--gpu",
@@ -76,8 +76,12 @@ def main(argv):
     parser.add_argument(
         "--input_scale",
         type=float,
-        default=255,
-        help="Multiply input features by this scale before input to net"
+        help="Multiply input features by this scale to finish input preprocessing."
+    )
+    parser.add_argument(
+        "--raw_scale",
+        type=float,
+        help="Multiply raw input by this scale before preprocessing."
     )
     parser.add_argument(
         "--channel_swap",
@@ -86,14 +90,26 @@ def main(argv):
              "RGB -> BGR since BGR is the Caffe default by way of OpenCV."
 
     )
+    parser.add_argument(
+        "--context_pad",
+        type=int,
+        default='16',
+        help="Amount of surrounding context to collect in input window."
+    )
     args = parser.parse_args()
 
-    channel_swap = [int(s) for s in args.channel_swap.split(',')]
+    mean, channel_swap = None, None
+    if args.mean_file:
+        mean = np.load(args.mean_file)
+    if args.channel_swap:
+        channel_swap = [int(s) for s in args.channel_swap.split(',')]
 
     # Make detector.
     detector = caffe.Detector(args.model_def, args.pretrained_model,
-            gpu=args.gpu, mean_file=args.mean_file,
-            input_scale=args.input_scale, channel_swap=channel_swap)
+            gpu=args.gpu, mean=mean,
+            input_scale=args.input_scale, raw_scale=args.raw_scale,
+            channel_swap=channel_swap,
+            context_pad=args.context_pad)
 
     if args.gpu:
         print 'GPU mode'
diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index 76eee4b2dbe..3f80e723643 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -210,7 +210,6 @@
 # off by default (i.e., categories that must be enabled by the --filter= flags).
 # All entries here should start with a '-' or '+', as in the --filter= flag.
 _DEFAULT_FILTERS = [
-  '-build/include_alpha',
   '-build/include_dir',
   '-readability/todo',
   ]
@@ -446,7 +445,7 @@
 _error_suppressions = {}
 
 # Finds Copyright.
-_RE_COPYRIGHT = re.compile(r'Copyright \d\d\d\d BVLC and contributors.')
+_RE_COPYRIGHT = re.compile(r'Copyright')
 
 # The root directory used for deriving header guard CPP variable.
 # This is set by --root flag.
@@ -1369,16 +1368,15 @@ def ReverseCloseExpression(clean_lines, linenum, pos):
 
 
 def CheckForCopyright(filename, lines, error):
-  """Logs an error if no Copyright message appears at the top of the file."""
+  """Logs an error if a Copyright message appears at the top of the file."""
 
-  # We'll say it should occur by line 10. Don't forget there's a
+  # We'll check up to line 10. Don't forget there's a
   # dummy line at the front.
   for line in xrange(1, min(len(lines), 11)):
-    if _RE_COPYRIGHT.search(lines[line], re.I): break
-  else:                       # means no copyright line was found
-    error(filename, 0, 'legal/copyright', 5,
-          'BVLC copyright message not found.  '
-          'You should have a line: "Copyright [year] BVLC and contributors."')
+    if _RE_COPYRIGHT.search(lines[line], re.I):
+      error(filename, 0, 'legal/copyright', 5,
+            'Copyright message found.  '
+            'You should not include a copyright line.')
 
 
 def GetHeaderGuardCPPVariable(filename):
diff --git a/scripts/gather_examples.sh b/scripts/gather_examples.sh
index dd6cef0df1b..3fc726065ba 100755
--- a/scripts/gather_examples.sh
+++ b/scripts/gather_examples.sh
@@ -22,7 +22,7 @@ for README_FILENAME in $(find examples -iname "readme.md"); do
 done
 
 # Gather docs from examples/*.ipynb and add YAML front-matter.
-for NOTEBOOK_FILENAME in $(find examples -d 1 -iname "*.ipynb"); do
+for NOTEBOOK_FILENAME in $(find examples -depth -iname "*.ipynb"); do
     DOCS_FILENAME=$GATHERED_DIR/$NOTEBOOK_FILENAME
     mkdir -p `dirname $DOCS_FILENAME`
     python scripts/copy_notebook.py $NOTEBOOK_FILENAME $DOCS_FILENAME
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 444e9cf4009..9fd1232a9ad 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -1,8 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <cuda_runtime.h>
-#include <cublas_v2.h>
-
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
@@ -75,25 +70,25 @@ const Dtype* Blob<Dtype>::gpu_diff() const {
 template <typename Dtype>
 Dtype* Blob<Dtype>::mutable_cpu_data() {
   CHECK(data_);
-  return reinterpret_cast<Dtype*>(data_->mutable_cpu_data());
+  return static_cast<Dtype*>(data_->mutable_cpu_data());
 }
 
 template <typename Dtype>
 Dtype* Blob<Dtype>::mutable_gpu_data() {
   CHECK(data_);
-  return reinterpret_cast<Dtype*>(data_->mutable_gpu_data());
+  return static_cast<Dtype*>(data_->mutable_gpu_data());
 }
 
 template <typename Dtype>
 Dtype* Blob<Dtype>::mutable_cpu_diff() {
   CHECK(diff_);
-  return reinterpret_cast<Dtype*>(diff_->mutable_cpu_data());
+  return static_cast<Dtype*>(diff_->mutable_cpu_data());
 }
 
 template <typename Dtype>
 Dtype* Blob<Dtype>::mutable_gpu_diff() {
   CHECK(diff_);
-  return reinterpret_cast<Dtype*>(diff_->mutable_gpu_data());
+  return static_cast<Dtype*>(diff_->mutable_gpu_data());
 }
 
 template <typename Dtype>
@@ -108,6 +103,12 @@ void Blob<Dtype>::ShareDiff(const Blob& other) {
   diff_ = other.diff();
 }
 
+// The "update" method is used for parameter blobs in a Net, which are stored
+// as Blob<float> or Blob<double> -- hence we do not define it for
+// Blob<int> or Blob<unsigned int>.
+template <> void Blob<unsigned int>::Update() { NOT_IMPLEMENTED; }
+template <> void Blob<int>::Update() { NOT_IMPLEMENTED; }
+
 template <typename Dtype>
 void Blob<Dtype>::Update() {
   // We will perform update based on where the data is located.
@@ -115,21 +116,95 @@ void Blob<Dtype>::Update() {
   case SyncedMemory::HEAD_AT_CPU:
     // perform computation on CPU
     caffe_axpy<Dtype>(count_, Dtype(-1),
-        reinterpret_cast<const Dtype*>(diff_->cpu_data()),
-        reinterpret_cast<Dtype*>(data_->mutable_cpu_data()));
+        static_cast<const Dtype*>(diff_->cpu_data()),
+        static_cast<Dtype*>(data_->mutable_cpu_data()));
     break;
   case SyncedMemory::HEAD_AT_GPU:
   case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
     // perform computation on GPU
     caffe_gpu_axpy<Dtype>(count_, Dtype(-1),
-        reinterpret_cast<const Dtype*>(diff_->gpu_data()),
-        reinterpret_cast<Dtype*>(data_->mutable_gpu_data()));
+        static_cast<const Dtype*>(diff_->gpu_data()),
+        static_cast<Dtype*>(data_->mutable_gpu_data()));
+#else
+    NO_GPU;
+#endif
     break;
   default:
     LOG(FATAL) << "Syncedmem not initialized.";
   }
 }
 
+template <> unsigned int Blob<unsigned int>::asum_data() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <> int Blob<int>::asum_data() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <typename Dtype>
+Dtype Blob<Dtype>::asum_data() const {
+  if (!data_) { return 0; }
+  switch (data_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    return caffe_cpu_asum(count_, cpu_data());
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+  {
+    Dtype asum;
+    caffe_gpu_asum(count_, gpu_data(), &asum);
+    return asum;
+  }
+#else
+    NO_GPU;
+#endif
+  case SyncedMemory::UNINITIALIZED:
+    return 0;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
+  }
+  return 0;
+}
+
+template <> unsigned int Blob<unsigned int>::asum_diff() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <> int Blob<int>::asum_diff() const {
+  NOT_IMPLEMENTED;
+  return 0;
+}
+
+template <typename Dtype>
+Dtype Blob<Dtype>::asum_diff() const {
+  if (!diff_) { return 0; }
+  switch (diff_->head()) {
+  case SyncedMemory::HEAD_AT_CPU:
+    return caffe_cpu_asum(count_, cpu_diff());
+  case SyncedMemory::HEAD_AT_GPU:
+  case SyncedMemory::SYNCED:
+#ifndef CPU_ONLY
+  {
+    Dtype asum;
+    caffe_gpu_asum(count_, gpu_diff(), &asum);
+    return asum;
+  }
+#else
+    NO_GPU;
+#endif
+  case SyncedMemory::UNINITIALIZED:
+    return 0;
+  default:
+    LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
+  }
+  return 0;
+}
+
 template <typename Dtype>
 void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
   if (num_ != source.num() || channels_ != source.channels() ||
@@ -143,20 +218,20 @@ void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
   switch (Caffe::mode()) {
   case Caffe::GPU:
     if (copy_diff) {
-      CUDA_CHECK(cudaMemcpy(diff_->mutable_gpu_data(), source.gpu_diff(),
-          sizeof(Dtype) * count_, cudaMemcpyDeviceToDevice));
+      caffe_copy(count_, source.gpu_diff(),
+          static_cast<Dtype*>(diff_->mutable_gpu_data()));
     } else {
-      CUDA_CHECK(cudaMemcpy(data_->mutable_gpu_data(), source.gpu_data(),
-          sizeof(Dtype) * count_, cudaMemcpyDeviceToDevice));
+      caffe_copy(count_, source.gpu_data(),
+          static_cast<Dtype*>(data_->mutable_gpu_data()));
     }
     break;
   case Caffe::CPU:
     if (copy_diff) {
-      memcpy(diff_->mutable_cpu_data(), source.cpu_diff(),
-          sizeof(Dtype) * count_);
+      caffe_copy(count_, source.cpu_diff(),
+          static_cast<Dtype*>(diff_->mutable_cpu_data()));
     } else {
-      memcpy(data_->mutable_cpu_data(), source.cpu_data(),
-        sizeof(Dtype) * count_);
+      caffe_copy(count_, source.cpu_data(),
+          static_cast<Dtype*>(data_->mutable_cpu_data()));
     }
     break;
   default:
@@ -201,6 +276,8 @@ void Blob<Dtype>::ToProto(BlobProto* proto, bool write_diff) const {
 }
 
 INSTANTIATE_CLASS(Blob);
+template class Blob<int>;
+template class Blob<unsigned int>;
 
 }  // namespace caffe
 
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index 7edb3a455ac..99b2292461e 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -1,19 +1,40 @@
-// Copyright 2014 BVLC and contributors.
-
+#include <gflags/gflags.h>
+#include <glog/logging.h>
 #include <cstdio>
 #include <ctime>
 
 #include "caffe/common.hpp"
 #include "caffe/util/rng.hpp"
 
+// gflags 2.1 issue: namespace google was changed to gflags without warning.
+// Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
+// 2.1. If yes , we will add a temporary solution to redirect the namespace.
+// TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
+// remove the following hack.
+#ifdef GFLAGS_GFLAGS_H_
+namespace google {
+using ::gflags::ParseCommandLineFlags;
+}  // namespace google
+#endif  // GFLAGS_GFLAGS_H_
+
 namespace caffe {
 
 shared_ptr<Caffe> Caffe::singleton_;
 
-
-// curand seeding
+// random seeding
 int64_t cluster_seedgen(void) {
   int64_t s, seed, pid;
+  FILE* f = fopen("/dev/urandom", "rb");
+  if (f && fread(&seed, 1, sizeof(seed), f) == sizeof(seed)) {
+    fclose(f);
+    return seed;
+  }
+
+  LOG(INFO) << "System entropy source not available, "
+              "using fallback algorithm to generate seed instead.";
+  if (f)
+    fclose(f);
+
   pid = getpid();
   s = time(NULL);
   seed = abs(((s * 181) * ((pid - 83) * 359)) % 104729);
@@ -21,10 +42,61 @@ int64_t cluster_seedgen(void) {
 }
 
 
+void GlobalInit(int* pargc, char*** pargv) {
+  // Google flags.
+  ::google::ParseCommandLineFlags(pargc, pargv, true);
+  // Google logging.
+  ::google::InitGoogleLogging(*(pargv)[0]);
+}
+
+#ifdef CPU_ONLY  // CPU-only Caffe.
+
+Caffe::Caffe()
+    : random_generator_(), mode_(Caffe::CPU), phase_(Caffe::TRAIN) { }
+
+Caffe::~Caffe() { }
+
+void Caffe::set_random_seed(const unsigned int seed) {
+  // RNG seed
+  Get().random_generator_.reset(new RNG(seed));
+}
+
+void Caffe::SetDevice(const int device_id) {
+  NO_GPU;
+}
+
+void Caffe::DeviceQuery() {
+  NO_GPU;
+}
+
+
+class Caffe::RNG::Generator {
+ public:
+  Generator() : rng_(new caffe::rng_t(cluster_seedgen())) {}
+  explicit Generator(unsigned int seed) : rng_(new caffe::rng_t(seed)) {}
+  caffe::rng_t* rng() { return rng_.get(); }
+ private:
+  shared_ptr<caffe::rng_t> rng_;
+};
+
+Caffe::RNG::RNG() : generator_(new Generator()) { }
+
+Caffe::RNG::RNG(unsigned int seed) : generator_(new Generator(seed)) { }
+
+Caffe::RNG& Caffe::RNG::operator=(const RNG& other) {
+  generator_ = other.generator_;
+  return *this;
+}
+
+void* Caffe::RNG::generator() {
+  return static_cast<void*>(generator_->rng());
+}
+
+#else  // Normal GPU + CPU Caffe.
+
 Caffe::Caffe()
-    : mode_(Caffe::CPU), phase_(Caffe::TRAIN), cublas_handle_(NULL),
-      curand_generator_(NULL),
-      random_generator_() {
+    : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(),
+    mode_(Caffe::CPU), phase_(Caffe::TRAIN) {
   // Try to create a cublas handler, and report an error if failed (but we will
   // keep the program running as one might just want to run CPU code).
   if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {
@@ -50,6 +122,7 @@ void Caffe::set_random_seed(const unsigned int seed) {
   // Curand seed
   // Yangqing's note: simply setting the generator seed does not seem to
   // work on the tesla K20s, so I wrote the ugly reset thing below.
+  static bool g_curand_availability_logged = false;
   if (Get().curand_generator_) {
     CURAND_CHECK(curandDestroyGenerator(curand_generator()));
     CURAND_CHECK(curandCreateGenerator(&Get().curand_generator_,
@@ -57,7 +130,11 @@ void Caffe::set_random_seed(const unsigned int seed) {
     CURAND_CHECK(curandSetPseudoRandomGeneratorSeed(curand_generator(),
         seed));
   } else {
-    LOG(ERROR) << "Curand not available. Skipping setting the curand seed.";
+    if (!g_curand_availability_logged) {
+        LOG(ERROR) <<
+            "Curand not available. Skipping setting the curand seed.";
+        g_curand_availability_logged = true;
+    }
   }
   // RNG seed
   Get().random_generator_.reset(new RNG(seed));
@@ -69,11 +146,13 @@ void Caffe::SetDevice(const int device_id) {
   if (current_device == device_id) {
     return;
   }
+  // The call to cudaSetDevice must come before any calls to Get, which
+  // may perform initialization using the GPU.
+  CUDA_CHECK(cudaSetDevice(device_id));
   if (Get().cublas_handle_) CUBLAS_CHECK(cublasDestroy(Get().cublas_handle_));
   if (Get().curand_generator_) {
     CURAND_CHECK(curandDestroyGenerator(Get().curand_generator_));
   }
-  CUDA_CHECK(cudaSetDevice(device_id));
   CUBLAS_CHECK(cublasCreate(&Get().cublas_handle_));
   CURAND_CHECK(curandCreateGenerator(&Get().curand_generator_,
       CURAND_RNG_PSEUDO_DEFAULT));
@@ -89,28 +168,30 @@ void Caffe::DeviceQuery() {
     return;
   }
   CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
-  printf("Device id:                     %d\n", device);
-  printf("Major revision number:         %d\n", prop.major);
-  printf("Minor revision number:         %d\n", prop.minor);
-  printf("Name:                          %s\n", prop.name);
-  printf("Total global memory:           %lu\n", prop.totalGlobalMem);
-  printf("Total shared memory per block: %lu\n", prop.sharedMemPerBlock);
-  printf("Total registers per block:     %d\n", prop.regsPerBlock);
-  printf("Warp size:                     %d\n", prop.warpSize);
-  printf("Maximum memory pitch:          %lu\n", prop.memPitch);
-  printf("Maximum threads per block:     %d\n", prop.maxThreadsPerBlock);
-  printf("Maximum dimension of block:    %d, %d, %d\n",
-      prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
-  printf("Maximum dimension of grid:     %d, %d, %d\n",
-      prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
-  printf("Clock rate:                    %d\n", prop.clockRate);
-  printf("Total constant memory:         %lu\n", prop.totalConstMem);
-  printf("Texture alignment:             %lu\n", prop.textureAlignment);
-  printf("Concurrent copy and execution: %s\n",
-      (prop.deviceOverlap ? "Yes" : "No"));
-  printf("Number of multiprocessors:     %d\n", prop.multiProcessorCount);
-  printf("Kernel execution timeout:      %s\n",
-      (prop.kernelExecTimeoutEnabled ? "Yes" : "No"));
+  LOG(INFO) << "Device id:                     " << device;
+  LOG(INFO) << "Major revision number:         " << prop.major;
+  LOG(INFO) << "Minor revision number:         " << prop.minor;
+  LOG(INFO) << "Name:                          " << prop.name;
+  LOG(INFO) << "Total global memory:           " << prop.totalGlobalMem;
+  LOG(INFO) << "Total shared memory per block: " << prop.sharedMemPerBlock;
+  LOG(INFO) << "Total registers per block:     " << prop.regsPerBlock;
+  LOG(INFO) << "Warp size:                     " << prop.warpSize;
+  LOG(INFO) << "Maximum memory pitch:          " << prop.memPitch;
+  LOG(INFO) << "Maximum threads per block:     " << prop.maxThreadsPerBlock;
+  LOG(INFO) << "Maximum dimension of block:    "
+      << prop.maxThreadsDim[0] << ", " << prop.maxThreadsDim[1] << ", "
+      << prop.maxThreadsDim[2];
+  LOG(INFO) << "Maximum dimension of grid:     "
+      << prop.maxGridSize[0] << ", " << prop.maxGridSize[1] << ", "
+      << prop.maxGridSize[2];
+  LOG(INFO) << "Clock rate:                    " << prop.clockRate;
+  LOG(INFO) << "Total constant memory:         " << prop.totalConstMem;
+  LOG(INFO) << "Texture alignment:             " << prop.textureAlignment;
+  LOG(INFO) << "Concurrent copy and execution: "
+      << (prop.deviceOverlap ? "Yes" : "No");
+  LOG(INFO) << "Number of multiprocessors:     " << prop.multiProcessorCount;
+  LOG(INFO) << "Kernel execution timeout:      "
+      << (prop.kernelExecTimeoutEnabled ? "Yes" : "No");
   return;
 }
 
@@ -124,7 +205,7 @@ class Caffe::RNG::Generator {
   shared_ptr<caffe::rng_t> rng_;
 };
 
-Caffe::RNG::RNG() : generator_(new Generator) { }
+Caffe::RNG::RNG() : generator_(new Generator()) { }
 
 Caffe::RNG::RNG(unsigned int seed) : generator_(new Generator(seed)) { }
 
@@ -155,8 +236,10 @@ const char* cublasGetErrorString(cublasStatus_t error) {
     return "CUBLAS_STATUS_EXECUTION_FAILED";
   case CUBLAS_STATUS_INTERNAL_ERROR:
     return "CUBLAS_STATUS_INTERNAL_ERROR";
+#if CUDA_VERSION >= 6000
   case CUBLAS_STATUS_NOT_SUPPORTED:
     return "CUBLAS_STATUS_NOT_SUPPORTED";
+#endif
   }
   return "Unknown cublas status";
 }
@@ -193,4 +276,6 @@ const char* curandGetErrorString(curandStatus_t error) {
   return "Unknown curand status";
 }
 
+#endif  // CPU_ONLY
+
 }  // namespace caffe
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index 2991c81f559..118929fbff5 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -1,19 +1,14 @@
-// Copyright 2014 BVLC and contributors.
-
 #ifndef CAFFE_LAYER_FACTORY_HPP_
 #define CAFFE_LAYER_FACTORY_HPP_
 
 #include <string>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
-
-using std::string;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
-
 // A function to get a specific layer from the specification given in
 // LayerParameter. Ideally this would be replaced by a factory pattern,
 // but we will leave it this way for now.
@@ -24,6 +19,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
   switch (type) {
   case LayerParameter_LayerType_ACCURACY:
     return new AccuracyLayer<Dtype>(param);
+  case LayerParameter_LayerType_ARGMAX:
+    return new ArgMaxLayer<Dtype>(param);
   case LayerParameter_LayerType_BNLL:
     return new BNLLLayer<Dtype>(param);
   case LayerParameter_LayerType_CONCAT:
@@ -34,10 +31,12 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return new DataLayer<Dtype>(param);
   case LayerParameter_LayerType_DROPOUT:
     return new DropoutLayer<Dtype>(param);
+  case LayerParameter_LayerType_DUMMY_DATA:
+    return new DummyDataLayer<Dtype>(param);
   case LayerParameter_LayerType_EUCLIDEAN_LOSS:
     return new EuclideanLossLayer<Dtype>(param);
-  case LayerParameter_LayerType_ELTWISE_PRODUCT:
-    return new EltwiseProductLayer<Dtype>(param);
+  case LayerParameter_LayerType_ELTWISE:
+    return new EltwiseLayer<Dtype>(param);
   case LayerParameter_LayerType_FLATTEN:
     return new FlattenLayer<Dtype>(param);
   case LayerParameter_LayerType_HDF5_DATA:
@@ -70,6 +69,8 @@ Layer<Dtype>* GetLayer(const LayerParameter& param) {
     return new SigmoidLayer<Dtype>(param);
   case LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS:
     return new SigmoidCrossEntropyLossLayer<Dtype>(param);
+  case LayerParameter_LayerType_SLICE:
+    return new SliceLayer<Dtype>(param);
   case LayerParameter_LayerType_SOFTMAX:
     return new SoftmaxLayer<Dtype>(param);
   case LayerParameter_LayerType_SOFTMAX_LOSS:
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 3e671704465..76889d8b70a 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -1,61 +1,62 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
-#include <cmath>
-#include <cfloat>
+#include <functional>
+#include <utility>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
-
-using std::max;
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void AccuracyLayer<Dtype>::SetUp(
   const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 2) << "Accuracy Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 1) << "Accuracy Layer takes 1 output.";
+  Layer<Dtype>::SetUp(bottom, top);
+  top_k_ = this->layer_param_.accuracy_param().top_k();
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
+  CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
+      << "top_k must be less than or equal to the number of classes.";
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
   CHECK_EQ(bottom[1]->width(), 1);
-  (*top)[0]->Reshape(1, 2, 1, 1);
+  (*top)[0]->Reshape(1, 1, 1, 1);
 }
 
 template <typename Dtype>
 Dtype AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   Dtype accuracy = 0;
-  Dtype logprob = 0;
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
+  vector<Dtype> maxval(top_k_+1);
+  vector<int> max_id(top_k_+1);
   for (int i = 0; i < num; ++i) {
-    // Accuracy
-    Dtype maxval = -FLT_MAX;
-    int max_id = 0;
+    // Top-k accuracy
+    std::vector<std::pair<Dtype, int> > bottom_data_vector;
     for (int j = 0; j < dim; ++j) {
-      if (bottom_data[i * dim + j] > maxval) {
-        maxval = bottom_data[i * dim + j];
-        max_id = j;
-      }
+      bottom_data_vector.push_back(
+          std::make_pair(bottom_data[i * dim + j], j));
     }
-    if (max_id == static_cast<int>(bottom_label[i])) {
-      ++accuracy;
+    std::partial_sort(
+        bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
+        bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
+    // check if true label is in top k predictions
+    for (int k = 0; k < top_k_; k++) {
+      if (bottom_data_vector[k].second == static_cast<int>(bottom_label[i])) {
+        ++accuracy;
+        break;
+      }
     }
-    Dtype prob = max(bottom_data[i * dim + static_cast<int>(bottom_label[i])],
-                     Dtype(kLOG_THRESHOLD));
-    logprob -= log(prob);
   }
+
   // LOG(INFO) << "Accuracy: " << accuracy;
   (*top)[0]->mutable_cpu_data()[0] = accuracy / num;
-  (*top)[0]->mutable_cpu_data()[1] = logprob / num;
+
   // Accuracy layer should not be used as a loss function.
   return Dtype(0);
 }
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
new file mode 100644
index 00000000000..b2ef91eab67
--- /dev/null
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -0,0 +1,59 @@
+#include <algorithm>
+#include <functional>
+#include <utility>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ArgMaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  Layer<Dtype>::SetUp(bottom, top);
+  out_max_val_ = this->layer_param_.argmax_param().out_max_val();
+  top_k_ = this->layer_param_.argmax_param().top_k();
+  CHECK_GE(top_k_, 1) << " top k must not be less than 1.";
+  CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
+      << "top_k must be less than or equal to the number of classes.";
+  if (out_max_val_) {
+    // Produces max_ind and max_val
+    (*top)[0]->Reshape(bottom[0]->num(), 2, top_k_, 1);
+  } else {
+    // Produces only max_ind
+    (*top)[0]->Reshape(bottom[0]->num(), 1, top_k_, 1);
+  }
+}
+
+template <typename Dtype>
+Dtype ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  int num = bottom[0]->num();
+  int dim = bottom[0]->count() / bottom[0]->num();
+  for (int i = 0; i < num; ++i) {
+    std::vector<std::pair<Dtype, int> > bottom_data_vector;
+    for (int j = 0; j < dim; ++j) {
+      bottom_data_vector.push_back(
+          std::make_pair(bottom_data[i * dim + j], j));
+    }
+    std::partial_sort(
+        bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
+        bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
+    for (int j = 0; j < top_k_; ++j) {
+      top_data[(*top)[0]->offset(i, 0, j)] = bottom_data_vector[j].second;
+    }
+    if (out_max_val_) {
+      for (int j = 0; j < top_k_; ++j) {
+        top_data[(*top)[0]->offset(i, 1, j)] = bottom_data_vector[j].first;
+      }
+    }
+  }
+  return Dtype(0);
+}
+
+INSTANTIATE_CLASS(ArgMaxLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index d08adc49eef..4cb852031af 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -1,13 +1,9 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::min;
-
 namespace caffe {
 
 const float kBNLL_THRESHOLD = 50.;
@@ -28,21 +24,24 @@ Dtype BNLLLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void BNLLLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* bottom_data = (*bottom)[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     const int count = (*bottom)[0]->count();
     Dtype expval;
     for (int i = 0; i < count; ++i) {
-      expval = exp(min(bottom_data[i], Dtype(kBNLL_THRESHOLD)));
+      expval = exp(std::min(bottom_data[i], Dtype(kBNLL_THRESHOLD)));
       bottom_diff[i] = top_diff[i] * expval / (expval + 1.);
     }
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(BNLLLayer);
+#endif
 
 INSTANTIATE_CLASS(BNLLLayer);
 
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index 75bea00e993..9895a06107a 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -1,13 +1,9 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::max;
-
 namespace caffe {
 
 const float kBNLL_THRESHOLD = 50.;
@@ -45,9 +41,9 @@ __global__ void BNLLBackward(const int n, const Dtype* in_diff,
 
 template <typename Dtype>
 void BNLLLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* bottom_data = (*bottom)[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 8036bdab675..b76d4b2ca14 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -1,21 +1,15 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_GT(bottom.size(), 1) <<
-    "ConcatLayer takes at least two blobs as input.";
-  CHECK_EQ(top->size(), 1) <<
-    "ConcatLayer takes a single blob as output.";
-
+  Layer<Dtype>::SetUp(bottom, top);
   concat_dim_ = this->layer_param_.concat_param().concat_dim();
   CHECK_GE(concat_dim_, 0) <<
     "concat_dim should be >= 0";
@@ -74,32 +68,40 @@ Dtype ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   if (concat_dim_ == 0) {
     int offset_num = 0;
     for (int i = 0; i < bottom->size(); ++i) {
       Blob<Dtype>* blob = (*bottom)[i];
-      Dtype* bottom_diff = blob->mutable_cpu_diff();
-      caffe_copy(blob->count(),
-        top_diff+top[0]->offset(offset_num), bottom_diff);
+      if (propagate_down[i]) {
+        Dtype* bottom_diff = blob->mutable_cpu_diff();
+        caffe_copy(blob->count(), top_diff + top[0]->offset(offset_num),
+                   bottom_diff);
+      }
       offset_num += blob->num();
     }
   } else if (concat_dim_ == 1) {
     int offset_channel = 0;
     for (int i = 0; i < bottom->size(); ++i) {
       Blob<Dtype>* blob = (*bottom)[i];
-      Dtype* bottom_diff = blob->mutable_cpu_diff();
-      int num_elem = blob->channels()*blob->height()*blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_copy(num_elem, top_diff+top[0]->offset(n, offset_channel),
-          bottom_diff+blob->offset(n));
+      if (propagate_down[i]) {
+        Dtype* bottom_diff = blob->mutable_cpu_diff();
+        int num_elem = blob->channels()*blob->height()*blob->width();
+        for (int n = 0; n < num_; ++n) {
+          caffe_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
+                     bottom_diff + blob->offset(n));
+        }
       }
       offset_channel += blob->channels();
     }
   }  // concat_dim_ is guaranteed to be 0 or 1 by SetUp.
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(ConcatLayer);
+#endif
+
 INSTANTIATE_CLASS(ConcatLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index 2820bf0dfdf..aea8b77e37f 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -1,10 +1,8 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -16,7 +14,7 @@ Dtype ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     int offset_num = 0;
     for (int i = 0; i < bottom.size(); ++i) {
       const Dtype* bottom_data = bottom[i]->gpu_data();
-      caffe_gpu_copy(bottom[i]->count(), bottom_data,
+      caffe_copy(bottom[i]->count(), bottom_data,
         top_data + (*top)[0]->offset(offset_num));
       offset_num += bottom[i]->num();
     }
@@ -27,7 +25,7 @@ Dtype ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       int num_elem =
         bottom[i]->channels() * bottom[i]->height() * bottom[i]->width();
       for (int n = 0; n < num_; ++n) {
-        caffe_gpu_copy(num_elem, bottom_data+bottom[i]->offset(n),
+        caffe_copy(num_elem, bottom_data+bottom[i]->offset(n),
           top_data + (*top)[0]->offset(n, offset_channel));
       }
       offset_channel += bottom[i]->channels();
@@ -41,26 +39,30 @@ Dtype ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   if (concat_dim_ == 0) {
     int offset_num = 0;
     for (int i = 0; i < bottom->size(); ++i) {
       Blob<Dtype>* blob = (*bottom)[i];
-      Dtype* bottom_diff = blob->mutable_gpu_diff();
-      caffe_gpu_copy(blob->count(),
-        top_diff + top[0]->offset(offset_num), bottom_diff);
+      if (propagate_down[i]) {
+        Dtype* bottom_diff = blob->mutable_gpu_diff();
+        caffe_copy(blob->count(), top_diff + top[0]->offset(offset_num),
+                       bottom_diff);
+      }
       offset_num += blob->num();
     }
   } else if (concat_dim_ == 1) {
     int offset_channel = 0;
     for (int i = 0; i < bottom->size(); ++i) {
       Blob<Dtype>* blob = (*bottom)[i];
-      Dtype* bottom_diff = blob->mutable_gpu_diff();
-      int num_elem = blob->channels()*blob->height()*blob->width();
-      for (int n = 0; n < num_; ++n) {
-        caffe_gpu_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
-          bottom_diff + blob->offset(n));
+      if (propagate_down[i]) {
+        Dtype* bottom_diff = blob->mutable_gpu_diff();
+        int num_elem = blob->channels()*blob->height()*blob->width();
+        for (int n = 0; n < num_; ++n) {
+          caffe_copy(num_elem, top_diff + top[0]->offset(n, offset_channel),
+                         bottom_diff + blob->offset(n));
+        }
       }
       offset_channel += blob->channels();
     }
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index 55966b54bde..df3e31ba84a 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -1,46 +1,88 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
+#include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/im2col.hpp"
-#include "caffe/filler.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Conv Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "Conv Layer takes a single blob as output.";
-  kernel_size_ = this->layer_param_.convolution_param().kernel_size();
-  stride_ = this->layer_param_.convolution_param().stride();
+  Layer<Dtype>::SetUp(bottom, top);
+  ConvolutionParameter conv_param = this->layer_param_.convolution_param();
+  CHECK(!conv_param.has_kernel_size() !=
+      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
+  CHECK(conv_param.has_kernel_size() ||
+      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "For non-square filters both kernel_h and kernel_w are required.";
+  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
+      && conv_param.has_pad_w())
+      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
+      << "pad is pad OR pad_h and pad_w are required.";
+  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
+      && conv_param.has_stride_w())
+      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
+      << "Stride is stride OR stride_h and stride_w are required.";
+  if (conv_param.has_kernel_size()) {
+    kernel_h_ = kernel_w_ = conv_param.kernel_size();
+  } else {
+    kernel_h_ = conv_param.kernel_h();
+    kernel_w_ = conv_param.kernel_w();
+  }
+  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
+  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
+  if (!conv_param.has_pad_h()) {
+    pad_h_ = pad_w_ = conv_param.pad();
+  } else {
+    pad_h_ = conv_param.pad_h();
+    pad_w_ = conv_param.pad_w();
+  }
+  if (!conv_param.has_stride_h()) {
+    stride_h_ = stride_w_ = conv_param.stride();
+  } else {
+    stride_h_ = conv_param.stride_h();
+    stride_w_ = conv_param.stride_w();
+  }
   group_ = this->layer_param_.convolution_param().group();
-  pad_ = this->layer_param_.convolution_param().pad();
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
+  // TODO: generalize to handle inputs of different shapes.
+  for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
+    CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num.";
+    CHECK_EQ(channels_, bottom[bottom_id]->channels())
+        << "Inputs must have same channels.";
+    CHECK_EQ(height_, bottom[bottom_id]->height())
+        << "Inputs must have same height.";
+    CHECK_EQ(width_, bottom[bottom_id]->width())
+        << "Inputs must have same width.";
+  }
   num_output_ = this->layer_param_.convolution_param().num_output();
   CHECK_GT(num_output_, 0);
   CHECK_EQ(channels_ % group_, 0);
   // The im2col result buffer would only hold one image at a time to avoid
   // overly large memory usage.
-  int height_out = (height_ + 2 * pad_ - kernel_size_) / stride_ + 1;
-  int width_out = (width_ + 2 * pad_ - kernel_size_) / stride_ + 1;
+  int height_out =
+      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1;
+  int width_out = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1;
   col_buffer_.Reshape(
-      1, channels_ * kernel_size_ * kernel_size_, height_out, width_out);
+      1, channels_ * kernel_h_ * kernel_w_, height_out, width_out);
   // Set the parameters
   CHECK_EQ(num_output_ % group_, 0)
       << "Number of output should be multiples of group.";
   bias_term_ = this->layer_param_.convolution_param().bias_term();
   // Figure out the dimensions for individual gemms.
   M_ = num_output_ / group_;
-  K_ = channels_ * kernel_size_ * kernel_size_ / group_;
+  K_ = channels_ * kernel_h_ * kernel_w_ / group_;
   N_ = height_out * width_out;
-  (*top)[0]->Reshape(bottom[0]->num(), num_output_, height_out, width_out);
+  for (int top_id = 0; top_id < top->size(); ++top_id) {
+    (*top)[top_id]->Reshape(num_, num_output_, height_out, width_out);
+  }
   // Check if we need to set up the weights
   if (this->blobs_.size() > 0) {
     LOG(INFO) << "Skipping parameter initialization";
@@ -52,12 +94,12 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
     }
     // Intialize the weight
     this->blobs_[0].reset(new Blob<Dtype>(
-        num_output_, channels_ / group_, kernel_size_, kernel_size_));
+        num_output_, channels_ / group_, kernel_h_, kernel_w_));
     // fill the weights
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
         this->layer_param_.convolution_param().weight_filler()));
     weight_filler->Fill(this->blobs_[0].get());
-    // If necessary, intiialize and fill the bias term
+    // If necessary, initialize and fill the bias term
     if (bias_term_) {
       this->blobs_[1].reset(new Blob<Dtype>(1, 1, 1, num_output_));
       shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
@@ -65,44 +107,44 @@ void ConvolutionLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       bias_filler->Fill(this->blobs_[1].get());
     }
   }
-  // Set up the bias filler
+  // Set up the all ones "bias multiplier" for adding bias using blas
   if (bias_term_) {
-    bias_multiplier_.reset(new SyncedMemory(N_ * sizeof(Dtype)));
-    Dtype* bias_multiplier_data =
-        reinterpret_cast<Dtype*>(bias_multiplier_->mutable_cpu_data());
-    for (int i = 0; i < N_; ++i) {
-        bias_multiplier_data[i] = 1.;
-    }
+    bias_multiplier_.Reshape(1, 1, 1, N_);
+    caffe_set(N_, Dtype(1), bias_multiplier_.mutable_cpu_data());
   }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
 
 
 template <typename Dtype>
 Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->cpu_data();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  Dtype* col_data = col_buffer_.mutable_cpu_data();
-  const Dtype* weight = this->blobs_[0]->cpu_data();
-  int weight_offset = M_ * K_;
-  int col_offset = K_ * N_;
-  int top_offset = M_ * N_;
-  for (int n = 0; n < num_; ++n) {
-    // First, im2col
-    im2col_cpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-                      width_, kernel_size_, pad_, stride_, col_data);
-    // Second, innerproduct with groups
-    for (int g = 0; g < group_; ++g) {
-      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-        (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
-        (Dtype)0., top_data + (*top)[0]->offset(n) + top_offset * g);
-    }
-    // third, add bias
-    if (bias_term_) {
-      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-          N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
-          reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()),
-          (Dtype)1., top_data + (*top)[0]->offset(n));
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->cpu_data();
+    Dtype* top_data = (*top)[i]->mutable_cpu_data();
+    Dtype* col_data = col_buffer_.mutable_cpu_data();
+    const Dtype* weight = this->blobs_[0]->cpu_data();
+    int weight_offset = M_ * K_;
+    int col_offset = K_ * N_;
+    int top_offset = M_ * N_;
+    for (int n = 0; n < num_; ++n) {
+      // First, im2col
+      im2col_cpu(bottom_data + bottom[i]->offset(n), channels_, height_,
+          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+          col_data);
+      // Second, innerproduct with groups
+      for (int g = 0; g < group_; ++g) {
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
+          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
+          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
+      }
+      // third, add bias
+      if (bias_term_) {
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
+            N_, 1, (Dtype)1., this->blobs_[1]->cpu_data(),
+            bias_multiplier_.cpu_data(),
+            (Dtype)1., top_data + (*top)[i]->offset(n));
+      }
     }
   }
   return Dtype(0.);
@@ -110,59 +152,79 @@ Dtype ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  const Dtype* top_diff = top[0]->cpu_diff();
-  const Dtype* weight = this->blobs_[0]->cpu_data();
-  Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  Dtype* col_data = col_buffer_.mutable_cpu_data();
-  Dtype* col_diff = col_buffer_.mutable_cpu_diff();
-  // bias gradient if necessary
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const Dtype* weight = NULL;
+  Dtype* weight_diff = NULL;
+  if (this->param_propagate_down_[0]) {
+    weight = this->blobs_[0]->cpu_data();
+    weight_diff = this->blobs_[0]->mutable_cpu_diff();
+    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
+  }
   Dtype* bias_diff = NULL;
-
-  if (bias_term_) {
+  if (bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_cpu_diff();
-    memset(bias_diff, 0, sizeof(Dtype) * this->blobs_[1]->count());
-    for (int n = 0; n < num_; ++n) {
-      caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
-          1., top_diff + top[0]->offset(n),
-          reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()), 1.,
-          bias_diff);
-    }
+    caffe_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
-
-  int weight_offset = M_ * K_;
-  int col_offset = K_ * N_;
-  int top_offset = M_ * N_;
-  memset(weight_diff, 0, sizeof(Dtype) * this->blobs_[0]->count());
-  for (int n = 0; n < num_; ++n) {
-    // since we saved memory in the forward pass by not storing all col data,
-    // we will need to recompute them.
-    im2col_cpu(bottom_data + (*bottom)[0]->offset(n), channels_, height_,
-                      width_, kernel_size_, pad_, stride_, col_data);
-    // gradient w.r.t. weight. Note that we will accumulate diffs.
-    for (int g = 0; g < group_; ++g) {
-      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-        (Dtype)1., top_diff + top[0]->offset(n) + top_offset * g,
-        col_data + col_offset * g, (Dtype)1.,
-        weight_diff + weight_offset * g);
+  const int weight_offset = M_ * K_;
+  const int col_offset = K_ * N_;
+  const int top_offset = M_ * N_;
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = NULL;
+    // Bias gradient, if necessary.
+    if (bias_term_ && this->param_propagate_down_[1]) {
+      top_diff = top[i]->cpu_diff();
+      for (int n = 0; n < num_; ++n) {
+        caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
+            1., top_diff + top[0]->offset(n),
+            bias_multiplier_.cpu_data(), 1.,
+            bias_diff);
+      }
     }
-    // gradient w.r.t. bottom data, if necessary
-    if (propagate_down) {
-      for (int g = 0; g < group_; ++g) {
-        caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-          (Dtype)1., weight + weight_offset * g,
-          top_diff + top[0]->offset(n) + top_offset * g,
-          (Dtype)0., col_diff + col_offset * g);
+    if (this->param_propagate_down_[0] || propagate_down[i]) {
+      if (!top_diff) {
+        top_diff = top[i]->cpu_diff();
+      }
+      Dtype* col_data = col_buffer_.mutable_cpu_data();
+      Dtype* col_diff = col_buffer_.mutable_cpu_diff();
+      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
+      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
+      for (int n = 0; n < num_; ++n) {
+        // Since we saved memory in the forward pass by not storing all col
+        // data, we will need to recompute them.
+        im2col_cpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
+                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+                   stride_h_, stride_w_, col_data);
+        // gradient w.r.t. weight. Note that we will accumulate diffs.
+        if (this->param_propagate_down_[0]) {
+          for (int g = 0; g < group_; ++g) {
+            caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
+                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
+                col_data + col_offset * g, (Dtype)1.,
+                weight_diff + weight_offset * g);
+          }
+        }
+        // gradient w.r.t. bottom data, if necessary
+        if (propagate_down[i]) {
+          for (int g = 0; g < group_; ++g) {
+            caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
+                (Dtype)1., weight + weight_offset * g,
+                top_diff + top[i]->offset(n) + top_offset * g,
+                (Dtype)0., col_diff + col_offset * g);
+          }
+          // col2im back to the data
+          col2im_cpu(col_diff, channels_, height_, width_,
+              kernel_h_, kernel_w_, pad_h_, pad_w_,
+              stride_h_, stride_w_, bottom_diff + (*bottom)[i]->offset(n));
+        }
       }
-      // col2im back to the data
-      col2im_cpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
-          stride_, bottom_diff + (*bottom)[0]->offset(n));
     }
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(ConvolutionLayer);
+#endif
+
 INSTANTIATE_CLASS(ConvolutionLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 51f5d159879..04ae13932d9 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -1,41 +1,42 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
+#include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/im2col.hpp"
-#include "caffe/filler.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  const Dtype* bottom_data = bottom[0]->gpu_data();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  Dtype* col_data = col_buffer_.mutable_gpu_data();
-  const Dtype* weight = this->blobs_[0]->gpu_data();
-  int weight_offset = M_ * K_;
-  int col_offset = K_ * N_;
-  int top_offset = M_ * N_;
-  for (int n = 0; n < num_; ++n) {
-    // First, im2col
-    im2col_gpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-                      width_, kernel_size_, pad_, stride_, col_data);
-    // Second, innerproduct with groups
-    for (int g = 0; g < group_; ++g) {
-      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
-        (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
-        (Dtype)0., top_data + (*top)[0]->offset(n) + top_offset * g);
-    }
-    // third, add bias
-    if (bias_term_) {
-      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-          N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
-          reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()),
-          (Dtype)1., top_data + (*top)[0]->offset(n));
+  for (int i = 0; i < bottom.size(); ++i) {
+    const Dtype* bottom_data = bottom[i]->gpu_data();
+    Dtype* top_data = (*top)[i]->mutable_gpu_data();
+    Dtype* col_data = col_buffer_.mutable_gpu_data();
+    const Dtype* weight = this->blobs_[0]->gpu_data();
+    int weight_offset = M_ * K_;
+    int col_offset = K_ * N_;
+    int top_offset = M_ * N_;
+    for (int n = 0; n < num_; ++n) {
+      // First, im2col
+      im2col_gpu(bottom_data + bottom[i]->offset(n), channels_, height_,
+          width_, kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+          col_data);
+      // Second, innerproduct with groups
+      for (int g = 0; g < group_; ++g) {
+        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, K_,
+          (Dtype)1., weight + weight_offset * g, col_data + col_offset * g,
+          (Dtype)0., top_data + (*top)[i]->offset(n) + top_offset * g);
+      }
+      // third, add bias
+      if (bias_term_) {
+        caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
+            N_, 1, (Dtype)1., this->blobs_[1]->gpu_data(),
+            bias_multiplier_.gpu_data(),
+            (Dtype)1., top_data + (*top)[i]->offset(n));
+      }
     }
   }
   return Dtype(0.);
@@ -43,57 +44,71 @@ Dtype ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  const Dtype* top_diff = top[0]->gpu_diff();
-  const Dtype* weight = this->blobs_[0]->gpu_data();
-  Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  Dtype* col_data = col_buffer_.mutable_gpu_data();
-  Dtype* col_diff = col_buffer_.mutable_gpu_diff();
-  // bias gradient if necessary
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const Dtype* weight = NULL;
+  Dtype* weight_diff = NULL;
+  if (this->param_propagate_down_[0]) {
+    weight = this->blobs_[0]->gpu_data();
+    weight_diff = this->blobs_[0]->mutable_gpu_diff();
+    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
+  }
   Dtype* bias_diff = NULL;
-
-  if (bias_term_) {
+  if (bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
-    CUDA_CHECK(cudaMemset(bias_diff, 0,
-        sizeof(Dtype) * this->blobs_[1]->count()));
-    for (int n = 0; n < num_; ++n) {
-      caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
-          1., top_diff + top[0]->offset(n),
-          reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()),
-          1., bias_diff);
-    }
+    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
-
-  int weight_offset = M_ * K_;
-  int col_offset = K_ * N_;
-  int top_offset = M_ * N_;
-  CUDA_CHECK(cudaMemset(weight_diff, 0,
-      sizeof(Dtype) * this->blobs_[0]->count()));
-  for (int n = 0; n < num_; ++n) {
-    // since we saved memory in the forward pass by not storing all col data,
-    // we will need to recompute them.
-    im2col_gpu(bottom_data + (*bottom)[0]->offset(n), channels_, height_,
-                      width_, kernel_size_, pad_, stride_, col_data);
-    // gradient w.r.t. weight. Note that we will accumulate diffs.
-    for (int g = 0; g < group_; ++g) {
-      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
-        (Dtype)1., top_diff + top[0]->offset(n) + top_offset * g,
-        col_data + col_offset * g, (Dtype)1.,
-        weight_diff + weight_offset * g);
+  const int weight_offset = M_ * K_;
+  const int col_offset = K_ * N_;
+  const int top_offset = M_ * N_;
+  for (int i = 0; i < top.size(); ++i) {
+    const Dtype* top_diff = NULL;
+    // Bias gradient, if necessary.
+    if (bias_term_ && this->param_propagate_down_[1]) {
+      top_diff = top[i]->gpu_diff();
+      for (int n = 0; n < num_; ++n) {
+        caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, N_,
+            1., top_diff + top[0]->offset(n),
+            bias_multiplier_.gpu_data(), 1.,
+            bias_diff);
+      }
     }
-    // gradient w.r.t. bottom data, if necessary
-    if (propagate_down) {
-      for (int g = 0; g < group_; ++g) {
-        caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
-          (Dtype)1., weight + weight_offset * g,
-          top_diff + top[0]->offset(n) + top_offset * g,
-          (Dtype)0., col_diff + col_offset * g);
+    if (this->param_propagate_down_[0] || propagate_down[i]) {
+      if (!top_diff) {
+        top_diff = top[i]->gpu_diff();
+      }
+      Dtype* col_data = col_buffer_.mutable_gpu_data();
+      Dtype* col_diff = col_buffer_.mutable_gpu_diff();
+      const Dtype* bottom_data = (*bottom)[i]->gpu_data();
+      Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
+      for (int n = 0; n < num_; ++n) {
+        // Since we saved memory in the forward pass by not storing all col
+        // data, we will need to recompute them.
+        im2col_gpu(bottom_data + (*bottom)[i]->offset(n), channels_, height_,
+                   width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+                   stride_h_, stride_w_, col_data);
+        // gradient w.r.t. weight. Note that we will accumulate diffs.
+        if (this->param_propagate_down_[0]) {
+          for (int g = 0; g < group_; ++g) {
+            caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_,
+                (Dtype)1., top_diff + top[i]->offset(n) + top_offset * g,
+                col_data + col_offset * g, (Dtype)1.,
+                weight_diff + weight_offset * g);
+          }
+        }
+        // gradient w.r.t. bottom data, if necessary
+        if (propagate_down[i]) {
+          for (int g = 0; g < group_; ++g) {
+            caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_,
+                (Dtype)1., weight + weight_offset * g,
+                top_diff + top[i]->offset(n) + top_offset * g,
+                (Dtype)0., col_diff + col_offset * g);
+          }
+          // col2im back to the data
+          col2im_gpu(col_diff, channels_, height_, width_,
+              kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_,
+              bottom_diff + (*bottom)[i]->offset(n));
+        }
       }
-      // col2im back to the data
-      col2im_gpu(col_diff, channels_, height_, width_, kernel_size_, pad_,
-          stride_, bottom_diff + (*bottom)[0]->offset(n));
     }
   }
 }
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 6d7392a6b2a..c089c9ba5df 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -1,67 +1,74 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <stdint.h>
 #include <leveldb/db.h>
-#include <pthread.h>
+#include <stdint.h>
 
 #include <string>
 #include <vector>
 
 #include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/rng.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::string;
-
 namespace caffe {
 
+// This function is used to create a pthread that prefetches the data.
 template <typename Dtype>
-void* DataLayerPrefetch(void* layer_pointer) {
-  CHECK(layer_pointer);
-  DataLayer<Dtype>* layer = static_cast<DataLayer<Dtype>*>(layer_pointer);
-  CHECK(layer);
+void DataLayer<Dtype>::InternalThreadEntry() {
   Datum datum;
-  CHECK(layer->prefetch_data_);
-  Dtype* top_data = layer->prefetch_data_->mutable_cpu_data();
-  Dtype* top_label;
-  if (layer->output_labels_) {
-    top_label = layer->prefetch_label_->mutable_cpu_data();
+  CHECK(prefetch_data_.count());
+  Dtype* top_data = prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
+  if (output_labels_) {
+    top_label = prefetch_label_.mutable_cpu_data();
   }
-  const Dtype scale = layer->layer_param_.data_param().scale();
-  const int batch_size = layer->layer_param_.data_param().batch_size();
-  const int crop_size = layer->layer_param_.data_param().crop_size();
-  const bool mirror = layer->layer_param_.data_param().mirror();
+  const Dtype scale = this->layer_param_.data_param().scale();
+  const int batch_size = this->layer_param_.data_param().batch_size();
+  const int crop_size = this->layer_param_.data_param().crop_size();
+  const bool mirror = this->layer_param_.data_param().mirror();
 
   if (mirror && crop_size == 0) {
     LOG(FATAL) << "Current implementation requires mirror and crop_size to be "
         << "set at the same time.";
   }
   // datum scales
-  const int channels = layer->datum_channels_;
-  const int height = layer->datum_height_;
-  const int width = layer->datum_width_;
-  const int size = layer->datum_size_;
-  const Dtype* mean = layer->data_mean_.cpu_data();
+  const int channels = datum_channels_;
+  const int height = datum_height_;
+  const int width = datum_width_;
+  const int size = datum_size_;
+  const Dtype* mean = data_mean_.cpu_data();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
-    CHECK(layer->iter_);
-    CHECK(layer->iter_->Valid());
-    datum.ParseFromString(layer->iter_->value().ToString());
+    switch (this->layer_param_.data_param().backend()) {
+    case DataParameter_DB_LEVELDB:
+      CHECK(iter_);
+      CHECK(iter_->Valid());
+      datum.ParseFromString(iter_->value().ToString());
+      break;
+    case DataParameter_DB_LMDB:
+      CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
+              &mdb_value_, MDB_GET_CURRENT), MDB_SUCCESS);
+      datum.ParseFromArray(mdb_value_.mv_data,
+          mdb_value_.mv_size);
+      break;
+    default:
+      LOG(FATAL) << "Unknown database backend";
+    }
+
     const string& data = datum.data();
     if (crop_size) {
       CHECK(data.size()) << "Image cropping only support uint8 data";
       int h_off, w_off;
       // We only do random crop when we do training.
-      if (layer->phase_ == Caffe::TRAIN) {
-        h_off = layer->PrefetchRand() % (height - crop_size);
-        w_off = layer->PrefetchRand() % (width - crop_size);
+      if (phase_ == Caffe::TRAIN) {
+        h_off = PrefetchRand() % (height - crop_size);
+        w_off = PrefetchRand() % (width - crop_size);
       } else {
         h_off = (height - crop_size) / 2;
         w_off = (width - crop_size) / 2;
       }
-      if (mirror && layer->PrefetchRand() % 2) {
+      if (mirror && PrefetchRand() % 2) {
         // Copy mirrored version
         for (int c = 0; c < channels; ++c) {
           for (int h = 0; h < crop_size; ++h) {
@@ -106,81 +113,151 @@ void* DataLayerPrefetch(void* layer_pointer) {
       }
     }
 
-    if (layer->output_labels_) {
+    if (output_labels_) {
       top_label[item_id] = datum.label();
     }
     // go to the next iter
-    layer->iter_->Next();
-    if (!layer->iter_->Valid()) {
-      // We have reached the end. Restart from the first.
-      DLOG(INFO) << "Restarting data prefetching from start.";
-      layer->iter_->SeekToFirst();
+    switch (this->layer_param_.data_param().backend()) {
+    case DataParameter_DB_LEVELDB:
+      iter_->Next();
+      if (!iter_->Valid()) {
+        // We have reached the end. Restart from the first.
+        DLOG(INFO) << "Restarting data prefetching from start.";
+        iter_->SeekToFirst();
+      }
+      break;
+    case DataParameter_DB_LMDB:
+      if (mdb_cursor_get(mdb_cursor_, &mdb_key_,
+              &mdb_value_, MDB_NEXT) != MDB_SUCCESS) {
+        // We have reached the end. Restart from the first.
+        DLOG(INFO) << "Restarting data prefetching from start.";
+        CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_,
+                &mdb_value_, MDB_FIRST), MDB_SUCCESS);
+      }
+      break;
+    default:
+      LOG(FATAL) << "Unknown database backend";
     }
   }
-
-  return static_cast<void*>(NULL);
 }
 
 template <typename Dtype>
 DataLayer<Dtype>::~DataLayer<Dtype>() {
   JoinPrefetchThread();
+  // clean up the database resources
+  switch (this->layer_param_.data_param().backend()) {
+  case DataParameter_DB_LEVELDB:
+    break;  // do nothing
+  case DataParameter_DB_LMDB:
+    mdb_cursor_close(mdb_cursor_);
+    mdb_close(mdb_env_, mdb_dbi_);
+    mdb_txn_abort(mdb_txn_);
+    mdb_env_close(mdb_env_);
+    break;
+  default:
+    LOG(FATAL) << "Unknown database backend";
+  }
 }
 
 template <typename Dtype>
 void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 0) << "Data Layer takes no input blobs.";
-  CHECK_GE(top->size(), 1) << "Data Layer takes at least one blob as output.";
-  CHECK_LE(top->size(), 2) << "Data Layer takes at most two blobs as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   if (top->size() == 1) {
     output_labels_ = false;
   } else {
     output_labels_ = true;
   }
-  // Initialize the leveldb
-  leveldb::DB* db_temp;
-  leveldb::Options options;
-  options.create_if_missing = false;
-  options.max_open_files = 100;
-  LOG(INFO) << "Opening leveldb " << this->layer_param_.data_param().source();
-  leveldb::Status status = leveldb::DB::Open(
-      options, this->layer_param_.data_param().source(), &db_temp);
-  CHECK(status.ok()) << "Failed to open leveldb "
-      << this->layer_param_.data_param().source() << std::endl
-      << status.ToString();
-  db_.reset(db_temp);
-  iter_.reset(db_->NewIterator(leveldb::ReadOptions()));
-  iter_->SeekToFirst();
+  // Initialize DB
+  switch (this->layer_param_.data_param().backend()) {
+  case DataParameter_DB_LEVELDB:
+    {
+    leveldb::DB* db_temp;
+    leveldb::Options options;
+    options.create_if_missing = false;
+    options.max_open_files = 100;
+    LOG(INFO) << "Opening leveldb " << this->layer_param_.data_param().source();
+    leveldb::Status status = leveldb::DB::Open(
+        options, this->layer_param_.data_param().source(), &db_temp);
+    CHECK(status.ok()) << "Failed to open leveldb "
+                       << this->layer_param_.data_param().source() << std::endl
+                       << status.ToString();
+    db_.reset(db_temp);
+    iter_.reset(db_->NewIterator(leveldb::ReadOptions()));
+    iter_->SeekToFirst();
+    }
+    break;
+  case DataParameter_DB_LMDB:
+    CHECK_EQ(mdb_env_create(&mdb_env_), MDB_SUCCESS) << "mdb_env_create failed";
+    CHECK_EQ(mdb_env_set_mapsize(mdb_env_, 1099511627776), MDB_SUCCESS);  // 1TB
+    CHECK_EQ(mdb_env_open(mdb_env_,
+             this->layer_param_.data_param().source().c_str(),
+             MDB_RDONLY|MDB_NOTLS, 0664), MDB_SUCCESS) << "mdb_env_open failed";
+    CHECK_EQ(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn_), MDB_SUCCESS)
+        << "mdb_txn_begin failed";
+    CHECK_EQ(mdb_open(mdb_txn_, NULL, 0, &mdb_dbi_), MDB_SUCCESS)
+        << "mdb_open failed";
+    CHECK_EQ(mdb_cursor_open(mdb_txn_, mdb_dbi_, &mdb_cursor_), MDB_SUCCESS)
+        << "mdb_cursor_open failed";
+    LOG(INFO) << "Opening lmdb " << this->layer_param_.data_param().source();
+    CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, MDB_FIRST),
+        MDB_SUCCESS) << "mdb_cursor_get failed";
+    break;
+  default:
+    LOG(FATAL) << "Unknown database backend";
+  }
+
   // Check if we would need to randomly skip a few data points
   if (this->layer_param_.data_param().rand_skip()) {
     unsigned int skip = caffe_rng_rand() %
                         this->layer_param_.data_param().rand_skip();
     LOG(INFO) << "Skipping first " << skip << " data points.";
     while (skip-- > 0) {
-      iter_->Next();
-      if (!iter_->Valid()) {
-        iter_->SeekToFirst();
+      switch (this->layer_param_.data_param().backend()) {
+      case DataParameter_DB_LEVELDB:
+        iter_->Next();
+        if (!iter_->Valid()) {
+          iter_->SeekToFirst();
+        }
+        break;
+      case DataParameter_DB_LMDB:
+        if (mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, MDB_NEXT)
+            != MDB_SUCCESS) {
+          CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_,
+                   MDB_FIRST), MDB_SUCCESS);
+        }
+        break;
+      default:
+        LOG(FATAL) << "Unknown database backend";
       }
     }
   }
   // Read a data point, and use it to initialize the top blob.
   Datum datum;
-  datum.ParseFromString(iter_->value().ToString());
+  switch (this->layer_param_.data_param().backend()) {
+  case DataParameter_DB_LEVELDB:
+    datum.ParseFromString(iter_->value().ToString());
+    break;
+  case DataParameter_DB_LMDB:
+    datum.ParseFromArray(mdb_value_.mv_data, mdb_value_.mv_size);
+    break;
+  default:
+    LOG(FATAL) << "Unknown database backend";
+  }
+
   // image
   int crop_size = this->layer_param_.data_param().crop_size();
   if (crop_size > 0) {
     (*top)[0]->Reshape(this->layer_param_.data_param().batch_size(),
                        datum.channels(), crop_size, crop_size);
-    prefetch_data_.reset(new Blob<Dtype>(
-        this->layer_param_.data_param().batch_size(), datum.channels(),
-        crop_size, crop_size));
+    prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
+        datum.channels(), crop_size, crop_size);
   } else {
     (*top)[0]->Reshape(
         this->layer_param_.data_param().batch_size(), datum.channels(),
         datum.height(), datum.width());
-    prefetch_data_.reset(new Blob<Dtype>(
-        this->layer_param_.data_param().batch_size(), datum.channels(),
-        datum.height(), datum.width()));
+    prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
+        datum.channels(), datum.height(), datum.width());
   }
   LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
       << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
@@ -188,8 +265,8 @@ void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   if (output_labels_) {
     (*top)[1]->Reshape(this->layer_param_.data_param().batch_size(), 1, 1, 1);
-    prefetch_label_.reset(
-        new Blob<Dtype>(this->layer_param_.data_param().batch_size(), 1, 1, 1));
+    prefetch_label_.Reshape(this->layer_param_.data_param().batch_size(),
+        1, 1, 1);
   }
   // datum size
   datum_channels_ = datum.channels();
@@ -217,9 +294,9 @@ void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   // cpu_data calls so that the prefetch thread does not accidentally make
   // simultaneous cudaMalloc calls when the main thread is running. In some
   // GPUs this seems to cause failures if we do not so.
-  prefetch_data_->mutable_cpu_data();
+  prefetch_data_.mutable_cpu_data();
   if (output_labels_) {
-    prefetch_label_->mutable_cpu_data();
+    prefetch_label_.mutable_cpu_data();
   }
   data_mean_.cpu_data();
   DLOG(INFO) << "Initializing prefetch";
@@ -239,14 +316,12 @@ void DataLayer<Dtype>::CreatePrefetchThread() {
   } else {
     prefetch_rng_.reset();
   }
-  // Create the thread.
-  CHECK(!pthread_create(&thread_, NULL, DataLayerPrefetch<Dtype>,
-        static_cast<void*>(this))) << "Pthread execution failed.";
+  CHECK(!StartInternalThread()) << "Pthread execution failed";
 }
 
 template <typename Dtype>
 void DataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!pthread_join(thread_, NULL)) << "Pthread joining failed.";
+  CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed";
 }
 
 template <typename Dtype>
@@ -263,10 +338,10 @@ Dtype DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
              (*top)[0]->mutable_cpu_data());
   if (output_labels_) {
-    caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+    caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
                (*top)[1]->mutable_cpu_data());
   }
   // Start a new prefetch thread
@@ -274,6 +349,10 @@ Dtype DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(DataLayer, Forward);
+#endif
+
 INSTANTIATE_CLASS(DataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/data_layer.cu b/src/caffe/layers/data_layer.cu
index 2ff9a292b3e..2ae1a640319 100644
--- a/src/caffe/layers/data_layer.cu
+++ b/src/caffe/layers/data_layer.cu
@@ -1,18 +1,14 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <stdint.h>
-#include <leveldb/db.h>
-#include <pthread.h>
-
 #include <string>
 #include <vector>
 
+#include "leveldb/db.h"
+#include "pthread.h"
+#include "stdint.h"
+
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::string;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -21,13 +17,11 @@ Dtype DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  CUDA_CHECK(cudaMemcpy((*top)[0]->mutable_gpu_data(),
-      prefetch_data_->cpu_data(), sizeof(Dtype) * prefetch_data_->count(),
-      cudaMemcpyHostToDevice));
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
+      (*top)[0]->mutable_gpu_data());
   if (output_labels_) {
-    CUDA_CHECK(cudaMemcpy((*top)[1]->mutable_gpu_data(),
-        prefetch_label_->cpu_data(), sizeof(Dtype) * prefetch_label_->count(),
-        cudaMemcpyHostToDevice));
+    caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
+        (*top)[1]->mutable_gpu_data());
   }
   // Start a new prefetch thread
   CreatePrefetchThread();
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index e1b69f363b4..0621b56ec6e 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -1,13 +1,11 @@
-// Copyright 2014 BVLC and contributors.
-
 // TODO (sergeyk): effect should not be dependent on phase. wasted memcpy.
 
 #include <vector>
 
 #include "caffe/common.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/syncedmem.hpp"
+#include "caffe/util/math_functions.hpp"
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
@@ -17,7 +15,8 @@ void DropoutLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   NeuronLayer<Dtype>::SetUp(bottom, top);
   // Set up the cache for random number generation
-  rand_vec_.reset(new SyncedMemory(bottom[0]->count() * sizeof(int)));
+  rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(),
+      bottom[0]->height(), bottom[0]->width());
   threshold_ = this->layer_param_.dropout_param().dropout_ratio();
   DCHECK(threshold_ > 0.);
   DCHECK(threshold_ < 1.);
@@ -30,7 +29,7 @@ Dtype DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  int* mask = reinterpret_cast<int*>(rand_vec_->mutable_cpu_data());
+  unsigned int* mask = rand_vec_.mutable_cpu_data();
   const int count = bottom[0]->count();
   if (Caffe::phase() == Caffe::TRAIN) {
     // Create random numbers
@@ -46,21 +45,28 @@ Dtype DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  CHECK(Caffe::phase() == Caffe::TRAIN);
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-    const int* mask = reinterpret_cast<const int*>(rand_vec_->cpu_data());
-    const int count = (*bottom)[0]->count();
-    for (int i = 0; i < count; ++i) {
-      bottom_diff[i] = top_diff[i] * mask[i] * scale_;
+    if (Caffe::phase() == Caffe::TRAIN) {
+      const unsigned int* mask = rand_vec_.cpu_data();
+      const int count = (*bottom)[0]->count();
+      for (int i = 0; i < count; ++i) {
+        bottom_diff[i] = top_diff[i] * mask[i] * scale_;
+      }
+    } else {
+      caffe_copy(top[0]->count(), top_diff, bottom_diff);
     }
   }
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(DropoutLayer);
+#endif
+
 INSTANTIATE_CLASS(DropoutLayer);
 
 
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index 3c25d6a1292..9bcd687bb5d 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <limits>
 #include <vector>
@@ -7,10 +5,8 @@
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/syncedmem.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -32,7 +28,7 @@ Dtype DropoutLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const int count = bottom[0]->count();
   if (Caffe::phase() == Caffe::TRAIN) {
     unsigned int* mask =
-        static_cast<unsigned int*>(rand_vec_->mutable_gpu_data());
+        static_cast<unsigned int*>(rand_vec_.mutable_gpu_data());
     caffe_gpu_rng_uniform(count, mask);
     // set thresholds
     // NOLINT_NEXT_LINE(whitespace/operators)
@@ -40,7 +36,7 @@ Dtype DropoutLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
         count, bottom_data, mask, uint_thres_, scale_, top_data);
     CUDA_POST_KERNEL_CHECK;
   } else {
-    caffe_gpu_copy(count, bottom_data, top_data);
+    caffe_copy(count, bottom_data, top_data);
   }
   return Dtype(0);
 }
@@ -56,19 +52,23 @@ __global__ void DropoutBackward(const int n, const Dtype* in_diff,
 
 template <typename Dtype>
 void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  CHECK(Caffe::phase() == Caffe::TRAIN);
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-    const unsigned int* mask =
-        static_cast<const unsigned int*>(rand_vec_->gpu_data());
-    const int count = (*bottom)[0]->count();
-    // NOLINT_NEXT_LINE(whitespace/operators)
-    DropoutBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, mask, uint_thres_, scale_, bottom_diff);
-    CUDA_POST_KERNEL_CHECK;
+    if (Caffe::phase() == Caffe::TRAIN) {
+      const unsigned int* mask =
+          static_cast<const unsigned int*>(rand_vec_.gpu_data());
+      const int count = (*bottom)[0]->count();
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      DropoutBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
+        CAFFE_CUDA_NUM_THREADS>>>(
+          count, top_diff, mask, uint_thres_, scale_, bottom_diff);
+      CUDA_POST_KERNEL_CHECK;
+    } else {
+      caffe_copy(top[0]->count(), top_diff, bottom_diff);
+    }
   }
 }
 
diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
new file mode 100644
index 00000000000..98b437eea98
--- /dev/null
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -0,0 +1,98 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void DummyDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  const int num_top = top->size();
+  const DummyDataParameter& param = this->layer_param_.dummy_data_param();
+  const int num_data_filler = param.data_filler_size();
+  CHECK(num_data_filler == 0 || num_data_filler == 1 ||
+        num_data_filler == num_top)
+      << "Number of data fillers must be 0, 1 or equal to the number of tops: "
+      << num_top << "; you specified " << num_data_filler << " data fillers.";
+  CHECK(param.num_size() == 1 || param.num_size() == num_top)
+      << "Must specify either a single (1) 'num' or one for each top blob "
+      << "(" << num_top << "); you specified " << param.num_size() << ".";
+  CHECK(param.channels_size() == 1 || param.channels_size() == num_top)
+      << "Must specify either a single (1) 'channels' or one for each top blob "
+      << "(" << num_top << "); you specified " << param.channels_size() << ".";
+  CHECK(param.height_size() == 1 || param.height_size() == num_top)
+      << "Must specify either a single (1) 'height' or one for each top blob "
+      << "(" << num_top << "); you specified " << param.height_size() << ".";
+  CHECK(param.width_size() == 1 || param.width_size() == num_top)
+      << "Must specify either a single (1) 'width' or one for each top blob "
+      << "(" << num_top << "); you specified " << param.width_size() << ".";
+  // refill_[i] tells Forward i whether or not to actually refill top Blob i.
+  // If refill_[i] is false, Forward does nothing for Blob i. We use this to
+  // avoid wastefully refilling "constant" Blobs in every forward pass.
+  // We first fill refill_ in with the INVERSE of its final values.
+  // The first time we run Forward from the SetUp method, we'll fill only the
+  // Blobs for which refill_ is normally false.  These Blobs will never be
+  // filled again.
+  refill_.clear();
+  fillers_.clear();
+  if (num_data_filler <= 1) {
+    FillerParameter filler_param;
+    if (num_data_filler == 0) {
+      filler_param.set_type("constant");
+      filler_param.set_value(0);
+    } else {
+      filler_param.CopyFrom(param.data_filler(0));
+    }
+    // Refill on each iteration iff not using a constant filler,
+    // but use the inverse of this rule for the first run.
+    refill_.resize(1);
+    refill_[0] = (strcmp(filler_param.type().c_str(), "constant") == 0);
+    fillers_.resize(1);
+    fillers_[0].reset(GetFiller<Dtype>(filler_param));
+  } else {
+    refill_.resize(num_top);
+    fillers_.resize(num_top);
+    for (int i = 0; i < num_top; ++i) {
+      fillers_[i].reset(GetFiller<Dtype>(param.data_filler(i)));
+      // Refill on each iteration iff not using a constant filler,
+      // but use the inverse of this rule for the first run.
+      refill_[i] =
+          (strcmp(param.data_filler(i).type().c_str(), "constant") == 0);
+    }
+  }
+  for (int i = 0; i < num_top; ++i) {
+    const int num = (param.num_size() == 1) ? param.num(0) : param.num(i);
+    const int channels =
+        (param.channels_size() == 1) ? param.channels(0) : param.channels(i);
+    const int height =
+        (param.height_size() == 1) ? param.height(0) : param.height(i);
+    const int width =
+        (param.width_size() == 1) ? param.width(0) : param.width(i);
+    (*top)[i]->Reshape(num, channels, height, width);
+  }
+  // Run Forward once, with refill_ inverted, to fill the constant Blobs.
+  this->Forward(bottom, top);
+  // Invert the inverted refill_ values to refill the desired (non-constant)
+  // Blobs in every usual forward pass.
+  for (int i = 0; i < refill_.size(); ++i) {
+    refill_[i] = !refill_[i];
+  }
+}
+
+template <typename Dtype>
+Dtype DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  for (int i = 0; i < top->size(); ++i) {
+    const int filler_id = (fillers_.size() > 1) ? i : 0;
+    if (refill_[filler_id]) {
+      fillers_[filler_id]->Fill((*top)[i]);
+    }
+  }
+  return Dtype(0.);
+}
+
+INSTANTIATE_CLASS(DummyDataLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
new file mode 100644
index 00000000000..8085b4644cc
--- /dev/null
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -0,0 +1,102 @@
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void EltwiseLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  Layer<Dtype>::SetUp(bottom, top);
+  CHECK(this->layer_param().eltwise_param().coeff_size() == 0
+      || this->layer_param().eltwise_param().coeff_size() == bottom.size()) <<
+      "Eltwise Layer takes one coefficient per bottom blob.";
+  CHECK(!(this->layer_param().eltwise_param().operation()
+      == EltwiseParameter_EltwiseOp_PROD
+      && this->layer_param().eltwise_param().coeff_size())) <<
+      "Eltwise layer only takes coefficients for summation.";
+  const int num = bottom[0]->num();
+  const int channels = bottom[0]->channels();
+  const int height = bottom[0]->height();
+  const int width = bottom[0]->width();
+  for (int i = 1; i < bottom.size(); ++i) {
+    CHECK_EQ(num, bottom[i]->num());
+    CHECK_EQ(channels, bottom[i]->channels());
+    CHECK_EQ(height, bottom[i]->height());
+    CHECK_EQ(width, bottom[i]->width());
+  }
+  (*top)[0]->Reshape(num, channels, height, width);
+  op_ = this->layer_param_.eltwise_param().operation();
+  // Blob-wise coefficients for the elementwise operation.
+  coeffs_ = vector<Dtype>(bottom.size(), 1);
+  if (this->layer_param().eltwise_param().coeff_size()) {
+    for (int i = 0; i < bottom.size(); ++i) {
+      coeffs_[i] = this->layer_param().eltwise_param().coeff(i);
+    }
+  }
+}
+
+template <typename Dtype>
+Dtype EltwiseLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const int count = (*top)[0]->count();
+  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  switch (op_) {
+  case EltwiseParameter_EltwiseOp_PROD:
+    caffe_mul(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(), top_data);
+    for (int i = 2; i < bottom.size(); ++i) {
+      caffe_mul(count, top_data, bottom[i]->cpu_data(), top_data);
+    }
+    break;
+  case EltwiseParameter_EltwiseOp_SUM:
+    caffe_set(count, Dtype(0), top_data);
+    // TODO(shelhamer) does BLAS optimize to sum for coeff = 1?
+    for (int i = 0; i < bottom.size(); ++i) {
+      caffe_axpy(count, coeffs_[i], bottom[i]->cpu_data(), top_data);
+    }
+    break;
+  default:
+    LOG(FATAL) << "Unknown elementwise operation.";
+  }
+  return Dtype(0.);
+}
+
+template <typename Dtype>
+void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const int count = top[0]->count();
+  const Dtype* top_data = top[0]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  for (int i = 0; i < bottom->size(); ++i) {
+    if (propagate_down[i]) {
+      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
+      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
+      switch (op_) {
+      case EltwiseParameter_EltwiseOp_PROD:
+        caffe_div(count, top_data, bottom_data, bottom_diff);
+        caffe_mul(count, bottom_diff, top_diff, bottom_diff);
+        break;
+      case EltwiseParameter_EltwiseOp_SUM:
+        if (coeffs_[i] == Dtype(1)) {
+          caffe_copy(count, top_diff, bottom_diff);
+        } else {
+          caffe_cpu_scale(count, coeffs_[i], top_diff, bottom_diff);
+        }
+        break;
+      default:
+        LOG(FATAL) << "Unknown elementwise operation.";
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(EltwiseLayer);
+#endif
+
+INSTANTIATE_CLASS(EltwiseLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
new file mode 100644
index 00000000000..eec8857c577
--- /dev/null
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -0,0 +1,67 @@
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+Dtype EltwiseLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
+  const int count = (*top)[0]->count();
+  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  switch (op_) {
+  case EltwiseParameter_EltwiseOp_PROD:
+    caffe_gpu_mul(count, bottom[0]->gpu_data(),
+        bottom[1]->gpu_data(), top_data);
+    for (int i = 2; i < bottom.size(); ++i) {
+      caffe_gpu_mul(count, top_data, bottom[i]->gpu_data(), top_data);
+    }
+    break;
+  case EltwiseParameter_EltwiseOp_SUM:
+    caffe_gpu_set(count, Dtype(0.), top_data);
+    // TODO(shelhamer) does cuBLAS optimize to sum for coeff = 1?
+    for (int i = 0; i < bottom.size(); ++i) {
+      caffe_gpu_axpy(count, coeffs_[i], bottom[i]->gpu_data(), top_data);
+    }
+    break;
+  default:
+    LOG(FATAL) << "Unknown elementwise operation.";
+  }
+  return Dtype(0.);
+}
+
+template <typename Dtype>
+void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  const int count = top[0]->count();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* top_diff = top[0]->gpu_diff();
+  for (int i = 0; i < bottom->size(); ++i) {
+    if (propagate_down[i]) {
+      const Dtype* bottom_data = (*bottom)[i]->gpu_data();
+      Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
+      switch (op_) {
+      case EltwiseParameter_EltwiseOp_PROD:
+        caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
+        caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
+        break;
+      case EltwiseParameter_EltwiseOp_SUM:
+        if (coeffs_[i] == Dtype(1.)) {
+          caffe_copy(count, top_diff, bottom_diff);
+        } else {
+          caffe_gpu_scale(count, coeffs_[i], top_diff, bottom_diff);
+        }
+        break;
+      default:
+        LOG(FATAL) << "Unknown elementwise operation.";
+      }
+    }
+  }
+}
+
+INSTANTIATE_CLASS(EltwiseLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/eltwise_product_layer.cpp b/src/caffe/layers/eltwise_product_layer.cpp
deleted file mode 100644
index b394450d6ae..00000000000
--- a/src/caffe/layers/eltwise_product_layer.cpp
+++ /dev/null
@@ -1,62 +0,0 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <vector>
-
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-void EltwiseProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  CHECK_GE(bottom.size(), 2) <<
-      "Eltwise Product Layer takes at least 2 blobs as input.";
-  CHECK_EQ(top->size(), 1) <<
-      "Eltwise Product Layer takes a single blob as output.";
-  const int num = bottom[0]->num();
-  const int channels = bottom[0]->channels();
-  const int height = bottom[0]->height();
-  const int width = bottom[0]->width();
-  for (int i = 1; i < bottom.size(); ++i) {
-    CHECK_EQ(num, bottom[i]->num());
-    CHECK_EQ(channels, bottom[i]->channels());
-    CHECK_EQ(height, bottom[i]->height());
-    CHECK_EQ(width, bottom[i]->width());
-  }
-  (*top)[0]->Reshape(num, channels, height, width);
-}
-
-template <typename Dtype>
-Dtype EltwiseProductLayer<Dtype>::Forward_cpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  const int count = (*top)[0]->count();
-  Dtype* top_data = (*top)[0]->mutable_cpu_data();
-  caffe_mul(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(), top_data);
-  for (int i = 2; i < bottom.size(); ++i) {
-    caffe_mul(count, top_data, bottom[i]->cpu_data(), top_data);
-  }
-  return Dtype(0.);
-}
-
-template <typename Dtype>
-void EltwiseProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
-    const int count = top[0]->count();
-    const Dtype* top_data = top[0]->cpu_data();
-    const Dtype* top_diff = top[0]->cpu_diff();
-    for (int i = 0; i < bottom->size(); ++i) {
-      const Dtype* bottom_data = (*bottom)[i]->cpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_cpu_diff();
-      caffe_div(count, top_data, bottom_data, bottom_diff);
-      caffe_mul(count, bottom_diff, top_diff, bottom_diff);
-    }
-  }
-}
-
-INSTANTIATE_CLASS(EltwiseProductLayer);
-
-
-}  // namespace caffe
diff --git a/src/caffe/layers/eltwise_product_layer.cu b/src/caffe/layers/eltwise_product_layer.cu
deleted file mode 100644
index 9c66033c20f..00000000000
--- a/src/caffe/layers/eltwise_product_layer.cu
+++ /dev/null
@@ -1,42 +0,0 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <vector>
-
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
-
-namespace caffe {
-
-template <typename Dtype>
-Dtype EltwiseProductLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  const int count = (*top)[0]->count();
-  Dtype* top_data = (*top)[0]->mutable_gpu_data();
-  caffe_gpu_mul(count, bottom[0]->gpu_data(), bottom[1]->gpu_data(), top_data);
-  for (int i = 2; i < bottom.size(); ++i) {
-    caffe_gpu_mul(count, top_data, bottom[i]->gpu_data(), top_data);
-  }
-  return Dtype(0.);
-}
-
-template <typename Dtype>
-void EltwiseProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
-    const int count = top[0]->count();
-    const Dtype* top_data = top[0]->gpu_data();
-    const Dtype* top_diff = top[0]->gpu_diff();
-    for (int i = 0; i < bottom->size(); ++i) {
-      const Dtype* bottom_data = (*bottom)[i]->gpu_data();
-      Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
-      caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
-      caffe_gpu_mul(count, bottom_diff, top_diff, bottom_diff);
-    }
-  }
-}
-
-INSTANTIATE_CLASS(EltwiseProductLayer);
-
-
-}  // namespace caffe
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index a894d470c64..17180d40b69 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -1,16 +1,9 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <algorithm>
-#include <cmath>
-#include <cfloat>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
-
-using std::max;
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -35,20 +28,32 @@ Dtype EuclideanLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       diff_.mutable_cpu_data());
   Dtype dot = caffe_cpu_dot(count, diff_.cpu_data(), diff_.cpu_data());
   Dtype loss = dot / bottom[0]->num() / Dtype(2);
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss;
+  }
   return loss;
 }
 
 template <typename Dtype>
 void EuclideanLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  caffe_cpu_axpby(
-      (*bottom)[0]->count(),              // count
-      Dtype(1) / (*bottom)[0]->num(),     // alpha
-      diff_.cpu_data(),                   // a
-      Dtype(0),                           // beta
-      (*bottom)[0]->mutable_cpu_diff());  // b
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  for (int i = 0; i < 2; ++i) {
+    if (propagate_down[i]) {
+      const Dtype sign = (i == 0) ? 1 : -1;
+      caffe_cpu_axpby(
+          (*bottom)[i]->count(),              // count
+          sign / (*bottom)[i]->num(),         // alpha
+          diff_.cpu_data(),                   // a
+          Dtype(0),                           // beta
+          (*bottom)[i]->mutable_cpu_diff());  // b
+    }
+  }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(EuclideanLossLayer);
+#endif
+
 INSTANTIATE_CLASS(EuclideanLossLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
new file mode 100644
index 00000000000..dd14f1995c8
--- /dev/null
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -0,0 +1,43 @@
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+Dtype EuclideanLossLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  int count = bottom[0]->count();
+  caffe_gpu_sub(
+      count,
+      bottom[0]->gpu_data(),
+      bottom[1]->gpu_data(),
+      diff_.mutable_gpu_data());
+  Dtype dot;
+  caffe_gpu_dot(count, diff_.gpu_data(), diff_.gpu_data(), &dot);
+  Dtype loss = dot / bottom[0]->num() / Dtype(2);
+  return loss;
+}
+
+template <typename Dtype>
+void EuclideanLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  for (int i = 0; i < 2; ++i) {
+    if (propagate_down[i]) {
+      const Dtype sign = (i == 0) ? 1 : -1;
+      caffe_gpu_axpby(
+          (*bottom)[i]->count(),              // count
+          sign / (*bottom)[i]->num(),         // alpha
+          diff_.gpu_data(),                   // a
+          Dtype(0),                           // beta
+          (*bottom)[i]->mutable_gpu_diff());  // b
+    }
+  }
+}
+
+INSTANTIATE_CLASS(EuclideanLossLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index e954030d260..81a506a8177 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -1,18 +1,15 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Flatten Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "Flatten Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   int channels_out = bottom[0]->channels() * bottom[0]->height()
       * bottom[0]->width();
   (*top)[0]->Reshape(bottom[0]->num(), channels_out, 1, 1);
@@ -30,10 +27,14 @@ Dtype FlattenLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   (*bottom)[0]->ShareDiff(*top[0]);
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(FlattenLayer);
+#endif
+
 INSTANTIATE_CLASS(FlattenLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/flatten_layer.cu b/src/caffe/layers/flatten_layer.cu
index 157eeb1dcdc..7233afb3718 100644
--- a/src/caffe/layers/flatten_layer.cu
+++ b/src/caffe/layers/flatten_layer.cu
@@ -1,10 +1,8 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -17,7 +15,7 @@ Dtype FlattenLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   (*bottom)[0]->ShareDiff(*top[0]);
 }
 
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index cff4f7c7318..5bad5f6a1f0 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 /*
 TODO:
 - load file in a separate thread ("prefetch")
@@ -7,13 +6,13 @@
   :: don't forget to update hdf5_daa_layer.cu accordingly
 - add ability to shuffle filenames if flag is set
 */
-#include <stdint.h>
+#include <fstream>  // NOLINT(readability/streams)
 #include <string>
 #include <vector>
-#include <fstream>  // NOLINT(readability/streams)
 
 #include "hdf5.h"
 #include "hdf5_hl.h"
+#include "stdint.h"
 
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
@@ -45,6 +44,7 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
     file_id, "label", MIN_LABEL_DIM, MAX_LABEL_DIM, &label_blob_);
 
   herr_t status = H5Fclose(file_id);
+  CHECK_GE(status, 0) << "Failed to close HDF5 file " << filename;
   CHECK_EQ(data_blob_.num(), label_blob_.num());
   LOG(INFO) << "Successully loaded " << data_blob_.num() << " rows";
 }
@@ -52,9 +52,7 @@ void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {
 template <typename Dtype>
 void HDF5DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 0) << "HDF5DataLayer takes no input blobs.";
-  CHECK_EQ(top->size(), 2) << "HDF5DataLayer takes two blobs as output.";
-
+  Layer<Dtype>::SetUp(bottom, top);
   // Read the source to parse the filenames.
   const string& source = this->layer_param_.hdf5_data_param().source();
   LOG(INFO) << "Loading filename from " << source;
@@ -115,10 +113,9 @@ Dtype HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
-// The backward operations are dummy - they do not carry any computation.
-template <typename Dtype>
-void HDF5DataLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) { }
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(HDF5DataLayer, Forward);
+#endif
 
 INSTANTIATE_CLASS(HDF5DataLayer);
 
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 9c5bb5a818f..1f682d5726d 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 /*
 TODO:
 - only load parts of the file, in accordance with a prototxt param "max_mem"
@@ -15,8 +14,6 @@ TODO:
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::string;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -40,25 +37,16 @@ Dtype HDF5DataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       }
       current_row_ = 0;
     }
-    CUDA_CHECK(cudaMemcpy(
-            &(*top)[0]->mutable_gpu_data()[i * data_count],
-            &data_blob_.cpu_data()[current_row_ * data_count],
-            sizeof(Dtype) * data_count,
-            cudaMemcpyHostToDevice));
-    CUDA_CHECK(cudaMemcpy(
-            &(*top)[1]->mutable_gpu_data()[i * label_data_count],
-            &label_blob_.cpu_data()[current_row_ * label_data_count],
-            sizeof(Dtype) * label_data_count,
-            cudaMemcpyHostToDevice));
+    caffe_copy(data_count,
+        &data_blob_.cpu_data()[current_row_ * data_count],
+        &(*top)[0]->mutable_gpu_data()[i * data_count]);
+    caffe_copy(label_data_count,
+        &label_blob_.cpu_data()[current_row_ * label_data_count],
+        &(*top)[1]->mutable_gpu_data()[i * label_data_count]);
   }
   return Dtype(0.);
 }
 
-template <typename Dtype>
-void HDF5DataLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-}
-
 INSTANTIATE_CLASS(HDF5DataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index e491697e17c..0d7590b1b12 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "hdf5.h"
@@ -12,7 +10,6 @@
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
-using std::vector;
 
 template <typename Dtype>
 HDF5OutputLayer<Dtype>::HDF5OutputLayer(const LayerParameter& param)
@@ -41,14 +38,6 @@ void HDF5OutputLayer<Dtype>::SaveBlobs() {
   LOG(INFO) << "Successfully saved " << data_blob_.num() << " rows";
 }
 
-template <typename Dtype>
-void HDF5OutputLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
-      vector<Blob<Dtype>*>* top) {
-  // TODO: no limit on the number of blobs
-  CHECK_EQ(bottom.size(), 2) << "HDF5OutputLayer takes two blobs as input.";
-  CHECK_EQ(top->size(), 0) << "HDF5OutputLayer takes no output blobs.";
-}
-
 template <typename Dtype>
 Dtype HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
@@ -62,12 +51,10 @@ Dtype HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const int label_datum_dim = bottom[1]->count() / bottom[1]->num();
 
   for (int i = 0; i < bottom[0]->num(); ++i) {
-    memcpy(&data_blob_.mutable_cpu_data()[i * data_datum_dim],
-           &bottom[0]->cpu_data()[i * data_datum_dim],
-           sizeof(Dtype) * data_datum_dim);
-    memcpy(&label_blob_.mutable_cpu_data()[i * label_datum_dim],
-           &bottom[1]->cpu_data()[i * label_datum_dim],
-           sizeof(Dtype) * label_datum_dim);
+    caffe_copy(data_datum_dim, &bottom[0]->cpu_data()[i * data_datum_dim],
+        &data_blob_.mutable_cpu_data()[i * data_datum_dim]);
+    caffe_copy(label_datum_dim, &bottom[1]->cpu_data()[i * label_datum_dim],
+        &label_blob_.mutable_cpu_data()[i * label_datum_dim]);
   }
   SaveBlobs();
   return Dtype(0.);
@@ -75,10 +62,14 @@ Dtype HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   return;
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(HDF5OutputLayer);
+#endif
+
 INSTANTIATE_CLASS(HDF5OutputLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index b9948252285..d2f20b3fc79 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "hdf5.h"
@@ -12,7 +10,6 @@
 #include "caffe/vision_layers.hpp"
 
 namespace caffe {
-using std::vector;
 
 template <typename Dtype>
 Dtype HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
@@ -27,12 +24,10 @@ Dtype HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const int label_datum_dim = bottom[1]->count() / bottom[1]->num();
 
   for (int i = 0; i < bottom[0]->num(); ++i) {
-    CUDA_CHECK(cudaMemcpy(&data_blob_.mutable_cpu_data()[i * data_datum_dim],
-           &bottom[0]->gpu_data()[i * data_datum_dim],
-           sizeof(Dtype) * data_datum_dim, cudaMemcpyDeviceToHost));
-    CUDA_CHECK(cudaMemcpy(&label_blob_.mutable_cpu_data()[i * label_datum_dim],
-           &bottom[1]->gpu_data()[i * label_datum_dim],
-           sizeof(Dtype) * label_datum_dim, cudaMemcpyDeviceToHost));
+    caffe_copy(data_datum_dim, &bottom[0]->gpu_data()[i * data_datum_dim],
+        &data_blob_.mutable_cpu_data()[i * data_datum_dim]);
+    caffe_copy(label_datum_dim, &bottom[1]->gpu_data()[i * label_datum_dim],
+        &label_blob_.mutable_cpu_data()[i * label_datum_dim]);
   }
   SaveBlobs();
   return Dtype(0.);
@@ -40,7 +35,7 @@ Dtype HDF5OutputLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void HDF5OutputLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   return;
 }
 
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index 24329fba028..bc3a593c769 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -1,16 +1,12 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
-#include <cmath>
 #include <cfloat>
+#include <cmath>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
-
-using std::max;
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -30,26 +26,50 @@ Dtype HingeLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   }
   for (int i = 0; i < num; ++i) {
     for (int j = 0; j < dim; ++j) {
-      bottom_diff[i * dim + j] = max(Dtype(0), 1 + bottom_diff[i * dim + j]);
+      bottom_diff[i * dim + j] = std::max(
+        Dtype(0), 1 + bottom_diff[i * dim + j]);
     }
   }
-  return caffe_cpu_asum(count, bottom_diff) / num;
+  switch (this->layer_param_.hinge_loss_param().norm()) {
+  case HingeLossParameter_Norm_L1:
+    return caffe_cpu_asum(count, bottom_diff) / num;
+  case HingeLossParameter_Norm_L2:
+    return caffe_cpu_dot(count, bottom_diff, bottom_diff) / num;
+  default:
+    LOG(FATAL) << "Unknown Norm";
+  }
 }
 
 template <typename Dtype>
 void HingeLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  const Dtype* label = (*bottom)[1]->cpu_data();
-  int num = (*bottom)[0]->num();
-  int count = (*bottom)[0]->count();
-  int dim = count / num;
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    const Dtype* label = (*bottom)[1]->cpu_data();
+    int num = (*bottom)[0]->num();
+    int count = (*bottom)[0]->count();
+    int dim = count / num;
 
-  caffe_cpu_sign(count, bottom_diff, bottom_diff);
-  for (int i = 0; i < num; ++i) {
-    bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
+    for (int i = 0; i < num; ++i) {
+      bottom_diff[i * dim + static_cast<int>(label[i])] *= -1;
+    }
+
+    switch (this->layer_param_.hinge_loss_param().norm()) {
+    case HingeLossParameter_Norm_L1:
+      caffe_cpu_sign(count, bottom_diff, bottom_diff);
+      caffe_scal(count, Dtype(1. / num), bottom_diff);
+      break;
+    case HingeLossParameter_Norm_L2:
+      caffe_scal(count, Dtype(2. / num), bottom_diff);
+      break;
+    default:
+      LOG(FATAL) << "Unknown Norm";
+    }
   }
-  caffe_scal(count, Dtype(1. / num), bottom_diff);
 }
 
 INSTANTIATE_CLASS(HingeLossLayer);
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 749ea3c2d6a..2dd7476237a 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -1,28 +1,58 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
+#include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/im2col.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/common.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Im2col Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "Im2col Layer takes a single blob as output.";
-  kernel_size_ = this->layer_param_.convolution_param().kernel_size();
-  stride_ = this->layer_param_.convolution_param().stride();
-  pad_ = this->layer_param_.convolution_param().pad();
+  Layer<Dtype>::SetUp(bottom, top);
+  ConvolutionParameter conv_param = this->layer_param_.convolution_param();
+  CHECK(!conv_param.has_kernel_size() !=
+      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
+  CHECK(conv_param.has_kernel_size() ||
+      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
+      << "For non-square filters both kernel_h and kernel_w are required.";
+  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
+      && conv_param.has_pad_w())
+      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
+      << "pad is pad OR pad_h and pad_w are required.";
+  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
+      && conv_param.has_stride_w())
+      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
+      << "Stride is stride OR stride_h and stride_w are required.";
+  if (conv_param.has_kernel_size()) {
+    kernel_h_ = kernel_w_ = conv_param.kernel_size();
+  } else {
+    kernel_h_ = conv_param.kernel_h();
+    kernel_w_ = conv_param.kernel_w();
+  }
+  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
+  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
+  if (!conv_param.has_pad_h()) {
+    pad_h_ = pad_w_ = conv_param.pad();
+  } else {
+    pad_h_ = conv_param.pad_h();
+    pad_w_ = conv_param.pad_w();
+  }
+  if (!conv_param.has_stride_h()) {
+    stride_h_ = stride_w_ = conv_param.stride();
+  } else {
+    stride_h_ = conv_param.stride_h();
+    stride_w_ = conv_param.stride_w();
+  }
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
-  (*top)[0]->Reshape(bottom[0]->num(), channels_ * kernel_size_ * kernel_size_,
-      (height_ + 2 * pad_ - kernel_size_) / stride_ + 1,
-      (width_ + 2 * pad_ - kernel_size_) / stride_ + 1);
+  (*top)[0]->Reshape(
+      bottom[0]->num(), channels_ * kernel_h_ * kernel_w_,
+      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1,
+      (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1);
 }
 
 template <typename Dtype>
@@ -32,22 +62,28 @@ Dtype Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   for (int n = 0; n < bottom[0]->num(); ++n) {
     im2col_cpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-        width_, kernel_size_, pad_, stride_, top_data + (*top)[0]->offset(n));
+        width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+        stride_h_, stride_w_, top_data + (*top)[0]->offset(n));
   }
   return Dtype(0.);
 }
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
   for (int n = 0; n < top[0]->num(); ++n) {
     col2im_cpu(top_diff + top[0]->offset(n), channels_, height_, width_,
-        kernel_size_, pad_, stride_, bottom_diff + (*bottom)[0]->offset(n));
+        kernel_h_, kernel_w_, pad_h_, pad_w_,
+        stride_h_, stride_w_, bottom_diff + (*bottom)[0]->offset(n));
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(Im2colLayer);
+#endif
+
 INSTANTIATE_CLASS(Im2colLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index 26bc1b97959..6b4c701073e 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -1,11 +1,9 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
+#include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/util/im2col.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/common.hpp"
 
 namespace caffe {
 
@@ -16,19 +14,21 @@ Dtype Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   for (int n = 0; n < bottom[0]->num(); ++n) {
     im2col_gpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-        width_, kernel_size_, pad_, stride_, top_data + (*top)[0]->offset(n));
+        width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
+        stride_h_, stride_w_, top_data + (*top)[0]->offset(n));
   }
   return Dtype(0.);
 }
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   for (int n = 0; n < top[0]->num(); ++n) {
     col2im_gpu(top_diff + top[0]->offset(n), channels_, height_, width_,
-        kernel_size_, pad_, stride_, bottom_diff + (*bottom)[0]->offset(n));
+        kernel_h_, kernel_w_, pad_h_, pad_w_,
+        stride_h_, stride_w_, bottom_diff + (*bottom)[0]->offset(n));
   }
 }
 
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index ed064d0608d..a0f03a8266d 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -1,14 +1,8 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <stdint.h>
-#include <leveldb/db.h>
-#include <pthread.h>
-
-#include <string>
-#include <vector>
-#include <iostream>  // NOLINT(readability/streams)
 #include <fstream>  // NOLINT(readability/streams)
+#include <iostream>  // NOLINT(readability/streams)
+#include <string>
 #include <utility>
+#include <vector>
 
 #include "caffe/layer.hpp"
 #include "caffe/util/io.hpp"
@@ -16,23 +10,16 @@
 #include "caffe/util/rng.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::iterator;
-using std::string;
-using std::pair;
-
 namespace caffe {
 
+// This function is used to create a pthread that prefetches the data.
 template <typename Dtype>
-void* ImageDataLayerPrefetch(void* layer_pointer) {
-  CHECK(layer_pointer);
-  ImageDataLayer<Dtype>* layer =
-      reinterpret_cast<ImageDataLayer<Dtype>*>(layer_pointer);
-  CHECK(layer);
+void ImageDataLayer<Dtype>::InternalThreadEntry() {
   Datum datum;
-  CHECK(layer->prefetch_data_);
-  Dtype* top_data = layer->prefetch_data_->mutable_cpu_data();
-  Dtype* top_label = layer->prefetch_label_->mutable_cpu_data();
-  ImageDataParameter image_data_param = layer->layer_param_.image_data_param();
+  CHECK(prefetch_data_.count());
+  Dtype* top_data = prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = prefetch_label_.mutable_cpu_data();
+  ImageDataParameter image_data_param = this->layer_param_.image_data_param();
   const Dtype scale = image_data_param.scale();
   const int batch_size = image_data_param.batch_size();
   const int crop_size = image_data_param.crop_size();
@@ -45,17 +32,17 @@ void* ImageDataLayerPrefetch(void* layer_pointer) {
         << "set at the same time.";
   }
   // datum scales
-  const int channels = layer->datum_channels_;
-  const int height = layer->datum_height_;
-  const int width = layer->datum_width_;
-  const int size = layer->datum_size_;
-  const int lines_size = layer->lines_.size();
-  const Dtype* mean = layer->data_mean_.cpu_data();
+  const int channels = datum_channels_;
+  const int height = datum_height_;
+  const int width = datum_width_;
+  const int size = datum_size_;
+  const int lines_size = lines_.size();
+  const Dtype* mean = data_mean_.cpu_data();
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     // get a blob
-    CHECK_GT(lines_size, layer->lines_id_);
-    if (!ReadImageToDatum(layer->lines_[layer->lines_id_].first,
-          layer->lines_[layer->lines_id_].second,
+    CHECK_GT(lines_size, lines_id_);
+    if (!ReadImageToDatum(lines_[lines_id_].first,
+          lines_[lines_id_].second,
           new_height, new_width, &datum)) {
       continue;
     }
@@ -64,14 +51,14 @@ void* ImageDataLayerPrefetch(void* layer_pointer) {
       CHECK(data.size()) << "Image cropping only support uint8 data";
       int h_off, w_off;
       // We only do random crop when we do training.
-      if (layer->phase_ == Caffe::TRAIN) {
-        h_off = layer->PrefetchRand() % (height - crop_size);
-        w_off = layer->PrefetchRand() % (width - crop_size);
+      if (phase_ == Caffe::TRAIN) {
+        h_off = PrefetchRand() % (height - crop_size);
+        w_off = PrefetchRand() % (width - crop_size);
       } else {
         h_off = (height - crop_size) / 2;
         w_off = (width - crop_size) / 2;
       }
-      if (mirror && layer->PrefetchRand() % 2) {
+      if (mirror && PrefetchRand() % 2) {
         // Copy mirrored version
         for (int c = 0; c < channels; ++c) {
           for (int h = 0; h < crop_size; ++h) {
@@ -118,18 +105,16 @@ void* ImageDataLayerPrefetch(void* layer_pointer) {
 
     top_label[item_id] = datum.label();
     // go to the next iter
-    layer->lines_id_++;
-    if (layer->lines_id_ >= lines_size) {
+    lines_id_++;
+    if (lines_id_ >= lines_size) {
       // We have reached the end. Restart from the first.
       DLOG(INFO) << "Restarting data prefetching from start.";
-      layer->lines_id_ = 0;
-      if (layer->layer_param_.image_data_param().shuffle()) {
-        layer->ShuffleImages();
+      lines_id_ = 0;
+      if (this->layer_param_.image_data_param().shuffle()) {
+        ShuffleImages();
       }
     }
   }
-
-  return reinterpret_cast<void*>(NULL);
 }
 
 template <typename Dtype>
@@ -140,10 +125,9 @@ ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
 template <typename Dtype>
 void ImageDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 0) << "Input Layer takes no input blobs.";
-  CHECK_EQ(top->size(), 2) << "Input Layer takes two blobs as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   const int new_height  = this->layer_param_.image_data_param().new_height();
-  const int new_width  = this->layer_param_.image_data_param().new_height();
+  const int new_width  = this->layer_param_.image_data_param().new_width();
   CHECK((new_height == 0 && new_width == 0) ||
       (new_height > 0 && new_width > 0)) << "Current implementation requires "
       "new_height and new_width to be set at the same time.";
@@ -185,20 +169,19 @@ void ImageDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   const string& mean_file = this->layer_param_.image_data_param().mean_file();
   if (crop_size > 0) {
     (*top)[0]->Reshape(batch_size, datum.channels(), crop_size, crop_size);
-    prefetch_data_.reset(new Blob<Dtype>(batch_size, datum.channels(),
-                                         crop_size, crop_size));
+    prefetch_data_.Reshape(batch_size, datum.channels(), crop_size, crop_size);
   } else {
     (*top)[0]->Reshape(batch_size, datum.channels(), datum.height(),
                        datum.width());
-    prefetch_data_.reset(new Blob<Dtype>(batch_size, datum.channels(),
-                                         datum.height(), datum.width()));
+    prefetch_data_.Reshape(batch_size, datum.channels(), datum.height(),
+        datum.width());
   }
   LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
       << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
       << (*top)[0]->width();
   // label
   (*top)[1]->Reshape(batch_size, 1, 1, 1);
-  prefetch_label_.reset(new Blob<Dtype>(batch_size, 1, 1, 1));
+  prefetch_label_.Reshape(batch_size, 1, 1, 1);
   // datum size
   datum_channels_ = datum.channels();
   datum_height_ = datum.height();
@@ -224,8 +207,8 @@ void ImageDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   // cpu_data calls so that the prefetch thread does not accidentally make
   // simultaneous cudaMalloc calls when the main thread is running. In some
   // GPUs this seems to cause failures if we do not so.
-  prefetch_data_->mutable_cpu_data();
-  prefetch_label_->mutable_cpu_data();
+  prefetch_data_.mutable_cpu_data();
+  prefetch_label_.mutable_cpu_data();
   data_mean_.cpu_data();
   DLOG(INFO) << "Initializing prefetch";
   CreatePrefetchThread();
@@ -237,9 +220,7 @@ void ImageDataLayer<Dtype>::CreatePrefetchThread() {
   phase_ = Caffe::phase();
   const bool prefetch_needs_rand =
       this->layer_param_.image_data_param().shuffle() ||
-          ((phase_ == Caffe::TRAIN) &&
-           (this->layer_param_.image_data_param().mirror() ||
-            this->layer_param_.image_data_param().crop_size()));
+      this->layer_param_.image_data_param().crop_size();
   if (prefetch_needs_rand) {
     const unsigned int prefetch_rng_seed = caffe_rng_rand();
     prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));
@@ -247,25 +228,20 @@ void ImageDataLayer<Dtype>::CreatePrefetchThread() {
     prefetch_rng_.reset();
   }
   // Create the thread.
-  CHECK(!pthread_create(&thread_, NULL, ImageDataLayerPrefetch<Dtype>,
-        static_cast<void*>(this))) << "Pthread execution failed.";
+  CHECK(!StartInternalThread()) << "Pthread execution failed";
 }
 
 template <typename Dtype>
 void ImageDataLayer<Dtype>::ShuffleImages() {
-  const int num_images = lines_.size();
-  for (int i = 0; i < num_images; ++i) {
-    const int max_rand_index = num_images - i;
-    const int rand_index = PrefetchRand() % max_rand_index;
-    pair<string, int> item = lines_[rand_index];
-    lines_.erase(lines_.begin() + rand_index);
-    lines_.push_back(item);
-  }
+  caffe::rng_t* prefetch_rng =
+      static_cast<caffe::rng_t*>(prefetch_rng_->generator());
+  shuffle(lines_.begin(), lines_.end(), prefetch_rng);
 }
 
+
 template <typename Dtype>
 void ImageDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!pthread_join(thread_, NULL)) << "Pthread joining failed.";
+  CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed";
 }
 
 template <typename Dtype>
@@ -281,15 +257,19 @@ Dtype ImageDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
              (*top)[0]->mutable_cpu_data());
-  caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
              (*top)[1]->mutable_cpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
   return Dtype(0.);
 }
 
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(ImageDataLayer, Forward);
+#endif
+
 INSTANTIATE_CLASS(ImageDataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/image_data_layer.cu b/src/caffe/layers/image_data_layer.cu
index 98047297d80..f61409cc38a 100644
--- a/src/caffe/layers/image_data_layer.cu
+++ b/src/caffe/layers/image_data_layer.cu
@@ -1,14 +1,4 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <cuda_runtime.h>
-#include <stdint.h>
-#include <leveldb/db.h>
-#include <pthread.h>
-
-#include <string>
 #include <vector>
-#include <iostream>  // NOLINT(readability/streams)
-#include <fstream>  // NOLINT(readability/streams)
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -16,9 +6,6 @@
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::string;
-using std::pair;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -27,12 +14,10 @@ Dtype ImageDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  CUDA_CHECK(cudaMemcpy((*top)[0]->mutable_gpu_data(),
-      prefetch_data_->cpu_data(), sizeof(Dtype) * prefetch_data_->count(),
-      cudaMemcpyHostToDevice));
-  CUDA_CHECK(cudaMemcpy((*top)[1]->mutable_gpu_data(),
-      prefetch_label_->cpu_data(), sizeof(Dtype) * prefetch_label_->count(),
-      cudaMemcpyHostToDevice));
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
+      (*top)[0]->mutable_gpu_data());
+  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
+      (*top)[1]->mutable_gpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
   return Dtype(0.);
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index ab6e67d73b1..fa01116ee36 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -1,16 +1,12 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
-#include <cmath>
 #include <cfloat>
+#include <cmath>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
-
-using std::max;
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -20,14 +16,24 @@ void InfogainLossLayer<Dtype>::FurtherSetUp(
   CHECK_EQ(bottom[1]->channels(), 1);
   CHECK_EQ(bottom[1]->height(), 1);
   CHECK_EQ(bottom[1]->width(), 1);
-
-  BlobProto blob_proto;
-  ReadProtoFromBinaryFile(
-    this->layer_param_.infogain_loss_param().source(), &blob_proto);
-  infogain_.FromProto(blob_proto);
-  CHECK_EQ(infogain_.num(), 1);
-  CHECK_EQ(infogain_.channels(), 1);
-  CHECK_EQ(infogain_.height(), infogain_.width());
+  Blob<Dtype>* infogain = NULL;
+  if (bottom.size() < 3) {
+    CHECK(this->layer_param_.infogain_loss_param().has_source())
+        << "Infogain matrix source must be specified.";
+    BlobProto blob_proto;
+    ReadProtoFromBinaryFile(
+      this->layer_param_.infogain_loss_param().source(), &blob_proto);
+    infogain_.FromProto(blob_proto);
+    infogain = &infogain_;
+  } else {
+    infogain = bottom[2];
+  }
+  const int num = bottom[0]->num();
+  const int dim = bottom[0]->count() / num;
+  CHECK_EQ(infogain->num(), 1);
+  CHECK_EQ(infogain->channels(), 1);
+  CHECK_EQ(infogain->height(), dim);
+  CHECK_EQ(infogain->width(), dim);
 }
 
 
@@ -36,37 +42,59 @@ Dtype InfogainLossLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const Dtype* bottom_label = bottom[1]->cpu_data();
-  const Dtype* infogain_mat = infogain_.cpu_data();
+  const Dtype* infogain_mat = NULL;
+  if (bottom.size() < 3) {
+    infogain_mat = infogain_.cpu_data();
+  } else {
+    infogain_mat = bottom[2]->cpu_data();
+  }
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
-  CHECK_EQ(infogain_.height(), dim);
   Dtype loss = 0;
   for (int i = 0; i < num; ++i) {
     int label = static_cast<int>(bottom_label[i]);
     for (int j = 0; j < dim; ++j) {
-      Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
+      Dtype prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
       loss -= infogain_mat[label * dim + j] * log(prob);
     }
   }
-  return loss / num;
+  loss /= num;
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss;
+  }
+  return loss;
 }
 
 template <typename Dtype>
 void InfogainLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  const Dtype* bottom_label = (*bottom)[1]->cpu_data();
-  const Dtype* infogain_mat = infogain_.cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  int num = (*bottom)[0]->num();
-  int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-  CHECK_EQ(infogain_.height(), dim);
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    for (int j = 0; j < dim; ++j) {
-      Dtype prob = max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
-      bottom_diff[i * dim + j] = - infogain_mat[label * dim + j] / prob / num;
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down.size() > 2 && propagate_down[2]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to infogain inputs.";
+  }
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    const Dtype* bottom_label = (*bottom)[1]->cpu_data();
+    const Dtype* infogain_mat = NULL;
+    if (bottom->size() < 3) {
+      infogain_mat = infogain_.cpu_data();
+    } else {
+      infogain_mat = (*bottom)[2]->cpu_data();
+    }
+    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    int num = (*bottom)[0]->num();
+    int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
+    for (int i = 0; i < num; ++i) {
+      int label = static_cast<int>(bottom_label[i]);
+      for (int j = 0; j < dim; ++j) {
+        Dtype prob = std::max(bottom_data[i * dim + j], Dtype(kLOG_THRESHOLD));
+        bottom_diff[i * dim + j] = - infogain_mat[label * dim + j] / prob / num;
+      }
     }
   }
 }
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index c60261e9486..a9e0f353e9e 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -1,21 +1,18 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "IP Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "IP Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   const int num_output = this->layer_param_.inner_product_param().num_output();
   bias_term_ = this->layer_param_.inner_product_param().bias_term();
   // Figure out the dimensions
@@ -48,13 +45,10 @@ void InnerProductLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   }  // parameter initialization
   // Setting up the bias multiplier
   if (bias_term_) {
-    bias_multiplier_.reset(new SyncedMemory(M_ * sizeof(Dtype)));
-    Dtype* bias_multiplier_data =
-        reinterpret_cast<Dtype*>(bias_multiplier_->mutable_cpu_data());
-    for (int i = 0; i < M_; ++i) {
-        bias_multiplier_data[i] = 1.;
-    }
+    bias_multiplier_.Reshape(1, 1, 1, M_);
+    caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());
   }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
 
 template <typename Dtype>
@@ -67,7 +61,7 @@ Dtype InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       bottom_data, weight, (Dtype)0., top_data);
   if (bias_term_) {
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
-        reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()),
+        bias_multiplier_.cpu_data(),
         this->blobs_[1]->cpu_data(), (Dtype)1., top_data);
   }
   return Dtype(0);
@@ -75,20 +69,24 @@ Dtype InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  const Dtype* top_diff = top[0]->cpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  // Gradient with respect to weight
-  caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-      top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff());
-  if (bias_term_) {
+  if (this->param_propagate_down_[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    // Gradient with respect to weight
+    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
+        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff());
+  }
+  if (bias_term_ && this->param_propagate_down_[1]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bias
     caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        reinterpret_cast<const Dtype*>(bias_multiplier_->cpu_data()), (Dtype)0.,
+        bias_multiplier_.cpu_data(), (Dtype)0.,
         this->blobs_[1]->mutable_cpu_diff());
   }
-  if (propagate_down) {
+  if (propagate_down[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bottom data
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
         top_diff, this->blobs_[0]->cpu_data(), (Dtype)0.,
@@ -96,6 +94,10 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(InnerProductLayer);
+#endif
+
 INSTANTIATE_CLASS(InnerProductLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index f139c23c310..e02107200cf 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -1,15 +1,11 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <cublas_v2.h>
-
 #include <vector>
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -23,7 +19,7 @@ Dtype InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       bottom_data, weight, (Dtype)0., top_data);
   if (bias_term_) {
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
-        reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()),
+        bias_multiplier_.gpu_data(),
         this->blobs_[1]->gpu_data(), (Dtype)1., top_data);
   }
   return Dtype(0);
@@ -31,20 +27,24 @@ Dtype InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  const Dtype* top_diff = top[0]->gpu_diff();
-  const Dtype* bottom_data = (*bottom)[0]->gpu_data();
-  // Gradient with respect to weight
-  caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-      top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_gpu_diff());
-  if (bias_term_) {
+  if (this->param_propagate_down_[0]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+    // Gradient with respect to weight
+    caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
+        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_gpu_diff());
+  }
+  if (bias_term_ && this->param_propagate_down_[1]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bias
     caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        reinterpret_cast<const Dtype*>(bias_multiplier_->gpu_data()),
-        (Dtype)0., this->blobs_[1]->mutable_gpu_diff());
+        bias_multiplier_.gpu_data(), (Dtype)0.,
+        this->blobs_[1]->mutable_gpu_diff());
   }
-  if (propagate_down) {
+  if (propagate_down[0]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bottom data
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
         top_diff, this->blobs_[0]->gpu_data(), (Dtype)0.,
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 1efb6235f98..48665221f1d 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -1,26 +1,25 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
-#include <cmath>
 #include <cfloat>
+#include <cmath>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
-
-using std::max;
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void LossLayer<Dtype>::SetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 2) << "Loss Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 0) << "Loss Layer takes no output.";
+  Layer<Dtype>::SetUp(bottom, top);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
+  if (top->size() == 1) {
+  // Layers should copy the loss in the top blob
+    (*top)[0]->Reshape(1, 1, 1, 1);
+  }
   FurtherSetUp(bottom, top);
 }
 
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index cfcc59c9feb..e77f6857c85 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -1,20 +1,15 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void LRNLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) <<
-      "Local Response Normalization Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) <<
-      "Local Response Normalization Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   num_ = bottom[0]->num();
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
@@ -85,7 +80,9 @@ void LRNLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       product_bottom_vec_.push_back(bottom[0]);
       product_bottom_vec_.push_back(&power_output_);
       LayerParameter product_param;
-      product_layer_.reset(new EltwiseProductLayer<Dtype>(product_param));
+      EltwiseParameter* eltwise_param = product_param.mutable_eltwise_param();
+      eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+      product_layer_.reset(new EltwiseLayer<Dtype>(product_param));
       product_layer_->SetUp(product_bottom_vec_, top);
       CHECK_EQ((*top)[0]->num(), num_);
       CHECK_EQ((*top)[0]->channels(), channels_);
@@ -124,7 +121,7 @@ Dtype LRNLayer<Dtype>::CrossChannelForward_cpu(
   }
   Blob<Dtype> padded_square(1, channels_ + size_ - 1, height_, width_);
   Dtype* padded_square_data = padded_square.mutable_cpu_data();
-  memset(padded_square_data, 0, sizeof(Dtype) * padded_square.count());
+  caffe_set(padded_square.count(), Dtype(0), padded_square_data);
   Dtype alpha_over_size = alpha_ / size_;
   // go through the images
   for (int n = 0; n < num_; ++n) {
@@ -174,7 +171,7 @@ Dtype LRNLayer<Dtype>::WithinChannelForward(
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelBackward_cpu(top, propagate_down, bottom);
@@ -189,7 +186,7 @@ void LRNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelBackward_cpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* top_data = top[0]->cpu_data();
@@ -202,7 +199,7 @@ void LRNLayer<Dtype>::CrossChannelBackward_cpu(
   Dtype* accum_ratio_data = accum_ratio.mutable_cpu_data();
   // We hack a little bit by using the diff() to store an additional result
   Dtype* accum_ratio_times_bottom = accum_ratio.mutable_cpu_diff();
-  memset(padded_ratio_data, 0, sizeof(Dtype) * padded_ratio.count());
+  caffe_set(padded_ratio.count(), Dtype(0), padded_ratio_data);
   Dtype cache_ratio_value = 2. * alpha_ * beta_ / size_;
 
   caffe_powx<Dtype>(scale_.count(), scale_data, -beta_, bottom_diff);
@@ -221,7 +218,7 @@ void LRNLayer<Dtype>::CrossChannelBackward_cpu(
         scale_data + block_offset,
         padded_ratio_data + padded_ratio.offset(0, inverse_pre_pad));
     // Now, compute the accumulated ratios and the bottom diff
-    memset(accum_ratio_data, 0, sizeof(Dtype) * accum_ratio.count());
+    caffe_set(accum_ratio.count(), Dtype(0), accum_ratio_data);
     for (int c = 0; c < size_ - 1; ++c) {
       caffe_axpy<Dtype>(height_ * width_, 1.,
           padded_ratio_data + padded_ratio.offset(0, c), accum_ratio_data);
@@ -244,17 +241,25 @@ void LRNLayer<Dtype>::CrossChannelBackward_cpu(
 
 template <typename Dtype>
 void LRNLayer<Dtype>::WithinChannelBackward(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
-    product_layer_->Backward(top, true, &product_bottom_vec_);
-    power_layer_->Backward(power_top_vec_, true, &pool_top_vec_);
-    pool_layer_->Backward(pool_top_vec_, true, &square_top_vec_);
-    square_layer_->Backward(square_top_vec_, true, &square_bottom_vec_);
-    split_layer_->Backward(split_top_vec_, true, bottom);
+  if (propagate_down[0]) {
+    vector<bool> product_propagate_down(2, true);
+    product_layer_->Backward(top, product_propagate_down, &product_bottom_vec_);
+    power_layer_->Backward(power_top_vec_, propagate_down, &pool_top_vec_);
+    pool_layer_->Backward(pool_top_vec_, propagate_down, &square_top_vec_);
+    square_layer_->Backward(square_top_vec_, propagate_down,
+                            &square_bottom_vec_);
+    split_layer_->Backward(split_top_vec_, propagate_down, bottom);
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(LRNLayer);
+STUB_GPU_FORWARD(LRNLayer, CrossChannelForward);
+STUB_GPU_BACKWARD(LRNLayer, CrossChannelBackward);
+#endif
+
 INSTANTIATE_CLASS(LRNLayer);
 
 
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index b2097eb99cf..eee12e66cec 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -1,10 +1,8 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -104,7 +102,7 @@ Dtype LRNLayer<Dtype>::CrossChannelForward_gpu(
 
 template <typename Dtype>
 void LRNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   switch (this->layer_param_.lrn_param().norm_region()) {
   case LRNParameter_NormRegion_ACROSS_CHANNELS:
     CrossChannelBackward_gpu(top, propagate_down, bottom);
@@ -180,7 +178,7 @@ __global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data,
 
 template <typename Dtype>
 void LRNLayer<Dtype>::CrossChannelBackward_gpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   int n_threads = num_ * height_ * width_;
   // NOLINT_NEXT_LINE(whitespace/operators)
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 60bce27b8c9..d1717fd4258 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
@@ -10,8 +8,7 @@ namespace caffe {
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
      vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 0) << "Memory Data Layer takes no blobs as input.";
-  CHECK_EQ(top->size(), 2) << "Memory Data Layer takes two blobs as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   batch_size_ = this->layer_param_.memory_data_param().batch_size();
   datum_channels_ = this->layer_param_.memory_data_param().channels();
   datum_height_ = this->layer_param_.memory_data_param().height();
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index 6486621d8aa..a9c7de6595d 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -1,16 +1,12 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
-#include <cmath>
 #include <cfloat>
+#include <cmath>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/io.hpp"
-
-using std::max;
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -32,26 +28,37 @@ Dtype MultinomialLogisticLossLayer<Dtype>::Forward_cpu(
   Dtype loss = 0;
   for (int i = 0; i < num; ++i) {
     int label = static_cast<int>(bottom_label[i]);
-    Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
+    Dtype prob = std::max(
+        bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
     loss -= log(prob);
   }
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  }
   return loss / num;
 }
 
 template <typename Dtype>
 void MultinomialLogisticLossLayer<Dtype>::Backward_cpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
-  const Dtype* bottom_label = (*bottom)[1]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  int num = (*bottom)[0]->num();
-  int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
-  memset(bottom_diff, 0, sizeof(Dtype) * (*bottom)[0]->count());
-  for (int i = 0; i < num; ++i) {
-    int label = static_cast<int>(bottom_label[i]);
-    Dtype prob = max(bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
-    bottom_diff[i * dim + label] = -1. / prob / num;
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+    const Dtype* bottom_label = (*bottom)[1]->cpu_data();
+    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    int num = (*bottom)[0]->num();
+    int dim = (*bottom)[0]->count() / (*bottom)[0]->num();
+    caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
+    for (int i = 0; i < num; ++i) {
+      int label = static_cast<int>(bottom_label[i]);
+      Dtype prob = std::max(
+          bottom_data[i * dim + label], Dtype(kLOG_THRESHOLD));
+      bottom_diff[i * dim + label] = -1. / prob / num;
+    }
   }
 }
 
diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp
index e9dbd0eb75c..3343b26cfc5 100644
--- a/src/caffe/layers/neuron_layer.cpp
+++ b/src/caffe/layers/neuron_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
@@ -10,8 +8,7 @@ namespace caffe {
 template <typename Dtype>
 void NeuronLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Neuron Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "Neuron Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   // NeuronLayer allows in-place computations. If the computation is not
   // in-place, we will need to initialize the top blob.
   if ((*top)[0] != bottom[0]) {
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index 7e880a27b69..30657b6c804 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -1,40 +1,105 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
 
+#include "caffe/common.hpp"
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
-using std::min;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
+using std::min;
+using std::max;
+
 template <typename Dtype>
 void PoolingLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "PoolingLayer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "PoolingLayer takes a single blob as output.";
-  kernel_size_ = this->layer_param_.pooling_param().kernel_size();
-  stride_ = this->layer_param_.pooling_param().stride();
-  pad_ = this->layer_param_.pooling_param().pad();
-  if (pad_ != 0) {
-    CHECK_EQ(this->layer_param_.pooling_param().pool(),
-             PoolingParameter_PoolMethod_AVE)
-        << "Padding implemented only for average pooling.";
+  // Set the max number of top blobs before calling base Layer::SetUp.
+  // If doing MAX pooling, we can optionally output an extra top Blob
+  // for the mask.  Otherwise, we only have one top Blob.
+  if (this->layer_param_.pooling_param().pool() ==
+      PoolingParameter_PoolMethod_MAX) {
+    max_top_blobs_ = 2;
+  } else {
+    max_top_blobs_ = 1;
+  }
+  Layer<Dtype>::SetUp(bottom, top);
+  PoolingParameter pool_param = this->layer_param_.pooling_param();
+  CHECK(!pool_param.has_kernel_size() !=
+      !(pool_param.has_kernel_h() && pool_param.has_kernel_w()))
+      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
+  CHECK(pool_param.has_kernel_size() ||
+      (pool_param.has_kernel_h() && pool_param.has_kernel_w()))
+      << "For non-square filters both kernel_h and kernel_w are required.";
+  CHECK((!pool_param.has_pad() && pool_param.has_pad_h()
+      && pool_param.has_pad_w())
+      || (!pool_param.has_pad_h() && !pool_param.has_pad_w()))
+      << "pad is pad OR pad_h and pad_w are required.";
+  CHECK((!pool_param.has_stride() && pool_param.has_stride_h()
+      && pool_param.has_stride_w())
+      || (!pool_param.has_stride_h() && !pool_param.has_stride_w()))
+      << "Stride is stride OR stride_h and stride_w are required.";
+  if (pool_param.has_kernel_size()) {
+    kernel_h_ = kernel_w_ = pool_param.kernel_size();
+  } else {
+    kernel_h_ = pool_param.kernel_h();
+    kernel_w_ = pool_param.kernel_w();
+  }
+  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
+  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
+  if (!pool_param.has_pad_h()) {
+    pad_h_ = pad_w_ = pool_param.pad();
+  } else {
+    pad_h_ = pool_param.pad_h();
+    pad_w_ = pool_param.pad_w();
+  }
+  if (!pool_param.has_stride_h()) {
+    stride_h_ = stride_w_ = pool_param.stride();
+  } else {
+    stride_h_ = pool_param.stride_h();
+    stride_w_ = pool_param.stride_w();
+  }
+  if (pad_h_ != 0 || pad_w_ != 0) {
+    CHECK(this->layer_param_.pooling_param().pool()
+        == PoolingParameter_PoolMethod_AVE
+        || this->layer_param_.pooling_param().pool()
+        == PoolingParameter_PoolMethod_MAX)
+        << "Padding implemented only for average and max pooling.";
+    CHECK_LT(pad_h_, kernel_h_);
+    CHECK_LT(pad_w_, kernel_w_);
   }
   channels_ = bottom[0]->channels();
   height_ = bottom[0]->height();
   width_ = bottom[0]->width();
   pooled_height_ = static_cast<int>(ceil(static_cast<float>(
-      height_ + 2 * pad_ - kernel_size_) / stride_)) + 1;
+      height_ + 2 * pad_h_ - kernel_h_) / stride_h_)) + 1;
   pooled_width_ = static_cast<int>(ceil(static_cast<float>(
-      width_ + 2 * pad_ - kernel_size_) / stride_)) + 1;
+      width_ + 2 * pad_w_ - kernel_w_) / stride_w_)) + 1;
+  if (pad_h_ || pad_w_) {
+    // If we have padding, ensure that the last pooling starts strictly
+    // inside the image (instead of at the padding); otherwise clip the last.
+    if ((pooled_height_ - 1) * stride_h_ >= height_ + pad_h_) {
+      --pooled_height_;
+    }
+    if ((pooled_width_ - 1) * stride_w_ >= width_ + pad_w_) {
+      --pooled_width_;
+    }
+    CHECK_LT((pooled_height_ - 1) * stride_h_, height_ + pad_h_);
+    CHECK_LT((pooled_width_ - 1) * stride_w_, width_ + pad_w_);
+  }
   (*top)[0]->Reshape(bottom[0]->num(), channels_, pooled_height_,
       pooled_width_);
+  if (top->size() > 1) {
+    (*top)[1]->ReshapeLike(*(*top)[0]);
+  }
+  // If max pooling, we will initialize the vector index part.
+  if (this->layer_param_.pooling_param().pool() ==
+      PoolingParameter_PoolMethod_MAX && top->size() == 1) {
+    max_idx_.Reshape(bottom[0]->num(), channels_, pooled_height_,
+        pooled_width_);
+  }
   // If stochastic pooling, we will initialize the random index part.
   if (this->layer_param_.pooling_param().pool() ==
       PoolingParameter_PoolMethod_STOCHASTIC) {
@@ -50,29 +115,47 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  const int top_count = (*top)[0]->count();
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top->size() > 1;
+  int* mask = NULL;  // suppress warnings about uninitalized variables
+  Dtype* top_mask = NULL;
   // Different pooling methods. We explicitly do the switch outside the for
-  // loop to save time, although this results in more codes.
-  int top_count = (*top)[0]->count();
+  // loop to save time, although this results in more code.
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     // Initialize
-    for (int i = 0; i < top_count; ++i) {
-      top_data[i] = -FLT_MAX;
+    if (use_top_mask) {
+      top_mask = (*top)[1]->mutable_cpu_data();
+      caffe_set(top_count, Dtype(-1), top_mask);
+    } else {
+      mask = max_idx_.mutable_cpu_data();
+      caffe_set(top_count, -1, mask);
     }
+    caffe_set(top_count, Dtype(-FLT_MAX), top_data);
     // The main loop
     for (int n = 0; n < bottom[0]->num(); ++n) {
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
           for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_;
-            int wstart = pw * stride_;
-            int hend = min(hstart + kernel_size_, height_);
-            int wend = min(wstart + kernel_size_, width_);
+            int hstart = ph * stride_h_ - pad_h_;
+            int wstart = pw * stride_w_ - pad_w_;
+            int hend = min(hstart + kernel_h_, height_);
+            int wend = min(wstart + kernel_w_, width_);
+            hstart = max(hstart, 0);
+            wstart = max(wstart, 0);
+            const int pool_index = ph * pooled_width_ + pw;
             for (int h = hstart; h < hend; ++h) {
               for (int w = wstart; w < wend; ++w) {
-                top_data[ph * pooled_width_ + pw] =
-                  max(top_data[ph * pooled_width_ + pw],
-                      bottom_data[h * width_ + w]);
+                const int index = h * width_ + w;
+                if (bottom_data[index] > top_data[pool_index]) {
+                  top_data[pool_index] = bottom_data[index];
+                  if (use_top_mask) {
+                    top_mask[pool_index] = static_cast<Dtype>(index);
+                  } else {
+                    mask[pool_index] = index;
+                  }
+                }
               }
             }
           }
@@ -80,6 +163,11 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
         // compute offset
         bottom_data += bottom[0]->offset(0, 1);
         top_data += (*top)[0]->offset(0, 1);
+        if (use_top_mask) {
+          top_mask += (*top)[0]->offset(0, 1);
+        } else {
+          mask += (*top)[0]->offset(0, 1);
+        }
       }
     }
     break;
@@ -92,10 +180,10 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
           for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_ - pad_;
-            int wstart = pw * stride_ - pad_;
-            int hend = min(hstart + kernel_size_, height_ + pad_);
-            int wend = min(wstart + kernel_size_, width_ + pad_);
+            int hstart = ph * stride_h_ - pad_h_;
+            int wstart = pw * stride_w_ - pad_w_;
+            int hend = min(hstart + kernel_h_, height_ + pad_h_);
+            int wend = min(wstart + kernel_w_, width_ + pad_w_);
             int pool_size = (hend - hstart) * (wend - wstart);
             hstart = max(hstart, 0);
             wstart = max(wstart, 0);
@@ -127,43 +215,44 @@ Dtype PoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (!propagate_down) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) {
     return;
   }
   const Dtype* top_diff = top[0]->cpu_diff();
-  const Dtype* top_data = top[0]->cpu_data();
-  const Dtype* bottom_data = (*bottom)[0]->cpu_data();
   Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
   // Different pooling methods. We explicitly do the switch outside the for
   // loop to save time, although this results in more codes.
-  memset(bottom_diff, 0, (*bottom)[0]->count() * sizeof(Dtype));
+  caffe_set((*bottom)[0]->count(), Dtype(0), bottom_diff);
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top.size() > 1;
+  const int* mask = NULL;  // suppress warnings about uninitialized variables
+  const Dtype* top_mask = NULL;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
     // The main loop
+    if (use_top_mask) {
+      top_mask = top[1]->cpu_data();
+    } else {
+      mask = max_idx_.cpu_data();
+    }
     for (int n = 0; n < top[0]->num(); ++n) {
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
           for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_;
-            int wstart = pw * stride_;
-            int hend = min(hstart + kernel_size_, height_);
-            int wend = min(wstart + kernel_size_, width_);
-            for (int h = hstart; h < hend; ++h) {
-              for (int w = wstart; w < wend; ++w) {
-                bottom_diff[h * width_ + w] +=
-                    top_diff[ph * pooled_width_ + pw] *
-                    (bottom_data[h * width_ + w] ==
-                        top_data[ph * pooled_width_ + pw]);
-              }
-            }
+            const int index = ph * pooled_width_ + pw;
+            const int bottom_index =
+                use_top_mask ? top_mask[index] : mask[index];
+            bottom_diff[bottom_index] += top_diff[index];
           }
         }
-        // offset
-        bottom_data += (*bottom)[0]->offset(0, 1);
-        top_data += top[0]->offset(0, 1);
         bottom_diff += (*bottom)[0]->offset(0, 1);
         top_diff += top[0]->offset(0, 1);
+        if (use_top_mask) {
+          top_mask += top[0]->offset(0, 1);
+        } else {
+          mask += top[0]->offset(0, 1);
+        }
       }
     }
     break;
@@ -173,10 +262,10 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       for (int c = 0; c < channels_; ++c) {
         for (int ph = 0; ph < pooled_height_; ++ph) {
           for (int pw = 0; pw < pooled_width_; ++pw) {
-            int hstart = ph * stride_ - pad_;
-            int wstart = pw * stride_ - pad_;
-            int hend = min(hstart + kernel_size_, height_ + pad_);
-            int wend = min(wstart + kernel_size_, width_ + pad_);
+            int hstart = ph * stride_h_ - pad_h_;
+            int wstart = pw * stride_w_ - pad_w_;
+            int hend = min(hstart + kernel_h_, height_ + pad_h_);
+            int wend = min(wstart + kernel_w_, width_ + pad_w_);
             int pool_size = (hend - hstart) * (wend - wstart);
             hstart = max(hstart, 0);
             wstart = max(wstart, 0);
@@ -191,8 +280,6 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
           }
         }
         // offset
-        bottom_data += (*bottom)[0]->offset(0, 1);
-        top_data += top[0]->offset(0, 1);
         bottom_diff += (*bottom)[0]->offset(0, 1);
         top_diff += top[0]->offset(0, 1);
       }
@@ -207,6 +294,10 @@ void PoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(PoolingLayer);
+#endif
+
 INSTANTIATE_CLASS(PoolingLayer);
 
 
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index 95bfaefc951..58f1997c38e 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -1,15 +1,10 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
-using std::min;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -17,24 +12,37 @@ template <typename Dtype>
 __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* top_data) {
+    const int kernel_h, const int kernel_w, const int stride_h,
+    const int stride_w, const int pad_h, const int pad_w, Dtype* top_data,
+    int* mask, Dtype* top_mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride;
-    int hend = min(hstart + kernel_size, height);
-    int wstart = pw * stride;
-    int wend = min(wstart + kernel_size, width);
+    int hstart = ph * stride_h - pad_h;
+    int wstart = pw * stride_w - pad_w;
+    int hend = min(hstart + kernel_h, height);
+    int wend = min(wstart + kernel_w, width);
+    hstart = max(hstart, 0);
+    wstart = max(wstart, 0);
     Dtype maxval = -FLT_MAX;
+    int maxidx = -1;
     bottom_data += (n * channels + c) * height * width;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        maxval = max(maxval, bottom_data[h * width + w]);
+        if (bottom_data[h * width + w] > maxval) {
+          maxidx = h * width + w;
+          maxval = bottom_data[maxidx];
+        }
       }
     }
     top_data[index] = maxval;
+    if (mask) {
+      mask[index] = maxidx;
+    } else {
+      top_mask[index] = maxidx;
+    }
   }
 }
 
@@ -42,16 +50,17 @@ template <typename Dtype>
 __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, const int pad, Dtype* top_data) {
+    const int kernel_h, const int kernel_w, const int stride_h,
+    const int stride_w, const int pad_h, const int pad_w, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride - pad;
-    int wstart = pw * stride - pad;
-    int hend = min(hstart + kernel_size, height + pad);
-    int wend = min(wstart + kernel_size, width + pad);
+    int hstart = ph * stride_h - pad_h;
+    int wstart = pw * stride_w - pad_w;
+    int hend = min(hstart + kernel_h, height + pad_h);
+    int wend = min(wstart + kernel_w, width + pad_w);
     int pool_size = (hend - hstart) * (wend - wstart);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
@@ -73,16 +82,17 @@ __global__ void StoPoolForwardTrain(const int nthreads,
     const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* rand_idx, Dtype* top_data) {
+    const int kernel_h, const int kernel_w, const int stride_h,
+    const int stride_w, Dtype* rand_idx, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride;
-    int hend = min(hstart + kernel_size, height);
-    int wstart = pw * stride;
-    int wend = min(wstart + kernel_size, width);
+    int hstart = ph * stride_h;
+    int hend = min(hstart + kernel_h, height);
+    int wstart = pw * stride_w;
+    int wend = min(wstart + kernel_w, width);
     Dtype cumsum = 0.;
     bottom_data += (n * channels + c) * height * width;
     // First pass: get sum
@@ -113,16 +123,17 @@ __global__ void StoPoolForwardTest(const int nthreads,
     const Dtype* bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* top_data) {
+    const int kernel_h, const int kernel_w, const int stride_h,
+    const int stride_w, Dtype* top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     int pw = index % pooled_width;
     int ph = (index / pooled_width) % pooled_height;
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride;
-    int hend = min(hstart + kernel_size, height);
-    int wstart = pw * stride;
-    int wend = min(wstart + kernel_size, width);
+    int hstart = ph * stride_h;
+    int hend = min(hstart + kernel_h, height);
+    int wstart = pw * stride_w;
+    int wend = min(wstart + kernel_w, width);
     // We set cumsum to be 0 to avoid divide-by-zero problems
     Dtype cumsum = FLT_MIN;
     Dtype cumvalues = 0.;
@@ -145,20 +156,30 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   int count = (*top)[0]->count();
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top->size() > 1;
+  int* mask = NULL;
+  Dtype* top_mask = NULL;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
+    if (use_top_mask) {
+      top_mask = (*top)[1]->mutable_gpu_data();
+    } else {
+      mask = max_idx_.mutable_gpu_data();
+    }
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
-        top_data);
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
+        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data,
+        mask, top_mask);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, bottom_data, bottom[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
-        pad_, top_data);
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
+        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, top_data);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
     if (Caffe::phase() == Caffe::TRAIN) {
@@ -169,15 +190,16 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       StoPoolForwardTrain<Dtype><<<CAFFE_GET_BLOCKS(count),
                                    CAFFE_CUDA_NUM_THREADS>>>(
           count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
+          height_, width_, pooled_height_, pooled_width_, kernel_h_,
+          kernel_w_, stride_h_, stride_w_,
           rand_idx_.mutable_gpu_data(), top_data);
     } else {
       // NOLINT_NEXT_LINE(whitespace/operators)
       StoPoolForwardTest<Dtype><<<CAFFE_GET_BLOCKS(count),
                                   CAFFE_CUDA_NUM_THREADS>>>(
           count, bottom_data, bottom[0]->num(), channels_,
-          height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
-          top_data);
+          height_, width_, pooled_height_, pooled_width_, kernel_h_,
+          kernel_w_, stride_h_, stride_w_, top_data);
     }
     break;
   default:
@@ -187,12 +209,14 @@ Dtype PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   return Dtype(0.);
 }
 
+
 template <typename Dtype>
-__global__ void MaxPoolBackward(const int nthreads, const Dtype* bottom_data,
-    const Dtype* top_data, const Dtype* top_diff,
-    const int num, const int channels, const int height,
-    const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* bottom_diff) {
+__global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
+    const int* mask, const Dtype* top_mask, const int num, const int channels,
+    const int height, const int width, const int pooled_height,
+    const int pooled_width, const int kernel_h, const int kernel_w,
+    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
+    Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
@@ -200,52 +224,65 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* bottom_data,
     int h = (index / width) % height;
     int c = (index / width / height) % channels;
     int n = index / width / height / channels;
-    int phstart = (h < kernel_size) ? 0 : (h - kernel_size) / stride + 1;
-    int phend = min(h / stride + 1, pooled_height);
-    int pwstart = (w < kernel_size) ? 0 : (w - kernel_size) / stride + 1;
-    int pwend = min(w / stride + 1, pooled_width);
+    int phstart =
+        (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1;
+    int phend = min((h + pad_h) / stride_h + 1, pooled_height);
+    int pwstart =
+        (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1;
+    int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
     Dtype gradient = 0;
-    Dtype bottom_datum =
-        bottom_data[((n * channels + c) * height + h) * width + w];
-    top_data += (n * channels + c) * pooled_height * pooled_width;
-    top_diff += (n * channels + c) * pooled_height * pooled_width;
-    for (int ph = phstart; ph < phend; ++ph) {
-      for (int pw = pwstart; pw < pwend; ++pw) {
-        gradient += top_diff[ph * pooled_width + pw] *
-            (bottom_datum == top_data[ph * pooled_width + pw]);
+    int offset = (n * channels + c) * pooled_height * pooled_width;
+    top_diff += offset;
+    if (mask) {
+      mask += offset;
+      for (int ph = phstart; ph < phend; ++ph) {
+        for (int pw = pwstart; pw < pwend; ++pw) {
+          if (mask[ph * pooled_width + pw] == h * width + w) {
+            gradient += top_diff[ph * pooled_width + pw];
+          }
+        }
+      }
+    } else {
+      top_mask += offset;
+      for (int ph = phstart; ph < phend; ++ph) {
+        for (int pw = pwstart; pw < pwend; ++pw) {
+          if (top_mask[ph * pooled_width + pw] == h * width + w) {
+            gradient += top_diff[ph * pooled_width + pw];
+          }
+        }
       }
     }
     bottom_diff[index] = gradient;
   }
 }
 
-
 template <typename Dtype>
 __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, const int pad,
+    const int kernel_h, const int kernel_w, const int stride_h,
+    const int stride_w, const int pad_h, const int pad_w,
     Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width + pad;
-    int h = (index / width) % height + pad;
+    int w = index % width + pad_w;
+    int h = (index / width) % height + pad_h;
     int c = (index / width / height) % channels;
     int n = index / width / height / channels;
-    int phstart = (h < kernel_size) ? 0 : (h - kernel_size) / stride + 1;
-    int phend = min(h / stride + 1, pooled_height);
-    int pwstart = (w < kernel_size) ? 0 : (w - kernel_size) / stride + 1;
-    int pwend = min(w / stride + 1, pooled_width);
+    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
+    int phend = min(h / stride_h + 1, pooled_height);
+    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
+    int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     top_diff += (n * channels + c) * pooled_height * pooled_width;
     for (int ph = phstart; ph < phend; ++ph) {
       for (int pw = pwstart; pw < pwend; ++pw) {
         // figure out the pooling size
-        int hstart = ph * stride - pad;
-        int wstart = pw * stride - pad;
-        int hend = min(hstart + kernel_size, height + pad);
-        int wend = min(wstart + kernel_size, width + pad);
+        int hstart = ph * stride_h - pad_h;
+        int wstart = pw * stride_w - pad_w;
+        int hend = min(hstart + kernel_h, height + pad_h);
+        int wend = min(wstart + kernel_w, width + pad_w);
         int pool_size = (hend - hstart) * (wend - wstart);
         gradient += top_diff[ph * pooled_width + pw] / pool_size;
       }
@@ -260,7 +297,8 @@ __global__ void StoPoolBackward(const int nthreads,
     const Dtype* rand_idx, const Dtype* top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
-    const int kernel_size, const int stride, Dtype* bottom_diff) {
+    const int kernel_h, const int kernel_w, const int stride_h,
+    const int stride_w, Dtype* bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
@@ -268,10 +306,10 @@ __global__ void StoPoolBackward(const int nthreads,
     int h = (index / width) % height;
     int c = (index / width / height) % channels;
     int n = index / width / height / channels;
-    int phstart = (h < kernel_size) ? 0 : (h - kernel_size) / stride + 1;
-    int phend = min(h / stride + 1, pooled_height);
-    int pwstart = (w < kernel_size) ? 0 : (w - kernel_size) / stride + 1;
-    int pwend = min(w / stride + 1, pooled_width);
+    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
+    int phend = min(h / stride_h + 1, pooled_height);
+    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
+    int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
     rand_idx += (n * channels + c) * pooled_height * pooled_width;
     top_diff += (n * channels + c) * pooled_height * pooled_width;
@@ -288,34 +326,46 @@ __global__ void StoPoolBackward(const int nthreads,
 
 template <typename Dtype>
 void PoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (!propagate_down) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) {
     return;
   }
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  int count = (*bottom)[0]->count();
+  const int count = (*bottom)[0]->count();
+  caffe_gpu_set(count, Dtype(0.), bottom_diff);
+  // We'll output the mask to top[1] if it's of size >1.
+  const bool use_top_mask = top.size() > 1;
+  const int* mask = NULL;
+  const Dtype* top_mask = NULL;
   switch (this->layer_param_.pooling_param().pool()) {
   case PoolingParameter_PoolMethod_MAX:
+    if (use_top_mask) {
+      top_mask = top[1]->gpu_data();
+    } else {
+      mask = max_idx_.gpu_data();
+    }
     // NOLINT_NEXT_LINE(whitespace/operators)
     MaxPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, (*bottom)[0]->gpu_data(), top[0]->gpu_data(), top_diff,
-        top[0]->num(), channels_, height_, width_, pooled_height_,
-        pooled_width_, kernel_size_, stride_, bottom_diff);
+        count, top_diff, mask, top_mask, top[0]->num(), channels_,
+        height_, width_, pooled_height_, pooled_width_,
+        kernel_h_, kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_,
+        bottom_diff);
     break;
   case PoolingParameter_PoolMethod_AVE:
     // NOLINT_NEXT_LINE(whitespace/operators)
     AvePoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, top_diff, top[0]->num(), channels_,
-        height_, width_, pooled_height_, pooled_width_, kernel_size_, stride_,
-        pad_, bottom_diff);
+        height_, width_, pooled_height_, pooled_width_, kernel_h_,
+        kernel_w_, stride_h_, stride_w_, pad_h_, pad_w_, bottom_diff);
     break;
   case PoolingParameter_PoolMethod_STOCHASTIC:
     // NOLINT_NEXT_LINE(whitespace/operators)
     StoPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
         count, rand_idx_.gpu_data(), top_diff,
         top[0]->num(), channels_, height_, width_, pooled_height_,
-        pooled_width_, kernel_size_, stride_, bottom_diff);
+        pooled_width_, kernel_h_, kernel_w_, stride_h_, stride_w_,
+        bottom_diff);
     break;
   default:
     LOG(FATAL) << "Unknown pooling method.";
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 85c84423aa2..8b5d8d16bd4 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -1,13 +1,9 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -49,9 +45,9 @@ Dtype PowerLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void PowerLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     const int count = (*bottom)[0]->count();
     const Dtype* top_diff = top[0]->cpu_diff();
@@ -99,6 +95,10 @@ void PowerLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(PowerLayer);
+#endif
+
 INSTANTIATE_CLASS(PowerLayer);
 
 
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index 9a25de72d36..0950b78b4ce 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -1,13 +1,9 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -23,7 +19,7 @@ Dtype PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     return Dtype(0);
   }
   const Dtype* bottom_data = bottom[0]->gpu_data();
-  caffe_gpu_copy(count, bottom_data, top_data);
+  caffe_copy(count, bottom_data, top_data);
   if (scale_ != Dtype(1)) {
     caffe_gpu_scal(count, scale_, top_data);
   }
@@ -38,9 +34,9 @@ Dtype PowerLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void PowerLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
     const int count = (*bottom)[0]->count();
     const Dtype* top_diff = top[0]->gpu_diff();
@@ -68,7 +64,7 @@ void PowerLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
         caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
         caffe_gpu_scal(count, power_, bottom_diff);
       } else {
-        caffe_gpu_copy(count, bottom_data, bottom_diff);
+        caffe_copy(count, bottom_data, bottom_diff);
         if (scale_ != Dtype(1)) {
           caffe_gpu_scal(count, scale_, bottom_diff);
         }
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index 7a33e556268..fca10a5aad3 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -1,13 +1,9 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -16,28 +12,36 @@ Dtype ReLULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = (*top)[0]->mutable_cpu_data();
   const int count = bottom[0]->count();
+  Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
   for (int i = 0; i < count; ++i) {
-    top_data[i] = max(bottom_data[i], Dtype(0));
+    top_data[i] = std::max(bottom_data[i], Dtype(0))
+        + negative_slope * std::min(bottom_data[i], Dtype(0));
   }
   return Dtype(0);
 }
 
 template <typename Dtype>
 void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* bottom_data = (*bottom)[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     const int count = (*bottom)[0]->count();
+    Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
     for (int i = 0; i < count; ++i) {
-      bottom_diff[i] = top_diff[i] * (bottom_data[i] > 0);
+      bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0)
+          + negative_slope * (bottom_data[i] <= 0));
     }
   }
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(ReLULayer);
+#endif
+
 INSTANTIATE_CLASS(ReLULayer);
 
 
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index 51e5ef26c81..a74428bfce1 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -1,19 +1,16 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
-__global__ void ReLUForward(const int n, const Dtype* in, Dtype* out) {
+__global__ void ReLUForward(const int n, const Dtype* in, Dtype* out,
+    Dtype negative_slope) {
   CUDA_KERNEL_LOOP(index, n) {
-    out[index] = in[index] > 0 ? in[index] : 0;
+    out[index] = in[index] > 0 ? in[index] : in[index] * negative_slope;
   }
 }
 
@@ -23,9 +20,10 @@ Dtype ReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = (*top)[0]->mutable_gpu_data();
   const int count = bottom[0]->count();
+  Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
   // NOLINT_NEXT_LINE(whitespace/operators)
   ReLUForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-      count, bottom_data, top_data);
+      count, bottom_data, top_data, negative_slope);
   CUDA_POST_KERNEL_CHECK;
   // << " count: " << count << " bottom_data: "
   //     << (unsigned long)bottom_data
@@ -37,28 +35,31 @@ Dtype ReLULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 __global__ void ReLUBackward(const int n, const Dtype* in_diff,
-    const Dtype* in_data, Dtype* out_diff) {
+    const Dtype* in_data, Dtype* out_diff, Dtype negative_slope) {
   CUDA_KERNEL_LOOP(index, n) {
-    out_diff[index] = in_diff[index] * (in_data[index] > 0);
+    out_diff[index] = in_diff[index] * ((in_data[index] > 0)
+        + (in_data[index] <= 0) * negative_slope);
   }
 }
 
 template <typename Dtype>
 void ReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* bottom_data = (*bottom)[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
     const int count = (*bottom)[0]->count();
+    Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
     // NOLINT_NEXT_LINE(whitespace/operators)
     ReLUBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, bottom_data, bottom_diff);
+        count, top_diff, bottom_data, bottom_diff, negative_slope);
     CUDA_POST_KERNEL_CHECK;
   }
 }
 
+
 INSTANTIATE_CLASS(ReLULayer);
 
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index a638684f3b6..24ab6a85c6a 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -1,14 +1,10 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -16,7 +12,7 @@ template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::FurtherSetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
   CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
-      "SigmoidCrossEntropyLoss Layer inputs must have same count.";
+      "SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
   sigmoid_bottom_vec_.clear();
   sigmoid_bottom_vec_.push_back(bottom[0]);
   sigmoid_top_vec_.clear();
@@ -41,24 +37,37 @@ Dtype SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
   }
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  }
   return loss / num;
 }
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  // First, compute the diff
-  const int count = (*bottom)[0]->count();
-  const int num = (*bottom)[0]->num();
-  const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
-  const Dtype* target = (*bottom)[1]->cpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  caffe_sub(count, sigmoid_output_data, target, bottom_diff);
-  // Scale down gradient
-  caffe_scal(count, Dtype(1) / num, bottom_diff);
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    // First, compute the diff
+    const int count = (*bottom)[0]->count();
+    const int num = (*bottom)[0]->num();
+    const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
+    const Dtype* target = (*bottom)[1]->cpu_data();
+    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    caffe_sub(count, sigmoid_output_data, target, bottom_diff);
+    // Scale down gradient
+    caffe_scal(count, Dtype(1) / num, bottom_diff);
+  }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(SigmoidCrossEntropyLossLayer);
+#endif
+
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
 
 
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 61004541fce..0e4dab76b79 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -1,14 +1,10 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -29,23 +25,32 @@ Dtype SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
     loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
         log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
   }
+  if (top->size() == 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  }
   return loss / num;
 }
 
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
-    const vector<Blob<Dtype>*>& top, const bool propagate_down,
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  // First, compute the diff
-  const int count = (*bottom)[0]->count();
-  const int num = (*bottom)[0]->num();
-  const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
-  const Dtype* target = (*bottom)[1]->gpu_data();
-  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
-  caffe_gpu_copy(count, sigmoid_output_data, bottom_diff);
-  caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
-  // Scale down gradient
-  caffe_gpu_scal(count, Dtype(1) / num, bottom_diff);
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    // First, compute the diff
+    const int count = (*bottom)[0]->count();
+    const int num = (*bottom)[0]->num();
+    const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
+    const Dtype* target = (*bottom)[1]->gpu_data();
+    Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+    caffe_copy(count, sigmoid_output_data, bottom_diff);
+    caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
+    // Scale down gradient
+    caffe_gpu_scal(count, Dtype(1) / num, bottom_diff);
+  }
 }
 
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index 88a7920fc18..0f8b582da2f 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cmath>
 #include <vector>
@@ -28,9 +26,9 @@ Dtype SigmoidLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* top_data = top[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
@@ -42,6 +40,10 @@ void SigmoidLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(SigmoidLayer);
+#endif
+
 INSTANTIATE_CLASS(SigmoidLayer);
 
 
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index aa8568abb3f..039796e1b22 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cmath>
 #include <vector>
@@ -7,8 +5,6 @@
 #include "caffe/layer.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::max;
-
 namespace caffe {
 
 template <typename Dtype>
@@ -47,9 +43,9 @@ __global__ void SigmoidBackward(const int n, const Dtype* in_diff,
 
 template <typename Dtype>
 void SigmoidLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+  if (propagate_down[0]) {
     const Dtype* top_data = top[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
new file mode 100644
index 00000000000..e182837c3aa
--- /dev/null
+++ b/src/caffe/layers/slice_layer.cpp
@@ -0,0 +1,139 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void SliceLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  Layer<Dtype>::SetUp(bottom, top);
+  const SliceParameter& slice_param = this->layer_param_.slice_param();
+  slice_dim_ = slice_param.slice_dim();
+  CHECK_GE(slice_dim_, 0);
+  CHECK_LE(slice_dim_, 1) << "Can only slice num and channels";
+  slice_point_.clear();
+  std::copy(slice_param.slice_point().begin(),
+      slice_param.slice_point().end(),
+      std::back_inserter(slice_point_));
+  count_ = 0;
+  num_ = bottom[0]->num();
+  channels_ = bottom[0]->channels();
+  height_ = bottom[0]->height();
+  width_ = bottom[0]->width();
+  if (slice_point_.size() != 0) {
+    CHECK_EQ(slice_point_.size(), top->size() - 1);
+    if (slice_dim_ == 0) {
+      CHECK_LE(top->size(), num_);
+    } else {
+      CHECK_LE(top->size(), channels_);
+    }
+    int prev = 0;
+    vector<int> slices;
+    for (int i = 0; i < slice_point_.size(); ++i) {
+      CHECK_GT(slice_point_[i], prev);
+      slices.push_back(slice_point_[i] - prev);
+      prev = slice_point_[i];
+    }
+    if (slice_dim_ == 0) {
+      slices.push_back(num_ - prev);
+      for (int i = 0; i < top->size(); ++i) {
+        (*top)[i]->Reshape(slices[i], channels_, height_, width_);
+         count_ += (*top)[i]->count();
+      }
+    } else {
+      slices.push_back(channels_ - prev);
+      for (int i = 0; i < top->size(); ++i) {
+        (*top)[i]->Reshape(num_, slices[i], height_, width_);
+         count_ += (*top)[i]->count();
+      }
+    }
+
+  } else {
+    if (slice_dim_ == 0) {
+      CHECK_EQ(num_ % top->size(), 0)
+          << "Number of top blobs (" << top->size() << ") "
+          << "should evenly divide input num ( " << num_ << ")";
+      num_ = num_ / top->size();
+    } else {
+      CHECK_EQ(channels_ % top->size(), 0)
+          << "Number of top blobs (" << top->size() << ") "
+          << "should evenly divide input channels ( " << channels_ << ")";
+      channels_ = channels_ / top->size();
+    }
+    for (int i = 0; i < top->size(); ++i) {
+      (*top)[i]->Reshape(num_, channels_, height_, width_);
+      count_ += (*top)[i]->count();
+    }
+  }
+  CHECK_EQ(count_, bottom[0]->count());
+}
+
+template <typename Dtype>
+Dtype SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->mutable_cpu_data();
+  if (slice_dim_ == 0) {
+    int offset_num = 0;
+    for (int i = 0; i < top->size(); ++i) {
+      Blob<Dtype>* blob = (*top)[i];
+      Dtype* top_data = blob->mutable_cpu_data();
+      caffe_copy(blob->count(), bottom_data + bottom[0]->offset(offset_num),
+                 top_data);
+      offset_num += blob->num();
+    }
+  } else if (slice_dim_ == 1) {
+    int offset_channel = 0;
+    for (int i = 0; i < top->size(); ++i) {
+      Blob<Dtype>* blob = (*top)[i];
+      Dtype* top_data = blob->mutable_cpu_data();
+      const int num_elem = blob->channels() * blob->height() * blob->width();
+      for (int n = 0; n < num_; ++n) {
+        caffe_copy(num_elem, bottom_data + bottom[0]->offset(n, offset_channel),
+                   top_data + blob->offset(n));
+      }
+      offset_channel += blob->channels();
+    }
+  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+  return Dtype(0.);
+}
+
+template <typename Dtype>
+void SliceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) { return; }
+  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+  if (slice_dim_ == 0) {
+    int offset_num = 0;
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
+      const Dtype* top_diff = blob->cpu_diff();
+      caffe_copy(blob->count(), top_diff,
+                 bottom_diff + (*bottom)[0]->offset(offset_num));
+      offset_num += blob->num();
+    }
+  } else if (slice_dim_ == 1) {
+    int offset_channel = 0;
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
+      const Dtype* top_diff = blob->cpu_diff();
+      const int num_elem = blob->channels() * blob->height() * blob->width();
+      for (int n = 0; n < num_; ++n) {
+        caffe_copy(num_elem, top_diff + blob->offset(n),
+                   bottom_diff + (*bottom)[0]->offset(n, offset_channel));
+      }
+      offset_channel += blob->channels();
+    }
+  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(SliceLayer);
+#endif
+
+INSTANTIATE_CLASS(SliceLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
new file mode 100644
index 00000000000..8e01131ec01
--- /dev/null
+++ b/src/caffe/layers/slice_layer.cu
@@ -0,0 +1,69 @@
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+Dtype SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->mutable_gpu_data();
+  if (slice_dim_ == 0) {
+    int offset_num = 0;
+    for (int i = 0; i < top->size(); ++i) {
+      Blob<Dtype>* blob = (*top)[i];
+      Dtype* top_data = blob->mutable_gpu_data();
+      caffe_copy(blob->count(), bottom_data + bottom[0]->offset(offset_num),
+                 top_data);
+      offset_num += blob->num();
+    }
+  } else if (slice_dim_ == 1) {
+    int offset_channel = 0;
+    for (int i = 0; i < top->size(); ++i) {
+      Blob<Dtype>* blob = (*top)[i];
+      Dtype* top_data = blob->mutable_gpu_data();
+      const int num_elem = blob->channels() * blob->height() * blob->width();
+      for (int n = 0; n < num_; ++n) {
+        caffe_copy(num_elem, bottom_data + bottom[0]->offset(n, offset_channel),
+                   top_data + blob->offset(n));
+      }
+      offset_channel += blob->channels();
+    }
+  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+  return Dtype(0.);
+}
+
+template <typename Dtype>
+void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (!propagate_down[0]) { return; }
+  Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
+  if (slice_dim_ == 0) {
+    int offset_num = 0;
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
+      const Dtype* top_diff = blob->gpu_diff();
+      caffe_copy(blob->count(), top_diff,
+                 bottom_diff + (*bottom)[0]->offset(offset_num));
+      offset_num += blob->num();
+    }
+  } else if (slice_dim_ == 1) {
+    int offset_channel = 0;
+    for (int i = 0; i < top.size(); ++i) {
+      Blob<Dtype>* blob = top[i];
+      const Dtype* top_diff = blob->gpu_diff();
+      const int num_elem = blob->channels() * blob->height() * blob->width();
+      for (int n = 0; n < num_; ++n) {
+        caffe_copy(num_elem, top_diff + blob->offset(n),
+                   bottom_diff +  (*bottom)[0]->offset(n, offset_channel));
+      }
+      offset_channel += blob->channels();
+    }
+  }  // slice_dim_ is guaranteed to be 0 or 1 by SetUp.
+}
+
+INSTANTIATE_CLASS(SliceLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index e9983608e94..61990ed9d97 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -1,21 +1,17 @@
-// Copyright 2014 BVLC and contributors.
 //
 #include <algorithm>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Softmax Layer takes a single blob as input.";
-  CHECK_EQ(top->size(), 1) << "Softmax Layer takes a single blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
   sum_multiplier_.Reshape(1, bottom[0]->channels(),
@@ -35,13 +31,13 @@ Dtype SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* scale_data = scale_.mutable_cpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
-  memcpy(top_data, bottom_data, sizeof(Dtype) * bottom[0]->count());
+  caffe_copy(bottom[0]->count(), bottom_data, top_data);
   // we need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
   for (int i = 0; i < num; ++i) {
     scale_data[i] = bottom_data[i*dim];
     for (int j = 0; j < dim; ++j) {
-      scale_data[i] = max(scale_data[i], bottom_data[i * dim + j]);
+      scale_data[i] = std::max(scale_data[i], bottom_data[i * dim + j]);
     }
   }
   // subtraction
@@ -61,7 +57,7 @@ Dtype SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   const Dtype* top_data = top[0]->cpu_data();
@@ -69,7 +65,7 @@ void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   Dtype* scale_data = scale_.mutable_cpu_data();
   int num = top[0]->num();
   int dim = top[0]->count() / top[0]->num();
-  memcpy(bottom_diff, top_diff, sizeof(Dtype) * top[0]->count());
+  caffe_copy(top[0]->count(), top_diff, bottom_diff);
   // Compute inner1d(top_diff, top_data) and subtract them from the bottom diff
   for (int i = 0; i < num; ++i) {
     scale_data[i] = caffe_cpu_dot<Dtype>(dim, top_diff + i * dim,
@@ -83,6 +79,10 @@ void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(SoftmaxLayer);
+#endif
+
 INSTANTIATE_CLASS(SoftmaxLayer);
 
 
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index a264a819b78..65b0e229d1a 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
@@ -7,10 +5,8 @@
 #include "thrust/device_vector.h"
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -50,8 +46,7 @@ Dtype SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   Dtype* scale_data = scale_.mutable_gpu_data();
   int num = bottom[0]->num();
   int dim = bottom[0]->count() / bottom[0]->num();
-  CUDA_CHECK(cudaMemcpy(top_data, bottom_data,
-      sizeof(Dtype) * bottom[0]->count(), cudaMemcpyDeviceToDevice));
+  caffe_copy(bottom[0]->count(), bottom_data, top_data);
   // we need to subtract the max to avoid numerical issues, compute the exp,
   // and then normalize.
   // Compute max
@@ -79,14 +74,13 @@ Dtype SoftmaxLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 // TODO(Yangqing): implement the GPU version of softmax.
 template <typename Dtype>
 void SoftmaxLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   const Dtype* top_data = top[0]->gpu_data();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   int num = top[0]->num();
   int dim = top[0]->count() / top[0]->num();
-  CUDA_CHECK(cudaMemcpy(bottom_diff, top_diff,
-      sizeof(Dtype) * top[0]->count(), cudaMemcpyDeviceToDevice));
+  caffe_copy(top[0]->count(), top_diff, bottom_diff);
   // Compute inner1d(top_diff, top_data) and subtract them from the bottom diff
   // cuda dot returns the result to cpu, so we temporarily change the pointer
   // mode
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index fecd7a520df..98cf14c4a28 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -1,26 +1,30 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 2) << "SoftmaxLoss Layer takes two blobs as input.";
-  CHECK_EQ(top->size(), 0) << "SoftmaxLoss Layer takes no blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   softmax_bottom_vec_.clear();
   softmax_bottom_vec_.push_back(bottom[0]);
   softmax_top_vec_.push_back(&prob_);
   softmax_layer_->SetUp(softmax_bottom_vec_, &softmax_top_vec_);
+  if (top->size() >= 1) {
+    // softmax loss (averaged across batch)
+    (*top)[0]->Reshape(1, 1, 1, 1);
+  }
+  if (top->size() == 2) {
+    // softmax output
+    (*top)[1]->Reshape(bottom[0]->num(), bottom[0]->channels(),
+        bottom[0]->height(), bottom[0]->width());
+  }
 }
 
 template <typename Dtype>
@@ -35,31 +39,46 @@ Dtype SoftmaxWithLossLayer<Dtype>::Forward_cpu(
   int dim = prob_.count() / num;
   Dtype loss = 0;
   for (int i = 0; i < num; ++i) {
-    loss += -log(max(prob_data[i * dim + static_cast<int>(label[i])],
-                     Dtype(FLT_MIN)));
+    loss += -log(std::max(prob_data[i * dim + static_cast<int>(label[i])],
+                          Dtype(FLT_MIN)));
+  }
+  if (top->size() >= 1) {
+    (*top)[0]->mutable_cpu_data()[0] = loss / num;
+  }
+  if (top->size() == 2) {
+    (*top)[1]->ShareData(prob_);
   }
   return loss / num;
 }
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  // Compute the diff
-  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
-  const Dtype* prob_data = prob_.cpu_data();
-  memcpy(bottom_diff, prob_data, sizeof(Dtype) * prob_.count());
-  const Dtype* label = (*bottom)[1]->cpu_data();
-  int num = prob_.num();
-  int dim = prob_.count() / num;
-  for (int i = 0; i < num; ++i) {
-    bottom_diff[i * dim + static_cast<int>(label[i])] -= 1;
+  if (propagate_down[1]) {
+    LOG(FATAL) << this->type_name()
+               << " Layer cannot backpropagate to label inputs.";
+  }
+  if (propagate_down[0]) {
+    Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
+    const Dtype* prob_data = prob_.cpu_data();
+    caffe_copy(prob_.count(), prob_data, bottom_diff);
+    const Dtype* label = (*bottom)[1]->cpu_data();
+    int num = prob_.num();
+    int dim = prob_.count() / num;
+    for (int i = 0; i < num; ++i) {
+      bottom_diff[i * dim + static_cast<int>(label[i])] -= 1;
+    }
+    // Scale down gradient
+    caffe_scal(prob_.count(), Dtype(1) / num, bottom_diff);
   }
-  // Scale down gradient
-  caffe_scal(prob_.count(), Dtype(1) / num, bottom_diff);
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(SoftmaxWithLossLayer);
+#endif
+
 INSTANTIATE_CLASS(SoftmaxWithLossLayer);
 
 
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 24a3c384c96..32f3e670f48 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -1,14 +1,10 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cfloat>
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -21,7 +17,7 @@ Dtype SoftmaxWithLossLayer<Dtype>::Forward_gpu(
 
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
+    const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
   // TODO(Yangqing): implement the GPU version of softmax.
   Backward_cpu(top, propagate_down, bottom);
 }
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index aa2b6f6a308..2786d3f7694 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -1,18 +1,15 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void SplitLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
-  CHECK_EQ(bottom.size(), 1) << "Split Layer takes a single blob as input.";
-  CHECK_GE(top->size(), 1) << "Split Layer takes at least one blob as output.";
+  Layer<Dtype>::SetUp(bottom, top);
   count_ = bottom[0]->count();
   for (int i = 0; i < top->size(); ++i) {
     // Allow the 0th top blob to be 'in-place', but no others.
@@ -38,8 +35,8 @@ Dtype SplitLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (propagate_down[0]) {
     (*bottom)[0]->ShareDiff(*top[0]);
     // Add remaining top blob diffs.
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
@@ -51,6 +48,10 @@ void SplitLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
 }
 
 
+#ifdef CPU_ONLY
+STUB_GPU(SplitLayer);
+#endif
+
 INSTANTIATE_CLASS(SplitLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index e2269b8beab..1cf15a79314 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -1,10 +1,8 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/util/math_functions.hpp"
+#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -19,8 +17,8 @@ Dtype SplitLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 template <typename Dtype>
 void SplitLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
+      const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
+  if (propagate_down[0]) {
     (*bottom)[0]->ShareDiff(*top[0]);
     // Add remaining top blob diffs.
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index 46d11d0a17a..0c8be3fac60 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 // TanH neuron activation function layer.
 // Adapted from ReLU layer code written by Yangqing Jia
 
@@ -18,31 +17,33 @@ Dtype TanHLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   Dtype exp2x;
   const int count = bottom[0]->count();
   for (int i = 0; i < count; ++i) {
-    exp2x = exp(2*bottom_data[i]);
-    top_data[i] = (exp2x - Dtype(1))/(exp2x + Dtype(1));
+    exp2x = exp(2 * bottom_data[i]);
+    top_data[i] = (exp2x - Dtype(1)) / (exp2x + Dtype(1));
   }
   return Dtype(0);
 }
 
 template <typename Dtype>
 void TanHLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
-    const Dtype* bottom_data = (*bottom)[0]->cpu_data();
+  if (propagate_down[0]) {
+    const Dtype* top_data = top[0]->cpu_data();
     const Dtype* top_diff = top[0]->cpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
     const int count = (*bottom)[0]->count();
-    Dtype exp2x;
     Dtype tanhx;
     for (int i = 0; i < count; ++i) {
-      exp2x = exp(2*bottom_data[i]);
-      tanhx = (exp2x - Dtype(1))/(exp2x + Dtype(1));
-      bottom_diff[i] = top_diff[i] * (1 - tanhx*tanhx);
+      tanhx = top_data[i];
+      bottom_diff[i] = top_diff[i] * (1 - tanhx * tanhx);
     }
   }
 }
 
+#ifdef CPU_ONLY
+STUB_GPU(TanHLayer);
+#endif
+
 INSTANTIATE_CLASS(TanHLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index 13bb001ffc0..b3daad1e638 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -1,4 +1,3 @@
-// Copyright 2014 BVLC and contributors.
 // TanH neuron activation function layer.
 // Adapted from ReLU layer code written by Yangqing Jia
 
@@ -13,8 +12,8 @@ namespace caffe {
 template <typename Dtype>
 __global__ void TanHForward(const int n, const Dtype* in, Dtype* out) {
   CUDA_KERNEL_LOOP(index, n) {
-    Dtype exp2x = exp(2*in[index]);
-    out[index] = (exp2x - Dtype(1))/(exp2x + Dtype(1));
+    Dtype exp2x = exp(2 * in[index]);
+    out[index] = (exp2x - Dtype(1)) / (exp2x + Dtype(1));
   }
 }
 
@@ -28,36 +27,30 @@ Dtype TanHLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   TanHForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
       count, bottom_data, top_data);
   CUDA_POST_KERNEL_CHECK;
-  // << " count: " << count << " bottom_data: "
-  //     << (unsigned long)bottom_data
-  //     << " top_data: " << (unsigned long)top_data
-  //     << " blocks: " << CAFFE_GET_BLOCKS(count)
-  //     << " threads: " << CAFFE_CUDA_NUM_THREADS;
   return Dtype(0);
 }
 
 template <typename Dtype>
 __global__ void TanHBackward(const int n, const Dtype* in_diff,
-    const Dtype* in_data, Dtype* out_diff) {
+    const Dtype* out_data, Dtype* out_diff) {
   CUDA_KERNEL_LOOP(index, n) {
-    Dtype exp2x = exp(2*in_data[index]);
-    Dtype tanhx = (exp2x - Dtype(1))/(exp2x + Dtype(1));
-    out_diff[index] = in_diff[index] * (1 - tanhx*tanhx);
+    Dtype tanhx = out_data[index];
+    out_diff[index] = in_diff[index] * (1 - tanhx * tanhx);
   }
 }
 
 template <typename Dtype>
 void TanHLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
-    const bool propagate_down,
+    const vector<bool>& propagate_down,
     vector<Blob<Dtype>*>* bottom) {
-  if (propagate_down) {
-    const Dtype* bottom_data = (*bottom)[0]->gpu_data();
+  if (propagate_down[0]) {
+    const Dtype* top_data = top[0]->gpu_data();
     const Dtype* top_diff = top[0]->gpu_diff();
     Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
     const int count = (*bottom)[0]->count();
     // NOLINT_NEXT_LINE(whitespace/operators)
     TanHBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-        count, top_diff, bottom_data, bottom_diff);
+        count, top_diff, top_data, bottom_diff);
     CUDA_POST_KERNEL_CHECK;
   }
 }
diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
new file mode 100644
index 00000000000..c932356019b
--- /dev/null
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -0,0 +1,34 @@
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+void ThresholdLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
+      vector<Blob<Dtype>*>* top) {
+  NeuronLayer<Dtype>::SetUp(bottom, top);
+  threshold_ = this->layer_param_.threshold_param().threshold();
+}
+
+template <typename Dtype>
+Dtype ThresholdLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = (*top)[0]->mutable_cpu_data();
+  const int count = bottom[0]->count();
+  for (int i = 0; i < count; ++i) {
+    top_data[i] = (bottom_data[i] > threshold_) ? Dtype(1) : Dtype(0);
+  }
+  return Dtype(0);
+}
+
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(ThresholdLayer, Forward);
+#endif
+
+INSTANTIATE_CLASS(ThresholdLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
new file mode 100644
index 00000000000..398d56e8765
--- /dev/null
+++ b/src/caffe/layers/threshold_layer.cu
@@ -0,0 +1,35 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void ThresholdForward(const int n, const Dtype threshold,
+    const Dtype* in, Dtype* out) {
+  CUDA_KERNEL_LOOP(index, n) {
+    out[index] = in[index] > threshold ? 1 : 0;
+  }
+}
+
+template <typename Dtype>
+Dtype ThresholdLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    vector<Blob<Dtype>*>* top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = (*top)[0]->mutable_gpu_data();
+  const int count = bottom[0]->count();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  ThresholdForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, threshold_, bottom_data, top_data);
+  CUDA_POST_KERNEL_CHECK;
+
+  return Dtype(0);
+}
+
+
+INSTANTIATE_CLASS(ThresholdLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index 862c0347082..ddff5555417 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -1,16 +1,14 @@
-// Copyright 2014 BVLC and contributors.
 //
 // Based on data_layer.cpp by Yangqing Jia.
 
 #include <stdint.h>
-#include <pthread.h>
 
 #include <algorithm>
-#include <string>
-#include <vector>
-#include <map>
 #include <fstream>  // NOLINT(readability/streams)
+#include <map>
+#include <string>
 #include <utility>
+#include <vector>
 
 #include "opencv2/core/core.hpp"
 #include "opencv2/highgui/highgui.hpp"
@@ -22,44 +20,38 @@
 #include "caffe/util/rng.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::string;
-using std::map;
-using std::pair;
-
 // caffe.proto > LayerParameter > WindowDataParameter
 //   'source' field specifies the window_file
 //   'crop_size' indicates the desired warped size
 
 namespace caffe {
 
+// Thread fetching the data
 template <typename Dtype>
-void* WindowDataLayerPrefetch(void* layer_pointer) {
-  WindowDataLayer<Dtype>* layer =
-      reinterpret_cast<WindowDataLayer<Dtype>*>(layer_pointer);
-
+void WindowDataLayer<Dtype>::InternalThreadEntry() {
   // At each iteration, sample N windows where N*p are foreground (object)
   // windows and N*(1-p) are background (non-object) windows
 
-  Dtype* top_data = layer->prefetch_data_->mutable_cpu_data();
-  Dtype* top_label = layer->prefetch_label_->mutable_cpu_data();
-  const Dtype scale = layer->layer_param_.window_data_param().scale();
-  const int batch_size = layer->layer_param_.window_data_param().batch_size();
-  const int crop_size = layer->layer_param_.window_data_param().crop_size();
-  const int context_pad = layer->layer_param_.window_data_param().context_pad();
-  const bool mirror = layer->layer_param_.window_data_param().mirror();
+  Dtype* top_data = prefetch_data_.mutable_cpu_data();
+  Dtype* top_label = prefetch_label_.mutable_cpu_data();
+  const Dtype scale = this->layer_param_.window_data_param().scale();
+  const int batch_size = this->layer_param_.window_data_param().batch_size();
+  const int crop_size = this->layer_param_.window_data_param().crop_size();
+  const int context_pad = this->layer_param_.window_data_param().context_pad();
+  const bool mirror = this->layer_param_.window_data_param().mirror();
   const float fg_fraction =
-      layer->layer_param_.window_data_param().fg_fraction();
-  const Dtype* mean = layer->data_mean_.cpu_data();
-  const int mean_off = (layer->data_mean_.width() - crop_size) / 2;
-  const int mean_width = layer->data_mean_.width();
-  const int mean_height = layer->data_mean_.height();
+      this->layer_param_.window_data_param().fg_fraction();
+  const Dtype* mean = data_mean_.cpu_data();
+  const int mean_off = (data_mean_.width() - crop_size) / 2;
+  const int mean_width = data_mean_.width();
+  const int mean_height = data_mean_.height();
   cv::Size cv_crop_size(crop_size, crop_size);
-  const string& crop_mode = layer->layer_param_.window_data_param().crop_mode();
+  const string& crop_mode = this->layer_param_.window_data_param().crop_mode();
 
   bool use_square = (crop_mode == "square") ? true : false;
 
   // zero out batch
-  memset(top_data, 0, sizeof(Dtype)*layer->prefetch_data_->count());
+  caffe_set(prefetch_data_.count(), Dtype(0), top_data);
 
   const int num_fg = static_cast<int>(static_cast<float>(batch_size)
       * fg_fraction);
@@ -70,24 +62,24 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
   for (int is_fg = 0; is_fg < 2; ++is_fg) {
     for (int dummy = 0; dummy < num_samples[is_fg]; ++dummy) {
       // sample a window
-      const unsigned int rand_index = layer->PrefetchRand();
+      const unsigned int rand_index = PrefetchRand();
       vector<float> window = (is_fg) ?
-          layer->fg_windows_[rand_index % layer->fg_windows_.size()] :
-          layer->bg_windows_[rand_index % layer->bg_windows_.size()];
+          fg_windows_[rand_index % fg_windows_.size()] :
+          bg_windows_[rand_index % bg_windows_.size()];
 
       bool do_mirror = false;
-      if (mirror && layer->PrefetchRand() % 2) {
+      if (mirror && PrefetchRand() % 2) {
         do_mirror = true;
       }
 
       // load the image containing the window
       pair<std::string, vector<int> > image =
-          layer->image_database_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
+          image_database_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];
 
       cv::Mat cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);
       if (!cv_img.data) {
         LOG(ERROR) << "Could not open or find file " << image.first;
-        return reinterpret_cast<void*>(NULL);
+        return;
       }
       const int channels = cv_img.channels();
 
@@ -214,7 +206,7 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
       // useful debugging code for dumping transformed windows to disk
       string file_id;
       std::stringstream ss;
-      ss << layer->PrefetchRand();
+      ss << PrefetchRand();
       ss >> file_id;
       std::ofstream inf((string("dump/") + file_id +
           string("_info.txt")).c_str(), std::ofstream::out);
@@ -246,8 +238,6 @@ void* WindowDataLayerPrefetch(void* layer_pointer) {
       item_id++;
     }
   }
-
-  return reinterpret_cast<void*>(NULL);
 }
 
 template <typename Dtype>
@@ -258,14 +248,12 @@ WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
 template <typename Dtype>
 void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
       vector<Blob<Dtype>*>* top) {
+  Layer<Dtype>::SetUp(bottom, top);
   // SetUp runs through the window_file and creates two structures
   // that hold windows: one for foreground (object) windows and one
   // for background (non-object) windows. We use an overlap threshold
   // to decide which is which.
 
-  CHECK_EQ(bottom.size(), 0) << "Window data Layer takes no input blobs.";
-  CHECK_EQ(top->size(), 2) << "Window data Layer prodcues two blobs as output.";
-
   // window_file format
   // repeated:
   //    # image_index
@@ -293,7 +281,10 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
 
   string hashtag;
   int image_index, channels;
-  while (infile >> hashtag >> image_index) {
+  if (!(infile >> hashtag >> image_index)) {
+    LOG(FATAL) << "Window file is empty";
+  }
+  do {
     CHECK_EQ(hashtag, "#");
     // read image path
     string image_path;
@@ -349,7 +340,7 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
           << image_size[2] << " "
           << "windows to process: " << num_windows;
     }
-  }
+  } while (infile >> hashtag >> image_index);
 
   LOG(INFO) << "Number of images: " << image_index+1;
 
@@ -370,16 +361,14 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(crop_size, 0);
   const int batch_size = this->layer_param_.window_data_param().batch_size();
   (*top)[0]->Reshape(batch_size, channels, crop_size, crop_size);
-  prefetch_data_.reset(
-      new Blob<Dtype>(batch_size, channels, crop_size, crop_size));
+  prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
 
   LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
       << (*top)[0]->channels() << "," << (*top)[0]->height() << ","
       << (*top)[0]->width();
   // label
   (*top)[1]->Reshape(batch_size, 1, 1, 1);
-  prefetch_label_.reset(
-      new Blob<Dtype>(batch_size, 1, 1, 1));
+  prefetch_label_.Reshape(batch_size, 1, 1, 1);
 
   // check if we want to have mean
   if (this->layer_param_.window_data_param().has_mean_file()) {
@@ -400,8 +389,8 @@ void WindowDataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
   // cpu_data calls so that the prefetch thread does not accidentally make
   // simultaneous cudaMalloc calls when the main thread is running. In some
   // GPUs this seems to cause failures if we do not so.
-  prefetch_data_->mutable_cpu_data();
-  prefetch_label_->mutable_cpu_data();
+  prefetch_data_.mutable_cpu_data();
+  prefetch_label_.mutable_cpu_data();
   data_mean_.cpu_data();
   DLOG(INFO) << "Initializing prefetch";
   CreatePrefetchThread();
@@ -420,13 +409,12 @@ void WindowDataLayer<Dtype>::CreatePrefetchThread() {
     prefetch_rng_.reset();
   }
   // Create the thread.
-  CHECK(!pthread_create(&thread_, NULL, WindowDataLayerPrefetch<Dtype>,
-        static_cast<void*>(this))) << "Pthread execution failed.";
+  CHECK(!StartInternalThread()) << "Pthread execution failed.";
 }
 
 template <typename Dtype>
 void WindowDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(!pthread_join(thread_, NULL)) << "Pthread joining failed.";
+  CHECK(!WaitForInternalThreadToExit()) << "Pthread joining failed.";
 }
 
 template <typename Dtype>
@@ -443,15 +431,19 @@ Dtype WindowDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  caffe_copy(prefetch_data_->count(), prefetch_data_->cpu_data(),
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
              (*top)[0]->mutable_cpu_data());
-  caffe_copy(prefetch_label_->count(), prefetch_label_->cpu_data(),
+  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
              (*top)[1]->mutable_cpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
   return Dtype(0.);
 }
 
+#ifdef CPU_ONLY
+STUB_GPU_FORWARD(WindowDataLayer, Forward);
+#endif
+
 INSTANTIATE_CLASS(WindowDataLayer);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/window_data_layer.cu b/src/caffe/layers/window_data_layer.cu
index bc49fef6545..6e8fa8b349a 100644
--- a/src/caffe/layers/window_data_layer.cu
+++ b/src/caffe/layers/window_data_layer.cu
@@ -1,9 +1,8 @@
-// Copyright 2014 BVLC and contributors.
 //
 // Based on data_layer.cpp by Yangqing Jia.
 
-#include <stdint.h>
 #include <pthread.h>
+#include <stdint.h>
 
 #include <string>
 #include <vector>
@@ -12,10 +11,6 @@
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
 
-using std::string;
-using std::map;
-using std::pair;
-
 // caffe.proto > LayerParameter > WindowDataParameter
 //   'source' field specifies the window_file
 //   'crop_size' indicates the desired warped size
@@ -28,12 +23,10 @@ Dtype WindowDataLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   // First, join the thread
   JoinPrefetchThread();
   // Copy the data
-  CUDA_CHECK(cudaMemcpy((*top)[0]->mutable_gpu_data(),
-      prefetch_data_->cpu_data(), sizeof(Dtype) * prefetch_data_->count(),
-      cudaMemcpyHostToDevice));
-  CUDA_CHECK(cudaMemcpy((*top)[1]->mutable_gpu_data(),
-      prefetch_label_->cpu_data(), sizeof(Dtype) * prefetch_label_->count(),
-      cudaMemcpyHostToDevice));
+  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
+      (*top)[0]->mutable_gpu_data());
+  caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
+      (*top)[1]->mutable_gpu_data());
   // Start a new prefetch thread
   CreatePrefetchThread();
   return Dtype(0.);
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index c39510def3e..db6b4ffefc4 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -1,21 +1,19 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <map>
 #include <set>
 #include <string>
+#include <utility>
 #include <vector>
 
 #include "caffe/common.hpp"
-#include "caffe/proto/caffe.pb.h"
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/util/insert_splits.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
-using std::pair;
-using std::map;
-using std::set;
+#include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
 
@@ -33,128 +31,123 @@ Net<Dtype>::Net(const string& param_file) {
 
 template <typename Dtype>
 void Net<Dtype>::Init(const NetParameter& in_param) {
-  // Create a copy of in_param with splits added where necessary.
+  // Filter layers based on their include/exclude rules and
+  // the current NetState.
+  NetParameter filtered_param;
+  FilterNet(in_param, &filtered_param);
+  LOG(INFO) << "Initializing net from parameters: " << std::endl
+            << filtered_param.DebugString();
+  // Create a copy of filtered_param with splits added where necessary.
   NetParameter param;
-  InsertSplits(in_param, &param);
+  InsertSplits(filtered_param, &param);
   // Basically, build all the layers and set up its connections.
   name_ = param.name();
   map<string, int> blob_name_to_idx;
   set<string> available_blobs;
-  int num_layers = param.layers_size();
   CHECK_EQ(param.input_size() * 4, param.input_dim_size())
-      << "Incorrect bottom blob dimension specifications.";
-  size_t memory_used = 0;
+      << "Incorrect input blob dimension specifications.";
+  memory_used_ = 0;
   // set the input blobs
-  for (int i = 0; i < param.input_size(); ++i) {
-    const string& blob_name = param.input(i);
-    shared_ptr<Blob<Dtype> > blob_pointer(
-        new Blob<Dtype>(param.input_dim(i * 4),
-                        param.input_dim(i * 4 + 1),
-                        param.input_dim(i * 4 + 2),
-                        param.input_dim(i * 4 + 3)));
-    blobs_.push_back(blob_pointer);
-    blob_names_.push_back(blob_name);
-    blob_need_backward_.push_back(param.force_backward());
-    net_input_blob_indices_.push_back(i);
-    net_input_blobs_.push_back(blob_pointer.get());
-    blob_name_to_idx[blob_name] = i;
-    available_blobs.insert(blob_name);
-    memory_used += blob_pointer->count();
-  }
-  DLOG(INFO) << "Memory required for Data" << memory_used*sizeof(Dtype);
+  for (int input_id = 0; input_id < param.input_size(); ++input_id) {
+    const int layer_id = -1;  // inputs have fake layer ID -1
+    AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
+  }
+  DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
   // For each layer, set up their input and output
   bottom_vecs_.resize(param.layers_size());
   top_vecs_.resize(param.layers_size());
   bottom_id_vecs_.resize(param.layers_size());
   top_id_vecs_.resize(param.layers_size());
-  for (int i = 0; i < param.layers_size(); ++i) {
-    bool in_place = false;
-    const LayerParameter& layer_param = param.layers(i);
+  bottom_need_backward_.resize(param.layers_size());
+  for (int layer_id = 0; layer_id < param.layers_size(); ++layer_id) {
+    const LayerParameter& layer_param = param.layers(layer_id);
     layers_.push_back(shared_ptr<Layer<Dtype> >(GetLayer<Dtype>(layer_param)));
     layer_names_.push_back(layer_param.name());
     LOG(INFO) << "Creating Layer " << layer_param.name();
-    bool need_backward = param.force_backward();
+    bool need_backward = false;
     // Figure out this layer's input and output
-    for (int j = 0; j < layer_param.bottom_size(); ++j) {
-      const string& blob_name = layer_param.bottom(j);
-      const int blob_id = blob_name_to_idx[blob_name];
-      if (available_blobs.find(blob_name) == available_blobs.end()) {
-        LOG(FATAL) << "Unknown blob input " << blob_name <<
-            " to layer" << j;
-      }
-      LOG(INFO) << layer_param.name() << " <- " << blob_name;
-      bottom_vecs_[i].push_back(
-          blobs_[blob_id].get());
-      bottom_id_vecs_[i].push_back(blob_id);
+    for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
+         ++bottom_id) {
+      const int blob_id = AppendBottom(param, layer_id, bottom_id,
+                                       &available_blobs, &blob_name_to_idx);
       // If a blob needs backward, this layer should provide it.
       need_backward |= blob_need_backward_[blob_id];
-      available_blobs.erase(blob_name);
     }
-    for (int j = 0; j < layer_param.top_size(); ++j) {
-      const string& blob_name = layer_param.top(j);
-      // Check if we are doing in-place computation
-      if (layer_param.bottom_size() > j &&
-          blob_name == layer_param.bottom(j)) {
-        // In-place computation
-        LOG(INFO) << layer_param.name() << " -> " << blob_name << " (in-place)";
-        in_place = true;
-        available_blobs.insert(blob_name);
-        top_vecs_[i].push_back(
-            blobs_[blob_name_to_idx[blob_name]].get());
-        top_id_vecs_[i].push_back(blob_name_to_idx[blob_name]);
-      } else if (blob_name_to_idx.find(blob_name) != blob_name_to_idx.end()) {
-        // If we are not doing in-place computation but has duplicated blobs,
-        // raise an error.
-        LOG(FATAL) << "Duplicate blobs produced by multiple sources.";
-      } else {
-        // Normal output.
-        LOG(INFO) << layer_param.name() << " -> " << blob_name;
-        shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
-        blobs_.push_back(blob_pointer);
-        blob_names_.push_back(blob_name);
-        blob_need_backward_.push_back(param.force_backward());
-        blob_name_to_idx[blob_name] = blob_names_.size() - 1;
-        available_blobs.insert(blob_name);
-        top_vecs_[i].push_back(blobs_[blob_names_.size() - 1].get());
-        top_id_vecs_[i].push_back(blob_names_.size() - 1);
-      }
+    for (int top_id = 0; top_id < layer_param.top_size(); ++top_id) {
+      AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx);
     }
     // After this layer is connected, set it up.
-    // LOG(INFO) << "Setting up " << layer_names_[i];
-    layers_[i]->SetUp(bottom_vecs_[i], &top_vecs_[i]);
-    for (int topid = 0; topid < top_vecs_[i].size(); ++topid) {
-      LOG(INFO) << "Top shape: " << top_vecs_[i][topid]->num() << " "
-          << top_vecs_[i][topid]->channels() << " "
-          << top_vecs_[i][topid]->height() << " "
-          << top_vecs_[i][topid]->width() << " ("
-          << top_vecs_[i][topid]->count() << ")";
-      if (!in_place)
-        memory_used += top_vecs_[i][topid]->count();
+    // LOG(INFO) << "Setting up " << layer_names_[layer_id];
+    layers_[layer_id]->SetUp(bottom_vecs_[layer_id], &top_vecs_[layer_id]);
+    for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
+      LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->num() << " "
+          << top_vecs_[layer_id][top_id]->channels() << " "
+          << top_vecs_[layer_id][top_id]->height() << " "
+          << top_vecs_[layer_id][top_id]->width() << " ("
+          << top_vecs_[layer_id][top_id]->count() << ")";
     }
-    DLOG(INFO) << "Memory  required for Data " << memory_used*sizeof(Dtype);
-    int blobs_lr_size = layers_[i]->layer_param().blobs_lr_size();
-    CHECK(blobs_lr_size == layers_[i]->blobs().size() || blobs_lr_size == 0)
-        << "Incorrect blobs lr size: should be either 0 or the same as "
-           "the number of the layer's parameter blobs.";
+    DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+    const int blobs_lr_size = layer_param.blobs_lr_size();
+    const int num_param_blobs = layers_[layer_id]->blobs().size();
+    CHECK(blobs_lr_size == num_param_blobs || blobs_lr_size == 0)
+        << "Incorrect blobs lr size: should be either 0 "
+        << "or the same as the number of the layer's parameter blobs.";
     if (blobs_lr_size) {
       // Check if this layer needs backward operation itself
-      for (int j = 0; j < blobs_lr_size; ++j) {
-        need_backward |= (layers_[i]->layer_param().blobs_lr(j) > 0);
+      for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
+        const bool param_need_backward = layer_param.blobs_lr(param_id) > 0;
+        need_backward |= param_need_backward;
+        layers_[layer_id]->set_param_propagate_down(param_id,
+                                                    param_need_backward);
       }
-    } else if (layers_[i]->blobs().size()) {
+    } else if (layers_[layer_id]->blobs().size()) {
       // catch: if a layer param does not specify blobs_lr, we should assume the
       // learning rate to be 1. Thus we will need to perform backward.
       need_backward = true;
+      for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
+        layers_[layer_id]->set_param_propagate_down(param_id, true);
+      }
+    }
+    const int param_size = layer_param.param_size();
+    CHECK(param_size == num_param_blobs || param_size == 0)
+        << "Incorrect param size: should be either 0 or the same as "
+           "the number of the layer's parameter blobs: " << num_param_blobs;
+    const int blob_share_mode_size = layer_param.blob_share_mode_size();
+    CHECK(blob_share_mode_size == num_param_blobs || blob_share_mode_size == 0)
+        << "Incorrect blob_share_mode size: should be either 0 or the same as "
+           "the number of the layer's parameter blobs: " << num_param_blobs;
+    for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
+      AppendParam(param, layer_id, param_id);
     }
     // Finally, set the backward flag
     layer_need_backward_.push_back(need_backward);
     if (need_backward) {
-      LOG(INFO) << layer_names_[i] << " needs backward computation.";
-      for (int j = 0; j < top_id_vecs_[i].size(); ++j) {
-        blob_need_backward_[top_id_vecs_[i][j]] = true;
+      LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
+      for (int top_id = 0; top_id < top_id_vecs_[layer_id].size(); ++top_id) {
+        blob_need_backward_[top_id_vecs_[layer_id][top_id]] = true;
       }
     } else {
-      LOG(INFO) << layer_names_[i] << " does not need backward computation.";
+      LOG(INFO) << layer_names_[layer_id]
+                << " does not need backward computation.";
+    }
+  }
+  // Handle force_backward if needed.
+  if (param.force_backward()) {
+    for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) {
+      layer_need_backward_[layer_id] = true;
+      for (int bottom_id = 0;
+           bottom_id < bottom_need_backward_[layer_id].size(); ++bottom_id) {
+        bottom_need_backward_[layer_id][bottom_id] =
+            bottom_need_backward_[layer_id][bottom_id] ||
+            layers_[layer_id]->AllowForceBackward(bottom_id);
+        blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] =
+            blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] ||
+            bottom_need_backward_[layer_id][bottom_id];
+      }
+      for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
+           ++param_id) {
+        layers_[layer_id]->set_param_propagate_down(param_id, true);
+      }
     }
   }
   // In the end, all remaining blobs are considered output blobs.
@@ -164,26 +157,251 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());
     net_output_blob_indices_.push_back(blob_name_to_idx[*it]);
   }
-  for (size_t i = 0; i < blob_names_.size(); ++i) {
-    blob_names_index_[blob_names_[i]] = i;
+  for (size_t blob_id = 0; blob_id < blob_names_.size(); ++blob_id) {
+    blob_names_index_[blob_names_[blob_id]] = blob_id;
   }
-  for (size_t i = 0; i < layer_names_.size(); ++i) {
-    layer_names_index_[layer_names_[i]] = i;
+  for (size_t layer_id = 0; layer_id < layer_names_.size(); ++layer_id) {
+    layer_names_index_[layer_names_[layer_id]] = layer_id;
   }
   GetLearningRateAndWeightDecay();
   LOG(INFO) << "Network initialization done.";
-  LOG(INFO) << "Memory required for Data " << memory_used*sizeof(Dtype);
+  LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+  // Don't display debug info by default.
+  debug_info_ = false;
+}
+
+template <typename Dtype>
+void Net<Dtype>::FilterNet(const NetParameter& param,
+    NetParameter* param_filtered) {
+  NetState net_state(param.state());
+  // Let the phase of the net be the current global phase provided in the Caffe
+  // singleton, unless explicitly provided by the state.
+  if (!net_state.has_phase()) {
+    switch (Caffe::phase()) {
+      case Caffe::TRAIN:
+        net_state.set_phase(TRAIN);
+        break;
+      case Caffe::TEST:
+        net_state.set_phase(TEST);
+        break;
+      default:
+        LOG(FATAL) << "Unknown phase: " << Caffe::phase();
+    }
+  }
+  param_filtered->CopyFrom(param);
+  param_filtered->clear_layers();
+  for (int i = 0; i < param.layers_size(); ++i) {
+    const LayerParameter& layer_param = param.layers(i);
+    const string& layer_name = layer_param.name();
+    CHECK(layer_param.include_size() == 0 || layer_param.exclude_size() == 0)
+          << "Specify either include rules or exclude rules; not both.";
+    // If no include rules are specified, the layer is included by default and
+    // only excluded if it meets one of the exclude rules.
+    bool layer_included = (layer_param.include_size() == 0);
+    for (int j = 0; layer_included && j < layer_param.exclude_size(); ++j) {
+      if (StateMeetsRule(net_state, layer_param.exclude(j), layer_name)) {
+        layer_included = false;
+      }
+    }
+    for (int j = 0; !layer_included && j < layer_param.include_size(); ++j) {
+      if (StateMeetsRule(net_state, layer_param.include(j), layer_name)) {
+        layer_included = true;
+      }
+    }
+    if (layer_included) {
+      param_filtered->add_layers()->CopyFrom(layer_param);
+    }
+  }
+}
+
+template <typename Dtype>
+bool Net<Dtype>::StateMeetsRule(const NetState& state,
+    const NetStateRule& rule, const string& layer_name) {
+  // Check whether the rule is broken due to phase.
+  if (rule.has_phase()) {
+      if (rule.phase() != state.phase()) {
+        LOG(INFO) << "The NetState phase (" << state.phase()
+          << ") differed from the phase (" << rule.phase()
+          << ") specified by a rule in layer " << layer_name;
+        return false;
+      }
+  }
+  // Check whether the rule is broken due to min level.
+  if (rule.has_min_level()) {
+    if (state.level() < rule.min_level()) {
+      LOG(INFO) << "The NetState level (" << state.level()
+          << ") is above the min_level (" << rule.min_level()
+          << " specified by a rule in layer " << layer_name;
+      return false;
+    }
+  }
+  // Check whether the rule is broken due to max level.
+  if (rule.has_max_level()) {
+    if (state.level() > rule.max_level()) {
+      LOG(INFO) << "The NetState level (" << state.level()
+          << ") is above the max_level (" << rule.max_level()
+          << " specified by a rule in layer " << layer_name;
+      return false;
+    }
+  }
+  // Check whether the rule is broken due to stage. If stage is specified,
+  // the NetState must contain ALL of the rule's stages to meet it.
+  if (rule.stage_size()) {
+    for (int i = 0; i < rule.stage_size(); ++i) {
+      // Check that the NetState contains the rule's ith stage.
+      bool has_stage = false;
+      for (int j = 0; !has_stage && j < state.stage_size(); ++j) {
+        if (rule.stage(i) == state.stage(j)) { has_stage = true; }
+      }
+      if (!has_stage) {
+        LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i)
+                  << "' specified by a rule in layer " << layer_name;
+        return false;
+      }
+    }
+  }
+  return true;
+}
+
+// Helper for Net::Init: add a new input or top blob to the net.  (Inputs have
+// layer_id == -1, tops have layer_id >= 0.)
+template <typename Dtype>
+void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
+                           const int top_id, set<string>* available_blobs,
+                           map<string, int>* blob_name_to_idx) {
+  shared_ptr<LayerParameter> layer_param((layer_id >= 0) ?
+    (new LayerParameter(param.layers(layer_id))) : NULL);
+  const string& blob_name = layer_param ?
+      layer_param->top(top_id) : param.input(top_id);
+  // Check if we are doing in-place computation
+  if (layer_param && layer_param->bottom_size() > top_id &&
+      blob_name == layer_param->bottom(top_id)) {
+    // In-place computation
+    LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)";
+    top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get());
+    top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]);
+  } else if (blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {
+    // If we are not doing in-place computation but have duplicated blobs,
+    // raise an error.
+    LOG(FATAL) << "Duplicate blobs produced by multiple sources.";
+  } else {
+    // Normal output.
+    if (layer_param) {
+      LOG(INFO) << layer_param->name() << " -> " << blob_name;
+    } else {
+      LOG(INFO) << "Input " << top_id << " -> " << blob_name;
+    }
+    shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
+    const int blob_id = blobs_.size();
+    blobs_.push_back(blob_pointer);
+    blob_names_.push_back(blob_name);
+    blob_need_backward_.push_back(false);
+    (*blob_name_to_idx)[blob_name] = blob_id;
+    if (layer_id == -1) {
+      // Set the (explicitly specified) dimensions of the input blob.
+      blob_pointer->Reshape(param.input_dim(top_id * 4),
+                            param.input_dim(top_id * 4 + 1),
+                            param.input_dim(top_id * 4 + 2),
+                            param.input_dim(top_id * 4 + 3));
+      net_input_blob_indices_.push_back(blob_id);
+      net_input_blobs_.push_back(blob_pointer.get());
+    } else {
+      top_id_vecs_[layer_id].push_back(blob_id);
+      top_vecs_[layer_id].push_back(blob_pointer.get());
+    }
+    memory_used_ += blob_pointer->count();
+  }
+  available_blobs->insert(blob_name);
+}
+
+// Helper for Net::Init: add a new bottom blob to the net.
+template <typename Dtype>
+int Net<Dtype>::AppendBottom(const NetParameter& param,
+    const int layer_id, const int bottom_id,
+    set<string>* available_blobs, map<string, int>* blob_name_to_idx) {
+  const LayerParameter& layer_param = param.layers(layer_id);
+  const string& blob_name = layer_param.bottom(bottom_id);
+  if (available_blobs->find(blob_name) == available_blobs->end()) {
+    LOG(FATAL) << "Unknown blob input " << blob_name
+               << " (at index " << bottom_id << ") to layer " << layer_id;
+  }
+  const int blob_id = (*blob_name_to_idx)[blob_name];
+  LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name;
+  bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
+  bottom_id_vecs_[layer_id].push_back(blob_id);
+  available_blobs->erase(blob_name);
+  const bool need_backward = blob_need_backward_[blob_id];
+  bottom_need_backward_[layer_id].push_back(need_backward);
+  return blob_id;
 }
 
+template <typename Dtype>
+void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
+                             const int param_id) {
+  const LayerParameter& layer_param = layers_[layer_id]->layer_param();
+  const int param_size = layer_param.param_size();
+  string param_name = param_size ? layer_param.param(param_id) : "";
+  if (param_name.size()) {
+    param_display_names_.push_back(param_name);
+  } else {
+    ostringstream param_display_name;
+    param_display_name << param_id;
+    param_display_names_.push_back(param_display_name.str());
+  }
+  const int net_param_id = params_.size();
+  params_.push_back(layers_[layer_id]->blobs()[param_id]);
+  param_layer_indices_.push_back(make_pair(layer_id, param_id));
+  if (!param_size || !param_name.size() || (param_name.size() &&
+      param_names_index_.find(param_name) == param_names_index_.end())) {
+    // This layer "owns" this parameter blob -- it is either anonymous
+    // (i.e., not given a param_name) or explicitly given a name that we
+    // haven't already seen.
+    param_owners_.push_back(-1);
+    if (param_size) {
+      param_names_index_[param_name] = net_param_id;
+    }
+  } else {
+    // Named param blob with name we've seen before: share params
+    const int owner_net_param_id = param_names_index_[param_name];
+    param_owners_.push_back(owner_net_param_id);
+    const pair<int, int>& owner_index =
+        param_layer_indices_[owner_net_param_id];
+    const int owner_layer_id = owner_index.first;
+    const int owner_param_id = owner_index.second;
+    LOG(INFO) << "Sharing parameters '" << param_name << "' owned by "
+              << "layer '" << layer_names_[owner_layer_id] << "', param "
+              << "index " << owner_param_id;
+    Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();
+    Blob<Dtype>* owner_blob =
+        layers_[owner_layer_id]->blobs()[owner_param_id].get();
+    const int blob_share_mode_size = layer_param.blob_share_mode_size();
+    if (blob_share_mode_size > param_id &&
+        (layer_param.blob_share_mode(param_id) ==
+         LayerParameter_DimCheckMode_PERMISSIVE)) {
+      // Permissive dimension checking -- only check counts are the same.
+      CHECK_EQ(this_blob->count(), owner_blob->count())
+          << "Shared parameter blobs must have the same count.";
+    } else {
+      // Strict dimension checking -- all dims must be the same.
+      CHECK_EQ(this_blob->num(), owner_blob->num())
+          << "Shared parameter blobs must have the same num.";
+      CHECK_EQ(this_blob->channels(), owner_blob->channels())
+          << "Shared parameter blobs must have the same channels.";
+      CHECK_EQ(this_blob->height(), owner_blob->height())
+          << "Shared parameter blobs must have the same height.";
+      CHECK_EQ(this_blob->width(), owner_blob->width())
+          << "Shared parameter blobs must have the same width.";
+    }
+    layers_[layer_id]->blobs()[param_id]->ShareData(
+        *layers_[owner_layer_id]->blobs()[owner_param_id]);
+  }
+}
 
 template <typename Dtype>
 void Net<Dtype>::GetLearningRateAndWeightDecay() {
   LOG(INFO) << "Collecting Learning Rate and Weight Decay.";
   for (int i = 0; i < layers_.size(); ++i) {
     vector<shared_ptr<Blob<Dtype> > >& layer_blobs = layers_[i]->blobs();
-    for (int j = 0; j < layer_blobs.size(); ++j) {
-      params_.push_back(layer_blobs[j]);
-    }
     // push the learning rate mutlipliers
     if (layers_[i]->layer_param().blobs_lr_size()) {
       CHECK_EQ(layers_[i]->layer_param().blobs_lr_size(), layer_blobs.size());
@@ -215,16 +433,35 @@ void Net<Dtype>::GetLearningRateAndWeightDecay() {
 }
 
 template <typename Dtype>
-const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
-  if (loss != NULL) {
-    *loss = Dtype(0.);
-  }
-  for (int i = 0; i < layers_.size(); ++i) {
+Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
+  CHECK_GE(start, 0);
+  CHECK_LT(end, layers_.size());
+  Dtype loss = 0;
+  for (int i = start; i <= end; ++i) {
     // LOG(ERROR) << "Forwarding " << layer_names_[i];
     Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], &top_vecs_[i]);
-    if (loss != NULL) {
-      *loss += layer_loss;
-    }
+    loss += layer_loss;
+    if (debug_info_) { ForwardDebugInfo(i); }
+  }
+  return loss;
+}
+
+template <typename Dtype>
+Dtype Net<Dtype>::ForwardFrom(int start) {
+  return ForwardFromTo(start, layers_.size() - 1);
+}
+
+template <typename Dtype>
+Dtype Net<Dtype>::ForwardTo(int end) {
+  return ForwardFromTo(0, end);
+}
+
+template <typename Dtype>
+const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
+  if (loss != NULL) {
+    *loss = ForwardFromTo(0, layers_.size() - 1);
+  } else {
+    ForwardFromTo(0, layers_.size() - 1);
   }
   return net_output_blobs_;
 }
@@ -260,16 +497,77 @@ string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss) {
   return output;
 }
 
-
 template <typename Dtype>
-void Net<Dtype>::Backward() {
-  for (int i = layers_.size() - 1; i >= 0; --i) {
+void Net<Dtype>::BackwardFromTo(int start, int end) {
+  CHECK_GE(end, 0);
+  CHECK_LT(start, layers_.size());
+  for (int i = start; i >= end; --i) {
     if (layer_need_backward_[i]) {
-      layers_[i]->Backward(top_vecs_[i], true, &bottom_vecs_[i]);
+      layers_[i]->Backward(
+          top_vecs_[i], bottom_need_backward_[i], &bottom_vecs_[i]);
+      if (debug_info_) { BackwardDebugInfo(i); }
     }
   }
 }
 
+template <typename Dtype>
+void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
+  for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
+    const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];
+    const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
+    const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
+    LOG(INFO) << "    [Forward] "
+       << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name
+       << " data: " << data_abs_val_mean;
+  }
+}
+
+template <typename Dtype>
+void Net<Dtype>::BackwardDebugInfo(const int layer_id) {
+  const vector<Blob<Dtype>*>& bottom_vec = bottom_vecs_[layer_id];
+  for (int bottom_id = 0; bottom_id < bottom_vec.size(); ++bottom_id) {
+    if (!bottom_need_backward_[layer_id][bottom_id]) { continue; }
+    const Blob<Dtype>& blob = *bottom_vec[bottom_id];
+    const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
+    const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
+    LOG(INFO) << "    [Backward] "
+        << "Layer " << layer_names_[layer_id] << ", bottom blob " << blob_name
+        << " diff: " << diff_abs_val_mean;
+  }
+  for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
+       ++param_id) {
+    if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }
+    const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
+    const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
+    LOG(INFO) << "    [Backward] "
+        << "Layer " << layer_names_[layer_id] << ", param blob " << param_id
+        << " diff: " << diff_abs_val_mean;
+  }
+}
+
+template <typename Dtype>
+void Net<Dtype>::UpdateDebugInfo(const int param_id) {
+  const Blob<Dtype>& blob = *params_[param_id];
+  const int param_owner = param_owners_[param_id];
+  const string& layer_name = layer_names_[param_layer_indices_[param_id].first];
+  const string& param_display_name = param_display_names_[param_id];
+  const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
+  if (param_owner < 0) {
+    const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
+    LOG(INFO) << "    [Update] Layer " << layer_name
+        << ", param " << param_display_name
+        << " data: " << data_abs_val_mean << "; diff: " << diff_abs_val_mean;
+  } else {
+    const string& owner_layer_name =
+        layer_names_[param_layer_indices_[param_owner].first];
+    LOG(INFO) << "    [Update] Layer " << layer_name
+        << ", param blob " << param_display_name
+        << " (owned by layer " << owner_layer_name << ", "
+        << "param " << param_display_names_[param_owners_[param_id]] << ")"
+        << " diff: " << diff_abs_val_mean;
+  }
+}
+
 template <typename Dtype>
 void Net<Dtype>::ShareTrainedLayersWith(Net* other) {
   int num_source_layers = other->layers().size();
@@ -301,6 +599,21 @@ void Net<Dtype>::ShareTrainedLayersWith(Net* other) {
   }
 }
 
+template <typename Dtype>
+void Net<Dtype>::BackwardFrom(int start) {
+  BackwardFromTo(start, 0);
+}
+
+template <typename Dtype>
+void Net<Dtype>::BackwardTo(int end) {
+  BackwardFromTo(layers_.size() - 1, end);
+}
+
+template <typename Dtype>
+void Net<Dtype>::Backward() {
+  BackwardFromTo(layers_.size() - 1, 0);
+}
+
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
   int num_source_layers = param.layers_size();
@@ -361,7 +674,38 @@ void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) {
 
 template <typename Dtype>
 void Net<Dtype>::Update() {
+  // First, accumulate the diffs of any shared parameters into their owner's
+  // diff. (Assumes that the learning rate, weight decay, etc. have already been
+  // accounted for in the current diff.)
+  for (int i = 0; i < params_.size(); ++i) {
+    if (param_owners_[i] < 0) { continue; }
+    if (debug_info_) { UpdateDebugInfo(i); }
+    const int count = params_[i]->count();
+    const Dtype* this_diff;
+    Dtype* owner_diff;
+    switch (Caffe::mode()) {
+    case Caffe::CPU:
+      this_diff = params_[i]->cpu_diff();
+      owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();
+      caffe_add(count, this_diff, owner_diff, owner_diff);
+      break;
+#ifndef CPU_ONLY
+    case Caffe::GPU:
+      this_diff = params_[i]->gpu_diff();
+      owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();
+      caffe_gpu_add(count, this_diff, owner_diff, owner_diff);
+      break;
+#else
+      NO_GPU;
+#endif
+    default:
+      LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+    }
+  }
+  // Now, update the owned parameters.
   for (int i = 0; i < params_.size(); ++i) {
+    if (param_owners_[i] >= 0) { continue; }
+    if (debug_info_) { UpdateDebugInfo(i); }
     params_[i]->Update();
   }
 }
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index ab3c2fecc5c..44bfb631a22 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -1,4 +1,4 @@
-// Copyright 2014 BVLC and contributors.
+syntax = "proto2";
 
 package caffe;
 
@@ -54,13 +54,55 @@ message NetParameter {
   // If set False, then whether to carry out backward is determined
   // automatically according to the net structure and learning rates.
   optional bool force_backward = 5 [default = false];
+  // The current "state" of the network, including the phase, level, and stage.
+  // Some layers may be included/excluded depending on this state and the states
+  // specified in the layers' include and exclude fields.
+  optional NetState state = 6;
 }
 
+// NOTE
+// Update the next available ID when you add a new SolverParameter field.
+//
+// SolverParameter next available ID: 27 (last added: test_state)
 message SolverParameter {
-  optional string train_net = 1; // The proto file for the training net.
-  optional string test_net = 2; // The proto file for the testing net.
-  // The number of iterations for each testing phase.
-  optional int32 test_iter = 3 [default = 0];
+  //////////////////////////////////////////////////////////////////////////////
+  // Specifying the train and test networks
+  //
+  // Exactly one train net must be specified using one of the following fields:
+  //     train_net_param, train_net, net_param, net
+  // One or more test nets may be specified using any of the following fields:
+  //     test_net_param, test_net, net_param, net
+  // If more than one test net field is specified (e.g., both net and
+  // test_net are specified), they will be evaluated in the field order given
+  // above: (1) test_net_param, (2) test_net, (3) net_param/net.
+  // A test_iter must be specified for each test_net.
+  // A test_level and/or a test_stage may also be specified for each test_net.
+  //////////////////////////////////////////////////////////////////////////////
+
+  // Proto filename for the train net, possibly combined with one or more
+  // test nets.
+  optional string net = 24;
+  // Inline train net param, possibly combined with one or more test nets.
+  optional NetParameter net_param = 25;
+
+  optional string train_net = 1; // Proto filename for the train net.
+  repeated string test_net = 2; // Proto filenames for the test nets.
+  optional NetParameter train_net_param = 21; // Inline train net params.
+  repeated NetParameter test_net_param = 22; // Inline test net params.
+
+  // The states for the train/test nets. Must be unspecified or
+  // specified once per net.
+  //
+  // By default, all states will have solver = true;
+  // train_state will have phase = TRAIN,
+  // and all test_state's will have phase = TEST.
+  // Other defaults are set according to the NetState defaults.
+  optional NetState train_state = 26;
+  repeated NetState test_state = 27;
+
+  // The number of iterations for each test net.
+  repeated int32 test_iter = 3;
+
   // The number of iterations between two testing phases.
   optional int32 test_interval = 4 [default = 0];
   optional bool test_compute_loss = 19 [default = false];
@@ -92,6 +134,13 @@ message SolverParameter {
   // random number generator -- useful for reproducible results. Otherwise,
   // (and by default) initialize using a seed derived from the system clock.
   optional int64 random_seed = 20 [default = -1];
+
+  // If true, print information about the state of the net that may help with
+  // debugging learning problems.
+  optional bool debug_info = 23 [default = false];
+
+  // If false, don't save a snapshot after training finishes.
+  optional bool snapshot_after_train = 28 [default = true];
 }
 
 // A message that stores the solver snapshots
@@ -101,33 +150,73 @@ message SolverState {
   repeated BlobProto history = 3; // The history for sgd solvers
 }
 
+enum Phase {
+   TRAIN = 0;
+   TEST = 1;
+}
+
+message NetState {
+  optional Phase phase = 1 [default = TEST];
+  optional int32 level = 2 [default = 0];
+  repeated string stage = 3;
+}
+
+message NetStateRule {
+  // Set phase to require the NetState have a particular phase (TRAIN or TEST)
+  // to meet this rule.
+  optional Phase phase = 1;
+
+  // Set the minimum and/or maximum levels in which the layer should be used.
+  // Leave undefined to meet the rule regardless of level.
+  optional int32 min_level = 2;
+  optional int32 max_level = 3;
+
+  // A customizable set of stages.
+  // The net must have ALL of the specified stages to meet the rule.
+  // (Use multiple NetStateRules to specify conjunctions of stages.)
+  repeated string stage = 4;
+}
+
+// NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available ID: 23 (last added: memory_data_param)
+// LayerParameter next available ID: 34 (last added: exclude)
 message LayerParameter {
   repeated string bottom = 2; // the name of the bottom blobs
   repeated string top = 3; // the name of the top blobs
   optional string name = 4; // the layer name
 
+  // Rules controlling whether and when a layer is included in the network,
+  // based on the current NetState.  You may specify a non-zero number of rules
+  // to include OR exclude, but not both.  If no include or exclude rules are
+  // specified, the layer is always included.  If the current NetState meets
+  // ANY (i.e., one or more) of the specified rules, the layer is
+  // included/excluded.
+  repeated NetStateRule include = 32;
+  repeated NetStateRule exclude = 33;
+
+  // NOTE
   // Add new LayerTypes to the enum below in lexicographical order (other than
   // starting with NONE), starting with the next available ID in the comment
   // line above the enum. Update the next available ID when you add a new
   // LayerType.
   //
-  // LayerType next available ID: 30 (last added: MEMORY_DATA)
+  // LayerType next available ID: 34 (last added: SLICE)
   enum LayerType {
     // "NONE" layer type is 0th enum element so that we don't cause confusion
     // by defaulting to an existent LayerType (instead, should usually error if
     // the type is unspecified).
     NONE = 0;
     ACCURACY = 1;
+    ARGMAX = 30;
     BNLL = 2;
     CONCAT = 3;
     CONVOLUTION = 4;
     DATA = 5;
     DROPOUT = 6;
+    DUMMY_DATA = 32;
     EUCLIDEAN_LOSS = 7;
-    ELTWISE_PRODUCT = 25;
+    ELTWISE = 25;
     FLATTEN = 8;
     HDF5_DATA = 9;
     HDF5_OUTPUT = 10;
@@ -147,24 +236,41 @@ message LayerParameter {
     SOFTMAX = 20;
     SOFTMAX_LOSS = 21;
     SPLIT = 22;
+    SLICE = 33;
     TANH = 23;
     WINDOW_DATA = 24;
+    THRESHOLD = 31;
   }
   optional LayerType type = 5; // the layer type from the enum above
 
   // The blobs containing the numeric parameters of the layer
   repeated BlobProto blobs = 6;
+  // The names of the parameter blobs -- useful for sharing parameters among
+  // layers (but never required).
+  repeated string param = 1001;
+  // Whether to require shared weights to have the same shape, or just the same
+  // count -- defaults to STRICT if unspecified.
+  repeated DimCheckMode blob_share_mode = 1002;
+  enum DimCheckMode {
+    // STRICT (default) requires that num, channels, height, width each match.
+    STRICT = 0;
+    // PERMISSIVE requires only the count (num*channels*height*width) to match.
+    PERMISSIVE = 1;
+  }
   // The ratio that is multiplied on the global learning rate. If you want to
   // set the learning ratio for one blob, you need to set it for all blobs.
   repeated float blobs_lr = 7;
   // The weight decay that is multiplied on the global weight decay.
   repeated float weight_decay = 8;
 
-  // Parameters for particular layer types.
+  optional AccuracyParameter accuracy_param = 27;
+  optional ArgMaxParameter argmax_param = 23;
   optional ConcatParameter concat_param = 9;
   optional ConvolutionParameter convolution_param = 10;
   optional DataParameter data_param = 11;
   optional DropoutParameter dropout_param = 12;
+  optional DummyDataParameter dummy_data_param = 26;
+  optional EltwiseParameter eltwise_param = 24;
   optional HDF5DataParameter hdf5_data_param = 13;
   optional HDF5OutputParameter hdf5_output_param = 14;
   optional ImageDataParameter image_data_param = 15;
@@ -174,7 +280,11 @@ message LayerParameter {
   optional MemoryDataParameter memory_data_param = 22;
   optional PoolingParameter pooling_param = 19;
   optional PowerParameter power_param = 21;
+  optional ReLUParameter relu_param = 30;
   optional WindowDataParameter window_data_param = 20;
+  optional ThresholdParameter threshold_param = 25;
+  optional HingeLossParameter hinge_loss_param = 29;
+  optional SliceParameter slice_param = 31;
 
   // DEPRECATED: The layer parameters specified as a V0LayerParameter.
   // This should never be used by any code except to upgrade to the new
@@ -182,6 +292,21 @@ message LayerParameter {
   optional V0LayerParameter layer = 1;
 }
 
+// Message that stores parameters used by AccuracyLayer
+message AccuracyParameter {
+  // When computing accuracy, count as correct by comparing the true label to
+  // the top k scoring classes.  By default, only compare to the top scoring
+  // class (i.e. argmax).
+  optional uint32 top_k = 1 [default = 1];
+}
+
+// Message that stores parameters used by ArgMaxLayer
+message ArgMaxParameter {
+  // If true produce pairs (argmax, maxval)
+  optional bool out_max_val = 1 [default = false];
+  optional uint32 top_k = 2 [default = 1];
+}
+
 // Message that stores parameters used by ConcatLayer
 message ConcatParameter {
   // Concat Layer needs to specify the dimension along the concat will happen,
@@ -194,16 +319,28 @@ message ConcatParameter {
 message ConvolutionParameter {
   optional uint32 num_output = 1; // The number of outputs for the layer
   optional bool bias_term = 2 [default = true]; // whether to have bias terms
-  optional uint32 pad = 3 [default = 0]; // The padding size
-  optional uint32 kernel_size = 4; // The kernel size
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in height and width or as Y, X pairs.
+  optional uint32 pad = 3 [default = 0]; // The padding size (equal in Y, X)
+  optional uint32 pad_h = 9 [default = 0]; // The padding height
+  optional uint32 pad_w = 10 [default = 0]; // The padding width
+  optional uint32 kernel_size = 4; // The kernel size (square)
+  optional uint32 kernel_h = 11; // The kernel height
+  optional uint32 kernel_w = 12; // The kernel width
   optional uint32 group = 5 [default = 1]; // The group size for group conv
-  optional uint32 stride = 6 [default = 1]; // The stride
+  optional uint32 stride = 6 [default = 1]; // The stride (equal in Y, X)
+  optional uint32 stride_h = 13; // The stride height
+  optional uint32 stride_w = 14; // The stride width
   optional FillerParameter weight_filler = 7; // The filler for the weight
   optional FillerParameter bias_filler = 8; // The filler for the bias
 }
 
 // Message that stores parameters used by DataLayer
 message DataParameter {
+  enum DB {
+    LEVELDB = 0;
+    LMDB = 1;
+  }
   // Specify the data source.
   optional string source = 1;
   // For data pre-processing, we can do simple scaling and subtracting the
@@ -222,6 +359,7 @@ message DataParameter {
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
   // be larger than the number of keys in the leveldb.
   optional uint32 rand_skip = 7 [default = 0];
+  optional DB backend = 8 [default = LEVELDB];
 }
 
 // Message that stores parameters used by DropoutLayer
@@ -229,6 +367,39 @@ message DropoutParameter {
   optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
 }
 
+// Message that stores parameters used by DummyDataLayer.
+// DummyDataLayer fills any number of arbitrarily shaped blobs with random
+// (or constant) data generated by "Fillers" (see "message FillerParameter").
+message DummyDataParameter {
+  // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or N
+  // num, N channels, N height, and N width fields, and must specify 0, 1 or N
+  // data_fillers.
+  //
+  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
+  // If 1 data_filler is specified, it is applied to all top blobs.  If N are
+  // specified, the ith is applied to the ith top blob.
+  repeated FillerParameter data_filler = 1;
+  repeated uint32 num = 2;
+  repeated uint32 channels = 3;
+  repeated uint32 height = 4;
+  repeated uint32 width = 5;
+}
+
+// Message that stores parameters used by EltwiseLayer
+message EltwiseParameter {
+  enum EltwiseOp {
+    PROD = 0;
+    SUM = 1;
+  }
+  optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
+  repeated float coeff = 2; // blob-wise coefficient for SUM operation
+}
+
+// Message that stores parameters used by ThresholdLayer
+message ThresholdParameter {
+  optional float threshold = 1 [default = 0]; // Strictly Positive values
+}
+
 // Message that stores parameters used by HDF5DataLayer
 message HDF5DataParameter {
   // Specify the data source.
@@ -242,6 +413,15 @@ message HDF5OutputParameter {
   optional string file_name = 1;
 }
 
+message HingeLossParameter {
+  enum Norm {
+    L1 = 1;
+    L2 = 2;
+  }
+  // Specify the Norm to use L1 or L2
+  optional Norm norm = 1 [default = L1];
+}
+
 // Message that stores parameters used by ImageDataLayer
 message ImageDataParameter {
   // Specify the data source.
@@ -311,10 +491,17 @@ message PoolingParameter {
     STOCHASTIC = 2;
   }
   optional PoolMethod pool = 1 [default = MAX]; // The pooling method
-  optional uint32 kernel_size = 2; // The kernel size
-  optional uint32 stride = 3 [default = 1]; // The stride
-  // The padding size -- currently implemented only for average pooling.
-  optional uint32 pad = 4 [default = 0];
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in height and width or as Y, X pairs.
+  optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
+  optional uint32 pad_h = 9 [default = 0]; // The padding height
+  optional uint32 pad_w = 10 [default = 0]; // The padding width
+  optional uint32 kernel_size = 2; // The kernel size (square)
+  optional uint32 kernel_h = 5; // The kernel height
+  optional uint32 kernel_w = 6; // The kernel width
+  optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
+  optional uint32 stride_h = 7; // The stride height
+  optional uint32 stride_w = 8; // The stride width
 }
 
 // Message that stores parameters used by PowerLayer
@@ -325,6 +512,26 @@ message PowerParameter {
   optional float shift = 3 [default = 0.0];
 }
 
+// Message that stores parameters used by ReLULayer
+message ReLUParameter {
+  // Allow non-zero slope for negative inputs to speed up optimization
+  // Described in:
+  // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
+  // improve neural network acoustic models. In ICML Workshop on Deep Learning
+  // for Audio, Speech, and Language Processing.
+  optional float negative_slope = 1 [default = 0];
+}
+
+// Message that stores parameters used by SliceLayer
+message SliceParameter {
+  // SliceLayer needs to know which dimension to slice across.
+  // Currently, SliceLayer only supports slicing across num (dim 0)
+  // and channels (dim 1).
+  // By default, SliceLayer slices across channels.
+  optional uint32 slice_dim = 1 [default = 1];
+  repeated uint32 slice_point = 2;
+}
+
 // Message that stores parameters used by WindowDataLayer
 message WindowDataParameter {
   // Specify the data source.
diff --git a/src/caffe/proto/caffe_pretty_print.proto b/src/caffe/proto/caffe_pretty_print.proto
index cfdce82c79f..225138a4274 100644
--- a/src/caffe/proto/caffe_pretty_print.proto
+++ b/src/caffe/proto/caffe_pretty_print.proto
@@ -1,4 +1,4 @@
-// Copyright 2014 BVLC and contributors.
+syntax = "proto2";
 
 package caffe;
 
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 4932968d0b6..d96d03942f3 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstdio>
 
 #include <algorithm>
@@ -11,21 +9,19 @@
 #include "caffe/solver.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
-
-using std::max;
-using std::min;
+#include "caffe/util/upgrade_proto.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 Solver<Dtype>::Solver(const SolverParameter& param)
-    : net_(), test_net_() {
+    : net_() {
   Init(param);
 }
 
 template <typename Dtype>
 Solver<Dtype>::Solver(const string& param_file)
-    : net_(), test_net_() {
+    : net_() {
   SolverParameter param;
   ReadProtoFromTextFile(param_file, &param);
   Init(param);
@@ -33,30 +29,138 @@ Solver<Dtype>::Solver(const string& param_file)
 
 template <typename Dtype>
 void Solver<Dtype>::Init(const SolverParameter& param) {
+  LOG(INFO) << "Initializing solver from parameters: " << std::endl
+            << param.DebugString();
   param_ = param;
+  if (param_.solver_mode() == SolverParameter_SolverMode_GPU &&
+      param_.has_device_id()) {
+    Caffe::SetDevice(param_.device_id());
+  }
+  Caffe::set_mode(Caffe::Brew(param_.solver_mode()));
   if (param_.random_seed() >= 0) {
     Caffe::set_random_seed(param_.random_seed());
   }
   // Scaffolding code
-  LOG(INFO) << "Creating training net.";
-  net_.reset(new Net<Dtype>(param_.train_net()));
-  if (param_.has_test_net()) {
-    LOG(INFO) << "Creating testing net.";
-    test_net_.reset(new Net<Dtype>(param_.test_net()));
-    CHECK_GT(param_.test_iter(), 0);
-    CHECK_GT(param_.test_interval(), 0);
-  }
+  InitTrainNet();
+  InitTestNets();
   LOG(INFO) << "Solver scaffolding done.";
 }
 
+template <typename Dtype>
+void Solver<Dtype>::InitTrainNet() {
+  const int num_train_nets = param_.has_net() + param_.has_net_param() +
+      param_.has_train_net() + param_.has_train_net_param();
+  const string& field_names = "net, net_param, train_net, train_net_param";
+  CHECK_GE(num_train_nets, 1) << "SolverParameter must specify a train net "
+      << "using one of these fields: " << field_names;
+  CHECK_LE(num_train_nets, 1) << "SolverParameter must not contain more than "
+      << "one of these fields specifying a train_net: " << field_names;
+  NetParameter net_param;
+  if (param_.has_train_net_param()) {
+    LOG(INFO) << "Creating training net specified in train_net_param.";
+    net_param.CopyFrom(param_.train_net_param());
+  } else if (param_.has_train_net()) {
+    LOG(INFO) << "Creating training net from train_net file: "
+              << param_.train_net();
+    ReadNetParamsFromTextFileOrDie(param_.train_net(), &net_param);
+  }
+  if (param_.has_net_param()) {
+    LOG(INFO) << "Creating training net specified in net_param.";
+    net_param.CopyFrom(param_.net_param());
+  }
+  if (param_.has_net()) {
+    LOG(INFO) << "Creating training net from net file: " << param_.net();
+    ReadNetParamsFromTextFileOrDie(param_.net(), &net_param);
+  }
+  // Set the correct NetState.  We start with the solver defaults (lowest
+  // precedence); then, merge in any NetState specified by the net_param itself;
+  // finally, merge in any NetState specified by the train_state (highest
+  // precedence).
+  NetState net_state;
+  net_state.set_phase(TRAIN);
+  net_state.MergeFrom(net_param.state());
+  net_state.MergeFrom(param_.train_state());
+  net_param.mutable_state()->CopyFrom(net_state);
+  net_.reset(new Net<Dtype>(net_param));
+}
 
 template <typename Dtype>
-void Solver<Dtype>::Solve(const char* resume_file) {
-  Caffe::set_mode(Caffe::Brew(param_.solver_mode()));
-  if (param_.solver_mode() == SolverParameter_SolverMode_GPU &&
-      param_.has_device_id()) {
-    Caffe::SetDevice(param_.device_id());
+void Solver<Dtype>::InitTestNets() {
+  const bool has_net_param = param_.has_net_param();
+  const bool has_net_file = param_.has_net();
+  const int num_generic_nets = has_net_param + has_net_file;
+  CHECK_LE(num_generic_nets, 1)
+      << "Both net_param and net_file may not be specified.";
+  const int num_test_net_params = param_.test_net_param_size();
+  const int num_test_net_files = param_.test_net_size();
+  const int num_test_nets = num_test_net_params + num_test_net_files;
+  if (num_generic_nets) {
+      CHECK_GE(param_.test_iter_size(), num_test_nets)
+          << "test_iter must be specified for each test network.";
+  } else {
+      CHECK_EQ(param_.test_iter_size(), num_test_nets)
+          << "test_iter must be specified for each test network.";
+  }
+  // If we have a generic net (specified by net or net_param, rather than
+  // test_net or test_net_param), we may have an unlimited number of actual
+  // test networks -- the actual number is given by the number of remaining
+  // test_iters after any test nets specified by test_net_param and/or test_net
+  // are evaluated.
+  const int num_generic_net_instances = param_.test_iter_size() - num_test_nets;
+  const int num_test_net_instances = num_test_nets + num_generic_net_instances;
+  if (param_.test_state_size()) {
+    CHECK_EQ(param_.test_state_size(), num_test_net_instances)
+        << "test_state must be unspecified or specified once per test net.";
+  }
+  if (num_test_net_instances) {
+    CHECK_GT(param_.test_interval(), 0);
+  }
+  int test_net_id = 0;
+  vector<string> sources(num_test_net_instances);
+  vector<NetParameter> net_params(num_test_net_instances);
+  for (int i = 0; i < num_test_net_params; ++i, ++test_net_id) {
+      sources[test_net_id] = "test_net_param";
+      net_params[test_net_id].CopyFrom(param_.test_net_param(i));
+  }
+  for (int i = 0; i < num_test_net_files; ++i, ++test_net_id) {
+      sources[test_net_id] = "test_net file: " + param_.test_net(i);
+      ReadNetParamsFromTextFileOrDie(param_.test_net(i),
+          &net_params[test_net_id]);
+  }
+  const int remaining_test_nets = param_.test_iter_size() - test_net_id;
+  if (has_net_param) {
+    for (int i = 0; i < remaining_test_nets; ++i, ++test_net_id) {
+      sources[test_net_id] = "net_param";
+      net_params[test_net_id].CopyFrom(param_.net_param());
+    }
+  }
+  if (has_net_file) {
+    for (int i = 0; i < remaining_test_nets; ++i, ++test_net_id) {
+      sources[test_net_id] = "net file: " + param_.net();
+      ReadNetParamsFromTextFileOrDie(param_.net(), &net_params[test_net_id]);
+    }
   }
+  test_nets_.resize(num_test_net_instances);
+  for (int i = 0; i < num_test_net_instances; ++i) {
+    // Set the correct NetState.  We start with the solver defaults (lowest
+    // precedence); then, merge in any NetState specified by the net_param
+    // itself; finally, merge in any NetState specified by the test_state
+    // (highest precedence).
+    NetState net_state;
+    net_state.set_phase(TEST);
+    net_state.MergeFrom(net_params[i].state());
+    if (param_.test_state_size()) {
+      net_state.MergeFrom(param_.test_state(i));
+    }
+    net_params[i].mutable_state()->CopyFrom(net_state);
+    LOG(INFO)
+        << "Creating testing net (#" << i << ") specified by " << sources[i];
+    test_nets_[i].reset(new Net<Dtype>(net_params[i]));
+  }
+}
+
+template <typename Dtype>
+void Solver<Dtype>::Solve(const char* resume_file) {
   Caffe::set_phase(Caffe::TRAIN);
   LOG(INFO) << "Solving " << net_->name();
   PreSolve();
@@ -66,54 +170,78 @@ void Solver<Dtype>::Solve(const char* resume_file) {
     LOG(INFO) << "Restoring previous solver status from " << resume_file;
     Restore(resume_file);
   }
-
-  // Run a test pass before doing any training to avoid waiting a potentially
-  // very long time (param_.test_interval() training iterations) to report that
-  // there's not enough memory to run the test net and crash, etc.; and to gauge
-  // the effect of the first training iterations.
-  if (param_.test_interval()) {
-    Test();
-  }
+  // Remember the initial iter_ value; will be non-zero if we loaded from a
+  // resume_file above.
+  const int start_iter = iter_;
 
   // For a network that is trained by the solver, no bottom or top vecs
   // should be given, and we will just provide dummy vecs.
   vector<Blob<Dtype>*> bottom_vec;
-  while (iter_++ < param_.max_iter()) {
-    Dtype loss = net_->ForwardBackward(bottom_vec);
-    ComputeUpdateValue();
-    net_->Update();
-
-    if (param_.display() && iter_ % param_.display() == 0) {
-      LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
+  for (; iter_ < param_.max_iter(); ++iter_) {
+    // Save a snapshot if needed.
+    if (param_.snapshot() && iter_ > start_iter &&
+        iter_ % param_.snapshot() == 0) {
+      Snapshot();
     }
+
     if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
-      Test();
+      TestAll();
     }
-    // Check if we need to do snapshot
-    if (param_.snapshot() && iter_ % param_.snapshot() == 0) {
-      Snapshot();
+
+    const bool display = param_.display() && iter_ % param_.display() == 0;
+    net_->set_debug_info(display && param_.debug_info());
+    Dtype loss = net_->ForwardBackward(bottom_vec);
+    if (display) {
+      LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
     }
+
+    ComputeUpdateValue();
+    net_->Update();
+  }
+  // Always save a snapshot after optimization, unless overridden by setting
+  // snapshot_after_train := false.
+  if (param_.snapshot_after_train()) { Snapshot(); }
+  // After the optimization is done, run an additional train and test pass to
+  // display the train and test loss/outputs if appropriate (based on the
+  // display and test_interval settings, respectively).  Unlike in the rest of
+  // training, for the train net we only run a forward pass as we've already
+  // updated the parameters "max_iter" times -- this final pass is only done to
+  // display the loss, which is computed in the forward pass.
+  if (param_.display() && iter_ % param_.display() == 0) {
+    Dtype loss;
+    net_->Forward(bottom_vec, &loss);
+    LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
+  }
+  if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
+    TestAll();
   }
-  // After the optimization is done, always do a snapshot.
-  iter_--;
-  Snapshot();
   LOG(INFO) << "Optimization Done.";
 }
 
 
 template <typename Dtype>
-void Solver<Dtype>::Test() {
-  LOG(INFO) << "Iteration " << iter_ << ", Testing net";
+void Solver<Dtype>::TestAll() {
+  for (int test_net_id = 0; test_net_id < test_nets_.size(); ++test_net_id) {
+    Test(test_net_id);
+  }
+}
+
+
+template <typename Dtype>
+void Solver<Dtype>::Test(const int test_net_id) {
+  LOG(INFO) << "Iteration " << iter_
+            << ", Testing net (#" << test_net_id << ")";
   // We need to set phase to test before running.
   Caffe::set_phase(Caffe::TEST);
-  CHECK_NOTNULL(test_net_.get())->ShareTrainedLayersWith(net_.get());
+  CHECK_NOTNULL(test_nets_[test_net_id].get())->
+      ShareTrainedLayersWith(net_.get());
   vector<Dtype> test_score;
   vector<Blob<Dtype>*> bottom_vec;
   Dtype loss = 0;
-  for (int i = 0; i < param_.test_iter(); ++i) {
+  for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
     Dtype iter_loss;
     const vector<Blob<Dtype>*>& result =
-        test_net_->Forward(bottom_vec, &iter_loss);
+        test_nets_[test_net_id]->Forward(bottom_vec, &iter_loss);
     if (param_.test_compute_loss()) {
       loss += iter_loss;
     }
@@ -135,12 +263,12 @@ void Solver<Dtype>::Test() {
     }
   }
   if (param_.test_compute_loss()) {
-    loss /= param_.test_iter();
+    loss /= param_.test_iter(test_net_id);
     LOG(INFO) << "Test loss: " << loss;
   }
   for (int i = 0; i < test_score.size(); ++i) {
     LOG(INFO) << "Test score #" << i << ": "
-        << test_score[i] / param_.test_iter();
+        << test_score[i] / param_.test_iter(test_net_id);
   }
   Caffe::set_phase(Caffe::TRAIN);
 }
@@ -261,6 +389,7 @@ void SGDSolver<Dtype>::ComputeUpdateValue() {
     }
     break;
   case Caffe::GPU:
+#ifndef CPU_ONLY
     for (int param_id = 0; param_id < net_params.size(); ++param_id) {
       // Compute the value to history, and then copy them to the blob's diff.
       Dtype local_rate = rate * net_params_lr[param_id];
@@ -276,10 +405,13 @@ void SGDSolver<Dtype>::ComputeUpdateValue() {
             history_[param_id]->mutable_gpu_data());
       }
       // copy
-      caffe_gpu_copy(net_params[param_id]->count(),
+      caffe_copy(net_params[param_id]->count(),
           history_[param_id]->gpu_data(),
           net_params[param_id]->mutable_gpu_diff());
     }
+#else
+    NO_GPU;
+#endif
     break;
   default:
     LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index fec37d6e9ec..7d25183701d 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -1,11 +1,8 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <cuda_runtime.h>
-
 #include <cstring>
 
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
@@ -14,9 +11,11 @@ SyncedMemory::~SyncedMemory() {
     CaffeFreeHost(cpu_ptr_);
   }
 
+#ifndef CPU_ONLY
   if (gpu_ptr_) {
     CUDA_CHECK(cudaFree(gpu_ptr_));
   }
+#endif  // CPU_ONLY
 }
 
 inline void SyncedMemory::to_cpu() {
@@ -28,12 +27,16 @@ inline void SyncedMemory::to_cpu() {
     own_cpu_data_ = true;
     break;
   case HEAD_AT_GPU:
+#ifndef CPU_ONLY
     if (cpu_ptr_ == NULL) {
       CaffeMallocHost(&cpu_ptr_, size_);
       own_cpu_data_ = true;
     }
-    CUDA_CHECK(cudaMemcpy(cpu_ptr_, gpu_ptr_, size_, cudaMemcpyDeviceToHost));
+    caffe_gpu_memcpy(size_, gpu_ptr_, cpu_ptr_);
     head_ = SYNCED;
+#else
+    NO_GPU;
+#endif
     break;
   case HEAD_AT_CPU:
   case SYNCED:
@@ -42,6 +45,7 @@ inline void SyncedMemory::to_cpu() {
 }
 
 inline void SyncedMemory::to_gpu() {
+#ifndef CPU_ONLY
   switch (head_) {
   case UNINITIALIZED:
     CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
@@ -52,13 +56,16 @@ inline void SyncedMemory::to_gpu() {
     if (gpu_ptr_ == NULL) {
       CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     }
-    CUDA_CHECK(cudaMemcpy(gpu_ptr_, cpu_ptr_, size_, cudaMemcpyHostToDevice));
+    caffe_gpu_memcpy(size_, cpu_ptr_, gpu_ptr_);
     head_ = SYNCED;
     break;
   case HEAD_AT_GPU:
   case SYNCED:
     break;
   }
+#else
+  NO_GPU;
+#endif
 }
 
 const void* SyncedMemory::cpu_data() {
@@ -77,8 +84,12 @@ void SyncedMemory::set_cpu_data(void* data) {
 }
 
 const void* SyncedMemory::gpu_data() {
+#ifndef CPU_ONLY
   to_gpu();
   return (const void*)gpu_ptr_;
+#else
+  NO_GPU;
+#endif
 }
 
 void* SyncedMemory::mutable_cpu_data() {
@@ -88,9 +99,13 @@ void* SyncedMemory::mutable_cpu_data() {
 }
 
 void* SyncedMemory::mutable_gpu_data() {
+#ifndef CPU_ONLY
   to_gpu();
   head_ = HEAD_AT_GPU;
   return gpu_ptr_;
+#else
+  NO_GPU;
+#endif
 }
 
 
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
new file mode 100644
index 00000000000..e11e3f2a981
--- /dev/null
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -0,0 +1,140 @@
+#include <cfloat>
+#include <cmath>
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/util/rng.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+class AccuracyLayerTest : public ::testing::Test {
+ protected:
+  AccuracyLayerTest()
+      : blob_bottom_data_(new Blob<Dtype>(100, 10, 1, 1)),
+        blob_bottom_label_(new Blob<Dtype>(100, 1, 1, 1)),
+        blob_top_(new Blob<Dtype>()),
+        top_k_(3) {
+    // fill the probability values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_data_);
+
+    const unsigned int prefetch_rng_seed = caffe_rng_rand();
+    shared_ptr<Caffe::RNG> rng(new Caffe::RNG(prefetch_rng_seed));
+    caffe::rng_t* prefetch_rng =
+          static_cast<caffe::rng_t*>(rng->generator());
+    Dtype* label_data = blob_bottom_label_->mutable_cpu_data();
+    for (int i = 0; i < 100; ++i) {
+      label_data[i] = (*prefetch_rng)() % 10;
+    }
+
+    blob_bottom_vec_.push_back(blob_bottom_data_);
+    blob_bottom_vec_.push_back(blob_bottom_label_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~AccuracyLayerTest() {
+    delete blob_bottom_data_;
+    delete blob_bottom_label_;
+    delete blob_top_;
+  }
+  Blob<Dtype>* const blob_bottom_data_;
+  Blob<Dtype>* const blob_bottom_label_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+  int top_k_;
+};
+
+TYPED_TEST_CASE(AccuracyLayerTest, TestDtypes);
+
+TYPED_TEST(AccuracyLayerTest, TestSetup) {
+  LayerParameter layer_param;
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), 1);
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
+  LayerParameter layer_param;
+  AccuracyParameter* accuracy_param =
+      layer_param.mutable_accuracy_param();
+  accuracy_param->set_top_k(5);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), 1);
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
+  LayerParameter layer_param;
+  Caffe::set_mode(Caffe::CPU);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+
+  TypeParam max_value;
+  int max_id;
+  int num_correct_labels = 0;
+  for (int i = 0; i < 100; ++i) {
+    max_value = -FLT_MAX;
+    max_id = 0;
+    for (int j = 0; j < 10; ++j) {
+      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+        max_id = j;
+      }
+    }
+    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      ++num_correct_labels;
+    }
+  }
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / 100.0, 1e-4);
+}
+
+TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) {
+  LayerParameter layer_param;
+  AccuracyParameter* accuracy_param = layer_param.mutable_accuracy_param();
+  accuracy_param->set_top_k(this->top_k_);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+
+  TypeParam current_value;
+  int current_rank;
+  int num_correct_labels = 0;
+  for (int i = 0; i < 100; ++i) {
+    for (int j = 0; j < 10; ++j) {
+      current_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+      current_rank = 0;
+      for (int k = 0; k < 10; ++k) {
+        if (this->blob_bottom_data_->data_at(i, k, 0, 0) > current_value) {
+          ++current_rank;
+        }
+      }
+      if (current_rank < this->top_k_ &&
+          j == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+        ++num_correct_labels;
+      }
+    }
+  }
+
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / 100.0, 1e-4);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
new file mode 100644
index 00000000000..fb3951c3098
--- /dev/null
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -0,0 +1,169 @@
+#include <utility>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+class ArgMaxLayerTest : public ::testing::Test {
+ protected:
+  ArgMaxLayerTest()
+      : blob_bottom_(new Blob<Dtype>(10, 20, 1, 1)),
+        blob_top_(new Blob<Dtype>()),
+        top_k_(5) {
+    Caffe::set_mode(Caffe::CPU);
+    Caffe::set_random_seed(1701);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~ArgMaxLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+  size_t top_k_;
+};
+
+TYPED_TEST_CASE(ArgMaxLayerTest, TestDtypes);
+
+TYPED_TEST(ArgMaxLayerTest, TestSetup) {
+  LayerParameter layer_param;
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_out_max_val(true);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_->channels(), 2);
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPU) {
+  LayerParameter layer_param;
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  int max_ind;
+  TypeParam max_val;
+  int num = this->blob_bottom_->num();
+  int dim = this->blob_bottom_->count() / num;
+  for (int i = 0; i < num; ++i) {
+    EXPECT_GE(top_data[i], 0);
+    EXPECT_LE(top_data[i], dim);
+    max_ind = top_data[i];
+    max_val = bottom_data[i * dim + max_ind];
+    for (int j = 0; j < dim; ++j) {
+      EXPECT_LE(bottom_data[i * dim + j], max_val);
+    }
+  }
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPUMaxVal) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_out_max_val(true);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
+  int max_ind;
+  TypeParam max_val;
+  int num = this->blob_bottom_->num();
+  int dim = this->blob_bottom_->count() / num;
+  for (int i = 0; i < num; ++i) {
+    EXPECT_GE(top_data[i], 0);
+    EXPECT_LE(top_data[i], dim);
+    max_ind = top_data[i * 2];
+    max_val = top_data[i * 2 + 1];
+    EXPECT_EQ(bottom_data[i * dim + max_ind], max_val);
+    for (int j = 0; j < dim; ++j) {
+      EXPECT_LE(bottom_data[i * dim + j], max_val);
+    }
+  }
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_top_k(this->top_k_);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  int max_ind;
+  TypeParam max_val;
+  int num = this->blob_bottom_->num();
+  int dim = this->blob_bottom_->count() / num;
+  for (int i = 0; i < num; ++i) {
+    EXPECT_GE(this->blob_top_->data_at(i, 0, 0, 0), 0);
+    EXPECT_LE(this->blob_top_->data_at(i, 0, 0, 0), dim);
+    for (int j = 0; j < this->top_k_; ++j) {
+      max_ind = this->blob_top_->data_at(i, 0, j, 0);
+      max_val = this->blob_bottom_->data_at(i, max_ind, 0, 0);
+      int count = 0;
+      for (int k = 0; k < dim; ++k) {
+        if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) {
+          ++count;
+        }
+      }
+      EXPECT_EQ(j, count);
+    }
+  }
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_out_max_val(true);
+  argmax_param->set_top_k(this->top_k_);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  int max_ind;
+  TypeParam max_val;
+  int num = this->blob_bottom_->num();
+  int dim = this->blob_bottom_->count() / num;
+  for (int i = 0; i < num; ++i) {
+    EXPECT_GE(this->blob_top_->data_at(i, 0, 0, 0), 0);
+    EXPECT_LE(this->blob_top_->data_at(i, 0, 0, 0), dim);
+    for (int j = 0; j < this->top_k_; ++j) {
+      max_ind = this->blob_top_->data_at(i, 0, j, 0);
+      max_val = this->blob_top_->data_at(i, 1, j, 0);
+      EXPECT_EQ(this->blob_bottom_->data_at(i, max_ind, 0, 0), max_val);
+      int count = 0;
+      for (int k = 0; k < dim; ++k) {
+        if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) {
+          ++count;
+        }
+      }
+      EXPECT_EQ(j, count);
+    }
+  }
+}
+
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp
index 40eee9c80f9..dbbee08d667 100644
--- a/src/caffe/test/test_benchmark.cpp
+++ b/src/caffe/test/test_benchmark.cpp
@@ -1,37 +1,27 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <unistd.h>  // for usleep
-#include <cuda_runtime.h>
-#include <gtest/gtest.h>
+
+#include "gtest/gtest.h"
 
 #include "caffe/common.hpp"
 #include "caffe/util/benchmark.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
-class BenchmarkTest : public ::testing::Test {};
+template <typename TypeParam>
+class BenchmarkTest : public MultiDeviceTest<TypeParam> {};
 
-TEST_F(BenchmarkTest, TestTimerConstructorCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  Timer timer;
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_FALSE(timer.has_run_at_least_once());
-}
+TYPED_TEST_CASE(BenchmarkTest, TestDtypesAndDevices);
 
-TEST_F(BenchmarkTest, TestTimerConstructorGPU) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(BenchmarkTest, TestTimerConstructor) {
   Timer timer;
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_FALSE(timer.has_run_at_least_once());
 }
 
-TEST_F(BenchmarkTest, TestTimerStartCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(BenchmarkTest, TestTimerStart) {
   Timer timer;
   timer.Start();
   EXPECT_TRUE(timer.initted());
@@ -48,26 +38,7 @@ TEST_F(BenchmarkTest, TestTimerStartCPU) {
   EXPECT_TRUE(timer.has_run_at_least_once());
 }
 
-TEST_F(BenchmarkTest, TestTimerStartGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  Timer timer;
-  timer.Start();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_TRUE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-  timer.Stop();
-  timer.Start();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_TRUE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-  timer.Start();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_TRUE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-}
-
-TEST_F(BenchmarkTest, TestTimerStopCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(BenchmarkTest, TestTimerStop) {
   Timer timer;
   timer.Stop();
   EXPECT_TRUE(timer.initted());
@@ -84,83 +55,31 @@ TEST_F(BenchmarkTest, TestTimerStopCPU) {
   EXPECT_TRUE(timer.has_run_at_least_once());
 }
 
-TEST_F(BenchmarkTest, TestTimerStopGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  Timer timer;
-  timer.Stop();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_FALSE(timer.has_run_at_least_once());
-  timer.Start();
-  timer.Stop();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-  timer.Stop();
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-}
-
-TEST_F(BenchmarkTest, TestTimerMilliSecondsCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  Timer timer;
-  CHECK_EQ(timer.MilliSeconds(), 0);
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_FALSE(timer.has_run_at_least_once());
-  timer.Start();
-  usleep(300 * 1000);
-  CHECK_GE(timer.MilliSeconds(), 298);
-  CHECK_LE(timer.MilliSeconds(), 302);
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-}
-
-TEST_F(BenchmarkTest, TestTimerMilliSecondsGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  Timer timer;
-  CHECK_EQ(timer.MilliSeconds(), 0);
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_FALSE(timer.has_run_at_least_once());
-  timer.Start();
-  usleep(300 * 1000);
-  CHECK_GE(timer.MilliSeconds(), 298);
-  CHECK_LE(timer.MilliSeconds(), 302);
-  EXPECT_TRUE(timer.initted());
-  EXPECT_FALSE(timer.running());
-  EXPECT_TRUE(timer.has_run_at_least_once());
-}
-
-TEST_F(BenchmarkTest, TestTimerSecondsCPU) {
-  Caffe::set_mode(Caffe::CPU);
+TYPED_TEST(BenchmarkTest, TestTimerMilliSeconds) {
   Timer timer;
-  CHECK_EQ(timer.Seconds(), 0);
+  EXPECT_EQ(timer.MilliSeconds(), 0);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  CHECK_GE(timer.Seconds(), 0.298);
-  CHECK_LE(timer.Seconds(), 0.302);
+  EXPECT_GE(timer.MilliSeconds(), 290);
+  EXPECT_LE(timer.MilliSeconds(), 310);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());
 }
 
-TEST_F(BenchmarkTest, TestTimerSecondsGPU) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(BenchmarkTest, TestTimerSeconds) {
   Timer timer;
-  CHECK_EQ(timer.Seconds(), 0);
+  EXPECT_EQ(timer.Seconds(), 0);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
   usleep(300 * 1000);
-  CHECK_GE(timer.Seconds(), 0.298);
-  CHECK_LE(timer.Seconds(), 0.302);
+  EXPECT_GE(timer.Seconds(), 0.290);
+  EXPECT_LE(timer.Seconds(), 0.310);
   EXPECT_TRUE(timer.initted());
   EXPECT_FALSE(timer.running());
   EXPECT_TRUE(timer.has_run_at_least_once());
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index 5d38e54ff75..adf7a4d38e9 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -1,11 +1,9 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
-#include "caffe/common.hpp"
+
 #include "caffe/blob.hpp"
+#include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -23,8 +21,7 @@ class BlobSimpleTest : public ::testing::Test {
   Blob<Dtype>* const blob_preshaped_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(BlobSimpleTest, Dtypes);
+TYPED_TEST_CASE(BlobSimpleTest, TestDtypes);
 
 TYPED_TEST(BlobSimpleTest, TestInitialization) {
   EXPECT_TRUE(this->blob_);
@@ -41,7 +38,7 @@ TYPED_TEST(BlobSimpleTest, TestInitialization) {
   EXPECT_EQ(this->blob_->count(), 0);
 }
 
-TYPED_TEST(BlobSimpleTest, TestPointers) {
+TYPED_TEST(BlobSimpleTest, TestPointersCPUGPU) {
   EXPECT_TRUE(this->blob_preshaped_->gpu_data());
   EXPECT_TRUE(this->blob_preshaped_->cpu_data());
   EXPECT_TRUE(this->blob_preshaped_->mutable_gpu_data());
diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index ecc117e3b36..df188fd319b 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -1,19 +1,22 @@
-// Copyright 2014 BVLC and contributors.
-
 // The main caffe test code. Your test cpp code should include this hpp
 // to allow a main function to be compiled into the binary.
 
-#include "test_caffe_main.hpp"
+#include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
+#ifndef CPU_ONLY
   cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+#endif
 }
 
+#ifndef CPU_ONLY
 using caffe::CAFFE_TEST_CUDA_PROP;
+#endif
 
 int main(int argc, char** argv) {
   ::testing::InitGoogleTest(&argc, argv);
   ::google::InitGoogleLogging(argv[0]);
+#ifndef CPU_ONLY
   // Before starting testing, let's first print out a few cuda defice info.
   int device;
   cudaGetDeviceCount(&device);
@@ -27,6 +30,7 @@ int main(int argc, char** argv) {
   cudaGetDevice(&device);
   cout << "Current device id: " << device << endl;
   cudaGetDeviceProperties(&CAFFE_TEST_CUDA_PROP, device);
+#endif
   // invoke the test.
   return RUN_ALL_TESTS();
 }
diff --git a/src/caffe/test/test_caffe_main.hpp b/src/caffe/test/test_caffe_main.hpp
deleted file mode 100644
index df64cbb41cc..00000000000
--- a/src/caffe/test/test_caffe_main.hpp
+++ /dev/null
@@ -1,20 +0,0 @@
-// Copyright 2014 BVLC and contributors.
-
-// The main caffe test code. Your test cpp code should include this hpp
-// to allow a main function to be compiled into the binary.
-#ifndef CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
-#define CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
-
-#include <cuda_runtime.h>
-#include <glog/logging.h>
-#include <gtest/gtest.h>
-
-#include <cstdlib>
-#include <cstdio>
-
-using std::cout;
-using std::endl;
-
-int main(int argc, char** argv);
-
-#endif  // CAFFE_TEST_TEST_CAFFE_MAIN_HPP_
diff --git a/src/caffe/test/test_common.cpp b/src/caffe/test/test_common.cpp
index 13c2d9514f7..0b3639c7706 100644
--- a/src/caffe/test/test_common.cpp
+++ b/src/caffe/test/test_common.cpp
@@ -1,24 +1,27 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
 
 class CommonTest : public ::testing::Test {};
 
-TEST_F(CommonTest, TestCublasHandler) {
+#ifndef CPU_ONLY  // GPU Caffe singleton test.
+
+TEST_F(CommonTest, TestCublasHandlerGPU) {
   int cuda_device_id;
   CUDA_CHECK(cudaGetDevice(&cuda_device_id));
   EXPECT_TRUE(Caffe::cublas_handle());
 }
 
+#endif
+
 TEST_F(CommonTest, TestBrewMode) {
   Caffe::set_mode(Caffe::CPU);
   EXPECT_EQ(Caffe::mode(), Caffe::CPU);
@@ -48,19 +51,23 @@ TEST_F(CommonTest, TestRandSeedCPU) {
   }
 }
 
+#ifndef CPU_ONLY  // GPU Caffe singleton test.
+
 TEST_F(CommonTest, TestRandSeedGPU) {
   SyncedMemory data_a(10 * sizeof(unsigned int));
   SyncedMemory data_b(10 * sizeof(unsigned int));
   Caffe::set_random_seed(1701);
   CURAND_CHECK(curandGenerate(Caffe::curand_generator(),
-        reinterpret_cast<unsigned int*>(data_a.mutable_gpu_data()), 10));
+        static_cast<unsigned int*>(data_a.mutable_gpu_data()), 10));
   Caffe::set_random_seed(1701);
   CURAND_CHECK(curandGenerate(Caffe::curand_generator(),
-        reinterpret_cast<unsigned int*>(data_b.mutable_gpu_data()), 10));
+        static_cast<unsigned int*>(data_b.mutable_gpu_data()), 10));
   for (int i = 0; i < 10; ++i) {
     EXPECT_EQ(((const unsigned int*)(data_a.cpu_data()))[i],
         ((const unsigned int*)(data_b.cpu_data()))[i]);
   }
 }
 
+#endif
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index 72e3c902cf1..c60b7f744cc 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -1,24 +1,22 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class ConcatLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class ConcatLayerTest : public ::testing::Test {
  protected:
   ConcatLayerTest()
       : blob_bottom_0(new Blob<Dtype>(2, 3, 6, 5)),
@@ -27,14 +25,17 @@ class ConcatLayerTest : public ::testing::Test {
         blob_top_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     // fill the values
+    shared_ptr<ConstantFiller<Dtype> > filler;
     FillerParameter filler_param;
     filler_param.set_value(1.);
-    ConstantFiller<Dtype> filler(filler_param);
-    filler.Fill(this->blob_bottom_0);
+    filler.reset(new ConstantFiller<Dtype>(filler_param));
+    filler->Fill(this->blob_bottom_0);
     filler_param.set_value(2.);
-    filler.Fill(this->blob_bottom_1);
+    filler.reset(new ConstantFiller<Dtype>(filler_param));
+    filler->Fill(this->blob_bottom_1);
     filler_param.set_value(3.);
-    filler.Fill(this->blob_bottom_2);
+    filler.reset(new ConstantFiller<Dtype>(filler_param));
+    filler->Fill(this->blob_bottom_2);
     blob_bottom_vec_0.push_back(blob_bottom_0);
     blob_bottom_vec_0.push_back(blob_bottom_1);
     blob_bottom_vec_1.push_back(blob_bottom_0);
@@ -55,13 +56,13 @@ class ConcatLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(ConcatLayerTest, Dtypes);
+TYPED_TEST_CASE(ConcatLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(ConcatLayerTest, TestSetupNum) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_concat_param()->set_concat_dim(0);
-  ConcatLayer<TypeParam> layer(layer_param);
+  ConcatLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_1, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(),
     this->blob_bottom_0->num() + this->blob_bottom_2->num());
@@ -71,8 +72,9 @@ TYPED_TEST(ConcatLayerTest, TestSetupNum) {
 }
 
 TYPED_TEST(ConcatLayerTest, TestSetupChannels) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ConcatLayer<TypeParam> layer(layer_param);
+  ConcatLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_0, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_0->num());
   EXPECT_EQ(this->blob_top_->channels(),
@@ -82,10 +84,10 @@ TYPED_TEST(ConcatLayerTest, TestSetupChannels) {
 }
 
 
-TYPED_TEST(ConcatLayerTest, TestCPUNum) {
+TYPED_TEST(ConcatLayerTest, TestNum) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  ConcatLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
+  ConcatLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_0, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_0, &(this->blob_top_vec_));
   for (int n = 0; n < this->blob_top_->num(); ++n) {
@@ -108,21 +110,11 @@ TYPED_TEST(ConcatLayerTest, TestCPUNum) {
   }
 }
 
-
-TYPED_TEST(ConcatLayerTest, TestCPUGradient) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  ConcatLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradient(&layer, &(this->blob_bottom_vec_0),
-    &(this->blob_top_vec_));
-}
-
-TYPED_TEST(ConcatLayerTest, TestGPUGradient) {
+TYPED_TEST(ConcatLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  ConcatLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  ConcatLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradient(&layer, &(this->blob_bottom_vec_0),
     &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index b08486e10a3..5a7ea80467e 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -1,80 +1,105 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class ConvolutionLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class ConvolutionLayerTest : public ::testing::Test {
  protected:
   ConvolutionLayerTest()
-      : blob_bottom_(new Blob<Dtype>()),
-        blob_top_(new Blob<Dtype>()) {}
+      : blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_bottom_2_(new Blob<Dtype>(2, 3, 6, 4)),
+        blob_top_(new Blob<Dtype>()),
+        blob_top_2_(new Blob<Dtype>()) {}
   virtual void SetUp() {
-    blob_bottom_->Reshape(2, 3, 6, 4);
     // fill the values
     FillerParameter filler_param;
     filler_param.set_value(1.);
     GaussianFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_);
+    filler.Fill(this->blob_bottom_2_);
     blob_bottom_vec_.push_back(blob_bottom_);
     blob_top_vec_.push_back(blob_top_);
   }
 
-  virtual ~ConvolutionLayerTest() { delete blob_bottom_; delete blob_top_; }
+  virtual ~ConvolutionLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_2_;
+    delete blob_top_;
+    delete blob_top_2_;
+  }
+
   Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_2_;
   Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_2_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(ConvolutionLayerTest, Dtypes);
+TYPED_TEST_CASE(ConvolutionLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(ConvolutionLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
   convolution_param->set_num_output(4);
-  shared_ptr<Layer<TypeParam> > layer(
-      new ConvolutionLayer<TypeParam>(layer_param));
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 4);
   EXPECT_EQ(this->blob_top_->height(), 2);
   EXPECT_EQ(this->blob_top_->width(), 1);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 4);
+  EXPECT_EQ(this->blob_top_2_->height(), 2);
+  EXPECT_EQ(this->blob_top_2_->width(), 1);
   // setting group should not change the shape
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
-  layer.reset(new ConvolutionLayer<TypeParam>(layer_param));
+  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3);
   EXPECT_EQ(this->blob_top_->height(), 2);
   EXPECT_EQ(this->blob_top_->width(), 1);
+  EXPECT_EQ(this->blob_top_2_->num(), 2);
+  EXPECT_EQ(this->blob_top_2_->channels(), 3);
+  EXPECT_EQ(this->blob_top_2_->height(), 2);
+  EXPECT_EQ(this->blob_top_2_->width(), 1);
 }
 
-TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolution) {
+TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   // We will simply see if the convolution layer carries out averaging well.
+  typedef typename TypeParam::Dtype Dtype;
+  shared_ptr<ConstantFiller<Dtype> > filler;
   FillerParameter filler_param;
   filler_param.set_value(1.);
-  ConstantFiller<TypeParam> filler(filler_param);
-  filler.Fill(this->blob_bottom_);
+  filler.reset(new ConstantFiller<Dtype>(filler_param));
+  filler->Fill(this->blob_bottom_);
+  filler_param.set_value(2.);
+  filler.reset(new ConstantFiller<Dtype>(filler_param));
+  filler->Fill(this->blob_bottom_2_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -85,53 +110,29 @@ TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolution) {
   convolution_param->mutable_weight_filler()->set_value(1);
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
-  shared_ptr<Layer<TypeParam> > layer(
-      new ConvolutionLayer<TypeParam>(layer_param));
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Caffe::set_mode(Caffe::CPU);
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // After the convolution, the output should all have output values 27.1
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     EXPECT_NEAR(top_data[i], 27.1, 1e-4);
   }
-}
-
-TYPED_TEST(ConvolutionLayerTest, TestGPUSimpleConvolution) {
-  // We will simply see if the convolution layer carries out averaging well.
-  FillerParameter filler_param;
-  filler_param.set_value(1.);
-  ConstantFiller<TypeParam> filler(filler_param);
-  filler.Fill(this->blob_bottom_);
-  LayerParameter layer_param;
-  ConvolutionParameter* convolution_param =
-      layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
-  convolution_param->set_num_output(4);
-  convolution_param->mutable_weight_filler()->set_type("constant");
-  convolution_param->mutable_weight_filler()->set_value(1);
-  convolution_param->mutable_bias_filler()->set_type("constant");
-  convolution_param->mutable_bias_filler()->set_value(0.1);
-  shared_ptr<Layer<TypeParam> > layer(
-      new ConvolutionLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Caffe::set_mode(Caffe::GPU);
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // After the convolution, the output should all have output values 27.1
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  for (int i = 0; i < this->blob_top_->count(); ++i) {
-    EXPECT_NEAR(top_data[i], 27.1, 1e-4);
+  top_data = this->blob_top_2_->cpu_data();
+  for (int i = 0; i < this->blob_top_2_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], 54.1, 1e-4);
   }
 }
 
-TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolutionGroup) {
+TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   // We will simply see if the convolution layer carries out averaging well.
+  typedef typename TypeParam::Dtype Dtype;
   FillerParameter filler_param;
   filler_param.set_value(1.);
-  ConstantFiller<TypeParam> filler(filler_param);
+  ConstantFiller<Dtype> filler(filler_param);
   filler.Fill(this->blob_bottom_);
-  TypeParam* bottom_data = this->blob_bottom_->mutable_cpu_data();
+  Dtype* bottom_data = this->blob_bottom_->mutable_cpu_data();
   for (int n = 0; n < this->blob_bottom_->num(); ++n) {
     for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
       for (int h = 0; h < this->blob_bottom_->height(); ++h) {
@@ -152,18 +153,17 @@ TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolutionGroup) {
   convolution_param->mutable_weight_filler()->set_value(1);
   convolution_param->mutable_bias_filler()->set_type("constant");
   convolution_param->mutable_bias_filler()->set_value(0.1);
-  shared_ptr<Layer<TypeParam> > layer(
-      new ConvolutionLayer<TypeParam>(layer_param));
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Caffe::set_mode(Caffe::CPU);
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // After the convolution, the output should all have output values 9.1
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int n = 0; n < this->blob_top_->num(); ++n) {
     for (int c = 0; c < this->blob_top_->channels(); ++c) {
       for (int h = 0; h < this->blob_top_->height(); ++h) {
         for (int w = 0; w < this->blob_top_->width(); ++w) {
-          TypeParam data = top_data[this->blob_top_->offset(n, c, h, w)];
+          Dtype data = top_data[this->blob_top_->offset(n, c, h, w)];
           EXPECT_NEAR(data, c * 9 + 0.1, 1e-4);
         }
       }
@@ -171,104 +171,122 @@ TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolutionGroup) {
   }
 }
 
-
-TYPED_TEST(ConvolutionLayerTest, TestGPUSimpleConvolutionGroup) {
-  // We will simply see if the convolution layer carries out averaging well.
+TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
+  // Test separable convolution by computing the Sobel operator
+  // as a single filter then comparing the result
+  // as the convolution of two rectangular filters.
+  typedef typename TypeParam::Dtype Dtype;
+  // Fill bottoms with identical Gaussian noise.
+  shared_ptr<GaussianFiller<Dtype> > filler;
   FillerParameter filler_param;
   filler_param.set_value(1.);
-  ConstantFiller<TypeParam> filler(filler_param);
-  filler.Fill(this->blob_bottom_);
-  TypeParam* bottom_data = this->blob_bottom_->mutable_cpu_data();
-  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
-    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
-      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
-        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
-          bottom_data[this->blob_bottom_->offset(n, c, h, w)] = c;
-        }
-      }
-    }
-  }
+  filler.reset(new GaussianFiller<Dtype>(filler_param));
+  filler->Fill(this->blob_bottom_);
+  this->blob_bottom_2_->CopyFrom(*this->blob_bottom_);
+  // Compute Sobel G_x operator as 3 x 3 convolution.
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
-  convolution_param->set_num_output(3);
-  convolution_param->set_group(3);
-  convolution_param->mutable_weight_filler()->set_type("constant");
-  convolution_param->mutable_weight_filler()->set_value(1);
-  convolution_param->mutable_bias_filler()->set_type("constant");
-  convolution_param->mutable_bias_filler()->set_value(0.1);
-  shared_ptr<Layer<TypeParam> > layer(
-      new ConvolutionLayer<TypeParam>(layer_param));
+  convolution_param->set_num_output(1);
+  convolution_param->set_bias_term(false);
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  layer->blobs().resize(1);
+  layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 3));
+  Dtype* weights = layer->blobs()[0]->mutable_cpu_data();
+  for (int c = 0; c < 3; ++c) {
+    int i = c * 9;  // 3 x 3 filter
+    weights[i +  0] = -1;
+    weights[i +  1] =  0;
+    weights[i +  2] =  1;
+    weights[i +  3] = -2;
+    weights[i +  4] =  0;
+    weights[i +  5] =  2;
+    weights[i +  6] = -1;
+    weights[i +  7] =  0;
+    weights[i +  8] =  1;
+  }
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Caffe::set_mode(Caffe::GPU);
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // After the convolution, the output should all have output values 9.1
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  for (int n = 0; n < this->blob_top_->num(); ++n) {
-    for (int c = 0; c < this->blob_top_->channels(); ++c) {
-      for (int h = 0; h < this->blob_top_->height(); ++h) {
-        for (int w = 0; w < this->blob_top_->width(); ++w) {
-          TypeParam data = top_data[this->blob_top_->offset(n, c, h, w)];
-          EXPECT_NEAR(data, c * 9 + 0.1, 1e-4);
-        }
-      }
-    }
+  // Compute Sobel G_x operator as separable 3 x 1 and 1 x 3 convolutions.
+  // (1) the [1 2 1] column filter
+  vector<Blob<Dtype>*> sep_blob_bottom_vec;
+  vector<Blob<Dtype>*> sep_blob_top_vec;
+  shared_ptr<Blob<Dtype> > blob_sep(new Blob<Dtype>());
+  sep_blob_bottom_vec.push_back(this->blob_bottom_2_);
+  sep_blob_top_vec.push_back(this->blob_top_2_);
+  convolution_param->clear_kernel_size();
+  convolution_param->clear_stride();
+  convolution_param->set_kernel_h(3);
+  convolution_param->set_kernel_w(1);
+  convolution_param->set_stride_h(2);
+  convolution_param->set_stride_w(1);
+  convolution_param->set_num_output(1);
+  convolution_param->set_bias_term(false);
+  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
+  layer->blobs().resize(1);
+  layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 3, 1));
+  Dtype* weights_1 = layer->blobs()[0]->mutable_cpu_data();
+  for (int c = 0; c < 3; ++c) {
+    int i = c * 3;  // 3 x 1 filter
+    weights_1[i +  0] = 1;
+    weights_1[i +  1] = 2;
+    weights_1[i +  2] = 1;
+  }
+  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  // (2) the [-1 0 1] row filter
+  blob_sep->CopyFrom(*this->blob_top_2_, false, true);
+  sep_blob_bottom_vec.clear();
+  sep_blob_bottom_vec.push_back(blob_sep.get());
+  convolution_param->set_kernel_h(1);
+  convolution_param->set_kernel_w(3);
+  convolution_param->set_stride_h(1);
+  convolution_param->set_stride_w(2);
+  convolution_param->set_num_output(1);
+  convolution_param->set_bias_term(false);
+  layer.reset(new ConvolutionLayer<Dtype>(layer_param));
+  layer->blobs().resize(1);
+  layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 1, 3));
+  Dtype* weights_2 = layer->blobs()[0]->mutable_cpu_data();
+  for (int c = 0; c < 3; ++c) {
+    int i = c * 3;  // 1 x 3 filter
+    weights_2[i +  0] = -1;
+    weights_2[i +  1] =  0;
+    weights_2[i +  2] =  1;
+  }
+  layer->SetUp(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  layer->Forward(sep_blob_bottom_vec, &(sep_blob_top_vec));
+  // Test equivalence of full and separable filters.
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  const Dtype* sep_top_data = this->blob_top_2_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], sep_top_data[i], 1e-4);
   }
 }
 
-
-TYPED_TEST(ConvolutionLayerTest, TestCPUGradient) {
-  LayerParameter layer_param;
-  ConvolutionParameter* convolution_param =
-      layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
-  convolution_param->set_num_output(2);
-  convolution_param->mutable_weight_filler()->set_type("gaussian");
-  convolution_param->mutable_bias_filler()->set_type("gaussian");
-  Caffe::set_mode(Caffe::CPU);
-  ConvolutionLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(ConvolutionLayerTest, TestCPUGradientGroup) {
-  LayerParameter layer_param;
-  ConvolutionParameter* convolution_param =
-      layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
-  convolution_param->set_num_output(3);
-  convolution_param->set_group(3);
-  convolution_param->mutable_weight_filler()->set_type("gaussian");
-  convolution_param->mutable_bias_filler()->set_type("gaussian");
-  Caffe::set_mode(Caffe::CPU);
-  ConvolutionLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(ConvolutionLayerTest, TestGPUGradient) {
+TYPED_TEST(ConvolutionLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
   convolution_param->set_num_output(2);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
-  Caffe::set_mode(Caffe::GPU);
-  ConvolutionLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  ConvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(ConvolutionLayerTest, TestGPUGradientGroup) {
+TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
@@ -278,9 +296,8 @@ TYPED_TEST(ConvolutionLayerTest, TestGPUGradientGroup) {
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
-  Caffe::set_mode(Caffe::GPU);
-  ConvolutionLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  ConvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py
index 0d8f5aa98e3..0516eff9158 100644
--- a/src/caffe/test/test_data/generate_sample_data.py
+++ b/src/caffe/test/test_data/generate_sample_data.py
@@ -14,7 +14,10 @@
 data = np.arange(total_size)
 data = data.reshape(num_rows, num_cols, height, width)
 data = data.astype('float32')
-label = np.arange(num_rows)[:, np.newaxis]
+
+# We had a bug where data was copied into label, but the tests weren't
+# catching it, so let's make label 1-indexed.
+label = 1 + np.arange(num_rows)[:, np.newaxis]
 label = label.astype('float32')
 
 print data
diff --git a/src/caffe/test/test_data/sample_data.h5 b/src/caffe/test/test_data/sample_data.h5
index a1f923a71ae..cb327573cae 100644
Binary files a/src/caffe/test/test_data/sample_data.h5 and b/src/caffe/test/test_data/sample_data.h5 differ
diff --git a/src/caffe/test/test_data/sample_data_2_gzip.h5 b/src/caffe/test/test_data/sample_data_2_gzip.h5
index 56c0a740ec2..b3d187cb1da 100644
Binary files a/src/caffe/test/test_data/sample_data_2_gzip.h5 and b/src/caffe/test/test_data/sample_data_2_gzip.h5 differ
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index 1eae1a4e068..208beed91d1 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -1,16 +1,16 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <string>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "leveldb/db.h"
+
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/vision_layers.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 using std::string;
@@ -18,15 +18,16 @@ using std::stringstream;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class DataLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class DataLayerTest : public ::testing::Test {
  protected:
   DataLayerTest()
-      : blob_top_data_(new Blob<Dtype>()),
-        blob_top_label_(new Blob<Dtype>()),
+      : backend_(DataParameter_DB_LEVELDB),
         filename_(new string(tmpnam(NULL))),
+        blob_top_data_(new Blob<Dtype>()),
+        blob_top_label_(new Blob<Dtype>()),
         seed_(1701) {}
   virtual void SetUp() {
     blob_top_vec_.push_back(blob_top_data_);
@@ -37,6 +38,7 @@ class DataLayerTest : public ::testing::Test {
   // all images are the same; else each image is unique but all pixels within
   // an image are the same.
   void FillLevelDB(const bool unique_pixels) {
+    backend_ = DataParameter_DB_LEVELDB;
     LOG(INFO) << "Using temporary leveldb " << *filename_;
     leveldb::DB* db;
     leveldb::Options options;
@@ -63,475 +65,326 @@ class DataLayerTest : public ::testing::Test {
     delete db;
   }
 
-  virtual ~DataLayerTest() { delete blob_top_data_; delete blob_top_label_; }
-
-  shared_ptr<string> filename_;
-  Blob<Dtype>* const blob_top_data_;
-  Blob<Dtype>* const blob_top_label_;
-  vector<Blob<Dtype>*> blob_bottom_vec_;
-  vector<Blob<Dtype>*> blob_top_vec_;
-  int seed_;
-};
-
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(DataLayerTest, Dtypes);
+  // Fill the LMDB with data: unique_pixels has same meaning as in FillLevelDB.
+  void FillLMDB(const bool unique_pixels) {
+    backend_ = DataParameter_DB_LMDB;
+    LOG(INFO) << "Using temporary lmdb " << *filename_;
+    CHECK_EQ(mkdir(filename_->c_str(), 0744), 0) << "mkdir " << filename_
+                                                 << "failed";
+    MDB_env *env;
+    MDB_dbi dbi;
+    MDB_val mdbkey, mdbdata;
+    MDB_txn *txn;
+    CHECK_EQ(mdb_env_create(&env), MDB_SUCCESS) << "mdb_env_create failed";
+    CHECK_EQ(mdb_env_set_mapsize(env, 1099511627776), MDB_SUCCESS)  // 1TB
+        << "mdb_env_set_mapsize failed";
+    CHECK_EQ(mdb_env_open(env, filename_->c_str(), 0, 0664), MDB_SUCCESS)
+        << "mdb_env_open failed";
+    CHECK_EQ(mdb_txn_begin(env, NULL, 0, &txn), MDB_SUCCESS)
+        << "mdb_txn_begin failed";
+    CHECK_EQ(mdb_open(txn, NULL, 0, &dbi), MDB_SUCCESS) << "mdb_open failed";
 
-TYPED_TEST(DataLayerTest, TestReadCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = false;  // all pixels the same; images different
-  this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 3);
-  EXPECT_EQ(this->blob_top_data_->width(), 4);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 100; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
     for (int i = 0; i < 5; ++i) {
+      Datum datum;
+      datum.set_label(i);
+      datum.set_channels(2);
+      datum.set_height(3);
+      datum.set_width(4);
+      std::string* data = datum.mutable_data();
       for (int j = 0; j < 24; ++j) {
-        EXPECT_EQ(scale * i, this->blob_top_data_->cpu_data()[i * 24 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
+        int datum = unique_pixels ? j : i;
+        data->push_back(static_cast<uint8_t>(datum));
       }
-    }
-  }
-}
+      stringstream ss;
+      ss << i;
 
-TYPED_TEST(DataLayerTest, TestReadGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = false;  // all pixels the same; images different
-  this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 3);
-  EXPECT_EQ(this->blob_top_data_->width(), 4);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 100; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 24; ++j) {
-        EXPECT_EQ(scale * i, this->blob_top_data_->cpu_data()[i * 24 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
+      string value;
+      datum.SerializeToString(&value);
+      mdbdata.mv_size = value.size();
+      mdbdata.mv_data = reinterpret_cast<void*>(&value[0]);
+      string keystr = ss.str();
+      mdbkey.mv_size = keystr.size();
+      mdbkey.mv_data = reinterpret_cast<void*>(&keystr[0]);
+      CHECK_EQ(mdb_put(txn, dbi, &mdbkey, &mdbdata, 0), MDB_SUCCESS)
+          << "mdb_put failed";
     }
+    CHECK_EQ(mdb_txn_commit(txn), MDB_SUCCESS) << "mdb_txn_commit failed";
+    mdb_close(env, dbi);
+    mdb_env_close(env);
   }
-}
 
-TYPED_TEST(DataLayerTest, TestReadCropTrainCPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_with_center_value = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        num_with_center_value +=
-            (center_value == this->blob_top_data_->cpu_data()[i * 2 + j]);
+  void TestRead() {
+    const Dtype scale = 3;
+    LayerParameter param;
+    DataParameter* data_param = param.mutable_data_param();
+    data_param->set_batch_size(5);
+    data_param->set_scale(scale);
+    data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend_);
+    DataLayer<Dtype> layer(param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_data_->num(), 5);
+    EXPECT_EQ(blob_top_data_->channels(), 2);
+    EXPECT_EQ(blob_top_data_->height(), 3);
+    EXPECT_EQ(blob_top_data_->width(), 4);
+    EXPECT_EQ(blob_top_label_->num(), 5);
+    EXPECT_EQ(blob_top_label_->channels(), 1);
+    EXPECT_EQ(blob_top_label_->height(), 1);
+    EXPECT_EQ(blob_top_label_->width(), 1);
+
+    for (int iter = 0; iter < 100; ++iter) {
+      layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+      for (int i = 0; i < 5; ++i) {
+        EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
-    }
-    // Check we did not get the center crop all 10 times (occurs with
-    // probability 1-1/12^10 in working implementation).
-    EXPECT_LT(num_with_center_value, 10);
-  }
-}
-
-TYPED_TEST(DataLayerTest, TestReadCropTrainGPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_with_center_value = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        num_with_center_value +=
-            (center_value == this->blob_top_data_->cpu_data()[i * 2 + j]);
+      for (int i = 0; i < 5; ++i) {
+        for (int j = 0; j < 24; ++j) {
+          EXPECT_EQ(scale * i, blob_top_data_->cpu_data()[i * 24 + j])
+              << "debug: iter " << iter << " i " << i << " j " << j;
+        }
       }
     }
-    // Check we did not get the center crop all 10 times (occurs with
-    // probability 1-1/12^10 in working implementation).
-    EXPECT_LT(num_with_center_value, 10);
   }
-}
 
-// Test that the sequence of random crops is consistent when using
-// Caffe::set_random_seed.
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededCPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-
-  // Get crop sequence with Caffe seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  void TestReadCrop() {
+    const Dtype scale = 3;
+    LayerParameter param;
+    Caffe::set_random_seed(1701);
+    DataParameter* data_param = param.mutable_data_param();
+    data_param->set_batch_size(5);
+    data_param->set_scale(scale);
+    data_param->set_crop_size(1);
+    data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend_);
+    DataLayer<Dtype> layer(param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_data_->num(), 5);
+    EXPECT_EQ(blob_top_data_->channels(), 2);
+    EXPECT_EQ(blob_top_data_->height(), 1);
+    EXPECT_EQ(blob_top_data_->width(), 1);
+    EXPECT_EQ(blob_top_label_->num(), 5);
+    EXPECT_EQ(blob_top_label_->channels(), 1);
+    EXPECT_EQ(blob_top_label_->height(), 1);
+    EXPECT_EQ(blob_top_label_->width(), 1);
+
     for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+      layer.Forward(blob_bottom_vec_, &blob_top_vec_);
       for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+        EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
-      vector<TypeParam> iter_crop_sequence;
+      int num_with_center_value = 0;
       for (int i = 0; i < 5; ++i) {
         for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
+          const Dtype center_value = scale * (j ? 17 : 5);
+          num_with_center_value +=
+              (center_value == blob_top_data_->cpu_data()[i * 2 + j]);
+          // At TEST time, check that we always get center value.
+          if (Caffe::phase() == Caffe::TEST) {
+            EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
+                << "debug: iter " << iter << " i " << i << " j " << j;
+          }
         }
       }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence after reseeding Caffe with 1701.
-  // Check that the sequence is the same as the original.
-  Caffe::set_random_seed(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        EXPECT_EQ(crop_sequence[iter][i * 2 + j],
-                  this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
+      // At TRAIN time, check that we did not get the center crop all 10 times.
+      // (This check fails with probability 1-1/12^10 in a correct
+      // implementation, so we call set_random_seed.)
+      if (Caffe::phase() == Caffe::TRAIN) {
+        EXPECT_LT(num_with_center_value, 10);
       }
     }
   }
-}
 
-// Test that the sequence of random crops is consistent when using
-// Caffe::set_random_seed.
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededGPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-
-  // Get crop sequence with Caffe seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  void TestReadCropTrainSequenceSeeded() {
+    LayerParameter param;
+    DataParameter* data_param = param.mutable_data_param();
+    data_param->set_batch_size(5);
+    data_param->set_crop_size(1);
+    data_param->set_mirror(true);
+    data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend_);
+
+    // Get crop sequence with Caffe seed 1701.
+    Caffe::set_random_seed(seed_);
+    vector<vector<Dtype> > crop_sequence;
+    {
+      DataLayer<Dtype> layer1(param);
+      layer1.SetUp(blob_bottom_vec_, &blob_top_vec_);
+      for (int iter = 0; iter < 2; ++iter) {
+        layer1.Forward(blob_bottom_vec_, &blob_top_vec_);
+        for (int i = 0; i < 5; ++i) {
+          EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
+        }
+        vector<Dtype> iter_crop_sequence;
+        for (int i = 0; i < 5; ++i) {
+          for (int j = 0; j < 2; ++j) {
+            iter_crop_sequence.push_back(
+                blob_top_data_->cpu_data()[i * 2 + j]);
+          }
+        }
+        crop_sequence.push_back(iter_crop_sequence);
+      }
+    }  // destroy 1st data layer and unlock the leveldb
+
+    // Get crop sequence after reseeding Caffe with 1701.
+    // Check that the sequence is the same as the original.
+    Caffe::set_random_seed(seed_);
+    DataLayer<Dtype> layer2(param);
+    layer2.SetUp(blob_bottom_vec_, &blob_top_vec_);
     for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+      layer2.Forward(blob_bottom_vec_, &blob_top_vec_);
       for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+        EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
-      vector<TypeParam> iter_crop_sequence;
       for (int i = 0; i < 5; ++i) {
         for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
+          EXPECT_EQ(crop_sequence[iter][i * 2 + j],
+                    blob_top_data_->cpu_data()[i * 2 + j])
+              << "debug: iter " << iter << " i " << i << " j " << j;
         }
       }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence after reseeding Caffe with 1701.
-  // Check that the sequence is the same as the original.
-  Caffe::set_random_seed(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        EXPECT_EQ(crop_sequence[iter][i * 2 + j],
-                  this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
     }
   }
-}
 
-// Test that the sequence of random crops differs across iterations when
-// Caffe::set_random_seed isn't called (and seeds from srand are ignored).
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededCPU) {
-  Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::CPU);
-  const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-
-  // Get crop sequence with Caffe seed 1701, srand seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  srand(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  void TestReadCropTrainSequenceUnseeded() {
+    LayerParameter param;
+    DataParameter* data_param = param.mutable_data_param();
+    data_param->set_batch_size(5);
+    data_param->set_crop_size(1);
+    data_param->set_mirror(true);
+    data_param->set_source(filename_->c_str());
+    data_param->set_backend(backend_);
+
+    // Get crop sequence with Caffe seed 1701, srand seed 1701.
+    Caffe::set_random_seed(seed_);
+    srand(seed_);
+    vector<vector<Dtype> > crop_sequence;
+    {
+      DataLayer<Dtype> layer1(param);
+      layer1.SetUp(blob_bottom_vec_, &blob_top_vec_);
+      for (int iter = 0; iter < 2; ++iter) {
+        layer1.Forward(blob_bottom_vec_, &blob_top_vec_);
+        for (int i = 0; i < 5; ++i) {
+          EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
+        }
+        vector<Dtype> iter_crop_sequence;
+        for (int i = 0; i < 5; ++i) {
+          for (int j = 0; j < 2; ++j) {
+            iter_crop_sequence.push_back(
+                blob_top_data_->cpu_data()[i * 2 + j]);
+          }
+        }
+        crop_sequence.push_back(iter_crop_sequence);
+      }
+    }  // destroy 1st data layer and unlock the leveldb
+
+    // Get crop sequence continuing from previous Caffe RNG state; reseed
+    // srand with 1701. Check that the sequence differs from the original.
+    srand(seed_);
+    DataLayer<Dtype> layer2(param);
+    layer2.SetUp(blob_bottom_vec_, &blob_top_vec_);
     for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+      layer2.Forward(blob_bottom_vec_, &blob_top_vec_);
       for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
+        EXPECT_EQ(i, blob_top_label_->cpu_data()[i]);
       }
-      vector<TypeParam> iter_crop_sequence;
+      int num_sequence_matches = 0;
       for (int i = 0; i < 5; ++i) {
         for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
+          num_sequence_matches += (crop_sequence[iter][i * 2 + j] ==
+                                   blob_top_data_->cpu_data()[i * 2 + j]);
         }
       }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence continuing from previous Caffe RNG state;
-  // reseed srand with 1701. Check that the sequence differs from the original.
-  srand(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_sequence_matches = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        num_sequence_matches += (crop_sequence[iter][i * 2 + j] ==
-                                 this->blob_top_data_->cpu_data()[i * 2 + j]);
-      }
+      EXPECT_LT(num_sequence_matches, 10);
     }
-    EXPECT_LT(num_sequence_matches, 10);
   }
+
+  virtual ~DataLayerTest() { delete blob_top_data_; delete blob_top_label_; }
+
+  DataParameter_DB backend_;
+  shared_ptr<string> filename_;
+  Blob<Dtype>* const blob_top_data_;
+  Blob<Dtype>* const blob_top_label_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+  int seed_;
+};
+
+TYPED_TEST_CASE(DataLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(DataLayerTest, TestReadLevelDB) {
+  const bool unique_pixels = false;  // all pixels the same; images different
+  this->FillLevelDB(unique_pixels);
+  this->TestRead();
+}
+
+TYPED_TEST(DataLayerTest, TestReadCropTrainLevelDB) {
+  Caffe::set_phase(Caffe::TRAIN);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLevelDB(unique_pixels);
+  this->TestReadCrop();
+}
+
+// Test that the sequence of random crops is consistent when using
+// Caffe::set_random_seed.
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLevelDB) {
+  Caffe::set_phase(Caffe::TRAIN);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLevelDB(unique_pixels);
+  this->TestReadCropTrainSequenceSeeded();
 }
 
 // Test that the sequence of random crops differs across iterations when
 // Caffe::set_random_seed isn't called (and seeds from srand are ignored).
-TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededGPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLevelDB) {
   Caffe::set_phase(Caffe::TRAIN);
-  Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_crop_size(1);
-  data_param->set_mirror(true);
-  data_param->set_source(this->filename_->c_str());
-
-  // Get crop sequence with Caffe seed 1701, srand seed 1701.
-  Caffe::set_random_seed(this->seed_);
-  srand(this->seed_);
-  vector<vector<TypeParam> > crop_sequence;
-  {
-    DataLayer<TypeParam> layer1(param);
-    layer1.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int iter = 0; iter < 2; ++iter) {
-      layer1.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-      for (int i = 0; i < 5; ++i) {
-        EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-      }
-      vector<TypeParam> iter_crop_sequence;
-      for (int i = 0; i < 5; ++i) {
-        for (int j = 0; j < 2; ++j) {
-          iter_crop_sequence.push_back(
-              this->blob_top_data_->cpu_data()[i * 2 + j]);
-        }
-      }
-      crop_sequence.push_back(iter_crop_sequence);
-    }
-  }  // destroy 1st data layer and unlock the leveldb
-
-  // Get crop sequence continuing from previous Caffe RNG state;
-  // reseed srand with 1701. Check that the sequence differs from the original.
-  srand(this->seed_);
-  DataLayer<TypeParam> layer2(param);
-  layer2.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  for (int iter = 0; iter < 2; ++iter) {
-    layer2.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    int num_sequence_matches = 0;
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        num_sequence_matches += (crop_sequence[iter][i * 2 + j] ==
-                                 this->blob_top_data_->cpu_data()[i * 2 + j]);
-      }
-    }
-    EXPECT_LT(num_sequence_matches, 10);
-  }
+  this->TestReadCropTrainSequenceUnseeded();
 }
 
-TYPED_TEST(DataLayerTest, TestReadCropTestCPU) {
+TYPED_TEST(DataLayerTest, TestReadCropTestLevelDB) {
   Caffe::set_phase(Caffe::TEST);
-  Caffe::set_mode(Caffe::CPU);
   const bool unique_pixels = true;  // all images the same; pixels different
   this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
+  this->TestReadCrop();
 }
 
-TYPED_TEST(DataLayerTest, TestReadCropTestGPU) {
+TYPED_TEST(DataLayerTest, TestReadLMDB) {
+  const bool unique_pixels = false;  // all pixels the same; images different
+  this->FillLMDB(unique_pixels);
+  this->TestRead();
+}
+
+TYPED_TEST(DataLayerTest, TestReadCropTrainLMDB) {
+  Caffe::set_phase(Caffe::TRAIN);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  this->TestReadCrop();
+}
+
+// Test that the sequence of random crops is consistent when using
+// Caffe::set_random_seed.
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceSeededLMDB) {
+  Caffe::set_phase(Caffe::TRAIN);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  this->TestReadCropTrainSequenceSeeded();
+}
+
+// Test that the sequence of random crops differs across iterations when
+// Caffe::set_random_seed isn't called (and seeds from srand are ignored).
+TYPED_TEST(DataLayerTest, TestReadCropTrainSequenceUnseededLMDB) {
+  Caffe::set_phase(Caffe::TRAIN);
+  const bool unique_pixels = true;  // all images the same; pixels different
+  this->FillLMDB(unique_pixels);
+  this->TestReadCropTrainSequenceUnseeded();
+}
+
+TYPED_TEST(DataLayerTest, TestReadCropTestLMDB) {
   Caffe::set_phase(Caffe::TEST);
-  Caffe::set_mode(Caffe::GPU);
   const bool unique_pixels = true;  // all images the same; pixels different
-  this->FillLevelDB(unique_pixels);
-  const TypeParam scale = 3;
-  LayerParameter param;
-  DataParameter* data_param = param.mutable_data_param();
-  data_param->set_batch_size(5);
-  data_param->set_scale(scale);
-  data_param->set_crop_size(1);
-  data_param->set_source(this->filename_->c_str());
-  DataLayer<TypeParam> layer(param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_data_->num(), 5);
-  EXPECT_EQ(this->blob_top_data_->channels(), 2);
-  EXPECT_EQ(this->blob_top_data_->height(), 1);
-  EXPECT_EQ(this->blob_top_data_->width(), 1);
-  EXPECT_EQ(this->blob_top_label_->num(), 5);
-  EXPECT_EQ(this->blob_top_label_->channels(), 1);
-  EXPECT_EQ(this->blob_top_label_->height(), 1);
-  EXPECT_EQ(this->blob_top_label_->width(), 1);
-
-  for (int iter = 0; iter < 2; ++iter) {
-    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    for (int i = 0; i < 5; ++i) {
-      EXPECT_EQ(i, this->blob_top_label_->cpu_data()[i]);
-    }
-    for (int i = 0; i < 5; ++i) {
-      for (int j = 0; j < 2; ++j) {
-        const TypeParam center_value = scale * (j ? 17 : 5);
-        EXPECT_EQ(center_value, this->blob_top_data_->cpu_data()[i * 2 + j])
-            << "debug: iter " << iter << " i " << i << " j " << j;
-      }
-    }
-  }
+  this->FillLMDB(unique_pixels);
+  this->TestReadCrop();
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
new file mode 100644
index 00000000000..4188bb68c9e
--- /dev/null
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -0,0 +1,199 @@
+#include <string>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+using std::string;
+using std::stringstream;
+
+namespace caffe {
+
+template <typename Dtype>
+class DummyDataLayerTest : public ::testing::Test {
+ protected:
+  DummyDataLayerTest()
+      : blob_top_a_(new Blob<Dtype>()),
+        blob_top_b_(new Blob<Dtype>()),
+        blob_top_c_(new Blob<Dtype>()) {}
+
+  virtual void SetUp() {
+    blob_bottom_vec_.clear();
+    blob_top_vec_.clear();
+    blob_top_vec_.push_back(blob_top_a_);
+    blob_top_vec_.push_back(blob_top_b_);
+    blob_top_vec_.push_back(blob_top_c_);
+  }
+
+  virtual ~DummyDataLayerTest() {
+    delete blob_top_a_;
+    delete blob_top_b_;
+    delete blob_top_c_;
+  }
+
+  Blob<Dtype>* const blob_top_a_;
+  Blob<Dtype>* const blob_top_b_;
+  Blob<Dtype>* const blob_top_c_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(DummyDataLayerTest, TestDtypes);
+
+TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter param;
+  DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
+  dummy_data_param->add_num(5);
+  dummy_data_param->add_channels(3);
+  dummy_data_param->add_height(2);
+  dummy_data_param->add_width(4);
+  this->blob_top_vec_.resize(1);
+  DummyDataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_a_->num(), 5);
+  EXPECT_EQ(this->blob_top_a_->channels(), 3);
+  EXPECT_EQ(this->blob_top_a_->height(), 2);
+  EXPECT_EQ(this->blob_top_a_->width(), 4);
+  EXPECT_EQ(this->blob_top_b_->count(), 0);
+  EXPECT_EQ(this->blob_top_c_->count(), 0);
+  for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
+    for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
+      EXPECT_EQ(0, this->blob_top_vec_[i]->cpu_data()[j]);
+    }
+  }
+  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
+    for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
+      EXPECT_EQ(0, this->blob_top_vec_[i]->cpu_data()[j]);
+    }
+  }
+}
+
+TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter param;
+  DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
+  dummy_data_param->add_num(5);
+  dummy_data_param->add_channels(3);
+  dummy_data_param->add_height(2);
+  dummy_data_param->add_width(4);
+  dummy_data_param->add_num(5);
+  // Don't explicitly set number of channels or height for 2nd top blob; should
+  // default to first channels and height (as we check later).
+  dummy_data_param->add_height(1);
+  FillerParameter* data_filler_param = dummy_data_param->add_data_filler();
+  data_filler_param->set_value(7);
+  this->blob_top_vec_.resize(2);
+  DummyDataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_a_->num(), 5);
+  EXPECT_EQ(this->blob_top_a_->channels(), 3);
+  EXPECT_EQ(this->blob_top_a_->height(), 2);
+  EXPECT_EQ(this->blob_top_a_->width(), 4);
+  EXPECT_EQ(this->blob_top_b_->num(), 5);
+  EXPECT_EQ(this->blob_top_b_->channels(), 3);
+  EXPECT_EQ(this->blob_top_b_->height(), 1);
+  EXPECT_EQ(this->blob_top_b_->width(), 4);
+  EXPECT_EQ(this->blob_top_c_->count(), 0);
+  for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
+    for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
+      EXPECT_EQ(7, this->blob_top_vec_[i]->cpu_data()[j]);
+    }
+  }
+  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_vec_.size(); ++i) {
+    for (int j = 0; j < this->blob_top_vec_[i]->count(); ++j) {
+      EXPECT_EQ(7, this->blob_top_vec_[i]->cpu_data()[j]);
+    }
+  }
+}
+
+TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
+  Caffe::set_mode(Caffe::CPU);
+  LayerParameter param;
+  DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
+  dummy_data_param->add_num(5);
+  dummy_data_param->add_channels(3);
+  dummy_data_param->add_height(2);
+  dummy_data_param->add_width(4);
+  FillerParameter* data_filler_param_a = dummy_data_param->add_data_filler();
+  data_filler_param_a->set_value(7);
+  FillerParameter* data_filler_param_b = dummy_data_param->add_data_filler();
+  data_filler_param_b->set_type("gaussian");
+  TypeParam gaussian_mean = 3.0;
+  TypeParam gaussian_std = 0.01;
+  data_filler_param_b->set_mean(gaussian_mean);
+  data_filler_param_b->set_std(gaussian_std);
+  FillerParameter* data_filler_param_c = dummy_data_param->add_data_filler();
+  data_filler_param_c->set_value(9);
+  DummyDataLayer<TypeParam> layer(param);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_a_->num(), 5);
+  EXPECT_EQ(this->blob_top_a_->channels(), 3);
+  EXPECT_EQ(this->blob_top_a_->height(), 2);
+  EXPECT_EQ(this->blob_top_a_->width(), 4);
+  EXPECT_EQ(this->blob_top_b_->num(), 5);
+  EXPECT_EQ(this->blob_top_b_->channels(), 3);
+  EXPECT_EQ(this->blob_top_b_->height(), 2);
+  EXPECT_EQ(this->blob_top_b_->width(), 4);
+  EXPECT_EQ(this->blob_top_c_->num(), 5);
+  EXPECT_EQ(this->blob_top_c_->channels(), 3);
+  EXPECT_EQ(this->blob_top_c_->height(), 2);
+  EXPECT_EQ(this->blob_top_c_->width(), 4);
+  for (int i = 0; i < this->blob_top_a_->count(); ++i) {
+    EXPECT_EQ(7, this->blob_top_a_->cpu_data()[i]);
+  }
+  // Blob b uses a Gaussian filler, so SetUp should not have initialized it.
+  // Blob b's data should therefore be the default Blob data value: 0.
+  for (int i = 0; i < this->blob_top_b_->count(); ++i) {
+    EXPECT_EQ(0, this->blob_top_b_->cpu_data()[i]);
+  }
+  for (int i = 0; i < this->blob_top_c_->count(); ++i) {
+    EXPECT_EQ(9, this->blob_top_c_->cpu_data()[i]);
+  }
+
+  // Do a Forward pass to fill in Blob b with Gaussian data.
+  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_a_->count(); ++i) {
+    EXPECT_EQ(7, this->blob_top_a_->cpu_data()[i]);
+  }
+  // Check that the Gaussian's data has been filled in with values within
+  // 10 standard deviations of the mean. Record the first and last sample.
+  // to check that they're different after the next Forward pass.
+  for (int i = 0; i < this->blob_top_b_->count(); ++i) {
+    EXPECT_NEAR(gaussian_mean, this->blob_top_b_->cpu_data()[i],
+                gaussian_std * 10);
+  }
+  const TypeParam first_gaussian_sample = this->blob_top_b_->cpu_data()[0];
+  const TypeParam last_gaussian_sample =
+      this->blob_top_b_->cpu_data()[this->blob_top_b_->count() - 1];
+  for (int i = 0; i < this->blob_top_c_->count(); ++i) {
+    EXPECT_EQ(9, this->blob_top_c_->cpu_data()[i]);
+  }
+
+  // Do another Forward pass to fill in Blob b with Gaussian data again,
+  // checking that we get different values.
+  layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_a_->count(); ++i) {
+    EXPECT_EQ(7, this->blob_top_a_->cpu_data()[i]);
+  }
+  for (int i = 0; i < this->blob_top_b_->count(); ++i) {
+    EXPECT_NEAR(gaussian_mean, this->blob_top_b_->cpu_data()[i],
+                gaussian_std * 10);
+  }
+  EXPECT_NE(first_gaussian_sample, this->blob_top_b_->cpu_data()[0]);
+  EXPECT_NE(last_gaussian_sample,
+      this->blob_top_b_->cpu_data()[this->blob_top_b_->count() - 1]);
+  for (int i = 0; i < this->blob_top_c_->count(); ++i) {
+    EXPECT_EQ(9, this->blob_top_c_->cpu_data()[i]);
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
new file mode 100644
index 00000000000..4c17dfd341c
--- /dev/null
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -0,0 +1,163 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class EltwiseLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  EltwiseLayerTest()
+      : blob_bottom_a_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_b_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_c_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_a_);
+    filler.Fill(this->blob_bottom_b_);
+    filler.Fill(this->blob_bottom_c_);
+    blob_bottom_vec_.push_back(blob_bottom_a_);
+    blob_bottom_vec_.push_back(blob_bottom_b_);
+    blob_bottom_vec_.push_back(blob_bottom_c_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~EltwiseLayerTest() {
+    delete blob_bottom_a_;
+    delete blob_bottom_b_;
+    delete blob_bottom_c_;
+    delete blob_top_;
+  }
+  Blob<Dtype>* const blob_bottom_a_;
+  Blob<Dtype>* const blob_bottom_b_;
+  Blob<Dtype>* const blob_bottom_c_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(EltwiseLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(EltwiseLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+  shared_ptr<EltwiseLayer<Dtype> > layer(
+      new EltwiseLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 4);
+  EXPECT_EQ(this->blob_top_->width(), 5);
+}
+
+TYPED_TEST(EltwiseLayerTest, TestProd) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+  shared_ptr<EltwiseLayer<Dtype> > layer(
+      new EltwiseLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
+  const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_EQ(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i]);
+  }
+}
+
+TYPED_TEST(EltwiseLayerTest, TestSum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
+  shared_ptr<EltwiseLayer<Dtype> > layer(
+      new EltwiseLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
+  const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_EQ(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i]);
+  }
+}
+
+TYPED_TEST(EltwiseLayerTest, TestSumCoeff) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
+  eltwise_param->add_coeff(1);
+  eltwise_param->add_coeff(-0.5);
+  eltwise_param->add_coeff(2);
+  shared_ptr<EltwiseLayer<Dtype> > layer(
+      new EltwiseLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_a_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
+  const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] - 0.5*in_data_b[i] + 2*in_data_c[i],
+        1e-4);
+  }
+}
+
+TYPED_TEST(EltwiseLayerTest, TestProdGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_PROD);
+  EltwiseLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+TYPED_TEST(EltwiseLayerTest, TestSumGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
+  EltwiseLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+TYPED_TEST(EltwiseLayerTest, TestSumCoeffGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EltwiseParameter* eltwise_param = layer_param.mutable_eltwise_param();
+  eltwise_param->set_operation(EltwiseParameter_EltwiseOp_SUM);
+  eltwise_param->add_coeff(1);
+  eltwise_param->add_coeff(-0.5);
+  eltwise_param->add_coeff(2);
+  EltwiseLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_eltwise_product_layer.cpp b/src/caffe/test/test_eltwise_product_layer.cpp
deleted file mode 100644
index 86d6fdc5334..00000000000
--- a/src/caffe/test/test_eltwise_product_layer.cpp
+++ /dev/null
@@ -1,118 +0,0 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <vector>
-
-#include "cuda_runtime.h"
-#include "gtest/gtest.h"
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
-
-#include "caffe/test/test_caffe_main.hpp"
-
-namespace caffe {
-
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
-template <typename Dtype>
-class EltwiseProductLayerTest : public ::testing::Test {
- protected:
-  EltwiseProductLayerTest()
-      : blob_bottom_a_(new Blob<Dtype>(2, 3, 4, 5)),
-        blob_bottom_b_(new Blob<Dtype>(2, 3, 4, 5)),
-        blob_bottom_c_(new Blob<Dtype>(2, 3, 4, 5)),
-        blob_top_(new Blob<Dtype>()) {
-    // fill the values
-    FillerParameter filler_param;
-    UniformFiller<Dtype> filler(filler_param);
-    filler.Fill(this->blob_bottom_a_);
-    filler.Fill(this->blob_bottom_b_);
-    filler.Fill(this->blob_bottom_c_);
-    blob_bottom_vec_.push_back(blob_bottom_a_);
-    blob_bottom_vec_.push_back(blob_bottom_b_);
-    blob_bottom_vec_.push_back(blob_bottom_c_);
-    blob_top_vec_.push_back(blob_top_);
-  }
-  virtual ~EltwiseProductLayerTest() {
-    delete blob_bottom_a_;
-    delete blob_bottom_b_;
-    delete blob_bottom_c_;
-    delete blob_top_;
-  }
-  Blob<Dtype>* const blob_bottom_a_;
-  Blob<Dtype>* const blob_bottom_b_;
-  Blob<Dtype>* const blob_bottom_c_;
-  Blob<Dtype>* const blob_top_;
-  vector<Blob<Dtype>*> blob_bottom_vec_;
-  vector<Blob<Dtype>*> blob_top_vec_;
-};
-
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(EltwiseProductLayerTest, Dtypes);
-
-TYPED_TEST(EltwiseProductLayerTest, TestSetUp) {
-  LayerParameter layer_param;
-  shared_ptr<EltwiseProductLayer<TypeParam> > layer(
-      new EltwiseProductLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  EXPECT_EQ(this->blob_top_->num(), 2);
-  EXPECT_EQ(this->blob_top_->channels(), 3);
-  EXPECT_EQ(this->blob_top_->height(), 4);
-  EXPECT_EQ(this->blob_top_->width(), 5);
-}
-
-TYPED_TEST(EltwiseProductLayerTest, TestCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  LayerParameter layer_param;
-  shared_ptr<EltwiseProductLayer<TypeParam> > layer(
-      new EltwiseProductLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* data = this->blob_top_->cpu_data();
-  const int count = this->blob_top_->count();
-  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
-  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
-  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_EQ(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i]);
-  }
-}
-
-TYPED_TEST(EltwiseProductLayerTest, TestGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  LayerParameter layer_param;
-  shared_ptr<EltwiseProductLayer<TypeParam> > layer(
-      new EltwiseProductLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* data = this->blob_top_->cpu_data();
-  const int count = this->blob_top_->count();
-  const TypeParam* in_data_a = this->blob_bottom_a_->cpu_data();
-  const TypeParam* in_data_b = this->blob_bottom_b_->cpu_data();
-  const TypeParam* in_data_c = this->blob_bottom_c_->cpu_data();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_EQ(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i]);
-  }
-}
-
-TYPED_TEST(EltwiseProductLayerTest, TestCPUGradient) {
-  Caffe::set_mode(Caffe::CPU);
-  LayerParameter layer_param;
-  EltwiseProductLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(EltwiseProductLayerTest, TestGPUGradient) {
-  Caffe::set_mode(Caffe::GPU);
-  LayerParameter layer_param;
-  EltwiseProductLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-}  // namespace caffe
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index d5e4107ac5e..511d38ccab5 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -1,26 +1,24 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class EuclideanLossLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class EuclideanLossLayerTest : public ::testing::Test {
  protected:
   EuclideanLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
@@ -43,17 +41,17 @@ class EuclideanLossLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(EuclideanLossLayerTest, Dtypes);
+TYPED_TEST_CASE(EuclideanLossLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(EuclideanLossLayerTest, TestGradientCPU) {
+TYPED_TEST(EuclideanLossLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  EuclideanLossLayer<TypeParam> layer(layer_param);
+  EuclideanLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
+      &(this->blob_top_vec_), -1, -1, -1);
 }
 
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index e8b556a66b5..e04b0fd22af 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -1,17 +1,13 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/filler.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
 
-typedef ::testing::Types<float, double> Dtypes;
-
 template <typename Dtype>
 class ConstantFillerTest : public ::testing::Test {
  protected:
@@ -28,7 +24,7 @@ class ConstantFillerTest : public ::testing::Test {
   shared_ptr<ConstantFiller<Dtype> > filler_;
 };
 
-TYPED_TEST_CASE(ConstantFillerTest, Dtypes);
+TYPED_TEST_CASE(ConstantFillerTest, TestDtypes);
 
 TYPED_TEST(ConstantFillerTest, TestFill) {
   EXPECT_TRUE(this->blob_);
@@ -57,7 +53,7 @@ class UniformFillerTest : public ::testing::Test {
   shared_ptr<UniformFiller<Dtype> > filler_;
 };
 
-TYPED_TEST_CASE(UniformFillerTest, Dtypes);
+TYPED_TEST_CASE(UniformFillerTest, TestDtypes);
 
 TYPED_TEST(UniformFillerTest, TestFill) {
   EXPECT_TRUE(this->blob_);
@@ -84,7 +80,7 @@ class PositiveUnitballFillerTest : public ::testing::Test {
   shared_ptr<PositiveUnitballFiller<Dtype> > filler_;
 };
 
-TYPED_TEST_CASE(PositiveUnitballFillerTest, Dtypes);
+TYPED_TEST_CASE(PositiveUnitballFillerTest, TestDtypes);
 
 TYPED_TEST(PositiveUnitballFillerTest, TestFill) {
   EXPECT_TRUE(this->blob_);
@@ -123,7 +119,7 @@ class GaussianFillerTest : public ::testing::Test {
   shared_ptr<GaussianFiller<Dtype> > filler_;
 };
 
-TYPED_TEST_CASE(GaussianFillerTest, Dtypes);
+TYPED_TEST_CASE(GaussianFillerTest, TestDtypes);
 
 TYPED_TEST(GaussianFillerTest, TestFill) {
   EXPECT_TRUE(this->blob_);
diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
index 52c567b0295..cbd01f245f2 100644
--- a/src/caffe/test/test_flatten_layer.cpp
+++ b/src/caffe/test/test_flatten_layer.cpp
@@ -1,24 +1,21 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
-template <typename Dtype>
-class FlattenLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class FlattenLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   FlattenLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
@@ -38,12 +35,12 @@ class FlattenLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(FlattenLayerTest, Dtypes);
+TYPED_TEST_CASE(FlattenLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(FlattenLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  FlattenLayer<TypeParam> layer(layer_param);
+  FlattenLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5);
@@ -51,10 +48,10 @@ TYPED_TEST(FlattenLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_->width(), 1);
 }
 
-TYPED_TEST(FlattenLayerTest, TestCPU) {
+TYPED_TEST(FlattenLayerTest, Test) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  FlattenLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
+  FlattenLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int c = 0; c < 3 * 6 * 5; ++c) {
@@ -65,34 +62,11 @@ TYPED_TEST(FlattenLayerTest, TestCPU) {
   }
 }
 
-TYPED_TEST(FlattenLayerTest, TestGPU) {
-  LayerParameter layer_param;
-  FlattenLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int c = 0; c < 3 * 6 * 5; ++c) {
-    EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
-        this->blob_bottom_->data_at(0, c / (6 * 5), (c / 5) % 6, c % 5));
-    EXPECT_EQ(this->blob_top_->data_at(1, c, 0, 0),
-        this->blob_bottom_->data_at(1, c / (6 * 5), (c / 5) % 6, c % 5));
-  }
-}
-
-TYPED_TEST(FlattenLayerTest, TestCPUGradient) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  FlattenLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(FlattenLayerTest, TestGPUGradient) {
+TYPED_TEST(FlattenLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  FlattenLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
+  FlattenLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index 9f793f2fce6..696165cdaa2 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -1,15 +1,14 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <cuda_runtime.h>
 #include <string>
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 using std::string;
@@ -17,10 +16,10 @@ using std::vector;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template<typename TypeParam>
+class HDF5OutputLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class HDF5OutputLayerTest : public ::testing::Test {
  protected:
   HDF5OutputLayerTest()
       : output_file_name_(tmpnam(NULL)),
@@ -30,7 +29,8 @@ class HDF5OutputLayerTest : public ::testing::Test {
         num_(5),
         channels_(8),
         height_(5),
-        width_(5) {}
+        width_(5) {
+  }
 
   virtual ~HDF5OutputLayerTest() {
     delete blob_data_;
@@ -51,9 +51,9 @@ class HDF5OutputLayerTest : public ::testing::Test {
   int width_;
 };
 
-template <typename Dtype>
-void HDF5OutputLayerTest<Dtype>::CheckBlobEqual(
-    const Blob<Dtype>& b1, const Blob<Dtype>& b2) {
+template<typename TypeParam>
+void HDF5OutputLayerTest<TypeParam>::CheckBlobEqual(const Blob<Dtype>& b1,
+                                                    const Blob<Dtype>& b2) {
   EXPECT_EQ(b1.num(), b2.num());
   EXPECT_EQ(b1.channels(), b2.channels());
   EXPECT_EQ(b1.height(), b2.height());
@@ -62,64 +62,61 @@ void HDF5OutputLayerTest<Dtype>::CheckBlobEqual(
     for (int c = 0; c < b1.channels(); ++c) {
       for (int h = 0; h < b1.height(); ++h) {
         for (int w = 0; w < b1.width(); ++w) {
-          EXPECT_EQ(b1.data_at(n, c, h, w), b1.data_at(n, c, h, w));
+          EXPECT_EQ(b1.data_at(n, c, h, w), b2.data_at(n, c, h, w));
         }
       }
     }
   }
 }
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(HDF5OutputLayerTest, Dtypes);
+TYPED_TEST_CASE(HDF5OutputLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(HDF5OutputLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
   LOG(INFO) << "Loading HDF5 file " << this->input_file_name_;
   hid_t file_id = H5Fopen(this->input_file_name_.c_str(), H5F_ACC_RDONLY,
                           H5P_DEFAULT);
-  ASSERT_GE(file_id, 0) << "Failed to open HDF5 file" <<
+  ASSERT_GE(file_id, 0)<< "Failed to open HDF5 file" <<
       this->input_file_name_;
   hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
                        this->blob_data_);
   hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
                        this->blob_label_);
   herr_t status = H5Fclose(file_id);
-  EXPECT_GE(status, 0) << "Failed to close HDF5 file " <<
+  EXPECT_GE(status, 0)<< "Failed to close HDF5 file " <<
       this->input_file_name_;
   this->blob_bottom_vec_.push_back(this->blob_data_);
   this->blob_bottom_vec_.push_back(this->blob_label_);
 
-  Caffe::Brew modes[] = { Caffe::CPU, Caffe::GPU };
-  for (int m = 0; m < 2; ++m) {
-    Caffe::set_mode(modes[m]);
-    LayerParameter param;
-    param.mutable_hdf5_output_param()->set_file_name(this->output_file_name_);
-    // This code block ensures that the layer is deconstructed and
-    //   the output hdf5 file is closed.
-    {
-      HDF5OutputLayer<TypeParam> layer(param);
-      EXPECT_EQ(layer.file_name(), this->output_file_name_);
-      layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-      layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    }
-    hid_t file_id = H5Fopen(this->output_file_name_.c_str(), H5F_ACC_RDONLY,
-                            H5P_DEFAULT);
-    ASSERT_GE(file_id, 0) << "Failed to open HDF5 file" <<
-        this->input_file_name_;
-
-    Blob<TypeParam>* blob_data = new Blob<TypeParam>();
-    hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
-                         blob_data);
-    this->CheckBlobEqual(*(this->blob_data_), *blob_data);
-
-    Blob<TypeParam>* blob_label = new Blob<TypeParam>();
-    hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
-                         blob_label);
-    this->CheckBlobEqual(*(this->blob_label_), *blob_label);
-
-    herr_t status = H5Fclose(file_id);
-    EXPECT_GE(status, 0) << "Failed to close HDF5 file " <<
-        this->output_file_name_;
+  LayerParameter param;
+  param.mutable_hdf5_output_param()->set_file_name(this->output_file_name_);
+  // This code block ensures that the layer is deconstructed and
+  //   the output hdf5 file is closed.
+  {
+    HDF5OutputLayer<Dtype> layer(param);
+    EXPECT_EQ(layer.file_name(), this->output_file_name_);
+    layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
   }
+  file_id = H5Fopen(this->output_file_name_.c_str(), H5F_ACC_RDONLY,
+                          H5P_DEFAULT);
+  ASSERT_GE(
+    file_id, 0)<< "Failed to open HDF5 file" <<
+          this->input_file_name_;
+
+  Blob<Dtype>* blob_data = new Blob<Dtype>();
+  hdf5_load_nd_dataset(file_id, HDF5_DATA_DATASET_NAME, 0, 4,
+                       blob_data);
+  this->CheckBlobEqual(*(this->blob_data_), *blob_data);
+
+  Blob<Dtype>* blob_label = new Blob<Dtype>();
+  hdf5_load_nd_dataset(file_id, HDF5_DATA_LABEL_NAME, 0, 4,
+                       blob_label);
+  this->CheckBlobEqual(*(this->blob_label_), *blob_label);
+
+  status = H5Fclose(file_id);
+  EXPECT_GE(status, 0) << "Failed to close HDF5 file " <<
+      this->output_file_name_;
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index a0ed113b36e..f903afb736f 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -1,32 +1,31 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <string>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "leveldb/db.h"
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/vision_layers.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 using std::string;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class HDF5DataLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class HDF5DataLayerTest : public ::testing::Test {
  protected:
   HDF5DataLayerTest()
-      : blob_top_data_(new Blob<Dtype>()),
-        blob_top_label_(new Blob<Dtype>()),
-        filename(NULL) {}
+      : filename(NULL),
+        blob_top_data_(new Blob<Dtype>()),
+        blob_top_label_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     blob_top_vec_.push_back(blob_top_data_);
     blob_top_vec_.push_back(blob_top_label_);
@@ -49,10 +48,10 @@ class HDF5DataLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(HDF5DataLayerTest, Dtypes);
+TYPED_TEST_CASE(HDF5DataLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(HDF5DataLayerTest, TestRead) {
+  typedef typename TypeParam::Dtype Dtype;
   // Create LayerParameter with the known parameters.
   // The data file we are reading has 10 rows and 8 columns,
   // with values from 0 to 10*8 reshaped in row-major order.
@@ -61,13 +60,12 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   int batch_size = 5;
   hdf5_data_param->set_batch_size(batch_size);
   hdf5_data_param->set_source(*(this->filename));
-  int num_rows = 10;
   int num_cols = 8;
   int height = 5;
   int width = 5;
 
   // Test that the layer setup got the correct parameters.
-  HDF5DataLayer<TypeParam> layer(param);
+  HDF5DataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), batch_size);
   EXPECT_EQ(this->blob_top_data_->channels(), num_cols);
@@ -79,49 +77,42 @@ TYPED_TEST(HDF5DataLayerTest, TestRead) {
   EXPECT_EQ(this->blob_top_label_->height(), 1);
   EXPECT_EQ(this->blob_top_label_->width(), 1);
 
-  for (int t = 0; t < 2; ++t) {
-    // TODO: make this a TypedTest instead of this silly loop.
-    if (t == 0) {
-      Caffe::set_mode(Caffe::CPU);
-    } else {
-      Caffe::set_mode(Caffe::GPU);
+  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
+
+  // Go through the data 10 times (5 batches).
+  const int data_size = num_cols * height * width;
+  for (int iter = 0; iter < 10; ++iter) {
+    layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
+
+    // On even iterations, we're reading the first half of the data.
+    // On odd iterations, we're reading the second half of the data.
+    // NB: label is 1-indexed
+    int label_offset = 1 + ((iter % 2 == 0) ? 0 : batch_size);
+    int data_offset = (iter % 2 == 0) ? 0 : batch_size * data_size;
+
+    // Every two iterations we are reading the second file,
+    // which has the same labels, but data is offset by total data size,
+    // which is 2000 (see generate_sample_data).
+    int file_offset = (iter % 4 < 2) ? 0 : 2000;
+
+    for (int i = 0; i < batch_size; ++i) {
+      EXPECT_EQ(
+        label_offset + i,
+        this->blob_top_label_->cpu_data()[i]);
     }
-    layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-
-    // Go through the data 10 times (5 batches).
-    const int data_size = num_cols * height * width;
-    for (int iter = 0; iter < 10; ++iter) {
-      layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-
-      // On even iterations, we're reading the first half of the data.
-      // On odd iterations, we're reading the second half of the data.
-      int label_offset = (iter % 2 == 0) ? 0 : batch_size;
-      int data_offset = (iter % 2 == 0) ? 0 : batch_size * data_size;
-
-      // Every two iterations we are reading the second file,
-      // which has the same labels, but data is offset by total data size,
-      // which is 2000 (see generate_sample_data).
-      int file_offset = (iter % 4 < 2) ? 0 : 2000;
-
-      for (int i = 0; i < batch_size; ++i) {
-        EXPECT_EQ(
-          label_offset + i,
-          this->blob_top_label_->cpu_data()[i]);
-      }
-      for (int i = 0; i < batch_size; ++i) {
-        for (int j = 0; j < num_cols; ++j) {
-          for (int h = 0; h < height; ++h) {
-            for (int w = 0; w < width; ++w) {
-              int idx = (
-                i * num_cols * height * width +
-                j * height * width +
-                h * width + w);
-              EXPECT_EQ(
-                file_offset + data_offset + idx,
-                this->blob_top_data_->cpu_data()[idx])
-                << "debug: i " << i << " j " << j
-                << " iter " << iter << " t " << t;
-            }
+    for (int i = 0; i < batch_size; ++i) {
+      for (int j = 0; j < num_cols; ++j) {
+        for (int h = 0; h < height; ++h) {
+          for (int w = 0; w < width; ++w) {
+            int idx = (
+              i * num_cols * height * width +
+              j * height * width +
+              h * width + w);
+            EXPECT_EQ(
+              file_offset + data_offset + idx,
+              this->blob_top_data_->cpu_data()[idx])
+              << "debug: i " << i << " j " << j
+              << " iter " << iter;
           }
         }
       }
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index 1725827755f..8f6f6f78cf1 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -1,31 +1,30 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class HingeLossLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class HingeLossLayerTest : public ::testing::Test {
  protected:
   HingeLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
         blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)) {
     // fill the values
+    Caffe::set_random_seed(1701);
     FillerParameter filler_param;
     filler_param.set_std(10);
     GaussianFiller<Dtype> filler(filler_param);
@@ -46,28 +45,31 @@ class HingeLossLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(HingeLossLayerTest, Dtypes);
+TYPED_TEST_CASE(HingeLossLayerTest, TestDtypesAndDevices);
 
 
-TYPED_TEST(HingeLossLayerTest, TestGradientCPU) {
+TYPED_TEST(HingeLossLayerTest, TestGradientL1) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  HingeLossLayer<TypeParam> layer(layer_param);
+  HingeLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 1, 0.01);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 1, 0.01);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), 0, -1, -1);
 }
 
-TYPED_TEST(HingeLossLayerTest, TestGradientGPU) {
+TYPED_TEST(HingeLossLayerTest, TestGradientL2) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  HingeLossLayer<TypeParam> layer(layer_param);
+  // Set norm to L2
+  HingeLossParameter* hinge_loss_param = layer_param.mutable_hinge_loss_param();
+  hinge_loss_param->set_norm(HingeLossParameter_Norm_L2);
+  HingeLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 1, 0.01);
+  GradientChecker<Dtype> checker(1e-2, 2e-3, 1701);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), 0, -1, -1);
 }
 
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
new file mode 100644
index 00000000000..ee684c00255
--- /dev/null
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -0,0 +1,127 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/util/im2col.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+// Forward declare kernel functions
+template <typename Dtype>
+__global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    const int height_col, const int width_col,
+    Dtype* data_col);
+
+extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+
+template <typename Dtype>
+class Im2colKernelTest : public ::testing::Test {
+ protected:
+  Im2colKernelTest()
+        // big so launches > 1024 threads
+      : blob_bottom_(new Blob<Dtype>(5, 500, 10, 10)),
+        blob_top_(new Blob<Dtype>()),
+        blob_top_cpu_(new Blob<Dtype>()) {
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+
+    height_ = blob_bottom_->height();
+    width_ = blob_bottom_->width();
+    channels_ = blob_bottom_->channels();
+    pad_ = 0;
+    stride_ = 2;
+    kernel_size_ = 3;
+    height_col_ = (height_ + 2 * pad_ - kernel_size_) / stride_ + 1;
+    width_col_ = (width_ + 2 * pad_ - kernel_size_) / stride_ + 1;
+  }
+
+  virtual ~Im2colKernelTest() {
+      delete blob_bottom_;
+      delete blob_top_;
+      delete blob_top_cpu_;
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_cpu_;
+  int height_;
+  int width_;
+  int channels_;
+  int pad_;
+  int stride_;
+  int kernel_size_;
+  int height_col_;
+  int width_col_;
+};
+
+TYPED_TEST_CASE(Im2colKernelTest, TestDtypes);
+
+TYPED_TEST(Im2colKernelTest, TestGPU) {
+  Caffe::set_mode(Caffe::GPU);
+
+  // Reshape the blobs to correct size for im2col output
+  this->blob_top_->Reshape(this->blob_bottom_->num(),
+          this->channels_ * this->kernel_size_ * this->kernel_size_,
+          this->height_col_,
+          this->width_col_);
+
+  this->blob_top_cpu_->Reshape(this->blob_bottom_->num(),
+          this->channels_ * this->kernel_size_ * this->kernel_size_,
+          this->height_col_,
+          this->width_col_);
+
+  const TypeParam* bottom_data = this->blob_bottom_->gpu_data();
+  TypeParam* top_data = this->blob_top_->mutable_gpu_data();
+  TypeParam* cpu_data = this->blob_top_cpu_->mutable_cpu_data();
+
+  // CPU Version
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    im2col_cpu(this->blob_bottom_->cpu_data() + this->blob_bottom_->offset(n),
+      this->channels_, this->height_, this->width_,
+      this->kernel_size_, this->kernel_size_, this->pad_, this->pad_,
+      this->stride_, this->stride_,
+      cpu_data + this->blob_top_cpu_->offset(n));
+  }
+
+  // GPU version
+  int num_kernels = this->channels_ * this->height_col_ * this->width_col_;
+  int default_grid_dim = CAFFE_GET_BLOCKS(num_kernels);
+
+  // Launch with different grid sizes
+  for (int grid_div = 2; grid_div <= 8; grid_div++) {
+    for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+      int grid_dim = default_grid_dim/grid_div;
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      im2col_gpu_kernel<TypeParam><<<grid_dim, CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, bottom_data + this->blob_bottom_->offset(n),
+        this->height_, this->width_, this->kernel_size_, this->kernel_size_,
+        this->pad_, this->pad_, this->stride_, this->stride_,
+        this->height_col_, this->width_col_,
+        top_data + this->blob_top_->offset(n));
+      CUDA_POST_KERNEL_CHECK;
+    }
+
+    // Compare results against CPU version
+    for (int i = 0; i < this->blob_top_->count(); ++i) {
+      TypeParam cpuval = cpu_data[i];
+      TypeParam gpuval = this->blob_top_->cpu_data()[i];
+      EXPECT_EQ(cpuval, gpuval);
+      if (cpuval != gpuval) {
+        break;
+      }
+    }
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index 7f677ca03d6..32cf6369361 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -1,24 +1,21 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
-template <typename Dtype>
-class Im2colLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class Im2colLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   Im2colLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
@@ -37,16 +34,16 @@ class Im2colLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(Im2colLayerTest, Dtypes);
+TYPED_TEST_CASE(Im2colLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(Im2colLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
-  Im2colLayer<TypeParam> layer(layer_param);
+  Im2colLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 27);
@@ -54,65 +51,68 @@ TYPED_TEST(Im2colLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_->width(), 2);
 }
 
-TYPED_TEST(Im2colLayerTest, TestCPU) {
+TYPED_TEST(Im2colLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
-  Im2colLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
+  Im2colLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // We are lazy and will only check the top left block
   for (int c = 0; c < 27; ++c) {
-    EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
-        this->blob_bottom_->data_at(0, (c / 9), (c / 3) % 3, c % 3));
+    EXPECT_EQ(this->blob_bottom_->data_at(0, (c / 9), (c / 3) % 3, c % 3),
+        this->blob_top_->data_at(0, c, 0, 0));
   }
 }
 
-TYPED_TEST(Im2colLayerTest, TestGPU) {
+TYPED_TEST(Im2colLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_size(3);
   convolution_param->set_stride(2);
-  Im2colLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // We are lazy and will only check the top left block
-  for (int c = 0; c < 27; ++c) {
-    EXPECT_EQ(this->blob_bottom_->data_at(0, (c / 9), (c / 3) % 3, c % 3),
-        this->blob_top_->data_at(0, c, 0, 0));
-  }
+  Im2colLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
 }
 
-TYPED_TEST(Im2colLayerTest, TestCPUGradient) {
+
+TYPED_TEST(Im2colLayerTest, TestRect) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
+  convolution_param->set_kernel_h(5);
+  convolution_param->set_kernel_w(3);
   convolution_param->set_stride(2);
-  Caffe::set_mode(Caffe::CPU);
-  Im2colLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+  Im2colLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // We are lazy and will only check the top left block
+  for (int c = 0; c < 45; ++c) {
+    EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
+        this->blob_bottom_->data_at(0, (c / 15), (c / 3) % 5, c % 3));
+  }
 }
 
-TYPED_TEST(Im2colLayerTest, TestGPUGradient) {
+
+TYPED_TEST(Im2colLayerTest, TestRectGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
+  convolution_param->set_kernel_h(5);
+  convolution_param->set_kernel_w(3);
   convolution_param->set_stride(2);
-  Caffe::set_mode(Caffe::GPU);
-  Im2colLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
+  Im2colLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-
 }  // namespace caffe
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 42a1d0358db..5232c1187c1 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -1,19 +1,17 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <cuda_runtime.h>
-
-#include <iostream>  // NOLINT(readability/streams)
 #include <fstream>  // NOLINT(readability/streams)
+#include <iostream>  // NOLINT(readability/streams)
 #include <map>
 #include <string>
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/vision_layers.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 using std::map;
@@ -21,16 +19,16 @@ using std::string;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class ImageDataLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class ImageDataLayerTest : public ::testing::Test {
  protected:
   ImageDataLayerTest()
-      : blob_top_data_(new Blob<Dtype>()),
-        blob_top_label_(new Blob<Dtype>()),
+      : seed_(1701),
         filename_(new string(tmpnam(NULL))),
-        seed_(1701) {}
+        blob_top_data_(new Blob<Dtype>()),
+        blob_top_label_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     blob_top_vec_.push_back(blob_top_data_);
     blob_top_vec_.push_back(blob_top_label_);
@@ -57,21 +55,21 @@ class ImageDataLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(ImageDataLayerTest, Dtypes);
+TYPED_TEST_CASE(ImageDataLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(ImageDataLayerTest, TestRead) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter param;
   ImageDataParameter* image_data_param = param.mutable_image_data_param();
   image_data_param->set_batch_size(5);
   image_data_param->set_source(this->filename_->c_str());
   image_data_param->set_shuffle(false);
-  ImageDataLayer<TypeParam> layer(param);
+  ImageDataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
-  EXPECT_EQ(this->blob_top_data_->height(), 1200);
-  EXPECT_EQ(this->blob_top_data_->width(), 1600);
+  EXPECT_EQ(this->blob_top_data_->height(), 360);
+  EXPECT_EQ(this->blob_top_data_->width(), 480);
   EXPECT_EQ(this->blob_top_label_->num(), 5);
   EXPECT_EQ(this->blob_top_label_->channels(), 1);
   EXPECT_EQ(this->blob_top_label_->height(), 1);
@@ -86,6 +84,7 @@ TYPED_TEST(ImageDataLayerTest, TestRead) {
 }
 
 TYPED_TEST(ImageDataLayerTest, TestResize) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter param;
   ImageDataParameter* image_data_param = param.mutable_image_data_param();
   image_data_param->set_batch_size(5);
@@ -93,7 +92,7 @@ TYPED_TEST(ImageDataLayerTest, TestResize) {
   image_data_param->set_new_height(256);
   image_data_param->set_new_width(256);
   image_data_param->set_shuffle(false);
-  ImageDataLayer<TypeParam> layer(param);
+  ImageDataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
@@ -113,17 +112,18 @@ TYPED_TEST(ImageDataLayerTest, TestResize) {
 }
 
 TYPED_TEST(ImageDataLayerTest, TestShuffle) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter param;
   ImageDataParameter* image_data_param = param.mutable_image_data_param();
   image_data_param->set_batch_size(5);
   image_data_param->set_source(this->filename_->c_str());
   image_data_param->set_shuffle(true);
-  ImageDataLayer<TypeParam> layer(param);
+  ImageDataLayer<Dtype> layer(param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_data_->num(), 5);
   EXPECT_EQ(this->blob_top_data_->channels(), 3);
-  EXPECT_EQ(this->blob_top_data_->height(), 1200);
-  EXPECT_EQ(this->blob_top_data_->width(), 1600);
+  EXPECT_EQ(this->blob_top_data_->height(), 360);
+  EXPECT_EQ(this->blob_top_data_->width(), 480);
   EXPECT_EQ(this->blob_top_label_->num(), 5);
   EXPECT_EQ(this->blob_top_label_->channels(), 1);
   EXPECT_EQ(this->blob_top_label_->height(), 1);
@@ -131,14 +131,14 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
   // Go through the data twice
   for (int iter = 0; iter < 2; ++iter) {
     layer.Forward(this->blob_bottom_vec_, &this->blob_top_vec_);
-    map<TypeParam, int> values_to_indices;
+    map<Dtype, int> values_to_indices;
     int num_in_order = 0;
     for (int i = 0; i < 5; ++i) {
-      TypeParam value = this->blob_top_label_->cpu_data()[i];
+      Dtype value = this->blob_top_label_->cpu_data()[i];
       // Check that the value has not been seen already (no duplicates).
       EXPECT_EQ(values_to_indices.find(value), values_to_indices.end());
       values_to_indices[value] = i;
-      num_in_order += (value == TypeParam(i));
+      num_in_order += (value == Dtype(i));
     }
     EXPECT_EQ(5, values_to_indices.size());
     EXPECT_GT(5, num_in_order);
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
new file mode 100644
index 00000000000..162d0e6c589
--- /dev/null
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -0,0 +1,66 @@
+#include <cmath>
+#include <cstdlib>
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/loss_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class InfogainLossLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  InfogainLossLayerTest()
+      : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
+        blob_bottom_label_(new Blob<Dtype>(10, 1, 1, 1)),
+        blob_bottom_infogain_(new Blob<Dtype>(1, 1, 5, 5)) {
+    Caffe::set_random_seed(1701);
+    FillerParameter filler_param;
+    PositiveUnitballFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_data_);
+    blob_bottom_vec_.push_back(blob_bottom_data_);
+    for (int i = 0; i < blob_bottom_label_->count(); ++i) {
+      blob_bottom_label_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
+    }
+    blob_bottom_vec_.push_back(blob_bottom_label_);
+    filler_param.set_min(0.1);
+    filler_param.set_max(2.0);
+    UniformFiller<Dtype> infogain_filler(filler_param);
+    infogain_filler.Fill(this->blob_bottom_infogain_);
+    blob_bottom_vec_.push_back(blob_bottom_infogain_);
+  }
+  virtual ~InfogainLossLayerTest() {
+    delete blob_bottom_data_;
+    delete blob_bottom_label_;
+    delete blob_bottom_infogain_;
+  }
+  Blob<Dtype>* const blob_bottom_data_;
+  Blob<Dtype>* const blob_bottom_label_;
+  Blob<Dtype>* const blob_bottom_infogain_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(InfogainLossLayerTest, TestDtypesAndDevices);
+
+
+TYPED_TEST(InfogainLossLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  InfogainLossLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-4, 2e-2, 1701, 1, 0.01);
+  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_), 0, -1, -1);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index 91917df6cae..5f9729c4f90 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -1,24 +1,25 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
+#ifndef CPU_ONLY
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+#endif
 
-template <typename Dtype>
-class InnerProductLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class InnerProductLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   InnerProductLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -37,16 +38,16 @@ class InnerProductLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(InnerProductLayerTest, Dtypes);
+TYPED_TEST_CASE(InnerProductLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(InnerProductLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   InnerProductParameter* inner_product_param =
       layer_param.mutable_inner_product_param();
   inner_product_param->set_num_output(10);
-  shared_ptr<InnerProductLayer<TypeParam> > layer(
-      new InnerProductLayer<TypeParam>(layer_param));
+  shared_ptr<InnerProductLayer<Dtype> > layer(
+      new InnerProductLayer<Dtype>(layer_param));
   layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->height(), 1);
@@ -54,43 +55,27 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) {
   EXPECT_EQ(this->blob_top_->channels(), 10);
 }
 
-TYPED_TEST(InnerProductLayerTest, TestCPU) {
-  LayerParameter layer_param;
-  InnerProductParameter* inner_product_param =
-      layer_param.mutable_inner_product_param();
-  Caffe::set_mode(Caffe::CPU);
-  inner_product_param->set_num_output(10);
-  inner_product_param->mutable_weight_filler()->set_type("uniform");
-  inner_product_param->mutable_bias_filler()->set_type("uniform");
-  inner_product_param->mutable_bias_filler()->set_min(1);
-  inner_product_param->mutable_bias_filler()->set_max(2);
-  shared_ptr<InnerProductLayer<TypeParam> > layer(
-      new InnerProductLayer<TypeParam>(layer_param));
-  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  const TypeParam* data = this->blob_top_->cpu_data();
-  const int count = this->blob_top_->count();
-  for (int i = 0; i < count; ++i) {
-    EXPECT_GE(data[i], 1.);
-  }
-}
-
-TYPED_TEST(InnerProductLayerTest, TestGPU) {
-  if (sizeof(TypeParam) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
+TYPED_TEST(InnerProductLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
     LayerParameter layer_param;
     InnerProductParameter* inner_product_param =
         layer_param.mutable_inner_product_param();
-    Caffe::set_mode(Caffe::GPU);
     inner_product_param->set_num_output(10);
     inner_product_param->mutable_weight_filler()->set_type("uniform");
     inner_product_param->mutable_bias_filler()->set_type("uniform");
     inner_product_param->mutable_bias_filler()->set_min(1);
     inner_product_param->mutable_bias_filler()->set_max(2);
-    shared_ptr<InnerProductLayer<TypeParam> > layer(
-      new InnerProductLayer<TypeParam>(layer_param));
+    shared_ptr<InnerProductLayer<Dtype> > layer(
+        new InnerProductLayer<Dtype>(layer_param));
     layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
     layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-    const TypeParam* data = this->blob_top_->cpu_data();
+    const Dtype* data = this->blob_top_->cpu_data();
     const int count = this->blob_top_->count();
     for (int i = 0; i < count; ++i) {
       EXPECT_GE(data[i], 1.);
@@ -100,34 +85,25 @@ TYPED_TEST(InnerProductLayerTest, TestGPU) {
   }
 }
 
-TYPED_TEST(InnerProductLayerTest, TestCPUGradient) {
-  LayerParameter layer_param;
-  InnerProductParameter* inner_product_param =
-      layer_param.mutable_inner_product_param();
-  Caffe::set_mode(Caffe::CPU);
-  inner_product_param->set_num_output(10);
-  inner_product_param->mutable_weight_filler()->set_type("gaussian");
-  inner_product_param->mutable_bias_filler()->set_type("gaussian");
-  inner_product_param->mutable_bias_filler()->set_min(1);
-  inner_product_param->mutable_bias_filler()->set_max(2);
-  InnerProductLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(InnerProductLayerTest, TestGPUGradient) {
-  if (sizeof(TypeParam) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
+TYPED_TEST(InnerProductLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
     LayerParameter layer_param;
     InnerProductParameter* inner_product_param =
         layer_param.mutable_inner_product_param();
-    Caffe::set_mode(Caffe::GPU);
     inner_product_param->set_num_output(10);
     inner_product_param->mutable_weight_filler()->set_type("gaussian");
     inner_product_param->mutable_bias_filler()->set_type("gaussian");
-    InnerProductLayer<TypeParam> layer(layer_param);
-    GradientChecker<TypeParam> checker(1e-2, 1e-2);
-    checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    InnerProductLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-3);
+    checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
         &(this->blob_top_vec_));
   } else {
     LOG(ERROR) << "Skipping test due to old architecture.";
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index 1923128dd71..3bd62fd9e18 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -1,33 +1,31 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 using std::min;
 using std::max;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class LRNLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class LRNLayerTest : public ::testing::Test {
  protected:
   LRNLayerTest()
-      : blob_bottom_(new Blob<Dtype>()),
-        blob_top_(new Blob<Dtype>()),
-        epsilon_(Dtype(1e-5)) {}
+      : epsilon_(Dtype(1e-5)),
+        blob_bottom_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     Caffe::set_random_seed(1701);
     blob_bottom_->Reshape(2, 7, 3, 3);
@@ -49,13 +47,13 @@ class LRNLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-template <typename Dtype>
-void LRNLayerTest<Dtype>::ReferenceLRNForward(
+template <typename TypeParam>
+void LRNLayerTest<TypeParam>::ReferenceLRNForward(
     const Blob<Dtype>& blob_bottom, const LayerParameter& layer_param,
     Blob<Dtype>* blob_top) {
+  typedef typename TypeParam::Dtype Dtype;
   blob_top->Reshape(blob_bottom.num(), blob_bottom.channels(),
       blob_bottom.height(), blob_bottom.width());
-  const Dtype* bottom_data = blob_bottom.cpu_data();
   Dtype* top_data = blob_top->mutable_cpu_data();
   LRNParameter lrn_param = layer_param.lrn_param();
   Dtype alpha = lrn_param.alpha();
@@ -112,12 +110,12 @@ void LRNLayerTest<Dtype>::ReferenceLRNForward(
   }
 }
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(LRNLayerTest, Dtypes);
+TYPED_TEST_CASE(LRNLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(LRNLayerTest, TestSetupAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  LRNLayer<TypeParam> layer(layer_param);
+  LRNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 7);
@@ -125,28 +123,13 @@ TYPED_TEST(LRNLayerTest, TestSetupAcrossChannels) {
   EXPECT_EQ(this->blob_top_->width(), 3);
 }
 
-TYPED_TEST(LRNLayerTest, TestCPUForwardAcrossChannels) {
-  LayerParameter layer_param;
-  LRNLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Blob<TypeParam> top_reference;
-  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
-      &top_reference);
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
-                this->epsilon_);
-  }
-}
-
-TYPED_TEST(LRNLayerTest, TestGPUForwardAcrossChannels) {
+TYPED_TEST(LRNLayerTest, TestForwardAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  LRNLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
+  LRNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Blob<TypeParam> top_reference;
+  Blob<Dtype> top_reference;
   this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
       &top_reference);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
@@ -155,17 +138,19 @@ TYPED_TEST(LRNLayerTest, TestGPUForwardAcrossChannels) {
   }
 }
 
-TYPED_TEST(LRNLayerTest, TestCPUGradientAcrossChannels) {
+TYPED_TEST(LRNLayerTest, TestGradientAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  LRNLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  Caffe::set_mode(Caffe::CPU);
+  LRNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 &(this->blob_bottom_vec_));
   // for (int i = 0; i < this->blob_bottom_->count(); ++i) {
   //   std::cout << "CPU diff " << this->blob_bottom_->cpu_diff()[i]
   //       << std::endl;
@@ -174,31 +159,13 @@ TYPED_TEST(LRNLayerTest, TestCPUGradientAcrossChannels) {
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(LRNLayerTest, TestGPUGradientAcrossChannels) {
-  LayerParameter layer_param;
-  LRNLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  Caffe::set_mode(Caffe::GPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int i = 0; i < this->blob_top_->count(); ++i) {
-    this->blob_top_->mutable_cpu_diff()[i] = 1.;
-  }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
-  // for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-  //   std::cout << "GPU diff " << this->blob_bottom_->cpu_diff()[i]
-  //       << std::endl;
-  // }
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
 TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_lrn_param()->set_norm_region(
       LRNParameter_NormRegion_WITHIN_CHANNEL);
   layer_param.mutable_lrn_param()->set_local_size(3);
-  LRNLayer<TypeParam> layer(layer_param);
+  LRNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 7);
@@ -206,16 +173,16 @@ TYPED_TEST(LRNLayerTest, TestSetupWithinChannel) {
   EXPECT_EQ(this->blob_top_->width(), 3);
 }
 
-TYPED_TEST(LRNLayerTest, TestCPUForwardWithinChannel) {
+TYPED_TEST(LRNLayerTest, TestForwardWithinChannel) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_lrn_param()->set_norm_region(
       LRNParameter_NormRegion_WITHIN_CHANNEL);
   layer_param.mutable_lrn_param()->set_local_size(3);
-  LRNLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
+  LRNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Blob<TypeParam> top_reference;
+  Blob<Dtype> top_reference;
   this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
       &top_reference);
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
@@ -224,56 +191,19 @@ TYPED_TEST(LRNLayerTest, TestCPUForwardWithinChannel) {
   }
 }
 
-TYPED_TEST(LRNLayerTest, TestGPUForwardWithinChannel) {
-  LayerParameter layer_param;
-  layer_param.mutable_lrn_param()->set_norm_region(
-      LRNParameter_NormRegion_WITHIN_CHANNEL);
-  layer_param.mutable_lrn_param()->set_local_size(3);
-  LRNLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  Blob<TypeParam> top_reference;
-  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
-      &top_reference);
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
-                this->epsilon_);
-  }
-}
-
-TYPED_TEST(LRNLayerTest, TestCPUGradientWithinChannel) {
-  LayerParameter layer_param;
-  layer_param.mutable_lrn_param()->set_norm_region(
-      LRNParameter_NormRegion_WITHIN_CHANNEL);
-  layer_param.mutable_lrn_param()->set_local_size(3);
-  LRNLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  Caffe::set_mode(Caffe::CPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int i = 0; i < this->blob_top_->count(); ++i) {
-    this->blob_top_->mutable_cpu_diff()[i] = 1.;
-  }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(LRNLayerTest, TestGPUGradientWithinChannel) {
+TYPED_TEST(LRNLayerTest, TestGradientWithinChannel) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   layer_param.mutable_lrn_param()->set_norm_region(
       LRNParameter_NormRegion_WITHIN_CHANNEL);
   layer_param.mutable_lrn_param()->set_local_size(3);
-  LRNLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  Caffe::set_mode(Caffe::GPU);
+  LRNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_top_->count(); ++i) {
     this->blob_top_->mutable_cpu_diff()[i] = 1.;
   }
-  layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index d0265767c07..d10e702e79a 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <stdint.h>  // for uint32_t & uint64_t
 #include <time.h>
 #include <climits>
@@ -7,6 +5,7 @@
 #include <cstdlib>  // for rand_r
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
@@ -65,8 +64,7 @@ class MathFunctionsTest : public ::testing::Test {
   Blob<Dtype>* const blob_top_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(MathFunctionsTest, Dtypes);
+TYPED_TEST_CASE(MathFunctionsTest, TestDtypes);
 
 TYPED_TEST(MathFunctionsTest, TestNothing) {
   // The first test case of a test suite takes the longest time
@@ -81,18 +79,6 @@ TYPED_TEST(MathFunctionsTest, TestHammingDistanceCPU) {
             caffe_cpu_hamming_distance<TypeParam>(n, x, y));
 }
 
-// TODO: Fix caffe_gpu_hamming_distance and re-enable this test.
-TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) {
-  int n = this->blob_bottom_->count();
-  const TypeParam* x = this->blob_bottom_->cpu_data();
-  const TypeParam* y = this->blob_top_->cpu_data();
-  int reference_distance = this->ReferenceHammingDistance(n, x, y);
-  x = this->blob_bottom_->gpu_data();
-  y = this->blob_top_->gpu_data();
-  int computed_distance = caffe_gpu_hamming_distance<TypeParam>(n, x, y);
-  EXPECT_EQ(reference_distance, computed_distance);
-}
-
 TYPED_TEST(MathFunctionsTest, TestAsumCPU) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
@@ -104,91 +90,116 @@ TYPED_TEST(MathFunctionsTest, TestAsumCPU) {
   EXPECT_LT((cpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestAsumGPU) {
+TYPED_TEST(MathFunctionsTest, TestSignCPU) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
-  TypeParam std_asum = 0;
+  caffe_cpu_sign<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
+  const TypeParam* signs = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < n; ++i) {
-    std_asum += std::fabs(x[i]);
+    EXPECT_EQ(signs[i], x[i] > 0 ? 1 : (x[i] < 0 ? -1 : 0));
   }
-  TypeParam gpu_asum;
-  caffe_gpu_asum<TypeParam>(n, this->blob_bottom_->gpu_data(), &gpu_asum);
-  EXPECT_LT((gpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestSignCPU) {
+TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
-  caffe_cpu_sign<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
-  const TypeParam* signs = this->blob_bottom_->cpu_diff();
+  caffe_cpu_sgnbit<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
+  const TypeParam* signbits = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(signs[i], x[i] > 0 ? 1 : (x[i] < 0 ? -1 : 0));
+    EXPECT_EQ(signbits[i], x[i] < 0 ? 1 : 0);
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSignGPU) {
+TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
   int n = this->blob_bottom_->count();
-  caffe_gpu_sign<TypeParam>(n, this->blob_bottom_->gpu_data(),
-                            this->blob_bottom_->mutable_gpu_diff());
-  const TypeParam* signs = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
+  caffe_cpu_fabs<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
+  const TypeParam* abs_val = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(signs[i], x[i] > 0 ? 1 : (x[i] < 0 ? -1 : 0));
+    EXPECT_EQ(abs_val[i], x[i] > 0 ? x[i] : -x[i]);
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
+TYPED_TEST(MathFunctionsTest, TestScaleCPU) {
   int n = this->blob_bottom_->count();
+  TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
+                                                   this->blob_bottom_->count()];
+  caffe_cpu_scale<TypeParam>(n, alpha, this->blob_bottom_->cpu_data(),
+                             this->blob_bottom_->mutable_cpu_diff());
+  const TypeParam* scaled = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
-  caffe_cpu_sgnbit<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
-  const TypeParam* signbits = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(signbits[i], x[i] < 0 ? 1 : 0);
+    EXPECT_EQ(scaled[i], x[i] * alpha);
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
+TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
+  const int n = this->blob_bottom_->count();
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
+  TypeParam* top_data = this->blob_top_->mutable_cpu_data();
+  Caffe::set_mode(Caffe::CPU);
+  caffe_copy(n, bottom_data, top_data);
+  for (int i = 0; i < n; ++i) {
+    EXPECT_EQ(bottom_data[i], top_data[i]);
+  }
+}
+
+#ifndef CPU_ONLY
+
+// TODO: Fix caffe_gpu_hamming_distance and re-enable this test.
+TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) {
+  int n = this->blob_bottom_->count();
+  const TypeParam* x = this->blob_bottom_->cpu_data();
+  const TypeParam* y = this->blob_top_->cpu_data();
+  int reference_distance = this->ReferenceHammingDistance(n, x, y);
+  x = this->blob_bottom_->gpu_data();
+  y = this->blob_top_->gpu_data();
+  int computed_distance = caffe_gpu_hamming_distance<TypeParam>(n, x, y);
+  EXPECT_EQ(reference_distance, computed_distance);
+}
+
+TYPED_TEST(MathFunctionsTest, TestAsumGPU) {
   int n = this->blob_bottom_->count();
-  caffe_gpu_sgnbit<TypeParam>(n, this->blob_bottom_->gpu_data(),
-                            this->blob_bottom_->mutable_gpu_diff());
-  const TypeParam* signbits = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
+  TypeParam std_asum = 0;
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(signbits[i], x[i] < 0 ? 1 : 0);
+    std_asum += std::fabs(x[i]);
   }
+  TypeParam gpu_asum;
+  caffe_gpu_asum<TypeParam>(n, this->blob_bottom_->gpu_data(), &gpu_asum);
+  EXPECT_LT((gpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
+TYPED_TEST(MathFunctionsTest, TestSignGPU) {
   int n = this->blob_bottom_->count();
+  caffe_gpu_sign<TypeParam>(n, this->blob_bottom_->gpu_data(),
+                            this->blob_bottom_->mutable_gpu_diff());
+  const TypeParam* signs = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
-  caffe_cpu_fabs<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
-  const TypeParam* abs_val = this->blob_bottom_->cpu_diff();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(abs_val[i], x[i] > 0 ? x[i] : -x[i]);
+    EXPECT_EQ(signs[i], x[i] > 0 ? 1 : (x[i] < 0 ? -1 : 0));
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
+TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
   int n = this->blob_bottom_->count();
-  caffe_gpu_fabs<TypeParam>(n, this->blob_bottom_->gpu_data(),
+  caffe_gpu_sgnbit<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
-  const TypeParam* abs_val = this->blob_bottom_->cpu_diff();
+  const TypeParam* signbits = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(abs_val[i], x[i] > 0 ? x[i] : -x[i]);
+    EXPECT_EQ(signbits[i], x[i] < 0 ? 1 : 0);
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestScaleCPU) {
+TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
   int n = this->blob_bottom_->count();
-  TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
-                                                   this->blob_bottom_->count()];
-  caffe_cpu_scale<TypeParam>(n, alpha, this->blob_bottom_->cpu_data(),
-                             this->blob_bottom_->mutable_cpu_diff());
-  const TypeParam* scaled = this->blob_bottom_->cpu_diff();
+  caffe_gpu_fabs<TypeParam>(n, this->blob_bottom_->gpu_data(),
+                            this->blob_bottom_->mutable_gpu_diff());
+  const TypeParam* abs_val = this->blob_bottom_->cpu_diff();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(scaled[i], x[i] * alpha);
+    EXPECT_EQ(abs_val[i], x[i] > 0 ? x[i] : -x[i]);
   }
 }
 
@@ -205,21 +216,12 @@ TYPED_TEST(MathFunctionsTest, TestScaleGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
-  const int n = this->blob_bottom_->count();
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  TypeParam* top_data = this->blob_top_->mutable_cpu_data();
-  caffe_copy(n, bottom_data, top_data);
-  for (int i = 0; i < n; ++i) {
-    EXPECT_EQ(bottom_data[i], top_data[i]);
-  }
-}
-
 TYPED_TEST(MathFunctionsTest, TestCopyGPU) {
   const int n = this->blob_bottom_->count();
   const TypeParam* bottom_data = this->blob_bottom_->gpu_data();
   TypeParam* top_data = this->blob_top_->mutable_gpu_data();
-  caffe_gpu_copy(n, bottom_data, top_data);
+  Caffe::set_mode(Caffe::GPU);
+  caffe_copy(n, bottom_data, top_data);
   bottom_data = this->blob_bottom_->cpu_data();
   top_data = this->blob_top_->mutable_cpu_data();
   for (int i = 0; i < n; ++i) {
@@ -227,4 +229,7 @@ TYPED_TEST(MathFunctionsTest, TestCopyGPU) {
   }
 }
 
+#endif
+
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
new file mode 100644
index 00000000000..311c7781be5
--- /dev/null
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -0,0 +1,127 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class MaxPoolingDropoutTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  MaxPoolingDropoutTest()
+      : blob_bottom_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    Caffe::set_random_seed(1703);
+    blob_bottom_->Reshape(2, 3, 6, 5);
+    // fill the values
+    FillerParameter filler_param;
+    filler_param.set_value(1.);
+    ConstantFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~MaxPoolingDropoutTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(MaxPoolingDropoutTest, TestDtypesAndDevices);
+
+TYPED_TEST(MaxPoolingDropoutTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
+  PoolingLayer<Dtype> max_layer(layer_param);
+  max_layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  DropoutLayer<Dtype> dropout_layer(layer_param);
+  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
+  EXPECT_EQ(this->blob_top_->height(), 3);
+  EXPECT_EQ(this->blob_top_->width(), 2);
+}
+
+
+TYPED_TEST(MaxPoolingDropoutTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
+  PoolingLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  Dtype sum = 0.;
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    sum += top_data[i];
+  }
+  EXPECT_EQ(sum, this->blob_top_->count());
+  // Dropout in-place
+  DropoutLayer<Dtype> dropout_layer(layer_param);
+  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
+  sum = 0.;
+  Dtype scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
+  top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    sum += top_data[i];
+  }
+  EXPECT_GE(sum, 0);
+  EXPECT_LE(sum, this->blob_top_->count()*scale);
+}
+
+TYPED_TEST(MaxPoolingDropoutTest, TestBackward) {
+  typedef typename TypeParam::Dtype Dtype;
+  Caffe::set_phase(Caffe::TRAIN);
+  LayerParameter layer_param;
+  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+  pooling_param->set_kernel_size(3);
+  pooling_param->set_stride(2);
+  PoolingLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+  }
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 &(this->blob_bottom_vec_));
+  const Dtype* bottom_diff = this->blob_bottom_->cpu_diff();
+  Dtype sum = 0.;
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    sum += bottom_diff[i];
+  }
+  EXPECT_EQ(sum, this->blob_top_->count());
+  // Dropout in-place
+  DropoutLayer<Dtype> dropout_layer(layer_param);
+  dropout_layer.SetUp(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.Forward(this->blob_top_vec_, &(this->blob_top_vec_));
+  dropout_layer.Backward(this->blob_top_vec_, propagate_down,
+                         &(this->blob_top_vec_));
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 &(this->blob_bottom_vec_));
+  Dtype sum_with_dropout = 0.;
+  bottom_diff = this->blob_bottom_->cpu_diff();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    sum_with_dropout += bottom_diff[i];
+  }
+  EXPECT_GE(sum_with_dropout, sum);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index 15f01bd41e3..f401bb0f4f2 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -1,9 +1,8 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <vector>
 
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
@@ -12,9 +11,10 @@ template <typename Dtype>
 class MemoryDataLayerTest : public ::testing::Test {
  protected:
   MemoryDataLayerTest()
-    : data_blob_(new Blob<Dtype>()),
-      label_blob_(new Blob<Dtype>()),
-      data_(new Blob<Dtype>()), labels_(new Blob<Dtype>()) {}
+    : data_(new Blob<Dtype>()),
+      labels_(new Blob<Dtype>()),
+      data_blob_(new Blob<Dtype>()),
+      label_blob_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     batch_size_ = 8;
     batches_ = 12;
@@ -54,8 +54,7 @@ class MemoryDataLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(MemoryDataLayerTest, Dtypes);
+TYPED_TEST_CASE(MemoryDataLayerTest, TestDtypes);
 
 TYPED_TEST(MemoryDataLayerTest, TestSetup) {
   LayerParameter layer_param;
diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index aa475ca27c7..3d1037baed0 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -1,24 +1,20 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename Dtype>
 class MultinomialLogisticLossLayerTest : public ::testing::Test {
  protected:
@@ -46,8 +42,7 @@ class MultinomialLogisticLossLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(MultinomialLogisticLossLayerTest, Dtypes);
+TYPED_TEST_CASE(MultinomialLogisticLossLayerTest, TestDtypes);
 
 
 TYPED_TEST(MultinomialLogisticLossLayerTest, TestGradientCPU) {
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 4c7f0e7f7ac..18bc9ad7838 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -1,56 +1,52 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <google/protobuf/text_format.h>
-#include <leveldb/db.h>
-#include <sstream>
 #include <string>
+#include <utility>
+#include <vector>
+
+#include "google/protobuf/text_format.h"
 
 #include "gtest/gtest.h"
+
 #include "caffe/common.hpp"
 #include "caffe/net.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
+#include "caffe/util/math_functions.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
+template <typename TypeParam>
+class NetTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class NetTest : public ::testing::Test {
  protected:
-  virtual void SetUp() {  // Create the leveldb
-    filename_ = tmpnam(NULL);  // get temp name
-    LOG(INFO) << "Using temporary leveldb " << filename_;
-    leveldb::DB* db;
-    leveldb::Options options;
-    options.error_if_exists = true;
-    options.create_if_missing = true;
-    leveldb::Status status = leveldb::DB::Open(options, filename_, &db);
-    CHECK(status.ok());
-    for (int i = 0; i < 5; ++i) {
-      Datum datum;
-      datum.set_label(i);
-      datum.set_channels(2);
-      datum.set_height(3);
-      datum.set_width(4);
-      std::string* data = datum.mutable_data();
-      for (int j = 0; j < 24; ++j) {
-        data->push_back((uint8_t)i);
-      }
-      std::stringstream ss;
-      ss << i;
-      db->Put(leveldb::WriteOptions(), ss.str(), datum.SerializeAsString());
-    }
-    delete db;
+  NetTest() : seed_(1701) {}
+
+  virtual void InitNetFromProtoString(const string& proto) {
+    NetParameter param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    net_.reset(new Net<Dtype>(param));
+  }
 
-    const string& proto_prefix =
-        "name: 'TestNetwork' "
+  virtual void InitTinyNet(const bool force_backward = false) {
+    string proto =
+        "name: 'TinyTestNetwork' "
         "layers: { "
         "  name: 'data' "
-        "  type: DATA "
-        "  data_param { ";
-    const string& proto_suffix =
-        "    batch_size: 1 "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 5 "
+        "    channels: 2 "
+        "    height: 3 "
+        "    width: 4 "
+        "    num: 5 "
+        "    channels: 1 "
+        "    height: 1 "
+        "    width: 1 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
         "  } "
         "  top: 'data' "
         "  top: 'label' "
@@ -81,56 +77,1595 @@ class NetTest : public ::testing::Test {
         "  type: SOFTMAX_LOSS "
         "  bottom: 'innerproduct' "
         "  bottom: 'label' "
+        "  top: 'top_loss' "
+        "} ";
+    if (force_backward) {
+      proto += "force_backward: true ";
+    }
+    InitNetFromProtoString(proto);
+  }
+
+  virtual void InitTinyNetEuclidean(const bool force_backward = false) {
+    string proto =
+        "name: 'TinyTestEuclidLossNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 5 "
+        "    channels: 2 "
+        "    height: 3 "
+        "    width: 4 "
+        "    num: 5 "
+        "    channels: 1 "
+        "    height: 1 "
+        "    width: 1 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "  } "
+        "  top: 'data' "
+        "  top: 'label' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 1 "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "    bias_filler { "
+        "      type: 'constant' "
+        "      value: 0 "
+        "    } "
+        "  } "
+        "  blobs_lr: 1. "
+        "  blobs_lr: 2. "
+        "  weight_decay: 1. "
+        "  weight_decay: 0. "
+        "  bottom: 'data' "
+        "  top: 'innerproduct' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: EUCLIDEAN_LOSS "
+        "  bottom: 'innerproduct' "
+        "  bottom: 'label' "
+        "} ";
+    if (force_backward) {
+      proto += "force_backward: true ";
+    }
+    InitNetFromProtoString(proto);
+  }
+
+  virtual void InitTrickyNet() {
+    const string& proto =
+        "name: 'TrickyTestNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 5 "
+        "    channels: 2 "
+        "    height: 3 "
+        "    width: 4 "
+        "    num: 5 "
+        "    channels: 1 "
+        "    height: 1 "
+        "    width: 1 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "  } "
+        "  top: 'data' "
+        "  top: 'label' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 1000 "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "    bias_filler { "
+        "      type: 'constant' "
+        "      value: 0 "
+        "    } "
+        "  } "
+        "  blobs_lr: 1. "
+        "  blobs_lr: 2. "
+        "  weight_decay: 1. "
+        "  weight_decay: 0. "
+        "  bottom: 'data' "
+        "  top: 'transformed_data' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 1 "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "    bias_filler { "
+        "      type: 'constant' "
+        "      value: 0 "
+        "    } "
+        "  } "
+        "  blobs_lr: 1. "
+        "  blobs_lr: 2. "
+        "  weight_decay: 1. "
+        "  weight_decay: 0. "
+        "  bottom: 'label' "
+        "  top: 'transformed_label' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: SOFTMAX_LOSS "
+        "  bottom: 'transformed_data' "
+        "  bottom: 'transformed_label' "
+        "} ";
+    InitNetFromProtoString(proto);
+  }
+
+  virtual void InitUnsharedWeightsNet(const bool bias_term = false,
+      const Dtype blobs_lr_w1 = 1, const Dtype blobs_lr_b1 = 2,
+      const Dtype blobs_lr_w2 = 1, const Dtype blobs_lr_b2 = 2) {
+    ostringstream proto;
+    proto <<
+        "name: 'UnsharedWeightsNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 5 "
+        "    channels: 2 "
+        "    height: 3 "
+        "    width: 4 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "  } "
+        "  top: 'data' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct1' "
+       "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: " << bias_term <<
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  param: 'unsharedweights1' ";
+    if (bias_term) {
+      proto << "  param: '' ";
+    }
+    proto <<
+        "  blobs_lr: " << blobs_lr_w1;
+    if (bias_term) {
+      proto << "  blobs_lr: " << blobs_lr_b1;
+    }
+    proto <<
+        "  bottom: 'data' "
+        "  top: 'innerproduct1' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct2' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: " << bias_term <<
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  param: 'unsharedweights2' ";
+    if (bias_term) {
+      proto << "  param: '' ";
+    }
+    proto <<
+        "  bottom: 'data' "
+        "  blobs_lr: " << blobs_lr_w2;
+    if (bias_term) {
+      proto << "  blobs_lr: " << blobs_lr_b2;
+    }
+    proto <<
+        "  top: 'innerproduct2' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: EUCLIDEAN_LOSS "
+        "  bottom: 'innerproduct1' "
+        "  bottom: 'innerproduct2' "
+        "} ";
+    InitNetFromProtoString(proto.str());
+  }
+
+  virtual void InitSharedWeightsNet() {
+    const string& proto =
+        "name: 'SharedWeightsNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 5 "
+        "    channels: 2 "
+        "    height: 3 "
+        "    width: 4 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 0.01 "
+        "    } "
+        "  } "
+        "  top: 'data' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct1' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  param: 'sharedweights' "
+        "  bottom: 'data' "
+        "  top: 'innerproduct1' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct2' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  param: 'sharedweights' "
+        "  bottom: 'data' "
+        "  top: 'innerproduct2' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: EUCLIDEAN_LOSS "
+        "  bottom: 'innerproduct1' "
+        "  bottom: 'innerproduct2' "
+        "} ";
+    InitNetFromProtoString(proto);
+  }
+
+  virtual void InitDiffDataUnsharedWeightsNet() {
+    const string& proto =
+        "name: 'DiffDataUnsharedWeightsNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 10 "
+        "    channels: 10 "
+        "    height: 1 "
+        "    width: 1 "
+        "    num: 10 "
+        "    channels: 10 "
+        "    height: 1 "
+        "    width: 1 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  top: 'data1' "
+        "  top: 'data2' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct1' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'constant' "
+        "      value: 0.5 "
+        "    } "
+        "  } "
+        "  param: 'unsharedweights1' "
+        "  bottom: 'data1' "
+        "  top: 'innerproduct1' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct2' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'constant' "
+        "      value: 0.5 "
+        "    } "
+        "  } "
+        "  param: 'unsharedweights2' "
+        "  bottom: 'innerproduct1' "
+        "  top: 'innerproduct2' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: EUCLIDEAN_LOSS "
+        "  bottom: 'data2' "
+        "  bottom: 'innerproduct2' "
         "} ";
-    proto_ = proto_prefix + "source: '" + string(this->filename_) +
-        "' " + proto_suffix;
+    InitNetFromProtoString(proto);
   }
 
-  char* filename_;
-  string proto_;
+  virtual void InitDiffDataSharedWeightsNet() {
+    const string& proto =
+        "name: 'DiffDataSharedWeightsNetwork' "
+        "layers: { "
+        "  name: 'data' "
+        "  type: DUMMY_DATA "
+        "  dummy_data_param { "
+        "    num: 10 "
+        "    channels: 10 "
+        "    height: 1 "
+        "    width: 1 "
+        "    num: 10 "
+        "    channels: 10 "
+        "    height: 1 "
+        "    width: 1 "
+        "    data_filler { "
+        "      type: 'gaussian' "
+        "      std: 10 "
+        "    } "
+        "  } "
+        "  top: 'data1' "
+        "  top: 'data2' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct1' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'constant' "
+        "      value: 0.5 "
+        "    } "
+        "  } "
+        "  param: 'sharedweights' "
+        "  bottom: 'data1' "
+        "  top: 'innerproduct1' "
+        "} "
+        "layers: { "
+        "  name: 'innerproduct2' "
+        "  type: INNER_PRODUCT "
+        "  inner_product_param { "
+        "    num_output: 10 "
+        "    bias_term: false "
+        "    weight_filler { "
+        "      type: 'constant' "
+        "      value: 0.5 "
+        "    } "
+        "  } "
+        "  param: 'sharedweights' "
+        "  bottom: 'innerproduct1' "
+        "  top: 'innerproduct2' "
+        "} "
+        "layers: { "
+        "  name: 'loss' "
+        "  type: EUCLIDEAN_LOSS "
+        "  bottom: 'data2' "
+        "  bottom: 'innerproduct2' "
+        "} ";
+    InitNetFromProtoString(proto);
+  }
+
+  int seed_;
+  shared_ptr<Net<Dtype> > net_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(NetTest, Dtypes);
+TYPED_TEST_CASE(NetTest, TestDtypesAndDevices);
 
 TYPED_TEST(NetTest, TestHasBlob) {
-  NetParameter param;
-  CHECK(google::protobuf::TextFormat::ParseFromString(this->proto_, &param));
-  Net<TypeParam> net(param);
-  EXPECT_TRUE(net.has_blob("data"));
-  EXPECT_TRUE(net.has_blob("label"));
-  EXPECT_TRUE(net.has_blob("innerproduct"));
-  EXPECT_FALSE(net.has_blob("loss"));
+  this->InitTinyNet();
+  EXPECT_TRUE(this->net_->has_blob("data"));
+  EXPECT_TRUE(this->net_->has_blob("label"));
+  EXPECT_TRUE(this->net_->has_blob("innerproduct"));
+  EXPECT_FALSE(this->net_->has_blob("loss"));
+  EXPECT_TRUE(this->net_->has_blob("top_loss"));
 }
 
 TYPED_TEST(NetTest, TestGetBlob) {
-  NetParameter param;
-  CHECK(google::protobuf::TextFormat::ParseFromString(this->proto_, &param));
-  Net<TypeParam> net(param);
-  EXPECT_EQ(net.blob_by_name("data"), net.blobs()[0]);
-  EXPECT_EQ(net.blob_by_name("label"), net.blobs()[1]);
-  EXPECT_EQ(net.blob_by_name("innerproduct"), net.blobs()[2]);
-  EXPECT_FALSE(net.blob_by_name("loss"));
+  this->InitTinyNet();
+  EXPECT_EQ(this->net_->blob_by_name("data"), this->net_->blobs()[0]);
+  EXPECT_EQ(this->net_->blob_by_name("label"), this->net_->blobs()[1]);
+  EXPECT_EQ(this->net_->blob_by_name("innerproduct"), this->net_->blobs()[2]);
+  EXPECT_FALSE(this->net_->blob_by_name("loss"));
+  EXPECT_EQ(this->net_->blob_by_name("top_loss"), this->net_->blobs()[3]);
 }
 
 TYPED_TEST(NetTest, TestHasLayer) {
-  NetParameter param;
-  CHECK(google::protobuf::TextFormat::ParseFromString(this->proto_, &param));
-  Net<TypeParam> net(param);
-  EXPECT_TRUE(net.has_layer("data"));
-  EXPECT_TRUE(net.has_layer("innerproduct"));
-  EXPECT_TRUE(net.has_layer("loss"));
-  EXPECT_FALSE(net.has_layer("label"));
+  this->InitTinyNet();
+  EXPECT_TRUE(this->net_->has_layer("data"));
+  EXPECT_TRUE(this->net_->has_layer("innerproduct"));
+  EXPECT_TRUE(this->net_->has_layer("loss"));
+  EXPECT_FALSE(this->net_->has_layer("label"));
 }
 
 TYPED_TEST(NetTest, TestGetLayerByName) {
-  NetParameter param;
-  CHECK(google::protobuf::TextFormat::ParseFromString(this->proto_, &param));
-  Net<TypeParam> net(param);
-  EXPECT_EQ(net.layer_by_name("data"), net.layers()[0]);
-  EXPECT_EQ(net.layer_by_name("innerproduct"), net.layers()[1]);
-  EXPECT_EQ(net.layer_by_name("loss"), net.layers()[2]);
-  EXPECT_FALSE(net.layer_by_name("label"));
+  this->InitTinyNet();
+  EXPECT_EQ(this->net_->layer_by_name("data"), this->net_->layers()[0]);
+  EXPECT_EQ(this->net_->layer_by_name("innerproduct"), this->net_->layers()[1]);
+  EXPECT_EQ(this->net_->layer_by_name("loss"), this->net_->layers()[2]);
+  EXPECT_FALSE(this->net_->layer_by_name("label"));
+}
+
+TYPED_TEST(NetTest, TestBottomNeedBackward) {
+  this->InitTinyNet();
+  const vector<vector<bool> >& bottom_need_backward =
+      this->net_->bottom_need_backward();
+  EXPECT_EQ(3, bottom_need_backward.size());
+  EXPECT_EQ(0, bottom_need_backward[0].size());
+  EXPECT_EQ(1, bottom_need_backward[1].size());
+  EXPECT_EQ(false, bottom_need_backward[1][0]);
+  EXPECT_EQ(2, bottom_need_backward[2].size());
+  EXPECT_EQ(true, bottom_need_backward[2][0]);
+  EXPECT_EQ(false, bottom_need_backward[2][1]);
+}
+
+TYPED_TEST(NetTest, TestBottomNeedBackwardForce) {
+  typedef typename TypeParam::Dtype Dtype;
+  const bool force_backward = true;
+  this->InitTinyNet(force_backward);
+  const vector<vector<bool> >& bottom_need_backward =
+      this->net_->bottom_need_backward();
+  EXPECT_EQ(3, bottom_need_backward.size());
+  EXPECT_EQ(0, bottom_need_backward[0].size());
+  EXPECT_EQ(1, bottom_need_backward[1].size());
+  EXPECT_EQ(true, bottom_need_backward[1][0]);
+  EXPECT_EQ(2, bottom_need_backward[2].size());
+  EXPECT_EQ(true, bottom_need_backward[2][0]);
+  EXPECT_EQ(false, bottom_need_backward[2][1]);
+}
+
+TYPED_TEST(NetTest, TestBottomNeedBackwardEuclideanForce) {
+  typedef typename TypeParam::Dtype Dtype;
+  const bool force_backward = true;
+  this->InitTinyNetEuclidean(force_backward);
+  const vector<vector<bool> >& bottom_need_backward =
+      this->net_->bottom_need_backward();
+  EXPECT_EQ(3, bottom_need_backward.size());
+  EXPECT_EQ(0, bottom_need_backward[0].size());
+  EXPECT_EQ(1, bottom_need_backward[1].size());
+  EXPECT_EQ(true, bottom_need_backward[1][0]);
+  EXPECT_EQ(2, bottom_need_backward[2].size());
+  EXPECT_EQ(true, bottom_need_backward[2][0]);
+  EXPECT_EQ(true, bottom_need_backward[2][1]);
+}
+
+TYPED_TEST(NetTest, TestBottomNeedBackwardTricky) {
+  this->InitTrickyNet();
+  const vector<vector<bool> >& bottom_need_backward =
+      this->net_->bottom_need_backward();
+  EXPECT_EQ(4, bottom_need_backward.size());
+  EXPECT_EQ(0, bottom_need_backward[0].size());
+  EXPECT_EQ(1, bottom_need_backward[1].size());
+  EXPECT_EQ(false, bottom_need_backward[1][0]);
+  EXPECT_EQ(1, bottom_need_backward[2].size());
+  EXPECT_EQ(false, bottom_need_backward[2][0]);
+  EXPECT_EQ(2, bottom_need_backward[3].size());
+  EXPECT_EQ(true, bottom_need_backward[3][0]);
+  // The label input to the SoftmaxLossLayer should say it "needs backward"
+  // since it has weights under it, even though we expect this to cause a crash
+  // at training/test time.
+  EXPECT_EQ(true, bottom_need_backward[3][1]);
+}
+
+TYPED_TEST(NetTest, TestUnsharedWeightsDataNet) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->InitUnsharedWeightsNet();
+  vector<Blob<Dtype>*> bottom;
+  Dtype loss;
+  this->net_->Forward(bottom, &loss);
+  EXPECT_GT(loss, 0);
+}
+
+TYPED_TEST(NetTest, TestSharedWeightsDataNet) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->InitSharedWeightsNet();
+  vector<Blob<Dtype>*> bottom;
+  Dtype loss;
+  this->net_->Forward(bottom, &loss);
+  EXPECT_FLOAT_EQ(loss, 0);
+}
+
+TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->InitUnsharedWeightsNet();
+  vector<Blob<Dtype>*> bottom;
+  Net<Dtype>* net = this->net_.get();
+  net->Forward(bottom);
+  net->Backward();
+  Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
+  Layer<Dtype>* ip2_layer = net->layer_by_name("innerproduct2").get();
+  const int count = ip1_layer->blobs()[0]->count();
+  const Dtype* grad1 = ip1_layer->blobs()[0]->cpu_diff();
+  const Dtype* grad2 = ip2_layer->blobs()[0]->cpu_diff();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_GT(fabs(grad1[i]), 0);
+    EXPECT_FLOAT_EQ(-1 * grad1[i], grad2[i]);
+  }
+}
+
+TYPED_TEST(NetTest, TestSharedWeightsDiffNet) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->InitSharedWeightsNet();
+  vector<Blob<Dtype>*> bottom;
+  Net<Dtype>* net = this->net_.get();
+  Dtype loss;
+  net->Forward(bottom, &loss);
+  net->Backward();
+  EXPECT_FLOAT_EQ(loss, 0);
+  Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
+  Layer<Dtype>* ip2_layer = net->layer_by_name("innerproduct2").get();
+  const int count = ip1_layer->blobs()[0]->count();
+  const Dtype* grad1 = ip1_layer->blobs()[0]->cpu_diff();
+  const Dtype* grad2 = ip2_layer->blobs()[0]->cpu_diff();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_FLOAT_EQ(0, grad1[i]);
+    EXPECT_FLOAT_EQ(0, grad2[i]);
+  }
+}
+
+TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  Caffe::set_random_seed(this->seed_);
+  this->InitDiffDataSharedWeightsNet();
+  vector<Blob<Dtype>*> bottom;
+  EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
+  EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
+  Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
+  Blob<Dtype>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
+  // Check that data blobs of shared weights share the same location in memory.
+  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  // Check that diff blobs of shared weights are at different locations in
+  // locations.  (The diffs should be accumulated at update time.)
+  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  // Compute the expected update as the data minus the two diffs.
+  Blob<Dtype> shared_params;
+  const bool reshape = true;
+  const bool copy_diff = false;
+  shared_params.CopyFrom(*ip1_weights, copy_diff, reshape);
+  shared_params.CopyFrom(*ip1_weights, !copy_diff, reshape);
+  const int count = ip1_weights->count();
+  // Make sure the diffs are non-trivial.
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
+    EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
+    EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
+  }
+  caffe_axpy(count, Dtype(1), ip2_weights->cpu_diff(),
+             shared_params.mutable_cpu_diff());
+  caffe_axpy(count, Dtype(-1), shared_params.cpu_diff(),
+             shared_params.mutable_cpu_data());
+  const Dtype* expected_updated_params = shared_params.cpu_data();
+  this->net_->Update();
+  const Dtype* actual_updated_params = ip1_weights->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_EQ(expected_updated_params[i], actual_updated_params[i]);
+  }
+  // Check that data blobs of shared weights STILL point to the same memory
+  // location (because ... who knows).
+  EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+
+  Caffe::set_random_seed(this->seed_);
+  this->InitDiffDataUnsharedWeightsNet();
+  EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
+  EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
+  ip1_weights = this->net_->layers()[1]->blobs()[0].get();
+  ip2_weights = this->net_->layers()[2]->blobs()[0].get();
+  // Check that data and diff blobs of unshared weights are at different
+  // locations in memory.
+  EXPECT_NE(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  // Compute the expected update.
+  Blob<Dtype> unshared_params1;
+  unshared_params1.CopyFrom(*ip1_weights, copy_diff, reshape);
+  unshared_params1.CopyFrom(*ip1_weights, !copy_diff, reshape);
+  Blob<Dtype> unshared_params2;
+  unshared_params2.CopyFrom(*ip2_weights, copy_diff, reshape);
+  unshared_params2.CopyFrom(*ip2_weights, !copy_diff, reshape);
+  // Make sure the diffs are non-trivial and sum to the diff in the shared net.
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
+    EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
+    EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
+    EXPECT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i],
+              shared_params.cpu_diff()[i]);
+  }
+  caffe_axpy(count, Dtype(-1), ip1_weights->cpu_diff(),
+             unshared_params1.mutable_cpu_data());
+  caffe_axpy(count, Dtype(-1), ip2_weights->cpu_diff(),
+             unshared_params2.mutable_cpu_data());
+  const Dtype* expected_updated_params1 = unshared_params1.cpu_data();
+  const Dtype* expected_updated_params2 = unshared_params2.cpu_data();
+  this->net_->Update();
+  const Dtype* actual_updated_params1 = ip1_weights->cpu_data();
+  const Dtype* actual_updated_params2 = ip2_weights->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_EQ(expected_updated_params1[i], actual_updated_params1[i]);
+    EXPECT_EQ(expected_updated_params2[i], actual_updated_params2[i]);
+    EXPECT_NE(actual_updated_params1[i], actual_updated_params2[i]);
+    EXPECT_NE(expected_updated_params, expected_updated_params1);
+  }
+}
+
+TYPED_TEST(NetTest, TestParamPropagateDown) {
+  typedef typename TypeParam::Dtype Dtype;
+  vector<Blob<Dtype>*> bottom;
+  const bool kBiasTerm = true;
+
+  // Run the net with all params learned; check that gradients are non-zero.
+  Caffe::set_random_seed(this->seed_);
+  Dtype blobs_lr_w1 = 1, blobs_lr_w2 = 1, blobs_lr_b1 = 2, blobs_lr_b2 = 2;
+  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
+                               blobs_lr_b1, blobs_lr_b2);
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  const vector<shared_ptr<Blob<Dtype> > >& params = this->net_->params();
+  const int num_params = params.size();
+  ASSERT_EQ(4, num_params);
+  const Dtype kNonZeroTestMin = 1e-3;
+  vector<Dtype> param_asums(params.size());
+  for (int i = 0; i < num_params; ++i) {
+    const Dtype param_asum =
+       caffe_cpu_asum(params[i]->count(), params[i]->cpu_diff());
+    param_asums[i] = param_asum;
+    EXPECT_GT(param_asum, kNonZeroTestMin);
+  }
+
+  // Change the learning rates to different non-zero values; should see same
+  // gradients.
+  Caffe::set_random_seed(this->seed_);
+  blobs_lr_w1 *= 2, blobs_lr_w2 *= 2, blobs_lr_b1 *= 2, blobs_lr_b2 *= 2;
+  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
+                               blobs_lr_b1, blobs_lr_b2);
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  const vector<shared_ptr<Blob<Dtype> > >& params2 = this->net_->params();
+  ASSERT_EQ(num_params, params2.size());
+  for (int i = 0; i < num_params; ++i) {
+    const Dtype param_asum =
+       caffe_cpu_asum(params2[i]->count(), params2[i]->cpu_diff());
+    EXPECT_EQ(param_asum, param_asums[i]);
+  }
+
+  // Change a subset of the learning rates to zero; check that we see zero
+  // gradients for those.
+  Caffe::set_random_seed(this->seed_);
+  blobs_lr_w1 = 1, blobs_lr_w2 = 0, blobs_lr_b1 = 0, blobs_lr_b2 = 1;
+  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
+                               blobs_lr_b1, blobs_lr_b2);
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  const vector<shared_ptr<Blob<Dtype> > >& params3 = this->net_->params();
+  ASSERT_EQ(num_params, params3.size());
+  for (int i = 0; i < num_params; ++i) {
+    const Dtype param_asum =
+       caffe_cpu_asum(params3[i]->count(), params3[i]->cpu_diff());
+    if (i == 1 || i == 2) {
+      EXPECT_EQ(0, param_asum);
+    } else {
+      EXPECT_EQ(param_asum, param_asums[i]);
+    }
+  }
+
+  // Change the opposite subset of the learning rates to zero.
+  Caffe::set_random_seed(this->seed_);
+  blobs_lr_w1 = 0, blobs_lr_w2 = 1, blobs_lr_b1 = 1, blobs_lr_b2 = 0;
+  this->InitUnsharedWeightsNet(kBiasTerm, blobs_lr_w1, blobs_lr_w2,
+                               blobs_lr_b1, blobs_lr_b2);
+  this->net_->Forward(bottom);
+  this->net_->Backward();
+  const vector<shared_ptr<Blob<Dtype> > >& params4 = this->net_->params();
+  ASSERT_EQ(num_params, params4.size());
+  for (int i = 0; i < num_params; ++i) {
+    const Dtype param_asum =
+       caffe_cpu_asum(params4[i]->count(), params4[i]->cpu_diff());
+    if (i == 0 || i == 3) {
+      EXPECT_EQ(0, param_asum);
+    } else {
+      EXPECT_EQ(param_asum, param_asums[i]);
+    }
+  }
+}
+
+TYPED_TEST(NetTest, TestFromTo) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->InitTinyNet();
+
+  // Run Forward and Backward, recording the data diff and loss.
+  Blob<Dtype> data;
+  data.ReshapeLike(*this->net_->blob_by_name("data"));
+  this->net_->ForwardPrefilled();
+  this->net_->Backward();
+  data.CopyFrom(*this->net_->blob_by_name("data"), true, true);
+  const Dtype *loss_ptr = this->net_->output_blobs()[0]->cpu_data();
+  Dtype loss = *loss_ptr;
+
+  // Check that combining partial Forwards gives the same loss.
+  for (int i = 1; i < this->net_->layers().size(); ++i) {
+    // Note that we skip layer zero to keep the same data.
+    this->net_->ForwardFromTo(1, 1);
+    if (i < this->net_->layers().size() - 1) {
+      this->net_->ForwardFrom(i + 1);
+    }
+    EXPECT_EQ(loss, *loss_ptr);
+  }
+
+  // Check that combining partial Backwards gives the same data diff.
+  for (int i = 1; i < this->net_->layers().size(); ++i) {
+    this->net_->BackwardTo(i);
+    this->net_->BackwardFrom(i - 1);
+    for (int j = 0; j < data.count(); ++j) {
+      EXPECT_EQ(data.cpu_diff()[j],
+          this->net_->blob_by_name("data")->cpu_diff()[j]);
+    }
+  }
+}
+
+class FilterNetTest : public ::testing::Test {
+ protected:
+  void RunFilterNetTest(
+      const string& input_param_string, const string& filtered_param_string) {
+    NetParameter input_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        input_param_string, &input_param));
+    NetParameter expected_filtered_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        filtered_param_string, &expected_filtered_param));
+    NetParameter actual_filtered_param;
+    Net<float>::FilterNet(input_param, &actual_filtered_param);
+    EXPECT_EQ(expected_filtered_param.DebugString(),
+        actual_filtered_param.DebugString());
+    // Also test idempotence.
+    NetParameter double_filtered_param;
+    Net<float>::FilterNet(actual_filtered_param, &double_filtered_param);
+    EXPECT_EQ(actual_filtered_param.DebugString(),
+       double_filtered_param.DebugString());
+  }
+};
+
+TEST_F(FilterNetTest, TestNoFilter) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterLeNetTrainTest) {
+  const string& input_proto =
+      "name: 'LeNet' "
+      "layers { "
+      "  name: 'mnist' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  data_param { "
+      "    source: 'mnist-train-leveldb' "
+      "    scale: 0.00390625 "
+      "    batch_size: 64 "
+      "  } "
+      "  include: { phase: TRAIN } "
+      "} "
+      "layers { "
+      "  name: 'mnist' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  data_param { "
+      "    source: 'mnist-test-leveldb' "
+      "    scale: 0.00390625 "
+      "    batch_size: 100 "
+      "  } "
+      "  include: { phase: TEST } "
+      "} "
+      "layers { "
+      "  name: 'conv1' "
+      "  type: CONVOLUTION "
+      "  bottom: 'data' "
+      "  top: 'conv1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  convolution_param { "
+      "    num_output: 20 "
+      "    kernel_size: 5 "
+      "    stride: 1 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'ip1' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'conv1' "
+      "  top: 'ip1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  inner_product_param { "
+      "    num_output: 10 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'accuracy' "
+      "  type: ACCURACY "
+      "  bottom: 'ip1' "
+      "  bottom: 'label' "
+      "  top: 'accuracy' "
+      "  include: { phase: TEST } "
+      "} "
+      "layers { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'ip2' "
+      "  bottom: 'label' "
+      "  top: 'loss' "
+      "} ";
+  const string input_proto_train = "state: { phase: TRAIN } " + input_proto;
+  const string input_proto_test = "state: { phase: TEST } " + input_proto;
+  const string output_proto_train =
+      "name: 'LeNet' "
+      "layers { "
+      "  name: 'mnist' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  data_param { "
+      "    source: 'mnist-train-leveldb' "
+      "    scale: 0.00390625 "
+      "    batch_size: 64 "
+      "  } "
+      "  include: { phase: TRAIN } "
+      "} "
+      "layers { "
+      "  name: 'conv1' "
+      "  type: CONVOLUTION "
+      "  bottom: 'data' "
+      "  top: 'conv1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  convolution_param { "
+      "    num_output: 20 "
+      "    kernel_size: 5 "
+      "    stride: 1 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'ip1' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'conv1' "
+      "  top: 'ip1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  inner_product_param { "
+      "    num_output: 10 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'ip2' "
+      "  bottom: 'label' "
+      "  top: 'loss' "
+      "} ";
+  const string& output_proto_test =
+      "name: 'LeNet' "
+      "layers { "
+      "  name: 'mnist' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  data_param { "
+      "    source: 'mnist-test-leveldb' "
+      "    scale: 0.00390625 "
+      "    batch_size: 100 "
+      "  } "
+      "  include: { phase: TEST } "
+      "} "
+      "layers { "
+      "  name: 'conv1' "
+      "  type: CONVOLUTION "
+      "  bottom: 'data' "
+      "  top: 'conv1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  convolution_param { "
+      "    num_output: 20 "
+      "    kernel_size: 5 "
+      "    stride: 1 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'ip1' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'conv1' "
+      "  top: 'ip1' "
+      "  blobs_lr: 1 "
+      "  blobs_lr: 2 "
+      "  inner_product_param { "
+      "    num_output: 10 "
+      "    weight_filler { "
+      "      type: 'xavier' "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "    } "
+      "  } "
+      "} "
+      "layers { "
+      "  name: 'accuracy' "
+      "  type: ACCURACY "
+      "  bottom: 'ip1' "
+      "  bottom: 'label' "
+      "  top: 'accuracy' "
+      "  include: { phase: TEST } "
+      "} "
+      "layers { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'ip2' "
+      "  bottom: 'label' "
+      "  top: 'loss' "
+      "} ";
+  const string output_proto_train_explicit =
+      output_proto_train + " state: { phase: TRAIN } ";
+  const string output_proto_test_explicit =
+      output_proto_test + " state: { phase: TEST } ";
+  this->RunFilterNetTest(input_proto_train, output_proto_train_explicit);
+  this->RunFilterNetTest(input_proto_test, output_proto_test_explicit);
+
+  // Also check that nets are filtered according to the Caffe singleton phase,
+  // if not explicitly specified in the input proto.
+  Caffe::set_phase(Caffe::TRAIN);
+  this->RunFilterNetTest(input_proto, output_proto_train);
+  Caffe::set_phase(Caffe::TEST);
+  this->RunFilterNetTest(input_proto, output_proto_test);
+
+  // Finally, check that the current Caffe singleton phase is ignored if the
+  // phase is explicitly specified in the input proto.
+  Caffe::set_phase(Caffe::TEST);
+  this->RunFilterNetTest(input_proto_train, output_proto_train_explicit);
+  Caffe::set_phase(Caffe::TRAIN);
+  this->RunFilterNetTest(input_proto_test, output_proto_test_explicit);
+}
+
+TEST_F(FilterNetTest, TestFilterOutByStage) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "  include: { stage: 'mystage' } "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  const string& output_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, output_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterOutByStage2) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { stage: 'mystage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  const string& output_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, output_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByStage) {
+  const string& input_proto =
+      "state: { stage: 'mystage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { stage: 'mystage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByStage2) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  exclude: { stage: 'mystage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterOutByMultipleStage) {
+  const string& input_proto =
+      "state: { stage: 'mystage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { stage: 'mystage' stage: 'myotherstage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { stage: 'mystage' } "
+      "} ";
+  const string& output_proto =
+      "state: { stage: 'mystage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { stage: 'mystage' } "
+      "} ";
+  this->RunFilterNetTest(input_proto, output_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMultipleStage) {
+  const string& input_proto =
+      "state: { stage: 'mystage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { stage: 'myotherstage' } "
+      "  include: { stage: 'mystage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { stage: 'mystage' } "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMultipleStage2) {
+  const string& input_proto =
+      "state: { stage: 'mystage' stage: 'myotherstage' } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { stage: 'mystage' stage: 'myotherstage' } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { stage: 'mystage' } "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterOutByMinLevel) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 3 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  const string& output_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, output_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterOutByMaxLevel) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { max_level: -3 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  const string& output_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, output_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMinLevel) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 0 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMinLevel2) {
+  const string& input_proto =
+      "state: { level: 7 } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 3 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMaxLevel) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { max_level: 0 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInByMaxLevel2) {
+  const string& input_proto =
+      "state: { level: -7 } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { max_level: -3 } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "} ";
+  this->RunFilterNetTest(input_proto, input_proto);
+}
+
+TEST_F(FilterNetTest, TestFilterInOutByIncludeMultiRule) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 2  phase: TRAIN } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { min_level: 2  phase: TEST } "
+      "} ";
+  const string& input_proto_train =
+      "state: { level: 4  phase: TRAIN } " + input_proto;
+  const string& input_proto_test =
+      "state: { level: 4  phase: TEST } " + input_proto;
+  const string& output_proto_train =
+      "state: { level: 4  phase: TRAIN } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 2  phase: TRAIN } "
+      "} ";
+  const string& output_proto_test =
+      "state: { level: 4  phase: TEST } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { min_level: 2  phase: TEST } "
+      "} ";
+  this->RunFilterNetTest(input_proto_train, output_proto_train);
+  this->RunFilterNetTest(input_proto_test, output_proto_test);
+}
+
+TEST_F(FilterNetTest, TestFilterInByIncludeMultiRule) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  include: { min_level: 2  phase: TRAIN } "
+      "  include: { phase: TEST } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  include: { min_level: 2  phase: TEST } "
+      "  include: { phase: TRAIN } "
+      "} ";
+  const string& input_proto_train =
+      "state: { level: 2  phase: TRAIN } " + input_proto;
+  const string& input_proto_test =
+      "state: { level: 2  phase: TEST } " + input_proto;
+  this->RunFilterNetTest(input_proto_train, input_proto_train);
+  this->RunFilterNetTest(input_proto_test, input_proto_test);
+}
+
+TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
+  const string& input_proto =
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  exclude: { min_level: 2  phase: TRAIN } "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  exclude: { min_level: 2  phase: TEST } "
+      "} ";
+  const string& input_proto_train =
+      "state: { level: 4  phase: TRAIN } " + input_proto;
+  const string& input_proto_test =
+      "state: { level: 4  phase: TEST } " + input_proto;
+  const string& output_proto_train =
+      "state: { level: 4  phase: TRAIN } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'loss' "
+      "  type: SOFTMAX_LOSS "
+      "  bottom: 'innerprod' "
+      "  bottom: 'label' "
+      "  exclude: { min_level: 2  phase: TEST } "
+      "} ";
+  const string& output_proto_test =
+      "state: { level: 4  phase: TEST } "
+      "name: 'TestNetwork' "
+      "layers: { "
+      "  name: 'data' "
+      "  type: DATA "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layers: { "
+      "  name: 'innerprod' "
+      "  type: INNER_PRODUCT "
+      "  bottom: 'data' "
+      "  top: 'innerprod' "
+      "  exclude: { min_level: 2  phase: TRAIN } "
+      "} ";
+  this->RunFilterNetTest(input_proto_train, output_proto_train);
+  this->RunFilterNetTest(input_proto_test, output_proto_test);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index 2210b4612ad..649f8f626d9 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -1,24 +1,22 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class NeuronLayerTest : public ::testing::Test {
  protected:
   NeuronLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -36,99 +34,101 @@ class NeuronLayerTest : public ::testing::Test {
   Blob<Dtype>* const blob_top_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
-};
-
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(NeuronLayerTest, Dtypes);
 
-TYPED_TEST(NeuronLayerTest, TestReLUCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  ReLULayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
+  void TestDropoutForward(const float dropout_ratio) {
+    LayerParameter layer_param;
+    // Fill in the given dropout_ratio, unless it's 0.5, in which case we don't
+    // set it explicitly to test that 0.5 is the default.
+    if (dropout_ratio != 0.5) {
+      layer_param.mutable_dropout_param()->set_dropout_ratio(dropout_ratio);
+    }
+    Caffe::set_phase(Caffe::TRAIN);
+    DropoutLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+    // Now, check values
+    const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+    const Dtype* top_data = this->blob_top_->cpu_data();
+    float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
+    const int count = this->blob_bottom_->count();
+    // Initialize num_kept to count the number of inputs NOT dropped out.
+    int num_kept = 0;
+    for (int i = 0; i < count; ++i) {
+      if (top_data[i] != 0) {
+        ++num_kept;
+        EXPECT_EQ(top_data[i], bottom_data[i] * scale);
+      }
+    }
+    const Dtype std_error = sqrt(dropout_ratio * (1 - dropout_ratio) / count);
+    // Fail if the number dropped was more than 1.96 * std_error away from the
+    // expected number -- requires 95% confidence that the dropout layer is not
+    // obeying the given dropout_ratio for test failure.
+    const Dtype empirical_dropout_ratio = 1 - num_kept / Dtype(count);
+    EXPECT_NEAR(empirical_dropout_ratio, dropout_ratio, 1.96 * std_error);
   }
-}
-
-
-TYPED_TEST(NeuronLayerTest, TestReLUGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  ReLULayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
+};
 
+TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(NeuronLayerTest, TestReLUGPU) {
+TYPED_TEST(NeuronLayerTest, TestReLU) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  ReLULayer<TypeParam> layer(layer_param);
+  ReLULayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
   }
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestReLUGradientGPU) {
+TYPED_TEST(NeuronLayerTest, TestReLUGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  ReLULayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  ReLULayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestSigmoidCPU) {
+TYPED_TEST(NeuronLayerTest, TestReLUWithNegativeSlope) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SigmoidLayer<TypeParam> layer(layer_param);
+  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  ReLULayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_FLOAT_EQ(top_data[i], 1. / (1 + exp(-bottom_data[i])));
-    // check that we squashed the value between 0 and 1
     EXPECT_GE(top_data[i], 0.);
-    EXPECT_LE(top_data[i], 1.);
+    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
   }
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestSigmoidGradientCPU) {
+TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SigmoidLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  layer_param.ParseFromString("relu_param{negative_slope:0.01}");
+  ReLULayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(NeuronLayerTest, TestSigmoidGPU) {
+TYPED_TEST(NeuronLayerTest, TestSigmoid) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SigmoidLayer<TypeParam> layer(layer_param);
+  SigmoidLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_FLOAT_EQ(top_data[i], 1. / (1 + exp(-bottom_data[i])));
     // check that we squashed the value between 0 and 1
@@ -137,59 +137,35 @@ TYPED_TEST(NeuronLayerTest, TestSigmoidGPU) {
   }
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestSigmoidGradientGPU) {
+TYPED_TEST(NeuronLayerTest, TestSigmoidGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SigmoidLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  SigmoidLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-
-
-TYPED_TEST(NeuronLayerTest, TestDropoutCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  Caffe::set_phase(Caffe::TRAIN);
-  DropoutLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    if (top_data[i] != 0) {
-      EXPECT_EQ(top_data[i], bottom_data[i] * scale);
-    }
-  }
+TYPED_TEST(NeuronLayerTest, TestDropoutHalf) {
+  const float kDropoutRatio = 0.5;
+  this->TestDropoutForward(kDropoutRatio);
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestDropoutGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  Caffe::set_phase(Caffe::TRAIN);
-  DropoutLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+TYPED_TEST(NeuronLayerTest, TestDropoutThreeQuarters) {
+  const float kDropoutRatio = 0.75;
+  this->TestDropoutForward(kDropoutRatio);
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestDropoutCPUTestPhase) {
+TYPED_TEST(NeuronLayerTest, TestDropoutTestPhase) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   Caffe::set_phase(Caffe::TEST);
-  DropoutLayer<TypeParam> layer(layer_param);
+  DropoutLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     if (top_data[i] != 0) {
       EXPECT_EQ(top_data[i], bottom_data[i]);
@@ -197,109 +173,46 @@ TYPED_TEST(NeuronLayerTest, TestDropoutCPUTestPhase) {
   }
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestDropoutGPU) {
+TYPED_TEST(NeuronLayerTest, TestDropoutGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
   Caffe::set_phase(Caffe::TRAIN);
-  DropoutLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    if (top_data[i] != 0) {
-      EXPECT_EQ(top_data[i], bottom_data[i] * scale);
-    }
-  }
-}
-
-
-TYPED_TEST(NeuronLayerTest, TestDropoutGradientGPU) {
-  if (CAFFE_TEST_CUDA_PROP.major >= 2) {
-    LayerParameter layer_param;
-    Caffe::set_mode(Caffe::GPU);
-    Caffe::set_phase(Caffe::TRAIN);
-    DropoutLayer<TypeParam> layer(layer_param);
-    GradientChecker<TypeParam> checker(1e-2, 1e-3);
-    // it is too expensive to call curand multiple times, so we don't do an
-    // exhaustive gradient check.
-    checker.CheckGradient(&layer, &(this->blob_bottom_vec_),
-        &(this->blob_top_vec_));
-  } else {
-    LOG(ERROR) << "Skipping test to spare my laptop.";
-  }
+  DropoutLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
+      &(this->blob_top_vec_));
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestDropoutGPUTestPhase) {
+TYPED_TEST(NeuronLayerTest, TestDropoutGradientTest) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
   Caffe::set_phase(Caffe::TEST);
-  DropoutLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  float scale = 1. / (1. - layer_param.dropout_param().dropout_ratio());
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    if (top_data[i] != 0) {
-      EXPECT_EQ(top_data[i], bottom_data[i]);
-    }
-  }
-}
-
-
-TYPED_TEST(NeuronLayerTest, TestBNLLCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  BNLLLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    EXPECT_GE(top_data[i], 0.);
-    EXPECT_GE(top_data[i], bottom_data[i]);
-  }
-}
-
-
-TYPED_TEST(NeuronLayerTest, TestBNLLGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  BNLLLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  DropoutLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestBNLLGPU) {
+TYPED_TEST(NeuronLayerTest, TestBNLL) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  BNLLLayer<TypeParam> layer(layer_param);
+  BNLLLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Now, check values
-  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
-  const TypeParam* top_data = this->blob_top_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
     EXPECT_GE(top_data[i], 0.);
     EXPECT_GE(top_data[i], bottom_data[i]);
   }
 }
 
-
-TYPED_TEST(NeuronLayerTest, TestBNLLGradientGPU) {
+TYPED_TEST(NeuronLayerTest, TestBNLLGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  BNLLLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  BNLLLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_platform.cpp b/src/caffe/test/test_platform.cpp
index c3868f34d9f..f3513e08814 100644
--- a/src/caffe/test/test_platform.cpp
+++ b/src/caffe/test/test_platform.cpp
@@ -1,11 +1,11 @@
-// Copyright 2014 BVLC and contributors.
+#ifndef CPU_ONLY
 
-#include <cstdlib>
 #include <cstdio>
+#include <cstdlib>
 
-#include "cuda_runtime.h"
 #include "glog/logging.h"
 #include "gtest/gtest.h"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
@@ -47,7 +47,11 @@ TEST_F(PlatformTest, TestInitialization) {
          CAFFE_TEST_CUDA_PROP.multiProcessorCount);
   printf("Kernel execution timeout:      %s\n",
          (CAFFE_TEST_CUDA_PROP.kernelExecTimeoutEnabled ? "Yes" : "No"));
+  printf("Unified virtual addressing:    %s\n",
+         (CAFFE_TEST_CUDA_PROP.unifiedAddressing ? "Yes" : "No"));
   EXPECT_TRUE(true);
 }
 
 }  // namespace caffe
+
+#endif  // CPU_ONLY
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index 41d4841f444..5be01f77b32 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -1,28 +1,27 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class PoolingLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class PoolingLayerTest : public ::testing::Test {
  protected:
   PoolingLayerTest()
       : blob_bottom_(new Blob<Dtype>()),
-        blob_top_(new Blob<Dtype>()) {}
+        blob_top_(new Blob<Dtype>()),
+        blob_top_mask_(new Blob<Dtype>()) {}
   virtual void SetUp() {
     Caffe::set_random_seed(1701);
     blob_bottom_->Reshape(2, 3, 6, 5);
@@ -33,22 +32,351 @@ class PoolingLayerTest : public ::testing::Test {
     blob_bottom_vec_.push_back(blob_bottom_);
     blob_top_vec_.push_back(blob_top_);
   }
-  virtual ~PoolingLayerTest() { delete blob_bottom_; delete blob_top_; }
+  virtual ~PoolingLayerTest() {
+    delete blob_bottom_;
+    delete blob_top_;
+    delete blob_top_mask_;
+  }
   Blob<Dtype>* const blob_bottom_;
   Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_mask_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
+  // Test for 2x 2 square pooling layer
+  void TestForwardSquare() {
+    LayerParameter layer_param;
+    PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+    pooling_param->set_kernel_size(2);
+    pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+    const int num = 2;
+    const int channels = 2;
+    blob_bottom_->Reshape(num, channels, 3, 5);
+    // Input: 2x 2 channels of:
+    //     [1 2 5 2 3]
+    //     [9 4 1 4 8]
+    //     [1 2 5 2 3]
+    for (int i = 0; i < 15 * num * channels; i += 15) {
+      blob_bottom_->mutable_cpu_data()[i +  0] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  1] = 2;
+      blob_bottom_->mutable_cpu_data()[i +  2] = 5;
+      blob_bottom_->mutable_cpu_data()[i +  3] = 2;
+      blob_bottom_->mutable_cpu_data()[i +  4] = 3;
+      blob_bottom_->mutable_cpu_data()[i +  5] = 9;
+      blob_bottom_->mutable_cpu_data()[i +  6] = 4;
+      blob_bottom_->mutable_cpu_data()[i +  7] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  8] = 4;
+      blob_bottom_->mutable_cpu_data()[i +  9] = 8;
+      blob_bottom_->mutable_cpu_data()[i + 10] = 1;
+      blob_bottom_->mutable_cpu_data()[i + 11] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 12] = 5;
+      blob_bottom_->mutable_cpu_data()[i + 13] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 14] = 3;
+    }
+    PoolingLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_->num(), num);
+    EXPECT_EQ(blob_top_->channels(), channels);
+    EXPECT_EQ(blob_top_->height(), 2);
+    EXPECT_EQ(blob_top_->width(), 4);
+    if (blob_top_vec_.size() > 1) {
+      EXPECT_EQ(blob_top_mask_->num(), num);
+      EXPECT_EQ(blob_top_mask_->channels(), channels);
+      EXPECT_EQ(blob_top_mask_->height(), 2);
+      EXPECT_EQ(blob_top_mask_->width(), 4);
+    }
+    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    // Expected output: 2x 2 channels of:
+    //     [9 5 5 8]
+    //     [9 5 5 8]
+    for (int i = 0; i < 8 * num * channels; i += 8) {
+      EXPECT_EQ(blob_top_->cpu_data()[i + 0], 9);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 1], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 2], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 3], 8);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 4], 9);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 5], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 6], 5);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 7], 8);
+    }
+    if (blob_top_vec_.size() > 1) {
+      // Expected mask output: 2x 2 channels of:
+      //     [5  2  2 9]
+      //     [5 12 12 9]
+      for (int i = 0; i < 8 * num * channels; i += 8) {
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 0],  5);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 1],  2);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 2],  2);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 3],  9);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 4],  5);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 5], 12);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 6], 12);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 7],  9);
+      }
+    }
+  }
+  // Test for 3x 2 rectangular pooling layer with kernel_h > kernel_w
+  void TestForwardRectHigh() {
+    LayerParameter layer_param;
+    PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+    pooling_param->set_kernel_h(3);
+    pooling_param->set_kernel_w(2);
+    pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+    const int num = 2;
+    const int channels = 2;
+    blob_bottom_->Reshape(num, channels, 6, 6);
+    // Input: 2x 2 channels of:
+    // [35     1     6    26    19    24]
+    // [ 3    32     7    21    23    25]
+    // [31     9     2    22    27    20]
+    // [ 8    28    33    17    10    15]
+    // [30     5    34    12    14    16]
+    // [ 4    36    29    13    18    11]
+    // (this is generated by magic(6) in MATLAB)
+    for (int i = 0; i < 36 * num * channels; i += 36) {
+      blob_bottom_->mutable_cpu_data()[i +  0] = 35;
+      blob_bottom_->mutable_cpu_data()[i +  1] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  2] = 6;
+      blob_bottom_->mutable_cpu_data()[i +  3] = 26;
+      blob_bottom_->mutable_cpu_data()[i +  4] = 19;
+      blob_bottom_->mutable_cpu_data()[i +  5] = 24;
+      blob_bottom_->mutable_cpu_data()[i +  6] = 3;
+      blob_bottom_->mutable_cpu_data()[i +  7] = 32;
+      blob_bottom_->mutable_cpu_data()[i +  8] = 7;
+      blob_bottom_->mutable_cpu_data()[i +  9] = 21;
+      blob_bottom_->mutable_cpu_data()[i + 10] = 23;
+      blob_bottom_->mutable_cpu_data()[i + 11] = 25;
+      blob_bottom_->mutable_cpu_data()[i + 12] = 31;
+      blob_bottom_->mutable_cpu_data()[i + 13] = 9;
+      blob_bottom_->mutable_cpu_data()[i + 14] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 15] = 22;
+      blob_bottom_->mutable_cpu_data()[i + 16] = 27;
+      blob_bottom_->mutable_cpu_data()[i + 17] = 20;
+      blob_bottom_->mutable_cpu_data()[i + 18] = 8;
+      blob_bottom_->mutable_cpu_data()[i + 19] = 28;
+      blob_bottom_->mutable_cpu_data()[i + 20] = 33;
+      blob_bottom_->mutable_cpu_data()[i + 21] = 17;
+      blob_bottom_->mutable_cpu_data()[i + 22] = 10;
+      blob_bottom_->mutable_cpu_data()[i + 23] = 15;
+      blob_bottom_->mutable_cpu_data()[i + 24] = 30;
+      blob_bottom_->mutable_cpu_data()[i + 25] = 5;
+      blob_bottom_->mutable_cpu_data()[i + 26] = 34;
+      blob_bottom_->mutable_cpu_data()[i + 27] = 12;
+      blob_bottom_->mutable_cpu_data()[i + 28] = 14;
+      blob_bottom_->mutable_cpu_data()[i + 29] = 16;
+      blob_bottom_->mutable_cpu_data()[i + 30] = 4;
+      blob_bottom_->mutable_cpu_data()[i + 31] = 36;
+      blob_bottom_->mutable_cpu_data()[i + 32] = 29;
+      blob_bottom_->mutable_cpu_data()[i + 33] = 13;
+      blob_bottom_->mutable_cpu_data()[i + 34] = 18;
+      blob_bottom_->mutable_cpu_data()[i + 35] = 11;
+    }
+    PoolingLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_->num(), num);
+    EXPECT_EQ(blob_top_->channels(), channels);
+    EXPECT_EQ(blob_top_->height(), 4);
+    EXPECT_EQ(blob_top_->width(), 5);
+    if (blob_top_vec_.size() > 1) {
+      EXPECT_EQ(blob_top_mask_->num(), num);
+      EXPECT_EQ(blob_top_mask_->channels(), channels);
+      EXPECT_EQ(blob_top_mask_->height(), 4);
+      EXPECT_EQ(blob_top_mask_->width(), 5);
+    }
+    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    // Expected output: 2x 2 channels of:
+    // [35    32    26    27    27]
+    // [32    33    33    27    27]
+    // [31    34    34    27    27]
+    // [36    36    34    18    18]
+    for (int i = 0; i < 20 * num * channels; i += 20) {
+      EXPECT_EQ(blob_top_->cpu_data()[i +  0], 35);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  1], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  2], 26);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  3], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  4], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  5], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  6], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  7], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  8], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  9], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 10], 31);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 11], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 12], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 13], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 14], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 15], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 16], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 17], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 18], 18);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 19], 18);
+    }
+    if (blob_top_vec_.size() > 1) {
+        // [ 1     8     4    17    17]
+        // [ 8    21    21    17    17]
+        // [13    27    27    17    17]
+        // [32    32    27    35    35]
+      for (int i = 0; i < 20 * num * channels; i += 20) {
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  0],  0);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  1],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  2],  3);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  3], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  4], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  5],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  6], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  7], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  8], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  9], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 10], 12);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 11], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 12], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 13], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 14], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 15], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 16], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 17], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 18], 34);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 19], 34);
+      }
+    }
+  }
+  // Test for rectangular pooling layer with kernel_w > kernel_h
+  void TestForwardRectWide() {
+    LayerParameter layer_param;
+    PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+    pooling_param->set_kernel_h(2);
+    pooling_param->set_kernel_w(3);
+    pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+    const int num = 2;
+    const int channels = 2;
+    blob_bottom_->Reshape(num, channels, 6, 6);
+    // Input: 2x 2 channels of:
+    // [35     1     6    26    19    24]
+    // [ 3    32     7    21    23    25]
+    // [31     9     2    22    27    20]
+    // [ 8    28    33    17    10    15]
+    // [30     5    34    12    14    16]
+    // [ 4    36    29    13    18    11]
+    // (this is generated by magic(6) in MATLAB)
+    for (int i = 0; i < 36 * num * channels; i += 36) {
+      blob_bottom_->mutable_cpu_data()[i +  0] = 35;
+      blob_bottom_->mutable_cpu_data()[i +  1] = 1;
+      blob_bottom_->mutable_cpu_data()[i +  2] = 6;
+      blob_bottom_->mutable_cpu_data()[i +  3] = 26;
+      blob_bottom_->mutable_cpu_data()[i +  4] = 19;
+      blob_bottom_->mutable_cpu_data()[i +  5] = 24;
+      blob_bottom_->mutable_cpu_data()[i +  6] = 3;
+      blob_bottom_->mutable_cpu_data()[i +  7] = 32;
+      blob_bottom_->mutable_cpu_data()[i +  8] = 7;
+      blob_bottom_->mutable_cpu_data()[i +  9] = 21;
+      blob_bottom_->mutable_cpu_data()[i + 10] = 23;
+      blob_bottom_->mutable_cpu_data()[i + 11] = 25;
+      blob_bottom_->mutable_cpu_data()[i + 12] = 31;
+      blob_bottom_->mutable_cpu_data()[i + 13] = 9;
+      blob_bottom_->mutable_cpu_data()[i + 14] = 2;
+      blob_bottom_->mutable_cpu_data()[i + 15] = 22;
+      blob_bottom_->mutable_cpu_data()[i + 16] = 27;
+      blob_bottom_->mutable_cpu_data()[i + 17] = 20;
+      blob_bottom_->mutable_cpu_data()[i + 18] = 8;
+      blob_bottom_->mutable_cpu_data()[i + 19] = 28;
+      blob_bottom_->mutable_cpu_data()[i + 20] = 33;
+      blob_bottom_->mutable_cpu_data()[i + 21] = 17;
+      blob_bottom_->mutable_cpu_data()[i + 22] = 10;
+      blob_bottom_->mutable_cpu_data()[i + 23] = 15;
+      blob_bottom_->mutable_cpu_data()[i + 24] = 30;
+      blob_bottom_->mutable_cpu_data()[i + 25] = 5;
+      blob_bottom_->mutable_cpu_data()[i + 26] = 34;
+      blob_bottom_->mutable_cpu_data()[i + 27] = 12;
+      blob_bottom_->mutable_cpu_data()[i + 28] = 14;
+      blob_bottom_->mutable_cpu_data()[i + 29] = 16;
+      blob_bottom_->mutable_cpu_data()[i + 30] = 4;
+      blob_bottom_->mutable_cpu_data()[i + 31] = 36;
+      blob_bottom_->mutable_cpu_data()[i + 32] = 29;
+      blob_bottom_->mutable_cpu_data()[i + 33] = 13;
+      blob_bottom_->mutable_cpu_data()[i + 34] = 18;
+      blob_bottom_->mutable_cpu_data()[i + 35] = 11;
+    }
+    PoolingLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, &blob_top_vec_);
+    EXPECT_EQ(blob_top_->num(), num);
+    EXPECT_EQ(blob_top_->channels(), channels);
+    EXPECT_EQ(blob_top_->height(), 5);
+    EXPECT_EQ(blob_top_->width(), 4);
+    if (blob_top_vec_.size() > 1) {
+      EXPECT_EQ(blob_top_mask_->num(), num);
+      EXPECT_EQ(blob_top_mask_->channels(), channels);
+      EXPECT_EQ(blob_top_mask_->height(), 5);
+      EXPECT_EQ(blob_top_mask_->width(), 4);
+    }
+    layer.Forward(blob_bottom_vec_, &blob_top_vec_);
+    // Expected output: 2x 2 channels of:
+    // [35    32    26    26]
+    // [32    32    27    27]
+    // [33    33    33    27]
+    // [34    34    34    17]
+    // [36    36    34    18]
+    for (int i = 0; i < 20 * num * channels; i += 20) {
+      EXPECT_EQ(blob_top_->cpu_data()[i +  0], 35);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  1], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  2], 26);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  3], 26);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  4], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  5], 32);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  6], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  7], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  8], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i +  9], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 10], 33);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 11], 27);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 12], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 13], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 14], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 15], 17);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 16], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 17], 36);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 18], 34);
+      EXPECT_EQ(blob_top_->cpu_data()[i + 19], 18);
+    }
+    if (blob_top_vec_.size() > 1) {
+        // [ 1     8     4     4]
+        // [ 8     8    17    17]
+        // [21    21    21    17]
+        // [27    27    27    22]
+        // [32    32    27    35]
+      for (int i = 0; i < 20 * num * channels; i += 20) {
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  0],  0);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  1],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  2],  3);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  3],  3);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  4],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  5],  7);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  6], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  7], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  8], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i +  9], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 10], 20);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 11], 16);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 12], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 13], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 14], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 15], 21);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 16], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 17], 31);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 18], 26);
+        EXPECT_EQ(blob_top_mask_->cpu_data()[i + 19], 34);
+      }
+    }
+  }
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(PoolingLayerTest, Dtypes);
+TYPED_TEST_CASE(PoolingLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(PoolingLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
-  PoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -57,13 +385,14 @@ TYPED_TEST(PoolingLayerTest, TestSetup) {
 }
 
 TYPED_TEST(PoolingLayerTest, TestSetupPadded) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(2);
   pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  PoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
   EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
@@ -72,13 +401,11 @@ TYPED_TEST(PoolingLayerTest, TestSetupPadded) {
 }
 
 /*
-TYPED_TEST(PoolingLayerTest, PrintGPUBackward) {
+TYPED_TEST(PoolingLayerTest, PrintBackward) {
   LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-  Caffe::set_mode(Caffe::GPU);
+  layer_param.set_kernelsize(3);
+  layer_param.set_stride(2);
+  layer_param.set_pool(LayerParameter_PoolMethod_MAX);
   PoolingLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
@@ -90,7 +417,7 @@ TYPED_TEST(PoolingLayerTest, PrintGPUBackward) {
   }
 
   for (int i = 0; i < this->blob_top_->count(); ++i) {
-    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+    this->blob_top_->mutable_cpu_diff()[i] = i;
   }
   layer.Backward(this->blob_top_vec_, true, &(this->blob_bottom_vec_));
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
@@ -99,87 +426,124 @@ TYPED_TEST(PoolingLayerTest, PrintGPUBackward) {
 }
 */
 
-TYPED_TEST(PoolingLayerTest, TestCPUGradientMax) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-  Caffe::set_mode(Caffe::CPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-4, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+TYPED_TEST(PoolingLayerTest, TestForwardMax) {
+  this->TestForwardSquare();
+  this->TestForwardRectHigh();
+  this->TestForwardRectWide();
 }
 
-TYPED_TEST(PoolingLayerTest, TestGPUGradientMax) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
-  Caffe::set_mode(Caffe::GPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-4, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+TYPED_TEST(PoolingLayerTest, TestForwardMaxTopMask) {
+  this->blob_top_vec_.push_back(this->blob_top_mask_);
+  this->TestForwardSquare();
+  this->TestForwardRectHigh();
+  this->TestForwardRectWide();
 }
 
+TYPED_TEST(PoolingLayerTest, TestGradientMax) {
+  typedef typename TypeParam::Dtype Dtype;
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pad(1);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+      PoolingLayer<Dtype> layer(layer_param);
+      GradientChecker<Dtype> checker(1e-4, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
+}
 
-TYPED_TEST(PoolingLayerTest, TestCPUForwardAve) {
+TYPED_TEST(PoolingLayerTest, TestForwardMaxPadded) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(1);
-  pooling_param->set_pad(1);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::CPU);
+  pooling_param->set_stride(2);
+  pooling_param->set_pad(2);
+  pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
   this->blob_bottom_->Reshape(1, 1, 3, 3);
-  FillerParameter filler_param;
-  filler_param.set_value(TypeParam(2));
-  ConstantFiller<TypeParam> filler(filler_param);
-  filler.Fill(this->blob_bottom_);
-  PoolingLayer<TypeParam> layer(layer_param);
+  // Input:
+  //     [ 1 2 4 ]
+  //     [ 2 3 2 ]
+  //     [ 4 2 1 ]
+  this->blob_bottom_->mutable_cpu_data()[0] = 1;
+  this->blob_bottom_->mutable_cpu_data()[1] = 2;
+  this->blob_bottom_->mutable_cpu_data()[2] = 4;
+  this->blob_bottom_->mutable_cpu_data()[3] = 2;
+  this->blob_bottom_->mutable_cpu_data()[4] = 3;
+  this->blob_bottom_->mutable_cpu_data()[5] = 2;
+  this->blob_bottom_->mutable_cpu_data()[6] = 4;
+  this->blob_bottom_->mutable_cpu_data()[7] = 2;
+  this->blob_bottom_->mutable_cpu_data()[8] = 1;
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 3);
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  TypeParam epsilon = 1e-5;
-  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[2], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[4], 2.0    , epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[6], 8.0 / 9, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4.0 / 3, epsilon);
-  EXPECT_NEAR(this->blob_top_->cpu_data()[8], 8.0 / 9, epsilon);
+  Dtype epsilon = 1e-8;
+  // Output:
+  //     [ 1 4 4 ]
+  //     [ 4 4 4 ]
+  //     [ 4 4 1 ]
+  EXPECT_NEAR(this->blob_top_->cpu_data()[0], 1, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[2], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[3], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[4], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[5], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[6], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[7], 4, epsilon);
+  EXPECT_NEAR(this->blob_top_->cpu_data()[8], 1, epsilon);
 }
 
+TYPED_TEST(PoolingLayerTest, TestGradientMaxTopMask) {
+  typedef typename TypeParam::Dtype Dtype;
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_MAX);
+      this->blob_top_vec_.push_back(this->blob_top_mask_);
+      PoolingLayer<Dtype> layer(layer_param);
+      GradientChecker<Dtype> checker(1e-4, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+      this->blob_top_vec_.pop_back();
+    }
+  }
+}
 
-TYPED_TEST(PoolingLayerTest, TestGPUForwardAve) {
+TYPED_TEST(PoolingLayerTest, TestForwardAve) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
   pooling_param->set_stride(1);
   pooling_param->set_pad(1);
   pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::GPU);
   this->blob_bottom_->Reshape(1, 1, 3, 3);
   FillerParameter filler_param;
-  filler_param.set_value(TypeParam(2));
-  ConstantFiller<TypeParam> filler(filler_param);
+  filler_param.set_value(Dtype(2));
+  ConstantFiller<Dtype> filler(filler_param);
   filler.Fill(this->blob_bottom_);
-  PoolingLayer<TypeParam> layer(layer_param);
+  PoolingLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_->num(), 1);
   EXPECT_EQ(this->blob_top_->channels(), 1);
   EXPECT_EQ(this->blob_top_->height(), 3);
   EXPECT_EQ(this->blob_top_->width(), 3);
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  TypeParam epsilon = 1e-5;
+  Dtype epsilon = 1e-5;
   EXPECT_NEAR(this->blob_top_->cpu_data()[0], 8.0 / 9, epsilon);
   EXPECT_NEAR(this->blob_top_->cpu_data()[1], 4.0 / 3, epsilon);
   EXPECT_NEAR(this->blob_top_->cpu_data()[2], 8.0 / 9, epsilon);
@@ -191,62 +555,41 @@ TYPED_TEST(PoolingLayerTest, TestGPUForwardAve) {
   EXPECT_NEAR(this->blob_top_->cpu_data()[8], 8.0 / 9, epsilon);
 }
 
-
-TYPED_TEST(PoolingLayerTest, TestCPUGradientAve) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::CPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-
-TYPED_TEST(PoolingLayerTest, TestGPUGradientAve) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::GPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-
-TYPED_TEST(PoolingLayerTest, TestCPUGradientAvePadded) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pad(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::CPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+TYPED_TEST(PoolingLayerTest, TestGradientAve) {
+  typedef typename TypeParam::Dtype Dtype;
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+      PoolingLayer<Dtype> layer(layer_param);
+      GradientChecker<Dtype> checker(1e-2, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
 }
 
-
-TYPED_TEST(PoolingLayerTest, TestGPUGradientAvePadded) {
-  LayerParameter layer_param;
-  PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
-  pooling_param->set_kernel_size(3);
-  pooling_param->set_stride(2);
-  pooling_param->set_pad(2);
-  pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
-  Caffe::set_mode(Caffe::GPU);
-  PoolingLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
+TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) {
+  typedef typename TypeParam::Dtype Dtype;
+  for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
+    for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
+      LayerParameter layer_param;
+      PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
+      pooling_param->set_kernel_h(kernel_h);
+      pooling_param->set_kernel_w(kernel_w);
+      pooling_param->set_stride(2);
+      pooling_param->set_pad(2);
+      pooling_param->set_pool(PoolingParameter_PoolMethod_AVE);
+      PoolingLayer<Dtype> layer(layer_param);
+      GradientChecker<Dtype> checker(1e-2, 1e-2);
+      checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+          &(this->blob_top_vec_));
+    }
+  }
 }
 
 
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index 99b127d3dbc..0c104c20b7c 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -1,27 +1,22 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
-
-using std::isnan;
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class PowerLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class PowerLayerTest : public ::testing::Test {
  protected:
   PowerLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -56,8 +51,8 @@ class PowerLayerTest : public ::testing::Test {
       if (isnan(expected_value)) {
         EXPECT_TRUE(isnan(top_data[i]));
       } else {
-        Dtype precision = max(Dtype(abs(expected_value * 0.0001)),
-                              min_precision);
+        Dtype precision = std::max(
+          Dtype(std::abs(expected_value * Dtype(1e-4))), min_precision);
         EXPECT_NEAR(expected_value, top_data[i], precision);
       }
     }
@@ -90,166 +85,85 @@ class PowerLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(PowerLayerTest, Dtypes);
-
-TYPED_TEST(PowerLayerTest, TestPowerCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestForward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerGradientCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestBackward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerGradientShiftZeroCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = 0.0;
-  this->TestBackward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerZeroCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 0.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestForward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerZeroGradientCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 0.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestBackward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerOneCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 1.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestForward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerOneGradientCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 1.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestBackward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerTwoCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.34;
-  TypeParam shift = -2.4;
-  this->TestForward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerTwoGradientCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
-  this->TestBackward(power, scale, shift);
-}
-
-TYPED_TEST(PowerLayerTest, TestPowerTwoScaleHalfGradientCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.5;
-  TypeParam shift = -2.4;
-  this->TestBackward(power, scale, shift);
-}
+TYPED_TEST_CASE(PowerLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(PowerLayerTest, TestPowerGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPower) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 0.37;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestForward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 0.37;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestBackward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerGradientShiftZeroGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 0.37;
-  TypeParam scale = 0.83;
-  TypeParam shift = 0.0;
+TYPED_TEST(PowerLayerTest, TestPowerGradientShiftZero) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 0.37;
+  Dtype scale = 0.83;
+  Dtype shift = 0.0;
   this->TestBackward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerZeroGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 0.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerZero) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 0.0;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestForward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerZeroGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 0.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerZeroGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 0.0;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestBackward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerOneGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 1.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerOne) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 1.0;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestForward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerOneGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 1.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerOneGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 1.0;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestBackward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerTwoGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.34;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerTwo) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 2.0;
+  Dtype scale = 0.34;
+  Dtype shift = -2.4;
   this->TestForward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerTwoGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.83;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerTwoGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 2.0;
+  Dtype scale = 0.83;
+  Dtype shift = -2.4;
   this->TestBackward(power, scale, shift);
 }
 
-TYPED_TEST(PowerLayerTest, TestPowerTwoScaleHalfGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
-  TypeParam power = 2.0;
-  TypeParam scale = 0.5;
-  TypeParam shift = -2.4;
+TYPED_TEST(PowerLayerTest, TestPowerTwoScaleHalfGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  Dtype power = 2.0;
+  Dtype scale = 0.5;
+  Dtype shift = -2.4;
   this->TestBackward(power, scale, shift);
 }
 
diff --git a/src/caffe/test/test_protobuf.cpp b/src/caffe/test/test_protobuf.cpp
index 182af2e4611..0c502d6dd36 100644
--- a/src/caffe/test/test_protobuf.cpp
+++ b/src/caffe/test/test_protobuf.cpp
@@ -1,14 +1,14 @@
-// Copyright 2014 BVLC and contributors.
-
 // This is simply a script that tries serializing protocol buffer in text
 // format. Nothing special here and no actual code is being tested.
 #include <string>
 
 #include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
-#include "caffe/test/test_caffe_main.hpp"
+
 #include "caffe/proto/caffe.pb.h"
 
+#include "caffe/test/test_caffe_main.hpp"
+
 namespace caffe {
 
 class ProtoTest : public ::testing::Test {};
diff --git a/src/caffe/test/test_random_number_generator.cpp b/src/caffe/test/test_random_number_generator.cpp
index 62daf6087fd..98424c06bfc 100644
--- a/src/caffe/test/test_random_number_generator.cpp
+++ b/src/caffe/test/test_random_number_generator.cpp
@@ -1,13 +1,12 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <cuda_runtime.h>
 #include <cmath>
 #include <cstring>
 
 #include "gtest/gtest.h"
+
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
+
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
@@ -16,9 +15,9 @@ template <typename Dtype>
 class RandomNumberGeneratorTest : public ::testing::Test {
  protected:
   RandomNumberGeneratorTest()
-     : sample_size_(10000),
+     : mean_bound_multiplier_(3.8),  // ~99.99% confidence for test failure.
+       sample_size_(10000),
        seed_(1701),
-       mean_bound_multiplier_(3.8),  // ~99.99% confidence for test failure.
        data_(new SyncedMemory(sample_size_ * sizeof(Dtype))),
        data_2_(new SyncedMemory(sample_size_ * sizeof(Dtype))),
        int_data_(new SyncedMemory(sample_size_ * sizeof(int))),
@@ -65,11 +64,6 @@ class RandomNumberGeneratorTest : public ::testing::Test {
     caffe_rng_gaussian(sample_size_, mu, sigma, rng_data);
   }
 
-  void RngGaussianFillGPU(const Dtype mu, const Dtype sigma, void* gpu_data) {
-    Dtype* rng_data = static_cast<Dtype*>(gpu_data);
-    caffe_gpu_rng_gaussian(sample_size_, mu, sigma, rng_data);
-  }
-
   void RngGaussianChecks(const Dtype mu, const Dtype sigma,
                          const void* cpu_data, const Dtype sparse_p = 0) {
     const Dtype* rng_data = static_cast<const Dtype*>(cpu_data);
@@ -114,19 +108,6 @@ class RandomNumberGeneratorTest : public ::testing::Test {
     caffe_rng_uniform(sample_size_, lower, upper, rng_data);
   }
 
-  void RngUniformFillGPU(const Dtype lower, const Dtype upper, void* gpu_data) {
-    CHECK_GE(upper, lower);
-    Dtype* rng_data = static_cast<Dtype*>(gpu_data);
-    caffe_gpu_rng_uniform(sample_size_, lower, upper, rng_data);
-  }
-
-  // Fills with uniform integers in [0, UINT_MAX] using 2 argument form of
-  // caffe_gpu_rng_uniform.
-  void RngUniformIntFillGPU(void* gpu_data) {
-    unsigned int* rng_data = static_cast<unsigned int*>(gpu_data);
-    caffe_gpu_rng_uniform(sample_size_, rng_data);
-  }
-
   void RngUniformChecks(const Dtype lower, const Dtype upper,
                         const void* cpu_data, const Dtype sparse_p = 0) {
     const Dtype* rng_data = static_cast<const Dtype*>(cpu_data);
@@ -188,6 +169,28 @@ class RandomNumberGeneratorTest : public ::testing::Test {
     EXPECT_NEAR(sample_mean, true_mean, bound);
   }
 
+#ifndef CPU_ONLY
+
+  void RngGaussianFillGPU(const Dtype mu, const Dtype sigma, void* gpu_data) {
+    Dtype* rng_data = static_cast<Dtype*>(gpu_data);
+    caffe_gpu_rng_gaussian(sample_size_, mu, sigma, rng_data);
+  }
+
+  void RngUniformFillGPU(const Dtype lower, const Dtype upper, void* gpu_data) {
+    CHECK_GE(upper, lower);
+    Dtype* rng_data = static_cast<Dtype*>(gpu_data);
+    caffe_gpu_rng_uniform(sample_size_, lower, upper, rng_data);
+  }
+
+  // Fills with uniform integers in [0, UINT_MAX] using 2 argument form of
+  // caffe_gpu_rng_uniform.
+  void RngUniformIntFillGPU(void* gpu_data) {
+    unsigned int* rng_data = static_cast<unsigned int*>(gpu_data);
+    caffe_gpu_rng_uniform(sample_size_, rng_data);
+  }
+
+#endif
+
   int num_above_mean;
   int num_below_mean;
 
@@ -202,10 +205,7 @@ class RandomNumberGeneratorTest : public ::testing::Test {
   shared_ptr<SyncedMemory> int_data_2_;
 };
 
-
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(RandomNumberGeneratorTest, Dtypes);
-
+TYPED_TEST_CASE(RandomNumberGeneratorTest, TestDtypes);
 
 TYPED_TEST(RandomNumberGeneratorTest, TestRngGaussian) {
   const TypeParam mu = 0;
@@ -396,6 +396,7 @@ TYPED_TEST(RandomNumberGeneratorTest, TestRngBernoulliTimesBernoulli) {
   EXPECT_NEAR(true_mean, sample_p, bound);
 }
 
+#ifndef CPU_ONLY
 
 TYPED_TEST(RandomNumberGeneratorTest, TestRngGaussianGPU) {
   const TypeParam mu = 0;
@@ -515,5 +516,6 @@ TYPED_TEST(RandomNumberGeneratorTest, TestRngUniformTimesUniformGPU) {
   this->RngUniformChecks(lower_prod, upper_prod, uniform_data_1);
 }
 
+#endif
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_sgd_solver.cpp b/src/caffe/test/test_sgd_solver.cpp
new file mode 100644
index 00000000000..1ec24b134c6
--- /dev/null
+++ b/src/caffe/test/test_sgd_solver.cpp
@@ -0,0 +1,345 @@
+#include <algorithm>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "google/protobuf/text_format.h"
+
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+using std::ostringstream;
+
+namespace caffe {
+
+template <typename TypeParam>
+class SGDSolverTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  SGDSolverTest() :
+      seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
+
+  // MockSGDSolver: an SGDSolver with public history.
+  class MockSGDSolver : public SGDSolver<Dtype> {
+   public:
+    explicit MockSGDSolver(const SolverParameter& param) :
+        SGDSolver<Dtype>(param) {}
+    vector<shared_ptr<Blob<Dtype> > >& history() { return this->history_; }
+  };
+
+  shared_ptr<MockSGDSolver> solver_;
+  int seed_;
+  int num_, channels_, height_, width_;
+
+  virtual void InitSolverFromProtoString(const string& proto) {
+    SolverParameter param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    // Disable saving a final snapshot so the tests don't pollute the user's
+    // working directory with useless snapshots.
+    param.set_snapshot_after_train(false);
+    // Set the solver_mode according to current Caffe::mode.
+    switch (Caffe::mode()) {
+      case Caffe::CPU:
+        param.set_solver_mode(SolverParameter_SolverMode_CPU);
+        break;
+      case Caffe::GPU:
+        param.set_solver_mode(SolverParameter_SolverMode_GPU);
+        break;
+      default:
+        LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
+    }
+    solver_.reset(new MockSGDSolver(param));
+  }
+
+  void RunLeastSquaresSolver(const Dtype learning_rate,
+      const Dtype weight_decay, const Dtype momentum, const int num_iters) {
+    ostringstream proto;
+    proto <<
+       "max_iter: " << num_iters << " "
+       "base_lr: " << learning_rate << " "
+       "lr_policy: 'fixed' "
+       "net_param { "
+       "  name: 'TestNetwork' "
+       "  layers: { "
+       "    name: 'data' "
+       "    type: DUMMY_DATA "
+       "    dummy_data_param { "
+       "      num: " << num_ << " "
+       "      channels: " << channels_ << " "
+       "      height: " << height_ << " "
+       "      width: " << width_ << " "
+       "      channels: 1 "
+       "      height: 1 "
+       "      width: 1 "
+       "      data_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "    } "
+       "    top: 'data' "
+       "    top: 'targets' "
+       "  } "
+       "  layers: { "
+       "    name: 'innerprod' "
+       "    type: INNER_PRODUCT "
+       "    inner_product_param { "
+       "      num_output: 1 "
+       "      weight_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "      bias_filler { "
+       "        type: 'gaussian' "
+       "        std: 1.0 "
+       "      } "
+       "    } "
+       "    bottom: 'data' "
+       "    top: 'innerprod' "
+       "  } "
+       "  layers: { "
+       "    name: 'loss' "
+       "    type: EUCLIDEAN_LOSS "
+       "    bottom: 'innerprod' "
+       "    bottom: 'targets' "
+       "  } "
+       "} ";
+    if (weight_decay != 0) {
+      proto << "weight_decay: " << weight_decay << " ";
+    }
+    if (momentum != 0) {
+      proto << "momentum: " << momentum << " ";
+    }
+    Caffe::set_random_seed(this->seed_);
+    this->InitSolverFromProtoString(proto.str());
+    this->solver_->Solve();
+  }
+
+  // Compute an update value given the current state of the train net,
+  // using the analytical formula for the least squares gradient.
+  // updated_params will store the updated weight and bias results,
+  // using the blobs' diffs to hold the update values themselves.
+  void ComputeLeastSquaresUpdate(const Dtype learning_rate,
+      const Dtype weight_decay, const Dtype momentum,
+      vector<shared_ptr<Blob<Dtype> > >* updated_params) {
+    const int N = num_;
+    const int D = channels_ * height_ * width_;
+
+    // Run a forward pass, and manually compute the update values from the
+    // result.
+    Net<Dtype>& net = *this->solver_->net();
+    vector<Blob<Dtype>*> empty_bottom_vec;
+    net.Forward(empty_bottom_vec);
+    ASSERT_TRUE(net.has_blob("data"));
+    const Blob<Dtype>& data = *net.blob_by_name("data");
+    ASSERT_TRUE(net.has_blob("targets"));
+    const Blob<Dtype>& targets = *net.blob_by_name("targets");
+    ASSERT_TRUE(net.has_layer("innerprod"));
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    const int num_param_blobs = 2;
+    ASSERT_EQ(num_param_blobs, param_blobs.size());
+    const Blob<Dtype>& weights = *param_blobs[0];
+    const Blob<Dtype>& bias = *param_blobs[1];
+    ASSERT_EQ(D * N, data.count());
+    ASSERT_EQ(N, targets.count());
+    ASSERT_EQ(D, weights.count());
+    ASSERT_EQ(1, bias.count());
+
+    updated_params->clear();
+    updated_params->resize(num_param_blobs);
+    for (int i = 0; i < num_param_blobs; ++i) {
+      (*updated_params)[i].reset(new Blob<Dtype>());
+    }
+    Blob<Dtype>& updated_weights = *(*updated_params)[0];
+    updated_weights.ReshapeLike(weights);
+    Blob<Dtype>& updated_bias = *(*updated_params)[1];
+    updated_bias.ReshapeLike(bias);
+
+    for (int i = 0; i <= D; ++i) {
+      // Compute the derivative with respect to the ith weight (i.e., the ith
+      // element of the gradient).
+      Dtype grad = 0;
+      for (int j = 0; j <= D; ++j) {
+        // Compute element (i, j) of X^T * X.
+        Dtype element = 0;
+        for (int k = 0; k < N; ++k) {
+          // (i, k) in X^T (== (k, i) in X) times (k, j) in X.
+          const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
+          const Dtype element_j = (j == D) ? 1 : data.cpu_data()[k * D + j];
+          element += element_i * element_j;
+        }
+        if (j == D) {
+          grad += element * bias.cpu_data()[0];
+        } else {
+          grad += element * weights.cpu_data()[j];
+        }
+      }
+      for (int k = 0; k < N; ++k) {
+        const Dtype element_i = (i == D) ? 1 : data.cpu_data()[k * D + i];
+        grad -= element_i * targets.cpu_data()[k];
+      }
+      // Scale the gradient over the N samples.
+      grad /= N;
+      // Add the weight decay to the gradient.
+      grad += weight_decay *
+          ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
+      // Finally, add any momentum.
+      const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
+      ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
+      Dtype update_value = learning_rate * grad;
+      if (i == D) {
+        update_value += momentum * history[1]->cpu_data()[0];
+        updated_bias.mutable_cpu_diff()[0] = update_value;
+        updated_bias.mutable_cpu_data()[0] = bias.cpu_data()[0] - update_value;
+      } else {
+        update_value += momentum * history[0]->cpu_data()[i];
+        updated_weights.mutable_cpu_diff()[i] = update_value;
+        updated_weights.mutable_cpu_data()[i] =
+            weights.cpu_data()[i] - update_value;
+      }
+    }
+  }
+
+  void CheckLeastSquaresUpdate(
+      const vector<shared_ptr<Blob<Dtype> > >& updated_params) {
+    const int D = channels_ * height_ * width_;
+
+    const Blob<Dtype>& updated_weights = *updated_params[0];
+    const Blob<Dtype>& updated_bias = *updated_params[1];
+
+    Net<Dtype>& net = *this->solver_->net();
+    ASSERT_TRUE(net.has_layer("innerprod"));
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    ASSERT_EQ(2, param_blobs.size());
+    const Blob<Dtype>& solver_updated_weights = *param_blobs[0];
+    ASSERT_EQ(D, solver_updated_weights.count());
+    const double kPrecision = 1e-3;
+    const double kMinPrecision = 1e-7;
+    for (int i = 0; i < D; ++i) {
+      const Dtype expected_updated_weight = updated_weights.cpu_data()[i];
+      const Dtype solver_updated_weight = solver_updated_weights.cpu_data()[i];
+      const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_updated_weight), fabs(solver_updated_weight)));
+      EXPECT_NEAR(expected_updated_weight, solver_updated_weight, error_margin);
+    }
+    const Blob<Dtype>& solver_updated_bias_blob = *param_blobs[1];
+    ASSERT_EQ(1, solver_updated_bias_blob.count());
+    const Dtype expected_updated_bias = updated_bias.cpu_data()[0];
+    const Dtype solver_updated_bias = solver_updated_bias_blob.cpu_data()[0];
+    const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_updated_bias), fabs(solver_updated_bias)));
+    EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin);
+
+    // Check the solver's history -- should contain the previous update value.
+    vector<shared_ptr<Blob<Dtype> > >& history = this->solver_->history();
+    ASSERT_EQ(2, history.size());
+    for (int i = 0; i < D; ++i) {
+      const Dtype expected_history = updated_weights.cpu_diff()[i];
+      const Dtype solver_history = history[0]->cpu_data()[i];
+      const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_history), fabs(solver_history)));
+      EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
+    }
+    const Dtype expected_history = updated_bias.cpu_diff()[0];
+    const Dtype solver_history = history[1]->cpu_data()[0];
+    const Dtype error_margin_hist = std::max(kMinPrecision, kPrecision *
+        std::min(fabs(expected_history), fabs(solver_history)));
+    EXPECT_NEAR(expected_history, solver_history, error_margin_hist);
+  }
+
+  // Test that the correct update is computed for a regularized least squares
+  // problem:
+  //
+  //            E = (1/(2n)) || X w - y ||^2 + (lambda / 2) || w ||^2
+  //   \nabla_w E = (1/n) (X^T X w - X^T y) + lambda * w
+  //
+  // X \in R^{n x (d+1)} (each example is a row, (d+1)th element is always 1)
+  // w \in R^{(d+1) x 1} ((d+1)th element is the bias)
+  // y \in R^{n x 1}
+  // lambda is weight_decay
+  //
+  // TestLeastSquaresUpdate works "inductively", assuming that the solver
+  // correctly updates the net K (= iter_to_check) times, then given the history
+  // from the Kth update, we compute the (K+1)th update and check that it
+  // matches the solver's (K+1)th update.
+  void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0,
+      const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
+      const int iter_to_check = 0) {
+    // Initialize the solver and run K (= iter_to_check) solver iterations.
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check);
+
+    // Compute the (K+1)th update using the analytic least squares gradient.
+    vector<shared_ptr<Blob<Dtype> > > updated_params;
+    ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
+                              &updated_params);
+
+    // Reinitialize the solver and run K+1 solver iterations.
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+                          iter_to_check + 1);
+
+    // Check that the solver's solution matches ours.
+    CheckLeastSquaresUpdate(updated_params);
+  }
+};
+
+TYPED_TEST_CASE(SGDSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->TestLeastSquaresUpdate();
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateLROneTenth) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  this->TestLeastSquaresUpdate(kLearningRate);
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.5;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 5;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 5;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index d8018be0c25..f5716c9e9a7 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -1,25 +1,24 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class SigmoidCrossEntropyLossLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class SigmoidCrossEntropyLossLayerTest : public ::testing::Test {
  protected:
   SigmoidCrossEntropyLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
@@ -69,7 +68,6 @@ class SigmoidCrossEntropyLossLayerTest : public ::testing::Test {
     targets_filler_param.set_max(1.0);
     UniformFiller<Dtype> targets_filler(targets_filler_param);
     Dtype eps = 2e-2;
-    int num_inf = 0;
     for (int i = 0; i < 100; ++i) {
       // Fill the data vector
       data_filler.Fill(this->blob_bottom_data_);
@@ -96,36 +94,18 @@ class SigmoidCrossEntropyLossLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(SigmoidCrossEntropyLossLayerTest, Dtypes);
-
-
-TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestSigmoidCrossEntropyLossCPU) {
-  Caffe::set_mode(Caffe::CPU);
-  this->TestForward();
-}
+TYPED_TEST_CASE(SigmoidCrossEntropyLossLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestSigmoidCrossEntropyLossGPU) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestSigmoidCrossEntropyLoss) {
   this->TestForward();
 }
 
-TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SigmoidCrossEntropyLossLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
-}
-
-TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestGradientGPU) {
+TYPED_TEST(SigmoidCrossEntropyLossLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SigmoidCrossEntropyLossLayer<TypeParam> layer(layer_param);
+  SigmoidCrossEntropyLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), 0, -1, -1);
 }
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
new file mode 100644
index 00000000000..ee8818781f5
--- /dev/null
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -0,0 +1,189 @@
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class SliceLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  SliceLayerTest()
+      : blob_bottom_(new Blob<Dtype>(6, 12, 2, 3)),
+        blob_top_0_(new Blob<Dtype>()),
+        blob_top_1_(new Blob<Dtype>()),
+        blob_top_2_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    Caffe::set_random_seed(1701);
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_top_vec_0_.push_back(blob_top_0_);
+    blob_top_vec_0_.push_back(blob_top_1_);
+    blob_top_vec_1_.push_back(blob_top_0_);
+    blob_top_vec_1_.push_back(blob_top_1_);
+    blob_top_vec_1_.push_back(blob_top_2_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+  }
+
+  virtual void ReduceBottomBlobSize() {
+    blob_bottom_->Reshape(4, 5, 2, 2);
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+  }
+
+  virtual ~SliceLayerTest() {
+    delete blob_top_0_; delete blob_top_1_;
+    delete blob_top_2_; delete blob_bottom_;
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_0_;
+  Blob<Dtype>* const blob_top_1_;
+  Blob<Dtype>* const blob_top_2_;
+  vector<Blob<Dtype>*> blob_top_vec_0_, blob_top_vec_1_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+};
+
+TYPED_TEST_CASE(SliceLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(SliceLayerTest, TestSetupNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_slice_param()->set_slice_dim(0);
+  SliceLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
+  EXPECT_EQ(this->blob_bottom_->num(), 3 * this->blob_top_0_->num());
+  EXPECT_EQ(this->blob_top_0_->num(), this->blob_top_1_->num());
+  EXPECT_EQ(this->blob_top_0_->num(), this->blob_top_2_->num());
+  EXPECT_EQ(this->blob_bottom_->channels(), this->blob_top_0_->channels());
+  EXPECT_EQ(this->blob_bottom_->height(), this->blob_top_0_->height());
+  EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0_->width());
+}
+
+TYPED_TEST(SliceLayerTest, TestSetupChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_slice_param()->add_slice_point(3);
+  SliceLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  EXPECT_EQ(this->blob_top_0_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_0_->channels(), 3);
+  EXPECT_EQ(this->blob_top_1_->channels(), 9);
+  EXPECT_EQ(this->blob_bottom_->channels(),
+    this->blob_top_0_->channels() + this->blob_top_1_->channels());
+  EXPECT_EQ(this->blob_bottom_->height(), this->blob_top_0_->height());
+  EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0_->width());
+}
+
+TYPED_TEST(SliceLayerTest, TestSliceAcrossNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_slice_param()->set_slice_dim(0);
+  SliceLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  const int top_num = this->blob_bottom_->num() / 2;
+  ASSERT_EQ(top_num, this->blob_top_0_->num());
+  ASSERT_EQ(top_num, this->blob_top_1_->num());
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_0_));
+  for (int n = 0; n < top_num; ++n) {
+    for (int c = 0; c < this->blob_top_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_EQ(this->blob_bottom_->data_at(n, c, h, w),
+                    this->blob_top_0_->data_at(n, c, h, w));
+        }
+      }
+    }
+    for (int c = 0; c < this->blob_top_1_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_EQ(this->blob_bottom_->data_at(n + 3, c, h, w),
+                    this->blob_top_1_->data_at(n, c, h, w));
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(SliceLayerTest, TestSliceAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // Slice at 2, 8: should produce output blobs with #channels 2, 6, 4.
+  const int kSlicePoint0 = 2;
+  const int kSlicePoint1 = 8;
+  layer_param.mutable_slice_param()->add_slice_point(kSlicePoint0);
+  layer_param.mutable_slice_param()->add_slice_point(kSlicePoint1);
+  SliceLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
+  ASSERT_EQ(kSlicePoint0, this->blob_top_0_->channels());
+  ASSERT_EQ(kSlicePoint1 - kSlicePoint0, this->blob_top_1_->channels());
+  ASSERT_EQ(this->blob_bottom_->channels() - kSlicePoint1,
+            this->blob_top_2_->channels());
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_1_));
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_top_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_EQ(this->blob_bottom_->data_at(n, c, h, w),
+              this->blob_top_0_->data_at(n, c, h, w));
+        }
+      }
+    }
+    for (int c = 0; c < this->blob_top_1_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_EQ(this->blob_bottom_->data_at(n, c + kSlicePoint0, h, w),
+              this->blob_top_1_->data_at(n, c, h, w));
+        }
+      }
+    }
+    for (int c = 0; c < this->blob_top_2_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_EQ(this->blob_bottom_->data_at(n, c + kSlicePoint1, h, w),
+              this->blob_top_2_->data_at(n, c, h, w));
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(SliceLayerTest, TestGradientAcrossNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Gradient checks are slow; reduce blob size.
+  this->ReduceBottomBlobSize();
+  LayerParameter layer_param;
+  layer_param.mutable_slice_param()->set_slice_dim(0);
+  SliceLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+    &(this->blob_top_vec_0_));
+}
+
+TYPED_TEST(SliceLayerTest, TestGradientAcrossChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Gradient checks are slow; reduce blob size.
+  this->ReduceBottomBlobSize();
+  LayerParameter layer_param;
+  const int kSlicePoint = 4;
+  layer_param.mutable_slice_param()->add_slice_point(kSlicePoint);
+  SliceLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
+    &(this->blob_top_vec_0_));
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index 3ba302d4c60..37685af6b12 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -1,25 +1,22 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
-template <typename Dtype>
-class SoftmaxLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class SoftmaxLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   SoftmaxLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 10, 1, 1)),
@@ -38,18 +35,17 @@ class SoftmaxLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(SoftmaxLayerTest, Dtypes);
+TYPED_TEST_CASE(SoftmaxLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(SoftmaxLayerTest, TestForwardCPU) {
+TYPED_TEST(SoftmaxLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SoftmaxLayer<TypeParam> layer(layer_param);
+  SoftmaxLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Test sum
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
-    TypeParam sum = 0;
+    Dtype sum = 0;
     for (int j = 0; j < this->blob_top_->channels(); ++j) {
       sum += this->blob_top_->data_at(i, j, 0, 0);
     }
@@ -58,7 +54,7 @@ TYPED_TEST(SoftmaxLayerTest, TestForwardCPU) {
   }
   // Test exact values
   for (int i = 0; i < this->blob_bottom_->num(); ++i) {
-    TypeParam scale = 0;
+    Dtype scale = 0;
     for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
       scale += exp(this->blob_bottom_->data_at(i, j, 0, 0));
     }
@@ -73,11 +69,11 @@ TYPED_TEST(SoftmaxLayerTest, TestForwardCPU) {
   }
 }
 
-TYPED_TEST(SoftmaxLayerTest, TestGradientCPU) {
+TYPED_TEST(SoftmaxLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SoftmaxLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  SoftmaxLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index 8b8be8e8b6d..bd39bd4420b 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -1,26 +1,24 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class SoftmaxWithLossLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class SoftmaxWithLossLayerTest : public ::testing::Test {
  protected:
   SoftmaxWithLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
@@ -46,26 +44,15 @@ class SoftmaxWithLossLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(SoftmaxWithLossLayerTest, Dtypes);
-
+TYPED_TEST_CASE(SoftmaxWithLossLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SoftmaxWithLossLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
-  checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_), 0, -1, -1);
-}
 
-TYPED_TEST(SoftmaxWithLossLayerTest, TestGradientGPU) {
+TYPED_TEST(SoftmaxWithLossLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SoftmaxWithLossLayer<TypeParam> layer(layer_param);
+  SoftmaxWithLossLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &this->blob_top_vec_);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2, 1701);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
   checker.CheckGradientSingle(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_), 0, -1, -1);
 }
diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
new file mode 100644
index 00000000000..a7dbf77fd95
--- /dev/null
+++ b/src/caffe/test/test_solver.cpp
@@ -0,0 +1,107 @@
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "google/protobuf/text_format.h"
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+using std::ostringstream;
+
+namespace caffe {
+
+template <typename TypeParam>
+class SolverTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolverFromProtoString(const string& proto) {
+    SolverParameter param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
+    // Set the solver_mode according to current Caffe::mode.
+    switch (Caffe::mode()) {
+      case Caffe::CPU:
+        param.set_solver_mode(SolverParameter_SolverMode_CPU);
+        break;
+      case Caffe::GPU:
+        param.set_solver_mode(SolverParameter_SolverMode_GPU);
+        break;
+      default:
+        LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
+    }
+    solver_.reset(new SGDSolver<Dtype>(param));
+  }
+
+  shared_ptr<Solver<Dtype> > solver_;
+};
+
+TYPED_TEST_CASE(SolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(SolverTest, TestInitTrainTestNets) {
+  const string& proto =
+     "test_interval: 10 "
+     "test_iter: 10 "
+     "test_state: { stage: 'with-softmax' }"
+     "test_iter: 10 "
+     "test_state: {}"
+     "net_param { "
+     "  name: 'TestNetwork' "
+     "  layers: { "
+     "    name: 'data' "
+     "    type: DUMMY_DATA "
+     "    dummy_data_param { "
+     "      num: 5 "
+     "      channels: 3 "
+     "      height: 10 "
+     "      width: 10 "
+     "      num: 5 "
+     "      channels: 1 "
+     "      height: 1 "
+     "      width: 1 "
+     "    } "
+     "    top: 'data' "
+     "    top: 'label' "
+     "  } "
+     "  layers: { "
+     "    name: 'innerprod' "
+     "    type: INNER_PRODUCT "
+     "    inner_product_param { "
+     "      num_output: 10 "
+     "    } "
+     "    bottom: 'data' "
+     "    top: 'innerprod' "
+     "  } "
+     "  layers: { "
+     "    name: 'accuracy' "
+     "    type: ACCURACY "
+     "    bottom: 'innerprod' "
+     "    bottom: 'label' "
+     "    top: 'accuracy' "
+     "    exclude: { phase: TRAIN } "
+     "  } "
+     "  layers: { "
+     "    name: 'loss' "
+     "    type: SOFTMAX_LOSS "
+     "    bottom: 'innerprod' "
+     "    bottom: 'label' "
+     "    include: { phase: TRAIN } "
+     "    include: { phase: TEST stage: 'with-softmax' } "
+     "  } "
+     "} ";
+  this->InitSolverFromProtoString(proto);
+  ASSERT_TRUE(this->solver_->net() != NULL);
+  EXPECT_TRUE(this->solver_->net()->has_layer("loss"));
+  EXPECT_FALSE(this->solver_->net()->has_layer("accuracy"));
+  ASSERT_EQ(2, this->solver_->test_nets().size());
+  EXPECT_TRUE(this->solver_->test_nets()[0]->has_layer("loss"));
+  EXPECT_TRUE(this->solver_->test_nets()[0]->has_layer("accuracy"));
+  EXPECT_FALSE(this->solver_->test_nets()[1]->has_layer("loss"));
+  EXPECT_TRUE(this->solver_->test_nets()[1]->has_layer("accuracy"));
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index 327bcf937ac..711669bacb8 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -1,27 +1,25 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <string>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 #include "caffe/util/insert_splits.hpp"
+#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename TypeParam>
+class SplitLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
 
-template <typename Dtype>
-class SplitLayerTest : public ::testing::Test {
  protected:
   SplitLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
@@ -47,12 +45,12 @@ class SplitLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(SplitLayerTest, Dtypes);
+TYPED_TEST_CASE(SplitLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(SplitLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SplitLayer<TypeParam> layer(layer_param);
+  SplitLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   EXPECT_EQ(this->blob_top_a_->num(), 2);
   EXPECT_EQ(this->blob_top_a_->channels(), 3);
@@ -64,91 +62,46 @@ TYPED_TEST(SplitLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_b_->width(), 5);
 }
 
-TYPED_TEST(SplitLayerTest, TestCPU) {
-  LayerParameter layer_param;
-  SplitLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    TypeParam bottom_value = this->blob_bottom_->cpu_data()[i];
-    EXPECT_EQ(bottom_value, this->blob_top_a_->cpu_data()[i]);
-    EXPECT_EQ(bottom_value, this->blob_top_b_->cpu_data()[i]);
-  }
-}
-
-TYPED_TEST(SplitLayerTest, TestGPU) {
+TYPED_TEST(SplitLayerTest, Test) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SplitLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
+  SplitLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    TypeParam bottom_value = this->blob_bottom_->cpu_data()[i];
+    Dtype bottom_value = this->blob_bottom_->cpu_data()[i];
     EXPECT_EQ(bottom_value, this->blob_top_a_->cpu_data()[i]);
     EXPECT_EQ(bottom_value, this->blob_top_b_->cpu_data()[i]);
   }
 }
 
-TYPED_TEST(SplitLayerTest, TestCPUInPlace) {
+TYPED_TEST(SplitLayerTest, TestInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SplitLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::CPU);
+  SplitLayer<Dtype> layer(layer_param);
   this->blob_top_vec_[0] = this->blob_bottom_vec_[0];
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    TypeParam bottom_value = this->blob_bottom_->cpu_data()[i];
+    Dtype bottom_value = this->blob_bottom_->cpu_data()[i];
     EXPECT_EQ(bottom_value, this->blob_top_b_->cpu_data()[i]);
   }
 }
 
-TYPED_TEST(SplitLayerTest, TestGPUInPlace) {
+TYPED_TEST(SplitLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  SplitLayer<TypeParam> layer(layer_param);
-  Caffe::set_mode(Caffe::GPU);
-  this->blob_top_vec_[0] = this->blob_bottom_vec_[0];
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
-    TypeParam bottom_value = this->blob_bottom_->cpu_data()[i];
-    EXPECT_EQ(bottom_value, this->blob_top_b_->cpu_data()[i]);
-  }
-}
-
-TYPED_TEST(SplitLayerTest, TestCPUGradient) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SplitLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(SplitLayerTest, TestGPUGradient) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SplitLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
-
-TYPED_TEST(SplitLayerTest, TestCPUGradientInPlace) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  SplitLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
-  this->blob_top_vec_[0] = this->blob_bottom_vec_[0];
+  SplitLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
 
-TYPED_TEST(SplitLayerTest, TestGPUGradientInPlace) {
+TYPED_TEST(SplitLayerTest, TestGradientInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  SplitLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-2);
+  SplitLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
   this->blob_top_vec_[0] = this->blob_bottom_vec_[0];
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 0ad8123f881..4f13981bd82 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -1,25 +1,21 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 using std::min;
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
 template <typename Dtype>
 class StochasticPoolingLayerTest : public ::testing::Test {
  protected:
@@ -49,8 +45,7 @@ class StochasticPoolingLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(StochasticPoolingLayerTest, Dtypes);
+TYPED_TEST_CASE(StochasticPoolingLayerTest, TestDtypes);
 
 TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
   LayerParameter layer_param;
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index cd7475898d1..b658871b3ee 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -1,12 +1,12 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
+#include "caffe/util/device_alternate.hpp"
+#include "caffe/util/math_functions.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -23,7 +23,9 @@ TEST_F(SyncedMemoryTest, TestInitialization) {
   delete p_mem;
 }
 
-TEST_F(SyncedMemoryTest, TestAllocation) {
+#ifndef CPU_ONLY  // GPU test
+
+TEST_F(SyncedMemoryTest, TestAllocationCPUGPU) {
   SyncedMemory mem(10);
   EXPECT_TRUE(mem.cpu_data());
   EXPECT_TRUE(mem.gpu_data());
@@ -31,37 +33,69 @@ TEST_F(SyncedMemoryTest, TestAllocation) {
   EXPECT_TRUE(mem.mutable_gpu_data());
 }
 
+#endif
+
+TEST_F(SyncedMemoryTest, TestAllocationCPU) {
+  SyncedMemory mem(10);
+  EXPECT_TRUE(mem.cpu_data());
+  EXPECT_TRUE(mem.mutable_cpu_data());
+}
+
+#ifndef CPU_ONLY  // GPU test
+
+TEST_F(SyncedMemoryTest, TestAllocationGPU) {
+  SyncedMemory mem(10);
+  EXPECT_TRUE(mem.gpu_data());
+  EXPECT_TRUE(mem.mutable_gpu_data());
+}
+
+#endif
+
 TEST_F(SyncedMemoryTest, TestCPUWrite) {
   SyncedMemory mem(10);
   void* cpu_data = mem.mutable_cpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
   memset(cpu_data, 1, mem.size());
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<char*>(cpu_data))[i], 1);
+    EXPECT_EQ((static_cast<char*>(cpu_data))[i], 1);
+  }
+  // do another round
+  cpu_data = mem.mutable_cpu_data();
+  EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
+  memset(cpu_data, 2, mem.size());
+  for (int i = 0; i < mem.size(); ++i) {
+    EXPECT_EQ((static_cast<char*>(cpu_data))[i], 2);
   }
+}
+
+#ifndef CPU_ONLY  // GPU test
+
+TEST_F(SyncedMemoryTest, TestGPURead) {
+  SyncedMemory mem(10);
+  void* cpu_data = mem.mutable_cpu_data();
+  EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
+  memset(cpu_data, 1, mem.size());
   const void* gpu_data = mem.gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
   char* recovered_value = new char[10];
-  cudaMemcpy(reinterpret_cast<void*>(recovered_value), gpu_data, 10,
-             cudaMemcpyDeviceToHost);
+  caffe_gpu_memcpy(10, gpu_data, recovered_value);
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 1);
+    EXPECT_EQ((static_cast<char*>(recovered_value))[i], 1);
   }
   // do another round
   cpu_data = mem.mutable_cpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::HEAD_AT_CPU);
   memset(cpu_data, 2, mem.size());
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<char*>(cpu_data))[i], 2);
+    EXPECT_EQ((static_cast<char*>(cpu_data))[i], 2);
   }
   gpu_data = mem.gpu_data();
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
   // check if values are the same
-  cudaMemcpy(reinterpret_cast<void*>(recovered_value), gpu_data, 10,
-             cudaMemcpyDeviceToHost);
+  caffe_gpu_memcpy(10, gpu_data, recovered_value);
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<char*>(recovered_value))[i], 2);
+    EXPECT_EQ((static_cast<char*>(recovered_value))[i], 2);
   }
   delete[] recovered_value;
 }
@@ -73,7 +107,7 @@ TEST_F(SyncedMemoryTest, TestGPUWrite) {
   CUDA_CHECK(cudaMemset(gpu_data, 1, mem.size()));
   const void* cpu_data = mem.cpu_data();
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<const char*>(cpu_data))[i], 1);
+    EXPECT_EQ((static_cast<const char*>(cpu_data))[i], 1);
   }
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
 
@@ -82,9 +116,11 @@ TEST_F(SyncedMemoryTest, TestGPUWrite) {
   CUDA_CHECK(cudaMemset(gpu_data, 2, mem.size()));
   cpu_data = mem.cpu_data();
   for (int i = 0; i < mem.size(); ++i) {
-    EXPECT_EQ((reinterpret_cast<const char*>(cpu_data))[i], 2);
+    EXPECT_EQ((static_cast<const char*>(cpu_data))[i], 2);
   }
   EXPECT_EQ(mem.head(), SyncedMemory::SYNCED);
 }
 
+#endif
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
index 9c9f8a74ae2..9b8e745b8a7 100644
--- a/src/caffe/test/test_tanh_layer.cpp
+++ b/src/caffe/test/test_tanh_layer.cpp
@@ -1,26 +1,24 @@
-// Copyright 2014 BVLC and contributors.
 // Adapted from other test files
 
 #include <cmath>
 #include <cstring>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
 #include "caffe/vision_layers.hpp"
-#include "caffe/test/test_gradient_check_util.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
 
 namespace caffe {
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
-
-template <typename Dtype>
-class TanHLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class TanHLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
  protected:
   TanHLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 10, 1, 1)),
@@ -39,45 +37,12 @@ class TanHLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-typedef ::testing::Types<float, double> Dtypes;
-TYPED_TEST_CASE(TanHLayerTest, Dtypes);
-
-TYPED_TEST(TanHLayerTest, TestForwardCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  TanHLayer<TypeParam> layer(layer_param);
-  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
-  // Test exact values
-  for (int i = 0; i < this->blob_bottom_->num(); ++i) {
-    for (int j = 0; j < this->blob_bottom_->channels(); ++j) {
-      for (int k = 0; k < this->blob_bottom_->height(); ++k) {
-        for (int l = 0; l < this->blob_bottom_->width(); ++l) {
-          EXPECT_GE(this->blob_top_->data_at(i, j, k, l) + 1e-4,
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));
-          EXPECT_LE(this->blob_top_->data_at(i, j, k, l) - 1e-4,
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) - 1) /
-             (exp(2*this->blob_bottom_->data_at(i, j, k, l)) + 1));
-        }
-      }
-    }
-  }
-}
-
-TYPED_TEST(TanHLayerTest, TestGradientCPU) {
-  LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
-  TanHLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
-  checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
-      &(this->blob_top_vec_));
-}
+TYPED_TEST_CASE(TanHLayerTest, TestDtypesAndDevices);
 
-TYPED_TEST(TanHLayerTest, TestForwardGPU) {
+TYPED_TEST(TanHLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  TanHLayer<TypeParam> layer(layer_param);
+  TanHLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // Test exact values
@@ -97,11 +62,11 @@ TYPED_TEST(TanHLayerTest, TestForwardGPU) {
   }
 }
 
-TYPED_TEST(TanHLayerTest, TestGradientGPU) {
+TYPED_TEST(TanHLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::GPU);
-  TanHLayer<TypeParam> layer(layer_param);
-  GradientChecker<TypeParam> checker(1e-2, 1e-3);
+  TanHLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientEltwise(&layer, &(this->blob_bottom_vec_),
       &(this->blob_top_vec_));
 }
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
new file mode 100644
index 00000000000..32dfbeeac92
--- /dev/null
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -0,0 +1,98 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/vision_layers.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ThresholdLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  ThresholdLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~ThresholdLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ThresholdLayerTest, TestDtypesAndDevices);
+
+
+TYPED_TEST(ThresholdLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ThresholdLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  EXPECT_EQ(this->blob_top_->num(), this->blob_bottom_->num());
+  EXPECT_EQ(this->blob_top_->channels(), this->blob_bottom_->channels());
+  EXPECT_EQ(this->blob_top_->height(), this->blob_bottom_->height());
+  EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_->width());
+}
+
+TYPED_TEST(ThresholdLayerTest, Test) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ThresholdLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  const Dtype threshold_ = layer_param.threshold_param().threshold();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_LE(top_data[i], 1.);
+    if (top_data[i] == 0) {
+      EXPECT_LE(bottom_data[i], threshold_);
+    }
+    if (top_data[i] == 1) {
+      EXPECT_GT(bottom_data[i], threshold_);
+    }
+  }
+}
+
+TYPED_TEST(ThresholdLayerTest, Test2) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ThresholdParameter* threshold_param =
+    layer_param.mutable_threshold_param();
+  threshold_param->set_threshold(0.5);
+  ThresholdLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  layer.Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
+  // Now, check values
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  const Dtype threshold_ = layer_param.threshold_param().threshold();
+  EXPECT_FLOAT_EQ(threshold_, 0.5);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_LE(top_data[i], 1.);
+    if (top_data[i] == 0) {
+      EXPECT_LE(bottom_data[i], threshold_);
+    }
+    if (top_data[i] == 1) {
+      EXPECT_GT(bottom_data[i], threshold_);
+    }
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index 9203f5583be..2abcadc0862 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1,12 +1,10 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cstring>
 #include <string>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/util/upgrade_proto.hpp"
diff --git a/src/caffe/test/test_util_blas.cpp b/src/caffe/test/test_util_blas.cpp
index 2e4c6795952..8770f309951 100644
--- a/src/caffe/test/test_util_blas.cpp
+++ b/src/caffe/test/test_util_blas.cpp
@@ -1,12 +1,11 @@
-// Copyright 2014 BVLC and contributors.
+#ifndef CPU_ONLY  // CPU-GPU test
 
 #include <cstring>
 
-#include "cuda_runtime.h"
-#include "cublas_v2.h"
-
 #include "gtest/gtest.h"
+
 #include "caffe/blob.hpp"
+#include "caffe/util/device_alternate.hpp"
 #include "caffe/util/math_functions.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -15,14 +14,12 @@ namespace caffe {
 
 extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
 
-typedef ::testing::Types<float, double> Dtypes;
-
-template <typename Dtype>
+template <typename TypeParam>
 class GemmTest : public ::testing::Test {};
 
-TYPED_TEST_CASE(GemmTest, Dtypes);
+TYPED_TEST_CASE(GemmTest, TestDtypes);
 
-TYPED_TEST(GemmTest, TestGemm) {
+TYPED_TEST(GemmTest, TestGemmCPUGPU) {
   Blob<TypeParam> A(1, 1, 2, 3);
   Blob<TypeParam> B(1, 1, 3, 4);
   Blob<TypeParam> C(1, 1, 2, 4);
@@ -30,8 +27,8 @@ TYPED_TEST(GemmTest, TestGemm) {
   TypeParam A_reshape_data[6] = {1, 4, 2, 5, 3, 6};
   TypeParam B_reshape_data[12] = {1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12};
   TypeParam result[8] = {38, 44, 50, 56, 83, 98, 113, 128};
-  memcpy(A.mutable_cpu_data(), data, 6 * sizeof(TypeParam));
-  memcpy(B.mutable_cpu_data(), data, 12 * sizeof(TypeParam));
+  caffe_copy(6, data, A.mutable_cpu_data());
+  caffe_copy(12, data, B.mutable_cpu_data());
 
   if (sizeof(TypeParam) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
     // [1, 2, 3; 4 5 6] * [1, 2, 3, 4; 5, 6, 7, 8; 9, 10, 11, 12];
@@ -48,7 +45,7 @@ TYPED_TEST(GemmTest, TestGemm) {
 
     // Test when we have a transposed A
     A.Reshape(1, 1, 3, 2);
-    memcpy(A.mutable_cpu_data(), A_reshape_data, 6 * sizeof(TypeParam));
+    caffe_copy(6, A_reshape_data, A.mutable_cpu_data());
     caffe_cpu_gemm<TypeParam>(CblasTrans, CblasNoTrans, 2, 4, 3, 1.,
         A.cpu_data(), B.cpu_data(), 0., C.mutable_cpu_data());
     for (int i = 0; i < 8; ++i) {
@@ -62,7 +59,7 @@ TYPED_TEST(GemmTest, TestGemm) {
 
     // Test when we have a transposed A and a transposed B too
     B.Reshape(1, 1, 4, 3);
-    memcpy(B.mutable_cpu_data(), B_reshape_data, 12 * sizeof(TypeParam));
+    caffe_copy(12, B_reshape_data, B.mutable_cpu_data());
     caffe_cpu_gemm<TypeParam>(CblasTrans, CblasTrans, 2, 4, 3, 1.,
         A.cpu_data(), B.cpu_data(), 0., C.mutable_cpu_data());
     for (int i = 0; i < 8; ++i) {
@@ -76,7 +73,7 @@ TYPED_TEST(GemmTest, TestGemm) {
 
     // Test when we have a transposed B
     A.Reshape(1, 1, 2, 3);
-    memcpy(A.mutable_cpu_data(), data, 6 * sizeof(TypeParam));
+    caffe_copy(6, data, A.mutable_cpu_data());
     caffe_cpu_gemm<TypeParam>(CblasNoTrans, CblasTrans, 2, 4, 3, 1.,
         A.cpu_data(), B.cpu_data(), 0., C.mutable_cpu_data());
     for (int i = 0; i < 8; ++i) {
@@ -93,15 +90,15 @@ TYPED_TEST(GemmTest, TestGemm) {
 }
 
 
-TYPED_TEST(GemmTest, TestGemv) {
+TYPED_TEST(GemmTest, TestGemvCPUGPU) {
   Blob<TypeParam> A(1, 1, 2, 3);
   Blob<TypeParam> x(1, 1, 1, 3);
   Blob<TypeParam> y(1, 1, 1, 2);
   TypeParam data[6] = {1, 2, 3, 4, 5, 6};
   TypeParam result_2[2] = {14, 32};
   TypeParam result_3[3] = {9, 12, 15};
-  memcpy(A.mutable_cpu_data(), data, 6 * sizeof(TypeParam));
-  memcpy(x.mutable_cpu_data(), data, 3 * sizeof(TypeParam));
+  caffe_copy(6, data, A.mutable_cpu_data());
+  caffe_copy(3, data, x.mutable_cpu_data());
 
   if (sizeof(TypeParam) == 4 || CAFFE_TEST_CUDA_PROP.major >= 2) {
     caffe_cpu_gemv<TypeParam>(CblasNoTrans, 2, 3, 1., A.cpu_data(),
@@ -116,7 +113,7 @@ TYPED_TEST(GemmTest, TestGemv) {
     }
 
     // Test transpose case
-    memcpy(y.mutable_cpu_data(), data, 2 * sizeof(TypeParam));
+    caffe_copy(2, data, y.mutable_cpu_data());
     caffe_cpu_gemv<TypeParam>(CblasTrans, 2, 3, 1., A.cpu_data(),
         y.cpu_data(), 0., x.mutable_cpu_data());
     for (int i = 0; i < 3; ++i) {
@@ -133,3 +130,5 @@ TYPED_TEST(GemmTest, TestGemv) {
 }
 
 }  // namespace caffe
+
+#endif  // CPU_ONLY
diff --git a/src/caffe/util/benchmark.cpp b/src/caffe/util/benchmark.cpp
index 0bd852182c8..566d06a8f5f 100644
--- a/src/caffe/util/benchmark.cpp
+++ b/src/caffe/util/benchmark.cpp
@@ -1,7 +1,4 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <boost/date_time/posix_time/posix_time.hpp>
-#include <cuda_runtime.h>
 
 #include "caffe/common.hpp"
 #include "caffe/util/benchmark.hpp"
@@ -17,15 +14,23 @@ Timer::Timer()
 
 Timer::~Timer() {
   if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
     CUDA_CHECK(cudaEventDestroy(start_gpu_));
     CUDA_CHECK(cudaEventDestroy(stop_gpu_));
+#else
+    NO_GPU;
+#endif
   }
 }
 
 void Timer::Start() {
   if (!running()) {
     if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
       CUDA_CHECK(cudaEventRecord(start_gpu_, 0));
+#else
+      NO_GPU;
+#endif
     } else {
       start_cpu_ = boost::posix_time::microsec_clock::local_time();
     }
@@ -37,8 +42,12 @@ void Timer::Start() {
 void Timer::Stop() {
   if (running()) {
     if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
       CUDA_CHECK(cudaEventRecord(stop_gpu_, 0));
       CUDA_CHECK(cudaEventSynchronize(stop_gpu_));
+#else
+      NO_GPU;
+#endif
     } else {
       stop_cpu_ = boost::posix_time::microsec_clock::local_time();
     }
@@ -55,8 +64,12 @@ float Timer::MilliSeconds() {
     Stop();
   }
   if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
     CUDA_CHECK(cudaEventElapsedTime(&elapsed_milliseconds_, start_gpu_,
                                     stop_gpu_));
+#else
+      NO_GPU;
+#endif
   } else {
     elapsed_milliseconds_ = (stop_cpu_ - start_cpu_).total_milliseconds();
   }
@@ -70,8 +83,12 @@ float Timer::Seconds() {
 void Timer::Init() {
   if (!initted()) {
     if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
       CUDA_CHECK(cudaEventCreate(&start_gpu_));
       CUDA_CHECK(cudaEventCreate(&stop_gpu_));
+#else
+      NO_GPU;
+#endif
     }
     initted_ = true;
   }
diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index 037410e29b7..c48f31f35d4 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -1,28 +1,29 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
 
 #include "caffe/util/im2col.hpp"
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void im2col_cpu(const Dtype* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_col) {
-  int height_col = (height + 2 * pad - ksize) / stride + 1;
-  int width_col = (width + 2 * pad - ksize) / stride + 1;
-  int channels_col = channels * ksize * ksize;
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    Dtype* data_col) {
+  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
+  int channels_col = channels * kernel_h * kernel_w;
   for (int c = 0; c < channels_col; ++c) {
-    int w_offset = c % ksize;
-    int h_offset = (c / ksize) % ksize;
-    int c_im = c / ksize / ksize;
+    int w_offset = c % kernel_w;
+    int h_offset = (c / kernel_w) % kernel_h;
+    int c_im = c / kernel_h / kernel_w;
     for (int h = 0; h < height_col; ++h) {
       for (int w = 0; w < width_col; ++w) {
-        int h_pad = h * stride - pad + h_offset;
-        int w_pad = w * stride - pad + w_offset;
+        int h_pad = h * stride_h - pad_h + h_offset;
+        int w_pad = w * stride_w - pad_w + w_offset;
         if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
           data_col[(c * height_col + h) * width_col + w] =
             data_im[(c_im * height + h_pad) * width + w_pad];
@@ -35,28 +36,32 @@ void im2col_cpu(const Dtype* data_im, const int channels,
 
 // Explicit instantiation
 template void im2col_cpu<float>(const float* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, float* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, float* data_col);
 template void im2col_cpu<double>(const double* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, double* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, double* data_col);
 
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_im) {
-  memset(data_im, 0, sizeof(Dtype) * height * width * channels);
-  int height_col = (height + 2 * pad - ksize) / stride + 1;
-  int width_col = (width + 2 * pad - ksize) / stride + 1;
-  int channels_col = channels * ksize * ksize;
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    Dtype* data_im) {
+  caffe_set(height * width * channels, Dtype(0), data_im);
+  int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
+  int channels_col = channels * patch_h * patch_w;
   for (int c = 0; c < channels_col; ++c) {
-    int w_offset = c % ksize;
-    int h_offset = (c / ksize) % ksize;
-    int c_im = c / ksize / ksize;
+    int w_offset = c % patch_w;
+    int h_offset = (c / patch_w) % patch_h;
+    int c_im = c / patch_h / patch_w;
     for (int h = 0; h < height_col; ++h) {
       for (int w = 0; w < width_col; ++w) {
-        int h_pad = h * stride - pad + h_offset;
-        int w_pad = w * stride - pad + w_offset;
+        int h_pad = h * stride_h - pad_h + h_offset;
+        int w_pad = w * stride_w - pad_w + w_offset;
         if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
           data_im[(c_im * height + h_pad) * width + w_pad] +=
               data_col[(c * height_col + h) * width_col + w];
@@ -67,10 +72,12 @@ void col2im_cpu(const Dtype* data_col, const int channels,
 
 // Explicit instantiation
 template void col2im_cpu<float>(const float* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, float* data_im);
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, float* data_im);
 template void col2im_cpu<double>(const double* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, double* data_im);
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, double* data_im);
 
 }  // namespace caffe
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index 6aecb0e5722..c90f93eb67b 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -1,5 +1,3 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <algorithm>
 #include <cmath>
 #include <cstdlib>
@@ -12,26 +10,30 @@ namespace caffe {
 
 template <typename Dtype>
 __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, const int height_col, const int width_col,
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    const int height_col, const int width_col,
     Dtype* data_col) {
   CUDA_KERNEL_LOOP(index, n) {
     int w_out = index % width_col;
-    index /= width_col;
-    int h_out = index % height_col;
-    int channel_in = index / height_col;
-    int channel_out = channel_in * ksize * ksize;
-    int h_in = h_out * stride - pad;
-    int w_in = w_out * stride - pad;
-    data_col += (channel_out * height_col + h_out) * width_col + w_out;
-    data_im += (channel_in * height + h_in) * width + w_in;
-    for (int i = 0; i < ksize; ++i) {
-      for (int j = 0; j < ksize; ++j) {
+    int h_index = index / width_col;
+    int h_out = h_index % height_col;
+    int channel_in = h_index / height_col;
+    int channel_out = channel_in * kernel_h * kernel_w;
+    int h_in = h_out * stride_h - pad_h;
+    int w_in = w_out * stride_w - pad_w;
+    Dtype* data_col_ptr = data_col;
+    data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out;
+    const Dtype* data_im_ptr = data_im;
+    data_im_ptr += (channel_in * height + h_in) * width + w_in;
+    for (int i = 0; i < kernel_h; ++i) {
+      for (int j = 0; j < kernel_w; ++j) {
         int h = h_in + i;
         int w = w_in + j;
-        *data_col = (h >= 0 && w >= 0 && h < height && w < width) ?
-            data_im[i * width + j] : 0;
-        data_col += height_col * width_col;
+        *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
+            data_im_ptr[i * width + j] : 0;
+        data_col_ptr += height_col * width_col;
       }
     }
   }
@@ -39,17 +41,20 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
 
 template <typename Dtype>
 void im2col_gpu(const Dtype* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_col) {
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    Dtype* data_col) {
   // We are going to launch channels * height_col * width_col kernels, each
   // kernel responsible for copying a single-channel grid.
-  int height_col = (height + 2 * pad - ksize) / stride + 1;
-  int width_col = (width + 2 * pad - ksize) / stride + 1;
+  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
   int num_kernels = channels * height_col * width_col;
   // NOLINT_NEXT_LINE(whitespace/operators)
   im2col_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
                              CAFFE_CUDA_NUM_THREADS>>>(
-      num_kernels, data_im, height, width, ksize, pad, stride, height_col,
+      num_kernels, data_im, height, width, kernel_h, kernel_w, pad_h,
+      pad_w, stride_h, stride_w, height_col,
       width_col, data_col);
   CUDA_POST_KERNEL_CHECK;
 }
@@ -57,40 +62,47 @@ void im2col_gpu(const Dtype* data_im, const int channels,
 
 // Explicit instantiation
 template void im2col_gpu<float>(const float* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, float* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    float* data_col);
 template void im2col_gpu<double>(const double* data_im, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, double* data_col);
+    const int height, const int width, const int kernel_h, const int kernel_w,
+    const int pad_h, const int pad_w, const int stride_h, const int stride_w,
+    double* data_col);
 
 template <typename Dtype>
 __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
-    const int height, const int width, const int channels, const int ksize,
-    const int pad, const int stride, const int height_col, const int width_col,
+    const int height, const int width, const int channels,
+    const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w,
+    const int stride_h, const int stride_w,
+    const int height_col, const int width_col,
     Dtype* data_im) {
   CUDA_KERNEL_LOOP(index, n) {
     Dtype val = 0;
-    int w = index % width + pad;
-    int h = (index / width) % height + pad;
+    int w = index % width + pad_w;
+    int h = (index / width) % height + pad_h;
     int c = index / (width * height);
     // compute the start and end of the output
-    int w_col_start = (w < ksize) ? 0 : (w - ksize) / stride + 1;
-    int w_col_end = min(w / stride + 1, width_col);
-    int h_col_start = (h < ksize) ? 0 : (h - ksize) / stride + 1;
-    int h_col_end = min(h / stride + 1, height_col);
+    int w_col_start = (w < patch_w) ? 0 : (w - patch_w) / stride_w + 1;
+    int w_col_end = min(w / stride_w + 1, width_col);
+    int h_col_start = (h < patch_h) ? 0 : (h - patch_h) / stride_h + 1;
+    int h_col_end = min(h / stride_h + 1, height_col);
     /*
     for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
       for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
         // the col location: [c * width * height + h_out, w_out]
-        int c_col = c * ksize * ksize + (h - h_col * stride) * ksize + (w - w_col * stride);
+        int c_col = c * patch_h * patch_w + (h - h_col * stride_h) * ksize
+            + (w - w_col * stride_w);
         val += data_col[(c_col * height_col + h_col) * width_col + w_col];
       }
     }
     */
     // equivalent implementation
-    int offset = (c * ksize * ksize + h * ksize + w) * height_col * width_col;
-    int coeff_h_col = (1 - stride * ksize * height_col) * width_col;
-    int coeff_w_col = (1 - stride * height_col * width_col);
+    int offset =
+        (c * patch_h * patch_w + h * patch_w + w) * height_col * width_col;
+    int coeff_h_col = (1 - stride_h * patch_w * height_col) * width_col;
+    int coeff_w_col = (1 - stride_w * height_col * width_col);
     for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
       for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
         val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col];
@@ -102,31 +114,31 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
 
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int ksize, const int pad,
-    const int stride, Dtype* data_im) {
-  // CUDA_CHECK(cudaMemset(data_im, 0,
-  //            sizeof(Dtype) * height * width * channels));
-  int height_col = (height + 2 * pad - ksize) / stride + 1;
-  int width_col = (width + 2 * pad - ksize) / stride + 1;
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, Dtype* data_im) {
+  int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
+  int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
   int num_kernels = channels * height * width;
   // To avoid involving atomic operations, we will launch one kernel per
   // bottom dimension, and then in the kernel add up the top dimensions.
   // NOLINT_NEXT_LINE(whitespace/operators)
   col2im_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
                              CAFFE_CUDA_NUM_THREADS>>>(
-      num_kernels, data_col, height, width, channels, ksize, pad, stride,
+      num_kernels, data_col, height, width, channels, patch_h, patch_w,
+      pad_h, pad_w, stride_h, stride_w,
       height_col, width_col, data_im);
   CUDA_POST_KERNEL_CHECK;
 }
 
-
 // Explicit instantiation
 template void col2im_gpu<float>(const float* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, float* data_im);
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, float* data_im);
 template void col2im_gpu<double>(const double* data_col, const int channels,
-    const int height, const int width, const int psize, const int pad,
-    const int stride, double* data_im);
-
+    const int height, const int width, const int patch_h, const int patch_w,
+    const int pad_h, const int pad_w, const int stride_h,
+    const int stride_w, double* data_im);
 
 }  // namespace caffe
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index b9aeb37c71b..270568cb5dc 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -1,18 +1,11 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <map>
-#include <string>
 #include <sstream>
+#include <string>
 #include <utility>
 
 #include "caffe/common.hpp"
 #include "caffe/util/insert_splits.hpp"
 
-using std::map;
-using std::ostringstream;
-using std::pair;
-using std::make_pair;
-
 namespace caffe {
 
 void InsertSplits(const NetParameter& param, NetParameter* param_split) {
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 44858f48a36..fd7454d4f98 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -1,28 +1,24 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <stdint.h>
 #include <fcntl.h>
-#include <google/protobuf/text_format.h>
-#include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/io/coded_stream.h>
+#include <google/protobuf/io/zero_copy_stream_impl.h>
+#include <google/protobuf/text_format.h>
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>
 #include <opencv2/imgproc/imgproc.hpp>
+#include <stdint.h>
 
 #include <algorithm>
+#include <fstream>  // NOLINT(readability/streams)
 #include <string>
 #include <vector>
-#include <fstream>  // NOLINT(readability/streams)
 
 #include "caffe/common.hpp"
-#include "caffe/util/io.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/io.hpp"
+
+namespace caffe {
 
-using std::fstream;
-using std::ios;
-using std::max;
-using std::string;
 using google::protobuf::io::FileInputStream;
 using google::protobuf::io::FileOutputStream;
 using google::protobuf::io::ZeroCopyInputStream;
@@ -31,8 +27,6 @@ using google::protobuf::io::ZeroCopyOutputStream;
 using google::protobuf::io::CodedOutputStream;
 using google::protobuf::Message;
 
-namespace caffe {
-
 bool ReadProtoFromTextFile(const char* filename, Message* proto) {
   int fd = open(filename, O_RDONLY);
   CHECK_NE(fd, -1) << "File not found: " << filename;
@@ -72,32 +66,44 @@ void WriteProtoToBinaryFile(const Message& proto, const char* filename) {
 }
 
 bool ReadImageToDatum(const string& filename, const int label,
-    const int height, const int width, Datum* datum) {
+    const int height, const int width, const bool is_color, Datum* datum) {
   cv::Mat cv_img;
+  int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR :
+    CV_LOAD_IMAGE_GRAYSCALE);
   if (height > 0 && width > 0) {
-    cv::Mat cv_img_origin = cv::imread(filename, CV_LOAD_IMAGE_COLOR);
+    cv::Mat cv_img_origin = cv::imread(filename, cv_read_flag);
     cv::resize(cv_img_origin, cv_img, cv::Size(height, width));
   } else {
-    cv_img = cv::imread(filename, CV_LOAD_IMAGE_COLOR);
+    cv_img = cv::imread(filename, cv_read_flag);
   }
   if (!cv_img.data) {
     LOG(ERROR) << "Could not open or find file " << filename;
     return false;
   }
-  datum->set_channels(3);
+  int num_channels = (is_color ? 3 : 1);
+  datum->set_channels(num_channels);
   datum->set_height(cv_img.rows);
   datum->set_width(cv_img.cols);
   datum->set_label(label);
   datum->clear_data();
   datum->clear_float_data();
   string* datum_string = datum->mutable_data();
-  for (int c = 0; c < 3; ++c) {
+  if (is_color) {
+    for (int c = 0; c < num_channels; ++c) {
+      for (int h = 0; h < cv_img.rows; ++h) {
+        for (int w = 0; w < cv_img.cols; ++w) {
+          datum_string->push_back(
+            static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
+        }
+      }
+    }
+  } else {  // Faster than repeatedly testing is_color for each pixel w/i loop
     for (int h = 0; h < cv_img.rows; ++h) {
       for (int w = 0; w < cv_img.cols; ++w) {
         datum_string->push_back(
-            static_cast<char>(cv_img.at<cv::Vec3b>(h, w)[c]));
+          static_cast<char>(cv_img.at<uchar>(h, w)));
+        }
       }
-    }
   }
   return true;
 }
@@ -111,6 +117,7 @@ void hdf5_load_nd_dataset_helper(
   herr_t status;
   int ndims;
   status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims);
+  CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_;
   CHECK_GE(ndims, min_dim);
   CHECK_LE(ndims, max_dim);
 
@@ -119,6 +126,7 @@ void hdf5_load_nd_dataset_helper(
   H5T_class_t class_;
   status = H5LTget_dataset_info(
       file_id, dataset_name_, dims.data(), &class_, NULL);
+  CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
   CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data";
 
   blob->Reshape(
@@ -134,6 +142,7 @@ void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
   hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
   herr_t status = H5LTread_dataset_float(
     file_id, dataset_name_, blob->mutable_cpu_data());
+  CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;
 }
 
 template <>
@@ -142,6 +151,7 @@ void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
   hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
   herr_t status = H5LTread_dataset_double(
     file_id, dataset_name_, blob->mutable_cpu_data());
+  CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;
 }
 
 template <>
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index ba2492aa635..974adf55e7d 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -1,8 +1,5 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <boost/math/special_functions/next.hpp>
 #include <boost/random.hpp>
-#include <cublas_v2.h>
 
 #include <limits>
 
@@ -34,38 +31,6 @@ void caffe_cpu_gemm<double>(const CBLAS_TRANSPOSE TransA,
       ldb, beta, C, N);
 }
 
-template <>
-void caffe_gpu_gemm<float>(const CBLAS_TRANSPOSE TransA,
-    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
-    const float alpha, const float* A, const float* B, const float beta,
-    float* C) {
-  // Note that cublas follows fortran order.
-  int lda = (TransA == CblasNoTrans) ? K : M;
-  int ldb = (TransB == CblasNoTrans) ? N : K;
-  cublasOperation_t cuTransA =
-      (TransA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
-  cublasOperation_t cuTransB =
-      (TransB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
-  CUBLAS_CHECK(cublasSgemm(Caffe::cublas_handle(), cuTransB, cuTransA,
-      N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
-}
-
-template <>
-void caffe_gpu_gemm<double>(const CBLAS_TRANSPOSE TransA,
-    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
-    const double alpha, const double* A, const double* B, const double beta,
-    double* C) {
-  // Note that cublas follows fortran order.
-  int lda = (TransA == CblasNoTrans) ? K : M;
-  int ldb = (TransB == CblasNoTrans) ? N : K;
-  cublasOperation_t cuTransA =
-      (TransA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
-  cublasOperation_t cuTransB =
-      (TransB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
-  CUBLAS_CHECK(cublasDgemm(Caffe::cublas_handle(), cuTransB, cuTransA,
-      N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
-}
-
 template <>
 void caffe_cpu_gemv<float>(const CBLAS_TRANSPOSE TransA, const int M,
     const int N, const float alpha, const float* A, const float* x,
@@ -80,26 +45,6 @@ void caffe_cpu_gemv<double>(const CBLAS_TRANSPOSE TransA, const int M,
   cblas_dgemv(CblasRowMajor, TransA, M, N, alpha, A, N, x, 1, beta, y, 1);
 }
 
-template <>
-void caffe_gpu_gemv<float>(const CBLAS_TRANSPOSE TransA, const int M,
-    const int N, const float alpha, const float* A, const float* x,
-    const float beta, float* y) {
-  cublasOperation_t cuTransA =
-      (TransA == CblasNoTrans) ? CUBLAS_OP_T : CUBLAS_OP_N;
-  CUBLAS_CHECK(cublasSgemv(Caffe::cublas_handle(), cuTransA, N, M, &alpha,
-      A, N, x, 1, &beta, y, 1));
-}
-
-template <>
-void caffe_gpu_gemv<double>(const CBLAS_TRANSPOSE TransA, const int M,
-    const int N, const double alpha, const double* A, const double* x,
-    const double beta, double* y) {
-  cublasOperation_t cuTransA =
-      (TransA == CblasNoTrans) ? CUBLAS_OP_T : CUBLAS_OP_N;
-  CUBLAS_CHECK(cublasDgemv(Caffe::cublas_handle(), cuTransA, N, M, &alpha,
-      A, N, x, 1, &beta, y, 1));
-}
-
 template <>
 void caffe_axpy<float>(const int N, const float alpha, const float* X,
     float* Y) { cblas_saxpy(N, alpha, X, 1, Y, 1); }
@@ -108,22 +53,10 @@ template <>
 void caffe_axpy<double>(const int N, const double alpha, const double* X,
     double* Y) { cblas_daxpy(N, alpha, X, 1, Y, 1); }
 
-template <>
-void caffe_gpu_axpy<float>(const int N, const float alpha, const float* X,
-    float* Y) {
-  CUBLAS_CHECK(cublasSaxpy(Caffe::cublas_handle(), N, &alpha, X, 1, Y, 1));
-}
-
-template <>
-void caffe_gpu_axpy<double>(const int N, const double alpha, const double* X,
-    double* Y) {
-  CUBLAS_CHECK(cublasDaxpy(Caffe::cublas_handle(), N, &alpha, X, 1, Y, 1));
-}
-
-template <>
-void caffe_set(const int N, const float alpha, float* Y) {
+template <typename Dtype>
+void caffe_set(const int N, const Dtype alpha, Dtype* Y) {
   if (alpha == 0) {
-    memset(Y, 0, sizeof(float) * N);
+    memset(Y, 0, sizeof(Dtype) * N);
     return;
   }
   for (int i = 0; i < N; ++i) {
@@ -131,16 +64,9 @@ void caffe_set(const int N, const float alpha, float* Y) {
   }
 }
 
-template <>
-void caffe_set(const int N, const double alpha, double* Y) {
-  if (alpha == 0) {
-    memset(Y, 0, sizeof(double) * N);
-    return;
-  }
-  for (int i = 0; i < N; ++i) {
-    Y[i] = alpha;
-  }
-}
+template void caffe_set<int>(const int N, const int alpha, int* Y);
+template void caffe_set<float>(const int N, const float alpha, float* Y);
+template void caffe_set<double>(const int N, const double alpha, double* Y);
 
 template <>
 void caffe_add_scalar(const int N, const float alpha, float* Y) {
@@ -156,25 +82,26 @@ void caffe_add_scalar(const int N, const double alpha, double* Y) {
   }
 }
 
-template <>
-void caffe_copy<float>(const int N, const float* X, float* Y) {
-  cblas_scopy(N, X, 1, Y, 1);
-}
-
-template <>
-void caffe_copy<double>(const int N, const double* X, double* Y) {
-  cblas_dcopy(N, X, 1, Y, 1);
-}
-
-template <>
-void caffe_gpu_copy<float>(const int N, const float* X, float* Y) {
-  CUBLAS_CHECK(cublasScopy(Caffe::cublas_handle(), N, X, 1, Y, 1));
+template <typename Dtype>
+void caffe_copy(const int N, const Dtype* X, Dtype* Y) {
+  if (X != Y) {
+    if (Caffe::mode() == Caffe::GPU) {
+#ifndef CPU_ONLY
+      CUDA_CHECK(cudaMemcpy(Y, X, sizeof(Dtype) * N, cudaMemcpyDefault));
+#else
+      NO_GPU;
+#endif
+    } else {
+      memcpy(Y, X, sizeof(Dtype) * N);
+    }
+  }
 }
 
-template <>
-void caffe_gpu_copy<double>(const int N, const double* X, double* Y) {
-  CUBLAS_CHECK(cublasDcopy(Caffe::cublas_handle(), N, X, 1, Y, 1));
-}
+template void caffe_copy<int>(const int N, const int* X, int* Y);
+template void caffe_copy<unsigned int>(const int N, const unsigned int* X,
+    unsigned int* Y);
+template void caffe_copy<float>(const int N, const float* X, float* Y);
+template void caffe_copy<double>(const int N, const double* X, double* Y);
 
 template <>
 void caffe_scal<float>(const int N, const float alpha, float *X) {
@@ -186,30 +113,6 @@ void caffe_scal<double>(const int N, const double alpha, double *X) {
   cblas_dscal(N, alpha, X, 1);
 }
 
-template <>
-void caffe_gpu_scal<float>(const int N, const float alpha, float *X) {
-  CUBLAS_CHECK(cublasSscal(Caffe::cublas_handle(), N, &alpha, X, 1));
-}
-
-template <>
-void caffe_gpu_scal<double>(const int N, const double alpha, double *X) {
-  CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), N, &alpha, X, 1));
-}
-
-template <>
-void caffe_gpu_axpby<float>(const int N, const float alpha, const float* X,
-    const float beta, float* Y) {
-  caffe_gpu_scal<float>(N, beta, Y);
-  caffe_gpu_axpy<float>(N, alpha, X, Y);
-}
-
-template <>
-void caffe_gpu_axpby<double>(const int N, const double alpha, const double* X,
-    const double beta, double* Y) {
-  caffe_gpu_scal<double>(N, beta, Y);
-  caffe_gpu_axpy<double>(N, alpha, X, Y);
-}
-
 template <>
 void caffe_cpu_axpby<float>(const int N, const float alpha, const float* X,
                             const float beta, float* Y) {
@@ -381,6 +284,26 @@ void caffe_rng_bernoulli<double>(const int n, const double p, int* r);
 template
 void caffe_rng_bernoulli<float>(const int n, const float p, int* r);
 
+template <typename Dtype>
+void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r) {
+  CHECK_GE(n, 0);
+  CHECK(r);
+  CHECK_GE(p, 0);
+  CHECK_LE(p, 1);
+  boost::bernoulli_distribution<Dtype> random_distribution(p);
+  boost::variate_generator<caffe::rng_t*, boost::bernoulli_distribution<Dtype> >
+      variate_generator(caffe_rng(), random_distribution);
+  for (int i = 0; i < n; ++i) {
+    r[i] = static_cast<unsigned int>(variate_generator());
+  }
+}
+
+template
+void caffe_rng_bernoulli<double>(const int n, const double p, unsigned int* r);
+
+template
+void caffe_rng_bernoulli<float>(const int n, const float p, unsigned int* r);
+
 template <>
 float caffe_cpu_dot<float>(const int n, const float* x, const float* y) {
   return cblas_sdot(n, x, 1, y, 1);
@@ -391,18 +314,6 @@ double caffe_cpu_dot<double>(const int n, const double* x, const double* y) {
   return cblas_ddot(n, x, 1, y, 1);
 }
 
-template <>
-void caffe_gpu_dot<float>(const int n, const float* x, const float* y,
-    float* out) {
-  CUBLAS_CHECK(cublasSdot(Caffe::cublas_handle(), n, x, 1, y, 1, out));
-}
-
-template <>
-void caffe_gpu_dot<double>(const int n, const double* x, const double* y,
-    double * out) {
-  CUBLAS_CHECK(cublasDdot(Caffe::cublas_handle(), n, x, 1, y, 1, out));
-}
-
 template <>
 int caffe_cpu_hamming_distance<float>(const int n, const float* x,
                                   const float* y) {
@@ -435,16 +346,6 @@ double caffe_cpu_asum<double>(const int n, const double* x) {
   return cblas_dasum(n, x, 1);
 }
 
-template <>
-void caffe_gpu_asum<float>(const int n, const float* x, float* y) {
-  CUBLAS_CHECK(cublasSasum(Caffe::cublas_handle(), n, x, 1, y));
-}
-
-template <>
-void caffe_gpu_asum<double>(const int n, const double* x, double* y) {
-  CUBLAS_CHECK(cublasDasum(Caffe::cublas_handle(), n, x, 1, y));
-}
-
 INSTANTIATE_CAFFE_CPU_UNARY_FUNC(sign);
 INSTANTIATE_CAFFE_CPU_UNARY_FUNC(sgnbit);
 INSTANTIATE_CAFFE_CPU_UNARY_FUNC(fabs);
@@ -463,18 +364,16 @@ void caffe_cpu_scale<double>(const int n, const double alpha, const double *x,
   cblas_dscal(n, alpha, y, 1);
 }
 
-template <>
-void caffe_gpu_scale<float>(const int n, const float alpha, const float *x,
-                            float* y) {
-  CUBLAS_CHECK(cublasScopy(Caffe::cublas_handle(), n, x, 1, y, 1));
-  CUBLAS_CHECK(cublasSscal(Caffe::cublas_handle(), n, &alpha, y, 1));
-}
 
-template <>
-void caffe_gpu_scale<double>(const int n, const double alpha, const double *x,
-                             double* y) {
-  CUBLAS_CHECK(cublasDcopy(Caffe::cublas_handle(), n, x, 1, y, 1));
-  CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), n, &alpha, y, 1));
+using std::signbit;
+bool caffe_signbit(float arg) {
+    return signbit(arg);
+}
+bool caffe_signbit(double arg) {
+    return signbit(arg);
+}
+bool caffe_signbit(long double arg) {
+    return signbit(arg);
 }
 
 }  // namespace caffe
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 184613c0a84..cec051e6bef 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -1,9 +1,8 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <math_functions.h>  // CUDA's, not caffe's, for fabs, signbit
 #include <thrust/device_vector.h>
 #include <thrust/functional.h>  // thrust::plus
 #include <thrust/reduce.h>
+
 #include <cmath>
 #include <cstdlib>
 #include <cstring>
@@ -13,6 +12,136 @@
 
 namespace caffe {
 
+template <>
+void caffe_gpu_gemm<float>(const CBLAS_TRANSPOSE TransA,
+    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
+    const float alpha, const float* A, const float* B, const float beta,
+    float* C) {
+  // Note that cublas follows fortran order.
+  int lda = (TransA == CblasNoTrans) ? K : M;
+  int ldb = (TransB == CblasNoTrans) ? N : K;
+  cublasOperation_t cuTransA =
+      (TransA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+  cublasOperation_t cuTransB =
+      (TransB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+  CUBLAS_CHECK(cublasSgemm(Caffe::cublas_handle(), cuTransB, cuTransA,
+      N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
+}
+
+template <>
+void caffe_gpu_gemm<double>(const CBLAS_TRANSPOSE TransA,
+    const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
+    const double alpha, const double* A, const double* B, const double beta,
+    double* C) {
+  // Note that cublas follows fortran order.
+  int lda = (TransA == CblasNoTrans) ? K : M;
+  int ldb = (TransB == CblasNoTrans) ? N : K;
+  cublasOperation_t cuTransA =
+      (TransA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+  cublasOperation_t cuTransB =
+      (TransB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
+  CUBLAS_CHECK(cublasDgemm(Caffe::cublas_handle(), cuTransB, cuTransA,
+      N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
+}
+
+template <>
+void caffe_gpu_gemv<float>(const CBLAS_TRANSPOSE TransA, const int M,
+    const int N, const float alpha, const float* A, const float* x,
+    const float beta, float* y) {
+  cublasOperation_t cuTransA =
+      (TransA == CblasNoTrans) ? CUBLAS_OP_T : CUBLAS_OP_N;
+  CUBLAS_CHECK(cublasSgemv(Caffe::cublas_handle(), cuTransA, N, M, &alpha,
+      A, N, x, 1, &beta, y, 1));
+}
+
+template <>
+void caffe_gpu_gemv<double>(const CBLAS_TRANSPOSE TransA, const int M,
+    const int N, const double alpha, const double* A, const double* x,
+    const double beta, double* y) {
+  cublasOperation_t cuTransA =
+      (TransA == CblasNoTrans) ? CUBLAS_OP_T : CUBLAS_OP_N;
+  CUBLAS_CHECK(cublasDgemv(Caffe::cublas_handle(), cuTransA, N, M, &alpha,
+      A, N, x, 1, &beta, y, 1));
+}
+
+template <>
+void caffe_gpu_axpy<float>(const int N, const float alpha, const float* X,
+    float* Y) {
+  CUBLAS_CHECK(cublasSaxpy(Caffe::cublas_handle(), N, &alpha, X, 1, Y, 1));
+}
+
+template <>
+void caffe_gpu_axpy<double>(const int N, const double alpha, const double* X,
+    double* Y) {
+  CUBLAS_CHECK(cublasDaxpy(Caffe::cublas_handle(), N, &alpha, X, 1, Y, 1));
+}
+
+void caffe_gpu_memcpy(const size_t N, const void* X, void* Y) {
+  if (X != Y) {
+    CUDA_CHECK(cudaMemcpy(Y, X, N, cudaMemcpyDefault));
+  }
+}
+
+template <>
+void caffe_gpu_scal<float>(const int N, const float alpha, float *X) {
+  CUBLAS_CHECK(cublasSscal(Caffe::cublas_handle(), N, &alpha, X, 1));
+}
+
+template <>
+void caffe_gpu_scal<double>(const int N, const double alpha, double *X) {
+  CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), N, &alpha, X, 1));
+}
+
+template <>
+void caffe_gpu_axpby<float>(const int N, const float alpha, const float* X,
+    const float beta, float* Y) {
+  caffe_gpu_scal<float>(N, beta, Y);
+  caffe_gpu_axpy<float>(N, alpha, X, Y);
+}
+
+template <>
+void caffe_gpu_axpby<double>(const int N, const double alpha, const double* X,
+    const double beta, double* Y) {
+  caffe_gpu_scal<double>(N, beta, Y);
+  caffe_gpu_axpy<double>(N, alpha, X, Y);
+}
+
+template <>
+void caffe_gpu_dot<float>(const int n, const float* x, const float* y,
+    float* out) {
+  CUBLAS_CHECK(cublasSdot(Caffe::cublas_handle(), n, x, 1, y, 1, out));
+}
+
+template <>
+void caffe_gpu_dot<double>(const int n, const double* x, const double* y,
+    double * out) {
+  CUBLAS_CHECK(cublasDdot(Caffe::cublas_handle(), n, x, 1, y, 1, out));
+}
+
+template <>
+void caffe_gpu_asum<float>(const int n, const float* x, float* y) {
+  CUBLAS_CHECK(cublasSasum(Caffe::cublas_handle(), n, x, 1, y));
+}
+
+template <>
+void caffe_gpu_asum<double>(const int n, const double* x, double* y) {
+  CUBLAS_CHECK(cublasDasum(Caffe::cublas_handle(), n, x, 1, y));
+}
+
+template <>
+void caffe_gpu_scale<float>(const int n, const float alpha, const float *x,
+                            float* y) {
+  CUBLAS_CHECK(cublasScopy(Caffe::cublas_handle(), n, x, 1, y, 1));
+  CUBLAS_CHECK(cublasSscal(Caffe::cublas_handle(), n, &alpha, y, 1));
+}
+
+template <>
+void caffe_gpu_scale<double>(const int n, const double alpha, const double *x,
+                             double* y) {
+  CUBLAS_CHECK(cublasDcopy(Caffe::cublas_handle(), n, x, 1, y, 1));
+  CUBLAS_CHECK(cublasDscal(Caffe::cublas_handle(), n, &alpha, y, 1));
+}
+
 template <typename Dtype>
 __global__ void set_kernel(const int n, const Dtype alpha, Dtype* y) {
   CUDA_KERNEL_LOOP(index, n) {
@@ -20,27 +149,20 @@ __global__ void set_kernel(const int n, const Dtype alpha, Dtype* y) {
   }
 }
 
-template <>
-void caffe_gpu_set(const int N, const float alpha, float* Y) {
+template <typename Dtype>
+void caffe_gpu_set(const int N, const Dtype alpha, Dtype* Y) {
   if (alpha == 0) {
-    CUDA_CHECK(cudaMemset(Y, 0, sizeof(float) * N));
+    CUDA_CHECK(cudaMemset(Y, 0, sizeof(Dtype) * N));
     return;
   }
   // NOLINT_NEXT_LINE(whitespace/operators)
-  set_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+  set_kernel<Dtype><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
       N, alpha, Y);
 }
 
-template <>
-void caffe_gpu_set(const int N, const double alpha, double* Y) {
-  if (alpha == 0) {
-    CUDA_CHECK(cudaMemset(Y, 0, sizeof(double) * N));
-    return;
-  }
-  // NOLINT_NEXT_LINE(whitespace/operators)
-  set_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
-      N, alpha, Y);
-}
+template void caffe_gpu_set<int>(const int N, const int alpha, int* Y);
+template void caffe_gpu_set<float>(const int N, const float alpha, float* Y);
+template void caffe_gpu_set<double>(const int N, const double alpha, double* Y);
 
 template <typename Dtype>
 __global__ void add_scalar_kernel(const int n, const Dtype alpha, Dtype* y) {
@@ -63,6 +185,54 @@ void caffe_gpu_add_scalar(const int N, const double alpha, double* Y) {
       N, alpha, Y);
 }
 
+template <typename Dtype>
+__global__ void add_kernel(const int n, const Dtype* a,
+    const Dtype* b, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = a[index] + b[index];
+  }
+}
+
+template <>
+void caffe_gpu_add<float>(const int N, const float* a, const float* b,
+    float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  add_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, b, y);
+}
+
+template <>
+void caffe_gpu_add<double>(const int N, const double* a, const double* b,
+    double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  add_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, b, y);
+}
+
+template <typename Dtype>
+__global__ void sub_kernel(const int n, const Dtype* a,
+    const Dtype* b, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = a[index] - b[index];
+  }
+}
+
+template <>
+void caffe_gpu_sub<float>(const int N, const float* a, const float* b,
+    float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  sub_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, b, y);
+}
+
+template <>
+void caffe_gpu_sub<double>(const int N, const double* a, const double* b,
+    double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  sub_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, b, y);
+}
+
 template <typename Dtype>
 __global__ void mul_kernel(const int n, const Dtype* a,
     const Dtype* b, Dtype* y) {
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index e079b422dfb..5415ca83b60 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -1,19 +1,14 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <google/protobuf/text_format.h>
-#include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/io/coded_stream.h>
+#include <google/protobuf/io/zero_copy_stream_impl.h>
+#include <google/protobuf/text_format.h>
 
 #include <map>
 #include <string>
 
 #include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
 #include "caffe/util/upgrade_proto.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-using std::map;
-using std::string;
 
 namespace caffe {
 
@@ -85,11 +80,13 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
       LayerParameter source_layer = param.layers(top_idx);
       if (source_layer.layer().type() == "padding") {
         // This layer has a padding layer as input -- check that it is a conv
-        // layer and takes only one input.  Also check that the padding layer
-        // input has only one input and one output.  Other cases have undefined
-        // behavior in Caffe.
-        CHECK_EQ(layer_param.type(), "conv") << "Padding layer input to "
-            "non-convolutional layer type " << layer_param.type();
+        // layer or a pooling layer and takes only one input.  Also check that
+        // the padding layer input has only one input and one output.  Other
+        // cases have undefined behavior in Caffe.
+        CHECK((layer_param.type() == "conv") || (layer_param.type() == "pool"))
+            << "Padding layer input to "
+            "non-convolutional / non-pooling layer type "
+            << layer_param.type();
         CHECK_EQ(layer_connection.bottom_size(), 1)
             << "Conv Layer takes a single blob as input.";
         CHECK_EQ(source_layer.bottom_size(), 1)
@@ -189,6 +186,8 @@ bool UpgradeLayerParameter(const LayerParameter& v0_layer_connection,
     if (v0_layer_param.has_pad()) {
       if (type == "conv") {
         layer_param->mutable_convolution_param()->set_pad(v0_layer_param.pad());
+      } else if (type == "pool") {
+        layer_param->mutable_pooling_param()->set_pad(v0_layer_param.pad());
       } else {
         LOG(ERROR) << "Unknown parameter pad for layer type " << type;
         is_fully_compatible = false;
diff --git a/src/gtest/gtest-all.cpp b/src/gtest/gtest-all.cpp
index 5ced66a90f1..926197419fc 100644
--- a/src/gtest/gtest-all.cpp
+++ b/src/gtest/gtest-all.cpp
@@ -601,7 +601,6 @@ class GTestFlagSaver {
   bool list_tests_;
   String output_;
   bool print_time_;
-  bool pretty_;
   internal::Int32 random_seed_;
   internal::Int32 repeat_;
   bool shuffle_;
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
new file mode 100644
index 00000000000..22d9eb19912
--- /dev/null
+++ b/tools/caffe.cpp
@@ -0,0 +1,233 @@
+#include <gflags/gflags.h>
+#include <glog/logging.h>
+
+#include <cstring>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "caffe/caffe.hpp"
+
+using caffe::Blob;
+using caffe::Caffe;
+using caffe::Net;
+using caffe::Layer;
+using caffe::shared_ptr;
+using caffe::Timer;
+using caffe::vector;
+
+
+DEFINE_int32(gpu, -1,
+    "Run in GPU mode on given device ID.");
+DEFINE_string(solver, "",
+    "The solver definition protocol buffer text file.");
+DEFINE_string(model, "",
+    "The model definition protocol buffer text file..");
+DEFINE_string(snapshot, "",
+    "Optional; the snapshot solver state to resume training.");
+DEFINE_string(weights, "",
+    "Optional; the pretrained weights to initialize finetuning. "
+    "Cannot be set simultaneously with snapshot.");
+DEFINE_int32(iterations, 50,
+    "The number of iterations to run.");
+
+// A simple registry for caffe commands.
+typedef int (*BrewFunction)();
+typedef std::map<caffe::string, BrewFunction> BrewMap;
+BrewMap g_brew_map;
+
+#define RegisterBrewFunction(func) \
+namespace { \
+class __Registerer_##func { \
+ public: /* NOLINT */ \
+  __Registerer_##func() { \
+    g_brew_map[#func] = &func; \
+  } \
+}; \
+__Registerer_##func g_registerer_##func; \
+}
+
+static BrewFunction GetBrewFunction(const caffe::string& name) {
+  if (g_brew_map.count(name)) {
+    return g_brew_map[name];
+  } else {
+    LOG(ERROR) << "Available caffe actions:";
+    for (BrewMap::iterator it = g_brew_map.begin();
+         it != g_brew_map.end(); ++it) {
+      LOG(ERROR) << "\t" << it->first;
+    }
+    LOG(FATAL) << "Unknown action: " << name;
+    return NULL;  // not reachable, just to suppress old compiler warnings.
+  }
+}
+
+// caffe commands to call by
+//     caffe <command> <args>
+//
+// To add a command, define a function "int command()" and register it with
+// RegisterBrewFunction(action);
+
+// Device Query: show diagnostic information for a GPU device.
+int device_query() {
+  CHECK_GT(FLAGS_gpu, -1) << "Need a device ID to query.";
+  LOG(INFO) << "Querying device ID = " << FLAGS_gpu;
+  caffe::Caffe::SetDevice(FLAGS_gpu);
+  caffe::Caffe::DeviceQuery();
+  return 0;
+}
+RegisterBrewFunction(device_query);
+
+
+// Train / Finetune a model.
+int train() {
+  CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train.";
+  CHECK(!FLAGS_snapshot.size() || !FLAGS_weights.size())
+      << "Give a snapshot to resume training or weights to finetune "
+      "but not both.";
+
+  caffe::SolverParameter solver_param;
+  caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
+
+  LOG(INFO) << "Starting Optimization";
+  caffe::SGDSolver<float> solver(solver_param);
+  if (FLAGS_snapshot.size()) {
+    LOG(INFO) << "Resuming from " << FLAGS_snapshot;
+    solver.Solve(FLAGS_snapshot);
+  } else if (FLAGS_weights.size()) {
+    LOG(INFO) << "Finetuning from " << FLAGS_weights;
+    solver.net()->CopyTrainedLayersFrom(FLAGS_weights);
+    solver.Solve();
+  } else {
+    solver.Solve();
+  }
+  LOG(INFO) << "Optimization Done.";
+  return 0;
+}
+RegisterBrewFunction(train);
+
+
+// Test: score a model.
+int test() {
+  CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to score.";
+  CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score.";
+
+  // Set device id and mode
+  if (FLAGS_gpu >= 0) {
+    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
+    Caffe::SetDevice(FLAGS_gpu);
+    Caffe::set_mode(Caffe::GPU);
+  } else {
+    LOG(INFO) << "Use CPU.";
+    Caffe::set_mode(Caffe::CPU);
+  }
+  // Instantiate the caffe net.
+  Caffe::set_phase(Caffe::TEST);
+  Net<float> caffe_net(FLAGS_model);
+  caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
+  LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
+
+  double test_score = 0;
+  for (int i = 0; i < FLAGS_iterations; ++i) {
+    const vector<Blob<float>*>& result = caffe_net.ForwardPrefilled();
+    test_score += result[0]->cpu_data()[0];
+    LOG(INFO) << "Batch " << i << ", score: " << result[0]->cpu_data()[0];
+  }
+  test_score /= FLAGS_iterations;
+  LOG(INFO) << "Score: " << test_score;
+
+  return 0;
+}
+RegisterBrewFunction(test);
+
+
+// Time: benchmark the execution time of a model.
+int time() {
+  CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
+
+  // Set device id and mode
+  if (FLAGS_gpu >= 0) {
+    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
+    Caffe::SetDevice(FLAGS_gpu);
+    Caffe::set_mode(Caffe::GPU);
+  } else {
+    LOG(INFO) << "Use CPU.";
+    Caffe::set_mode(Caffe::CPU);
+  }
+  // Instantiate the caffe net.
+  Caffe::set_phase(Caffe::TRAIN);
+  Net<float> caffe_net(FLAGS_model);
+
+  // Do a clean forward and backward pass, so that memory allocation are done
+  // and future iterations will be more stable.
+  LOG(INFO) << "Performing Forward";
+  // Note that for the speed benchmark, we will assume that the network does
+  // not take any input blobs.
+  float initial_loss;
+  caffe_net.Forward(vector<Blob<float>*>(), &initial_loss);
+  LOG(INFO) << "Initial loss: " << initial_loss;
+  LOG(INFO) << "Performing Backward";
+  caffe_net.Backward();
+
+  const vector<shared_ptr<Layer<float> > >& layers = caffe_net.layers();
+  vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
+  vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
+  const vector<vector<bool> >& bottom_need_backward =
+      caffe_net.bottom_need_backward();
+  LOG(INFO) << "*** Benchmark begins ***";
+  LOG(INFO) << "Testing for " << FLAGS_iterations << " iterations.";
+  Timer total_timer;
+  total_timer.Start();
+  Timer forward_timer;
+  forward_timer.Start();
+  Timer timer;
+  for (int i = 0; i < layers.size(); ++i) {
+    const caffe::string& layername = layers[i]->layer_param().name();
+    timer.Start();
+    for (int j = 0; j < FLAGS_iterations; ++j) {
+      layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
+    }
+    LOG(INFO) << layername << "\tforward: " << timer.MilliSeconds() <<
+        " milli seconds.";
+  }
+  LOG(INFO) << "Forward pass: " << forward_timer.MilliSeconds() <<
+      " milli seconds.";
+  Timer backward_timer;
+  backward_timer.Start();
+  for (int i = layers.size() - 1; i >= 0; --i) {
+    const caffe::string& layername = layers[i]->layer_param().name();
+    timer.Start();
+    for (int j = 0; j < FLAGS_iterations; ++j) {
+      layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
+                          &bottom_vecs[i]);
+    }
+    LOG(INFO) << layername << "\tbackward: "
+        << timer.MilliSeconds() << " milli seconds.";
+  }
+  LOG(INFO) << "Backward pass: " << backward_timer.MilliSeconds() <<
+      " milli seconds.";
+  LOG(INFO) << "Total Time: " << total_timer.MilliSeconds() <<
+      " milli seconds.";
+  LOG(INFO) << "*** Benchmark ends ***";
+  return 0;
+}
+RegisterBrewFunction(time);
+
+int main(int argc, char** argv) {
+  // Print output to stderr (while still logging).
+  FLAGS_alsologtostderr = 1;
+  // Usage message.
+  gflags::SetUsageMessage("command line brew\n"
+      "usage: caffe <command> <args>\n\n"
+      "commands:\n"
+      "  train           train or finetune a model\n"
+      "  test            score a model\n"
+      "  device_query    show GPU diagnostic information\n"
+      "  time            benchmark model execution time");
+  // Run tool or show usage.
+  caffe::GlobalInit(&argc, &argv);
+  if (argc == 2) {
+    return GetBrewFunction(caffe::string(argv[1]))();
+  } else {
+    gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/caffe");
+  }
+}
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index 7cf5fe5f222..fe3497fa87d 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -1,7 +1,6 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <glog/logging.h>
 #include <leveldb/db.h>
+#include <lmdb.h>
 #include <stdint.h>
 
 #include <algorithm>
@@ -12,32 +11,74 @@
 
 using caffe::Datum;
 using caffe::BlobProto;
+using std::string;
 using std::max;
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
-  if (argc != 3) {
-    LOG(ERROR) << "Usage: compute_image_mean input_leveldb output_file";
+  if (argc < 3 || argc > 4) {
+    LOG(ERROR) << "Usage: compute_image_mean input_leveldb output_file"
+               << " db_backend[leveldb or lmdb]";
     return 1;
   }
 
+  string db_backend = "leveldb";
+  if (argc == 4) {
+    db_backend = string(argv[3]);
+  }
+
+  // leveldb
   leveldb::DB* db;
   leveldb::Options options;
   options.create_if_missing = false;
+  leveldb::Iterator* it = NULL;
+  // lmdb
+  MDB_env* mdb_env;
+  MDB_dbi mdb_dbi;
+  MDB_val mdb_key, mdb_value;
+  MDB_txn* mdb_txn;
+  MDB_cursor* mdb_cursor;
 
-  LOG(INFO) << "Opening leveldb " << argv[1];
-  leveldb::Status status = leveldb::DB::Open(
-      options, argv[1], &db);
-  CHECK(status.ok()) << "Failed to open leveldb " << argv[1];
+  // Open db
+  if (db_backend == "leveldb") {  // leveldb
+    LOG(INFO) << "Opening leveldb " << argv[1];
+    leveldb::Status status = leveldb::DB::Open(
+        options, argv[1], &db);
+    CHECK(status.ok()) << "Failed to open leveldb " << argv[1];
+    leveldb::ReadOptions read_options;
+    read_options.fill_cache = false;
+    it = db->NewIterator(read_options);
+    it->SeekToFirst();
+  } else if (db_backend == "lmdb") {  // lmdb
+    LOG(INFO) << "Opening lmdb " << argv[1];
+    CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
+    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS);  // 1TB
+    CHECK_EQ(mdb_env_open(mdb_env, argv[1], MDB_RDONLY, 0664),
+        MDB_SUCCESS) << "mdb_env_open failed";
+    CHECK_EQ(mdb_txn_begin(mdb_env, NULL, MDB_RDONLY, &mdb_txn), MDB_SUCCESS)
+        << "mdb_txn_begin failed";
+    CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
+        << "mdb_open failed";
+    CHECK_EQ(mdb_cursor_open(mdb_txn, mdb_dbi, &mdb_cursor), MDB_SUCCESS)
+        << "mdb_cursor_open failed";
+    CHECK_EQ(mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_FIRST),
+        MDB_SUCCESS);
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
+  }
 
-  leveldb::ReadOptions read_options;
-  read_options.fill_cache = false;
-  leveldb::Iterator* it = db->NewIterator(read_options);
-  it->SeekToFirst();
   Datum datum;
   BlobProto sum_blob;
   int count = 0;
-  datum.ParseFromString(it->value().ToString());
+  // load first datum
+  if (db_backend == "leveldb") {
+    datum.ParseFromString(it->value().ToString());
+  } else if (db_backend == "lmdb") {
+    datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
+  }
+
   sum_blob.set_num(1);
   sum_blob.set_channels(datum.channels());
   sum_blob.set_height(datum.height());
@@ -49,28 +90,61 @@ int main(int argc, char** argv) {
     sum_blob.add_data(0.);
   }
   LOG(INFO) << "Starting Iteration";
-  for (it->SeekToFirst(); it->Valid(); it->Next()) {
-    // just a dummy operation
-    datum.ParseFromString(it->value().ToString());
-    const string& data = datum.data();
-    size_in_datum = std::max<int>(datum.data().size(), datum.float_data_size());
-    CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
-        size_in_datum;
-    if (data.size() != 0) {
-      for (int i = 0; i < size_in_datum; ++i) {
-        sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
+  if (db_backend == "leveldb") {  // leveldb
+    for (it->SeekToFirst(); it->Valid(); it->Next()) {
+      // just a dummy operation
+      datum.ParseFromString(it->value().ToString());
+      const string& data = datum.data();
+      size_in_datum = std::max<int>(datum.data().size(),
+          datum.float_data_size());
+      CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
+          size_in_datum;
+      if (data.size() != 0) {
+        for (int i = 0; i < size_in_datum; ++i) {
+          sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
+        }
+      } else {
+        for (int i = 0; i < size_in_datum; ++i) {
+          sum_blob.set_data(i, sum_blob.data(i) +
+              static_cast<float>(datum.float_data(i)));
+        }
       }
-    } else {
-      for (int i = 0; i < size_in_datum; ++i) {
-        sum_blob.set_data(i, sum_blob.data(i) +
-            static_cast<float>(datum.float_data(i)));
+      ++count;
+      if (count % 10000 == 0) {
+        LOG(ERROR) << "Processed " << count << " files.";
       }
     }
-    ++count;
-    if (count % 10000 == 0) {
-      LOG(ERROR) << "Processed " << count << " files.";
-    }
+  } else if (db_backend == "lmdb") {  // lmdb
+    CHECK_EQ(mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_FIRST),
+        MDB_SUCCESS);
+    do {
+      // just a dummy operation
+      datum.ParseFromArray(mdb_value.mv_data, mdb_value.mv_size);
+      const string& data = datum.data();
+      size_in_datum = std::max<int>(datum.data().size(),
+          datum.float_data_size());
+      CHECK_EQ(size_in_datum, data_size) << "Incorrect data field size " <<
+          size_in_datum;
+      if (data.size() != 0) {
+        for (int i = 0; i < size_in_datum; ++i) {
+          sum_blob.set_data(i, sum_blob.data(i) + (uint8_t)data[i]);
+        }
+      } else {
+        for (int i = 0; i < size_in_datum; ++i) {
+          sum_blob.set_data(i, sum_blob.data(i) +
+              static_cast<float>(datum.float_data(i)));
+        }
+      }
+      ++count;
+      if (count % 10000 == 0) {
+        LOG(ERROR) << "Processed " << count << " files.";
+      }
+    } while (mdb_cursor_get(mdb_cursor, &mdb_key, &mdb_value, MDB_NEXT)
+        == MDB_SUCCESS);
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
   }
+
   if (count % 10000 != 0) {
     LOG(ERROR) << "Processed " << count << " files.";
   }
@@ -81,6 +155,16 @@ int main(int argc, char** argv) {
   LOG(INFO) << "Write to " << argv[2];
   WriteProtoToBinaryFile(sum_blob, argv[2]);
 
-  delete db;
+  // Clean up
+  if (db_backend == "leveldb") {
+    delete db;
+  } else if (db_backend == "lmdb") {
+    mdb_cursor_close(mdb_cursor);
+    mdb_close(mdb_env, mdb_dbi);
+    mdb_txn_abort(mdb_txn);
+    mdb_env_close(mdb_env);
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
+  }
   return 0;
 }
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 2d4d4c77b52..0ddafa0c5fe 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -1,18 +1,23 @@
-// Copyright 2014 BVLC and contributors.
 // This program converts a set of images to a leveldb by storing them as Datum
 // proto buffers.
 // Usage:
-//    convert_imageset ROOTFOLDER/ LISTFILE DB_NAME [0/1]
+//   convert_imageset [-g] ROOTFOLDER/ LISTFILE DB_NAME RANDOM_SHUFFLE[0 or 1]
+//                     [resize_height] [resize_width]
 // where ROOTFOLDER is the root folder that holds all the images, and LISTFILE
 // should be a list of files as well as their labels, in the format as
 //   subfolder1/file1.JPEG 7
 //   ....
-// if the last argument is 1, a random shuffle will be carried out before we
+// if RANDOM_SHUFFLE is 1, a random shuffle will be carried out before we
 // process the file lines.
+// Optional flag -g indicates the images should be read as
+// single-channel grayscale. If omitted, grayscale images will be
+// converted to color.
 
 #include <glog/logging.h>
 #include <leveldb/db.h>
 #include <leveldb/write_batch.h>
+#include <lmdb.h>
+#include <sys/stat.h>
 
 #include <algorithm>
 #include <fstream>  // NOLINT(readability/streams)
@@ -22,6 +27,7 @@
 
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
+#include "caffe/util/rng.hpp"
 
 using namespace caffe;  // NOLINT(build/namespaces)
 using std::pair;
@@ -29,51 +35,102 @@ using std::string;
 
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
-  if (argc < 4 || argc > 5) {
+  if (argc < 4 || argc > 9) {
     printf("Convert a set of images to the leveldb format used\n"
         "as input for Caffe.\n"
         "Usage:\n"
-        "    convert_imageset ROOTFOLDER/ LISTFILE DB_NAME"
-        " RANDOM_SHUFFLE_DATA[0 or 1]\n"
+        "    convert_imageset [-g] ROOTFOLDER/ LISTFILE DB_NAME"
+        " RANDOM_SHUFFLE_DATA[0 or 1] DB_BACKEND[leveldb or lmdb]"
+        " [resize_height] [resize_width]\n"
         "The ImageNet dataset for the training demo is at\n"
         "    http://www.image-net.org/download-images\n");
     return 1;
   }
-  std::ifstream infile(argv[2]);
+
+  // Test whether argv[1] == "-g"
+  bool is_color= !(string("-g") == string(argv[1]));
+  int  arg_offset = (is_color ? 0 : 1);
+  std::ifstream infile(argv[arg_offset+2]);
   std::vector<std::pair<string, int> > lines;
   string filename;
   int label;
   while (infile >> filename >> label) {
     lines.push_back(std::make_pair(filename, label));
   }
-  if (argc == 5 && argv[4][0] == '1') {
+  if (argc >= (arg_offset+5) && argv[arg_offset+4][0] == '1') {
     // randomly shuffle data
     LOG(INFO) << "Shuffling data";
-    std::random_shuffle(lines.begin(), lines.end());
+    shuffle(lines.begin(), lines.end());
   }
   LOG(INFO) << "A total of " << lines.size() << " images.";
 
+  string db_backend = "leveldb";
+  if (argc >= (arg_offset+6)) {
+    db_backend = string(argv[arg_offset+5]);
+    if (!(db_backend == "leveldb") && !(db_backend == "lmdb")) {
+      LOG(FATAL) << "Unknown db backend " << db_backend;
+    }
+  }
+
+  int resize_height = 0;
+  int resize_width = 0;
+  if (argc >= (arg_offset+7)) {
+    resize_height = atoi(argv[arg_offset+6]);
+  }
+  if (argc >= (arg_offset+8)) {
+    resize_width = atoi(argv[arg_offset+7]);
+  }
+
+  // Open new db
+  // lmdb
+  MDB_env *mdb_env;
+  MDB_dbi mdb_dbi;
+  MDB_val mdb_key, mdb_data;
+  MDB_txn *mdb_txn;
+  // leveldb
   leveldb::DB* db;
   leveldb::Options options;
   options.error_if_exists = true;
   options.create_if_missing = true;
   options.write_buffer_size = 268435456;
-  LOG(INFO) << "Opening leveldb " << argv[3];
-  leveldb::Status status = leveldb::DB::Open(
-      options, argv[3], &db);
-  CHECK(status.ok()) << "Failed to open leveldb " << argv[3];
+  leveldb::WriteBatch* batch = NULL;
+
+  // Open db
+  if (db_backend == "leveldb") {  // leveldb
+    LOG(INFO) << "Opening leveldb " << argv[arg_offset+3];
+    leveldb::Status status = leveldb::DB::Open(
+        options, argv[arg_offset+3], &db);
+    CHECK(status.ok()) << "Failed to open leveldb " << argv[arg_offset+3];
+    batch = new leveldb::WriteBatch();
+  } else if (db_backend == "lmdb") {  // lmdb
+    LOG(INFO) << "Opening lmdb " << argv[arg_offset+3];
+    CHECK_EQ(mkdir(argv[arg_offset+3], 0744), 0)
+        << "mkdir " << argv[arg_offset+3] << "failed";
+    CHECK_EQ(mdb_env_create(&mdb_env), MDB_SUCCESS) << "mdb_env_create failed";
+    CHECK_EQ(mdb_env_set_mapsize(mdb_env, 1099511627776), MDB_SUCCESS)  // 1TB
+        << "mdb_env_set_mapsize failed";
+    CHECK_EQ(mdb_env_open(mdb_env, argv[3], 0, 0664), MDB_SUCCESS)
+        << "mdb_env_open failed";
+    CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
+        << "mdb_txn_begin failed";
+    CHECK_EQ(mdb_open(mdb_txn, NULL, 0, &mdb_dbi), MDB_SUCCESS)
+        << "mdb_open failed";
+  } else {
+    LOG(FATAL) << "Unknown db backend " << db_backend;
+  }
 
-  string root_folder(argv[1]);
+  // Storing to db
+  string root_folder(argv[arg_offset+1]);
   Datum datum;
   int count = 0;
   const int kMaxKeyLength = 256;
   char key_cstr[kMaxKeyLength];
-  leveldb::WriteBatch* batch = new leveldb::WriteBatch();
   int data_size;
   bool data_size_initialized = false;
+
   for (int line_id = 0; line_id < lines.size(); ++line_id) {
     if (!ReadImageToDatum(root_folder + lines[line_id].first,
-                          lines[line_id].second, &datum)) {
+        lines[line_id].second, resize_height, resize_width, is_color, &datum)) {
       continue;
     }
     if (!data_size_initialized) {
@@ -88,23 +145,54 @@ int main(int argc, char** argv) {
     snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
         lines[line_id].first.c_str());
     string value;
-    // get the value
     datum.SerializeToString(&value);
-    batch->Put(string(key_cstr), value);
+    string keystr(key_cstr);
+
+    // Put in db
+    if (db_backend == "leveldb") {  // leveldb
+      batch->Put(keystr, value);
+    } else if (db_backend == "lmdb") {  // lmdb
+      mdb_data.mv_size = value.size();
+      mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
+      mdb_key.mv_size = keystr.size();
+      mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);
+      CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
+          << "mdb_put failed";
+    } else {
+      LOG(FATAL) << "Unknown db backend " << db_backend;
+    }
+
     if (++count % 1000 == 0) {
-      db->Write(leveldb::WriteOptions(), batch);
+      // Commit txn
+      if (db_backend == "leveldb") {  // leveldb
+        db->Write(leveldb::WriteOptions(), batch);
+        delete batch;
+        batch = new leveldb::WriteBatch();
+      } else if (db_backend == "lmdb") {  // lmdb
+        CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS)
+            << "mdb_txn_commit failed";
+        CHECK_EQ(mdb_txn_begin(mdb_env, NULL, 0, &mdb_txn), MDB_SUCCESS)
+            << "mdb_txn_begin failed";
+      } else {
+        LOG(FATAL) << "Unknown db backend " << db_backend;
+      }
       LOG(ERROR) << "Processed " << count << " files.";
-      delete batch;
-      batch = new leveldb::WriteBatch();
     }
   }
   // write the last batch
   if (count % 1000 != 0) {
-    db->Write(leveldb::WriteOptions(), batch);
+    if (db_backend == "leveldb") {  // leveldb
+      db->Write(leveldb::WriteOptions(), batch);
+      delete batch;
+      delete db;
+    } else if (db_backend == "lmdb") {  // lmdb
+      CHECK_EQ(mdb_txn_commit(mdb_txn), MDB_SUCCESS) << "mdb_txn_commit failed";
+      mdb_close(mdb_env, mdb_dbi);
+      mdb_env_close(mdb_env);
+    } else {
+      LOG(FATAL) << "Unknown db backend " << db_backend;
+    }
     LOG(ERROR) << "Processed " << count << " files.";
   }
-
-  delete batch;
-  delete db;
   return 0;
 }
diff --git a/tools/device_query.cpp b/tools/device_query.cpp
index 5040b8ee9d1..03799e52b53 100644
--- a/tools/device_query.cpp
+++ b/tools/device_query.cpp
@@ -1,23 +1,7 @@
-// Copyright 2014 BVLC and contributors.
-
-
 #include "caffe/common.hpp"
-#include "caffe/net.hpp"
-
-
-using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
-  if (argc > 2) {
-    LOG(ERROR) << "device_query [device_id=0]";
-    return 1;
-  }
-  if (argc == 2) {
-    LOG(INFO) << "Querying device_id=" << argv[1];
-    Caffe::SetDevice(atoi(argv[1]));
-    Caffe::DeviceQuery();
-  } else {
-    Caffe::DeviceQuery();
-  }
+  LOG(FATAL) << "Deprecated. Use caffe device_query "
+                "[--device_id=0] instead.";
   return 0;
 }
diff --git a/tools/dump_network.cpp b/tools/dump_network.cpp
index f29e150b048..90895fdc969 100644
--- a/tools/dump_network.cpp
+++ b/tools/dump_network.cpp
@@ -1,10 +1,8 @@
-// Copyright 2014 BVLC and contributors.
-//
 // This program takes in a trained network and an input blob, and then dumps
 // all the intermediate blobs produced by the net to individual binary
 // files stored in protobuffer binary formats.
 // Usage:
-//    dump_network input_net_param trained_net_param \
+//    dump_network input_net_param trained_net_param
 //        input_blob output_prefix 0/1
 // if input_net_param is 'none', we will directly load the network from
 // trained_net_param. If the last argv is 1, we will do a forward-backward pass
@@ -13,17 +11,16 @@
 #include <string>
 #include <vector>
 
-#include "cuda_runtime.h"
 #include "fcntl.h"
 #include "google/protobuf/text_format.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/net.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/util/io.hpp"
 #include "caffe/solver.hpp"
+#include "caffe/util/io.hpp"
 
 using namespace caffe;  // NOLINT(build/namespaces)
 
diff --git a/tools/extra/resize_and_crop_images.py b/tools/extra/resize_and_crop_images.py
index 0ab75dc2ae5..c844f590c06 100755
--- a/tools/extra/resize_and_crop_images.py
+++ b/tools/extra/resize_and_crop_images.py
@@ -3,7 +3,7 @@
 import gflags
 import os
 import cv2
-import PIL
+from PIL import Image
 
 # gflags
 gflags.DEFINE_string('image_lib', 'opencv',
@@ -37,9 +37,10 @@ def resize_and_crop_image(self, input_file, output_file, output_side_length = 25
 
 class PILResizeCrop:
 ## http://united-coders.com/christian-harms/image-resizing-tips-every-coder-should-know/
-    def resize_and_crop_image(self, input_file, output_file, output_side_length = 256):
+    def resize_and_crop_image(self, input_file, output_file, output_side_length = 256, fit = True):
         '''Downsample the image.
         '''
+        img = Image.open(input_file)
         box = (output_side_length, output_side_length)
         #preresize image with factor 2, 4, 8 and fast algorithm
         factor = 1
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index cdad6676d7f..49e8f98971c 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -1,19 +1,18 @@
-// Copyright 2014 BVLC and contributors.
-
 #include <stdio.h>  // for snprintf
-#include <cuda_runtime.h>
-#include <google/protobuf/text_format.h>
-#include <leveldb/db.h>
-#include <leveldb/write_batch.h>
 #include <string>
 #include <vector>
 
+#include "boost/algorithm/string.hpp"
+#include "google/protobuf/text_format.h"
+#include "leveldb/db.h"
+#include "leveldb/write_batch.h"
+
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/net.hpp"
-#include "caffe/vision_layers.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
+#include "caffe/vision_layers.hpp"
 
 using namespace caffe;  // NOLINT(build/namespaces)
 
@@ -27,14 +26,20 @@ int main(int argc, char** argv) {
 
 template<typename Dtype>
 int feature_extraction_pipeline(int argc, char** argv) {
+  ::google::InitGoogleLogging(argv[0]);
   const int num_required_args = 6;
   if (argc < num_required_args) {
     LOG(ERROR)<<
     "This program takes in a trained network and an input data layer, and then"
     " extract features of the input data produced by the net.\n"
-    "Usage: demo_extract_features  pretrained_net_param"
-    "  feature_extraction_proto_file  extract_feature_blob_name"
-    "  save_feature_leveldb_name  num_mini_batches  [CPU/GPU]  [DEVICE_ID=0]";
+    "Usage: extract_features  pretrained_net_param"
+    "  feature_extraction_proto_file  extract_feature_blob_name1[,name2,...]"
+    "  save_feature_leveldb_name1[,name2,...]  num_mini_batches  [CPU/GPU]"
+    "  [DEVICE_ID=0]\n"
+    "Note: you can extract multiple features in one pass by specifying"
+    " multiple feature blob names and leveldb names seperated by ','."
+    " The names cannot contain white space characters and the number of blobs"
+    " and leveldbs must be equal.";
     return 1;
   }
   int arg_pos = num_required_args;
@@ -91,75 +96,94 @@ int feature_extraction_pipeline(int argc, char** argv) {
       new Net<Dtype>(feature_extraction_proto));
   feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
 
-  string extract_feature_blob_name(argv[++arg_pos]);
-  CHECK(feature_extraction_net->has_blob(extract_feature_blob_name))
-      << "Unknown feature blob name " << extract_feature_blob_name
-      << " in the network " << feature_extraction_proto;
+  string extract_feature_blob_names(argv[++arg_pos]);
+  vector<string> blob_names;
+  boost::split(blob_names, extract_feature_blob_names, boost::is_any_of(","));
+
+  string save_feature_leveldb_names(argv[++arg_pos]);
+  vector<string> leveldb_names;
+  boost::split(leveldb_names, save_feature_leveldb_names,
+               boost::is_any_of(","));
+  CHECK_EQ(blob_names.size(), leveldb_names.size()) <<
+      " the number of blob names and leveldb names must be equal";
+  size_t num_features = blob_names.size();
+
+  for (size_t i = 0; i < num_features; i++) {
+    CHECK(feature_extraction_net->has_blob(blob_names[i]))
+        << "Unknown feature blob name " << blob_names[i]
+        << " in the network " << feature_extraction_proto;
+  }
 
-  string save_feature_leveldb_name(argv[++arg_pos]);
-  leveldb::DB* db;
   leveldb::Options options;
   options.error_if_exists = true;
   options.create_if_missing = true;
   options.write_buffer_size = 268435456;
-  LOG(INFO)<< "Opening leveldb " << save_feature_leveldb_name;
-  leveldb::Status status = leveldb::DB::Open(options,
-                                             save_feature_leveldb_name.c_str(),
-                                             &db);
-  CHECK(status.ok()) << "Failed to open leveldb " << save_feature_leveldb_name;
+  vector<shared_ptr<leveldb::DB> > feature_dbs;
+  for (size_t i = 0; i < num_features; ++i) {
+    LOG(INFO)<< "Opening leveldb " << leveldb_names[i];
+    leveldb::DB* db;
+    leveldb::Status status = leveldb::DB::Open(options,
+                                               leveldb_names[i].c_str(),
+                                               &db);
+    CHECK(status.ok()) << "Failed to open leveldb " << leveldb_names[i];
+    feature_dbs.push_back(shared_ptr<leveldb::DB>(db));
+  }
 
   int num_mini_batches = atoi(argv[++arg_pos]);
 
   LOG(ERROR)<< "Extacting Features";
 
   Datum datum;
-  leveldb::WriteBatch* batch = new leveldb::WriteBatch();
+  vector<shared_ptr<leveldb::WriteBatch> > feature_batches(
+      num_features,
+      shared_ptr<leveldb::WriteBatch>(new leveldb::WriteBatch()));
   const int kMaxKeyStrLength = 100;
   char key_str[kMaxKeyStrLength];
-  int num_bytes_of_binary_code = sizeof(Dtype);
   vector<Blob<float>*> input_vec;
-  int image_index = 0;
+  vector<int> image_indices(num_features, 0);
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
     feature_extraction_net->Forward(input_vec);
-    const shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net
-        ->blob_by_name(extract_feature_blob_name);
-    int num_features = feature_blob->num();
-    int dim_features = feature_blob->count() / num_features;
-    Dtype* feature_blob_data;
-    for (int n = 0; n < num_features; ++n) {
-      datum.set_height(dim_features);
-      datum.set_width(1);
-      datum.set_channels(1);
-      datum.clear_data();
-      datum.clear_float_data();
-      feature_blob_data = feature_blob->mutable_cpu_data() +
-          feature_blob->offset(n);
-      for (int d = 0; d < dim_features; ++d) {
-        datum.add_float_data(feature_blob_data[d]);
-      }
-      string value;
-      datum.SerializeToString(&value);
-      snprintf(key_str, kMaxKeyStrLength, "%d", image_index);
-      batch->Put(string(key_str), value);
-      ++image_index;
-      if (image_index % 1000 == 0) {
-        db->Write(leveldb::WriteOptions(), batch);
-        LOG(ERROR)<< "Extracted features of " << image_index <<
-            " query images.";
-        delete batch;
-        batch = new leveldb::WriteBatch();
-      }
-    }  // for (int n = 0; n < num_features; ++n)
+    for (int i = 0; i < num_features; ++i) {
+      const shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net
+          ->blob_by_name(blob_names[i]);
+      int batch_size = feature_blob->num();
+      int dim_features = feature_blob->count() / batch_size;
+      Dtype* feature_blob_data;
+      for (int n = 0; n < batch_size; ++n) {
+        datum.set_height(dim_features);
+        datum.set_width(1);
+        datum.set_channels(1);
+        datum.clear_data();
+        datum.clear_float_data();
+        feature_blob_data = feature_blob->mutable_cpu_data() +
+            feature_blob->offset(n);
+        for (int d = 0; d < dim_features; ++d) {
+          datum.add_float_data(feature_blob_data[d]);
+        }
+        string value;
+        datum.SerializeToString(&value);
+        snprintf(key_str, kMaxKeyStrLength, "%d", image_indices[i]);
+        feature_batches[i]->Put(string(key_str), value);
+        ++image_indices[i];
+        if (image_indices[i] % 1000 == 0) {
+          feature_dbs[i]->Write(leveldb::WriteOptions(),
+                                feature_batches[i].get());
+          LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
+              " query images for feature blob " << blob_names[i];
+          feature_batches[i].reset(new leveldb::WriteBatch());
+        }
+      }  // for (int n = 0; n < batch_size; ++n)
+    }  // for (int i = 0; i < num_features; ++i)
   }  // for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index)
   // write the last batch
-  if (image_index % 1000 != 0) {
-    db->Write(leveldb::WriteOptions(), batch);
-    LOG(ERROR)<< "Extracted features of " << image_index <<
-        " query images.";
+  for (int i = 0; i < num_features; ++i) {
+    if (image_indices[i] % 1000 != 0) {
+      feature_dbs[i]->Write(leveldb::WriteOptions(), feature_batches[i].get());
+    }
+    LOG(ERROR)<< "Extracted features of " << image_indices[i] <<
+        " query images for feature blob " << blob_names[i];
   }
 
-  delete batch;
-  delete db;
   LOG(ERROR)<< "Successfully extracted the features!";
   return 0;
 }
diff --git a/tools/finetune_net.cpp b/tools/finetune_net.cpp
index c1cd788a1fb..81c0c354dbf 100644
--- a/tools/finetune_net.cpp
+++ b/tools/finetune_net.cpp
@@ -1,33 +1,7 @@
-// Copyright 2014 BVLC and contributors.
-//
-// This is a simple script that allows one to quickly finetune a network.
-// Usage:
-//    finetune_net solver_proto_file pretrained_net
-
-#include <cuda_runtime.h>
-
-#include <string>
-
 #include "caffe/caffe.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
-
 int main(int argc, char** argv) {
-  ::google::InitGoogleLogging(argv[0]);
-  if (argc != 3) {
-    LOG(ERROR) << "Usage: finetune_net solver_proto_file pretrained_net";
-    return 1;
-  }
-
-  SolverParameter solver_param;
-  ReadProtoFromTextFileOrDie(argv[1], &solver_param);
-
-  LOG(INFO) << "Starting Optimization";
-  SGDSolver<float> solver(solver_param);
-  LOG(INFO) << "Loading from " << argv[2];
-  solver.net()->CopyTrainedLayersFrom(string(argv[2]));
-  solver.Solve();
-  LOG(INFO) << "Optimization Done.";
-
+  LOG(FATAL) << "Deprecated. Use caffe train --solver=... "
+                "[--weights=...] instead.";
   return 0;
 }
diff --git a/tools/net_speed_benchmark.cpp b/tools/net_speed_benchmark.cpp
index 36a00779f60..cd16e8d0984 100644
--- a/tools/net_speed_benchmark.cpp
+++ b/tools/net_speed_benchmark.cpp
@@ -1,101 +1,7 @@
-// Copyright 2014 BVLC and contributors.
-
-#include <cuda_runtime.h>
-#include <fcntl.h>
-#include <google/protobuf/text_format.h>
-
-#include <cstring>
-#include <ctime>
-#include <string>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/net.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/util/benchmark.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/solver.hpp"
-
-using namespace caffe;  // NOLINT(build/namespaces)
+#include "caffe/caffe.hpp"
 
 int main(int argc, char** argv) {
-  int total_iter = 50;
-  if (argc < 2 || argc > 5) {
-    LOG(ERROR) << "net_speed_benchmark net_proto [iterations=50]"
-        " [CPU/GPU] [Device_id=0]";
-    return 1;
-  }
-
-  if (argc >=3) {
-    total_iter = atoi(argv[2]);
-  }
-
-  LOG(ERROR) << "Testing for " << total_iter << "Iterations.";
-
-  if (argc >= 4 && strcmp(argv[3], "GPU") == 0) {
-    LOG(ERROR) << "Using GPU";
-    uint device_id = 0;
-    if (argc >= 5 && strcmp(argv[3], "GPU") == 0) {
-      device_id = atoi(argv[4]);
-    }
-    LOG(ERROR) << "Using Device_id=" << device_id;
-    Caffe::SetDevice(device_id);
-    Caffe::set_mode(Caffe::GPU);
-  } else {
-    LOG(ERROR) << "Using CPU";
-    Caffe::set_mode(Caffe::CPU);
-  }
-
-  Caffe::set_phase(Caffe::TRAIN);
-  Net<float> caffe_net(argv[1]);
-
-  // Run the network without training.
-  LOG(ERROR) << "Performing Forward";
-  // Note that for the speed benchmark, we will assume that the network does
-  // not take any input blobs.
-  float initial_loss;
-  caffe_net.Forward(vector<Blob<float>*>(), &initial_loss);
-  LOG(ERROR) << "Initial loss: " << initial_loss;
-  LOG(ERROR) << "Performing Backward";
-  caffe_net.Backward();
-
-  const vector<shared_ptr<Layer<float> > >& layers = caffe_net.layers();
-  vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
-  vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
-  LOG(ERROR) << "*** Benchmark begins ***";
-  Timer total_timer;
-  total_timer.Start();
-  Timer forward_timer;
-  forward_timer.Start();
-  Timer timer;
-  for (int i = 0; i < layers.size(); ++i) {
-    const string& layername = layers[i]->layer_param().name();
-    timer.Start();
-    for (int j = 0; j < total_iter; ++j) {
-      layers[i]->Forward(bottom_vecs[i], &top_vecs[i]);
-    }
-    LOG(ERROR) << layername << "\tforward: " << timer.MilliSeconds() <<
-        " milli seconds.";
-  }
-  LOG(ERROR) << "Forward pass: " << forward_timer.MilliSeconds() <<
-      " milli seconds.";
-  Timer backward_timer;
-  backward_timer.Start();
-  for (int i = layers.size() - 1; i >= 0; --i) {
-    const string& layername = layers[i]->layer_param().name();
-    timer.Start();
-    for (int j = 0; j < total_iter; ++j) {
-      layers[i]->Backward(top_vecs[i], true, &bottom_vecs[i]);
-    }
-    LOG(ERROR) << layername << "\tbackward: "
-        << timer.MilliSeconds() << " milli seconds.";
-  }
-  LOG(ERROR) << "Backward pass: " << backward_timer.MilliSeconds() <<
-      " milli seconds.";
-  LOG(ERROR) << "Total Time: " << total_timer.MilliSeconds() <<
-      " milli seconds.";
-  LOG(ERROR) << "*** Benchmark ends ***";
+  LOG(FATAL) << "Deprecated. Use caffe time --model=... "
+             "[--iterations=50] [--gpu] [--device_id=0]";
   return 0;
 }
diff --git a/tools/test_net.cpp b/tools/test_net.cpp
index c5819ec71b7..92e14eeebaf 100644
--- a/tools/test_net.cpp
+++ b/tools/test_net.cpp
@@ -1,57 +1,7 @@
-// Copyright 2014 BVLC and contributors.
-//
-// This is a simple script that allows one to quickly test a network whose
-// structure is specified by text format protocol buffers, and whose parameter
-// are loaded from a pre-trained network.
-// Usage:
-//    test_net net_proto pretrained_net_proto iterations [CPU/GPU]
-
-#include <cuda_runtime.h>
-
-#include <cstring>
-#include <cstdlib>
-#include <vector>
-
 #include "caffe/caffe.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
-
 int main(int argc, char** argv) {
-  if (argc < 4 || argc > 6) {
-    LOG(ERROR) << "test_net net_proto pretrained_net_proto iterations "
-        << "[CPU/GPU] [Device ID]";
-    return 1;
-  }
-
-  Caffe::set_phase(Caffe::TEST);
-
-  if (argc >= 5 && strcmp(argv[4], "GPU") == 0) {
-    Caffe::set_mode(Caffe::GPU);
-    int device_id = 0;
-    if (argc == 6) {
-      device_id = atoi(argv[5]);
-    }
-    Caffe::SetDevice(device_id);
-    LOG(ERROR) << "Using GPU #" << device_id;
-  } else {
-    LOG(ERROR) << "Using CPU";
-    Caffe::set_mode(Caffe::CPU);
-  }
-
-  Net<float> caffe_test_net(argv[1]);
-  caffe_test_net.CopyTrainedLayersFrom(argv[2]);
-
-  int total_iter = atoi(argv[3]);
-  LOG(ERROR) << "Running " << total_iter << " iterations.";
-
-  double test_accuracy = 0;
-  for (int i = 0; i < total_iter; ++i) {
-    const vector<Blob<float>*>& result = caffe_test_net.ForwardPrefilled();
-    test_accuracy += result[0]->cpu_data()[0];
-    LOG(ERROR) << "Batch " << i << ", accuracy: " << result[0]->cpu_data()[0];
-  }
-  test_accuracy /= total_iter;
-  LOG(ERROR) << "Test accuracy: " << test_accuracy;
-
+  LOG(FATAL) << "Deprecated. Use caffe test --model=... "
+      "--weights=... instead.";
   return 0;
 }
diff --git a/tools/train_net.cpp b/tools/train_net.cpp
index 7c6f23e6240..622bca311c8 100644
--- a/tools/train_net.cpp
+++ b/tools/train_net.cpp
@@ -1,37 +1,7 @@
-// Copyright 2014 BVLC and contributors.
-//
-// This is a simple script that allows one to quickly train a network whose
-// parameters are specified by text format protocol buffers.
-// Usage:
-//    train_net net_proto_file solver_proto_file [resume_point_file]
-
-#include <cuda_runtime.h>
-
-#include <cstring>
-
 #include "caffe/caffe.hpp"
 
-using namespace caffe;  // NOLINT(build/namespaces)
-
 int main(int argc, char** argv) {
-  ::google::InitGoogleLogging(argv[0]);
-  if (argc < 2 || argc > 3) {
-    LOG(ERROR) << "Usage: train_net solver_proto_file [resume_point_file]";
-    return 1;
-  }
-
-  SolverParameter solver_param;
-  ReadProtoFromTextFileOrDie(argv[1], &solver_param);
-
-  LOG(INFO) << "Starting Optimization";
-  SGDSolver<float> solver(solver_param);
-  if (argc == 3) {
-    LOG(INFO) << "Resuming from " << argv[2];
-    solver.Solve(argv[2]);
-  } else {
-    solver.Solve();
-  }
-  LOG(INFO) << "Optimization Done.";
-
+  LOG(FATAL) << "Deprecated. Use caffe train --solver=... "
+                "[--snapshot=...] instead.";
   return 0;
 }
diff --git a/tools/upgrade_net_proto_binary.cpp b/tools/upgrade_net_proto_binary.cpp
index 928fc52dc27..d7a62e32441 100644
--- a/tools/upgrade_net_proto_binary.cpp
+++ b/tools/upgrade_net_proto_binary.cpp
@@ -1,12 +1,10 @@
-// Copyright 2014 BVLC and contributors.
-//
 // This is a script to upgrade "V0" network prototxts to the new format.
 // Usage:
 //    upgrade_net_proto_binary v0_net_proto_file_in net_proto_file_out
 
 #include <cstring>
-#include <iostream>  // NOLINT(readability/streams)
 #include <fstream>  // NOLINT(readability/streams)
+#include <iostream>  // NOLINT(readability/streams)
 
 #include "caffe/caffe.hpp"
 #include "caffe/util/io.hpp"
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index 8a77f752cc2..1176585b02a 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -1,12 +1,10 @@
-// Copyright 2014 BVLC and contributors.
-//
 // This is a script to upgrade "V0" network prototxts to the new format.
 // Usage:
 //    upgrade_net_proto_text v0_net_proto_file_in net_proto_file_out
 
 #include <cstring>
-#include <iostream>  // NOLINT(readability/streams)
 #include <fstream>  // NOLINT(readability/streams)
+#include <iostream>  // NOLINT(readability/streams)
 
 #include "caffe/caffe.hpp"
 #include "caffe/util/io.hpp"