diff --git a/README.md b/README.md
index 1c648c6..0418e50 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ https://user-images.githubusercontent.com/8482575/120886182-f2b78800-c5ec-11eb-9
 
 ## Why *flowTorch*?
 
-The *flowTorch* project was started to make the analysis and modeling of fluid data **easy** and **accessible** to everyone. The library design intends to strike a balance between **usability** and **flexibility**. Instead of a monolithic, black-box analysis tool, the library offers modular components that allow assembling custom analysis and modeling workflows with ease. *flowTorch* helps to fuse data from a wide range of file formats typical for fluid flow data, for example, to compare experiments simulations. The available analysis and modeling tools are rigorously tested and demonstrated on a variety of different fluid flow datasets. Moreover, one can significantly accelerate the entire process of accessing, cleaning, analysing, and modeling fluid flow data by starting with one of the pipelines available in the *flowTorch* [documentation](https://flowmodelingcontrol.github.io/flowtorch-docs/1.0/index.html).
+The *flowTorch* project was started to make the analysis and modeling of fluid data **easy** and **accessible** to everyone. The library design intends to strike a balance between **usability** and **flexibility**. Instead of a monolithic, black-box analysis tool, the library offers modular components that allow assembling custom analysis and modeling workflows with ease. *flowTorch* helps to fuse data from a wide range of file formats typical for fluid flow data, for example, to compare experiments simulations. The available analysis and modeling tools are rigorously tested and demonstrated on a variety of different fluid flow datasets. Moreover, one can significantly accelerate the entire process of accessing, cleaning, analysing, and modeling fluid flow data by starting with one of the pipelines available in the *flowTorch* [documentation](https://flowmodelingcontrol.github.io/flowtorch-docs/1.1/index.html).
 
 To get a first impression of how working with *flowTorch* looks like, the code snippet below shows part of a pipeline for performing a dynamic mode decomposition (DMD) of a transient *OpenFOAM* simulation.
 
@@ -78,6 +78,9 @@ The easiest way to install *flowTorch* is as follows:
 ```
 # install via pip
 pip3 install git+https://github.com/FlowModelingControl/flowtorch
+# or install a specific branch, e.g., aweiner
+pip3 install git+https://github.com/FlowModelingControl/flowtorch.git@aweiner
+
 # to uninstall flowTorch, run
 pip3 uninstall flowtorch
 ```
@@ -90,7 +93,7 @@ and install the dependencies listed in *requirements.txt*:
 pip3 install -r requirements.txt
 ```
 
-To get an overview of what *flowTorch* can do for you, have a look at the [online documentation](https://flowmodelingcontrol.github.io/flowtorch-docs/1.0/index.html). The examples presented in the online documentation are also contained in this repository. In fact, the documentation is a static version of several [Jupyter labs](https://jupyter.org/) with start-to-end analyses. If you are interested in an interactive version of one particular example, navigate to `./docs/source/notebooks` and run `jupyter lab`. Note that to execute some of the notebooks, the **corresponding datasets are required**. The datasets can be downloaded [here](https://cloudstorage.tu-braunschweig.de/getlink/fiQUyeDFx3sg2T6LLHBQoCCx/datasets_29_10_2021.tar.gz) (~1.4GB). If the data are only required for unit testing, a reduced dataset may be downloaded [here](https://cloudstorage.tu-braunschweig.de/getlink/fiFZaHCgTWYeq1aZVg3hAui1/datasets_minimal_29_10_2021.tar.gz) (~384MB). Download the data into a directory of your choice and navigate into that directory. To extract the archive, run:
+To get an overview of what *flowTorch* can do for you, have a look at the [online documentation](https://flowmodelingcontrol.github.io/flowtorch-docs/1.1/index.html). The examples presented in the online documentation are also contained in this repository. In fact, the documentation is a static version of several [Jupyter labs](https://jupyter.org/) with start-to-end analyses. If you are interested in an interactive version of one particular example, navigate to `./docs/source/notebooks` and run `jupyter lab`. Note that to execute some of the notebooks, the **corresponding datasets are required**. The datasets can be downloaded [here](https://cloud.tu-braunschweig.de/s/sJYEfzFG7yDg3QT) (~2.6GB). If the data are only required for unit testing, a reduced dataset may be downloaded [here](https://cloud.tu-braunschweig.de/s/b9xJ7XSHMbdKwxH) (~411MB). Download the data into a directory of your choice and navigate into that directory. To extract the archive, run:
 ```
 # full dataset
 tar xzf datasets_29_10_2021.tar.gz
@@ -109,6 +112,34 @@ echo "export FLOWTORCH_DATASETS=\"$(pwd)/datasets_minimal/\"" >> ~/.bashrc
 . ~/.bashrc
 ```
 
+## Installing ParaView
+
+**Note:** the following installation of ParaView is only necessary if the *TecplotDataloader* is needed.
+
+*flowTorch* uses the ParaView Python module for accessing [Tecplot](https://www.tecplot.com/) data. When installing ParaView, special attention must be paid to the installed Python and VTK versions. Therefore, the following manual installation is recommend instead of using a standard package installation of ParaView.
+
+1. Determine the version of Python:
+```
+python3 --version
+# example output
+Python 3.8.10
+```
+2. Download the ParaView binaries according to your Python version from [here](https://www.paraview.org/download/). Note that you may have to use an older version ParaView to match your Python version.
+3. Install the ParaView binaries, e.g., as follows:
+```
+# optional: remove old package installation if available
+sudo apt remove paraview
+# replace the archive's name if needed in the commands below
+sudo mv ParaView-5.9.1-MPI-Linux-Python3.8-64bit.tar.gz /opt/
+cd /opt
+sudo tar xf ParaView-5.9.1-MPI-Linux-Python3.8-64bit.tar.gz
+sudo rm ParaView-5.9.1-MPI-Linux-Python3.8-64bit.tar.gz
+cd ParaView-5.9.1-MPI-Linux-Python3.8-64bit/
+# add path to ParaView binary and Python modules
+echo export PATH="\$PATH:$(pwd)/bin" >> ~/.bashrc
+echo export PYTHONPATH="\$PYTHONPATH:$(pwd)/lib/python3.8/site-packages" >> ~/.bashrc
+```
+
 ## Development
 ### Documentation
 
@@ -151,21 +182,24 @@ If you encounter any issues using *flowTorch* or if you have any questions regar
 
 ## Reference
 
-If *flowTorch* aids your work, you may support our work by referencing the following software article:
+If *flowTorch* aids your work, you may support the project by referencing the following article:
+
 ```
 @article{Weiner2021,
-  doi = {10.21105/joss.03860},
-  url = {https://doi.org/10.21105/joss.03860},
-  year = {2021},
-  publisher = {The Open Journal},
-  volume = {6},
-  number = {68},
-  pages = {3860},
-  author = {Andre Weiner and Richard Semaan},
-  title = {flowTorch - a Python library for analysis and reduced-order modeling of fluid flows},
-  journal = {Journal of Open Source Software}
-}
-```
+doi = {10.21105/joss.03860},
+url = {https://doi.org/10.21105/joss.03860},
+year = {2021},
+publisher = {The Open Journal},
+volume = {6},
+number = {68},
+pages = {3860},
+author = {Andre Weiner and Richard Semaan},
+title = {flowTorch - a Python library for analysis and reduced-order modeling of fluid flows},
+journal = {Journal of Open Source Software}
+} 
+```
+
+For a list of scientific works relying on flowTorch, refer to [this list](references.md).
 
 ## License
 
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 47e0f35..f859a35 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -25,11 +25,11 @@ def setup(app):
 # -- Project information -----------------------------------------------------
 
 project = 'flowTorch'
-copyright = '2020, flowTorch contributors'
+copyright = '2022, flowTorch contributors'
 author = 'flowTorch contributors'
 
 # The full version, including alpha/beta/rc tags
-release = '0.1'
+release = '1.1'
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/docs/source/flowtorch.data.rst b/docs/source/flowtorch.data.rst
index 0f22dc3..7e4827c 100644
--- a/docs/source/flowtorch.data.rst
+++ b/docs/source/flowtorch.data.rst
@@ -58,6 +58,14 @@ flowtorch.data.tau\_dataloader
    :undoc-members:
    :show-inheritance:
 
+flowtorch.data.tecplot\_dataloader
+----------------------------------
+
+.. automodule:: flowtorch.data.tecplot_dataloader
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 flowtorch.data.selection\_tools
 -------------------------------
 
diff --git a/docs/source/notebooks/dmd_intro.ipynb b/docs/source/notebooks/dmd_intro.ipynb
index 8d5b91b..49b71e6 100644
--- a/docs/source/notebooks/dmd_intro.ipynb
+++ b/docs/source/notebooks/dmd_intro.ipynb
@@ -91,7 +91,7 @@
     "$$\n",
     "The same problem may be posed with a time-continuous state $\\mathbf{x}=\\mathbf{x}(t)$ instead of discrete one. Then the basic formulation of DMD, mapping a given state linearly into the future, becomes a system of linear ordinary differential equations (ODEs):\n",
     "$$\n",
-    "\\frac{\\mathrm{d}\\mathbf{x}}{\\mathrm{d}t} = \\mathbf{\\mathcal{A}x},\n",
+    "\\frac{\\mathrm{d}\\mathbf{x}}{\\mathrm{d}t} \n",
     "$$\n",
     "where $\\mathbf{\\mathcal{A}}$ is the continuous counterpart of $\\mathbf{A}$ (the connection between both operators will be established in the following paragraphs). The initial value problem has the solution:\n",
     "$$\n",
@@ -145,11 +145,11 @@
     "\n",
     "### Steps to compute the DMD in practice\n",
     "\n",
-    "**1. Compute the SVD of $\\mathbf{X}$**\n",
+    "**1. Compute the SVD of the first data matrix**\n",
     "\n",
     "First, we factorize the data matrix $\\mathbf{X}$ into its singular value decomposition (SVD):\n",
     "$$\n",
-    "  \\mathbf{X} = \\mathbf{U\\Sigma V}^{\\dagger},\n",
+    "  \\mathbf{X} = \\mathbf{U\\Sigma V}^{\\ast},\n",
     "$$\n",
     "where $\\mathbf{U}$ is a $M\\times M$ unitary matrix, $\\mathbf{\\Sigma}$ is a $M\\times N$ matrix containing a smaller diagonal matrix of singular values, and $\\mathbf{V}$ is a $N\\times N$ unitary matrix (refer to the [Linear algebra with PyTorch](linear_algebra_basics.ipynb) notebook). What the SVD essentially provides us with is an easily controllable means to perform matrix approximations and a potentially better coordinate system to represent the system's dynamics.\n",
     "\n",
@@ -157,17 +157,17 @@
     "\n",
     "As mentioned above, we can approximate the discrete linear operator as:\n",
     "$$\n",
-    "  \\mathbf{A}\\triangleq \\mathbf{X}^\\prime \\mathbf{X}^\\dagger = \\mathbf{X}^\\prime \\mathbf{V\\Sigma}^{-1}\\mathbf{U}^\\dagger.\n",
+    "  \\mathbf{A}\\triangleq \\mathbf{X}^\\prime \\mathbf{X}^\\dagger = \\mathbf{X}^\\prime \\mathbf{V\\Sigma}^{-1}\\mathbf{U}^\\ast.\n",
     "$$\n",
     "However, to reduce the computational effort, we first represent $\\mathbf{A}$ in the span of the first $r$ left singular vectors $\\mathbf{U}_r$, where $0<r\\le N$:\n",
     "$$\n",
-    "  \\tilde{\\mathbf{A}} = \\mathbf{U}_r^\\dagger \\mathbf{AU}_r,\n",
+    "  \\tilde{\\mathbf{A}} = \\mathbf{U}_r^\\ast \\mathbf{AU}_r,\n",
     "$$\n",
     "where $\\tilde{\\mathbf{A}}$ is an $r\\times r$ square matrix that is [similar](ttps://en.wikipedia.org/wiki/Matrix_similarity) to $\\mathbf{A}$. Note that this projection is only meaningful if $\\mathbf{X}$ and $\\mathbf{X}^\\prime$ do not have full rank, e.g., if there exist coherent structures in the flow. Combining the two expressions above and using the rank $r$ approximation of the SVD yields:\n",
     "$$\n",
-    "  \\tilde{\\mathbf{A}} = \\mathbf{U}_r^\\dagger\\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1}\\mathbf{U}_r^\\dagger\\mathbf{U}_r = \\mathbf{U}_r^\\dagger\\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1}.\n",
+    "  \\tilde{\\mathbf{A}} = \\mathbf{U}_r^\\ast\\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1}\\mathbf{U}_r^\\ast\\mathbf{U}_r = \\mathbf{U}_r^\\ast\\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1}.\n",
     "$$\n",
-    "It is important to mention that $\\mathbf{U}_r^\\dagger\\mathbf{U}_r$ yields an $r\\times r$ identity matrix, whereas $\\mathbf{U}_r\\mathbf{U}_r^\\dagger$ in general **does not** yield a $M\\times M$ identity matrix. After completing this second step, the reduced $r\\times r$ operator $\\tilde{\\mathbf{A}}$ is much easier to deal with.\n",
+    "It is important to mention that $\\mathbf{U}_r^\\ast\\mathbf{U}_r$ yields an $r\\times r$ identity matrix, whereas $\\mathbf{U}_r\\mathbf{U}_r^\\ast$ in general **does not** yield a $M\\times M$ identity matrix. After completing this second step, the reduced $r\\times r$ operator $\\tilde{\\mathbf{A}}$ is much easier to deal with.\n",
     "\n",
     "**3. Compute the eigen decomposition of the reduced linear operator**\n",
     "\n",
@@ -187,8 +187,8 @@
     "$$\n",
     "\\begin{align}\n",
     "  \\tilde{\\mathbf{A}}\\mathbf{W} &= \\mathbf{W\\Lambda} \\\\\n",
-    "  \\mathbf{U}_r\\underbrace{\\mathbf{U}_r^\\dagger\\mathbf{AU}_r}_{\\tilde{\\mathbf{A}}}\\mathbf{W} &= \\mathbf{U}_r\\mathbf{W\\Lambda} \\\\\n",
-    "  \\underbrace{\\mathbf{U}_r\\mathbf{U}_r^\\dagger}_{\\neq \\mathbf{I}} \\mathbf{A}\\tilde{\\mathbf{\\Phi}} &= \\tilde{\\mathbf{\\Phi}}\\mathbf{\\Lambda}\n",
+    "  \\mathbf{U}_r\\underbrace{\\mathbf{U}_r^\\ast\\mathbf{AU}_r}_{\\tilde{\\mathbf{A}}}\\mathbf{W} &= \\mathbf{U}_r\\mathbf{W\\Lambda} \\\\\n",
+    "  \\underbrace{\\mathbf{U}_r\\mathbf{U}_r^\\ast}_{\\neq \\mathbf{I}} \\mathbf{A}\\tilde{\\mathbf{\\Phi}} &= \\tilde{\\mathbf{\\Phi}}\\mathbf{\\Lambda}\n",
     "\\end{align}\n",
     "$$\n",
     "A more widely accepted definition suggested by [Jonathan Tu et al.](https://arxiv.org/pdf/1312.0041.pdf) is:\n",
@@ -199,12 +199,12 @@
     "$$\n",
     "\\begin{align}\n",
     "  \\tilde{\\mathbf{A}}\\mathbf{W} &= \\mathbf{W\\Lambda} \\\\\n",
-    "  \\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1} \\underbrace{\\mathbf{U}_r^\\dagger\\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1}}_{\\tilde{\\mathbf{A}}} \\mathbf{W}&= \\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1}\\mathbf{W\\Lambda} \\\\\n",
+    "  \\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1} \\underbrace{\\mathbf{U}_r^\\ast\\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1}}_{\\tilde{\\mathbf{A}}} \\mathbf{W}&= \\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1}\\mathbf{W\\Lambda} \\\\\n",
     "  \\mathbf{A}\\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1}\\mathbf{W}&= \\mathbf{X}^\\prime\\mathbf{V}_r\\mathbf{\\Sigma}_r^{-1}\\mathbf{W\\Lambda} \\\\\n",
     "  \\mathbf{A\\Phi} &= \\mathbf{\\Phi\\Lambda}\n",
     "\\end{align}\n",
     "$$\n",
-    "Therefore, the computation suggested by Jonathan Tu et al. yields the so-called **exact DMD modes**. The modes obtained using the original definition are commonly called **projected DMD modes** because the relation between both eigenvector matrices is $\\tilde{\\mathbf{\\Phi}} = \\mathbf{U}_r\\mathbf{U}_r^\\dagger\\mathbf{\\Phi}$ (we obtain $\\tilde{\\mathbf{\\Phi}}$ by projecting $\\mathbf{\\Phi}$ onto the POD modes/left singular vectors of $\\mathbf{X}$). However, if the last column of $\\mathbf{X}^\\prime$ is in the span of $\\mathbf{X}$, both definitions yield the same result; refer to remark 5 of the article by J. Tu et al. linked above."
+    "Therefore, the computation suggested by Jonathan Tu et al. yields the so-called **exact DMD modes**. The modes obtained using the original definition are commonly called **projected DMD modes** because the relation between both eigenvector matrices is $\\tilde{\\mathbf{\\Phi}} = \\mathbf{U}_r\\mathbf{U}_r^\\ast\\mathbf{\\Phi}$ (we obtain $\\tilde{\\mathbf{\\Phi}}$ by projecting $\\mathbf{\\Phi}$ onto the POD modes/left singular vectors of $\\mathbf{X}$). However, if the last column of $\\mathbf{X}^\\prime$ is in the span of $\\mathbf{X}$, both definitions yield the same result; refer to remark 5 of the article by J. Tu et al. linked above."
    ]
   },
   {
@@ -456,7 +456,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1280x640 with 2 Axes>"
       ]
@@ -505,7 +505,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1280x480 with 1 Axes>"
       ]
@@ -586,7 +586,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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SKhtQJmpxdRyXF6dcXFyeekLjWUwmE7dv3yY4OJigoCD++ecf/v33X65evcr169e5du0a4eHhL3w/s9nM+fPnOX/+PBs3brTZljlzZiNkBQYGUqBAAQoWLEiyZM+7YyuEEMJeooeqGTNmsGvXLn777TfMZjORkZFs27aNbt26GbcFASZMmECuXLlYvXo127db/jn19vamXr16tGjRIkH1qXqRJH/FSin1CfAJsF9r3fKJbUWx9KoyA7W01pui1kef0mal1touU9rE1ytW9qK1JiQkhEuXLnHx4kWCgoKMAPbPP/8QEhLyWudVSpE7d24KFSpEoUKFKFy4MIUKFcLX1zfBXkoWQoj4KPotui+++IKxY8dSoEAB6tSpQ8aMGVm9ejWnT5/m8uXLVKtWzSZcgeVhqStXrmAymfDx8SFNmjS4ubkB8SdUvekVq0QVrKKC0LRoq3IBaYEgIDja+rpa63+jjhkCDAZ+1VpXfMY5o0/C/CtwHcvtv0zAacBukzAn9mD1Ivfv3+fChQtcuHCBv//+m8uXL3PlyhWCg4OJjIx85fOlS5eOYsWKUaxYMYoXL06xYsXw8/OTsCWEEG9o7ty5tG3blqpVqzJ27FijT9WDBw/YsWMHw4cP58CBA1SrVo3JkyeTK1curHkj+ndwfBxLJbcCbaUASj5jvV/UH6uXTi1a6/FKqb+Az4ASWJ4SvIxlCptRz2keKl5R8uTJjatO0VmfQjxz5gznzp3j/PnzXLx4keDgYJsZ0J9048YNNm3axKZNm4x16dOnp1ixYpQsWZKSJUtSokSJBP8EihBCxBWtNZGRkWzZsgWA3r17ExgYaDzF7u3tTbVq1fDy8jIGtHfr1s0Y0G42m22CVHwLVfaQqK5YJXRJ/YrVq4qMjOTChQucPHmSU6dOcfbsWS5cuMC9ey/d/QKAvHnzUqpUKUqWLEmpUqUoWLAgLi6J7XcOIYSwj/v371O2bFmCgoI4ffo06dKle+o23sOHD5kwYQIDBgwgWbJkVKxYkfHjxxtPC8ZnciswEZFg9ea01ty6dYsTJ05w7NgxTpw4wdmzZ7l169ZLn8Pb25vSpUtTrlw5ypUrR8mSJWVwvBBCRAkLC6NChQocOnSI5cuXU69evWfe0rtw4QIFChTAzc2Ne/fuUaZMGTZs2ECKFC89WYlDyK1AIaJRSpE2bVoqVKhAhQoVjPW3bt3i2LFjHDt2jOPHj3Pq1KkYr2w9ePCAn3/+mZ9//hkAZ2dnihYtSrly5ahYsSLly5eXtg9CiCRJa42npyfvvPMOhw4dYufOndSrV89oam19BYxehfXq1ePHH3+kTp068T5U2YNcsYpH5IpV3NFaExQUxJEjR/jzzz85fvw458+ff+6YLSulFIULF+bdd981glaqVKliv2ghhIgj0UOS9UpU9Nt9GzdupGbNmgCMHz+eHj16PHXspk2bqFWrFj/99BMlS5Y0vifj44D16ORWYCIiwcqxHj16xIkTJzh48CBHjhzh+PHjL9V1XilF0aJFqVKlCpUrV6Zs2bJ4eHjEQcVCCGF/0QNUZGQkd+7cwdPTk8jISJtfIq0d1wG+/PJLmjVrRpYslglJ9uzZw4ABA7hw4QJbt24ld+7cQPwPVSDBKlGRYBW/mM1m/v77bw4ePMjhw4c5evQoN2++uLOGp6cn5cuXp3LlylSpUoW33norXvRmEUKIF4ne/HPx4sWsX7+e3bt34+XlRbp06ejQoQN169YlefLkAIwaNYr+/fsDUKxYMfLkyYOPjw/Lly/nxo0bTJ48ma5dE9bE9RKsEhEJVvGb1prg4GD27t3LwYMH+eOPP15qUHyGDBmoWrUqNWrUoEqVKtLeQQgRL0W/UtW3b1++/vprwDLjRXh4OLdv3wagS5cudOrUiQIFCgAwb948xo8fz5kzZ3j06BEAvr6+DBgwgI4dOwIJ40qVlQSrRESCVcJiHae1d+9e9u7dyx9//PHCVg9OTk6UKlWKatWqUaNGDYoUKSJXs4QQ8cro0aP54osvqF27Nn369OHtt9/m7Nmz/PTTT/Tv3x+TyUSrVq0YMGCAMR/g5cuX+ffff/nzzz/JlSsXmTJlIl++fED86aj+siRYJSISrBI2s9nMmTNn2LVrF/v37+f48eM8fvz4ucdkypSJWrVqUbt2bSpVqoSXl1ccVSuEEE87dOgQH3zwAcmSJWP16tVGR3WrpUuX0rNnT27cuMGIESPo16/fc8+XkK5UWUmwSkQkWCUu4eHhHDp0iF27drF3716CgoKeu7+npyeVK1emdu3a1KpVi0yZMsVRpUIIYbFixQoaNmzIoEGDGDJkiLE++tirOXPm0K5dOwD27dvH22+/beyXEIPUk6SPlRDxlIeHB2XLlqVs2bIABAcHs3PnTnbv3s2RI0eMsQhWYWFhrFu3jnXr1qGUonTp0nz00UfUrVvXZhJTIYSILefOnQMwmiJHRkbi4uKCs7OzcUvv448/ZtOmTaxYsYLLly/bBKuEHqrsIeHc9BQigfP19aVJkyZMnTqV7du3M3HiROrUqUO6dOme2ldrzW+//Ubv3r3JlSsXhQsXZtiwYRw7dgy5yiyEiC3WuyWrV6/mwYMHNtN7OTk5Gb3+MmfODMDx48cB5HspGglWQjiAm5sbZcuWZeDAgWzYsIHFixfTtm1bo9fLk44ePcrgwYMpWLAgAQEBDBw4UEKWEMLuPvroI/z9/Tl16hQrVqwwrqxrrdFaG7cDvby8cHZ2pmTJkoBcqYpOgpUQDqaUIm/evHTu3JmlS5eyceNGPvvssxifGDxz5gwjRoygYMGCBAYGMmTIEE6ePOmAyoUQCY3ZbI5xm8lkwsfHhxo1ahASEsKkSZP48ccfefDggU1w+u2331i0aBHZs2cna9ascVF2giKD1+MRGbwunnT37l22b9/OL7/8wqFDh577lGGBAgVo0qQJTZs2JXv27HFXpBAiQYg+AP3nn3/m77//xt3dnRw5ctjMrXrmzBm6d+/Oli1byJs3L9WqVaNr166kTJmSgwcPMmrUKHbv3s23335L27ZtHfVxYo08FZiISLASz/Pw4UN27tzJ5s2b2bdv33NDVtmyZWnWrBkNGzYkTZo0cVilECI+iv603oABA/jyyy+NbU5OTnzxxRcMHz7cWHfixAmGDBnC1q1bCQkJIXXq1Li4uHDz5k2UUowZM4aePXs+de7EQIJVIiLBSrys0NBQfv31VzZt2sSBAweIiIh45n4uLi5Uq1aN5s2bU6dOHZnDUIgkbty4cXz++efky5ePmjVrcu/ePWbNmgVA586dGTNmjNFPLygoiJ07dzJnzhyuXLnCw4cPqV69OrVr16Z27dpAwmv++TIkWCUiEqzE63jw4AHbtm1j48aNHDp0KMYxFKlSpaJRo0a0bt2akiVLJqrfMIUQLxYaGkr16tVJliwZ48aNI3/+/ABs3ryZJk2aEBISQocOHRg/fjyenp42x4aFhREZGWnMEQiJM1SBBKtERYKVeFO3b99m48aNbNy4kVOnTsW4X548eWjdujUtWrTAz++1vjuEEPHck7fozpw5Q0BAAKtWreLDDz80nvRzcnJi+/btNG7cmBs3btiEq+jjsp51zsToTYNV4ouaQiRhqVOnplmzZixatIg1a9bQtm1bfH19n9rvzJkz9OvXj6xZs1KtWjVWrlz5wul3hBAJh8lkMgJQWFgYDx8+JDIykjJlylCqVCnAcsXJus+7777LsmXLSJcuHbNmzeLTTz8lPDzcJlSBtFV4GXLFKh6RK1YiNmit+fPPP1mzZg2//PILDx8+fOZ+6dKlo1WrVrRt25aAgIA4rlIIYS/Rb9FNnTqVzZs3ExwcTPr06dm8eTNLliyhcePGxv7Rr0JFv3L18ccfM3ny5KduCyZ2ciswEZFgJWJbeHg427dvZ+3atRw6dCjGBqPlypWjffv2NGjQIMl9qQqRWPTr14+vvvoKJycnPD09CQ0NBaBVq1aMHDnS6J4OtuFqx44d1K1bl7t377J161bee+89h9TvKHIrUAjx0jw8PKhevTozZsxgw4YNdOzY8ZmTPe/evZtWrVrh5+fHZ599xtmzZx1QbcxMJhNeXl64uroSEBDAkiVLHF2SEPHKypUrmTx5MjVq1GDz5s0cOXKEfv364erqyvz585kzZw537twx9ldKGb9oVaxYke+//54ZM2YkuVBlD3LFKh6RK1bCEcxmMwcPHmTlypXs3LkzxtYNlStXpnPnztSuXRtXV9c4rtLW9evX6dOnD0eOHOHo0aO4uroSHBz8zHkXhUgKnhxkPmrUKObMmcOqVasoWLCgsX7mzJkMGDCAW7duMWzYMLp27YqPj4+x/VmD0xPr038xkVuBiYgEK+Fod+/eZf369axatYpLly49c5/MmTPTsWNHOnbsSIYMGeK4QluPHj0iR44c/Pvvv6xevZoPP/zQofUI4WjDhg3D1dWVTZs2kT9/fqZPn47ZbMZsNhsTKs+ePZu+ffty+/btZ4arpE5uBQoh7CZlypQ0a9aMFStW8O2331KpUiWb2e0B/vnnHwYPHkzWrFlp0aIFBw4ccFC14O7ubkzFceTIEYfVIUR8sGPHDoYMGcLYsWM5f/680XNKKYWLi4vR465du3Z89dVXpEmThkGDBjF9+nSb24LizUiwEkI8RSlFkSJFGD16NBs3bqRz585PXZ16/PgxixYtokSJEpQuXZolS5Y4pGVDkSJFAAlWQpQuXZoRI0bg4+PDP//8w88//8z58+eNW3tOTk424WrUqFFkyJCBAQMGMHbsWGm5YicSrIQQz+Xj40Pbtm1Zt24dEyZMoESJEk/ts3fvXpo1a0b27NkZNWoUt2/fjpPatNZs2rQJkGAlkjaz2Yy7uzu9e/fm448/Jlu2bJw+fZrvv/+eW7duGfs9Ga6GDh2Kk5MT6dKlw83NzVHlJyoyxioekTFWIqG4fPkyixcvZsOGDYSFhT213cvLizZt2tCzZ0/8/f1jrY7x48fTq1cvY/n27dsyVkQkWdZB5o8fP+abb77hm2++wWw2M2jQIJo1a2YzIXv0AenHjx8nMDDQUWXHOzLGSggR57JmzcoXX3zBpk2b6NWr11Pd3R8+fMjUqVPJkycPdevWZdeuXTH2zHpdJ0+epF+/fqRIkYKUKVMCz79qde7cOWrUqIG3tzdp06alS5cuRl8fIRID69UoNzc3evXqxWeffYZSimHDhrFo0aIYr1xZQ1VM84yKVyPBSgjx2pIlS0bTpk1ZvXo148ePN8Y7WWmtWbNmDRUqVKBs2bKsXbvWLl/eERERtGjRgvDwcCZPnkzJkiWBmIPV3bt3ee+997h9+zbLly9n4sSJrFy5kpYtW75xLULEJ9HD1aeffkrv3r1RSjF8+PBnhqsnjxVvzuXFuwghxPM5OTlRvnx5ypcvz+nTp1mwYAG//PILkZGRxj6///47H374Ifny5aNPnz40bdr0tcd0DB8+nEOHDlG/fn1atmzJyZMn2bJlC3/88ccz9585cyY3btzg4MGDpE+fHgBPT0/q1avHoUOHKFas2GvVIUR89GS4Ahg7diyjRo3CZDLRsmVL0qZN6+AqEy+Jp0IIu8qbNy8jR45k3bp1tGjRAm9vb5vtJ0+epE2bNuTKlYvx48fz4MGDVzr//v37GTVqFJkyZWLmzJkAFC1aFIj5itWGDRt47733jFAF8MEHH+Dt7c369etf6f2FiGsmk+mVj3kyXPXp04eHDx/y+eefExQUFAtVCisJVkKIWJEuXTp69OjBhg0b6N69+1O/IQcFBdGrVy+yZ8/OyJEjuXv37gvPGRYWRsuWLTGZTMydO5fUqVMD/wWrU6dO8ejRo6eOO3HiBPny5bNZ5+LiQp48eTh58uTrfkQh4oS1o/rixYs5derUSx8XPVz16NGDXr16MW3aNAoXLhxLlQqQYCWEiGVeXl60bNmSdevWMXDgQLJmzWqz/datWwwYMIBs2bIxcOBAmzEgT+rTpw+nT5+mS5cuVK1a1VifK1cuUqVKRUREBMePH3/quDt37pAqVaqn1vv4+MRZawghXlX0Bz4mT55MixYt+N///vdKc3dGD1eDBg2iY8eOgAxUj00SrIQQccLV1ZU6deqwYsUKxo4dS/78+W223717lxEjRpAtWzb69OnDtWvXbLZv3bqVqVOnEhAQwJgxY546v3XgfEzjrIRISEwmk82cfTly5ODtt99m3bp1DBo0iDNnzrz0uayD0qMPTpeB6rFH/pcVQsQpJycnKlasyPz585k+ffpTTxKGhoYyZswYcuTIQe/evbl+/TohISG0adMGZ2dnFi5ciKen51Pnfd44Kx8fH0JCQp5af+fOHeN2ohDxRfQJladNm0bNmjXp37+/cdv6+++/Z8iQIa905UrEHQlWQgiHUErx9ttv8+233zJ79uynOrqHhYUxbtw4cuTIQdmyZQkKCmLQoEEUL178meezPtn3rGCVL1++p8ZSmUwmzpw589TYKyEcSWtthKovvviC7t27c+3aNbp3787XX39N//79AVi2bBn9+/eXcBUPSbsFIYTDFS5cmGnTpnHy5ElmzJjBnj17jG0PHz7kxIkTeHt7ExYWxq1bt2w6SFs1adKEJk2aPPP8NWrUYOjQody4cYN06dIBsG7dOh48eEDNmjVj50MJ8Rqst//mzp3L6NGjqVy5Mt988w0FChQw9qlcuTKdO3dmxYoVODk5MXz4cHLnzu2oksUTZEqbeESmtBFJTVikmSt3IwmL1Hi6KLKkdMHTxYnTp08zbdo0m4BllTx5cj777DN69epF8uTJX+p9QkJCKFCgAFmzZmXgwIHcuXOHXr16UapUKdasWWPnTyXEm2vdujWLFy/m559/pmLFimit0VqjlEIpxa5du/joo4+4desWjRs3ZsiQIeTJk8fRZScKbzqljQSreESClUgqLt+NYNP5h/x6KZzwyP++gzxcFO9k86BaLi+ypnTl5MmTTJ06lb179z51jrRp09KvXz86d+6Mh4fHC9/zzJkzdO/enV27duHh4UGDBg0YO3bsU322hHC00NBQAgMDuX79OocPHzYC05MDzlesWEHDhg0BqFevHuPGjSNr1qxGABOvR4JVIiLBSiQF686EMv/ofZ73zaOAVoWSUztPMgCOHTvGtGnT2L9//1P7+vn5MXjwYFq3bo2Li4xuEIlDhQoV2LNnDxs3buT999+32Wb9d/vKlStUqFCBx48fc/XqVVq3bs2cOXOMfSRcvR6ZhFkIkWCsOxPKvBeEKgANzDt6n3VnLJMkFyhQgGnTpjF79mwKFSpks29QUBDt27cnMDCQlStX2n2yZyEcoVixYmitGTNmDBcvXnxqu1KKrFmzkiZNGipVqkTWrFmZN28ekydPNrYLx5BgJYSIE5fvRjD/6P1XOmb+0ftcvhthLBcuXJjZs2czefLkpwbrnjlzhvr161OmTBl2795tl5qFiC0x/QLw+PFjANq0aUPBggXZu3cv06dPJzg4GIDIyEgjNG3YsIFjx47x6aefsmDBAgB++uknIiIi5BcMB5JgJYSIE5vOP3zhlaonaWDz+TCbdUopSpcuzZIlS/jqq6/IkiWLzfa9e/dSvnx5Pvzww1ea/kOIuBK9+ee///7L6dOnjatS1onJc+fOTbNmzfDw8GDWrFmMGDGCkydPGre79+zZw8SJE8mQIQMAAQEBpEmThiNHjnD//qv9AiPsS4KVECLWhUWa+fVS+Gsdu+NSGGGRT0+/oZSicuXKLF++nP79+z/VgmHt2rUUKFCAjh07cvXq1dd6byHszWw2G32qvv76a95//33y5ctHmTJlaNasGdeuXUNrjaenJx07dqRLly4kT56cmTNnUqJECZo1a0aTJk2oVq0aP//8M59++ilFixbF1dUVs9lM3rx5SZ06tdwKdCAJVkKIWHflbqTN03+vIjxSE3QvMsbtLi4u1K1bl7Vr19KpUyebruwmk4lZs2aRO3duRo0aRXj464U7IezF+mRfv3796Nu3L0FBQRQoUIBHjx6xdOlSGjRowIEDB4iIiCBlypT07t2bMWPGULt2bUJDQ1m6dCk//PADadKkYdq0aXz66acAjBw5kjt37lCyZEkiIyPlVqADyVOB8Yg8FSgSq6PXHjFs553XPn5wBR/eyvBy/03cuXOHmTNnsmbNGiIjbQNZtmzZGD16NA0bNpTf6IXD/Pjjj7Rs2ZIKFSowbNgw8uXLx4ULF2jfvj2//fYbxYsXZ9KkSRQvXhwXFxfjCb8dO3YQGhpK6tSpSZkypTHf5rRp0/jiiy/IkCEDW7dufWqic/Fq5KlAIUS85+nyZiHG0/Xlj/fx8aFv376sWLGCihUr2my7dOkSjRs3ply5cs9s3SBEbDCZTDbLhw4dwsPDg2HDhlG4cGGcnZ3Jly8fixcvpk6dOhw8eJAePXpw8OBBm/FYFStWpGbNmpQuXZr8+fPz+PFjBg4cyKBBg3Bzc2P16tUSquIBCVZCiFiXJaULHq8ZrjxcFH4pXr0/lZ+fH2PHjmX27NlPdaT+7bffKFmyJK1atZLxVyJWRA9T1jFVXbp0Ydy4cTx8+JCWLVtSuHBhY8yV2WwmW7ZsTJ48mQ8++IADBw7QvXt3Dhw48Mzberdu3aJcuXKMHDmSHDlysGvXLgIDA+Ps84mYSbASQsQ6Txcn3sn24u7oz1IxmyeeLq//VVW4cGEWLVrEkCFDnhrgvmDBAvLkycPYsWONx9yFeBPHjx/n/v37Rliy2r9/PzNmzODzzz/nu+++4/r164BlzJVSCicnJ8xmM35+fkyePNm4ctWrVy/27t1rcy6ANGnS0KdPH0aOHMmaNWsICAiI088pYibBSggRJ6rl8uJVr1kpoGouzxfu9yJOTk7UqlWLNWvW0K5dO5sxjPfv3+fzzz/nrbfeYsuWLW/8XiLpOnToEAULFqRChQqEh4fbTEFTokQJpkyZAljmrgwNDeXhw4cARmiyhqssWbIwadIkPvroI/bu3UubNm2MIBZd/fr16dOnD76+vnHw6cTLkmAlhIgTWVO60qrQy02abNWqUHKypnS1Ww2enp506tSJlStX8t5779lsO336NFWrVuXDDz/k77//ttt7iqTDw8MDLy8vfHx8bG4FPnr0CLDcCrSGq5UrVzJr1izgv0AV/ecsWbIwfvx43nvvPdq1a0fGjBmf+Z7W24wi/pCnAuMReSpQJAZOkWF43L+Ic+RDTC5ehCfPjtnlv6tOrzNXYGw5ePAgo0ePfipIeXp6MmDAAD777DP5b1G8kqCgIFKkSEGKFCmYO3cuH330ESlTpuTx48dG889Zs2bRqVMnAKZOnUrnzp0By5Ur61Uu68+hoaEkS2b570Dm/4sbMglzIiLBSiRkHvf/Jt3fa0kT9DPOpv+6pZucPbnlV4UbOeoQnjwHYJneZvP5MHZcCrPpb+XhoqiYzZOquTzteqXqeSIjI1mxYgUzZszgwYMHNtvy5MnDtGnTqFSpUpzUIhKP8ePH89lnn1G1alW+//57UqRIwaNHj4zv9m+//ZaOHTsCMYer6CRUxR0JVomIBCuRUKU/vxy/EzNQz7kOpVEE5e/E9VwNjHVhkWaC7kUSFqHxdLU8/fcmA9XfREhICBMnTmTdunVPbWvSpAnjxo0jU6ZMDqhMJARPBp+jR4/SpEkTTp06RfXq1Vm6dOkbhysRN6SPlRDCodKfX06WE9OfG6oAFJosJ6aT/vxyY52nixO5U7vxVgZ3cqd2c1ioAkiVKhWDBw9m7ty55MqVy2bb0qVLyZs3L1OmTHmqJ5EQ0XtNWS9WFCpUiBUrVpA/f342btxIkyZNuHfvHu7u7saYq/bt2zNjxgwAunbtaoy/klCVsMn/e0KI1+Zx/2/8Tsx4pWP8TszA4378HRxesGBBFi9ezGeffYaXl5ex/v79+3Tr1o0yZcrw119/ObBCEd9YB5APHjyYsWPHGuEqf/78fP/9988MV9b2Hh06dDAGsXfv3p2//vpLpqNJ4CRYCSFeW7q/177wStWTFJp0F3+MpYrsw8XFhSZNmrBq1SqqVKlis23//v0ULVqUfv36ERYWFsMZRFKzb98+hg8fzpYtWwgNDQUsV7ICAwOfGa7c3NyMK1ft2rVj4sSJjBw5koIFC8pYqgROgpUQ4rU4RYaRJujn1zo2zZUtOEXG/1CSNm1aRo0axZQpU8icObOxPjIyklGjRlGgQAF++eUXB1Yo4gtfX19q1qzJL7/8YlyBcnZ2RmsdY7iKfluwW7dufPHFFwBPNQMVCYsEKyHEa/G4f9Hm6b9X4WyytGRIKEqVKsUPP/xAy5YtbfoGXbhwgcqVK9OmTRvu3Hn9SaZFwhI9+Fh/9vPzo2vXrgCMGDGCXbt2ARhXn54MV82bNzfC1ZNBSsZYJWzy/54Q4rU4Rz58w+Pj/xWr6Dw8POjevTuLFy8mX758NtvmzZtH/vz5Wbt2rYOqE3HFZDIZwefRo0c2IahatWp88cUXhISEsGrVKsLCwmzGS1nD1VtvvcX69eupVasWEREREqQSGfl/UwjxWkwuXi/e6bnHv/lUNY7g7+/PvHnz6N27N56e/32Gq1ev8uGHH9K4cWNu3LjhwApFbLJesfz0008JDAzk119/5ebNm8b2unXrEhgYyNy5czl58iRKKZsrUoGBgSxatAhfX1+qVq2Kq2vc9GsTcUeClRDitYQnz47J+fXCkcnZk/Dk2e1bUBxydnamcePGLF++nJIlS9pss97uWbp0qTzdlUgtWLCAiRMncuHCBRo0aMDnn3/Otm3bAChevDj169fn3r17dOvWjVu3bj11RapAgQIcPXqU/v37A8jfk0RGgpUQ4rWYXSwd1V/HrSzv20xzk1BlzJiRKVOmMGTIELy9vY31N2/epGnTptSrV++Zk+eKhC1Xrly4urqSPHlycuTIwfz586lWrRpDhw7l+vXrDB48mCpVqvD777+zbNkyIiMjnzpH6tSpAemonhhJsBJCvLYbOeqgebV/FDSKG9k/iKWK4p5Silq1arFixQreeecdm22rV68mMDCQlStXOqg68aaiX03SWqO1pmzZsnz99dc8ePCArl27smHDBooWLcrQoUN5//33mTJlCl26dMHX15dly5YZ7Ree9bSfhKrER4KVEOK1hSfPQVD+Tq90TFD+TsacgYlJ2rRpGTduHF999RUpU6Y01t+8eZP69evTrFkzbt++7cAKxasym81G8Hn06BFKKaPzfo0aNShWrBjdunXjrbfeYt26dUybNo1//vmH7t278/nnn+Pi4sKePXsYOnQoIE/7JRXy/7IQ4o1cz9WAK/k7v/DKlUZxJX9nm7kCE6PKlSuzfPlyKlasaLN+yZIlBAYG8tNPPzmmMPHKrEFo5MiRfPrppwQFBeHi4gJA7ty5adGiBffv32fgwIF4enrSqVMnDh06RJs2bTCZTFy6dAmwPDV6+vRph30OEbdkEuZ4RCZhFgmZx/2/SXfxR9Jc2WLT38rk7MmtLO9zI/sHifJKVUy01mzevJmvvvqKBw8e2Gzr0KED33zzDcmSJXNQdeJl7dy5kxo1avDw4UOyZs3KuHHjKFasGNmzZwegatWq7N+/nx9//JHy5csDlumPDh48yNSpU1m1ahXjx4+nR48eDvwU4lW86STMEqziEQlWIjFwirQ0/3SODMPkYnn6LzEMVH9dN27cYNiwYfz+++826/39/Vm8eDElSpRwUGXiZdy/f58rV64wfPhwvv/+e9zd3alcuTLNmzenUaNG7N27l7p165I/f/5nduHfu3cvpUqVAiy3FuV2YPz3psFK/h8WQtiV2cWThz75uJ+uKA998iXpUAWQLl06Jk2axIABA2z6Xp07d44yZcowbNiwZz41JhzLOtDc29vbaJ8xe/ZsKlWqxE8//USTJk3o3bs34eHhVK9enV9//ZWxY8cax1vHYlnbcUioSjrk/2UhhIhlSik+/PBDli5dSsGCBY31JpOJwYMHU65cOc6dO+fACsWTT+xZ7+ZEf2rv448/Zv78+UyfPh0fHx+++eYb+vXrR0hICG5ubmzZsoWzZ88C/zUStR4voSrpSJT/TyulGiildiil7iilQpVSR5VSfZRSr9ziVimVTCn1hVLqoFLqnlIqQil1VSm1XimVeJ4ZF0LEOj8/P2bPnk3nzp1t5hzct28fRYoUYeHChQ6sLumKPk3Nli1bGDlyJLVq1aJ9+/Z89dVXNr3I0qRJQ8eOHdm4cSM9e/bk7NmzrFmzhsePH7N161aZ1kgkvjFWSqkJQA8gEtgGPADeA1IBu4H3tdYvNUmZUioNsBPIH3We34AQwB8oGrXbJK21XUYlyhgrIZKOkydP0r9/fy5fvmyzvlmzZkybNo0UKVI4qLKkJfotusGDB/P111/z6NEjm31y5szJt99+S7ly5WymoAkNDeXChQt88sknHDlyhPv37zNmzBg+++yzOP0Mwr5k8Ho0SqkPgdVYQtA7WuvDUevTYglZBYFxWuveL3m+iUB34BCWQHY72rYawFrABSittd5rh/olWAmRhISHh/PNN9+watUqm/U5cuRg6dKlT02XI2LPoEGDGDFiBOXLl6dHjx6kSZOGo0ePsmTJEvbv30+mTJmYNm0aderUwWQy2VxxDA0NZfbs2fj6+lK/fn0HfgphDxKsolFK7QfeBgZorUc+sa0csAt4BGTQWt99ifP9BRQAGmqtlz9j+xagCtBLaz3eDvVLsBIiCdqxYwdDhw7l/v37xjoXFxeGDRvG//73PxmfE8vWrVtHw4YNKVasGDNnziQwMNDYFhERQaNGjVizZg2+vr5s2bKFfPnyGVe6rK/Rp6aRgeoJmzwVGEUp5YslVAEseXK71no3cAVwB2q85GnDX3K/my/eRQghnq1ixYosW7aMIkWKGOsiIyPp168fVatWlfkGY9nhw4d5/Pgxn376KYGBgcbUNZGRkbi6urJ8+XIqV65McHAwPXv2JCwszAhO1tfog9wlVCVtien/fes30m2t9d8x7HPwiX1fZGPU6/+UUqmjb4i6FfgucBX48VUKFUKIJ2XIkIEZM2bQuXNnm3+Yt27dSqFChdixY4fjiktErG0QrCIjI9m1axdKKaPpp8lkQimFi4uLcdtv0qRJZM6cmfPnz3Pr1i0HVC4SisQUrKwtnS8/Z58rT+z7IqOBzUAx4JJSapNSaplS6iDwE7APqPgytxWFEOJFnJ2dadu2LbNnzyZDhgzG+qtXr1KpUiVGjBjxVDAQL89sNhtjo44cOcKDBw9wdnbGzc0Ns9nM9u3bAYxpa+C/tgmpU6fGx8eHCxcucOHChbgvXiQYiSlYJY96DX3OPtZ5JV7qcRutdShQGxgLJAOqAo2wBK1bwFYg+FWKVEoFxfQHyPgq5xJCJE5vvfUWS5cuNaZIAUsoGDhwIFWrVuXatWsOrC7hsl4J7NmzJ0WLFuWnn35CKUWdOnVwdnZm27ZtT/UTs45DTp8+Pb6+vqROnRo/v9caeiOSiMQUrOxOKZUJ2AN0AwYAOQFvoASWJwUHA7uVUsljPIkQQryGFClS8M0339CrVy+bKyi//PILhQsXZteuXQ6sLuGaP38+c+bMoVy5cvj7+wNQqFAh/P392bRpE7NnzyY4+L/fl63Bavv27ezatYvChQuTIkUKEtODX8K+ElOwsj5O87xZTb2jXu+95DnnYxkQP1Br/aXW+m+tdajW+gBQC/gLKAS8VPsGAK21X0x/sIzXEkIIwDIgumnTpnz33XdkzPjfBe2rV6/y7rvvMn78ePkH/gWevHX6888/kyNHDmbMmEGxYsUAKFWqFD179kQpxddff82oUaPYsmULYLnKtWvXLkaMGEFYWBjt2rUjbdq0NoPVhYguMQWri1GvWZ6zj3XbxefsAxhPGVaJWlz65HatdQSwImqx8ktVKIQQryEwMJClS5dSoUIFY53JZKJXr140aNDApk2DsGUdIzV27Fi2bdvG2bNnadSoEfnz58dsNhvBq2PHjowfP56UKVMyffp0ateuzfvvv0/VqlWpUaMG27dvZ+zYsTRu3BhAAq2IUWIKVn9EvaZRSsU0OL141Ovhlzhf1mg/x3SFyzpoPXUM24UQwi6SJ0/OuHHj6N69u81TgytXrqR48eIcP37cgdXFb4sXL6ZPnz40adKEs2fPkiyZ5caGUgpnZ2djnsDu3bvz7bff0qxZM8xmM1u3buWXX36haNGiLFq0iF69egGW8W5yxUrEJNEEK611EHAgarHpk9ujGoRmwdIgdMNLnDL6oPSY2h+XinqNqb2DEELYjVKKli1bGpMAW505c4YSJUrw/fffO7C6+KtevXp8+OGH3Lhxg5CQEPbv309ISIjNBMnWcFW/fn3mzZvHqVOnOHjwICdPnmTt2rU0bWr5Z0Waf4oXSWx/O76Meu2rlLLO5Wed829a1OKU6O0RlFJ1lVKnlFK/RD+R1voy/wW1iUqp7NG3K6WaY3lCEJ7RkFQIIWJLsWLFWLp0KYUKFTLWPXz4kMaNG/P5558n6ZYMT372iIgIPDw8WLp0KXXq1AFg27Zt7N+/32Y/a/d0sATYXLlyUbRoUXLnzk2qVKkAy+0/CVXiRRLV3xCt9RpgEpZB6nuVUhuVUiuAc1jmCdwDDHzisJRAXiDXM075MZau6vmAk0qp7Uqp5UqpY8BCQAGLgMWx8HGEECJGadOmZebMmTRr1sxm/dixY6lWrVqSbWJpHVO1ceNGo3N6ZGQk7u7uLFu2jI8++ohr167RqVMnDh06ZHOs9QpWTLf55PafeBmJKlgBaK17YLmS9DtQBsv0NUFAX+A9rXXYK5zrGJa5AkcDZ7A8IfghkB5L49BGWusWWkYxCiEcwMXFhU8//ZQvv/zSZn7RrVu3Urx4cY4ePerA6hxn4MCB1KxZkz59+hAZGYmLi4sRrhYvXky9evW4ePEiDRs2fCpcCfGmEl2wAtBa/6C1fkdrnVJr7aW1Lqi1Hq21fvyMfedprZXWOnsM57qmte6rtS6ktfbWWrtqrdNrratprX+I9Q8jhBAv8P777zNv3jwyZcpkrLt48SKlS5dm2bJlDqwsbjx5+y9PnjykTp2aCRMm0K9fv6fC1aJFi6hXrx5///23hCthd4kyWAkhRFKTO3duFi1aRIkSJYx1YWFhNGnShH79+hmDsxMb61x+YGn+2apVK77//nseP7b8Hj127FgGDhz43HDVtGlTDh48+Ly3EeKlSbASQohEImXKlEyePJmWLVvarB81ahR169ZNdP2utNZGqPriiy/o2LEj+/bto3z58nTo0MF4km/06NEMGDAAk8n0VLhq2LAhZ8+epWrVqly/ft2RH0ckEi4v3kUIIURC4ezsTPfu3QkICGDo0KE8evQIgB9//JEyZcrw448/kiPHy85DH79ZB5NPmzaN0aNHU6NGDUaNGkXBggWNfapXr06LFi34+uuvUUoxfPhwm3A1f/587t69S7ly5UifPr2jPopIRJSMu44/lFJBPj4+vuvXr7cZiCqEEK/j1KlTfPrpp9y4ccNYlyZNGlatWmXTxT0hi4yMpE6dOmzfvp3t27dTsmRJo4GnNXgtX76cRo0s3XH69OnDiBEjcHFxISIiAldXV5veVNKnSvj5+REcHBwcNdXcK5O/PUIIkUgFBASwcOFCAgMDjXW3bt2iUqVKzJ0714GV2c+dO3fYvXs36dOnJ0+ePJhMJiNUWS8cNGjQgNGjRwPw9ddf88UXXwAYrRisQUr6VAl7kL9BQgiRiKVNm5ZZs2ZRvXp1Y11kZCQff/wxffv2TfCD2l1dXUmVKhV37tzhn3/+wdnZ2aYflfXzlSpVimTJkuHl5cW4ceP48ktLP2kXl/9GxEifKmEPEqyEECKRc3d3Z9iwYXTr1s0mPIwePZqGDRsSFvbS7f3inVSpUlGsWDHu37/P9OnTuX379jP3K126NDly5DAmUR4wYAA//vhjXJYqkggJVkIIkQQopWjVqhVjxoyxGcO5cuVK3nnnHa5du+bA6l6P9VZf06ZNSZcuHevXr2ft2rXG04/Rb/OtWbOGkydPMnToUGbPng1YGqlGP48Q9iDBSgghkpCKFSsye/Zs0qRJY6w7cOAAJUqU4NixYw6s7NVZr76999571KpVi8uXL/Pll18yd+5crl27Ztzm++2335g+fTr+/v48evSIAgUKALBnzx6j35UQ9iLBSgghkph8+fKxYMEC/P39jXWXL1+mbNmybN++3YGVvZ7UqVMzbNgwGjZsyOXLl+nbty/vvPMO/fv3p0ePHtStW5ft27fTpUsXcubMSerUqXFxcSFr1qy4ubnJ2CphVxKshBAiCcqQIQNz5syhbNmyxrp79+5RtWpVlixZ4sDKXo+vr68xhU2BAgU4c+YMo0aNYvLkyZjNZqZMmUK3bt0AGD9+PJGRkZQsWRKttdwKFHYlfaziEeljJYSIayaTibFjx7J8+XKb9aNGjeJ///tfgruaEx4ezqNHj1i/fj0PHjzA19eXjBkzUrx4cQCmTp1Knz59yJEjB5s3b8bX19fBFYv45k37WEmwikckWAkhHEFrzcKFC5k0aZLN+k6dOjF58mSblgQJldaa4cOHM3nyZAB27txJvnz5HFyViI+kQagQQog3opSiZcuWjBw50iZEzZgxg48++ihBt2PQWvPvv/9SqlQphgwZQubMmdm1a5eEKhFrJFgJIYQAoGrVqkydOhVvb29j3bp166hSpQp37txxYGWvTymFu7s7devWpW/fvqxfv56AgABHlyUSMbkVGI/IrUAhRHxw4cIFunXrZtPbKjAwMEGPSTKZTJjNZlxdXR1diojn5FagEEIIu8qZMydz584lR44cxrrjx49TpkwZTp8+7cDKXp+zs7OEKhEnJFgJIYR4Svr06fnuu+8oWLCgsc7a6+rAgQMOrEyI+E2ClRBCiGdKkSIF06dPp1y5csa6W7du8e677/LLL784sDIh4i8JVkIIIWLk4eHB2LFjqV27trEuNDSUGjVqsHbtWgdWJkT8JMFKCCHEc7m4uDBo0CBatmxprHv8+DH16tVj0aJFDqxMiPhHgpUQQogXUkrRvXt3unbtaqwzmUy0aNGCadOmObAyIeIXCVZCCCFeWps2bZ6a6qZr166MHDnSgVUJEX9IsBJCCPFKGjRowLBhw3By+u+fkAEDBtC/f3+Z0FgkeRKshBBCvLLq1aszduxYm95QX375Jb1795ZwJZI0CVZCCCFeS4UKFZg0aZLNTBHffPMNn3zyCWaz2YGVCeE4EqyEEEK8trfffpupU6fi5eVlrJs2bRodOnTAZDI5sDIhHEOClRBCiDdSuHBhpk2bRrJkyYx13333Ha1bt5ZwJZIcCVZCCCHeWIECBZg5cyYpUqQw1i1atIgWLVpIuBJJigQrIYQQdhEQEMCsWbPw8fEx1i1dulTClUhSJFgJIYSwG39/fwlXIkmTYCWEEMKucuTIIeFKJFkSrIQQQthdTOGqZcuWEq5EoibBSgghRKx4VrhasmQJbdu2lT5XItGSYCWEECLWPCtczZ8/n86dO0uHdpEoSbASQggRq3LkyMGMGTNImTKlsW7WrFn07NlTwpVIdCRYCSGEiHW5cuVi+vTpJE+e3Fg3adIk/ve//0m4EomKBCshhBBxIk+ePEyZMsVm+psxY8YwZMgQxxUlhJ1JsBJCCBFnAgMDmTx5Mh4eHsa6YcOGMW7cOAdWJYT9SLASQggRpwoVKsTEiRNxd3c31vXu3ZvvvvvOgVUJYR8SrIQQQsS5YsWKMWbMGFxcXIx17du354cffnBgVUK8OQlWQgghHKJMmTKMGDECJyfLP0Vaa5o3b87GjRsdXJkQr0+ClRBCCIepXLky/fv3N5YjIiL46KOP2LVrlwOrEuL1SbASQgjhUHXq1KFXr17Gcnh4OLVq1eLPP/90YFVCvB4JVkIIIRyuadOmdOjQwVi+d+8eVatW5e+//3ZgVUK8OglWQggh4oX27dvTsGFDY/nq1atUqVKF69evO7AqIV6NBCshhBDxglKK3r17U7lyZWPd+fPnqVatGvfv33dgZUK8PAlWQggh4g0nJyeGDRvG22+/baz7448/+PDDD3n06JEDKxPi5UiwEkIIEa+4ubkxduxYAgICjHXbtm2jTZs2Mq+giPckWAkhhIh3kiVLxqRJk/Dz8zPWLV26lH79+jmwKiFeTIKVEEKIeCl16tRMnToVHx8fY91XX33F9OnTHViVEM8nwUoIIUS85evry8SJE20mbf7kk09Yt26dA6sSImYSrIQQQsRr+fPn56uvvjKmvjGbzTRq1Ij9+/c7uDIhnibBSgghRLxXrlw5+vbtayyHhYVRs2ZNaSAq4h0JVkIIIRKEjz76iI8//thYvnnzJtWrVyckJMRxRQnxBAlWQgghEozOnTtTvXp1Y/n06dPUr1+fiIgIB1YlxH8kWAkhhEgwlFIMHDiQQoUKGet++eUXunTpIj2uRLwgwUoIIUSC4ubmxrhx4/D19TXWzZ49m3HjxjmwKiEsJFgJIYRIcFKlSsXEiRNJnjy5sa5Pnz6sWbPGcUUJgQQrIYQQCVT27NkZM2YMzs7OAGitadasGX/++aeDKxNJmQQrIYQQCVbx4sXp37+/sfzw4UNq1arFjRs3HFiVSMokWAkhhEjQPvjgA5o1a2YsX7lyhbp16/L48WMHViWSKglWQgghErzu3btTunRpY3nPnj107txZnhQUcU6ClRBCiATP2dmZUaNGkS1bNmPdnDlzmDRpkgOrEkmRBCshhBCJgre3NxMmTLB5UvCzzz5j+/btDqxKJDUSrIQQQiQaWbJkYfTo0caEzSaTiQYNGnDp0iUHVyaSCglWQgghEpUSJUrQq1cvY/nWrVvUqVOHsLAwB1YlkgoJVkIIIRKdRo0aUaNGDWP56NGjtGvXTgazi1gnwUoIIUSio5SiX79+BAQEGOuWLFnCxIkTHViVSAokWAkhhEiUPDw8GDt2LKlSpTLW9e7dmx07djisJpH4xVqwUkplUEo1VUqNVUotVEqtj3odq5RqopTKEFvvLYQQQgBkzJiRr776ymYwe8OGDfnnn38cXJlIrOwarJRSrkqp5kqp34F/gIVAL6AZUCPqtRewCPhHKfWbUqqZUsrVznU0UErtUErdUUqFKqWOKqX6vMn7KKXqKKV+VEpdVUo9Vkpdj6p/kD1rF0IIYV/FixenZ8+exvKNGzeoX78+ERERjitKJFp2C1ZKqRbA38B8oCRwE1gHjAJ6Ax2Az4GvgJ+A20ApYAFwQSnV3E51TAB+AMoC+4FNQFZgNLBNKeX5iudzU0r9AKwBKgPHgRXAMSAX0N0edQshhIg9TZo0oXLlysby77//Tp8+fRxYkUisXOxxkqgrVCWwhKlJwDyt9dGXOK4w0AZoAsxXSnXRWpd5gzo+BHoAD4B3tNaHo9anBbYB5YDhWILey/oWaIAlWLXXWt+M9n5OWD63EEKIeEwpxcCBAzl79qzR02rChAmULl2ahg0bOrg6kZjY64pVbqAPkFVr/enLhCoArfURrXUPIAvQF8jzhnX0i3r9yhqqot7nJtAlavETpVTKlzmZUqoS0BLL1amG0UNV1HnNWuu9b1izEEKIOJAsWTLGjBmDh4eHse7jjz/m1KlTDqxKJDb2ClY5tdbjtNaPXudgrfUjrfUYIOfrFqCU8gXejlpc8oz32A1cAdyxjPd6Gd2iXidoreVmvBBCJHA5c+Zk4MCBxnJoaCj16tXj4cOHDqxKJCZ2CVZa63vx4DxFol5va63/jmGfg0/sGyOllDNQKWpxp1Iqo1Kqp1JqulJqglKqlVLK+w3qFUII4QBVq1alUaNGxvKJEyf45JNPHFiRSExivY+VUip9bL9HlBxRr5efs8+VJ/Z9npyANTiVAs4C44FOWMZxzcMy6P69VylSKRUU0x8g46ucSwghxOvp2bMn+fPnN5bnzp3LwoULHViRSCziokHoP0qpIgBKqUFKqdpKqWyx8D7W6cxDn7PPg6jXFC9xvjTRfv4OOITlVmNyoDCwAUgHrFVK5X6lSoUQQjiUq6sro0aNwtv7vxsPnTp1kvFW4o3FRbCqA/wb9XN7YC2WKz13lFK/KqUmKaXaKqWKK6U8Yj5NnFPRfg4GqmqtD2qtH0QNzv8Ay6B2bywD71+K1tovpj/AVbt+AiGEEDHy9fVlyJAhxvLDhw9lvJV4Y7EerLTWP2mtr0b9nAVIi6Uf1FDgApYWCFOx9Jy6/wZvZT022XP2sf5q8jJjuaLXMu/JgflaaxMwM2qxMkIIIRKcihUr0rhxY2P5xIkTdOvW7TlHCPF8cT5XoNb6ttZ6u9Z6gta6jda6KJbAUwho/Qanvhj1muU5+1i3XXzOPtHPZ50G/UIM+1jXZ3qJ8wkhhIiHevToQb58+YzlOXPm8MMPPziwIpGQxYtJmLXWkVrrv7TWi9/gNH9EvaZRSsU0OL141OvhGLZHr+kBcDpqMW0Mu1nXP4hhuxBCiHjOOt4qWbL/bni0b9/eaCQqxKuIF8HKHrTWQcCBqMWmT25XSpXDcsXqEZaB5y9jedRrTLf6qkS97n/J8wkhhIiH/Pz86Nevn7F87949GjduTGRkpAOrEglRoglWUb6Meu2rlCpqXamUSgNMi1qcorW+G21bXaXUKaXUL8843yTgDlBDKdUx+galVGMsk0pb9xNCCJGAVa1aldq1axvLe/fuZdiwYQ6sSCREcR6slFKuSqnCSqk2SqmJSqkd9jq31noNlpDjDexVSm1USq0AzgEFgT3AwCcOSwnkxTKh8pPnuwk0AsKBGUqpY0qp5Uqpw8BSLE8ODtdav+wVMCGEEPHY559/TtasWY3lkSNHsmvXLgdWJBKaWA1WSqmUSql3lFI9lFLzlFJHsDxtdwiYjWXKmPL2fM+ouQcbAb8DZbBMXxOEpSXCe1rrsFc8389YBtbPB1JhaR+RFcvtxKpa60F2K14IIYRDeXl58eWXX+Li4gKA2WymSZMmhISEOLYwkWAorfWL93qZE1lut5XF0jzT+id6I9DofaFCsAw2Pwwc0lovs0sRCZxSKsjHx8d3/fr1uLu7O7ocIYRIshYtWsSECROM5caNG7N06VLHFSTijJ+fH8HBwcFR/SVfmYs9ilBKNQHmAq7WVdE23wX2YQlRh4HDWuuY2hcIIYQQDte0aVN+//139u3bB8CyZcv44IMPaNKkiYMrE/GdvW4FDgLcsHQO/wHLbbcd0bYv0lr301qvkFAlhBAivnNycmLw4MEkT57cWNe5c2euXLnynKOEsF+wygUcAXJorZtorb/WWr8HfAa4A/OVUsuUUqns9H5CCCFErEqfPj39+/c3lu/evUuLFi0wm80OrErEd/YKVpHAEa314+grtdbjgWJYxlM1BP5SSsn0L0IIIRKEypUrU7NmTWP5119/Zfz48Q6sSMR39gpW3sD/nrVBa30SKAWMADIAm6LaLMjobCGEEPHe559/TsaMGY3lfv36ceLECQdWJOIzuwQrrbVZa33jOdsjo9oSlMPSU6obcEgpVcQe7y+EEELEFm9vb4YPH45SlueyHj9+TIsWLaQru3imOG0QqrXej6UNw1QgH5Ymnv2ee5AQQgjhYEWKFKF58+bG8uHDhxk1apQDKxLxVZx3Xtdah2utuwFVgevA8LiuQQghhHhVnTp1Inv27Mby8OHDOXr0qOMKEvGSw+YK1FpvxTLNzBJH1SCEEEK8LHd3d4YOHYqTk+WfzoiICJo1a8bjx49fcKRISuwSrKIGo6d51eO01iFa6xZR50inlJLJjIUQQsRbgYGBtGnTxlg+fvw4Q4cOdWBFIr6x1xWrrsDfSqlRSqncr3KgUiqvUmoMcB7obKd6hBBCiFjRrl07cuf+75+60aNH88cffziwIhGf2CtYvQ38iaXlwiml1G6l1FClVDWlVDallDeAUso7arm6UmqYUup34ASWRqJHo84jhBBCxFuurq4MHToUZ2dnAEwmE61atSIiIsLBlYn4wF7tFv7QWpfD0gR0L1AGGAD8BFwA7iqlTFjmDbwArI/aXhL4HWigtS6vtT5ij3qEEEKI2JQnTx4+/vhjY/mvv/7iq6++cmBFIr6w6+D1qLkAywJFsTzttwd4iGVSZuufh8BuYBhQVGtdTmu90p51CCGEELHt448/JmfOnMbyiBEjpHGoiJ2nArXWR7TWQ7TWFbTWybF0ZvcFkmmtk2ut34nafiQ23l8IIYSIba6urgwaNMh4SvDx48e0bt0ak8nk4MqEI8VJuwWt9UOt9b9a67C4eD8hhBAiLhQoUIBmzZoZywcOHGDChAmOK0g4nMP6WAkhhBCJQceOHcmSJYuxPHDgQC5duuTAioQjxWqwUkq5K6WyK6WSxeb7CCGEEI7i4eHBwIEDjeWwsDA6duyI1tqBVQlHiZVgpZRKpZRaDjzA0p/qnlLqnFLqe6XU50qp95RSKWPjvYUQQoi4VrRoUT788ENjefPmzfzwww+OK0g4TGxdsRoL1Is6/1UsTwLmBBoAXwE/A7eVUmeVUjKljRBCiASve/fupE6d2lju1q0bd+7ccWBFwhFiK1jVAMKAklpr36gnAwOAZsA3wK/APSAX0CiWahBCCCHiTIoUKejdu7exfOPGDZtlkTTEVrDyBrZorQ9aV2itz2itl2qtP9dav6e19gFyA01iqQYhhBAiTlWpUoUyZcoYy3PmzGHXrl0OrEjEtdgKVsdfZiet9XmttdyEFkIIkSgopejbty8eHh7Gug4dOsh0N0lIbAWrecA71jkChRBCiKQic+bMdOzY0Vg+deoU48ePd2BFIi7FVrCaBRwD5iml3GLpPYQQQoh4qUmTJjbT3QwdOpQrV644sCIRV2JrShsN1AYKAr8ppepIwBJCCJFUuLi48MUXXxjLDx8+pFu3bg6sSMSV2OpjlR3YC/hjmZB5FXBHKfWbUmqiUqqFUiogNt5bCCGEiA+KFClCrVq1jOW1a9eyYcMGB1Yk4kJs3QocD+QFwoEDwGnADSgFdMMyBuu4UuquUmp7LNUghBBCOFT37t3x9v5vuHGXLl0IDw93YEUitsVWsHoHuAMU0FqX1FrnB1IC5YGewGIsYSsZUCGWahBCCCEcKnXq1HzyySfG8qVLlxgzZowDKxKxLbaClROwTWv9t3WF1vqh1nqP1nqS1rplVNhKBVSMpRqEEMKG2Ww2fo6MjHRgJSIpqVu3LgEB/41+GTVqlAxkT8RiK1j9AXi9aCet9QOttXROE0LEOpPJhJOT5Stv//79LF68mFOnTjm4KpEUODs706dPH2M5LCyMXr16ObAiEZtiK1hNx9LHKkMsnV8IIV6ayWTC2dkZgHnz5vHFF18wY8YMjhw5IuNdRJx46623qFmzprG8YsUKduzY4biCRKyJrWC1GcuTgCuUUj6x9B5CCPFCZrPZCFWTJk1i6tSpZMmShTFjxtC4cWObDtlCxKZu3brh5fXfzZyuXbvKLelEKLaC1W0sY6fKAEeUUj2UUlli6b2EECJG1tt/8+fPZ+HChZQvX55+/fpRrlw5ACxt94SIfWnTpqV9+/bG8okTJ5g+fboDKxKxIbaC1X3AD1BAFuAb4KJSKlgptVYpNVApVU0plTaW3l8IIQznzp1j2bJlZM2alS5dupAnTx5j2927d/nrr79YtmwZu3fvJiwszIGVisSucePGZMuWzVgePHgwd+7ccWBFwt5cYuOkWutUSqlcQDEsDUKtr5mwdGQ3OqYppS5rrXPERh1CCAEQFBTEzZs36dy5M/7+/sb6HTt2sHbtWnbv3m2sa9iwIe3atcPHR0YxCPtzdXWlV69e9OjRA4A7d+4wZMgQJk6c6ODKhL3E1hUrtNbntdY/aK37aq2raK3TADmBBsBo4Gcsva6yxlYNQggBEBERYfN69epVZsyYQZ8+fdi9ezc1a9akfv36eHl58cMPP7Bv3z5HlisSubJly1K6dGljefr06Zw9e9aBFQl7ipUrVjHRWl8ELgIrreuUUhKshBCxKnPmzKRIkYLZs2ezd+9erl69ys2bNylSpAgtW7Y0xlv5+voyceJEjh07RrVq1dBao5RycPUiMfr000/Zt28fZrOZiIgIPv30U9avX+/osoQd2OWKlVLqnFJqmVKqj1Kq8qs8Cai1vmyPGoQQSVv05p9PCgwM5H//+x958uQhKCgILy8vvvjiCwYNGkS5cuWMY60D2YsVKwYgoUrEmpw5c1KvXj1j+aeffmLbtm0OrEjYi72uWOUEcmC5zQeAUuoScAg4HPV6SGt9y07vJ4QQhuh9qoKDg7l+/TqhoaEkT56cvHnz4uHhwfvvv0+hQoUASJkypU2bBScnJ44ePcrq1avx8/Mja1a5kC5iX8eOHdm4cSMPHjwALPMKHj161Pi7LBImewWrhlgGqFsHqacGskf9+ci6k1LqCtGCFpawdcNONQghkqDooWrBggWsXLmSf/75x9hepkwZKleuTO3atcmQIYNxey/6cYcPH+bbb7/lypUrDB48mFy5cjnks4ikJVWqVLRr144JEyYAcPz4cRYuXEjr1q0dWpd4Myo2ergopbLxX9Cyhq3orRWiv2mw1lp+PQSUUkE+Pj6+69evx93d3dHlCBHvRR8DNXnyZBYsWICvry+1atXCbDZz+vRp9u3bh7u7Oy1atHjqH6xHjx6xadMm5syZw7Vr1+jevTtNmzZ96txCxJbHjx/ToEEDgoODAct4wPPnz0vjWgfy8/MjODg4WGvt9zrHx1a7hUvAJSzd1wGIahAaPWgVA9IDvrFRgxAi8bMGnzVr1rBgwQLKlClDt27djJYKJpOJbt26cfToUS5evMjNmzdJm9byO97ly5cZMmQIf/31F1mzZmXgwIHGlCNms9loLCpEbHJzc6NLly70798fgH/++YcJEybQt29fB1cmXlecPRWotb4CXAHWWNcppXyxBCwhhHhlWmtCQ0PZunUrXl5etGvXzqZP1Zw5czhw4ADlypWjQYMGRqgCyJo1Kx988AHFihWjUqVKBAQEABKqRNyrUqUKixYt4uTJkwCMGjWKDh06kDp1agdXJl6HQ789tNbBWusfHVmDECL+e/To0TPXK6W4efMmf/31F6VLl6ZgwYLGtlmzZjFr1izKli1L586dCQwMBODSpUvGGKwPP/yQ9u3bG6FKay2hSsQ5Jycno2EowL179xg2bJgDKxJvQr5BhBDx2r59+xg0aBCXLl165va7d+8SHh6Oq6urse7bb7/l22+/pWzZsk9NYTNlyhSWL19uTH7r5uZmbJMxVcJRihcvbvRTA5g2bRoXL150XEHitUmwEkLEW6GhoUydOpVt27Yxc+ZMrly5YmyzPnjj7e2Nu7s7p06dAmDevHnGlaonQ9XWrVvZsWMHHh4ecmVKxDuffPKJ8fcyIiLCGHclEhb5ZhFCxFvJkiWjV69eFCxYkJ9//pkpU6YY4cp6dSlHjhzkz5+fixcv0rJlS6ZOnUqFChXo1KmTTag6cuQIc+bMIUuWLJQvX16ClYh3/P39jQcoAJYtW8aJEyccWJF4HfLNIoSI1woXLsynn35Kvnz52LZtG5MnT7a5cuXk5ETr1q3JmDEjJ0+eJGvWrDRr1oyAgADjqtahQ4eYPn06Fy5c4OOPPyZ//vyO+jhCPFeHDh2M29pms1meDkyAJFgJIeIt6zioggULMmzYMAIDA9m+fTtTp07l8uX/ZsMKCAjgo48+Im3atPz777+sWbOGU6dOcejQIRYtWsTAgQM5fPgw3bt3p1atWsB/txKFiE8yZcpkM9XNunXrOHDggAMrEq8qTidhFkKIl2UymXBxsXxFHTlyhPDwcNKmTUuKFCn45ZdfcHFxoUOHDmTJkoVUqVJRu3ZtvLy8+P7779m4cSMbN240zpUlSxYGDRpE7dq1AWmpIOK3Nm3asHbtWsLCwgD4/PPP2bFjh2OLEi8tVjqvi9cjndeFsIgefKZPn87q1at58OABuXPnJiQkxGiXUKVKFTp16kSWLFlQShEREcG9e/dYvXo19+/f5969e5QtW5ZcuXKRI0eOp84tRHw1bdo05syZYyxv3bqVSpUqObCipONNO69LsIpHJFgJYWvOnDlMnz6dd999l+bNm/PWW29x/fp1/vzzT0aPHk1ISAiVK1emc+fOLzVxskxTIxKK+/fv88EHH3D//n0A3n77bfbt2yd/f+PAmwYr+bVNCBEvnT17lu+//57UqVPToUMH3nrrLQDSp09P5cqVGT16NFmzZmXr1q3MmjXLZsyV2Wy2ebWSf5REQpE8eXJatWplLB84cIAtW7Y4sCLxsiRYCSHipVu3bnH79m3KlSuHv7+/Mdjc+lq0aFG++OILADZv3mzT58p6q09u+YmErGHDhqRKlcpY7t+/vzx0kQDIt44QIl4KDQ0F/gtS1qtP1qtOWmuKFy9O48aNAdiyZQvffPONdKsWiYaXl5fNVatDhw7ZPJQh4icJVkKIeMnLywuAbdu2cfHiRZydnW22WwNXunTpSJYsGQEBAezevZugoKA4r1WI2FK/fn18fHyM5QEDBshVq3hOgpUQIl4qXbo05cqVIzQ0lO+//57r168b26I/2RcZGUm2bNn45JNPGD58uM18a0IkdJ6enrRu3dpY/uOPP1i3bp3jChIvJMFKCOEQTw4sf9b2qlWrkj59ejZt2sSyZcuMNgvWUPXnn3+yYcMGMmTIQMmSJalWrdpLnVuIhKRevXqkSZPGWB48eLBctYrHpEGoECLOmUwm49besWPHCAoK4vLly7i5uVGuXDnSpk1LqlSpKFu2LBcvXmTFihUsX76cY8eO0aJFC9KmTUtwcDALFizgypUrtG/f3ub8MmhdJCYeHh60bt2acePGAZaGuZs2baJ69eoOrkw8i/Sxikekj5VICqLfxpszZw7Lli3jzp07xvaMGTNSsGBBOnToQPbs2bl79y5r165l06ZNnD17FqUUSinjqtSnn35K06ZNHfJZhIgr4eHh1K5d2/hvRfpaxR5pEJqISLASiV30Bp1Tpkxh/vz5ZM+enZo1a5IsWTK2b9/OpUuXuH79Onnz5mXo0KHkypWL8PBw/v33X3788UfOnz/P9evXKV68OMWLF6dixYqAdFQXid+CBQuYNGmSsbxt2zbeffddB1aUOEmwSkQkWImkYvPmzQwYMICyZcvSrVs3cuXKBcDDhw85fPgwc+bM4a+//qJIkSIMGTKEzJkz2xwfERGBq6ursSyhSiQFoaGh1K5dm3v37gFQoUIFfv31VwdXlfhI53UhRIKhtcZsNvP7778D0KJFC3LlyoXWGpPJhJeXF6VKlaJr164EBARw7Ngx4x+OyMhI4zzRQxXImCqRNCRLlszmtvfOnTv57bffHFiReBb5NhJCxBmlFA8ePODPP/8kderU5M+f39hmHczu4uJCYGAgFStWJCIigh07dhjrhUjqGjVqRLJkyYzlIUOGOK4Y8UwSrIQQccrV1RUXFxcePHjA6dOnAZ56dNzDw4OqVauSKlUqLl++TEhIiAMqFSL+SZ48uTHbAMDPP//Mn3/+6cCKxJMkWAkh4ozZbEYpRbZs2Xj8+DEHDx4ELLfyrE/5aa3RWpM6dWq8vLwwm81PdV0XIilr3LixzTjckSNHOrAa8SQJVkIIu3tywmT4b4C5h4cHlSpVAmDmzJmsX78esIQr61ODSimOHDnCtWvXKF26NMmTJ5eGiEJE8fHxoU6dOsbyypUrZY7MeESClRDCrkwmk9FS4c6dO9y8eZNHjx7ZDDCvVq2aMU3H0KFDWbVqlc1xR44cYd68eWitjXYK0q9HiP80b97cuJJrMpkYPXq0gysSVtJuIR6RdgsioYve9mDRokVs3LiR27dvkzp1aurXr8/bb7+Nn5/lCeabN28yf/58li1bBljmBsyQIQNeXl5s2bKFmzdv0rNnT5o1a+awzyNEfDZw4EA2btwIWMYlXr58mXTp0jm4qoRP2i08g1KqgVJqh1LqjlIqVCl1VCnVRynl+uKjX3juGkopHfVnqz3qFSKxsIaqqVOnMnHiRM6fP49SijNnzjBu3DhmzJjBmTNnAEibNi1du3alZ8+eJEuWjH379rFmzRqWLl1KsmTJGDhwoBGqZO4/IZ7WsmVL4+fw8HC++eYbB1YjrBLdFSul1ASgBxAJbAMeAO8BqYDdwPta67DXPLcPcAzIBCjgF6115Tev2ji/XLESCd7evXsZOHAgb731Fm3atMHf35+NGzfy448/cuzYMSpUqED79u0JCAgwjjl79iy3bt3i3LlzBAQEkCFDBrJkyQJI808hnqd79+5GLysfHx+CgoLw8vJycFUJm1yxikYp9SGWUPUAKKm1rqq1rgfkBv4CygHD3+AtJgMZgBlvWKoQidaFCxdQStG+fXsKFCiAh4cHH3zwAV26dOHtt99m586dfPvtt0arBYDcuXNTqlQpmjdvTvHixY1QpbWWUCXEc0S/anXnzh3mzZvnuGIEkMiCFdAv6vUrrfVh60qt9U2gS9TiJ0qplK96YqVUXaAZ8A2w/00LFSIxeNYtuitXrlC4cGHjipTJZMLZ2ZnixYvTvn17I1zNmjXLJlxZ2yxEJwPWhXi+YsWK2Vz9HTdunNw6d7BEE6yUUr7A21GLS57crrXeDVwB3IEar3jutFiuUp0GBr1ZpUIkDiaTybiatHfvXuN2n4uLixGIIiMjcXZ2NtooFC5c2CZczZ49m1OnTgEYbRaEEC9PKWXzgMeFCxeMFibCMRLTHBFFol5va63/jmGfg0CWqH2XvsK5pwNpgY+01uHy5S+SOq218aj3zJkzmTdvHmaz2fhNOU2aNFy9epWMGTMaY6SeDFfOzs7s2LGDBw8eMGrUKFKlSuXATyREwlWlShUmTZrEjRs3APj666/54IMPHFxV0pVorlgBOaJeLz9nnytP7PtCSqnGQH1gstZ6z2vWJkSiYv3lYt68ecyePZvMmTPTpk0bypUrR5YsWbh16xajR4/mzp07Rld1pZRNuGrdujX58+enfPnyEqqEeAMuLi4209zs2bOHQ4cOObCipC0xXbFKHvUa+px9HkS9pniZEyqlMgJTgfP8N37rjSilgp6zOaM93kOI2GIdL6W1JjQ0lJ9//pnSpUvTvXt3/P39CQkJ4ciRI8yYMYPdu3czduxYevfujY+Pj83TfUopihYtytixY42+O9bQJYR4dXXr1mX27NmEhVkeev/666/5/vvvHVxV0pSYrljFhlmAD9BOa/3Q0cUI4WjW23/79+8nNDSUW7du0ahRI/z9/dFakypVKsqVK8fnn39Ozpw52bJlC2PHjrW5cgX/hSgJVULYR4oUKWxu/61evZpr1645sKKkKzEFq/tRr8mes4931Ou9F51MKdUKqA3M0FrveLPS/qO19ovpD3DVXu8jRGyZMWMGn3zyCcOGDcPNzQ1/f3+b7S4uLhQuXJg+ffrYhKuQkBCb24LRSagS4s01bNjQ+DkiIoKpU6c6sJqkKzEFq4tRr1mes49128Xn7GNVN+r17agu7sYfoG/UtmLR1sttPJEk5M6dGzc3N/bv38+9e/eMAbMmk8nYx9nZ+alwNWrUKG7fvi19qYSIJdmyZaNMmTLG8syZM3n8+LEDK0qaEtM33B9Rr2mUUjENTi8e9Xo4hu0xHfPOE3/yRm1LFW2dx6sUK0RCValSJUaPHo2npyehoaEsWrQIsFypela4+t///kfWrFnZtm2b0VpBCBE7og9iv379OsuXL3dgNUlToglWWusg4EDUYtMntyulymG5YvUI2PAS5/tQa62e9QdoE7XbL9HWX7TPJxEi/itXrhxffvklXl5e/PLLL8YcZc7Ozk+Fq0KFCtGnTx+GDh1q89u0EML+SpUqZcxcADBhwgTHFZNEJZpgFeXLqNe+Sqmi1pVKqTTAtKjFKVrru9G21VVKnVJK/RKHdQqR4JUrV46RI0fi6enJ0qVLnxuuSpYsSY0alr680hVaiNjj5OREo0aNjOWDBw9y4MCB5xwh7C1RBSut9RpgEpZB6nuVUhuVUiuAc0BBYA8w8InDUmK5tZcrDksVIlF42XAVnYyxEiJ21apVy2Yi5kmTJjmwmqQn0X3Daa17AI2A34EyWKavCcIy4Pw9rXWYA8sTItEpX768Tbiy3nqw9rsSQsQtb29vatasaSyvWLGCO3fuOLCipCXRBSsArfUPWut3tNYptdZeWuuCWuvRWuunHo/QWs+LGiOV/RXObz2msl0LF8KBnrxF9yqhyBquvL29Wbx4MSNHjgSkjYIQjlKvXj3j5/DwcObMmePAapKWRBmshBCvRmtt3KL7999/AYwpaF5W+fLlGTJkCAC+vr52r1EI8fL8/f0pXLiwsTxjxgy5ghxHJFgJIYwrS+PGjaNdu3b88ccfxvpX+TJ+5513WLt2La1btwZe7aqXEMK+6tevb/x87tw5tm3b5sBqkg4JVkIkYdFv/4WFhREUFMT169eZOHEihw9b2r29arjKnDmzcW65FSiE47z33nv4+PgYy5MnT3ZgNUmHBCshkiiTyWTc/vvrr7/4+eefyZo1K15eXpw5c4bx48dz5MgR4NXDFcjTf0I4mpubm838gT/99JPMHxgH5JtPiCTIbDYbEyrPmDGDnj17MmbMGM6cOYOPjw9KKU6dOsWYMWPeKFwJIRyrbt26xs+RkZEyiD0OSLASIgmyXk2aM2cO3333HUWKFOHbb79l+vTpLF++nAEDBlCmTBnOnDkj4UqIBMzPz48SJUoYy9999538NxzLJFgJkUSdO3eO5cuXky5dOjp27EhAQACRkZG4urry/vvv88knn1CxYkXOnDnD2LFjX3vMlRDCserUqWP8fP78eXbv3u3AahI/CVZCJFF37tzh5s2bvPPOO+TOnRuz2YyLiwtgae6ZO3dumjVrhr+/P2fPnmXChAk24UoIkTBUrFiRFClSGMvTp093YDWJnwQrIZKo69evAxAcHEx4ePgzB5sXLlyYgIAAzGYzJ0+eZPLkyZw8eTKuSxVCvAF3d3ebTuyrV6/m7t27zzlCvAkJVkIkUfny5SNVqlRcvXrVeFIoevuFyMhIwNKbKkeOHJQpU4Zjx47xww8/cO/ePYfULIR4PdFvB4aHh7NgwQIHVpO4SbASIol4csoaHx8fsmbNyt9//82iRYsAy6B2rbXNbcEzZ85w9+5dmjRpQkBAAL/++iu3bt2K8/qFEK/P39+fAgUKGMtz5851YDWJmwQrIRKpJ4PUk+OifHx86Nq1KwBr1qxh9OjRREZGopQybgv++eefbNu2jbfffptSpUpRqFAh7t+/z2+//RY3H0IIYTe1a9c2fv7jjz/ktn4skWAlRCIUvfnn5s2bmTp1Kj169GDdunWcPXvW2K9o0aLGhMkrVqzgf//7H8uWLePcuXNs3LiRsWPH8vfff/P2228DkC1btrj/MEIIu3j//fdxc3Mzlr/99lsHVpN4uTi6ACGEfWmtjeafU6dOZd68eca2ffv2ERgYSLNmzahUqRJg+bL19PRk8ODB7Ny5k507d9qcr0ePHsb4jP379+Pm5kZAQEDcfBghhN0kT56cd955h59//hmAJUuWMGbMGOP7QtiHBCshEhnrLb+FCxcyb9488uXLR9OmTblx4wZHjx7l119/5ZtvviEiIoJq1aoBUL58eWbNmsW+ffs4cuQIDx8+xN/fn6JFi/LOO+8AsGjRInbs2EHhwoXJmTOnwz6fEOL11apVywhW165dY8uWLVSvXt3BVSUuEqyESCRMJpPxm2dERAQ///wzxYsX57PPPsPf3x+Ad999lyxZsrBo0SImTpwIYIQrf39//P39adasGWaz2ab9wuLFi1mwYAGpUqWif//+NhO7CiESjpIlS5I2bVpu3rwJwOzZsyVY2ZmMsRIikbCGqv379/Pnn39y+fJlmjdvjr+/vzGQ3c/Pj1atWtG6dWtu3rzJxIkT2bRpk3EOa4sFJycnIiMjuXr1Kr169WLy5Ml4eHgwY8YMsmfPHuefTQhhHy4uLjZB6qeffrJr+xSTyYSXlxeurq4EBASwZMkSu507oZBgJUQCFB4e/sz18+bNo2vXrmzatImUKVOSPXv2p54OTJUqFc2aNbMJV5s3bwYwWixYfz548CBXr16lUqVKTJs2jVy5csXehxJCxIlatWoZPz969Ijly5fb7dy3bt2iYcOGBAYGcvr0aVq3bs2NGzfsdv6EQIKVEAnM4cOHGTFiBMePH7dZHxkZiYuLC+nTp+fHH3/kn3/+4caNGzg5OT3VVf3JcDVmzBjWrVv31HvVqlWLUaNG0a9fP/z8/GL1cwkh4kauXLnInTu3sWzPZqHp06dn3rx57Nu3j0yZMhEREcGePXvsdv6EQIKVEAnI/fv3mTZtGps3b+bPP/+0uRrl4uJC/fr1adOmjTG4fPHixVy9evWZ57KGq+bNm3P37l1MJpPNduu5s2XLRrJkyWLpEwkhHME6thJg9+7dMX5PvC53d3cqVKgAwJEjR+x67vhOgpUQCUjy5Mlp2bIlbdq0oWbNmjg5ORmDUAE8PDyoWbMm9erVI0uWLOzbt4+1a9fG2Ck9VapUtGjRgtmzZ/Phhx/abHvW3IFCiMShatWqxhPEZrOZhQsX2v09ihQpAkiwEkLEU1prACpUqEDHjh1JkSIFkyZNYvjw4Zw5c8bYz9PTk1q1atG0aVNSpUrFsmXLWLFiRYzhKnXq1BQqVAh4ulu7ECJxypgxoxF8wHJ125601saDMRKshBDxklLKCFfOzs5cvnyZnTt38ttvv7Fw4UKbjuoeHh7UqlWLli1bkjx58heGKyu5SiVE0hH96cCjR49y+vRpu517woQJ7NixA4BLly5x584du507vpNvUSESCJPJZDPfX9asWfn8888pUqQImzZtYv78+TGGK29vb5YtW8aqVatsbh0KIZKuSpUq4erqaixbJ2N/UydPnqRfv36kSJGClClTAjFftVqxYgV169Yla9aseHl5ERgYyLhx44iIiLBLLY4gwUqIBCD6NDWbN28mKCgIsDT7a9euHW+99RabN2+OMVy1atWKlClTMm/ePJYsWRJjuwYhRNKRIkUKSpUqZSz/8MMPb3zOiIgIWrRoQXh4OJMnT6ZkyZJAzMFq7NixuLu78/XXX/PTTz/RtGlTBgwYQLt27d64FkeRzutCJADWK1Vjx45lw4YN/PTTT8a2EiVKADBz5kyjH1WrVq2Mx6mt4cpsNjNt2jQyZ86Mh4dHHH8CIUR89P7777Nr1y4Azpw5w7FjxyhQoMBrn2/48OEcOnSI+vXr07JlS06ePMmWLVv4448/nrn/unXrSJcunbH87rvvorVm4MCBfP3112TIkOG1a3EUuWIlRDxj7X7+pEePHrF//358fX1xd3dHa22MuSpRogQdO3Z87pWr2rVrM3v2bOrXrx8nn0MIEf+VL18eNzc3Y/lNBrHv37+fUaNGkSlTJmbOnAlA0aJFgZivWEUPVVbFihUD4J9//nntWhxJgpUQ8ciRI0f44YcfnjnQMywsjNDQUHx8fHByckIpZTOg/Vnh6ty5c8bxnp6expyB8vSfEALA29ubMmXKGMvLly83vlNeRVhYGC1btsRkMjF37lxSp04N/BesTp06xaNHj17qXDt37sTNzS3BzvQgwUqIeOLq1at8/vnnxhQzISEhNtvNZjORkZF4enrarI8pXP3888/Mnj2bU6dOPfVe8vSfEMKqSpUqxs/nz5/nr7/+euVz9OnTh9OnT9OlSxeqVq1qrM+VKxepUqUiIiLiqdkinuXEiRNMnDiRDh06kCJFileuIz6Qb1ch4om0adPSqFEjUqdOzbfffsuGDRtswlVISAihoaFGsIreKf3JcNWpUycCAgL45ZdfuH37dpx+DiFEwlK+fHnc3d2N5Vd9OnDr1q1MnTqVgIAAxowZ89R2a7+smMZZWd28eZMPP/wQf39/vvrqq1eqIT6RYCVEPGA2m3FxcaF169Y0adIEJycnZs+ezU8//WSEq0ePHhEREUGaNGkAy1UnrbURsJRSPH78GIC3336b9u3bM2rUKJvL/EII8SQvLy+b74m1a9e+9LEhISG0adMGZ2dnFi5c+NQVdXjxOCuwTNdVvXp1Hj9+zKZNmxL0NFoSrISIB5ycnIxw1bRpU1q0aIGzszPfffcdP/30E+Hh4SilMJvN+Pj4ABhjrKxtGADc3NyMK1flypWjcuXKgIypEkI8X6VKlYyfz5w589LNQj/55BOCgoIYNGgQxYsXf+Y+1sHoMQWrR48eUadOHS5evMjmzZvJnDnzqxUfz0i7BSHiiSfDFcDChQuZPXs2AK6urri4uLB//37Cw8N59OgRJpOJ8PBwIiIiiIyM5M6dO9y9e5c+ffqQL18+m3MLIURMypUrh4uLC5GRkSRzhV1Lx5O3RSNwTw7pAsDd+5nHLVq06IW3Dps0aUKTJk2euc1kMtG4cWMOHDjAtm3byJs37xt/FkeTYCVEPBJTuJo/fz45cuQgMjKS33//nd9//z3Gc3zyySc2oUoIIV7E29ubRu++RRnXE7R4y5XkLIWFSwHQbslQbzWGEu0hvX2/W7p27cqaNWsYPnw4JpOJvXv3Gtvy58+fIAewq9d5rFLEDqVUkI+Pj+/69ettBhKKxMtsNj/zalJkZKTx2+OSJUtYtmwZN27coGzZsrz77rtkzZoVd3d3zGYzSimSJ0+Oi4sLYWFhxiPKWmubKXCEECIm6c8vx/f4dJye85WhUaiqI6F0V7u9b/bs2bl06dIzt23fvp2KFSva7b1elp+fH8HBwcFaa7/XOV6uWAnhICaTyRgfdfnyZcxmM+Hh4QQEBODiYvlP03rlymw28/333/PXX39RunRpcubMaczBFT1AWX9RiimwCSHEk9KfX06WE9PhBb+HKTRs7mdZsFO4unjxol3OE59IsBLCAcxmsxGq5s+fz9q1a7l9+zZhYWHUrVuXatWqUahQIZRSuLi40Lx5c5ycnFiwYAEzZ84kIiKCmjVr4uPjY3NVyvqzhCohxMvwuP83fidmvNIxenN/VK737H5bMLGQb18hHMAafKZOncqUKVO4efMmuXPnxtnZmZUrVzJp0iS2bdtmPM1nvXLVsmVLnJ2dWbBgAevXr39mh3YhhHhZnqdXW65EvQKFJuTX6bFUUcInwUoIB9mzZw/Lly/nnXfeYfr06Xz77bdMmzaNypUrc/LkSb777jt++eWXp8JVq1atePz4MZMmTeLatWsO/hRCiITKKTKMzFe3vtaxnid/gEcP7FxR4iC3AoWII9HHVIFl6ggvLy86duxI7ty5AShcuDCpUqUiVapUrF27lrlz5wKWHjNOTk64uLjQuHFjwsLCSJMmDQEBAQ75LEKIhE+FXMBDh7/Wse7mMML+OY5njpJ2rirhk2AlRByxhqpp06bh5eXF6dOnKV26NLlz58ZkMqGUwsnJiezZsxutFmIKV+3atTPGU8lAdSHE67h99/4bHR98/Qb+OexUTCIiwUqIWPD333+TNm1akidPbrP+jz/+YO7cuTg5OZE8eXLKly8PYHMlCyBLliw24WrBggWYzWaqVKmCk5OTzYB1CVVCiNcRytPTz7zS8frNjk+s5BtZCDs7fvw4DRs2pGfPnoSH215mL1KkCN26dSNNmjTcvXuXv//+O8bHja3hqm7dupw6dYqpU6cSFBQUB59ACJEUhCfPzgPt8VrHPtAeqPQyFOFZJFgJYWcuLi44OTnh6upK9Aa8kZGRALRs2ZJGjRqRMmVKTp06xY4dOwgNDX3mubJkyUKjRo2oVasWzZo1I2vWrHHyGYQQiV+m1Mn5UZd7rWPXUYFcvhnsXFHiILcChbAjs9lM3rx5Wb16NSlSpMDT05ONGzdSpkwZUqZMaQxgb9WqFc7OzsyaNYtZs2bh4eFB7dq1nzmje9asWfnss8/w9rbM1SUd1YUQ9uDp4sSZjLUxX/sFJ/XyLRfMWnEzoAXJ3CVCPItcsRLCjpycnNBakzlzZry9vVmwYAGDBg1ixIgR3L9/H2dnZ+PKVfPmzenQoQPOzs5MmjSJdevW8fDhw2eeV0KVECI2FAoIYGRks1c6ZqSpGVXfezeWKkr4JFgJYUfWp/usChUqRObMmdmxY4cRrqxzAIIlXHXs2NEIVz/++GOM4QqQUCWEsKusKV0xFWjE8IjmmPXzv1/MWjE8ojmZqn5GngzJn7tvUibBSgg7iT5NzYoVK7h27RqFChXi66+/xs/Pj23btr0wXE2dOpU1a9bEOOZKCCHsrXaeZJgKNKLa49EsiKzy1ID2B9qDBZFVqPZ4NJmqfUa78jkdVGnCoKIPrhWOpZQK8vHx8V2/fj3u7u6OLke8pmnTpjF37lwqVqzIiBEjcHd35/Tp0/Tt25egoCDee+89BgwYQPLkyYmMjDQmXF6yZAmTJk3CZDKxePFi8uTJ4+BPIoRISi7fjWDz+TD2X7pDFtMVkqlwQrUHp8NTc+P4b4ztVIc2H1VzdJmxzs/Pj+Dg4GCttd/rHC8jz4R4Q9HHPf3666+sXLmS9957j5YtW+Lu7m4MaP/qq6/o27cv27ZtA3gqXDVt2pSwsDBSpEghoUoIEeeypnSlfVFXmr/lTdC9TDx8bOZ/n/Xk1oVj6IhwDuZ2ShLB6k3JrUAh3oDZbLYZ9/Tvv/8C0Lp1awIDAwHLuKjo4ep5twXbtm1LgwYNjHMLIURc83RxIndqNwpl9KBErvToCEs/PusvheL5JFgJ8QasXc/Hjx9P//792blzJxUrViRfvnzGPtapap4Vrr788kvu3buHi4sLT96Wl47qQghHK1nyv7kAT506JRO/vwT55hbiDf3999+sWLGCHTt2cPbsWe7du4fZbDauQlk9Ga6yZcvG1q1bGTBgABEREfLEnxAi3ilRooTN8saNGx1UScIhwUqIN+Tn58eXX35J2rRpCQkJ4fz588Ytvidv50UPVyNGjCBlypQUKlQIV1dXB1UvhBAxS5cuHTlz/vcU4ObNmx1YTcIgwUqIN+Tq6kqZMmXo3bs3fn5+XLlyhS+//JKwsDAjSEVnXRcQEMCKFSto27YtwFO3AoUQIj6Ifjtw+/bt8l31AhKshHhJzxpMbl3n6upKqVKl6NWrF5kzZ2bbtm1MnDiR8PDwGMMVQMqUKY3zyK1AIUR8FD1YXbt2jZMnTzqwmvhPgpUQL8FkMhlh6PLly5w4cYIrV65w//59Yx9XV1dKly5N7969yZgxIytXrmT8+PExhiv4r5O6DFQXQsRXRYoUMZofA2zYsMGB1cR/8m0uxDNYB55rrY2JkwHmz5/PJ598QqtWrWjevDlDhgxh165dxnEuLi6ULl2aPn36kDFjRlatWsWECROMcGUymRzyeYQQ4nUlS5aMAgUKGMs7duxwXDEJgAQrIZ5w6NAhvvnmG27cuIFSyghVU6dOZcqUKdy9e5dChQqRMmVKdu/ezaBBg2yelHkyXK1cuZJx48YRFhZm81ufEEIkFMWLFzd+/u2336TP3nNIsBIimjt37vDVV1+xfPly5s+fz82bNwHLF8mqVauoWLEi3377LbNnz2bixIm0aNGCBw8eMGTIEJvL49Zw9b///Y80adKwZs0ajh8/7qiPJYQQb6RYsWLGz3fu3OHYsWMOrCZ+k2AlRDSenp60bduWXLlysXLlSr777jseP37MrVu3APj444+N6WZy5MhBly5d6NSpE2azmWHDhj0VrkqVKkWfPn0YMGCAzW98QgiRkLz11lvGvKYAW7ZscWA18ZvMFShENB4eHrz77ru4uLgwY8YMVq1aBUBwcDClS5c2Oqpb5wd0cXGhbdu2aK2ZOXMmQ4cOBaBGjRqAJVy9++67xiB1s9ksA9WFEAmOh4cHBQoU4MiRI4Cl7ULv3r0dW1Q8Jd/wQjzB3d2dcuXK0alTJ/z8/Ni0aRPnzp0zwlFkZORTrRHatWtHx44dMZvNjBw5kvXr1xvbou8roUoIkVDJOKuXI9/yQjyDh4cH5cqVo3PnzmTIkIEbN25w8OBBgoODnzmvH1jCVZcuXXj8+DFDhw7l4sWLcV+4EELEkujjrEJCQmTcaAzkVqAQPPsWnTVcAcybN4/Tp08za9YsunXrRtq0aY3bgdG1adOGhw8f4u3tTfbs2eOqfCGEiHUFChTA2dnZaBuzfft2ChYs6OCq4h+5YiWSvOjNP0+ePMmePXuMbdZw1bp1a3LkyMGWLVv47rvvuHnzJkqpZ1656tq1K61atQKe3a1dCCESIk9PTwICAozl6D38xH8kWIkkLXrzz6VLl9K3b1/69evHzp07jX2s4apDhw74+vqyZs2aF4YrKxlTJYRITAoXLmz8vHfvXscVEo/Jt75IssxmsxGqpkyZwvjx43FycmLgwIE2Ywm01ka46tSp0yuHKyGESCyiB6ugoCCCgoIcV0w8JcFKJFnWq0mzZs1i/vz5lCtXjlGjRlG5cmWSJUtm7GcdR/VkuFq3bh2zZs0yOrQLIURiV6hQIZvl7du3O6iS+EuClUjS9uzZw8KFC8mXLx9dunSxGT8QGRlJcHAw+/fvJyQkhIiICDw8PChbtiydO3cmTZo0rF69mkuXLjnwEwghRNxJnTo1WbNmNZZl3sCnyVOBIkk7e/Ys4eHhNG/eHH9/f2P9vn372LZtG1u2bOHBgwfkz5+fBg0aULlyZTw9PSlbtiwRERFERkZKR3UhRJJSqFAhLl++DMCBAwccXE38kyivWCmlGiildiil7iilQpVSR5VSfZRSrq94niJKqS+UUr8opa4ppSKizrlLKdX1Vc8n4p/z588DGO0TIiMjmTdvHv/73/9YtWoVOXLkIHv27Jw4cYLvv/+ekJAQwHJbsEqVKtSqVQuQp/+EEElH9BYLJ0+eJCwszIHVxD+J7oqVUmoC0AOIBLYBD4D3gNFAbaXU+1rrF/4tUEq5AIejFh8AB4BrgB9QGigHtFRKVdVah9j5Y4g48tZbb7Fp0yYmTZpE7ty5OX/+PH/99Re5cuWibdu2VKlShaCgIPr378+JEyf4448/qF69OoAx8B3k6T8hRNIRPVhFRkZy4MABKlSo4MCK4pdEFayUUh9iCVUPgHe01oej1qfFErLKAcOBl53g6BCWQPaj1vpRtPcpCGwGSgDfAB/b6SOIOPbuu+/y559/8ttvv3H8+HHSpUtHhw4dePfdd/H390drjZ+fHzlz5uTSpUvkypXL0SULIYRD5cyZE09PT+NK1a5duyRYRZOoghXQL+r1K2uoAtBa31RKdQF2AZ8opYZrre8+70Ra60jgmYNntNZ/KaX6AAuBxkqpjlrrCPt8BBGX0qZNy2effUbjxo25cuUKxYoVI02aNMYVKKUUBw4cYMeOHfj7+5M6dWoHVyyEEI7l7OxMYGAgBw8eBKSf1ZMSzf0LpZQv8HbU4pInt2utdwNXAHeghh3e8o+oV08grR3OJxwkVapUBAYGUq1aNdKlS2czXurIkSN89913RERE0KhRI9Kmlf+rhRCiQIECxs/WgCUsEk2wAopEvd7WWv8dwz4Hn9j3TeSOen0M3LbD+UQ84eJiuZC7efNmRo8ezaFDh+jatStVqlQBkGagQogkL3qwunr1KsHBwQ6sJn5JTLcCc0S9Xn7OPlee2Pe1KEs3yD5Ri+ujj78SCd/BgwdZuXIlW7duJW3atAwYMIA6deoAz56sWQghkprowQrg999/p379+g6qJn5JTMEqedRr6HP2eRD1muIN32swlicDHwB9X+VApdTz+v9nfJOihH34+flx//596tatS7Vq1ShatCggoUoIIazSpk1LunTpuHHjBmDp/SfByiIxBas4oZRqCQwCzMDHWuuzDi5J2FnGjBkZPXo0Wmu8vb0By+0/CVVCCPGffPnyGcHq0KFDDq4m/khM/1Lcj3pN9px9vKNe773OGyilGgBzohbba62Xv+o5tNZ+Mf0Brr5OXcL+kiVLZoQqQOYCFEKIJ+TLl8/4+c8//5Txp1ESU7C6GPWa5Tn7WLddfM4+z6SU+gjL04ZOQEet9ZwXHCKEEEIkWtGD1a1bt2QAe5TEFKys7Q/SKKViGpxu7Ut1OIbtzxTVeHQZ4Ax01lp/+1oVCiGEEIlE9GAFlgHsIhEFK611EJZpZwCaPrldKVUOyxWrR8CGlz2vUqo28AOW8WidtdYz37xaIYQQImFLkyYN6dOnN5b37dvnwGrij0QTrKJ8GfXaVylV1LpSKZUGmBa1OCV613WlVF2l1Cml1C9PnkwpVQNYgSVUdZJQJYQQQvwnb968xs9Hjx51YCXxR6J6KlBrvUYpNQnoDuyNCkuhQCUgFbAHGPjEYSmBvIBH9JVKqfTAKsANCALKKKXKxPDWvbXWN+31OYQQQoiEIG/evOzatQuA48ePO7ia+CFRBSsArXUPpdQeoCtQBnAFzgNfAeO11o9f8lReWKa/AfADWj1n3yGABCshhBBJSu7cuY2f//33X27fvp3k51RNdMEKQGv9A5ZxUS+z7zxg3jPWXwTkGXshhBAiBnny5LFZPnz4MJUrV3ZQNfFDYhtjJYQQQog44uvri5eXl7EsEzJLsBJCCCHEa3JycsLf399YPnLkiOOKiSckWAnx//buPE6K6t77+OfHLGyD7CCLKETcEAFZRMWHoKAEhIABXKMg4hI0ah5jvNeNRzBR49WIxnvFKKDXHQWXiLiAQVRAEBVRRBSQJY6sMjMMMzBznj+quunp6R5mqaFnur/v16tf1VWnzulTZ3r5zalTp0REpNIiTwd++eWXCaxJzaDASkRERCotssdq7dq1FBUVJbA2iafASkRERCqtU6dO4ecFBQV8//33CaxN4imwEhERkUqLDKxA46wUWImIiEilNWnShObNm4fXv/jiiwTWJvEUWImIiEiVRPZapfoM7AqsREREpEoiA6vVq1cnsCaJp8BKREREqiQysPruu+9S+spABVYiIiJSJUcddVT4eWFhIRs2bEhcZRJMgZWIiIhUyZFHHlliPZXHWSmwEhERkSpp3rw5DRs2DK8rsBIRERGpJDMr0WuVygPYFViJiIhIlUUGVt9++20Ca5JYCqxERESkyiIDq++++y6BNUksBVYiIiJSZZGBVXZ2Nnl5eQmsTeIosBIREZEqO+KII0qsp2qvlQIrERERqbJ27dqVWE/VAewKrERERKTKGjVqROPGjcPra9euTWBtEkeBlYiIiAQistdKgZWIiIhIFbRv3z78/Pvvv09gTRJHgZWIiIgEIrLHKlXvF6jASkRERAIR2WO1adMmiouLE1ibxFBgJSIiIoFo06ZN+Pn+/fvJzs5OYG0SQ4GViIiIBCIysILUnMtKgZWIiIgEolWrViXWU3EAuwIrERERCUTdunVp3rx5eH3dunUJrE1iKLASERGRwBx++OHh5z/88EMCa5IYCqxEREQkMJHjrFIxsEpPdAVEYjEz0tPTqVOnDmaW6OqIiCQl5xzOOYqKiigqKgqkzMgeq82bNwdSZm2iwEpqHDMjMzOTxo0bk5WVRZ066lgVEakOzjkKCgrIyckhPz+fwsLCKpfZsmXL8POffvqpyuXVNgqspMZJS0sjKyuLZs2aJboqIiJJLyMjg4YNG/Ljjz8G0nMVGVht376dwsJCMjMzq1rNWkNdAVLj1KlTh4YNGya6GiIiKcPMaNSoEWlpaVUuK3rKhS1btlS5zNpEgZXUOGZGRkZGoqshIpJS6tatG8iY1hYtWpRYT7UB7AqspMYxMw1YFxE5xIL67o08FQgKrEREREQqrW7dujRu3Di8nmpXBiqwEhERkUBFng7ctGlTAmty6CmwEhERkUBF3tZm27ZtCazJoafASiQJnXDCCWRlZbFhw4ZEV0WkwjZs2EBWVhYnnHBCoqtSYVlZWWRlZSW6GgnXtGnT8PNUm8tKgZWIxFWbf+BEaruFCxeSlZXF4MGDE12VCouchzDVeqw0QagIkFe4n++27iGvYD8N66bzi5YNaJipj4dUo8I86mz7BgpzITOL4hbHQqbmbwNo27Yty5cv17QrtZgCK5EUtfanPJ5dtpnXV2azp/DAbMMNMtMY1rU1F/Vqx9Gt9GMnwbFt35CxYgbpq2Zh+/LC211GQ/Z3GcW+HmNxLY5NYA0TLyMjg2OPTe02qO0iA6vt27fjnEuZaXR0KlBS1szFGxnx2Ce8sHxLiaAKYE9hES8s38KIxz5h5uKNCaph2b7++msuueQSOnToQIsWLejduzcPPfRQ3NtRfP3110yZMoWBAwfSuXNnmjZtSocOHTj33HN5+eWXS+1/1VVX0aVLF8CbhyY0diR6DElOTg7Tp0/nwgsvpFu3brRq1YpWrVrRp08fJk2axK5du6rl+Guj9GXTqD/9TDI+m1kiqAKwfXlkfDaT+tPPJH3ZtATVML7Iv/ucOXMYNGgQbdq0oVWrVgwcOJB58+bFzbtjxw4mTZpEr169aNmyJYcffjj9+vXjwQcfJD8/v9T+ZZ2CXrt2Lddccw1dunShWbNmtG7dmuOPP57f/OY3PP300+H9pkyZQlZWFtddd13cei1btoysrCw6d+7M/v37y90WS5YsYeTIkbRr147WrVtzxhln8NRTT5WZZ9myZdx2223079+fTp060bRpUzp27Mjo0aNZsGBBqf0HDx7MkCFDAFi0aFGJz15ku2zdupVHH32U8847jy5dutCiRQvatGnDGWecwQMPPMDevXvLfVxBigysCgoKyM3NTUg9EkE9VpKSZi7eyH3vfHfQ/RyE97us7xHVXKvy++ijjxg5ciR5eXl07NiRM888k+3btzNp0iSWLl0aM88jjzzCzJkzOeaYY+jSpQuNGzdm06ZNLFy4kPfff59PPvmEe+65J7z/qaeeSm5uLq+++ioNGzZkxIgRMctduXIl1113HS1atKBz5850796dXbt2sWLFCu6//35mz57N/PnzS1wllIrSl02j7oJJB93PcOH99ve6snorVQlTpkzh3nvvpW/fvpx99tmsWbOGxYsXM2rUKJ555hmGDx9eYv9169YxdOhQfvjhB1q0aMHZZ5/N/v37WbhwIbfffjsvv/wyr7/+eonBzvGsWrWKQYMGsXv3bjp37szgwYNJS0tjy5YtfPjhh2zZsoXf/va3AEyYMIEHHniAF198kcmTJ9OkSZNS5U2b5gWwl19+Oenp5fs5fOWVVxg3bhxFRUWccMIJdOnShU2bNjFx4kS+/vrruPkmTZrEwoULOf744+nRowcNGjRg3bp1zJ07l7lz53LvvfcyceLE8P6DBg2iXr16vPvuu7Rq1YpBgwaF0yI/S++99x4333wzbdu2pVOnTvTu3Ztt27axbNky7rjjDv75z3/y5ptvUrdu3XIdX1Ci/57Z2dk0atTokNYhUcw5l+g6iM/MNjVt2rTdG2+8ccg/BDVJvXr1aN++fbm/6Cpq7U95jHjsEyryzjdgzlW9a8Rpwb1799K9e/fwl/mf//zn8P29vvzyS4YOHcr27dsB74foyCOPBOCDDz6gffv2dOzYsUR5a9asYdiwYWzevJn333+fXr16hdM2bNhAly5d6NChA1999VXM+mzevJk1a9bQv39/6tQ50Am+Z88ebrjhBp599lkmTJjAgw8+GGg71Ca27RvqTz8Tq8C7zmHkj5tfY04LhnqrmjRpwuzZs+ndu3c47e677+Yvf/kLnTt3ZsWKFSXyDRgwgE8++YShQ4fy5JNPhu8DunXrVkaOHMlnn33GmDFjePLJJ8N54r3vrrnmGp5++mnuuOMObr755hKvk5+fz/Lly+nXr1942/jx43nhhRe45557uPbaa0vsv23bNo499liKi4tZvXo1rVu3PmgbZGdn0717d3JyckqVuWDBAkaPHh3uIYruoXn77bc56aSTOPzww0tsD/V+5efns2rVKtq2bRtOW7hwIUOGDKFfv3689dZbMeu0evVqdu/eTZ8+fUps37lzJ2PHjuW9995jypQp3HDDDQc9vv3797Np06ZAerk2btzIyJEjw+sfffQRp556apXLPRTat2/P5s2bNzvn2lcmv04FSsp5dtnmCgVV4PVcPbe8Zswe/Oqrr7Jp0ybat2/PlClTStw09cQTTyz1gxNyxhlnlAqqAI455hj+9Kc/Ad4pnopq164dAwYMKBFUATRo0IC//e1vpKenM3v27AqXm0wyVsyoUFAFXs9VxooZ1VOhKrj11ltLBFUAN910E40bN+bbb78tMRnkRx99xCeffEKDBg2YOnVqiZurt2zZkocffhiAWbNmlWt27tBl++ecc06ptPr165cIqgB+97vfAfCPf/yD6E6EmTNnUlBQwIgRI8oVVIXy5OTk0KdPn1KB2oABA7j88svj5j377LNLBVUAp5xyCldeeSX79u3jjTfeKFc9Ih133HGlgirweozuv/9+gIR8/g477LAS66k0gF2nAiWl5BXu5/WV2ZXK+9oX2fzhrE4Jv1rwgw8+AOC8886LedXURRddFA6UouXm5vL222/zxRdfsG3bNvbt2wfAjz/+CMC3335b6XotXryYjz76iI0bN5Kfnx/+IcvMzGTbtm3s3LmzXKd7kk5hHumrZlUqa/qqWRT2v61GXS0YGvcTqW7duhx11FF8/vnnbNmyhfbtvX/0Q+/VgQMHxgxeevToQdeuXVm5ciWLFi3i/PPPL/O1e/bsybx587jhhhu49dZb6devH/Xq1Stz/1NOOYUlS5bw7rvvhk+nFRcX88QTTwDeWMLyCh3PmDFjYqZffPHFPProo3Hzb9++nXnz5vHVV1+xa9eu8Ofvu++84QaV/fwVFRWxcOFClixZwo8//sjevXtxzoU/g1X5XFdWVlYWZhauw9atWw95HRJFgZWklO+27ik1UL289hQW8d3WPZzU7rCD71yNQv/Zh07xRWvatCmNGzfm559/LrH9zTff5Oqrr2bHjh1xy969e3eF6/PTTz9x8cUX8/HHH5e5X05OTkoGVnW2fVNqoHp52b486mxfQ3GbHgHXqvKOOCL2WMPQ+JnI00hbtmwB4KijjopbXseOHVm5cmV437LccMMNfPzxxyxYsIARI0aQkZFB165dOf300xk1ahQ9e/Yslefqq69myZIlPPbYY+HAau7cufzwww9069aNvn37HvR1Q0KfvXjHE+8zCTB9+nRuueUW8vLivxdycnLKXZeQtWvXcuGFF5Y5vqsyn+uqSktL47DDDgt/D4WGJ6QCnQqUlJJXUP4rf2KpbFCWaFu2bOGyyy5jx44d3HjjjSxevJgtW7awe/fu8AB1oNTpkvK49tpr+fjjjznllFN47bXX+P7779m5cye5ubnk5uaGT3+k7HjOwipeDVXV/AGLPuV7KDVo0IDXX3+df/3rX9x222388pe/ZO3atTz88MP079+fG2+8sVSekSNH0rZtW95++23Wr18PHBi0XpHeqqpYsWIFv//97ykoKGDy5MksW7aM7OxscnJyyM3NZerUqUDlPiOXXHIJX3/9Nb/61a+YN28eGzZsCH/+yvon6lCIPB2Y6LocSuqxkpTSsG7V3vINMtMOvlM1Cw1u/eGHH2Km79q1K2ZvVX5+PsOGDWPy5Mml8qxdu7ZSdcnLy2PevHnUqVOHl19+udSVV3l5eWRnV+7Ua9LIrOLtTaqaP4FC79V169bF3ScU7EQO2j6Ynj17hnun9u/fz+uvv86VV17J448/zogRI+jfv3943/T0dK644gruuusuHn/8ccaOHcv8+fNp1qwZo0ePrvDxrFmzJu6touJ9JmfPno1zjquvvjpm8Bc6FVhR33zzDV9++SUtW7bkueeeK3XBT2U/10Fp3LgxGzd609Wk0hgr9VhJSvlFywaVDo4aZKbxi5YNAq5RxYUG6L7yyivhMRqRnn322VLbdu7cCUCHDh1KpTnneOmll2K+VmZmJkDcOX52795NUVERhx12WMzL2Z9//vnU7anyFbc4FpdRuTFSLqMhxc2PCbhGh84ZZ5wBwLvvvhszwP7888/54osvqFOnDqeffnqlXiM9PZ2RI0dy1llnAd70H9Euv/xy6tWrx9NPP83UqVNxznHppZdSv379Cr1W6LP34osvxkyP9dmDA701sU6j7t27N9xjHC30+Ys3N13oc92mTZuYV1G/8MILMfMdKpE9Vqk0n50CK0kpDTPTGda1fFcARRt+UuuED1wHGDFiBG3btmXjxo3ceeedFBcXh9NWrVrFfffdVypPaBbrOXPmhAeqg/eFPXnyZBYvXhzztVq0aEFmZibZ2dkxu/JbtWpF06ZN2bVrF88991yJtKVLlzJp0qTKHGJyyfRmVK+M/V1G1aiB6xV12mmn0bt3b/Lz87n++uvZs2dPOG3btm38/ve/B2DUqFHhAe9lmTZtGmvWrCm1PTs7OzzNQ6zgpUWLFowZM4YdO3Ywffp06tSpw4QJEyp8PJdddhlZWVksWbKk1CD1hQsXhgfERzvuuOMAL/CKHEe1d+9ebrzxxnCvXbR27doBXs9TrH+ijj76aNLS0li1ahULFy4skfbmm2/yyCOPlPvYqkPkVaCVGT9WWymwkpRzUa92VPTGCgZc2LNddVSnwurXr88TTzwRvoS9e/fujB07luHDh9OvXz9OO+20Uj1TQ4YMoUePHmzevJnu3bszatQoLr30Urp27cqDDz7IH/7wh5ivlZGRwZAhQygqKuK0005j3LhxTJw4MTyRYVpaGrfccgvgTcg4YMAAxo0bx6BBgzjrrLMYPHhwzF6yVLOvx1hcBd91DmNfj7HVU6FD6Mknn6RDhw688cYbdOnShUsuuYQLLriAk046ieXLl9O9e3f+67/+q1xlTZ8+nZNPPpkTTzyRMWPGMH78eH79619z4oknsnnzZvr378/QoUNj5r3mmmvCzwcPHlzmQPN42rRpw8MPP0xaWho333wzp5xyCuPGjeOcc85h6NChjB8/Pma+0B0SPv/8c7p06cIFF1zAxRdfzPHHH8+cOXPC00JEO+KIIzj55JPZunUrffr0Yfz48UycOJE77rgD8ALGq666iqKiIs4991wGDx7MuHHj6NevH2PGjOH666+v8DEGSYGVSIo4ulVD/jjoFxXK88dBv6gRk4OGnHHGGSxYsIDhw4eza9cuXn/9dTZv3sxtt90W89Ya6enpzJ07l5tuuok2bdrw/vvv88EHH3DSSScxf/58Bg4cGPe1pk6dyuWXX46ZMWfOHGbOnMnMmTPD6RMnTuT555+nb9++fPvtt8ydO5eCggIeeOCB8CDhVOdaHEvhgDsrlKdwwJ01ZnLQqujYsSOLFi3ipptuolmzZrz11lvMnz+fjh07ctddd/HOO++U+2rRO++8kyuuuILGjRuzdOlSZs+ezerVq+nVqxfTpk1jzpw5cScW7tq1a3jKh6oMWh89ejRz585l4MCBbNy4kX/+85/k5uby0EMPlbhzQaQmTZqwcOFCJkyYQJMmTXjnnXdYunQpZ511FosWLeKkk06K+3rPPPMMY8aMIScnh5dffpmZM2cya9aB6TvuvfdeHn30Ubp168Znn33G22+/Tf369ZkxY0Y4AEuUyMAqlW5po5nXaxDNvO6p7pnXQ2Yu3shf3/muzGkbDS+oqkm3s5HaK33ZNDIX/L8yJwt1GIUD7qyRt7OpzRYsWMCwYcPo3Lkzn376acrcELgigpx5HeDxxx/nscceA7wA+/vvvw+k3OpW1ZnXEz9gRCRBLut7BKd3asZzyzfz2hfZJaZSaJCZxvCTWnNhz3Y1qqdKarf9va6k6Kj+ZKyYQfqqWSXmt3IZ3lisfT3GJkVPVU1SVFTE3XffDcB1112noOoQSdVTgQqsJKUd3aoht//qGP5wVqfw5KGhq/9qwkB1ST6uxbEUDvoLhf1vo872Nd48VZlZ3tV/tXigek309NNP8+GHH/Lpp5/y1Vdf0aVLFy699NJEVytlpOqpQP1yiOBdLZjoGdUlxWQ2rFEzqiejRYsW8cwzz9CkSROGDRvGfffdV+1DDOSAyMBq7969FBcXJ3SC2UNF7zAREUlKjz32WHiMjxx60fdxzM/PLxFsJavkDx1FRETkkIsOrMq6T2IyUWAlIiIigVNgJSIiIhKQ6MAqVa4MVGAlNY5zLuXvLycicqgF/d2rHiuRGsI5F/O+WCIiUn0KCgqqNbBSj5VIghQXF6fMfzYiIjWBc46cnByKiooOvnM5Rd9BJKgZ3Ws6TbcgNU5RURG5ubmkpaWRlZWVEvOeiIgkgnOOgoICcnJyyM/PDzSwysjIKLFeUFAQWNk1mQIrqXGccxQWFrJz505+/vln3X5CRKSahMZVFRUVBRpUAaUmYy0sLAy0/JpKgZXUSBpnJSJSu6WlpWFm4XFbqdJjpXMsIiIiEjgzK3E6MFV6rBRYiYiISLWIDKxSZfB6UgZWZjbazN43s51mlmdmn5vZzWaWcfDcMcvraWYvmVm2me01s3Vm9rCZtQq67iIiIskicpyVTgXWUmb2N+BF4HRgKfAW0AG4F5hvZvUrWN4oYDEwCtgAvAoUA9cCX5jZ0YFVXkREJIlEBlZBD46vqZIqsDKzEcD1QC5winPuHOfcb4DOwEqgHzC5AuW1BWbiDfK/yjnXxzl3PnAM8L9Aa+BZ02VrIiIipUROl1NcXJzAmhw6SRVYAf/pL+9xzn0a2uic2wb8zl+91swal7O8G4AGwLvOuWkR5RUB1wA/A72Bs6tYbxERkaSjwKoWM7N2eEEOwLPR6c65RcBGoC4wpJzFjiyjvFzgNX/1vApVVkREJAVEntBRYFX79PCXO5xz6+Lssyxq37jMrBEQGj+1LM5u5S5PREQk1UT2WGmMVe3T0V/+UMY+G6P2LctREc/jlVmR8kRERFJKZI9VkDd4rsmSaeb1Rv6yrLv35vrLwypQXlllVqQ8AMxsUxnJ7Xbt2sXw4cPLW5yIiEiNtXPnzvDzqVOnMmPGjMRVppz+/e9/A1R6OqVkCqySgnOO7du3b0t0PZJQM3+5I6G1SF5q3+ql9q1eat/q1QwgJycnOycnpzYMtGoFVPqeaskUWOX4y4Zl7JPlL3dXoLxQmT9XsTwAnHPt46WFerPK2kcqR21bvdS+1UvtW73UvtUr1do3mcZYrfeXR5SxTyhtfRn7hGyIeN4hgPJEREQkySVTYLXCXzY3s3iDyXv5y0/jpIc553YDa6PyVbo8ERERSX5JE1g55zYBn/irF0Wnm1k/vB6mAuDNchY7u4zysoBh/uorFaqsiIiIJKWkCax8f/aXt5jZyaGNZtYceNRffcQ593NE2kgzW21m78Uo72/AHmCgmU2IyJPml9cEL5h7O8iDEBERkdopqQIr59wcYCreoPLFZjbXzGbhndLrCnwI3B6VrTFwLPCLGOVtAcYCRcA0M1tsZs8Da4DfAtnARS5VJucQERGRMiVVYAXgnLseOB/4GDgN7/Y1m4BbgDOdc/kVLO8l4BS8032d8G5zkwb8HejmnFtbRnYRERFJIabOFhEREZFgJF2PlYiIiEiiKLASERERCYgCKxEREZGAKLASERERCYgCKxEREZGAKLASERERCYgCKxEREZGAKLCqJmY22szeN7OdZpZnZp+b2c1mllHJ8nqa2Utmlm1me81snZk9bGatgq57bRBU+5pZDzP7DzN7z2/bfX6ZH5jZxMr+vWq7oN+/UWUPMTPnP94Nor61TXW0r5n92sxeM7MfzazQzH4ys4/M7I4g614bBNm+ZtbQ/45YZma7/e+IH83sDTMbXh31r6nM7Fgzu87MZpjZSjPb73+Ob6tiuQPN7E0z22Zm+f5t5u7278lb+zjn9Aj4gXePQQfsA+YBLwM7/W0fAPUrWN4ovywHLAVeAL7z138Ejk70MdfG9gXS/TwOyAHmA8/5Zez3ty8BmiT6mGtj+8YpuymwGSj2y3s30cdb29sXyARe9PPvAd4DnvXfz9nAtkQfc21tX6A5sCriO2Ke//27POK746FEH3MC2jb6cVsVyrzRL6MY+Jf/Xv63v2010CLRx13hY0p0BZLtAYyI+BCeHLG9BfCFn3Z/BcprC+T5+a6M2J4GPB0RbFmij722ta8fWC0DRgN1o9K6Alv88p5M9HHXxvaNU/7/4gWtj6ZiYFUd7QvM9PPNjv4Rwjsr0TfRx11b2xd4yM+zDGgWlTaEA//wpkQbA1cAfwUuAo4DnqpKYAX08AOq/cCvIrY3AN71y56V6OOu8HElugLJ9vCDHAfcGiOtn5+2F2hczvLu8/O8EyMtC9jlp5+T6GOvje17kNe6hAO9ABmJPvba3r5499l0/nt6bIoGVkF/P5zl51mZKu/RQ9y+K/08o+Okv+2n35joY09Qe8+oYmAV6ml9PEbakUCRn35coo+1Ig+NsQqQmbUDevurz0anO+cWARuBunj/7ZTHyDLKywVe81fPq1Bla6Fqat+yrPCX9fH+401q1dm+ZtYC+B/gGyDlxvxAtbXvdf7yb865fVWuZC1WTe27t5z7bSvnfuIzs0xgqL8a6++1AfjQXx0ZnV6TKbAKVg9/ucM5ty7OPsui9o3LzBoBR0flq3R5SSDQ9i2Hzv6yENgRQHk1XXW273/jBafjnXPl/bFKNkF/P6Th9VgBLDSzw83sBjP7bzP7m5ldVmsH/1ZOdbx/5/rLP5lZs8gEMxsCDMAb5/padEY5qGPwTvlBkv2+pSe6Akmmo7/8oYx9NkbtW5ajIp7HK7Mi5dV2QbdvXGZmwM3+6hvOuYKqlFdLVEv7mtkFeBdgPOSc+/Bg+yexoNu3E95wAIC+eOPWogOpv5rZBc65+eWuZe1VHe/fe4E+wDnABjP7EG/4xdFAT7welfHOuZ8rXFsJ/Q12Oedy4uxTK3/f1GMVrEb+Mq+MfXL95WEVKK+sMitSXm0XdPuW5U7gVL+8W6pYVm0RePua2eHA3/GuYv3PylctKQTdvs0jnj+Bd6Vab/91ugNvAi2BV82sc6ncySfw969zLg8YBtwPNMQLsM7HC6q24w2w3lyZysoh/T4/pBRYiUQxs0vxxgEVA5c7575NcJVqs2l4Uyxc4Zzbk+jKJBmLeL4Z7wKWZc65XOfc58Bw4Eu8XqxU+ecgUGbWBq9X6jrgNg70EvbBC2TvBBb5wzZEAAVWQQt1ZzYsY59QV/3uCpRXVpkVKa+2C7p9SzGz0cCT/uoE59xLlSmnlgq0fc3sMrz/9v/HOfd+1aqWFKrz+2FG9Olq51wR8Ji/OrBcNazdquP7YSZeL+Dtzrk/O+fWOefynHOfAOfiXTXYDbipMhVOcdX+fZ4oGmMVrPX+8ogy9gmlrS9jn5ANEc874H2Iq1JebbfeXwbVviWY2Xl4V6fUAa5yzj15kCzJZr2/DKp9Q1fy9Daz96PSDveXPSPSLnDO/ViOcmur9f4yqPZdj3cpugHfx9kntL1NOcqr7db7y0Da17/KcJC/+lx0unNun5nNwpvzbiBe75WU33p/2cTMGsUZZ1Urf9/UYxWs0OX5zc0s3mC7Xv7y04MV5pzbDayNylfp8pJAoO0bycxGAM/jTbx6jXPu8UrVsHarrvbtBfSPehzrpzWJ2FavIpWthYL+fsjFm74C4k8HEtqeGyc9mQT9/u0Q8Txej0lo0HqzOOkS3zd4cwRCkv2+KbAKkHNuE/CJv3pRdLqZ9cOLwAvwBpaWx+wyysvCO9UC8EqFKlsLVVP7YmbD8CaqS8cLqh47SJakFHT7OudGOOcs1gMY5+/2XsT29cEcSc1UTe/f0KnqeKf6Qj0uS8tZXq1VDe0bOSj9lDj79PWX8aZ3kDicc4XAP/3VWH+vI4HT/NXZ0ek1WqJnKE22B/FvqdCcOLdUwDtlshrvRya6vMhb2kyI2J7GgdsJ6JY2lW/fIXhftMVE3DIoVR9Bt28ZrzOW1Jx5Pej3bwu8OdYc3unryLQLOHBPxiGJPvZa2r6hmdy/Ao6KSrskon0vSfSxJ6i9Z3CQmdeBa/32fSpG2skcuKXN4IjtuqWNHqXeLKH7SxXiTTA3iwM3AV1E1E1AI35k1scpbzQHbgq8GO+UVSrfhDmQ9gVa4c2s7PDmS5lRxqPW3Qg00e17kNcI5UmpwKo62hevVyrf3+dLvF6sTzlwg9y7En3MtbV9gROBrX56PrDAb98vI9r3aVLnH9uT/d+g0CPUNhujtreJyDPJ3+f9OGVG3oR5Ad5NrkP3adVNmPUo8WYZg3en7p/xziOvBP4EZMbY96A/THjzprwM/ITXw7IeeARonehjra3tizcBqyvn46hEH3Nta9+DlB/Kk3KBVXW0L94s1jOATXgBxTa80yxnJ/pYa3v7Aq2Be4DP8caq7fO/h98CxiT6WA9xu/6yot+XHCSw8vcZiBcEb8f7Z3cN8GegUaKPuTIP8w9KRERERKpIg9dFREREAqLASkRERCQgCqxEREREAqLASkRERCQgCqxEREREAqLASkRERCQgCqxEREREAqLASkRERCQgCqxEREREAqLASkRERCQgCqxEREREAqLASkRERCQgCqxEREREAqLASkSSmpmNNjMX47HbzD42swlmFvh3oZn1MLMiM3s4Rlqame0xs31mttrMLqpg2Y3NbLuZLTEzC67WIlJVCqxEJNn19JergZn+4wXg30BfYBrwSDW87sNAPjA5Rlpz4EVgFXAsMMPMWpa3YOfcz8BfgD7ApVWvqogExZxzia6DiEi1MbN3gIHAH51z90dsrwP8HbgacMDRzrnvA3rNUcBLwF+dczeXsV9dYB3QBhjpnJtTgdeoB/wA7Ac6OucKqlRpEQmEeqxEJNmd7C+/jNzonCsGJvmrBnQP8DVv9JdPlLWTHwwt9Fcr9PrOub3As3hB2fkVrJ+IVBMFViKStMzsKKCZv7oyOt05l43X4wNQGKeMZmb2H2a22Mx+NrO9ZrbKzP5kZukx9u8BnAYsds59U45qrvCX3cuxb7QZ/nJiJfKKSDVQYCUiySw0vmqnc25zdKKZdQTS8YKqJTHS/w/wNfBn4HBgAfAv4EjgHuCVGIPHR/jLdw9WOT/vYH+1+8H2j+ac+wzYCvQxszYVzS8iwVNgJSLJLBRYfRkn/TZ/eb9zbmtkgpl1B94AmgJXAp2ccyOcc+cAx+GNjRoGjIwq80x/+XE56ncD8Ev/+ZFm1rQceaKFXuesSuQVkYApsBKRZFYqsDKzdDPrYmZPARcCtzvnbo3MZGZpwFNAI2Csc+5xf0wWAM65TcDd/uqvol6zh7/8uqyKmdnxeD1hu4Gf/c3d4+x7rpkVm9nhMZJX+cuTY6SJyCGmwEpEklko2LgmNH8VsA8v0BoBnOucmxIj3/lAV+Aj59yzccpe5y9bhDaYWUOgob+6PV6lzCwDeBqoB1zHgdOQ3eNk+RTo7Zz7MUZa6HVax3s9ETl0Sg28FBFJBmbWgQNBz4t4c0rVAdoD/wevN2qWmf3CObczKvsof3maH4yVJTJv44jnOWXkuR2vN22Wc+4pv/fqbA70dpXgnNsCbIlT1m5/WZnTiCISMAVWIpKsQqcBdwMXRp7KM7NT8QaiNwWuAP4alTcU4LwE7DnI68yJeL4r4nkjDgQ9YWbWB/gPvAlKr/I3f+ovu8d6ATPbBPzDOTcpRnIomIsODkUkARRYiUiyCgVWyyODKgDn3Mdm9r/AeOBiSgdWrfzlFc65UsFRPM65PWaWh3c6sDlRgZWZ1ccbu5UGjHPO7fCTQoHVcWZWN3KyTzNrDrQDPovzss39ZXZ56yki1UdjrEQkWYXGVy2Nk/4Pf9nNzDpFpYUGk59YidcNBUknxEi7D+8WNo865+aFNjrnvsPr7coAukTl6eYvP4/zeqE6Lq9EXUUkYAqsRCRZhXqs4gVWS4CN/vPzotLe9Jd/N7MjojOaWRMzG29mraLT8E4xApwalWcg3kSeq4E/xsgXmig0epxVN7yer/Ux8kS+zvw46SJyCCmwEpGkY2btOXA6L2Zg5bwbpb7sr0bPRXU73lV/3YG1ZvYvM3vWzF4ysyXAj8BUYAelzfGXgyLq0wSYDhQBv3XO5cfIF2+cVTfgCxfjxq7+LO/NgaXOuX/HKFNEDjEFViKSjEK9Vf/255yK5yV/2Tdyjig/SOkBTAa+8ssbBfTDO103Dfi1c24/UZxzK4CP8GZDP97f/Aje1Yh3OeeWxalL6FRe96jt3Yh/GnCsv/x7nHQROcQsxj9BIiJSBWY2Ci9oe8A593+rUE4GkAtc65x7PCqtHt6pzH1Ax8gB7yKSOOqxEhEJmHNuFvAhcJWZVWXizuOATGL3WF2HN0/XfyioEqk5FFiJiFSP64D6eOO1KqsbUEzUvQ7NrDFwC974saeqUL6IBEynAkVEREQCoh4rERERkYAosBIREREJiAIrERERkYAosBIREREJiAIrERERkYAosBIREREJiAIrERERkYAosBIREREJiAIrERERkYAosBIREREJiAIrERERkYAosBIREREJiAIrERERkYAosBIREREJiAIrERERkYAosBIREREJiAIrERERkYAosBIREREJyP8H2LHU2ShXMZ8AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 960x640 with 1 Axes>"
       ]
@@ -643,9 +643,9 @@
       "Mode 2 has a growth rate of -0.000 and oscillates with a frequency of 0.600/(2π)Hz.\n",
       "\n",
       "noisy dataset\n",
-      "Mode 0 has a growth rate of -0.134 and oscillates with a frequency of 2.315/(2π)Hz.\n",
-      "Mode 1 has a growth rate of +0.075 and oscillates with a frequency of 2.796/(2π)Hz.\n",
-      "Mode 2 has a growth rate of -0.002 and oscillates with a frequency of 0.605/(2π)Hz.\n"
+      "Mode 0 has a growth rate of -0.130 and oscillates with a frequency of 2.315/(2π)Hz.\n",
+      "Mode 1 has a growth rate of +0.081 and oscillates with a frequency of 2.802/(2π)Hz.\n",
+      "Mode 2 has a growth rate of -0.041 and oscillates with a frequency of 0.601/(2π)Hz.\n"
      ]
     }
    ],
@@ -684,7 +684,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1280x480 with 1 Axes>"
       ]
diff --git a/docs/source/notebooks/linear_algebra_basics.ipynb b/docs/source/notebooks/linear_algebra_basics.ipynb
index d43f1b1..066581f 100644
--- a/docs/source/notebooks/linear_algebra_basics.ipynb
+++ b/docs/source/notebooks/linear_algebra_basics.ipynb
@@ -1184,7 +1184,7 @@
     "\n",
     "A Hermitian matrix is a complex matrix that is equal to its conjugate transpose:\n",
     "\n",
-    "$$ \\mathbf{A}=\\overline{\\mathbf{A}}^T $$\n",
+    "$$ \\mathbf{A}=\\overline{\\mathbf{A}}^T = \\mathbf{A}^H$$\n",
     "\n",
     "Example:\n",
     "\n",
@@ -1229,14 +1229,14 @@
    "source": [
     "### Unitary matrix\n",
     "\n",
-    "A unitary matrix is a complex square matrix whose column vectors form an orthonormal basis. The matrix product of a unitary matrix with its conjugate transpose, denoted by $\\dagger$, yields the identity matrix:\n",
+    "A unitary matrix is a complex square matrix whose column vectors form an orthonormal basis. The matrix product of a unitary matrix with its conjugate transpose, denoted by $\\ast$, yields the identity matrix:\n",
     "\n",
-    "$$ \\mathbf{Q}\\mathbf{Q}^\\dagger = \\mathbf{I} $$\n",
+    "$$ \\mathbf{Q}\\mathbf{Q}^\\ast = \\mathbf{I} $$\n",
     "\n",
     "Example:\n",
     "\n",
     "${\\mathbf{Q}={\\frac {1}{2}}\\begin{bmatrix}1+i&1-i\\\\1-i&1+i\\end{bmatrix}},\\quad\n",
-    "{\\mathbf{Q}\\mathbf{Q}^\\dagger={\\frac {1}{4}}\\begin{bmatrix}1+i&1-i\\\\1-i&1+i\\end{bmatrix}}{\\begin{bmatrix}1-i&1+i\\\\1+i&1-i\\end{bmatrix}}=\\begin{bmatrix}1&0\\\\0&1 \\end{bmatrix}= \\mathbf{I}$"
+    "{\\mathbf{Q}\\mathbf{Q}^\\ast={\\frac {1}{4}}\\begin{bmatrix}1+i&1-i\\\\1-i&1+i\\end{bmatrix}}{\\begin{bmatrix}1-i&1+i\\\\1+i&1-i\\end{bmatrix}}=\\begin{bmatrix}1&0\\\\0&1 \\end{bmatrix}= \\mathbf{I}$"
    ]
   },
   {
@@ -1330,11 +1330,11 @@
     "\n",
     "The SVD is a factorization of a real or complex matrix that generalizes the eigen-decomposition of a square matrix to any ${ m\\times n}$ matrix:\n",
     "\n",
-    "$$\\mathbf{A} = \\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\dagger$$\n",
+    "$$\\mathbf{A} = \\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\ast$$\n",
     "\n",
     "- $\\mathbf{U}$: is a $m \\times m$ unitary matrix; the columns of $\\mathbf{U}$ are called left singular vectors\n",
     "- $\\mathbf{\\Sigma}$ is a $m\\times n$ matrix containing a diagonal matrix of dimension $\\mathrm{min}(m, n)$; the diagonal elements are called singular values\n",
-    "- $\\mathbf{V}^\\dagger$ is a $n\\times n$ unitary matrix; the columns of $\\mathbf{V}^\\dagger$ are called right singular vectors\n",
+    "- $\\mathbf{V}^\\ast$ is a $n\\times n$ unitary matrix; the columns of $\\mathbf{V}$ are called right singular vectors\n",
     "\n",
     "example:\n",
     "\n",
@@ -1348,7 +1348,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 840x840 with 3 Axes>"
       ]
@@ -1365,15 +1365,22 @@
     "     [3.0, 4.0],\n",
     "     [5.0, 6.0]]\n",
     ")\n",
-    "U, s, VH = pt.svd(A, some=False, compute_uv=True)\n",
+    "U, s, VH = pt.linalg.svd(A, full_matrices=True)\n",
     "S = pt.zeros_like(A)\n",
-    "S[:A.shape[1], :] = pt.diag(s)\n",
+    "S[:A.shape[1]] = pt.diag(s)\n",
     "\n",
-    "assert pt.allclose(U.mm(U.conj().T), pt.eye(3), atol=1.0e-6)\n",
-    "assert pt.allclose(VH.mm(VH.conj().T), pt.eye(2), atol=1.0e-6)\n",
-    "assert pt.allclose(A, U.mm(S.mm(VH)))\n",
-    "vis.plot_matrices_as_heatmap([U, S, VH], [r\"$U$\", r\"$\\Sigma$\", r\"$V^H$\"])"
+    "assert pt.allclose(U.conj().T @ U, pt.eye(3), atol=1.0e-6)\n",
+    "assert pt.allclose(VH @ VH.conj().T, pt.eye(2), atol=1.0e-6)\n",
+    "assert pt.allclose(A, U @ S @ VH)\n",
+    "vis.plot_matrices_as_heatmap([U, S, VH], [r\"$U$\", r\"$\\Sigma$\", r\"$V^\\ast$\"])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/flowtorch/analysis/__init__.py b/flowtorch/analysis/__init__.py
index 26f9377..4e23403 100644
--- a/flowtorch/analysis/__init__.py
+++ b/flowtorch/analysis/__init__.py
@@ -1,4 +1,5 @@
 from .psp_explorer import PSPExplorer
 from .pod import POD
 from .dmd import DMD
-from .svd import SVD
\ No newline at end of file
+from .svd import SVD
+from .svd import inexact_alm_matrix_complection
\ No newline at end of file
diff --git a/flowtorch/analysis/dmd.py b/flowtorch/analysis/dmd.py
index 700d537..d52e3d9 100644
--- a/flowtorch/analysis/dmd.py
+++ b/flowtorch/analysis/dmd.py
@@ -2,7 +2,7 @@
 """
 
 # standard library packages
-from typing import Tuple, Set
+from typing import Tuple, Set, Union
 # third party packages
 import torch as pt
 from numpy import pi
@@ -32,10 +32,14 @@ class DMD(object):
     tensor([-2.3842e-06, -4.2345e+01, -1.8552e+01])
     >>> dmd.amplitude
     tensor([10.5635+0.j, -0.0616+0.j, -0.0537+0.j])
+    >>> dmd = DMD(data_matrix, dt=0.1, rank=3, robust=True)
+    >>> dmd = DMD(data_matrix, dt=0.1, rank=3, robust={"tol": 1.0e-5, "verbose" : True})
 
     """
 
-    def __init__(self, data_matrix: pt.Tensor, dt: float, rank: int = None):
+    def __init__(self, data_matrix: pt.Tensor, dt: float, rank: int = None,
+                 robust: Union[bool, dict] = False, unitary: bool = False,
+                 optimal: bool = False, tlsq=False):
         """Create DMD instance based on data matrix and time step. 
 
         :param data_matrix: data matrix whose columns are formed by the individual snapshots
@@ -44,28 +48,93 @@ def __init__(self, data_matrix: pt.Tensor, dt: float, rank: int = None):
         :type dt: float
         :param rank: rank for SVD truncation, defaults to None
         :type rank: int, optional
+        :param robust: data_matrix is split into low rank and sparse contributions
+            if True or if dictionary with options for Inexact ALM algorithm; the SVD
+            is computed only on the low rank matrix
+        :type robust: Union[bool,dict]
+        :param unitary: enforce the linear operator to be unitary; refer to piDMD_
+            by Peter Baddoo for more information
+        :type unitary: bool, optional
+        :param optimal: compute mode amplitudes based on a least-squares problem
+            as described in spDMD_ article by M. Janovic et al. (2014); in contrast
+            to the original spDMD implementation, the exact DMD modes are used in
+            the optimization problem as outlined in an article_ by R. Taylor
+        :type optimal: bool, optional
+        :param tlsq: de-biasing of the linear operator by solving a total least-squares
+            problem instead of a standard least-squares problem; the rank is selected
+            automatically or specified by the `rank` parameter; more information can be
+            found in the TDMD_ article by M. Hemati et al.
+        :type tlsq: bool, optional
+
+
+        .. _piDMD: https://github.com/baddoo/piDMD
+        .. _spDMD: https://hal-polytechnique.archives-ouvertes.fr/hal-00995141/document
+        .. _article: http://www.pyrunner.com/weblog/2016/08/03/spdmd-python/
+        .. _TDMD: http://cwrowley.princeton.edu/papers/Hemati-2017a.pdf
         """
         self._dm = data_matrix
         self._dt = dt
-        self._svd = SVD(self._dm[:, :-1], rank)
+        self._unitary = unitary
+        self._optimal = optimal
+        self._tlsq = tlsq
+        if self._tlsq:
+            svd = SVD(pt.vstack((self._dm[:, :-1], self._dm[:, 1:])),
+                      rank, robust)
+            P = svd.V @ svd.V.conj().T
+            self._X = self._dm[:, :-1] @ P
+            self._Y = self._dm[:, 1:] @ P
+            self._svd = SVD(self._X, svd.rank)
+            del svd
+        else:
+            self._svd = SVD(self._dm[:, :-1], rank, robust)
+            self._X = self._dm[:, :-1]
+            self._Y = self._dm[:, 1:]
         self._eigvals, self._eigvecs, self._modes = self._compute_mode_decomposition()
+        self._amplitude = self._compute_amplitudes()
+
+    def _compute_operator(self):
+        """Compute the approximate linear (DMD) operator.
+        """
+        if self._unitary:
+            Xp = self._svd.U.conj().T @ self._X
+            Yp = self._svd.U.conj().T @ self._Y
+            U, _, VT = pt.linalg.svd(Yp @ Xp.conj().T, full_matrices=False)
+            return U @ VT
+        else:
+            s_inv = pt.diag(1.0 / self._svd.s)
+            return self._svd.U.conj().T @ self._Y @ self._svd.V @ s_inv
 
     def _compute_mode_decomposition(self):
-        """Compute reduced operator, eigen decomposition, and DMD modes.
+        """Compute reduced operator, eigen-decomposition, and DMD modes.
         """
         s_inv = pt.diag(1.0 / self._svd.s)
-        operator = (
-            self._svd.U.conj().T @ self._dm[:, 1:] @ self._svd.V @ s_inv
-        )
+        operator = self._compute_operator()
         val, vec = pt.linalg.eig(operator)
-        # type conversion is currently not implemented for pt.complex32
-        # such that the dtype for the modes is always pt.complex64
         phi = (
-            self._dm[:, 1:].type(val.dtype) @ self._svd.V.type(val.dtype)
+            self._Y.type(val.dtype) @ self._svd.V.type(val.dtype)
             @ s_inv.type(val.dtype) @ vec
         )
         return val, vec, phi
 
+    def _compute_amplitudes(self):
+        """Compute amplitudes for exact DMD modes.
+
+        If *optimal* is False, the amplitudes are computed based on the first
+        snapshot in the data matrix; otherwise, a least-squares problem as
+        introduced by Janovic et al. is solved (refer to the documentation
+        in the constructor for more information).
+        """
+        if self._optimal:
+            vander = pt.vander(self.eigvals, self._dm.shape[-1], True)
+            P = (self.modes.conj().T @ self.modes) * \
+                (vander @ vander.conj().T).conj()
+            q = pt.diag(vander @ self._dm.type(P.dtype).conj().T @
+                        self.modes).conj()
+        else:
+            P = self._modes
+            q = self._X[:, 0].type(P.dtype)
+        return pt.linalg.lstsq(P, q).solution
+
     def partial_reconstruction(self, mode_indices: Set[int]) -> pt.Tensor:
         """Reconstruct data matrix with limited number of modes.
 
@@ -79,11 +148,30 @@ def partial_reconstruction(self, mode_indices: Set[int]) -> pt.Tensor:
         mode_indices = pt.tensor(list(mode_indices), dtype=pt.int64)
         mode_mask[mode_indices] = 1.0
         reconstruction = (self.modes * mode_mask) @ self.dynamics
-        if self._dm.dtype in (pt.complex64, pt.complex32):
+        if self._dm.dtype in (pt.complex128, pt.complex64, pt.complex32):
             return reconstruction.type(self._dm.dtype)
         else:
             return reconstruction.real.type(self._dm.dtype)
 
+    def top_modes(self, n: int = 10, integral: bool = False) -> pt.Tensor:
+        """Get the indices of the first n most important modes.
+
+        Note that the conjugate complex modes for real data matrices are
+        not filtered out.
+
+        :param n: number of indices to return; defaults to 10
+        :type n: int
+        :param integral: if True, the modes are sorted according to their
+            integral contribution; defaults to False
+        :type integral: bool, optional
+        :return: indices of top n modes sorted by amplitude or integral
+            contribution
+        :rtype: pt.Tensor
+        """
+        importance = self.integral_contribution if integral else self.amplitude
+        n = min(n, importance.shape[0])
+        return importance.abs().topk(n).indices
+
     @property
     def required_memory(self) -> int:
         """Compute the memory size in bytes of the DMD.
@@ -101,6 +189,10 @@ def required_memory(self) -> int:
     def svd(self) -> SVD:
         return self._svd
 
+    @property
+    def operator(self) -> pt.Tensor:
+        return self._compute_operator()
+
     @property
     def modes(self) -> pt.Tensor:
         return self._modes
@@ -123,12 +215,20 @@ def growth_rate(self) -> pt.Tensor:
 
     @property
     def amplitude(self) -> pt.Tensor:
-        return pt.linalg.pinv(self._modes) @ self._dm[:, 0].type(self._modes.dtype)
+        return self._amplitude
 
     @property
     def dynamics(self) -> pt.Tensor:
         return pt.diag(self.amplitude) @ pt.vander(self.eigvals, self._dm.shape[-1], True)
 
+    @property
+    def integral_contribution(self) -> pt.Tensor:
+        """Integral contribution of individual modes according to J. Kou et al. 2017.
+
+        DOI: https://doi.org/10.1016/j.euromechflu.2016.11.015
+        """
+        return self.modes.norm(dim=0)**2 * self.dynamics.abs().sum(dim=1)
+
     @property
     def reconstruction(self) -> pt.Tensor:
         """Reconstruct an approximation of the training data.
@@ -136,11 +236,45 @@ def reconstruction(self) -> pt.Tensor:
         :return: reconstructed training data
         :rtype: pt.Tensor
         """
-        if self._dm.dtype in (pt.complex64, pt.complex32):
+        if self._dm.dtype in (pt.complex128, pt.complex64, pt.complex32):
             return (self._modes @ self.dynamics).type(self._dm.dtype)
         else:
             return (self._modes @ self.dynamics).real.type(self._dm.dtype)
 
+    @property
+    def reconstruction_error(self) -> pt.Tensor:
+        """Compute the reconstruction error.
+
+        :return: difference between reconstruction and data matrix
+        :rtype: pt.Tensor
+        """
+        return self.reconstruction - self._dm
+
+    @property
+    def projection_error(self) -> pt.Tensor:
+        """Compute the difference between Y and AX.
+
+        :return: projection error
+        :rtype: pt.Tensor
+        """
+        YH = (self.modes @ pt.diag(self.eigvals)) @ \
+            (pt.linalg.pinv(self.modes) @ self._X.type(self.modes.dtype))
+        if self._Y.dtype in (pt.complex128, pt.complex64, pt.complex32):
+            return YH - self._Y
+        else:
+            return YH.real.type(self._Y.dtype) - self._Y
+
+    @property
+    def tlsq_error(self) -> Tuple[pt.Tensor, pt.Tensor]:
+        """Compute the *noise* in X and Y.
+
+        :return: noise in X and Y
+        :rtype: Tuple[pt.Tensor, pt.Tensor]
+        """
+        if not self._tlsq:
+            print("Warning: noise is only removed if tlsq=True")
+        return self._dm[:, :-1] - self._X, self._dm[:, 1:] - self._Y
+
     def __repr__(self):
         return f"{self.__class__.__qualname__}(data_matrix, rank={self._svd.rank})"
 
diff --git a/flowtorch/analysis/svd.py b/flowtorch/analysis/svd.py
index 3f7b07f..ad2aa4e 100644
--- a/flowtorch/analysis/svd.py
+++ b/flowtorch/analysis/svd.py
@@ -1,12 +1,95 @@
 """Classes and functions wrapping around *torch.linalg.svd*.
 """
 
+# standard library packages
+from math import sqrt
+from typing import Tuple, Union
 # third party packages
 import torch as pt
 # flowtorch packages
 from flowtorch.data.utils import format_byte_size
 
 
+def inexact_alm_matrix_complection(data_matrix: pt.Tensor, sparsity: float = 1.0,
+                                   tol: float = 1.0e-6, max_iter: int = 100,
+                                   verbose: bool = False) -> Tuple[pt.Tensor, pt.Tensor]:
+    """Split a data matrix in low rank and sparse contributions.
+
+    This function implements the *inexact augmented Lagrange multiplier
+    matrix completion* algorithm to solve the *principal component pursuit*
+    problem. The implementation is based on the Matlab code of Isabel Scherl
+    (link_), which is in turn based on the LRSLibrary_.
+
+    .. _link: https://github.com/ischerl/RPCA-PIV/blob/master/functions/inexact_alm_rpca.m
+    .. _LRSLibrary: https://github.com/andrewssobral/lrslibrary
+
+    :param data_matrix: input data matrix; snapshots must be
+        organized as column vectors
+    :type data_matrix: pt.Tensor
+    :param sparsity: factor to compute Lagrangian multiplyer for sparsity
+        (typically named *lambda*); lower values lead to more agressive
+        filtering
+    :type sparsity: float, optional
+    :param tol: tolerance for the normalized Frobenius norm of the difference
+        between original data and the sum of low rank and sparse contributions;
+        defaults to 1.0e-6
+    :type tol: float, optional
+    :param max_iter: maximum number of iteration before to give up, defaults to 100
+    :type max_iter: int, optional
+    :param verbose: residual is printed for every iteration if True;
+        defaults to False
+    :type verbose: bool, optional
+    :return: tuple holding the low rank and the sparse matrices
+    :rtype: Tuple[pt.Tensor, pt.Tensor]
+    """
+    row, col = data_matrix.shape
+    lambda_0 = sparsity / sqrt(row)
+    # low rank and sparse matices
+    L, S = pt.zeros_like(data_matrix), pt.zeros_like(data_matrix)
+    # matrix of Lagrange multipliers
+    Y = data_matrix.detach().clone()
+    norm_two = pt.linalg.svdvals(Y)[0].item()
+    norm_inf = pt.linalg.norm(Y, float("inf")).item()
+    dual_norm = max(norm_two, norm_inf)
+    Y /= dual_norm
+    # more hyperparameters
+    mu = 1.25 / norm_two
+    norm_data = pt.linalg.norm(data_matrix)
+    sv = 10
+    rho = 1.5
+
+    for i in range(max_iter):
+        temp = data_matrix - L + Y/mu
+        S = pt.maximum(temp - lambda_0/mu, pt.tensor(0.0))
+        S += pt.minimum(temp + lambda_0/mu, pt.tensor(0.0))
+        U, s, VH = pt.linalg.svd(data_matrix - S + Y/mu, full_matrices=False)
+        # truncate SVD
+        svp = s[s > 1.0/mu].shape[0]
+        if svp < sv:
+            sv = min(svp+1, col)
+        else:
+            sv = min(svp + round(0.05*col), col)
+        L = U[:, :svp] @ pt.diag(s[:svp] - 1.0/mu) @ VH[:svp, :]
+        # print(L[0,:])
+        # Z is the residual matrix
+        Z = data_matrix - L - S
+        # update Lagrange multipliers
+        Y += mu*Z
+        mu = min(mu*rho, mu*1.0e7)
+        # check convergence
+        residual = pt.linalg.norm(Z) / norm_data
+        if residual < tol:
+            print(f"Inexact ALM converged after {i+1} iterations")
+            print("Final residual: {:2.4e}".format(residual))
+            return L, S
+        if verbose:
+            print("Residual after iteration {:5d}: {:10.4e}".format(
+                i+1, residual.item()))
+    print(f"Inexact ALM did not converge within {max_iter} iterations")
+    print("Final residual: {:10.4e}".format(residual))
+    return L, S
+
+
 class SVD(object):
     """Compute and analyze the SVD of a data matrix.
 
@@ -20,6 +103,14 @@ class SVD(object):
     :type s_cum: pt.Tensor
     :param V: right singular values
     :type V: pt.Tensor
+    :param L: low rank contribution to data matrix
+    :type L: pt.Tensor
+    :param S: sparse contribution to data matrix
+    :type S: pt.Tensor
+    :param robust: data_matrix is split into low rank and sparse contributions
+        if True or if dictionary with options for Inexact ALM algorithm; the SVD
+        is computed only on the low rank matrix; defaults to False
+    :type robust: Union[bool,dict]
     :param rank: rank used for truncation
     :type rank: int
     :param opt_rank: optimal rank according to SVHT
@@ -46,17 +137,34 @@ class SVD(object):
     tensor([ 99.9687,  99.9996,  99.9999, 100.0000, 100.0000])
     >>> svd.U.shape
     torch.Size([400, 5])
+    >>> svd = SVD(data, rank=100, robust=True)
+    >>> svd.L.shape
+    torch.Size([400, 5])
+    >>> svd = SVD(data, rank=100, robust={"sparsity" : 1.0, "verbose" : True, "max_iter" : 100})
+    >>> svd.S.shape
+    torch.Size([400, 5])
 
     """
 
-    def __init__(self, data_matrix: pt.Tensor, rank: int = None):
+    def __init__(self, data_matrix: pt.Tensor, rank: int = None,
+                 robust: Union[bool, dict] = False):
         shape = data_matrix.shape
         assert len(shape) == 2, (
             f"The data matrix must be a 2D tensor.\
             The provided data matrix has {len(shape)} dimensions."
         )
         self._rows, self._cols = shape
-        U, s, VH = pt.linalg.svd(data_matrix, full_matrices=False)
+        self._robust = robust
+        if bool(self._robust):
+            if isinstance(robust, dict):
+                L, S = inexact_alm_matrix_complection(data_matrix, **robust)
+            else:
+                L, S = inexact_alm_matrix_complection(data_matrix)
+            self._L, self._S = L, S
+            U, s, VH = pt.linalg.svd(L, full_matrices=False)
+        else:
+            self._L, self._S = None, None
+            U, s, VH = pt.linalg.svd(data_matrix, full_matrices=False)
         self._opt_rank = self._optimal_rank(s)
         self.rank = self.opt_rank if rank is None else rank
         self._U = U[:, :self.rank]
@@ -111,7 +219,8 @@ def s_rel(self) -> pt.Tensor:
     def s_cum(self) -> pt.Tensor:
         s_sum = self._s.sum().item()
         return pt.tensor(
-            [self._s[:i].sum().item() / s_sum * 100.0 for i in range(1, self._s.shape[0]+1)],
+            [self._s[:i].sum().item() / s_sum *
+             100.0 for i in range(1, self._s.shape[0]+1)],
             dtype=self._s.dtype
         )
 
@@ -119,6 +228,18 @@ def s_cum(self) -> pt.Tensor:
     def V(self) -> pt.Tensor:
         return self._V
 
+    @property
+    def L(self) -> pt.Tensor:
+        return self._L
+
+    @property
+    def S(self) -> pt.Tensor:
+        return self._S
+
+    @property
+    def robust(self) -> Union[bool, dict]:
+        return self._robust
+
     @property
     def rank(self) -> int:
         return self._rank
@@ -126,7 +247,6 @@ def rank(self) -> int:
     @rank.setter
     def rank(self, value: int):
         self._rank = max(min(self._cols, value), 1)
-        self._cols = self._rank
 
     @property
     def opt_rank(self) -> int:
diff --git a/flowtorch/analysis/test_dmd.py b/flowtorch/analysis/test_dmd.py
index 1387133..234f64f 100644
--- a/flowtorch/analysis/test_dmd.py
+++ b/flowtorch/analysis/test_dmd.py
@@ -27,13 +27,46 @@ def test_DMD():
     assert dmd.amplitude.dtype == pt.complex64
     assert dmd.dynamics.shape == (rank, data.shape[-1])
     assert dmd.dynamics.dtype == pt.complex64
+    assert dmd.integral_contribution.shape == (rank,)
+    assert dmd.integral_contribution.dtype == pt.float32
     assert dmd.reconstruction.shape == data.shape
     assert dmd.reconstruction.dtype == data.dtype
     partial = dmd.partial_reconstruction({0})
     assert partial.dtype == data.dtype
     assert partial.shape == data.shape
-    parital = dmd.partial_reconstruction({0, 2})
+    partial = dmd.partial_reconstruction({0, 2})
     assert partial.dtype == data.dtype
     assert partial.shape == data.shape
+    top = dmd.top_modes(10)
+    top = dmd.top_modes(10, True)
+    assert top.shape == (min(rank, 10),)
+    assert top.dtype == pt.int64
+    assert dmd.reconstruction_error.shape == data.shape
+    assert dmd.projection_error.shape == (data.shape[0], data.shape[1] - 1)
+    # robust DMD
+    dmd = DMD(data, dt=0.1, rank=rank, robust=True)
+    assert dmd.svd.L.shape == (data.shape[0], rank+1)
+    # unitary operator
+    dmd = DMD(data, dt=0.1, rank=rank, unitary=True)
+    operator = dmd.operator
+    shape = operator.shape
+    assert shape == (rank, rank)
+    diag = operator.conj().T @ operator
+    assert pt.allclose(diag, pt.diag(pt.ones(rank)), atol=1e-6)
+    # optimal mode amplitudes
+    dmd = DMD(data, dt=0.1, rank=rank, optimal=True)
+    dmd = DMD(data, dt=0.1, rank=rank, unitary=True, optimal=True)
+    assert dmd.amplitude.shape == (rank,)
+    assert dmd.amplitude.dtype == pt.complex64
+    # total least-squares
+    dmd = DMD(data, dt=0.1, tlsq=True)
+    assert dmd.amplitude.dtype == pt.complex64
+    dmd = DMD(data, dt=0.1, rank=rank, optimal=True, tlsq=True)
+    assert dmd.amplitude.shape == (rank,)
+    DX, DY = dmd.tlsq_error
+    assert  DX.shape == (data.shape[0], data.shape[1] - 1)
+    assert  DY.shape == (data.shape[0], data.shape[1] - 1)
+
+
 
 
diff --git a/flowtorch/analysis/test_svd.py b/flowtorch/analysis/test_svd.py
index 89d9068..239d0f4 100644
--- a/flowtorch/analysis/test_svd.py
+++ b/flowtorch/analysis/test_svd.py
@@ -4,7 +4,17 @@
 # flowtorch packages
 from flowtorch import DATASETS
 from flowtorch.data import FOAMDataloader
-from flowtorch.analysis import SVD
+from flowtorch.analysis import SVD, inexact_alm_matrix_complection
+
+
+def create_noisy_low_rank_data():
+    pt.manual_seed(0)
+    L = pt.ones((100, 20)) * pt.randint(-5, 5, (20,))
+    L[:50, :] = pt.ones((50, 20)) * pt.randint(-5, 5, (20,))
+    S = 20 * pt.bernoulli(pt.ones_like(L)*0.1)
+    S -= 20 * pt.bernoulli(pt.ones_like(L)*0.1)
+    return L+S, L, S
+
 
 class TestSVD():
     def setup_method(self, test_method):
@@ -24,7 +34,6 @@ def test_init(self):
     def test_optimal_rank(self):
         svd = SVD(self.data)
         assert 1 <= svd.opt_rank <= self.cols
-        assert svd._cols == svd.rank
 
     def test_reconstruct(self):
         data = self.data.type(pt.float64)
@@ -33,4 +42,35 @@ def test_reconstruct(self):
         assert pt.allclose(data, svd.reconstruct(self.cols*2))
         err_r1 = pt.linalg.norm(data - svd.reconstruct(rank=1)).item()
         err_r2 = pt.linalg.norm(data - svd.reconstruct(rank=2)).item()
-        assert err_r2 <= err_r1
\ No newline at end of file
+        assert err_r2 <= err_r1
+
+    def test_robust(self):
+        X, low, noise = create_noisy_low_rank_data()
+        # test if robust is True
+        svd = SVD(X, 20, True)
+        assert svd.robust
+        assert svd.S.shape == X.shape
+        assert svd.L.shape == X.shape
+        assert svd.U.shape == X.shape
+        assert pt.linalg.norm(low-svd.L) < 20.0
+        # test if robust is False/default
+        svd = SVD(X, 20)
+        assert not svd.robust
+        assert svd.L is None
+        assert svd.S is None
+        # test if empty dictionary is passed
+        svd = SVD(X, 20, robust={})
+        assert not svd.robust
+        # test passing arguments to Inexact ALM
+        svd = SVD(X, 20, robust={"sparsity": 1.0, "verbose": True})
+        assert bool(svd.robust)
+        assert pt.linalg.norm(low-svd.L) < 20.0
+
+
+def test_inexact_alm_matrix_completion():
+    X, low, noise = create_noisy_low_rank_data()
+    L, S = inexact_alm_matrix_complection(X)
+    assert L.shape == low.shape
+    assert S.shape == noise.shape
+    # very rough test to see is low rank tensor was found
+    assert pt.linalg.norm(low-L) < 20.0
diff --git a/flowtorch/data/__init__.py b/flowtorch/data/__init__.py
index debf469..8b90adb 100644
--- a/flowtorch/data/__init__.py
+++ b/flowtorch/data/__init__.py
@@ -1,7 +1,9 @@
 from .foam_dataloader import FOAMDataloader, FOAMCase, FOAMMesh
-from .hdf5_file import HDF5Dataloader, HDF5Writer, FOAM2HDF5, XDMFWriter
+from .hdf5_file import HDF5Dataloader, HDF5Writer, FOAM2HDF5, XDMFWriter, copy_hdf5_mesh
 from .csv_dataloader import CSVDataloader
 from .vtk_dataloader import VTKDataloader
 from .psp_dataloader import PSPDataloader
-from .tau_dataloader import TAUDataloader
-from .selection_tools import mask_box, mask_sphere
\ No newline at end of file
+from .tau_dataloader import TAUDataloader, TAUConfig
+from .tecplot_dataloader import TecplotDataloader
+from .selection_tools import mask_box, mask_sphere
+from .outlier_tools import iqr_outlier_replacement
\ No newline at end of file
diff --git a/flowtorch/data/csv_dataloader.py b/flowtorch/data/csv_dataloader.py
index 73ada0e..3802bc2 100644
--- a/flowtorch/data/csv_dataloader.py
+++ b/flowtorch/data/csv_dataloader.py
@@ -10,7 +10,8 @@
 settings automatically.
 """
 
-# standard library packages
+# standard library 
+from os.path import join
 from glob import glob
 import re
 from typing import List, Dict, Tuple, Union
@@ -33,8 +34,7 @@
 
 FOAM_SURFACE_KEYS = {
     VERTEX_KEY: ["x", "y", "z"],
-    WEIGHT_KEY: None,
-    FIELD_KEY: ["f", ]
+    WEIGHT_KEY: ["area_x", "area_y", "area_z"]
 }
 
 PANDAS_SKIPROWS = "skiprows"
@@ -55,6 +55,17 @@ def _parse_davis_header(header: str) -> List[str]:
     return re.findall('"([^"]*)"', header)
 
 
+def _parse_foam_surface_header(header: str) -> List[str]:
+    """Find column names in OpenFOAM *surfaces* output file.
+
+    :param header: header line containing column names
+    :type header: str
+    :return: list of column names
+    :rtype: List[str]
+    """
+    return list(filter(None, header.replace("#", "").strip().split(" ")))
+
+
 class CSVDataloader(Dataloader):
     """Load CSV files from different sources.
 
@@ -77,13 +88,13 @@ class CSVDataloader(Dataloader):
     torch.Size([3741, 5])
 
     >>> foam_data = DATASETS["csv_naca0012_alpha4_surface"]
-    >>> loader = CSVDataloader.from_foam_surface(foam_data, "total(p)_coeff_airfoil.raw", "cp")
+    >>> loader = CSVDataloader.from_foam_surface(foam_data, "total(p)_coeff_airfoil.raw")
     >>> times = loader.write_times
     >>> times[:5]
     ['0.001', '0.002', '0.003', '0.004', '0.005']
     >>> loader.field_names
-    {'0.001': ['cp']}
-    >>> snapshots = loader.load_snapshot("cp", times[:10])
+    {'0.001': ['total(p)_coeff']}
+    >>> snapshots = loader.load_snapshot("total(p)_coeff", times[:10])
     >>> snapshots.shape
     torch.Size([28892, 10])
     >>> vertices = loader.vertices
@@ -165,7 +176,7 @@ def from_davis(cls, path: str, prefix: str = "", suffix: str = ".dat", dtype: st
         return cls(path, prefix, suffix, read_options, False, dtype)
 
     @classmethod
-    def from_foam_surface(cls, path: str, file_name: str, field_name: str = None, dtype: str = DEFAULT_DTYPE):
+    def from_foam_surface(cls, path: str, file_name: str, header: int=1, dtype: str = DEFAULT_DTYPE):
         """Create CSVDataloader instance to load OpenFOAM surface sample data.
 
         The class method simplifies to load data generated by OpenFOAM's
@@ -178,24 +189,32 @@ def from_foam_surface(cls, path: str, file_name: str, field_name: str = None, dt
         :param file_name: file name of individual CSV files, e.g.,
             *p_airfoil.raw*
         :type file_name: str
-        :param field_name: each CSV contains only one field; this parameter
-            allows to prescribe a descriptive access name; defaults to `None`,
-            which chooses the generic field name *f*.
-        :type field_name: str
+        :param header: line number in which to find the column names (starting from 0);
+            defaults to 1
+        :type header: int, optional
         :param dtype: floating point precision; defaults to `pt.float32` (single precision)
         :type dtype: str
 
         """
-        field_key = [field_name] if field_name is not None else \
-            FOAM_SURFACE_KEYS[FIELD_KEY]
+        folders = glob(f"{path}/*")
+        times = sorted(
+            [folder.split("/")[-1] for folder in folders], key=float
+        )
+        filepath = join(path, times[0], file_name)
+        with open(filepath, "r") as surface:
+            content = surface.readlines()
+            column_names = _parse_foam_surface_header(content[header])
+
+        weight = FOAM_SURFACE_KEYS[WEIGHT_KEY] if all([key in column_names for key in FOAM_SURFACE_KEYS[WEIGHT_KEY]]) else None
         read_options = {
-            PANDAS_SKIPROWS: [0, 1],
+            PANDAS_SKIPROWS: list(range(header+1)),
             PANDAS_HEADER: None,
             PANDAS_SEP: " ",
-            PANDAS_NAMES: FOAM_SURFACE_KEYS[VERTEX_KEY] + field_key,
+            PANDAS_NAMES: column_names,
             VERTEX_KEY: FOAM_SURFACE_KEYS[VERTEX_KEY],
-            WEIGHT_KEY: FOAM_SURFACE_KEYS[WEIGHT_KEY],
-            FIELD_KEY: field_key
+            WEIGHT_KEY: weight,
+            FIELD_KEY: [col for col in column_names if col not in
+                FOAM_SURFACE_KEYS[VERTEX_KEY] + FOAM_SURFACE_KEYS[WEIGHT_KEY]]
         }
         return cls(path, file_name, "", read_options, True, dtype)
 
@@ -304,9 +323,14 @@ def weights(self) -> pt.Tensor:
         weight_key = self._read_options[WEIGHT_KEY]
         snapshot = self._load_csv(self._write_times[0])
         if weight_key is not None:
-            return pt.tensor(
-                snapshot[weight_key].values, dtype=self._dtype
-            )
+            if isinstance(weight_key, list):
+                return pt.tensor(
+                    snapshot[weight_key].values, dtype=self._dtype
+                ).norm(dim=1)
+            else:
+                return pt.tensor(
+                    snapshot[weight_key].values, dtype=self._dtype
+                )
         else:
             shape = snapshot[self._read_options[FIELD_KEY]].values.shape
             return pt.ones(shape[0], dtype=self._dtype)
diff --git a/flowtorch/data/foam_dataloader.py b/flowtorch/data/foam_dataloader.py
index 4a59c1f..d2da801 100644
--- a/flowtorch/data/foam_dataloader.py
+++ b/flowtorch/data/foam_dataloader.py
@@ -73,16 +73,21 @@ class FOAMDataloader(Dataloader):
 
     """
 
-    def __init__(self, path: str, dtype: str = DEFAULT_DTYPE):
+    def __init__(self, path: str, dtype: str = DEFAULT_DTYPE,
+                 distributed: bool = None):
         """Create a FOAMDataloader instance from a path.
 
         :param path: path to an OpenFOAM simulation folder.
         :type path: str
         :param dtype: tensor type; default is single precision, `torch.float32`
         :type dtype: str
+        :param distributed: case is considered distributed if True; if None,
+            the case type (parallel/serial) is determined automatically;
+            defaults to None
+        :type distributed: bool 
 
         """
-        self._case = FOAMCase(path)
+        self._case = FOAMCase(path, distributed)
         self._mesh = FOAMMesh(self._case)
         self._dtype = dtype
 
@@ -320,21 +325,27 @@ class FOAMCase(object):
     .. automethod:: _eval_field_names
     """
 
-    def __init__(self, path: str):
+    def __init__(self, path: str, distributed: bool = None):
         """Create a `FOAMCase` instance based on a path.
 
         :param path: path to OpenFOAM simulation case
         :type path: str
+        :param distributed: case is considered distributed if True; if None,
+            the presence of processor folders is checked to evaluate the
+            parameter; defaults to False
+        :type distributed: bool
 
         """
         self._path = check_and_standardize_path(path)
-        self._distributed = self._eval_distributed()
+
+        self._distributed = distributed if distributed is not None \
+            else self._eval_distributed()
         self._processors = self._eval_processors()
         self._time_folders = self._eval_write_times()
         self._field_names = self._eval_field_names()
         if not self._check_mesh_files():
             sys.exit("Error: could not find valid mesh in case {:s}".format(
-                self._case._path))
+                self._path))
 
     def _is_binary(self, header: List[str]) -> bool:
         """Determine if the write format is binary.
@@ -904,7 +915,7 @@ def _compute_cell_centers_and_volumes(self, mesh_path: str) -> Tuple[pt.Tensor,
         :return: tuple of two tensors; the first one holds the cell centers and
             the second one holds the cell volumes
         :rtype: Tuple[pt.Tensor, pt.Tensor]
-        
+
         """
         points = self._parse_points(mesh_path)
         n_points_faces, faces = self._parse_faces(mesh_path)
diff --git a/flowtorch/data/hdf5_file.py b/flowtorch/data/hdf5_file.py
index 5fc55a6..4cba09b 100644
--- a/flowtorch/data/hdf5_file.py
+++ b/flowtorch/data/hdf5_file.py
@@ -9,7 +9,7 @@
 """
 
 # standard library packages
-from os.path import isfile, exists
+from os.path import isfile, exists, join
 from os import remove
 from typing import List, Tuple, Dict, Union
 import sys
@@ -20,7 +20,7 @@
 from flowtorch import DEFAULT_DTYPE
 from .dataloader import Dataloader
 from .foam_dataloader import FOAMCase, FOAMMesh, FOAMDataloader, POLYMESH_PATH, MAX_LINE_HEADER, FIELD_TYPE_DIMENSION
-from .utils import check_list_or_str
+from .utils import check_list_or_str, check_and_standardize_path
 
 
 CONST_GROUP = "constant"
@@ -107,21 +107,21 @@ def load_snapshot(self, field_name: Union[List[str], str],
         if isinstance(field_name, list):
             if isinstance(time, list):
                 return [pt.stack([pt.tensor(
-                        self._file[f"{VAR_GROUP}/{t}/{field}"][:], dtype=self._dtype
+                        self._file[f"{VAR_GROUP}/{t}/{field}"][:].copy(), dtype=self._dtype
                         ).squeeze() for t in time], dim=-1) for field in field_name]
             else:
                 return [pt.tensor(
-                        self._file[f"{VAR_GROUP}/{time}/{field}"][:], dtype=self._dtype
+                        self._file[f"{VAR_GROUP}/{time}/{field}"][:].copy(), dtype=self._dtype
                         ).squeeze() for field in field_name]
         # load single field
         else:
             if isinstance(time, list):
                 return pt.stack([pt.tensor(
-                    self._file[f"{VAR_GROUP}/{t}/{field_name}"][:], dtype=self._dtype
+                    self._file[f"{VAR_GROUP}/{t}/{field_name}"][:].copy(), dtype=self._dtype
                 ).squeeze() for t in time], dim=-1)
             else:
                 return pt.tensor(
-                    self._file[f"{VAR_GROUP}/{time}/{field_name}"][:], dtype=self._dtype
+                    self._file[f"{VAR_GROUP}/{time}/{field_name}"][:].copy(), dtype=self._dtype
                 ).squeeze()
 
     @property
@@ -148,6 +148,18 @@ def weights(self) -> pt.Tensor:
             self._file[f"{CONST_GROUP}/{VOLUMES_DS}"][:], dtype=self._dtype
         )
 
+    @property
+    def connectivity(self) -> pt.Tensor:
+        return pt.tensor(
+            self._file[f"{CONST_GROUP}/{CONNECTIVITY_DS}"][:], dtype=self._dtype
+        )
+
+    @property
+    def edge_vertices(self) -> pt.Tensor:
+        return pt.tensor(
+            self._file[f"{CONST_GROUP}/{VERTICES_DS}"][:], dtype=self._dtype
+        )
+
 
 class HDF5Writer(object):
     """Class to write flowTorch data to HDF5 file.
@@ -244,7 +256,7 @@ class FOAM2HDF5(object):
     >>> from flowtorch import DATASETS
     >>> from flowtorch.data import FOAM2HDF5
     >>> converter = FOAM2HDF5(DATASETS["of_cavity_ascii"])
-    >>> converter.convert("cavity.hdf5", skip_zero=True)
+    >>> converter.convert("cavity.hdf5", ["U", "p"], ["0.1", "0.2", "0.3"])
 
     """
 
@@ -256,21 +268,28 @@ def __init__(self, path: str, dtype=DEFAULT_DTYPE):
         :param dtype: tensor type, defaults to DEFAULT_DTYPE
         :type dtype: str, optional
         """
-        self._loader = FOAMDataloader(path, dtype)
+        self._loader = FOAMDataloader(path, dtype, False)
         self._dtype = dtype
         self._topology = None
         self._mesh_points = None
 
-    def convert(self, filename: str, skip_zero: bool = True):
+    def convert(self, filename: str, fields: List[str] = None,
+                times: List[str] = None):
         """Convert OpenFOAM case to flowTorch HDF5 file.
 
         :param filename: name of the HDF5 file
         :type filename: str
-        :param skip_zero: skip zero folder if true; defaults to True
-        :type skip_zero: bool, optional
+        :param fields: list of fields to convert; if None, all available
+            fields are converted
+        :type fields: List[str], optional
+        :param times: list of times to convert; if None, all available
+            times are converted
+        :type times: List[str], optional
         """
         file_path = self._loader._case._path + "/" + filename
         self._remove_file_if_present(file_path)
+        # this is currently redundant since the loader is initialized
+        # with distributed set to False
         if self._loader._case._distributed:
             message = """The direct conversion of distributed cases is currently not supported.\n
 Workaround:
@@ -286,7 +305,7 @@ def convert(self, filename: str, skip_zero: bool = True):
             print("Converting mesh.")
             self._convert_mesh(writer)
             print("Converting fields.")
-            self._convert_fields(writer, skip_zero)
+            self._convert_fields(writer, fields, times)
             print("Conversion finished. Writing XDMF file.")
             writer.write_xdmf()
 
@@ -369,22 +388,28 @@ def _get_cell_centers(self, job: int = 0):
     def _get_cell_volumes(self, job: int = 0):
         return self._loader._mesh.get_cell_volumes()
 
-    def _convert_fields(self, writer: HDF5Writer, skip_zero: bool):
+    def _convert_fields(self, writer: HDF5Writer, fields: List[str],
+                        times: List[str]):
         """Convert convert OpenFOAM fields to HDF5.
 
         :param writer: HDF5 writer
         :type writer: :class:`HDF5Writer`
-        :param skip_zero: skip zero folder if true
-        :type skip_zero: bool
+        :param fields: list of fields to convert; if None, all available
+            fields are converted
+        :type fields: List[str]
+        :param times: list of times to convert; if None, all available
+            times are converted
+        :type times: List[str]
         """
-        field_info = self._gather_field_information(skip_zero)
+        field_info = self._gather_field_information(fields, times)
         for job, info in enumerate(field_info):
             print(
                 f"Converting field {info[0]} at time {info[1]}, dimension {info[2]}")
             data = self._load_field(*info[:2], job=job)
             writer.write(info[0], info[2], data, info[1])
 
-    def _gather_field_information(self, skip_zero: bool) -> List[list]:
+    def _gather_field_information(self, fields: List[str], times: List[str]
+                                  ) -> List[tuple]:
         """Gather field information for parallel writing.
 
         - check if field type is supported
@@ -392,9 +417,9 @@ def _gather_field_information(self, skip_zero: bool) -> List[list]:
 
         :param skip_zero: skip zero folder if true
         :type skip_zero: bool
-        :return: list of all fields; each list element is a list
-            with the entries [name, time, shape]
-        :rtype: list
+        :return: list of all fields; each list element is a tuple
+            with the entries (name, time, shape)
+        :rtype: List[tuple]
         """
         def load_n_lines(file_name, n):
             lines = []
@@ -406,17 +431,21 @@ def load_n_lines(file_name, n):
         field_info = []
         mesh_path = self._loader._case._path + "/" + POLYMESH_PATH
         n_cells = self._loader._mesh._get_n_cells(mesh_path)
-        all_fields = self._loader.field_names
-        if skip_zero and "0" in all_fields.keys():
-            del all_fields["0"]
-        for time in all_fields.keys():
-            for name in all_fields[time]:
+        times_to_convert = self._loader.write_times
+        if times is not None:
+            times_to_convert = [t for t in times if t in times_to_convert]
+        for time in times_to_convert:
+            fields_to_convert = self._loader.field_names[time]
+            if fields is not None:
+                fields_to_convert = [
+                    field for field in fields if field in fields_to_convert]
+            for name in fields_to_convert:
                 path = self._loader._case.build_file_path(name, time)
                 header = load_n_lines(path, MAX_LINE_HEADER)
                 field_type = self._loader._field_type(header)
                 if field_type in FIELD_TYPE_DIMENSION.keys():
                     field_info.append(
-                        [name, time, (n_cells, FIELD_TYPE_DIMENSION[field_type])])
+                        (name, time, (n_cells, FIELD_TYPE_DIMENSION[field_type])))
         return field_info
 
     def _load_field(self, field: str, time: str, job: int = 0) -> pt.Tensor:
@@ -437,7 +466,7 @@ class XDMFWriter(object):
     def __init__(self, file_path: str, hdf5_file: File):
         """Create XDMFWriter instance from path and file.
 
-        :param file_path: path to DHF5 file
+        :param file_path: path to HDF5 file
         :type file_path: str
         :param hdf5_file: HDF5 file instance
         :type hdf5_file: File
@@ -607,7 +636,8 @@ def create_xdmf(self, filename: str = None):
         :type filename: str, optional
         """
         xdmf_str = XDMF_HEADER
-        times = list(self._file[VAR_GROUP].keys())
+        times = list(self._file[VAR_GROUP].keys()
+                     ) if VAR_GROUP in self._file else []
         if len(times) > 0:
             for time in times:
                 xdmf_str += self._add_grid(time, " "*12)
@@ -629,3 +659,37 @@ def create_xdmf(self, filename: str = None):
         )
         with open(self._path + "/" + filename, "w") as file:
             file.write(xdmf_str)
+
+
+def copy_hdf5_mesh(path: str, from_file: str, to_file: str):
+    """Create a copy of an flowTorch hdf5 file containing only the mesh.
+
+    Sometimes, it is helpul to create a new copy of an existing hdf5 file
+    that contains only the mesh, e.g., to create a separate file for
+    POD or DMD modes.
+
+    :param path: location of the flowtorch hdf5 file exclusing the filename
+    :type path: str
+    :param from_file: name of the file from which to copy the mesh
+    :type from_file: str
+    :param to_file: name of the file to which to copy the mesh
+    :type to_file: str
+    """
+    path = check_and_standardize_path(path)
+    from_file_path = join(path, from_file)
+    loader = HDF5Dataloader(from_file_path)
+    to_file_path = join(path, to_file)
+    print(f"Copying mesh from file {from_file_path} to {to_file_path}")
+    writer = HDF5Writer(to_file_path)
+    datasets = {
+        VERTICES_DS: loader.edge_vertices,
+        CENTERS_DS: loader.vertices,
+        VOLUMES_DS: loader.weights,
+        CONNECTIVITY_DS: loader.connectivity
+    }
+    for ds_key in datasets.keys():
+        data = datasets[ds_key]
+        writer.write(ds_key, data.shape, data, None, data.dtype)
+    xdmf_name = f"{to_file.split('.')[0]}.xdmf"
+    xdmf = XDMFWriter.from_filepath(to_file_path)
+    xdmf.create_xdmf(xdmf_name)
diff --git a/flowtorch/data/outlier_tools.py b/flowtorch/data/outlier_tools.py
new file mode 100644
index 0000000..39b6840
--- /dev/null
+++ b/flowtorch/data/outlier_tools.py
@@ -0,0 +1,55 @@
+"""Helper tools to detect and replace outliers in time series data.
+"""
+
+# standard library packages
+from typing import Callable
+# third party packages
+import torch as pt
+
+
+def iqr_outlier_replacement(data: pt.Tensor, k: float = 1.5, nb: int = 3,
+                            replace: Callable = pt.median) -> pt.Tensor:
+    """Detect and replace outliers based on the inter quantile range (IRQ).
+
+    :param data: time series data; time is expected to be the last dimension
+    :type data: pt.Tensor
+    :param k: factor controlling the detection sensitivity; smaller values
+        increase the sensitivity; defaults to 1.5
+    :type k: float, optional
+    :param nb: number of neighboring points in time to consider when replacing
+        an outlier; points in the range i-nb:i+nb are considered for each
+        outlier i; defaults to 3
+    :type nb: int, optional
+    :param replace: function mapping the neighboring values to the value with
+        which to replace the outlier, defaults to pt.median
+    :type replace: Callable, optional
+    :return: clean dataset with the same shape as the input data
+    :rtype: pt.Tensor
+    """
+    initial_shape = data.shape
+    if len(initial_shape) > 2:
+        data = data.flatten(start_dim=0, end_dim=-2)
+    elif len(initial_shape) == 1:
+        data = data.unsqueeze(-1).T
+    shape = data.shape
+    q25, q75 = pt.quantile(data, 0.25, dim=-1), pt.quantile(data, 0.75, dim=-1)
+    iqr_k = (q75 - q25) * k
+    outliers_low = data < (q25-iqr_k).unsqueeze(-1)
+    outliers_high = data > (q75+iqr_k).unsqueeze(-1)
+    outlier_indices = pt.logical_or(
+        outliers_low, outliers_high).nonzero(as_tuple=True)
+    clean_data = data.clone().detach()
+    print("Detected {:d} outliers ({:3.2f}%).".format(
+        outlier_indices[0].shape[0],
+        outlier_indices[0].shape[0] / (data.shape[0]*data.shape[1]) * 100
+    ))
+    if outlier_indices[0].shape[0] == 0:
+        print("Nothing to do ...")
+    else:
+        print("Start to replace outliers ...")
+    for row, col in zip(*outlier_indices):
+        i, j = row.item(), col.item()
+        clean_data[i, j] = replace(
+            data[i, max(0, j-nb):min(shape[-1], j+nb+1)])
+    data = data.reshape(initial_shape)
+    return clean_data.reshape(initial_shape)
diff --git a/flowtorch/data/psp_dataloader.py b/flowtorch/data/psp_dataloader.py
index 8690c90..3ec2ef6 100644
--- a/flowtorch/data/psp_dataloader.py
+++ b/flowtorch/data/psp_dataloader.py
@@ -27,7 +27,6 @@
 INFO_KEY = "Info"
 PARAMETER_KEY = "Parameter"
 DESCRIPTION_KEY = "ParameterDescription"
-WEIGHT_KEY = "Mask"
 TIME_KEY = "TimeValues"
 FIELDS = {
     "Cp": "Images"
@@ -60,6 +59,9 @@ class PSPDataloader(Dataloader):
     >>> loader.zone = "Zone0001"
     >>> loader.zone_info["ZoneName"]
     HTP
+    >>> loader.mask_names
+    ['Mask1', "Mask2"]
+    >>> loader.mask = "Mask2"
     >>> cp = loader.load_snapshot("Cp", loader.write_times[:10])
     >>> cp.shape
     torch.Size([250, 75, 10])
@@ -82,6 +84,8 @@ def __init__(self, path: str, dtype: str = DEFAULT_DTYPE):
             raise FileNotFoundError(f"Could not find file {path}")
         self._zone_names = None
         self._zone = self.zone_names[0]
+        self._mask_names = None
+        self._mask = self.mask_names[0]
         self._info = None
 
     def _time_to_index(self, time: Union[List[str], str]) -> Union[List[int], int]:
@@ -175,10 +179,46 @@ def zone(self, zone_name: str):
         """
         if zone_name in self._zone_names:
             self._zone = zone_name
+            self._mask_names = None
+            self._mask = self.mask_names[0]
         else:
             print(f"{zone_name} not found. Available zones are:")
             print(self._zone_names)
 
+    @property
+    def mask_names(self) -> List[str]:
+        """Find available binary masks in the HDF5 file.
+
+        :return: list of mask names
+        :rtype: List[str]
+        """
+        if self._mask_names is None:
+            keys = self._file[self.zone].keys()
+            self._mask_names = [key for key in keys if key.startswith("Mask")]
+        return self._mask_names
+
+    @property
+    def mask(self) -> str:
+        """Name of the currently active mask.
+
+        :return: name of the activated mask
+        :rtype: str
+        """
+        return self._mask
+
+    @mask.setter
+    def mask(self, mask_name: str):
+        """Set active mask.
+
+        :param mask_name: name of the mask to activate
+        :type mask_name: str
+        """
+        if mask_name in self._mask_names:
+            self._mask = mask_name
+        else:
+            print(f"{mask_name} not found. Available masks are:")
+            print(self._mask_names)
+
     @property
     def info(self) -> Dict[str, tuple]:
         """Get iPSP metadata valid for entire file.
@@ -236,5 +276,5 @@ def vertices(self) -> pt.Tensor:
     @property
     def weights(self) -> pt.Tensor:
         return pt.tensor(
-            self._file[f"{self.zone}/{WEIGHT_KEY}"][:, :], dtype=self._dtype
+            self._file[f"{self.zone}/{self.mask}"][:, :], dtype=self._dtype
         )
diff --git a/flowtorch/data/tau_dataloader.py b/flowtorch/data/tau_dataloader.py
index 79ad660..b07bcba 100644
--- a/flowtorch/data/tau_dataloader.py
+++ b/flowtorch/data/tau_dataloader.py
@@ -9,8 +9,9 @@
 
 """
 # standard library packages
+from os.path import join, split
 from glob import glob
-from typing import List, Dict, Tuple, Union
+from typing import List, Dict, Tuple, Union, Set
 # third party packages
 from netCDF4 import Dataset
 import torch as pt
@@ -19,67 +20,128 @@
 from .dataloader import Dataloader
 from .utils import check_list_or_str, check_and_standardize_path
 
-
-IGNORE_FIELDS = ("x", "y", "z", "volume", "global_id")
+VOL_SOLUTION_NAME = ".pval.unsteady_"
+PSOLUTION_POSTFIX = ".domain_"
+PMESH_NAME = "domain_{:s}_grid_1"
+PVERTEX_KEY = "pcoord"
+PWEIGHT_KEY = "pvolume"
+PADD_POINTS_KEY = "addpoint_idx"
+PGLOBAL_ID_KEY = "globalidx"
 VERTEX_KEYS = ("points_xc", "points_yc", "points_zc")
 WEIGHT_KEY = "volume"
 
+COMMENT_CHAR = "#"
+CONFIG_SEP = ":"
+SOLUTION_PREFIX_KEY = "solution_prefix"
+GRID_FILE_KEY = "primary_grid"
+GRID_PREFIX_KEY = "grid_prefix"
+N_DOMAINS_KEY = "n_domains"
+
+
+class TAUConfig(object):
+    """Load and parse TAU parameter files.
+
+    The class does not parse the full content of the parameter file
+    but only content that is absolutely needed to load snapshot data.
+
+    """
+
+    def __init__(self, file_path: str):
+        """Create a `TauConfig` instance from the file path.
+
+        :param file_path: path to the parameter file
+        :type path: str
+        """
+        self._path, self._file_name = split(file_path)
+        with open(join(self._path, self._file_name), "r") as config:
+            self._file_content = config.readlines()
+        self._config = None
+
+    def _parse_config(self, parameter: str) -> str:
+        """Extract a value based on a given pattern.
+
+        Every line of the parameter file follows the structure:
+            parameter : value
+        This function extracts the value as string and remove potential
+        white spaces or comments (#). The separator is expected to be a
+        colon.
+
+        :param parameter: the parameter of which to extract the value
+        :type pattern: str
+        :return: extracted value or empty string
+        :rtype: str
+        """
+        for line in self._file_content:
+            if parameter in line:
+                return line.split(CONFIG_SEP)[-1].split(COMMENT_CHAR)[0].strip()
+        return ""
+
+    def _gather_config(self):
+        """Gather all required configuration values.
+        """
+        config = {}
+        config[SOLUTION_PREFIX_KEY] = self._parse_config("Output files prefix")
+        config[GRID_FILE_KEY] = self._parse_config("Primary grid filename")
+        config[GRID_PREFIX_KEY] = self._parse_config("Grid prefix")
+        config[N_DOMAINS_KEY] = int(self._parse_config("Number of domains"))
+        self._config = config
+
+    @property
+    def path(self) -> str:
+        return self._path
+
+    @property
+    def config(self) -> dict:
+        if self._config is None:
+            self._gather_config()
+        return self._config
+
 
 class TAUDataloader(Dataloader):
     """Load TAU simulation data.
 
-    TAU simulations output results in several netCDF files, one for each write
-    time. The mesh is stored in a separated file with the extension *.grd*.
-    Currently, the loader only enables access to field data but not to boundaries.
+    The loader is currently limited to read:
+    - internal field solution, serial/reconstructed and distributed
+    - mesh vertices, serial and distributed
+    - cell volumes, serial (if present) and distributed
 
     Examples
 
+    >>> from os.path import join
     >>> from flowtorch import DATASETS
     >>> from flowtorch.data import TAUDataloader
     >>> path = DATASETS["tau_backward_facing_step"]
-    >>> loader = TAUDataloader(path, base_name="sol.pval.unsteady_")
+    >>> loader = TAUDataloader(join(path, "simulation.para"))
     >>> times = loader.write_times
     >>> fields = loader.field_names[times[0]]
     >>> fields
     ['density', 'x_velocity', 'y_velocity', ...]
     >>> density = loader.load_snapshot("density", times)
-    >>> density.shape
-    torch.Size([1119348, 10])
+
+    To load distributed simulation data, set `distributed=True`
+    >>> path = DATASETS["tau_cylinder_2D"]
+    >>> loader = TAUDataloader(join(path, "simulation.para"), distributed=True)
+    >>> vertices = loader.vertices
 
     """
 
-    def __init__(self, path: str, base_name: str, dtype: str = DEFAULT_DTYPE):
-        """Create loader instance from TAU simulation folder.
+    def __init__(self, parameter_file: str, distributed: bool = False,
+                 dtype: str = DEFAULT_DTYPE):
+        """Create loader instance from TAU parameter file.
 
-        :param path: path to TAU simulation files
-        :type path: str
-        :param base_name: part of the solution file name before iteration count,
-            e.g., base_name_ if the solution file is called base_name_i=0102_t=1.0
-        :type base_name: str
+        :param parameter_file: path to TAU simulation parameter file
+        :type parameter_file: str
+        :param distributed: True if mesh and solution are distributed in domain
+            files; defaults to False
+        :type distributed: bool, optional
         :param dtype: tensor type, defaults to DEFAULT_DTYPE
         :type dtype: str, optional
         """
-        self._path = check_and_standardize_path(path)
-        self._base_name = base_name
+        self._para = TAUConfig(parameter_file)
+        self._distributed = distributed
         self._dtype = dtype
         self._time_iter = self._decompose_file_name()
-
-    def _find_grid_file(self) -> str:
-        """Determine the name of the grid file
-
-        :raises FileNotFoundError: if no grid file is found
-        :raises FileNotFoundError: if multiple grid files are found
-        :return: name of the grid file
-        :rtype: str
-        """
-        files = glob(f"{self._path}/*.grd")
-        if len(files) < 1:
-            raise FileNotFoundError(
-                f"Could not find mesh file (.grd) in {self._path}/")
-        if len(files) > 1:
-            raise FileNotFoundError(
-                f"Found multiple mesh files (.grd) in {self._path}/")
-        return files[0].split("/")[-1]
+        self._mesh_data = None
 
     def _decompose_file_name(self) -> Dict[str, str]:
         """Extract write time and iteration from file name.
@@ -89,30 +151,98 @@ def _decompose_file_name(self) -> Dict[str, str]:
             iterations as values
         :rtype: Dict[str, str]
         """
-        files = glob(f"{self._path}/{self._base_name}i=*t=*")
+        base = join(self._para.path, self._para.config[SOLUTION_PREFIX_KEY])
+        base += VOL_SOLUTION_NAME
+        suffix = f"{PSOLUTION_POSTFIX}0" if self._distributed else "e???"
+        files = glob(f"{base}i=*t=*{suffix}")
         if len(files) < 1:
             raise FileNotFoundError(
-                f"Could not find solution files in {self._path}/")
+                f"Could not find solution files in {self._sol_path}/")
         time_iter = {}
+        split_at = PSOLUTION_POSTFIX if self._distributed else " "
         for f in files:
-            t = f.split("t=")[-1]
+            t = f.split("t=")[-1].split(split_at)[0]
             i = f.split("i=")[-1].split("_t=")[0]
             time_iter[t] = i
         return time_iter
 
-    def _file_name(self, time: str) -> str:
+    def _file_name(self, time: str, suffix: str = "") -> str:
         """Create solution file name from write time.
 
         :param time: snapshot write time
         :type time: str
+        :param suffix: suffix to append to the file name; used for decomposed
+            simulations
+        :type suffix: str, optional
         :return: name of solution file
         :rtype: str
         """
         itr = self._time_iter[time]
-        return f"{self._path}/{self._base_name}i={itr}_t={time}"
+        path = join(self._para.path, self._para.config[SOLUTION_PREFIX_KEY])
+        return f"{path}{VOL_SOLUTION_NAME}i={itr}_t={time}{suffix}"
+
+    def _load_domain_mesh_data(self, pid: str) -> pt.Tensor:
+        """Load vertices and volumes for a single processor domain.
+
+        :param pid: domain id
+        :type pid: str
+        :return: tensor of size n_points x 4, where n_points is the number
+            of unique cells in the domain, and the 4 columns contain the
+            coordinates of the vertices (x, y, z) and the cell volumes
+        :rtype: pt.Tensor
+        """
+        prefix = self._para.config[GRID_PREFIX_KEY]
+        name = PMESH_NAME.format(pid)
+        if not (prefix == "(none)"):
+            name = f"{prefix}_{name}"
+        path = join(self._para.path, name)
+        with Dataset(path) as data:
+            vertices = pt.tensor(data[PVERTEX_KEY][:], dtype=self._dtype)
+            volumes = pt.tensor(data[PWEIGHT_KEY][:], dtype=self._dtype)
+            global_ids = pt.tensor(data[PGLOBAL_ID_KEY][:], dtype=pt.int64)
+            n_add_points = data[PADD_POINTS_KEY].shape[0]
+
+        n_points = volumes.shape[0] - n_add_points
+        data = pt.zeros((n_points, 4), dtype=self._dtype)
+        sorting = pt.argsort(global_ids[:n_points])
+        data[:, 0] = vertices[:n_points, 0][sorting]
+        data[:, 1] = vertices[:n_points, 1][sorting]
+        data[:, 2] = vertices[:n_points, 2][sorting]
+        data[:, 3] = volumes[:n_points][sorting]
+        return data
+
+    def _load_mesh_data(self):
+        """Load mesh vertices and cell volumes.
+
+        The mesh data is saved as class member `_mesh_data`. The tensor has the
+        dimension n_points x 4; the first three columns correspond to the x/y/z
+        coordinates, and the 4th column contains the volumes.
+        """
+        if self._distributed:
+            n = self._para.config[N_DOMAINS_KEY]
+            self._mesh_data = pt.cat(
+                [self._load_domain_mesh_data(str(pid)) for pid in range(n)],
+                dim=0
+            )
+        else:
+            path = join(self._para.path, self._para.config[GRID_FILE_KEY])
+            with Dataset(path) as data:
+                vertices = pt.stack(
+                    [pt.tensor(data[key][:], dtype=self._dtype)
+                     for key in VERTEX_KEYS],
+                    dim=-1
+                )
+                if WEIGHT_KEY in data.variables.keys():
+                    weights = pt.tensor(
+                        data.variables[WEIGHT_KEY][:], dtype=self._dtype)
+                else:
+                    print(
+                        f"Warning: could not find cell volumes in file {path}")
+                    weights = pt.ones(vertices.shape[0], dtype=self._dtype)
+            self._mesh_data = pt.cat((vertices, weights.unsqueeze(-1)), dim=-1)
 
     def _load_single_snapshot(self, field_name: str, time: str) -> pt.Tensor:
-        """Load a single snapshot of a single field from the netCDF4 file.
+        """Load a single snapshot of a single field from the netCDF4 file(s).
 
         :param field_name: name of the field
         :type field_name: str
@@ -121,9 +251,21 @@ def _load_single_snapshot(self, field_name: str, time: str) -> pt.Tensor:
         :return: tensor holding the field values
         :rtype: pt.Tensor
         """
-        path = self._file_name(time)
-        with Dataset(path) as data:
-            field = pt.tensor(data.variables[field_name][:], dtype=self._dtype)
+        if self._distributed:
+            field = []
+            for pid in range(self._para.config[N_DOMAINS_KEY]):
+                path = self._file_name(time, f".domain_{pid}")
+                with Dataset(path) as data:
+                    field.append(
+                        pt.tensor(
+                            data.variables[field_name][:], dtype=self._dtype)
+                    )
+            return pt.cat(field, dim=0)
+        else:
+            path = self._file_name(time)
+            with Dataset(path) as data:
+                field = pt.tensor(
+                    data.variables[field_name][:], dtype=self._dtype)
         return field
 
     def load_snapshot(self, field_name: Union[List[str], str],
@@ -159,34 +301,40 @@ def write_times(self) -> List[str]:
 
     @property
     def field_names(self) -> Dict[str, List[str]]:
+        """Find available fields in solution files.
+
+        Available fields are determined by matching the number of
+        weights with the length of datasets in the available
+        solution files; for distributed cases, the fields are only
+        determined based on *domain_0*.
+
+        :return: dictionary with time as key and list of
+            available solution fields as value
+        :rtype: Dict[str, List[str]]
+        """
         self._field_names = {}
+        if self._distributed:
+            n_points = self._load_domain_mesh_data("0").shape[0]
+            suffix = ".domain_0"
+        else:
+            n_points = self.vertices.shape[0]
+            suffix = ""
         for time in self.write_times:
             self._field_names[time] = []
-            with Dataset(self._file_name(time)) as data:
-                self._field_names[time] = [
-                    key for key in data.variables.keys() if key not in IGNORE_FIELDS
-                ]
+            with Dataset(self._file_name(time, suffix)) as data:
+                for key in data.variables.keys():
+                    if data[key].shape[0] == n_points:
+                        self._field_names[time].append(key)
         return self._field_names
 
     @property
     def vertices(self) -> pt.Tensor:
-        path = f"{self._path}/{self._find_grid_file()}"
-        with Dataset(path) as data:
-            vertices = pt.stack(
-                [pt.tensor(data[key][:], dtype=self._dtype)
-                 for key in VERTEX_KEYS],
-                dim=-1
-            )
-        return vertices
+        if self._mesh_data is None:
+            self._load_mesh_data()
+        return self._mesh_data[:, :3]
 
     @property
     def weights(self) -> pt.Tensor:
-        path = self._file_name(self.write_times[0])
-        with Dataset(path) as data:
-            if WEIGHT_KEY in data.variables.keys():
-                weights = pt.tensor(
-                    data.variables[WEIGHT_KEY][:], dtype=self._dtype)
-            else:
-                print(f"Warning: cell volumes not found in file {path}")
-                weights = None
-        return weights
+        if self._mesh_data is None:
+            self._load_mesh_data()
+        return self._mesh_data[:, 3]
diff --git a/flowtorch/data/tecplot_dataloader.py b/flowtorch/data/tecplot_dataloader.py
new file mode 100644
index 0000000..4e9a141
--- /dev/null
+++ b/flowtorch/data/tecplot_dataloader.py
@@ -0,0 +1,291 @@
+"""Read Tecplot data via the ParaView module.
+"""
+
+# standard library packages
+from os.path import join
+from glob import glob
+from typing import Callable, Union, List, Dict
+# third party packages
+import torch as pt
+from paraview import servermanager as sm
+from paraview.vtk.numpy_interface import dataset_adapter as dsa
+from paraview.simple import VisItTecplotBinaryReader
+# flowtorch packages
+from flowtorch import DEFAULT_DTYPE
+from .dataloader import Dataloader
+from .utils import check_and_standardize_path, check_list_or_str
+
+
+class TecplotDataloader(Dataloader):
+    """Dataloader for Tecplot binary format.
+
+    The dataloader wraps around `VisItTecplotBinaryReader` available
+    in the ParaView Python module. One level of blocks/zones is expected
+    under the root block/zone
+
+    Examples
+
+    >>> from flowtorch import DATASETS
+    >>> from flowtorch.data import TecplotDataloader
+    >>> path = DATASETS["plt_naca2409_surface"]
+    >>> loader = TecplotDataloader.from_tau(path, "alfa16.surface.pval.unsteady_")
+    >>> loader.zone_names
+    ["ls", "te", "us"]
+    >>> loader.zone
+    "le"
+    >>> loader.zone = "us"
+    >>> times = loader.write_times
+    >>> density = loader.load_snapshot("density", times)
+    >>> density.shape
+    torch.Size([300, 3])
+
+    """
+
+    def __init__(self, path: str, file_names: Dict[str, str],
+                 reader: VisItTecplotBinaryReader, dtype: str = DEFAULT_DTYPE):
+        """Default constructor function.
+
+        :param path: path to snapshot location
+        :type path: str
+        :param file_names: names of available snapshots
+        :type file_names: Dict[str, str]
+        :param reader: ParaView reader for Tecplot binary format
+        :type reader: VisItTecplotBinaryReader
+        :param dtype: tensor data type, defaults to DEFAULT_DTYPE
+        :type dtype: str, optional
+        """
+        self._path = path
+        self._file_names = file_names
+        self._reader = reader
+        self._dtype = dtype
+        self._zone_names = None
+        self._zone = self.zone_names[0]
+
+    @classmethod
+    def from_tau(cls, path: str, base_name: str = "", suffix: str = ".plt", dtype: str = DEFAULT_DTYPE):
+        """Construct TecplotDataloader from TAU snapshots.
+
+        :param path: path to snapshot location
+        :type path: str
+        :param base_name: common basename of all snapshots, defaults to ""
+        :type base_name: str, optional
+        :param suffix: snapshot file suffix, defaults to ".plt"
+        :type suffix: str, optional
+        :param dtype: tensor data type, defaults to DEFAULT_DTYPE
+        :type dtype: str, optional
+        :raises FileNotFoundError: if no snapshots are found
+        :return: Tecplot dataloader object
+        :rtype: TecplotDataloader
+        """
+        path = check_and_standardize_path(path)
+        file_paths = glob(f"{path}/{base_name}i=*t=*")
+        file_names = [f.split("/")[-1] for f in file_paths]
+        write_times = [name.split("t=")[-1].split(suffix)[0]
+                       for name in file_names]
+        sorted_names = sorted(zip(write_times, file_names),
+                              key=lambda tup: float(tup[0]))
+        file_names = {time: name for time, name in sorted_names}
+        if len(file_names.keys()) < 1:
+            raise FileNotFoundError(
+                f"Could not find solution files in {self._sol_path}/")
+        return cls(path, file_names, VisItTecplotBinaryReader, dtype)
+
+    def _assemble_file_path(self, time: str) -> str:
+        """Assemble path to a single snapshot.
+
+        :param time: snapshot write time
+        :type time: str
+        :return: snapshot path
+        :rtype: str
+        """
+        return join(self._path, self._file_names[time])
+
+    def _parse_block_name(self, meta_data: str) -> str:
+        """Extract block name from a reader's metadata.
+
+        :param meta_data: output of `GetMetaData()` as string
+        :type meta_data: str
+        :return: block name
+        :rtype: str
+        """
+        lines = meta_data.split("\n")
+        name = None
+        for line in lines:
+            if "NAME" in line:
+                name = line.split(":")[-1].strip()
+        return name
+
+    def _create_tecplot_reader(self, time: str) -> VisItTecplotBinaryReader:
+        """Create instance of `VisItTecplotBinaryReader`.
+
+        :param time: snapshot write time
+        :type time: str
+        :return: reader for Tecplot binary data
+        :rtype: VisItTecplotBinaryReader
+        """
+        return self._reader(
+            registrationName=self._file_names[time],
+            FileName=[self._assemble_file_path(time)]
+        )
+
+    def _load_single_snapshot(self, field_name: str, time: str) -> pt.Tensor:
+        """Load a single snapshot of a single field.
+
+        :param field_name: name of field to load
+        :type field_name: str
+        :param time: snapshot write time
+        :type time: str
+        :return: snapshot of the requested field
+        :rtype: pt.Tensor
+        """
+        reader = self._create_tecplot_reader(time)
+        field_names = self.field_names[self.write_times[0]]
+        reader.PointArrayStatus = field_names
+        reader = sm.Fetch(reader)
+        wrapper = dsa.WrapDataObject(reader.GetBlock(0).GetBlock(
+            self.zone_names.index(self.zone)
+        ))
+        return pt.from_numpy(wrapper.PointData[field_names.index(field_name)])
+
+    def _load_multiple_snapshots(self, field_name: str, times: List[str]) -> pt.Tensor:
+        """Load multiple snapshots of a single field.
+
+        :param field_name: name of the field
+        :type field_name: str
+        :param times: list of write times to load
+        :type times: List[str]
+        :return: tensor holding multiple snapshots; the time dimension is always
+            the last dimension
+        :rtype: pt.Tensor
+
+        """
+        return pt.stack(
+            [self._load_single_snapshot(field_name, time) for time in times],
+            dim=-1
+        )
+
+    def load_snapshot(self,
+                      field_name: Union[List[str], str],
+                      time: Union[List[str], str]) -> Union[List[pt.Tensor], pt.Tensor]:
+        """Load snapshots of single or multiple fields and write times.
+
+        :param field_name: single field name or list of field names
+        :type field_name: Union[List[str], str]
+        :param time: single write time of list of write times
+        :type time: Union[List[str], str]
+        :return: snapshot(s) of one or multiple fields
+        :rtype: Union[List[pt.Tensor], pt.Tensor]
+        """
+        check_list_or_str(field_name, "field_name")
+        check_list_or_str(time, "time")
+        # load multiple fields
+        if isinstance(field_name, list):
+            if isinstance(time, list):
+                return [self._load_multiple_snapshots(name, time) for name in field_name]
+            else:
+                return [self._load_single_snapshot(name, time) for name in field_name]
+        # load a single field
+        else:
+            if isinstance(time, list):
+                return self._load_multiple_snapshots(field_name, time)
+            else:
+                return self._load_single_snapshot(field_name, time)
+
+    @property
+    def zone_names(self) -> List[str]:
+        """Names of available blocks/zones.
+
+        :return: block/zone names
+        :rtype: List[str]
+        """
+        if self._zone_names is None:
+            reader = sm.Fetch(self._create_tecplot_reader(self.write_times[0]))
+            root_block = reader.GetBlock(0)
+            self._zone_names = [
+                self._parse_block_name(
+                    str(root_block.GetMetaData(i))
+                ) for i in range(root_block.GetNumberOfBlocks())
+            ]
+        return self._zone_names
+
+    @property
+    def zone(self) -> str:
+        """Currently selected block/zone.
+
+        :return: block/zone name
+        :rtype: str
+        """
+        return self._zone
+
+    @zone.setter
+    def zone(self, value: str):
+        """Select active block/zone.
+
+        The selected block remains unchanged if an invalid
+        block name is passed
+
+        :param value: name of block to select
+        :type value: str
+        """
+        if value in self.zone_names:
+            self._zone = value
+        else:
+            print(f"{value} not found. Available zones are:")
+            print(self.zone_names)
+
+    @property
+    def write_times(self) -> List[str]:
+        """Available snapshot write times
+
+        :return: list of available write times
+        :rtype: List[str]
+        """
+        return list(self._file_names.keys())
+
+    @property
+    def field_names(self) -> Dict[str, List[str]]:
+        """List of available field names.
+
+        The field names are only determined once for the
+        first available snapshot time.
+
+        :return: available fields at first write time
+        :rtype: Dict[str, List[str]]
+        """
+        time = self.write_times[0]
+        reader = self._create_tecplot_reader(time)
+        return {
+            time: reader.GetProperty("PointArrayInfo")[::2]
+        }
+
+    @property
+    def vertices(self) -> pt.Tensor:
+        """Points in which field values are defined.
+
+        :return: list of points
+        :rtype: pt.Tensor
+        """
+        reader = sm.Fetch(self._create_tecplot_reader(self.write_times[0]))
+        wrapper = dsa.WrapDataObject(reader.GetBlock(0).GetBlock(
+            self.zone_names.index(self.zone)
+        ))
+        return pt.from_numpy(wrapper.Points)
+
+    @property
+    def weights(self) -> pt.Tensor:
+        """Weight for POD/DMD analysis.
+
+        This function returns currently a list of ones, since
+        cell areas/volumes are not accessible via the reader.
+
+        :return: list of ones
+        :rtype: pt.Tensor
+        """
+        reader = sm.Fetch(self._create_tecplot_reader(self.write_times[0]))
+        wrapper = dsa.WrapDataObject(reader.GetBlock(0).GetBlock(
+            self.zone_names.index(self.zone)
+        ))
+        # volume or area are not contained in file; therefore, a tensor
+        # of ones is returned for now
+        n_points = wrapper.Points.shape[0]
+        return pt.ones(n_points, dtype=self._dtype)
diff --git a/flowtorch/data/test_csv_dataloader.py b/flowtorch/data/test_csv_dataloader.py
index 294846e..1e10cfb 100644
--- a/flowtorch/data/test_csv_dataloader.py
+++ b/flowtorch/data/test_csv_dataloader.py
@@ -3,21 +3,30 @@
 # third party packages
 import torch as pt
 # flowtorch packages
+from flowtorch.constants import FLOAT_TOLERANCE
 from flowtorch import DATASETS
 from flowtorch.data import CSVDataloader
-from flowtorch.data.csv_dataloader import _parse_davis_header
+from flowtorch.data.csv_dataloader import (_parse_davis_header,
+                                           _parse_foam_surface_header)
+
+
+def test_parse_foam_surface():
+    header = "# x y z  U_x U_y U_z  area_x area_y area_z"
+    columns = ["x", "y", "z", "U_x", "U_y",
+               "U_z", "area_x", "area_y", "area_z"]
+    assert columns == _parse_foam_surface_header(header)
 
 
 def test_from_foam_surface():
     path = DATASETS["csv_naca0012_alpha4_surface"]
     loader = CSVDataloader.from_foam_surface(
-        path, "total(p)_coeff_airfoil.raw", "cp")
+        path, "total(p)_coeff_airfoil.raw")
     times = loader.write_times
     assert len(times) == 250
     assert times[0] == "0.001"
     assert times[-1] == "0.25"
     fields = loader.field_names
-    assert fields[times[0]][0] == "cp"
+    assert fields[times[0]][0] == "total(p)_coeff"
     n_points = 28892
     vertices = loader.vertices
     assert vertices.shape == (n_points, 3)
@@ -25,27 +34,40 @@ def test_from_foam_surface():
     # should be a tensor filled with ones
     assert pt.sum(weights).item() == n_points
     # single snapshot, single field
-    snapshot = loader.load_snapshot("cp", times[0])
+    snapshot = loader.load_snapshot("total(p)_coeff", times[0])
     assert snapshot.shape == (n_points,)
     # multiple snapshots, single field
-    snapshots = loader.load_snapshot("cp", times[:10])
+    snapshots = loader.load_snapshot("total(p)_coeff", times[:10])
     assert snapshots.shape == (n_points, 10)
     assert pt.allclose(snapshot, snapshots[:, 0])
     # single snapshot, multiple fields
-    snapshot = loader.load_snapshot(["cp"], times[0])
+    snapshot = loader.load_snapshot(["total(p)_coeff"], times[0])
     assert len(snapshot) == 1
     assert snapshot[0].shape == (n_points,)
     # multiple snapshots, multiple fields
-    snapshots = loader.load_snapshot(["cp"], times[:10])
+    snapshots = loader.load_snapshot(["total(p)_coeff"], times[:10])
     assert len(snapshots) == 1
     assert snapshots[0].shape == (n_points, 10)
     assert pt.allclose(snapshots[0][:, 0], snapshot[0])
 
+    # test case with multiple fields and face area weights
+    path = DATASETS["csv_surface_mounted_cube_xy"]
+    loader = CSVDataloader.from_foam_surface(
+        path, "U_plane_xy.raw")
+    fields, times = loader.field_names, loader.write_times
+    assert fields[times[0]] == ["U_x", "U_y", "U_z"]
+    # face area weights
+    weights = loader.weights
+    assert weights.shape == (35154,)
+    assert (weights[0] - pt.tensor([5.31517e-18, 2.85608e-18,
+                                    0.00985272]).norm()).item() < FLOAT_TOLERANCE
+
 
 def test_parse_davis_header():
     header = 'VARIABLES = "x", "y", "Vx", "Vy", "Vz", "swirl strength", "vector length", "vorticity", "isValid"'
     columns = _parse_davis_header(header)
-    expected_columns = ["x", "y", "Vx", "Vy", "Vz", "swirl strength", "vector length", "vorticity", "isValid"]
+    expected_columns = ["x", "y", "Vx", "Vy", "Vz",
+                        "swirl strength", "vector length", "vorticity", "isValid"]
     assert columns == expected_columns
 
 
@@ -58,7 +80,6 @@ def test_from_davis():
     assert times[-1] == "05000"
     fields = loader.field_names
     fields = fields[times[0]]
-    assert len(fields) == 3
     n_points = 3741
     vertices = loader.vertices
     # DaVis files have only x and y component
@@ -73,13 +94,13 @@ def test_from_davis():
     assert snapshots.shape == (n_points, 10)
     assert pt.allclose(snapshot, snapshots[:, 0])
     # single snapshot, multiple fields
-    snapshot = loader.load_snapshot(fields, times[0])
+    snapshot = loader.load_snapshot(fields[:3], times[0])
     assert len(snapshot) == 3
     assert snapshot[0].shape == (n_points,)
     assert snapshot[1].shape == (n_points,)
     assert snapshot[2].shape == (n_points,)
     # multiple snapshots, multiple fields
-    snapshots = loader.load_snapshot(fields, times[:10])
+    snapshots = loader.load_snapshot(fields[:3], times[:10])
     assert len(snapshots) == 3
     assert snapshots[0].shape == (n_points, 10)
     assert snapshots[1].shape == (n_points, 10)
@@ -90,6 +111,7 @@ def test_from_davis():
     loader = CSVDataloader.from_davis(path, "B")
     times = loader.write_times
     fields = loader.field_names[times[0]]
-    assert fields == ["Vx", "Vy", "Vz", "swirl strength", "vector length", "vorticity"]
+    assert fields == ["Vx", "Vy", "Vz",
+                      "swirl strength", "vector length", "vorticity"]
     snapshot = loader.load_snapshot("swirl strength", times[0])
     assert snapshot.shape == (n_points,)
diff --git a/flowtorch/data/test_foam_dataloader.py b/flowtorch/data/test_foam_dataloader.py
index 33cf21e..2ccfe76 100644
--- a/flowtorch/data/test_foam_dataloader.py
+++ b/flowtorch/data/test_foam_dataloader.py
@@ -157,6 +157,9 @@ def test_distributed(self, get_test_data):
             case = FOAMCase(get_test_data.paths[key])
             distributed = get_test_data.distributed[key]
             assert distributed == case._distributed
+        for key in get_test_data.distributed.keys():
+            case = FOAMCase(get_test_data.paths[key], False)
+            assert case._distributed == False
 
     def test_processors(self, get_test_data):
         for key in get_test_data.paths.keys():
diff --git a/flowtorch/data/test_hdf5_file.py b/flowtorch/data/test_hdf5_file.py
index e7831d3..1847666 100644
--- a/flowtorch/data/test_hdf5_file.py
+++ b/flowtorch/data/test_hdf5_file.py
@@ -7,7 +7,8 @@
 from h5py import File
 # flowtorch packages
 from flowtorch import DATASET_PATH, DATASETS
-from flowtorch.data import HDF5Dataloader, HDF5Writer, FOAM2HDF5, XDMFWriter
+from flowtorch.data import (HDF5Dataloader, HDF5Writer, FOAM2HDF5,
+                            XDMFWriter, copy_hdf5_mesh)
 
 
 class HDF5TestData:
@@ -65,7 +66,7 @@ def test_write_xdmf(self, get_test_data):
         case = get_test_data.test_cases[0]
         case_path = DATASET_PATH + case
         converter = FOAM2HDF5(case_path)
-        converter.convert("flowtorch.hdf5")
+        converter.convert("flowtorch.hdf5", None, ["0.5"])
         del converter
         file_path = case_path + "/flowtorch.hdf5"
         writer = XDMFWriter.from_filepath(file_path)
@@ -76,10 +77,12 @@ def test_write_xdmf(self, get_test_data):
 
 
 def test_conversion(get_test_data):
-    for case in get_test_data.test_cases:
+    for case in get_test_data.test_cases[:2]:
         case_path = DATASET_PATH + case
         converter = FOAM2HDF5(case_path)
-        converter.convert("flowtorch.hdf5")
+        # test all fields, selected times
+        converter.convert("flowtorch.hdf5", None,
+                          ["0.1", "0.2", "0.3", "0.4", "0.5"])
         del converter
         filename = case_path + "/flowtorch.hdf5"
         if os.path.isfile(filename):
@@ -93,12 +96,25 @@ def test_conversion(get_test_data):
             hdf5_file.close()
             os.remove(filename)
             os.remove(case_path + "/flowtorch.xdmf")
+        # test selected field, selected times
+        converter = FOAM2HDF5(case_path)
+        converter.convert("flowtorch.hdf5", ["U"], ["0.1", "0.2"])
+        del converter
+        if os.path.isfile(filename):
+            hdf5_file = File(filename, mode="a")
+            var_keys = sorted(hdf5_file["variable"].keys())
+            assert var_keys == ["0.1", "0.2"]
+            assert list(hdf5_file["variable/0.1"].keys()) == ["U"]
+            hdf5_file.close()
+            os.remove(filename)
+            os.remove(case_path + "/flowtorch.xdmf")
 
 
 def test_hdf5_dataloader():
     path = DATASETS["of_cavity_ascii"]
     converter = FOAM2HDF5(path)
-    converter.convert("flowtorch.hdf5")
+    converter.convert("flowtorch.hdf5", None,
+                      ["0.1", "0.2", "0.3", "0.4", "0.5"])
     file_path = path + "/flowtorch.hdf5"
     loader = HDF5Dataloader(file_path)
     times = loader.write_times
@@ -129,3 +145,24 @@ def test_hdf5_dataloader():
     assert pt.allclose(Us[:, :, 0], U)
     os.remove(file_path)
     os.remove(path + "/flowtorch.xdmf")
+
+
+def test_copy_hdf5_mesh(get_test_data):
+    for case in get_test_data.test_cases[:2]:
+        case_path = DATASET_PATH + case
+        converter = FOAM2HDF5(case_path)
+        converter.convert("flowtorch.hdf5", ["U"], ["0.1", "0.2"])
+        del converter
+        if os.path.isfile(os.path.join(case_path, "flowtorch.hdf5")):
+            copy_hdf5_mesh(case_path, "flowtorch.hdf5", "mesh_only.hdf5")
+            assert os.path.isfile(os.path.join(case_path, "mesh_only.hdf5"))
+            assert os.path.isfile(os.path.join(case_path, "mesh_only.xdmf"))
+            hdf5_file = File(os.path.join(
+                case_path, "mesh_only.hdf5"), mode="a")
+            assert hdf5_file["constant/volumes"].shape[0] == 400
+            assert hdf5_file["constant/centers"].shape == (400, 3)
+            assert "constant/vertices" in hdf5_file
+            assert "constant/connectivity" in hdf5_file
+            hdf5_file.close()
+            for f in ("flowtorch.hdf5", "flowtorch.xdmf", "mesh_only.hdf5", "mesh_only.xdmf"):
+                os.remove(os.path.join(case_path, f))
diff --git a/flowtorch/data/test_outlier_tools.py b/flowtorch/data/test_outlier_tools.py
new file mode 100644
index 0000000..fdb9315
--- /dev/null
+++ b/flowtorch/data/test_outlier_tools.py
@@ -0,0 +1,30 @@
+# third party packages
+import pytest
+import torch as pt
+# flowtorch packages
+from flowtorch.data import iqr_outlier_replacement
+
+
+def test_irq_outlier_replacement():
+    data = pt.tensor([
+        [3.0, 2.0, 4.0, 8.0, 1.0, 0.0],
+        [3.0, 2.0, 4.0, 5.0, 1.0, 0.0]
+    ])
+    clean_data = iqr_outlier_replacement(data)
+    # the shape of both datasets should be equal
+    assert clean_data.shape == data.shape
+    # check if outlier is detected and replaced;
+    # the number of elements in the second direction
+    # is even -> PyTorch returns the lower median
+    assert clean_data[0][3].item() == 2.0
+    # decrease sensitivity
+    data_clean = iqr_outlier_replacement(data, k=2.0)
+    data_clean[0][3] == 8.0
+    # use only the two nearest neighbors
+    data_clean = iqr_outlier_replacement(data, nb=1)
+    assert data_clean[0][3] == 4.0
+    # test with 1D tensor
+    data = pt.tensor([3.0, 2.0, 4.0, 8.0, 1.0, 0.0])
+    data_clean = iqr_outlier_replacement(data)
+    assert len(data_clean.shape) == 1
+    assert data_clean[3] == 2.0
diff --git a/flowtorch/data/test_psp_dataloader.py b/flowtorch/data/test_psp_dataloader.py
index 369c550..ff9d654 100644
--- a/flowtorch/data/test_psp_dataloader.py
+++ b/flowtorch/data/test_psp_dataloader.py
@@ -18,6 +18,15 @@ def test_zone(self):
         loader.zone = ""
         assert loader.zone == names[-1]
 
+    def test_mask(self):
+        loader = PSPDataloader(DATASETS["ipsp_fake.hdf5"])
+        masks = loader.mask_names
+        assert masks == ["Mask"]
+        loader.mask = "Mask"
+        assert loader.mask == "Mask"
+        loader.mask = ""
+        assert loader.mask == "Mask"
+
     def test_info(self):
         loader = PSPDataloader(DATASETS["ipsp_fake.hdf5"])
         info = loader.info
diff --git a/flowtorch/data/test_tau_dataloader.py b/flowtorch/data/test_tau_dataloader.py
index 29f508e..2d5fbfa 100644
--- a/flowtorch/data/test_tau_dataloader.py
+++ b/flowtorch/data/test_tau_dataloader.py
@@ -1,62 +1,185 @@
 # standard library packages
-import pytest
+from os.path import join
+
 # third party packages
+import pytest
 import torch as pt
 # flowtorch packages
 from flowtorch import DATASETS
-from flowtorch.data import TAUDataloader
+from flowtorch.data import TAUDataloader, TAUConfig
+
+
+SOLUTION_PREFIX_KEY = "solution_prefix"
+GRID_FILE_KEY = "primary_grid"
+GRID_PREFIX_KEY = "grid_prefix"
+N_DOMAINS_KEY = "n_domains"
+
+
+class TestTAUConfig():
+    path_0 = DATASETS["tau_backward_facing_step"]
+    path_1 = DATASETS["tau_cylinder_2D"]
+    file_name = "simulation.para"
+
+    def test_init(self):
+        config = TAUConfig(join(self.path_0, self.file_name))
+        assert config._path == self.path_0
+        assert config._file_name == self.file_name
+        assert len(config._file_content) > 0
+        assert all([isinstance(line, str) for line in config._file_content])
+        config = TAUConfig(join(self.path_1, self.file_name))
+        assert config._path == self.path_1
+        assert config._file_name == self.file_name
+
+    def test_parse_config(self):
+        config = TAUConfig(join(self.path_0, self.file_name))
+        empty = config._parse_config("Not in the File")
+        assert empty == ""
+        relaxation_solver = config._parse_config("Relaxation solver")
+        assert relaxation_solver == "Runge_Kutta"
+        n_mg = config._parse_config("Number of multigrid levels")
+        assert n_mg == "1"
+
+    def test_gather_config(self):
+        # backward facing step
+        config = TAUConfig(join(self.path_0, self.file_name))
+        config_values = config.config
+        assert config_values[SOLUTION_PREFIX_KEY] == "sol_files/sol"
+        assert config_values[GRID_FILE_KEY] == "grid_files/PW_DES-HybQuadTRex-v2_yp-50_s1.15_ny67.grd"
+        assert config_values[GRID_PREFIX_KEY] == "grid_files/distributed/dual"
+        assert config_values[N_DOMAINS_KEY] == 96
+        # 2D flow past a cylinder
+        config = TAUConfig(join(self.path_1, self.file_name))
+        config_values = config.config
+        assert config_values[SOLUTION_PREFIX_KEY] == "solution/solution"
+        assert config_values[GRID_FILE_KEY] == "cylinder_scaled.grid"
+        assert config_values[GRID_PREFIX_KEY] == "dualgrid/dual"
+        assert config_values[N_DOMAINS_KEY] == 16
 
 
 class TestTAUDataloader():
-    def test_find_grid_file(self):
-        path = DATASETS["tau_backward_facing_step"]
-        loader = TAUDataloader(path, "sol.pval.unsteady_")
-        grid_file = loader._find_grid_file()
-        assert grid_file == "PW_DES-HybQuadTRex-v2_yp-50_s1.15_ny67.grd"
+    path_0 = DATASETS["tau_backward_facing_step"]
+    path_1 = DATASETS["tau_cylinder_2D"]
+    file_name = "simulation.para"
 
     def test_decompose_file_name(self):
-        path = DATASETS["tau_backward_facing_step"]
-        loader = TAUDataloader(path, "sol.pval.unsteady_")
+        # backward facing step, serial
+        loader = TAUDataloader(join(self.path_0, self.file_name))
+        time_iter = loader._decompose_file_name()
+        assert len(time_iter.keys()) == 1
+        assert "4.69800000000e-02" in time_iter
+        assert time_iter["4.69800000000e-02"] == "23490"
+        # backward facing step, distributed
+        loader = TAUDataloader(join(self.path_0, self.file_name), True)
         time_iter = loader._decompose_file_name()
-        assert len(time_iter.keys()) == 2
-        assert "2.9580000000e-02" in time_iter
-        assert time_iter["2.9580000000e-02"] == "14790"
+        assert len(time_iter.keys()) == 1
+        assert "4.69800000000e-02" in time_iter
+        assert time_iter["4.69800000000e-02"] == "23490"
+        # 2D cylinder, serial
+        loader = TAUDataloader(join(self.path_1, self.file_name))
+        time_iter = loader._decompose_file_name()
+        assert len(time_iter.keys()) == 1
+        assert "6.0000e+02" in time_iter
+        assert time_iter["6.0000e+02"] == "1000"
+        # 2D cylinder, distributed
+        loader = TAUDataloader(join(self.path_1, self.file_name), True)
+        time_iter = loader._decompose_file_name()
+        assert len(time_iter.keys()) == 1
+        assert "6.0000e+02" in time_iter
+        assert time_iter["6.0000e+02"] == "1000"
 
-    def test_properties(self):
-        path = DATASETS["tau_backward_facing_step"]
-        loader = TAUDataloader(path, "sol.pval.unsteady_")
-        times = loader.write_times
-        assert times[0] == "2.9580000000e-02"
-        assert times[-1] == "3.2190000000e-02"
-        field_names = loader.field_names
-        assert len(field_names.keys()) == 2
-        assert "density" in field_names[times[-1]]
+    def test_file_name(self):
+        # backward facing step, serial
+        loader = TAUDataloader(join(self.path_0, self.file_name))
+        file_name = join(self.path_0, "sol_files/sol") + \
+            ".pval.unsteady_i=23490_t=4.69800000000e-02"
+        assert loader._file_name("4.69800000000e-02") == file_name
+        # 2D cylinder
+        loader = TAUDataloader(join(self.path_1, self.file_name))
+        file_name = join(self.path_0, "solution/solution") + \
+            ".pval.unsteady_i=1000_t=6.0000e+02.domain_0"
+        assert loader._file_name("6.0000e+02", ".domain_0")
+
+    def test_load_domain_mesh_data(self):
+        # backward facing step
+        loader = TAUDataloader(join(self.path_0, self.file_name), True)
+        data = loader._load_domain_mesh_data("0")
+        assert data.shape == (13936 - 2118, 4)
+        data = loader._load_domain_mesh_data("89")
+        assert data.shape == (14472 - 2638, 4)
+        # 2D cylinder
+        loader = TAUDataloader(join(self.path_1, self.file_name), True)
+        data = loader._load_domain_mesh_data("0")
+        assert data.shape == (4510 - 173, 4)
+
+    def test_load_mesh_data(self):
+        # backward facing step, serial
+        loader = TAUDataloader(join(self.path_0, self.file_name))
         n_points = 1119348
-        vertices = loader.vertices
-        assert vertices.shape == (n_points, 3)
-        weights = loader.weights
-        assert weights.shape == (n_points,)
+        assert loader.vertices.shape == (n_points, 3)
+        assert loader.weights.shape == (n_points,)
+        # backward facing step, distributed
+        loader = TAUDataloader(join(self.path_0, self.file_name), True)
+        assert loader.vertices.shape == (n_points, 3)
+        assert loader.weights.shape == (n_points,)
+
+    def test_write_times(self):
+        # backward facing step, serial
+        loader = TAUDataloader(join(self.path_0, self.file_name))
+        assert loader.write_times[0] == "4.69800000000e-02"
+        # backward facing step, distributed
+        loader = TAUDataloader(join(self.path_0, self.file_name), True)
+        assert loader.write_times[0] == "4.69800000000e-02"
+
+    def test_field_names(self):
+        # backward facing step, serial
+        loader = TAUDataloader(join(self.path_0, self.file_name))
+        times = loader.write_times
+        assert "pressure" in loader.field_names[times[0]]
+        assert "density" in loader.field_names[times[0]]
+        # backward facing step, distributed
+        loader = TAUDataloader(join(self.path_0, self.file_name), True)
+        times = loader.write_times
+        assert "pressure" in loader.field_names[times[0]]
+        assert "density" in loader.field_names[times[0]]
 
     def test_load_snapshot(self):
-        path = DATASETS["tau_backward_facing_step"]
-        loader = TAUDataloader(path, "sol.pval.unsteady_")
+        # backward facing step, serial
+        loader = TAUDataloader(join(self.path_0, self.file_name))
         times = loader.write_times
         field_names = loader.field_names[times[-1]]
         n_points = 1119348
         # single snapshot, single field
-        field = loader.load_snapshot(field_names[0], times[-1])
+        field = loader.load_snapshot(field_names[0], times[0])
         assert field.shape == (n_points,)
         # multiple snapshots, single field
-        field_series = loader.load_snapshot(field_names[0], times[-2:])
+        field_series = loader.load_snapshot(
+            field_names[0], [times[0], times[0]])
         assert field_series.shape == (n_points, 2)
-        assert pt.allclose(field_series[:, -1], field)
+        assert pt.allclose(field_series[:, 0], field)
         # single snapshot, multiple fields
-        f1, f2 = loader.load_snapshot(field_names[:2], times[-1])
+        f1, f2 = loader.load_snapshot(field_names[:2], times[0])
         assert f1.shape == (n_points,)
         assert f2.shape == (n_points,)
         # multiple snapshots, multiple field
-        f1s,  f2s = loader.load_snapshot(field_names[:2], times[-2:])
+        f1s,  f2s = loader.load_snapshot(field_names[:2], [times[0], times[0]])
         assert f1s.shape == (n_points, 2)
         assert f2s.shape == (n_points, 2)
-        assert pt.allclose(f1s[:, -1], f1)
-        assert pt.allclose(f2s[:, -1], f2)
+        assert pt.allclose(f1s[:, 0], f1)
+        assert pt.allclose(f2s[:, 0], f2)
+        # backward facing step, distributed
+        loader = TAUDataloader(join(self.path_0, self.file_name), True)
+        # single snapshots, single field
+        density = loader.load_snapshot("density", times[0])
+        assert density.shape == (n_points,)
+        # multiple snapshots, single field
+        density = loader.load_snapshot("density", [times[0], times[0]])
+        assert density.shape == (n_points, 2)
+        # single snapshot, multiple fields
+        density, p = loader.load_snapshot(["density", "pressure"], times[0])
+        assert density.shape == (n_points, )
+        assert p.shape == (n_points, )
+        # multiple snapshots, multiple fields
+        density, p = loader.load_snapshot(["density", "pressure"],
+                                          [times[0], times[0]])
+        assert density.shape == (n_points, 2)
+        assert p.shape == (n_points, 2)
diff --git a/flowtorch/data/test_tecplot_dataloader.py b/flowtorch/data/test_tecplot_dataloader.py
new file mode 100644
index 0000000..900687f
--- /dev/null
+++ b/flowtorch/data/test_tecplot_dataloader.py
@@ -0,0 +1,106 @@
+# standard library packages
+from os.path import join
+# third party packages
+import pytest
+import torch as pt
+# flowtorch packages
+from flowtorch import DATASETS
+from flowtorch.data import TecplotDataloader
+
+
+class TestTecplotDataloader():
+    path = DATASETS["plt_naca2409_surface"]
+    loader = TecplotDataloader.from_tau(
+        path, "alfa16.surface.pval.unsteady_")
+
+    def test_from_tau(self):
+        file_names = ["alfa16.surface.pval.unsteady_i=1600_t=5.6805000e-01.plt",
+                      "alfa16.surface.pval.unsteady_i=1610_t=5.6955000e-01.plt",
+                      "alfa16.surface.pval.unsteady_i=1620_t=5.7105000e-01.plt"]
+        times = ["5.6805000e-01", "5.6955000e-01", "5.7105000e-01"]
+        assert list(self.loader._file_names.keys()) == times
+        assert list(self.loader._file_names.values()) == file_names
+
+    def test_assemble_file_path(self):
+        first_file_path = self.loader._assemble_file_path("5.6805000e-01")
+        true_path = join(
+            self.path, "alfa16.surface.pval.unsteady_i=1600_t=5.6805000e-01.plt")
+        assert first_file_path == true_path
+
+    def test_create_tecplot_reader(self):
+        reader = self.loader._create_tecplot_reader("5.6805000e-01")
+        assert type(reader).__name__ == "VisItTecplotBinaryReader"
+
+    def test_field_names(self):
+        field_names = self.loader.field_names
+        assert "5.6805000e-01" in field_names.keys()
+        expected_fields = ['X', 'Y', 'Z', 'density', 'pressure', 'cp',
+                           'cf', 'cfx', 'cfy', 'cfz', 'yplus', 'eddy_viscosity']
+        assert field_names["5.6805000e-01"] == expected_fields
+
+    def test_parse_block_name(self):
+        meta_data = """vtkInformation (0x1a1812a0)
+  Debug: Off
+  Modified Time: 358329
+  Reference Count: 2
+  Registered Events: (none)
+  NAME: ls
+        """
+        block_name = self.loader._parse_block_name(meta_data)
+        assert block_name == "ls"
+
+    def test_zone_names(self):
+        zone_names = self.loader.zone_names
+        assert zone_names == ["ls", "te", "us"]
+
+    def test_zone(self):
+        assert self.loader.zone == "ls"
+        self.loader.zone = "us"
+        assert self.loader.zone == "us"
+        self.loader.zone = "none"
+        assert self.loader.zone == "us"
+        self.loader.zone = "ls"
+
+    def test_vertices(self):
+        vertices = self.loader.vertices
+        assert vertices.shape == (300, 3)
+        self.loader.zone = "te"
+        vertices = self.loader.vertices
+        assert vertices.shape == (30, 3)
+        self.loader.zone = "ls"
+
+    def test_weights(self):
+        weights = self.loader.weights
+        assert weights.shape == (300,)
+
+    def test_load_single_snapshot(self):
+        density = self.loader._load_single_snapshot(
+            "density", self.loader.write_times[0])
+        assert density.shape == (300,)
+        with pytest.raises(ValueError):
+            _ = self.loader._load_single_snapshot(
+                "none", self.loader.write_times[0])
+
+    def test_load_multiple_snapshots(self):
+        density = self.loader._load_multiple_snapshots(
+            "density", self.loader.write_times[:2])
+        assert density.shape == (300, 2)
+
+    def test_load_snapshot(self):
+        times = self.loader.write_times
+        # single field, single time
+        cf = self.loader.load_snapshot("cf", times[0])
+        assert cf.shape == (300,)
+        # single field, multiple times
+        self.loader.zone = "te"
+        cf = self.loader.load_snapshot("cf", times[:2])
+        assert cf.shape == (30, 2)
+        # multiple fields, single time
+        cf, cp = self.loader.load_snapshot(["cf", "cp"], times[0])
+        assert cf.shape == (30,)
+        assert cp.shape == (30,)
+        # multiple fields, multiple times
+        cf, cp = self.loader.load_snapshot(["cf", "cp"], times[:2])
+        assert cf.shape == (30, 2)
+        assert cp.shape == (30, 2)
+        self.loader.zone = "ls"
diff --git a/flowtorch/rom/cnm.py b/flowtorch/rom/cnm.py
index 6b5230e..a54878f 100644
--- a/flowtorch/rom/cnm.py
+++ b/flowtorch/rom/cnm.py
@@ -299,9 +299,12 @@ def _interpolate_trajectory(self, step_size: float) -> pt.Tensor:
                              "to interpolate a trajectory")
         times = np.arange(
             self.times[0], self.times[-1]+0.5*step_size, step_size)
-        prediction = pt.empty((self.encoder.reduced_state_size, times.size),
-                              dtype=self._dtype)
-        for dim in range(self.encoder.reduced_state_size):
+        if self.encoder is None:
+            state_size = self.cluster_centers.shape[-1]
+        else:
+            state_size = self.encoder.reduced_state_size
+        prediction = pt.empty((state_size, times.size), dtype=self._dtype)
+        for dim in range(state_size):
             spline = InterpolatedUnivariateSpline(
                 self._times, self.cluster_centers[self.visited_clusters][:, dim],
                 k=min(3, len(self.times)-1)
diff --git a/flowtorch/rom/test_cnm.py b/flowtorch/rom/test_cnm.py
index 17b055d..ab0c1a9 100644
--- a/flowtorch/rom/test_cnm.py
+++ b/flowtorch/rom/test_cnm.py
@@ -130,3 +130,8 @@ def test_predict(self):
         assert prediction.shape == (cnm.encoder.state_shape[0], 4)
         prediction = cnm.predict(self.data[:, 0], 3.0, 1.0)
         assert prediction.shape == (cnm.encoder.state_shape[0], 4)
+
+    def test_encode_none(self):
+        data = pt.rand((2, 20))
+        cnm = CNM(data, None, 1)
+        prediction = cnm.predict(data[:, :1], 10, 1)
diff --git a/flowtorch/rom/utils.py b/flowtorch/rom/utils.py
index ce1d0f0..4c86990 100644
--- a/flowtorch/rom/utils.py
+++ b/flowtorch/rom/utils.py
@@ -69,6 +69,6 @@ def remove_sequential_duplicates(sequence: np.ndarray) -> np.ndarray:
     :return: sequence without sequential duplicates
     :rtype: np.ndarray
     """
-    is_different = np.diff(sequence).astype(np.bool)
+    is_different = np.diff(sequence).astype(bool)
     return sequence[np.insert(is_different, 0, True)]
 
diff --git a/flowtorch/version.py b/flowtorch/version.py
index e35d817..1a3e615 100644
--- a/flowtorch/version.py
+++ b/flowtorch/version.py
@@ -1,3 +1,3 @@
 """flowTorch package version."""
 
-__version__ = "1.0"
\ No newline at end of file
+__version__ = "1.1"
\ No newline at end of file
diff --git a/references.md b/references.md
new file mode 100644
index 0000000..92908bd
--- /dev/null
+++ b/references.md
@@ -0,0 +1,2 @@
+- Nils Rathje, Philip Ströer, Andre Weiner, Tobias Knopp, Axel Probst and Rolf Radespiel: Experimental analysis of longitudinal vortex dynamics,AIAA 2022-3305. AIAA AVIATION 2022 Forum, June 2022, [https://doi.org/10.2514/6.2022-3305](https://doi.org/10.2514/6.2022-3305)
+- Andre Weiner and Richard Semaan: Simulation and modal analysis of transonic shock buffets on a NACA-0012 airfoil, AIAA 2022-2591, AIAA SCITECH 2022 Forum, January 2022, [https://doi.org/10.2514/6.2022-2591](https://doi.org/10.2514/6.2022-2591)
\ No newline at end of file