IMDB Sentiment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Analysis on IMDb Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download and extract the IMDB sentiment dataset:\n",
    "    \n",
    "    wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
    "    tar xfz aclImdb_v1.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we pre-process the dataset, converting each review to a sequence of word index numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000 words in vocabulary\n"
     ]
    }
   ],
   "source": [
    "# Vocabulary: All words used, starting by the most frequent\n",
    "with open('aclImdb/imdb.vocab') as f:\n",
    "    vocab = [word.rstrip() for word in f]\n",
    "    # Keep only most frequent 5000 words rather than all 90000\n",
    "    # Just saving memory - the long tail occurs too few times\n",
    "    # for the model to learn anything anyway\n",
    "    vocab = vocab[:5000]\n",
    "    print('%d words in vocabulary' % (len(vocab),))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def text_tokens(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub(\"\\\\s\", \" \", text)\n",
    "    text = re.sub(\"[^a-zA-Z' ]\", \"\", text)\n",
    "    tokens = text.split(' ')\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def load_dataset(dirname):\n",
    "    X, y = [], []\n",
    "    # Review files: neg/0_3.txt neg/10000_4.txt neg/10001_4.txt ...\n",
    "    for y_val, y_label in enumerate(['neg', 'pos']):\n",
    "        y_dir = os.path.join(dirname, y_label)\n",
    "        for fname in os.listdir(y_dir):\n",
    "            fpath = os.path.join(y_dir, fname)\n",
    "            # print('\\r' + fpath + '   ', end='')\n",
    "            with open(fpath) as f:\n",
    "                tokens = text_tokens(f.read())\n",
    "            X.append(tokens)\n",
    "            y.append(y_val)  # 0 for 'neg', 1 for 'pos'\n",
    "    print()\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train = load_dataset('aclImdb/train/')\n",
    "\n",
    "# We are cheating here - this is a test set, not a validation set.\n",
    "# This is just to make results quickly comparable to outside results\n",
    "# during the tutorial, but you should normally never use the test set\n",
    "# during training, of course!\n",
    "X_val, y_val = load_dataset('aclImdb/test/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25000, 25000)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train), len(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bag-of-words Linear Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bow_onehot_vector(tokens):\n",
    "    vector = [0] * len(vocab)\n",
    "    for t in tokens:\n",
    "        try:\n",
    "            vector[vocab.index(t)] = 1\n",
    "        except:\n",
    "            pass  # ignore missing words\n",
    "    return vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_bow_train = [bow_onehot_vector(x) for x in X_train]\n",
    "X_bow_val = [bow_onehot_vector(x) for x in X_val]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def best_train_history(history):\n",
    "    best_epoch = np.argmax(history.history['val_acc'])\n",
    "    print('Accuracy (epoch %d): %.4f train, %.4f val' % \\\n",
    "          (best_epoch + 1, history.history['acc'][best_epoch], history.history['val_acc'][best_epoch]))\n",
    "# (Note that sentiment.model is the state after the last epoch rather than best epoch!\n",
    "# Use ModelCheckpointer to restore the best epoch.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bow_input (InputLayer)       (None, 5000)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 5001      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 5,001\n",
      "Trainable params: 5,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Fitting...\n",
      "['bow_input']\n",
      "['activation_1']\n",
      "['bow_input']\n",
      "['activation_1']\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 6s 241us/step - loss: 0.4524 - acc: 0.8331 - val_loss: 0.3621 - val_acc: 0.8713\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 4s 151us/step - loss: 0.3132 - acc: 0.8880 - val_loss: 0.3164 - val_acc: 0.8794\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 4s 153us/step - loss: 0.2728 - acc: 0.9000 - val_loss: 0.3003 - val_acc: 0.8815\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 4s 150us/step - loss: 0.2501 - acc: 0.9080 - val_loss: 0.2938 - val_acc: 0.8815\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 4s 153us/step - loss: 0.2346 - acc: 0.9139 - val_loss: 0.2931 - val_acc: 0.8809\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 4s 169us/step - loss: 0.2233 - acc: 0.9178 - val_loss: 0.2916 - val_acc: 0.8807\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 4s 165us/step - loss: 0.2144 - acc: 0.9214 - val_loss: 0.2928 - val_acc: 0.8798\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 4s 154us/step - loss: 0.2068 - acc: 0.9246 - val_loss: 0.2958 - val_acc: 0.8789\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 4s 166us/step - loss: 0.2005 - acc: 0.9261 - val_loss: 0.2973 - val_acc: 0.8784\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 4s 168us/step - loss: 0.1956 - acc: 0.9297 - val_loss: 0.3010 - val_acc: 0.8770\n",
      "Accuracy (epoch 3): 0.9000 train, 0.8815 val\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Activation, Dense, Input\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "class BOWSentimentModel(object):\n",
    "    def __init__(self):\n",
    "        bow = Input(shape=(len(vocab),), name='bow_input')\n",
    "        # weights of all inputs\n",
    "        sentiment = Dense(1)(bow)\n",
    "        # normalize to [0, 1] range\n",
    "        sentiment = Activation('sigmoid')(sentiment)\n",
    "\n",
    "        self.model = Model(inputs=[bow], outputs=[sentiment])\n",
    "        self.model.summary()\n",
    "        self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "    def train(self, X, y, X_val, y_val):\n",
    "        print('Fitting...')\n",
    "        return self.model.fit(np.array(X), y, validation_data=(np.array(X_val), y_val), epochs=10, verbose=1)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.model.predict(np.array(X))\n",
    "    \n",
    "sentiment = BOWSentimentModel()\n",
    "history = sentiment.train(X_bow_train, y_train, X_bow_val, y_val)\n",
    "best_train_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bow_input']\n",
      "Good story about a backwoods community in the Ozarks around the turn of the century. Moonshine is the leading industry, fighting and funning the major form of entertainment. One day a stranger enters the community and causes a shake-up among the locals. Beautiful scenery adds much to the story. [ 0.7639569]\n"
     ]
    }
   ],
   "source": [
    "test_text = 'Good story about a backwoods community in the Ozarks around the turn of the century. Moonshine is the leading industry, fighting and funning the major form of entertainment. One day a stranger enters the community and causes a shake-up among the locals. Beautiful scenery adds much to the story.'\n",
    "test_tokens = text_tokens(test_text)\n",
    "print(test_text, sentiment.predict([bow_onehot_vector(test_tokens)])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def plot_train_history(history):\n",
    "    plt.figure()\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "    plt.xlabel('epoch')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.legend(['acc', 'val_acc'])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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QCpEBwbKrLnhtV13wx+Wu+qB7br8/N7lPgOQHJ7yce9tQ/mYfEW6n2q3u/sTe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\nQkREBvX/Aea44+UxXjx0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbe00877050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bag-of-Words with Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bow_input (InputLayer)       (None, 5000)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                320064    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 320,129\n",
      "Trainable params: 320,129\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Fitting...\n",
      "['bow_input']\n",
      "['dense_3']\n",
      "['bow_input']\n",
      "['dense_3']\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 5s 202us/step - loss: 0.3288 - acc: 0.8603 - val_loss: 0.3015 - val_acc: 0.8716\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 4s 167us/step - loss: 0.2355 - acc: 0.9034 - val_loss: 0.3123 - val_acc: 0.8681\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 5s 186us/step - loss: 0.2022 - acc: 0.9166 - val_loss: 0.3357 - val_acc: 0.8622\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 4s 180us/step - loss: 0.1786 - acc: 0.9287 - val_loss: 0.3396 - val_acc: 0.8639\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 4s 167us/step - loss: 0.1515 - acc: 0.9400 - val_loss: 0.3764 - val_acc: 0.8614\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 5s 181us/step - loss: 0.1226 - acc: 0.9529 - val_loss: 0.4110 - val_acc: 0.8582\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 4s 179us/step - loss: 0.0916 - acc: 0.9665 - val_loss: 0.4509 - val_acc: 0.8571\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 4s 168us/step - loss: 0.0634 - acc: 0.9796 - val_loss: 0.5089 - val_acc: 0.8516\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 4s 167us/step - loss: 0.0400 - acc: 0.9898 - val_loss: 0.5814 - val_acc: 0.8519\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 5s 186us/step - loss: 0.0238 - acc: 0.9952 - val_loss: 0.6323 - val_acc: 0.8505\n",
      "Accuracy (epoch 1): 0.8603 train, 0.8716 val\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Activation, Dense, Input\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "class BOWHiddenSentimentModel(object):\n",
    "    def __init__(self, N=64):\n",
    "        bow = Input(shape=(len(vocab),), name='bow_input')\n",
    "        hidden = Dense(N, activation='tanh')(bow)\n",
    "        sentiment = Dense(1, activation='sigmoid')(hidden)\n",
    "\n",
    "        self.model = Model(inputs=[bow], outputs=[sentiment])\n",
    "        self.model.summary()\n",
    "        self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "    def train(self, X, y, X_val, y_val):\n",
    "        print('Fitting...')\n",
    "        return self.model.fit(np.array(X), y, validation_data=(np.array(X_val), y_val), epochs=10, verbose=1)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.model.predict(np.array(X))\n",
    "    \n",
    "sentiment = BOWHiddenSentimentModel()\n",
    "history = sentiment.train(X_bow_train, y_train, X_bow_val, y_val)\n",
    "best_train_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lMMYcWbCjoSqAB1yuxbikoqqWl7/YzEtfZLGnuparT0nm3vNOpGecLRVujAlO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\nY0yjLCyMMcY06v8D4wNHJaDdIHkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd1746bcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overfitting!  Ideas:\n",
    "  * Apply dropout\n",
    "  * L2-regularize the weights of the hidden layer\n",
    "  * Different N values?\n",
    "  * Multiple hidden layers, with different activation?\n",
    "  * tf-idf !\n",
    "  \n",
    "Can anyone fix the bag-of-words model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bow_input (InputLayer)       (None, 5000)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 128)               640128    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 640,257\n",
      "Trainable params: 640,257\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Fitting...\n",
      "['bow_input']\n",
      "['dense_5']\n",
      "['bow_input']\n",
      "['dense_5']\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 5s 195us/step - loss: 0.4958 - acc: 0.8481 - val_loss: 0.4592 - val_acc: 0.8628\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 5s 186us/step - loss: 0.4398 - acc: 0.8758 - val_loss: 0.4450 - val_acc: 0.8684\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 5s 191us/step - loss: 0.4247 - acc: 0.8797 - val_loss: 0.4358 - val_acc: 0.8673\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 5s 194us/step - loss: 0.4172 - acc: 0.8786 - val_loss: 0.4341 - val_acc: 0.8688\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 5s 181us/step - loss: 0.4061 - acc: 0.8806 - val_loss: 0.4229 - val_acc: 0.8703\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 5s 191us/step - loss: 0.3995 - acc: 0.8823 - val_loss: 0.4275 - val_acc: 0.8681\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 5s 182us/step - loss: 0.3896 - acc: 0.8869 - val_loss: 0.4179 - val_acc: 0.8706\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 5s 192us/step - loss: 0.3840 - acc: 0.8861 - val_loss: 0.4130 - val_acc: 0.8704\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 5s 183us/step - loss: 0.3799 - acc: 0.8870 - val_loss: 0.4285 - val_acc: 0.8631\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 5s 181us/step - loss: 0.3741 - acc: 0.8876 - val_loss: 0.4163 - val_acc: 0.8664\n",
      "Accuracy (epoch 7): 0.8869 train, 0.8706 val\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Activation, Dense, Dropout, Input\n",
    "from keras.models import Model\n",
    "from keras import regularizers\n",
    "import numpy as np\n",
    "\n",
    "class BOWHiddenRegularizedSentimentModel(object):\n",
    "    def __init__(self, N=128):\n",
    "        bow = Input(shape=(len(vocab),), name='bow_input')\n",
    "        hidden = Dropout(0.5)(Dense(N, kernel_regularizer=regularizers.l2(1e-3))(bow))\n",
    "        sentiment = Dense(1, activation='sigmoid')(hidden)\n",
    "\n",
    "        self.model = Model(inputs=[bow], outputs=[sentiment])\n",
    "        self.model.summary()\n",
    "        self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "    def train(self, X, y, X_val, y_val):\n",
    "        print('Fitting...')\n",
    "        return self.model.fit(np.array(X), y, validation_data=(np.array(X_val), y_val), epochs=10, verbose=1)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.model.predict(np.array(X))\n",
    "    \n",
    "sentiment = BOWHiddenRegularizedSentimentModel()\n",
    "history = sentiment.train(X_bow_train, y_train, X_bow_val, y_val)\n",
    "best_train_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GloVe-based Model (let's start with averaging)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download and extract the official pre-trained GloVe matrix:\n",
    "\n",
    "    https://nlp.stanford.edu/data/glove.6B.zip\n",
    "    unzip glove.6B.zip\n",
    "    \n",
    "We decided to use GloVe over word2vec as in our experience, GloVe carries word semantics better and works better in semantic-sensitive tasks.  But the difference is not huge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading GloVe vocabulary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may need to restart your kernel here if you don't have a lot of free memory, to get rid of the bag-of-words vectors from before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Embedding dimension means how many elements each word vector has.\n",
    "# Common values are 50 and 300.  Generally, larger vectors have more\n",
    "# capacity to carry information (meaning).  But the training process\n",
    "# is slower, both vectors and further layers take more memory, and\n",
    "# there could be higher risk of overfitting.\n",
    "EMBEDDING_DIM = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "# let's create a dictionary of each word in the pre-trained GloVe embeddings, saving its location indexes \n",
    "import os\n",
    "GLOVE_DIR = \".\"\n",
    "embeddings_index = {}\n",
    "f = open(os.path.join(GLOVE_DIR, 'glove.6B.%dd.txt' % EMBEDDING_DIM))\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Found %s word vectors.' % len(embeddings_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# a matrix that is indexed by our vocabulary, containing\n",
    "# GloVe embedding for each vocabulary element\n",
    "embedding_matrix = np.zeros((len(vocab) + 1, EMBEDDING_DIM))\n",
    "for i, word in enumerate(vocab):\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        # words not found in embedding index will be all-zeros.\n",
    "        # also, [0] is reserved for padding\n",
    "        embedding_matrix[i + 1] = embedding_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking how many words have no pre-trained GloVe word embeddings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0076"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1. * np.count_nonzero(np.all(embedding_matrix == 0, axis=1)) / len(vocab)  # OOV portion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are representing reviews as sequences now.  However, each sequence must have the same length on the input in Keras.  We will pad shorter sequences by zeroes from right, but what should be the maximum sequence length?  Let's find a compromise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Longest: 2470, Average: 233.778560, Median: 174, 90% percentile: 458 tokens\n"
     ]
    }
   ],
   "source": [
    "lengths = sorted([len(X) for X in X_train])\n",
    "percentile = 0.90\n",
    "seq_cutoff = lengths[int(len(lengths)*percentile)]\n",
    "print('Longest: %d, Average: %f, Median: %d, %d%% percentile: %d tokens' % (lengths[-1], np.mean(lengths), lengths[int(len(lengths)*0.5)], percentile*100, seq_cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def vocab_indices_vector(tokens):\n",
    "    vector = [0] * seq_cutoff\n",
    "    if len(tokens) > seq_cutoff:\n",
    "        # Remove the middle\n",
    "        tokens = tokens[: seq_cutoff // 2] + ['SINGLE_PADDING_IN_THE_MIDDLE'] + tokens[-seq_cutoff // 2 :]\n",
    "    for i, t in enumerate(tokens):\n",
    "        try:\n",
    "            vector[i] = vocab.index(t) + 1  # reserving 0 for padding\n",
    "        except:\n",
    "            pass  # ignore missing words\n",
    "    return vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_indices_train = [vocab_indices_vector(x) for x in X_train]\n",
    "X_indices_val = [vocab_indices_vector(x) for x in X_val]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GloVe averaging model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "seq_input (InputLayer)       (None, 458)               0         \n",
      "_________________________________________________________________\n",
      "embedding_1 (Embedding)      (None, 458, 50)           250050    \n",
      "_________________________________________________________________\n",
      "global_average_pooling1d_1 ( (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 250,101\n",
      "Trainable params: 51\n",
      "Non-trainable params: 250,050\n",
      "_________________________________________________________________\n",
      "Fitting...\n",
      "['seq_input']\n",
      "['dense_6']\n",
      "['seq_input']\n",
      "['dense_6']\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 3s 133us/step - loss: 0.6879 - acc: 0.5726 - val_loss: 0.6785 - val_acc: 0.6280\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 3s 125us/step - loss: 0.6704 - acc: 0.6473 - val_loss: 0.6655 - val_acc: 0.6472\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 3s 125us/step - loss: 0.6581 - acc: 0.6589 - val_loss: 0.6551 - val_acc: 0.6557\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 3s 123us/step - loss: 0.6482 - acc: 0.6655 - val_loss: 0.6470 - val_acc: 0.6612\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 3s 124us/step - loss: 0.6402 - acc: 0.6713 - val_loss: 0.6401 - val_acc: 0.6670\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 3s 125us/step - loss: 0.6335 - acc: 0.6774 - val_loss: 0.6342 - val_acc: 0.6721\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 3s 124us/step - loss: 0.6276 - acc: 0.6819 - val_loss: 0.6291 - val_acc: 0.6760\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 3s 124us/step - loss: 0.6225 - acc: 0.6856 - val_loss: 0.6245 - val_acc: 0.6800\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 3s 125us/step - loss: 0.6178 - acc: 0.6881 - val_loss: 0.6203 - val_acc: 0.6847\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 3s 123us/step - loss: 0.6137 - acc: 0.6929 - val_loss: 0.6165 - val_acc: 0.6879\n",
      "Accuracy (epoch 10): 0.6929 train, 0.6879 val\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Activation, GlobalAveragePooling1D, Dense, Embedding, Input\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "class GloveAvgSentimentModel(object):\n",
    "    def __init__(self):\n",
    "        self.model = self.create()\n",
    "        self.model.summary()\n",
    "        self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "        \n",
    "    def create(self):\n",
    "        seq_indices = Input(shape=(seq_cutoff,), name='seq_input')                    \n",
    "        seq_embedded = Embedding(input_dim=len(vocab) + 1, output_dim=EMBEDDING_DIM, weights=[embedding_matrix],\n",
    "                                 input_length=seq_cutoff, trainable=False)(seq_indices)\n",
    "        avg_embedded = GlobalAveragePooling1D()(seq_embedded)\n",
    "        sentiment = Dense(1, activation='sigmoid')(avg_embedded)\n",
    "\n",
    "        return Model(inputs=[seq_indices], outputs=[sentiment])\n",
    "\n",
    "    def train(self, X, y, X_val, y_val):\n",
    "        print('Fitting...')\n",
    "        return self.model.fit(np.array(X, dtype='int32'), y,\n",
    "                              validation_data=(np.array(X_val, dtype='int32'), y_val), epochs=10, verbose=1)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.model.predict(np.array(X))\n",
    "    \n",
    "sentiment = GloveAvgSentimentModel()\n",
    "history = sentiment.train(X_indices_train, y_train, X_indices_val, y_val)\n",
    "best_train_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Questions:\n",
    "  * Predict sentiment from mean embedding, or mean sentiment from each embedding?\n",
    "  * Projection to a latent \"sentiment predictive\" vector space first?\n",
    "  \n",
    "Other ideas (for pretty much all further models):\n",
    "  * Trainable embeddings?\n",
    "  * ...what about embeddings from scratch, by the way?\n",
    "  * Just more epochs?\n",
    "\n",
    "Advanced idea:\n",
    "  * Could we visualize importance of individual words towards the predicted sentiment?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maximum in a \"sentiment predictive\" space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "seq_input (InputLayer)       (None, 458)               0         \n",
      "_________________________________________________________________\n",
      "embedding_2 (Embedding)      (None, 458, 50)           250050    \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 458, 64)           3264      \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_1 (Glob (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 253,379\n",
      "Trainable params: 3,329\n",
      "Non-trainable params: 250,050\n",
      "_________________________________________________________________\n",
      "Fitting...\n",
      "['seq_input']\n",
      "['dense_8']\n",
      "['seq_input']\n",
      "['dense_8']\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 4s 155us/step - loss: 0.5546 - acc: 0.7292 - val_loss: 0.4599 - val_acc: 0.7936\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 4s 144us/step - loss: 0.4316 - acc: 0.8094 - val_loss: 0.4171 - val_acc: 0.8119\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 4s 145us/step - loss: 0.3946 - acc: 0.8284 - val_loss: 0.4106 - val_acc: 0.8125\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 4s 147us/step - loss: 0.3717 - acc: 0.8409 - val_loss: 0.3843 - val_acc: 0.8272\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 4s 146us/step - loss: 0.3566 - acc: 0.8474 - val_loss: 0.3786 - val_acc: 0.8289\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 4s 147us/step - loss: 0.3432 - acc: 0.8526 - val_loss: 0.3736 - val_acc: 0.8318\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 4s 147us/step - loss: 0.3328 - acc: 0.8572 - val_loss: 0.3817 - val_acc: 0.8264\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 4s 145us/step - loss: 0.3251 - acc: 0.8625 - val_loss: 0.3688 - val_acc: 0.8352\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 4s 144us/step - loss: 0.3184 - acc: 0.8652 - val_loss: 0.3665 - val_acc: 0.8362\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 4s 145us/step - loss: 0.3121 - acc: 0.8683 - val_loss: 0.3675 - val_acc: 0.8357\n",
      "Accuracy (epoch 9): 0.8652 train, 0.8362 val\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Activation, GlobalMaxPooling1D, Dense, Embedding, Input\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "class GloveHiddenMaxSentimentModel(object):\n",
    "    def __init__(self, N=64):\n",
    "        self.model = self.create(N)\n",
    "        self.model.summary()\n",
    "        self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "        \n",
    "    def create(self, N):\n",
    "        seq_indices = Input(shape=(seq_cutoff,), name='seq_input')                    \n",
    "        seq_embedded = Embedding(input_dim=len(vocab) + 1, output_dim=EMBEDDING_DIM, weights=[embedding_matrix],\n",
    "                                 input_length=seq_cutoff, trainable=False)(seq_indices)\n",
    "        seq_hidden = Dense(N, activation='tanh')(seq_embedded)\n",
    "        max_hidden = GlobalMaxPooling1D()(seq_hidden)\n",
    "        sentiment = Dense(1, activation='sigmoid')(max_hidden)\n",
    "\n",
    "        return Model(inputs=[seq_indices], outputs=[sentiment])\n",
    "\n",
    "    def train(self, X, y, X_val, y_val):\n",
    "        print('Fitting...')\n",
    "        return self.model.fit(np.array(X, dtype='int32'), y,\n",
    "                              validation_data=(np.array(X_val, dtype='int32'), y_val), epochs=10, verbose=1)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.model.predict(np.array(X))\n",
    "    \n",
    "sentiment = GloveHiddenMaxSentimentModel()\n",
    "history = sentiment.train(X_indices_train, y_train, X_indices_val, y_val)\n",
    "best_train_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ideas:\n",
    "  * Averaging rather than maximum?\n",
    "  * Do other N values make a difference?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolutions on sequences of embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "seq_input (InputLayer)       (None, 458)               0         \n",
      "_________________________________________________________________\n",
      "embedding_3 (Embedding)      (None, 458, 50)           250050    \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 456, 64)           9664      \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_2 (Glob (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 263,939\n",
      "Trainable params: 13,889\n",
      "Non-trainable params: 250,050\n",
      "_________________________________________________________________\n",
      "Fitting...\n",
      "['seq_input']\n",
      "['dense_10']\n",
      "['seq_input']\n",
      "['dense_10']\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 6s 240us/step - loss: 0.4946 - acc: 0.7489 - val_loss: 0.4146 - val_acc: 0.8111\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 4s 168us/step - loss: 0.3692 - acc: 0.8385 - val_loss: 0.3745 - val_acc: 0.8340\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 4s 167us/step - loss: 0.3295 - acc: 0.8583 - val_loss: 0.4009 - val_acc: 0.8249\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 4s 167us/step - loss: 0.3007 - acc: 0.8734 - val_loss: 0.3925 - val_acc: 0.8296\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 4s 167us/step - loss: 0.2730 - acc: 0.8872 - val_loss: 0.3785 - val_acc: 0.8374\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 4s 168us/step - loss: 0.2498 - acc: 0.8979 - val_loss: 0.3955 - val_acc: 0.8358\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 4s 167us/step - loss: 0.2358 - acc: 0.9030 - val_loss: 0.4073 - val_acc: 0.8319\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 4s 168us/step - loss: 0.2138 - acc: 0.9129 - val_loss: 0.5104 - val_acc: 0.8086\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 4s 166us/step - loss: 0.2093 - acc: 0.9157 - val_loss: 0.4437 - val_acc: 0.8274\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 4s 168us/step - loss: 0.1935 - acc: 0.9227 - val_loss: 0.4514 - val_acc: 0.8310\n",
      "Accuracy (epoch 5): 0.8872 train, 0.8374 val\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Activation, Conv1D, Dense, Embedding, GlobalMaxPooling1D, Input\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "class GloveCNNSentimentModel(object):\n",
    "    def __init__(self, N=64, size=3):\n",
    "        self.model = self.create(N, size)\n",
    "        self.model.summary()\n",
    "        self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "        \n",
    "    def create(self, N, size):\n",
    "        seq_indices = Input(shape=(seq_cutoff,), name='seq_input') \n",
    "        seq_embedded = Embedding(input_dim=len(vocab) + 1, output_dim=EMBEDDING_DIM, weights=[embedding_matrix],\n",
    "                                 input_length=seq_cutoff, trainable=False)(seq_indices)\n",
    "        seq_conv = Conv1D(N, size, activation='tanh')(seq_embedded)\n",
    "        max_conv = GlobalMaxPooling1D()(seq_conv)\n",
    "        hidden_repr = Dense(N, activation='tanh')(max_conv)\n",
    "        sentiment = Dense(1, activation='sigmoid')(hidden_repr)\n",
    "\n",
    "        return Model(inputs=[seq_indices], outputs=[sentiment])\n",
    "\n",
    "    def train(self, X, y, X_val, y_val):\n",
    "        print('Fitting...')\n",
    "        return self.model.fit(np.array(X, dtype='int32'), y,\n",
    "                              validation_data=(np.array(X_val, dtype='int32'), y_val), epochs=10, verbose=1)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.model.predict(np.array(X))\n",
    "    \n",
    "sentiment = GloveCNNSentimentModel()\n",
    "history = sentiment.train(X_indices_train, y_train, X_indices_val, y_val)\n",
    "best_train_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model still sucks.  What problems does it have?\n",
    "\n",
    "But the model below is awesome.  (Stolen from Keras' `examples/imdb_cnn.py`.)\n",
    "  * What are the differences to the model above?\n",
    "  * What exactly makes it so awesome?\n",
    "  * Did we beat the bag-of-words baseline yet?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "seq_input (InputLayer)       (None, 458)               0         \n",
      "_________________________________________________________________\n",
      "embedding_4 (Embedding)      (None, 458, 50)           250050    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 458, 50)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 456, 256)          38656     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_3 (Glob (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 354,755\n",
      "Trainable params: 354,755\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Fitting...\n",
      "['seq_input']\n",
      "['dense_12']\n",
      "['seq_input']\n",
      "['dense_12']\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 6s 241us/step - loss: 0.4191 - acc: 0.7960 - val_loss: 0.2955 - val_acc: 0.8770\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 6s 224us/step - loss: 0.2512 - acc: 0.8972 - val_loss: 0.2800 - val_acc: 0.8869\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 6s 222us/step - loss: 0.1829 - acc: 0.9278 - val_loss: 0.3035 - val_acc: 0.8794\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 6s 223us/step - loss: 0.1251 - acc: 0.9532 - val_loss: 0.3131 - val_acc: 0.8812\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 6s 225us/step - loss: 0.0889 - acc: 0.9672 - val_loss: 0.3449 - val_acc: 0.8795\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 6s 222us/step - loss: 0.0610 - acc: 0.9780 - val_loss: 0.4176 - val_acc: 0.8774\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 6s 221us/step - loss: 0.0499 - acc: 0.9812 - val_loss: 0.5348 - val_acc: 0.8638\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 6s 226us/step - loss: 0.0415 - acc: 0.9850 - val_loss: 0.4720 - val_acc: 0.8758\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 6s 224us/step - loss: 0.0351 - acc: 0.9878 - val_loss: 0.5985 - val_acc: 0.8618\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 6s 224us/step - loss: 0.0320 - acc: 0.9885 - val_loss: 0.5249 - val_acc: 0.8746\n",
      "Accuracy (epoch 2): 0.8972 train, 0.8869 val\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Activation, Conv1D, Dense, Embedding, GlobalMaxPooling1D, Input\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "class GloveCNNAwesomeSentimentModel(object):\n",
    "    def __init__(self, N=256, size=3):\n",
    "        self.model = self.create(N, size)\n",
    "        self.model.summary()\n",
    "        self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "        \n",
    "    def create(self, N, size):\n",
    "        seq_indices = Input(shape=(seq_cutoff,), name='seq_input') \n",
    "        seq_embedded = Embedding(input_dim=len(vocab) + 1, output_dim=EMBEDDING_DIM,\n",
    "                                 input_length=seq_cutoff)(seq_indices)\n",
    "        seq_conv = Conv1D(N, size, activation='relu')(Dropout(0.2)(seq_embedded))\n",
    "        max_conv = GlobalMaxPooling1D()(seq_conv)\n",
    "        hidden_repr = Dense(N, activation='relu')(max_conv)\n",
    "        sentiment = Dense(1, activation='sigmoid')(Dropout(0.2)(hidden_repr))\n",
    "\n",
    "        return Model(inputs=[seq_indices], outputs=[sentiment])\n",
    "\n",
    "    def train(self, X, y, X_val, y_val):\n",
    "        print('Fitting...')\n",
    "        return self.model.fit(np.array(X, dtype='int32'), y,\n",
    "                              validation_data=(np.array(X_val, dtype='int32'), y_val), epochs=10, verbose=1)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.model.predict(np.array(X))\n",
    "    \n",
    "sentiment = GloveCNNAwesomeSentimentModel()\n",
    "history = sentiment.train(X_indices_train, y_train, X_indices_val, y_val)\n",
    "best_train_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we make the model more awesome yet?\n",
    "\n",
    "Ideas:\n",
    "  * So what about the Glove pretrained embeddings?\n",
    "  * Project the embeddings to a more convolution-friendly space?\n",
    "  * Average pooling instead of max?\n",
    "  * Is 300D embedding better?\n",
    "  \n",
    "Advanced ideas:\n",
    "  * What about _both_ average and max pooling?\n",
    "  * `relu` activation is popular in computer vision CNNs instead of `tanh`.\n",
    "  * Some people recommend `rmsprop` optimizer rather than `adam` for CNNs. Does it matter?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recurrence on sequences of embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "seq_input (InputLayer)       (None, 458)               0         \n",
      "_________________________________________________________________\n",
      "embedding_5 (Embedding)      (None, 458, 50)           250050    \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, 64)                22080     \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 272,195\n",
      "Trainable params: 22,145\n",
      "Non-trainable params: 250,050\n",
      "_________________________________________________________________\n",
      "Fitting...\n",
      "['seq_input']\n",
      "['dense_13']\n",
      "['seq_input']\n",
      "['dense_13']\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 540s 22ms/step - loss: 0.6910 - acc: 0.5092 - val_loss: 0.6826 - val_acc: 0.5236\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 536s 21ms/step - loss: 0.5446 - acc: 0.6851 - val_loss: 0.4029 - val_acc: 0.8170\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 527s 21ms/step - loss: 0.3816 - acc: 0.8287 - val_loss: 0.3633 - val_acc: 0.8353\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 527s 21ms/step - loss: 0.3472 - acc: 0.8488 - val_loss: 0.3377 - val_acc: 0.8502\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 518s 21ms/step - loss: 0.3239 - acc: 0.8586 - val_loss: 0.3275 - val_acc: 0.8560\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 523s 21ms/step - loss: 0.3009 - acc: 0.8702 - val_loss: 0.3432 - val_acc: 0.8504\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 526s 21ms/step - loss: 0.2890 - acc: 0.8768 - val_loss: 0.3201 - val_acc: 0.8607\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 520s 21ms/step - loss: 0.2680 - acc: 0.8852 - val_loss: 0.3279 - val_acc: 0.8537\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 524s 21ms/step - loss: 0.2549 - acc: 0.8939 - val_loss: 0.3133 - val_acc: 0.8665\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 529s 21ms/step - loss: 0.2428 - acc: 0.9001 - val_loss: 0.3242 - val_acc: 0.8631\n",
      "Accuracy (epoch 9): 0.8939 train, 0.8665 val\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Activation, Dense, Embedding, GRU, Input\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "class GloveGRUSentimentModel(object):\n",
    "    def __init__(self, N=64):\n",
    "        self.model = self.create(N)\n",
    "        self.model.summary()\n",
    "        self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "        \n",
    "    def create(self, N):\n",
    "        seq_indices = Input(shape=(seq_cutoff,), name='seq_input') \n",
    "        seq_embedded = Embedding(input_dim=len(vocab) + 1, output_dim=EMBEDDING_DIM, weights=[embedding_matrix],\n",
    "                                 input_length=seq_cutoff, trainable=False)(seq_indices)\n",
    "        recursive_repr = GRU(N)(seq_embedded)\n",
    "        sentiment = Dense(1, activation='sigmoid')(recursive_repr)\n",
    "\n",
    "        return Model(inputs=[seq_indices], outputs=[sentiment])\n",
    "\n",
    "    def train(self, X, y, X_val, y_val):\n",
    "        print('Fitting...')\n",
    "        return self.model.fit(np.array(X, dtype='int32'), y,\n",
    "                              validation_data=(np.array(X_val, dtype='int32'), y_val), epochs=10, verbose=1)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.model.predict(np.array(X))\n",
    "    \n",
    "sentiment = GloveGRUSentimentModel()\n",
    "history = sentiment.train(X_indices_train, y_train, X_indices_val, y_val)\n",
    "best_train_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you take the time to train the model, it seems to really suck after the first epoch.  What could be the problem?  (Imagine the input, and how the RNN processes it.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model below fixes the problem - how?  What is the most essential change?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "seq_input (InputLayer)       (None, 458)               0         \n",
      "_________________________________________________________________\n",
      "embedding_6 (Embedding)      (None, 458, 50)           250050    \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 128)               44160     \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 294,339\n",
      "Trainable params: 294,339\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Fitting...\n",
      "['seq_input']\n",
      "['dense_14']\n",
      "['seq_input']\n",
      "['dense_14']\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 306s 12ms/step - loss: 0.5319 - acc: 0.7134 - val_loss: 0.3534 - val_acc: 0.8473\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 307s 12ms/step - loss: 0.3012 - acc: 0.8737 - val_loss: 0.2861 - val_acc: 0.8796\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 307s 12ms/step - loss: 0.2445 - acc: 0.9011 - val_loss: 0.3012 - val_acc: 0.8715\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 303s 12ms/step - loss: 0.2094 - acc: 0.9182 - val_loss: 0.2831 - val_acc: 0.8879\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 303s 12ms/step - loss: 0.1752 - acc: 0.9330 - val_loss: 0.3280 - val_acc: 0.8724\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 307s 12ms/step - loss: 0.1453 - acc: 0.9461 - val_loss: 0.3160 - val_acc: 0.8874\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 308s 12ms/step - loss: 0.1195 - acc: 0.9563 - val_loss: 0.3404 - val_acc: 0.8826\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 306s 12ms/step - loss: 0.0907 - acc: 0.9699 - val_loss: 0.3737 - val_acc: 0.8775\n",
      "Epoch 9/10\n",
      "25000/25000 [==============================] - 305s 12ms/step - loss: 0.0631 - acc: 0.9806 - val_loss: 0.4440 - val_acc: 0.8740\n",
      "Epoch 10/10\n",
      "25000/25000 [==============================] - 304s 12ms/step - loss: 0.0398 - acc: 0.9894 - val_loss: 0.5192 - val_acc: 0.8696\n",
      "Accuracy (epoch 4): 0.9182 train, 0.8879 val\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Activation, Bidirectional, Dense, Embedding, GRU, Input\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "class GloveGRUSentimentModel(object):\n",
    "    def __init__(self, N=64):\n",
    "        self.model = self.create(N)\n",
    "        self.model.summary()\n",
    "        self.model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "        \n",
    "    def create(self, N):\n",
    "        seq_indices = Input(shape=(seq_cutoff,), name='seq_input') \n",
    "        seq_embedded = Embedding(input_dim=len(vocab) + 1, output_dim=EMBEDDING_DIM, weights=[embedding_matrix],\n",
    "                                 input_length=seq_cutoff, mask_zero=True)(seq_indices)\n",
    "        recursive_repr = Bidirectional(GRU(N))(seq_embedded)\n",
    "        sentiment = Dense(1, activation='sigmoid')(recursive_repr)\n",
    "\n",
    "        return Model(inputs=[seq_indices], outputs=[sentiment])\n",
    "\n",
    "    def train(self, X, y, X_val, y_val):\n",
    "        print('Fitting...')\n",
    "        return self.model.fit(np.array(X, dtype='int32'), y,\n",
    "                              validation_data=(np.array(X_val, dtype='int32'), y_val), epochs=10, verbose=1,\n",
    "                             batch_size=128)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.model.predict(np.array(X))\n",
    "    \n",
    "sentiment = GloveGRUSentimentModel()\n",
    "history = sentiment.train(X_indices_train, y_train, X_indices_val, y_val)\n",
    "best_train_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are overfitting.  Can we improve generalization?\n",
    "\n",
    "Ideas:\n",
    "  * `GRU(N, return_sequence=True)` would return a single output element for each input element, rather than a single output for the whole sequence.  What cool things could we use this for?\n",
    "  * Any ideas to borrow from CNNs?\n",
    "  * Could we stack multiple GRUs on top of each other?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining CNN and GRU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go wild and take a look at an even more advanced model combining several ideas.  Can you explain what's going on here? Is this madness worth it? Which further improvements might be most desirable to test?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "seq_input (InputLayer)          (None, 458)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_7 (Embedding)         (None, 458, 50)      250050      seq_input[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 458, 50)      0           embedding_7[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_15 (Dense)                (None, 458, 64)      3264        dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 458, 64)      0           dense_15[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 458, 64)      12352       dropout_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 458, 64)      0           dense_15[0][0]                   \n",
      "                                                                 conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_4 (GlobalM (None, 64)           0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_2 (Bidirectional) (None, 128)          49536       add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "sentiment_by_hidden (Dense)     (None, 1)            65          global_max_pooling1d_4[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "sentiment (Dense)               (None, 1)            129         bidirectional_2[0][0]            \n",
      "==================================================================================================\n",
      "Total params: 315,396\n",
      "Trainable params: 315,396\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Fitting...\n",
      "['seq_input']\n",
      "['sentiment_by_hidden', 'sentiment']\n",
      "['seq_input']\n",
      "['sentiment_by_hidden', 'sentiment']\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/10\n",
      "25000/25000 [==============================] - 1047s 42ms/step - loss: 0.5901 - sentiment_by_hidden_loss: 0.5373 - sentiment_loss: 0.4826 - sentiment_by_hidden_acc: 0.7392 - sentiment_acc: 0.7476 - val_loss: 0.4038 - val_sentiment_by_hidden_loss: 0.3823 - val_sentiment_loss: 0.3274 - val_sentiment_by_hidden_acc: 0.8323 - val_sentiment_acc: 0.8634\n",
      "Epoch 2/10\n",
      "25000/25000 [==============================] - 946s 38ms/step - loss: 0.3874 - sentiment_by_hidden_loss: 0.3742 - sentiment_loss: 0.3125 - sentiment_by_hidden_acc: 0.8425 - sentiment_acc: 0.8664 - val_loss: 0.3651 - val_sentiment_by_hidden_loss: 0.3371 - val_sentiment_loss: 0.2976 - val_sentiment_by_hidden_acc: 0.8588 - val_sentiment_acc: 0.8724\n",
      "Epoch 3/10\n",
      "25000/25000 [==============================] - 931s 37ms/step - loss: 0.3332 - sentiment_by_hidden_loss: 0.3287 - sentiment_loss: 0.2675 - sentiment_by_hidden_acc: 0.8644 - sentiment_acc: 0.8889 - val_loss: 0.3490 - val_sentiment_by_hidden_loss: 0.3193 - val_sentiment_loss: 0.2851 - val_sentiment_by_hidden_acc: 0.8656 - val_sentiment_acc: 0.8832\n",
      "Epoch 4/10\n",
      "25000/25000 [==============================] - 948s 38ms/step - loss: 0.3013 - sentiment_by_hidden_loss: 0.3011 - sentiment_loss: 0.2411 - sentiment_by_hidden_acc: 0.8757 - sentiment_acc: 0.9020 - val_loss: 0.3201 - val_sentiment_by_hidden_loss: 0.3130 - val_sentiment_loss: 0.2575 - val_sentiment_by_hidden_acc: 0.8685 - val_sentiment_acc: 0.8935\n",
      "Epoch 5/10\n",
      "25000/25000 [==============================] - 1038s 42ms/step - loss: 0.2738 - sentiment_by_hidden_loss: 0.2822 - sentiment_loss: 0.2173 - sentiment_by_hidden_acc: 0.8866 - sentiment_acc: 0.9147 - val_loss: 0.3215 - val_sentiment_by_hidden_loss: 0.3108 - val_sentiment_loss: 0.2593 - val_sentiment_by_hidden_acc: 0.8705 - val_sentiment_acc: 0.8962\n",
      "Epoch 6/10\n",
      "25000/25000 [==============================] - 1065s 43ms/step - loss: 0.2577 - sentiment_by_hidden_loss: 0.2716 - sentiment_loss: 0.2034 - sentiment_by_hidden_acc: 0.8904 - sentiment_acc: 0.9198 - val_loss: 0.3290 - val_sentiment_by_hidden_loss: 0.3118 - val_sentiment_loss: 0.2666 - val_sentiment_by_hidden_acc: 0.8706 - val_sentiment_acc: 0.8902\n",
      "Epoch 7/10\n",
      "25000/25000 [==============================] - 1038s 42ms/step - loss: 0.2325 - sentiment_by_hidden_loss: 0.2570 - sentiment_loss: 0.1811 - sentiment_by_hidden_acc: 0.8966 - sentiment_acc: 0.9308 - val_loss: 0.3355 - val_sentiment_by_hidden_loss: 0.3179 - val_sentiment_loss: 0.2719 - val_sentiment_by_hidden_acc: 0.8692 - val_sentiment_acc: 0.8940\n",
      "Epoch 8/10\n",
      "25000/25000 [==============================] - 1030s 41ms/step - loss: 0.2211 - sentiment_by_hidden_loss: 0.2480 - sentiment_loss: 0.1715 - sentiment_by_hidden_acc: 0.9011 - sentiment_acc: 0.9332 - val_loss: 0.3501 - val_sentiment_by_hidden_loss: 0.3197 - val_sentiment_loss: 0.2861 - val_sentiment_by_hidden_acc: 0.8723 - val_sentiment_acc: 0.8925\n",
      "Epoch 9/10\n",
      "12224/25000 [=============>................] - ETA: 7:11 - loss: 0.1980 - sentiment_by_hidden_loss: 0.2361 - sentiment_loss: 0.1508 - sentiment_by_hidden_acc: 0.9080 - sentiment_acc: 0.9431"
     ]
    }
   ],
   "source": [
    "from keras.layers import Activation, Bidirectional, Conv1D, Dense, Embedding, GlobalMaxPooling1D, GRU, Input, add\n",
    "from keras.models import Model\n",
    "import numpy as np\n",
    "\n",
    "class GloveCNNGRUSISentimentModel(object):\n",
    "    def __init__(self, N=64, size=3):\n",
    "        self.model = self.create(N, size)\n",
    "        self.model.summary()\n",
    "        self.model.compile(optimizer='adam', loss='binary_crossentropy', loss_weights=[0.2, 1.0], metrics=['accuracy'])\n",
    "        \n",
    "    def create(self, N, size):\n",
    "        seq_indices = Input(shape=(seq_cutoff,), name='seq_input') \n",
    "        seq_embedded = Embedding(input_dim=len(vocab) + 1, output_dim=EMBEDDING_DIM, weights=[embedding_matrix],\n",
    "                                 input_length=seq_cutoff)(seq_indices)\n",
    "        seq_hidden = Dense(N, activation='tanh')(Dropout(0.2)(seq_embedded))\n",
    "        \n",
    "        seq_conv = Conv1D(N, size, activation='tanh', padding='same')(Dropout(0.2)(seq_hidden))\n",
    "        # Residual skip-connection: The convolution just fine-tunes per-word Dense projection.\n",
    "        seq_hidden = add([seq_hidden, seq_conv])\n",
    "        \n",
    "        recursive_repr = Bidirectional(GRU(N))(seq_hidden)\n",
    "        sentiment = Dense(1, activation='sigmoid', name='sentiment')(recursive_repr)\n",
    "        \n",
    "        # Inception: Also try to make even the hidden representation good enough to predict sentiment.\n",
    "        max_hidden = GlobalMaxPooling1D()(seq_hidden)\n",
    "        sentiment_by_hidden = Dense(1, activation='sigmoid', name='sentiment_by_hidden')(max_hidden)\n",
    "\n",
    "        return Model(inputs=[seq_indices], outputs=[sentiment_by_hidden, sentiment])\n",
    "\n",
    "    def train(self, X, y, X_val, y_val):\n",
    "        print('Fitting...')\n",
    "        return self.model.fit(np.array(X, dtype='int32'), [np.array(y), np.array(y)],\n",
    "                              validation_data=(np.array(X_val, dtype='int32'),\n",
    "                                               [np.array(y_val), np.array(y_val)]),\n",
    "                              epochs=10, verbose=1)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.model.predict(np.array(X))[-1:]\n",
    "    \n",
    "sentiment = GloveCNNGRUSISentimentModel()\n",
    "history = sentiment.train(X_indices_train, y_train, X_indices_val, y_val)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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JfUTGli5OUHwSeaIof5qCikOyBqc8v2NIKmBTqfs6EvvgoEIOtfNouRk3iU+JIgTFRpInuRlkdc+qNI5hW8pJiyTjG5fiw6t0m0H2HTtGlsj6C8O2YR29juye1YXn3WLAD+REaAhZNaWzIIsVVvWyk26QCW3LCnz6jyGjBzQTeHD9g5M3Of6As7ykIcsHNScl63tKYfYehG9vzn3z7J+sy5dQPmL9lvc7hiVX6O+yeivVoWfku6alBRxM9rfK9mZYL0S5bcljXn2RyJs+xbYDkG7+x6g3czAEGAIMgX8LAq+kr8wDDKv7Xj0/S2SX8+61SHkGPU1vMZqz18DR8dbJi40k9w3O0+chfr6CX4kRDqqAOXiRbaeukpvh4SSc/90il0IPU/O/ykGvGUMQuQ+EwRpADI1EyAFX8hNwsqTQREQpL5MDApFSMDZC8wkQU4MDKQYhhgoYZx3wWug+aoJZHFiPmruFXAgLU1htqdR9lUKZ4fiayltO0nk6wK/us4eKkBgmHEDk0lEe/hcGOqIRBM54xPTFW0nwoT+NZNt6LZnye3B8Hj+Aes93NVXoOOsz4mCZS+dHmfUwiq+KAmYtL12sZHxlSsBlKhMhz8migc3pAFetTsiIKYZ50QlxlANCTpG9E32P3Lt3j9wMP0NW+39O0+Xy/qWBAsrx4A6ViuWstJL4jCwa+DoNM7zOQJT55M9dKU3RhtNl1hCVB0/FNOSGNhIvC1dFmOItpiF/yo17mMdIwIZrn1qiUyj0Lo1my+QUuIsGeIbzhky0CkuIhm1JnufgONG4vYYsaC9Ys5MmbATe1tCLnTIno3xS4si8rorJEjkmxu5nghEbew+y5ZJkffKm3sgQXxYyy5Vpsis7NsrakzFfpY81+RJjmuojxPBX8eQsHYpXgkzfLVy9kZMmu57DpGEiDTWAwWFoWC9E2W3Jo7m+SOSrfGrJmkHVhbZn1QSGkgt7YwgwBBgCfxcCr6avNJ876/tegZ8lssu/KyPWhVNBOH9uwpebAOT+RONU8snh1Pgocj4khBwPCSFXbj2mcQvS8Wr3b+SE4NMPGqKepyc8+V9dNG3xEWZt/EtcE5Q9zRiCsCuLFu0NDtLJYv5bnXYuZeHZvAtGDv+YZmHlsRgsndwH7zRujJ6jZ+On9sK2vydBoYgxY9CEMgCQ8iCc39rF+TVtKm0bLPVmC3qmIvSv+/IovJs/g+PkjeNRl+A/5lO07dgX6/+6ge/blae0387fI5w9yo7GT598B9h74MdZQ+Cu39Jo5/ImJswRzgtxkTbtv4g8T/RYzSsbf876nM9jmbZLMaVfIyofUAafjfxc9l54ztjdo1G7ei3UqlUL9TxbYeiMdTSxXotOY/XkDgaXzyZi9UQBm0rdV2FcN/m5lwoY/es+9CouGLLYMPJLnIzNovxER+MuPqITGVdn4fD1FP5dG3OKP3jafeYfCJjlTWmWrToM4SRYNkJ2rOX9+3dsSMPzcqREh/OHVDm65g1fz4ucD8+GI/pOmU5ptVemYfuFBPoOJGKz/2J+7/WQ3pwsOjjbmdgTi3RcPqLvL5y8UVa/lQ9wRYtebXmemREn8UAjGvuwjl6nN9iijVReeN32y0lATgyyZVKbckbuW8xv0Wz9zXz0aSJtFa778TCMLa03bvNiN67GCGfc5KZbsnSWpMClbF2+TMn6d/if2bKC33JZuftSTOpakxfBzgmgxprs3OCkskWVK+MmrTr8HSKrpFkEni315wS0f+FBgljfVEiZF0OAIcAQYAi8cgTs3WqgVx/h6Mve3YEIj41H5IVd8C7fBBu1PbDvVx9kPwnCR9P2oeyHs/FVx5pA9jOsn/opSpaviXe9vPChlxca1a2CrlN26MdOBZeNV6uAOXhhz6VI3Lt3C7du3cK9e/dwM/wM5o/vWHA5solTNpISEpAg+z1PTkZSXArS9YfCE5KSkZacrKBJ13IToIXwZ6e/edu5L1q/K7dM5wC3EvozRDlxyLDQWmRpz86YN7AD6jf4DIPaS4P8tPh4aHLN56GMV0e8U112vsGxCr5ZoT/UCODBlu24l5YLza2T/P5Z5Ebg0z7d4ePjQ38feX9PQbqxOhRJ5pO0mpc2JhgDV0TxaUyY2JXfz0sTBOBYVCa/PkCnSVGUJVf2KZpkPE/R8BQvtKn8u7JOCGFy3nJ3jQGrEBV9G5dCj2PruqUYPUAYoJWs74nAce+j/IeTEZEkDdQ0EYepdZ72H3kayc0Z2hjq95GQRG4Eth8xtqDoVud9ycoigLVbTvP0F7Zv4Z8fe/dAj169qZhP9i7B+dgcEO1NBCy6gDJt56N5NWN8aAQDh5Od3rIhgKLFLTMYkUOA0o17UsMLHMsf5u6mnVnS5R2YeSkJ7aaPtmDvtSs+9vsOXg088cX0IahVQuzC0hEfLykv0plNK+lFFi/mo8lH3yAsJpVHwN69EfatnYHX8jRUko7gDcK5upM/duTbwCc+PujfYwB8BjTC4iS9IZTcCFy8JVhmMoDYwlcr82Uh18Im00btResxmwEHL2z61dfk2VOp/ApbogLgnxOCm/eFcwYFwI2xYAgwBBgCDIECQcAVny89gPGNS+HxHn80qFIBtVt8gshec3EvIQBvl9Bhw5Sv+JQWLhwLd0RjQvO6+HzOdt5vZfAdEKJF8MrR2DdvCT/WLRCx9Exe7XfO3g216tVCTcUgpibq/rIajzbUwS2tfC64ILNpnleuJhxdq7xDV4mMqQ+gzRvGlhm5VYvIXUONBs7G8dV97Ipws+HCioUqBdFAxylZCrwkSosLz7EKvt5wGF/zUbMRcWIr5k/0xYYraRIzEy4XN+MAl5qt+cE0N2jm/rgx6/2Iq5RwUPd2qJSppVb5mjVbyIcRrRauddqYHHSJDKzl9SJJMsH+euUSIhuzz4vrB6PlmD0maXZ+8xF2fmMcvPFaMgZ4ljIOAPB+qyaoUa02UK02mjRvg08Hj8L0yXvQo2lPnAWQcOJnvNPWBTGX/VHODtARaUWrwZuvqfJs2qkLMG0fHxb5WL5qJJJXwCeje+PHwat4jxMLd+P2pLex68e9QLGRaO9ZHEUd2/IrL/zgPzcCm4/cRL16JxH4IgsTh3yUZ3mIKXHPpw/kSmCea5l8VAd+MasMBs8eh5kdZ/B+T3b74uCtfuhd1xG7lvzI+433ac0/8/pXq81wnLo6nCfLTIjEH+sWY9A30qSAYXxr6EvVEiztcaZoufJqWv1nDJ27A7PG9ECXQZIia5iG+E60TxF6VCin4g1GoWWzN5GmFa+kaIbF7wgre1qtMzo2MO5TRD6WPK3JlyX8Cp8mDnOHD+WT+fX4JrSuLCnzRAc62QWiESaXjPq9dISfPVr4YtqQgr3qip0NjFgUhgBDgCHAECgwBOzd6mFBWAImRkVBoyMoVqI8Xq8kGDZLuvInvtwQjTd8AjDA0xUbv2qIhZeFMfn03bfh2/YtXo4PBw5GvRHNEHY3CQ2a5O+7Lc+YxWN4eaT8uImaQmFXEe2/8sabLYTtKPnhb1tc22BIjEkFN7sPO0AxgLBIiEzcCj0GoI5F25osYpkH0cPLBzDjy3684vWZ/5+4t60sRtXvRLeUqUXXqi76FKFWzLg4HHov01/y0bkVlaljxlg1qDdMN8tKXuGXzlEWlm/holGscqS9ND1JYHwPGFCufjfsOLYAFd8fy6fDbRPcd3k8hjRxR1SoJPeVG0+Btipb+pxcqXxXTkUgbZqxwtSw/aeo57BGsKSnXYsWrS8hNekFesz9RG9tsAr6/Ngdi4cF8LzWDR2F517h/CrEJ+3qUf6WOFKfPbeETJWmelsfDCv1o7BSCmDmquPwnlIcCzY9xBs+69FWvtKqykHmmf0MgQtno/fkZfzWRW5l3en4DHSeLKz8ySgFp4X03Krj5iuBaOXxCbVMuPqbnljt54H1J/ZgkJd5q49El4GELEExXbTCH1+20N8BZyRQAXlYmK8CSi0fbLKxZ/aX+D44HvMP3sZIL/nKPsBZOPyocwUE7Yzl0zDVI2em65co7T1QtYzK7FA+JGRRGQIMAYYAQ+D/EQFHVK4p7f4ScpiIJb4T+WMzq+f3geb6erqTihvHTuoqp3eEu4MdKpYpWqDgiPt3CpSp9cwc4T17K0Z6qa8CWM/PuhichnzwWQKSkpLoLzktDSmxp+gZqcN3n+NlmhTO0T47NYoqGqKJZDFlJ9WracVQ7qmDJiETZbza4A23wi6GRARM7YNqTbpgw43OOH43BZun90WNKuWlcxdy0fJ0u6Jhay9KJe7a4jwST+5FRAGdh7CYFxGUP0Eg05dwU4EBNBu6iZa1WO6ajDQcXfIZT/b2uEBw72KY+PyiqfVnD8s1aEXrEcf84iXhstlytd6kImVwGrzKX6nqHjSuV4fmtL7JSR0rNcXQblLbSb0ezgf7fCyZlm/cZQBvkp4PyAnBvhMpqPTxADRQuadJztvQXbOFdLkz8qzjBrEdq+Or5b7U88airihevi2v6Mwc29XgfBwlM3IkXN+DTm5VeeVr6Nx9SM/aj65N3kTZUuoDcmvp3et0w+Wn1/HzMOmeNW5r7eAPauKXQ3eM5JF7yM8ybT16RR5U4G5r81XgAljBMHjpQHSfvh/Ttl3HxE7yD5vIxEE695erQYJG3quINDnQpOnbiUM1VCqtfqWDSP0qn87/gcvCXyWeLC2GAEOAIVCYCETuW8Yffej2/WK0Le+A66ek3RXTZvRTjLWykp7gbE5LVC79f6mAmYdZbRuc+RjWhjqihLs73GW/Um5uKFG6lKCgOPdF5fLuKOpmSCM/A+OAIm7SvK14lsi0JJmIj+EUB8vP35jmZT4k+vACDJizjSfaevE3tKllfMeOeQ6Goem4ejJE8HSqjtLyrUI5IQg4IG1HVMbMxraJrTBy3WWlt6k3C3kVLyPNpu8/Ztmg18nFVVHeXNm7urihpIuw4tS0dhX+XV4nOHcRqYhNSW2xf5mykjK3f8MpJKucjXsRfZmuUDq4mBpwlkTnwQMV6bo0mo3WdfQGH7haVqmVQknjiPv187J++6xsXJzxQtxap0ja7ItnjyHUsIhIyM02dWls4UoReYwfWvfnMeHO3C2c3MV8HqykJ9poHD4eiSLl6mHSivOICduPQY2kba1Tp+4zeQGwmB/xGTwnAJEmDOVoY4JQ1/ULk+EiD5NPK/Nlks8rCAjdMBbtx/yJiZsuY1ZvD6MUU2Lu4LGmCNoN0huU4c5UPRLO3smJFXe6eHVE1UKfuJKnbsbt5I1G1aU6YoaSBTEEGAIMAYbA341A9j3MHjCH3z0zcyx39EF2D6XLEHRtJhnP4kQNCZgHFPNCpQL+5hT20ku+YM4iwog0KToeLxWD0xd4HC0cZjdWzkwNUvMhClHdh2fA0AVvNZas7y3dcIKegTIg5F+Trh/mte82HzdUaNpqtPnzy8Tp3VsFFk7eqG0wUBCNjJhKwxhf7tDXc4QfEw6dD/rFh9/mJl8ZWf7lIOy6pTc2IGMcumEi+iw4C+824gy4ellZy6vq201oKrumr8aNNMnQBRfg5CQpuaYuQqYM9I5c6XiWYZDV7/dOBlIliovc8l1hG5vrm82pMsJZBjwXJVjFkyfwNPohfR3c2XjwKgbWatUTLZ2l1b8hY7pSS5QCjSu6+w4Tyfnthz0+kFbgpADzrlK1POmK3IWrkmzmY0mhdi6eGPVjL8kDwISJvQ1kVQRDvtKWfv8yNWTRuJmB4RLFSqjAw1r65Jv70LH7Rqpkvd7YG+svR2LhJ5V5hkoLi4ZyAnYuNdCmo95KqHYtek7ZYtQP5CaHw9ezOxyGdjY4DwvkvXJuW76MJVXzyUbSkyd4mpxhEJiNuJj7iIqKNcqLAaHRa/gOP7w7eAmmBFzH/P5S/ygS5iSEostbb2N/ZDpqtepK6/CRo9dFEvpMuRtK2xFnbMfU5fE0gp36iigNN+GwtI9ISdEbUbExHRPJM2+GAEOAIaKU8iAAACAASURBVPBqEbCxD8urr9RpnuPhk3iLvxt58VMFxQbZz6+dh41pmRi17kc0oIa8BO5lWtZDBdmiQk7CaYzzO4XpGwfzZ/dVZbDRs9AVMJKRiqc6/WH9XA0MP+3m5BZNUXPnZrYeF8yk52oeIWBqa97MMxc39mgM4mSzzLma5zQ9tcJMiH8qJGnnBrsCPTjtiA6fj6PbvB5sHIypAeqrMSkxx9D/nUH83tORvd4xgkCXqVdejGSUKSxGYYB63oStjnwiugPYsPOS4NQ8woZZs+mARtgCl44UjXIrnFZjrBRc275SOMfj3BdTBgnyl/ZoJ5nYzo3AJx4tsP7YDaRrtUhLiMSG2X34gVg7/yB0ri6szJgqK2t5cZYAvymrPyuVtQUdB/6Eh7ROpON4oGRsIynmAVKzFdq8Ef6We8jKA4BxfcvG/QsB6NFjLmXJrfZ41xeMeNi51MXUZYIFHo5gzMw/qGVAIUIc1s9cxDu5LZEda0krWpSh3mHv7oFhQ6oLb/Ye6NP5bUMSVPugG1X4bNl+yDG0d6vJn9Xh3MdOhBvIK0sy+xlvjCLx7BXEG1jrbNV3OB1oc4ZCfNpJK5gChxwkpEoacNoLadkt66Vg+IWjC/xmld6qZDaiLmzDjLH6iQbeMEwO0hKSobWSPtfJDdDMxnGFMlwBvfTKq4vHu3jd7CyYq7SSAyBiUT90GL8GdxPSoNWm4MbprfCu1AQb09/Ecr8ewiZOKXs4fOwGD8HTy7sRoO/zZKhSp7U4iOZS1PsIjm02jv7cE2UqV0al0sWwMUw663dpw0hUrF4TtWpVQZW2P+CJQXlSoQwcdw/PQ4NeP/G+ZUk4Vq5cKfutw7qV8/FBlVY4W8QPPRu7w969Kfy+bsnT75m1ABdlFkO5Gcotc2YJKRSfhM87qpyXNEgf2RF4atCfGZLI38XJqIcxwjk0eZixOxHnjoTy3m/06SizxmlMyXwYAgwBhsA/GoFC6Cszn5xGm7IVUa1yBdTosZpOaqrhYF3fa8DBStlzk0Px9ei1QPFJmNxPnNR2QfW6wsR44vkHyNIPEbkdMV937Ir7nv4Yr78yxSD1/L0W5KViSl46kpZ0n6yb0pnLCv197LeB3I9PJVqdTkmu8nZlvS+Nx/Fo3LqN4l3Ol7vANVenIec3f0Npyn44g9xNyqKcU2KvkuGNStLw2buvE/EaV0okc9AL4py8ydXUHFmIaeclWfqcfM26jSZ/7AkmYWFh/EXGM4Z9QtOXX/omcsxMuqGQcf7B21TG9NiLUpi9B9l8/qEYjZjLG3/hs6wMStb3pDLIMeTcLX7ibguXLrXl/MYs3kluR8eRpKQnJGjlaCGukzc5fDedps85YkNXmOTL8eHKI1Z/y3heZWUNLy7txPBtyrQdvMjEmVNI3zbu1F+8/JWT5cgDpexiRsTL/cxfMqwjL9OSSHjQcnqhLMfTtcFnZP6KTSQgYD1Z6D+BeDcoTtPmwsu1+ZpcTzSsRxryx/CalO5jvz/Js4wskpl0n17EXPbD2eShBbezPz69iOfDXdKtnjtCdk19n6eZtv22mGWrn4/1F27DZQi58zLXIL7Y7nvTPPXx+5M8THpJ6zEhOirHiLVhNH6ujsM1gRwP+I7GFXE7f/cZ0eqEC8/5G+tl9Vlerhw99+MvEbf3IFeeXRFuuLeQ/uS534W0XYaQkLvPedly0u6TeQOr8v6jNkmXOVLBjRzPFRdJizLJn/MPChcQc1Ezog8p8jvQ14d/n7zbdBlxfZM1OHD9l7k+ghDlxd095nL9APen9OfyoLy0XU9m8Lh7TKiL8jybcssxzc24SYaVKsrnv3L3+fr+QkdOif0OQP64lGCQmvSaHH2Wv2BTTGv82jNGF9ZL1IKL64uCZfxRbCS5HPfCkEzxLr/c/vdLSYow9sIQYAgwBP7pCBR2X5kYtlL6rjl4kYsqY2hb+l4OV1tkF8pDGnssCI5RFJE4fuK+HdyY99yxLcI31smbXIjLVNAW1AsKipEhn7TrARL4ssGP+GGcbmZwIfFKIZsMFDhuEBseF0uW9axCYO9BJs39g1y89YBo0q6RXsWdVdMc9FsoEQfWYvr06dyXRKhUDE4GqoCpDjQlKQ1dCbeOkpHdm6jKwqXLKWUHwx4ZRuMHYmqDybd8A0ncgz2q/CzJW27GQ+Lf4zVF/D5+a8jjl0mKwX+Vbj+QGP2AOj32Kpk/fpAijogZFzc6zXDgLWQnPny3ajl8s2I/SdVHobiq1AsuP+KfJbxEWu4ZH36AfNrQVSmzyxCyaO4I3q9Cm35k+fajvDIgjyd3i/VkYsB1ubfCnXFPvSxEfBRPew/S0/d7ciA0UqaAKNgRQrTk9OYfSUtnB6XsAPlmxRGTypQhF27wyg3KJ++WBveGNInh6/g0zsYZKoKGlOben5Hvmpbm+fwS/ERBaK7d/y4bOGdE7yEwmNgwVEQUOAKk12KhbhgO7t08u5CAkCiSbFAuSw7e4WWzhl78aHh5FjMqi+/XnTFThgoYeMVl26whRjyqdh1Njt0UFDsphpYcmN1FQeu37oIUbMJlTb7Eem2IKWj/J32YOJpfT0nlenXzWIVs8nJUF02qH0bpGbZ5J29yxWBSIjfjAVk8/COaptgnujYYooKdXgLdXaq4qaX5mX+Qqqg3Ar+l6RjFc/ImZx6rf3hFTMq0nU/7NdUEmCdDgCHAEPgnIfCK+kptrDSxyPWTSQZDRpv6XhtlF+HX3Avk+3tT/fbd47+Tj9s0pN+EL/z/MDnWFXnm52nHRc7fGlrhx85Ifo74tBewc3JD5Uql+W073MXJRdzLoXgBGkUo6JykJTzEg/tPoYUTnJwA52IlULFyZZR1K1hLKpbKnfTkITQ6R5QsXQ6l3MTtc5mIjXmGHKeiqFypvJFdO5KtRUJ8EjJ03NZEJ4O4plLORvyTeD6OnZMTypSvlI9yspZXNn9htibtJbTZQMWa1VAc6Xj4JIve/WBK6r/fX7gQXPOS237nhLKVrceNO6vjUvkNlHQU7psyzlMmoqOeo2rNykZlbUxr2ifp+nqU8fycv8j5/tGJeZ/HMc3KphCiTcHDuFQ4Obmhgr5P4BhlJD/F07QslChdCWVpHQcspefqexZceGMrGZpkJCWlIZur9xVeQykXU5iazgK3Bz4+6QV/1USREqVR0d30uSSufSZnEBQrXR6vuVvWR1iaL9MSykMyERsVjcyir6FGJaVRiezsbMSeX4bqXuOw9VoyPjVxF56cW37dqU/uI+qZcCdLkZIVUCefdTa/8ojxczWX4V2+Ob99e9vNZPSuq34voEjPngwBhgBD4L+IAPc9jonLRNVa1fMxBiw45HSap7h19ync36iPqu7iGLjg+FvL6V+hgFmbKUbPEGAIFD4CIat88cGw1ZjIGVige6kLP12Wwt+AQPZjzOzUBN+f642HKUv198v9DXL87UlmYuNXbTBwxXneqqOaYZG/XUQmAEOAIcAQYAj84xH4B68f/eOxYwIyBP7TCHj5LsXu+Kfo3r8+XnO/beJ+p/80RP8XmeesNo6o0QIrX3yIMw8W/IeVL+DShsm88jV8xRlVq47/FwXOMsEQYAgwBBgChY5AoVtBLPQcsAQYAgyBvwmBIug2LRAH5/XFpM518NXKs3+THCzZwkRAl5GKop//gFvP9qJlZefCTOofzDsVe2b3RbPBSzBr22X8Nkyw1vgPFpiJxhBgCDAEGAL/YATYCtg/uHCYaAyBfz4CRdDp6z9xr4U3Nlwxvvvtny8/kzAvBIpUeh8LF7yfF9n/ebgWcdq3EXI3Be/n+yL7/3OoWPYYAgwBhgBDIE8E2BmwPCFiBAwBhgBDgCHAEGAIMAQYAgwBhgBDoGAQYFsQCwZHxoUhwBBgCDAEGAIMAYYAQ4AhwBBgCOSJAFPA8oSIETAEGAIMAYYAQ4AhwBBgCDAEGAIMgYJBgClgBYMj48IQYAgwBBgCDAGGAEOAIcAQYAgwBPJEgClgeULECBgCDAGGAEOAIcAQYAgwBBgCDAGGQMEgUIhWELORnKyxWUpnN/d/xM3ZNmdAJaJO8xzXLh5D4P4H8P3hG9QoaqdC9fd4vXhyCYvnByDJ3QOjxw1BNbd/jmwcIqlPInHx0nXEp2fC1bUCXn+rDurWqYwiSMSuDWfQsl9XlHeyA8lOR4pGpwqinWNRlHJzMQrTaVKQnk0M/B3h7u6GjORkaA1CACHMyNuEh06bjvQMpUx2jq4o5WbLTeyZ+GvffhRt/jHql+dMgufdzkq4u8PBhGx5e2fi8e1wBAXuRsWeX6NL3VJ5R/nPUmQi8sI5XIt6Ch2cUK1OIzRtXAtF8olHhiYZ2mzrmUh1jJWhhF42Hl2/jMt3ovAii8CldBU0atIMb5QrKpGoujJx9/J5XLufCnfXbCRklUWrNl6oYmE/SbTPEHzgCup17ojKefb76m3a0VGLoMXToO00Fz5NyvJSkmwtom+F4cCWg3hzwNf4yGz7TMeJHUGo6NUVdcv9V68TUC1c5skQYAhYgIBO8wjnzv6Fx88y4VLaHRWrVEdDz7o2j9Vt45eNB9cv4NL1GP47W6ZiBbxRpyHeqlTCghwAXJ+ZosngaU2NjXKS72LH3ouo0747PCsVs4ivTUSkkP5yM66Rls4O3KjWpt+y8/GFJNmrZ3v3+O/Ey7OYhIOTN7mamvPqBTGT4tkl3ah8n68NM0P5qoM0ZM+8L6hs8vrk5tmFjBvbkw/beC2ZFywxPECVlo/n4EWOPEg3ysBfK/urxtl18zFZ1rOKatjRx1ojPuoez4lfE3djHi5DSNTLXPUoJnxz0iLJ9+3KE9h7kOMPXvBUXDvrVdzZmL/Y7py8SYQtdU33iPw87CMF398vJZiQjHnnpN0gE9qWVeDF1bmyH84msVnWlbMCTd1d4lOiiBFfeTsw5e4wJ5CVoQzM7KQbZGqP11Sx/Mx/B0k1UUzp0WfJoEYl+HiuDT4jY307CDzsPcj6kIeyFFScuufkeMAcUs/BnsDBy6J+/0GQv6qMfDnbe5CzcZmE6B4Q/x5NFXR5t89nQv8BkA3n85BbJSvMiyHAEPjvInAp8Efa33ToP0Do07hxhnNfcvhuitXA2MJP0Yc7eJF+3RpTmfrP3UeMR3dysVJI0MrpktwAKdfmaxL2WBhLCZQ6EhW2n46pFp56ImdQ4G4UOEc9Q21sMFXAuIHynBWbyN5Dh8iWFaMoYOIA5dChQ2RbwEoysnsTGvb7paTCEu2V83166y9ycM8GWqiwdVBciJLLlZBRm8ILMSVrWOvI4dkf0zoxdO4O8jAplaQmxZDD62ZQf64eKeqL7hGZ+0klRfiC4CizCedmPKCDE8NBc27GQ/Lb8HcV/Nr5B5nlJwYmhq1UxONkXX/KvCxiXPkzJ+mSMBB3GUKuJxor7y/ir5LhjUpKabkMIecfWN8p0jR1z8m5Y0Hk1ym9Kc+8B3g09n/MkUIWflKZx+k939UkUceN5J+TTVM68369FofajIdCwXbwIr+s20UuhIWR0NDTJCQkRP87Ty6FbKTlxA/UnbzJhbg4VoZ65Ln209HFUYmROEmhf3JtOtugpLLjQ+h37D3f9fQDHxk0l/Jac/6pQSxCcnXJ5Nh6ZR9lWb//nHzXtDTlzZelTM63fAMFGfn2uVehUFrWPjVk44SWPP+lwTFGcjMPhgBDgCFgiMC5lUOFPsm5Lzmtn/zlvnG/Dawm+Nt7kMN3zas/cp628MtJkiab+889TMQp8NjQtbS/bOMXSP3l6eVmREoT4fYeZF3weRK4RNJFXu/aj4zx9VEoZ8191qvykvPNr7vQFLDEy+t4ULzGbaYfLV5Y3V1JEQHItpvKQrsW+C0fb7nKRy2/mf1742ul1ZS/TQHTkV1TvYna6mJO2n2yK2A92bD9JEnhB5B/L1pc6jlJ5+ng5/1xe40ESo8OpoOqHw4qBxPyuNwgpsfcY0bxlR7SwGeXSkci1mc6IHLwIn+pKEJKnlryu4HiVqbtfJMz7cq40hu3usKvgth7kCMPxG5HChddf63Sd5IA8cnHoF/kxz1f3tpMOzfLBnjy2P8Nd5oMI3Ellsu5LnoPj13l7kuVfaAVsGREH+I/Cl7jNpAkEys0HLuL68fQcuLq6K+ymTtWhoTsn+rF4zNs0T7yLCOLKx3y7N5Zo1XLnYrvkZb8MbymgKtzX3JDsWKtDDNcZeZW4tt2n0HOXgsji4e1EHhY0O/HXVjB03qPW0b27Nmj+G3fvp3cjM9U1J7Hx+cJvPlJKEtXqFPowOkPtqqtwJO9MAQYAkoEdLGHaB9jNDkv36HhNo3Em/lGiVxt46cjOye0EuRwGULuG+wqubp5LJXRcCxISAr5qX0FGr5atrsuMVyYIF+4bTfZvn03OX5qBx1T7r9reqwl5iW/z8JTwMIFBcxwMKuY0VX7aOgLtMPc0/nN2z8svuZvV8Aen17EV8J/y0Bavno0aIX6tkhRMVJbZTi5pB9tdFBptPIKknZrO09bY8Aq1VkP+dZGT08XnnbQb+ZXNsTBM1XaAGL9YDyF1psReWwNLQwFLFHfjrk8/FvqjbxcX4VbjpFcARMVszJtl1qtdIty823A3oOcM6Psy+smV05t/PYqVnLk8v0XyzA34yY/6ae6ap37SJoZBcgC2aqQvP32UPkeyXGdGHBdLDKj55X1vkI/lKcCpv9GcH1VrgUjGUJImo3tMyctTBho5CmTUXaYB0OAIfCfQUBHDszuIvRfDl7kbJzx7puTP38mhAOqk/tKqGzjx+1E4LdxA6Sbv/FkOtefiUeeDCe5I4NmUvm6Gexc4hWw4pOo4ihOgLX2s2yHkzJv1r8VrhVEt2loXrO4dWfTHKvD+8uu+NSrlnXx/vHUthhcKLhMaWOOoU/biTxDZydjQxQFl1IBcnKSDoof2HYICVwzMvgr3agH5jQri8cGRi44slZ9h6Ols978hHYtlu+ONIgtvYZsWsq/jB7axYTRhCxKHB4umOXYMH4VIjNUhNJThmxajps5uRjx7XDUcxCamtZKuzSxJ1Zh5I7HQPFJmDa4MZWBOf5BCOikunE+7L5esHTsWb2Rd3fp1QwlbLRpU6zM62j94WC87a7eVRPtLYxrNUQCo/gkbJjZJR9GVyRW/y+u5JvBCNS2wKKx7Y2zZFcFIxd9R/1Phz2k7sv7NvLtl/No51Wb+ouOUtUbo6OLYMdq/drDSDPRFeiyTASIjPRPze0goa1r1+KXWYuxNeg8npkwKCRGVZr2EX3zftq7Nca3s3oDugMY/dOBvCMwCoYAQ+A/iIAWkZevCPm2d0NJlaFj3Q9bU1wWrwtGJn1Tc9jGL/VZFO2LS5Uz1ins3Wqi5wfl+AQTj03C6agX+sRTsfOX5YLbwQtTRyq/AbrUNMCpAopx32cSjRkfjQecvDFvkpJOLScF4VdoVhBL1x8MkmaLiI7o88s2GjEx5ip2/bEET+t+g2ld7LFs1kJceKhBjVYDMXlYewhFkYkbpw9iz67DCL4aiVKVa6F61ZroPvBLeNUpQ3lxjozkh7hw7DBWbY7F3B3fofTDMGzZthsnw2NgX6wy+g4bh06NKyricC85yQ+wfdNa7Dt2HS/gCs8PO+K9t4oi1/0ddG5e1Yj+4eWj2LRpB0KvRcKxUn180q8t7j1IN6DLRsSJvQh7qgWnnpEsoG777mhcuRg4q4Sb9l2Hm5tgQ00eZsAEz26fxc7tO3E2LAoZbuVR961G+Kj7x/CqX4UnfXp5O9q905dW4JO7N8IlshTS04uiU99uqODC1b5sPL59Bbs3rkPS2yMwo58Hnt8+i8OhMbCT9CBoNI7oMrA3b8mLk3HbvggUcRMIsp2ro0+v92QKTCYiThxA4N4jCH/+AsVRFPVadcaQQWKahjkx/f78+DS0GFgS59eO5K0dSpQl0X36TFQvV03y0rscyr2LiaPexdkFZ3if+Z+vwYReP+M1g8FwriYcv/5yDig2Et3eNS57OeMFm1Zix5AROJuVA2jXYvX+yfi5t/HgLFdzGQtnH4JLo9kY3qs6lv+0Qs7GQnciVk/+iacdsfQzI7ktZGJE9uj6SWzfshUhN56iQoUKKF6mBlq0a4uPvJqgpKMBOLLYnOJOtNHYuHQNjobH8CEV63fBpDGf6uuQRPwq2hlnWe7orkAEHjyG5yiHcsWK4a3GzdCuYyc0qFZaEkbvslUmI0YGHiWr1uUVfa5ObNt0CDP6voZdE3riqxXnAZchmDLoHYMYlr+6VOuIE0c7moiQiU3jP8bGNOmTt/Pi96jqVDBlyCX6MiES+3buxdGgM7ifSvDmm2+g9rve+HJQeyOlMl/4ZifiyPY/+bKM1xRHpXpv4QOvzmjbsgHK2mQtVILM9Y0uOH22C94uoa7ElihbnhK3bVFT707HXydOCW4HL9SrWpLSiA57t2po/X4ZBB2NQ2LICdxPHo+GpdXTEOOYfmZi9+IFNHjZd+OxTP82ZvFOfDeiO0qbaZscqVsxR6TEnMP6P3YgLDIe9sXK4J0PuuCzT9uqxm3euy/w9Z84MXcKgkd2QLvK+bXXScVnDoYAQ+D/AoEXSHkoWAw0lR1HJ1calBiViEwCFDH5CbKNX8bzBJpGrjTfSf0ABxQpIakzcamczMWRq4nBydOJPF2Z1l1RWzGRmYl9i5bg7cHLeD3i6h+LsDIlA8NX+KOZzf24TCRLnNYvmuUvRp5bEHn2z8nelT8Q7wbFualD/tdp6DeKs2OiRSnOMtz4xqV4mirdZpB9x46RJeM70njiAen06IN0b6fIc+rMCZRO9OOeYhwxpxnReoMi9h5kQcBRcvLQH3S5s8dcw21oGrJ9Vh+Br4MXWbpuE1ngJxky4NOh2z5SyJnAXykvLkw8n5UZf5WsmTdVNUyUSzjo/6mQlpM3+WXtOjLDtznN08d+gSQ9V0OWD2pOStb3pP6cFT1+G51zXxIeG0U2zJtIl3c5GcTzQ+FByxX+HOZc/gUjA4Ro42+QX6cMpnzHr7tAtz5x1mpEq3At+o8howc0o3TcYXTRip+UFxVX7iMytrRUB3jsnPuSQ9eeqRCre2XcC5TSBYiaVZsb+nOHPotNb3sVtxttu/mI7JxQW+JZfBJ5orJbSNyTPHl3NMm49yel57ajKU89qsvN+YpbRrl8b7spWHk0TU1I3lsQn5G1EzoJsjh5k2WbtpFl/p9T2dTKRcw3J8N3v61QtkF92yzacDrF4FW1s9gLm2jb8B63gDfi82lDV5oX+SHd/MhkDm8apntgXE8BUqXbD+RhVi6J2DCMl6tNmzZE/HF4mtu2RnmbcdzZK22t4PiZ2qJqbRmKSYbv/V7A096DfL92Fzm6ZxnFnNtKK55Jyy++2tgQWq/6zVxO1swdQcuRy1fj1m34fsj67btiTsw/xfbPpSWdSX4mGcOg/bUhH9m2cjNbdGm7NMmHEO67aNZIiJM3CVLpA+Rlq+jjZYY7UGykusVVWf+qtq3HMLfsnSHAEPivIfBM2qJtwnq3fEyf9/jGNn4PZGddxfGpYUnQflbWF8tl67VIOb4TjwhsvKaR+t9iI43OlxmmU5DvhXYGzJSQckC4D576mYTnZOe6peRrn/b8h1jc+1mz/2QyWVQw9CZ9T/7clX6sg+NEG1YasqB9Rd6/SKOf+fMXuRkJ5FroPmpOmEub+42au4W3Kia3iFKpu/wckJasGVSdp/0lWDJJmR0vGIhQVgbZ/tZiI8ltmflvuaUWQ2tY5g5SR2weR/OnxEo6BF7bdxUdDJHcR2RYqaI0zn69QQn5WQH5ORWie0p2rltJZgzTm1aWKWBcGaZdl8y6cwNtcdAllm9OmoCDcs+svpHZe5AtlyQLYTflA0YLD2wm6c9mieUlPjv6/mJ0IF2USflUDpKMz+PojW/kYeBCHOhwjVVu1ICTR14v+LTFgY1zXxKZlas4p5F3ByVJf3ap/gybiY5PohRc8g5IWS+5cA09eM+VY4zMoICibjr3JVdk543EfIu4V+02mQRfu08e3TqpaEsiBq+incnPPM3efVsGwzOyaODrtO6LZ/Rsl0nG2qxTMu8t4jTnYASdjHiZFENObP9FMZkxb9tZ8jTtpVmu5gJ1scEKfsb1WoptbRlyMeVGbKr77KHM5Ocyxcmi/OErKTryc1aiMSYRzy9nziTL90RQOQrSQc84yCZTctJkCpHJ9ie1KU5OZf8sSUjbpRkFjKPW6TJIalI8uRF2mqyZq5wU43Fw8la0TS6OYdm2GDCZ7DgUTDYYKLHc2VbjiR/p2wZZ3iXJmYshwBD4byOgoRZ+uT5IbQJbPGPLhec9vrGNn7yfc2k022gcypXRRfGsrawvlvfjI9ZJ1r05f86w2Rs+6/niFa1t/3DwHi3u1Pgocj4khBwPCSFXbj2m/gXp+IcqYEIWFQPdYiPJQ/5gspZEhl0gV+7F8YNKUdHilBr53VrnxLudDPzl5tZXHpNbzpNZSnEZQu7QAapUYaYrBnuEHJ5ajzT1D6HlkXFPsHomfIwNzejL+Bt8iOWVy/AjLlec5GF0dcTIOhchR2Z/QAehv+mtXJlLg8uAHGvl4F0nWQIDiNyCDBdPHFzIrfPd2evHp69UyjjqZ9JKQR4KDwWVEJJ0azedeecHIrLZ3Xnb/6IDXXkcuVs8WCnGla8mibMgnNJtPECRuIj4CebuZWXJdzpKy4aPT//M518cUIpxLeugxDSlescdKjVUfEUq+VMsCy4dZRkSEhe6mNYJhQKuZ0DNwgKkoZ9kcVIue+dxGxTGJFIvSyb2DY2gFF47e04tGqmVGdex0nvR7D3IiceS1TjrZZKjq+6+F/K7at00si6XK1l/zb/5b0lp4eu0icPRosS2lKFc0ZKXrfyDZljHbMFXLpviKgkizZQaHqoW81UQT07RFFee5P2rYqJQpY8V05a3OXl8MZx7UhqDfl9Oo+rWPTe6A9GcgZVuFGzfQgAAIABJREFU/vsUfaHcVD5XT5QWHoUUqWx51CFV+ZgnQ4Ah8H+PgNzIhlpfzFn7FRdJLNmlYAs/+YQg15cZjkM5q7Zb9ddrcOFSX6yhCyjcDrlrj+PIndCdQp/v3Je/I1W0yshdP8SPs3RPyTrZ9Tv8NxYg/I6yAi5tWzesW7K7Md80GTrpbMOgBUNQ1Y7bWFoEbzZ+Bw1rcvv2XfGx33fwauCJL6YPQS26xz8d8fHZNH1pZygAO/2t1s590fpd+dktB7iV0BvKyIlDho4brwp/RJPDO2Z1rwP/zecgHu9712cxJreurqfKxt7l83h30YbT0amxu95ffMj4i14WPNUPWWciaM0iPnaPWUNRr6hyw23bMeuwZt5crAo4AZ8mZS1IBZBjrYzgCO8R06jXz/JDluQxAr7djErdV+G96uL5gXQEbxCMD5z8sSN8fHzwiY8P+vcYAJ8BjbA4SY9ebgQu3oqjfM053Ot0w+nUxwhcMsmIbHLvd9B+yg6zBz/Lv9Mdfk2k8pi26BCEEs3GsU2/8TwnjfpIf57QKAkDDy5mSQyY4Uf9uUOfey4n699TsX3hIsDeA5MGv0dpbHE48/UdcHErAmdlEVvJLh2BP/8sxHHyRqPqxjfGN+/tQw2WXP1ls6pxkc8+/0Rx7qdIhZrUuEhRQ4kKqZ1pIg7j26NCvWn/kadRmdm7eWKo30eCNLkR2H4kQpLMBpmkyMau0A1jUcvrC/484KxtB7B4WAtKNPCDuXgqdSHQ3LmKwBdZeMMnAEPbvk7pbHGcWjoBMy8l0ag/7NuI98pb1pVbWoalPTtj3sAOqN/gMwxqL51xTIuPhyZXljEqhW19qzy6s+LsWhk0/KAeH8ydr3qgyZWTFpA7G9unTkSQNhvt/IMwxExfqfiGFFDqebJxLIOuX6/Bzb0zKemJxYfxRPZtogEAunZuqTC+8uZHE/DHcPFMG7DnlKwtyCNy7pwQ3LwvnJUwDGLvDAGGwH8XgVaDvqJjA26s8/GENXis0YFkp+PG6a1o/dYn1L4AZ2Qsx8TnQUTQFn727i0wY0ZnkQWGtvwQW0LvIwfZSEuIxIZZ3dFnwVkaLjlc8fnSAxjfuBQe7/FHgyoVULvFJ4jsNRf3EgLwdgkdNkz5iidfuHAs3BGNCc3r4vM523m/lcF3QIgWwStHY9+8JbiXVrDfIcu+2lJu/jZXbqa6KlKrzXCcunoNa6Z/AseESPwxbyTs7NzQffpW87ISDXQmPmRcROmD64oWvdpSXt/1b4mKDfthb9hjuNVuh97vi0qcFk+io3m60tXLKwaqNHJBObKf4uIeYRBaooSaRZg38MXXkzG0X2ujAaotIpSu34kqMJG/r0BYvFAJkyKCeYVKrrwQ7VOEHhUOTBZvMAotmzVDi/r10eBdT9T3/AaLFy/mf3Pn/oYuDSxTDjmZ7Vwqoufon5GTdl8x0OXCTvzYCxPW/WUmaxUw+LtxNDxyzRycjs0B0YZj5S+CoYTuXmI5UjKzjsote2JsaQn77xbv5ZVAze2jGL8zFm+P+R7NLRwUqyaU/Rzhx4QBkbWWE1X56T1dPN5FeTfjZm/v7kmtCCHnCZ4kGbe3LJ1g/VHk71y6EurprcCJfkbPAm5nOs4ajf6vwZuviU7Fs2mnLvQ98rF0eJd6WiwTjWHkiNznj3cHL+H9f95zG9N6d8aIGbOpQooX8+G38oI+Xip+nzqOV8rX/thLZqTGiG2eHvGX16P1mM2U7u1xgfimk+UKncVl6FgFX284jPCrm9GlrisiTmzF4MYl4V6rvWCAhkqg4rAGX2meDJIFSY6nI97yfFtgbucGJ4VyppKmDV6xZ5ah72/nUKbtfGyeplfa9XyIDkjP0X9siUYxIScllY7ws0el10Jy1f34G6wZpJ/o00UjKcPUCEeYVpLEcMSH/UfTV/XD6zQY9oWAscSduRgCDIF/IwIO5d7HzvN/UNFDFg1F1RLOsHdyg4dXX8X3oHPXvC3+2sqvw9R1+G34u4IcuRH47N2acLRzQsnytTF4hmDJtWT9IrwVwyZvSka47N3qYUFYAh7fu41bt24hJjYRURsnoGYJeyRd+RNfbojmJ0YHeLpi44h2WHg5hU9j+u7b8G37Fr/o8+HAwaiHMwi7K018UkDy4TAeieWD2d8WNfsZAn8eBZfytTFoWgz2XIrEwXl9C0ycd31XYOOElpRf+rU/0a1pVbQatozORhLtfZw9LAz2CnLATBOVOXIz0hCjNR4gy0gK2FkBA/yGCTxzQhBw4CrvPrX+J97Km1x5IboMJHAWAgEsWuGP4WPGYPLkyfrfWIwZM4b/TZ48HJ6V9KuRAmeL/tu7vYExK07j1MqhCvrlw/xx0+TABHirUz/4lNCv0uVGYP2+S7h3aj8/+91j5ud4w9rBh10VjFg1hsrwYONghMamQjRnP8u3o2I2mhJa6CDZaUjUGQ6oLIxsQJaruY99B57xvtqI83iiupogsyLEzYY/SjXgovZasHXQknYWFXqOCnLlxlPqVjhkVpmunIowaR5cEceKl8wnx1C7q2C6vLVfECZ1FVaIHCu1xfKFUr+z4asWOBidiceHl/BK+fDlG9C6ssykqBVpcqTchMFIL3075DyKjcTeOZ/kq54B5svw4eUDGNy4DOp/2BdZPVbi3t2j1PS6leKrkhcrLVkdXT5qLR7IJsVi797m41Tq/BFqGqzyqzKzwlO8loPbrXDl0ASUM1hh5iwcftS5AuUoTchRL96Rma7XIO09ULWMmzKwwN6KoMcYacXdlCxqyZUoJZhmVgtjfgwBhgBDwBIEyjfuh8yk+9i/eT0WzZ0FP7+Z+HnFVkTcvQ7/Hk0piz6dPajbnMM2fmUw/LcQ3Ak9jLUrlmCmnx/8/X/CzmNXcfnQQj651OuZeOvz/vCgu+FEKRxRuWZt1KlTB69XEpWzRCzxnchPjK6e3wea6+sxcEUUH4GblBO/6wIHR7g72KFiGaO9PmICNj2t6cttSqCwIyVc34OB7/TiB9JD5+7Dwsld+FWfi5cL8mPoigG/nES91qvg02M0XW49u2oUav0Vh9vnZqJKtjRj6lKQSasAaCe7Uiw2QdDWVcgK1KtOh8/Q0WU+j/PycQcxo6cLNq98gB4z1yiUF042V/2dV1uPXsGXLT60UY44zOs5Ca1+26CyvcoRXr6rcAou+GCYcH8Xd5/NneiXqFdXWpVSJOxYC6PnD8RG39W8Nzc43qAn+PLTZgpSS1/e6jYEPiUWUDPgrauU4qNW7r4UHUzJYSFzO5caaNOxPAJ3PEZ+65O9a2k0etsNQWHCNkkiW3WQxHFFow4fADsC+BmkZm8pr2+Q6ArTlXc7K1frTSpABrdMofJXqroHryRwW8u8OjQv8NXoh9elrQ5d29ZVSPDBVzMxbMYu3pwtF+D9hnBxCncdgb9vfu5xy8S68T34bYxiglvPz0YNk4pJJmJjUlGxmmRiXYxn2TMRAVNHYMCcbYBzXxy/uwJtapXklUCxfVvGxzyVS7V2OL9+jLCa+HIZvHpXwoFlg6EN24guP4QADl7YtPDTfK0aGkqQkxCKHnU64qzTeESdm2nCbL8DxC3AyNUgQZMNlDBUnnOgSdPXQYdqqFRa1jEbJprfd9mdiKrN1wT/4tXq0LZggoR6Ozv964cDNC/MwRBgCBQsAs7ub8D7szcUTDmlZcauS7xfmbZL8b4V9/7axs8RbzXvwP8kQTKxvJe06+Ub3/YWTUpG7lvGb+Xv5r8dbcs74Nw2aTfDtBn9FOOGrKQnOJvTEpVLF6wC9u9eASOP8UPr/rxSUGPAKqp8SQVTMK57Z44gKs0ejT8egYj0WGyfN5Iyzrg6C/uvPYd90WKo5iJ8gGP3bcfNJNv2ihp+BE190sVBUPCspbhkYl9q3F8BWBRoZt8/zUXeDu5szfhpnQTCtOno8H4TfjD4lRnlJXhOgOpZIo6JNiYIdV2/MBmeq4nDiYNb8N3KkyaF8xoyRlrVAvA09aVJWi6gSa8hdC+zSMgpSx/Q82uir4VPvVJnSO03vmeBbP3MIsJWI60mExbe5WooivBuVwpVqusVU90BnIxQO3uXjitH9PceAXD+G8ZilrSzMmWlGf39G04hWWU31ovoy3yfwGXeQd8m1YGxzTfh7h0asUhxgw7ZsRbmHNdf/EipgD+3TTJaZZEF5+m8ueN7fLFCvOQZ8Fl8Gp96Cgq/WmRtzH5UqV4BESb6BrU4cr/owwsE5QvA1ou/8cqXPLzg3I5oMWgxAgfX4Fk+3jMVDapURvNu3+Jzv2W4mxCMNtWtXyk3JV9ucjg+r9UaQfbDcPvJXBUFNh23b0cjB65oN8hHYGNiRVhxx4xXR1RV2dprSg6r/fWXfXNbiF+3Ip0X0eG0LVSrZLq+cJePqp0NtVpOFoEhwBD4jyCQiOUTv6V5Xb5ggEJpoQEWO2zjF39hpXCBPQBOD+hj5iwvFSX7HmYPmMNPNs8cy10kLbv30WUIujaTdmZwcUIC5gHFvFDJir6XpmXG8a9WwNLvX6ZGHRo3MziQT8wPxs1gYhCUjm29P0XgVeE8DncWqdfXv+LxacF4A0d84q/7gGNVNOum37KSE4Jl2y4a8FGqUnYmtrw9jFXeXv38STzlIypndi610bFXJcFfdwB9Rv5uNBDlttj0fH8QojKNR9JOFs0P0GSpw2vAl9QdHq41ML4hBIkrN/ybdi16TtliZCCDGwT5enaHw9DOJrcWiVuAgme0xYF7otkTmrzgcHBBaUd9/uw9VC9LlcfgDnL6fa281HaEb1eLlCWx9MQyEPk26TVMuSXLbRo+fV/9bBIXx/LVLFc0av0BnwxnhOChyrZB7pLcY0H7cCnK+PC88upEV7z3sXSA9beAk3ojJGIuuP1tKYg8IayQcUv4dYyW8AHjeiOiIuNjs9Oydub6ZnP0Ki6sRGivTMO5KMlQj5j00+iHohODLdwSQSNY4LCTWUT5Ye5uapRHjFq60WCcXdpPfOWfC5dtNWqjKVHnEHxZ2BqqIDZ44SYrevcRDPxwQWU/nI3Fo1sZUClfT29aChSfhNIGK2SWlWEmTu/Wn6F18kZtA6Mt9GyUMkmb38J3zESv9fexNDiKs8pLf2vnjEAtd7U6lo24mPuIioo16lvMCUG0tzCyrRd2vjEUd54vRW2VOh6ydADqvhsI7rRjrVZd6YTNkaPXjVin3A2lys2EiV3zHnzY2bo1IhtH1wjr9b0Ht4O7wXZJI8FkHo8i9ZMFTt7o20FaPRZJUlL0kzE2yyZyYk+GAEPgv4NANg79PIoaxBq+IlR1QlCneY6HT+It6Kct42eIb+aT0xjUeqLg7dwXe5d9YdF47vzaefzOpVHrfkQDg+9AmZb1UEH23cxJOI1xfqcwfePgfE2iGsrOvb9yBczOwUk63MzNtDsJW3TUhHNyEi3rAfZFjD/EWS+lA3GB36xCBL/qlI2oC9swY6xkhCMbOUhLSOYHnbpM/WDezg1KJUjG3yCs2HuuWLnmqGLQWrlVD2qE4d23uUPwRdB20AiaDW6L29JDkfQ9Peo4Nu/Tn1nJ1eBRtCQ7IG1t+W3lJsTqzzLdv7wbNTp+Q3ncufNY7y6CLl+Op/73N/miTtvRCL58FwkJT3Bu73I0qdkBZ4v4YUa/OgKdbN/K4WM3eL+nl3cj4Lgwq54X1lwEl2ofYlnPKjTdkb5qlgNlM8cAIhb1Q4fxa3A3IQ1abQpvNce7UhNsTH8Ty/16mFEFXeDmJpRJl/pjcCVeMr4gChB7dgdVwF0a9EWDShKOIo3hs+3QUZKXc1/0am2Z8Y2HEdf4eFcjlANme/cm0soggGlrBxg1Ul2qpFQnxqRavJpVv6WggHHbK69HSzyEDMTBv14TtOvUFc08JuMRIShWRGrO3VpLlus4+gZ9xtHVwshV/bAjXLl1NfrknxTLhX6f0C1f8nrxKF4jYccZTkt6gptaoWIZts/Camd2LnUxdZlgtYgTZszMPwwUoDisnylYCOUMVHSsJW1JtUUmRYb1L2Ve4w7mCn9Pdvti0ooTig/Ms9vBCNh9WCThnyGLBqFqo9E4FHYX6VoNIi9sQ8va72PB6RgFnfFLHPx79qPbnrntgAe3+5kZhGfj+r656DDtFMq0qIbSTnawvgx10CToFVvdAWzYKWwx0WkeYcOs2VTpELaApiNFI2zDswXfnITj+KyPP7/VsLguCXeiohAT8wRPExKQnJwMjUZp+IXD59KGkahYvSZq1aqCKm1/oOdwjbGT+WRHY9rHH2DFlVRUrP8aLm5djZUrV9Lf+vXrMWd8J3wwZg9+DPyU/4jbuzeF39fC2d89sxbgomJXQzq2zJklJFB8Ej7vaIEhlOwIPNVjJZMMQCaClk7Ghx92w6hZv+FWgrKviz2zhjcWwp35m/nVO8qosrdcnYPsjTszeBdLvlzM+41Y6Y+3DQYaQCLOHQnlw9/o01FmRVjBhr0wBBgCDAEJgezHWDL8fXSevIX36+YfhF+HNZfC9S5OOWpTtiKqVa6AGj1Wmz6LbSE/wwRizqxF49dbC98jJ28cv/O7Sh9nGAvITQ7F16PX8hOUk/uJZ9ZcUL2usAsj8fwDOkYj2mh83bEr7nv6Y3xXyaKsMVcbfQrYrL1Jdrk6HUlNiiHb5w3hNg3RX0vfX8iN2ESi1ekUcbMyksmx9ZMpXdkPZ5CbccqLS7m7WsQ7XESe4n0E4jv3LFm/CIG9B/kr5joZ3qgk5Tn/4G16b0p67EUpzN6DbD7/UC+PdJHvsMWH6V1Rt48tF/gY3Bn2+/AalD+Xdov+Y8h34wZSv5L1Pan77XGBfBryi+x4ue09iJdnMUonzwv0dxcQoiW7pr6vTsPh6+BFjj/QUkwzog8paAf6+vDvk3ffJrk6DTm05EsaXrn7fBKdmkXjyh30/jGXISSK3pUmp+DczxWX9ynk15f9fNmFd4axxXd6R40+zo/rjpD78YkkMT6GHF43g8rLla38HjIxvvpTuny0m/8xdRKZr06nIXdCt0r1zMmbBF66r6iv3D0SfL1z7iu7P46QrIwM8uzeWTKhbVlJVoD4rT1BEiy6hFe6C2nE2jCZVNytrXf5iwRFbP3WbiX+PV7j0+Hu4kjNVZJzb4pLrR28yJ5Lj0g20ZFbx3+n8snLJVeXrKgXNQYsJY9fivVCS86vH0PjFWs4idxKFO7cyky6IbUlgBRGO/tjeE2a9sd+f5JnGVkkM+k+vYiZu9PjYZYEgm0yGWPI+eQkXZLqg75ulm/zBZk7dxb5olsTKtfCbQfJqimd6btYVvSp6DvU0tKRI/Oki+b5eM59yfqAALJixVqybt06/rd2hfD8ffFs8mlDV5peN/8gYmsZyu/04tKV91tUfn3eW/x0mtiKr/weMEO+4rubZxcSEBKlB0h5Bx9HI14IrYYg76d7Su+OE3mafBr0a1zfLF5sz/WLsXyd0pFTK0dTnI3ufJMJkhx9VtFOx689Q6ReWSRMIXOayfoIew+yaPc58oTv54RvYPEGo8jlOOlOOzFmYrh0Fx9HcyD0BklISiIPw4/QNsjdD2acpvKybeUdbCJ39mQIMAQYAiICWnL92P/Yuw6wqI6u/UoTUVDsNfYWu8ZoYoIixhKMJZYYFVsSNWqsiQmxRdT4aewde8OKvSZiQ7FFUMEuCipYQOqilAXmf+bevXPL3l0WBH811+fBnXrmzDtzZ86ZcmaTOPfRcWrfdSHS6Ff6liSVR/9NyFCkyR49IfPLqDtkpeSdruZ9Z5BbMUraQmrlr57JzfP8pO8AE8JkW4CMXLibnDu+ja+rrTu5qDL2KinnxP/GHmJWTujKCZC+oC0IjWqKlZBe+RDlveML2ERI0wiTdZzkUWQaPme9N3ssTqBFf2sM9iXPw8QHlKVx/ZdfYI89l27VSlYOTVfC9RcSGKmcFOPJjmlyJZOm/eF/C0j/Rk4cjYGe88np4DCSItE5o4KNHxv+ffNF8jR4HZenbd/xZNuRc+RRVIKknani9IsRX7RDBht1mBRyaHpHWVrPdRcJVczUlFbKs7KDcgXrwziBou2sMxI+1JzqOFTo9BM5fvOFWgajsDu+oznFeenmdWR4F1GwlbaRel2NSMkCBKFvzz1zTy/TLDrCHvo2CJtC2fIX3/Vk249VifTBWvow4O6xn8nwFvJyv4oHwmUMSjwRJ2ZzNNRef5c+nizQbjF4CYkwqRgTkhQeQCZ5fGHEF20X/3vxrGTZQ7SKuu+5GUY29K9kRIM+hn75+kHV/pT731kKObNlpuojyL+u+IctlNAKmerjWfPE4DBypESeI8PcihljAJBvPJdKFov05ILvXCNM1McOeTHSR4+F9s3Ob/9Fm8RHqbPRhtcTMkhm8iOm0AtlfuO5mkS8ipU9zF6+8wxy5/Yho/rRPJbhm0JmlBYXxYSy1H6F7/XqllEy3MVHN+X4Cb6s5h5pWR4Ljce1zOQwsnBoO1amMF4WajDI9Fimv8cUNyl9wf2t11GBPe5XupghpOF+reqSmetOsrlRlonzpJCL+5aSXq7OjD+W39adbD5+xziLIUTAUe1xVZOZtAgNAQ2B/xQCKZH/khlj+otjvLULmbziIHmWLC5wqgGSEiku+tMxhnvkmBCSM3p0vp9LencuwcY5KhMfC3ysVrTJMF2oL5ff1Jh378Qa8pVrQ1bGd14bSXii+XqaLMyCiHw0TQ43z96abCQlHo+eJ8DW1hGlyhZlx9qS457iaWIanIqWRXHDcbacMJ2SkgJ7e3uQ9BT+WMyrNNg5OKFMCfFxXyVdfUoS4mIT8So5GdZOJVChhBMSnjwBKVoGRezVD/FT+tFRUdybM4WLlkURR1vQuj3TWaFMCePHc4Uy6YN40VGxXL6CTiVR3FlhGEBICCD2ySPEJRM4FC2JMmbSSbIYOZ/fvw6Uq4NSJuohzUDPAEfFvgQ9qJbfqShKO1t+D4KkPMON8AzUrVWOI5kc9wKRz58h8RU9omOLEhWqoUIJ03WV8iF3pyL8/jOUqVqRHbWTx79NviQs616bu2S69HwUhjUXjVBQLhOjHyH88Qvo9UCh0pVQs6JgYtV8HfS6eETFJnDtYufgbLZ/maeUe7E5+c6AdMRGR0Nn6BPFy5VFQeNrj7nHpIISxT/ymYCjE0qXK4fCKm+j0W/04cNIjk87p1KoVrEkG6cUJN8qLx0vdHobFC5aghuPeOaohcVnyLAtgHJlX68ecbf34asG3RD+5SycXTMItnGxiH2VwPVnotcjPi4CIf8ex5jJyzHOJwRzDEdG0tPTEXl+KSq5jMb2a3Gq9w9yG8iEJw9w/xl/dDd/4VKoVbVcrrYhHSsfRvLjGzXwWah4KVSqWA75LezPdHyMTeTHWjo/lSzhbJK/TF0Q3Es2447v7LgZhx61zRjoyG0gNXoaAhoC7wwCqU8uY51vIErWqIaa1WuhWtVyFstNVAZ/+DwVFapVYvNyzuil4+Ku9QhOLYzqVWuiZo3qOZJf9bqnuHXvKZwr10MF1fvFb7ZZ3gsF7M1CppWmIfBmEciMC8Q3FT6FL+GNB9SQXBB9s5xopWkI5B4CTAnI7I7r0T5mzu+nYt7HFXC+7wHsHGm4a5AegakdmuCPcz3wKH6xCVPyucfr+0UpFZt+dEW/FecxbnMQ5vRp9H5VT6uNhoCGgIbAO4CAeGv/HWBWY1FD4L+IADX0senGYbRIX4GGn07BY8ljtf9FPLQ6vx8IhBzcyF+gJjq8NBhzUavZ7b8XYty/0RjSrSEXTa2oDi1RA3+cboqzd+dpypcaaGbCLm8YzylfQ1ec1ZQvMzhpURoCGgIaAnmJgKaA5SW6Gm0NgVxCwL6iG/6++zda3p6JDwr1VrUKmUtFaWQ0BN4IAoWLGh771h9CszJN8OvCzQgICsb9+/dxK+QS9m5ZgkFuxVC7/a/cu2dtyvFWcfXJCSgwcAZuPduPFuWytnz6RirzThSSgH3Te6HpgEWYtiMIy4fwFh7fCdY1JjUENAQ0BN4zBLQjiO9Zg2rVeb8RoGZR18zZjFp9x+KzXHyg9v1GTavdW4lAegT++u5TjN/42CR7hRoMgs+aqejURHz+wmRiLSILBJ5j5cSVqD1gJD6vVjiLtFq0hoCGgIaAhkBeIqApYHmJrkZbQ0BDQENAQ8AsAo9DAnDs5Clcu/EIr5IJrAo44IM6H8HV9TM0q1fJpCEJs0S1SA0BDQENAQ0BDYG3GAFNAXuLG0djTUNAQ0BDQENAQ0BDQENAQ0BDQEPg/UJAuwP2frWnVhsNAQ0BDQENAQ0BDQENAQ0BDQENgbcYAU0Be4sbR2NNQ0BDQENAQ0BDQENAQ0BDQENAQ+D9QkBTwN6v9tRqoyGgIaAhoCGgIaAhoCGgIaAhoCHwFiNgk1e8kfQkxOv0MvL5bAqhiKOtLEzwqKUX4mi+grapSEqW0xPi+V8b2NjYokABe9hYXKsk3Lz4L27cf4p0u4IoXao8qtX9EBWcCyDqyiGcT6uPzs0qcOQT4+KQIS+Q+RycnVVeBk9HXJyOpREcdo7OcIAxNjSexhW0mHd1+k7OzhZfWifpKQi/FYhD2w6jet9f0K52EYFNs78vn1zGwjk+iHWui59GD0JFx3xm02uR5hBIxaUDB1GgWQeUt01VTSj0C3N9UNnuyXFxSFFQM/f9KZK+9d5X0WE4dz4IUUmpsC9aHo2bNUUl5wI55lsNL8AGzs6OFtPUpyQZjVHvCubvwzedqXuKCwGXER6rQz47O3xQpSE+alxNZWyWN6nQl5LtCiIlNgml6n0El3qV5InM+B6HnELIq6r40jBXmE6ajschQQi6cx8v0wjXbxs1aYrKJaT9Ngkndx1FaZdOqF1CM7FvGkstRkNAQ+BNI0DS4xEceAV37z+FHrZwLFoKdY3GsKy50uvikZROskzo4OiM/CoycczDG7h07TbiklKRz64gKtasgybBh5p/AAAgAElEQVT1sh7raYFU7o3XJXNlK+UmgaGMuHvYtf9f1PqiC+qXdRCCc/+X5NG/mOB1FF35n7ULORuRqlqianpDfo+Fp8nGoVXltJS0Jf62fUeSjfvOkSTVkvjA2FuHSPeCdqo0e44aTdrb25Cibt4GGjqytFt51bS0jq6e+0m6oqzM5GscDSUGNQevJ4/VsAFIndHGdBRkmTfizF+q/IzzCWFpTDr0YcSr60ey/GsuR5tMrowIWNSZ5R24NlAZrfktRCAj8S75o01JAqu65Pjtyyb7Y2UPH5JOUsz2QXm7m+ivdr3InVeZFnL39ia77PsH63/S72vhkdAcMm0CL4Aci0ixkOYL4tnE2Zgv+0Hk/juA+bv+TV/2nWmMPZ0TbN3J/mumxrYU4uf9E8s3YNRo0sLOmvNX6buYPE8z/61EhwaQSR4fc+nbzrpgtp+kx94gE7qWYWVJ++23XrtIAivqGT8mAGTD+UdmaWqRGgIaAhoCbwqBW0eXkQ+trbgxzLF+R/KVaxE2nv0w64BZeVvO4zPya/FCLK90LFS6uy+Uj6tUZvprSHOWt3NnURaFXS9y4l68vCiZL54c9Z7E6kDLKuH6CwmMeClJpSf3Aw8yWWz+6SeSuNx3IvdJihQz9XFk57RvGFi0wg4NfyaRZia2hxc2M4C+8txKXiSzmYkQ/Qs5Pau6ZO3hc+RaYCA5fXw/mTG6m6ysAg0nqQo/6VH+bKKF/SByOPABSUxMJA+CT5JxXUozGuW6LJZ1qvAzy1kcrYt9o+lm60KRuHt0Fsuz2O++CA4hRFpXruNZu5CA5xmyNOqeeDL/63KMLs1LBYaIV2nqyZWh+hfk3PH9MoEgOwrYJe8+rOwRm4OV1DW/BQhkxF4mHk75Ce1/ITFCm+vIprEtGLa0XWf6PZRRiwrey74PGk/7aKzkE5Em1oUeZmlHrzhLLFUlpDTeNndMkGFhx6ou2Xb5Kcfe0+Ad/Pds606uJwhYZp/zzORHZPnQT2T4t/E6ahGhmEBvWT7aNutPy793iwj9PyV6l7/pU4t6G2FP8Wd/VnXJSZWFv39md+LTWNUlu67F8cjrw5giberbooqXdJ6g5XgoBAVpM9JvnS7oMX6kvBnctJ+Ji3jiOLBY8f1L6WpuDQENAQ2BN4FA2InZ/Phl7UJ2XePnXVruk0DD3MuNgWcsYsXcZotyjJy487aE5jM2NlPdIEZvEHz0L8imsR14/ux6kStMnhKzZibfZXnpgvc6v/PEd9EINiZ/0Kk3GTnYg8lLlI9mHuvzXGbKUwWMr348+d8XpVhFacVqDN5ipmI6Mu+L0gQmlJHEW1tEWna9yA3F6nJS+GmmvdKyWnkaC1B/T/+Kp2FVlxyLEKc9nt8Ucnh2Ly6eKnBPFMItm7TpxGntQi6pNLbY7IREnFnA0aJ1VpZEiM5Ikeo6K+tOnHhrJ8OgcL38nHvV+ShpsRa5I4SPCiDZUcAyEh+QPT7ryYadp0i88BFYVKKWiCKQkXiDV76s6pJ/wuRqEd05le7MygcgHr+AxRKBs+DPRn1UQDkz+SZPy3EiiVL0YyHNu/WrI6v7V+L6u3xxRE+2/fgpF56dfqxWd6bgCUKyBd84ISlkjUJxK+Y2R7KroVbS2xX2rn7T+kg/ftK0H0T8gh9zY2xacjQ5vn4yGyPpPFBZMZkmh+5j8UNXyHfxk0N9WVx/RRwds5cP7Ub+53OS/Ht8NZuwzSlgBye4cPSGLDhAniXTRTI9eRYaQMa6FWflUB5335Se2Ygny/tV5OI3ZuN0wtvVqzRuNAQ0BN55BDIfk1FFC3JjkfykDV+zq1tGcXF0QyJrOUNPto+twaUfuXA3CbkVTAIDA9lfUPBNErDPcMJFoQOEHfXi8tFF6wfKTRz9PV6mAshcP+WulVwHkcrKMcH8wun8HXvJzp17yYnTu9hi2cF7ctksL9rxDShgOjaR0ElG+Btm8uianuyZ8Dl3dERtNVumPZtY8ZYqFnRbUn7sKp5X8Cgvtu5GChwPskExsnUnVxUr6hmJgayBaF3UOqTYUClMYNwurLCKkYRO5iuGVuYxsarL/9r1ymIVn3ZgcZdEUMDWXI6VUbbEkyg5Cvm6gqsl5WlpKALx7CihqW9AqmDRQU25w5UedZwJfrQPLjGxTS4oE6bKeffaQ1ywkCtg4nf2uv04JtiHjVH169tz7v7L5ccglLglhx+RtQdtEzl/yhyaP7cQ+Hf9YJOLdXePTmVtSRfTROFAT3aP/YyPs3VXWUSTHElVm+wF5tMMCxxmdsCERRDVndTMx+KqLEDmKXa72FxjYp4T2NB+NQQ0BDQE8gqBjETxOo3agjDbFLF2MZKXlTxR2YXOjxN3mL4qIyhayjmUyUUOw8kjpQImuaKh3MSQzgOdFSdaOAWs4M9sbnh+cQXHn9rGjbIuueF/I1YQM1/y5ivq17dnl9iWDWqC1ReimV902KB89dqiNweuMh+1Q3t7w809okOynmIq/LNGfidDnP4Q/j73WIiQ/BZC7/HjgYyHSEuXBAOwcmyMMRM7sMC5P6xAmIw+i0L6k7OYt/kR7BtNxxf11A1cZEQbDItkXuczpm3Dst03RCIKV0b0eSxacgElXb/D8C5NkBAiGG4wZSJEQUDiNWfSRJJMc+YiApEnV2L4rgig4M+YOKCxKuUmX3vgQ2v+00y5MhGnbr+UpbMu0QK//VCZhc1dfRJCL2CBSIff5pmAVV30aNtADH6nXRkgOr6fRx69gshk/ruOu32U+85g645m1Yu+Zg3TWP7gYN6MyYYxK3HXUBaLlDj8Ny/DzYxMDPttqNhuxvZ3JDk0Z+4gkIB/ftuODnNn49OSxlNZ9XbDMeUjvj8kh5xGZFwmV2ymLhgrl13g3MVcXFHTWZm3EJq2bcmzmLIWxwNfqLJLMrIeQeNu+sE3pTkWjPrCmEa+8hi+YAoLPxP4iLmpg841v03rAegP4af/HZLFaR4NAQ0BDYE3jcCWff6QSyPA/YunODbs67fFB47KsVTOYYZNeez1PYeJPerKI5gvCYdXreR8vXq0QUEWLnG8WorTtxIkAQD0D3DyaBQX5tK8qiQuAbvnLuP91i6YMFw+DusTEgHbUnCgduRIOCa3GwMqR8z+WZ5OQjBXnebRysWiKnush9/hg0xAoaR/cBmIc1H8pCgtiqQqm1gam7U7LeoejqYYNKd8jlAxosKIjG1TEav8jZWwEg06YN7U8aoW/loOHCbW49VSbPV7wOhJHf8eXMcJZiM8u8PZjKHAqn1nYImnqNQtG+6NxyaUuovbVyAgLQO/TRiKxqXjpMW9ltvRwQbxD89hwbRx8PDwQP8ho7F0ix9iVS3VpCPi9r9YMmEYvLYYFEdF6U9DTuN/v/+ITm6d0X/IEMxYtBmX70eZtCQZ8/AqVk8bhGm+d0BS7nG0KR9TvI9JPvhU3DizB3+OHYrWrVvja4/BGPv7LPjfjlGUTr3peH7/KjZMHw6vLbe5eGqtbJmhfj+OmSrJl467Fw/hjzFDubp7eIzC/qBnKjSBjLgwbFs8CX26dEGXLn0xedFmHD26C4cvGvchYwIxWDX+f1zwsMXfooyJPpG/7Cf4vnVJln2Z70Xm5h35UaH8hywsbIs3AhXfERUy1y17gLKdRqJpOWuWFrAcw+S4Rzjluwq9v/4DjwnBy4eXseaviYb+8RuOmMCIKyw9Bv9sXYLBHl05nIb97oXtRy/jhcIyqoQxC5yFUadlUz5dyloc+zcCT4O2oGjtLtx3Nm79/1DHKXeGtHmbvdHCzoBbylqsOnhXlb9MXRDmTz/CLbIM7f4Zx4dqQhaY1/inIiwkAEsnDOS+Jdre5/ZvxK9Dh3DtNnnRnhx80zmlyVc6u2MBgypLR0F033cQywd9bCJlIZSsyFuwKt6yLSoaFK348GA2P3zWsi6cVL7Dig0/ZzQP+99h7uw6ClXuiDMB6032S6fi4nfuJhMc+JKa9ejFOU7O+h1+kcbLLNnlR0uvIaAhoCGQHQSsHEuhUR3eGvCDzYPRZ9pBtuBL5Yx5Izdw5Kq2rK86lkrLsnOugc7dPjFpmTYj+gq2HHjKLRx3bFlFmhUV6zRkfo/GnXEsXNQTLmxZAd+X/OJp3ariImym7iFOneHlw2KtOikW21JxYMEi1BlQk1P0rm5cAO/4ZAxd7IWmRXNHjmAMm3LkxjaaeRr8cY5ibou5e1/sMp/hOKLaPSvuQriJYxeyI4gqd8DoES/h7DzVaZVn/ymv0i1Jmob+USsu7FKf+QoReudDapVR7ZgY0YeRIUUKcMcclccYRfI8NmW7rCORYYc5PgR+Zhw2tuhGj7NwhhschpMnmclkVbcKLE9Ojl5JsSxcrz6jJfDA/ToMZ4ZM6D2RDbPHyY5bGd990JNji77nadm6k5Wb15H+jZxE2lZ1iUsDvqyNl2+T/d4ziHsD/nwxLa/DD7/K7kDRe3YUP2r9Zkxj3upO+c6TyYHjx8miMe0Z3dXnhYuhKWTf7O9YOKXpMmoJWeJpuKQpOQZLL2MeuRYsHgOVxgFEeWw0OdyPN/ZgVZfM8zlGTh3ZyIy5dM3CChptc+E+IOVpx03DpX+xM8hcN3x/E+ugPEYr9C0Jv8PWyY2h3NnPH7+acVg04mEphknhh2XHbCm/E6aOFfmRlCviLrKfEunP2rD31GVk9axhsryNW7lyfUh5xECkYNp1Z7+njBbljf5xFhAz73HlOtVvSVxdXbk/l/oOnKET+TFk0/SFb2LHzcdk99iaYlkm7toJ59/H7w0nyaFbWXo63klv9NAS8xb/FLJXYfCoQufxZEQX+f1bihW9nyYcazX/TeeMpohudsYCU5YKRWrZdkm+E+k8IJ2DTB0vFfoBj9di1ft80vuaxuOgZdxKv/MdsjtghvyS+xedvY5bRlRLpSGgIaAhkIsIXFNYHnYZvYrcDg0iv7cpwc15VfquZHPK6xQrzO9qd6ipIQ1BBuTmfau6ZOv5G+SCxALuBiYH8lxIx+juC+T2FYSjk5uu6QizWu4w3Ph+2etUKIu8b+QOGDXhTgUSwdSu0mrV56N9ZUY5LFbAOEtjN0hYaCgJDb3FCcQ9G0rMW1KzlGFSE5MCGjqyZmgVJixxjUkFOWsXsnzfFRVjGUI+8Vc6QdP8SgVIELY/N2taXqqc6tndIEpP7UKjIFTzl8ZTZAY8lOWLnJp2KevQvO94suuIH9mgEJjpx8UJk/qnZPc6bzJ5SFuGnVLwYObxJXcr6IfDKaOC4G4/iHh5LSC3E6LI7nWLyS8eX3D0BBOnVfuMJ+MHN+PLMChgp/4yWCwDiN9zwZyJwWALQPI3+ov1rxfht8mpffNkiiK9C7j5+FUSHRtFjkpMT3Ntb+tOdp6/S3TJceT8limsbnLDKeI9I+klz/So85wSpsRBDXV2hlnlbqEyfUYsT1fom1JriMI5ZSGO/srvuIh3GKX3KC3FMDM5mly7cECuOANkxKxt5GJgoMx6UNkuK2XfLiHPyJSPinIYSs9iX5MqlAD5fupUsmzfdWW1s/SHneCN2rC6O04k92IF658pJOzWWTLNYBqcpinfeQb5NzRCwaPpYoRvgg7Kr6QGf9Qu9wrCsV0vcjctk0jvVKopYHmN/7Pw2+TACtGsOq1/oQaDyJ7T58m//rvYJWUazoT9LL7pHNE0wJu9sSDn1itNtSa1diuMKZP2igta7Ds0c3dLmJwpVqYWCqSTuyXfvxqfh6Z35McbEwo+XewTDM/AZBo1ylqYhoCGgIZA7iFw2vsHJhvRcVH4G7Y2tywsi3dvlQvKQi2oRVmpkTKBB2qYQ25Ons8hvb8mpUnD6WYGXZij/wTDfNKNj4So++S8vz854e9PrtyKEFjI1d83qoCJK8LyXSoK4rjNQaxi2VHAWAMIHUIwZmHtQoLMmnSPJ5t//5J1IikdusMS/Fz9vTLGJJFbVpGusFLjGsKkuUnV+IZARVTAKDZKU9ZLZZYNX/CCLVNsxM5KeX9dBayz1wGZ4ik1n0/pSy10SQVTueBhykgCIRESi4tLTsstNkrpgV6wzKQm+1LI3cCL5Eroc85YCWcZk7axQnk5J5jEV4QTyQV5+vSB7C0mPb9TwrW5XS/F23Riu0p3CqQWKyftlZpGJeTvCR+Sj7z8hUY18StiI6drIrlU8AIIVXT474da/KvKLRbs8NvKduBoXYS+po/kjULUGe0raVNRWbUUQ6lpcu/j4k4a3WVmlk3tB8mM3AgKDN8npYZhRBOyaqtbplAQwqmJeO8xxruYVDgWFnaEtNc3DOG+6+KtLbHKJOTifwX+eaM2knoado6kZQkKhqBoCnlp3Y0VsDeDP1vJ456mmCt7JuOF4YIx5U86GdGaS79B+TdNxNXBbNEU+7tSgTE3Fshb4/V8wkKfso9cWikKEkoLiEKJUuXKuC35VNI0SswEOuZ+6SKLYJ7e3PjN+FVYBTNHW4vTENAQ0BDIbQSk70XSeYT+DVn4t8ULnOb4yYg1PA+lYh1amo8aJxLeaxR4oAvsZ0xstgiyOJXrr0U8J3cu7ObHXYPBOyovUTpUXuBOhuifknW/9zDSDb7y9DU61SLlKyfuN3TQUXkAsjCGeB/DkCIFWMTcvo2xycRlZ5ZIxbHh/A3cDQ6C3xFfzPcaixY2t7hUH+IsGpeyhteWSyq5aFBh9JlxCI+CD2GYWzFZmoh9Xqhf9gv8EyqeMZUl4DyF0ddzHAsO2zQAfob06U8uckYBCjSchHYmjG+wjAZHBgGKNu7GLo3T4Bmz9rL7T7FBuzD1cizaTPopT86ndvqyBaQ3haq3G4uNQ8XLjPtOi3e9kvWm7yLY5VO5UAGgWJWarMqnrsjvVEjp9Z83CBU4GvlRvfHHaFiV3pEohK88p8ClQX18N2kQqrF7PkmIihKtpEjv+kkvyDtXqohSBSR82ZSGa3v+7kXZL1vjo3J2jDfAGo5Otpw/xv8kHunEO4qCAYhpXWrBa8s51jafeCzE+FaVJDTUnQI29o75YSdhRz11frTvP4xFPdm/CP9GZoCeuV68JhzFWvVAJ7eu6P1VGZZm5boznPvKod28UYhvXCRtmn0Mkc/wArxdL7T6pAIrR4oRMp4rjNyIyexspZUshoYt+XtrFNcwCa5iDnVXZlww+peqjiHzj6B468nwO7KKJYzc+xPm7L/P/PTOU9B5P86/yns0SkhZkKTK2kmNfRRG38meLGnM8Z+xL0i4d5mAnfMXcGfVfx7wKUtj2vHm8C9kMODy+WetUFbSBvkK5Gfs6RJfMTd1SL9BWYTBkxOaQn9X0jM3FijT5tSfcn8/Wo3cAli7YPOSwSbvJlhJP/2cFpajfOnYOWEcdxetjddRDGpSPGsqGf64+UDtvmvWWbUUGgIaAhoCr4MASQnH8ZPG8rT3qHaoP2Qrk4dyWkbo2SOcfQP7Br3wUUVxrlLSu3vOn0snC0/bhs+rNsPu4HhZMJUdBy4+hDGNi4DK9Q3Kl0LN5l/jbvdZCI32QR0nPTb8/iOXZ/78UXBGOMY2q42Bf+7kwrz97oCQFPh5/4QDsxchNFGUBxUF5cj7/6SAAfnsq2PB1d2iMQsA/T4ZgOBkAgf7QpZVxq4XPmpQG9XrNYJb+24YPWkuzqbGYPPvX7LL8FP6NMP843LrUlLiFep9iaV+L3Dbf42MF2T4o12d73HDDODlWn2LUUVFOy1z157gSF/avZYXgH/pabEAaM0JisUwYPpoxt6TvYNx+BZVAlOxZ9FMLnyMRysWb8qRFheGC2fO4OLFi4q/s7gUFGrCEIbSiqINWvf5iRWRKRqHY2FqjjRCFw6AyMMBzEod9ecvVR3dC/LSTnF7g2CvQiAzVd2yWDXXoTh99RpWT/oaNtF3sXH2cOTL54guk7arUJEHpZixSJeqS4WowsnzUZ+o1BVC8+5uLMGUPi1QumFv7A+MgGPNNujxuVRBYclER/oLBB/nhSdz/IgZgLKfdGSYIfM6dp26ifvnDnKDz8AhXyA/8qNN/x9YljPLtuBO4lMcWrkLsB+EDgqhLscYEh30JozC0MJFjDjbJ4yf84FS4zQ2qFG/Dh+XzxG2EsWAZVBzpIfj5zYtsSkxlVO+Ag/+Abf232PPBNFIwrSeE9l3GntlK/qtuA9Xz/3oUk38NtVIWxJWrkU32Tc+ZeF+7gKy7vYxjNkdiToj/0AzFQt8arTfCP6Sgk19S5Ik2XZmh+brjgXZZo5leI5ZQ/nvYsmJzWglW2Cha52i8qkzYRQmKfwGu9RNx1+6QJab/yLPLkWv5edQzG0OtkxsZzFpK0u/G4spagk1BDQENATMI8AvgtbC5D1PMW5zEF7FXsVYN3HR6O7K3ugz7W8TsqV52nxsEvw2bOKc3wzpYNJo3YUNo1C7/RhOvrkVk4DLvn+IxDOvo1vjzjgVKRdWrRw/xLzAaESE3satW7fwMDIG9zeNRVUnK1B54fsN4ajs4YO+9Qth07A2mB/EK3GT9t7GYLcaVHpF634DQDd1Au/FiuXlguv/TQGjvNtXbI9/js0Tq6E/BPdvZyIk3pxILCYH0YEYCYZ0Z2ujfCdp5v4stfOanw/CladnRYGXFpO2DX9tvSopUOHMVx4Dl3zPAk/O8caVqAfYMWUft/L6TXvRUh1LlIWjkpuHbGdw6soTeBUdwO2oUUuSbpVMrwwIpJPCjuETFxc0b95c8fc5mrluw0sLhQmnIiUEkhb+2qNiJYPynLYNK/eI5vQzdU9x02CZsrWLuBtmIWE+Wfoz+P41AvYla6L/xIfYd/kuDs/mrYRli04OE38yeAU2jW3Bcidd24rOH1XAZ0OW4olRP2TJOAdJT0SMXqnkytMofXSRos/oZix4Sb/e6OI+letbPd2qc+HVWnURn1xI24YR/X7kd0p/74nKasJaHmPoULQ043fZiLWyJxoi7/HWKMt+2Q5VpTuSLIfSkY69f/TjB0Sruti3dQo+MOTrNG62aKUwbRu+/mU7MsgzeLUZAdj1wuLf3JXEcubPVx7DVo5keelO94XIBPhvXsyFTRvcXrLLyJKZduQx/qYLftMxeTwWmKxOOvZN/x5/+EVhzuHbGO5ivDBStWkbltvUDphesstf/+MaKJjjnVRWFHOkPDyOb9zGgZ6QuHJkrMWLdIyA5tAQ0BDQEHhjCCRg0fdfcougVAad2qcRCjg3wFy/MOyd9g3jYt/k9jhg9tQYS2rkyIy7zls/BNCznWGhVpEq9sp6fDJgERe6/eJc1CrqhCbdpiAu9LAoC2T4Y4jXLmalUSRhg3JVa6JWrVr4oKxgJTEGiwaP406xrJrzDXQh67nFW5qHLoz93Ekqp9rA2TofShcTT+2JtHPu+n9VwCjb5VxH4dSi3qwGEfsm4NtRa4B8vNlLFpEtRzG07d+e5Yg5E4Bww05W5NmF+H46v1PFEhgcdiU+wfbH5+DhJCo5F4JCVBpTzFm/6yBRadMfQuNSVbEw9iVqfDcSjXNiytKmEn5cNpgVcGNBJxQs6cbtqE0d1cmk+U6WAYCtQ1F82bAVOnfuLPv7qrUrPBe2NXkcR0qDugtWrCUK98pIVb8NOk/0YfjN7VMPc/ZdRdTDq/hzSF+uDp29DuDr2tnfmYgO2YcOjhXQY/xS/DDrAJLSDqJTk+ooXuR1+olqJcwEFkLfuacQuH+pbLc0YOUIVPt4Ch6ZeSsqn30VduzR3mKWbeDSbZDIT+Z1DsMa3w1FfUPfoqs7g4c1Z2n89u3j3P2+NDbN/SYwtK/YBufXGxSWV0vh0mMmgiOf4NL+Weg4w58/Eja/p0X9GOQZTi0P4upDjyVULyEOV1bOzbFyyy+s3nQFzsaqDPftzTs8y6TZb5YhG44anQexPk2ztSpfhKtLuS6L0TYbfflN4J+NauVx0rwbC8wx7re4H7pMOoiJO0IwroN0ApXkkmzZRkYrj6zw6dKTxWPW5UuXzJ6SLSlK6cyIvoCutdojwHYMrp+bigpqiyTKTBK/na2EeUm45tQQ0BDQEMgLBDLjbsH3IP8sz7jv20re5iqEzhM3YM8EcbFz92Hxqkp2eAk9u5872UMXpZqaOH4YcnwPR1L5rm6Rqh3gd/cgU8LurvPBbTMn1wS+7h5Yyi1Wd/5jIdxKWiPk9DEhChMn95bJyWmxTxCQ0QLlir5nChitccufvLG6v+IODTFzbozBZLkjn2Gie3j1Etb8MVX1/TFKzcq5CX5dwZ8Jpf4X918g1cyOUT77+hjxRzcjRn4d3DLHk7ZMqTNQphp5x8bORuWoBTjW6o5DV05i7969sr/9x0/gzwHGgrkaDRr2UvJeTsWy6g9JK/NS/NaHH2bBv3RphFKVGmH2g1ZYc+Q69k7qmH1cSARmtOrD3Zeo0ncl5o/vKBkEWFF57gg9+w/uJ1qh8VfDcD0pEjtnD2dlJl+dhoPX1B9sFRIJR7JSdKlIM9OnhPT0t2jDVrIdURo2pGcriQJjg5YDvpNmARyGo1V9RXu9MQxt0Lz/QvgO4N/woAsqDcqXQ7POv2Gg51Lci/aDayXTR1ClFclMisWdV7wgXLCovaTOfKoPu/2G+V+Xk2YBXaEb6faBLOy1PTbV8NOcfkZkPMd0s7wfvjH8jdj8fwvIk7HATG3o8ZQvRm7ljshMU3noM/7hHUTo9ChaW/ymrpy+jkSVbzH0kj8rqVUL8S4sC8yBgx7jGVitFY5aDcHtJ7NQxWgXOAm3b4ebPsZj645GlZxyULKWRUNAQ0BDIGcIxIcFsztXTgXtFUTyo9OvXtlcqFeQgHj8sPtAdxMnAhIQ9A9//6xYxcJGd+jpabrZc8XdOGUJRv70UEzv+yf36PLUUfRaTxIunTzNJ7MfhE5NxZM8NNDfZzbg4IKyWTw0bVROFgHiknIWCfM2uhC+Wy03ymFpeaqHFUk4fwzQQKTcly1Q0TDZ1WrxBej9roGTtpuc6GwLiYpOvY/VH+qU8u5HvSEAACAASURBVPdZ/+9lOyIo+DM6NBbPx0rTWuLmlLqZ3WVJx47rYfJcLJ9QakJDljXHnsd3DcYybN3Rqy1/5C0rYvQR5Z/bdEP5zjMQmZZJrWxyf7qrWzCovfrWclY0kx4EcTsbNF3jpvXlQq/kPkdWdNTiLd+NSsKOHj3he5W/x5XPvjS6/7IEEWeWM7InL0nvPLFgg6MQGrVqybmVxj2UKWX+fJXw7YQuYpD9IHT8VD44FK3bRnZPqfNvXxutrOclhiJzvCt411R0X/8Ai/3us/an/WDtn8NQzZk3cKLMk5WfGsDYH6LcrSiMkev3s5UvSiNsy1rsDVQ+op2EUweOm3iAWF6ywJ1yp6FJ9yHyScZxInp+LhpAkVMBlP3qTeKv5OX/y5/9sSAdzx8+wP37kWZPHajVJ3iXJ3c85XefEMzp08goCd156lijDg7eTQJs6Df1BZcm5tR+3IlTXqyWTMaOE/GVBWO5VX6h5xgVzQWQlFsY7uaC3ZV/wJ0Xi1GTGRIS0/sv7ovan/giRQziXPHxz/mQ1zoVoiCqeTUENAQ0BCxAoGDpqky+TXypHJ3ove949rC98ki3XvcCj55EmR3PpccPv/6yrgmOCqLyx024uJiHCaoL2LHhgXzefI7ye+kqFM+vnc0dqRyxbiYaKMbiYi0+lBlty4g+g9GepzFp0wATyqFKARYG5b0Clv4MF45FIybgCqLM3ZOxqYaFNw6whjbFv+zyfj5HOChWEfW6x1gxrDcT2Ckd6Uq1bQH+jhI9sjTCO0BFCXuOrYuWsuK7t6/P3KYc1iVcMGXUJyx62OJvUcaSOwNmsPms11BRsHQYDo82yrsMGYhOEC8bJr5UVUUZT1k5MvVyBY4KT4u+X8hlG+btJTvSZWsrHtGUCx7pODL3B+7OToPKRRB1/w4ePHyIJ0+iER0dh3idDqkqbJqmx3Od9kq8+Oj760pcj6UCUzruX9yByaNEIxzpyEBidJyhTUWBSCkMA2Jc5LGHqivgXMn5HCHsnFK/w6eF4L36mKzPlPusK1N+PqljfuelXgteAYP+EELCE/nKWfB/s67iDmurMT1RQ9Hnka88+vxPPMbbvXNjI6o5wVCfarACqsBBih89KizFKCP6BL79xos7alhQH4s79+/j4cMneBodjbi4OOh0KgO4Ebd8gFWhoqjpIPY1j2bjcDFSNKCA9Bgc27qNrc5xuTL80f2jMhi1aA/CohOhiwvD+gkd4NrVC8/U7bvISn90/Rrnv3pdrsTR3ZwxEzuwtBPX9jUajPUJYpsqJ4k3hT/RpyMpg1co5N8mYO47MxeXM5rZHwsubxiO0pWqolq18ijvNiPLe5VCY9z7ezYadP8f5y1OguHt7S35W4d13nPQsvxnCMjviW6GUwQteo/k55oMf8xcfU4gxf3GhvhyBlaoJ6tJV8D60cNIGQ2ZJz0cE79qiRVXElC6Xhn8u32VhD9vrF+/Hn+O6YCWI/dhpm9P+QITYnDunwscucrftJdYgJWVoHk0BDQENATyBIH8JSuiSUFeZpritcVIXkqLTWJy+5efise+U5+cgWvx0qhYrhSqdF1llE9gVjh+CMeJaFHV1PUUG1SvU57LknJlIrZejBayG37T8Yrwmx7UsrVoKVuRDEBm3AX88tNa0I2S8b0Fhc8elWrzp3ZizocxBY9afvylfSc8qO+FMZ1y5ySEjKOc2K63LI+eJMY+kNnT/8ZzK3kU+0ryNpExJeEBY+VbRXp9MomJvE4WDmlOD4ywv/6jZxIfHx+ydsUiMmrw1yxcSDPV56KsEOk7OTRN1T5TyOngeyQuMZY8CD5JxnUpzWi4eu43y6uUcOKtnXw+i95qsQQbPdkz4XOO5rC1gayoTL2evEqMJid8xAeDaT1KuP5Czt97RlL0LGmWjphgb1bXgg1GkEMXbpDo2FjyKPgfMrRRYS6Ovg+WIqGUqdeRI4u+Z/nKdZlDwhOEh3DFt38E/NV+v/Fczd4nSkuOI8fXj2f0ireeTG4+fyUpUf4OkUBPeGBV8NPfwvXyE1jVJVcTMkhU8F72VgStW0iMSDMu9DCLo+l3XXvKynsZdUN8bV0WJ767Rt+9EN60u318Gc+74j0sRlDmEN/CkrapLImqx/AGnOStL2UyfaQf/+gsfUctjb6jJv+n7PcUL3MYXnoYwvoATTvn8G32LSRF/ivGWdUlW84/YoVJ38KSto3U7Vi/I/Hxv8/ymHMIbzlJ8//oOZPM9BzK+K/adwbxO7JBbFPJ+CDkG+cTYq4YotfryJ0L29m7THT88b38gKToxQ9KeF+Nvjly55WIcVpyMnkWGkDGuhVn/ZiW67n2JIlO5Pvdm8FfTy5LHrym75qI32YK8Zd8Z58OXkWeJfN1MP9N54ym9N08oQ3UfsWxQP7mGk0rfwdRvfnuHVc8zK3S9kK5IzYHy4hc3fIbay9vP74/pkddZg9W0wfope++STNTzPykD7o7DCdBz19Kk/Bu/VPxzTwzvHE82g+Sv1dICJE+yM6/TWdchBaiIaAhoCGQlwiESd5xpXOHMK9Eh55i42WNwVtksqLsXVtrF/JvQoYKizqyvF9FbhwW3tNUSWQIesa/hUvHUau6ZP3pO5xMkqmPI0clY7H0zVpjWqJcPc9P+rYpIUz3AMjIhbvJuePbeHnA1p1czPJdYOOSLAnJs4eYE0N82OQmTIDCr7lHJynT3OO61i7kBhNyLBPsBfqNXHuTP1fsIWGxgmIggUIfxnWYeqPmkdWzxjEhTsjL/Vq7kAX7rjCBU5LbjJN/aLWyh0+W+SzFJjl8n9HDw8nh/KNxMn4lE3v3hRfM8KiMSiEX9y0lvVydjdvK1p1sPn5HloGWrSa0U16Ezpx4a6UxLQl/At/0Y9W/uiYKvIo0SuVXKWgJQnxc6D5ZeYsOB7EPWihL+KWvnN/c7ylLL8TVHLyeXPdfrhpHeU0lfPuWbtXKKA1VfgMjs3q4m4dSeITWvpHh0T8ZwqY9V9b3JfSR6iei3K9IrCNzm5YgDT2PKsJFr6UYzlnvrdrONQb7kudhcrwF/PovF/pdCplRmlfehThTv3vuCWqsyKOxS0/8vEUFXUqL9oE1R8TvNDXqKpnm8bFR+3iuO5vFNyl5JFnRD+WP8PKPYMu/MT3ZPfYzozIZn5IHwvMW/5NkabfyqnwcCnvGP96tqBtVMi9fP6ja1pT/eX63ckTzumGizc5YkE4IubpllIz/rOYJQiQTsrJuSr+tO7kSYywA3Duxhinu3AKOId+IWQdMKl83JEoua2ehPFt32cPu0sfMjdIKeQy/HgvPGHV/AZOcPF5uREwL0BDQENAQyCECof6biHuDgrIxWhjTJq/4hy1KC+RTDA8c0zR0/OIeORYiDb/SR5V33IxTxBp7M5MfkZUqjyTTMip0Hk/878UbZ5KE6EJ9Of5Njad0PvjKtSGr43deG0l4okmhS0I5Z858NJtsS+yt8KTi4cMEVKzIP5ab2yxF3L4O20p1UMo+H0h6CqIin+B5TDz3HpSdQ1FUrlYJBXNgbOpV9CPE2JRABefctZSS2/VXo5cc9wKxiS8NGDihZAnn7BvLQBI2j+sJj3lHQB+w617fEbExMUh8xR+VTHuZgMePb+DImilYd64L7seuVrmIrsYdH0ZS4vHoeQJsbR1RqmxRxl9y3FM8TUyDU9GyKO4oHi80TSlnMSkpKbC3t+f6DHec7lUa7BycUKaEeGcwa8pJWNa9NobvisDS81EY1twyU/8k5QXCnluhSkXBhKpxSbT/JdiUQRkz96zyGsO42/vwVYNuCP9yFs6uGQTbuFjEvkqAXg8QvR7xcREI+fc4xkxejnH0vg47AmBcH2kISUnCw8hI6Li+ZIvCxUqinKQPSNMmRj9C5LMEpKMAylSqmKd9QlquJe68xt8SHt5MmpyNBenp6Yg8vxSVXEZj+7U49FQak8kD5kl6EkJv3YOOOyJvizKVa5n9hvKABVWSmboguJdsxt2v2HEzDj1qKwzrqObSAjUENAQ0BPIKAXpP95FEXnZChWpVUdhG/c4Nlc0ePk9FBZMydSqinsQgHbYoVbYEk+my4p7eLQsPfwodFSxgiyKly6EKMy9vOrde9xS37j2Fc+V6qGBGTjJNIXdj3lIFLHcrqVF7MwicX9kXnw7xwdAVF7B8iPh+lbL0V9dXoWDDzbgee1J2t0yZ7n31Z8YF4psKn8KX8Bfyje50vaMVZwJjZndc516ZN3XFNBXzPq6A830PYOdI0/3kHYVBYxtAjseC9AhM7dAEf5zrgUfxi42Myfx3wE3Fph9d0W/FedCHT9UMi/x3sNBqqiGgIaAh8P4hkIN9nvcPBK1GuYBAeiiW/+LLEdIlGIw3qJDN1N3DjDETUOenNailsD6jkvy9DKIGHTbdOIynNdqh4adFcedS9t8DehuBCTm4kbeGZKvDS/rotpOdKpu3/16Icf9G49iehqrxWuA7jkAOxwJqpn1YlebwftkaZ8Pm/YeVL+DyhvGc8jV0xVlN+XrHPweNfQ0BDQENATUENAVMDRUtLPsIWNujqA3tTqnw+dUNd86Nx7hBX6JujXIoYJuOqIf3cDHgCHf0DLbuuOjjbvF2c/aZeftz2Fd0w993/0b3Wu3xQaF7CHq8AY1Kqissb39teA4LFy3GO/SH0KxME4yf9ys6fV4fpQsXRNqrGNwJuYT99PjpiVh4LDyDNuVEC4fvSh01Pi1AIIdjQWpyAgoMnIFbE0ehVk4esbeAtbc/SQL2TR+CLpO2Y9qOIEzsYWxS/+2vg8ahhoCGgIaAhkBWCGhHELNCSIu3GIGHZ1eg0ufiI9ZqGUfM2o1JY7qgpOFhbLU0/6UwauZ0zZzNqNV3LD6z8IHitxaf9Aj89d2nGL/xsUkWCzUYBJ81U9GpCW9S1mRCLeKdRkAbC3LafM+xcuJK1B4wEp9XK5xTIlo+DQENAQ0BDYG3HAFNAXvLG+hdYy9T9xT+J4/B/8JVRMbwRxELFquCj5s3R4vPP30rLj6+a5i+a/w+DgnAsZOncO3GI7xKJrAq4IAP6nwEV9fP0Kxepf/0zue71pavw682FrwOelpeDQENAQ0BDYH3GQFNAXufW1erm4aAhoCGgIaAhoCGgIaAhoCGgIbAW4WAKTNlbxWTGjMaAhoCGgIaAhoCGgIaAhoCGgIaAhoC7wMCmgL2PrSiVgcNAQ0BDQENAQ0BDQENAQ0BDQENgXcCAU0BeyeaSWNSQ0BDQENAQ0BDQENAQ0BDQENAQ+B9QCDPzNCT9CTE6+gr1ab+2aBAgQKwt88zFkwV/J8JT9Y9xZUAPxwOLgTP8V1RMAc1N9eO+WwKoIijvRFVvS4eSelEEW4DZ2dHRZjmVUcgFZcOHESBZl+hmu1LpKgn4kKdnJ3fAqMWqbh78Ryu3X8KPWxRsVYjfNS4GnJqZN5Un7NzdEZBi4eLdMTF6YyQk+L18sllLJzjg1jnuvhp9CBUdMzH0ut1L3Dz6gXs2XMd/Wb8iioFxDiWKJccyboXuHv1DPbsuZPnZeUSywoy6XgcEoSgO/fxMo3Avmh5NGrSFJVLFFCkU3pTcS/oPK49SIBzoXREpxXHZ64uKC9pB2UOqZ+kPIPfoSv48Mv2KJfN9rl+cjcuP7TCl306M4us8Q/P4cStgujcvsFb8E1Ja6q5NQQ0BDQE/v8QUJfpjPlxcHRGfovnaOP8GXH3sGtfAArVdsOXzSoYJ+BCcjrf8ORIegridcmcRyoPSAvj+Nj/L2p90QX1yzpIo3LXTfLoX0zwOiqBZ/nnVL8l+dFzPjkd/DiPOPnvkX14YS1pYWfNsC/mtpgkZOYMh5hgH0bHqD2tXcg/YUlGhC9591HNs/tmnFFaLUCOQEbiXfJHm5IEVnXJybDnZGm38qpYcm1h14tcT8iQE3jDvozEG2SsW3EjHou3nk4i03LW6UyNHXVG7yfpFtYv4sxfRjxRzMb5hDAKAYs6szQD1wZy4fdOLJd9O7B1J1fzBOMU4uc9nnxobcV4yLuyWJVz3ZEee4NM6FpGrINkzP/Wa5fJcScpPID0b+TE5SvU4FsyanBbnoZVXbLe/5F5PvUvyAmfP3nsrF2y3T4xQYa5SZE33H8Bx0PNwStJVM66rnm+tVgNAQ0BDYF3DoFn5NfihVTHeKVM2H3hhdeoXTyTd7rOOqNKJ6fzDU8snhz1niSbc0u4/kICI15KytKT+4EHSfeCdlx9559+IonLfSdyn6SUYgo5v+VXWcP9z+ckuRZ4kezxWUwGti4qi3MZvcHkhC2lqrnNI5CZHE1O7lxC2tvbcPhSBcxYTTJPQxarf0xmfV1W1lbz/O7Lkig9mclhvCIBkNcRxpV032d/Ruxl4uGUn8B+EAmJERWr+MhzfLgg3NoPIufD4t8CKOLJ/K/Lcf3i08GrSIyeSq0vyObfv+TCXm8wJuThhc2ywRLWLiTguYiLaQBEvoQJokrfxSTiVZosi3ShYMTmYC4uKfIOOX18FxnmVozv77bueabkPr0VRPyObGDfKVXA/r8VahlAWXhofxXGGAFn5W8br6NGSnN6lD9Tcj8dvJ6NTXePzmJjzOrzT41Kz9THkePrJ7M0XFnZxCwz+SYZUqSAybaNvbWX63N0zHqiKWFGbaAFaAhoCPy3EDC1IKoc66l/4s7bOQbn6pbf2NjuoaLI5XS+oQxlJt8lnk2cefpWdck6v/PEd9EIVt4HnXqTkYM9ZPJGM4/1JCXHtbEsYx4rYLTi18RJ2q4XuatYFacrztKGNKX5WlYdLZWIQApbTXhtBYwQkhF7nglNtL26zjouFqXqekGmfMQr2HvuvZb6p0pdLVAfeYS0ajPnnVTi6U4Sp3xZ1SX/hBl/9gGLe7PvRG1wUsMjr8MSb21hPG26Ju5u6sP3ceHlurym4k90TMETxghLxofEWzsZX4Xr5efcq85HGcGRkfiA7PFZTzbsPEXiOeVRTHJl/WCeRjYFfJGC5a6/p3/1xsqynKusUx6c4MLxPWTBAfIsmSq3evIsNMBoR3T3Ten3n0I2Dq3K19euF7nxSqrlyOOUyijdjXfrMpkEXAskC4c0zwFmKWTH77X4fHQxw0TbCrv+VfqufCfHkqxbTkuhIaAhoCFgCQJ6sn1sDW7MHLlwNwm5FUwCAwPZX1DwTRKw7w9+TLV4gdS43Fehh8VxGSBqMk7O5htaVjz53xelGH2pLBAT7M2Fz9+xl+zcuZecOL2L6SsH7xnLYcacv17IG1HAhO08UxNewOLvGTg0jXxSfr0K/ndz63JVAaM4nlokKgF0l+aBQpmWYi0IwVSIyftuTEt+we245YayKa3Hm3GLW+/DDEfhlOVeWvkD+0bUBidl+jfhl66MSRUwQTF7naOvPP86smJoZb7eVnX53yyPXdIJowXDSlDA1lyOzRYkDG8TQnq2iGWR+E2WlQUrFkfTnSQ6rtMdLqN/mY/F1UaAzPN7yJIkhx9hq4xqyrS0T0mPizICBkdOFOSwo16sX3AKvZm2FRY8+i9/nSM1Sq41v4aAhoCGwLuDQHrUcW7MnLhDPLqv5F4YV3O84KqYL+jYrJRxcjrfUF7vHp3Kxv3OivmKU8AK/syOnD+/uIJL28pTZV5TVjwX/G+FFcR6bVrLLrbp9RQD7V9uIpCRC5B+1msoWthZ82ylrMWyvXdNsui/eTEX99MPHXNsjMEkcaOIdOyd2B9/+EUhv2N+vMYdUCPKbyIg8uRKDN8VART8GRMHNH4TReZOGfo0Rud84AODOwn7Vm3i3B27N4XTa9quyIg2GPLJvM7TT9uGZbtvsHKVjozo81i05AJKun6H4V2aICEk1ZAkQ5lU878GAnE3/eCb0hwLRn1hTCVfeQxfMIWFnwl8xNxBBzbhZkYm52/jUpOFC44ilRqjvcEw0/q1fyPRxLilTzMRIRBS/GZEn0HfTlNRd/QG/L1upCLW2Nu8z0/cWLdh+Pc4FSn2c+OUWoiGgIaAhsD7iUCGTXns9T2HiT3qmqhgEg6vWsnF9erRJkeG3o7NGY6ZgXHYcvo4hhRRN9yU0/kGSMDuuct43q1dMGG4fL7SJyQCtqXgQOUUEo7J7cYAtu6Y/bM8nYnKv3bw2yGrGlnME+qVihtnDmPfnr/hd/UuipSrhkoVqqJLv+/hUquYkIj9xjy8ij0bF+Fp7V8xsaMVlk6bj4uPdKjyWT+MH/KFoXNkjyYl/jTkNDZs3YZzF5/AuVpp1KjzOdq5t0WjqiVVrWXlPh/piLh9Bfs2LUNys4kY19YO21etxKFL4bBycMAHVZuh98C+qF3CjmEhdVClJH8+4M6Z7VjrcwhPXlLhpSi6j/gZnU1ampFS4N3WJT7BuBGfIGDeWS5gzsDVGNv9L5RRCNmZumAsmXsOcBiOzp+UlhF6FX0X+7ftxKFLt7nwQsWroZPHd+jQuLwsneB5djsAu3fuRkDgfSQ7lkTtGo3QrstXcKlnSJ8eg3XjO2PQ/AAuS2LYSWza6QwH/UsUqdpaZkmHWk07tscXvoeP4wVKoISDA2o0boo27TugQcWiQpHsl6TE4/KpA1ix8j7+2D4FL0+sxdzNp5DmUA7f/+KJz6sVRkZcGHZuXosDx0PwEoVQv3V7fFqjADKdP5aVzYgaOWKwavz/uNBhi781wtIo+WsEZBf7rIoqXKE2J6QGpGVgx+YjmNyrDPaM7YYfV5wH7Afh9/4fZ0XCoviqfWdgTIWzGDHzCJd+2XBv/NZnMSrYKjoegIvbV4DyM2/CUBT2/SYL+vx3tXfTOsTWGYbJvU1NMjwZajVx84EQODqK9h1TdTEo9KE7enxehZWV3X7GMppxPA45hZ3btsP/xlOUKlUKBYtVQfM2bmjn0gSFbQQcEnBy12E8TRWVE5KWhIJV26ALx18qAnZtR2iqDWzpfJOWhqquPdG8YvatPBWq3BFnAjqijpP6Gp5T8ZKsNm7NqxrcSbh08jTvtnbBhxUKszSCw8qxIlp9XgxHjz1HjP9JPIgbg4ZF1csQ8mT9m4DFQ79FAGmPG396IG0HP1aYy2fl/BFGjmiOgHkBGDHnb1yb/5XqWG+OhhanIaAhoCHwLiNg51wDnbuZrkFG9BVsOfAUsKqLji3FOdB0DnlM1CVvtB2/Hx4LA/Ht507YrVdfKM3ZfANk6h7i1JkYrtBirTqhprN0LknFgQWLUGfAUk43uLpxAbzjkzF0hReavvacI6+nSV8u7KKZJUHvgGV1BPGGr3j5Dg7DyaO0TEKtwY1pXIRKEqR858nkwPHjZNGY9pyfhomXtF+Q/d4ziHuDgiyuww+/imXSs/4Ga1eW0xSqpCfHFhmOR9q6k5Wb1zHLXZQHaqnOpUF9rtyNl2/nPh/6p8Rn2jB2ZIcr09pFdheLCzPUUW6RUDyCWKHzeLLUqyfDh+UByJwj4vEgodbmfpNDfWV01KzECO3psVBuyeayr+GssFVdMva3obJ69Zn1t+KoIjXmYODZ1p3MXbuOTB7cjJX9lacvd3mfu69hVVdGq359vk2GrhCPD0Ve3Mxwcx89j+zw8SY9G4qWfaTlR4X8QyYP+ZqVRfuPp2d/0c/dgTtDksP9eJpWdck8n2Pk1JGNrIyus8SyzeEZcYa3vEbbZIcZK5HsmJrK9rw5+kJc9rAXcmXxqw8jo4qK353Qr8p3nsF9w9c3DOEwc3V1JcIfTWPuaJm8RL4Pl+2yjkSGyc+IzzgcKk/KXbS9yd+jcxhOnmQmk1XdKrA2W3M5mqWnd782zB4n6zPKIw8Mb8kxtbTER8Rn2jeMJux6kcXrdpCA4GeMdnb6mZBJrSwhjpBnZO3YDnyZtu5k6eYdZKnXQJEHW3dyIoy34pQee1e8G0XHBIC0H/wnOcX405OgI6vFfm8/iAQ9TxWLykWXMAbw/Vq4A/aM3Qs1dRydEHHconml7SZlzzxm0pSE/Gu4zycchbQ07539hqMrr3G3Qc6J5tMQ0BDQEHh/ELiz35ObZ4q5Zf/ufUYirxvYN5rOHQGU6grK+TgrxNTnG94GhaB/dF8gl0eFqxKbrulEWxUOw81ercmKj+zGv/E7YMr7XXGhh5nQSidcQWg/9VcnJmT4PReMT+vIvC9Kc+H5G/1luCD9guxet5j84vEFFy6Yda7aZzwZLwjsBgXMcpo8jMyUta07uWSwSketqTArWlTIsR9EvLwWkNsJUXnARzw5u38rmTyEN9Es1K1Cp5/Idr/z5NyRVTLsUPBnieUuuSBDsZ284iB5FBVJjnr/xLAVOr/lHUdO1/iej8H4hsKYRNiJ2VyZ1FperOHePcVSULIpfzPZXRHxMj41CS2kJ5mPZdgfZMY9RIMjaueQhUv1tIzpe6VWep6RBf0+YFgI9z2eBB0kC2ZN4LAtXI9X5mjeCV6/M6G9+4LTZHX/SlzeuX6iqdL0KN5YiaUDiHDXhAqk5sydM6ExBwpY9rC3vCdQ5YAzmW8Q9ilGfx6+zqzevYp9SE7unMswo/GzdwSQp4mvLCyE72u0j6UQPbvTSOmo9VtBYB66gpqUT5EZ8JAJ8vqnZPc6b/ZdUXrK9mJ4S9qFWvb0Mphcn+oToFgwICS7/UwAQVqW3PCEjizvV5HrYwUaTiIPJQYrIi+sZf2WKoJXmNVMHeuXtF7SvimUx42DVnXJaYssSgq5svd7aHpHnj/JmEQnXGY1UYKrnLJYZ8q/rN0kCU1jJklECBHuorp6ik8YWJ5XNDKjpvDLS9J8GgIaAhoC/yUERFlw2DreirDltTfIeBI58XUUMLX5hvIinXOkPNJwavSsssd6jmXBEJZ0nE+Iuk/O+/uTE/7+5MqtCMurlo2Ub1YBs3YhO8/fIOHhoeRa4Emy5PcBohABENHqlKhoKQXTc8IbU4oJ/JXEIhu3i5ZJpfwUcjfwIrkS+pzQlVVBebOMpmiBTSnURxiUCSogLDktt66W+3wQIqUpNdtM2zklpO+umgAAIABJREFU0rALYxCCxR0p8eOgOzhbJTsA5rCwpO8IFxVp/emfdOdGWFUo22UlMy9N9GG84qQi9D2WXIyvM9qXE97ZrpCRlTRC/pnekvWZ5axOYl3VFELBAo6MJ0NFhVUYri707a0IYUdAVOpo3Pi9/I7Ly6gH5MKFQPLiVSIT8CfJlDpC/p7wIfnIy98CKMU+RleQmKKpkpMJjSrKgkpyMSib2IsZzbtC/dfIlX9DX9jI2sSQP/Me241ezBRs87TFWFEBo3soMYG8xSKh3y2VWTY0KP5ssUTsEzS9miAv/a7MKWA30zIJVay5lTRbd/L3PbUnAF4wS0vZ62eEsLaV7LZRDJ5fWMj6utTIiYDPOW/RMEtDz/1CMEkO5a1Q0norVyaFSU741limXHRQi6mCoiXFXSibaz+Vb1tggeFhot1oOpZGgZlAg/sVdmgVq5oW5eV2VMXTG6rGRmSFaR4NAQ0BDYH/DgIZsYbnRCRKlKW1FxZLf94hLohL5wflfGyOrqn5hs8jLkjSk3TXIp6TOxd28/OTwaAXtZ5N5yT69Agng+mfknW/92BzLzdfAUQ4dWWOl+zGSQ9EmjymmGsRGf7o8UkdVKpUDQ2auGLEn+s50qVce2PpzgAEb/rBcGm/EL7ynAKXBvXx3aRBqMbuGSQhKiqdsSO9wJasFy7bA/3nDUKFfPReRH5Ub/wxGlal9xGyT9OOo8GKY45iVcTL46eu3GHh1JEXfEhpVm9cT3bRMX9ZN8xfMojxEHRVvPAuBJb9qi+6NikueAHYo8bHTZhfiiMLNOMo+XEXeDZxZikmLjgC/uRuOo5vXs6F/zyiHeNTd+sUd7YWmdfR85su8PDwYH/t3P9gdG6suoBYkoKjqxdwYV2n/YAPCwj3W/hkbiPXYfXsWVjpcxIesjoxMrCWZNFd/xu/HXvORX7Rrj7jSUht5VgfP3i2472Z17HzH4OxBxiMP9AYW3f0b8ufb3YoURnNmjVGsQL5QHR8rad1qQWvLefw0kD0E4+FGN+qklCE2V+hj9k75oedhG+zmbIRmT3sLSN8YcMoVHP5jrtrNW3HISwc0pxl7NdyFp7SocvwT3fnKnxfpqGyhw9+cPtACM72LzUiU7RxN0z5SLyrN2PWXoZ5bNAuTL0cizaTfrL4/Lb0uzLJkHUpRAQeQscPPseRaoMRFLEfbasZ313KeT8zVXISfP/6i4+0dUejSk5GCZv18GBGca7O3YK7yTzw9lVbY2k3/o5kzPGfceK+0DOBh6f3ce3x20C3PLrTlI6dE8bhaEo62ngdxSAT3yitTHbHHSMAsgg4PGsAFsa+xPbz01FZ5b4gzS7OJuaJXTl93aRBEPM5tVgNAQ0BDYH3D4HQs0c4GcC+QS98VFG8F51VTdOfHEfXrlNRpe9K/NFDlKVpviSDgaasaIjxWc03hTBw8SGMaVwEEfu80KB8KdRs/jXudp+F0Ggf1HHSY8PvP3Lk5s8fBWeEY2yz2hj4504uzNvvDghJgZ/3TzgwexFCE3kDUmL5r+fK6znQiLv1p2+gWckMpL3Sw8rBCcVLlUJpZ0ejdNVch+L01aFceGr0XWxctxD9fzVYMzFKLQ/ITJUIz5Ko7NJMI7xAE3k4AJHJw1HDoAzkL1Ud3QvacYJMcXvTF9hziw9JFaBGs17br/Gh9RrOutilG7eRimYyy4OpulRO0BA/ERuUrFhWSjab7lIYMGU0ZnbiLZ3dXf0nzkzuiZbFguE9lzfA0MWlAqP54PpV5u7fpQ3KpqZAUJebNp3PxZGUFBSq5Qqn9Gf4dx+vMDk5FWT5BIeVY2V898t4wZvlr56IFswaVC+jmv6jDh2BiQe4uLsR0appCLXMKVMGC6F5dzfgmA+XfkqfFvhr9rfwWTMbnZq0gWJcUaWJ9BcIPs5fEE3RqSd53dBsYW+BAnj3gBc+GbCIY+uvfbfxc6eaSG+RH96r2/LW7V7Ogad3d6wf2gzUAtGaCaO5C7prZ3aX9cns1otXqothwPTRmNp+Mpf9yd7BOHyrN3rUtsGeRTO5sDEerbJL2nz6lLVo+8laLs2IX4ajUUn1Navc6mdqzNjX/QQlHY3LtXKuj24tSyDg2DMg4wmexOpRoxw1xFMInYePxPBd/HeydOtFdJlELc0mYe9Kb8BxItrVK6JW1GuHRZ5dil7Lz6GY2xxsmWhY2DBQJXrJBEt0SDb6pmjCJAQHHMsFPhbCfeJpjFkXhJ715XW1FVY68jnCQfZNmy6WLpBIF3ZMp9RiNAQ0BDQE3ncEkuC3gbd2/M2QDnC2QHbgEXmOGf1746btANxZ+b1sQTyfjQMKWfPznFV+aiYq63/m5hsht5Xjh5gXGI1x9+9DpydwcCqJD8ryC7mxV7bi+w3h3AJx3/qFsOnHhpgfFM9lnbT3Nga71eDcrfsNwIfDmiLwXiwamFlUFMq09PfNKmB2vdC0aW3UsnDSQ/oz+M6fjh7jl3K7EPsu34Xticn4cvw2S+tnnM5imvaoWKkQnz9tG1bumYA5BitpmbqnuJnCr522VjGlbFyoSojFfKjkVQTlL1URH9rb4ObLNCRF6RTKliJxLnlrdOgND6c/sSkxFXRna/2ByyhX+W9u5bvrrIGyFee0pFdcqVQomzBypFnT5NSC4sMUdQU6J6zfv3COZbty4ymgtgtja2hnANxK98R2ZnkUCH4yeAU23QmHxzzeqlrSta3o/NFWtBi8BDuWDENZE6vuQn6SnogYE1Z/hDSv+5sd7LMqK/XJcdQ0KN2tPI9yyhfNY1PWDcvm90KrkVs4Eht+bI6e7VNQ/84ijNkdiaEr9qMVpxhkVULW8ZXcPDCkyEx+RxXA1JUn4P57Qczb/AiVPdbDrZK4zJA1teylWNK3Nlo3TkLX2sYLA7ndzzJ1D3Dg0DOOwZTr5/FEl4kS7CSAwLc18jsZhvAMf9x8nIBW5UpwkeU+74EhRaZwOPlNX4V7v7micvRZrj1GbO6JEhZPmEJZWf+mPDyOb9zGoUDDSbhyZKxRGdTCYbsvS+Ho7kiOmKnJJzXJsC9lVRcVihkvzmXNSRJ2zud3Dy+d2wevuzuRYshUoEA+bJu6mvelbcOf4yuhTIFU6Iu0xh/jO8oEAmk5dIGEe8ojD3CTlqO5NQQ0BDQE3nYEMuOu89YPAfRsV8didmOvHOGeC4LVJexYPAOv4nnZkJ7Kyog5zG1qUGJHt8/FxKhK0EVmwv3n31RPnWQ138iZskG5qvLdNiAGiwaP4xaIV835BrqQ9ei34j6XjcqqdHFZ/GcDZ+t8KF1M3Uy+mC57LlNzYPaoWJqa6GC8k6CeOTpkH/p93J0T6H+YdQDzDZPjv0E5mZD5MrJH0wadJ/rAY3ULTsmY26ceShe8gn4NAe8xfbnV/s5eB/C1ijCmXiMxNHt8iPlMu0SFpbVrU5NChOn8OYixqYaf5vTDpsGruMxU6N5gIPN9z6aqBGNO7cf16DH41MQuAs2UT7LwERnNr0SoErMwsES16ixlMl2CV/lXpFJd7u0hemzKpW0zi5Qvnkwh9J17Ch+2WgmPrj+x940CVo5AtUvPcfvcVHxgZrEhn30VuLYvCd9dEbDPebdWqRENSkdstDC4AZZgb4IQC34UIprv7uRWm4VTR8sfp2LI5D1MMXKvbM/F2zeaDq/Bufi2mU0l/LhsMLx7L+To31jQCQX5E6vYOKrTa+2yySokeOx6YeWK6hg8aBoX8nXjkbgTu5rthgvJcrufWRUqikZ1HHE0MI4rgqielSuERm1bArt8uAWqpjUkT3PYVMJ3s/vCm36fadtw6PwstArbA1i7oF8HyydMoX5Z/WZEX0DXWu0RYDsG989NVX0iALCGcOQWmTpE69IBJ+XTGRnQJRq+U+uKKFtUMiBkxYQkXjgeHLBqKsReK0lgcK5fwD8Bkb9RWUz6pSNgQsGiT3loO2DG+GkhGgIaAv89BELP7ueOH9LFtqbZOH4I4URS5nVM+lW47mGM3/OTWzDjJB9ec8BYtK0mT2PZfCPPo/TdPbCUu7bQ2Wsn3Epa49wO8eTFxMm9ZXJgWuwTBGS0QLmiuauAGZ9rUXL5/+EnEZjRqg+nfNFzooLy9Vqs5ICmlXMTrA8/zIr9pUsjlKrUCLMftMKaI9exd1LH7N+jyAEfjAETDpL8Ck8NOymWbt2aIJWt4CbdB7E7KELGcl0Wo6WpXYgMf/gcEo8jCnn433TsGPcZRmy8ybah/aYtxmUTZ26fX/LBAl/TH7BAu1hxfkeA+g9uOI04yf0kIc3L8CCur1G/tb3lAl/o2X9wP9EKjb8ahutJkdg5e7hAEslXp+HgtRfMb8ohHHNN0aUim2/LmiLJhUddXIZipVvg9ivDmWULsB++Lsgszeh74n3H/AUVA5FNNfx5wviI8NYdPxvthJgtxILI+l0HcUeApUnpilXHxuK9RGnc67pb9Z2EgxNceDIpa9G691Kj+0C53s/yFUH5SoadNv0hnLrOH8uV1yUJV/4xvKsFwE6xnNa4i3hHbIyrOxoNWIk6P41E41x+4yQzLhgDq7XCUashuP1kFqoYLTok4fbtcGSgENr09+CrYNixk9dH8W6LS3tUUDl6qcxj7LdFi5/mY/3/sXcdYFEcX/wHCKKAiv1viTVWxBaj0YjdqMROolGxxd57YouxR2NDo4INjWIs2HvEhiJYsGBXVCxYQOqhHBww/292b2d3r8AdxVjW78Ob+ubNb3Zm3pvyxscHPjp/vvu2imOWpRPmeW6Bj89GrJnSkHsrUZ8WH2Lo/qixtEq4goCCgILAp4uAePzQrZ+rWfO7Y7UOOLh1o8GxeYv3XFTTHkFs0GsGtu70gbe3NxqUlp84MX2+SacFUkIxp9c8buFy5mh6bUHyRqVtf3SoJ3+/1t9nIZDXBSUyNR8Z5+ODVMASHl3hLk9TtuvU0zGcQMRVfePV0o/JDE2ifoAJLbuiVMe5CE9OoxYjuT/Vta3o3yZzq8iZ4UNaG0vdBWMAwmoEfQyv7/f6j8lm/+4Kz5GlYwNMnthGyh6GDeqgtwNXoUFDlmbVgD7Yc0c0CiBEBG0aj25LAuDaujXauGnvp2kOodvw9XpKE9167tq4Dx4m6Uic1LyIzk6S/Zf1mbCuvjoN5x8Kt8+EkoGXYaLhkr7t9PETU0pdCdjxw4/wvcbf4bKwLQ63iX/h+VneCAlNeeriI2kGA2571G7ahAunj84+VWXTBc+UMMxrNwWFmg2GS/NvWbkZYt9MuuXOsjGHhXB3BoDUAIaQoGDtvghY0UPwcr9LV27Xa7/Yh+fhd4U/XidLbKLHwtYZI+a7yVKPG/9DBufQrWTpTfYQFZITreA6exNGF+QngvC9IzFiBf8YuUAn+78zezRs304gj9U+p7WGblgQQGJx/xS/Q1apX09U0TmiSB9OH/mz1hhMGr9YYcz4hkb1BqF3H+JlTKKkgIydRH0Hw1u4YHe5gbj3ZgUq6/BAKfiv6IWq3/hyxwArftuBKUD/Hr+hV0DsgyC2GDJufAfZKqReYhpgodPhuUS58XX77ujTowd66Px17fATvDaO5EnlcoJbf5qmD3q7NdTbPU1TxbGFLYNlK4EKAgoCCgKfGQLS44ddMpCX6Lzy9EUEu/NP5STXn/oYHJt79uzMXaWhcFb+tgN+cuuBvn37ok5J0c6CufONsaYJ3LCQO9k2wns+aurMWYUaVUMxySJiauRZjJl8BtM39zVL2TRWtizcXLOJ5qZPjQ9mJompOXThPa306ESFeNN9Cv7Ptj+5wb1xoyGhQdtFWpwZ+mQSFxHNmS4XzJ/TfH24d4DkJZhPU0MOaU2eu45ZSa7euUMehoWR8PAIEhERTWLi44laIy+D+rKfD/q+kIgHfc9G9k8wMw4QeZxoRp2+IRShfXtLyHtwqiuPbzrmoIW06f0mhvEmPLn2sulO7kneKmL50p7JH+y1dCLefjeJKjGRxEXcIxtn8w8uC6aeX50VzW9TukWbjSDHg++TiIhwErBvJf+ulMM0SZ1Ec+7iEwNvyDavLeT5uzRydeNo9j3Rpw6EZ2F5/l6RyXUduXi5aW4RP/rOkn69+DLLufuwd68EesLjxIbeYGKYaB1S0+qGTI0L6aVv2HVeIH9QUEhDf1/eOc4eC+f6gZnYS2npugXTsULfHLL6pOwtLFr2sBaFGNZCOruaI8jhy/eJKjGe3AvazrVf22UmPFStecA/rGzbX+9xxJQIrQlcOk5oH2+X8xsre6Nsqc5zETRten31qvbxXvF7IoR+68JbfLRu0jdDKL3MfWeEsDdMdPpiWqL2YWntWLj9eoysisL7bpSXQ4/VsjjBIx07DI0DNB0tR3isktJa4fdQyJ7+r+YxmdKyCNfeFXrNJVu8vYinpyf78/b2JnPHtOHixTf+NOTgVBf+G2FPBgjFSJ4OkLwfJsRKf5kpeUsncvy54bpL00vdLG96JuwJIdJxSPrUhpSW4lYQUBBQEPicEBAeX4ZMBtNHQB0uztH0aZY4HRlUN0eGZugzNd/olkIINVvfyMaK0Hdzn3LPVdE0GrJnamN+Xso7nD0JRN/+pG/V0ndH03smSL8U00Jy8B0wDYmLfkJ8ZnfjK6UVItqN2UQeRcQRjQHlRWCZNoTwjowgxEkFHyEsf43cBJZO5PLrKHJi4yRWTuHmv5Hbr+WPvZpL81pcHHvnSSjP0G+3yetIeDL/ZSUnxuQAH6kyBYzyQMu8GfaavA67xAQg+oaBwAfFMTL0tEyoWnNGFKreRlzmBVttm8zecUVHiRBawpRfNXv4teOsE0YzhAd5svYxhCNtM5F/tdgZtDzK8li5kJMygVNDto+rxOgXa9aD/FjLXiKYq8jfQyqw+PaT/yGvEpNJUvQj9hAzxe+pth1pZ4wMDSBDaudneShGcoVbFBYHexxjSt3dE6v4PLb9DShthuARFcBhG+gDwvJ/yYkqctd/s/zNLUsnMnTyfE7Q9fDwIF7e3mSD55/k5451Gb8UL0FoNA97eflSX2r0Zb1+WbTZz2TBgtmyspfuOEzWTGkn40XWfhlioyHx0Y9kb3F0m/wPeRr9TvKdigOmFLc0jYa8i48kJ31myMov0mwiCXzwirVhmkZFjiwfwNKU7LSIhMUlc9WNi7hJpmofXKZ8z9l7g8Fwy/dXlofG/eL5L4lNFAYzc78zQmLDr8m+M1qW8Ow8LTT6zk6xPCsXsu/yM5JCNOTOyfUsfNFh/o06xqTMoX0fDSAjthh+LFO6CEDrJD5yLyMk92hesnfPZG1rqL/a9icPJQszVOETHrOnuPP9XkPOSB6I13tPTlJ6TFiAbPwau+GcbCFAktSg09THz/9d2IHDWPcdSINElUAFAQUBBYFPHgEVWd27DDcuprcQTGGQzStWLuRSXGq66KSrgGVhvpEXKsoNS3TeJWXvzwJklMducv7ENl7esXYlF14L78PKqWXVl2MKmCrUlwkIhiZoN4/0V8AfnFgmy+/g/D3x8X9IYiSPjFK6Hnv36gmFQnl0R0gqzJhKc/nhexyu8XfWyHgQ6Or+Vhq0lWje6SuNQrqs8iFdxRZoSn8HLtgl084fSx43lqajr34/kwp0EmEpo/ZI70MT+NvzQL63pJsnImSvTCkUePvF86CB1REqIE/Uw79Br7kkxEBnSAyTP0hNd134nVOBCzU5u3W+XJHR1p8K0VLOb24do1cux6vswUH+Ye/iTZvqpaXCfnC46R1WeNhbf5VF8ni4pK0E3NL91Xl81jzsBcz0f9Xh5w3uclFeuk1eKVn40JAg38WyHSOaxhRs4m/46GEq1FX6sG9i2D4i3aGi3Mp2ZA1gRr9z3Z0sgTb9nThriOGybfuTO6/vst1SaR7Kw002uZj+nbGdGF0+tQ9ECugnhAWQ6e6t9Pgq3WEk8Tf4KLSQk/+95TuGW6gydvogNf6WTKHRf8xcTo/6Lnr11ONHhomkTu4e+ju2dGXRY8h3jIawwGZfsz85cfuNfoE0RPOAKW6Gyvpp1lHD+XRC2c5h3uEyxVCWTNh9BYj8wW9ZKsWjIKAgoCDw2SBAT7Rxu0eSBV5jlVdrHzimY3WhFotkMqqhPFIFbLyPuOhJ02Z1vhHKE/QSyo+hHbkHJ9eT9s1qsXnp51l/k7D4DLbuBOKZ+LWgeWRnEj8gD1HH4unrOFhbO6BYiYLM4EVizEu8jE9GvoIlUNjBdKMJtGqm00zAlvE/wn3JEdDH2NycHRAdFYX4d/y7Uslv4/Ds2S0cWT8D3uc74WH0OgOXz42DaTofQPSNjSjk3I8jNmxDMP5yL4+n4XGc3y4TGBjnKrMxSQh7+Ar/q1BG7x6FPsUURLyIQKJGAwtraxQqWgJ2+le5WDaSkoDIiGjuzSC7fEVR2FHH+ANLybftk/AoWOTKg+JlShjhhVoHjISKa0drFC6ZfvkS8npOtVoNW1tbkBQ1YmJiOJo2efPhf0XMNQaRgFVuVTF813OsDIzAsAai4RC9QrMUYB726RUVH/kU4a/iuCcPaJ2LlyyJ/Lb6DUnb78mTcB6bfMVQsUxR1o/To//xx2XfdyZgoVHFIiJawNwR/yui/zizkFb2mxKFm6FqOFUpKQuWe1KQon6DxR1qYbndbNzfM1DvLqc8ffb44l48wsNXvLXT3PmLoUqFkh/E9xG4ZhAaDl7LPW1w4+8+7wWL7EFUoaIgoCCgIJBTCCQh4kUUUmCNYiWKZDhWU1n9yesklK5YNl05L6e41aWrUb3EnQcv4ViuBko7mqc76NLKDv8HrYBlRwUzSyNwTS80HOyDIZ5BWD2YPipr+N+7m2thV2sLbkafQnWdy3yGc5gfKlXA3D2C8Pco4/yYT13J8SEgkBYTjG6lG8KX8MYMhEe/PwTeFB4+DwRCT3rgyxZjMH1vKGZ1rPB5VNpALdUP9yNPxY6AbX/cfb3WoGERA9mUIAUBBQEFAQUBBQGTEfggrSCazH1OJUwJxeqJvhx1VZy+xT6h2DTVA8wdOxXVR07Qs0AmpFF+FQRMQYA+ebD51mE0SvFErYYz8EzzwW5Mm1IdJc1HhkDQP79yyleXBccw4zNWvuhD8IPq/AjYdMeFJ4ry9ZF9xgq7CgIKAgoCHw0C+meGPhrWc5BRK1sUzEWhSYLPLy1w7/wkjO/fDk6VSiKPdQoinjzAhYAjGPvbau4dgQs+rhluxWaF2zzWuVn29/nOFytUcbwXBGzLtMCx+8fgVqUNvrB/gCvPNqF2UQPvDrwXbpRCPh8EUpDwJg3L9t3E6A6Ze17jU8Aq5u5htK/ZAVerjcWdEwtQJZvfS/sUMFLqoCCgIKAgoCCQPQgoRxCN4PjknCfKNh5qJJYPHrFgN6aP7YSi1hbppst8ZBIeXAnE9mW/YPrmizwZa1ds3j8V9apW+Izu02QewY8xJ1GHYf2iLajSaxy+LSu+gfEx1kXhWUHgY0HgxUUfeF0tikmDWyl3vj6WRlP4VBBQEFAQ+EgRUBSwdBouTfUS/qeOwz/oGsKj+KOIdoXK4+sGDdCoccMcv8RHH52b2GMUIouUR8G8vCCe9u4dot89QlJhd3gv7a0ICum0nxKlIKAgoCCgIKAgoCCgIKAgoCDwoSGgKGAfWoso/CgIKAgoCCgIKAgoCCgIKAgoCCgIfLIIKEY4PtmmVSqmIKAgoCCgIKAgoCCgIKAgoCCgIPChIaAoYB9aiyj8KAgoCCgIKAgoCCgIKAgoCCgIKAh8sggoCtgn27RKxRQEFAQUBBQEFAQUBBQEFAQUBBQEPjQE3qMZ+gTcvnAJtx6+RIqNHYoXK4WKTtVQ2jEPIq4eQmCyMzrWL20yPi9vnMEtTWW0rFPc5DzGEpKUBMSqNMaiYZHLHgUczHs1O+7FfVy6fAMRCUmwty+GLypVQdUqJZEbUdiz6Rwa9eigtZ6YgpgYldGyM4qwcXCEpSoGar2EueDo6KAXaixAo05AQqIcg8zUm6efhIsHDiJP/faoaP3WAG9SLnIhT548sLXNqU8xCRd2rcOGfx/Apdsw9GxeSVq4me4k3L9wHtcfvoQG1ihTpTa+qlMR4iMBZpIDkBhjqO1EOvkcHXP0iQOxJNNd1DhNUMBlhEWrYGFjgy/K1zIJh3eRj3E+8AoSbeygjk5AsRpfwaVGWZMLfnbjNG68q4B2JowTGnUsEhJ131LLhYTnJzB15Rv8tbo7gncfRXGXDqhaRDH1b3IjKAkVBBQEFAQUBD4rBDSqWCSk6M6n+hDkdXBEbjNEuczKErol3zy1G5efWKJdz44ZWiUnKWrEqhI5Esbkq9SYB9i1/xKqtOoE5xI5aImavId/0XcOETc7G9p6en8/jh5D2tjmIgVbeJEEU3lJe0ZGF7QjsJtAXqSZmsl4uqgQbz2+pLz2WR1kPLNejIrsW/izQXoOzt+TMaO7cnGbr8dwOdMSr5NGNlYG00t5MOZeFfiYrOxaymD+48/VetwZDnhDJtd11Kdh2588fGcewKnx98nvLYsSWDqRU49fG+XNUH1adOxJ5nnuIY8i3hlmMxOhqfHBIr423ck9M+sjFJkaf4uMa1FYD6PCzeeQ8GTzMBJoEqJKHx+b7uRmXKqY/ANwXfadr4cB15bWrmT/9UgjHKqJn9dIlq/v6DGsTcr3WkFeZ4BfZGgAme7+NZe/9QJT+uIrfnwwMN5QXlvOOkoIecV/pwDZFPjUCN9KsIKAgoCCgIKAgsDnjMAr8kthezZ/G5LdhDA3D1PmZx7LzMkS+u0QdUUrv1u5kGvpykux5KjXdFLNypLVpUiziST4+VsJUQ15GHyQ6StLz7yQxGW/E9lPUk4xJcKfCVuw7U8OBz8i8fHx5FHIKTK+U3EGRMlOK0xWwF4HebB8f2UbQCqZkEg/qDGeJ03mia+1hhyb057xNnDBLvI0Oo7ERT8hx7x/Y+GU9vrL0VwWdbgfw4cqaPM8t5D9R46QbZ4jZOmpoH/kyBHYNmUtAAAgAElEQVSyw8eLDO9Ul8UJdNISn5LVQ75h4bQMXtCUt4chX1SwlywfzbvxzENDSdMNS42+TNzz5Sa0nW9EiYpDcvwjsqBLCVaGfc2fyJ4zgeR6cDA5fcSXLJ01VNYpaPkjFuwmcZnVayRcUgWXKviUJuXrUQbCviSrxBlLlnYpydFoOGgtidJQxt6QLVPacWHmDDoSoswZG36ex01QGGz7k8DHsSz+Q3GcXt6DtSGHp8Cv8EuV7udJeuz+u7ADn8/SiezSLjwQzWOm9NO+H22graniJR0jaJnuJgzwz88uS5fPQ4+FhQkV2TyuEZd2hd8TPb6VAAUBBQEFAQUBBYHPGYGMNiikssC0nXdNgiqzsoQu8bTE22RwgTz8fG/tanTBOi3xPpM36OaAt18g8V0uythfdOhBRg1yl8mh9d03EkFS0C03u/w5roAxhcTSiRx/nqLDt5ocXtidAy9Prekm7mapyOreZZiAZY7iplO4nlcmrFu7ZqBN62UnqdGBTJlqPGa/XoKEMD+mDMw9zAt8gvbuMmarXNnTPGBaOP3Ad9yW7w9e9/2Vw2BV4EtWjkCLdQgrF3JRogixhDKHmqzXUdwKtVhktvJDd4g45cvSifzLBFyxoKsbB7E2K9Fpjd6HnaaJIed2LpZ1ALuaI2SKnEjNPFfohWPE23sLCbjzxryM2tTxd7Yy3oWdSxqlCdvHhWfHNxiwQlRuTFEyMlWRLGTShPvxbWPbn/iFPCO0JycnRpITG+ULC+V0Bq3EUB4j+k0O8QyWcZAY6stw7aMTR3cHVw/pSv7wOUUunVjHvouMsYklf7QqRmDlQlbt2Ev27dvH/vbu3El2HbnG8S4yEsvGk78vG9vBE1MrLgUBBQEFAQUBBYHPAwEN2T6uEjdPj/LYTW7cCSHBwcHs70rIbRKw73d+HrdyIQGvxYV3Y/hkVpbQp6cmO6ZUYTIEjCpgWplAu1C8NjCCkYoK4Tcflu7YS3bu3EtOntnFZPSDD3Ja/SIkhxWwWLKklXaXy9qV3DJ4/EvF7y6YqPAkhh1hwpigaOx5IFdOGLpmOqgCxo5KGm1M40SlO0n6AiWfT1CShF0TYXVBtw4yXrgdMx3hUPOAU3haLzjLGIoK8WEfo7OzLefO6PikITzNVyhi2VG6YRvkQrbA3MU1AxlvhVoY3+1Uh/uzDsC1bzYdMxX4yMyv0EaUH6kCJihmtD5Z3a2T4pOxkpGZWmQtzyWqQBsZYO8fncnali6kRLDdLA3ZPe5bPs7a1cBigOQIZnq7k8m3Wb/MCBuhrYZ5h5hcYXpMldslzUSfN7kQJaGCgIKAgoCCgILAR4RASsQJbv6etuOGUa4fH53FpTFVbsycLKFfvFCuoAcYU8Ck8klH7vqBSItTwOwmMJnl9QVPri5NJ9NrCjn/L4etIFohdz7tjTzNIRw7/0z37hwAe/SYNAlIfYLkFAPROkH+W1bhdmqaLHTJypNIlYX8Rx5r8TL/oR1HEEmbUudfwdqdMa9eYTyXGrxwmIb6Fex0UmbgzVUWrgM64EeXipKEycwdEsKb5dg0dg3u6xkjYMkg4Dns1yGoZsV/DmozbYKEn1qD4bueA3YTMK1vHZF4Jly5SzTGpjN/iTnfLsKcTTdE/3/h0oi4BgY/0nKQgH1rN3Pu793qIZ/Ff8HY+yozDv/+uh1tFy9Ew6L6Q8aX3w3HjK8Kcswk3jiD8Bi+f6apQrBmVRAXXsilGSo76ua1R73WTfhKqDfgRPAbgxUiqXLjMAYTcYFJ2LN4Juf6e9kyrPDagUt3wzMcGywd6uDX2T8AmkMY+cch4+SVGAUBBQEFAQUBBYHPBIHUXKWw1/c8pv3gZKTGCTi8dg0X1/2HlshYis2cLKFbeGrkWfTqMBNOYzbhmPco3WiJPw67F6/i/VYumDq8lSQO0MTFA9bFkJfKbyQMv303FrB2xcIJ8nSyTNno0ZWIspG0PqlxLctgrb++ElakZlssmTkJZRzSl2KJOgRr5x9DiU5rEPbgMCvg7IpFuBItV8pY5H/keHNyGhr0/gsRGl0tLD86TZ+JUU3KcJwVrNEXJH42/pd+1Q3UIhe6Ld6BnxvoW4FcssULjWys+DzqDVh78L6B/ECa6gqWzjkC29pzMMTtWz3F1mAmvcAorJ30Bxc6bMVPmaiHHkEU/bov/h5SgUWsGroMj2RKZBJund2DeeOGoHnz5ujiPgjjpiyA/90olkfq0Khe4vz+dRjY+9d0lVFpHqk7f+mqDM8dW47gdeJLeA5tDfclRwDb/pjS52tp8v/QnQRqDej3sUPQxd0d7u6DMN9rL16rdb9Bc1m0g9u+g1jd31g97VG0DG8pqHCT1iijVbRiw0JwVM2vqnzbxMmgklqmVmPGzGH/e8ydGUfCw2MYsCmMy5pwfQNGDemGr6uWQonmA3A05HW6JOv/0J2LP7VgCvzCk9JNq0QqCCgIKAgoCCgIfOoI2DhWQseu3xi19JwaeRVbD7wELJ3wfZPyJsCROVlCTjgOK4b8hADSBtvnuaOohb4NcCF9muoJTp/l5cJCTTvoLAIn4cCy5ajetzKnOF77exm8YhMxZMUs1Cv4flSjHC7FHq0GDhSw4H4HNfkCgxYeRLTEpKWF7ZcYO809Q/ORdw79A9+3yfi5b2uUqdgYS1pplY9Uf6zccUlWzn/hKejUEqMLimsAj7aMRDH7HnrCX9X2w/BTA9NN7ptbl1J12mH8CHFnbFG/dXhpQAa/cfBvTkAeNaMXviygVdjMLCz83BbMvBzN5WrawJQOaEoBufFdv4liQvUGBIfFcf401QOMq1scTi5dsPpRMYybNg3NijzD0vm/oknVwlgf9Irlu3d2O0Z0/go2+UqgUceBWLftJhL1FGKW3KjDyrE0vrK35eKpYl08bwkM9QxEqY5z8TR+HTQ7h8LCwoJTBqlCSP+of8LWm0ZpZndEasxtjG9ZCjWad8WxyNwohXvYsmUtpgzpjOL52uNU2LssFJkLlb7+1vgCScpLhJzgBzmHkqX41SQA8W8iWZkFihRgbqkjVx7RiL//v9cQb+A7laZPz33nxA6D0RGn1qNtzeLoMXs33hpMAdh+UYfvu2k38deGACOplGAFAQUBBQEFAQUBBQGKwMOgIwhITkWhZn1Rp4R4Asw4OpmTJaT0Lm+ahLG7w7HkyCpUy2MBTbJxocHCGrDXnuxq1r6+bBFYdXc3Bu56hl/7NQPd3Jk8ZCWQdzgm9a8tLS5H3TmsgAFffjcO64fIBfO1v7RHIdum8Nx/LcPjQWLtX2PLfC9ux6FX6y+4o4udJ49j0fSo3eNMCNeMQHY4LEphRsBGOaXkbZzw13bwEtyJFI+yyRNlry9JUwBtBs4Qib5dhH9OvhT91EWew3vEOsCmOwa0+wKad8ZXEeQZ5b4n1y7yAdauqFQynzwyC77CVb9BG8nbYKp3/E7KWa8JWHollqO8cc1v+L55c4xcspMp48OHbdEK8Um4d+0ZKn/Xme1e0UxmPFEhcp8rDxzriIo1jZh3+CbC9k5BaWsLlG8/Bad2LsZr/zM4deoU97dwRwAmtBd38URiOeF6jemtvsWSU8Wx7fJLBG7xwPLNF3F7P38cjx6ta+483+CR2OzgJjXmGc6q+F2jXl2/ZatlL25dY+TTjHz61taiAmbrAFiZvRPMikC9QRuRmBiPV2F3cWrfJozsVU+MBPDPb10xdPk5WRjzWBRBjfZFOO++BUcMLliwtIpDQUBBQEFAQUBB4LNGIAF+m/hrGN16tZYpN5mFxZgsIdBT3fVFvb5r0GzyfoxqQfWA9P8RDZCgvbJUNH9+lphejxhevx/KuW9EL2d7HF88jduMmOs7FuWseSEkPvIRgs6examzZ3HtbjjLm52OHFfA6B2v/quvYMuUdnK+U/0xtGNtlO00AzcijEhnkhzRV49gfnAMOs/sh0p5eIDKNu4M93xaAU69Ad6Hhfs5kozv2elYxQ3Rd/bKhH7KwtE141GtaG786XvJDKUzc8wnazTIU6Ud/mhVjBGYN3+rbHchPGAbPKLfovPs4fjS2gKm3rJhBDlHAi6eOsO56B2fLxyy83N6B1Wa7spGAq78Kyp8hW0Fad0eDdxacHwk3TyNxyp6HDU3OoycgJFDJqBH+//J2TbD9/DsBnybuyR+94uQ5SpVtBh7IDmP4xdo2rUDqmkVxhV+TzDxh4Yo7pBHlienPPcPeHB9o+kvi9CtrngktWr7weKO7Nu9uPYkZ47WndvmyR1fLdlpBSZ0EJVO69yi0mqX2/BD5vZlq8PNjl85M/fuoT6euWBr64BiZSqjaYfenBL68s4hRp+m3zx2KE6HGxpvcsO5kfbct/oiHkd+WEea9euqhCgIKAgoCCgIKAj8NwikxYjHDzs1rZQtTBiTJTjiKWGY3qgvt0u1fub3TP5Kr2BLh/Jw61aKS7J/ry9CwiNw/8IeuBati83qzjjwlztSXhzFd9MOoHDzORjapgKQ8gobp/6I/EUr4BsXFzR3cUHtqqXQYcouoydo0uMhvbjslJjTKSc/es49hKchhzCsRSFZuuf7ZsG5RCv8G2rscBBNnoRDa+YAVi6Y0LehmD9XRYxbPoT5/5y5DTG6MjuLfX8OxyodcTbuOXyXT9ArdNIPX6PVlF3IGVFYKI6aJMmPXr9NFgIQdWIC9l2J0frjsHPpMu7crgxPltp0h40FrwTZOuSGjaAPmZ7deMoUICZVtzHt0X7yDLjUdMbP0/ujYj7h801ARIRowUW+y5U51ZIyFrRpNCq6/Mxtsc/ecQgegxswfns3WSDbJVHdu8Ydjy3n7oOBJqzMMEJZdoirUKfnt4G7uzt3/6tn515w71WbU7K5ItJu4tKd9O9BZYYV9cP9aDpqK9c3t/w1yOgqmKUppxMyw0AGeYpXaYcdb25ibB3tEci0mzgR+Dj9XKn+uP3I8H3C9DMqsQoCCgIKAgoCCgKfPgKh5/jjh7Y1u+OrMuJJlszWPCNZ4vCCvpw8sz1wDtul0i1LlAKFGHv0W3GIm/+prlGzVDFUbtAF990WIDTSB9XzabBpylAu8dKlo+GIMIyrXxX95u3kwrz87oEQNfy8RuLAwuUIjc/ehVm5rCrwnEO/pWu0w0q/Nxh1dgO6NBsoGn1I9cd31QfgJgeIIFSLTKS8OIc/1lKhqSTePbuGC48FoToP3kkW1tVXp2HPlcHoX7ewmBkpeHT1Cl4kE0iScvHJyQSlneqirKNujCR7Jp0WtsXRdeSfSO07DH9N7IHRXrw1OEru1Hw3jPvyAlb2M2bUIJOF6mQr2agrRhecyoTwGR778ePffZB89zh3hrb6GA/UN2DVToeMcW/KG3b3J+u7F/JiLPLk53aUbr+V71ZUbDYEZ67xSndS5H387e2BPr9ordzISWTJd//ALHzTdzlH4899dzGhQ2WkNMoNr3Wt+e/27SJM9nLDxiH1AcRh/dQxnEK7Yb4bO4KXJQZMzEzULxF0nL9rZVdzBBrV+xLxauE4aT14fM1rxWq1DdrUlPYLEwtIN9lrLBjC3/H86+QWNC2po2UR8d6ZSiX0WTnBhLBbnOLKhyaB07mzU5EHQO+Yzt26Fkur/MAVc/cFf4RVzoncZ6k9hiAPVXwKAgoCCgIKAgoCnzsC4sJvt8Ft4ZjlOTt9WSL8nAdcp53BWO8r+NFZfp/cWlj5t3BAXu3pOGnrWDpUw5LgSIx/+BAqDUHefEXxRQneanP01X84w1104ZweRdw8tBa74jJ9710MasHv7DXv3RfVhtVD8INo1JTpF9KSzHe/VwVMYK9y4/64+rIqepZrKgpfydvw5z8TsXGwvhnzgF0btMqaP1p9VVcgY/B3jsch9Py7j0QIVmP7YFdMuWTYxPWywAiMbsDf/TBIMIuBlg7lMMrzLGrVGYYmg9cyaqsGz8Lw7ge4S4QsMLsdFqUwbM0oeLjN5yg/3twXQfM7IWHLCs4/e1Abk7ZxjbFFUuIRpcmZBwASQq+K34aVC6qU5jsMx0vKK/gunYMfJq3kTIbuu3wf1id/Q7tJ24yxalZ40osTqNyBv0PXdPJRTvmiBHKVaIFVS7vzOz4ANg1tgB/bqOF8bzmn0A7x3K+vhJhVsvmJiSYRkcl8GyzznIUBDRzNJ5KpHCnYN2cAdzRz0eG7GO6ib1SmQr2WAHw46sZ2wDQacS/Y+etKsMvyQG64Mnkqt+LuCY47/grv52CoYT6UUAUBBQEFAQUBBYGPGYG0mJu89UMAP35XPYtVyUiWSMDOpX9yZVw8vw+z7u+EsMScJ48Fts1cx5efvA3zJpXF//IkQVOgOX6f9L3ELH4ulKxQWYfPKCwfNJ5bOF+7qBtUNzait+dDLk2hFouY3MdnygVHKwsUL5S90kOOKmBUa51xugbWTWuuU3HApsg32P7sPPqWbYTN8bwQFnTlBpJQR6I8AUgJxfppu7hzn5dvTUZxixRItxktrK0RGbgWX7n9zpXx2GcR/Gd1R6uy4pZo6cbN0LJEsqQxeHYSLCqjSSXxYp4ekyYEaFSxeGudHwVsI7Cw6wR8u3qTgbeScsFl0BqcgS2aDOaVH2oY4V7YO1SrKt6TMaE4s5NU6tgf7vmWMIybluJXD+h9ndZZLNvCtjyatSkK313PQQ0oZOe/wH2iMmXr3BpfFuF3RiNv7EPvr924C5MDFxzAUm0nu3Ql+xh4ekO0gtehRVVZtZoMnYnBv+3hzJXSCNdyvHVEasp/1iD9xQNZ5mz1pCA68h0cHUQrP9uPX8WABvp9LVuL1RLzW9EbnaYfxLQdNzC+re7Apk0kGV3CIw3vOqUkigpYqeJFs7QgkH49JW8Spp+Qi7WxljBvQnoliYKAgoCCgIKAgsDngEDouf3c1Yw8taajXhaPH5oiSxAVv8gcsHYmROlMH+mNy/gnkXLXLoHpE78H0lnQvX9gJWfBu+OsnWhR1ArndxxnBKf91kN2nSI5+gUCUhuhZMGPSAGjFvLW/74b/Qc1NaCUAJaOdfGL51Bs7rGMq/ibh2+QRIDcEtDCzu7hlIc+nv1Rt2xJBpDU8UWXPhhcYAEvFKfdxMrNAWg1XRBE7dFr8Q70kmbINncc/qxUFvvHHsX5oXlx6vA2HPfqh+OsbHlBLv1HwX3iGqYMvYyjR7RyVgFDrooYuag3Ng8Sd98oV5PHds2WkpMJf09LrUoCtQaaHTsY9CwwvRQp/Ju/rD+K0G+CPMfcpj055at8rzVM+RLSZddv5APxParcdjodLldFzDu5Cl51+smK+2fHBJ5HWWjOeSIurEKxhmtx9QVvhYiW5DfPB/cnNGNGaqSlq58cRe3qO7Evcp3BeGnajNz0blyrUf9g/JYrmG3ggcbYJ/eQULA8SlVtisEF8nD98uqZm4if9p1sUKPlhF70Z8U1bSQa8GCB2eZIRVI8v3RTv1YG1pOsXVG7bPZZ9My2KiiEFAQUBBQEFAQUBP5TBMTjh279XLMk95gkSziWRKORS7Gxb5reNaLc9hZY+oM7pwzSt8jmrfoVZRxSkGLzpUyP0IMrJRRzes3jTlDNHN0UgGhQjr7t2qGeaMyM5vX3WQjkdUGJbDU0B+hfuNLjNPMBVahVsVR/9Ju+3ajlP2t78chUja91H2uNwqZ5i7gtwv6daxlnxKIshixzZ/H7Zi/B9Wy+LMeISxx3Dnhg6qs4DGpREZYOZfBdu2Lw+60FDhkzKGJli4K5tCvrlk6oVjpru28SVjincJNNd/W+rttgmUl3OEzDj42NWwY0fTfLHrWbNuHKjvI/haec9UFdrgz79exraJOlxYRgUJ0fWaZKg7ZiqAvPa8KjK+w+W516znIFUnLfiGXOpMNCOFMMYO6CvXqWbwrW7ouAFT1k1Jeu3K5nACb24Xn4XRHfJZNlyIonJQzz2k1BoWaDUb5oRW4XkiOn3oCuU7bpGXjhMHXuBKuB7VCBOyOdgtdPHuHhw3C9tBmxFbJrMnc3borPDSzqqf9eRmpkEL6vVB0H7ycAucrip6m8ZcGo0/txL0b3Aqtk0HOYhvZ1Mr6jZmnEmmJGfKufnAE9fkhfuW/kJFoHleaLjdUaKbHIvt1UKX3FrSCgIKAgoCCgIPAxIyA9ftilnVO6VdGo3uDpiwiDcobJssQDDb5u3x19evRAD52/rh1+gtfGkTwPuZzg1p+m6YPebg3lJ+l0uAzcsJDbCBnhPR81mTE3PlGhRtVQTHKXLDXyLMZMPoPpm/tmSdnUYYHz5qgCZp3Hnivk/poeGOEVYEAJe41/lq9kfLm1cWZu6qBbhNT8d6UBU9AoA2MRzl0Gi6bfNYeweKP5DzMTzTu8lNxpkh51lDGWEoWjK0aiGr0nZNMd9b+k95Ns4eDAq0Df1xiFqwZM64cH7GIKBLUcUzOdh+ssrKzZ+wW0bBtr/qibjA8dz9Ob17mQazflQj/daRw7rS1LPW1DL70PSRMXz+KjnsRxu1ksIB1HjUa8AkaPVN4IE2noZmEXJamBeIfceqsTtKOe9l0Cl+J12A5h+V6Lceqv7qwjJb/jH3ymtH1/WYOb0VSgT8HDCzvw2+jtrMgUpCI+Mkb7vQlqKbXG4ABrE4wrFPqfaFL1xd5BmOB5SjaAvLrrB5+9x1h51OG/rA9K1x6JI8EPkKBW4f6FHWhUuTGWnH0iS2fM8y5OtFCYoDZssILmpWX3/bom9x19/0ND5LOwR8s+4uLDzWU90HrsOjyIjIdaHYtbZ7fDtURdbE74Eqsmd+aO+F3eNBzFy1ZAxYqlUKrFXLww8f28B8cWoqYbv8VfmITAy8tL8ucNb69FaFLqWwTknoyudfiFlUY9RqEafQgx1R/z152XVT/6hi93d44GZjS4CW95PH1i+D2OpBdnMaBzS3RxH4QNR3XfF3yN5eN4S0cTfBajlsFX7qNw/l/eUE65bm0kFjZlLCseBQEFAQUBBQEFgc8WAeH4IV3Ib1TB+AkuOic3K1wcZUoWQ/nOa2XPIGVGljAG+LsErQV1okJyoq7lbP1caTFBmDhyA2A3AZN6CAqkLcpW5d8rjgp8zORfog7DxDYd8Mh5FsZKntjRp5rJEJKD/9ISr5M2trkoItxfhZ4zyJmQByQmPpo8CjlFxncqzuKaTd5PUhgvGvIwaAupZmXJxZfstIjcj4iTxLOEnCM5MZFE3tnL0gvlrTnz0GgeOQUNiYt4SLzGtmX8UBr1Og4gCz1WE0/P5WS5pyfx9vYm8ycPkZVTzn0jUWuJXVwzUJZ/vve/5FFEFImKeEKOef8mxlk6kX8fC7nknKRpNCQu+gnZubC/mB4gjQYtJrfCo4hao5FnIIRoNCpyL2i7iLW1K/G9/EiWVhN+hOfbpju59y6N0aDYvQoNIONaFJaVN3nDKRIZ/46lM+54RSbXdeTyDtsQrJdMo0kkz+78S/rUzifSt3Ih0z3Wk507t5LVHgvJyF6txDj6rVi7Em+/m3ptp/s90TYSvhGhzelv/hq5CSydyLW4VBITdoa42dkw+ssP39Ojq8t0avRlEUvtt1u02c9kwYLZ5OeOdRmtpTsOkzVT2jG/lAfObdtfhrVuOdSfnKgid/03k0Y2ViIdSycydPJ84unpSTw8PIiXtzfZ4PmnrGxKf8ftGC3JN2Rpl5Jifi3PUn4WHQ7Vpo0lf7QqJku7MjDCEGuysAcnlsnySGnrukdsCZHlvbb1V5bXy+8hF5cScZm458vNhZfvtYbEiZ+kLG+aRkX8vEay/Mg7nFx5/VaWhnqigr3ENACp0GsuuRT6nDy+c5qMrVOAi5vsfc5o26dGB7I2WH85Wo++EqAgoCCgIKAgoCDweSOgIqt7l+Hm084LzqYLhWxOtnIhl+JSufRZkSUMFRiwogc/91u7cjKfoTRimIbsmdqYS7/E74kYTAh5flaUcUZ57CbnT2zj5UBrV3LhdZIsbXZ5kF2EDNLRPOaErBqjl5B1C8YbFJZh5UKW7bsqE4zi7+zkAdURJDdfFwROaWkasn1cJYPpqWBoSCmQ5qbu+Bs+RvPrCpe6/vE+dxi5e75jOMF/5RZvMryTKKhL8zToNZeEpNOYF716pstLoRYrdIRVFVnSSlRkpWXRtAmMOw3ZNrQCcfMIYiGEaMjucd8aL8+kD5qQ5ycXcjRsa88h0TJB2jhvUj6pu06zDmTo5KXk36D7TKGVMMqcup3Xwfl74uP/kMSE7pPVY/nhK2yg0C2L8hkh45ORZw51+HkyrEUhGU2BTrfJK8nt14JyqiFBvov1vu0izSaS4PCMOq3p+Ahls9+8w8mjZGklYsmO2XKlnaYt3WEkOXH7DasXdVzbOlpWr/WXI2Xx+p5XZMZXBWV5GB86fZQqz1ej+IFWSufByfVMweEUZG2+EQsO6HzPYq5bvqLipleetSs591zENzX6OlPodNOW6vgbOR8qx0AshXcJmBRqscgoP7p5FL+CgIKAgoCCgILA54JAanwwm8fFBWDDtVeHH2EyA51Xedkw67KEbmmPtfInXZx9KNlc0E1H/apQX44nY/M8lVPaN6vF+P551t8kLF4qZxmimvkwC5o1k5tnJmV7fvcmrMtWRzFbC5AUNSLCX+B1VCxnydAmb0GUq1gWdp+IwTGifoVbYalwqsIbC0mMeYPw168Q/46+ZWWNIqUronQRHaMOJqH4oSdKwCq3qhi+6zlWBkZgWA6a9adIEHUsnr6Og7W1A4qVKMgs5yXGvMTL+GTkK1gChbXHQbOKXHzkU4S/itN+r/lQvGRJ5LfV/2BJSgKePAmH6l0ybPIVQ8UyOWnRz3it6FHOiOi3HL+58xVEcWom0cC/lJQUhAeuRFmXMdh+PUbvbQ0DWbIcRDEKvfOAe4uD9of/lauC/2XrG3z0bttTNr4AefC/cmXxP8f0+1ya6gpci9bnjLvsuB2DH6rK3xnJcsUVAgoCCgIKAgoCCgIfPQJJiHgRhRRYo1iJIkz2Mt0DtCEAACAASURBVFYtKpM9eZ2E0h+InK9RvcSdBy/hWK4GSmer7GEMgfTDc1wBS794JfZTQSAtJhjdSjeELxmIe29WZNnS3qeCywdbj5TnmNm2Ln4//wOexq5AaRPuxn2wdckSY0nYPLQZensGglp1NGRYJEvklcwKAgoCCgIKAgoCCgIKAjoI5KgRDp2yFO8njAA19LH51mE0SvFErYYz8MxEww6fMCQfbNWoVcQhRSrh9zP1cO7+ks9Y+QIub5rEKV9DPM8pytcH+8UqjCkIKAgoCCgIKAh8WggoCtin1Z7/aW1sy7TAsfvH0OTufHxh38OgJcj/lEGlcA4BTWIc8vSbizuv9qNRSZvPFJU47JvTHfX6LsfsHVewenCjzxQHpdoKAgoCCgIKAgoCCgLvGwH9yyzvmwOlvE8KAbsyLXA45gHWL9qCt++oIf/PVcD/cJs1d4nGWLqk8YfL4HvhTI3X6urwfxCLxhWz9z2+98K+UoiCgIKAgoCCgIKAgsBHi4ByB+yjbTqFcQUBBQEFAQUBBQEFAQUBBQEFAQWBjw0B5Qjix9ZiCr8KAgoCCgIKAgoCCgIKAgoCCgIKAh8tAooC9tE2ncK4goCCgIKAgoCCgIKAgoCCgIKAgsDHhoCigH1sLabwqyCgIKAgoCCgIKAgoCCgIKAgoCDw0SKgKGAfbdMpjCsIKAgoCCgIKAgoCCgIKAgoCCgIfGwI/AdWEFMQ/vAmrl+9gzgUQFH7d3gdnQtlqlRHLeeKsPsPODK/0ZLw/G4IjvruRfGuE/F91QLmk8jhHIkxT3HhxAH4PSqByZM6w86k8pJwYdc6bPj3AVy6DUPP5pVMykUTJcbEQJ1O6nyOjhm+mi7PnoDbFy7h1sOXSLGxQ/FipVDRqRpKO+ZBxNVDCEx2Rsf6pbksGnUsEhKJPLvJPlsUcEhFrEqjl8PGwdGM7zEFMTEqPRrG6p2meox9uwNRqI4LXGqU0suXmYC3L67jWAjQpU1NLrtGFYuEFOO4WOSyRwEH68wUxeVJVL3E1QA/HA6xN+Mby3RxH3XGd5GPcT7wCiISkmBbsBTq1K+Hso55slinJMTEvMsUDfG7/PDHskxVMBOZ0lQvERRwGWHRKljY2OCL8rXwVZ2KyJ0BLaFtE23soI5OQLEaX8GlRtkMconRz26cxo13FdBOO56JMRm5UvD6yT0EX7qMy1eCEHwnH/7c+gcq5bHgMpozB8Q+OY+Td+zQsU1NM8fpjHhU4hUEFAQ+HQQMyzm5cqlx1GMa1G0XwL1uYZOrq1E9w/mAi3j+is6LjiheqixqOVc1Q+7ii7p5ajcuP7FEu54dUdSaH/+MMUFS1IhVJXLR4jwoT50a8wC79l9ClVad4FwirzwyO33kPf4LPbud/FjLnkqEhv8sncjSnQFE/R55MqsozTPy5+DvZLyvvxxpFomcTnz/6FJSzcqS8VioxQoSl2ZaqanxwaSRjRWf16Y7uffOxIxERVZ2LcXK1Gtfm+7kZlyqaUwQQqLvHCJudjYG6f04egxpY5uLFGzhRRK0FC+s6GQwrR4fBr673LX/JGEh3gbzVx+zn6SYyPXzs38apDHe54ZBChdW9ODS0/JNbR+DhLSBIfuXcPSaTd7Pkl1cM9AgTwIufVYHsbTmOJ4EbRC/E4CY842ZU86nkvay7+8G28HjSGiWqnht62CDdIX2Nfpr6USuRj354MeyLIFjZubLvvMNY2ntSvZfNzbGq4mf10iWr+/oMaxflO+1grxOTn/8jAwNINPdv+byt15gTl9UkxsnNsvm0pF/rCeng+9zY2Jm5oAw/2UcH5UHrSER6bNtJrJKcgUBBYFPBYHHR2ex8U5vfrF0IgGvk0yuqnTMbd2zlyi32nQnxx7Emkwn6opWfrNyIdfSlTNjyVGv6WI5ACnSbCIJfv5WUpaGPAw+yOTPpWdeSOKy34nsJ2mIYizZMqUdazi7miOI3/VHRK2hadXkVehFMls7EdFGLdz8NxKaLpCGyngPYZo35PyJo+SvKT+wunxoClhS9FNy0Ps3xh8VjgVFJSOE0hKvc8oN17Fs+5NHGQgQuvRiw88T93y5Wdmw7U8CH5vekSi9lAh/JsTQ/IeDH5H4+HjyKOQUGd+pOKNdspNQLw05NOd7Fv7D6N/J1p17yZEjvnJeAOKx9zA5vM+XrJw1jnVCkQ4hT4K2sHAOAysXEvDaFMUxliztUpLxQPNSAez5u2QJRGryPDSUvIjmw4SBTGifNE0MuR0SQl7GS/NIsqfjDNw4iit72IYLBlKpSeDWX2S8DVl90uRvwgBBkpYYSU7t/It9K0IdDKX93MPY5GDpRLZdfsnB8TJkB/+NW7uatTChi6VUwXYdPZ8c9A8kwcFBxN/fn/1dCA4m64eUl7W/u8dZQj6CsUy3vjnlP72cXwzh+ryBRRpYOpFTz/UFi38XduBxtXQiu67H8OxpHpPJdR25cDq2RBtQZqjiJR3LaLnuHqYpYOrw82RYi0KsPQcv201eJcoLyewcEH1nLzf+FW4+h7yQk8wp6BW6CgIKAh8NAm/IjK8KsrFHd7ysNMjX5AXr817axWGb7uTsY0EBekNW9y7DxtRjDzKWXNMSb5PBBfLwedKZT9MS77NxmY7n3n6BxHf5CFaXLzr0IKMGucvkv/ruG3N8M+g9KGCxIqgAaThorcFJiX6DZySriVT4vvshKmGEkHd3trKGez8KmIbsmepKVgZGmNhV37CPzVzhOPTCMeLtvYUE3HljYlnyZAHanR1zhAophWNz2rMOePy57v6Tmhxe2J2Lz1NrulZIUPHKj6UT2X1bKwRpCV7dOIi1UzmdzkQ7JO24hVosknyPWloSIazzgrNS9gy64+/sZOXkr8EroGt12ioqxIvV69gDNUmND+Q6+/S9YYRoHrBBpNbkowbLMBZ4eetojm7jMcYHPzpI0V1DbsC0ds1glchYSbrharbrae43pkvp0/WryLo+ZTncpYo+IRqybWhDLjzz44eGbB/XiNDJRFDsDOFIF1WkiyK2tefIdjje/1hmiMv/LkwT7sdPurb9iV/IM06ASE6MJCc2iotYtN/ojh+Jofv4/gSQIZ7BsgokhvqyuD46cYSoyOohXckfPqfIpRPr2IRvigImKEhcP86g3QnJ3BwQFeLD8V6+15ps2ZmXAaN4FAQUBD5aBF5f8OTGBtcxK8m+fftkfzt37iS3I/QXqQxVVhN+hI2PI7aEyJNoHojzlcM02VwlT0h9arJjShVGC0YVsFjyR6tiLJ1UNhPksqU79pKdO/eSk2d2MVnp4IOcP4uX4wrYLd8xrOLIO5w8TXdXRS4Am6NR6zdOzoVESY6sZV6AMp2/52f54yGmlyUeCXzfwrF0Vd4UoUKOQixZ0kq7y2XtSm4ZPAKp/UaYIqHi8pRz99FbfZHyYgiHm1vHcALsxShhl0tFPIeU479XSyf+N8Pjk1pBWKu0CQrY+svRsqrFS5R2OlD4XQ8iowvakbVnAsmCLiVYHzFHAWO7K7b90z0uSoVwdqTT6CAlY9cEz3/3jZnA3AeSRBzP5AqYmilmpvdp3SrFknn1CpOak/10IyR+lWzxy9BOzvseyyTMfRDOS3SRxshO9/2jM1m/pAs+4tE8Ddk97ls+ztqViOOHUCWxb9CFRKMnCZJvs36Z0VgpOxkAkO3CjptQpN6vyIOhsU8vuSRAWETL7BFlCSnFqSCgIPBJIKAdT+h4lpaV7XHJiSUj4+7pP39i4256mw7CKSJuQYrKX0ZkG+k43nGWfIGbU8DsJrCxXVAym5q5EJ7ZJs5ZK4gpofij/2p2ZW3YX/1ROt0LcvboPW0OS39/jRuOhSUx/+foUD85gW4txnNVt7G2/cQhsELufForLJpDOHb+mYH62qPHpElA6hMkp/DRNM+An741+/J4hYbN0LzjYFR0FLtBaqTWGEfaTZ548jas2n3LAB98UGpkIJb/FYSizX7G8E51EXdD+F5TZXkcqvyEV6E3cGjnRswdURgtazaAR/RbDGzyDRbHtMUK7x04F3wbp2e1luUz7nmNRQPHcdFztk9hF++Np8/ZmFQ6ZCr/dBBIBVHx30H40asI1xqKibl7FEu2PAWsXVH/y4I6eUz12qF843oY1MHZaIbr/0zD0L+fsPhp233RtKQN8yuOOPz763a0XbwQDYuKY4CAy5ffDceMr/j2SbxxBuExaVxUmioEa1YFce5CLs1QWTJ+8HntUa91E96p3oATwW94t87/JFXf8I9OEq03Cot6/oCAZP5b6rM6CD8655zhpwY9R6KRjRU2DR+A0+HJhllSQhUEFAQ+GwRUd49i+K7ngHoDFs/2wPajgXhlwHBZxoCocf/KVT6ZpQPyGxBpqzZvysh4ePtBkKhYIIDUyLPo1WEmnMZswjHvUdIoHXccdi9exYdZuWDq8FayeE1cPGBdDHmp3Q4Sht++G8vNywsnyNPJMmWjJ0dtDt4/sgGb47XwWTrhh9ZOGbJesHZn/NHqV/x6/DWXds3mALSb3lybLwmPb1zG4W3rEF37V0x3K4vz+7dj3+EAvHj7DuXqdcGYYZ1QMJe+FZR3kfexf9tOHLp4l6NlX7giOrj/jLZ1smaBjipFRB2GzSvW4XgIL+wUr/E9Joz6EcVs9fmghVOrVzu3bYf/rZcoVqwY7AqVR4OWLfCdS13kl/D+8spOtPy6O26n8hP/6b2bYXu/ABIS8qBt945G6WvB4n5yO+QGSAJO7/oHe46fRfQ7gmKV6qLDDz/BpUoxaVLOrVG9xKVTh+DtG4qJXvP/U8F+XMsysD/zFANdeGuHArNFarbFkpmOKONA8bXHMF9DipqQ2vivbZkOOLFbP75Cr7kYW/ocRsw/wkWuGu6FX3uuMLh4cGG7JycYLZk6BPl9u+kTk4QUq+CEdhWqwNmpLM6u9sFRNa9Bjho9GO7f1UZ+W9O7Y/i5bZgfHAPY9scPrcpLSsm8k6hf4fgeX/gePoE3KIIiefOiUp16aNmmLWqWMa4o0G8stwVw7+x2bPA5hBdvqTZWEG4jJjBLlQJXvGW2Y1izNRwLds1AwafB2LZjL06HPIFl3pLoPngM2tYpLiRnv6kxj7FzywYcOHEDb2EP5+Zt0LBSHqQ5fm3QepypfYwvIPPjCmPQoCM/qjepBxw/wE1cxy/9Dgf7syhRtyeXevzff6B6Pn3B3yApvcBc6Lb4sF6oEJDwcD9q9fAQvKg8aCOmuVVmfkMOc8cyOqYe2L0fx4+ew6M4gi+/LIfK37hiQJ9WyCcb+rKG78sbZ7Dpn204f+EFHCsWR6XqjfGda2vUrlDU7EUXeb3t4LbvIHpW/VoezHz2KFomL3A5GoWbtEYZraIVGxbC+u63TZx06spnLlOrMQAfznPY/x4GNCjCqJrreH3Oh82HsJuA+YPrm0XC3DnA0vErjBrRAAFLAjBi0TFcX9o+izibxa6SWEFAQeCDQiAJez2WMI5WzhiLlVrfKI/dmGFE5mYZZI63iH3KWyCUBUs8uaztmS/qYRSSCDj5ggUiDiuG/IQA0ga35rkjeUeAGKXjSlM9wemzUVxooaYddBbLknBg2XJU77uSsxJ+7e9l8IpNxBDPWahXMLPzsg4DGXkzu3VmSr6LXj3ZViK92GvoQrI+HfHeBNVJ6fGJuLREsnd2N0aLhpfuOImM6CSe6xS2IeV3enjqzAqZpRMZ9+sQdu6e5um54JjZF+2kx3ZmrPZkx0gEHuiveEdJWsNXZMO4tnw9rF3Jyi07yMpZ/cR6WbuSk+xCooqs6lOf5K/hLMZbOhFnZ1uCDI/FicdPKC9cHsm9JoHPaTtEC313/beR4Z3qimWxI35S/jN2S4/9ZXSsxhA16XaxwOfABQdIlMb8bW8pL6Ydw+FxK9HJm4Q/PixiAZC5h/Ut1tG7Vdz9mrzDyYu0RLK2a2mWR/doWVIEvTxfh8UP/GMVl7fHzJmi0RFLJ2KaZbxX5PeWRTlaundTDGFqyhHE8AtbGB+uY5aQHT5eMitr+v1E/MZoX1w560dWN6Hd6O+iI084lhLCDrOz1UL81JnjDOZZF8gbqxDqkhjmx/Nm6USW+Bwnp4/8zXjtrGc9zpw+ps7SuCLwl97vvf2TDdaRa+e0B9zYkc+5CWnWrBn35+Kcl9Bja6ZbIDVQuuYxd7xVwDm98SKzY1nIfq1lR0sn8vuGPeT4vpWsTUTjE1nFV0OOLx/A42ftStZs8SZ9aucT8bR0Ii41+fHx75ywRqt5zO5mSvvZ45MLGQ/GjulJceXnMP12kvZL42OleFyVtie12rXZczpXb2dnZ+6bGTp5pYH7F2L/pPlMmQOkHN7brz1+aeSYkDSt4lYQUBD4dBGg4xS7Q25AjqRH/47q3L83jsYrZp+A5jNktVA6LhqS27hj4wBZ4sfLFkzOM3AEUUrLbZn8Pr9wLWTzdRVhdcw73PiRceOVynRMDt4Bk98/kN+BSJ9fBihnEZFX3F6F3SUHPEWTv3RSsa/Zn+w5E0gu+e8SL+4BZMdt0XqKMFlKjX9QAwxj6xRgk+h8bUOmz5UYK51cKR9UAKVWHZ/dOS0TEBb7SU1YinhQ5eyJ5H5TeNAGxgsVlq6yO0mExEvum23O8Ny/wCM/+Qr3kejdj+ke24nfkX9k/FHe+bsEarJv+Z9k+eo5TIgydp5WKMHYr7TtjAsVxnLTcJWe1TbKJ72nsXrfVb17XulRkvJiqCPr5+Vxo2nVRMOMTNDydY0X0LyCkMJfwlfLLCHqKmBRwctZG3OGPdIec8L3ltuJsjY2hU/6XIAwIJqCsXQQMtSuwsV7Ws85e+9KYHlFlvX+gvEtFzblAh7N+5vnQfI0IpwclRjTEXCjlhOvBx3Q+/5GLNhGqKU+qUWiEp3WSBZFRAFU2p9SIgK5b1Vef/P7WGbHFQlI6Tofn+Tvb3LfMP2OHaaRB1pLmPQS8eM752QWYEt1nEsuhT6X1D9d8gYiNeTgVFfWZrRc6aVj3QyZGctSo3nsKe2y7vsYyahgraEZgBkMygq+7GkHyT0rwXgOw9O2P5k1a1mOGGyi966EJz2m7xUXYIQ7UpQH+ffHoCDC5E7TGJv7pP3SGB26yMPubwrCj6UT6dixo6yN6Rh/QCYEmTsHiLxTl5R/Q4tP8tSKT0FAQeBTRkCjSSRx0RHkVvBZsm7BeDYusnHY2lUmtxrHQrwXTfMaMvMuHfN05SHB4Bl9bkcw0cbkPAMKWGq8qDwO8xYNftBwunhOF9boP8H4m3Ssi4t4SAL9/clJf39y9c5z41XKQkyOKmBS09wUSFPfO2KA0glHoiUzLZUz872YhEsMerzRWmihjcqAFlYwLZ3IGR1z4s8k7xlUT8eCnCFspUJLuzGbZPWKuyIKIW4S08KvgzzYhGlIkWJmOQFSS/KWk7QsXaHeEG98mEQ45nbVJNZcNM/Y7gnFSrqySwVC9p6XgY/ZeHlijLTtjAkVYmpjLvmzBayTA6RUx99IiIlvTUh50e3IhksWFTCqwksFSsqD/EKo1iQrEw4lmANEt61So6+TxVMmk8XepzjhWrjsSb89+u/JhT3k1zFjyM4g+e6PIT6l38RfJrxTIRX09BWwN8xCEFV8xKULvmQ6UDEBUGaOW1JfKxfyj2wHgjeMwrWbpP9SitJdca8T/AoWX5LEUpFsB0gcsKfLlENCjk2tRr6a5c8gymwfM3tcYSUad6QlPiVeY7W73YLgrBXGdcfBm5v497zoKQHR0INx2unFCAZ7hD7TecGJ9JIT6bdk6lgm7RfSMU462Un7fubwFdtdV4F5LtmB+uuMqZZh04XBYKRgnp6WL20z6biiawFRICTtc8bGHmkaKV4CDforVYSokrX88FWmnKfGP5IZ8KE7p6KRK0n/NGsO4EuX8tZS5+K6lD/FrSCgIPAZIqB5Q/Yt/JnJtHS+kSpF6SEiNbJBT6xJx1aaTxO2jyl4srFfONmhs0vFxmODMqt4oo7Kjtefvyb3gnbzC9jak2SCVUZ2Sk/zknhLnpoS5tL2k3315KP06mlK3Hs66JjRQUjT4+2teJYbf9sUJSQGPSzy5GZEVPHvOLfqzmnuTCfSbuLHbp3g7u7O/r5z/Z2lv7U2CNH0U8rEv5/6dZHdAchdrAKqaXnMw+glwPfPP3mftStql83HYgRH/R/cuYvP1H9t8VbcT8wkQwJB7W8hlzb4uqyIDXKVwi+e2kuJAB5v24nQeP6OGWDqpXCdQrLdmx895x7C05BDGNaikIz6832z4FyiFf4NfSsLz24PNSpRsE5Xdgmf0p+7YC+EUqOv7MLMy9FoOX2kSeeFLR2dMW7uPIzr2xS0NUIv8PfLXt0IQzwBvvi6E+YvXQq3+vr3n3TrZm0hGlLIY2fgFqtuhnT8qpvH2P2SVt85c2ehpcktHZwxcPJ3fFDaTez8V2ucRJKoRPte6Fy3sCTEFpW+rsv8spttFtpX5W26o+k30rt9VnDIZ83nSX2NRI34/QuGLGZ3qoJZW8+zNvjG3QOTmpbVlpO1PmbOuMIqZsSRFhOCPsW+xOClR1C4+W/wO7KWpQzfOxKL9j9kfiAJVwL9OP9arzEoIrs7JUlmgjMtJggDWk1gKfPUmg6vicL9WRZs1GHaWAYUdG6Hhb1bo0bNn9CnlXivLD4iAqo0sd2kBWUGXxsLw2AUKi+WefrqPWkx2eZWP9yPpqO2AlYu2PLXINkYLy3EUuyK0uBsc2s04hX08j1GYVDbWtz4QQuwdCiHSdsDMLiAdqZRb8Bmv0d6ZZs3B+hlx9UzN7kxSj9GCVEQUBD4LBHIVQgdJq7D7f0zWfVPeRzDC8m8zSJ0HN/2Gcpk3agTE9B+3Do8V2lAUhJw6+x2NK3Uhdk9UKsAwcDX4QV9OcNl2wPnoJxE9peS19plkwTZo9+KQxhbpwCo7FizVDFUbtAF990WIDTSB9XzabBpylAu/dKlo+GIMIyrXxX95u3kwrz87oEQNfy8RuLAwuUSWVlSRBacOaiAWcPBQStQAbB1AKwMz6d67EdGvGRh1MrUFw76bKYlZawsPLp5jdHp06klvqpRAzW0f4OXLIWHhweWLViAdVt/5CbY5JjHCDp7FhcuXND5O4eLV0Iht2vHk07WqFkZ1GFTsASqpWNMwdbpGxQ1UB8qoHdtor2onfoCL6Izrp+sYCMeirvuP9sKTWWKhf5HK8+RGVzkFDLnK12jHVb6vcFd//VMqeUopfrju+oDcIspjpmjn14u/lsthL5zxrBkL/YOwuE7VAVLwp7l87nwse6ixR6W0ARH2a87cnVq3aQy7EzsFwLZl4+lSpChr1JImfGvhohWzmp++T+DGb5q+z0Lv/88krkFR5IqCfJvKBeKlikhRBv+JSpo0hmsRaXNHg3cWjAaM3o2QvFaPbA/+DkcKrfED42lShyfLCt9zJRxhTFjyJEShgktm3DGh6jyFXzwd7RoMwB7plKjDPy/2T9OY99u9NV/0NvzIZpN3o9OFe2EJJn4jcPyAW7MOAQsneC7Z6pZCp3JY1muUpi46RhCrm3F91XtcfPUdvStkx+OFVsxS33GKmAOvsmEV+bCDwcwC5KUbu5iX8LNjtd8CttqFXpjBWYq/DUWDBnI5fzr5BZ9y5GEX+CjCVRGLIElhN2C71uhbyUxISJT7GgzNf62LlO+GJ1cZTFgmTvzXn2gb3Exq3OArUNuk+duxojiUBBQEPjkEaja/hes66NdBNWEIdqEjQOrIo2xO/Bvho3/soEonc8GltYOcHLpLptD2nWox8nm4ec84DrtDMZ6X9GzAGttoxWgLByQN4++MGXpUA1LgiPxPPQu7ty5gyfhUXi4eRwq5LMEnX8HbApDOXcf9HK2x+ZhLbH0SizH2/S9dzGoRSU646B5776ohnMIfhDN+M4OhyjnZAc1GY3ccG7YCNgUxoVGh0Vwq2gZC5sJoplKLmfuTFtgSk7gJ8pCLRZh6qhRRlcxBbajHx/HNy6DBa/8N99sxMVOy5CGoV2kNNUjHDj0iqOnvhmIF6o0FNGzfiYxwZ7qj9vP4tC0ZOYtZwnM0xUE/X+5mXUvGpfRR5CQLbjoc2FqSOXG/XH1ZVX0LNdUFGqSt+HPfyZi4+A6ppLJVLqyLdwxuMB8ficVwMw1J+E6xY4zI17OfSNaSHcXzSihRP1+uJXSz4wcYtK4V/pClhhrnuth0HmW4eqtl0CLL5ifOSRWibjV8GnfmdAPWO4sO74Z5InN98LgvoS3dpRw/R90/OofNBr0F3b8NYzbCf8v+5hYwRTs/b03P4BbOmHfPzPwhXZC6DB+IRr9+S0/uSRvQ5eJHXDbsxlmtRwB2HTHil9dRTKZcF3eNAljd4eznOM3/4N2mfw2GZEMdsSfXjmE3wb0wKar8fhp1j8I3VEYI2q0FZVAkVAmXLYoU1ZrDSt5G9bsmYpFPXgrummql7ittSDa3EXcDctEIQaypGDfnAH43S8Ciw7fxXAdC6w0Q4V6LZmFQ2M7YNKdK+evK5m9yMIY0whKHBAfJyp+LB5A5TrfAFjDBYmnLsQUWZ0D2Cq0vmwjFqK4FAQUBD5DBHKj86jJGLCJl5szkiUFgIrW6YGk6G9w/Kg/Qp89w+vYNBQsUwVtW1TD7kn98Nuey1zSbu3omJ+AnUv5E2QXz+/DrPs7IWx75MljgW0z1/Fkk7dh3qSy+F+eJGgKNMfvk76XnOjJhZIVdOeKKCwfNB50sXLtom5Q3djILYZSYlRnmNBBmj4XHK0sULyQoRFWqJX5v6biZT5lACW+rMcmKvqOyuPINPzPwHsrMuLkDe6eFLVMl9a1sizsRZ3ej5uRYw2+9SIt2zpvQbSr1RTWZfJLg5GmioeTe+tM82FpXxC1qzvgKDUbTg+UyrcLtGXZozZ9O2aXD/cOQb1K8qN3Moay7LFHraYuwK6tHCWD7EjKyClc+CJSEB35DgWL5ANd5ZhxugbWy63xVwAAFWRJREFUTdM/NmVT5Btsf3Yefcs2Yk8bBF25gSTU0V8VlvCeZWeushi6ahC8tCa9by3rALtlPNW/R3fI2bKNMF+hQUMAwrE2KyOpTAsuUvFLljCRGN51LVDWCW1sc3GCtUvr+pnuB6wgsx326LX4NKo1XQP3ziPZ8YSANSNQ8eJr3D0/E198CH2MvMLp1Ve42tnW7I4vi4g795aODbBm60RUd/uDi7+/pgdy8TIzlvgtyII5ekB11xf1+mqJASjfaw1mapUVQ1AnxrzE21yFUVhyQsFQOuNhUfCZOgy95u3glMeTDzzRrGJ+EHUIhKOGxvOaGpMLHaf5wH0d398X96yB4nZX0bsW4DW2F/cNdJx1AF2qZmXXUJ8XvxW90Wn6QUzbcQPj20onYElayawZHsmvlkpiOWdKonh0sFTxzJvLL1DOmTuuQ98A8//3Gt5O+lYiVPClGu61uhzp+k2fA6gZe1NPr+iWovgVBBQEPnEErMVz2BnJklIkbBzLwfWnctIgTgkSlK9CLVagcQU6view9zQD1s6EcaPzwMZl/Pyau3YJTJ/4PZDOotH9Ayu5ayQdZ+1Ei6JWOL/jOONl2m89ZHJOcvQLBKQ2QsmCH5ECVrJZd4wuOJU7t4lUfyzaeB67J33LKmnIEXFVe2+LRlo6YVRPY2+0GMptJCzVHz6HrqFhP0O7JSnYMb4pzjgtx8p+bjh01c0IkSwEWxRAqbJ2AFXANIdw+uZr1HLRPe6VgKv/nmGF2EgmeRaYbY4EXDvtz1OzLouCBrZtpUU5VMkhXABEXFiFYg3X4lrMdby9dhHrf9+N/oOaGlSWLR3r4hfPodjcg9eA3jx8Y+CNCCnn2eN27twfbnarxd037QrJ93Ucs6cAc6lIRrnEt8JakLlE+PSFCou7rAc3nUHMqG/hqDNovQ27wnY1rGzFY8WZK9H8XKHn/oWFc0vUaT8MNxO6YNeKOfhhEv8SSeK12Th4fSSGNfjv+1haQjTuveMFb7uCtnrKebWuv2Jpl82ynSq6izrK0K6jqTClhGHiN73F1Nau2OXxs56QLiZIwY6BDeBZzweBv6Q/Fot55K6wY0t45QvA9kurOeVLniJ7fLS/bww7jM0F+SOoEzvVxkT68l/Nn7D+yFT0b1M9ewrSUgnaNPr/7Z0JeI1X/se/CbciGBWKoI/YqVg601GlFMOMoZJqbWMp7cyI/u1NpVLUY02ppehfEltoBWNM0RY1ROyiVTHBCEkkQSKL3EhuxI17k/N/zvve+673JrkJT9N/fvd57vNu5/zO73zes75n+WHQjN0I3HkZS0bqbVY+TL2JAq/WaNGpn7DuituMcTYinPijrXwF0K93mwrr6Va7PrwNNYAnxciJSS1zFknpFnaUapS/DnC0NlQpic6JABGoxgRso/R86r+j5ULlJ5ODjYFzJecb14y3dYIM6D19LbZPKoG29VGrrhvWjpwgzixx98XyjXPRsp4V1ufaaWyHSWLFE2silo5fLgx2LJrJl5EU4MdoW/vb4334/V69Fv905ErAsy+aOVg+pJHs0qX8idYlb+V13ASzdn0sOd4//zNcMdo3fJBuK04y8cXkD6Xr4SHr9PPvpadln4gjBaK7jX+biP3C+h21v5gdgRi95hyG9nfytVPtXHdl0E2Q1CYR7qUueg0bIvkNjTypX0/GHuJWtDhC1v69ceiom6II6MOSRDo9cTT/H9YHiIsSjdNNXD3BoYFhpwKf5gNrCpYP+QQN+wegVT13dOw9CLyj/t6Cf+j52MI11JU7PV16ODaC+jRV5LLcPLpiWoi6Y/5h4EhdR0UdbuVGptSy1FfPt+0qjEjxuxev3FE/FK6KcOtiNL774YLDxfOK/hvqtntVWk9jjp2P80nyl3u74PspchiThCkB9ifi0WEaUzupxFUB9o4chX1XxPTq5tEUI+Z8iXtnQiWZ0T/yjQeeTh6ThFbyhC8u/vaqdnSkPmZs/1ZagMyDSN61DQd+Fqcny0EW4OR3UTBaHW9oIbsTpzzyjoD99+XxzeheihHJ4uwLWHngHgZ11H4AclS+OCrLinDmwD/E4AxD0UGzoVCBzWi8XZ/KHJk5AR8NfAct/Jch7UkJ37FX+Juu7HLa+bKYHiAxPgn3c2Um5dEh7l/BeG3SenwSeRWrxr2s81KcHYM323fG97cKgJo++Mu8QYIbPrviZq62TlNU5vXmY9hvlRvU6EQLN9xrOWLNy55OmLxwsOjp0SqcjbdvAyTLeZyXJ130666fQuwwf5ZRB5SY8nDfUrn1pZJSdEIEiMD/UwJWHNuyQ4jbyEkDdW0iXh7fSc+CvlWhxWHFkc+nSRuCTQmLUaz1qoUew8Zg4tixGKv5v+P3F4Rvny4Kq+mLEe9zNxPx7oheug+gyhAvbFspzKSaFhGCbpq2dsPeL6GJYlCiOPsMZgWfwoKvJ7m0ploZnrPzZ9wBA3z+FIT982zrGyyHMGhkCJIcbp6Qg4gP30GIbZpeowFLdTt4MYsV9gpeW2EZDPJOf/ZnXr4DMdPLNkWl5Bre9u2J7VHXUWA2Iz/7FnYsHS1UugMX/4AhfISqnD9lWHez1Iusio3p0voEux5cbLfRszDhN6KOfPrRv+LUjbOUk7vFkUIAa4PflhOPorV8NOq6oOH9ywcQeUK/25Uj9c0mfdL/zz/DxTVNz43BJxOVI4yKRoBbPRic7DTjKBz7vcK8TPspCszOJ8dkxB/HpB7dhDi/ObKX8KXDUFtc88H5TAs/56ATlond6+022IERg7tKYZV1wqfRlDmoaM1AzLFs5JyLRZZmg4jXx0yRG86eUzFhoHbzh2Jk5ynWazxSvLiylHPxuXu9NvjTkCaCr6joOGlXQLuYtLMb0KHnAPj9uRfm7YqHWy15o4JmQ/3RVlHg8AbevP8VdwHi/mcs+kojLxPbF4kjjp1n7cNgaaMIOa3w9Z2Fmr5CVmq6qI5bPbgp0pGlyNaA1NznzX/pp3nm2asuwrccU6WH5q8Pl/L2a53FRmdF85ir5Yqkp+aETzXu4CmXQxNeDcTFNMW6HWsOju3eo1pkzD84jHjFGzPX70dydj5MucnYPu/P6D98MTKcZx8h5JTotRi+7IykxYR1ZzBVN7IuPcaT3P9iecBoYfqeTwsv4YHrZZkFpmxbmWI5hB3fiHP1Laa72LFkqTRSKk5nLcBDvruVi+W2qLEVR1b/XVhP163V88hKuonbqalIT89GdnYuHppMKNJkMWa+gbHezdGuU1s08/LEl1HlKyMTjq5EN9vU0EYsDuHh4Yp/BCLCV+GNFq/jXK1gvGMb9e49doa4MVDxaYRskddRct2NV/dJo5xlVdr2+uxOqrx+T35j4lnfcXL+nLt8l+ajShFORG4QHdabj1F99B1r1+oAUdSDm7FSOh36RsU+TmrjQddEgAj82ggU4YcNQRgwwB/TloTiRrbcxuExSTu7BWNCzwOeU7HoA2VbEihKP4P+jZqiZfMmaD18s6bcUnCw3sP6KX0wJGiPcNN/8Q/4MuBVhYPSTwsLbG0KZsKTcmwCwncLnjN9G1DnIwRJU/U94NOptRBQzoVkPLG1Z5g5BXMG++F218WY7VfxmQxOY1Ceveor78bCTimMs3Kjumv3nmRJaTnMaExnsVF72KjudXmUhf+w4N3MWKIN1cIu7ZsrueF79qfkPbE5MrPT24OkZ9zocsZjUUBaTJh03y5feWw04FOVPTFtqNrrEksuO7L+b5LM1uM3sHuFsh4Xts+Qnnl2/4jdyCmSRBhv/FN6xhkcvHSXWZmF3TixVbq/6rBs8JN7fJxyRHrG9X538gThOkhjE0kKRDhR2GEC2Ix137D4lEyBtWQk1zCUHU1QW33KTTkl23wC2PrDNyVjd2r5+qsnj00s/vTXsiFn/i7dfdkHwSEsLCyMrVu3joVHRLBtYZ+zv/r/ThWnvTYDokp7QTyubcYtZKfiElhuvpHdjotmgW81lfyVZnOC63I77pAqLlyXsMOxLDf/sYM4WVi+8bbK9sPo4N3sjrFQ4dbC9s/rI4T/P9t+lgCUWCysMD+bnYhcKOnGdX+h/xx2ISGDmS2S06d6ItlCUtnMEoO4tmuWpAtPg3sjl0vXWvtkog8T+2pKG8kNz38Zj5+wIuNtyRAzz2+yjSHGshNPqvhuOpUkxe9R1iWVYfQley8LHIuM19mUl+tL4aw6HC/xLUj7SX7m7st2XbhjkyfbMwpYd1SywxEftVGUo4m/63msYuWKFFnNid12FE8D9j/PAyHBUyTbJm3GL2PHj+xQ5xWFe+4vMPKqRrL60px2Wud//Gdb2c6ICBbh4L9s9kRJH172nMssYhUty5S23Liu9bt0lWVr4tHzs5MVKre5QXalHUk7S+1xdPAWqfxW2ifj7mq9/LnOxoyaImMJURpD2Rr9leFN2ykb8uRyruyS66Pw42L6tyrSfuvxm5yGX2IxsePKOtFzKruc+UirnnStDIvnzxwLr994vWerb9x92TcaQ8xrBsnlZXnrAHuA/17pJ7xTlR0e+0M6EgEiUE0IPGTLf99ILt/dfdkXB86z9KxUdjRCbHPX6TaNXXZgm1VVHtfoy37KK9YwM7OrUV+LNrl4uctlH7ymcVP25bkNY0X9NDZHHfuU23FrjivtkDKmtKHJy8vzUXtE3QxD2UUH8XMs37W7z9AQs16R3MRzbGnAH+WXqansXvEPYsfj7uo9MrkRpqwQ+fmh5Ay25wO58Sg9Vxhly4o7oGos2t18HPa90wrSgRJMaZzSLsN+3P/fZLZjoo8+bgo9uMyClHNswYRBOncv+k1npxMeOgjWzA4tfVPlPjjiogN36lsFaVfYKmWjS8GaN1pS8pU9XBMLfbelKgx7vDxeLo9xWHWHz+63XEelUT1LstBw7zJzjWNr6zwONfqyLw7GSg13dayZ8I4Ge9R0GBe7PtpOSP7VSKfulW4fpxxUGQbnYWs7yPYw7EeloVqtrpW7zmALX/ES9F59PF0lqth4SZ/e3X3ZdkUnSeVBuDCzM7tCdI16Ho+Pw/4tdXy402SFEXN7PPmRG/W+q/zIoEhz/gtXSx0QpZ/2k/exzOSDDvlPDI1hvCHOG5JN+/XTueGd3J/T5A8c9jiVP49Vrlyxh6c+WtjxcPljkDKu9bq+ybYekdNuUdYVtmRCD128giPOOk3f9rBufrtI508ZVqnnz41hVzNj9WnE9r7KKsu4kenFw71V4fMy5V6hUdWR54YvF/mr3dn1Kk+5nX9jkyoMu1/tsf3kXQKv4vzrqo4/N4CsNfJp5yce5Tyklam7NgxlsTnaBgRjCSe2Snmmfpdakr7TVnznNOzrig+JjsI5e0+fprm+idoPXLb39aJ/EIu5p++8uVYHKMhYEiSOauPzCjd0SgSIQLUgoBxUUJVX7r4sJCLaaTlnTpMHD7jBZfugijntJ8Y/CL5Uw10sL2v0ZZ+GfS8NmrgKNfnESlGO51SWVKhs1+olmRL3CW4dGYDmrnl5Pqx/d6kc/+virzRtZb3Mytxx456dDo89owcW80NkZeYhP98oGFx1M9RG0xY+8G7wdHcYUatvRVZ6Fh5bLHAzGNCwcTPUKXNOmlrC07yymB4iy5gn2E96zrMBvF/QG2dWhmdMv4PcxwyeXo1d4sSsZmRncc58PpMB9b1ewPMV3v1MqdGzOb8Xfw0Gn85o4uEGrntWWjoycx7aOHmhVVufX/S9PZtYV0yq8ep2NOz6nrBl6u1jgapde/ii0pT4FBgLn4Dnr9adOqJ+Tc3uGg6D5btSZsNUyKcaGNCo+S+bT7iKZrMZHh4eQnrIzc0VdHvO8zfwfkFeD+goKq7mMUcyKnqPmQuQmpYmcazfsDGaN/PSrRjl8vOz7yAtg5cFteHt07ISuxNWVNuK+eNlkslSU1OmFCEtNQPFhtpo3qziu//x9LszcBQmrDkCbgxzRNd6MObkIF9Il8CTR3m4e/c6jmxdiIjzbyHJuAWthXn7VljND7DarzvW11mCW/v/XsqGJBWLt9YXNyCaeCMBJmHasgHerTrCu4FiSq3WQ6Wui5ASnyDkay6mTkMfdGgpTid1JtbVOuDCpsnoFbAZfIOYq19NfOb8nOlN94kAEagaBPhartS0DKH85Zsl123UBD4tm6NWGW1ovuNuamYRXlS024rSLyFi389o3L4tOrTriLZtmstLbp5xdC2m+7iRcB8NWnXBi8+sjC5/JH6RDlj51SOXRIAIlEbg9KbJeCNgMwL55gHSfObSfNAzIlD1CVzYNB69AiLBF2OHlrIeoPDaZtTpvhPXjNHSVv6JJ9ah3R9mYcGBRCz2fwbz9qs+vgpraE76FrXb+gMe7yM+czM6KNaLVlgoeSQCRIAIEAEdgTL6rzr3dIMIEIEqRKDv5A04kHUfb43rAu8G8c5tF1UhnUkVIlAqAWsiQufsE5yY8vS7/tn9lpgSsGz2PHSevlXaNTZm91y8NnYF3l5xFAup82VHVa5jiSkOk387SrDtdjGVOl/lgkaOiAARIAIVJEAdsAqCI29EoGoQqAX/+ftwuNYkDBnSEYlhZxEa0LtqqEZaEIGKEKjhAa+avGoqQuTHf8DN80EIfH8IfNs3R22DFVmpCbh47ghmfxoq2HG5GDnUNrXTioIHJfji4DXM9Hu6NsIqEo1fk5/c+MMY1s0PsS/Nxo2oFehYiimDX1O8SFciQASIQFUlQFMQq+qbIb2IgIsEks7sxI7Yxlg8448u+iTnRKBqEUg9GwafPvL26460m7biGyyY/RYaK8wcOHJH98omkP5jJMJjGyMoYBCt+SobF7kgAkSACFSaAHXAKo2QBBABIkAEiMDTJlBiuo/T0cdwOuYK0nLEqYh1GrZGj5490btPryqxiPppx5nkEQEiQASIQPUgQB2w6vGeKZZEgAgQASJABIgAESACRIAIVAEC7lVAB1KBCBABIkAEiAARIAJEgAgQASJQLQhQB6xavGaKJBEgAkSACBABIkAEiAARIAJVgQB1wKrCWyAdiAARIAJEgAgQASJABIgAEagWBKgDVi1eM0WSCBABIkAEiAARIAJEgAgQgapAgDpgVeEtkA5EgAgQASJABIgAESACRIAIVAsC1AGrFq+ZIkkEiAARIAJEgAgQASJABIhAVSBQ05kSbm5uzh7RfSJABIgAESACRIAIEAEiQASIABFwgQBjTHBNI2AuQCOnRIAIEAEiQASIABEgAkSACBCByhCgDlhl6JFfIkAEiAARIAJEgAgQASJABIiACwScTkG0D5G5IIucEgEiQASIABEgAkSACBABIkAEiEApBP4PW3xJXpZRskUAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we do even better? Yes - let's look at the literature.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "...and https://arxiv.org/pdf/1605.07725.pdf (source of table above; 5.91%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final challenge: What common idea is shared by all of the best models? Can you implement one of them based on what you learned today?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}