├── .ipynb_checkpoints └── LocalizationModel-checkpoint.ipynb ├── LICENSE ├── LocalizationModel.ipynb ├── README.md ├── car-detector.tf-1000.data-00000-of-00001 ├── car-detector.tf-1000.index ├── car-detector.tf-1000.meta ├── checkpoint ├── localize.h5 ├── non-vehicles └── .DS_Store ├── test1.jpg └── vehicles └── .DS_Store /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Henrik Tünnermann 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /LocalizationModel.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "Deep Learning Vehicle Tracking: Starter code\n", 8 | "======\n", 9 | "\n", 10 | "This demo shows how to approach Vehicle Tracking with deep learing.\n", 11 | "Using the data from udacitys \"Vehicle Detection and Tracking\" project. \n", 12 | "The images provided (car and non car) shoulb be placed in ./vehicles/ and ./non-vehicles/" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 1, 18 | "metadata": { 19 | "collapsed": true 20 | }, 21 | "outputs": [], 22 | "source": [ 23 | "import glob \n", 24 | "import cv2\n", 25 | "import numpy as np" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 2, 31 | "metadata": { 32 | "collapsed": true 33 | }, 34 | "outputs": [], 35 | "source": [ 36 | "cars = glob.glob(\"./vehicles/*/*.png\")\n", 37 | "non_cars = glob.glob(\"./non-vehicles/*/*.png\")\n", 38 | "\n", 39 | "# Generate Y Vector\n", 40 | "Y = np.concatenate([np.ones(len(cars)), np.zeros(len(non_cars))-1])\n", 41 | "\n", 42 | "# Read X Vector\n", 43 | "X = []\n", 44 | "for name in cars: \n", 45 | " X.append(cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2RGB))\n", 46 | "for name in non_cars: \n", 47 | " X.append(cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2RGB))\n", 48 | "X = np.array(X)" 49 | ] 50 | }, 51 | { 52 | "cell_type": "markdown", 53 | "metadata": {}, 54 | "source": [ 55 | "### After loading I do the usual pre-processing\n", 56 | "\n", 57 | "I do not use a validation split, but I have to validate that the model works well in localization independently anyway." 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 3, 63 | "metadata": {}, 64 | "outputs": [ 65 | { 66 | "name": "stdout", 67 | "output_type": "stream", 68 | "text": [ 69 | "X_train shape: (15984, 64, 64, 3)\n", 70 | "15984 train samples\n", 71 | "1776 test samples\n" 72 | ] 73 | } 74 | ], 75 | "source": [ 76 | "from sklearn.model_selection import train_test_split\n", 77 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.10, random_state=42)\n", 78 | "\n", 79 | "\n", 80 | "X_train = X_train.astype('float32')\n", 81 | "X_test = X_test.astype('float32')\n", 82 | "print('X_train shape:', X_train.shape)\n", 83 | "print(X_train.shape[0], 'train samples')\n", 84 | "print(X_test.shape[0], 'test samples')\n", 85 | "input_shape = (3,64,64)" 86 | ] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": {}, 91 | "source": [ 92 | "### Define Keras model in Classification mode\n", 93 | "\n", 94 | "In the model definition I do not use any dense layers, but analogous Convolution2D layers. Also, since I do not have classes, but -1 and 1, I use the mse metric for loss. Adding more than two classes is a bit more tricky because softmax needs normalization along the correct axis (see bottom)." 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": 7, 100 | "metadata": {}, 101 | "outputs": [ 102 | { 103 | "name": "stdout", 104 | "output_type": "stream", 105 | "text": [ 106 | "Train on 15984 samples, validate on 1776 samples\n", 107 | "Epoch 1/20\n", 108 | "15984/15984 [==============================] - 1s - loss: 0.4897 - acc: 0.4974 - val_loss: 0.2045 - val_acc: 0.8238\n", 109 | "Epoch 2/20\n", 110 | "15984/15984 [==============================] - 1s - loss: 0.1575 - acc: 0.8622 - val_loss: 0.0891 - val_acc: 0.9234\n", 111 | "Epoch 3/20\n", 112 | "15984/15984 [==============================] - 1s - loss: 0.1002 - acc: 0.9214 - val_loss: 0.0683 - val_acc: 0.9448\n", 113 | "Epoch 4/20\n", 114 | "15984/15984 [==============================] - 1s - loss: 0.0784 - acc: 0.9405 - val_loss: 0.0525 - val_acc: 0.9566\n", 115 | "Epoch 5/20\n", 116 | "15984/15984 [==============================] - 1s - loss: 0.0651 - acc: 0.9503 - val_loss: 0.0585 - val_acc: 0.9555\n", 117 | "Epoch 6/20\n", 118 | "15984/15984 [==============================] - 1s - loss: 0.0568 - acc: 0.9574 - val_loss: 0.0419 - val_acc: 0.9662\n", 119 | "Epoch 7/20\n", 120 | "15984/15984 [==============================] - 1s - loss: 0.0526 - acc: 0.9621 - val_loss: 0.0366 - val_acc: 0.9747\n", 121 | "Epoch 8/20\n", 122 | "15984/15984 [==============================] - 2s - loss: 0.0468 - acc: 0.9633 - val_loss: 0.0382 - val_acc: 0.9707\n", 123 | "Epoch 9/20\n", 124 | "15984/15984 [==============================] - 1s - loss: 0.0425 - acc: 0.9691 - val_loss: 0.0451 - val_acc: 0.9662\n", 125 | "Epoch 10/20\n", 126 | "15984/15984 [==============================] - 1s - loss: 0.0402 - acc: 0.9717 - val_loss: 0.0305 - val_acc: 0.9775\n", 127 | "Epoch 11/20\n", 128 | "15984/15984 [==============================] - 1s - loss: 0.0389 - acc: 0.9720 - val_loss: 0.0364 - val_acc: 0.9724\n", 129 | "Epoch 12/20\n", 130 | "15984/15984 [==============================] - 1s - loss: 0.0350 - acc: 0.9740 - val_loss: 0.0294 - val_acc: 0.9820\n", 131 | "Epoch 13/20\n", 132 | "15984/15984 [==============================] - 1s - loss: 0.0333 - acc: 0.9772 - val_loss: 0.0265 - val_acc: 0.9786\n", 133 | "Epoch 14/20\n", 134 | "15984/15984 [==============================] - 1s - loss: 0.0335 - acc: 0.9770 - val_loss: 0.0290 - val_acc: 0.9803\n", 135 | "Epoch 15/20\n", 136 | "15984/15984 [==============================] - 1s - loss: 0.0299 - acc: 0.9785 - val_loss: 0.0277 - val_acc: 0.9831\n", 137 | "Epoch 16/20\n", 138 | "15984/15984 [==============================] - 2s - loss: 0.0302 - acc: 0.9789 - val_loss: 0.0245 - val_acc: 0.9837\n", 139 | "Epoch 17/20\n", 140 | "15984/15984 [==============================] - 1s - loss: 0.0280 - acc: 0.9808 - val_loss: 0.0266 - val_acc: 0.9820\n", 141 | "Epoch 18/20\n", 142 | "15984/15984 [==============================] - 1s - loss: 0.0251 - acc: 0.9834 - val_loss: 0.0246 - val_acc: 0.9837\n", 143 | "Epoch 19/20\n", 144 | "15984/15984 [==============================] - 1s - loss: 0.0257 - acc: 0.9825 - val_loss: 0.0229 - val_acc: 0.9854\n", 145 | "Epoch 20/20\n", 146 | "15984/15984 [==============================] - 1s - loss: 0.0254 - acc: 0.9822 - val_loss: 0.0225 - val_acc: 0.9848\n", 147 | "Test score: 0.0225085995835\n", 148 | "Test accuracy: 0.984797297297\n" 149 | ] 150 | } 151 | ], 152 | "source": [ 153 | "from keras.models import Sequential\n", 154 | "from keras.layers import Dense, Dropout, Activation, Flatten,Lambda\n", 155 | "from keras.layers import Conv2D, MaxPooling2D\n", 156 | "from keras.utils import np_utils\n", 157 | "from keras import backend as K\n", 158 | "\n", 159 | "\n", 160 | "def get_conv(input_shape=(64,64,3), filename=None):\n", 161 | " model = Sequential()\n", 162 | " model.add(Lambda(lambda x: x/127.5 - 1.,input_shape=input_shape, output_shape=input_shape))\n", 163 | " model.add(Conv2D(10, (3, 3), activation='relu', name='conv1', input_shape=input_shape, padding=\"same\"))\n", 164 | " model.add(Conv2D(10, (3, 3), activation='relu', name='conv2', padding=\"same\"))\n", 165 | " model.add(MaxPooling2D(pool_size=(8,8)))\n", 166 | " model.add(Dropout(0.25))\n", 167 | " model.add(Conv2D(128,(8,8), activation=\"relu\", name=\"dense1\")) # This was Dense(128)\n", 168 | " model.add(Dropout(0.5))\n", 169 | " model.add(Conv2D(1, (1,1), name=\"dense2\", activation=\"tanh\")) # This was Dense(1)\n", 170 | " if filename:\n", 171 | " model.load_weights(filename) \n", 172 | " return model\n", 173 | "\n", 174 | "model = get_conv()\n", 175 | "model.add(Flatten())\n", 176 | "model.compile(loss='mse',optimizer='adadelta',metrics=['accuracy'])\n", 177 | "\n", 178 | "model.fit(X_train, Y_train, batch_size=128, epochs=20, verbose=1, validation_data=(X_test, Y_test))\n", 179 | "score = model.evaluate(X_test, Y_test, verbose=0)\n", 180 | "print('Test score:', score[0])\n", 181 | "print('Test accuracy:', score[1])\n", 182 | "model.save_weights(\"localize.h5\")" 183 | ] 184 | }, 185 | { 186 | "cell_type": "markdown", 187 | "metadata": {}, 188 | "source": [ 189 | "# Turn the classifier into a heat map\n", 190 | "Now this is the fun part: since the model is all Conv2d I do not actually specify image dimensions (although that is useful for training). And I do not add the flatten() layer in the end" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 9, 196 | "metadata": { 197 | "collapsed": true 198 | }, 199 | "outputs": [], 200 | "source": [ 201 | "heatmodel = get_conv(input_shape=(None,None,3), filename=\"localize.h5\")\n", 202 | "# that is it!" 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": 10, 208 | "metadata": { 209 | "scrolled": true 210 | }, 211 | "outputs": [ 212 | { 213 | "data": { 214 | "image/png": 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MoYNGUOqHDzdoGBMKUQzc+XC07PSD5wLwqSVvAWDn1tkALHzG1w3tic93uLZlr7ZLKdWd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\np5cOhT7w6SoKJ7l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ZEGVcG7ZBxkj1nKNqp6WwtwShBAwxTU0YR781+rPWuPM0lm7weXLYr/c19xho\nAdq4mSYW+P6uZTeSZBfYIqZG+GWjL8PgLDzMV9qas7a538jT9g2CdwtjEDBrYbOO4mjqkH+2kUHw\nvIXSMs7lrgUqEtavMOUQhN3HEjWg4lkdQqzNqjPtubiC0xn3ru9rVK6yz1FTGsTKnkQ2chIs8sYd\nooJbeVSSZKui6vBwwvZjQIrxfuh9NPgKFg+FTL7WM5ltra3PqoWpKYnWllAGBKCW0DGZs89NrR7s\nUBxdiB4HI3swejgYPRaSWCNKYvj8rTQ+8aqSrZaJ9OgWmXI382Ih7fwdaf7xzP5dbQyzQwbGKETD\nbDJnUA3oo0WPlJW2ntJRyGncGIajListP4UWh0++m4uG+66xunjeaI7bs9ebYHdDBlwRzxk13Ywg\nrp2O0ScjjeGXw0wdy8TXDrWjCTm72uGu1rg7ZnLMdyrS/CSd2dRV4lK/PvAWmjNxbMtxWumaeYVm\nP7bNBHdkbO8mZWUbnib0xwzJhtG74OwY/FENOYS0JobxN+03+b9vPT9vT+IWwjgPqSjknq3btNjB\n4h7DWt/Okg2qrhTsRrqMfV0xfRHXezrkY3NO03B3seok67+P9HNoXxN82SLvosghCjlZboztC99f\nClZVtx6dnwCYnEnrEhE97kAvxQMHME3fy460vbMaKHcn+56v9DIMXw49NIw+qGZpJUwjlC0yz8Lh\nJ+KhgPRNI9rNuNDwmubgmtVYOCpgnPtby64c0+iNrDzpSE0r04qmcKjEpgJh4+ajt2NODi9V8hE/\ng4AENCEksZBEK6ZUB1y0Y/gAMkEqVtSoutWhO4Ik08PS8pRIxaMQWmmHPja0hd21jHB0j8LjOGex\nDtdN7nwLX4dp8PTNqVCylYWe6cJlfNZYsyUciqMQzjbg63VhUwVuPUipLNVqn6NRljiYXw8tlWQa\nGCSPxEktzC20xeq1kUZoQkQgp1VlSKkGoFp7lMnzLKo4RLWqJd83ncFoHtbLOjwxNOHUwl0tadDM\neVv5KVkbMxlJHgCozoiBSXKrujkypYhuyeqVWR1yi7FqaykrxTXJeQz3HYR3zxh2K7N4v5IViFua\ncA8oUpoCY+9JaAt6GEqBiDtpK23tVzFBOIlXsszrswZEhEKxKrKqTSClZNnmx1Gz6F1QhXZevW/h\nNytUJs9poNZW80jVM6IHRaGqF97Lti9i/YtlbJKmaShYWJFqZa7FI34ODw+7MMq9HIj6uvhShbjN\n7cNAYoe7WuWDAAAgAElEQVRRWH106Vp66dl6sUiKdO1jpRnFo8QrEw7lEVSg7qQixTDN88yuZD7S\nvMGaGO+1dqy1j1HDVi3NxA44ZzdcTWRdpKn6pBfDCZjGxCa17Lo0YOimyfX2xHWhuTeYRwL7xYTm\nEG8N+EEswXyGzg/vEfHa61FvPXweVclZdsrDrjHtKYRz9Q04YOPNWZeTZdXCyurY1ajb5t2hCOFr\nDkEBNZXIHPDe93ivwTpmucUSyNHeaeJgu2WwlVcW4Pj37vctWkKkHXgxzuFotYyQTHyWhvHvGuhY\nTsP2Sf/ehNg4n9kFXRmqwrY2LP6ZVCz71S0FMUw+LDml+klhpkG3QnWusIwWX8zjdlkwtMPGXTwv\nRVXtQJAac5BAC1XslIEURedQigbsZ9p80YDoQHJulrVZ3/j9iUIhS8S2h9Cy/S/1qKUY7W7jWI3J\nJ68P1QJkpRdpG9fDWJp59Pe13Ii2MAb+oXokYq61KQnbsjQB1hTCVVvf2vJ8O3o4GH2YNsiuv2Ew\nlcMMNu0+ake0glLOx0MgxOJuuJqbnklZOeCQXedjp8hGjE0Yz4oypKERt4kSbafnpSytQmY839K2\n02qDx3urBuzTGb+NzNqkbU7Fjh3g1RGsGmay4kwjrhvaTUqyqsO9y7Dsqx6mGu8cr2mOPRkzZ7vP\nQUQM4x9M0niXD7dpMzEnI14bQik0JYZ4+GTx4QcHB0cgghV0UEsYx004KT1uPzleWoYTsUxztuui\nz0sLuRsYfSud6/NWagtHbE5OtdT16hYNEYWxwwhGsvUQa7KadjgqKsfsCXXNzuYFszBj/Bw+ijMK\nwqKJsdrb2/OkrNH/0LPH27oZtPGxHPIKYgpFDPx0rjUzNTgy2UE5RVFRUp7sRLgKe0zMep+NFsgb\nlprZZ8siE5AM9zIztzlZR19Lt3oTE344h3YFTF26h6Aax36EcXLsi9J5Tat4WSvJ7+3raKDU/VF9\nrZvyo6rtpDVVteAAF8DiCYq9H2sloM9NCISjmfEPSg8JozfyaOgjLoaxQ02a+aEAgk9IHGyhvsGk\nNudYOmajhOk4VnQMGiXtCB2MkrTh4qtnj2GNlUpiEsfzvWRC1Gwx81bIZTjwpAa37Az+OJx2GXIC\nVGtLy27twhZFZKc2KzDnXs9n2LjVMRZLa4eSj2LRHmBmzDp16Ks5onXdxpG5BGVo2p64hYF6KK0a\nbJdU2slBkQg0CsQxTb0uHuFkrWlZueb8HYQvPcmoCTWHRap7q1O2qIpoY6UyJdnBkm1+JukZtwnp\nIDwGlWmlwRuxphPCNM8WgTOeOORTPnu4ZE22RrKOgrRDEbgF1Q6pqOoKzzqiptZq4coxVsGYFUvq\nkgQStqDXlGKd/azD+rY58Hlblh6a2fwC8d2akSaUnISKICoIBm1OYhj1hYPnePlTP87d1z7P+cvv\n4nBzgSsf+ZdY5plSU4ukAVoEWqyPUDa0SstXmPPEUovBj0Jbh8nHeRoSncLxqZ6Fauc32N4LJl9F\nICUWqilv6iHbS9+D1TPgR4r1nZLnbni1WIsIdFgySsu9jYae6CGZIn5k5Go9Phg9PIxeKiUruZpZ\nGvidbeEdp6BEinBp2YlJPSkpYoFlA1LJVTGz1LSx4qp/1c4cx8SFGNSCksWicbIGHuebK+CTnWSY\nlZBSmF2bWBJoisMhQlOXpgEDQ+G0UfvqJYqDFsU0Vm2vMYYQ94s50VbaQQguBWo1LNbbrq5aW+kH\nY0uTM5UlNkLqwlXomXt94Wor9mTVRSVAydVmCC21WUbBzIMn+xpeWRhCh4TwVPHIGJ3WCkDb/HkI\nRdM+t6EJV//OGLXdm71qXDsfRfEa8KwpoJadUMuxHYgzJenMePX90MfVZ6Z9rNbFcRCV4epd2YDa\n/AzWJ0tOiqqm88DocWaVU4KhoqlHlA/zVdshONkXdvGmzFO/TmtXCo4c/iLClolaFosuE0G2YrkW\nOXN6OeDGS5/k+kuf5Pa1L3D/3mvcXCYuPfX1TI+9j6SHoJntpJSamWbzKSxbO9xDtCsIZShVLNip\nUGZ5ur9iGGdJie3AWG09YSU5kpWtiKQ3detzVumaeYUUFVcBIk6+uK8p9ZwLVfePSfAQ7RFWngIT\nYx3+gjEUdtdyjTNol3p8oMRb0YOnVn1FyZ0sFba5tky7JIpQm1nbNezS6psg1U65iT4nj2mX6hss\nJGCExbkmOkkcE9v2cmDZ3Uow5tEKWmXsuf4Thv+YGbhe6PZ+qT0WW9wB23/6PSMTZGjTysQU064l\n4TDR8U6mI4yk3d8doQGTIOphhNpGo1ks0uukj22K3IFm6os75lII3g6VSTWtLk9ut/p7RhM5+tZy\nxsVAEknZcfZk4afxDFHmnJiSMOdk41J67ZZod1szYU3pesxJxhS3QwhhFKsKBtb6nXsBrd1SAG1T\nD+Z9jGPMR5RaGLXm+K61Jw6j2Zn3aW8izWl1zxi2GcXPwnEZEUdTyr1Ew46lGqUEMlbOYWyHrXcx\nqyagsQqT2rVx/aiJxj0pS1ufGw6ZvPJrrQuHs+2ZUweHnOUa25svovdvcLh/m1vXX+Xi3oazUriY\n7/Lap/8+P/d/fj/LCz/HWezM4aK1+62G/JHWdrFEpohmizW/a41nMZCSWsjimbPJEuqKSf9eLoFu\nGUSJhDaWifZZW3uJ9n6DeaRl6LcidQNE20ouq8E582Zq63xEFeJ9kgxqkiNayFvTO2r0IvIXge8E\nXlXVj/pnjwJ/HXgf8Dzw3ar6ptjI/hfA7wDuAX9QVT/5Tu+YVdjPlbPOFAx+GbRSL5wUWFxoz1TD\n9EOKqqpp+mBZbcG0xKIUglQFbaGQUcOE1SLPvmhG6EY99DI0oSNRXBYKRMRiaysjbIkSrZzuanxz\nE1zQF2VzdO1AIhVllkjMcBO7aeNdw49+7C7wtWAZPnOIJ8q/xv3xfW1lobVp99ZH11Yk+diEwNxx\nVPqvyUtZgB2PZ6niDjGZetPOHJA4x1fsTUmH5ypEMkn0aZ7nZtpaHoNt0ZSlC7cIeaX7O5ReGyXP\niVpt3ixaqvt4Aseo2vuN3z8KlygwlnJnMKUU8hSx+z62DA7zajJbXJtEeiLeOH8oK4df2yN+oIAk\ng+Kq1J6j4bCDDuvPnrXWvk340tZcUj/XzbXiBodVPxJvR0mx/gfMg+3hPFO98HvWLXm7cJZXee6n\nPsb2xotcOLXwyivXuHDhMqXs8eq113nhl36a+vwBLz7/U8zbA17/hXuklDjz3m/m3mp9AjkxD2s7\n+hZZqxq+rh0lDB+LkWlPDoGpDhbqCjqzdWz1eJzxEs7zClOP8rPzXX2v7JwHXGtlKdtVwME0TZbd\nX20htCMQxc7cbb48P2ciCqA9KD2IRv8DwG/b+exPAB9X1WeBj/vfAL8deNZ/vhf4bx6kEYdJuZRO\nsUyZmQRJUfGqgqP2mRIp4dp6YUqQ0kQlUXSh1sXqtksli4WumQZc0GSJF5bKHOc+GjyiWto7rW6J\n/VhGbv/dIgWSD1s6unh2Bj7JZD8pkdLEJDPZ/5MqTDI500vH3H0Un7dxCCulkrSS8YgXtc9kR8MP\nU3HUEEdNumm9QhNEq2tSsmJd/tYQvKM2u2uR7C6q497d2uba6OqYPWdwk7D62X3e7rN3BYudvLRu\nrwWsOCPNx1tTkaU4p15NdWz3rtbaLL9QQlK/Bg8ZlCl7Qt96dMIimjzbO8+TpesP2drjWPnBtcjU\nf09z6tbT0I9d63FS+8m1Mo1j5ZaZPVLbT7OqZSg4ZiZsm3eZBE3a2zPMUVRhFIVZK4/IIadufYqb\nn/4Yy9UfQ25/gft3b/Doo4/ywtWX0JQpZctP/OMf5vD6pzi1XIdyn4Nbn+eFn/+HbF+/ymkTpWZR\nt/E5ChmJGBSSpUdSHUdHLBIfxyKWJ1NEWIbrY68MM9jeH/k2VEWLrp4ptRfYm8QYNUnaeIaisbuf\nxn0Y2n9US931C7wdvaNGr6r/UETet/PxdwG/2X//y8DfB/64f/4/qI3eJ0Tkoog8paovv907RISD\nKiS97/VAqmvozmgDJKxqGX85obl6HHQITLH6G3EMWLEDRBTYpInFtRzx9y1DrGsWc3AacwhJTTOD\nVfCaHNaOpMXaor2WiJ2TsfhCWXvSQxOrdJO+QyHuwPV3iZXSw8p5RLaenfduLD6bUAotT8ycbsYE\nw7+xYNRNT184lh9gcEucNBTOvlg7K80vCVNjxPaGOpj0qu4cTKBaG84tKq1ueRSIU9G+8Ry3Dd9I\nihDIii3+nXUcDsjW9zDdNZzqkC3UybRs93nYtesInlE4jP6XimCHV5tVp76hFu01hSyqx8aiVtec\nhWZdiiaSRFR990Fk6Yx9XPvr3w1WoHopAFW3dPqcm+a+dsBHLSIzftRqREXinyf+4Ad62/j3EM0q\nukp8alZcw5l76ZHd9tpIQ3JrJJdEyQdQhQ1ukVBIy0K+90t87id+kOX2dXR7n+vXX+Xpp9/DC899\nnq/78Ed47fotLp67yHvedZEbr7/K4WEFDnjztRs8Jk/AzVfYPPo0QqKQSLqwyETSvAIxqlvUcXCK\nWa42Z91CdcYasJbUtr5FpNWFiqgqhn7n1J2xagtnNSY1uyU58AERg4W0MpR09vWjFrRRRVsF2KiL\nVF0ZKW59ZjfEdo8zfCf6cp2xTwzM+xrwhP/+buCF4boX/bO3ZfRJhGlO7B9C1somT+2EIMPbfaET\nGq3RauP6Jmvng0628FXVTi/akX7GmMIbXm3yhuXSapmrmcQZS4pIbrF3U9610GAY9A05ar21VrIm\net6vG8MiDesXEXcSR0jkkKWrPZRTJFGozsGPTvZoxq47bRykxXs7E2laQyxwWPWBxrLWwqNHtsjK\nHJHGrYf3am7Qx1u3df39mPIOFtmxur6125lqK0LVGzMly0kQjoeljlI4WuPIP/zdqTH6YPIBw0Sf\nk09HSnGAjK4Kj3XnaTT/aBvExzKYeygSyfvboKDde4bHZIQ4kiyUDEYBo+HD8b4F1CLdNxP+htEh\nKIPP4yh5rY05kdlj5oBTZZ/9w3tMp84wpy1f+NSPs3/7Bndev84sBlfs7+9z+bFLvHrtKnf3D9nM\nwpvlLsiWU6dOsbe3R9LEK1ef59Y/+Xv8pic/yGZznsPJ8keSR7ONFuFqXHBoN2eD5Fxlsv0c+44m\n6Een/XEkzotiLuKz2Cd2v8+x7uwZwREDj4xaXJlM63YH1JembNF68ZyhTasQ6wegf+aoG1VV2a08\n9QAkIt+LwTtcuPw4tS7k6RRZ7wNeDc9rhYsKh9st8zybU8vPdI365aGtItKYezRIUmoDPi7ijTtD\nqjPipRRPstBmvm2rH7hAHMcWXN43emCzzkhQRUh+CHnfID05qTbtzlvkFTD7RLdaJuJxuM5kc01t\ng5sQ6IvNInXGDVjawjWty6NspDrGbXq8wVoeZ+2buKqu4J94js8ZkQg2Mv7IVjRm5jCPpAYlmEzT\njv265j7WH4oY9ZEZRj5AWSzZrB2gSWiTEBm9VcRxVX+HxoZUrz3iAisFhtzXwsggsghEIa6Uhwgj\nmuwKuCbaAXg4pcFQrQRCylRdfBxcGanaII6xHb5827t8Jfs71hYImBU3vn9xgCGTu5yL58dY5DUz\nCQE+pewKzfogFG2My/ICUruv+wmino4QgQyVR5LCrV/m+ud/hjdfeY2zT7yfD37k67j2xU/xxrWr\nPHnxLDdvvclm7zR3797mypXLANy/d8ClS5e4dfsur7z6qsFhecPdu/vcP9zjcrnDT338b/K+j/56\nHnnP+yk5Wfz7sEYtA55VG8d1CZAngSqr6qKjFSzD2msRMMM7oJdslpQwl846D2P0HXVGXYmIuSlZ\n/Xorc7y1McetiyFMNtSCVgZcw9dHOw/jQejLZfSvBCQjIk8Br/rnV4Fnhuue9s+OkKp+P/D9AE+9\n7+tVEswVaivopE3rRpRTe5t2olM42pojS71WzqhVDtJ8xH7juxShYxgzaxq8eHnXUXsJE0t6TKsK\nLctvjLJomg3h6KM5AMmGX4/mezh5mtbaGDignolKNe1We7aeakUHsDBCBgPLi2cnMTgimEs7RNzF\nl5VRcAM83lusjtBx2m/1olkp0vvdRyBqqd8hatIQvkoSclW8Dp0lI3lYYFsPMS/D76H5rE8PS6tN\n2yITBse5f2FlEFw4F7VaIaUlJq0tr/h3UrUSyh6zbSGlspqjEQIIp2b2mPUQvmaSK5Omli7vcmZV\nk6bNkysUAStYYbOwHFjRcdbaLKBqzEZTdjO/g+Y2hl4GOtmZzNEPyxo2mLDV2482YzIy4XtmGDtj\npFFR0+PQRTlf3+RHPvYD7NV9bt26w+b6yzzz1B4Xzp3h+kuVbTlks7fH2bNneeONN7hx4xZ3bt/j\n8uXHuXlnC5q4dfMeV65c5vyFs1x49DKw4cNf/24+d/UVnjpzm9df/yKnHnsPJWcQO28CMQYtdA3e\nFCB8vSuSLLNWUeZpYtnJEI/fdx2dmiKiqbZaOn3Pd+HX5j4lskZJ6faQ4YGAVra1EEmSQml7sDo6\nERm9gSTUJM36n2S3bMtb05fL6P8P4HuAP+v//s3h8z8qIn8N+Dbg5jvh8wAquWnMFWlSNmquaMWc\nWHEykQh5yl5jZK2lhBStqkRS1SypQTptgw+JMIL9GYon4rW26cJjLLfQ8WyfhBY5oczzzKJWJ16z\nTZSIhRcutfZDMnArpCUihXncQ+OMsZhjbrvdGiNsWkJPSqq6kNWsDq2FKKuAF15KYpsBgnF6BnIG\nqcYkyKaZFMYqfZb1umhpsewhAJr/oh1eXEmJVl/fh9EPlVAWKUzZj8FLHnkjdpJPciYdpZ9FXND4\n/FakaTG2kcSdUlvUPOotLyDNmwY7bL1tEb8vFCjJa4Zbb/LARAWhlvtWwTBlFi1UuoKQ6LX3rRy0\nC2mx8ru1VBas5rplTy++IfvhHSLmw6lSDVLyeV906e9xDVQja1Lx2ko9WzXtao94LLjNSjs+L5SB\nZVnYTFgxPxHzBZGoOuRwyLQ6TMae7/XxdaFqHIO3NUsLK6WweOz+jKJln8/90o8ylS0XLl3msJ6i\nJuWzn/4pzuzB3Ztv8Nj5pxAKb77xGmfOnCPnmasvvsqVJz/AYTF/zuNPPsWVxx/j1deuc+Z0YpPh\n//nE32U5OOT6i5/m4hNfx2/4rn+bfbnCIWechyygiSqZVOxEraVWUoZc7HSzWippnklqdXjamMc4\nhpCvW9/jgwWZBBYPUU7SLKYpe05PzS4UR7gxosQSmm29hj+JKVOXxcIxVU1Yh9WIW/YePRQWd3LQ\nbq14vDM9SHjl/4w5Xh8TkReBP40x+B8UkT8MfAH4br/8h7DQys9i4ZV/6EEbcsQUHszp3cSBYCRR\n1qCZ3TumuGCZbRk71T1quNda7eDjQRNPbsrW1KNTxzNO4xCHERYKh0kIl+wOxNAGBbCUZ3MSp2xY\nfmChYIWfqpgjproVExl1hCblDD7PCd2all7Q1qZIDe/MVz1T0eptt+fR4ZwklqGoXigqS0YpTSs0\naMxw1LKYkFkcQklu8YRZbC/dxfW9zLNYTZTNNLMtSzv5qEa532H+WxBfUq+nkr0uf8HqjZgDfWql\noLMd+JJSO4i7lC1jRJTBIgpaGtOSPDiQpce2S0oGfSQv4+DafDtVLBkDDpO7laPeSYGPukrVIbUN\nhe1222vSRyTWsM51KL+cUnK4z0G1OuSJOO1aA+NpaAbRQS3hPI+MYmtDYP/VlQLbTz07NDOEK2M+\nLhOsgBYvrdvPMAjFJKkw5cLL177I/v4+myuPc+4R5Y2b9zn3yKMc7N/jm/+Fj3Lu9B739+9y+9Y+\nFx+9xN079/j6r3+Wx596inv3Cyln7u1f4OaNN9k/UK699AXe/cQVLp49xeVnnqAs+1y6dJ/nfvxv\n8cQHfgPzo+9hO52GebYicC5AwXw0SXBfiR07GmWzrVQKzSpLiGd99/IQ3WqrJCbjmKWS1Eq2hGDt\n+8qs/RLMOa4RK+ZXtABWw0dQ8pQpmHYuKGkSBDvP1+bI1xi0Pll7+t55EHqQqJvf9xZf/dZjrlXg\nj3wJ7zfyFttgtVJC43Pb942xJKEutRX4Dy1+1yGTB6dT3DvPc6sLE9dH5cS4tXgsa3Ok5gyltuPW\nAkGNwwZg5X5kypmlLo4NB0OUxtwkeTq11xYvoXliTj8rbhbQUvE699VrclgYZWRStuPx2jj5iTza\nk2vi31ELkKTMZIvhdlOcUpjnDdvt1lO+LRM1i5LniEEPpqRtbPOcqMvgyHPoZk7SYJxVwSelxXXX\nYjVSLN/BmIdKWGtqpxgN8FsphXme2yEvRRdSjmfh0I5bhTUK45mDNSvG4Ihl1OukoxiMJcago/zv\n5EpB9aPjNLJzEdN242BoNcYcNZLUlY2lFFKeSZONkdDnCjUrbp56TZxaeoXGlBKa1LD+ZGUyVGvz\nb7TzDrR4MawoqCUN+slp5vDwkGmyQmeLn2oUmcVW/8miY9ShuKxm3YVjUpKXEYi1WRay2AHcEV0l\nKaNlwzd/87fwc29+nuc//8tcfvzdnNqc5kMf/CAvfPEGe9Ml7tx4k8PDQ+7cucP5i+d45Mwed/Q+\nL7/0RVT3OHXxCt/0a34LH/+//y6PP/Eo73v6/dy79QpPPXWB84/MPHL+Md586WXK/ms8/8lrbM98\nmI/+xn+N+9sz3EeZNpnwq6kqqfQyy+Ho3MzJFUULSNDiVSeHkiix3pZlsdIaUTgueZRR8nsm41v1\n0PhGCJVpisON8L2QKa74qK8ByQm2BfG5MUVyaaGU22Vpa9WEuAcWvEVm9lvRw1MCwalpzQ0DPHp6\nEHSIIKhVP3TKygp2qaWXvbU4Wecjhgc0jDtw1shci+MJA++ekI7CrzBhLL7YNd08TZ51CiDMsx2f\nR5P0zgjENlFx5hOWiCpWti+Zg6vl6KWp1/po7TVnQRQss3ER2/zkJqwC0hBR5jk7TlubBk3Nzki3\nbaHbYgdxh1PU0FGPW2wVDVWJrCgRgQxaC0tZEAwfplYi7FFrRdVyJnIeLDXt66B6WF87jxSlUpAM\nRRcUafPTIuDVD6Euvlm9yElEvuSULSkrrMHwRUjAetumgQnJjqMj+QEi7kHIHhlFJafZrCPUYZJ+\nwIV6Om7OljxTFr+e0koHKJHPsetLqs2KmnJupyWJmKUXe6LNbVOWQgVRry0DQjVB4gqHzf0QiSZW\n46d4FFdz1ifzEeWcqZQmuESUvU1iu92y8b0w54xIJR0WLp47w7ueusKcKvfuHfDoY+/ixS8+Ry1b\ntC7cvn2bWzduU1W4ffMu85y4c/cuj5x9nJQ2nDv3KG/e2fKRj347l86e4uc/8XEunr/Ea69co1w4\nxeHhAafOneO0KKe2W27cf5Gbz/80F9/1EabTj7GvQz6EiLfb5wg1Bqz2exK3mFLullTqVm2pWyv1\n7eGZUUNLkysi7qeKIwE1wmCH4IgQuEXDcvQYeMEtR/eQDH6f7AJGPJQr1njZrfr4gPTQMPpkKh2w\nPtpvZDijpM2pl0m1MqSjE8/qvQiBEdMcqRa9cTT8sMFEDfNkhf9HG60Nfg87VoaIWxhHa6FUv8+c\nNJGlCOEQi/rjYRXM7VgyFyuDpaIuHILhL7X280EB99G7Jqbm2hugm5yTQwmb1nZjGuplWXRom2mR\nMjDg1tdhXhoM5rV6qvRTkAyHjhR724DZHaVJukVWIlbdN0FYKqKeVJVoAkvEYDazimpzUjeh7LkX\nbR0BWu0QZ0naipfhEdUpWbEpcfPdYInasxeln0KG98cUhzhbNEPAezLWNUpoWqjFHKFFi59GVRHN\nDd6i9mMox3Gecmf8u9ZqzJtdHwx+iKBqbc5Ngej+E6HXwA+FxX5ynpiqxdeH0jEqO5jXBF0OmPPk\n41BAK6f29vnRH/5hzpWb3L2zz97pCzz9zJPcu/sa5f4+h/v3EWBZKoe1sj1U7t+/x+HBPi9f+zRn\nzz3GZT3HlQ98C+cunWEvLZw/d5FJ7nL+4kUuXjzLrRsvcuu2ReS88do9nn7vh7j1ws9w543r7J99\nL0999NfaMZAibhWnwXfka8ch1iibvWhXDKxOjXQfoWPsWSoqk8fce2x7Xchi51mUUj0oxNbytEks\n255BbA5j9fpV2iyFqD4b19k7zWrdlsPGs0jSLVAZA2zfmR4KRm9ZeDNwaHiVmgMqNPLZN3wJDRw3\nwxpG7RimdudKzR1rt+qIviGOKTAFNHw7QuHAWEAcnGALxxh2rXEaVFgDUZ1I185I+veh9WipLGIJ\nPtklfyt9mhKHtXily86goq0CLVNRtbZkpwj1ioVJBGVJ+DJqE25TNjNbs52SE8xwmiYrdpWFyHiy\nbFkTemnqmK2ARQMoDSuckyWtVKxdeXAq4bCYenvbWGMwhuKHYyTTioPBlwRTMQ2yUlDttWaqZ3fa\nxu2W3JTTSsjWuiCSzWIRw9/H2uQhjJSKeuRIVmEisbg/xoyAbm301PVqZXdTO0qEMY/CBG5p97U1\np1imLCBamVAkb5jSsHlTF6BFl8a823Mc4kpqOO+BF7LLqWv8Ify3jvc2DSUsgUEwaG+grdEEWeKA\nRLP4ZoRKIVNYXnsJfeM1Xrr+Bk++71keefJpVAVZDqDc59VXrvHImcfZO/coL7/wCqdP3+XGm9eg\nVO7vH3Lq1GlOycz9A/MLzacukze3OX3mIpJmPvXp5/nVv+bXcv/edZ5/6SU+8oGLXLp0hZeufo55\ngvsH95GsnL98jjOPbLh9+w0O3rzNuTNnuZiFGwU2zMiibEPwirQaWVutaK3OU/DzoSviCkgNCzBn\npFYyC+IWZFUrAZEEyGYJ2LnMxYqTOea+9fMMTFDgyVcdzpn8LAyDeiz6STQUJLNaYy+EAhoZgG93\nitZx9KVd/RUjZVkOATNzoU/KPM9t40YKcXZsNIorbVJmb5rZ5GlVxxtgTF/PSCtAFAIhTp8KWjmF\nq9Sp4Y4AACAASURBVLbiTfM0NSYzOonj2iN4mWum3SG4drg1ysnwOYhyJe2Q6LF0wZTzSmvftXhG\n2oW61JOrhB6COBbHGu/bFX5jYbC3o4wv8JzbmHWKEFFtv+NtMhMZ14r7+8CyXINBWTSD3V/K1guY\njenn/qNRBqIS4YRWGiM5tGfvS4nWnloX7JAY/0wsZlBESbm2e6bZKiiOcxxhi2O/SrLTpapYn6Ok\nwJyETU6rz8Sd0uNwRVkO8VwK+66u3tM0vyTrstXtGdr+bkIPUyxa6v3OtVF8a0xAFNeKRQRNFoW1\nSYrceY3t65/lCz/3CV7/wufYv3GDTCYJ3L51j0uXnmJbhM2pPS5evsjN2zdQ4OrVl/nlzz7HufOP\ncnd/y5nT56js8eab93n3u5/l1JkrvPb6TR55ZI8bN1/n1ddf4V3vfZL5zMzd/TsA3DssLJq5+tLL\nVJSXXrpKKbd549XPcvD6c7z+wmc5paYU5cmUkLZfVFuRt5RSW2+twGAdhILzHNnZx/F78JFxL8bf\nWXTFH4LG38dxj3ujNItZ8sO+kdr8UF8OPSSM3jbM5GcxjnUdxoMAJme2WewUoayYSRPQgjP5podI\nH8xpqBwHdOar2qoV7tYsz9IFSpuIodVNAA0hWjklY8gKqWrTeFMyRrNbw2R8jhUsE7LaoRGz78bJ\nTcyZo0x5hLTGZ+9WV8ySmDycMfDeiOaJ2huTWxwhVIIyvTqiltqsFhn6e2RGj1lZ0cYY4wbXqZqA\n8MiHmM8WYuq1VRhw7NiIfZOaj6Cf7mNtnubsFRxdAPh6aT87Y5VTOrJB23ypUIuSpmyCQ1mNRdBG\n8qrCo+V5pNY36NEyEVFhUTadiaedMR3nezWWss4fiaiw+Jn8/N/jnjkyquxznt1SbZbxmECVLXxQ\nD+8xTYWrL3+Rx556gvOPXqDU++zfsozXK0+9h89dvcbd7SGHZcutO7e5c+8upx+5yNmLF2Ha43PP\nv8yNO1uef/lNZD7Pk08/y8uv3uZ3fde/wTd+4zfxnqeu8MhGOb0RXn/jNZ77wvO8/Np17h5s2T9I\n3Lm7sHfqPI9eepyD7SHX37zB448/zu1bV/lHP/RXOM0BSKGyNWXKx22z2UAypWSWxJSsvlYUznsr\nphxjEWM8J1doFDZ5aopOxEfupjKN85dSajynfe+Q/2h17EZW1Wph24ufFFffQfka6SFh9GayGzwx\nOPToCzpCuLIPQEjkyTeLln4wdwzmqIGLWDJORNKIM7j4bsR+471TykzD/SNDPY5hj9fFImpHIQq9\nrjtd0MTvVTxWP86iHUL2QuiNTG4Xr939fexXZIUGph8+gXhOMKtW3mFwCuZhjKLv49hC88E2hhML\ncCz4ddyiRaVZL0CDuEJgjqWfdw+TjveGkNGk7hcIAWJwgxZz4QI7fowd6G5nnkfMtG/QaWVxhka/\na12Fz2Gc36ZxuzU1vs+ezUpLHJ+3O967ay6K0Y33xr/F98XuPWN7dvu5e13AC0knZhXk4JDlYOHu\nQeHmvUMOS+XCmdNcObtHXu4wkzg9n+XpJz/I/f0tRTNV97h184C79wqXH3uaNJ2nlA3zmUdhOsed\nfbh48Sl+5Ef+MQf3D3nxhS/wyKmZKxfOcfHCFT74oW/gcJu4deuQsiSuv36bSuZjP/J3Kct9Xnju\nBfbv3ePwzhs8eS4j9+8yayHVQgrIb8ocLgtpmszgVqsrNCVTFlLuCkhazU0f/xYOq51PRdh2lPPe\nZerjvWPZ6921MKW0KnQZ+zL2fjxnPHznQemhwOiRzKRbNG3YeIEuhRaNkOkOUvG41zomPIllvIXD\nsgpM1TDylqUW2Lwz/Ck0n8litadpainFxcMpD7HwwVqL4fHJYJDA3DYRg+zOOaRHdqTmC/CNGPEP\n7uCqMmzGsDzEnS0ZqMGMoURkdqK9I8xOC/2cOCiLab5TboxXpWvC7jn2JDBtdW2qVtKcLTIJPJZc\nyCm38Lqq2p3B/v4y+BVUq6Md0hKuJrUQN6TpqUjgi00Y26EuCxUcE0YrG60U4tzSGdXMRhb279zi\n/NnTJElsRdlS2JPTaMpUxRgKBybAFVgOuP76NU5v9jh74QqFiTRVizhx+EKrZ6BaPl7rV5SJjbkW\nC5EwQShrZhmO6mACW9EWtZWRlvgVUR12qE1iNA9teno5jr62pWl7rdBVMBvoIbc2mcyS7JQqYg0A\nc/b1x+q50K076PfYmra1QCnsTbPP8UKSwv2Du9y6e8DXfdO3snf6MvOZi/zMJ3+Ujzxxhpde+gXu\n3n6DD334ozx+5d188aWXefKJJ5lz4erV57hw+Rmev/o6+dQZPvThD3HttVtweo+7t+8w58q9wy0f\n/PCzvHTtKrI94JHz5zh/7iKPP32FH/uxH+PgziGH2/ukpOzfeZXHHrvC/UPlzt0bfO6zz/H13/jN\n3Lp3yI/+X/8jH/i27+T8k++nTgvhW0pZWcqBMXe62yI5MKjZ+EFp9ab6nJiTPzULvSuWPn7JrMxg\n5jkiq1yJG/1uqtrOhh3rzgesJAgqqRU7bTkyaIOEvxR6OBg9cYgIoFadfFEPl1SIw4CNVVq8L6Je\nCa4/I8p2WgikMadwxMYgR111aeUgbeAWr78SSSBgUcU4kzaHSD8kwLTitWm/YM4ULdWONHPnXFxP\nsYXS6qekZIdHc1TbnKaJg4MDJGKCh/ru4n0KxjAevHBEW0/S6tdHFuZWa7MumrbpzDwnKwKmvjHU\nYaSotbEu9+DjniwFPRKMbK66BbDUitYtiYCMEkW3TMyUunUHprDBQtDkcJ9P//Q/Ye/UI3z4W76V\nopVTB1s+9cmf4NRe4r3PPMW8yRzeu8+9+TyXnn4fyAQKaTKfzunlgNsvfpb7L1/lqQ8+ixzeY35k\nw6JC8dIPVqRktlA5T7jK89SLRwktx2CMTpKdUsOBsSqm+U7Fono0NqQL8ggFHbdogwldMdnWCGfU\ntvEDUtput2bS40fcjVpdsiSc5BCCCmxFWbSS45CL0iWLQHPmj+unMZxYh65NTjJDWhAqn/ns5/mG\n9z/N3Zs32Tt9kUvnzvHYR5/hcz/5t6n7r/FInjm4+yr3T21449rzzLLwvmc/zEE5w6s3b/Hbf8/v\n5Wd+/hf48Df+Kl7/pz/Bhz/yEX72kz/G9u4dVJV3vedprr1ylds3XuPTP/sZfvFTP8Ply/8ij5y9\nzMH+LTZTpmxvcmYv8fjjj1OKcvb8Yzz62Lt58eU3mPceYcrnuf7KF3nsmQ9wmMXzD5RIEDuunHdT\nYkohzVPb4yufFrvWvx55TjtIpOx8JzR+k1JqiWjLYu9LFWtDDotBwTGOyN+JnJRIrnxQeigYvcVW\nm2POajKJeZVdm2GIZLD/e4ml0Fw8WgJoZ0OmHZikLAEbdChFUvKaIDZhKmLM1xd61EUxi8APOYlE\nq50+iES2G5Y4kgTd9rjxKRtTt+NhaFFAo1kXTLtWCz1LLTLDNA/z7juuLNlr0mCHSdNDSHtWIyvN\n0cqbmFO2uBAyLNtLLiRQrVaSQ7zgWmDQPvrZHXZWImDYLJML1OoC1xXFw8P7pDmTZou/tySWYlpQ\ntXLPSYCilKykgztcyTBdf5XtmVPs1QPu1wnRwsULZ8h1n1df/Czbg3vcun2DJV/i1737Xdxb9vnC\ncy/wgQ89a9Da/uu88plP8PilCxxc+xRf/PQhH/m234LOp5jFIoN0svT8w8gcVmUatPNi4Ssdahrn\nmy4o2wA7893kyawhLASvWUJ+QIzWPqZNuKdwnHoiT0uE0mZZtJPOlKZAxHPUX98WZlU06artI3wW\ncJFpkGtoScSSvHRyKElhKcrka+jbfuNv5NYr1/jM87/Ib/r296LlVV5/6ZOwXOPi6Zm79+9Q9JCf\n/elPcvrsGVIpvPZS4dKlZ3jsPb+KMxffw379AumRy3zLt/9Wzpyeufz0dX7yH3ycJ598ir/xt/8O\n5d5tvv7Z9/Dqy4c888xlfvbnf57HnnqG+fQ+Tz5+hWtf/EUuX0ioVC4/eoF57xx7px/lXU9/HT/y\n9/4R3/ab3k/ZbFiWLVU2zUKLCC0d+hpjE3CdWff+XdvoPdotZSufgNAi8AArNUJXDGrUl1Ibtyiv\nMk0T28NDNpPlKcx5ndSZkuVX4EpBFMmzeQ8r859H6Ab3MosgaTKhhw1yQiA5dotS69ZuEIwzSd80\nqtodUwMuaclUG7QubTPWJAMD8yPEtDNmgDm+1Rou3GM96Sttz77k/rI1fBtpWYWShW1goj6JWotr\n11iWoan35mRzpmyZlsYoisRxeamFnCevHElABQNzMpjA2+qw1iwTNRyXzr9soVt9lWw1Mo0hZ3GB\n0B3OoiZqJ0leA0gxJ7o5yw/u7bM3bxARyvaAedpwWMTeu1Sohbt3b3P+/EU7MyBM1rtv8OKnfpon\n3vMUUu9yePMuV3/5Mzzz4W9iqwcohdN7G85dPMUbrx5w+tGzvPzqHbh5nXOnTzMf3mRz/zZnzpzl\nhau/zIXzlds3nqdUuH1XyIe/hs/85MfReze5f/8+H/7W38Lh2cc5c/FR95H+v8y9WZAs15nf9zsn\nM2vvqq7e+/btvt13x73YCIAkCBAbSQw5lKiZkWazxiOFLVt+GCtshxy2whF+cFhh+8V+0IMdMeEZ\n23Io5BnHjK1ZKHI4JAGQ2EhiuQtw96337tr3qtyOH87JrKwGOAAj/ICMyOjqzKzMrLN851v+3/8T\npPDo94ZkC1OMoqD+kXiIJcfZ0kqpmF88+hwak19BIuNa6EQrE1dKas2WGX/RuFYCRGDcZQaFEVm1\nkXYXCaDIip0Y/8ZVp7VPHTjU3x1D87R15hPR20XWV1xY3LaJuKJ0nNuAMYXEVZCbWeBLL71MZlhh\n6+471HcvIYctOsEQJ5NlplzEHWTxwpBubYt69ZCB2OXkwy9jKcXLX/0qh9Uq88srSBXywle/zrVL\nl9g4+xCv/+gHLBSn6Pe7HFa2aTQ6pHJzZHIL7B9u0+m1cTIWg2GHfr9Pp91g6KcpzuRZXT/DmXMN\n7ty8w5e+doFCBtreAHCwLStuYE+GJGG5ybq+0XwwUyZ2T0btP3bFTiLvIpkTx7MIEaF2wSgBfgTf\nlQLLcUB5GDMUP1TYlkCpMRowjgUIE48y7rTIpS0nX/lv3D4jwVhBKDGmva4OZaN3nTVmYQkbsEE4\nCJmC0GhESkJoEpCsqDJRAvEgIthSiGXJGGUiCEEFOvkhIi5SxIFe2whIKzK/o4UhMeGFDHSChRA6\nCUcok3ChSFsSKXVykoWGemmzXWEprR2JUMPVTKEgHCGwza6v0Qa2Jp0CLIElbGzpIJVEhQKJhZ34\nrvYU6AVCiqgoMsaS0VmbCpAh+hka2gvCLBIQQ+8k2g1mKeIKOUL70pBSxPf0wwDH4N8BctkMfuAi\nQ49Bp8mw38NRPmmpsFVAOOyDNyToNHFCFzsYkfVa+Lu3kO1drl76CadOrpORglGnQxh4ZJ2sdovY\naWYXVhgGIzKpNFINOdy+zbV3XmNUv829K2/jtWucXV+nslun3+lgSSjlCzj9Fp3d66RGFTLeAfvX\n32b31iVdqUsKslLh+D433v8p9Z275IUiZYU4MoppWGMGU2mDsLRlacaEY4Lv8WIropiNtvYi5I2l\ndLuOK3xZMQJKKENBLA2kljH81zLLhw48a+tXmUQpqVQ8dqK+scxciO4RwwoRiGDsxojeLypjpxGY\n+lm2GsGwhl/fpbl7hyweBAIpHQppgVR1UrQpZGyy2TT9/pBus0+9Xqc3cOn3RlgZi0a7inKrXHnn\nLzk+5eFV7lHbukp99yZW6DLqj7j4yOMUCkV++zd+i+WFMoHXo9MdkskXmVk4wamHLvLyN15gZqaA\nH4zojUasbZyl1RswvzDLsaUSw2ENQZdw1GJlJkvB72I3a6QtZSrWuSZHQM9XaeRN7LYysFiJ2WU0\np0JNvoeGvibr7cbtq0tJjXc0XFJIjWq1TBwrtpJlCilSWFikLWnGhZZ7ERdOZBxLM4cjEr5QCcJf\nQHx/JjR6AUjp6FRkwph2GCItWcQ8NJFJmzRbosCUUFHRhbEWFtWZlZG/PnpeYjVXxuaNTLWoHmMy\nOAXEDImxuWeiBmqcZqlX4DDCqhuO9tAH89620YBVJDARoIL43ZSxVEKj2dlKQxCDWFvQ3xcSo0GE\nsZ9VxL9Dd2vMLQMTRSRkGGmK0cIVjn3+AjN0jamJMAtFxMkcLWw6+G1r+5UglNi2xLIkBK5O0hIQ\nui5Za5rQ9fGUJBA+FiNyasTO9cs8fP483VaVnc0r9Ko7ZB2HY8c3KM6WONy1mc6ncfs9rHyJ0+cu\nMKjVCALF9FSRrZsfUC7Novo1zqzMsrPb4mdv/RVzU1nSi3M89/xXuHb1dfZ2qxw/eYxhb5+U10PY\nkLF89h/c5AsvP4Xt+ghH4g3bpFSf2ZxFt7rN4twsIgCFJJ2bIrQgjGMlhhcFPaEtA02Nx4WJj2iX\n0Ni1J5UygkOiJMb9FcaLRaRGaGvTjDMFdnxe90ecNWyu10r7eHxGQeTomG1b+Envk9TwQoSOWylj\nPSK0giJChQgUtj/gnde/y/HlJYZNl1q3zfTyOpm8x+G9n9DeepdRd5/hoMZ0LkOhUOSw0sIPO0yX\n59nZ3WJ7e4/CVIlMasTqnMO3/+//kUx+mlYvYO/BDRpr59g4fZGnH3uUenUPu6BIyS63b3zIydVl\nsrlVjq1dpNttcv36G7Tr26SdFKn0PJffu8zKSplizidwd3j7R3eYm1tn9uQMf/ZH/4LS7CK5qQUy\nC4+y8fhzhFJiCT8WktFcDsxqKoXphHCswcf9IcQYmpxwW0b38AKNvomCrMp4H1SkQEb1qY1rJ57z\nBKZfo6dJQzhnhICKlCsT4BcKYUGSpvyTts+IoA/xA4EjAnxlNNPYNNVmkzTCL3KNwKRJbUnjw4pM\nLEXMXxM9A0Rc3itZoAAYY+9llNikxi4RszlHIH7aSja1ZAFL2LHrR4Q63V0KgbDHiVxhbAJqk9xK\n8raj6Uox5QS1JSMJMZMSPSh0E4hY6B6FhUZ/xwVPxlmSiGSCWMTAp1EoganiJTHZlea7UhHXzRQJ\ngRS4I9K2hYPA1yohUuh8YseRBP0BneohpfwUwtE+/TAMyImA+/du8Nj5Mxw+uEFl+yYlq0ug2qRk\ngRvXrlCYrbM4v0BoS7J2CoKQjG3T6rXoeCHHF2fZ+mBAIS0QozabN3bY33vA8hRU7l+lmDpHfX+f\n0UgwP7fM7HSJNEOm8hlGwy47h/sc23iSav2Q43PHqO1u0qnu0x32WFlZwQ8Dbl26zMapDV599TW+\n8eu/jS8M7j9a1MNQC2ApdKKNaTNL6JT6mFpZUyQaH2sAygAKjNBWJvitIk1aET8jKpoeI5ZM+nwU\n5BWRj1iMmUTH8RkdjFUKgsAgF5J1C0x/W2g6CRHqt5KE2PjIoMb7b73G2eNLNJsd0oVpbEdRefAB\nM7k27s4ltm69S+gPWFxeoj/0kVaW0rSDZafp9bV/vNVuMD8/y/xcgeGgQsaW9DstRj1Bt1dkWJrn\n/bff4tkvfZ77N68StoeMhjdZPzFNrdqm3b7OdMeh1+ng92sIX1CenWEwaPDMsw9jSUG7uU8qlWKm\nJOi2bzBVmOP4vEM2r+h29tnp2px49DkQDkgV05QkIZOR4LDRHafMohwNeGn88kLp+XvUpRfBHolc\nzwaKG28qNOf0TIYoM1vARHA4CXhQcX9rddeKXXO/yPYZEfQB0rKRKkAoxwiYxHkVgBgL+4nVMNJ0\nVaQVRQPZjn2PoAd9xCQZCeZI2AsYM1+a9o4qwicFZxznioNbAhkVFUloCFHhX8sWeF6gBasQ+hqD\n2BBEJtmR3yG039RO2BS2pbn3hQLfcM1LIWPHm0x0vMY8m9+cJBxLbEEkxM2AjiqCKDEmW4raJxJr\ngggvbvy5gYcIfQatNoVCjka9yvTsLIQWKVtiOYLdyj7BoIfrDclNlUhJh06jjrB81o4fQ3otGO0Q\nDvfJTmcY+h526JNzFHnb52Bvk5JyaMr7YEmOL85TdEKuf/Aznnz8HAvzZfrdJpXDBlPFPGdOnuOV\nV7/PdGmGg+3buP0R9VoFwhFu0CHnWDgpQadnc9AMubC8TnG6zLB9iBV0cXI2gZthamYBlI/lpLl+\n9RLThRx729uUj51ASRKLrhkPRBq7brhQaYitSAoCE3MJQ4FtkF9KKCwi6ojx/bRr0fQnOkAcEuoi\nH7YYF+DRssIoHDK2yiIBFlX+ihQaW0urWBipifGtx3OoFI4IsMM+d69fod1q8ReXLvNbv/nvcOvW\nHUa9KqdXMnx4+VVk0CSXcdi8/wDbTlHM58hPFclkM3QHAWE4QpFidn6e/FSBeqPJYDAily/heiGz\nswtIO+THr/wFZ86cpfLAZ6Vcp9vaJZsJuXv3Gp4nCQOLDzstEGlEKJiZLpNJS6byUwThkFa7je+G\neIHC9TxarTZBkGZh6Ri3714jDNIsbBwnHfRRloMXGk4pMf5rG/EgpOaOiuZyjIefUBrRgVZFvFCD\nljGBGF+nEuicCLIabdLE7VDGLUuSWXbsbQjRtSCEMjJKBrGMEknQ/SdsnwlBjxBkQxdXpg380MOy\nbQJPp64HKnLXCJ06LgCUxguro7caD9wkYicW/JjvmiQKQl+jE+KlVmtXusSgvqc2tQyfDWDJaPWV\nxnyKOsZo61ICIcrDFBlIQrESk1+KmDo1skQsDF1pIh6gVBAXNJAEusRddDeljDvAZEgKAfgTmoZU\nBs5lRInmfYlWhrFyEPsjk8qF0pMhYmhEhBCGulhC4IPy8EdDHAKCbpeB7zIzO09AwOqJE7jdDoE3\nIBhmkSmPhXKBw9s3sMM+u/v77N+/iaNGuJ6P6w1p7LWZWVggHPUY9kes5m16wyrFbJ7uQRu/fcBS\nKUW/VaPb7XL23EUCtYnvu2zt7fPUk1+i3ajj9gdkHcn62jF67Sa+36Pa7dLqDRgEBf7hf/RfsLm/\njyMFIhwxCnxmlpY4cXENFXhs3r3F6rElQt+n1u5y9+49vri6bgKlGhanOU8UwnI0C6FpMivSzjFw\nWzM+gnGTImTkIAu0qxFbA7KU5vCJFRqDHrMQcaa3JcbV0OyIvloY8jmhFXcpdVxIGbptABFG+RsG\nIijGFcEspTVLS4A37HO49QDlwueefJrnvv6r4Lo8dGaJoL/PweZ7FDIB16/eJ5tJcWLttB79QtLp\nD0hnHfoDF0EK31OcPn2K0aBN4FvksiUy6TyD0ZCtrR2wcpw6UeLcRoZ+8yqW5THs1WkMPEauxPc0\n62do3BVT+RwrKwvUqtv6dwmLB/f3GLmKQr5EEAgO6222d9t8PjdHGNjkchn2t27T3r7G9PGTjOwp\nPCLMmbaMlXGH6bYckwtG8yiUIrYCLCHjBQI1pkPGLNBCmjwTwLPEeI5KK/ZUaPewgdZG8u0IXlIp\nhcTGEpoiOYxI0aSeuL+IUv8ZCcaONRStDWuKzjgxxQQ4pWVMXxPskIYHQqe/a/x3xBCRLJ+stXHz\nPSLukiTvtIp3GU0S+dH3i9Lykyt9fM6Y8Mixr01axO8e74ZfRdP2BoTK1z46c14L/Sijjvh6K5m5\npyBaVI5m4iXfN9q0O32MmY94X4QVIULG7zW5G6Fhje8fB6eDkNB3sVRIo1ZhKp1mKpshl87QHfSR\ndopQSM5euMD0zAyLMyVGjRrD2j50a6zNZAj6TUJvhCMthkMX3/fJ5XI4jsNB9QDX7VPIpwncHvdu\nf4Dba7C/dRuphmRSktDzcN0hliNxvYDTZ8+BbTE1XUJK6HTa7O3ssLg0S61WodVqkc3NcPr0BYql\nWYYDH88b0GnUmZqaIl2YIrQlVjrD2Ycu8Oabb3J4uM9MucTXnn+OtFQ4KsASAYcHO4YFU4yDbGZL\nZkBH2ddg+ICiTOPINYnJ1lZjXLQ0ikLUJ8IE8lN2FMwN42Di2F0TTIxjPWZEvEuYGH9CqHg8x8yI\nJjM37Tjk0hbZfAFp57DtAoVcilsfvMn2vQ/wR2163TaO41CrNahUKpqy2EnHHOy2lSGXLVGYmkeI\nDNV6h06ny8F+BdtOUa83cEc+x5ePsbqyQuCNqDeq7O/u4dh5hEgz6If0Bj4Ptg5odYbMzS+SnyrT\nbDcYjXrcuHGdmzfu0+7biNQMtS74doHS9BJOpszId8jl59jebXNy4wTv/eSHvP+T17CD4UQW68Qc\nNpvNuO0in3oE7Ehef/T7OnQ2pkeekG9ynOWu5YWh+zZFqg2rdbzHHENq7G6LNstk3n/a7TMj6C0Z\nccnoDDTH0f6tkEmekGhwRsatJcYpwknOEqG0RjOOgJtNCpPwMsZBx8GRIwHeo7vufBU3WrQwRMRT\nmqYhYa6Z94zeX5KYdNEgCHUWZczNIh1s2zYJZGEsCMYuqvFPiZBDljVerCxrcjCCWRysJFeHRg6E\noY/jjFOwx4ufMovMmH8lQiHoDF19/zDwyGXSzBQLeIMeu1v3yKVTZLNZPAG1bpdbDx7wV9/7Dr1W\ng83bH1K7f5OM3+LaOz9iJitYXZzDEpLKYYO1E6eYm18gm83iui7FqQIZB7qNA3rNHXbvXubYTJrZ\ngsVo0ERammysVm8yu3yM7UqDerdPeXGZ8vwCtUad0vQUm5sPCIRgpByWVk/T7A+4/eAOp06f4L2f\nvQ1hwOb2Nl3Xw5c6s9VTIU9/4SmmC1mKWQcx7DKqHdCvHRIO+gy7XQgDpKVdOY6Q2JaFY9sxp1GU\nuSzQkEzbUGekbMdg2IN4nFhS+8aFAtcU9dAp+mPhIZQu+2gpTcsc7RGfzdF+TBL4RdwqE2MiMc5j\nLDgKx5JYluD2jasc3LuF6B+SGm5RznYpOF0alW2G3Q6e66JCQXl2nmw+T7PZpNfpozzBVL7MaAhz\n88vce7CPEBkKpXmyUzN0+h5eYPHI41/ED2F2rszm5l3a7TbtzpDtnX3a3RGeL+kPPEJlI6TDAWTl\nQAAAIABJREFUYOiRzeXYrxwibMHi0gq9fgontcR+xeeRJ18ilBmOnzzNU194FmSWzNQsp88/TIhi\n0N/j7s13kb5n+KR0bYiUZRPZy1LaSGEjlMQRjq4bEIqYaiWCocYLqBHcVpSNDdjCjl2dUo1J1aRR\nqpQKYtI7LAjQ/0eCP1B+/FlaxDJMSIVjWYYq4ZOJBpPbZ8J1Ewf9TIDCkpo/2kq4PZJJRUCM9ZX2\nuJ5qchBbItA1Lg2GVakxR7xlWbh+VF5PGfw3cfBy/KwkTzem8lA0IYAwwcMOKJPpFtW1jdA/yfce\n88OomE8fEi4nqRBoP6FuE7MgRNmYkUtAjImTpAVeoN/D93W5uOjeSa1F/x4+8szk50gAmIORAxFh\nhEQQBNqV49ikUikuvfcOtiV45OIF/MCl12mRnV/AU4pyeZa0CnjozClsJyBrh5w6vsj9q/fJOCH+\nqE2n06ZQLDF0R4yGIdncFPu1AyrVKtLO8fabb3Hm3HmWy3muvvs6s9ljNJtdWgMPJ53n3uZ9yovL\nLK2f4vadexw2NhGOwxefegr14RUCFeIrhZObQdqCzPQyh/evkjo4ICUVTz76ODsHB4ycPLlMFgG4\nwxHt1iGZYESruo836vPh5UssrJ5k7tgyluVwbHUVYRmhYMaezpgMQWonmbAE0tLJ9cpQT9tSC24p\npYkW6XFiGzdkhOoJUUgT3FOmk4Mw0Au/ZWFJpZfeUOclJPlQIox/MlAffY7OC6HrIstIYKkoQVDg\nqoDC3Bz7e3dw/APy7g2GvV0cx8MbdKkdVsgXZ5ktz7G0nEWi6HRbOm/DG1J5cJv1jbS2HgLF55/4\nHFeuvkeoJCKVIpQOK6unyRcXafdC0ukp5ueXCfw+w6HLpfc/4OSpswxHPtMzi7TaW4y8EbX6IaNR\ng+LUNJ1WhX4vxA0zrJ08z6mHZxl4IfuVNmtrx+i5Q+rNFtK2WVheotXZx6HP8spxLNUHp4SPIFBG\n847noskKtwSB8rEtSSCNBaYUtnH3RE6vEAwfFqBC7d5RWhkMhSBlOfFCHXyMYE4mtMWWn2XFQtyP\nqtuZcWALqWsbjKfmp9o+8VIhxKoQ4odCiA+FEB8IIf4Tc3xGCPE9IcQt87dsjgshxL8QQtwWQlwW\nQjzxya8hENLRlMMiQIZ6AtjSwk4EOZNMfHbK0TCmALP6Gq0KEzgRCe0+gZON+EYcx9FCWUTukQgC\nJeNnRRqVJSLCIa2Zx3wUR8w2xxQ5H1cDGq/4MSTPWCSR6SVta+IeGmpnyvfJcdQ+JheTNpadAmmb\nBco2flsBYUjaceL3jjDwKtDPjSwSS9hYhs0zhkseEQohGgEUaR7R/0IIUtLCsiSpXJ5nX3iBYrFI\nu9tif3+XXqdNSuk8AcdJEfgWm7fvUrl/h1sfvsPW1jX6gxYEIbXqIesnVrh27SpOOsvI9am12gxG\nIU88+Tls2yZv27SbLVYWjvPUI0+QzxdZmD/G+toGxXwR/ABb2FSrXV76+q+ztHqKt3/2Drdu3eWh\nRz/HSAlS2Wlml07w5Ze+zr39Bk8+82UG3R5bD3YJFTz95eeYmZkj9APSCJTnxuiU/sil2WwSKsXs\n4jzKskllsmTSOY3KsmU8xqQEyx5biWA0cSyksJFKaj95Ymxod5guRqPHDjiWIGVHOphEWDaguYxU\n5CJUNiIUONLBshxNEW3GviM0JluZBAyBhS6coY9H8yTrpEhbti4eIi2kBSkkHoLQTvPiiy8ylXVp\n16/Ta++xv/2A69ev0+oN8AKfgTti6LoMXY8wgFF/hAwU2VSaB/duUmtsEQYDdrfuMRq4NDsjclML\n7NdaBLbFYaXJyy//Es1m03AT2bSbHseOnWZrt0G11eORhx9jaWmJ9ZUl8inBTLmMN/SoVTvMLa7x\n1Bdf4HOff57TF59g7fRFzj/6JZpdRWcY8mB/ByeTYtjrMepWyWUUw3aDm5feIhw2dbzCUqjAM32h\n56gUY5dwGPpmIY1w9tF8xMinKN4RddfYRZMy58fWulaiHMvCMVaBhFhDt6SDQFthup9lfF3SjRR5\nJaxPr9B/KteND/xTpdQF4Gng94QQF4B/BnxfKXUG+L75H+CXgTNm/8fA//JJDzDhjtjUiXzbkV89\nTm4KfWxL6MRRI8A0j/hYyx//MAshrPg/oSBlR0T/mnzLV5E2LjXhP8S7SPi0dX3QyfTmaEtqUkLo\nxK6xpqyPjSe90QVCzRkSVQ/SiSsWUR3QpE8wMr0/suozTsyJhEZswpvBFVkESc1ds39OmvlxmyUy\nPCOtc7wAJdwCwmifAbiuz+raOsvH1njmmS8zGo1A6LTu0B+xvrrI1196jmLeZuPEGnfuPMBybHYr\nOwxHPpVKg+XFZbyhR284IJ1Oo1RIpbqHEkM21tcZdHuM3B6VSoVUOkut3uCw0qDXG9Fq9yjPLnDs\n+Dr9gctTX/wyX/+lv82tG7c5s34aGQhc1+Xhi4+xt1PnzNkLnDh5jue+8jInT53i5p3bdLpD1tdP\naoK1UZdO84DydI6VlWM89aVnyE6XOfe5J8jPzjE1Ow+OFU9oW40VCd14ElvYONLBFrY2400bRn05\nTquykNLW/D7CwrKc+HhkpSbstiOzRmJZDlLa+n5Hn5FgGT1q7Ubj9ug5mxQgyaZypJ0Up04ukstJ\nRm4PaWmLt1wuM1OeAyS97pDy9BKttsu16/ewnByF0jTpTI75+UUWF5expc5lUUjWTp4lV5rlzPnH\nuHL1Qw73b4J7SC7lI8UIhUeukGc48LCtLOXiHFtbWzgpSbV6iJJgWdBsNimXZ2lUa4yGfV794Q+p\nVerYVo4Xv/K3mTt2gceeeoELFx+l3aySsnymiyUI4Mp77/LGj19jfiqPjbFwLSeOyUkpYyqDyPU1\ndo0lFC5hJeaoMDUNdGA2qdwl96RnIprL0bW2beMHbuy6tqWMmXOja7UCpmLl8P9X141Sag/YM587\nQohrwArwK8CL5rL/A3gF+C/N8X+p9Fu8JYSYFkIsm/v83E1nHBrTMumTFpOa8/i4IfMaY0Y+ek8E\nSliIyEhW0kTqDXRQRoWsx7QHE8800XhUxFBnTbyDFTnlIkCN0P5vKXRCkSSqeWsT4ZzD0J+wTIQy\nmHUFpvqyTpket/84lpD4tUKYFHv0+0UmvmXZBKGPFFKnusvxIIxcWJE7SxN7iTglPm5b0xZBEMRF\nUTCvpwhMgRSHdBp67TppJLev32ZpYQZ30KZRPWS6PMNoOOTtK+9Rtj0e3LnEQ+fPEpSL3L/1AcqW\nVJsNDqoVlpaWUIFHp9tjfmkF39/HSUGukKJyuEMuleHah1eoHx7gZFL4IkW+PM3G4gnE3QcEoURa\nDvV6k0xKYds+ld27tCr3STkeQklef/MNBmGRx55+CV/ClWvvc/H0CdxQ4foBI6+rXV/dFunAp767\ny25vwMLqKuWFZY6dPMVAWfimxgBYcWDVj4qDf4xA/Tj32MQEFQLLHlNYCHS/ZFJpTRQnNMzWVybD\n1QTkDdcBKqLn+NjnfPzzj1pv0TtZEoQMQUq8dp3LP/s+zeombq+N7/ocP7bC0B3RbvWx7Cxt6bF/\nUGVl7RSWnSUMR7hemvv391g/WSKrMqQch73dB0zPLHLhoS8ytbDC4WGdp3qCjKjxs7f/LbXKAaPR\niP5AkUoXSaUyLC4do9o4YO/gEJRLtpCn2+3x4MEDUpZDaaqIIsVjD59FOA8g6NPv2bCwxJef/1u0\nmrs89eTTNA7mee+nP2Z+egbbsvm7v/ZrvPrWh/x3/81/zX/4z/45wk7jh76uJZzom49zb0rjytTI\nG5sgNIuxyaxWodQUBaFBCSXaOYqxSJOkppSK5U/0BPvIO0ghNIurSrh8pRWz6/4iXDe/UDBWCLEO\nfA54G1hMCO99YNF8XgG2El/bNseO3usfCyF+JoT4WafdmGjQUIlEqGlS65h8+Y8GTGPNJtZoJ10T\nwARHRMx9brTgWBuOZHjSESZEvEe1R5OBkjFSRWnN3VQtivrjqPtpIgeACEVkaXcKRwNlRnCo8b3G\nafM6OGdLoQOEBq2j6R3CGLFkW7qUoCDUZGJMCqdk+x0NTkeYeol2T+nMQEUhl6FRq3Hz6nu0qvtY\n3pCw3yWfsslJwSPnT7N170PswOMnr7/G2uoyjz32GEEgGAXQ6I7wRYq+r6g2OwSh5OLDj+EHgpSd\nZthvIcSITquJnU7hZLNkCtNgZVheXUfaaWr1CpaE6XKBXrdFygmZyivefvOvyWQkdirDb/z273Li\n5AZpR9I43ObWB+/wxo9fo9Fq0ndd0rbDsNvC8l0cf8TW7Wvgjcg6FjOlomY0RJf98wZD7WuPhbli\nDMj7yDifiJMcbefxsXEQ3LZ00p5laCUsAWnLji1ZTQan9zHKZrzF8SiO7Am3YTTGk+AGzevp4nsD\nbDHE67cZdjtM5adYWFhiOHQJfUU6nSYMQ0qlEgeVGvuHLTyVIpWd5vqtTY6tbNBu9RFCs0suLs1g\n2XD79i1+9vZVcukiK8fW2N7d5frNmyjLQdpZRsOQrc09LMvi+OoxJIJmo4fvKdLpEsO+ot/zCIKA\nqakS8/Oz/MWf/yk7m9fZ275Gu3qXct6nfnife7cucfP6JX72kzdJO5K9/V3q9Tq3b9+k1aiSckRM\nEmdbMqYciOcUfGTXEMooA90wfsbCRC/CgtCU4xzP/agyWNRXMUKKsbcioliQQiXAJyKuv5HcYpfr\nL+C6+dTBWCFEAfgT4D9VSrWTg1QppcTR0fYJm1Lq94HfB1g7fV6LPINVFeqoFvLRWxtShL/hfTVV\nrE7zDwnEeGAbBCegEEq7dYIjWa96JbaO3POIJpSAWuoLdLELMJmSlsl8DE303Lw5jIMwOhwsjHUg\ndIKccY/4qHHqu9Hyo9RrHdwbv9dRDSRKww5RutanSCR2ILRFxMcvoDCmT5CYUnWJxU8qiS9c0rYk\npSTHFmfh/EkONm8zO79AWvncvnIVFfosTqdJOVDdq/Hk5x/l2geXqR5WWJxbZmnhBIf1OqPAwSck\nOwW7e4dkcidYPb5Ou1Vn2G/T6NQYDgKy2SzDkcdhvcXaxnnu3L9POpen3e4hrZB6bR/fG2ETsjhX\nopC1abVaWNkSQ8/mxPpp/uLP/x/u3b7CuZPrrG2sk5suUiqVmCnPIfw+t69e5tjyHIPmAWK6RG1/\nj+LsHCIICF1fF/LwQAR+HCjTliMxrUY0Tsb5E+P+/jhtWnco8XXa6hIm8Urj7y0DuxXSEJdZ0TiN\nlItxyU19H4Ew+cyx2y9RVvPjzH4hNH9S2k6TSqcoZHN4hRK1yiGlUonRsM/U1BQ7u1sIO8vq8Q2c\n2ohmx+XkybPsbt5h5PnUGlWef+HLqHDErTtXsZ2AMJB0OgdsbR1goVhaKXLy9Ck+uFKh2fEY9od0\nOiOKhSJhGLL14C4vvfQib779NrMzeaq1DsMRzM0uI8WIXC7H1s42KysrtHp9drc/wB0t8Zf/Zhvf\n90ml4WD3Lrbw6HRdpovz9PoeBwcHWLbg7/+Df2BcLZpXJoj74qOafPR/RPgWk5wl5p+2+k2ANupf\nc04mExeViovlRGyU+qkJeRq7osdJbZHmH42xX8RtA59SoxdCOGgh/6+UUn9qDh8IIZbN+WXg0Bzf\nAVYTXz9ujv1N99eMbmalkma1s0zSh4PEQcbY13iFmyAQOtI5Cay9VCBFYEjIAmxLjfHD6KLU0Woa\nPVsKhSVDBD6WCOJJ81FN7KMDQqlkZ0WCebJj4niC0bh1nUhBaGlNXAiFM3Hr0CxuwUTsQhxdbBhb\nFFLouqShtHRwWtqaHVRaJkdhrFVEuybOtBhX/ApxRKiDTwLSIgCvx7DVxBv1CIMhtvJISUG7sosa\n1Hnj+9/GDoc8/PBFyotLBKHF4vIC1WqVWq2CEIpWr8/syhozyxsEosDJM4/z6OeeoTfyaXX6FGeW\nsJ0Cqew0S4urhGHI2upJMqkspXwOd9Diwb0PUfS5/MElcrksq8dWmSuVOdw7oNkZUpw9Qb58nPdv\n3afnedy4+QGn1hb4tb/1VcJggCDk5JmL5EvTdHttlD/EUQO6tR1sNaBcTHPryrtcfucnjJoVDu7e\npFer4Q17RDVuQ5MDEY0XaYUI5WGJAFuGSHwkfnxM4scW1sQuJ+mqfRGArTH5QkBgBZACZY8FOQZ9\npb2Rvn6+CEyiVhgn1I3jOWOjFCFMZqUhzrI0BXYoQAoXy1a0GnUOqi16vSHD/oi9vQP29vboNuts\nHF9ifrrAU08+ysn1U2TzRc5euMh0ucTTX3qEXmebdnubmXIBicWg08Qd7OKPrvPBe9+lXb1P4HsE\nXopWx6fdV2SnSgyGLscWp7HpsLt9nfMPnSI/vcA3vvX3aHYG/MZv/n0WZpepN1qcPXuWw2oVIQTl\n6QWazSb3795kZ28bx0njepJmG3rDDFgZlBAUZmaoNrrcebCts83jguxRSUgMOZyMSwVqQjgfS4Q4\nFlhCk51FuxDa5aWRkCEChW3JyfZGaRJDpSaOJ++hhIVKKJdSjkkF7agPZYiQ2lIPE7lCn7R9okYv\ntAT5A+CaUup/Spz6M+AfAv+D+ftvEsf/YyHE/wV8EWh9kn8ezOoWlVQyGaRj/5bxUZlGUnFjjd0M\nltDaTeRfVgkNCYhJivRnFbP+EWvC49ORNmahE4pU1FlqrJElMfhH2ivOVI386EopZKIUYPQMIM5W\njTVxDOFYvBDEbzXxe/R5hSTQVouUsQahvxpdL44ysE4sDJEWmHwvQYgwJmqEA48qtYSeR6O6T2mm\nRDYlqT/YwW3WcIIRtnKZmXJ45qkLZAoZ3F7btJ9DpzdienaG3f19HMfBdT3863coTM/hpLP0BwNS\noSCdS9NstzixsU6/7zMYujTbHd6/dIkzZy/gjfogFZ7fZ6o0Rbtd5+FzG6SsAIHLwmyBu/fucHbj\nFLsHTZ792jeR02dptVrs7Nzisa8+y5s/ehWARrNDZiGFlDa1gz0Kls/e9h0W5mcoZFOgPOaWZ5lf\nWMHttbl98xpPPvcCxfIc2A4oPTYtxlnJAjQ65oivd6L9VTjhglNKIWwZQ2KV0Eyr4+9ahEQFw/Xi\nYhlUWew+Ck2hHBPwCY3mGEN9AV1DNenKDGJhr0notDVdu3+T/t77uKMh+/uH5NOSB1ubpBwLIRTP\nPPM0/X6fXrdKo3OAk1/Bd23ubl3j3NnjNGo7COWSyaSoVCu0mz0cO81U2mVxxqVUnqHduAfk+fzT\nL1JvdpkpzfDTN37AwmIZIX0sa0TgdqjuD7m9VWWmvEQ2leXyu+9xcHCAEiEDd0AQKPYO9lhYPMGT\nn3+eN996l7/3W7/DD/76Ozzy6IscP36cymGdy1fe4aGLT9BotvjP/vPfZX7lDLvtJoWZBU0R7QtC\n5ceQ62hL5iSNodYhMiE6I6oVLX9MuQkBYiIWA4FlrjWLimUQc0mlbzxHieNqyedHmr+YfM1P3D6N\n6+ZZ4HeBK0KI982x/wot4P9YCPGPgAfAb5pz3wa+CdwG+sC/94lPUIpQ+YSBhUWILccJTZYlUT5o\nvPu44n0UCNWFACS+YWSMTaGQ+LyeEEf89Ii4gyBaYcdVmfQTA53phyJQofFTC6MREX8PjiBWlEAH\nX6OqSxDl60bc5ZHPTzK5WCjlI4ROWInQL8nzWm9QpCwLRWCoZfXbRvjpcdvogaH5z41QN1TQ+t1F\nvIiNfz94oaEwU2NeF195WFJgS4+iDdMpm93tB0znHHZ2Wsigz1RGsXP3Ko3OkKnZ4yx1T1NeWiWd\nK/C5R87z2is/5PyFR3GHI/LZAqdPn+bqtRt0W016Q4/bt+9zeuM4lYMd3n/nLbL5IqfXzvPmW69z\n9vxDXL58mVOnNuj2BwRCsnH+YdY3TvLDH3yfv/yT/52VtTO0Goe8/LVvUMzn+O53v8sf/dEfY+dn\n2dy6x5kTK9y5dYtqtcrUzBIXHnuCXiDZvP8A4Y64u3ub4aCNUFOMBj5z84tYBZ8793cozi1w7Pgq\nmXTOBLXH8ZMk1xBSaArgI8H9qCqRZVu4YSQw0JQf0eRNVK6y0PkU8YJBxFNqilIQxrEY0FacFQpT\nsk4v8BpiGSKiikfGjSmQhmYhUQMVsNG1CDp7N3Ab24TugKW5Mu1WnYX5MnPzZbLZNK43pNvtkspm\n8DyX6s51XF9x7vxJht0WBwdbLM4v0O2NqNXbFHJTZLNpDg4OyGQdqoc7CHsKIWd48fHn8O7eYeSP\nCC1Btlig02uzsbpBq9XiweY9+m2XO9evkLND9jZvc3JjmZn5Od69fJlOx2M08tjf2ede+QH5TJFC\npszLX/llfvjKX7FfadNs9fnaN3+XuYVZDvd2uH7jLrt7FWrtPp9//hsIW2LZWaQyvFUJWRErQKEd\nKzwRO2w8x8Q4jyLqbzMjSW6OYZ8M0XPRx9XfTRZJspIBYUDZE8+xRECgRFxI5dNunwZ182MSeuWR\n7asfc70Cfu8XeAejpaAdScFk42nN2ZpoxCh9Wz8Qk8LNpOBOlPlLdkKc/amMtm1w5FEkPHkNmHqp\nwhCERYVBJn8vSil838e2UybxRaE+pheSgyeGXIpwonVtNY7ER/7+qFCx/rnjRUi3DZhlIw78RKUE\niZ4jQ6SwiKvrqHFhkphe1/xOkWwjIWIYqyMlTuDT2t/j0ttv8NDDD9Hvt5C2YraQYu/eJmsri7z7\n/pukUgXcbhWp1tjb3WLp2DHOXXyU7/zVdzmoNHj+mS+xeX8LC4vjx1ZZXVnjnfd+RqlQ4vTGSZQ3\npN/tUSzm6fZ7fP4LT/Nvv/3nnFxb4dFHH+fK1Q/xLZud3QqDThtv2MZz+7QqGUrlRUZDmF07zvzC\nEtlRSGDZLC6UadYb7O9tkUnnWFldBzuNPxoghGJnZwfLGzE/P0/EXVKvN9mpdlhcPkaqNENxukyn\nNySfyeNYk4pDrNUfQbpEWxIiKyfGacJvz+TY15juiJpWu9vCMETJyQBdrLVLoziYx6tQZ5hH9wyF\ngFBnoatAYNvEgkcKga0shAxJW0MC4SKUx8jtc/6h0+TTDkOvT6/XQQhBtpCn2eowGCrSTpaXXvgy\nly69hwqH5PN5Nrd2WFg8RsrJYqcc2t0OB4d1Tp5aw22PqOwdEKg2r776KlgBhUKGXCGLGyoOKg1q\nlSbz87MoIXDSNjdvX+fi2VMcXy5zWNuj1a6wvn6CGzc2kT0fKQRXLr2P7RRpN1uUylk2t+6ztLLB\niY0NUrk87f6I3//D/5UXvvQsb/z4u6ydPIUdvEgmm6Xr+3E9i8giO+oONZ/ieZ8Mqk8okUkfe8Ky\ni+5nMXbVRfeCCNUDUozl3Vgpi2SIjt8kYwSfZvvMUCDoxIBJczTaAnyETVzcwxKCmFdGO/JxTApy\nrP0aH33kz7ZsMXnMAFmEJcwiEcT+8hhJI3RCgyMkdphIbDiy2xJStkSoYCJgE7FHHkVZJDs/mcDi\niDFWXQiD3ze/L/qtjrRw5BhaJYQVY/+j94u+ExVjgaSLZuzfjeF5pg2QmiVRiJCUDLFN8kjgNbH6\nVcLWLt39+1w4vYTo1xgcHjCfz3PvxnVCv0c2ZTGVcpBqSKu+i9ve59zKMm6rT6fZ4tjSCoHrcenS\nJbJZhx+99grtepV6tcJMaYoTJ09gWRZnTp0lk7Xo9ZvU63XsVIFz585x5+5NAuUy9Ia02w2qB/dZ\nnCtRyuTxBkMcKVlZXOLRhx/hBz/4IWurpzm1cY6vvfhLVHZ2ePutH7O+foLpmUU2Ns5z785dWq0W\nuVyG1bUlZmaK9Ho9AgXZqSL1bg/h5MhMlSnOzJMtTDM9M0c+kwXlQeiZYi/Gbx4jp8axpGTfR+Mh\nKUiinI3omrgPY9efHpex8I7uzTjfI7peSoktdfEKR2imSyUVgQy1z58wHg/CBo3jl/GcUni4rW2a\ntX227j9gc+sBpzbWkVLR6evMV9/3qdZaHFZbuB54bki5VOLBvVsU8g7tdhMVWoShoNcb4CvIFUoI\nK0V/6GKni2SnpplfOM5Xf+lbnDl7iuJUlkG/w8bGBraTY2puGbIF7u3X6A48pvIFBv0+rX6Hnufi\npNM0G10q+1Xu3d3CdT0arTqNRoOZcpbN+5f58//3X+N7fdxek87BPvl0iqW5Mv/0n/wTirkUCzMz\nPHJqnde/80fcf+cNujsfMqxtY436pNAxqQihdHT+wmSOSYxgSsCvP24hiLZo/idrz+rzulfjsWHG\ngoQEdco4L+cX2T4TFAjJLfqBganjeRSp8HHJUdEWsc4JKYmKPScbHoh968IgZJKNlnx28lgSzaDd\nSUcTFqIAmfzI837ean/UygiPvEvy3aNNu14SCS4Gf528bkILSQwwXaggmLgmKlYRub+0vaCwsUH4\nIEP8Xpe961fIqw5q1MNtNqi2K2TSBfK5Am6/giOGjHotfvr2TdxRn/mFRVoHu7zz1qtMl2eZyvhc\nfe+nyNAnm0lz7cpl6oeLBMMh17ttTp08x7Dfxe20yIdDep0W5XwOJQQ9XGQKNjY2mMoJPvzgKqNB\nh3Q+T63e5u6dG+QLObL5HHs7W7huQKfTIp0NOajcx/d9ao1N2q09VpbnOKwe0O35dF5/nVRhlouP\nPEq/26JROcDyXV564QV+9Nrr3Nu7y289+QJb1Q6luUVSpSKWsPCGI0adDlZKJyuN3D6pdFaPidgq\nHJMO66aeFBA2oIQaC/gE+mLcd1EcaDLmFPfpEY3z521JP2/MuxTFudBWnzClKm0CDna32dvZZvP+\nfQiH+GGACj0q1Rr7u3scW1klCGDQ8+j0+oQBSDuD7YA77CGQNBs9hMpSq3bJ5qeoVvs4mSIIm153\nRKvZp9MTXLt1j1/51b9Lp9dmc2uLwTDgN3/j3+Wd96/x3DPPUasf8Id/8Ptkcxb5jovnK6x0jpvX\nb9Bud2nUu9hOhpEbYkmHbFZiyYD/81/+AeXZaVrdDn/n7/wq16/d4oOf/ojDaoWF+RKpt8k1AAAg\nAElEQVRv//jHLM5O89PXv48QFsvlAofbN0lNlzlx+mEKM8dBOKSyU/hCaIBIYm5H7QrEYAiNi1dx\nXyflVVIeRLIjUGpCyz4612OffzR/EwvGx8UGP2n7zAh6IbT/0bI0P7dtT/rBor+xkEpo/cmAhm1Z\npoC3NfkdOdmQScH4UVdRwt+eWGyOvu/4+x9PMHT0PsnnHT0ft8GRlT+5sEVuK1ATUL6jxZ+PCv7J\n+6vYpDzKvxMfCw1dMgpv2GYqFbJz8w4zxTTesEXKDvC6B9QP77J9x2NhbhY7C5CjVh1RrVY1T1Ho\n8cf/+g/ZOHWOwcBFWg6FQpFACYozs2zfv0Npaprd7U1OnV5l2G5p3DwetXqVRquDb2XwBgtU9u5g\nC5f93Xs42QJ5O4PvDrh35yb5bI58foqUI5B4XLv2Hs8//zyvvvYaI7eLlc3hqhG37tylNLPMf/Dv\n/x637+1zbG2DD969TCEnyNgDdrZv09i/i5PP8tLXv85ercLKxkNsHhywXioipcBJp1B4KCFjGo0I\nPpccG5Om/6QlF7W5hhLr05FPP3JJhhzlQYpAB+Pg0FElIqmMxILGWJ1KoCuLJcc7k37eMAwYDXvY\nQuJ5PjMlnbxUOazTbHS4cPFR6vUmtWobb2QRBCHZfA7X9fFdn+npEpaUVEddUtkCjhOysrauaS2G\nPtMz64w8sFM5gs6Qw0qDu1t7fOXlb9IbdKlVW/zPv/+H/Mqv/S6HtQGLi6f4ysvfol3bpVQqc+/O\ndaq1JrZd4ML5M7z73mVsW1GtVlGhcbfVKhSKU2SzeXwVcri3zc6DO4zah9y9f5fqfJlvfO2LhMri\n7p1b5NI56pUt1s5fpNlrcfkn32NxaZXi3DrltbOITElr9yi92sYxjWjl1XFA0yWmT6wJBTUpqyKv\ng82Ydjqe9wnlNXLlSZSu5ma2QH1UFn2a7TMn6JMj76gA+3nfgzEVQdwIKunbUqgjDEBJ6+DjhH7y\nePR5cuL9/Hsl/z+q4X/c9z/udyQHys9bCD7u+R97/+i3C13AO4KS/TytQFgSEQSkhUCmUrxz6xpl\nx6bdrJF3BK1mjUG3g2Vrwq7hQCe8Db0hTi5F4EPg2lhSsXFylmplh43106ytn+bW3S2sTI5qZ8DG\n2Ydo1uoEfsDMdJnbB1u02weUSlNkyzmqnQbusIXb2mNpzsH3AqYKCxzUGgz6Vbygia+g26+wllun\n1thnqpjDliHXrl0DJEoW+Oa3fh1lZ/je919leWmFe1u7pFIpfvi9b/Otb/wSQrl8+OF7tFs9Tjz+\nGEEoqXVdysUicwtLTC9a1Httitk80vCRBCE6yzmVBiVNEE0Y5Ew0ZhPBzuT4ifqSsa6vGAME9Dhw\n4vGk+5SJMRFphlGcBSYtw3GMRRm3QsRFn3gnDRUyGr3EzqfITOXxwoDVE6eQQZt+b0Cz1eH4yjqZ\ndBEpBqBsyuUyKJtOt0G/00WpgNDX8OVuNwACOv0BMpXnyy+9zF//4DWeffYbbD64w97uAQrJ7/zO\nPyIzU+ZP/+zPqR5UyKVznDl9jrfeeI1MusTy4gLzi0X+1f/2bc6sL9PrdBFK0qjXWTu+AUFouGMk\nhdI0nW4f13WxUzky6QJBEPDd73yH2VKRa3v3eeSRh2i2Gvzo1VeYLuU5dWqDlZVV3n/vA6qvH/C5\nLzxNZnGZZqcN7m3ubm7x1Fe+ZSKkUdzL9JbpuND0jTKnlGKizyeVSG1RRwWHAiEJTY4KajKOE1vt\nGH+80f9DIf7Gufvzts+Ij14QcclE21HhdlTITX5WcUZrlPEZO53jnY/c7+ixj9yXSXKyowEa7ccL\nY9xylGE4zqw1XOIk3u/IFpEXKeN2CoLgI26p6PlaCwwTWh8xA2Iyy/Hoe0axh6MmXzKrUmBhS4sU\nIcLr0mtX8IYtprIZzp07x9beHu2BR740w8WLn2N1dZVet4kIh3Q7DYJwSBi6NBo1qrUDWs0K1cND\nCtlppvI5QjWi12/x6GMPc+bceX709k/JTpeZXVgik81z7949VteWmJ7NM/L7HBzsUixkePKx83Tb\n+/QHLdrtOtXKHqVCmqksnFpfZn1tjlxWUq3tksvYDHtdBv0Oo9GATsdjfvkkIz9Pu2cxXV4hX5zh\n3oO75As5mtUdBt0Kb/3o+wzaHb7wzLOcOv8IfU/x7PMvsry8rAN0ImS+VCTwPZQaIKXuJ8tJxdmR\nOsBvfLuGu+ioBRqNs4hq2NJdr3dU3KNJrvhxtraGEEf54oRBPPaizyrw4/PROUkUsA/j8Rrv5pgm\nB9dC6Nj6QxRnZhn0WgzdkLv3t0lnC3hByO7uHqNhQMrJ0252GA2G+L7P0BsycnsszJdJ2Y4pSOOT\ntgSHu1t8/3t/hZNKsXtQZ2+vThjYTE/PsXH2NEII+iOftROncaw0m9tb+IMOB5u3QI34yY9fJZOz\n6A17BIHi1o3bjEYDrt34kPLsDCnHolCcwnVdVBBSLpf52ssv89DDF3n2+ReYLpcZBT75qWmqtRaj\noUu9Xmd7exOEYnt7m063j6dCOu022bRDIZPCtm02TpzAsa2PyIvQzBeBFRPIxVh3FeLAxG4rhRWG\nOErXjLbiPtc5OtJkxis5WQ/DEhBITC6FmugvS2g8/6fdPiOCXpng56Qg/jg+h6T2mgxIWsrFET42\nHpZyYyE23v9mc8dWCluZSRLxscvJeyT5vEEH36ICEHr3JvajbhrbMEZOBnLGQbWI8zr6nULoNO0o\nVXusHURQqwQ6IzFAoomdTKtPFhYZ/wb99JSSWCpEeB4/eeUV6pt3yYQDatv3GFQPKafTtFodmo0e\nI1dw5+42jUaDUmGKdDbD0B0yGmmo2FQxj21LRl6fdN7mscfOIaXP/Vsf0KxtcfXKW5Rmivy3//y/\np9sPWD91AcvJMXRHbO1usbe3Q7vTYNht4bptdnduMxq2aHeq9AddBoMBOztbjNwu3qhJGPQ5f36d\n0ajDndvXaTSrzM7NkS9M88QTT2GnizQ7LsXyEs+9+HXeePM9vvnLv4I36HP+9En+8s/+hHwuxZkz\nZ8jlCrx/6SqPP/4EBD4ZfFo72+zduol0XfLZHJlMhlQqQzqX/agVeWR8Rp8jrvIk2RzwESsvOe4t\n1MQeXZMc99HnCSGUsIItoXRwVmpGzORCosdMaIr9WNgEBD446TyZbJGZhWWEtHn8iSfJF6Zx7AzV\nihaQzVaN/qCDF/RJZ2BjfYmHHzmDHwzodrs4tmRufoZisYglFZ1mhcr+Nu/99KdU6g32Dg44vnqC\nV155hdmFBYpTJZaWlqhWK2ysHmN76y6zc0X6vSZnzp7EH/WpVfbptJs0m00smWbQG3J4eKipkd0R\njggpZB3yGQcZKpYXltjbPWSqNM9DFx4Hy8b1A1w/wM5kkFaaGzfvcP/BDvl8nnK5TLfbod/vMjs7\nz87BIZvbu4DAsdPx3I8CpEnhL6VmCI3dNEkwR2KuJ4uK+IkYopBKB/IDpXN8zLgIZKRI6vtZVlQP\nI5yoi/Fpts+M6ybWmFEx/lQZcynCksdmq9Rl3MIg0K4IBaF04oaPTFwppYE96sICQRDEKcUw9u3b\ntg2+G+NnI7+bEJNCd+xX+qhfVJvC9uT/Eg39FIwhjxFJUcK0k0IiZWSiYRj/TIebbCdLRBh97d7x\nwgAlzKBLIDyUUh/x431UoER+XP27NCJDo4Y21hbZvfMB5Qtn2b17BXtpgf+PufcMkiw7z/Sec/1N\nW1lZvrraVPvpnu4xPQ6DcSAMB1wQC9Bzl6HYCIlB7SqkEP/pl6CQQhEKRUiitCuuKJEhihSXJEAs\nCRLeDAEMMIOx3TPTprq7vMmqSm+vv0c/7s2s6p4hOPtDETgRFV3ZdevmrTznfOcz7/e+7foOi0fL\nzM0ewTYE9WoXRZV0Oi00PeHVNwyDVq1GGEss20DoeS5cuEBlb4XJcpY4aFHbX8GLNXbqXTZ39vn5\nT36OrbVNtnb2+PgzF9muvMNYKUun3URTJH7QJwp6yDjENE1K41NEYQvDsokCH1vXaDRrOP0ux4/N\nceXKFVbW9rmxdJsXPnGed6/d4dj5h3C9iK9/8zv88q/+Cr4f8uqPfsT8eB498pkrFxnPZ6hsbhCi\n8cRHnsPQVZavX6Ne3UdVDHpugAwCFs6cxzAtYiHTbkmGucHk8x4W5uJolJYRo7k4vF4OIqlhsTVh\nzEhTAjK6B4cPJHDg+2o9h+f0/u+T1NC9KTrtPqdJQaCICB2fv/3rL/LsC89jZy3mZhbYXr5FbxCw\ntl6htl/Bd9oUC1mCUOfUqVOMFYrs7e0xOVXCC1wGgwFCBStjsbfbIuO6zMzMsL6+ysB30C2dE6cW\n2arUmJyYYm1thVBR6TWafPpTn8JWYyrLy+zsbpOzdCwdOt0Gp09f5iNPPcH+zhZ37ziUy2XW1rd4\n+PJlmvUqnU6HqfExTNMmlmBlM9y+8x7HZYCdy9PsdNHsPFq2iFAVes0ARZpkxwo0epKnn3yYTMZi\n+e4S3V4bRIiiwBOPXSHKzqS7PU4P6GEK9OD7g8a3xC4FQTDSKIi4N/UcyXubLQ/vTSETCLNURKJZ\nK5IUz7AnJonUI4Z9UlL8h/noPxuGXsAwIELEqIoBHBSV4OAgUFU1zYPeyxcihTjgmFCVERb6wKgn\nzU9hnORQE9bIZNMpCBhRGA8f6jDXzb00BmLE9njwJ0hIYIrpxoplytkz9PCiZPNpHAicC5G2NR/q\nvooP7W9VVZFh2g2ZYqVVTSUMw7QRS4JQRmIIMmVWVMS9RmL4TAc54rQIl/4dqpIcajoqeqlIPQww\nA4eiHnLn2ssMelUMu0CnUeH2zjqWGWAqCq7XRY0EnucRRRHF8TIrq2u0213Gxgr8+McvMzVdplTI\n03d6FKxxvP6Aj378Eb7x7R/wx5U1fvWXf4sjRyeo1tdo1Hdp1DcJ/QhTVxkbK7N06wZH54/Q7fpI\n2UFXTRRFo91q0el3yeZsctkC7U6PtY03yRZmiVGpVBsUShOYhkIUe5w+uUjgO0xP5KlurXB26hJR\n0MVQQCOm265y6dITvP3j75PN2NT39+h120zPzqBHCjkjxu13UHyfWqvNkeOLo47jUBzMX/LPvR67\nKuQo95786P1EZ4oiEeKnoykOcr3vR2cdHh8EOkhe3GscQr/Nzsq70N3EaC3x4y/f5sGHnqDfaPD4\nY09z47030XSV3Z1NXKfPlSsPAJJOp0MY9xkv5xAypN/tUW900HULzw+Ym18gjFV6rkMkE23hnC4Y\nDHoIGWLqClcef4pieYqfvPxtlpauY6samipYmD/KlRdfZH+vxle++S2eefppfN9ndW0Zy87SbHVZ\nXFwkm7Op73uMl/L0Bn3GJ+aIUSlPTLG5t8fN26v87n/131DZ22d9fZ1saZLXf/IqCyceABGjKzrZ\nTIGnf+6zVGvbvP7WDWZmipTGx3nn3Wv0+x7GxCnOTR0jJqEuODzi1NMWEmJSTqpUUlJJARNylBaN\n03WgIDlw7sK0wJtE4VrSNxNLDCVp0hzt1eHBIBPt2VgIoji6p0j7j42fCUMv0py6EAoaGnGaZx4a\nrWGoFKfrVFUF6n2Pnoh0C2S6ERQJiqISDsPh1BDqqVEc2lZNS9AlQ0DlAY87I6M7PHCGZEQjSeVU\naETKaCQCnlrQxLimXpkiUzidknSmJoLRoz/+njHUHk3AGHGC9U/zvsNnGSKShp+RSN7gUCg5RGgM\n0zVJZ6wYBXsKiEP0CyT8HKoSsnz9KrksvHPtFSKny6Dfwcxa1KpV5mZmYXIcFYdmY4fJqTGy2Sz9\nfp/x8TLrmxWefPJJ1la3GHgumZzNTmWLufmjTE0K6q0GCwtHae5vMz9pcvX6m3zz7wJUPLJ6iYmy\nTTCwKM4UqbfaVDY3yOg2vYGPUARbWxVmpueQSkB+rIgXBhw7eoJarYYidObnj9Hqx4xNjNPpuFx6\n8FH2qvscPXWKZsfD1hWOzU1iThhsr99gsmjT7fh0GlVMVWXlzk263S5L7+1QLhXRFIWsYdFvNHnr\ntR9x+amPUW07HD9+HL/fRs/kUFUTGTjsVKrMLRxNmFFlkMzPcP7T9XAgxX24KS814IltQBl1qybl\n2ShO5l4XhwTf7zPsIr33wWGerqFIue9gSNI2yXUqm6s3eedHX2fMdNnbXsFQDdZvgesqzEw+wHZl\nl8sXT1PIquQzY8SEdDsdfNdhe2uL06fO0Xcc6vU27VYPRfWZnp1ifGKSN16/xsKRE0zPTRM4IaXZ\noxw7doZr797iueeeodVpsXTzHdzQpbm/hWOaZDIlJkKFm+/d4cTxBT7y6GP89V99mWeffYL11dus\nr28jYwjDEKfTIGsbGJbJbLFArlRGVU2uXl/igUee5NLcUYxsiZMXZlg4c563X38VYVzlI8+/gDtw\nKI+V2N9r4YUq5ZmTfPSTn0bXYvqdKoZl0e72mSr5hE4b1SqM6ApGztKwMK4wUsKTMuaAg0iMkHHD\naE4RB3MkZcKEipDomo7v+yhCRyoxkjjpSI9iwMNQEmcvcHtce/NNIpHhwkNXUDMWH3b8TBj64VCF\nTNJbygEC5TCmXZGkilDqCE9+P0IliqJUKCQ1iiJJ35CKCBy+9vC431s67FkP8+FSygOcPqRcFkke\nVAh57y+RIinkwT1Gpfn73hc+oIB6388/qFg8fD0SPpfpxmbIUAkgiIfdv+mzDDlTFJlEEEmnnmR7\nfT0xzEWbfqdPMZuhND7NOzfe5OTiGaJYYlkWoeeiKAql8QKe51Aaz+P5PWbnJtja3sTOZ+g4fVwv\nolSao1icZWO1wqnT59jY2KJWbzO7MM0Lz15KaIU1yfb2PoYGx44fYdD30XUdRVGwLBNFUfHcJCSO\noggVFWfgYVoZms0mum5gCR0hVLrdDiE6Fx48ia6b3L17Fz2XI5sts3p3iVLO4O61u0yXbAb9HqHX\nRxERhp4jjAJs0+Li+QfwvQECaDRqqIbBpSuPYNg6otki8hyErqLbGeI4xPO6FIs2QnpJBDc8lId2\nWcb3cK2OMjQHE3JoXaQWH5GkEtNDOGluOsDpH16/yfyq6a8eTtMNU5Cjd2ZohCBmYXaavYkSTn2F\nk8cXcByH3Z11uj3o9ZsUCzaGKZiaLqOpSYozm7URMqZcLuN53sjbtO0sjhtSLJao1+s888zTvPLK\nT3jskUfZ360TBKnEpR/RbrSxLB0ljli9c4PLly5y7dq7OE6dZ55boLVfQcSCMAy5eOECx44s8Euf\n/zzf++4P2dqu8NTjj6HKiM31ZQZ+QBBqNFouY8Usp84+zNZOC82eIFZ0JBpB5DF/bBE3gDfffA9D\n17kdrfDUUx9la3efbD6HZo1RLNjMTc/x1qvX0LSQze0dosxtFhbPUCqV74ngh+7YqNqlCEjz9CNA\nhXpY/jRltRWH4NDDwz9KayUiSlE7JASMIkYJe9y6+jKVrTWmSyXatTrjEwtIr4mWm32fvfiHxs9E\nMVZAqqWZhD0jxrhYjsQWhqgRESfGO45joig6ZNiT1IauqCknzcECH3rA92Nb7ymAkTDWDYV8k5GE\nZ0m3YnLwJHJgQ3kwyWFemdHfk95TSwuviWqNHIVwo6LOoQLP/c+T5FjVIW6CEXf1IcZOoci0YJz+\nDUrydw9ZPodjJH845LtWku9Fym4ZohBJBSuT5dTFSwgrw/yp89zc2seePEpklLDzUxTGJ1lZXWbg\n9BM0gkzYEmvVCp7fx3H7hHHM9s4uCJ3l1S1abR9FzeK4ETdv3iGXy9Bo7KBEfQpWRDkfMFESTJQy\nyDhgeW2VzZ1Nur0edi6P5/vUqg00YTA5OY3nhQwGHrVai1arg6HbSW+cTGQlVUWnXC5z+fJlXN/j\n/LmzvP3qKyxMjWHjId0mpaygtreC9Fso0kEhIAhdysU85WKOTqtK6PXxfZ8Iwf5+g1s3lxCAqcJX\n/+Yv+JM/+rfoMiTyHDK6yfbqMrub68SBN5rroeh7EiGm3PKpEyJgxH2u3jf/I/UnmRbUlUPoKCHf\nt2YO/3tYFUlV1QOsv6KgawqqoqAqAl1TGSuNY2byGJk8EtjdqVDd3+f0yRMUsxlOnZghDnqUy1km\nJsfodbt0Oj1U1cQ0LEzTxDAsJspTBGFMGElcx6darfPGG29g2ya7lS2KxTyNWo1mq447aPKD7/0t\nhuixu3kLv9/m+rW36XfaDAYDNtdXKZcm0FWNB8+eZzxX4NiReVaW7vDpn3+RKAipVqtcv3kLOz9G\neXoOL4zY3q2SKZZ5+PGP8LEXP8Pzn3oRzdJQNEm328Qu5PjsL38eoetcefJJoljwpS9/ham5BUoT\nMwSKRq3dY2O3yvFT5zh97jKPP/UsFx+8xFgh9779fbjrfWhbhtDW4dyoI1TbveiY0c9TpM4wcyFJ\n4JNSSbSFVSVk+87b7K38BK+2RHfzGrJ1h/7WVUxnD81pfGgb+zNh6CFFsCDTAkSErkoOKF5TSKE4\nMN7DD/pwd+jhvP2wGDXkiRkaxffRHAyNJffWBA4q5UO0QrLJRmgG7jPI4kCabTgkEcgIZMAI/XIf\nLbCAJP1CWrwbPuPhotqhL0XGKDIeoTGG0KzhMyYIgFTMQCZPcbiWwQe8v6IkFNDFwhhjpXGOP3CJ\nZiD5xOd/kyef/ww//9l/xnd/8CoraxtIoN1t0e506fYHdHsDohgqu236/QAZa+RzE7zw/CeZnjpC\neXyaRx69zAMXTmLZglh6BP6AH/zwZap729T261x76z0cz8fzQmIp6DsuqibpdLuMl8c4sjCHaers\n7tdpdXq0OgMQGt2+y87uHrv7ewhF8t71a8Shx0SpwGs//j6dZpXAG7B4fA41HlDfuUt1d4NqbZdI\nQhhL/BAcP2BnZ4det01ldwPLEJimRhh7tLsdjp84wfzCPNffvcr8TJn52QlKOYuVu9cpFUxMTXD+\n1HF69X2k00dEAboKMvBSOF1iYBUhEudF05LUXjLBaRRwIHiRXAuamhhlIWOQPoJgyIbxPoGM0eE9\ndAhksvZkHB4c7pID6l1iIkXhFz77T2n3PG7dXSOXH+PypYdZXb7L+toyIvZx+x1MXWNnZxdNtRn0\nA9bXtpJGo0YD30s81YmJCeI4ZGdnlyiMcRwPRTFotJp0e218r00xrzFWgLmZPKt33yUOHTynjzNI\n8O9EDu9e/QmN/RWmywa1/WVMvU8waPBLn/8spWIBgPWVVVw/IJCC3VqNWFF56MoVrr5zjQuXHuSx\nJx9jdm4SIWNCP6CQK1LMZjhz6iyf+9wv0R/4fOTZFyhPTTI9PcnkVJlMxmBp6Sa3bt1iZXWdd2/c\noTwxg66bGPpBimToYA69cDGchw+wMWIEuUzkI4cUGQdUGZKhEImqipTiJUYjQgvbmGGDlXdfxak3\nUSW0e118N2B7e5tvf+2LvP29L35o+/qzkboZRZYJs95B+/ewSQBAphBEgBgZCzQBghBEUlJNbpUa\n65HIQhIhSDks2n5w+gMRHQqth+GtlpzII3rYFO8sAKJE7V1JUMjJuyfNSKMc66GIfCjkG4uDbtvE\nCB88h6IoCYkaMnmeQ52MBx76++GmMk7pbdPnPHzPIaYbIJYhaeN7mk9UUzHpgHZlk8rObY7Nz7O9\nvYnj9FFUg6XlVRoNh3/5n/0u0aDGUtSh09yi3miRL+UY+BFRrFIan+bEyXPcvrNNEMSsb+6Sy5cx\nLIP/+4/+D/qddbLZLEQqR4/Msrm5SbPtYNka41MzNNs9lFgniiOKxTxSSjK2ztTUOJubFba3KkSx\niWkn1AiqUMjli/Qdn1ajToTg0YcfodsPuX3rGi9+5leo1dvsbK5wdPYYd6+/gx/02d/d49jCKbbX\nblFptOgNHMbKM5TGxtE0ELFHf+DgOB6anUdoFtl8jk59n16zwcqSysRYCRlrtPb2aNe3MHWTN197\nBT+IyJgGRxZPUtmuIoTK8aPHCKWa1J2GrIjwvvm8HyHDUE5SClT1QMcUhtHjgcNzwLclRnvlA9cK\npA6TRMXnzo2rtPdWmJ2dJWzVcLpdtrx1SmWbmek5MnkNVfXZ3t4kjgSVnT0cx6FQLOEFkjAQ2JMF\nlpdXiSMYL5XQdRW30cHSLRrVBrZtMjt3hOWVl/n+S9+iYFvU9zv0ekV2qzXCGNqdDrlcHlWNyWdh\nd/cW3//xBkII9vb2UGSdsfF5ZudOkzUt/DCGMCabyfP4U8/wlW98g52dHTK5AtX9OscnJ4hjaDQa\nNOstFhcXqdfqaJqBoqvky2WWl+7w4AMX6LU72IUMJ48u8s5bb3HmwoO83fNpO30y+RyhVIniEE0Z\nNqelwAaZksfJOKl1CZLUaTJB6XQMHcf4HlswAmLESkI/naZ21Ags3UeJO/z9t75I2GvitOvUG310\nXUXXFIQwCTxBv9Gia3x4HP3PhqGHUfFJIJHDQpUQxHGUGP/7wAj3KzaN9kcaJifarUmqByAawqGA\nWEapAfxgiNrB68PFrQRFM6QdHiF87vm9KM2LcjCrHGpoIkUHfQClwuFaw9BSC00ZGfv7kRyHn3No\nNIY1iMPXDfOxwAEaRxGpsHqAISJUv0V7d4nu7jJvrl2jVqvz4MOP065u0e/5yCDAsizWN2rMzM7i\n9PbIF8usr1dQDZVCIYG37e/VaDWbnFg8Q7PR47lnnuPqtZ/gawG9ThdVVcnlcgwGA5577nk2tjeB\nkFwux3vX32Fu6ggbG5sUijkunH+AdrtNpbJHIZdn9soRao0e7U4fK5MjkjGu6xLEPtOzc2SzWZrt\nNt1uSLvdpddp4wx6jBUySDz2qy0UVTI/v8DmxiaakaPd7XLugYeQiuDu0i08p4PvJCpKQiRdrrqp\nsrpyl71Gi/HpCZaXHZ766AscP3kWP4757je+NWraefDihSTVFrlYhqC6X4W5WUBB01SkoiFjMYJK\nJiMezUlyCCTGIVbTaG4YJR4SFThcyB3SqwwPiWFd5t5xGHWV4Od9z+Xq1auobgLa+foAACAASURB\nVIMxM8Zx+4kqGjFB6JLLW+xWN1IBDUGj0WJyYppOp8Ps7CxrKxsoisabb76NbWXpD7rYpkGpNE0u\nV2B1bZPLly/Q7XYJAw8Ap+9iaTqdbp+B4zE+Nc3d1a1RPUZVYmr7eywePYWiRtRqNfygxw9+9BKz\nsycJo7cQSkiv38E0TXZ293jrvT/j0See4oknnuUv/upveOn7P+AXZ6YoFAp8+5vfYW56hsnSOPMz\ns4QyZmNjg06jxYsvvkhlc4ON7S1O586wV93nhZ/7GO1mg09++kWWbt1laWmJk2cujOprapoyVhV1\nNE/iUFQlhzzz6f5LgTIH3criACwBaSZBigROLmJUNUCJ+3z3a39O1N3F7fU4vXgaGaZIp0jQ73Xw\n3IgTs8cYeA4fdvzMGHoYFixBiZU07RGjpYRLiHtbhCH5wIav1fR7GScHhBTJoo+IU3ijRNUSCKYi\nRcqHc4i24NBzDA2mrukEoZdE1zHEQyOfslPG8t78vFDSzUmCsz2A1ykjzVUh5UhAOP2rRxt8OOSQ\nlyf1zg8TI33QATHUxz2M2pCHP081OUSGC22EZiKkW73Lnde/TeS0ibwGE+NFnN6A9TuvkB0/hqaO\n0Wy22VgLmZ6e5Nrr71BtNInjkLGJaWq1fWqNOrbpgKxj2yq6NmC8JGg17xJ6+3Tr+xw7skAul2Nj\ne5ter0dlfI+N9U3GSkVU1eTk4ll0Ref8uQyKolDZ3Esk4XSVarWOYWbodrtk7QwbG5sYtkUkgVhy\ndGGeyt4uzVaH8uQUlmUxVsjz/Ze+z6c+9Snu3lxif2+Xubk57LE85x54mFpjn05fI1eYQ1VDFk+4\nRH6XjJUnk8mxvVNDhDBVmmB+7ggnTizwg1d+yMTkPNffeZ0TJ07TaHawFeg7HR5//ApX33mbp55+\nhl5rn4yZYWosS+R3WFlepd7u8MTTzyKEAoqBqihE0eFDO1mDqjpcG/EhqOxBjn507dDDHxZ8SWlr\nZVKUjxhGvwzD2DQ1mJR2TdPk13/lV3nrR99gd/U6uqriDPoEccBErsTAaROGiWB7s9WhUW9xavE0\nx44epdXo0On2se0sUSjodrtcuHiWwHfIF2yCIODSpRNYlkK12iKfP4Uz6HHk6AkGgz4D18c0BZqm\n47kuAhVkRBw6fPyFjxCFPTrdiG6vjxcEeG5EZX8b11XI5kwa7RjbtlhdvoudH+P6u+9xcvE8pxZP\nUihPoQmFb37t6zx6+SKnFhfJ2hmEEqJEku31FebnF3AHPU6ePImUknqzQbFYwg1cjh0/iSIEcwuL\nIMzUYUx4/ofp4QS9pIz6e4b8QzJVnhJpQXXYT6EKEqf1UI0uiQpAEI2AHkocsLezTnOvgha2sQyN\nTmeXTAa6PZ8oTtJCD1w6g+P2CILBB9rRDxo/Ezn6YX6c4ckm40Q+MKXvFWk4GidVtxRJkPxuUtgg\nNYgCTVPRhEBREmKoUfu3di/NgaaKeyQLE/mw5EvEMZoQRHGQFLbSIpqSOtvD64b5UkPXMHRt1HpO\nLBNxZ6EQhweUBiPjO3rupFFqJEeWfikyqVVwD1yLf/Drg+6hqgf3Hr6fKgQqakJ5qiioCqzfvUW/\nvcPu+k2cTo27t69D3KPX2WX55lvEQYeLF07RrG3z7tU3CcOY8fI0lx56nL29NkEoKI1Nks9ncdwu\njtvCGdTodTbZ3bmJDLtMjBcZL5UIgoAL586jCI3N9Q3G8uM4XZfd7Qq727vEYVJY77U7tBot4jBm\ncnKaudl5XD8ka5nous7ExARRJMnniuQLY3R7DopiEcYqm1u7TE5McefOHVzX4dTiIpapYZs6rXoD\nTdOJ4pjtyh7F8RKLp8/guC75fBYzYxOGId1uF8MyUVXB7u4Wb7/1Oq36DqYSIv0eGeEzXy7w2COX\nKGQtxgoFer0exWIRiMnYOdrtNo16jcrONooM0IhYW71DnIpGj/KyI8MxLNrFaJoyyuMOC/73dzcf\npuAeCtGPakmKTEAJSIiT/LyqkJb2034VxUil95YJXQfbMrBtk3w+SyZrsbe3h+sN2N7eRkrJWDHP\n1bffod9z6fc8ZmcXyGbzHDlyBF3XsW2TIPCI44BabY8g6ON5bWZmxhgMmuRyNr1Ok8HAQVMU3L5D\nq9FOwBUKZEyDj//cMwycVrpHNRw3prLbIoxV9nbrdDo91lY3gISCWVNBV0Iir88X/92f8pkXP8ni\niWPMlkuEgx53bl1nZ3OVH7z0LfqtGmvLS3iDLks3rhE4A3RVsLa2khQ+VZXyWJlqo0kkFISqIZUE\nFROnn+1Q1JtDn7cQBxKk91NMIGTi/KVfh2sqihDp/A6FyWMMVSOIVY6duoii58hlx2i3qkhcymWb\nY8cmOXpskt3KKoNenUGv9aFtrPqFL3zhQ1/8/9f4vX/9b77w/D/9Z0CSA08qz4zahYddiKquJVDJ\ndASBNzJkQij0ej2yVuJRZCwTU9WIwwiZNliNPHCURI81jlEV5V7JLw4KvUN4pBDJ5pEM0S0p1jk1\n1sPQLKmUKygoaIqaFGMUFUVTGUoSvq+ZifcXXIFR/8Dh8YHIHPH+Jpr7f5Z0z4oR/FIBFKGhypCs\ncLhz/Ud4vTaO20XoOlq6AC0jw/rqFhlTp7qzweREka31NcrlcSJpMlaaolZvsVvZpVrdw7IsxscK\neJ6LoZk09qtYlk7GMjB1C98PCMIQTTeo15vkc3l8z0dXNVRFUKlUsPREl2B6ehZF0XAcB8dxQFHZ\n391PkCKGxaA/oDBWSud9wO5elWPHTnL6/DmEqrOyusZYcYzA62BZCqWxPJOTZXL5Ijdu3qTRbPD5\nz34W07Lodpt0mlXa7Qb9XpdYCMIoIpvL0+11yGRtGtUKigyJowjhe1Qq26ysbmBaNnYuzxtvXWP+\n6HGOHD1KFCSiJROTE4yXxxl0WyiGxtETi2iGgSI0tFTIRlUEWsqncxgRBUqCwx5CJg+tjYQSI1lj\nypBSQ4mREjRVS9elmjbiHDgBihiuDRWkYNDco7q5TKO6Sb/TSCCPmoKiCtrtFoapMzc3n3qnoKoG\n6+ubIFVAI45hY3MDXdcoFnO47oBOp4Nt2gDoukEsAxRF4eSJU2xu7dAfeCwcmScOQ8IgQgiF0vg4\n+YLNzHyZbq+B63RYXl5hMAgJohghDUwrS6/r4bkxfhhRyGbRFMnpxeNYtkEYBLx97T0uXLzEXmWX\nfr/DhfOnkDJgr7JNrVKhWtvHD3ympydZWrrN7Tu3ufDAeXLZHLppIoQgn8uj6nqS3kw71pERasJa\ndwg0kXyuqhCJQzasI5ISi4jEw0+63ZO6WxzG6bynBfTUWRweJIqMyObzHDl2nFOLC+zurNHvtGk1\nG7huH89z8LwelqVjZ0zCyGXpdrXyhS984Q/+MRv7YTRjLeAHgJle/yUp5X8thDgB/DlQBt4EfktK\n6QshTOD/AR4F6sCvSSnXftp7uI7DG6+8TOTGXLh8FkVRMA2bd9+9zqkTi3zvpe8AcGT+KK1WizAM\nCdw+tmkAAjWFkHmBj+u6ZGwLx+mPIGZBFCdGXtGZnp4ml8kwNTXF0tISTzzxBLqu8+prbxCGIR99\n6iNg6CCjZGOMZMWGHnOy+UbFtDRdEouYKK0lqKOTWyEmTpAWaWEmFuqhXJ46Ki4k0KrhLcXBATOc\nB7jHoN/fRp2kf5Kr45QUSzkM/ZRJoZrhfUTiQdhWDlsvMHtmktXlJXwnot1qoesG7fY+x06cYXPt\nBt1OB0NzmZmdYOA6qBH0ugOe/9gLbG9vsLezgeu02Nne5djRedx+n2w2y9TEBMSSbrdLJGOW766k\nh6XBoO/huSG6aaHrCqbm4voxg24HmXbH9QYOkRRM2QUy+RyNVpNWZ5D8v5psK9sqUJ6cwfNDlpdW\neOjRK3TbPXK2Qqe5iyJCBm0HM1PCMDJ87GMf5bW33uS7f/89rjz8GK1qA9+LAAPDzuA6HlLRaXVb\n+DKg77ZZmJ2jUqkQxwGDQQ8/jOhHXZwg5uLjx/j0L1xkZ7+KqmhU600WjhyjkM+yub1BrjTJdCGP\nZWXQzITtMoqGDVBJkv3+GkxCRqaky+MgEhUpb86ww2rYzS2liqoOVxJACIdQXelNiZApXbGk02uj\nZnTa3S7BwMO28wx6XSIizEwWRRP0+31yuSyDgUvshBimjR+A7HtMT08zMeEh8XGdAVEY0ml1yc4V\nieKIfr+f7JuoR7/nMjs3zc5WhdDrMV7Ko6kWy2tbTE2W2NvfQRcxuiqpt7sgFTIZiwcWL9FodKhW\n22RyJeqthHnUj3xUESMiDz12mZ/MoBs+3//an9B1XDa3dnjy6Y/yzttvQRhx4ewFJqdm8F2Po5cv\n0Nqrcv3mTX7+E59M0XAp8YqiQppSEzKAVJktOlSTS+YoGoboI/UpGQ9rJkM487000KYi6fbaRFFE\nPp9HURLHNYFhKqDoySEuVV558yrblT2Cbh/HczEMjXavy1ixzGAQsby2jJ358Jl38dNartPFJYCs\nlLInhNCBl4H/Avhd4MtSyj8XQvxb4JqU8veFEP8SuCSl/B0hxK8Dn5NS/tpPe4/J6Tn5/K/9DprU\ncOMBppFFxglDoKZpDAZJg45haERxiG3byMBHFyCjhHo3QhLEAXEQks/lGDhOiiHW8cMYP/SIEAgl\nScG4voNIIZGaqqNoSXOWIg7h7UlCupn5OU6cOMHs/CxhkPDCQEpAJlO+ekWmiyHF8R+SMpRCEsow\nSQUpetrJmqRSGCJrUosepPw9SgonHRbhFCXJ84+wQVGMrusJfjdlsYuRI68dGOH2P2hIAVrQw2+s\nsXL1u8howI3r73Jy8Sxr26vouslg4NKoNxkbG2Msl6PZqGFZBoWxEqZh0x947NcbnD13jh+89C2y\nWUG/1yJra5w+foqtzU0mJ8vEcYxt21R260gZMVYusXx3g0wmR6edpEk0XYXQozhepJjLE4eS3b0q\nXhhhZGykEGQsm+s3bqFoOnY2j+u6nD3zAL1+gKoYTM7MIl0X33cRmk7k9MgWVVynw8rSCrniFMeO\nn2HgOpTKE9TrTWI/SQOcPn2C5btL6IbKfrWKYWdwXJfxyXG2N9c5e/oMlUoFw7AIBiGKnkHqRf7F\nf/qfs7qzj5XNI1QdTVcIHY/zD5zj3WvvMDM3S3fg4bo+p06fRTV0InlwaA8P7Pi+fXiQzkxfjxyL\nIf3wvXN5vwB8ct8RuUbaiRERDem7JYxZIb//e/8tq++9hd/vMl6a4MjCDFHsslPZwB10KZdLGLpK\nu9UninQsK8PebouBG6DrOoNenRMn53AGbRQhcb2AufkjVKtV8rksYegjiCkWyiyvbGOZeUI/wFA1\nPvXpF/mzP/sSXuigWYKnnrzEfnWLY8eOcff2MpaZwXEDkEla7saNFXQzgxf5nDt9inLept+so2sK\n+XwO07bQLJMglHQHDs7AZ3d3n3wmz0OXH8HOFsgUSghNRagGbSfgc5//FerdHrplI6VkfX2LI/NH\n4ZA4+2EU3wHYITiYm/S6OFZQREJlIFCJRmLj6RxIj0plDyEE01OzafPnwX2FmlLSywA9rvN//m//\nPW6jTj5roGqSWMR4bkQuV0BRYyQ+f/VXb78ppbzyD270dHwYzVgJ9NKXQ+ZNCXwM+M30//8Y+ALw\n+8Bn0+8BvgT8ayGEkD/lRJFxIn0mgxBLUQkcDyEUdE0jDPzEG45DiAWGqhC6SbU5Y1tIJU6KWqlW\nqqppeI6LECpRAFEY4YU+XuAihAaKxDJUQj9ANwWqqidcLU6IZZioqkbkh0iSgz0ENu+us3prGc2E\nWAoy+RxHjizw6EMPEUUx3X6P3d0K58+fx3H62IaJlEn6IwgiTNsicF18x8e0QtbW1hLjmS+wtbGJ\nYRh4nsf6+noCK8zkKIwVqVS2adTqnD9/nitXriS7OU6UtxRDQUofQ1eJpBh1HooU9SPlgcf3Pphd\n5KMo8MPvfYui7rF55yZCi4kUnf1ml6MnLrK8vEwmN0l3EGFk8tTaDQQR9UaV3b0dFo4epz9wGSsV\nsC2NK48+jOO2uHb1DWZmjtBuNbAsgziO8cOA/Z06ujDQDQXLgIuXTnP9vVvk8nmOHF1I8uGb2xh6\nho2tfbJWBi+S6LZNpztANw0QOkEIxUKBMIgpFMaoNers7rcojU0wv3CCtdVVMpbJ1k6F3/rN38DI\nRLzx+o+4cuUK3UFEs1Ulk82yv1shcAOmpmYYuA6mafLg5YdYvnsDXdcxDZt6o4Votjl99gKKAg9c\nuEylUqHlJx791GyZv/7Kv+ehRx6jVtlkcjpB2Kyuvcc3v/6XbO5U+MxnfomPPvdJLCuTrFMlIuHy\nGFbnE69eSRK3o/kS8oAwQQiRsFak3uIw2kuyOkm0NuTbOSjWC2TsAweEegJl1K6PlLz22mt8+sV/\nwl/sVehoNg9eepRGc5c48pCxyszcAvuVHSzTRFEtms02rVaXVtvDcxPN4lxWo1gok89m2K1sUx6f\n4vrNO8zNzVFttnH6HQI3YHY2oN9v47sCYkFuqsRbb76DqkhyWRM36OH7Lo1Gg3w+Rz6fRddset0m\nA6dLGAgK+SyNZhvdMvEGDrVBn6ylIkOJIlVCJ6Eqdl2XvGWBCHn0whlkrNKq79OsN8gV6hRKY6ia\nzqDn8Op3v0nT8ShOljl56jSmEqMqMX4QoJtmsncOkQweNESp9wAkpEzpo2WQeOfEaIpEyvAAnaMp\nWFmTbreLqiWR/u7uLpOTk4kxVk0iJUTGLu9ee41+p02n1QMyGKZOJmMR+k16nRaO2yGb0/8x8z0a\nH8r3F0mJ/03gFPBvgGWgJaUcJsy3gPn0+3lgk2QRhkKINkl6p3bfPX8b+G2ATDafnmRJ+BN4HpqR\n5MzCMDkVoyjG87wRzS9hwCBKik5hGBMSpQwDCdNcKJOFGCGJZIhUNKIoQb24roepapiKiecGaKjE\nxEnOMOWzl1Kii8SARiIRH/aciGw+h9Prs3z7Npurd4jjRPXFMDRuXn2VYj7HM08/TatRJ58v8sOX\nf0yz06E4NkHf8RCqRhyH6LqeYHFl2nyR5v113aTr+TT299A0lYxtcffObdbu3iWfz3Px8iVK5fFR\nWioMQqRUUFVBEKUbWxgHWP57Nn8yDF0hjHzcXpvAqyOlZNB3sLNjCC3PzMxRrl27ycTUAgU/YmZh\nnnffruAPeuxtb3BycZG1jVWy2SwREY1m0jnYqFc5e+o0hpEcoM1mk3a3Q+BHLBxbYGNlA0036PU7\nuH4Cq6xXu1T3G4yPjzExMcWd5RXa7S4TpXE8EdOuNZAkeekoFJhmBtcJCIKAM2dPs1droukGmWye\nxx57jNreNroi8LyAb337Jc49cIyN9Z2kXiMspmYm6Q9cNM2kPFNmZXmZ6blZ2r0u71x9m/J4Dk1V\n8bwkNbGzu8fHP36Bt15/g1s37zI9O8fFy4/S67u0+z5PPfkkX/vq3zA5NcV3v/tVslmbRqvJb/zG\nb5DNFMnlJ7EMAxVBRJQUYbkX2ptEZSTRXfI/KTZ+GJkliI3hcZ1WcQ5w2aT9HcmmGxkWRVUTeLFI\nDgk5rNUACMnFixf58z/+3wmCiHarxxtvXeP82UVazRrnz5zn1dd/jDvoMTFeptfv46QOGFFMFATk\ni0WECLh1a4nTp44Rx9DtD+j3PXTTwvB9Qs1l9ugk09PTRJFKEKrU91tIKQmihFk2kzM5NjWP67r0\n+30GvT5O38NzW8RSo9fpkc0VKZUKmLZBp+dS3d8lZ5pYZpnBoM+wf0XRNTShEQcRxWIiFN5otZmf\nO06MJAg9uu06YQy1eguv66Blc3hOl4xQqLV6ZO0czVabEydPE4u0xC0OQ1TTzz/Ny49QcYQkxHFp\nSi6lXh8y1Zqqxps/eZUoipifKmPpGnduXCX70CPcuHGDZ558Fl33aXZ22Fm+wcx4AUuFrc01SsU8\ncZxDVzUUAY7TZ+HoCWDtH7Tbh8eHMvQyKTE/JIQYA/49cO5D3f2n3/MPgD8AGJ+clk6vSxSGZDIZ\nTF1DEZLA6WPqBr7voqsqDNXR4zRtQUTfCxBCRajg+B6mZiI0DRGHgEIUBEihERMTCYkmYnQ0DEVH\nRjGWljZFhWkBNpJ4vpegbYyklTyOE2CUJhR67V5SQY8jFENBU0F6PVRTYMgeO7cqfHPrVRRCHD/A\nMEsogYliZdACgQyiVLEkQEQqqqIQRwpDyuDI8xPIlqoS+0HCQ4MCCvR7HV790ct4YYBhmaOw0LZy\n2Fmb8fFxHnro4aTLMA0LdV3D9/1DrfcKYSSxDZ1PfvoXuPXWj+hlNV5++Qf89u/8x/y7v/grjix4\n6EaWyt4ezz77HF//+jeZmjrK7u4abuSxtb9NLmMRC4tBv8r+rke72cLSLYoTBbr9BtVaHc/zcB2f\nQqFA4MdMzCTdk7VGnSiKCCOTUAh6A5fdym1++Vc+z5vXriFR8GWApWcpTuQRmkomm6ff8ygWC3zq\nxU/TbndxB122NisEXojruuzXqpw9c56//cqXGCsU8f0Ba6sb+L7GIPDIWDEbG1uMlSZ59vlnee/a\ne3R7A2S1xubuDvV6HakmSK+5mSnsfBFDz3L1jWtoikrohxTsIr2ew87ePuNT0/zr//V/RFUFncYu\noVR44tmP0hkMOLl4mtfeuMrlS3MEUXSQ40VFKKQIsgMEWBgniKOhY8MhTxEJanwojSAEB93aB4W9\n0RgWbYUgHvHo3ntNTCK3+Yuf+zx/9Pv/M5l8gWajTRBEnH/gIlff/gkf/eizLN1YYvnuLR5+9BE6\nzT7NdhuhxHQGdbrdPkiHsZLNdmUHGUNGt7Asg9p+lVKpiKYUmJgq4/p9jixMs7FeoTSRpdHdo93T\nMGyF8XKeXNbE83tcuniRRq1BoZBjs13h5OI5mo3bDDyXY8ePEAUh9WaTXqdLv9ej27U4MjOD6w4I\nwxBds+n2BpgSujtVDMukVJ7EsDIoqk5+LMfAdclmLWqtDkvLtzl3+hwPnnmSIAzZXrlB5DlcuHwZ\nVYPQ9dFNLeHMkVHSbihBkVHSAKeSCL4oSY79sIc/7JdIGhpTKVC/Q3m8yP7mLb71ta9iWxqVu28Q\nhxFn52wa9QrblWVUmoxNCHKFLK16zAPnFzFMjWajzfZ2hV63jWlnPrS9/Q/C0UspW0KIl4CngDEh\nhJZ69UeA7fSybWAB2BJCaECRpCj7D983loSRTxwFuG6MrZoJtCmIEVGYsrsFuL5HlMq1TYwXCPwg\nzacn0m5hJBGEKQomSrRREUm0kPJHEIUIPeGrQcZEfoRUQRWJWpDruiPjHgSJeIguNHQEHpKsIRHh\nAEsXKFGbbqNG1gYliHB6LcZUhdrmPrlcLqETtnz2ax6+r1KcOIJUUhGRKEBPFYoCUghmGq4rSgLL\nVJSkcw5AhjLx+qSCJjQCL0TRVDKWTb/XpVHfZ3d7i/euvYNQFYIwxDAMLl26xMLCAoZhYKY1DkVR\ncR0fQ8/y2GPP8Gd/9A4PPfI0X/ry3/Hccy9Q21sjn7Ox7AwnF8/ym78xy/de+ir/5DOf4++/A7EM\nMHQbXddptyqEsU+pVGR+Zp67d5YwrURqrl5rYpeySAntRgPVVPF9l6nJGQYDj3feW0FTs9i2xuzC\nCX7/D/+IbEanPD6RwGw1Sb/fxcpmCPwB9VoVK2Pz9ls/Jp/Nsbq+Rn8QYGcylMbG+Nu/+Qon5me4\ncPY8jUYDXde5c3slKdT7LsbkBGMTE9SbHW4trVJtdlhYPMfO/h679S7/6l/9l3znm9/i+LEFdBlj\nWjoZe5Cgu6RCsVBmfWMbM9/j5OnTVOs1MqZGeXyM48eP4zgeb7/+Go8//Qzf/fb30KwcUXwvnBaG\nfRiH02ukKLAoUZ9SBCGH2RCBtF50r3bwwRgiv+5trkvgyMP3H3KYDw8TKSGKBcWxKXwnpllvcWd5\nGcteZGZ2mqWlJbrdLo898TjbWxX2dhuEMiabKzE+PoaQCoaeYe7oJDJyaDYauN6AcrlMqVSi223T\nH/Qx6jpSRkRRj4iA3FgRZ+BhWTk0TWFze4vZeBJVi8nkbBqNBlPlKeam5slZOaYmJtmp1fB9F2cw\nQIljJsvjtBUFVdfYT/s64iAmjBRidAZOxGDgMJsdQ9MtrIzN8RMnEZrG5tYW5ak5Xnv7Op/45KeY\nLI9z68ZV2q0Gpqpz673XsS2F3fo+t5ZW+MXP/xJSEajqQY5ekMJXZZTMUZzIJya5do0YmfRERMNi\neUS3usHOyjWWb3S4aQoib0BxfBZTBdPQuXvjO0RxQN7UGMQ+Rs7AcSVnzs3TcyrktRzTswWKpRP0\nezNY5vsRfP/Q+DCom0kgSI28DXwC+B+Al4BfJkHe/EfA36S/8pX09Svpz7/30/LzpEtRxUiq31LB\ni31cx8O2ExWfwOmBANvQiZG02gNcb0AUhKiqThD6xEqyCZxBD1PTUUTCImeZmYQ/B1BlhKmqiMgB\nJUnLICP6rT6GXcCLQoRhE8ZakueOEk84VmIMFQgCTN1FxFX8QZvA72GbKk5/QKtRQ1ey2LaNpmVZ\n3djHNksIpU3PDTGKDbSBgtDKqEYORU0qaH7g4HvhSEXK9T1M00x4rtMir6qqIJIGmygN72MZE3kR\nbc9PsMhqQsxmWAb9wQBNUQl8nzffeIPXX3sNTdOwLAvfdxk4PhPlcXTg8tljjJfLfOVvv8xHn30e\nz2nSbNSYmZqm3+2ys75CGEmKOYuL586ii0/w9a9+hSeffpQbS7eYnFik3Wrg9F0qu9uMjY1RLBbo\nd7osLOTY2dlB1w0atTrFUhEjY1GrNdnfq2FoNgPHp1prsLu7Q7aQIZ+1iJWQI/NzxFHA2toaYT/A\nzprEwufM2XOsri0zO13g+PEpwOLdG+sEXp/P/MKLvPL975OzDJ5/5lmuvfcep86c5/bt25x/4DKe\n5xALg6nZo5Sm5jjzwMO8/MqrPPr0eaxsjoee/BiXrjzL3s4mf/mn/xemcKtpOwAAIABJREFUDrHv\nsr6+welT53GCiGPHT1GeniKbz3Bn+TaqofPCxz7OxsYG1eYev/XP/wXf+PZLaGaG5648zdjY2EhB\nbJR6OWSMh6+TTmU5EhrX7xMcT66TqOrQiHNPKuGw1OUoEriP315Jo4BEbUqi6Dkmp47ym//8P+GL\n/++fkrFzzE2WUBjgeR6KGnJkYQqI0S2bwvgUtm1z++5dZCgZH58gV7Tp9x3CaEA2l6M7SDRl6/Um\nmq7T7foEYYtcLkMcxzQaXcy+z9TEJK7vEDvQ93xqzRalQp5atYOq2FR3mxw9usjGxhaZrM2J7AKV\n3R1MXaXd7JDN5On3uxiGxunzF7hx4wazs3M06y1MK0On12WsPEXf81AMg/GJMps727iez+nTZ9nZ\nr/PkEx/hwoXzvPzDl3BabYQiyRVMHjx/jrxts7W5xlOPPULgDDBz+cTIp9OmKSpxHKBEDlurd7j5\n7lVOnVhMurIHA6ZnZ5g/eprCxAS/97/8TxgKZITL9LiBZUxRyFrUmg36Toem20eN4dj8HL1eG09X\n0G0VGUlcr49haWQyBTqtLkEUImIVScRg8OEbpj6MRz8L/HGap1eAv5RS/p0Q4gbw50KI/w54G/jD\n9Po/BP5ECHEXaAC//o++g4xx3DaaqmLpFjKMsUwTpCRwXOLQw8zYCYpEURnP5YijOPmZGtDr9ZBC\nxbDMtL6lIWRMxtARgYOmqhiKQ6dewwlc/MBFKBqZjEV1f5/yWBFb6dDY22dsdgE7N04YRsRBiPb/\nUfdmQZJl533f79xz99wza6/uml4Gsw8GowGxLwyCJII0FDJNUuYDbIohL+FXhSPkBz8oHJYUJuUw\ng6bD4QfZWhx0WDBJkSIJUiRggAABEoPZMTPo7um91twz737Pvef44Vb3ACQtz4PMgM5DdUd2VnVV\nZt3vnvN9///vL13KNCHKEzqhzXo+RlKAqAlc0GVFHuUMehdYzFZYlqCoLPr9XTy3xTJaEwQ+i8kp\n0WrNzpU2pnQRwmCs6hxCZTWSMZXj+y623cwdaq2xzo/430fglBJRNwO1qmqGyI7VhD3XeYWomzeq\nLJqBki0EWtVkZQKWhSck6WJNmae8up4yvnOTvb0L3L97l/X8BEPJvTs36Po+X5ndxgt9DAW/+ivf\npK4y9vb3+LNvfR3HcRn1Nx/KJ9NozOZwi9lshislvV6P4XDUJFDteMwWU7IsQ9HgjnVlKKTEEjW9\nUYet3SGdrsfXv/Y6WAW+61GoHOmEJOmKjc0ub117ncB3uXX7Gr7vs3/hKrs7Iw4P7/Otb/0ps9mE\n7fe9j3arxbVr13jhIx/nwqXLWK7H9saI67du8+ylR9k/uMLLr73Gpz7zYyyimI2dPb7x4iu8/6kn\n2X3kEbYuXuT4znWEqcCWhP0+n37meabLFd+99h3ysuD4dExt4NW3rvPiiy+yub3DL/3yr/KxT32a\nT37yh7l4cKUZnp4TUd/FUsC7Msjm70LygEJ0/tCfU+EI8X2f88AY92Dg/u6/P5BrwvfKNptfnuaP\nB5x0Iyy0cQhafT780U8QRxN+5ze/QDcUSLemKioSk5EmJVkuKEqBkBqlHvwcTaKSUhptNKZShKGP\n0Q7zWYTtCColyIRG2AZHOki7i7QapIIQglpIVA3LZfP/DPtdev1NkmUMplHcyaJAW5LQd/Bdj+ee\nfZa33/4udeVw9col3nzzTba2tpBS0h32qRQoXdPbGKIr1UAIbZu6VpyNT7l3dEir1cFowcc+8kF2\nNjZYWZI0TSnKmlWc8+Qzu1S2wyMXD8hUA4YTVmNmbGaBJZKC3//df0E6vk8Rr5jeeQ1dKgI/5P53\nNd8WEqfTxSvnUGQMLu3gWzXL9ZRkVVFVzZxq1N/EkTZZntHqtvEDi/HskDD0cFxBUdWkRYx0LFRZ\n4Uib4XCDk5Mj3ut6L6qb14Hn/5LHbwEf+ksez4Gffc/fAecacg2WYyG0phY1dV7iSRtHOhjPayD8\nwmDqqjkGVjWWZVBlRpEnjWzSapj2na7PYrpEhC1caSF1zfG9u2z2Q1SZIYzCsS2mp4e0goAsXjT2\nd9sgkjG6jFit1mjp0+/3EXWCyhacziJUkTEY9AlDn2i9otNtEYYhSa7QRpIkBZU2dDo9LMvGxmE8\nnSP9Fiopac+njLY9BA7UBUgHaTfGIGlZ1FWNruvmeI3ESI1tOYiqUfEIy24ud0s2PUDHRhuNfjBE\nFhpHSGpj8JxGIGV0iSorjLZodzxsS+AajRKSos7o725x9X1XuHnzLd584xX2toaoqqAuJDv+gDKK\nsBzNejnh4GCPydld0ihnvlxiiyfJsgxf+iQ6ZxXFBJ6LZXtkRcVyuWQ4HOI4nOufKwadDkuzJM8z\nfL/C80Mmswm4JXHi8MIPXWJ8ekKpzmf9xmY6nRO0PDaGI87Ozmh3QuKk5N79IxaLFFXmtEKPVhiw\nt7fD/ZNjRru7jHa2cNttDg8Pmd5qTidet4sIff70pVfpbO3zyU9++hyL3CJNU9otn+ef/yGeePQK\nX/nSF9nZ22dra5vVasHtG9eJ0ohOr8MnP/VxxuOIa9fv8JN//d/nE5/4FI899gQnx6eE7T6FyvF9\n/y8Y2v7ykPiH18/3Pefd3fn36+wF7+7mzfkHwbsyXS3ezUL43iUeJJxgATWOZ/Pbv/NFDvY2+dZL\n3+bOnTt86mPPkxcRSVFSVha2bOO6kihfoCqNtBo2japrrLLC9SzqypAmiicvXubo6JigFVKWJU7Y\nCAOUqlGlIQhamEohaNLSyrLJHijLAtdxGJ/NOatWCCMZT97h2acf52R6wsHBPnmeMhxtMhtPcR2b\nixcvIoRh0OuiigctV0OaJPiOx+X9y6yjJW+/9QZvvv0F/ubP/YccjyegKsbjMWm05s3XXmE2OWM2\nmxGnKRubezz31x4nSUs+9MKHWEUJQadFmcXUxkJYLr5jQCgcaVgc30GqFF3mCFvg+A5JukZKQY2g\nrWsubLms1zmeyDg+PkNKSRRFBIFH17bxpI1l22zvbHB6ekxZG9qtLutkgRaGVhBQ5IqCmkopdGVY\nLOpzJ/Z7Wz8YrBtjGjqbZTVwJN/Gti1sy9AkL9YopR72IauqatoZdU2exgijKPIcz3UwRpOs5mid\nM5/NOdjbY3F2hEPJep6CURSFIhQW0WqFKjI8z8GVHrPZmG6/hzaNEcd1MgZBm7PxGIcS4UiEcTk9\nPaWqKkbDPr5fk+cVRQFRWtDtdBh0u6yWCZ1uc0HYtk0rCJmtIgJXUhcF0tagBXWdUxQZjnNupFEF\n0nGoVd3wORDUFA3oTUjq2jwEqqm6bNQ3loupKypdN7A38a5jt1QKW1p4noesDU6VQD1H2mDLAM9t\nkyWKzeEmx3ddPvWpT3F4+7tonVPka27fnKCqjBde+ADPPv04Z6eHDLtdLm3v8drr32E1W6LKmrpu\nZhpB0KEsDJalm6Box2M4HDI5OyGKIgYbI7I44an3PU5eK2oNZ9MZ+xe2uHPvNpauWYumMOSZwWiJ\n6wSs1xGbG33Gp3Mc4bFepLi+RzyZkReCq1ev8vbbb/OxD36Iy5ev8oXf+C0wFvPZEtf3uXPnLg4W\nhRF84uM/zGtvXeenfvpneOYDHwA0+zubBIHH//Vrv8YLL7yAQHLt2jsIEdDp9Gh1hyzWh9w/OqGW\nFbUUHH37Vf77/+FXkW77PDTcRSMYbW1ijKAoGxlrM1gFeKB/f9DE+Ys+h4c78O8xxok/Z7z5vuc9\nfOCByUc8dJPz5z5LWs0QVgiB0RqjK4Qw/PiPfZp/9o//F04P77K/v8vt27fxA4krPKJlxhNPP0lW\nGrobW9y8cZ1Wp4OuKoLQQ1caVRpUpdEazs6mGC1RZYEqz7MihEQgWUfRuW9FkMQZYeg3vhK7mRk5\ndoiNhx0GZHGJqgpOxgsuX7lEXRfYUnD7nRvUZc3Z2YTHn3qS5XoFolHkVbrGaIvhxgZJVvLNb38L\n17WJ84wrVy/x2muvUBtJEAQcH96n02nxlS//Pq12iEby/vd/kN5gE6ycP/7qV/niF7/Axz72CS5f\nvcJ3r9/i5HSCBn7sRz7NcODx1iuvkizGtF0HXSs6rWYDmHuSVbxiYxiCLMnzFZubPcZnYxaLGUrV\nHFy8RH/QxbYtlCpxpGA6nROGbaQDWR4zn61RSjHoDdHaEAQBq2yFLV2klLjuey/fPzCFPo9WGKex\nBJdxo2tOlQIsalM9HEI16hFDpTIsBLZQpGWEqQWO1Wp6nUpT5gV5ljU8bWFIi4SsyJqEpKri+PiY\nqlTMojVbWxvgegw6I3zfZ7lcIoSkNIqTo7tYrkAbiyhaA+AFbbLlsnF3piWbG9voaUQkciaTCY7t\nkUYx/f6AVqvFZL5gNmswqbevvcnFy48xGG6CsXClSy0sKlVSGUNZVnTtECltpDAoU1EohZA2lVIP\nTHtYto0QNkVa4jnNa2PbNpYQVNo0/oGiwAiB5wVYwuDbClmtSJMztJ2CFNS6Ra0S7t68iS1zBHDl\nyg7T6RTb6XPr9jV2tzZwPYuyiGmHHtIIktWKq5cu47ghaVbiOB6LVcR8sWa9THAchytXLiGE4fR0\nynqdYITDahnhSMnLL7/MhcsHFEqxWs3PT0ED5tMZcZQT+DYbw00qZTg+mWCMxHe6ZKIgiiIODg6Y\nLKc4to3tOFgWOK7ktddeQ2ho9Xoc3rzFOm2G06PhJr7n4Le6/Bf/6X/G/iOP8B/87N8kXU1I4xW/\n//KL7G5t48mcr3zp90gyxfPPPUuaZbz48kt86/WXCFstCgqefvID/MJ/8rdJUoXrtXD9EDAopfAd\nD4Okrkvanouua2p0szExFjVgPchufdh+Od/Rfw/V8vs09Dys47xrgfreNs67KhzE9zzD/PkbSdPS\n0VpRVxWzyTHSVLz87T+h33Hphj7K1sxnc3y/Q+iHlBmYGnw/xGQFBweXmE5OuHP3JtI21HVDNnXt\nkOGg04DzLEVZ5VS6oW8a3cg6bdslTbPGr2I1RkbXE3iugyU71JXCAjrdkM3NEVG8JlovuXEjYntj\nQKlyqjxja3OveT3qim6nzVpHZFmFLit63RFlmbJYLDHCwvN7PPuBpwgCn3t3j3jssSfY3N7l7PiE\n9XJF6LtYFGzsb7G5s8U3vvFN4mhCtxNy9epV3nzlq1x74084GS9o9/pI2+bVb5Uc338HC0G+XuH3\nu/QGXSxhkWYxSpe0ut45fsJghR7SFixXM6qqZHt7jziO2d3fY3x2gutaTOZT2ucmwLLKqKqKqnRI\nk5p0vcJCMBpYuLJDp9MhaLloXfFe1w9EoRcYhu0QrXLSdM1gMCBNEyxpkWXZwyImEFhaYzk2Qkt0\nVRM4NrLfGGiMUdhO06t0HEGxiMnzAJWtyZKITqfDYrFgONxAVYY8afrA+vxrlnXFfL5ESpvhcMhi\nNeP+vTt0Oh0qXTPsjzgbT3EDga4t4lyx4bdJ4pi6KFBpSui6SGHI8oTlckFeFriujSWbk8ijV96H\n70tElVLVBku6IAXRcoXrutTaolKyycY0FbpWlLXGclwcxwHdeAOEdrEtB9dzwTKY+l2OjiUMuq5x\nbXmOWjAYo6irmHRxh9XylI4PnWGHLF4yPjtie7h3zvCoWMUr/LZkOh2zvbtFpQrG41OyNGJzMMQY\nQ1E3JjZtCnRdo7BI05Sz8SkHF65SliXaCKJ1TFWXWNJFWIqgFZKnCb3RFt3OgHm0YjJdEoZtsnyN\n0W4zqwla1JXBdf3GbSyatpjGpt3qU1eC0XCbu/fv4IdtonjNer3Es3ySouTu/SPSvEDVmuliSbsV\nsL01YuRIPvPDH+HHP/sTfOUrX+HwnVc4OrzDCx/8ANOzd7h75z7PPPch3v/I+7h79z6TxZrpYkl/\n0GW2innqqaf4/Of/FhujHS5caFNUNY5ouCfRaoXXAauuePEbX+PmzZvMlgtsJ+SZZ5/jo5/8JLbt\nf18Bfpc/LxBWU1QBHsaN/oW9/IP1/+Z6/je7oTU10jK8886bXHv7dXrtAN/S3Do7ZrmYMR2f0u0F\ntDsttKppt9sIbYgXCwBUFrNaTOl0AmpdYtsOUZTQ6/WojWg8HbaPqqLm5my7D9OX6rrGCI1G4Adt\nSpWBcUiTpu3Sabcos5wkWVFVOevVks3NEd1uF9sxZKsMPwxYrReUKsd1XfIkJi8ydna3qaqKZBXT\nH24xfPQyf/byy5ye3OWxxw74+tf/mDQp+MiHPsr927d49OpVvvvdt4lXcz7xsQ+D62IoGJ/doRvY\nhH6H+fiYKIoYDofoak2elITtkNvvLAhbPqqs6Y+GVFVJWuTYtsVyucR2LS5s7GEJzWIxYTjqM5lO\n8YMWW9t72NJnOp2S5ym9Xodbt95hc3MTrTXraIW0bc7O5qyjjLIwBH6Huirp+A5FWVKpGNsZNLr9\n97j+PxEIfxVrMBiaT37iMxRlSVEpHKeJ7FutVoxGQ5RSWFYz+LHE+VBSQFkppGxuAg+DrrVB0KQT\nCTQbvZDQ1ty5dZtLl64QxzG+7xOtUnzfpdNpsYoXRKuGl57nOdoY+oMeRlQ4tsW9e/dod3rNKSPN\ncV2fs8kEYwyPXLpIlRUMBgOyrMCxm9ZOt9tHCYOUHqfjCUHY5uIjB7SCsIF0PUAoYBHnBbZ08ByH\nOE/xw04zwHRdaqMJwxCteSipFI5DUWpqVRF6PsaSFConDMNz96TEsuRDiWZe5jg26HRCOr1BHC15\n6qmr3Lt/AyMbPsnFg32M0ERRRDsMcDyXNE3pd3vMZzOoNUWesL+zy3K+xrE9hGhgbbZtY0uPJEmo\njUQbG98NiJOkUYNoTZSscRxJqx2SRDFpmvL44+8jSSJWUczJeEGaKVqtLmVZYDuaIk3Y3NxGIFmu\nEsKW37T0bJv1OqbdDlmtVuxd2GUyXnH3dnOcn0znuE7AZD4jy1Icx6XXCTm4uIPnO+R5Sl0bXCdg\nMRtz5cqlBrfhudw4Ouanfubz/Kvf/UOe/8AH+dDHP8JXv/pVrly5ytnZGZ/73OdoBSG2E5xH9lkk\nqyVf+vIfcfP6NZ555imS1ZLp+IhoteYDz38EZaCsDJ/9ib+B8D0w9vdwih704s9tT+ZdLlJ1PkwV\nD7EF9UMzX1aVeJ53/jUenAK+3wFtTI0jGmSxZTWSW0toqAtcC85Oj3ntpW9R5hGHh/d4643vkMYL\n9nc28DyPqqpYzWNs6TVGwSQlVzlCCMq6ZB1F2J6PlIK8UGgtaLe6jUdCKaqqxHFBWA5l2QwfBRJt\nKlotvxk9V/W7CABDU8BtaHe79Pt94lUMQtNtt3kQk9nrdEnTFCEEq2hNt9vHcuzm2nZcpJDMFxF7\nFy7wxpvf4SMf/SCO43B0dATCYXd3n+vXbqJ1xWq54KMf/SidXsjJ6RHCGLIsaxADdeO6L8uSUpeU\nVYXjuXS7fbIkJz8f9MazM577wDMYoamqZobY7XYQVk2e5xweHmNZFmVRceHggGs3bnJxbx/XDXHd\npu2aZRmBF5LlCYXKyVJFHGXcvHlCb7BDy3VpBxZ1VTEY9bB9i1YP/rd/9uK/HQTCX8Wq6xqlCpSq\nEJZAG0mcFpRFRZblVGVJEIQkcXaeEeughcaSDjk1fquhMSZJgqgr+r0u3ZaD0RVSVMxnU3rDHpVu\n+BxRFGFZkvV6zWq1wPFsqrqmKM973kKga4PtSaS0MUawWDTMFwDX99na2mK5XrOYr/Bdl3uHRwSe\nj+8FBK0Qy5bI84vy4OCAvCxQZcZapQ9t1I4lCYMeQri4XkCe53iuRVlEKF0jZHD+CwJKKbI4Jex1\n0EqyihLqUiGHQyogKxJUFSOFjW0sGkJhw/WvRIWuBI5Vc/mxR7l38y5VZdNqbeJ4NovpdaJVCbK5\nGItCkSQZ0/kM3w1ot7r4ro0q26hSYzsBAklyLu8KQ0mexeR5Trvbp8g14/EpBousVBzsH6CqirDl\n0yB2LeIkZblcM5+Pcf0WnuehTYOjuHz5Eqdn9+jvbhN4wXlcHVTKUBYlVZXSPUcDO67/EGPR73c5\nOrrPYLhFWVb4vo/v++zsbKOKhOVqzpbTwegUKSR5lrO7vdPcyCzDZLEkq+DN77zN3/ipn+bxJ56l\n2+3yH33+KoNBj7quKVWOY3tUVRPXV2UZokq5+fYb/PXP/STr5YKzu+8Qr2eoPKfII0pVMdjcwXMt\ntLAw0lDXZfMePRieGtMM2s8lfFqcB+4IgRE1aH3edjn3XGjV0BBtl5rzoS0VD/KFwWAbgdEKowq+\n9dKLPP3s83QCF2kJ0BWh4+BYkrSs2dvZpy4Lrr/9nfONCAgckizFlZq8LEizBCkFlWkKmu/7+K0m\nIL2ua4q8IopXjddDNrpzx3FJ4pSyajYuRZbjh17DlbIsam2oyuKhEbLTbeH7NtrUrFYryjynPEdH\nX9y/QJqlBEHjbxkOhwjZ3DSzvKQuKhbrhCLNaHf73Lxxg2eefhJdliRpSl0pFqsFp6entNtdNgZ9\nLKvi5PQ+2myiipKqUmR5Qp4mbGxscHp61NxIbPADl6QoWR4ek6UlRsCg32E0GhHHawqV43kuQegx\nm581+Qv3moCWB7O0yWTC7u4ueZEymy1wHI/BYIBBkpdlA5KurQanbTIs2ydNSkIvJEoKfNciWqd0\nZEi0/ncseEQbw2QZARZBEBAEHvlqRafTwZGSq49dASw8L+DlV16l0+0wmc/QlSIrCorKxRYN98GV\nAlVE1NmKbrdFup5TVRk7W48wny44PTnDlhJLNs7Y4XD4EHBmhKGsajqdVmPOyksG/S6j0QjXCwjD\nkLNJgwwYjycgmvDlsqrRCKTtNCEc7Q5lkpHmzU7f81za3UZHrIqKqsgavb0lsXTF0489ykuvvY7j\nusTriP7GJiYvyfKSvCzIkvT8hmM4nc7Y37+IbYHjOVRlRqFKhNDkcXMENlIiaHTzRkAWxyihsXTF\n6dEE17I5OkkI/Q5VIdjZukySrrAkWLaLbTe7o06rzWqxpNNqcTyZozVIIbGlTyf0cWw4PDkmCAq2\nRhv4voUqCrqdNmErwJIuZ5MV2rJIi4r+qM14fEpRlsxWBRdVRdjuoWpNp9NDqSX9Xp/7927Rbod4\nnoPt2kymU1RdoVIDonl/pvMJtm2j0oKdHQvLsRltd1ku1qRpTFXBer3G931ef+N1nnniCt2whetI\n8szgepLx6YT1POfO3TM2tlp4rRZKVVx78y1ef+NteqNtHr3yNBubQ3q9Lh/96Idpt9vouqZMIn7v\nt77AE49e4ktf+iK+4/LyN7/ckD8tgWNKalMxObmFMnDnznVc1+bDn/hRdGUjpYMlbGoecOQbToqF\nwRJWk24lLbQwKNXo5wWa8dkJv/GFf0FVKX7+b/1t+sNRI6s9l3FaVqNgE0Y38wtL8t/+4i/hSMnk\n+B55FnF2dsLzzz/P008/zfuffz93bt1kd3uHL//+v6bfGSEszWq14rnnHme5XOM4DkmSUFQZ8TI+\nb9PUJHmF7bybbhaENgZBkZXUdcN4yYsMx/YxdU1V1rRarWae4rms1zG10pSloi4Vu7vb2I51bnY0\nFHmK67r0Om3OTsacnJwReC6nasrO5gZREjOZzWl3O5ydjNFKM+p16e326LR7bGgFlaKIFRUlltHY\nUoARWKZEypp2ywVTslxM0VqzWi4py5KiTAgih/ddvczb168RdgPcMCROMqQdUFQlUdZ8f5ZS7F/Y\nwlYWk+kZhi5aa05Px2xv7xKtEzqdDmlacHh0grBX+I6L77fpdfucnkzIz088ttV4Inq9ARf2D7hz\na4LjNBu9na1NfL9BUr/xxit8+OPPveca+wPBo//7/+Dv/7393T1UmbGcj7GFphXYVGXKbH6G5wja\ngcfJ0SGWbWOoybKU0aBP4Do8sr9HWWboImd71EalcxyrxtKKaL3GljbxOiJNcjqdPnGSYrseda2p\nK4WUFo7rocqSsihI0pSqqul2W7TCNmmSYTAUWY7WDVd6OBhQKMXW5gbz+bwZntSGsNVFVYat7R2O\nTk5ZR2tUXbGKIk4nU2xpE7Y7CMtF1QZtKoSwEOeuxwqbJCtIsobtXWQKpWrm8wWu2xzVZ7M5rpTo\nukZXJZ5j02TeNk7iuioal3FZoFRBksRN/140TkZpW1i20yQ42RZ7WwMOjw7p97v4gY/rObTbHSxL\nkqUZ/W6fxTym3xuRFzWtVofJZMbR8SmuF9Lp9MmykiTJGI42UFWJ63uUpSLNCkLXx7IFhSpZrZe4\nfovxeMlnfvQzfPf6NZI0w/EcfF+ytTUkSdZ4vkeaZ4RhG9fzKaqaLM+wbAmWhev5HFw4IM1LprMF\neV7S7QxYryPyojmZuZ6H6zpsDntQl817NVsRxylZqlClQBvNRz7yApaU+H6A53uYumY5H9Nv+1TF\nmrpYkyxnvPHKS/zZ1/+YP/3ql3n5m39CPD/l7s03caymrZVEMZUq+cmf+Cw3rt8gzXKi1Yp1FBEn\nMXme8spLL3FwcJl+r48wGq1r7HOBAXUjGaYqyZOYtu/hSokEhK6wtOa3f/0LzE4OabmSb37t63zq\nEx9H6vMcBK3Pw6Ur/o9//k/Z3uzT74X8we/9DqNBh9nZMdFiikpTqlLxzo3r+K6HZeDaW6+zv3eB\nC/sXmJxN6HQ6tFo+d+7fxojG06FUw1LqDweo2mDbDqWqUJUhSVIwglprdK2R0sK1bTDguz5ZrjAG\nQs8nbHcoy5ponaIK1SCQjaAVttB1E6buSAtb2tTakGc5pjLYdnMja7Xb1LrG8316/RHz+YI8rTA1\n9AZD8qJkMh3jODYqL6irCilFkwMsJdTgeTZJFLMxHJIVKY4EtKGqK1zXAWE4PjwiDEMGw37TppEO\nO3sXOT2Z0+l0kVKSJmsevbRP4LskaYLnerTCDrPpmqKomnwAIRqNfl6xWK7R2mLQH+DZAZim7VUr\nje349HtdwrARlUhL0u20WS3nJMmCRx+7gOeBlDX7B3tYsua1N44h601nAAAgAElEQVT/7fDo/yqW\nIwUffqZLrTcYbnYRlkur0+f2nXuslzF5WTCejrEdSduV5EoTuA4YxdNPX+Gd69cJpSYMocqW1CrD\nkQJp2wSeS6UERtgMh32KXFGVCsvO6Pd62E5DsizyhMB3ydMMz/URCNbLiCRK8bxmCCqlg2/ZTQ+y\nVLQ8F2qN74ekaUqlNJmV89hjTzBbLKkqheO5LFZLkiznkUuPcno2w2A/hJpN44w4BWUMlTaUVORZ\nRui16AYdkI2jt+fZLKMYdZ5Y5dkDpFG0fRsjFY4RaNkoHMo8R9hNDLhSGks2xE5VlIRBiG3ZTY/T\nQJKuuba4x+VHLhLHa+JVTlbkdHpd8rzi4OAx8iJlMNqjNoLZ/AxLyIYmaUl0bXH/aILnNvF9167f\npL/ZpW9pZrMI3+5QFAW6yjkdT0mynK6R+KHLi6+8Si1ccAyttst8dkZROsTJiiRVZKVmvjik3xux\nXmWoqgl7Wa9XeK7LaCAoC02almhtWCzWGC3hHHuRJAnxOqbf9tjaHHJ8/6RpL7XbzOdzer0hYFhH\nc3Z3Nrh/eMx8nLBYTHni8ctcHPh4gca1IlARZaEJHA/pWGRJCqKi1iUa2fRzqwJqzW/++m+AsUE3\nrKRa50ihWU5OePSxJ/mDP/hNfv7nfwFjBBYSlWo816Yoc3w/4GR8yq/92j+mrg3Scvg7f+e/hLpG\nmJrF5D6eXRNNp9i2zS/9N/810vN56tln+Kmf/lmSJONPv/k1zu6/w6/9rzf4mZ/+KRxTYOuaIl5j\nW5LAcVmPT6mFxUurFZcuHeBLQVYktIZ9hCyxLIv7h7cYDbrkqqTbbeMFbmNyqgy2aHrOjayxidOz\nHLARrFWBEC7KNCa+stRYRpBkGZ7tslgcs16vkY6PbQscx8F1fdIkQ1gaaklrNKAsS1arJYNBj0sH\n+8xOTtjb2cEPWyzWC6I4pSwSag3L1bpR9aiC8fiMvZ1d4iTDlgLHajIpbEsQpwX9/gjQbPQGmKpm\nf2eXLMtYruaoIqHb7bNe1zz99NNYjk1R1XitLstVwjt33iTLSsanY/wwwHMF2mquhw1/g6JQnI2n\nLFcJWVzwoQ99gtdef5U4TqjrmrxUdLyQwAuwrIDFfE68ikmzgkuXLiMQ+K7H8ewQKSW1UXz4o88Q\nJwuCwOB4DmlaIKWgLOv3XGN/MAq9Lbm0P+L+yZTV4j7IgFu3v0uaa3QtQdiYWpzLpXxaHQ9RVwQd\nn3hxxtWDTS5sbeC5Di+/+gpVIWm3QnzfJclKvCAkK3KiJCLwfMKWT39jwN7ONt/5znfIi5T93T3q\nyqCqnI99/CN88xt/xuVHHzkPmwDf99jb28NxHO7du4fneeRlxnw+BWPY2RpRqIper0eexyzns6aY\no9nc2Ga1WpOtVtiuS17VFBWAQLoWUbJG2A4GWKwSVJFT2DHdVsjm1gDf8ykDm343bBjgaUq/F2JR\nU6sSS4DSiqoGKW28Vps4TVB1iSUdWr6HqStGvTZZmlIZzcW9fY6O72EJj9rYTBZzup0OaZHzvkef\najgyWc0bb91ka2OD+/eOsG2XKFqzsdlHKUW/36UV9hDLFatVhK4UpS5oKUWaJQShRxHnnJ1N6Pfb\nCCE5ODggSYtmoBrF5HmGlBWnkzGO0Ny6dYe93X1u3TpFOB697pDlKmqckWFAmucUSlPXJffv3EPY\nkqqq2djYIlonYEnKomC5WBGEPtubQ+oiZ7VY8cQTj9NttXn1jVcJw5A0T1kuVqzjFUdH91ksIlwv\nAGO4eGGLIo+wjUueNbJe23JAK3IjqGuNsGpsAaoqyfMGl4E2WLop2rqG6XqG3/IBmE4mTKcLdvcf\n4X/87/4hWmv+7t/9r/it3/1XrFbzBtHbajWU1kI1cZXG8E/+519pWEW2Q0dK4qrGa9lUlWrCZgx8\n40++xmq14vOf/zzv3LiGLQS9Tpv//Z/+EzrdHmVZsl6v6babdqi0bCw0qsgp84x2J2C+mvDmd19l\nY2PEar3AdsB2PGQpGU+mCHEO+KtpMiDqEtcPcLGaeVRdUVY1vuvie2GD/4aHsmhbWCRxjOM4+F6A\nFha+76GUoixL4nVCux0yGm42s6myZHNzkzJrTiBBEKB1o5kHi8ViieuEVFXj2QiDgCxpxBaHh4f0\nOi2ksJoWsO3SCTtIt2i8CZZpwkts0ZgVbcFiMTsXQfgErkeapsxWS6TnczJd0u72mC6W7O9dYHLW\n0GX7wx6WlEymc+azSZPcJh06rQ6T8ZxX3/gOdQPbAgOD7gDf92kFberKUKsK1/XpdAdEUYRSivV6\nxWDYo6oq3rlxjbOze3z6Ux8mjhsyaBwnaK3p97vvucb+QBR6g+H+6RGrrORsep/A96kri35/i1r7\nzBZLPLeL60qSeI6Ukn6/jWVDHs3YH+3wyF6ffqfN6f0OWleUueL0dExlGrdolMS4tkO8XvHhj/xQ\n0zLI13QHIVc2LjAbT9BGY9kF2hSMNrqsViuyNCcvKopSMRgW1EazXi8JQo8kXeIHPq1Wi/V6hR+2\nuH3rLS5fusp4cp9Or98QA1VOx7EIPJukKMlXMyzpkKY5YauPqTWOV+N4Hgd72xR5QuhKfvzHfoQX\n/+xPSOOYXrfLYjZBK01bWng6RSIoa0WWV0jHRWUl63yFsB3anQ5QUemSuqjxAw+lEqoyBSO5fe8u\nQhhC36Mq7POEoJRWu8/R8SlJpmh3Bsxmc+artAFF5Yqnn32G8dkhly8dsJzNWa9ntIKAH/vM5/iN\nX/+XPPns49y7f5PdrYtkaUnQa8H5ULHd7iJoEMCe5zGbzdjY2MB2DLUuzneMNut1gucFzOYptshI\n0hwvaKGNi8DQ8h3idYTTDxBCEIaGJM74kR/5Ub76x1/DdX2qskYKSRi26QwGDHo97t09pFQ5nU4H\n6Vh4EoaDEXkZEYYeH/jAc1SqQBtBvI7YHA2hKs6DTGyErKkqTVE3xSuKVnieQ78/RISSvKhwfRdV\nNVGCjtsQRh3hUpR5U2ikxKicwHPIVc4//p9+GSEESZbjWJDHJYvFAhsbS0IQBEyOpzieizCQJ2nj\nl9AOeZoQtlpNJKYxfOe1V/g/LXjhuWf50pf+kHi5oFKK9WKJ53l4XlNU0bpp2WiF7blMpmPefPu4\n0f53PPJ8hedC2G4zm82Yzxd4nk+ZlxRZQZxkgCRsdx+mJLXaHbqdgOVySaGaFqjveVgWFKU+T06y\n0JUi6PTxgxZZoSjKDK3N+TXdp9dr4biSXJVoAVVdE8cx1bBHv98njRNMXiAcB0s0fX6MIPB92u0W\nG5tDKgWM4OTkiHa7zfF4wsGFHaKsIE1T0iLH9ST1ufQZaWFbMBptcnw6Zr6McV2f6ekZbhhQqQrX\nc/B9l83NEYNhtzk1CxifTQk9iaRk0BthNAReyGw245FHLlNXhrysyMuawA2I1xG7W9sUaUYSpai8\nIEpynFzR6bRotULClsfGqHG9PvP044w2uizWM9arBGl5GC3x3IA0U/+Gqvr96weiR/+P/tEv/r3h\n0ObkbEac5RRZAcbi3r1jkrxG17Bcx7h+QBh66Lrp0S+WCwJXYlFz9coV/ugPv8h0vmA6j4jiiFan\ni2U5RHFMGLQoyoJW2+f+4SFbWxvkRYbvN3z0sOXgepKd3c1Gq1pXxGlOXiiW6xilDcvVijt3b4NQ\nbG6NENJw5col8iJld3+XxXJGu+Vz8cIOXuBRaU2WxNSq4D//hc/zzJOP8sJzT9LreLQCi7YvmU3O\nsOqSqkwZDnsU+YpO6OE4AlOXnNy/x2K5xrM90DXboyE//KmP0W97rOZTWkFIFEWMz8ZsbW2zWC7o\ndbuoqqbSFUIayjLFsgxFmQMCSzZDs1anRaEq6rpJALKwsG2Xsq5xHRdtDHEcN5mkCIoiY3OjR7/b\nwqJCWhC4Lu12i0op+oMe9+/fbYIbypLRcEhZVk3/vNtjHcUEYUgQBszmc6q6UW7ouknPqVSN54ak\naU2UFKRJ1Th6LUkYBEiryVpVSvHI/kWKtDjXnhuyPOf27Ts8cukS8/mCxWpFURSUWcrP/exP8/Kr\nr7KM40bZYsBxHaKoYROl6RqjS7a3NvA8l6qq2NrcxvdcVvEcY4lGRWW7eEGL5XJBt9tBCxhuDLEd\nlywtqBXnDsYW6yimqjRCCqI4xnEdBDS6dEs+zEMuioK8yImzjCRKMAbyvGhMUMKi1W6dh1zoc6et\nhdGGolRYQlAqhRA2pa5ZLuYYYRifnhBHEdP5jCxt0AICgefZuK6D65y/jrXGWBaL1RzP9ZqdfTug\nzBMwmjJTgEAVBZa0ecBgz/KKstJ4XsB6FVOWCkENVePOdmwH13XIk4RaVQ2MrzZUqkbppt9fqJpC\nlejaYAmJUhVbWyPCVhMPqsqaJMmwHYfd7W1832tQ0XWTGasqzXIZYVsS13epqwpdlbi2S1EqFosF\nm5tbWLZNFCdIabNarrBdD9tuDHaVrknzjFYrpMxLPC9EG4tSmSaEsckFxXFsxtMpRakIwjZaw7Db\nxw+aG9ujVy9TpBndXo/lek2alxgD9w6PUXXdKMq0oa41o1G/yTsoK+IowfZ8sryhvwaBj+c7lGVO\nv9emqkqkY+P5TZKcH7RIk4LTkwkImyzNuX13/u9Oj/4BL11ri4Pdg4Z9MpnghxZZodC1piwExixp\nhR7SwGijh5E2m1tDzg7v8A9/8Zd57Mo+luMwGG08DJS+df0a7W6PLM4QFihpUdU1JycnFKrg4OAC\nSFB1ge/7pHlMp+0iHYlln9/tXQ/LsZmvEjzX0Bv2cXxJy2mxTta0e20Oj+6BKdjc2KRUKWHLZjor\nqesCqwZblPihzb/87d9E6ZrR5gajocXP/ex/jNEWmdJEcYoSFjeu3+Tw8Jjbd25S14YsK3nn1l06\ngccjF7a5/t03+eTHP8w7b7/Fv/fjn+P2vbsY4fCvv/x/8+G/9iw3Dk/xfRdZK4xlQEuqumycuEGL\nJE5x/IDVOkagMdqnVjaO18ZQI62QXr/BLAdeG8uyCDyJ49ik8ZSNQZ+iiIiyhF63izaQrOYIaXP5\n8kXKIsXxbJbLJRv9HaQMuHfvsAmBqWvKSjHaGHJ0dESSNNm+jmshg4A014TtHmfThI2NEbu7uxze\nvYfRmtl8ioVgMBiQpxmbm5vkRcrlZ67w7ZdfZhWteenbL/Lo+55gMpmhEfS7Pe7fv0+hStZp1uQR\nOzbSsXAch24vZHe/z+A81NyXDpbUFGVMaRm0UQz6AzACaXlEUZOFq3XjbxDSJs9yLMum3+9QlKrB\nRKsmBtMYgxc0ckLP80iTrAlD1xZGwHi2QNWGzUEXYaDIckxd4XsBSZJwf7kiDEMsx0KVCuec0lob\niPOkCWipCqIoRinFfDImWXsMBwM8v41tSW7evEEQenREC7/lokUTlpPXiiJLkLbAZBm+45JGMWhz\n3k7RtLstLj9yhTjLieMULwgZT97E80J8L6R7sXEzK1US1eV5frOPFIJWeD5srBr1DbWmxrBKUhzH\noyga7XhdN/b+qiqpKoe6rsjTkna72xjh5gsG/TbDfg9HWkRRxGS6pN3qIWwLC8PGsE+322j4F/MV\nAGfjRpkVJyl5nrM52iCKEja2RqyjZfOe+CFlVZOmJcvoCIRNnmuEA9Ix7I82kVIinRCDTRyX3L99\njKDJxFC14qVvv8re9gbv3LgFQNju4LouGxuNMijJCkxdE4Yuy+Uc5TfpWVmpcIWk3enQ6bRJs5hS\n1ezv7VCqhKJMCFs90ixDWIY4yeh2BhQ5VOfzqve63vsz/39clrDA8tje3maxXJMmFXVlc/nSE7h2\nH2MCllHMeJGxiAyzOOPmvfus4yXXbr1DbblcfeIxZNgiaPUatK2CNClphV08N8RxPOIoYTgc0gpD\njKAZzHVCqqJitYyJ05zeYESWFdSqYnNzkzRrhk1Hx6eUVYHXapFXFVlRUuSK8dmUO7cP8dwAgCiK\niaOUIi2wHcNPfPazlKrimy+/zj/45V8hqg3t7RGFzqlUjFQpdhXh12t6TsUjPY/07Ai7aswxJRLf\n71HhsI5Sbt46ZLLK+PXf/l3OFhO+8o0/4vqN1wj8ClMlJNEcRxbUVQJ12bDUhSFarZowZa0YDXvo\nPKMqmkQfIQRxEuG5LVynRalqsrQkTTOCwMO26kaZ5Dj0+hsUuebw6BRtCRQV+/v7lFVjkCmynPHJ\nKVkUk6xWpGmMKlOkBZbRzMcnXNzbp+2FBH6bLFUUeY3KDfE6w7ZtkjRm0G/T6YSUecJg2MWxBK5l\nGAwGKFUxnU5ZzU+J1jO2d4a4niDwJXv7Ww24Thi2N0f86Gc/wx9//U85Pp5TlBZRUrJaZyzWEZUu\nOTq6ietAp+vT7bbodru0WgF5EQMaLWymizXS9cjzvIFVCcV8PcZxBbUqWS7XTMYzqropwOtVQqff\nA8vGlQH9Vh9bSExZM1/MkM7/Q927B1uW3fV9n7Xfj/M+99xnv6Yfo9Y8NGgEEgEhCySMLUPiciqA\nreBSEEXsgMtUgpNK2WBeLuxypYwJJoRKFWAoMAgRZJBsJZIQwkhoNBpppBnNjKb7dt/uvq/zPme/\n9157r/yxjsYkZctDgTHZf3R137p9bve5d6+91vf3/X6+DpksGU/nGJaFlCVRlJCmuS4/UYKsKill\ng2m7VArGkxl5VpKVBfUmpNbvDcmKikqCKSxarQ5ZVmBgYgiL2dmEw8M7SNnQVA2ObbBcjsmSmCTN\nMS2LqqqYLhImy5hUwnKdUVYC22lhGjayUERRRplXeJ7PfL4kbHeoGxhP5pyfTlCVxLc9ULr0tB3q\ne8G0LUqlKDY4ccN2MJWNLGviKIJmo91bFllWsFqXLFYV67jg7HzM/QdnlIWkqiHPSqq6IUorkrzG\n9UIsxyYvCuaLNbUwWKwi7h+fUdQ1cVZQ1LBKchphIDBYrmLthIoipCx16XkWk8QZZdOglE2S1wjb\noREWnucjLJPFeqXb0lYrptMlgd9FNRY72wfs7+zwusdey+XLFxlu9TVrCkmep1R1SbvdpikLktWc\ndivgwsEBvXaLuiyo8gzV1Di2yXwxJc9jet2QyeSMPM9xTAfPNVFNQxzlhK5L3VTUdYVjmyxW0ate\nY/9MSDc//uM//kNbQ5+qVJujq15EHxyfE68r1lFBmld4YY8oSRG2wXp5huOZQI1pGAy3R6xXCfNV\nhBA6KmzbDlLW1LKi022RpBENNbIucRw93DMNi6bW9rA8y3WptzLp9QdE64gsy3A8j3avRZJFhK2Q\nfr9HliTM5yvqBpKkQBgmoddivc5ZrRNuvXwH2/WI4xQvaPHs88/hdzrsHhwgVUVdS7I44sMf/Ne8\n+Wu/lqO7R2RFhmsIvv5tb+Ol24fEeUFjwM2bN0nTNbJM+eon30BeFczWM5bzBWWW8ra3vpm6lly9\ncZWXD2/RoMirCikrlvEK01Ds7G5T5Dm27ehEsQGubbKzu8NquaKUOZcuXObu3Qd4QUhelORljpQ5\njmXSarWIopj1Yo0sK4LA4fLlS0zG2jJqu4H2Q9eSMPBZLubs7e2zt3vA8fEJV69dY7aYYbk2dS0Y\nT+ZMZysefvhRLMunabQ1zzQs8rzcpJcXOJZNVUiSNGd7e4v5bE4ja1SjeOL1jzCbj5lOz8iyiKzI\nGG1vkWUJ/UEfpRpu3zmkqhvWSYZUGuFbK4Vp1ISBxfVrl+i2Q4osZR1HmJZBuTn6+75HEIQEG3pg\nU9fkeckqWuK6DpZlI6uasmrotHtEUcLp2QlSVpSVJElSolg/5KSscWyLreEWalM87XgeWZ5RFiX9\nfo+yKlGN5pnZtkMcZ9SN0klhy6bX72r5yrKJ45gsy0iyHJRBnJdEm9wAQpEXOVuDPrKsuHHjKq3A\nRxqam5SWkqJqaDCxbAth2qzXK92XrCBOEqI4pcgqWt0+0/kM1/c5GU9wgxazxZL5fE2e1ciipNvp\nYm/6DjSFQw8el+s1Rd5QlpL1OsK0bGzbIS+LV1DJusxDn/TquiEvChSKra0RpmVTFjm2aRMEPnGS\nkqQ6gyKbBqlAmCaddo/xbEGSFTTCoqoKLNehUTVBGBDHMb3BYDOEFXS6XYQhaLVDZN2Q5QVNrdMM\nZV0jTIvQ98jzlHwj37qez3KVEMc5QdAiTWNsR7Cz06Xf9UiTBZ5rEfi+vncKieO4KKXwXJfLly7i\n2jau7WAKA9Ow6LY6JFFELWs6rRaKamNrDQGFY9s0NAhh4dk+ZVGSpQVVVdLqhHR6bV566fT/P9KN\nLj+2iOMEKQu63QGDwYBOBwop8IIueVXy3Bdu4fsunqlojbr4vsm9e3c52LvAyec/h6EEO6Nd8vU5\nrU6HJItpD0POjk+Q6xRMie2AZbk0SpLEBb2eYD5f0eoOWK+XjKdLqrLBna102q8pONjfoSgzZN0m\nTVa49i7xcoUftsmyjFJKFquIPM2I45jhaBsnCLULwxDYvsPuwQ6mbRClSxbLKVUW0/VDOts7fPpz\nz3Hn7gOE4+CHp6R/8Blcr8XWqMdyvmA6vs1waHMwOmBv4HNx7yFeeDnn4mjAo4/cpKnhtz/wQaJa\nguMgsBC2h2dbbPV7RMsF56dnhO0WpdRWREM05GVJMc1ppETJCs939f+nLlDUmo9TVnhmi7u3v4hl\nOShZsUrXdLfavPjiiwhhcHw6IUmLV04GyIKd0Ta1NLh+7TV87vlbzOZr0iRH2ALPzynLkl6vz+np\nhOl8hWNatNo+x/MZuzsjLGHQ6W0hS4lsBFWtODmbEgQBSika12GxWvPu7/5v+emf/ikKWdHpdMiy\nbOPA6CDLivF8QeD62K6LLHJM08R2LGyn4eKFXTzPI89z6romSjIaaWvwXWVwcjzTdXiWhRf4FI1C\nCoXnhyhMbCfgwfEpjq1nHWDQ6fSI4xjX9xiOWqRxxny5pioynN09nX72A0zH1qfFskJKydnZGb7v\n61mAabJaxxRVhSpLlGpoBSFJsklCNjWG41BXFZ6nO3R9N+D+4V0MWaNaLqsyZ9TvMNjuMFuOSaIV\nuazBsqgkxGmGbRd0w4A4LQlaXebTBTs7I0QDZZETZSv6jeLgwiXyKt/IqzoI5XkeWSyxLZc8LzGw\nMAwHgUtZNhRVRVNblFWF3wpJ8gzDAMex2Nvb4fT0XHcu1LpA+0vpeMMyqWuBKSyaSmIIPddQSpGm\nOqBUVZXW/OtqU5upEQtFUeJ5LkrV7A8GTKdj0kZiO3qu49gmaZaQpjG7u9ukWfQKS6tRDb3+gGo2\np8hTHUi0HDzHIUljdrb69LsDnn7mebI8Znt3RByvMa0ReZ7i+z4PHjxACYO6MahKRVmkDAZ9+oMu\n3VaH6WxMmZXUZQWNwDUchv0+nb725Dd0KYqCKE4YDPvITbH4YrFCNYIkjnG9AHeDvq6bV4+v+bOx\n0BuC5XJOUdTs72xv4tw1aZpgOQGqTjGo6Xdsdne3kDLGtF1kXRCGIePpOWBgCpOj+3f1YMjalHnI\nHNvXpQNbO1t02yGyrKiVgdMPmE0X1FKwWkeYhj7KVrXEBoRtMep0cT2bs/MHJPGaS5cukWWaLqcM\nhW3b+L6vG69QtLoBbmBx7eErnJ+MybKEdRLT6fo4ttD/NhrCdhdqgRO2ee6Lh+RlRVpFuPaaMPRZ\nrRakaYpSgqqWyKbh0t4uSbHgjY+9kdc/+ThPf+YZ3vev3o+wLRolcIOOjosnKdu7PVbzFXagf1jl\nhnUlG021LIsE3/VewTE4tkHoexgG1I2OpI+GHao8Igx9uqHH4e27DPptAqdNWVZE6YrAbzPY3WWx\nfABAVUq6YQvPD4milA99+HexTI/zyZyy1mnNJEm4eGGfe8dTFouILC1wez5pUtAK++R5SW0I8qqk\nlhqU1TRoyeAPoSru3D3i/e//V7R7fawsIy8qqrKgLCWCAs/zcRxX7/Q2vJjd3V0QFa2godsL2dvZ\nYTGbcHp6ijD1brOUNQiTrEjxSw/PEFRVrU8clkWaZVRVQaMMiqohSyOGvSFZllNUJfP5kr2DEM/z\nSKJYO2aE0FJGKQnbFmmckKQ5eZ5TFQX97e1XLJCqEXQ6HZQhGI8ntHyfXqerPemVfkj67RallNAo\nkjSnzCt2trf5mZ/6CXb3tnnqqU/wnvf9S9IiR5hgOR6uKclkQ1KUrKNEs5BGkJcNjcxJs5QtAyzP\nx7BNxtMJURRx9OAI33fp93uMp3MC3yNarmiahrDTJfRcmrogSlOoG3zf21R4an5Ps+l4LmSFZTkk\nSbJBjdsb1o8Aav05VQ7KpMxyWkGI5VgEoYdoatqtPVbRnO3tbV56+RCFQRC0NlKMZG9vF1VX2pJZ\n5tqf73sYRc7B7h6NqlBT3UudZRnr9ZrBYEAUxYSdDl4QcnT/AVevX+PevXtIS7DVa7O3PcQJQs7P\nF1y/eoPz8Yw0r8FwGJ8vKdIF/U5Aa+PsMkyXJCs52D0gLQp6jWCdpCzmK9qtgNANKEtdkeoFLlEc\n6/cy9BGKTX7C4sWXXuTKxUu4js9kNse2HNKywndtkqxgtli+6jX2z4R082M/+iM/dOHCSLcPFZXm\nwzsutmMRBBa2Y6JkRq/rUVUxeZmRFQnrKMEwNEDLMASz+YQg8On1OtiW1rMcy+JLThPP8zc3cElR\nlsRxBoZB2A5RTYMhjM3xTWLZFq12wOHdQ9qdFpVqsC2DNMmwTAvDElius0kLNgShg+NZCBNqSgwL\n6qpispjQKInrOyyWC1zPJvQDDGXgOwFJmrFcl5xNlsyXKxAmeZlSSw2NCjohUkKWlZimxcl0xqc+\n/zk+8vGPcXRySl42VMqgRuhdlGrwPBdZVZi2TVNLXTHo2JimQZHnmnPi+9S17tUUpoHnely+dIl7\n925hGhIpY7J4iaFqOi0Hz3HZ2RlRyZxeL6RRYFsWfhCSlQUYNueTCaPtbRzLIS8kq0VEmtdIZVHK\nmrKShEGIaVjs7lzEtVxu3bqH67cwTYMszbh+7Qr9bpc4jiJkPd8AACAASURBVKlrwXqVIVWF67n6\nIdIdUOVaOhKmgWXbLJdrqlLS1I2er9Q1spJcunKZbnvA2WSMaVlcONgjiVeEnoXrNlw82EGJQj+U\nllqi84IAKWsEJlma0253qRsoCm3LNEx9IpSNIk5L8rxiOl2gsGl1OviBr2mjpkUQ+LieR5qXmKYB\nArrdLqaANEkxTQNhClzPxrB00XScpjh+QFlLGiGoyopKVlRNjR+45EWK7XnEyRrL9lis1iR5SV42\n5EXJi198gZuPPsbpZMLvP/0pllFM2OqQl5Kw06GqFEWpKGRNXSmKoiRJIgSK7e0Rs+mcMGhpQJ8A\nWZUMBn163Q5REtHUEttxSWIN8EujmDhaEQQ+Tak7asNOR4PAyhLbMlGNAMMgjlKqQrJO1igElmnh\nOBZbW0O2t7fo9tp02qFGGQeubt0yIfB0gY7reUjZsF4lFJWG0HmuhywrDg4O6HVaxOsIy1TISpeb\nyKp6RT4bjkbM5nNMwyRKEy7sH5DGMaKBna0hgWvT7YVESUToBTiGyd7+FiiDdRRz72TB7XsnHFy4\nwHI53/THCubLOVeuXAEh8PwAyw6YL1Lc0N+4hQyauuHs9IwgbFNVNbPJDMO0MExby0Z1w/hcL+ag\n4W5hq4VhWjTSZLVYEQQOeZVjuyZlVeJ7PnfuTv9kpZtNleDTwLFS6puFEA+h+2KHwKeB71BKlUII\nF/jnwBvQpeDfppS6++Veu64bTk/OoGmoHcnFS3vMJucopbh48SJFkWOZEGU5SZ6iBMyma1ZpjGoE\nvuNz+coeju3i2LoAO000E32ZaohYnudIqbA3keQk0VqfYSoWyzFB0MIwTaL1EtOykE3NIl6ytTsk\nLSM8B4Qy0TAKreOnWaSfwE2OsSES1o2kKGvd3Vrk7O3vb2SBmLDd4ex0QqfbxhEWy2RNHJVkWc18\nnnHhwoh1lNIzAnzfpddzWa9L8lKSphXtnomUELg2UZzgmwrlengbDk5d16hGUFU5pm1QZpneHdc1\nw+GQuqxI05TA9TAxqVVN4Ho0jSCOIxpVYLsNVdUwGnbZGvRpioxOy0FVkjxL6IYeSuikrWV4zFdL\nusMtkrTQjTfK2NhYOwxHPtPJigdnJ5h+SJnDO/7i2+mGHgY2H/jgh6BpaCpJUTe4rsub3vTV/M7v\nfIST4zGYJrbl0uuEPHT1Mut1zmKxIGwH+IHD+Oyc2/MjHV7LNbb2q9/0Ndy5cwdZlhzdvkWUK9rt\ncEMAbbh+/RplseCxx68Q+jrAM1tMNA21qmlMSZIXBK6WUWbzFf1+H8fxdD2epcuoHcfDNB2iZE3Y\n0jJhVUvCoEUYtFhGa6QyWK5XGkfbaeP7LgpJWegZiTAE3VaXs9MxoqsprINhTyesixrXtsnKglbg\n0x+0sVwLr3FwfJesyMnLCmHZuE7AerrGFAbn65y/9f1/j7PzE3YO9olWC8pSD/bPx1O6nQHTkzFl\npU92rVbAm974RqpKM5WuXXkY2xR83Zu/hs8+80mOT+7R77d56qmnsDwN+ctjTbCsqgqpJAYNRZWj\nhCD0Q5paF8I1SqAwyLMM07HZP9hlOV1jljayqTdyTUVRFHieQ93UJEnyyvcLFJUsWK5iXezR6iAL\nSVpoqSdf5zRZoWcVgYdBmzDUbiXDMDQIMC+057ysefnWXeI4xzQ8sjzmVnaI71l02i3mixmtVgtZ\nVnRaAc9+9nm+6g1fSZ5mHB+f0h0ekCQ5YHAyPqfIdDgyK3JcJyArJMN+h9lizWSSIKyQaFURr3Km\nLNka9jEIUbXDS7de5vqVyzSGyTLKieOYsOWDcDg+mRC2PCwPeoMuWVby4P4ZjzzyCLPJKS2/he3Y\ndDshqyh+tcv3H8l187eBF/7Qn/8R8E+UUteBBfDuzcffDSw2H/8nm8/7slfTNHRbXS5euMz+/j4v\nvnALKTUh7+TslHUUsViuyfOcoig5PTnTDBW/Q1nUBEFIkSuWi5TzsyX3jibcuX3G7cMxp2drzs4j\n0kzh+X3yHOKkRKEj/K7v4AUuEkVSakRxUZVYrochLBzH2WiJmrZXVgXL5ZzDu7dJixzbM6ipMC2o\nJayjlPUqY7kuaLe7LBYRJ8cTyqzBUC6D/i6NNElzSZY1LJYx7XaPGzcuU9cNZSFBmNox1O4znU5Z\nLFasojVn43NkmVNmGe0gwLcdmroiipZa3zQgyyNqFHGckueaTVLXNacnZ6zmM0wl8TwD37O4eGGX\nQb8NogBR8ru/92HW6yVVVTFbLDgbT1iuYtZxzipaslrOWSznJHlGjWIyn2lU7HxBIyW9Xo+sKFlE\nKSeTCcqyuHTlMqZpc3w6ZjxZ8ZGPfJyqFuRFwVvf+lY818OxbepKH+8/8ju/y+1bd+kPd1FYrKKY\na9eu4bqupg8u55ycHjMeT1+RzeJYS1x5XnH79h1mswX/zbvfjWwaHAssUzAcdAkCl7yIsT2bo/t3\nOT47J4pjzfMXDdhCUxAbRVE3eK02jtfGC7o8OJlxfLLk8M6Es/GK8SRC4eF7ercsLBM/DImzlOli\nSVkp1uuUPJN0+yPyQpLmkkZYFHVB2HFxPIHtmaRVxul4gWHZGJZFf6tP0AoxLJOHb76G2WJB3TRs\nb28zns2ZzhYI08EPWvpBXul2p1LWnE/mrJKMqjG5e+c+soLFPKIsJapU7I12efiha7zxK76Cf/3+\n3+YtX/tmtgZDtgYjLh1cwrMDTOHw7Kefpa4VR3fv8fLhbdzAxTFtyryg0+kgDKXnONRgGhSyxnI8\n0qzifKZnXYtVwmS2oJQNaZIzny2pmhqlBAKDXq+HlLpUaLFYsFwusW0bwxB6qO+7tAOf69cewjAs\nuv0Rk8Wa5TrCshwGvT5SlmxtaSa8aW6qNF2HBkVVN/o9VwaLeUySlViWQ6vTpt/v0+n0dCtY07BO\nYtIkoygKRK24fu0hsjxBKcFjjz7OeDzFwCRwbGwB7Y5PWaWouiQvEkzRcOfwEJlVGrecZChZs1pE\n5LlkPospJXzuuRdRpo3ltqilIE0LTo5nzKcZZ5Ml+xeuYAiT7eGWttsWGbUqOTm7T2MIjTmPCm69\nfMTtW/de9eL9qnj0QogLwC8A/wD474FvASbArlJKCiH+M+CHlFLfJIT44Ob3nxBCWMAZMFJf5guF\nga8u7oa0wxbreIUb+ASezfb2lrbRJYX2LAtBZ9Dn2c9/lq3+Hpajtc800U9FA5PFYv3KolFLxXA4\nwrAECEnoBwyHA9bLJYv1BMcV9PohVVWRSs3tsA0Ly3LIs5J2K6ChZrFYgGkQeDZFVuqUZydEqobt\nrR6ySjUnvjF1mjaXdNoD5tOl1iFr/TBrt0O8wKWsMkxhsrO1w+HhIbbl4zgOAokyDfI0otvtEq0S\n/KCFZbnEac7JyQNuvvbaK1HwNE0xbYdBp/1KNF1KSSM0vrZpvlTnZ1PlBXmWcP3qRWSd0zQNu7u7\nhGGbF774PFleopQgjQtkDb7vUpYljmHSVBmjQUgjc4oiJylzRrs7lFmBbbmbzk+JG3RZrVNmyxVl\nVhD6PoHXYjqPuHVvjGgElmVw48YB3/KOv8Rnn/kMH/u9T+J4beqyBlts3Bc1w8GIZbTGcSw8VzEa\nbiOlZLlesV6v6Q0G1FW2kQgkSmldO01Tatnw2OOPcnh0iMx0PuLKlSskWQymgSKn33MoioIkXtPt\ndmlqg0oZyEqRpnrn5pgW3c4WcRyzXiVIBSiDNNcNaELoYpcij7l8ZR/TNJGVYrVOWS6XupOgqihq\nXZxtmTp63/ZttkdDLMsEy+P24RHzRcTWsMPu7jaL+ZQg7FI3CtU0pBsN9zU3rrFaRZycneO6Pv5G\nYlmvEpZxgu+4uI5FEAT0ej12d3eI4hVVkbO3s8Prn3gdzz77eS7uH5BlGS988SWuXbuBISzmywVp\nGhN4LSwhcD1BLjOOTw5ZrSekaYptmhr7vH+JO3dOOT2f41o26/UagY1pe5sTbYEQekamGrmRGUKG\ngy0m07Eu+hGmvg9Mc7PGKPzAJQx9bFvjGdJszcMPX+P+vXs4ts9b/tzb+NCHPkISJURRxI2Hr1AU\nGa0NWTaOYx1EU2pDmJ3TYCJrPSvwfBPXNXE3FXz7uzsEvk0aR+T5Zl6SV1x66AoHuzukRcl4eoZh\n2BzdOyHNFLXQGydl6Pc5Tdb0eh77233qqqSuauKk4v7xGYHrUdWKh2++FlPA7Tu36Ha7jEYjBHD7\n8BClBMtFxP7+PnWZsb+zxcPXL3N8/DKDYZciWROEbdZJSprl1MIhy0um84jz8wXLSP6J8uh/Avgf\ngfbmz0NgqZT6UsXJA+Bg8/sD4D7A5iGw2nz+9A+/oBDiu4HvBjAMwe7OJVqtDsxMptMp7bbevchG\nsYwjskoP4GarNao2cD2bXq9LXkjarS4YPvOZ3klZhtasg05IUeX4hocwbUpZEUURlmsRhD5CaMhQ\nVUqyLKURilavhZQ6kr1epZsF1KDMC7phSFTG2K6FsASe7ZJliSZg2gamMHA9R7clxbl2JzguoI+R\n1A3np2Msy8AwLIr8DN8LqZsKywbDCpjNJliODYZLQ4zjWniuy3K9pqobHhyfs7e/g2tbdLo6zFRT\nY1gGsi5oBKi6Is5zqqreoE8bhsMQYXSpmwLTAMe2QJXM5trtcT6e02BhCBvH0eEgy7RZRRFCGIR1\nQ+jYtGyLltdluciopWJ7K0AIRV2VVFXNKl5RZjl5VmMaeuB56VIfqRzuHj2gkjVpmvMv3/8Bbt54\nmH6/T1GU1DSUZU1V1XQ7XdIsxnEcsizB87us0xxkRSvsMpkt8cOA6TRFllI/ZFybsNtlFWn2+fHp\nmcbWbvXxfZeijFHUuK5JUTbkUrGKE8qixFcGUZxT1wrD0IPtxWLF1tYWmWxoDEt7q4sKWSmqQlIW\njaaOCkEpJWleE4YOjZAErZD5YsVsvtSDv0ID5Bph00g9UK3Ekl6vQ5GllEWDEjbT2RqBhawltYzw\nvIDVMmK1XhAEHkf3z+iELZQUGJ7Fxb19DGERZzkXL13m889+jjiOGY1GvOuv/3WOjx9weHgL3/dx\nXIvJmfbo3793ShwnvO6Rr2A8H9NuhwwGPfZHuxRlirC0u8auoZIFrVaHvb0DBsMReZaxXC4p84Iq\nL7A8RxepOI4u6ahqbNNBCbQNNIkBg7KqGE/+beF7txNQSI0HL6WO8juNwWoV0e22GPQ6tFseSRxr\nyasRfOITnyBOC5I8o6rh9PSU7eGAoioxKy23Ng1sDfqcnk+pagBNB7WEYGvQJ2y5TM7PMBAUSYJF\niOO2OX1wjGmahGGIYxoUecZsOqGSkqqsuHzpOnGy4JFHHma+ynnqs59DUGNbDQd7I6qqoNVyCUNN\nnnzk5mWmixXHD84xREWjFCjJcrkkywptsRQWyyRBGSaz2YJe4DObLngxS3EdidNtceHSPm9/+9t5\ncDbm1/7P38YUCul6rOKx7pl+lS1T/8GFXgjxzcBYKfVpIcRbX9WrvopLKfWzwM8CtNuBUqbB2XSC\naTl4rRDLcVnN1kRZSdjqaf1M6t3baNvHMh3u3rmPwsIwLO4f6Z2CahQlCtM1EKaLY5m4vk9RFGRp\nSZbNuHnzBkmxxnJMVqsF63VKp91h0O9Q1QqxqcBznRZFmSOExc7ekKZOuHBhn6xIsYSB0UBdQ9Xo\nJqnGUKyjFYFrUhSSeJ1RZpuSBttDhB7dzpbeAVkO56dLrl65QFalhO0uR0f3MUwL2wrI8grD8piM\nF/QGBr3eFienc6RUPLh3zPXrV6jyjVaq9ACylpqwmecFZVUDBnVVsr97WVP2lGR3Z0jg269oo9pS\nukIpQSlB1hXpOsUQKU2jddg8T8liGHYduu0OnuVq7TXPORvPsISBaZqM5+fUBox29plNV1i2wxdv\n3WY02mFne0Sv0+fwzi1EoyiznJduvQymgeMFRMmKsmmwbJu0KPE8Bz/wMW39kOx0OkRZynS2wPM8\nxmdjAsdh5/IV6krS6fa5d3yM4zi02m3Ozs64+fANbl5/iEcfu4nt2aR5wm/91vsIw4DVKoLGpNPb\nIUkUsrZIk5K6LvFcn2hV4lgV8/nxJjeg4VlKGBrBXDVESaEftEJx7+ScrWFXn5zSDNcJmM+WZElG\nbTUEfkszk/IapQpQFqqJMQ2X5SqhFeiauen4nJs3bzLaHmKZDrdu3aLX62EYFuv1GgvJwzcewbZN\n8rRgMAh585vfwtH9B1y5coXFYoFhGPzKr/4q//dHPvDlb8KP/vHv41dwK/m//diXoIrVH1qD2u1t\nVqsVigZTaHqrUoqHblxksVgReD7js3MaWSOLivUyQqEZ9e22RbROuHfvLg26xKM2JY7tkZUVJiam\nWZPnOieglMCwHBzHwRa2/pkIfXZ2Rownxzz++KOk0Zp0leBZJoVMePTRRwF07sULOJ1MmI1ntL0W\nZVqQiSXf8c5v4+VbL3L1yg3e/pa30u228VwL0xL4js08OsdE0G63eeTmo7z40m1+8qf+GYeHd4nT\niMWqwrI1TVe3idXIokYATVHR3x0i6oKmSLi0v8fXPvl6rl2/hFCKzz/9adquizQdVKF48rHH+Pzn\nvvCqv0+vZkf/tcB/LoR4B+ABHeCfAj0hhLXZ1V8AjjeffwxcBB5spJsueij7773qWlHjUtYV0/Mx\nYdvn8O49hGlgmA5pXmNYBfPFjGGvj5SS8/MxcVKRpfmGpVHz6GOPEWcx0+lUe2l3tae5NwxwzC2O\njo6oFRzdv8uVhw5Is5jx+AzXcRkMe6RpSlULHMPl0qUrPP/ci9RNw8GlEcKoUZVgPJkRtnyCoMV8\nuqDf7VElijTXRclCBSSxpCoU4GAYDv1+n6YG1cBymRNFFUJIkiTn1uExg2Gb+w/O9dBJCXbtFmma\ngGwwLYPTkxl1I6gkrFcZCs2PKauUONZduIZhaOnJVlSVQZZq9xLKxrJMsmTBpasHZMmK2SzGdV0C\nv6Xj3abA8R2ySGuYu/t7mIZNnlfcevmQdrvN3oUuvbZFkeWkWUFZ1CgMonWGbdu4XkCW14SdNifH\nE9KswnEqTMtjMl1StGpQJv1+DyFqJpMZo50d/WALQsrJnFo1NLJgf3+48bIXOLZHEOiOTSHgDW98\nA1/4whcYdLo0RUm0iPir3/ptfOz3/w2zszG1Y3Ph4kUm4zHH9x+wvz3iwsFFhlsd/uDp32fvwi5x\nmlCuJKtVzLbjsVzGeG7Icrkmzwr29g5wXZ+TkxNc12U46NFu6ZBOkpaUZa2bz2SD4/qb01nFahax\nuzMiWUe84U1P4nkBTz/9NHEW89hjj1HX8PQznyN0A1zbZjlLGI0C9nf3ubS/j2VZPHThEt/wDd/A\neDbl2Wef5fHHn8C2dRvaYrEgWq24f/8Iy3LxXY88q/ik/AP621uMRiNefvllTNPm05/9+KteBP40\nrsVyDOhyoFpJiipnb28Hz3M42N8mjRM836ETbrFaLTQ6xIDJdE2v10HW6OTvZhB75fJlFss5dVZB\nU7FYxZimxgnIBpJED86XsyU3rl+naaQO0vX7JFHMYrlg0O5zPpniBjaKnO2dfX7qf/9Fdi9cQYkG\noWOuVNTYSm0KfWtQNigDhEDVNcLeLKMCVNNoNIGCiw+/mbe9450Ytv2lNhgQUOeSVRTz6+97D3/j\nu7+HGw8dsDvsQZly5co+Nx+6wJvf9Dpc1+Z4OuY9v/E+Ll97DZWckiQRVVWDSviq172W+x/65Kt6\n//9InbGbHf33b1w37wHeq5T6F0KInwE+p5T6aSHE9wCPK6X+hhDi24G/opT61i/3ur7vKWGVdLt9\nTFOws72FrAt9dM8roqSgrivyNNrsbiBelSAsPC9gtpyxvT3C8y39jUwSbMMkzzK6nT4vvPQivc6A\n8/NzLl06YGd3wN2jW4xGQ87G5+RZgylM0qwgSQodmLI9iiJHAZ2ew+WHdmkwWCxn+vhcS+7fn3Dp\nwg6rSMOyLMumyCvWa02/a7c6OtIfx6xXMd12j/lyoWvo9PuJYRh4vkG/32M6m2s2ShAiGqUTlWVF\n3UBV1YiNPTJoOWyPOviBjW2b2Ja5YXevqOsGy/SwnZA0TohWS3p9lycef4hLD11A5jllpWUlgcXz\nX/giVeNSK4FlBxoFEUeYpgXKwrI0gfHJ113h3tFLdFptVtF6wyUXxGt984x2drhzeIxhWRiGzWyZ\nYJomrusSRRGOZeK5IWHg4Tgm5+cT4iRlMNrm5VuHm+rDmieeeBTX0157JXVOQQixGdQJhGUzny1p\neT5K1kzPJ/zVb/1WzqcT/uBTT+G3WpRVxXDQZzTskxUFvmvS6XgkWcQqXukCZ88HYSErQV1Brzvk\n8PAur3/yK3nxxS/y1rf8OT760Y/Q73aZTs/4xj//VpZRzGC4zXSy5N7RCZPpQnedWjZNVVLLgk47\npNdpcf3awwyHQ9brNT/8I38X1/X54R/9cVzXx7Uc0jTBtk0e3L/PZDLR+YCiIEkSXMdHNjVBENDp\ndDg42KPdahG2WsRZzjPPPMOdu/fpd3t83dd9HbZjUJcVslE0DWRZxnt/85df9X39p3fZ6CpJ3Z/o\nWiZ7+zv4jq737PUGzJYLkjgjjlNMU2Bagk63Ty1LlFJ0ewPWq8UrrKEk0chey7IwUfiexzf/xXfw\niU98nC+89EU6nTbD7pBGVSyjJVujAZYAx3FIkxgnCPj7P/IP2dq/xt//0X/IR97/nv+0b9Hmevq9\nP8L5+Tm/8/FnWKwL8qJGmA6FrDi6e8jD12/QCTv89Hs++qo0+j/OQn8Vba8cAJ8B/mulVCGE8IBf\nBF4PzIFvV0odfrnX9XxXBa2adquLEAa7u7tMpxPqzYC0qRWuH2BYOllHY3Dv/jGW6RPHGRcv7SDr\njH6/S9jpspiv6bQHLGYzpuMJu/t7rFcJq/WcJ1//KIqSNIkARRTFuG6H5VKHhCbnujLPsgx2dkYc\nn5wyHLUJWy7rWCNipSyxDMFqmbG7rdvslXKZjVNqBQf7e8RRRJIn9HoDqqLUUsH/69KGp7Dt0TQN\njzxyk9l8yv17J9AogtBje2uEbbvaebNcgmFSS0mr43H12gUcxyQvUqDGc02EMHFtjyzLWS5SijjF\nNhVXro54/etfy3IxRjWWDnw1Wg+fLyKyUjBfJMRZReC6GEaD43hs71zg6M4ZDx6c8KY3PcrZ8V1C\n32FrKyRea3a7EDZxlOL6HvPZil5vQNjuURQVp2djHMfTVYtNQ1VVeI7NeDzGc1zm65QwaBOnCTqA\nDl/z1Y8xnZxhmS6Gqcizkvl8wcHBRcbjMa12l7KWVHlFv9XRick4IStTvv2d385ivqJSQg9tl0s8\ny0SoiqtXL2AZgguX9zk9GXNyvsAN27hOiCwqBoNtppM5R3fukRU5aZrSarUwLUW/3+W/+MvfTLvb\n5Z//0i9hmxZ1rWh3ely+fJmy0VjiuqxYzZbIsiDPc/3vrApMpWvipNRahs4B6GLo0A/Y39/HcmyC\noIUyhF6EslLbii2LqqlpByFpkbOMImoluHBwhZdeeonA87h+7TLr5YKi0j2mjuXycz//v73q+/pP\n6zKw9E5ZmCilsAV0u20aDF3cIyWO6VA3lfaoK10Eb7n6Z9Y0Teq6RhhKO38wUUqQpDmNkniOTRSv\n+eZvfDudfpePfvSjZHnB7u4+9+/fx/ZcRv0uplDYtsnZZMyvfeBj/M8/9pMox+Hjv/4ftKP/qV7f\n/x1fSd2YtNpDev0R57M563XEi889T7fdIQgCfuXDz/3Jl4MrpT7KRtnbLN5v/Hd8Tg78V3+k120U\nW4MhRVExGg2p65pet0sUrQgCjyTJyIuMXtjDdW1Wi0jfFEoQdBw6/Ra725e5f/8+61XC8YNzypFi\ncnZOmVe8/MU72LbF657QOlwjazzPIwh8Ll68yKc+9XmiqCFNMgxhAQ2O4+D7PlvDAVmesL29zcnZ\nFJnDaHeoB6HWgtHOgOVigW23uXLxNbx865DpbEVVljRKEUW6WQZDIJRCAfoXXUaQxCmdbpvDw7uE\noU+nHbJcRly/eg3Pc5nPNb1wsVxrlrmSdLttpKxoVEaW62GVsEzqqkAYWqqRZUwYWGyP+himIkk0\nNjeJY9ZxhFKKIOyCEmRFgRJQlgXtls/1K5fBsDg7nbG3v8N4POX4ZIxr+6zjlNHuiDBEV6DVBZUs\naDLFQ1cv85fe8S38hb/wDn7jN36Tr3jyDczma+brmB/8wR+k3W5vNG6fVRSjgCRLX/k5EMDDD9/g\n5PiQ9jDkiSee4PnnnycIAm7efJg4yYizlNHWDnenhwz7A6LlCs/zKOuCn/xnP83JvQf8/lPP8HM/\n9wtcvPQQT3/y4/S7HW7fvo9jW4wnK7aGO6znOeW04DWvHTEc9IhXCZ32kG/8ptdycnbG0dERBlBV\nJbPpgn/xK+8Fw9AcpqKilg11fc4Xnn2Bsmk2Q28Tx3Ew0YErYRrUdQWiwXc9BoMB+zu7tFotXNfd\nIIcbTNMkl+qVxKhSiu5wi0KWeGGAjGPm6zVN0/D4E6/n9p1DzudTdvf3GJ+ds5ivsG2Bb3kaB71+\n9f7qf9/1lnd8Gx/7wK/yP/zAD/C//OiP8q7v+yF+/id+6I/1mlu9NnmRkGQVptBqhutqBC+hQ7zK\nEYbAtsA2XX1aahoqmdPpahy3bRpIxaac3MK2XNIoRjQNmHDl4mXWyYpPPPVxlDBo97p0B30enGoZ\nrpT6a8/XK973f/0O3/f3/gE4Hb6c0/wtb/4v+di/eS91LTfdzXoQ/ydx/eW/9l385i//H/yd/+nv\n8I//0T/m8Jnf4OqTfwWAg0s38B2f5Toi8F26gc9nn/40vmezNejy5Fc9ya98+LlX9XX+SDv6/1iX\n5znq6rWhLl5uNOfa2JTknpycYJomxycT9g72GI2GfOELL+E6IePxgpuPXSEIPJqyYDpb4zpt8kLx\n4OgIY9OI47gulg2vvXkDRc7xg7s89vhrCX2X+XKNeMGjPwAAIABJREFUYQS88MIDJuPxK38HGtrt\ntj4aqobtnSG2Y5GXFbPJHC9w2drx2NkacXY8IfC7yBLuHt3HtDyEAqkKlGpAQbfXwvM83VIEmKbN\ndDrXOGUavXs0BYZQVFVJr9Mhjte0On2iKMGyDLIiQ9YlvV5IfxiQpms6fR0SMRCoutE757yiE4Rc\nuriveTmrc3zP4ex0TBRlyE0Zt+eHPHhwQn+4xdn5DIFNuxNiCpjMpggcZGPR7QxYTMZUMkcWEsMG\n37bwfZ+y1I09VVXxd3/gB/ilX/xlZrMZvd6AvJR0ulu88WvezIc+/GHuHR1tOPUj7h0dEUcpYhNg\nq+saoRq+9797F5995lN4QUCr1eLo6B5qU0mXFeUrvusoWtHrdPnOd72L3/j191CWJd/5ne/iY7/3\nSSQGu/sXaHc7dNohhmHw4vNf4LnnnqPIU8pCslykCNvFDwMs09EDsRoMS6CajdXWFLi2juzrZLGF\nYZmbInoDUyjSNMYwFaajizMef/xxWn6AUmJjw9OwNs/zUI2kyjOk1Lz+L6GLhRCE3cHGMptRVRVx\nltDaJCPX6zVXr93Ach2eeuopTNtiHee0g5Boteahyxd53WOv4aWXXqKpFcK0+Plf+Jn/hHf0v/v6\nvne/E8e1uHjxIkFbW0BN02e0vYVsaixDSzp5njMY6JMwGBRSF3/UsqHIcurGYjab6USxYfDSSy9w\nenrK8fEpaZqys9XjhRdeYLlas4r0wHsdzV+RewaDAe/9wAf5ru//AfLGQinBdLLg6KkP/qd9g/4/\n16/97A+z1etvakeh224jG4Vra6fdRz7yIf72j/3in/yO/j/W1WzQppYpyMoCw7VwbZe0KDTQSBio\nZs5krIFjaVIS+i0eefQK164+RFmWfOqTT7OOCq5d3SJPlqAsGgWO7VHXDTcevk5ZlihVvOK5TaOY\nqpL0B0OiaAUCmkbDqQCiSMstwoTJZMbVaxcxHcF8BlcujkBVnJ+OmYzXlNUKoUApg1rmXL28j1IV\n1oZ7XtUK1egjK5icnZ2RpRkCE9u2sUyhNXsaLl+8SJqs2Nvd4ux8wsHeAWHoE4Qu52f3aHUDLLth\ntLWPlA15VlHmFa5tEoQO3V5AkWYMBz0sy8C1PepC8cRjT/L7n/gUrcBFVpA1BVuDIV//9W/nt3/7\nAyRxRjtsce/OXf7m93wvP/lP/1cs22A5n6Dqkm/8hrfxmc88S5InZKnkz7/1m/j0pz+NKeDRRx/l\nnd/xXfy1d34nwrZpyhLDsPnY732CX/m193Lp4AJnxye4tsN8fI6JwkDR7bRZrVagahTw3OdfJAw6\nSCkpk5xu0KGqtK++1+tx7do12q0A27UYDAZYpuBvfe/f5N69u5ydn3Kwv00Y6tTgc59+nm/4+m/k\nN9/3WyjT4Xy65pFHHuW5555DWR65bCAt8H0DwzCQtUTUDb5rYzoWplAYqsazDCxDsbc3oNfrMRj8\nP9S9d7BlV33n+1lh733izam7b3erk6SWEEqgACgLSQghjDDJOPEYMBSFeR6PAdcYB2yMmXkYY2Pm\n4Smwn8HGBgNGBGMhMEhCEpJQaKXOOd984g4rvD/WvrfBY4EoYSOvKlWp7zl3n3P3Oeu3fuEbhqjV\nGkSRQizjtgVoGbaT0kHTxlpLnhsK44KJhtZUkwqFTRmfnGZycpK9+/eRZRlL7U54blHQaDQYqQa2\nb7e7xPXXXRecsyoJa1evodfr0azUqFbrrBkfR0jPo9ue4Pzzz2VmZobFxdYz3pNv/b/fxf/6kw9w\n+Yuv4I6vf5vnvOA6Hrv7tmd0zVfcfD29bpuiyHjs8W3s25Fx6MBh9h3YTxzHDAwM8L37HyY3cPXV\nl7LU7rJt2zZAcMHzLmBmZobdOw4SVwJq7LLLXhi+O0qyatUqqolg3ao1ZGmPV9x8A3GSMD61KrTz\nrGP19Boy47n0sst5+2/8Fi7P2HjaaiqR5o5DO5/yfb/kup/nn2771Eom770h4Eye+fqHz36an33V\n6/j7v/sbXvPa1/OFT/8Nr3jd6wGYXTTs27eDbrcLpTR1vV5ncmwUHUnWrDvjab/OsyKjjyPlN22s\nE8c11q6fZmlhnpMzC9RL6VdjPTOzS4yMjGG958TJ4/gCTj99nMHBCfbvP4DSMSqqYjLLsSPHUUmN\nWq1Gu7VIpZowWK/RHFTESSA6KFnQa3WxVoWe7lKXpcWUPLUIEfBhQXAp9I7XrV9No16h053D5RlT\nYyO0Wi1kMsCeA8cZbAySptDtLTIwEHPRc89m5uRROu0emzafwa59Bzly7AQOcCXs7H1/8Hv81u/+\nPtYYKpEOcCvjmZgcIY5jrr3yCj77+c+SZ4H5uHHzOM9//haazSbtbsrFF13Gpz75aZxzJflE0Gg0\n2LRhA9+9516uuPJqTszPojNL7i2XXvRCXH8BS8xie44Dh0+ioogLzr+IBx58JEA1sw5zi3MkScLs\nTIuTM/OgI/KsX9LSg+l2IwmZso4jjDFcffllnHP2WVz6gkv45jf+mc1bzuAb3/wOt37ldi656GLu\ne+C7nHvuOThvmRyfYLAxwD1338/w+CBrplZx5plnMDIywtDwQBi8pym2MLRaLeZmT9JttUNm7y3N\nRp2xiVFu/eKXGZ8Y5YLnX8BH/uyj/OVf/jW//dvv4brrrmNoaIg9e/bw0pe+kr/+28/wwCOPs9jp\nAhKpwmyj2+0SRxWSqEKiJN6krJkc5eytZwWkTbPJQnuBJIop8gCljKQCkVNt1Gn3gg1c2g+ZJ4RW\nTSDohAMgjkvtEhtaNOc+9zx27dkbzEOsLclmKb1eSpZlaBVTa9SJtCTPU5w13HzzjXzxi1/CWk+c\nVNBxYBKbvDg1iFQKrYMEh3OOv/7UJ/7jNvDTXHd88RPgAyEuriRBV10nKKVoLy4xPjVJL+0jPCTV\nSim5LMhzQ7/fLyGuhoXWAt1ul167x9GjR9m2Yxe7tu9ACclNN93It+74F558chdr1q3l8OHD/OIv\n/V98/a7vYgvHZ77yFa6//BpGp9fQT9MgrxxX2bX9SY7s2//TvkU/sHxvCbTg4N69PP7448SJQhiw\nJsh9VOs1Lr7mFf95MnqlAh16dnaRtNel389oNJosLrXpZylDg6NMTVSCFr3wRBEMjtSo1mrs3buP\nPDelE1AVrTWTk5NUqgPkNkAMFxbm8FFBfSCIDHlXYAvJhtM28/iO7aCDY9XAwBAzJxdpLS6V7+zU\nIXj40FHe93vv5m//8qNs3rqFKIo4kiTsPnAcKQS1RpVW5zhxxTM6UuFtb3sDX/3iV1lc6nL7v9xB\nqw9RJJFKEyehPVSt1ssgL0tbvYhVU2OcnJlBKsHx48dJdMKZmzczNjaKFylKDJDlEfVanZmTi4yO\nrCJSil5acHx2jsPHUo7OHWCp2+BLtz9CXKmSd1ostRa4897drJsYpJZIohjGyiGVQLHt0Uep15qc\ntm415z33uSileOzR7QwMDbGw1EbaUEKOjIzQaDTotIIPqVIRw8PDrF27nn6Wc9ed9xDHDdqdjEsv\neSHXXvcSvLG8/udfHYxJ8j7HjxxnbmaW6158JYePHsFaw/YnnqRRT7AuZ/369ezdu5drrrmG27/2\nVdauXcuG09aTpilHjhyh3V7i5ptv4gMf+ABXXvUrXHbZZSwudGg0GrzxF9/Ann37eXjPIywtLfHB\nP/0TljoZWZEH5ycriGTw3MzznIpWKGEZqFcwhcealAcfvBctQyBuDAQD5nq9SRRFgQqvNCrSyDgB\nr8mkIFYRaT8IeXkbUB1ShmFipENPudNts3fvXoy1NJsD1Ov14P8qBSY3FHlO7vs4W1CtxSRa4ZTn\nK1/6IjZLeec7382HP/xn4PyKUJ0vmZqmyEh7ORKBkM88eZPxCC6fBz0KZg7iUch/KEr6Ry5jDEoL\nrA8G30IIMp9CAUKL0nQlDErzPKXVoqyAA5ei222TJAmREoyNDFFdtZqJ8VHOOfs5xFFEvVIlqkRc\ne9ULEV6WktQ1+rniwPF5juw/zJOPPMr0xAQTYxMMjw6xZdPpnHfeeZx91pmMjf/b7/tfZ/LeFwgR\nPaN7sbw++Ae/za//1nspFmeIhsbJ544Tj04B0F2Ypygs0gk2b9zCF/7xc0GLvlahVklod3s/4uqn\n1rMio6/XEv/qV13B/j0HGR0b5PjJebq9jH4vI3OGCE1WOGZm5ogrEVJ6anVNrdYg60ve/a538atv\n/680Bwbp9wPiYWhkmKLIyG1OvV6jUVUY22VydIxer0fWW+Kjf/pR3v5f/xteadLCgY84fnQeQWjB\nwCmEADh+4edeyde/+DluuPoK0sJwcqnLwaMn2HPgGKtXTxFJQRRLLn/hRVx/1RXcftu/0GgO0M0M\nc4tdvJBEkaLbaYVyzFp27thDVI3o9bp0llro8hCwhaOSQCUOf6cXBHp4EhNFCoRDoJiePg0nHJXm\nJE/uOUxmHFLHnH766Tz22BMkOkKK4PcqjMPlbW658TJm547SarVoDDTBa6r1JmmaMTk5SawFo6Pj\njI6OMjQySrPZpKpj8jzHubD5FhfnWVxc5OTJWZyx7Ny7l2q1Sr+XlVIEfaqVCGcKpFakmUXKoAvu\nioKX3/RSPvznH2LHzr0cPRHu+b13/hN/8scf5Oabb+a2227nLW95M3fccRe33347b3rTm1i9eor9\n+w8yOjrO/MIiX/6nr6zocue5wRpBq9MmiiJ6WUBRtTs9Ci/p9UN/33qPEsHtKlKCZqVCv9cm0hKp\nHHWtsK5g/bo1DA4NMzW1msOHDzO/2EGqOLTZ6sM4SrmGwpVmF0XZy6+gKFaCvHOOpFLD2YJEqzB7\nUgSiWFKlmlSoRHEY3pcYbO891uSMj48hpKdWqQfN8zhB6wASaA4O4b2n2+8xNzfH4GDQzRdC0O/2\neNu73vlT3dP/1tpx39dwxmJLnXVrLU5QQlMNzlgKa/A2lLzLUgZecApxI0SphyMDkikvsNailKIS\nx0itEZEiT8PP47jJ+c97EcnwCPfdcQ8XX3UFOAHKk3eDPIiIYrwtkFH933zf/2eg/8kNYx/97r2c\nc/El3PONb3DpNddw5z9/jcuuvwGAI9sfwcmI22/7BjMzJ3hi53ZOX7+aXq+DjiRJXOU33/+R/zwZ\nvTGW17725/jt//47TMoR6pUqraU2WdoLutu9Ps44pPcI75heM02v1wGrccaWU3dYWmqX+iOBYBJF\nik0bNvAnH/kQAzVPpRJx9pnPCS2KXk6v1ePrt93Bb77nd7nrnrtJU0uj2UQJiKIqq1atClo6vR5T\nUxMMjYxx4SWXYGSN2mCFYblILy2o1Zqk3R69rMv8zAL/8Pdf4B/+7gtUqxLvBTqq4GREklSIIonw\nlvHREU6eOBFc76sVRkeHUNOriGLYunUrrcUlTN7jzDM2s+/AQc4880w6vT7bd+yhn7YRIphIS2ER\nSUS316FeqXLJBedTTyK+dtvtSGsYHKiR5oaLLr2IM7aczpbTphloSMAyOjSCtWGQmpkiqAB2+tjc\nMr80z/Yd+5idvT8EyCxQH7UOXxnnXGhN6ChkaYUhbiSkoiBNM7RWaOU56+ytfPozn2NkYhprPLHS\nCG8YHRmg1+4wPz+PJCBupJRs2LSZj3zko9x8888gdcSlL3gRCMW9332AEydO0Gq1iCs1oqRCmtoQ\n2HWCEJJOv0dhHJ1SzM05h7cO4R0RhrQ1z9lnb2FoaIDhZp11a9egZdA8GR0ZZn5xgfHREYYH61Rr\nCUeOzaCjagiovYx+Zul1+/SMI9JJ8CHodJE6AhGybOc8WrmgP19qwyCiIL2daMZHR5idm+HoseNI\nHZNEEdUk+AB4L8J7FgSKvAZvLMZYjLdlm0aW3IKgszM6PMJzz97E3Nwc7XYHb2GkOfzT2cg/Ys0v\ntlFAFEXU63VU2fbDOpJYB4ilK8D5MBx3oW1qS3iqdaESMMYF3wRrGR4eZmZuPhh+CInzEts3XHDJ\nZTTGp/D9FEGENZaD+/Zz8VVXBriPEqS9HvHQAPhwgDzVWkmGvf/Bf/8E1n0PPQhcwkOPPgZcQ1YU\nK4+1en1GmhVGqjW2Pu/57NvxBFvWr2dgqEk/65Om2dN+nWdFoNc6BOYitxw+fJTptWtZmJunPjGG\njCNsPaZWHSTNSqKRlpy+aQsPPvQEA/Uahw8dYGRkhA2bzkBFmsHBQUZGJxgdHaEwfY4cOM4xEaMj\nyR3f+jK9XkpraYG/++TfML1hmpNzx9i4YStDg2N87WtfxdmcRkOyd/e+8HrC027Ns+eJB5E6RnpF\npAWrp4e58qorsIXjoe89yMSqLZy19Ux27tzJ+nWnEdcSbr31VtatW0+9OczMzAzeeLJul2oSsXXz\nxUxOTyGjgKZ40QsuY37+KEWaMTHaoNu2FFmQqTV5wdjwGK++5TxqzVqp4z1BszGMjwV5FhHpoJ6Y\n97pccfEleB/K4eMnZuh2WhzZs5fdjz5I4aHXWaRWHaJWqVOYYBCtIoUzgXBVFAVRoml3+ggAGwKX\nMTpACWPB8NAg115zFXfffTcXX/QcWu029913P2masmb1OJs3TLN+3WqmpqbYuXcfjUaTo4cOc+45\npyN1wa/92q/xmX+4lQuf9zzOOvtMjh09SaQTJiZXc+dd9/Dd732PIg8BLo4rOC8wPmL+xDxOBq9R\nawWdbi8oFrrACMZbsl5wEooSxxvf/CYuuuA8BioVapEg63eJIoEqkzKbZXhvOW3tGMZYnLM4m7N6\ncoJ+YciygHvPUoMZHKDwDiE1Skr0WNAb8r4oD0FZDv1DMFBK4YzFuQxR5AhbIfE5ayeH8cjwmMkw\nhcPjkUIgEGAFAo9WilhFFB6KwlG4FJP30D5AORGa1nxKvRox2Bih38/IsqcfAP4j166de5BAL02D\nmYgQxCouW4CCer2GExBJRaSDcmwlTmjUqisqk0VRoFWoUKMoAuEYHh4HG2wFs8Lw5JNP0hhei0fj\nlUcE0RvOOfs5uKJAeqCAowcPU4s3IioRivgp37csGbKiRMwt//snsQYGQhWxOHsSgN0796w8FqkK\nf/sXn+DWT/4tyjsaQ3V2jw1x1XXX0GhWV4iXT2c9K1o3IyND/lff9os89MBDrF69mjQvkKJCt9tl\nfnGGudkWs3MzZFmf9lIbEGSFp1Gv4JyksIFBmaYpzgt0pcINt7yKoeER5mbb7Nm7Cy1yXG6oNark\nueHsLVv42pe/hNeCqek1dFsdlpbagaSkDb3uIoNJxObN06yZHCaOY2Zn5zl901qUUtz9nftKW7S1\nzC+26aU9RkeHWbduHZ1Oj7nZeZrDYyBDlhaEtxKGm4MMDAzQbDYZGRlhdGKi9NpURErTaS9SWEMc\nJdjCse/AfpIkoSgs7Xabbr9Xqjh2KIoiOBIh8D6wUCUCrAn2aDLgu6M4tAa8dygEKBVcf3SA9hkE\nwjr6WQ8pHFdd8UK2bNjEV/7pa2x7cge1ShOvZCmXoKjVamzeMM1FF5xDohT7D+zl4OFjHD50lMce\n30a1WmF4pM6rXvFyxsbG2LnrIA89upsrr7qcShyRZz0eeeQhDh86wfGZeay1DDSa9NMuSkYgZWA3\nRxFp7lCRpt1uU4kTXJn1ZqZAiMCUXVycxxQZeIvJ+1x+yfP4+de9munpachTev02Sgk0Itghekuk\nJZVIIwqDNWlw8UJgnaKwnsIHN648D9WBMQ5nPVIrcB7roSgK6tUKgiCCZ0tmqrUF3nuMMRTWIKVc\nyfCVkowPDdLq9PAEpJUpCrKyLQYB+SXLDFNIWR4kniiJg42ic/SLHOUJn7cUCC0RZSaM0Fz/mjf+\nm3vtp7k++ecfQCpwRagGhQgWkb4c0C6vSAlQEuk9zlqiOFkZOC/zD6SURFEAVmgd0agH8IWxMLFq\nLbv2H+PCF1wKeQ/6fdAR1oGKJN6GfeGxyLKX7wA9OPYffk+O79nO1KYzefLeu9l6yQuwWYFKwoFy\nYvfjvPa6m2h2MiKncInlgpuu4YXXX42zGdYbrn/12//ztG4WF5b4yJ/+7yCLWwnyr6GCMziTkdSa\nGBuMc2kG2YBut8fq1avJjA2Sws0mR44cYc26DVz4/EvYc3KRY/NLNJqjtApD1l7glTe9jHZniQfu\nu5807zO/OM/g2BBZkTMxMUWkYpYWZjl762lc89qX8eB3vsWlzzuLtNem18sYGx0m7edIqXnxi69n\nbHSSsYnVTK6ZxjofUClShT62E7RaLdIiD/08oZibm8OmOd1ulywr2LVvhvsf3Uu/36fXS+l3ujSr\ncXBsAkDSzzorksO1WgPhQ4vD+oC2SCoB/WLyAo0pnxdU9IaHB4NjT7/P5z//jxw9Ocdia4nB5iCz\ns7OcddZZrJ1ejxcSLRVEgkocc/qmDShgy6aNDI+tYmmpyx1338PAQIMDB/YxMjLC3t3buObKS7FZ\nj80bN/D+97+Ptes3cOWVl3HhhRcSa6jVahw6fJDtO3eyc/tj7N21nXqzEYhb3S6Vah3rJXMLC8wt\ntFDCU603g3aRA4UsxdMsxipavQxPCLB50QPnyYuUSMH1117JLTffhMx7RFoQR5KlmUPUVLiKNZ44\nqeF9gcJCGvDY3gZnptyA8QIjq+GQdD44/7jv6xUT0E/4EFAFll63A1JQr9dxgBceoUQwMA8/wfhg\nZq6FxwlBL8940U0v5e5/vg3vHTa3SCVIkvA5S5ngbKhkrA/XUZEOrWWtqSiNtjE2L1babspF4AJp\ny/r8Ge/J5sjptOd3EjXXULSPEFfGydOZZ3TN4cFB0l6GUaEH77zHOxPcprRGyGBr6ZDMz8/SqNWo\nJgnGWqz1aB1mMcaxotEkF5fbtWUFpSu855afR8Y17n/gAZTLcanB+hSdxJjcr7T0lmcFWj91Nv/v\nvT7+8Y8D/5PPf+ELwAuQ8lSWXo0i/vHbX0VZRX31WpCWJ75zJ8dmjgaOxo+Roz8rMvpKEvvrrr2I\nPO0wNjaKtQX33/cw3hk2b1lPux+c1yNRYebkPOvWrePIkSMgPL00w5eUcITmRVe8mHsfeJCecaV4\nkkMpiS9yalpivQXvMVnKr//q2/n0Zz/DutPWMzE2ycaNGxkdHqJaCf6piQp9wMMHj+FR9E1WGvJK\njHFkWXBw6vSCyUe72wkD5CKn0+9BPyeKokCKUkEEKSoHdFEUIT1Y6VZ6kUqA8g4tw7woz3NOzs8x\nPDwc5A10hHcGIUOL4NixY0jpaA4NcvYZZ3D8+NGg4yI0d3/3bs7cvIH3vPudHDt+kk///ed5cudu\n6gNNzjvvPM7a+hyazaBjv7C0yCMPPcyRk0fB5Lzvd38LRchAf+03fpPVk+uw3jA3N8uZZ57Jpk2b\nmFozyvjICCODQxw8eJA77/4OO3fsQeoIay3VemVlgJZlGVka3IN0khDFFbIsEK2ESlbMuW2WYh3U\nB4fIshwZJ8FJqNfBGUOedRHeUuQp559zNm/4hZ9jbHyYTnuBpMzUanFCFCm8tzghA15fSuIoQuHR\n2hBhEEVBlkNPCLyIyKzHeYWOoxI5E4L68meT53nIzKVEEQwuvA3sZqlDL74wBhAlSc5TGENmgjz1\ncgYrhEBGkjiucOVLX8r37rgDWwTFyywrEMjSrPpUluucw+BxeASSio5QQmP9qet6ITDl+/YCXv5L\nb3tGezKqT1F0j6PiCWx+kiQZI8tmf/Qv/pD1qY/+MbWkQjUJbZuxieDtmudhUJ4XQXoiTVNyY8jz\nnMHGIJ1+Z2UugQzEwPB3h9i1TLhzCCZXr+eNb3wb73jHr7Nzx+N87M//jLTVAWkRsQ7PsyGJXK6O\nlIwQ0jO+8enj0n9Sy9sMoRJ8niLiCra3gKqFGYvvnACvoR5UAJAO25rn27d9FUeBcJ4Xv+Zt/3ky\neiHgzDPW0m3N0+4s0UtzIi0YHRlncmKMardHvTaIlpos7aBUzthYI7Qlyszx3HO2ctrG06nUR3jd\n614HwMBgnVq1TqOWMDQwzGC9xmJ3KfT58GgveM9vvpsjx06Q9bssLS1Q9DvkaYan3GhecuzEcRZa\nC5ycmcM6jZIxednXVWWjVxDKbIdHRjrAQGOFs4akEkpDqQXKe3Qk8eRIH5wJrTAhgAtBs97Ee0+7\n3WbPnt2sWr0+sFGlo184BCYMj6Tm6JHjHDl6iE1btvCan30N5zz3fD74wQ+xZ/d2TtuwgZe85KXk\nJmNkeBBb9LnxJddxYm6WB+5/kG/9yx2sXbueTqfDxZdciHUFo6Mj2LwX/E6ThKXWApdcfCEb153O\naVvWIoQIWfqhQ+zds5e//eSnqCUV4koNKTVKV1lYDIJnuZX0szSUxc4gZUzhFd5AZtIyiBqElKRZ\naHVoqUFAr9fH4cm7QUrZFxlpv83aVRO8+KqreP4F51KNBYl0mO4iw1Ud7n+k8Dh0pHAoorJlIoFY\nQKIEEQJTGNr9nJ7XCKGwOJSuUU0q5N4gCOQtjzsVDFQYfrLcQ/dgrEWLU2zOYCXp8QSyVMDKB4cj\n7xxChGF2RSqEdXz71i9Rr9bodTpE1YSK1ORlli5ECPBB8HBZETEgVXIniCUB4qkkxlmkUkTVKs4U\nZMXT0yj/YSuynoKQeFhAS8Uz7fzPzy0wD6WPs2dmbjYYy1QqxHGMVjFjpb9BVliy5e9FNQqifkJg\nvUdIs3LAORO8DIT0CC9JkiqDY2P8xrvfxf/zgT/EuVCxGQp8fqpCg7JFBmjpQD/1MPby8y/ljofu\nWUHduKKNjJpP+fwfZ3lngARX6srLpHLqsaSGkwonBFrECJ+jaxWstXR6barq6UM8nxWBvlqtkPb6\n4CPGR9ex4bQRrr/uZ1i1ahUjIyMMDA4HWd1KFVuEnmdufWkUIVlYWArGInlB4SRFYennGWmnTa+1\nxKF+H4B+vxvkXpcW6XfTFS/ZxcXFQN6IKxSZQalAlNJxIHMURUGn2yWJYyIt8CIljvWp/ikgRcg0\npAQhHVLKUFr6kOGG7M9RFDmWgCkWQpHZgKSR/nB3AAAgAElEQVSQwjE9NcotL38Z3aVF7r33vsAK\n9BaPIs8NlaQCKKSS1Jo1zjnvHG551S2sWTtNN3Xs3LWdyTVTaK2Zn5/lzLPOoD5Qod/u8IpbbuLj\nf/kpqgOjPPzww+hIcuH5z2XN5CjTqye55HnnsWrVKgYHGhzat4+jx45x/4MP0u50uP+BBxmbGKe9\n1KJSCWJcQngQikIoXOEwRUY/zWl1U4y1uFYvkJua9bI/bUlzgyxjkLEWpTTOG3JH0BHxnl6vj/UW\npQQ27TM81ODNv/LLnL5pAxWt8DZolSSxIli5h3vtZWivKKWQKiKOI5wzJEISYdEuRWDoZ55uanCy\nhvKSQmiiKMF5yIogQpampzxRvSs/S6nxeIyzmDKjxEFcjXEqPDeU0z5k/TLC2aCNLmxZRfqyerMu\ncC9qNTq9Lueefx579u5nfn6eOI6J4xpFnoe5g1BBHReDghWpixSDzsKcwvswOwiDX0Osnjn0T4rw\nQSVakxeAe+aV/+joaKmplIc91euTtwqUkqRZDylCH74aaSr1xsoAVkq5IqmdxDFKBH+CiYkJHn/8\ncdIipyhy+mnBxNgo11x+Ofd+5w4mp8bZvX8/A7U63js0Eo9fuWfeC7yU5B7U980I/vW6/MoruOMh\nVlA3wv7kuiBChraRpBzwft/bsP0C48JMbilN6Xc7zM4cB62p15uo6D9ZoJ+aWs1rX/tfEGi8CVmx\ncwGi1u+lzLZbFFmO8J5+v0/WT+mkBYtLS/TSUPotdTtkWUavF3q3tUadfr+Pt0GgrLBFGOSIkBVJ\nL1ewzlJFSBRKaOJ6tAK1Et4isCAcjUSitUQIE4KJNAgdDgTvPdKH74EtN3wo82NMETa38Q4hPMKH\nfvGyQqYBsjwnljkXnnc+0hcoESwQK0md55x7ISMjI8RxBWMcRw8dZOfOnRw5coRer8eD9z8YBJ+W\nOlx//fV84TP/gBCBpzm9ahV52oJajQ0b1rN69SSvuOVVvPNX34rWwVB85549PPbYo3ztK19kcmwi\nkG+cRKhgLNJPcwaHxkj7hoXFDtWqK++bIk1TVO4whSNNcyrVOiKKwZvQvhCKtHA44bBZESS5Tem9\nax3YoOtTFBlZ2qFWSTBFynnP2cqb3vBLVERGox4IbvWqIDOewliEN9SSJiHXDeHeex+GkkLgE4nN\nDRUPFdkjUimp9Rya6TM0NIKqN4ni4FokiryUXHaYoiDP85VWQAjmIbe33uKFJ3eGWCiklCRJHGYw\nJmx8KQTOh3bO8mce2jxB7wbrKKxF+vDcxcVFjDHcc889KwHNmtBfr1WqNOpVrPH0s5RYxsFQ29vA\noHYWCyQ6ChUGYHw4QIR85oG+0w/kqHb/OADd7OQzvqZxlqwfbCyRAiNC9WusQccVnPMUFoq8x8JS\nsAWEkIWjQnI0OjREogLEdPfegyRJQrM5zNjYCKun1zA4PM75FzyXb9/xTUZGRrjnu/fykutvoLu0\nBLgw5JXh+62ELN+L/6H9brtspFcedjZ75jOQ5ZXOnQTW0J07AZzG0okjwGYAFk4eJTVBQqPf79Pv\nZxw7dpJaXENECWNjT394/KwI9ItLLb70zbvptVK63f4PyNoKIejlBdUkwZVBQghRGjqHHnBhDcpa\nlIZKEspx6TwVHWGFRQKDlRjnLJEUiEghfEAyOJNTUwInBFIVaC3KYRyYXvAYPbBvL+OjY4ikvlLG\nSykppAIhkEKghcSZcohXHlKIgKQw3pEXBdVagjcFSVwNypu1OtVmnampKdZMDjA1MUqkoF6JOWPz\nJlqtFrffdlso1Todtp59NrGMqFUTVq+aROGDOFZe0NwgGalZ/uLP3sfG0zYwPj7OzPGDRJGmlxse\n3fY4oPjEJz5BHEUE1q8IWvcGao1RDp5cCPr53aC4qbWmUR9gZmEpUNWjGlnhcc6Sm15gNraDHZxz\nQGTJC0Nu7ArevrAO6x0GQZ4FpIjNg6KjcSBsQS2OuOzSC7jpxmsYbtapKKjGHoRGaw/o8p46KrEE\nYnr9PkKGDF4oFYJCiXSJ8w5VmdKoB3RWrz9AIWsMTg7jrcJKhTGBka3jiDwzZYbnygrOrlQHvvQM\ncDZc21qDlWGo6oxlsNEMVYkpkU7OBfKPDMQepMOVh0YkJRFhmK6VWmkLLROBQhUYvtMGRz1JMMrg\nRQXnQOYOnSTgBUY4jAmiacKDQ2OtQ1ro/xj46v/IpbUmKis8Y215qMqgxOocRWFwOJyIkMKttL/C\nARgOyJkTMwh/CnWVl+5jtXqFarXKOedewK7dO9i0ZQuXXPICPv2pv8GYAhlp0rzPQK2OUIFspaRC\nInDYgF56irV+/VoAHvzufcCLWIFH/QRWr+R8tLvhM+v2T+HoZxY7ZC6nnwZnvW6nx4mZWVZPTWLS\ngvndB5726zwrAn2/n7Jj516kiHA2XwkSAM556qVZsFAC8OAd3pmQjcca7yOUcyAd3hfhcRxSWLQq\nST0IdDWmKIKhifeCNM1YXFqk0+lg0g6dTosPfej9mMwwN7vEn3/s47R6fTrdHmvWbQEd4YzDl2WW\nLeVpnTHLOVVQKZRihbk3NDRSSgSsZXLVBBWtOHr0KAcOHODRRx/lwH2HOOfc53LHNw/zvt9+J4GN\nKxA4Zk8cQwrNuReey/z8PKsmhmktzrN16xmsmpxgbHSERqNGTSuKtIOUmrn5BfYf2Ms3v/lNvvfI\nNiqNBspLvBNUqk2iuEGnn3P4yCGiKGJ4aBSTp3gpSNMUHQXZWGMtlH6e/X6GUWWrpijIjaUwpZKk\nF2gdNnFWFKTGggievcHaEbIsp5uHSqsaxcQKdCR51ctfziXPu4BEQ1VDuzVHsyKJ4uDxiSegEKTA\nWQ8i0OdViWcWCqQOGkGyHLqO6T51OUNKnVY+Qp6BUxHOC4pegfUG5z1CSJQKGfQy47ff75PEFZwP\n9m74YAbtnUMKiZKSqoyRiHI+pMnSFCvcChIqwC8BAVmWsaxGoKNopfQvjCEv2ZwQnhew5AE+ODAw\nwHmXXcZ93/gGzUqdSmExUuBKcxmBRHuQSpFbiymF8rz3OOtKL9Fn3+r1AnxXa70CW9UiIpbhoE5U\n2Je5NTjr8eUBWVhLgcc5iUqC2Jy2NvyOc/SyjJMng4H51rPO442/8hY+97nPsW3bNrY9+jBaCR7b\nvYu5xTm63S55nlOrNZgcHWHV5ASTq6YYn5h4yvf9ll97BwAXvvBF4T0PP/Vzf9w1um4DAKs2h0Hw\nmtO3rjzW6vXx3pPlBc56KknC4PAoT2x/svSifvrrWRHohRChHFaOJK5T2Pz7HpOhfysE2JANL7fI\nvM2QPiOOg/hRb6kTsgVjGBgaJmnUOXp8BploKs0Kd931HfLCcujQIcanJhEohkotkyT2AYLl8iBH\nqxxbNqzj8R27aXtPvzBIG4ZjSIcUGi0kSS0MTyIkzYE64+OjjI1OUanGRCqm1W6zfft2br318yy1\nWyRCMdRsMDAwwPjoCGm/zXA1pjoxDqqCNV2iRDMYaf7kg3/E297xTt72tjexfsM0tWqMlgqT97Cm\nQErBwuwxvrfvIIcOHOTAgQN4L0hqg0yuXsvU2tM5ePAAlShGqzAE7fR7SBFhiVEiGCojY0yR4bzA\nGhXahDJGqIBj90KRGosQDucdxhZYJ3AiKK97VFDtzMAagfMFvX4/ZFFpD5OGQCacYdP0BDddfxXP\nOWsryqRUKwa8QwpotedpNCuk3T6R1kh1ipjiZWiTCRKEXK6aQgYsTR/yLgNJQjNq0840i3mNpayD\nR6F1AU4FdIWQ6BKX7r1HicDU9dagvcA7i7PBVF2WLRAvBL58Ta0DG1sIEVolSuOtpd/vU68HqKuV\nhKF7pUpuDbFUK/LKUii8shRZQZFnCB2hdIJ1rLzmwsIC3/qnr3HlS1/OEw/cj1haINYxufOMjY1g\njKPIguF05GyZ+YZzZFma4dm41q2dRuHJsoy03w+OWp0weBdCIJQO7VUf0DHehL9NRoooDvvNGIPx\nBlEEC1IvBSoO+8kYwwP33cuN0+sYbjZYWpjh5MmToCWr1k1zYvZEQMAJR60SPGV1HNHtdnnkkUeB\nVT/tW/QD68DuvRRlAqN0zMLCEi7vk3YLuu0OcfT0iVvPikAvhaeiw+bxposuM+JQzgpkFEp3qTTW\n5DTrdYYGR5hcNcFf/dVfc3J2ntVTE7z21a9kbHKMXrfP4nybJ57cwfr16zl85BiPb9/Do0/uZs30\nNL3csv/AEaTQHOAo3huGGprNG1bjZSjJpfClXrZC6OCJqiQ0anUmJyeZnl5Ho1YhN4ajhw+zd/ce\n9mzfyQP3z/Gym17O0UcOMDczy9p104wPNph+0aUrMwEtwHhDFEUM1Bwb1k8wPHg6SycPI7TEmZw4\nCofXe97zHj772c/S7ra4+KILWVpYZH5+Fgk0BwfJsoJ+alFC4n045YtuRrMXNlKeh4PLOVBaMD+3\nxMTEVIl00EF3PaliCOSVqBqhictgISiKMDSNdGDE9ooM6xxCRDhv8UpQOE9heggPRdbHFgVKSmzh\nqUjH637uFVx7xaUUeUosA2JE+C6qVg1DMhW0hDZsWI+KNN4muCK0U/JS9yQcvhGRBpxF49B6AF+0\niVlgsN6irhc41l/FYjpO7gTOhhaftZ5ISaIo2Bo65yisoVqt4q0nzwO6AxUqGO89Og56KpStuOVW\nYlEUREiSWrD76/TC91XroHejdbwCe6Rs6ZmixDx7gSktHJNELX/5yfIiDBP7tkSXeEy/4N5//jKX\nXH01tjBsu/8hqo0Gvgzsbdcl6/cRQgQOhAwJk9P8WIzJ/8h13Q1XA4FPcmDf/jBPw1EUll6vx/z8\nPAcP7kOhaDQaVKtVZKRJomWdG4V2gbjmK56itBc0NlRUTlkQnv/xh7/Pa3/udWx7/DGOz87wjTu/\ng5aCRCectm4dw8PDqGWYp1R0uhmL/4cD3E9/HTh6mPsfeIgv/OMXiVQgjb3/vb/D1NQUCIP3T/9A\nf1qBXgixH2gTZsLGe/88IcQI8PfAacB+4NXe+wURJpkfBm4EesAve+8f/KHXRxCJALuKqhHeF9xw\nww3s3LmTXq/HK15+C9see4R+v8+unTvJs6Dh4oqc173m1fzmf38v8ydmuTWpc+zEcQ4dPUIvNQgE\nlhIS50EKzdHDx4KuigiomPGRUS648DwmJhps3rim7AlqKlHE9NRUoKgXOevWr2b7tm08ue1BYqWR\nUlKtxGzcuJHJyUkuufAcBq94AbV6zP/84IeZXj3FL//SK0PLSKuAyvEeb32w64s0RZFxwTmbmJmZ\nodlo4IxlYanFd+68iyNHT5aDWoiSmKFohIcefryEC/rgqrTYI80NUaSQ3q8wKCu1GtX6IFZorFMY\n6+n2U4xTWCeZW+oQRUkQZ5IxNvdoFLXmEE4qrHOlbqfAeoh0GFxmpsB6ReE8Js8xLgwGnQ1Y8TiS\nVDUMNAe47qrLuPbFV1PRoIRFKgtxyRAtM2UrPMKF1ojWGic93hqsDYQkGWmkd4gSRaKFRmtN2jfE\nUUzsjjGkZhlJlui5GvNmK6gxorpB2hyR1SlkEAur1hpkWUEUBX0VV+p797P+qeG7EOAoFSXDoE6J\nMPiz1qJ9SDgKYzHdLirSZb85R4gEqYOapCyHfN57ijwnjqqAwzgbVC7TNOj7yADLHWg2AhEsC71q\nYwwugcXFeR68405a3T5XvvRlYHPu+vrXqdVqyEhRlzXSNAt6PgQWqfME0btn4frSF764cv8lgmaz\nSb/fXWmDrZ2cZM34OGlJGDTOMjc3x+yJOYx3DAwMMDU1hSBk/MYYjA097eVZB8CF557D+/7g92j1\nMwrreetb34o1gksuuoiFhbnQKqslvOjSF3Hx8y8Kmb2QfO5vPsUrX//zP7X786/X7/z+7zM1uY7J\nqbW4wlIv4bNCxCitUT8GvPJpEabKQP887/3s9/3sfwDz3vs/EkK8Gxj23r9LCHEj8HZCoL8Y+LD3\n/uIfdv2ztp7h/98PvR+Tp3R6KWm3F3rBpQtPURiaQwM4LNWkBghEqWgHIdvTKmZ+fp4sK1A65tjs\nDDYLJe3k5DgyCjC6VmsxDBkrCUNDI9RqFazLyfMetYaiUdF4K+m22vR7jt/63fey2E255oZr2bpl\nI6smJtBCBiRPnlJNIgYGBpBSknY76Fjz0MOPMjQywulb1ge6Nr6UMSiCS1JumZ2bY2Zmhu3bd3Hk\n2Al0VCGp1pHKk6UBepk7Q6vTAbkMnQt4aqkERZYTRYESXxRFEM8ioE+iOLgmTU6u4uFHtxGpgMuv\nJDU6vYyBoWHSNMWYvBxmytCScj60YkrZhrCZAmoJL4MekQ3mGMs6/dIXRB7GR2q8+lW3cOam9QwP\nDLIwd4x1a9YEnLDL8ZoVLLotZQFyE4TAyjcevldSriBegm1oCAhShQEqtosqCgajWYabHbLC080G\nmVlULPYsVDRSNmjUB6lUTGC/lkPaPDfggpRAKId1kAZOs5XWSuFs6Qsc0FWqPPKyLBDhQjunRGxJ\nVQb6FIFCRfHKIbH8WQRFSvUDnrHhMYfHBicyE6hQUZlAeC8obE41qTA40CTWkswGTfZwgAjiWpWB\nemOFxetNHrgHefg7Ln/5L/7Iff3vtbasWcvYqjEaSZWR4UFWTY6zdt0Uw0ONlfuzHFxteX91eaCq\nsh0HQfzMFAWFCQd1FEUcO3mCubkFduzYgTGGVVOrqdYqwaDIeyq1hEatTmYd23ft4uTxOVpLXb59\nxzeZmWsR6dAhMMZirMc6HyQWpCPLCpxn5T399JdgoDHMlo2bufmmm5iaGMMWLawt6PZ7aC35b+/5\nw5+cOfhTBPodwJXe+2NCiFXAt7z3ZwghPlb+/6f/9fOe6vrr167xv/G2N6CiwGqsl7T+oAEjqFar\nFCajmsR0Oh2UFrQ7KfMlPE14T1aE8i3SCZVKmMDXGk2aA0PgFXnRJy9SHnvsMY4cOcLunTvo9VK8\nddTrVSrVmHe8/S2MDgWsflF4FhbbHDpygo987H9z/OQM/99ffhRvLAJHJYqxJohdWVugY4UUPlD2\n8xAkrXUk1YTFxUX27t3Lrl07mF3o4YVkdnaWpFpjqN5ESr2iXBiyx0B6WWr3yMqMvVavhoGkE3gC\n21SqaMXJKEmS8LsmSDEMjgwjpWbfvn0470n7QQQqiSsYK+i0uhhcwJ8rWWKLLc5JnAttkzQvsEWO\n8JZ+LzhzWWNIlARvWLtqjNe/+hY2rluDJluBHFpriUtbOK0lHouOywG2tQFtU7Y14NSGXv4mFkVp\nDl3VCOuIZA0dBZaryvYzVGkzXC9YyJu08nH6Ji4JNFW8kkgdAgZQVh2efjelyHKkVqH3XyqhGudP\nSRyU/wkhsIVFyuXWjw06SiZkjEpFiBKmOzAwEFo7+DCMlTJgossALLzEigDVlSX8c/nvc2Wy4gqD\nM44kSVaw48ss2ko1olKpIF1A2ERxzOJSl37aZaAxuBIcI6kw3pWm7wUX3/CaH7mv/73WX/9pICo1\nGg2sFyvoOSFE0PC3gfjXbS8F2WetV7RsVIlGUiIkNFJKpAj6SlESM7+0yFBjZOUwzXNDq9th9+7d\ndDqtlc9kcHCQOKmWEgcaykN3mdTWL3Luvfc+vvfQw2xfFhITgsL4Z0mgD/vnrW9+O6/6meuxeUqt\nEtPp5XS7bWbmjpNmfd7xrvf+RJmxHrhNBM7xx7z3fwFMfl/wPg5Mlv+/Bjj0fb97uPzZDwR6IcSb\ngTcDjI2OsG56DcYKpAKBWqFEZ1nGsZPHoCzvVwZNKjBi69Uq9WqN1dNrGB0ZZ6nT4dixY+zZf4CH\nH9vGo088zuyJWbJ+SrNZZ3BwkGazycYN61m7di0To2M0mw2SWDMyPIj0BV5KnDcMDw4wNDKGRDA0\nNMQd37mLq6+4HA1UophIVspBkqTb7lCp1LDCs2fvAZ544glOzM/S7XaDDkoZyPERzsHiUsaobiBl\n6J9LofDOUxiHNZYit2F46B3CS/J+aDcoISlMFoZUImSeXgiKvPwdpeh2u8hI02wOAiFwyhK2t4xN\nLqwBGeCV0nksroQPOqwNvc/cW4zJcd7QrCYoDC5vcdPNL+XyF17K2HCDWFo0YHwIfnmelhtalwM2\nR6SjH1BUNCa0NZbFm5bFpgrrUSoiiRO898RoiqgAIVA+Z1C2mJxaxBaLdN0mimgYKYcgK/DG4nxE\nEulQoQhBkaWlaIAIwZIwQIWgGZN2Oxj/r2RnvV/x3gUX5G/LYecyQa8oilJtsU6aBkE0h8cLQaRU\n4GToAHfUUpVQShn4DVJSqVTxUqzwL5y0iEr49zKZKK6Eg7ufBsMUjSRS4bWTOHjcZmmOkhonglxA\npVI5xeAt19c/+3GUkFz9s2/4oRv8K5/8KIhohUwkhODG1/8XvvhX/2uFc3HTL7z5h17jsx/74zAX\nKquhPM/xK34O4W8XziO1oMgsY2Nj5Mv+uV5gCguFDfo2ziICgAuvFP12QdVUUUlMmvVK7kNIapq1\nKhddeAGFyeh2u7TbbTqdDiePzYKX1Ot1qtVqSIaSCO89o4OD3HTtNdx47bV4L+ikfR574km+9e07\neaD02x6v13jHO97B6uk1fPDPPsbR48dYaHdKwoxhuaoFCA3VwKEQAfhcPhIH6r/3oCtEUUS1WscJ\nSOIKvbRPo15j9sghvM9Wrrn8OVx/w3VBfkPA/OIinpyJyTFOP2MjY2NjvONd7/2hn8nK+3uaGf0a\n7/0RIcQE8HVCa+ZW7/3Q9z1nwXs/LIT4MvBH3vu7yp9/A3iX9/6Bp7r++rVr/Lve8st4pYm0pNvL\naDTq4XTHgo6YmJhgbHScWmlusZR2OXxgPzue3M6BAwfo9bucPDFDvTmAMY7FhRZXXX0Fp522DoVn\ndHggMDp9cP+JKxHW5FTjBO9CCd3rdfDC0c9SisJiCke1PsBXvvYv7N1/gE7eQyOoxhEvu/FGVq+Z\n4rEnnmDXrl3kpkAYiKsVPAGHH+jdNRZbS+S5oV5rBsKXMZycmSGOK2xYOx00YErp03baY7DRJMsy\nOr2UVquzErCjOMaaHCmh0+sG2QEZkZkCKcMwsJYEeOfmzZtptTulg9MpzZQ4qlBYEaCQaYoTHu9Y\nYXYCoEKmKa2Fok89iXnjL7+O5559JsJlNKtxedA4lLB4Z1BJrcSen4KWLgccpQKL1buyF67L4Zrz\nKwbazgWClVAaLz3C2YCIwTEgO4wlxxiszTLXHeRkq0rHjDO70COuN0hiiTMepatEcYBdLr9+nhYh\nE5chk5dSUjiLzYsV3aDv79FHOrRmhCewcz2nhrFZvuIcFWQdZGnknSKjkDPJUnQuKS0EhZQr+ka+\nfA0LK/fa5wbvHFmer5C1tD51WKlSNbRSSvkuV7jLapp5ntNutxFKMNBs8v9T9+axlmXXed9vT2e4\n976xql5V9dxNsskmRUk2KdKKRIqiTCUeFY+x4kFBDDh/eAgCIbETIInhQTCQ5A8BRowgtoNAiWLL\ncaQoQRzFDhJZsGHEpiExjkzS7KZ6rlfDm+69555z9pQ/1j7nvRZsq40wAHWJQnU93neHc/Zee61v\nfd+3VqsVpMwnvv93/or7+hv9+N/+yn9Jzmo+GC7XVzw8fUzf99S1VNq2rmTykzNU1uLjtVJ1ggv7\nvkNNGTgZVdl52EiMUTyfihmZtRZV9A/TIWecZbVYChGhrMWHjx7N13xiNxnj5t+pqor1pgOj+cF/\n698B4K//V3+BP/Wn/wyb3vPwfE1CM/hADON1zl96MlmEJKK8RZOLYYSiIiNkg2kWtZxeSv6tdZkt\nKgZ488tqmSW9f3SHppYkan+1xCTPr/m2b+VbPvphFosFf/iH/71vXEafc367/P1QKfWTwKeAU6XU\n/RvQzSSdext49savP1N+9s98KG04eepptHM45zi8dUJdOy4uLvj617/OV37xK/zkT/1PnJ6eslgs\nuLxcs1y2VNbx9NP3Obl3l+eefpo7d+6QQ2S5XPKnf+RHeOXll/ip/+En+GN/9A+jdVGl+oBSnhwC\ntTOslhVKGSpnOEx7jEHMt0II1O2Kq/WOT336k7i64vGTSy4vL6nrmv/9b/896lpMt9adYLwqZeqg\nOLl9zPmTMxR7bLZXeB/ISRErTfCZ7XZH141k5dj0nozlfL2lrRwhZnFSTFpgBQW7vhc/9mEgZPHH\nj9nQdSNta6Ra8JEUc6HXwddee53ej4SoCD6Tik9KP/SElNgNvrBDxB1Ro6isQhHIYUBHxb/6G7/A\nd3/6E6wqh6sTtenRJhHjDoMRWqSFdrVHLhx7oEBQIqKaNlIOzF4vEw4eY8Y5CZzjOMq4cAU6R6zK\n2DRwp32L2805WXU82j7NmT/GL2+hB8uLH6hBR2pTiQDOKYIfGMaRXdqhtUVXmqQDUWmUE4hAh0By\njhw9Vl8LlwBSVhhjpXlvNZSmqnNuFtXEnMrmvTH9yMvBoktQ3263xY7BSH+kabDloCFBLhbI01Qo\nVMa6AltYTSy+OqHct1w83J02jP1AXbe4RUVOS5arxWzdKwZnmf/7Z//n+V7knPn2z/8AAP/HT/8Y\n6/V6FiPlnNGqiAxNmn8WY5zFgBPs8tv+wB8F4Kf/2/98PsyNdhilZHBK+R7ToJC6drz4wnPXIrAQ\n6IP8Hb0X1WcJ9JOGwFoJ6uLzE+f+ikqKnDKVrQjB433EWoG7UvIz3dZ7z3K5R13XdL3w0A8ODrj/\n1L25sth1YqJ3cXHBen3JYrHgwbtvz4f4HPd8x7//7/4wyjrGwbO/f0jXSf+waZecXV7w2utv8LM/\n+7O8+85bdLuB3a5HAZuSM1VqoulKb0quhWT7zjli8tja4n3g9tGRHFjayVS5nDGLFcvViuPjY4zW\nrNoFGcPXX3+Ate9fAf0rBnql1BLQOed1+e/vB/4U8NPADwF/rvz9P5Zf+Wngjyil/grSjL385+Hz\nACEG/ubP/T0evvuAr3zlK9TVAlcZ+gMcTk4AACAASURBVH5H27YcHa547pln+c5PfIIXX3yRykrJ\nLxvDC3+Za6fBYRj4oR/8HSwd/On/+D9kCAMheZy1RcEaGXxPTJ6L9RNiUGw3I8v9Pd5+5x1+7u/+\nnSJMadCqotsN8yLc3z+g9yOXV1eY7Th7jou7ISyzYtv0hAgxJvwYWa+3XG62vPDiCmljKlRxLgmF\n2iczNDPbbkfOap6pWbmGpGAIA7EwVLTWpAzaWGkeZcEVszU8Xm/JyuIacVf0/Ui4IeyaNmhCg1Zo\nlTGDZ2/hsKrnt/zm38D3fNenxTjNe2qnsbVGxQRpxFUVKWeqypKyB6MZwij2FTkTQrEQyBLgUxSF\nacqSrQ1hQGsJ8hgNXmGJDDlTKycMHEYadc7T+69zWD/g4eaER8MHUO6EXdDsQkCryKaT5mjPIJDf\nNmMqRwoZrS273W6usoxW7HY7XnvnNS7XVzx17z6He/vCdgmBrK7LZfCzWjUET4pxrgR9iFCosak4\nLGZj57GB3nuqupb1YoTT7po9UUwrRQxBAr1ixo9TiCzaZXFoVDP9c2pKd13PZuzR+hbt0RJrFLWr\nUUajnWDZKSU22618Z6WkArBOGuk3fGq+97f+/vcbG/6ZD53lT1vVBXpMs53AxJCqqrIeit2B0Zaq\nqVmUqm4sMEtUnquLdWnQWppFi3O1QGBZxmCm6hoyCyEQMPjsUUzZ/+Rg6clKs9lsuLq6Eu2GUqzX\nWyonDeC6dmQCTdPy7LPPMo4jZxfn3H/mabH0uKHd+J1/6If/P1+rv/wXfnRu7Ld1cyNWyPe11s5Q\nYVVLsjQMnpyg63ps5d7jvbTZ+fkw/hd5vJ+M/i7wk6W8tcCP55z/V6XU3wd+Qin1B4HXgd9dnv+/\nIIybryH0yn8+OAg8eXLGw7Mznnr2WT72yke5/9Rdbt8+lqYggdZVWCsBui6nvSsy5hhh2G6oGzuX\nh0otOLnzXWIGdHlJN/QsVkvGXlSxb7zxBl/+ymu8+uo/YRgG2nZJ5yWzcE0NGJRZEf3I3uGKfvBz\nxkSMMxulD51MXUqJwXtWC7HffXx2IXTDSvDEVILI4ycPobg1isOl4Wq9papFuCQUSBh8MQDTmr29\nA7quE/ZRCKA1uTBEUkrEBBgtwb4wO1IcGHZi2hSjGKkplTFaS1aaE4umYVlb4rDjt/7mX893ffqT\n1DZzdHQgDpwojFaEsQMDRIM1FTEHbFWjlaauFjLi7obBnHO1NGBLM3RqMGct5ecUyIxxaCW8/xjH\nUuB6GiJ36x1H7ZdJKXDZvURYfiumOYa4QetMZWRerinc8RwTMUgD0/c9l1cd4ssvMFbMiRxy4W3D\n0cEhi4VATT54ab5l5qxzDrbeY63wglTB8uu6BlWav1lRNQKzTHRMpdQMEc1QeRS21G63Y3t1xa2j\nWzPzBEAZzTAGhmLYl1KaRVnoTF07tKZUBjWLtmXIEWsVxiACuDTQNC11LUSEsR/QVvZIzon/62d+\nAkhcXFwx9MJmG4Of+17WWs4uruZeWNM0JBVFa1Ca1H3xeOmDiI2GccBkabQaNQ1ISe+BwlS+vucx\n59KAT7RtS9M0DN5zsHcIMRGy9OUgEWMCpWSojjKMfpyb2DFGLAp8JmVhz0yeU9ViyVhsr733c5XT\n5SmYuhLwxzkx3FstUWj2V3vvUeX/3E//GH4MdP3A5eXlrMHQWnB/56RRPlVAWtk5CP+uPyRq2hh2\nLKqalCIh7iBn2saw2+04PxvmXkbf9zRNVQ4aef29/QXGGA72Sy/Ie9pGDrtuu/sXmiT2Kwb6nPNr\nwLf9U37+BPi+f8rPM/AvZIZdNw2f+NS38Z/82R/hv//xHyeMHd4PxORROnL+8DGLxYK2bamcY7Xa\nRytPCILVnZ4+YrtlLsO01uz6yLsPT/l/fvHLXFxc8fjskv39fXwIVFVFzGDcktot8TGSVeL84oKT\nkxOs1mhbg3USZJPHVqv5JqaIDAPRAokYbbAxYW1FCIH1tqOp6gKPKPpxoF0uJBClTNaGMSZSPzB6\nT+UrdPHEzlGCd90spFoIUTL3smnGkGbrgZxhjILPj6O4cXbDiLPVrDYM3mO1pqkdlkhdGVaLit/3\ne3+Qb/vwB2mdoWlATLpHlIIQxPclhJEQe3LIGO2ISmGqWhqTRPrtODfuUlYYHUvFkFDGziU/MAc/\nozV9L0KZTI9PChS4sMWmjqfqr7LfvMvF1Qm7eJdLXmQTDX73LuMYBd7JitpVaGcIQXzaQwn0IYyz\nZ4wxjqQVxMRQbITv3zmZG9NMTpNKaJzKGmxdzZ9bJjaVIdZBjMRUMvgogrBc/qdKwJiyzukg1kqx\nXLakLLMIDvf22WsWIspK10236YAIPrHtt1xcXKAQPrwymkWhDwJsNls0mqoRsy0bzayfiDHSNA27\n3Q6rDSl6UgyEYopltaKphKUTY8RFi9F2vl57e3t0my0hRalYilWy92I3smiW5V4HYUnljLG2INAZ\nYzSpCMXKl5vnGhgne9VoTSo+/+PYE7NiVdTlMiy8RSET0EiJvu/pgyekgLYKkhJ2nhErcO89lAz5\n8vISn3e4ia1TrovWuth3BPp1h/eX3Do+EDjNSuVHlnvubjhCfuYbUP00i1ogsKRl3KHS+GEUtfo8\nNeu4XOdIXQgK3ksQH4ZeEqUcqSpLu1gQY2SxXM1Q6ft5fFMoY5vK8eqXv8S3vPIi3eaMnCLWaowz\nrFZL7hyezKIVmS7Vz8wTYSis2PQ9v/jlX+Ttt9/m8ePHaLucA1BKieXqkF0/4uoKH6cNBuIpb6iV\n4e6dO6QY6fuB2lWMIRBSxDg7ZwdZKxHGpJGUyjxT6wTrTQqtLJWz6JLVrrcd1rqSWWUS4jlujBVf\nkroCLY6dVlvQAWUMY6GkxQRjGVHns2DIGVveW9gyRI9SknW1lWPwslFrZ1GVhdCTQsfnPvPr+B2/\n/Qc43l+gVUbFkVXjGOKOOMpriClyGeSgRGQm1q5C39NZnB6H2JexfMyahhACjS32Auk6s/PeYzRz\nMBIV6YBWDSkotPKs8hl39s5Y1m9z1d3jSf4wQ6zZGY2KAd8rUkF7jLboAn9pnUhlvaQ00QvjjPtm\nxBY3lkx9wp7HGCQIKfFZiSnPZfscOMnCKBp9YZDA6IPQLstzpwxQ7IUrvPd0ux6l8pwtq8I2Ser6\nc02Pvu+lOZ0zGIGZqqGRZjEabUzpf0TatialVNTMYueQyiAdreQ1J1hg+h5tuxT9xm5HNooUE1kl\nUgos28lyWjGOwm7a29tj8KNUoeUeTti1H2X/1bYWkgRA1nPmPlUj02M6wPx0KDgnWb21pBRoqpqg\n8o3mqGKz2XL71t3iP5RYOocrjerLy0suLi7Q1lHXMkVtudojBQ9J8Prd4AuVVc9BO8bI5eUlVVWx\nXC6lOgkD/eUVtmhUXC2e+AbFz/zVv/QNGcX4l370RxjHem5OV/q6iQzSp5nXpFYYaxnGcD0Lo6yv\nSX8RY+TR+SM5/HbX9/n9PL4pJky9/MGX8p//z/4kh3sr/BDQypdp7yPDsMNWK4Zh4PLyivV6zVe/\n+lVef+sRVVVJpmItyV6r5dq25epKmiLee6zSKKtnjrK1luQFp5vEO3OZZ4WeeLR/QMqB7XZLXRdL\nW6WIWdP1A08enxMLdDBxtp12QOJquwFgf3+fzWYjhlUI7m5cwxALwyWAXTQYM/ljZ4jMcnqttUzF\nCZK5hDJ2ToZcGBnKUZSySkdqJ41l8QgRfP0P/L7fw/d/z3cz9ht03NE6gzaUJuANTD1I538MHuPq\nGa922tBtduhCS5ssnTOCtUOhJxoREDlbIK7JMnjKqLKwWcI0KUplWpNRJBb1mhebr5L117nMH+XJ\n9kMM7JHjgi705CHSJ7mfrZXyVltHQtN1HdudzM/NCmEMZdngVW0h3bAcLp81A5tdJ3qLSnog02Ya\nxpG2aeag5ZTQM1OSa5My1MUeYWKBTM3GqboRXa8M9lYqk3ElQ5NmYh89psyLHccRn2JRh9ZzRRSL\nl33O4jVfKYOrDFqDswatZDi6tsLOyFHGDU6DyVWGuqmum45XG3777/+99FeX/ORf+wkePjjllVc+\nNh9GOYt4yFqLcVYcV4cw9ypyzgwxzZOZQhjRRqGipi1B0mqxjJ6qlCnQ22L9MWk1KM/RCEMmZ9GH\nhFQGrmDec+iqsjemAyNFODs7K9fegVOonBh3I70PHOy18x6aPv/EZpImdCCU+2PKve3668AZYxTi\nRpQ9VVV2dqxNKdE0DZ/97OfIOfPo0SlvvvWGXDctFb33nquLSzb9ZrYhAbBGKoi6rgv7yBWDPSUE\nhdLnmOLY1MO5uLiY33s6AGWSWeaP/5n/9BsnmPr/+/HSC8/lP/kn/hiV1SwWKx6ePuZrr36Vq6sr\n3nz7XSg0KGesNAlTxpcyawokMauZI261wfvEo8dnjGMgx8RqtRAcfLvl8PCQFP2Mrxlj8CGUsjKx\n3W64desWVmm6oScraJqGMAp7JmfFo0dPSFmGSUwNKFWoeCEIR92ZGwfLBPuUxpEvij9bT0pKTYxZ\ncPAo1soxQ+d7qSoKlpeKMGz0Q2G0CPfcKfDbNd/ykY/w2e/6BN/z2e/i7NE7NJVmtWyI0QuThMkb\nXxa+zhRBS8ZUEjyyAqMk4Ohi5yzccTM3lrK+Ls8Fly7WAcq+h8VSV4Xv6DVDXqONOIlqtWNfdTyz\n/wbOnNIPiavhWdbhA6yTAyP4vTWGbivNKGUTfTdA+UwqaTyRVLLLGLL0d73HGKkAw+hl2HrJXFFq\nztgFfpENE7Ng+e1Ek9RTZVJokFmG1MSQMM5Iv6N44cRc3D7L902qAhLOqMIKiXPQEVZJNb9mSonN\nZkPSqow7LGrbJOV7ShCkCKGpHbUzOKPFGKzvWe0tWa1Wc/UyBeWc5bgx5tqHRzLmflY2ex+ZbLen\n388pCfSXhGYs843L2MdyqCiMwJ8xksoBaa0t9OXSpyjYeEKVg1/+7VSZN4EEVD90czU0NU9j6YNN\nn8sYERSKb5O57k+FwiTz46xclsSL+frKOpdMPRYYTvZbmg+TEKRhXNc1lXVcXl4y9tLPEYFfQhth\nyezv71MZi6uXDF4+z8c//jGefvF5sBUEz5f+wRd55623yX6cCSDjGAhhZNvvePjkjDEEsegr39lW\nFcu2vV53E9uq2HxP4xZjjFSuoS9W1D/8J//cr55Av1qt8nd/+pNUE45rDDkmxhBkA2rp8lfWsawl\nOIfczzfSOYErul4YB23dYE3N2+++g/dR7HGXC2LO+Cg+G5SminOO5XLJ1MwxxvDWG29ycvd2gX3E\nelhrzW7bl8NBAv2YBOMd/DizDqasQBpt19S0qXGSSpbW9z1DiHMDZsreUtZzBjD4iM9+ZspYozl7\n8JDV4QGts6zahhBHnj455o/8oT/IvVv75DDgjMZYJIvXULnr189JzZnobDVQhqtYa1ms9rFWFy+c\nLNOjQqArG3LK4kFgAlPK0oxsTFeVUWhJcHPvI94PKB3xcYfTBzSxpzFv8tH7j6jUE9b9CafDSwzm\nPmNuSFpeu++22GINkMjsxp1QU7c9aDNTk2MOBZ7JGDKXl5fkLOvCKk22moPDQyrnaNp29oePUaZF\nGWOgfK8w9nPm6L1nb7WUrzNBUVkRc2S5WEhjNmW2u07gGVV8fJSlbdtZYTz5rV9nlWkOaFOwnzI6\naysq6xiTcOSfnF2w7jqMEfinbSpuHR2Iv1E5pJarxQwh1WWtjuNITtOalmsxjmPJMMO8BiZ++t7e\nnihDC0wSQxLTOpgh0msMe4I9M7rcq2nvwPUhaYyh4H/o8vmmZmNKZSgLfh7DOP2uKKzr+WCcPF0m\nKqhWMulLFWvmvjRqlZJ+WMiBcddfUzq7AV/gzP39faxxwiRTeb4ec2P8xl5VSsn93W5JKczXqq5b\nDldLbCWQrE+e1WoJTI14y6c+9SlWd29BjLzx6i/xC7/wC1gfZqU22s6H8sXFBQ8fP5LhPY2Iqqae\nzHQQUO5D9CPeR8ZBDul/+z/6s7+aZsYqtn1gkyVrXNSS1dRNzfZyLbh4hnHYUTtDCnEe8ef9SLCR\nPgZ2fQ9A5Woqp7l9+4RhGDh/coY1FX4chLJkbWFRBFa2YvAScMMg5XVCEaKi60WZe2wdTePISjP6\nLA1Lsmz6mNDaoLQmK0PImcpqQkpYUzGWhsnoC3ccJXQ+bbDOkrK4PIIE/5Ay0Qt0pYwMq1ZaYVWm\n0Zq9tuKwtnzLKx/kD/y+34PKmTtHDTp5yFtMnYlBMY4JUzm0VehkMFYqgqptCGGkchZXaIGZiHGy\nwfoxMAwjl5frgi1eMygkSw/zv+vayYZTYEvVNcEcKYHWlrrW1JXm4PgIFXrYvcOR/Sq39s+Iw44v\nv7XidHNMWLUos2a3PcXomuXeqmSkBuM0YRhkPmgOKKNZrzdQGt3jKGyqtpF5muJ7otjf32cYBvYO\n9ucD1yi5Fv2wk55CNQ31vj74q6q6rsLSLxPZaItxImAC0EZx6A5muqSSZUEMg6wzjMARhX3U9z0U\nGG8SSG23WzCKyjisHlDLPYwVZoaxipQiOcpzuy6wWi1omkqsQYaR7aYDlecKVU/aBZRwmUIiRqEO\nrlYTqUDN33nCgTfbHbGQFXwY8cVeQxldRFrT8HNZr5JpRxYLIQ7sdrvidmrmJAwKX3wYZohLsn2x\nfpig1+k1tts1Iae5SnHO4cxYLKYVxly/LsrjYyQEP3+e1aIViM0HTk9P2Ww2gGZ9teWDH/wg3XaH\nUj3j+YirrsVSw07igo+Brus4PDycD/KqqhjHnvPzS3ZDj9aXPHokgqf7J3epFlIxVaYmxUg37vg7\nP/u3CV2Pq2v60PPyKx/h5Y9+DN00kOEX/8EX+cpX/zGNa3j63tM889RTom8p9s1d1/Hqq6+itebg\n4ADnhFFonOX2ndsMgwT89/v4pgj0uXh0TCWLVxrnNBcXF2TEm0JboSNu+kEUoIWni1aEJM0WVSCQ\nYfCksJ1NpqbTOmexEDBK45MsYh8DYRc5Pz9Ha03jKvHJ3m4Jpes/9J5FuyIkCptFMnPxDFTklGQo\ntDYg7VGs1uyKC19KSWh+ZGJMjD7OGcyUiUxZZCZR19cdeWO0NIxtRaUVX/iNX+D7Pv9Z7t07ZOy3\n5DRS65rgI9u+w9UVlRbxjTG6DFnJhBhpl41kllZRl+xE6IOSXe6GHeMgLA2DKcG6VABZ9AJWCdSk\nKFn3OBR4QrLHHENxFZSMvm1rnA5cvP0VDuqRk8N3OWoeMG4jT7qP0Vd3Wdw1XFxBt95wcf6EyjRc\nXT4BLLWVvod2esZJbWVo25Z+56mqSiws6ppq8gna9YA8v10s6bYSTNqmYRxFyLNsF9CK1kEqKcne\nppmicwY/Zepl/fTjQNwFxuIttGjaeVygNganDVEnxnHKaIX2enl5WSpHCFFK8emPMYbKOhaLJZV1\nOCdDSPaXK8YcOTw+JvRD+UzXTc+u60pTGna7AYVmfbURyf9ygSpY/kQJJgpzo6oags/EJLS+hw8f\nSoO3alDIoXZ8fMz+/v6cUM2e8eXaSEUUCMHT92K2NsEvExtEoBtJelLxpCJHxphwtpb9QGS72XK5\nkWapMYa2qum6Tdm3CaIjW3XdfDS6HDbXzeKZKVX+VIdHLJq2xBXFxdWGx6fyPVerFZVz1K6aK4dU\nyAxWi6/O6empwCTWsbdYcnznmNVqxXrbcX5+zoOHD2fqqLWak3t30HhcXZGz0K3tskE5x4Fd8s6b\nb/HwrbexlWNxuM/Hf82389FPfhuomst3H/ILP/8PaLSmreu5mvyWj36UR48ecX5+zhuvv8U7b75F\nNo66fmseVPN+H98UgT5NUvic6b2U5QEZAxfjSDZqziz7OFLXNRebjt1ux/7qoOBX5XSuLcPgWYcd\n+8XT+vBY3BpzFGxSa00eFCRN8HLIRJ9KQK1JqmKzC4Vj39IPER9For/reyrnUFB4/ppUsrmcA9EH\ncrYy27Pg2MMYSEmMrXzxsghBFmBKsdADg3DQkYwspYG2MeB3/Prv/Qzf972f5XBvgQ69NDxTj3UZ\n7+Hi4hxjZMHXVc1me8VeeyA9hJgxOaEzbLdbmqZh2UjAyVqYJd16O2dZIQcosvsZdy1Nw5v2vcMw\nYIzjcF+ggpDlQFLK4nTAjDuMXqH0lioO3D14wL3VW0DPebfPk8fP8u5ujx0ZZQb8mKmc4dbBodDr\nSoN6yrDbtgWlcK4urqbXkOOkvvXeo8t4yRikkT7GIEZmVjBoV9uZPVNVFSiB3xZ1S8ypYN8TTHUj\nuCB2JXVdU5kl1hrGUSrISBaoKkX6NBBLpipCF00/ylzj8/PzuU8jazZxsLePbRy2yPq999J/0RXW\nWSotk7uwksCQcnltgyv3aNkuaOprHcUw9ixXi9kJUhWvIaMzMarZvkEbR7uw3LtnZ8xfKgGxbpjW\nf86KGDxoucZ1XTOEQg5Ahn+gNEPwVJVwwAUy2ZGSkAeGMXCxvqJpKvYPVkJdLVCWMY7BjzTaYoxk\nx87VKCUQVDSZsOuoKglXi6ahamS8YhhHdEmKQJMLq+maYivBsG6X3L17h24SU7mGvu9mBphBSAwY\njR8Ch4fi7jI1ut944y2p+LSjqVq+41s/QdYitut2Gx4/vmDsB2G71TV13dIuG5wLhFqU59o6ut3I\nZvuAv/3wbwHShN12Pb/2Oz7J8x/+sEw1W294/fXX+Zs/87eAxO3bt3n2hed5Tim0cbz++htsNh3b\nze59x9hvCox+uVzmV155ZQ4gJyf3pJTcbHBOhkHY4v3QVHUx8iqNOSiNSlnkMoFHNlrbtrR1LaUx\nsBuGWQwSowSvODV2ClZrrZVMSWtSwXl9GDg8PIBULIFrGWDRj5Gg8jXvtzxs4fH24yAZchkYbVAE\nxBwrZoF2FKk4EILVikVbE/ot3/WpX8sP/KZ/mUp7SCONs1RWgbZYB/YGHDCWwQvGyEbsup4Mc4Y1\nWfBOzWulBMbZ7bYcHhwwjv08J3Vy/pzww6mxBoIRCtyQ5fsYdcN6tyGpnpgNi6YhK0Xajqx4nefv\nvEljfokcGjbD8zwKL9PpQ0Yvtrp+7DCpIjFACe4ZmbXbtkvi3PvQpQmYZfhEWbtTKT8Mw9x7sEag\njzEmhjBQmesmq1KKWLjhqTToxjHM/3/f96QyqtKWQ286QLTWtE3NFP8FTpDGb8zTWhI8WGVYLJZC\ntc15/nxhNk2DPFF9iYQysCSlSE4aV4LU9FxjDP2NSgCEJLCoF1ROzerta/tu+XvqR7hqskHO83XI\nOc9Q1cQ+G0vjOHhRaE7ZvB+H2SBvMmXLQdbNzPYp7JrGVaWHIPts8CM5KUbfc35+PmPeoHF1TQjT\nPhAaZtu2UKqqGbapBBKaRJPTZ87zGhULDQptcbpuUxIj17ZYEpR9PxZ18/ZKYsTlZs3l5SUxp3L/\nFhwdHbFYrLi6upr1PHEMDN5ztVmzWIj4axrOM7Fj9I37Z+sKnWX4j8qJse/mg3SyGI8+4OqG7U5Y\nbp///Oc5vvc0AJsn5/zUT/0UX/vyP6ZdrAhB1tj7Zd18U2T0gPBIy2Z6/PixZFQocgSyLs0fxXYr\nZaJxMqOzdtXMCJmw4WEQ3/eUECwwStDo+5GDg7ZYAIjqUOVJtSdj3gYvvt45JzCKIfZUtWWMI8nn\nwqjRYAxRScBWBmKWST8hjPR9kA2gFUkJRmq0JiRxQaxrR6OcDDO3DdHv2FtUfOZf+g4+/rGPcLBq\n2asNqzYSo6Kpl+QcSWFk3G1ZLg5JQGWkObpQEnCF0WHEHS8V73qtpXHJtZFWCAGC4uDgCG0U7XIB\n3UCO8pztdj0Hz67rWCwWMxcYRPThnJmzlxgj3W5gGAdqt8BGTxMzt4+/xmH7TyBeMI7P8tB/kvN0\nTMyRJluiHnGVQhnBZ00WGmouM1uddTPDIiUIYeTRoyc0dYtxlr4bZvrcOIrh2DQyRRccXGweLCGB\nM2bWPkzsIh8jPnhUwZ5DCLNXP0Auh6j3ntu3b89ZotbMFDjwxcpA3rtp2xkCEQVqodcVHxc/H44y\nzcx7j3YVoRdxTNd7rLYkwJSsd/al0df2vFpr1ustV+dXMqB8HHnhhRdYLdp5LvJ0z2JMpDyUw8sV\ngZAGJcEw5kD0ErD7wXNxccHdkxPefffdwrTRWGdo2pbFYkHXSTY8Va27nWSXPkpVq7UWyqfRpbm/\nFHsGXXNyclKUv5mDo0O6fpwNxkyxfxbNgFylMEZiEVChlAxiz9cMmlw0BMJecXOgn66XQEmFW18Y\ncrEkMZO9d1MgzsXeitu3b5MKtHt+fs7DB+/gR1DGyGS22nG4d0BCKNSTQvb09AFdt+P48IhFu+T1\nN18vFZUkVnt7e8TRE5OX/ljl2PRDsdDQqNayGTuaxqFS5h/+nb/Lph8Yg+fx4zP+/he/yNH+Ec+9\n9AHeffcBDx5N9mK/8uObItCnPJ3c15Q1jXC7bW3xhdExydKh+JzHRFKB2jlCMdCaMiqFYdePWDvK\ndPkMxko2qFQmJDl5jbKzuGfamFMmq7CE4OkKzS1jiCER0jBDB2OIhEFw1l3opXETZPKSSiJ4sk7E\nLc5UhD5gYmJv6cix57O/7jv4DV/4XhoTqVRCVRqT84ztEiN97IhRgslqb4GPkT6MpCB0TGNVySAT\nWkMqGaMqmZYq2eybb77J4eEhVhvxqkmBkITLDpCVpq4sxlzjsIeHhzMz5NpvZKTvO2LMc8mMUrT1\nAsuOhkc8d+sJlf4qqYuch49wEb6Vi7EmmzMqfchG9fgQSENm6DwpD2XAivjvAMIvzb5seOHMD/1I\nyjBcCl0UJKB2ndgenF1d0rYt905O5B5mUeUaYwQjLpS2vhc4ZYyBVbvA1bXYGJcgIYFM1uLEZ54y\ncqPFd3+CHqyDtmmxURhK0/tppcgJmbJ1c23qau5HpWKUNusUtGbPrIjhulJRSlFlsUqwparyZQ5v\nLvRI51wx/Luk22w5Pj7marPmpzOMawAAIABJREFU9u3bnJ+f0zQNh/t75CTw6DTDYFr3lOleTbMg\npYHlcsmw68hRoLyUM83RPskHccsEtHMM5XppW4bjDIJzT5YSwNwL0np53Uid9j5isyAV1fUc4BBC\nOSjEr94pxRgGhtLLqp0cDDFnxtLo1Rr8brxhxyALRJrACe93WCWN5Riz+COlhCZJ4hYjzhli9JLB\nX1yQgnzf6C2Ht2S0aNM0NE1daJ09qVyDqqo5OjpGo3j8+Aw/jiRj6NcD9+/fl2q1rsnUQgfdCQvq\nyaMLXFWJ8K00siXh2nJ+dVXMz6Bq9+hD4pdef0uqKP/+0ZhvikCv1XQqK7S2VHUtmXJl8SQR6Ihh\nDKnYoIYA2joCoLXBakNdtyUYRZHGZ0XCMIYRxeQNI5upH4QqqdDkJKd1SqCMlZFsSqOyAiWGXKMX\nn/icFboMiu6LOnW5XBH8SFNgoskvRRvJfJXSsqD8yO29lqfuHPGF7/sMH//Wj+LSgCZidKaycsob\nJXis9562rVF2MbNPpofLzcw11kjQCONQSlTJcEWkBD6OVJXl7u1b8stK4Seoyij82KO1pakn+Xcq\nzIlYGCPXlMzp+qZsMHkkJzlocA1WXXGcB54/fgvSq2Qcj8fnON19kC5loEflmk0QbnZKYc5MNYYU\nI2BIKhexElRthdKWvh9pFwt0sVbousu5CTwMYjHhh5Hlcik+NmWtaCXmsPMsg0K5005m97Ys5iZc\nSom+YL6r/X2BgIqit4WZZuucQAZaZSrrME5LgNJAWYsxRnalgeqMxdWVVIspoa2U8ZMzpLDAMnWh\nIKrKQcyCySPzGdAFvlGKw/19zh4/JiYJel3X44zi6OioCJ4c3Thw9+5dvPccHBwUvxxhycQU5ia9\n1lowecCYGu+FOmqtpe97Fqs9Hj58yOHhISFEtl1HGKRPNmkQtNYyN1eBbRv53LGwgAptOZPKrIlC\nI6bYEnupblrniFmEYjPMqCAkD0zzozV1UcjL9+6KNUNpqKaEMwafEyHEuRIa+hGjREy2HgbW3RZt\nDfurJZvNBus0YZCe3NnZmbBu9g/YPzxguVxycu8ehor1+oo79+4So+fyYsPDhw9JZA4O9mXXpCiQ\nbr/DOs3zLz5H9Ilh2BHCSLtc0Pc9m43odCrXzPMHNptNYT5tZqg5hEBTLwrpwQs6oS0Pzy6praMt\nvbb38/imweif/9CHaG1F3/fvWYQyQUrPJetN57bFYkHMgsc3VVs6/oGrqytCYZJUjYggFnXD5eUl\n1loODg54fP6YaqZZZlI29H5kUk5OmN4U3CalYiSL90xKkBODH1kuVmijCDERfZJMMCdyHIq3CBwu\nG37P7/5dPP/UbSoDq7bBjz3GKMZBKIK1M4zFG7+u24JhJvb29kiTz295TNll27bkFEpQVoJfpwIb\nhFx8fQSqMErjqiKsMbZULiIymjjJMQS0ltcXnYtseKPrObAAECFpg3UVVkfqcMadxTnP3vsyMbxN\n8ie88+hZHvUvsbULrHazVfLEo45RhE1KqVLK1uQcZ4jDWWmWX222nD6QTaW15vT0FGVkRsH5kyfs\ndjvEGmNL1TQ899wL8hFLhjYFiSmLttYW8yyw2byn4RpKY3wWHeU4V3rTxDMlliu0VU3lpt+XrFcO\nz+u1A9LQm0R50uyM5KSwpSeUssx79SHMM1VV8aOZeifTY7JQEN/2Xpqv3pNCEDogcqg9OT8vMEri\nheeeEcwbsRuZRH0Txm2ceU/1ABNnXc2Z5WazoS/486pdFJookDK3bt3CWKFpJi2Tw4wqUGq+1muE\nEJhUjRPE5oxlMli7+R2nvyecfep7yGdTM8RmjCFRoKNyQNiigL95v7HXamatNaEf+OpXv8rT9+5z\nfOuQfAMie/LkCU/fvSf3uojTgpeEQrtpfoGY1J0+fMjDhw85OTlhsWzYbnbyfZF5CstGVLpt2zIW\nIsl04J89uSiN25q2bRmGUaaElab6er0uQ8vlvbpxANHBM44jdVXxl3/sx3/1CKYWy2V++gMvcdgs\n3yMimR5Wu+sbnlNx5xMHPm1LZ75gqVVV0e9GQskcbF1RGUvlXLFVmHzDBR8dxxGrkaHX3hfTrIli\nN7GBCp2sjBWQgGOJORcKojABnE4M3Y6jw32G7Zrv/nXfzvd893dz+2gfqxJOK0YvjpeqBK3s5XtM\nC203jFTWzY3UMXi0Zm6QTh4q0zWy1hLDCKRZxBI8MztmCnCTAlIw+4wvop2mEQpb1/XzDFRhFEkg\nlkyJa2c+5P5UdY01LV13wb7Z8q3PPaKpfxFi4MnVC5z2d7janpDcARkvUFfxrA8hYJTGOo11MnFL\no2iXKxaLxQ18VfotF+eX1HXLg4fvMo4jrm5ZLpdst1vOzs6E9z0Mwn1uW/p+vAHzJS4uLvAxcH5+\nzu3jW6xWK9pFTW0d/S68Z73Z2jL7jwC2HHpTRWCMoVku0FqJQtJK016a4fK71lxTZydYBZjVs957\nhr7ni1/8It/xqU/N1hw3FaHSQNRF8v5e87Pp+tR1zW63Y73dkmOchT5VVXF8fMyDx4/oup7tesPL\nL78sWeCiJvoRays55I2oXJN0hefEZvqTUmIsdOOMmk3AJsinKj5OEzvK1TLcZ7PZsFpJAjTdcxHs\nFUGVmgRX+j3XGwpZofRepnt48zrmrOZDLiHvdX5+LpTUfdFM7HY7+TxFEKmLQGn6HPurBTkJUy3k\nNNsqTwdpKsG267r5Gk2NX/ncsh60cZyfn8uhaeQ+XVxc8PDhQ3JM3D66TQhFa1LWlrCKyrrXmrF4\nEWmtGcph2tQLdrsdm27LMHjG4NkNXmZLlOrt0aNH/Nh/86so0DdNk194+WUc+j0/n4KUKhDEZAeL\n0YStR1tVbmrHME4qujLkAPWecW03X89ay24cpPGXigAIxxjEvErZ6+blTcXfdalamA1aBplUTnOw\nXDDuzvjwyx/kD/7Q7yf2W5a1wWro1ut5kLOqigQ8RDSqWOFafEzYMju1dpWc2HWNLjCBNWZmKkyD\nGWbGS/bz9xtHoXzeDAxOAVoYAdIQDigz6QtSYZAUFka8ZniEOFL0QqTiOGitpm5kcx+owDO3ztir\nvg75NXKseHj5IS74MF7t0/sBpR3RJ/rBz1ng8aEMWNBGRvqpyRhLu/l9JCOTUngy6truOnGYLDYR\nr7322kz3E1GJox+EdjaxqyaMN5YpRJO7pdWGbrOhbZazYEgphU+yCSevIUovRyk1H0KTCtUWfx/t\n7EzxnFwrp8BV1zWhBC3KXgshsFlv+drXvsaHX/mIUEBvMJy0EoMrsZuw8+9MNr1TEFZqsku47sd4\n78tYy4Zq0Qq7zHu22y3OappK/M/39vbY27su/afvNVUt3nuMu2aNxBDYbDtyzjx48IDbd084Pj7G\nFphwSiSqqpqpmiklmlaqjIk6OldW5X2TujbuUsU3vx/jfA+mx5ShhxCwhfq56Tq6TrQjU8UaY5Re\nTjn422IrYGwzVzDOOXQeGKNUvdtdB0mgppOTkzlGTIE/3yAxhJRwriYloUzHnApZJJHiMB8UXdcz\njj3jLsyK5AmpUPa6XzMZzsWSRMk8ZZl9LQSDRqqXcSBlxcWm53K95uHDh5ydnfHzX/qFXz2sG60N\ndSUZa2UrMUsqp69KeYZXYmnaOutQjZ6bg9Y1YIusPCdpcEWxTfApyUxVH4Uni5TSrqlxWgzMUojE\n3JcbmalMNfvVoBQjmSprMIrGVAXqGVkZjXGZ3/T9n+ML3/c5cuiE15t3mMZC8igMR0cHYsykM9pp\nFqXEJuV52MTU2JQRduKoKNbD1zhoCPn6upSgctOTuq5rlLYEPxQOvFBFMRU6RzbdtmQ5xTxr9OUQ\nUxgtJk9Jabrd7j3TomIUTN5ax3a34/TxmwwPvsLv+MIzVPbnyXFHzjVvnX6Qtf0Qg1qQFLh6RRg9\nKUds5TheCRuo6/tCVR04Pj6eK7GcCrUwK9brNYMfOT09lUbi4SHWVvTjSB83bHcdu91QsvGBp5/e\n5/Hjx3g/CAc7ZfGNKc3UlBIJSNkwjhFDJiTN1bYrvRupjoa+Y29vj9o6urWsicWiAZUYR7HD6IvH\nyW4cCu6fsNbMFeO9p58qP7N0u93snKm0JhavIAnWFcGDsUIrnBhBWYndgVEiuFJKlQNFzfsgpVR0\nIVqEYkpgfJc1R8cNlxcXbLrHhKTnKVdKZ/pux/27d1ipNGfTU7XrYyB0oqlomgbtpqEihe9eAvLH\nPvbRQlGO+H4obq1CZ52avFPzWRKLiJs0GCVYi+1bFAgwB3KMhKKqdaVxrikwTuHEJzKubiBFjHM0\nywanbxNLoFU5Y6uKq6ur+RoL1VEovFq1ZX7xlVSGzhFQVM2CzfaKNHpOHz2We94I7bG2wqgJOWEr\ni8m5wKOTvYQtRJIaP+b5MG7bmsVigV/KPhKoaXzP4ZWzqOFXyz3Zy34USnBI7AYRsykjQ4ZcXfHa\nq7/E47NLnpyf8dRTz3B8cMzPf+kX3leM/aYI9GIktkUpTa96lC7Zd0Ju3o3yThVVXDf4WWqdisJ1\nUsBqrWdqni1B2+gKVaX5OeM4gnNi1FQ56qmcRCicbbtkKDJ2NNgEi6aWgQdpxFrHv/L5z/D5z32G\n88en5H6N0pFKS2aVEOzbWEPKQXxMnJnnhU6Kw3Echcp2934pbQP46+mROV+rEafve9NbZNpwE643\nZYV938+Z5/R7E5Xsl0NjMcrQhBiLGGfw8/tNiseQPRfbNZvTS546Tnz75+5SuX9ETlek/BxvPzqm\nMy+xSQtM1WCUeIQYJZknOc1j2MZeWBJ7e3tst7tZB6GUbM7gI1dXFzw5v5jx2vPzc/qSsU4GVhN+\nbIzh61//OmdnZxwe7nNwUKMU1yZYMfDu6QOpKLRltRLBzsHBgVDsinVACNLzSD6x84PQe3VmNw7S\nAzEGHQI5QwwSnKYALk35JVpruk7YHTlTrJ2vcf+6FsaFNfUMB90Uok2VgVIyxk9rqQCngwOtUKW8\nFxO9kgQlGd+46a6Drc+JpnL0w4h2DmLk+PgYHzNf+9prnNy+w4svPk9MSex7tZqbmr8cq58++wQv\nTYGyaSrJbGOkqaRB2zTNvB7Hblv6RYG6aqgqh9ZmhmSsnYaHFyuCwZOIM+wiSU+BlBSFDFEqYWOp\nrIx4FLsIYd4cHBzM3yHGTNNIoK3rmsvLSyCRSibeVOKCusx7gIaSaO12MoYwVmKaNlGJZS/GUtFP\nSZfFOo3RzTWLiWsL9GmvLhaLufrRWrPre1xVzwNxYhYlsXOGqqmFiKEMT548oR/8bLX8gQ98iJyk\nwny/j28K6Kaum/zM8x8AwGDEE72cyDHGuaSbPLCttQw+zyWgGDhJpqQKdjaXtVkCmh/DLFkGGIt3\niWC+lTjcFH680iLx11mjydQLR5Uyq1bzQ//6v8YrH3oRVGTXr0nDwKK2VNYQxijDQ0oTqGpEVRfH\nItU/PCDr67JtGvEmm9vQ9z3OFoGMmjBJPTdeb+LsNwPDXNLOXHcJCtZaYRVoB/oaf52uwfT8nEWa\nrpQSJW1xL5zexznHMIxE37Ef3+WV59/CmddQ2XOxPeHNx3dI9qP0ZolZrBi7HVcX5zNsMmUrcG1P\ne5NmNx0mcvgVJWU/0oc4e7yLSVgqGome87NL6uJ6uFy2c1UgvvxqPhDnOQbFGKsrsMKyacVvJnrh\nmEfBtltX/L9TKplcFMqnvv6cIOyXyatmwo51YejYqimmeTCEa6vc5XJZvFdkbT1+dMbzzz/P6mBf\n/PkNc0Iz9wzKYZ1Lg1ZrUWJP+0Jl4a4nNEap2dGx6zpOHz/i4nJNs1xKQjB6ck6kGKmMxlipnD71\nye8g54yrZc2MxbrD1LWYtJUZBVPTerPZzPdrUUwGp/uYuW58yz4VCuLF+TlVUTXLYdixf7CHsW6+\njroMQWnbltPT0+sGcvHBkRnHYss9rU1fRnBOMIsAl6bcd5lo5b1nnEf4yfXcDRv6vufs8RPu3btH\n0+7Rtgs2uy3j2FOZCleZWe06u3Um+QzKMKvGkxKBVOOqOSnzXkSeAmNNzpOS8a/XMjpxW9biBJdR\nCCBTxT2OI6++9jqnp6d0XcfR0RFHd+6xLFPnNpuOv/bX/8o3DrpRSh0CfxH4FiTZ/DeBrwB/FXgB\n+CXgd+ecz5Xs4B9Fxgl2wL+Rc/6Hv8LrowE/igQ5a4FeYhEdZa0J3s981RhFMq/s9eT3jND+pkA7\nlgxKUyb/BOHLhnBNLQNmpgJagl5tLTF6KmvIuw3jsOX3/uAP8b3f+WnG3ZVoW8OVLOw40rRi2FS1\nq8LHL01QYzBK4SqHXSznfsF02m82Gw73D95zHeq6RhVmdUiZYYjkHCQQJSkTJx/6KXOfFMFTlaCU\nom3rwvOXqVc3n3+TxTDZGkz3IBf2QyxDlQUu0QzjDrq3uds85OUXHgBvkbPj9OKQty+f5cnumMWq\nxuTM43ceoHREZXEgvequsLaSLM26mVEjPPYwY8PS3Eql+rIsl46lfu+BsN6KEVi37dnteqy2LA9X\npCSTeeS7SVMarXC1HFjr7YamXpBS5sn5mdAo+56Liws2xQdHIDDNMGy5f/cplFJs12tWC2FN7B8s\nS0YnTKVNGe2WUsIX/6Wmld7K4fExY99zcv8E69x8oIYQik2yZbftODt/zK2TO+xGT9PY4jevyUnh\nC389DENh2heWiqvwZc6wQfx5rLNMXv+ZiLFyqNw1wppabzYkI7TPjBIPJOeIKfHC8y9xebXh6PCQ\ncYhcXp5zdHTAMPR06zWr1QpnhdmitODPiz2ZbhTJdMOAMxaNoW1XDL6/dpLsewY/4spQdvFzkix7\nudzDGkfOiTB6tJVkZ2pU3r59u9hsmHl920oSH5WvGTWr1UoGBJU/lGooxoxKmZBlv9sknyEmEY9N\nIsC95QoNrLcXvPvgbRarfYFcYsREsc7YDf1cBUsFnQnRc3ZxwdnZBc888wzGVFxeXs59is1mw9j7\nUgGJKvrq6mruVeSc0eWw3Gw2ckBnWbvGVoQQ+Sevfp3Ry3q5c+85Xv7Qh4gp8e7jh6yvtgxltOP7\nebyvjF4p9V8DP5dz/otKqQpYAP8BcJZz/nNKqT8BHOWc/7hS6jcCfxQJ9J8GfjTn/Ol/3usvFqv8\n0ge/DWUUVluMAl0Cgi6N0elELZ9Hst+ptMuZy81aGkiFVtn3A76UxWL+BP1uJBcTtLEfQEUq17Ks\nK0IKkCPd5pyn7h3z237rb+HXf/bTbK4usBRhkvFzlWGMwdWi3p0y1Kk5PGU+N+mZIGwLVy1mP3iZ\n9p3nIHAdvMe5/zBJqqdsoqoF+7sJvYTSkJyeI5xpPW8QhfilO1eLoVsZtjI1jlAjpGJ7oDJVsni1\nQzcV68eXLKotv/bZf8Ry7x3SsCMoxxsPXmK034k3+6RsWA+DzPS1Tt4vBHa7gcrVKP3erD2lxOhl\n6PZkL5BzxgdpWnU7sWTYbrfELDzxGGVs3TAMc0bkXMXJyQl15WgrJ3zodC3+AvHVv7y8JCboul1Z\nbwsSmtdff53NRsRobStq0qF4m8cY2V/tsWgkeGsUL7zwAu2igSlL9Ds2myv2VweFjbJlbynMsZAS\nTVuhtKY2FltfJxdKi3Pol770JT72sY9LP2a7wxrFO++8w72n7lO3LY8ePeLo+KDc05rKGMH7tbiE\nOudI5PfY6koAlAatMYZ+23P6+InAWCju379PCCM+eep6yXLZ0jg5EFVlMUlsrWNO6Mrhxx6bFcaK\n5cW1j7uMdJwCqymUY1tU2D6GuSn7zjvvcPv2bTabDY8ePaFtlhitOTjY4/DwUBIvxRwohQl2g+pZ\njOeGmMhWY5XFD/0McaESuVR/Y0gM404a9t7jY+ZqsyaFyHK55Pj2kQTfIILMKfvuug0hZtbrDaeP\nH6G1MFvu378viRsJTaZtWzbdjkdPLmYvqAl6o9hpbDYbUcH2/j002/PL8/c0pBMSQ8biErq+2vLq\n17/OejsQopgkHh8fcnJyQgiCDHTjIIlfqVr/xt/4yW8M60YpdQD8PPBSvvFkpdRXgM/lnN9VSt0H\n/s+c84eVUv9F+e//7pc/75/1HnXT5vvPfJhcYJbaVWI0FiXDh2u64DiOYjTWX2cOk2fEVPY652ax\nSiaxbBckMnXVFAplYlEvaCrZOCYH7hzv8Z2f/hSf/MTHMUSi72lqmdTujAw9mQYwTEMkjm4dz41i\nP8iouSngTl4kU7N1WrSuatgWtz9ioiludX1/7a8/LY5pUUwwjDTq0nwtpjJx0SzfQ6c0rozCK2KU\nFD0ZbhwcglkeHQn7JUdFyhlXafFcKeXvHpZD9QZP3X6Ac38f4gDxiCfrV7h0H2Ht73DRbWiaxYxN\n1k4aysIYcfP3vnkNUkrCKZ8WQL72Zj87O6PrB3bdwGZ7xdHREW+98wBrK3KSAdlTID48PGR/f592\n0bBcthhzTT2MZQiMMwLFWFfT9wMXFxeSCJRqQuXMputm47SqERHWxdk5y+WSlAPJB5599tn/l7p3\ni9U0S+/6fmu9x++8z7tq17Grz3OyPRPP2MHgGYTBA0wE4agoF6AQLhKcEKFABAqgkFxEMlEcASIQ\nCIriJAQuosQmBgwYxobxMJ6M3e6Znp7uqq6qrr1rV+3Dd37Pa+XiWWt9u4ziGMk3vaXRdO+u2vv7\nvvd9n/U8/+d/cB4zFcZYjo6OOD19Sppp6rJha2sLrXHFpWMymQQoJvPwRBxJjqoSiuLZs2fcvXeP\nZ8/OuHXjOlWxpixrPjx+Qr/f58nJCa+/8SpJmhJZWZb7z8s/EyrSYbLw3a+n/loriWV13aLiiPl8\nTqKFu193ddAG2FaomUbB7vaE8XAk6uhI0c9ymkq8dZTGdayxg6lUKDhay2GdRjFtJ74/aS/n0YMP\nOD4+ZjAQVWxdt6RJ7qZfG97H7v4evV4vHJgbWNEETn7jYKr1uiRzTKf1ek1bl85pM3GNwZquk2St\nLOsxmoyFfa4Uq0KW70kiUYRiQiiHQdu2FFVD6XZIXsiU5TkH+3v0XXaEIqI1KngOXVxcYK0l76UB\nUqrrmmpdhRrlm8OrVNSybsPk+c233+H8/IKybrFWMdzaZjAY0R/2WMxX+DhMlHj1105f8VM/9X/8\nukE3LwHPgf9RKfVdwC8A/zFweKV4PwUO3T/fAB5f+fsfuu/9fxZ6P4Z1bgs/b1eCC7c1qlMvFEBA\nTmizEU75TFf/DLRV5zoaTRyllHVBEucU5Zo0jmQx2hWsZ3N+95d+J5/5nk+hEX+VTLdcPDthf2+X\nWCtUZyirklVnwyLTUx/X67ULKRZf9KZpguBrsVgEOp4/fLquo2mNUKWMYWs0RlklToiqoW4kni3L\nshCTeJXbC6Jy9QEIYfnMhg4qNC8oyjVZkjqaZIlWcfDkKMuavb29AGMR5SgtGKLuLLtJQi9vGUYf\nstX7AK0/gHZMbQdcNp/ivPcJmUZiJR1sY+ishJBbK2Hpgl9GG6zfm0p5QVSaYj2WGSUURcF0OpUF\nXl0HxexysXZdU0rdtMLe2dvl9PQUrTXD4ZC8l4VuU1nLcrUiy3pYY6g72cH4B3AwGEhSlFVsbW1R\nroUymLvOvV4bYjd5nJ2dMRj26PV6PHl6IlnEWrE93grxbnt7uxwcHLgHenOgTadT+v0+/UHufr6w\nWJwdnGD84zFZknDz5hF1Jdd6NBrx5hsfBysH2dNnz4jTiCyK6PfktSRRQhTHbgcRhwPUUxqjyIqX\ni5WgmbZtMV1Lr5dxevqc7e1tsjghGQ4pioJVsRaPneWSh/cfcP3okCSK6Q367OzsyJI5ykAZZ1cS\nufcKXbvRasi9Ks+0L5S3bt0iyzLu37/vmjNp4Ib9EVtbY7rOYm3HBw8ehYO7P0hDhxzHKZGDdOhk\nob9YLJh3LaPRiJ2dHeePY3n+/DlbOzvBk2g0moTAFTk4JOC9LMUC4vnz52HJ6g+irGpompaL8ynG\nKvqOEXN8eipNnb+foyyQHvxU8PTZ8yB4A8jS3sbsTSnGox5RkmBAFNiJ7CJ+/H/7X3n1ldcDdPV9\n/+b3M1tIcV+uCopKXFA7I6r8q4K6X+vXr6XQx8CngR+x1v68UurHgP/sVxRqq/zx/Gv8Ukr9UeCP\ngnCpOyv2rZHSkjvTOdhB65CUAxsIwHQWG5a0G7Mn44RA8nOga2uGmVit9nsp88sz3nzzNf7A7/nd\n3Lp5wHo+J0kjVkVDEkW0TcHuzrb4qptIhFUGLJqzi3MODg5IFZRVxShLWayWoSMfjUYAtEYUuYvV\nUuT41pL1cmFMoANTpq5rIuckGbIgHYfZu2/6myR48GjFcik8aT9hRC6+zx924/GYLMmZL2cOa56H\nQ2GyvRN4+CCKwsG4T9t11EUD1ZrejT0y9YxJ7210+x3oGsryBtPmdZZ8nCquUJ3YNHetyEQVBtsZ\nqspg09R1862bTkTyHkWRs5twDCq5DwI8BZrBaEzbKXoDUQWnac56veb84pJR3gs+NYPBgGvXrjGZ\nTOhMi1IuV7fryNPUCbFM6HjrbsNJHwwGqKjm4cOHHB3sUxQruq5hf3+f6XROliTUzqHU46rr9dp9\n5sJmms9lT6P1fuD9y0STULYd62ItUxqTMNpvb2+750V2BJ6G2LgH1nuw+Ps8z3OOrl1juV6LoV3T\noLQmH/fCc+TZPJtuXgcBnpVfhoojbNsyny+o6pp33nmH7/7kp1ivJQSl35c0o62tlCzrsV4vSWLJ\nSa6ajtjlJ0RWY7Gsi4Jevjk83fPs3kuLsYa2dqwXd4+tViv29va4efMmaZozny4oioLRaCSFy0GJ\n4rbahN2RD/XRnYbOMJlMGI4nmHbTKdcOot0/PJRlpvtMdbwR6PnPKE1zlIpYFRVp3qerG4p1RdpL\n3TI4QkeWw+vXOD09Dbqc2EbBluTy7IyqNsGCQzv6Z90YOrNhv9V1EejLcRy7UKGafr/ParXil7/2\nFvcffsDnv/BD7Gxt03a9l0XjAAAgAElEQVQ23CNtO3NTfSv7ORmh3H6pdtkb/xr19tcA3VwDvmKt\nvev+/Tcihf4Vfp2gmzTr2d1rr6DUhjporUWsZiQoxOPWgSXCRuRhrZWu3nlwtLVAHkkaQ9cSafjc\nZ76HL/7W38TOJIeuQXWGySjn+MkThqMRo60JdVESK8gSWSQ2TqidZKnzx1ZhKXOVZuZfg8egfRfh\npw7/8IFYDXhW0cXzMzSb9xRFEWkmHcHFxQVFUXDz5k2ePHnCvXv3HIZuNjef928xG8gCRFZeNTVR\noiWbsm6C50lv0HemZmmgHyrbp66nDLKcUTrlIP7nZGkBPMDalKq+xlnzgzw3ExoVQ2mFTdJU2FYR\npQpcLqrvvpUWyplSCmsUkUrwFrEWA9piWvkslWMW1W3HYrUijlOStEfT1NS10CkXK6EN7uzscHx8\nTNu23LpxXRgv1tBWJVEs9sMGiZwj0uJFrzWmg6ZpN6O0lWSnPI6YTqfEqYvaK2pUpNnf38cAi+XM\n+ZeUDHt92rYhz3tMp3N2dnY4O3tGpC0vv/wyw2Gf1Mnt0zSmXBesiyXbOzvhUOulG0O13e29MG0s\nFjNiHQlEhbhKXl3cKSUFWxkJ0tZaOvgoedF6WAqboTXGxWZG6EiW351pOH7yVGIQdczWzg7rsmR/\nf591ucZ7wRvT8ejhQ8rVmtHWhOtHR6RZzGTQd4VLLIzPzs7Y2d1lMBgQO4sBYxuHjzvbAyMQSp7n\n7OzskKYpz5+fk8ZyyAmtWiiq/jnwkJd/xq3tiJRmvlwF7jtmY60wHI9kWqhq1mVBV9VkvZ5AgKs1\nSZIxHA4Zj8c0rewOGtOFw9q2HXXXcn5+wXA0whgYDsfBXllovAvKsqZYV6yLJXXHC89t13WkaS4i\nRw+t2s2eTWBWuf8fPXpEURQcHFzjtTde59HDD4miiPl8idWb4BtAcqXTFNNKQ9fZ1rHIIqJY8fd/\n6v/69VPGKqW+DPwRa+23lVJ/HvCSuvMry9gda+2fVEr9DuCPsVnG/nfW2s/+aj8/STM73r+DNZ1w\n2R03GMe6EHX2ptOJIjE88rYBtpVg60gplGnppwmTQc5Ld2/x7/yB30UWd5ydPOS1V+7Jh+0yL/2X\n4I9JoMHF0YaCuFEh+m1+EwQrsZYi4vmx/hDwXXjlPM799/I83xwSrfjtRzoO9DxvaSBL1yT8ft8t\nRe6/edVgWZZB+QcyVq5WIniZz+cMR6NwI/Z7PTCd8H3jfKNMtC2xrsjMiMy8za39d1HqHSIMrCdM\nm5c5XX6GVRRjo4SmhaZeU9XGCUMGDuuW11pVFXGaCL85jjA48c+VB7kzhqoVT6KmrJhMJuFw297d\nxypZJM+mC4qiZD6fM5/PWJUl/SxntVqxvb3L66+9jIRfa5T77JVSIjgpCskJdsU9z/OQftQ0HWki\nPv1N14Sdy5Mnx+RpJt5CbkpsG/EVL4oySP/rumY0GLK9vU1RrkHFzGaXbDkTrL39HbketqMoVvSS\nnKKqxPUwTdFY3n33Pe7cuUPWy123J7BTkiSYduNvEw5jl5Pq7w8pPhJucv36dUzbOMhgQwRojSgt\n/XTcdOK7//TJCZqI1bp0y3h5GPJ+n7JakyfiOUWkaeqOl156ifOL5yRJzGsvv8IwzzC2dQVOlNze\nIsBDF+Fa240/f5qmKGdHLLYlHbGKg2LaY9vKmQbO5/Mw2XgRl7DkDIWzUt7e3mZdLOXvNqKkNo7B\nslwUdFiml3OyLOP27dui9nZyb+NCXJqmCQZjRSG2wLPpwu0EG1rT0XTqBa2K/7JWfpbXtMjPld3c\n5WyK6Qjv17+fXm/AtWvXmc2mgXJrrWXlJgYde+M3cIzyUJuMkcB3T7P+B//g//x1LfTfjdArU+A+\n8IfdHfW/A7eBhwi98sLRK/8S8MMIvfIPW2u/9qv9/DhJ7WjvtiRcKsKyCUdJFAsEEywQjBFWTqQU\neSonaBZ3VKsFX/wtP8jv/7d/F/XsTDzTU4WOLJGNg9cIQN1s+LcASnsfDRPEGr7wli7XsnWGX8ot\niL03jOcBm9bfQI4l4P5dTvvUdbdX7X5butaERbKXSWutA1NFX+HNA4FNA1zhb+sr2HYN1gZan07i\nwO9N3aRSNSVlO8W2EarrGFjF7cNz9ie/DN0zqEu6KOb0+Ws8XX6KdV9jmz5WmdCpFGWJUpuAC1k6\nOy8dpUjSyEE1ckA2tkIZBVYzX60oyprLy3P6vV5Qoj558oS9g2vgtAanT884Pj4OKtSt0ZinT58K\n9t0f8srLd1HK0s9TvI2zZ5x03UbGDg6/blqyfi+Mv0mc0pgKReSEV3GwpfCHdpw4RlDdkvd7oSjF\nOiLWisnONtPZkuVyzmg0pOs6zs/OODzcZziU0Iq2rqncYZLGwiqTaXTjnw+OlRXFgcZ5NWdBHnz5\n3mq1QiexLDWtYNP7u7shf9XfK51j4FSN3Bfz2RIVx7SdWHCcn89oGonWE9gkYb1eB83CYrVEKYEK\nZXJNGY+HbG+N+dxnPkNRrMReREkxjxSMJlshiziKZPkJQlKwWq7/VZ59W9ehq1+tVvR6vZD12/ru\nu9lYfORpJvkPvBhG3jQN66Li8vKS2XTF5XzGs2fPwWqW60I8eIgoC7Fw3tnZYbVakKc9eRaNCxIv\napquRVlFUZWsViuy3oA4zoPg0JiWCAuRDsU7yzJGfSFFnE9ncs/VFZcXMz744APKsuTVN16nLMVh\nNY6S0CQGIaGzFze2xbrGUrnnR0cupL22pGnsJkTLP/zpv/fR8bqJ4tTm2zco1ytGo1HoYv1D0An7\nV5aQyBtMLGQRrBZT/uDv+7187I2bbI16PD1+xI2j64zTmH5/iFWRqAKVnJbWx8M1cphkzgdcJSmm\naQNEpBABjN+SS0cvXilRIvhhGm+Ws2HUcgwIIIgzPJbYdR1VLTLnfr//QvI8hheybZV21rpOHONx\n7FCA4k0WrvcnN0b4wZ7L6yMBrRXOtXFMHtsqhoOEdlkQtcfcOzphMr4PzQyoadodHp3e5sx+HKv7\npNGAdVOEAlhXLXUrfGd/APoFcRQlAW/t9wTvtQqSPHHaCMvZ5SWr9Zo8y8SEytgNZ9rAs7NzptMp\n61UTbBy2tnZYr5dUVSXFszV87M3XqKqCPH3xEFQqcst4wfmbpiNCrl9jmjBxFYVYJQunOg3XsnSh\n8J6q21pDHCWB2eJHddu1XMymrIuae/fusbu7zdOnJ7QOwrp+/ZDBoEfXNKS5dIx5mrJeLgAYDoes\n12vquqU/HLBer0ljf6/Z8Jn6RsKL3ppGYvyaugvEgNPTU1555RV+5md+htu377KzswNarI79z/j2\nO+/RIQrXmzduc3Ex5fT0lMvLS7IsY1VIkY+1UBz3Dg45PT3F20mfXS5R2jLq97lxbZ+drTHD4ZDZ\nbMrL9+463UXMdDplMhowGo3CJOzNwPzUFYRsbEgEURSxtbXFo0ePXOfbC8Zi1lqqohQWWpqQ9bMA\n3fpnw1jFyckJTz485cGDB+gkcXuGGmMjrNFcu3Eknfp6xWgsz+B8tsQYUQc3TcPFxSVay+JcuWd8\nNBpJgVWKpq0Y9UYMh0PhwGM5v3jObLogSRKWyzVKKb729V9gd2efo6Mj2TlEMetVsdk3srF6ATZJ\nau7gBMSenY0BXNU2pFGMVnIPf+QK/XDvpaDODAHIrtDL6Z+SJakzAzP0E8MXf+g38tlPfwJFR4xi\n2E/oTMNssSDX3oJVE+Upqe5RFFdodM4cTNwgNdOlLIaUsVhfqN2E4TsHY6DrGqyL8bOdCcXXY+St\n8zKRTozw+v2fIXaK3ap2KkcDHSTRhob5+PFjOtPy+uuvO96smxTEriz8TN9NeCinl2YvjJD2iuDI\nNPK6sJauLRlkEVFxxs39U/ZG74M9ARvR2T2+c/I9mOgaK1IsA1pzgWkUdSPQjFWeeeEoo26y8GIo\nq8TtMk0SrBU4TjrUjtl0wfHpKZezS+7evsvu1rYcjq38ufPLGXGa8e6777IuOglaKSvyvM9kMgrd\n37WD6wxHPbEhts7sywWHgMjJkyQJfz6NJHSmowvsKa9x8PdX4G1bS1dLF1kUBZeXl9StUCb9JJYl\nsWNeFehY/FV2drbI8pR+X5wHjZHs0V6WELnPSFnLerlgZ2cHi6FYlzx/fs7+4QHluhBKpxN++YbH\nX99w37mC2TRdKHbPLs7Z29sLymKBCyQk3ZMFIp1ycnLCe+/d5/XXXyfvDzk7O+PJkyckSYRBcevW\nLc7Pz8mSlOFoQhRLp12WJcfPxT4A26FNy9ZwQN7v03UdvSzhs5/9XkajEXGiyRK3O/ML1iuECn+v\nGCM+Ov4zX6/X4Tnyz5I8wwl1WYpOI8sku7br0BEvPH+oSKwyyprZdMHjxx9S1uJaqqMcpVNad4D2\nIgVKmE1pciUG0Mo1n14uuVhIZ17XNTdu3BKqZZbQ7+coIzu5NE2xkTOUa+W1/KN/9E94+PAhn/70\np5mMt0ORzgdDnj87QykJ2PEUYGv8stbtj1qvFJZUOh+lWNc1cZbSVpKvEeuIv//TP/nRKfRxktnx\n3j2IJFlJ0m4MaZyQRJpYCR5968YNfvDzv5FPfvxjmOoCbS39PKauCoa9oTxY2yMZ9VoXwm2EUuUl\n7m0n/HZN5Iq+jGNKx/9K0dZhKbLx774qqFFWwih8x3/Vp0RZKM0m19IXFY/xXV0oW2vRVjzZ265x\nrJwmsDk669+LIbI+WHrjXZ6mOfP53LE25Pf0BwOBHExHP41QOqVpDFqvSbVmVz3n1uG30fYRmimY\nmKbd51sn92h6343WEU0nMYjrdSmsmisPX4CS3KGmtHVQgiLL+xR1RaK0iMkiEeGcX0xZFWsuZwva\ntuPWrVuA25EYS1nWRJFitlixWKyI4pRnz56xs7PL/v4h7777Drdv3yLSmtFoEA5g7+sjEFnsbIUl\nSi+OCR24n6yCfYRSklBUiw+KteqFe6BtOlAbNXNZlvT7fS6nM7IslQS0OGbYG6Bizdtvv00WRxJI\nv7vH/v4+UaJD3OVqMePmzZs8eP89jo6Owv0vdrsb1pQvDL7QCwNFUzq83jpIRmMoqoqiqvnKV3+B\n6XTK937vv8E3v/lNXnnpHvsHu7IcRGHbjsvFknfffZeXX36Zp0+fopRw0auqoqoqZosVW9u7Lq1M\noZw9tm8qWqNYLGe8+vLL3H//Oxhj2NnZ4fDwUHYl2pKmGVGkuXnjMLz23E0zURQFr6qes07wh5bc\nTHJ9YsTITGvtvJfWxG5vk0QpOo7o6NxhV6OupMtVVSX8dJDFbGdpasP9Dx4xnS+wiBdRpJTocaqW\nDuUMBG04aNvWhKYtz/sMRxM+uP9ARGh1BdFmD6KQz/AXv/F1+v0+H3/zdUnoKhuBoVqZIptWdgKe\n/5+keZhmer0BdV1u9DLODtzbg/v3RySCxzROsG3HT//0T3x0Cn2S5na0dycIXrSCGAttw73bR3zp\nd3yBawf7GNOS9xJ0BFUhfiWRhtRx2ReLBXEcESeaVEuqj0+ysUYFmbpgpZkrpLI48hQzT5PzEE4c\nxwHL94pS/z8ZcfNQRPyXd53TzjPGWxH7B9cXmnDS57ncrErTtLU7BExQtorS0nmZV3U4TADH9sn/\nldecJAmxG/2VUpjIElnotQX7g0dc334HeACdhjplVlzjg8s3YPwq1gl61uvSuTqmRM7HBQiHVZhc\nHN4ax8I6quqWJEld+IoJTKH1unTuhWJtkaYpy9k8KDsNQlus65pef+ggmy2yLOfx4ye0bcW16wdB\n0COdURR40Uopl0gEaZqjVUwUW7d8lw65s2KwFUcpaZRSNyVpkl+5GzeL+K7rWJXiE545YVuSJCzm\nS8pK8PCylCXt3t5OmBKqqmI4HHN8csL1o0OqonSQCDx79oyja9cDROkPf3+feljO4+3+dSsnlV8s\nFoLxao0BkiRjVdU8efqUr371q7R1w2e/93vZHo65dnRN7pm6ljB5HYV9SlVVvP32O4AU8yRJePTh\nMbO5UIK3xluBUjoeS4LSu+/f52B/n+n0gjjSLJdz7r70Mvfv3+eTn/gYn/zkxynKFYnWkrNcykJc\nrscmPvGqmLCfO6GjW9paa8mSNExiURTRyxIhLkQR2mXpGmMo64L1aiU5rJ5+rDZe9LPlgunlnKaT\n598oxXyxYrZYQic/e7kqWawLqqYO11gpJdYPiewsTAeN2QSnNE2HdQvstm1Yl8XGBbfr2N3Zot/v\nMxgMhKShfLiRDov/zoq7KvhGRJ55/0zjCSFXUIOu60h7udshQKKjj1ahj5LUHhy9TlPPGWQpr987\n4g/8/t9DU62ga0gj6YhOnj6h3085vHYgm3h0SKG3WlFVJZdn5/T7fW7evAkQ1KV+wWSsnPaRExBZ\n24UFKBAwTX+aewXuaDQKqe4BylGG1C38PJNHxj95Pb4TF0sGWeIN3cKmbcXyNiwMO5li6qZwXSfu\n74pfdtM05FlfciWvcJcBR89biBDGcbP98ldeq0HblEmy5PrW+0T269j2PmrYpymGnD6/wcXyZbre\nK0RxR1EKe8dz+QcDhx+7h9XDF/71w9UQCVF/Wgcpyb5BU5c1s8VcME7nZ1IUFculwE/D4ZDx9iHH\nxycYxx7a2h65lCxve9sxGPYY9Pp0nbBnPD7rpyYV4VxBJYYtzZyHkXONtKambeQQHQxGZImjqjqP\ne+UC6v2BLwKzMizMjTFczubEicAEg4EEt6eRYLBKKYpqzXpVhokriWXpNh4MeX52ytZY2DlAoOTi\nHBn9tOZTkuq6DiIhzzrxuQtb4zEG6Ixmvl5TNTVf/qc/S55m/Ibv/35+8id/ks9//jfR7+Xy+ty1\n2uDkGdPplOPjY6bTKXVrgnPptYNrVE5Z7WmUWdZjOOyzXMyo64r9vT3iLKWuG56fnjKeDHnjjdfA\ndiyXC7YnI5qm4tq1a4Fz3jk2k9+HeVbY1WkYwLTOIdIId77Xy3jvvffkuY9zt3+oOD9/jm3aAGF6\nncWqLILOZDZfClKgEtJej0ePHtE0Mi0sVyUdok717Bj/zPruu2utRItqTduKd03TVIH95POntY4Y\n9PqI0CvGsLE/aNuWKElliuw23fpm4Q5Kd2Cd1xcS8dgUa4bDYSj2dSdqWtsZIhQ/8zM/9REq9FFs\nP/8bPs8f/H2/k61ByvTyDBXBZGsohlzEkimrxBEwTiKU47FiLbbtiNKEXi8Pnhe+UIPHAy04GbSl\no58P3CHggw9qmqZz4/MVHxj3pZQKIpwoTV4osqZpaa8wAzoF6E0I9VWYRjlcTrq+YVhKtZWEXVtc\nEIpjXRSVPAxp3hd+vl9IuYcdIHXB0BsqngqHl5injcj0Cdd775PZbwBT2qbAjG/y+MPbrJrvRSUp\nxGNaY6nrksV8xuXlJev1kjfeeIPheBR48m3bUjUdvTTbfD4QCm7dNILjRgmtNZRlxXqxDt2v/0yK\n1VoWoalw2deVReuEs4sL9vb2uH3rkKcnz/jOd77D3t4+JyfH9Pt9PvmJjwX1o8efg72uu2TT6Zyt\niXjdz2YztJaoQdqGwXhEmmdCeYwjR9nTgY3jBTCdazKu7j2kYIkCdF0J9TPPcybDEXXdYIC8L4fD\nbDan3+8xHAxo25rFdMbOrlgl1HXNcDjcWBSjg9mb90uR0Ow6LN0zl0mc93uiyHYxh6CprObp06cc\nH59wsH8NZSXibza7ZD6bMuz12d7fDx11mqZMp3N2d3dp25bHjx/zwaMPObpxi7feeksS3BwBorWG\nKInpGoELu7pie2fCtYMDnk8v6eqG8/Nz0jSha2u+8IUf5PXXBB7a3x6zXC4FxooiGrsJGullGT4I\nxt+74cu4CEBjqMuKf/RPfpo7d+6QD/p8/gd/C1/56s/T0VEsV3z4wQP29vbC5NV0liSV6EZRkAu0\nqXWMVgnr5YpvfucD2S30R5w8e07dNuGgFUhmk2qllOgbzs/P0Q4GNJ0871GcOu1IQu2EZaZrwo7R\nusbGN2dJJMw3jBweHraKogRraiziTqq0cxJ1+QDgoCJrBLpqWrSFL3/5H350Cv1Ld27bH/3zf5Kq\nqVFRBKaWhJw0papL+kk/+HO4+AiiKCLWCbF3i2QTfDwYDMiSNNAz225jdWywzuArEe8IjBPRbHyj\nvbQ54GK4Lt7BLhtOfRc4w3VdB1GMTh2HvO1C8fNFKXHB0WGr7hamxt0kuBFWa0Pbif+60jFRlKDj\nmGK1esFXfjabsbPl4vF6wjGGGB0r0AlRO2PLPmF/8oSoewvFGrqGygz4cPY67zwacHjju0iyAWUl\n3UIU6UAJ9YyPums5PDwMeCGdCXRQP476z6ssS8qy4nI+C3zptjHURR0mAxVHNFVB7KIg67qlbg0X\n5zPGW9tsb29zfnbKYDjk1q1b4s0zGgaraqsUkbt1vQiqbVusJHSzXK9EIFPVLBYrdBRxdnnJ3mgs\nHupKlm55P5NIybIErcX4LYoYDEa0brHpJ7AklQI1cjCftZaiWDN2QeI4uq34KbXUjUEp8UePYsXp\nyVOuXz9EuUJijCHr5eFw9rYBRVHI7iVOGA+H4sfisksFSlTguvLWyGK8aSUQ+5///Fd4+OhDer0e\nX/ziF6nKNav5gqosmOzu0uv1AtyYJ7lbMIp9cNNZfu7n/gWvv/YGb731Fmk/59mzZ9StCIhS58eu\nlRWLjyxle2fiDrUZ8+mM/cMDqnLF93/us+zt7JD1Uz58+IjxZMj5+Tm7u7scHR3R7/d5+vQpBwc7\npGnupmGJxPSxf9ZaukqUpMvlksPr13np5Xs8Oj7lzquv8tN/7yf4+ld/nvPTp3zfZz8nn79nRLkJ\nc10W7tkTC2drLefnF5yfzdg9OOQbv/hLArOqmMVa7AawmtYosjwlTWNM01K1cviWRcV4PKZ1nlv9\n4ZC2a0RfY3xWcEPXbWzQQ6GOImKtwXiLdVf8faKZW7yHxatrAvx1r+salbgoR8fo++f/7O9/dAr9\ny/fu2B/9z/84RinKqgqYoPDIFap1rzFy1EhtqcsKpSJy5+fi3fNArEsVvICLe8Vk13UhZLtpGqxx\nSjO9MQ5bLBZEkeRP/ko8fblcBiw1cgtA3wX4v+/j0eI4Dko5PxJqu2EKeMjJGEPExohMaSjLtUAC\nuXTNSZI5PD4Nh4xSykFYinUxpyobtrd3IWroJ2N6asnh6Anj+OsY+wG6jbB0mNUeH1bfx3l0jyRO\nWa9a2lZ86DvbUlXSVb7//vscHx/Lsmg4CAXplVdeYbFacvfuXSl8oxHW8dYDnc6xKJZL8f1uG0Os\nNWVRB77/eDJ0xVIYT+tS8lG1OxSfPXuGMYabt44YD0fEmhehIjaLPM+lro2weyLHiilWkkS1Lkqs\nViTu2os1RUeUaJRFIDgVhfzWtjWURYWOdRD8+NjCLMnCglFHCtNJ4bPIfTEcjKibgjjJOT19Sp7n\ncqhkCaPRILiPRlEUIA3///7zW5cFeZqReJiQK9GaSoVM09qZmPnP7RtvvcVXf/5rHFw75I033uDo\n+iGr+YK6rHjr7V8OHfwbb7zBYDAKIr4nT56wXK+5c+clPvjgETj+/YfHTyiqju3tXU6fPRMzucmI\nxUKohEfX9uhlOUWxZjabsXewD7ZjtZhz48YNev3UcdZXHOzusSzW0vggwRnffvdtvuu7vis0CnEc\nhwyJREe0dUPnU5m02Eh84vt/ALoWYpmGo67gb/21/4GqKNkaj4Pn/2K1ojfoMx6PmU8XriGxpFnu\nINEKVETbdjw7e05dtazKivlihbXeimQl1E4HJSk37ffznNFoJOwqt7A3VmPthsnldyF+gkErV8a8\nKWG0aZzCVeaF+8BbYvj6ImJDQR201vzsP/0IQTev3Ltr/+s/8x9Rt4KzJVkc3qx21sXy74Suu2kq\nBvkgYJy+YIpXRishC1e6ae28QHzB9p0m4MYwHUYsb3Dku3vAKSqbwNrQWtO51xiCvLs2mByFMGG7\nwekC7ncleKNqGx4/fsydO3epC4fjDwfOwldhlSx3J5NJYCr4G8Nzquu6xboxfjLZJsWw179kt/+Q\nUfLLwBxsBybj8uIaj81n0MlNTKKwJMynVaCyyqElGLnH5LXWFM6IbWdnR/j6mRS6qqrEvM1BSf4Q\nbKuaxWLBxcWF5N4qcQHtmpaqbh22bSmbMryfy+mSwWDI+fkZTVtx9vyCV197mb29Pfqp2ApkmYiW\nDLC4vKDrOjE2c94/SZbTdJvprWuEAx4nKeuyplgtsKaj3+87CwHo5z1iJVbC/rX4BZly2P90OmVr\nazswrjz0kiQxGu0UmitZnmnhWititFZUbYsyhuVyyb2X7oR7z3PFY7XRAfiDL+nndE0bDjaPByda\nXCijWA752rmRyv1gadqW1lp+4id+grzXY3dnh09+8pMspjMGgwHT6ZTKNVNKKcd1F+hsOp3ywYNH\nWKs4ODjgcjbHKvjg4RMaC1GaOIYYbG1tMRwM+PibL/PuO9/m8PAArRTrquTa4b48x5FmMOqjleLB\ng/uALH5v377NyckJk8mENNb0+3l4duM4FatpZWT6bdoAF/ogGavgB37rD7NZZNVABFHE3/kbf5PZ\nbMZsNuPg4ICqqXl+fs4gy8MOKYlzOit6i6JqhKGlDPPZks4ovvntd5nNFsRuGRtFEbWzSk6TLDjU\nAoGR5aEaXx+ufvndjr+OvmHyATn+yz/D/r/L9zYogmdlCd8+RsURX/6Z//ujU+hffumO/Yt/7j+h\ndgUyMCjaVpSJnWU6nXL94DBI/8eTAdqZeeV5DkkUPrSiKNCuw/TjUOH868GLmixdJ4UfZej3hqFQ\n+T8X6c0D6B/wq8yT3pUoL2strU+6saCMUBv9yHWVURHHMYmLU2uVdXx8CUroXGycRtGZFoP4aON4\nu/71+wLsDyBFTJS0jMYpu/qYVw4fEKsPUabAmgjV9jieH/Gk+hR1eoskkUKWZRltVYeOylronJf4\nVW1A5ny3/cHlqYroOuAAACAASURBVHN+MumM7CSKomA+n9M1daAj1nVN0biEL2frPBiOJWTGdq77\nyTBWiw9K6ichRZxoYq3IkogodVYRnagOq0KcQr3tg7WWyWiEdUZxHi5TKsJYzXJdMr14xtnZGVvj\nCTePrlPbijTOmAxHLJ2FbXplEeq9cuazJbu7e2Ex64v9xcU5k8mEyXhMVdZijIchzTR5NuK9994j\nShOOjo5oypJr1w+oinLDWuJFqq2/l+puY8R1dR/UVjXFek2ai6WAAdbrkkRHlE1NnGRk/T7vvfce\n/+IrXwFr+fjHP06a5GyNhQjw/PlzR10sXWMzCg3LbDZjZ3uPum55fn7B22+/TWMUjUUWgUCeiAHY\naDRie5zyiU98gq999avkvZQoiRn2RswXUw4P98mdta98hjNu37kjVtQrsSK+deMaXde45fgAb+Eg\nBIiB+O5nOSqKJBinrEiymPPnF7z6+mvcvnuX0eEhErkZi5W2Mfz3f+kvMz07Z75YkA/6tHUp1gE6\nw1rNcNinqiTTWcURw2Gf4+OnfO0XvkGW91mva+7ce4mHjx5JaIrL7sUVZ8/AM8Zg2OT6eshzuVyL\nNYfaJIYlTruzsVHQ4RpXVUWep+G58g2j1nGgB/vDQhpg8Wb6uZ/7CGH09+7etv/Vn/oPIJJFXuQ6\n5jwXvFSZDaXPsyvappZCG8lJetUOIFKOFukuTqQ1lVM8+r9vbEukRdmZ99LgY6HVBu65OlL7guoX\nocYY1i55xtsRBxjBbni9rcPc/YPt+d7WWorlSkK0kwQ6Kdy2M7KH8PFkbuz36UzWtOT9segBTIdG\nMjjL+oLtWPPKzXMOd06w63dQqqZtckyX8f75XU6L1zifJ8RZj+39HXm49cYQCwivz9+wg8GAuikx\n7WaEbJqGNMs3DAl3Q3ZdF1SMZbl2EAUuI9X5obgbfz69pPEHGxI6UTfCvplOnWNkteLVl19CY4n1\nJlGsrhuaFmzXiiGZO0zzPCdLEi4vLxkOxmEi6feHvPPud4i8iMdBJlmWEkfiI+PZLG1Vo531r2gY\nvH94zmy6cL4rmwlSu063LEvqouTk5ITBeMTdW7c5Pj7mZ3/2Z/mhH/5tnJyc8IlPfILBYEDkmF79\nfp+HDx+yvb0tVgJsdBUrl/LloTrYOFVmWcbl5SXj8ZjVakV/OGA+F4fSx48f0+v1GA6HfP0XfpGP\nf/zjPHjwgFdeeYX33nuPwWDAxcUFd+/e5fLyMmDCflorCrlWVdXw4MED8v6A9x48whDTdDVEWnYe\nWmNbQ6rlGen1esxml2R5ws2bN3j/O++xu7fN66++QpIknDx9ws2bN2mrmqOjIxZrMSjrZxlR7LNX\n5drMFhvnVnmO5XMRz/mI1k3BPobv+dMP+Xf//T/Gwe07YFu0kj0eFn7v7/y3ePPVV4iTDB0hOQRa\nIj6TJGM0GVOVDX/zb/0tbt26JZoApemPd0MjBfDk+CllU6PQiOLdhIbL1x3p6oV907aS1+trhkcn\nxNbZ34fyLOAOgasU7Q3aoF9QnnduCewPg3/2lX/80Sv0Ookx1oZQ5CSJ6eomnGgbhap0xlVVCe6a\nJHRWAkaudkURstTQSlG58HC/XIydgX/XdWR5EjjrvtB7bM2Pzv6mCrz2tuVyPmM0GtHv9/EqNp+G\nUyxXDIfDwIcNohD3+gGashJGQxQR4aTuFuI0DuZOEphchUJvrJJxMpZDLOsN6KO4Pk6I2m/x2mvv\n0dVPiGgwa43uUr559jG6+PuYmYy6BaVi4jwjy2NiraDd7BeiKCJ24RseYvKft3bjo0TWbbB4wRPl\nGnn+s3Eybq2cgMl9rj0HsVycndNaePL0iZiIGcO6qEPEYNM03Lx5nSyN2d6e0Dk6G67DCcHf7jX7\nHUiexlTOOVEmKclnPb+cUrcSJHH79m2319Eh+Frw1pW4pUYRp6en7O/vY610UgIFXBPvk7iHwXJ+\nfh4OGWttsOK9duOI09NTnj17htaaxx885Ad+4Adk4dk0XL95jX6/H5KyJpMJqd6wM6y1lHUVptWu\n60icYMgzQ5almKSt12v29vbEQ8gxgJ49e8bBwQG2s8Gq4lvf+hb37t1jPp8TxzHf/OY3aVuxf9ja\n2iLPxSyu6zpGowlf+5dfZzzeErix6bBRymy+ZL1eU7XiE6Xd8jrRkZihpRmH+5LK1FQVp89OePP1\n12Q6i+Di4oKb148YDofMnY9OokXyL5OfxDui0xeChLrObBhkW1tcv36dp0+fcnIiGQGL6Yzf8sNf\nZDQZ85t/+LdhW4EioziGzvCP/8E/5Gf/yT9DK9ljlGVD2XaMx2Pe+859fvGtX+KN194IMMxgOKZp\nGm7cuEGW9XjrrbeYFU3o4jEquJ36rh68VbNyNUARY+n3e+GADj7yrSVKUkwtGo1hfyDCu+HwhYND\n/jkOClyAdbkKDa9Sir/+43/to1PoX7l3x/7FP/cnBMuNIgqXEKOdI2Icb7y2g7rUWQkkOgoPvHWZ\nluv1mq5ugmLSGkM26IfuW7qRzp2qMdZKB22MQbEpHnG2ETz5g8BDMOLE96Ifd90J5altW0zbyYjr\nfHGuLmz9RU+jOHT8vVTwd4UN8WxXQwsSJ4Iq2wZlO3rpAKUiVNwRr475nnuXTCa/CNWarqmIbITN\nJ7z97mv07/12kv4Wy9mattHoxGPjmrJcs5yt2N3dDcWyal2Y+WiEUtBWNXGcUFWb/UPkEreU3YRt\ntM6zRgya5GcNB2MRkFlLEsUhtHm9XlM3IkgSLn2L0hLYcv/+A46OjpiMRxzs7pD3UqztwEoXI8Zk\n8lD+SqWuaWsa02GNGGrVZUO/PxSh0XDIarV23bxAQ1jPxz6n1+tx/OyUwWDAzZs3qYuS4XAUUq18\nF94hDpyeZ+3hq9bT6DrDbCle9k+ePOHoxh2R4Xt4aTLg4OCAw8PDMLFtj0ZMJpPQWMRxzNOnTzk8\nPHS+9l1oeLTWTOdLIsQIb+wOyl6v94Lysiyq4PzYG/S5uLgQO+i65unTp/R6PU5OTqjrmv39fcqy\noOsMq1XB0dERg16f/mBEnOWcPnvOB48ecuf2S/w/b32T87O5MMF0yY1r17lx/Yiubcl6UpiePPmQ\nm7eO2N/ecgflJdvb2+SZFHExxRPzO58IVRSFHM5IeLinS965I9PH2dkZRVFwfjF3tMlU3lPdom3L\nsJ/RGMPv+NIX+dLv+72SsBZpIh1j6ob/9kd/lPVyzZPjpzx8+Jj5YiV21ApMJ/dwbyAogW0tZ88v\nAgmiNeKIqZFsZZ++4Retch9I3ZgMR+xu79LP4vA8lKV4GjV1x8VsRl21xE55fHh4KFGT7UZ46etM\n1fECvdI4nY93ZP3xv/e/fHQK/asvv2T/mz//J6i7VoKMu47dre1gK+sXUeu1bPYXiwWD8ZCbN2+K\nY5zD81vXbRtFMKRKvGe78pFnOixofSCACNDkYDBXfKZ9jKGHWryppk9mCswZV7j9eNW2YoWcJ6lk\n1HrTJVckfcGPULRWIBpcglXs4CvPzPG5uJHWgVdNrEh0iq5nHB203N19i9R8B2whwhBTU3ZHfLj4\nfprxZ2lbg04gtTlNazF0WC287KqpKddl6OYmkwmJWyR3V96nV3xGkZaELgfVXF0yrddr6lYWxP2R\nYPPrlWDaiXJcYYeZl2XJ87MLameh6x9aay17u/us12tRQ3eNUyw6NoNyMF4UhdcVqKpWUpXqtqV2\nLImm7ijXpRSKizPefPNjsuMZj7HWkLqFWxLLtNfPs2BsJrx8MVUry5Io1oxGI1brgrQnnfb2eMJw\n0A+eOKPRiHrl1LQ96a7HW9us12unoN1Dmc2E55e6h0fXw/g+m82COdhgMGB3d5eyXL8QRDObi8e7\nh6L8M9I0DZeXl3z7299men5JlCR8+tOfZjweMxgMePbsGf1+n5OTE84vL0K60ng8Js8zzs8vSNMc\naxTW1MznS9CKOM2JlWa6mIONePDgMTvbe3zszZcE/sx8glob2CZlU1Ou1nJ4KRfMkopFRObEX2W1\nDp/3er3m5PiUyc5O4N5bazk5Pg2TZV3X4kbpbI+bpmG5EqgnzxI+/alP8o1vfB2rFX/5b/x1rJVr\niO2g6fj3/tAf4d333ufo8AZZL6dtO4paoiv967ZWkSf5lbCZSnYZvT67O1tsD8fUxeIFgZscws6c\nrIV+f0yWJYE+7LUY7713X6y9iYnTBOuYc74B8vdxURQCiQ364V4sioLYRqG5yPOcv/J3PlId/V37\nX/ynf5w4jen1M6p14wqxFNrBqC83fa/PM0fxUhru3r0bCmHblqyLJVnaE4my3ZyOlo7OJ1Yp6yhZ\nGuMw0OCS6eAhf8Fbu5FVd12HQUI+/JcyNhwscaxpW8P9+/d59dVXw8N3FV+N4ziwVbx6FQiL3ufP\nnwslzY2tcRyjkg5FRoQijVOM0ZAYMqMZ2m9yuPNzDHgOFNBqsAkXxV3uzz9D2zukl4zRkZN2h3Fw\nQ+EKh4+fTHTM9PKcKIoYDoeB251HSWAdXZ1KIsdgMkrehzGGxsgBMMh7L/DQ/UFrjLMN7nSAe46P\nj7lxdMDjx08YDScMBkP2drcAOD09lcBoJdNV127GW6XEdMt3wirSbvmq3OG6WdADJInD890DlDjT\nOmtFqdhVdVBvKjQ6fhF6k/vAhpSgKIpIrEK5iEqlFFZv7idPyQQCkQAbhekycWHafpHvcfmg9H0B\nHhNYACVBJN42IE4kkL3rrCgrPYVXgdbeNVKEVd6Ow1OSG/c7smSDJydxxs7ODpfzy1BwyrKkLAyD\noTiSHu7v0lQVRoubZNu2DIdDvvzln2NnZ4cvfOEL/NIv/RK72ztu35bz1i9/g8YaDg8Pefz4MefP\nnvPmx15nb28v7CKKoqAuGx5+8IjOeUn1hr2w+1kulyyWa27duc39+x+wXKzJ8iSoYuM4xnYbNtz+\n/i5/9i/8Bb71i19nZ2eL/f1D/vSf/jMcn5xI0IeCxnRErcC5o+GQw8NDdsc7nD5/hlWKg2uHKIsE\np8Qxl+cX/PLbXw8HsaeDz2ZT1uuC8WiHKEpE1KSlOaiqivW6YGtri8l425nUxXgFNrglqyPs5HlO\n03Qh08JfM39P+fvrL//tv/nRKfTbk2372z73BdI82yzBuk1WaudggDzPA4QSpUnAx9I0BSXfz7JM\nPK8jibrzuHyaRQFbb9saXZdoLT7PO3vbvPnm68Hzwxc3/8FaK0tDa80LNCdlNjzujhd55Ff9Uvz7\nSJKELJalse+UfeeuHDsIeKE7y3pDhqM+WZaijaVYFeT2mHv7jxjq91DmgbwuW0Kyz+Mn+zytPgn9\nN9B5TC/rY12oil++9fNe+N1RFGF0FIqiBfquc/SHUxRFtFX9gsdN52TnxnXTq0ImgsVCQpbrcuMz\n7kdPrWUkT5x9rGdj+IPU0tHUnbuOPXCxbD4boHWufsZ4Ottm3+G/sjxmuVwRu0Qw/7PDf0/FeqAo\nV6KcdG6FIkLLoDNczmeMRxOBagZinbFarQBZnNu2C5CIX9YXdRWW/ypW4dD7ldqCKIqYzRaBv64d\nC8m4JT9IU+A7XO9bFCmZ6JTGMTeE2qq8J4rdHEj+ngJF17kQdG3JoyxQFUWjYqgcySCNYzo8bOQa\nHZdM5u9jk6jwGgd5xmQ8prWym3r06BHD/oCqKGi6jmvXrrGzs8ODBw+4ceMWVVWAknxYIOgcNIr9\n/X3qttmw25qWsmgoS0l4Mq1YkPgJ7uziXFTWdRXSurwNyJtvvsnt27f58MMPSZKEd955hx/5kR/h\nc5/9HpzDHbaoePj4mJ//yr/k4cOHWDriVpE6ZXISybJVgmnE8K5wsKWHCpNok1Pt/fzzvjRofs+X\nJylbW1uhnjx6eExVVeFeWjq3Tm+PslqtwnSwXC45P7tERWIK9/LLLwdo1d+HWmt+7H/+CBX6na1d\n+6Xf9CWslwSrDd3MGEOsXUHCLVmTmEhteOrytREeiOWqdD3+g+vlGcZ5UjRtxSSTVKB1McNYJ2by\nm3NjBJ+22iUNiQvmsC85pfuH4kzY1qJiTPNNfJiHm5bLZfCMl6WVLFNF6LVhD+GWyd57WykVvNm1\n1vSGOTqxrBcVXbkiWj7gk68esx2/DarD0qDqDqu2+dbJ68y776ZKU5rWMt7doqgjtDvgIqXQKFaV\n5Nv2er3NQ95aF+YQo60OPuce5vKKzYuLCyngVpSxU+dl3roMzwgVFMKeVhqhsI4J5a+XtRbrHiYP\nUV3lC8vfE9pl13UoNJ0zklK4PU2kww7FGBPEY00nAeld14Vlt1+a9lI33cSixegnPZI0om2kueis\n+LH08j5FUYX7KorlkDLGMOoPwgHjPXRaK0XbF3r/Hr2lhi/4VV2TeH8kJxwCKF1RNWajON7sQ6LQ\n6UfxRsDnqZdd1zEeTURR222sOExniRMXrI2Y06VpGpa4i8UsJGmZtgt+UGmavxDU7u9Xq6XQR0mK\n7STeL0qisBurqoqos+IJpX3eg1hHdF0rjZeDXMqqou7EedUfZj5ohK7FKA3W4eZXAre11kRJ7F5b\n6QpvvPHSuULaABMOWBt1KFnJkMQZbQu2NRgHNZZ1Fd6/X4B7r6urnvf+e1eDifxB7p8Za+W+nk/n\nwS8pctYHSikuLqbuc5X6tL29TRzHjCZbjEaj8LyVRc26rAKUo7Xm+ekziqoMuRN/7e/+3Y9Ood/d\n3rW/6zd/SUJCnEti4A5HoBzM0FoT6I10GwtgsQ5w1rJtLYs9pehMEzorY1qX4CN/P4vFrzuJANWG\nB8YXBYBIOzMtZcQJs+0cRa8j6+XUa2d4NhmGxZoPCPcdun+NvqP3uPZVIyfYhItc5UwnSUKSjliW\nl6wWU/L0gs9ce8T25AG6XtCZISauieMDvv3+NjPzBk0WoaMRVmmIUpo2pnH8cO9lb7CBTnp5eUnr\nclnTrMdyuaStDfv7+xRFwWKxCFx1//pFaCMRaLG78dMoEupgFNE2zaZz83sQZDHVmTb46G/vbYcD\nzxcuX5C11iQ6CsWyKAryQf/K4lWHqcMYE/xzFLHQOjvo94cU1Yazbq0VSmos/7ws1ox6eWgorFGo\nSFO1HVmaA7Ks9g+edUXfaz0CC6xpZaHnppzVSvzmPaurcYeOZ8Ak8QbW81OhP+CstYzH49DlBQWu\ncgloWhg5aRKHYtPVDToW3xgxiyvIexlda1AaV4QLLLLA9CZpcZwQJQIf7GxtU9Xi1Ojv/zTPJO7R\nwT39fEhzRbgUaY2K5Bp0iOdUtZaAmq3tbXcvb+y7dbRJdDNuovSwTl0LdXO9XhOrOEyTHZbYbMLH\nu65j6SbF3qAfsgxAprvt7W3Qm8nGx+5ZmlCQjZEAnDzvY1rDer3CROqFwwQl7rT+52o2xn1SnwhN\npP+frzVtK7Rf223en1KbicErbOu2Dr9TDhSxMJG9k4uVdNnG4ERq/YE0Rw5y/C//yl/9NRX6+P/v\nDyilXgf+9pVv3QP+LPA/ue/fBT5AogQvlVzdH0MyY9fAH7LWfv1X+x1aa9Kk73xfLHmSBtrjarWi\nUc7as5aOp60bsngzjiu1ySxV2oISb5ter4dWLTrRWJ1g85jEYZZZlpFuD8nTJDBu/M/yuHKWDjGm\no7YOEuqE2aNjySkd9wXDjpKYrrPUVcVzx3rQWtO0G1Wfh0CusnSujtq+UPlCF7o1u8baFGxOFl0y\nf1LSNddp60NiFZPGCUurKPUuXXtJZjPqaCY/u6uJupImyl5gh9Bs1L0CSwmLoGtb+r0eKouYXU7l\noNIRWZKSjNMXsOqydLCCo6Bq02Jsi2lbdKSxLeE9SXHLIdKkWc718YiqKsSqwNEclVIsFjOuXbvG\nYjFzJmIR1opCOO/FxNqgUM7Xz9LYjjjWWKtI06Erlh1JommbjrpZErlgbK8JiHVC1dS0be1w0I3h\nlOmgaxvWVU2aZKzXKyIUTVOJk2c/x9qGVVk5l1BHv42d8Zfbvwx6YnGgrKZrGgwxWkekbmppGtkX\nNE1DnjgDPiVwgNaa1UIWoN6tUiAyHfY4nTVsbwnu672TIheB6MM7iqJgfjmlP+g5+4aKyWSbs6en\nsjyNE1rbYl3QzvnZKR3yO8u6CswOZSFPM7d4lMOn7Wp5xtIU1frwDDmsxpOJY69500HEckQJLGjU\nRnAIBJq0P/Tk87ABr5YkpTjYMydJIhnDpkNrIVQk/TgcPk3XUi5KB9fZ8DuSdCNEspKEQ9s6Rl3d\nMBj2MKaj7Tqsw/e7Kx5ONZZYaazVWGOoa6k/yoqlQVkVtJ04oxpj6GV5mFI9iWK1XIrQ0juhsiFn\nbG9vY4xy1icW0LROVzLoiddPlqQURRGaBr/j+7V8/Wt19EpCQp8god//IXBhN+Hg29baP6WU+u3A\nj7AJB/8xa+3nfrWfuzPZsj/8fb9hM8IrEx4+gCzZ4Iq+QOpoY4cQRRHjQT9MAyLe8Rmi8jM7JbJ8\ngVJAvNMdbhrLQaEiLUXPCM6mnCpSGSvLFRd2IF365vV3zgP9qrUBECigftS+iov7LtZYyQLNkpS2\nlHR6bw/QtC1d46EDmTh6ibMd9sIvxwzxHWkcx3RWB8wbLLaVWL11WZLn/QCRxNotCBuXg+oOo1jF\n1A4nzvvyWrqmDiP2dDpFdTKSeqMvpTZ8en9PRXFMXTdgZPT3B6j/Oanj619cXEh3bDqBauqGKJIO\nsKgr0lQ6otV8EdgWmWN5XIXMtNZcXs5ojOwKPvWpT2GN5v33Hvj7NxxUg8EgsHi8ZbK/x7w/vt/Z\neIZD01ThMOn1euSD3HXeGwhRipvbDTTOEM74KVWudc+JhLpOTNOytEdbbEysyrJEp5tucrVaYV1X\n66eRXp69IG7r5X0p/EkSmCDKinDIs1OMEgzcNDLNeRpt27aSO7uzi3JU5q2dCaZqwms0xpDEeYBC\nPFunKApHN22J4pjcGZ+Fa9O0bv/hoJ10MxWKGNKGzj0wuNjQkHUUhcSlDcd8I3xr2paqaoOvVNd1\nKIflG2MktCSWhsi/JvEIasKkspivAoSy/H/bO7cYy7Lzrv++fT37XKurumu62m3HPT0hkp+IFQVb\nIBQR4jiOZSsoQo4icEKQEQiJy0PIyE+8EYIQCSAcC4gCOE6cC8SyElkQwiOGhAQz4/HgnvTMdHdN\nd9f13Pd1LR6+tdc5PYw93dbMVFfr/KVSnbPPqVN7nbX22mt93//7/2czOllCNxv4a6zf71OVhe8P\nay1FWTIej/8/raI2x6dJYa1j0AWeePYO6Jgz8ID0dCuJ3iqYAiznK6+FNE11MRCpZAXAv/j8r781\nK/rX4fuBl6y1r4jIx4Hvc8d/GfhvwD8APg78O6u98t9FZEtE9qy1r32zDw1DYe/ikOPxKZ0YBEsc\nr74Qay3JoOtXoFEUIcFq+9s0DVVZMBwO3B04wrqkZ3tjSELBdnTAh0mIrSvKyrjYYENtIGgCoiR0\napeGunDJR8eAaX0vl0t3N3ehpjAMtXq0WlHLsix7INTUTq7rWzxjjPeJtHWDqbQ+4Pj4WG8ocUwv\nG2Jtw2SsA3DpQlZhKEBEFIcUtYqSLZcLwJKkfUJXYIMYOnFHE9Q2Upd7V4KfJBFhJFS1inYd3Fev\n1u2tLeIoVaGu4ES3ik3hOOAjpWEOtCqznlRcv36dg4MD8nyJadrCEI3TJrEKSJm68VIF7aDtdFMt\nwCmKlROSCTF1hbURTd2Q5xXzWU6326epYTQcYG3DYqESwYPBANNYlguNjyZZh6DWyeXw8BBsyOHh\nIYPBiLLMSdPM5UMMIpYoiX1yVxVTI583ALWPbGPVbZIyzbo01nLvnkoJhE4Qrw29mED11FVK2BDG\nCQ0GzS4ITV0TRx3yYglGODw4oJd2XZxXvzvT6GRVliVZp+vZPoHzPC6LCtNYP+kupguCQGP9oMzg\n+WLu/YmPjo5Ylpo7asqKixcvEscx88mU0WjEoNujKipidMEyPpn4sFLLqCny0j+fzWYcHh56OYh5\nsXATmk5eTz31lDJpHOupqgvnmhQ9sIq3deOvl8DCYrGAMOBoeqiG3qKm5UmSqOeDrTFNhbGWqgqw\nBNgGolS9X5MkIUsjls5w29qGIIq9d4G/YRhLJ0npdLp0kszTr9M0VSpoUVMZQ9VUVFVJXWnepJ2P\nBoOBL/RrBe9adk3TqNKl2JUt4mym9RttTUbLPtvd3eX0eOzDge0C8Pj4GIAAdcJKkwTbGIq6oCMd\nL7j4sHjUif4TwOfd46fWJu+7wFPu8buAW2t/c9sd+6YTfSABdVUwGnT9oG4NNOq6pnEr/PXEYBzg\nRMtCLcRJIl+5VjcNjVnJC+s2tPaVhcEyIA7Fr2Z0BRUjRshLLQIKbADOkxIX8w+S1F/41lpPOZRg\nJWZUOOOGsqwJogeNHkSE0tmKtayNlq6IsdimIeuvZGTrpqGqdKVJm9QMBCOQO+ejpmnAKgtjONhS\nw5C5ToJi1FezZbiIaGwwL5SHPJ/WhJGasLeKf/1uF1tDWRdghMn4lCROubB1QY0axlrUcnxwQhRF\n9PoZL3zt624FpRPmcrnEWOH+0THD4ZAru3uMx2PiOKbXVx+AxTL3FnZJkoARXt2/w3w+pZd1fYLa\nV+xGNWXVcPf+Ia2ERWBhLjk7OzuOzbAkiCOdNIKA8XiGSMjFS5d1F2Us737Pd/jPFRFe299nOtY8\nRJzGlHnxADe/2+1zfHhEnheOa95hcjLR3E2t6pd1VVMXtY+tH41PSeMIEzR0ktizKYxRLfFqkZN2\nklXuRiKayrAolyuGjhif3K6L0u3qnERtqGGelt5praV0zmNJEmkexCUCB4OBK/gzXN67wmwyJQkj\nFvM5oYT0u33GJ2PP5GlZTgSwNdrh3r17dLtdTk+mXqul2+1zcHDAtWvXuHfvNeq6ZtjrM55NyRKV\nDr+3/xrdbtcnlY0xSGAJauNX2+0Ot71xtGEO20A/6zoDDoNtAo4Ojr0cdJukHk9VuKyTxIzHY4Zb\nI91Rr11vV4letAAAErNJREFUYRi5fJTxbBYNG+kut2lWfs6r8K114pghHbfaLmWV11ID8JnfHYRh\n6PNZrbptWZZg1LO6NUBZr+QOw5CmNtx69Y7fjcRJ6As6d3Z2PMvIGENttNYkDnU3s5wvKOvqoSfu\nh57oRSQBPgY8+/rXrLVWRB4pqysinwI+BTDo9ej1+271m1DZEgkDbUgAwkpcq2UB2LUEjCZjVQRM\nE39qur3OGdeBpdZcWAjddrbVYbHW+q1YIjHWKt2wrmtip0lhGw11xNFq22yMoS50Mo6cIUl7h29c\n/LeNpbUiWe3F2Sa9NEmsHTgZz3TCWJY0VuO8xtYEovSrOO3RBAaRiEBCrAQs5yrzayrDwd0DylJL\n260ovQ670udpu0msIUo0hJQXOf1+39O3jFnthLYv7NA0DdevXyfPc27evOkLd3T3siTPC681Y43Q\nzfoYLHuJVqBO5moDV5Yl+/v7/uY26Pf0AnUTZJp1uXRpB2MMs8mcutYkOsBstsDUled07+7uEorG\nMvO8pGkMRVER2ZCXbnydonKyBLMFUZQQxymdTsJLL93k6tWrTKdT9vb2qMvKtWPKeDymqkpfUKTm\n4roSCyQCO2M2XXB8fKz86a0RRVEROoGpqmpcjFwNJwhRgTfHxjk5OSFOInppF9OoHlDgpGoDQh+G\n0t1Qs2KyhCEYIemkLvwRMi8XbhdRqjiXEaxtiGPVvVFqoGE8neniJorVpjEvyBstFEscRVCMJZ8v\nuHDhgq83yBc5d6b3SNOU3UtXmM1mlJUW1hVFxenphBs3/oR8qSqPw9GAXidjPp9y584ddnd3GY1G\n/voDNYxvY/N1XauURxxRujbWVe0sFF0ldKg1MFhDHIdMp2MCIEq6HB+fErqwS1uVWhUFWa/rGWw6\nzvQ7rZuaw6NjzzoS1LCm1Qhq6x3anFF/0KVwyXMR8cYzZVlydHTkdwij0Qhr9bN6PVWdDQKV+mhq\niJLQ+0e0DLGqqlSjKBs+UD9w9V1PM51OuXv3NYqiYHt7G1zIp31PL+s6o5PokWL0j7Ki/yHgf1lr\n77nn99qQjIjsAffd8TvAu9f+7qo79gCstZ8FPguwt7trwyggiGLEWiITQG0JzCpZiVhsoLHy2XxB\nHK0Mn621hLS65iMVpXKrsiCO1cAaqJvar+TyZek5sTaw2LwhC1KkFhp0xW4qzbI3VhCJMUaIgpjl\nfEmv16PTSckXBSIxYSAkoeOfFxVZkpB0e06LvSbtZiSdTJO/aBxyOp0SSkCxzCnQgqNQhCgI6HS7\nlLVW3rWyyXEYMpnNuHfvwMfokkS9T0ejkadrnZ6MmTrvzzAKCKgp84KiqtnZ2dHwQdNQlg2NqUjS\nHqayJGHKbDaj19M4fG0aojCmPxry/PNfc/kJTZDq96iUv14/86uatqLQWhh0OkRRQjdOsahGTysP\nrBWhA+KoQ1UXzGZTTbomGmdtstrr/HS6WgTXG4zcVlg4PDxma2uLslTHqjTV1ebR0REXL1/h/v37\njPo9Bv2Sfn+o9NGmYXhhG4theGGL3rBHP+uT5zkvv/yyKyvvcnT7Ncfbbhj0R45lNPBjp90yT5z0\nr6VmOZ8xOT3RsS2Bet3GKcuyxKA3/Es7287O0LD09QSGOIrIi4rLly5z9epVwjDk8OSUF557XlfL\nwz5NXRNWMaZWuwoxQmADojCiKJbUFXR7HebzgiBKERtgizlxnEBgqI0lDhM6PTVaKcol8/nSUWMN\nnU5Xq1+Nhh30e1ZtnvsH+zQIiQhbwwFZr0NV6e5pNNxlkS/VfSmOiMOIvb090o6SHOpGtexPT0/d\nNVU7KXLdtdJYeh1NVsbOs7UtdjS14ZVXbpGmGZcuXSJNMx/aUD8BSxxF1EWuypYLy97lXe7evcvR\n0Yk3L+r3Oxgz4cLWiCzLtMAujhnGKcb9r2St7kJE5ZbbyTVNlX3kKbGhity1OaednR116Lr5irJn\nkhiLKo3u7OwQhiGTycxP6ru7F+nvDDgdaxhn4OQvXn7lli4M04wwSigrtc5sx53esAUJYdgf+F34\nw+Chk7Ei8qvAl621v+Se/xxwtJaM3bbW/rSI/DDwt1klY3/BWvu93+qz93Yv2U/9pR92d3JLXa2S\nJu2WF1GfxriTEQYRgaNDtXzSycmpvwiLuvLMnZYO1ybS2rtjGzdvP7/j+NVtmKUsS704XEwuDEPP\n849dfgC7KqBQSVKnuWMbZfLYVVKqlUIIMJ5O1u12/a4gTVMv6VvXNWEcMxwOGZ+cMB6PaRot0mlD\nC7EzNw/DkDjteDPpNI0JiPzWWFckerM0LllcFbkPaZVlSVk33uJQaZG6he4N+nTSjDt37hA7NlDf\nhV4AptMp/b6uoBpTsb+/z4Wh0uryKqcqG8+UqB2bqT2vpmkYZisvzNlsSlEsGQ6HGnN2lZBZlumq\nryzpOv+BVhOlE+vKhlAZIuPxmOvXrzOZzta41BriiCOVNlgsZ24nZXzOJwgCbz7djgsVtNP4b/va\nycmJnk8Y+nhrG17c3t4GNIE7nSoVdWXonhPFwUrCgJWc7Xw+ZzQascjnLBa6KmyaBsLIaz0VReFN\ntNuVqkUZZprzsE5+12JpfFKvm8U+b7C/v8/ulXcz7HUZn5xyeHjodp46/kajAYvFgv6g62+wgPbh\nQC09v+tPPUNZltx8+WUa4/jhrj4iThNmsxmJS+YClHXF9WeuYc3qOqiK0rPMBoMBcZr4kKpnhYGn\n2Or31MGYmtoaf26TycSHN1Inx2EFJ3Koi4XLT+0xX8x8HqgNI2nhWUixLJXlgqGp9bqUwBXQJR2y\nLNM8D67iGusTuEVR+B1Lm1NLYpXp7g8HVLUhDvX/9Xo9Z4mKeu7OZrqLLGtvrdnSr9t6Cc/GKxa+\nlkLpy6sCQGstP/8fvvDWJWNFpAf8APA31g7/I+ALIvJTwCvAX3bHfwed5G+g9MqffLPPt1a12xuX\noV7F18JVEZNV+QKxKM0yDlZaF7MZcRjTmNqFRAKVvw0DyqZGTLgq73bVpy1vvY3PtbsDWPFjI8dw\nQIR8sSROUtJOotZuZhUa0phuRRgnytETDQ+15ctdp1vfyRLEFXHUwUqYyBcuJatBP51OmU6nNFVF\nKAGIGpC3+ibGGCISv21s439FUXH/7m2CQCejra0tRqOhsiMcGwWMK/SpGI1GzBanBI7vnGU9H9IK\nw5AyL4lc2EWpp4Vf2atxdslstmS5XGCMcDKeYi10ewN6fS1SK8qGOInoxInncV997zXKvGAymWKt\nYWtrSFWnLsSkuYUw0Is6Noay0JxB66U6nU6pk46np9UurPPCC8/zPd/7Zzg6PCZfLjFNSVWUlLmG\n1zpZxyfM9DvI/cqvDT+1NwBjYDqZeDbHpUuXfB8tFmraXBQFV65ccWGfytVOBNR1SRgnvory+vVr\nvPLqTeazpb+gJ5MJ3W5XC7FYsYKsXRWdBUGg5tC1TpSTyURpoqEakNRNw2hrgK1rZovZA+yPZV7q\n5JskjLa2XZLv1OvhGxOQxCrUNpmOaRrDclGQJhlpkjkWj07OGMutV++Q5zmFC1NEQcitW7d5z3ve\nQ1VUREHEYqEFhFtbW17eoq0UbSevVq7A1ye4MdxWmPqkKCt5inbRYowhjWK2R1u+WK393pqm4db+\nHYajbYqiotdfcOHCDmEUsxWI2/m5/1s3FEvt78VyyvbOtl8ILhYLqlLrPcJYtY7axKtpLFEnYjwe\n+8KzdhIuciUE3Lt3j8l0TpokJPEqF1eVuS+g3N3dJeuqZ3Dr+rWeXG0r4/Ni4a95zSeW3lh+tZh5\niDn8UeiVbxfetbdn/8pHf4Ag0q0fLlNPs1p1hVHgtnTKSKjXqI7WWqSB2lEqGxqfZGkaZZQEti1E\naBgMBp5DfXJyogMrTtYutLYyd+WYpCt6yNJkrbQ/8nHUYb/L3OnTe3NgK/6ibivukjik62z52jDG\n7du3vbBTHMde5AjQTHvrGbnMSTKNFbaZeyFkuHVBS+aduNN4cqpxvFCNjbOOxhkrbyjSyjWsmEuB\nC60kSaJmG94QQ/xKslU+VDlVQ1lWZFnmJZyrUp2biqJg59JF7ty5xcWLF7l3/y7z6cxPOu3qpWUO\naFJ2SZyE/sbbDmLV02m9BiK/ko6iiMgGHBwdKte4p9WqxlZsX9zl8PDI1VEoB7vdfuOKsLa2thyb\nqPYr4OFw6C9c/YnJ84WXj82yzBd7tcyp1uC9lcwoigLbVFgJ6XR1dR4FrSesMJ1OybIM0AXAcrnU\nz3P9YBp8gU1VLJUzXtdeJVRDCbqrXSzVF3eRz+mmfZcn0vbWTenVV9ukbJFXnB4f0u/2lJfe3Vpx\n2EUII6VjTiYTnn76aU7GpyRR7AkL3f4AsTCfTzW8sZxxenrKtWvXKIpCGSVlyeXLl0l7HW2HrQiD\neK1gTcfe/v4+WZaRdDSP0y6y2t9tBWo7Htr6kna8tnRaES2kHHR7VKYhLwtOjmc888wzxFHKZKp+\nBWGwKtDS4jc1Cjk6PuTq1ct0Ou21qDIFg/6Qfr/PItcE+XQ6d2NXKPKlJxK0dEpjDFGo7KBFrmSE\nXifzbdE8hXFsLyHLekymU195a4xhmWth1Lp4HajEgi/CcmHTlvb5z3/lHFXGisgUePGsz+MtxkXg\n8KxP4i3Epj2PN5609sCT16a3oz3fYa299GZvelR65duFFx/mrnSeICJ/8CS1adOexxtPWnvgyWvT\nWbbn4Rn3G2ywwQYbnEtsJvoNNthggyccj8tE/9mzPoG3AU9amzbtebzxpLUHnrw2nVl7Hotk7AYb\nbLDBBm8fHpcV/QYbbLDBBm8TNhP9BhtssMETjjOf6EXkwyLyoojccFIKjz1E5N0i8vsi8jUReV5E\n/o47vi0i/1lEvuF+X3DHRUR+wbXxqyLy/rNtwRtDREIR+SMR+ZJ7fk1EvuLO+9ecsB0ikrrnN9zr\n7z3L834jiMpj/4aIfF1EXhCRDz4B/fP33Hh7TkQ+LyKd89RHIvJvReS+iDy3duyR+0REPune/w0R\n+eRZtGXtXN6oTT/nxt1XReQ/isjW2mvPuja9KCI/uHb87Z0H2/Lis/gBQuAl1LUqAf438L6zPKeH\nPO894P3u8QD4v8D7gH8M/Iw7/jPAz7rHHwF+FxDgA8BXzroN36Rdfx/4FeBL7vkXgE+4x58B/qZ7\n/LeAz7jHnwB+7azP/Q3a8svAX3ePE2DrPPcPKvV9E8jW+uYnzlMfAX8eeD/w3NqxR+oTYBv4E/f7\ngnt84TFr04eAyD3+2bU2vc/NcSlwzc194TsxD551x38QFUprnz8LPHvWA/LbaMdvo1pALwJ77tge\nWggG8IvAj62937/vcflBVUZ/D/gLwJfcBXa4NmB9XwFfBj7oHkfufXLWbVhry8hNivK64+e5f1qf\nh233nX8J+MHz1keo9ej6pPhIfQL8GPCLa8cfeN/j0KbXvfYjwOfc4wfmt7aP3ol58KxDN9/MpOTc\nwG2Jvxv4Co9uxvI44Z8BPw0Y93wHOLXW1u75+jn79rjXx+79jwuuAQfAL7lQ1L8WFeY7t/1jrb0D\n/BPgVdTEZwz8Iee3j1o8ap889n31Ovw1dGcCZ9ims57ozzVEpA/8JvB3rbWT9des3prPBXdVRD4K\n3LfW/uFZn8tbhAjdTv8ra+13A3M0LOBxnvoHwMWuP47exK4APeDDZ3pSbzHOW5+8GUTk00ANfO6s\nz+WsJ/qHMil5HCEiMTrJf85a+1vu8D1RExbk2zBjOUP8WeBjIvIy8Kto+ObngS0RafWQ1s/Zt8e9\nPgKO3skTfhPcBm5ba7/inv8GOvGf1/4B+IvATWvtgbW2An4L7bfz2kctHrVPzkNfISI/AXwU+HF3\nA4MzbNNZT/T/E/hOxxxI0KTRF8/4nN4UIiLAvwFesNb+07WXvgi0LIBPorH79vhfdUyCDwBj+y3M\n0t9pWGuftdZetda+F+2D/2qt/XHg94EfdW97fXvadv6oe/9jsxKz1t4FbonId7lD3w98jXPaPw6v\nAh8Qka4bf22bzmUfreFR++TLwIdE5ILb5XzIHXtsICIfRsOgH7PWLtZe+iLwCceIugZ8J/A/eCfm\nwbNMYrhx9xGUtfIS8OmzPp+HPOc/h24xvwr8sfv5CBoD/T3gG8B/QV23QBOb/9K18f8A33PWbfgW\nbfs+Vqybp91AvAH8OpC64x33/IZ7/emzPu83aMefBv7A9dF/Qhka57p/gH8IfB14Dvj3KHvj3PQR\n8Hk0v1Chu66f+nb6BI1733A/P/kYtukGGnNv54bPrL3/065NLwI/tHb8bZ0HNxIIG2ywwQZPOM46\ndLPBBhtssMHbjM1Ev8EGG2zwhGMz0W+wwQYbPOHYTPQbbLDBBk84NhP9BhtssMETjs1Ev8EGG2zw\nhGMz0W+wwQYbPOH4f/K1TGLgXrSAAAAAAElFTkSuQmCC\n", 235 | "text/plain": [ 236 | "" 237 | ] 238 | }, 239 | "metadata": {}, 240 | "output_type": "display_data" 241 | } 242 | ], 243 | "source": [ 244 | "%matplotlib inline\n", 245 | "import matplotlib.pylab as plt\n", 246 | "\n", 247 | "def locate():\n", 248 | " data = cv2.cvtColor(cv2.imread(\"test1.jpg\"), cv2.COLOR_BGR2RGB)\n", 249 | " \n", 250 | " heatmap = heatmodel.predict(data.reshape(1,data.shape[0],data.shape[1],data.shape[2]))\n", 251 | " \n", 252 | " plt.imshow(heatmap[0,:,:,0])\n", 253 | " plt.title(\"Heatmap\")\n", 254 | " plt.show()\n", 255 | " plt.imshow(heatmap[0,:,:,0]>0.99, cmap=\"gray\")\n", 256 | " plt.title(\"Car Area\")\n", 257 | " plt.show()\n", 258 | "\n", 259 | " xx, yy = np.meshgrid(np.arange(heatmap.shape[2]),np.arange(heatmap.shape[1]))\n", 260 | " x = (xx[heatmap[0,:,:,0]>0.99])\n", 261 | " y = (yy[heatmap[0,:,:,0]>0.99])\n", 262 | " \n", 263 | " for i,j in zip(x,y):\n", 264 | " cv2.rectangle(data, (i*8,j*8), (i*8+64,j*8+64), (0,0,255), 5)\n", 265 | " return data\n", 266 | "\n", 267 | "annotated = locate()\n", 268 | "\n", 269 | "plt.title(\"Augmented\") \n", 270 | "plt.imshow(annotated)\n", 271 | "plt.show()" 272 | ] 273 | }, 274 | { 275 | "cell_type": "markdown", 276 | "metadata": {}, 277 | "source": [ 278 | "### What else is there to do\n", 279 | "\n", 280 | "- Lower false positives further. As you can see from the heatmap parts of the tree are a bit dangerous you could consider grabbing a few patches there saving them and adding them to the non-vehicle data\n", 281 | "\n", 282 | "- If you try to run this model on an car image that is way larger than 64x64 pixels you will get funny results. Therefore you will need scaling or region proposals to really catch all possible. But the good news is that a scaling factor of 2 is usually okay (as you can see the cars in the demo image are also to big but detected just fine.\n", 283 | "\n", 284 | "- Optimize for speed by only using sensible image areas sizes.\n", 285 | "\n", 286 | "- Make one box per car. This is called non maximum suppression. Some DL architectures do this out of the box (SSD, YOLO in this simple model you have to do it yourself in code)\n", 287 | "\n", 288 | "### More than two classes\n", 289 | "\n", 290 | "For more than two classes you need the softmax classifier the way it is currently implemented in keras only works in flat arrays. However there is a github project with uses Alexnet, Resnet etc to generate heatmaps in the same style and they also implemented a \"Softmax4D\" layer. You can check it here https://github.com/heuritech/convnets-keras (very interesting read). Using this our model becomes (something like this). \n", 291 | "\n", 292 | "Since these customlayers are not maintained by myself I will show a direct tensorflow version below." 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": null, 298 | "metadata": { 299 | "collapsed": true 300 | }, 301 | "outputs": [], 302 | "source": [ 303 | "# You need to adapt the Y vector to be [0,1] and [1,0] instead of -1 or -1 for this to work\n", 304 | "from customlayers import Softmax4D\n", 305 | "\n", 306 | "def get_conv(input_shape=(64,64,3), filename=None):\n", 307 | " model = Sequential()\n", 308 | " model.add(Lambda(lambda x: x/127.5 - 1.,input_shape=input_shape, output_shape=input_shape))\n", 309 | " model.add(Convolution2D(20, 3, 3, activation='relu', name='conv1',input_shape=input_shape, border_mode=\"same\"))\n", 310 | " model.add(Convolution2D(20, 3, 3, activation='relu', name='conv2',border_mode=\"same\"))\n", 311 | " model.add(MaxPooling2D(pool_size=(8,8)))\n", 312 | " model.add(Dropout(0.25))\n", 313 | " model.add(Convolution2D(128,8,8,activation=\"relu\",name=\"dense1\"))\n", 314 | " model.add(Dropout(0.5))\n", 315 | " model.add(Convolution2D(2,1,1,name=\"dense2\"))\n", 316 | " model.add(Softmax4D(axis=3,name=\"softmax\"))\n", 317 | " return model\n", 318 | "\n", 319 | "model = get_conv()\n", 320 | "model.add(Flatten())\n", 321 | "model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])" 322 | ] 323 | }, 324 | { 325 | "cell_type": "markdown", 326 | "metadata": {}, 327 | "source": [ 328 | "# Now for the tensorflow version:\n", 329 | "\n", 330 | "fortunately tf is nowadays much more accessible than when I wrote this notebook, for the more than two classes version non tensorflow is now a reasonable option:" 331 | ] 332 | }, 333 | { 334 | "cell_type": "code", 335 | "execution_count": null, 336 | "metadata": {}, 337 | "outputs": [ 338 | { 339 | "name": "stdout", 340 | "output_type": "stream", 341 | "text": [ 342 | "X_train shape: (15984, 64, 64, 3)\n", 343 | "15984 train samples\n", 344 | "1776 test samples\n", 345 | "Epoch 1 Batch 250 Training loss = 0.505 Acc:0.812\n", 346 | "Test: Epoch 1 Batch 250 Training loss = 0.427 Acc:0.845\n", 347 | "Epoch 2 Batch 250 Training loss = 0.472 Acc:0.833\n", 348 | "Test: Epoch 2 Batch 250 Training loss = 0.392 Acc:0.831\n", 349 | "Epoch 3 Batch 250 Training loss = 0.411 Acc:0.854\n", 350 | "Test: Epoch 3 Batch 250 Training loss = 0.272 Acc:0.893\n", 351 | "Epoch 4 Batch 250 Training loss = 0.318 Acc:0.875\n", 352 | "Test: Epoch 4 Batch 250 Training loss = 0.290 Acc:0.877\n", 353 | "Epoch 5 Batch 250 Training loss = 0.279 Acc:0.896\n", 354 | "Test: Epoch 5 Batch 250 Training loss = 0.149 Acc:0.945\n", 355 | "Epoch 6 Batch 250 Training loss = 0.144 Acc:0.958\n", 356 | "Test: Epoch 6 Batch 250 Training loss = 0.088 Acc:0.971\n", 357 | "Epoch 7 Batch 250 Training loss = 0.169 Acc:0.896\n", 358 | "Test: Epoch 7 Batch 250 Training loss = 0.080 Acc:0.972\n", 359 | "Epoch 8 Batch 250 Training loss = 0.115 Acc:0.979\n", 360 | "Test: Epoch 8 Batch 250 Training loss = 0.071 Acc:0.974\n", 361 | "Epoch 9 Batch 250 Training loss = 0.070 Acc:0.958\n", 362 | "Test: Epoch 9 Batch 250 Training loss = 0.048 Acc:0.983\n", 363 | "Epoch 10 Batch 250 Training loss = 0.082 Acc:0.979\n", 364 | "Test: Epoch 10 Batch 250 Training loss = 0.047 Acc:0.985\n", 365 | "Epoch 11 Batch 250 Training loss = 0.033 Acc:0.979\n", 366 | "Test: Epoch 11 Batch 250 Training loss = 0.051 Acc:0.984\n", 367 | "Epoch 12 Batch 250 Training loss = 0.084 Acc:0.979\n", 368 | "Test: Epoch 12 Batch 250 Training loss = 0.053 Acc:0.981\n" 369 | ] 370 | } 371 | ], 372 | "source": [ 373 | "import tensorflow as tf\n", 374 | "import numpy as np\n", 375 | "import glob \n", 376 | "import cv2\n", 377 | "import numpy as np\n", 378 | "\n", 379 | "cars = glob.glob(\"./vehicles/*/*.png\")\n", 380 | "non_cars = glob.glob(\"./non-vehicles/*/*.png\")\n", 381 | "\n", 382 | "# Generate Y Vector\n", 383 | "Y = np.concatenate([np.ones(len(cars)), np.zeros(len(non_cars))-1])\n", 384 | "Y = np.concatenate([np.array([[1,0]]*len(cars)), \n", 385 | " np.array([[0,1]]*len(non_cars))]) #note that this needs to be different now\n", 386 | "\n", 387 | "# Read X Vector\n", 388 | "X = []\n", 389 | "for name in cars: \n", 390 | " X.append(cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2RGB))\n", 391 | "for name in non_cars: \n", 392 | " X.append(cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2RGB))\n", 393 | "X = np.array(X)\n", 394 | "\n", 395 | "from sklearn.model_selection import train_test_split\n", 396 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.10, random_state=42)\n", 397 | "\n", 398 | "\n", 399 | "X_train = X_train.astype('float32')\n", 400 | "X_test = X_test.astype('float32')\n", 401 | "print('X_train shape:', X_train.shape)\n", 402 | "print(X_train.shape[0], 'train samples')\n", 403 | "print(X_test.shape[0], 'test samples')\n", 404 | "input_shape = (3,64,64)\n", 405 | "\n", 406 | "\n", 407 | "def model(data, train):\n", 408 | " norm = data/127.5 - 1\n", 409 | " c1 = tf.layers.conv2d(norm, 16, 3, strides=(1, 1), padding='SAME',\n", 410 | " kernel_initializer=tf.truncated_normal_initializer(stddev=0.001), activation=tf.nn.relu)\n", 411 | " c2 = tf.layers.conv2d(c1, 16, 3, strides=(1, 1), padding='SAME',\n", 412 | " kernel_initializer=tf.truncated_normal_initializer(stddev=0.001), activation=tf.nn.relu)\n", 413 | " p = tf.layers.max_pooling2d(c2, 8, strides=(8,8), padding='SAME')\n", 414 | " \n", 415 | " drop1 = tf.layers.dropout(p,rate=0.25,training=train)\n", 416 | " \n", 417 | " c3 = tf.layers.conv2d(drop1, 128, 8, strides=(1, 1), padding='VALID',\n", 418 | " kernel_initializer=tf.truncated_normal_initializer(stddev=0.001), activation=tf.nn.relu)\n", 419 | " \n", 420 | " drop2 = tf.layers.dropout(c3,rate=0.50,training=train)\n", 421 | " c4 = tf.layers.conv2d(drop2, 2, 1, strides=(1, 1), padding='VALID',\n", 422 | " kernel_initializer=tf.truncated_normal_initializer(stddev=0.001))\n", 423 | " sm = tf.nn.softmax(c4, name=\"output\")\n", 424 | "\n", 425 | " return c4,sm\n", 426 | " \n", 427 | "def loss(data, correct_label): \n", 428 | " logits = tf.reshape(data,(-1,2),name='logits')\n", 429 | " labels = tf.reshape(correct_label,(-1,2),name='lables')\n", 430 | " loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))\n", 431 | " train_op = tf.train.AdamOptimizer(0.001).minimize(loss)\n", 432 | " \n", 433 | " correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1))\n", 434 | " tf_accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", 435 | " return loss,train_op,tf_accuracy\n", 436 | "\n", 437 | "\n", 438 | "\n", 439 | "\n", 440 | "batches = (int(np.ceil(X_train.shape[0]/64.)))\n", 441 | "\n", 442 | "\n", 443 | "with tf.Session() as sess:\n", 444 | " \n", 445 | " tf_data = tf.placeholder(tf.float32, shape=(None, None, None, 3), name=\"image_input\")\n", 446 | " tf_labels = tf.placeholder(tf.float32, shape=(None),name=\"labels_input\")\n", 447 | " tf_train = tf.placeholder(tf.bool,name=\"train_input\")\n", 448 | " \n", 449 | " modelout,sm = model(tf_data, tf_train)\n", 450 | " l,train_op, tf_accuracy = loss(modelout, tf_labels)\n", 451 | "\n", 452 | " sess.run(tf.global_variables_initializer())\n", 453 | " \n", 454 | " for epoch_i in range(20):\n", 455 | " for batch_i in range(batches):\n", 456 | " train_loss, _, accuracy = sess.run([l, train_op, tf_accuracy],\n", 457 | " feed_dict={tf_data: X_train[batch_i*64:batch_i*64+64], \n", 458 | " tf_labels: Y_train[batch_i*64:batch_i*64+64], \n", 459 | " tf_train: True})\n", 460 | " \n", 461 | " # Display the loss after the epoch\n", 462 | " \n", 463 | " print('Epoch {:>3} Batch {:>2} Training loss = {:.3f} Acc:{:.3f}'.format(\n", 464 | " epoch_i+1, batch_i+1, train_loss, accuracy))\n", 465 | " \n", 466 | " train_loss, accuracy = sess.run([l, tf_accuracy],feed_dict={tf_data: X_test, tf_labels: Y_test, \n", 467 | " tf_train: False})\n", 468 | " print('Test: Epoch {:>3} Batch {:>2} Training loss = {:.3f} Acc:{:.3f}'.format(\n", 469 | " epoch_i+1, batch_i+1, train_loss, accuracy))\n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " saver = tf.train.Saver()\n", 474 | " saver.save(sess, \"car-detector.tf\",global_step=1000)" 475 | ] 476 | }, 477 | { 478 | "cell_type": "code", 479 | "execution_count": 17, 480 | "metadata": {}, 481 | "outputs": [ 482 | { 483 | "name": "stdout", 484 | "output_type": "stream", 485 | "text": [ 486 | "Populating the interactive namespace from numpy and matplotlib\n", 487 | "INFO:tensorflow:Restoring parameters from ./car-detector.tf-1000\n" 488 | ] 489 | }, 490 | { 491 | "data": { 492 | "image/png": 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493 | "text/plain": [ 494 | "" 495 | ] 496 | }, 497 | "metadata": {}, 498 | "output_type": "display_data" 499 | }, 500 | { 501 | "name": "stdout", 502 | "output_type": "stream", 503 | "text": [ 504 | "Car: 79\n", 505 | "NonCar: 11883\n" 506 | ] 507 | } 508 | ], 509 | "source": [ 510 | "import tensorflow as tf\n", 511 | "import cv2\n", 512 | "%pylab inline\n", 513 | "\n", 514 | "\n", 515 | "\n", 516 | "with tf.Session() as sess: \n", 517 | " saver = tf.train.import_meta_graph('car-detector.tf-1000.meta')\n", 518 | " saver.restore(sess,tf.train.latest_checkpoint('./'))\n", 519 | " graph = tf.get_default_graph()\n", 520 | " \n", 521 | " \n", 522 | "\n", 523 | " tf_data = graph.get_tensor_by_name(\"image_input:0\")\n", 524 | " tf_train = graph.get_tensor_by_name(\"train_input:0\")\n", 525 | " tf_label = graph.get_tensor_by_name(\"labels_input:0\") \n", 526 | " sm = graph.get_tensor_by_name(\"output:0\")\n", 527 | "\n", 528 | "\n", 529 | " testdata = (cv2.cvtColor(cv2.imread(\"test1.jpg\"), cv2.COLOR_BGR2RGB))\n", 530 | " #print(testdata.shape)\n", 531 | " \n", 532 | " img = sess.run([sm], feed_dict={tf_data: testdata.reshape([1, 720, 1280,3]), tf_train: False})\n", 533 | " \n", 534 | " threshold = 0.95\n", 535 | " \n", 536 | " \n", 537 | " img = np.array(img)\n", 538 | " imshow(img[0,0,:,:,0])\n", 539 | " show()\n", 540 | " print(\"Car:\",np.sum(img[0,0,:,:,0]>threshold))\n", 541 | " print(\"NonCar:\",np.sum(img[0,0,:,:,1]>threshold))\n" 542 | ] 543 | }, 544 | { 545 | "cell_type": "code", 546 | "execution_count": null, 547 | "metadata": { 548 | "collapsed": true 549 | }, 550 | "outputs": [], 551 | "source": [] 552 | } 553 | ], 554 | "metadata": { 555 | "kernelspec": { 556 | "display_name": "Python 3", 557 | "language": "python", 558 | "name": "python3" 559 | }, 560 | "language_info": { 561 | "codemirror_mode": { 562 | "name": "ipython", 563 | "version": 3 564 | }, 565 | "file_extension": ".py", 566 | "mimetype": "text/x-python", 567 | "name": "python", 568 | "nbconvert_exporter": "python", 569 | "pygments_lexer": "ipython3", 570 | "version": "3.5.2" 571 | } 572 | }, 573 | "nbformat": 4, 574 | "nbformat_minor": 1 575 | } 576 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Vehicle-Detection-and-Tracking 2 | This is a deep learing approach to Udacitys "Vehicle Detection and Tracking" project in the CarND using CNNs. Just follow the ipython notebook. 3 | The the the dataset this was model was trained on can be found in udacity's repo: 4 | https://github.com/udacity/CarND-Vehicle-Detection 5 | 6 | Specifically I used: 7 | [vehicles](https://s3.amazonaws.com/udacity-sdc/Vehicle_Tracking/vehicles.zip) 8 | [non-vehicles](https://s3.amazonaws.com/udacity-sdc/Vehicle_Tracking/non-vehicles.zip) 9 | and some additional samples generated from the [GTI vehicle image database](http://www.gti.ssr.upm.es/data/Vehicle_database.html) -------------------------------------------------------------------------------- /car-detector.tf-1000.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HTuennermann/Vehicle-Detection-and-Tracking/12ae22cd3fafe3221959f7af21d1b5b6d557fb11/car-detector.tf-1000.data-00000-of-00001 -------------------------------------------------------------------------------- /car-detector.tf-1000.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HTuennermann/Vehicle-Detection-and-Tracking/12ae22cd3fafe3221959f7af21d1b5b6d557fb11/car-detector.tf-1000.index -------------------------------------------------------------------------------- /car-detector.tf-1000.meta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HTuennermann/Vehicle-Detection-and-Tracking/12ae22cd3fafe3221959f7af21d1b5b6d557fb11/car-detector.tf-1000.meta -------------------------------------------------------------------------------- /checkpoint: -------------------------------------------------------------------------------- 1 | model_checkpoint_path: "car-detector.tf-1000" 2 | all_model_checkpoint_paths: "car-detector.tf-1000" 3 | -------------------------------------------------------------------------------- /localize.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HTuennermann/Vehicle-Detection-and-Tracking/12ae22cd3fafe3221959f7af21d1b5b6d557fb11/localize.h5 -------------------------------------------------------------------------------- /non-vehicles/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HTuennermann/Vehicle-Detection-and-Tracking/12ae22cd3fafe3221959f7af21d1b5b6d557fb11/non-vehicles/.DS_Store -------------------------------------------------------------------------------- /test1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HTuennermann/Vehicle-Detection-and-Tracking/12ae22cd3fafe3221959f7af21d1b5b6d557fb11/test1.jpg -------------------------------------------------------------------------------- /vehicles/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HTuennermann/Vehicle-Detection-and-Tracking/12ae22cd3fafe3221959f7af21d1b5b6d557fb11/vehicles/.DS_Store --------------------------------------------------------------------------------