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Starting with a random network, optimize the classification confidence of high-confidence examples.
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Things to try next:\n", | |
"\n", | |
"- What happens with very large and very small numbers of classes?\n", | |
"- What happens with multiple softmax output layers connected to the same network (\"competing\" representations)?\n", | |
"- How do the `search_size` and `items_per_class` variables affect the results?\n", | |
"- Does a more \"centered\" activation like `selu` affect the results?\n", | |
"- How does different pooling, batchnorm, dropout affect the results?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"env: CUDA_VISIBLE_DEVICES=0\n" | |
] | |
} | |
], | |
"source": [ | |
"%env CUDA_VISIBLE_DEVICES=0" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"\n", | |
"from utils.show_array import show_array\n", | |
"from utils.make_mosaic import make_mosaic\n", | |
"from utils.plot_images import plot_images" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"float32 0.0 1.0 (70000, 28, 28, 1)\n" | |
] | |
} | |
], | |
"source": [ | |
"from sklearn.datasets import fetch_mldata\n", | |
"\n", | |
"# load MNIST\n", | |
"mnist = fetch_mldata('MNIST original')\n", | |
"x = mnist.data.reshape(-1, 28, 28, 1).astype(np.float32) / 255.\n", | |
"# y = mnist.target.astype(np.int32)\n", | |
"\n", | |
"# shuffle images and labels\n", | |
"indices = np.arange(len(x))\n", | |
"np.random.shuffle(indices)\n", | |
"x = x[indices]\n", | |
"# y = y[indices]\n", | |
"\n", | |
"# verify the data is formatted correctly\n", | |
"print(x.dtype, x.min(), x.max(), x.shape)\n", | |
"# print(y.dtype, y.min(), y.max(), y.shape)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"valid_n = 10000\n", | |
"train_x, valid_x = (x[valid_n:], x[:valid_n])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"Using TensorFlow backend.\n" | |
] | |
} | |
], | |
"source": [ | |
"import keras\n", | |
"from keras.models import Model\n", | |
"from keras.layers import Input, Dense, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization, Activation\n", | |
"from keras import backend as K\n", | |
"\n", | |
"def build_model(input_shape, output_size, activation='relu', batchnorm=True, pooling='max', dropout=0.2):\n", | |
" pool = MaxPooling2D if pooling == 'max' else AveragePooling2D\n", | |
" \n", | |
" input = Input(input_shape)\n", | |
" x = Conv2D(32, (3, 3))(input)\n", | |
" x = BatchNormalization()(x)\n", | |
" x = Activation(activation)(x)\n", | |
" x = pool((2, 2))(x)\n", | |
" x = Conv2D(64, (3, 3))(x)\n", | |
" x = BatchNormalization()(x)\n", | |
" x = Activation(activation)(x)\n", | |
" x = pool((2, 2))(x)\n", | |
" if dropout > 0:\n", | |
" x = Dropout(dropout)(x)\n", | |
" x = Flatten()(x)\n", | |
" x = Dense(output_size, activation='softmax')(x)\n", | |
" model = Model(input, x, name='model')\n", | |
" model.compile(optimizer='adam', loss='categorical_crossentropy')\n", | |
" return model" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"CPU times: user 548 ms, sys: 56 ms, total: 604 ms\n", | |
"Wall time: 386 ms\n" | |
] | |
}, | |
{ | |
"data": { | |
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2cCDwS8AjwD8Dl6SU1o3wPnOB+cDrgeeBHwBLUkpNo4uI2A1YBJwG7A88DtwInJ9S2tSs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\nkiRV4BuRvDK6qrJYmgAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fb3d07ba908>" | |
] | |
}, | |
"metadata": { | |
"image/png": { | |
"height": 250, | |
"width": 383 | |
} | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"model = build_model(x.shape[1:], 10)\n", | |
"%time y = model.predict(x[:10000])\n", | |
"for i in range(10):\n", | |
" plt.hist(y[:,i], histtype='step', bins=20)\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"from IPython.display import clear_output\n", | |
"from collections import defaultdict\n", | |
"\n", | |
"class LossPlotter(keras.callbacks.Callback):\n", | |
" def __init__(self, skip=1):\n", | |
" self.loss = defaultdict(list)\n", | |
" self.skip = skip\n", | |
" self.name = 'loss'\n", | |
" \n", | |
" def setup(self, name):\n", | |
" self.name = name\n", | |
" \n", | |
" def on_epoch_end(self, epoch, logs={}):\n", | |
" self.loss[self.name].append(logs['loss'])\n", | |
" if epoch % self.skip > 0:\n", | |
" return\n", | |
" plt.figure(figsize=(12,8))\n", | |
" for key in self.loss:\n", | |
" plt.plot(self.loss[key], label=key, lw=1)\n", | |
" plt.legend()\n", | |
" clear_output(wait=True)\n", | |
" plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": { | |
"scrolled": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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l/NdSyttKKdckeU3maibeuN9nz0pyQ5LPH3TGnyf5XJIzk9xQSvmj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\nrkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7fb39022a208>" | |
] | |
}, | |
"metadata": { | |
"image/png": { | |
"height": 467, | |
"width": 708 | |
} | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"batch_count = 100 # how many batches to run\n", | |
"search_size = 1000 # how many samples to predict before selecting exemplars\n", | |
"items_per_class = 10 # how many exemplars for each class per batch\n", | |
"output_size = 10 # how many classes to use\n", | |
"\n", | |
"model = build_model(x.shape[1:], output_size)\n", | |
"loss_plotter= LossPlotter()\n", | |
"indices = np.arange(len(train_x))\n", | |
"exemplars_y = np.identity(output_size).repeat(items_per_class, axis=0)\n", | |
"\n", | |
"try:\n", | |
" for batches in range(batch_count):\n", | |
" np.random.shuffle(indices)\n", | |
" y = model.predict(train_x[indices[:search_size]])\n", | |
" best_y_indices = y.argsort(axis=0)\n", | |
" \n", | |
" exemplars_x = []\n", | |
" for i in range(output_size):\n", | |
" class_exemplars_i = best_y_indices[-items_per_class:, i]\n", | |
" class_exemplars_x = train_x[indices[class_exemplars_i]]\n", | |
" exemplars_x.extend(class_exemplars_x)\n", | |
" exemplars_x = np.asarray(exemplars_x)\n", | |
" \n", | |
" # alternatively, this works for items_per_class = 1\n", | |
"# exemplars_i = y.argmax(axis=0)\n", | |
"# exemplars_x = x[indices[exemplars_i]]\n", | |
"\n", | |
" loss = model.train_on_batch(exemplars_x, exemplars_y)\n", | |
" loss_plotter.on_epoch_end(i, {'loss': loss})\n", | |
"except KeyboardInterrupt:\n", | |
" pass" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": { | |
"scrolled": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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7W6zJFxFJ3fV0e//ZnrL65fFYn640wNP6rswH8I4XPA/xsJYtb5qeykhQE7Scvok2GJea4QneqRIt1lw+yXPXVRnESb5TKR9aJgN5718Q1PJ1BjPHBweH7j4gmBAw2FyWJCJyVnt3WzIK7ldjKkqG2wkAhcCh7ZOWvOpJm78qW4H4HE+AoFWqBVZf97Xcmg9r0Rz5W13JKlVavLPPbHh8uvV+cD/jPyN9TS5WB3tVRN+LvKu7UHoIuFujurEwTZdWUWWfpDlpnqY1r1sw/qyKv8V9PXr06BH8Mi7+MgBg8+gA3Xkjre68xVdz1SjF/L1UV/TK6Zto87sLAeheUlFH8fuRHlwVQUR0VKLBuOwUQImeqzIMiAAac2NPQKYEjMWXJ4nI6Y8DetuQ4rO9noiabzx9RMoDb77ZXddYK63efPPkT5+/+WaQA/voQPTdj8i+Xr16+dPVGz25EADGOWsO7m7uAOYN92jzTWtuDgd7v38mn0grnarBKiUf2bSm5FXDM7OGP1cjSlFJsMRMPLJ8SL3gvMeyHwLAjGdKBkRFP2YG8/5rXAkertE4TvubLwIrtLltUQZjuh8+r8iRWeJFqXKnSk+FDqmGX2ym6PnkfZHDQce/ERFporfmGeCsu87GVf3xv5dzrDTmf2RN6RoLMbmGNyniXO2bktMB7AqO7j5+NAqMG5wAAgfNSzMk7WJ9aWKxWUA3jzVFMNZLkTV6cax5FNAX3DTcweBPLwvUtdTMCqnJhmXXBioMKQVs9rxwimAcRYqs0YtjTYsU4PPYf+EULQFosn6iyvX+IHTkvMifFZb/NyVCRdbo5B9nTclnGvwx619oV2JNeW82adFoHEWKrNHLOWZNEflVkfwDpAjGRfIPkCIYF8k/QBQYx77fkOLiCwdWDFzJ7ihybbvrrvMtx/6pxIXidYug5Tptwt5uQZo/k/YcafWvtNNKfmRWUOb235SGjWpZf/pCULcOCliw/jdldiRozYmIqHZkgr5Z40W6Ck4qMQK6VVOi0ej4Vy37b4vtqjeZjX0NTkGAxQFhkNuTgU8FOHW7T3X5GSM75uughLuIJSG/rsMaBrvKjK2JQ0iXnmbJu4AmZ4roYWBUwOFL3jgAAJG7AhvlAR94j2mmMWVJt7D4aB5OWuwbHhasBws44mVp+XsSYM2C1FOapHL62F2x41HyDzpVDeag22jILrZhfI5N8WwYl/oRONJ/HwuAk5MbeL84CcCrEQAJ8721bFOs9ABO8vVAKBJPofhIJChde2wK84wjzAqurM55Oxw+6aeDUuRdIDBFm4hoA3NAun2TBJP9Iy8wXy4P/irsgFt1/sMTJqybsE6TtPJCwR4EZStWH5JVKCJRaln0EhcKheKCrbktQ5sf32wvdmpP2TElmIWOiCjErI3ldsk/52Fc8gNk9qtMdN+nw74FsKyl62sXv5KLRVVLVO2TASDVnQQ6mQEcblbhko+ngie6VDZZXijeNx433gXm8TSTuTDR5nezOqdDahip2qpQig7jgexjpyKiXXxsyvAnek3ZxJkX+rXUIemvwPjKDcYDe8j74i3X9cuslMvuK3wsPuB1fdfvxuvqLb+KGj76ebJAW48VMl5lgdaUnY0zmtTK0geA5ZpTEk3n/bry/7a5/KO23WZOsnPGLGtKxMRvk5nvNilduvRfn2Y1x6R7zLSn2ObNmzdv7kpXKXHvu59KRET2v/turaDodM3ibTveOG7q1KlTj4oBOBvGtbHLqgkq//kW4KhrAfE0kPM8EVGfWfC8zi/LBPD2+URExV91FX8QUbw1BsdFvP5xT0TwHNHTeRBgo1ntZt+qG48jnBplPO7PSApe4iSKY2Z/+UOXOQarU9scDiCwJZqJxeU9h7zAqTBhG4CcH1PgZVdutlwAPER30y8+GNdZnS8S//FFO30w/rha9SXJDAYHMkeH4iORSHwoZHacD8YtgbSgKyGisnATL7ik/EaeptN1YdY6I/WT+TP7g2lNxQ1O6uOkD+oFIDkuLhQKhdyv4+bMfDR++fLly+PTmBm8UxkZSyunBD6rrQK58cBhiR++t+VogZE0CaDwDSJXEZNSMHTpeuC46gCeBsySiUqTgAQ17fwEgHbmpLDEt3BVZIQU6HrmeY1T2MgM7Lh0TQrzyhi1c4joOoTDx6O4d/n6Sp0KzHxQoyM6yj8EUk1e4j+hGziXPr0HwJypTatsBVxPQok+Z5H10Gw8ROSH8UtJImc+rkA02kvY+YxAIDgy8JlnIv4xPBSJROLjiChkvsZ8MF7gzQt3pGk0GEewR8dtWWY9soLeVURENIxPOzl3hjUlNvDp0/NO51mge8YeCeLiQ6GIR+zGl+TZKdzmfxTwVcgJnzbEILpSSdHFhrcAyMsDsJvIgXHJxa66t/PnAQ4NfLlJhQ5z1ZMA3nZiGb1Y/XjBXtdwrD6BngKa0cwrrfzWxrMZey51OoeIiNqd1N8l+tdhXB/8Q+AM7RLgiKcu2AWc7ikADj1bnCr+CkxX6ytKfIvCMTG0PbkM+WBc/+08Of1xLBGVXizbXarrN4LByS9VI7rf68jGxdujl80O5IVxnY04qeM/mAP8qssr7Y7snhoV3cOsoeghqpbJc5xPhjWlDj9BRHRb794fHk8FgC1WWmq8F8OuXOcn8/QwpuustLeGvbeeAT5XhngTxlsXLOjSuXPnxv2A7LuJHBhXhLt8s8J8pe50AIBNVhHLTQkA2qq6WUqA7nlX4b5r/HVV81bczOyUzZe7BwaTsXqps8LhcHDpC1FUGJ83mfm0JjG+3B4wjwrCcaOzwIfuFHbHmvqTsxjAoQuJSlwIzFZfnuW+wdGeRLR9NBGV+d0N47dFUgw7q3l84/sFgo1GDG5jxD3tVGYSoYgOxk8AQZzgREQDcnGyokZ3U66G2YeI6FNO1pLqvsGsDIz+eUPsRynK2BkfCcVHIvGhSCgUCoXivMSJjW8aayL497HxYIdNzSOPA1Cy2WZv/q5vu6pVzdfppUcB48myYPwmzrRyte/pcFHExgMKc9EkF4ynqFSERNVU1qCQa1bnupD6QIb6c+8ATcjdOZV/CYc3x2ouLxqM6eIMPqqZw5T88mhiAY8KulcjALg9e8uaYj32AUB4eyMiGglsVktHS3+NHXWIqNGzxYnobajvgXo/58ka08y2ApWQ4PoEMGYbj+n9WW4CwHjTnSAKKYxrXuAkAb54kim5Dn2AT4ZyjjYLsu5+/kOnK7OGl6tFnAGRir4KMjw0MNGKWZ8XaEvgPTB2SamlwDEjbm7B+H0PM7EK4+vggvFFRxQYP8UuKkKqBna2BvBYrsK4l0U+bUp947P7NZ4dxnrNJUSFMf3M2gomIhqdxWMCVngqpAO/11KPWNb0Q04CgO1ERN0YR10u+3fYU4eIqBIR0XUZaiJ4vc0i60wvuvQCcfEjCiTZHBWrCzYqrx2bnc1yLkNua0x5I6x1cT8HDrUKVhFt0ceM6TOwdreV98FqVakfxuc/mAHs0lSzRiPWGAlO1Tnr3wMLNdGYEjMBmFVsFow/kv3uL/WUIdaf7SSi7jRw8VER+3meDLh46SslKq8sD4zVCd9ID4xjjvhhTIfC0Pq4+QFxXlt+ZqzX7hpC1PgIdwo4/B6AR9QDljWRnJdmAt82IqLueUhWS93KzpC8Hs7H5MgC50OdLSLTrKGkrrhhDLZG4Oobwer6XnwkEgnFmZP7kLMxgrtvLtqLhMCICxFN8twUVYZEyfEtfyCi7dPzEXHRfvhgXOl7AJN0K6FRBuO+BXBYDDzSJVNbIFt2BkzybHJgLDjodhj7wA7bXgdAWdl0ORUCuDk2RjnzspAfxvZnL4zrZAXAuMn+COZqXilTAO37jwYjAh3BKRHRenwccLQLgIw45YBlzVf7Rxt+MfXIxlbXDmg9HFZHokpf5GUqo9lmken2zH2gyCG14VcbrfnkEMgcVWNNiOJDcaTOkt19swwIXo4koknI0akaHGctMxCN4rPaN9xHvMHlp/lgfCeAJTrHOl4/GDdNZLBmMK56EDipSX/4CnBCpBaMF3rdk/2w78CfwNg97I1y9qOKBuN3gI2q7lFk1yZf53yIcFizMPAC45SWw+3abJuHMlBGcWHA3nblPgNwVnlzWtY0mJgOtCKq/EM2vrtHbXJbOpza9SsXo0AZ5R8Ii0Pu8HJixKIC8sj94GSXjxtnh9rI5Yi5+uauHJzSkaPMPq6H8T0qq61H6udyEPMJERHdkcHvuw54YXzfZsAZ+bwSpSR7DzOGB/sNMUtd0zCXvJYLrLNpQx3fGPPUp+2+zEX2qa8DkGu/bSvPBpIut9tb3FemlPjKcSo8MHaVl9f3rLTtNTi+vFeLcPiX4MugYyp3olcOgqME66j5cQ5aqi0/G8Bw571oWVMlG3ijOJWPx4En3LGM0YCF4lIvZyNV9cg3i1hZGmXaJYoEG9QmWfSRA2euR955A+BPbjEkLh3QFlVM5/laTpUvmLUx42m8zT1ieu5UlW3AyRG6vZ+iEGs8C2iXzfsAOFo1SFNmWERkm41CG8a9Acy1xxWquk9hO74OACZZiWhPAoedQM93wAGVT6Aa3DCOI+WTci0xR1h1iAaD3yXyw7hbajhbc5OfgH4fwj+DMf3EiUGHb8tyvXgsa2oBP5xHldbgS28izgycMm/dZb8CK1wvpgL52fTTan0kIomBFetvJnPyS0EKMoLHDordfXNWX8Y/AkjX+QY1svULeLcW6N9gsckZHsZX952qshA4od37xp60Bpw4jZk1/N3dMoCjrYI0Zb+F4KAS5bdgXPYwgK3W6lzLHVjoDErXAQBy+hAR0ZiVrsXoFsnAQOU5vRhONbdrAdq7Gv0ac6FNNDW4AAsaEgV4XMvD4bB3Rz1DHgEKRwRqiOggeH+UXcWqbgqGMd2fAoTHWLNty5rPkNeW3itAvg8dYoK+3vAkHHjB7V4VyLTiRFSj/eRjEZEzgSgeAv1Y7I1RqX3zWljbrM5aQEcLSyOAPhpVmcn6Bbwma3i755DrTt27JxqKo4UpPmXmQMZIomo7oU48VHkSkBSVpMhejL7kEIC0qV1LEFHbJUhUUoPKNp4XBpCxZ8++PXsKgUNq4OU5BvZfZt3ABrutRWsicyUnPmRmHHsuZXChYG/TOjH16z+6Fzhqxv+8MK622UU1q0jMj0BuoIaIDoLn6XdAv2w+sybbbE4BgOHWbmumNYJ154/Owhn/VrqF2FC587RTp84WnHjWu94wW2T6+PHjUyIiEckMSh2lpmArZOQTX5K20jfFPgYHs1caW59pn+APmRdoeNHejLKAdxXne0l41TvVKR04qUdxlDDFrWBODc41bXkIwIeBV9K2EJ5kKidRs8arCwHg4IH9+zN9vAOnnYVsZz3PkCdPATj0Wetq1apV67UbLmoN11Kk90oGgZmzE5nBbEaxA6KRL+WHT7b0HiQiopuBsG7j3NfB2bokriqPpDNv1G0qcGs6gEfd1hzB2Y3AV3H+b48GAIS3fR+wKnxFvr1DYc4XgWNxw2SBJk4bMgYAVdScBCCQboeIaBfwpfYJ/pD5i+CYWNn1fESH/jJj2Mder6YN/I7kaVH2m48SpvgMzMGEejG/6lBc9pFdwOnLXcfUyugLRyebUD32oueRfeGVdRaMf/dGsi6bluMkJLldrzh7ZT1e8ZItmXHE2vfviJVdETChPRTWRA7r7HDt8+CSl8BBjl7JunXrdvudOWeNDsVED5y29paxrfkAwJGOQcxojT+eP3/+fA1BZ98MERHBvlc1m1jP1Y7F/rzWvwHjUfoylmQ+prny58DBzhsR3cjsS7B2rGm/BvjUq1bENbV3S+PUiG42OhXAz4EX8i6ABM9VuAv8y147ZPmQIUOuCMg5uKjtAODatm3bBvhPzV49kpcPAAXJXpoj05+ICzQ15pUR4N3LP7rBdioDLvgB7RpI3zNa1rGXwOwjYyVqauShZOjrG4joLt9oXPfzz18P3GLlT6TNW2+99dZb2q3W3xSFf98lgXfeDeOQ7qy7UEunIvr0Bx1WV+JHbQgjdl8UGNfMBrZG26A8ymA8isHBr9qYLcCIQC7CLnlAincF5z/GUxH75ptvvhltyeEvScDdiwtr/Pxocv7k79sE5L42ZWasei3KAt+fWfMflOoJ0FY1xQfc+XOs3sKy5l5EWTOkAB/fkWLzuXBacLL9k7B2fvZIqQNAygM+a4roVvTyf2vNFci+7+98/xztm3HABD29d9ScoBrMuv0Lv9PBuOTOoCyEIhhHkf9baxqe0m7QFyjnaN88HCW+R9Fzgkr8yLoq7Nq7jz4ZPE4/nzvT/9IvgnEUKbJGL3/ZmlDo/9IMU4pgHEWKrNHLOWZNEWtQkfwDpAjGRfIPkCIYF8k/QFQYV/oUWPHppwODgtmlfxHZN2CANsVXK4NFIl89Fvi0dPxgrES+uljzJHX/Tbe3H1GsP4XBlhHQ7kJKj67QL9/RWF2awn9E+vfrp9vHjCo+91xv/TZIWqkJaJOHS4toEseI6JWInopvSmaU7IhzVxxXve8Os+J6nXfxtH6tjguNxeYzT/gzS1p069atW7eAPHQqM2jChGVnAOArf41zuSGZxjm/utMxRpk4jMZHWqMvTWftCt6HvFq7edMU2ao9JyXm3uk+oJnGNO7XL8qmRcFS7iswJwfyBjZ5b9seIJBw5U+seYqVwke3lP8+StJxsbniz3Iy5WIxt08NkJIrzzibjZ9bUzwHxtfn2MQBHsKG+hsT0u2sCS/7V+nGe438mEVf+9DTCcDSy+5/cS+wuZVHV+FrAPnbAHVFXu2cxqf1tY+0Rg/jVsdYW0/6opfyRJVEftl9IOhWlfvqaArz2/qzBElM204MZv7Vr6o8wtwDU1M8W7XD8KlTLwm05oFTzLptt+ohyta53UR0JZz0hmTr6q3pWcDuvnMWxreAmXnvdmb2rJO8DUWOf+rO137eUf30yDWutLJyK4HljxNRtT55yPUMHA8C2Htd+RsmZqLweysn29U5axFIGkhERGP5qI4SlW7klbrhuGl2RmXtObfxMben4r9VPddsZCR3vbq2V2HKdePkhD/7omLbQ8b44K8evM3hwQk4XbXXNiSJSGRFoDVPMeuK2GgIAN0elrRXduqSIC5OF+0epzGrlXfGvwfjV+IfIKK733hj4BtvvDFJIpH5iicTF4qPGLxf8SGLh9GU8yo2mCQiW5fJb+8bYmxWqsaNwXz02arnN7tpLM66VldSjH7OWPzjgTPwVs8oMEb20fYqju8BFptJSt0ZbnqEW88Ae+sTEXXeBKw3ceGm5Dir39u8CyI6agNqyXynTjddlgdWxRARdWHu6zrguVUl6s/K4uSEztdU9jSsOXDpDdWqVWs94DcB/DDuFM/M3HcK81yP5t4jGQLsu6zlfuBrt+qO4UT3JYus7NfrVZEAa6jSYuZZmrKhCxKjwLj6LvlWlwT3uvhet7Z8Dey1XcpIhIji3OkScSHXil2LKUaaakTSBwwYUFnRvAIUZGRkFMCmYlNzFX1sQ85vjLehZje8j8gF4xliVwZE4KqAM2H8NRF1AHBcrQ1++Ad1qMbcBU7HllmNBNs7GQXcop5zArDXfHt1DAPfK51jy2c5WtaELqxl6KAae7V8qfSQiPY2dfUOiW5rYr9g8MGLiQZ7aWefybD6VYAlHmWdECLADx2o8WEvyWXltYCsuOMSoqHIH+pS3Vkon8yRPaPiGlGxJZHVfmuIvmBmnd/wLoB4XcL1QMnXzfAaZmOtRkWNBZhvf7JgrCe4mpbhQkZqyg479b5ipg+NCozj9Sc9aH77zL79+/btOwrgzDVEKoxbZeJX61mbx2fUKIEC4xI/wV0CuAgAjm7ctDENALC83eX2aFcVcEhGbwsjV0mvu6sAeN76cHcY4U+r2J3jfEkydDlzMUkYqFERfcVaGDc6JB/qksrLTmRupR5wWTMWJmVChwxPROM5Kdy1a9eZlF1rX33tTLrHd6q/jhk8oSxRF2Y3AWaPtcDc16sTUeW12OpSVd8nIoWfVCAiekgyr/RZQ1RsIfMhXdzlJ+BXbclpjmi77i3gQ42q3LaI/OGMUaY1IS/mnAYCAAULFixYkGyCr9Dmjmv8K3asnDt37tx5c9+65JI1wO9q1mEojkKmxLtPWrKzIa2IiKj6RGC0YY0F42JvscNi9Wge/+RkLb0MYAVWly1BRFR8McCfOz+5CMCwNkR0z3cAgJ9z7WeuKqCMW8NdU+cPgA2OX9cpB7i6GPlu1SbouPZoEA7pHFS6hQ/pVPSwSGWdri2zKziiWNNqbEHydw9XIqJWYw+6c92v3CcbGxBd1JCose89sHYPM39zdQWih/fwSdfgWXczkGYUdxcb6YZxtY0iciyOiIja75DpVT3WEBG9znxW5zaEGIXaWMRnkqfruZgCPaFWE0iq4sA61sQZgIuLs9msDRm09oH27a8lImrRvn37V3MBJXG82pW1nZfFz8qQ5pV4X/WbI7enWsxrNoxrMP/u4Op1ZqduPx1IadvoXvPTjRlAuu0rPJALHDKuvGyTfQAAFcZKLVQDL4zVOqnOmUauuudWvRPRMR9RG4E2zHsLc3edrorIKzpdzGreqyLDsabxN0g2nt3SD2GJq3js1m2y2iwpb5kUWe1CwYUzwZyc0JKIKMSc0MFRFb/seSDNTJxtfTTN5agMFZFvzfqEYbKnkscaIiIC88mgiyj5SLHYBOh9g2qnZKxOd6/I9zrdcMgw5WNQZniUZDZqewa6qspOqUjWLRGE9EUXdP4GWJXPNoxfdO35MFeFsQC7lHlLHyjsg90AvGN9aJEAKCznTyND8cHKfo3jzijwtafc72lgGPk6pz6gXQIBa4maSu9gLVRLrZZBOh3NZHynfLStueoIp5ukBvV/TXdVV8RmYIuJ4ouP44A7cjCTwTzdspcXKDCuBMAu/ngUe5WimuKDRTK6mu/vUvsstgt33zwBZrc7bbVuWewRIPdezSWWfk9ytH2aimwdFdFDYWxRB/EAGMdrAUdE16cDyYGVHtekIU9XjOatp1el4veARUNhwbhSGtDC+co7ahLpcUCtfHxAC2MqNRTKNOkD987WrQCHmeIrD4w7Gg6IH8ZaPO7l1RraO6Kt0Ad275H9sTrdC3CRmNnWrGG2EDiTN1ZSvhLzC9LNwq2Lh+CwmwFgLRCBSbYzMgKo+cWVReR3a51tsexTYNxeJM+qmCzxnVjzC3ffzI1gjabGmZZHofCISZL5Ol0XwUqNqswfgIsV2Q/jKMVKRDQPOBWI4nIzgV26VtFcigcADDW7wIJxFzeLzxvqaDwALifXBeMeLhhTs1MuGOeoUYE6h5B2vqNzEyaU3waQP8T1g34lr6meFJe2BL9viYjoapGAKj1DYnZjlTLXsKw5inQLxW2BTmqLoQ61ySwkeXzVNcyYb7z1Km90c1G33gmsMIbuEu1XpB+62FFVSJBsm2HrAcmzXh6uvrkqC6x7q9yWHbCrlCVv5Im3Vt+W4wLdqmBzkRWuA34Y6ytHiYq/lOvZhsOSUnOBQl32QFBlrSV35gA/Ww+yBePHmZ91vlJilQrjNpnAQXt6X/wh1XGIuGFMh10wdo24JZcDVRTdbtcLeH0QjKmtPhhfcYuejD4ajGMO6GFMDcHKrh6mNY1Tki2apPrrPAt4F020wogPZHsn+T3Tmb8x8Nnis2NI7qo8ITfDGpxLNICbzrG3iP3u65noEJtafXMpEdEbjKNBpdpERL8Ah3VrQzGbZEdgpSYR1cyMbNWoSm70Lhf4MBt1MH4XOHJ5oOYF6Mtj46KMxaXWAEfsh9+C8XIVt1T7qOvjtwBskPeCOhoPiQLju9wwLv4z1tura+U3qLsyEJXYhN8oAMbevWIUGR2035Ih30SBMa0X7Y56ROBVzgfTmn681QLgV3xSM8uv6QpHERFVPM6cOpiIqOJdKczpahF865PAJ5cQEb0xHtj3vuI007fyVaz55/37xeFFs/rmSiKqdYiVGK5HolE/PyWize56Htppw3XAKveykfdORZ3ftZiHxOBQ9bX5IgkasqJoKKa1InyzY40B44sy+XdlfbI581FlIvMEgNxZxYmIivc6CyDHPoHbN6Z2qQqMq8JyDImI6HoXaedHwF6lhqtj4BSP2gLaNefGKVoYP8Zh7eIIrZdVWh2BjzkenGlNPB80H/u7Vh/Unfc5yfJkhvVjTm1NZK7izVWRSr0B3E5U95kjAsD9tq3f0HpVtiyQ+c5dUfvmVdYTkQ8CEptqdDRXsrT7Au1BVscWwao7vET8vjsV0r/+6fqTOKkJVb8BHLg4WBVtfC//gKBQ+T0Txp+6t1Grus/18QkAwKHZDzww5xAApDsF1lVXAuPueLkhEVHDZjedBBwSiEqnscqJTTVLwO9KNkb9vcDeryy/InaiMen0dk7pTeivuRKiNfyVDuOHIlu0zdZHosA4oj5rXhh3iueRmj7vcdoVjiIi6n8G6R2ocb8QIjj+kQvF9CaApIULNwOR3ITPNRkgl+TIfGWPM7VvJuhh3DYN0HDb1aSqm2S+bnHv4kLkrq8UrLvPy//ru1NRPON2J1Gg2fytdQbgXaG3JFpN9YsQ/K7+ugHjfsxpzzjpmc+muZKxqnc/41pXVFfxehgQf++9994zUrUeV+w/hROdTKd60BFsdrHrNEkDsPvrjh07duzY8RdgUUkK8Li6ij6vYhhEw2JHryBVy5lxW36OLi+MaLM6ETOt6Y/DTxMRPYqIhqelxLxsdPcuDs5jLkg8msLMmH+FR6dsjKV/TEdIsgo5pW8qZTOzZpVuPJCkYSkqVWaeHNS+3caKRHQ5hZPktAfgnjsVZTC+Otk14VClwmnB4lbBumgojlkF2R2rWmPAuMZOZs48lfD226G3h506BQ6/42o3PV1B8SFlMkjFB7sQnv6UmgT/PoAxnTt37vzjesZeT0c0GZetNDQYQH0wrn86p7suQb7eNqzWqDqDNyzVZKkXS5dWmmZE/QJgXOE4FyQeTUzM2vScZh17SNAAuDrXzHs9+L4vEedra1zoe76Wb+0R5LkiVErffMTMmsTOlgm+LCNH6ojvneHIKuCYbqSehHc8Rzx3Sj8YX5UOaNi/y4yFoE6wLqpjvBRgF+WKNcWr95Nro7Ik70jf+VSW2fE5P7tD/C0GqTB2cyuVcTKS1o/wB90bfrrN1P78puGK+zvjw4h+QfoupOvG3KnM+3U0p9ukk0ZD1DjFD2NqvY0ZvHWVLsJx/1mZ4D/a4Qgzpyf07xzkh8zZunXr8uuuu05PVHVFmmwqqe7QqfTNWmbWpFPMAfB5sIqok4i+7GOVfkfIhkfyvAlV7julZ6O4KgtI1uw28jYgYzQpo9H4LZ7K8a4n2Kt4JZq9840B4+yx1zULWOdpVwgA+Olmr6JYpZYL1wI5nRYuvP1xT0FSySoDl3+CWcOGPRDs/5V7dNiwYcPeqWINV34YV9x0vKH3mC1gHcRr7jgUVI5CRERd5CXtGemTgw7qbGs6dp6e+KSWMutKwcig7M/Gffv27RBw/K/J/SLyh3qPlb6pvo11NMU9oSfwpstOfxu0B7Mhb2mpj6lnks8r8ME4LrBlyVmAa6tERUp/D8zWJBvERVwc7y6pfdi3YPIf4qmo9fDD0ahj/rKcYzUFf8maeqcF9/z51/6u9JL8PW4O3/9d37Rb4Zuiua3RurGtAPwePG8s9T50NNxmzrHmnK/DR1RURLcSRf6aNe9A5mnmVP+OlLzeE+A9l/tGB7naCXCvwStyFcAz9afXOxUrgc0eV6QIxlHkr1nzAVZEoWL9z8k53DdxOhjH5iD7RU25TZ0dSNeePj5KGPrSIcu9CUxFMI4iRdboxesbxwV/7ZZXOv4XjCmCcTQpskYv55g1RaxBRfIPkCIYF8k/QIpgXCT/APmrMC730iL5cGhgmK/k8xGJxL+kS1So1a1bDxnSLaA6pkMErr0iPdIFC7X1HXu/0RJcUcWftNs+0r49msXPvyrln0BXXdXFexI5rmvX42Akkvf3NzGJKreuP6FdGiqxVLw8BJYU+7gwpN1z5lGRtZW1vxgX+qu2/bfFctXPb2RJABVHlb7HAUCSgnD1grGgnD3d37B4lcsX/W6oM3p4sgdifkhHwndHuBXF2BB3TRymsXZd6eEsfT49VUNecIo2Ee0VHWlWf8CbaBQwjWm8AszB2YxNDoaB4F3eLpucEQYQyAz0YH/Z/txzgfQn5Z6+nS57+ne5TGPNNldBiUuicbg9LCK6+ucWuUBDbZpHnGPDOTbFs2Bc9SO2pOeN3oSsUj8COLuPBSt9y+OlxhZYeROn+ngXbJ9V8y0Gu25/zA/M719M1/GJNdty1wwL2KX0RdbmTAwS0W2JTlQN0C5E75UfNZp+zAWeHXj9t+qBdNbB+JM0I8UpQNX9lNUFft2buQCAnQEP3iN/4Oy6IwC2dwq05skcZh3jxM3Qw/g5PYyrvwjs1b/nQk4w938G48vvfX5QnQti3QctGJ/voJiZDzzlBvIAAKuvoA8FmOdNdLkVwKJZc4zbNMtVWFP82QwASD7Y55pr3k0EXGmOP3DBGCKqbyYjHfFTinRhbc5En4IoMC67GhN1ui+lQMMa1I+Z2Z1s4btV96UzgznIZ9kTBn49BYSf8WpajrfSMjMv8ahKNftawGcyAUzynfFuJwHwlyBrii9h/l03cH4D4EaNrr/IT5pUhiob4Mtlc0QZjB1r4kK6HTwzIyPbvtGYKG7gG97c6IGDBg0aNOhr+Vkk8t2gQYOUe/KdwZi1/r1vn+vdu3fv3r2fc2VkvXgGIoXhg7sKCgoKZlswtWDciN2ySsVxqUVA6q1E9G7nk0h178lZLg34qTid12HUKQBYpvZPFQDAISOpqhmgEGNRh3QeQ0RUn7Gpb99fzeIhN4wROawjBtkTBcbUSQ/jriIaR6XfwjeY97pWnDzAuXJhtpECOM3XuMmHguO3l78f/o1T2yZBgFP3rQc2eZt1AwTf0Z0ATniH40WFAI4fdkZxL4wbsZ8UzpZvgCXlg1UXnpXCdrp2P8HDY6eKuj5sWGNB2JKQ+vUMIA85GRl5ERRkZGQcdCZP9vvbkkKH4E9JxTb/n7PNbnhFmrgk31zNVmE86kFDEpmZ1WrudrBf0zfAg+PHgZ+M57di/wJAZc2pOhNA1sYWRFSt9aCDgFoDapVZVN4YeYSIfjM+ekfjFF2S9xJZ72dMtsTk7guSriIB7KxERK0r1P6D3S8MlzWlB2RFCn4/gQgw3dv2wlmQk4OJvvDD+J0TgGBii0rrfdQ4Ze86Biy8uhzdCQBuDpQa3dKAghGXfhIVxjk6Ru2L9wMfa3QjRbQT0VePQjcxJIpTsxwiEbJQrPJfqXkQNodbxPhf2hhL40DVljxLl+uDseOLXZEpXjEIGSwYb2Vnd9oWtzLzQScxpeRiYJOZ/Fd6NLBG8Y+L/4Ld9lP/YwGQ73iziwFgIxF9MGIpMOIn4KCT7Qo260srzehLRBWOcxCMk3V1Wq1Flmo3FewE+PaxNKXiXtmhZWif6aapcFlTdiLz8Vcr7gkYjctefwTYfSUZtbcuGJe/+TgAeagMPQyc9tT/t4I5q7zT7zePALCjF1HpBYC5n6EXxiOYde//iw4C0FUbJIhoNxv+yCaF9Is7ccIcjUOh+Dj7WMidPP/43LkrAWDHvLlz56YBTgrFA2u+qBcTExNzzbgr+o0bN26XevnXvP+RFsa/icgrH026vHbt2rVfnXRARMwBy4KxMOfat77YzTnMaXZedkUojFaXA0h3BsKWSs00Ufc8NblUABxqcXGfkwz8Uqvke+pp3CXIRLSCV5DnVpU7rq3uoNYiog0adfJQg6jyTJSbOJCRpaaVK9aUnsj8a2zFOceW+GBcZgxwxvBiPvTC+H4AOPt1SaL18O7g/voBy5XwwTi2Tx6wrSYRUenzv4PBHuGB8U1g1qSj081A4V2azOJuImt1HvXlp3BGm1fvzikOnOKFvEfPv/LKK6+sTUTUrLsCY6rgivPWWee6/GL167f84Ycfflj4ww8/rHN1TeXY2Fhn8Grth3GEWe3/h5nZ5nGtCJyyS21LvnTcrJsjIqKW7pnESGCN3X0RAO+8tw0AfqtNFB3GXfhYRW/nfCXalPnWonVyiS7K0sO48m59YXR9hotqVbFmJ/P886nlsdbdwewijys5FjhpxC6aHAVyVBgMSgI4uTUR0XrAvX1489PADOMd54NxXwA7LjD+LjM3EMa1NoDH6OD4WxQOtzkiA3S62AToc+fdmfFBMI6LxrhyFXQJbRW+B7A5WPcY4A+EmnKBiMh2wxoHxtOUKMP1OxUWoRCg0qRfmqQQZHhg3B5KuboAKGQAR68rSX8OY+7r7Zx++uqOYqNE5mh0RCnacluijZEMHSuwHsZTwB+UILqnFc0w/nJkPHDKdKRugudW7YYVgWt+wgPjSw4DJ8yu8sL47kJgm4liGgkktnJbQ0Q0l8G6Cq6XsvVobCdyshgRVQ5wyW7ahvSZOv56z0gbBFg93xrRTfFe99+S8+cBCAczWcelI2D2a0jFmSKy1fAQFRizGti4eZsD499EXGRsXcWhsnnIDeOSv3hgDAD5BjOCG8YRj8PQBejr7ZzaehhTi7NyXKs8Lae0KcAbRW7VqCr+ClaZNWxreiEy2vhrIiIFrsKkKSLymfHnJcckEnk+WHefyGnX3fgasA98Bah1oM2zgR0W9+PzhVZtnatvSq1B5GfNZVQ/BSTrmI8PiUGo2SmgJusQ8K7/qCmRP4VxtMG4+Qkg1RtvJCKiq+cAOkqCSrmANnB4t4jIq6Y1jm/sgjFNdGD8PtzM7l0BG8aXe3SzkW0ba8L4mBndiT4ab+fMO3ydA/aHUy35TmSMTvcrREOMQDRfD2Ma6X64LGtapPJ+Y3DsmMoFr6otLjsD3GkMbK1rbwSOK8txzzKAe4mIqMkRT2S4vYiYVRGlnzsjbipCoKCXqXt7J5DV1GUNERHdwXxSE6aotA7ADs0VXlUQpap0nfu165I4z0jrR2xclMG4ZNskdaciRa5YCyD/++BRpxkApAUPxreclkhku+lBWDB+ygPjrgkuGLti+iqMHwGSlVKrMusUHBgw/rSVeSWjosI4kRPI1zkjoxDD3iiSV1mj+wD6QvZ22dJdp/vIbZVlzRfMsURE1DGF+XdXi8nACmuq2eCgK6bW4hRw/D0jVP444KLGqr0SmGiGEtoAUKkIaR2w2/xzNICCO93WEBGVXMF8kIKlbSHAmsrIYvNEvtPsF3JepzDCvwfriOI91RiBjJoh7zFLHofjernksjQAbuZVRy7aBzjsuW5pvkpECrtY1ihx40RnkfWOk+yCseshVWHcEsD7TqLF3ep8fBEAnLQ2w2kGD4w3qPkZj2bxO+SHMbMWxhdvF9GtcrSMAmNaI2nexXZLgmFcZRtPJiKi61OZJ7t8x2fDWGa9vhtsBuYrj3QKgJ/Mv7fARXJDvYFcYyApPeIwkKgQ5tF7eRZr5+WjC4FU+1YpfXMzu+ioXXJDoX5QbSgiOtKs8wEs1ej8pc9BVIQhXesrMkRONAtQXDYbQG5/DaniDhGR3wOfuuanRcTZ5NCCcbE7mDnH8gdK9mLmZOs7LQF1davqEqCX9eH8xYCy79ck6+4QGcRYWGB+iPkRQIazQ5KbJ9X66OmcXsw3aGlMGxTIbM1CVdmtonurEvUT0RUytwQnKw63ac2TvL0GEdFzWWz+ZcsbDnfTGweBjSo/mgCnTLfujXycdqFnvkVvd9t8APtcTqMA8dWIqOyk4wBy7XHV6ZvatIJ5ke763gUSW2p0IZFpuhL/W6DntqR4b4FnABWhtjGdBjAq4HizrQDWXh+gITLTTY4HTjkv+lIiImNibWus1KArDjBz9jXXxBK1vOZJZt5hrwNXBLDjPuvTlT8Ch50npFwqkLuwrRE5GhjGaeXHegAwVyCf/AlwEWO5yNLu6hfZ05DI1zn1mZfr97/70p5A+WR1RAtV6iiiYbEhmmeTwzvWVNyJ6URUehoi2OPp1DeA5calDygEvnctZYsF8fq/FHqTlQQF3Ymo7jMZAmCoR4dpRJe88jMAfB+0g2utW/KZe1GwtE3TD6pX5Ylo16FjAXyr0cX5Bl/vAR/OHWl9gpFwVUCU/+ozANbqMmd7CoCCwNynCptFRAqcNS6nFu+G7czMvOjhhxOZmdc674DiAw4B6d90LUN0eddv0+GmgGyfCgD7P7vpppveOonTKjFWFQArr336jZcOH84BgLeUoPcjWbzffF3e9WoW7zGmUJ7OqbgFEC03ZlyqnI4NVvUWHccHUd29+h13+wNKlpthTdmVvKAkNXiWGRu8k+ZnwsDyu5pePW9eBHjOTRwcAY7f1LTp4Hl7I8B8ry69YYMPFm4GIki8w51SJsCS6z85DgCYpCzF2X1T7pKRzAs0MeOy8wEt2WZIZJMuWZrejNLQH4P461SEV50WJLQKUDSaEw3FNE4ABJLKlf9KRGTnvcqvOyWlN+9Qc4Nc6wc9vweAQ/sPnAGAQ1e6Yo7tVX43F8t56e/gEpc3t4KZ16557dlha3OZ2Vzm8PbFaI6y/x3dI7JZw8grUdLxl8gO3b3sxzzf+WRa8w7z8mXHmflzf6LSmELz2iKZz1dyqyazdd0RLPTolPyXkV6fMWKpwkmPqk6T0zeNkph1BccXAvhOQ8XW5rTIy8EqooEJwCuahgFMVJ47pfWMy9+fBPk10MnZExXFXTIE2BeYjPCYREQK1ToMtTL6hp69DQif7dmzp/tVXvIlI20ewPcvVvactdU366ye/86tKX3xHgXF413vlbsSMm3WuH0WVH38xszZ2ugwldomkn5voOoM/tBMyInaSaSLRuViIrSsKbvaoLZ7JchN2xsGAE5/3u/hTU43cuVzFzzv9eHXm12S9pWfitCGuDtrVOmb4cwHdZTxLwLZurTAN0TkCY2O5kC/f41vkdlf4K+BcYVJAL4JzH659Szwu3ZTuLhUiD+zlYiI2qdKRNxvVHeBf/EbbrjhhhtuCCJyLnvFkEwUDBlyRZBJxS8bBgBrB/jCf5e+Yd2U1x/23ss7fzVgvKmvQ37v6a7YBaxNVSGip0Q0G6q9pt+olOrv18L46YLdSmjEsqbVQgYfah5c7zUKOPW6ht31mddff/311wPiuw/mAMjfOjEoJPr8NgAJvy9q4elpp28uW5R3i7eVJRfiRy0zRKOlslibE98zeBZGRERxfox6F0M0JD9VfgcQWHBzXTqQr90xlmYC4my07pK9IiLiit8V8VS4JVHNN/g/tKZrr169HtXoYp588smAApP/ed+4xGVNlA2T5pn5eT55nIHF+vOfBSKngh/IpxILI5LQw2VNEYz1UmSNXlzWRNm9bp6/kMCUcRxeEKwhIjoL6Pl7L3rrLTfrbxGMo0iRNXrxjsa67/XSwph661w7IqLxwG8a8rcga4pgrJcia/RyjllTRLdSJP8AKYJxkfwDpAjGRfIPkCIYF8k/QP4yjBuPBiKhwFWXx3ciElz8FokPhfSnrPjEhx9+GKqlLQ2dhigbMeqldXpEe8oukqVdHYjWTpW2/g3C/k0ZE8FtGlXFPtOmvfAvnDIUdQrmT/WxZUggRRcREVXbhbf+BVP+K6JeT6f4CIDfgpLDuh5gZnCSP+Oo2b48ZmQ+628Tp2XiICKq8uROYy36p9fthXV37+4L3hW65utrJDLxbu1i8whAC8em+ciM0s6doRF0r0sNGHMG9/mPExHRncFcbH8m5b/3FhFYUqzxfhEJfxxsTbQU3ygZZxRl5yTqdZyTdK1aMdtLGedYpMKGcfmJ+/YZWQ7I3LfPk6/SeJiV/pC7xnOGuocMTUbPiy/2LDe7OWW8P/0Wg5P35IKZD8VZ5vwFGF981FjdnqrL7xmhz9QiytAPNiPg4Rrx21xqwCIA2BLUvET73unYqalzrdxx4taDoWBd7E0MzggaO2qONjhFpgZaExdlH0+KthM5kbZhlePM2hXiyZxls5f8j2Cs+VkHxh8xc+6e21YdYjDzYTUXs1y7I4zM355MSGNmfsGdzPI6W7tCgr8f7crJjgrjOkcYcy6o3uyyB5j5bKMgK4NhvBXIHjEiXbNUT0QDC7FYk09P9Jm+LmoEcNpVLuq1uc5rPxpP0MmA1q1mGck+AU9QyUYTE0RE8oJKAM4bk8YM5uV+1dPZIjueeSUsPwdZo8/HoWjDrXEqHcanMQ/V0dg0OaQQYxnW3Pt0WEQkIiI7n37alVnUniUS+WiQIZ6t2Nv0O2xyCUUAzOvfT5sh5DM8GE02jAcdYg49SkSVX5g1gxmHlF/uwsz4johaT2H2EETF7mA+2JRgQnm0qlNeeiHfXn3zmO8sQ0RUssYOxr5Y00z1K8EwnoyPyhOV/h2ndBd7EtrXPn0G6Erxbsv0tPN01nUHrTSnIeSV2944Cex7//08/2Bfu+sGkbRfX4sXCfnaxfaJN7ou1ZeXU3OACI+qRJQjqQHWGJkMmkuJtq5GcVGU4EM6SBWfxex48JEIEcVsdyXipq8b6iRWsqopzP9uwABnlD/j7Idr/Cdpm51R+S0m9a7wqAVxAEqtEUUiwSOjBeOWp5htqoDbmcF77O+0OQOYtFCtTgCRzeqFrmHmD6mZBeP5aoFdlFLZ4gPg3PDmSRGz+szdv4MDYRz7QhUioi/hdW9siQbjPp6caE871wjvtua1sABIXbQoyyppseW25RlmjvdZL4wr3bVdcneEahN1k6xHvL94+xbrRZbqTfSs8J1IQkciopxgp+Jf9imiuNRvR/QMHy2ZzziMaJEIET0Nr5y0KQXX+HRZNo5Xr1qVfPDw+lWrVq3OPXiQwcyHLB2YeddZG+LMzM8qv+oV87gJ4z5KCSmVe+1CcJ7NDtKPrdGY6MojDP5deS3vY55XjkaDOelUxnDGOOV1bvxKKBTyhytqMTM3tz51Bx+rb3eOLU8wP6nr1PO+1c+aT3qdXEXamjSrQTICKFBLil3WvF6A7U+1anUx0X64XZYSLxYAkpsVBOM2ySLbjdjIavGWMpR6HcycNu/WCaeUUcOQ9gdFxpQlImoflsd81pAxGMdpriSaTxHSD+JVUqBP757LrBQpRyJEVHwDvpy245tp06ZNm5ZpYNW+YWU/GTcutuM4Qw4AAN5UznbNxY3LEhHdefHFnTvvV9B3Jp2ZC9PS0tLOpKWlpZ1xwdgPZPOoA2MXjcApZjvMc5iZGckG6C7+xlUM2iGNeRTR9fEvvHBZy7srLwc7m4DHRSKRkOUfewaHWszMjos53KTVc3fw+af4iK83TWmMqDDO0enaAht0cYzLjwPqHuaKNXeuyscWoy7uglNuGDcZBwC/d/saT9Jtbqei+CNZsrJ2OSKiiushnsBZs2kM5hnXE1E8e/yUHmE5ao4j82VWcY81pnVaOMZFi1NEcUUe4yhFM+BDymbUxklatS9GjQxf+qr295xUYeySKrW/BcK6rHz6Sh1EY4cMGeKkyzbwwJi8SDYOOTB2dbL6Gcz7poMnGv5CSzA7CXYvwj38RAC7XSjg9yyZxszsVJjWMvn9PN86wbmB5S9EtBVoqlH9GYxFSxowSQfjF/KBCWbC1SpMUCeyzY8BwLoyVPWxYuUXIO1ORfe2yG+ml/WYyETX3Pi8PslAZKpxEbMPu0N9T+TJYpP96vYs6eqxhoiM3o3TXEcoaiROrxvK0CVB04vgvcrHgGfhFWhhTHdlKNyvXukPoG+wquYS5jO3a9o5uLJg/CL2ub7R4QxUGHemvmnWGAzmBPt7yez2X0PKqG7COD7OGJhd4wPY1bAW84byFABjeEYwS5pnYXdtHfXprVFgXCnxX4Dxl4XAsyZ2LzwBlZfmksPAia2LjbKXMcBWR1XsPcjhWOPvuidlk5uPahAzb7/QCBoOwkKXrn6mTDSJEqoeEXO+4a9+04648VF8iijojz2uh/GVGWC1oNwP4xtzAJwOXlw6fwX0MG51hvlohWDdY8zs4+93bPCPxh7nbJ8zGkfcBFcR9dmBB8aP5ykUhpGI02eeXh8BQA0xjTe4kTyd8yIiGkq9BwHg1MDggsS4Qj2M6U2J6NhWNDCuOF5ku8Wp1vioTFa+ctE+hYutRYIruXaQyFEzGlxqgxxx36i6h5g/bmj9Ddf7tpzYNBstNsv8Ei5rTIni4UYlU4s2GC8DtNQHLyLyjfrZ9wtlFwFI10yt7wWQqWFOrHkMzL9pfjaBeZNu1Al0Ktww7pTJ7tHYkc3MTvzQC2MaqnxWoeuZV09ldj3cLY3zeDqnj3Y0jvl8+vStwK6bA7WTosEYotv3hXoUBsC4WG9gi/W8lPwFOWpn7BBJsG5P6zMiihvXXyTLrFGosEjSY12/1Gwas+0Pv87xlVTlx5Jn8qzVzJaTF7mssSQaHKMMuHFRdA2OB6+aEhGVW+S5FT4YjwMAHaveTCC/d7Cq9HsMzrkjUFdqWiHzi5pzBk/x3BEfj2+s3rnRKox/8/jGdLtulcw9RHicir8NYyKiMg8dxOEmQSQ4kxB+RdeqM4z4VaDk4mtl/mdY8xCw2RoQan6ALNWleDAXsKhkS4xHbn/F/V0lMJ264qPEO70bBmyuZv8d7yIxaJRhsswVb3xIkuyqU1/1W5zmIv7VwfhdJezlldnMua65iOcnynxeCCBX4+bdkoJcTe0vDWMGBzMulx/DnBvSrcYEwfhFuADTQZ2quWFcGcwFtiPfh5ldC9cva59o13C8wwPjh3Uw1jOVEtEFh4CgrbG6ukdVl9SHHNVVx1MucINqMRHRJMenanIYUF0K6g3+zpzDNZkFDFZ19y63mDvfEfnZHaN7PI9DJooH7c9LctN/rpKkikRE9SeLJDlvYm/1WzTPWKeLi9IsJpW5h065j9ldau65U+8BQJ5mE4cmKcjVDUdjAUQ+bxisY3YR9XoMUOIGwQG38t+DHYYQF4wrbwSz8+54PI9ZTW6ofFibWeLy5royuzj/pwfCuGE2tLutEBFRnV+Cl6RzkaLbUKBmRhRGoYNAN+eTYY3gmDGBazAoAVjsegSes2vQW4/3crFZUur9PFnuGaZ2Zqwy7tztiw+B/3DdxetZBhMR3bwkkvd1EGsQRfeMo+iiuRRUi5l126I8kukdndy/0Xg3AGiekCbTAR3959gCZn4umAHpWwCvBmo8IHZg/Hge88X2hOkjxkJnfw91TldnC9wzvn3MPNV53fRhdWiJU5egXd1b/oM0INtm2m0EgwbPewO2/AmMKRRMHukeVd3yiSBLp3sA6g8a1hxEzg1EVG/wYUHWU+6B/IwN43HAJ4GseRV+EPnZ+7I1IxOdx6QxI+Qei/pIbmUq23XoSSl0LZsrfeML/KgSxW+IuvDnnXWr8gfzYPcR953aBaBgSfDurRXWAwnBRB6lh4H5pIb8/y0wJ+ieKw2MaSgzW3kUgw655hyPZDGnHU18pUuXLu8lMSPhWoWzYxIAnOpjpNRU7uNOnI2PRCLxIVM8XV97A2PlOy1LUtmWLZ/cybyiDPlh/AbytZSCREQtjwePxttckS+3dBfk6rjYHwAKnQmgYc37wOHNmzcfAWSFN1T9KQr6lCCi1gsjSAwci+kpkXhfotJvGNfHShvw6qZJNnVaJSKH3QtqSt/ER4FjNM84yooJ0U5m1gx+12XxXk9erHqiYj1yAe2A0xXQEJjVfI/BOZqNtBvvBSdrVg18KxE2jJsZGwm+2OcdROCZc6xwJXKsdsWrynVkZuYz+/bt27/vMCOpjaL0LBy6X2g35YKZly9bw8zgDXFGA7e9b4D1e0MT0UKgZtDxB/S9SiVE161EDwD4UDGfiKj2TjMnYMvjlbzffw7Ap0OHDj0KORTE30vUvlCs9D1Fks1sASTddaFXt1eQA5GDt3pyl9XBJzpU4zSqaCsm1AXMX2jSpV9jHuX7GefvZwDgkIbfv2UysCV4b981zNj0YKCKmh9isGbPZP96mpOoedlCC6iHFy50ZxvWe/mYDeNkb0yl/K178qz8FuYJKoopLt6FYs97sNlM+9HYOKKiaaD7K2+4l44UObFo5MiRJ4AfA8tXSi5AWDs53KqHcckFfhhT7dH7gH2jHwuYhpeyII5ng1NDK/whMsSHVBrHzHxwz5Db2vibZItI/v6nfI+M0zfeF5vna9EQHtLp6BXwKg0zVt39zN5HUfmV27MAJAR7FFRjCqDh9m18GNjku0xTDjFjQrlgnX9VWOGpOO/9PRkM5qSARd6KfX10a4o8FjJhnBQ8Hw2FQoEBouI1rho+fPjw4cO7WBjw3oJS0G2b9KbBnLpAE+LpqNsygog6Zu7Wzf+oYwCMicqUL6+pNbl8CgCkLQ6ivSMiqpkmIv79WVp+yjPYj24iInp66tT3gjLXnb75k9y2OJ0qFC0HucP6pFYa1XxmH7m3Y03JpQC8rrMt2wAg0DFsfICZP9I0q5zOPF1zb/0o9tCt3PMC+FPt5i//BfkbNQW17p5wfMxDmjXM/4z8FWtK9+rVq5duk3GimGMi47Xbov6r1sTFh/4jp/w3xLamys+Auo+UR7YBKAxc3HgoNwqMh3qX1dy/rU2bPzfkHKvw+vetaSjykW6Xgr8p52jfNDwBYJKGGJloG8ChYNXA3IIEXebXg3n8lfaV6febimAcRYqs0YtjzdWfYa6WbpaeOXagu073kDabnOgM87i/YU0RjPVSZI1e/q+t+Yy5/V//duS8c6pziqRI/iUpYg0qkn+CnFOj8f+/Xpx/T4qs0UsRMWyR/I+lp0Sj6/6L4oVxXEFc1O/X+rDVv/ArXQD018Z4r5aDwZl6RfL/kNy9LaIjoiP6TL+T2QM6BRHRaxnBtR+X9ohgu7pO6345lDsFbW0EEVHzndps/LvfD1hZNaRDIjMv18G42357m5Nz7FVVZI1WfNY03AItnyJ1llOxGlXxY1kx2l+pmSbBhcMTGHzUWVXxBNwqLkA0GMf0zcV+zY/eL+ihaRa7Npf54DXBeSfFnt8He9vpc/xW/Q+k0c6dQcRgFSrGjBERkc/8G2TUHz1qVMHiUYH7iZe4aqRBNxmw526tbqMiEpGBvpq5Upd0aHk0sl8k7ORbevumqiB/pa5q7qbs41o2ssmiSQ4iIpohe4Ib/sxQ80I9MO4DnNTWzRP1Zd7YKVjVtUAOBW4pSY1n7gHnvBOsJHocQGa/yqY55wBwHHEDp0ePHj16tPq754jTrVNFlXsft0aa+5g7+a25JguCwhMnThRggsctbDc/y6Dbec931optJy+0+PZWe5Ulqiw1s5z2LOpSxTXk3JYhGQYnYvZE66nx3Kk7tkvB99rLWQzNxoFEVeWUnsCt/VkJXud/upCPqoByw3hEukqY5ZOxubxO81Q9nKtLq2v1DTPe1j5xrx4EMm2KPqVzngBMzuTIFm+jzq8REVHFvXv9jsoeIw+l5SR99LxtYeQ577ESzoX1Xl3aa02Z2Pg14XAY4YPNX4/39WzXLl27jISgq280qjf19J4vtHZopUEiZ5u/cvXpIBj3BPaPfYCINuOMJw2sr8UatfB+t4LabTMhnHxvkh/GrwFAwS8mlF2kiV3Fkc/Jbw1RrfWQj7WX01m0dQrFnxc/IZ4llX6T3YFJbsW/Z3YhygXjqjuAYZottYnoCSAnsMCBqj6TBQQXKdPT0dLx6i0JC7Y6faZ0Dvhns9ICmz2tap05Xp+IqCbgy7qpd8qgWPoRizS/SdQzwIublmjVvsSkyM1ea0aFEQ6Hwwj/8vgUb0HOzBkIm/94sTEkQ0RytXbopP5mTjltvfXWHWnstYbogs5GqunlZzDa07jGhJMZqczMHJ7m9itGMnPGy/VCP9SgRK+pddZkI/u1a66usTEAxuWenpS968EXEkRErDHeBePa64Dvtbs/0xZ5TXupomcNoamSEewa9PQmDrlgPBWAb7tcS6o8Cd56bbDuBXjKKR1pnMK86fFgHdUfB8gKxbtzOqcL+GbrLy+Mh2AbEQXDeJJJ3/RtFKb6nkBzz6ELc8UqAO0seWXd1pQZlR9G1i+tG3abUanyYvfzMRICgWDmjJkzBO6czAs3iYiMNT5UG6mk7Hbq0yfZqP2I4JSPiuN55gcvtwaMdUGjsSmV5gEBJeCX1Hzooalr1jF/6zo8OPHnfkRET5eeABnqbnIBzBriXwNgTESxFdqcEhH53sqeUK2pPTKgiMWRm7wFtY5U/FRmaTOn4rJFw/07LxqMm2UBb+nKqektxlYNmcanmcAgzSg+mjlH61G8ByBbHamdztmO0+ZUY7IXxn3Dy4zODIKxwIhCPuWDcYvxVsZ/AIy/E6lk/7nfY8174XB4hfmKjgtDZULojzDCqx/o/AARUefVUCZBVG+/iBz4wHgt1lotuyzFsH2ZCik08/7H3C+5kFptu45nua1x5Px5wGgdv33DeOZ7AzWxTzNv84yBF1gjWIOXswEs92WsPZogIjLTfhRVa14HELDZgCnVZ0PHXkX9ZI02N65puszSsO0lMS9zHVBgXHkVMF+L4vHZvFEzFqvwr9ezp1pBVqI++KSm2IrotQJgvasIye6cDwqzrLn0SbjZj0oug6kKgHFjMXO0P/DBeLxdSToEf3iHh8WyyriC8l03yEtuay7aDawwD7VKQaSD06z+GsFM54L7r4XYhSWX/CEiK633+hCRNEszziphsv91UU5VzHDDOO061RpHGuwBEjVvz8pjzjK/Hzi0NP6Bmb2sgBc4L+KXAQ8XFxE9dFhEZPZF9gHFmmvyHWqwAOmsr1SJzT+l5Vq4dIck6PYTSGJ2e+IKjLsDeMnXgq4kIir/QS6z5s3Q6DQwyMDAHavTgCxl34D6DD3TwHVhAHcSVR040CrxtDtnKyzmj16IuHnFSi6zwPuiH8ZvRMSA8VQ55bnDNoyLb8VwT7O24WzTC3v3tIgFPdOal8PhpdbD9nbYNRp3CSvFIsZnO5p/SHUlm59UYNyHmZk/feGFF154YZaf3XgN81rnutYxP6ZaY0nPPglAfCCKS90xYTNz4a+Bs5XGiWD20X9dAKAjEdXp+FEKsNvr576XKyJyTKnEVayZHxXF5ddoog1E9GJEPy+cJ8didbokZndxhwLjKSLba1KFWz8SiXz3/mNWHOdKIqIngI0diC7/MCsCTFU5gKnCWmBwcSKiEZmFhmNl6+psQWQGUYf5APBbP3co43IRKaxZ4e73t4mIWbVjd84WNn2+inu44E5XO/oWJjnfcB+MYw5L2Ji9paDgIrfOhnE75Hur/z8R043sUThf7J0fTGsQDluLjJV/DYdVVomIMvoS0UxRPv+owvgqUWAM5l+dRhczmGcqZwGzUmT9ThCMm00/KQBwKwVIo6eZmRMDt2quHAIE+MI7GlecC/xMbSatBAD44jg5IiLrVJpBx5o6B2W7dkwlel2+1aluzN1YgogaNA5o/qpweyKq2iYgW6IXMN99RIHxZKBd+zFZEAOL0xSn5Q5w+nvUdvpx5uO73e71eeNF9tWnkhVDa0UkNzNXRGzlMObld7yTmG7Wo+aqKyuV3wawc0yB8XPeTRMsGLeYw/yJ5yoezkbqxImPtfig0AfjJpBkIorpVCslwKnIffDBBx+8hp7AUW/HfCI7qhARtTv72oYCr//nwLjWT+FwWKWeG6GMvkRtj0D5XGOZiAwra8gSN4wPKhOCkhd6iPAigALjm4BJqjVERLQLxm3aHlAXfv9BMH9ya2BMafxe04/J9kaVS+xGODcfAH7qWtrnjOSIiBTkpM8fbSHctqbE28BT5t9V27X0eaUJp4JLxonoZ3mD6JYBGfKHb0ntpmwZSFS6a5oEVAavZfbEqB0YV94HvHkagAAnjrpjFp+Ax9eZB04eeXNsxZsPqH1eCyi4garNAgSFmxtetRiwnYpWa5iRvI0Zyas2MJhPKpHuyWaE0nxq9lVydc4W3t+ozmOPTUxl3u67Id0WmhQP+MUzwWkCCX97LOUs0sMBMAYA5CbnIuwtDmtVKKsfqNRiQsb8Tvy70zleGK8Me2Dc1TUaj4RAGVaniltsGN+2yu3tj3PD2D0al1jOqS0Ua4iI7ocUnBn4YWZYdvsAUG0vItg+6vVGjQIqMhPBR2bMmDEjC3vcDUs/nA4AKHzzvsr+ZjQlbF0EFhsTE9uai4GtVYmISlw1+SjgeQVSB1lMRLfvCsipiD1zvBQ9KiIi3jWiErNkTR1quVxEMnx+02VZzEPoyyFPtbIPOTCubcLq5LrOnetV3+WC8Umcfmkj54y4gIjoqWyVxmwb8gfQZacBnP7jgdJvJeHw87Zj3jaFGcwTEjpfQ+WeBHOqTRNEVQ5bME58p3Pn5ebPKTDGmVRj4Ij1XT8Vu+DlUaNGPZWDbzyKJsZTIQB8MA7BllQf3co7IpKWJT9VmCWOJ25aswrY1aNHjx4pxqMzVWlWb7USgO2PMMLKBFcLY6/0iQZjGs78nGINERGteb0tEdGPftgQTbE2zdh8p0836KOXY4iI7gSrM/HSTx4we2aFb3sSQ/ouOGhdhhE2V2E8mYio+NsA/PZskdeIJuULzvhGo6EykzoVyImtcszLANBJ5Gai7VI4+ZRc4G13DTO/sgjMeZmbzPmaA+OYkwCATxoTUYVHUlUYdynMmpDL6ww7auxxkdIJ9lGFbwCcbFrvxp+A1c66WocfmBlvG5wA3TeCebID46vMfjvybkMiesYLY5oSQd7Jk0k52KhdjafsiC/M03j1qlUf92xMdFq8e7HUiy+Y3umqq66aeNjvVBBdN+o0Jw2nJjn5Tpea1nTODoeN5Y+Mpye7R2OaKZARXeoRdekyEgJRVxVKlnkuR0REtg4tc100GHu4gz5iPvCCedNvv+++V5n3K9aoEgjj8y+99NKhXzIzn71F94uD5bC6BHwBgPzjA48zkPdy8AoXlY9p9WT3zX4YvwUjcPQTACDZk8lROfl4+Wt3iYic9Z13pLx9weL0ljUf94eHP5DvStL7kXDX5idyfbkhITvMY29Y4PaNAYxtTM26/QGcGOq8ryciAnxm/F2rI35TgSV4l8YCwPQlCQDmtHJUbabCXMBr0280EME3yrtjjIHiYbFEdP4zZ30wpmeH3EhE043NFIIlCxoSC6IbC62lbEdKmg7pjCAYE1VtSURviXebFiLq/osB4zmd6G0PjK8ZEUYYq+2VPE/WYbfevXv37n2RZ4rnlR3u0bh2JjPvWvfTjcPXr8szOJlUaxQJhDEREZ1Xrtyrrr32Gs+c+YLtR8R4FqMXAXiHiGYDwE7XiYo/oMRuS84WEWP1x7ZmsgHjkADAqQ7klotx/PFszH4oHhN9No6UXgdTqtEjaeLjlPhDLiNKlsEtjop/pvqNA+ND1gqnF8ZI2pIM4IQKkPcZzA/Uadmy5cPTwezihxH0oB8tJ/fs42owu/UUZs546tXExDPMzDO+U5M5njN+7emuLbp2/R7AocquzrFkuq46nIgoa4uGVoaoE/wwtkQDYyKi0pvVHG7bmsozf/n1+C+fViAfjI1Ysf3PUd0+RqVG62HceadnUeq1/UpgedvaLZz/MAX0TdmPk32jnyINt6kwHs1sb08V85E7NeiOs3i9VRkiqtwqBOS46pU7SILNXNVkrIiYVKW2NQOAfd9UeDgsAOCjnYgtEJH4jxIkwT/Ij5RCWf3oDsgj3nBd/Xy5gCiXxx2RlT7nP26L1TfhfdabU4FxX3XO5WK/qv0HmPmQEW/YOt41dRBs7rrZaJcz3k3GV3ugwv2W43FVK+0zf+4MAODQJe7OMSUm1f3QuKRs9lqt7l14B0ZH3tbD+E454qPpNsVYg3g7HN7voRys90DnBzqv7vxA59UIzyCdDIkyGvso+xt0Wrpz586hne7t1KlTFWrKvN1rDRHRbQC0QXlquIdZSfOayYxcg9mz4mjmDJXmqBuQdpPxZ6uhnkzdDiL7r6xV+YJLYu9YlyUiYk7gVd8YSEiGAL/c4i/0n2g83/Ft/RaOFBGRSGLINxe9QOTVjh9BRCb6V0CG2I/4DvuYAuMSgy0YLxtYyx0BqG6yrR1bOaOnJ8a31Z43jY/1/mC9PRaMj/b3Ecc8sk95amaZ0PDeqlrMn/mv35SnoIVxmT+iLPLfpofxMBcrfMBrPATXKp4q9ddIlMSClwuERwe7+WMiiEaqT5VW8Hc+a5p2nXAa+ELBzQdJSYcHPlChQoViVKbCzSvOMi9QAmcvAAJs7FS/fqdNQIarwqHC58CZw+9de+211167BEhTF6o7iIicOpgtWVkiIthmjqpOwK23MVmX+JuDCMKaiWR/9njgbivPi4jItIB4XPGlBsJlUsCq8qNHTRiPibWPqTkVdaYDAJaV9z8BxWvUqFGjZo3KZX3B6KpbAABTHwtaT3ooNHcez9UUfpRpMiQJgCwdMqSX1d8+GNs+eYBEgXE1Eb8vZslALYxjsiQ4xG/LS/lhLIuN9efyEnUJI6z9TaKdEXFtfuPI9fDs5uaV8UmtLWtK9uzZs+fy/fv3nxYAT6ijn72p57cTE5iZk0aqa3ExA3Odte9kD+VcFbgkVlF1cEdb7KxTpW8u/g5A4HhLRPQZpunWlPVSZci+zbPHtC8TWCla/bUsZh7VURlq/326ldLlLxzSs/x/qDLVPxqn69Ltib7Qw7geAqYUlgzSwvieyFL1QoKS+D82JnsBjUe6V/S8cmdaYX6gW1FqGjhXux2dx5o3baxJ1ug4V+ZMx4//UFxq3u19/31mw3iWt0/PqzRq4Qr7zO+ofVC67xRYGD686Al77nOOFThoc4H+qhQUHNJuF/pvS2far93gnPS7LZEm+mnIH1SpxY5ATdnIBglUOPJl19pE64N2FukrxaLd2sUxbyQHbtfV8FGij7VUfh4ZU/bOmjmRWUQ0LOJJZF6+vGI1oiuvITovQs+VaOyd/j73yaAHafPlM5JW/ezdoS6S0Y+q3tpsIBHR4u+2q31QMJpiwlXjdqbUb/j0jGhvm/+xnFtPlceaJ7ScuxTVqbgh2mj8IVI0eWEr5aZo1hAR0XXdH388sDg2SpwiqlyMiL4W88+s+Z/JOWbNuQ3jir8+FfxFIqKKOXrfGGmarFIiuvDnocGKC3K2u9y4v3erRnz4L6G4+mIPRb9OzjHgnFvWnNsw/t/Kf8Wa25n96b1B8v/DvvnL8u/7xkXy78nuvkSJ/2sj/t+Xc+upKrJGK0XW6KWIw61I/gnihXGlzdBMjj6RiC4u+vygKBGuYj0Sfv5XLCuSf44MjnhrHxQp33dVxFuN4shGqaxTtTgBe0XV83L4GbwwqBKxzHuFvtIRS1qlCoI3ryYiaityQJ/A4pZz7FVVZI1WAqwpP0RaB3yTiIZk5ekrpyssAs7o9vko9myeNlZV+mdmsxLSG6l4IQ88K8jRuAQANJX6Y+AUnfmk0UE54OWvceSysdu2/h5rfzznb9X/UFRrqtS6a8ROAKEAQr2nH6IO/fv169cvmMK00Us/i0jhIC0hiVaa9vt8Liw6D3/fNJ3LycHbOMbsYf37uPwiYKUuOlp8rMzTMRg03sT8tvm3B8YlVjCCt3H/FcDg4PrXpqkiP+vWzUssED1nTJmbkhjMG+10o/8ocOK928CVsvKdD2zSNip16rRN/3LuwniplXO125cd1jgl1yp+PJ7ooZUsPnTBArNoDCcDt3fVS9OvssBgLWlkm2TZpaH/m8ysSacios+AFZU1uhKfyIpKGh3tAr9t5+K4YXwzc+4bQQ0rbQDyNfRu40SO6NIpSwwWcS+NqSf9lZnfHzLEKVJwOqffmYh5zq+9icN32XSQsYpz5JN4375Tb+YZycSxZzZqmZrKitirGK72xZ8+FhFZ5l3iaHSJuuj75orntfb8Lal+++2333777eq5VWu+YAYnpx8Cf+5t+dBu3n1s9e6tx46BOdc9grQDIEsnTJgwIQfwFQcZEgqF1A3rLbkNAkEEEQ2M70/mXZrs76uAH4I1RNRTsEWzzyXRx7JfU3Ndov4EnB1kf3TDuGwS80F/GyJaAPjIHUx5LCwS0hnSWvQwrrmE+Yx7bmh3zuXp2G289i4Je/PCEpFipku3ZdbQIxFRxLuXZ8xW6URERDeJfm2wrDjFeMqtOq/lLGMQO+3mG3o4V75T8hN37nzHfbpbfxGJfNHb9zOxJ75QxtE6njG1w+j5BmXc1NGjletxvlBx7vbpD15wce0dAalT9TrXa0n1Ylq27DzCm8d83ph8PF+CiEYBC32uo7GXbMTXb0REbyYz+Lfxuzi5m98aInoUEpQuRURUBzxLm1HQ+HjkjHa99a7sRB31cSdmtYDPBeMS3zLnBXNj5AFzg1dKGieI/BjsExFVOSQiq4NfX5WWMGd0dx+zO2c60Nf4qwmOeRom2vWQ3aPAON63sXJIfjT+GCO+jagHfFnN+KOW5Ntp8c6tqjkDwM6n7/84yZ04WnUv/xF2ymwaJLkJUHul5AAAwhNbj01xTXJi9vCWmffHEhFR49FZW1yzh2uTLObDcDi8/zGfNURU1vB7P+QoZQXUxQtjoicP1yai9qeR7Y0thaLs7k30piD7yzZDIjjSJMAaagqdX0z1t2O1NkuxzCac0e1TTHdnFwTmKRNRYzCr/emC8QvMHJyt9kLYJoT3ykERuSRYRfSSyLp7gycTZX5lZiuCV3no0IZESuecBsyH7fEAGG8w3f6ZfqfLcojj/Duy/iame/KDbPRaM11M8r1H5LB90D7BU3uBzKcqEVE7pDVRml0iS+kCJxn+XXHB+LlsbF3QvEmTkT8hJQw3NNYwmI8P69Kly9gUZle9fYeksAJjWG5KUGwggjwdC0TFfhEg3edz1bqdqO1pYJZXER+JxIdCZseFvNr7du1sQnMZ9mZ1LmvKz9X6xXW28w/67K4Q0E2nq3tc7taoBqfh2BVqPE2F8Z1nmKcH/mTtDQBmEL3x3nvvvefOxurOIqPLEFGVpzZu3LhxsItw6fwjYnGJV2v77Tuu4XwSc0bn84iISk07k86c8Rk5ndM804bxTB+Mj0bWGH9U3usr/gpZtyDkG1PuEzEp8dIk1XuF8yImjF8JgPFTOQh/YTyqDQB188xLRDWg7Do3jHch914iInr28JRcH4wz9qgsborPPTrsgvH22m5rHGm8Avyl96Ah9V89zuCfgliFynySBQz33WfLIfY//qaU3yVYYn9yfelRhibUVnlvFL+YWudAz361TGZo9puPPYGwm7xAhfE3zMkXE93z0t6DB9298zMA1Cs3OQwAWSvVUzwUFoklIppmpFaz43dTieG2R9HmVxH5Tem7y5KYjfK829cwM2/LYHI656pszDViHxOyAkZjsx6y3Gr2vhrtmxDyjSlvi5hPYKqc9jSjVWIyc73qh3HPXMB6szdwj2OXyBjlUz0R1+i3y2LLLXvhIIWFhoiI1uIoPfzV0cTExN0PUX/rwSQyyv3xLhHRlYcBRHDs9uqKNba0+CKdNVtYxL6+h5lX3xIQcavW7giQ/aZugAz0KAxpeAq88Rnni47mesGbwW3qhDg5ysbv27FFQydAdG/OYU0sJeZnPuah4FBg/BTAQ8u/EzGmMtPVb60DgBpjrPKAsEJ68zAbML73jEUr41xRRZGkmkREMd9miYiI0q1jmbcY3tRWZk55ttQKFcZ9AeNBejgTehjXTPFy6jkOsf921EuVr4zihVTJ8XhrF+aI+eTP8sG46k6EG1uhxguAHdWcdpdIgfoLImrcsdSeg2Y0oFjV9ShwJ2POYSUfqJ9aqIxwOGxwIjfbbA7JtzrWmNJo9NZUDt7Colz9N/eAeV/Qkuv5U/cJkN03QEVEcf75hCXV27QxakMtzkTVmo/hdcEtqQ3WV9nSZMnSuqMlt+Tr2BvqM4/wHFJgnMn8dYXZDOb965nPqtwB6wBgnlOs5QxBdXaLSKyB4pwNAyaLSuFWUSSBiIieE5Hl21QYPwegPhHR9RFgUxW6ZjJAPhi375OBIBibv9+J2U3TGxeJRCLxcRQ0GBN9JLKyOhFRqrBnIvuifGNO3Ff7YPws8uyJaNUtAJQYV50kKEDywPgJO62/HnzOaJeoMDacgSctz+KoY40pjzGYeWxQjWoXZsamvoHTqpFGCW/u0KDYmB7ETb9KABv/WCOYas1G0Tm4T+OYEdmp+Eti4tMe5W2C+4guurqlvx2V/EKeI6JL33rIvyDRiY2XcEWFGMeG8QNh3n/RPOb0L66vXm4Ls1pbuc7C76YlS34BkG5Pc9qziMRSpZ0RyelFVFdEnOuzYNwmS369pfRTKoyPMSfUIKLGKeBNNanMl5zRm1ww/mXJhg2ZQACMj1qO0UigmytU5Zpt+2Bc/keRMzOvINokp4lIjZuuFKuPl8gZG6aRCBEVnwUzwEGVm20GEnaq572PE7rZkSsPjIvvPtzD+NI6AGH3emvLdBeMezgfLBi37OM4yLY1ljRPinjemLbRABAwSBMR0UOHDvV9acNZmwJfkXh7CPDLVwKB7Pp4icCaySnWNM0yIsbVn/G2K3fCKE+sGRLAW6pYOUtWlLzotwzk/3C57xdvlLOxN35+uFAk5MVx/wiuoRL1X82Q4w2tYzaMLzzG/HUn5qzniIrdWhgM47O3EZWaEAY+tTSDRERi6SkR+ZEMGPtG42q/yokLiD5WYQwODyIiGsnY1ZCuncf8kto5dc6av7h5SMBonD26bds2jduW/pZ5jyvg5oaxr29Kv3VCRGRNvqQS0XXKsspgObnr/fdvufLKK1ny7W0IIhEiqgr8Xp6IqFmPzQDgIZRaJzKnx9VXX30hEfV0w5h2ofBRothHtmHtcB8f+1Hlrqp/WzCuu8NA8FuBMKZmp5gP+BaqYn5IBP90x5+ksLQ+iUIv93Gc/fDHEVGcWzkQDH6vHH0JDoBxP2wkovJzBd4AUBdOqUd0y8wUYx7rhvEjkHufOAxJzcZ6H6v8Qpm6U3JWD3r3hHi958bga6gTM4Nttk0bxjcy80cmi/drzPy76l+bMF5RgYjoqmTAZp0cKSKZ9W47K3Lyeip1wVsiaU65pQHjmOVGiOCIG8ZGlfBbjIFlXk9jHuELuAHAwqp3G7RMqiSCmfNSeCPYGzeODmOiCrdM+P1ARGQJEZHSd6Vun/z1StO5X6+cjYhKfgRsXrVq1apUYMleH4zr/mC0Or1+/fqTIr+oXs59aUjcvDkB6TOrD4InUkGfwdnzI1G9wxsA4Pb5ACLIfJZuBwzODc8FNf6J2btkGPMDMyZod4O15U5guedQnL30ETgi3/9Me6LbDNpd8ljzOX9JdP9cBidUc7eaw8PpxmVgXvDcNVM5yT0dnQs5BvxyW8nrA6Juv4jk9qtORF/5YDwYbzRdZoR37rWtUWF8dRLzoocfXljInO+6lpuNl3tVIrr6tmQA9rrc9yKy76WDItKlYt13JCJQVqwMGLcWWVmaLhwTluedhSNwRhMiohaM44nMPOfuqq7OeX39+vXrn7i1Ks0HnvBcx51OmIqfca9/OTcjIPZpWfWkyOiA4+dVrly5cuXvZaR9xLCmlLlfXNKbTUvU3a0yvxIRUfHKA5JNyRZJdu0o8y0ApP7xKFGjbC+M73L84Rtz1Tt8uxNrm/Eo0Xxrxud9LlsfY0/VeMVezKmd9VFaS4oNtPfTdElcKBSK10YqiD5n5yFWYYw3qV9y9sB+SjzOkDV89JdcxoIrilOdw2oMi4honUQkqxcRdRcXOwgRUeUCSbyPiKh31mDvauP3GS2XMcZ/wfbygQfGHT4w8VE4wb0kU2UZAGR++eWXX2YCwFJ7pbuNTXwr60UkkqUSgFWMRHjnRV+JfPH8J2dFpigGjWXe17JBgwaTLRbTjCs8nWP3EXx7h3To169f/379+o0EAnejMlZVQ0EqIqKWefKhTkdr5Tv7b9OayjVfH/lhzXuqEhF9in361LAP5Cd3n1d7eNbRo0aUb7wXxrTRhrFrukfXpxowPr3nwkqkzPh8fXOvyRhrS7yLelcvLYHwPRpdnBvG1Z1VjfJDRInyukbjhI/BP36czJ7BmIZBgOXv1KjZaYI/r+J3SMbtRJXfznaF4omI6CbJbUFEFafyaN9kdP7Z+08g81tgfsAU70aLcwbMB32rJ10KoUih8gqcmaeSyrjL6s97QkSyCkzd8+q0ujcrv8ecOuAub+eYcpnfN7ZlpDdS4Ugk4k1vc+R2e4+GAAn7RmOXfAz9hpv0gTts7JJeWOFJ465o7yEx1s3/Yi5/mJDxwrh0JTI68qJZ3t3EIkBQlI2IqMLDD9vJIBcm6OkP4tyjcdOErIHPGAt3b+5izHZwqvTNm1kRRBDBRt+GmvWmgnkPUlIY6VO8b4mBkKOhb/cdh5uilYiI7hK5ltqPTkp/2J80vC470UCN4k/aML7Mppw59X0A2ejKdBvEx6e7NhW+YnZ+oQnUwiRPDsx5T9gA393NZdBNS2wYL/jy0fMtd+4/CWNdK7ojOozvVU7iUzcIWMa15RHx7lPmyJVp8LNmm7LGNRpT06PhMMKJ5jjogXGDnw5M3zh9+vTpXx5gZvdKNJhTEo8mruhiigKcRsAbROdVqFDhzd8zgCmqwxln4TbOmCLHOaohiCDyXlNqOEQg+EoZbdW+GQgG//aSdywmon5YncJgXjDJ3zN9DPq+zNOjGvp0pXfK0hOQX/waortMf3K3wtBsV0Zvjxvam4i2bj3+eRBPz/V3PW+4RbtH79jg0mzqQi3M+M7cPZ5WkWmL+hDVfWLVgfiZ7gj5LxtGXkFE9PFh2vJn23j++if6vyv6QgQiItISX/6pXKbfj4g2nq38V0+z56daVGy8VfR/1yIi5bytOtIF511OROdFiBJ3uxoO7N64Kp0XqXsdnRchovNGOElLrYjua0QlHicqJmd3LvlYXcZ8O059XH9Tt/eZ+FT1iLzRd2+DmEgk9bHNvjV8Q9Lyhn7vvfWmJN1csRQRJQWQNC5bk9hmX+rc7ScDmhV89ukt+6fM3hE0Fi3afDlR9oSZBzLUo/pR638g/vGveV6U0ViXrhR9NBY5rY9IhWW9vdFU8GisbmDilstEz9Pa9ky00XiQTmXM+JTRuPUym2x32TRvom69zgsSEhNOpqYi82RyQqKSUdHIfplmj23iaeXkZ0Z8mcZNUxCBQHBkoDv3R+2bZ3yZVv9tOffpVmYiU7c59kjWJpZEh3GUbJWwnLYdlaDQ8xjAx3FrST35Wj8eH8QK3eRwbRR/m8p9mBt+QrGm9Ku/Zg0f3qNGzRq66p7arVv3vaO2exWv5IunARSMGtXSt+0JEcUFZsoTEVGDbuPHj/9N9YoNOccqY855GD8FBL12iIhGuvZhcUkoKozf1luQmO8MmkHnaAWs0GwBS2W/E20hAx200t38soZ/0RtE9GHQ8sf/Ws4xa855GFcbMUfHcBYFxv9X1lCJIdisjcyWC/XVnuygZ1MNRTxTPK+UvzU44PY/lXPMmnMexv9D+Y9ac1BXBEb04nKt2/x/Zc2/LeeYNUUw1kuRNXo5x6wpIr8qkn+AFMG4SP4B4oNxxff9u/v+uTwQ8YVkiqRITOkN/dYX0aVMq2fH52QFbv1zl4tS0OvjjMG+KKetNHx4ULJ+uY2Sq92onShOk6gzIn5WH9eBc8zjOsetuatf/zOHvdst/5fEZ027jMwgZBAR0TF+NcqpHo9s0anu2SJZobbByFKzW71TvK6TpfAn99dV3paumyWQVqObRFnAMgoLghRgXvBvbFPwfy3nhjULThnrMS5rynUZm5iYxWDeH4jjhvNwoHlAMed9vZ/7SEQioqewDJa4UCik1pt6+6ZxCufd4GtlyGf4Norv2uiYliCi/rKk/r7SAFNuzNXDOOY0ZLPn+85vVPkiT2RNQL1qt9NyRMfvYtZ4hQIUcWBmV1/bnVNziv30XJsRxcvpe0afVKaVuyO//aXvWda0LTzp5Kj0/1mXjkRUAd7E2f+AfGwSmajAaTHJ2f41iB8ybhcz+NBX3rTNtVkwOdzO+AqHQiETqf7EeTWN28SxF8ajmXUBxVvOZmnHaaKG+7BYt7v9g+Kri7Jlmcp/4YHx28C+oKwiIiKq8qNIeElcgGaKSFAatiHasZhOgvl+F3mF/cUnYBWRxKzzLdXW+zHTTN/ujS26XR+uSBcvkWcdI1Wy4jKk3+7WlH79E4t+IydiJ/Fa1iyAs8d6pV04VNVpWHfQ58pwUYF9MC7ZG4gfGEDU6MKKFzlPKsQFN5v8OGonrmEGc0FCGnOC78ylOv6aZ2bArvOozgJS8NHDQ9KBSW5NXEQVTzMj3yJkZNVbh1zfuCuLN/hYEQ2pelbD4UNERPV/8VM4WdI373Pt4v4troxZN4yfysH+oDV3w9IDIjI1SNMgX1Zp9xAJaVHcpdC7j7rdOTFn7NqIl5HvHVOeFDHLd45EvEQVxbobhIklDyDfk/zQ94/PmxAR3QLAs53ipUC6mUqYjUTrWkxr6h6Ds8f6NYBSfURPMSt16D4Yl7puMTOD/cQh8WpCZMgzd6iWUui8oF+Wpao1RGTC+OCTFB8E424M5tRf53Q+6+O+Onvq/aHPEFFDYKtbE7WMMd5+zELBMK64SVPBQESdgYc1KqKaU2W9biyumri/sq5d90RmHYdbx8OYr2NjK/5atoj0Dnw6For4iB0tCXkyWBXphQj2uCutrM6pDZvuYjy+I49cJNKdiIjKpYrn3Vj85YNGWsNDgLfA4QOz+CoYxjC5xLIBa+w0rCn+nrozfUeM3DDfadin8EclG34ez3adtvYYZk7duy+NfensrvIUL4znywknb+Nls0hXBc5LX6ATEY2MqMwAplQ9y+BnGxJdlOGDcV0zO+krH4zj4iPxoYjpV2jLmJyUKzeMfwGv1i3Rb9Z6G0Qlv5MVWoLaCbk6Z5san3C4jYncMJ6Ug7d0g+o1S0Sws2sgil/IlMmaF4rxPooL1HRMZR93k9U5NwBmukSjTD8jzYey1rDzLvHAuNKVs8yc/4cAb0JZBxPGVX79WzBuBOyxHa1KCRhxwHlmL01zpS0l8ffqx9LLmPnrVkQDfTAOeWGsoqJWekSpPwiCMRWvS0T0EUzmJZeM3PFZXSKqfQzo6tcSUaWvTiGzR6DK8AHjNDqlNsRlTa9cbNXRDT4YZn0Ma4LoZxrlj7yuU9Eu8G517qfA+Ik8zNeh+PpMkfCCYF3McUnVcnvaneJLBay4kS3upjb2Q2B1zvPIucD6C95SjQq7xaxdjJdDruen7q6CkPnns/jNU8XV5qjFlDIT+N6t+xMYO9WANYGTB+yNk+lzVmHc8Cx/oHwsNYb59OPliCjm1VpEYxe/4wzc7jd3yDUAFv9Alio3IhDGREQ05Bg26ah16KL1zH8Evq8figfwlaZZfJTBWB2oVWva5HKGjhfz6lO8/XyNjj7EXt3bn+jTRG3VY7sMWeM64MD4wkIs1VC/EW0VCb9JRN2mTZs2bdq1rqqyuyxG4OtH3+d9QZgXbiRmu3unJTOjb53HRm/dkr5ny2ONDHNMawbb6WA/YKc3BtJPxGRRzxV3vvko2JQ/OzDM0+wxAEaR4UzAs9/0pUDY3IE+G3Az43hhDIWqfj6rz/bDzCotdm3m2U7tTvX9zDaq4nwwDjmf3hVR/SEtjBsfYSwMDhCVee2LDPDooNTAxoPzAfyhob/W+IB2FMM2U7Gm8kLAywhky6fMY3S6hnvy9Ntp3Jj1kE7VIYW3uf0NG8a1RbgtEZUa+v777x+U3fXVSN89GYKBRDFT8yUiIuIyepXsLUlEpUcUipx43P2DoUgk4oRrXKrxzJx16x9WLcOZl4mcznkZh41bXikP77pPSRctFTHeYFewuF9lA1UYe5/zxwB0a9iwPrVJ8sH4BuAP889swKpVDIbxMSXKOZ/Vsu2H2eVUvMlznHGo+nJWYOz2KShe/XQ/ZJn6jumngfHgb5iXB3BSlIqLm7mGmTEz8GV+EED6M55XeZxjVtBKlR3GiLMPKdaMZNYW6FQV7NbpaIue3p2qn11fVaMqt4bZE/Z2YAysICpX+zcBCk4DmOicpVm6yHyimJ+t+lDXCkihPENEld4yqkrd/ROKREJOKMdl9JcAsqcCiMD8T02ncx4AQuWIiB6HD8aDRWRQ49JEtXaJuKtfKyVndTfvXTCMAWS+9zV8MB4PWNtkRIdxW0DdrMcHY6esmsqnnXGIEmPimXm7PW+znWGTGkJBSP3kPHdc404NjI8Gk1xdtNmqNg/2VgXA4UsDFKHA0caw0hM0dlvzA3sZIR1ZBtYSGL8QOWV0SLWAeGSvrHpERKWbvbTfM4+hELMR3q3zmcULZ8G42CvIfYEaLABOj+1xT80eK/NhrxCev1Tk62JUeYhEZF3Tpk1fUvitiNpwQTsiGiCSO+vlI+Le/lgNmrthXPIHq7i/YHO68VcPp3OqbAWOf/XVV1+vBbZtPtbdaVeh+osiItnH3y/9o8hXDd2P7EAgY9Gicdc0CYBxispR4C7hLpkDPF/llUGdBg0alB8AY5ti5dEUdw39fIcw8tKXfkxyUYZVZCUgOoWZeZS/X7yrCkTvyn633cFORauxBZwZFMi6tRCInN1xGJHghaEOqSJyJEARPNoYEkdm3bTybeuvNvZWMnf18/3kDmzXxdMoUboQUbkHZ6Uc95U/NMgcQ0Qx72yU7BMZnhjwJmTXJqI6j+61Y8cWjC8C3qLz9+DUWLNycDjsYtbRItKEyv8mIk9WIGqwR5Ta6LoH5Sciujks4bZU/HtJd7nH6j1y98/VNkfFJ9T6pdzDzPyc0jk1kkREJCIivPExZXQqX+3+uQk5IlKwaYNIf8/yUOnxBkqTux7ywThZhbH7Ca8Pl7hhTM8XyMnnGjVq1Gj0IYm4XwALkD1q1KhRo86adLpwwTjZKt68LyOfp51UYBzvg7HdNyJSWFiY/YkhtzVrNlzSVGsMafwNmH+jIHlpZIcOzSiO4d36x5SLrwFO+lcHlMBxcDs3+YfzrcERI6pUZxPg21FoB2YQUVxcXCvf2a4rPFuFqMQPkvVZ7mivcsTJksViB2ZlflKp4o+n3YP1XeCPiSgmCcxW9ZcF44NYW+v2DXjCLn9d6sB4gsjz5503RmRbrfOIyrwrorznB4s0Jqp4XAq6UfFpIuLyKhSPyhMb/dCG8StUZ2j+ImNZ2umcCq+sS01NTctH+DHySZOXv0sXEZFwF4+mTPv27cctW7ZsWaYPxq3eMeUrH4z7myA8uH769EIfjGli2FALjj3hdioeyLBJY47+9PJwN4HjCMwZMmTIkCH5ESBnySVJcLiKlDWzUJzbNz4gPvnMZQ0RlWt3hMFr61HF+rq6wDjWlo2X+hYI2OPNfJaiwFgNqDjfSsTuGCJqkeSn1CPagRlXDdkB5swd3k1K7pLZRGUXRIbHdMqf7dHVTf2i5jw51K8F0dUpniW3ddhakajFHgbbC+AWjAU9zt+AX8wQ9pW398+B/W6bINKeSonkX0FU7NL9IouVvhss28sR3SoylxrvFJEf1ChGnOL1eWDclZl5PTNz0ivGPC+umN//O+9nBO8MRRVvHSs2fXyA+J0KW/zLH+OBE289WLlyudL0KDDJujzbmqcOAMD2CXfXremGMd37JTPz4S9GXFqX6GE3jGtkWRgv3HERdeEsZaknLhSKj0SsKGScgo9bX+9dqe/dt1z77N2vv/7666/vyha5wmVN5Y9GGg7ZNx99FM/fBKcrxE7kLF0UoMySQBjHxRH5V2JUCQWOxj9jTwzR/emc/YEfxjvFmvnYe5hYcn3hqfPLfy9/tB6Uv90L8Xfl97ScEZWISnyQPtUdd2uchtVE1NYYBM3R34Hx5gFA2ubNm7d8MG1zOrBohg2DISJHXr1SZHOrVk/PEsmcoU6PHxRZHVtyguy79plDIpjjXpWLt99Rngg/1chRZ3fWep4XxmWBbaSRliLbtBHCOkf+DoyLde1qrUFdU+DsteRYU7pcuXLlShGRF8Z0Xrly5cqZuF/phjG1e23v3r1790754kGixidZRzjlI05zSU/Zdb5izdOH3d2G4GrU2cwndGQc7YCj2lCu3zeOs9kr4gN944rgpEHzs5gLk5i9y2A7wMz8/HT28s0R0Qk59KOICH7wLVeMFDndlojqLk0a6NUdFXzTtOleQJBk1TBaMO5lO4YCAAuvVfyRcl+7XnFTXCetkaqotnlXjYxYY5zJquRSTWebN4Q5CQnj3J1jSFkob2KPvCBymU5H9+HvwFiR9oVI8eRUqOKDsSpr2Z8aZJ+Xo8M4pNPdZEX6IxEierpA7TYGJzucKmWaN2/e/HoiotqTgVQXu8edCxf2aWg8qrW/d6KLPkPifT6FJtlC+XNwkoEaMaZeLhkHZubkPOblvsXqdmdERE7NDPAax0jyBeXKX7MweamfwXudTaj6qx00tyMVj43+xoLxstHXupk8Lk62gRqePa6S+6zVF1lAzl/kC1SqGX6eO3VDinI/rrq5trdziOhPYaznQI0G4/XRYFw+x7uK55Ka0TjcosF4LPMtOl3UN7kbxv1YhfHB1auU9Kd7mHlfXaIaXY4xJ7kneK1PAviCiOiaZGCXj04jPkLW3fK8F1ypb4EBN4pZzmDesLRzAN/CEsPWvNcCxv86kvF82SDnvl1Y5HiWRA70D8hTtjjcFjznLP0oi9HFy5d/sd7AKuXL+6N47Z5cvUVk6RdffBEUBGwQEhGRXwLXI50uiPNoLpi7fapxjcemnW9/2/2d6DDO1M1vosP4SDQY3x7+Mxjnt9K1XcuFwUEuosanmQMYHk2JBuNyRxKqO9YYo/HCzoa4I8M5zLwutPlUGoN/9S7gtTwMhM+mnz2bB2z3uRvxkUh8yItUxzg/it198yZvnnFvMIlR7UnMW99pFpjbHyOaopBrwpL92YrPngteANnIzDj2rG5fvGhSpmyVKrrdzaNLXHx8ADcYEVHZG+KTnns19s4alR1z/DCeqtuQaoTM0/9qq6XQrnNGhTFlY6e9oXYgjPGk76gh5bayljWx3Epeon/oosGYDpi+k2FNIjP3DeaTX2KP1Htf9C/gXfLOIfNt6yQ6Kb8fJWhsv1HVu6j2TZ3DrHt8o0qFoyu1Pvrfk3Ocp6LEVoQ1G67TC9FgTBug3Rc6PiqMlwRN8WypfEwP4+Gcrd1RPWYrx2l/0jf/dcn8LWV11ril9t1j5zJ44hvNgkexhtsBoHcv/+peXBQQk0UY687sPzcKvCw5x2FMIfyhW1pvIfodLoke+xeneDTMWXoOulUDsEqXlDryY20yC13O3EqrpLgAn8sn/5fAiY840b+/JkUwjiL/uc5pkapPAYwqj8DOSvsP3qo6+6PBmOJD2oCbLecYcM4ta/6pMP5PSJE1ejnHrCliDSqSf4AUwbhI/gFSBOMi+QdIAIyrTEP2X9le7T8lN6+J6FkuiuT/FSn2wukt9wfzSjQ+oy8BoWcKdLugEdEtazNb6XSN17ztfPC56g3WMJ+9wHuUiIh6HwPaBWqSZJnu11qczIwW5K43LBvJVvHzOTZxKLLGLbFDkqyguc+aEmNk/Z1fBQYyG4/O0jPNtQ6naWtH6M5c/KHbhyL2oJP85I9UXH6cmYPppl4BM/v3fieia7KQqfm1Ej9bJadBUvb+08BB29Jz4FYpcs5aU+5LIALMaRmQVFKu/siRJxDxbbVOVH7I558LVnye8vkQ3zreX5Ims5h1MD5/lKyLoX2+jUGJqNwajjIY/5gWzEJARFQlwVsK40ixxfjR/uCD8QfJzPtfCGz3ZQGY+fEA1ZUrgcKbAhRExTaInNFuxl16EoDDjRxz/MB5IjJey4DUYONP2nUz+jWo4MySHTnebbUC5FyFcbNT1qrzoTGxnm/1X2Ntp83eauUhDOufU0Er9a1SEHm/lfb3K08+xLzTaujpm1IrJe9SosBxNcTs5XZypGfkG62uyo+SoG3Z36pzJ/LD+INC8IzgxagvmcE8IsBBOP97ABKcT/C0SHrgU0FEVHIHgIPK4qgfOC3TAO863tUPm6PQWGC+7txd5Dediqgni3dn2DcHm39U3TGtrMYa/ZrxbZ/PnfNow6a+41W7pUfk425aZhytVGvrZa5zfnssM+/bt/cQM/NMd4J3uxR7V3jO8yzjfw6x/0nxLw7VOhQOI3xw+fLYK5Yv96WAtZy1gJnPHNs18yqPNUREpUdKbiAJMRFRRL99IdXMEt0GRVRpiacG39VQMM9JUnHDuOSHAG+LDWz3SgFz6tgPg9zcaQC+/TD35gDVBWdEdNvaUYtDAA6pS/x+mDwE5Hg4FbqkWWQha4ClupN3k7W+Yw3MTMKKh6WHN1VbzL6usU6sSk1Xem2UQrWmb240xrgs9xBfuvGiVUb2692BKaVN+07aJyJyalIfj6bLsMyccHpGf7Uc0/npFcyTShBV6Dwrg3mvq3SgKTPj2IgR3fYwe4mx2vyW/Nv4l1q37vsbOCBrOtbYqtr8j0d5ZS6Yf7mtzZ1/8EqPNUREN4v4KMosqQMOyCg2pPinMlQbKxsrckNxnXJK+EvFM3LD+CZm9rKUmNIeQKq31sSQqjsANCneLmjI6S5yLFZjSclvgMLhrrtgdU4lO63tIeBTT7ulgjNGevJJP4xfnFvZ+CMIxm+ZeUZXWoVBjowXcxvxOIQtDiT7VgUmjltSfQgERwZWf0bg3q2z9AynSvWn6t5mZQasywAKkpOTp6QUZrgKdd4/msfMzNumPB3nHHXB2Ozrr5nZ/eg8nfh9GyKiWYCHWUcRQUBsaHZ0GO95qV01ohv2BsG4zC/ym+8CTam/HbO01Gh1JVunOq9zWHbrTkrd84+oH10wfuY4H/OyQphyrz8T25IqSwAMCn5sau8V7mT82aR1a/eQVHoH4C0JNTun7q4Mk8OHvgK8o/myiBiUUyER8TzoHcUisHnLD+OahwqMhMLRctpTp9A8XQxVqYmwYy4O7V4UGF+/EYx3qxHRHHaNxhUmAMDpgwcPZgK4s7K7WcUlAqy6x4j7jIWaeDskn5nPTrnkkup0NP0H+046P33f2VFmcu8LPhib0iLRPxo7AsZL3mPtjkeDMV1Wk4jowj+Yn/BYQ0TviATPjIhoKGcElwsSUfk1hTfqdFVFDmknPuX2uHmKVBg3T9ZWLzRNAIffCdZdDmBvJSKimJ49V+13bQHyssg0IiK6f2qWyB8qVVXZSYBZ2FqsWTNzEmh2zijAgucO/5R7qWAXERFNBza4H41SGyJi+jY/Rn73WvqOmBt0H/dxkfdBmuHcdFRcccMaG7+BMK6+GzLnmWpERE2zRA23dwOAcdcR0RwAWFZZbdb8JGTe5ZbxA1wwTuekZQ8a89NvM52JmvLTl5heYftdzF8HDSB3nWDm7PsCNERUfohgo2+nlh4AcOAkgAj+CKg4JSKatC+Xp8R4raGyP8vPup1fbolCjEUdZKtOVXWD5GvnVDTN84iqMJ4B/tX0GOM8adczGTyUiKhUXJyXGXCKxV5y0yoAWKRk15fcKwWNiMp+mgIREWElHnM/YGx42LrvYmCGMab4YfwOsM8TxF4qMEhdpgPT3KpbcaaDeVsX+0bjWqfNGUPpJPFUIlU8YPGMh0xWCMcaG7uBw/EScDeL83Cjy+M8AWBcaSKiur3SAewf4ACuzBKgh/MEDoAyOXyk4JQzmLzMZyw2+YB5Q2bwYFznswIwa/dTH8JgP4v7y+FwOHwyNbxjxfTrA+Nx5d8+kcv8/Pl+a26W/ToU00w9MRZVPJihq4mgF0Q0wyYR9Qxvck9RHRiXnobI8VgiqvTCh/ERLFdrPcpuYvxYguj5ZXuY895zjYCPFQJzihGVer/AcAOVacknIuOIYvqLSOa6jx/dJL/bnnDcaWB2caKY/tkAgJS6RAqMz1gvoocADHBfxFIxdpFudMIH4z5YbP5VPF6+8Fx8DzlWkYhKNZst4tn49hnADKVvgVOj/hdgfBsUJrCNLs59AU5bdV2/AgCcKsbmkv2A883rzn6vtBvJCU6R0X3MlqnuX77y2h9OZDPzFN+cu+ImszC4Uzmviqr3+3w3BAKfhhx/4l6/kujaZZnMzP195AdEtU6Jl2fClpop7Itf23KRaMveaZTkaesaK67gK91HHBh3Y95/J1GNLw8Y8Rr1vfIrcPLWS/qnmkXlOUq5arGvRMKXEVX4yKo6VWC8TM42pU4HRKRfUyLqJWKv5kwwIm2Vfwbwx5RDwOzSZHVO2UOAxW37MvCDp8prmchJIiq9SUSmu1V9cOaVhjExpWJiLoR43kkt8mQinVchtFlEvM7Ta0CoVY3KRA/xMcdrdjsVtmOhNkzmOQ5YNsIDY5Njo83JPA+MB5huERERlVmf2lZpd5aVTQa6B8K46XcZufnMzDzbP8/vZ1cOz/ZOrapvtCLHS3zDpwPjTB/ZrLncopKUKtbEitzrs8KUhW4eYreMzjWYQ+P8VZx3FcpPvoOW9DYGqweesiNuNowrbmR+hWL67gHzsSXLmacp7U4xz69/lhk8b8ZGMCvkuBcB6Et0Z4Gg8NM+B4B4Zxi/rJD7Uu0tguQXziMimurAuEo20JaofDyAjedTpeVAFbI6pyKArN4dOnTo0OHLTN9gTN8Jwl/dF7cX4huNRxr8BBsAQJq7dTGpMiq02HjU3ExzRGXWQIBTM99OBkbYR92jcCQSIR+MRXEH2iS7RuMIkH4fPThp0qTDZrjCcYAH4KgTDP9GFn6vuLH9w87uStenMlvF+MoPbzYoKsI7g7avucuGMc/2jMdK5HiXNwjwhQ1jXO895QfMfOJ4Eh/90I5GKdbcJgsrExFRvdBob6qCYBJRzAQI9vqC6s3Cx4io2qvrOez7xTVSUI7q9u/fv7+/Fq3k+oLrqeQbR9lhqbRh/BHz9upVfmHG6VB96ugajcukcPYdu5izZ7UsRmXbsVo5OQZAu4ojcyCnQlR2A6BEj8cKOlVLETFHnpvT5YzFHdYJWHIelVgDZPcsTlT8ZxRUJhXGinjZmtvkwCJG8MK4jUFcGxER+dHTjDYYqtdqZ0V86/RXj/10iXlW50XnCbjZfyrtwNbduf7jZHaNxusBFOYWmhfxOwDn4amyGutsB3SVwDPF44PvNyOiuPfTmXmc1xqiFQZQk14NrEb+gnn8RyMWMJg9/K99Gfzb+PHj57DBg6ZKu1nACuN166sPjR35dQ+i8wfz7gAYx50R42m6dbOIJzh6F9KbU+mfjcfKG3foIMeoWP80+eLCed5hhbYKv7c5LCIi3T0R/jKfydmrnt0oADDRdFItGJffyNyFxjNzqCoRZfEhJSLVhXnszczbjTfxzSqMy+4HMt9YCyDhIio30Q26sbKF3hJZap5qnmTa4ZUPgBDRI8CB2kRU4m2TqNJ0KpZGhTF118K4zG8CIPnMwu6v2V6yLTVnL1ky9rEydKtIcLpJ3efnS2Su89m5VREXopUmgpTPP//88893Q3ZtzHKNxvXX2leQc+K+tgBOOCuHrbYi/bObiGr27PnLWYE6xaO2P5jNDH5Dy/1QX+Mjp+wFM/NofbU1HRV1zxJD7jfXkjcKfIWMZa6/vmaH6+85FcZ0r8qUOvuDYDxOMq4mIqp3RHww7ocpRBP4yIxrrtnmI8Z6T8bTLZJxa3Fa4M2caHZaRCQlMXGmRAo8T9WNkCQA8sNq4FOTqsKCcS0ANR8Ecm4nopjFkQI1OSjEPLYX84Trn3+uLD2cqq7WVwSQmgrg2wup8UQg+WGlY8fK581Y9sUSEZWuPCosNok2XQesanhLIfAqUY0r5wO4UOmcEpdffmO3y++aM+euy18D4nz9edPS7bJ36YAPxEVRS0RU8e3tG6dcXked7PlEC2Oi5shVKEOCluw8ML71lOlt7mxdzuMbU/nVAIDfJowjomaAa0eiynPt90bSJFGneEQlrz5pcL99NubOk5opHj2yKcWL44qN1YXdRDCPoGDZ6I5VVH67u/Xn20FOhSFlewaOxuNkHxFR+2QREY8L0A/vE23kGURP5/pIAOZJz7qZvzcgapzqrX6/TUTkj7pENcK+1MmYtMgvIqk1rzgudsaRDWNmrvEh84NEZd9N5QIXD0Y8MPaiRAA7X2+3HxFsdlBgvf8P33bvyiTglMpaTWMFI0WeIKIrOk46KBFx9iQ5/zhwYheATUuW7AaQNq6cu3MsCVjFIyIqW7O8UeIcoDN+2z/cWDJcjlTWqJojQfn0F2BMdP0zGzcueaYpkTdSQVTvhhtuuOEG413khTFR+2HDhg37YNiwB2pNgpeqoPpVU+6sU6dOMaLPOH+IxpoWz7GLw6XcnJTctXO6mP7wjSnQ0mffliwqjUfcT+FsM1X2plQ3jG+/3QHfVTujwfhmEZF+niB2G8HN9A72jpyAbF/874BcvTivMlHtXb5dPm4TKby1DtE14/wBixYQYGrMFQcwx5eoWfon5uWzmFcvXbbGl6g5kfnHi6rf1rNnaAcz41clxlPcpMxKPiFA2gz3A9f0qIgU3tNr8s5cEREZpKSqveLyG8687e0cSzQwJiKDeE6n+x3aPQVzZb1OdQvUBdy/BGNFPKOxS/wwduQIRmvbXZbEKcq+KCVud9zdy752w7iT4TEfH0+N+/X/JZc9uxU1feYZA7rVPwZDjTgqy3bLPVO8VZzzxx9//DH18qZdn0hRR3cvjOdlisgUT/YLEfjIzYZd/lHlwInmklP1uWmFB33kMJX2CzaERnyfK2Ffl9bPSv1m8MM/FRa84jjN9hTvdYUbbL+Hi6t5JnPmqVOnMsDM2X1c7EFPWUgUrPDlBk0vFJGwiAgfPvxcSTXhsuTLx20QF4yw7s3fg3GLHKCRRrdOosA4pFN96tpqXgdjHY43BsVjTSkzAcArwTopjLJH73jmjxxrXuI/bm7fokuXLh+v+yKFmY8o41idbHvKYPybp7zir48gAtl1RZvbVggErhW+CACs6tGjx8oIABdPzSqT2zYtBcy8xEnjUmGMB6gvi0gA41rIhNXmx/x5FW/KZhGRQ5Mr+696cJYZvg3IKvpCAGCd6wosGJe86pQB4+Gv1vBF1Ic5EE/19Pj5Oy0sDgnI4nr5qGHM2oCdtiv2X4afhw0b1q2Kcxl/D8aUFHiZRER1z0C3n/ZFBaKtmhknycqnQLxGgzG82ykq8h507467JGBzO1uan+b8h21r0phzMk/bLFdHXJyio50kTSNurOiW2LnGDAa71qnfDyvpFFkuQulaF041gQxOHaVMRJ1OqLw5Ei6AyP76AXnhJeq9ko4FIx4JSg46f4WISKfgbJwbVoqITKgXcM6yk9MLfnBnkv41nooSN40FM28de1Mz3zrlA0OGDBkyZKkmY7LmbVky+7HGWh5ijwTCeKW29UltvnHMUe3AeK/IXt2Oa58iX6G20sJY02cbI/rRmKokamEc0ebwEBF9bg7HkQgRrWe2YcVJo9wphxVHZakwHqVuNvZVBBErZpz8sXvxo9VBB8a/eMsiipUv3+u99957b8gbriD0OVZScK7TrbRM9afN26KHcaMCbd1M63BACqcpbYB8ZxT/2zCONhrTYa0n73oF+OSiTGPP3EiEiCqMsmCc/15b/+uv9kd7TBgvGNHEPQLOsUbind18KfPNf8k2YHziZv2am1uKYBxFAjrnpbOPaIuYvjLCzkHynT6xZNkpbXlTpR1Ii41mTVSnYkm00ZjaHP7XYEzjz5zRWvOfkgdWIDzn1Oe6vUb9UgTjKPI/75y+OcudD8HW6GHcHtoUGSKiK7I+CVZ4CPw18j/vG5ecY9YUwVgv/y1rfog+Gpvy/8+++WtSBOMoUmSNXs4xa4rIr4rkHyBFMC6Sf4D8dRiXGif3RFGHxLeDmSOvzJKfdboOLyZDX3VVJP+PSAzP12+MVbEXAnZpIiKi8x5Pe8m3L54hTedENmhLTitFXJsb+X2c2QDG+NsNBA5p7aQuOdCtRZV6IROALgD2chqYrYW6c8zjKrJGKz5rhov4Ys4xVv5dP7AOxo8DCN5sttkRqEUMHrletivW+EbjKyfeHxgaLXkr0S7fUUtqv1tm/4saXWhMBaK49wN1lS++sBLRyXTtib1S4mllqfzoE3+53f/X3rWGR1kl6eIugUgEIsyIkhERCSCgAyJyCaOIIKg7BMT7MK67BhKR6wLDYIMioAJ5nAyy3NXFETUkjICIkMYgcpGAEEwImGACmJAb6UASkq63en9891tkfRzXmYf6Q7qrv6+L8719Tp06VW9dufT+3LuFxz+H3JgXCoXemh/ZzBlw94VCIX/I73O9rnVSUlJSUtLkbm6cOoNTNwGbpnjxmF0zgnZfdryrwagzlZU5lMp3rqfi14+7qm66REReNaetF5Elg9MK2Ii3ixjMhc6q2ocB9HG8q0rjHfDsfLSoDHKqj3vXtAZ+ZsYx/YRb//mE6xnmvSo7OC7TJD/gzu/5A9JsR2Cmt7ZKT7f8B89/xmjGetKKmMRszdQNSqryQtfTn+lazpUzy0Erx4pxXtVt1QktcSYzyq78Y36FotyqJarbxuZRCTibmGkytxZ/cNdEHhLTeZNVWk6x8PtZ5X4R04m6NeDWINbPzOBCZxfbtmXwTkSn+4EtHqr7ioF1jkwMVRLBzIeN2ll9cHoFF6l/jb/stRwR5YsDjrM0/A86Mcuu06S/yAXPezaqEs2eK4bx4OEHQ0DKcKemYceXX9798pMu8yINK/lGzRUcd3mjy01bhLdv336kT3c4zdZc1oB66EaXKydfxoWSAIAvHct8yBPHEQXMRYWF6+cmljPbemr+eluFnhy22clTQUT31NgLP0zS4xy8UgNeg3c/rdfgPTfSf0uOKcfDCuNnGcyc6FKS3TYdKHDnviKiFkU46/IMiYhuO486n0exxeDUMnBgvPlXpVnzZ2hdrbOKvb6VKN9Bm9Im73t1H7oIebbirz471Ty7ieIo/qep2kHsdLmo5fBaH9WUVR/XrVo1p59zxY0tVTNr4myKhh3XqPmGKx2ZuA8UA+oS9W/V5S51Q4uOMzMjfYiLNTqM3XM/R8W1o1sy66APoi7+UMjn8/lcOuC9yaxWL3Z4n3OsOh9z7en807ufPQ3mWIc1RL3SpcCTbq1NPp/x4DfuUIagZ9wgDtjoddMn62pdEzWJiD6rZHDAUd5HRA3WA6s8s0ZabPFqCB4+0o9az17hU5jBlo2hPjglFdpyWeKAcTt995ovp2y6iRC1Cv2Q/pcqt5Rr6fK9JGSHcZuTovrZRwxqUONRXTsp45jI5UtBEcm11r9HdD5ZqiWIXcyxZN00TBAJlpaWlgZFVtsY7locQPAv2sbkW+RY83ia3f/c9mp1+iseYreGaGbN119++eWXdYC7W0lE1L/Si1OdyO+EcRoXaF5BNttSnDa9tFTd84NVAlQLjBt8JJ5dOIlWsidXxR7A67pGi2o504v9KirP+uRNMI7cWslclu3kkyGiNYB3/3l6Drjg7LRORPQMvFEcOziFGT6Lv6ENTnSVDl4njMdVjFeff74jAX6RDt5iiNWr6C8ajPuJncOCIr+tUtaovnUGqbhmTdNnUuXSrvmP30wDHl//nZUjJOIDNcVx59KlS5dZMiAbJoiUzyEiGpMjss66mV4LGEyq38LiO96ceJyZ3397/fpSrjrF6S5dSodHENF1lfXAOHw6UOYkXCEiZU52vAcoMO72CS7aSw4eVv55EiGNdcpizQr5xLuv+3Pglzwm1d8UeZbvhO06hHTPe/5JvrHk2ppgnMjM7F5NGB/ESn2PNughm6f7zGVcsLMFqx8tA+zLvi6xgFoPTUSklD9pgzMSBoxr7fdO0UM7Tt9Yh/HAyxBr4rxPL3icKHK37bohGrXjABF9tVKtabpG+G3dL1pmgXFcmjoRP3ATEfXZstJQNUwQqVPdmHEiEm/G8R3FKDEI36wwnlbO4NxuDYlo4OK5bzNfb7bGuH/rAwAOkauEd08FSuwMH4r4XH3j0dW8f14k0bAzzA6Xi4iIRqZfZGixUbM1N5fJMncziGhSQI56pSHuAbzqySYCNZ6nCXEBsXrvBoyfBVCmoDgqIeEFswdx0zlkaBHAexbWIMtCjX8be2UE3x0AVnp1fWixinn/eCKKHj41J+dk2Q4idxg7HLx19cA4RULK3jBVJGSBcdvjIup2yQnjhp+pXvY1yU4YvypFj+ifHFNrhnFkKoAQ0v6TiChqeAhI0YMOHUXqtIBP1AoRMcWq7rwAmMhE1wPntL/brwOQq1cq0ztAO7M1ivScPz8RgIv3S0REt444B7hWI8bE+EKhkN+puGVBFaOsqKi2rnCjC21As7vvLmRmjLSSRhIRXZcvKyJczSCi8KNc7BVquj+IAy08dLu8TxqIvpIj1ut0GA+uZOZNRNRn+MkzzMgzuTPzRNS4wXOfKTsWEzngjVkITGtEjXrGxb0yPe635innQ5HT6izWY9asWRZAR29l3t+WiObnKtUKuZ3JDcY9qhxPaoUG4xtKHTB+UpuNU4BDliDfnZBtqnFTHDBuKupls8UUOFKsiay+ZHhMY2uS80wwHh4MBoN4vxURUWRaEMFgUC9yXixilFk/VGsZtBnAIdOZV/MMowZwHfNHpl4prU4xTzdZo8jfNBaXBc5T2LB+/aYfBpDxmAONMfXE24heygYzf+0ai2/3MoOZ18RqVpusuUHEM9Le7iizZ7RhsYjLMZsiCwC39hxERDSZ+SXrOxqMI7Yy88dtqfNDRWoRTLbxoX8HFDv//j2AA6cA44k02gGkUtiibHVcPzEVxwWAe4haTg1UTikCUBUw8epH5ypMuX/O1YtuyAxjLVwwEpfsRXWKUxF55xIWB4xvg3xFRNRsH2BrKvGtfKFslq495Qnj7l+JGEYq1nxiEMU1nVLzznXHTTB+NhgMBju1IqIWb+RamYGbnDTDmL4TWWdE3c7hvMWZ3IfyKPXPPGZzRKg9s7rhMgFnhhan+Nq5XPdXWV7WufjF/vpQTNR6AzPzchdNrzPKIzJ4dkzb390ysyE1j47W6X1N8jCzZ7V4z7NeiwnRg99jl2dIYZsoAcpBo7QSVw3GfmbmThRr1HKVGm7yvQqMH/xCkJXYPWy2GcaDgO1Ne2sgBlCToql6VOP0b2jIVqVOFwBY93Z6ngdGU0RCCAo5Tkip8NEGp1c1UhoTEXVJhX2Ld81hkSlLM5Ri1WxbNXajjRJ8b/bUdSUARltVi0Q4kJyYuPVdkZO2U/zGObKJiAZ8a1lpFGuOyavq65t3XBzV5E6zU2HgdrlWlqmxDjU5Z4bxvdUiZ40vFWw2btK+d+9jwAn11XjmZBNrygxmXmOyhoiI5uujvc1+cNK+UNWsd4GxNhn7nCpq9duk0hDqLiD0mjPFoUB9RNitIdWwZpZIx+sXZIiIHFtiC2OHh3DpQSIKfx74yl5WvA34vDm17dy5s9Pr2Au82Gbyw89MPlfoaOLR7rKMJaJrP62Tc6pOg3EIQCJ1RgjwExG9AFO6zsA6jKOI/UHwwRHU5IUL2KgvZRF5qOj5MgDkr4+NjY1dXGrsPWcB8+n2AGqTJ3Xp0qVLl0SDJ5MSmJmofxqYOe+uTp0+sM7GNFpELq4LiYiNUYeIUsSQ6rl2rUCtb99sU7RINS47Yb9qmdTGDFhcKyJiPH4bjKOPZnQjWmiH8Wyi1Vu2IBgMBucEg8H9mm6iSPVG9Sx1yHHRHTPFyDMLO3To0GHUhg0bNnwNAChXVeFrmSunaY5NHz/zMccWbyIjKyPjsJtvfH2ahvDO5JCYkN/n7hoPezOTOeetwf2jd4CdZ4p/1+kfRljGhohop+Q/c0REaotFxHZCNhn8FFF44mEwc75VF7YHeHrtxiwA5xfafnPzGZWP1oIBAKdusCqHyNlfEUXk8IdSrh7yaTAG87G2kVsZvH8UEVECGzMndS+vHUrpwLlRRE2eBb4zYqBTUfPUuwCKJym7vGtXGzBeDXShx1E9QX09EnV6tc5xLhhGq4sYnDGsa4vOM8EFw8yD0/TF7DJAzqcedfCQ0aMK53dV+oeiT5SGjK5SOaUcEcJrfv/7uZsPb05KFXEsnN0VeB8JSqFRh61Y85asjSCiNvOCMxoR0ULzxSpTrrbPG24u4g/zi0jWxqFDh074PEtExHRYmwy7BIy4xfu1CAGZr73+2gdACNkjzNaoQ7AomqjRY1Uuq/I185N6937eHcaKxDim45i9tXVfzO3XkohoOXKcvkGbDXv2LLhrw0HG+W52a6aIiMinwwf++iWx5zFOBvejAUeV34DNR+4N4JS2UveyqKLOAAePaEOTbcur2C5xRAMyJHaIkTZgwNgXsZUZgYFERHHHzTCegELqWIHdHYk6vQXARNUu+CttAua0piE3EdHdmbjwqqHDqsdLoQ1ZSz8q9Os+4NJB9CozeCUNTtF9cUsYp1+/fjc0+CjopCJ5bPv2v8wZE0XtS5yH0UQPbTh/vnivV6C7GdG9Tpeams+79I1cXnB7law23lSsuQninzBh9ne7lQFZKKYUKIMUOHNiK3XHpyub+HaJWczoeM+E4N3z5p2xOPIzDh8pLWQGc9Z/gD+xWEOmFb9xwAbjvpnq7n4ocNqLxIAoxha763WQ9+mu+isojPC6sDujsrfFGiLqVhoSiQujxpE75ZIt62YyeMJbAeaDfZgP2m7WW/nfCwAUWVMn+gJYcazgkPKJ96zXdSyVOOpeHpzV95xox6YmGLOfmTGeiKITD5vzKjrWoHL2+8AjYYuLAkDVONMeW7C2zSbgi+Liuukz3ym+LIHBuu4dxYr/6tq1a9euXUf6UWKM+YcAEs4DIRQp/vG6rtbBUWW4d9yZXHMqVGlbz3nNGy4wJmreNCyMnhIxPQjVmkdPi0hVuso1YoHxGQXG1dljf0Wk7PjMS2fT+5J3Ze9JTk5eW2aDcZPwGcuXL39i0OTw8PBmRPvsqTPdOw8dOnTo0HCyw7hX+p+1NgmNP7M5FY0+xlkiIhq6F3BklPjNtKBmResTvMoIavwNFxwH56pMymadv8t0i77FIm/cl/CpSLGdb/m5SgCCE08cUnxks5hhbFtx+wLYkVZZBADVk2yM4v1Fomi11O2txVotFqXBeHC5clNjy2Ucq9+szhqnAyGg7lPLFJ8InC42zSyHTWnOPf6qhYUUhtMS03lepkEawuDAx52a2AdHked/NhgTEdGsUFaE8Uqzpk3C66/r4ZKFEmN8InppaTCYu1RreY1gMDjMesfm17ckoh5lIm97Vyj0OOeVAeaA8XhAPTRuuQ1Aunk71mA56hYS0R1VcCSUkC8U8quW22bjJKN9QKuBQMjajPHWyf47iIgGTykHsEQLH5hvcVdARAQbxjjjJr/TnnGWo59Os906jMfbEu5b/I8OqIye9uu6XZL97xaISOVE/T09bpxihhUzdhmPo/nSCmi/nNM2ktqGfzT7d7O6WPjd6A9JFRW1CoxrD000LxxP12jfV5Cds0k/p/u5YBzloTpg4aFyzXCLP2UJRw8cNsyIK7jAWJFxIt6ZXOQyGxsW2WD8uwqgoCvRfQnfASiw5nc8cgR1y2+J3AfAcazqCylpxjE+n22Pt5o5M23smDFjkv2ZzJxnTbg8xrx2zJh0JVHTODO0jM0B+X7v3kfczL9RwdPm1W2cunkKpvyJMY4A9wMqok6scLkuTfHRLpq722owjkzJLjRgXLjSsnUctkyJqC2b4mgZ0ajv03/6vGDd58l9+/Z17dA2eubMmbNmzrQ3KX4qPo05KT4+3jWnQpeNPxLGlOrc4mnyhogHg++VwHiMeCQqEPUJwaOS4YdgvPmA10nX6zqrmGrNBAD5+/ZdBICz9oOuR44AlRcAbHcEzfSjD0dRxPUZQYOjr+Yz29jkmzgqqwynwXKPG095HWNEZGc9/sTUDq45FY1uf6V02Su+Ji6qW4sAFMdf53rG13R+VvWa1JQRprfMGW49k5jhi4+Pj4935hv/POIAzl6ciPL+eL4zcqZJih7Bckg9TsUVwHihN4y3IBjc6WzGTETN8yR0yevclYjae5YGHLdv8WJytcWPjzvaLYctVVT3u9DeGTjWnAtdxu7KBjPziV2LBtsvM2BcuduEnF9YSdUvnKdiBnDGu/ojXryS9SnFm/o46qBM9FCtEnOCn+uj2lk/jD3m3JO2Ld6VSpQjUkGRfgWq+W5kog3GA8Awdzfc5wuFQj7XCqbW0dHR0dFuqTFvMvMmTmZOM09/V2FcnzgGp2Nuib1lxJXJHG8YU9gZz3PO+q0hItpfL4xL7M6TKidFDtbTp8NTRoCX2K2JmDYt7cKSaY7ORj+rXIVxPfLTDU6vquwf/tAPyP/VqbgTQa8vfbFiyY+ZjInGLtbzBv5Fn9RPIf+yMP4pxNWa8d4w/sfKP8HY/L/JVRjXI1et8ZZfmDUNflHmXJWr8qPkfwFUn/rfsJxSHgAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<IPython.core.display.Image object>" | |
] | |
}, | |
"metadata": { | |
"image/png": { | |
"height": 284, | |
"width": 710 | |
} | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"y = model.predict(valid_x)\n", | |
"best_all = y.argsort(axis=0)\n", | |
"mosaics = []\n", | |
"for i in range(y.shape[1]):\n", | |
" best = best_all[-25:,i]\n", | |
" mosaic = make_mosaic(valid_x[best].squeeze())\n", | |
" mosaic = np.pad(mosaic, 1, mode='constant', constant_values=(1,))\n", | |
" mosaics.append(mosaic)\n", | |
"show_array(255*make_mosaic(mosaics))" | |
] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.6.2" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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