{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# [DeepSphere]: a spherical convolutional neural network\n", "[DeepSphere]: https://github.com/SwissDataScienceCenter/DeepSphere\n", "\n", "[Nathanaƫl Perraudin](https://perraudin.info), [Michaƫl Defferrard](http://deff.ch), Tomasz Kacprzak, Raphael Sgier\n", "\n", "# Demo: part of sphere classification\n", "\n", "This demo uses the whole datataset, smoothing, and the addition of noise.\n", "\n", "**You need a private dataset to execute this notebook.**\n", "See the [README](https://github.com/SwissDataScienceCenter/DeepSphere/tree/master#reproducing-the-results-of-the-paper).\n", "But you can use it with your own data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 0.1 Load packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "import shutil\n", "\n", "# Run on first GPU.\n", "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n", "\n", "# To get the CUDA profiler (do it on the CLI before starting jupyter):\n", "# export LD_LIBRARY_PATH=/usr/local/cuda-9.0/extras/CUPTI/lib64\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from deepsphere import models, experiment_helper, plot, utils\n", "from deepsphere.data import LabeledDatasetWithNoise, LabeledDataset\n", "import hyperparameters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = (17, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 0.2 Definition of the parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### A) Non tunable parameters\n", "These parameters are fixed or the preprocessing script has to be modified." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "Nside = 1024\n", "sigma = 3\n", "data_path = 'data/same_psd/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### B) Tunable parameters\n", "These parameters can be changed.\n", "\n", "We choose to work in the noiseless setting by setting `sigma_noise = 0`. This allows this notebook to run an acceptable time. In the noisy case, the training of the network needs considerably more iterations." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "order = 2 # 1,2,4,8 correspond to 12,48,192,768 parts of the sphere.\n", "sigma_noise = 2 # Amount of noise for the experiment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1 Data preparation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1 Data download\n", "Set `download` to `True` to download the dataset from zenodo" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "download = False\n", "if download:\n", " %run -i 'download.py'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Data preprocessing\n", "Apply the preprocessing steps.\n", "1. Remove the mean of the maps\n", "2. Smooth with a radius of 3 arcmin. (`sigma` parameter)\n", "\n", "Set `preprocess` to `True` to execute the preprocessing script." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "preprocess = False\n", "if preprocess:\n", " %run -i 'data_preprocess.py'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us display the resulting PSDs of the preprocessed data. We pre-computed the PSDs for faster execution." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "compute = False\n", "if compute:\n", " psd = experiment_helper.psd\n", " data_path = 'data/same_psd/'\n", " ds1 = np.load(data_path+'smoothed_class1_sigma{}.npz'.format(sigma))['arr_0']\n", " ds2 = np.load(data_path+'smoothed_class2_sigma{}.npz'.format(sigma))['arr_0']\n", " psds_img1 = [psd(img) for img in ds1]\n", " psds_img2 = [psd(img) for img in ds2]\n", " np.savez('results/psd_data_sigma{}'.format(sigma), psd_class1=psds_img1, psd_class2=psds_img2)\n", "else:\n", " psds_img1 = np.load('results/psd_data_sigma{}.npz'.format(sigma))['psd_class1']\n", " psds_img2 = np.load('results/psd_data_sigma{}.npz'.format(sigma))['psd_class2']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The PSD of the two classes is almost indistinguishable. \n", "\n", "Spoiler Alert! This is the reason why PSD features are not good enough to classify the data." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ell = np.arange(psds_img1.shape[1])\n", "\n", "plot.plot_with_std(ell,np.stack(psds_img1)*ell*(ell+1), label='class 1, $\\Omega_m=0.31$, $\\sigma_8=0.82$, $h=0.7$', color='r')\n", "plot.plot_with_std(ell,np.stack(psds_img2)*ell*(ell+1), label='class 2, $\\Omega_m=0.26$, $\\sigma_8=0.91$, $h=0.7$', color='b')\n", "plt.legend(fontsize=16);\n", "plt.xlim([11, np.max(ell)])\n", "plt.ylim([1e-6, 5e-4])\n", "plt.yscale('log')\n", "plt.xscale('log')\n", "plt.xlabel('$\\ell$: spherical harmonic index', fontsize=18)\n", "plt.ylabel('$C_\\ell \\cdot \\ell \\cdot (\\ell+1)$', fontsize=18)\n", "plt.title('Power Spectrum Density, 3-arcmin smoothing, noiseless, Nside=1024', fontsize=18);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Data loading\n", "The following functions will\n", "1. Load the preprocessed data\n", "2. Create samples by dividing the complete spheres in patches (based on healpix sampling). See the function `hp_split` of `experiment_helper.py` for more specific informations.\n", "\n", "The function that load the testing data will additionally add the noise to the sample." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "x_raw_train, labels_raw_train, x_raw_std = experiment_helper.get_training_data(sigma, order)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "x_raw_test, labels_test, _ = experiment_helper.get_testing_data(sigma, order, sigma_noise, x_raw_std)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2 Solve the problem using histogram features and an SVM classifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1 Features compuation and dataset creation\n", "The following function prepare the features for the SVM classifier.\n", "1. It splits the training data into a training and a validation set.\n", "2. It augments the training set by adding different realization of random noise to the sample\n", "3. It computes the histogram features for the training, validation and testing set.\n", "4. It normalizes the features in order for them to have a mean of 0 and a variance of 1.\n", "\n", "The features are computed using the function `histogram` of `experiment_helper.py`.\n", "\n", "We use 10 different noise realization by setting `augmentation=10` in order to increase the number of training sample." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/defferra/miniconda3/envs/scnn/lib/python3.6/site-packages/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n", " FutureWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Class 1 VS class 2\n", " Training set: 765 / 771\n", " Validation set: 195 / 189\n", "Computing the features for the training set\n", "Iteration 0 / 20\n", "Iteration 1 / 20\n", "Iteration 2 / 20\n", "Iteration 3 / 20\n", "Iteration 4 / 20\n", "Iteration 5 / 20\n", "Iteration 6 / 20\n", "Iteration 7 / 20\n", "Iteration 8 / 20\n", "Iteration 9 / 20\n", "Iteration 10 / 20\n", "Iteration 11 / 20\n", "Iteration 12 / 20\n", "Iteration 13 / 20\n", "Iteration 14 / 20\n", "Iteration 15 / 20\n", "Iteration 16 / 20\n", "Iteration 17 / 20\n", "Iteration 18 / 20\n", "Iteration 19 / 20\n", "Computing the features for the validation set\n", "Computing the features for the testing set\n" ] } ], "source": [ "ret = experiment_helper.data_preprossing(x_raw_train, labels_raw_train, x_raw_test, sigma_noise, feature_type='histogram', augmentation=10)\n", "features_train, labels_train, features_validation, labels_validation, features_test = ret " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 Classification using SVM\n", "Let us test classify our data using an SVM classifier." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimal C: 0.03162277660168379\n" ] } ], "source": [ "error_train, error_validation, C = experiment_helper.err_svc_linear(features_train, labels_train, features_validation, labels_validation)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The validation error is 11.458333333333332%\n", "The Training error is 10.188802083333332%\n" ] } ], "source": [ "print('The validation error is {}%'.format(error_validation * 100), flush=True)\n", "print('The Training error is {}%'.format(error_train * 100), flush=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now compute the error on the testing set. To avoid complexity, we do a small mistake that advantage the SVM classifer: we do cross-validation on the testing set.\n", "\n", "While this is wrong, the spherical CNN still clearly outperform the SVM classifier." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "_, error_test = experiment_helper.err_svc_linear_single(C, features_train, labels_train, features_test, labels_test)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The testing error is 10.104166666666666%\n" ] } ], "source": [ "print('The testing error is {}%'.format(error_test * 100), flush=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Histogram features visualization\n", "\n", "To get a grasp of what is happening, let us plot the histogram of the data." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cmin = np.min(x_raw_train)\n", "cmax = np.max(x_raw_train)\n", "bins = 100\n", "x = np.linspace(cmin,cmax,bins)\n", "\n", "fig, axes = plt.subplots(1, 2)\n", "x_hist = experiment_helper.histogram(x_raw_train, cmin, cmax)\n", "plot.plot_with_std(x, x_hist[labels_raw_train==0], color='b', label='class 1', ax=axes[0])\n", "plot.plot_with_std(x, x_hist[labels_raw_train==1], color='r', label='class 2', ax=axes[0])\n", "axes[0].legend()\n", "axes[0].set_title('Histogram - Noiselss case');\n", "\n", "if sigma_noise:\n", " # Updating cmin and cmax does not really affect the features. \n", " # We keep the same as in the noisless case in order to have the same x axis.\n", " x_hist = experiment_helper.histogram(x_raw_train+sigma_noise*np.random.randn(*x_raw_train.shape), cmin, cmax)\n", " plot.plot_with_std(x, x_hist[labels_raw_train==0], color='b', label='class 1', ax=axes[1])\n", " plot.plot_with_std(x, x_hist[labels_raw_train==1], color='r', label='class 2', ax=axes[1])\n", " axes[1].legend()\n", " axes[1].set_title('Histogram- Noisy case');\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These histogram are normalized in order to get the final features" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plot.plot_with_std(features_train[labels_train==0], color='b', label='class 1')\n", "ax = plot.plot_with_std(features_train[labels_train==1], color='r', label='class 2', ax=ax)\n", "ax.legend()\n", "ax.set_title('Histogram features');" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# ax = plot.plot_with_std(features_validation[labels_validation==0,:80], color='b', label='class 1')\n", "# ax = plot.plot_with_std(features_validation[labels_validation==1,:80], color='r', label='class 2', ax=ax)\n", "# ax.legend()\n", "# ax.set_title('Histogram features - Validation set');" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# ax = plot.plot_with_std(features_test[labels_test==0,:80], color='b', label='class 1')\n", "# ax = plot.plot_with_std(features_test[labels_test==1,:80], color='r', label='class 2', ax=ax)\n", "# ax.legend()\n", "# ax.set_title('Histogram features - Test set');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3 Solve the problem using PSD features and an SVM classifier\n", "Solving the problem with PSD features is very similar than solving it with histogram features. Hence we are not describing each step.\n", "\n", "The computation of the PSD features is actually very expensive. Since the classifier will also fail miserably, you may just want to not exectute this part of the notebook. In order to reduce the amount of PSD to be computed, we disable the dataset augementation by setting `augmentation=1`. Nevertheless, we use augmentation for the results in the paper." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/defferra/miniconda3/envs/scnn/lib/python3.6/site-packages/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n", " FutureWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Class 1 VS class 2\n", " Training set: 765 / 771\n", " Validation set: 195 / 189\n", "Computing the features for the training set\n", "Iteration 0 / 2\n", "Iteration 1 / 2\n", "Computing the features for the validation set\n", "Computing the features for the testing set\n" ] } ], "source": [ "ret = experiment_helper.data_preprossing(x_raw_train, labels_raw_train, x_raw_test, sigma_noise, feature_type='psd', augmentation=1)\n", "features_train, labels_train, features_validation, labels_validation, features_test = ret " ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimal C: 0.1\n", "The validation error is 26.822916666666668%\n", "The Training error is 0.5859375%\n" ] } ], "source": [ "error_train, error_validation, C = experiment_helper.err_svc_linear(features_train, labels_train, features_validation, labels_validation)\n", "print('The validation error is {}%'.format(error_validation * 100), flush=True)\n", "print('The Training error is {}%'.format(error_train * 100), flush=True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The testing error is 32.916666666666664%\n" ] } ], "source": [ "_, error_test = experiment_helper.err_svc_linear_single(C, features_train, labels_train, features_test, labels_test)\n", "print('The testing error is {}%'.format(error_test * 100), flush=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 PSD features visualization\n", "\n", "To get a grasp of what is happening, let us plot the psd features. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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vfKWtrEgkgu9///u4cuUKHnvsMRw/fnzblXMhO4Ve3wGcOHFCnjt3bqersTVKJeD0aaC5rgHxOPCJTwAez87Wi2EYhmEYhmEYZhu5evUqHnrooZ2uxh1Hp3YRQgxKKU9sdi57yreL1cYN9pQzDMMwDMMwDMMwm8BK+a1ig1T6DMMwDMMwDMMwDAOwUn7rYKWcYRiGYRiGYRiG2QRWym8VHL7OMAzDMAzDMMw9yJ2al2ynuNn2YKX8VlGtUsI3hmEYhmEYhmGYewS3241kMsmKeRMpJZLJJNxu9w2XwfuUbwedOuTyMv38wR8ADkfH0+r1db9iGIa595GyfTtJhmEYhmHuePbt24dIJII4OyBXcLvd2Ldv3w2fz0r5rUDTgEIBKBaBcnlF865Wafe0ri4gGgWuXgWeegq4CaMKwzDM3UmhAJw9C3z4w8Du3Ttdm61RLFK9BwY2P5YNDgzDMMw9isPhwMGDB3e6GvcUHL5+K3A4SHBzOEgTbzI1Bbz9Nn00Pw/k8+RMZxiGeV8hJVklGw3gypW7IweHZQEXL5IhoVRa/7hoFBgeBv7+74FUavNy63UgkWibKxiGYRiGeX/BSvl20eoRcTqBbJZ+12orH6dSJH8tLwOZDNDbC0QiO1BXhmHuHsrlrSl3N0ujAYyO0uC0HtUqYJrtn0m5vlJdLq8tT0qyUMbjQE8PjZHZ7PXVtVYDKpXrOyeVAiYn2z9rNIB0uvPxptne7pEIkMsBLtf651QqwNAQEIvRcVevbl6voSHg3XeBt95qV8xrtY2V/zuVSmVtH2EYhmEYZkNYKd8uWteVu93A4cOAYZBQ2vy6UKDQ9elpkmE9HpLx2EHCMMy6XL1qh9jcSiYmgGvXgEuXOufJKBaB118nL3Ark5OkVHbi8mXgzJl25XJxka7T20v/GwaQTLZ/3/p/J86fB954A1hYaDN8bsjVq1T3YtH+bHYWOHXKVr4TCTIYvPoqXePNN2ngrtXIYNHdTeP76hCnZJKOy+XIQBsKAX4//Z/LrV+nXI6MEwMDZLFdWLC/u3ABOH166/fXSqXS+RneDs6epeeryGZ5kmMYhmGYTWCl/FbSopTX6yQj+XwkE3q99mEbyWwMw7yPqdXI66ppmyuq18vS0sr4BNMkBXX3bhqgWhVXgJSqc+fI+7u0RAOaYnGRlNnVymOpRHV2OukcgAZBpdzqOn3m9dpKbqVCyvDgYPs1VpebTpPSe/48lbcZ1SoNtG53+64Y8/P0WTRK1xscpPJcLjre6SSFPZEgr7phkDU1mbSV3lqNjBKXL9Oxrdk7XS7yhF+8SOevJh6nMgEgEADm5qjcfJ7KknL9XTxU2HunMn/3u/Z2MU1gfJwswjfKRhERrXUqFOh5WhYZGV5/ndpGUattHI2xFbJZUvw56y/DMAxzj8BK+a1E11c8BK3y6sAAyZMAyXezsyRH3Y2RigzD3CLqdVLShACCQVJsfv97W5FWVKt22Hm9vtbKl0isVXALBeCdd2yveC5HSqOudzYAzM+TwhwI2EqjqmOxSOcVCmuvq2m2sgnQdSoVUnYVbjeVV6+Tsq1ppNAtLnZul0KB2sTtBnbtouPWC5duNEgxVIqt32+vGVKZN8NhMnxks3Rv/f1kKOjqsg0GCwu2JVXX6XrFIh0/Pk51zmTajwPIY25ZdM1OIe/RKFlqAWqTcpnuLxYjZd3no2M6MTlJ0QKLi1S+8kaPjVF/mZqylwWMjVE9r161n91qKpW1fauViQkKsd9IEa5UqH1qNTIAXLpEzyiRsJ/R+DhFAKw2/FwPExP0PnQyStxrZLPUt+6GvAsMwzDMDcNK+a2kRSmv1zsn4vX7Sf4dHKQoz9XydKPR2cHCMDeKZd2jDqZc7s4SXOv1G18L3mhQGPD4OCmNbjcNFtVqe4izlOSJnZ6mEPczZ+hHeSKzWfp/Zqa9/FTK9vim06TcKA+v309KeGsniURIwQRoXFPlK8XUMKgMpZjn82QoCAZtZVN5zrUO045a3xONklIbCpHS2Wnwa/VGaxods1rRzGTo+tPTZHwYGqL7UgYAVR8hqO61Gt2jsWpDEo+HFORYrF3Z1jRq22SSrKo9PVSW8q634vdTOasVyEqF7tnlsj9zOKjt5+bImOHx0P2aph0iD9D/c3NkQBgcpJ+REbq3XI7q6vPRZ+UyHdvXR+XHYmvbdH6ePNqnT3dWzKWkMtLpjdf/K8OAz2c/f9WmpZJtJHE6bzzLaaNBbTkwQMaGe3Iwa1Kr0Tjw5psU/bBeLoPbhZRkBLoZg0orqk/cLhoNqv+93GcYhrlrYaX8VrJKKe+EELS0UslLrdGFjQbJ2WfPct4cZnsolUhHO3fuHpNLqlV6WTopHDvF1BQ1diJBnr2NXuJ8vl2xjMfp//5+W6lxu0lBn5+3Q4SjUVLW+vrIs+t0kiKnlPCFBfp/tZK9vEyKm99P2c8jETt8Rymuqj7VKimQShH2em3Pcy5ne8OvXiXFbnHRDnVXCqoQpMwtLtrXacUwSOmLxUihczrpmpOTpJC0hjsnk3RPCoeD7nNhwVYKL18mg8boKLBvH7VPq1Kdz9tKeWubKK+1Qgiqr9/ffqzHQ20/M0PnCEHX2L9/7b2p41X0wfIynZdOr7XUKmNEtUr3JQQ9t1SKQuQvXqTjMhmaILxeWnKwbx+VOzrablzJZskgoWn2c5qZodDvkRGamKpV6gNdXXReq9FHUS7TsV5vewRDLEYTlDKGVatUZ5+PlObW/T7LZVLmGg26z9lZu09KSe2zFaOaMgT5fNT/NlNUTfPuHOzSaWojy6JoA6+X8gysJ0x0ol6nd3+7jJWRCI2zIyPXf24+T5ETqv6pFEX+XLq0cf1GR2kM2I57SCbJSLfdS4EY5n2AadIQcCf5Pu41WCnfDtab8A1jZTH5VhIFBwK09FKFuiu5PJu1l2TG4ySXXc+8zNybxGLrR/h2QkqSf0yT9MSddrpsK7kcvSg3o5TX66Q8r+fdvp6ZyLLs9crKk7m4SC/06rXXlkUK1zvv2C/2/PxaBREghatSoeRkg4OkcIXD9nfKo768TEpQJELKVrVKg8f0NNVDecp9Pttb1boW2uGwO9fqdTVuN3WeapXK9/no+H376FrnzlEna62/x0PXVuu6VxMKUVlOp62odnfT8ygWbSU7FqM2bPUuB4OkaL77Lh1fKpHRYvduUmYMw16/DtC56gVQRgOVwG21lxuwlfJWvF4alFvDz4H19yV3uWxDwNAQvYiXLq0tV9OAPXtIwVfoOhl4hLDLWFpa247BIN2XimgAyIOfy7X3EcD2xk9M2MqwYVAZqw04gN0HAgHbKKRC9xcW7BCvYnFttAFAn+XzdJ6m2f1YhebH4xSK3ylUv9PSC9XOLtfayJFWLIsUuvHxteWq469XwqzVqO1upWSaSNBSgfFx21jidtO1lTDQynre5miUxhU1LloW9fvx8Y2XKnQilaJEiXv3Up+5ngSEpkmCi3qHazV77FpaWj8/QL1O77YK4b9Z4nHqf53akLmlWNb1b5jB3Fmk0yR2cB6sWwcr5bcaKYFGA6VSZ1mlFeUUUUse5+ZI3gsGbYP58DDJRJ2cGfcKpnn98sKNYFnbF4V3uymVbF1vqxHSsRgdGwqRLLveUtW7kmSSbuxGPSDKYjE+TkKs2r/w9Gl6EbNZCh89dWp9a0YkYocoqzXSPT30Au/fT57kN96g67SSy5GQapokNNbrdB+tnt1W+vtJaRsYoJ/ViqRakz08TL91nco6d468Thcu0GCjFJueHlJKWwkE6H6kpJekVdkUgq4xOEiCcqvX2u0mpXJ1eT4f3Vur4t+KrpMCrZRHgAbMgQF727SJCeC99zqfq7zFkQi13XrKsarL8jIdpxRbXe/swV+P1hCnja7VitoKrtGge1Vh7Zuh+nUwSNdKp0mpWG20cbupPq3LA3Sd2q/VKBEO0/Pp7aWJZXzcvnelLBeL1B9HR+la2SyVoev0XE6fpn6Uy9G5ahAqFDo/Y6eTjk0mbYNKdze9C4UCTWrBINWl0bAVtFKJoi9aQ91jMfu5+f1kPFLrvN5+m0LaFdksvYuzs/T9xAS9F1LSfb75Jp1jmtSXVaK9jZidpX7YqiQ2GltX0jMZMjKtdx3TpO/DYer/rYJDMLg2WV8kQs+jk2IejVI5U1P0/8wM3fOVK2u3B9yIuTkaF9VyBGUg2gq1Go0V5TKNXZEI3UO9bvfZmZnOSnc2S+2qEkw2qVRu0KicSlG/V32GuW1MTlI3ZYdSBzKZju9jpXJnKcDp9CZ1kvLujEq6g2Cl/FYjBGCaKJXWl0db6eoi2f3SJZJffD6at3I5mkcqFZpTpqc37/uWdRvC3m9BdrrhYZIbbvWcOTNDOtZ23cKtHItMs/1ZLizYjq1WGXS9c6emyFGhnGh+P8lr94xcEotRY1Sr9r7Xpkk3qKQA01xfkEylSPDv76dGGhoiBSGft7cJczrpIbdu96RQgrRSuFMpWxFyOkmodLlIEYnH22e1TIYUKZWcK52m62yk7HVal91KdzfVQSnHfj8J+EqZ7+nZ+Hy1zlqFB7d6plX59Xq7R3cjhLA96TdCOEzPprd3/borY8TU1MbKrvLaZjKdPeNbxem8vvM9HhK8QiHqG50iBjqhDBYul11Gvd6uaN8ImkZ1qFbbDUC6TorT0BApsZcukVKmjgmF6LnH4/badRVVofZxX43LRe0dj9vPxjCo/a5epc/DYarLa6+Rl9g0aaArlexEgdWqvfRC1VUp22fP0jVmZuyBbWmJrmeaFHI9OkpKdTpN77GSMOfmSHG8eJEMBOthmlS+SgajGBwko5c6pl4nJfTy5fbt4KQkhXhsbK1kWyjQ52r5Qqf+4XKRwWRhgY6bnKTyWpeb1Os0iWYyNA52ddHvbJYEh74+MpwtLGxNQKhU6Bn19rYbsVYnn8tm270FpRI918uX6bueHnoW8Xh7BIAQdF+dcgyo3RuUkbBZ39FRCoy5rvlLJWd0u+nvHVjqpFYCtVEu0zPcKFfDnch1eE5M07bDqG6aSNxZCueOMjZGfaBSaXulhofbVwftNCroal3fx9WrZHl5912aP3grzOuGlfLtYj0BWkrANJHPb01+MwySPZeWSEZXaBrNYyrCslKhMXxycv18OSMj5ARYoywuL2+PiziZJC/GNq7PqlbtHZda9aftMDBYlj2PSEmylWVtPjdbFsmlGzkWVJTk7OzW62OaWxuzpKSBWa0DV3JhMEjysLJedjpvedne5SkYbJepGo0biAhUHiZFrUYXaXayaJTmltWUSs1DisUbT/DUytISFWqaJJC3vmATE/Y2TOfO0YOp1ejvU6c6K+bT07bn0e2mBzo7S4Ko2tpQhTFns2tDN5V0oTJ6rw5rBuhcl4sUivPn7XZU66jVWu4LF67Pa9sJw6C634ziZhjUzq2KlEKttd7MOLDdGMbG1+zqonbtFPrfSk/P5oaJ7SYQoNDfTkrrVlHLDW62f7TWaXU7KM9qIkHGAF2n/t+qJKpIAZU8L5ejwaRS2Th8PZ9v/z4Uov6v6/a6/GCQjpuepoFu1y4yMBUKNKGt7gPhsD2YDwzQwFYo0G+VKyEYJIWxt5f68ugoGRLCYeozw8N0/MAAKeudJhul2FsWlanyKmSz1FbJJLXTxYvkVR4fp8GwVclXy2zUHvYAlTcyQuPU5CS1/epIk1a8XpqQpqboRwkFalybn7cjYpRxz+mka9RqdvtZ1toJIJNZOymprflaxxKvlwwZb7xhT4yXL9O9V6tU9uAgTVyJhN3HhKB76+5uf4ateSpaSSToeen6ShihZdEUoh5z27Np1fJKpfbkiq3W93DYbo+tavbKsn0TDA2RvtLGpUvU14eHb6rsDYnF1lWiq1WaFq/LJlCpkOFMhStYFs27asHx1FRbe6fT9o6S6hU4e5baotOrZln06oyOdlBIFxa2vvzgJrwO+Tw9khvx7FvW1pw9jQbs99AwIPMFnDljbyiiNq0oFkFtut72ny0ySaVy66IRVGBUR7lRjbdKRozFqMOz5/y62BaJSgjxr4UQMSFEx1FFEP+jEGJCCHFJCPHYdlz3bqFRNVEubx6+rtA0msNaZaCuLurzSlFXS1WvXKEtYCnmAAAgAElEQVR+v/olNE17CWvb4FCpUOhdJ29fJ4rF9Ufr+Xmq1EaehRbGxzc/VOn3mmbLGJZFVX71VZIzbtSgrHYQKpXop1qluVktF1iPxUWq97Vr9uRy4YLtvFH1TqVoXtrKGFQqkXy5no7YikqonE6TrqeSUrf2p9W7Uak6vfsuXau/f61RaMMEyJbVeUKbmCBPlnoIanujRGLFiTw+btt8CgWq72uvNcPlL18mgbX1Iap1kq3CYL1uH2NZ9j7RAH3+7rs0q1+8SFbm3l76zuOhOvb12dtcVSr0kqTTFK6y+qaVR6dVkVOeWV0nxaFVcVEh3ZZFluFMhhpbhXWmUuuvnQZoVlPhC40G1Usd29tLitJ6oeu3ExVOfD2D107jcFAbbhZS7nZvLXR8u9lqqPtG569OWrfd6Dopp+qdUmHUG+F02tvrrUdX19pICSFI6W5VQg2DjlPrjA2D3rWhIXpvWpc4qGsrZRugyUMNmKZJ57vddtLEQMBediEE9Zk9e+gdNwx6J1vXyCvGxmgy6Omx97FPpcjYp5L9LS7a+5vOzQH330/vuSpH5U1wu20lNJ0mo4Iac9S4sx5+Px2jlFtl0EskaAydnKSoFMBu12CQxsLWMc7tblduSiUyDLz3XrvhVUUbtKIiLOp1ul4iQROVrtO4OzZm53VQ/UihooZWP8NisV2QUZFN6thwGBgfRynfgGXRYy6VQNe+epUUltawsaEhuh+lOLYKQk4nPeeRka1ltbcsus/WrQc7sXrtnYqagL0qqVgE6snmUo5SifrQrl3Un1QdW70ShcLNrefL50nYOH++o7amuuzERPODrQgwKrJMtVs0Su1/6RJpsufPtymQi4vUhdxuOnVujv5fb4OSWIxeiYkJIJmQVH6tRicMDZEAtlrrtSx7a89ymQp49dXNn229vsYyICWJKmr1zkr5W1Qw5+ZILNpoDX0q1ex60fLKMrPycg6pFN2ukumEAErp5ran4+NrNeJqlfr5pUuQkmwlK+L9ZkYJlXy1SZuNKp9vO9806fvWV3/F86O2Rm00aHzy+WjsSSTWFzLj8etLMqCEyTslbOAWsV1ujr8G8B9s8P1/COBw8+drAP5qm65757DBy5pNmptGo26GrrfLImqnoV276D1Y7azO57EycbWFCKl9gNfJdGtZ9nJa1Gqk/Lz5ZnshzXXyiMVIOIjFVspSzZBO20Yz1GqolCXGxmgOrFZBL6Nah9d88dWSy0DAHrwB+q2WgmWzVJ3NnK1S0oCuqt1o2CH/aomkit7M52l86GSxLZdpvu/upjYfGaFyFhbob+UBVjtX1Wr2XNq6NFKRyZAhRS0BdDrp76Eh+5gVS2c6Dbz9NjIJE5pGuqEyqrfqiIYB5OMVUlJbhMho1HYSdUKFsK/pBlLScx8cbP9SCXwej62szc+TkjA3h2LRjrpMp21nyfh4My/WbJW+UBdWXLxIguDly5iebj73kRGyWKhtsk6ftteCxmJUB7U3dOu6S7/f9kaqUOeuLurvSoBVnv0LF+h+ZmZsT91WUBajWIwmyvPnqR38fnqgWwmXUInNUilqKHVtTbs5T+p2Yhj22mOG2YhwmAa0jTy8Pt/mEQwKw7AVZYAGsdaEdRuhFNTFxc7vkqbRvBUI2J+1vvuGYRvazpyhMWh2lgZeZfBRCWDOnyel3u+nQW5mhsrv7SWF1OGgMUGNzQsL7VvdSWnvbX8zhi91z5cu0fXV2iZVprrn1vb3+drXMCnDYrVqe7iUQbSTEaivz34+g4NUh0CAxtWZmRsbN1Rm/dV/AysGk9ISTeoOB5BbLNJctbREeTuSSZo8CwV6hl1dNOGq+2jtD93dNIZL2e4taHE1ZrM0FclMczmUrq+1oicSNA80GvT7zBlb+W1xjbeKUJWL1+ycHKrvCWErXGNjpF2pLfE28NCr6DmAbvPs2aY+rJQYFelUKpGmuYq5OZIjEwnAnJihAjrIs3NzTUXRsqjPNpdFWBZIcOvqovczGqVnEYsB9TrqdXoVle0oHqepT8l5nZzeajr1eoHETIHkg0jEFtxcLupnKlxC7Wjx+uukiL/+Oj04l2tjB5TKQ/HOO20CoPJF7drVdNrUaiSDdDI85vP2WurRUch0BjMzdIqSXTtFcl+7Rt1kaaapmLpcqCxn4XRSN87lbJthYSFrR6eNj68kjz57FihGm9/FYigvZlCpUHtbi8u0u0Enl306Tc/s3Dn6yWZhmnSL588DslSmfjw9jWyWmne1/lwug27uzBkaH1fvZgJQn7hwYa3lpVik/t2aH6NlPfpKVOX8PDWkMsoODa0I/6XS9eWavFvYFveHlPKUEOLABof8xwB+JqWUAN4RQoSFELullNeRO/ruZXLUhH8LssT1ouZJv5/er74+27GQTNL8obal3b27eZIKqy2XMT5UQlH4sX8/za2RCJWTzZI89HhXhHq9z0cvUE8PcPAgDewej+2utSwsTxYwnw2uLGONxZpzfaWCfVOnUOg6DE07CACIR6rYN/EGHVir0SB78iQSSQ25HOlYpgmk4g0gkUZkvgcej4AQ9kB+/jxw7BjdV61Gc0S5DHzwg3TPySTpcKEQ8MQTdE+NBo0Rs7NUfWX89/loXPJ46Pt6HXjwQarW2bPUjmr5aCpF82VfH43FMzN0jWyWZJ5qldqwXKbPAgHgxAnApZtomBLnzjkgBM1drXpkNAp8wLcEtxs4c20XwmHghDkKRKNIlzLweHrhctE1VuNyAYXpOJCbhukNINn/4ZUlnxtFuarNASIRkt9CoeaYms/b1uUrV6iyKuMuQAcrz7ZhUAPGYkiHqtA0Fzwe2zKuvPSWBeRmC5A+QPj9wMICzA98CCgWYSSTwJ49qC/GcS1SRXfYwsnyop1ZOR6nQhYWyAOkZvitKq+GYa99Ngw74100Sj/K+7hV1LrIWo3eiWqVXjyn0y5/M+VBhY9eu3Zz65pvNXeKgYC581nP+rcduN1bX4Pv9dqGtxtZoqD2lK9WaSwcHaV3enUERnc3HaMUXZ/PVgRbCQRo7Lx8mSYh5QWv12mcXVzc2JixFQzDThS5WVSDQq1hSqfp3qJRajuvl+5XaQXKur8ePT10Lyppzt69N3YPmkZCejIJHD1Kbbv6upqG7GwaDkcX3G6gMJ8C3LrdfpZlL5PQtJW5CVNTdtK71uv19lIbLC/bnlaV0O5jH8P4uIbZWeC+4iICapyOx+m8Wo0UwsFB+tvppMm00bDnrHh8pdxYLAinE6jXJMxkBgg07OUHAE3W09M05qpylFJSq5FWpHa1aOmHY2N0+Mc/btuSUing/vQ43PMTNLGrpVPLy6in8oDfD0chjbzRhWJRoL+fmqxybRp+i0LzZDAE0TABIVAo67hwAbi/r4TjpbdWkhul53K4kizjyXoRQgknai61LMhMFhOJXkhpP0r1CmiavWpBStC1pESl4UAySU1smkA6UgR00E0ODFA/U8teYjF7O0yVGFRt/6iIxewdRpJJYN8+VBsGBgeBD/Vm0FMsUoWGhoBHHwUKBaRibmiac8WIUJ2KwFWp0HVCIeDAASpb7ZLw2GPUxsPDKPdkUKmcRChEj1OtaHM6gYceonYhpV9DTw+QnisDIcqrUFnMwdFD9z07S11D04BSNA2EHTSWLC8DsRhmMwOYmQEGMin4nE5A01AZnYWmhSnT/ZUpeItFktUOHbKXslSrJPBaFl0gGAQuXkT64EdRq3ko5c1UAiHLgjUzh8HZD6BcETh61G5SIajrB2Pz9vaWu3Z1DscMBoGhIch/7x8gljJo59aFhZVsw9WDD9KmKxcvAJUKZnedxHxUx5NiCFomTZ1k7156N3fvBq5eRd7dh9NvGwiHgSefvI4x5i7gdsUk7gXQGrgcaX52zyvlpqWhkK4huOfWXUPNoa++Si/wkYcamJvVEAgIaBoQi5oo9peQqbjRv5SAY6AHlUwZs1eKMPb6Vwybc3MkT+zZA8SjdRTnJ+Db3dQePR56Kd57jy5YLK5MhGZD4Np7eWBvEKEQfdXT05xTBmPY662hMDID1/4DcLkFoueXsDcACGX1rFaRHo1haGHXSiIywwCspRiqr76NtPYP4D9oK00OB82t588DD36gjmjMQLkiKLrIbODhvhjmJ/zo6gqg0SCHq5Q0NjgcpCyXSjSGALYDp1y2nRenT9Nvr7fdodIqO4VCdlJb5RTo6rLzEPX20iQ5fKGOR+tnUchYqIun0L97bWiiBgvFt4dQNyyYgX8fqfkqSjINTyiM/EwCnqPrex1cLqB8JQ58aACJS1G85fggPtYzBjN3AEZ44zDXUMjeclZFDhySMezX9ZWbkdMzGJ10oCs7j4Eun33DlUpbsp6lSzH4+vevTGQNU8LtFitlo1RE1anBbRhoVOp497cZeMw8jns0CFBxTllEKVpBPajB0d2c1VS2atNE49IwtHIJYqCDdWKLWBIoXZ6C3+G4MaHd66XJWSXgavUiaZrdsTajq4skoa14/xiG2RpqyzUhbiyngsuFYiSNhl5CcPduO+fDak+2Yaz9bL13WWVHav3e6aRJTNX5Zulksd0Mnw8YHUW9JmEkUxD9zXnW4UD88hLqmgt7thJBtJUstpsRCpHi7HajcXkElssDx+qweY8HxasJeI4cgsMB1KIJyIfcWKmhw4H5i0kEy8sIhZsW6e5u8i6u3hpRoeuQpony9DK8U8M04WezqFyZQnzpIPxegeLYAgIfDJFQoLSlqSlSlr1eqvuVKyvLnfIXJzGqu3DUBJweFxrDVxHNPgp/txOFeAWVogX0OGmiVEsNVPTEe+9R33A4SCk3DFh1E5mhOXTFxzHhP45cYC9CIaDbVcT0pAeWFEifuozFajd8vn2oFEzkJuNwH9pHE2vTuCo1HRdfjcNyFvBR6x1EQx+Bw0HX12tlVHIV+Hd5MffeEqbhxUn5NtxGA1Hfo/B4wshOxGD1N6A1vTzZpSyyzhwqYWDNYiC3G0vno5gye9u6ZqvNyDCAeqmO0u/OwWdmIZ1OTIWfhKa5V7zEpUQa5oEAjEKB2lt5GrxeVEZnoRcycLR6pFYhITDzXgL9pRn4LNqJYdp9HPF4CJ7pZfTsctJ7ubwMOXIFYm4W2ex98O5/hM5vWCgOz8C1r+m1GBmBTGeQ6H0Q/skJeHw+Mq4Hg0B3N8oLaYhgDT6fE0tL9Cj7+4H5iSoe0KNwJJeQjwlorpNwOARKmTzqYQccDgfKqSycAxYADfE4yeWWBVQX08CupvEmHEZ58ApmZA/27jWQHU4Bj1BeifLwMow9DVjVOsrJDLyH+mFOz0HmK6gspnDe+QQe903AL2V7JEsmg8w7V+HuewyWBSQvRRC6L4Tych6mKMLw+RGZs6Drmnq0WJ7IY09x0U76qQwWHfoBikVkTw3hTOpRHOlbxodKE0BvL0qRJN58pYj7+sr4UG4JFgRmoikUpRdFZBE4tIsUChUequtANouxU4tw+fffk1vs3VGJ3oQQXxNCnBNCnIt3SvpxF1KXOvTq9mcoX01/t4k+TwHhRhJzP/s93JMjcDiauyFND+PKy29g6t+8jYUFCr0rmw44CykEAkCfM4v86QsY8BdXHGO+4jJii5Y9yDXj56uhfhoUW1y9RemBnliC308fqd8uF4D5ORRdXcjGqvDrZbhdEtb0LEp6gJSSUAh5PYTxX15EIDPf5gxxZxaRyLvgWJpbI7M4ncCAv4jU3/wejomrtDtRl4ns7wdx7d+cR+XMOfjcDZq49Cx6RMpeths2sbtnbdyLx2N7jAeCJez25doU8tVoGrC7u4pd4cqKTKLyH3m99HdXyELlvUuYHy0jOV+Er945s1oAeaQSFlIpIGCm4crHkclqqGgeGJnEhjKbYQBIp1B3+pBYMtFXmEb6jcvwJjdf66+87/09DfT5S/DWMlh8cxplV4huMBxGTu9C8tQwopeTsNxe+6ItbviCFkRjfAoet4QQQCgxAXHmFAJee02Ss5BGTdBDyFTckJNTqI7PoaRROeWKgFHKwZFYQkm6AcNANO1BGiTIWl4/rpwrIZreosdsHVJlD66+uoCy4wY9e0Ig7ehDwX2TYd1q7ezNrjNmGKadcLizgLgVNA0zoxVMXczaa9G3Gna/Hrq+JrGHDIWRNb0wwzu3PER6fZgdr+L8j8/RuNoci2ruIGZen8b878ZQctxgO14vhrGyLeLUlSqunYpBetqNyqbhRj2egcthkUM0l0FNs+eDsuFH5NQUZoaykC73Srn17n7IrvWjEVJFFy7/HxeQzFL+gbKvB+N/N4pQcgpBkUcqZq5sB5io+jHz9+NIV92wepvykMOBgqsH9VAvpMuNuZE88ufHkCs6YAXDmB/JwjV1heQiVFEs0fOv9O5rN8h0d8Pq6QNCIZjeIGbHq6j7wsiaPlz72zHMx5xIvTMGc2QU8d8MYvInryEYn0S3SGP57AwqF67C524ggDzSiYbtqWxS0gNoTM/BGp9ErN6F7Nlr6PKRLOSRJeTyAjVXAMtnZ2HMTGBxsozltAOp376HLl8NzuQSSrDfhWJRwpNdQqm21ijT8PixNLiAPn95Q/nFk11CYiqLoqcX0QWJ1BuX0BO0174bxSxqwoV40YvJcWvFAGR5fBg9tYxoRJIgs04kTUEEMP+7McxdLcLq34XlhI7sq+9hb08FtdlF1Jwkf1T8vbj463lEsz5UZxbhcZLsEpIZxBeqdF3DQDU8gNnBBMb/tzOYei8JGQojGjcwdSEDyx9EJiPhl7TEYVe/he4uCV0HfAtjmPjVCObGKkiMpxESOfv+QIJ3qSThRgXdQRMH+oq0EkWXsHJ51LXm2OFyYXGmCl9qDm6nhVoij4bhAjQNuawFv1aCz8ojm6MEj5OXSrj2/01gfiQP7eoIpl6bQT3Q/i40/CHkx5fhd1QRdFaQns7AcnmQL2hwFtPoMvKQb5xCyEm5DXwuE5Uz55A2KWojgxCm5/V1l+CYoR5ELy5jt4wic/oy8o5uQNeRzupwpxaQOjWMjAwhb3qhR+fgb2SQzTU7jc/XlrS2oIdgXpuA33tvri2/XZ7yBQD7W/7f1/ysDSnljwD8CABOnDhx96Ts22A9uSkct14plxLeK+dg5GkrpcZACFpxHsXyQUjDgb7GEswP7Ia3UsVi3Y/9DSBj+uHPLcAx54ArMklRatcyqBx6GGawC33pcSyWgthdobGuUKBInfkI8MAH6Ja7u5trsk0vPLllOBZnYXn80PMZmF29AARc1Twi8X7Uqjm46gVYxToctSJi2X6YScBwAIWCC3pfF4KRSyh2+9AIdQNSwlNOYdbsRaCegGnW6eAmWiEL/9gFoNcBLT+N+kgJ0jAQ1NMo+HYhXIihlk9BFPPwzFIKz+LRJ9AIdcM7dhFaIYvC45+0LedSwkgsQdQqEI06XPOTEFKifOBB1PcdamvrlbB9sw7f8NsQloXi0SdJCLAsOKIzkB4fLK8fRmIJASuG+WI/tHwWXV2LqGGtd9ZXTyNb0GEZToQDc0A+h6gMwll3Qi/F7YRFHRC1KoRpIlvQUao5MJCfQD7Yh15zCSV8aMOuo+cz8Fw7D1gNCIsmoYzTjUzegDDIcZFMueH3V1Cu+ylkKUjGy3Saool0HYhlXXBZWaCYg+Xxozc3BeGso5RLwuwmM7lRzKAiXQgCiBaDCNZiqFaBbKUfPgCZqgd+Mw4U00hXuuCqABNLfnhywIkBinBI6f2o5IE9N5GjIV4JIB/0I1MUcPnXJhbeDNMErk444XACJx6/8XqUK8BiFNh/H+C4S/KoMczdwOQk6ToHD17/uY0GkHf0QOq0brNctpNU3Si1GoUZHzxo6/fJJHB5xIX776Po0pulbgIC17c0PZMF5rIhhA8AkSywuxmpns7psLwBABLpggPe26CXV6u05GzXLiAm+wA/UCq320PKNVr2o1XLsBxOaPUqqo0g1CKbQtUB4fOgqDtQLJK+XC4DF4eAgf712zlaDEH0+TG2rOPYADAzq6Ps60NPcQbVsoZCWV9Zvzo254HD6UFkHBATwKEPkA3owogDu3YBuwaArBZGt55FvNYDMwZEit3oF4soVD4ENyooNxPDT09rePQxGv+lpD6SSJCH1O8XGE30wpUGCmUfxC43ptI6Qs4cvIV5wOmAdagfem4KDaRQdAbgsSqwCmm4KknkSg5ks0AuD+xvOuMzJSccZgaGDowt9SNkxIFEFPU9B+Azs8gWNASKBizdQG9xBlHZA0R0dAeKMBMRVEtZpEo9cNWbm5KUJEKeJHKmZ41UUyhpqAkXQuODKH34hG0kMeuA0FYm3d5qBNFcAJEhASnD6NXiMKNTqN7/IZLLyjlUZRdmU24USsCeCuBxA8WSQN43gHJdQzADNMzOaQySRTe8wRpSCGBoCCiVPOj2l4DpYRQbNRQqBro8wGxERyXYj7FFwG/FoFcKsPwhBEpLyJScK17Zy8MCDbMLXXsrSOWDWFwEpmIBSAkEUkC64IS/moBpdcF38TTMcB9q+z6AvvoC8oFdSOcEnLU6fOUEajIIvZxHTYThlfQOOGUV+tIS3JFJ5E98CsJqQLMaqNU1OJwUsBqtdqO/MYZSPog6JKo1AQdITnc3ikA9h0yB3oGEPgA9BFjVOgYaC0j5ehGJChw8QPcjJbC4JNCwAGc5C2HWUZUCxSIQL/vQJSLQ0wV45SLylSxMvwdGMQuPq4LJaD8e2wXML7uQzLhW9IXVTE8DeQTRGxtBzQuMjDnw6KNAvBZET34CdY8bM0tuuFwS/lIMjnQByzUv9lgr0fr44AdpHF6IO+FsZIBEFCVnP4A7ePnfDXC7RMFfAfhTIcT/BeAkgOz7ZT25CQN69SYyZ24BPZ+BkU+vKD8AgGIeej4Dy0nTldAEhNcNK0UKVa5owOHS4VycRiMQAgwHRKUEz9VBQDcgrAaEO4jFJSAUtCOzwqHmNrkmDfQPPACkMwJGdwiemWuQzfVn7vlxNFxuWF1uxGKA3+2EnklCh4QedmJurl146O420HAE4Z69huIjT0DUqvC7Teg+A56ChVIhi0bTo6CVi/BdPgvL7YF0e2F5A9ArRYiiiUa4Bx4BaPDCPTkCrVyE2d0PrVyEMzqNqtMFPROHAKAXsmiEuqFnU/BeHYQw65A6VYquJeGZuQroBuq77wMAuCeG4UjHUDj2FIzkErRqBVI34Jq9hsoHj8NIxeCZGgEMB6QAhCVhdvWgRwPQ5YeeWIDV3QvL64flJQutqFbgWp6D7PVDOpxwpqKA7kBGBjEfAZwuwKoUYflDJDFqWpsmqFXLkDop0HVfGFagBq/DCT0dh6hVIZ3rrwt2zY4BQoPl9wMaTZLO5prwZJKiq90ewNkXRjVPn/n9tMxS5btREZ4hn456JglhWRBWA5Y3AGd0hvqlacIty8iW/QjXgFwW6N7dj0YVWI5RSFu67EGoHgfcwHJcg9Pd3C67ubRzdpYMAqUSReTdSPJsKZvLPnsEotHm0lMAR45sXTFWiXBVvSoVCjFrRtjj2jUS/jZLkB2JANPNxIbXs6SdYZj1qTd3HtE0YN/+6zd4VSqA5STJMhYjgTIQAB55pD1Ku1QiAVmtVrEsijYeGFgrmC4tAYtLgMsNHH6APltcAoLNJaLbYZgbvUb3fuzo1qPhlxYBrwcw9ObWSyXA39x23hmisPBkcuNl4irXVjBI01K5Arhd12+szGSoHLU7ZaVCbdymlJcACECrlGjNsrCvDQCZNGAEfaib9Gx8PipP15rtvH9tpH3dBPI5IBzWV3a0a1hAb48OkTThWppB0eVDLkftKiW1kd8HmA1gcoKUcocBJOKA1QAMrwuGrw/pDJAvAIGgAIoCej4Dh1lEuW4gGqX5o1ig8/N5UshDIYoEVlueLi+TsuYP6QjrABBEq49QOgwY2RR8A33QKhqsuXHopTxy7hBGR6kNe5oOlOgi4NrTD4cD6LYAzQpDi06jvvt+uIoJZOHB7Czg7A3BcgPKnyrqQbjmx+HwWZibF0imgYceBEy3H2GZQbIQwmr7VyoFiHAIWjUJ19w4KocpHNw3/C4aXj8qHzwGUa/BWcogvMuWXaXVDcfSHKr7HoCo16DDQiKlkcLqBEpFUsrjccDhcdCce5WexYkTAKS9EYquA/EE4O4jw0291lyCaHVBzySgdQWxtGQn+1XLLh15HdVcmmSYRBQFbwCLi83EvpYKwnHD20zy6/XROzQ7A9QcXvjTi5C9/dBLReilIrRaFRAavD4Brw8Q/gAQm0e9bzc02UClppHRRwJarQxnchFapQi9lIfUNFgQKzLP5CTgC+hARYNrdgz15nelEmDpDhiFNIxcCmnhxtw8VpIEAw400I+ABSxE6P0KNRNGT88APT0eiKU5iEoJZV8AsRiQr3vQay5BlJMww73QCxmYvbug59NweBzIVIFkc7MZh2PtzpkAfbe8DHT3e2DVBfSQCzJHY0yp5oBr1y44QQbCQkGgN+yBZlaQEb24dIneH6cDmJikpfyJBNC1uxvGtUFYji7g0x/f+iBzF7AtSrkQ4t8C+CSAXiFEBMB3ADgAQEr5MoC/A/AZABMASgD+s+247t2AqTlhVJLtCV62AylhJJfRCHbBEVuAdLRbi6THQ9/7AoBuP2aVBK5YBMJdQVgtE6d0e9Fwe1e8skGLFIdFzc4TAtDAZVk02N13H02KoZAbpmh5G806hGlCBD3oAwDTD315DrAsNMI96O8gNEi3F3oqBr2QXYk+cDrpc+fCDKpON/RUDO6FSUiHE1KFUgsBy9Oe0czy+KEVczDDPYCmwfL64UjHYLk8gNAgNR16NolGqBvu2WuwXG7I0NrwNjPcC8/UCBrhHohqGc7lOVgOJ9wz16Bn4mgEugBdhzMWRW3PQTiWI7D8IVieDuGOmg7pcsMzdpESmhx6GNLpgmf0AqBpcIXI1my6KQGBr0pt2+8VqORpe0q9JsoAACAASURBVDDf8Fk0/GGUP3R85ZmLSgler0AyR4OvNOy+oBdzMJ2dNT5Rq8LIJtuNOaDnnErRANu6ht7jBZaW7fX33c3kgF4vdW/h98EZj6IuJaRmwPL4YCSX4B67iEZXPxxOEprCIVtgc7moP46MUP4F4fNBGgYaRWByioTWatVOoKu2oS4U6KdSIYGxVQhdnZeoUqEIj/v2NzPiWyQ0ZrKAYTXzI8Uol8JWKBbtrKizs/bObd3dJNSqrPcqF0wn6iYJcD09NMncqUp5vU5tuZ1DF3PvISW9T37f9iwvXk3dBAr5tTnUOlEu2cnRt3pO2/lNo5vDScqRz0fvwfg4JWpSY9f8PI1/KjVFPk/HCEHzokJtAaUyXB88QJ/nm8nkK5Ubq2cr9ToJv7K5e5RaprzhOSZFO6ll7ppGdSoVaWzt6bGNmBsEamF+nhTfo0dJIL9wgcbCw4evry+ojTmUMbPRoD7VOjYmU4DTY0ArZAE/GZAjERqf+vqorm4PYJh2KoBUiu4lnW7KPWG7zWZm6HiVA8vrJSO4rpKShbqgl4twBt2Ix+2ErwpDpzoXi/auc9FFoL/PVuCrNTLqSNMDR2oZME3UDReqTaNBNkt1ymToPtSW7PG4vVxX17HuUjrLH15R0i2PH3o2CakbCHYbqFbs3fuWl0kp9fvs5w3NAS2fhl7IwsilYfh7UK2uzTsoHU5IlxvS70KPQW2azgCWywvZ5UW1KS80GvTOKOXJ4wUaoguO+AIqBx+EkBJaKQetlEfl0IehFXNYE2eq6dAaJvRCFsJqwHBKJJLkMzAMIJWmZ7UQBbqbWwWbJiBqwNBFezMBr5feg1rVvmdDGfI1DY2uPrgl9alUyn7/dJ2WdTiX5tAIdkGYdQS7yIiiNR1TitX5J3M5wBtyQi9l4IzOQDqcJAdlEhQB2tKeRi4N18I06ga9c34fYBlOGJkktGIelssDrZiHdLnhdNjZ+0slej4NJxkWEO5GJgNkc4C7ywNHYhGiXoMr1IdEHAivGlc0jfqqin41dJKzhMMPI7UMqRvwdvciGm3mguzqpShKy4JWpErouRQspxsOAczN2n6iQqF9HKtWyUnh8zfH5Kax0+dt34AHsNtVIoAGAgg1KMKotxmCkUwCY6PUn4XDQK1nF0SqiHuN7cq+/iebfC8B/Ffbca07lnXMwvW6gAYLWj6DRvgGkkopTJO84WHKAGskl+G79BbM7gHopRzMUHvcjuXywsjEoRey9jpgkEVvacmehDrSnH2bS4ohrbWTq6aRRVjthramLMMB2RJuDsOANAxA6Cse2c7XdsBILsFy2xlGLa8fRjoOx8UzkEJDwx9sC2VfD8vXsmZYCFi6A+65cdR7dkFYDTiSy6j374OWz6KxSjFdQTcgHU64ZkehlfJo+MOQThccySVYDudKW0mnC67ZMRjZ5IpHv2OdvH7A6wcaJjyTtD1JI9i9xqgC2Nu5WhUfnPEIZHwB0umCns+Q1/9+Ck03siloHid6VtkBpNsL9+QwqnsOob5r/xr3iZ7vvL4d6JwM2Gg+NpXTxNDp2U9MUD2l0w09GYOrVlmJAjC7+uDIJOGMLUC63KjVyNLb6kVWSYzDYcBqri8PO8hbYTQz36vktgAJUVNTJFRJAJUqTb4eN01c164BH/6wvSWwZZEFW8D25gD2JGCaNMHv3r017042S+V6vSRI+fy2IJvNkZCWSGyslCcSdvLTTNY2JEhJFv87IZy90SCvUVcXRcQwzHqkUqSMPfDA+v0+39x3N7DBjhAKlU8rHKafpUUK7X388c3zIhZLzV3BHKTQrFZ2paSxoaur8+4U6TS938pbq4TuZNJWoKQk5aCnh8abo0ftrc+XY/bOE3v2AqNjZCw1dJoz8wU7XFlQ0mUsLpLSdaO7oqnQ2lCIDJADA0ChaBtJGg3a2vO++2yvcqHQLgd4vcDsHIUBh5ptrL4rldYm1x+foLZZXKTvFhbsVDPZLCm8hw9v/R5UVJbC5aKIKkW9TkbdUNANkU2iLjQ4XDp03d4WXeUBM3R6XuPjdkSVaO5rrvrP4iLVWYj2ezNaxRPDgUYgDI+kPq7rNN630rrteigEBFsyjbfOo5bbAyMdAyDg7w7BApWXaDo3Egm7rl1ddlBcTy+whWlphUbTuG8AMHw0t87OArU6ecxXY2k6XLOjQMOCP7z+lVodH1qz3gpdp3cql6PnWK225mTTAEgY+f+fvfsOj6pKHzj+vdNnMiW9EkJHbCC49l5Ze1t1dV17+6nruru2tax17aKurr2CvWNBAQsIFkBAlCqEhCSkTjLp0+/vjzOTyaQRIDT3/TyPzy7JlHNv7tw57znveY8P3WRGQ1P/bqjD5KvtCNSS3stiw1pRDJEIBrcDC4lzXFcL3joVSMYLrZvN6tx1HjxqboYVK/vegUbTEkFfZ7rFhslbjW3tCjCaMBj6t0FC/DqKBi1YqkoJZeaB0ZQUkMdFnB4slSWE3amxiS2IWGyYGmrQ0Ila7Zga64ikuDE6jNTUqHuTI97PMxiIpGdji6j+iwZkZFgxVteim81JOwx1ZTar/6wW1c9SfSuNcIZK+zGh2mM0AgYTOiaIRjC1qJE/U3MjEZcHp1UNSLlc6vPZ2KiyUeLWlal7jM3a/f3t9r678UZjcvs9qWrAtWNw6jdai2c76Pr9tgWCgMOBpaqU9v4G5bH1zRGXp2M22FxTjmPVYlomHELElYqlqpRwWhaGoJ+I3dnj1iGaHkVrbyHsTHzjmIyJrVP7w2REbUfRA7NZdQD6+1rRfuwLF0lxdcw2d6xBAiJpmz+VGHWnEXWrHpqOCWNDLea6yg1+uCNOD6aGWnSjiagrEWx2fYylrpKIxda/m4XRpG6A/djAXrfZMdWqYdpwRg5Y7VjWlxIoGAZGE6aGGjWI0fV47Slo/nZsxUsxtjYRHDQsaQbfXFeZNGDTH6me5CZ7PKpDGA+ydbsDwqHEIIPBSMSVqnqjBiPusPqy7rzTlqZ1T3nStOTOUedrzGZVL+dIUTMadbXqZr377qoT3tamZt5DocRWu7m5Kl20pqZ7WrnJBMFYZyL+pVpdrTphI0eqL5r2dhX467rqeMQ7j/EOXjikOuWNjaozH9+zvacdxZpboHgNuGO19PRo4hyuW6dmWsaNU4MM21JTkzqG2lq1FnZHmC2PRpOvx97Et0QdiKLXW1tZuepIbsryjf7QdTVwa7fH0mpjGSlZfdQji6fb1tf3HpSvWAEGTQXWnTX4VAp1/LOixwKgkhIVAE0Yrz63bo9aNrPrrskpzYGACsDiNU58PnW/sNnUz8NhNXjniwXRrW0qWMvIUPeMcARK1qpBOasN6ryJgYPO96X4NuS77qpmmaMR1eZIBEpK1Yycy6U+N8uWqXtYMBRLZ471tKxWqK5S7905GPb51KDGkCHqePLze782m5vV+xXkJ7Zsbm9PpOvqUdXO8nL1uR0yJLHzmtGYuMd565LvqxaLOleWlOR7r2ZQg42dA9d2vxooiUTUOXM41LmOp5sbjSqrKn58ndPfw2rnq6SB/kBA3a87B1AmkzrWUFgNYsS2J0azWjE2NaiMN4sVh13Nbq+vTL5nOp3quybeibda1HHk56vrubw8qX5UnzRNBaLhcJegvYfH9fp1bjCihYIQiWDpFKQ1NUF5RffMtHi7+nq//ohfo46UntsWdXrUTK6r/7uApDhUUO6OnVu3W31uTLF6ho2NyX9L3WLDVFdJODUTHY1oiktNSoTDPfbtoiluDE31aGhE3Gl0vp3HU9N76nd2HtRyuaCPOr0bFHGnYWzxdQxybIyoK1UtN+yjb6dbbYStuZh1dV02NqplD4Z2r1reaLVj8nnRQkE0uw1DWH1Gug6OmYzqfhU/9lBWfr8DVrM5ltLcy++SGIwQCceWjqj+nEbi76xpqn5BPJMwvhtbTwNBsPE1Okx9ZIv8lkhQPlB6KfYWCqpAxeSr61cARjSKbc1SrBVrCGXk0bbb3gBYqstVyktdJVGLDVNjPeH0bJVu3otwWs+zv5tbSDbO5UqeSRgQRhNaOISpdj3hrC24jxyAZsC2bhURxwY+6ZrW5+x3/DGhTWlvP2+eSefCYAQ9gqmpAd1sQQsGwNlzJR7dZiditWGur8bUUEPr2P3VYEc4jKm+hoh743MmOzc5vtdoXHyGvJtYdkRPuwhtis7vmZqqOvGBgOp8xteFOZ2JtmoapKWqn/f0ZW6xJGZ82tvVbH4koj4rFevV5zg+49Z5V5Y4m111GiMR9f4tLapIUeegPD7zUV6mft65s9XWlphtMBhUh7k/KahbUk2N+mwHg+qc9DXjsL2oq1OZG3uM73tQY/UaFbyMGrX12hYKJbaR3tQBjtZWWP0rBPwDk73Q2qquvc4pwvE0bKNRnZ+SEvXZStsn+bPb3q46brqeSFtsaOh5pVYkoj5DkAiyQAX8ixerAHPUKPUZLC1VQXRqqmqf16u+Z9LTVVtXrVKDVvHP9vr1Kki1WmDkKBXgumLj1NGoeo/mJjVjPXqUmhlKSVHH2dCggvR169R7xDPDevr72O3qteO783QOqmtqEttep6aq9ofDqlPq6dSJdjjUvSoUTtyHNC2xLGfVqlhgbOl9h7N1qg4oK2Lp6harCpLinWiXSwXELpe63oYMUe1IT1ftD4Vjg5l13T/TPQ1mpTjU8/PzEuelPXa/6lxYq+tMpcetzm9VtbrfxR+7cqW6nsaNSww8tPdSdkfXwd8OhtgulE4XasIhElJLr+KzwsbuM56dZ7BBLUdoidVBif+9NuZzmNrzV+xG6bpUDGLbPJdseCBxc/T52vEJgo1gNqu/Z/w7TNOSz0/XbJao3Ym5thItEka3WNEtNqI66PENy3sQdfcczW2t7yHdbNlwv68v/ezbaZrKTvF6Y58hZ6Kvp4UCmBr8hDPzcPbxfZY0+L+FR5qNzT607osOOrJnFy5Sg6+QvFRR9I8E5VtYOAwGswFCUbRQsM+iWwAmbzWW6nWEsgdh9NViaG9F1zQMbc2EPRmYa8qJWu3o28mVPqABeUwkLSs2Erdlby4RT3q//ibbI92WgqV8jVomsKH2a2q02dDoxVJZQmDITmrd/lY4x1tLfJQ2GOx5S2FIrNfrSUqK6sDGtivFbAGXTXXALVa1Nu6XX2JBfQ+n22RMpKKC6rQ0+tRAAKjn/fST6mw3Nian1JotarYwFFZ/DrtddZg3Jyhva1Od3xEjVYDS7ldrvwYNip0HU98p8pGISs91u9Q5DQSSO0MtLWo2cPjw/m/1Hi8auTmdz9o6dTy93XeqqlR2UnNT70F5JKJqCGha9/oDW1JtrbqGzOaNryHQ2qrOW3OzSvmrq1PnfnO+BnQdVv2qUoT32jtxvurqEmslV61S14vBoAInl0tdW7W1KjgsKFDBXucx6WAI7F0+Z/HK1bqugk+zU537khJ1XcUD75JS1Y74TFgorNYsdw6A4/UuHI7E2ttMVZeTFcuTgy2LRQ1wNTSo6tulpYmdQyIRdQ3rOmTnqPPg8/W9ttvjUfeHtrbke0xmZqKNnWc4u6Ztxmez62q7XwPxcx5f65yW1n22KhhMpNDH36+hAWpbE59DkykRoDY0qMc3Nqpj9vtVkSerrf9lbsxmNXCxcqUa9DCb1Mxe13ts1891PEXWFlJZEmPGqOvH50tkQ8QD9fh66q4MBpWGr+vJs9QRZypaKLBRH16TUZ2/UEjdJ7qmoW8VPSzdM5n6fw/dnmzUDH7s72SuLiecqQYAOmdC/q+z29W12fUzFfHEZhi2k/6+hq5qFvSym3b83rmdNHeHJEH5FhYKJUaNNlQJG8C6vpiIM/aNqxkwNtSq/DHNoGaRIxHsq3/evBG87Z2mJRWn25LvsyMG5KBS040+tairv9dC1JWqCotkD1Jp+z2sY99RxWeXN5WmqQ736tWqIxlPk+o8WxWObVXb2xdO522R46mzgwapzmc8qAkE1e86v4bdpgKhhno1OGA2q85053X0ndXWqlTBvFx1f8nI6N43rapSaZHFa2DsWLX1WjwlPxyGzCxVPRdUcATJr9HSkhizMZkShZ/i1q9XfYXiYvVF3LWwnrVL9WW/XxVpycmBnXbqfkzxdb4ZGd0797qu/i7Nzaooz9AhPc8SB2KFEd0udZy9zTS2tyfS1wOBTU8Dr61T6/h62oanJ16vCqbKK9Q10N7eexs7a2tTAzqjR6vrIsWhzuem7kAQ52tUtRDMZhWQ2m3qnHi9ahDKZEyk0/p8iWyJlSvVe7tcav20ZkjuTIZDQJf+diCg/lfT1Cy/y6mu40af+pvHi6Hp0eRZH2esEJYruTwIbW3qOmlrU58BVyxASE1LfN+COj9VVWrm2ulMXMPx4DjeiezvmtF4MbCmpuTgfWMGdtxude56u4/Eg+CSUhVcdw7AG5sS7YhLTe07wC4rS7TP7VHZCNHoxmXMpXrU9bK2WGU0NPp6XprT2/G43Sr7YuhQ1XanU1V8jq/Pr6zqeQbUFrs3BpzJ15hutW1SUKdp6jPU+ZoRW0ckLVNNCfdVU+h/lMPR82C1brb0nl++DegGVem/t36zBOObT4LyLSwYSszqaOFgn4/V/G0YWpo6io5FnB6sZavR9Kjatoz4LPJWnN4R262NHpgxGInaHDh++QFDMNBtXfyOzBYr8qZtxsfCZOq7Y74xafdmswoi161TAaXLrdrY0xev0Zio9hufGdN1NTvaNSiPRlUgHI1CfWy97JgxycFdJFaAMSNDBTQtLerf6el0JJ011CfSWJcuVW3YeefEa3i9ibaYLYnt7+Jt8NarjrbPpwIjp1PN9vv9Kr160KDkPaJbW9X5a/D1HEA0N6tZ2by8RDvilXwNse2MUpxqD2Cvt/sscX296mzH6xM0+NSgRiTSPXBtb+9UGTkWlIfCKiW363q93kQi8OuqROrxhmYc49tGeTyqrYsXq5/bHd0Ln9XWqRnW+MBQU1NshrNCBWvu2LKh1tbux9a1Sna7X6XGFg1JzISXlalANZ5VoqPOb06Oet2elmeYzSogtDsS1X9BtaGmOrkd4XD34w8GY3UiYuuEMzLU8cSPMV6A1NNDinDXn5ljlYgzM9X13fmrsOvsndGoPnPxc9K107gpX6N9FVDqrw11Xj1uFYyur1BbscWzEcrL1d+g62v1dm+y2ztlEqDOz4YK5fXVpuqaxLaUG1Mt3mxW11lxsQrmLZbEfSlepK6nmVdbbI1/U6MaUNhcmqaOQeLCbWRrTLaILSaS4lbLRn5DfcftjXxCBkIv68khlr4enykPh3p8TEfxj8rSLj0Mlf8V1QzJZQolIBebKOpwogUDhO0pv6nryGpVHcXtqRCI0ZjYCika7XtmqWu7LdbYfpxdOr6treqeEg+KQiFVMTkjIzYTH4jVY4kFvgajKgzWNRDWdTVLGq+Yqmnq+Xa7emx1TSJYtJjV0oB4SQy/X81oxivfxtfD/7wkUUiqvEIFCvFKrPHiW6FQz+vTm5pUYBZf82o2qeMvKUm8ZnyQIBJRxxkf7GxrU2nI8W2D4mvbFi9W7zd+fHIQFa+uHQwmAoKaavVee+3VPWW4pET9rHOxqtbYTizRaCLY7ksgkDh/8QCrpUUNrHQOytvbVQq2zaYKohkMaoDB41HtNpli1YZjSx46z9JXVqm2jhurjlfXVaZEVZU63mHD1DGXlanzb7cnrok6rzrf4XDPt4V4JWybLTmgidcViZ8zTUukqnfWGrtG4ss3/P7kwlY2K9j62c+zWtVrRKOq3sOGZny3VEG8LSm+Rre9XWWlxAci2lr7N6MfZ7Ml9lEfiDa5XSpTYlMSzDzu5CKf7lhm0oayFFxOdS1vbsEzUNdCTXX/s1uEEJ2YTKrYsNhiJCjfgqLRROcVgxHN30M1k3CIlMVzVIVF9O5bm9m3xcIn8Vu2o6bsb8jm7PO7JXg8KvjZlKJeKQ41e5mZlViXXlungrjOs2LxGfmSEqipVcW0DIZEoOJ0qirJXYNgo1F18uPFntraVJBotydS1+PtjleIj6/jjQeyoMYKm5sTae7xv0F6mhqQiKej+3wqQAiHew7K479va1X/ud0qePR4ek7h71xg0udTEzBdCw3Fq++vXqMyAkaOjM3WN6jzE9UTwXVdncpq6ryPMSTP1uflJQLWlpbEuud1ZbCzU1V/9vthZA+p9Z0LWcUrNDscahazqCjxu3jgHQyqQNaZolKF40F0fIbVZlPZDoGAKkqYna2ugWhEXTdDhqjAtaFBzYBXV0PhYPW36lr5GlTAtGJFIrW4q/g2V+sr1LUZF183HGc0qgKHwaAKmPPzVBDW2qIGEiwW1Safb9PHBM1mdf693uQ9iH+L4vUlGnzqGh6I2eLN0VcBug3pXHMDYsXZMjecNdDfNPn+sFpV9XUhhNge/XamyrZDkWji/+smE4ZA96Dc1NSAIRQgkpalqqX/hmYvhfhft6lVtuPB0dJfYjNkbWoG1evtHjSlpiYqpWdmqv084wGryahmynqqslxWpoLQeJXiOq/6XWNj93RYTUvsS9u5yJPVktib1twlmcdgVMFXfEbaZFKd+vr65NfWdVUh22KODRY0J/a67Skgj68pjqup6V7ULb5VlMulArja2sSWTfGsAUtsQCEcVufBZlOP7aytPdHG1k7v6YutqU2JBc3l5Wrdfm1NYo1+XCSizl3XEg4mkzrGzoMcNbE9rg1G9bp+f6K9nYMXk1ENOvz6q3rO8hWJoK2mRj2nrEz9nePXYFlsu72eiuSZzeo6crl63+LSYlEDGd22yulyTH6/mp0vXqMGDOIV0C2dlmbE19VvKl1XgxA74iz4xsrMVGvL09IGZrZ4eyJrUIUQIkFmygdKD98ukU5r63SjCUNsptxUs56IOxXd5sDo8xI1/zZnLoUQm85qVQFjZaUKtkymnteDGgzJP+9Px91sTl5ra7OpfZxDIRVAdg12XC41852amghI46/T2qoC7a7BXLyAnd2RuD1araqqe+d0+kAgkVEUL+zU0tJ7imw8fTk3VwXVrX2k9MbTgP0BlWkAiboDZrN6nfhsud2mHuN0qv+iUTXDazAAmqroHk+lbWpKDHSkpqrZco1Een+8dkAwCEuWqJ/1lMmhaSpgjVfdjq/V1TTVlq4F8zqz2dSsc9e9lkMh9bdqbEwUNotXDdf13jNK+tqdAPq3NMRsVpkOrS1qRrW+Xu1g0HmjB7dbnfPNmeF2e9RWWf8LQbkQQoj/DRKUb0HRpJlyM4ZAG1ooiGPVIgL5QwkM2xmTrwbdKj0LIUR3drsKbNra1F7oA6lzWmg8YCouTl7rG2c0qkJrK1aoQLNzJfZQKFbRvcs6TatVtb2qMjG7ajSqAC0eTEJsjbqeeE5dnQqAe1v3abWq569Yof63P+mtVktsvXqwU+FNTb3v+spEam19vap0Hk+1j29TZTCowZG8PDWIEO20l7XRqAJMg0EFm4FAIiivq0tUyO/tWOpqISszsfUTJDIKSkp7Dzx7q9jrcqmUe3eXiuWbWuBrY8TT3HVdZRF4UtWseedZcZNpw2vwN/g+xq23X7EQQgixNUiu9EDpodhbJNLpH0aVvq72h45iaqhBCwUx+tvVtgdCCNFFvEp4Y2P3PY8HmtutAsHe9hG3WWPr1Z3dn9fbgIHBCFXVya/pdMKvqxMz1G1tyat2MjL6XmtqMCSqgaen929rp/g67ubm5PNotqig0RmbBU5PVzO8drsqPBffPcNiUYMVvsbkQYSOcxN7TDwwh9hWT5V9ty8+6NLertrReYAhJUVlEGxsmrfZrM5hX2nmW1J8rT0ktlXbnL3phRBCiP8FMlO+BSUF5bFpGVNtpUpb97djaqhF76NyuxBCpKX1ucHDgOmaBt+TngLM+Jr0nqR6IOpKDrotFjUL/euvag/1eJG3uP6sM92UWdKejs2Z0nMatdmstoftHMA7HFBeFtuXvZc0b6tVHU9BgUrB75zK3hNNU2n6P/6Y2F6t82sNZJGrrWVjqoMLIYQQQpGgfAuIRBIFhTrTolHM9VVEUjwYw/WY15f8ZithCyEGzo5cEKmn2pUOh5oh9vnUjLd7O9rKrjfxWW1/oHthuTirVRWU++EHNfDQnzXPvQ0MCCGEEOJ/h6SvbwFtbWr/zVAouTMdSXGha2rvoGiKG2NrE9EUd+8vJIQQv1FWq9qurHMRsO2d3a6C7d5msDUNUtNUir+kbQshhBCiv2SmfCB0yS2NRlVg3tycXM1WtznQbaqXpltsRNI3Y08YIYTYgaWkqPXX23rv5Y1ht2949vu3tm2VEEIIIbY8Ccq3gHBYVeBtad30fYqFEOK3rj9F2oQQQgghfut2kKTBHUskooLySFiCciGEEEIIIYQQvZOgfKB0WjweiYDJDM0tia1hhBBCCCGEEEKIriR9fQuIRMDlVNv87MhVk4UQQgghhBBCbFkyUz5QOhV7i8SqCe+Ie8wKIYQQQgghhNh6JCjfAqJR0OTMCiGEEEIIIYTYAAkdt4BwBCRrXQghhBBCCCHEhkhQvgVEwrKWXAghhBBCCCHEhklQvgVEo2pNuRBCCCGEEEII0RcJHQdCpyJvEFtTLjPlQgghhBBCCCE2QILyLSAS2dYtEEIIIYQQQgixI5CgfKB0mhrXdZkpF0IIIYQQQgixYRKUbwG6LmvKhRBCCCGEEEJsmISOA6XTuvJwBNkTTQghhBBCCCHEBklQviXoEpMLIYQQQgghhNgwCcq3gEhE1pQLIYQQQgghhNgwCcq3ACn0JoQQQgghhBCiPyQoH2DxpeUSlAshhBBCCCGE2BAJygdCpyJvup70TyGEEEIIIYQQolcSlA+waHRbt0AIIYQQQgghxI5CgvIBpiOp60IIIYQQQggh+keC8oESj8QlfV0IIYQQQgghRD8NSFCuadpETdNWapq2WtO0G3r4/XmaptVqmrY49t9FOWm+ZwAAIABJREFUA/G+25VYJC7p60IIIYQQQggh+su0uS+gaZoReAI4EigH5muaNlXX9WVdHvqmrutXbu77be9kOzQhhBBCCCGEEP01EDPlewGrdV0v1nU9CLwBnDgAr7tD0pH0dSGEEEIIIYQQ/TMQQXkBUNbp3+Wxn3V1qqZpSzRNe0fTtMKeXkjTtEs0TVugadqC2traAWja1qdL+roQQgghhBBCiH7aWoXePgKG6Lq+OzADeLmnB+m6/oyu63vqur5nVlbWVmrawJL0dSGEEEIIIYQQ/TUQQXkF0Hnme1DsZx10Xffquh6I/fM5YMIAvO/2o1O+ui7V14UQQgghhBBC9NNABOXzgZGapg3VNM0CnAlM7fwATdPyOv3zBGD5ALzvdkkCciGEEEIIIYQQ/bXZ1dd1XQ9rmnYl8DlgBF7QdX2ppml3AAt0XZ8K/EXTtBOAMFAPnLe577u9kvR1IYQQQgghhBD9tdlBOYCu658Cn3b52a2d/v+NwI0D8V7brVgkLunrQgghhBBCCCH6a2sVevufEZWZciGEEEIIIYQQ/SRB+UCJT4/LTLkQQgghhBBCiH6SoHyARWWfciGEEEIIIYQQ/SRB+QCLyiy5EEIIIYQQQoh+kqB8gOlRWVMuhBBCCCGEEKJ/JCgfCJ0WkUeigATlQgghhBBCCCH6QYLyARaNSEwuhBBCCCGEEKJ/JCgfYNEoGOSsCiGEEEIIIYToBwkfB0psIXk4so3bIYQQQgghhBBihyFB+QCLhGWmXAghhBBCCCFE/0j4OFBixd7CEpQLIYQQQgghhOgnCR8HWCQCmpxVIYQQQgghhBD9IOHjAAmF1f+Gw1J9XQghhBBCCCFE/0hQPgACAVi1UgXmkQgYjdu6RUIIIYQQQgghdgQSlA+AaESnpRV8PgiFOgqxCyGEEEIIIYQQfZKgfIBEwlBVJTPlQgghhBBCCCH6z7StG/BboOtgMquZcj26rVsjhBBCCCGEEGJHIUH5ANE0DQ2pvC6EEEIIIYQQov8kKB8AsS3KycjYtu0QQgghhBBCCLFjkXndAaNv6wYIIYQQQgghhNjBSFA+AHSJx4UQQgghhBBCbAIJyoUQQgghhBBCiG1EgvIBIluTCyGEEEIIIYTYWBKUD5D+ZLAHghK6CyHEb5W5ppz0T17B0NK4rZsihBBCiB2IBOUDQI/qG5wpn/x5FntevAcfzU3fKm0SQojtWerXH1B0+wWYa8q3dVMGTO5L95LzxmPkP/0vKTYihBBCiH6ToHyA9NX9Ckfg+U9yiUQ1Jr1dQCi81ZolhBDbHVN9NTmv3I9j9RIyPn55WzenX4zNPob/42RyXrqvx98b2ltIWbaAqNmCa/EcdVwSmAshhBCiHyQoHwC6Dmi9z5XP/dlNTYOFkw+qo8prYcb8NJaV2PnkuzTpswkheqbruL6fjmf21B0zuNN1XPNmYi1dmfRjQ2sTec/fDbqOv2gUKUvnJf3etnY5zoWzIBrdsu2LhHHPnYaxsb5fD0//dAqW6jLSv3gbS8Xabr9PWfI9WiRM2bX/oel3h5H91uMUPPFPiEb6fF1DazOpM9/BVF+9SYcBYGz0Ymysx7K+hPRpUzA11G7ya22WaBTnom8261iEEEKI/0Wmbd2A3zpdh+c/ziU7Lci/zlvHwlVObnh6KADhiAYUc+y+Ddu2kUKI7U76p1PIeeNRALRwGN9hp2zjFqnZ4qjZim6zA+D6YQbZb/+Xygtvom3MnkmPTfn5Owb95waiZiurnvoS3WLFWraaorsvwdjaRNWfrwM9Su7kBzHVVRLOzMNasoKiOy7AEA7hO+h4Ki+6tduAZ8pPc/F89xltI8fiWPUTKUu+pf7oP+I96aKe29zUgGvhLFrGHUA4NbPj5xkfv0z2O0/SNnJ3Sm95HtuaXzA31NC852EdAwmBITt1HHfazLdoHTMBx8rFeL79lNo/XJH0Pq5Fswg7PbSNGkvb6D3wT32B7Hefon3ITtQfd27PJ1TXGfSf60lZOg//zLdY++83wKDGyg2tTZjrKgkUje72NJOvjuzXHqFpnyMxNvvIe/HfaJFE8O/55hPW3jEZTFv3Kz519lTynr+LYE4hpf98mkGP/oP24btS/efrtmo7hBBCiB2NzJQPAF0HrZcE9h9XOlmw0sXFx1dhMetcdGwV4YhGUa4fpz3C6zOztnJrhRDbO0v5GrLffoKm3x1G26ixZL33NFoouE3bZFv9MyOuPpbhN5yGoa0FohFyXn0YS3UZ2a890u3xnm8+BsAQCuBYNh+A7NcmoWsaa++cQsORp9M+ciwA9jVLAUj76j0wGGk45CRSZ3+Ee+6nSa9pbKxn0GPX45k7jbyX7sW14CvCaVlkv/sUzsVzurXB0NrM0FvPIe/5uxh601mYvFWJ9sVe2/HrEjLfe4Yhd17EoMeuZ8jt5zP01nMYeus5pPz8PQAZH72I0d9G1Z+vo230OJyLvkl+o0iYlJ++pWXs/mA0gcGA96SLaB53IJkfvYShtanHc+r+fjopS+fRNmostopinEu+7fjd4Af+wrCbz+7xuDI+fB7Pd59ROOnv5D93J20jx1LzhyuoPuMqKs+/EVvZr6R98XbiCdEI6Z+8QtY7T6IFAz22xdhYj+Zv6/F3/ZX69QcAWKrLGHn1MdiLl5E+4y2sZavVA3SdtBlvdlwb4n+bsakBk69uWzdDCCG2CxKUDwDz268x4uVbGHrjGd06UB98k0GKLcKpB6svnlMO9vLOnct447YVXHx8JQtXuSivsWyLZgshtlMZn0wmarZSdd6N1J58CaZGL+5vp/X42JSf5lLw6LW4v/sMx9L55D11K6lfvDOg7TF5qxj02HVo4SBmbzWeb6dhX/0z5oZa2kaNxV6yAlvJCgwtjTgXzsbQ0ohr4Wx8Bx1PxObAtWg2poZaUpbOo+GoM/HHZqD9hSOImszYi5dCNIrrRzWjXXX+jbSNGkfuK/djqSzpaEf6tClooSBr7n+H4rteZfUjH7P2jikEsweR+d4z3dL8c16bhKm+hspzb0AL+sl/8lbQdcy167FWllJ95l8IZhWQ9cGzhNOy8B14PLbipTRPOIRQ9iByX74P29rlpH/+Bg2HnERw0HBaxh2IrWw1lsoSMj54jrSZb+P+YSamlkaa9zo86f1rT78CQ3sLmR++kPRzc3UZ+U/eQu7L9+EfPIp1NzxJ2J2OZ9aHAFhLV2Jf84s65k8nJ/8xdB3Xom9o2WUvfAccS+O+Eyn7+yN4Tzif+uPOxXfoKbTsshdZ7z/bEQxnvf0kOW88RuaHz5P78r3d/76+OoZfezLDrzttkwNzQ3sLtuKl1J58CQ2HnYquGag540p0TcM1/0sAHCt+JPeVB8h/5jYs5Ws26X3Eb4OxqYHh/ziJYdf/AWOjd1s3RwghtjlJXx8AtofuwhSIYNB0ciY/oGZLNA1dh1mLPRyyhw+bRXUWNQ12HtIOwDH7NDDprUFMmZ7NORNrSE0Jk2LfwusohRBbnKm+mpzJD2JsbSKYM5iGI07rMQ25J4b2VtzzZtK43++JuNNo22Uv/EWjyPh0Mo37H5uUkmyqr6Hg8RsxBNpxL/gKAF0zkDr3U3SzhcaDTki8cDRCxtQXMYQCBPKG4Fr0DYa2ZnVTMhhpOPRkWsYf3C1dXAv6KXzwagz+Ntbe/Tr5T96MZ84nmEeNI2oyU3H53Qy/7lTSZryFrXQlttKVRE3mWAr6iRjaWnAunksgfxiartO095GdTpSZwOCR2NYux776Z0yNXponHAIGI+svvY2hN5/N8OtOI5SWTfOeh5L69fs07XMUwbwhSW30HvMn8l66F/uvS2gfNRaiUdK+fJfU2VOpO+48fEechm4ykf/8Xbi/nYYhoO7BLXscRNvOvyP1y/eon3gWwYKhVJ13PbrFhnPxHAof+itDbz2HsCuV2tOvjD3nQHJef4Th152W1IZgZp6693cSKByB7+ATSf/8dZr2OQr/sJ3RQkEGTfo7ltr1tI0eR/VZ16i/1X4TSZ/xFsZmH55vPiZqMtNw1JlkfDoZS2UpwbwiACxVpZi9VdSdcEHPSxo0jepzr6fozosY9s8zCeQVYa0speHQk4nanaRPm0L9xLMJFI7oeErajDcxtrdibG8lddZUGo4+M+m6yX/yFkyN9ZT/5T6iTk/s51Hsv/5EYNBwoilubGtXoOk67SN2o3W3fag65x9gMuNa8DUpv3xP3SmX4Jr3RcfLps7+iJqz/krKku9IWTqPuhMvJOpwdj+eHhibGnDN/4LmPQ8l4sno13M2l23NL+S8/ih1J5xP6+77bZX37Hjvtcsx1Vf3+PncUXlmT8XY3gqAa/5X+I44bQPPEEKI3zaZKR8Amt9P/W4HUXfKpVhqKjDXrgeg1mfG22Rm7IjWHp9XkBXkxAO8vPJ5DkdesxuH/XU35i9PdEqq6s289WUmzW3qzxSNQkmVdYes+STE/5LcF+/F+dNctFAI9w/TKbrncszVZf16ruuHGRiCfnwHxwJqTaP2lEuxri8h86MXEw/UdXKmPIgWibDmgfco+9skyq/4N6ue/pLWXfYi96X7yPzgOWyr1Yxr2sy3yX73KTKnvkjB0//CsXKRCsRam7BUFFP4yD8YesufuqVmZ374PLbyNVRceS+BwhE07vd77Gt+IWPaFFp33YdwZi7Nex1G6uyp2EpX0jzuAAzhEG0jdqd95O60jN0fc3012W88ir9wJMH8IUmv7x+6M7aS5bgWfIluNNEy7gAAQtmDKP73G9ScfiWBwhGkz3gT3Wii9g//1+2cNe5/DBGHk7QZb2Kqr2bozWeT+/J9tO78O2pPvVQ95qATaB+6M9lvPYHn288IZhUQzCvCP3QMVRfeRLBA1frQLTYAWsbuj++gEwi706m44h4irlQAgnlF+PY/Rg1IXHYHpTc+Rf3Rf6T8b5NU6noXNWf+hXBqJoUPX4O5upyMqS9gqyim4qp7KbvucYKDhqv2HXAsWiSMZ/ZUPN9Oo2WPA/H+/mx0o5HB913BkNvOw1JRjH3lYgDadhrf6zUUzCui+J43qDntcsLpOdQddx5V515P3fHnEbU5yHzv6Y7Hav52Ur98j6Y9D6V9+K6kfflOUsaB68dZeL6fTsryBeS8rmocGH11FD54NUPuupjB9/4f6Dr2YnWdtQ/bWQWOJjMArWMmYC9eiuZvJ2XZfFp234/m8Qfj/u4zjL46Bj36DzI+nUzWO092Ow5jo1cdb6f2mHx1DL7nMvJeupfCh68ZkCKInlkfkvXWE72m9gNkv/k4jpWLyH3pvi1fhDBG87eR+f6zDPnXnyl85B+4v/t8q7xvT4zNPgoeu57MD57b+CfrOumfTCbz3achElaDZl9/QNvoPQhmFeBcMrfbU7SAn7ynb0u6VjeXc9E3pE97dYPFF4UQYluQmfKBEAyimywdM2HWimJC2QWsWKeKIe00uL3Xp95y7jp2H96KrsOrM7K56pHhTL5lJUU5AS6+fyRrKuzM/dnNo1cX868Xinh3ViaXn7Seq06t3CqHJrZPT7yXx9S56dx9cSl77tSywcfrOkyZnsXn89IpyArw9zMqyE4LbYWW/u8xV5fhWvwNtadeRt1JF2GuKWforX+m6J7LaR53IN4Tziecnt3r81NnfUggfyj+4bt2/Kxl/ME07ns0mR8+R+uYCWi6Tub7z5KyfAE1Z1xJKKeQUE5hx+MrLr+Twof/Rta7T5H5/rOU/f0RMj6ZTOtO46m48h7M3mr8RaMSQWQkTOqsD0n//HUKH76Gqj/9g4ajz8RSvoaMT17Bd+DxtO6+LwBN+x5Nzpv/AcB36EkAeI89F1vJSpr2Opy6Uy7FVF9DxOkBg4HWWJBtCIfwHXpyt+NtH7YzaV+8Q8a0V2nZbd+k2dJwZh7e48/De/x52IqXEnF6CGXld3sN3ebAd/BJpH/2Ks4l30E0SsVld9K071FgMKoHGQzU/PFqiv59Keb6aupOvLDvWUdNo/LiW3v8VeVld1B1wT87Avi2nffs8XEA0RQ36659jCF3Xcywf56BFgri2/+YjsGHuEDRaNpG70HOG48B4DvkZCKpmdSecilpM9/BVryMnFcfJpyWRdjp6Zg5703Ek4H3xAvxnnhhoi1OD/VHn0XWB8/iLV6Kf9gupH31HqaWRuonno25toKCp/9F5nvPUH/0mURT3GR8/DLB7EE0TziEjGlTCLvTSJ01FYO/jdbR40lZuZCUX37AtmYpwZzCxEx6TNuYCWR+/DLu+V9gXV+C76ATCWXm4Vo4i8KH/4YWCtI6ejypX72H94Tzcc3/Euu6X2nbeU9yXn0YU6OXmtMux3v8eWR++ILaai4aoWnvI3H/MIOUX36gdbd9MFeXEXGlJV0/htZmPN99RutO4wnmFWFbu5yozdExEAJgW/0L+c/dGXuCgdrTLu92Lg2tzThWLCSYU4iluoyUpfNo3W2fPs9/j3Qdc025uobj12UXmr8NjCaMjV6G3HkR5vpqmvY+EtuaX8ic+gJN+x7dv9nyaJS8Z25DN1uoOv/GXt+vv3JefQj3/C9g/he0jplA++g9+v1c5+I5HUUro/YU/EWjsFSXUXvyJThWLcb97WcqWO80qJX25TukzlG1BwIFw2ne+whAfZetWW9jeL5/o5IGLBVr1SAOELXa8B12av+fLIQQW4EE5ZtL19FCQaJGM8HcwYBKL4QDWVHqAGD04N7X6DlsUf54hNq+5qCxjZx1x05ccM8o9hjVwpoKO7sNa2XGgjQ+nJPOe7NVmt5L03I45+gaUp0y2rs5an0mvlvq5ogJPhy2HWfZwLzlTp54XwUmD75RwBu3rdzAM2DmglTumTKYkYPamT4vjbXrbbxx24p4oWexAfZVi7GVrsJ38AkdgVhv3D/MBMB3wHGAmvEtu+Yhcl6bRNrX72OtWMO6m57peLwWCpL+ySvY1y4jnJqFY/XPKvW3S4+z6twbsJWsYMjdl6jXTcui+qy/Uj/x7G5tiHgyKLntJUyNXgbfcxmDH7gKgMqLbiHiyeie8ms04TvsVBoPOI78J28md8qDmOvW41i5mIjdSc0fr+54aDgjl7K/PoilqoyWPQ4CVJp28b1vJR7TadAhnJpJ5YU3Yy1bnZj97yRe7A3otia7M/+wXXr9HUDtKZdgLfsVU6OXyktu61i33lnbmAnUnnQR9l+XUH/k6X2+3oZs6DroLDhoOGtvf5mcKQ8TcbqpOu/GHh+3/uJbyXv2TvzDdu4I+rwnXID3hAvImPoC2W//F4DmPQ7c5DTm+olnkTrrAwof/CstY/fDPW8mLbvtS/uosbSP3B33vJlkffAs6dNfp3nCodiLl1J53g007n8srvlfkPnxy/gLR1Jxxd2Esgcx4upjSP3qPRwrFnYbaAD199UNRrLeehyA1t32IZhbSCTFjX3tMnz7H0PdyRcz/NpTGPKvczHHtlNL+/p9QmnZtI0aR9a7T+Na9A32Nb/QtNcR1Jx+BaGMXBwrFpI2/U0MrU0MeuKfBDPzKL7nTXSbQ1W2f+TvpKxYCEDUYsMQ9KtzOvEsas78CxhNZHz2KhGHk/bhu5E24y3qjjsP3WbHWraa/KdvpXG/YwhlF6DpUSrPu4FBj9+I55uPeg3KU5Z8S/YbjxHMKaT6T38nnJHb8bvs1yaR8dlrNO9xIOV/fTA5UA6HyfjkZbLee4ao1YYhGCBqtVNy0zO07zQez+yPyH/2dhwrFtK203is5Wsw11aghUK07HFA4nrUddCjuL+bTmqsmGHL+INp2W1fBj9wFZaqday74b8bHNRJPqbv8Mydhvf3Z+OZO43MqS9Sdu2Gg3JDazNRm53M954mmF1AMLeIrHefIpyaSdidTvPvDgODgbQv38VWujLpM+6a/yX+waNAg5zXJ9G6+z5E7U4mf57Nva8Wcut5pZx5uKrV41rwFbaSFdQdfz66tefPZerX76MbTYTSskmf/ia+Q0/5zSwFEL89kShU1FoYnLNtC7zGVXrNXPLASA7cvZHrzqro8TH2lYuxr15CwxGn9/o5FH0z3nbbbdu6DT165plnbrvkkku2dTM2LBiEu+6iacR4/L87iIxPXiGYN4TW3fbl1RlZ+IMGLjquf3u2ulMiHLKHj4+/zWDJGidnHFbLvZet5Y0vsvj0+wyc9ihP/WM1787KIis11Gta/NZgqq+m8KFrsJavoXW3fbdZOzZVJApn37ETb32ZRW2jmcMnNG7ya3kbTcz6ycOCFS6qvBaG5vU8gr+u2sK7szIZkufHbt30lMu7XimkudXIBcdW8/G3GZxwgBdPSu8DNLoO1z81BIctyof3LCM/M8jrM7PZqaiNYfm9p2vucKIR9YftbaQhEu71d2rbrNkQjRBxpyc6a7pO6hfvMug/N+D6aQ620pU07Tux4/dawE/GtClkfvQiWiRCoGgUuZMfIJSZR/0xf+p4/XBmHr5DTyZqMpM260Ma9z+GqNOtAofHrif9y3fRQiGcS+cRyB1M1QU3ddvOSrdYaZ5wKBgMNO1zFJWX3Eb7TuN771hqGlGbg7ZR40j55Xua9ziY+t+f3XdH1Giiea/DMTXVkz7jLUyNdVRefif+oWOSHhbMH6LWbvezU+sfshOtY/frMb074krF7K0iaraqQCmW9rzRTGaaDjgW3+GnJW191lXbznvSdMCxKnDbiqJOD037TaRlz0N73aos6vTQeNDxKuDrcm4DBcPI/OQVAFUsb3jfgxS90S1WWnfdB1vpShyrf6Zt1FiqLr6VqD0FNI2mfY6mZff9sK37Fdei2fgLR1B1/j/BYqV5ryNo3X1fak6/gkhqJhiNmBtqSZ39EYZgAO/v/0Rg8Kjk9zNbSFnyLbb1JYTSsqk94yowmWkfOoaow0XtH/6PcEYO5tpKUlYupGmvIyi94b/4B4+k5qxraNz/GFwLvsRaUUz1WddQc/Y1ajbeYMTY1kLarA9wz/+CSIobc6OXqD2F9tHjSPnlB7I+fJ6aUy+jbcwEgrmDqZ94FhF3Oukz3sSxchHB3CKyX3+UhiNPx3foyaR/8Q7B7AICQ3Yi7/k7SVmxiJTl8zE2NWBsbaTq3Bsw1dfg+e5zGo48A91oJP2z10HXCWfkYmhpZMidF6FFdazr15I6+yNad9+XiCcdU301BU/eTDg1E/va5QRzCgkMHoWhvYWiuy4m76V7SFm2gJbxBxMoHIEWDlN+1X34R6lBq2BuEWkz38K6bjXuBV+R89okPN9Pxz1vJpbKEpr3PhJDWwtDb/ojmR+9hOf7zwkMGoZuMGD2VmMItquaBe2tWNetpvGAY5OuMUtFMWkz31bZDvaUjp+bq8spfPAvhLIKqLji32jRCGlfv0/UYiXiSiPiSsW5cBaF9/+FqM3esY1g+rRXKbrnMjI/eglzQw1V51xLw1FnkjrnY8z11VT/8Wr8I3cn4kolY9oUgrlF6p6C2gkg59WH8B12Kg1HnE76jLdwLp5Lyx4HcufbO1Hjs9DabuTkg7y4FnzFoEevxbFyEVGzhfadxmMtW83g+69Et1gJFI1SdRGevYPWXfaicf9jSf3mI5r2OkLd6/v1odExtLeimwe+KK+xqQF78TJCmXk7xiCBrmOuLlMZKZr6PrWt+QWzt4pwRg5EwmR8/DLmuioCg0cO+Nub6iqxVJcTTvsN7VwUjXScy7gXP8nhikkj8TjD7D5883bFGAj/fT+frxalsni1k5MPqiMtWEPa568TTs8hmuJW955bz8G1eA6OZfNxz5upst/GH0x0C3zX6lGdSHuQvP2GDvhrbwm333575W233fbMhh4nM+Wby69G3qMmC2gaofQcTPU1ACwrcTCmaOM+TEPzAnx031JWl6tZcoMBrjurnIfeGMTfzihnn12aGTuihbe+yuSco2t6vYfrOkR1MG6hmdCcKQ/hWLkIx8pFNO19ZFKq7ebQdXjmo1ys5ijnTux+fKvKbHz8bTqnHVK3WSOIn3yXzq/ldnLTg3zwTQYXH1fFkLzuAepPqx28+GkuUR0uO7GSnYe08+GcdN78IovbLyjFlRLhD7eMwduUCCROO6SW2y9Yl9T2aBSunDSC1RV2PpyTwe0XlLK81MHQPD9779zc69+xvsnEXx4dzt47N3HVqZUUr7cya3EqV5y8nuP39/LE+/l8vcjDOUfXdnuurkNLu4GfVjtZVpLCHReWYDTAsfvW89g7+bw6PZsj9tz0wYj+8Ac1rn50OEaDzqSrirFatkxBBNvqXyh44p8YAm2U3vJct0JguS/eg2fOJ5Rf/UBHGra5ah0RTzqgUXTnhVir1gEQzMyn8sKbiHgyyH71YZxL59Eydn/aR+xG1rtP4f5+ukohjUYY/MBVOFYuIuzJwPnTXKylK7GVrqLqT//osZ1N+xxFzpv/wT1/Jt7jzsP149e4Fs6i+o9/pf6YP2GqqyTiTO11lDmcnp00a90fgaJRrHnow/4/wWCk6vx/4jv4JHSzJakg2JbSW5q4SIg6PVRcejspS+fhO/D4zXqtQOEI1t3YfQ03AJqGf8RulP7zKRzLf1QDMrFBhHB6drelF/FCdkCvA7T1x/wJ+xM3UXfCBR2BR9sue9G2y14dj6m86Gbqf382gYJhauDpgGM7frf27tcx+Ns61vV3vO6Rp+OZPRUtFKTktpfJfeke0ma8hff3fyL9s9cIezKoP/bPScFU074TaR+xK7kv3MOQOy4garZQ//uzCXsyCBQMI+2r92jddR+ci+fi2/8YPN9/jnPpPBr3nQgmE40HHkv6F2/jnqcyYnJem0TUYuPXxz8n9ZuPMba3UnrTs+gmM4PvvZzC+6+k5I7JpE1/E6I6pTc/y6DHrifr3adp3vtIMj5+GXvxUry/P5v2UeNUocMevhB0q426Ey4g543H0I0mqs+4iradf4f7u8/I+Ow16spW41i2AGvVOiKxoLr6nGtxLJtP1gepgBo2AAAgAElEQVTP4VzyLW2j98B3wLHkP38XntlTaTxELT8hHGLw/Vdhrq8mdfaHrL3zVXWudZ2Cx28AoOyvD6JbbNQf/Udc82aS8+Z/yHrvadbd8CS5r9yP2VtN7pSHaBl3AGZfHdlvPkbbqHFquUBWAU37/R40jeK7X8PsreqYFQ+nZhLIH4prwVfUH3sOgKrHoeu0jD8I/5CdKPvbwwx67HoyX3uUFesmArC81IHzuxkUPHkT7cN2AXTc30/He9JFqg7GulVkv/4oTfschW3tMlVEcq8jaBszAf2V+3Av+Iq6vCKs5WsIFI7Eufgb8p6/m9YxE1h/+Z1JA4jZrz5MxuevU3n+jZud9q6FguS8cj/htGy8x/6ZIbefh6WmguqzrlGDplvZqjIbVfUWDhrbpLbdjETQbfZeH5828y1yX3kA3wHHUnnp7Ziryxhy+wVoepSyax7G/usSMj9+CYBgbmFH37C8xkK6O4zDFmVVmY1wROsoetyTQFDr1l+wlK9h6L/OxRD007jPUVRd8E+i9u7FIY2NXtKnvUrzXkfgH7Zzn8dvri7H/f3n+A4+UQ00Ar+W2Vha4mDi3g0dRZo3KBJGi4Q3KoMKwFq2mqI7VDaU9/jzAHXsL32WA8AbX2Txp6O69+/iLBXFZL3zJHWnXMpXvj1YVurggmOqBzQLUtdh+vxUBuf4WVdtY84SD3//8XKcS77FPe8L1t71Kq4FX2L0t+Lb/xhVaNZoQouESf90MjVnXTNwjekkFN4BBrE20oAE5ZqmTQQeBYzAc7qu39vl91bgFWAC4AXO0HW9ZCDee5sLqEBOj83uhNOyMDfU0NxmYF21jZMP2vitPlJs0aRZ8FMP9nLqwYnXOf3QOm56dgiT3irggN0bGTeiFYs5+cYx6a0CXpuZxTPX/sr4Uf2bUQ+GNeYvd7LL0DZSnRHMVevInfIQofRsqv/0946bTcrP3+Oe/6Xq+Ex/A/e8LzYYlJfXWPhsXhpnHl6Ls48K8x99m86jbxcAai3+iEHtPPD6IPbftYm9d27mwvtG4W00M31+Gu/fvazXGWddhzk/u2ltN3DYhEYspsTjIlF46oM8Rhe28cx1v3L4X3fjtZlZHDi2iZJKG388ogaTURXVO+/fo3HaI0SiGhcsd3H9WeX864UiwhGNSx8aidsRpj1o4LnrVjGysJ2XpuXw4qe5tLYbWV7qYNyIFu64qJQ5SzysrrBz0oF1fPxtBmfdkUitHV7Qzj2XlLDrsMQAjrHZh7m6nCmLj2LhKicLVzk5bHwjb32VicWsljyku8MU5fqZs6TnoPy2Fwfz9ldqNHlQVoAT9q8HwGSEs46o5aE3B7GqzMaoQj/vfJ3Bo28XcNz+9Vx/Vnmff8vOTPU1mGsrqBs8gWseH4bDGuX+y9d2XI/PfpTLN0vUGtPXv8jion2XYWhr6Vbsqy9awA8GQ6JjHS/sFOu4pk1/g5zXHiHsTsPgbyf35fspv+o+cl/8N+GMXJrHH0Tal+8CkPXOf2ndbR81u/LYdQQz8wll5WGpLqfisjtA18n8+GWK7rsCgLDTo9ZXH/kHAJw/fk32m4/RPOFgPHOn4Vi5iPUX3kzjgcdR+ODVZHz+OlGLjab9JvZ4LOHMPPxFo3AumoP3uPNI/fI9Qhk51E/8Y8fvtxcb6syIra/pgGOTgtUtymiibde9N/iwQOEISq9/gqjDFRvk6q75d4ez4rmD+s6CMBh7HQDSzRYiPcxSRjwZrHnwA9B1dKuNhiPPoHDS38j64FmcS76l9pRLe5zdbDzgOPyDRpDx6RSa9j6iI7Oi4bBTyJ38IIMfuFIVWDztcoKDhpH+6avUH6Uq0vuH7UIgfyhp09/AEFCD8oagH/cPM/DMnkr78F3V7CxQdt3jFN1xAUV3XKD6BXsdTih7EDVnXMXg+68k+43HSP3qfRr3+32/Oq71x5xDYPAogrmDO2orhLLySZv5NqmzPsS++mf8g0dRcttLGNpaiHjSCeYUkv756xiCAar+fB2BwhF45nxKzquTCGfkqvvhwlmY66upPfUyMj58nrzn7qT8rw+SsuRb7CUrWH/xrYRiy/Oi9hTW3jEZW9lqBk36O0PuVDULKs+/kdxX7if7zf9gL15G2J1O2TUPdasx0NPymYYjTiP3lQcoePxGak6/Eve8mYTSc/DH6vS0jt0f30HH4/l6KlokxN5j/CxY7iB78oP4h46h9ManSJ39IbmTH8RSUUzKT98SzMzDUleJc+Es7GuWEjWZaRm3P1G7k/aRu+P6fjr21T/j/Gku/qLRWNavJWp34vlhBv6hY6g/9s/qb9veQtpX7wNqMqJ159+pcx8L2p0LZ5P54fP4Djmpx5oZXXnmfkra1x+o///Nx1jq1hNJcZP5wXP4DjkxKcg0+erQAu1J9UI2STiE2VvV7XV8LUbOumMn2vxGXrhqIWe/fioGfxslt73U8VhdV9/j85a7+MeZ5Uyc/VHsOKZRe9rlpH3xLpoeJezJoHDS3wBo3PdonIvmkD7jTdYP35Xp81O55j/DGJQV5G9nlHPtf4cSjWq8efMSDvnpMUIZuUk7Sdz1SiHvfp3JE9es5pT5N+D59jN8Bx1PytJ5RK02Go74A+nTpuD+YQZRh4vqs66h8aDEQGXuS/fiXvAVnu+ns/rBD3rNTALIf+oWHKt/xrHqJ8qufYyyGgtn3rYT7UEjT0/N46Zzyth/tybSZn2AY8WPVP/xmm73OVNdJYPvuwKTr47Sm57pyBbZkGBII+u7zzH620j/dDLvFvyFNevtpNgieBvNHD6hgS9+TKN8bYjRzfNpG7Nnt/tZ9tv/xfXj19DSwkUr5gEwPN/PoePVhMuaCht5GcHel2jqOlrA320gJhDU+LXczpghbSwrcVDptXL3xSX85718VixsJeXn7whmFWBbt4qUn+aq4qnZg6i89HZ8h55MKCOXnDceU7tsnH5lvzLgZsxPxWKOcvC4pj4f9/1SFw++XsDoXB/v3bDBl92hbHZQrmmaEXgCOBIoB+ZrmjZV1/VlnR52IdCg6/oITdPOBO4Dztjc994uxILyqFFdcKG0bAI//sJVj6jOxS5DBj7t5Lj9vHy50MNzH+fy3Me5OO0Rhub58bUY+ceZFQzN9/PCJzlEdY3H38vnhRt+TTxZ17GWryGYlZ+Uvrm20srf/jOMlWUOCjIDvH39fCbcdyWmJi9aKIix2UfFVffhmfspOa88QCB/KLWnXYa1fA3OH2fx3i63MDjHz+CcIJEo/LDMRXZaiBEFqtPy9yeG8XNxClX1Fm45fZWaCewyG7Cu2sKkNwsYXtBOfZOJVz7LxmaN8tkP6Xw0V+33HtXh5j+v465XBvPM1Dyu/oOqdL9oVQo3PzeE9oCBMUVtVDeYWVaiZgtGFLTz54nVuFMi7Dm6hdk/uSmpsvHIX9aQlRrmmH0amDI9hynT1chkfZOJq/+wnkfeKsBg0Hn3ruUEQhqn/2sMNz07hKJcPzecXcaVk0ZQ5bXw0JXF7LdbszrOMyooq7Ey7Yd0CrMDfDAnk+z0EPOXu8jLCHD7BaWcO7GGn4sd7Dm6hZ/WpPDYO/lc/MBIPrp3Kd8vdbGu0sL9C8/Cvm4VoZypjB58OOvrLPx7ciFLSxyceICXdHcYgAN3b+Sdr7PwBzUsJp2qejN5GSFKq628+3UmRbl+8jOCXHnK+qSBm1MPqeOJ9/O579VCctKCfDAnE7s1wsvTcrjWNImR816j6s/Xdcwqx2lBP9aKtfiLRmFob2XoredgavQye78nmbNErTGcMLqZc46uJRSGd77O5JBxPprajHz+lZW7pp2DyVfHqr89xV/nnIbdGuWJ/LtJ/2Yq6y+9Hf/QMRh9dRhiHRFTQy1Dbj0HNANr75yCsa2Jwf++jHBaNmXXPoZz0TfkTn6Q5j0OZP0lt+GZ+ym5Ux5i+LWnYGpuACDj08mE0rKon3g2Oa8/ogomvfsUEVsKxtZGLHXrqT35Epr2PwaA5j0PI+OTl9HCIeqPOSdpdq7mrGso+velZL/5OO7vPqdt1DgaDz4RNI2KK+8hc+qLtO66d7cZvc5axh1IxtQXsZauUltFnXjRZhdhEmJb6k/wvsnLEjZAt1j/n723DIzq7Lq/f2c87u5GCO7uVgoUdyjWQrHiUty1OBSXIsWLOwVKkOIWIBBC3DPRiYxkZt4PA4EU2tI+7f3vfb+sLyTh2HXNmXOuvffaaxX/nFepLlpnDxyPbkFvYV0cSL8PGt/SJA2dW+Jv2Y06YHfhR+RJ0WQ16WQSGmzTj4zWfd+8rwSBjNZ9cN80C4CEkd/idHAdLjsWI9JpSe77ZpWo8QokacgcPFeMA5GYtFcicvnla5FXsS725/ZikMlJ6zrswwYrCO/0suutbMmrXL+YrZDafYQpifEqcCiydyZq4QEEnRadiycASUNm47V0NJ4rxhI99wdsLx9D5+CCsm1/DFI5LntXYnXnEnZn96KzdyGnzqclr0MiRe0XQtLg2XgtHYmqSkOym3RCHheJ/YUDGAWB+PGr3wnIfwtZjTshVaZge/FH/O9fQaRVl2BVgKntxP6nA1ThHp/Vc8Uy/BZyVQYJ/SdiVJiRV7Ee7FyC8/41iNX5JA2eheuOb7ENPY4sKYaCMtWLA97cWi1w3fEtJEaRW60xFo9voXX1Jm7SetzXT8fh5A7SGnblWZo9dWKOINKqiR+5BPdNs0yVWnUBBcGVyWjV22RJqdOgiA5H4xlAYVAF0BdhFhmGVJmMwcKaguBKxee2vn4arbMneksbzKKekF23FVktuuM3ow+2Fw9TWKqiyVVDq0GeHAsGA3ET15RglbwPT2PMGLEyAEszPdsnPME55wUaryAQBLyWjsLy8U3u9VvJqHt9aVU7k3b1Mjl7y44Ctendozp6CZnSJCDsum0B8RO/A0Hg1A07VrwqlKxYK6VD0jOymnTE7uKhYtvJ3OpNSe05GveNM00aIgOn4/zDcmxDj5HXaRJztlfA3qoIZY6E0asDcLDWkZErJWvfWRwjtgIgzUhGHh9Jpm8V9l1Yjt4gcPdAFF9EH0bj5oP9+f0YxWLixywnv0Idcqs3xfLhNSwfXcP1+wUUBFdG5+KJPN7U3lHoXwazqKdY3zxX/G5/jauPrJmz3YseZR6xKDLMxHR7dB2zF49YetrEHpk3MIa1h90Y9G0QE5vdZsGFeQhGI5LsDLIatcfhtIlNovYJxvbnIwhaNSKNGscT20kcvgCziIeYRTzgtPcgHsTY0a9lKhZ5KVjfOI9RJmdeznDWH/cg0fmu6SuVl8PG5fk8wET5L+1TwNhuiVy4a0fAurF4J19HVakeCSOXgETCwl2ePHsOvyT+AoBlxH2syEWFNenn7+NxZR3fecxn8tGq+Lqq2T7lOU62RSXmQdBqcF83Fau7l4n/cib7JL2oWUaFo00Rw1cEcC3Mhg4NlNhYFCERG2hSNZvb4Za43b6EYDSSMGoJXktG4rprKdK0BJTtvwRBKBaBzKnXGuub57F8fJO8SvWQx0dikCmKn0Nv48AlR2ZsNelcbB73lE+z96J19aYwuDKifBVeS0ZiMLPgdueFjPuuAiKRkQ7VVb/7nfhvxN9RKa8BRBqNxigAQRD2Au2At4PydsDMVz8fBNYIgiAYjf8D5l6v6OuvK+UZcne8ND9xO9wUEFYu9cfK2H8WUgmsHhVFSqaU8BhzfrprS3KGjIxcCePW+uFgU4SlmZ6ODZWcP12E/bwx5AVXY6kwluGqBfhfWIPWxYvYSesosHHj/G1bZm3zQSoxMKZbAqsOepD23QFkyiRipm/F7MUjUzAzth0yZTIFpSqSOHQeRpmCvEp1cd25hEXfCqSalWHxkGh+OO/EtTAbpGI9z0u3wy32JkLeT0BF9JdCCb7QnfxyNYkfvQzzFw+RJUUz4sVwDl7zQCoxsHrUSy7es2X9UVPV8Ku2yUQnK7j8wIZFQ6JpUT2b+y8s2HrKhbb1MniZqGD8Wn8cbHRUDVbxNMYCkcjInC9isLLQs2CnF9O3+AIgFhnRGwQqBubRrGo2AON7mCrDfu5qXsSbseWkC35uas7dtmN4x6RilfItEyM4c9Oebk3S8XDSsm9WOFqdiEpBb5gIIhGs+DoKZY4UJ1sdUzb5sPGYaRwz+8cilUCwdyHBrxT5fd00lPfPp8OUMnSYXIaMXCk1uIkZEQA0Td2NRacqqHUiNh5zQyI2MKhtSvH56lXI5eA5a9KP32R72qfsvu5L/1YpZKkkyKRGdk59jqNNyQcxgK2lnqEdkli2zxOJ2MDAz5Lp3SKNdiN9CTq5EplBjfu6qUTP24NRLEaqTEasysZl93LkybHkla+F3toOSY6JwVH+7nbK+3+OmdzAlWNZTLvUjRS5DznZp+jcWEmSUoZ65wGkmCr65ltWcyZ7IHZk4SRei0SvxX7bUs59soSOOzojKswnpe9ErO5cQpptEvNx3r8aaUYqkpxMJKos/L/piliVTX5INRJGfmsSK2vcEfszu5Epk0ntPhJBr8Pu4o+k9JtEQXBlnA5twGvFOCQ5GSQNmkl+SFXkiVElfIeNCjOUnQa/97tXEFKV3JrNsT+3F6NYQkrficULR4OF9QdRy1WV6uF4dAueqyciGI0lMvwf8REf8X+ASEzS4Dk4nNxBVtPOGCys/tTuRpmc2CkbMYt4aNJAeI1fJZBz6rVClpaAUSJFVbUR0vQkXHYvxygIxSrhr5FXpSExM7/HIDcrrjYDJA6ejf25veSXrVFCDO6vILtBW6xvXwQw0cR/hV+3HBQ5uBI/fhX+k7rhM2cgElUW6R0GgUhMZsse2Fw/jeeqCQDFfvPvQ0GZajzfcLm4EpneZShGqZSCkGp/Tp1eIiGtx0iyG7XHa8kIBL2erGZdSp7rlSBkfeEazas1Qb3JFIzkv0oI6Vw80bh6Y3UvFIPUpJuQU7dVsY1kWvcRxcfKatwRSXYGOid3shu1hyKdqfItCCjbf4nv7AGcn3mVsakTibQ7jcbdj7yqDUmUL8Du3F70Ng5Y/3IG76Wj0Dq6ETdpHT5zB+KycwmJwxfguWw0isSo4vPpzSy41Wkxe2LrsPLZPZTtB5LVrAsWj2+iqtoIo1xBfpnqOB3agFEsxmBmidqnFIWlKmF1+wJO+9YQPnQ13tf28MCjNdfzK/JV9iJkqkwuVh/PwVu+HLtqj1xmJEkph3nz8U85QlKD7tzxaEfI45sAWOz/nisFIyh8GsWIo13w1HfDx3UG9SvkEHD+EmonT7I/7YnrjsX4f9MFrZ0r+1N2EexdQK/maSRtMbVsZLbogSw5FsdjprnNataFIkdX4iavLx5zToPPsL9wgPvrbpKtqse+WeHk5ks4cd2egW1T+O6QG0E3z6Jx9Ubn4Fp8LKv7VyhPXywr+FPm0Qn0UgXRc35AqkxCMBiK2TTqwHKoA8uR3bQTAWPbY/nDerbXXMuA+zsxyM2IH7cKn9kDcDq0Eesb51DER/L480XMvtuKo1cdMBoFNGnXAYibsAbvhUOQ7NzKuej+fN0pkQ4NMmhdO5NvNvgiXDQFocrP+uN4fBsWT26hcfdFVJiH5cNrFPqXIWnQLGx/Pozduf2MmGbD8bRxKAqyCBMHsFI/kibP1vLZ44UIRlPFukjsj4W+Kw7Jj8lu8Bm2ocdpwGV863vyMNKSyZ/H4+OqobblI/yTr6P2DMDqwVWCRrQk3asKh54eoSGXEaEhvf2XOB3ZTFMuklWpEX0efYO1IQq3B1vwc6tESoaUESv8OdR2O1KhiILSlbG+cR7Ho1tMei4SKda71jGpcBJlvFVM+/QO18Js8HJWczjUxCJqXDkbGws9VYPzCLx6hUJrFzRegWS26FbsxpLzq7aqvHI10VtYY339DIqoJzgd3oRREFC2+4LYT78iPUdBKd0ThMgIvts/hgoBeWTmSsnfsg/3rDkYBRGJIxZhdeMc5pGPTPOWtBZtUV12Tn6KtyId+DBWwn8L/o6g3AN424A3Afh12rx4G6PRWCQIQg7gACj/hvP/P0XcCw3eQHSWLe5AgtGDAHRUc46nVWsTFf2fgqu9Dlf7nGKaSrZKzJdTXRBlKOnU36Qq3uLMQlyeheLyLBRHbAhiPbn+FVEkvsRs7DB+0PfjAZXw93Ni2Yho3B115CVk0+zaZpIrf0phUAUKA8sjzs3E8vENktv0NamWvmpYyatYF3YuoZP8BCcVnbm77GdyRLUZ001F1umb+D05CcA05qIcM486y2YjoMfy0XWkX/TBx2AKPtuSgKzZEnp/ko6vqwZXBy0nr9viYF3E4HbJyGVGivQm6jXAuO6JJD9MJXLiJqKNvpT168uqcfHFFeS30bRqNslKU9Li5C/2FKjFjOySWNxz42BTxMLBMQCkZkqR3blG4Ibv+cRmLP0+fUPpKeNbSBnfxBK/vw8iEcWB/KTe8Wi0Imwti+jU6P23u7+7hmHtkzh9MI+AICd6xv1IkUZMrE9DmseeR1Qmi2BfLVbmesr4FuDh+KaXvkaIig3CEFoc2Y6C+pw3O0WDU9/gSgouTVa+NyB/jS/bpNKsajYWZvriDOo4z33I4tQkfjENlx1L8BndAZnhTa+91taJjFafY3f6B0RGA+mf9CJb6kTlEyvoXuYhDsGOVF/aH7kqBj/jS2YqFtKgYhvSs6WY7zxAvG15aN8Or+/nMsLvOA4ZL5Dkaslo2BGHy4fouL4FeokcdWBF3L43dcEk9ZuEKCkB13M7Tb/3Ho/BzgGPNZPQOHqQOGxeMZXQKJMTM2Mb8uRYk4+zIJDRdkDx9StrtsI59EcK7DzIqf0JSKR/mjKeNGgGau9SqP1C/pKQjdq/DAXmDpinxpNatvF7Lb4+4iM+4q+hMKiCSdn8L0JvbUdetUa/v5FIXMI6LatxR6RpCRSUrfFelsz7nAMMljYoO371l6/zbeRXrEty/8noHN0+WACryM6J1O4jcd8yhyKpGVmv6cNiCckDp+O1eDgaD3+ymnT+/QO9RQ02WFiR1mvMXx0GWjef4naEXzfF6m0dSZO6U1d6FyvzRtRSPCDR6FdClC6rYiNcU3aQVa6eqZ2hRTcs711G5+JlUnovvmYp6V2Glvj9NQqDKpDkXpUOSevZIWlFQNZdohuOLmYpvE42ZDXrguXdy2Q3bk+RvQtpXb/GY8N0/MZ2wGBmQeKQuaj9SiPJVuKwcyUVd40jkk4IGMmp0xK9tV2JNqf0ToPxXjAEvYU1cVM3Fb8X8r1D8Px+LlXHm5wpKnOYaDriyiYA3C8kckl2gvoVc5n8eTw/bsul1v0jGMRS3EP30oDTFMhsSG/Vl+Ajq+gbdJXOLxZinhLNABYR1bQ9pcqZ0+jcJV54dETStBNiVRbmzx+gCL/PHMNQYvqtpmpwPsqtR0k180Pr7ktG6z6YP7tHfmBFCkKqvvNZqv1CyHAtQ/MX3zGwZafi9VKtsqbqZsty8dS9HkqEfz/EA/pheT8UjXcpvL/pwUD5Tly7fEHVR8d56VYXo1yB1sP/vfdMka0jKc164X5qC1n392HPadKa90RvZYuy3Rd4bJiBOEuJQSrDd+UoosRV6NNST/em6cjHHSPJqhQa7yBSWvTB88dVdLa/QP9WdgDIpEaGdkjG/eY+4h0qktdlKFp3H0SF+WQ17gQSCaLC/OJ7MKdeGxzO7GZ2zOcoMDH1vtSv5655bdqGzSezSlMyew7HfPEsxqUtQuJkgzjdgLL2Z2huPqCJ4QpBA+uVyAH2dDgLeZAwehmK6HCsbl/A7eZ5ponnYyPJR6NVkPlpb6yP7qSd1UVUpRzxe2BKCH1WdAh1l9FokJO+ag/+y6eVmLuCgHIkDZwOBQX4rBpHW47xRdwWWm84xddmG+k7rxpjVvtx/7kZg9qaWBRVg1VUJJRIx5pIBIGs5t2QZqah8Q56dy0jkZJeqw2uF3ab5qdGc4xSKU5HNvPsaASXjK1pIkxHbtQwlzhs+4zheaSIHjvXkupZFXOJFs+V4wFI7TIMdUoWDa8cYHDzMAI9tGj/fHfwvx7/KqE3QRAGAYMAvL29/2DrfwfURjmh1CcJV9yBREy0jEuiJuTSnWw6/v4B/kY4kMFdoQ0KEkkWTUVFA2ryI98xlFrcYDYzSMWZ3sJhDCSyhsHMxETBKxCVJ1FYQBGuTBfmIkfDTGEWowAjAtMkC7kmtqabNJ0OQgavnxkvCUBLCNOEecwSfYuCJAyChHRhKHLhCOGU5jzNGSqsI0mojzdhfMV6rMllDCtYzHiKkDCZBbQS6ckSuqLDCxfSeCFqi15sTxzfYUSOhCJe37IeQhLXhZ5IjTmIMJAvbCNRWI6edxdEYvT4EU2gkEVz0TlEogLSGYyedxWaPYQk9tMNKYW05Bfi2Y+R3xY9+SPYCLnsEfVCL7ImlbGYZBfexTfixSxnNXlCFXQkcZGm3BZ1YQoXqSbcRSeUYrywFLVQmgLeeCKbUUg3YR9ZRlsacIUksScSTEmaCsIccvj9PsUgUSQGwRw9ph6/rsJ+EvDgB3FfTtGSPsatRBJAGOUxIOKlUI0mUj3Pjf0J5hl6oTV+QjyTWEFnDiI3+uLDfQawlc4cYAjrSaYlXkIqQdxklTCDAqEH/dnIOGEpMpK4RXXWiFYwl9t4E894FnJT3ItPWU4ONthKOpMi6GhONkociRMNxkli4Ftjd6xE5qwXkrDnTfJBKXYhTupDBfL5tQzIN8JCXKnCHaEFSxD4K4RajWDGMulEykgKqM6fZ8IYEPMVG+jMDo4IU5nwF67hIz7iI/49KBSZM0a6mppiFY34Z8Uz3wcjAt9Lv8RNoqXmn3gm7ZT0I5SaKAV3lgoiHF49Rx8IlakpSiFIomG98OI/u1AUhN9UIQ+jPBWEMDRABR7xkAq8rb08V5iKhMok0ZpZ5JIiuNJOEoa3RM1SovlQ7avljOFbenFHqIEaObuE3nSmJLHzkTi5ozwAACAASURBVLgSFyWN6CRS4kQRe0U9KURDBR4xR5hPb6kZZSUFpEvKsknUjF3U53N2EUp91KJAvCgpVHtDVJu6QgIKQcwmUQJumBL7u8R9aM0RgnjBWJayiIkMYhM76c0R2rOPbiSJvMgXapMqjGMs6ynAjIqicM7qG+NPNLOYh1HUmimsZaEwBVdC2chABrCVL9mMQaiCBQWcpSWfIma+ZAZ3JJbUYBtLGU2scJZCoRIVuMQOvqKuIHBe1JJpxkQCRWLWE//OuxZBYILwLVtozQzRHLIo2aLRgvNIKeIkrWkhmHFe0oVS4kKe0Zyu7CdHqI4PcSxlLK1+fexfYaloHBM4zkImEYUfy5jGMFTsEfdmPy3JEpyxELScN9bjHtXIFrqSRSd8CWUzo2kIbBANYRCH2CwMIpkdGDFpKJUhnADusURYQGtBYK/0c/J0YjqKlIgBtdQS2at7I0woTwFlqc0NHlGeVYxgMwM5I2pFFrZ8JWxiiETFz8JgZjOYacIcCjDjJrUwpw4NuEwaRnhrNhsSSjyexAi+PJeW4ZzkS3owjBGsJl+w4Rp1sRasuUktGhCKWjCtayewiMVMpIVwDhlFeDKNfXRlO32pzxUu0JTIvLqMUyciFopoxSoO8UbEcB5TSOQQJ0RtMRfdI060FjVlCRRe4k4yh2lAE0AtmDFOuoJgSSFtyXzns5ktzKAxWhLx4JJ4ElKJGAtOso5h1CeUy8aGROHHQDYTLTSkBs9wQslQyfc8F5elO/N4TjBSaVeKhGQWsYfhwneoGPgHd8V/J/6OZ20i8LZ6hOerv71vmwRBECSADSbBtxIwGo0bgY0A1apV+6+gtgulgmhIKLPsHlKNIp4KJusgi5QoLLbNJ6dOy3/MekeSkYIiNgKru5eQZqQiTY1HkqNE5+CC856VyJJikBp1XAvsR1pwP3zE69iS252ffvbDw9GdxMl7EGxUWN36Cdfti/Gb1pvMVr1x+uUgP3v3ZtP9ajSLesbDlxasP+qGk62WqZt8OXHdnkWDo3GyLeLqIxvimMh2dT/0WJAw8ltsLxzEZe8qDBIpc4IPcyPOjRGFq/FeOhKdRMG+om6ElBXRe1xTHJ5ZEpUgIzM6DIdze7C7eJDYaVuwvB+KPDUOUuNwPLoFWXIslg+ukjRkNqrqTXHeuwqpJo/oRfuRJ77Efd10fKf3oTCoPIqYZxhFYjJb9sRgYYXLrmXF3rdGQYRgNCBPeEnstC0gEiHOycR5zwq07r7IE14iMepI+mom7htm4nDmB1OfDCCPeYb1zfNkN+mEzskdecwzRDqtqYfsbRgMSHIyKLJ1xGXXUmx+OQOAxivovUIwsqQYk3ertT2WESZP3e0s4Vx0C6YwEKsnNxA9CMXx2FaMYgmRSw4XV3fNw+8iN6g5+dl31FcewemX42R82guxKgfXKwfJ69DnXU/qV7A/sR2XfasxiiVktPqcrBbdCEi4zGrRcL7ZGIC1uQ8zvrXEX4ByShlZKgmLd9uw/qicmiEWRFtV5PxZOwxGb9rKK+P/9ByGlwoKrZ0JVXTCRSGjVdxpVA+vIX3Vq/ZdVg8itgVjZv0VI6PmADBZtIWdl7xI8TnHvBahbNgxgIJnEhr2HsSVu7bc3GwNQFzdbSRnyLi3yxJBMGJhaSAuTczE9TI2jItEJDKJFXafWZr4NAVT+sRRVCSw96ITU/vEUSkon303/ZBaTiAnU8Lpm9HUKqMiJkVO9dJ5H+xGM32LD8euOSCVGDg4J5wgT/WH7fgKj6PN2VXQiUuObUh6IqO3Mgx3R92fOsZHfMRHvB9Pos35/rQLXRunUz3kzyfNcvPFhEWZU7OMqpiZ9WsYDLDphCtSsZH+rVI5cMmR7add2HXWmatrH75jURkRr8BMbsDL+U0gpioQse+iE9VLq6gY+H/TnrkWZs3kjabwNHTNw99lSL2GMkfCot1e6C28yc2XcOBSIoPbp6A3wLQtPqTnyEnPkXPwZ8diP/D3wWh8E0PnFYrYfMKV6qVV1C3/5/s9E9JkDF0WiE4vvNN6pcyRcEdXkYb6i7wsyMND/ZLN9Ma5UITFK/HY0488iWIUiicGJmkf8MM5Z8JjzQmPNad17SyaVTO1rOmKYMtJVzwctXxWN7PEGB5GmrMsqRuDnVcTkHaDHbaj2Xk7hE4dnnL3uSW7zjnjaKPj8BUHCjViDoc68P3kCJbs98LOZzJVh0ehXBrIqFVvVLjN5Hou91hP/bjdfHFtDp9eN6dn83Ruh1vSoGIuMqmRNYfcycAKcYGRPnMtKB+Qj7OtjpM37NnrE8raIWFor5bilpcTpfPvcyPjK4Q8S57X2Iz7nWMm4dHwO0hU2RxyHkxkmg8L6xyju+c1Zu0fiPGQiLpmrWgecQy9RM5a2+nU4xkBT86RL85DI8jZktSSgst5rDjggZezBn3Q5xSmL8Fl/xqymndFZtSyo6Az7slyNp1wJRlrkp/D3YgsqgWX/K49jTFja3IrhgZ2pcqZHRTUboq4IA+ba6dQtu2P0+NL5Ijt2BLTlBMrDcWCsH3oSmv1KRT712BA4LvkjjTRppGWJcWIgI9LSZecjBwJ638KIr3CWRbWOsrUez05dsWfzzuFse6IG1onD7xdNDyPNyP06wM0vbcY+9O7cDhlYt4tVQ3CPUHLunMB5PisZlVsBzSndpHR/ksEnRanwxvRieUsVg4g9nARaw6ZqsG7f3ICIzyPN6dqsIq5A2M5es2RNGExP3h/w4TUVVxXV2Gt+GsUeRlcKDOeH+/5cvCugCV+TBOPxDntCadoxdGbrjhoGtKBfYg3z8YsMozkAVMpLFWRoMwb/EgL5q8K4GmMBZZmeuyCRtD9xT4U+nxOMwrNWWccjQ2YqppH/nMr0gQXVhhHMVWyCI8DK5EqUygIKEdWq+m0FBRUD3FH9MCGhFNGRqwMQC41kmE2iKmFUyn0DOLhJ+OptWUQ/lN6IEsztXc6HtlMwpjlWDw3rVH3ZTSjCUXsveDEtlOm9ptqpVUl1jK6IvjhF39ia23Aw0nDseMm3aYv27QguvVJFDlpKDUhpEcaKDp0GNftixDnZhFnW4F1Ma0QBGg9fBL3r9tzcbct4E17+9bUurKHqPpN0SpKtuX8L+DvCMpvA0GCIPhhCr67Az1/tc0xoC/wC9AZuPg/0U8OyF9pzGiKxEARYZrS/CjrTku/CCye38M84kGJftW/BUVFeK6eiNW9ywDozS3Runqb+umGzkXj7of/5B44nN5FftkaTPvGlB9OZxjtjRDUOBxvFw3WFnqMyMmt1xp1QFk8Vk/Ced8atI7uWA3vg8tCHf0XlKJQI6JRpWxWj37JwZ8dWfyDJ4MWB7Fv1jNCH9oQ7dSN2L656Fy9TDSxyvWxeHqHIltHvvRy5QujkcKZJtENVeN2nOkUg6WZHrEI6pRTUaccpDKTzA4D8Jk/GPcNMxDnZqKqVB+jXIHjMZMQiF5hgfuGmaRlK7G5fhpluy/QuvuidfclzsYRt82zMX/+ALVPKSRZ6bhvMYn4qD38UQ6Ygt7SmoJSlbF8dB33jTOxunMRVY1muOxZjs2108XTq2w7gJx6bbC8exmHE9vJbtAWQafFZ+FQxPm5WN25RFqPUXiuGIdgNJAwbD6qWi1MOxsMeKyeiPWdS2idPJClJ6Js2x/z8Lsmhdb6nyFPfInZyycUBFfGLOoJTj+uw6AwJ2r+Hiye3EaaHMutm+1QJluS7lwW61sXkGYkUxBYAUXsMxyPbiXliykAWD68jkEqp0z7YJSSaeR0GYjO0Q1ZShw2107hM3cgRQ6upHccRGGpSsVjFKuycTq0kfyyNdDZOeN4fBv25/Yi0heh+bQtLje0TO8Xh5uD6QHram/6t1ZZFRFxZpQPyEdVIObGU2ty8iQoKzen7I3FAKT0HsvxT15AUSC60Y7YXjqMuEBFgXsAGXn+kAs2AzqQ+8sNDHIFtk5N8LqiYUA/sAysyvGyT8lXiwn0UNOsWjadp4UgEmBs9wSy8yT0nReMk62Ord9EcOGuLTO3+TBurR9zvojl+DV74tMUWJrpmbfjDdtm+hYf+rdKpUAtZufUZ0zf4sv8HV4IIsjJkzCmWwJftjElbjRagZ3nnNEVCXz+SVoJt4AHLyw4ds2BLo3SOX/Hjvk7vNg66QWCYFqQbj/tQs0yKqqV/u1gIPShDSLByIoRUXSZHsLRqw4MaZ/ym9t/xEd8xIfBYIBxa/2ITVFwLcyac8vCftft49fQFgn0nV+K53HmfP5JKpN6v9+J4tQNu2KXkACPQn68bKpO6Q0CZ2/a0bXJmyD26iNrBi8JRCY1cmzBEzxfBeaTN/py4a4d5go9pxY/KW55+is4HPom8Xryuj19P00r8f9ZKjG6IlHxOdKzJQxbFki+WsyB2eEs3OXFwcuODGqbwu7zzjyJtmDJ0Ch2nXNm8wlXOjdSvjdBcT/CgsFLA2leLZu5A2NZ86M7O866sOWEK1u/ifjgpIjeAOuOuLHzrDNqrQhdkYi9F5wY3jG5eJsHLyx4RAUkBh1Wdy4iwsgjKuD01IqmVXNITJcRlWRGvQo5XH1kw40n1hy/bk/tsrnEpMg5HOpQHJT/eNmRVQdNn9/VMGtCH9rg7axh44QXbDzmhqWFkewZK4hOf0Z8TANefG/GlUfWfLPeD41WoFArplJgHv1bpTJurR+fjC1PkV5g8ZBofF01HFnwlDvhliRnyLCy0FMjRIWtpQcqxmOjdOXEdSnXH1tz97kVnRul071pOtfCbBjdNYHKQfnM3eHF0xhzzqfL0RsEFgxSYudhwZhuiUAAOQQwnAxMta3ypFQoT3bjjnismoDa1pFS47uwJyOccv4aREI1qj3K5/YzKxI6jyDvoZLc2i3YUy8V24v1kW+7gjwljifeLXkW58CMrQ5UL63i+8kRCAJkXh+Cx7qpuG2Zi8o9mCtJ9fH5KY1b4VZ80TqFA5cc2XfBCR8XNdO3+OBkq2Na3ziOXDElrg1DhqGffQHvxV8j0hQi0qqxfHAVSW4mtwJ7EBFpRUQS9G2ZyosEM1x8q2M8Jcby0XUSfesQHeNJz9n2RMSZIRYb2Tj+BTXL5PE8zvSZ/HzfFq1OoHtvCTlubejkpWPPHTFfrwggKsmMeQNj3nJBsiS5xmxy67TEdfsiEkOaEXk5iKFLNWTkSKn+dQi5Z5vgePx7iuyccDyyBZkyiehmX5L+kzNrDkG1YBXtG2Sw44wzdlZF9G+VwqFQR7pNL02BRkzjqrVJGrmNHuGWBD3LI85vGdaxYQR82p4z2Y85+Ys9cqmBzMK+OB/fwhGnwRz42Qk32rFWGI7tK3V7152LSRy2AIVKSYxLTZ7GWFCnXC5rx0Qik8rJ2NUD+fMwtscPIP1HB1oI9REZDVg9uEpC2c7UFBeS7DuA4GNL0Lh6kzhqCQ1t1YCpiNC2XiYtamQxcmUAVx7ZIBrQlUSZkfyy1bGxcSDvVm0sw34hu/5n6C2tsT+3D1FejqllQW7PlZwKvEx8ys5zzrg6aEnJkHHsqgOD31rL3HhqTW6+hFa1M2lSJYcW1bNRyAwEeKgBS7SWlpRBQxl/SJcOx23rPACKxk5imjIeH1c1dcqpqF02lwELS2FlpkfafQjGpdfwXTKcpGZ9gLfaUv4HIPwdsbEgCK2AFZi4uVuNRuM8QRBmA3eMRuMxQRAUwE6gMpAJdH8tDPdbqFatmvHOnTv/52v7p5GWBi4uML7NU/p3K6T/giC0OhF7xt0l+KtGpHUeQka7L/7Wc9qEHsN902yUbfqRX6GWSQDlV0IsTvu/w+78fuInrH63kvtbKNJhEX6XQr8QDJY2RCYqmLvdG2c7LTP6xxX3x1+6Z8Ow5YEMbpfMlpMu9GyWzsRev2+jJU1PwurmebKbdnqvr+RrWF87jcd6U99L7KT1aDz8cN67ivxyNSkoUx2/ab2R5GSgcfUmeu7u3/RzxmjEIuwGInU+qioNS86PQY//N10xSqTEj1tF4Ji2ZDXtTF7FusiSY8lq1hnEEqQpcfhP7oFBYYZgMGAUBNJ6jcZt81wEfRE6B1f0FtbIUuNJHDYfnYMrtpePYn9uLzk1m6OIf0FhQHmSv5iCZdgNvJaOIqfOp1jfPIegf1NJUXv4kzxoZgkLqvRsCVFJClqHL8HpyGYAoufswvbSIWyunCBy+XH0Ng4EjOuA1sWL+PGr3pkC120LsL10CACdgxtRiw4UKxU7HP8e5/1riJq/12SR8/MRnA+ue2XNM+p3P8u3kZAmIyZFQf3AZDzXTsYgU5A0ZE6xbYfjoQ04HTb1vqX2HM3j6v3IzpP8rj/pr5FXKEIkUGzpoSsyaQsIgqlKs+WkCyv2e+DhpEWZI6GMbwGrR75kyiZfXOy1NKuWzZeLTBZFwV4FHJoXzqkbdoxf64+LnRYXey1Poi1YOfIlBoPA+qOuxcr9TrZaRnZJokP9DIxG6DUnmCSljNPfPuHoVQfmbPfm26FRfFozi+HLA/j5gS0WCj0/rQh7p1r2Gl2nl0YqMfLD9OcMWBhEQpqcM0se/62+oh/xEf9JPIy0wMJMX+y28T68XY38O6F/FXOLRaYAeNC3QfRtmcr2My6M7prAwM9S37tfVJKcHWddaFEtq9g9Y+8FR2Z/74O7o4a0LCnnlz3m/B1bdpxxYcWIl5TxLcRohA5TQijSC6i1IlIzZegNAtP7xbLrnDO2lkXsmmbSSolJltNtZmkUMgPKHClt6mSyaHAMt8Mt6Ts/mE4NlRy7Zk+bOpnMGxj7h2M1GuHRSwvcHTXFOiC5+WIafl2BTg2VPHxpgQDsnfmMQo2pgqzMkdD2m7KotSIOzA7H313NwMVB3I+wYMmwaBpXyeHUL3aMW+vPxJ7xrDjgQY0QFevHRXLpvg3DlweyeEgUbepklZjz+DQ5AxcFkag0vVMWDo5m2mYfmlTJJjzWnCK9wJH5Tz8oKbL/oiMzt/nQsFI2E3smMG+nF9HJCs4ve1x8zyzY5cnjn1K4p69EfkhVLMLvUtEiAqmnK5snvOBwqAOzt/vw49yn9J4TjKu9juhkBYuHRBEWZcG+i05cW/sQC4WBPnNLkZ4jxc1By40n1vi7FxKXKsfJVkdyhpwRnRKLg4vX86vRiZCIDRyaF467gxYzuQFBgDM3bVl72J0ujdPfa036a+y76MisbSaVaVvLIrLzJDjY6FBrRFxYEYb1W++NxHQZ+WoRpbz+HBvr11BrBWJTFMUCs68hys8lcPRniAvzefr1dzTcMZBCjYh9s8Lxd39VkTYacTixHfPwO6R1G0Grje14HmeOIBg5t/QxO846s/OsS7GHNUCXRumcvWVH3fK5LB0ejdXti3isnojOyZ207iPw+G4KGAxETN3BgtvNcHPQ0qflm0SSy84l2P58mPjRyxh+pRtnbtrTrl4Gt8ItKdSI6d40nfVH3SjSC0glBib1ji/B5hi61PQudrLVcn7Z43csg99G52mleRpjQc0yuWyb9AJpWgL+k7oj0qrRuPmQ+vl48svVZNd5Z26HWzGlT/w7CbTYVDlfLAwiI0fK9inPqRDwYcwXQadl6SE/Np9wxcq8iLCv1qBIjsZgZoHbtgWoqjTA6l4oD2YdITSjHPUr5rzjm/56rPVLpRAaYWJQxo9ZTl7l+gDIEqPRObj+pve8wWC6x22tSq5XBJ0WecJL1D7BKGLC8ZvRl6SB03E6tIEsz/K4PDxPoEchkYlmrB4VyY4zLihzpJxY9IRncWY42epYvNuTn+/bcmXNw3c8598HqzuXMEhl5Fes+7vbmT+9g9Pe1RQ4++J448QfHvffAEEQ7hqNxmp/uN2/tWD93xKU5+SArS2M/PQZX/XMp9X4sgR7F7D862j8x3dE6+H/p0VnRIV5yGMjKCxVEURibC8ewnnvStK6jyC7SSd8X1lyRC3c//urHIPhHbGUvwuvHwQAu6Y9+2Av9D+E0YjDye0YpCaRll+PT5bwEpsb58hu8Bk653dtFT4U1tfP4LFuKkU2DohzM3n57aH3eoEqop5gf/oHBIMBZdv+aHyCsb52CrtLh0nuPwmDuVVxouA1shp3IKX/5JLXbjTiN6UHivhI1F5BJH8xBUXMM7TufsWCZO+DWJWN+/rpFAaUQ9lxELLkGAImdCa9/UBy6rUicFwHUj4fR9b7rH+MRkTqAswiw/BePJzk/pNNXqD6IgLHtHtlAbPuL8/hh0DQqvFYMwlEYhKHzX+vZ/DfgVvhloz7zp9CjYgfpj97ZxEzYZ0vZ27as/zrlzStaur5DI81w8VOh0RspOPUEJNqLeBgo2Nm/1gcbXQs+sGLB5GWtKyZSZVSeczf6V2ceS/SQ6/ZpXkcbY6Xs4a4VAXt6ys5csWR6X1j6d7sXcpnaqaUxiMrMKJzIoPbpXD6hh1jv/Nner9YujdVkp0nxkKhR/ovUPuISZEjERmLK3sf8f8e52/bcivcivE9En53ofl3wGCApzHm+Lurf9vjFpOdZusJ5ZBJDfy88tE7izuAyw+sGb/Wn8mfx9G+/rt9h2AKOJMzTJaOv34c6g2gKxLeWZBmq8R0mxmCRiewZ8Yz5nzvTVi0BRdWhDFkSSBRSQrOLQt75/v00x0bvtngR4FajJlcz0/Lw7C11NNxagiCACtHvOSTseXp2iSdw6EO6IpEtK+nZP5XsTyNMaPztDJM7xuLEYE5272RSgxc/e4h+y86sXSfJ8cWPsFMZqDf/FIUaETsn/2MnWec2f2TM2eXhjFmjT8pGTJOL3nMqoPubD/jwtD2yVQNzisWwnof9vzkxJzt3pjJ9YzvkUCtsiqOX7Nn3RF3Ds55ys2nVny7xwsHGx35hWK2TYrgl8dWrPrRVBWuUy6X9vWVTFjnz9Q+cfRsbgoiNVqBZmPKk5EjxcFGx6G5T3GyLcJggA5TypCpkrBvZjjujjoKNQIDFpbiYaQlErGRDeNf8M16X9KzZcilBk4veUxqpoxes4OpXS4XSzM93i4aRnVJQhBMtP2MHCm+bm9oyO0nhyCVGNk/6xmCAAd/dmD6Fl8Oz3tKsHchd55bMmRJIA3LKjnxwANBr0dvbsniXg+ZvMmfGiEqivSQmSvl5OInjFrlz/k7dlhbFPHzqkeEvbSg7/xgln/9kuql82gwvAJftUvmq7YphEWZUzEwn3O37Ji62ZcaISpWj4oscc/M2OrNgUtOTOgZT79fsRD+LAo1AlM3+eLmoGVI+2Q++6YsKZkyRnVJLOGs8p+CLOEl0sw08ivUJl8tokgv/GZCGWDbKWe+3ePFJzUyWf51NFFJctpMLAfA0uFR3I+wKLaXfXttKE1PosjaHqNcgTw+Egx6NK986N8HQactXi+8Fvl9GmNGj1ml0RWZ2JtzvozF3rronedFSqaUjcdcaV07k6rBv782jUxUcOyqAz2apRUzA2WJUSiiw1HVaFbCcvH3oNEKaHSiEkmVD8HrMX3ZJoWvOyUXjz1gTFuk2UrUHv5EL9z/u/sv2+fJqC6J1Cj8GUXMczJb9f57M6BGI4GjP8MoEiNLTyR5wBQ63pxQnNA6tuApB382JdZ6NU/jh/POmCv0FKjF9GyextQ+8X98jj8JvU6PNi2bKhNb/O3H/ifwMSj/D0GtBjMzGNo8gmGfq6g2sBJdGyuZ2CsB9+8mYxb5mJfLj33w8eTxkXgvGoYkJ4PsBm1J7TWGwFGtERfmo1dYkPj1Qry//ZqU3uPI+uS3PVj/acSnyRixIoDyAfnMGhD3j1RA/lEYDPhN640iLoKsRu1J+WLqXz6UWJWN2YuHCPoiDAoLk0XLeyZEmpaI5f1Qcuq2+mD/1vfBc9lozCLDyGraGacjm4lcduz3FbyNRnxn9kWcl8vLxQexuXIC9y1ziR+9jLwqDf7ydfzboNEK6A3CewMIoxEKNaLfDC5SMqWEPrDB30NNBf/84oDHaIStJ11Yus+UAKoRomLrNxHFuS5VgYhNx125F2FJy5pZ9GqeTrtJZbCxLGLn1Ih3zrPzrDMLdnlxcvFj/Nw0GAwwcHEQN59aUcq7kOdxZgR7F7J3xrP3Bl3ZKjH7Lznh5qDl01qZv9nz+jaik+WMX+tHteA8vvkNOu6v52rZPg+2nHRFIjaybuyLv9Qf+mewaLcn0UkKVo54+UEZ9f8/Iq9QRI1BJv/XWQNi6dL4r5uXxKXKWLbPkxcJZpT1y2fy5/HYWr5ZTMYky1n4gxehD20o75/P7hnPEIsgO09MXKqccn4Fxd+BpXtN9wrAkqFRtKqd9c75Pp9birvPrfBw1HDuVfXzYaQ5Vx7Z0LdlKlbmBubu8GL3eWc6N0pnQs8E7jyzonqICgHoM68UUUlmTOsb9xYVFdYfcS0OOJ3ttKRlyRjaIYnhHZO5/MCaIUuDmN4vliK9QGaulMql8rj73JKNx9wo75/PyC6JDFwcxOB2yTSqnEO3GSHFCbVBiwO5GmZqNakYmE9UsoIrax7y7R5P9l5w4vLqR1go9Gw85kblUnnUKaciM1dCs9HlcbHXkpMnQW+ALRNfUM6/gMR0GZ+MLYfnqwTe688wJ19Mr9nBRCWZKlnt6ykp1IpISJczb2BMcYJRqxNoPLI8LvY6LBR67j5/Y/fWoGIO68dFkp0npuOUMuSrRagKJJT3zycjV4KXs4ZGlXJYtNuUeH77M32NJ9HmnPjFnh5N0/B2eZOIi0xU0HNWaXxc1fww/Tk7zzqzdK8nX7VNpkWNLEJ8Cjn1ix2zt3szrENScbV47WG34v5bgBUjXlItOI+uM0qTpJSz8Kto2tbLJCNHQv3hFUuwGtKzJTT8uiIjuyRSr3wuPWcH4+GoZdOEF9Rd0wOzqCfkh1QlbvIGjl61Z9IGUz/94HbJjOicxJNoc0at8mdA61R6NEtHb4AGwytQq4yKOuVymbbFlx/nPiXEp2Tl+LfqGHoDpGdLi9u4/k68trZtUCmnxOfxb4XRLZL4TwAAIABJREFUaEpoB3qqkUlMz+qbTy3R6wXqlFeh1QmsOeSGu4P2vYnp/yteJipQ5kioEfLhOjD/dhRqTAnHt8djf3InLntXktz3G7Kb/YEDwn8AzrtX4HB6FwAvVp4k1uDF3p+c6NBAib+7huw8MU1GVkCtFRHsVYBMaiQ7T8K2Sc//Ec0cvU6PNiOPKuP+O+jrH4Py/xAMBhCL4ctGkXzZPY9agysxvkc8/Vul4XDie5z3reH5+ksf5JcqTYnDd/YAjBIpBaUqYXPzPGrPABQJL0kcOg/3ddMQjAb0CgsiVxzHYGH9Hxjh/y7EuVmYh98hr3J9jLLfoMH/C2H2/D6+c03KkwVBFYidvvUP97G8dxmv5WPRuHojS0+i0C/EtN//ylvtH0boQ2uexpjzeYu0YlGh38Law258d9iNn1c9KqaYPo0xY8UBD+4+tyTIs5C9M58Xb1+gFrHmkBthURa42Ok4dcOe2V/E0LlRSS3MArWI3nODeRZrEo70c1Mz8LPk36w8gom22Gt2acJf7bNj6vN3BHneRpEeZmz14XCoI+3rKwmLsiAnT8LJxY+xMn8z7lvhliSky+lQP+ODbqGzt2yJSlLwZZuUd6qWCWkyWowtD1C8UP8r0OoEJm3wRZkjZdaA2BKVuLex44wzDyMtmN4/7ncrQn830rKkLN/vga1lEWO6JbyXDZGSKWXeDi/K+xe8UzXbftq5OKhqXi2LlSN/twPsN5GvFtFxShmyVBKqh6i48tCaxpVzWDkyCqPR1Ot89KoDcqmBuuVzuXjPlvmDomlVK4uOU0OISjKjSikVi4bE4Gyrpdno8pTxLeB+hCXNXvUWv41CjUDNryphrjCQmy/h+MIneLloaDqyPBm5Uro2STcFeGPKY6HQk1vwZmJ8XdV4u2gIfWiDv3shUUlmtKmTwcA2Kfi5q2kxpjz+7mo+rZnJtC2+ONjoOLnoCdYWegwG6D6zNI+jTa0ogmDEaDTdrB0bKJnWNw65zMiw5QE8eGFBteA8rj225udVj7A0MxAWZc7o1f60q5dBoKeasWv82Tn1GSNXBlCtdB4rRrx//s/esmXRD174uKiZ0T+uxH04fYs3B392okaIis0TI4qTagYD5OSL2XTcle9Pu2Ku0CMSQC4zsHViBEFe6mKK+aYJEdQuq+LeC0uS0mVodCI+rZVZTBPXFgkYDHD8mgMztppo0itGvKRRpRx6zg4mIV3O9skR71CZfw8/3bFlxMoAvu6UyIFLpv7hbZNf/OF+iUoZDtY6uk4PwWCEIM9CLt6zxc6qCAE4u+wxl+7ZMHp1ALunP6NS0JuqZtfppRGJjIhFEJcm5/iCJ9ha6bE7vx/XHYtJ+mIqOY3aA3Dkij2Poy0Y1SXxN+nyc7734lCoI94uGtRagTNLnnx8/X3EvxcGA5KsNIocXP9fXwkAkqx0vJaOIq9CHdK7DnvvNlceWXP3uSVftE4psV74J/C/GpT/C0iS/90QiUAqNaLVi4hJMdFcvJxNL2G1t4mao4iLeK+PI4CoIA/biz8iYMT24mEA4iatR+vkgTwpBkX8C3JqNie39ieYRTzA/qcDZLTp8zEg/xugt7ZDVbP5/+vL+NMoDK5MWrfhWF87TVqPD+v/zqvSkOQBU7C69RMFpauaPHY/rkg+GA0q5tKgYu4HbduiRhZrDrlz4ro9/VulkZ4tYcjSQIr0Ao0r5/B156QS25srDEzoaTKsMBohKlnB7p+c3wnKF+/x5HmcGWvHRFKkF1h7xI3JG/1Qa0XvqCOrCkSMX+vP1UfWGIwCS4dHMX+HFxuPuVLm6yiy88QlaMJGI5y/Y8u2Uy48jLRkWIckhnZI5km0Od1mlmblQY9iCpoyR0K/+aZnm6udljrlVcSlypi22Zfm1bPo3SKdQo2AXGpEJDKJTI1eHQCAs52OTg1LjutexBuNibO37X4zKE9Ik2FhpsfuPfRogA3HXDl90x6Z1EDvOcEsHhJN7XKqErd5WJQ5C38wBbZBnoUlRGn+SSSmy+g1J5j0bClGo4BOL7yX0jd9iw9XH9lw4a4dVUrlUa10HnsvmISUYlIUVAtW4eao5fpj67/co733Jyfi0+R8P/k5NULyiqvNT6LNSVTKOHrVgV7N0xjQOgVXex0dpoSw9aQrSUo5UUlmdGqo5PRNOzpOCaF2WRXp2TK6NI5DJMCDSIt3zvcg0vL/Y+++4yQ7ywPf/06onKurc5wOk7NGWUJIIiOTscFgE4xhseGy18AazHpZR1iwvdc47K6vE77YYGTQkswaIxDRBoEJRhkQ0mhid1dXrpPP/eOtru6a7p6gac2MpOf7+fRnprurq06dU3XqPO/7PM+L5+v8x5ce5jf/epKv/yDL6AmbxVqE8QGbj91RYrGqLkU+9jv38Imv9HHfwwmu3FnnA/8wwk+Ox3nNc47zlpce5Q9vHeHvv9DP579V4DlXlTlejvKun3+Ymy+rsm2izWDR6aaP6jr8j7f+kFu/WOKKnXW2TbT59n1pUnGfg1ub3X336med4FX/to1//laBVz37RDeo2zPd4vP//QeAeg1rWsh7PjROuR7h+ddtvEDuM6+o8MwrKuv+7t2veZiX3rjAtvF2T5aLrkMh4/OffvYIr3zGSfJpn2OLUV7529t4wbt28rNPm+db96UZH7C4elcdXUcNrq2T/bs8e/nCpyxw+7fzxCIBNx2sYBrw0d+4Fy/Qurc5W087VOFph5b4o05Wwm+97idn9XejJTXj/sYXHuOtfzzNj48meNOLjrJrS5M3/v4cd3wnx533ZkhEfXZt6U0zvvlQpdtM77de95NuWcTS015KY981PeVrL7i+fNrBSYAXP3WBD98+wAOPJHjHKw7Lx5+4tOn6JROQA3iFfh787b897W2u31vj+r1nd50k1idB+SaIRUNcX+f+wyr9bLnZjTWpmkvFThOUD//l75D9xj8D4GUKHP6V/44zrEa3H/7VPyb1g29Qv+xGAE78/H9i8adejVd44i0DIM7N4i2vZvGWV5/T31RufOG6S7KJzTU7anHlzhp/etsI374vww8eTNJoGfzdu+874+yUpsGLb1jgd/5mgnseSnTTK79xd5qPfqGf1zznOE89oGribzxY4Q3vn+P3PzLGTQernFiK0LZ1Dm5t8Ot/PsXX/j3Lzz79JNfvUx+Uh0/E+H9uHeWaX9qH4+rMjrYZH7AJQzixFOWeh5KMlGze84YHeX4nMN493eLlT5vnw5/v55ary+yfa/LZfy10t/fWO0pcs6fOX3xmiDvvzXDnvRkeeCTBbV/u4+pdqlnU8lI3AB/9Qv+aoPwHDyZJRH1ecP0iH/9yiZa1UmZQaRh84d/yfOveNJ/4ah+FtMdtv3t3NwNhme1ofOT2fm46WOFtL3uE171vjte9byvPvrLM+3/pQXRdDTz8/kfGKGZcBooun/6X4hmD8j/4+xG+/L0cv/26h9g9vdK8531/N8onvtrHe97wE4aLDl/8Tp7nXlPuBiGrtSyd179/DsvW+dhv3cNtX+nj//unQZ51xVJPl/4fHYnz1e/neOMLjvKhzw1w6x0lZsfavPdvx4lHAvrzLm//2Ue468Ekn/paH4dPRnvSjE/VtHQ++68FvnF3hj3TLX7umSdpWTp/+Y+DXLenyhWdztiveMZJ/vwzQ3zw/wwwvxRhtGTzzlce7qbxvvY5J3jH/9rCH388wTOvKPNbr3uIX/ypY7z9T6f53J0FpoYsbthf5f6HE3zxO3nqLb1nluRb96bRtZBbrinzV/84yL/enSEeSVHIqBUUnv323dz+7QIvesoCoyWHX3rhSsft6/dVuf9wgqceUKm9b3/5EV7znBP84n+b4+NfLjEz2ubGzvth9fFZ1pfzeo7xegNrl+9o8NafeYSHT8T45RceXfN7UAHzvpkm3/1hmoGCw/X7Ht1a5Iaugv3TWU71nBm1+PR77+J/fGKYv/1n9Zn/G6996KzbxJgG/M+3/bDnZ7oOUf3RZUe+6+cOU22YbB1vc83ucytneeblS9z93OO0LJ3X3XIcwwgZ6nO49Yslji7EOLitsSZz5GefdpLv3J9mtGTzolUlC2jao+ons3OqzXvf8CA/PhbnZU87c0M2IYS40CQo3wSxKHzl3n6+dL/JYMFhckjNlPu5Ptx8ieQD32fpmS9f83eR4w+T/cY/M/+C11F+5ssJY4meRlh+ro/atc9Z+QNNwysOPubPRwhxft796of5z38+xU+Oq/rbVz3rxFmniz736jLv+7sxbvtyHzt+TtWAf+AfRhkp2bz5xStBg6HDr7/6YZ7/zp3c8qu7aLTV1Fs25VFrmrztZY/w2ueudJ5+9bNPMF+JYDk60yNt7vhOnmNldb4x9ZDfeO1DvOiGhTV1jW95yRG+9J0cb/7DGT7y7nv59Nf72DbR4uBcg9u+UqJcM/nMvxR55hVl7ns4ya1f7AfgK9/PcdeDSb7yvRx9OZdXPesEf/D3YzxyMtrTPO6uB1PsmGrx9MsrfPj2Ab7+gyxPO1RhvmLykl/fwXwlSjQS8MwrlvjcNwv8yW0j/NfXPNyzjf/0zQJL9QiveMZJpoZtPvPf7uL//fQQf3rbCDunWvzCLSf4XKdJ2q+/6mE8H97zoQkOn1Sdsz/1tSIvvXGhp2b0znvS/PmnVTfb/+sDM3zqvXeRigccWYjy159VMxj/4ffmumnRn/p6kU/87t1rgqY//vgwDx6L85fvuJ/tk23eMniUO76T5y0fmOaGfVXe9OKjjJRcPvm1IroW8rKb51msRfjkV/vYMmThuDofefe9bO8M0MQiKuD99n0ZJgbXn7E9Xo7w87+tUpWzSY/P/Esf5bpJPBKwVI/wplWvo0wy4CU3LPA3/6Q+W37lZx7peQ7PvqrMbV/uY6EW4dd+Ts3uTww6fOjX7+WL38mzb6aJoa8ExXc9mOppVvbNezLsnGqRTgRctbPOrXf0d5/naL/De97wIF/7fpb/+6ePrHke4wNOz9reAKWcx1/92v18+PP9PPfq8qb0Mv2FW9bv0L7am19ylN/+4ARvetHRs+rlsBn6ch7/+ecPc9OBCkcWYrz4hs2v0T1bg0WXD75rbZ+Ms6Hr8NaX9R7fl9yw0K05/5mb1gbJmWSwZlDhfD3a0hghhLgQJCjfBL/82hZ//3c+JxtR3vGKR3rSohoHbyD31c+gt5sEiRQEAcl7vo09MUfu6/+HUNOo3PjC82r8JYS4tEwN23zo1+878w3XkU/73HSwwqe/3sdbXnKUBx5J8J0H0rzzlQ+v6T49OWjzm7/wEB/5fD/PvGKJUt7l418qsX9OraG7WjQS8q5VKdOvec7ZdRFevjh+xW9u44Xv2kmjbfD2lx9m+2SbD98+wH/5i0lalsHPPfMkw30OX/1+juv2VnnW23bzia/28ZXvZ7npQIVnXbHEH/z9GP90Z4Ff6AwWeD7c81CSn75xnkPb62RTHrd/O8/Nl1X4L38xSa1p8jfvuo8DWxsYOvzWX3vcekeJNzzvGPFoQDrhY+jwl/84yPRIm6t2qmAwFg355Rce44HDCf7o4yNsnWjzm389wfbJFj990zwPn1ClRl/5Xo6P3N7PD48kVEr01WX++rOD3HJ1mW/fr2ZF3/uGn/Da927lf9w2zNtergYoAD7yX1WqdT7jkU/7vOdD4/zrXZnu8lqgGqp96HODvOSp891ANRkP+O9v/hHv//AYn/1GkYdPxvngu+7jU1/v49o9NfrzHi+4bpGPfqGfD3xslD3TzW5ADioToy/n8qXv5njwWIwg0HjLS4/0zDS+90PjLNZM/vId93Plzjrv/ssJ/uyTaoDh6YeW1izZ88pnnOTDt/cTi4T89CkN5CIm69YPR0x4xuUradq7p1X68fd/lOLb96X5yBf6efvLHuG7D6R5zXPUbPVNl1W49Y5+glDjBderAYXnXr3Ec9dpDnc6+bTPGy9Q6cGyq3fV+cz77rqgj7lMvaYe22aLF9qLbljgf31SDW49/fJzO/5CCPFEJEH5JnjLL7a5pfBvpCdLa35XecpPUfjCx8jf/g+Ub3mValLyod/D7RvELM/T3Hm5zH4LIXq85jkn+NydBd7/kTFOlCNkkx4vumH9WdHnXVvmedeuzACda4BzNmZGLd7/yw/yxt+bJZvyeN61ZbIpj1za4wv/lmfLsMWBOVWnu9wV/Pq9tW7a7Y0Hq4wNOOyfbfC3nxvglqvLDBZdfnw0juXo7NrSwjTgxgNVvvidHH/xmUG+9N0873zl4Z4U79fecoJb7+jnBb+2k3rLpD/vsG28zf2Hk7zvjT/uGRDVNJVJcOc7dvGG98+RiPn83i/9GEOHqSGbkZLNb//NBKDqb2/7cok/vHWUoT6nu92//6Yfc9WuOi/uzCS/7OZ57vhujskhi70zrW5w67gaH/iHEf7pzkJPUP7HHx/BNMKeDAdQqbR/9c4HumsW/9qfTXF8Mcp/erkaNNk32+w2NnvlM3oHT3Rdddu+7csrnzePzMe46WAFwwjJpXw+d2eBN7/4SHcg4D///GGSsYBG2+BXX7G2ln1swOG231FrSp/rkj7L8mmfbeMt/uhjIwQhhKHGOzpdsZeD9+v31njdLcfIJP11083Fk8dQ0eV//+7d+IHWXYpKCCGezCQo3ywbdLG3ZnbT2HsNfZ/5Gyo3v4S+z34IP5lGbzbQwoClp//0Bd5QIcSlbu9Mi1c/+wR/9Y9qJulXfuYRUqdZK/pCuH5vjU/9t7tIxgL6cqqm+80vOsrvfUStkXpq46SfunaRL/xbHlDrIwO89WWP8Jr3bOUZb93N+3/pQSp19RG0PMv64hsW+MRX+/iDvx/jKfuqvOLpvQHpaMnhN177EP/7K30c7Cxv9dV/z/HTN82vOxhRynn83bvv5dNfL/KsK5eYHlGlRZoGz75yib/4zBBzY21+8xce4qaDFY4uxPjZp5/kq99XjTRv2K+2+00vOsonv1bkDz46yjfvyfDym3vTbaORkGv31PjSd3PdBmyf/FqRT3+9j9fdcmxNDfyyF9+wwK1fLPGpr/Ux3Gdz82WV7vb92dsf4IFHEuvWQf/iTx3n4RMxbrmmTKVh8oe3jvK5O1dq/ccHbF797JVMiWgkPONyeMv75nw8//pF3vd345RyLn/+q/fzi++bY890i11bVACu6/ArP7N+3bZ48tmywQoJQgjxZCRLom2Cpfvn+eFHv016qn/d3yfv+iaT7/0l6gefQubfvszh//h72GOzROaP0Np95QXeWiHE40EQwF99dhDL1nn9846tu4TWpWCjtX2DAD74fwbYOt7uWef84RNR3v6n0/z4aJw9M01+dCTOHR/4925Q/6XvZnlkXtXPnpquf6ozrT9/Oi1L50OfG+AZly9tuHzaar/11+N8+HY1g/7R37hnzUzv8nrJt/7mPXz6X4p88LODHNxa5y9+9YHTrr1+bDGiGuldU+6uSX2u7nkoge9rVJsGX/5ejpfeuNBtOHohOa7Gx75U4uDWBtsm2o+6Q7wQQgixkSfqkmgSlG+CMwXlBAEzb3sh0fkjOIPj/Oh9H1v/KlYIIZ4EfnQkzvPeuZMw1LjlmkXe98afXOxNOqOjCxGe985dXLa1wf982w/XBJvlmsn1b9rLlmGLHx9N8PKbT/KOVx6+ZAdThBBCiMejJ2pQLpHhZjjTVICuM/+i1+NlChx/5VslIBdCPKnNjFr8h+cfoy/r9qRZX8pGSi5f/ZPvrRuQAxSzHvtnm/z4aII9003e+XMSkAshhBDi7MglwyY4m/S82nXPpXbdcx/7jRFCiMeBN7/4GG9+8bEz3/AScqZ0+l99xWE+9qUSr/+p4xds2SwhhBBCPP5JUC6EEEJsAtWR/eEz31AIIYQQYhXJo94k0stGCCGEEEIIIcS5kqB8k4QSlgshhBBCCCGEOEcSlG8CVVN+aXaxF0IIIYQQQghx6ZKgfJPIWqxCCCGEEEIIIc6VBOWb5BJd7l0IIYQQQgghxCVMgvJNoOkyTS6EEEIIIYQQ4txJUC6EEEIIIYQQQlwkEpQLIYQQQgghhBAXiQTlm0CavAkhhBBCCCGEeDQkKN8sEpkLIYQQQgghhDhHEpRvFmm/LoQQQgghhBDiHElQLoQQQgghhBBCXCQSlAshhBBCCCGEEBeJBOWbQdOkpFwIIYQQQgghxDmToHyTSEm5EEIIIYQQQohzJUH5JpBZciGEEEIIIYQQj4YE5ZtFInMhhBBCCCGEEOdIgnIhhBBCCCGEEOIikaB8E2gaUlQuhBBCCCGEEOKcSVAuhBBCCCGEEEJcJBKUbwapJxdCCCGEEEII8ShIUL4JJCYXQgghhBBCCPFoSFAuhBBCCCGEEEJcJBKUCyGEEEIIIYQQF8l5BeWaphU1TftnTdMe6Pxb2OB2vqZp3+18ffJ8HvPSJTnsQgghhBBCCCHOzfnOlL8DuD0Mwzng9s7362mHYbi/8/W883zMS85yTbltg2Vd3G0RQgghhBBCCPH4cb5B+fOBD3b+/0HgBed5f49bmhZSqUC1erG3RAghhBBCCCHE48X5BuWDYRge6/z/ODC4we3imqZ9S9O0f9U07QkZuLsOZDIQi0EYXuytEUIIIYQQQgjxeGCe6Qaapn0eGFrnV+9a/U0YhqGmaRuFo5NhGB7RNG0a+IKmaf8ehuGP1nms1wOvB5iYmDjjxl8qNF1D02FkBI4fB98H84x7VgghhBBCCCHEk90ZQ8cwDJ+20e80TTuhadpwGIbHNE0bBk5ucB9HOv/+WNO0O4ADwJqgPAzDPwP+DODQoUOPm/lmXYdsFkolWFgE35OgXAghhBBCCCHEmZ1v+vongVd1/v8q4BOn3kDTtIKmabHO/0vAtcDd5/m4l5RoFHZsh0gEIqaaKRdCCCGEEEIIIc7kfIPy9wJP1zTtAeBpne/RNO2Qpml/3rnNDuBbmqZ9D/gi8N4wDJ9QQTmsdGCPxSAILu62CCGEEEIIIYR4fDivJOswDBeBm9f5+beA13X+/3Vgz/k8zuNCJyo3TQnKhRBCCCGEEEKcnfOdKRenMCMQPG6q4YUQQgghhBBCXEwSlG+WzjpopgGhzJQLIYQQQgghhDgLEpRvMl1fqS8XQgghhBBCCCFOR4LyzbAqCtf17qS5EEIIIYQQQghxWhKUbzJd9qgQQgghhBBCiLMkIeQm042LvQVCCCGEEEIIIR4vJCjfZLomNeVCCCGEEEIIIc6OBOWbpROJ64bUlAshhBBCCCGEODsSlG+WTiQuM+VCCCGEEEIIIc6WBOWbTLqvCyGEEEIIIYQ4WxKUbzLpvi6EEEIIIYQQ4mxJCLkZVuWrG4b6VmbLhRBCCCGEEEKciQTljwHThCC42FshhBBCCCGEEOJSJ0H5Y0CCciGEEEIIIYQQZ0OC8seABOVCCCGEEEIIIc6GBOWbZVVdeSQCvg+2fRG3RwghhBBCCCHEJU+C8s2yqrNbLA6VCtQb4LoXcZuEEEIIIYQQQlzSJCh/DMTj6t++IrRaF3dbhBBCCCGEEEJcuiQofwwk4pDPw9CQSmMXQgghhBBCCCHWY17sDXhCWFVPDpDLwe7dKqNd01bqy5PJi7R9QgghhBBCCCEuSRKUPwY0DWIxFZRHIqq+3DBUV/Zo9GJvnRBCCCGEEEKIS4Wkrz+GNA1KJQhRqeyt9sXeIiGEEEIIIYQQlxKZKd8sp6SwLxsbg74+NWt+9OgF3iYhhBBCCCGEEJc0CcofY5GI+nLdDeN2IYQQTwRhiLE0T2hGCLKFC/7YhCHokgAnhBBCPN7Ip/dmWbVO+XoiEchkwbIu0PYIIcSTgObYmAvHILj4S13orQZ+toDm+2f8TNhsZvkk5tJJNFs+ZIQQQojHGwnKL6CRYVm3XAghujxv/WA6CNCb9bMKbPVmDS/Xh9GoPQYbeG40u409PouXL6FbF+5kr7ebeLkizd1XYTSqF3xAQAghhBDnR4LyzXCWeemFAkRj6jpUCCHOxFw4jrl44mJvxmMjDIksHsdcWljzK2NpgSAWx6gunvl+NHAHxsBzN77NhTrpahp+OodXGkKzLlxnT81q4oxO4+f7cIYm0OuVC/bY626P6zwxBwYCH4LgYm+FEEKIJyAJyi8gXYfREWg0LvaWCCEudZrVxk/nCGLx0wec61muL74QzhSk+OsHxEajij0yRWiYPfehuQ5hPE57+0FAO/39+x6hEcEr9G88OOp5mIvHMdYLVFcFWXq7ecYUeL3VwFw8gbbOLLjmOgTxFJgRvGwBLfDV457NwMKjoDeq6I2q2l9GBC/XB4A9No3muRctKNZcB6NWxqiWIfDRnCdOOn3k5BEii8cv9mY8oRj1yobnCCGEeDKRoPwCK5UADfyLX/4ohLiE6e0mzsgU7uAYRuscRvLCEHP+KEZl/rHZMM9VM6GoC2pz4RiaY6/83ve6AaHe7gSxq+qcNautAjbXwRnZgpfN9/y91m5gD00SRqJ4xQEVLC8HmEGgLuI7gxS61cIr9BPG4oSR6LoX90arrmauPaf3F76HuXgCc2kezWqpYPIMAbRutbCmd6Kvczw0x8bvNHcL40na07toz+7tbvea21utlZ+H4bkFJmGIbltgmBj1JazZPWAY3cfeMH3e99Ab1bN/nEdBb9awR6ch8DGXFjBqS6cf7AgCdYwvdUFAEE8SdvazOH+a1Vav5cf4NSmEEI8HEpRfYNEoDA1KbbkQTxqPdhYoDPByfXj5fjXzeZZ0q4lXHOzMQJ8SDIUhRmXxvGYvzaV5FfCHIbgO9pYdGPWl7ux8ZP4oRkWlpGvtFu7w5EoQG/gY7Tr2yBba2w8SpLMEmTy6uyoo9zz8vJr1dUvD6FaLyPwRzKWTGLUlvExBDQZUFtDbTdz+UYBOILpOyrjv4QxNENI7k27UKrRnduOnc5iVBawtOwgSGRUgeh5Geb5ntllzLPxkBmd4knA5e6FXh5HmAAAgAElEQVRnht/GT+e637sjk7hD42q77N4TvuY6GO2mGpwAjPK8KlM45bWi1yoYSyfXPCXNsfFyRRp7r6F+2VPx+gZ7fu8OjqNbawNds/N46830L2cUnFejuE7mgTM8iV/oJ0imcQdG0durHi8MOwMr6rka9Qq61bo0G9R5rjouYaj2ebZAaEYviaaCTwSa3cLL9aFJSYAQQkhQvmnOYb2zYlEtkba0BOXyY7hNQoiLSm9UMatl9Oa5NSHTbIsgnSWMJ/DTOfx44rSBtEoZXlIzoVYLZ2QKrzjYGwyhUsb9RApj1fbozfpZ1yBrVgs/X1L3bbVAN7BHp3FGtqDXl9BsCy9XXEmf1zScwXFABbdGvYo9sgVnYrYbSPrJrDohgkpHj0QJkhkAvFyRIBqjvWUHYSSGFvhYs7tpHHwK7akdtHZf2Q3gvXz/2n0UhmiE+Lk+gnhipQygE1R5A6NYk1vxcn24xUGs2d3orQZGvUKQTPUcN73VwBkc6zynCaInHlEDA7Wl7mMF8cSafebl+9FsC82xu3XmequBPTAGYQCeRxiLYW3Z2ZtiH4ZovoOfKa5JvdcdCz9XBNMkjCfXecw+AsPEWFoJ9vV2Ez+dxZ7YuuZ1AWA0Krh9Q6pR3Cki80dWnueyIFhTq29Wy9gTWwnjSVpb99PYew1eYaAnE0Jv1dW21Tv3F/i4xUG0dQYRLhS93Vy3g7/RqBLEE2h2G921CTJ5/GQazT3HchKxLj3wcUtDG9/gidiXQAghNiBB+UWQToNuQCIB2azMmgvxhON7mEsn0V2b9tZ9KtX4HC4w9XYTZ2C8842OPbVj4+7igY9RW8IZGMWsLBCYMTXDXhxEs3tnjjXPwZ7aRqjpnaDKRfMctDA8q7p1o9XAHp7CLY1gVBZUHbNhqOZivodut3EGxgkSKYx6BS9Xws/k1aBlGKIFHm5ppOc+w1i8O6ZptBq4fcMra22bEZoHrscZn6O54zKau64gjEQJ40nc4QlVS97h5/sIzUhvIF0r4+b7VSp8roTe2R9Gs44zOE4YieIX+mkefAqYJn4mj1scQPMcnOEpNNcG31NBcRDgFQcAcIfGaW0/SOPA9aAb3cGAMBJbs8/8dBbCAKO+hNGsqv3ue3ilYYJEErNWxh0YxesfXjPzHqRyWLO7VbnA6tlEz8VP5dY8VpcZob39MpzRLVjjsxi1CnqrgTW5HT+VZXmQpPdAhNjjc3iFfvTaymixZqsMgVOzNczKAmZlYWUgJAwJAWd4onMDU+3TZBq03owDe3IroHXfE27/CPqq56c3KhiLJ1QHft/DLJ9EWz6uj0GgprWb6nmfMiChhQF+Kqfev66Ln8x29oWzwT11dN5b4kw0/GwRTdPWHtfAJ3LySDfrRgghnugkKN8s53ChYBiwZzds3wFjY71rl3tnWN622VS3EY9PMvD/BBQEvTXVqNRoa2Saxt5rcftHsIcnVWDhnuFiHtSLJPDxCqXuj7xCP8FGNdP1KvbYNPaWHTT2X0dr9xUqGEpl19xviIafzuNli2iOhVEt057ehTM8sW7dut6sqZpx20JzLIJIDK/Qj58rEsYSuP3DahckUgTROJpjE6RzuP2jGNVF3MEx0HW8XAlzaR4vUyBI925XEI2vhIiei1/s7/l9GImCphGkc91Z8XV3WyRKa89VEKrAT6+VCbJFVW8N+IX+blCO6+ANjKx7P9b0Lpp7r+7O5JtL87jFQdpb93dnpcNIFGdsmiCZxprapoJHUE35ThEk0qCbOP0jOIPj6O1Gd5+5/WNodhuvOKj2YSa3MptuW2rGO5HCHpvGqK1OqwrXfazV/FwRe3Ib7tAEWuDhZQv4uSLBOjPry1kNQTJNe24vXr6EsTTf2Y42zsAYQSLVff0uB+rN3VeiWS3VcM6x1QCMGel9/vFkzxiA1gnE/GxelQoYJn62sOo14KEFAa09VxEaJkatjDU+i+44mIvHiZw8snIifRSlIZrrYCwt9A6AAPbYDPop9xei4RUH0AJfjSvF4gTJzBnfx0Z1AbN8clM7teutxuN/6RbPXSll8Vz8WJwwGsOPp9YM+hj1Kvb4jHrtyAenEJccvVGVUp5NJkH5RZJOQyIOuZxaJq1eV6nszQYsllcawbnuShBeraqAviY9UTaN68LCwvkNdDiOOn4AtRocP77+NUSzqY5xZROOX7vdeb2cQ8an74Ntn/l24tyY1UWVirt8wRyGaAS4I1PdANSe3klr+0GMWvn0S2V5LsbSPO7geG9asq7j9g2vbYjle+B5akkwIEjnCJJptRnxhAogOjOZmm2pRmSmidc3hFk+SZDN45WG8bLFtQFOEKA7Nu25farut1GjvXUfmCZBIkVj37V4fZ3UU03DLQ2DrgI7d3CM5u4re2aWQ13Hntq+5imH0ZiaGe8EMH4ifcZ9vpEgkcKa2oZZXUQLQtozu9X9A162iJcpYi4cJ0hne+q/e7YnnsDPlzqB9wz2+CzW7G41k70OP1NA81z1OKcEpAAYBs29V2Nv2YnXN4TRaqjGdLE4zvAEzb3XdLfFHp7CaHVOJp6Ln84D4AyrLvXLAT1ohLG1qfLrPp9YnOaeq9Wx07TO8Uv3NtdzOvXwuk4YjWHN7iWIJ9EcC8331EBLaeX1pzdr2GMz+IUSrb3XoDm2arqXL63dADNCkFKvQ8118ONJwmgMtziIUV/CyxZVyUIirQaK6hWs8Tn8XJHm/mtpXH4zzsQc1tR2NbAxMIrebnb7Ciy/n7RWA72+/snVqCxgLqgO/EatjJ9Mr5RwBD6hGVElDtH4yvu4U0rhp3OduDAkiCUI4wmVWbLhDg8BDXdwbNXxWvmdUS2r4LRZWxu0hyHG0nx3kGfZ8gy++Rh18j9bRnURo7y2x8HZ0Kw2RqNGqGnorQa63cbPqddLkFpnoCNQ5zUvV1yT8VOrqc/A1fdtLp7AqJx5/+itBnpt/XIdo7K4tkxDPCrmwjHMpceo2WiHUVlY814RmyMMT3+9qDk2mu9hVE/zfvE8WULyHElQvhnOoZ78VLoOc7MqlX3XLrjiCpiagqUKVCoqtb2iMicJAjW7HolcArPlYahOho/zkftGQw2MNB/lMnVhCLU6RKKdLAZP9QxorxN32Tbs2weGfn7HLwjU/W/bdm5BdrXaO4BwKseBxuOgCfIlpZOya49s6QZTequOUxrpBoPLvNIQra37VXCyTn245joqIJnchrVO8OoXSuirLlz1Zl3NyE/v6Abip3KGJ9E7L27dauIWVRDtFfpx+0doT+8GXVdLeZ3CqC1hj25Rqdo7Luup3wbUgIO+8hHiDE3S2n4ZYTSmgq6Rqe7vveIA9ctvVjOpa564hp/KolutTmr62QWbG/GKgzj9o7Rn96iGbMtMk9aeK2nuv5bW9svO6rxtT2xddyBhtTCewE9l8fL9G94mSKbVjGA6R2iYagAD1PJppaHutniFfgLTVLPFhAQJdVzCaIzW7iu7wVwQT6kMgrPk54o9gzxeoZPK77no7ebagNowsMdnVzIA4kn8fAl8F71ZJ8jkupkEQSKFn+tDb9bWP76A0z+GUa+itxrd5+5nCxCG3RIEp39EPZ6m4fV3shg0rfs83ZFJrG0HcPtHVZDmOlhT27vvO6PdRPPdNYNLmm0RxOI0DlynMh627FTHtDMzqzkOXjqrBpaKQxjthkqZryzg5UsEsYRqRGZG1OBBJLZe8v/K47k2QSqLMzShmtetCuD1+hJ+KoO5NN/JAliuqw8wakuqkWG+pM4PQdDtzaA7Nq1tBzrLI64MGujVMwSRnoexNN8b8J7uM9v3Nu5d4bmEuoGfzp51t3zNandnxo1WndaOy7Cmd6rVDhy7+5rz0rnexw0C0Az1vlruX7G8GT4E4arPWN/DaNVp7r6S0IysHNdmrdtIEVgZEPF9NMI1+8RcPKFei4ZxdhlN50mvVR4fqw6sQ281TjtDqjkWQTKzUiZ1OqtW8ziXxzcXjxNE4yvZT+dBc6z1m18+idVq6vrY6Rwaz+8ttdWtFl5xEOg9vpHjD6vypzAksnjsnMpPGk2ZOJKg/BKQz8OePepfXYexUdi9G8bG4cAB6C/B/Dz09anZ9f5+WKdPzwWl15cIYgk1cn+B1xitVuHkyfUDW88/94B3eGTjJerCUAXbp06M2Lb6m1YLSn0wM622K5uFkVH1+yBQfxsE6sSWTEEmA8PD57ZW/dJSb7DcasHgoFpeL5U6u5OY76sSz7171aDOqYF5GKqfBf5Kz63NtNHn8vJg00URBJ21lM9iA4IAvVlfc/GwnK7t9Q113we6beEOTax7N97AqAp21ksVb1SxZnbjjk2rg3UKNZsaojdrKl3Yc2kcvF4FvxvwigOgaWqmzQ/wOynxYSxOa+/V3Zn8MJ5QgwjLF7S2RWjoOCNb1P2UhlRjsdMI4wkVYG5knefU3c5cH0at3FMj/qgZBu0dl60/s61p+Jl8b7C+CZr7rsGa2nbG24XRGM29V+OMTq9/AzOCPT6negMYkZ5U8yCZpr3toGrkt8Hr62x5+X4018GsltEcG6NVw8/2Hl8/X0ILAkJNU0F5Jo+fzqF5jlrqbdWAjD02jZ/r2zD7wB0YxekfRvO9bnZFkMrSntvXzabwSkOgG1jjs6cdcAjS2W5mgjM8hZ/KdvobFHGGessw9HYTo1XH2rKLIK3q893RLZ3tVHXMeieIBjXwhediVMq4fcPqOJkmfiqDW+hXgwSxeE8ff61Z6wmMdauNVyjhZwoEqYzqaG+1VZmL59Ge20v9iptp7TiEFvidJcEqKuAOA+yp7bj9o+itejdd3+kfJkhncUvDatCgs9ycMzJNGEv0BBR6u4lRPolZPoHRqKqSiU6jQM22MKsLK4MBPS8KV63MYFvrBouqTGYWZ3RGBUULR1dmQgN/3Zlqo1lFbzfVeTISxc8W1L7XdLQgUP0WOq8Fvd1UTQk9F93uDBLpuhroCQKMpXmMygKODXmV1EEYglkr096yEz/fhzswgtFZQtGw26Bp3XO2Xq/gFko0916FMzTeE+gbtUXaM7toz+3FHptFP7V/RxieNoDWHBtz/ujZXwt5Hlrgrfs5sNl8H6xzCHT02tKGGQdhSLdppFFZGfDoHrtOoK63GtjDk53Bv9NcrIYhZmVBleZ09t2ppWDLjFpZvc8CH822sMZn1QoeyythoEpBH82Eh1GvqsyZzrWAZa9cFnjeysTYappj91wP6K1G7woS6w2InYNGJ7PyXI7dRvezuHjuEy5BoGKN5UC8XlP7ojsY5rm4fUMqo2q5rMmx8DN5NF+9tt2B0bOetPR8df9ne238RC1oOa+gXNO0l2qadpemaYGmaYdOc7tnaZp2n6ZpP9Q07R3n85hPBpoGhTyMj0EspurOk0kV7AEUCmsDpyBQQdWFmkHXPA9rdjftLTswNkgZ3Ay+r06Iy8/L99WbcWJy7ZvX86BaUSfm5RTx5Q7368VdlgWZLOQ6Ja62rVLZV58EqzV1PFZ3ya/XV7ar1VJBdiYD0zMqyyGXVR9e5bI6fpWKOpH1dyaiSiX12WXbKmiv1dZJdw9D1ZjJUvex+nPKdaGvc1+FwspIJmwcX9q2um08Djt3qjKI1bP5jYZ6HiMjj77xYKu1/t9Wq1Be6j2OyxYWNncFAq1VX2kItVoYolcXe5ov6Y0KQTSG3jh953G91cColQmS6TUXs7rVwi0MECTTaKhmaUE0vuGMIaA6i596oIIAOvWrGwmjMeyxWfx0Dq/QT2vX5et23j71b1rbD6oZwh0HuzOv63H7RzCadfR2A81p095x6JxmY8+Hn+sj1PTOyPvjkBlZP3V9HX4mf9r96nbqt53hyZ7AF9SMd/3QTbjD5xeU+50u4s7gOK1dl2ONzqx5zYaRKO3pXVhTO9R2aBqtnZfTOPCUNZkZfr6kmt5ttA9ME2vrfuqX30SQUp310TScseluRkkYT1I/dCPu6JbTbnuQSOEVBrCHp8AwsGZ2oYUhzvCUCviXm7CFIVq7SWPv1d3BqNXbo5bQa3aa5nWC8lQWzfcgEqG9bX/3ebZ2X4k1vbO7X/xVgYDRbqoMllV17l622ClbuIbW7isxGlWMpXns8RnCeLLzlcAtDqig1fNoz+6lcdlTu8vImdUyXr5E/dBTsaZ3AeCVhlVgvbRAe3onzsQs1tQ2FVAsD6hZTay5vbS2X0b98huxJ7epzrKeh9GoYI/PqYyCUw9RbYn2tgO0dh5CazV7Z649Fwwdr39YZQ8kUqppYDyJZlsrWTXFgW6TQM1q42eL2KNbiJw8rAaSdB3MCM7IJG5puHv+8tM57Knt2GMzmPUKWru9komRyuBni+p9EY3hNh3yeZVd6LVsgmhc9a5ADe7hOWoViGwRe2SqG/jqvoszPkcYT+JnCt39pbca+Pl+3OFJMAz1uJrWMxOs15c6y+MdVysaLBxTQWiH0ayqPgyrUqlPzZIAFcgZ1UV1HEa3EJqmGvBtN9YuCRiGGEsLGwapZ6tahUb9LAe/O1kUfirbkxZeq6nA7uRJaJUt9T5Z1bxRbzXUyg2dsgAtDPCXm41aG2Re0Pn8LA5iTWxFb1TRG1VVKnXKAIhmtQnNKBoqg8sdHMOZ3EaQymAPTWJWyzhO59qvona7Xls6bRZJEKjrj0Y96GYv6e0mjU7rhnJnlc9aTV1/Nxqd8o3qYifrovN/1LKOoWmiuU73+sOol9V9rlMqYVQWMRc3qHNEXV96LmzdBq2zCKb1VgOjuki9DidO9Ja/hgFs2957Dbn2Ab11Bw/6+3s3cXoamq3lCh2NIJbALY2svMesNs7gOEE8pQYEh6c6JXR293kvH5NGo7efVrsFQ0OqtPdsJpoadZXl+kRzvjPlPwBeBHx5oxtommYAfwI8G9gJvFzTtJ3n+bhPKuk0XHYZZDrXQZmMWu98dTBWqaifVzvvf8dZP0Cy7PVTq0EFTPPz6uS7TNVqHe9ZK1ez2gSZvLo46ht8TBo9LI8SLlVUBsFyHX2rBYMDKsA99WHrdZiZhYMHIBrp7JMsjI6q/6+2PAM+NKT2ZSKhbjM21nsSDHwVxPb3q/3SaoEfqEyG7dthclKdGDQNtkytHJvZWXUC27FD9QywLPU8QD3WzIwKjDMZlQFRLquv5dFMvVlDcyycSpORURWYr846THVisUxm5eeVijp+qwNfz1+ZqV9+/FgM5uZWXh/Lvx8ZgfQ68eLZCENotVe95joXFHptCd+H3btU0F9ddRzabSj2QTzx6AaTwrD3PWDbYDZrGK2GOk6r7lMtbZRaWYYJ0D1PLQ/lqbps1bRk7ZPXrBbNPVfR2nlIXch3LhwIQ/C9bpDlp7JElua7y2ZtJIgnQNewLAj9AGPxJJGFYzhD42cMgu2JOdrbD2LN7jlt4L+aXyhhz+xas5b1qdz+UTTPRbdatHZfddb3vxn8bIHW3qvXr0l+sjFNGgefgjM+u+6vw3jivEqmABUw7rsWa3Y3fiaPtW3/mgEA6Ky1vnoAwIxsnGVgGKd/TE1bU9Kxhmme1XNr7b5CZZSgBhjqh25U2RyZPH6miFk+SWThKG7/MMFpZu+X17Vfrs8PozH1Htuys2d/hJFoz4CDOzDW6TxvE8QS+Jmc6tSPahq3vKQfuo6XL+H2D+OMTeOMz/Vsg1pKsEIQTxCkMt3942fyWNM7sbbsUNvfeWw/k6e550qae6/uDl74uT7a07tWsn40A7c0rN7vZgRME3dglMj8I1iT27FHpwlR08xGZVHN8LkOQSyOVxoiSOdo7bmyU57mqlnw6iLtLbs6+8GkceB6Vec/vbN7LnSGp7Ant6K5nfXnW3Xs4Umc4Umc0elu3wsAe2o77e0HVnaEYWBN78QemyHUdZVRkS2o32kard1XqCyHftVPIB5X10VhrYE9OtN97QWJNKCh2yrY8zOFbsDhx5PdQZbVGSi61cJelbkSRqI4Q+M9AbbuebS3HcCa2Io1u4fmnqvxckUVPAYBITru8CSe7bGwALRaGM3ayvKCnioLMBpV3P4RFQQOjONliuo2vq/6MqzqQaC36gTJ1LpLFK6nUlXXKOvNrKYzK4FOo6GCa8umG3G12+rz0mjVcYfGVYmBrbI7PE+9FWZmYetW8FuOGlDRTfRqGXPxOG7fINbMLkJDxyyfwEvnCZJpFbyfkt68vFomoJpc9g3hlYbRfR/dtbHHptVgWWe/mScfwWhUaM/swhqfRbMt3OXyFsAdmsArDmAv1hgfh6FBaFY9tDDsrCqyfvZCs9m5fmo6+Oks7sAYutPGcWD7NhgeUgMaugETExA4HqCBYaompgOj+Lk+laXiOrS37qe592rVFNKx1HKhU9tVveLydUUQqABWAz+VW9MrYdlyNmR/SV1fnmmiZHkQw7U8BgdXJqyaTRifgL6ieh4bXduZSwsY9Uo3ePZ8VZKZTqtj5XUyLUsltX/rdXWeC2NxvL4hNZAZhuC7+Jk89tgsXr6k+rh0msqqTIgQLfDxHQ/X7Z299zyVAVMqQXvjcRxAvXZNUx2XJ5qN8wnPQhiG9wBqOYuNXQH8MAzDH3du+xHg+cDd5/PYl5zzvUg6g9XXO4YBs3Nw910qfTkM1Ztn2za49171QrcsFZjaNiS8OprTphkv4fk6ug5WzSHtlPHTWcJ4EsdRI4JzW2GpExxmNVVb19x7DfEffl+l6zgWum3R2qkSI8J4kiCZwW9btP046dP0aKpUIWJC1l0gjCVoksJ21GCDYagTTzzeOQm4qv4a1M/u/JY6obguFIrqeUdj6vtIpHMtoqlUcsNQM9b33w9Tk+q2zaZ6TumU+ptaTZ30ip3P/eER0HQYH19JjfdcyOZUEL1tmwpa77lHparHYuqrtEEMMbgq/tk6px5v9b4ZGlJfoJ7v0JDKAPjJT2BhEfpDG29oFP2Hx0inUhQKKmiPRiGRVP+C2gboBKAaTE7BiePqRO75UFlSP9c1tR+WZbOQy6tMgiBQJ7dEQt0W1Ih+aEYINV2lCLZreHqUciuOaaqT53LWgqkF2FZIIW/gOOBX60S9Nu7wBMb8cUw88nmTfL4zaOB4RLSAdjuq+icsqVHpdEo9rm638VMZlqoGsRikIg6a567M8nY6RS9nItidE3SjFmBocYxEFLvh4jV8CtGmqoN2Heyt+0nefWc3C8HLl/BKQ7jzA5jlk3jFATX7EY2pYDudB0LVcbkzk2aPzZC665t42YK6CNa0bgqmMzSJ0azjldZJm14ljMSw2tAOgXKFcMsEXq6kZnkuoiCZprnrctCNDWvUHzPa6bMEnnTWCZAfl4/xWDnl87Yb7GsarR2XoTsqBdtfDuzW4eVVdkbPuQXUzPIZOKPToBsk7/om1vQuQtPEOH4EzfNV47rVgw+aRnv7wXXvx88Vac/tVYH36uek692Z+TV/c+rAlabhjkxhVhaJVBdws4U1x9aemMPtG+r2hPAzOczF43j5fsxamdCxsMdmu9vg54q0Z/eQvO87oBu0p3b0rlbQuX8/X6K55yo14NLpBRFkcxiVBfxORgOmSWvn5WfcpwCYJu25fWh2uzcLqLNdQSaPFvyIaBRSaajYPsaqYxzG4gSxOGajpiYO4km0TmM5e1WZTxBPgqapZRvzpTWlOV5xkOjRh7AsSBgOfjyhUu9X3c4mJPWDb6B5rgrQUlmcFqSHwJ5vEMxMEj3+MHq9ghaGBKZJc+ch/EI/TO9S57xCP/FHfkhj/3X4qQzp731dDSwEPpplYe3aTezHd6nP42is+9kVpDKq3KC2BJEodiyLaehs366uC6NGgBE4OFqceFxdpxy/v06IiesmmJ2Fw/c1yehlatF+/EicSgUGdRevMECQzmKPzxA7/CNq5gBDQypItW04Evoqm6NviMjCURr7r1M9SUyT1q4rMaqLKuOps5pDEI2juY7qFxKqgQPDUNcPJUL8dJYgmcbL5DFadZyhCWJHf0K5DEmrijm3C69/WPWuyBTUKhyr39emiTM8ifbQt0mn1bVN+cEmzswImCbR44dV0Oh7uMUhKlVNLXASquu6++YtvOSIGlxHQ9dCkkmN0TF1LTg22lnG2G7hDg/jZQsk7/sOzvAUertJ8q5v4gxNdM8h9tgcqX//V6xZ1WTUyxahbREmkkQrC3iZAs7kNjTXIf6Te3AiSSLtKprv0Yj2oeudbMjO5cDoKNx9t5qt77796hXCSJRmkCSquejxBF6mSORohbGxNOV5j8iJ4xCWyKTj6Lp6Dm7bJRbTWKqbeJ66z4jXRssVcQbHif/oB/jRfhxbXZdHo+prOatU0zrXyMd9goipjmkkij08SezoTzqNXjOE+Vj3fOFnC0SPP4SuG3jFQfxkmuCBH9E3WqJWB88JiPotwjBNPK6u5R56aONThO+ribO9uyDy2IZdF8WF+FQeBQ6v+v6Rzs/EeSjkVWO4RkN9TXYyHScmwLbUjO/0DLSXLNXFtH8U92SFuTk1S+stVrAn5tA8j2B+gVZbNZEbGuzcR9NDs9u09lyFny3gDE1hLhwjNKM09l/bcwHtDI7ROtFAI8Q7fBRz4RitZtid+dWsNvWqTy4LQa2BZ8ZpLzbRdBjoh8Z8i/bxCkGoZnubjZChYXUSSac7I3R9YC210UKfdFqdHCYnVHo5zQaNJZfBQRWggzqhHTqkRkKX94u/VFfbdrLBtu1qtnh5sGN4CParxtIMDIBzooJTbjC0KrjOZFQjvoFzjB1yOXUi24imqeeZSaul8qY6qfle3xBhGBKLqVnucGkJ/+QifauuH+Lxzuj20ZP0l1QWwXINWaWiZuyLBXViTZ6S6TwzrfbTnj0r2xePgWk31Ej/4kkW5wPKJxwCx8M+UWVsFGJRNbK+VOlkMJw4QXjsOMVCSDEf4NYsmgeuw5rZRTM7TNZoomnLgyYh3vEFtEoZI3TV4EAOtPmTqiaxWcUtDeMenSceC3ErTTSrRWgYNJ7G9qEAACAASURBVB5Zonq0ibl4nPaxJYpF2L0HXNvHrrTpyzg09SyNSIFszEZv1PA1k+oR9Zrzc0W8XB9BvY7uWFhbdgDQ3rqfxuU3qRnoqW04pRHs8TmMahlzaR57aHLlYjXfR/2Km2lvO6A6og+Od2ex3IFR6gdvOHNAaxi0wwT9RRfPVu9NNat1XmOkm8LvLHcmxOOWaaqL/L7B02eemBGs8VmcwbFzL9PQdZzhSazZPTjDk3ipPEsnXOonm6rs4By4o1s25T3nlYZUQJRbO1IcRqI9TRqd4SmCWIL23B7VYM9qrenn4PWP0Np9JY1913SzEtbj5/t6g9XRGdB02lv3P6pzmtc3uGGfjKCT0RCNQjwWEqKvWeLPHRjHT6TwU1nVXDGVQW/VVTC8TNfVUnfLnwOnDPL4qQz1eohtg3NyCWfVgEX3Nrk+1XMjDHFGptTSjqG6VnFdcAbHCTUd3bZo7LuG5mVPXdmG5caOxQGsia24fYMq2OwMlpjVMmEiqVLwx+fQ6xXqdWgfUzXV1Ufq2MeX1JKHfUM4xxYYGlbXhSOjYB1dRG83sRYaDA5CKhFCq4lzYon+ARWkG3ZL9WSo1Nm6FZJxH9tbWUrTGZ1RfSaqFQqdGDga7WT2R2LYk1tp7r2mk8mhjnOQyuCOTK0069Q0lZper2AszdNeshgchCuvhOktoeq502ky2t66j+buqwjjSZpmhrhm4dk+fmdZSAAMY92BNr/z+1hcXecYgaMGugsD6O0GfiyOMzhO82iVoSGVwbh9m7r2SJgediyrltSMFsjGLAxD9W86eFBdS0ciEA1t7GQBr3+ExqEb1fVEcUC9DsZmVl6//cPY49Pdvh9uvp/KsRaV4xZ+IkVrz5WdrJ4ctUrA0mKA33Zw9RhOzaLeULtzeRInm1XfB/VGpzFnAwwTt9wgCEKa802c4hCtWIGUYakJK7uOnR/AtOpqsgXIZwLChUW08iIRw2N8Qh1Lq9zELo3i5Uuq43o7wGta5DqJctlMiHWi1s3UjcehL23TjKxk0tlT23EGxrCHp9ZkQwWJlMrcLVvdBrOuHVAoqEwA7/gCgRcQtapqEqYz4RZUqpiLJ6gv2mqloaoqH3GOLTAyoq7Hn4jOGJRrmvZ5TdN+sM7X8zd7YzRNe72mad/SNO1b8/OP7VIKm+4irKOZz8PWkQYz/dVuanI6DZdfrgLWYj4k6VZYmrqM5sA0McMjm4VMOkTXNdqDUzQOPoWKVmRusE5/aSVAHI4tcbKwrXsydEvDnExPU5/etyYd0EqViJkB00NNaslh5tPTJBon2bnVI1k/wVJVw1yaZ3rSZyDToty3FUtPMTduMzkRErHq2O2QrZM2eecE5uJxhoq99S0DmRbNhTaD5iKRzud8fz9sKVQpL6pmL6PDp3SBXFXemE5DSm+zMLKPRNjqCWyXLX/m9qUsHN/AcNrk0he+zX0xZePG0jjJvPowiEIq6hKiYxtJ8rGVlCfDgEykTd1PUko2SSZhPFcjVl9gerDF4KCqS9q3J1iTzJFMqpHi3KpJGk2DfLTF4sQB5gtzjKXKTOZrzOfncKJpBvIOW4bauA8fY7qvyvbROuXYEFZuiLTZJh9t0siOdOtGm7EimdjKsSzGm7RSg8wP7WUoUcU0IWnYeJEEi9kpKjOHsKZ3UksOsWWwSUZvUpk6QH16P2bokgoblLccwnU1JocdEnEYNBeh2WAg2cBJ5WnFCgzkLDRD42RqC4X2ERYTKqV8KTmGfaTMyeL2leDZMNQHiabhjs1gT+/AGZumvO1qjiVnsEq9IyphJEoYjdE4+JRuYL+88862c7iXylJK26DpeIkn6KeLEJc4d2waa+u+R/fHndnsIJWhRZJcNkTTNNzMxRnUspJFjobDWKkzl4B4pSEah24kjCexxreyNHdFt+ljV2cmd6P0/w3vu29Q9Q449f42gWfGl1f1I4aDF0+vKZtwRrfQ3Hdt9+e1/lkWM1NrmhC2JnZQ23Pd+oOoZgQnnmUyV6Gh53rSpbs0DWt6J/VDN6r7ME1CM0o+aROYMYJkmnJ2ioX01Ia9P8J4gvaOg92B3WrfDOVmnOqe62h0noNXGqYcHyXZOIHueyykt5AK6thBRA0MTe/ENRMUkupzdqjPw8OkMn0ZEbfJ4EBIDBs3U6SV6GMw0yYWg4gZUitMYpgqwBnN1KmkRlf2p2HQmNqNGTikkuHyUyaRADtUn4Hd/hCnO2YDozQPPoWTfTvxGhbDhTaGAfmEjZvIdgP6MJ7svmZqiWHGCk0MHdzoxn1QuvsxGgdNJxYJMU1IxEMsPYmfzrEQH6M6vJPW0Ay65zA+FlIsqutHgGQyxNbV53Y1OUxffCWnOh5fSTpJp6Gtp1QGwKpVMRqX3dAzKBVGorR3Xt7dNy0zR18hYCBeo1xYlY0STxPqBrP9VRYTYyxmpxkvNNi33WbnVnf1YWAyX2GpZlB7pE7lmEV99gC1WD+zw02KaYdatB/bSJJKhug65NMe5dgIicTK4cwYTZr5UeYLc4wkq2yZWi6LDWlFcoSxOPP0w+GHcct10qbKIe8zq+C6ZIyV/dKXsmhEV53ndB1r617s1ddDHUEyTcOOork2bTNDGE/ixjOkTJvi/9/emcbIdWX3/X9f7fvSVdVVve/NJpsUKVGiFkqyZuIZZ5LANuIYMwYcI3DgfLCBBMgXJ1+c7UPyITGQIDHgJAM7QZKJ4WRgIxnEMZwZj0Yz0mg0kixRK0VRQ7LJJtl7VddeNx/+7/Z7VV3VC5cukTw/oNFVr6reu+/e886959xzzw1XUPHHsTZyColgDZbF+p7Ml7G5qbA6vIhQeQ2nTrbgWVvB6tgZVL0RZGL7xLc/wOxrlGut/5LWerHL3x8d8BrXALhHtSP2sW7X+l2t9Vmt9dlsNtvtK4Kbchm5TAtDYz5n0UmthsD6MqzNdXiKGxh9dgwrrRRWa1FkZhKwKtvwVEqITWWxvu3HzRUP/CdmkQ5uO1kXajUMTfhRTI7urMtdK/mhHzuDW5XYLv/DeiOKgdkUErXbaI2OozI+j5mvzCHlL2H2y1MY/uvPYPavziNYvI30gIVVNQA9OYVIdRXejRUMPzmM+FPzSGxdw+z5QRz/2mmEKu2LwOOqiLmfP4HhUWdhjCoVMTSskP9rT2LqyzMIbPdO6qGqFRTmYrgVGEFhcWDPrKcxbCF8eg7ZJ8bgrx79HpjhVhHN3BC2G36EE36g0UCwUURycRjBhUnElKvsrRYygx54j80iZm0D9TrGRlo4/nPzGB5RsDbX4VtZhn/l+m7H0c4m7e1rrjIZhZV6HNXCJIbG/RhIa6wH8sDICMKNTaQ8m1j85TMYyjWRCmwDM7OoDeQRUduIeCpoZgZ3Tln1hBF19dtRqwwMD2MjkEN2gFv+hPQ2fOPDaM0fx22V5fr/sXHEWxvITwRxq5HCcjGC7PEsCvMxXG8NIn3+OCL1daDRwPCEDzNfmkassYpGNIVmIIKQLiNaiGMtPIz0+ePYTnE942ZoEPlffAGbybFdS820ZnXcusWlAptWEpnnj2OtuDt5VasFLK/5sbpu7Tp+EOqRFCKVVQSGM6jU91mLe4RUKvcn+74gfB4xyavulPV1O2MwokhlvYjMDmGrcrCEf/earUYI1vPnsdbc3xheXweuXbfQaAC3iiHc9A635ecw3Mk2w9vbwI1bnvuiR2otL4JxP1SzgQCqaESTu7q1jS0LS6tO3oMV7yCKs4+jUm33St/aCuLqVqLrkuNWi0n1ct4VBBZnsVXqPlTe3ARurnh26qkVTyJaW4PKZlBvKKwMzGFj5ETPXegaDfY3po5XfYNQX3gJV9ejuLlKY3WrZEEvnsTcSyPILeZwzTOGwmwE/vlJbG17sL6h4J2eQKTFBGOh2gZCx6dwZXsAw89OwFdaR0hV0Iil0BgaQxTMZBbPBLBcTSIxmoBVLSMVqaGcanc+3C5HMHgsDU/NmQgI+IEqAgwBP4BslErA9c0IIgtjOP43TiCmOaYKtYqoZYZ2zuE+XzWSRszaRiQfw3Zj/yiWWsOCLxmB1WDCxXjCQkmHUa5aqJw4ixVPDqu1KPJPjsJXXGPF24vsIxGgrCkv9VCcUQUGl3BFI0AZ7U73VgtYvqnaEpOZcYSR/03EkRzwID8ewEbACbMs1zwITA4jEyqhlh1BMTCAZKSOeH0Vscrttmtn4zWM/pXHMPe3X8DI157HUjGOWmEcsdYGBga92PIkUUFwJxoylQRu6EEkEmrnPBFPFfVkDtvxAtLxJkxWuHTWg00dQ7MJVKeOY+aXzmHiy/OIgkZ4IlLHyNk8x242QW8D9Uh7zpmNjd25mwCgWlPAsWMYeGwEmzrGZyWbR0hvI6q3YE2OY7kcRzrt3G/Gt4nUS6dxOziK0WdHECstY/orc9iIDME3PY5o6+Hdm/4o4iVfBzCrlJoEjfGvAvilI7ju0XGf15N3pVRiDPG5c4xXfu01Tn2urzM25+JFoFJB+sVxzN8ErlwBBhbngLdeBQDkn17Epx8xYdnQUBLWzTNckF6vA+UyQqdPYaHhxYUL3He7XucM/NWrXO8Riznh0M0mEH/6BDxbSZwdHYDyWPAHZ4CFGfgBjAFAcxJAEZFYEslrfoxNDkE16sD6OvLH5pD3+YArZ+EvFOg5vfw+FafHwyy6Hgv5k1nAyjlZ7UolWOfPYzoeBmoTwHcu29lK7AXqGxtOyvHNTQw8+Ri+kFCINOaB116lUk6n2X5bW7RIMhkoaDz2Uhooh4BXXYtbajVeOxzGnovnO6nV+LfXb8yetOvrUB4LsfkhXLoOTA8ngWoRqNcx/8wAdDAE9bKr06hUkJ1NYWBxAJ7vguU7doyL6nM54M//HDuxZ5UKdmKZAFqeExMUDp+PU+a1GpLDETw170MsBgTUOQSaTcxcCWIgMgR18VMgW0BkbhjIx6AaDZwJJtBc0fBc0PB4NKYei+HCp1zv3QyEEXJpGY+lcerFFMo6gMg1ZlJRzQZOv5SCynKAsrwMTDyRhufCANLjE3jCw04vXzgDj6XxYlEhERsEXk8Cy8sInTqFUDaLZnUIzWICLa3g9/qQn8ij5vUjc/I0Iq9QJDw+ylEtwfwAuRwH5k17O46pKYrE1hbXcnk87eubtracbPkTEyxrq0Xv7urqjgjtrPnvhtbMNuyzgPTiEK7eYtni8fbojqOmXnd2FsjlHuwlx4JQr++dN84YSva28IeOtF5fp35YXQWUsuB/8Rnk/D5cfpvP8kEplbATEXU31GrA1JQPGx/s/qzV4j2aa9RqXL516RLve26Or905Ukp2bppmk7q8V6ZjY3wY3VUsUo9eusTcKocdHpntRjuXW5nPwtkoUK/B06whOJhAtcou3v2dWIw6NRDg/c3MAB9+6HxPa+q3kyd5vHNJWrkMROeG4ckrzCQz+P6ru8Nly9xxDTMzwCef8PNALgVV/AiJuUFcXGF9ptPsawIByoW7nU2umfV1/g8GgTNPWJhc5zmLRV7nzBkL/sQpZGrAqUtAcuIFeMsevPkWl6DNLmagfvw+G1YpzLw4jOQWkE2OAq98BkspjJ9KwjeYhvWRBWxtITGXh1pXSD8xCXzwA/gLBcSRwPa2M5TMZoFCdhj44AIPNhoIJgOotzy4dYvyMTrqtHGjwWPhsNN/lErAM8/Yw5BGBviJH7h5E5bfj+jUILbt79+8yd/n8wDicQSDacSC47i83j506vasVqvAQCEBVG8BjQaiY2nUWx40ShzjXrnCus4+Mwu8tWbv/8oxVzBkoeEN8m0kirC2DdlGgx18OAzE4wjFfWh4AiiVOLRMp9k2ySTfGxna2GB519Z4TFseBF54GtG0B/G3vbh1y5HP0Sdm4csUMLOVRLEIRELHeb2f/IRfiESAYhGefBbjixTARA4Y2QIyAxl468cR8iehL3hRRwjBAAUzMpxAshFEXCdgHhCfV+PEuShUIorQ5YydRGoWoVYMrUsWtraAwlQI0WMjiK6tAa/RQ+LzApNPDgJvLvEG63UEoz7UW45CWF/n0LJU2t0+5TIwujiE8MAQmq/zslMLKVgrHwMKOHE+heS6D8nrUcpvswmkUph/OoXJGhDwnQS2pxGPRPBiDfDUs1Dfw0M7e3BXRrlS6ucB/BsAWQD/Wyn1ltb6y0qpIQD/QWv9Fa11Qyn1GwD+BIAHwNe11hfuuuSPGmbkb16XSsBzz1HLa03teeMG35uU4PZIfybOdeRKZQCLCWfiQwP44qjr4SkU+Nvvf5+/LRQwbrEjefNNKv54nOvY83nghz+k7qjX2ZHEhmKAOoaewbseD3D6NDwAnp/iIAbo2Ppm0vX++HHg7bep7Ws1WkA+H7OtXb/O1/G4M/rx+7n4Z2mJ2n9ri73lp59SKwwMQBXyiFoAkACef5693vIyv18uOz1DJsObMlku6nVqWK+XFXDhAj83xnay3WMIgL2pycBnMpJVKs6owDgBAKdH05plHhlBbi2Ii9eAyNQAsHx7Z12BCgRYJqP5KhWo4WF4wgFakVeuOFnmwmHep9dL2fjoI8co39igzCwscAT18ss7PaqVy+0koQPCO80BBIHhF514KLvuwwAQiAEX2A5j8yGkR4B33gFSKS8CpajjREqnEcsGEQMAzxgFCYAnHgEsFp3Ft4DnnoMC4KRN43XpY7AYe7WxQeG0LHhefB7+ly1Uq4D/sScRSCWRtAdAExPAK6+w+UyOgU8/dQbE587Z29ZleXsFV662ZBI7A5VymevM1tc5kNXa6YSbTTbfT37SOwEgYCdfzCegps4hm8wh/mN20teutScI7EajQVHYL9H1nbCxwXbe3uY9DBxh3rlWi49JOi3OAIDqw+ttNzbuNSar8oNY3531Y+d+hNZU4Uo5qrtX0N3WFrtKs42SGfivr1MnRPaIml1ZoT5YWABeZ+5IhLJRhEKA//3dA9OVFafLclOpUB8Ui+3PfqvFrikS2f2bYpHnMkk+zfabXi+7x48+curDcPs2dYbHw/v0+6m/ikXqqmyWfnxDvc7PnnuO333rLdaL6erM+Y3h3mg4+sLv57Iordkdh8PsPlMpp8y90JrftSyex9ShuV6tBkSG4kD1GqA1BkbDuLjqyEGjwWtMTtIQNnstDwy078ZRrXKYMzHBrtHod0O5DExNBYCRScQ1P6tUnC7c66X8nD3L83zyie2QncgA5WFkZlJ49/vsks3QqtEA3n3Xad90mmUaH2dfuboKnDnDts1m+dkrr/D3xiHi99PnDniRDAIvvWRKHGXjX70KnD2LcCqAsRQAbVv6xSLGj4WAhB9ozQAXLiB9ZgjnFZBMDgLpZ4F4HCOrCu+84yTPXVwEPM0U39h7hQXz6Z1+aHra8eknk5TzXI7P4OAg6zkSceYF4PUCTz/NmwsGMbDkx+0P+ftQiEW9fRsYGbFgLZ5Homyh8R0O+ew5A1Qq9lpw12BzextITaWA5WtAq4XQ/AxatnPKhKo3m4A/FKBQa82Gf+01+DLDQM1CqQRkC154qkkKQLHIDvGTT4ByGcHhNPQaD8/MMCkvwOforbccfVqrAadPAz/6kTPzHx2KQ3mZvPj6deDjj1l/iawfSKUxvhMJPuUI/NtvO1mKT57cuVfL4hiEWXlnEAYQuQxsbHgQyMSAldsIPfEEns8AwaUcL2Y/eLmJMK2+1Jkdb1usBQSXeJlhk+3LPAzGk+d+8FdX4TtzBt4LHjSbdsJfzeHYJ5+wWpViO2WzdnLmlCuTewMozMWAV1pAJILoYATzgwCaA/xxvQ4cPw6lTLGtHeUcCICJA06dYgXvpaQfUO42+/o3AXyzy/ElAF9xvf8WgG/dzbUeaTY2bNetnflsc5NWhemtlaKQXr1KTWhZ1ERpZ83HTgc95IQn7ZoZiESAF15oG6nl81T8Ho9zjnSaytNkMZ/ZnQNlTw703eFhR5N+8gkNR4DKwbJ44RMn2n8zPs46uHmTBvrcHHvOup3K3T36DIfZc1+96vTkp05xhDVrb1tjWfzOm29ylHHsGCtNa/ai4TB/Vyqx7m7dcnqWapWfX7vGc6RSTKFpPmu12KNXq+xRJiZ4PdvaymapeBOJBPDpNkcVZuQxOMh7TCRYdiMHCwu8lru3MiPNZNIZlWzam6+fYAZYBIOsq/ff53uX3Oyi13SSx8MOzOcDlEI0yr5XKQCXR+jImJhgGxlSKTv9p6+9zAfF728fcVsWZmfZGajB9qmPQoEDHtPpmKb9wQ/oxU8kes8ETU9T/29usmqGhyleAAcgN244Ppzxcaez7kWtBuRyFjA0hACA8+cdo9TsJtANrSlixiB395V7YcJzfT7X4KgLrRbFLJ/n4Mo9CL/fbGw4szN7id+jQLXKv1Kpu1F+J7O63c5x8yafg1CIsm2ClNxwFtiRG7PVzmEChdy4fcvdMIFO+53D+DwLBapPE8Dk8VA9njzJ67zyCr/brbz1Or+7uclnOBplvTSbjjFh2NnCSTnlfOopZztNE4RkWSzTjRuOPjHXtwOh2p6pzU3qeTuwDT4f72Fzk6rt9m1n5jEaZXfm9bLs29tsF+OXnJ1ld5RO85jXy3totXjO556jUXj9OrtTj4fRb4ZEwjH4NzepL809PP448N3v2vse23ooGGR9Pfssy/nxx7zWgr2t/cgIo4w2NjjsuHHDmU00s6JmOGKycgP8XTBIYy9l24NLSztdC6KFOPDJZUBrZMdC+PCWY7Qbx2oyyd9VKqyXSITtU6uxzcplJznu7Cz1u7FDTNCacayajNM/+hG/Wy47wXq2PxiFAmfcFxZiQOE8Ulrh3DnqUiOTANtwaYm/Mf2E2ZbV6213hGYyzjDmQJw8yS+7+1KlWLjPPnMegslJIJeDikZBVat2CphOO06eU6eM/omwAjY2gFIJ3sk8qh+xnhcXnf7x+nUOAWZm+NxVKpRR91wLgLYHK5FwHNuzs7ze9eu2XFgWIhHOK5TLwBtv8HYWFljX5jZXVylX6ckE8JMqkEohOJbDaMnRb20YBZTLAckkAjOjUO/zGidOAKiPcrIgm2Xht7eBDz5A8Pk5DFhOdMRnn7Ft02m+v2BPNc7NOf3o5ctsQ6NXo1HeZ7VKp1vPiBojWOvrfAj26rjB6y8vA4HUMPBJBcjlEPSD9/Dhh+xMEk5CPni9O68te36jXHaNgUyISankeNMCAT7oY2NAoYDEVaeNFxb48cAAHfpa81ybXFGxM1Q2w9hw0k9hcStm94/3G3gMDVGJPWi5xw5A/9P9CvaitCKfUMty3MXNJnuiWo0K98IFCnGt1j6VB1Dip6e7n/8wdHFldw4MlaKuetsO09vxrt1rTJzX4mL7selpasTOacVIhCOEep0awbJo+JpCdxKN8jfr63zIUyngi19sHxWOjbECcjlHoRmHSCjEdnv9dfbk4TCVWL3OHn9ykkojl2ObGbepcSjsMZ3q9dqdmU60W4EAtf2VK859GcXmTtnZSSzG+9rYYL2cO9feWw0PM97QyOGd0LFp5E6Vj42xzJ2CZFm03O8hLp9TGx7P7sHN0BAHnr1+Y8jlKBbb26wytyjFYvYe7dv8XjhMMTJ+mm5Uq7v7WOMk+Oij3jPUpRIf+4UFqoy336YoxWK9HV2mTz15kt58MyNkDIRYzAnfNRn6lWIn/fLL7VsOmnLeD+p1Diw+6BJ6eycc9Szw8vLuQXUvVlYoG71mwTc2ONNy6VJ7cI35rFbjfd1NJMPmJv2Mm5ss++QkDSW3Q6Rhbz1rDLF63QkJ75TvYpHyVCh0N6obDQ6etebvjJF68ybl0xhKGxuUt73ubXub6sQEG21tsb7MLOTiotN1nD7tzEK6761p77trZjCN0V0s0v4olWgcZDIsz9IS7yufZx2EXdtSzszw+0bWMhl2T60WDVGPh46/cBj43vecrsCMizMZnvOVV1gvySS/MzfHbqzV4ljVROo89RTL/eqrHCM3m/yuCa+emWFdRCL8vFymPzkSoR4oFrvrvJMnuQpua2v3LiOBAM9x4QL1w+IiyzQ4yPKGQuzqMhnH7xqLOQ6LY8f4uZlVM6uljD7a2uL/apVj7Xqd8m/aZGKCry0LiGTDwEf2LF82iOFhR47M7JzZtaVSYfmU4nnffnsnCntn3D8wwLrZ2mKZNzZ4D+7nbnSU92LmPCoVntO0udnaM5UCoBQs1d5lG4zzt1Khrk+nWRfdIqS8XnbTB6arBYodI3znwXSPGToIhdhdl0odMmLGYNvbCHtC8F5y6iIS4XDmo49Yb0rxHCZSYS8nq1myZYa0fj+HcO7nPx5nuwzaG5SMjtKJtbLiPL8LC4AnEuWDZje+a3K5Oz4f8MIL8Hq9iNrh7ckkAE+e50kmnbUPlgVkszibd6r67FlHBsbGeK+hkDM0n5nhszczs/vSCwt8Zns6IP220Xr5Mh/MfWayhobs9tLjfGGUUyzmRHeayaYuuINOd0inGU44b28Rmc/b+wxPAHCM7lbLGcrGYnyGMxk+86++yio0YmnqDMBub00iwQc/FjuY1/f4cT50DxlilN8rDrtwyu16X1mhwC8vO5sCTkzw6b5yxdlT6/Jl9hwHnSK7j4yOYmerrv1C0u4509N8oLtptM6Heb92GRmhUX36NN93ntPEBHae071HSCzGUd/58xwNGdet3+94LEzsYrHI3x90XzWl2qczACrLWIyjxsHBg8W4ms3br1zh+To7b6+XGnNr696HBFlW7zIeufA4OKGA+2McxZ1EuD0rtredSXszc9BZjSYBjGV1d3wXCuwDb93i552zlmbWx8zoPP44DY7l5d5rwEsl9qmRSPugdHOT97+yQhGqVDgQMo9LMMi6efdddrDLyzx/ItG7KTc3KT5DQ/a2OR0rbsyMXzeUcvr8/WZT98PkLzTnNDSbvF8TFncQ35NZN7zX9itm5rpXMic3lYozExoMskxbW84OCOWyMwAtlajyFJ/65QAAE5xJREFUTX0bY/7xxxnhcRg6w5mbTcrrxASv2WxSnXTe++ysM1trZCmdpnFpJlMAyn8mQ/XWLeJkdZVjqEiE6tbM+hojKJvl+zOu1Ca9IkaM4ZZIcPbszBmqcRNC7O4aEwmqu+98h+c364D9fseocMszo1h4LBJh/Wcydn6MpnN99zU6ndJmy87NTZZzetq5xvw8Dd9k0glXNsb+c8/xd6+/zuc/kWjXE5cvO2HCHg8NZ2P0u+UzleLsomnn9XWnTcJh4MUXu0daxGIsg73KaBdmNrhe5z2PjTnPaSBAO8aNUry/et0x9j77jNcetvd+NvsvV6uUj2yW5zRONa35WaHg6jIbdgXbC9bn5miUm+gJ448/fpwya3TOyAhff/wxx/Lm+TfhwO++Sz1njEo3Pl/7PEinDkwknCDDg2CC03pFZ91TenVePegMQGwjHIYfbNfOWX33HIMJzd9Pd3o8HHYYgxZo19kG4yg2euzkSfaTH39MMdjpaw+bINp+EI4fp5xR51jtXhLjzUJ7Zmz3/VqWE8zp/lmvOQezlGRPRka6e3b2wig3N/bM9qET1hQKfCCMspuacjxeoOy+/74T8AiwDTMZ9ivGNnCPTfZ8PoL2HnRGge7H3XqmP6eIUX6vOOyWaDdu8H80SkE/c4Yjm0uXKNlcBN7eOy4ussc2sXl9pHPd7ZFyLxfVjoxwJHanD7fp0YtFJ47p00+797ZmAeLc3N0Zo8ZNe/v23ouXO5mddby+3YjFHt7NH+8TlkUH1Weftc+8mFUKppkbDRpVo6NOmF4nZmC7tMTOrjNJUqvVLlam03/vPa6S6BxImwGMOZ5MOmqqWuXg64MPnLWSnYOKoSHOSNy4QXU0NERj0Mzwra05M+2As9avXHbCaxMJdtirqxwT7MwmuTB5KYJBivPW1p2HRwM0QhYX+Xi4w4U3NjhYyOdpGPUy/u1clwiFnARZpt66GfLFIo2MpaXu59Sa7WMiFKanWa8mWiEQoAGXSDjrVC2L5TaGvgmBfuopJyDGhOLuh1nb6/U6Y02T7sIkGOvMvG1mNAsF/r90yZn1isfpsLl4kW25vs5jMzPAj3+8+/omfNqMLz0ex0kzP09DD6CsmhVL777LwZxJHBWJUHbMrH0yybI3m/TPKkW/arfQ/kCAKvfqVT4vV6/y+TIzaeZZNG1nZu9MiGqlwvusVp00InupyVCIv9/eZv25n/WhIfpu3etvAZZ5ZMRZx2uMU8P0NOt5dNTp+gKB9tVA3fB4dndtey19CIV6ryTyeungqNcP3n259dX0NOX45k0+L8Zx5762uWcTQWCWS7TVt1mXbFsCwSDL9Rd/wTYz5e90llgWzxmLsU3d92Cerffes7cgvQO/9GGHZGYC8kFkv+iyaJRyZ3TMXhzGMWH6w0KBetzoqLtln+jwB587ySA7OEgvnXkYOpw7xnZ2O7CUYqSDaadnnz3kUqv7Fnb74CBGeT8w8W/hMEeOjz9OKXZ55LqSTgM//dN9N8gfKg4zXdoL05aAK46oC+k08KUv3Zts/cHgnXlR+7FTwEOOnZtvp7+yLIrUG29wUJxK0SidnubfXp1UMEjjeH2dRqQxAs169m6DxclJhpIWizTqzCSSmb03/XEoRDG1E/QimeQMweuv20lnOgZHHg/XFZqUDmZ25+JFJ9nR2hqvYcLfR0fb15aa3IKtFgfOP/jBbsPJGD2A45y/U6PcJOkyM2tvvEHjZ2DAyeybSrG93JMAbkxexpUVOlDicdavSf/g3nXCsnh8aIh1X6vtdrhsb7PtzExeNstzm8zW09OMeDCRBMaIikQcZ0CpRDvE1Fs67QRW9cKEm9frHDhtb/M8xqh3j9P8fh6rVp3VVCdOsO1MVKF7dczwMNt5eZl1OTvbW72UyzSGjDGZyzlb5+TzPE+lQmPJtJ0JTzWhkUtL/HxiwnFQKNVu2OwVPDY56URLRiL8rqlns/Z6dZXPjakXn4/tUSyynJWKs1PDXvKp7NDla9d2l8lyTcJ16yYsq3u4ayDAAW6/uZuZXZ+Ps50mW7px1hjDvPO5GR5mSP/gYBdHQYcHMpFwogP2o9fEsWW1r5QT7o4nn7y/ww2lmCpIuE8YD2UPIhEur+icDHC3+Z2kCnrUEeuuH2xtsdc+c4YxT4dZwysG+YONGMUPHd2W8ufzfLQnJjjbNjtLA+KgXuOJCSc5f6NBY6/XgDEU4uxqNIqd5ELForP21s3IiBMGHQjQ4Bkc5G+7zf4NDHAGyQyY83kOopmd2Nlb1uR/SaWcsEWzLnNry1lxMTOzey9TszOhuZ5lOQN1k1SqVOIMmwkw2tjg8ZWV9vXA4TCN10CAr595ho+cmXE2Tg0TIAOwPEtLTsKhbJbOh1qNr/N51sGxY87M3eoqr33tmnPficTO1rdtlMtOjkOzPjKXo6MlEHBmU2/fpowYFW+cKEtLNFDcg59UavfsthuzhtuynOsvLDhbLHWL8iwU6GSpVjlB4l4b3Grxc3e48vw8nTCnTrFeTTKy1dXd9+++npHBTMb5zfp6+6zx449TFp57jobciRPsLmdn+f9u1KhxHrm70lyOddMZtjww4CwvMLPlTlbg3szO0kiU7rodk1cUcJbxlErORiduslk6rB7kGeVHGcuS4c7DjtuJKdwbZKb8XnAYzWP2BBgcPPRaH0EQHgyUogEWDLLT6hzs70cy6exwsLXFWb69Qirda/rMulRzHjdDQwxFNomTzLrPzjXHvYhEaByurtJIu3yZa5FnZmikG2M6n6dxNT7OUPHz53l8dJSG7M2bTjh0q+U4Nfx+ntck8TF5B2/d4nf9fhqp6TRnScxmC+m0kwXWXU9eL89ntmYyA/9EwslubSILNjZY3ydO8Hsm14/BJGUyM9zPPMPymfBJM6NuKJd5/VaLn42MOPWctHfdMakxTp6kQdyZ8Gl+nmXnloQO7u1lzH26MdmMp6ZYL8bhks9zFrrbDJNJ0nziRPuMaCBAGekM8TQy5MYkXCsW2/esdfudBwboRDLyms3uXn6RSNAYN9yLHKZ7MTHBMnbeYybjJKarVBwH0H7d9oO61dxRY9q+W9CXvYuqIAjCI4MY5UfN6ip7oCPJ8CEIQj/x+7uHo+6HZdGgNaHgB80LCNCw8HhohHYa8qGQs8Wh+1qH4eRJztL6fDRmLl6kAWiS1zz5pGNkj4xwdtEYmz6fs03t2hrXIHdGGgwPc6Aejzvb1MzOOqFwkQiNtFCIv11a4nHjCOkkl2PyWXekgUkW9M47dFQEg0wGduKEE57eGWXg8fAcb73FmfRkst1oN2H6gLPPc6PB85kM+cbxkUjQMDYhzJ2Jkgz5fPfERya79MoKZcwY+gANx2aT9diZaGlhgdfulsgrHAa+8IXdx4GDL/WLRp0tkZJJ1v3t2+3tolT7WuhCgU6EfuYuNRELnQwMUJ7DYSeCw71HuHB3mC3PDpMaRRAE4WFFjPKjpF5nzy7xWIIg7EM+T2MyFDrcChezBVOvXIh3u8e1O4XC2Jhj/Bncocp+Pw1BNz4f//J5Godmq3qDSeJlMpV3ZqJ3G9dmpz+zTrVbAj1jHHbbRdLsmKg1Q6X32yM9n2daiG6ODLcDZHPTMdw9nt3fN3vd3imWxZB6y2Idv/yyMyu9uclydst0b3KI3k9SKd733JzjUNgrCiMW273BxOcFr9dxiAWDlJmDJgcW9icep3Oqm+NJEAThUUOM8qPEpAU+SMpcQRAeacJhDlgDgcPPZh9lEv073QhBKa6B7zRwPB6Ga7/5Jg36vZwIZleUpaXeRq7fz/W9e53HhLAfhF5tYbZZM1uMZTKHc6YcFnew1cwMs5in03RQ3I3Bf7d4PMyn8DAark88cfdOLcFBqfvvJBIEQXhQkO7lXnGQEYhJvSsIgnAADrsW/UGjl4E7NESj9iD+S7O2fS/VelSG1Ows8OqrNJiP0jFi9pC+dYvryPu9s+HDaJAD/Q2xFwRBEB5uxCi/V+y3T3m1yrg3WYwmCIKwLwcNKBoc5HZRnfsx94NcjmHl4fDRGqaBALOgr672d5ZcEARBEIQ7Q4zyo0BrxjT22r9aEARBuCM8nu5JuvpFv9bHFgqfr3oQBEEQBOHgiFF+v7l9mxmLvN7Px1SOIAiCIAiCIAiC8LlBdtK8F3TGKW5vA8vLzr5B8/OczpFt0ARBEARBEARBEAQXMlN+PygWaYAvLXGB4dAQ/2Q9uSAIgiAIgiAIguBCjPJ7TavFWfEnnuAWaLkcUwzfz71xBEEQBEEQBEEQhAcSMcrvFa0W0GgwoVsuBwSD/cv4IwiCIAiCIAiCIDwQyJrye0EgABw7xpnxYhEYHe13iQRBEARBEARBEIQHAJkpvxcoBUxNcd349jaQTve7RIIgCIIgCIIgCMIDgBjl95JgkH+CIAiCIAiCIAiCcAAkfF0QBEEQBEEQBEEQ+oQY5YIgCIIgCIIgCILQJ8QoFwRBEARBEARBEIQ+IUa5IAiCIAiCIAiCIPQJMcoFQRAEQRAEQRAEoU+IUS4IgiAIgiAIgiAIfUKMckEQBEEQBEEQBEHoE2KUC4IgCIIgCIIgCEKfEKNcEARBEARBEARBEPqEGOWCIAiCIAiCIAiC0CeU1rrfZeiKUuoWgM/6XY5DkAFwu9+FEB4YRF6EwyDyIhwWkRnhMIi8CIdFZEY4DI+yvIxrrbP7felza5Q/aCilfqS1PtvvcggPBiIvwmEQeREOi8iMcBhEXoTDIjIjHAaRl/2R8HVBEARBEARBEARB6BNilAuCIAiCIAiCIAhCnxCj/N7xu/0ugPBAIfIiHAaRF+GwiMwIh0HkRTgsIjPCYRB52QdZUy4IgiAIgiAIgiAIfUJmygVBEARBEARBEAShT4hRfpcopX5GKfWhUuqiUuo3+10e4fOBUuqyUuodpdRbSqkf2cfSSqk/VUp9bP9P2ceVUupf2zL0F0qpx/tbeuEoUEp9XSl1Uyn1ruvYoWVEKfUr9vc/Vkr9Sj/uRbj/9JCXf6SUumbrmbeUUl9xffYPbHn5UCn1Zddx6bMeAZRSo0qpbyul3lNKXVBK/V37uOgYoSt7yIzoGWEXSqmgUuqHSqm3bXn5x/bxSaXUa3bb/3ellN8+HrDfX7Q/n3Cdq6scPWqIUX4XKKU8AP4tgL8M4DiArymljve3VMLniJe01qddW0D8JoA/01rPAvgz+z1A+Zm1/34NwO8ceUmFfvB7AH6m49ihZEQplQbwWwDOAXgKwG+ZQbbw0PF72C0vAPDbtp45rbX+FgDY/dBXAZywf/PvlFIe6bMeKRoA/r7W+jiApwH8ut3WomOEXvSSGUD0jLCbKoAvaK0fA3AawM8opZ4G8C9AeZkBsAbgV+3v/yqANfv4b9vf6ylHR3onnxPEKL87ngJwUWt9SWtdA/ANAD/b5zIJn19+FsDv269/H8DPuY7/J01eBZBUShX6UUDh6NBafxfAasfhw8rIlwH8qdZ6VWu9BuBP0d1wEx5weshLL34WwDe01lWt9acALoL9lfRZjwha6+ta6x/br7cAvA9gGKJjhB7sITO9ED3zCGPriqL91mf/aQBfAPCH9vFOHWN0zx8C+KJSSqG3HD1yiFF+dwwDuOJ6fxV7KzDh0UED+L9KqTeUUr9mHxvUWl+3X98AMGi/FjkSDIeVEZEd4TfscOOvu2YwRV6EHeww0TMAXoPoGOEAdMgMIHpG6IIdGfEWgJugw+4TAOta64b9FXfb78iF/fkGgAGIvOwgRrkg3B/Oa60fB8O3fl0p9YL7Q81tD2TrA6EnIiPCAfgdANNg6OB1AP+yv8URPm8opaIA/geAv6e13nR/JjpG6EYXmRE9I3RFa93UWp8GMALObh/rc5EeaMQovzuuARh1vR+xjwmPOFrra/b/mwC+CSqrZROWbv+/aX9d5EgwHFZGRHYeYbTWy/agqAXg38MJ+RN5EaCU8oHG1X/RWv9P+7DoGKEn3WRG9IywH1rrdQDfBvAMuPTFa3/kbvsdubA/TwBYgcjLDmKU3x2vA5i1Mw36wUQFf9znMgl9RikVUUrFzGsAXwLwLigbJnPtrwD4I/v1HwP4m3b226cBbLjCC4VHi8PKyJ8A+JJSKmWHFH7JPiY8AnTknvh5UM8AlJev2tluJ8HkXT+E9FmPDPZazf8I4H2t9b9yfSQ6RuhKL5kRPSN0QymVVUol7dchAD8N5iH4NoBfsL/WqWOM7vkFAP/PjtbpJUePHN79vyL0QmvdUEr9BthBeQB8XWt9oc/FEvrPIIBvsn+DF8B/1Vr/H6XU6wD+QCn1qwA+A/CL9ve/BeArYHKLbQB/6+iLLBw1Sqn/BuCnAGSUUlfBDMf/HIeQEa31qlLqn4KDIAD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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ell = np.arange(features_train.shape[1])\n", "ax = plot.plot_with_std(ell, features_train[labels_train==0], color='b', label='class 1')\n", "ax = plot.plot_with_std(ell, features_train[labels_train==1], color='r', label='class 2', ax=ax)\n", "ax.legend()\n", "ax.set_title('PSD features');\n", "# plt.xscale('log')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# ell = np.arange(features_train.shape[1])\n", "# ax = plot.plot_with_std(ell, features_validation[labels_validation==0], color='b', label='class 1')\n", "# ax = plot.plot_with_std(ell, features_validation[labels_validation==1], color='r', label='class 2', ax=ax)\n", "# ax.legend()\n", "# ax.set_title('PSD features - validation dataset');\n", "# # plt.xscale('log')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# ell = np.arange(features_train.shape[1])\n", "# ax = plot.plot_with_std(ell, features_test[labels_test==0], color='b', label='class 1')\n", "# ax = plot.plot_with_std(ell, features_test[labels_test==1], color='r', label='class 2', ax=ax)\n", "# ax.legend()\n", "# ax.set_title('PSD features - testing dataset');\n", "# # plt.xscale('log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4 Classification using Deep Sphere\n", "\n", "Let us now classify our data using a spherical convolutional neural network." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.1 Preparation of the dataset\n", "Let us create the datafor the spherical neural network. It is simply the raw data." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/defferra/miniconda3/envs/scnn/lib/python3.6/site-packages/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n", " FutureWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Class 1 VS class 2\n", " Training set: 765 / 771\n", " Validation set: 195 / 189\n" ] } ], "source": [ "ret = experiment_helper.data_preprossing(x_raw_train, labels_raw_train, x_raw_test, sigma_noise, feature_type=None, train_size=0.8)\n", "features_train, labels_train, features_validation, labels_validation, features_test = ret" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The spherical neural network will uses a Dataset object that need to be initialized. The object `LabeledDatasetWithNoise` will add noise to the raw data at the time of training. It will slowly increase the amount of noise during `nit` iteration." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "training = LabeledDatasetWithNoise(features_train, labels_train, end_level=sigma_noise)\n", "validation = LabeledDataset(features_validation, labels_validation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Building the Network\n", "\n", "We now create our spherical neural network. We use one architecture, a fully convolutional architecture (see the exact parameters in `hyperparameters.py`), for all the problems (that is for all configurations of `order` and `sigma_noise`. A smaller `order` means more pixels per sample, that is more data for a prediction. It translates to higher accuracy as the network is more confident about its prediction (as they are averaged across spatial locations).\n", "\n", "For the paper, we selected a conservative set of parameters that were providing good results across the board. To train faster, diminish `num_epochs`, or interrupt training whenever you get bored. To reproduce all the results from the paper, the easiest is to run the `experiments_deepsphere.py` script." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "ntype = 'FCN'\n", "EXP_NAME = '40sim_{}sides_{:0.1f}noise_{}order_{}sigma_{}'.format(Nside, sigma_noise, order, sigma, ntype)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#sides: [1024, 512, 256, 128, 64, 32, 32]\n", "#pixels: [262144, 65536, 16384, 4096, 1024, 256, 256]\n", "#samples per batch: 64\n", "=> #pixels per batch (input): 16,777,216\n", "=> #pixels for training (input): 32,212,254,720\n", "Learning rate will start at 2.0e-04 and finish at 2.9e-05.\n", "NN architecture\n", " input: M_0 = 262144\n", " layer 1: cgconv1\n", " representation: M_0 * F_1 / p_1 = 262144 * 16 / 4 = 1048576\n", " weights: F_0 * F_1 * K_1 = 1 * 16 * 5 = 80\n", " biases: F_1 = 16\n", " batch normalization\n", " layer 2: cgconv2\n", " representation: M_1 * F_2 / p_2 = 65536 * 32 / 4 = 524288\n", " weights: F_1 * F_2 * K_2 = 16 * 32 * 5 = 2560\n", " biases: F_2 = 32\n", " batch normalization\n", " layer 3: cgconv3\n", " representation: M_2 * F_3 / p_3 = 16384 * 64 / 4 = 262144\n", " weights: F_2 * F_3 * K_3 = 32 * 64 * 5 = 10240\n", " biases: F_3 = 64\n", " batch normalization\n", " layer 4: cgconv4\n", " representation: M_3 * F_4 / p_4 = 4096 * 64 / 4 = 65536\n", " weights: F_3 * F_4 * K_4 = 64 * 64 * 5 = 20480\n", " biases: F_4 = 64\n", " batch normalization\n", " layer 5: cgconv5\n", " representation: M_4 * F_5 / p_5 = 1024 * 64 / 4 = 16384\n", " weights: F_4 * F_5 * K_5 = 64 * 64 * 5 = 20480\n", " biases: F_5 = 64\n", " batch normalization\n", " layer 6: cgconv6\n", " representation: M_5 * F_6 / p_6 = 256 * 2 / 1 = 512\n", " weights: F_5 * F_6 * K_6 = 64 * 2 * 5 = 640\n", " batch normalization\n", " Statistical layer: mean\n", " representation: 1 * 2 = 2\n" ] } ], "source": [ "params = hyperparameters.get_params(training.N, EXP_NAME, order, Nside, ntype)\n", "# params['profile'] = True # See computation time and memory usage in Tensorboard.\n", "# params['debug'] = True # Debug the model in Tensorboard.\n", "model = models.deepsphere(**params)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# Cleanup before running again.\n", "shutil.rmtree('summaries/{}/'.format(EXP_NAME), ignore_errors=True)\n", "shutil.rmtree('checkpoints/{}/'.format(EXP_NAME), ignore_errors=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.3 Find an optimal learning rate (optional)\n", "\n", "The learning rate is the most important hyper-parameter. A technique to find an optimal value is to visualize the validation loss while increasing the learning rate. One way to define the optimal learning rate is to search for the largest value looking for which the validation loss still decreases." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "# backup = params.copy()\n", "# \n", "# params, learning_rate = utils.test_learning_rates(params, training.N, 1e-6, 1e-1, num_epochs=20)\n", "# \n", "# shutil.rmtree('summaries/{}/'.format(params['dir_name']), ignore_errors=True)\n", "# shutil.rmtree('checkpoints/{}/'.format(params['dir_name']), ignore_errors=True)\n", "# \n", "# model = models.deepsphere(**params)\n", "# _, loss_validation, _, _ = model.fit(training, validation)\n", "# \n", "# params.update(backup)\n", "#\n", "# plt.semilogx(learning_rate, loss_validation, '.-')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.4 Training the network\n", "\n", "Here are a few remarks.\n", "* The model will create tensorboard summaries in the `summaries` folder. Start tensorboard with `cd summaries` then `tensorboard --logdir .`, and open in a browser tab to visualize training progress and statistics about the learned parameters. You can debug the model by setting `params['debug'] = True` and launching tensorboard with `tensorboard --logdir . --debugger_port 6064`.\n", "* You probably need a GPU to train the model in an acceptable amount of time.\n", "* You will get slightly different results every time the network is trained." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "step 9 / 1920 (epoch 0.38 / 80):\n", " learning_rate = 1.97e-04, training loss = 7.15e-01\n", " validation accuracy: 48.18 (185 / 384), f1 (weighted): 32.01, loss: 6.97e-01\n", " CPU time: 33s, wall time: 52s\n", "step 18 / 1920 (epoch 0.75 / 80):\n", " learning_rate = 1.93e-04, training loss = 6.70e-01\n", " validation accuracy: 48.96 (188 / 384), f1 (weighted): 32.35, loss: 7.15e-01\n", " CPU time: 61s, wall time: 100s\n", "step 27 / 1920 (epoch 1.12 / 80):\n", " learning_rate = 1.90e-04, training loss = 6.34e-01\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/defferra/miniconda3/envs/scnn/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " validation accuracy: 49.22 (189 / 384), f1 (weighted): 32.47, loss: 8.39e-01\n", " CPU time: 88s, wall time: 146s\n", "step 36 / 1920 (epoch 1.50 / 80):\n", " learning_rate = 1.86e-04, training loss = 6.20e-01\n", " validation accuracy: 49.22 (189 / 384), f1 (weighted): 32.47, loss: 8.60e-01\n", " CPU time: 115s, wall time: 193s\n", "step 45 / 1920 (epoch 1.88 / 80):\n", " learning_rate = 1.83e-04, training loss = 5.86e-01\n", " validation accuracy: 49.22 (189 / 384), f1 (weighted): 32.47, loss: 1.23e+00\n", " CPU time: 142s, wall time: 239s\n", "step 54 / 1920 (epoch 2.25 / 80):\n", " learning_rate = 1.80e-04, training loss = 5.66e-01\n", " validation accuracy: 49.22 (189 / 384), f1 (weighted): 32.47, loss: 9.67e-01\n", " CPU time: 168s, wall time: 286s\n", "step 63 / 1920 (epoch 2.62 / 80):\n", " learning_rate = 1.76e-04, training loss = 5.00e-01\n", " validation accuracy: 49.22 (189 / 384), f1 (weighted): 32.47, loss: 8.74e-01\n", " CPU time: 196s, wall time: 332s\n", "step 72 / 1920 (epoch 3.00 / 80):\n", " learning_rate = 1.73e-04, training loss = 4.25e-01\n", " validation accuracy: 49.74 (191 / 384), f1 (weighted): 33.61, loss: 7.30e-01\n", " CPU time: 222s, wall time: 379s\n", "step 81 / 1920 (epoch 3.38 / 80):\n", " learning_rate = 1.70e-04, training loss = 4.02e-01\n", " validation accuracy: 54.69 (210 / 384), f1 (weighted): 42.35, loss: 6.64e-01\n", " CPU time: 249s, wall time: 425s\n", "step 90 / 1920 (epoch 3.75 / 80):\n", " learning_rate = 1.67e-04, training loss = 3.00e-01\n", " validation accuracy: 51.30 (197 / 384), f1 (weighted): 35.35, loss: 1.67e+00\n", " CPU time: 275s, wall time: 472s\n", "step 99 / 1920 (epoch 4.12 / 80):\n", " learning_rate = 1.64e-04, training loss = 3.13e-01\n", " validation accuracy: 51.56 (198 / 384), f1 (weighted): 35.92, loss: 1.30e+00\n", " CPU time: 301s, wall time: 518s\n", "step 108 / 1920 (epoch 4.50 / 80):\n", " learning_rate = 1.61e-04, training loss = 3.14e-01\n", " validation accuracy: 51.56 (198 / 384), f1 (weighted): 35.92, loss: 1.10e+00\n", " CPU time: 327s, wall time: 564s\n", "step 117 / 1920 (epoch 4.88 / 80):\n", " learning_rate = 1.58e-04, training loss = 2.87e-01\n", " validation accuracy: 59.64 (229 / 384), f1 (weighted): 51.35, loss: 6.14e-01\n", " CPU time: 354s, wall time: 610s\n", "step 126 / 1920 (epoch 5.25 / 80):\n", " learning_rate = 1.56e-04, training loss = 2.68e-01\n", " validation accuracy: 66.67 (256 / 384), f1 (weighted): 62.25, loss: 5.69e-01\n", " CPU time: 380s, wall time: 656s\n", "step 135 / 1920 (epoch 5.62 / 80):\n", " learning_rate = 1.53e-04, training loss = 2.38e-01\n", " validation accuracy: 69.53 (267 / 384), f1 (weighted): 66.22, loss: 4.81e-01\n", " CPU time: 406s, wall time: 702s\n", "step 144 / 1920 (epoch 6.00 / 80):\n", " learning_rate = 1.50e-04, training loss = 2.32e-01\n", " validation accuracy: 72.92 (280 / 384), f1 (weighted): 70.63, loss: 4.23e-01\n", " CPU time: 432s, wall time: 748s\n", "step 153 / 1920 (epoch 6.38 / 80):\n", " learning_rate = 1.47e-04, training loss = 2.40e-01\n", " validation accuracy: 93.49 (359 / 384), f1 (weighted): 93.46, loss: 2.43e-01\n", " CPU time: 458s, wall time: 794s\n", "step 162 / 1920 (epoch 6.75 / 80):\n", " learning_rate = 1.45e-04, training loss = 1.91e-01\n", " validation accuracy: 69.79 (268 / 384), f1 (weighted): 66.57, loss: 4.88e-01\n", " CPU time: 484s, wall time: 840s\n", "step 171 / 1920 (epoch 7.12 / 80):\n", " learning_rate = 1.42e-04, training loss = 2.27e-01\n", " validation accuracy: 92.45 (355 / 384), f1 (weighted): 92.40, loss: 2.33e-01\n", " CPU time: 510s, wall time: 886s\n", "step 180 / 1920 (epoch 7.50 / 80):\n", " learning_rate = 1.40e-04, training loss = 2.14e-01\n", " validation accuracy: 91.93 (353 / 384), f1 (weighted): 91.86, loss: 2.31e-01\n", " CPU time: 535s, wall time: 932s\n", "step 189 / 1920 (epoch 7.88 / 80):\n", " learning_rate = 1.37e-04, training loss = 1.80e-01\n", " validation accuracy: 69.79 (268 / 384), f1 (weighted): 66.57, loss: 4.82e-01\n", " CPU time: 561s, wall time: 978s\n", "step 198 / 1920 (epoch 8.25 / 80):\n", " learning_rate = 1.35e-04, training loss = 2.21e-01\n", " validation accuracy: 91.67 (352 / 384), f1 (weighted): 91.61, loss: 2.15e-01\n", " CPU time: 587s, wall time: 1024s\n", "step 207 / 1920 (epoch 8.62 / 80):\n", " learning_rate = 1.32e-04, training loss = 1.84e-01\n", " validation accuracy: 94.01 (361 / 384), f1 (weighted): 93.99, loss: 2.03e-01\n", " CPU time: 613s, wall time: 1069s\n", "step 216 / 1920 (epoch 9.00 / 80):\n", " learning_rate = 1.30e-04, training loss = 1.65e-01\n", " validation accuracy: 93.49 (359 / 384), f1 (weighted): 93.46, loss: 2.03e-01\n", " CPU time: 638s, wall time: 1115s\n", "step 225 / 1920 (epoch 9.38 / 80):\n", " learning_rate = 1.28e-04, training loss = 1.48e-01\n", " validation accuracy: 95.05 (365 / 384), f1 (weighted): 95.05, loss: 1.80e-01\n", " CPU time: 664s, wall time: 1161s\n", "step 234 / 1920 (epoch 9.75 / 80):\n", " learning_rate = 1.25e-04, training loss = 1.22e-01\n", " validation accuracy: 97.92 (376 / 384), f1 (weighted): 97.92, loss: 1.44e-01\n", " CPU time: 691s, wall time: 1207s\n", "step 243 / 1920 (epoch 10.12 / 80):\n", " learning_rate = 1.23e-04, training loss = 1.77e-01\n", " validation accuracy: 97.40 (374 / 384), f1 (weighted): 97.39, loss: 1.41e-01\n", " CPU time: 716s, wall time: 1253s\n", "step 252 / 1920 (epoch 10.50 / 80):\n", " learning_rate = 1.21e-04, training loss = 1.28e-01\n", " validation accuracy: 89.84 (345 / 384), f1 (weighted): 89.75, loss: 2.37e-01\n", " CPU time: 742s, wall time: 1299s\n", "step 261 / 1920 (epoch 10.88 / 80):\n", " learning_rate = 1.19e-04, training loss = 1.45e-01\n", " validation accuracy: 94.27 (362 / 384), f1 (weighted): 94.26, loss: 1.77e-01\n", " CPU time: 768s, wall time: 1345s\n", "step 270 / 1920 (epoch 11.25 / 80):\n", " learning_rate = 1.17e-04, training loss = 2.01e-01\n", " validation accuracy: 97.66 (375 / 384), f1 (weighted): 97.66, loss: 1.47e-01\n", " CPU time: 794s, wall time: 1391s\n", "step 279 / 1920 (epoch 11.62 / 80):\n", " learning_rate = 1.15e-04, training loss = 1.65e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 1.26e-01\n", " CPU time: 820s, wall time: 1437s\n", "step 288 / 1920 (epoch 12.00 / 80):\n", " learning_rate = 1.13e-04, training loss = 1.40e-01\n", " validation accuracy: 96.35 (370 / 384), f1 (weighted): 96.35, loss: 1.55e-01\n", " CPU time: 846s, wall time: 1483s\n", "step 297 / 1920 (epoch 12.38 / 80):\n", " learning_rate = 1.11e-04, training loss = 1.15e-01\n", " validation accuracy: 94.79 (364 / 384), f1 (weighted): 94.78, loss: 1.59e-01\n", " CPU time: 872s, wall time: 1529s\n", "step 306 / 1920 (epoch 12.75 / 80):\n", " learning_rate = 1.09e-04, training loss = 7.18e-02\n", " validation accuracy: 97.66 (375 / 384), f1 (weighted): 97.66, loss: 1.23e-01\n", " CPU time: 898s, wall time: 1576s\n", "step 315 / 1920 (epoch 13.12 / 80):\n", " learning_rate = 1.07e-04, training loss = 1.76e-01\n", " validation accuracy: 96.35 (370 / 384), f1 (weighted): 96.35, loss: 1.35e-01\n", " CPU time: 925s, wall time: 1622s\n", "step 324 / 1920 (epoch 13.50 / 80):\n", " learning_rate = 1.05e-04, training loss = 9.73e-02\n", " validation accuracy: 98.18 (377 / 384), f1 (weighted): 98.18, loss: 1.16e-01\n", " CPU time: 951s, wall time: 1668s\n", "step 333 / 1920 (epoch 13.88 / 80):\n", " learning_rate = 1.03e-04, training loss = 1.37e-01\n", " validation accuracy: 97.92 (376 / 384), f1 (weighted): 97.92, loss: 1.24e-01\n", " CPU time: 977s, wall time: 1714s\n", "step 342 / 1920 (epoch 14.25 / 80):\n", " learning_rate = 1.01e-04, training loss = 1.49e-01\n", " validation accuracy: 94.27 (362 / 384), f1 (weighted): 94.26, loss: 1.61e-01\n", " CPU time: 1003s, wall time: 1761s\n", "step 351 / 1920 (epoch 14.62 / 80):\n", " learning_rate = 9.92e-05, training loss = 1.32e-01\n", " validation accuracy: 97.40 (374 / 384), f1 (weighted): 97.40, loss: 1.20e-01\n", " CPU time: 1029s, wall time: 1807s\n", "step 360 / 1920 (epoch 15.00 / 80):\n", " learning_rate = 9.74e-05, training loss = 1.30e-01\n", " validation accuracy: 98.18 (377 / 384), f1 (weighted): 98.18, loss: 1.15e-01\n", " CPU time: 1055s, wall time: 1853s\n", "step 369 / 1920 (epoch 15.38 / 80):\n", " learning_rate = 9.57e-05, training loss = 1.05e-01\n", " validation accuracy: 97.92 (376 / 384), f1 (weighted): 97.92, loss: 1.22e-01\n", " CPU time: 1081s, wall time: 1899s\n", "step 378 / 1920 (epoch 15.75 / 80):\n", " learning_rate = 9.40e-05, training loss = 1.00e-01\n", " validation accuracy: 88.80 (341 / 384), f1 (weighted): 88.64, loss: 2.28e-01\n", " CPU time: 1107s, wall time: 1945s\n", "step 387 / 1920 (epoch 16.12 / 80):\n", " learning_rate = 9.23e-05, training loss = 1.40e-01\n", " validation accuracy: 97.66 (375 / 384), f1 (weighted): 97.66, loss: 1.07e-01\n", " CPU time: 1133s, wall time: 1991s\n", "step 396 / 1920 (epoch 16.50 / 80):\n", " learning_rate = 9.06e-05, training loss = 1.17e-01\n", " validation accuracy: 97.14 (373 / 384), f1 (weighted): 97.13, loss: 1.19e-01\n", " CPU time: 1160s, wall time: 2037s\n", "step 405 / 1920 (epoch 16.88 / 80):\n", " learning_rate = 8.90e-05, training loss = 1.04e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 9.68e-02\n", " CPU time: 1186s, wall time: 2083s\n", "step 414 / 1920 (epoch 17.25 / 80):\n", " learning_rate = 8.74e-05, training loss = 1.48e-01\n", " validation accuracy: 98.18 (377 / 384), f1 (weighted): 98.18, loss: 1.05e-01\n", " CPU time: 1212s, wall time: 2130s\n", "step 423 / 1920 (epoch 17.62 / 80):\n", " learning_rate = 8.59e-05, training loss = 1.29e-01\n", " validation accuracy: 97.92 (376 / 384), f1 (weighted): 97.92, loss: 1.07e-01\n", " CPU time: 1238s, wall time: 2176s\n", "step 432 / 1920 (epoch 18.00 / 80):\n", " learning_rate = 8.43e-05, training loss = 7.71e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 1.08e-01\n", " CPU time: 1264s, wall time: 2221s\n", "step 441 / 1920 (epoch 18.38 / 80):\n", " learning_rate = 8.28e-05, training loss = 1.30e-01\n", " validation accuracy: 97.92 (376 / 384), f1 (weighted): 97.92, loss: 1.08e-01\n", " CPU time: 1289s, wall time: 2267s\n", "step 450 / 1920 (epoch 18.75 / 80):\n", " learning_rate = 8.14e-05, training loss = 1.03e-01\n", " validation accuracy: 98.18 (377 / 384), f1 (weighted): 98.18, loss: 9.14e-02\n", " CPU time: 1315s, wall time: 2313s\n", "step 459 / 1920 (epoch 19.12 / 80):\n", " learning_rate = 7.99e-05, training loss = 1.30e-01\n", " validation accuracy: 96.88 (372 / 384), f1 (weighted): 96.87, loss: 1.16e-01\n", " CPU time: 1340s, wall time: 2358s\n", "step 468 / 1920 (epoch 19.50 / 80):\n", " learning_rate = 7.85e-05, training loss = 1.10e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 9.45e-02\n", " CPU time: 1366s, wall time: 2404s\n", "step 477 / 1920 (epoch 19.88 / 80):\n", " learning_rate = 7.71e-05, training loss = 8.65e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 9.23e-02\n", " CPU time: 1392s, wall time: 2450s\n", "step 486 / 1920 (epoch 20.25 / 80):\n", " learning_rate = 7.57e-05, training loss = 1.36e-01\n", " validation accuracy: 98.18 (377 / 384), f1 (weighted): 98.18, loss: 9.06e-02\n", " CPU time: 1418s, wall time: 2496s\n", "step 495 / 1920 (epoch 20.62 / 80):\n", " learning_rate = 7.44e-05, training loss = 1.08e-01\n", " validation accuracy: 98.18 (377 / 384), f1 (weighted): 98.18, loss: 9.05e-02\n", " CPU time: 1444s, wall time: 2542s\n", "step 504 / 1920 (epoch 21.00 / 80):\n", " learning_rate = 7.30e-05, training loss = 1.21e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 8.54e-02\n", " CPU time: 1470s, wall time: 2589s\n", "step 513 / 1920 (epoch 21.38 / 80):\n", " learning_rate = 7.17e-05, training loss = 8.05e-02\n", " validation accuracy: 97.66 (375 / 384), f1 (weighted): 97.65, loss: 1.13e-01\n", " CPU time: 1496s, wall time: 2635s\n", "step 522 / 1920 (epoch 21.75 / 80):\n", " learning_rate = 7.04e-05, training loss = 7.54e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 8.68e-02\n", " CPU time: 1522s, wall time: 2681s\n", "step 531 / 1920 (epoch 22.12 / 80):\n", " learning_rate = 6.92e-05, training loss = 1.51e-01\n", " validation accuracy: 97.92 (376 / 384), f1 (weighted): 97.92, loss: 9.25e-02\n", " CPU time: 1548s, wall time: 2727s\n", "step 540 / 1920 (epoch 22.50 / 80):\n", " learning_rate = 6.80e-05, training loss = 7.09e-02\n", " validation accuracy: 97.92 (376 / 384), f1 (weighted): 97.92, loss: 9.57e-02\n", " CPU time: 1574s, wall time: 2773s\n", "step 549 / 1920 (epoch 22.88 / 80):\n", " learning_rate = 6.67e-05, training loss = 1.20e-01\n", " validation accuracy: 98.70 (379 / 384), f1 (weighted): 98.70, loss: 8.36e-02\n", " CPU time: 1600s, wall time: 2819s\n", "step 558 / 1920 (epoch 23.25 / 80):\n", " learning_rate = 6.55e-05, training loss = 1.18e-01\n", " validation accuracy: 96.61 (371 / 384), f1 (weighted): 96.61, loss: 1.13e-01\n", " CPU time: 1626s, wall time: 2865s\n", "step 567 / 1920 (epoch 23.62 / 80):\n", " learning_rate = 6.44e-05, training loss = 9.67e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 9.04e-02\n", " CPU time: 1652s, wall time: 2911s\n", "step 576 / 1920 (epoch 24.00 / 80):\n", " learning_rate = 6.32e-05, training loss = 7.85e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 8.36e-02\n", " CPU time: 1678s, wall time: 2957s\n", "step 585 / 1920 (epoch 24.38 / 80):\n", " learning_rate = 6.21e-05, training loss = 1.12e-01\n", " validation accuracy: 98.70 (379 / 384), f1 (weighted): 98.70, loss: 8.24e-02\n", " CPU time: 1704s, wall time: 3004s\n", "step 594 / 1920 (epoch 24.75 / 80):\n", " learning_rate = 6.10e-05, training loss = 7.88e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 8.94e-02\n", " CPU time: 1731s, wall time: 3050s\n", "step 603 / 1920 (epoch 25.12 / 80):\n", " learning_rate = 5.99e-05, training loss = 1.07e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.96e-02\n", " CPU time: 1757s, wall time: 3096s\n", "step 612 / 1920 (epoch 25.50 / 80):\n", " learning_rate = 5.88e-05, training loss = 1.24e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.92e-02\n", " CPU time: 1783s, wall time: 3142s\n", "step 621 / 1920 (epoch 25.88 / 80):\n", " learning_rate = 5.78e-05, training loss = 1.17e-01\n", " validation accuracy: 97.92 (376 / 384), f1 (weighted): 97.92, loss: 8.83e-02\n", " CPU time: 1808s, wall time: 3188s\n", "step 630 / 1920 (epoch 26.25 / 80):\n", " learning_rate = 5.68e-05, training loss = 1.32e-01\n", " validation accuracy: 96.88 (372 / 384), f1 (weighted): 96.87, loss: 9.79e-02\n", " CPU time: 1834s, wall time: 3234s\n", "step 639 / 1920 (epoch 26.62 / 80):\n", " learning_rate = 5.57e-05, training loss = 8.64e-02\n", " validation accuracy: 98.18 (377 / 384), f1 (weighted): 98.18, loss: 7.85e-02\n", " CPU time: 1861s, wall time: 3280s\n", "step 648 / 1920 (epoch 27.00 / 80):\n", " learning_rate = 5.47e-05, training loss = 7.69e-02\n", " validation accuracy: 97.40 (374 / 384), f1 (weighted): 97.40, loss: 9.27e-02\n", " CPU time: 1887s, wall time: 3326s\n", "step 657 / 1920 (epoch 27.38 / 80):\n", " learning_rate = 5.38e-05, training loss = 1.01e-01\n", " validation accuracy: 97.40 (374 / 384), f1 (weighted): 97.40, loss: 9.36e-02\n", " CPU time: 1913s, wall time: 3373s\n", "step 666 / 1920 (epoch 27.75 / 80):\n", " learning_rate = 5.28e-05, training loss = 8.49e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 8.15e-02\n", " CPU time: 1939s, wall time: 3419s\n", "step 675 / 1920 (epoch 28.12 / 80):\n", " learning_rate = 5.19e-05, training loss = 5.87e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 8.04e-02\n", " CPU time: 1965s, wall time: 3465s\n", "step 684 / 1920 (epoch 28.50 / 80):\n", " learning_rate = 5.09e-05, training loss = 5.77e-02\n", " validation accuracy: 98.18 (377 / 384), f1 (weighted): 98.18, loss: 7.84e-02\n", " CPU time: 1991s, wall time: 3511s\n", "step 693 / 1920 (epoch 28.88 / 80):\n", " learning_rate = 5.00e-05, training loss = 7.09e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.77e-02\n", " CPU time: 2017s, wall time: 3557s\n", "step 702 / 1920 (epoch 29.25 / 80):\n", " learning_rate = 4.91e-05, training loss = 1.09e-01\n", " validation accuracy: 98.18 (377 / 384), f1 (weighted): 98.18, loss: 7.78e-02\n", " CPU time: 2043s, wall time: 3603s\n", "step 711 / 1920 (epoch 29.62 / 80):\n", " learning_rate = 4.83e-05, training loss = 1.16e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.71e-02\n", " CPU time: 2069s, wall time: 3649s\n", "step 720 / 1920 (epoch 30.00 / 80):\n", " learning_rate = 4.74e-05, training loss = 1.01e-01\n", " validation accuracy: 98.18 (377 / 384), f1 (weighted): 98.18, loss: 7.78e-02\n", " CPU time: 2094s, wall time: 3695s\n", "step 729 / 1920 (epoch 30.38 / 80):\n", " learning_rate = 4.66e-05, training loss = 1.31e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.34e-02\n", " CPU time: 2120s, wall time: 3741s\n", "step 738 / 1920 (epoch 30.75 / 80):\n", " learning_rate = 4.57e-05, training loss = 6.79e-02\n", " validation accuracy: 98.18 (377 / 384), f1 (weighted): 98.18, loss: 7.91e-02\n", " CPU time: 2146s, wall time: 3786s\n", "step 747 / 1920 (epoch 31.12 / 80):\n", " learning_rate = 4.49e-05, training loss = 7.23e-02\n", " validation accuracy: 98.70 (379 / 384), f1 (weighted): 98.70, loss: 7.63e-02\n", " CPU time: 2171s, wall time: 3832s\n", "step 756 / 1920 (epoch 31.50 / 80):\n", " learning_rate = 4.41e-05, training loss = 6.86e-02\n", " validation accuracy: 97.14 (373 / 384), f1 (weighted): 97.14, loss: 9.07e-02\n", " CPU time: 2197s, wall time: 3878s\n", "step 765 / 1920 (epoch 31.88 / 80):\n", " learning_rate = 4.33e-05, training loss = 7.45e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.08e-02\n", " CPU time: 2223s, wall time: 3924s\n", "step 774 / 1920 (epoch 32.25 / 80):\n", " learning_rate = 4.25e-05, training loss = 1.17e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.07e-02\n", " CPU time: 2249s, wall time: 3970s\n", "step 783 / 1920 (epoch 32.62 / 80):\n", " learning_rate = 4.18e-05, training loss = 7.08e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.20e-02\n", " CPU time: 2275s, wall time: 4016s\n", "step 792 / 1920 (epoch 33.00 / 80):\n", " learning_rate = 4.10e-05, training loss = 1.03e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.32e-02\n", " CPU time: 2301s, wall time: 4063s\n", "step 801 / 1920 (epoch 33.38 / 80):\n", " learning_rate = 4.03e-05, training loss = 6.00e-02\n", " validation accuracy: 98.70 (379 / 384), f1 (weighted): 98.70, loss: 7.81e-02\n", " CPU time: 2328s, wall time: 4109s\n", "step 810 / 1920 (epoch 33.75 / 80):\n", " learning_rate = 3.96e-05, training loss = 6.94e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.28e-02\n", " CPU time: 2354s, wall time: 4155s\n", "step 819 / 1920 (epoch 34.12 / 80):\n", " learning_rate = 3.89e-05, training loss = 6.96e-02\n", " validation accuracy: 97.66 (375 / 384), f1 (weighted): 97.65, loss: 9.24e-02\n", " CPU time: 2380s, wall time: 4201s\n", "step 828 / 1920 (epoch 34.50 / 80):\n", " learning_rate = 3.82e-05, training loss = 7.22e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.90e-02\n", " CPU time: 2406s, wall time: 4247s\n", "step 837 / 1920 (epoch 34.88 / 80):\n", " learning_rate = 3.75e-05, training loss = 6.32e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.11e-02\n", " CPU time: 2432s, wall time: 4294s\n", "step 846 / 1920 (epoch 35.25 / 80):\n", " learning_rate = 3.68e-05, training loss = 1.13e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.91e-02\n", " CPU time: 2458s, wall time: 4339s\n", "step 855 / 1920 (epoch 35.62 / 80):\n", " learning_rate = 3.62e-05, training loss = 1.40e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.98e-02\n", " CPU time: 2484s, wall time: 4385s\n", "step 864 / 1920 (epoch 36.00 / 80):\n", " learning_rate = 3.55e-05, training loss = 9.87e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.03e-02\n", " CPU time: 2509s, wall time: 4431s\n", "step 873 / 1920 (epoch 36.38 / 80):\n", " learning_rate = 3.49e-05, training loss = 7.18e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.68e-02\n", " CPU time: 2535s, wall time: 4477s\n", "step 882 / 1920 (epoch 36.75 / 80):\n", " learning_rate = 3.43e-05, training loss = 4.09e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.73e-02\n", " CPU time: 2561s, wall time: 4523s\n", "step 891 / 1920 (epoch 37.12 / 80):\n", " learning_rate = 3.37e-05, training loss = 8.88e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.30e-02\n", " CPU time: 2587s, wall time: 4569s\n", "step 900 / 1920 (epoch 37.50 / 80):\n", " learning_rate = 3.31e-05, training loss = 7.56e-02\n", " validation accuracy: 97.66 (375 / 384), f1 (weighted): 97.66, loss: 8.62e-02\n", " CPU time: 2613s, wall time: 4615s\n", "step 909 / 1920 (epoch 37.88 / 80):\n", " learning_rate = 3.25e-05, training loss = 1.14e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.23e-02\n", " CPU time: 2638s, wall time: 4660s\n", "step 918 / 1920 (epoch 38.25 / 80):\n", " learning_rate = 3.19e-05, training loss = 8.43e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.69e-02\n", " CPU time: 2664s, wall time: 4706s\n", "step 927 / 1920 (epoch 38.62 / 80):\n", " learning_rate = 3.13e-05, training loss = 1.68e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.95e-02\n", " CPU time: 2690s, wall time: 4752s\n", "step 936 / 1920 (epoch 39.00 / 80):\n", " learning_rate = 3.08e-05, training loss = 9.05e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.81e-02\n", " CPU time: 2716s, wall time: 4798s\n", "step 945 / 1920 (epoch 39.38 / 80):\n", " learning_rate = 3.02e-05, training loss = 1.00e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.82e-02\n", " CPU time: 2742s, wall time: 4844s\n", "step 954 / 1920 (epoch 39.75 / 80):\n", " learning_rate = 2.97e-05, training loss = 7.78e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 7.54e-02\n", " CPU time: 2768s, wall time: 4890s\n", "step 963 / 1920 (epoch 40.12 / 80):\n", " learning_rate = 2.91e-05, training loss = 6.80e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.64e-02\n", " CPU time: 2794s, wall time: 4936s\n", "step 972 / 1920 (epoch 40.50 / 80):\n", " learning_rate = 2.86e-05, training loss = 1.00e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.57e-02\n", " CPU time: 2820s, wall time: 4982s\n", "step 981 / 1920 (epoch 40.88 / 80):\n", " learning_rate = 2.81e-05, training loss = 6.67e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.56e-02\n", " CPU time: 2846s, wall time: 5028s\n", "step 990 / 1920 (epoch 41.25 / 80):\n", " learning_rate = 2.76e-05, training loss = 6.90e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.44e-02\n", " CPU time: 2872s, wall time: 5075s\n", "step 999 / 1920 (epoch 41.62 / 80):\n", " learning_rate = 2.71e-05, training loss = 7.83e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.69e-02\n", " CPU time: 2898s, wall time: 5121s\n", "step 1008 / 1920 (epoch 42.00 / 80):\n", " learning_rate = 2.66e-05, training loss = 9.80e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.53e-02\n", " CPU time: 2923s, wall time: 5167s\n", "step 1017 / 1920 (epoch 42.38 / 80):\n", " learning_rate = 2.62e-05, training loss = 4.13e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.42e-02\n", " CPU time: 2950s, wall time: 5213s\n", "step 1026 / 1920 (epoch 42.75 / 80):\n", " learning_rate = 2.57e-05, training loss = 6.28e-02\n", " validation accuracy: 98.96 (380 / 384), f1 (weighted): 98.96, loss: 7.68e-02\n", " CPU time: 2976s, wall time: 5259s\n", "step 1035 / 1920 (epoch 43.12 / 80):\n", " learning_rate = 2.52e-05, training loss = 5.58e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.58e-02\n", " CPU time: 3002s, wall time: 5305s\n", "step 1044 / 1920 (epoch 43.50 / 80):\n", " learning_rate = 2.48e-05, training loss = 5.78e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.64e-02\n", " CPU time: 3028s, wall time: 5351s\n", "step 1053 / 1920 (epoch 43.88 / 80):\n", " learning_rate = 2.43e-05, training loss = 6.29e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.37e-02\n", " CPU time: 3054s, wall time: 5397s\n", "step 1062 / 1920 (epoch 44.25 / 80):\n", " learning_rate = 2.39e-05, training loss = 9.03e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.45e-02\n", " CPU time: 3080s, wall time: 5444s\n", "step 1071 / 1920 (epoch 44.62 / 80):\n", " learning_rate = 2.35e-05, training loss = 1.38e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.67e-02\n", " CPU time: 3106s, wall time: 5489s\n", "step 1080 / 1920 (epoch 45.00 / 80):\n", " learning_rate = 2.31e-05, training loss = 1.33e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.38e-02\n", " CPU time: 3132s, wall time: 5535s\n", "step 1089 / 1920 (epoch 45.38 / 80):\n", " learning_rate = 2.27e-05, training loss = 7.19e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.34e-02\n", " CPU time: 3158s, wall time: 5581s\n", "step 1098 / 1920 (epoch 45.75 / 80):\n", " learning_rate = 2.22e-05, training loss = 5.98e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.46e-02\n", " CPU time: 3184s, wall time: 5628s\n", "step 1107 / 1920 (epoch 46.12 / 80):\n", " learning_rate = 2.19e-05, training loss = 5.49e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.45e-02\n", " CPU time: 3210s, wall time: 5674s\n", "step 1116 / 1920 (epoch 46.50 / 80):\n", " learning_rate = 2.15e-05, training loss = 6.90e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.73e-02\n", " CPU time: 3236s, wall time: 5720s\n", "step 1125 / 1920 (epoch 46.88 / 80):\n", " learning_rate = 2.11e-05, training loss = 8.96e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.43e-02\n", " CPU time: 3262s, wall time: 5766s\n", "step 1134 / 1920 (epoch 47.25 / 80):\n", " learning_rate = 2.07e-05, training loss = 1.56e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.49e-02\n", " CPU time: 3289s, wall time: 5813s\n", "step 1143 / 1920 (epoch 47.62 / 80):\n", " learning_rate = 2.03e-05, training loss = 4.31e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.86e-02\n", " CPU time: 3315s, wall time: 5859s\n", "step 1152 / 1920 (epoch 48.00 / 80):\n", " learning_rate = 2.00e-05, training loss = 5.34e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.31e-02\n", " CPU time: 3342s, wall time: 5905s\n", "step 1161 / 1920 (epoch 48.38 / 80):\n", " learning_rate = 1.96e-05, training loss = 4.06e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.33e-02\n", " CPU time: 3368s, wall time: 5952s\n", "step 1170 / 1920 (epoch 48.75 / 80):\n", " learning_rate = 1.93e-05, training loss = 6.35e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.25e-02\n", " CPU time: 3394s, wall time: 5998s\n", "step 1179 / 1920 (epoch 49.12 / 80):\n", " learning_rate = 1.89e-05, training loss = 6.65e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.42e-02\n", " CPU time: 3420s, wall time: 6044s\n", "step 1188 / 1920 (epoch 49.50 / 80):\n", " learning_rate = 1.86e-05, training loss = 7.93e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.27e-02\n", " CPU time: 3446s, wall time: 6090s\n", "step 1197 / 1920 (epoch 49.88 / 80):\n", " learning_rate = 1.82e-05, training loss = 7.74e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.25e-02\n", " CPU time: 3472s, wall time: 6136s\n", "step 1206 / 1920 (epoch 50.25 / 80):\n", " learning_rate = 1.79e-05, training loss = 7.50e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.33e-02\n", " CPU time: 3498s, wall time: 6182s\n", "step 1215 / 1920 (epoch 50.62 / 80):\n", " learning_rate = 1.76e-05, training loss = 6.42e-02\n", " validation accuracy: 98.70 (379 / 384), f1 (weighted): 98.70, loss: 6.65e-02\n", " CPU time: 3525s, wall time: 6229s\n", "step 1224 / 1920 (epoch 51.00 / 80):\n", " learning_rate = 1.73e-05, training loss = 6.12e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.48e-02\n", " CPU time: 3551s, wall time: 6275s\n", "step 1233 / 1920 (epoch 51.38 / 80):\n", " learning_rate = 1.70e-05, training loss = 1.00e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.28e-02\n", " CPU time: 3577s, wall time: 6321s\n", "step 1242 / 1920 (epoch 51.75 / 80):\n", " learning_rate = 1.67e-05, training loss = 5.98e-02\n", " validation accuracy: 98.70 (379 / 384), f1 (weighted): 98.70, loss: 6.81e-02\n", " CPU time: 3603s, wall time: 6367s\n", "step 1251 / 1920 (epoch 52.12 / 80):\n", " learning_rate = 1.64e-05, training loss = 5.92e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.45e-02\n", " CPU time: 3629s, wall time: 6413s\n", "step 1260 / 1920 (epoch 52.50 / 80):\n", " learning_rate = 1.61e-05, training loss = 5.63e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.14e-02\n", " CPU time: 3654s, wall time: 6459s\n", "step 1269 / 1920 (epoch 52.88 / 80):\n", " learning_rate = 1.58e-05, training loss = 5.75e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.13e-02\n", " CPU time: 3680s, wall time: 6504s\n", "step 1278 / 1920 (epoch 53.25 / 80):\n", " learning_rate = 1.55e-05, training loss = 8.75e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.35e-02\n", " CPU time: 3706s, wall time: 6550s\n", "step 1287 / 1920 (epoch 53.62 / 80):\n", " learning_rate = 1.52e-05, training loss = 5.56e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.74e-02\n", " CPU time: 3731s, wall time: 6596s\n", "step 1296 / 1920 (epoch 54.00 / 80):\n", " learning_rate = 1.50e-05, training loss = 1.07e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.29e-02\n", " CPU time: 3757s, wall time: 6642s\n", "step 1305 / 1920 (epoch 54.38 / 80):\n", " learning_rate = 1.47e-05, training loss = 7.64e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.25e-02\n", " CPU time: 3782s, wall time: 6687s\n", "step 1314 / 1920 (epoch 54.75 / 80):\n", " learning_rate = 1.44e-05, training loss = 2.61e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.16e-02\n", " CPU time: 3808s, wall time: 6733s\n", "step 1323 / 1920 (epoch 55.12 / 80):\n", " learning_rate = 1.42e-05, training loss = 1.48e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.13e-02\n", " CPU time: 3834s, wall time: 6779s\n", "step 1332 / 1920 (epoch 55.50 / 80):\n", " learning_rate = 1.39e-05, training loss = 1.12e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.18e-02\n", " CPU time: 3860s, wall time: 6825s\n", "step 1341 / 1920 (epoch 55.88 / 80):\n", " learning_rate = 1.37e-05, training loss = 1.02e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.07e-02\n", " CPU time: 3886s, wall time: 6871s\n", "step 1350 / 1920 (epoch 56.25 / 80):\n", " learning_rate = 1.34e-05, training loss = 6.99e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.10e-02\n", " CPU time: 3911s, wall time: 6917s\n", "step 1359 / 1920 (epoch 56.62 / 80):\n", " learning_rate = 1.32e-05, training loss = 7.60e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.02e-02\n", " CPU time: 3938s, wall time: 6963s\n", "step 1368 / 1920 (epoch 57.00 / 80):\n", " learning_rate = 1.30e-05, training loss = 8.83e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.11e-02\n", " CPU time: 3964s, wall time: 7009s\n", "step 1377 / 1920 (epoch 57.38 / 80):\n", " learning_rate = 1.27e-05, training loss = 6.36e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.30e-02\n", " CPU time: 3990s, wall time: 7055s\n", "step 1386 / 1920 (epoch 57.75 / 80):\n", " learning_rate = 1.25e-05, training loss = 5.36e-02\n", " validation accuracy: 98.70 (379 / 384), f1 (weighted): 98.70, loss: 6.53e-02\n", " CPU time: 4015s, wall time: 7101s\n", "step 1395 / 1920 (epoch 58.12 / 80):\n", " learning_rate = 1.23e-05, training loss = 8.28e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.02e-02\n", " CPU time: 4041s, wall time: 7147s\n", "step 1404 / 1920 (epoch 58.50 / 80):\n", " learning_rate = 1.21e-05, training loss = 7.91e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.11e-02\n", " CPU time: 4067s, wall time: 7193s\n", "step 1413 / 1920 (epoch 58.88 / 80):\n", " learning_rate = 1.18e-05, training loss = 4.83e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.21e-02\n", " CPU time: 4094s, wall time: 7240s\n", "step 1422 / 1920 (epoch 59.25 / 80):\n", " learning_rate = 1.16e-05, training loss = 1.28e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.96e-02\n", " CPU time: 4120s, wall time: 7286s\n", "step 1431 / 1920 (epoch 59.62 / 80):\n", " learning_rate = 1.14e-05, training loss = 6.44e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.05e-02\n", " CPU time: 4146s, wall time: 7332s\n", "step 1440 / 1920 (epoch 60.00 / 80):\n", " learning_rate = 1.12e-05, training loss = 1.21e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.03e-02\n", " CPU time: 4172s, wall time: 7378s\n", "step 1449 / 1920 (epoch 60.38 / 80):\n", " learning_rate = 1.10e-05, training loss = 9.79e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.98e-02\n", " CPU time: 4198s, wall time: 7425s\n", "step 1458 / 1920 (epoch 60.75 / 80):\n", " learning_rate = 1.08e-05, training loss = 7.59e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.25e-02\n", " CPU time: 4224s, wall time: 7471s\n", "step 1467 / 1920 (epoch 61.12 / 80):\n", " learning_rate = 1.06e-05, training loss = 1.45e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.07e-02\n", " CPU time: 4250s, wall time: 7517s\n", "step 1476 / 1920 (epoch 61.50 / 80):\n", " learning_rate = 1.04e-05, training loss = 8.53e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.04e-02\n", " CPU time: 4276s, wall time: 7563s\n", "step 1485 / 1920 (epoch 61.88 / 80):\n", " learning_rate = 1.03e-05, training loss = 7.03e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.97e-02\n", " CPU time: 4302s, wall time: 7608s\n", "step 1494 / 1920 (epoch 62.25 / 80):\n", " learning_rate = 1.01e-05, training loss = 9.81e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.00e-02\n", " CPU time: 4327s, wall time: 7654s\n", "step 1503 / 1920 (epoch 62.62 / 80):\n", " learning_rate = 9.89e-06, training loss = 8.56e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.03e-02\n", " CPU time: 4354s, wall time: 7701s\n", "step 1512 / 1920 (epoch 63.00 / 80):\n", " learning_rate = 9.72e-06, training loss = 6.64e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.09e-02\n", " CPU time: 4379s, wall time: 7746s\n", "step 1521 / 1920 (epoch 63.38 / 80):\n", " learning_rate = 9.54e-06, training loss = 6.75e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.20e-02\n", " CPU time: 4405s, wall time: 7792s\n", "step 1530 / 1920 (epoch 63.75 / 80):\n", " learning_rate = 9.37e-06, training loss = 9.36e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.13e-02\n", " CPU time: 4431s, wall time: 7838s\n", "step 1539 / 1920 (epoch 64.12 / 80):\n", " learning_rate = 9.21e-06, training loss = 7.28e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.00e-02\n", " CPU time: 4457s, wall time: 7885s\n", "step 1548 / 1920 (epoch 64.50 / 80):\n", " learning_rate = 9.04e-06, training loss = 7.60e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.98e-02\n", " CPU time: 4484s, wall time: 7931s\n", "step 1557 / 1920 (epoch 64.88 / 80):\n", " learning_rate = 8.88e-06, training loss = 7.12e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.93e-02\n", " CPU time: 4510s, wall time: 7977s\n", "step 1566 / 1920 (epoch 65.25 / 80):\n", " learning_rate = 8.72e-06, training loss = 7.64e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.92e-02\n", " CPU time: 4536s, wall time: 8023s\n", "step 1575 / 1920 (epoch 65.62 / 80):\n", " learning_rate = 8.57e-06, training loss = 2.81e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.96e-02\n", " CPU time: 4562s, wall time: 8070s\n", "step 1584 / 1920 (epoch 66.00 / 80):\n", " learning_rate = 8.41e-06, training loss = 8.04e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.10e-02\n", " CPU time: 4588s, wall time: 8115s\n", "step 1593 / 1920 (epoch 66.38 / 80):\n", " learning_rate = 8.26e-06, training loss = 1.01e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.14e-02\n", " CPU time: 4614s, wall time: 8161s\n", "step 1602 / 1920 (epoch 66.75 / 80):\n", " learning_rate = 8.12e-06, training loss = 5.86e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.91e-02\n", " CPU time: 4640s, wall time: 8207s\n", "step 1611 / 1920 (epoch 67.12 / 80):\n", " learning_rate = 7.97e-06, training loss = 8.23e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.00e-02\n", " CPU time: 4665s, wall time: 8253s\n", "step 1620 / 1920 (epoch 67.50 / 80):\n", " learning_rate = 7.83e-06, training loss = 4.46e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.92e-02\n", " CPU time: 4692s, wall time: 8299s\n", "step 1629 / 1920 (epoch 67.88 / 80):\n", " learning_rate = 7.69e-06, training loss = 4.42e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.88e-02\n", " CPU time: 4718s, wall time: 8346s\n", "step 1638 / 1920 (epoch 68.25 / 80):\n", " learning_rate = 7.55e-06, training loss = 1.07e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.90e-02\n", " CPU time: 4744s, wall time: 8392s\n", "step 1647 / 1920 (epoch 68.62 / 80):\n", " learning_rate = 7.42e-06, training loss = 4.77e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.02e-02\n", " CPU time: 4771s, wall time: 8439s\n", "step 1656 / 1920 (epoch 69.00 / 80):\n", " learning_rate = 7.28e-06, training loss = 1.05e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.87e-02\n", " CPU time: 4797s, wall time: 8485s\n", "step 1665 / 1920 (epoch 69.38 / 80):\n", " learning_rate = 7.15e-06, training loss = 1.29e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.85e-02\n", " CPU time: 4823s, wall time: 8531s\n", "step 1674 / 1920 (epoch 69.75 / 80):\n", " learning_rate = 7.03e-06, training loss = 6.29e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.13e-02\n", " CPU time: 4849s, wall time: 8577s\n", "step 1683 / 1920 (epoch 70.12 / 80):\n", " learning_rate = 6.90e-06, training loss = 5.80e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.06e-02\n", " CPU time: 4875s, wall time: 8623s\n", "step 1692 / 1920 (epoch 70.50 / 80):\n", " learning_rate = 6.78e-06, training loss = 1.07e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.88e-02\n", " CPU time: 4902s, wall time: 8670s\n", "step 1701 / 1920 (epoch 70.88 / 80):\n", " learning_rate = 6.66e-06, training loss = 6.38e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.89e-02\n", " CPU time: 4928s, wall time: 8716s\n", "step 1710 / 1920 (epoch 71.25 / 80):\n", " learning_rate = 6.54e-06, training loss = 1.08e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.85e-02\n", " CPU time: 4953s, wall time: 8762s\n", "step 1719 / 1920 (epoch 71.62 / 80):\n", " learning_rate = 6.42e-06, training loss = 7.15e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.83e-02\n", " CPU time: 4979s, wall time: 8808s\n", "step 1728 / 1920 (epoch 72.00 / 80):\n", " learning_rate = 6.31e-06, training loss = 5.41e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.95e-02\n", " CPU time: 5005s, wall time: 8854s\n", "step 1737 / 1920 (epoch 72.38 / 80):\n", " learning_rate = 6.19e-06, training loss = 1.10e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.98e-02\n", " CPU time: 5032s, wall time: 8900s\n", "step 1746 / 1920 (epoch 72.75 / 80):\n", " learning_rate = 6.08e-06, training loss = 4.54e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.81e-02\n", " CPU time: 5058s, wall time: 8946s\n", "step 1755 / 1920 (epoch 73.12 / 80):\n", " learning_rate = 5.98e-06, training loss = 9.96e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.83e-02\n", " CPU time: 5084s, wall time: 8992s\n", "step 1764 / 1920 (epoch 73.50 / 80):\n", " learning_rate = 5.87e-06, training loss = 6.18e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.82e-02\n", " CPU time: 5110s, wall time: 9039s\n", "step 1773 / 1920 (epoch 73.88 / 80):\n", " learning_rate = 5.76e-06, training loss = 7.71e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.86e-02\n", " CPU time: 5136s, wall time: 9085s\n", "step 1782 / 1920 (epoch 74.25 / 80):\n", " learning_rate = 5.66e-06, training loss = 7.80e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.82e-02\n", " CPU time: 5162s, wall time: 9131s\n", "step 1791 / 1920 (epoch 74.62 / 80):\n", " learning_rate = 5.56e-06, training loss = 4.86e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.81e-02\n", " CPU time: 5189s, wall time: 9178s\n", "step 1800 / 1920 (epoch 75.00 / 80):\n", " learning_rate = 5.46e-06, training loss = 6.50e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.85e-02\n", " CPU time: 5214s, wall time: 9223s\n", "step 1809 / 1920 (epoch 75.38 / 80):\n", " learning_rate = 5.36e-06, training loss = 6.64e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.83e-02\n", " CPU time: 5240s, wall time: 9269s\n", "step 1818 / 1920 (epoch 75.75 / 80):\n", " learning_rate = 5.27e-06, training loss = 2.60e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.15e-02\n", " CPU time: 5266s, wall time: 9316s\n", "step 1827 / 1920 (epoch 76.12 / 80):\n", " learning_rate = 5.17e-06, training loss = 5.70e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 6.04e-02\n", " CPU time: 5293s, wall time: 9362s\n", "step 1836 / 1920 (epoch 76.50 / 80):\n", " learning_rate = 5.08e-06, training loss = 7.17e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.93e-02\n", " CPU time: 5319s, wall time: 9408s\n", "step 1845 / 1920 (epoch 76.88 / 80):\n", " learning_rate = 4.99e-06, training loss = 7.91e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.78e-02\n", " CPU time: 5345s, wall time: 9454s\n", "step 1854 / 1920 (epoch 77.25 / 80):\n", " learning_rate = 4.90e-06, training loss = 1.58e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.81e-02\n", " CPU time: 5371s, wall time: 9500s\n", "step 1863 / 1920 (epoch 77.62 / 80):\n", " learning_rate = 4.81e-06, training loss = 4.91e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.83e-02\n", " CPU time: 5397s, wall time: 9546s\n", "step 1872 / 1920 (epoch 78.00 / 80):\n", " learning_rate = 4.73e-06, training loss = 4.18e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.83e-02\n", " CPU time: 5423s, wall time: 9592s\n", "step 1881 / 1920 (epoch 78.38 / 80):\n", " learning_rate = 4.64e-06, training loss = 7.88e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.85e-02\n", " CPU time: 5449s, wall time: 9638s\n", "step 1890 / 1920 (epoch 78.75 / 80):\n", " learning_rate = 4.56e-06, training loss = 2.71e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.78e-02\n", " CPU time: 5475s, wall time: 9684s\n", "step 1899 / 1920 (epoch 79.12 / 80):\n", " learning_rate = 4.48e-06, training loss = 8.61e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.91e-02\n", " CPU time: 5500s, wall time: 9730s\n", "step 1908 / 1920 (epoch 79.50 / 80):\n", " learning_rate = 4.40e-06, training loss = 1.37e-01\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.84e-02\n", " CPU time: 5526s, wall time: 9776s\n", "step 1917 / 1920 (epoch 79.88 / 80):\n", " learning_rate = 4.32e-06, training loss = 5.87e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.80e-02\n", " CPU time: 5552s, wall time: 9822s\n", "step 1920 / 1920 (epoch 80.00 / 80):\n", " learning_rate = 4.29e-06, training loss = 6.98e-02\n", " validation accuracy: 98.44 (378 / 384), f1 (weighted): 98.44, loss: 5.79e-02\n", " CPU time: 5566s, wall time: 9842s\n", "validation accuracy: best = 98.96, mean = 98.44\n" ] } ], "source": [ "accuracy_validation, loss_validation, loss_training, t_step = model.fit(training, validation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see below that the classifier does not overfit the training data." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot.plot_loss(loss_training, loss_validation, t_step, params['eval_frequency'])" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN\n", "INFO:tensorflow:Restoring parameters from /mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN/model-1920\n", "The validation error is 1.56%\n" ] } ], "source": [ "error_validation = experiment_helper.model_error(model, features_validation, labels_validation)\n", "print('The validation error is {:.2%}'.format(error_validation), flush=True)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN\n", "INFO:tensorflow:Restoring parameters from /mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN/model-1920\n", "The testing error is 1.35%\n" ] } ], "source": [ "error_test = experiment_helper.model_error(model, features_test, labels_test)\n", "print('The testing error is {:.2%}'.format(error_test), flush=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5 Filters visualization\n", "\n", "The package offers a few different visualizations for the learned filters. First we can simply look at the Chebyshef coefficients. This visualization is not very interpretable for human, but can help for debugging problems related to optimization." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN\n", "INFO:tensorflow:Restoring parameters from /mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN/model-1920\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "layer=2\n", "ind_in = range(6) # Should be None if layer=1\n", "ind_out = range(4)\n", "model.plot_chebyshev_coeffs(layer, ind_in, ind_out)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe the Chebyshef polynomials, i.e the filters in the graph spectral domain. This visuallization can help to understand wich graph frequencies are picked by the filtering operation. It mostly interpretable by the people for the graph signal processing community." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN\n", "INFO:tensorflow:Restoring parameters from /mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN/model-1920\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.plot_filters_spectral(layer, ind_in, ind_out);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here comes one of the most human friendly representation of the filters. It consists the section of the filters \"projected\" on the sphere. Because of the irregularity of the healpix sampling, this representation of the filters may not look very smooth." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN\n", "INFO:tensorflow:Restoring parameters from /mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN/model-1920\n", "4 6\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib\n", "matplotlib.rcParams.update({'font.size': 16})\n", "model.plot_filters_section(layer, ind_in, ind_out, title='');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Eventually, we can simply look at the filters on sphere. This representation clearly displays the sampling artifacts." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN\n", "INFO:tensorflow:Restoring parameters from /mnt/scratch/lts2/mdeff/deepsphere/deepsphere/../checkpoints/40sim_1024sides_2.0noise_2order_3sigma_FCN/model-1920\n" ] }, { "data": { "image/png": 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