Human Activity Recognition.ipynb

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   "cells": [
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     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from sklearn.decomposition import PCA\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "from sklearn import svm, grid_search, metrics, preprocessing, linear_model, cross_validation\n",
      "from sklearn.learning_curve import validation_curve, learning_curve\n",
      "%matplotlib inline\n",
      "\n",
      "## Utility functions :\n",
      "def plot_PCA(X, y, classes, azimuth, elevation):\n",
      "    \"\"\" X numpy array containing data, \n",
      "    y numpy array containing the labels, \n",
      "    classes a list containing the label names\n",
      "    azimuth, elevation float numbers \"\"\"\n",
      "    nClasses = len(classes)\n",
      "    fig = plt.figure(1, figsize=(10, 8))\n",
      "    plt.clf()\n",
      "    ax = Axes3D(fig, axisbg='white', azim=azimuth, elev=elevation)\n",
      "    pca = PCA(n_components=3)\n",
      "    X_redux = pca.fit_transform(X)\n",
      "    print(pca.explained_variance_ratio_)\n",
      "    print(\"Variability of the compressed dataset %.4f\" %sum(pca.explained_variance_ratio_))\n",
      "    cmap = plt.cm.Paired\n",
      "    colors = np.linspace(0, 1, nClasses)\n",
      "    for n in range(nClasses) :\n",
      "        index = np.ravel(y==(n+1))\n",
      "        ax.scatter(X_redux[index, 0], X_redux[index, 1], X_redux[index, 2], c=cmap(colors[n]), label=classes[n])\n",
      "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
      "    ax.w_xaxis.set_ticklabels([])\n",
      "    ax.w_yaxis.set_ticklabels([])\n",
      "    ax.w_zaxis.set_ticklabels([])\n",
      "    plt.title(\"Data plotted on the three first components\", y=1.03) # y prevent title overlap with axes\n",
      "    plt.show()\n",
      "\n",
      "def plot_validation_scores(train_scores, test_scores, param_range):\n",
      "    train_scores_mean = np.mean(train_scores, axis=1)\n",
      "    train_scores_std = np.std(train_scores, axis=1)\n",
      "    test_scores_mean = np.mean(test_scores, axis=1)\n",
      "    test_scores_std = np.std(test_scores, axis=1)\n",
      "    plt.title(\"Validation Curve\")\n",
      "    plt.xlabel(\"$C$\")\n",
      "    plt.ylabel(\"F1 Score\")\n",
      "    #plt.ylim(0.0, 1.1)\n",
      "    plt.semilogx(param_range, train_scores_mean, label=\"Training score\", color=\"r\", linestyle=\"--\")\n",
      "    plt.fill_between(param_range, train_scores_mean - train_scores_std,\n",
      "                     train_scores_mean + train_scores_std, alpha=0.2, color=\"r\")\n",
      "    plt.semilogx(param_range, test_scores_mean, label=\"Cross-validation score\",\n",
      "                 color=\"g\")\n",
      "    plt.fill_between(param_range, test_scores_mean - test_scores_std,\n",
      "                     test_scores_mean + test_scores_std, alpha=0.2, color=\"g\")\n",
      "    plt.legend(loc='lower right')\n",
      "    plt.show()\n",
      "    \n",
      "def flatten(somelist):\n",
      "    return([item for sublist in somelist for item in sublist])\n",
      "\n",
      "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
      "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
      "    \"\"\"\n",
      "    from sklearn examples :\n",
      "    http://scikit-learn.org/stable/auto_examples/plot_learning_curve.html#example-plot-learning-curve-py\n",
      "    \"\"\"\n",
      "    plt.figure()\n",
      "    plt.title(title)\n",
      "    if ylim is not None:\n",
      "        plt.ylim(*ylim)\n",
      "    plt.xlabel(\"Training examples\")\n",
      "    plt.ylabel(\"Score\")\n",
      "    train_sizes, train_scores, test_scores = learning_curve(\n",
      "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
      "    train_scores_mean = np.mean(train_scores, axis=1)\n",
      "    train_scores_std = np.std(train_scores, axis=1)\n",
      "    test_scores_mean = np.mean(test_scores, axis=1)\n",
      "    test_scores_std = np.std(test_scores, axis=1)\n",
      "    plt.grid()\n",
      "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
      "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
      "                     color=\"r\")\n",
      "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
      "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
      "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
      "             label=\"Training score\")\n",
      "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
      "             label=\"Cross-validation score\")\n",
      "    plt.legend(loc='lower right')\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Human Activity Recognition from smartphone sensors data\n",
      "\n",
      "# Introduction\n",
      "\n",
      "We try to recognize activities of 30 individuals using sensor data gathered from their smartphone. Human activity recognition can be used to measure physical activity of users of a smarphone or some wearable as smartwatches. This may be used to compute how much calories one burns during the day, and to display advices if the user does not take enough exercise to remain healthy.\n",
      "\n",
      "## Dataset\n",
      "We use the Human Activity Recognition Using Smartphones Dataset (Version 1.0) that can be found on [UCI archive](https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones).\n",
      "\n",
      "It is experimental data : 30 volunteers (19-48 y.o.) performed six activities (walking, walking upstairs, walking downstairs, sitting, standing, laying) wearing a smartphone (Samsung Galaxy S II) on the waist. As the smartphone was worn on the waist, there is no rotation issue as there would be in the case the smartphone is \"worn\" in the pocket.\n",
      "\n",
      "Signals come from the accelerometer and the gyroscope of the smartphone (captured at a constant rate of 50Hz). They were filtered to remove noise and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The acceleration signal was separated into body and gravity acceleration (using another filter). \n",
      "Body acceleration (from accelerometer) and angular velocity (from accelrometer) were derived to obtain Jerk signals.\n",
      "Magnitude of the three dimensional signals was calculated using the euclidean norm. Fast Fourier Transform was used to compute frequency domain signals.\n",
      "\n",
      "At the end, we get a set of 17 signals, and for each of them, several features were created :\n",
      "```\n",
      "mean: Mean value\n",
      "std: Standard deviation\n",
      "mad: Median absolute deviation \n",
      "max: Largest value in array\n",
      "min: Smallest value in array\n",
      "sma: Signal magnitude area\n",
      "energy: Energy measure. Sum of the squares divided by the number of values. \n",
      "iqr: Interquartile range \n",
      "entropy: Signal entropy\n",
      "arCoeff: Autorregresion coefficients with Burg order equal to 4\n",
      "correlation: correlation coefficient between two signals\n",
      "maxInds: index of the frequency component with largest magnitude\n",
      "meanFreq: Weighted average of the frequency components to obtain a mean frequency\n",
      "skewness: skewness of the frequency domain signal \n",
      "kurtosis: kurtosis of the frequency domain signal \n",
      "bandsEnergy: Energy of a frequency interval within the 64 bins of the FFT of each window.\n",
      "angle: Angle between to vectors.\n",
      "```\n",
      "\n",
      "Additional vectors obtained by averaging the signals in a signal window sample. These are used on the `angle` variable. There are 561 features on the dataset :"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!wc -l ./data/features.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "     561 ./data/features.txt\r\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!cat data/activity_labels.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 WALKING\r\n",
        "2 WALKING_UPSTAIRS\r\n",
        "3 WALKING_DOWNSTAIRS\r\n",
        "4 SITTING\r\n",
        "5 STANDING\r\n",
        "6 LAYING\r\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "y_train contains one label / line\n",
      "\n",
      "X_train contains the 561 features / line\n",
      "\n",
      "The whole thing should fit in a pandas dataframe as it is 63Mo :"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -lh data/train/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "total 256304\r\n",
        "drwxr-xr-x@ 11 maryanmorel  staff   374B Nov 26 22:20 \u001b[1m\u001b[36mInertial Signals\u001b[m\u001b[m\r\n",
        "-rwxr-xr-x@  1 maryanmorel  staff    63M Nov 26 22:20 \u001b[31mX_train.txt\u001b[m\u001b[m\r\n",
        "-rw-r--r--   1 maryanmorel  staff    62M Dec 11 21:49 X_train_clean.txt\r\n",
        "-rwxr-xr-x@  1 maryanmorel  staff    20K Nov 26 22:20 \u001b[31msubject_train.txt\u001b[m\u001b[m\r\n",
        "-rwxr-xr-x@  1 maryanmorel  staff    14K Nov 26 22:20 \u001b[31my_train.txt\u001b[m\u001b[m\r\n"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!awk -F' ' '{print NF; exit}' data/train/X_train.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "561\r\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "561 columns separated by spaces (' ')\n",
      "\n",
      "Problem : there is one space in front of positive numbers (in order to get neat columns when you look at the text file), it messes up with pandas parser. We use unix command line utilities to replace `'  '` (double spaces) it with `' '` (single space), and remove additional spaces before the first column :"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!sed 's/^..//' data/train/X_train.txt > data/train/X_train_buffer.txt\n",
      "!sed -e 's/  / /g' data/train/X_train_buffer.txt > data/train/X_train_clean.txt\n",
      "!rm data/train/X_train_buffer.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!sed 's/^..//' data/test/X_test.txt > data/test/X_test_buffer.txt\n",
      "!sed -e 's/  / /g' data/test/X_test_buffer.txt > data/test/X_test_clean.txt\n",
      "!rm data/test/X_test_buffer.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train = pd.read_csv('./data/train/X_train_clean.txt', sep=' ', engine='c', header=None)\n",
      "feat_names = flatten(pd.read_csv('data/features.txt', sep=' ', header=None, index_col=0).values.tolist())\n",
      "X_train.columns = feat_names\n",
      "X_test = pd.read_csv('./data/test/X_test_clean.txt', sep=' ', engine='c', header=None)\n",
      "X_test.columns = feat_names\n",
      "X_train.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>tBodyAcc-mean()-X</th>\n",
        "      <th>tBodyAcc-mean()-Y</th>\n",
        "      <th>tBodyAcc-mean()-Z</th>\n",
        "      <th>tBodyAcc-std()-X</th>\n",
        "      <th>tBodyAcc-std()-Y</th>\n",
        "      <th>tBodyAcc-std()-Z</th>\n",
        "      <th>tBodyAcc-mad()-X</th>\n",
        "      <th>tBodyAcc-mad()-Y</th>\n",
        "      <th>tBodyAcc-mad()-Z</th>\n",
        "      <th>tBodyAcc-max()-X</th>\n",
        "      <th>...</th>\n",
        "      <th>fBodyBodyGyroJerkMag-meanFreq()</th>\n",
        "      <th>fBodyBodyGyroJerkMag-skewness()</th>\n",
        "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
        "      <th>angle(tBodyAccMean,gravity)</th>\n",
        "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
        "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
        "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
        "      <th>angle(X,gravityMean)</th>\n",
        "      <th>angle(Y,gravityMean)</th>\n",
        "      <th>angle(Z,gravityMean)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.288585</td>\n",
        "      <td>-0.020294</td>\n",
        "      <td>-0.132905</td>\n",
        "      <td>-0.995279</td>\n",
        "      <td>-0.983111</td>\n",
        "      <td>-0.913526</td>\n",
        "      <td>-0.995112</td>\n",
        "      <td>-0.983185</td>\n",
        "      <td>-0.923527</td>\n",
        "      <td>-0.934724</td>\n",
        "      <td>...</td>\n",
        "      <td>-0.074323</td>\n",
        "      <td>-0.298676</td>\n",
        "      <td>-0.710304</td>\n",
        "      <td>-0.112754</td>\n",
        "      <td> 0.030400</td>\n",
        "      <td>-0.464761</td>\n",
        "      <td>-0.018446</td>\n",
        "      <td>-0.841247</td>\n",
        "      <td> 0.179941</td>\n",
        "      <td>-0.058627</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.278419</td>\n",
        "      <td>-0.016411</td>\n",
        "      <td>-0.123520</td>\n",
        "      <td>-0.998245</td>\n",
        "      <td>-0.975300</td>\n",
        "      <td>-0.960322</td>\n",
        "      <td>-0.998807</td>\n",
        "      <td>-0.974914</td>\n",
        "      <td>-0.957686</td>\n",
        "      <td>-0.943068</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.158075</td>\n",
        "      <td>-0.595051</td>\n",
        "      <td>-0.861499</td>\n",
        "      <td> 0.053477</td>\n",
        "      <td>-0.007435</td>\n",
        "      <td>-0.732626</td>\n",
        "      <td> 0.703511</td>\n",
        "      <td>-0.844788</td>\n",
        "      <td> 0.180289</td>\n",
        "      <td>-0.054317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.279653</td>\n",
        "      <td>-0.019467</td>\n",
        "      <td>-0.113462</td>\n",
        "      <td>-0.995380</td>\n",
        "      <td>-0.967187</td>\n",
        "      <td>-0.978944</td>\n",
        "      <td>-0.996520</td>\n",
        "      <td>-0.963668</td>\n",
        "      <td>-0.977469</td>\n",
        "      <td>-0.938692</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.414503</td>\n",
        "      <td>-0.390748</td>\n",
        "      <td>-0.760104</td>\n",
        "      <td>-0.118559</td>\n",
        "      <td> 0.177899</td>\n",
        "      <td> 0.100699</td>\n",
        "      <td> 0.808529</td>\n",
        "      <td>-0.848933</td>\n",
        "      <td> 0.180637</td>\n",
        "      <td>-0.049118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.279174</td>\n",
        "      <td>-0.026201</td>\n",
        "      <td>-0.123283</td>\n",
        "      <td>-0.996091</td>\n",
        "      <td>-0.983403</td>\n",
        "      <td>-0.990675</td>\n",
        "      <td>-0.997099</td>\n",
        "      <td>-0.982750</td>\n",
        "      <td>-0.989302</td>\n",
        "      <td>-0.938692</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.404573</td>\n",
        "      <td>-0.117290</td>\n",
        "      <td>-0.482845</td>\n",
        "      <td>-0.036788</td>\n",
        "      <td>-0.012892</td>\n",
        "      <td> 0.640011</td>\n",
        "      <td>-0.485366</td>\n",
        "      <td>-0.848649</td>\n",
        "      <td> 0.181935</td>\n",
        "      <td>-0.047663</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.276629</td>\n",
        "      <td>-0.016570</td>\n",
        "      <td>-0.115362</td>\n",
        "      <td>-0.998139</td>\n",
        "      <td>-0.980817</td>\n",
        "      <td>-0.990482</td>\n",
        "      <td>-0.998321</td>\n",
        "      <td>-0.979672</td>\n",
        "      <td>-0.990441</td>\n",
        "      <td>-0.942469</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.087753</td>\n",
        "      <td>-0.351471</td>\n",
        "      <td>-0.699205</td>\n",
        "      <td> 0.123320</td>\n",
        "      <td> 0.122542</td>\n",
        "      <td> 0.693578</td>\n",
        "      <td>-0.615971</td>\n",
        "      <td>-0.847865</td>\n",
        "      <td> 0.185151</td>\n",
        "      <td>-0.043892</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 561 columns</p>\n",
        "</div>"
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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
        "0           0.288585          -0.020294          -0.132905         -0.995279   \n",
        "1           0.278419          -0.016411          -0.123520         -0.998245   \n",
        "2           0.279653          -0.019467          -0.113462         -0.995380   \n",
        "3           0.279174          -0.026201          -0.123283         -0.996091   \n",
        "4           0.276629          -0.016570          -0.115362         -0.998139   \n",
        "\n",
        "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
        "0         -0.983111         -0.913526         -0.995112         -0.983185   \n",
        "1         -0.975300         -0.960322         -0.998807         -0.974914   \n",
        "2         -0.967187         -0.978944         -0.996520         -0.963668   \n",
        "3         -0.983403         -0.990675         -0.997099         -0.982750   \n",
        "4         -0.980817         -0.990482         -0.998321         -0.979672   \n",
        "\n",
        "   tBodyAcc-mad()-Z  tBodyAcc-max()-X        ...         \\\n",
        "0         -0.923527         -0.934724        ...          \n",
        "1         -0.957686         -0.943068        ...          \n",
        "2         -0.977469         -0.938692        ...          \n",
        "3         -0.989302         -0.938692        ...          \n",
        "4         -0.990441         -0.942469        ...          \n",
        "\n",
        "   fBodyBodyGyroJerkMag-meanFreq()  fBodyBodyGyroJerkMag-skewness()  \\\n",
        "0                        -0.074323                        -0.298676   \n",
        "1                         0.158075                        -0.595051   \n",
        "2                         0.414503                        -0.390748   \n",
        "3                         0.404573                        -0.117290   \n",
        "4                         0.087753                        -0.351471   \n",
        "\n",
        "   fBodyBodyGyroJerkMag-kurtosis()  angle(tBodyAccMean,gravity)  \\\n",
        "0                        -0.710304                    -0.112754   \n",
        "1                        -0.861499                     0.053477   \n",
        "2                        -0.760104                    -0.118559   \n",
        "3                        -0.482845                    -0.036788   \n",
        "4                        -0.699205                     0.123320   \n",
        "\n",
        "   angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
        "0                              0.030400                         -0.464761   \n",
        "1                             -0.007435                         -0.732626   \n",
        "2                              0.177899                          0.100699   \n",
        "3                             -0.012892                          0.640011   \n",
        "4                              0.122542                          0.693578   \n",
        "\n",
        "   angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
        "0                             -0.018446             -0.841247   \n",
        "1                              0.703511             -0.844788   \n",
        "2                              0.808529             -0.848933   \n",
        "3                             -0.485366             -0.848649   \n",
        "4                             -0.615971             -0.847865   \n",
        "\n",
        "   angle(Y,gravityMean)  angle(Z,gravityMean)  \n",
        "0              0.179941             -0.058627  \n",
        "1              0.180289             -0.054317  \n",
        "2              0.180637             -0.049118  \n",
        "3              0.181935             -0.047663  \n",
        "4              0.185151             -0.043892  \n",
        "\n",
        "[5 rows x 561 columns]"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now read the labels :"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "classes = pd.read_csv('data/activity_labels.txt', header=None).values.tolist()\n",
      "classes = [item for sublist in classes for item in sublist] # flatten list\n",
      "y_train = pd.read_csv('data/train/y_train.txt', header=None)\n",
      "y_test = pd.read_csv('data/test/y_test.txt', header=None)\n",
      "y_train.columns = [\"activity\"]\n",
      "y_test.columns = [\"activity\"]\n",
      "y_train.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>activity</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "   activity\n",
        "0         5\n",
        "1         5\n",
        "2         5\n",
        "3         5\n",
        "4         5"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "(7352, 561)"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_test.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "(2947, 561)"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Data Analysis"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>tBodyAcc-mean()-X</th>\n",
        "      <th>tBodyAcc-mean()-Y</th>\n",
        "      <th>tBodyAcc-mean()-Z</th>\n",
        "      <th>tBodyAcc-std()-X</th>\n",
        "      <th>tBodyAcc-std()-Y</th>\n",
        "      <th>tBodyAcc-std()-Z</th>\n",
        "      <th>tBodyAcc-mad()-X</th>\n",
        "      <th>tBodyAcc-mad()-Y</th>\n",
        "      <th>tBodyAcc-mad()-Z</th>\n",
        "      <th>tBodyAcc-max()-X</th>\n",
        "      <th>...</th>\n",
        "      <th>fBodyBodyGyroJerkMag-meanFreq()</th>\n",
        "      <th>fBodyBodyGyroJerkMag-skewness()</th>\n",
        "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
        "      <th>angle(tBodyAccMean,gravity)</th>\n",
        "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
        "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
        "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
        "      <th>angle(X,gravityMean)</th>\n",
        "      <th>angle(Y,gravityMean)</th>\n",
        "      <th>angle(Z,gravityMean)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "      <td> 7352.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>    0.276993</td>\n",
        "      <td>   -0.017695</td>\n",
        "      <td>   -0.109141</td>\n",
        "      <td>   -0.605438</td>\n",
        "      <td>   -0.510938</td>\n",
        "      <td>   -0.604754</td>\n",
        "      <td>   -0.630512</td>\n",
        "      <td>   -0.526907</td>\n",
        "      <td>   -0.606150</td>\n",
        "      <td>   -0.468604</td>\n",
        "      <td>...</td>\n",
        "      <td>    0.125293</td>\n",
        "      <td>   -0.307009</td>\n",
        "      <td>   -0.625294</td>\n",
        "      <td>    0.008684</td>\n",
        "      <td>    0.002186</td>\n",
        "      <td>    0.008726</td>\n",
        "      <td>   -0.005981</td>\n",
        "      <td>   -0.489547</td>\n",
        "      <td>    0.058593</td>\n",
        "      <td>   -0.056515</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>    0.059623</td>\n",
        "      <td>    0.040811</td>\n",
        "      <td>    0.056635</td>\n",
        "      <td>    0.448734</td>\n",
        "      <td>    0.502645</td>\n",
        "      <td>    0.418687</td>\n",
        "      <td>    0.424073</td>\n",
        "      <td>    0.485942</td>\n",
        "      <td>    0.414122</td>\n",
        "      <td>    0.544547</td>\n",
        "      <td>...</td>\n",
        "      <td>    0.250994</td>\n",
        "      <td>    0.321011</td>\n",
        "      <td>    0.307584</td>\n",
        "      <td>    0.336787</td>\n",
        "      <td>    0.448306</td>\n",
        "      <td>    0.608303</td>\n",
        "      <td>    0.477975</td>\n",
        "      <td>    0.511807</td>\n",
        "      <td>    0.297480</td>\n",
        "      <td>    0.279122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>    0.001189</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -0.999873</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -0.995357</td>\n",
        "      <td>   -0.999765</td>\n",
        "      <td>   -0.976580</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -1.000000</td>\n",
        "      <td>   -1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>    0.263429</td>\n",
        "      <td>   -0.024863</td>\n",
        "      <td>   -0.120993</td>\n",
        "      <td>   -0.992754</td>\n",
        "      <td>   -0.978129</td>\n",
        "      <td>   -0.980233</td>\n",
        "      <td>   -0.993591</td>\n",
        "      <td>   -0.978162</td>\n",
        "      <td>   -0.980251</td>\n",
        "      <td>   -0.936219</td>\n",
        "      <td>...</td>\n",
        "      <td>   -0.023692</td>\n",
        "      <td>   -0.542602</td>\n",
        "      <td>   -0.845573</td>\n",
        "      <td>   -0.121527</td>\n",
        "      <td>   -0.289549</td>\n",
        "      <td>   -0.482273</td>\n",
        "      <td>   -0.376341</td>\n",
        "      <td>   -0.812065</td>\n",
        "      <td>   -0.017885</td>\n",
        "      <td>   -0.143414</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>    0.277227</td>\n",
        "      <td>   -0.017219</td>\n",
        "      <td>   -0.108676</td>\n",
        "      <td>   -0.946196</td>\n",
        "      <td>   -0.851897</td>\n",
        "      <td>   -0.859365</td>\n",
        "      <td>   -0.950709</td>\n",
        "      <td>   -0.857328</td>\n",
        "      <td>   -0.857143</td>\n",
        "      <td>   -0.881637</td>\n",
        "      <td>...</td>\n",
        "      <td>    0.134000</td>\n",
        "      <td>   -0.343685</td>\n",
        "      <td>   -0.711692</td>\n",
        "      <td>    0.009509</td>\n",
        "      <td>    0.008943</td>\n",
        "      <td>    0.008735</td>\n",
        "      <td>   -0.000368</td>\n",
        "      <td>   -0.709417</td>\n",
        "      <td>    0.182071</td>\n",
        "      <td>    0.003181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>    0.288661</td>\n",
        "      <td>   -0.010783</td>\n",
        "      <td>   -0.097794</td>\n",
        "      <td>   -0.242813</td>\n",
        "      <td>   -0.034231</td>\n",
        "      <td>   -0.262415</td>\n",
        "      <td>   -0.292680</td>\n",
        "      <td>   -0.066701</td>\n",
        "      <td>   -0.265671</td>\n",
        "      <td>   -0.017129</td>\n",
        "      <td>...</td>\n",
        "      <td>    0.289096</td>\n",
        "      <td>   -0.126979</td>\n",
        "      <td>   -0.503878</td>\n",
        "      <td>    0.150865</td>\n",
        "      <td>    0.292861</td>\n",
        "      <td>    0.506187</td>\n",
        "      <td>    0.359368</td>\n",
        "      <td>   -0.509079</td>\n",
        "      <td>    0.248353</td>\n",
        "      <td>    0.107659</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>    1.000000</td>\n",
        "      <td>    1.000000</td>\n",
        "      <td>    1.000000</td>\n",
        "      <td>    1.000000</td>\n",
        "      <td>    0.916238</td>\n",
        "      <td>    1.000000</td>\n",
        "      <td>    1.000000</td>\n",
        "      <td>    0.967664</td>\n",
        "      <td>    1.000000</td>\n",
        "      <td>    1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>    0.946700</td>\n",
        "      <td>    0.989538</td>\n",
        "      <td>    0.956845</td>\n",
        "      <td>    1.000000</td>\n",
        "      <td>    1.000000</td>\n",
        "      <td>    0.998702</td>\n",
        "      <td>    0.996078</td>\n",
        "      <td>    1.000000</td>\n",
        "      <td>    0.478157</td>\n",
        "      <td>    1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 561 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "       tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  \\\n",
        "count        7352.000000        7352.000000        7352.000000   \n",
        "mean            0.276993          -0.017695          -0.109141   \n",
        "std             0.059623           0.040811           0.056635   \n",
        "min             0.001189          -1.000000          -1.000000   \n",
        "25%             0.263429          -0.024863          -0.120993   \n",
        "50%             0.277227          -0.017219          -0.108676   \n",
        "75%             0.288661          -0.010783          -0.097794   \n",
        "max             1.000000           1.000000           1.000000   \n",
        "\n",
        "       tBodyAcc-std()-X  tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  \\\n",
        "count       7352.000000       7352.000000       7352.000000       7352.000000   \n",
        "mean          -0.605438         -0.510938         -0.604754         -0.630512   \n",
        "std            0.448734          0.502645          0.418687          0.424073   \n",
        "min           -1.000000         -0.999873         -1.000000         -1.000000   \n",
        "25%           -0.992754         -0.978129         -0.980233         -0.993591   \n",
        "50%           -0.946196         -0.851897         -0.859365         -0.950709   \n",
        "75%           -0.242813         -0.034231         -0.262415         -0.292680   \n",
        "max            1.000000          0.916238          1.000000          1.000000   \n",
        "\n",
        "       tBodyAcc-mad()-Y  tBodyAcc-mad()-Z  tBodyAcc-max()-X        ...         \\\n",
        "count       7352.000000       7352.000000       7352.000000        ...          \n",
        "mean          -0.526907         -0.606150         -0.468604        ...          \n",
        "std            0.485942          0.414122          0.544547        ...          \n",
        "min           -1.000000         -1.000000         -1.000000        ...          \n",
        "25%           -0.978162         -0.980251         -0.936219        ...          \n",
        "50%           -0.857328         -0.857143         -0.881637        ...          \n",
        "75%           -0.066701         -0.265671         -0.017129        ...          \n",
        "max            0.967664          1.000000          1.000000        ...          \n",
        "\n",
        "       fBodyBodyGyroJerkMag-meanFreq()  fBodyBodyGyroJerkMag-skewness()  \\\n",
        "count                      7352.000000                      7352.000000   \n",
        "mean                          0.125293                        -0.307009   \n",
        "std                           0.250994                         0.321011   \n",
        "min                          -1.000000                        -0.995357   \n",
        "25%                          -0.023692                        -0.542602   \n",
        "50%                           0.134000                        -0.343685   \n",
        "75%                           0.289096                        -0.126979   \n",
        "max                           0.946700                         0.989538   \n",
        "\n",
        "       fBodyBodyGyroJerkMag-kurtosis()  angle(tBodyAccMean,gravity)  \\\n",
        "count                      7352.000000                  7352.000000   \n",
        "mean                         -0.625294                     0.008684   \n",
        "std                           0.307584                     0.336787   \n",
        "min                          -0.999765                    -0.976580   \n",
        "25%                          -0.845573                    -0.121527   \n",
        "50%                          -0.711692                     0.009509   \n",
        "75%                          -0.503878                     0.150865   \n",
        "max                           0.956845                     1.000000   \n",
        "\n",
        "       angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
        "count                           7352.000000                       7352.000000   \n",
        "mean                               0.002186                          0.008726   \n",
        "std                                0.448306                          0.608303   \n",
        "min                               -1.000000                         -1.000000   \n",
        "25%                               -0.289549                         -0.482273   \n",
        "50%                                0.008943                          0.008735   \n",
        "75%                                0.292861                          0.506187   \n",
        "max                                1.000000                          0.998702   \n",
        "\n",
        "       angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
        "count                           7352.000000           7352.000000   \n",
        "mean                              -0.005981             -0.489547   \n",
        "std                                0.477975              0.511807   \n",
        "min                               -1.000000             -1.000000   \n",
        "25%                               -0.376341             -0.812065   \n",
        "50%                               -0.000368             -0.709417   \n",
        "75%                                0.359368             -0.509079   \n",
        "max                                0.996078              1.000000   \n",
        "\n",
        "       angle(Y,gravityMean)  angle(Z,gravityMean)  \n",
        "count           7352.000000           7352.000000  \n",
        "mean               0.058593             -0.056515  \n",
        "std                0.297480              0.279122  \n",
        "min               -1.000000             -1.000000  \n",
        "25%               -0.017885             -0.143414  \n",
        "50%                0.182071              0.003181  \n",
        "75%                0.248353              0.107659  \n",
        "max                0.478157              1.000000  \n",
        "\n",
        "[8 rows x 561 columns]"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is signal data from an experiment : very clean, very regular, no missing values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_train.activity.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "6    1407\n",
        "5    1374\n",
        "4    1286\n",
        "1    1226\n",
        "2    1073\n",
        "3     986\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There is a slight class imbalance : Class 6 (resp. 2 and 3) is more (resp. less) represented than average representation. This could have a negative impact on our classifiaction, but oversampling seems complicated given our features, and undersampling is costly as we only have 7352 samples.\n",
      "\n",
      "There are many variables. We do not hope any correlation plot : it would be useless (too much information in a limited space) plus it would a good way to explode the memory. Let's try a PCA to get a synthetic look at the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pca = PCA(n_components=10)\n",
      "pca.fit(X_train)\n",
      "print(pca.explained_variance_ratio_)\n",
      "print(\"Variability of the compressed dataset %.4f\" %sum(pca.explained_variance_ratio_))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.62555992  0.04913095  0.0412155   0.01874993  0.01694941  0.01272099\n",
        "  0.01176714  0.01068966  0.00969418  0.00858032]\n",
        "Variability of the compressed dataset 0.8051\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Keeping three components seems to be reasonable, let's plot the data according to these three components"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_PCA(X_train.values, y_train.values, classes, azimuth=59, elevation=-21)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.62555992  0.04913095  0.0412155 ]\n",
        "Variability of the compressed dataset 0.7159\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FixejXbt2WLRoEVasWCFORR43bhyWLVuG9PR0jB8/HgAwe/Zs3H333UhLS8OyZctQUlKC\n1157DQ899BDS09PRo0cPvP322wAAvV6PTz75BG+++SYyMjLw4YcfYsSIEW6f81//+lfcddddGDp0\nKPLz8xEfH9+isq3SNrvbD2PGjMGePXuQlpaGP/7xj4qPke4LT9uhRL4fWuuxKv+6VqvFihUrUFlZ\nifz8fGRmZuK+++4Tg+G4ceNwyy234Le//S2Sk5MxePBgbN682ePPd/dvpee2dOlSdO3aFSkpKfjn\nP/+Jd999FwDQu3dvjBo1Cvn5+UhPT1es4rto0SIMGDAAZWVlyMjIwNSpU8HzPHr27ImlS5fi4Ycf\nRmZmJlauXIkvvvgCOp3Oq+et9DyvuuoqdO/eHddddx0mTZokVil+7LHHUFpaioKCAhQUFKC0tBSP\nPfaY2/0h9cQTT6B79+74zW9+I1YPrqqq8up75ceVzWbD1KlTkZmZiQ4dOuD8+fNYuHCh2+8nhBBC\nIpWmlWlP0dFEkBASFFqtFgcOHEB+fn64nwohbXLkyBHk5+eD4ziPI+iEEOID79Z+EEIU0acxIYQQ\nQgghhBBVoIBKCGkzbwsEEaJmdBwTQggh6kFTfAkhhBBCCAkcuutFiB9oBJUQQgghhBBCiCpQQCWE\nEEIIIYQQogoUUAkhhBBCCCGEqAIFVEIIIYQQQgghqkABlRBCCCGEEEKIKlBAJYQQQgghhBCiChRQ\nCSGEEEIIIYSoAgVUQkhEOnfuHHbt2oVWejnj+PHjGDlyJD777LMQPTNCCCGEENJWunA/AUIIkeI4\nDjqd+1MTYwyfffYZZsyYgYSEBGg0GmRnZ6OkpASDBg1CaWkp0tPTYbPZsHjxYjzzzDN48MEHcf31\n14dwKwghhBBCSFtoWhl98Dw0QQghATZ+/HgsX74cpaWlKCsrQ2lpKUpKSpCamor9+/djwoQJyMnJ\nwYwZM5CRkQHGGM6cOYOtW7di27Zt2LZtG/R6PdavX4+cnBxMnz4dv//975GUlASNRhPuzSOEEBL9\n6MOGED9QQCWEqArP8zhw4AC2bNmCLVu24JdffkFlZSWMRiP69OmDIUOG4Oqrr0ZZWRni4+NbfK/T\n6cTf/vY3bNu2DdOmTYPBYMC2bduwfft2NDc3Iz8/Xwy+RUVFMJvNFFoJIYQEGn2wEOIHCqiEENXi\neR7vv/8+nnnmGfzxj39Eamoqtm3bhoqKCsyYMQODBw9GXFwc4uLiwHEcOI7DoUOH0LdvXxiNRpef\ndfDgQWzduhUVFRXYtWsX7HY7evXqJY7UFhQUwGAwUGglhBDiD/oQIcQPFFAJIarDGMP27dsxadIk\n9O/fH1OmTEFycrLLY5xOJ+x2OxwOh/j/Wq0WcXFx0Ol0iIuLg1ardRs4OY7D/v37xdC6Z88eAED/\n/v1RVlaGsrIy9OnTx+OaWEIIIUSGAiohfqCASghRlZqaGsycORMHDhzAggUL0Lt3b8XHOZ1OWCwW\nMMZgNpuh0+nE0Cr9w/O8OMoq/HEXWhljsNvt2LNnjxhaq6qqYDAYUFhYiNLSUgwaNAjdu3eHVktF\n0AkhhCiigEqIHyigEkJUwel04vXXX8drr72Gf/zjHxg+fLhiCOR5HjabDQ6HAyaTCXq93uOU3ECE\nVovFgh07dohFmI4cOYKEhAQUFRWJI62dO3em0EoIIQSggEqIXyigEkLCijGGjRs3YsqUKbjyyivx\nyCOPuBQ/Eh5nt9ths9mg1+thMpnavFZUGlo5joPT6QRjrMXU4Li4OGg0GrehtaGhAZWVleKa2BMn\nTiAtLQ0lJSXiSGv79u1pPSshhMQeOvET4gcKqISQsDlz5gweffRR1NXVoby8HF27dlV8HMdxsFgs\n0Gg0MJvNiIuLC/hz4XneZaQVgOJIqxLGGGpqalBRUSGOtJ47d06xRyuFVkIIiWp0kifEDxRQCSEh\n53A48NJLL+H999/H9OnTce211yqGNp7nYbVawXGcV9N5Ay0QofXMmTPYtm0btm7disrKStTV1SE3\nN1ecGlxSUuJ1j9a1a9eCMYZhw4YFdDsJIYQEFAVUQvxAAZUQEjKMMaxduxaPPfYYbr75ZowdO9al\nHYzwOJvNBrvdDoPBAKPRqIpRR8aYOD1YmBrsdDqh0WgUpwcr4Xkex48fF0dZKysr0dzcjG7durnt\n0Xrw4EFMnDgR27dvx8svv4wbbrghlJtNCCHEN+H/wCIkglFAJYSExLFjxzBp0iTExcVh3rx56NCh\ng+LjHA4HrFYrtFotTCZTUKbzBhJjTHGkVWh3I/3jKbQq9Wjt3bs3Tp8+jZ9//hkTJkzAxIkTYTab\nQ7yFhBBCfEQBlRA/UEAlhASV1WrFokWLsHr1asyePRtDhgxRDGpOpxNWqxU8z4vTeSOVv6GVMYZ3\n3nkHs2bNQnFxMdq1a4cjR44AuNSjVSjCRD1aCSFElSigEuIHCqiEkKBgjGHFihUoLy/H6NGj8Ze/\n/EUxTEmn8xqNRhgMBlVM5w00aWgVpgfzPA+tVttiarCw7vaLL75Az549UVpaKn6/Uo9WvV6PwsJC\ncU1rjx49qN0NIYSEV/R9iBESQhRQCSEBV1VVhYkTJyIrKwszZ85Eu3btXB7DGBOn8+p0OphMppgL\nVvJ2NxzHAQA0Gg10Op0YXH3p0Xr48GEkJiaiqKhIHGmlHq2EEBJSFFAJ8QMFVEKI1+rr6z1WnG1s\nbMT8+fOxceNGlJeXY+DAgW6n81osFjDGYDabY3qaqrwglMFgcJkezPO8YuVgX3q0pqamoqSkBGVl\nZdSjlRBCgotOroT4gQIqIcRrt956K9avX4+SkhKUlpaitLQUJSUlyMnJwUcffYTFixfj3nvvxejR\noxVH7Hieh81mg8PhiOrpvN7wZQRZqXIwY0yxcrC70Eo9WgkhJGToREqIHyigEkJ8Ul1djS1btmDL\nli3YunUrNmzYAJvNhoKCAgwePBhlZWX43e9+B5PJJH6PsH7SZrNBr9fDaDTG9JRTjuNgsVig0Whg\nMpnaNIIcih6txcXFSE5OptBKCCG+oZMmIX6ggEoIaZPa2lrMmjUL+/btw/jx41FbW4tt27Zh+/bt\n+Ne//iW2iNFoNOA4DhqNBvHx8apvGxNMQgEkjuPESsWBCn/SHq1tDa08z+PEiRNiEabKyko0NTUh\nPz8fZWVlKC0tRXFxcYserYQQQlzQCZIQP1BAJYT4xOl04s0338Srr76KRx55BLfccotL4BGCktVq\nFdurCOcab8NSNJGvMzUajSEJeNLQKp0eLBRh8qVHqzA1WOjR2qtXLzG0FhQUeD1dm+M4fPvtt7jh\nhhsCvbmEEKIWFFAJ8QMFVEKIVxhj2Lx5MyZPnowhQ4bgH//4BxISEhQfJ0znlYYx+QifEJg0Gk3U\nhlY1Vir2t0crcClkVlVVYcuWLaisrMSePXvAGPPYo5UxhtWrV2PSpEnIzs7GypUrYTabQ7XZhBAS\nShRQCfEDBVRCSKvOnj2LqVOn4vz585g/fz7y8/MVHyeEMa1WK07x9cRdWIqG0MpxHKxWKwC0eZ1p\nqPgbWoUgvnv3bnGk9ddff4Ver0dBQQFyc3OxfPlyXLhwAU899RRuvvlmmiJMCIlmdIIjxA8UUAkh\nbjkcDrz88st47733MHXqVFx//fWKwYLneVgsFvA8L66tbCtPodXbaanhFMx1pqEkvA7SqcE8z0Or\n1bZ4HTy1uzl8+DBmzZqFdevWoUePHmCMISkpiXq0EkKiXeSd9AlREfXe0ieEhA1jDN9//z2mT5+O\nm266CV9++SWMRqPi46RrK+Pj4/0OY9LRU+nvkYZWh8Ph87TUYJPvC0/9YiOBu9dBOkXbZrMp9mjV\naDSw2+2oq6tDx44dsWPHDqSmpoIxhsbGRlRUVKCiogIfffQRTpw4gbS0NBQXF4vVgzt06BDR+44Q\nQgghbUcjqISQFo4fP45JkyYBAMrLy5GTk+PyGDWsrQzEWspAPY9w74twUlpXzBgTR7yF0VZPI621\ntbWoqKjA1q1bxR6tWVlZKC0tFUMr9WglhEQQOlkR4gcKqIQQAIDNZsPixYuxcuVKzJo1C5dffrli\nIHA6nbBYLGCMwWw2q2ptZahDayStMw02aW9XoTCWdHow4FrBWaPRuA2t7nq0ClODqUcrIUTF6MRE\niB8ooBIS4xhjWLVqFebOnYuRI0dizJgxikGLMQar1QqHwwGj0eh1W5FwC0ZojZZ1poEgtBMS1h/r\ndDq365SpRyshJEbQSYgQP1BAJSSGHThwABMnTkRGRgZmzpyJzMxMl8dIp7Dq9XoYjcaIn8La1tAa\nrn6masTzPGw2W5tvWMjbDrU1tLrr0SpMD/alRyshhAQInXAI8QMFVEJiUGNjIxYuXIiff/4Z8+bN\nQ3FxseIFvHQKq9lsbrVtTCTzVLVW2G6HwwGdTgez2RzxIb2tpCE90DcshNAqfQ0C1aO1X79+4nrW\nvn37xvR0bEJI0FFAJcQPFFAJiSE8z2PZsmV46qmn8Le//Q2jR49WDJ00hfUSxhjsdjtsNhuAS5Vt\npaE1Li4OOp3ObQGgaCIdSY+Li/Oqz22gfm+ge7RWVVVBr9ejsLBQXNPao0cPr4N2fX09du/ejcGD\nBwd6cwkh0SG6PxAICTIKqIREiT179mDz5s0oKSlBnz59XEaIdu3ahQkTJqBnz56YOnUqUlNTXX6G\nNJDp9XqYTKaoD17uuAvpStNSlVqtRFNoVVsxqECEVovFgp07d4prWg8fPoyEhASPPVrtdjuWLFmC\n+fPnY/To0Xj66adDudmEkMgRHSd/QsKEAiohUWLTpk147rnnsHXrVpw8eRIDBgxASUkJevfujX37\n9mH//v1YuHAh+vbtq/j9wuiYVqsN2eiYGrVlnWm0hlahAJLT6VT9SLq/r4G8R+u2bdvEHq1FRUVw\nOp344IMP0Lt3bzz++OMoLCwMw1YSQiKEOk+UhEQICqiERKH6+nps2bIFS5Yswffff4/ExEScOnUK\nffr0wezZs3HllVeKF+vCaJLT6RTbxqg1hASTfAqrv+tMIzm0+lsASS0CEVq//PJLzJkzB3a7Henp\n6WCMISsrCyUlJRg0aBBKS0uRkZERkfuHEBI0dEIgxA8UUAmJMowx/PLLL5g8eTIuu+wyTJw4EQkJ\nCaivr0dlZSU6d+6MrKws8WIdALRaLQwGQ8ysp5QTprAGu7er2kOrfIp3NFRslvP2NRBG0j/88EOY\nTCbcfvvt4v8LPVq3bduGiooK1NbWolOnTtSjlRAioDc/IX6ggEpIFDl37hymTp2Ks2fPYv78+ejW\nrZvLY4QqqRaLBXFxcdDr9S0u2tUUmIJNDcWg1BBaw1UASS3k61k5jgNw6caNTqeDTqcT17O66/F6\n4sQJMbRWVlaiubkZXbt2pR6thMQmeqMT4gcKqIREAY7j8Morr2Dp0qV49NFH8dvf/lbxQtjpdMJi\nsXgcKVRDYAo2tfczlb8GHMeBMRaU1yBUo8dqxxiD1WqFw+GAwWCAXq93Ca6Abz1aDx06JBZh2rlz\nJ/VoJSR20JuaED9QQCUkgjHG8MMPP2DatGn43e9+h4ceeggmk0nxccLFd1vWFLoLrb70p1QD+ehx\nJI0UKlWt9Se0RlIBpGCSTmvW6XQwmUyKoVPo0Sp/DQDvQ6vQo1UIrdSjlZCoFXsnU0ICiAIqIRHq\n5MmTmDRpEpxOJ8rLy9GxY0eXx0inbnq6+G6LSAut3oweRxql0Ar8d2qqdP8Lr4G0AJIaR49DJRDT\nmgPVo3XPnj1iaK2qqoJOp2tzj1ZCiCrE3kmVkACigEpIhLHZbHjmmWfw+eefY+bMmbjyyisVL37D\n0btSjaFVDetMQ8lTaAUuBXWhAFKkjB4HkjCKbrVaodFoAv7eCERotVqt2LFjh7im9fDhw4iPj0dR\nUZE40tqlSxcKrYSoV/R+yBASAhRQCYkQjDGsXr0ac+bMwR133IExY8ZAr9e7PE5tgSxcoVVekdZk\nMkV1MHVHOlIIXAqqQvVmpf0fzftIOoouBNNQbG8gQqtSj9bU1FSUlJSII60dOnTwanuqq6vx008/\n4fbbbw/G5hJCKKAS4hcKqIREgIMHD2LixIlITU3FrFmzkJWV5fKYSApkwQyt0hEyrVYbUetMA81d\nAaRArKcdlSdBAAAgAElEQVSMJNKbNmrp6xqIHq21tbWoqKgQpwefPXvWY4/W2tpaPPHEE3jttdcw\nduxYlJeXh3qzCYkV6vzwJSRCUEAlRMWam5uxcOFCrFu3DvPmzUNpaanb6bwWiyWiA5m/o0xAdK4z\nbYu2jKJHY2iNtPW2gQitSj1au3TpAo7j8N133+EPf/gD5s2bh9zc3DBsISExQ70nGkIiAAVUQlSI\n53l8/PHHePLJJ/HXv/4Vd955p2Lo5HkeFosFTqdTDGRqvgD3lbehVaPRwGazqWqELBzkrVL8DWSR\nGlqlbYSE9bZqen6+8KblkPC+l7/WjDH85z//wfTp0zFgwAB06tQJx44dQ1NTE/Lz88V2N0VFRYiP\nj4/J9wwhQUJvJkL8QAGVEJXZs2cPJkyYgPz8fEybNg1paWkuj1F7H89gkoZWjuPEC3YA0Ov1qqse\nHAretkoJ1O8SQhPHcWJw0mg0YQ+tgajMGwncFcKS7nfhHLFx40ZkZGSgpKSkxfcr9Wjt2bMnysrK\nUFpaisLCwpi90UNIANAbhxA/UEAlJEQuXLgAs9mM+Ph4xa9fvHgRc+bMwY4dO7BgwQL079/f5THy\nPp5mszliR4b8IV9nKgR0f6YHRyK1rLd1N9IdqtAa7Mq8kYDneXAcB4fDAY7jAMCn/e+pR6tQhIl6\ntBLitej5oCEkDCigEhIiixYtwsyZM5Gfn4/i4mIUFxejpKQEBQUFWL58OV588UU89NBDGDFihOJF\npNPphNVqBc/zMb2+Ul6JVamSMRCYNa1qFq6KtN4K1f6XFoJS434IFfl+EG5UyKcHy0Oru/1FPVoJ\n8UvsnYQICSAKqISEkN1ux65du7Bt2zZs3boVP/30E/bt24fc3FwMHDgQAwcOxN13343MzEzxolG6\nrjCW11dKC960dT9EQ2hVY0VabwVy/ws3bJxOpyraKYWLL/sh2D1ahdBKPVoJoYBKiD8ooBISBufP\nn8f06dNx8uRJzJkzBxzHiT0OH3nkEaSkpIhFT4QqnrE4bRFwbZ8T6II3kRJao3Xdsa/7P9Iq8waL\n0+kMSGGwQPVoraysFCsHHz9+HKmpqSguLkZZWRnKysqQk5MTk68TiVl0sBPiBwqohIQQx3FYsmQJ\n3nrrLUyePBk33nijx7YxQrVOoSiNGgrRhEo411d6e9Gu1WqDvv+lhX+CXQBJLTytaRXeEwaDISZH\nTUMR0OX7n+M4lz7FOp3Oqx6twkhraz1aCYkydGAT4gcKqISEAGMMP/30E6ZOnYrrrrsO//u//wuT\nyeTyOE/9K6UXjdKLR8C15YdSy4lIIl1v62mdaSiFuhCQvPCP2WyOyoq0rRFG0IWALr1ho8aR7mAJ\nd+ucQPVoraioENe01tXVITc3V2x3U1JSguTkZI+v34EDBzB37lzMmTMHXbt2DeYmE+KP6DsJERJC\nFFAJCbLq6mpMmTIFFosF8+fPR6dOnVweI5/GajKZvLrI9qVPZSSE1kCsMw2lYIVWeUCPxcI/3gT0\nSJme7Q9pMFXbCLq/oZXneZw8eVIMrJWVlWhsbFTs0Xr8+HHMmzcPn376KcaNG4fx48cjKSkpDFtN\niFci84RDiEpQQCUkSOx2O5599ll8+umnmDlzJoYOHepxOm8gR8mULtqFqZHeXDiGWrDXmYaSP6E1\nkgsgBZI/lXndhaZIC63y3rZGozEiRtDl+1/oU6xUOdhdaD106JA4NfjAgQOwWCzYsGEDrrrqKkyc\nOBFDhw6N2fcGiRh0cBLiBwqohAQYYwxff/01Zs2ahREjRuC+++5TnKIqDSNC25hgXnCpNbQK6yvD\n2ccz2LwJrcLFvMFg8HoEPdoEqzJva6FVmDqshhs2wppjm80WNe8JpWMf8DzLg+d5bN68GSNGjMAd\nd9yBESNG4NChQ6isrMTu3bupRytRu9g7gRMSQBRQCQmgQ4cOYdKkSUhMTMTs2bORnZ3t8hg1VWPl\ned7l4tGXKXr+iPVprEJoFaY0CwWAYqkQliAcI8f+Tk8NxvORTmmO9qrdnkKr8NpotVpcvHgRubm5\nLb5X2qNVGGndv38/9Ho9CgoKxMrBPXv2jPr3DlGt2PkwIyQIKKASEgAWiwWPP/441q5di7lz56Ks\nrMzlolZeldZsNqvy4snThbt8mmRbLtwjbZ1psEindgthRAit8ot3wHW0SY3Hjq/khX/CPXIcrtDq\nz5TmaCGcF+x2u7h/5bMMhJFu6tFKIkBsvYEJCTAKqIT4ged5fPrpp3j88cdxzz334M9//rPidDw1\nVqX1RSDW9UXTOlN/+Dpy7EshrEjZn/L1lWoq/CMXzNAarCnNkcTTWlvq0UoiGB1QhPiBAiohbbR3\n715MmDABXbp0wWOPPYa0tDSXxwh39aNxtFB64S69iFS6eAxXP1M1CeTIcaRWb5b2dI2Li4vYYyEQ\n1WtjvRiWNJj6ciwEIrQq9WjNzMwUKweXlZVRj1biLzp4CPEDBVRCfFRfX4+5c+eioqICCxYsQP/+\n/RWn8woX4mofIQok+cUjx3HgeR4AxCl6Op0OWq02JvYHELr+lWoPrcJNCgBRub7Sm9Cq1Wpht9vF\nYljhXH8eLvKbFEaj0e9jwd15RxpahfOOrz1aS0pKMGjQIK96tBIiQQcKIX6ggErI/+E4Do2NjUhN\nTVX8Os/zWLp0KV544QU8+OCD+J//+R/FoOF0OmGxWABE54W4N6QjxwaDATqdzmXUI9qLAalhtFAN\noVU6pdloNMbUNFZh33McB4fD0eJmjRrbPQWTtDpxKIpABatHa9euXVFWVobS0lIUFxcjPj7ep9fu\n/PnzaNeuXSA3lahTdL+hCQkyCqiE/J+9e/di0KBByMrKQlFREYqLi1FUVISioiJUV1dj0qRJKCoq\nwqRJkxQbxEuncNJ6Ms/rTH3pFaqmKareUvNoYahCK01jVV5rC0CV7Z6CRV6dWBgxDce2BSK0Snu0\n7ty5E3a7HT179hRDa0FBgeLI+J49ezB37lxs3rxZrDhMolrkv3kJCSMKqIRIOJ1OHDhwQLwA2bx5\nMzZt2oTk5GQMGDAAxcXF+MMf/oCCggLx4l0eysJdiTRchNFCjUYDs9ns82ihGkb7/BWpRW8Cue/l\no+exOo3Vl/WVbekTqnZCMLXZbKquTiw97jmOc7lhIK0crPTcOY5DVVUVtm7d2qJHa9++fVFWVoZ2\n7drhgw8+wJo1azBhwgQ88MADSExMDMOWkhBT14FOSIShgEqIAo7j8Nprr+GNN97AhAkT0L9/f3Ga\n1+WXX47BgwcDADQaDXieF0OZGi/Agi2Y/Uy9CU7COrNwjjhFa+scX4ITgIipzBss0mmsWq3Wr/WV\nkRxapW1zInFat7f73t3sEIfDgW+//Rb/+te/sHnzZiQlJaFTp04oLi4W29306NEjIguEEa9FzgFP\niApRQCVEgjGG9evX49FHH8U111yDcePGwWw2uzyO53lYLBZwHCdegAr9K6N1qp5cOCsUK11AhmOa\nZCy2znG374FLN2wMBgP0en3UHvdK5NNYgzWtW+2hVRgxjbQZBN7wdt8LRdH+/e9/4+LFi3jooYeQ\nnJwMq9WKnTt3imtaDx06hPj4eAwcOBBlZWXUozX6RMeBT0iYUEAl5P+cOnUKkydPRnNzM8rLy9Gl\nSxeXx0grsipNX/S0xkneBiFSL9zU2sNSegEp/D1YNwzUUAAp3KShDIC4pi4W1lVKSUcLQz2NVS09\ncqVT26NpBoEn8n0vTA8GAK1WC71e32J6sNL3Cz1ahZY31KM1qtCLRogfKKCSmGe32/H8889j2bJl\nmDFjBq6++mrFCwIhkPjay9NdaPW2b5+acBwHi8XS5nWmoeZvURQl0kAiTOuONUKlak+hTC2j3MGi\n1vXGoQytTqcTNpstpgthSYuB6fV66PV6l2Pf1x6tlZWV4kjrmTNnqEdrZKIXiBA/UEAlMYsxhm++\n+QazZs3Cbbfdhr///e8wGAwuj5OvsQxE9UVPzeblFzNqGJ2UXoxH+lrbtt4wUGsgCSV/K/NGQ2iN\nxOrEgQ6t0n0Qq4WwvN0Hns713obWs2fPioX7KioqUFtbSz1a1Y9eDEL8QAGVxKTDhw9j8uTJMJlM\nmDNnDtq3b+/yGOl03lBciKqx9UqsVGRtLbQKr4vBYIjJKs3SIlCBPg4iJbQGcx+EgxBahampns43\nQmiNtn3QFvJ9YDAYfL6JKD3XC/tfer4Rpga31qNVCK2B6tFKAop2PCF+oIBKYorFYsGTTz6Jb775\nBnPnzsVll13m8gEuXV8Y7jWWrY18SO/CB/ICXk37IFyEC1G73d7iAj0Sp2a3lfQmTSiLQKkptIZr\nH4SDp5tkwteF84Hap/cHWiCCqSeB6NF6+PBhcWrwzp07YbPZ0LNnT3F6sLserSQoaCcT4gcKqCQm\nMMawfPlyLFy4EH/+859x9913K15gSdfWqXl9YTAv4CNtnWmgSYv/yNcbezNdT9r2JlLJb1AYjcaw\nHwdClexArif2RK3FwEJJmEFht9vFfSu8BrFyo0YaTEN9g8Lf0Op0OrF//363PVrLysrQr1+/Vj/n\n9u3bhxdffBGLFy+G0WgM1uZGm+h7MxASQhRQSdTbt28fJk6ciI4dO2LGjBlIT093eUw09LH0t4Kw\n0DonGtaZtpW8Iqs36419mZqt9oATqnYpgRKMIlhUobnlqLFSOPd3XWUkCGcw9USpcrD05qS0crC7\n2UF79uwRpwfv378fer0eBQUFLj1ad+/ejfLycnz33XcYN24cHnnkEcTHx4dpyyNOZB74hKgEBVQS\nterr61FeXo4tW7Zg/vz5KCgoUPzAjuY+lt4UBNJqteA4LubXlQkFTwJRAEl+AS/8HVBPz0q5cLZL\nCaS2hlbh4t1ms0VEOA8G+aixLyPn0RJa1RpMPfG2R6vSdgij5NIerRcuXMCZM2ewf/9+3HrrrXj0\n0UfRv39/1e8HlVHvQU5IBKCASqIOz/N477338Nxzz2Hs2LG4/fbbFT9YY3Uqq3AhKYRS4WJGo9FA\np9O5hNdo1lpf20D/Lm8rqYYytMZCdeLWZhdoNBo4HA7xXBDLwTSQo8aRFFojMZh60pbQ6nQ68dJL\nL+GZZ57B2LFjUVRUhL1794o9WlNSUlBcXIzLLrsMt9xyS9hfM5WjnUOIHyigkohy9OhR7N+/H0VF\nRcjMzGzxNcYYKisrMWnSJBQUFGDKlClISkpy+RmBHi2LRNJwLlyMqq2CcDCpZX2hp9Cq1G4okPs9\n1iuySo8Bxhg0Go0qqwcHUzimM6sttEZbMHVHeq6RVm7WaDTQarXi69LU1ISUlBQkJCS0+H6e51FT\nU4PKykocO3YMDz74YNS+LwKEdg4hfqCASiLKDz/8gFmzZqGiogKJiYkoKipCUVERunfvjg0bNuDw\n4cNYuHAhevXq5fK90gvSWLwgB3wL596M+EkLAkXCvvRUAElNglkEK5aq0rrjbtQ4GGta1Uht05nl\noZXjuKBXzI6VYOqJcAw4nU7xPKhU9E043oW/C8sAIvk9EAK0cwjxAwVUEpEYYzhy5Ai2bt2KN954\nA1u3bgUAWK1W9OvXD8899xy6d+/eYo2l2kNJMAVyKquaWoD4QlqhORLXWPpbxVYto8bhJL1B420x\ntGgKrZFUBMub9fNtCa08z8Nut8f0DRqO48RgKn8fSJeACDcLTCZTi0DK8zwF1NbRziHED+r8ZCLE\nC6dOncJzzz2Hq666Cm+//TbMZjNOnz6NyspKZGZmtij6AgA6nQ46nU4cGYyFD1d5u5DExES/L8aE\nu+jSCrfS8CRc/Egv4qUXlaHe720JJWok7HdpoJBX9JTvd2HfC6NFWq0WCQkJMXeDRj6dOSkpyetj\nQFib7ct+D+fxrkQIpsJ05ki4QdPafnc6nbDb7WJoFarXurtZIA+mgTgXRhrhM1EImPHx8YrHgEaj\nEcO7cDNDzccKIST60AgqiTinT5/GlClT0NDQgPLycuTl5bk8Rj6NUafTuVRVVVrjF00XLMLFCICw\njJQEawTEl98fqgJIaiIPTxzHAfjvBb+aCtMEWyinM4f7eHcnWqozu9PaCLdWq4XT6RSn8sbazAFh\n/wjHgNFoVFzaIdy4BdBiWm9zc7NYPEzA83xMjjz7KHreZISEAQVUEjEcDgdeeOEFfPDBB3jssccw\nbNgwt33ehBFDdxcjvvSujLS7x2ouAuVNgRTputa2/g5vjoFoJl9jqXSDRq3VVANBLdOZwxlapcHU\nXSiJVtIRY2HdPKBcwTZa94l81NzXYCpobm4GALH/qXB+TUhIiLnzqo+i88AiJEQooBLVY4xhzZo1\nmDFjBoYPH46xY8fCYDC4PE66xrAtrSKk4Ul6MQ/AZdqeGi9sInXEMJA3C4RgGmutgwS+TGdWWzXV\nQAhHVVpfBTu0xkLbIE+k50FpL9doWkvsSaCCqUD6mWq1WmG1WqHX65GWlkYB1bPIPYgIUQEKqETV\njh49iokTJ8JgMGDevHlo3769y2OERuMOhyPgawzdVbJVU1GgaBwx9LWCsBDMhLVV0TaNsTWBujnh\nKbQqTYlXi0gq/qMkEKFVGkwjea11W7kLpq19T7SEVm/XGXsbTAUWi0Vc6yudIk1TfFul7gOGEJWj\ngEpUyWq14qmnnsJXX32F2bNnY/DgwR6n84a6GqNa1puFe51pKHm6WQBcCq7CeuNIuKAMhFBMZfVl\nhDvUF6zSi3IAMBqNUXNzwttzDADY7faILwLWVm0Jpq39vEgKrdKbMwACFkyFm35C9fukpCRxv/I8\nD4PBoLrZCSoT/oODkAhGAZWoCmMMX3zxBcrLy3HnnXfinnvuUQxd0g9ktUzlFC5s5BfzwRh9UvM6\n01CQX5Tq9XqXNZby/R1JU1VbE+6prEJolR/rgOs6v2Ct4Y724j9KpDcLhAJYQkXyWCyAJb05428w\n9UQ41oXWK2qYRSPvZevu5kxbg6nQL1woMpWYmNjiMRRQWxXdb0BCgix6h1tIxKmqqsKECRPQvn17\nfPzxx8jIyHB5jJqDmXCRKCUffRLaUQDKU1W96ckoncrpS7uMaOBt2xx5JVu1VFQNBI7jYLFYoNFo\nEB8fH5ZRc+noqdBuSD7CbbfbFUOrNxfJnsTyGksh7AvHtcFgEPe/sN8dDkfEryX2RB5MQ9E2SanF\nk/S8LpyT5KFVCIyB3O/yYBrIEVNhOq/BYEBycjLi4uLE0XlCCAklGkElYdfQ0ID58+dj06ZNmD9/\nPgoLCxU/bIWLEmEdTKRebLVlXWs0rjP1lTSYtWU6s6cRbqUApTZCMBNaPERKMFOaHtyW0ado6Wfb\nVvI+np6WNERrASzhM0CtBbCU9jkQmFkFvo6YCr+jtfeVcF4RgqnZbG5xXAn7PCkpqcV20ghqqyLj\njUWISlFAJWHD8zw++OADPP300/j73/+OkSNHKl5wCcFMq9Wq8qIkUJSmTAqjfsL7NBYvzKXBLNBT\nOSOh3VA0BjOl49zdOj9h1oDD4Yio6tSBEqherpEaWiMhmHrib2hVGjFVeo38CaZGo9HtTU+HwwGL\nxYLk5OQW20QBtVXqeRMREoFoii8Ji+3bt2PixIno168fvvjiixYffgJhylGsVGaVTyMTtp/jOHEa\nnzSsS6cH+7Oudf/+/fjll1+QnZ2Na6+9VjWjhzzPi8EkWMFMGkQF8vWVwZqq2hrp9kfbdG5h3wnH\nNeA6LVs6HV6r1baYzhoL5FNZ3U1n95anY12N04PlwTQUU3mDQelYl+5zT+cX4X2g1Wrdtk6TBlNv\nzklO56V2bMJ5NSUlpdXjSmkgo5XBDUII8QuNoJKAq6mpwZAhQzBw4EAMHDgQRUVFGDhwILKzs1Fb\nW4uZM2eiqqoKCxYsQJ8+fVy+P1L7eQZKa9sfyFG/5cuX48GxD6Nb+wKcuXgMpZcV4p133w5rSA3U\niFGgn1Oo2g3Jp7OrYftDSR5M9Hp9i30vjLTKQ1S0nCNCWfzH3e8P50hrpI+YtoX0/CIUv5LWKpAW\nwBLOBW0ZMRWCqclk8vq8wnEcmpqakJKSIv4fz/NicTriVnSckAgJEwqoJOCcTif27NmDyspKVFRU\noLKyEpWVleB5Hnl5eSgsLMRVV12FoUOHokOHDuKHqlAu32KxxOQ6S3/WmXoKUKdOncK/X30e589U\no29hKcY+/AgSEhLAGENux04YUToBOen5cPIc3vl5Dp596UnccMMNQd5a5W0IZ2XathAu5D1VEPal\nAFakbX8gebv9amnxFGhqfv1DEVrlwVRYYxkr5NsvTKF1Nz04Pj7eq2AqfKYKhQV9rd/gdDrR0NCA\n1NRU8f8ooHolck4+hKhQ7Jz9ScjExcVhwIABGDBgAO68805s2rQJkydPxoABAzBo0CDs3bsXH330\nEQDgxhtvbDGdCbjUNibWPvikLTPaUplVehdduu9qa2sxfcKD+NNADQZcnYSPNq3CjKknMHfBInAc\nh8bGBnRIywMAxGl1yE7ugtOnTwdy07wibRsUrsq0baFUUMndVFV3F/PSPobhrMwbLr5uv1AtW/oY\neQEsNU1VbY10jaFWq1Xl6+/P9ODWio7Jg5katz+YWtt++bIPAGhsbGx1v8qDaWJiouqOfUIIcSd2\nPgVIyJ05cwaPPvooamtr8corr6Br164ujxHWWTocDvHDtrm5OWIqq/or2G1zduzYgV7pDoy6ojsA\noHfHFFz3xCbwPA+z2Yw+vfthfdUXGNLzFpy9eBwHTlWisHAuOI4LyT6PxpYhvgQo6bYaDAax72Cs\nCFQvU29aPLU1QAWLPJi7W2OoVr6EVnfLD6QjxrEeTJXW2Aoz3ISpvNIpvu7eJ8I+Fc6p/gZT4SYa\nIYSEUux8GpCQcTgceOmll/D+++9j+vTpuPbaaxUrDkqnsyYlJbVYW+PLRU4kBppQ9TM1GAxosDjF\nC5xmmxM8u9SiwGg04sNl/8Ht/zMSTyz/FEajEc888zR69+4tXuAEa5/LCyAJ09WilTxACcFcKICl\n0WjE0dZoOs7dkQbTYLXM8TdABSu0yoNpNBWA82af22w2caqq8L7Q6/Uxc2NGeu5318dV2sNU2KfS\nc4AQGqXHjFBtVygqGKgRU3cBlUIrISSYKKCSgGGMYe3atXjsscfw+9//Hl9++SWMRqPL41qbzulL\nZdVgFKkJJuk627i4OL8rc7amuLgYSwztMeXtSgzINeO7Xx249f/dKb4uOTk5uPOu0di8aQt69uqB\nW2+7FWazWXyu0jWW0mqTSqNQ3uxzeQGgYG+/2sgr88qDuad9Hg2hNdwj5t4GKGmBmkDtc2EUPRAj\nxpFEPvInzM4QzkHS0BrKGwWh1tZgKvzd3c8UPk+EWTGhaEMV7ccsIST8qEgSCYhjx45h0qRJ0Gq1\nKC8vR4cOHVweE4zprO6K1Khx7ZlQRZExFrLpfOvWrcNdo++Bw2GFKc4JXmfCRx8vQ0VFBfR6Pb77\nZg32VBxC7/a/wbGaPTCmc/jqm9Ue1wB708NS2P/SAlhqLQATCv5UJg5lBeFgibReru72OdC20BqK\nEWO1Uurj6a5dirfVySMptMqDqVJVZl+DaW1tLcxmM2w2m/h5Esz3VE1NDdLS0lqczzUajeINaCKK\njTc4IUFCAZX4xWq1YvHixVi1ahVmz56NIUOGKE7nlY6a+VpF0Ffy9X7SipNKrSmCfaEY7HWmgoaG\nBhw/fhzt2rVDVlYWqqurcdUV1yJRG49U01kUdIzD+UYO635tRq/OV8Fib8Leo1sx4ZZXoNeZwBiP\nt36chdffeQVDhgzx6Xd7qqyq1WrFi/tYK4Aln8oeyJYh8psy/lQQDhb5iHEkt4xqLbTKexJrNJqw\njxiHk7fBtLWfEamhVZgBEchgKuxToUhSqEZMKaC2SWy80QkJEpriS9qEMYaVK1di7ty5+NOf/oTV\nq1crXnwI04+0Wm3IGq23VjBFaeqk0sWlv+SjZsFaZwoAP/zwA8b/7wSYdIngmQNjH74PK1asQJo+\nF7WNW3Ftz2QAwMlaKxL0DBrejoJOQ/Hr8R3QaC7tK41GC6PeBIfD4fPvVyoMJIwacRwnvu7Nzc0x\nscYyFJV5hfAvDfy+VhAOFvmxHw1Tud1VypavaRVGSgV6vR7x8fGqmMURCvJgajab27zt7qZkS49z\nT9Pgw3HMyYOp0rEvDabSIl2egqndbheXxgifp6G62ae05pUQQoKJRlCJz3799VdMnDgRmZmZmDlz\nJtq1a+fyGKE6r9PpFKezqu3DTX6hozQKJb2w9/b5y9eZBms667lz5/Di8y9j187dWP/zemSldIaT\n52DSJ+Bc/QnYnc1IN6fiT4MsmHFzNix2J5788gwqT1hxfd9s/PPHZpxpYOiS2QtFXYfhyLmdONJQ\ngU2bNyAhIaHNz4sxBqvVqjhqJh8REf4OwGWfq3W6amsCVZk2UJSO8WC1YJHOlojFXsbCekqHwyEW\n/lHr6HagyYOp0Mc0FNvny5TsYB2P0tkC7qbxKwVTT/tHeD9ZLBYx7Ov1etTX1yMhISFkVY/r6uqQ\nlJQkfo4J22AymULy+yNUdLyxCQkTGkElXmtsbMSCBQuwfv16lJeXo6ioSHE6r7Q6rZqrs7obEZFf\n6Ci1p5CGKCnpOtNgtk2wWq0Y9+Aj4C6asH3vTsTrHGDcMTh5Dc43xyPJnIFmux42xwX0zErF+QYb\nHE6Gsjwzdpy0Is1sw5/KeDz7XQ2MmTZsPbsc+fld8dWi1W0Op94UQPJmREQ6ChVJF/Rqnc7pru1N\nIKvZytcYh2q2hFpIp/EbDAYkJycrnhuVRrcj6RhXooaqxErncl9GWv2ZwSEPpt6OmHobTIXRUuk+\npdYvhJBoRwGVtIrneXz44YdYvHgx7r33XkybNk3xAziU1WmDydsLemmlT2GdpdPpFFu4BPMCbe/e\nvais2IVkQzsY0IS7r0zBjYU98esZK2Z/vg/HL5xHdmpnNNv0WLqpFgM7G+HgeLz2Uy0OnbPjlXUX\nUCIv5wcAACAASURBVF3nQFq8Fpy1ARs2bW7zc5FeoLZlKrenqZPCPg/ndNXWRGLLHG+q2XpzQR+K\nqcxqJl9j62kav7vzihqmZLeFGoKpJ8EOrcEKpjabrcW5VGkab6gDKgViQkioxc6VBGmTnTt3YuLE\niejVqxc+//xzpKSkuDxGGDnieT5qL1A9XdALI8bCBY3NZhPXXSpVtA2EjRs3Ij4uFb8tvAtb9i3G\nrcWdUNN4Cl0z4tAzi6GmKRG/6XETNlatwrZjh3HHP0/A4uCh0zKkxcfhL5en4lyDE6/+UIO6w7/i\npZdexn333evzmib5dNZArokSLujcXdDzPO8yui0f5Q6maFtn6et6P+lFq1CZNpK33xfehBNvtBZa\nhX2uptCq9mDqSSBCq/C+D1YwFWYgqK2YHAVUQkgoRV+SIF778ccf8eGHH2LgwIEoLCxE//79xTUl\ntbW1mDVrFvbt24cFCxagb9++Lt8vXWsYCa0jAkk+apiYmNhifY50VMRd6xtpcQxv2Gw2/PDDD2hu\nbkZ9fT06tusGB2dHo82J6trTSDXb0DFVD32cE93aaWG11+Om4r/gvR/noF2iHtlJJhypacCs4dn4\nbd9E8IzB7mR4+ft6LF3yMTZt2Iy33nnDq9cwVJWJ5bwZ3fbUUzEQxZjk6ywjPZh6onRBLxz3wvRU\nAGIBl0ifrtqaUNyU8GVKdihDq/ScByCigqknvoRWIaTFxcUptgtqSzC1Wq1ile/ExESvbvCGYwRV\njgIrISSYKKDGsJycHHTt2hXr1q3D888/j6qqKnTr1g0pKSlITk7GFVdcgXHjxrn0NJW3zojmC3Ql\n0nWmSqOG/q75UwpRNpsNY+97EBeqLUg2p2Hv8S2IYwb01BTiQqMTj68+hlsHJuHweQf6dzTipv56\nzF+9FD/u1cKoc4BnWjh4K05f5FB12oqiXAPmrDyPjYeaodVokWzMwsb1m3DkyBF07drV7bbL1xgH\nszKxt3wZ+fNnXau/U5kjXWtrbCN5umpr5L0sQ33O82ZKdrBmEwjHvdBzM1qCqSfS0CoUumKMwWAw\nIC4uDjzPi/sEgLivha/7GkyTkpJUPfPIXSCmyr6EkGChKr4EwKUPmp9//hnjxo1D165dkZaWhj17\n9mD37t3YuHEjMjIyxIsjoQ2JUJ03VsjXGvo7YixcYMr7/CmFqM8//xzvvLIctwz+C5otFhw4thPL\nN74Ge3MNemdrkWLWonuWAb8fkIzSLka8s/Einv72PAw6DQo6mjDj95lIMMbhu32NeHz1OXA8w/AB\nSbijLBU/H+Tw4vd1MCZk4bOVy9CrVy/F5xoN1VmlFVWlU4XlF/TyEBXMqcxqJx0t9/W4l09XFfZ7\npIRW+XEfyD62wRDIvqGxGEylhNkYQuErpboCwvEtvEdaC5rCZ4jVahV7grflM7SpqQlxcXEhq6Lb\n0NAgvvcFPM8Hvad5hKMdQ4gfYiddELfOnj2LqVOn4vz583jrrbeQn58vfk0oBORwOMS1UMLdVGFK\nX1tasUQSeXXaQI0aSi8aWysOVF1djbT4LJw6dQq1NRfhtBvQ3GhFTrIW1/VJw8Fzjag6Y0fPbBt2\nnrRgyboavDSqA97aUIcLTU4s/voCRg1KwfV9EvFZZT06pOixdn8TxlyZjrsHJ+CLHTVoNJnRrVs3\nl22XrjeL9FFDpWnVntb8CaMnwsVYLE1j96UAkDuhqCAcDNL3fCRVJW5tpFWpB7TSTQIKpv8NpkrH\nvXQqr06ng1arFdvrKBECrPAZkpycHBHHk4CKJBFCQo0CagzjOA4vv/wy3n33XUydOhXXX3+9ywes\nRqNpEc6EO6attWKRh9ZIvLgJ15ROpeJAgwcPxrtvLIPWkoQOafnYsG8l0hOy0Wg7CJ63Y8cJGww6\nhoWrz8Jq59G/oxG/HLnUO2/csAzEaTV4Ye0F9OtgRGGuGR1S4rDhUDMmfHQa44ZlwMbxuOLysha/\nUz6VOVovUpVClDCdVVrsSrjADMa6VjUJ9jrLQFUQDgZ5u5xoKPrm6xR4gcFggMFgiLp1xO74Gkzl\na0yVQpw0mArthwLxGUJVfAkh0S6yP3lJmzDGsG7dOkybNg033XQTvvzySxiNRpfHCW1jlEbOvBkZ\nkV9kytdEqfmiR1qZOJxTOn/88Ues+XYdzGYjcrpkYNWPbyBOw9A9E7i9JB2/HNbj3U21WPrXXDy7\npgYjilPA8QwrdjRg6caLeGdMLrKSdLhocWJgJzPWH2zCnOFZGPN2NcZfm4EUcxxmf3EWZpMB1Ud+\nBeDflM5I56mPr3xKtvT4ViqAFWn7LJzFn3ytIBzo/S0EU2EULBqCqSfSNZY6nU68GQVAXFvsdDrR\n1NQUcf2IfSVdW200GmE2m30Kpkp4nofFYhHPI5E2YkoIIeEWvZ/ARNGJEycwadIk8DyPt99+Gzk5\nOS6PkQYUYZ2pNxcj3lxkqr14SqDXmfrjm6+/wQuLXsPA/KE40VCDn37cALvNgS4ZRjw7sgQ8fwp/\nGdwR975zAit2NaKwkwkPXJWOiuNWdEjRY8Gqs3BwDDmpemQn61DX7ER1HYe/L63G365Iwx+Lk6GL\n0yIrWY/5X9YgJS0DVqvVpQASx3H45ZdfYLFYMHDgQKSnp4dlfwSTN+HM05Rs6UwC4fiWX9Cr4fhW\nIh81VMt0VqXqqoB3+9vbECWfwm42m1X7OgWDdG21UlVawHV/C4+P9NAqD6ZK/Yt9CaZCqJfe4EpJ\nSQnKTR61jKBSkSRCSLBQQI0RNpsNTz/9NL744gvMmjULV1xxheKHcaArtLq7yHTXzzJYrUFaI19n\nqobKxB+8twxXDrgFnbLz8dJ/5iArsQs65HZFTf23uNh0ALo4DTqmGpBq1uLDrXXITTWgwerEdX0S\nUdbFjOxkPR54vxqTftsO5xudWLWrAYY4hrMNPACG/WdsaLAy2Bw8TtRymHv//8LpdLbYdpvNhvvH\n3IW6Y7vRLlmPWeeAfy/9CD169AjrvgmUQIQz4cLV3fHtqdVQOMOQPJxFyqihN/u7tZtgsV4AyJtg\nKlDa30rr5Nt6kyDUhOcbqGAKQDz2mpqaYDKZghZMBRqNRqwPESo0xZcQEkrqvxohfmGMYdWqVZg7\ndy5GjhyJ1atXu0xXFS7WLBYL4uLiQhLOWpsiLC3mEcy79WpvHWJ32LD65w9x7NRhDOh0Bc5ePA4b\nB+yqtqCksxkfbanF9pNW3HdlOnpmG/HN3ga8vaEOPbIMGNYrAdUXOXy9txENFiesdh7dO5pw0WLB\ni2tr8UlFA7KSdNh61ILu/UvRq1cvOJ1ONDY2ivv5xRdfhPZsJT4Zmw+T0YD3N5xB+cwpeOv9T8K9\na/wmncIe6HAWjFZDgSSvShzp4czd/lYqfiXdTuk6y1jgSzD1RGmdvLubBPJZBOG6KROsYGqxWOBw\nOMTziNJymUhHa1AJIaFGATWKHThwABMnTkR6ejo+/PBDZGZmujxGutbSbDaHtX2G0hRhoGVrEE+j\nI0oVWj1RyzpTdwqLB2DxE4+jU3pvOJ0crPZG8M5jGHtVOt5YfwbvbKxDvdWJy7vH49aCJHy7vwlO\nJ8OafU3gnDzGXJGOtzbU4dczdhR1MiHJZMeRC3YwpkVpFzNeHp0DXRzwWWU9nvy6Av/vtt/jy+9+\nQFxcHJxOJ95/7z3866XFeHhoAhrqLsBhjseg/CS8vPG4WDwoEkON/HUPVThra3EgaeExf5+ndNv9\nCSiRQB5ahXDmdDrF97pwPhHOHWqoIBwM0nCm1MM2EHy5SRDKmQTC6y683wMRTIWf6XA4xJ/Z1NQU\nsvdSOKb4hnrElhAS2yigRqHGxkYsXLgQP/30E8rLy1FcXKz4gSx8wIZ7rWVr3LUG8WY0SuliQ03r\nTN1Zu3Ytnly4CO1TuiPJlIGO6d0wuNdwfLd9H/aesiHZqEVhJxO6ZBjw6xkrnlt7AZ3S9Lj3ynSk\nJ+jwxY56HF5+BicvOvDn36Th271N6N3BjHYJGizbVo/SLmZotUBGog43DUjCK+tqUXfyAEaPHI3/\nfPgfTJ0yDR+//zoeuiYdX+1qwD1D0tDc1IQ3frahT79BsFgsAblJEEpqLP7kzbrtQKz7k6+3U8O2\nh4p025XCmfxcItwAA0JbQTgY5MFUKZwFkzehVV4BPhChVfgdrY0WtyWYWiwWcBwHk8mEhIQEj1V8\noxXHceJSIEIICQYKqFGE53ksW7YMTz31FMaMGYMVK1a4jEZK19yFulJnIPnaOkG4mBem9KplnakS\nnucxbfIM9Mwug15nQrwxCTzLxr6TG1HXXIsdJy1otjkx6XftYIjT4oH3qnGmnsPsm7Ow55QNp+s5\nxBu0yE7Wo/oih6qzdvTNMeF4DYc1+5owMNeEb/Y14p4hqahp5PDBlovomxOPc40GrFm7Fv9+/d/Y\nuK4SWclpGHtNDzTbD2DI4wfh4IEu3fvh3RefQlJSkqqmrLa2P/3t5xlKnooDKa3783RhLw/loQ4o\n4SRtG+Jp28NdQTgYwh1MPfFl+ruvoVW+tri1YCq8z3wNpomJiWHdn+EqkiRcOwjvqVgJ5ISQ0KOA\nGiV2796NCRMmoHv37vjss8+Qmprq8hhpK4FIKYjiC08FmYTiT8ClC0zhAkhpbVSoLzwOHz6Mqqoq\nJCUloaysDFarFRdrG9C/22+xoWoVenUowf4Ta3BtnwSMHWrEsRpgxY56PPrJGRR0NOHuwSmY+flZ\nbDtmwRNfncffrkzHny5LwfKKBiSatLjzsnT0yDLg9Z8uYMvRJnA8Q3WtA5c9fhCJRi0SDDrcXpqJ\n1362w2gwYfeuPejZoQS7DpzB17tr8Mj1XTGkRwJmfW3Ha2+9L1bxbctNglBe2Kux8JU/Wlv3Jy82\nJkzL0+v1qltbHUzyUK7UNqQ13lYQVltFW28q06qRN9PfW7sBFoxgKrzGQtD3FEyjdQRVOMc4HI4W\nAZ0q+BJCgim6EkqU+vXXXzFnzhwUFhaKf7KzswEAdXV1mDNnDnbu3ImFCxeiX79+Lt8vHUEK1voj\ntZKvsRXWG7Y2rS9UPVs3btiAT/69GIO6GrG31oH16/qif9FvcLG+Ds32RpR2uw4/7/0cOal23HVZ\nKmqaOPx80I7/vTYDndP02HrMinc21sHhZHj8y3Po3cGEG/olws4xDOxkxmULD6JDahaMBgO6ZDTC\nEKfBlBsycVtRMtbsbcRd/z6BCw0M7/5iREpSJ7TvyqN/QX8s27oaNw5+GAu+ehEzPzsOB4Dn//kW\ncnNzPW5PIEf/2kqtbVOCQT4aJUzdt9vt4mvA8zyampoAwCVEqXH0r61CMVLuSwVh+XkkmOssIzWY\neuJNaBVGiQU6nQ4Gg8FlXXlbgqmwjEENI6ZyoQjDwnnUYrGAMQatVovk5GRxP0RjGCeEqAcF1AiQ\nnp6OYcOGYfv27Vi5ciW2b98Oo9GIrKwsZGdno6SkBPPmzUPPnj1bfJ98BEntUxsDqbU1tt6u/Qv2\nxeayd17FhN/noXNWMhhjePKT3XhiwQ9o///ZO+8wN8pr/39Go15W24vbuldsY2N6J6EklEsPSUho\nCXBJQsoNJLkhHdK5BAgJIUAIvfeOKQaMccW9rbt3vb1pJY00RfP7w79RxrJ2V1u0K63n8zx+7GfX\nu5p5NaM53/ec8z2Fk1i86SUULY6eaMVX4iLgFli1R2HOGDdHT/SiqDqXHRnk2kfD3Pe1UVz5r1oK\nvXZCUoKErlPfqaElYPnOOg4fW8gzKzuw2+D4yT6icoJ51R4OG+Oh0zuTcCjMrPnj+dPtf8Dr9fLR\noo95e82jeAuqcBUX8ujjD1NdXd3v8+wp+2cEnAMtEc7XsSmDQbo5rrma2R5sUoXpUGfKh9McaCQK\n054wPhuMa1RVVQRBwOVyJSsGjM0p2L8hY4jW3oSp2c3e2NDsS6/2UGdQs/VaxmeJ4XDu8XgAkp+r\nQ3EMFhYWFodG9JbnlJSUcPXVVwP7HwjLly/nu9/9LuPGjWP8+PFs3LiRZ555hocffpjJkycngx7D\n+n4kZ5BSGUhZZ08lwunKKNM5f/alRFjXdaLhLqqKxwOQSGgUu3V27tyJiyK0RCNlfoEvLShmc4PM\na+vCaAmdDimBqum4HQIlfpFSn52gW8RmE0joOk8u72BKuYv7PmqjusjOw0va+MOb+8uBZU1nW1Oc\n8SVOOiWVvR0Jnnj070yfPv2AY3vwoftZv349siwza9YsvF5vZm9AHzAC+9Q1STdqCHru/RtpY1My\nJdNscW8lq6kO2UOZ/esvxvUhy3LOlXD3p88ytWqjJw41YWpg3oQC0t7rxmaMcU0ritJjmXdqprCv\nwnQ4yMaxGa0w5pFrxtoqijLor2dhYWHRE0IvO2DW9lgOUVtby3XXXYcgCNx2221MmjTpgEDG/PA2\nduuNuX8jubTPwAjUjV3fbIryVCFlBJ3QNxOVu27/HWXhNcwbLbBi7Xb+vSTMZzWt+FwKMyttqDpU\nBOyMK3ag6zpvbgwzptDB7DFuxpc4eX1dFx1RjdaISkWBg7suq+L5VSHaohqf7Ylyy9nlFPvs/PSF\nRop8DjqjCjtaFI4c72FrU5wOxcPG7bU5fy2Y19i87obxla7rOJ1OXC5XzoiUbJKaLTYC9cH63ebs\nn3mtzdd0JkIqG5h7yu12O263O2/f81TRavzprorA6K891NyYDREZj8eTGdN0wtT429yDHY1GCQaD\n3f5Ow5fBGLPW3/U0BG42NvNS0TSNrq6utF4TfcWoNjKME9ONXFNVlUgkcsA6JhIJ7HZ7zo1nyyFG\n/o1pYZFFrAxqnqDrOq2trUiSxLPPPguALMsHZOyMXXkjUDf3WhpBULrSvuEeoD5QhmOuZboSYej7\nzNaLv3wFV37lS/yloQ6HPUA0UUhCb8YpqjRHRKoKHESVBLtbZaqCdnQdrjuxkA0NCne+2wIInDM7\nwPYWmfV1MTqiGl8/toh7F7Wyu03lR8834RSh2CfitOvUd6rYBIgpCaJxnYICH7FYLFnGlauk9v4Z\ngbqiKMn32wja+pORyieynS3uS/ZvKB2bU8uYR0JlSF+NxmB/2arb7c77c8+EVGHaXcbU+Du1xzTd\nBrxxHRlZ2IEK03zF+Aw1twB1t8k1Ug2gLCwschdLoOYJRtA3fvz4A4J0swA1xqcAyZIcI2B0OBzd\nlq2mlpsNpGx1KMnFWa7dzWztrtfypRdfZvKoEympnkhl0Rj++dptFAcq0bTdnDengB+fVcp7m8P8\n5d1WFm+P4rbDDU/UAwKiANecWEyZ38758wI8+HE7P3y2AQFoDGn8+8oxzK/28FFNhG89Xs+4Igce\np43rTy5E1nRW7YkR9BVgs9m4+YffZ9XSxYwaPYYf/ew3ac22cgFz9swIqlKrCHqbaWkWsMN9vfQF\n80ZMdy6l2aInw5rUzS8Y3FEsZmE60k2v4MBybKMKBkhWB5j7LEdCD3E6UoWpURGTrpQX/nN9pj6r\nzMIqXW/lYN5DRsZ2KBiIYDQLU6fTSUFBwYi+nywsLPITS6DmEZIkHZDpMgJzURRRVTVZcmM8uI0A\n0sB4eJuDn+6yJOb+v9QgyAg+hysISmcIk8sZstReS0VRWLx4MR0dHXz4wceIHeWotnpiXSrxeJSg\n20tCEzl1mo8XVnexsT7OVccX8e6mCJ/ujHLuXD8+l43HlnawoS7OlAqdV9Z2cdxED3PGuHhhdZjZ\nY+ycMs2Pru/vV3WI8MaGLv5x+WiOmuAl4BZpDGmEqo/jpGOPoFRv4hdfKGNr4zouveBs3nz3I8aO\nHTuMq3YgqfN7e+q1zMT8Kl1wP9zXdXekzvPMhY0YOHCtuxvFktrX2peKjUPJjTkV83vekyNxd+7Y\n+Vod019havy7u98Zj8eRJOmg3spDCWODS5ZlnE4nwWCwT/4MVgbVwsJiKLEEah4Ri8Vwu91pv2cu\nb0rFEKlmwWq25jdEqxEIOJ3Og37eHASl9qQN5RxRw2UxX82fVFXlD7/7M827w9gTHpZ9shKfA0YV\nVZAgiCRHaO3qpMibYE1tjCU7ovz1sir2dqgE3Tbiqs4ZM/3sbFU4bbqfH5xegtshcmpznG8/UU91\niYOQpBGVEzSEFJ5a3sk7G8N87ZhCPtke5ekVnRwxzkNLWEXVBTZt2kjtnt0s/M1UygJ2Tpqis6q2\nkXfeeSdpzDWcDIYzbybmV+mu69Ry7KHGPM8zW2NTskFvo1h6c7UFDhAph5Ibc6bC1KC32bjZdBAe\nTFKz5MZIsNT/0x9havzt8/my2i85lCKuL69lfH4alUZ9Eaa9vZ4lWi0sLLLFofHUzzNUVeW0005j\n5syZybmns2fPRpIkXC5Xn3+f+WFkFnRGibC5VM9sMGEWrj1lW1NLKQezvM/A7Fppnmeab6xbt44d\nG/cxf+IpbNy0kYmlhRw3oZXyQBur9uxiV6OIPxDnzFkBnl3Rgc1m4431Xby4ugtVhy5J446FLcwa\n5Sah62yqlzmy2o3fZSPoESgP2FlfF6PQY+OUP21HSQi89K1xFHpEvnVqCWfduYt/f9rBhzUSoViC\nuLYRhyjQFdUoC9hxiAJhSc0J44tDtddyuMemZIO+rLWB0ZZgGGHl4/2eKX0Vpj2RbQfhwSRVmKbb\njOivMDXcaAEKCgqyeBbDR0/3hbGZq6oqbrcbr9c7qO/tSL4fLSwshh9LoOYot956K2vWrGHlypX8\n61//YsOGDQQCAaZOncqtt97KYYcdxnHHHUdZWVm/HxS9ZVuNwDzTbGtqKWUmJkG9kdpnmu/jFNau\nXcu+3U3Yw2vZUbeZuaOdTK8sREkILBgbY+mOVr4428dv/qucSDzBpfft4S/vtfHHiyoYW+Tgr++3\n8cqaEJICx0708NSKTjbVx9ASOjbBhtsucNMZpTzyaSdOOyR0KPPb0XQQbQKlfpG/LGzh+lMq+OqJ\nM6hr2svtb2mcc89u/veLZaytjbF4l8YdZ589bGtk3owwHCWHu9dyKGaIpvbXjgRh2hPmzS9BSD/T\n0tzXmk5I5fNnAQxdlrynHmLzJuNQbcb0VZianxc9CVOzG61x/3R2dg7acffEUGdQ05E6y9XtduP3\n+wftvUsVxFYG1cLCIltYAjUHsdvtnHTSSZx00knJr2maxh133MHevXuRZZmHH34YURQ59dRTDwhi\njV3xwRCtvWVbDVL7WlN70noyCerOuCa153AkBOtdXV08/cRz7KrfTSwq47J7qW2rpyUUISprqIkE\ngqAypsjO1sY4NkFgQqkLpygwpdyFaIMrjyvktXVdKFqC7c0yJ0z2cO+H7YgCFPvt3HFpFevrY7zw\nWRf3f30UNz3XyMtrujh2oofXdkbZ3apwzEQfc0Y7icWjiILGqdN8LNsZ5acvNhKSEtxxzz8pLi4e\n8vUxZw5zaTOiuxLh7vr/+lpKORLdaTMlkyx5pj3E+SRac6F8uy+bMTA4JmOZGF6lE6a93T+GME11\nozX/rpFMtme55sM9ZWFhMbKwBGqeIIoiRUVFOJ1OrrrqquTXjWBCUZRkgG/MPjUHEsZDPpvZ1lQH\nQ+M1u+uTSg06zQG+cQ5DnUHLFpFIhJ//9NfE2xwU+0ZRFhiLQ3SyemeUEydDkddOa1TF7xL5uCZK\necBOWUAkpiSIxPePhfE6bYRiCaqCdr7/+RL+saiNhz7ppCzgoNwvsqkhzuIdUew2gWK/SMAjcudl\nlfzm1Wb++FYzakLn9xdUsGpPjLW1USqCraDrNIRU/nRxFUdN8HD5A7Us+WQxl1122ZCtTb5mDnvq\n/+tpM8YsCgyBdqiZAJmFaW+OxJn2EPd3g2AoyfXy7XRrPRgbBNkQpsZaGsI0F9xoh1IMG1UGxr0E\n2R2ZY2wcWxlUCwuLocASqHlEunmVRr+RpmmIoojT6Twgy2mMJDAyoKmiNZvZVnNPq/l4zcLVnG01\nhqprmpb8mnksQLoS4VwJPHtj6dKlEPFSFhjFvlgtbeEGfK4AdptARFb59mllTCh28MDidu5+r5Vd\nbQoTSxxEZI1drSq/e6OFyeVOPq6JcP1JxVx8RCFLdkjsaJa55YtlOO0Cq/bE+O3rzfzs7DJ2tyo8\nvaKT8+YWcMxEL8t3SXicAtuaZBAEnv8sxMZ6ma2NMaZVOPnh50t4ZX2YDimBDZ1oNJr1AD/f3Jgz\nIdWxGQ52xzZKKQ3Mw+4PhV7LwSrf7q7XMt0GQbrxWUN5reW6MO2JvlYQpK6xoijJecWDJUzN8zsz\nEaZDcV8N5X1rrJcx93koZrlaTr4WFhZDiSVQ8whJkjIye0hXugXpgzcjU5lOtA5UuKaKVuNvo9fM\nfLyqqibL3YzRAubjTjdv0byDn+vjFCKRCBu3rCIe2o4cb2H6aD+iWMCmvTEmlXqYPcpNh6Ry2Gg3\nY4sdeBxQGrAz3e+i2CdTGbSzplaiJawxd6ybzqjGvg6FqRVOqgoduOwCWgKau1Rufq4RLaHz4OJ2\nbn+7BZdd4MtHBtnbofDXD9q4ZEEhT147jrp2lS/ft4suSePYP+5kVKEd0eHh3PMvTG56ZMMkyDxK\nwhj7MNyZj2xiXjvjOrfZbMley3QBfrpZxPmMWZhms3y7tw2C7mbjZmvTyyxM86k6IBN6cxBONc6D\n/Zlz43Pb+P/m35WpMM10fudQ3zfZFnDmkTm6ruP1evtlnGhhYWGR61gCNY+QJKnbMTOZ0FPwli7b\nmipaByPbmhqcGa+lqmqy/8zYcTf/TLreVrNoTR2nkC7AH64gX1VVXnnxNeTOrcwb62VmRREnT/Ox\nrUkmJntYWxfn050RorKOx7Hf6CgcT9AYUtncEOe28ysYXeigK67x6Ked/GVhK7NGu/l0R5TtzXYu\nXRBkbJGDpbui+F02Zla5WLgpwgXzChAE+KgmgqTqOEQBmwC3nF2OaBNZvUfiiAk+jpvk58VVHexs\n1bj+uzdy2mmnHXD8mWwQZNoDaC7tTDdKYqTSU+awp3EsueK22l9SZ7gOR1/xcBhfjWRh2hPGkTkE\nnAAAIABJREFUZ7ksy9jt9qRzbDoHYVEUk+vSmzCVJCk5vzMXSnnTkc3r2lzObLfbCQQCRCKRIV0H\nK4NqYWExlBwa0eEIobc5qP0JgLrLtppFqznbmipYByvbajgCG8GMEUCaZ7ca/y/VlKmncQpmk49s\njL/JhPXr17Nl7S7mjqsmGmukQxJYXxelwO1m7lgHf3u/kzveaWVcsYM9bQqhWIJin0hNUxwBKPTY\nkBSdioADuwhvbuhiyY4IDnG/GP3W4/Voif2Bg8susGxXlOtPLqIhpCHJCY6e4GHTvhiFPjuCYOP4\nP+xk9oQSlm9t4qnrJnLy9ADfO72cOb/ewWGz5yTfZ/OaG2vWV5Mg49+GkclwOPMOJ30VaJmMCEmX\nARyIcU02yAUToJ7IlvHVoSpMDcdlQ5imVkUY16b5ug6Hwz0KLGNTxxCm/ZnfCel7J7PFYAu4/pQz\nZxNLoFpYWAwVlkDNIwaaQe0L6cbApMu2po6A6G+21RxA9GbIZPzb3MdnLitOHX9j/LxR3tybmBrs\ngDIejxOXNNbubqK6qBOn3UZjyMYHW5oJx3QEdHY0y+xsVYjGE3zntGKOneglIif489st3PhUPTec\nUkI4nuD5VSFuv6SSvW0Ke9tVbjqjlHZJQ9N0Lr1vDzYBErrAMytD3HhaCVVBB396u5nN9XG+fVoJ\nn58R4M732gjFBdxuD9/49x7OmBVg4cYwJcWTePCeJ1ixbBW3/Px/u12HRCLBzp07URSF6urqA/qi\ne5ptaRYDxtrnkmgZTFJ7Dgc617K3DKDZZCx1E2YoS4TzudcSei9bTZfVNtbXKN8+1IRpb4I8XY9p\nT0LHLExdLle/helQM5j3WKo4TydMhzqj2Z351Ujvm7ewsBgeLIGaR6QzSTIYKhOI7gLlTLOt/Q00\nejNkyiTbmpoBTCemzJmpVPHa337L1avXUN+6h+piN8dNclIWsKFqCdqjGntbFb44p4CL5xWwtSnO\nPR+0M7XChaTsL/c9cYqPfyxq5fa3Wyj127lofgFzx3jQdYFOKYGagOpiJ5sb4oDADScX8caGCEdU\ne7jq+CKW7ogi2sDpEOiUNG48LcCMKhc3PtXA7ReW8ce3mllbKxHV/Nx06R+w2UReWvwgn3zyCSec\ncMJB56MoCnfe8Vd2bNqL0+HG5lH5yS03UVZWllxzY72Ma8LpdOJ0Og+4PlJdQHO9hzhThiqDlqmz\nrbEpk656YDCPayRnDnvKahvlrMbnhuGsKstyTrQXZIv+CtOeHGA1TUOSpOSYqcESpkMp5Ab6Otla\ng8EgdR2tkl8LC4tsYgnUPGIoM6iZYg6UzXSXbTWX5g5UtEL/sq3GMafLtqZmpnoq8+vp2M0Otc89\n/RwFnkKi8XrcDjfzxpYQlcMs2hpB02XmjXXjdtoo8Iioms7uVoXZY9yEpAQfb4vy5aP2O/bqQETW\n0XQ4eoKHh5a047K3c0S1h79/2Mb0ShclAQdnzvLTKSVYsUvihsfr+e0F5ZQHHDywuI2fv9zI5ccU\noqgaZ8/288DHrTz1zbGc+pcm9jRuZ9KYGZQEqmhtbQVg5cqV/PbWPxDqDHHmF05n9pzDqN/WwdnH\nX47NZmPtlmU89siTfO8H3znovFOdedNtEJgzU+Ye4nSiNZeD/FwZlZNJifBgGl/lynkPNcZ1bgg0\nw/Cquxmiw9VeMNgMhjBNxehLVxQFt9ud7FvNNwbyfvZnDSyBaGFhMZKxBGoekYsCtTsyzbbKspx8\nyBqurkORbTWOwcg8m/+k67fMZL6lccxGECcIAp2dnXS0RJlTfTL1ratZsXszs0Z18umOCBv2Sdht\nAi1dGg4RnlsV4qL5Bby1IUxNk8zKPRKNnTLTypycMzvAij1Rnl/VSXtEZWyxk2hcZ3VtjJfWdFHm\nF3GIML3CyfyxHi68dw972xXOOszPcZN8qAmdX5xTzkl/2smK3TFCksa/Pm5lWoULmwCxeBSAjq5W\naltrmDLlcrZu3cpXL/sax0+5iAmlpTz76EusmLaSeRNOS67x6PLxrKtblHTm7ctMz0zElDnIH6ys\n9mCSKshz0ZE4GyZBZmE6UkYEZYK51zKdQOuurzXdhlc+VQ9kQ5jC/jEpqqridrvx+XxZc3bO1Qyq\n8ZmpqioejydrazAYWILYwsJiKLEEah4Rj8eHtcR3oHSXbTXmn4qimAwAjfE33Zky9Yfesq2G4DSP\nwTFer7vetJ4yJgCPP/4EDs2PXbDTIUVZuCnEqt1h/C4bJ03xc+IUL//6pIPWqMbeNoX/PrmYL87x\ns6NJwSnCG+tVluySmBFPsKNZQdMSjCmy8+yqDsIxnboOUDQd9AQFHpG/LWrHJsCJk728s7GLIyf6\n0BI6DptATUOciJzg2IkeEgk3v3q1iT9cWMVPXmikviPOW8sfx+f38a0br2PmzJncddddTK04ijkT\njgPgTPfXeezj31HqH8v0ibNx2J1s2b2WCdOrCYfDCIKA1+sdkDNvJmKqP+Y1g01/BHku0V+TIGMD\nxhAq+Xbe/aU3YdoTxmdHX6oHhvp67o5sZUwlSQJIbm7k+rMrEzI9B/NnRyKRwO1292sNhqMH1RKo\nFhYWQ4UlUPMISZJG5Myz7sZtdJdtTRWsAwniesu29lQibA7GjOM0HGoTiQSvvvIabzz3PlI8SmtX\nPUJiH8dMDGIXZOKazg2nFlPgFlET8PAn7bRFVR5b2sH8cR4+N93H85+FGF3k5I5LKvj7onZsgoDL\nKbJ0p8SCcR4SCBw/yYuiJnh6ZYgtDXE2NcjYbQJepw0lAZ/URLjt9WYmlDr550dtaAmdD7dGOH2m\nH7fDxr+XtDO/2ofXaefue+9g5syZSYHpcrmQVSl53nFl/xzeo0+Zw0tv/wtBsDF2fCXnX3g5brc7\nOSZosMmk3zK1jHygpavdYYzRiMVigyLIc42eTILi8TjxeDz5dfNMy3wpxe4r2coUD3Updl9JdWEe\nDGFqiDLDydsoZ81Hd93eXqs7QyFFUZLi3O1243Q6B2SelguCMR82xy0sLPKPkRNZHQJIkoTX6037\nvVx4UPWXdA+4THtbu8u2DjSIM/dNGhhi1ewIbJR4iqKIy+U6wKHyH/f8EzUqEomHqGlYjdeRYFyR\nQGXQw8JNEbSETk1TnHHFDqZXuagPqSgabGyIc//H7YwtsjNnjJvnPuvC7bTxxo3VrK6NcetrzcRV\nndlj3FSXOLj97VZKfHZq2xX8bhuPXTOWyeUuXvwsxK2vNbFyd5R3NoY5cYqX5bskYorOg4vbKfDY\nuPTIQrY0aMQTdrq6ug4ImM8//3z+9td7eW/t0wQ9ZXy2eyE3/fR7fPXyL3PmWacjSRIVFRXJ/jsD\nWZZ55OFHWb1qLaWlJXzjuqsZPXp0v96HnugtyDeuj8Gab2me4ZpNQZ5LpMsU2+32btcZRka/5XCU\nMHdXPdDdOmej5N08FmkwhKmxoSNJ0kHZQkmShszcbyjoaQ1kWUaSJARBwOPx5OWYrVwRxBYWFocG\nlkDNI1RV7TFbk28PvP6QSW+r4ayZrWyrOWAzgkbDpVZVVQD+ds+9tNRKFPsq6RLX4nHEUTWBFXs0\nfnFOGUu2R/npi00sqHZjtwk8t6qTc+cGuXBeAZKSYGq5kw9rIizeJlHkE/nVuWVEZB2f08Y5c/zc\n/1E7o4sc/OT5BsaXOBlX4mBqhYOtjQplATudUY2TpvrQX4P2aILlP52EaBNo7lI4+c+7UDSd1i6V\nn73YjNdbxAlHXEx9ff0B51tRUcGbb7/OP+69j472Tv7wvVs59dRTCYfDFBQUUFZWlnYt7/i/O9m8\nYi9zJh1LY30dP/juzfzj/nsoLCzs17r3hXTXB2Q2Zqg74ytj5MOhNMPVEKZGL3Vqpri3dU7Xb5m6\n1rm4hqnCdLhLmDMpeU/nit1X0ZoqTNONRRqIMPV4PAPKFuYjxrUUi8Ww2WzJzZ3BWgNLMFpYWIxk\nLIGaRxxKD/e+kEm21QiYs5FtTQ0ia2tref/NTygPjEFTVnLzGUEOH+vg2VXtPLS4hese2UepX6Qr\nptHQqdDUpVLqd3DCZC9HT/RQ26ZS16GwrjZGVNaxiwKvrw/zhVkBdF1nS6OMrCV47NMOinx26jpU\nCr0iKxrjdMV12iMqowudrN8XQ9F0Cjw24qqOTdAZXeikKmjHKUJDl5/DZ53MuadczpvLHmXcuHEH\nnVtVVRW/+OXPk0E70GM2SVVVPlj4IZef/kMcdiejy8fTsqyO1atXc8opp/RrfQeDdMKzt5JKo3Rb\n0zRcLhder3fE34OpJcx9zRT31m9pLsXOJbfmXBOmPZFJyXumfdrZEKZGGauu6zkhTIdSyBmfGbIs\nE4vFktn3kdAGkG4dR/rnoYWFxfCR/5+aFkB+94Fk69jTOfKas61GIJcu2zqQkrm2tjb27N6DqDu5\n6HAHE8pECjwJxpeIuB02JpQ6kRSdYyd5qGuXqeuAWaNdvLUxzNETvHhcAgs3RfA4bRR6BS5bEOSF\n1V2sq4vhc9pojWgUe+3YBIF/XTkGmwC3vd6M22FjV2ucL923l2kVLtbUxrjl7DLufLeVx5d1cPH8\nIE8s29+HmkBkxsT5yHGFB1++jRNPPYZnHrqHx/5xO4cffTJfvfIbOByOPhsB2Ww2EARUVcFh3z++\nR1HjBwTSuUJ32SkjaFcUJXkNGEF8qvNqvt5zqRjCNB6PD3oJc3/cms1rnM0S4XwSpr3R3Tp35z5u\nfL87syvjszK13z4dqf2VmZSxjrQMoNHyEQqFcDgcBAKBrArT4TBJMs8Zh/xuLbKwsMhtLIGaJ1gP\ngsHBnH1IFzD3lG01B8s9BV6vvfIGAXcJxd4qWiLL6JJivN+k8OyqTj433U97VOXI8R7cdoGGzv3i\nuEvSmFHl5vrH6ghJCVojKidP8bKtWeaxZZ2Azqigh1On+Tmi2s27myM8tbyToyfs70m+7qRivv90\nPSdP8bF0V5SPava76o4vcfLIVWO47J97ue31ZqqLHXgcNoKlE/nFzbcRCnXy0vuPYuus4YfnTSXo\nc/Hv9xbx5GMOLrj4sj4bAdlsNi657AJef/Expo2ZT3PHPhwFGvPnzx/Q+zYUpM70DAQCyaxranbK\nCPrTZQHzbdyKubfW5XINSQlzpqWr2RrJkg/jgQYDQ7QaaJqGJElompZ8nzVNIxKJAP/pazWEbibC\n1MgWQmbCdKjJtpAzDKUMAzGfz3fAbO2RjhWbWFhYZANLoOYRPbkDWgyMTLKtiqIkd8lTAzfjvZFl\nmQ1rNjNz7AIiUYmPtoZAT9ApqcysdNES0agucTK90sVjyzopcImML3ViswlsbZRx2wU2dcioms7K\nPTGau1ROmuqnK64xrtjJpDInTruN9oiGrOoomo4gQGNIxWUXOGdugM2NcWQVXHb4+oO1TC534rIL\nBAWB7c0yXbEEY6pkHnz8r5z/xcuIxcKcPMfHqNL9JcTnHjmaO99fypcvv7JfWbSrrr6SMWNGs2bN\nOsaXTuPiiy/sdjxSLpCJIU4mWUBDTEFuzmxNxciY5kpvbSalq92NZDFvFPTEoSJMUzG/1y6X66B5\nm8a1rKpq8k+q+ZkZs/GPzWbrlzDN9wyq0ZtueBAEg0FCodCQXU/pMprZfr18fr8sLCzyC0ugjiBy\nLQDOlFwtT+4t22oEzLquE4lEaG9v5x9/u5+aLTuYWFyExxEggZsl21oJegQiMQ2P08aZs/ys3B1j\neqWTaFxnRpWLsw7zs3pvjJiis6khThyNyqCDYp+dn32xlJV7Y9z1bhstYQ3QeeTTDgB+9UoTTrvA\n48s6uen0Ep5a3klbWOWX51Vw/uEFdERVvnTfXi4/Jsif32nj6uOLcDts/H3RDlasCrFuyyqOOv4I\nGjpbURQVHZ361gjBotJ+l+UKgsAZZ57BGWeeMRhvQ9YYqFjpTxYwF4yCzKZP+dBbO5CRLObeY+O9\nzse5tf0lVZime6+Na9ZmsyVFqVE9kkqqMB1s459sMNjCyqg4UBQFl8tFMBhMXmMjXcSN5HOzsLDI\nLSyBmifkqog7FDEHwUbmzOPxcNtvfk+kTmRC5WHU1++gK7qSL852sLdVZG+7ipoQqSq0U9+p8tle\niR+fVcbH26I0hlQicoJX1nYRcNsQbQJBr52vHBngd2+2ctNzjQiCwNmH+Xh0aQeSomND56xZBSzf\nFWVXi4LXBc9/FmJPm4ykJLhsQRDRJiBg55Rpfv6+qJ3vnFrC/5xRiqrpTKlw8tDiTra3dDJj5lfY\nXredv762ab/Z0t4EX//WdYTD4UEtq8wVzA61RpA9WGIl0yyg2SgonWjNxjqbDXHyQZj2RCabA+YR\nQ8bPOJ1OHA5H3pVh95XehKmxJul6TLsTpqmOtLnYV55NDFdiVVVxu90HZaGHmuHoQTVjXGOH2nVg\nYWExNFgCNU/obkcbLPGaC2zdupX33v6Q02ZdRoFdZF3n63z1aCfzx3q4e3eUH5xeiprYP390UpkL\nmwBbGuK0hFVW75V4f0uYrxxVyIlTfKyvi/F/C1v47RutnDnLzyULggRcNv73hUZkDW48rYSorLOu\nLs6R4z2UBews2hqhpUsCBMYUu1i5W+JzM/xE5ASLtkQIxRJUBu1o/78kuCJgx+MU+NV5pby69CP+\nfv/DrFmzhng8zo9nzqSiomLYBdVgk+pQ6/F4hsxds7csYDZniRo9cj05tY4EUisejPU0l+1rmkY0\nGu3V3TZfMa5vY+ZoX4RpOgxhKknSoDvS5kO2sac5rukYasfgoRao5rnIxkZXrr+HFhYW+YklUPME\nSZJwuVzDfRhZIZ8fcEag9/orb1LkL6Ut1Eyhv5gir4zb4WFNbZTzDy/ghCk+FFWnyGPjZy83EZUT\nbKyPc+ZMP2fOCnDPB62MLXLQ3KXy7yUdXHJEkLc2hDl8jJvmLo2uWIJ549yoiQQ7WxUCbhvjS+18\nsl3i2hOLmFTmZFerQktY5bw5AX74bAOTy51sbZRpj6iUB2z8/o1mppQ7cTsEbn6ugTNm+PbPVvXv\nd5s84ogjksGqEcBnIqgGMn9xqDAbAQ2mQ+1ASJcFhIHNbE39PYYb8UgWpmbMZds9lfL25G6bTrTm\n8roZ59KT0VVfhKlx/0uSlByVkm1H2mzSHyGXi+NyhhNDqKuqSiQSSQr1fH52W1hY5Db5+cQ5BJEk\nKaeNZgZKvj/4FUWlqKiUFdveIaFCwK6yao9EeUBkYpmT7U1xCr0iS3ZKlPntfGG2H79L5P0tET4/\nw8VR4z1saYyzs0XmmhOKOHysm6aQSkzVEQBFTbCuLs6yXTEaQwkK3DYSOnxumo/ygIMpFS6eXtGJ\nxyHwyrowv7uggg37YqzeG8MuCigqfGlBAf/7QiNdMQ1F1XlzQxetKyKcebbIPf/3WyZOO4wzzjr7\noMDVCGjN2SmzMEp1P04nqAZj3mx/MPdb5oIRUCZ0N7M1E0Fl7rdUFAWHw9Hj3NqRQqow7c15OtXd\n1vgd5ms5WxntwSJ1NFB3wtRc4pypK6+iKAiCkNfCtD8M1JV4pGVQzethbG4Eg8EDzAEtLCwsssGh\n8+TJc2KxGG63O+33rBLf4UcQFWKtH/HF6U421YepaRQZHbSzu03hiWWdXDS/AFnTeXlNFzedUUp5\nwE5FgZ0Ct43msMpXjy7kxifrcTkEjp/swybAZUcV8qtXmvC7BLriCZyiwJqfTWZtXYw/vtlCfaeC\n1yVQXiBS16Gws0XmjJl+lrzbys3PN1IRELHbBAR0fnB6KcdP8vGzc8rZUB/n2ofrOHWaj4piH5tr\nP6G0dBq7F6/nieZmrvrm9QBJh0gjk5cqWs0llb0ZSRkOyDA0Drcjqd8SMhdUqqoe8P9FUUy2B+Tz\n+XdHX4VpT6Rz8obuM9rDZXrVH2FqZJG7Oz4j2270mBobG9lkqMRcJq+T2mObi+NyhpLU9TA+P8Ph\n8CG7JhYWFkOLJVDzhGg02q1AzXfyfRe2pqaGd557lG8eU4JD0FlQXcSjn4ZYsnMf44tFTpjs5YOt\nEbY3xfE6BcaXOKgMOtjbprC3XeGNdV00hzViSoKWLo3Hl3VQ5hdxOwW6Yhof1UiML3Xy4BVjSOiw\noNrL+FIHcTXBk8tDBD12GkIqNU0ymxpaKfWLTKt04neJbNjXSYnPxmvrwkyrcFFeYOdfi9upKLBz\n0oxKKgttXHx8IfcvbuaWK07hWw+8xVevuBqn05nMuqVm34xRO8bf5oDF/DOGaDVmAqaa2AykfLU7\nDqWyVkN42Gy25Jo6HI5kX1h3M1v7MpYlVzH3wg1UmPZGTxltY22Hokfb3EMNpC1V748wNWZ4OhwO\nCgoKkps7hwKpPbYDNX/K9wxq6vVg7jk2qgksLCwshgJLoOYJkiSN6Axqvh1/LBbjgw8WUV/byFtv\nvUXA4WXyqMlEpQitoUY8TpldbR7CUog97TIxRacrluCc2X7u/bCd8w8vYG+7wr2L2hhXZKc8YOfU\naT7W1kqs3B3lgcU2bILAmbP8eJ02GkIqcTWBrOnsbovzyXaJcEzDJsCd77Vw9fFF/OXSKm57vRmH\nXeCVNWEULcEjV4/lsOoi7l7YxLWP1mOzCYQknWtOGY8/UIDTKeMQbSR0HR1A6P29MIJ1c2+fIVbN\nWT2D1GxraoYqk/JVs6DqyWVUluVDsqw13Zicvo5l6W2Nc4GhFKY9kcnoG3OJ8ECqBrItTJ1OJwUF\nBcmfGUohMlQZ1NR5oeaMcb732A4GqTNdzdeDQT6YWllYWIwcDt1P5DyjpxJfi6FF0zQeeuBhIs0C\nFcWjkDsctIYFPty8m2J3Jx1RB9uaVA4bpXHh4cV0SgmW7pTY3RqnQ0pw0lQfe9pkVu2OURYQ0XSB\n35xXzowqNxFZ4/L7a7lwXgGKpoMA2lad9qjKb19vZlqli9V7Y5w4xcv0She/f7OZu84vZ9YoL6v2\nSmxrjhNXdYIeER2R7zzVwOUnOoioNmx2J9f/z8/pbO9i5Ycvs0AspKmliY8/2sWkCdXc+cpmjjzp\nrH5lENKJQSMoNAJ3Y+1Sf8YI2FOD/UzmiRrZQ1mWky6jh5IwzXSmZyZjWVLXOJdGDJlHAwmCMGzC\ntCcGe41Tz3mwhKkkST0KkaFiOK6ndBnjwTz/fMug9jTTdaiOwcLCwiIdufWEt+iWnjKoFkNLQ0MD\nDXvb+dyR/0VMlhhVPobPNn3Kfe83U+L34XIEsds0LjzcR1kgyrlzvMwd7eKXrzby2Z4Y4bhOVdDO\npHInVUEbCzdHmVm136E5HEswoczBT19s5IhqDzrQ0KkypcLFpQsK+OXLzdx7+SimV7mJKQlW75W4\n49025oyReHJ5O6omcPJUH+OKHbyytgtRgO11LVx7cjkTSh0s//BNbv/bQ7R96SJeefZxImII+2SF\nWEkR4ybP4POnn4kkSclSxd5MVXrCHOh0l201i1fIPNtqzgIaP2cIt3xxX+0rqdnDgc5vNa9zrs1s\nNR+LWaQN5WigwSDTNZZlOVmGbdw3Roa7N2GayT1qzpBlIkRGmujQdZ1IJJITwny4MWfk+zLTtTsx\nOtKuFQsLi9wgf570hzgj2SQp345f13UEBOJyjGdfuJtArIZzD4N1+7z4vAuYVDGHjXueR0m0ICBQ\n6LWxbl8Mu83GxEoHPrfA144JMq7Ywb+XdFLfoXLZ/XsJekTK/Xb2tCqMLnLgcQgcN8nHjEoXb27o\nYm+bSqFvvyuwTYASv51IXGdXi0xjSEXX4axZfh66an+v6qnTfHznyX1MKHbwWa3MJacvIPJRE2vX\nruXEE0/kmhu+d4ARSmqJrhE467p+kGDtb48o9C/bag70jeOy2WxJkZbOfTV1/I3ZSTifGOrsYV/L\nV7PhcJvvwrQ30q2xMbpJluXk/zEynqm92ZluHvVVmBqvOxQMRfbNbB7mdrv7nCHsK7mcQU0dndPb\nTNeefk++fYZaWFjkHyPniT9CaG9vZ968ecyZM4c5c+Ywe/Zs5syZQ1dXl5VBzRGqqqooHVXASwuf\noETdwkkzSmkOScwaHeCF1XuxCfPpiLRS0yihKHYeXary1sYI88d5CMc12sIqP36+kURCp7ZDoTIo\ncu7sAiqDdt7fEqE8YOecuQXc92Eb25tl1tbGGF3o4L6P2hBtAtc+uo/rTixmR0ucT7ZHQUjwt4tH\n8bV/1TKjykUknsAuCkytdCEpOs9+FqK6OMbd777BsYdP5li7/aCeLDhQBJoxi1YjiDacYfsaMHdH\nJtlWw0XVGJ9iCFOzEE11XzWPv0k1C0otrcy1oMuc6RhukZZJ+WpPpleZbgykE6b5uKnQF9JtQBjn\nbKyxcf0aZd29CQtN05Akqd+lm/mOqqpIkoSqqtjtdhwOB16vd7gPa1DJVKCaHXmNe6o/DsUj+R60\nsLDIPSyBmmMEg0Heeecd1q1bx9q1a3nqqae45ZZb2LNnDxMnTmTdunXMmjWL8847j/Hjx2Oz2awd\nzSFGFEWu/uYV/PmPf0Tp6EC0leN2eLAJIl6Xk5ZQHQ5biPnjCtnZovDQkhY8TgGnQ6CtPUF5wAGC\nRk1DjLiic9wkH1WFIlPKnZw42ceNT+3DYYPZY9z86rwKfvJCA08u78BlFyj1iVx/UhFra2OoCR3Q\nufG0Uu79sI3PT/fzxPJOzp0bIOgR+dmLjdhtAjeeVsIjn3bgtCV4dfFmIsJvuTMaYfqcI/ifH/+c\ngoKCA86vpqaG7du3EwwGOfLII5PjStKJk0yyrQMRGKlBtaqqJBIJHA5HUhwJgnBQb2um429ycdZl\nJqY4uUAm5atmh1uzyE3dGMik33KkYS7ZNkabdLcBYbhhq6qKLMvdrou5p9DtduP1evslTPO1bNNY\nT1VV8Xg8+P3+5HU1FORST6au68RisaQRlM/nG/A9ZZxfarm5hYWFxWAj9PLhYn3y5Ah33303bW1t\nFBYWsn79ei666CLmzp2bfFAIgoDD4RhQFmu4MHrqBmLvPxzU1NRw89UXcMncKkQUPtlwSTu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2XNxi14vH5Q4vz3tdcc5DrcVzZu3MjMBccxuno8ACd/4b9Y+PRD+Hy+ZFattraWZcuWoQNHHXkk\n+/bt45kXXiIajXLYzBlcc9WV+P3+Pr92b72txuubA0KzIOpu/E2qIZNZhPW1/H6kCtPuMPoAFUVJ\nlluanZrNLsKxWCzZJ9gXYZppmX0+ZQPTYWRMJUkaMkfiXF0zsztzvsyzzdW1tLCwGHlYAjVP6CmD\nOhIwB9gG2TJlGiii3c+zSzt4e8lO4rLCpkYBSRcQ4x34nAKfm+ljR5PKVScU0yVpTCl30S5pnD3b\nj9dp446FrextU7AJUOQV8TptjAqK/H1RO4UeG9ua4kwsdVLodfPxdolplU66YhpNrR2MLnLQHtHY\n1yFRVSDw8JI2mkIKAbeNIq+diaU2HKKN/z65GE2Hf181hok/3co/Lq/i7Y0R2sIaP3+5idKqai69\n4kscc8wxfTp3h8PB3LlzB3U9Ozo6eOypZxg9ZSZuf5BX3n6PM04+HqfTyfMvv0qoK8zoqkouufD8\nA0Tf6tWr2bJ7H5d+4zs4nE7WLP+Up597nhuuu3ZAx+P1euncWf+f42ttwePxJMtBd+/ezR13/43J\n844i1Bni6Z/cgsvt4mvf+RHFpWV8+NYr3Hf/A1x/7TcRRfEAt9/B7G3tafyNcX+YM3ndZVsVRcnI\nMOhQF6bpSnnhP2XVgiAQiUS6FfqGMJUkqV/jUvIZs/GTruvJZ5ksyyPqGspEwKX2Grvd7n63D1iC\n0cLCYqRyaDwdRwDGGIp05Ht5TXfH31PfYG+GN9kyZQqFQmzZsB2Xcywu9xyqCsvwlzdRt+dlvjDV\nR4XfxuNL2/C5bOxuieOwC0iyhssuEPTYOX6Sl6DHzg+f2UdchaMneonEEygaPHt9JYIg8O8lHby/\nJcxvzqvgoSXtfLwtzqRSJ794pYn54zysr4vhssOlRxRwRLWX5rDGm+u7WLUnxsXzC3htXRc64HEI\ndMV0FFWn8oebEUUbipbgiJP/iz/dcVfOlE9v3LiRyglTmTP/SAAKgoV8uHgRLe3tHH/GuVSOHsOG\n1at4/Kln+ObVVyZ/rrm5mTETp+D4/8Jr4rQZvL5i8YCP54QTTuDDT/7Ea08/gtcfZNu6lXz72muS\n33/jrbeZe8Ln0JxuXCWjGN8VYdErz6InNDweDyefeQ4P3X4rTqfzgF7GSCRyQF/pQDdWuht/Y/yt\nqupBBjvme0oURdra2ti3bx+jR4+mrKysW8Mg2H/fOZ1OnE7nsFcxZJNMhKl5wyGTUl7zHM9cH5di\nfr8H+h73ZPwky/KAj7WvxzKc12x3o2Ly6T6yBLGFhcVQkbtPSQuLbsjETdjow0tnytTfMmFd12lv\nb+edd17GJUvEvSXU1gbBLjK5zIVblHDZBUr8Ip/tifHUik7OOzzAtiaZrY0yE4od7G1XeHpFJ4Ve\nO7KmM7bYwWtru7hwXhCn3UY4nuCoCR6e/6wT0SZQXeLksz0Sz/13NS+sDrGhLk5DSOWKYwqpCDo4\ndpIXl13gw5oIp0zzYbfb2NYk872n6zlmgpf7P27na8cEeW9zhHA8QVEwwJ//cjdOp3PIA8Tu0BIJ\nRPE/H0V2u532jg6Ky0dRNWYsAIfNO4KNq5YSjUbxer0AVFRU8OnaD5h31HE4XS5qNq1nVFXlgI/H\n7/fz0x/dzCuvvMLC995HT+i89977TJw4EafTSXtHBys+Xkqoqwuny834aTOpnjaTV599kq9+479p\nbWmmoOA/IkQQBDRNw+Vydet2PVhl7MY9kXpvmLOtRqD++uuvc/udd1M2agzN+2r58Q9/wNlnn31A\nf2UsFkPTtGT21RDa5vE3qU7C+YrhnKqqKk6ns9/C1AjgzSNC8mWO52CRiRgbKrEzlNdkunNKHRUz\nmJnz4ehBTTe71hKtFhYWg82h8bQcAfT0kLUeDvsx99UZPV3pyoRlWT4o29pbNkvTNB579AlefvZF\nxrvb+NLJRxAMlPDSyp28vnYP80tijCktp6a+iQK3jd9dWM6f32mlpjFORE6gagm+8mAtAZeNEv/+\nsk+7AO9tCiPa4NlVnZwwZb/w+qgmwqQyJ7vb4rz4WYjxpU72dajMHuXmiLEe3t8SpjmsUVlgR5J1\nOiQNLQF722QCXgfTRvl4Z2OEmKxz+dFBmrtUWiMaok1Hk2Ls2bOHyZMn54yYmDZ1KqueexF/QRCP\n18va5UuYPWM662p2JJ1SQ50d6Jp2QB/2nDlz2LZjB0/ddxcujxe3qHPtNVcPyjFFo1He++hjIgmR\nsVOm89Gajaz93g+45+47qd1bS2HFGI45+1jiMYkX77+TwuJSatZ+Rs3mjUQ727nlRz9M/q5EIsHO\nnTt58aWX2VNbR2lpCdd94xrGjBmT/H66MvbBNA1Lzba2trZy+513c8Nv7qRyzFga9u7m9z//PgsW\nLKCoqCi5uWO323G73QcZK6X2XsqyfMAx9zSeJdcwnJAHKkzhP4IhGo0mR4QMdI5nut+fq6SWMfel\nv3akYR6bM9jXgYWFhcVIxxKoeUAmAUkuB4A9MRRD53vLtqYrE07NaC364EN2rGtgQvloZnqiuB12\npGgb08oEXlXD1DTCgvE2aholygN2WsIqPges3xenyCtS4LZRZLPxP6eXUBm0s7VJ4cfPNzCp1EFj\np4ysCVzz7zrKAiI1jTIep8CHW6NMKLGzYleU3W0KXofA/33URnlAJBTTeGpliE93STR0qrRGdTY3\n2dgZshONCfgKq1i0vZkVuyXaIio3nVGKpOos2yHxy1t+xN//+RCCIOREX3N5eTkXnftFPl22nBZV\n49jDZzFv3jy0V1/j9acfpbi8isa9uzjnzM8nezpramrYsWMHoyor+f63jiORSFBWVjZomYmNGzfS\nGZU568tXMXH6YcjxOA/d/muWLVtGZzjM3GNPp6SiingsxqgJU1jzyQd8/cabOf6Uz7Nv93Zefv0l\nTj/9dGRZ5te3/ZZlq9agJBKUVI5GL3RyzgUX8YPvfIvNNduRYhJHzp/PpZdcfECfaOr1OZimYfX1\n9ZRUVCGKNv4fe+cd3VZ5//+X9h625SHvmXhkOXuQkJAdwkwpG8pov5RSoIxCgW52yy5Q9ioQZgYz\nezk7dpzEdrzjLcvWsLW3fn+k8i+EsJMQU7/O0ck5VqR79ejq3vt+Pp/n/X7yT7fSa+qiv7+fhoYG\npkyZMnBTLRAICIVCA/vk9XpRKpVIJJJvjL+JxbPAyY+/+TYcLUw1Gs2XHJlj/36bVt7YZ3e73d8q\nIuRUJiaEv+13dKpXi7/r5/mh23G5XCcsNudY2ztZnOoTJEMMMcRPh1PnCjLEN/Jj39CdSE72Z/uq\nKImvqmY11jeTlphDdyhIn7MKX8BNNBKl096HUKigrU/A8xvq8Ycj9DpD2NxielwRxmRKkUuEHLIE\nyE+SYtRLSNKI8QWjSEQCKtu9qOVi7lqUyLgsORZXGF8gwh9X9hAIhXnw/BQaLWGe2tjPgY7DVdGp\n+Qp6XRGGpyh4dmMveYkS9EoxXq+X0jQBl09J4LOqftb1i+l1h1hYpEYmFTImU87YTAV/+KSKJ//5\nHKFwkHlnzmL69NNO6tgfi/T0dH7234pijLMWn0lxUxMOhwPjjEmkpKQQCATYtm0bqzdvI694NJaG\nA+wqr+CGX193XMWAWCzG6/GQnJYJHBYzungDu3fvxuN243b0I5T0sPb911Hr4xk2ajw7169h6vTT\nGVE6ge2rVmK32/lw2XKE2kTOv+5WDGlZfPz6cyAUkZiZx98efJhLf3M7WWnp7NxVRii0lCsuvwz4\ndhFNZrOZ9vZ24uPjycg43Ar90UcfsXXHrsMRNGIRTrebnKwsrr7qF6hUqoEs5bS0NHpNnTx02685\n47xLWHj5ODaueIdHHnuC9/5rnOVwODhw4AAajYa4uDjuvPseOjq7kEml3H3n75k5c+bAvh45ESQW\nizGbzezfv5+UlBTGjBnzhVipY8XfHDkpdKI5EcLU5/Ph9/sRCAQolcqvdVz/KXHkZ/+uVcKfmtiJ\nOfLC4UmkbxsV80P4qY3hEEMMMUSMIYE6SPiqi1Ds7z9l8XoyOVZGZDQaJS3DSGVZEyOKTuPDFbtp\n6mrA7fPQ6UxmRPYsDjS/g14aZUaBkvNKtbxX7uDiCToWj9ISrxIiF8PySicTsuUUGuW8sMWOWBBF\nKhLi9IVp7PHzs7FaPAE/e9t8ZMSJEQDXv21i1nA1/pAQsdJAyNtNhy2I0xdBKRVyzhgtXX0hItEo\nRUY5nXY/I4wizhyRwvR/NCPXSNl2yMvt85NIi5PwxHorSQkZzJl4Hh6vi82rNpKaaiQvL+/HGfBv\n4Oj9ikajfPTZapZcewO6uHii0SjL33iRmpoaRo4cecz3+D6/kTFjxiATRvn8vTcpHDuJptoDVJRt\npK+wBJvTw/svPEF8kpFJcxeTWzwaW4+JPRtWccd1v0Ct0RLxH45OOtTWxpjZZ7Nnzx42rHwfh92K\nrcdMNBJBF5/IgfKdWHt7yR89nrJNn3HF5ZcRCoWw2+1Eo1HKy8vxer2MGjVqoC1bKBSyfft2Hn/6\nWZLSMuk1dXL+WYvw+/1s2FHOGedeyKtPPIzWkMyMBWfx2duv8sbbS8lIz2DKpAncdsvviIuL49IL\nL+C1d5cxaupMIpEwF//6Fh664XJMJhPd3d3c9LtbSc0bhq3HTK+pg5/98iZ+c+Z5dBxq4oEH7qKg\noIDMzMwvrG1dtmwZb779NuXlFRSVTqCrpYkF8+bw6D//+aX4myOrrbGJIOBL61qPV7X1RAhTr9dL\nIBBAKpWi0+kGXHx/6hzZvjoYqsUnSsgdKyomluv6U7wmf9U4DonkIYYY4ngzJFAHAT/lk/+P7az4\nbRAIBMyecwa1NXWs37OS/pCUVlsSQiSMzpxO9aGPuXRiHLsP2SlMkVFklBMM95NjkOANRlDJxBi1\nYvq8If7yUQ+hSBS9QoRELGbJeBVdfQHKGj3csNSE1RXG7g4xu1jNtacpeGu3g+e2ODAYs+izNvHG\n1elUm/y8u6efVdUONDIhswvVPHmREU8wyjMbrTy8qpenLjIiFcH54/Qs3WFhwZOHmJanxuKVMvX0\n8wCQyxQYdGmYzeZTVqAeTTgcJhgKodbqgMPfjUYfN1C5OJJIJMKKlSv5YPlH9FqtJBkSuOryS5k1\na9Yxj7mGhgZ27t6DVCJl2tTJ3PX727j59juoP1CBpdtETslosopGMmXhufzrz7fR3d6CxdSJTKFE\npdGSNbwEfWIS6bnDWP7iE9x1990MLyxky+pPOFh1gHkXXUUwGGD1O6/S1dLE1PnnMHrqLA7VVPLR\nW6/QbzaxYPHZ+Hw+cvLyqamuJm/kWNS6OJ7494uMGp5PbkEBY0aO4NkXXmLYuKlYeszEp2XzxtL3\nUMmlXHTT3bicTrx+P1f96nfUlm9Dl5TCgst+SWHhcD5b+hovvvQS11x9NZMmTeLdFR8Tr9chk8vx\n+7z4fV4UCgX3PvAgi6/+LZNmzTvcpnz9FYj/G4WRkZtPTtFIGhoa0Ol0/PVv91JRWYnP5yUYgR5T\nF7975CXyR5USDvj561XnsX79eqZPn/6FaqtIJDpc6f2K+JuvqrYe2eL8bY+ZmNmTTCYbiAyKEdtu\n7Hj6JmF6pDg7uoXzRFe0Tqax0LG2Ew6H8Xq9x619dbBe274uKsbj8Zy0/fgq06ITub3B+p0NMcQQ\ng4shgToIiBnFDPHjYbfbCTg7yNL34LV209vjQyNRsLfuWYSCEKZ+BTKxiGV7nWjkQhRi2N7spccZ\n5sOKfj494GLxKC0pGjEb6j1YXSHml6gozZChlCnZ0uBha6OHhh4fv59vYMlYHZ5AFKsrzBmFOsoa\n20jRiTE5wmysd/PaL9LpdoS48R0TcokA/nvjMDFbwYpKJ39a2QPAPz4zcf3p8eiVIv7+qZXMtAJa\nO1qAKOFIhD5XLzrdsSuPpyIikYjCYflsXvUJ4087HXNXB90tTeSeu+hL/3fTpk1s3r2P0rnnEAkF\n2bN5Ha+89Q5qtZoJEybQ0tKCy+XCaDTS1tbGw088TVbxGMKRMJ+sWk28Ts3p8xXNKkUAACAASURB\nVBdz+vwz+dvtNyKRyumz29m9+QWIRknNyScSCXNgxxYQQDgYZHjpRHQJBrRxBlZv2ExtfQNej5dx\n885FpdGBAOb+7Ao+eP5x3E4HWz75gMTUdMwd7Vz8f78lNa+Q8rKNrH73dQrHTeaMn12BMS2NfSWj\nee/phxkz5yxef+cDqqur6e13o9TqsJg6sPX2EAr4sT3wZ0ZNnQVRiESjHKqvZfTUmcgUSmory7Hb\nbCzfs50Lf/5zioqKKMjO5KUH7mbY6Ans376BRQvmodVqMZm6GT56HAASqZT8kaUcqqth2rzF+H1e\nTK3NGAwGbrz5dyiSs/jNA0/z+0vP4tbHXubv1/6M4aUTiETCSOVKcotGYjKZBtYPx6JrjpXbGhOi\nR1dbY9E3sWpr7D2O1SIcE5ZHC1OlUvmNwvTrDJ2OtzgbTIRCIbxeL6FQCLlcflzaV0/1icljcbQ7\nsVKpRCwWH9OdeDB+vu/LkGgdYoghjjdDqmcQ4PF4vnJN0//ahfDH4v3/vMiiQiEatNgzsrmzZStj\nUqLMK0nF4/fx7p5echMlmJ1BHlljxagV0WYPsrfNy7BkGdMLlKikQn4xNY7STAW3vd9NrkGCPwRm\nRwCDUk67vR+ZRMi/NthYvteJRCwgEIzgCbgREcLthxc227j/vGTyk6Wo5UJuOiOBh1dbuG1eCKVU\nwMvb+miyBGi3BxmeLOM3M+P581nJAGQlSLnpnf2UWWsIRlzk5A6jeHQuxcXFP/LofjcuvehCVnz0\nER+//hw6rZZfXXUFCQkJAPT399Pf34/BYKCuoYk4YwY1e3eRPXwEWUUj2Ve2nh27dtNl6uZgUyv6\nxCR6Pv6Mg9UHKJ17DuOnzaJi51Z2b9mI3RcmgV4eve8v6AzJjJoyA7lSjUgsoaeznelnLkEklhAO\nh/jolacRS2SMnzmffds2oVBruPaPD9NaW0XVltWEfW4gilAops/ai6vfhkYfR2HpJCrL1uHzuknN\nLsDt9lA6Yy7bVn2ESq0lFAricrlIzcpHplCTmGxEGZeAVKlBEwnjbKxFKJZw44PPUL5xFT2dbWz5\ndBkioZD3nv0nMrmCqt3b0Or01O+vYNzMeUSFIn553a956P77uOaqX1BRUUFbWysXnr2ICy+8kFAo\nRHFRIRtXvseCi3/Bug+XsmvNx0TDIaKhIF2tTUybOI68vDz2H6jiH396bEAcauPiScnMZuPypcw4\n+wK62w5RvXsb9975u6/MbT26chrjyHWtscdXVVtjbZYxk7PY81KpFIVC8SUH4iOFaWx/hoTpl4mN\naygUQqFQoFarB+X15odW/o5ea3s8o2J+CD+2SVKsknwqt3cPMcQQg5Mf/ww7xDfi8/l+sqYbg2Xm\n1dLdQXqJAVObnSxjFjrFFuYVa8k1QIc9wuxCFab+EH+cEcdfP+7l/DE6KtvdzB+hweULo5ULqTMH\n2NvuxaAWIxHBe+X9zC3WoFMKeXmblYk5Cm6fZyAciXLjUhNzilSsr3OjVQj52VgtFneYHc0eakx+\n8pKkRKNgdYcRCmD03xuRiASEI1HuWWwkXiHg9x+YOGeMZuAzxCmFGPUiXr+uiLOfXs9vb/81paWl\ng+6GU6lUcuXll2Oz2WhoaMBqtZKYmMjeyko+XrUWhCIaaw8iiEZxR2DK/HOZMGs+1RU7Sc4p4K23\n3yE9K4sJ02chUajRpedR9d77jJgtpHz3Tlob6pkweyEpGdm4nQ5UOj21e3ejVGtQxyWQOayIOEMi\nUrkCuVJFYmo66XmFaOMTWPnq03icDpZcdwsymZz0/OFYzV2sfPNlTne5kSkUlG9ag8GYwcTZi2g7\nUEGCTIYkGKSutoZEYxqO/j7czn6qd29jeOlEbGYTjdV7EYkE9HZ3s23FB6TJ5fzcOJy8WWfw/trP\n2fDWi8gTkujpasftcKBUqjC1NOJ1uwn5vSAUccP9T6HVxzNp5lweu+N6Lr78F+QML+JAxW40Gg3r\nN2/Bardz84038uc/3sPvbrud3579CukFRSy8+Coa9+8h1NfD3bfefNhlORwmGong7LOjTzBw2oKz\n+fefb+XMy/6Pd555mLcevw+hAB544H5GjBhxzO/yWEIv1rIYq5ge3cIYq3QeXW09Mrc1VtUKh8M4\nnc4vidzYa+GrhWns/WJrCk+G6c235UROTMYEvMvlIhqNIpfLT4gwHQztot9nre1g+Fw/hNjxEask\nCwSCn+z9yRBDDPHjMSRQBwE/9QrqYNj/pLQsKhoayVIqsPTZcflCeMJSwpEwEpGQxh4/GoUEtz+K\nRiqgzuyjot3HhGwlEOWQNYgAONDhp9XuwheMcueCRHY0e3hzhxOzI8CDS1LQKoRIRQIumqBjTY2T\ngiQZt8w14PSFeXt3P+l6MX//pIdDlgBRYGOdm1d+kYbZEeaGt7u5YVYc10xPJAp09B1e85qdIEWv\nFHHDUhPXzEijwKgm06A8qceOxWJh1Zq19DmcZKWnMXfO7AFH2e9Dd3c3b73/IcbsYYTDQVavfxZb\nv4uFF1zGvx99iLBMhamtBQBrTzebPl2OQqkmOS2b/eFNqFKyCMu0rN+48XAmrUzO3m0bycgdhqPf\njlgmQ62NQygS09ncgKm1GbfLSXxyKp3N9bgc/fR0tpFXMoamqkrcjj5GTjmdyrJ1GLPzSMnIQSpX\nEAoFUeviyC4awdbPlyMUCElMz0Qfn0hr5W5SutuZl5uHJiuTjS8/Rdq4qfSaOll4ybWYWpv5+PV/\nE41G8HvcpKSl8+I9tzA1Ts+03DxGqFXs372N4vh4Nu/dyfSrf0tiWgbr3v8Plp4u5l94FQqVhl2r\nltHZ3sa+bZtoqa9BoVRiNnVy/V8eIz4xienn23nr8fu47eGnef5vtzOutJRx48bx+1tv4Zr/ux6H\ntZeNH3/ArMXns2vNR9Q3NPDm2++gVqs479yzefIPv2HczAW47DbwOdm7+gMmT5jAr669msmTJ3+r\nKKNoNIrVasXr9ZKSkjIQY3MksWpr7N/YsRuNRgmFQkSjUSQSycAa09jrj85AlkqlA/msx+LodlaV\nSnXKnKNO5H7EDH+8Xu9A9fmnYvbzXUXjkZMTp3LV/GSL4djvr6+vD7FYPJAZfCqOzRBDDDG4GRKo\ngwCfz3dK5FX+L3PBZdfyzCP3sqyxCpdXiC8s5l9r28lJEOHwRWju8WHUS9je5KDN4iMvWU52vIR6\ns59krRinL8zyfU4kQmi3BZGL4Yl1VrISJIzLlNPYE6DDHiJdL0IoFlBj8mNyhDktX8rH+528ut1O\nsVFGe1+ICVkKNtS50MhF/PuyPAyaKHq9ArG0D5VajUKlxuN2MSlXRXqihtuW2XC6vWTGy2ixellX\nZaPTHqRiz14+/3gtWp2Gc85fTGZm5gkZO7fbzWtvvk3BmInkjUulZt9ePli2nEsuuvB7v+fW7dsp\nHDuZYUUlAPSYuxH6wrz/n5cx9/YiVyiZee6FREMhzB1tqPXxeNxOtn6+nJIJ00jJzCEsFDJxzll8\n8NwjeD0uKjatpnpnGYlpGbj6bGQXjsDRZ6Onq51+m4UPn38cvSGJjPzhZOQXsu69N1jzzqsoNToM\nqel88Nwj5JeMoXDcZD567VnGzZhLW2Mte7espbB0Em6Hg9FTT6e5Zj8WUweN9l7OOO10LE31GBVK\njJEeerramDT3LEZNnk7A56P/3xYCPg9Nna34fT5GKhUY1Sqae8yM1agx6PS0+P1kFo8mOSMbiVTG\nzHMuZMfaT5g858zDAoMoK197lqaafYedbL0eRCIx1h4TKp2O3OJRKDUafB43QqmCx5/6F1MnT6aq\nuoqSyTO48rY/43Y6eO4vt2Lq6uKZF17i59fdit3Sw/oVS/nt9dfR0dnJuIVncO65TyISib4Q1+T1\negcqlqFQiCef+hd7KipINBi45eabyM3N5T+vvML+TZtQSaVEtFpuuuuugbbtGF/VIhx7xHJbg8Eg\n8P/FnEQiGTBjirUQH0t0HSlMv28762CsnsXaNL1e70A2ssfjGRjP/xViEx2xlubvOzkxGI+Bb+JI\nwQ58oZIcmzAaYoghhjieDAnUQYDX6/1B1aZTmcFwYevq6mLT+jJSs8egTEhn1Yr3yYsPMS5TTbFR\nzn0fd5OoFTM5VwlR6OoLkKQRkaoTs7fdRyAcxeYOMS5TSSQaJRwBpz+CLxRlQ52bQFiASqng8TVm\nFFIhQgF0O8JMHVvItkMtHDT5WHl9Jql6CZ32IBe90M5fz07kt0u7abCEcAUFvLTLyoSpM3h+6x50\n8m4UQj//XGNnYkEu8qiVq6fnYel3s6nOxXWvt7Lo/CtwdEUYP3wOtj4Lrzz3H2687dfExcUd9/Hr\n6OhAHZ9I4cjRAEydNYf3XnpmoJr1XfH7/Rw61EqONpmA349UJsOYlsG6Tz9BoFCx+PJfUbl1AzKF\nkpxhRXR3tLJt1Qr6rRaMWbmYmxrQWsxEpRJq3B7svWb8HjclE6aRmJrBwfLtdDQ38Mr9dyMQChCK\nhP9t5c3AYbPSdagJBODzeRk+ZgJzL7iczkONeB0OBCIhpdNno4s3ULF5LQd2bOb6e5+gYORY9m3b\nyOdvv4w+MQmdSo3pQDlbNq5GL5ORkp6JvL8ffWIKXc2N9HS0oos30FS9D5lcgT4plcnzzsJZsYNR\nOTl8tmc7b9RUE5TI2O/2MHzxBcgVqsNVRKkMkVjMu88+wuLLrqZiy3rOufq3jJp6Olp9Am8+9ncy\nhxWxaeW7XH7rn6nZswOH1co/77gBgzGd/Gmz6XQ72Fy2jWv/9A/6++zIZDLyRo6lu70VXVIq29d+\nynX33I+tpxuXy8Utv/vdl76nI3Nbw+EwNpuNc847n65uMzK5gsIx47nsyqu45w930Ll9O3+fMweZ\nRMKqqir+8+KL3HTHHd94LBxpinTkOfLIauvRJjZHE6saxmJCBus6y+/K0YY/KpVqYKy8Xu8J3/7J\nFHJft62jK8eD6Rg4kWN4LMEul8txOp1fmCQaDOM0xBBDDD6GBOogwOv1DrX4/kjYbDbee3M5Ockl\n6HXp7Nn0JDMyA8zMz2Bfq5U+jwBXIMKVUxO4fLKePywzkxkvRYAAhz/CrEIVFa0++r2QkRBHd18f\nnmCU5y5Lo9AoY+nuPu74wExhipB7z0lBLRPx0CoLVlcYs19NTUcEvUyAQS3GE4hg0IiIV4u49T0z\nTl+Ee1d5CIW95OWVMnXYLLQaI2/VH6CrpYkr5kwnGo2QLg0SASZOnkJavosupY+QL8ro0knIJDJU\nRjUmSxttbW0nRKCKRCL8R8TABPw+iEa/lbGGx+Oho6MDgUBAZmYmbrebV994k54+Fy3r1tLZbWb0\nyBLMHa0kGuJJKR6PSqsnMTUDp91GV1sLqdl5uJ1O7D3deD0uUv1uzh01HZFAyIHqfXSIRXhFYlRa\nPRZTJ0KhCKlMTigYIM6QTNGEKWxa/g6jp85kx5qP6eloxe1yolRryR9ZSlpOAbaeboaPnYijz8by\nF5/AYEzj0MH9aOLiUWl1tDUeJCk9E4lUhnN/OZMSE5lUPJI9zfUkKVUcPFjNQacTT8VO5l5wJQG/\nj3ef+ScqtZpAwIdco0MoEiPNK2R5Sz3CKGwwmUiePIukuAS2fr4Cr8eNNi6BAzu3MHnOYjT6OD5/\n6xVMbU2kZucdbsUTCcksKKLX1EFHcz1P33Mj/TYLCoUSlVbPtfc8gEwqo6WhFplyKZauTpLTs3DY\nrJjbW5hx1hLmX3QV7/zrIcpWfYRAwBfWiEYiEXbt2oXVamXEiBFkZWUhFArp6Ojg9ttvR6xN4OGn\n3iIU8PP0PTeiNSSzYd06psfFIfrvmtHSzEzWV1QQiUS+1lX364hVW79KnMZuvo8UpjKZ7JQ+Fx3J\nDznvR6NRfD4fPp9voE3zaMOfn2IV8GiOXkepUCiOS9X4VBHe35cjI3SOFuxHGowd/ZohhhhiiOPJ\nkEAdBPyUTZJOddra2tDJk0gzZtLUUs/YTDUqvxatQsyZo5J4fXsv4Sgk68TsbPHQbAlw77nJxKtE\n1HX7eWNHH829AZotUayeHvwBLwVJMkamKVhz0Mnbu/q5elocZkeIF8v6ePaSVC6ZqGNPqw+pXIFC\noaLPbWXXIQ+lmQqaewNYXWEevcDI7R+YcAdDnLvoWs6afQEAqYZMlGoFI4edQaejHrezh1qXg2EZ\nWWg0Gg4esJCWnc+hVhuVVbspGT4aqUSG0903cMMWa8f8vuLgaHJyctBs3cbGzz/GkGzkUF0N06dM\n/EaB2t/fz/KPPkGdkEQkEmVX+V6UMglaYyaLLp3H3l07KNu4nrJPP6Rw+HCigEYmxtvXS0pGNhtW\nvovF1IlEKiU1O5e4xBQ0unhGpKTg7rfjdbuQhUOo5QpCIQsKlYq4xGRmnXcR0WgEh81GY9VelEoN\nap2eT954jsLSSfzsulsxd7TwzlMPUbF5LbbuLpLSMjmwYwszz7kQn9dNY1UlQqEIn9fNvm2byMgf\njtNup6eplhtmzcGo0eDzeHA7+9ns8pI9YSqTtXHs2fg5CpUan8+NTC5nelEJs/MKONTazMp3X0GV\nXYBUoaCmq4vUnOGY92ylOCcXTSTA5jdfIKFoFDPPWkLOsCIsPWZaag8QCgQo37yaibPPxN5jpmLL\nWqzmLtJzC+hoaiAlIR6pSkNIICISjhCJglQmx5iZy4Zlb9LRVEdrfTVSuYLzrvo19l4zxux89mxe\nh8Pcyd3XvwEcFqd33PkHDja1kpKZzUOPPMbf//xHdDodbzz6KIX9/RTFJfLZMw9z9k33MOGMhWz7\n5ANcyfFs6e5mfGoqKcnJVLa1kZSWNlDROjJ+5usySr+NaDuyKhSJRFAoFAP5lYOF77uvRzvRajSa\nU8KJ9mRPsh4t0I+sHP8vc3RF/esE+09hYnyIIYY4tfnxr05DfCM/dZOkUxmJREIg5AfAZO7Ebe1F\no1dxsNdDwG2lrN6GxRnm6Q02cg1iioxSIpEokQgUJMnocYZo7AkxPlvJnGINLRYR62vdVHZ4eHKd\nlb+elYxCKiBeKeSRNVY+rXLSbAniD4ZpOLCHNDVcPT+HO5e14fJHUEkF/H5BItOHKVlQomZdnZ22\n1rqB/Y3XG1DKVQjkQlJT5uIP+Fi3+R0e3eTGWHMQBzp6bTWYDx2g48DnvPm+jIXzLiW70EhRURFw\neL1RIBD4gjg4+vFdEIlEXHbJxZSXl9PvcLJw5rRvFW2zq7yc1PyigXWme3fvZNNnK8gdNQ6f18PY\nSVNwuZzsDvqZu+QSTKYu/vWPB3C5XEhlcmy9ZoQCIZ5oFIFASG7JaBy2Xg45vIzMyEAkktDU3U2/\nz49YLEamUNJUXUnh2IlIpEq8LheG1DRWLX2FlKwckjOyqd27k5ryHShVasLhIMbMHNT6OLZ8+iHt\n9TV82GNCplAiCvrJEYnIitPR+OEb7JQpken0jJsxF12CAZVMTntDLe5wCL1EhNHjxOnqZ/jo8Xz2\n1gsEAn4K0jO5fPI0EtUaJhSWYOk1Exw3ha2fL6ewdBIZKalMDGSRIBYRSUpEKhSy29XHaTNm0tJQ\ny5a3XiLqcjHujIU4bDbefuJ+rN2dhEMhxs9cQNDcQakxmfSkRFbVNyBIz+HtJ+5n6qLzqN5VRp+1\nl5se/jebVryDqbWZqQvOIRQBV5+Nsk8/RCMW8Ng/HyY9PR2ArVu3UnuojZsffgaxRMKh2mr+dt9f\nmDaimGtHj6ZXo0WTaOST5ib2bd9IXeUePOZORowqplMo5Ja33yY5JQVtbi433XQTKpXqCy3CsTiZ\nWGX1SNEKX38ujLVxxlyBv6sw/SHV3B+b2Drg7+JEezI4mWMZc3T2eDwnXKAPtgrqd4nQGYzH/xBD\nDDE4Ef3lL3/5uue/9skhTg779+/H6XQyceLELz0XW2t1KsyEfx/C4fApvf86nY7qmv3sKd9Na3Mn\n7WYLSomPPqeX+m4fIpGCeSUaEjXQ7QhR1eWnOEVGJApttiA7mr1AhPvOTWZcloJEjZh9HT5e3mrH\n4gpz/lgteoUEhy9MXbefj/Y72dfhQyUVcvciA71OPy2WEI9eWMwb27v4x5IUJucq6HWGeHqjDU8g\nSlNrA/l5Y0hKSKGqoYJhIzJZfM4Cum3tiBRRLrjk5yxacjklE8/A5Q1iq/6cFTfkcuW0BCIhL+Um\nB/c++BBSqRSxWIxEIkEqlSKRSAYqqbHKUyAQ+MKN/pF5kl938yISiUhPTyc/L4/ExMRvNfbVNQcx\npGah1mgIBPzsLi/HarHi9AVpaaxHo9Ox8r23mXbGPEpGjqK+sQl/VEhiWhYzz76QfmsvuoRERk2e\nQemM2XS1NOL3ehDEG6ioPsDO2moqurpIGz0eW48Jn8dNfHIKppZmerraAdi0fCmZw4u59Oa7KRo7\nibikFNoaqskdMQaBQEhGfiEyuZJIOEQoEEAkkaEiQoFIwF1nzKUgOZkEuZym7i50+UUUjJvMls9X\nEugxYenvo9Jk4ufjJ3J68UjytBrKdmzB4nYzavIMXF1tLBgzDplIRNDv52BjHe6UDELBAKNPO4OA\ntYdpObn47FaEQhE6Yxqrynez7qMP6N+6ickaJbkqBX2BAPN+eRPjZs7D7/Eilkiw95go8bk5c9rp\nKIJ+4sIROtpbSAr62fD5R3SYOuiz9uLss1FXuYdzrr6BjcuXsnPdp1RuXY9ILKazvZ3PVq0mHAoy\naeJEKioq6HEFGDVlOgDauAQ++s+LpMfpmZeTgzHRQG31AUw2Gx9sWofHambJxAncNncus0eOZGx+\nPhs7Ovj7448PtJofKURjx2bM9Ch2XMbOIcFgkFAo9KXjMhwO43a7CYVCA62c37adNxgM8q/HHuOh\nP/6R9998k2A0ysjRoxEIBLjd7i9N2IRCIYCB6JsTQayj5pv2P5bf6vF4Blp5ZTLZt5pgimVbnmgh\n6/V6B1yXTxSxqJhgMIhIJEKlUiGXy0+Y82wgEEAsFp+USYBYO+736bAKhUJ4PB48Hs93Ghefz3fM\n38+peg3/Efnrj70DQwwxmBkSqKcQnZ2dLFmyhMrKSjo7OwmFQuj1evbv308gEGDs2LFfes1gF6in\n+v6LxWKKSgrZunULI4ePZ8LEWexrtbG75hAjswykxYu5dmYW8VoxMoGfFkuQ7c1exEIBm+rdnFuq\n5mB3kCWlWnIMclQyIdVdPrISlGgUIqyuECWpCqq6fDy32Y7DFybXIEUiFvKLKXHMHKbiiXVm8pNE\nlDW4Wbavjw/3OnmxzE6/N8LZozRcOVXPg0s/ISgOU1CSyeKzFxEXF0fJiGLKyrbw7yce55MVK0g0\nGmltbmC8upXxORqEQiFapZhllS4uv+qXX/rsXycOjozwOFK4xhxVj5zVd7lcmM1mBALBl8y+ent7\nqa2txW63o9FoaGxsxGQyIZfLCYdC1Dc2kpKaTn1DPQ11dcyeN5+cvDz27dvH0pefI+j3I9fFk5WV\nzeaybUgUSuIMSXhcDvqsvciVSno628gaVozTbiWveAyauARCMhkF0+fii4QJh0IkZWSTVzKKtOx8\nZEolbXXVNFZV0NXcyKTZZ5JRUEgwGEChVFGxZR1T55+DTKGgft8eotEo0xaeS3JGNkRB1FJPcWo6\ns4tL0MnkOAMBqkwmOp1O6jevQa1W0+EP0CoSE+63UapUIHU6cHa2Ud3WQovTQUpaFg63i+aDVUj9\nXioP7GVvIExy8SiqdpVRMn4qIoWSzsZaNOEwMn0cn9fX0eFyMzwzh4vGlhJ1OcnOzEEUDPDeqk8Y\nNmYCFZvXUDp9NvlFo4izmFBEQvh9PhLlcqotFmSRMGqJGJPDwajTZuNoa6GnpZG04cWYO1r5/ROv\nkjW8hH1bNzD3gssZc9ps3njx36SlJDNixAheefEFhpVOQK3Vs+b9N5ETZMrUqVTu3UtJejooFHzS\ncogrfnUt11xzNfa6OiZkZiIQCIhTqdja1sbMRYu+1jzL4XBgMplQKBQDbYiBQOALOaWx49Lv9xMO\nh1EoFCiVSkKh0MDx/FUcKW7/89prtK9bx4Nz5zI/O5u3Pv0Uj1TKLbfdzh133Mmjjz2GXC5n0n8n\nD4PBIAKB4IQKVL/fP5DleixiwsPr9Q4I0+/axnyyRNZXiZ3jwZHjIBQKT1qWbTAYRCgUnrRr2ndx\n+Y9NNHo8Hnw+H1KpFJVK9a0nLmLbO9YEyal6Df8RGRKoQwzxAxg6o5xCxMXFccstt3DgwAE2btzI\nU089RV1dHWq1muLiYjo6OiguLmbWrFkYDIaBCsJQ282JRS6Xk5qWRkFWPkqlCqlUg0CgRy9X4PF5\nCIYCOP1h1HIxw1Jk1HT5qev2c+F4HQa1mGikjyc32LhsUpSqTi9bGz1cP8vI7OJ47v+4jfOebUEk\nEPDgkjSmD1Njdfr5/QcmbJ4gGqkAsyPEX1a2E6cS4w8JOS1fyT2LktDKhVzxSgejs8XMKFCwv3ID\nAU8bOXlZADz/3PPs3riGi8ekEY3CP/58N7PPPp8VB7xcNDmMUirknd12Cksmf+uxiFVKj76ZibVi\nxh4xsVpTU8OaTWVo4+Jx9fexcPZMRo0ahUAgoKmpiY9Xryc1J49+2yHue/BhUjKzSUxMRsxGLrlg\nCR6vlw0ff8iOnTtxeLw47FYys7NJSDJyziVXMmb8RD548zXef+dt2ltb6enuomTCNCymTibMnEc4\nEkEskbB66avEJ6eyf/smCkaNRanRs33VSuwWM9FwhNyS0Yw7fR59ll76bb0Ys/MQCIT4Mj1UlK0l\nvWA4KRk57C1bR3xiCvkjSlGqNexY/RGZ+UXYe7oRikR0tTSgDYep72hn/Z6djB1WRJPFQofHg0Qg\nZEnpOKZNmEK/18vblRUcbGlCBuhCAcJE8QeDnJ6egblyJymjJ2L1e3l6x3aEUhlodNT95wW8bhcV\nm9eSWzKKfb0WNrYfImN4Mbsa6pGr1LQ01tGtVjBseAkGYxrdtTUkSpQ8gn21JwAAIABJREFUc89N\n5JaMIj1vOMGAn7LubnA5iFNrWF5Xy4xUI2dm51Bt6aWnoRFRdwdF0SBZKclsf/UZXGotCIRU7yxj\nxlkXMGX+2Wi0OqRyOc+/+DKffrySO269mfvuvhF/IMDwggL++fBDxMfH83Y0yv07diBXqbjhb39j\n/PjxuFwuPohEKKuvJz8piS0NDaQOH45KpQIOV75efe01yrZtR6/TceMNv6G1pYX//OtfJCoUWINB\nfn3nnZSWlg4cm8e6Sf62xjehUIhXX3iBrWvWIBSJWPTzn7Nvxw4uLy5GLZOhlsk4My+Pex96mPRx\nU3nun69g7zXz8G8uo6iwkDlz5nzr39GJ4FTObz1ZHMv8SqVS4fF4/ufG4kiONj76vjFK8L9hoDXE\nEEP8+AwJ1FMIpVLJwoULWbhw4cDfQqEQf/rTn4DDM7PLli3DaDRSWlr6pdaymHnIYLoQDxaBPawo\njzWrPyIpLo229naCURWf7OslSeXmtU125BLQK4UY1GIeXKLj9+/38Pg6C+EICAVReh1B7lluQi4R\nkpMg4f1yCw3dTvZ1eJFojKhFPpJ0h6sirdYgTb0Barp8PL/ZRmmmnA+vy8TqiXDe061cMlGPRCxA\nJRdxwXgdu1s9mGx+bjyrkIJUETfe8n+Mzkukp7aJ8wvVFBtkyORauvs91FUfoGTsPMbfuxKFVExy\nei7PP3rfDx4fgUDwpZZAj8fDhrLtzD7nAnT6OPr77Hyy4j1SUlJQq9WsXr+BsafNJCU1jWXvvYsk\n3khG4Wj8HhfhoJ+1GzZy5WWXotNoqKqtY8KC80nLzGLN8ndpbaxj1rxFyOUKps6YydMP/hWXzUqi\nWILF56ZPIMJkTAOhEKupE5ejD4u5E4/DQUtdNSKRkPxR4xl/xgIa9pfTsHsrH7Q04A/48SnUhAF7\nbzdGkYhCiZCafz3Auy43XgSc96ubqdi8hnUfvElcYgp1lbvYve4zVEoFI0RRxg4vxO33sbK+jqUH\na2j1+XGLpeQKBKRqtUSJEvR58fSYEAtgWXMzQqIEBALOHD2GwjET6Whp4qX1qwlnF3DB3Q8ybPR4\nyj75kHef/QcTZi2gZs82OprrsfeY0CUkUtvSgibewLSF5+Lss7Fi3SfMDQaJ1B1ku8tD+uQZ1OzZ\nRmpWLsa0dOr3VVDfZ6fb2kvI6yVBJuVXI0YiFYlw+H0MVynp7GhhdkkJyelpPOpyUd9n48lrziWi\n0TNu7mKEAgFORz+NlXtorqnmtltv5aGHH2b+/Pn4/X5kMhnd3d309fVx3s9/zhXXXPOF37pareaG\nP/yB159/nifeew+JSMSEGTOw2WwkJCTwyKOPsmnXXuZecDnd7a1cdOmlFBsM3Dd7NkadjnqzmUce\nfJAnX3vta4/L2Daj0ShdXV1IJBKys7O/dN758L33sO7YwWPz5+MLBnniww9xiMW02GyUpKYC0GK3\n09bZybWPXINQKCQh2ciEuYvZvXv3jyJQjyXIjkdEymATIUcKMDg8qXhk1XiwrQs9XtuKGULFzO+O\nh1PxYDs2hhhiiMHJkEA9xRGLxWg0GrKzs1m8ePHA32MVq5iZTaxidSxjm9g6wiG+HzabjUMNbcTr\nkujo7MBi72FiyRykUilVTbv5cO96DKoQc0vUFCZLabGEyIgXMz5Txog0Jf9cY6G8zc+vpscxJU+J\nAFhR2c+75f2oZUJCgR5yxk7ldx9UkaWPUtXhps3i5/5Pe3H7o5w/VkcgDGqZkMwECdubPYxKl2Nx\nBtlU72ZPq5+0JD3nTMoiEIqgjPbz29kl3N/bhUYuosfh4NWNjQiJYA/2MmHiJJZ/volgMEh2dvYJ\na3lzOp3IVRoSEgwAJCQY0OrjB1ouA8EQOr0eu9WKydTF5LlnkpxowJCYxKdvv4rA7yYSiVBdU8vU\n2QuIS07FZDaTkJLGrs3raGlspGrnNto62pk27yw0pla89XXE6/XY/X7slh4sjn6SM7JZeMm19Fl7\nKN+4CoVcidXay8TZC0hITsVlMaNUyJhgNJIxrJiylmYO2PvwdQW4dNYiCrJz6bdaUGzdyJqOTur3\n7aFqdxkzFv2M7MIR+LxuPn/7ZWQ9nSyYPI2JObm095hpdrho6urEq9YyY8E59O7bTWdnO06ng+pe\nM3EeF1dMnYYoEqG5t5cNJhMGkRi/z4NKrcKgVCDKzsPV3cX6XU+we/VKFiQmktPXg1csxKXR4HE6\nCPj9eG1W5l54JaXT5yCVyXhr3x5quzooGF5MnkjCrg2f88e77uSzVat56LdXEAmFyVErmWkw4HE4\n2NzdzdauLgxKBYFIFJvLTaFex4LcXFY1NlAYH8/Fw4ahTk7l31s3s/rlpzDt2UZ/n40xMil3zV3A\nxh07efTBB7n97rvxeDxcefU11Nc3IOizkRwfT2peHtfdcssX1tInJibidjq5vLSUCdnZlLe18dh9\n9/Hnhx7i3fc/5A/P/Ad9QiIjJ06jtnIXGpsZo04HwLDkZJQCwUB7+Nfh8/l49L776D54EIFQSHJh\nIbfcddcX1u5V7dnDRIOBBrOZ3MRE5mZns1Mu5/3ycur7+vCHQrSJRKRmZdFYVcnEM4xEIhFaD+5n\n9uglJ+AX9NXEBFnM6XgwuhHDDxc738V59qfM0RO938X46Hhtf4ghhhjieDIkUAcBx1pjEqtYxaqm\nsTVbR7pextZhHel6efTjf+1C/n0o37OXJG0244uziEbB0muhqfMgcyefR03jes4fG8/+dgvZ8WJG\npEnZ3uwlFIYUnQyHP8qs0Xl0ePoYlhxhfJYCiQjqzD5KzH5UchE2V5jm6l2cddH/sXvbZuTqTjTu\nAMWpMuYWqdhY7+GiCWE0ciGpegmPr7OypdGDyxel3hLBmJrOVfPT8fhDBEIRhAIBWqWMy+aN4A8v\nlBEKhJmRpWVsqhptQhL/Xv8xpRMmMn/+/BMiTtvb2zGZTMhkMgIeF73mbhKTU7D0mPG5Hej1eoRC\nIcPyctlXvovs3AKkMgXW7k6yMzOJAlZrL7lF+ezfv5/Ojnbsbd2kDwsRCgap3rsbrUBAXHszeTo9\nrn47+3aUMTfVSEAIal0cKfEGXt+yEbdURlZhCa5+Gx5zF7PT0hGEguztbmfvWy+hy87H2lzL3OQU\nDDIpEbeT4mQjtU4nCQmJ5OTk43X0EXL2oQoGyNHr0bTUY/D70HS1YIj42dPWig6I1+px23ppUyoJ\nBYOoRELUUgmpI8ZQNH4KIrGELVvXEznUTIpKyaiMTHLTMnH3mmgRCrB53HxeW0O+pReNUIjZ4cS8\neiVZpjYyAl6KMtNxR+CcvFySiPJK1QHyJRIkcXHUej0Eairp9LjY19lOjlyGnhBqay9ZcjlbO1po\nbW1j9szTmTNrJts2b6bAbufSsWN5Zv16kuRyXjtYw+LcPLZ2dlBnt+MJBljX1MgBi5UZaWmHzyOh\nIPOyciASpdjvpjfkZ+HIEYycPI1kmZR3du7E4/Fw7/0PoErOpNjt4/p583G2HeKgw8m1l17GL35z\nPbfcfDMAJpOJqM3GWXPmIBAIWKzXU752LZ2dnQiA8H9NhwBkEjkdLhdmh4NkrZaq9nbaLRaqq6sp\nLS0daA0+Fh++9x56s5mb5s9HKBDw8s6dLHv/fS6+7DLg8A19bW0tnRYLufHxdHg85GZkkDVvHr/8\nzW/Ys2cPYrGYWyZPprq6mgsvvoSK9Z/Ra+okJUHPpZdeitfr5Z2lS+np6mLEmDEsXLTouP++YjmU\nLpfruGZ3DjYikciAMI1FxXzTut9Tpap5vLd1JLEIpWAweMIcm7/q8w2WbqghhhhicDBkkjQI+Oyz\nz8jPzyczM/NLz4XD4QGxCt/P2OZIR9YjW6JOBjFnzVMh9uCrqDlQi0YWh1KhQiAAqUzGtsq19Lut\nuFwtlCR5mFmkYf1BJ+9X9LO2xsXFE3WUZigQCkVsrvfQaOqju9/D9Hw1MqmQv37cQ2dfiCSNBLFI\nQKZeyKotFcwrVvDKTdPYU32IiydoGZupoLEnwAOf9fLKtj4OWiTotUqiQjlmn5xb7vgjtZU7qDzY\nwsura9l80EZtp4dtB3vITNGTm6pjdWUXF4wykpqSjFatxtLvImrIYty4ccd93MvLy/lk7UaCYiV1\njU1IBREaDlZRXVlBa8NBzlu8iJSUFACyMjMwt7exf285bU0NpKel0dnexqbVn9HZWEenqYsd5ZVs\n3LyFhvpaZCoNVeW78LrdTExNY2RuHi6bFbVISIfDgdvhYOyoUmRyBTavF3FeIW0tTRSMGo/f68Fg\n72WYTovZbOKc7Gzk7n4UHiddne2MMaZSnJ6B3+Wg3uWiwx/EauvF32MixdmH1OPmgKmLQMBHqlJF\nulSM0+Vg7oQpWLq7yAh6MKRnoYxGaTd14vS46HK7GZGcQkQkprb1EAKhEEVqJuW1VSTLleji4kiX\niBEEg3x26BAT09JJUio42NtLo8tJllqNRCJlYrweLVHGJyXhCgaRhUMIoxEabVZmlYxkSmYWveYu\n1D4v8nCYrrZD5AujlBoM2IIBBJEIrmiU/U2HWLV2LQ3bt6O12uju72PZgQNMT02lKC6Ovb29rGhs\nQieTccPEiWSr1SxvbsLm80I0SqIujqzikdR3dSBPMKCXyRAAWXI5KkMSbY21bGltYfXWbeyrqmL8\nrAUY7RZSPS7Sc4dTWjyS7T09lB+sZWRxIYmJiTz66GOUrV6Dwe3C6/EQl5DA6sZGTluwALFIxNLX\nX0IolvDJWy9TV7mDK3/5S95Yt471Bw/yyubNjEhIoHnXLj7bvJm5Cxd+qUoUO7esWrGCKVotKTrd\n4fOjQEBVXx9TTz8dgLKyMmwVFVyYnc3YxETE4TBL6+vx+/188MYb9NlsnHX++SQkJJCens7Pliwh\nKU7LmfPnctcf7gTg9zfeiK+igqJgkDXr19Pc28ukKVOOy+8qVil0u90DFdMTmd8ZM3s60cY3MaOe\nbyvkY5E5brcbgUCASqVCoVB843nsZLgrxzgZRllH4vV6EYlEP8j46LtwtMNzTLAOZcl+iSGTpCGG\n+AEMCdRBwMqVKxk9ejRGo/FLz8XWnn7dBfpIY5uvEq6xuIZgMEggECAUCn0pSiT2XseTowX2qUgg\n4ONgdR16bTw+vw+zrY1zLliIL+TG4XQgDfYwJV9DNBREKRGglAjZ2OChxRqkvNVHRUsfU/OkqKRC\nXiiz8fF+B7XdAW6eY+Dec5NZMELDZ1Uuuu1erpuqgJCPA60O5GIoNsoozVTiicrRF8/lw48+Z8bs\nhUybfSbX/t+vefKBe3j+qlyum5eDUSvmlbUtXDw+h7iIj9c31LO10UXJiJGoRREKjfF4gyFW1lqZ\nc/YS8vPzj+u4h8Nh3lj6LvPOu4ic/AJyhxWxfcvmwy29ShUigYCCvBwMhsMtvyKRiIL8fCaNH8f0\nqVNoaaxne9kmhhcWkl9YjNnahzcYJiktkzHTzgCBEGt3J8UTT0PlsjF65AhcTifmzjbanE6yzliI\n1eXCI5GxrfYghTPnU72zjFAoSF+vGVFXG9JIBGU0QoZYiFMgQhgKgkRKm9uNrc9OQ28vm7u76bVZ\nSM7M5VDNPmTRCG12O3KREKNKzWm5+eRrtOxoaSbQb6fL1EFJspER46ZQ1d7GhrqDuMNh0uLiEAE+\nu4Ueq4VUYxqZoyeQkVdIZfk2ZEBVawvr21rJUqs5o7CIeImE0sREAgIRs1JS2NttYlyiASHg8vsx\ne9ykKJWsa2vDFoVxSUmkqFQc7OogV6tDHfKjFQrIVqvJ1mjIU2t4o6GBqSPHUFFfS5JcwbVjx3FW\nehrCaIR4uZzSxETS1GoSFQqq++zcP306o/MKiFcqaXM4WNdlorq/H5FWS72tj7XNTVwxbxGhPjsd\nUTCZuzF1trGtox1ncgZZ46ZRsWUjuoREXN1d5BAhM384nf+PvfMMjKM81/a1vVetdtWrJUsusmzL\nNrZxo9jGgAHTQkIKhEBIQhoncPhyCDmBEFIhCeQkEBI6AUMgFBs3jG25yU2yimX1Lq12tb3vzs73\nw0jHgClJMDE5uv7t7OzOzLuz78w9z/Pcj89DndeHPiuH4Ngo27a9Sd3Bw2RqNIz6/ew9fpxHamvJ\nmz2bi9aupbe3h+MtzRx8/WVmSQQumj6dXYcOUbZgAcc6OrgyN48FjixmOnLZe+ggx9wulr0tOMcZ\nn1t6envpaW5m1tu1pC/X19PictHa0IA3GMQfCGBzu1laXQ0qFRarlZeOHOH6adP4cnU1cZeLR197\njQvWrqWhoYHGxkZycnJYtmwZMpmMhoYG6l56ibvPOYepDgdnFxbyi5df5pKrr/5AR+IPY7yGMBQK\nASd8CgRBQK1Wn9Y585Nyov0wR+JxxnuY/r0tUcb5JAVqKpVCFMXTvq3xNO/xh8zjhlCnO6J+Kofn\nSYF6SiYF6iST/BNMpvh+Chi3df+4OVm4nnwj8n6OrO+ubz05xfjfmRkzZ5BIJGk+ehipTMbC5XMo\nKy9DpVIxZWoxW19J8YcdzVhVKYxqKdcttrC3K8yu9ggapYR1cwycV6Gj15OiIEPBk3t9GNQyzi7T\nopBL0IpS5hZqqOuJMOyLY1SP8sWFJm55dpCuMYFUWmR3v4JXttyLUqmksrISgGPHjpFlklGWfaL+\nbjgEy4uNFKqTWM1Gplg1/PHIKMFQkNqYgr1vdhGIJli1dh3nnHPOxz5OyWQSJFL0b9cDppJJBkdG\nufyaa5laOQ2vZ4zn1z/FlMJ8FCoFpcWluMc87Dt4GFEUybXbmD6jCqMjn4aDdcQSSYqnVzPS00E8\nHmXBeRczNjyA1eHg6MFdGOoP4x8cpMPnQzCaqV6+ir72Y2Tm5KP1+6nfvZ2solJmzlvMkR1vcLCz\nHUU0QjqZpGzmLPKnTOPIvlpc4TBrVl2MPxJh246t9LtG0WZkMtrRyjSdni/OrCIVj/PEsRbm2x1k\nGw1E/WmKjSb2tR9HIpExYrOTEfBhkYIoV1CRm09hpoPW7g6isTiry6aSIRHZun0jFasvJSSX0+l2\nk6fTMRyOUGYy0T3qRAJU2myk4wMIghZvNEKzy83ZDgcbentocI+xoaeH0XiCLyw7B008zKZjzTg0\nGuZk2nijp4dsrRapQklbMIRdrcKaYcOTSGJUqVGkkkRTAiqVEp1SRVQQ0CgUaJRKTGoNCokEfyJB\nLBREh4g3Hkdlz+aCq7/Ahmf/zPlXr2JqcTl/2b6VUpMJ0WDi1bZWchEhu5ArvnE7BpMVpVrDs7++\nl4LcPDrco1QFwzhlcuwz5tDy8rNER/Jp6B8gIsLPb/w6P3n+KaqtGUyVQOfx41x/3fUMjPnJyM5j\naU6Iy0pLKcjPR+3xsHXDBvxjY5hnzqRs1jykUglzvR7uf+xx7rzzznfMR+MP1y678kp+fuwY39+y\nBSGVorm3l1uWL6dIrea1F19EWVGBc3SU89NpMu12Nu/ejUGtZmVFxYnPV1Wx+bXXeOg3v6Fh82Zq\nMjPZOjbGwfPO45u33koikUB/UsRKo1Agl0gmhNHfy8kprAqFAoPB8H+yjce7nYn/0TYxEomEdDp9\nGvbwk+fkc2PcX+J015iezKRJ0iSTTPJJ8H/vivcpJBqNvq9APR11H6dyZB3f1sdd3zoues9kJBIJ\nc2vmMLfmRB/aVCrF73/zC2RjzeSY5GQYlNQP28kzjLCkTE0wmsZuUOAKCVQ4lOSYFMRTcG6FjqIM\nBa/UB7DqZBwbjqNXnjj2rcdCXDTTwEPbx5hbqEEukzK7xMKSqiy6RsI0BhT88fePUVxayCWXXYxG\no8HhcDDsS9E6GKB3NMJbTaNIkmmkooAckVQyhVqSRgj7+OUT65HJZOj1evLy8ibcLj8KTqeTaDSK\n0WjEarW+73pqtZosm5X6g/uZNrOatmPNIIHCklIAmo820NTWRd/YMBAmtsmPwzGdL3z1FuQyOZv+\n9iI73tzGeVfnkzdlKl2tTfjdo+RNqaCz6Qi7os/jdg6z69UXyCrIYrerl+7jLRTNORtf/SHam45g\nzcyiYe8OWo/UoVAqceQVsu3FpwgFXKTUOnaMjqIAEh4fU4xBtg0NIYoC+w/tZ3jMRXGmnTKlnEBa\nJKyQEY+Geb6lhRqHg7FolHa/l6jegM/lRCeTopJI6AqHeflYC63RCP5wBGtGBu2xOEcbjmBARBTT\nREaHKVOrSA9007S/Fq1UznybmaPOEQwKOfVuN3qlilhaYFt3F2lRZN/gIDOsVgaDAbbEY2QolWiU\nCuaVVfBq01Eeq92BSkhRYbFwYWEhUlGkJjOTx48f53MzrWTo9Gw4fowjbg8Zag03LluBv6+L5r5e\nfBoV3YEATWNjtPl81Dgc7Hc6EaQy/tR4lCVuFyPRKMfdLopLpjJj7gI2PvwA2//nF8TTaQS9iUTF\nLDwuJ0Z7Jl2uMQyjLjbd+U3MhaUoS6ai0em49NKL6evpYcuOnRgybLQ9+ye+PbuastJyLpmzgHv+\n9gJPb3iZRQ4HVxUV0T46ii0a4ec73uLe1/dSt3UDpXKRLucoTx08SFIUGUskWFRYyO7+Acp9HsKi\nSJ3XR0o4kfVxqht1jUbD9++5h/b2dvbt28eMujounDULgByLhf+ureXcq67ie089hUoqRWG1YrXZ\niKdSqORygrEYvkiErS+9xJ8uuQSzRkM0meTm11/n4nXrmDFjBr8RRf569Cgzs7PZ2NFB+ezZGI3G\nj/w/g/ea25yqhvCTEgifxDZOdSzjpn+xWOxjdSb+pDhdYlgQBOLx+MS5Mf7QwufznRFjM1mDOskk\nk3ycTKb4fgp4+umnWbVq1SmdKscbz38SIu+fqW999/ecvP8flqJ8JhEKhdiwYQPdda9y86oSqkod\nVBeZeXVvF263m1gyjUEtpdeTRCaRMBJK0eVOsnaWAatOxoamEIIoQSmT0DycYF93hD/v8aGUS/jt\nNTm8cChAw2CCsYjA72+cQ2WugYe39WG0zefs6ovoau+ltbORuTVz0Gq1WO25XPf//kB9qxMxnubA\nYAiFJE1SgNfaPMzPN7Kve4xrvng9lZWVEzfMH3Xc6xsa6OgdJJaW0NHZhVwiYrFY3nf90pISWhvr\nObBnF7GAB41KiSXTwf66/RxuqMdRUMjaz19IfkkJxxtbyMgppapqDgqlAo1Wx5H9e+g8fgyZTE48\nGqHl0D7CwQCxaITethZkMpF4JEjzwd24XX4iiTS+MRcFZZW0NRyiq6mBnrYWhFSCmmXnE/J7mDqz\nlLySUhwF5bS1HCWJBI1KTQQR58gIF5SVMSXTzgyNipExF5lqNV86fw1jQoospZLdPV10+f00ej14\nJTIkyRgmuYy+UJh5ublopFLCcgWNoy4CqRRZSEhFgqzJy8OhVnNOfj5DoRBWuZy3ujoZcDkRgNHR\nYaZbzPzn3LmUmUy81NlBk9tNUhAIJZIsz8tjSW4uFxUV0eb10RYIkBJFookEQ4EACiFFjd3OLJuN\n6sxMooJAdyCARqEgiISjI8N0+bwgpjHIZPT292BXq/FFI+wcGmJ1YSELHA7avF52Dg7S4/eTBGSI\njMVjDAgRFmbnEAqGePXFZ/jGwrNYXVTEtKwcWgb6aOtqZ6S3E5Mlg9mz5nLDvLM4t2IaGreTjds2\nko6EyQ4GWZKTg0IqoaWzE5VUyrklpZRPr8ZksdLQ28OR7k6ikQhHnSMMCmnmzJzN6431nPfFm/GN\nuXjjr8/S53KxtriIs7OyUCsU+ID9fb1sGhikIRTFmUiRk5nB56+99j03yePn+LjLq9PpxHv8OHPf\nrukPxWLsHhrilu99j4rqauavWMHnr7uO/pERXtqxg1GvlyebmiiYM4e+9nbKLRZyTCZUcjl7+vuZ\nvngx+fn5nLV0KRuOHGFbXx/22bO59Y47UKlUH2leEQSBaDT6gSmsyWQSv98PgFKpPK1z5rg3wOlO\nUx0XWzKZbCJlNRwOv6OX68eRsioIAoIg/FPp1h+V8RTfj2tbqVSKSCRCNBqdMIQ6+dwYH8NP6kHv\nqeqTJ1N8T8lkiu8kk/wTTEZQPwXE4/HTkuL7cXBymvDJjLtNjgvU90sTTqfTEzWwZ/rF7VhLC888\n/Au8/c2EfGO8tSfC9JIsCkqmkk6EKXWokCDSNJQgFBf42goLP3jFzWgwxV2vjGLRySi1KSmwKnhk\nl4cLZhhxh9OMBAQ+U6Plqj/0EU5JWDMni00tQdY91EUikcBmm8J3Lr8FmUzOvJlL2LDnGcLhMDqd\njoajR9Er5cwszOD8mUXsbh/hiV3NGFUhiiwa9g6EsFrMJJNJnn/uOWrf2kY4GiO/qITKykrWrVv3\nvjehPp+PwdExZi9YhEwmIx6LUb9/NwUFBe+bTmYwGLjm6qsmXu/bt59f/uqnxFIixsws1EYTzkEX\nKpUCvy9I/f69xIMR5AoFEb+XMdcoKYkM775d5BQUM3VmNQPdnVgzbJgsJiQkUanj6Mx28kumkxZS\nRMMh2hvrmX32uai0KoZ7O0nGUxzasZW80myqFs1my/o38HlC6Iwm8lNxZslBFfAiU8lxezxYgBKt\nlulGA75EArffQ/9AP3YpZOt0OLQ6BAl0KdS4BoeYaTRydnY287JzEJNJDnd24hBF5KkEJSY9aoWO\nC6fNYF9nG60eD754nN83NTItw4ZEJqPe6SQmCKzOz8eiUhHSaFhbUoIzHGZFfj61Q0N0+v1Mz8jg\nsMvFfucIJSYTl5aUEhNSxMJB4oLA58rL2dTfTySVIiEIvDU4yOrCQpzRKBlKBblZWSxwOEim0wST\nSQ44nfQFg1hVKpKCgE2l4uqyMvaNjlKg1/Nibx/ytEB1lh2pQolCIiPg9pGhkHNWaSmdg4MowlHO\nX7wc08q11L+1mcHudpLhAHuPuWjv62FkzEWl1UpeUSHfPessDvX3s0Ktxmm1kmcw8FRDPWVVc+lz\njjAslTEai7HQbictlXJkoJ9NHe2EkfLYfT8gOjZCPJ7AIpdi1WiOpRc0AAAgAElEQVRQyuVcW1nJ\nd2prySwu5rhzlKbd2zFnZJKIRbjhxht55A9/OOXNeiQS4f777qNx/34G+vvpGRpi7Zw5bOrsZP55\n5/HNG25A6vMRSiSYsWQJ3739dmpraxkaGmJeNMq29eup0mh4Yc8eNjQ2cl5lJU5BoLi4GIDc3Fzu\nvPdeBEH4QEfhkxkXpslkEpVKheltE6d3s3XLFn794x+jEEXUZjP//YtfUFZW9pG2caYzXmf779Iq\n5uOIcJ8cRR6vL/1H05s/biZTfP85JBLJ5OBNMsm7EEXxPRP+pED9FBCNRt/TZmacM1XYfZBwPbm+\ndbyXayKROKPrW5PJJOsff5BLp4nkzS2kuV3ClpYRCjM1/O6v+6gsyWKGRUU66mNlpY6XG4J4I2kS\nqTSukMhYKMYXFlrwRkXeOh7mstlmGoZERMMMVq6ew/M71qNIRZiVp8brD5OXnc3v/vwXnnr8T7y5\nYQNPvfArLjjvC5gMZtLpNHK5nD8+8jBvrn+Cc/JVxGJhfru5nhvPqeLJ2hbOLXegVigQhDS17jRb\nNr3BthefYkm2gm6nj+e2biTPbmXjq3/j0cefPGU0JplMotZokclkhEJBOtrb6Ojsor29Hbvdjiie\niKZ+UCTH5XZhzy1g9sJltLe14iiaQt3OJpQqKZGIiEIhR6o1kxAS1B9twj6lgvzSSowWGw17thPx\n+8nJz2fheasIJ/yMjTrZt+VvOHJK0RlNVC9egT23gAPbN7L9pb9QXl3F2RddSiTgp/XgEWQKOa88\n8SpBr58F512Md7iPnP5O7HoDSGCm0URTbxcFRiOBeJzWQBCTBB57cwv5RhNamYRLqqpQyRW0ut08\n19lJe1okFo9TY7XS5R4lT69joS0DTzKJO50m32SidWyMPx46gC8SxheL0R0IMMtipcZhZzSZYmFm\nJn9saiSRTpNMp+kPBhmNRAglEujkcgwKBc1jYxQYDPQFgzg0Gs4rKKDMZOa59jbyDQZMSiVvDg4y\n1Wxm7/Awbw4MsDA7G38iQSAeZ67djkGppNhkpsvvQyeX0xsMsiI3F71SSYPLhVomI5RMsn94mIqK\nCpKJOEaNhoYRF8XZuUS0GvoSKfRSGXc88ywauQylXM6wCNdf/3Usa6/gN//1bXSxKFUGLbn2TF4L\n+MjT69FLpagVCqKhEOVGI0aplJkZGTi9Hq558H7sej1KmYypZjNRqZRSg5Fv1dRweNTFAw0NDBzY\nyfmZmVjycjjgdFKclYUnGOSQ08lIIsH9DzzAf97xfRavvYaLv3QziViUn37ts/zsZz/DpFYjUyhY\neeGFlJeXA/Dju+4ism8fXy0uhqIiHmlu5om+Pi6+8krq9+9nqUbDFQsXkkil+NGbb7J161ZWr14N\nwFeuuYbbFyyg2GKhs62Nn+3eze/a2vjVQw9hMBgIhULs3buXSCRCdXX1hwrUv6e2sre3l9/fey+/\nXL6cIquVjc3N3H3HHTy+fv1pnR9PtwgZn//D4TByufy01lF+WkTVeBR5vARDrVZ/aH/bM+HYzpTr\n9KeFf/XvNckkZxLvN39MCtRPAYlE4iOnip3pvLu+NZ1OT6R4jUdbz8T+rYFAABUJdNIEIc8I+WY5\nwVCEe17uwTFlNnNm5lKVk2bL63/lhSMB3joeYVtbnLjMwE23fof1Tz7MwT4fyVSSb66wsKMjTKkj\ng1ani0Q8Sc3sZRTLGvja+fno9XreaPTwrZu+SKU1zj0Xa2nub+TXf/g2Zy1ay/JVi2loaODX9/2I\nb8xzIE0nceiUjPrD/GlnC2ZbDrXDoJaliAkSZsw+ixf+8jR3nJ1LzDPKfLuNuJCmyKFj17F69u3b\nx+LFi99zzEajkXgkRH9vD41NTaiNFmz5Raz/2+tYrFbsdjtqGaw6/7z3jfAH/H4c+UXYHFm8+frL\n7Nr0OqGgn8LSKaQSIlULziIzvxCNzoDH7SYRC5FTVEJO8RRiET/JRJx9m15jzDuI1zOM1+0m6AuA\nMEpRxRxyS8pQKFWUTK+mcV8trUcOMe+cCxjo6GLxmkvxjbkIeNy4R4Yw2TLR6HRE+zuJx2OoNVq8\nqQTNo6NIk0lc4TCJVIqZVgvRWJSORBy5REKNzQbJJClRJNeaQe/gIB6JyM6hQSwqFQV6PSaVigyN\nBmkkykA4jEmtYSASocpoAoOBXJ2OUDKFTW/A5/VQYcvAZjTxYmcn+0ZGqHe5kEskiMCenTspNhpZ\nmJ2NVi7HrFLRk0jQ4fURTSYxKBQsdDiIv52R8EZfH0aFAq1CwbUVFTzf3k5XIECx0choNMpUiwWV\nTEa3388Mq5XLSkvZ2t/PzIwMWjwekuk0RUYjz7a1IZdKuXvBAjb29bGpr5fC4jIyzWacfd0sLyjg\nppkzGY1EeKq9nVce/jV+nwd9Mo445qIprGJVQQFXlJWxb3gY/9gYpj17yFAqOeR2Y1eryFCpMDiy\nsYbCzM+wUZKTw57ODkb8fm6aNg2pRIJOKqHIaKDAauUzs2YR9fsZjcf50Y4dTHM46JZK+crttzN9\n+nTa2tu49Ls/AkCp1pBbVsnul17im8uXE0+l+Mltt/GD++9HoVCw49VXeWjZMsptNjrdbipkMt7Y\nuZPOAwdIqVRce/nlJ75HLqfGbqe/p+d/z2OvlwKrFY1KxYyqKlbEYmRefDFlZWV4vV5uv+UWClMp\nlDIZzz3yCPc++CBFRUXv+T+8Oyr2UWorOzo6mGmzUfR2/ffKigoeefFFwuEwer3+I8xefz+nU/QI\ngkAsFiORSAAn6oPP1Ayhf4R/ZOzebXyk1WrP2JTZ96sbnmSSSSb5OPnX54tM8qF8UJT03+XCMF7T\nolQqJyIK4z3uxtO9xk0iwuEw4XCYaDRKPB6fELOncyyMRiMRQcHhlk5kEhGHWYPFoMGqV3L1579C\nffsIjzzzCuG4QCgmIpdJcYdFNFo9/cfqSKUExoJxvnK2BZlMilkjZ0F+gqUlMZrq36Sro4XZJWZy\ncnMwmoxMzzMx2NfJ5xdl8ljtKJsaXWhlfooqHZy/8jzu//H3ydTLkKRTWLQyAokU4aTAvo5hCqwq\nFsyoYMXyq7jvB0+Rl1WEz+fDO+ZGFNNIAAki4WAQWTxEa2vrKY9ZpVJxVs0cmg7uIxAMY7fZyLRa\nyS2rpKBiJovOWYnGaufgocPvO25VVVU07NnJT79/K3ERlq69msUXXEYsGiM7vxhThg3PqJOx0WHi\n8RjJRIpU6sSDioB3DKvdSjDgp7HuMCpNNmZLMTpDBqNDXXQ21xMO+IlHIwz1dCCTy4mFo/R3dhGP\nxjBnOkglk6jUWvQmM73Hm5EpFDQGwwxI5YwqVOxqbyNbr8MZClKg01JjzySYSlGanUO23kCxxcKh\nkRGkgoBOLiMUDjLbZGBVQQFD4TADoRAH3W4CiQTn5+WTp9OiksnZPTiAWhSJCCmm22wsLiyiNxJm\nOBYjJgg829KCXqVEEEU6/H5kEglfr6piUXY2WVot/kSCZTk5VGdmUmYykavTcczn5ZHmZnzxON5E\nglAigTsW46jLxZsDA/jicY66XJRaLBQbjXT4fHT6fNx/+DD/09jIlv5+Sk0mOnw+VDIZyXQaZyTC\n8txcluflMRqNkqXR0uh2U6TXk69WMzDcjzDmwiCTUWAwEBFFDEolFUYjrrpaFN3tfO6ss4inksx3\nOLikvJw1xcWsyMsjKZHwVFsbT7a1sWN0lCW5uewfGuJYTzfriovIt9moG3WhMFlwx+L0hkK44nGQ\nyTg+MkI6GCQ4MkIgEmG+w0G92812pxOXVMqqCy4AoGLqVPZveRWAeDRK+65tXFdVxXmVlVw4cyZr\ncnPZ+sYbtLa2YtfrGYlGkQBuv59oIsE3qqt5eMUKRI+HP731Fq1NTTTU1/Nmezt5hYX/ex7Pm8cz\nhw8TT6XocrvZPTLC9OnTAXjphReYI5PxveXLuWXRIq4qLOTJhx+e+Ox4VCwQCBAOh1EqlZhMJtRq\n9UcSIA6Hgw6vl8jbgq7d5UKmUqHVaj/0s2cSqVSKUChEIBBAIpFgMpnOWBH2SSEIAuFwGL/fjyAI\nGAwGjEbj35Xi/K+IoJ5qe/8u9yKTTDLJmcFkBPVTwIdN/J/mC/wHie9/tr715GjrPztGCoWC5Rde\nxf/cs5uWITkD7lEKcqyk0bDh1ZfQ+low6EWEFOzrjnDTEgu/eXOMkZFhVs7IpDJTgyatxhkUaHPG\nuW6hhUF/iuyMfLwRN7t7XPzg6Xa2HRngtiuqePnQGHFByvUPHeCOC2yUO2zc/doohw/sxXv55Wjl\nKc6bk8uLdb3cdJYNbzhJVExhUEmJekdp8oxhcw6gN9jY8uaLWJUS/nxwkBWFBvb1B6kfCXNRuZUe\nT4SHH/otn/vc504ZxbBarSxcMJ9Bb5iKqeXU7d2DzZGDRBQAyMzKYbCtaWL9UChEMplEp9PR1NRE\n78AgI4P9aExW5p97ITqDEUumnUQ8intogK0vPkN5dQ1yuZyB9laSyTjJZIqje94iLSQZG+0lFglQ\nOrOaGQvmcHTPbqz2AqLhGE37dxLy+8jIykFwDlKGiFavY9eTDyOx2FBrdZTNqqGz6QhdzQ1Mn382\nXS1HGRx1kjNzDpWrLmS6VsfArje5fMoUKk0mVFIpT7e3MxwKcVFxMc0uF0N+H28NDiKKUGU2kxRF\nlublMxQK8vujRykyGsnUajnsdhGOJyiyO0hJpPRHIlTk5DAaTyDK5Bh0eh5rPEqmwYjb60Ell3NF\nWRmeaJShcJiNvb0MRyJcWlJCi8dDrl7PUDiMSi4nnEzS6HIx3WolkEhQajQSSCTYMTiIRa3m7Oxs\nDrpc3LV/P4UGA2qZjAyNhqFQiJQoknq7BvW5jg6uq6ggkkzS4vWysqCAMrOZFzo7mWG1Isqk1Njt\nvNLdTbffRzCRYHZmJlmZNhJCGm8kgi8WI5FOc15BAf54nCe3bUMURdyxGB0eDzFBQCqV4lOqcRSX\nsVIhwZdM8nJnJweGh/n2uSsR3E5qe7vxRqP0BYMMx2LcuXs3K4qL6fd6cWi1DIZCjIXDRFIpXu/p\n4cLSKaycPoO/NR7lP7/3PX73hz/wwP2/YvGSpWx/+Tli4RA2jZoxl4tUKoVcLkcllyOkUpjNZqw2\nG893dHDE6aSut5c8vZ4Ly8tRy+Usys3l2YYGBjwewskkY+k04knmbt+87TYeuO8+vvDyy+gNBr58\n220TqcN+j4dys3liziq0WKh1uyfqCKPRKHAiXdPj8bBjxw5kMhlnn3025rc/90FMnz6dBRddxNdf\ne40ik4nmsTG+fdddp70e8eMSHONR41OlM39S165PUsR9lG2lUilisdiH1h5/XNv7OPk0329MMskk\nnx4mBeoknzo+an3rx92/NS8vj7RchzVDy9J50+gaHOPI/i5aWo/z8ytyiMeVzC008vAOD3854OeK\nuSZiKWjoGkKpyuRwey/Tc5R4wil6xuK4w2li6TEO9gzwnWVmKrOy+esRP+fcuZUlK1ZTVZNBTnA/\nV8wxkRJEfnlVDhc9XMfGjRs41NxDKFtFsyvCpm4fdqMMhUzCJVOtzCm2oZHBb/f0s2nPC5AIsnJq\nJo/u6UAQ0nijKcLJNE83uvj++ZU81uihr69v4ob73eTm5tLcup0erZaUkOL44QOce/75iKLIUH8f\nGW+7+h44eJCO3gGUKhXHmxvJn1KBJdNB8Yw59Hd34MjJw5aVTcuRg6TTaTyuEfJKp+L3uIiHQ8xY\nsIS+jmMcr69DKpWgUCrRmpOodXLUGg27X3+DY4cOIVcoyMwpQm804nEOoBQFFppMiECBVsP5Uin7\nhoZo2rmFjsYjxCIhzrvyi4iJGGm3k+xMO10tRymeNpMRr49YLMK0zEySKQGTQk6BXk9r/wD+aBS5\nBPIMBhY4HAgidPr99IdCiIhMy7Bh1WhIptMccjqxqNXMyclhV3cnC20ZDMQSvHGsBSRS+gMBUsCl\nU0/01TwQjbA4K4tz8vI4MDxM7fAwpUYjJUYjcqkUpUzGYZeLcCqFTCJBI5dTbbMxKzMTgPUdHaTS\naeKCwGUlJYxEIszJzMQXi3Fufj4SiYSeQIAKq5VZmZkYFQr6QyFe6e7m5a4u1HI5UqDO6cQVjXJo\ndJSFDgev9/Zy5759pEWRz5aX0+zxYFapqLHb2TowQH2Ti3AySYHJxMWFhSQEAXc0yhVTpvCLQ4dI\nCALlZguv9XQT0RkYjkR4treT2XY74UQCuVRKZHSYvUNDfKmykky1mga3m983NaFTKKgdGMCq03FL\nVRXlGRk829JCh8+HKxzmv669Hq1OzzK1lh9seIW6ujo2vfIKGSoVSy79DHNWrGTMOcxff3YnU1pb\nUWk0/K27m1tvuomKigqmLFxI/4EDjAgCjV4vX6yqQi2X443FODY2xoqyMr63ejUquZxBr5dN27ez\n5sILgRPmX3f++MenfKA2q6aGFx94gOq8PNRyOS80NzN9zRr8fj8SiWQiC6S7u5v/941vMN9sJi4I\nPPfoo/zq4YfJfPs3/aD57hvf/jbnrlqF2+3mS1lZFJ4U3T0d/LMi5NPeKuZ08O4xUb0dBT8TjI/+\nHs6EmtdJJpnk359P18z4f5APM0E6U02S/hWM17cqFApUKhUajQadTodOp0OpVE64Bp8qTXi8Jc77\nXXi9Xi//84u7OKdUiibh4uWtdRw51svN52RTnqVBq0ijUkg5PhxlOJCk2KZk0JeieTCGVIxzvL+X\nMruaaAoiCXiqzo9RI2XY66EsU8KaGVrOKtVz9yU5aBVS4tEAay68mJioIiHKEeUqkqgIh0I8/9Bd\nfHe5CoMkjFQKdX0RTDoF9QNRtncHeGRvHz/e2oVCFJFIRVxeP0d6nFRn6UgDX5+fxaOXlmHRKNAo\npPgicUwm0/uOq16v59zlS5FEA2RolZTYTbQ3HGTnpteQxILMnTObvr4++pxjLDpnJbMXLCaalpGR\nU0B+YRG5xaWYrDZe/tOD1L21mfbGw9TXvklRxXTmLV/FvHMuwJyZTSqZQCKRsvD8i3AU5KPUppFI\nk2SX6Kl9/a8cP3KEqbPnM2XGbIrKZ1A0dRYWhw2lLEUk6CPq9zA/N5tiawZzi4qpcGSj1uqIRSP0\ntxxB39PGYnsGy/NyCXW0sP4399FZX8dQOMyW7m5GQkFavV72Dg8TSiTY1NlBr9dLo9vNn5pbaBxz\n80ZPN7sG+rl7/37u2F2LFAlLcnLINxhISyS83tHJ/AwbWoWCmRYzhQYDgpBils2GXaUiQy4lGAlh\nUigYjURIp9OUWyx4YjFSoki52YxcKiWWStHq9fK3zk7sajWXl5aSo9cjlUiotFr56owZzHc4yNbp\niKfTdAeD+GIx1k2ZwmWlpXyxooK8t+sTZUAqnebcvDzmOxxcUVpKpkbDLJuNSCrF7MxMCvR69jlH\niSQSuKJRVhcVUWwysbqgAIlEgkom48KycqLpNCFB4Ja5cxEBmVSKUank2NgY5WYzrV4vz7W3UaDX\nY5VI0BktaOVyHDodS/PzydLrebOvD4VEQoXZjCceZ2FWFsVGI2uKivhOTQ1CKsVxjweLSsVnpk6l\nOjubtEyGQq0hkUqyb3AItTWT3913H3PjcW6urMDc1Ur7oX1Mm7uQHiHNyz4fOyUSvnrnnUybNg2p\nVMptd97JFbfeyoqvfpXb7rmH++rruWnjRm7YsIHBdJqzSkspsdnINZvxRaNoTpFCe6q5dsU557Dk\nmmu4dft2bnjtNUw1Naxdtw6dTofRaJwwuXnqkUe4urCQbyxaxK1LlrDEaGT9s89+5PmtsrKSJUuW\nkJ2dfcYKBFEUicfjBAIBotHoRHTw/dKZPymx86+MoL7fmGg0mo9FnE4Kxkk+Th588EFqampQq9Vc\nd91177teKpVCr9dTV1c3sezpp59GKpW+Z1llZeXE61AohF6vZ82aNe/5zqKiIrZt2/ae5W+99Rb5\n+fkTrxOJBOvWrWPJkiUEg0F++MMf8vnPf37ifalUSlVV1Tv+F//1X//1juNJJBL86Ec/oqKiYqI/\n/Jo1a9iyZcsHDc8knyCTEdRPCf+uIvSTENjv7tk2vt2TI66CINDR0UHtlleIRQJkF03lvNVr0ev1\nSKVSnnn6aXJSHVx/bhHxRJINdd00utVYjFounp/PPa+1Y9VKGQulaBmJUVOgId+q5EuLzAz5BQ5u\ncGHNNZNlUvH8AQ/xlIg/kqbApmYslMKklqKQywjEUshlEjxePxdccAF//N0D/HSzl2QszPMHfPhC\nSX7/jVKm5Wj46lIrS37eTU9IQ2L6F1Hu/C1fqM6kwqZm0B/nJ7WDaOPtzMvWsq7SSiIl0DIa4bV2\nH0ik+KIpfrvfyZduuBGHw/GBY2gymVi4YP7E61AoRDqdxmAwIJFICAQCWDMdyN9OqdQZjEQiJ1Ib\nEZJIZDKUWh0NtdvpaT+GVmdg9tnnEPJ70RrMmG2Z9LQ2MXvJOWQ4smlvPAQICAkVISGGzxllzbU3\nM61mIWqNls3PP85wbw9pUSR7ZhlD+5qZkmHDkV9En8eHWW9E4m1h9tIVvPr4MXxtLUxZdi5qpQKt\nBGZk56C1Z5Gv1RCRyKhta6XVH4BUCrNazYKcHF7t6GCu3U6uTkenz8e2vj6UMhnnFhZSYjDQ7vPR\n6fdTOzyCTCJBFASKDXriQopio5EioxGrWo0zEuFvvX3UZGfz9PE2ctUq1HI52TodD9TX44pGSabT\nnJ2VhVWjQS6VclQmo3ZoiKkWC3kGA3/r7KTT72fvyAjLcnLYCzS43VRarahlMgwKBYddLs7OyWE4\nHEYURXRyOYddLgoNBvINBpLpNIgi+UYje51ONvT2cnZODus7OjjicqFUKvnJylW81HgUh0ZDt9/P\nVIsVVyTC33p6iAsCXfE45TYbfzh8GI1MRqvXyxSzGYVMxi2zZtETDNIZDNLrD+CwZBDUaJjlcPDN\nWbPQqtWUm81cv2kTmWo1B5xOahwO4qkUQ+EwSKXUTJ/Oo01NbB8dZeTAAbQaDYMGA4MifH39s2h0\nBuT2bHweNzVTp3BBdTW7EwkaDxygrm4PW5/+IxVTy7j/4Ycnbv7H55hAIEBOTg7V1dVYLBYWLlzI\nGxs2oNHpqKqq4vc/+xnSujrkUimbR0b4wa9+9Z7/QSKR4LE//pGWI0ew5+by5ZtvJjMzk0suu4yV\nq1dPZGmM9xs+mYDPR95JKb35RiO7env51o030tPRQW5hIf9x552UlJR8xJntzGFchI23ijmTTX4+\nKU42PpLL5f82Y/J+gnhSJH8yuN1uAGw222n5/tzcXO688042bdo0UZ5wKuRyOYsWLWLnzp3Mn3/i\n3mDnzp1UVla+Z9myZcsmPvfiiy9SUFDAW2+9hdPpfMe9x0cpx4rH41x++eXEYjE2b96MRqM55WeG\nh4f5y1/+wjXXXDPx3SdzxRVXMDw8zJNPPsns2bMB2LZtG6+//jrnn3/+B+7DJJ8MkwL1DOeDJv3J\nC8I/zrvThD0eD7vfeI6LqjOwW+zsaexiy4aXueiyq0mn0xxrqGOxVY1ZpwSdCqtBRbAvxlg4TXOv\nF71GRk2pAZUkxRyvhteOBvjhWgcmjRyVUuDcCgMbmwN0uxQ8eE0u+VY5T+z1E0pKcQYFvvXcMGeV\n6nijOUyWWYkxw05723GeeeEVVp27DGMqzNUzMmhxxrj7VTe//WwWBo0MlVxCwB/gyiuvZNdrL2A3\nKHBHkqjkUvKMSuRSgRy9imjyRHRYq5DS6o7Q5IqxYvXFfPnLX2bJkiV/9/i92z3UaDTS1tdKKlWO\nSq1Gq1Kw561tNDc303H8OKlEgtlLlpOIxVHrdfR1tDHU00l2YQk+1zDNB/ag0WhQKpX0tjfiH3NT\nUDEFuVxLSgiRjo9gMBmJR8PIFQr8Yy70RjNFOdPpb29jxO/DjwRh715ys/MIplIMRiJEj7dgsWcT\ndjoRutuRalRkKuUgiqh9HhRxNXlWK06ziWGvh2KVkkUOB0fcbuY5HGRrtczJzGSWzcYRl4tMjYbP\nTJ1KIiWgUygYDIcJxONcVVbGrqFBCgwG9gwPk63TEUsmichkKKVSFGKacqOReoWcFo+HhdnZbO3r\nA4mERVlZqORyUkAgkSAtisgAtVxONJnk9j17KNDrKTAaiQkC7T4fA6EQKrmchVlZRFMp5jkcdPn9\n7BkZ4frKSg6OjvJGXx+CKFI7PIzF40EmlXJRYSF6hQJvPI5GJmN9e/vEb3h5QQGJUBCDUkm9241V\npWLfyAgjsRixtMhALMrS8nKcw8PsjkZZWTaVzr5+UqLIspwceoNBpBIJBXo9m3p6wGwjPjpMMp1m\nV38/SYmEZ48dQy6REIjHeaChgXl2O61eL85wmCXz5uGWSCidPZuRYIgXW1pQqTXMyrTz8J8e5bY7\nvk9naxPmtmbKMjLYe/w4NyxaxOa+PqaazVxYWIhLIuGl4WE2b97M0qVLJ4yEXnn5ZV76858xKRSE\nZTK+etttvPTMM+zYtg0hEuGt3FyKZ85EmD4dmVLJPStWnNKF994f/pDo0aNcWVZGc3s7X7/uOh54\n+GHMZjNGo3FClJyKOYsW8Zf168kzm4mnUrzY1sZwPM6Xysu584IL2Nvdzfe/9S0efe65f7kB0ski\nJBgMIpVKT9k6J51OE4vFiMfjp71VzD/DJxllHPdH8Pv9KBQKDAbDaR2Tf0UN6qRAPT2IokggEMBg\nMLwnuh6Px7nmc9eyefMmAFauXMWzTz/1sXd4uOyyywA4ePAgAwMDH7ju0qVL2blzJ//xH/8BQG1t\nLbfffjsvvPDCO5Z9//vfn/jM448/zg033MDGjRt56qmnuPXWWz/yvkWjUS655BLkcjmvv/76xLGf\n6ty77bbbuOuuu7jqqquQyWTvWGfr1q1s3bqVjo4OcnJyJpavWrWKVatWfeT9meT0cuZdSSZ5B+OR\nvg/i0/5E9kxgaGiIEquUgqwMAJbPmcJvXj2GSqVCKpWSn626BPoAACAASURBVJ9P7eEmRKGTbLOK\nHe1xesMaNjT4KNDEWDfbzDFnGqVMztJKI9taowwHIJoSyLRZKc5Toe0bY0ExGDRyAjGRaxZYuPHJ\nIQwaBY1uJYF0GlGmIKXQ8p1LZjLU8BoDPRWIsQjfWeGgPFNFgSnGti4fj9Z6kUol9HkSVOep+OVP\nf0xMlCFojCiiXlJCmnAyzYJcLbW9AUrNKrRKKUedEVYUGUEiI5WM/UPi9FQUFBQw4hxl7/YtKJRK\ndAopAbcTS0YGAZ+LJRevQ63VUzwzn9HhDhLRAL3tTYwO9jDc00siHmXm/MXUbniZ7uPNTJ1VQ15J\nKT2tLSRiUYK+IP1dxwkHgojA6FA/c5evxGSxkV1Yyvrmo8QlPprDIToG+hmJRDBMmUp7/SFyzSZy\nzCb6QkGm27NwDQ3Q3N9PudWC3GLFFepjeGSEaFrEpVJwZFTC4txcuv1+TEoljWNjJ0RVIkGuXo9O\noUAhkaBXKFHLZMzOysIdizIYCtMfPLF/L3V2YtdqqbbZiAkCBQYDHT4v11ZU8GhzM50+HwqZjOU5\nOcyy2RBEkXafjxydDplEQvDt+ulWrxe7VsuXKiuZZrVS73bzp5YW1G8L36QgoJbJSIkiVrWaPcPD\nHPf5kEskhBMJrqusRCOXs3NwkEgyyaa+Pjr8fpKCQKXVikdIE9DqUHjcpNJp1FIpWrmcw6OjRCUS\nZk+rYqbVRu3RI5yXm8vXzj+fvbt3s7G7hwy9niuXreCZ7VtPmEqVlFBjd1A/Msxxr49MZR9TtRra\nggG8sRiBeJw5djvn5ueza3AQmURC7fAwAJF0mu+98goqo5FpixbhGnTx4Gu1yOQKnv3NfWzbvp3z\nz17EsS1buLC0lMFQiJ09Pfy/F16ge3CQHy9eTFQQyIxGqQuHefj227lXFFlxwQWs++xn2fD449xU\nVUUilcIXDvO9G29kcUEBC81mblm+nL5gkCN+P/FwmK/cdNMpz/FIJMLurVt56YorkEulzM7JoXn7\ndjo6Oli+fDnAROuUU3H1Zz9LwO/nlldeQSaTsXjNGoQNG7hk5kwAVlVWsrGvj+7u7gmH4PfjkxAD\nsViMO777XXbv2EFaFLnossv473vvnWgJNt4qRqlUYjQaP7Af8vvx75SeerLxEfBPGR9N8n+PxsZG\nLrn0MkZGRlCpVDzx+GNcfPHFE+/ffc+PGRjz87stRxBFkYfu+Br33PNj7r77RxPrJBIJHn30Ubp7\nepg/bx6XX375P3x/+FH+l0uXLuX+++8HTkR2w+EwV155JbfddtvEsmPHjrF06VLgRE/nnTt38sQT\nT2AwGHjwwQc/skCNx+OsXr0ai8XC+vXrUSgUH7j+ZZddxnPPPcdjjz3Gl7/85Xe8t3XrVs4666x3\niNNJzjwmBeoZTjKZRKlU/qt347RxptycqFQqfOHkRDqgNxhBofrfGiGrI4+ORJLukSS7j3to82q4\n+vovEz76IvkGHbkWmFaiZP3+YVqcAmX5Fn6/w8Wych2BYzHqXRqWziljZ1MLRTkKZjigfTCCNypS\nkmWkcURgMGVnSq6Nx757IVqNClEU+dPmY6g1avyRE46iM3JUbG6Hn25ys3q6nue+kk9Zto6pd23h\nJz9/gId+eR8p7wjeSJwMrYLa3gCCKPKT2kE0Cinzc/WsnWrFGUrxyz27/6GxGhwcJBAIYLfbycjI\nmFg+f14N0yorSCQStLS0cKT5GB7PGPFohJYD+8ktLSMpjNF6ZB8mh46Qb4yg141Ko8KSaWfK9Cp8\nYy6QSFi29koKykvoPd7Kpr88Ren0KloP7yeVFIhHoyiUSspnziUzJ5+WA3uoNptYXZBHttlCbzhK\nNC3SMOYimoxRkzWFYnk20niSXUcOEA4EKdZpqbZYaPP5sGk0XF1eTo8/wFAoSEIQUEql7Hc6iaVS\n6BQKGt1uzCoV4WSS/6mvZ77dzjGfj7FYjLFolLAgUGw00BUIUGO3Y1AqGQyFJnqLriwoYK7djl2r\nJSEIpEWRhQ4HeoWCUCrFPLudV3t62NzXxxSjkdFYDE8sBkCOXs8s24m61lydDqNSSbHRyAGnk839\n/azIzSWVThMVBCwqFTdMm0a718vGvj4yNRoa3W4EUWQoFKIxFqPcbGZdRQURIc2UrCweb2igwGik\nzunk4Ogo061WoqkUSKVkh3yYFAr2yiTU5DvYt38PWbm5ZIyOcjyZQhUao9RoRC6RMBwO85uGerQK\nJaX2TKrNRr5VU0NQEPj2G29wbl4ea4qKyDcY0MjlHHA6SabTKKVSFmRnc8m0aVhyc7nn9Q1Y55zF\nY7+8B9fIEBqtDt9gF6mRYe456yxmZmUhiiLBWIzjMhkhmYywVIo8GqXMYkEllXJ1aSld0Sjh9nYe\nuv9+RK+Xx7ZtI0+vp93vZ9TpJHvaNHI1GuwGA6FkkvkGA385duw953s6nWbjhg00HDiAy+PBFQqR\nbTQil8lIi+JHio55PB5GRkb4zLXX8tVvfAMAl8vFWy+9RIfLxTN1dfS6XBwdHUXzxz/yre98h4KC\nglN+1yf1QPIPDz6I2NbGjs98hqQg8K033+TPjz7KZz772Y/FffbfgVOZQWk0GgKBwCc2LmdKBHWS\nfxxBELjo4rWsuf6bLL34Sjoaj/DFL13HkcOHJgzR6g4cYOkln0GhPBE1XLL2auo2v/iO77jw4rW4\nQ1HKqxew/vs/4MDBQ/z0vp/8Q/v0UeaZ+fPnE4lEOHr0KJ2dnSxZsgSNRkNxcfHEsqKiIvLy8gB4\n8sknmT9/Pnl5eaxbt46vfe1r1NfXU11d/aHbCgaD7N+/n2eeeeZDxSmcqEO9++67ufnmm/nCF77w\njvfcbvc7Uos9Hg+lpaUTpQoflNo8ySfHpEA9w4lEIu/bxPzfxSDpTDiG0tJSGg9P5cUdx7CblLSO\nJFiy+rPAiSfjA8cPcd83L2VwcIi6VifxrhDPP/sE185McXaZnb0tg0RjKfp9ErpCOkKxGGa9nk6v\njKggRUiE0UZ6yNWneK62n18HksiUWirzDHQM+fnaQgtjIScvHu7G7V9OgUZ1wvRJKuHCdVez/pWn\n6PHECcYEmlwRbDopB3qjrHygB6NGhiDIWbpsGVPKylh7wWrkosg0nYJZWTp29gSQSyFLp2DtVCsS\niYRWd4R4/P2jPe/Hxk0b2X90FyabHu//Z++8w+Oozr59b+99V1pp1WU1S5Z7A2MbXBK6KSHw0VtC\nQhJSCW9CCIRASCCNkoRACKaHagzY2IBxr7LlJlu9r6Rdbe99vj9cXlNjCEkMr+7r0nVJ2tmdmZ2Z\nc87vnOf5PcNhLjrnUhoaGo6+rtVqCYfDbNmxE7nOjESlQWuw0rp7B137dyOSJkGUobB0MlKZjPxi\nBzpDKV37dyGI0qSSMYxmKyJxhlg0jESmRK3VEQ0HmLngTMZNmEx/ZxvrXn2B/o6DqLRa+nZuZpzJ\niM1goFCrQZHLsc/nw5BOYhELGOUZBhJJynNiiqRiRhQyFpTUoJBIkUokNFgsiMViagsLeLx5FwPh\nCLtHR5ldYEckgDceZ4bdzprBQXQKBesGB+kOhRADJxcU0BEK4Y5GKVSrOcXhYLzJxEgsRp7Nxubh\nYbzx+CH32nSarcPDRNJpziovZ25hIdtdLvyJBD3JJIORCP5kks5QiGqjkavq6ugLhdgwPMwejwej\nXM5QNIpWKmWcwYBMIuHdgQFygoBcIuE0h4Ndo6OsdzppsFrJ5XIsbW3lgspK5hcVsdPtZnl3N6c4\nHBTpdSQzWbLAdJuNvmCQeCbDT6dPp0yvpy8U4v69e3mzu5t8k45QLoMnGKFUoyUci7Hb5yPtdHJG\naRm1ViuXVlczFIkgSCS85fEgjkSYVFSETKHAms1iVSqxKpUEUyns2SxqqZRIKkWhRoMzGuXy2lp0\nEgnB/n6WFDl4Z+cWgiYLi2/4ES07NrF77UrKdVoC8TibBgYwKhToZDKmzpxJcVERv3ngASqUStp9\nPkLJJJ5EArlMxuIZM3hszx7UkQhLzzwTnULBnuFhrhkYQAS0+nwsKi8nnk7T5fNhKyj4wD3/1z/9\nidZVq1hcVka8ooJLn36aWxYupMPnI6BWM3Xq1I99Zla9+SYP3n03BWo1w/E4P7j9duafeio2m41T\nlyzhqvvv58qqKmYUF1OpVrN91Sq+3tTEw08//ZEi9d+NSCRi365dfKumBrlEglwiYUlZGWu2bePS\nyy//TN1nP48mScfm3IpEIpRK5VEzrNwx5Yn+U4wJxs83w8PDRGIx5p79FQDGTZjMuIZJ7Nmz56hA\nLS8r4+COzUyZeyg/srVpC+XHpCFs3LiRrt5+fvHUCiRSKaddcBnfPWs2P/3J/3xoTvw/43juKaVS\nyYwZM1i/fj3d3d1HI7LmzJlz9H/H5p8+8cQTfOMb3wDAYrEwf/58li5delwC1Wq18sADD3D55Zej\n1WpZvHjxP33P6aefTlFREQ8//PB7xplWq5XOzs6jf5vNZvx+P11dXVRVVf3Tzx3jP8OYQD3Bicfj\nqFSq//ZhfOERi8Wce+EltLe3E4vFOP00B3a7HTgkUEUIyORKnl3bwfQiEScXRBl2eukLFuIKZ2mo\nKOSOFw6SX97A1MoiFKEOTq4xk8jCc+/sp94mpcAg5udnV/HEZg+IZfzhbQ897jhfnW6i3ZVgJJhG\nJ01x06+f4YFbLqXL6eONDftoamoim8nQH8qQyWbQyGAklOOpa4o4q1HHmy0RLnl0ALFYzK/vvgud\nXESZXsVXG6yIgFKDgseb3cQyOX7+bj+JjEA6J6BUq4nFYsed7+Z0Otm2dwNnXDYPhVKO1+3n+Ree\noa7uzveE93V2duKorMGchZb2Lky2fNR6PdFgkN72/UybO5+Csip0BhMHdm5kROQCMgR9Q5CLMdjV\nT9G4SsQSgZbt2wh4R5DJVJTW1BMNRxAhoqSqluaNaxjobCXscSPEEziDfkxAJptjJBojm83ij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JpxUXs3t0lOU9PaRyOVRSKTlBYEZeHnMdDupNZjpDQdqDQbx6PYraWn54++1857vfJ69+BmKR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\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olPZxMNLyOLIpyFBGTSLqKhCkFkmSBjjIZkK9TSWm+Mqp0MFgEK1Wi9FoRKfTjfp+JiKo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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110999dd8>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_PCA(X_train.values, y_train.values, classes, azimuth=-93, elevation=-7)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.62555992  0.04913095  0.0412155 ]\n",
        "Variability of the compressed dataset 0.7159\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PSj3mRETUO9a8ayDLM9L8d+VwaL22eoBq9fqnRsIVq56j0TNl5Xl4kiTB5XJZ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\nkUiE1atX1/47kUjUbieR7MVu7MIcqZ4Glsry9nzdqSyzPTjQAibtPUDvW2Z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J5duquGvW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\nAAAJF7KyyYYYAgIdAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11296d4e0>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are two big clusters that can be separated by an hyperplane. Each of these big clusters are compound of three smaller overlapping clusters. The first cluster contains activities involving walking, while the second contains activities for which the subject stays still.\n",
      "\n",
      "According to the frist three components of the PCA, each of the three walking activities seems to be well expressed by the data. Laying seems to be well separated from other still positions (as the gravity effect is measured on a different axis : the smartphone is horizontal while it is vertical for sitting and standing activities.). However, sitting and standing seems to be harder to seperate. This is quite intuitive, as they are quite similar activities from an accelerometer or gyroscope perspective.\n",
      "\n",
      "Nonetheless, these figures gives us great hope of finding some good classifier to separate our data (remember we are only using the three first principal components, accounting for approximately 71.6% of the variability).\n",
      "\n",
      "Having one component explaining 65.5% of the variability also accounts for some high correlation in X_train.\n",
      "\n",
      "#### What are our options ?\n",
      "1. Use principal components as features\n",
      "2. Use Support Vector Classifier. As $n << d$ and the PCA shows easy-separated data, the linear kernel may be powerful enough to find some good separating hyperplane.\n",
      "3. Use regularized logistic regression (LASSO or Ridge)\n",
      "4. If we're still stuck, try SVM with non-linear kernel"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Modelisation\n",
      "\n",
      "We use cross validation on 5-Folds. Parameters are selected using F1-score as this performance measure avoid giving good scores to naive models in case of class imbalance. Performance of each model is evaluated on test set (train test consists of 70% of the data) looking at precision, recall and F1-score. For more information on cross validation on K-Folds, cf. _The Elements of Statistical Learning_ (T. Hastie & al.). For more information about the performance measure we use, cf. :\n",
      "- Precision and recall : https://en.wikipedia.org/wiki/Precision_and_recall\n",
      "- F1 score : https://en.wikipedia.org/wiki/F1_score"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "K_folds = 5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### 1. Logistic regression"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### On the compressed dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pca = PCA(n_components=50)\n",
      "X_redux = pca.fit_transform(X_train)\n",
      "print(\"\\n Variability of the compressed dataset: %.4f of the total variability \\n\" %sum(pca.explained_variance_ratio_))\n",
      "logit = linear_model.LogisticRegression()\n",
      "logit.fit(X_redux, np.ravel(y_train))\n",
      "y_pred = logit.predict(X_redux) ## No fit_predict method\n",
      "print(\"\\n performance on train dataset : \\n\")\n",
      "print(metrics.classification_report(y_train, y_pred, target_names=classes))\n",
      "print(\"\\n\\n performance on test dataset : \\n\")\n",
      "y_pred = logit.predict(pca.transform(X_test))\n",
      "print(metrics.classification_report(y_test, y_pred, target_names=classes))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " Variability of the compressed dataset: 0.9309 of the total variability \n",
        "\n",
        "\n",
        " performance on train dataset : \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "                      precision    recall  f1-score   support\n",
        "\n",
        "           1 WALKING       0.98      0.99      0.98      1226\n",
        "  2 WALKING_UPSTAIRS       0.98      0.98      0.98      1073\n",
        "3 WALKING_DOWNSTAIRS       0.99      0.99      0.99       986\n",
        "           4 SITTING       0.90      0.87      0.88      1286\n",
        "          5 STANDING       0.88      0.91      0.90      1374\n",
        "            6 LAYING       1.00      1.00      1.00      1407\n",
        "\n",
        "         avg / total       0.95      0.95      0.95      7352\n",
        "\n",
        "\n",
        "\n",
        " performance on test dataset : \n",
        "\n",
        "                      precision    recall  f1-score   support\n",
        "\n",
        "           1 WALKING       0.89      0.98      0.93       496\n",
        "  2 WALKING_UPSTAIRS       0.92      0.86      0.89       471\n",
        "3 WALKING_DOWNSTAIRS       0.93      0.92      0.93       420\n",
        "           4 SITTING       0.94      0.85      0.89       491\n",
        "          5 STANDING       0.88      0.94      0.91       532\n",
        "            6 LAYING       1.00      1.00      1.00       537\n",
        "\n",
        "         avg / total       0.93      0.93      0.93      2947\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### On the original dataset :"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "logit = linear_model.LogisticRegression()\n",
      "logit.fit(X_train, np.ravel(y_train))\n",
      "y_pred = logit.predict(X_train) ## No fit_predict method\n",
      "print(\"\\n performance on train dataset : \\n\")\n",
      "print(metrics.classification_report(y_train, y_pred, target_names=classes))\n",
      "print(\"\\n\\n performance on test dataset : \\n\")\n",
      "y_pred = logit.predict(X_test)\n",
      "print(metrics.classification_report(y_test, y_pred, target_names=classes))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " performance on train dataset : \n",
        "\n",
        "                      precision    recall  f1-score   support\n",
        "\n",
        "           1 WALKING       1.00      1.00      1.00      1226\n",
        "  2 WALKING_UPSTAIRS       1.00      1.00      1.00      1073\n",
        "3 WALKING_DOWNSTAIRS       1.00      1.00      1.00       986\n",
        "           4 SITTING       0.97      0.97      0.97      1286\n",
        "          5 STANDING       0.98      0.98      0.98      1374\n",
        "            6 LAYING       1.00      1.00      1.00      1407\n",
        "\n",
        "         avg / total       0.99      0.99      0.99      7352\n",
        "\n",
        "\n",
        "\n",
        " performance on test dataset : \n",
        "\n",
        "                      precision    recall  f1-score   support\n",
        "\n",
        "           1 WALKING       0.94      1.00      0.97       496\n",
        "  2 WALKING_UPSTAIRS       0.97      0.95      0.96       471\n",
        "3 WALKING_DOWNSTAIRS       1.00      0.97      0.98       420\n",
        "           4 SITTING       0.97      0.88      0.92       491\n",
        "          5 STANDING       0.90      0.97      0.94       532\n",
        "            6 LAYING       1.00      1.00      1.00       537\n",
        "\n",
        "         avg / total       0.96      0.96      0.96      2947\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The model performs better without dimentionality reduction. As the time-complexity in the number of variable is reasonable considering the number of variables in our dataset, we decide to keep the original feature set. \n",
      "\n",
      "Regarding performance, sitting and standing activities are harder to seperate as seen in the 3D plots. As sitting has a poor recall and standing a poor precision (compared to the other classes), we can deduce that lots of activites labeled as sitting are identified as standing. Resolving this issue may be hard.\n",
      "\n",
      "There is some overfitting, thus we try a L1-regularized variant of logistic regression to prevent this."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### L1 regularization\n",
      "To prevent overfitting, we use L1 regularization (L2 regularization gives very similar results in our case). For more information about regularization and the influence of the parameter $C$, cf. http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression, or _The Elements of Statistical Learning_ (T. Hastie & al.)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "logit = linear_model.LogisticRegression()\n",
      "C = np.logspace(-2, 4, 15) # Exponential exploration of the parameter space\n",
      "params = {'penalty':['l1'], 'C':C} # L1 Penalty\n",
      "train_scores, valid_scores = validation_curve(logit, X_train, np.ravel(y_train), \\\n",
      "                                              \"C\", C, cv=K_folds, verbose=0, scoring=\"f1\", n_jobs=-1)\n",
      "plot_validation_scores(train_scores, valid_scores, C)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Efaie8vxy65BnGEafpjM12LOqejDwt0wb0y3efRcqKnbt4AdQWel6hQ/Ozkiv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\nSXZpZgAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11288fba8>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The model still exhibits overfitting that cannot be reduced by penalizing the coefficients without decreasing the performance on cross validation samples. The cross-validation score is quite variable (+/- 2%) depending on the 5-Folds sampling."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "logit = linear_model.LogisticRegression(penalty='l1', C=C[np.where(train_scores == np.max(train_scores))[0][0]])\n",
      "print(\"Optimal C parameter : %.4f \\n\" %logit.C)\n",
      "logit.fit(X_train, np.ravel(y_train))\n",
      "y_pred = logit.predict(X_train) ## No fit_predict method\n",
      "print(\"\\n performance on train dataset : \\n\")\n",
      "print(metrics.classification_report(y_train, y_pred, target_names=classes))\n",
      "print(\"\\n\\n performance on test dataset : \\n\")\n",
      "y_pred = logit.predict(X_test)\n",
      "print(metrics.classification_report(y_test, y_pred, target_names=classes))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Optimal C parameter : 193.0698 \n",
        "\n",
        "\n",
        " performance on train dataset : \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "                      precision    recall  f1-score   support\n",
        "\n",
        "           1 WALKING       1.00      1.00      1.00      1226\n",
        "  2 WALKING_UPSTAIRS       1.00      1.00      1.00      1073\n",
        "3 WALKING_DOWNSTAIRS       1.00      1.00      1.00       986\n",
        "           4 SITTING       1.00      1.00      1.00      1286\n",
        "          5 STANDING       1.00      1.00      1.00      1374\n",
        "            6 LAYING       1.00      1.00      1.00      1407\n",
        "\n",
        "         avg / total       1.00      1.00      1.00      7352\n",
        "\n",
        "\n",
        "\n",
        " performance on test dataset : \n",
        "\n",
        "                      precision    recall  f1-score   support\n",
        "\n",
        "           1 WALKING       0.94      1.00      0.96       496\n",
        "  2 WALKING_UPSTAIRS       0.98      0.93      0.96       471\n",
        "3 WALKING_DOWNSTAIRS       0.99      0.98      0.98       420\n",
        "           4 SITTING       0.96      0.87      0.91       491\n",
        "          5 STANDING       0.90      0.97      0.93       532\n",
        "            6 LAYING       0.99      1.00      1.00       537\n",
        "\n",
        "         avg / total       0.96      0.96      0.96      2947\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As seen previously, the sitting and standing activities are note well separated. There is a still a huge overfit despite the regularization.\n",
      "\n",
      "We try to improve these results by using a SVM classifier, as the hyperplane found by this model is often better at classification than the one found by logistic regression thanks to the margin maximization (for more precision, cf. _The Elements of Statistical Learning_ T. Hastie & al.)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## 2. Support Vector Classifier\n",
      "\n",
      "As the data is likely to be separable by an hyperplane in the original feature space, no need for nonlinear kernel here. We user `linearSVC` (SVC with linear kernel implementation of liblinear. Complexity is $O(np)$, i.e. this model is less complex than logistic regression that is $O(np^2)$. For more information on SVM and the influence of the parameter $C$, cf. http://scikit-learn.org/stable/modules/svm.html, or _The Elements of Statistical Learning_ (T. Hastie & al.)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linSVC = svm.LinearSVC(dual=True, tol=0.0001, multi_class='ovr', fit_intercept=True, \\\n",
      "                intercept_scaling=1, verbose=0, random_state=42)\n",
      "C = np.logspace(-3, 3, 15) # Exponential exploration of the parameter space\n",
      "params = {'C':C}\n",
      "train_scores, valid_scores = validation_curve(linSVC, X_train, np.ravel(y_train), \\\n",
      "                                              \"C\", C, cv=K_folds, verbose=0, scoring=\"f1\", n_jobs=-1)\n",
      "plot_validation_scores(train_scores, valid_scores, C)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6MgwjexnTwXFVjYnIF4FncN1x71XVDSJyRTL/LuBQ4AERUeAN4LKMS3wJeEhE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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aaa88d0>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "linSVC.C = C[np.where(train_scores == np.max(train_scores))[0][0]]\n",
      "print(\"Optimal C parameter : %.4f \\n\" %linSVC.C)\n",
      "linSVC.fit(X_train, np.ravel(y_train))\n",
      "y_pred = linSVC.predict(X_train)\n",
      "print(\"\\n performance on train dataset : \\n\")\n",
      "print(metrics.classification_report(y_train, y_pred, target_names=classes))\n",
      "print(\"\\n\\n performance on test dataset : \\n\")\n",
      "y_pred = logit.predict(X_test)\n",
      "print(metrics.classification_report(y_test, y_pred, target_names=classes))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Optimal C parameter : 19.3070 \n",
        "\n",
        "\n",
        " performance on train dataset : \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "                      precision    recall  f1-score   support\n",
        "\n",
        "           1 WALKING       1.00      1.00      1.00      1226\n",
        "  2 WALKING_UPSTAIRS       1.00      1.00      1.00      1073\n",
        "3 WALKING_DOWNSTAIRS       1.00      1.00      1.00       986\n",
        "           4 SITTING       0.98      0.99      0.99      1286\n",
        "          5 STANDING       0.99      0.99      0.99      1374\n",
        "            6 LAYING       1.00      1.00      1.00      1407\n",
        "\n",
        "         avg / total       1.00      1.00      1.00      7352\n",
        "\n",
        "\n",
        "\n",
        " performance on test dataset : \n",
        "\n",
        "                      precision    recall  f1-score   support\n",
        "\n",
        "           1 WALKING       0.94      1.00      0.96       496\n",
        "  2 WALKING_UPSTAIRS       0.98      0.93      0.96       471\n",
        "3 WALKING_DOWNSTAIRS       0.99      0.98      0.98       420\n",
        "           4 SITTING       0.96      0.87      0.91       491\n",
        "          5 STANDING       0.90      0.97      0.93       532\n",
        "            6 LAYING       0.99      1.00      1.00       537\n",
        "\n",
        "         avg / total       0.96      0.96      0.96      2947\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This linear SVC performs better than the logit, and is faster to compute.It still has a hard time to separate sitting and standing activities. In order to address this issue, we could try to :\n",
      "1. Try to obtain more data\n",
      "2. Improve the data representation of the signal contained in the dataset\n",
      "3. Improve the model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_learning_curve(linSVC, \"Learning Curve (SVC)\", X_train, np.ravel(y_train), cv=5, n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Te7FSr2TykMnMHDlTBgp2UwLLtwZCTIGlXEPXBFdKYVZmbGZbcAsY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\nREU8ESHucXvdOL3OhOqaKwhC5JDBhnF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       "text": [
        "<matplotlib.figure.Figure at 0x111805cc0>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As seen in the learning curves, the performance in terms of training example starts to level off at 5000+ training examples. Obtaining more data would help to reduce variance of our score, but may not be sufficient to improve the model performance dramatically as we would still have the standing/sitting representation overlap in our data.\n",
      "\n",
      "Trying a nonlinear SVM (e.g. using a RBF kernel) would be very costly in terms of time-complexity as this model would be $O(n^3p)$ and would imply at least one more tuning parameter, making the cross-validation step way harder.\n",
      "\n",
      "Further work on signal representation seems to be a more efficient way to improve the model, as we saw that these two classes seems to present serious overlapping with our features (cf. 3D plots). If we manage to find a representation in which these two signals would be easier to separate, this would solve our problem. This could by done by using spherical wavelets rather than Fourier transform in a scattering network fashion to perform the classification (cf. Stephane Mallat & al. work http://www.di.ens.fr/data/software/scatnet/)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Conclusion\n",
      "\n",
      "Our best model is a linear Support Vector Classifier. This model can be seen as a first step toward an health monitoring application for smartphone or wearable. There are still some issues to face in order to launch a proper product :\n",
      "- In our dataset, the smartphone is fixed on the waist. In a real application, it would be in the pocket, and thus its position with respect to the body would vary. We should try to detect the position of the smartphone before the feature computation step in order to take accound of an eventual rotation. \n",
      "- It would be useful to detect if the smartphone is carried by the user, in order to avoid activity quantification when the smartphone is simply carried by a car.\n",
      "- We should try to recognize other activities that are quite common such as running or biking. These activities would be relevant for our health application. \n",
      "\n",
      "Adressing these issues would add complexity to our task. In this fashion, scattering networks with spherical wavelet discussed above would be of some help.\n",
      "\n",
      "The model we developped is time stable, thus it would not be useful to refresh it regularly. However, we should try to obtain a broader dataset in term of individuals in order to take account of the variety of human bodies & movements. As we are using smartphones to collect the data, obtaining more data points would not be very costly in term of aquisition or storage. If some retraining would be necessary, it could be done automatically as the whole parameter selection steps are done by our code without human intervention. Some checks should be programmed in order to verify that our exploration of the parameter set is wide enough, and to send alerts if there is some drop in performance.\n",
      "\n",
      "As we use a SVM, the model is not very scalable, but as seen previously, this is not an issue as the learning curves show that increase in performance from additional data would not be very significant.\n",
      "        \n",
      "Such application could be thought as a smartphone app financed by advertising revenues, or as a SAAS (software as a service). In the SAAS fashion, the app would be accessed by an API to allow developpers to integrate some health analytics in their application. Pricing would be set according to the transactions volume (e.g. number of API requests)."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}