diff --git a/04_measuring_quality_of_a_classifier.ipynb b/04_measuring_quality_of_a_classifier.ipynb
index 6d710bcb426b28480c9a376bc021f03878a2a9a7..38332a8799c195dcf9825a36083d80268991e80f 100644
--- a/04_measuring_quality_of_a_classifier.ipynb
+++ b/04_measuring_quality_of_a_classifier.ipynb
@@ -153,11 +153,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Up to now we used _accuracy_, the percentage of correct classifcations, to evaluate the quality of a classifier.\n",
+    "Up to now we used *accuracy*, the percentage of correct classifcations, to evaluate the quality of a classifier.\n",
     "\n",
     "Regrettably _accuracy_ can produce very misleading results. \n",
     "\n",
-    "This and the next chapter will discuss other metrics  to asses the quality of a classifier including the possible pitfalls."
+    "This chapter will discuss other metrics used to asses the quality of a classifier, including the possible pitfalls."
    ]
   },
   {
@@ -314,7 +314,7 @@
     "\n",
     "1.  A classifier predicts labels `[0, 1, 0, 1, 1, 0, 1, 0]` whereas true labels are `[0, 0, 1, 1, 1, 0, 1, 1]`. First write these values as a two columned table using pen & paper and assign `FP`, `TP`, ... to each row. Now create the confusion matrix and compute accuracy.\n",
     "\n",
-    "2. A random classfier just assign a randomly chosen label `0` or `1` for a given feature. What is the average accuracy of such a classifier?"
+    "2. A random classfier just assign a randomly chosen label `0` or `1` to a given sample. What is the average accuracy of such a classifier?"
    ]
   },
   {
@@ -335,10 +335,10 @@
     "1      1           TP\n",
     "0      0           TN\n",
     "1      1           TP\n",
-    "1      0           FP\n",
+    "1      0           FN\n",
     "\n",
-    "TP = 3    FP = 2\n",
-    "FN = 1    TN = 2\n",
+    "TP = 3    FP = 1\n",
+    "FN = 2    TN = 2\n",
     "\n",
     "accuracy = 5 / 8 = 62.5 %\n",
     "</pre>\n",
@@ -469,7 +469,7 @@
     "    </tr>\n",
     "    <tr style=\"border: 1px black\">\n",
     "        <td style=\"border: 1px solid black; background: white; padding: 1em \">FN = 5</td>\n",
-    "        <td style=\"border: 1px solid black; background: white; \">TN = 9900</td>\n",
+    "        <td style=\"border: 1px solid black; background: white; \">TN = 9990</td>\n",
     "    </tr>\n",
     "        \n",
     "</table>\n",
@@ -519,11 +519,11 @@
    },
    "source": [
     "<pre>\n",
-    "TP = 3    FP = 2\n",
-    "FN = 1    TN = 2\n",
+    "TP = 3    FP = 1\n",
+    "FN = 2    TN = 2\n",
     "\n",
-    "precision = 3 / (3 + 2) = 60 %\n",
-    "recall    = 3 / (3 + 1) = 75 %\n",
+    "precision = 3 / (3 + 1) = 75 %\n",
+    "recall    = 3 / (3 + 2) = 60 %\n",
     "F1        = 2 * (0.6 * 0.75) / (0.6 + 0.75) = 66.66%\n",
     "</pre>"
    ]
@@ -686,7 +686,6 @@
    ],
    "source": [
     "from sklearn.model_selection import cross_val_score\n",
-    "from sklearn.metrics import make_scorer, confusion_matrix\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "\n",
     "\n",
@@ -697,6 +696,10 @@
     "    print(\"{:.1f} % of the beers are yummy\".format(100 * sum(labels == 1) /n))\n",
     "    print()\n",
     "    \n",
+    "    # NOTE: metrics given in `cross_val_score` as strings (names).\n",
+    "    #       (In order to use metric functions, as these imported from `sklearn.metrics`,\n",
+    "    #       you need to transform them first into estimator scorer function using\n",
+    "    #       `sklearn.metrics.make_scorer()` function, e.g. `make_scorer(f1_score)`.)\n",
     "    for metric in [\"accuracy\", \"f1\", \"precision\", \"recall\"]:\n",
     "        scores = cross_val_score(classifier, features, labels, scoring=metric, cv=5)\n",
     "        print(\"   {:12s}: mean value: {:.2f}\".format(metric, scores.mean()))\n",
@@ -709,7 +712,7 @@
     "print(\"balanced data\")\n",
     "assess(classifier, beer_data)\n",
     "\n",
-    "# we sort by label, then removing samples| is easier:\n",
+    "# we sort by label, then removing samples of one class is easy:\n",
     "beer_data = beer_data.sort_values(by=\"is_yummy\")\n",
     "\n",
     "print(\"unbalanced data\")\n",
@@ -730,7 +733,7 @@
    "source": [
     "## Exercise section 3\n",
     "\n",
-    "1. Play with the previous examples, use different classifiers with different settings\n",
+    "1. Play with the previous examples; for beer data try `SVC` classifier with different `C` and `gamma` settings.\n",
     "\n",
     "### Optional exercise\n",
     "\n",
@@ -739,7 +742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {
     "tags": [
      "solution"
@@ -751,15 +754,15 @@
      "output_type": "stream",
      "text": [
       "OPTIMIZE SETTINGS\n",
-      "score = 0.921  C=5.0  gamma=0.5\n",
-      "score = 0.913  C=5.0  gamma=1.0\n",
-      "score = 0.925  C=5.0  gamma=2.0\n",
-      "score = 0.943  C=10.0  gamma=0.5\n",
-      "score = 0.933  C=10.0  gamma=1.0\n",
-      "score = 0.933  C=10.0  gamma=2.0\n",
+      "f1 score = 0.921  C=5.0  gamma=0.5\n",
+      "f1 score = 0.913  C=5.0  gamma=1.0\n",
+      "f1 score = 0.925  C=5.0  gamma=2.0\n",
+      "f1 score = 0.943  C=10.0  gamma=0.5\n",
+      "f1 score = 0.933  C=10.0  gamma=1.0\n",
+      "f1 score = 0.933  C=10.0  gamma=2.0\n",
       "\n",
       "BEST RESULT CROSS VALIDATION\n",
-      "score = 0.943  C=10.0  gamma=0.5\n"
+      "f1 score = 0.943  C=10.0  gamma=0.5\n"
      ]
     }
    ],
@@ -777,7 +780,7 @@
     "\n",
     "from sklearn.svm import SVC\n",
     "from sklearn.model_selection import cross_val_score\n",
-    "from sklearn.metrics import classification_report\n",
+    "# OPT, cf. from sklearn.metrics import classification_report\n",
     "\n",
     "\n",
     "results = []\n",
@@ -788,7 +791,7 @@
     "    for gamma in (.5, 1, 2):\n",
     "        classifier = SVC(C=C, gamma=gamma)\n",
     "        test_scores = cross_val_score(classifier, features, labels, scoring=\"f1\", cv=5)\n",
-    "        print(\"score = {:.3f}  C={:.1f}  gamma={:.1f}\".format(test_scores.mean(), C, gamma))\n",
+    "        print(\"f1 score = {:.3f}  C={:.1f}  gamma={:.1f}\".format(test_scores.mean(), C, gamma))\n",
     "        results.append((test_scores.mean(), C, gamma))\n",
     "        \n",
     "# max of list of tuples considers value of first entry\n",
@@ -799,7 +802,7 @@
     "\n",
     "print()\n",
     "print(\"BEST RESULT CROSS VALIDATION\")\n",
-    "print(\"score = {:.3f}  C={:.1f}  gamma={:.1f}\".format(best_score, C, gamma))\n",
+    "print(\"f1 score = {:.3f}  C={:.1f}  gamma={:.1f}\".format(best_score, C, gamma))\n",
     "\n",
     "# EVALUATE CLASSIFIER ON VALIDATION DATASET\n",
     "\n",
@@ -834,7 +837,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.6.8"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
@@ -863,12 +866,7 @@
    "title_cell": "Table of Contents",
    "title_sidebar": "Contents",
    "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "336px"
-   },
+   "toc_position": {},
    "toc_section_display": true,
    "toc_window_display": true
   }
diff --git a/08_a-neural_networks.ipynb b/08_a-neural_networks.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b6e84c14fa5536ac27e1d881cec6814766d6cda9
--- /dev/null
+++ b/08_a-neural_networks.ipynb
@@ -0,0 +1,3596 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "    \n",
+       "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
+       "    \n",
+       "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
+       "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
+       "    \n",
+       "    @import url('http://fonts.googleapis.com/css?family=Lato');\n",
+       "    @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
+       "    \n",
+       "    @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
+       "\n",
+       "    \n",
+       "    body {\n",
+       "        font-family: 'Lora', Consolas, sans-serif;\n",
+       "       \n",
+       "        -webkit-print-color-adjust: exact important !;\n",
+       "        \n",
+       "      \n",
+       "       \n",
+       "    }\n",
+       "    \n",
+       "    .alert-block {\n",
+       "        width: 95%;\n",
+       "        margin: auto;\n",
+       "    }\n",
+       "    \n",
+       "    .rendered_html code\n",
+       "    {\n",
+       "        color: black;\n",
+       "        background: #eaf0ff;\n",
+       "        background: #f5f5f5; \n",
+       "        padding: 1pt;\n",
+       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
+       "    }\n",
+       "    \n",
+       "    p {\n",
+       "      line-height: 140%;\n",
+       "    }\n",
+       "    \n",
+       "    strong code {\n",
+       "        background: red;\n",
+       "    }\n",
+       "    \n",
+       "    .rendered_html strong code\n",
+       "    {\n",
+       "        background: #f5f5f5;\n",
+       "    }\n",
+       "    \n",
+       "    .CodeMirror pre {\n",
+       "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
+       "    }\n",
+       "    \n",
+       "    .cm-s-ipython span.cm-keyword {\n",
+       "        font-weight: normal;\n",
+       "     }\n",
+       "     \n",
+       "     strong {\n",
+       "         background: #f5f5f5;\n",
+       "         margin-top: 4pt;\n",
+       "         margin-bottom: 4pt;\n",
+       "         padding: 2pt;\n",
+       "         border: 0.5px solid #a0a0a0;\n",
+       "         font-weight: bold;\n",
+       "         color: darkred;\n",
+       "     }\n",
+       "     \n",
+       "    \n",
+       "    div #notebook {\n",
+       "        # font-size: 10pt; \n",
+       "        line-height: 145%;\n",
+       "        }\n",
+       "        \n",
+       "    li {\n",
+       "        line-height: 145%;\n",
+       "    }\n",
+       "\n",
+       "    div.output_area pre {\n",
+       "        background: #fff9d8 !important;\n",
+       "        padding: 5pt;\n",
+       "       \n",
+       "       -webkit-print-color-adjust: exact; \n",
+       "        \n",
+       "    }\n",
+       " \n",
+       "    \n",
+       " \n",
+       "    h1, h2, h3, h4 {\n",
+       "        font-family: Kameron, arial;\n",
+       "\n",
+       "\n",
+       "    }\n",
+       "    \n",
+       "    div#maintoolbar {display: none !important;}\n",
+       "</style>\n",
+       "    <script>\n",
+       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
+       "        return false;\n",
+       "}\n",
+       "    </script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# IGNORE THIS CELL WHICH CUSTOMIZES LAYOUT AND STYLING OF THE NOTEBOOK !\n",
+    "from numpy.random import seed\n",
+    "seed(42)\n",
+    "from tensorflow import set_random_seed\n",
+    "set_random_seed(36)\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import seaborn as sns\n",
+    "sns.set(style=\"darkgrid\")\n",
+    "mpl.rcParams['lines.linewidth'] = 3\n",
+    "%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "%config IPCompleter.greedy=True\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
+    "from IPython.core.display import HTML; HTML(open(\"custom.html\", \"r\").read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 8: Introduction to Neural Networks\n",
+    "\n",
+    "\n",
+    "\n",
+    "<img src=\"./images/3042en.jpg\" title=\"made at imgflip.com\" width=35%/>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## History of Neural networks\n",
+    "\n",
+    "\n",
+    "1943 - Threshold Logic\n",
+    "\n",
+    "1940s - Hebbian Learning\n",
+    "\n",
+    "1958 - Perceptron\n",
+    "\n",
+    "1980s - Neocognitron\n",
+    "\n",
+    "1982 - Hopfield Network\n",
+    "\n",
+    "1989 - Convolutional neural network (CNN) kernels trained via backpropagation\n",
+    "\n",
+    "1997 - Long-short term memory (LSTM) model\n",
+    "\n",
+    "1998 - LeNet-5\n",
+    "\n",
+    "2014 - Gated Recurrent Units (GRU), Generative Adversarial Networks (GAN)\n",
+    "\n",
+    "2015 - ResNet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Why the boom now?\n",
+    "* Data\n",
+    "* Data\n",
+    "* Data\n",
+    "* Availability of GPUs\n",
+    "* Algorithmic developments which allow for efficient training and making networks networks\n",
+    "* Development of high-level libraries/APIs have made the field much more accessible than it was a decade ago"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feed-Forward neural network\n",
+    "<center>\n",
+    "<figure>\n",
+    "<img src=\"./images/neuralnets/neural_net_ex.svg\" width=\"700\"/>\n",
+    "<figcaption>A 3 layer densely connected Neural Network (By convention the input layer is not counted).</figcaption>\n",
+    "</figure>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building blocks\n",
+    "### Perceptron\n",
+    "\n",
+    "The smallest unit of a neural network is a **perceptron** like node.\n",
+    "\n",
+    "**What is a Perceptron?**\n",
+    "\n",
+    "It is a simple function which can have multiple inputs and has a single output.\n",
+    "\n",
+    "<center>\n",
+    "<figure>\n",
+    "<img src=\"./images/neuralnets/perceptron_ex.svg\" width=\"400\"/>\n",
+    "<figcaption>A simple perceptron with 3 inputs and 1 output.</figcaption>\n",
+    "</figure>\n",
+    "</center>\n",
+    "\n",
+    "\n",
+    "It works as follows: \n",
+    "\n",
+    "Step 1: A **weighted sum** of the inputs is calculated\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "weighted\\_sum = w_{1} x_{1} + w_{2} x_{2} + w_{3} x_{3} + ...\n",
+    "\\end{equation*}\n",
+    "\n",
+    "Step 2: A **step** activation function is applied\n",
+    "\n",
+    "$$\n",
+    "f = \\left\\{\n",
+    "        \\begin{array}{ll}\n",
+    "            0 & \\quad weighted\\_sum < threshold \\\\\n",
+    "            1 & \\quad weighted\\_sum \\geq threshold\n",
+    "        \\end{array}\n",
+    "    \\right.\n",
+    "$$\n",
+    "\n",
+    "You can see that this is also a linear classifier as the ones we introduced in script 02."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 281,
+       "width": 389
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the step function\n",
+    "x = np.arange(-2,2.1,0.01)\n",
+    "y = np.zeros(len(x))\n",
+    "threshold = 0.\n",
+    "y[x>threshold] = 1.\n",
+    "step_plot = sns.lineplot(x, y).set_title('Step function') ;\n",
+    "plt.xlabel('weighted_sum') ;\n",
+    "plt.ylabel('f(weighted_sum)') ;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def perceptron(X, w, threshold=1):\n",
+    "    # This function computes sum(w_i*x_i) and\n",
+    "    # applies a perceptron activation\n",
+    "    linear_sum = np.dot(np.asarray(X).T, w)\n",
+    "    output = np.zeros(len(linear_sum), dtype=np.int8)\n",
+    "    output[linear_sum >= threshold] = 1\n",
+    "    return output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Boolean AND\n",
+    "\n",
+    "| x$_1$ | x$_2$ | output |\n",
+    "| --- | --- | --- |\n",
+    "| 0 | 0 | 0 |\n",
+    "| 1 | 0 | 0 |\n",
+    "| 0 | 1 | 0 |\n",
+    "| 1 | 1 | 1 |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perceptron output for x1, x2 =  0 , 0  is  0\n",
+      "Perceptron output for x1, x2 =  1 , 0  is  0\n",
+      "Perceptron output for x1, x2 =  0 , 1  is  0\n",
+      "Perceptron output for x1, x2 =  1 , 1  is  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculating Boolean AND using a perceptron\n",
+    "threshold = 1.5\n",
+    "# (w1, w2)\n",
+    "w = [1, 1]\n",
+    "# (x1, x2) pairs\n",
+    "x1 = [0, 1, 0, 1]\n",
+    "x2 = [0, 0, 1, 1]\n",
+    "# Calling the perceptron function\n",
+    "output = perceptron([x1, x2], w, threshold)\n",
+    "for i in range(len(output)):\n",
+    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
+    "          \" is \", output[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this simple case we can rewrite our equation to $x_2 = ...... $ which describes a line in 2D:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def perceptron_DB(x1, x2, w, threshold):\n",
+    "    # Plotting the decision boundary of the perceptron\n",
+    "    plt.scatter(x1, x2, color=\"black\")\n",
+    "    plt.xlim(-1,2)\n",
+    "    plt.ylim(-1,2)\n",
+    "    # The decision boundary is a line given by\n",
+    "    # w_1*x_1+w_2*x_2-threshold=0\n",
+    "    x1 = np.arange(-3, 4)\n",
+    "    x2 = (threshold - x1*w[0])/w[1]\n",
+    "    sns.lineplot(x1, x2, **{\"color\": \"black\"})\n",
+    "    plt.xlabel(\"x$_1$\", fontsize=16)\n",
+    "    plt.ylabel(\"x$_2$\", fontsize=16)\n",
+    "    # Coloring the regions\n",
+    "    pts_tmp = np.arange(-2, 2.1, 0.02)\n",
+    "    points = np.array(np.meshgrid(pts_tmp, pts_tmp)).T.reshape(-1, 2)\n",
+    "    outputs = perceptron(points.T, w, threshold)\n",
+    "    plt.plot(points[:, 0][outputs == 0], points[:, 1][outputs == 0],\n",
+    "             \"o\",\n",
+    "             color=\"steelblue\",\n",
+    "             markersize=1,\n",
+    "             alpha=0.04,\n",
+    "             )\n",
+    "    plt.plot(points[:, 0][outputs == 1], points[:, 1][outputs == 1],\n",
+    "             \"o\",\n",
+    "             color=\"chocolate\",\n",
+    "             markersize=1,\n",
+    "             alpha=0.04,\n",
+    "             )\n",
+    "    plt.title(\"Blue color = 0 and Chocolate = 1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MVW+NmW29Lb5n0XMjv4+e8SDa36aYyUiXiTbF8dxALj9XK6VyHcPcfCeldHYL8QeMTW4l4GWl1LFKqfq8fS+llDovb/8Pa60fW8K6ANBav4iZn6UTMFblTRAZzcMyGjN3zceYSTWr/bnP3d05SCmVm3gUpdQySqnrgUPztPl5ztnMVso/ppGFayrmWapxLfaZi38ZzCAVD7bRNkEQqoBYwgRBiJ0CI/E4mB+sG2C+dxqAY6Irzm1xKeYq/Q7AF0qpDzATKS6PmXPiGFo8KKu1vifywZ8FPBT9WPoKMxJQ7mHiy7XWo0uJR2t9W3RF/XjMMwWfYK4mK8wPpk9p3nm6Mor1YMwPxM8xc6msHx2HeRhLU7FRm9BaN0RX2icBOwGfKKX+i7nqvxHmuL4NHNTKlWhbOAlzBX5n4HOl1HuYmeaXwVydH1LC8xxEz6hcjnkY+whgb6XUx5iH73OjPD0f1dPWw9VtorV+XCl1LWZ+kdFKqY8wP4p/jbn7Nx3Ytp37nKWU2hUzG/w2mFHzbo727WM+N7mO2N8xxy4ODsXcPfk18G6UgwZgY8w5+Rmwb74dqlqfe0x+D8Pk8D2l1PsYu9Z6mGPzFsYWtlykyY3A9Z9ItxLwP6XUF3kj2R0Sxb8V8IFS6l3M8V4fqI/i2qvIiHCCIFQRucMiCEIl2K7F3zaYTsZ/MDNXb6S1bm2uhmZorZ/ETHo3EWP52AAz/8URWutWJ36MJqrrh3luwMM8C+JgfvwP1lq3dZW35f5OwEww9yzmh/aGmE7QNcBmWuvP87Q+5gfiQcAzmB9rm2DuENwK/EZr/RAloLV+P2r7cMydiPUwM5y/DpwJbB1Nfmct0Xwxm2Pm4ZiBORZZzHw4W7YyClZr+7oeMwng1Ggfm2B+xI7GfE4GYeZO2Vgp1Z4hh1ur71xgCMZC2AszSMAYzGf68yJFi+3za8x5cSCmM/4DJq8bYK7yjwT6aq2P1Vo3lhtDVOe3UZvPw1ifVgfWwVi/LsF8Jt8uUK7DP/eRHW9TzDM+n2M6SKthOipnA1sDT0fygXnlPsB0rj7CDJu8dvS8Wu6YbxXF/88o/g0wz9LcAfxfNCGoIAgW4oRhm/N0CYIgCIIgCIIgVAW5wyIIgiAIgiAIgrVIh0UQBEEQBEEQBGuRDosgCIIgCIIgCNYio4TlEc0JcDJwFOZhPA8zzOPDwDVa6wUl7mc94DLMbMvLYSbiuhO4tZSRcARBEARBEARBMMhD9xFRZ+VJzIzMczCzLjdiRlVZOnq9i9Z6Xhv7+T/M6DU9gJcxo6PsHO3jAa314ZWKQRAEQRAEQRDShljCFnE8prPyNrC+1no3rfWewLqYsfa3xgz92CpKKQe4D9NZOUJrvb3WeghmuMq3gcOUUvsV24cgCIIgCIIgCIuQDssijo6WZ2qtv8pt1Fr/gLGJgZkMqxi7Y8acf0FrPTJvHzOA30UvT4+ltYIgCIIgCIJQA0iHZRE/AO8DrxV474No2dbMyf2j5WKzZ2utc/aw7ZVS3Ze0kYIgCIIgCIJQS8hD9xFa64FF3t4yWn7Zxm42ipb/aa0aYAXMTMGvlt46QRAEQRAEQahN5A5LG0TPpVwevXysDflK0fKbVt7Pbe9dbrsEQRAEQRAEoRaQOyxtcxWwI/AdcE0b2qWiZWsjic2Plt1iaFc+bwJrYkY3+zDmfQuCIAiCIAgCwDqY37GfAL/pqEqlw1IEpdTlwPnAQuDA6OH5YuTmWGltrGinxTIu1gR6Rn+rxLxvQRAEQRAEQchnzY6sTDosBVBKZYC/AicCC4AhWuupJRSdEy27tPJ+52g5t7wWFqy3ZxAENGYDwjAkDMFxwHVM3yjI2+Y4TkU0ldpvEjXFygRhyPfffssXn39GGPpN2iDq5rqO+QtC87fSiiuy2q9+hedlwPWMKPAx/WIHXBeCIO91R2qqWXf1NXWeCwQ0NgZWtk9yXlxT5zll5q/6MdRqzpufe+mNM605L//cS0acydKUUsajvr6p65D7zdshSIelBUqpbsCjmBG/fgYGl9hZAfga2BRYETPiWEvaesZlSfkQWKWh0eenn+fR0OiTDQIyrkvn6IO1oCHbtK2+zquIplL7TaKmzTKZbtR17sGll/yBqVOnkvUD/GwDThjSOeNQ70GDDwuzIZ0yDquvugrDLr+anfcwY0MEDfPAbwCvHqeuM2HjgqbXbn3XDtNUs24bNMv1rAN/IT/+1GBl+yTnxTXL9vDKyp8NMdRqzvPPvTTHmdacl3vuJSXOJGlKKePWd2WF3j2J6NBHEOSh+zyUUssAL2A6K18AfdvRWYFFo4NtWGDfDrA+4APvltfS1sn6QeRHcwiBIAgJgrDZtkppqlm3bZpSyqyy6mo8NnoM19/0V3ouvSyO4+K54LlG47lQnzGvv/nma44//khOP+1Efpw5A4cABweHAPyG5q9DH0K/YzTVrNsSDZa3T3JeXFNW/iyJoVZzTtx1WRpnWnNuxXen7Xmw7ViEfrGfoBVFOiwRSql6YDywOaZD0Udr3drwxK0xMVruU+C9PsDywEta69lL3NA2yHgujgMhobEiuQ6u6zTbVilNNeu2TVNqGdd1OPTQQ5k48Vn6998LP4Bs6BDgkg0dGrLgBwAhfgCPPPoIO+zYh/HjxxKEASEuePWEuISE5rXjgeM131YpTTXrtkSD5e2TnBfXlJU/S2Ko1ZwTd12WxpnWnFvx3Wl7Hmw7Fo5XqZ+vbSIdlkVcDmyDubOyk9a66JwrSqm1lVLrK6V65m2eAvwX2F0pdUKednng1ujldfE2uwAhONGy1W2V0lSzbts0pZSJVldYoTf33HM/d/3tPnostxINTh2N1BFGz77k88MPMzjl1JM59dSTmfHdt4u9LwiCIAiCkCbkGRZAKbUscHr0cgZwvVKqoFZrfXi0+iywOnAMMCJ6L1BKHRu9d6dS6jjMcy07AcsAd2mtn6pMFBCGxorkhyF+EOI4xoYENNtWKU0167ZNU3KZwLz2owf09xowiM226sNVV1/JE4+PIuO5eG6I43p4oY/nmv1kXJjy/GT6vTKd8y8aygFD9scJsuC4i27ZBlnz57jGf5r/Oi5NpfabGI1D6EfbrGyf5Ly4Jiwvf1bEUKs5zzv3Uh1nWnNe5rmXmDgTpCmlTBUtYdJhMWzFopG9Nov+WuPwYjvSWr+mlNoac8dmZ2Bj4H/ABcDfym9q64TR5XvfD/CDwFzJrzfv5W+ry7gV0VRqv0nUtKdM1g/w/aBJ0617Dy4ffjV77TmQoReexcwfviVwXYLQxQ/Ng/mu4+A5MG/ubM4552zGj/kHVwy9jFVXX4fQ1Ejo++BnAY+wzoX813FpKrXfhGgCPwt+I6HvW9k+yXlxTdn5syCGWs15s9ylOM605tya707b82DbsWBx10dHIR0WQGs9EdqXBa31GkXeexfYv8xmtRsHB0LwPJcQs8xZj1puq5SmmnXbpim1TCYsrNlxpx14+rmX+OOfr+LhB0eSDc0VDtfxCcKQbOAQhCGZDEybPp199xvMqWecy1HHn4LneTgZD8I6yHg4YUCY/zqqrGxNpfabEI3rdSIM63AyoZXtk5wX17heprz8WRBDrea82bmX4jjTmvOyz72ExJkoTSllcj9UqoBbtZqF2Mk9yO1FD3jnlvnrnutUTFPNum3TtKdMpoimR4/uDBs2nBEjR7HmGmsRhgF+AI2hQ2Po0hg9mJ8NYM68+Vw+/HIGDd6LD/73P3Ay4HnRsr756+hhurI1ldpvUjRuBsfL2Ns+yXlxTbn5syGGWs15fu7SHGdac27Ld6ftebDtWMhD90KsFHiwe7FtldJUs27bNKWUya22sd8tNt+Sp8ZN5vTTz8b1MjRS1+qD+f/85xvsttsO3HzT9TQ0NCIIgiAIgpBkpMOSMmyfe6RWNCWXCTEz3Zew306dOnP++ZcwYeJzbLjRJvhOBlw3ulNjNLl5XPxsAzfddC0HHDCYd95+ExnDvgPmgrC0fZLzCs4FYUkMtZpz4q7L0jjTmnMrvjttz4Ntx6KKD91LhyVlJGHukVrQtKcMYfvq3mST/+PxJ57i7LPPpT5TR9YPms3d4gc0/Wn9HvsOGcQVV17J3PkLkTHs49dgefsk58U1ZeXPkhhqNefEXZelcaY151Z8d9qeB9uOhVjChFix3QZVK5oYLWEty9Rl6jjxxJMZN+5pttxy66IWsSAIuO22mxmw1y689tprCIIgCIIgJAnpsKSMRNigakBTCUtYIc2aa6/DI4+PZeiwK+nUtQe4Lo7jNlnDwNjE6jPw5Refc+SRB3PpJecze9bPcjs9Jg2Wt08sFGIJS2vOibsuS+NMa86t+O60PQ+2HQuxhAlxkRQbVNo1lbSEtdTUZzIcedQxPDV2Ejtsv0PTSGL5NrGGrLGIQcjIB+5nx52245lnn0Zup4slrNZzXlb+LImhVnNO3HVZGmdac27Fd6ftebDtWIglTIgV221QtaKpoCWskGaVVVbhnhEPcNONt9Fz6WWK2sS+/vorDj/8YM4883f8MPNHBEEQBEEQbEU6LCkjETaoGtB0lCWspQYc9j/gYKZMfY3+AwYWHUnMc2Hc2CcZNHB3xo0dTZhdWJlbyCnXYHn7xEIhlrC05py467I0zrTm3IrvTtvzYNuxEEuYEBdJsUGlXdORlrBCmt69V+Cvt9zBTTfeRq/lli86ktiPP/7IaaefwrHHH813332H3E4XS1gt5bys/FkSQ63mnLjrsjTOtObciu9O2/Ng27EQS5gQK7bboGpF08GWsEKaPfr1Y/Lk5znw4MOKWsQAJk2axG6778wjjzxE2GxHgiAIgiAI1UM6LCkjETaoGtBUyxJWSNOj59Jcc+1N3HvfKFZadY2iI4nNnzebSy+9iCMOO5BPP/lQbqeXoMHy9omFQixhac05cddlaZxpzbkV352258G2YyGWMCEukmKDSrum2pawQprt+/ZlzJhJHHnUMUBYdCSxadOnsfMufbn9jtvJBuZmsNxOF0tY1eu2zZZiSQy1mnPirsvSONOacyu+O23Pg23HQixhQqzYboOqFY0FlrCWmq5LdWHoJVcw5smJrLOuKmoTmz9/PsOGXcQB+w/kgw80giAIgiAI1UA6LCkjETaoGtDYZAkrpNl8i62Y/MxUTj3tLJxMp6Ijib3z9lvsN2RvbrzhWhrmz7Xn9rUlGixvn1goxBKW1pwTd12WxpnWnFvx3Wl7Hmw7FmIJE+IiKTaotGtstIS11HTp3Jmzzj6XRx8dw8YbbVJ0JLHGbJYbb7qe/nvtxlv//jdW3L62RIPl7auKbSBBmrLyZ0kMtZpz4q7L0jjTmnMrvjttz4Ntx0IsYUKs2G6DqhWNhZawQpoNNtyAxx4fw4UXX4bXqVvRkcTef18zZP/B/PFPVzJv/vzF3hcEQRAEQYgb6bCkjETYoGpAY7slrKXG8zKc9NtTGTv+WX6zxbZFRxJzCBjx97sZsOfOTHvxBbtvcYslLH6N7e0TS1jN5Jy467I0zrTm3IrvTtvzYNuxEEuYEBdJsUGlXZMES1ghzZprrcW9Ix/isqFXsVTXpYqOJPbZ51+w/4FDuOCiP/DzL79g5S1usYTFr7G9fWIJq5mcE3ddlsaZ1pxb8d1pex5sOxZiCRNixXYbVK1oEmIJa6nxHJfDjziSKVOns8uuu7c54eTDDz3IwIH9eObpSYu9JwiCIAiCUC7SYUkZibBB1YAmaZawQpqVVlqF++5/hOuuv4Ueyy5fcCSx+oyxjH3//XecfPKxnPzbo5nx/Xf23OIWS1j8GtvbJ5awmsk5cddlaZxpzbkV352258G2YyGWMCEukmKDSrsmqZawlhrPc9lnnyGMfepp9tpr78VGEsu3iPkBPDF6NDvutC2jR48mCAOqfotbLGHxa2xvn1jCaibnxF2XpXGmNedWfHfangfbjoVYwoRYsd0GVSuaUsrkVm2NIVpfrtey3HzT7dxzz4Mss8KqRS1iP/74I2edfRq//e3xfPvNV4u9LwiCIAiC0B6kw5IyEmGDqgFNGixhhTS77tGPSZOnsN+BhxedbNJzYcqU5+nXbxdG3nsXYZC19za4WMLap7G9fWIJq5mcE3ddlsaZ1pxb8YbUAD8AACAASURBVN1pex5sOxZiCRPiIik2qLRr0mIJK6Tp0bMHl11xFfffO4pVVvlVq5NNQsjsOXM47/w/cMihB/DpZx9j5W1wsYS1T2N7+8QSVjM5J+66LI0zrTm34rvT9jzYdizEEibEiu02qFrRlFImt2prDEU0fbbbjqefe5kjjj+FrNupVYsYwGuvvcbAgXtyx+23kvWzBTWCIAiCIAiFyFS7AUJ8hKGx9fhhiB+EOI6x9ADNtlVKU826bdOUXCYwr/3QvhhK0XTq3IXzLxjK7v0HcumF56HfewfP8fFcB8f18EKf+kxIxoUg28B1117JmPFjufbaG9lgnTUhyILjgt9g1nOvc7ed87dZqXEI/Wible2rgMb29rVLE5aXPytiqNWc5517qY4zrTkv89xLTJwJ0pRSRixhQhyE0aVv3w/wgwDfD8yl8ALbKqWpZt22aUotk/XtjaHUODfeeBP+8fhYzjr7XBy3jsCtI3AzBG4djb6D6zh4DriOw7///W/699+VG//yJxbMm0fo+wSOS+j7hH7WLHHMX942GzWBnyX0G61tXyU0trevPZpy82dDDLWa8/zcpTnOtObclu9O2/Ng27EIKeyi6Ai8YcOGVa1yITaOBtbIZgPmzW9ocu5kPJc61/RJ/TBs2uZFD3nHranUfpOoKbVMl66dAGhoyFoXQ3vj7FRfx47b70D/PffmX2+9xXczZtIYhLgEeE6Ig2PuzoQQhgFv/usNnn/+aTbYaBNWXnlVcEKcEJy6DK5XZ74Ww+yibY5rnWaprvVAyPzG0Mr2VURje/vaoena2SsvfxbEUKs5b3bupTjOtOa87HMvIXEmSlNKGa+Opbp1if7r8xkwgg5CLGEpIvdQdBCGBKGDFz00DeC5TtO2SmmqWbdtmlLLeK5D6NoZw5LGudFGG/HoP8Zw94i7ufHaPzG/YQGNjgOhS2NobHAA2QA++PAj9j9wPw4/5gTOPev3dOtcB06Gpgf7nAx4QbSshyBY9NoGjZvB8XxwAjvbVwmN7e1rj8b1ysufDTHUas7zz700x5nWnJd77iUlziRpSikjD90LsRJCs4ekC22rlKaaddumKaVMbtXWGJYwzoyX4bhjTmD8hGfo02d7Gqlrde6WMAz52513MGDPXXnppZcQBEEQBEHIRzosKSMRc4/UgCat87C0N87V11iLB0Y9wfArr6Fr92XAdXEct2muFjDzt9Rn4Ouvv+D444/kgvPP5uefZoJN49OXMheEpe2TeQuKa8rKnyUx1GrOibsuS+NMa86t+O60PQ+2HQt56F6Ii6TMPZJ2TZrnYWlvmfpMhkMOPYynxk5i1513JQwD/IBm87c0ZGmau2XUI6PYYcc+jJ8wHmvGpy9lLghL21eVsfoTpCkrf5bEUKs5J+66LI0zrTm34rvT9jzYdizEEibEimX2oJrVlFImt2prDHHFGcKKK67IHXeN4LZb/8ayy/UqahP7/vvvOPbYIzj1tJOYMeMHBEEQBEGoXaTDkjJstgfVkkYsYYU14DB4n/14YcqrDBw0BN/JgOtGAxAYTc4u5rkwacJY9t57N554/BHC7EIrb6dTxbqttQ0kSFNW/iyJoVZzTtx1WRpnWnNuxXen7Xmw7ViIJUyIC9vtQbWiEUtYcc3yy/fihhtv4fbb/kbvFXqT9YNmFjE/oOnvl19+4Zzfn8URRx3GV199hW2306li3dbaBhKkKSt/lsRQqzkn7rosjTOtObfiu9P2PNh2LMQSJsRKAuxBNaEppUxu1dYY4oqzFc1OO+/CpEnPc9gRxxS1iAE899xz7NFvV0aOvI8gDBZ7XxAEQRCEdCIdlpSRJHtQmjViCStd0617D666+loeeOgxVlltraIjiS1cMJfhw4dx6EFD+OhD3XG3ysUSZkfdttlSLImhVnNO3HVZGmdac27Fd6ftebDtWIglTIiLJNqD0qgRS1j7Ndtssy1PjpnIccedhONQdCSx1954nV123YGbb7mJhmxANW+nU8W6rbUNJEhTVv4siaFWc07cdVkaZ1pzbsV3p+15sO1YiCVMiJWE2oNSpymlTG7V1hjiirNETZcunbnwgksYP+5p1t9g46I2sYULF3LllZcxZN+9ePfd/yAIgiAIQjqRDkvKSLo9KC0asYSVp9nk/zZjwqTnOPOs83DrOhcdSez99/7LAfvvy3XX/pEFc+d0+O10KrRfazW2t08sYTWTc+Kuy9I405pzK747bc+DbcdCLGFCXKTFHpR0jVjCytd07tSJ004/k8cfH8em/7dp0ZHE/CDLX2+9mT3678wbb/yTjrydToX2a63G9vaJJaxmck7cdVkaZ1pzbsV3p+15sO1YiCVMiJWU2IMSrymlTG7V1hjiirMMzbrrrcsjjz7JsMuuoq5Lj6IjiX344UcceNC+DB8+lDlz5yz2viAIgiAIyUM6LCkjjfagJGrEEhavxnU9jjnuJMZOeI6ttt2x6EhirhMycuT9DOi/K88/9/Tit7TFElaexvb2iSWsZnJO3HVZGmdac27Fd6ftebDtWIglTIiLtNqDkqYRS1hlNKuvvjp3//0+rr7yWrp36150JLEvvvqSQw49gNPPPJUff/6FSt1Op0L7tVZje/vEElYzOSfuuiyNM605t+K70/Y82HYsxBImxEqK7UGJ0pRSJrdqawxxxRmjxnUcDjzoYKa++Ap79B/Q5oSTj4x6iH577MykSRMWe08QBEEQBPuRDkvKqAV7UBI0YgmrvGaFFVbknntGctMtd9CzV++iI4n99OMMzjjjFE753Ql8//WXsd5Op5xb8EnU2N4+sYTVTM6Juy5L40xrzq347rQ9D7YdC7GECXFRS/YgmzViCesYjee5DBgwkPFjn2bw4CFFRxKDkAkTx7PjLjvw6KOPEoQBsdpSbL/dLxaKgpqy8mdJDLWac+Kuy9I405pzK747bc+DbcdCLGFCrNSQPchqTSllcqu2xhBXnBXWLL3M0vzlupsYOfJRll95taIWsV9++Znz/nA2xx57JF99+fli7wuCIAiCYBfSYUkZtWoPsk0jlrDqaPrutAsTJk7hoMOOKTqSmOfC9Okv07//rtz9t1vxs0tuE6DSt+lt09jePrGE1UzOibsuS+NMa86t+O60PQ+2HQuxhAlxUcv2IJs0YgmrnqZb925ccullPPTgo6y5xppFRxKbO28eF118IfvsuzcffvgRS3I7nSUok2iN7e0TS1jN5Jy467I0zrTm3IrvTtvzYNuxEEuYECs1bg+yRlNKmdyqrTHEFWcVNFtttS3PPvcSp5x2FoHbqahN7PXXX2OvAbtz221/pTHbuNj7giAIgiBUD+mwpAyxB9mhEUuYHZpOnTpz4YVDeeyJ8ay3wSZFRxIL/EZuvPE69h28J+/8+/WSb6dT6dv0tmlsb59Ywmom58Rdl6VxpjXnVnx32p4H246FWMKEuBB7kB0asYTZpfn1JpvwyD+e4Jxz/oDrZoqOJPbue++x19578cc/Xs28+Qso2ZZi++1+sVAU1JSVP0tiqNWcE3ddlsaZ1pxb8d1pex5sOxZiCRNiRexBdmhKKZNbtTWGuOK0QFOXqeN3J5/G089M5debbV3UIub7PnfceSv77NOP1197ZbH3BUEQBEHoODLVboAQH2FoLDF+GOIHIY5jLDNAs22V0lSzbts0JZcJzGs/tC+GtOZ8jbXX5YFRoxl5//1cf92fmDPrRzzHx3MdHNfDC33qMyEZF7764nMOO3Q/DjzsGC644FKWqnchyILjgt8AgUPoZ802v8Esc+/nbp3nb0uDxvb2tUsTlpc/K2Ko1ZznnXupjjOtOS/z3EtMnAnSlFKmipYw6bAUQSl1NPB3oK/W+qUSy2SAOUCnViRfaa1XjaeFzQmjS8u+H+AHgbnKXG/ey99Wl3EroqnUfpOoaU+ZrB/g+4F1MaQ554QOBx96ODvvvDPDLvo9U6e+QODWEbguQejS2NhAJ8/Bc8B1HP7+93uYPHky1w6/nL7b9QE8wjqXwM+C30jo+4R1Lvg++FnzvmkBYf62NGhsb187NGXnz4IYajXnzXKX4jjTmnNrvjttz4Ntx4LFHQkdhXRYWkEptS1w8xIU3RDTWfkIKOQl+bGcdhXDwYEQPM8lxCxztpiW2yqlqWbdtmlKLZMJ7Y0h7TlfbbXVefChx3n00Ye5YvjlzJ07h2yYxfVcgjAgGzgEYYjrwjfffMnxJx3HvoMGc96Fl9GrUxdcrxNhWIeTCXHCgDDjQVgHGQ8nqtzJ35YGje3ta4fG9TLl5c+CGGo1583OvRTHmdacl33uJSTORGlKKZP7p1oFpMNSAKXUEGAE0G0Jiv8mWv5da31lbI0qgdxDx0EYEoQOXvTAMYDnOk3bKqWpZt22aUot47kOoWtnDLWSc89zOfjgQ+mz/Y5cevklTJ4wjtAPaHQcCF0aQ2PdA8gG8ORTT/Lciy8x9PI/ctThB+J4GXAC8OohCMALwMnQ9HCik1m0LQ0a29vXHo3r4Xj+kufPhhhqNeduZlHu0hxnWnNe7rmXlDiTpCmljDx0bwdKqVWVUvcBjwEe8N0S7CbXYflnbA1rLyFU7QHnatZtm6aUMrlVW2OIK84EaFZYoTe33HQ7f735Lnr16k0jda0+mD9z5kxOOul4Dj3sEL75Zkm+JgRBEARBKBXpsDRnOHAE8AawDfD+Euwj12H5V1yNag8yJ4cdGpmHJbma/nvtxXMvTGPIfgfhOxlwXRzHbZqvBcz8LfUZmDh+PNtt14dHRj1AmF2I1WPsy7wFBTXIsUhsnMRdl6VxpjXnZZ17CYozMRqZhyVRvA8cBWyttX6nvYWVUg6wKfAtMEgp9ZpSarZSaoZS6iGllIq5vYshc3LYoZF5WJKtWW65Zbnm2uv52133svKKKxOGAX5As/lbGrLgBzBr1izOv+A8Dj70QD7//AusHWNf5i0oqEGORWLjJO66LI0zrTkv69xLUJyJ0Vg+D4sThmHbqhpFKfUCsCMljhKmlFob+DB6GQAvAz9j7rqsCswG9tRavxxzU18AdvQDc+V4YaNPYzagLuPSpT4DwPyGbNO2TnVeRTSV2m8SNba3T+IsXbNw/lyuuGwot956K65XR8ZzyfoBQbaBzh7Ue9Dgw8JsyNLduzLskgs48eTTqO/aHQB/4TzwF4LXCbeuM0HjgqbXXqeuidPY3j45FhKnxCnHQuKszLHwOnXFcZvudUwBdqKDkDss8ZKzg30FbK613kFrPQhYE7gO6A6MUkp1rlQDstk8m1GYZ3fJ21YpTTXrtk1je/skztI1S3Xrzg033MgLL0xh3fVUk03MDJoAsMgi1rBwPpdcMpR+u+/Of//zzuLWh6B1K0tiNLa3T46FxClxyrGQOCtzLKpoCctUreZ08hiwGuBrrb/ObdRaZ5VS52F6opsD+wAPx115Y6PPTz/PoyHr0+gH1HkunetMihc0Zpu21We8imgqtd8kakot02PprhCGzJw5x7oYJOfNNUptwpNPTuDmW27mjttuIusHeI6D47hkw4CGbGgGU/FCXnvjDTbfYkvOPPP3nPLb31LvhuDV49R1JmxcAH4DePW49eYOd9Awr2mb7Rrb29cezbI9PCBkxg9za/5YJC3O5XrWNeUuzXGmNeflnntJiTNJmlLKuPUhK/TuSTWQOywxorUOtdZf5HdW8t4LgPHRy80r2pAQZMQoCzSllMmt2hpDXHGmRNOpU2fOPOscnnxyAhtvvGnRkcQaGxu55pqrGTyoH2+//RaCIAiCICwZ0mHpWL6Nll0rVYGMGGWHRkYJS7dmg402ZvTYiQwddhmZTl0LWsRyfx/+T3Pwwfvxx6svZ97sWThYPEpMDY6qQzn7sSSGWs15IetKGuNMa87LOvcSFGdiNKWUqaIlTDosMaKUOkUpNUoptVsrkjWj5ZeVaoOMGGWHRkYJS7+mS6dOnH3O2UyZMoUtNtuCrB80G0XMD2j6CwKfO++6g9377cQrr7yCtaPE1OCoOsixSGycxF2XpXGmNedlnXsJijMxmlLKVHGUMOmwxMtawIGYoZGbET1of0D0cnJFW1FN20yVLTtWaUopk1u1NYa44kyrBlh7nXV4aNTjDB9+DfVdl27VIgbwySefcsihBzB06EXMmj1rsfcFQRAEQVgc6bAsIUqp1ZRS6yuleuVtvhvwgcOUUvvlaeuAm4HVgQla639Wql1iD7JDI5awGtFE+XMclyOOPpbxk55nux12LTrZpOfCqFEPsVf/XXh68nissgTUoIUCORaJjZO467I0zrTmvKxzL0FxJkYjlrDUch/wHnBqboPW+l3g7OjlP6KJI/8BfAwcj5mY8uhKNkrsQXZoxBJWO5pc/jKey6qrrsrtd97DtX++kaV79mx1skkI+fqbbzjiyEP53SknMmPmT1hhCahBCwVyLBIbJ3HXZWmcac15WedeguJMjEYsYbWF1vomYHdgErAusDcwD7gS2FJr/X3FG1FN20w167ZNU0qZ3KqtMcQVZ1o1udW8Mq7jsO+Q/Zgy9VX2HrRv0ZHEAB5//DH699uJcePGEDarTBAEQRAEkHlYiqK13mkJ33sWeLYCTWqTQlYWoNm2SmmqWbdtmpLLhDSzhNkUg+S8BE1e/lqW6dVree644+88NW4cQy+/hBnffRONJGasEJ4bRnYx+OXnHznnnDN58qmnuGL4n1mx9/KYrtGiW/A5CwUtLDEdrqlm3bFrHEIWtxnV5rFIWpzeotylOs605rzMcy8xcSZIU0oZsYQJcSH2IDs0YgmrHU2+JaxQmX79+zN+7NPsv/9BRUcSg5Bnnn2GHXfZkQcffBA/8EmsbSBBGuRYJDZO4q7L0jjTmvOyzr0ExZkYjVjChA4nNH1hwiLbKqWpZt22aUopk1u1NYa44kyrJrfaRpkePXvwpz9ex8MPj2bFVdcsahGbPXsWF138B4466jA++/STxd4XBEEQhFpDOiwpQ0aMskMjo4TViCYvf6WU2Xb7voyb8AKHH3MCjutRbCSx119/lb322pXbbr2JbONCEjWSTII0yLFIbJzEXZelcaY152WdewmKMzEaGSVM6EjEHmSHRixhtaNpyxLWssxS3bpy/vmX8Mgjj7PuOusWHUls/oKFXHb5pQwc1J/339ckxjaQIA1yLBIbJ3HXZWmcac15WedeguJMjEYsYUKHU03bTDXrtk1TSpncqq0xxBVnWjW51SXY72abbcnTz0zlzLPPI/Q6F7WJvfnmmwwc2J8bb7yehQ0Ni70vCIIgCGlGOiwpQ+xBdmjEElYjmrz8Lcl+6+rqOffcC3niyUms/+vfgOtGI4kZTc4u5rkQhlluu+1m9h3cjzffmN7+2/1ioSioQY5FYuMk7rosjTOtOS/r3EtQnInRiCVM6EjEHmSHRixhtaNpryWskGbDjTZk1KjHOO8PF5HJ1BcdSex9/QF7D9qbK668nDlz52GlbSBBGuRYJDZO4q7L0jjTmvOyzr0ExZkYjVjChA6nIy0xHbXfJGpKKZNbtTWGuOJMqya3GsN+M16Gk044maefncpmW21f1CIWhiH33H0Xgwf3Y/q0lxZ7XxAEQRDShEwcmSLC0FhO/DDED0IcJ2ya8C5/W6U01azbNk3JZQLz2g/ti0Fy3r78xbXf1dZYm3sf/AcPPfgQ111zFbN+/gHP8fFcB8f18EKf+kxIxoVvv/6So448iH0PPJyLL76M7p0zEGTBccFvMOu517lb+fnblkRTqf1WRRMS+tH2mj8WSYvTWZS7VMeZ1pyXee4lJs4EaUopI5YwIQ7C6NKt7wf4QYDvB+ZSboFtldJUs27bNKWWyfr2xiA5b1/+4tpvGMD+Bx7EmHGT2HWXXfFDh8CtI3AzBG4djb6D6zh4DriOw8iR97Pjjn14ZvIEQj9L6PsEjkvo+02vQ8zM0vnblkRTqf1WQxP4WUK/UY5FAuPMz12a40xrzss995ISZ5I0pZQJWfyOf0fhDRs2rGqVC7FxNLBGNhswb35Dk9Mk47nUuaZP6odh0zYvekg4bk2l9ptETallunTtBEBDQ9a6GCTnbWvy81eJupfu2ZMD9juQNddam2mvvcb8hY00BiEuAZ4T4uCYuzMhzJs3h8kTx/H5p5+y2VZ96NatGzghTghOXQbXqzP/asLsom2O237NkpSxVNO1sweEzG8Ma/5YJC3OpbrWL8pdiuNMa87LPvcSEmeiNKWU8epYqluX6D8UnwEj6CDkDkuKyD3U60UP8uaW+eue61RMU826bdO0p0zG0hgk5+3LX6Xq9jyX/fc/kAkTnmOP/gMIIvtZY+jQGLo05j2Ynw1g7IRx9Ou/C489MZoQDzwPnAy5ByhxMou2efXNX5eiWZIytmrcDI6XkWORxDjzc5fmONOa83LPvaTEmSRNKWXkoXshVkIo9FBvs22V0lSzbts0pZTJrdoaQ1xxplWTW+2Aunv16sVfrr+ZO28fQe/eK9NIXasP5v/888+cdtpvOf74I/j2268RBEEQhCQjHZaUIXNy2KGReVhqRJOXv46qe9fdd+fZ51/m4EOOwHcy4Lo4jts0XwuY+VvqMzB1yvMMGNCPB0aOIPAbQeYtWEyDHIvExkncdVkaZ1pzXta5l6A4E6OReViEjkTm5LBDI/Ow1I4mjnlY2qtZZpmlufKqPzFixIP8apVfEYaBsYTlzd/SkDU2sblz53DJpRcxZL9BfPzJx8i8Bc01yLFIbJzEXZelcaY152WdewmKMzEamYdF6HAqaEtpU1PNum3TlFImt2prDHHFmVZNbrVK7dtmm22YMPE5TjrpFBzPK2oTe+WV6eyyS1/uuP1WstnqXSUTBEEQhPYiHZaUIfYgOzRiCasRTV7+qtW+zl26MHTocMaMmcR6aoMmm5h5eN9ocnaxxoYFXHvtlRx00L689+5/xEJRri3FkhissIqIJSxdGtvPvQTFmRiNWMKEjkTsQXZoxBJWO5pqWMIKabbYYkueHDOBU085g4zrkfWDZhax3EhifgD//e87DB48gD9few0LGrLUsoWCcvZjSQwV11jaPuKuy9I405rzss69BMWZGI1YwoQOp4NtKR2y3yRqSimTW7U1hrjiTKsmt2pJ++rr6jn1tDMYM2Yim266eVGLWNbPcsMN1zFw7914881/IQiCIAi2Ih2WlCH2IDs0YgmrEU1e/mxq33rrb8DjYyZw/kVDqe/SrehIYp98/BGHHnoAVw6/lDmzZ1FrFgrK2Y8lMVhhFRFLWLo0tp97CYozMRqxhAkdidiD7NCIJax2NLZYwlpqOtXVccLxJzF69AS22WqboiOJhWHA3ffczS67bs+UqVOoJQsF5ezHkhgqrrG0fcRdl6VxpjXnZZ17CYozMRqxhAkdTgUtJ21qqlm3bZpSyuRWbY0hrjjTqsmt2tq+EFZfY3VGPvgo115zI9269yxqE/v888856KAhnPeHs/j5518QBEEQBBuQDkvKEHuQHRqxhNWIJi9/VrYveg0Ohx52JFOmvsKuu/cvOpKY58ITjz3CwIF7MGni+NRbKChnP5bEYIVVRCxh6dLYfu4lKM7EaMQSJnQkYg+yQyOWsNrR2GoJK6RZeeWVueOOu7nuuhtZZullio4kNmPG95z8uxM46eQT+f77H7DCsmCbLcWSGCqusbR9xF2XpXGmNedlnXsJijMxGrGECR1OjHaSdmuqWbdtmlLK5FZtjSGuONOqya3a2r4CGgeHAQMGMnnyFAbvu39RixjAU0+NYY9+OzF69GOEzSoTBEEQhI5BOiwpQ+xBdmjEElYjmrz8Wdm+Ipplll2Wm26+g7v+dh+9Vly16Ehic2b/wvnnn8uxRx/GV59/3LZtwHbrg1jCUhEncddlaZxpzXlZ516C4kyMRixhQkci9iA7NGIJqx1NkixhhTS77Lob48ZO5pBDjig6khiETJk6hR133pF77x2BH/hYaWsQS1j8GkvbR9x1WRpnWnNe1rmXoDgToxFLmNDhLKFVJBZNNeu2TVNKmdyqrTHEFWdaNblVW9tXoqZb924Mv+Jqnnh8LGusuXZRm9i8eXMZdtklHHboQXzy8UcIgiAIQqWRDkvKEHuQHRqxhNWIJi9/VravnZqtt9mOp599kRNOPIXQqys4klh9xljG3vz3PxkwYDduvuk6GhsWYI2tQSxh8WssbR9x12VpnGnNeVnnXoLiTIxGLGFCRyL2IDs0YgmrHU3SLWEtNd2WWorzL7iIUQ8/gVp3/cVGEsu3iC1Y2MCVV13BXgP24L///S9W2BrEEha/xtL2EXddlsaZ1pyXde4lKM7EaMQSJnQ4JdpAKqKpZt22aUopk1u1NYa44kyrJrdqa/vK0Px6k00YPWY8vz/vQsh0KTqS2DvvvM3gwQO47rprWLBw4WLvC4IgCEI5SIclZYg9yA6NWMJqRJOXPyvbV6amrq6e004/hzFjn2HjTbcoOtkk+Nx1120M2ns3Xpv+UtvWAkvsEZSzH0tisMIqIpawdGlsP/cSFGdiNGIJEzoSsQfZoRFLWO1o0mYJK6RZT63Hgw89ykUXDaW+vnOrk01CyIcffcw+QwZx6bCLmT1nDlZaH8QSloo4ibsuS+NMa87LOvcSFGdiNGIJEzqcViwepdhAytZUs27bNKWUya3aGkNccaZVk1u1tX0xajzX49hjTuDp515iqz47F7WIAdx/370MGtifqVOeL/i+IAiCIJSKdFhShtiD7NCIJaxGNHn5s7J9FdCs+qvVGXH/KK646i/06LkMrU026bnw9Tdfcdxxh3PG6Sfx048/YI31QSxhqYiTuOuyNM605ryscy9BcSZGI5YwoSMRe5AdGrGE1Y6mFixhLV+7rsO+++3PxInPssce/YtONukHMOqRUfTdYVsmTBiHFdYHsYSlIk7irsvSONOa87LOvQTFmRiNWMKEDieEZnaOQtsqpalm3bZpSimTvXafSgAAIABJREFUW7U1hrjiTKsmt2pr+yqc8969V2TEiAe486576bHcSkVtYj/8MIPfnfJbTjvtd8z4/rvF3hcEQRCE1shUuwFCfIShsW/4YYgfhDiOsXcAzbZVSlPNum3TlFwmMK/90L4YJOfty5+V7euAnIchDNh7MJtt1YcrrxrOk6MfjUYSC3FcDy/08Vyz34wLzz87kWnTp3HBxcPYb58hOEEWHJcmq0GQNX+OC35D89exa0JCP9q+JPupePss0VjZPmdR7lIdZ1pzXua5l5g4E6QppYxYwoQ4CKPLnr4f4AcBvh+Yy6AFtlVKU826bdOUWibr2xuD5Lx9+bOxfR2V8+49ejL8qj9x55330bv3KgRuHYGbIXDr8EOHIADXcfAcmDtnFmeddSbHHnMIX37+KaHvE+KYP98n9LOEvk/guM1ex60J/Cyh37jE+6l0+2zR2Ni+/NylOc605rzccy8pcSZJU0qZkMXvnncU3rBhw6pWuRAbRwNrZLMB8+Y3NLk2Mp5LnWv6pH4YNm3zHKcimkrtN4maUst06doJgIaGrHUxSM7b1uTnz8b2VSPna621JocffiQ/zprN2+/8l8YgJAwCPCck44KDY7QOfPXllzzxxD/o3LUbm2y6Oa7rQJjFCcGpy+A6Ljjhotdenfl3GZOma2cPCJnfGC7ZfircPms0FrZvqa71i3KX4jjTmvOyz72ExJkoTSllvDqW6tYl+g/AZ8AIOgi5w5Iicg/EetGDs7ll/rrnOhXTVLNu2zTtKZOxNAbJefvyZ2v7qpHzHj26c9llVzJi5MOsvtoahGGAH0Bj6NAYujRGD+ZnA5gzbz6XXXEZg/cZwP8+/BCcDHhetKxv/jp6CDQ2jZvB8TJLvp9Kt88WjY3ty89dmuNMa87LPfeSEmeSNKWUkYfuhVgxFy+r+jBuVeq2TVNKmdyqrTHEFWdaNblVW9tX5ZxvsflWjB3/NKeddhaul6GRulYfzH/jjdfZdde+/PWWG2lszCIIgiAIOaTDkjJkTg47NDIPS41o8vJnZfssyHnnzl244IJLGTf+GTbY8Nf4TgZcN7pTYzSeC54LfraBG274M/vvP4j/vPNvrJ8LwvZ5FVI+VwVx12VpnGnNeVnnXoLiTIxG5mEROpJqz89g8/wRth4LmYcl2ZpanIdlSfa76aa/4YnRYznrrHOp8zJk/aDZ3C1+QNOf1u+x776DGH7VVcxb0EAl5ySgnP3YPq9CyueqIO66LI0zrTkv69xLUJyJ0cg8LEKHE4ITLVvdVilNNeu2TVNKmdyqrTHEFWdaNblVW9tnWc7rMnWcdNLJjBv3NFtssVVRi5gf+Nx6600M2GsXXn/9dQRBEITaRTosKSNJVpE0a8QSViOavPxZ2T5Lc77WOuvy6BPjuHTocDp17QGui+O4TdYwMDax+gx88flnHHHEQQy99Hxmz/oZq2wptls8Um6bIe66LI0zrTkv69xLUJyJ0YglTOhIkmgVSaNGLGG1oxFL2JLttz6T4aijj2XMUxPZvs/2TSOJ5dvEGrLGIgYh94+8n5123p5nn3sGa2wptls8Um6bIe66LI0zrTkv69xLUJyJ0YglTOhwQnCiZavbKqWpZt22aUopk1u1NYa44kyrJrdqa/sSkPNVV12VEfc9xI033EqPnssUtYl99dWXHHbYQZx19qnM/PEnBEEQhNpAOiwpI+lWkbRoxBJWI5q8/FnZvoTkHBwOOPAQpkx9lT32HFB0JDHPhbFjnmDQwN2ZMH4MYXYhVbOl2G7xSLlthrjrsjTOtOa8rHMvQXEmRiOWMKEjSYNVJA0asYTVjkYsYfHVveKKvbnt1ru48cZbWW7ZXkVHEps5cyannHoyx51wLN9/9z1Lao+gBE1ZFoo0aCxtH3HXZWmcac15WedeguJMjEYsYUKHE5prloRFtlVKU826bdOUUia3amsMccWZVk1u1db2JTTn/fr1Z/Lk5zngoEOLWsQAJk6cwG577Myjjz5M2GxHgiAIQlqQDkvKSJtVJKkasYTViCYvf1a2L8E577n0Mlx73c2MuPdhVlxl9aIjic2bO4tLLrmQIw8/iM8+/QixhNWGbYa467I0zrTmvKxzL0FxJkYjljChI0mrVSRpGrGE1Y5GLGGVrbvvDjvw1FOTOeLIY4Cw6EhiL097mZ123p477rydbGBMDBW1pdhu8Ui5bYa467I0zrTmvKxzL0FxJkYjljChwwnBiZatbquUppp126YppUxu1dYY4oozrZrcqq3tS0nOuy7VhWGXXsGToyew9jrrFbWJzZ8/n6FDL+LAAwbxv/99gCAIgpB8pMOSMmrBKpIEjVjCakSTlz8r25eynG+x5dZMfmYqvzvlTPDqi44k9vZb/2bIvgO4+aa/0DB/LhWxpdhu8Ui5bYa467I0zrTmvKxzL0FxJkYjlrDkopQ6WikVKqW2b2e5lZVSdyilPlZKzVdKaaXUJUqpTpVqa45asorYrBFLWO1oxBLWse3r2qUL5/z+PB59dAwbbrBx0ZHEGrNZrr/hOvYcsDtvvfUWhawO5LaJnSRxcRJ3XZbGmdacl3XuJSjOxGgst4Rlqlaz5SiltgVuXoJyqwLTgVWBN4F/AdsBlwO7KKX20Fo3xtnWxQjBiZatbquUppp126YppUxu1dYY4oozhZoP3n+PN/71Oj/PmUWGenbaoS9rrL2eNe2rqMaC9m240YY8/sRT/P3uO7jh+mtp8LNkCQidBpwwXwzvvfc+Q/YbxAnHHctpZ55Pt/quCMnlA/0eb/3zZebO/gWcTvTZaXeU2qDazRIEoYJIh6UASqkhwAig2xIUvxXTWblEaz082t9SwGhgN+B04Lp4Wro4hWwWQLNtldJUs27bNCWXCSHfEmZTDJLzwppXpk/nr7dcz6uvvIzjeThuhjDIcvnlPltvsx2nnHoW2223ndUxpCXnmUwdvz35NHbbfS8uuPA83njjFRzHxXP8PJtYSH0GHAL+fs/dTJo8mav+fDN9+myHg0NIczuE6R4tsj4029ZSsyRlkqixpH3Tpk/jpltu4pXXXiHjOniOgx+GLLjsUrbeug9nn34G222zTeLjtEJT8brLPPcSE2eCNKWUEUuYHSilVlVK3Qc8BnjAd+0sr4C9gY+Aq3LbtdZzgeMAHzgttgYXoJatIjZpxBKWTs2jox7m2GMP59XXphMEWcIwhDAkDEOCIMurr03n2GMP5x+PjLI2hjTmfK211+K+Bx5m2KVX0rVL16IjiX362ecM2W8Qvz/vHH7+ZTZmT2InsT3OUaNGceyxR/Dqa682y2fO/jdt+jSOPOpQ/vHYqETHaY1GLGG1p7HcEiYdluYMB44A3gC2Ad5vZ/l+mG7oU1rrIP8NrfXnGHvY6kqpDWNoa+uEphGERbZVSlPNum3TlFImt2prDHHFmQLNi1OncOFFvycIg2YanOZlgjDgggvP4cWpU6yLITaNhe3zHJcjjjyKF6ZMY+dddmtzwsmR949g6623ZtKkpxd7T7CLF198gQsvOo8wDIrqgiDg4osvYNrLL3ZQywRB6Cikw9Kc94GjgK211u8sQfmNouV/iuwf4NdLsO+SqNXRg2zTyChh6dP85fprmrY5roPrZnBcp+DrELj+hmuti6EWcr7KKr/i/pGPcu11N9N9mV5FRxL77tuvOeywwzjrzJP54btvkRGG7Izz+r/8mZwtJTdZaKF8ei6EYcgtt9yQyDit0sgoYbWnkVHCkoPW+o9a6/ta3h1pBytFy29aeT+3vfcS7r9NxCpih0YsYenS/O+D95k+/aXFLGDFXk+b9iL/++B9a2KopZx7nsu+Q/Zj7FNPs+eeexcdSQxCnhwzhp122Z4nn3ySIAwQO4k9ceoPPmDa9GnNLGCFLGH5+Xzl1Vf44MOPEhWndRqxhNWexnJLmDx0Hy9LRct5rbw/P1ouycP8bVJX57H88t1Z2OjTmA2oy7h0qTcpnt+QbdrWqc6riKZS+02ipj1lQqBXr+7WxSA5X6QZ/Y9XzUkWAk60LOH1m2++yuZbb2ZFDLWY855LL8XDDz/ApHGHctoZZzDzhxmtjiT2448/cuZZp/LM5H5cc/0NrLaKua7kL/TAXwheJ9y6zgSNda2+9jp1bbNMEjXVrPuRx19lSXjzzdfYdqtNEhOnbZrK121+JvXq1T3lcSZHU0qZ3LZqIHdY4iV3ZyZs5X2nxTJ2stk8m1GYZ6HI21YpTTXrtk1je/skzvZpfpk1C8dxW7WAFXrtOC6/zJplTQy1nPO99t6bV197nYOPOBpcF8dxmyxE0NxmNHnyZLbeckvuuvN2Ar+xuXUlaCj+upDdJQ2aKtY9e9YvreYq9zr/L7dtzqyfExWndRrb2ydxVudYVNESJndY4mVOtOzSyvudo+XcSlTe2Ojz08/zaMj6NPoBdZ5L5zqT4gWN2aZt9RmvIppK7TeJmlLL9Fi6K4QhM2fOsS4GyfkijefWE4YBYRjihCE5C5jruq2+DsMAz61n5szZVsRQ6zn3gwwXX3w5e+zSnwv+cAZffP4p2dB0LLNhQEM2xMmA54XMmj2H3558CvePfIhrr/4Tq/9qFfDqceo6EzYuAL+h4Gu3PgQgaJiXKk1V63Y7Ndm9PG+RJSyXq0VWsOYa3M7M+GFuYuK0TVPpupft4QEhM36Ym+o4k6QppYxbH7JC755UA7nDEi9fR8sVW3m/rWdc4iEEJ1q2uq1SmmrWbZumlDK5VVtjiCvOhGv69t2xaRvtsIT17bujNTHEqrG9fUU0222/Pc+/MI0TTvodWae+6EhiL7/8Ev333JW77/4bWT+72PtC5Wk699pdboeYWyIIQjWRDku85EYHa23Y4txUvEsyAlmbhKEZMccPQ/wgxA/DJntE/rZKaapZt22akssE9sYgOV/0et311mfbbbfHcV0c18Nx3cgC1vrrPn36su5661sTg+R80esuXboybNhVTJz8NOusvzG4XjSSmIPjZvDcRVYjv3Eh1193FQcfMJD3//sWBNlFf35D89eRhSJ1mirWrdZbjz7b9iHjQsZ1yESWsPzXuVzltm279dast87aiYrTOk0H1B36tRFnYjSllJFRwlLDxGg5SCnV7NgqpVYDfgN8prV+txKVh9ElRN8P8IMA///ZO+84K6q7jX9n5u5SFRsSSyKWOCb6GrsCUpUqRcSOUcSOChY0aixYsaMCNmyY2EWqVBEUG9bEmtFoYolRI4JI3b0z5/1j7ix3YXe4u7edmfk9fPYz55z7nDnnmcd73dl57jmu5/9JsY62YnHKObZunFz7pF19NYjn6+rnn3cRViqVuSGx8JRXU16/bqVSnDdipHYaxPPanH323Y9Zs+dw1tnnASk8swLP9I+uMvA8MA0Dy4APP/iAXr26c9eYW1izahXKdfEME+W6KDftHzH8n6y2OHDKPb/zzx9JyrKwDN+PdHqdL6bh+xR4lTJNzjnrnEjq1IlT7LE9N41yq2OvM0qcXPooNnwSXSrIDUsjYdv2b2zb3s227a2CNsdx/oV/02ID12RxWwAPABZwW7HmZGCAAssysUwTyzL9OEQdbcXilHNs3Ti59klZ+moQz9fVD+7YiWuvHo2BQnkupmGiPBflebXqBorrrrmRgzt20k6DeL4hp3nzZpx/3gXMmDWfXXffiyqVokqZYJiYJnhKkfb8I6QZd884jjluEH//4G+YysNIWRhmhX8M/hef1RYHTrnn17FjZ66+ZjQeJp5SpFK1fTFNME1QGFw56jradewUSZ06cYo9tmmlMKz464wSJ5c+Btm529JCblgaj0eBT4Bz1ms/G/gO+LNt2x/Ytv0s8BnQHZgF3FOsCQV7EliZvQqCY3bZMo2icco5tm6chvRJaapBPN+Qc8yxx/LghIkcuP8B+PuueP5fnpSH56U5cP8DeHDCRI4+5hhtNYjntTnB++///m8Pnp00jREj/0Rlhb/IgutBtTKoVibVyqAqDWkPnM/+yaCjBnHVtVezcnUVWBYYKYJ9C/C/EZ45VtauR5GjwfyOOmYw9014lH33P7DGBzdz0+J6cOBBHXj4kcc54shjI61TG06xxzZTGFYq/jqjxMmlj+zDEh84jvOFbdsH4D9h6Q3sAnwB3AXc4ThOuuiTKMMXWYt+3ihycukTFHXVUCidMeIc1K4dB3eYxL8//5S3332LZSuWk6KSLp060nbnXUl7nvYaxPNatJq2lJXitFPOoHf3nlx68Xm8/sZrmS/kmzX7t9T0U4oJ993DgjnPM/r6G2nfsQuC4qJ9+/a079iFz774F39/51VW/vIzGE1o36U7tv27dasZCQSC2EFuWELgOE6XRr72NXByEaa0UaRdL/P/Xv/Bnef5tey2YnHKObZunJz7KPy9IjTUIJ6Hc3b6rc2+B+6L6ymW/rQCyzRwPaXN/MTzHDhZ779szg5td+KZSdP5618mMvqm0axcvRrMdOZJjb8ngWUqKlPw7bdfM3ToCQw66lguufw6WrVqRbBvAXjgVtWuZ760GimOZvPbdZedaX/AHig3zZKlqzEqmkHWfhFx0Rlvz/3AkRF7nRHi5NJHvnQvKBRSlolhgEJhGNREH7LbisUp59i6cRrSB6WnBvG8Yf7pOj/xPJxTn38py2LIkKHMmv0CXTp3RWVWG0srAw+TdCYi5noAiieefIJOndsze85sFCYKhcIEq7J2PROziBRH0/mREJ1x9ZwSjiWeF+halDESJjcscYTy74VRIW3F4pRzbN04ufQJirpqKJTOuHKCoq7zE8/DOUE1hLPtNttxz70PMOb2cWy++ZZUU1Hv3i3ff/8dQ4YM5tzhZ/K///2IQCAQCAoDuWGJGeqKPnieqtVWLE45x9aNk3MfBdmRMJ00iOcN80/L+Ynn4Zwc/TMMk/4DBvLiwjfo2+9wXCMFZmaFP9PvY5nU/MyeOZ1+/bozdcqzqPRaDDwMMvGXzN4GtdoyUQxtOZrOj4TojKvnlHAs8bxA10IiYYJCQaIienAkEpYcjkTC9JpfoSJhdZ1n66234s677ubu8RPYunWbDSJirkfNz7Jlyzj/ghGcOOQEvv32W8oS8Yh5bIaE6Iyr55RwLPFcImECHaHAyBzrbSsWp5xj68bJpU9Q1FVDoXTGlRMUdZ2feB7OCaoNPE+3Qw5h7twFHH/CSaERMYD58+fTvUc3Hn/8L3jK2+B1gUAgEGwccsMSM0hURA+ORMISwsnyT8v5iefhnDz8a7nJpoy+8Xb+8tizbPvrHcE0MQyzJhYGfkysMgVr16zkmmuuYvCxg/jicwetYiARjs2QEJ1x9ZwSjiWeSyRMoBkkKqIHRyJhyeFIJEyv+RUzElbXtWjfvj3Tps/h5KGnYRh+HKy+lcQWv/Um3Q7pzLjx46h2PYoe8Yh5bIaE6Iyr55RwLPFcImECHaHAyBzrbSsWp5xj68bJpU9Q1FVDoXTGlRMUdZ2feB7OCap5XotmzZpy+WVXMWP6HOzdfh8aE1uzZg3XXXcVRww8jE8++RiBQCAQbBxywxIzSFRED45EwhLCyfJPy/mJ5+GcPP1bv77X3vsxe+5CRpx3EWZF09CVxD75+EOOOnIAt992I2tWriDyURGJhMWLI5Gw5HEkEiYoJSQqogdHImHJ4UgkTK/5lToStn6fpk2aMHzE+UyaNIM/7LlX6EpiaTfNuPFj6dm7G++88w4Fj3jEPDZDQnTG1XNKOJZ4LpEwgY5QYGSO9bYVi1POsXXj5NInKOqqoVA648oJirrOTzwP5wTVIlyLXe1deebZqVw56npSTTcJXUnss8/+yVFHD+SGG65mxcqVG7wuEAgESYfcsMQMEhXRgyORsIRwsvzTcn7ieTgnT/821sc0LU459UxmzHqR/Q/qGLqSmGkoHn10In37HMJLC+cTuaiIRMLixZFIWPI4EgkTlBISFdGDI5Gw5HAkEqbX/ModCaurT9u2bXnokb9y/bU307JFy9CVxL76+muOOXYQ510wnKU/LyeviEfMYzMkRGdcPaeEY4nnEgkT6AgFRuZYb1uxOOUcWzdOLn2Coq4aCqUzrpygqOv8xPNwTlAtwbUwDYNjjzuelxe9QfeevTe64eSTTzxGzx5dmTN39gavCQQCQdIgNywxg0RF9OBIJCwhnCz/tJyfeB7OydO/xvRp02YbHn74ce4cey+ttmoTupLYT0t+YMTwYZxz9un88O1/0DoqIpGweHEkEpY8jkTCBKWEREX04EgkLDkciYTpNT8dI2HrcyzLpG/f/jw/fR79+h0eupIYKGbOep4uh3Tk2WefxVMeWkZFJBIWL45EwpLHkUiYoORQYGSO9bYVi1POsXXj5NInKOqqoVA648oJirrOTzwP5wTVMl2LzbfYjDvGjOPRR59iq21+HRoRW7ZsGRddfD6nnjqEb//z9QavCwQCQZwhNywxg0RF9OBIJCwhnCz/tJyfeB7OydO/Qs2vc7dDmT3nZY4+bkjoSmKWCa++uoiePbvx8EP34rnVaBMVkUhYvDgSCUseRyJhglJCoiJ6cCQSlhyORML0ml8UImF1cVpu0pIrR13D4489Q9sd2oauJLZy1SouvewSDh/Yl88//4IGRTpiFJshITrLwpFIWPI4EgkTlBwKjMyx3rZicco5tm6cXPoERV01FEpnXDlBUdf5iefhnKCq0bU48MB2zH/xFc46ewSuWRkaE3vzzcX07nMo9957N9Xp6g1eFwgEgrhAblhiBomK6MGRSFhCOFn+aTk/8Tyck6d/xZpf06bNuPzyq5n03Ex2sfcIXUnMc6u5445bGTTwMD56/x2SFJshITq1jQdJJCxeHImECUoJiYrowZFIWHI4EgnTa35RjYTVxdnzD3/gmUlTOP/8izDNVOhKYh9+9BG9+vTipptuZPWatSQhNkOhx9JUZ1k4EglLHkciYYKSQ4GROdbbVixOOcfWjZNLn6Coq4ZC6YwrJyjqOj/xPJwTVDW+FpUVlZxz9gjmznuJPfY+IDQi5rou9943nsMP78nbby3e4HWBQCCIKlLlnoCgcFDKjxa4SuF6CsPwowdArbZicco5tm6cnPt4ft1V+mkQzxvmn5bzE8/DOXn6V0oNO+6yK48/PZVHH53InbffzIrlS7EMF8s0MEwLS7lUphQpE7756kuOP+4IjjlhKJdeeiXNKwzw0mCY4Fb55aAeRDyy29bnNKZP0TkGys20FWosLXWWiVP0sdU6/2KtM0KcXPpIJExQCKjMn+hc18P1PFzX8/9kV0dbsTjlHFs3Tq590q6+GsTzhvmn4/zE83BOvv6VUgPK4PjBJzJl2mwO7tARVxl4ZgWemcIzK6h2DUzDwDLANAweeuhBunY9mEUL56PcNMp18QwT5bo1dYXh/2S1rc9pTJ9iczw3jXKrCzqWjjrLxSn22Nn+xVlnlDi59FFs+GS3VLBGjRpVtsEFBcMQoG067bFqdVVNoiBlmVSY/j2pq1RNm2UYReEU67xR5OTap1nzJgBUVaW10yCeb5yT7Z+O8xPPwzlNmlUCjfevXBq22GJzjj3mOLbb/te8tvhN1lS7VHsKEw/LUBgY/tMZBb/8spyZz0/jv9/+h30OaEeL5i3AUBgKjIoUplXh/wqi0uvaDLM2Z/16Ln2KzGnRvBJQrK5WhRtLQ51l4xR57OZNrXX+xVhnpDi59LEqaNGyWeaTiC+BRygR5AlLjBB8edPKfGEzOGaXLdMoGqecY+vGaUiflKYaxPOG+afr/MTzcE4+/pVTg2WZHH/8CcyaPZ9Oh3THy8TaqpVBtTKpzvpiftqDyVOn0LPXoTw/ayYKCywLjBTBF2sxUuvarMrwei59is0xUxhWqrBj6aizXJxij53tX5x1RomTSx/50r2goFBk7opD2orFKefYunFy6RMUddVQKJ1x5QRFXecnnodzgmqEr0WbNr9i/Nh7GTf2Prbccmuqqaj3i/lLlvzIaacNZdiwU/nfDz8gEAgEUYHcsMQMsj+DHhzZhyUhnCz/tJyfeB7OydM/LTQAhmHSu09fXlz4GgOPOBrXSIFpYhj+3i2W6fezTKhMwQvzZnNY3x5MevZJVOZL61Hcq4JCj6WpTm335JB9WOLFkX1YBKVEfev5y/4M+l4L2Ycl2hzZh0Wv+TWUo/M+LA3lbLXVltx62x1MuP9htvnVNijl+ZGwrP1bqtJ+TGz58p+56OILOebYI/nq66+J4l4VFHosTXWWhSP7sCSPI/uwCEoOBUbmWG9bsTjlHFs3Ti59gqKuGgqlM66coKjr/MTzcE5Qjdm16NipM3PmLODkk09FGUZoTOzllxfSpUt7Hnlogr8amUAgEGgIuWGJGSQqogdHImEJ4WT5p+X8xPNwTp7+aaGhHk7zFi25/vpbmDxlJjvutEtNTMz/8r7PCeJia9es4vobRjF48NH88zMnMrEZCj2Wpjq1jQdJJCxeHImECUoJiYrowZFIWHI4EgnTa35JjoTVxWl3UHuef34up592FiYGaderFRELVhJzPfjb397hsL49ueOuu6hKe9TEQDSNslDosTTVWRaORMKSx5FImKDkUGBkjvW2FYtTzrF14+TSJyjqqqFQOuPKCYq6zk88D+cE1ZhfiyZNmnLBhRcxZcrz7L77H0IjYtXV1dx88w0M6N+TDz/8AIFAINABcsMSM0hURA+ORMISwsnyT8v5iefhnDz900JDAzi/32NPpj4/hwsvugyrslnoSmKfffoPjj56IDfdeB2rV/yCjlEWCj2W7pEd3eJBEgmLF0ciYYJSQqIienAkEpYcjkTC9JqfRMLCOc2aNGHYsHOYMmUW++y9T+hKYp7nct/999C9ZxfefPNNasVCNIiyUOixdI/s6BYPkkhYvDgSCROUHAqMzLHetmJxyjm2bpxc+gRFXTUUSmdcOUFR1/mJ5+GcoJrAa7HTzjvx1NNTuP66m2jWvGVoTOyLL/75NxIhAAAgAElEQVTFMccO4uqrL+eXFb8gEAgEpYbcsMQMEhXRgyORsIRwsvzTcn7ieTgnT/+00JAHxzBMTh56OgsWvkbHTl3rXUmsMuVHxp544nH69OrG/Bdmo0OUhUKPpXtkR7d4kETC4sWRSJiglJCoiB4ciYQlhyORML3mJ5GwhnN22GEHHn7kr4y+4RZabbLpBiuJBRExUPzn228ZfMKxDDvnTH78aRkKhUTCYsiRSFjyOBIJE5QcCozMsd62YnHKObZunFz6BEVdNRRKZ1w5QVHX+Ynn4ZygKtcCA4OBRwxi9pyF9D6sf2hEDOC5Sc/Qq2cXZs2agVr/ogoEAkGBITcsMYNERfTgSCQsIZws/7Scn3gezsnTPy00FJjTeuutufe+hxk3/gE2a/2rOiNiwc+ypUs4//zhnHXGUH749mskEhYjjkTCkseRSJiglJCoiB4ciYQlhyORML3mJ5GwwnB69+nDzBnzOOKIozaIiLneus0mQTF33lw6de3Ek08+iac8JBIWA45EwpLHkUiYoORQYGSO9bYVi1POsXXj5NInKOqqoVA648oJirrOTzwP5wRVuRZ1clpt1opbbh7D448/R5vt2oZGxJYv/5lLL7uIIUNO4Ksv/73B6wKBQJAP5IYlZpCoiB4ciYQlhJPln5bzE8/DOXn6p4WGEnA6dOrMzNkvcfxJp2CYFvVtNmmZsHjx6/Tpcwj33TsWN13c+A0FOo8WcRzdOBIJSx5HImGCUkKiInpwJBKWHI5EwvSan0TCiuN5i5bNueyyq3jyyUnssvMu9W42CYpVq9dw1agr6Ne/F47zKQqFRMIixpFIWPI4EgkTlBwKjMyx3rZicco5tm6cXPoERV01FEpnXDlBUdf5iefhnKAq1yJnnfvtdwDzXniZEeePxDObhMbE3n33Xfr168nYsXdSVV21wesCgUCQK+SGJWaQqIgeHImEJYST5Z+W8xPPwzl5+qeFhjJ4XlnZhIsvvpzJU+ew6+57ha4k5nlpxo+/k4EDevH3dxcjkbCIcCQSljyORMIEpUS5YwM6xxp0vRYSCYs2RyJhes1PImGl83z3PXbnmWeeY+RFl2JZFaEriX3yD4fD+h3G9aOvZeWq1UgkTHOORMKSx5FImKDkUGBkjvW2FYtTzrF14+TSJyjqqqFQOuPKCYq6zk88D+cEVbkWjdaZslKcdcbZzJu/iL32ax8aEfM8jwcm3M+AAT144/VXN3hdIBAI6oPcsMQMOsUGdIs1aHktFEgkLMKcLP+0nJ94Hs7J0z8tNGji+Q477sxjT03mz1feQIuWmxK2ktjXX3/DH/94NBdfNILlPy9FImEaciQSljyORMIEpYSOsYEkciQSlhyORML0mp9EwsrnuWWaHDf4BGbNfoGuXbqGriTmevDoXybSqXN7XnjhBRQKiYRpxJFIWPI4SY+E2bbd27bt52zb/si27bdt277Dtu0dN9JnkW3b6WLPLbZQYGSO9bYVi1POsXXj5NInKOqqoVA648oJirrOTzwP5wRVuRYF1bndttvz2OPPctfY+2i5WevQmNh33/2X004/mQsvHM6PS5Zs8LpAIBBAkW9YbNseBcwABgC/A/YBzgU+tm37go103/CTTRAKpfxH965SuJ7CVarm0X12W7E45RxbN07OfTx9NYjnDfNPy/mJ5+GcPP3TQoOmnisFg448hudnv0T33oeDaWWewhgYZgrLXLeaWMqEObNm0KdnZ6Y+9yTKTYOX+cnEUmrqXhrcKvDS63ghnFzOk1efuHJKMHaNfzHXGRlOLn3iGAmzbbsLcCX+jcds4ALgUuBvQBPgFtu2H7Vtu3zPl2IGlfnzlut6uJ6H63r+1a+jrVicco6tGyfXPmlXXw3iecP803F+4nk4J1//dNCgs+ebb7EFN986hrFjH2CLLdrgmRV4ZgrPrMBVBp4HpmFgGbD852WcffYwhp01hO+//QbluigM/8d1UW4a5bp4honnplFudSgnu54LpzF94sop9tjZ/sVZZ5Q4ufRRGMX5BTYHpIp47nPwHxSPdhzn8qz2m2zbPh24AxgMtLJt+2jHcdYWcS6JgIEBCizLROEfg0f167cVi1POsXXj5NonpfTVIJ43zD8d5yeeh3Py9U8HDVHwvGevHnQ6+FWuv+EaJj/3LGmVBsPENFw8pUh7Bp5SpFIw/8UFvPPWW5w/8jKOO/FUTNPESFmgKiBlYSgP02qCUhUYKf/XKmADjsqu58JpTJ+4coo8tmml1vkXY52R4uTSJ3hjlwHFjIS1A1YAo9Z/wXGc+4FOwA9AX2CmbdstijiXRGDdFx/9L0QGx+yyZRpF45RzbN04DemT0lSDeN4w/3Sdn3gezsnHP100RMXzLbbYnNE33sqEB//Kdtv+GqU8XA+qlUG1MqnOfDE/7cHyFSv48xWXceRRh/Ovf/8LjBRYVuZYCWYKw0r59cyXgzfgZNdz4TSmT1w5xR47278464wSJ5c+ZfzSfTGfsLQG3nccJ13Xi47jvG3bdgdgHtAFeMG27V6O4/xcxDltFLZtHwpcBuwJVALvADc6jjMnx/6/Br4KobzqOM7BeU80DAr/oZ0KaSsWp5xj68bJpU9Q1FVDoXTGlRMUdZ2feB7OCapyLUqqs1379syaPZ87bh/NhAn3Zr6Qb5LGQxlVZOO1116la9eDuXTkxZx80kmkyvf7kkAgKCOK+YRlJRD61MRxnC+ADsDHwIHAy7ZttyninEJh2/YQ/Buo9sCbwOuZ+c3OxNhywd6Z4/vAY3X85HTj01jovlZ/UjiyD0tCOFn+aTk/8Tyck6d/WmiIqOfNmjdn1KgbmDZtDr/ddTdcIwWmmXlS43OszJfyq6vWcPPN13LccUfwj08+qtkvAmRPjijvT0IJxxLPZR+WMHwC7GLbdqswkuM43+HHw94E/g9YBGxdxHnVCdu2twHuBX4G9nMcp4/jOD3xb1iWA3fatr1dDqcKblhudhznhDp+ri2OAh9RWKs/CRzZhyU5HNmHRa/5NZQj+7CUV+f++x/A1GmzGHbWuViGSdr1au3d4nrU/Hzwwfv079+HW267jTVrq/HPVOJ9KJLCkX1YksdJ8D4sL2TOf8zGiI7jLAUOARYAu2R+So1z8VcvG+M4zodZc3sLuBloCuTylCW4YXmn4DPMFQqMzLHetmJxyjm2bpxc+gRFXTUUSmdcOUFR1/mJ5+GcoCrXoqw6m1Q2YfiI85k2bTZ77rkP1VTUu3dL2k0zZswtdOzYgbfffhuBQJAMFPOGZQr+59KFtm1vdBzHcVYCvYGpmX6lRq/McUodr03OHHvncJ698Rcb+LQQk2oo4hYbiCpHImEJ4WT5p+X8xPNwTp7+aaEhRp7bv/s9k6fP4uJLr6CiaQswTQzDrImGgR8Tq0zBZ59+Su/eh3HD9VexcsUvSDwoelEpEqIzMpykRsIcx3kXOBQYBjTPsU8VMAg4HhharLmtD9u2DeD3gIcfZVsfn2Ze2z3Dre88WwC/yfAvsG3777Ztr7Jt+1vbtu+3bXvbIky/FuIaG4gaRyJhyeFIJEyv+UkkLNqeN62s5IzTz2Lq1FkcuP+BNSuJZcfEqtJ+REwpjwcefICu3Q5m0SuLkHhQtKJSJERnZDiaR8IMpVTZBtcFmRuNJcD/HMep8/sztm1/j//dmlaO4yyvh3MIfhQOoBp4CagC9sdfNe07oIvjOE5hFbAQ6Ox6/l+v1la7VKc9KlImzSr9heBWV6Vr2ppUWEXhFOu8UeToPj/RKddCdMq10F2nZcJjEx/h4osvZtWataQs/zsuXrqKphZUWlDlwpq0/3vM6aecyHWjRrHZlq0xK5riVa8Bdy1YTbCa+H83ddeuqmlbn9OYPnHl6D4/0Vmea2E1aY5h1jzreAnoQolQkCcstm1vnmf/YYWYRx4IVjNbFcJZnTm2DOEE31/5CLAdx+nuOM5hwI7AE8Cv8FcKKxrS6ayYiqoj+qCKxynn2LpxdJ+f6JRrITrlWuiu0zBMTj3tNN7/8EN69O4TupKYZcJjf/kLHQ7uwMznZ4BX/0piNW3rcxrTJ64c3ecnOstzLcoYCSvUPizv27Z9guM4LzWkUyYi9TB+dOzuAs2lMfAyRxXCMdY71oUxwCTgF8dxfgwaHcdZadv2qfiroe1r2/ZBjuO8kc+E60J1tcvSZauoSrtUux4VlknTCt/iNdXpmrbKlFUUTrHOG0VOrn023aw5KMWSJSu00yCeN8w/HecnnodzWrZqlpd/OmhIgufNmrbi7vETmDJ1CtdfcwVL/vcdVuaGJq08XM//X7frwffffcexxx1Pr8MGcPWVV9N6i1ZgVWJW+hyvahW4VWBVYlQ0RVWvqbeeS5+4coo99habWoDifz+ujLXOKHFy6WNWKrZuE7r4b9FQqO+wbIe/8eP1tm3nFHCzbXsw8CHQvUBzyAcrMsdmIZymmePK+giO47iO4/wr+2Yl67VVwIuZ6r6NmmWuUJm7KhXSVixOOcfWjZNLn6Coq4ZC6YwrJyjqOj/xPJwTVOVaaK/TwKBfvwHMmfsS/Q8/InQlMYCpU6fQs2cXpk2bjFrfdIFAEDkU8kv3JnAJ8Kpt2zvVR7Jtewvbtp8BHgWC27QlBZxHY7Ac/6ZlK9u2N3jqlGnbCljjOM6yPMb5LnPMaRGCxiAJK8lEgSOrhCWEk+WflvMTz8M5efqnhYaEeb7FllsydtwEHn/qaVpvs33oSmK/LF/GxRdfyKlD/8h/vvkSWTGqARxZJSx5nISsEnYq/i/8Bv4XzP9m2/ZJ65Ns2+6L/1TliAzXAJ4Cdi/QPBoFx3EU8DFgAbvWQbHxr9UHYeexbfsq27aftW37/+qh7Jg5ftPYuW4MSVlJRneOrBKWHE7gn67zE89llbA4en5Yn968+sorHHPMYMJWEgPFgoUL6NS5Aw8/8jCu8s8jK0aVf/UsEqIzMhzNVwkryA2L4zgPAX8AXsW/CWkJPGTb9hO2bbeybbulbdsP4u+x0ibD+Q/Q33Gc4xzH+V8h5pEnZmeOh9fxWtA2cyPn2BN/Weaj13/Btu2tgR74q4ctaOQcc4PyLzAqpK1YnHKOrRsnlz5BUVcNhdIZV05Q1HV+4nk4J6jKtYieTmDTVq244fqbmPTsdHZou1NoTGzlyhVceulIjj/2CP797y8QCATRQsEiYY7j/Bv/S+WX4S/la+D/4v534H1gSKYN4F7g947jzCjU+AXAw8Aa4E+2bdd8x8S27f2Ai/FXCbs7q31n27Z3s207+9tH92WOF9q23SGL2xJ4CNgUeMBxnO8oEiQ2oAdHImEJ4WT5p+X8xPNwTp7+aaEhqZ5nedeu/cG88OIrnHLqWSirInQlsXfeeYv+/Xtz373jSa9djTZxHN04EglLHichkTDAj1Y5jnMjcADwN/wblN8AbTPlT4DOjuMMcxznl0KOnS8yN1wX4t9UvG7b9izbtmcDrwGbAKc7jvNDVpf5+HoGZp1jLnA7/pf3X7Zt+2Xbtp8D/gUcBiwCRhZTh8QG9OBIJCw5HImE6TU/iYQlx/Psz86WLVpw2Z+v4IknJmH/1ibterUiYq5HzU9VVRU33XwDffv35uOPP0GhKHscRzeORMKSx0lCJKwO/AR8mSmr9X6qijRm3nAc526gH/AG0BH/+zivAN0dx/lrjue4EP/J0qv4+7L0Av6L/5TmkMxqYcWF8u8OUSFtxeKUc2zdOLn0CYq6aiiUzrhygqKu8xPPwzlBVa5F9HQGxfU4f/jDXkyZNosLRl4CqWahK4m9//77DBjQhzFjbmXN2rUbvC4QCPRBofZhAcC2bQMYAVzDus0Yq4EfgW2B3+GvInYXcLnjOKvrPFEZkYmpbTSq5jhO25DXngGeKeC0ckZdj9OBWm3F4pRzbN04OfdRkB0J00mDeJ4DJ8s/LedXBI7u82sQJ0//tNBQAo6W88vybn1ORUUlI867iB49+3Hp5Rfz9/fewTBMLMPNiokpKlOgVJr77rub2XNnc8ONd7DfPnvh3xqti78E8SWyYjOx5xR9bANF7ShSPHVGiJNLnzhEwmzb3gP/ycRt+F+6N/C/u7I//o3Ko5k2CzgP+MC27a6FGl/gQ2IDenAkEpYcjkTC9JqfRMKS4/nGPjvt3WyeeOJZLvvzlTRt0iR0JbHP/vk5Awb2Y9Q1V/LLipVoGdnRLR4kkbB4cZIQCbNt+1rgHWA/am7DuAnY33GcDxzH+cVxnCH43/f4IcPZCX+zyftt2960EPMQZKD8C4wKaSsWp5xj68bJpU9Q1FVDoXTGlRMUdZ2feB7OCapyLaKnMyhu5DyWaXHKyafz4oJX6XBwp9CVxJRSTHzkYfr378WilxciEAj0QaGesPwZP15mAJ8DnRzHudRxnOpskuM4U4E9gClZzacAHxVoHomHrCSjB0dWCUsIJ8s/Lecnnodz8vRPCw1J9TzLu1zOs8MOO/LU01O5YfRtNN9k8zpXEqtM+SuJffvtNwwdOpjzzzuLZUuXoM0qTrqtGCWrhMWLk6BVwgz8ZX3/4DjOa/WRHMf50XGcI/CXOV6e6bdtAeeRaEhsQA+ORMKSw5FImF7zk0hYcjxv6GenZZkcc+xxzHh+Lod0O3SDlcSyI2KuB088+QSdOrdn9uxZKBRlj+zoFg+SSFi8OEmIhAHfAr0cxzkr11WwHMd5FPg//OWBBYWE8u8CUSFtxeKUc2zdOLn0CYq6aiiUzrhygqKu8xPPwzlBVa5F9HQGxUacp02bNtx3/8Pcc89DbLrlr0JXEvvhh+85a9jpDB9+Nv/74YcNXhcIBKVBoW5Y/i+zB0mD4DjON47jdAeGF2geiYfEBvTgSCQsIZws/7Scn3gezsnTPy00JNXzLO8acx4w6NNvALPnvEyffoPqjIhl/8ydO4tevbrw7FN/wY/JaBrriVBUioTojAwnCZEwx3GW5tl/fCHmIZDYgC4ciYQlhyORML3mJ5Gw5HheiM/OLbbcnJtvHcMD909k6623qXezSVAsXfYzw88bwUlDBvPNf75Gy1hPhKJSJERnZDgJiYQJdILy/36ECmkrFqecY+vGyaVPUNRVQ6F0xpUTFHWdn3gezgmqci2ipzMoFmisrt0O4cWXXuPoE04JjYgBLFq0iMMO68nERx7EU16dHIFAUFgUdONIQXmhlP8Y3FUK11MYhv8IHKjVVixOOcfWjZNzH8+vu0o/DeJ5w/zTcn7ieTgnT/+00JBUz4vw2dmseUuuuno0PfoM4MrLLuZfnzuZzSYNDNPCUi6VKUXKhHTVaq6/7kqmzpjBrbfdyU6/3ha8NBgmuFV+OagHMZrsNt05RR9bodxMe6x1RoiTS5+oR8IEekBl/lTkuh6u5+G6nv+nozraisUp59i6cXLtk3b11SCeN8w/Hecnnodz8vVPBw1J9bwYn52u67HPPvsxZdpszhp2Lsqw8MwKPDOFZ1ZQ7RqYhoFlgGkYLH5zMYcc0on77r6LqjVrUK6LZ5go10W5af+I4f9ktenOKfbYnptGudWx1xklTi59FEbhf3nNEdaoUaPKNrigYBgCtE2nPVatrqp50p2yTCpM/57UVaqmzTKMonCKdd4ocnLt06x5EwCqqtLaaRDPN87J9k/H+Ynn4ZwmzSqBxvung4akel6Mz87setMmlRzSpRuHHNKDt955lx9/Wka1pzDxsAyFgeE/nVHgeS5vvfk6r7zyEnvsuRdt2mwDhsJQYFSkMK0K/9c8lV7XZph6c4o8dvOmFqBYXa1irTNSnFz6WBW0aNks8y7hS+ARSgSJhMUIwZcIPaXwlIGV+ZIhgGUaNW3F4pRzbN04ufaxTANl6qlBPG+YfzrOTzzfOCcf/3TRkETPi/HZWVefvffeh8lTZ3L//fcx7q7bWFu1hmrDAGVSrTzcTNQs7cGHH3/M4QMHMPT0czhv+HCaVVhgpKj5orKRAsvLHCvB89bVdeMUe2zTwrBcMLx464wSJ5c+8qV7QUGhyNwVh7QVi1POsXXj5NInKOqqoVA648oJirrOTzwP5wRVuRbR0xkUS6CzsqKSM886mxkz5rLPPvtRTUW9X8x3PZdx4+6g72GH8PbbbyMQCAoDuWGJGWStfj04sg9LQjhZ/mk5P/E8nJOnf1poSKrnWd6VSufOv92VZ6fM5PIrrqFJ803ANDEMf+8Wy/Q5lgmVKfjqy39zwglHc/Woy1jxy8+g034bmuxPQkJ0RoaThH1YBPogZcla/TpwGtJH9mGJNkf2YdFrfg3lyD4s0dVZ6M/OXPo0qajg5KGnMm3abDq0OxilPFyPWvu3VKXB9QAUEx+dSJeuB7Ng4Ytos9+GJvuTkBCdkeHIPiyCkkOBkTnW21YsTjnH1o2TS5+gqKuGQumMKyco6jo/8TycE1TlWkRPZ1Ask87tf/1rJv7lCcbcPo5NNt0sNCb2zTdfc9xxR3HhyOH89FNe+2wLBImF3LDEDBIb0IMjkbCEcLL803J+4nk4J0//tNCQVM+zvCuXTjA45tjBvPTyGxzasw+ukQLTzCwK4HOCuJhlwrQpk+jXrwezZ01DpdcS+XiQRMLixZFImKCUkNiAHhyJhCWHI5EwveYnkbDkeF6OSFhdnG222YZ775nAnXeOZ4vNtyTterUiYq5Hzc+SJT8y7OyzOPX0U/jhh/+hUEQ2HiSRsHhxJBImKDmU/7cfVEhbsTjlHFs3Ti59gqKuGgqlM66coKjr/MTzcE5QlWsRPZ1BUROdBgY9e/Zm7twFDDrq2NCIGMCsWTM5tHsXJk16GrX+f5QCgWADyA1LzCCxAT04EglLCCfLPy3nJ56Hc/L0TwsNSfU8yzuddG62+RbcPmY8Dz/yOG22/U3oSmKrVi7nz3++hJP+eBxfffkFkYsHSSQsXhyJhAlKCYkN6MGRSFhyOBIJ02t+EglLjue6RMLq4nTq3IUZM+Yy+ISTNrqS2CuvLqJL14OZ8MD9pD2F/y8C8SCJhMWLI5EwQcmhwMgc620rFqecY+vGyaVPUNRVQ6F0xpUTFHWdn3gezgmqci2ipzMoaqyzeYvmXDPqeqZOmclOO/82NCa2atUqrrjiUo45+nD++c/PEAgEtSE3LDGDxAb04EgkLCGcLP+0nJ94Hs7J0z8tNCTV8yzvdNe5/wHtmDd/EWcNGwFWZehKYn//27sMHHgY48fdQdXqlWgdD5JIWLw4EgkTlBISG9CDI5Gw5HAkEqbX/CQSlhzPdY6Erc9p3qwZIy/6E08/PYXf77Z76Epi1dXV3Hb7LfTp25P3338fhULLeJBEwuLFkUiYoORQYGSO9bYVi1POsXXj5NInKOqqoVA648oJirrOTzwP5wRVuRbR0xkUI6Zz9z324LkpM7jksisxK1uEriT28ccfM/CIftx882hWr1mzwesCQZIgNywxg8QG9OBIJCwhnCz/tJyfeB7OydM/LTQk1fMs76KmM5Wq4KxhI5j+/Hz23OeA0JXEDDweemgCfft04/XXFqFVPEgiYfHiSCRMUEpIbEAPjkTCksORSJhe85NIWHI8j1IkrC7OzrvszF8ff5qrrriWZk2bha4k9q9/f8nAI/px8SUXsXzFShSKsseDJBIWL45EwgQlhwIjc6y3rVicco6tGyeXPkFRVw2F0hlXTlDUdX7ieTgnqMq1iJ7OoBhxnZZhcuJJJ7Pwpdfo3KXbRjecfHTiQ/Tq2Y2FCxds8JpAEGfIDUvMILEBPTgSCUsIJ8s/Lecnnodz8vRPCw1J9TzLuzjo3H773/DY45O4+ZY72WSzLUNXEvvh+28588xTuOD8s1nyw/dENSpFCceSSJhEwgSaQWIDenAkEpYcjkTC9JqfRMKS43nUI2HrcyzLZNCRRzF9+jx69uoTupIYKKZMnUKXbgczffo0POURtagUJRyrnDojw5FImKDkUGBkjvW2FYtTzrF14+TSJyjqqqFQOuPKCYq6zk88D+cEVbkW0dMZFGOos/XWW3H3uPuZMOEvtNpq29CI2JIlSxg+4mzOPvsMfvjuvxu8LhDEBXLDEjNIbEAPjkTCEsLJ8k/L+Ynn4Zw8/dNCQ1I9z/Iurjp79O7D3Hkvc/ig40JXErNMePHFF+jRsyuP/fVhlJcmClEpSjiWRMIkEibQDBIb0IMjkbDkcCQSptf8JBKWHM/jFgmri9Nqs1Zcd8NNPPLQ42y/3fahK4kt/+UXLhx5PkcdPZAvv/wKhULnqBQlHKucOiPDkUiYoORQYGSO9bYVi1POsXXj5NInKOqqoVA648oJirrOTzwP5wRVuRbR0xkU464zU+/YqRMLFr7GKaedSdqoDI2JvfLKInr1PoSHH34Q1yvfX8QFgkJCblhiBokN6MGRSFhCOFn+aTk/8Tyck6d/WmhIqudZ3sVaZ1a9efMWXHPNjTz5zFTa7myHriRWXbWGm266nmOO6o/z8fvoGJWihGNJJEwiYQLNILEBPTgSCUsORyJhes1PImHJ8TwJkbC6OPvtux/PTZ7BsGHnQiYaVt9KYu++9zd69OrOHXeMYW1VFTpFpSjhWOXUGRmORMIEJYcCI3Ost61YnHKOrRsnlz5BUVcNhdIZV05Q1HV+4nk4J6jKtYiezqAYd531cJo0acKFF/yJmbPmY++xT2hErLq6mjvvup0jj+zL+397d4PXBYIoIFXuCQgKB6X8R8iuUriewjD8R8xArbZicco5tm6cnPt4ft1V+mkQzxvmn5bzE8/DOXn6p4WGpHpehM9OLXVuhGP/fg+efm4GDzw4gfF33s7qlWksw8UyDQzTwlIulSlFyoQv/vkZRx01gD8OPZORIy+lqeWBlwbDBLfKL9dXD6JABeMolJtpL/pYIZxyjq0bJ5c+EgkTFAIq86cY1/VwPQ/X9fw/zdTRVixOOcfWjZNrn7SrrwbxvGH+6Tg/8Tyck69/OmhIqufF+OzUUefGOAYmJ598GlOmzebAA+XrKeoAACAASURBVA7EVQaeWYFnpvDMCqpdA9MwsAwwUNxzz3i6dWvP4tdeQblplOviGSbKdeutKwz/p0Acz02j3OqSjBXGKefYunFy6aMwCvp7a0NgjRo1qmyDCwqGIUDbdNpj1eqqmifHKcukwvTvSV2latoswygKp1jnjSIn1z7NmjcBoKoqrZ0G8XzjnGz/dJyfeB7OadKsEmi8fzpoSKrnxfjs1FFnQzhbbbklxx83mNat2/D64reoSntUewoTD8tQGBj+0xkFy5f/zPRpz/HTkiXse0A7mjVtBobCUGBUpDANs3bdqvB/VVXpgnCaN7UAxepqVfSxQjnlHFs3Ti59rApatGyW+S+OL4FHKBHkCUuMEHxJz8p8MS84Zpct0ygap5xj68ZpSJ+UphrE84b5p+v8xPNwTj7+6aIhqZ4X+rNTV50N02Bx8smnMHP2C3To2AUvE3esVgbVyqQ664v5aQ+efPpJevY6lHnz54ORAsvKHCtr1zNfwC4Yx0xhWKnSjBXGKefYunFy6SNfuhcUFIrMXXFIW7E45RxbN04ufYKirhoKpTOunKCo6/zE83BOUJVrET2dQTHuOhvJ2W7b7bn3vge57daxbLbZFlRTUe8X87///jtOOmkwI0acyY9LfkIg0BFywxIzyFr9enBkH5aEcLL803J+4nk4J0//tNCQVM+zvIu1zjw4hmFy+MAjeHHhG/Q5bACukQLTxDD8vVss0+9nmVCZgpnPT6df30OZPu05VHotsg9LwjiyD4uglEhZsla/DpyG9JF9WKLNkX1Y9JpfQzmyD0t0dSZ1H5aGctq0ac3Ycfcwbux9tN5qa5Ty/EhY1v4tVWk/JrZ06VJGnHcuQ4aeyH//+18UCtmHJSEc2YdFUHIoMDLHetuKxSnn2LpxcukTFHXVUCidceUERV3nJ56Hc4KqXIvo6QyKcddZQM6h3bszd+4Cjj/+jyjDCI2JzZs3j+49uvHkk4/hKQ+BoNyQG5aYQWIDenAkEpYQTpZ/Ws5PPA/n5OmfFhqS6nmWd7HWWWDOJpu24tZb7+Lpp6ey/a/b1sTE/C/v+5wgIrZm9QpGjbqCE44/in998SkSCYs5RyJhglJCYgN6cCQSlhyORML0mp9EwpLjuUTCGs/p1Kkzs2a9wEknDQX8lcTqioiB4o3Fb9C1WyfG3z2eatdDoZBIWAw5EgkTlBwKjMyx3rZicco5tm6cXPoERV01FEpnXDlBUdf5iefhnKAq1yJ6OoNi3HUWkdO8eXMuvfRynn1mKrvu+rvQiNiaNWu49torOXJQX/7xj08QCEoNuWGJGSQ2oAdHImEJ4WT5p+X8xPNwTp7+aaEhqZ5neRdrnSXg7LX3fjw/ez7nDr8QI9WkzohY8PPRhx9w5KD+jLn9ZtauWoFEwmLEkUiYoJSQ2IAeHImEJYcjkTC95ieRsOR4LpGwwnGaN2vGeedfyLPPTuf/9thzg4iYm9ls0vUg7aYZO+5OevU5lPfeexeFQiJhMeBIJExQcigwMsd624rFKefYunFy6RMUddVQKJ1x5QRFXecnnodzgqpci+jpDIpx11lizm6/241nJ03j8iuvxWrSst6IGIDjfMqgIw9n9I3XsnLVqg1eFwgKCblhiRkkNqAHRyJhCeFk+afl/MTzcE6e/mmhIameZ3kXa51l4FhWitNOH8bzs15k3wM6QMhmk6ahmPjIwxzWuxsvv/QiEgmLMEciYYJSQmIDenAkEpYcjkTC9JqfRMKS47lEworredsdd+ThRx/jumtuokXzFvVuNgmKr77+mqOPOYLzLxzBsuW/oFBIJCxiHImECUoOBUbmWG9bsTjlHFs3Ti59gqKuGgqlM66coKjr/MTzcE5QlWsRPZ1BMe46y+y5ZZgcd/xgXl70Bod27xm6khjAE4//lZ49ujJ33twNXhMI8oHcsMQMEhvQgyORsIRwsvzTcn7ieTgnT/+00JBUz7O8i7VOTTz/1a+25ZGJTzLmzrvZdMutQ1cSW/Lj9ww/90yGn3smP/z3WyQSFhGORMKiBdu2D7Vt+0Xbtn+0bXu5bdsLbNvu2cBz7Grb9hO2bX9t2/Yq27bft237HNu2i369y/0IWedH3LpeC4mERZsjkTC95ieRsOR4LpGw0npuWSb9+x/O89Pn0bdvf9Ju/SuJgWLG89PpekhHnnvuOTzlIZEwzTmaR8JSZRtZQ9i2PQR4GFgLvAhYQFdgtm3bZziOc38O5/gD8DKwKfAq8FbmHGOBg4ATijL5bCgwMsd624rFKefYunFy6RMUddVQKJ0x5Hz6j094+923WLZiOSkq6dKpI2133lWb+RWVo/v8cuUEVbkWkdK5/nuva+dO2Lv9LnY6y86pp88WW27OnXfczRH9j+Cyyy/mx//9QBoPZVRhqNpvsKVLl3LhyBHMnDGZa667ie3a7opA0BjIDUsGtm1vA9wL/Awc7DjOh5n2/YEXgDtt237ecZz/hJzDAB7Fv1n5o+M4f820t86cY7Bt25Mdx5lULB11PfoFarUVi1POsXXj5NxHQXasQScN4nndnDdef53x48aw+I1XMSwLw0yhvDTXXONy4EEdOPuc8+nQoYPWGsTzDCfr/Zf4axEBnfW9964eVcVBB7Xn3BEXccBB7SOvUwdOLn26Htqd2fu/xE0338gTT0zEMEwsw82KiSkqU35MbNGil+jZsxsjL7mSE088GQMDRe0okn97tC52VKutWJxyjq0bJ5c+EgnTAucCTYAxwc0KgOM4bwE3A02B0zdyju7AnsDC4GYlc47/AcMy1eGFnPT60O0Rsk6PuHW9FhIJiw7nmaeeZOjQE1j85ut4XhqlFCiFUgrPS7P4zdcZOvQEnn36KW01iOcSCYuizrD3nlIer7/+CieeeAyTnn060jp14eTaZ5NNN+Gqq6/lsb88ww6/2SF0JbEVK1dyyaUXM/CIfjif/RP/TBGIQSWFo3kkTG5Y1qFX5jiljtcmZ469G3sOx3FeBX4ADrZte5NGzTBXKP9eGBXSVixOOcfWjZNLn6Coq4ZC6YwBZ9HLL3HZn0fiKa8WB6N2H095XHrZhSx6+SXtNBSMo/v8cuUEVbkWWuvM6b2H/xTgiisu4bVXXo2kTu04DehzULt2vLjgVc4cNhzXrAxdSWzx4jdo3649d9xxF2k3vcHrAkFdkBsWaqJcvwc84JM6KJ9mXts9w60Pu2eOH9bzuoN/zX/fyKluFDqtKqLbqidaXgsFskpYNDi3j7mlps0wDUwzhWEaddYVMOaOW7XTIJ6vx8l6/yX+Wmisc2PvPcMw/R/TwFOKcePviKROnTiN6dO0aTOuuOIannl2BrvYu4euJOamq7juuus48oi+fPT+u2i9MlZSOLJKWCSwOX4cbInjOFXrv+g4Thr4EWgOhD0d2SZz/G89rwftbRo5z40iCo+Qk8CRSFi8OJ99+g9ef/2VDSJgYfXXXlvEZ5/+QxsN4nndHImE6a0zl/eeUl7mx297Y/FrfPGZEymdunHyOe9ee+/NM5OmMmLEhRiGFbqS2PsffECvPj255ZabWb1mLVrGoJLC0TwSJl+699Eic1wVwlmdObYEljfyPNnnKDgqKixat96EtdUu1WmPipRJs0rf4tVV6Zq2JhVWUTjFOm8UOQ3po4CtttpEOw3i+TrOlGcX+28yBRiZYw71995bzL4H7qOFBvG8bk7w/pNroafOnN9767W9996b7HfQvpHRqRsn//O24JprruTEE47mzDPO5L333qt3JTHXdbn7nrG8tGAWd469m3aduvntay1w14LVBLOiKV51RU3datK8IJxinTeKnFz6BG3lgDxh8eFljiqEY6x3bMx5cjlHXkins2JGqo7ogyoep5xj68bRfX6is2Gcn5cvJ4ic1BdLqSum8vPy5dpoEM/lWkRRZy7vvexIWNC27JflkdKpG6dQ57Xt3zFn3nyuvm40zZq3wDDMmlgYmZhYsJLYZ599Tq+ePTlvxHBW/PIztTaX9Kpq1+vagLIxnGKdN4qcXPqUMRImT1h8rMgcm4VwmmaOK/M4Ty7naDSqq12WLltFVdql2vWosEyaVvgWr6lO17RVpqyicIp13ihycu2z6WbNQSmWLFmhnQbxfB3HMisJIieGWhdLMU2z3rpSHpZZyZIlv2ihQTzfkNOyVbOa91/Sr4WuOnN57ynl/60wm5Myar/3dNepG6fQ5x101GDatevI5ZdcwCuLXiKt/BvNtPKoSiuMFFiWIu3B2HHjmTJ1OreOvpGOHdqDVYlR0RRVvQbcKrAqMSsVAF7Vqpq2xnCKdd4ocnLpY1Yqtm7TinJAnrD4WI5/s7GVbdsb3MRl2rYC1jiOsyzkPN9mjr+q5/WNfcelMFD4j3BUSFuxOOUcWzdOLn2Coq4aCqUz4pyOHTvXtAWRk1zqHTt21kZDQTm6zy9XTlCVa6Gtzpzfe+u1dezYKVI6teQU+Lw7/KYtTz8zlVtvG0uzTbYIXUns66+/4qQhg7n00pEsXbZ0g9cFyYPcsACO4yjgY/yd7evahtXGv1YfbORUwepgG6wCllldbDfAzYxVcCjlr9rhKoXrKVylah7RZrcVi1POsXXj5NzH01eDeL6u/ttdd6Ndu4MxTBPDtDBMMxNLqb/evn1HfrvrbtpoEM/r4Hj5nUcLDTH3PJf33rpImN920EHt2em3dqR06sYp1nmVguOO/yOvv/U2h/buC6aVWUnMwDBTWOa61cRSJkyf+hx9enZl1vTJ4KXX/WTiSrXa3KqGcxrTJ66cXPrIKmFaYHbmeHgdrwVtM/M4R3ugNfCK4zi/NHx6G4fK/BnDdT1cz8N1Pf/PGnW0FYtTzrF14+TaJ+3qq0E8X1c//7yLsFKpzC9JFp7yasrr161UivNGjNROg3i+ISf7/Zf0a6Grzo2991Tmn2FaWFYFZ551XiR16sYp5thbb92GBx58mNvGjGezzbbCMyvwzBSeWYGrDDwPTMPAMmDpTz9y2umnMGL4Gfzvu+9QrovC8H9cF+WmUa6LZ5i16rlwGtMnrpxc+iiMQv7a2iDIDcs6PAysAf5k2/a+QaNt2/sBF+Ov8HV3VvvOtm3vZtt2dpjvJeAjoLtt26dlcVtn9b2tWAIMDFBgWSaWaWJZpv9Ito62YnHKObZunFz7pCx9NYjn6+oHd+zEtVePxkChPBfTMFGei/K8WnUDxXXX3MjBmUiKThrE8w052e+/pF8LXXVu7L1nBP+UYtRV19Lh4PaR1Kkbp9hjV6Qs+vXtxwsLXqN3v0FUqRRVygTDxDTBU/53WjylSKVg1uzZDDiiL5OnTgbl+b9OpywMswIjZWEqr1Y98+t2KKcxfeLKyaWPgSrkr60NgtywZOA4zr+BC4FNgddt255l2/Zs4DX8vVdOdxznh6wu8/E3mRyYdQ4PGIr/fZj7bdt+w7bt5/A3jNwTmOA4zvRiaQjWRbcya6EHx+yyZRpF45RzbN04DemT0lSDeL4h55hjj+XBCRM5cP8D8PeC8Py/PCkPz0tz4P4H8OCEiRx9zDHaahDPa3NSeZxHFw1J8DzsvaeUR7uD2jHxkccZdOSRkdapC6eU773WrbfktjFjufveh2jT5lco5eF6UK0MqpVJtTKoSkPag5+W/czIiy/kuOOP4utvviHzbf3MsbJ2PbOPSCinMX3iysmlj+zDogccx7nbtu2v8J+odATWAq8A1zuOMz/Hc7xp2/aBwDVAV2AP4DPgUuCBokx8fSj/qWutG+H124rFKefYunFy6RMUddVQKJ0x4hzUrh0Hd5jEvz//lLfffYtlK5aTopIunTrSduddSXue9hrE81o0uRYR0Vnfe69r507Yu/2ONVXp2u+/iOrUhlPi916nzl2YM2cBt9x0LRMnPpT5Qr5Zs39LNhYuXEDnzu254pJL+OPgwZjl+z1aUCLIDct6cBxnBjAjB17bkNc+Bo4s4LRyRtr1Mu9//8Gd5/m17LZicco5tm6cnPso/PXqNdQgnodzdvqtzb4H7ovrKZb+tALLNHA9pc38xPMcOFnvv8RfiwjpXP+91yRl4Xkqdjpj7Xk9770WLTdh9OjbGHD4IC66eCRffv01mOnMkxoPMLBMhWXC2jUrue66K5k9cxrX3XALu+y6G8GeIWTtGVKrza0Kr+fSJ66cXPrIl+4FhULKMjEMUCgMg5rHr9ltxeKUc2zdOA3pg9JTg3jeMP90nZ94Hs7Jxz9dNCTV80J/duqqM66eh7332rfrwPPPz+PUU87AwL+pSSsDD5O0MnA9an7ee+9t+hzWg7vGjaMq7eEvwWASRJoU5ro2qzK8nkufuHJy6VPGSJjcsMQRyr8XRoW0FYtTzrF14+TSJyjqqqFQOuPKCYq6zk88D+cEVbkW0dMZFOOuM66eB9UQTtOmTRl50Z+Y/NwMfve7/6Oainr3bqmurubGG69j4OG9+eijDxHED3LDEjPU9fg1l0flheCUc2zdODn3UZAdCdNJg3jeMP+0nJ94Hs7J0z8tNCTV8yzvYq0zrp434L23x557MX3WPC4ceSlWZTPI7L1jmWCZPscyoTIFzj8+5uijBnLLzTewetVKUC4GHv46cutiT/XWM3uPJJKTSx+JhAkKhbg/Qo4KRyJhyeFIJEyv+UkkLDmeSyQs2p435L3XrEkThp19LpMnz2TvvfauWUksOyZWlQ5iYmnuuXc8h3bvxOI3FyORMImECXSFAiNzrLetWJxyjq0bJ5c+QVFXDYXSGVdOUNR1fuJ5OCeoyrWIns6gGHedcfU8qDbwPDvvsjNPPT2F6669kWbNW4TGxD7//HMGDOjDlVddyooVKxFEG3LDEjPE+hFyhDgSCUsIJ8s/Lecnnodz8vRPCw1J9TzLu1jrjKvnebz3TNNi6Cln8OKC1+hwcBdcIwVmZhPYrIhY8PPEY4/Sr19PXlo4XyJhEgkT6IK4P0KOCkciYcnhSCRMr/lJJCw5nkskLNqe5/vZ2bZtWyY++hg3XH8zm7bclLRb/0pi//3vfzh56Imce/5wlixdhkJR9giWbhyJhAlKDgVG5lhvW7E45RxbN04ufYKirhoKpTOunKCo6/zE83BOUJVrET2dQTHuOuPqeVDN8zwGBkcMOpI5cxfQs3ff0IgYwKRnnqZnj67MnvM8av0JCbSG3LDEDLF+hBwhjkTCEsLJ8k/L+Ynn4Zw8/dNCQ1I9z/Iu1jrj6nme7731Oa23bsP9EyYybvwDbNb6V6EriS1b+iPnjTiXYWeeyg/ffoPWMS2JhNVAblhihrg/Qo4KRyJhyeFIJEyv+UkkLDmeSyQs2p4X47Ozd58+zJwxj4EDjwxdSQwUc+bOplPXjjz99NN4ykPLmJZEwmogNyxxhAIjc6y3rVicco6tGyeXPkFRVw2F0hlXTlDUdX7ieTgnqMq1iJ7OoBh3nXH1PKgWYaxWm7Xi1lvu4InHJ7Hd9r8JjYktX/4zf7rkQoYM+SNff/UlAn0hNywxQ6wfIUeII5GwhHCy/NNyfuJ5OCdP/7TQkFTPs7yLtc64ep7ney8XTucu3Viw8DX+eNIp9a4kVpnyI2OLF79Gnz6HMOH+cbjphK4kJpEwQSkR90fIUeFIJCw5HImE6TU/iYQlx3OJhEXb81J8dm6yySaMGnUtf33sKXZquxNp16s3IrZy1WquuPJy+g/ow2ef/ROFouwxLYmE1UBuWOIIBUbmWG9bsTjlHFs3Ti59gqKuGgqlM66coKjr/MTzcE5QlWsRPZ1BMe464+p5UC2Rzv323Z/pz8/lnOEX4JlNQlcSe+edtzmsbw/Gjx9LVXXVBq8LygO5YYkZYv0IOUIciYQlhJPln5bzE8/DOXn6p4WGpHqe5V2sdcbV8zzfe43hNGnSlIsu/jOTp85m19//oc6IWPDjudWMHTuGIw7vw/vvvYnWUS6JhAmiiLg/Qo4KRyJhyeFIJEyv+UkkLDmeSyQs2p6X67Nz9z324OlnnmPkyEuwrIp6N5sExceffEKfvn0YPfp6Vq5ajZZRLomECSILBUbmWG9bsTjlHFs3Ti59gqKuGgqlM66coKjr/MTzcE5QlWsRPZ1BMe464+p5UC2TzopUBWedeQ7zXniZP+zbLjQi5nke90+4l8MP78mbi1/f4HVBaSA3LDFDrB8hR4gjkbCEcLL803J+4nk4J0//tNCQVM+zvIu1zrh6nud7r1CcHXbahcefnsJlV1xH8xabUN9mk5YJX331NYMHH8klfzqfX5YvQ5sol0TCBFFE3B8hR4UjkbDkcCQSptf8JBKWHM8lEhZtz3X47ExZJpZpcvwJJzJr9gt06dwldLNJ14NHJj5M5y4dePHF+SgUZY9ySSRMEFkoMDLHetuKxSnn2LpxcukTFHXVUCidceUERV3nJ56Hc4KqXIvo6QyKcdcZV8+DqkY6t9/u1zz+xCTuvOteWrTaKjQm9u23/+GUU4cwcuR5LPnppw1eFxQecsMSM8T6EXKEOBIJSwgnyz8t5yeeh3Py9E8LDUn1PMu7WOuMq+d5vveKpVMpOPKoY5k9dxGH9uoHISuJWSbMmDGN3j07M33K0/jxKU3jXhIJE+iGcj9a1fkRt67XQiJh0eboEGsQzxvPkUhYdHVKJCzanuvw2Vlfn9Zbb8Udd45n/NgJbLFF69CVxH5c8hNnnHUmZ5x5Ct9//x1axr0kEibQEgqMzLHetmJxyjm2bpxc+gRFXTUUSmdcOUFR1/mJ5+GcoCrXIno6g2LcdcbV86Cquc5evXuz4OXXGXDUCaERMYB58+bRu093nnziMZRSdXIEjUeq3BMQFA5K+Y83XaVwPYVh+I9DgVptxeKUc2zdODn38fy6q/TTIJ43zD8t5yeeh3Py9E8LDUn1vAifnVrqjKvnmnx25tKn5SatuOHG2+l92BFcdcWf+Orf/8QyXCzTwDAtLOVSmVKkTFi7egVXXnExk6dP45ZbxvCbbbYGLw2GCW6VXw7qQbwqu62cnFz6SCRMUAiozJ8EXNfD9Txc1/P/RFBHW7E45RxbN06ufdKuvhrE84b5p+P8xPNwTr7+6aAhqZ4X47NTR51x9VyXz85c+xxw0EFMnTaHU049HQ8Tz6zAM1N4ZgXVroFpGFgGmIbBK68solu3jjzy4H2kq6pQrotnmCjXRblp/4jh/2S1lZOTSx+FUYhfVxsFa9SoUWUbXFAwDAHaptMeq1ZX1TzVTFkmFaZ/T+oqVdNmGUZROMU6bxQ5ufZp1rwJAFVVae00iOcb52T7p+P8xPNwTpNmlUDj/dNBQ1I9L8Znp4464+p5vu+9culs1qwJPQ7pQacuXXnz7XdYsmw51Z7CxMMyFAaG/3RGgeumeeP1V3jjjVfZc+/9aN16azAUhgKjIoVpVfi//qv0ujbDLB8nlz5WBS1aNstcDb4EHqFEkEhYjBB8gcxTCk8ZWJkvjwFYplHTVixOOcfWjZNrH8s0UKaeGsTzhvmn4/zE841z8vFPFw1J9LwYn5066oyz5zp8djb2vAfsfyBTp89h/Pix3Hv3XVS7HtWGAcqkWnm4mWhZ2oP3/v53+vbvw5lnX8A5w4ZRaVlgpKj5AruRAsvLHCvB89bVS8nJpY986V5QUCgyd8UhbcXilHNs3Ti59AmKumoolM64coKirvMTz8M5QVWuRfR0BsW464yr50E1wjqbVDZh+IgLmDp1FnvuuQ/VVNT7xfx0Os1tt93EgP49eP/vf0PQcMgNS8yg03rmuq0rr+W1UCD7sESYk+WflvMTz8M5efqnhYakep7lXax1xtXzPN97Ounc7fe7M3n6LC665HIqmrYA08Qw/L1brKz9WypT8M/PPuXY445k9A1Xs2rlCmQfltwhNywxQ8rSbz3zJHIa0kf2YYk2R4e9BMTzxnNkH5bo6pR9WKLtuQ6fnYU6b9PKSs48YxhTpsxk/333RykP16PW/i1VaXA98DyXCQ/cT9duHXj1tVeRfVhyg9ywxBEKjMyx3rZicco5tm6cXPoERV01FEpnXDlBUdf5iefhnKAq1yJ6OoNi3HXG1fOgGjOdbXfckcefnMSNo2+jRctNQ2NiX375JYMG9eeyP1/E8uW/IAiH3LDEDFF/tBoXjkTCEsLJ8k/L+Ynn4Zw8/dNCQ1I9z/Iu1jrj6nme7z2ddRqGyYknDWXhS6/TpeuhuEYKTDOzWITPCeJilgnPPPU4/fr1YP4LcyUSFgK5YYkZ4vBoNQ4ciYQlh6NDrEE8bzxHImHR1SmRsGh7rsNnZzHH3n777XngwYnccvPtbN5qM9KuVysi5nrU/Hz//XecdvrJnH3uWfzw408oFBIJqw25YYkjFBiZY71txeKUc2zdOLn0CYq6aiiUzrhygqKu8xPPwzlBVa5F9HQGxbjrjKvnQTXmOg0M+vU/nNlzFtJvwMDQiBjA5MmT6dWzC9OnT0Gtf8ESDrlhiRni9mg1qhyJhCWEk+WflvMTz8M5efqnhYakep7lXax1xtXzPN97kdGZadtyq60YN/4B7r3vEbZosy2ErCS2/OelXHTRBZx+6kl8+5+vkEiYD7lhiRni+mg1ahyJhCWHo0OsQTxvPEciYdHVKZGwaHuuw2dnqcfu3qMHM2fM4+ijjwtdSQwU81+cT6fOHZj46ERc5Z9HImGCeEGBkTnW21YsTjnH1o2TS5+gqKuGQumMKyco6jo/8TycE1TlWkRPZ1CMu864eh5U466zDs4mm27C6Btu4Zmnp/KbHXYMjYmtWPH/7Z17uCRVee5/1XvPADIIKFcDXjBmISqKCFEZonKTi1zEKEQEPSIiGonKE4gSA/IQoxJOCATkokA8cBSVIJfRwSgX43gb5MQbsIxREEXlohDlNrO76/xRtWZ6Nrt7V3dX7fpq1ft7nnlqVfVbXd9X76zavXp9Vf17TjrpfRzxxj/nrrt+RpvRgCUy2jC12gSNSsJaounzz2R88ny4ZkL/TOTQE1FoBQAAIABJREFUVs/7vIs6z1g9n7DvNSbPIZrdlv4ZX71hBW992ztIpxbBkCeJ3bLy2xx00H5cdOHHmXn8UVQSJhpPm6ZWLWtUEtYejYWyBnk+vkYlYc3NUyVhzfbcwrWzbh+WLNmQk0/+O/7vpz/Pc579HGa6g58k9vjjj/MPHzmdAw/en9tuu52UFJWEiWaTQpIvB26rSlPnsa1piuwTmlZzKCvPWDWhaTU+eT5cE1Z1LpqXZ2jGnmesnofV2PMsqHnRC3fi6muX894TTiKdWn/ok8S+973vcfDB+3PWWWfy+KpVT3g9VjRgiYy2Tq1a06gkrCWaPv9MxifPh2sm9M9EDm31vM+7qPOM1fMJ+15j8hxBs3jxerznvSdy9bX/znN33AmGPEksTWc4//xzOfjAvbll5TdRSZhoHG2eWrWkUUlYezQWyhrk+fgalYQ1N0+VhDXbcwvXTos+bP/c7fnMZ67kb97/QdZbvHjok8R+/F8/4cCD9ueDp5zMHx55jJQUlYSJ5pBCki8HbqtKU+exrWmK7BOaVnMoK89YNaFpNT55PlwTVnUumpdnaMaeZ6yeh9XY8xxTMz01zTFHH8sNN67g5bvtPvRJYmmactGF57P/vnuwYsUKYkUDlsjQ1KoNjUrCWqLp889kfPJ8uGZC/0zk0FbP+7yLOs9YPZ+w7zUmzwk1z3zmdlzxuas5/e/P4EkbbQpDniR2zz13c/TRR/L+v3kfD/32flQSJkyjqVUbGpWEtUdjoaxBno+vUUlYc/NUSVizPbdw7bTuQ6eTMD01xV+88Qiuve569nzVnsx0Bz9JDFKu+OwVvHKPP+P6668nJUUlYcIuKST5cuC2qjR1Htuapsg+oWk1h7LyjFUTmlbjk+fDNWFV56J5eYZm7HnG6nlYjT3PEjVbbbUVF1x0Keee+wk2esqWQ58k9pvf/Jp3HPc23vOed3Pfffc94fUmogFLZGhq1YZGJWEt0fT5ZzI+eT5cM6F/JnJoq+d93kWdZ6yeT9j3GpNnyRpIeM3Br2X59V9jv9ccCkOeJDbVgeXLl7Hvq1/B5z57OWlvBpWECTNoatWGRiVh7dFYKGuQ5+NrVBLW3DxVEtZszy1cO637MEjz1M2ewhlnnsVFF1zKVltuNfRJYr978CHeffy7OOJNh3HPPfeQkqKSMGGDNBuHkw7ZVpWmzmNb0xTZJzSt5lBWnrFqQtNqfPJ8uCas6lw0L8/QjD3PWD0Pq7HnWbFmjz334uavfZM3HfXWoU8SA7jhhq+y9z57cNlln6KX9p7wunU0YIkMTa3a0KgkrCWaPv9MxifPh2sm9M9EDm31vM+7qPOM1fMJ+15j8lwAzZIlT+ajH/3fXP7pK/mjp28HQ54k9vhjD3P66afyxsMO5af/dTsqCRO1oalVGxqVhLVHY6GsQZ6Pr1FJWHPzVElYsz23cO207sMompe+9GVcfc1yjj76WHopQ58k9p1bVrLn3nvw8Y+fx6rVq1FJmKiHFJJ8OXBbVZo6j21NU2Sf0LSaQ1l5xqoJTavxyfPhmrCqc9G8PEMz9jxj9Tysxp7nAms22GB9PvD+D3LddV/m2e4FQ0vEHn/8cT52xj9w2GGHcNuPfvCE160xXXcAojzSNJsq7KYp3V5KkmRTicA626rS1Hlsa5rC+/Sy9W5qLwd5Ppp/JuOT58M1E/pnIoe2el7BtdNknrF6buTaad2HcTXP23Enrrz6S1xwwQV8/Nyz6HVnmEq6THUSks4UU2mXxdMp0x348R23ceghB3D0sX/Ju9/1l6w/DSQd1pR/9Wayf/3bakAzLBGR5kPubrdHt9ej2+1lQ/A5tlWlqfPY1jRF95np2s1Bno/mn8X45PlwzaT+WcihrZ5Xce20mGesnlu5dlr3YVxNpzPNMccex79d9UVevNOL6aYJvc4iep1pep1FrO4mdJKEqQSgx9lnn8VrD96bW29ZSdrtkpJk/7pd0u7Mmm11MXXqqafWdnBRGm8Bnjkz0+ORR1etmSGcnuqwqJONSbtpumbbVJJUoqnqfZuoKbrPBk9aD4BVq2bM5SDP59f0+2cxPnk+XLPeBouB8f2zkENbPa/i2mkxz1g9n7TvNSVPC5ottticI444ko033oRvr7yFVd2U1b2UDj2mkpSEJNs3gYcefIhrrrmK3z70IC/ZdTfWW7wI0hmSFJJF03SmFrHhkg3yd+Yu4FIWCM2wRES4GWsqvwErLPvbU52kMk2dx7amGWWfaaM5yPPR/LManzwfrpnEPys5tNXzsq+dVvOM1XML107rPpSlWTQ9zbHHvpNrl32ZXf50N3p5Kd7qNGF12mF1/tstMz2Y6fW45NJLedUeS7np5psgmYapqWxZ4033uoelD+fcG4D3AjsAXeAbwGne+++M8B67A18bIrnce/+miQKdj2ygrJvX6tYU2Sc0reZQVp6xakLTanzyfLgmrOpcNC/P0Iw9z1g9D6ux52lM84ynP5OLL/kUV33+c5x+2t/y8KOPkCYdZuiRJqvo5+67f87hh7+OIw5/Ax848QNsutkW1IlmWHKcc6cCVwDPA24EfgDsD6xwzu03wlvtlC+/AVw+x78VJYU8J3qeuQ1Nnb8lYDLPWDV9/pmMT54P10zon4kc2up5n3dR5xmr5xP2vcbkaVCTJB1ef9jh3HDTN9hzn/3pJtNDf7vlC//2OQ48cB+u/9J11HnTvWZYAOfczsApZPV4u3nvf5lvPwD4AnCJc2477/0jBd4uDFhO9N5XOjiZi+mpDr20S/Z87mw6EGDtM7uTyjR1Htuapug+SQL9vyVgKQd5Ppp/FuOT5/NrJvHPSg5t9LyKa6fFPGP23MK107oPVWqe9rStueD8i7hu2XWcdurJ3P+be5hKEpKkw0zao9vLhjzdHtx//30c965j2fOaa/n8lZ9jvfWye8gWEs2wZJyQL08JgxUA7/0yshuKtgQOK/heOwE94D/LDHAkUkjy5cBtVWnqPLY1TZF9QtNqDmXlGasmNK3GJ8+Ha8KqzkXz8gzN2POM1fOwGnuexjUJCfvtdwBf/vJNHPrnh7GaRUN/u2XZsmtZuXLlE7YvBBqwZOxLZt81c7x2Vb6ctyzMObeY7P6XO7z3D5cXXnE0tWpDo5Kwlmj6/DMZnzwfrpnQPxM5tNXzPu+izjNWzyfse43JsyGaTTZ9Cv901nlcfMnlbPG0baHTIUk6a8rCYG2Z2MzMDHXQ+gGLc25rYFPgl977380huSNfvqDA2z0fWATc6Zw73Tl3u3PuUefcz5xz/+ic26SksAcSpsqzKT/WPD2if1tVmjqPbU0zyj79ZQ2WcpDno/lnNT55PlwziX9Wcmir52VfO63mGavnFq6d1n1Y6HPxile+iuuu/TJHHHEUadqj24OZNKFHh5k0odur+lPskM+39R3aDFvny18NeD1s37LAe4X7V/YHXgHcDPwC2IWs7OxA59xS7/19Y8Y6lEWLpth88414fHWX1TM9Fk132GBxZvGjq2bWbFtv0VQlmqret4maUfZJgc0228hcDvJ8NP+sxifPh2sm8c9KDm31vOxrp9U8Y/XcwrXTug91nIuNN9mQ8847h7ce9Rccc8zb+emdd615khjJKjoJtRDlgMU5dzmwcwHpVcAX8/agG+ofy5dLCrxfGLDcDLw+DEycc5sBnwH2BM4HXlfgvcZiZqZvqjzNpgGBdbZVpanz2NY01uNTnjoXylPnQnm2M0+dCxvHtqaZvb7b0t357q23ctrpH+acfzkX0lXZrAz1EOWABXgG4ArotgbCBFc6j7aIR+8FzgZ+5b3/fdjovb/fOXcU8GPgtc65rb33g2Z0xmb16i6/e/ARVs10Wd3tsWiqw/qLMosfWz2zZtvi6alKNFW9bxM1Rfd58iZPgjTlgQf+YC4HeT6afxbjk+fDNUs23mAi/yzk0FbPq7h2WswzVs8n7XtNybNJmkH7/OW7T2Dp7nvxtx/4a370g1vn/bBcFVEOWLz3S4tqnXMvzJsbDJCsny/nvYnee7+abFAy12v3OOduBXYHXgwsKxrjyKT56Codsq0qTZ3HtqYpsk9oWs2hrDxj1YSm1fjk+XBNWNW5aF6eoRl7nrF6HlZjz7NpmgH7PP8FL+Cqq5fxyQvPJVcsOFEOWEYkPMZ4qwGvz3ePyyj8Ol8+qYT3mpO5ngwBrLOtKk2dx7amKbxPSjb9ajAHeV5A0+efyfgq0FiPbyTNhP6ZyGEBNCbj6/Mu6jxr0ljve43Js0Ga+faZnl7E8cefwJM3qmfo0PqnhHnv7wfuBbZxzm00h+S5+fIH872Xc+5s59xVzrktBkielS9/MXqkxaj7CRN1HduaRk8Ja48m+Gc1Pnmup4TF6nnZ106recbquYVrp3UfrJ2LTidhww03rOoj7FBaP2DJWQ5MAQfO8doh+fKLc7w2m91y/RPexzn3fLKb8h8AvjtemAVJIcmXA7dVpanz2NY0RfYJTas5lJVnrJrQtBqfPB+uCas6F83LMzRjzzNWz8Nq7Hk2TVOw79WBBiwZHyez4qPOuWeFjc65A4C3kJWDfbp/B+fc9vm//vKuC/Llh51z2/dpNwcuIRsUfcx7v6qSLJh7Sq/XS9fZph9gMnQuUugva7CUgzwfzT+T8cnz4ZoJ/TORQ1s97/Mu6jxj9XzCvteYPBukKbJPr5et1YEGLID3/lvAGcA2wA+dc9c4524ErgV6wBHe+8dn7XZ7/m/Xvm2fAD4PbAF8zzn3Fefc1cB/Ay8BPgucWWUu1qcT26JRSVh7NBbKGuT5+BqVhDU3T5WENdtzC9dO6z5YOxedTlLlR9ihaMCS470/iWw25XZgL2AHsid5vcx7f2PB9+gBbwDeAXwfeDnZb6/cDhwDHO6975Ye/GxSSPLlwG1Vaeo8tjVNkX1C02oOZeUZqyY0rcYnz4drwqrORfPyDM3Y84zV87Aae55N0xTse3Wgp4T14b3/V+BfC2qTAdtTstKwC+Z6vWosP2GiTZo6n3RjMs9YNX3+mYyvAo31+EbSTOifiRwWQGMyvj7vos6zJo31vteYPBukKbKPSsJEaVifTmyLRiVh7dFYKGuQ5+NrVBLW3DxVEtZszy1cO637YO1cdDpJbZ9vNWCJkRSSfDlwW1WaOo9tTVNkn9C0mkNZecaqCU2r8cnz4ZqwqnPRvDxDM/Y8Y/U8rMaeZ9M0BfteHagkLDKsTye2RaOSsJZo+vwzGV8FGuvxqSSsfI3J+Pq8izrPmjTW+15j8myQRiVhYkGxPp3YFo1KwtqjsVDWIM/H16gkrLl5qiSs2Z5buHZa98Haueh0kto+32rAEiMpJPly4LaqNHUe25qmyD6haTWHsvKMVROaVuOT58M1YVXnonl5hmbsecbqeViNPc+maQr2vTpQSVhkWJ9ObItGJWEt0fT5ZzK+CjTW41NJWPkak/H1eRd1njVprPe9xuTZII1KwsSCYn06sS0alYS1R2OhrEGej69RSVhz81RJWLM9t3DttO6DtXPR6SS1fb7VgCVGUkjy5cBtVWnqPLY1TZF9QtNqDmXlGasmNK3GJ8+Ha8KqzkXz8gzN2POM1fOwGnueTdMU7Ht1oJKwiEjTbEqvm6Z0eylJkq6ZvuvfVpWmzmNb0xTep5etd1N7Ocjz0fwzGZ88H66Z0D8TObTV8wqunSbzjNVzI9dO6z5YOxcqCROlkObD4G63R7fXo9vtZcPiObZVpanz2NY0RfeZ6drNQZ6P5p/F+OT5cM2k/lnIoa2eV3HttJhnrJ5buXZa98HauagLzbBEREICKUxNdUjJlmEKb/a2qjR1Htuapug+06ndHOT5aP5ZjE+eD9dM6p+FHNrqeRXXTot5xuq5lWundR+snYu60IAlIsJNU700pZcmTOU3TAFMdZI126rS1Hlsa5qi+0x1EtKOzRzk+Wj+WYxPns+vmcQ/Kzm00fMqrp0W84zZcwvXTus+WDsXuulelEuaz9qlQ7ZVpanz2NY0RfYJTas5lJVnrJrQtBqfPB+uCas6F83LMzRjzzNWz8Nq7Hk2TVOw79WBZlgiw/ozvNuiqfO3BEzmGaumzz+T8VWgsR7fQv4WhIkcFkBjMr4+76LOsyaN9b7XmDwbpCmyj266F6Vh/RnebdHU+VsCVvOMVWPhtwTk+fga/Q5Lc/PU77A023ML107rPlg7F51OUtvnWw1YYiSFJF8O3FaVps5jW9MU2Sc0reZQVp6xakLTanzyfLgmrOpcNC/P0Iw9z1g9D6ux59k0TcG+VwcqCYsM69OJbdGoJKwlmj7/TMZXgcZ6fCoJK19jMr4+76LOsyaN9b7XmDwbpFFJmFhQrE8ntkWjkrD2aCyUNcjz8TUqCWtunioJa7bnFq6d1n2wdi46naS2z7casMRICkm+HLitKk2dx7amKbJPaFrNoaw8Y9WEptX45PlwTVjVuWhenqEZe56xeh5WY8+zaZqCfa8OVBIWGdanE9uiUUlYSzR9/pmMrwKN9fhUEla+xmR8fd5FnWdNGut9rzF5NkijkjCxoFifTmyLRiVh7dFYKGuQ5+NrVBLW3DxVEtZszy1cO637YO1cdDpJbZ9vNWCJkRSSfDlwW1WaOo9tTVNkn9C0mkNZecaqCU2r8cnz4ZqwqnPRvDxDM/Y8Y/U8rMaeZ9M0BfteHagkLDKsTye2RaOSsJZo+vwzGV8FGuvxqSSsfI3J+Pq8izrPmjTW+15j8myQRiVhYkGxPp3YFo1KwtqjsVDWIM/H16gkrLl5qiSs2Z5buHZa98Haueh0kto+32rAEiMpJPly4LaqNHUe25qmyD6haTWHsvKMVROaVuOT58M1YVXnonl5hmbsecbqeViNPc+maQr2vTpQSVhkWJ9ObItGJWEt0fT5ZzK+CjTW41NJWPkak/H1eRd1njVprPe9xuTZII1KwsSCYn06sS0alYS1R2OhrEGej69RSVhz81RJWLM9t3DttO6DtXPR6SS1fb7VgCVGUkjy5cBtVWnqPLY1TZF9QtNqDmXlGasmNK3GJ8+Ha8KqzkXz8gzN2POM1fOwGnueTdMU7Ht1oJKwyLA+ndgWjUrCWqLp889kfBVorMenkrDyNSbj6/Mu6jxr0ljve43Js0EalYSJBcX6dGJbNCoJa4/GQlmDPB9fo5Kw5uapkrBme27h2mndB2vnotNJavt8qwFLjKSQ5MuB26rS1Hlsa5oi+4Sm1RzKyjNWTWhajU+eD9eEVZ2L5uUZmrHnGavnYTX2PJumKdj36kAlYZFhfTqxLRqVhLVE0+efyfgq0FiPTyVh5WtMxtfnXdR51qSx3vcak2eDNCoJEwuK9enEtmhUEtYejYWyBnk+vkYlYc3NUyVhzfbcwrXTug/WzkWnk9T2+VYDlhhJIcmXA7dVpanz2NY0RfYJTas5lJVnrJrQtBqfPB+uCas6F83LMzRjzzNWz8Nq7Hk2TVOw79WBSsIiw/p0Yls0KglriabPP5PxVaCxHp9KwsrXmIyvz7uo86xJY73vNSbPBmlUEiYWFOvTiW3RqCSsPRoLZQ3yfHyNSsKam6dKwprtuYVrp3UfrJ2LTiep7fOtBiwxkkKSLwduq0pT57GtaYrsE5pWcygrz1g1oWk1Pnk+XBNWdS6al2doxp5nrJ6H1djzbJqmYN+rA5WERUSaZlN63TSl20tJknTN9F3/tqo0dR7bmqbwPr1svZvay0Gej+afyfjk+XDNhP6ZyKGtnldw7TSZZ6yeG7l2WvfB2rlQSZgohTQfBne7Pbq9Ht1uLxsWz7GtKk2dx7amKbrPTNduDvJ8NP8sxifPh2sm9c9CDm31vIprp8U8Y/XcyrXTug/WzkVdaIYlIhISSGFqqkNKtgxTeLO3VaWp89jWNEX3mU7t5iDPR/PPYnzyfLhmUv8s5NBWz6u4dlrMM1bPrVw7rftg7VzUhQYsERFumuqlKb00YSq/YQpgqpOs2VaVps5jW9MU3Weqk5B2bOYgz0fzz2J88nx+zST+WcmhjZ5Xce20mGfMnlu4dlr3wdq50E33olzSfNYuHbKtKk2dx7amKbJPaFrNoaw8Y9WEptX45PlwTVjVuWhenqEZe56xeh5WY8+zaZqCfa8ONMMSGdaf4d0WTZ2/JWAyz1g1ff6ZjK8CjfX4FvK3IEzksAAak/H1eRd1njVprPe9xuTZIE2RfXTTvSgN68/wboumzt8SsJpnrBoLvyUgz8fX6HdYmpunfoel2Z5buHZa98Haueh0kto+32rAEiMpJPly4LaqNHUe25qmyD6haTWHsvKMVROaVuOT58M1YVXnonl5hmbsecbqeViNPc+maQr2vTpQSVhkWJ9ObItGJWEt0fT5ZzK+CjTW41NJWPkak/H1eRd1njVprPe9xuTZII1KwsSCYn06sS0alYS1R2OhrEGej69RSVhz81RJWLM9t3DttO6DtXPR6SS1fb7VgCVGUkjy5cBtVWnqPLY1TZF9QtNqDmXlGasmNK3GJ8+Ha8KqzkXz8gzN2POM1fOwGnueTdMU7Ht1oJKwyLA+ndgWjUrCWqLp889kfBVorMenkrDyNSbj6/Mu6jxr0ljve43Js0EalYSJBcX6dGJbNCoJa4/GQlmDPB9fo5Kw5uapkrBme27h2mndB2vnotNJavt8qwFLjKSQ5MuB26rS1Hlsa5oi+4Sm1RzKyjNWTWhajU+eD9eEVZ2L5uUZmrHnGavnYTX2PJumKdj36kAlYZFhfTqxLRqVhLVE0+efyfgq0FiPTyVh5WtMxtfnXdR51qSx3vcak2eDNCoJEwuK9enEtmhUEtYejYWyBnk+vkYlYc3NUyVhzfbcwrXTug/WzkWnk9T2+VYDlhhJIcmXA7dVpanz2NY0RfYJTas5lJVnrJrQtBqfPB+uCas6F83LMzRjzzNWz8Nq7Hk2TVOw79WBSsIiw/p0Yls0KglriabPP5PxVaCxHp9KwsrXmIyvz7uo86xJY73vNSbPBmmsl4RpwDIA59ypwCnAtt77X4y4758AHwKWAk8FfgJcCJznve+VHOo6TE916KVdsum7ZM303dopvaQyTZ3HtqYpuk//tLi1HOT5aP5ZjE+ez6+ZxD8rObTR8yqunRbzjNlzC9dO6z5YOxcqCTOGc+4Q4OQx930hsBI4HLgLWA5sC5wDfKqsGIeSQpIvB26rSlPnsa1piuwTmlZzKCvPWDWhaTU+eT5cE1Z1LpqXZ2jGnmesnofV2PNsmqZg36sDzbDMwjn3TuAsxjg3zrmEbFDyZOBI7/1l+fbNga8ARzjnrvLeX1liyOtgfTqxLRqVhLVE0+efyfgq0FiPTyVh5WtMxtfnXdR51qSx3vcak2eDNNZLwjTDkuOc2945tww4F3gI+P0Yb7M3sCNwUxisAHjv7wPema8eP2msw7D+hIm2aOp80o3VPGPVWHjSjTwfX6OnhDU3Tz0lrNmeW7h2WvfB2rnodJIqP8IORQOWtZwP7A/8O7Az8Nsx3mPffPmF2S9471cA9wJLnXMbjRtkIVJI8uXAbVVp6jy2NU2RfULTag5l5RmrJjStxifPh2vCqs5F8/IMzdjzjNXzsBp7nk3TFOx7daCSsLWsBM703l8L4Jwb5z2ely9/OOB1D2wB7AB8e5wDzIf16cS2aFQS1hJNn38m46tAYz0+lYSVrzEZX593UedZk8Z632tMng3SWC8J04Alx3v/1yW8zdb58lcDXg/btyzhWHNi/QkTbdHU+aQbi3nGqpld1mAtPnk+v0ZPCWtmnlVcOy3mGbPnFq6d1n2wdi7qLAmLcsDinLucrKxrPq7y3r+/xENvmC8fGfD6o/lySYnHBPhjgPUWT7PVFk+ml6b5txfQSbL/XLO3VaWp89jWNIX3mZ5iydabmMxBno/mn8n45PlwzYT+mcihrZ5XcO00mWesnhu5dlr3wdq56OOPWUCiHLAAzwCK1HRtPb9kJMJvrKQDXk9mLctiCUCS/0eaSp749rO3VaWp89jWNNbjK0tjPb6F1FiPryyN9fgWUmM9vrI01uMrS2M9voXUWI+vLI31+BZSU2SfPsr+8n0oUQ5YvPdLazr0H/LlBgNeXz9fPlzycX8GPCs//k9Kfm8hhBBCCCEgm1lZQvbZc8GIcsBSI/cALwK2Au6Y4/X57nEZl51Kfj8hhBBCCCFMoMcal0t4OtgOs1/If1Rye6AL3LaQQQkhhBBCCNFUNGApl+X58pA5Xns5sDnwde/9OD9KKYQQQgghROvQgGVMnHPPds5t75zbuG/zzcCPgL2dc8f0aTcHzstXz1zAMIUQQgghhGg0GrCMz1eB24HXhg3e+x7wVrKb3y90zn3LOfdvZD8YuSNwUfhhSiGEEEIIIcT8aMBSMt777wB/ClwJPAfYB7gLeAdwXI2hCSGEEEII0TiSNB30kyFCCCGEEEIIUS+aYRFCCCGEEEKYRQMWIYQQQgghhFk0YBFCCCGEEEKYRQMWIYQQQgghhFk0YBFCCCGEEEKYRQMWIYQQQgghhFk0YBFCCCGEEEKYRQMWIYQQQgghhFk0YBFCCCGEEEKYRQMWIYQQQgghhFmm6w5AjIdz7lTgFGBb7/0vRtz3T4APAUuBpwI/AS4EzvPe90oOVeQ4594AvBfYAegC3wBO895/Z4T32B342hDJ5d77N00UqMA5txfwAWBHYDHwXeAj3vvrR3gP9bMamNQ759y2wM+HSFZ475dOHKgYinPuLcAlwO7e+6+PsN/TyP427g1sTeblZcDHvPePVxCqmINx/HPOTQN/ANYbIPml936bciIU/TjnpoDjgDcDzwWmgJ8CnwHO8N4/VvB9Kvu7pwFLA3HOHQKcPOa+LyT7wPtkYAWwEngVcA7wUkAfdiugb4D5e+AGYFNgf+DVzrmDvPdfKvhWO+XLbwA/m+P1FROG2nr6/tA+TubVFFkfWe6cO9Z7f2GB91A/q4EyvGNtH/s+8IOclSJ4AAANrklEQVQ5XvclhCqG4Jx7GVlfGXW/bYBvAtsA/w+4FdgNOA3Ywzm3j/d+dZmxiicyrn9kX+atB/w38K05Xv/tJHGJuckHK1cDB5ANGL8FrCb7W3UacIBzbg/v/SPzvE+lf/c0YGkYzrl3AmcxhnfOuQT4FNl/piO995fl2zcHvgIc4Zy7ynt/ZYkhtx7n3M5kg5W7gN2897/Mtx8AfAG4xDm33XwXg5zwYepE770GJyXjnNsaOB94CFjqvf9hvn0Xsj7yz865ZcHDAe+hflYDZXiXE/rYx7z3l1cWsJgT59yhwKXAkjF2P49ssPJB7/3p+fttSHad3Qs4HjiznEjFXEzoX+h7l3jv/760oMR8vI1ssPJ9YP++zyibAdcALwM+CLx/0BssxN893cPSEJxz2zvnlgHnkv1B/v0Yb7M3WZnETeE/E4D3/j7gnfnq8ZPGKp7ACfnylP4PS977ZWQX9i2Bwwq+105AD/jPMgMUa3g32Td8/xQ+8AJ471cCHwPWB94+z3uon9VDGd7B2g9N3y09QjEQ59w2zrlPAVeSzYz9ZsT9HfAasm/nPxy2e+8fBo4mK8N9d2kBi3WY1L8c9b16eEu+fM+szyj3k5WJARw+z3tU/ndPA5bmcD5ZCdG/Azsz3tTovvnyC7NfyL+tvxdY6pzbaNwgxZzsC6Rk31TM5qp8ud98b+KcW0w2ZX5H/kdYlM/APkJxr9TP6qEM7yD70PQH4MdlBCUKczpwJHALWfnIHSPu/2ogAa6dXSvvvf85WXnYM5xzO5QQq3gik/oHawcst5YVlCjE/WR+zXU/bbgOPm2e96j8755KwprDSuBM7/21ANmXSSPzvHz5wwGve2ALsg/F3x7nAGJd8jKVTYFfeO9/N4ckXNRfUODtng8sAu50zp0OvA54JvBrsm+1TvfePzhx0C0ln9LegWwG6/Y5JD/OX3uecy7x3qcD3kr9bIEpyzvn3FOAp5N9YHqfc+5I4DnAg8B1wKne+3sqSEFk18I3A5d573tj/I2br9/dAexCdq29bawIxTAm8i/vwy8i+3t2kHPu7WQ3fz9GVlJ0qvde949VgPf+wCEv75Iv53u4U+V/9zTD0hC8938dBisTsHW+/NWA18P2LSc8jlhLmec8fPu0P/Aesid4fJ1sQHQC8O28XlSMx6ZkJUUPeO9XzX7Rez9D9k3Uk4Bh3xKpny08ZXkX+tiLycqK7gVuJPty7xjgu27Mb4vEcLz3H/Hef2qCJwmp39VICf5tR3b/w1bABWQDlRvz5eHASufcbqUEKwqRDyJPy1fnu/ek8v6nGZYacM5dTlbWNR9Xee8H3uQ0Bhvmy0E3dz+aL8e5Wa41jOIf8MW8Peich0cFFjnn4cPUzcDr89rQcGPcZ4A9yUoHX1fgvcQTma9/wLp95H/GfB/1s/Ipy7vQx34EHOi9/xmsuXH7IuAvgMuBl0wUragC9btmE/reL4HXeO//E9Y86vgjZF/KXeGc++Oij9gVE/Nh4BVk9yOdMY+28v6nGZZ6eAbgCvzbetAbjEn45mNQKUsyaynmZhT/5jvngSLn/L35+x4YBiuw5sa4o4CHgdfmZWhidIp4VaSPqJ8tPGV5909k3/S+MgxWYM2N228j+zC1s3PupRPEKqpB/a7ZXElWjrlrGKzAmtnRE8luxP8j4JB6wmsXzrnTgL8he0T8G/o/cwyg8v6nGZYaqPFHx/6QLzcY8Pr6+VI3dA9hFP/y55JDCec8//2AOW8E9t7f45y7FdidrJxlWdEYxRrm6x9QzC/1s4WnFO+8913m/n0jvPePOOduILuxeGfm/p0IUR/qdw0mv6/s7gGv9ZxzXyTrdzuTVRSICshntM4le6LiY8Ch3vthP1YdqLz/aYalXYSbRbca8Pp8NYhidMIjAhfinP86Xz6phPdqI/9DdtHdLL9or0O+bTPgsXkebqB+tvCU5d18qI/ZRf0ubtT3KsY5twS4lmyw8iDw6hF+1Lry/qcBS7sIT294wmMd85urtid7Vr2eoFISebnWvcA2Ax7n99x8Odcvaq+Dc+5s59xVzrktBkielS/ne5qHmIP8G77byH5D4E/mkDiya+Z8XqmfLTBleeecO8U593nn3KCn9qmP2WVgv8spfK0VC49z7l3OuSucc3sNkKjvVYhzblPgJrLHE98N7F5wZiVQ+d89DVjaxfJ8OVcN6MuBzYGve+/H+VFKMZjlZB+k5np0YPDii3O8Npvdcv0T3sc593yymxYfQD+6NQnD+khRr9TP6qEM73Yke2jFG2a/kH9RsA+wmuzpRcIWwf+DnHPrfLZxzj2d7Pp4l/deXxTYZDuyfvfm2S8459YHXp+vfnkhg2oD+W+8hZK724CX9//4bkEq/7unAUukOOee7Zzb3jm3cd/mm8mefrO3c+6YPu3mwHn56pkLGGZb+DjZjWgfdc6Fb4lwzh1A9guzvwI+3b9D7t32zrn+6e8L8uWHnXPb92k3By4hGxR9bK7HuorCXEJWt3uSc27Nk+Cccy8hu/HzUdb2FfUzW5ThXehjJ/Q/QjUvlbiY7LGrn/De/xpRG865p+febRa25Q9JWE42m3Zan3ZD4BNk10f1OwPM5R/wSbJv4I9wzr2uT7sIOIfsYTdf8t7rC7nyOY3sxz7vJnvgyNBZrLr+7iVpOt/Di4RFnHN3knXgbef6z9X3+v/y3l/at31X4Ktkj5b7Nlnd4SvJfsfgIu/92ysNvKU45z5K9qHpEbLzvxHZ4wJXA/t672+cpQ8d81Xe+5vybR3gCuDPgVXAf5DdwPaq/P0+C7wxv3FYjIlz7p1kNx2uJvMqAfYge0jJUd77y/q0d6J+ZoaSvDsTeB/ZU29WkP1+y+5k98D8B1l/Hfb4ZFECzrmbyK6Ru3vvvz7gtQ9570/t274dmWdbkZWoeLJvd7cGvgQclD91SlTMmP4dD5xF1m9XAj8H/hTYhuyHKV/hvb93AcJvDfmP5f6C7Gb5W5n7h3cB8N6/Kd/nTmr4u6cZlpbhvf8O2QXgSrJfcN4HuAt4B3BcjaFFjff+JLLZlNuBvcjqPJcBL5s9WBnyHj2yKfN3AN8n+0O8Z/6exwCHa7AyOd7788jK7r5F9kF1F7If6Ny7/wPvPO+hflYDJXl3Alk/W0FWRrQv2SzoicCeGqzYxXv/U2BX4FKyEpQDgN8B7yd72pEGK4bx3p8N7A1cT3bdfA3Zl3x/D+yiwUol7MraJ3u9GDhiyL+hVP13TzMsQgghhBBCCLNohkUIIYQQQghhFg1YhBBCCCGEEGbRgEUIIYQQQghhFg1YhBBCCCGEEGbRgEUIIYQQQghhFg1YhBBCCCGEEGbRgEUIIYQQQghhFg1YhBBCCCGEEGbRgEUIIYQQQghhFg1YhBBCCCGEEGbRgEUIIYQQQghhFg1YhBBCCCGEEGbRgEUIIYQQQghhFg1YhBBCCCGEEGbRgEUIIYQQQghhlum6AxBCCCEmwTn3FOAHwNPyTR/23p88QPtW4JP56j3Ajt77B6qPUgghxLgkaZrWHYMQQggxEc65fYEv5aszwM7e++/P0jwT+D6wEdAD9vbe37CQcQohhBgdlYQJIYRoPN775cAF+eo08Enn3FR43TnXAf4P2WAF4AwNVoQQohlowCKEECIWTgD+O2+/BPirvtdOBJbm7VuADy5gXEIIISZAJWFCCCGiwTm3G/A1si/kHgZ2ADYBVgKL8207ee//q7YghRBCjIQGLEIIIaLCOfcR4KR89WrgGcCL8vWjvfcXD9jvWcBewK75v+cBU8CHvPenVhmzEEKIwegpYUIIIWLj74D9gB2Bg/u2f27QYCXnr1i3jEwIIYQBdA+LEEKIqPDerwKOBFb1bb4bOHaeXe8HrmPtgOfKSgIUQggxEpphEUIIESN3kg1Awm+z9IDusB2896f3rzvnDq8kMiGEECOhGRYhhBAxcjZrByuQ3cfyzzXFIoQQYgI0YBFCCBEVzrmDgTfnq7cDd+TttzjnDqonKiGEEOOiAYsQQohocM5tDlyYr/aAo4G3A+GRmBc65zarIzYhhBDjoQGLEEKImLgA2CJvn+O9/6b3/j/y7QBb9rWFEEI0AA1YhBBCRIFz7ijgtfnqncDJfS+fBPwybx/qnDtyAUMTQggxARqwCCGEaDzOuW3JbrQPHOO9fziseO//Bziu7/VznHPbLFR8QgghxkcDFiGEEI3GOZcAFwMb55su9t5/ZbbOe38tcEW+ujFwcb6vEEIIw2jAIoQQoum8C9grb/8KOGGI9njggby9d76vEEIIw+iHI4UQQjQa7/2/AP9SUHsvoKeECSFEg9AMixBCCCGEEMIsGrAIIYQQQgghzJKkaTq/SgghhIgc59xuwNV9m5YA6wGPAo/0bd/Je3/3QsYmhBBtRvewCCGEEBmLgKfOsX2D/F9gamHCEUIIAZphEUIIIYQQQhhG97AIIYQQQgghzKIBixBCCCGEEMIsGrAIIYQQQgghzKIBixBCCCGEEMIsGrAIIYQQQgghzKIBixBCCCGEEMIsGrAIIYQQQgghzKIBixBCCCGEEMIsGrAIIYQQQgghzKIBixBCCCGEEMIsGrAIIYQQQgghzKIBixBCCCGEEMIsGrAIIYQQQgghzKIBixBCCCGEEMIsGrAIIYQQQgghzKIBixBCCCGEEMIs/x+ec9SQRSPSKAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 284,
+       "width": 406
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the perceptron decision boundary\n",
+    "perceptron_DB(x1, x2, w, threshold)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exercise section\n",
+    "* Compute a Boolean \"OR\" using a perceptron\n",
+    "\n",
+    "Hint: copy the code from the \"AND\" example and edit the weights and/or threshold"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Boolean OR\n",
+    "\n",
+    "| x$_1$ | x$_2$ | output |\n",
+    "| --- | --- | --- |\n",
+    "| 0 | 0 | 0 |\n",
+    "| 1 | 0 | 1 |\n",
+    "| 0 | 1 | 1 |\n",
+    "| 1 | 1 | 1 |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculating Boolean OR using a perceptron\n",
+    "# Enter code here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true,
+    "tags": [
+     "solution"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perceptron output for x1, x2 =  0 , 0  is  0\n",
+      "Perceptron output for x1, x2 =  1 , 0  is  1\n",
+      "Perceptron output for x1, x2 =  0 , 1  is  1\n",
+      "Perceptron output for x1, x2 =  1 , 1  is  1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 284,
+       "width": 406
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "# Calculating Boolean OR using a perceptron\n",
+    "threshold=0.6\n",
+    "# (w1, w2)\n",
+    "w=[1,1]\n",
+    "# (x1, x2) pairs\n",
+    "x1 = [0, 1, 0, 1]\n",
+    "x2 = [0, 0, 1, 1]\n",
+    "output = perceptron([x1, x2], w, threshold)\n",
+    "for i in range(len(output)):\n",
+    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
+    "          \" is \", output[i])\n",
+    "perceptron_DB(x1, x2, w, threshold)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exercise section\n",
+    "* Create a NAND gate using a perceptron\n",
+    "\n",
+    "Boolean NAND\n",
+    "\n",
+    "| x$_1$ | x$_2$ | output |\n",
+    "| --- | --- | --- |\n",
+    "| 0 | 0 | 1 |\n",
+    "| 1 | 0 | 1 |\n",
+    "| 0 | 1 | 1 |\n",
+    "| 1 | 1 | 0 |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculating Boolean NAND using a perceptron\n",
+    "# Enter code here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": [
+     "solution"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perceptron output for x1, x2 =  0 , 0  is  1\n",
+      "Perceptron output for x1, x2 =  1 , 0  is  1\n",
+      "Perceptron output for x1, x2 =  0 , 1  is  1\n",
+      "Perceptron output for x1, x2 =  1 , 1  is  0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 284,
+       "width": 406
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "# Calculating Boolean NAND using a perceptron\n",
+    "import matplotlib.pyplot as plt\n",
+    "threshold=-1.5\n",
+    "# (w1, w2)\n",
+    "w=[-1,-1]\n",
+    "# (x1, x2) pairs\n",
+    "x1 = [0, 1, 0, 1]\n",
+    "x2 = [0, 0, 1, 1]\n",
+    "output = perceptron([x1, x2], w, threshold)\n",
+    "for i in range(len(output)):\n",
+    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
+    "          \" is \", output[i])\n",
+    "perceptron_DB(x1, x2, w, threshold)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In fact, a single perceptron can compute \"AND\", \"OR\" and \"NOT\" boolean functions.\n",
+    "\n",
+    "However, it cannot compute some other boolean functions such as \"XOR\".\n",
+    "\n",
+    "**WHAT CAN WE DO?**\n",
+    "\n",
+    "\n",
+    "Hint: Think about what is the significance of the NAND gate we have created above?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multi-layer perceptrons\n",
+    "\n",
+    "\n",
+    "Answer: We said a single perceptron can't compute a \"XOR\" function. We didn't say that about **multiple Perceptrons** put together.\n",
+    "\n",
+    "The normal densely connected neural network is sometimes also called \"Multi-layer\" perceptron.\n",
+    "\n",
+    "**XOR function using multiple perceptrons**\n",
+    "\n",
+    "<center>\n",
+    "<figure>\n",
+    "<img src=\"./images/neuralnets/perceptron_XOR.svg\" width=\"400\"/>\n",
+    "<figcaption>Multiple perceptrons connected together to output a XOR function.</figcaption>\n",
+    "</figure>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Learning\n",
+    "\n",
+    "We know that we can compute complicated functions by combining a number of perceptrons.\n",
+    "\n",
+    "In the perceptron examples we had set the model parameters (weights and threshold) by hand.\n",
+    "\n",
+    "This is something we definitely **DO NOT** want to do or even can do for big networks.\n",
+    "\n",
+    "We want some algorithm to set/learn the model parameters for us!\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>Threshold -> bias</strong>  \n",
+    "    \n",
+    "Before we go further we need to introduce one change. The threshold which we saw in the step activation function above is moved to the left side of the equation and is called **bias**.\n",
+    "\n",
+    "$$\n",
+    "f = \\left\\{\n",
+    "        \\begin{array}{ll}\n",
+    "            0 & \\quad weighted\\_sum + bias < 0 \\\\\n",
+    "            1 & \\quad weighted\\_sum + bias \\geq 0\n",
+    "        \\end{array}\n",
+    "       \\quad \\quad  \\mathrm{where}, bias = -threshold\n",
+    "    \\right.\n",
+    "$$\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to algorithmically set/learn the weights and bias we need to choose an appropriate loss function for the problem at hand and solve an optimization problem.\n",
+    "We will explain below what this means.\n",
+    "\n",
+    "\n",
+    "### Loss function\n",
+    "\n",
+    "To learn using an algorithm we need to define a quantity/function which allows us to measure how close or far are the predictions of our network/setup from reality or the supplied labels. This is done by choosing a so-called \"Loss function\" (as in the case for other machine learning algorithms).\n",
+    "\n",
+    "Once we have this function, we need an algorithm to update the weights of the network such that this loss function decreases. \n",
+    "As one can already imagine the choice of an appropriate loss function is critical to the success of the model. \n",
+    "\n",
+    "Fortunately, for classification and regression (which cover a large variety of problems) these loss functions are well known. \n",
+    "\n",
+    "**Crossentropy** and **mean squared error** loss functions are often used for standard classification and regression problems, respectively.\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "    <i class=\"fa fa-info-circle\"></i>&nbsp; As we have seen before, <strong>mean squared error</strong> is defined as \n",
+    "\n",
+    "\n",
+    "$$\n",
+    "\\frac{1}{n} \\left((y_1 - \\hat{y}_1)^2 + (y_2 - \\hat{y}_2)^2 + ... + (y_n - \\hat{y}_n)^2 \\right)\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "### Gradient based learning\n",
+    "\n",
+    "As mentioned above, once we have chosen a loss function, we want to solve an **optimization problem** which minimizes this loss by updating the parameters (weights and biases) of the network. This is how the learning takes in a NN, and the \"knowledge\" is stored as the weights and biases.\n",
+    "\n",
+    "The most popular optimization methods used in Neural Network training are **Gradient-descent (GD)** type methods, such as gradient-descent itself, RMSprop and Adam. \n",
+    "\n",
+    "**Gradient-descent** uses partial derivatives of the loss function with respect to the network weights and a learning rate to updates the weights such that the loss function decreases and after some iterations reaches its (Global) minimum value.\n",
+    "\n",
+    "First, the loss function and its derivative are computed at the output node, and this signal is propagated backwards, using the chain rule, in the network to compute the partial derivatives. Hence, this method is called **Backpropagation**.\n",
+    "\n",
+    "One way to perform a single GD pass is to compute the partial derivatives using **all the samples** in our data, computing average derivatives and using them to update the weights. This is called **Batch gradient descent**. However, in deep learning we mostly work with massive datasets and using batch gradient descent can make the training very slow!\n",
+    "\n",
+    "The other extreme is to randomly shuffle the dataset and advance a pass of GD with the gradients computed using only **one sample** at a time. This is called **Stochastic gradient descent**.\n",
+    "\n",
+    "<center>\n",
+    "<figure>\n",
+    "<img src=\"./images/stochastic-vs-batch-gradient-descent.png\" width=\"600\"/>\n",
+    "<figcaption>Source: <a href=\"https://wikidocs.net/3413\">https://wikidocs.net/3413</a></figcaption>\n",
+    "</figure>\n",
+    "</center>\n",
+    "\n",
+    "\n",
+    "In practice, an approach in-between these two is used. The entire dataset is divided into **m batches** and these are used one by one to compute the derivatives and apply GD. This technique is called **Mini-batch gradient descent**. \n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "One pass through the entire training dataset is called 1 epoch of training.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 720x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "plt.figure(figsize=(10, 4)) ;\n",
+    "\n",
+    "pts=np.arange(-20,20, 0.1) ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Activation Functions\n",
+    "\n",
+    "In order to train the network we need to move away from Perceptron's **step** activation function because it can not be used for training using the gradient-descent and back-propagation algorithms among other drawbacks.\n",
+    "\n",
+    "Non-Linear functions such as:\n",
+    "\n",
+    "* Sigmoid\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "f(z) = \\frac{1}{1+e^{-z}} \\quad \\quad \\mathrm{where}, z = weighted\\_sum + bias\n",
+    "\\end{equation*}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jBPj98o8vr0GtkqScSZsCc/GWfjraWzOuRpLypRZh/TXl7R0JbZ8qb187zzmmytv15VH2mQbK2xOLqE2SlHMPP5389n6ZU2AkqboLTEMIBeAKSmH7kYRDHiu3XRlCKMQYiymneojSxaTbgL8KIfw2cBB4CXB7+Rz/pZpaJUn5MzE5xeN7BxPbnK8uSdWPrK+mNAXmeIxx1gK5McYJ4BjQA6xMO0mMcRz4SWA/8NPALuAscBellWVeG2P8dJW1SpJy5unDZxgdn5y1v7O9lR2b+upQkSTlS7VLN64ob8/NccxwedtL6YLTNLuAjwFvo7Ts4xHghcBm4O0hhO/GGJdkKkxHRxvr1qX+LrFk6vGcjc4+q4z9VRn7qzK16K9vPnw4cf+Vz1vLpo3L7+JSX2OVsb8qY39VrhH6rNqwPj3XPG16C0DhvO0sIYS1wN3AFuDGGONXy/s7gf8K/BKlOfFeZCpJy8jDu5PHYK7YsSbjSiQpn6oN60Plbfccx3SVt2fnOObtwGXAO6aDOkCMcTSE8GvADcANIYQbYozfqKLeRGNjEwwODs9/YI1M/xZ39OiZzJ6z0dlnlbG/KmN/VaZW/VUsFnn4yeOJbZtWdS2r/z98jVXG/qqM/VW5evRZf383HR2VR+9q56yfphTYBxJWcZleP30AGIkxnprjPK8sb794fkN5Pvtd5R+vrapaSVJuHB0cYfDsrMudaCkUeN7m5TcFRpIWo6qwXl7d5WGgFbg04ZBQfo4fzHOqVeXt7LtiPHd/R6U1SpLyade+5DGcbRt66exwfXVJgtqss35nefv6hLbpfZ+b5xyPlre3nN8QQmgFfqT84/0VVydJyqVd+5PXHPCupZL0rFqE9Y8AI8A7QwgvnN4ZQtgJvIPSajC3z9h/UQjhshDCzHfjD5W3vxVCeOmMY9uAPwBeQGkt9i/XoF5JUg6kjaxfstWwLknTqg7rMcanKC232AfcE0L4fAjhTuBblNZWvy3GeGTGQ75E6QZKb5hxjs8B7y2f4xshhG+FEO4AngB+AzgM/FSMcfZivJKkhnNuZJz9R5PXHXBkXZKeVYuRdWKMtwO3At+mtHLLdZSWYrwpxvjRBZ7j35XPcRdwOaUpMVOUlm68Nsb4cC1qlSTV35MHTieu+bu2r5M1fV0JLZLUnKpduvEZMcbPAJ9ZwHEXVnsOSVJje3zfYOL+ixxVl6TnqMnIuiRJldi1PzmsX7J1VeJ+SWpWhnVJUqYmp6Z48oArwUjSQhjWJUmZ2nfkLKPjs9cL6GxvZev6FXWoSJLyy7AuScrU7kPJo+rP29xHa4sfS5I0k++KkqRMPXUwPaxLkp7LsC5JytRTB88k7r9w48qMK5Gk/DOsS5IyMzY+yb6UmyHt2OTIuiSdz7AuScrMniNDTBVn3w6pb0UHq1d21qEiSco3w7okKTNp89V3bFxJoVDIuBpJyj/DuiQpM7vT5qs7BUaSEhnWJUmZeSpl2cYdm7y4VJKSGNYlSZkYHp3g0PFziW2OrEtSMsO6JCkTTx86w+xLS2FtXxd9PR2Z1yNJjcCwLknKRNqdSy90CowkpTKsS5IykXZxqeurS1I6w7okKRNzLdsoSUpmWJckLbkz58Y4NjiS2HaBYV2SUhnWJUlL7ulDyVNgNqzpoaerPeNqJKlxGNYlSUtuz5GhxP1OgZGkuRnWJUlLbs/h5JH17RsM65I0F8O6JGnJ7U0ZWd+2oTfjSiSpsRjWJUlLanRsMvXOpdvWG9YlaS6GdUnSktp3bCjxzqWrV3Z651JJmodhXZK0pPYeTpkC46i6JM3LsC5JWlJpK8Fsd766JM3LsC5JWlKpK8GsdyUYSZqPYV2StGSmporscyUYSVo0w7okackcPnmOsYmpWfu7OlpZt6q7DhVJUmMxrEuSlsyeOS4ubSkUMq5GkhqPYV2StGT2HHG+uiRVw7AuSVoyqcs2Ol9dkhbEsC5JWjIu2yhJ1TGsS5KWxODQKKfPjs3a31IosGVgRR0qkqTGY1iXJC2JtFH1TQM9tLe1ZlyNJDUmw7okaUnsO5q+EowkaWEM65KkJbHvyNnE/dvWGdYlaaEM65KkJbE/ZWR9yzrnq0vSQhnWJUk1Nzk1xYHj5xLbtjqyLkkLZliXJNXckZPDTExOzdrf3dnG6pWddahIkhqTYV2SVHP7jybPV9+ybgWFQiHjaiSpcRnWJUk1l7YSjFNgJKkyhnVJUs3tSxtZ92ZIklQRw7okqebSVoLZ6kowklQRw7okqaZGxyc5cnI4sW2L02AkqSKGdUlSTR08fpZiwv5VvR30drdnXo8kNTLDuiSpptLuXOrFpZJUOcO6JKmm9h/zzqWSVCuGdUlSTaWtBOPIuiRVzrAuSaqptDXWHVmXpMoZ1iVJNTM0PM7g0Nis/QVg81rDuiRVyrAuSaqZtPXV16/upqO9NeNqJKnxGdYlSTVz4Jjz1SWplgzrkqSaOXD8XOL+TQNOgZGkxTCsS5Jq5tDx5JH1zWt7Mq5EkpYHw7okqWZSR9a9uFSSFsWwLkmqieHRCU6eGU1s27jGkXVJWgzDuiSpJg6fTB5VX9vXSWeHK8FI0mIY1iVJNXHwmFNgJKnWDOuSpJo4eCL54tKNXlwqSYtmWJck1UTayLp3LpWkxTOsS5Jq4uCJtGkwjqxL0mIZ1iVJVZucmuJwSljf6Mi6JC1aW61OFEK4EXgXcBXQAXwPeG+M8QsVnGMF8A7gp4AdwDngbuDdMcbv1qpWSVJtHT01wuRUcdb+FV1t9PW016EiSVoeajKyHkJ4M/BF4HrgXuAe4KXAnSGE2xZ4jjXAt4D/AKwEPgfsBW4F7g4hXFeLWiVJtXcw5c6lG9f2UCgUMq5GkpaPqsN6CGET8EFgENgZY7wlxngzpbB+GvhACGHLAk71fkqj8n8LPC/G+MYY49XA24FO4MPV1ipJWhoHvXOpJC2JWoysv5VSmH5/jPHB6Z0xxvuA9wFdwJyj6yGE7cCbgCeBN8cYx2ac5z9TmlKzIoSwrgb1SpJqLG1k3YtLJak6tZiz/pry9o6Etk8B7wFeC/zOHOd4I1AA/jTGOOte1THGndUWKUlaOofSRtbXOLIuSdWoKqyHEArAFcAU8EjCIY+V264MIRRijLOvPir5ofL23hBCL/DTwAuBCeBLwN/P8VhJUh0Vi0UOpIX1AUfWJaka1U6DWU1pCszxmVNXpsUYJ4BjQA+li0bTXFzeDgAPAn8O/CrwrymNzn8xhDDX4yVJdXL67BjDoxOz9re1Fhjo76pDRZK0fFQ7DWb6+83kIZWS4fK2l9IFp0n6y9uPAE8AP0MptF8F3A68Gvgz4J9XU2yajo421q3L/neBejxno7PPKmN/Vcb+qsx0fx0cHEls37Kul40b+hPbmpWvscrYX5WxvyrXCH1W7cj6VHk71xSVwnnbJNNDL2PAjTHGe2KMZ2KM3wRuBs4APxNCuLSqaiVJNbfvyFDi/q3r8/8hKEl5V+3I+vQ7dPccx0wH8eSlAp7b9tcxxlMzG2KMh0II/xv4WeAVlObB19TY2ASDg8PzH1gj07/FHT16JrPnbHT2WWXsr8rYX5U5v78ef+pE4nGrezvs0zJfY5Wxvypjf1WuHn3W399NR0fl0bvakfXTlAL7QAhh1rOX9w0AI+eH8PMcLW+fSml/urwdWGSdkqQlkrZs42aXbZSkqlUV1ssrtDwMtAJJU1RC+Tl+MM+ppts3p7RvLG+PprRLkurk4AlviCRJS6UWN0W6s7x9fULb9L7PzXOOz5e3bzh/hD6E0AG8qvzjNxZVoSRpSYyMTXDi9KzbYwCwcY0j65JUrVqE9Y8AI8A7QwgvnN4ZQtgJvIPSajC3z9h/UQjhshDCzCUC7gLuBy4B/iiE0Fo+tgX4z8AO4IsxxliDeiVJNXIoZVR9bV8nnR2tGVcjSctP1WE9xvgU8DagD7gnhPD5EMKdwLcora1+W4zxyIyHfInSDZTeMOMck5SWazwE/CvgiRDCpyhdTPpWYC9wW7W1SpJq62DKzZA2OgVGkmqiFiPrxBhvB24Fvg3cAFwH3A3cFGP86ALP8QhwDfAn5V23AO3AnwIvKv9SIEnKkbSLSzd5cakk1US1Szc+I8b4GeAzCzjuwjnaDgO/Xv4jScq5tJF1Ly6VpNqoyci6JKk5pYV1l22UpNowrEuSFmVyaorDKReYOmddkmrDsC5JWpRjp0aYnCrO2t/T2UZfT3sdKpKk5cewLklalANpF5cO9FAoFDKuRpKWJ8O6JGlRDqVdXLrGKTCSVCuGdUnSosw1si5Jqg3DuiRpURxZl6SlZ1iXJFWsWCzOsca6I+uSVCuGdUlSxU6dGeXc6MSs/W2tBQZWddWhIklangzrkqSK7TsylLh/w+oeWlv8aJGkWvEdVZJUsb1HziTudwqMJNWWYV2SVLG0kXXvXCpJtWVYlyRVbN/h5JH1zY6sS1JNGdYlSRXbmzKyvsmRdUmqKcO6JKkiw6MTHDs1nNi2cY0j65JUS4Z1SVJF9qeMqq/t66SzozXjaiRpeTOsS5Iqsi9lJRgvLpWk2jOsS5IqkrYSjMs2SlLtGdYlSRVJX2PdkXVJqjXDuiSpIqkj615cKkk1Z1iXJC3Y5NQUB46mhPUBR9YlqdYM65KkBTt2aoSJyeKs/T2dbfT1tNehIkla3gzrkqQFO3j8XOL+TQM9FAqFjKuRpOXPsC5JWrCDx88m7t+0xikwkrQUDOuSpAVLHVl32UZJWhKGdUnSgqWOrLtsoyQtCcO6JGlBisWiI+uSlDHDuiRpQU6fG+fc6MSs/W2tBQZWddWhIkla/gzrkqQFOXgseQrMhtU9tLb4cSJJS8F3V0nSghw8kTwFZqNTYCRpyRjWJUkLkjay7sWlkrR0DOuSpAVJG1n34lJJWjqGdUnSghxKWbZxsyPrkrRkDOuSpHmNjE1w/PRoYtuGNd0ZVyNJzcOwLkma1+ETw4n71/R10tXRlnE1ktQ8DOuSpHkd8M6lklQXhnVJ0rxS71y6xotLJWkpGdYlSfNKu7h004Aj65K0lAzrkqR5ObIuSfVhWJckzWlyaorDJ11jXZLqwbAuSZrTsVMjTEwWZ+3v6Wyjb0VHHSqSpOZhWJckzSl1CszaHgqFQsbVSFJzMaxLkuZ08ITLNkpSvRjWJUlzOnjM+eqSVC+GdUnSnNJG1jca1iVpyRnWJUmpisVi6sj6ZqfBSNKSM6xLklKdPjfOudGJWfvbWlsYWNVVh4okqbkY1iVJqdLuXLp53QpaW/wIkaSl5jutJCnVgZRlG7eu7824EklqToZ1SVKqgykj61vXr8y4EklqToZ1SVKqtBsibXNkXZIyYViXJKVKm7PuyLokZcOwLklKNDo2yfHTo4ltWxxZl6RMGNYlSYkOnUieAjOwqpvuzraMq5Gk5mRYlyQlSr+41FF1ScqKYV2SlCht2cZtG5yvLklZMaxLkhKlX1zqyLokZcWwLklKdDBlzvo2V4KRpMwY1iVJs0xOTXE4Jaw7si5J2TGsS5JmOXZqhInJ4qz93Z1trFrZWYeKJKk5GdYlSbMcSJmvvnltD4VCIeNqJKl5GdYlSbMcOJYc1jcNrMi4EklqbjW7q0UI4UbgXcBVQAfwPeC9McYvVHHOzwOvAV4VY/xqLeqUJM3vYMqyjZvXGtYlKUs1GVkPIbwZ+CJwPXAvcA/wUuDOEMJtizznWygFdUlSxtJG1jcP9GRciSQ1t6rDeghhE/BBYBDYGWO8JcZ4M6Wwfhr4QAhhS4XnvAj4g2prkyRVbqpYdGRdknKiFiPrbwU6gffHGB+c3hljvA94H9AFLHh0PYTQAvwlMAY8VIP6JEkVOHl6lNHxyVn7O9pbWNPfVYeKJKl51SKsT09VuSOh7VPl7WsrON87KU2n+dfAoSrqkiQtQtpKMJvWrKDFlWAkKVNVhfUQQgG4ApgCHkk45LFy25XlY+c731XA7wKfiDH+dTW1SZIW56Dz1SUpN6odWV9NaQrM8Rjj2PmNMcYJ4BjQA8x5f+oQQgfwV8Ap4C1V1iVJWqTUkXXnq0tS5qpdunH6nTv5SqSS4fK2l9IFp2l+n9Kyj2+IMR6tsq6KdHS0sW7dnL9LLIl6PGejs6mvOXIAACAASURBVM8qY39Vxv4qOTo4mrj/sucNPKeP7K/K2WeVsb8qY39VrhH6rNqR9anydvY9qZ9VOG87SwjhpcD/A3w0xpg0912SlIFiscjew2cS27ZvzP+HmiQtN9WOrA+Vt91zHDO9dEDi96ohhBXAXwAHKa0sk7mxsQkGB4fnP7BGpn+LO3o0+QNRs9lnlbG/KmN/PWtwaJSh4fFZ+9taC7ROTXL06Bn7axHss8rYX5WxvypXjz7r7++mo6Py6F1tWD9NKbAPhBDaynPUnxFCaAMGgJEY46mUc7wFuAh4APivIYSZbVeWt78VQvgl4M9ijN+osmZJUooDKeurb1jTQ2tLTe6jJ0mqQFVhPcZYDCE8DLwIuBR4+LxDAqWpNj+Y4zS95e1V5T9Jbixv7wIM65K0RFLvXOrFpZJUF9WOrAPcSSmsv57ZYf315e3n0h4cY/xdSss1zhJCuAt4NfCqGONXq6xTkjSP9JVgXLZRkuqhFt9pfgQYAd4ZQnjh9M4Qwk7gHZRWg7l9xv6LQgiXhRD6a/DckqQaSl9j3ZF1SaqHqsN6jPEp4G1AH3BPCOHzIYQ7gW9RWlv9thjjkRkP+RKlGyi9odrnliTVVtqcdafBSFJ91ORqoRjj7cCtwLeBG4DrgLuBm2KMH63Fc0iSltbQ8Dinz866vx2FQukCU0lS9moxZx2AGONngM8s4LgLKzjnjfMfJUmqhYMp89XXr+6hvc2VYCSpHnz3lSQBc60E46i6JNWLYV2SBMDBtPnqXlwqSXVjWJckAa6xLkl5ZFiXJAHpc9Y3DTgNRpLqxbAuSWJkbILjp0cT2zatcWRdkurFsC5JSp2vPtDfRWdHa8bVSJKmGdYlSanz1Tc5X12S6sqwLkniQMp89c3OV5ekujKsS5I4eCx5Gowj65JUX4Z1SRL7jg4l7neNdUmqL8O6JDW54dEJjg2OJLZtMaxLUl0Z1iWpyaVdXDrQ30V3Z1vG1UiSZjKsS1KTS5sC46i6JNWfYV2Smtz+o8kj61vX92ZciSTpfIZ1SWpyjqxLUn4Z1iWpye1PmbO+dZ0j65JUb4Z1SWpig2fHOHNufNb+1pYCG9d6QyRJqjfDuiQ1sbQpMBvX9NDW6keEJNWb78SS1MTSLi7dss756pKUB4Z1SWpiqReXOl9dknLBsC5JTSx12UZH1iUpFwzrktSkporF1LuXOrIuSflgWJekJnXs1DCj45Oz9ne2tzLQ31WHiiRJ5zOsS1KTSpsCs3lgBS2FQsbVSJKSGNYlqUmlXVzqfHVJyg/DuiQ1qX2pyzY6X12S8sKwLklNau+RtGUbHVmXpLwwrEtSExodm+TwiXOJbdvWO7IuSXlhWJekJrTv6BDFhP2rV3bS19OReT2SpGSGdUlqQntSpsA4qi5J+WJYl6QmtPfwmcT92zeszLgSSdJcDOuS1ITSRta3O7IuSbliWJekJjM1VWRfWljfYFiXpDwxrEtSkzl88hxjE1Oz9nd1tDKwqrsOFUmS0hjWJanJ7DmcfnFpS6GQcTWSpLkY1iWpyew5knJx6XovLpWkvDGsS1KT2Zs2su58dUnKHcO6JDWRYrHInpRlGy9w2UZJyh3DuiQ1kcGzY5w+Nz5rf2tLgc0DPXWoSJI0F8O6JDWRtItLN63tob2tNeNqJEnzMaxLUhPZm3Jx6TYvLpWkXDKsS1ITSRtZ92ZIkpRPhnVJaiJPH0pbttGwLkl5ZFiXpCYxNDzOkVPDiW3bNzoNRpLyyLAuSU0ibVR9/epuVnS1Z1yNJGkhDOuS1CR2HzyduP9CR9UlKbcM65LUJNLC+o5NfRlXIklaKMO6JDWJp1KmwRjWJSm/DOuS1ARODY1y8szorP2Fgss2SlKeGdYlqQk8dTB5VH3z2hV0dbRlXI0kaaEM65LUBFIvLt3kxaWSlGeGdUlqArsPeXGpJDUiw7okLXPFYjF1GsyFGw3rkpRnhnVJWuaOD44wNDw+a39rS4Ft6724VJLyzLAuScvc7pQlG7eu76W9zY8BScoz36UlaZl7ypshSVLDMqxL0jKXuhLMRleCkaS8M6xL0jI2OTXF7pSLSx1Zl6T8M6xL0jK298gQo+OTs/Z3drSyeaCnDhVJkiphWJekZWzXvsHE/Rdt7qO1xY8ASco736klaRnbtT85rF+8pT/jSiRJi2FYl6Rl7PGUkfWLtxrWJakRtNXqRCGEG4F3AVcBHcD3gPfGGL9QwTleC/wb4DqgFzgIfB54T4xxX61qlaRmcOL0CCfPjM7aXwAu2mxYl6RGUJOR9RDCm4EvAtcD9wL3AC8F7gwh3LbAc/wm8DngRiCW/w7wK8D3QwiX1aJWSWoWaaPqW9b10t1Zs7EaSdISqjqshxA2AR8EBoGdMcZbYow3Uwrrp4EPhBC2zHOOK4D3AEPAy2KML4kxvh64GLgdWAd8pNpaJamZpF1ceolTYCSpYdRiZP2tQCfw/hjjg9M7Y4z3Ae8DuoD5RtffBLQC/yXGeM+Mc4xTmhZzFHhxCOGCGtQrSU3h8f2nEvc7X12SGkctwvpryts7Eto+Vd6+dp5zjAEPAF8/v6Ec2HeXf9y8mAIlqdmMjE2w98hQYpsrwUhS46hq0mIIoQBcAUwBjyQc8li57coQQiHGWEw6T4zxd4DfSXmOFeXnAPAiU0lagCcPnKaY8I7b39vBQH9X9gVJkhal2pH11ZSmwByPMY6d3xhjnACOAT3AykU+xzsprQxzX4xx72ILlaRmkjpffUs/hUIh42okSYtV7XIAK8rbc3McM1ze9lK64HTBQgi3UFoOcgp4R8XVLVBHRxvr1i32d4nFq8dzNjr7rDL2V2WWU389nTIF5uqwoWb/zuXUX1mxzypjf1XG/qpcI/RZtWF9qrxNnN5SVjhvuyAhhB8D/o7Shae/GWP8asXVSVITmpic4tGnTyS2XbFjTcbVSJKqUW1Ynx666Z7jmOnJkWcXetIQwi8Af0apvnfHGP+/xZW3MGNjEwwODs9/YI1M/xZ39OiZzJ6z0dlnlbG/KrPc+mvXvkGGRydn7e9ob2FlR0vV/87l1l9ZsM8qY39Vxv6qXD36rL+/m46OyqN3tXPWT1MK7AMhhFnPXt43AIzEGJPXEJv9mN8H/julEfXfKF98KklaoEdSRtUv3bqKttaa3AtPkpSRqt61y6u7PEwpWF+acEgoP8cP5jtXCKEQQvgw8NvAKPDTMcY/qqY+SWpGjzx9MnH/5ReszrgSSVK1ajHEcmd5+/qEtul9n1vAef4Q+EVKo/U3xxg/XoPaJKmpjI1Psmt/8rX8l19oWJekRlOLsP4RYAR4ZwjhhdM7Qwg7Ka3gMgzcPmP/RSGEy0II/TP2vQb4DWAC+LEY49dqUJckNZ0n9g8yMTk1a39PZxvb1+d/1QNJ0nNVe4EpMcanQghvA/4UuCeE8CVKK7/8SPn8PxdjPDLjIV8CLgD+JfA/yvt+t7w9DPxqCOFXU57uP8YYk26+JEkCHk6ZAhO2r6KlxfXVJanRVB3WAWKMt4cQ9lAaSb+B0pzzuymF6y/N9dgQQg9wXfnHLcDPznH4h0m+U6okCXjU+eqStKzUJKwDxBg/A3xmAcddeN7P5yhdoCpJqsLw6AS7DyYvQ3b5ha6vLkmNyDW8JGmZeGzvKaaKs+9R17eig81re+pQkSSpWoZ1SVom5lqysVBwvrokNSLDuiQtE66vLknLj2FdkpaBk2dG2XtkKLHNsC5JjcuwLknLwP1PHEvcv25VF+tWdWdcjSSpVgzrkrQMPLDreOL+qy8ayLgSSVItGdYlqcGNjU/y8NMnEtuuvtiwLkmNzLAuSQ3u0T2nGBufmrW/s72VS7etqkNFkqRaMaxLUoN7IGW++pU71tDe5tu8JDUy38UlqYEVi0XuT52vvjbjaiRJtWZYl6QGduDYWY6fHklsu8qwLkkNz7AuSQ3s/ieSR9Uv3LiS/t7OjKuRJNWaYV2SGtj9u5Lnq7sKjCQtD4Z1SWpQp4ZG2bVvMLHt6oudAiNJy4FhXZIa1HcfPUIxYX9/bwfbN6zMvB5JUu0Z1iWpQd376JHE/TsvXU9LoZBxNZKkpWBYl6QGdOL0SOoUmOsuX59xNZKkpWJYl6QGdO8jyaPqq1d2cvHW/oyrkSQtFcO6JDWg+x49nLj/usucAiNJy4lhXZIazJFTw+w+eCax7UWXb8i4GknSUjKsS1KDue+R5FH1gf4udmxyFRhJWk4M65LUYNLmq193+XoKToGRpGXFsC5JDWTP4TPsPTKU2Paiy5wCI0nLjWFdkhrI3Q8cTNy/YU0P2zf0ZlyNJGmpGdYlqUGMT0xxz0OHEtte+vyNToGRpGXIsC5JDeIfdx3j7MjErP2FAlz//I11qEiStNQM65LUIL76D/sT9z9/x1rW9HVlXI0kKQuGdUlqAAeOneWRp08mtt1w1aaMq5EkZcWwLkkNIG1UvW9FB9dcMpBxNZKkrBjWJSnnhkcn+OaDyReWvvzqzbS1+lYuScuV7/CSlHPfeOAgw6PJF5a+8prNdahIkpQVw7ok5djk1BRfvG9vYts1Fw94YakkLXOGdUnKse/Foxw/PZLY9qPXbcu4GklS1gzrkpRTxWKRz3zr6cS2Czau5NJtqzKuSJKUNcO6JOXU/buOs+/oUGLbzddt846lktQEDOuSlEPFYpFPf2t3YttAfxc7L1ufcUWSpHowrEtSDv3j48fYffBMYtstL7nA5RolqUn4bi9JOTM1VeSTX38ysW31yk5e+nzvWCpJzcKwLkk5c89Dh9h/7Gxi2y0vvoD2Nt+6JalZ+I4vSTkyMjbB333ticS2gf4uXuFNkCSpqRjWJSlHPnvP0wwOjSW2ve5lO5yrLklNxnd9ScqJg8fP8oV79yS2bV3Xy0uu3JhxRZKkejOsS1IOFItF/uoLkYnJYmL7z9x4CS0trqsuSc3GsC5JOfCNBw7y6J5TiW3XXjLA5ReszrgiSVIeGNYlqc6OnRrmb770eGJbR1sLP/3qSzKuSJKUF4Z1SaqjyakpPvzZRxgdm0xsf90NO1i3qjvjqiRJeWFYl6Q6+sRXn+SxvcnTX7av7+WmndsyrkiSlCeGdUmqk+8+eoQ7U1Z/aW0p8Is/foVLNUpSk/NTQJLq4ODxs/z3zz2S2v76G3awbX1vhhVJkvLIsC5JGRs8O8Yf/90DqfPUr9yxhtf+8AUZVyVJyiPDuiRl6NzIOP/lf/4jh08OJ7av7evkV/7Jla6pLkkCDOuSlJnRsUn+6O8eYO+RocT2ttYCv/aGF9Db3Z5xZZKkvGqrdwGS1AzOjUzwXz/5ALv2DaYe87M3XcqOTX0ZViVJyjvDuiQtsZNnRnn/x+9n39HkEXWA17xoO6+4ZkuGVUmSGoFhXZKW0L6jQ/zR/7qfE6dHU495+dWb+KevuijDqiRJjcKwLklLoFgscvcDB/nYFx9jbGIq9bidl63n526+jELBC0olSbMZ1iWpxs6NTPDR/xP59sOH5zzumosHuO3WK1z5RZKUyrAuSTVSLBa595Ej/O2XH2dwaGzOY19+9WbedPOltLa4KJckKZ1hXZJqYM/hM3z8K7t4+KmT8x77+ht2cOv1Fzr1RZI0L8O6JFVh35Eh/v6bu/lePDrvsR1tLbzp5sBLX7Apg8okScuBYV2SKjQ1VeT+Xcf48j/s56HdJxb0mC3rVvCrr3s+WwZWLHF1kqTlxLAuSQtQLBbZc3iIex85zHceOTznUozne8U1m/mZV19CR3vrElYoSVqODOuSlGJsfJK49xQ/eOI4Dzx5nCMnhyt6/Ka1PfzczYGwffUSVShJWu5qFtZDCDcC7wKuAjqA7wHvjTF+oYJzXAr8HvAyYC2wC/gQcHuMMX2hYkmqUrFY5PjgCE8cGGTX/kGePHCapw+dYXKqWPG5Ottb+fHrL+DmF22nrdXVXiRJi1eTsB5CeDPwEWAU+DLQCrwKuDOE8Csxxg8t4BxXA18H+oBvAveVz/EnwIuBf1GLWiU1t9GxSY6fHuHY4AiHTpzjwLGzHB0cYe/hMwwNj1d17o72Fl79Q1u5+Ye309fTUaOKJUnNrOqwHkLYBHwQGAReFmN8sLz/OuAu4AMhhM/GGPfPcY4C8JeUgvqbYowfLe9fVz7Hz4YQPhVj/ES19UpaPorFIuMTUwyPTTIyOsHw2ARnRyY4c3aM0+fGOXNujMGzY5w5O8aps2McHxypOpAn6e1u5xXXbOamndvoW2FIlyTVTi1G1t8KdAL/73RQB4gx3hdCeB/wHuA24HfmOMdNlKbPfHU6qJfPcTSE8GvA3cCvA8sirE9OTnHg2FmOHR+a3TjHN+5pTcVi5V/TL+IhFFMqWNS55vx3zm48cW6cInDq5LmE4+d6ooU/x0JqS39MZQ+a+99f+YPOb1l1cgSAk4Oz+2u+J0rtm3lqnpwqUpwqMlUsMlneFqc47+fpv5dWVCnOaJucLDI+OcX4xLN/JqZ/fs7+SUbGSn+GRycWNU2lVi7e0s+rrt3CzsvW097mdBdJUu3VIqy/pry9I6HtU5TC+muZO6ynniPG+M0QwhHgZSGElTHGM9UUW293fXcvf3/3bs6OTNS7FEmLsH19Ly+6YgPXXbaedau6612OJGmZqyqsl6evXAFMAY8kHPJYue3KEEIhxpg2BHZleftgSnsE1pef6zuLr7i+Htp9gr++6/F6lyGpAr3d7YTtq3jB89by/B1rWNPXVe+SJElNpNqR9dWUpsAcjTGOnd8YY5wIIRyjFLRXAqdTzjN9O7+DKe3T+zdUUWuqjo421q1buRSnfo549+4lfw5Ji9fW2sL2jSu57ILVhAvWcNmFq9m0dgWFQqHepeVSFu+by419Vhn7qzL2V+Uaoc+qDevTt+KbY2Is0wsT95Ie1uc7z8xzNKz+3s56lyA1vZYCrOnvZv3qbtav6WHb+pVs27CSCzauZMOaHlpdalGSlCPVhvXptc/nusKrcN52MedZyDkWbWxsgsHBym52shjPv2AVvd3tS7IahdSsWlsKdHe20d3ZSndHG12dbazsaaevp4OVPe30r+hgZU8HfSs6WL2yk9UrO2etfT49snL0aENfEpMZ+6ty9lll7K/K2F+Vq0ef9fd309FRefSuNqxPL2cy11VW0xM8z1ZxnoWcI/c2rO7hnf/8Wr7x4GEe23OS8fHJ5APn+JUkvWmOlpSm1EfM+fyVniy9ae6ZBc9tbG8vhauJieR7Y1Vwqhm7Ky96rudJ78/kljnPtYhfS2dO1Wgv39Y+9TU2z/NU+m+BUmhuKRQoFMp/f+bnQvlnSj+3FGgtPNveMqOto72V9tYW2ttaaCtvn/kzY39XRytdnW30dLbS1triNBVJ0rJVbVg/TSloD4QQ2mKMz1niJITQBgwAIzHGU3Oc5wBwDbAReDShfb457Q1jy7pe3vpTpX+OvwEvnKMGlbG/JElaHqqanFle3eVhSncsvTThkFB+jh/Mc6rpVWCumHWC0oozlwGT5eeSJEmSmkItrqS6s7x9fULb9L7PVXGO64F1wN2Nvsa6JEmSVIlahPWPACPAO0MIL5zeGULYCbyD0kout8/Yf1EI4bIQQv+Mc3wNeAi4KYTwyzOOXTfjsX9Yg1olSZKkhlF1WI8xPgW8DegD7gkhfD6EcCfwLUprq98WYzwy4yFfonQDpTfMOMcU8AuU5r9/KITw7RDCJyndDOkq4M9jjJ+utlZJkiSpkdRkQeEY4+3ArcC3gRuA64C7gZtijB9d4DnuBX4Y+ARwCfCjwNPArwJvqUWdkiRJUiOpdjWYZ8QYPwN8ZgHHXThH28PAT9aqJkmSJKmReas+SZIkKacM65IkSVJOGdYlSZKknDKsS5IkSTllWJckSZJyyrAuSZIk5ZRhXZIkScopw7okSZKUU4Z1SZIkKacKxWKx3jXU0z5gy9RUkYmJycyetKOjdOPYsbGJzJ6z0dlnlbG/KmN/Vcb+qpx9Vhn7qzL2V+Xq0Wdtba20tBQA9gNbF/q4Zg/rp4D+ehchSZKkpjEIrFrowW1LWEgj2A3sAIaAXXWuRZIkScvXxUAvpfy5YM0+si5JkiTllheYSpIkSTllWJckSZJyyrAuSZIk5ZRhXZIkScopw7okSZKUU4Z1SZIkKacM65IkSVJOGdYlSZKknDKsS5IkSTllWJckSZJyyrAuSZIk5ZRhXZIkScopw7okSZKUU4Z1SZIkKacM65IkSVJOGdYlSZKknGqrdwHNIoTQDbwN+CngYqAIPAr8D+BPY4xTCY+5EXgXcBXQAXwPeG+M8QsZlZ0bIYTfBX4H2BZj3JfQfgPw9TlO8bEY479YovJyab4+Kx+zs3zMdUAv8BDwgRjjX2dVZ96EEP498O45DvnlGOOHs6onb3xfqkwI4U3AX85xyH+MMf52VvXkUQjhzcBHgBtijHcntF8K/B7wMmAtsAv4EHB70mfncjdXf4UQtgF75nj4N2OML1vC8nIhhNAKvAX4eeByoBV4Evhb4A9ijCPnHZ/rz0LDegZCCL3AV4CdwAnga5Q+5F4M/DHw6hDCT8QYJ2c85s2U/mMcBb5M6YX2KuDOEMKvxBg/lOk/oo5CCK8Hfmuew64tb78F7E5o/2ZNi8q5hfRZCOEm4LOUvmH7GnAOeDXwsRDClTHG+fp8uZp+LX2KUp+cb1eGteSK70uLMv16+iJwJKH9HzOsJXdCCC8B/mSO9qspDcT0UXofv4/Sa+5PKH2GNtsgzJz9xbOvtweAHyS0x5oXlTPloP73wI8BQ8C3gXFKr5d3Az8WQviRGOO58vG5/yw0rGfjtygF9S8BPxljPAUQQrgQ+D/A64BfAv6svH8T8EFgEHhZjPHB8v7rgLuAD4QQPhtj3J/xvyNzIYRfA/6I+V+r029Q74gxNlUwP99C+qz8Tc9Hyz/eFGP8Snn/RcBXgXeFED4ZY/zeEpebR9cCI8BPxRgn6l1MXvi+tGjT703/0r55rhDCGyl9u9yb0l6g9K1EH/CmGONHy/vXUXrN/WwI4VMxxk9kU3F9zddfZdOvt/fFGD+25EXl0y9RCuoPALdM/3cXQhgA/jfwEuDfA/+uUT4LnbOejZ8vb2+bDuoAMcangLeXf/zpGce/FegE3j/9gVg+/j7gfUAXcNtSFlxvIYTLQgifBf6UUjg4M89DrgWmaOJRqgr77E3AekrTg74yvTPG+ATwm+Uff32pas2rEMIq4ELgfoP6LE3/vrRI1wCHDerPCiFsDSH8JfAJSt/OHE459CZK062+Oh3UAWKMR4FfK/+47N+nKugveDasN+NAy7Q3l7f/ZuZ/dzHGY5SmxsCzmashPgsN60usPAXmceDeGOOTCYc8Vt5unrHvNeXtHQnHf6q8fW1tKsytDwK3UPrq+IWUpg8lCiF0AFcAj8YYz2ZTXi4tuM+Y+zX2aWCS5f8aS+IHXTrflyoUQtgBrMLX0/neQykkfZfS1IRHU45Lfc2Vv0E9ArwshLByKYrMkYX2F5Tew4Z4Nls0o2OU+ujehLbzM1dDfBY6DWaJxRiHgFfMcch15e0+eOZrvysojRI/knD8Y+W2K0MIhRhjsYbl5sl9wB/GGD8NEEKY69jnA+3AUyGE9wA/QWl09BClkYj3zPxGYxmrpM+uLG8fPL8hxng6hHAA2BZC2BBjnGsUZ7mZDutnQwh/AbyS0qjLY8CHSbkYfLnzfWnRpl9Ph0MIf0LpQ38r8DSlr95nXejWJB6l9I3zR2OMU3O8V6W+T5VFSv99XgF8p6YV5suC+iuEsAbYDnwf+Lfli5svAU4BnwF+N8Z4IJuS6yfGeOsczc/JXDTIZ6FhvY5CCJ3A9CoA03PuVlP6qvlojHHs/MfEGCdCCMcovUGtBE5nUWvWYoxvn/+oZ0x/IN5C6Rejr1H6D/E6Sivw3BpCeFn5q9Nlq8I+21TeHkxpPwhsAzYw91euy830a+ntwAFKAWATpW8q/hh4ZQjhnzZhYPd9aXGema8OnAS+AeyndA3Tu4HXhBBujDEO16m+uogxvneBhy7kfQpK71PLVgX9Nf16+yHgBTz3s/CXKX0WvjLGuOwvMk1SHnSYXulrOnM1xGehYX0RQggfo/ThPZ9PxRj/Xco5CsB/p/Rb7yPlvwOsKG+TVqGYNv3G3ksDfCjWor/mMf0G9TXgn06H8vLFJH9L6aruD1IacW8IGfTZ9OssLSTMfI01rEX04/Rr6QPA22OM4+XzXE3pwqQ3Av+KuVdjWI6W3ftSRqZfTx8HfmF6ml55cYE7gOspTXF4W12qy7/5XnfL4n2qhqZfbw8Bt8YYdwOEEFYAfw78DPAxSr8sNqP/RGlA7zDwB+V9DfFZaFhfnAuAOecYlG1K2lleVujDwM9SGm15Y4xxtNw8PWI319fIhfO2eVdVfy3Ab1Aa9TwYY3zmosoY47EQws9R+or+DSGETTHGtN+e82ap+2wSmGu6QqO9xtJU2o8/DOwAHprZNzHG+0MIv04pYDVjWF+O70tZ+EngecCumd9IxBifKi+D+X3gthDCb07/YqjnmO9152vuud5PacT4TPliSgBijGdDCL8EvBx4YQjhxTHGb9eryHoIIbyb0gWjo5RW+pr+pr0hPgsN64tQzQ0Fyr/h/g1wK6ULAH80xjjzYpGh8rZ7jtN0lbcNcTHlUt+Aofwhl3gxTYzxQAjh+8ANlL4a/OxS1lIrGdy04iywKoTQlTJntqFeY/+3vXsLmaqKAjj+N3rJShDs8hJSiMuioquQpaXihcDIAoku0kNG9dQF6qGHIKIi0AcDocx87iJBgYXYxbQLPVQkCPtNUCqQqOxC1EM9rD05jjPz9YV+c+Y7/x/I4OwZORz32WfN3mevNchkz2Od+Rz0fOxOcmCPiDizZZuZp924NBXqtXVgQNvXEXGYXGKfT86G6ngT9Tv7XJdaq6VfnRFKKb9HxAfkRtWrydzj015EnE5m9G+xlgAABIlJREFUSLufTMl7Wymlu4DiWNwLzQYzhSLiPPJRjTXkc2RL+uTuPEoOUHNqJ+v9N04H5gB/tGTT5MnwfX2dOdKjaJbOJqPzB7RP9Bxf69QfhZ0MO23rS45Lp4Zj03COUydXq/pbzcb3Dhmo/wSsKqW82/OxsehjButTJCLmAp+Rv2j3A9eVUk6YSalLMQfIXKrz+/1T5P9bv8pkrRQRmyPirYg4d8BHLqyvhwe0t1Fn9viS3oaImEWmtTrSpkwwETE3IrZFxNYB7WcB55DPMA5LizntOC5NXkScHREvR8Sb/X7gVJ2xyRzs/Q0bp2YAC8jVrr6rF20TEU/V/nbZgI+05l4YEbPJokargUPA4p4Z9Y6xuBcarE+Bmk5pN3mhfEx2mmEXy3v19dY+bZ33dp68Ixx715Pn5YR0TRFxKbnp5gfMddxtWB9bQwZlbetjR4H1wH0RMa9P+z31dXddbm4bx6XJ+RVYS25sPyF9b0SsJlcj9rchnd7/NKzPLSJ/PO/r3qvUcpeT/W1db0OdzFoJ/AV82Ns+ndTaKzvJydEDwKLuQm49xuJeaLA+NbYA84CvgNWllJ8n+Px28tmqJyLi30wWEXEN8Dg5s7flFB3rOHqpvj4bEQs6b9aS1NvJi+2FfinnWmwHWVDk3oi4ufNmRFwEPE9u6No0omMbiVLKj8Ab9a/bajVTAOp1+Ay54e3ZERxeEzguTUJdjeis0rwYEf8WvqulzDvn6pmpPrYxsod8ln9FRGzovFnH9s752ziKA2uozr3wsYi4vvNmXRV8FZgFvFJK+b7fl6eRp8niUYeAmyaYHB2Le+GMv/+2dsWpFBEXk4PNDHJW/dCAjx4ppTzS9b2HyE0RfwHv1+8vIzcFr+8uvdwGEXGQzOxxQe+FFxGnAa+RmRf+JHMZ/wYsJXM+vw7c2bbZ0GHnrLbfwrHy1XuAX8g0lzOBJ0sprQtKaxCwl3ys4wfgUzJl1xJycuPhUsrm0R3haDkuTU5EnAHsAm4gZ9r31aalZN76TaWU1qdtjIiPyNWHxaWUfT1tC8m+dhZZ9+BbsljZbGBrKeX+KT3YBpjgfG0EHiUnFj4hq3kuJldx9pIThsNSsI61+iTDYXJT8pf0L+IGQCnl7vqdxt8LnVk/9W7kWMqfJWS6xn5/1nZ/qZSyhVyC+Zy80K4lB/oV3hCPVwvUrAMeAL4hl0eXkxfpBuCOtgXq/0Up5W2yf+4iHxW6kTx/65owOI1CTee1EHiOfC59FXAFeY6WtTlQB8elyarFjpaTKeMOkkH6IvL83W6gPrFSyhdkStUdZF2SlWQF2AeAB0d4aI1U+9Q6MlC/knxm+zty9Wv5dA7Uq4Ucyx50FYNjrrs6XxiHe6Ez65IkSVJDObMuSZIkNZTBuiRJktRQBuuSJElSQxmsS5IkSQ1lsC5JkiQ1lMG6JEmS1FAG65IkSVJDGaxLkiRJDWWwLkmSJDWUwbokSZLUUAbrkiRJUkMZrEuSJEkNZbAuSZIkNZTBuiRJktRQBuuSJElSQxmsS5IkSQ1lsC5JkiQ11D8wTMVZVN/cSQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 251,
+       "width": 373
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(pts, 1/(1+np.exp(-pts))) ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* tanh\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "f(z) = \\frac{e^{z} - e^{-z}}{e^{z} + e^{-z}}\\quad \\quad \\mathrm{where}, z = weighted\\_sum + bias\n",
+    "\\end{equation*}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 251,
+       "width": 386
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lineplot(pts, np.tanh(pts*np.pi)) ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* **ReLU (Rectified linear unit)**\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "f(z) = \\mathrm{max}(0,z)   \\quad \\quad \\mathrm{where}, z = weighted\\_sum + bias\n",
+    "\\end{equation*}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 251,
+       "width": 380
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pts_relu=[max(0,i) for i in pts];\n",
+    "plt.plot(pts, pts_relu) ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "are some of the commonly used as activation functions. Such non-linear activation functions allow the network to learn complex representations of data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "ReLU is very popular and is widely used nowadays. There also exist other variations of ReLU, e.g. \"leaky ReLU\".\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "Why don't we just use a simple linear activation function?\n",
+    "    \n",
+    "Linear activations are **NOT** used because it can be mathematically shown that if they are used then the output is just a linear function of the input. So we cannot learn interesting and complex functions by adding any number of hidden layers.\n",
+    "\n",
+    "The only exception when we do want to use a linear activation is for the output layer of a network when solving a regression problem.\n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise section - Google Playground\n",
+    "\n",
+    "A great tool from Google to develop a feeling for the workings of neural networks.\n",
+    "\n",
+    "https://playground.tensorflow.org/\n",
+    "\n",
+    "<img src=\"./images/neuralnets/google_playground.png\"/>\n",
+    "\n",
+    "**Walkthrough by instructor**\n",
+    "\n",
+    "Some concepts to look at:\n",
+    "\n",
+    "* Simple vs Complex models (Effect of network size)\n",
+    "* Optimization results\n",
+    "* Effect of activation functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction to Keras"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is Keras?\n",
+    "\n",
+    "* It is a high level API to create and work with neural networks\n",
+    "* Supports multiple backends such as **TensorFlow** from Google, **Theano** (Although Theano is dead now) and **CNTK** (Microsoft Cognitive Toolkit)\n",
+    "* Very good for creating neural nets quickly and hides away a lot of tedious work\n",
+    "* Has been incorporated into official TensorFlow (which obviously only works with tensforflow) and as of TensorFlow 2.0 this will the main api to use it\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<center>\n",
+    "<figure>\n",
+    "<img src=\"./images/neuralnets/neural_net_keras_1.svg\" width=\"700\"/>\n",
+    "<figcaption>Building this model in Keras</figcaption>\n",
+    "</figure>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_1 (Dense)              (None, 4)                 12        \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 4)                 20        \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 1)                 5         \n",
+      "_________________________________________________________________\n",
+      "activation_1 (Activation)    (None, 1)                 0         \n",
+      "=================================================================\n",
+      "Total params: 37\n",
+      "Trainable params: 37\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Say hello to keras\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Activation\n",
+    "\n",
+    "# Creating a model\n",
+    "model = Sequential()\n",
+    "\n",
+    "# Adding layers to this model\n",
+    "# 1st Hidden layer\n",
+    "# A Dense/fully-connected layer which takes as input a \n",
+    "# feature array of shape (samples, num_features)\n",
+    "# Here input_shape = (2,) means that the layer expects an input with num_features = 2\n",
+    "# and the sample size could be anything\n",
+    "# The activation function for this layer is set to \"relu\"\n",
+    "model.add(Dense(units=4, input_shape=(2,), activation=\"relu\"))\n",
+    "\n",
+    "# 2nd Hidden layer\n",
+    "# This is also a fully-connected layer and we do not need to specify the\n",
+    "# shape of the input anymore (We need to do that only for the first layer)\n",
+    "# NOTE: Now we didn't add the activation seperately. Instead we just added it\n",
+    "# while calling Dense(). This and the way used for the first layer are Equivalent!\n",
+    "model.add(Dense(units=4, activation=\"relu\"))\n",
+    "\n",
+    "          \n",
+    "# The output layer\n",
+    "model.add(Dense(units=1))\n",
+    "model.add(Activation(\"sigmoid\"))\n",
+    "\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### XOR using neural networks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 320,
+       "width": 332
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a network to solve the XOR problem\n",
+    "\n",
+    "# Loading and plotting the data\n",
+    "xor = pd.read_csv(\"data/xor.csv\")\n",
+    "\n",
+    "# Using x and y coordinates as featues\n",
+    "features = xor.iloc[:, :-1]\n",
+    "# Convert boolean to integer values (True->1 and False->0)\n",
+    "labels = (1-xor.iloc[:, -1].astype(int))\n",
+    "\n",
+    "colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.xlim([-2, 2])\n",
+    "plt.ylim([-2, 2])\n",
+    "plt.title(\"Blue points are False\")\n",
+    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\") ;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Building a simple Keras model\n",
+    "\n",
+    "def a_simple_NN():\n",
+    "    \n",
+    "    model = Sequential()\n",
+    "\n",
+    "    model.add(Dense(4, input_shape = (2,), activation = \"relu\"))\n",
+    "\n",
+    "    model.add(Dense(4, activation = \"relu\"))\n",
+    "\n",
+    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
+    "\n",
+    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 350 samples, validate on 150 samples\n",
+      "Epoch 1/300\n",
+      "350/350 [==============================] - 0s 957us/step - loss: 0.6689 - acc: 0.5343 - val_loss: 0.6602 - val_acc: 0.5533\n",
+      "Epoch 2/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.6577 - acc: 0.5371 - val_loss: 0.6518 - val_acc: 0.5533\n",
+      "Epoch 3/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.6494 - acc: 0.5429 - val_loss: 0.6444 - val_acc: 0.5533\n",
+      "Epoch 4/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.6421 - acc: 0.5457 - val_loss: 0.6373 - val_acc: 0.5667\n",
+      "Epoch 5/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.6351 - acc: 0.5571 - val_loss: 0.6306 - val_acc: 0.5733\n",
+      "Epoch 6/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.6285 - acc: 0.5800 - val_loss: 0.6240 - val_acc: 0.5733\n",
+      "Epoch 7/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.6223 - acc: 0.6057 - val_loss: 0.6177 - val_acc: 0.5933\n",
+      "Epoch 8/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.6163 - acc: 0.6543 - val_loss: 0.6112 - val_acc: 0.6467\n",
+      "Epoch 9/300\n",
+      "350/350 [==============================] - 0s 58us/step - loss: 0.6100 - acc: 0.6714 - val_loss: 0.6047 - val_acc: 0.6800\n",
+      "Epoch 10/300\n",
+      "350/350 [==============================] - 0s 51us/step - loss: 0.6036 - acc: 0.6886 - val_loss: 0.5979 - val_acc: 0.7000\n",
+      "Epoch 11/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.5970 - acc: 0.7086 - val_loss: 0.5914 - val_acc: 0.7267\n",
+      "Epoch 12/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.5907 - acc: 0.7286 - val_loss: 0.5849 - val_acc: 0.7333\n",
+      "Epoch 13/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.5836 - acc: 0.7286 - val_loss: 0.5777 - val_acc: 0.7667\n",
+      "Epoch 14/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.5765 - acc: 0.7514 - val_loss: 0.5706 - val_acc: 0.7733\n",
+      "Epoch 15/300\n",
+      "350/350 [==============================] - 0s 47us/step - loss: 0.5693 - acc: 0.7543 - val_loss: 0.5637 - val_acc: 0.7600\n",
+      "Epoch 16/300\n",
+      "350/350 [==============================] - 0s 47us/step - loss: 0.5617 - acc: 0.7686 - val_loss: 0.5562 - val_acc: 0.7667\n",
+      "Epoch 17/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.5535 - acc: 0.7829 - val_loss: 0.5483 - val_acc: 0.7733\n",
+      "Epoch 18/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.5453 - acc: 0.7714 - val_loss: 0.5400 - val_acc: 0.7800\n",
+      "Epoch 19/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.5375 - acc: 0.7971 - val_loss: 0.5321 - val_acc: 0.7933\n",
+      "Epoch 20/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.5290 - acc: 0.8171 - val_loss: 0.5238 - val_acc: 0.8000\n",
+      "Epoch 21/300\n",
+      "350/350 [==============================] - 0s 47us/step - loss: 0.5204 - acc: 0.8229 - val_loss: 0.5154 - val_acc: 0.8133\n",
+      "Epoch 22/300\n",
+      "350/350 [==============================] - 0s 59us/step - loss: 0.5121 - acc: 0.8343 - val_loss: 0.5070 - val_acc: 0.8267\n",
+      "Epoch 23/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.5036 - acc: 0.8371 - val_loss: 0.4992 - val_acc: 0.8333\n",
+      "Epoch 24/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.4952 - acc: 0.8657 - val_loss: 0.4909 - val_acc: 0.8333\n",
+      "Epoch 25/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.4868 - acc: 0.8657 - val_loss: 0.4825 - val_acc: 0.8333\n",
+      "Epoch 26/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.4780 - acc: 0.8686 - val_loss: 0.4741 - val_acc: 0.8400\n",
+      "Epoch 27/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.4695 - acc: 0.8743 - val_loss: 0.4661 - val_acc: 0.8600\n",
+      "Epoch 28/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.4613 - acc: 0.8857 - val_loss: 0.4582 - val_acc: 0.8600\n",
+      "Epoch 29/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.4534 - acc: 0.8914 - val_loss: 0.4509 - val_acc: 0.8600\n",
+      "Epoch 30/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.4455 - acc: 0.8943 - val_loss: 0.4432 - val_acc: 0.8667\n",
+      "Epoch 31/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.4376 - acc: 0.9000 - val_loss: 0.4358 - val_acc: 0.8800\n",
+      "Epoch 32/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.4298 - acc: 0.9057 - val_loss: 0.4283 - val_acc: 0.8933\n",
+      "Epoch 33/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.4221 - acc: 0.9029 - val_loss: 0.4211 - val_acc: 0.9000\n",
+      "Epoch 34/300\n",
+      "350/350 [==============================] - 0s 47us/step - loss: 0.4144 - acc: 0.9057 - val_loss: 0.4139 - val_acc: 0.9200\n",
+      "Epoch 35/300\n",
+      "350/350 [==============================] - 0s 51us/step - loss: 0.4067 - acc: 0.9057 - val_loss: 0.4068 - val_acc: 0.9200\n",
+      "Epoch 36/300\n",
+      "350/350 [==============================] - 0s 51us/step - loss: 0.3992 - acc: 0.9143 - val_loss: 0.3995 - val_acc: 0.9200\n",
+      "Epoch 37/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.3917 - acc: 0.9114 - val_loss: 0.3924 - val_acc: 0.9200\n",
+      "Epoch 38/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.3846 - acc: 0.9171 - val_loss: 0.3857 - val_acc: 0.9200\n",
+      "Epoch 39/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.3774 - acc: 0.9200 - val_loss: 0.3790 - val_acc: 0.9200\n",
+      "Epoch 40/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.3703 - acc: 0.9171 - val_loss: 0.3721 - val_acc: 0.9333\n",
+      "Epoch 41/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.3631 - acc: 0.9257 - val_loss: 0.3656 - val_acc: 0.9400\n",
+      "Epoch 42/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.3561 - acc: 0.9286 - val_loss: 0.3586 - val_acc: 0.9400\n",
+      "Epoch 43/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.3488 - acc: 0.9286 - val_loss: 0.3517 - val_acc: 0.9467\n",
+      "Epoch 44/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.3416 - acc: 0.9314 - val_loss: 0.3445 - val_acc: 0.9533\n",
+      "Epoch 45/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.3343 - acc: 0.9343 - val_loss: 0.3376 - val_acc: 0.9600\n",
+      "Epoch 46/300\n",
+      "350/350 [==============================] - 0s 61us/step - loss: 0.3274 - acc: 0.9400 - val_loss: 0.3308 - val_acc: 0.9600\n",
+      "Epoch 47/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.3204 - acc: 0.9371 - val_loss: 0.3242 - val_acc: 0.9600\n",
+      "Epoch 48/300\n",
+      "350/350 [==============================] - 0s 53us/step - loss: 0.3137 - acc: 0.9400 - val_loss: 0.3177 - val_acc: 0.9600\n",
+      "Epoch 49/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.3070 - acc: 0.9486 - val_loss: 0.3116 - val_acc: 0.9667\n",
+      "Epoch 50/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.3007 - acc: 0.9514 - val_loss: 0.3058 - val_acc: 0.9667\n",
+      "Epoch 51/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.2947 - acc: 0.9486 - val_loss: 0.3003 - val_acc: 0.9800\n",
+      "Epoch 52/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.2886 - acc: 0.9543 - val_loss: 0.2947 - val_acc: 0.9800\n",
+      "Epoch 53/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.2830 - acc: 0.9486 - val_loss: 0.2894 - val_acc: 0.9800\n",
+      "Epoch 54/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.2773 - acc: 0.9600 - val_loss: 0.2841 - val_acc: 0.9800\n",
+      "Epoch 55/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.2716 - acc: 0.9686 - val_loss: 0.2789 - val_acc: 0.9800\n",
+      "Epoch 56/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.2662 - acc: 0.9657 - val_loss: 0.2737 - val_acc: 0.9733\n",
+      "Epoch 57/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.2606 - acc: 0.9714 - val_loss: 0.2688 - val_acc: 0.9733\n",
+      "Epoch 58/300\n",
+      "350/350 [==============================] - 0s 51us/step - loss: 0.2558 - acc: 0.9686 - val_loss: 0.2641 - val_acc: 0.9733\n",
+      "Epoch 59/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.2507 - acc: 0.9714 - val_loss: 0.2595 - val_acc: 0.9733\n",
+      "Epoch 60/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.2457 - acc: 0.9714 - val_loss: 0.2547 - val_acc: 0.9733\n",
+      "Epoch 61/300\n",
+      "350/350 [==============================] - 0s 58us/step - loss: 0.2408 - acc: 0.9686 - val_loss: 0.2501 - val_acc: 0.9733\n",
+      "Epoch 62/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.2362 - acc: 0.9714 - val_loss: 0.2458 - val_acc: 0.9733\n",
+      "Epoch 63/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.2315 - acc: 0.9714 - val_loss: 0.2415 - val_acc: 0.9733\n",
+      "Epoch 64/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.2273 - acc: 0.9771 - val_loss: 0.2373 - val_acc: 0.9733\n",
+      "Epoch 65/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.2228 - acc: 0.9771 - val_loss: 0.2335 - val_acc: 0.9733\n",
+      "Epoch 66/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.2189 - acc: 0.9743 - val_loss: 0.2297 - val_acc: 0.9733\n",
+      "Epoch 67/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.2148 - acc: 0.9771 - val_loss: 0.2258 - val_acc: 0.9733\n",
+      "Epoch 68/300\n",
+      "350/350 [==============================] - 0s 61us/step - loss: 0.2111 - acc: 0.9771 - val_loss: 0.2221 - val_acc: 0.9733\n",
+      "Epoch 69/300\n",
+      "350/350 [==============================] - 0s 65us/step - loss: 0.2072 - acc: 0.9771 - val_loss: 0.2184 - val_acc: 0.9733\n",
+      "Epoch 70/300\n",
+      "350/350 [==============================] - 0s 59us/step - loss: 0.2037 - acc: 0.9800 - val_loss: 0.2148 - val_acc: 0.9733\n",
+      "Epoch 71/300\n",
+      "350/350 [==============================] - 0s 57us/step - loss: 0.1997 - acc: 0.9800 - val_loss: 0.2114 - val_acc: 0.9733\n",
+      "Epoch 72/300\n",
+      "350/350 [==============================] - 0s 70us/step - loss: 0.1962 - acc: 0.9771 - val_loss: 0.2079 - val_acc: 0.9733\n",
+      "Epoch 73/300\n",
+      "350/350 [==============================] - 0s 69us/step - loss: 0.1929 - acc: 0.9800 - val_loss: 0.2046 - val_acc: 0.9667\n",
+      "Epoch 74/300\n",
+      "350/350 [==============================] - 0s 57us/step - loss: 0.1895 - acc: 0.9800 - val_loss: 0.2014 - val_acc: 0.9667\n",
+      "Epoch 75/300\n",
+      "350/350 [==============================] - 0s 60us/step - loss: 0.1862 - acc: 0.9800 - val_loss: 0.1983 - val_acc: 0.9667\n",
+      "Epoch 76/300\n",
+      "350/350 [==============================] - 0s 56us/step - loss: 0.1833 - acc: 0.9800 - val_loss: 0.1953 - val_acc: 0.9667\n",
+      "Epoch 77/300\n",
+      "350/350 [==============================] - 0s 55us/step - loss: 0.1803 - acc: 0.9743 - val_loss: 0.1927 - val_acc: 0.9667\n",
+      "Epoch 78/300\n",
+      "350/350 [==============================] - 0s 63us/step - loss: 0.1777 - acc: 0.9800 - val_loss: 0.1901 - val_acc: 0.9667\n",
+      "Epoch 79/300\n",
+      "350/350 [==============================] - 0s 62us/step - loss: 0.1751 - acc: 0.9771 - val_loss: 0.1875 - val_acc: 0.9667\n",
+      "Epoch 80/300\n",
+      "350/350 [==============================] - 0s 59us/step - loss: 0.1725 - acc: 0.9771 - val_loss: 0.1852 - val_acc: 0.9667\n",
+      "Epoch 81/300\n",
+      "350/350 [==============================] - 0s 67us/step - loss: 0.1697 - acc: 0.9771 - val_loss: 0.1824 - val_acc: 0.9667\n",
+      "Epoch 82/300\n",
+      "350/350 [==============================] - 0s 70us/step - loss: 0.1675 - acc: 0.9771 - val_loss: 0.1798 - val_acc: 0.9667\n",
+      "Epoch 83/300\n",
+      "350/350 [==============================] - 0s 77us/step - loss: 0.1647 - acc: 0.9771 - val_loss: 0.1773 - val_acc: 0.9667\n",
+      "Epoch 84/300\n",
+      "350/350 [==============================] - 0s 56us/step - loss: 0.1627 - acc: 0.9771 - val_loss: 0.1748 - val_acc: 0.9667\n",
+      "Epoch 85/300\n",
+      "350/350 [==============================] - 0s 57us/step - loss: 0.1598 - acc: 0.9771 - val_loss: 0.1725 - val_acc: 0.9667\n",
+      "Epoch 86/300\n",
+      "350/350 [==============================] - 0s 66us/step - loss: 0.1578 - acc: 0.9771 - val_loss: 0.1703 - val_acc: 0.9667\n",
+      "Epoch 87/300\n",
+      "350/350 [==============================] - 0s 57us/step - loss: 0.1557 - acc: 0.9771 - val_loss: 0.1680 - val_acc: 0.9667\n",
+      "Epoch 88/300\n",
+      "350/350 [==============================] - 0s 49us/step - loss: 0.1536 - acc: 0.9771 - val_loss: 0.1660 - val_acc: 0.9667\n",
+      "Epoch 89/300\n",
+      "350/350 [==============================] - 0s 55us/step - loss: 0.1517 - acc: 0.9771 - val_loss: 0.1641 - val_acc: 0.9667\n",
+      "Epoch 90/300\n",
+      "350/350 [==============================] - 0s 49us/step - loss: 0.1496 - acc: 0.9771 - val_loss: 0.1621 - val_acc: 0.9667\n",
+      "Epoch 91/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.1478 - acc: 0.9771 - val_loss: 0.1602 - val_acc: 0.9667\n",
+      "Epoch 92/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.1463 - acc: 0.9743 - val_loss: 0.1585 - val_acc: 0.9667\n",
+      "Epoch 93/300\n",
+      "350/350 [==============================] - 0s 56us/step - loss: 0.1443 - acc: 0.9771 - val_loss: 0.1568 - val_acc: 0.9667\n",
+      "Epoch 94/300\n",
+      "350/350 [==============================] - 0s 56us/step - loss: 0.1427 - acc: 0.9743 - val_loss: 0.1550 - val_acc: 0.9667\n",
+      "Epoch 95/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.1410 - acc: 0.9771 - val_loss: 0.1533 - val_acc: 0.9667\n",
+      "Epoch 96/300\n",
+      "350/350 [==============================] - 0s 53us/step - loss: 0.1395 - acc: 0.9771 - val_loss: 0.1518 - val_acc: 0.9667\n",
+      "Epoch 97/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.1382 - acc: 0.9771 - val_loss: 0.1503 - val_acc: 0.9667\n",
+      "Epoch 98/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.1368 - acc: 0.9771 - val_loss: 0.1490 - val_acc: 0.9667\n",
+      "Epoch 99/300\n",
+      "350/350 [==============================] - 0s 53us/step - loss: 0.1352 - acc: 0.9771 - val_loss: 0.1475 - val_acc: 0.9667\n",
+      "Epoch 100/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.1340 - acc: 0.9771 - val_loss: 0.1462 - val_acc: 0.9667\n",
+      "Epoch 101/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.1329 - acc: 0.9771 - val_loss: 0.1449 - val_acc: 0.9667\n",
+      "Epoch 102/300\n",
+      "350/350 [==============================] - 0s 59us/step - loss: 0.1314 - acc: 0.9771 - val_loss: 0.1435 - val_acc: 0.9667\n",
+      "Epoch 103/300\n",
+      "350/350 [==============================] - 0s 71us/step - loss: 0.1302 - acc: 0.9771 - val_loss: 0.1422 - val_acc: 0.9667\n",
+      "Epoch 104/300\n",
+      "350/350 [==============================] - 0s 130us/step - loss: 0.1289 - acc: 0.9771 - val_loss: 0.1408 - val_acc: 0.9667\n",
+      "Epoch 105/300\n",
+      "350/350 [==============================] - 0s 61us/step - loss: 0.1274 - acc: 0.9771 - val_loss: 0.1394 - val_acc: 0.9667\n",
+      "Epoch 106/300\n",
+      "350/350 [==============================] - 0s 59us/step - loss: 0.1262 - acc: 0.9771 - val_loss: 0.1379 - val_acc: 0.9667\n",
+      "Epoch 107/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.1250 - acc: 0.9771 - val_loss: 0.1367 - val_acc: 0.9667\n",
+      "Epoch 108/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.1238 - acc: 0.9771 - val_loss: 0.1355 - val_acc: 0.9667\n",
+      "Epoch 109/300\n",
+      "350/350 [==============================] - 0s 51us/step - loss: 0.1229 - acc: 0.9771 - val_loss: 0.1343 - val_acc: 0.9667\n",
+      "Epoch 110/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.1218 - acc: 0.9771 - val_loss: 0.1333 - val_acc: 0.9667\n",
+      "Epoch 111/300\n",
+      "350/350 [==============================] - 0s 52us/step - loss: 0.1206 - acc: 0.9771 - val_loss: 0.1322 - val_acc: 0.9667\n",
+      "Epoch 112/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.1196 - acc: 0.9771 - val_loss: 0.1311 - val_acc: 0.9667\n",
+      "Epoch 113/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.1186 - acc: 0.9771 - val_loss: 0.1299 - val_acc: 0.9667\n",
+      "Epoch 114/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.1175 - acc: 0.9771 - val_loss: 0.1287 - val_acc: 0.9667\n",
+      "Epoch 115/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.1166 - acc: 0.9771 - val_loss: 0.1276 - val_acc: 0.9667\n",
+      "Epoch 116/300\n",
+      "350/350 [==============================] - 0s 56us/step - loss: 0.1160 - acc: 0.9743 - val_loss: 0.1268 - val_acc: 0.9667\n",
+      "Epoch 117/300\n",
+      "350/350 [==============================] - 0s 49us/step - loss: 0.1149 - acc: 0.9771 - val_loss: 0.1260 - val_acc: 0.9667\n",
+      "Epoch 118/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.1142 - acc: 0.9771 - val_loss: 0.1251 - val_acc: 0.9667\n",
+      "Epoch 119/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.1134 - acc: 0.9771 - val_loss: 0.1244 - val_acc: 0.9667\n",
+      "Epoch 120/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.1124 - acc: 0.9771 - val_loss: 0.1235 - val_acc: 0.9667\n",
+      "Epoch 121/300\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "350/350 [==============================] - 0s 45us/step - loss: 0.1119 - acc: 0.9771 - val_loss: 0.1226 - val_acc: 0.9667\n",
+      "Epoch 122/300\n",
+      "350/350 [==============================] - 0s 60us/step - loss: 0.1111 - acc: 0.9771 - val_loss: 0.1217 - val_acc: 0.9667\n",
+      "Epoch 123/300\n",
+      "350/350 [==============================] - 0s 55us/step - loss: 0.1106 - acc: 0.9771 - val_loss: 0.1211 - val_acc: 0.9667\n",
+      "Epoch 124/300\n",
+      "350/350 [==============================] - 0s 61us/step - loss: 0.1097 - acc: 0.9743 - val_loss: 0.1204 - val_acc: 0.9667\n",
+      "Epoch 125/300\n",
+      "350/350 [==============================] - 0s 70us/step - loss: 0.1089 - acc: 0.9771 - val_loss: 0.1196 - val_acc: 0.9667\n",
+      "Epoch 126/300\n",
+      "350/350 [==============================] - 0s 61us/step - loss: 0.1082 - acc: 0.9771 - val_loss: 0.1189 - val_acc: 0.9667\n",
+      "Epoch 127/300\n",
+      "350/350 [==============================] - 0s 111us/step - loss: 0.1077 - acc: 0.9771 - val_loss: 0.1181 - val_acc: 0.9667\n",
+      "Epoch 128/300\n",
+      "350/350 [==============================] - 0s 61us/step - loss: 0.1073 - acc: 0.9771 - val_loss: 0.1174 - val_acc: 0.9667\n",
+      "Epoch 129/300\n",
+      "350/350 [==============================] - 0s 57us/step - loss: 0.1067 - acc: 0.9771 - val_loss: 0.1168 - val_acc: 0.9667\n",
+      "Epoch 130/300\n",
+      "350/350 [==============================] - 0s 65us/step - loss: 0.1059 - acc: 0.9771 - val_loss: 0.1161 - val_acc: 0.9667\n",
+      "Epoch 131/300\n",
+      "350/350 [==============================] - 0s 54us/step - loss: 0.1054 - acc: 0.9771 - val_loss: 0.1153 - val_acc: 0.9667\n",
+      "Epoch 132/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.1047 - acc: 0.9771 - val_loss: 0.1147 - val_acc: 0.9667\n",
+      "Epoch 133/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.1044 - acc: 0.9771 - val_loss: 0.1141 - val_acc: 0.9667\n",
+      "Epoch 134/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.1037 - acc: 0.9771 - val_loss: 0.1138 - val_acc: 0.9667\n",
+      "Epoch 135/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.1033 - acc: 0.9771 - val_loss: 0.1132 - val_acc: 0.9667\n",
+      "Epoch 136/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.1028 - acc: 0.9743 - val_loss: 0.1127 - val_acc: 0.9667\n",
+      "Epoch 137/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.1023 - acc: 0.9771 - val_loss: 0.1121 - val_acc: 0.9667\n",
+      "Epoch 138/300\n",
+      "350/350 [==============================] - 0s 56us/step - loss: 0.1020 - acc: 0.9771 - val_loss: 0.1114 - val_acc: 0.9667\n",
+      "Epoch 139/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.1013 - acc: 0.9771 - val_loss: 0.1109 - val_acc: 0.9667\n",
+      "Epoch 140/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.1007 - acc: 0.9771 - val_loss: 0.1103 - val_acc: 0.9667\n",
+      "Epoch 141/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.1006 - acc: 0.9771 - val_loss: 0.1098 - val_acc: 0.9667\n",
+      "Epoch 142/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.1000 - acc: 0.9771 - val_loss: 0.1092 - val_acc: 0.9667\n",
+      "Epoch 143/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0992 - acc: 0.9771 - val_loss: 0.1087 - val_acc: 0.9667\n",
+      "Epoch 144/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0991 - acc: 0.9771 - val_loss: 0.1081 - val_acc: 0.9667\n",
+      "Epoch 145/300\n",
+      "350/350 [==============================] - 0s 52us/step - loss: 0.0986 - acc: 0.9771 - val_loss: 0.1076 - val_acc: 0.9667\n",
+      "Epoch 146/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0979 - acc: 0.9771 - val_loss: 0.1070 - val_acc: 0.9667\n",
+      "Epoch 147/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0975 - acc: 0.9771 - val_loss: 0.1064 - val_acc: 0.9667\n",
+      "Epoch 148/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0972 - acc: 0.9771 - val_loss: 0.1059 - val_acc: 0.9667\n",
+      "Epoch 149/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0970 - acc: 0.9771 - val_loss: 0.1054 - val_acc: 0.9667\n",
+      "Epoch 150/300\n",
+      "350/350 [==============================] - 0s 41us/step - loss: 0.0964 - acc: 0.9771 - val_loss: 0.1051 - val_acc: 0.9667\n",
+      "Epoch 151/300\n",
+      "350/350 [==============================] - 0s 58us/step - loss: 0.0960 - acc: 0.9771 - val_loss: 0.1047 - val_acc: 0.9667\n",
+      "Epoch 152/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0958 - acc: 0.9771 - val_loss: 0.1043 - val_acc: 0.9667\n",
+      "Epoch 153/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0957 - acc: 0.9771 - val_loss: 0.1040 - val_acc: 0.9667\n",
+      "Epoch 154/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0950 - acc: 0.9771 - val_loss: 0.1037 - val_acc: 0.9667\n",
+      "Epoch 155/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0947 - acc: 0.9771 - val_loss: 0.1033 - val_acc: 0.9667\n",
+      "Epoch 156/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0944 - acc: 0.9771 - val_loss: 0.1028 - val_acc: 0.9667\n",
+      "Epoch 157/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0938 - acc: 0.9771 - val_loss: 0.1024 - val_acc: 0.9667\n",
+      "Epoch 158/300\n",
+      "350/350 [==============================] - 0s 47us/step - loss: 0.0940 - acc: 0.9771 - val_loss: 0.1018 - val_acc: 0.9667\n",
+      "Epoch 159/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0932 - acc: 0.9771 - val_loss: 0.1014 - val_acc: 0.9667\n",
+      "Epoch 160/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0929 - acc: 0.9771 - val_loss: 0.1010 - val_acc: 0.9667\n",
+      "Epoch 161/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0927 - acc: 0.9771 - val_loss: 0.1005 - val_acc: 0.9667\n",
+      "Epoch 162/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0925 - acc: 0.9771 - val_loss: 0.1002 - val_acc: 0.9667\n",
+      "Epoch 163/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0921 - acc: 0.9771 - val_loss: 0.0998 - val_acc: 0.9667\n",
+      "Epoch 164/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.0919 - acc: 0.9771 - val_loss: 0.0994 - val_acc: 0.9667\n",
+      "Epoch 165/300\n",
+      "350/350 [==============================] - 0s 52us/step - loss: 0.0916 - acc: 0.9771 - val_loss: 0.0990 - val_acc: 0.9667\n",
+      "Epoch 166/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0913 - acc: 0.9771 - val_loss: 0.0986 - val_acc: 0.9667\n",
+      "Epoch 167/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0909 - acc: 0.9771 - val_loss: 0.0982 - val_acc: 0.9667\n",
+      "Epoch 168/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0906 - acc: 0.9771 - val_loss: 0.0979 - val_acc: 0.9667\n",
+      "Epoch 169/300\n",
+      "350/350 [==============================] - 0s 41us/step - loss: 0.0902 - acc: 0.9771 - val_loss: 0.0975 - val_acc: 0.9667\n",
+      "Epoch 170/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0901 - acc: 0.9771 - val_loss: 0.0973 - val_acc: 0.9667\n",
+      "Epoch 171/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0896 - acc: 0.9771 - val_loss: 0.0970 - val_acc: 0.9667\n",
+      "Epoch 172/300\n",
+      "350/350 [==============================] - 0s 47us/step - loss: 0.0894 - acc: 0.9771 - val_loss: 0.0966 - val_acc: 0.9667\n",
+      "Epoch 173/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0894 - acc: 0.9771 - val_loss: 0.0963 - val_acc: 0.9667\n",
+      "Epoch 174/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0888 - acc: 0.9771 - val_loss: 0.0959 - val_acc: 0.9667\n",
+      "Epoch 175/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0890 - acc: 0.9771 - val_loss: 0.0955 - val_acc: 0.9667\n",
+      "Epoch 176/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0883 - acc: 0.9771 - val_loss: 0.0952 - val_acc: 0.9667\n",
+      "Epoch 177/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0883 - acc: 0.9771 - val_loss: 0.0950 - val_acc: 0.9667\n",
+      "Epoch 178/300\n",
+      "350/350 [==============================] - 0s 56us/step - loss: 0.0882 - acc: 0.9771 - val_loss: 0.0947 - val_acc: 0.9667\n",
+      "Epoch 179/300\n",
+      "350/350 [==============================] - 0s 53us/step - loss: 0.0876 - acc: 0.9771 - val_loss: 0.0944 - val_acc: 0.9667\n",
+      "Epoch 180/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0875 - acc: 0.9771 - val_loss: 0.0941 - val_acc: 0.9667\n",
+      "Epoch 181/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0875 - acc: 0.9771 - val_loss: 0.0940 - val_acc: 0.9667\n",
+      "Epoch 182/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0869 - acc: 0.9771 - val_loss: 0.0936 - val_acc: 0.9667\n",
+      "Epoch 183/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0872 - acc: 0.9771 - val_loss: 0.0933 - val_acc: 0.9667\n",
+      "Epoch 184/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0867 - acc: 0.9771 - val_loss: 0.0932 - val_acc: 0.9667\n",
+      "Epoch 185/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0863 - acc: 0.9771 - val_loss: 0.0930 - val_acc: 0.9667\n",
+      "Epoch 186/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0860 - acc: 0.9771 - val_loss: 0.0928 - val_acc: 0.9667\n",
+      "Epoch 187/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0861 - acc: 0.9771 - val_loss: 0.0925 - val_acc: 0.9667\n",
+      "Epoch 188/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0858 - acc: 0.9771 - val_loss: 0.0921 - val_acc: 0.9667\n",
+      "Epoch 189/300\n",
+      "350/350 [==============================] - 0s 49us/step - loss: 0.0857 - acc: 0.9771 - val_loss: 0.0919 - val_acc: 0.9667\n",
+      "Epoch 190/300\n",
+      "350/350 [==============================] - 0s 47us/step - loss: 0.0855 - acc: 0.9771 - val_loss: 0.0916 - val_acc: 0.9667\n",
+      "Epoch 191/300\n",
+      "350/350 [==============================] - 0s 60us/step - loss: 0.0851 - acc: 0.9771 - val_loss: 0.0915 - val_acc: 0.9667\n",
+      "Epoch 192/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0847 - acc: 0.9771 - val_loss: 0.0910 - val_acc: 0.9667\n",
+      "Epoch 193/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.0846 - acc: 0.9771 - val_loss: 0.0908 - val_acc: 0.9667\n",
+      "Epoch 194/300\n",
+      "350/350 [==============================] - 0s 59us/step - loss: 0.0845 - acc: 0.9771 - val_loss: 0.0907 - val_acc: 0.9667\n",
+      "Epoch 195/300\n",
+      "350/350 [==============================] - 0s 79us/step - loss: 0.0843 - acc: 0.9771 - val_loss: 0.0902 - val_acc: 0.9667\n",
+      "Epoch 196/300\n",
+      "350/350 [==============================] - 0s 58us/step - loss: 0.0842 - acc: 0.9771 - val_loss: 0.0901 - val_acc: 0.9667\n",
+      "Epoch 197/300\n",
+      "350/350 [==============================] - 0s 67us/step - loss: 0.0838 - acc: 0.9771 - val_loss: 0.0898 - val_acc: 0.9667\n",
+      "Epoch 198/300\n",
+      "350/350 [==============================] - 0s 57us/step - loss: 0.0838 - acc: 0.9771 - val_loss: 0.0895 - val_acc: 0.9667\n",
+      "Epoch 199/300\n",
+      "350/350 [==============================] - 0s 61us/step - loss: 0.0837 - acc: 0.9771 - val_loss: 0.0893 - val_acc: 0.9667\n",
+      "Epoch 200/300\n",
+      "350/350 [==============================] - 0s 74us/step - loss: 0.0833 - acc: 0.9771 - val_loss: 0.0892 - val_acc: 0.9667\n",
+      "Epoch 201/300\n",
+      "350/350 [==============================] - 0s 74us/step - loss: 0.0830 - acc: 0.9771 - val_loss: 0.0890 - val_acc: 0.9667\n",
+      "Epoch 202/300\n",
+      "350/350 [==============================] - 0s 64us/step - loss: 0.0830 - acc: 0.9771 - val_loss: 0.0884 - val_acc: 0.9667\n",
+      "Epoch 203/300\n",
+      "350/350 [==============================] - 0s 60us/step - loss: 0.0828 - acc: 0.9771 - val_loss: 0.0882 - val_acc: 0.9667\n",
+      "Epoch 204/300\n",
+      "350/350 [==============================] - 0s 51us/step - loss: 0.0825 - acc: 0.9771 - val_loss: 0.0880 - val_acc: 0.9667\n",
+      "Epoch 205/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0825 - acc: 0.9771 - val_loss: 0.0877 - val_acc: 0.9667\n",
+      "Epoch 206/300\n",
+      "350/350 [==============================] - 0s 47us/step - loss: 0.0823 - acc: 0.9771 - val_loss: 0.0875 - val_acc: 0.9667\n",
+      "Epoch 207/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0823 - acc: 0.9771 - val_loss: 0.0873 - val_acc: 0.9667\n",
+      "Epoch 208/300\n",
+      "350/350 [==============================] - 0s 57us/step - loss: 0.0820 - acc: 0.9771 - val_loss: 0.0872 - val_acc: 0.9667\n",
+      "Epoch 209/300\n",
+      "350/350 [==============================] - 0s 58us/step - loss: 0.0818 - acc: 0.9771 - val_loss: 0.0873 - val_acc: 0.9667\n",
+      "Epoch 210/300\n",
+      "350/350 [==============================] - 0s 47us/step - loss: 0.0816 - acc: 0.9771 - val_loss: 0.0872 - val_acc: 0.9667\n",
+      "Epoch 211/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0814 - acc: 0.9771 - val_loss: 0.0867 - val_acc: 0.9667\n",
+      "Epoch 212/300\n",
+      "350/350 [==============================] - 0s 58us/step - loss: 0.0816 - acc: 0.9771 - val_loss: 0.0865 - val_acc: 0.9667\n",
+      "Epoch 213/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0812 - acc: 0.9771 - val_loss: 0.0864 - val_acc: 0.9667\n",
+      "Epoch 214/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0814 - acc: 0.9771 - val_loss: 0.0863 - val_acc: 0.9667\n",
+      "Epoch 215/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0811 - acc: 0.9771 - val_loss: 0.0862 - val_acc: 0.9667\n",
+      "Epoch 216/300\n",
+      "350/350 [==============================] - 0s 47us/step - loss: 0.0808 - acc: 0.9771 - val_loss: 0.0858 - val_acc: 0.9667\n",
+      "Epoch 217/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0809 - acc: 0.9771 - val_loss: 0.0857 - val_acc: 0.9667\n",
+      "Epoch 218/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0809 - acc: 0.9771 - val_loss: 0.0856 - val_acc: 0.9667\n",
+      "Epoch 219/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0801 - acc: 0.9771 - val_loss: 0.0857 - val_acc: 0.9667\n",
+      "Epoch 220/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0803 - acc: 0.9771 - val_loss: 0.0855 - val_acc: 0.9667\n",
+      "Epoch 221/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0803 - acc: 0.9771 - val_loss: 0.0853 - val_acc: 0.9667\n",
+      "Epoch 222/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0801 - acc: 0.9771 - val_loss: 0.0850 - val_acc: 0.9667\n",
+      "Epoch 223/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0798 - acc: 0.9771 - val_loss: 0.0848 - val_acc: 0.9667\n",
+      "Epoch 224/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0798 - acc: 0.9771 - val_loss: 0.0847 - val_acc: 0.9667\n",
+      "Epoch 225/300\n",
+      "350/350 [==============================] - 0s 58us/step - loss: 0.0795 - acc: 0.9771 - val_loss: 0.0845 - val_acc: 0.9667\n",
+      "Epoch 226/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0795 - acc: 0.9771 - val_loss: 0.0842 - val_acc: 0.9667\n",
+      "Epoch 227/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0793 - acc: 0.9771 - val_loss: 0.0840 - val_acc: 0.9667\n",
+      "Epoch 228/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0792 - acc: 0.9771 - val_loss: 0.0838 - val_acc: 0.9667\n",
+      "Epoch 229/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0791 - acc: 0.9771 - val_loss: 0.0836 - val_acc: 0.9667\n",
+      "Epoch 230/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0791 - acc: 0.9771 - val_loss: 0.0835 - val_acc: 0.9667\n",
+      "Epoch 231/300\n",
+      "350/350 [==============================] - 0s 49us/step - loss: 0.0792 - acc: 0.9771 - val_loss: 0.0834 - val_acc: 0.9667\n",
+      "Epoch 232/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0784 - acc: 0.9771 - val_loss: 0.0834 - val_acc: 0.9667\n",
+      "Epoch 233/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0788 - acc: 0.9771 - val_loss: 0.0832 - val_acc: 0.9667\n",
+      "Epoch 234/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0786 - acc: 0.9771 - val_loss: 0.0828 - val_acc: 0.9667\n",
+      "Epoch 235/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0784 - acc: 0.9771 - val_loss: 0.0825 - val_acc: 0.9667\n",
+      "Epoch 236/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0789 - acc: 0.9771 - val_loss: 0.0823 - val_acc: 0.9667\n",
+      "Epoch 237/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0781 - acc: 0.9771 - val_loss: 0.0822 - val_acc: 0.9667\n",
+      "Epoch 238/300\n",
+      "350/350 [==============================] - 0s 54us/step - loss: 0.0783 - acc: 0.9771 - val_loss: 0.0821 - val_acc: 0.9667\n",
+      "Epoch 239/300\n",
+      "350/350 [==============================] - 0s 53us/step - loss: 0.0778 - acc: 0.9771 - val_loss: 0.0819 - val_acc: 0.9667\n",
+      "Epoch 240/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0779 - acc: 0.9771 - val_loss: 0.0817 - val_acc: 0.9667\n",
+      "Epoch 241/300\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0777 - acc: 0.9771 - val_loss: 0.0815 - val_acc: 0.9667\n",
+      "Epoch 242/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0777 - acc: 0.9771 - val_loss: 0.0814 - val_acc: 0.9667\n",
+      "Epoch 243/300\n",
+      "350/350 [==============================] - 0s 52us/step - loss: 0.0776 - acc: 0.9771 - val_loss: 0.0814 - val_acc: 0.9667\n",
+      "Epoch 244/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0774 - acc: 0.9771 - val_loss: 0.0814 - val_acc: 0.9667\n",
+      "Epoch 245/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0772 - acc: 0.9771 - val_loss: 0.0809 - val_acc: 0.9667\n",
+      "Epoch 246/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0773 - acc: 0.9771 - val_loss: 0.0808 - val_acc: 0.9667\n",
+      "Epoch 247/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0770 - acc: 0.9771 - val_loss: 0.0808 - val_acc: 0.9667\n",
+      "Epoch 248/300\n",
+      "350/350 [==============================] - 0s 49us/step - loss: 0.0771 - acc: 0.9771 - val_loss: 0.0806 - val_acc: 0.9667\n",
+      "Epoch 249/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.0770 - acc: 0.9771 - val_loss: 0.0805 - val_acc: 0.9667\n",
+      "Epoch 250/300\n",
+      "350/350 [==============================] - 0s 60us/step - loss: 0.0766 - acc: 0.9771 - val_loss: 0.0805 - val_acc: 0.9667\n",
+      "Epoch 251/300\n",
+      "350/350 [==============================] - 0s 53us/step - loss: 0.0769 - acc: 0.9771 - val_loss: 0.0803 - val_acc: 0.9667\n",
+      "Epoch 252/300\n",
+      "350/350 [==============================] - 0s 49us/step - loss: 0.0766 - acc: 0.9771 - val_loss: 0.0802 - val_acc: 0.9667\n",
+      "Epoch 253/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0768 - acc: 0.9771 - val_loss: 0.0801 - val_acc: 0.9667\n",
+      "Epoch 254/300\n",
+      "350/350 [==============================] - 0s 48us/step - loss: 0.0763 - acc: 0.9771 - val_loss: 0.0799 - val_acc: 0.9667\n",
+      "Epoch 255/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0764 - acc: 0.9771 - val_loss: 0.0797 - val_acc: 0.9667\n",
+      "Epoch 256/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0760 - acc: 0.9771 - val_loss: 0.0795 - val_acc: 0.9667\n",
+      "Epoch 257/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0764 - acc: 0.9771 - val_loss: 0.0793 - val_acc: 0.9667\n",
+      "Epoch 258/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0759 - acc: 0.9771 - val_loss: 0.0793 - val_acc: 0.9667\n",
+      "Epoch 259/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0760 - acc: 0.9771 - val_loss: 0.0793 - val_acc: 0.9667\n",
+      "Epoch 260/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0759 - acc: 0.9771 - val_loss: 0.0790 - val_acc: 0.9667\n",
+      "Epoch 261/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0760 - acc: 0.9771 - val_loss: 0.0792 - val_acc: 0.9667\n",
+      "Epoch 262/300\n",
+      "350/350 [==============================] - 0s 41us/step - loss: 0.0756 - acc: 0.9771 - val_loss: 0.0790 - val_acc: 0.9667\n",
+      "Epoch 263/300\n",
+      "350/350 [==============================] - 0s 52us/step - loss: 0.0756 - acc: 0.9771 - val_loss: 0.0788 - val_acc: 0.9667\n",
+      "Epoch 264/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0756 - acc: 0.9771 - val_loss: 0.0786 - val_acc: 0.9667\n",
+      "Epoch 265/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0756 - acc: 0.9771 - val_loss: 0.0786 - val_acc: 0.9667\n",
+      "Epoch 266/300\n",
+      "350/350 [==============================] - 0s 47us/step - loss: 0.0752 - acc: 0.9771 - val_loss: 0.0785 - val_acc: 0.9667\n",
+      "Epoch 267/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0753 - acc: 0.9771 - val_loss: 0.0784 - val_acc: 0.9667\n",
+      "Epoch 268/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0752 - acc: 0.9771 - val_loss: 0.0782 - val_acc: 0.9667\n",
+      "Epoch 269/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0751 - acc: 0.9771 - val_loss: 0.0782 - val_acc: 0.9667\n",
+      "Epoch 270/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0751 - acc: 0.9771 - val_loss: 0.0779 - val_acc: 0.9667\n",
+      "Epoch 271/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0750 - acc: 0.9771 - val_loss: 0.0779 - val_acc: 0.9667\n",
+      "Epoch 272/300\n",
+      "350/350 [==============================] - 0s 45us/step - loss: 0.0750 - acc: 0.9771 - val_loss: 0.0778 - val_acc: 0.9667\n",
+      "Epoch 273/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0748 - acc: 0.9771 - val_loss: 0.0777 - val_acc: 0.9667\n",
+      "Epoch 274/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0745 - acc: 0.9771 - val_loss: 0.0775 - val_acc: 0.9667\n",
+      "Epoch 275/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0748 - acc: 0.9771 - val_loss: 0.0774 - val_acc: 0.9667\n",
+      "Epoch 276/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0744 - acc: 0.9771 - val_loss: 0.0775 - val_acc: 0.9667\n",
+      "Epoch 277/300\n",
+      "350/350 [==============================] - 0s 52us/step - loss: 0.0746 - acc: 0.9771 - val_loss: 0.0773 - val_acc: 0.9667\n",
+      "Epoch 278/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0746 - acc: 0.9771 - val_loss: 0.0772 - val_acc: 0.9667\n",
+      "Epoch 279/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0744 - acc: 0.9771 - val_loss: 0.0771 - val_acc: 0.9667\n",
+      "Epoch 280/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0742 - acc: 0.9771 - val_loss: 0.0770 - val_acc: 0.9667\n",
+      "Epoch 281/300\n",
+      "350/350 [==============================] - 0s 44us/step - loss: 0.0741 - acc: 0.9771 - val_loss: 0.0769 - val_acc: 0.9667\n",
+      "Epoch 282/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0741 - acc: 0.9771 - val_loss: 0.0767 - val_acc: 0.9667\n",
+      "Epoch 283/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0742 - acc: 0.9771 - val_loss: 0.0766 - val_acc: 0.9667\n",
+      "Epoch 284/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0738 - acc: 0.9771 - val_loss: 0.0767 - val_acc: 0.9667\n",
+      "Epoch 285/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0737 - acc: 0.9771 - val_loss: 0.0765 - val_acc: 0.9667\n",
+      "Epoch 286/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0738 - acc: 0.9771 - val_loss: 0.0765 - val_acc: 0.9667\n",
+      "Epoch 287/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0739 - acc: 0.9771 - val_loss: 0.0762 - val_acc: 0.9667\n",
+      "Epoch 288/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0737 - acc: 0.9771 - val_loss: 0.0762 - val_acc: 0.9667\n",
+      "Epoch 289/300\n",
+      "350/350 [==============================] - 0s 41us/step - loss: 0.0736 - acc: 0.9771 - val_loss: 0.0762 - val_acc: 0.9667\n",
+      "Epoch 290/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.0738 - acc: 0.9771 - val_loss: 0.0761 - val_acc: 0.9667\n",
+      "Epoch 291/300\n",
+      "350/350 [==============================] - 0s 50us/step - loss: 0.0734 - acc: 0.9771 - val_loss: 0.0760 - val_acc: 0.9667\n",
+      "Epoch 292/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0733 - acc: 0.9771 - val_loss: 0.0758 - val_acc: 0.9667\n",
+      "Epoch 293/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0737 - acc: 0.9771 - val_loss: 0.0756 - val_acc: 0.9667\n",
+      "Epoch 294/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0732 - acc: 0.9771 - val_loss: 0.0755 - val_acc: 0.9667\n",
+      "Epoch 295/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0730 - acc: 0.9771 - val_loss: 0.0755 - val_acc: 0.9667\n",
+      "Epoch 296/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0731 - acc: 0.9771 - val_loss: 0.0752 - val_acc: 0.9667\n",
+      "Epoch 297/300\n",
+      "350/350 [==============================] - 0s 46us/step - loss: 0.0730 - acc: 0.9771 - val_loss: 0.0751 - val_acc: 0.9667\n",
+      "Epoch 298/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0729 - acc: 0.9771 - val_loss: 0.0751 - val_acc: 0.9667\n",
+      "Epoch 299/300\n",
+      "350/350 [==============================] - 0s 42us/step - loss: 0.0731 - acc: 0.9771 - val_loss: 0.0751 - val_acc: 0.9667\n",
+      "Epoch 300/300\n",
+      "350/350 [==============================] - 0s 43us/step - loss: 0.0729 - acc: 0.9771 - val_loss: 0.0751 - val_acc: 0.9667\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Instantiating the model\n",
+    "model = a_simple_NN()\n",
+    "\n",
+    "# Splitting the dataset into training (70%) and validation sets (30%)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    features, labels, test_size=0.3)\n",
+    "\n",
+    "# Setting the number of passes through the entire training set\n",
+    "num_epochs = 300\n",
+    "\n",
+    "# model.fit() is used to train the model\n",
+    "# We can pass validation data while training\n",
+    "model_run = model.fit(X_train, y_train, epochs=num_epochs,\n",
+    "                      validation_data=(X_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\"><p><i class=\"fa fa-info-circle\"></i>&nbsp;\n",
+    "    NOTE: We can pass \"verbose=0\" to model.fit() to suppress the printing of model output on the terminal/notebook.\n",
+    "</p></div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 269,
+       "width": 389
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the loss and accuracy on the training and validation sets during the training\n",
+    "# This can be done by using Keras callback \"history\" which is applied by default\n",
+    "history_model = model_run.history\n",
+    "\n",
+    "print(\"The history has the following data: \", history_model.keys())\n",
+    "\n",
+    "# Plotting the training and validation accuracy during the training\n",
+    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
+    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
+    "plt.xlabel(\"epochs\") ;\n",
+    "plt.ylabel(\"accuracy\") ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "The plots such as above are essential for analyzing the behaviour and performance of the network and to tune it in the right direction. However, for the example above we don't expect to derive a lot of insight from this plot as the function we are trying to fit is quite simple and there is not too much noise. We will see the significance of these curves in a later example.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Before we move on forward we see how to save and load a keras model\n",
+    "model.save(\"./data/my_first_NN.h5\")\n",
+    "\n",
+    "# Optional: See what is in the hdf5 file we just created above\n",
+    "\n",
+    "from keras.models import load_model\n",
+    "model = load_model(\"./data/my_first_NN.h5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the training and validation in the example above we split our dataset into a 70-30 train-validation set. We know from previous chapters that to more robustly estimate the accuracy of our model we can use **K-fold cross-validation**.\n",
+    "This is even more important when we have small datasets and cannot afford to reserve a validation set!\n",
+    "\n",
+    "One way to do the cross-validation here would be to write our own function to do this. However, we also know that **scikit-learn** provides several handy functions to evaluate and tune the models. So the question is:\n",
+    "\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "    Can we somehow use the scikit-learn functions or the ones we wrote ourselves for scikit-learn models to evaluate and tune our Keras models?\n",
+    "\n",
+    "\n",
+    "The Answer is **YES !**\n",
+    "</p>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "We show how to do this in the following section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using scikit-learn functions on keras models\n",
+    "\n",
+    "\n",
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "Keras offers 2 wrappers which allow its Sequential models to be used with scikit-learn. \n",
+    "\n",
+    "There are: **KerasClassifier** and **KerasRegressor**.\n",
+    "\n",
+    "For more information:\n",
+    "https://keras.io/scikit-learn-api/\n",
+    "</p>\n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "\n",
+    "**Now lets see how this works!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We wrap the Keras model we created above with KerasClassifier\n",
+    "from keras.wrappers.scikit_learn import KerasClassifier\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "# Wrapping Keras model\n",
+    "# NOTE: We pass verbose=0 to suppress the model output\n",
+    "num_epochs = 400\n",
+    "model_scikit = KerasClassifier(\n",
+    "    build_fn=a_simple_NN, epochs=num_epochs, verbose=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's reuse the function to visualize the decision boundary which we saw in chapter 2 with minimal change\n",
+    "\n",
+    "def list_flatten(list_of_list):\n",
+    "    flattened_list = [i for j in list_of_list for i in j]\n",
+    "    return flattened_list\n",
+    "\n",
+    "def plot_points(plt=plt, marker='o'):\n",
+    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
+    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
+    "\n",
+    "def train_and_plot_decision_surface(\n",
+    "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
+    "):\n",
+    "\n",
+    "    features_2d = np.array(features_2d)\n",
+    "    xmin, ymin = features_2d.min(axis=0)\n",
+    "    xmax, ymax = features_2d.max(axis=0)\n",
+    "\n",
+    "    x = np.linspace(xmin, xmax, N)\n",
+    "    y = np.linspace(ymin, ymax, N)\n",
+    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
+    "\n",
+    "    if preproc is not None:\n",
+    "        points_for_classifier = preproc.fit_transform(points)\n",
+    "        features_2d = preproc.fit_transform(features_2d)\n",
+    "    else:\n",
+    "        points_for_classifier = points\n",
+    "\n",
+    "    classifier.fit(features_2d, labels, verbose=0)\n",
+    "    predicted = classifier.predict(features_2d)\n",
+    "    \n",
+    "    if name == \"Neural Net\":\n",
+    "        predicted = list_flatten(predicted)\n",
+    "    \n",
+    "    \n",
+    "    if preproc is not None:\n",
+    "        name += \" (w/ preprocessing)\"\n",
+    "    print(name + \":\\t\", sum(predicted == labels), \"/\", len(labels), \"correct\")\n",
+    "    \n",
+    "    if name == \"Neural Net\":\n",
+    "        classes = np.array(list_flatten(classifier.predict(points_for_classifier)), dtype=bool)\n",
+    "    else:\n",
+    "        classes = np.array(classifier.predict(points_for_classifier), dtype=bool)\n",
+    "    plt.plot(\n",
+    "        points[~classes][:, 0],\n",
+    "        points[~classes][:, 1],\n",
+    "        \"o\",\n",
+    "        color=\"steelblue\",\n",
+    "        markersize=1,\n",
+    "        alpha=0.01,\n",
+    "    )\n",
+    "    plt.plot(\n",
+    "        points[classes][:, 0],\n",
+    "        points[classes][:, 1],\n",
+    "        \"o\",\n",
+    "        color=\"chocolate\",\n",
+    "        markersize=1,\n",
+    "        alpha=0.04,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural Net:\t 484 / 500 correct\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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G+EZnZ+cv8jOKUkOmLEtFp4ZA7rWUkmcOdPHEnl5MxvPUgQs0lml84IaltDVUTdkOzCzt5MPXLaMycJrH918gHesXBPjVzavwGtq4bWefVik6M9GW4y6dK5VYpczsz+l72ptxHIfHO9IfqwAPbmxky/L5M+orUz2bUnTMvpOYB7bB8Ven9F/yGCNP/g3ej34FIyfpDzNL0dEkcM1D8NZ3M7Rf4Gu/O3++TUrRsUMDcGZXhnalR7/ugzjCKNm0E0dkWXlFiryNJdlX9sgFIo89DNFJLkQGTmO//h1Gdj+K593/DU+guuj+LLRWKTrZcz2XK99sjP9Lx0en+I7Ozs6dra2tm4g9EbgNWAscBv4Y+ObMTZ0tiJSlLAGdapv7tqSE77xyeMp85u4Rh69tO8rHNy/iuoUNU7Qp0nyemRYC7lu3mK2rmtlxrIc9py4yErHw6hqrGqu4pbWRhjLfjMZbOnpmvrpSdLoUndoyHzMp3nX98npmsg+EENy3fhFXLW/gpf3dvHTscsqOBmxdVVv443UWpOhIIPzGT2B/NveFwkQPvoy3dfOM7Zlpio4mbcrbbmZ4pA8OPjGt5d67/xBPsAYnT75NTtEJu81LnwTj1s/gjacTlWqKjre8nqxq7FXPz9tYEr4i1E/k0b8Ec5on0uE+zJ/9GZ4HPo/mLy8Z36oUncwpuOWdnZ1PcvkXINNtlkzxXQfw0AzNUpQgT+0/k/HLit/ZcYoF9wVZWJl+0iFXB10agj6Du1Y3c9fqlvgnqQHN3CAXvprrJELwVK5bOY9th7J8RA9sXtU4/UoZ0FQW5MPXL+dD1zmELAchwe/REGKyJ0z5RUDJp+iM7H4iy+A+zsFnoXXzzO1BMJMUnYQOXvcg0domrP1PwcAkr7gt3Ij36vfjrajPr29JaODCiez9u/AafOvuxVPTjJMm7aTYx1BC62U1UN868d2NafC23pLHscR8NbrrkemD+wTWAKPvPEL5DR8tGd8WRsePqVn+Qzh7L00USciUZano1BAocx21nIzziBM8vfs0n7wlke6V2qZKO8lcK1/NRLdUBGmp0DkzZOOWu9vqqPYn58TP3CYhBEGPnvH6+dClnqJjDfbCvp8zI4Z7sBEIoRU1RSdZe5fdgHfZJiKXTmN3HwHLgmAF/pY1aP5KHMOLY5n59XNyFR0rNdEyM7SbP4V3xY1olomT1EeppuggQVtzB86LLgL82pVo9Uvztj+kpmPaJhx7xZ3zj+7AuvZDEAiUjG/zrVWKjqIkiNXBp6Tq4NtO4rPsa2u/dsL9S1i7ukYYCEepmKSCjeNIRNy+XI95rmnlq8yPdQmT1pD/yM3LefiJQ7hh89JK3r2uZU76Xdom0rGQdgTsMJghxqaIt6JF19GO59LuF1eER8Djm5k95ijSNmP/z9EYPdVN+MrrQeg4uo5mRXPa/pQ6OhL7fzQMwQrI4uGWUV4XO2ZS+pBRAYYF5mjRj6FU7WtcTWjJTXAik4Dai/emX550jLnSMioYPZTpeyXjiRx5HV/bLSXj23xrafnAiYLjVyk6iuIgZSwPODF77OXAoHjYtkQKDdtODnrcc+Ssu6nWx7brG2LD/JqJdsXnrJjh+49XBMpXmWE7xAP8id81l5Xxu3cs42vbjk3bTkuFxp0bFnLVgtqxC/a5hm1LMKNI28IJjyLs2J1cRwhEfMDF1BzekZNxSseGaGhG9lgh0B2JjIZLxj8z8q0ZhkgIBwnN6+GMy4mtfLVowWqc8MiEPmxTwzHDSDNa9HFOpj0b34ep6XDspfTj06vw3P4pdG859iRjzJW2TY1I31kXjr+MHD4/qf/nqnbMMI4ZQWqz+0dQBfizGCEEAgdNEwgh47PHFhuB7TjoupiRPWHLfXoDgGnZk/ara7GgtTR8VNooX2WGrsUC/HR+WlVXyZ/dv4aXD3Xz/MFL44otVhpw3dIablg1j/llAWZyMTwb0HWB9HgRuoHmL0OYsVr/mu5B2NGiaOvCCcyujtidZTclJdPRfDWazzdj24xABcK2EBFZED9gRrAHe7FsE3QPetV8NMM76fpCM4h2dWB37YVI7GmFqF+Bd9m1iPgT2wnta+Ag0fxl+JZdQ+TNn4A9mLlf225H8/phkjHongCa4UGE7Lz6KFut6x481z+EteZ2op074PgbYA0AOjSsQGu9Fc/CDegy//brngCaN8tJ0Dz+2HlbQr7Np9Y8fjSPDyFn94+gCvBnOUIIhABN04ilxMD4YKHQOv5HXkvYlF1bFX4vU9afTkO5zzNpv5om4naJcZ/nS49GbV473suRnkEipk2Zz2Dd4jo2ttShF8iGbHWhfTVbtaaJeICf/jiv9Ht519rF3LtuEcOmTcS2KTc8BH3p/vSWxthyrYXuQWgGQvchRfzRBxIML8TzpwulI2c7sN/4MQydJpdo6+4DIzhzOz1BhBYFw86rH5zIAOG922D/0xCv+RIFEGXQfjf+tlti1VPi64dPvIHz2g/jAepl5LEdRHZ+D9a/j8D6u2K105P7koAtweNHeAJoN38M54V/yMypZY0EVm+NtTPJGIQ3gNS9YETz4qNcaaM6gLb5o2ibPgxIHMOLlsi1N7zxdxPya4PwBvDOX5zFrypoNc3gCZSMP/PuK8MHmhcclYOvKDqpgZjMWNuO5J2zfXRdGMa0Hcp8BhuXNTAvGHDVzkSdapu77dcuruX1U+4nIV5ZV5WmXzHD8WSmbQd+/s6JSaqoRHi7a4Tvc4oPbGrh5qXzM26z8LowvprtOl2ZzMm0EIIqv14SdhdFy9hLtcUukxk+9BLOzn8j57RsxFe/KCfjykWZzOm0feE4kSf/lkmfXMgR2PcI4f1P4bv3D9BrFxHe/xzOrh9M4QAL9vyY0MhZAjd+PGZ7vK/UmWyDLWsJ3fAJ7Ne+PbVPK1vw3/376LoOaXxRqmUyS1ELx6JiyQaGcDkJmggSaFmLLKGx5N1XqkymYjbjSMmzB7p4as+5CVf0j+47z4paLw9uWsKiqsxnis0lGxprCYiThGTm29yyvBqfRycWWExETPpp7nAcyf994SAd59JXQDaBH7x+hpHRKPe0t4x9LqXk6KUhdhzoobt/FAnUlvu4YeU81jXWool8Wz+ewvY2O0lcBimmR0DRy2RGew7mJ7hfsJayW34dKbRYIDpTmxHkokxmOm0N9hJ98sswYerAFOQokccfRtz4UeSUwX0SR18hVLGQ8vatl/slYQNjdgSWX0+0fhHRA9vg6Mvj2wjOQ1t9F4GVNyKnnV1XMO6CsujlFUtZC4Smw5rboePxzPYnwJrbEZqOLLr9hfUVSYvZigrw5wQyZTm1dqTkWy92srt7NG2LRy5G+asnDvE7ty9j9bxqV+1fJjUEylxrmuD917fw/dcnqd88CQK4c20T6X2R/9KPj+05NWVwn8wv9vbSVFvGusYauodG+cZznfSmXM10DY2yt/sEQe0Ev37b8gxm682VVmUy86OzPx9muy6FMpnWWzMsg5lKRROsu4/A0muRMy6NmZ8ymZPp6M6fMG1wP0YE+arLi6J3foq95jaEpk0sk5lkh1a/lOCNH8e57kOYwxcQZgTNX46oXohum8gMxlNKZTKlZWGP9GI7NoYniF5WW/Ryj5P5KrDhvYR6DsPFw9Pvy5oVBDa8O2/HYqlqVSZTUUKIlKWcUj+6++SUwX0y//DcMb5wfzv1QbezXqbalsk24/VNS+czFDL5xZ6pS2ZqwO/fvZK6QGKC5MnaFGk+z42OWg5PHeyb0s5Unt5zhroKP3/52EGmeld/1IGvbTvKp7cuZe2C2rzYP17n11dzRbtJ0cl8vTmqi5yiYw30wKUj5Ap908cJrLgRR2ixvnKYTpPPFB07NAg9u1yONupy/QjmqbfxL944aYqOJm0cM4oz0ANSopVV46tuGbPVsd3PzlrMFB1rqI/wwe1waDtgAWADVDZD2x2ULbsOoXuKn3YS95WuQdndn2Xk5W/DmbfT78aFGwnc/Bvogpwe37NBqxQdxaxk1LR42mUg+nzHWT547dI8WTQ196xpoakmwFO7uzjRP/Gu0/WLKrh3wyIaynzTtpUaauWSnafPu97m+CWTv3v8wJTBfTL/vP04/+fBCiq8Htd9uSWfvporJC6DFNMjoKgpOtHuDO5WZoJWhnHHZ/DPW54/mxHkK0UncuKd3PhhGqzzx2Dx1aSm6ER7D2F1bIMzKRcZi67Ht+Z2PHWLXY5NMO6CssDpHOHTe7Fe+MfJnTDYBTv/lZFDzxO847MQrC6obVP5SugGwds+g3PpDOHO7XB6P5gR8PmhaS3+tlsxqhbEnrZNmSI1V3X8mJrlP4QqwJ8TyJRlev3qUfcTSL1w5BIPXLUIr+F2NszUECg7va6xlnWNtZwdGuXY+SGilk25z0P7ghrKxlUjmaqd/KadnDrnouxbEiMuy+y+cqibe9YudGWbe61SdPKjc3M+zEZd7BQdx8ymdgiAAG81VMxDW70F3+KrkZ5Anmd/zV+KjhPO7u+Ua6zIZfttC5sw5hs/hiPbJ1//1E4ip3YSaX8Pvmsfyng8xUzRCZ87gpMuuE+m/zSjT/0Nvvd9AYqYSjSZr7TqFoKbfgVn8/iqPqkzBhfL5qL5CpWioygZRMpSptWHurObQOrUwAgrxirUpG9/4iXv9DZlqpsqymiqCGbZjnC5vjttFWiSsecOnOeetYuytjMznV9fzRWtUnRc6CKn6Ggef8ZPysax7n7K1997ORUHgZNnm/NaRUcrUMDiLRuzH8ckvPsXcHSKyZ4S7H+UiOahfP3dZDKeYqXoSMB58VuZ+2O4m8jeZylfe3tB7SwFX81GPVdSdGZ3FX+FayKmndV24Sy3KyVSQ61cUuHPf9oMwIgN0SwnAXNDPn01V1A+yhwB8btkJDlOMj6FI3/a27I6K7t9LWsKaufYRVFyik4O2/fUJZ7+5Rff4vVj/Vr9PZkF9wn2PoI9MkBmYxOMu6As0H4yzx2G8IXMxwTQ8Wz8FCjw8VRkX81OLcYtZiuz99JEkYRMWabXfm92d3CCXiOj9sdrkcU2+dL5TTvZsKRuktr3+cG0bbyGlrFt7rVK0cmPLqXzobC62Ck6WsU8qGuDvoNkTEUzet2yIqQq5C9Fx1i4DpMgkFmRhawIzkevXznmt+ghF8F9nFDnSwSveaBkU3Six3e694t5iejF03jqlpRMik6hbZgtWqXoKEqIlKtOZFq9ZmEt+3q6XLVuAIuqyjJqf+Ilr5tt8qnFDLadXi+rrWReQEwodTkVqeFepgTGXrKdnb4qNX1qcJgX95/lZN8IpuVQ4fewcXkDNy6Zh9+jpd1Wpei40EVO0QGBcc0DWE9nHuDr13ygKHamS9Exew8T7TmKtKJIfzmB5jXowWr3fW14N+z+UcZ+oP09sP8J4rVhpvfbpo+M8xvHXsm8rwTHXkXb+N7STTsZyS7VlZF+tNriVKRRKToTtR0aJHxiN4T7QTPQ6xbhb17NXEnRmb2WK7LihsUN/Mcb7gL829vqMPTZn82VGmrlmg/duJSvPXcso3W9wM0ra3ju8CVXfVzdXBaf9CqbS4PMybevSoGBSJRvbu/k+KXx1ZkuhE2Ov3WWn7x1lg9sbOS2VU2Tbp//vVD6nBsJ8ULHWTq7BwlbDgGPYMOiOm5etYCagHdsPQFFn+jKO38F3PRJrFemz53Wrv1lfAvXFaeCCILkFJ3wqT1Yex6H/pPjbAwBNG3As/EBfFWNGbcfWHsnob4jEyvZTMbi6whcfT92cxvRp/+W6YJ8/YZfw9fSPuY36djTbjMpkYsZjkcw7oKyUPtJz/LOruEprJ2l4KsS1NZgL9G3fgxd46tK2cCIUYmx6X7qb//krP8hVAH+nECmLNNrr6Fx//p5/HxPb0YtG8CW1saM2x+vU0OgYur8p520zavm1zYv4l92nGIqvMAf3tdKhdfjOsDfsqYRN/u7VH1VbD0UNvk/v9jH8DSxx0/e7iYatXJUuaiUzoeZ6Yhp86+vHJ4wn8ZAVNJz4AJPHbjAzUur+OB1y9A1UfQUnYT2LrsByuux3vop9HUygarl6Nc+gK+xLeM27ZFLRI6+CaF+EAJRvQDvypvIvmLK5RSd0XeehL0/mWhnghccLLcAACAASURBVLO7Mc/uxrnzD/AtyMxmaXgJbPkUobd+AgefSd92270Ern0AaXgx5rXC+75IdPdTcPzFievO34Dn6nvx1C8b36+R7btJIqMUpaKlndQsnhAcZoJW3Vy0SaPc+ko6JqHjb8GFU7HzNVCPb+kG9EB10VNoZqLNi6cwH/8zxv9dS8IaZGDHdwlf7KLyoS+73selhArwZzlSSqQEx3Fwxiq5SMZdqafoO1qbuDgU5uXj05dM+717VlLh8+A4zpRtJrTjSGwn8Vlm2xRCO45ExO3LZ18bm+uYf6+fZ/ec4a2u8cGPBmxdWc1ta5qp8nkByXvWNvDovsxq6F/dFGRZTUV8DLPfV8XU336pc9rgPsEv9p1nyfxKVtZVjGvHcWIXQlOdG6V6PsxEm7bD3zy1h67hND+QcV4+PsCl0QP85q1tSNtEOhbSjoAdBjMEQgdpx9I4Cqi9tQsx3vX7OJe6iXZ3ghkGw4u3cRVabQuaFY1tM0079shFIm/8J5zbO27cEoi8/h1YcTuBq9/r3k5zFGmbhDpfmjq4T8J+9m+w7vsfaDXNGfUlrCjBqx7Aar+HaOeL0NMJkVC8Dno73lWbMXQPWBZShsCKYvir0G76CGx8L9Fzh3AiETTDwDNvGaK8blK/CWmDtxaiLt9Pql6S0T6QUQGGBeZoQY8h3/Lriez7mbsxNbSje3wZjSsfOlNfSSvC6DuPwsFtEJ+4K0Hk7e9B43o8138QzV9ZtHM4W+0M92M+/iVIF9wnj/XA8ww/83dU3ftH7vZzCaEC/FmMlLE8YMeRSCmSArLpEDx0zTKa551n2+4uLoQnrrGxuYz7rl5IvT+A46K+nG1LpNCw7eRgpvjYTjzEyqpWnjsay8r42I2r+IBlc3JgmEjUpszvYWllOYYee0yasOOOtmZMy+apg1P/AG5oDPArm1YUxP5UX50cHGbviT6GwiaGJmiZX8m1TfV49NLZv27oDYc41Oduds7n93Wx/Ja2cZ/ZDvEAP/12pXo+zIRHd5+cNrhPsP9cmOcOd/OB6iVgRpG2hRMeRdixtChHCEQ8d6fQWvdXEFh6LbYQ6PHP7fBIRtvaA92Yz/49MHHyvTGOPEeo9zDG1k/jMbwZ22aFQLMd5FuPZOTjBNG9j+G9/ldc+QEhCKy6BVbdMt4PEqQZndxvEnzzVmXsN5ZvggNPuBoLS69HRkPTjsE2NRwznNbWfGlN98L89XBuT+Zjar254Ha69ZW0TSLbvw4DJ9KPo3sP5s/2Iu/8HJ7KxqKew2519OBzuJmZOfTa96jY8ltoweqMtyklVIA/ixFCIHDQNIEQEs1lmvzmxfO4aVEDxy8NcbJvGMuSlPkNNrTUUuaJ5wq6twrbcdB14dqefKJrsfCqkDaVew3aG6qZeBc0GcG71y+mrbma7R3d7O4Ojft2WbXB1rXNbGisuVzBK88kfHX00iA/fu0Y3SMpNp8Y4od0cU9rLfeuW4Smza7A9ZWDPa632XcuzIhpUuG7nFeua7G9OfUxVZrnQ7ZEbZvnj/S72ub5vef44KYl4PEidAPNX4YwRwGBpnsQdnRWaRwb87mvM2Vwn2DwNNbOH+Ld+smM2zcCFYSOvQXOsCs/c3oXbPoVhMdbMr7SdA/eVZuJugrwvQRWbIodK9P0oXsCaIYHEbILPjbvrR8n+vhfwUgGk0fWt8HIINZwL57ahUXZH5n4KvTKv08d3I8hsbb9X4wHvxA7n0vgOJtOC6HBoR0ZjC0Zm9CuRyjb/AmX25UGKsCf5QghEAI0TYOxqVySA67ptGBFfRUr6quYiJt2Ejp2p1LTEjbNpK3c6VgQKpKC0eLak6pXNlSzcks1oxGLvkgEx5HU+L1U+r1MTn599fbx83x129E0fcd4qvMipy+N8umtq0tqX0+nuy5lVyKwdyRCVcA31o6miXiAP9XYS/N8yFa/dSKzdLJkhmw4fG6Y1loDofuQIv7oAwmGl0Suer61I22iXR04oyPg9eGta8Gobh5bx+rvInr2CESHwBPEWLIeb1n9hHaiB14CZyhzB/TswRq5hFHVlJnNniAjpw649jNAtO80RvPagvt2Kq2V1cHm34IdX89oDPp9/y/CV5FRH8IbQOpeMKIFH5vmr8T/7j8h/OI3oWfvpGMZ48JBuHAQEzArWuCq+wkuvrqgNk/nK2v4Ipx6O6N9BIAcIXz0TfxX3VsSx9l02h4+D9L93/7I8Z0qwFcUk9SgNfWOcTF0qm3FtkkUzYa+0TAvH+ph/5l+RiM2AZ9Ge3MNt7TOpy4YGLd+0Och6DOK6quevhH++tHMAoyO3jA/e+ck79+4pKg2u9F2Ng+mADsl1/5KLJPZ2z/+CVOmnOoboLW2imKUyZTDfYT2PQ5HxtdjjwLR2uXQtAbOdsDF8Re01q5/x6pfhfeq92IsaB2bwTRWMtId0YPb8V7/SxnZrNkRMEdc9wEgQoMlVwYRxyQwfyXmbZ/FeuHb6S+O/LX4tn4avXZhxmNIV/rRvHCcyOEdMNgbO4Qr6vCuvBnPvOVouRynx0v5Hb9DdPg80YMvwIUTMNI/9SRYQ2fgpX9k9Pw9lF/z/oLtj+nKZIYPbnd/wB18FrH29lhJ1xI65ibTIuzyiVgcGcluu1JABfiKK4bUUCvfWLbDD3Ye5bWT419m7jcdujv7eLazj02LK/nI9cvjufmlwaNvH3e1/rZDF7lnbQvBsfr8pU110AsX3eXgA1QFxz9NSVwyXkk4MrsRW4kXjePVdGJI8l0Oz7x0GvOxh4FJXjSCWFCfEtiP48Ihos/+NeZNn6Rs6bXIyKj7GUwBug9mbj8CzRNI39YUCI8/rvLv24x13B7vglV4PvIw5undWIdehuHzsfy2igb01i34F7QhhIjl7WfchyD5AtTq7yL60rdg4Mx4x/QdJnriNaLl8/Hc+pv4appzOk6jcgHeaz+AHRok9J//PbOddfApRgM1+NfeUaD9Md5XE9Y552ISuAThPpzQILq/vAD2z0xfPjfcIXzlWW1XCqgAf04gU5alolNDoGLqwpZ+tB3JPz3fQeeFCFPx+slBLg7v53fvaGf8VAPF8VXINHn+YN+UNk/GK8d6ubOtqWB2zkRft7yBt8+4uyvTEIAFZYEZ2lBK50N2ujIw/iInU2orguAJ4HjLYyk6tg04eS2HZ4cvYT72Jdy8VJcO+cq3CFfUoQdrsmsgHHJRHtGLf9FaIh3bXHdjNLeV3kyltoVDBE3TwePHu2gj3kVX4xheNMuMrWd4kZaJdNlHcunH2MXcF6d20PC5WInEd/0xnvqlOR9zqONZUivPTIWz68fY6+4i+5KqOSyTGcouddF2TPRSO+Ym0aJ2IWjlrt9t8S27MSu/lAJz4LUvxeWrclFCulTsKI5+uuPMtMF9gsN9UZ7ef6boNgOcGsjuj/yRnoGSsD8T3b6ghgqXtza2rm1GiPHHtrwCz4drlzaQDRuXzAdPEKkHEcKLJiSadOLpElZedGS/u4oZ02G9/XN0I7u7gATLMrffjlC+ZANoZe76WHQNhhHImz+z1o6JDmi2nfM+hGPFZv61wphPfSVjV5lPfgVhhnNqj7DC4DrNxSJ6+JWC7I+Er9KtQ8Dl8RZH17ylcZxNo3Xbgvbb3Q1O8xK4+oGs/FIKqABfccUgpl8lJziO5NkMa9sneGb/+Xi99OISNjMsDp9CJMvtioEmBB+9dVnG6y+uMti8ZN6Ezwt1PJUSNQEf7fPdpY/curwar0ePXSBpOpc9J5mQrpMjLW0bDjzvbnDTcf4gTmQYqha637Z5PRnbj0DoHrSr7nfVha/93rz5MzeaPLQbu/gMn9oF1vTzulwmROjYGzm1x7x4kowqK6Ugz+zOmQ1T6+SL9knWaVrr2nbK56MFKgtk//TaDg8yuu9Zht/4McNv/YTRQy/jmNGxdYKtt7q6cA7c+DG0wGQFSGYHKkVnTiBTlqWiRYnYIYHCpejs6b5IZvfuLxMFdp/tY2NLXV5tm06XebObgr3MZ+Tdtlzq9nnV/PrNi/n/Xj7JVCypMvjtO9ckvSMxV86H7PVD1y3lwKMdZDIlQ7kO961fSCxslaAJpK7jSAF5nNHT6j0Crs/C6Ql3dcKau+HVb7nazte21VWKjmZH8a3ZSmjoAhyeYrbZONotn0GvW1QSqRBTpujkKe3EPvCCq/0B4Bx8Dqf9jpzZY0ezfFoUChUkrWq6FB1f21YiHY+7s731LqThxUmkWhXpODPDg5ivfBe6dk0wcXTnv8LKrXg3/RKGvwrPvX+I+dhfMF0qlX/d3ZTf/jvu/FFiqAB/TiBSlrIEdKptxbZJzGBbd7qnP8syjP0haCmcnZPpJdUV+AREkmO6DFi7qK5oNmerN7bUsej+cl482M3zhy6SPOTFVQa3tjdxXUtdUnnL8e1ciVV0QNBQHuCP7mvlK090Ep7iOKn2wufubac8PneARIAjEbaFJmRshrA8Vc7ItmLGdAhzBH/rTYT2PgrDGdQ/B1h+K55gFcjMxqvZEYTjoEtJcNMHCdcswNnzOIQneTembiXGNQ/ib1iScfvFqKIjAGHbIPJTGYZLx93vzKEuhBUhVxVgdMNDVs8x/T4KUflouio6WqCCyMqtcHh7hnbXUrbiemSRjzNrsBfzF38JTFHh6/B2oj2HMN71X/BVN6G970+J7HoETu6cZGUN0dhGsLkVOdoPlROf3s4WVICvuGJIDbXyhcwy1cYuxDS102DoGvdc3cTP3z6b+TbAtQvrpl2vFKkP+nlw41Lu37CIi5EopiOp9BhJE1ql35diym/nNi2VZXzxwQ28cuwcz+3rYTApM6HOD7e0LeDmZfPxe2M/MePPvaQLhjxVzhBeH/lAGH6E7iFw5+cIPfUwhKZ5Ib1pA4FNH4n/J0P7EbH/xz8PrrwJueImwj2d2OeOgBMFTzn+hWsxqhtxNAMcK/P2i1RFJxbl57oPEf+X5d9OmSRmaI9Ruyi7Nz4WrMmZDZn5Kv06Zdc+xMjIJTibSBtKg16J/+4/QBgeZBGPLWlahJ/+ClMG9wmGzjL6wjcov+uz6BX1lN/8CZzrPszo0Tfh+MvQfzq+ooPs7uBidwc89U8ENryH8rs+h14++37jVIA/J5Apy1LRqSFQMXXhUnTKAtmViywPepPaKp6v3n3VIp7edXbKu7PJvP/aJgxdK6rNM9WGrjEv6M94/ex0KZ0PM9dBj86drU3csaqRE/3D7DjYw86TQ/SF4afv9PDTd3q4bmE5W9ubqGkop5ApOlrtEvKB0bI61n55Hf73/nfC7zwJh55lQoBpVMG6ewm0b0UafpcpDLEUnXEpPULiWXQVvqb22DrxCjTODH1l2RHE6CDC64dgbV72RSFSdLKpjgI60uPLWXqJ5g3C8lvh6IuurPC23YyDnnu/p/HVlOlAuo7vrt8nsvup2HwP5sBEg1fcivfaD6AZwRkffzPVkRMvQeRS5s7uPUDk0ln0ectBQnTgHOz6AeP/xiUhLULv/JToyTep/cS30asaM++rBFAB/pxApCxlCehU24ptk5jBtu70NQsb+I83M78DnuDaRfUFtTOdLi/z878/tJHP//Dtae9I3bemgS0rGotuczH0lZqik6pfPnaOH77RxWS8cXqYN04f4t3dA3zklraCpehohgeWb4ajOya1KyvqV+KtmIcT70vzBAle/xBi43uInNmHNTKIpmkY1QvQG1vRZWzGXsel/YkUHS1PKTcyMkL46Ks4B7fBYPfl8QXnoa2+k8CKG9BgdqXorNgEh1yWFV1+Y24nvZI2/vY7CLsJ8Nvfg4FWGik6CW2blLdvQa65lfC5Izh9XYANwWoCLevQPD4cURibp9Py4HPu9jlgHnwGYXgJv/wduHgko23sS2e49L3foe5TP0To2d3AKwYqwFdcMaSGWvmizGdwbUs5b7qotX5NSxllJTRR1MJ5FXz+fWt5cvdpdhyfeBdncZXBXetbuKp59j22zBWJS7ErmVdP9qYN7pN5bPc5NCG4Z9V8xl0k5PExvn/1XYRzGOB7r3rPpH0J3YN/8dU4moHmxF7cc4QG0snOfgTJKTq59Ik1fIHwk38DkYsTBzjai/PW9xnZ+wTed/0B3or63PRLQpOH/S0Agb91C2GXAb6v9bac22NUzEO//bPYz/399AYsvo6yDfcVMMVFMNW5Jy0LJzKCo+kIXxlCCHxNq9EWrARIOr7zbWdmWtrRiROaZcKpDqLH3gCXb0xY5w4R6dyOf81d7vssEirAnxPIlGWp6NQQqJi6sBNdPXDNYvae2Z9RHQ8v8L5rlhTMtul1zFc1fi8f2bSc92+02d9ziaGwidfQWVJfTlNFsATsnG26lM6HmeuQafK9V0+TKb94p4f2xnJafPlP0UGCVt0Et/0ePP93GduYlht+HWPBmgKlJEySopOD9p3QAOFHvwT20NRjjV4i+vM/Rzz4RfRg9axI0dGqGqHtLjg4fcUhAJbfil67MC/7z7NoI9q9f4K58yfQ1zmxb1EGG+4n0H4bUlCwtJZ0KTrR3iNY+5+DrjfGTIygQevtGO134g3W5d22bLSdRUlSwGU51fGMvvHDWRXg61/4wheKbcNs49eAJbbtEIlkPmNdPggGvdiOZGQ4guNIYrPIJ67QRVG0lCClQIjYv2LZkaoDZbGX7sKjZkH6DXh0Niyt4e2j54kmx0cplOnwh+9eTUOZvyB+yMZXhi5oqipjSV0FC2vK4i+gFt/OYmt/fJ+FRyNp1ynV8yEX+sWj5zjY7S7vWUbDrJ/nRbdGENIB6cTSByAv2qicj1jQhn3xHIQny9X1wcKrQQiITBL41izDc+Ov4l+yMa92JuuAT0c4DpFwNKfth974IfRllpIAFnZoAN/Cq2bcr7CjCNtG03QE5NRfAZ+OwCEaCuNpbMMMDcHFk1MPbckmym74ZTQh8rYvPYEKvCtuhKWbcLxVUNMM89owVt9JYPPH8Mxfjk5hjqfJfCUAKS1Cr30X543vw1BqSqmEvmM4B57F9lfjrV1YMDsz1gisvY9Nva9zjD3YQ9mtn4r/LS8Mfr8HPTbN/UngX9xsq+7gK9IStWzePHOR8/2jWLZDdbmf6xbXU+kvbirJiGny1uk+Rkei6IZGc105bQ2VsT/YU1C4UzLGgrIAX3jfVbxyrIfnO3q4lHQ7v8YLW9sXsHn5AvxG7AWrUqLQvpqNCEptrxWW1w/1ut7mtRMD/NL6amKzLUgK8Ujf27AM733/hUh/N+bxNyE6DJoXo34xvsVXIw0fmmNh9p0kcvYQmCGEN4jR0o6vOvZSnVPINAQEuU7RcawwHH3Z3c46sRPn2g9DoHJmNozta/Lgr8sXnEIIAjd8FGvhBsz926C3Y/x45rVhtN2GZ9HVCGkXZF8aVQvwrr8HYFwKV3Eqz1z2FUhCr/07HHlp2sPA2flvhHQD34qbimBzei10D9SthL7D044hZ9gW2CYY3unXLQFUgD8nkCnLmemoZfOL3Sd5/vDEO16P7OpmfWOQh65fSm3AN2Hb8To1BJqZ7hsN8/O3T/LWmZEUq85RrsPd6xdw26rG+NV1ajuFTdFJaL9H4/bWJm5b1chg2CRk2wQMnUqfJ+kuQG7338x1cXw193Vuz4di6wvD7qt+SwQRW6IXIEUnVesNy/DULIx9Hq9CI5PW0euX4V/QhpaoqlK0CXxyn6ITPvWW630FMHpiF/61d87MhgKk6CTSTqThxdO8Hk/zOkxzBDlwDiSIqnl4vBVF3q/F1cm+ip4/BkcyfxnYfvXbWMs3YRRgQi43WltzO85LBQzwDV/sGJ4lqAB/liNlLDXHcRycsfrrkuQrdTc6bFr83TP76RpOX1d4T/coHT/bz3+9r5XG8sC4dhxHYjuJ9pys7UjVXUOjfPmJQ2lfixm24T939XC0Z4BPbF41YXIix5GIuH25sCcbXeHzUIEBiHjqRu78k0tdCr6aDdpxYhdCjpN+P+brfCgFnVBuEMjYHTAnAo4EaccqrQhd6YQ2R5G2Gft/rtocPJ/F3gJGzoMZmpkN0ZHY/6PhWJs59JeMCjAsMEcnrKMb/lhuvtBxdH3Sda4knewrq+Mp14dCtONFjJWbSmIsCe1rXE2oogWGsnjZNgt8KzYXND1npqgAfxYjZaxUXyz/XiQFZNnzLy8fmjK4T2ABX3mik//1wDr8xuXDyLYlUmjYdnIwMzNGTJO/nSK4T2Z3d4hHdp3g/VcvG/e57cRDrOLPJVXyKF9lhh2rgjiln/JxPpQKjVUGJwbcvYfklQ6GtJDRKDgWjhCI2MtDU2p7qA/z/HGwTYTPj3f+KqSvLKNtZ5u2QqA7EhkN56xN7KymYALHwQmPzMgGzDBEQjhIcOyc+ss2NRwzjDSjedsfc0UnfOVEQnD67bS7PC2HXkAuubokxpLQupR4b/l1os9+FaLT1MOvWQmXZna3P3j9R6ZfqYRQAf4sRgiBwEHTBEJIxm5aZ0nX8Cgd5zOp+xIjJOH1k73ctrIp2Spsx0HXxYztSbDjcI+rGQJfODrAPetMypPKTupaLLzKlU1zmSvBVxdDEV7u7OZwzyARyyHo1dmwtI4bl8zH78ls4LoWC/Cn9lPuz4dS4ebVCzjxmrs7Z7e01aN7/QjLC46OpnsQdhQQk+poz2GsvU9D36GxNiTEqlMt3oS3/R6Myvpp25lN2ghUIGwLEZE5a1M0LEMecL+PtZoWNH/ZjGxAAweJ5i8Djz+n/tI9ATTDgwjZJbP/SlUnfCX7+t0fCAAjAwivtyTGkqwNbz287/NEdz8Bnc8Tu/2YhKcKVt+Br/12Ii9+C7p2ZTV879Lr8S7dlJ3vioQK8Gc5seocxFNSErcSk+8UZq5f6kia9CRDXjxwjjtam5Paid2p1DSS0mSyt8mRku0Hp5kOfhJePdrLPe0tY+1omojblegjO3uuBD1bfOU4ko7eAXoHQkigvsLP2gU16HH7J9s2ajl877XDE9/jGLU4tuscj+w6x3vXzePuNc1JVW8mt0HTRDzAn+o4z+35UEwtJew/188rB3s4PxTBke4f8dyyugWhjYDmA82JvawWz+tN1aMHXoK3v5++sZOvEz25E+fuP8JoWp22nVmnPUGEFgXDzlmb3qXXEHmpDGTq+0tT4cW37HqkJzAzGyRgS/D4YaZtpWjhDSB1LxjR0tl/cS1xiPadxuk6GHuC4q/C29KGUdFQFHsSvhLegItjIAlNByNYEr5N1ZrhJXjdh5DXPEjo7AEY6AXNQNQ24Z+3HCEEjuGNPU3KAk/zOqo//LeIWXaXRgX4c4LUQExmpTt73NeH7QvDqGkT9Bgpbabalp1NvSNhRrNIFdnf1c897QuT2hRZ23Dl6dL2lWXbbDtwlmf39xKSjMPLCe5YU89da5rxjqWOxbY1bYevPL2PU4NTJ3v9Ym8vw2GTD1yzbEp7rqSZbI/3D/GNbYcYzLL0NMCn7lpJjd+LCA1MO5Nt+OjrUwf3Y0isp7+EfeNvEFy0DmH4CjKDpnAsoj2HMAcvoEkHvaIOo3kNsZ//mbWfl5lsbQux7i7knp9mvsPa70QXwvVMvKm6EDPZlsKsqsk6eux1rL2Pw9D4m2bRtyDasAbPte/DqF1YUNsSvtK9AWKzr7hM26prKTk/T9BCEGxZi9Yc+zw2464NUmBdOA69Lh9jCYPyLb9F2eZPZH9hVERUgK8YI5RlmuZrx3q5dcUCDD01mJk5ISu7uQbC0YlBXO6tm7uUqq/Cps3fP7uPU2lyv6PAEx0XeOfkRT73rnXxC88YP911YtrgPsHzhy+xZP4FrmlJP1Nv4jJornP04iB/+/TMclc/sXUpW9qauXjuEuMuHiYpgSelxNn5Q1fty1e/ycirAlZuwdd+D1pZddr2Z6KlYzHasR0OPAvRWKqDE/9nEoD2Oyhbd0/sbnW2fSHIdZlMhCDYfhcjPYcmlo+cjLqVlK2/Lzc2kNDkfH8wdkMil23OTI/u+jnsfzS9b893YD7RAVt+F19LewFti/lKCAGrt8KBp6c4ACaird5aUn52q6MHt7saL4BnxY2U3/bbrrcrFWbX8wZFGmTSMnsdyLL603/u6uYPf7SLF4/2JLWZI5t0PSubfIY2oU2ZA3uuFF2qvvrmiwfSBvfJdI84/NO2jnilolh1qBeOTPMSVgrP7E7kl+fC/uL7LhsdtRy+OsPgHmDv8b5Ym5pA6rGZbBNlIB1hjNPhc4chcjGLXiQc3k7kp39M+PzRtO1nq23bZuTpr8LuH48F9+MJwf5HGXn8r7DsyAz68iKFlnP7pe4lcOf/A4s3T+3Ghdfiu++PkLovNzZoHmwEjpbb8TjCGFf6Mdf+ykaPHH516uA+CfOFrxEZ6CmYbcm+8rVtdXdqGVV4llxTMn7ORnPUfalY88gryMSL6rMQFeDPCUTSMnu9YXFt1hbYwH+8cZan9p+ZsR3Jel5ZAH8W9rS31OTMBqVLQx+9NMxBFy+BH+83OXghlnb26kn3ZQLPDNmcHRpNa490ZX/p+NGNfu1kb+ora1nx5slB+odipTGFbaEJiSYdNCuKJq1x2j79zoz7s57+Mk5/16TtZ6tDL38T+jqn73zwNNEnvxJLicimLzuCcOyc2JyqdSEI3vJx/Pf/GbTdCeULwFcDFY2w6k787/0i5bf+BrqUuevXMdEBzbZzPh7hWGh2JKdtZquFYyJ3/czVcWrufaxgdib7yhOsRdzwiYzt9N31u+i2VRJ+zlYj3c26DYC0kWbI/XYlggrwFWPc0to44zYe23+BjgtZvqU/CZomuH1NvevtNq9cMOEzMcl6iskpRV+9uD91OvXp2b4vtk13n5uXCy9zaortStFHuealjp6ctbXtQOKJSMJzkvEpHHFtZrevUgnv+tnk7WehzYun4IyLC4+LR4j2HMyuXwTjUnRyYH+qNirqKL/mAwTf/6eUP/QXlN//Pwlu+jBGZUMeiLnVdQAAIABJREFU+yUP7SZflObPX5noaNd+MAdwxck3cSKJwDPfdo73VWDlTRg3fZIpw0BPNb57/wRPbUtRfTtTHT7xZvoxToMwsrnFWBqoHPw5gUxZZqcbynxct7CcN05ncaWbxLbdp1l7VxWxPyYzswngllWNPN1xIeM7ibcsq6LCl3jpN9GOmp01c12avnrnjPvjcv+52N0X08os9z4Vy3Eysi0znZvzoZC6eyR3kyGcvTAMS+vHUnTSzmSr5+gHtesdzMgwHm/5+Paz0GbHdtfdm/u34Wlqz6Lf3M9km9cZUrEJnz2I3P80XDgVe7G2ogaW3oS3/VY0zVOwmWyL6QvzbBZ1SIFw73GCC9fnfz9N4ivvshvwLLqK0Im3ofNFGL4ACKhtRl99O8bijei2hVMCx1m2WjoS681Hsto3esuGWVc5JxkV4M8JRMpSZq1/5YYVDIUPcvB8duWkAI5esrgwGmFeyiy32eoKv4fP3dvKXz/RyXThRvv8AA9du2ySdgQzseHK0qXpq+xC9Fg5zeoyH+D+AqEq4Elrz5VRRSd3SMm4FJ10VXSMeSuxjryQkz7NY2/ha908rv2s9Mksamef24eQibSUIlfRyZN2BnoIP/fV2Iy3yQyFYM+Pie75Max7P4GVN835KjpY2T15EuHBgtif1le6h+CKG9GWXw8kVZ5B4NhmSRxnM9FWzwGIuC+1DRC89oNZbVcqzN5LE0VeMHSN375tDQ9sWEBwBkfHyUu5ecyeYHFVOf/jvWtYu2DyUlU+4P718/nUltXxOugTyX3IMncpRV9l97p1LM1r4/KGrPpbPa867fel6KNc48thW/UViTvzSRcSkzxO9y2+ilgZvxwQSfwdmryvjLWr+vFJOLb7vhDkO0UnF9oaOk/4F38+MbhPZe8jhPY/HdvtczhFJ1Yj3j2Xyy/m284S8lUBdTTLJyv4KvGvviO7bUsEdQd/TiBTljPTmgZ3rW7ijrZG/uapvZzod/+anWkm7rfmxiaQzCvz8+mtq+kPR3nr5AWGR6N4dI3GujI2NNYmTcw0WTulmXZSmro0fbVqnp8Dve6eLC2tiZWGWlgRpKVC58xQ5s8BtqyswdCTqzFlZmd6LXLYVmH0jStq2O6y+lA6bl3dBDgZpOjosOFB2P2DmXdqBHKSvpFV3XCIV6+Zmyk60ef+mficwtPT+QzhRVfhbW7PqR2llKKjNa3BOfx8Zv5IwmheXRD7S8lXBdXRUdf7BKDyxg8iPLOv9n0yKsCfE4iUpcyJ1gQsrq/gRL/7H/jKYOIOXG5tAkm13xefPdfNtsLl+tlpy5a8fbaP1zvPcXEkiiYEC6r83LKmEb9H58WObg71DBE2IeiFqxfXcktbI/VBX95tKzVfudVb1zZx4LljuOG2tZdnWf7w5uX89ZOHMtquwoB71i4c23Yye66EFJ1bVy/ISYC/rM5Lc0Mll85dmjZFBwSBdbcRGjoFx16ZUb9Gw8LcpD80rYazu911XrsCXUrXE0XNhhQds+8kDJ125Q6n4xm0+SvnbIpOoHkNI0YlWC4mjFx0DYYRKG6KzhzXZBmk+2obY0/g9NkbJqsUHcWUXL3MfQUbDVhVV5l7Y2ZIaqiVa/Z0X+SPfrSLf91xis4LEc6HJOdGHXZ3j/K1bUf58pOH2HlqiP4ohCVcjMC2Qxf5ws/38/3XjmA7Ms8WZk6+fZUNaxqqaa7IPFGnPiC4qvFy6del1RX89m3Lpt2u0gN/eF87ZT7PlOuVoo9yzbyyAHe3pZ/sK1M+fdfaJH8lXUikebQuhEbZjR9DbHgIyPIuWvl8PPOWT9tXJtpYc6fr7o3VW7PrF0Gpp+hEDr/owhNxut7BGbubmiubBJkcT4XQQmjoGx/I0BkxfGvvK6CdpeOrQmpPUxvZEFx81az/Iz97L00USciUZe708ppy6vzQ5yIzYuvK6qRZbXNvU3Y6v2knb53p49svnyRbXjkxyGD4AL+1pQ0t8eOeBzsz06WZoiME/PYdq/mrx/YxYDIlZRp89u41XC6AEGtnzfxqvvhAOy8c6Gb7oYvjXtyt9sDW9gXcvHw+fo+e1HeuxlLs/Zqdfu+GRUQtm+1H3Je/1YHfum0px/oG2XX6AlYoQlulSaMuSJuiE9eahMC6u7H/f/beOzyO67r7/9yZ3dkCgOggCgESJMglQVIsIiWSEqt6s2Q7corixInjVNtx4sRvfs7r902eN051nhS/TuL4tRM7cRx3WZLVJRaJRSRFUWIFwd4AEgTRt87M/f2xu+AC2DaLXewC2u/zSPvlYubcc8+9s3PuzLnnrHyI0Nn9mMe3Q296b2AAxLJHkDYNUw/FlW+Fq03L0Staof9ceo1rVdjn3p5hmM00CNG5eS3tcYhFyHcTRWvOmh6FFnbiWLQR7/AAHH8mpS3UzZ9ErWqeMt0KzVZTxW0NSwlplRBM/02k0roaW3kDmJnu/CoMFB38aQ4pJVKCaZqYo0+AJWNW6pPkH1nfyj9vT+/G5gQ2L26I6GJmVY/JcNOUCGRObNTvD03KuY/iaLeP7ae62LqwPmu6ZcNWumFw6MpNTncN4AvqlDjstLdUsmx2BYpQLMufDC/T7PzRI8v40dvnE6ZzXd3k5sNrWilzaJE+jJVT4dB4fOVcHlk+h+u+AP6QQammUut2ISKLq3TmiWmGF0KmmXiem6aMvJkRFNL1YJV/aHUry5r72XG8i6PXxq72G9zhnl2LCXWdZYO18yu4PhgY89uhmiFKgzdYXDrC3fOraZ1TFX6NLlSQRlwupIqzsR3ZuAzf/v+Gc7tJibZtuOatxgz5UspPhwtp4Nz8Cfwv/S34U2TkUEqxP/R7CCMYdhCsthvyIo1Q+N+T0Dmn3Ex/L8sYBAMQ8mZNDxkUYNOzKnOy3LnqIQJllcgjz4M3zgbkmsWotz+Oo6JxSse4EG01FVzoQcTKx5D7v5X2NK2+/QNgBsF0TusQnemreRFh5z7ikEkpYpyS7MJTXcEvrp/Df+69nPQ4t4BPPbiQErsDw4h1ZvIPw4y4WNlL6z2K3ae6siZr+9FuNs2fTcA0OdHTz7AvhGZXWVBdRo0rmzlNEiNqK8OUvN55leffvT4uRaWPPecHcXKBx+9sYn1z3ZToFYXLZuOpOxfyxGqdAxeu0zcUACTlpQ7uaK2j1GYn7Fwnl6MIhXp3NPNF+BqyUpXcMIk4+EmOMSRSKFm5HvqDQfae6mLXyZtEayuWqbB5SS3r5s+mVMvtz3lbdTltGyvwhnT6An4QgkrNgduuAoKQYeLVQ2g2le4RH//w8ukJMoQ0sUmDm/1DPHegj7W9taxfFM5wZAqBiAxAIm5f9QQh5yw48SJh64+HCksfwbnobmTQl5bMdLmq2nHc+2kCh38Clw/FN1L9crRVT4DNhQwFM2pL94FqSmTQn1X9s8qdZWCxphMAiiNju8TjRkjBDPmzKjMb3Nm0DNm0FP+N89BzDowg2EuwNy7BVlaDIcSU61yotpoK7pyzHF/fvdD5Kqkg1jyFvbIRMxRAKjlwGKYQRQd/GkMIgcBEUQRCSHJZj+GO5hrmVJWw/cgV3ro49smpE9i2tIZNC+tx2lQM00RVRU71sQpVCbtX2dZJSsn2kzezJq8/BF/deYKTNyZm7FhQaeOBlc0srk2cujEbCCeOkXz/4Fn2nE+8YcwPfPetKwyPBHmgvSnhcblCmcPGtkUNxH/ynHuoSrjF5HNKZOV62H6qix8fnhgWMWTAc0d7eO5oD7+4rok7WnK/2Cp12Ch1lDDW7uBQFBx2jT5/kH+M49wDSKGgCxsBNFQh2HFmGHepm5XzqlBUe/ipNyIuN0MBQmfeBt9NaFoJ/pGw8RUbaE5EwxKc825H2h0oSeRMhquaA+fmT4B3AP+5QzDUA8IEdw1a2x3YNCcgMCfRls1VhjB0REBmXf+s2WHhBoxrR6xNnMr52KsasquH3YVisyN8RkHYJZYLBM7m5SiNntE5kat5Od1tNRXceceHCVbNwTzy0/hvVqoWoKz+AM6aeSh2J4rdgZAF5MRkgKKDP80hhEAIUBQFRstAxTo52eNzykr46IZFPLnW4PLgCEHdpMRho7m8JBI3fitcQVGiOmVfj0x4OIWmiEmlmR353oDMIHlecsRz7iFcQOyftp/jiZX13Lu4MaVuk7HVy+9dSercx+KnR3torCphRWN0Q2t+x3qquKKIiIOfbJ5P/np47dTVuM79ePznviuoqsra5pqM+pMtvv14V9xn60D4jSMKOnZMBAFUdnYOsGxBPYrdCZH4YGzaKJdC4H3rvyFR8auyZpTVT+Csnhs+PuZccsSVEhvuZfdi2jSUaIy/TYMIn5R8uxuhBMFm5Ez/yXJt3ip8e8rAHEow0nHQfk84o0kW9RCaC6lqYAsWhF0KmRdtpeFceBdy4XoCNy5iXjkORgDss9BaFmMrbxy9hoXNAYpWjMEvohAw3mmVOeVOu0pbdXmK48frlludUnORE/m6nPpXeE8f7maW2x7ztDa7/TIl/GjPOUs6vXj4Eisao9lW8j3WU8OnIk1mry/Ijw91ky6+ufsiyz5UiUtTJ9W3THlQN9iVJK2mQKIIiapIFEw0wB8Mce5KPwvnVE1IdSf1EIGX/h76k6RHHbqE+cKfEdryKRxNSwsmPV+mfDqkyQSBbdtvoL/6pcTjEovZS3HNuQ2RZT3er6kfi7aaHHfWzEWpngPEVO6Nud4EJkLq0z5NZsFo7vF4Pgb8G7Cxo6PjzTTPsRGuP58oOPlKR0fHnOxoWMR0x3hXKxso0ZKnUswVfrjvEmuaa0ffnGQT7124wVCKLDXjcWnQ4MrQCE1lJVnXp1AhCLu2ucSbHdb3d+w5f517FjXkQJvUON83RDpL3qjdROT/ndeHWdhcHfmDBCFAwsi+/0ju3McgtOPL2B7/ImJW3QQ504ojGJMmM9/6JOBagwex5VOEdnw5+cA0rcS59mcRisiBHoJbv+yFYZfC5altZQZH8J7aCxf2g3cQhAp1LWiebaj1nveRnSN2yoXTMIUoCAff4/GsB1L8SsRFO2Hn/gywL87fsxccXdCQ4z4LhY93gfLJc5P6UVUEKxvdHL6aWbW8TDFiwrHumywfzfOevX6dvJzZZXP62iBNZbc2rWZLn0Lh14Z99Az7EQJqS51U1ZZZlGP9eth5MkXGljh480RXxMGfeht5g2O3Y4+HRGBKgWkKMBUCpoqGgU+fmN7OGO6FCweSyhsPX8cOnOueGiNn+vFpkCYzwu1zbkN98kv4T+6FYy+BGbM/q3ElyrIHsFfPxQyMgKJClvV4v6Z+zIWtvO+9BO/+gAm41Evw0jtQ0oDjvk+jllbnvS85txUCKWzhBc40Rt4dfI/H8yHg34HSDE5fFfn8t46Oji9mTalpBzHuUxYAH69bvnUSkzg3Od+yrInDVzuZahy52MfyhuqkumXCdSOzsKOQHs0Uk++xzh43pcn+i73sOHaFy4NjndeFted4aO08Fpa5ECnThUYRvz0pwR8yUBWBZgvfVHTDyGh/x41oip082MtpT74pLV6Ijk0xcaqgjHud7u3YmV6HY3FyB2L1ByNvtkRBhAPM1BCdKFe0UtwrHkAsvxcZ8mOYBjbNgVDsmEIB3R+eIYYBovDCTuRQD75TO+DKCQj5QCuBxqW4PJtQSypzYju97zKh62eQwSC4SnE2eFA0d95CdLwHfgjHnk1+bY10EXjmi7ge/2OUkuqCmX85sVUxRGdy8Hg8c4A/Bz4KeIFrwGyLYqIO/ttZVK2IGYrxrla20FY1iztayth/0cKGsyxgxG8xjiZNuJyZhR35A7nRJ1/QDZOvv3GSI92+uH/v7PHT+fxJbp9Twi9v8MRs4E4fFwaG2XH0ypic/iUqbF1Sx7oFtRnrni/MrUjvOY2MfIrI/1uq3Ux4VX79TAYahNAHu1HLG27JiZVZQFxKk1DvBcyAF2nX0MqbUCJZeKZDiM54LgQIzQmKDWHqt46JjrggB21HFnJYP1eaOt69/wnnx7389/XCwEV8J16ABXfjuvMXsjYe/qsd6Ieehv7zY5r0Asxfj7b8UbTSqhyNU3xbBa93pnbuozCH8e36Bu6HP1cQcy53PGKnXDkNU4R8Lk3+jLBzfxD4VcIhOpk6+AmSEr9fIMd9FgoXBaKHBHJbnfWpdW0YZidvX45ffCkX0GxKjB7Z68vaubX8aH/ymgfxcLXfmxN98sX/Y29nQuc+Fm9fHsGx/zS/sK4thcxb14NpSr574Ay7z01MJj5iwHNHr/Pc0esTrqB0UOOK1/bUcJdmY93cMvZdiL/YjReiU24XLGqO89o/lFl+KtM0Czpkw/QP4z+xA46+DNyaXzpA6zocGz+Mc1bjtAjRSYsbOiYBlAIK0ZGGju/l/wu9J5NPpjNv4hvsx3X/JxFCyVxPqeN79V+gK8mzyLN7CZ7di3zwj7DXzJ+yEJ3gsdS54cegt5PAQBeOWQ1Z063Q+EwJ0clnks+TwC8Dd3Z0dFhMqAsej0cAK4Fu4AMej2e/x+MZ8ng8PR6P5zsej8eTQsQMgoj5LBReKHpMDVcVhY/dtZDf2jaf9rpRD2sUa5rLuG9xNVXjtoPPrbDzM2saJxyfDlobynPSlzK3lpE+3QP+nOiTD35hYMTSYm3P+UG6hqPOWurr4TtvxXfux8Oqcw9w95LoBtv82O7e25oT6hYboqMqJppisL6tCpcSDdHRUaSJogfBmdmGbdXuGCOnkLjZdwn/038KR39CrHM/inP76P3WH+Lt3FMwOk+amyFUQDGMrLchTB3FCFg+1/fuT1M791H0HMV/9JWM9RS6D98zf5rcuY9B6MUvIYd6psRW+Afh6rtpX1tRGMdfL4y5lSM+JkRnGiNvT/A7Ojr+cpIi5gOzIv99FdgNbCf8VP/ngEc8Hs9DHR0duyfZTlxomo3aMZvsph5mpIRmVW0ZId1AmuGn1CKP/zdNiWGG68+okdzz+dbp1v/DtsplKxvryrl7aQsBf5Cbw0FURVBZ5kCz25DAryDxBXQCQQO30zb6/fmeYQ5eSC/vPJG+PLx6Pna7mvVeXLs5krYesTCBytqyKR/ZQNBgR8dV3jrRxaAvhGZTWNRQzv23zaWhpjQjmd9/54Ll/h8838svb10yRk686+H4pZvstTDWVvHYmgU4HSpTOQax/6+qncXnHhP89bPHEuoYXbjc2VbDQ3fMQyH8hFFIE6REKir2xWsZuJZYRlyU1VEzpxUFOSonVmY+ue4doPv5L0Eo9cKu76WvUP2Bz+FsWVEw+mfKZUjFDAgUzYnQXFlvAwQVFaVpH29KE++J1yxNK3nyNco3PAqqZkk3Uyh0P/sVGEo/1S3omJ2vUbPt4zm3VaDrMhmlhxi+Zsnm044TflhRW1uGYp+aCvK5wHQu0xUNz7kC3N7R0bGpo6PjA0Ar8LdAGfBdj8fjzJeCUwUxeruUQGwoSv64KBA98sUdTjuNNSXUVbmx29Uxx7gcNsrLHGO+f2LtfKzg8TWNaHYlJ/rPKsksBr/crU2q3UAwRN+gH78/mPa5L7x7gV/56pt8c8dZTl7zcXVQ5/zNIC8f6+EPvnOQv/zhAby+kGV93uiwnr1m1/HrCWXGXg8vHDpnWXa6+N2HPTgcat6vgRXzavizn1nJorqxP7/REJ1ZDhuPrZ3Hh7a0IxR1NHxACmWUuxdvstz/0tseQNocY+QUCu879FJazn0Uvdu/haHaC0b/jLlqwxQKUtEKQqehC++CmTr0bgwC/YxcPmG5rZHuDsyL1p+QB46/Tsgwcm4TU0rLugEwBbrllTMzQnSm7/Zg+CHQAhgdHR1Xo192dHToHo/nc8AW4HbgCeC/s914MKgzMGDxRyLLqK4Ov8Lu7RlG1w1MM3qxSqIr0KnmpikxI08so9Vj860TCCprywDJzZ7hScnJFa+wKfzCumb+a98lUmFlYwnbFszOWV8qa8tYPNvFyWvW5veKlir6eoYttaUbJgcu9bDzeNeYLDXVTtjc3sCG1tk4RxdCY899/uhFnj8ap+R4DI5cHeEPv7WH/+/RFbg1W1q6mdJMK5f7ePhM6OsZGiNz/PXgCxm8ncFm7GoH9AaSH/Oxu1pYOKvU8hjkilfYFD69bSldw15OXB/C7w9hBvy0lZbRXq6jYDDY2080g8VowZkYLlZ9BPnO99KwEOCuxZyzhoHevqQy88GFHiDw7kvp9SOKoW5unDiIs7Y17/pPhqP7kcEAQpNg82e1jYpyNwqSgb6htM8dvnDa2jhEMHDpLMHyNkt6Du/5SUZtAfSeOICrqT2ntpLGxHDStOAqZ6BvMGu6FRqvqq5ASJ2e6/15f4JfXu5C0zJz1aetg9/R0SGBuN5QR0eH6fF4nifs4N9ODhz8QkBQN9h+9ArP7jtDd78f04S6WTbWe2ZzR0tdZBNmEVGI1IfkFRvm1VHmtPODfWfp9U/8uwAeXlbLA0ubySBhiyU8tGYuJ3+aZoxqBOtb61IfFIM+f4B/fOkYPb6JT5F6/fCjQ128eLiLzzy8hMbSsTeikz39KZ370XaC8O29nXxi85K0js+laQeCGWYaEvDFJ5azp7ObnSd7GI6shSo02Lykng0LZlOiFebTpoZSN0tb6wDBzWt9CN8NFP/NcHxrimwW7iVbGfEPwYkXkjfiqMJ1/++h2DTMvGfgmMiDPWcgg6Sn+rmDUNuad/0nxYlyctDGuIdI6Zzbbz2BAACmbkk3KU3oei+ztiBcO8BKvzKwlVpaCZXzoS+9YnJRqG0bsqxbofGInQrdaUiBaevgp4Fo0Js76VHTFEcv9/FP3zzBoE8f8/2FAZ0L+6/w3f1X+MTmedzWEEm5NYqp5CKPbY/nsaEEhaBPfL68voJlj6/izM0hDp/rZdgfjimfN3sWa1tqsKtKWnImxyW3t9bRVnWW0zfTc0o+uLoe1+iT9tRteQM6f/vTo/Sn8He9JvzVcyf4wmPt1JTcCvd49T1rN+l3u7z0+fxUuhwpdRNC0FCi0DVi7Tn+vIroz2ni60GM+Xf6UASUO+08tLyZh5Yn3sRaKPN4PJcQ7ruiIB2lGLofYdowMUdvrnEzWwiJ886fJTS7DePIS9B7irFwQvs9aCsfRigaZiI5eea6L8MUut6hgs4IlBYvoCw6/vMH4co7mY2Fu9KSbqY+ueKH0lGa1bFPZCvh2YLcZ8HB1yqxNa/CNEJZ080KN7z9BC68BwEvKAJbXStq49LsziuKITp5hcfj+R1gE/C1jo6OeHmeWiOfGS7XCxdHLt3kb54/ljR+TgL/uvM8H7/bZEVjdcy3uX09H95UGP3OnJSsbHLTDMdBF0IYUzp8fmUZ8yvLJnw/FfpHbfXrWxbzz68f51z/2EXkeDy8tIatbfWWdPvpexdSOvdRGMD39p3hN7e2A9Dn83OyJ0W8Shy80dHFo7e1pNQNJBvb6/negavjRSTFxiUNkY3via+Hci2z/Q21ZY5pMXevj3h542Q3Ry8P4A2AywFLm8p54s4FzK4qJZwXwIZQNBQzCEYgXFxIqCAN0INxuWN2G9R7CI7cwLxxEWnoUFKBs7b1VkGlkDelnHzxjJd2tsLuV1o8OBL+dzDyWjKLbcigAJuelo2MwRuYb341k1EAwNHUHp6raeomMhrwW7BXNmV17BPZyjl3Bb7TS+DGibT0st31Swg9fTtki+tDPQTfeQ66Do/RRwd0RzXitgdxzl+XHVvpDjCDYDqLha7yhPnARwi/9xzj4Ec21j4Z+efLU6xXTuEL6nz5lZNpb475+psX+fMnynHbpmaoDUMihYJhxDo6+YdhRlyszIq0vq8QtZWmqHxy2zL2XbzGG8e76faOnXPLZjvZsryJtopZMc5naoQMk51n0t9sCHC8J8BNf5AKTePKYGZ7Xy7f9KY9/muaa3n6wNW0gypKFFjZUD1B/vjrwSZU1swp4eBla5mK7lpSX9BzNyRNvrOvk0NXxj619AXgjbMDvHH2EOtay/nQynloAoRhIgwD0+9FGOGVnikEIvK7logrWin2hnColSEE6DoSPa1z88kVd0VG+zqY1YSM1AMolL5Y5YT8EPBhIsE0stqGEVIwQ/60bBQ8uSOTEQijZQ1CguEfmSBfIvH3XkSM9IFQELNm4yiPlPTRKiHYZ7292cvCm8+zOPbJbGW/66OE9n4LrqcIy7RXoA9cR1TOGb27T8UcCvWew9jxlcR6BXqRB76Nr+cs9tUfmrRuZsiPGQoglQL+0U0D08LB93g8LYRDbW50dHTciHz9deB3gac8Hs/THR0dP4wcaydcNGsu8EJHR8eMqnK7u/M63mDyJ6rjse/cNe71NOVIo/EQGKaJqgqUAtoCoCphp7WQdCpUxNpKUQQb589m4/x6ro/46PcFUFWV2W4npQ4bmSzijnZlcMMD3j7Xw31LmkbTw1qFbphpj79TUfjdBxfxNy+ODwmJj0895MFuGx+SBvGuhy3LGjl4uTNtvWfZYFldxa1w5gKDYUr++fXjnO2b+ErGoV9jvfcN2n3vUdo1TM8+OyW1Hkrb70WdsxjFWYIIeQGBotoRRnBG8uCZCxnZ1tayHKFpedd/MhwFTCSKswTszqy2odpdKDY7wmckPV4IFTr3ZDQGUIJj1RMIzTFWJuA7th1ObodQ/+iVLwHfrDmIxdug/QE4bH0LoP22B8PXxhTZSlVLsW39LfxnDyIP/EdixUL9cOg7hPovYVv7JEJkYX4E/fgvHEZefi/8dkHVoG4RzgVrUBwlSP8Qxo5/Sc9w5/cSKqvHvnTLpGyl2J0odgdCTm+HYVo4+MC3gM3AnwJ/AtDR0XHc4/H8PvD3wA88Hs8B4CJwJzCHcCGtj+VD2Vxi+wkr+XTDePPkde5fMoexzliuePhJZdg5VNI8J/c8mtFHGd2dml99CpknslV9mZv6skRbWtKXP+hN97n4WAx4gyiKoLIks8y3lSUxIExNAAAgAElEQVQOS3NybkUpn390Mf++4xRXh+MvKuZXa/zihgXUlbjGnHuLT7we5lWU8eiyWp5Lc5Pwb93nQVVjY0ELZ64A/PidCxOde2mydejH3DWyc+z3po557RDeawfRHdU47v80jvJGQIJNg0h88IzjfRfJBLq3H61yTv71nwyXgCHB7gS7K6ttCM2FVDWwBZMeL40AcQuLpQHbh76A6q4eI9MMeQm88HcweD7+SYOXkfu/BU1rAQdgIaRw3a9gn70IM8vjkdJWho48kmIzexRn38TnrMS9+rFJ6eY/vRf2fZsJG9C7j+B/74ewaBtIlXCgZpo48mPk0nsQNi1zW9kcoGhgFmPw84aOjo5/9Hg8x4A/JOzY3wZcAL4I/GVHR0f6pSinCbr7rW/cuRmA8A1ZcuvGnGseRaHEB091/6czz62tRIavUWyqAghaykso12DA4jph7cJolp/0dW4sdfP5R1dxYWCYvSe7uTEcQAioKXXw6No25tSV0dczmIbMKML/fnBZMza7ytPvJF6wuwR88kEPzeWlaek6ldyUJgcv9bL96BUuDY67+UrJg4M/ZI33DSQicsZEW5iBXvRn/wT7I1/AVtEIenA0Xd1M4xiZLWqVoA9F6nnXf1J9N0PhXxXDAJHdNoSpoyBT2kgE46QlSwdqGZq7aox8Yer4X/tKYuc+FlcOQPNquPQO4WsgOcT6j1Myfw1IM+vjkcpW/s694L+RWLnxOP4sLN2KQmlG+gy/9wIceSZ5G6deT1+fGATP7cfVtiFzW8VWsi3G4E8eHR0dWzL822vAazlQqSBhWIh1joWUMquv+C8MDLPreBed3YOEDChxKKyeW8X6hbMps2vZayiLEEBQN9l/qYd3z91gJKCjqQoL6sq4e3EDlc7pW7Eu28jiVJmAxsrMElvVVISfkgsh2Lq0PqlzPB6VDvDUlGfULsDc8lLm3rmAWGc3WhU5U9zraeLOuXXsPt3FvtM99PlBBeZUaWxsb2B1YxVqAcaUhQyTr+08wfHr8Z2m5sBx1njfjLu8GQ+JxP/mNyh99I/Jf2q8HHLnLEs2jkJxlY5aqmD6YpWP6k8O2hCMWYAmOF5xlGRkf8oqJ8gMXjkON9IL3QPg0iHsWz9N6J1nof9c/GMqW3Ft/U1ESVU4HWdOxiOxrSRgnoyXqyQ5fKf24LrtAcv6BC4fT+3cTwLGlSPQtn6Strr1MV1RMA5+EemhqtTB9UFrTyNcIuZ3doxLYp0PB0L8646TE17JD+kmzx+/wfPHb7BtYQUfWDkXBTWprKnlkpePXOTfd0z8gT19s5eXTvayuqmEp9a14bCr4859v/HcphT1VJdRZoMha1tJuLO5ZlTWpgX17Dt1je4RmfykCJ66a37WroHMuZjwtzKnjQeXNfPgsuYEmhfKnIgwafKNN04mdO4B7vC+CciYJ/fxZMZg4DKB3ouo9Z7RG23BpHjMErfNW4N+emdCm8WHhtq4NLLnpHD6YpkXQJpMadegaZX1FJnzNkyQGTpu3REOdXfifOJ/Y1w/Tej0HhjoBVVARSO2JZvRXFU5H49kttL9gzBy3XK/5KV3MVc9almf0JEXLbdlCQEfplCLaTLzrUAR1nD3otn86OAFa+d4qrnlXMSs4C1yb9Dgb144ErcIUyxe7+xnJKDzi+sXIkT0CeTk2p4s/+H+s/zoreRVYg9dGeHay0f47AO3odniV099f/DJz5VkXAjBPcvqefpw+k/gNy2owGG/lWdes6l85v7l/OMrRxPGx0fx8Y1zWVxXmfW+yFFOGsene1zmXDcMrvsC+EMGJXaVuhIXQmR3LDt6hzjSnTiWWRgDeAInCIfmpLLFLYQ6d2GvaS2IkJJccEddG7qrBnwWQiDat6GaRkHoP91DdNCDaO3bCFp08N0L7xwjRwa90GOtACAAFw6i3P5BbJVNONZ+JE711NyHYSW1la/fep8A9GEUi/rofZegN/0kAxnB4USZRJhTMUSniLxg65J6fvL2RYw002QC3O1pyErb/73/TErnPoq3Lg7jmXODO1qsVTfNBY5d70vp3EdxZcjkBwfP8Qvr2nKsVWEjsSuWHWzzNNDZNcCxa6k3vglg0Bdk55lu1rXU4rCHF42lDjufe+g23rp4g53HurgaLe8aOWfroio2LW6gxp2b0Kuo65xv9PkD7DrZxesne8dsRSu3w5al9dw9vw5Xhvn3x2PnseS1ASqNmyiZWGXgBgUTUpIDLgTY7/x5Qju+nJ49nJW42+8tGP0nxYlyctCGYMwiMsnx9ro2gq13wrm30hoCZc1TKJpzTGVkmWksv2+kAMYjsa2EmlniAlS3ZX30Gxcya8sClPqlaetTDNEpomBQWeLgqQ3z+dbuM2kd/9jyOqpdGpFZG/NpjQ/4gxy6bG3P8utHrnJHS21G7WWTv/qutVpne84P8PiqICUO+5TqWTg891V/FSH4xCYP/7XvNPsvJZ9XEjh81cvhq16+f+AK93mqeGzFXBRFYFMV7mqt467WOvp8AYaDOnZFocbtiGzKzY3+mfHxS4LJ846eAb78WvzfgoEQ/ORwN68d6eb3H14Sk+kns7YMU3KkK/kmfxGZP6lljoMsoJCSHHH7nOWE7vp12P2vSW2IrYzaD3+eETEr7zpnhScI0TEUBf+5dzBP7YaRm2CzQ2UDypL7cFbPTasNK5VsFQmuDR/DFxJweV/yMVj1CzgXb5lQGVk6M4zlV+15H49kthKVjYALy5mG6tus65PhPkIrcLStK4boUHTwpyXuX96EYUq+vTd5eelHl9dxf3sTY1btGfJ9Z6zH510eMrg67KOx1D2ptifDe70+OnutZ7DYc66H+xY35kXn/HMxiXPT45cGh9l5rCulcx8Pr3TcpHvAzyc2LY7klg/LrHQ5qHRFn0Tl3lb5DtG5ODic0LmPxbABf/v8Cf7nY7dR5oytXWCtXV8odenhPrUKEwURvkUmkBkHFTWWX/VPJy5MnWDXSbhwEMrqYaiPCakTlRJo38rsdY9i19z4BrwFo3+2Q3QCl44Q2vUNYNwT8YGLmOffwlvRgnPTr6PMqstaiE6Uu7b8CqHzK9APPw3D18a2v2ADDs821KqW+DKFAiV11uPVG9vyPr+T2srQYek9cOw5S91yLdqEsKiP6nBbSXppHa5aVNXGZDIRFUN0isgrHloxh60r5vDMgXM8//YlYvcr3t1azqZljTSWRB3ryeNqn/X0nABX+70RBz8/uNhnrWJoFJdvzLgMq5aQxBWbNF46fpln37uW+sAkONLt5YWjF3nktpYsaWUd0WVQvvD9PckX+LEYMeDFo5d4ck1rxu3Z0sjoY6oVnHIsYXHguCXZ2oKN5D+EITc82HMafefXwd8bv/P2CsTaD+OeuxqhKNicZWHnpED0z3aIjv/sQfQ9X08+Ifov4n/mz9Ae/wJaaVWSNgRjFpEpdDL1AL63fwind8Vvd6gPoWpJ5SiebZiHrBWv0hbfUwDjkdxWLs8mfMdeBNLMftCyBrW0akwIUzr6aA0efFjMbW8FjmxknorYKZc3wilA0cGfxmiqLuHj97bzcHs9gyNBTEzcNltMgSI57jNznulrNcMws6qHVa4bmekd0qM/PlOvc/557kJ0Xj05eec+iheP3+C+9jlotkILxUnExy8JMuddwyOc60/9RD0WO0/38fjKuRnby2FTKFXDbwSS4S33BhYHjiEh8hQ/nswYlDag1s3PewhDLniwuwP91S8lN1ioH7nn6wQrm7BXzsFUNRQjOKkQg4LiMSE6hrc/tXM/Cj/BV/4B9YN/hhBMOkTHDHjxv/DXMHQlcZPXT+B/9k9QH/ufOMqb48rRFt6F/9DTTHj7kAgV81HrFuR9PFLZSnFXYrv/s+gv/1XqPpXPw7XhYxnNUelwwcKt0Gk9G1FakEpa8yGprZgZITqFl2S5CMtQhEKp006ppsVU6oxdrU+eV5Zkltu+stSRVT2s8nKXPSO9y0c3Zk69zjOVD/iDPH04O849hH+T91/syUtfwu1bOT67bR86ayEbSwyOXevLuF0hBNuWzk7ZxiXHMt523Y0gmY1ufePY9AlUKSMhDDqKNGcEJzCM/urfpz02oZf/EWGEUIwAwjTyrn/WuBlCBRTDIHT0lbTtAYD3Osa1kwnbEKaOYgTS0sO/62vJnftRmBjP/jUEhuPKsdlsOB78LOPnclxolbi2/WZBzO90bOWsbcX+yBegZmHiPi3ahuuRz6Gqasb6uJbfD2pZurPAGqrrJ2+r2BCdaYyig19EWrhjofVsOG4F2qoyK/CSLSyonoUzjd/h8bi9rTb1QTMYGZgsJXZ3pp8WM11cuD6UdZnpIhc2ShcDXmtP72+dl1lF1Sg2tKV28BGCF8p/hr3ujUltpDiq0B75X9grGwDJ2HCO6c99p/eSdrgDQKifwMV3ABGRVTh9yQaX0oAO65VJgx3bk8iNXTQmblvvvwLdRy206sfXuSehTHv1HLTH/hSqkzjCzatwPv6/UF1lSXWbOp6erexVLZQ+8Ps4H/0TWPooLLgbFm6BNR/F/ZF/oHTtkwjVnlJOMq66ynA+9PugZN/Jty/aPCndwhBjPqYriiE6MwJy3Gf2+ZxZJcwpU7k8lP6K9p6ldYSjhXKvXyKuKoIti6t58USC+Nc4qHbAgsqYOL4p1jn/PDchOns7M3vqnAxB3ciKblPDRdZkqRk+mglnFsq83VLNxu9sm89XXk8R/y8UdpY/yYaHP4rrxGsEL72OPtSLsDmwVy3EteQ+XHNvQyoGZigAFFBISZa4PLE9uY3iQD+5C3PNAzMyREeGvFha8ERx43JCW6QbohM8vtNys/L4qxjL7kUmkKlUt+B+6HPog1cJntoP3h5QBMxqxtF2B6q7AtOmYeqh/I+BBVtFuVLRiHPtz6BE9bdpoIcmZBbKlCsVTWg/++cE330Zjr/KhAw+CzYi6hYh96Yb0gWUz0OtXzRpm8+UEJ2igz8jMG7VicwJf2rTQv7qp+kX+ThxpZ+G6hKWz66MLIxzq18ivq19DgfO36TXF+u0JMbP372AcIGuqdWzcLiYxLmJed/kHh7HRanDnnU90+X5zKLTWFMGZwewipbqkknrsKSuks/c18a/7zxNf4IxbSm38aubFlFT4oLmpVTWfh4pof/GcDirSmAQJTSEDIVDBkAUTNaXbHCp+8HfY3l86L0QCdExmUyhnkLi0Sw6ZqY55KWR0BZpZ9HptrbpG4BgH8I/hHCKpP3UymZjW/P46PemUCDowxzswlTsqM6y8BPvQs6ikyduUx1oqx5GrniQYP8V8A8jVDtKzVxsavi3fXjoBhz9SRoD5sS5+deykq2omEWniPcdmstK+L37F/IPL3eSvHZoGKdvBjm94xyLa7v41Y2LOHVjiJ5BH9KUVM1ysqKhCrs63gHKPtx2G3/6kTv4k+/t50YKJ/9X757L4tqKnOtU6MjFqOQib8LK1posS0wf0WVQPnBHSw3f3W+tvkNjqUpLeWnqA9NAW/Us/s8HV3OiZ4C3TnVzcySIIgSzZznZ2N5I86wSYq0jACHizKrRryT5zzKSRW5kOtNDYaPEhujkuy+T5ZHxFc4MwzFKspBFJxiIIzg1ZCiAcKXf5+C1UwSPvQZd747KCAK0bURbvA2tvD4tObnhadoqD1woSriStRl+w2MqNjB1QOJe9Sg+mx15+AeJB8pdh/OeT2Irq7ac1SexrWLMNU1RdPBnBOS4z9zxBVVl/MUHl/Pm6Wu8dOQ66TyUPdnj53M/em/C94ILbFlYySO3teC0R1+F5UbvyllO/uaXNvCTt07z+rFrDI4LYb57fjlblzQyu8yVMx2mD89NiE7TLJWLg9lz8aud0FZdlhXdpoaPXxJkzh02ha0LK9ne2Ue6uGd546TbjeVCQHtdOe115XFaG/ubJIHRIliKghQgUcI3chEOs8p3CEM2uXREi4pZhFbOjM2i4yiB2mXQYyUWHph356RDdHC4QLf+xks6SxOG6MRyQ1Hx7vkmnHkzvqDTbxA8/QbBlT+Le9k90yJEp5C4a9kDmIs24O/YD2ffgJEBECrUzUVdcg+25hUoRvbCh4ohOkUUBKSUSAmmacaksgzfTnPFXXaVexc3sv3YdYLpPMpPpDuwvbOPdy/28dmHllGmZV6EJxU3TYndprBtUSNbFzbQNeJlOKDjsKk0lDjRbOFLwTTNnOkwXbhphosUZXs+3bV4Nhf3XyVb+PCd80bnfzb1TJebZnghlGzOmKbEMKPfZXduPXZbC2euDXBxMPVFeNe8Waxtrsnb/JamiYzMLUwdYRhIIwCGH0K+8I1UGuGQjpnC590J599KOTZjsOAOCHmRRigsq1D6MhkeHAn/O+hHLN2K3GHNwXfOW5XQFjIowKZDyJtcj5bVcOJ5a2NRMQ9FqJghX8p+BnZ/E87tTi3z8HfxKuBcsmXKxyNtWxUoVxQN57LNKIvXg1AxVTWcrUqomHrqMbJkK90BZhBMZzFEp4j8QEo5etOUUkxJCegojvb0MzwJ5z4WNwPwDy8d5Y8eXoES7zV+FmCYERfLBBDUu93gjnXEctLstMRYW2UPtzfV8AOukln+l7H42TubaK+tnNI5Px6GScTBT3KMIZFCidSDyO7cVoTCp+5ZxvcOnuVAkorADy2p5v6lzXmd40akbdMETIkIBRGGjun3IozwjDCFQERWazOB2+avQ7fo4Gtz16L7hlBNiQz6C6Yvk+GE/BDwYSKxVbUQmr0Urh1LzyArnkRIiQz64rZhhBTMkB8ZCibVQ517O4ZVB3/+nQnbjeWhmxfSc+6jOPRd9Mal2DVXUp2zzdO1VZFLzJAfMxRAKtPbMSg6+NMYQggEJooiEEKSRqHJrGAkGOL7e85nVeZ1Hxy81MO6eWmk4csAqhJ2r6bKRtMZubKVpij89n1t/MMrpzOWsaLBxb3Lm5hbkaMcyhagKmEHP7mdBIZpoqoiJ3PPoSh8dP1CHl0V4M2OLk51DxIImbg0lRXzqlnfWofLrpLtxYVVRLP+hG0gEHYNIW0ozhJEyAsIFNWOMIIzhtvq5qO3rodze9Mz0pKHsFXUYXOVIQwdEZAF05fJcBQwkSjOErA7sW36OL43/g26jyS3x4qfwblsW9I2VLsLxWZH+IykethdjRhtW+D0jvTGoqQe14J1CFVN2U/9zJ70ZMZAv/geWvvGnNo9U1sVeRDF7kSxOxByejsMRQd/mkMIgRBEClxFV5uxN/Mw7/WGHYDdnTfxRg6rdsLGxfVsmF+HW7MlPHe8nC89f4yhDLKdpcL39l9lw/z6tPSwysPVfUVMld/syp9JPJe2Wlg9i88+sJB/eaWTkQQPR+zAr25uZXFdOad7BxkO6DhtCvOqyihz2McdnT9bKYqIOPhKkuPNyLGpjpscr3Y7eXxVK6mRH3sJRSG8aBSg2EBXEaoDKSKvQZBg0yASHzxTuGvDL+EzBFxM4QQufhjX6ifCJrO7EUoQbEbe9Y/HzWAA09+HadOwO2YhbFry431DBM8chNBI+PvyGhybP0Hg2mk49hL0dIy1xby7sS/dir2yOZyaMYlOEoXAQDfB/j4UZymifE7C4113/hy+4DBcPJh8LNy12B/9Hwh7aUpbSARcOpRcXjyc2Q23PTCl4yc0F1LVwBbM+xwqdC5sDlA0MIsx+EXkHeMdMTmGv3LiCj95d2IF0V4/PH24m6cPd/MbW+azvL5ywrmxPKib/P1LuXHuAXTCC4hqtzOpHplxkQOZM5Xn1latlbP4iydXcaS7n90nuuke8CGB6lIH6xfVcfucmtF87UvqKgvEJhN5PtNkTj8Oo1VtTRMhQWCiGHpBpe3LBXdt+mX0y7cTOv46XB8XmtK0Em3JPdjqPeHjpSjINJnCNAhePU7o2OvQc6sPOsCCDdgX34OtonHMuYZ/EP+BH8LF/YxHYP9/Qttm3Ft/C4wQIf8gqlBQS6qRdmfEFon7r/dexH/iFbyn3xgruKQeZck9uBauR4Gx5yIp2fhxRjrb4fhLMDz+nuiA9m24lz0Idlda85LAyIS+pQXvwJTP+0JMk1movJgms4hpgZeOXebZI9dTHvfVHWf5za3zWTY7cYrIPeev05dZtrG0sftUFx9Y2ZoT2eNdrSISI9e2UoRgRUMVKxoiKfCSOsWFiegyqIjUSDiio3+Q5DtVX664ECqOpnYcTe3oAR9y5AagIMqqsdmdAGNT+yEopDSZ0tTxvvl1uPh2/DE8s4fQmT3oqz5CSftWEAJj6Ca+5/8aQkky15zeifdaB+4H/xB7VctoikSZQif/uQPoe74eX+ZIN+bBbzPSuQvnfb+H4nCNOVcAbs9GxML16DfOExy4hjB1VHcltsYlqJE9YGmnWhQZhnAoUddrKsdSMOZ3tgDmVuHyiJ2mx60oIYoO/oyAHPcZ5lcHvWk591F8bftZvvTkSuxjymSGZUop2X40exlQEqGr30ei/kyO5yb148zkRVvlho9fErx/uATeT2kyE6ZrLK1CdYZTuyauclo4aTIlJr6d34DLCZz72JF+53sMqw4c7VsIvPT3yZ37KIa68b7+VRyPfj4tnQKXj2Ikcu5jMXAJ/4tfwvXoHyNs9glyFAlq7QIcDUvGVGq1WgFVaCWAiwlVWFOhomnK01VO5zSZUz/vi2kyiygYiHGf4dvpzhNdlqQYwIGLN9jQGt3oKkdlDvqD9GZYiNAaok8ZbrWdHZ4LmTOVF22VDp+KEB1TwrFr/ew6fpXLN/3oBsxyCe5sq+OuBbMpceQutWx2ObxfQ3Ss8kIK0Ql2d8JlC5mADv4HAUVYq+Lbewqj+yT2mrlJdZK6H2P3N9OXO3wV/8ldlCzdmjsbYcDSe+DYc+nrBShLtxVDdAqYF0N0iihomKZk9znrhT12n+yOcfBvYUTPUeD9OHQP+PjjH77NSAhcNmhvmsXm9npayiefNWW8q1VEYhRtlRrRZVCu0D3i459ePsHNcWFxPq/kmfeu8cx71/jgynruWdyUQy2yg4TzafQPkvy/li8QjqBQQnSCJ15LZ3jH4tAzlk/Rj78Kmz6eVKfglRMQ7LckV556Hdm+BZTc2cjl2YjPioOvVeJoXh6WMaXjKrh1weV/bk0Fl6aBGRwBxY7UosXn0rVVjLmmKYoO/oyAHPcJI6HMso33DOlj5ES5Q52aV1U9vlttD+nw1oVB3rowiKfGwa9t8uDSbHH1S82LYSeFZCtTSjpvDNAz5EcAs8tdLKgqQ0Qdmxy1m1+eft+uDfn485+eIFUW5h8f7iakGzy4rDmHek+eS6AYopMuL4wQHamH4Oq7WIZuzQkH4NKxlKEjxumJm3VTwttDcLAbtaY1Z/YS7krY9Duw6ytpqWS/71NIu9NyONBk+fslRMdQ7AS7O8KLxiuHbxleKYOl9+Fcche4q5PbimKIThEFAzHuM+qgTVaeHOWVLo0SFUaMSQnOGB03Avzdy8f4gweXjVadjdUvNRcWj38/89zZSjckr3dc5bWj1yakyazQYEt7PVsXNaIqhT9euQzR+X87O1I691E8d7QHz5xKWitKs9a37HMohuikxwslREf6BsdPtRzCn3oe+PoyEz3Sj1KRW3u5W1YQ2vTbhHZ9A0gQy+qoxnHPb2OvaMTMR9jJ+yBER5oGgR3/ApfjpC41h+DIj/Af+THqts/galiU2FYzJERnemfxLyIhSu32jM6rLYs/mRUh2NZeZ1lenWu8g5M5rg4bPHP4YsbnZ0+TmY9c2MofMvi7V47yzHsTnXuA/mA4betXXj9GyCj8CoK5mk9n+oboslgmeio2wE8G0WVj3D8AIEGIIo8uimJDdPKkj5iit7YAqNEQzCQ6KZk5WsJmmxJ7OZqXU/LzX0Js+DjULYaSOiiZDU0rsG/9NK4n/wJ7ZWPG8iUQvNaJ98hLDB/+Kd4TOzG8fRbkxF6FBTLXs8illIzs+lp8534MJMbrf0fw+pkkMsWYj+mK6bs0KSIGctxnuGLkhnmz2HPe2lOYuxbXj5ETy+9aOJvnj1wn3Yf4KxrcPLWhjX9+7QTn+jMLGRqPHaf7+MBKHc0WvfnE13UiL4bo5NNWUkq+uuMEF9KYB6duBPjm7g5+bdPirOqQfy7SOu7N49ad9UOXh/m5oB5TsG6yumaXh93W8Nwqhuik4oURoiOcswiXnbP6220DLO7ZalmRMHREmga+E7vg+gmLeoShVDZOne1UFceijSjz14W/t2mTytKDBImJ7/Q+eOeZMZuXTSBw6DsEZi/DvvbDKcOQZnqITvD8QbiSfkhZcOfXUT/yl8gZHKJTfII/IxBvhS7YstTa5jsVWNtSM0FOlJdqGp95cFFasppKFX5pw0Lcdju/98ByPr5xLm3VjjHHaJa0u4WDl2/G1a/Ic899QZ2rwz6uDPnwBvW0z33z3HU6e4Oki8NXvVwaGMl7f5Nxaen49OV2DVhMuRdBrz+Qtb7l1F5xQnQUaaLowSKXZiREx8i7PiomLLnH+kRc9rDlU+ztW+LqIYJefK9+GQ79F6QdtBaD5lXY7CWFMa4ZcGHq+Pb/N+z9euLMRNeOEnruT9EvH00qU5g6ihEoiH7lgpvHX7c2N4J96FeOx7dVbIjONEbxCf4MRmOZm0eX1fLc0fRSln1i6/xxOfAnorWijD9+dDHf33eOUzfiV73avKCcD6ych8MeXv0qQrCqqZpVTTUEdYORUAjNZmPPmW5+cnhihd1UuNHvtXwO3HI5ikiN8bY6fXOQ7Ueu8G7XWNsvb3CzdWkji2pmxZXz7tWbvHj4MpcGrWdh2nHsKh/dkN6CMh8QRB78ZBlmhtFJppkLbbKDhNfe6B8khZB1oyA4gjEhOnnUx+XZhO/Ei+kMcRjuWpy3PYT/yhHoO5feOXNWYa9qAVOfoIfvzX+Da8dSCEgMrf2+CTJzyc3ACN7OvdB7BkIhcJVim7saR9NSUP1AzXMAACAASURBVFTLMr3HX4dT29Pqq/7ql9A/8H+wldUkkBm78M7/3MomN7z90NuZlp1iETq7B/ucZQlsFWOuaYqigz8jIMd93uIPLJ2DIgTPpCh49eub58VUsZ0oJ5Y3lLr59L1L6Rn2c/BcD/3eIKoiqK90s7a5Gruqoijxz9dsCpot/CTfNGL/lj4MU5Ksz/F5MUQnE1tJKfnRofNs74y/we1Il5cjXafZtKCCJ9e0ImLiGdOtopwI71wc4qMbCsUm2eDjlwTxeWWJxtVh60/xK5xaFnXNLpdAMURneoXoIEGU1sDG34Y3/onUUNHu/RRoLpz3fAr/C38NI93JT6leiGvjx+O2Heq7BBcPpNFuAqx+ClvtQswpsJchBN63vgOnd01QQz+/D10tg7t+CXfLSgsyFeThH1vqsv/4q7jXPfW+C9EJZbohfLAvQVjUzAjRKTr40xxSSqQE0zRjnuCFb6dRfu+SJlbNq+HNk1fZ3dk/use/UoNNS2azfn4tbrs9cv7Yc5PxareDB5Y2jfneNCWGaUa+M5OeX16aWZDOrBLrupqmRET0S+f49zOPtdWz715I6NzHYteZfmzKOZ5Y1QpI9l+8MSnnHiAIBT1ephleCJlm4nkevh6i3yW/HqL8joW1HLtmbTP5/AobZZqtYO0lTRMZmVuYOsIwkEYADD+EfOEbqTRADxZ5yIs0QuF/F4A+zpblhO7+DYw3/x2I/9YWdx3a5t/A5q7CDPlQVBXng3+A/8grcOpVGL9zSymD9m242rcgJMiQb0LboRMvx28rJWyINT+Hw3MXhLw5t5E0JYFXvgz9ZxKrZAzBrq/gXfWzuD0b05IfOHcQy3sZTu/EXPEYOEsm6hkUYNOnxCZTzYWZ4R4/hfDvz3hb6Q4wg2A6p3UWnemreRFh5z5y05RSJH1FX+lw8NiKeTy2IpEjkh2dDEMihYJhxDoz8XFbfRVw2XIbq+fWTtB3JKTz1rnrHL3UhzcQwqXZWNJYwfq22ZRpGoYZcbEKPzlL3hG11TWvn1c6bqZ93uud/dyx0Mdsl4OfHLA+rvFQyONlmEQc/CTHWLgeolg+uwonFxMl24uLjcsaC95WELGVKRGhIMLQMf1ehBG+OZtCIKR833PdB6opkUF/QehjCoFW14Z84k/wXzoKFw6EU1ZKARUNqAs2oNTORwVk0Dd6rgC05fchF20gdPG9sCOl2FHKa9BmL8JUVNB1JHr8ts9mkIMfcD7xZwhFxfCPTImNfId+kNy5j8U73yVYXotSPS+lfC4eTiQlKYLdp7DVL5og0wgpmCE/MhTMuU2mmgtbSUa2orQSM848MUN+zFAAqRTwj2oaKDr40xhCiPBGNUUghIwJi8knBIZpoqoipT5OReXu1lm8eS7912srGlyUa/bRf5tS8tP3Lk50RL065/pv8PzxG9zVOotff/A2VCW1TkWAqoRd0d0nuyyfu/tkF0ubKxnKwt6kRdX2gh4vVQk7+Ml1TP96iEJB8LEtrfzLjnNpHb98tpPVDVW3MrwVIKJbe8I2EAi7hpA2FGcJIuQFBIpqRxjB9z23ucoQho4IyILQJ8oFDpyL70ZZeAcgMFU7SuQYM8m5KCDmrURxloLdOXoOKdvObLO50BwIRZ0Su0hvP5x/y5J++qk3cG5dmlI+RobV46URvq7GyVTtLhSbHeEzcmqT/FwzpegNy6HriCVT2RdtjWsrxe5EsTsQsoBvQGmg6OBPcwghEAIUReFWloHYO/1U8/CTSkWJ6pT8nA+snMfRy++RThZNJ/DknQtQlMhrfwnf3tvJgUvDSc/bfW6QkWcP89nHV4+em18bFTYP20iw77T1ipT7zg6gZskr37Z8TkGPl6KIiIOfbJ5bux6ifFl9JZ/YJPnarvMkw8pGNx+7axHqhM3xhWGjUaYoCCJzS7GBriJUB1JEXoMgwaZBJD74fc3tboQSBJtRGPpMlkvAkGB3gt1l4fwM0m0C2EvCU28K+uY/+5p1/a6+i2kEUWzu5PIdpdZlA8JVHtfOQnMhVQ1swZzaJF/ctvwBdCsOfkUr9tltmHFkCpsDFA3MYgx+EXnHeCdIFgAfr1v849wOG3/wyHL+8eWjXPfKhD0s1+B3H2iPbCQMn7vrTHdK5z6Kw5eHefrgWba2zs5yP2/xkUCI/Rdv0DfkxzQl1eUu7mippcShZr2t3HIBSEshIlGEAG9w8jUPZrsV2mdXpNAzvzyXlWwBVjRW8cUnytjd2c1rx3rGRD8vb3CzeWkjnuoyhFDSlpnP34NiJdv0eKFUss0WxwyFR94wQFg4f/ZiuHYUS6iYj4oBcor6djPN0JxxMK6fQ2vwJJUv5ixFXtpnWbajem5cmTO9kq2zdgHD7Q/C8TSzPmlO9O4ObPWeibaaIZVsp6/mRcwYVDg1Pv/ISg5ducHOY11cGLj11KaxVGVzez13zK2LpPAMLwKklLzyrrWCQM/tv8zGllpsKVKBWsVIKMSPDp7nrQvjQ436+OHbV1nbXMqH186nNCa0qNAx3i21Aqd9cj8rpSp88r52lEKOOSG6DMotyp0aDy9v4aFlc/DqJrphUmJXsY1WGc21BtlBwpEc/YOkENLtFQRHUChpMrPCR8cXS+fb27cRsujgi/atMe1NQd/0zMJopB5MqaezdTW+vf9BON1AmmjbhGLXMOPKFNy64ApofmSRl6x8nBHVBUfSyD50/QTBV08Q9NxPyZoPIibYKsZc0xRFB39GQI77LBQ+3gVKzG2q4I6WWu5oqSVkmPhDBg6bimZT4pwjOXVjIK2wnlgEJBy+2sua5poM+zORD/iD/NVPjzKYRJcDl4bp6HqPzz2yjApX4aYyvMXD2WHqSwTdI7F/S41aFyxqrGDXGevhPRCufvxz6xZQ5oguhgrFJtng6V8P47kQghK7CnarFZwLg0tgbJpMgVTsmIqdYprMwk2TmRVu6JgEUBQVLJxva2onVFKfOtVmFFolWtuGSFarKeqbqyw93cbDXZkyXaW0u2Dl43D4+2mL1drvTzhvZnKazFEuJM7bHwfPXfhf+ScYOJvaaB0vM6LYcN/+xIxLkzm9dxAUEYGI+SwUnvn5dlWlzGlHs6kJj7l0M7NiV103vVnoW5ibEr78yrGkzn0Ugzr831ePY0o56Xanim9qb0zdsXHYtLSR2xoqcVv8Zal2whefWM4nNi+mzHErDKsQ7JCI56qS7Uzlt0J0JEJ1hDOqCAVF5r9qa0HxAqlkmzVuhlABxTAsna9KcDzwe6BVkhLCjfPBz6CaxpT2zd5ye2rdxkMtxVE9Jy35Je3bYNHWtMTa7v0sWll1QpkzvZJtLMc/lJ5zH8WJ55FDPTOukm3RwS9iWsI0MktfFcrwvHg4dq3P0hPu7hGTI12pc8oXAgSwbm6tpVd8CrB+bh2KInjk9iZL7X1syyLKnZnVRcgXROpDiogg1uUPP8FXkaoDKVTCT8kkY8M53r88cOMC11//OsM/+mO83/4Mwz/+3wwf/D764PWsyM8fx/I5akkV7sc+D82rSIi5K3E9/gVsZXU564PhGyLYc5bg9dMYI/2j32uNS9JbgMRiyRaEEv1lTd6uEFCy5iOIO38ZXDVxhAH1y3A8/AW0hsUpZMZehYUyJ3LD/R07JtopBXyndo6z1a2P6YpiiM6MgBz3WShc5KyNUldm8exlTjvZsteOo9b2AADsOHaVFY1Vk2o39zwcoqPZFD55Xxt//8pp0sEn71mA0x5+ZrB5wWx6+728nkaRrI+ub6a1opRsjUvh8txdD4XOJTAaooMAm4ZUFJAlmIFhmCnhKJPgZsCLf8e/Mnz9GGPg9ULHawQ7XiPYuh7X+l9CFojOafEMQ3SiXHFV4N78W4RCI4SOvwHea2AIKK2mbvU21MpGBnr7c1KxNnD1OMaRl6Hn1pjoAFULYdkDuJpvQ1n385i70qn0C9grcbbfYznsxLFkK6LtLkLXTqFfvxAuluAow75gNXZnRVpy3hchOlHeuSe98YhFx17MO38hbCtmRohO0cGfERi36kQWAB+vW3bbWNlYzX9lUiSrtZZbjtbkdOi4kaCqYxJ09gYn3W7u+S37tFWX8/v3L+T/be9MGIpUZoOPb22jrbp8jJwP3T6fuspuXjh8hYE4+8SaZ9l4/I55/z977x0ex3Xd/X/uzGxF75UdJAiwiVWiSLGrUZaL3N/YcRzb+cWvS5zEr5M3xbET/xwnTuwoeZM45U1cUtybKtWoQoqS2MQCkmDvAAGC6Ftn5r5/7AJcLLC7M7sLYkHt93mk/WI4c+65597ZPffOmXNYWFmcQ323zic7i87txeFmSJMk8rxHQRgGipCRClg5ko1jKrgMDOF/5q+hL0UF43N78PsG8dz7aZSRcL8c0H9SsujEcYejANfSewGBGQ3tchZ6kdGwk6yOhx7E98YP4WSCNJg3TsHLp/DPWE7B+o/hW/MR5BvfST52jlLcD/4umsODmY5uGKg1TZi1C0aPR+xgre+3exadm2MXIJLPzSbMwWiITj6LTh55TCm8Lo3VMwotp8kEWNZYSIXHNYlaWYOUEjH6KDA3Eavd3PIivvLICtqu9bH7RAfXByMLm4pCF+taallcU4aSoDvr59awbk4Nx7v7OdvRR8iQeJ0ai2eW0Vg8kudZTmpfJgsjrmoeqZF4tscsBnIoG8et4lKC3nuV4L6fpnbuR3DtKP6jOylctGnK9R/hEgh2tBM+sRO6L4I0oKAYZt2Je94qhFAjQ531tkcWjdntj//ALxM797G4dJDhPd/Fe88nMCrqCbY9Cxf3jT1HLYKWzbhbt6A5XFM4TpNjq5zjpLvrLuJsFWOuaYq8g39bQMZ95gqPd4Gyyx9ePos3L7VZXqv/6oZmMCdDH3uI+Pa5OmaSkR/s2OOKgCW1pSypLSUxJpYpBLRWl9BaXWLp/NuXT+79kMtcAjdDdKJHFIFUVUwpeKuF6EgM/KdehaNPwZDFLDGxOPIUxqItCCGmvC/6YCehHX8Lge6xOvYPwOHLBA7/BFrfhnv5Q2mF6CS14ySEnRi+Hjj2pPWxOP8Gwdb7cFXMxbvhN9CNj2B2n4+kwvSW4CxrRAgFU3Ni6uEpG6e3QoiOPtBBeLgfHKUQtpnJrWRWzP2ZD9HJI2cg4j5lDvB43bLfRrnXxeff1sLfPHEcf6xPEQcV+JN330FtRSG93SM7/pnrsKy+gENXhxM3PAGW1Hoybnfyucjg2rcOz4fo2Ps+GBuiIyLZdAz9LReiI/QAvhe+BV3HSBvGAHrnCVy1zWPkm/5+zOE+UASioHK0KvVk9cUc6CL0y68C/uT6HnucACbeFe/Mqh6TEXbiO/Gi/eFoewZl/UcBgaa6UGqaIuMRDSUiB4qW3a4hOlJKgsdfRbY/D32XbI/dCNSWzfkQnTzyyCU0FHr50ruW8srJDl441o0vJkmOE9jcUsGGhfXMri8lsoOYPWxaVM+hq6dsXbN5kf3Uk1OBeLc0j/EYcVXzSI3E8ylmMZALj/dvQSiL7+V/zcy5j8Lo7YTaBUii4THHX4COw2NPmrcOV/NW1IoZk9KvwCv/RkrnfgTHniTUuBh31Rxbbeh9nQTOvg7DN0BVEUX1uObfjeLywuiiMYt9O7vX8hiM4sIbyPUfRWTZvtnlk2CrKeYyrDP84j/Zr3gcD+HFPWclMh+ik0fuQcZ95gqPd4Emhxc4NR5YPIP7FjVybciPL2zgcajUFHhQlZtfaDLL7TaVF9JU7uT0DWuVBueVOZhfWZxVHSaHZ99WeQ636n6YTB7SdV670M2u4510DpmYQLkL1i2oZn1TDQUux4TXSiAfoiMJdp+Fq3FOeJowpcQA/C9/Gy4myBpyZjfBM7thxa/gbd2U3b70XoJeG7nGAf3o85hbftNSG3r/FUKvfA/6zoyRIYHAoZ/AzDUU3vcxREF5dsNOAumlMjYNHaG5cmKevRVCdAzFQfDlv8vcuQcc9/8WUnXmQ3TyyC1IKZESTNPEHI0vj/ycTgU3TYlhjhwzb7keNQWeMccjVQ0FpikRUf2y2e4nNi7g0WfbuDoU69SMR12B4BObFo6O1620iV0+WbaaiHcN+3n5eAcXbwyjGyZFbgcr51WxoqECTRUZy59MbpqRhdDIHEt0zlTeD9nkx7t6+daL54mf6TeC8NiRLh470sV7VtayYV7duGulaSKjc2v0uGkgwiEwg5F3Y6QRyboi1NuWm4efIltQPIX4X/13uPhG6pMP/Cc+qeNtvidrfTGOPmdf6av7MYd7UFyFSdsId58h/MxfJZd18Q06v3+Oqvd+CcJ69sYJJxCw3zczen0OzLOJuAwJ0HQI+3JCn0x56PKR8U+s7MJdgbrxN3CU1I4ZO6m7wAyB6c6H6OQxNZBSjv5oSiliHLKpg2FIpFAwjFhnZuphmFEXK3t1rgBwqRqfu28pLxy7zHMneiI5kmOgAVuay9i2aCZORcmJMUqFybJVLIbDYb63+zQn4lONDgY53n2Z73OZ993VwJrG6slTIkMYJlEHP8k5Fu4H05Qc6rrB6yeuca0/CAKqipzcuaCaO+orUcWtvY8CusFrF7pov9yHP6TjdTmoLHTy8tmBlNf+eH8npoQNc+vGHB+pLzfGVqZEhIMooRCYOqYQiOjq93bkEgkdh1La0DIUBc5ZcO5HcPAHGI2LkE5vVvpFj/00xQB631UcZTMSytXDAfRnHrUmbKib7l/+NZ6Nn8y4P6O8vBFuWKv9MQpHKehhJOEpn2eJuBFWMMMBZDiUE/pkwqVpIF+0WHtgDNxQVAGeMpQFa3FWz8cUyjibmOEAZjiIVCbxR/AWIO/gT2MIIRCYKIpACImSE3WJBYZpoqoiR/SJQFUi7tVk6ORWFLYvncn9ixs5eq2P6wMBkJLKEg+La8pQFcilxU4qTKatAIZDYf7yiTb641dDMdCB/3rtCsGVJhvn1U6OIhlCVSIOfnI7Jb8fTt8Y4J+fOzNuv7C3J8TJPZf5IZf5+Ja5NFcWZ0/xBDCl5MkjF3nmxI24fzGgy/qO5k8PdLK4voxK782UtGq072NtIBAOF8LpBFNFUR0II1In4nbkUk8jN3ciLLgX045zH0Xw4iHci7elp78pCZ0/CBf3Q2AYBi2m9YyD0BwIpythe/rpN7CVx7znLPpQF47yGVkZJ6VlE+Zumw7+ovsj8zgH5lkirjo8KJoD4TdyQp9MuP/kG4Bhb4wAXB68D/0epupAicpkojngcKM4XAiZQ05MGsg7+NMcQkTKWSuKQiQEAMY6k+nzC33DvHSsg8MXBwkADqCl1sOmRfXMrywmkss9/trITqWijOiUuR7Z4CPZJJTRmPzst6UoKssbKqCBJJhaO1jhk22r7756OqlzH4uf7O9gXnURM0sKY47mhq0URUQd/GTzPPH9cPJ6P3/73Nj44ngEgb9/4Sz/c/NcWmtGUpRmvz9SwvdePcX+y9brSiTDK+2dvHvlnJstKQqRRWPMd4aigsMJigsUEzQnROODpxsP914ifPJVGOgAFCiuwdF8N47SxtFzhJaGQzIRPJU4VjxM+PuftX/tuTdg+cO2++hr3w37/pu0nKo4KEU1oLknbE9iwvFnbcsMn9qFY91HszKWrjmr8L/+I9D7Lbau4mzdCIojo3YnmwunB6k6QQvlhD4Z8fbnbc8RAIIDCefeGFtpLlAiGw/TGXkH/7ZAvCMmM+Ih3eTfd53kSKdvTCth4HCnn8OdZ5hV6uCTm1sodGkJ5MTrlplOmXORAzpMFz55turyBThuYzcYYOfRDj6ybv6k6JMJzyRNZkjX+YcUzn0s/mHnWb7+7mV4HNqk9OeFk1ez5twDvHyql0dWzEaIm4ua2zFNpn7jEoFd34YbcWN57QjhU88RrmjCuf4jaIWVkWsLamE4jbz3IyhpwLPts5hhay/2j4OvfzQVoNU++vb/HI78PH2dY1HehMNTRKK0kWbIbz9/OcC10yhZTEXpfPB3CT32Z1h5kuDc9jk0Rcv59JO3S5pMs+9K+veQ6rE0T26XNJnT+/lDHlmHbpj83fNt45z7eFzoC/P1pw7jC1ncis0BxLtaeSTGZNlq1wn7X8x7Lw3m5DzLxEavne8e975GKuw515VBi4lhSsmOQxk4nRPAAPzhm7u9I679eMQsNsT04nrvVUK//NJ45z4WPacJ/eJL6H0dkWtbtiU+NxnK56Gs+wQF2/83qrsYoaa5s6iqtvoYvHo8e849oLRuTdqe1NNcuITDCWWmw7XSOtwP/xGUzU7cpqcS1wO/j7OmadLmmZSSwKVD+F78FkM7vsHQs3+Lb//PMIZ605AZexdO/f2TLjeG08tyBEDVbIttiTEf0xXTd2mSRwxk3Gf6/LnjVzjXay3+sScAPztwnl+5qymBTEE2dMoOz6d+zAVbne8eJB1cG/Yzxxn7dZUrtrLDb94PLx/vwC5ebOtgy4LYDDXZ0a+ts3dM/YhsIfJe3M2ZdDulyZS6TmDHN7EWsmIQ2PEonvd/DdfCewju+wmWc8c7y3E/8mUUzYWpOZF6GIlEesoAj3U5Iyipt9Xf8NFn7MlPhpolOObdhWnoidt2FaQn2+XGzPIcUkrq8T70BwT7LmMc3wl9XZEd3ZJKtPn3oNYvQjXCmJM0z3wXj8Ar/wZm3JO1rmP4jz8F9Xfg3PQJNMVpSebtkibTzGBHXW3dZmme5NNk5pFDiFt1Rn9O7XLTlDx3NK7ceArsOT/AIyuNCUIH4nVLT6fscZHBtW81Pnm2MtLMIhQ2ci/FZCYhOp3D9u1wIwhSjmwyZa8/l3vsVWO2Co9TJbb/t1OITvD8G2DYWKzq/YTP7cc5dw2uB3+b4FNf4+Y7UwmgFOLa/r/QVI1xIS1GCFo3w7EnresAOGKqdabqoxzshu7jtuQnRM1iPBs+hjDCydtWHVAxH3rsFRBk1sqshujEcldpI8pd/wOIqUyLwDQmZ24JI8jw3h/B6ZeT9/nqm4R+/mW0h34fxel9y4ToOAur7C5ro9Bw1y5AvIVCdKav5nlkHW1dfelk/+X1C91saqpLfeIUI97VyiMxJstWxV4nlt+wjb3O45wEbTLDiKt6KzHRkiFThPUsvfwZg3VzSlBi0nsm1jlmsTFaRTL3uXHiJds20dtfxDnvThzlMxAP/TGB3d+FvnMTW6VhMe41H0IUVoCpT6iDZ8EG/HYcfHcFzobWmGqdyfsY7k0vDeYY1C1Dbb4HR9lMhKZZsq/SsgVzlz0H37NgQ8r+TAfuO/EK7Ps+lrMI+brw7f42hZs/aUH+yAI7fT0N3wD+k7vgwgEIDoPDAdXzcS3chKN8xi2xlVpQClUtthef6tbPRBODWGkraqdp7jTkHfzbAjLuMz3e1Z887j4Revr9CXSId4GmkudDdHLBVivmVnKkw156vUo31Hjdk6LPreU37wevgu2wGA0YTWyURf28boc9RSxgY8vYUCIJTFaIjtQD+E+/Dsefg4GoU6oUQfNaHK334vCU2paZktus4ApAz+lROUpZA+53/glG9xnCp3ZDfw+oQEkdlXc+iKuwkt6+4eQhJIUVKOt/A3PXP1tq3rntU0hFsx4KYWT23ou6+XM4Zt0BIT9mYCiSNclC2455dxI8+iz0WbOxY+nbI9Vssz3Gt5j7Dv4C2h63b+irhwn7ehEl9cnvkwxCdCQG/n2/gONx+oWAc90Ez71KsKIZ532fQlM9k24rddE2jBdtOPhl83DMWIaphy329/YI0cm/ZHtbQMR8ps9l2svViWRmR6c8v734ioZKbmZHt4aNi+tjUrLmTl+krfPH/n33/PKU/Y7HuqaySenPilmVtnVJhu2tVdQXj8RSJ7FXTIiOIs1o6Ihui+vX2vF///Pw+rdvOvcA5iAcf4bwT/4XvoO/QEgjLfkTcaHHFWezgXiZrtJ6Cle/D++9n6Jw829SuOKduAorEKZhSR/vrBWoGz8NwpO40YIqnG/7E5wldbb6q7kLE8u0ANXhiMg0w6iAYlgbA9XQ8W77FJTMSNmGo3k9Vfe8NyvjOpU8dGF/es59FMETL6VsS5g6ihG0rZswdfx7/mO8cx+PnnZCP/tTCAxOut08DYtg/mZrxlGL8Gz6hC35Y0J0pjFyZge/ubn514B/B+5pb2/fZeO6euBPgHuBOuAi8B/AX7a3t6f/TfwWREWRXdcrgtLC9K671RCpT8kjismylaoIPrh+Ft/edcHS+Q1FKuvn1EySNplBMHYv3A7uaa7jufb4glLJsaFlcsLgyj0uWqrdttOXToTtrZU8uGS8Y5Z4Po38i8Tu4/pQ9xn0Z/8qtVJtjzMsdbwr321LfiIuhAo4iWxf2oHTWluIyN8W9XHNXIZ4/9cJXDiAcfpV8PdGKooV1eJs2YSjegFS1RKG+iTijup5hIQXZHpPdrWSWsZmKMFy24rLS8GDX2D42E44/sL41JklM1Fbt1J9xyYYTcWa2bhOJdeP7kjLxqO4ftFCW7ELbOu6BS/shzMWXbLAdXx7/5vC9b8+6Xbz3PlB/A43HHsqsT6FdXi2fRbVW4JpS37UTtPcacgJB7+5uXkt8HdpXNcI7AEagYPAAWAd8KfAlubm5vva29uzWD4wVyHjPtPjS2pKUbFfymTNnMoEOsS7QFPJ8yE6uWKrVY0V+Ffr/GDvFZKhvlDhM/e24lBjfpQmQZ9bx2/eDxVeFw8tquKJNmsvtd/bXE5NQWyYUnb1e9ea2Rx//IQlXWCse6sA6+eVsmFhHbVFIzvJY/WUQDZDdCQG+rM2StUfe5rg7NW4ymdakp+Sz10NZ3dbbx+AEL4zr+Ode2cK+U4UI2QrK4wiwTXvLszmDSgjYQiaEyWadSetLDISaNkMx56w2U9gxkpwFUVkGjomQRSLITqjXFNxr3g7Ysn9hLtOo/sHUJCopXWolXMi/VS1aZ8ZJth/FXrP2bdxLPTwpGXR0dtesKfLhb2EV78P1rGr/QAAIABJREFUUVg1qXaTmhPvikcwWjcRPL4Hzr0KvoFIwbHauWit21AbliDSyHR0u4ToTLmD39zc/AjwbSCd54H/QMS5/+P29vavROUVAD8HtgGfBf46O5rmMkTcp0yLa6rKluZynrWxs7i8oYAi58gLkLEy43VLT6fscZHBtW81Pvm2umdeDXOqi9h59CqvXxggFtVewcZF9aybXYU2mu97qm0ynmeSRQckDyyegWFKnj5+nWTYsqCct98xa1L7U1/o5XP3zefRZ05FfusSQAV+6/4FzC0rRDciC8HIAix1/7OZRSd46UgkDMcGjKNPo9zz65bkp+KuhZsJ2nbwgVf/lZAicM9amVC+YgQRpslkZYWxw72LtuI7/SqE7OUedy26b1R/zHBk5A0DRBp6YKJWzxubweY2ygxjdCapo2AVmiNldqR0bKX3XLD8LkQsQidfxbP0wazYR+pBjOvn0fs6MU0TrbAUZ80CFECRBoqrBHX5dpQ77gduZjoyQ358bz4Blw5AoD/i+NfMw9W8CbV8ZnJb5bPoZIbo7vtXgQ8DPuAaYPlZfHNzczPwNuBMVA4A7e3tw83NzR8DzgKf4S3h4GcP9y+eweFLfVyz8AZggQrvWTPnFmiVHcS7Wnkkxq2wVWNRAR9eO5/3rAzT5Q8SNkyKXRrVBd7oGclczanHiKua9vVC8LZls1g0s4yXjnWy79JYh3V5fQGbFtczr7w4w5asoam8mC+/YzE7j13hpVO9Y5I4qsCmBWVsam2gzO0CJJoaExqRAonnU8xiwMYj9PApy1GcN3FxL2b4QygOi6EyycJXyhoIzl0LZ/fYVkPf9R3kjGWguSaWj8BOiM5kcsXpwbX9CwSf+joELW78LP8gjoqZN+UwIpNJ0DVucZkjITe2eDgLQQbdZzFDPhSHK6u2ihRosw85cCVj+0gJ/pO7MY89A8PXRmWHgbBSAC1b8C7aNu5+lsDwkR3IwxMUaBvqIHhmF1QuxLvp4yiugiS2ijHXNMVULk2+QsS53wf8OpEQHTvBtpHlGjzW3t4+xhttb2+/2NzcfABY3dzc3Nre3n4sSzrnKGTcZ/rc7VD57QcW8X+ePcblwcTBOmVO+K37WylxOZPIjHeBJKYpOdzRy2unrnFjKIgiBHWlHu5pqWNueVFW+jAxz4fo5KqtPE6NWdO+iJUVPv5+AJhTVsScdUX8D92gLxgJfClxOnE51DgZk69rucfJu1fO4e13zOJS/zD+sI7XqTGjuGACh966fAlkNYvOQHpVffXAAJqzJithAp61v4rfH4KO/Ta1COE7dwDXwg0J5NsP0ZlMLkrrcL/zSwTanoejv0jSLw1x14dxLdw4NltJuiE6FvjtULyJghILcyYVggRO7MK79P7s2irdPCyGmZFNDEUl+OI/waV9E8s3h6HtMXwX9uHe/nnwVoxe69/7U2h/Orl+10/ge/xruN/+B+AtH28r8iE6meIE8BHgP9rb283IhrwtLIp+Hk0ifzWwBLhtHXwpJVKCaZqYo0WEIj+n6XKvpvH5B5ZwtPMGLx3r5FTPzZfJZhSrbGytZUVDJZqqYJpjCxCZ5kgxI0GkkMvNfzvW1c93XjqHP/b3H7g8OMTeS6eo8cAnti6k2uvJuA/x3DQlIqpftmTerjxvK2vcNCMLofh7wOr9EM81RaHS4xo9nkzuZHNVCGaXFo45nok+0jSR0bk1etw0EOEQmEEwJUgjEtIh1NQ8VZGoRAiHItfbaSsBF0LFcfcHCP/EroMP8vRumLd6YvlhH9IIZ03PbHAFiXfxvRhL7yd88RDmmT0w0A2qCq5imLMaz6w7EKoDM+wfKyc0HPk7FH2JO4v6yZAATYewb8ptlC531jbZfl17Qhx5GtlyT+Ql8CzZSnF707vTvCUQPw9s8OCe/0rs3MdiqIPAjkdxbf8CmAbBjhOpnfsRBK4T2PVd3Jv/v/G20l1ghsB050N00kF7e/vXMhQxklIi0TOkkeO5mYIjC5BSjv5oSiliHLJsQLC4ppzFNRVIKQmbBg5VRYzuQgrMCe58w5BIoWCMqTwKhzpv8O+7kuc/v+aH///xE/ze9oXUet1Z7AsYZtTFStMveCvhVtrKlJLBUIgwkkLVgVubPpl7DZOog5/knAT3w1sNRtRGwZ52jOs9SGngKHaheSpRQiEwdUwhEDLyHZaSe8ph2P4uvnC4kCH/qBxp6oSDfjRVQzjdmEKxrgNgDNir/D2K4euYgeEJZep+UE2JDAXs2eQWcCkEruomqG7CEAJ1xA5CgKEjjfHjSDgAQT8mMrKoy6JORljBDAeQ4VDO2MguRwiYsSISK54J5BDh6+dxlNRnzVZqUTWmWgzGwAQNJoajcXnC+Z1yTId74cyL1hvrv0jw1Gt4Zi7DOPqsLT25+ibhnqs4vcVjdDDDAcxwEKlMb4dh+i5NYCTRcqIcXiPVjDNL5psATqdGVVXRZIi2DNM0EZhU1hQTCuvI6I6imML/m6ZENySaKlCUSMzf4HAwpXM/Agn8684TfPOj6xFCsdw20uRqj4+BQAiXqtBQWYTTocadCZU1RVNuo+nx/8m11eBQgB1HL7HjQAcBeXP8m6tdPLBqNotqy+gZjOz4VRS78bq1HLDJuFmHjNrJzv0w9Xrf2v+bUjJ04Dl6XvtP/B2HGNkgUDApKqrEuXATxUs3IlwFCEMHKZGaIyl3rthK/zPWs/4AMGcNFXV1SNPE33WG/kM7kOcPAtHMYVoRriXbKGpZixRgYqI53KjOgoT6hLyS5K9IJ4DTTUVNVdI+lrk8Ke0wHbgM+jCCw6gur60xtsoVzUGZ0zPl/cyE6+vfS/d/H8FyBdsEKPJqeCrLsmqr/hX34dv7Y+tKVM6jat7CtG1y4/RO+x0/twdXdTX+G6dtX6pdO0rZmreN0UFRHSgOF9UVJdH3GqYnprODP7K0kgn+XcR93pYQQqAIUBQFGdkjAW7us99qrmCiIlAUEIqCAJ5ps1fy/Lofjl7qZdnsypTthUM6zxy9xFP7LtIbV/VgS0sFDy2fTV1F4ZTbZbrw7l4fu0934vOF0DSFprpS7phdhaqIrLV1+MJ1vvbLNiZCe1eQ9ifbxx1fMbOI7Stn09JYnjO2Svd+mGqdbiXHNOj6/pfpa4vEbt98PiNRkIQHrzG09wf4zu6l+n1fxOGIPLmTqhOhhBLygqa76H/+u2BYz6RTtuZtSEXl2vP/gnn6tfEn6IMED/6MroM/G3u8fjElqx6iYMYShBBj9FGdDZbbH4P+K3S99F+U37EVZ1lDyv5Oaw4I00Q4CsHhyQ2dcow7K2ZS8f4v0vODLwPpVxBW3YUIpyerupWseAjf6X3Qe96SDlXbPpaRDoHootsWes7Q9+Q37F8HBPuuIuK+d4SigeIkH4M/dRiKfiYq4TcS4zE8GY2HQjr9/f7UJ04iKioiDzF6uofQdSMnYqZNU2KakToriiKQEp7alzzf+UT45etnmFEwMoQTt3fDF+BvdrRxI0E5sxeO9/DC8R4+sm4W962YA0hudA+Nk5Pnggv9Q/z0tbOc6Y3fQerAI45z/x21bF1QH02KkX5bp3oGefTZU9jFgYuDHLh4hA3zSnnPqjkoscVtpshuZVWRx7q93QMJz4m/H3JlvG8VH3ju7/G1/TL6d/w5I38DPWfp+sFXKHjgdxBCGZsSMQHXtn4S/Zm/xBJa7iXgqKH3sUfhsk0H4upR+n95lP66JRRs/A2k5hqjD033wOlX7MkEjLYddLftQFnxAdytW0ZllpQVIkyTvn6fJTvkOkcPIENBhFOCFshqG6UlXhQk/b2DU97PjLlWheuRrxJsexbabYaaROFXywj0DmbVVgQGoazWgoPvQLv/d/GrZfgT6GCJ+ybFZUuMgJ/+3oExOpRXlCKkTndX35Tv4JeUeHA603PVp0/A63hcjX7WJvj3VDH6edwC+HRjTAiGVVzqSV5V0x/S+cZTiZ37WHxn9wWOXrwe607kEYO2rl6+/lT7BM59BH4JPz/YyXdePYUp0xjMKKSUfPdl+859LF4+08fPD5zPSEa2kJ9PyaEPXsF35AfWL7hxmtCVNkAyNr3ixNxZMx/ntt8GHMnltjxIwfJHCF46bN+5j0XHEYZf/Gfk6D0Q0ce9cEv6MgHzwPfxjRZvjy5+YtNkxrQ1vTmTIDdu0ZwT/UyfqwVlFK56BM87/xzbmL8FoTqSyLdvK2P4Br7HvgJnJ3jiFQt3BZ53fQVn9Txb8ifko3V1bhEKyifQR4z5mK6Yzg7+SPac1gT/3hL9PHILdJlixPzg5AyP/K2biVNtJkPYSN7GzvYr9NkIV/znZ45Gf5in2i65xbuG/fzjC9YKmey7NMhTRy6l3Vb79YFxYVTp4IVTvXQO+dLSYep4ruhx67h//1NEIilTnX8T4WPPj6aHNIWWkmu1rbg/8New+iNQELvX44HmbWiP/DnelY9EUgQefW5ce7bReQT/5aNjdFBKG2HVhzKTu/e76KFgVKYTKRRbdshprjgwEJhK9tuITf045f3MIheFFTBvs60ppC19IKu2MqQk+OTXIWihyFmgB/++H2Wl79TMT/MmSg/qgvXjbYXIp8mcYozkQnp7c3Pz78fmwm9ubp4JLAcu3P458OHmMnPqH8nHL3kLtBS7awlQ4IJzfUM4FYXqAjeOmIqmpgk7j9l7ta3HD8cv91Lrnqjq7luXP3fEXvjUU8eus62lMZqj3V5br5+8Rrbw8vEO3rd6nm0dsskzrWR7u/Pgld0pzplge6z7BMIIjVapBJGy2qWiOvE2r0NZcBcgotldIlmLIo/cdczAEPSMf7cjLRzdgdLYOkaHwub1DDtcyD3/TropPIMnd1GweFtOVbLNBs+4km0SfjtUsk3EPXe+F/9gJ3QdTzl3HJs/i8NTmtQOdm3lP7sXgj3WJ/ClfZg3LqEVVWbUd1fzFoLn7BeQSwvF9bjKZ46zye1SyXZa7OA3NzfPbG5uXtjc3Fw5cqy9vf0cESe/GfjTmHMLgH8lUnwxX8V2iqGpCkvqvKlPjMONIPz10yf58ydP8Ns/epMf7TtL13DknYezfYPjculbwavtV1Of9BZCIGzw6nl76c8A9lxIr8hQny8r2Z4jOpzpy5qsdDGBe5pHDEx/f1rXyZCfTMIcROz7GdHjhi+L86XnZGTBENeup+kuCt7/KMrqD6clVp57ldHFTz5ExyKPXShOXd8kEOxsZ2jfj/Ht/jZDe39E4NIhpDTTlilUBwVbPwUtD8T0MQ6lc3A98AVc9S1Zt5Vx4oWJ20yC0PGdafd3hDsqGqFyoe2204Fr7YeT2IqEZp8umC5Lk+8CG4EvA1+KOf4pYDfwh83Nze8A2oG7icTfPwX8461Vc6og4z5zhUd+pDa21nGk40y80rbw0uleXjrdy0fXz0pbRt9wiNywS27wC33WM5DE4tSVPjY1jYRDWG9XEdn7tgwDUpqIEUfIog5Ty6eTrpnzSGaKZOfE/h3zL04PMssVQ00lu4/aw/5+HJ6ScW0pDnDOXUVg7/fsCx3oGQ3RyaVKthnz27ySbeD8XszXfwjhsYtI/eQL6Hhg5SN4WjakNacVCd6Vj2CseoTQyV3IGxchrIO7GMe8VahVc1H0MGaWbSXDAei7YHHixuDCIcw7P5ixbZ33fZrQL/4MhrP31Dce6rbfQa2aN7GtyIfoTDna29vPNjc3ryGyg/8g0AScBf4W+Jv29vb0801NK8StOpE5wG/q1lxZwrI6L4c6EpUssI5/33WBh5dUpXWtU1OYervkDg/oaYYS6CNFm+y1W1/mpf16FoLwo4js1E6dDfMhOsm5o2wxxo1LSc6ZYMFXWIOqaJhZDHkQegC9I7OXu+OhCiVhCI1Id9tPdaJIMx+iM41CdPxHn0O++cMkg+qH/f+Jf7ADz5r3R8Y0nbZQKGhaC9w9NvPMJNlKBtP8rQ4HsjIWmuJEe+h/49v/Qzjzanq6TASlAFo342reiMNdCAnv4dsjRCdnNG9vb9+U5r9dAj46CSrlkSUIIfjo+gX8266THM6Ck//i8fSqR86qKc647dsJ3jRTb3mc6e1qrF9Yy85TFl7YsoAa79RHF44scfKYGN7VDxE485Sta5TmrYAEIUZ31TLh4evnCO94FGTm3zuxUAsqE7erOkAUgLSZ7q+sjtHFT2yIThbsMKWcEc4ktBG7ULy1fQtebUvh3Mfg5Av4SxopXLB2CsfDuq1G8sLbhmska3nmOisOF4V3fRh95XsJntqDHOwEw4CBLuixv2DXHvxDXGUNCCEwFQ1MPYkOUTuluVbPFeSMg59HJpBxn7nCb7pAmqrwiQ3NHLxygxfbrnI2QUpGKxjUoaFQ4cqQvR3oba2NBIdjd5Cz12dTSo5d66PjxjC6KSnyOlnRWBHjROfCeIzls0oKSActjWUxsqy3W1PgYX6Fk1M9mcfi39NSk5YOU8vjlwS3N3fULsVZt4pQx94E58T+DeDGOX9t1kJTwjcuEX46jXSDqTB7fSSMKJEOQkLrvdD2c1tilYWb8iE6NvlUhugYBx63Nb4c+DnGgnURB3MKxsOOrRSnF4oaYdBekUoalmR9LPCW4Vl0b+S45kQOXCP409+zp1fdUrSa+Ug9nPi+jbUV+RCdPHIGIu5T5gCP1y3y8tuKxkpWNFbQHwzTGwjR2efjP/Zcwi4KPU4YSp4rPxabFlbg9ToJDo84l9nppylNXjjZwXOHOxmKywj6/TeusGZmEQ8vn0mZJ3nRrqngTk1lU1MpL562/gKiAFbPrOKms2qv3V+7p5mvPnaE4fSypwKRt+fXzq5JW4ds8ekQonN5YJiX2q5y+PIAPgOcQEt9ARsX1dNUXjj2hdQs6yAElL7ji9z4yW+jd5+e4JxY22g4Hvo8mqqR6LG5HS5Mg/DL32Iy4Fi0OWUYgqt5HUE7Dr4owDNjCSIfojMtQnT0/k7os/lemTGAfrUNV33rlIyHXVupLVsw3viurS46WzZO/lh4igk2roDLB6zrteheFDu2uk1CdKb+OXceb0mUuJzMLinE40hvhSyAh5dUWzq3oUjl1za1jnO1MoVhSv7lpRP8/OB4534Eb1wc5Cu/aKNzeGqrHifCltYGW18C77ijBoea/tdGidvJ/354CY1F6e+MfObepmiazqlFtudTNhHSTf7vKyf42pMn2HNhgGEj4lYHgTevDvPos6f4q6ePMBRK/0maFSiecsrf87eULnpHtAjPBOdUteB++Is4ymdFj0gyzW4S7j4NQ+lle0ranxUfxFE+M6UOakEp2rqPW5bruP+zCCWy8wmCMSE6KdqaPpxJkBu7ULx1/Qlds1Y7JB7hzlO3VM9MbOWeuwZcI0WgLKBhGVpZwy3pi3fth8Br7fdfLH0XzpqmNGx182O6YvouTfKIgYz7zBUuUp6nqendQZoiuH9RIx63xo/3Xk2YeXpFQwG/clcTDocSbTF7/fzxvjMc6UztuAeBbzxxjC+/cyke58iPeHZ0yJSXe1187r75fOOZ1DGNm+eXsbW5PuN2S90OvvDgUnae7OC1U9foGY6kmSt0wkAIEm3uF2vw8S3zmVtelLEOU8NT3w/Z4IYp+fsXjnHmRvJQqAv9On/w08N88K5G1sysYuy6LXv6KO5i6n7lT6kZ/CxXX3oM/cYFMA0cxdU4F9yBU9EAM6shCaFTu5P2PR2Iuz6Cu2mdZR2cc+6MhES8/C0S5sbXSnDc+2nUmvmYejh6bT5ExyqfqhAdM5zmwlifunG1bStNRXvb76P/7M/ATJFxrWwenns+fuv65SrE+c4vEnr+H+FaW2K91vwangXrLGUZGmMr8iE6eeQM4ladyBzg8bpNfF5DcXpx4DMrCwHYMK+Ou2fXsPdSN4fO32AoEMahCubVFLNufi1lHic3dy6y18/+QJhXzlrP8+0zYfe5LrY112dNh2zxueXFfPHhVh7bf4GDV8e/GFjlgfvvmMlds6oybitsSJ4/foXn27rG1TII6vDAoioaqgp4/WQ3Xf1+hBBUFrm4u7mGRTVl3EyLOfV2y9UQnRfar6R07kdgAv/52mV+/NplHlhWw9aFDShiMvQEpaiKgjvfe/O4qSP83Sj+bjCzG47CgL1CeKNY/avQdQJ6L0EoBAXlKHPuxNV0F6qqYjd8yDtjKcYH/w+hs69jnH4VBq+DqkJxLcrCjXgaWhFCHZMNJR+ik/shOqrbk3AjIimcnikb13Rs5fSU4njnH+E/+DM499pEHYKWrRQs245QHVnNfpWKa4oT57ZPow9cI9D+Clw/D0YInIUoc1bhmbsGqbnSmhu3S4jO9NU8j9sCpR4nrdUejnXZC2G5e/7NsvSaqrB2dnU0LhvGOxkRxLtamWD3qU7b1+xs62Trgrqok5pbqC7w8LENzQyGdM4O+OgfDqIHdWZXFTG3rJBsWC8QNvjb59q42D9x9lqfCU+0dTOrtI/PbF2E25EorWluYGSpkUswpeS5o/ZzRweBXxy6xsXrQ3x0/YKs1iyA8SOn951Bv3wShrrQFANnfTO4i0Z30jLOGpKm+o6KelwL1gJgKhqKqY/y5Fk3EnOhOfA03QVNd42TKSaSiWBMiE62bDJVnBHOJLQhGPMdcYv65mxoJZ2gS9fMpVM4HunZSvUUU3j3R9BXv5/Q2X2Yvl4QKlpJNa5Zy5Gaa+J5fIu4VlyD984PjLmvRrjMyFYx5pqmyDv4twVk3Geu8HgXaGK+ZUk9x563/sLSHfVeSt0j8bxWdZLRI9np2+GLNyzrO4L+ENzwB6nwxqYgy4VxusmLnBqbWiJxlL3dgynPt8qllPzTi8cTOvexuNAX5l9eOs6nt7be9A+yoMPUc2v3Qyb8RFdfRi8wH7wyTPWhizx8x6ys6iYBgSR4dj/Db3yPUMdeFEwcmDiJFC4Ts9bjWr4dtbAy88f4RZXQc9p2/0Vp3YQhDKHhHvSjz8HZN0AfAJxQ1YRYch/uuhaymxklH6JjlU9ViI7wlEDdCuiw/qInRY2oFXNsh4vkiq3wluFeuClyXHOiWMxIMx15PkQnj5yAlBIpwTRNTHPkhzXyczoV3DQjMcCRY6alaxZUFLN9USVPtqV+rF7mhA/eOS/aV+v6maZERPXLRj99wfS8qOFQmDK3Kys6TBbPtq1AcKpnwFZ6zPbrQU5f72deRYkl+Z3Dfo5f6SUcNnA4NRbVl1Bd4M2a/hNx04wsGk0z8TxP537IhHf2ZZ7vfceJHra2NuDW1KzpJk2T689/j56d3xw9LlFQMEdDHZQLuwhe2IPj3t9GrZ4XCe8QKkjDNtfm3YV+fqKQgiSoakVRnRD2jcqRegj//p/A6ZfjTg5B9zHkC8fwe6pwbv2fKCW1Gek8ysM+pBGO/J2JnFzhoeHI36Fo1rMstiFDAjR9zJjdqr5pyx9Et+Hgi1WPTImeuWCr6cal7gIzBKY7H6KTx9RAykgcsGlKpBQxDtnUwTAkUigYRqwzkxr3tczA6VT5+cHE4QUakZcwf/9nRyjUYNXsEta11FPpSl2UwzCjLlZ6xVvHweNUIGRfmFNTs6bDZCHbtgJ4qe2q7WtebOtgzvqSpOec6Onn6QOXOB/3ZOBnBzuZU6rx4MqZLCibnAJnhknUwU9yTpr3Q7rI1jfAnnNdbJxXlyVp0LfvGTp2PkokcVvEwRdIDCAaIRv9zyD87DcxH/gDnN7IuJlCIKS0xbXienRvJfisx+Ir89diBoZH5RhA+I3/gssHk1/o7yb0+NdQ7/88zoLStHUe4bofVFMiQ4GM5OQKJxyAoB8TCaaR1TaMsIIZDiDDoVveN8VThrL+E5i7/iV+RozH8g/gKJ85JXrmgq2mGzfDAcxwEKnk+I91CuQd/GkMIQQCE0URCCFRciLpqcAwTVRV2NZny/w61s+t4bUL3Rw8081wdJe80xf94Ys5d1CHnaf72Xm6n43zSnnXillJ44ZVJeJAZMtGrQ2lXGm3F6bjFVDpcUVfYsxdZNtWAIctZBuKx6FOf1IdXjl7jR/tS7xwONen8w/Pn+UDq+u5e05NwvPShapEXNXkdkr/fkgH5YWu1CdZwOmrfWyenx0HXxo6XS9+E2U0k0zsLn8kbGnswsTAOL0bseodgEBRHQgjZIsrRgjHPR8nvONr1pScuw7XjGVI7aac8MXDqZ37UQQx9v0Ice+n0tZ5hGueIoShI4IyIzm5wlHARKK4C8Dhju4m+5FSguZGqGrabagOD4rmQPiNKembu3Ex+oN/ROjo03Bp3/hpUb0Yx7L7cVTMwpzi8ZhqW00nrjjcKA4XQuaEU5U28g7+NIcQAiFAURRupmKL9SBvNY/sVCrKiE72rncrCpua6tjUVEevP8hXfpEkBVYUL53pIxA2+NDapugLrOPlK4qI6iXGHE+Xb1hYx7M2Hfyti6vR1NiYvqkcp8Q827bKBIl0OHatL6lzH4vv771KVbGH5qoSi3pa44oiog5+snme2f1gly+pLcPBBTLNbh/QzdF5kKlugTMHMPs64+otyOgZggmfO5x+Abnq3QjNAZoTonHDdrijai7m276I8fjXIdkrkQvuw7P63QghkLFy2p5PfM1E6DmJ7htAK65JW2eQ4PAilBBoRmZycoVLwJAYYT/hoy/AsWeBSLhOGKBxOcqiB3BXzbXdhnB6kKoTtNCU9VOrmImy9dMw1Euo8zimHkZRVRy18xFFNSjR9KdTPR65YKvpwoXmAsUJZj4GP48pR7wTJHOAx+tmX9Z3d50mOK6vE+P1i4O0zrzBysbKBDJFguPp8TKPm7Wzi9lzfsCSfm4B65tGdkSnemxS8ezaavycsIOJZT62/6ItKU8cuETz/aUW9bTGczFNpqaqbG2p5OnjaaaJjKLQ7cDuPJB6GMN3FUwdxV2B4o4sqIKnX4meZ29OGN2ncdY2Z5RKz1XaiPjAXxA4dwDjxIvQfz4q3QML1uJu3oBSUh+rXBHmAAAgAElEQVQ5X968Vh/sgv5ztu0WOrkL58p3ZaTz7ZgmM3jxEHLff0xstMsHMS8fxDfnLgru+jAK5HyazAm5y402awWmUEaP38q0kZNhK/36eQLn9kFoEBQnWsVMXLOW2xqj6cjzaTLzyGMScW3Yz6keq+59BC8evRp18CdGJm7mRHj/6nn0DB3j5PXkeqrAb21fSIErvshV7iLbtmqqcHHa5njOr3BOePzywDCXBlJn44nF2d4QnUN+agtTv69hFSPub67h3tZGDl7o4Zovfe1aZlivYKkPXMS39wn8Jx9HBvoZmT3OujvxrnoPhs96vYhYmCEfIMk0lZ5QHXia7sRcsG7CVHrmBNca6ebRH+jIXGcEuZom09SDBE6/htl3EfQweEpxzV6JWjk74bXBy22JnftYnHuNYWniWT9SAdiKToIxC8ccsFHucuu2Cl+/QHDvj8dlotIB/VU3LH6AgqX3RabplPdrsmwVY65piryDf1tAxn3mCo93gazz3Sfs55k/1xema8hHdaFnApnZTZMJEk0VfGpLKzuOXuLZY9cnDItYUuvh3avnUFngJnfHafJttbG1ltOvXMAONi2qn1Bm26VeW3JGcPTyDWoXTizz1vD07wc73OVQ+J37F/N3zx3j8qCBXSjA6hkVMXITt+U//jL9z/4JGOFx54Q6XiP02GsoxTUTXpsK0lkwZaki0361zpRZ0DnzNJmm7idw8QgM9yE0DaV2Hq7i+rRtInUd/4GfQvuz47ocPP40FDWirfsQzsq5Y641hIKx69+s2+/8GwSaNuCpbbak31SlyZyO3Kqt/FePI5//RpJBCsDRnzN84xyeLZ9E5lAfs2Yr8mky88gZiLhPmQM8Xjd713f2p1NGBLqGg1EHP16mYDL6qSqwfeks7ls0gzev9nCt14dhSgo8DlbNrqLE5ZyUdieTSwnnO/s5cvE6hmlS6nGyqLYMh6qkLXNZXQWV7gtcj2bKS4Vqr2BJXdmEMgMhe7v3IwiFR5zd7NgqF0N0RniBy8EXti/jSGcvzx66xPk+6zZ7+x21OEbfFUncVuDsPvp3/DGMPN5OcL450JWk74nhKmtMGaYi9CCBCwcxei6CGQRXCc45K9FKaic83yp3FJalV6m0uCrj0JpMQnSMgWv4Dj8Oo2FREWsbgK9sDtri+3DOWmlLpgz58D/7Tbh+KnG/By+jP/01xKbP4GhcMnqt//QbYPONEHlkB0rNPEv65VSITo5zK7bSB7tTOPcxuHoI//6f4V357pzpY9ZslQ/RySOPyYNM8zpTJr4ytUuRPjRVYdWMSphhz4nJJUgp2X2+i5efPMTVuBAYhfNsWlDO/YsbKXA6bMtWFMFn71vEXz7RxlAKz6lIg8/cuyhhViS3M72vLaczu7sxI0vGXIUiBMvqyllWV8bZviG++fTJlPpuWVDO1ubU2XOkoTOw82sgJymN3Oy7UFzRJ3ETPEKX0sD35hPQtgMIjbk01PY4ofJ5uFa/B7W6ady1VrhWUgdF9TBoL72rq2mt7bbGcQTphOiEe68SfPIvGXmBdRx6z6G/8k/o1x+kcMXbLcv37/nP5M59DMIv/h3iHV/FWVgGSOSp3bbsB8C1I5ihAIrTbUE/wZiF45SHduQyT22rwLHn7I3V8R2YS7ejaM4c6WM2bRVjrmmKvIN/W0DGfU4e7/EF2Xu2i56hIIoQVJV4WDu3Ksbpiz0/3gWyzisKXdBlcbs3BhXe2CJSsTKzH3ZyO3FTSr736in2XhpiIpjACydvsO/cDT6/fRHlnkR2TszLvS7+4O2L+dEb5zh4ZXjCdlY0FPDeNXMpck00nyK8uaGUx450jbs2FRbWxRbNsqZzdnn690OmfG5pIV98WyuPv3mR/ZfHj3GNV3D/skbWzKqyJDN4Zi/mQGfSc6zz8dCWPZQwlEAaBv4XvgWdRxILuHGG4I6/QGz9HTx1LRPKScVZ9AC8ZiO8pHgWavnsLFQqtR+iY/r7kzv3sTj+FEOeUrytm1PKNwK9cM6ekx46thPtzvcBEgbTe5dBDwyguQryITpZ5KlsZYYDExR0S43Aqb14WzfmRB+zZivyITp55AxE3KfMOr8+7OeHr5/jWFd86Ew/P3+zkzUzi3jPyjnopsmNYAhVEVS6XXjGOP7W21u7oIZdZ+29nFftFTQUFySQKRIcz3MQ/PLNCwmd+1gMhOHRHW384duW49TidoQs8GKXi4/ds5DBYIjXz3fTMxBASklViZc1s6socmkp5cwuLaS2QKFz2Pru8YxijcbiQst6WuG5HKIzEa8q9PDR9Qt4X0jn0NVehv0hNE1lTnURs0sKbMn0H3kqS7qNh7bhkziLahKGEvj2/zi5cx8D+fw3MN/+FbSiStuP6z1Na/Cfex2upU7VCwrODR/NSqhIOiE6vmPPY8m5H8GBnyCa16XMhuI7scu6zBGceg6x8mGE6gQtPRdDQbHU/3yITvZCdPQb9jKTjaLjMErLupzoY9ZslQ/RySPX4QvpvHq2k/ar/fhDBh6HSuvMMtbOrsHtsF7AoWPIx188fpxkUbxvXBzkjYuHxx1f0VDIpsX1zC0rtKX7rNJC6gtVrqaK54jBxkX1Sf89sUvx1sZgMMxz7T2Wz+8JwJ4LXWycl37xqCKXg23NDdG/4p2/1Hjbyln868vnLLe3fcVMW/pZwciSMRfgC+l0+fwYEkrdTio8E2cgAihwOrh7djXp2H0EZji9F529Sz9I4PzzMXH5N6GUNuFc/U6c1fMmzGwDMpJZp91eGEHg+PMUrnkfdh/XC6FRsOk3Gd71f+HKm4kbUApw3f851LIGMHXL8rMVoiNNHY6/aMsmECJwbh+u+euSy++yFpozFhK9vxNH+QworYWhDtsSVG9pYp3G8LhNhikP7chlntxWMmwvy9kowiMhcrnQx2zaKsZc0xR5B/+2gBzzaUrJL988z3PjCjGFOd7dwU/2d7C9tZIHl8wgUhgqVsZYHtJ1vvlkcuc+GQ5cGeLAlZM82FLJ9qWp24vlv7qhia892W6pnaZyJ+vnjDgtE8nMh+gk4rtP2v8BfvHoFTbMrUaM+QK8dTrfUV/GO5b5+MWhayl1fdeKWpbUjuTAv7V6juXxS4LM+bneQZ46cIlj3WN3bxuLVDYtqmPNzKpowarstiuIT/lqjXvuuJeiTb9B8Ow+wpePIHQ/mubA0bQItagWJRxMGuISOPUGtnFqJ8bKR5AOz4Qyk3HFAZ7NnyR4/SJm29Nwaf9NuUX1sOh+PLNXIDRXFsME7IXohHovgbSflMC4dAhz4abk8sOhZCISyzZCqEJFab0P03I14CjmbEA63EgL9sqH6GQvRAdXgb1xGoGj4Lazfz5EJ48cghj9NKXk315p582rvqRXPHnsOjeGQ/zKXU1RJy3W8b7JX7twHV8W3qN76vh1nE6Ve1sax7WRiDcWF/K7Dyzg0adPJl1gtNZ4+Pg9C1BHK4VOJFMkOJ7nh9JIO9nth/5AmNIxsfi3Vv97WxqpLHbz+P5LXJtgktYWKDy8ahbLEmTjyZRPZYiOlPCdV0+yL0FY1eVBg/947TKvn+rmNzctxOXQJpSTLtfK5xDq2GfrWuH0ohY2IlQN9/y1uOevBTOMCA6ghAeR4eHUoRYd1kJz4mHcuIhWPT/tR/fuylkoGz6O5OOYpkQRIIRys6hRFotS2Q7R8VsrtjcOoQBKKvkub1qiNYcXRZq4qufi91aBr9vytY5FmyyH3ORDdNIL0ZGmQejCm8i+q0hMREEF7sbFhHFjK9QLELOW3nb2z4fo5JGTeL79akrnfgSvXRhgRvW1pKEWL7XZ391NhF8cusadc6opdjtSnxzFnNIivvbuZew538WLbR30xHz3LK7xsGlxPc2VJXE7yRPDwilvSQwH03s+49N1SnGlPnESsbyhguUNlZzpHeTk5V4CYQOPU6O5oYQ5ZcXRs2RSGeliZMl4q2GYkr948hBXh1KvvE/1BPmnl07w6a2JsxKlA8/KB/C1/cjWNaWtD6G4vCS02qh6koSP0PX0wghkOJRYpg0uAKE5ENkIxclSiI5wpHkPOpwp5Ssz7sC0+L7DKLQS1JLaiG5CxbnhE4Se/qqlS8Xy9+Eom2Ej1EkwZkE55aEducwFph5m6M0n4NjzxD71kYAfoKQB+q9YGqsInLjnrroN7R+dU9Pcacg7+LcFJACmafLMIXsFop59M3GohW6YE+6MZoJdpzrZvmTGaBux7SXibofK5vl1bJ5fh26YhEwTt6ZO4LAkk5MP0UnEXZoCQfvj7BrNi39r9EzG55UVMm/cex65odtNxC8J0uPfffWkJed+BCevB/nnl44zu6qIRTPKmFEc+yg+PR0cFQtw1t1JqOM1a9cKhYr172Fo9N+in4qCFCBRMBUNhAEkyWyjpRlG4C6a8sf+1rm9EB2lem56NqluSSnf1XQn/r3fsyd30YNIxcFIiI1SPgNl2+cxX/pXCPclvm7Vh/As3JDdsJM8H+WGbtDxsz+H7pOJx8CWcw8sf3ekIJ0ezok+ZovnQ3TyyAlIGXlcb5omhzpu4Jepr4lFXxhOdPXRXFU6IpGRZas/bK9AiRXsOdXNA4saIHbXxQZXBLhVFSSY0rR8rWlKBBLTlJbOfyvx+TVFdNjMWOQGSlyOt7Q9TTOyaDTNxPPQNCWGOXLM+nxNxC/0D7H/8sQpRpPhaKefo51+Hj/SRX2h4O2rZtNaXZKRPkUPf4EbP/wsRt/llOfXb/8SWm0zZvfg2HNMHWEYSCMIRgDC/siPqjRAD43nM5ZAp82YbjxoRVXIsH9imbnGwz6kEY78beF8RVVh7no4ay/jjXveyoi9k8gXQoVl74dDP7Am1FGKc8FqCPtuygkNo3nLEO/4Y0LdZ5EndsKNq6DrUFwGs+/EuWAtmqJZ7vMIlyEBmj62vakevxzlnc9+J7lzbxcL7sPbvA5zutxXduaV7gIzBKY7H6KTx9RAykgcsGlKpBRcuZ46zeFEuHh9iPkVpeOOO5XsT4/+EJiTVBsnGQwz6mJNQdu5jnWtdbxs08Hf0loJUjDq378FYZhEHfwk5xgSKRQMI9a5Tx8vtdkrvDQRrg5JvvXiOd69so575tSmLUe4qih9z6P0P/GXCePxhaeU4nt+h6I7H8IwJ7CVKRHhEMLQMQM+hBHZVDCFQESL1sVyR30LYRQiiyWLaF4PegjTCE8oM9e47gfVlMhQwPK1jnl3E7bj4M/biJBgBIZTync2rSbk64JTO5PLVItxbPpNMCXSCI3KIRyAoB+JxFE5B3XdbAAMIVCjbRhx11i1lxFWMMOBSAhWhna/nbnR34E8dyD5+MWjbA70nht/vKgBmrfgmbkUGfLnTB+zyc1wADMcRCrT22HIO/jTGEIIBCaKIhBCEjbSm4yGYaJMkDVTUQRzSzXO2ihznwoaTNjWZENVIm7HVLSd66jzelhS4+bINWsvVynAuvm1b3lbqkrEnU1uB4FhmqiqyIq99qWxe58IP9nfQU2xm4VV4xf3VqEUVFP5vq8Tvn4C376n0AfOIgmjUIGndRvuhXcjNDdqtO/jbSAQDidCaijuAkTYBwgU1YEwQuO4qhYSXvouOPwTixq6cbdsQThdCWXmGtc8RQhDRwSl5WsVRwPmPb+J8cq3UpukZgmeVY8ghHXdvCvfSaCmCfPo89B3Nk6gBgs24160BcVVgCEUMI1R+ShgIlHcBeBwZ9VeqsODojkQfiNnxi8XeeDgSCidDZjgfN830C8ewQwMg+bCUdmAo7QeM0f6NVlccbhRHC6EnN4/cnkHf5pDCIEQoCgKRd7Eua+TodDriqbSg7G7jILNSxo4+8qFjPUcwYxyJ8qYX3lxS3ikfyJhP9/q/NfWN/ONZ45wZTD1IvFz982nZFye9dzpS7Z52DDZe/E6u050cq1fRwIVBQr3rZrFxoX1KeZzZOdeUch43huT8LjkqYOXaX0gNstQYh2uDwe5NuTHNKGiyEV9oXf0HFd1K67trQnbEYpCZIEtxspXNNBVhOpCiugjESRoTojGVcdzz5L78Yd9cPypFL1zo739D1EKK1PKzCnu8CKUEGiGrWtds1YQLPojjD3/Db1nJrCHAxZvx7XyHQjDsK2be/ZqzKZ1mDcuol+/iCklqrsQZ10z4eAggaPPQ/tLQPRFaK0YWu7FOX81aG5wuMHhyaq9hPP/sffmcXJc5bn/91Tv3bPvM1pn0bRmNNply9ZibV5kDLYxEOILhNwbCFkIJEDIJwmXJNzkJiHhJiRcbvILAUJCgCQGbGy8y5Zly5ItWZIljdTa99k1e+9V5/dHd8/0zPR0V/V0z/RI/fyhftRTdc573jrV9dapp97XgTRZwRzMneOXi/xae4pzJQEGLyLsRdibNgESzWxFiWrtc2ZcWeLCbAPFClpeg5/HnCNywVy7pJKfHk2dF3wy1i4qH2tj8iP21bXlLCq6ztWhzKzi3zNWjGq2ddNiFvuaf9xmMfP5B1bx5NHL7D2XOG1mU5mVX7i7gbpC17TtJOPeQIg3L/VwrXeEkKpS7LSxrrEy+nJsbvhhMj/d3c8/7LkwJU1rx6jGv+y9yL/svcjHNy/mjkWVKdqMIX2bMpkFJ4aLAyE6R/zUFNgT9iulxtGOfl45cYMLN4MT9q1xKWxvq2PTksq4m5fpxy8TnYeahpAg0FDUsL50e6gUrHsEb/kStJPPJ5YRNG3FsfIhhKts3qXwS6eSbYzbyhajvOcLhAc68F86AoFBMFkR5UtxLl6NMFnQ1PCM7DQXVmItrEITCkJT8Z54Efnuj6ceg/AQHH+C4PEnEOs/gmPZ3SBmtzprnkc4aRaxEt7BaOar3BlLPk2mfsxfy/OYgnKHjZYqO6e69eexXbvARaHNgpQST+8g524MEAhLnFYTrYtKWVJSyG/uauVvnj9Ol1fOyL5CM6ytLZtRGzNB5sOjWwtWs8KHNjTw8V0O9nmuc/5qP2FNUuywsKGximqXg0hwZgzBsMaP37nI6xcmZ9DwsvdcP5UOeHxzE80VxRkZR6ZwumeAb+yZLEeYin954wraJti4uDLltjOBEIIqp6B7hufhZLR3DFCzbKoWX5OSHx48z/5LifOsd45q/PDgNQ6f6+bXd7RiNU+/2qXr3BvbSKInpZ29fj3KktWE+m8Q7LuCCIdQHC7MC1dhjl6UYxVxtcAI3vOHYKQr0o6rCmfjHSj2Al19zSpHYCRNZiJuLqnFuW4Riha5NdUUc1ZSe3qPP488/tOUh1Ye/j5+qzWyGpxRf8VuGnPo+OUitznAry99djyE1X6b+jY6p+Z50JAP8G8JyLHP929Yyqmfn9a1lwAeXruEA5e6efqdawxMXKDjmZM91LoUHr1zCV98cBUvtV/n5VO9BBO2lhq/eb87ToMr4/4yGzyfJlMvt1lN3LtyMetrSpkImXD7ZDwYVvmbF05wdUhlOvT44O9eOsevbF3C2gXlGR1LujwYVvnmy4lkDonxr/uv4K4sosRhTdK+mLF997TU8F+HM1ebAiAQiGXLmtjXk0cuTRvcx+NsX5BvvXaaX9/RghCJxygBET0PJ/RlME1mIm4qW4StqnFMPqCZrWNp+8JhH8H934fLB6fY7T36H7BoA5ZNH8VicRnuN3vcWJrMueLBkR5dwX0M2pvfIdywEXMGU1rm02Tq5Ata4fw+3ccKgOKlt2QKTF3zilsjTabpj//4j+fahvmGXwaWqqpGIM0CQZmC02lF1SSjI4FoJh0otFlprnVx8MLNpPuagM8/6ObIpV5+fKQT/zTx10hIcujSAGWFZu5tWci9rbUsrbRTX+li5cJi7m2pYv3SEk5e7ickE7dR4RB89oHlLCyKl2KIWeUOV6QYjN8bmjMb5gvPpK/+df9ZTvfoezx85MogG+pLcFmtszreRHz/pR5OXDdWIdRukiyrjqWejLQjJUgpou/KCGZqX22Rkz2nOo3kkEmJlYtKqC+PnZuRvvp8Qb69/7LuNnpGwzTWuKhwxaQ+E+13uCLH1O8NTvgeTUWEAyhaIPoZiuwpteij8vS55hsk8OSfws1z0xs+dAPt9H7M9esxWewZ6Xem3GEzITSNgD+YE/ZMx/1HfgY3L6acG/FQLcXYKhZmzA6HzYRAI+jz54xfcpK7ylHP7jV0rExr34+lpC437J9l7nTaMNtdBDQLYo4lOna7BVMkS8Fl4LtG9p3frwjnkRBN5UV85dE27nWXTTnAVuCBlgq+8v42rvd7eba9V1ebP3zrOmf6BjEpgraaUrY11rC1oZplFUW0VpXyFx9cx69sXUJLpY1qJ9S6BGvrXHzm3ib+6OG1LChMr+R5JiFSb5JHFJnwVb8vwKFrxlK37slAGsh0oElJr9fP1aFRer1+Xj9lfJX81dM9WbBsImwWE5+5f1lG21xRNzWLzhse4+NPVvU6PuRPuhEAkvHKe+lxKSW+l/8egonfJ5kAdQjfS19HSnXG/WaEI5gg0ZlrexJwKSWcfRXDOPdahm2Kn1lz75dc5ZbSWkz1a9ENVxW2JWtRvf2Eh/vQwoGcGcvscDHhY74iL9GZx+ga8PLUoUu8dOQyQ9GFsQWFCve01HLnkkoeXbuU965azLVhL/5gGIfVzIJCJ2aTgpSSpw9dNdTfs+9cofm+tuj/4pfrJYoCaxeUs7q2FE0jQdaQqfvMLs9LdGbbV/vPGKuqDPD6hUEeWxeO03Nnd7yjwRD7znbyysluRme4LO7VIKxqmE3x8z6+XzEjW2O8oayQLz7YzLdfOUOv/tdtEqKx1EK1K05nG8Vb5/sMt3W804smJcqEi+L4TMqWRCcRD107AYPX9Bs/0onv2kmsDXfOqN/M8NyX6GiqD0O1CGIY6syonCYv0dHPq+77NB1P/G/oSyU9dEBNM94f/Q4Q92i/sg3Tyvuw1i1H5tC4ssFvFYlOPsCfh5BS8tPDV3ji0OUpf7s+rPGDt67z47eu8+ndzdSXFLC0pDC2J7Fb0lPdAxhNjHO2L0jPaIBKlw3iV00S8hhSbTdbXMxg39uNZ8ZX57qHSQcdo36WFBdkeYxwbWiEv33Wg1+mZWZCRDJZZv98WFxcyB89so7zN4d480w3PcN+kBKzonCmT3/GjIc2LCHR8R5Js4i1L6jislkS2By5aE7pK50sOjq42v6SYdu19pdRFq+Z80weM8miM2tcpnvSmDJ2jBWZz6JjhJvMTmo/9CU69v4ITu2BRG/TlS+DvrNwPkHRtJ4TqHtO4Fu4Dsc9n4hm9Zq7cWn+YfwXDqINdUZ+Rxxl2BvvQHGWznxe5bPo5DFX+I+3LvGzI8lX3wPA1547w+/ubmbJWIA/jjM3Jmc00YfTXYNUNlSlte9cY3Kolcf0yISvguHpX6xNhkCa+xlBr9fPX/3cQyZ7UohkIpotCCFoKi+i6e5Y9qFI4LznbAc/Ppxa6vSRuxbSXJ44c5FFQDiNGG668euaT2MbSWacCaNLX6KBCej1zLzfTHAEEyQ6c21PAi5MViKCT4MpF0pqoyRTNsVuGnPDL7nNBYrZSsG6R5Br3ovv6rtoAx2RbZzlmEwC9c3vpj6G197Bt/efcG3/VcQcjEULBvEe+ckUiZgEfMf+C+rW4Ljzw5hcJTOcV+Mf8xX5AH+e4UzHYMrgPh7/tOcM/+v9axHxOjPAH0oz+ArGlv1l3LfTcaFzu9ngeYnObPvKaTPDlAzyqVFgNce1lZ0x/vTQxYwG9wCbG+ID7UT9zs75sHNZDVWFNp4+fIVrw1NHWV9i4X3rF9NcWTxtO/WVdtoNpNsFKLGCxaQkbFMCsynRIc2jqyrmmfWbEZ77Eh2EBPcu8KQqNjYJy3fkJTpzxCf4ymTC2rRpLPOUqpgIfO/X9B/H60fw3ziNbcGKWR1LWA0RfParMHx9ettuHMX309NYH/lDlNJF6fmKvEQnjznACwZfQhwIwuneIVoqS4j+MgPgtKZ36OXYo9n4G4ZEHJ3bzRYXM9jXGNc0iT8cxmY2Y1Jmr99p7ZGSk539HDjbzc2RACCpLXGyeXkNjWWFWfPV6voKTnUb0EETqZVQW+DMmA2J+FAgxNEbxnNCp8K21gVRNvfnQ1tNKW0PlXFtaJTTNwYIhVWsVjMrakuoGXvhffp27llRR3t36hoA8di+IrY6m3j8synRARtjFVV1Q2BSQ3MuqZgXEh0EtpatBAwF+CYc9RvyEp054sl85Tt7EDCmy1PbX0Spa5nVsQRf/Eby4H4MfoLPfg3HB/4s+i6gQV/lJTp5zDa8gTBvX9CX9SYe+093RgP8cbQuKuWF08ZfpHvyWBdLKwtZlmNFifRgcqiVSWia5N2Om+xt7+BsnAZ6QaGJba213LG4Mrq6Obs40zfId145x/CkhfSrQ8O8dWWYaqfgk7taqHE5Jvw9E766c1ElPzxoLMDfuaI67mlTdnD4ivF5nwrvaa2kpiC9QmDZxMIiFwuLYjdMoNe+FVUlVDoEPT5925uATfXTS/d0HdGxjSRpPboP+AhcO47qHYGSahi4osv2MSxck1a/GecIcl2iAxJTYSXKho+gHfq+Lveatn0KYbZARgtuxW4ac8cvRrgW9BIc7ECEgyi2ApSyxVkcy/S+kuf26zqGE9DVjhYcBXvRrPgqdPMq9LTrty84gO/8QQqW3Z2mr+LcNU+RD/DnEfpGA6iRt/gMoWvQR3TWjn02lhVSboe+NLJwfP2lc/zhe5dHV1rH25zKRZK/zTbPnkRnOBjkGy+e5Prw1KwS14dV/v3gNZ4+fI3f3t1C1ZQgMHu8vWuAb76SfBW2yyv5s5+18/sPLaeu0EkmfWU1Cx5bV8uP39GXcrHYAlubambcbyo+Mppe2fbpsLulnAdXLozrYz6cD8m5EPCb97bw5z9r17UO/pn7mnBap898JIFsSXRU300Ch38GlxK8GGgA5pZ751xGEeHzQKIT5fbl2/EKE7z9veTOvdNQTi0AACAASURBVPsTWBauzK7sJIf8kooHBq+jvvscXDow0U9KIay4D/uK7WBxzJqvGElv0SPsH0Zxlc+K3wKn9xg38MTzaM1bjfuKvEQnj9mGTL1JImiaRNNiF9fxO/iHNyzmO68bXOWK4qm3L/HJbS1T2tQ0Gb0JEUTSqMWvHM4d1zSJIOaHzLXvD4X52rPHU6YrHArDXzx9ij98Xwuljvi0hNkZ70gwlDK4j0ECf//caf7X+1ejKEpGfbW9qRqvL8hzp5JfQApM8NkHWrCaRMaP0WQ+JXtrmti5vJwHVi3GgYwWtJp6juXq+aCHlzls/P57W/jeG+e40J/48X2VA37pniYWFxcm/I2JcalpSMTUbbQwQlWRagBUP4R8kYuqVCNSmxQ83HeV4HP/B5hhvtBKN5ayxciQT1e/WeUhL1INRf4/VzYY4M7GOwkvXknQsw/O7wdfb+T4FtbCsu1YF68ETYNg9BhlsG8ZFGAOQ8ibE77Qw30XDiHf/tfE81AbhuM/xn/mVSy7P4diL5wdXylpBrJaOHLOzoYPr6fx4ry3C+kbQlhsxnwVtoEWBM2el+jkMTsoK7CiiFgqPv2oLLShJUhZvLqmnEfWBnjySJdhW453+en3Bym2WiZ8r6oSKRRUNT6YmXuoWjTEymQJUOD5E1d15yIPAv/51kU+sbUls0YkwBvnjB3TYRWOdNxkbW1Fxn21e8ViFlYV8vKxa1wcmKgVMgPbm0vZ0boQl9kcF9xnD1VlLsD4itX/2LKIpsoSpAYOs0J5VTESGOiZvuJtrp4PelFit/GZXW10en286emga8iPpknKXFY2Lq+hvqiQyI1M8nbU6N+nbKdJRCiIUMNofi9CjdxIaEIgou/7TMfDQR/h57/OjIP7woXY7voohPxo4dT9ZpuHfWDSJDLonzMbjHKEwNG8FZq3ogqBKfq9KgSE/BDwoSEjlYsz2LcaUtBCfmQomDO+SMaD145PH9zHI3CT0NNfRXnwd1Gs9qz7isIqGDVe3E4xO9H8o7Pjw6DPsH0AqncAk6PIUF9ayI8WCiCVDAcMs4x8gD+P4LJZWF9fYViHf/fymmlXLXctq6Ojd4S3ro4atufd6zfZ1lg96VuBqmmYTCJjK6WZgEmJhFeZtCmsauw5ayzd6IkuP8OhIMU2a+YMSYBX241XVX29vYP1Cyqy4qtV1aWsur+EHm+AK/2jhMMqRU4ry8qKMJvitaHZx9qaMn7AFUMJ/hwCVtWWoYiYUyQmJbIWndxPuXk+GEVdgYMPrK8n8Yp/asReP5nqA4GwWBHSjGJ3IUJeQKCYLAg1mJSHz7wB0vjv1hiUAmjdga3tPkzR8ejpN9vc7ChEqGFEQOaEPTPlKKAhUewusNgz2ofJ4kAxWxA+dc7HmYoLYUJ756f656c6RODCQZyr7s++r1bei9Z5TL9tAA2bURwFMFs+tLvAb/xm3uQqQZgthvpSLHYUiw0h5/GPNvkAf97hvhV1hgL8QnMko8ZYUYoxjPNChxUwfqH0B8KTqtWOyxCmVrJN3PdscX8gzPHrfXT0DGMzm1hWVUi50z62TSTTzAB7229wsSdAACgyw/r6UrYtr6VirNrneJvtHYNpqaYOX+rl3pYFGR9jjAfDWlpVWa/1h1EUgaJE5ooyVpI0c7ZVFzioLpj4Qu9EZH8+KArc11bJMyf03wTdt7Iasyn+MXbEP5EAP9k8z83zYba5UBQiN42TfocUM4RNCJMNKTTGNMFmK0S1wom4RIWTxotZUbcG6toQJTXYq5sQQkEzWyGaLjBVv7PCLU6EEgSzmhv2zJRLQJVgsUd05RnsQ1gdSJMVzMG5H2cKHuzwQGjQ2Hw9vRe57r1gtmfVV5a6lQQK62BYf5Y+a+uuiF2z5cMla8Fj8JwvbkTYi4z7ymwDxQpaXoOfxyyidUEJ97fV8cIJfSfiJ3Y0TVh1HL+4jnOrOb1JbLHEpk+yVb251RN3Dnt57t2rHLo6MsX+5ZV2HliziFK7hW+8dIreSRlDhsLwytl+Xjnbz/amEh5b3xC9UYq03+dN72XN/tFg1L7sjD2cZpXJiDgiFoDNzfGaLf5A6yIudA1zqif1ilBbjTN6Q8aEdmTCYHa6fhNv1+8L0DvqRwqocNopc1izNua55ZDJNJnhoS6QU8/plAj5KXBvQRNKpM0cTEU5X9Jk6uVoociRV1UQs5f6Mde4vPim8fmqDaP1XMBU2ZRdX6lB7Nt/Hf/P/hRd6WXXfxRrce2snj9291b8BgN8pW1XWnMjnyYzjznDRzc3YjEpPHNs+hSECvDpe5toLC9K2d6SqkJoN55+s76ywPA+s4nTvYN846Vz0/+9x8/pF8/qauvVcwP4Quf46F1NkQx2gDIleNMHJb3ddMOe5g1biW2cZ9nEOYeiCD61fTn/eegib1ycflVta0MxH9zQGHdjN46p3+iDlJGUqq+c7OBc38SLaX2Jhe0rF7Curmxsnt0K0DWUsY0kqdLYyTT1uIR8utqfU45gPqTJ1M3HjilZ6CN205gD40zFe9NLaKH5RjDNgq/MRRXYH/5D/C//Xxid7h0ugemuX8K2bEuG052m5ubCKqi/G/TeKBUvxLZk7bTjTe2rOHfNU+QD/HkIRQgev7uBhzcu5WeHLvHCO9fG9MTldtjWWsum+irslunT1sXz1uoSXAqGZB0VdmgojQX407U/OQSaPd4xMpo0uE8HBy8P0bKwlw2LKgBJdZE95T6JUFZkZ9zWzI9dEbC2zsWRG8ZkV3c1VkTbuT2q/ppNCo9vbOTBVUHeONPJyev9+EIqDouJFQtL2dJcQ7HdOmn/mfQr0KTG9988x8ErwyTCxYEQF/dd4tiCHj6+uZmJpRNyx3dGuQQymSZTWpPJvJLAbJsH6ScznyZTVSwEu88RPvcGDPSBSUBpHeaWHVgLqrI7LjWMRgBFMUGG+5gvaTIlGgwZqwkSg2a2zZqvlKIabB/8S9Trxwmf3gO9V0FKcJbCsi3YG9ajZMiedLht6ycIeIeg62Rypzkqsd33WaTVgRaV3+XTZOYxr7C4spBP3reCh9sWEAyGkVIi4iQkEaTmioDda+t44rB+/d3utYsQIlaWPlH7Mei3I5P82aPp/ZimwisnO6IBvmB5VQlOBbwG9e4bl1YxfvOTHT9sb6vjyA19Tydi2Nxcw/gqz+wer7nkJQ4bD61ezEOrlxjaNx2JzhOHL00b3MfjneujWA+e46N3N2dlzLPPIZMSHUtBJUEcgMGV/NrlOS99ybREJ9R3lcC+b8No50RfdLcT9rxEuKIZx5ZfRimoyEt0ssSDnWeMzdM42ErrZtdXahBLdRNULxuXsiFyQ9amgWvXb+BtfxV58kUITU5yYYXlO3Gu2o1isaOl66u8RCePXIKiiBmlNdzeVMONvhHevDR9yr8Ydiwr5a4l01etnGsMB0K8cy0Nfa4OXB4I0T3qo8rlRBGCe9uqeOrdbt37372kCKcl+6ddY1kR6xcWcFinH97bVhW3Wj05LM0jEWKhql50jvrYe65f9/YHLg+zrW2URYUuw7blGnTNp7GNJKkeoQtFgRW74OTThuxwRove5IRkYxYkOsHu8wRf+OvkTuk9g+/JP8X68JewFpRmflzEOFnwV+ymMYeOXwIevJFixXk6lC5FsReg3Ua+SsWFUHCt2Inadh9q5ynC/V1oEswFxdgWrECabSgzlg9F/TTPL4T5AP+WgJz0aZwLAf9tYyMVhdf52fHEAasJeHhtDTuba3W2PzkEmh1+qtNY6kqj6BryUeWKSAR2LV9A+7UBzt1MnXSx3AaPrV/KbPnhY3c3ob5xhqM3vEntus9dxgMrYi+RSuD2kOjMNn+t3Xie6b0nrvPRu5fNin16eL/Pz+tnOjl0oY+hAFgVqK9ysL21FndlcdwTxIn7SmB6iY5AKhY0xYKRSraWtnsJnXwWUNGFxp3gKEHT2f58l+iE1SDBF/5Gn2+kl+CTX0I+/CdYiqrzEp1M82Dy3+BpUeXOmFRr3vhKJ5dmK+aaVsw1LWhmK0o4hMzUvCIv0ckjZyAmfcq0uBCCB1YsZId7AW9d7cFzrZ9AWMNpNbF8QQkbFlViNiWT5YxzTZMc6+jjnQu9DPpCmAQsrSxkc3MNFU7bjG1NxkcDE4spZRqRWkyRvkwK/MbOFfzrm2c4cn16zXt9iYVf29GCw2omm2OP52aTwq9sXc7Rjpu8evw65ydVI11V62T7ilqaK0om7SumbTPPx7lRic7B88ZvPN++PMxH75774yGl5GfHLvPC6YkFwkIanOj0caLzAjUuhd/Y1UKZM1GlZkgs0ZEIkw20MMqYHABdj9MtFifK7t8l8NxXiaQjTYKaNhx3fSjnpRyQOYlOsP01dN/8RA4Goaf+J+q6x7G37shLdDLJLem9r2Uqr8uYVCuXfSU1lcCFt5Adx8HvA6sdU00L9vo7UGDW7clLdPK4ZWE1K2ypr2ZLfUwrDpMv1slwpLOPH71+ZUp9ybN9fbx4uo+2age/tKU5a1IVmyW7d90VBbYJ/7eaI4H09eFRXmvv4NiVQUZVcCiwvLaAba01NJQVJVjdzD6EEKytK2dtXRmD/hA3/QEUoMJlx2WxTGuPviN9e8Po0UwnqaoKaFJmPfNSKvzHoQvsS3GD0jmq8b+fOsmXHl1JSZzcC5LMJ0VBaiYw2SBsIlIuPozex+mW8iWI934Z/9v/BV0nEnRgh5X341r5AFIxz3rmj7Q4goxIdE69mPR4TQftnR/gVcwUuDdlZlzEOFnwV+ymMYeOXwJuqWkhdNp43QZrlfuW9pUEvO2voh15kskVqdXLbzF68N9h9cO42u5FzKptUT/N8wthPsC/JSAnfc4d33+xix++nfxl3RNdPv7i6WP83ntW4bKZM25HU1Vh0v5ngiqHYEGRK2G/CwqdPL6xkcc3Trf33B6nYruFYrtFhz15iU52eHqISFsm307MHj92oy9lcB+DH/iXfR4+e1/bhHYiI5Bx7cY+BZitSEUB6UILjIBBSYJSUof9wS8gBzsJXHgHfINgMqOUL8DSuBGTqpGpR/ezw2cu0ZFqAAI3dR2zhDj0r4SXrkGxF94yEh2paQRvtKP2XIGwH2yFmJs2YHWUZv24mheuIKQUgpb6BfsxVK1CFJRnTFKWCxIdLeQl7O9FUUMorjL8R58Dz3NJnBCCY08wOtKD4+6PIGfJzrxEJ48cgpj0KeeE3xgeTRncx3AzAN/bf5Zf39GScTuqXA6aym1TcoxnAtvb6qJsbn2dXS5msO/tw41KdBYXmbgyZEQyEUFYg8hDqbkZ5x6dRfViONsXpGvET3WBnfjxJ5ToIIlU7VAQqooiJGhpShKcxVjadk7M/KGquttRR28SHu1HkRJTQXnaWWWklIiwhiKl4X0hMxIdLTzz376AZx+uVbvnvURHAv7jL8GJZyAw8SX38LH/IFzVgm39Y5jKl2RV/iHueAx58F90+9+y7qGMymnmUqITvNFOuP1FuHZE9/gn4PxrBEoWYW/ZlpfoGMD8tTyPnMOeE9cNbX+yyzeWkSbT2L1mId94+XxG22yusLGlvjqjbeYqROpNbnvEQlW92NBYxZUjxl+0fetKD5vr5yZrVa/Xz3kdL5BPxv4znbx/3dKx/+ubT3FB/yw9ipdS4r/yDtqpl6F3UlrZ8mWYW3dhW7Q6kks4STtaYBTv2f3geRX80fcUhAOaN2N1b8daWK7fNgQzleiINDXf8ZDn98OqB2buZ2KcLBzL2E1j4m2khNED/wbn35h+oN2nCDz7Z5h2/Q6OmuYM2jaRO5q34B3uhvZnU/revOlXsFQ2ZFhSltxX2eBSwujhJ+B0enKxeGgnnkW2bJsl+6N+mucXwnyAf0tATvrMDL80MMKrJ25w/NoIAcACrKhzsn1FHY1lhYi4H29/SOXAZQOPH6PYd7qTD6yvz6jdAMsri/nAulqeeCd1QLWyxsGpTh/JXs1trbLziXvcKGOFhzLr69zieYlONnihI72f2wsdg9EAf/bt7hpOr2Jsx0Asa8j4TEos0YlyRSBNJjQpMCrRSZdLTcX3xrfh8luJB9F3lvC+s4SX3oVt269O247/8hG01745dX/pA89LBD0vEVzxMI61D+mUGGQgi46QULMGOo/qOFrTwNudmQwucyjR8R/9efLgPg7qy39D8OE/xVxUlbU551z3GL6iWuSRJyHQN9WI4gbMdz6Gtbo587KTOZDo+A/9NCPBPQCBm/g7z+CocWfd/rxEJ48cgpj0KWfE/SGNb+/z0N498eIeAo7e8HL0xjkaSq18ansLLpsJEHR5J79Sqw+XekYyZvdkvqO5jhKXlZ+8dZmbCZ5Y2wXsXlPLruYaAmHJ/kvd7D15g764oayqdXLPijrc5YVECntl3s7c42IG+94+3KhEJ6zFB7X6EVRjGWJmf5xamjaP7yXivksytzSJUMMzk+gY5N6D358+uI/HpQMEzHYKNn54SjuBS4cTB/eTcfIpfELDuebhWZHogMDcdi/hmQT4mDKSwWWuJDoyMArHnzI04mD781g3fiSr88/VeCdq00bUzjMEO89CKICwObAtXoVSsjCyfRaKSc22RCc8cANOPZPGvJsesvMsSnVj1u3PS3QyALfbfS/wB8AqwAocBv7C4/E8r3P/RcCVJJu84fF4tszY0NsIIVXj7146wZXB5KkmL/QH+drz7/LFB1dht5gJqelV2Up3P71Yu6CcNY+W0x0I886FLvoGfdgsJhpqilhTV45JiQQcA8EANcUOHt/UQEWBgwKLBZtZTArqbx+I1Jvc9ujoHeGF41c44OnGHwK7BVpqC9netoBFRVOLUxXY0vu5ddksMzU1bZQVpCf1KHNNzDSlbz7FBf1Zlg+EB67DuX36B3TuVcLuezCX1I61I9Uw6qv/T38bJ54mtHQdtuLa5HYimKlEBySWmmbCdavgxrv6bYxHSd2Mbci2REdKiffSMUaOvgSj/ZFsqaVVWJdtJTjYi7E0ocC5fWjrPgg2Z1bnnxAmrNVNWKub0BRztDATGSpoNR2P3WCn14462ofXsw/6r4GqQkEx1vqNmGpbErYZPPWqMd/rgRqTC2b7NyLqp3l+IZyzAN/tdv8y8B0i2eP2EKmjtAN4zu12f8rj8fx/OppZG/18Fzie4O+eDJg6DyAnfabPXzhxLWVwH0O3V/LkkUt8+M5GiqzpTaUihyVtW/VyIWD5olKWLyqlv2dcRqRpktcvdLH3ZAcdoxNvNJaVW9nZVsfK2rKs2pab/PaR6KiaxkgwssJYYLWgKCLlvsGwxvf2Ty0gFgzBwSvDHLxymuWVNj5xz3KspvErRHNFMelg5ZJS3ePJNK8rcFDpgB6DSp2Ny2LvDIzPpFyS6ATb9xobEOD37MV510fG2vFdPABJhX1TEWrfi2XTR1PYmZlCV9JsxXHPp/Dt+SZ0p1FJtWlbTkt0gt1nGXniW+CflC1o8BLBSzqezEyD4M2rmBe2ZXX+zQVPV6Kj+Ufwv/4d6Jh0o9gNwQv7wVKKedv/wFrjnrAvntfTPgbTwlYyKxKjvERnBnC73bXAPwCDwBaPx3Mi+v0dwEvA191u9zMejyfVW5uxAP+rHo/n+1kzOIchpURK0DQt7nF65HJqlIdVjRdO9Rrqf9+FQd63Jky5w0aZFYy+j7dmafkEu6WE072DvHriBqd7AmNWrq51sm1FLY1lhWmNTdMkAjnWVzAc5h/3ejjbl9jgs31Bzu69xNaGfj64vj66EDWxTVWTHL3Rx42+UUKqRoHNzLqGSiocdkO25Rqf7Ku5ticb/OrQKK+euM7b1yYWJ9u8tIh7WmuoLXAl3DesSv7+5ZNcHEge2J3uCfDXzx7jcw+0YRIKILCYBJuXFvHGpaGk+8ajwATLK4rRNC2j4zfC72mt5YnD+l8OrrTD0pKCCTZLTUNG51bCvjQVEQqCFohUkpNqRN4hTNnhF9IIPs4dgA0fGmtHnkmjjQt7kXc8hkhmW8iLVEOR/89wvEKGcez4VXxnDsCRHxgy1b5kTUZsIDga+X8wqn3MwPEL3jiJtufrxv2vA5pvAEK+GdkpQ34Cl99FO7cfvDcj072gBpN7C9a6luzP7wRcBgWYwxDy6t5X8w0RfObPx18cT4RQP+GXvobc+iksi9eMt0Oa1XuTwLy41ZD9afsqbAMtCJo9L9FJA78F2IA/jwX3AB6P52232/1V4E+BXwX+KEU7sQD/cFaszHFIKccumlKKtPWyMRzr6jf6QBOAt672smVJNdtWVPOTI12691OAdQvK0aKL58PBEP+w5xTXRyaupkvgaIeXox3naSw188ntrdhNxu6sVS3yG6tpEb9967Xpg/t47LswiMNyhfesXDz2nSYlezzXef5ED6FJ2z99spdl5VYevWMJCwoKDNmYK4j31a0GKSXPnrzCC6cT5wh/49IQb1wa4uFVVexsXjDl7y+fvp4yuI+h0ws/O3qZR9Y2oqqRYHfnyoXsv9SO3jP10Y2LkDJSSXausGlpNUfO93BB57g/sm3ZlLkTU+JNO6c0iQgFUIJB0MJoQiCiY84GN7ryHkEAGfSNtzOo/7duwlCHb6LYC6a1LewDkyaRQX/GxmttWE/IpCAP6VwH2/QJhJQTx5umDYT8EPChISM3cjMcjxb0Zi24BxCKGc0/mrad/k4P7P83JhduwtuD2n0cn7kYsfW/Yy9dkFb76XI1pKCF/MhQUPe+wT3/L3lwHwd13z/CA3+I1VWSeuN0ULYMxeIwZH/acyzkRwsFkMr8vgjOVYC/O/r50wR/+wmRAP9B9AX4I8CZzJk2fyCEQKChKAIhZFyGl/RwczC9F2UHBn0oCmxpqOaNU11062zmwxsXYLVEjPaGwnzt2ZMMTI6YJ+F8f5i/f/EEn3tgJZY4+UMqmJRI0Koo0N49yOle/Y8aXvDcZIu7hhK7DU2TfGvfadq7p88zfbYvyF89d5ZP72qguTw9WcZcIt5XtxqeOXaVFzypCwA99W43Qgh2uWvHvtM0yYsnDT7hujjMe1aFcFjMKApUOmz89v1N/M0L51Lu++jqKu5cWGGov2xAQfBrO1r5p72nOHsz+Qn6WzsbaCieemNris6l6eeUQFhsCKsVNBOKyYJQg4DICk8Xwmobb0dRIppvo23Y7BPbmWSb2VGIUMOIgMzo2B1Nd+K32tH2/3MS68yYtn0Ky8K2jPWLAhoSxe4Ci33G7QbOvWnc6QZgrWlGWh3p2XatHfZ/K3kH4UHkK39L+L4vYClfnPW5HuMmiwPFbEH4VF3bh/u7YOCCId+pF99CrHsvIKBwIQxfM7R/MljWvx/F7poVXykWO4rFhpDz+yI46wG+2+0WQCuRn8ZTCTY5E/3bCrfbLTweT8KlK7fbXQYsBt4BPud2uz8GLAMGgKeBP/Z4PMYqtMxDCCEQAhRFYfxqE38BM8rTMgJFEdgUM5/dvYKvP3+S7hSa3cfW1bA5Lqf8U0cupwzuY7g+ovFC+zXet3pJvBFJeURbHbHztXbjucj3n+3ivauX8JMjl5IG9/H4xssX+MrDKyhzxr9wOJNjMzs83le5YE8qLqXk0sAIvcMBEFBdZGfxhCAzsn33iI/ndQT3MTx5rIs76ysptlsBONE1iPGM8HDkRj9b6qui5yg0lhXxJw+38ty713gzgVynucLG/asXsrwy/uZwbn3ttJn5zH1tnOwaYO/JDk71jN/FF5phZ2s1mxqro5Wpp7YjFCV60ygS96WYwGIFxQaKBmYrRLW2WeGFi2E4WX6GBCipB7N9vJ3iGujTL7caG7GzFIQyvW0WJ0IJglnN+NjtDRsJN9xB8PTrcHYfDHaCMEFxLbRsx7F0PcJkRstkvxJQJVjsYHHMvF2P8fcndGPZLoStAJmGbVrAh/r6P+ruKvTSNzE//rXM+EQHF1YH0mQFc1DX9kGd6UUn4MzLyA2PIRQTrLgfDnzbeBsJYNr1O1iqm8bmpRYYxN95DhEYRrG6MC1oQTE7M+crsw2UyGLDfMZcrOCXEpHn9Hg8ninXS4/HE3a73b1AFVAITPcLGpPnrANWAnuBa8AdwCeB97nd7u0ej+c2eNF2ciCWnua2rNiRVu9FLlu0LUmh1coXdq9m/6VO9p7son/SEV63oIAdbbXUlxaN9e0Lqew3oEsGeOVUHw+2LcI8JtVJNc6IfaoG7XofMcThrQt97GhZyCtn+1NvHIe9pzuiBX9yQ3uuj4scsCE1VzXJq+dusPdk55Q0qJUO2NG2gC0N1UTuUwSvne7EKN4428l7VkZuJLsG0ssJ3z/kj7M98lnutPORu5bxgfUqp7r7GQmo2MwKDeUFVLhi52Hu+BpACGirKaOtpoywquINq1gVU4JsU4nagVxKkylW7EIe+A5GYGrZPiFtpKl5K+qbBh8eL9uJCUmyNIiZSpM5bfpMoWB1bwb3lolVf2M8w/1mMk2mUIOJ88dnBAqOFbvS9rvvzGvGupNeghcPYWvcODvVWY2myey9mIYPNeRwL6aiShz1G/Ad+AGQ3u8mAI1bsK54AGthBUiNUPdZ/MefhavvRFxIJE+SCgQbN2FvfQCluDafJjOKubA89vZasjcwYjOigNQB/kngfR6P5yKA2+12Af8EPA58H9gwI2ungdVqprKyMBtN64YWFbSWVRYSCqtITUYvl+n9u7PUyb+9ftmwDv8n73TgC2vsXrWY8mIHqgYfqCvmQ5vd3OjzMuALYFEEC8sKcTgsU/p+ud34Y7wgcMXnZ0N9jYERgrMwvbR/o0E40jVgeL+9Z27y8XtXYDaZZnRsZv/fyLzKBUsS/RsMafzVk4c52ZH4Z6THB//x9nXOdw/z2w+txmQy8eZ5YzdnAAfO9/LRnW1IwHbZanh/AKvDQmllYTQl69Tx1NUV6xrz/P8XSisLE/9VDSFGfSijXlDDSMWEkBpImRVeuPoeOt75MQQH9R1EQAkhTQAAIABJREFUSxHVq7agmCxj7RSt2sSNA/8OUn8AU7nxQWzFBcntRIAQlBQ7s+6H2eAyZEILCBSrHWF1zKytgGA0tZvTgEL5B76Eo2ZR2rZ5jaRdjUI9/zql63fO2vEAQUlJivkX5SNpvacCRU4T1ujcDXzgd+l94iu69it9+IsoCLSgD6wO7JX1mGzj58Do+bcZee7vpm/g/H7859+k7P1/gLNu+cx8RWQBorKyEMVim77PHMdcCIxiOhKZZJvJS9KJ8DdAA7A9FtwDeDyeUeATwHVgvdvtvmsGts4LiDFXSiA+xaExbjYr7F41LpvRCwk8+24Xn/23t9l/5gaRtToJQrCgwkXrojKWLSjF6TAn7Lu7L72f7M5+r+FxWszJptT0sJrh5EVj+muIvMp3rWfYsJ15noRLydefOTJtcB+Pw5eH+McXT6BpKoFkvzjT4KZ/vN+qovSecJUU2hG54rtc5UJBmkxIIcbS+UmhZI1jL6DysS+S/BITg0L5L34JYbFPaEdY7JQ89gUd+0fguvujmMuXZnVcOclNZjShIBXrjNvCmt45CHYoqkn4F9GwgcqP/jnWhSvTtk1VFPCm8WSh+1LuHKdJHGt6748JZ/FYO7ZaN+W/8BWwlU6/g72M8g/9Mfb6O7AvXoWzaSP2+g0ojsKxdrwdZ+hPFtyPQXLzJ39GoP/GzPxAPk1muhiJfiY7U2PLrNNGfh6PRwUuTvM3r9vt3gN8DFgPHEjDzqQIBsMMDs7g0VMGUF4eeRjS1zNCOKxmJK3h9qZqDp7tpteXRjQEfPPFc/j8IdbWlk3S207ft8+XjrIZRkcC3OyJTafkYyutLAQkwwM+ii0wqFPvH8OiUjvD3vTsvHFzmCLdUqK55zFf6fXtbPOzvYMcuRqzLTVeP9vP5uYe3dtPRswP9QXpBRctZYXc7BnRfT7ciry0sgCJiNahSLCNFkb4BjH5RkBTE0tHMs1NZdjf+2X8r30bhq6SECWLsW/57/hkCYH+oantOBZh2fU7hF7+BkzJqTUOZe0vIhq2MNjXn9K24tIChKYxMOidHT9kmRP2I4MBhFWC2T/zdhvuggsGL+kr7sW1+iFCvRcI9lxFhP0IWwGWJaux2AvwSYGm49hMx1UtnfxzAKquOZEJXlLsREEy2D+sa3tl4Rq07kSvSSZB8VJGgwoEh8fbtFTh+NBfEL7RTujMazDUA4oAVzmmZfdgr1tOQCj4kvjB++q/GzKj57UfUrDtU2n7qqy8BCHD9HQPzPkKfnGxA2uadYbmIsAfIhLkV7jdbrPH45nwHMjtdpuBCsDv8XiMayLGERPcOmfQxm0Hh9XM53ev5BsvneT6cHo/Wt/fd5nVHyyNe0kzOUrSlM2UFRg78QQghGB7aw1PHjOmx96+oo5XT3aQ7CI+HVxpnpxzifSec8wOXj1p/N35fe0dFFlgyODhq3GNe8JmMbGtqZS95/RLfdbWOSmwzF0l2lxBqvkU6j+D+tZ/Qc85QAVHEZamzZGc4Yopch+AJNMVK83FNTgf+TJqz3kC596AgZ7Ic+2iSixNW7GVLwKSVxi11TRj/fDXGL14CM68CgPXAA0c5dC0BVvzVix2l37biEh0sjHeOeHEOBlp19a8k4DBAN+2/B6EAGtlA+bq5vHKsYoZtPDMx2myRI5b5Av9cJbO4vGYtMCQYnt701143zFWO0G07kzYvhAmbLXLsdUun1i5VzEjUvg/PHAdbp43ZAfXjqD6hxHOkhn4Ks5d8xSzHnl4PB7pdrvbgTuBZqB90iZuIj+xiSrTjm/kdv8RkZdr/8Tj8STatj76mbk8TTkLOelzZrzQZub33rOK4x39vHLiBucMVq8KAkdv9LFhcaWu/u5cXMF/HTIWtCnA6royXe1H+LgcYFNDlaEAv8IOLVXFdA36aE+VHmgSrEBdodOAnbnAc7uS7TEd0pzJOHxxkPtWVvH08W5D+21tqZ1gw3tWLeLo5X5dT4DswPs3LI3bf3IAcPtwCYjo3Ir/Xh3pYPDpvybc8SZWNKyM60ZD194hZClG2fFr2KuagOxVrzRVNOKsaEAzW1HCocj3ZitajKdqx2LC3roTpXnr2L5ptUPmKtnmDM9wJVtTxVKovwcu6nypdeWjiMIqg8fAeNVgGrfAeYM6/MbNuVvJ1mqC9R+Dw/+qbyzFDVibN6OpWkbtD14+kbC7VPBfPY6tdWd6vuLWkOjMVZLP56Kfjyb4W+y7n6doYxXwAeAXJv/B7XZXAfcTWW59JU0b5xFE3GdmuCIUVteV8bGty9Ky6O1zMUlE6v6cVgsblxRhBDvd5ZhNiq72J3OXzcJndjXq6scGfPr+FQghuLve+PsJu1orxl6uzMZxut14rGCSUQSALU2JNbjTQQHuXlo5wQaX1cIX3tNGRYqHTsVW+OL7WiixxzJMzb3v5prLSd+rIx30/fA3CXa8Fff9JIQG0V74S0JXj6FIDSUcRJHhW5urAYSm5o49M+VaCBOgqGrG2nVsehzqdbxe17Ib16oHZmWcNvf21PZMgqN506wdD6GFUdSAoX2dLVuh7X2pB1K6FOf9v4lJVTNuPwHjqWgB5NWj4B9Kz1fxWXTmMeYqwP8OkTJvv+d2u9fHvnS73RuALxLJovPNuO8b3W73crfbHf/WRyzh7OfdbvfmuG0LgG8DRcC3PB6P8dx4eYxhIE19/IBBvfr71y2lSOfzpGqnwu62RYZtig8hmiuL+fzuZiod0wQWQEOphT98pI0KZySas1tMPNRWqbs/K3BPc51hO3MB03tlbqFT9TUFJqDAZuFXti7Rvc9v7GrEap66glPqsPGl963h07vdNFZMlInVuhR+ceNC/ujhtVS50n0h8NZDfGgPIKVk4OmvoA3re6IS2vsNVN8QEyUftyhHMEGiM9f2ZIyTsbaEYsa16eOUPfQFWLiSKVi4FvP9X6Rg3SOReTcL47SU1oF711RbpoFY8yFM9sJZPAbxZ6H+fQtWvwfrvZ+D6rapg3BWwtrHcT3weRSrMzv2m9PLXkbHu/h/+BlG3v5PpBZOw1fjH/MVcyIO9ng8l9xu9+eB/wu86Xa7Xybiyp1Rm37J4/HE//K/DCwB/jvw3WgbL7jd7v8DfA54ze12vwH0AluJaPj3AfpTHMxryEmfmeOmNCOq8SKz+vorsJn53YdW8HcvnKQniQpmcZGJ37y3FbslPu+2nr6myk7qSwr48sNrOH9zmANnuugbCaAIQW2Jg83uGmoLnZPakuxesZCbI4GEBYriYQJ+50E3hfbYKZb5Y5M9nrsSHSFgQaHC9WFjS/kN5VZAsnZBOZ+8R/Ct1y5NaDkeFuDXdjXiHis0NdUes0lh07Ia7l5WQ3fnIP6wis1kwmqevGYSv69IOrZbmUfCVjn2/9D1dwh1HI/bZrqjMQ6f53XsdzwW3TSHJCgZ53mJjh6uSHDUr6W2aSP916+gegfQzCYsthIUmzMNadTMueOOD+FTgXMvJ5/Mqz6Eo+1+tFm0zbBEJ46ba5ajLFyFHOpGHepClRKzvQBz6QKk2YYMh5BZsl/UNCPbn0nuz2Q4s4fRvmvYHvoi6Bz7rSLRmbO3/zwezzfdbvcVIiv2W4k8RX8d+DOPx5Pi7Bhr4/Nut/sA8FtE8uKbgHPAV4G/9Xg8xt+InJeYdNeJzBhPdxVyUXmskqj+/kodNv7n+9ZxrPMmrx6/zvn+8cPXWuVgW1strZXFpC6qk4iLhN8LIWgqL6Lp7mJd7Qgh+G8bG1lS3cOLx67Rl6Bm1h2LCnnv2kWUO3OzYFG6vsoVvq21jn8/aOzVmu1tC8bGtbqunK99sJS3rnRz4Ew3PcMhkFBTYmXz8hrWLSjTVUBNRrnFpGBJuX0MuePH2eUQX+jKe/iZMT5xZTEJTr2MWPselGg+72wXw5ornu1CVzIcQI70oQVGkRY7JlcpQjFnbVyZLHQ1XfEms82J2ebKWrEuI9x514cJ168leHrPWEGmMTRuwta8A1P5EnQXnMqwr2bUr6MQi6Mozs8SLcv22+vc+KylEDRex2QMfWcIvPE9Cu56XJ+v8oWuZg6Px/M08LSO7ZYm+dt/Av+ZQbPyiIPTYmb9wgIOX9OflhBgS2ttWv0pimBtXTlr68oAgaZJFAUmBg3pQUcIoa8dIdhSX83mpVWcvznE1b5RwqqGy25h9cJyXBbzjOzMBWTKV9nAHYsreOrQNUZ0yiPLbbCyZmIeZqtZYUtDNVsaYrr8yYFpasTC0zxASkmo8yihK2eRYT/CUYxt2TrMhREp3eT5FB42mBUDQBtF+r1gcwCSWc8GM1scwQSJTobal8EQvvMH0NpfBN942tigcELLdmwtO1DsBZkfFzFOFvwVf3OYI8dPCKxVjVirGgmrYRjtB0VBOEswKZGFgGRZmbLHc9NXqbgQZpRV70E79H1mhPOvoa19GMXm0umrOHfNU8zfW5M84iAnfWaW72yr4/A1/SXZlxSbWFAQv3qdft+KknobfTzzshMhiKz+l09+QTi7xyP7PHclOhBZMf/Mg8v5y6dPp6y6bAd+6/7WqHZ/ro/L5FuCW4P7Tr3G6JHvEu6a+BsxvFdgq9tCwY6PISs3Ei/RkWqAicdDogeqkChpSAzmF8+8RCfkGyT09F+CP0E9COmF9p8TaP856v1fwFrVPC8kOkhmJDuZDY7dgWK2R76fA8nQfPJVMm5zb8M3dAPOvDJ1/hqA17Mf5+rdeYlOHvMDUkqkBE3TMlLoKhFfVOTikVVVPPlu6hfirMAvbV0WtWW85HOmbTLKNS1SXTdbPrqV+HzwVY3TwR881ML33zjLhYHEJdWXV9h4fFMjpXZrVsaiaZEbIU2bfp5rmkTVYt/lzvmQKT782ncYPfJtxlcHJ27ju/E6vh+9jevxr+J03xM9DhJsFWhci9t+vB53MigmM4S8kQuvVCMSEJ1caiECV46jDnSABOEowLZ4FTiKDLWTdR7yItVQ5P8ZaFML+Qk99b8hPJjSv+EX/hpx/+9hqlyauXEFRyP/D0b1jBn0lwwKMIfTnhO3E5/PvhLhII61j+FzVMGxJ4nkaEkD3achvDO1r8I20IKg2fMSnTzmBlJGdMCaJpFSxAUxmceO5josVnPSfPULCwS/vK2ZYqsdVY0PZuYeqhYNsdJMs3g7IRd9pWqSY1036e33omkaBQV21i0o5zP3ttHt9/PWmS56hv0oQlBVZOfOpirK7ZGnSNk6L1SNaICfZBtVIoWSc+dDJuA98TzDR/6FSDK2JDcE4RAXf/T7NH7yO2hKRLpnbdhJoOPdsW0EkZulpEeqwo0MhZAyspUmBEIHR1Pxe/ZC+x7iAwMJ+A99HxasxbrqIUyOYt1tZpOHfWDSJDLoz0ib/nef1RXcxxA6+CPEvZ/O2LgI+SHgQ0OCpmbUX2pIQQv5kaHgnB6z+cBvBV9Zm+5AqV+Lv/359FbzQwFk0JeyLy3kRwsFkEoOXQTTQD7An8cQQiDQUBSBEPFyluzgnoZq7lpcwYFL3Ry+0MugL4xZgSUVBWxtrWVpsSu6YqlhMoms22MEJiUSduSSTbmKXPJVWNV4of0aL57qmyLH+a9DN7hjoYtH1i/l4TVLmRpcZhcmJdJLcj+JnDwfZgqphvG+888oRIrapFzxD47Sv/cH2HZ9DgDXyp2MvvVNCPmIBfjxCuFEMLXuRLG7EGoQECgmS0ouhMC/5/9B1+R6inG4foTgjTNYH/w8Smmtofazwc2OQoQaRgTkjNsk6Idzb+g7qDEMXkLzDWAuqszIuFBAQ6LYXWCxZ9RfJosDxWxB+NQ5PWbzgd8qvlLUIJYldxBKJ8C3FyCstpR9KRY7isWGkPP7Rzsf4M9zCCEQAhRFISIBgImXycxyu9XM9uY6tk+b3z2yUqkoMZuyY4dRrkSLTSljaT/n1p5c5rniq2BY8vd72rnYP30yrLevjXLyxkm++FArFa746lPZt1NRRDTATzbPc/N8mCn3XzgKgx0oQK+pgredm/DYWvArDqxagKbgGe7w7qc23DG2z5Dn51Ru+QSKoxjFUUzx3b/F8GtfBSQKckJPU1CzEuviNUizjViqO8zWlDyw//vJg/sY5CjBF76O9cN/iWJ26m4/K9ziRChBMKszbjN44zSReo/GEL50DOu6hzMzLgmoEix2sDgy6i9hdSBNVjAH5/aYzQN+K/nKXN1ACBcwamheK/V3gNme2ldmGyhW0PIa/DzmHJMDMR0ralnnk22ba5tEDtgwX3hu+Op7+88kDe5j8Grwt8+38+WH18QVpsq+nRND0vl2PsyMBy8cIIyFnxV/gJPOTRNGGDbBu5Za3nVto8F3hA8M/gCbjEgDgp3nsNevBwSu9Y8gA6OMHPxmAj/FoWoFru2fjDw6N5BiT/UOwIW907c7GeFBgqdew9qyVVf72eKZTJOJN83Ugr6+jKVxnI00mbOdcnI+8lvOVyvvg+M/NTCpbTgWr0boOK/yaTLzyGOeIUkIkcckzLWvOkd9HL3h1b39QBDeutITl/Yy+4jdBt2OCPt9/KD041y2rwIpGU+HGAcpueBYy3dNRfyPvm9iIYwMTFxxK9j0ONbGNvwHnkC78NzE/cubMLu3Yam/ExGTAhlIsefzvGZ8YO0vIFu2Igz2lVGOIGNpMpU0VyDHgpqZ2xAe6o3khL9yPPougB2qGjC17sRe1xoZatp9CCbcgM7VMZsX/Nbylb11B/4zeyGg7yZW3PWLkXoPun0V5655inyAf0tATvrMFT45BJpLntupH3OBSyk51T3AqaOXGBgNoIU1llQWsKmxmgKbZVbtea19+pe5p8MrJ26wpaF6Vu00xnPpfJgZfyPYxGV7PdMG9xC9WEp6rI28XPgAu4efRlhdU9q0Vq/E+sgKtJsfQ3a3IzUVUViBxV4MzCC94LWTie1KBn8vYd8wJmfJLZEmUylflDKVbEKU1s84naJExffmDxNUdfVDdztqdzujjkqsD/w2SsmC9PqYx6kfZ5vfar7CUYx19+cJPvNXqV8iX/VBbMt36P4dyafJzCOHICZ9yhzgk22ba5vEDPa99fnxzpv84PWLDE3KOHmsw8tT73Zz99IiPrShEatZ6G5zJvzk9QGMossr8Yc07BZTVm2L8dtVoqNJyd7w0uhwUixxRf9+yLmZe/2vYa1bFvvDxPY1icnmwlSxFDQ1WilzhlKCsC+5bdOZHPSiRFNnzneJjrm4llDJEhi4bMgH9sb1M/K/0FS8b34XLh5M3pGvh+CTf4bt0S+jOIsN93fLyU7yEh1j87ugHPOjX8Z78iU49QpT0mdWtmJZ9QC2GrcheV9eopNHHvMMKUKR2xYHLnfzb29eTbrNm5eGuHbzOL9z/wqs5uz/bPiCae6nhqMBfvYhiC783GZo7x7Ab/RsUlycX/wRFkZX5RMjLujPxGN8qwP0q7zGrbDMcaVcBBmT6Egwt95LeP8/63dA8w4UiwO0cNr9Bi4fTh3cxyC9BPZ9F8sDn02jP0HG580ty29NXyk2J84NjyFWP0Sw+xxqYATFZMFSuhBRVIWS1jyO+mmeBw35AP+WgJz0mSt8cgg0lzwv0UnErw+NpgzuY7g6FOYHB8/z8c3NWbfNbgFvQJdZE2A3KXFt5Y6fI5j98+H60CiHzvcw6AthVgS15S7uXloVdxNkvM2OvmHSweiCNUx7bBSBNJnQpIBMVXCtXQEDV4wZaSkFZyma4JaQ6CDB2rCRcPelBFKZBChpwLH+gzPuN9z+ijG/954mONyLuaj6tpad5CU66XNFgqWuFZPZijLDisF5iU4eOQQx6VPmAJ9s21zbJGaw763LXzp+HSN4++oIjwZCFNusWbVtxcIS9p03JtOpdgocFktW7EnEc1mic3lgmB+9eYErg5M0V5eGeOJwB9uaSnl07RIsJv1Zh0K9p/C+/Qze6wNQ8H6MQnGVMu15qEmEGkYRMlI5LAOP8R3uLfhOPWvMyLYHMKGCTL/fmfJMSnRi3HHXL+BzuOD4U9OPfdEGXJs+hlBMhuQMk3m4/yr0nzfmdyB45nWs699/28tO8hId41wbvUmo+wIy5EfaC7BX1kdSXabrq7xEJ49cRVjVeOf6TTpvjhJSNYpdNtYvqaDUYZ1r0+YUk0Ot2x3eYJi3r44Y3m//2U4ebFucBYvGcc//z957x0lyXfe933urquPksLMzsznN5ohdAAsQwAJYcAFQBAMUSIqKtkwHPZO2n/QsP8vys/xky0+iaMuUHi1btESLlESKETkuwiLuAtg8i815Z2Yn93Squtd/VPdsz0yn6gndM5jf57Pbv6m+de65p29Vnbr33HPXtXp28O/d0DpN2mRH2lWtNJzs7uePX8jvYO0/3ce5rkG+/NCmjNSi2aG1Zvi1bxJ5988AQTiwtSS96moCBUpkOP1TMHVvhBtg+W44d6A4BY1qAh3plJ9To0NJHMFUhuiARgiDqs0Po9bex8iHb8HlQ5CIgOGHljUEO+5B1CxATCIsJ82dwe7i7D0eg9dKqE8w1f1m7vK5Z6vkzUvEjz0DFw+O6UoRJKy5F//Gfe56mpJslWGuWYp5B39OQANgOw5PHbnEcydvMn6D5R+8f531CwI8fvtyFoSDY84rhSutiCRslNZU+SwMKbKUH+8ClZPPh+iM51eHvG0SksaZG4OwMS2r+HqTjuJUzwBDURu/JVnWUEV90J+1fGtVkK1toaJTZdZYcMfSBZ70mXk+/ddDXzRW0LlP4+Kgw7fe+JBf+VhHXpnDr36TyME/Gz3eETsKahhkVVH1pHHP6lYSkXTc1bi6piNEB43/Y79KfLgXuk8WVtAZIn7zMrJl9aTrraQQnTHcX0Vg68PIjQ+6x6cgnGECLzWsQWnPbZ7rYSdTyTNt5UR6SEaHMVAYNc0QaqgYPYvlI+cOwWt/kqszwamXiJ/aj3r4/8JqXDYfojOP2QpB0lH88QvHOd2be3Xi8a4Yv/vjE/yLRzpYUhNmzNt8kbwrEuWVE1d55XT/mJeInYuruXdDa0ruWN281jE9XEzi3LnJE06mk1U8kk5aVnF1DcaTPH/sCi+d6mV8jesXBHlwcxtrmuomnPsLu9cw8MKxgptdhSR8Zd8GfKYsSp+p4pUYovNK53W84NCVCI9FEzQGfVll2n1niBz872T+3hY2O0be4GDV3qLruX15LeGQn0QkkV3/HCE6wzGHs129RJM2AVOwsqmWqrDhnlPMlLuC4D2/SvR7/woovHJbP/f7OPv+JVbj0jkVojOT3ArVFGHpLKhp9tzmuRx2MuXcSRDpPMDIu09B31nA3Wc7CdC+Hd+GB/A1ryy/nkXwxPVTeZz7TCiST/0nzMd+B1ndPB+iM4/Zib9683Re5z4NBfzRk538u89sIuzzFrKz/8x1/vad7DHb71wa4p1LQ9y7so7Hti1DTnBsyo/K06i8qA5YhQtlQZWH864Oj/CHT5wkluNd4nhXlOPPn+Gz21vZs6ZtzHc+U/JPH9zIM8cu8dzxHuws5+9aUs1jO5ZlrAmYOaRd3kqB7SheOnnT83mvd17jk1uXZv1u5J0nsh6/b/h5Ov0bGLbasn6fiYCAX7x3bRHX3y2nv3cgyv7Oa5y+PkjSkRhSAxpHXWPlwiB3rWph0YLa0ZG3fFPu0eMvUoxzn0b8re9gPfIbeWVOK0cw1SE6M8nN+kUkatthwNv6Hv+qO0uoTzDmZbEC2l+JXCdtrv7kq+hLh7Mb/8ohElcOkdj0GKGtn6gInfPxxLvfzdGLsiFJ9OiThHb/ksd+detjtmLewZ8DuDkc460Lg0WXTwCvfXiDj29YlHFU5+Wvnb2R07nPxP4z/cA5PrtjORNdoHLy+RCd8by9OkhYQmR8PFcBbF3WmCErt/yhWJI//MnJ8ZmJs+J7h64RDpjsWtI8Ro5pCB7dvISPb1jE+1d76eofQSlNTdjPbYubCPkzdtwsoE/5+fReD93RWNaXoELovDYAWyfK1FoTPftU1rqCOsqv9H6d/9nwawxYqfUY6U2vMja/qjLgK/vWUVsdIH0dZtU/I0TnTNcwf/XGVaROIpDElYEPB4EgrgxOX49y9sZ5Ht2+lM1L6oDcU+6OlHDiBW8G6TtHvP8aRvOKrDJndYjODHGx9gH0W39RvM1rlmE0LEN5rG8+RKcwd6RF/OU/ghtFbPx25IeMWNWE1t9bMfqP58meszBQXOa3UZx5Hfv2L2BKq6i65kN05lER0Fqzv/Oa5/OeP9LFA2vbkOmRojzT/yNJm++8XfxozP4zA9y2cpildVV55c4kV0oj0CilJyVnOrjWmjO9gxy50EskbmMZguULa9ne3ohpiEnLz8fvW9fEE8d6KBYGsLWtIWXH/PKfP365KOc+jb85cInt7Q1IISfIlAK2tzdCe+OY40qpgm2cLq6U+9KYTwelNI5KH5teXaOJUtx797xsbdDJKE50MGe9VWqIL/V8leOBzbwZuotufyp9qhA0B+De9Qu5Y/kCfIaBVgqdug6z6q8cRDLBzd5BfvTmGQxHYkkn9b2JxTiuNU+8e45qaxHLm2tAO2An3AdyBk9c7sTL6H0ayTMHMOpas8qcdp4cQTtJ9++ZrHcKeWDZDqJn34Xu40XZ2/zYFyE54rk+nRBg2iWd+1HhiYsfFOfcp3HoW6hlm5G+UEXoP54nzx4s3IYsSJx/D3PxxuL6le0HlQAVmA/RmUd5oLUbB3zscp/nc6PAzVicRn+hzBZw4GyXZ/kvH73CF3d3FC44Q3BUysXyOFo93Tja3ccP3jpPzzhP+PXzQ3yby+zb0MSD6xYhpmmu8O7VC3m1s2fCDra58PiuNgSioB1trXjplLd+GQM+uNbHloWNns4rFxxFysHPU8bRaCFxnEwHenoQtEq7nVf5zaxt0Bgo8q9rEMCG2AdsiB0miUVM+gmEqln8+LdHyyjl2gry2EppRDLOa52XkE6cgAJTKDQSQyWzcydo5XY3AAAgAElEQVTJm8cusmz3SpQQCO2+vGdyPeD93gXAUA8qFskqc7q5HQVDaXQiNqP1TiU3tCaw++eJHfhWASc/gLnnS8hALTqZ8Fyfk5SoZKykcz8qXB95No/9syPW+QrBjnsrQv/xnKh3fweA4ZtF9xOVjKGScbSsMIfBI+Yd/FkMIQQCRcIurRMmbAcZLFzuzVM3PMs+eDXKF4VOzRCUH4Z0XRMpCxadMRw418V33rma83sHeOJYD9f6o/zi7tVMhynDPpOvPLyOP3zqBEMFnPzHtrRw17IFRck93zM0IZNTMTh8rodtbbPDwTek68Lm71MCRykMQ0x731sQ8lNrwvjU94WwaVljdt2kD6uuFac/3UfzzyL4ieNXMazqNRPkGam/c9tAEE3Cia4EhrBIShNLuKP2+fjlIYeeiKaxqQrhJACBNKxRTsBbpp9R+ILIQDirzOnmZrAa4diIuJ7ReqeaC/z49/4TktdOoI6/OHYUuboVOh4guHQrwvK5YT0l1GFYQaRpIaJORbS50riyk9D3off+f/kwYtNDZdc/G8cX8t4egEANwucrqi5pBZCWH6EryGEoAfMO/iyHEIKgz4SI4/ncsM9CyswQELLyvmhpusVtTdiflpe/junm6XZKWRn6nOsbyuvcZ+LQlQitJ67w8MbF06JPczjIv/7kZl4+eZUXjvcwfgPZ9QsC7N28iNVNNeO+yS0zkvDeHwGiSVUxv1EhLqVIOfgyT3mVKluo3FRwg/s3LuT773vLpHP3ipacuoU7HmP4ra9nfKcL8qrNn5pwXxHSXXaf834jDU72RLGxUAjiGOjUUv1C/NTNOHe2LoBUHDamb5SbLStKWpdA/XKwglllTju3QgiZANOZ2XqngQvTh691Pap+CSJQBYaJVAohBMr0IVIpOkutQ/iCaMMHZqJi2lxJXMeLD78cg5FBMANl1z8bly1rUKf3e26S2d4BZqiouoTpB+kDNR+DP48yY8uSBs73eYvDr/NBfcCP+4DNPzJnCEjqLEIKwBjzMM9fx/Tzwu2cSf7sYW8ZJp4+2s2D69o97TzqhYd8Fo9sXsK+jUvosx3643FGIgmW1IWpDVieZfqt0m6MM53qcjK8EtNk3rVyIS8dv05/gqLwU5sWEPRZOWUGtz7E8MH/AXa8KB1kVROBNbuZeL2RsleO61Bp4okEhtRIFD7ATE2PF+KObSNzpL3zVTVjN6yE3jPFGSSF0MpdZUsFONvTZI7nqKT7yysHpIFEMVU7Bc+nyczPdanThpZVsf0vuGQzkQNB0B5GHpvW4As3Ft1P5kqazNk9/zAPAO5es9DzOXs2LEQUGfPRVud9B1wT8JuV1b2Ka+30YyCW4Mi14jZwSsMB3rlY4miMB0gpWNVez84VLWxaWE9toLTdj5fWlxYasaJl/CyBdyRsh9fP3uAH753jewfP8dyJK/THivR4PaBS+lMmApbBl/dtoKqI96t7Vtbx0PpFecsYoSbq9v07EEVcy6afuk/8G4Q5sc+kXft8sCyD9DiC8PC/IQW34tf0BG5uKD5fPwCr70X6/HllTisn3Z4ZrnfaOdMgN7NnVUo7K4dLfzVQRBzuePiriV04hD2YXsNS/rakuZAmYtPDnppjbdpXQr+69TFbMXtfTeYxitqgxZ7VDbz0YW9R5asMuGtFC4w+TsnL71rbwtk3vKWlund1XeoForg6pp9XTprMMz3FpzTNxAfne9jQWpfhdFeurap9pqedaNO4c3npu9EmbMWPP7jASx9OXIT1ww9usHFhkMd3LqcpHJhw7szwmbkemkJ+/vUnN/HM0cu8/GHfhLUQbVWSvZsXsXNJ07jzs8sMrL6Tup/6Awae+zfo6EDWMrK2hbqH/zW+1i1Z5WhApPpW1rqkYE1LHc8pAWpiasx8fOmC6rxp73xLd2B374WTz1EQNcsI7viZMqeonP1pMsdwx0YRR0oDpriO+TSZBbjQsH4vHP9Rtt6eG31nsV/7BgCJ+lXITQ/hW7GrYtoV2PwQ0d6rcPnNwm3Z8jhW+0Zv/Yq5kSbT+J3f+Z1y6zDb8EvAMsdRxOOlpaWbKoRCPhyliQzH6Wip5WrvINeHCu/6+c8fXZ9yEjNHP3LzhdVBXjp+HS9R1b941wpCPrPoOqabB8N+AGIjybLrc74vwpHL3p387ojNiye7OHqpG9MnaasJZ8zCVJ6tGuuCHDhd/MZLe1Y3sHlxQ0l1xZOKrz57hMN5Zka6hm1ePdXNlmV1VI9u8la6rQLhACCIjcRzltEatBbugnghipI7FdxnSta1NfDguoUsbQ6wsiXMtiV1PLK5lUc3LaW9NuxJptnQTnjLZzDrVqJHRsASyFA9Vv1mqu/5dWr2/FPMmvaccoJhX8pWiexllCIoYlzrvsHgSAyBG34jhc7LW2osdzZS69TUOqDVBG61rSMpAnDjBDmx9DaC9/8jDMPIKWcmeNBvIJQiHkuUTYep5MJJIBwHKV27TmUdQb+BQJGIxsrezkrloq4F50QRL7e5EOtFX3gHeySCr7XDDfsoc7skAmP5VmwloOsMKQ99LKxaxO0/T1XH3Z7lh0J+zECYuLIQZQ7RCQQsDDdLwQXgm17OnR/BnyOQUvArH+vg5Q+v8fzha1nTHt65tIZPbF/qeddP05D8o72r+Opzp4sq/7O3t9MQ9Bctf6YgCheZEYT9k7vsLg7Y/OUblzjQeYN/uGc9AWvqQ6GmwlZLa6v43K52vl3EHgodzQE+tW1ZyXX9zwOnuDxU+BXUAf7oyRP8209tIeib3OiMwMtVVB5YhmRza/qlCSajsbACBDc8SHDDgyk5mTLz95ji+pPg7jUtXOoaSJV3LZyP39PR4v4lxOgoXLadKQUQ2rIP1n6M2Om3UBffA3sEDD8sWEWw42MY4XqUNEHZOeXMCEcwJkSnHDpMJSfNmYY6BGP6YaW0uYK4Ea7Huvcfk9z/X5kUTj3HSCBMeNPHK6JdQphUbXkEvfEhRs69i+7qhGQcfGGsxVvwtW9AG1aJ13OqT1WK01Ai5h38OQENuJsB3b+mlT2rF9LZPcDl3tSunyGLre2NBCxjTPlbn4X5ysYavvLQav7k2Q/zbl70xTsWsWNRU+q88S5QOXnlhOisaZx8nDnAmd4EX3/xGF/eu3Fc+sHKsdVdK1qoDlr8zYHz9OeYXLp/dT2PbVs2mkrRa13XhiN5R+7HY0TBgXM3eKCjzXNdk+OVdD3MLHfvBjrju3FlUjvZLmqo4uEtS/juwWtYKn+Izr0b2lmzqNHT7qfSFyawfg9q88eRqQwu6WwuXndRnT4+H6JTLJ8P0SmOW4u3UvWpf0XfE38MycwwO2/Qh3+Ave4+CAYrol1owDTwr9uDXH23e9z0Ie0kejL9ivkQnY8qfgkqL0RHKY27V4NACGgKB1nRVMPK5moW1VVhjnpPmSMe3nhDMMAD61tpqbOIRKIkEg4G0Fpj8tCmhfzqnStYXF9VlpCEQrySQnRMw2AgMsKl/vEJKb2jL6ZorDZZNGbX4MqyVUt1iD3rWlnZEsQvNI0hg8V1fnavaeJX7lzJpkXp3WtLk//kB5e42Odlz1y40TfMnrULEZOot5JDdCqNFxOiIxLDGHaElho/y5pC9EVjRGIJBGPDchbWWDyyuY2tyxvLHv4wH6IzH6IzW3hN80KqbnuUWHgRSjkgwxArbt1eJhxfDb6mpRXTrung8yE68/hIwpCCnYub2Lm4OXXEHZtLc3cr+sqEKFxkxrB30yJeP1f6SEomXj52jTuWtUyJrDSm2lZCCNYuqGPtgvrUkbH9ZjI4edW7HfviMJywqfaXliUIXO0rt7dXFvL3J+2mo8v4e2lLDb/UHKK7P0pn1whOIoFhGixrrqa9MYggtatlauStEkIG5kN0cvD0byyYhjpERu+qoDZXJBcIIfG3r8ffvp5kbIj4934Dr9AX34ONeyuoXdNjKzI+ZivmHfw5AT3us1L4eBeonLxyQnTAzXTy6W0tfP8977sEj8flIYe+aIz60XUPc8tWhXg0kXm8eMRth2r/R/V6mFmugewhOhrsGMKJo3UCpQVkhKY01ldxR3PDmHAaPcnp98rn8yE6xfL5EJ3SbWUnvc16jiI2WPa2TLutmBshOvMO/pyAGPepK4CP163cOolJnDs9vLUunclk8hhI2NQHA1OkW+XZKh8P+yXDI+OTQRZG0ExneSqt3krc6KpyOWTd6ErZiGQMaUfQiQhSp8LCKmRTHakdEkpz4doA/dE4EkFTTYDFDSGkmJ565+xGV44DYmrrmN/oqnRbGdLwlBlvFFYw58Zyc4XPlY2uZq/m85iHR4x3tcqN5qpA4UJFwpJT27pKs1U+bFpcz43O4tNxAiwMy0lnM0q7qvMojPz9SYN2QOhbf5d9il4zYjsc6rzOoYt9DMcUhtSAxlGSxiqT7cub2LFyQWq3yCnUAcF8iE6xXGT0rgpqswfuRAcY6dwPN86AHQN/CLloK/5Vd4JhTJutjFADiBBob3uV0LKiIuw2H6JTGPMO/pyAHvdZKXy8C1ROXnlhJ83hAEtqTS4OTH6xdmPAn1HH3LNVPv6xtS0879HBv3d964zrWVnXw8xyDWQN0ZECrW00wk1RKRyg/FP0I4kk39x/gb7hCAImZPLpGbZ57sh1zvTEeXzXIkwpPpIhOlppkl0fYp87BPF+MH3Q3EFgxQ6k6Z8P0clnOztJ9O2/gjOvMR7q+jGi7/4v2PI4wU173XSPU2wrbflg7R448cSE+vPBt3ZPxdhwuvh8iM48KgJau9lzlFIZC1zdx2k5uFIaR6WPqbLpMZ4rpREp/SpBnzS/b0Mrf3HA2y7B47FzURifIVNtm7u2ysXr/X7uWFrNmxeGKAb1Pti5uAmlJtc/lXJfhPLJqdTrYaa5Vgqd6lu3jmtIDCPjw2hnBLQDyZj7UNWOG95RBm4nE/zt6+foH0zgk05KXxOLifzslZs8fyjJvq2Lpk6H5AjaSbp/l9EOhXj80gfot/4aYj2Mwbk3iL39TWhej+/+X0M6tntOIhXzPYV66IQA04bkSMXYpRiunTixZ78GNwvsLfPBd4nG+gnc9tlpsZV/9W7iXhz8hZswg/WoZLTsNpxOrm0/qASowHyIzjzKA6316ENTa1ERGWwcR6OFxHEynZmpQySZpCsaZyRuUx0waQwGCJtWYb1UysXyHqo9rdjW1siRtpu8d9XjNGkG7t3YNqXtqlRb5cNP71hJf+Q4J3sSecuFJfyTj6/HlHLS14ujSDn4Y48rNLGkg08K142dxuthtsBJ2WiMrZSDSIxAbABUAuUkEW6uX5QQZeMXrvXT3zdAQIEpFBqJoZI5+amLEe5ZWoWvKlByvfH+a+gzr0HPeUacJIRqoXUj5orbsaxA2W0ynkfPHYT3vpP/R+8+TuKvvwwbP4mvfRMK7f7mGbKcwS6SFw/ByCCYFtS0Yi3dhjB9RenhJCUqGUMnExVhl6J/7/d/XNi5T6PzeWI1bQQXb5pUvdlsZZg+jLv/Ac5r/39hPYLN+Hf+DDoRrQgbTidXyRgqGUfLWfQQzIJ5B38WQwiBQCGlQAg9brOjckHgKIVhiCnTx3YU717u5an3LtOXxX9b2WCxd1M761vqJ36ZgiFd96oybJQJwS/uXoP/7dO8eXHY89mf3dHKkpqpW6wLlWyr3JBS8KU963jp1HWeP3KDSJb78sdW1PLw5kVU+UpPjZkJQ7oOvpSgtObYjX5ePnqFD3tv7ejV5IePrV/AncsWYM0mg04x0ttwjDeBECANCRhIy4dwEoBAGlbZ+NuXL5EQFklpYqVChgrxg9ei3LWp0XNdeqSf2CvfhJudYw0T64Pe89jHfoK98TGCGx5AmuWzSSa3e84Vdu4zcfRHJG6eB8sH0RGwTAg3QNcFGDg3oXjyg++SXP0AwW0/hbT8eXUyrKBrl6hTdrsUy7Vtw9lXircfQOd+xModRclXyQQqPgSOwgjXIf2hvLbyL9pA/KHfxHnzr2Awx2zy8l34dn0OI7VvSLltON1cWgG37+nZfc+ed/BnOdwNdEBKiRsCAGNHCmeauyOVUqZ1mpzc4XiSrz17lGuR3KOtZ3qTnNl/nt3Levm521emNk0aK0dKkdJLjDleCVxKwc/vXsP9m0d45dg13j43QP5xaJDAF+5czO1Lm8d9MzX6VKqt8nGJwd517Ty4to0TXQNc7x/BUZq6sJ8tbfX4zPHxlJOrV0o3qjxmK77+4nHO909cS9ETh++/18WT73Xxzx9dS1t1aErqnm1cSIn70ihSx1PXs2VCzABTuPHbqfjgcvHYwHXO3nQQ2iKOicbVO46Rlx/vjnOXFfRUl1JxEk/9IcS6yYujPyTqJAjc/rNltw+mj+TxF/Prmw3XDnsr/+ELRHsu4P/Eb+bVSfiCaMMHZqLsdimWR8+86t1+gxewh29i1i3KKT/RdQZ15KmJtl6yG2vj/Yimppy2strW4f/kb5PsvUjy9NsQvemGptQtJ7BqFzIQRpk+SKWrLbcNp5sL0w/SB2o+Bn8eZcd4R0xn5VorTnQP8srxa5y5MUJSQ7UfblvWwN1r22gM+nOe652P1827rISt+KNnj3E9j3OfiQPnB7HM8/z0bcuzyBRMXdumh7dVhfm521fxc7ffOn5pYJhXT1znYu8wSVtRF/azY2UzuxY3YhqGJ/nF88q3VT4uhGB9S13GjM701KUR2LbD1547ypWh/FO5ceA/PnGS//uTG2gOTVU609nE4VaaTDf9o3DikIgg0VABKSGdSB8jP/5DzOrPA+BTDmZqit4HebmdTHhOHZjY/+eFnfs0TjxFcuEagm1ry2YfEKhIL1x6tzidJ4u+08Rf+W9U3f3LOXWalWky+8+XZA7n5iV8tW0TZAqVJPL2X8PpHLMCFw+QvHiAgZ0/Te3tj+W1lb++HWvXZ2/93kK6vAKuz5nk82ky5zGrcDMa578+e4yu6FhnuS8Oz3X28lxnLw+saeCxbUvd/M4VgJdPXeN6tliLPNh/uo+7Olpoq54YtlIZrfKGxbVhPn/HqtRf452n6cNstNVMQwA/ef9CQeceAGeIFvsSrz1xlkfvWoG1YNNonPFHAWP6k1II7bgOvnbcf2jKnRov+v4PMOl3tdUqpbP7sluI+0zDU13OSD9cOeTJhs7RZ6BtbVHytWOT7L8CySjSCiEbFqd+hMnZKnnzoiedJ40Lb+Ns+zRGuC6HTumXxsm3bca4uhXC5wXasbPKjLzz3dzOfQaG3/lbtOVHLL+rMuxQ0TzVp2b5g3DewZ8T0OM+x/K+aIzf++ExCu1b98KpXiLxJF+4YxVi1MnPLrM4Lko+X2nNi8dK2+X15WNXM5zitMzZlfqxvHzeVsVwrRVPv5vf4alOXuRjw6+wMXYQHw70Qu9fg6xqJLjmU4R3PYYMNlREe6aTa2A0TaYU6EQcoZIorXBT0ZU3NZ6KDsP5t/EBDbEbdPsWTkiNmY8vaar2VG/01AE8o/skyZF+jHBjTvnJaD/Jw8/CqRe4FbIJ4IN1ewls3APhppJt5ZRh5X305KuEbvvU3EmT6c+9ViwfRLB2QurUeP8l+PClomVEDnyLQNtWZKCm/HaoYD6fJnMeFQQx7lOP4d86cKagc5/GmxeGWLekjx3tjRPkeOPjdfN2/rneQYadIpUe34bzg3z+jvEyBZNrz0eJz9uqGH7iSj9DeQbj1o68yWMDf52y6Nj1MWr4JpFD/4Po2adp+PR/wKxbUfb2TC93H5ogUiP4GqGSSCeZM2RAOTaxpAJhE7QEhphYZqp47OLBUT03JT9gf2BBwbAcl2tMqbl9eYO3EJ2B0lLjqv5rWKH6rDITN06TfP4PcpyZgBNPEDv5EtbDX8GsX1SSrUzLz+R37fCIrk5y7epbSoiOTkZJXHwf++Qr0HMRUFBdB8t2E1qzG+kPT2v4h3/5DuLHn/RoBEmgdQ1inB2co897Nmes81WqNj9c9jCYSubzITrzmBXoikTp7C7WvXfx8pErKQe/fOgfKbTMNDcUoJTOWCTqQmQvPo8smLdVYXQPR3N+tyR2hM8MfBsAlVqQmQ2q/yq9f/cbNH3+G8hAwzRoWRkY234NKh1ukHEsNT3eMxDlzXO9nLzSR9JROEoS9gm2LK5n54pmqsPmmPJTwdXIwKh26/iQ9+yd9MkG0i+7YrQV47lg9cJqGmr8KC/1OiWOXqjs4UzJ3sskcjr3GdAjJJ/8fczH/i1GVb1nW1nNy7EJQNFDRlMAJ5ZHp9RLI8W1Idl7mfhz/wXsgbF1DEbh8PcYOfw95PbPEdhw/5T1rfHcqmsjXr8c+s4Vb4M19yMMa4wcrTWcK2Em6OwbsHnflLdrbvFUn5rlD8LZnQNoHinojM+x/PVO72Eu5/qTdA2n87JPlFkcL+WcW3y8c+4VQkyUqSfVno8Wn7dVYZ7z5qk1+wZ/5C4eLeJ6UAPXiLz5d2Vvz8z0J+VuIGNH0U7SDTkQJkoYONLileM3+NOXzvDW2UGiSXCUJK4MhhOaA2d6+U/Pnubtc72joQrpcyfLEbf20rCAR6Pfw0iOYCuB47ihOLYSo/qkeaPl8PCOZZ7rJVxamAbhuqwy4+98z4OQJNEPflSSrbQvBGvvL033UmGGcuqUGaJTqA3x/mvEn/rdic79OKhD32bk8AtT1reycWP3zxfffqMa3/afmiDHKTGWn8jUXz9zjc+VEJ15B39OQGR8juXXB3KPMubDjeH4GDlaa2xH561rLC+2XHbeXpdOJ+gdjQEQqXy9k9Fhns/zfLy9topsaE2cYoHTNa58flnRUz9y82NXSNumg2uEG54Tj2A4EaQ9grTjSG0jteKNIxd5/XQXhlT4pJvBZjy3hMMLR67wwbnuVEiMe+5kudXYTiaaiPMLiT9nXfwElrQn6BNihO0j7/DFjZKwoT3X61uxC8/wN+CrWzRBpuq/At0nvck6/ybEh0uyVWjD/WBUe9e/RIjFW3LqJJSNdOIF9RZ2nORLf1J8pR98B6f3wpT0rWzcX78Y6/4vF9bDX0/g0f8T0wxOkGOIEt034Zu2ds0VPiZEZxZjPkRnjkNpXbhQjvNsR3Hoai/7j13jQv+t0YL1CwLct7Gddc21TFfCnQXhIMvrLM71ex+luHf9wqzHp0nVOYl5WxXG8oU1tFQZ3Bi3WGRDzGPOb0BFekneOIyvfftUqVdRGNuftPvg1AqkCRoGh+O80tmFIQWpV4HUWdn580evsXpRPVWme3yy0/K+tnUkrVpI3hrdrUWzz36BoeEX+JB1REQVEk2V7mctp/Eb1QSX/jKjMxZewjSaVpAIL4TI9eKNuG5v6n47VmbirLdsPGnELx3Gv/ouz7aSgTCBh/8ZsZ/8e5iBiPzgqjvz6JT5Mplb78TVkzBSZErSFOInX8S684uT7lu5uL+1A/Oxf0/05H7ofJExtvTVw7r7Ca25G+kLZA3/EtIAs7bgjMQENLZPeVtmH5fuPxRgTOSG6Zaf5Q/CeQd/TkCP+7zFG6v80OU9XlKi+Z0fvk9/llD4410xjr94hhX1Fl/as46QL7MbZeogsupULL9/Uxv//dULnvQWwO7lLVlkzmeGKZ7P26oYLgQ8ctsS/vzlc2QiqKIZ5XTGv/xyVSResMxs5RoQaDeDjmGgtICMjCBvnr3uKWuNo+CDC4PcucpNBjDZzBlSAxs+Du//DeNRDWznxNhm4ZbXVgCV2vzHU71CI+/5e6infndCfVlRswT/pgdRWWQSGypOxjg40ZGS7Sbr2rF+9v8j+fKfwY2jJdVfFDZ8CgI1qBx6FJtFxznhcedYgLMHcG7/AtqavmwtorqZwJ2fR2z/FPbAdRxlY5oWRl0r2gyAnczZdtCwfi8c/q6nZsm1eyZk46l8LkAKlzvp39osjQtQvip0KixPGybacde2pLkK1qD9NRCd3R7+fIjOnEDuqfE717R4ltYQgL945XxW5z4TZ/uSfO25oyRsnUWHyU/pb2tv4r5V3mJVf/3BVQQsY8p0mOfzPBfXCO7fuIQNLUEy4Qh/lvKF5Qqfv2LaNl32QmmEYyPF2LCW41f6cobl5OJHL/ZM6bR8eP190LaFotC6ifDG+ydVb6BxSXFhGvUrCH38n2JonVUORmlxwtIwJqW/ZYWoevAf43/4X0Hz+pJ0yItV9xDc9nD+UIoiQ3QYulqSCjrSOyNhIYYU+OvbCDQtw1e7EENTXB/q2O2tQVYNwcWbKyIMpmjuJJGGgZAW0gggzBDCCiONYIm8GmFVo/0N6GAzBBrQwaYxXIWbwBdi2kIUZgjzI/hzHEvrqmirMrjqIeekz5D06uLyHV8ZUrx48gr7Ni4uVcW8+OyO5YT8Bk8e68lbLizh1x5czcqGGiYOtbmY3ZfqzGLeVoUhACkEf/+eDr7z9lnevDAIwEVfO9u9Ln2RBuaCpVOuY6VAgJv1Y0xMqwYhSNiaaNJGkF43UzhEBzRD0cQYOaOjmiVyIQTB+75E9N2/hVMv5m7MqvsI7/xphBDoqQjT+MzvE+18BY6/CHrkVj0NKxFr7yO0dDtCypxZemTTUkrJTm82LZkSu1kNizD2fQXiEey+yzhOEtMXQvjDxJ76A0j0eVOsZhHGhr0Elu9CCwla5dEj82Uyj66l5u/XU9O3povLQDXWvf+E5P4/Lqo5TZ/5DWLSmEE9JUhB1jCYYrmw0NJCSx/K8CNM123VAkTqUe+NC7ThB9NH+l6CzOhDUrgZi2a5cw/zDv4cgR73OZZ/8Z5V/McnO4uStKTG4OKgt4Ulzx/t4qH17anMN5k6jP/bOxcCHtm0hHs7Wnn11HVeP9VNX+q5bgDLG3w8sKmdDQvrUjvw5rLFfNhJ8XzeVl64aUh+/s5VPLgxyisnr3Hk7N3EB3+EX6fS+43+yy0rsPhejFBj3jKzmWutIRlFOCNonRgbopNKheklREcgQFtTvsGRtnyEdpN1sVsAACAASURBVH0OZ/PDxE++AVcOQzICRggWbcC/7m5E1QK0nURPUb0iVEdg5+OIrZ9ARfupqvZjGkGGEhJl+grWZa3YSfzV/wl4eKsML8RYsGJKwyikL4zZsgZp+pCpsCXf479L4v2n4fhzTEit2boDc+MeVCKKig4jJJgNSzAWrEQWad9iQ3QINcFIF54RrqugMJXs3Fq8BXX/V3Be/JOJNk7D30jTp/8ZVssaRm72FyFfTEFIjA+QaCnRvjDacV/UsoXE5OcWQphoYYAviFap+4uU6PSLm2eenvXK7S/MBcw7+HMCYtynHsMX11Txzz6+hq89c4p8rvva5gArF1Rz8Zi3xUgx4Hj3ABtb6jLqHa+bnhQP+3zs27gkNVNQihzhsfxHmc/bqhiuRzmAYGF1iJ/ZuYKf2SkY2v9zRA59k1v2zGNTIQjd9Xj+MrOdKxuSUWRiGJ2IIHXS/c5OYApFQ9jkZsQuYmOpW7y1zsTLBkdeuPRXY2zdh9yyFxAoIUfLqOnaYAeNEawjUF2FUAoZHynuXAfE5n3ow9+nWBib9mFoPX1tSW+MJS3MzXtRHXfhDHWjknGkaWHVtUGw9pZNS7RvsRtdyY67UT3Hi7YPAG1bMA3ftNtoKniwtQPnC18leeEQ9odvwHA3GBKqWzA67iOwsINAXQidCmfK35+SSMN1qKURcFNOC5BCIoQqnksLpAWGH6wgOh0RLkWGk14sB+St813osY66V17wGTj7Me/gf0Swor6a3/vMZt44183+49fojd/6LjMrznfePlOS/JvDcfAe7j+jmBuXrIuekRiX+kdIOooav8nqprpUBpKpwVyy1XQh7Y5nQ9Xdv4zddZbo5VcLSqnZ81v4WjZPsXaVBQHuKL52GLEdunqGSSYUps9kYZ2frUsbefH4jXTJVP/Lz3cty5jxqIBwiSnjCPdvD+eGNu4l0nseLr9X+MdYfT+BlbfPXLsAISRW83Iw/UjlZovxtDFYTp7pjOUu71+6leiBEGNCoArAt/7+mbPRFHAhTQJLb4PlO1EYqX04FAoDgU7tyCpcp3tGQmIEwvC7I+/S5NaSTz0hJMYTn0fRmHfw5wT0uM/sPOQzeaCjlQc6WknYiqRSBE1jzKZSJTt2OpsO412gcvK5EXZy/EYfzx2+zIc3x66AtoA9axt5YF07YX+urEYfLVuVkwvDoO4z/w9y/zcZPvE9SAxNKGc0LKb6ri8TWLWrYvSeLq6RdPUP8czBC7x/5jqGSiCAuDKosjRr2mtxhIlWNsWE6FQFfCxf2oxy3B1xKyVcYmq4D+kkPGU6kRqC9/0Dou/8EDqfIic2P45/2yfQztSFGDmxXuInXofO/bdSNoZbYM39+Nbfg5QWijhSGjDF9io2REdbPow9v4bz4h/ltk0mlt2NuXB93gw2U8f9IEoNgyk+JEaFw2hp4sQHxhyfyKcyJEanRt4ryRcohmf+PXsx7+DPcmil0I6NUgplpwJwpICM6a1s3JQCEw3KRnHreGO1iZHaIc/diF0X5AjBgmqfq4PSOErjXtCK/NNgM8eV0gg0Km2LMutTCn/6yEWePHGTbEgCz568yeunbvLPH11HUzBQcl1zwVYzwZVyX4SUytHPhSR8zy/ju/2nSZ54HefKYbQddWOuO+7Ft2SLu3gy1/lzhms6z17gGz98HcOOIh0bn7BT35vgOJy6eAMfJhJn9LiVgwcNxc/tWIxlx8BOgDBAO3OHJ0fQTtL928O5QhgEbvskbNhD7MxbcPUkOHHwBaFtM8Hl2xGWH2VHp0znkaPPwwdZdtGN3ID3vk3ivW8jdv99rKZlkEjFh0+hvXRCgGlDcqRgeX/zcmJ3/T3U6382Ud9MrLgL/x2fK0Kmcv8B2OrWQl5PXIDWaCOQumwk2tCpYXCPXFgo6UMbfjfFppkR0qI0TrAKISSO3xhzPDvHdcyVqxOQ+t7wyNMyZtc9TintzjjOcsw7+LMYWim0HUcno+h4fHQnzNJXl8PuFh8vOu5oYzqBQD6ukAR9FitrAq5z72i0kDhO5gVdfjhuWGDJyRTKjf3nrud07jMRUfDVp07wW49uImiVlj5vtttqpuAoUg5+njKORljV+Dc9hLF1H5kPEo2gyGRVsxrXhgb50x+9S50z7I7cKxtLOmgkhkpiCoVG4jhJLEtjSIOYfet4Zpkav+bTOxbTXCVxYhFE6iGshJgz3I6CoTQ6EStJjoEguPIOnFV3YqSOO0KAVuhEdMr0jJ18GY4/UfD31wf+G4ltP4tv8SZQzpTay0lKVDKGTiaKKu9v6SDx8G/jnH0DTh0AHbmlaNtWzI67seqX4sSjow5eVplCoLQfDDcTi1KCtCPsiQsDBx+IEEhjUs/uMSExZITEpAbcHGG5Azdpt290IC4HV8AcGcn2CqUFSjPrnfx5B38WQ6CQKonhxFBOFOm4NzmdcUPyysOm4PZWgw+uDKK0SG3mpnNyG5MH1zRikURjgADHSWIY0p2ZKzCTMH2cW4tycNcbibGHZg2SjuL7B68VXX7IhgNnr7N3XXtJ9c1mW80kDOk+/vLbSeAohWGIj6w9nzh0AQBHSySChAighDsin5QmVgZ3tMPSxio2LmrgyKWbDEYS2BgsqDbZuriBJW31WKkFutKwEE5iznEzWI1wbERcV4Q+2bg92FOUcz+K9/4asXw7IhCeVN3YCbr7Yxy/Nkw0nqAq6KOtuZr2Wj8Bszg5VrAaf/2n0Ts+hXIchB1H+IJoabkLTYVGYiLycWkgzCq09Lvx5YZMhcTgkQuEPwTS3TtDZDzHSuMgctxo5u/rxUMKjRQgZnmqzHkHf1ZDgpNE6ASoGMKJA6kYulRsail83/omrnf1cWP4VlYLW8msfGVzDXctq0Ym+tEihEgmEckE0vIhlA/huGFD2jBmkJtuDKFyYwhRys0cIIxUCACp6ceZ5Cp1Z828u4qi+MEL3tO7vXS8m73rF6VShxZfl6uuAETG2oziz/0ocSlFysHP95uqVNlC5eYm74/GOHl9hKDSKC1Jaos4BhqJgKz8dFecT+xoYv3SBYBGZaRdVKYPUhzTRzp2eU5xK4SQCTCdytAnC0+cfg2viF86SmDDAyXUZ4EUXO6J8vQH17gxGMNWEksqNHHsDyOELc3OZY3ctWUxPiMVnmiY4IzjAsjYxVQYZuq54ZbRRXOB8IdBuiP4In2PhxJ4emPGFDJvE5Pho5i/r3vlUopZ79xDmR38jo6OB4HfAjbjZkA7CPyHzs7OZzzIWAP8W+BuoBE4DXwD+HpnZ+fcngDXDkI7CCeJtBPgxAAByr6V8qoEXmU6/NLHlvA3b1/kRt8wABKJmdpOJc3XLapj37Y2rOQQSIlKDoGTQCYS7q6chjV6v1Ewc1waCOlzswDYUYQwIJa6Kcfd9kxuKtQrT02dKiO1uUZGNoHRm0tu/sH5wqE54zHswLWhEdprqjzV5XLhsXxurnVqlEpMncxK4ePTZOYvX2y5ucQVRy/cwKfj+LAxhIOUqqgUmEcv93DHygZXVgWlI5wJLp24myZTq4rQZzwXyoZT+/GM068jOz7mrb5UysYTV4f51jtXEVIirCBKSxIitVZIS4aF5uWLEc7HLvELd6zCNET29I3SQFhVaMMNiSktZWOaZw7Y6GlM2ThVvPh78KXBEQ6cvE7XYBSEoLk6wJ1rFrC0rnqGdS4XH3/fnp0om4Pf0dHxS8CfA3HgRdyVGXuApzs6Ov5BZ2fnN4qQsQV4BagBXgfeScn4L8AdwM9Pi/IVCX3rcwrSblX5DX7xnlVc7hnk0LmbnOseQSvwmZIVLbXcsbyBxtoAShqg7NQItZniCdAmkPobbn03ExwQuAvUpNAIBTKuQEhkYhiEu05AKF0StyM3iXW+BpEuEAZUtxJcsxsZqs9+rrRc3YwAOvOhUGS4UTwew1BJil30nOaReCLDPuPlk3eudjK3t75onNdP3eDVUz1EUpMYtRbc09HM7jWtVPusSUivHKQfl/PIAaVIJuKYOoGh7dF+KUb/z50CMxFPX8tTcz+bVRyB1zSZM8l1YgSK3DtXZ/xL9l3nvZPdBPwm61qqCIUtitnFtCfi8JcHb5CUgVGnONeasBPd8ONjN3lsx/KSdzH1xGcZCt3Xr0eifPPlTi4POWOOd3bHeO1sP+3VBr947xraqoLTp+Q8pgxlcfA7OjpagT8FBoC7Ozs7j6aO7wSeB77W0dHxRGdn55U8MgTwF7jO/Rc7Ozu/lTrenJLxhY6Oju93dnZmWeI/RyDcKW0tZGrHualNGadNH4ubalncVDNhmlzayewpxBx72lKiFc9NsG2QCuUIpGNDUqOEibYjo+Vlhr2K4br/OvEDf4m6cXjCTxF595vIRXcS2P15ZCA87lwfUkg3K4MOIVJxmMWGHtUySFVyBKUFhkhlbimCV9sDiGgii0xzTBo0MkeklAIn4X6XyqZExnRyIb7/w2t87+C1UWdOpl44huPwxOFrPHn4Kl/cvYTblrXkkDN+YbCeQ3z8K8Ec51JgYiOVjXYUjgZBcTvWSl/h1Idzl3tPkzn1PE/qRsNXlHuvcd11J/U5gORHZ5JIkYATI6xYUMUjW9poqw3nTdn4/LHzjBg1JIVv1JPPN8DxwkWbvTuCBAP+Encxnas8f/rjK0MR/sMTJ8d8Ox5Xhhz+35+c4Dcf7mBxbXiG9Z9Jns8KswflGsH/dcAP/F7auQfo7Ox8p6Oj4/eB3wV+Dfg3eWTsxQ3teTnt3KdkdHd0dPwj4DXg/wDmroOvHQQKoZXrTE7Tro5eOCrpTnA5DogyTyk7ApTj7niYlEhpIEsMY7L7LpP48e8B0exhjgCX3yD2o06Cj/4mMlg/To4eDWPyGnq0rtrmxtV+tNJI6T7ECnFLSprEIEZ0eKLMbCFMcGuUq8RwpjdOX+fp964S0tlH19L8e68ew5cYYsvipnFyBNoKgOnmXlaxXtBJhFWL9AWpnOlbl8+H6BTgSrFiQRgLG1PYmKL4XWqX1PunbZfaSudlD9ERbt5yYYZAMCHUhZAPRwTQOpX2Mgc0rnOfRKKQXDUXEfE1jDrjH/QJDr/cw6/eW8Pm9oasYTAx2+GVKwpthNCi+NWhr57r5aH17WUOiSkvjyUVvfE4aE2d3089Imd523H4z0/nd+4z8bWnOvm9n96GZciKae/U8vH37dmJcjn4+1KfP8jy3fdxHfyHye/g55TR2dn5ekdHRxdwd0dHR3VnZ+fQ+DJzD/rWZzmnckd1oLx6TOCZF623c7Vjk3jqD4Bo4Z8h1kv0xT8h+OhvjavLGRvGBEXzrUtqONB5lYQSqd0JNY6Sefn2pXX4TZExCp8/hCkzrKiUcKbhqM0zB08TIHfGpUz+gzdO0tG4iZAlb8mRkmS0j/ixl4icfAI1fH30d/O33UVg12fwLd6OMI0ZzMiU3qRlItKPy3nkgmZRdZDWGovum5C2mGvN3Lwh7KO9qcq9Ziri3jHDHEHpITqSgmEvhbiUaOlDm0GQ5sSXehOsdfsYOZ7t8Z3567tSFRIHg4PhXSg50eX4xquX+M1HQizOXC+UCoO5ODiMEt7T/Z65MQjrS8siNttxtm+Il45e4b0rkTHHty+p4eHtS2gN+Cecc/DyzdFwymIQA9651MPuZQsmqe08phMz7uCnQmvW4177J7IUOZX6bkNHR4fo7OzM9QzdkPo8muP7TmBBqq63Ste4gjHNITol8YoI0Sl9x8NsPHruLUZ3aCwGfeeJd50hsGDVlOgfCsCGRY28eXagYGhDXBlUGZo7VjZ7C2EiIySphHCmtzu7sJLuNvC5Mi6N50fPXGLnyqZROfHLh4m/+Ec46Ak3Jn11P9Ef7EctvI3Ao7+JNHzu8WnNyCTQGZkyxockaSe9m7Aid9iSzWheaWkWHeqUneuMF47M22Ilcu2uxbGj3Le6gW93DSKFKNh3fTjcuboFbfpQ6ZDACrqPTD0XIEXq3pm6N1kBpGOjDH+RO5imuACVkSEmd9hLIT6x348PdTF3PY59/MfjfveJ0Knx+mER5lRwR85yz7x/ib93z9oJ/Slhl5YnI55Me6uVcj1MP9da88ThCzydY7+UQxcHOXTxKHcureFzt69MZdNxz91/vPg0zGm8fPQKu5c1T5n+lcXz9+vZgnKM4Nfjhud0d3Z2JsZ/2dnZaXd0dPTgOufVwGAOOa2pz1w9M328ZRK6VjbmQ3SK5kLZSHRJ0/668yXPP406+jRyz5dK0tkZ6sEe7gKlEFVNyFAtezcvpGckztUedzIqV2hDQAo+s3MxVSEL5SGEyfUO7JLDmY6cv44P93LOlnEpGz924Qa3L6sBZWNfP4nz4lcxyJHpLQV9/V3iP/xtwvu+jBDG9GZkEgaOHQUz+yY0RJOurx2L5A5hchxEMoGwfGAbZcvENONcOYh4BMOJsL4lwCe2LeDFw5eA/CE6d69qYNOSWnQFhMpMO09liNHCQBoBhNCuP20FwVSp7JBZMsHk4jOYIcasX01491cYOvC1gv3BweDv6j4HIvfi+vevjjAYT1Lj9405P+wvzUWpDqbrqpDrYQb4M8cu5XTuM/HGhUFM8xw/u3PF6LkXB+z8J2XB1eH0Zpblb/vU8/TfsxvlcPDTKzNG8pRJx0JUkdvBLyQnU8aUw+czaW6uLlxwGqGScQAaGqpIjCh00gCt0dJAaFUWrpMGKi6QvgDCFyybHtk4COrqqjydi5NkuP+C9x/nxhlPdSWGexg59RbDp9+FnjNjRMVbOqjf+hBfeug2nj10nndO9+Ak0jeiW6ENrQ0hHt+1jEULambUVrbjMByzMeRYfQrxoWiSutoQSkiufv8vRr8teGvtOYF1/SBVa+6e5j4D2mem7pJu2BKo1DNAQnIINDRWjTuewZVto2yFNG2kaeQsV5AbFtoAbVhgBUC4QVlCSNCqvFwaiLQ+6f+dJCISRUYC4Jg8tG0JDdUWzx26TG/EntAnqgImD25ZzB1rFrjfVMD9oiAXMrVJICDcNRmeuHQXrGrTjzYCINyNAhUCtKZmYbVrJ61TF0UhLsAKghUCIW79FtP0f8Mnfpm+miDXnv1PY8L/MhETAb5d9wtcDWzI+n0mrseTLF3UOKaOusYw4Rc+JOLR/7xr0yIamquntf2V8P+N3mGOXuujbyjGT452F22fV8/08+gOzZKWGrQ3045BXVNVKpqqEqwxdf+n0dxcPW7/ktmFcjj46Tm3fP1KjPssRU4xMmY3UiE6SIGSFgh3RDGdhaEcHGWjhHQXcZZBD60VSpoYyp6a9iTyLyTLCWfYXRSWR75jWEQ7X2Xg4DPQfSq3rBud9D3TCctv55F9/5AHti7jgwt9XOnqJ+FoqsJBti+ro62xynUWZtrmgqIyo4znQS3R0iBy5ThEejyZt/+95wmvvXf624hGJkZcLkxkul8VybFj6GgEAlUorTyfP8ody33QiBG0HUZoGyEEGhOhk2XkEm1VoU03w4kWBun1Jlq6DjCp8LjbVrWybUULH14f5tSFbiIJB5/fz9qFYToWNyB9ATeDE+W4hyXdRfBmOuwMlGHm4BZSGO6AhhVG4Lgvg9JEaNsDtxCGgRamu7kV7gsTwkBopzQuzVRIvs5wWJg2Xn/Pz1G18R4G3voxfcd+RLL/GgrBDbmAg6HbeT94O8hQUdf0SMwNycqsQ0jBx7e183fv5EyoNwF+AbtWLJiR9peLH7/Uw3cPnKazq8TnE/D0Bxf4tYc2ARAQEPPo6RswuvyjEmwytdyjMSoU5XDwh1Of+RKpBlKfkTxlCskpRkbJSCRsBgaKWHQ5jWis8yFQ9PYMkYwOgx0FBErI0WngmebYMXQijvBpMGMzUreKRxg5dQBOvQyxXtc4Mgwdu/F33I8VrqOuNoREM9A35Em+SO9G6xVmDQN9gznla+UQefV/wOVDxcs89xbXntRU3f0rdCwIsa7ZP0bmYP8wSoxM2p6l2Ko5LInE3QW9xWRJ8QENYZOBviGGDz7n3b43z3Lz4llkTcs097FIzjINNe4umQOD8SKuhwSYvknqpF0nFFGeDeSycWHgmKGJYUzKBieCER8G7VBbX4VQiuGBEVpCBq1rm8e0MTIYQYloee5b0kBKN1RGmzK16By0UNm5xI1FN3xoy8TtzaQWZ1seedqQI65woL65Co1BX/cQaZfDRTE85rH8VPAa5G2fp/G2L6C15m/fOcMrZz2sWUohmbDp6x6eUMftixt55tCVoheBfnrXIgZ7R6aobZXHX/7wKt896D1mfjxe7uzl8W2uG7V9aQ0HzucKlsiOnUtrsv5ec4E3ttQC0N09VPYR/NraID5faa56OTQfxHXOmzo6OiZonTrWBMQ6Ozv788i5mvpcmOP7QjH6cwz61ueYbDaFuVauk6ySsYz3Vu9ysmbRGfeddmyipw8w/MZfMvzKNxh5838Rv3rc3fG0hPpiF95j5Lv/Ag7/3S3nHkBF4MRzxH/wL4kcec6NOsG7fCGAhRvxjGXb88qPvP2/vDn3aVx4m0T32Un+NoW4wKutti2pHz2n2P+3LG5w5UT6PJkgDRXtn2Y7FOAiZadJXA/euEqFQmhQSfdfubmTQKgE0h5BJoeQyQgyMYx0RhCOzWgmnFE7leF3GsPdLDEIA6SV4pabNcYIoowAyleNsqpRVjg7N4NoK+xumCTNW/+QJXCDW9eaCzHhyOyBEIKORfUlndvRXJP1eNhn8ZVH1hMswig/tWkBdy+fu8vuPrjWOyXOfRoJ231rum9Dm+dz7y3hnHnMLGZ8BL+zs1N3dHQcB3YBa4Dj44p04L54HCkg6ijwCG6WnJfHCHAz9azFTcM7Xv7cwSSy6DjSInG9E/v483Dl/QyZVbBxL4G1d0G4qaCcYrPoONJk5NCP4dhPuBVd5SJ5ej9Jqxbu+AKhpduKrm/k0hF4reCGx+jDf0dfwEftnZ8tKYuOse5BnOu5kjVlh7lxb866kr0X4cwbnuRlInHyRWTbeu+/TZG8lIxD21a38uqH3SiliwrREYZFx5JGd5S7xHEGJa2yZkC5FYJVuVmlBoainL05RNxOELZMVrY3EjKneAMpNNIeGT0+mmVJmq4jjR4NiSnr5k1mADDc0CFfGO0owN1MKXPjt8yFptk3SpJumWnKaOS+DumM76ZW/nTzTa31BMV5oplfFcDm1hA1fotcbV4YDvDbj23iuWOXefnDvgkbba1u9LF38yLWt9RNeXsqif/g7XNMJdLrptqqQ9y5rIY3ihzF37m4isU1IXL9XrOfe+i8FYxy5cF/GtfB/xQTHfBPpT6fLELGb6TKf33cd7uBZmD/nM6Brx13RM9OoJMREhfexxnsBi2QVTX42ze5ZeyE+6BNca0gfuBP4UqWEWQ9DEe+T+zI9xH3/TrBllVjzi3IExH373Tsup1AI4i/lKO+NJID8OrXGRl8nNC6+wrWpx0bXv7PRZtq5O3vEFy1BZyQt/bYCXwLVhCtXwV9p4urbOlOzHCDu3NtFpnJ488XrXdWXHzX3S7+f7P33vFxXeed9/fcMg29EwRJEGwAwSpSjRKrClUsyV124jhxNvGunH0/6/TEu3GczWaz3rxxym7iN3HWie1NHNtxkSLF6lQlJUoUi1jBDjaAIHqZfu95/5gZcABMuXcKZgbC7/Mh8ZuZc895nueeO/Pcc5/zPEbIti5WuAwK0MJJ5U/EXZrKh9bW89z7VwENndjK7UwuhOSjty7GLQMQVqGmGQa6bJtB81RCyJdT3e1wGYrEUhMK2boeZkO+Cz1DvHWmh54BLyFTQ1ci58B47xKdLWXsXNVIeXV57mVQ1cQ85EUaUTvN8nnCCEdWyqXA1MuQqgupuUCL3lhOhsoo0bWH6PumJBJpnIjH2sYyiRCd49lzaZpIBKYps+qnkPyRzQv5lwOxh+zpsXv9wqi+U/sypcnZgTH6xvwIKVmzpJYPrVvM6YERxvxh6mrLWF5bgRKKuPymmfvzUSz83NA4N3IYFVwe2dM9Oc8ev3UZ/uBpDl1Llf8E1i9w8zO3L0t4vuYKN02JlKXv5BfKwf8HIs7577S3tz/f1dX1HkB7e/ut0fd9xDnt7e3tywEd6Onq6ooF970GHAfub29v/3xXV9ffRds2xB37tdlQplCQpokZ9DJ24CcYh54mPqGQCfhQYdlWnGvuQ9FdmEKAaeDf+x3oO56+/1f/N4Ht/wFH/QpMIRDRCZ+KE/JDwBfZLmYaCCnxnXwptXMfjyM/JFBRj9q0IuV4vrP2V8AH3/0prnWPpdUhEde2/gLh178BI5dTD9LQgeuWj2H6J5Lb6MI7tmWfDmN8CEVzZKRLOm6ElEi4Viho69i1zR7UcD0vHu+NbvpTUM0QmjAnuUsXPLCuhdYadbJ/ZeltmGdftWeApjUIoWOksHO+uak5kQqY/oCt6yHf8r17vp83T/ahqSaOaefAMEJcvDrBd3oH+cjmJbTUembFXmEfqKZEBv2zfJ4UhKohUTGkBsKFFA4iP33xjnzUyS+ClTsjujxtTl+mLiHcvbSJkTE/L3QNpm37C1uXsKiifIq+YWny6uke9hzrwzvtlFRosKuzkR0rF1LXWIlARmPB5zZOXkpvSzvYubphis0Fgp/fsorOa/28cbyHS6NTNz0sKhdsX9fCbQvrEaK052c6mFJgSkreyS+Ig9/V1XWxvb39N4C/Bt5qb29/mcgt1D1RmX6+q6urL+6Ql4FW4BeBb0X7MNvb2/9d9LNvtLe3/xKRuPydRHLt/11XV9fTs6NRYSDDQS7/0+9iXHgvSQsDzr9G4PopXLt/FcVdTfDCAUvO/WQPb30fPvZfUTQHwggCAkXVk/LIwpZEcZVF0vkF/XDyZVt6GadeRW/dkHq87gO2+gSQZ96ATZ9AiNQ6JOKauxz9/l/F1/UaHNsDctoPirsOOnbjXnUnQghI0o9QNCKRY9lBLauMpOqzKL8drupuHob6hQAAIABJREFUFE1H+Azbx3YuX8DS1gW8f6GfY5cHGfJJVGFQWaazbkkDnYsq8eiRkLLYsY6mZfjrVsLAGcv6a2vvQzicOdfdDldcDpASxaVZvh7yLdPp7hu8dmaIkOJGF5FV+5CizeSm5Hvv9fGFHUspK8+/HTV3BcIIIwJyls5TGBQFRXFGc8K7EO5q0DyIyZzuxQk19mChdLPzAfDIhlYaqt3823tXGU6Q5rKtSuWx29tYXlNB/I2VP2Tw1y+foHs08ffkWBj+9f0+jl8e4vcevx1NU0reVlbgDdrPVZ8Kd69oSmA3wR2L67ljcQP9Xj99Yz4QgoYyFw1lkaQOEZS245sOipAogshveQmjUCv4dHV1fb29vf0SkRX7bUAAeBP4711dXZY8wq6urnfa29vvAP4Q2AWsBc4AXwL+T14ELyJc+ef/TDCpcx+Hiev49/wNzo98BbPrdXuDhIYJ9l1Ab90I0ZhsNEdyLgFDRpx73Y3/wruAzS+m/lMY/jEUd03y8Uas5/yNh5QgdFdqHZJwITQ8Gx7B2PRRjKvHMMYGkKqOVlmHXt+G1JyIaPXNZP2I6Kp7VnDUYAQmEIqGcFWA5rStSyouHG6k6gAtmFE/LpeDO1e7uXP1IkzNgRKrSBrHpx/r3PF5Aj/5w5k3Tomw5iM4mtfmTN9MudCj2VCCmuXrIZ8ymaqDl08PE8JBgMj+HAHJeVjl1fOjfGjT4vzbS/cglCBoxiyM5SYWa4+jHIQOQkVxeEi830MUFRdK5PxEKo0WXp5s+Ja2Jm5fUs+ZwTEu9I4SNiRup8a6RbU0VbhnHCOl5JtvnE7q3Mfj3FCIv37uKL/22C1RW82ubrPN3RlmUkmEJ3a2UeGK/y2aOW5juZvG8mRJCovDJvniiiJK3rmHAjr4AF1dXc8Az1hotzTFZyeAT+RQrJJA6PoZxg6mNd1NjHQTOPmarVXSGIyzr+Ns6cyokq28cd72eABG7xmcSzclH28yXtYeItVszawrUOpNK6Bp5c10e1Kmrxwbs1HNUhi6mJFdAAgOEf7x70Zum8oaoH03npW3o2jOglf9zZi7K1A+/J/x7fn/YDR5GJTY+Enca++fXdmScGEqgESxcT3kU6YrPf2M+fyoivVUpV3XhrhnbRNuyKtsihFAmGb21146LhRAjTj2auSmSqLGLYfHfrRlEXOIlAATRSJPdlwIhRU1Vayqq5p20zLzmLODY3T1B7CKA92jXOwZoUqLq7RbBDrng69oqeZFCyFP6VCpw7OHr9C9cIytq5qpck2tHjzP4+doaaOgDv48Mof33e/ZP+jUnswGmxhhsqIFMjUnxom8Nqx/WcdDhoNxfUXGMCYG8Z15C8Z7SVY5MTV0hO5Mr0O++eJN2Tn48Zi4AQf/Ce+xn+J64NdQqptzIOe0H+FZso9aVoP7sS9j9J0m2PUa9F0E0wBPJbTejmfFHShOTyS2vVDnLp7HXtu5HvIo04XrY8ScwsjI6XnIkFwb8LF8oTO/9iJqr7ydDyXyT1EihaNU1800lpOr9pJSwNxwLTLDq8etb8yN4bkj3Xxq87I8SFNc6GyopkqHkZC941SmBoWOhmB0OET3cD/Pnuhn2/JqPrG5LZpRZx5zCfMOfokieHav/YNSrIymhpJ5mky9IqMRpbtqMk1jKDhB6LVvQm+6zKmpoa9/ECk0ZDod8si9Fw5EcvbnGsEh/E9/Ff3xP0J3VGQlZyZpMnPFpeZAa2xHa1w1I7yHcAizgOeu2NNkjoUFYVNgp5qwA4NA2JwFe+UrTaYAVcF0VCCFDihIZxnEYu8nV8FjKH4euR2ScZ8Vj2zZ89Tn48g1+3Up958Z4lObi0G3/HIh4EObFvHd/VewiiWV6ozNstPxxrlhBsZO8sSu1SjxixI5kLl0efzr0sW8g1+iMAOzmDWgpjFlGEKqkARH63qCXS/YHtK1YCWKDGN4Rwj96x+DYa/KXiJUb9iJzyxcaIf/8vvw5t9mrUdyeAm9832cd38uKzkLEqJTgrzYQnTKVGkpLGc6d6pYvr6LKkTHCKGoGlJxIPQKpOqJbqiNr60QcZdLi8NcCtGZ+UwicbvJrIs2EUzR51zjd7U10T/i44VTA6TDugUejvamTnkZw4k+Py+fusb9qxcVXMfi4NPnbGniA7D3fG5CODyZHdiywfYhzpU7sF81EhACvWE5VCQrNpwEK7aj6C4k4NvzVzlx7iu3fhZHVUwOqzrkjkvTIPzGt7LWIy0u7scMTGQpc/wX3OzbqmS4iNrJxvWQT5mWNpRH5JGS5sAVdo09x8+OfZNfmvgrPjn2f7l9bB/lRiyMJ/K/ogia68ryb69JO+Wiz1g12mgFWsUZCcvRHNGQnNL+cZ477oU93Fw9tocPmq0e29jKz965mKok+RoWlCl8fnsr5/qsOfcxvHj0elxu+3nMBcyv4JcoHEtvw3/4KXsHVS1FW/sA4atHbB2j1i/NOERHkaDe+imMV/7S4oAazvUPRSq+9pyEEeuPI5Ni82eo2PhgwcJOTFUneH4/SJtVStwN4LOfLcjbfRhP+/aSDNEpJV5sITpLFjVQd/AU24d+zCKGpsyJSsZYwGFu8x/mPf9a9nm2E5AqaxaU43E6SidEZ0o12nKE0KMVaGMLHnLa39LjkdshyVzQZSYXKdstrdK4OGJvf9Wa5viKqrmUtXj5XUsb2NJaz8m+Ec72jOAPGXgcKp2La1lWW8GRa4N4bSah8JpwtHeQDQtrZ1WX4uTxr0sX8w5+icJz26dtO/hizX246tsYX/MhOP5vFo7Qce34ZVuP1ROFJLgXrmbizn+HfPvv04yn4Xzwt9DdNZjhIMaJl23pNwXuOlixFVf7NjRnWcHDTswMCnNl4twD4B3OKhSi0LYqFV5sITrKeD+fGPoBDpIX7xbAbRxD94Z4veJ+tq+sz23YTD5DdOZEhhwrHD6IITog2LGuhYtvdmMHD25uLZA+heVCCDqbqulsqpnR5tqg/b0MAD1D3qiDXxw6Fo5Pn7OliXkHv0ThWLQOT8dWvKfetHaApwHX0k0AlG14hAnNBUd+lLx9WQOOe7+IVlEHSCxntSDGmfKZe+UWjOpGAseehyuHpg0mYNVOnGseQPdU3ezr+jlruk2D+2e/jhqVxVS0aMad+IvWhj654v5YAeZZgKJmKXOBbVUqPPY67n1jfADvqVfg0iHwjoPugIalaJ33oi/oyKtNA3u/ndK5j51ZCWyki8WLb6OuyjU79iJqr4z6mTsZcqxgbrgWmWHTwjqe0rsZtpgpprlCZePSBob7M3No5ypMI7PrwTDmcHnaDyDmHfwSxpLP/S/O/PmnMa6fTt1QrcT5wK8jHR7McAiExHXLo7DyLvxde+Hsm+AbA02HmlbUdffiWNiB1FyR9th4lJ4iJEGtX45n5xcIhSYwes8ig37wVOFqaENoTkzNMXU8w2ZISxQGIKaFlxQ87GQWt7so1QuzCoUouK1KhMeH6EjDwPfOP8PZ16aejJAPrh0hfO0I4arluO7/FRRXRc5lMrz9lrNMxRzIuqtvYm7aMUv2yiRER8y5DDlWeOR2SMZ9VjyyZc9TnzNVgS8+2MlXnz5BugTLHgV+72O3IkRu5oE3GKbf58eUUONyRPPDZ9dnoXiZRycTlLljxxWPLoXh8a9LF/MOfglDdZez9N//DRd/8jWMY0lCblpvx735E6juypmFmJweytfvxtzw4OT7Nws3Yblwk92QBF0vw7lo7bTxZj66x1UFfvuFPVRFmRFeUuiwE+paYOSSbV3ABfhttHfjXrQGMUdDdKRpELx4EHOkB6SJKKvFvWQjisNdsBAdEQ7ge+MbcPlg6lMzcg7/T7+K50NfQqE8pzJ5u/bamCMxeS5iDl9Dq2oqvhCdOZshxwqHD2qIDkBDmYvf+/Bavr//PMeSZIFZv8DNp7asoLrSnbV8F4bHeeXYNQ5emZqZrq1GZ+eahdzSUosiSmvObV5Szw/f68EuNi9piLLi0aUwfPqcLU3MO/glDtXppnLrLxLY8CiBs3sxRvtAShRPLa5lt4G7EsUMA5JZCVsgxknZLjx8ncClI8jABKgaeuNytJa1cccLaN0MXS/aM0jzeoRQQU7XOf6inSVbxHHnqm0EztuMw19+d2Sj7bEnrR+zdjdiDoboSDPM+OFn4MQe4m94JODd/21YfjeuWz6O4nTPnmzR1/4z+9I79zF4b+B995/xbPv3uZVp1H6BIABjrA+tqjH/9iJqL8vHajMz5Ew6vHMbc8O1yA41bidP7FzNkM/PO+dv0D/mBwkNVW7uWNZIlUsnZqls7PX88Ss8fbQv4WcXhkJceLOb/Y3X+fyO1ehq6SQdrHDqbGop5+BV6+m0N7WUU+HU+SBcYx8UzDv4pQyhIlEioQLOcpwrtwNyaoGg2Q5hSJM1JNR/gdD+H8DQ2SmqhE5ASKmAWx7DvXo7UtVxdOwgaNPBVzvvK2jYiemfwH/+AIz3AxLKG3C034lWvxIql8Co9VV8ffV9KHVLCPR1Qd/J9Ac0rMa5+TFMw8xKl2IL0ZFhP75n/wJGUuzJOLcX/4X3cX74P6OW1c1aiI5EIt9/Nv25icfFdwnf+XNomid3MpmZxc4appmH4lOJuI0QHQGmszyyci+0OZUhxwqPmEAyN/WdfpOWmte4nTywZhGJEbGRjGtvR6aXu64lde7jcaLPzzffOMV/2NGRs3Cg2eAfvnUJ7189gZWcRCrw2OYlcccXXv7C8vjXpYt5B7+UIY3IBtJwEMJ+CHlBqJH3w8HC8OBE5HUwusoa91mg+zBGqkJP5hi890/4hi/ivOMzaK4Kgiu2wdk3rNmjoQNHfRsy5JshnwwK0MJ5s5EZ8uE/+CRcfHuGWMH3/pHg0jvQ7/wZQi/8ObHSLCmx8XH08gbMcBD3jn+P791/hovvJG/fejvu2z+DDAez1iXftrLLfW/8fWrnPgZzjMBzX8P9yJdnRTYZChPovwi+9E7CdARPvI62envuZPLUpB80AVR3ReT4fNsr5EUaIWtjqQ6kVDCFK7Jyb8JUx6NYHuPnh0vTRCKiOckLL0+23DQlhhl7z8zpGKYpEdEx7BzrDRn85FAvVnGs18fR3iHWNlXnVP588hqnk994aBV/9uxpUu1ZdgC/+uBKal3OOTPncjGvpCx9J3/ewS9hSNNEGiGkGUIGvBCKOI6mEIjo5JxtTsgPAR8mEkxj8rPgaB+m1Squ5/YScFbj7tiJa90j+MdHoPf91MdUteLc8nMQ8mOGZ8pnhBTMkB+ZBxuZ3mGCL/8VBIeTy3dxP6FrXbDjCXjvBzCe4sdl46dwL7sVGfRhCoEqJe5NnyTYfj/G2b3QexJ8XvCUwYJVqMu34iirASOI6Q9lrVc+bWWXGyM9cM1G3QZfP76zb+FYdmveZTM1J8G+y9Zli8dgd07tqy2+hfCZV+zJoFejltVPzrN82ivsA9WUyKA/fXtDYuohpFMB84MRlhOPWCKTDB/KFB0MQyKFEs3QktsAJMOM3jbYtNW+c9dtj/X6iR46G6ptH1dINJeV8QcfXstb567zWtcNxuI8fTdw79oGtixfQJmuzpn5lguYUmBKSt7Jn3fwSxhCURCqjlB0hNMNInL3qag6wggWhKOAiURxlYHumvzMPP2aPeVOPAtr7kNRVZz3PEHw7FvIU6/A6LTCV6466LgH5+rtCN84vrNvwfgNwISyBvQVdyCcHlTdjaLpCJ+RU52FUAm8+OepnfsYgsPw3g9xPfwlwgMXCJ96HYavghGObChedkfEsXd6Eo6luxfhrPko8DFMVUeJ2TbH5zJftsqE+w/ttzdvAM7vQ+nckX85nTqIDONyVQ3hcORMJkfjUsJVrTDSbV2GtQ+iOJ35tVGUa+4KhBFGBGT69oqOoutIRXwga63HQr2VOaO7wDBNVFXkXCc1ksjKdr/vnrNfY+TUjQBh08ShqbaPLSQqnBq7Oxfx+PYOBka8XB0Yxa1q1Hkc0zYPzyMGRUgUQTQkq3Qx7+CXMoQayQWtOcBw33yUrTkgGj8961wChgQ9UogGoWEGx+FSivCShDDxX3kf9/ItCM2Be9V2WLWN4Oh1zKFrIA1EVROO6sXI0Bj+N/4JrswMjwkdfZJQ8wbKH/hlVHcVaMGc6hy8dhJGr1pXa/waoRvncbasQVu88eZeibh9E7KQ509oCIcbqTpybquM+FULew+mY+QSUlFBeHIuj2mE8Z/dDydepMdrfxVwEuVNoOVWPm3nLxN+6svWxq9diXP9/dHl4lk4l7oHoQRBM9K3VwRCdyK0WCrM+B/Zuc+FokSd1rmie2TlXlFAmeKJZz9GzEaRv9aPHfVl5tB6wwYuR8xtKhb7WuOqqtBYW46eMEd+8chZDFxRRMk79zDv4Jc2pIHAREgTxQhDjtMaynAAY6Ab0z8e2fRatQDFVZE6HWSCNJmhvvOZqXe9C2XZHVPGcFQ2oVTUA5EUm3JiCP+zfwL+geQd9Ryh71v/idqPfxnF1ZxbGx17wbZexomXUBauLnjKyWS8mNJkErKTIjQO3tHIU6QcyhPsOUX4+T+HtBm608Ox4rac29dR0YT64JcIPP+XIBOnFwSgcTWenU+AYczaubSVJlNG6gpIU4IiKHQ87uxz+CCnybTHM7ORrmFpK9R06IqSY/lnk8+1+ZRvW5U+5h38OQN586+FNHTGxCDertdh5FpkFa+iBueyO1EbVyCDIbyn30CeeAmCQ5MjhAFaNqKt3Y2rvi1x/5MyMPmZDKZwNlIh6EupgwR8r/xVauc+zj6DP/pD9Ht+FWdzuyUbWeJ9x+3rdf149uPmlcd/wRVYHqcbfParVAa6D6MtvwOhaTmRJzRwifDzX7UtR0I0rEarbIpWWM6tvfS6Jaif/B94z+6HrpdgIm4D8IJ1qJ334mpuRxDdMzNb5xKB9TSZsWM+mJgbrsXsIRN7LanzMNxj73fJAZQ5S9tlmp9bHyyU9mz9oCMuTWYsRSWkSePoG8O/91vQM23Tai8EzrwGrgYwDQgmKTB19TDhq4cZ3/gpPGvvtZQmUzrLM9PP4UmZVs/f0wXD9jY5hvb8BTz8ZdTG5UltZIdnitlJTTgH0mS2rJlZHdYKDvwjEwd+hP7wb2Z9rqUpCbz2DfsyJISK466fza/tHCqu9btROnchjSAGJqqiI4SCqTmQ4RBy1s+lvTSZUtXiVu+J+zv3ecQEkrmpu7DYzirPLE3mttULeL/H3pPlXR11KCWUJjNXtrLCg2GD6xM+AmGDMqfGgjJ3SaUUTWSruYB5B7+UYTNExxzvx//MH6euDuu3uPno8PcJON04V96VNkTH2bCMTNbwtYVrUj7Sl8eez6BXCO3/HvpDv5WbMIQZP1hWoFqv6PkBD9Fxte/An4mDD4CP0E//CB75MlpV5qFZ4d6T4OvPUIZ4eHA+/OuT6U9nxY6KihB6NPSlcHNuPkTHDof5EB2rPDMbdTRU0+AW3LARi7+1vTkP8s8mz/18ujHh55UTV3n93NQkE1U67OxsYtuKZly6WgS62+XT52xpYs7s05+HvPl3SqjMTe7f8zepnXubMPZ/F2nGymhMH5fJ14rugOXb7XUuPDgXb0jQbxy/fiozwQfOEB6OVf5M0b8V3rLR/viL1mc/bl55/BectWPDw1cZP/gU43v/Hu/ebzFx4hXM4ETW8mjVzbDolhkmtA5J6PV/yEqG4Nm9WYwPuOvglk/h+vT/RK9pyViOkuYIpoTopGwfOyYzSCnxhsIM+4OEjdLL/Td33IvZQSa2EkLwhftWW17h/KXtS6lxOzMYqbiQy3n1fu8g//Xp4zOce4CREDx15Dp/9PRh+r0Z7qOaR9aYX8EvZdgI0QkNXITBs6n7s40gvu7DeNpuuzlWkkq22sZHCJ973XrXtz+OVB2kCiXAUo2+JJJfOIh2y6Mz+rTLlbUPYF49ZGtsrfO+OROiExroJrTvuzA89XG3BLyHfgBt23Dd8TiK5sxYHve2X8L37J/D8Dlbdp7E6GUCg5fQ69oyk2HUyh6PmRD3/gaOukXIcBjFXQ66G7NQFaYLzvMfojMWDPJmVy97TtwgfmG2o8HJzjULWdNUPfUewkKfheARE0js6F46XFhsZ5VnHnbSWObivzzayd+9copr48lvBN0CvvP6RX6gXWTVgnJ2rmmmraYiR/LPJs9diM7ZgVG+8eoF0mE4CF/76XF+79H10/YvFItNkvH416UL9Q/+4A8KLUOp4XPAUsMwCQQydzBzAY9LwQx68Y6OIoN+hBmM3KFLMxJqEcf9h34Cw1dSd5gJQkGcbZsnxxJGEGEYKIqKgEk5FN2NaFqFcf6t9H2ueQTPmvtm6DCdB4++RMZOfkUDzkVrU/ZvhStlNYR7zoLXYghHfTvudQ9FHp1lMW4+udupIjAJ+vyp7d/bFdl46r+5EXsGhi8RvnwUve12hFAykkcRKurKLYRN4EY3YFizdRxMBM6WdRnZJHR2H/jsP/nSO3ahuSsTXg+FPsezzd1OFWGaBPzB9O2FilTdkTSiIpZzPH5deyY/cX2Yrz7bxZkb3hnfCP1egwMXh7l4Y5iNi+tQJ1M1pu6zUNxdFqmP4PcGs+qnWLiUIGUk7WAkLjt3Y7jLIqvqfm8oo37KHBrbVjWzelEFZjCIIk3KdTDDklDUxwsTSfQZNKFnNMhb5wY5fW2AdYtqoznxi8fW+bRVPP/rl04wkao8bhyCJggZoqO5Zlb1zYZ7yl1oqkIwEKbQ6TJdLh01UhyjG/iWnWPnQ3TmDOJWexI99s4wVWVaBMaTjMsMORyNK3A98hVoSRJyUbkYbevnKd/4ocQ6TOdtWYRuqI70/VvgQih4dj0BNW3px6xZiuu+/xg9PLtxCx2iY/rHCL/4tfQ6A4xcwfvGN7Ozs6pTvvER9F1PWBtzOkb7MxoXIaCqPqMh1bLYcYmvhw8UR5CvEJ2zg6N8/ZX0328n+/z87WunMM3iXp2Lv/rmkR65sFVbdQWfvWslv/XQeurKXUykiew6OxjkT559H1+osIt8dpELW10YHuO619419FLXIP5wadlqLmA+RKeUYSeLTjD73N2JZdCnhnIkCdGJyaFUt+C6/4vIsRsELx9FBgKgKugLVqA2LkcJhzCT6TCNq2vux7hg4YlAItQtzVmWGMVRhvvB38T3/otw/HnAN20wJ3Tuxr3hQaSzrOjDNKyE6Hi73iRaWc0aet4nOH4DR1lDdrJpGcbBmkbGYVHaqu2EL8wsopYSTeugvBYz6Et5PXxweH5CdKQ0+dYrZyyflq4bft7u7uOutsakfRaaR0wgSaW7YUoOXe2n68owvlAYt0Ojo6WajS11k5Vwi0GXmVxYbGeV5zYzzGtnejjaay0lxIAfvrf/LL+4tT3rcWeH58ZW752NS71rERL4zR8e4d5VNexcvXDafoZisU88j39duph38EsZdrLolFXB8EjuZWhcNiXbSqIsOgllcpWjr7wLMxq2AcJ2ZhFn9SK8izbClcM2hRa4l27KbZYYRcVzy8OI9fcRvHaS0OgAQkq0qnq0RWtRo+POWvaULHi6LDrCDMPJPbanSvjYS7hu/2RWsullNZkFZfWfxn/6TVyrttoe11nXRriyGUZ7LA+nd96DYud6mOM8X1l0uvpHGbYYKhDDayd6og5+4j4LzyFVFp1XzvbwzIFrM8qtvXVxFCeXePTWhexc0VwkukzVK4Jc9pt6ftjhUprsOW79Ggd478oEO4fGaaspz7Fe+eC5sdWoP/OV+JdPD/Hy6SG+eP9KVtZV5kHHXPHpc7Y0MR+iM2cgb/5N8NhbLNmUl1GdHTuSjEtCOXLNy+7+JShfYE/ojvsQemwFIbfyCEXFuWgtnrX3UrZmZyTOX9Fy1v/s8PgvuJltTO8wGGPYxvXYSmvmsqllNVC/yv7YgPnOd/CeftP2uEKAa+vnrQ+0chfOhR0FuR6KliOwFqKjgBKLu0+P/aftryZeHTPom5j+lK14kMq9ePLQBX6UwLmPIQD88MA1njp8MS+yFSNy5YqdGxxjKIMH3V97/jTD/gzK4hYAubCVpmTfy1++eIar4xkWwJyHZcw7+KWMaSE6ptAmH4FP586Obbkff+FGRHXz1LEUHQOBqSSWI9dc6i7cH/4KVC23JnPdStybPjYrspUqjw/RSdTGMDL8MQt4s5LNQMXXcxoCWThn73yHkG/MtgxK7SLUR78CSpqibR0P47790wW7HoqXOyLfUynbODF1D4bmiRTHUxSmLFwk4APjmaXgG/QGk/ZZaC6TvP/WxT5e6rK22fvFUwO8fbGv4LpM5bNrL7v82nDmDuczh7pzrlc+eC5s1VJXNkP/TPCjt2P7ZorHPjN5aWM+RKeUYSNER9HLYO2H4dhTuRm7shnP1l+cMVahQhLcj/42viPPwtEnk4qste/AecsnEKI0QmVAYE4MYvrGEboDUVaNImXex00boqN7Mpsz7qqMw6KM4Wv4X/7fMGbvEXoiBE7uoXzTR2zL4KxqQTz+P/B3H8I4+SoMdwMGqJWw4g5c7dtRKpuiYSaFvR6KjVsK0ZFhhOaJZNBRHNxcf4rO+QT8ZkYce1DFtKdURcVheoiOlJJnD9nLgvbsoSvc0drIzYqihdcrglz2mzvdZBY+3dvdY3z8VgO3ruVMnlzykCF562wvPYMT+CcCVFe42LSwFqeu2e5zS1sTPznUm9Ym6XC6P0D/hJ/6MnfO9c3NvCp9zDv4cwZxd59CRF9O5e6NH8IX8kLXi6m7KmuEYBBCMwtYALB4M54tn0XRHZjTx5qUgaRy5IMLoVK+/gHM1bvwnt0PVw5ByAeKDgvaabp1N1pFHSNDo7MiTzZcmiF8597B6HoFhi/dtLtaiei8B3f7VnBX5VGGac7PtDaquwKqFsPI5aRTKCFaY2Fi9uQxJgYJPPnfmLl5OUOcfBE2fTgj+whVw73sNsxyI2guAAAgAElEQVQVW6itdIGUDI4FUKIF3zK9HqSEYM8pQtdOgeED3Y3WshZn43JEkczLrDiCKSE6idpIAVIBRcPqD+zCGg9nBuzHVTSWu2wfM1tIpPmZwVEGbao5EICzg2PRWOfiwYg/yN4zvRy7PIg/bOLSVToXVrG1fSHVLkf6DqYhV65YbVl2hawOXxtkS2tjjqTJDYJhg397v5tXTg/NSInwT1xm+/JqHtnQisdhPSzO49DY2lbFmxey39N3sLuf3Z2Ls+5nHokx7+CXMuxk0UEiNQee2x4n0NyB8f7zMHh6an+ueljzEO5Vd4Eq8PecRp7bBxOjIDSob8XZuRVR3gTJst2kyaKTd+5Qca3fjdK5K/K+5kAJh9AqPGkzwxQDN/xDBH76p+BNEFtsjCKPPon36NMoD30JV11rXmSwkkVHtO9CvvOd9HM0Do41OzKyv++Nb5Ez5x4AA0NKZJa2kkIBQer2Fq4H74X34N0fQGBq+EX4xHOEXfUomz+GY+VdRTNHM+NWsuhoKIqCFEwLzyEpv6u9idfOpqjDkACrG1xUuRyW+i8Ej9wOybjPJOd7R8kE53tHWFlXHEWZDFPygwMXefP8dMcwzKWRAZ47OcBdSyt5/LZlaFNSAaXqN3dZdDqbqtEBm3u2JzE+ESD+nGUrT7bcGwrxZ88dpXci/vOpeP3cMMeuDPObD62lcsrNVer+P35rG90DR7k8aiTt2wrGfKG0YxWGJ7dZKWHewS9lSAPMcCQUIOyHkBeEGnk/HEzKnQvaMVs6kcPXCY9dx5QCzVOOVr0YqWmIcBCkinPBSpT6VhAqpqpGsoIIFTPkS95/cCLyOhiNjY37LDxwieDloxD0gqqjNi5DW7QOjJAlubPhMihAC1u2USG46R8n8NM/AV+6olkG5rN/RPjBL6FVtxTEVq7WTfiOPZf4RiQROh9GEZpt+4cHLsPA6fT920XQF1k9zsJWMhSO/A6EQhldD4SDeI+9CMefSS6nvx9z7zfwj/bj6dxR8DmaMQ95kUbUTsnaCIGUClJxIsPhyNM3IObyJuLNZW5aq1S6R6w7GjvWNGOa5pR+pARfyMCUJh5dR0mTvSefXJomEhHN1x95P5hhvvVgKDyln9nWBSSmKQkZJt98o4tT/an37+y7OMrA2Am+sKvT0jkwTYmIjpGtrALJro5aXjhlv6gdgFBEwW09OYekyd/sOZnSuY9hMAB/+fwxvvTIRhSL4VyqgC/ev4bvvn2Og1cn0o6RDKogJ+cu19w0JTKbmK0iwbyDX8KQpok0QkgzhAx4IRT58jSFQEQnZyquOstwOJdhCIEqJYT8mGFrxybjhPwQ8GEiwTQQUhLsv4Bx6F9hbGoMqXEKDMpg/W5cK7ZgCiWrsVNxI6RghvxImzbKBQ+Hg4QvHoBzb8LEjYjyWhW03YZjxZ2o7ipMIQgefd6Cc38Twf3fQ931KwWzlbrjCYxXv55e5qV34urYiemfsC1P8PTrlu1hB9I0MpInnpuaE6mA6Q/Yuh5inwUuHUrt3Mfj6I/xeSpwtayd1bmbs2vAB6opkUF/8vaGgeLSMUNBpG79x/UXtrXzP545YWnl9Z6VNXTUV086FYP+AHu7rvHqmeEp9ZFvXVTGts5mWisrEneURxjRWAozLqbC5dQTN04Dl1Of0k8hYBiSV8/0pnXuY+gaCPLSySvctzp96IZhRlyzXOl4z+pFvNE1iC8D3665tqzgto7h7NAY54esP4u47oMjvUNsaKq1fIwmVH5+yyoeDfh55uAl3svA0W+uLy8am8XDlAJTUvJO/ryDX8IQioJQdYSiI5xuEJG7T0XVEUawIBwFTCSKqwx0F4ELBzD3fTOFFhPw/k/wj/fhvPMzCDOUF/lU3Y2i6QifMat2CfR0Ybz29Zlqh0fgzEsEz7wEGz6Os3MnTKZwtIihCxi+UZTahQWxle4uR3/4d/Cf3AMn9sD05H1VbbDmXjxL1gECMplPY9ZveCxj6R0oDldG8sRzxeUAKVFcmuXrIfaZUDTkiZdsiS1P7kG0bS7o9Z0p19wVCCOMCMjk7TU3QtVRdAdSEZZzvNV7nHzpkQ6+/tIp+lMk1Wl0RX6wzw2NsqK2nLcv3eCf919N2PbAlQkOXDnLtmVVfHxzGznIDGgZsegURYnI++7lft7qup5RXxsW15LhPuScwZTwwrEbto554Xg/961elFZ2NRIllzMdPYrKlx7t5Pf/9YSt4wTQOziBR1dprcpNlpls8Prxa7aPeeP4NW5ptu7gx1DndvLzd63k/FOHGbJ2DweADmxaWPj5mQiKkCiC6Ab10sW8g1/KEGpkQ5rmAMMdLSwqI6+jsc6zziVgSNBdhMb60jj3cTi/l0BVK541u/Iin3C4kaoDtOCs2SJw/Uxi5346jvyIwGgfYD/lX7D7IK6m5QWzlSI0PJs+inHbJzCunsAYH0LqLhxV9WjVizA1B0Qr92YkTx7gWPcwaK6sbSX06KpqULN0PaC7Jz8LDVyCCZtO2+hlQqM3UBtze75nhesehBIEzUjRxoVQnQhVRWjxG23jf2QT88ZyD1/58C103Rjh1RM9XLzhY3xa1E6fH/rODvPK2WHcWNvV8cb5EYS4yOO3LUsrQ664UJTIqrSU/P2bZ3i/J7P0je31ThrLYxlK8itzKn6yZ9B2XHsQOHp9iFta6lKOEQnjEdG/uZG71uPigY46nj81YFleCfzk8HXgOs1lCg9sXMSti+tzIk8m/GSv/d+Ss4OhSXtmMu7uDS18/93EN8yJsHttA7oWv7k3/VhhQ3JlbAJfMIzHodFS4Ynu18itDRVFlLxzD/MOfmlD2qhkO0s8Pi2gcehpe/oc+iGiYwtC0Wc99WOuuQx6MV6y4NzHcOENe7aKwT+IUgy2MkBvWgFNgsnqxFaqlqabT5W1YG/xLzVW3Y+jsjEnsgkzshE0lf2Tpck0r9pbIYwhfO0Ueu3igqe9tMstpck0QwjFgZCR8MObBa8iTybTcSEUOhpraG+o4m9f6+JYb3LH2M6W7dfPDXPbijHaamLZaKzJkzkHieC7+89l7NwDPHZra57ltMZ7hjLbIN8z5I06+KnGEHmR++H1S7jQP87pfvsZmnomTL619xK9q708smFJzmWzwjPdKBw2JJoa7+RbH3frsiYu9Y3xVnf6DeHrFnh4YE0sBCt9/0O+IK+cvMYrpweJD5rRgV0ddezoaKbK5Uzbj3Ve+s49zBe6mkOQN/9OSc9XGG4GxuHyAZs6BPF3H8qTTNO+tPKsv//CATJZkbcNRS95W6XijuV3WTSEBazahfv2T+ZOThG1k6X2TH0dzqBkJiDDgaK4vm3zSTulaCNF5F+W+Me3zqZ07jPBqxmEPGQKAXT3jvLOpQyqRUfxH3Yuo7U6TVG2WYKRYZC1acj0jciPK6Yqgl/Z1cmWpZmnGH3uZD+vnklctyMYNuid8HFt3Is3mNkG6lTIdOV2avYiexBC8LN3LufhNQ0p2+1cUc3nt3dEN/Smx9mBUX7/qWPsmebcQ+RG5oVTA/z+k8foHhnPTPA5jPkV/FKGzTSZs8KjaQFD/RczUsm4fhpz2Z0FSf2YSy5PvpaR/rZR0VLytkrF1YZVUNZkP5wlHjUr0bd8Cr12CabmwIyGDGUrWzZpMnFk6DjoZUWS9rI40mRO5xeHx9mfhWOcDO9dmeCzhhHnANmXzSqXwHNHLmYk562Lytm9cRELy2PF6PInp1Ve5spsg3CZOz6LUrIxcpcmczrXVMFn7lzBwxsCvHm6l5PXRrgyEp6RTz4Vnnqvh23LF6BGQ4i6h8d49fg13r081Rld3ehi55pmOhurmer3Zib/mmYPR2w+/VlRG586NrNxhRA8vG4xu9qb2XehjxOXh/AFw7gcGh3NVdy9ooky5/TzmrzPa6Ne/uLFs2llN4CvPdvFlx/ppKHclbH8N7m1m8tix7yDX8oo4hCdUDDDvOUhX+rH+CUSosNYLuNKksOz4vbSt1UqjoFz2y8SeO6r1gzStAb3zicwguOoQkNxlyOFmrOQoVyF6DgWdxA8bP98uxavznlI1mzw2QjRAcH39p23b1SLGA8ZVKuqLXky47DvtP1UjTrwua2rci7PiD/ApWEvIcOk0qXRVlMZdVit9bN5ST0/PGi/8umtS2IrwanGsC6HN2gwHAyiALUuJw7N2rmscTt5dMNSdnca/MaP7F20IeDg1UFuW1zP88cv8/TRxGmFT/b5Odl3gdsWl/NzW1bZsm8ivnPNQo70pHeM47FzbQt27JmKux0697Yv5N72lqz6+cmBi5blN4GnD13i323LxTUw5S6rZDHv4M8ZxN0NCzG5KjbrPCqD4vRMSTtnGY6yPMkXf9HOgi1mAyu2oThcM6unlpqt0nC9rhVj928SfuEvgBSPsxesp2zH5xGahnDUT1aXlfmSLfbawvUQMenNz7TqhQTrVsLAGQsnOorGDrSKhjyc79m4HqL2StUmyxCd0UCQK6O5D3eIQbUYUpA1pEw1y5Mi07jrZDg3OMpL71/l6LRwJ5eAe1Y3sKOjmTJH+tX5MofOphYPB69aX1G+paWMCpeOle/SVGdFSsmx60O8euwaXdPi6e9srWTnmmYWVVoLZTo3lNmToZOXBxn1BZM69/F49/I4qnKWn9uyMqOxYlhRV8GyWgfnB62ltWnyCNYvqMlqzFxjwBfgZJ+9MNeDV8f5ZCBEhTM/CRpKDfMOfimjiEN01AUrM3LwlZZ1aR7jF3fYiTF2g0DXq2BkVnmStjvhwtvp25U147r18ZK2lR2uLVyL9sk/xX96Hxx/HsJx1TCb16OtuR+1ZV2kLsQsyZZtJVtt84cJv/Cn6c91FNqmj+TlfM8Oz3+IzsneYcu2zARlemzF15o8GfOsbySyl2HP6R5+fDBx/Lhfwk9P3OCN0zf4jYc7qfe40vb5yC2tHLl60tJvggp8eHMr1mydPEQnbJh8e+8ZDl1LnJ/97e5R3u4e5aMbF3Bvx8K0Y3kDmd1CjXiDvJPElsnk2tI+yvLaipTypOJCCJ7Y2cHXnjvKdW/qm6RqB/yn3Wui6SpnYX5b5IcuZvYU/NDlAbavWJClDOlvLEsB8w5+KaOIQ3SE4iC47G44v9e6PkoF7pZORAmG6Egp8e/9HpzZk/n5XLGNstt/hglXDZx8Nnm75vW4dvwymqqR67CTogvRiecOJ+Vr78Fcdx+EfGCEkM4yIoEcAtOYXXmyCdEhHMTVsBz/Xb9MeN//STs1lK1P4Kprzcv5ng0+GyE63lBGzwwtYeeKahQlPv4+vTyZc1ha6+CixdXXyWOqY6vp2cnwzuUbSZ37eIyF4c+ePcHvPbIBj1NN2Wety8VvPdzOXz7blbKIlEvArz7UTr3HPeX45FwkfF9KyXf2JXfu4/GTw71ousqO5U0px3I5MnOXhn32ziPAayd6WL61IqU86bjHofPbD23gmfe7efXMUEKXdeuyah7d0EpZmvNXCD4RyOxp3IQ/lAMZZulpXZ4x7+DPGcTdeRdBiA4CHGsfInh+X5xsqSFu/zhCKHmSL/6izW3/UsLEW9+GC/st6ZkMrvZdCCHw3Pox6LwH75m90H0YguPgcELDKlwd29GqFmAqGpjhkrNVrriiOUBz5NkOaXgWITox7mrbTKCyjtDhf4PeYzMnRfN6nBs+hNqwrHB65oITtVeqNlmG6Lh1NX2jDLF9dXPe+p4OAezetIRvvGQvhnr7muxlNE3Jv+y7ZLn9aAheP9PDg2sXpW27sNzDf/3oevae62PP8V7G4vy3MgXuXdvE3SsWUOawdx4TzZiugVEO2qis+i/vXuX2xXW4U4QcLavJrKrxWAZlcQ9eGedzpozL758ZnLrKxze38eiGpZwdm6B3cAK/N0hVuZPNi+pw6TEX0L6M+YaeYQUsPYtMQHMN8w5+KaOIQ3QURUWpXYS2+zeshSGs+yjOjp05y3Aynecz7CR4fn/Wzj1rP4ZSsxgz2qfirsaz/mHMTR9BidlEc6CEQ5Nt8nUuizFEpxh5tiE6Ma4uWI1+3woMbz+B3nPgG0c4PWiL16A7KwuuZ254/kN02hszT2mYCp+8dSGNZW7b8mTKJXD3ygV8+6Wz02tDJ0WZEqkKmq0Mh68NpFxhT4SXj/exu7NlWkXSxP17HBr3r17IfR3NDHgD+MIGbk2l1uOcljbRqtyJQ3RePXbVnhLAvgt93Nu+MO6dqX16HCq3LS6fkQEnFQQQzCxLKL5QOC7jzEx57HCHJrh7VeQGcOjGWNr2xcAX1pWRSRGUxfXlOZDB5kVQpJh38EsZxRyiEw1JcDUsJ/zIV/AffhquHJypQ3Ub2voHcS3egJlH+fIZdmKeeDmr0yjWfwTX+geKJiSmaEN0ioxnG6IznSvuGtRlt0++HykYNjfOwWyE6NS4XXQ2uTlxPcMMXgnw6TsWsbWtybIMUsKJG8McOd/PRCCMrqm0NpazZWkTLj191hYpJV5fEH/Q5In7V/CXFlIECuCLD69Gy0GGnyMXrVdvjcEnoXtk3EIhsJsSCyGoL3NlJSuTTxqnvh8yJMd67c+B/Wf60mZ9uX/9It69fMpynw+vbWDP8RuEM/AXVSW+Qmt25zWZrYqZr2+uxS0u2rrhrHHCqvrKHMgwfc6WJuYd/DkDefNvkYToxD7Tqhbg2fUFgpfeJ/z+v8FQb+TzyjpYcTfOBatnQb74izZ3/YdHemDoQmanrOMB3O3bUctrMYUC0iz8+cujreYcz0GIzgeGE7VXqjY5KHT14MbFnHj+tOX2u1bVsKShgjdP9tIzHCRsQl25wh0rm7i7rSFlyMZ0HLo6wL+83c3otL2YBy6P8aP3erh3VS2PbWydzIkeD28wzL7zfbxyvJeRuOMb3eAPQbLkQIsrNT63YyVNZZ7EDWxizJ/ZRtLxDI/LBaZbczyUmSzD3vRL7QsrPHx+Rxt/91r67/w7Wit5cM0izvSO2q6I6xbg1HIfalJKbquiCHavX8BTR6ynWL1/fQtClJKW+cW8g1/KKPIQnVhIguEdIPDi12EsLrZTAiNeOPCPTBz4R8TtP4+z8568yZevsJPgmP0VLwAWb8Z1588gZiHkplhsNdd4rkJ0Phh8dgpdLasp57NbFvN/37pMOmxpreDjm9oAuG1xfZJW1sZ9/WwvPziQutrty6cHuTI4wa/c0xnn5Eu6h8f5i+dOJ0xz2RddiK5xwIqmckZ9kVYNlS7u7ljA4soyW3Km444M45d1LT7DUKqxRFbyzeQzQ3TUDP07bUoWmeR8Q3MNv7Zb4yf7L3BxZOadl1vAQxsXsGtVM0IItnYs4PSb3bZk2bW6Puqo5tdWxc7v7Wim+8Yoh6+lT7F6Z2sF25Y15kiG+Neli3kHv5QhjcjGu3AQwn4IeUGokffDwcLw4ETkdTCSv9YYvUHgmf8JMvWGJ/nOd/AHAng67sqLfDIoQAvn3kYheyszkzCMSCaYQp2nQthqjnEZCkd+B0Kh5G2CEwT7zsD4EAgVxV2Gs2UNSHdR6DBrPORFGlE7JWsjBFIqSMWJDIdBia2eS7DxmP22xXVUuHX+9d2LXBmbuSpbocCHNi/krrYmTFNa6jMVPz80nta5j6GrP8CTBy/w0U1tgKRnwsf/+1z6Jw5DQTh3fZzffWQ9rrgCTbmQP54vbSjnWAYhTi0VrqgsM/s0TYlhxt4zcyZrTH8RHSP2vkfTUKIj2cHCakdSHabztupyfv2BtfRM+DhycYBxXxCnrrGksYJ1C6pRhIKUIKXJ+gU1lCndTNgQaMvKJkwz/7bKZf/54p+7eyVPH7nEy6eHSIYHV9fy0NolkzbPha2kLH0nf97BL2FI04zk/TZDyIAXQpF0XKYQiOjknG1OyA8BHyYSTIPgG98knXM/iSPfJ1i3AL16Uc7lM0IKZsiPzLGNhMOV2b2+uwLTP1Gw81QIW801bmpOpAKmPzCjjUTiP/cOnHgJQjfzs5uAD2Dp3TjX7EZxeopGn3zysA9UUyKD/uTtDQPFpWOGgkg9ux/XVbVV/OYDG7gyPs7Jy0NMBMI4NYW25iraa6sQAswMNz9Ox5PvXLDV/pWzwzyw1sClKXx/7znLxw0G4eUTV3hobatdES3jjhVNPHPM3sbGjQs9uDU9qT0NQyKFgmHEO165gWFGbxumjC3YtaompUOYCFs7m23PiSa3h92rPUxxEqXEnDJ9BV/YvYo/tXAjB/ALW5dQocffbOQGiW1VvOgeG2fviR4u3BjHMGFxpUqVSyMQNgmGTVy6yuolNdzZ2oRLU5AyktEuFzClwJSUvJM/7+CXMISiIFQdoegIpxtE5EtGUXWEESwIRwETieIqIzTaB8P2Hk2Gz+3DseWzOZdP1d0omo7wGTnV2dHcTsBdD75+W3o6Vm5DcZUV7DwVwlZzjSsuB0iJ4tKmXgPhIP63vg2XEmwqj+HiXgJXj+F84NdRqhqLQp98cs1dgTDCiIBM3l5zI1QdRXcgFQE5CEFeUlnGkjWxSqXTV+qyQ1f/CN998zxDQfvH7u/uo3NhFeeH7eX63nNqkAfXLiFfmQCrHDp3Lqng7UvWq7beu256Bp3pEBimiaqKNO3sQ41Eyc3od1t7sy0Hv0qHtU3VZJmVMimWVJbxOw+u5G9fPsNwki0COvC5ba2sa65N3CBLJLNVsWHYH+Qbr5yc8fRtMGhwedQA4BO3NrN92fS6BbmDIiSKoOTj+ecd/FKGUEHRQHOA4Y4+k5SR19H46VnnEjAk6C6M0/vs69T9LuaWz4Luzql8wuFGqg7QgjnVWWgOWL0bDn7Xuo41y9Hql2IW8jwVwFZzjQs9GkIS1Ka87zvwZGrnPobQCIGX/hfOT/wxaJ6C65NXrnsQShA0I0UbN0LzIFQNoWnc/PGO/5EtDv7elQH+wWZcdTy6+8bwZbAxNUTkxmLtgpqksgXDJu9cusGx7sHIkwtdYeWCKu5e2US5Q5/Rfjr/1B3L6Rk5RneC+PLp+PTtLbTNyA8/vc/Iyr2iEFcsLPHYdnkkT7yIyxcf+Vtf5uIX7lrCty3k9BfAf9zdEc1ClDvZpvPF1RX8t4/dwqkbI7x5spfeER+mlNSWObmzvYlNLXXTNmDnliezVTHxEX+Qrz5znHT7nX94oIdw2OS+jljWo9zbqtSde5h38EsbssjTZA6m3+iWUK3RPlTNVTKpH8vatzBx6QD0W3sE67rrM6nTBRaYz6fJzDxNpjneD13PW5/s/gECp96gvP3ugutT0DSZQgEUUHSE6kSaEuI2ot78ES48vzY2kZVzDxAyJEMTGSz9A0Pe2HEzZXvp1FWeOtw7Yz3z1I0+nj7ax7bl1Xx809I4Z3amjrqq8qu71/LDAxfYe2EkoQwVGjy+pZVbWuqT9jOTx5DL8yGStrltST26pvLt1y8k3MAMUOeCL9zfyYLJOgf5nUNCKKxurGF1Y3Xex7Jjq2Lh//D66bTOfQxPHr7O8gVVtFVX5EGe6XO2NDHv4M8ZyJt/C5kWb1IGiKR9zACmkQf54i/a3OosFA3Pvf8R7yvfgL7jyfUSHvQHfx2tujn3di8RW80pHnsd977/1JvJz38ynHgB2X43otD6JOGmf4Lg9bMYIR+q5kBvWonw1Njrh6i9krZRkEIF1RH5qygY3j58B58leOUIEh+CSpwr7sS97l4UZ36KWlnBy0ftF1GajgqXTiyniV2MjCXe2P/U4Yu8eCp1Vq83zg3TO3yC/+fetQnTdcagqwo/c8cKHrslxN6z17nUP07QMKlwatyyvIHOxvyFs9hFKjE2Lqxl7Seree9KP/vP9DE0EUQRsLDGw9bVzayqqyBSPf2DgSI5ZQlxbczL2UF7N72vHLtG29b2PElU+ph38EsZxZ4m01UFiReAUqO8uvRSP+oqzod+i/DV9zGOvQS979/Ux90AnbtxrbwDXJV5q9ZbMraaIzxRmkwuH048p1PB20fYN4bqyf28z4aHR3sIHvopXH57UtRw9B8LNqJv+hB6bWtu0mQqAun0gO5GmiajP/0avtNPgzSmmCrY8yZjb/8V5Rs/R9ndn7m5pkDU/nnm3mCY/Tbi05Nh/dJarg5MAKO2jz3fFzvmpmzvXb6R1rmP4cxAkGeOXOTDtyyd0c90XubQ2N3ZQnLYtaGw2M4qT5/6UVMFd7Q2cEdrwwzpcyNDqfDiTpP5xske7OLglXE+HQjhcWo5lif+deli3sEvZRR5iI6ycgvm9SP2dKpdge6synlV21kJOzGC6E0roWkVhgAR8CI0B1J13AzhKIJwiaKw1RzgCSvZhixmjJoGERhHcVcWjW6hnpOEXv7z5AL3Hib008PIu34ZR1z13YxDdKSONEzMsMHQj3+fYM++yOeJwgpCAcbf/VvMiQEqdn8xLl94XJs88cuj6fNxp0OFBp1NNbgdKhzts338eCB20xOpfPvKmR5+fNCec/RS1yAPrWvFocVsDPm3YQy57Hf2zv1s8PFAkNFwGIdQqHE5otVsc9V/cdvq8mBm353XfX7anOU5lmf6nC1NzDv4cwby5t8iCdFxLtmATykD0/qFq3XsyJN8037I8mwLITQUhzsyWpGEWhSrrUqWJwjRQc+soqjQ3YXXJ8rDw1dTO/dxCO/7P1BWjatxeer+idoraZvIa+9b/5dgz1uWxvae+Bf0JRtxr95pqX0uEAgb6RulwSfubEURIprP3j6MaPpEU0q+tfc0B6+M2+5DAu9cvsHWtsa0bYsdpe6KmabkwNV+XjveQ3dcih0F2LWqlu2rm6lzO3MyVjHbysgwLWimx30QMO/glzKKPURHd6Nu+0WM1/7Kmj71nehtt0dyjM+HnRSMz9sqixCdpg4Y6U48v5NBlENZbV7mfSY8+N7TtsQP7/8h5mP/JU3/aUJ0BJhmGO/xHxP9MO5vcu498s+4V++w3D5bXqZn5pTH8InNzWxeVAdIqpyOjPqo9ugAPHnwQi3ptvYAACAASURBVEbOfQxXb4xBWyOzYbebEBbbWeXFHXaSjnuDYb7+8omEFXFNItWPXz49yC9ta+WWlrosxy1uW1V7HFwetV9krdqp50Ge+Neli4I5+O3t7Y8DvwZ0AgawD/jDrq6ud2z0sQ14PUWTf+rq6vq5rAQtZhR5iA4iiHvRGvx3f57w3r9LrUvTWty7nkDFBJl7+Uop7MQcH8D0jyN0B6KsBkXKWZWhlGxVbCE6js4dBE/byKIDsG533ua9XW74R6HHQorPeIxexBi8hF7TklWIjvfMPmRglMjaZXTOQ0oe6jlO6MZJ9IbVltpny1urK9AhaVaWZFhYrvD4XctZUVsx2We5U2dVvZPT/faqYd/R3sR4IMSeM/YKOU1HeHLlM/92m4pc9iuyOLawPGxI/vrlE5bSkX7zjW5+5R6NzsbqLMYtblvdtqKRo73dyY2QAAvLVerzkgFp+pwtTRTEwW9vb/8D4CvAGLAHqAEeBh5ob29/rKur61mLXd0S/bsPuJDg871ZilpCkDf/FkmITuwz19JNBOv/mOCpV6DrVab8PNZ3oHXuwrloHVLVwQznSb74i7bANkrApRnCd24/xqlXYSQud7NaCZ278LRvA3fVLMlT3LYqGp4gREeraCC45Fa4dABrcOFq31Y0tg5ePmZR7qkIXD6CXrMwef9E7ZVUBqD/YkZjh/uuRR38/ENTFXZ11PGCxQ2tACrw2w9tQFNjNy43sWPNQk6/dsFyXw5gc0s9L528YvmYZKhw6ekblQBK1RV788J1S859DP/4xjn++8c23fyZzQDFbKuNC2vxKN2W02QC7FjTnD+B5gBm3cFvb2/fTMS57wbu7urquhp9/0PAk8A/tLe3L+vq6rKymynm4P92V1fXB8iZj6LYQ3TiPlP+f/beOzyO67r7/9yZ2YpeiMYCggS5IMFOiqTYRRWrS5ZtuSW2Yzvxm9hJHNtJfskbp7okthMn8c8lsZ3YzuMSxyWWZHVKpERSVCPFBhJgASsAgiD6YuvMff/YXWDRdmcWC+wCxPd5yP1i995zzz0zs3vmzrnnFFXhvuVR5IZHCPv7ESEfijMP3EUo4RBykvXL5rAT3d9F4MmvwMAYG+70Xjj+KwaOP4Fyz5/hLKmedH2y2VbZxMcK0TFUG64tH8LX1wVd50Yfz2EQ2O75DOQUZU1mJT1gbTV5EIGB8cNvMBeiE/v+in4Y95qYy1Bw2PtdvgD7m9o41dKDP6jjtqusWFDMttpycgcf55uXP5LfVlfFvtM3MGupd2yoQlNjq6fDZa6qKGRVhYtjbeZCEz64YyGqAicuT2z1HmDdovgc9kwRFybbmeXZHXYyHpdSsvdEC1bQG4KG9i7qy4tSHDe7baUogt/YvpB/33cBM6gpsrG5ek6crPSeVzMBmVjB/3T09a9izj1AY2Pjrz0ez/eAjwLvBv7ThKy1RELVUshNNwMwDUJ0xmonXAUozsiu96nKKpOtYScEBwg89WUYuJ7kYOsYT30O/d6/jKyU3oS2yjY+ZhadcBBFVcm5+4/wHv4VND439uGcU49z06NoBeWTcg3IsD+l0C5Fs5FS9QrNPn74DeZCdHAVRd6zGEqg5EUc1bAu+e/Xz/HKhRGpJ31hLhxv54nj7dzhKebBNQujOdzNyR/J85w2PnX/Mv7piVNJnfz7VpSxo7ZiXJlCKHx4ex3fO9DIWy2J17Q+uHUBq6sicdgDQfMrv2Nhfr7GvPzcUfpMLo8hnXKtnSvZwtv6B+jwYxmvn+2IOvgz01arKkv4wFaDHxxIXIG41AWaIvjc428hBJTlO9m2rJL6ssLoE450nFfTH5lw8O8mYsXHxvjsl0Qc/HtI4uB7PB47kfj9042NjanlV5pRiLuLzbIQnYzrJGH4RZsN+kT4wIlnwZvMuR9C4LUfY3vbp25KW2UdHyNEJ8aFouLe+Chy5T34zr0C3huR9535OBfdgsgvQ0ljOJqUOv7LxzBOvwDtp4dOmKrVqMt246xYiplCWvbyxaTgd2Arn3gWHVvdRjjyQ0vjClcBjgWr0A3JN15sSBrP/nxjJ13eIB/auoSJlKKfm+vmsw+v5Jljl3j5/OhiHzWFNu5aM4+VFcUMTnAcaKrCR7Z7ON3Ry76TrZxoG3L0VeD2uhK2eSopdg1tynXZVCB1J/+dty5KuW+2YTq6YkPViK2h25viE7YopoOtNs6fQ80Duew71cq+s13Drp5SJ3T6ocMHHb4hG7YP+DjRdp4CO/zO7qVUF+ZNveJZiCl18D0eTyWRePsrjY2NYz1jjP0yrTQhbgVgAy54PJ7PAe8AFgJtwM+BzzU2NnZPWOlsxjQK0cl06EE2hp3oigINL1g75h1nCPZdRylecFPZKhv5eCE6w7jdhb1mI4orF2wulEkIxdG9XQSe/Sr0j5ELveUoestRvCVLcN7xcXC5EspUiudBwULouZD4PIyHmo9WtSJJFqDkITpqYTX2yq34Ww9EBcf/tI/NXUvuQ9gcPHX0ounNqm9e6Wfh2TZuWxIfv5t8rJG80Gnj3RsX8/BanVPtPfT6g9hVlZqSXMrzXCP6JJYphGDZnAKW7Sogr8iN1xeip3uAHIeGMuxGJNJ++dxCLvR0mJrvSHxs50IWF8VW783PNz1cmGxnlmd32Ml4XEuxDPBQ9eGZbas5OU7euaGGh9dW0+kPEDIkVzu9/OCVyyRCTxC+/HQTn37bEmqKYk6+uXHb+nwcaGrjWo8Pp9NOVUkem2rmUFOWuarZE8VUr+DHvlHHq8oRe7/chKxY/P29wE5gH3AFuIVIGNADHo9nW2Njo/nl0ekGqUc2pYaDEPZDaACEGnk/HMwMD3qjoSfRdcBM6TGCy6AALZwdNory4JVGSGG9NHjmAM51c24qW2Ujl6Fw5DciFMrY9WD4egg+8UUIJonJvnEG/5NfwXHvZ4ispo8vU6y+D/nS182fkGseRIT9iXUODSD1qJ3GaqPakaEg7jt+C99PjyEDfZDkcbpaNBf3re8mGNJ55pT5Ta8Az73Vyo5F5YxVJKu5u5+XTrbS2OYlpEOOHdZUF7K9riqaj3x4e5uqsKqyaNj7hmEk1X/ceSmC/BwH4YEgSMlQmu+hNrcuKePJBmsOfqVb8JHbPJTlODEGhVrTLRVuGDKaq1wQiahN3xiGIRHRMaZqPungZbmp5bWvLHSlfG5NR1spQlDqcuANhfliEuc+Hl975gx//47V0b0vice6MeDnv/af4Xx3/BMxP4cv9fLEkassrcjnIzuWMLc4x/T42YIJO/gej+eHwHoTTX8JPBnl4wUbxrydXBPyYg7+PuBdMUfe4/GUAj8Bbge+RWRlP+2w2zXmzMnsYyA94EPqIQoL7Ph7JQgFqUgMdFQtMxxACgPh0MGWOT1Gcj3kwwj5yXdmhz5SkRDsGvdCSAQ12EWe/eayVTZywx9GKpBnF2m5HkLe63S/9TJ6ywnw+cBmR5u3nPz6naiFFWP2vfbqT5M79zH0XUE/+RRF6+9POLecxcvw9j3MwJH/TSrSVreLwpWbUaVMKDPs60M1JHkuxmyDKjByNYyqVeR84F+49KM/RvfGHsCO/mG2ly2i+j1fQStdwCvnWoetxZlBrw7XQmGWVhZHQpcQeH1BvvrEYc50DA+fCAThhTPdvHCmm7vqS3nvtjoURYn2Sv//scI9+SV547YpIJfbl5Wwx+SNjQJ89l2bcLvtk6Jzov913cCQCoowUFUlrdJ1I3JmFJTkTfGsJvZ/AbmsnZ/LkcvW6hg8sKGWgiLXTWUrgeSlty5YslMQaOwbYMviioSSO7oG+OITp0gUMNXU1svfPnaUL/3GZmorCyzpkWmkYwW/GvCYaFcJg/u3kn0fm3l+9UfAvwKtjY2NfbE3GxsbOzwezweAJuDtHo+nsrGx0Vod72kCoSgI1Yai2lEdOZGQASlRNBtCD2eES0VFF6A63AhHTsb0mA5cdaW2IiBsTjRXXsb1n+XpuR5A0vHi9wmf2T/8QIcg3PQynU0vQ/U6KnZ/BNXmGOyr+/uh+XVL5074xF7E5rejKFpCvQs3PoBWNIfe/f8DwTEiHYWb3FvfTl79LrDZJ24voYLdgVAF7up1LPn4j+l+9Qk6j/4P4e5WYj8JjorFlKx7H3nr3obqjFQNvtLeN1o/E7h4o49lc4sRgM8f4i//+xAdSZLZPHuygx7vMT5x72oQgkyuc/7mzmW09xzmeJLNuQL4m0dWkZ9rz4ieAkFYN9BUgaJM7djZzO9bv5AjFtLSrqxyU1bijjqn6dNHNySBQAhNU7HZ1KyxTzx//k3zq/cxPH/4ItuXVIwr05CSL/7qjYTOfQxef5i/+u83+O7v7cRpnz71YSesaWNj4zazbT0ez+oodY3TxBl9TbpptrGxMUTEiR/rsxaPx3MY2A6sA35tVkezCAbD9PRYr7qWTpQUuUDR6OwNEgwYEDQAiaFJlHBmOEEw/BJFCtAzp8dIXlToRqp2unu7s0IfkARyUsvhG3bNpWtguEzZ20G4uw0D0Jw5aMXzkZo2Y2yVjbwkN/J11dkfSvl6kOj4nvkaXD+Z+KBfPEzb/3bjuuczSHvkuA6csubcRxDg2pnjOOevTj7PqltwvHcr4ctH0a8cBb8P7E6USg+2Rbdg6AY9PokRSm6vghI3QgnS1e0du40iMUQIQj4gALjR1j3KnNWPoPe3IINehCMfNS+Sa7/PK8Eb+Zno709t42F/j5+eG5EV1P/c35TUuY/h1Qu9zD10hl21lSgpxlInQtGcPATQeT35jctvb/Pw9MnLPHeyY8ziW8vLnDxySw1Fdo3uG6lXvZ0IDMPAMEBRQIl5+GlC0ZxI4bAuE7bKNlQ47dxbP4cnTyaPIs5V4X2bFg+er6kg3lZSSho7etl3soXjcSla8zXYtbyMrbXl5Nht4wubQuiGpDuFPckXOoJ03xjflTza0mn6mgfo6PPz+KHz7Fo2tbn3Cwpc2FO8qZjqW5FYWsyKcT5PFqNvBW3RV3caZGUn5PRMk5kJPpmpH4URxgj0Iw0dYXcjNIepvo78KgYKF0B34pRgI+Gu3TgoJ9BymtCJ54c5iCEg5C6FujvJWboFodqyxlYziY+bJtPC9eA9uSe5cx9D13l8R/4X9/p3RuR425L3GQOypwNl3vgpLYdxPYitcilUejCEMvi+oeuW7GUmTabUdaQhieSwjKyzCdWGVrCAROt7BTmpxTPn5dgBQV8gyJtXrDlOvzjcxi8Ot7G8zMltK+dSV1qQpvR8sb9E9O/E7RVFcO/KBdy1fD5vtXbSeqMf3ZDkuu2sX1BCkcs5bt+p5zGkU25yG2Uzv3flAhx2lV8eGf9arim08dFdHvIc9mF9rfOIrUK65D/3N3KsdfSTn94wPHasncePtfOJO2rxlOZPuU1Gct1IKXFvNECSceW/fMr69+eehtYpd/Angil18KPhM+3API/HkxcfWhNFrBzh8WSyPB7PvwLzgY81NjaOUSGImujrxEv+TQvIodfZNJlj8NgXXPpk6r5efI0vwakXwYisFAQAylegLt+Ns2oZyVITqsvvQD/4H+YPc+0OFLsTHeg/8hg0jFP0eaADDv8Yb/Mh3Hd8Apz5GbXVjOQJ0mSauR6kIZEnx8mTPx5OvYBc8yAoKojUVkMjak2xvRBII0y4tx3dCKHZc1Hd+XFtYu2sY93CUn511PqP9eqqYgBeOTfWz4c5NLT7adhzjuXlLj66fSl2beI/qSPdYTPQVIUN80phXkmchNTsOd2Qir2yCbd7qti6uIKDze0cPttOXyCMTVWoLslh54oqFuTHZzyaGKSUfHvfaRraEy9dS+Brz5/lU3ctYVFxZrPI2LXUvueK7Ik/P3/deoKL5uv9GIaclCd3k4FMBBM9DXwAeAD40YjPHo6+PklybCUSfvME8N34Dzwezwoim3BvAG9ORNmsxmyaTNM83akfA5ePo+/917GPy7UT6NdO4C1fgWv3/0Haxk9NaK/ZjO9aE5zbP7aseORW4drwLgyh4jvx4vjOfTy6mhl47us4Hvq/IM3NfTZNZhrTZCa4HkJXGyA0Ood6YoTxXTqGu+YWyCmz2DcCUVCWpOpsernu7aD9+FOEjj0J0dztIYCCaqi/E9fC9RE/X9XiVu+Je03MS1x2ls1xcsrCD/bm6rxoLnnJte5UtroPR8M1H9988RS/f3t93I+/Of1H8ujtEFbtMD24MNnOLJ8+qR8TcaemsHtJBbuXjBXckK7zQHKgqTWpcx+P7+07w988vDauZkRm7LO5Oo9DF62FYW2uTVypObVKBBDSDRyKmmLvqUV6A+LM4ZtErPwPHo8ntsqOx+O5D/gQkfCcH8d38Hg8ddF/8eE2/xZ9/YLH46mLazuHSJEsFfhSY2Njqscx+zEiREeRYRRpREMG0stF2E/o8lsMvPB1/D//cwYe/xsG9n6T0MW3EEZoqL0RQgUUXZ8UPVLlwgij6IG0yAxfOTG+cx+Payfw7fkmIuwfV6aKTs7m98PyexPLqlyF8/4/Q1U1CHjhrR8nbh+P7vOEzh7KiK1mMhdGNJwpUfsE14PReTXpoRsTnZdRZBh3jZnkZSMg3DgrPVNmo+DFNwn88s8IHXsMRhZm6rkIB7+D76kvgb8/EsY0mE4Rhj9JSszfsWnwpyQpnALuX1M92NeQMlkXUzhzI8iBC9dN65yIywn0zV6eLXrcvPzpNy5iBZ0BOHMjPmVtZvTfuWKuJb0Bti2NhdKMLTMvheVth6ak/EQhE5hyTRsbGw8BXwbmASc8Hs9jHo/nReBxIll23t/Y2Dhy19Sp6L+Nce99B/gZUAYc9Xg8z3s8nl8B54ANwE+Bf5zUyWQV4u7yh4UGTJwHr5/D95M/JbTv69ByFPpaoOcKXDmC/uI/4/3FZwnduMiYIQmTpJN1Hn+Rpy5HSoPwvu9gGu0n8Z+PbYYcW6YA3OvfjvsdX0GsejsU1oB7DhTMgyW7cd7/N+Tu/l0UWySe1nf2FfPjR2E07LEw3/TYasZzEbWTqfaM/kymFlsa66fYnbDIdI6DCOrvQChqEl3Tw4Ntpwm//K3kOnU1M/DcvyDDqa/FVOS6+PTdS5M+ks5R4TP3LacwripscYox/GNh38mWCcuIv/pmkRyztjKHju4BmjutX2OHmq5NgjbWMD8vhx2LC023f3BVOQXOxDE6G2qKLeuxoaaUiVTAnmpkJN9PY2Pjn3o8ngbg94E7gD4imW7+qrGx8bBJGYbH43kU+B3go8AWIjcIJ4FvA99tbGxMz9JMtsJiiE5woAvZeTXiIOSXYs+rRIjEIQbB1tOEn09yn+S/QeDpL6Le+xfYCufO6BCdwNXjELJWIFme3IOxaHNS+YqrANeqezDWPcRgxVPNjhIOYcTP5UIKUWdd5wkHfCjO3NkQnTTxiYboiNwiUvqCyikbPDb2ze8h2NIIfhP1/AoX4Vj3YLTQzcTnr/d3EGg6AJ0tQBjySlCXbsdROB+JQXjff5qektHVjP/UPpyb3weDVom3TnJeU5jL3z28kpfPtPLCyY5hJeTyNNhdX87W2nLcNm1Y3421ZTxz2lqhrPHQ5jVo6fNSlee2rH+MS2A2RMcsnxkhOlPBr/ellvWvo88fJytz+r9zQw1h/RwHL/Qm1Pee5aXcuawqqc7b68p58UxnQlkjcUd9laX2mUbGEno2NjZ+H/i+ybZj3jJFHfh/Yyhc5+aCiSw6MhwgePEI4VMvQMfwrKK+vAqUpbfjqNuKEls3iutr+HsJP/8vptXRn/wK9nd9HhVmbBYd/fwB68ep5wKy/zpqTnF65hVM/AU3Lvw9KA73bBadLMmi45q3Au8ohyc5nDWrB4+NptjR7vsTBvZ8A7qbx+9UXo975++AYUx4/oa/H/+B/4K2Y8PHaAW96UUGChcgFm+HkMkCXFGEGx5D3vIehBp9whD/FMkkz3PauXflAu6uX8D1AT/+cBi33UapyxF9uDC6b3mui9piO2dTWN0cC50DwaiDb11/Bv+KreOnZofs5DGkU+5Ms9HkcSXFlWdVUcgGOytC8L7Ntayrjab3HJEFaMP8XHbWV1FTmGtKZlmOm521Rew7a+57anPtHGrLM1vc1CqmT8b+WSRB3N1qNDOFlAa+V38CZ18au0tfG8abP8R38XXcuz+OYrMP9gWJv+kQo+JmE8KPv/kIzgWrot+9Q7Iyz2M/BhOU47W6KTICfaATNacoPXNJ8YtaqA6TY6XJViO4NML4LryBfv418PVEVrSL52Hz7MJRWJnWsaaExx+P8doM2nB0O6HZYekuaHrR/EGcvw7FVQBGeFCO4swl594/wX+tCb3heWg9TWQbqwMWrERbdjvO0oUAGBOcs+7txffk30MgwY9i9yXkmz80PSUZ/RfquUS4+yy2kmXJuiSFogjKc12M/oEfG+/ZWsvnH29I0MLC2BN8hD+x3jcfZu1lDpWFqWUMrygYr2xRZlBXWkDdzgJ8wRDdoRACQZHdhmPEkzkzeMe6GgYCIV5PUlF4/cISfmeXZ1qF58Csgz+9kSREZ+DwL8Z37uPR0cTA3m/huuuTyLjH8DRYTOEHyMaX0BesnrEhOqluW5GKLS2ZS8Jdl6E/lbR+NsgtxoidK1NiqyHub34d48B/MOqGsfM8obMvESquxbH7dxC5ZVlzziTjEw3RQYJzzQP4zx+GsJkbRyeOWx4dezwhsc1fjaNyeeT9aGhXjBsxPoE564qNwIv/lti5twhJJK5Sj74a/f1QIuM+ZUp4RY6Tz9y9lH9+umnMglFWUDYY05+aPpFbEhn32dTZYfK5MNnOLJ8N0THL83MdrKzK4XhL0jqiw7C1rjxOVnbMBcBl13ANKwBlXY6iwAe2LKH+yg1ePNHCxZ7hv0+L5uRw18p5bKktmzapMeMx6+BPZyQI0ZH9nXDKRBrFGNpPEb56EtvcFShSj1wGZuJ6R6Lv0owO0aG4Em6MWUA5IWy5pYxb5MckN/pvEHzqq9aPCcDyu1CRYEKHdIfoBM4cwHj1+4n16zxL4Fefx/HAX6C4ctMybraH6ChSR7G5cTz45wSe+UfwJrhx0/Jx3v1pNFchRobmHGw7Dd3nTZ9yZjDcwRcILXPhANWFeXzhkVUcvHCdvSdb6UqhQO6iYjulOWM9ObDCYTZExyyfaTaaTC64b8NCjj9msrAesKBAY15+bpboPzlcCNgwfw4b5s+h3TvAtb4ARQU5zCvOJd+mpr368lRi+mo+ixGIu8MWAl+TiZX7EQideoHBsIIJpY6T0e+TuIsp4zz2YzAxOY4lO8waYQhz10Zi3yc4F3/DHtBTK8nuWLbTwljpsRVIwt0t6Mmc+xhCPQT2xrbTZMM5k4SLqJ1MtWfcz9ScYnIe+Czqpg9CwfzhNsmtQNnwPpzv/DxaQVlG5xw+FcvElF7IwX8aanH1pIxhFi67xu1Lq/i7t6/nS4+sorrQZqn/bSsmvgkv/uqbRXLM2so8VlWXctuSIlNt7cCHdy6dXIWyDGU5LlZWFLF64RzmluYm75DlmF3Bn85IFKJz4Q3r8tpOoCsKSA2EJFJKQE/Wa4ROueiIGRuioxbPh8LF0H3OtEmUlfdMeFwpwymFTAGot/0hIq/MdJhGOkN0gg17rSnbeZZAdwu2ovkZP2eS8XSE6MS4IsGxZCvGstvA14sMDSCdeWiKPdImGmajD3QQaL8IwX4Uew7agpVoimNK5kzrGWvH0iIcC3aiukqIDhj3mhnutmt8dOdSPverk5hZzN+yMJ81lUVMVH8JzIbomOWzITpWbfXIuoU4NZWnTnUwHood8Ik7l1HqdmaR/lPJ4/+evph18KczpB7ZbBcOQtgPoQEQauT9gdRWemXfDbA5InIWb4Nz+6wJWLwpok8wmqQuHBzSKYNcBgVo4eE2sihHBnz4m9+AoIUYxuX3YS+ZP6FxCQcJ3bgEqUQHly7BUb4EI+SbUlsRDiINHc7vtaxy6NQebBvfl/FzJun5EApHfgdCofHbB72WrwcFCfZcDKEMHoNg22mMY09B+6lBOxlEqjEGF27EXn8XSuHcyZ1zaGLR6QbQTDknWcmALRcpFBzhAWqNM9RwDvctD0RTeAJRNzfTvMBh50/vX8bXnzvFjQRe/u4lRTy0ZgFSgpRylBwrXBoGEjGYzjQb7DARbhgSfbCAmZHWMQxDIqJjZHqe2c5jtpIS7lk5n1uXlHGgqY3XznfSE4ys2C8sdbBjeQXLy4tQhMAw0nu8pgs3DBm9jqc3Zh38aQxpGEg9hDRCyMAAhCJp3gwhIquFuvW8t0Y4hIye7ErNJgyLDr66cB0EBjCQYOiI6EViCJFRrocUjJAfGWcjK3L87Wdh/3ew9ESj/n5cnp3ofu+E9Te81nLvx0MGfVNqqxgP96ayGRi4ccWyzpnghuZAKmD4A+O2IeSHgG9C14Pv3Ktw9Gfj2+vCawQvvAbbfxfXnEWTNmecTvBb26AXQys57LXfRb9WiK6DphpIFLq0Ei7Yl4Pq5J2ORSxKsfbXZKLY6eQv7l9Nw40e9p9s5fz1ACEgzwYbFhayta6KYocDKeXEIhuj0KM2MLLQFqlA1yVSKOh6vLOYJtlG9LZhkmwlpeRKv5f2vsgNelmek3l5OYhpGBg00lb5djv3rKjmnhXVjHJypcBIw7k8XWFE5z/dnfxZB38aQygKQrUhFBvC4SISViNQVBuUL4SWY8lEDIejGC2vGKEHAYG9vAb/ktvhjMnY2+X3YSsqxwj4UJw5YHMOylJUW0a5anOhaDaET7fcN9h2BvabLLWg5kP9HdiXbkHTbGnTX3HmkNJvmM2JsDumzFbDeKpZB3x9lnXOBFecdpASxamN2wYFDGTK10Pw4luJnft4vPxN9Hs/i5Y/Z1LmzKJboeEJa8eyuJZWCvm5dw1Ci+S3DykaNqEDkgBOdGFjACdf33Oej92uUTfHfMXKqYNgZVkRq91/AgAAIABJREFUK8tiIThjrf6lB2p0Z9w03ts3AgLdMFBVkfY5qZEoubTLNQzJ/uZr7D3ZSod/+GelTthZX8n2mumVWWWybDUToQiJIph2aTFHYtbBn84QKigaaHbQXZGnn0jQ7GjL7yRs1cFfdifYXBCNvUaz49r4KD4hoemFJH3vwXHLuyDkA12CzTlKVia5sLuQqh20oKW+0pCE937bvA31XhyLNyBySyEa854O/bWyxZYqEgyifDlozimx1Uiu5JemojEEu5CKLavOn7G4sEU3YAa18dtLUr4epGpHP/y/1kzX8Bzatg9PypxtK28nZNXBr7+bJxo0vIrEjo4AAkT2DgkghIoR/Vsi+MaLzXzlHatx2tQ4IeKm4kJRoo6YyAp9Js4jK/eKwoiMJBMfI2ajIUd74jLDusG/7Wvk1PURnn0UHX74+ZutnLzUycd2LcOmKmPKyTY+GbaaqVxRxLR37mE2i870xog0mYoMo0gDJRzEUVY7OiNHQthxL9kcTfk3JEfFIPeWR9Hu/GOYt2Z0t/nrsb/tT8hd9xCqHkQxQqiAouujZGWSCyOMogcs9w02vwamttgNIXD6xbTrr2kOWLTVkh4A7iWbpsxWI7nNkQ+FiyzrDBBsfiPj50wyLoxoStFE7SdwPeitDTBwzZrhLhyCQP+kzNlmy4Hl95jXpWQpzVThDwaxKzqaYqAqxjCuKRJFyEhSIiKPww9ciIV2xTu4NxeXWaBD+nm26JGY/+Dg2XGd+3icvh7gBwfPZIXOs3yy+PTG7Ar+jIEcehUCIcC1++P4HvsC6L1Je9vf9kkUu3PcSpf2iqU4yxZhBP2EfT2RPOnuYnC4UeKqag7pwLD+mefxF635vvqZ/RaPA3BqH3L9uyyPlYzb6+8ieP6AeT2W3olid02Zrcbiom4X8pD13On62YNQuzmLzp8xeOzvRG0GbZik3Rg8fOmoZbsBBFtPYa/ZOClzzln7IN7QAJzZl1iJklqcd36Cw69cAmJnkyDmuo7mQ3i5oY3bl1SmNPeZgJnhWkwd0mmviz39HL6auKppPI5c9XKhp5+FBTlp1GLyMHtu3VyYdfCnM5JUshU5xdje+TeE9n4brjWMLSN/PurOD6MVzMPARLo8Rw5KTtFQlcwU0wJONU859WNnSwoHJohuhBGKLa1zUUqqUXb8HsZL30iuQvlKHLe+B0M3ps5WY3B1bl1qoUW97Rk/Z5LxdKbJHIsTGEjFcoT9PrRJmrMiwb3p/QzMXQ3Hn4IbI1JnOkqg/i5cdTuQdhfXukPohkLAUKMhOmIYDxHbzDbk7nf4Ihkshj8hlzcNl8BsmkyzPL1pMveeuIpV7DtxlYVbl6ZNh8njsylFrdhqJmDWwZ/OSFDJNlZ90mbLwXHH7xPuu4H/zAHobQFDQk4xjtpbsZVUYwiFdFUtTVa5M1M81eqsQyuY1qCEAyg2kfa5uOevJHjHpwm+9lPovTzWyLD8bnLW3I/U9Sm11VhclTI1B18VCSvEZgNPRyXbhNxmT8VyqDb7pNvOPW8FytxlhPs6CPVeRxgh1JxilJIFqDISc22EgwihR8JyAE2JbBOP5xqS8IgQHYg5uWLYXzcPj93siBT6ZjOPIZ1y02ujw5fNr97HcPhyPx9M+7wmg8+082mybTX9MevgzxjIodcxHrFreSW4NzwSDacBQ9GGh9bI8fta4oM6kF65E+bxF62Fvvml0G01NaBA2NxENpelfy728lq0h/4S/fp5AhcPg78PNBtq8ULsi25BVSIbFOUk2kpKSfD6WcKnX4COixAMQm4hovoWHJ6tKLZIgRTFmWfRdlHklmfBOZOET3KIjlJWi9FkMoNVHOxli6fMFlpeKUpBxdD3ilBADp33OXaNHl8wejZFHIyx+RBsgDJ8+f6mws0789SQTntZSII8oT6Zwuy5dXNh1sGfzkgSopMRPtNCdBZvgzcvWjsutduRNofpyrGpcrV0Ee7SGgzNPhQyFa14Opm20r2dBJ7/OvReGj7v7h5k90X8R38Gq9+Je+VdSJsdFm+Hcy9bMqHi2ZHxcyYZn+wQHdvijQT2/wCwUM+ibBkib07W2GjF3EJeauqwFKJz6+JYmkwZN7Gbh0sg0yE6Id1gIBzGoajRjEbpki9MtjPL0x12MhGk9xh0+QK8fv46nd4AihDMKXSxqXoObruWoszktgqGDRqv99DnD2HXFBYW51Ka45y0OWYvT9c5kVnMOvjTGWOE6OgDPfib9sPVYxDwguaAyjqcnp0oBZWTHrYw00J03LUbGXjzR1i54J11u7I+vCRVW+m97QQe+wKEexIb4ejPGND9uNc8iMNzGwErDr5WgGv+SmQW2WQsPukhOjqINQ8g3/qpadPZV92LIo2ssdG6hcUcONduKURnx7Kq6GzinoDcVDx2syNS6Js6NwyDI61dvHSyhXOdwUFdih2wq76SLTXlOG2KJZkj5xVBOvVOr41qimw0d1mr2FxTaEvrvG74AvzPq82caBu5B6ebn7/ZypaF+TyyflHcjdfEbdXjD/D8yavsPdM16pduaamDu1bPo25OAVLC2c4+Xm5ooa3HjyElRTl2Ni8tZ01VSbSGQ6avn3SdV9Mfsw7+DIGUOt7XfgpnXhz9YV8L/qYXYO5acrb+ViR/92Q9tifGmbwxxuBGyIev6QDyxnnw9kSqecxZjLZgLY6SeQy/aM3LVzQ72raPEt5vMhd+/f1oBZXjZiPKFq73d+Nr2geXj0HQB3YHlC/DWbcTChaNa6vAS99N7tzHcOIJgpV1OMsWE1z1duSxX5rqZt/92wghJhBiNEV8kkN0EAJ3/W14+1vhrIkbpA2/gb28NvN2ieO5bhs7llbw4ql2Yg5GxCojeQR3eEqoyI1mfpqhaO7u46WTrZy/3kfYgHynxvpFJWxZXIHbpmXEtfAGQ3ztuZNc6RsdcNIZgF8cbuXXh1v5w3uWsqAgxbC7SUI67bWjvorm/Rct90kXWvoH+PITp0l0i3HwQi9n24/y6btXkWNXE7QcjbFs1dI/wD8+cWrcZNBNHQGa9pzjjqXFHLvSRfvA8GuzzevnVPtFnOIiH9pZw4qKYks6zWLyoP71X/91pnWYbvgQsFDXDQKBlLYPpg1ulx094KO/p5e+Z/4FLhxI3KGvjVBLI2rtFqTNCRKkEBjRgjzp4NIwIiEididSc0zKGEgIeTsI9l5H93XiP/Zrwi//O7SdhJ4W8HXCwA24fgbj7MuETu1HutxoFUvx+0OWx1ILqwjlz4NLbyS2b/3DuNbcD0JJ+3zTxQ0MfK/8hPAr34HrZyHYB7ofgv3QeYFw0176Oi7hqt1EIGgMt3lfG/oRk1VVozC8XrTFm7GV1xJS86DteILWNpS3/Sn2siVZYatk3OXQQCgMRLeyTMb1ICRo81YSVvOh7SyMtWXZNQdl62/h8GzPCruM5PNKc3C4XJxt7UHKSIiOQCKlIChVDNVGWNjYWlfFw+tqhu6LZhi6fUH++dkTPNPQQUtvEF8YAjr0BgxOt3l57tQ1bKpBfXUpAvANWKu/kSqCYZ1/fPo4Lf2Ja2XrwIGzN1hXU0iu3fzaoJQSOXhPnN6D68qJbET3p8lWFbkuDja2ETBZNjxPg/dtrElLRdtgWOcLj50kYOLe1huC5mtd3Lq4zLT8sWzVFwjx94+dNFXp5fwNH94Edx5h4I0L3VQW2anMd5nWKxvhznWgqQrBQDjt56xVOJ021MijkYvA96z0nXXwreNDZIuD71QwggPcOPRz9BOPm+vk7yIcCmCvqIuEYwBII21c6EGErqMoKgLSOgZGGH/TAUKvfA/9rV8iz76McfYAdI2IBR8JI0DwwhH6285hn7sSJboR0IoOWlEVWu02wqoLOlpBxh5hq7DkNpxbP4h94TpU0m/TtB0bI8zAvn+Hi4cS26u7Fe/5o9hrNgyzle/Yk3CjOXHfkfBew7ZkR+RJSFnNkA17OkD3RexXUI265iHc2z8c2bSZBbYyw912FSEN/L7gpF4PCqCV1WCvvx1ZOBfDUQhFlVDuQVv7DlzrH8FeUJ41dhlL/xU1JdTPz8MfCNHp9aMKI1rgSmXFghIe2rSYzUsWIBSNoXVGMWN4jz/EF584wQ1/Yu+t8ZoXwwixfH4p/oHYd8zk6vZ8QwuHr5pPJNDS0cvmxeWm5UsJUkYqg0acpfTNwZXjAMA/EEqLTEUorFhQxMGm6yTz8TXgM/ctJ98Ry3Y1sbnsb27n6NW+JKMOocuns3xeHoVOhyn5Y9nqmRNXaOywsMfHBI5c6mFLbSlOTU2oTzZzd65zRjj4syE60xxSSgJHH7PW6fSLyLUPgxKN4ZvAY3hp6PgvHSF89iD034iExuSUoNXtwjZ3RUzLCY2BEBgBHwMvfANunE3dWBeP4BX/Re7W30pJB9WVT+7qezHWPogIRdY8pM0Rl0FkYnOcbO47/RJcfcucrW6cx3vkcXI3PDIkp7s1JbOH+65hd+aOsuHojE5kf1hOPI/9nagNMZ6knQkuFBVn9TqMmo3DbCcmIxtWujmCeSX53L9WYfdqgeELggQl143LkY9hz400naH4/v4mvCbTrfzvGy2smF9KhcOWvPEEYRiSPQ3tyRvG4WxnkHavj7Jhmy8zh3S7X+W5Lv7iweX86OA5mjrGXtteUuLg/VsXU+pO30r1vpPWa67sO9nCwq0e0+3jbRXWDV44fcPymGZwsKmNe1fNN92+xx9kf1Mrh8510B0ABVhQZGP78krWVRWjqdZCkWYRwayDP50hVLwXj0Fvm8WOOr4LR3Av3gyknikj0NKA/sI3gRFlvXuvEm49RthZiv2O30MrnDehbBy6ohB4/hvQNQHnPoYLrxGovxt1zqKU9YkV/InopmH4vSB0pKtoQjInk+uKDePks9Zs1fgc+toHkTZXRI5u8rn1CBgSDKFmhR2mUxadmcXtKHoQQ6jYHXYUNbK4YNgcSEVBCiKLA8S7+TODX+v3jesojodfv9HMR7YumXTdmm70MpDCZf3q2Ws8sLo6hXFFyrqOzSeneFOp28kf3FFPe7+PV85c43pf5DduTp6TW5eWU5bjMiXHLA+Gw6Ni283gdGu/hbGG26q5qz+1GiUmsKfhOvesnDci3G60blJKnm24wuPHh99k6kBzV4jmA5f4H3GJ3797KQsKcseVk35u/VhkI2Yd/OkMqRNqb06tb+dllEUbADE6O0jYjz7QhSEFijMPxe4a1SZ06S30ZBVV/R0En/gc4r7/i1o0P+VsHL6mV9Pj3EcRanwRW9G8CWUHkX3X8TXthVMvMiwuevEWHEtvQy2pzoosJjEebDkJgU7Ltgqefx3H0m0ROXkFkMKCj+YsyKrMLtMmi84M4ooeQBjG6PPACCGUyF4DaRjRp4oQ+YEVM4Lvb7S6AANHLvfR4w9R4LRNqm6d/anFrnd548OHzI5LCn2ScTGBvsl5Wa6bh9YunDT5Me5PcfHEH8bCWMNt1R+wli3ICgKAL6jjHnwKNbZuTx6/xFMNHQll+SR86akm/vTeOubnu8eUk34+8pydnph18Kc79BTvwY3Y82JJ7FF6qOsqgVMvQPPQZt0gQOUq1GW7cVZ6EEgMfx+hZM790EAEfv0PON/9ZRTNPmw8s1w27EltjuPh/GG49QOWdIjnA00H4LXvjy373EEC5w5C3dvIWf8wIgX5k8HDndYf/wLofW2Dcmw1txK68Jo1AQXz0fJLMzr3mRKiM605UXuNfF+KyL8ZjKtdI9MdmsM1ry/q4E8eUt0bmk2FyLJHk9Th0lJzxXIsnh7xtnJokxv2Ekqyi6G5uy+pcx+Pf99zmr99eC2ZjomfTph18KczhIpSMCe1vu45wwoZDRx7Bo6OkyGl9Rh66zG8Czfj2vpB/I0HLQ4WxP/ff4iy8+M456+29Hhf93VCz4XU5jgeDG/K4Qa+swfHd+7jcfoZvAjcGx7JihCJSFRjCtD1QTna3OWEbIUQ6jbfv/5t1ouLTROe6RAd3d9F4NoFCPSh2Fxo8+vRNHfG7TI2HwrRGf6+hjLDQ3T0FFdnh/pNnm6Vhe6UdCsrSDVERZhsZ5ZPTojOVHObKlhYqHGh29qC3ZrqQgtjDbdVdWGOpbGsIkdLXJDrheNXLcnrCsCp9h6WlxfGvTtZxyX+7+mLWQd/OkPq5C7eyDXhAGntUautZiWEBkCo+Bv2ju/cx+PCIXwYcOFkSuoa+76Of+tHsS9cHwlfECpIPSE3+iZjE5ACIZ9pHWLc8PUjD/2n+WFOP02oei1q6QLLY6WdO1PcDObMH7SVECrKjg9j7Pknc31LPTiq1wyeZxmb+yRxGQpHfgdCofHbB72Rv4PRfSppGDvU0Yx+9NfQemzQ1AaRp23Buauxrbg3O865eB4aQOpRO8W/LxQkNqRiIMNhUBI/0p+OvMBlA4vFkwAKnHYMw5hU3ebnuyl1QseIbVTJsGnRHNO6GYZEN2LvpXc+hiEjT5UNOSE52cC3L6/kwsHLWME2T7npuY+0lcumsrbKzZGW1J4wJcLKcieKYFzd/GGDIxYyN8XwckMLtcV5dAeC6IZBvsOOyxa7kUjveSXl9HfyZx38aQxpGAhFQSy7DdnwtPmOc5YhVAcyFMQIDsDhH5nvazVEYwSMA98hXFyNzRbJwGAIgYheSGPyFFe/EqJ4MYbfm3jcMXiwyUI11ihCp55H3PJuy2Olm9tKaxIWTxkP9qr6YbbSCqsIb/kwxsH/SNyxdCnOLb+JEfQPflFmau6TxQ3NgVTA8AfGbUPIDwEfBhIMfcJj+y8fR77+g/HtfvUooatHCW3+MK6qZRm3UYyHfaAaEhn0D70vBAYqQlMxDIGchEs9G7BhSRmHWy5Y6lOZp1DmdsU5SJOHXSur+Nnr5kP41s11k6PZTOum6xIplOgTifSGV+hG9LZhBpw7aypKeNZ1mWsms1ZumJdDqctleu5j2Wr3qnkcaWmyrGsybK+vSqhXhz+1vR8nrvn51M+PDnvPU2pn+/JK6suKEGk6vwwpMCTT3smfdfCnMYSiIFQbeevfSe/ZVyHYZaKXhn3ToyjOHIQeJHD2lUnXcyTCl05gX7YFECiqDaEHx+VacVVKjmkiqMtvG5y/GR1inEtvWh/s0uuIrR9CyLClsdLN8QXHUTABKlagFZZhjJDpXLAavezzBJoORjYZy7gVoDnLEMtui+zXEAIyNN+p4IrTDlKiOLXxzxkFDCSKMwdszgmNF7p2JrFzH49D/0H4rj/GVjw/K+ylufIQehgRkEPvCw3hzAdbDsLmRExyTHCmYLXaKMDdG6ojEUtTgO0LyzhzpYujrck9y3wNHt242KJuAt0wUFWR9jmpkSi5KbPVZEJRBL9/13L+6ekGOpP4v3WlDt63qdbSvMeyVXV+Du/bNI8fvXolJZ3HwtoqN56S/BEZdIYjVTd8LHe7sSNI40sXWV3Zzge3LEVTJ+7kK0KiCKZ9vP+sgz+dIVRQNJTcYhwP/yWBx78AvusJOjix3fcnaIVzI1UmhQbNE1uRTwmNL8DKOwAJMT3G4UKTsHgXnNubnrHdZdgXrkNqzoTjjsl7UwsXkgJQ3dbGSiOX6ISe/WeLWqvYN70XNOeYMtVcF86N70KsfRAZ7EOXElVzomiOSAXWcChj850qLmzRcJKgNn57CegSbE6wuSY0XviIyWJ2UYTe+jW2uz6ZHfayuRFKEDR96H1FRBx8e96IAlfMGH6518tXnzuHFSwtc7J72Tx6Or1TpudHttfx0zfOs/98z7h6LSzQ+NjuZeQOZkYxKz+ycq8ooAzzSCeud6SCrIirJJs9xz4VXuhy8uf3r+aZE5d5obGTkaUT8jXYvaKC3Usrx6iem5qtttSUke+y8dNXmse9sbh9aTEuu8oTJxL5F7Cmys0Hty6NFWYaV59ipyOhnFRwtNXHD145w0e2e+Ic89TPq+nu3MOsgz+9IXUEBkIa2FxFaA99Fv/5N9BPPQ99cY9cncWIuttxeLahaXaIT1U3MP4X+qTBfx0luqJtJsWefflugulw8LV8XHd9ElWCkULKP1SNUd+4JqAYBoowP990c//lY0lu/EYjd/tvQG7x8HNlLPkYYM9FEUrk/UlOhykNg2DzGxiNe6HjAhAERzEs3ojDswvFXTBltp3KNJnhzstw44y1E6+9Adl3HSVvTnamyZQ2pG4gUYhsAjcfIzsduJTwrecasYIyt+D/e3h9tE6AmDKdFUXwno2LuXNlgJdPt3H4wg28AXDYYEl5Djvr57KoKG+CY8WQzjlMnY2mijttGg+treG+VQs42d5DZ38ARUBFoZulgyvj6bXViooi6h8u4mxnH0fOX6c/EMauKsyfk8vm6jIctkjdirr5xew9cZU3LvcTj6WlDnbWV7GqohAhlFHyR/Icu43l5S4azMYjmcRbLQPRjbhFSXVIbqvpj1kHf8ZAIlQbriW3Yni2g68HIziAtLnRnDkIIlUvGVn1UlVTclrTpbOZFHtaYSX65g+hH/peyiMpC9dQuusjDIQ00+OO4oWV1p0s4UZoDpB66uNOkIdP7bVsr/7mw+Qu2Jwxncfioc4WAs9/DYIjsvgEOqHhaQINTxNYfi85ax6I5P+bbN1ifydqQ4wnaZeEBy8Obai1gsDlEzjrd2f++BG117D3Y5/NTJy81k2PxfjCQrcdpyNzP8slLicPr13Iw2urSeykZw+yV7OJQVMVVlcWR/8a6YSmhkS2EkKwpCSfJSV5jHfsFxbk8qGtHt4TCtMZCKJLSaHdRp7Dblm3XfVVNFyz9nTLDPaebIlz8G9uzDr40xlCRaIghTKYkg+ilVadBSjOfAzNjgyHkIyTwq5wLrRbSHsIgANq1kOz1XSZUWgFllMnOmq34re7MV76D0ZVzo2heAksWAdtTaAPgCsf8ubiWLqZ0rnzMVQ7xo1uS+PGc+HZgTxo0cFftgup2TGiISsZSVN4/ZQ1nQGuHM+q9JbBvnbCT36OpD8gDU/i1UM4b33/pOs2lWkyCVlMcxKFERzIiuM3ZppMAVLVosnYY8c1/vhOb/6r15uxiqaOAL39AfJzHcxEmwytIqdL7sxIkzk1PD22MqTkTEcvLzW0cvGGn5AB+U7YtLiUbUsqyHfaTclZNiefzdV5HLrYRzrR0O7n808cpjzfxRZPBcvKCuLCbczbaiZg1sGfzogL0VH0MFgIe4lxW91OQu0W017W30nOqrvxCgPOH7Ku97LbLIXoxLh7/ir093+V0KW3CJ9/Fby9YLNB0VzsdbdhzysFBMaK2wf7GtHQEWGEUZApjRvj7gVr8B7KAcN8ei+XZzsixdAG3d+Hv+kgXGsEPQQ2F8qCNbgWbYwENZgKI0n98cxEbJXWsBwgvOfrmP7SbXyO8PxVOMtrJ1W3Ka1kq6b2VS00e0L9Mh+ioyMNGefkj7VyOP24lAat3tSchPZeH3m5Toac4eyZ18R4DOmUO9NsNJl84rZqH/Dz9WcbuDFiveGGH5482cGTJzt4YGUZdy2flzSUSAjB+zbVoirnOdCc3lDh1n5Ja/8Ab7Wcp8gBH9ntYWFBjoX5jjxnpydmHfwZAzn0auHxuX1ePSF7kckMPBE46nYgFJWczR9koKIeefC7ljR1LN2Wkq4gEYqGc8EaWLAGQ9FQjEhhkDHDj4bx+IvW+rgIgVA17Hd8nOCzXzI1T/XW30J1F0bSJVoYSxph+l/7MZwdnZbTaDuO97UfwtpHyV2+M7nOAiLxzVbzyMXlI89wiE7oevPwPSUmED7xHJTXTq5uUxiio1XWEj5uyQQA2CtqM378Bn80b6IQnYFQihXGo5gZ7sXUYNZW5jERW133+vnC4w0kO7MfP95OIKTz4JrqpDIVRfDeTbVsqetn38kWXrs0tJqfbwNfiAln0esKwFeeauSP7lrC4uL8CUqbXph18KczEoToxH5Yk3Ih0e7+FOHHPmtqSGXbxxB5ZZGQEyFxLN2O3+eFIz8xp/OG30DklU55yIpU1EiIzgTDTrSyWsJ3/1+MZ77KsPSQI7Htd3EsXIdhVU8Zxvf0P0NnotzEBhz5Cf2+XtwbHk4uf95quHLE3PGJQizelDUhOsEzL1nSHYC2Y4QDPhRn7owI0VEr6wm7y2Cg3bwN8uehltZk/PhF+M0VohMIp56YvSzfFZUzs2wSgTDZzizPnhAdfyjM2Ru9DATDuGwqi4rzyXEkruY6tXxitvru3sakzn0Mz56+wdK5hdTNKTAlv7oghw9sWcJv3ioJhA00RaCpCt/ae4oTbenZiPu1Z8/wpXeuxj6YjjexrWYCZh386Yw0hOiAwJ5fjnbfX+Lf+03wjpdtxYlt50dxzFsxKgNN7vKdeDGQR36aUF1l3Xtxe7allMFmojwdITox7iytRrznHwhcOkb49D7obQdpQE4xSu0WnLWbIuE0KYzVf/gXSZz7OJx+kmBZNfYFaxPKtNftJmjRwS9eeweBLAnRSTk9af91FId7ZoTo6EHUNfejJyswFgdt9X3DQ2LiZMpwCH/Tfug4A6EAOHKxzV+LvapuUkJ6brYQHbct9Z/WvFwHMk0hFdnFY0in3Mzb6LrXz7PHL/PKhV5GYsO8XO5aM5+qXHfG9ZyIrS729HOlz1q454snWqi7rdDSWEIoOG1DGbV2LK/iRFt6NuKGgVcv3WD7onIT+ow8Z6cnZh38GQM59JrCo3StsJKch/6WQPtZwo0vQu81COuQW4y2+FZsC9ejjnrEPsRzlt9GcMEagqf2QNM+iN3rCxfU7cDh2YUtpzBl/SbO4y/aicsUioZz4TqMRRuHhQkN8hRkGsEgnHre0lEPn3wOe/W6hPJtZbUEy5ZDe4MpmUr1apwVSwh09U7SsbDIUy5xakyublMYooMQuGpuob+7DRqeTD71FQ9FQtlGyJHSoP/IY9DwNEPfGRGEzr5EyFGMsvFR3AtWp9deRO017H1G6ZCtGAiGOd910uxGAAAgAElEQVTVTyCk47KpLC7Jx5GgMJfTprKgQONSj7VQnTVVOTPEtZg6ZNJeTR09/OvzZ8f9/I0r/bxx5RS/vbOG1ZWZz+ySqq0Onm6z3OfkNR99wRB5dlvyxuNg2ZwCih0kLfxlFi83tEYd/JsDsw7+dEY6QnTiuZBo81Zir6iLvK/ZUWKhNCYywShFVTg3vBNjxd0IuwNsLtRYuwxnkklXiM5kcv+F17Hs8Nw4S7CvHXtuWcLjar/zDwg++WXoSrIaUlxL+dv+ILtslVsEndbMAkBOyfCQkDTrNpUhOjHuXvcwA/kV8PrPQB+9YoitELH+ERx1O0ZdbxId33PfhGtHR/eLIdCJ8fK36N/wftx1O2/6EJ2WPi/PHbvC6yPyfgNsW1TAnSvmUuJ2jtl3V30lPzh4eVS/RHhkQ3XUNJJstcnEuDDZzizPXIhOS/9AQuc+Ht/e18yn7tJYVJw35XoO8dRt1dKVIBw1Aa73+8grjrmZ1scVAn7vzmV8/olTwz5JFS39etwYiW01EzDr4E9npClEJ518MCQBBaTMePaOyQjRmTTbdV5K6TTQ25tRcooTyteEgu1tn2SgYQ/yxB4wRqYmU6FsGbaVd6KoGuiBrLGVrXYboUtvWDNKqQebM4/JLLw1pSE6cdxduxmxaAPBqw2EWk9B2Be5ma5agbPSgxDKmGFw/UceT+zcx+ONHxIuqkQrX3rThugcbbnBt1+6MK6J9p/v4cD5Hj45uHlvuJz18+awJ6+FqyZDG3YuLqTY7Yr2Fgw5w9ljk4nxGNIpN3M2evyNi1jBL1+7wKfvXjXleqbDVhN5hprquFLC6Y4e9p5oSbO7bUafkefs9MSsgz9jEHdXmsmQikEdyKweo3j8RZsN+ozBjRQzb+ixPAOJ5QtVI2fl29BX3k3g3CGM0y9Cd+xHSof2E4T2nODqGxXkr38QWbkWkWmbSLBXeAg5S8HfYdokav2dpmwyIT7FITrDjqUQOObVY1uweliImBgnk5QMhaNhOeYRPPEsWoUnPToTtdew92OfZR/OdfUldO5jkMBXnz3DZx9YTnmOc9hnqiL4xO31/NMzx7ieZJ/g+nk5vGPDImCmuBZTh0zYq8cf5LjFzZ/N3SHavD4qclyTpFVypGqrklwHF7ut57MpcjlSGq8vEOIbe05xuXdi2ahGIjVtpi9mHfzpjHSH6KSDpzkkIV18OoToYI9tSLKInGJL8wp2X8E49F+MW8K4p43eF/4danfh2vxeZKbtIyTq7t9Ff/LvzNmj+ha0hRsw9MkNCctEiE6qPHDhZSynSm09Rsjfi82RnwYdpleIzs8PnccKnjh8kY9s94ySk+fU+NN7V/PCqavsaehgZChxuVuwe0UVW2rKoveDMmoaSbbZJD1cmGxnlmcmROfo1dQ2/h+90EFF/bwp03M4T91Wm5bM4fCV0WFqibCgQKPEZY+TZW6sgWCYLz95PG1x9/HYtLjQpD7xf09fzDr40xlSj6z6hoMQ9kNoAIQaeT8czAwPeiN/B6OVMDKlxwgugwK0cHbYaBxuq15J6LSJDZTDoGIvqzE9Lz3Qj/7E5xnXuY/H2b34nPk4V9+TcfvYiqoQt/8h4T3/f2Lda27Fdct7kGHfpOsmQ+HI70AolPXXg2xvtHheRRBqPYNt7rKJ6xAaQOpRO8XeV+3IUBCp6VMaohPWDd64fJ1Xz1yn0xtZlawqcrK1roL6sgJavH7Lm2OPXPVy5noPi0tiebaHxrWrgrtXzOeu5fNo7Oim0xtEVQSVBS6qCyMx2VJGQhIApGEgERiGZKpsMpncMCS6EXvPSOsYhiER0TGmcm5eX2rZ2ft8wYwd14nYqq60gFwF+i2sEexYVp7SWL98s3lSnHuAbcsqTNnfMCQydkFOY8w6+NMY0jCQeghphJCBAQgFgUgGFxE9OeN5yNeHfv4V6LwaCevIyUOZvw61wjN4eo/X1ywn5IeADwMJhj4hWenkekjBCPmRSWyUSa7llBLKmwd9VxIe92EoXRJxkkyOFTjxHKac+xhOPIa+aBNCs2fcPraCeagP/jWBc2/Auf3gj1tFm78OUbsDZ9FcCAcx9NAoOVIaGIqaNn0MzYFUwPAHsv56wJ9iLmlvd1qumbAPVEMig/6h93WJYQsiHVP3Q3r0Wifff/niqGcZXdf8nLx2gTwVVs5PrRjOv+w5x73LS7lz2byh6KxhEHhKC6E03vkd3UqPvjfWZ9MRui6RQkHX4537NMk2orcNU2wrVVNS6mfXlIwd14nZSvDBXYv4+gvmnmwtm+Ng3dxSy2P59DCvXBy5Pyw92Lm4gDKny5ROhhQYkmnv5M86+NMYQlEQqg2h2BAOF4jI3aei2hB6cJBLfx/+134Clw8PF9AJxuXDGFoBxpb34ahaNqqvVY4CBhLFmQM254RkpZOrNheKZkP49KzQZzyubXo34ef/0fxJ0HGaUOdltPJFyY9N0A9nD1o5xQAIXT2FbcktWWEf1e7AueZulJW7kTLiRKqxVZeR8w0HCXU0Ez61D1ri6gAULUbx7MBWsw4hjZT1UZx2kBLFqWX99YA7vuCMeYiCEoTdPmEdNFceQg8jAnLofcWGYrMhFREptjzJePNyB99/JfHNc58OB8fIZ24WTzZ0IJHcs2J+yjLUqC2UKbDJ1ECgGwaqKtI+JzUSJTfltqotzweuWe5XXZafseM6UVt5SvP5P7tq+Nbe5oTtVpY7+dA2D5o69s1cx4Cf41e68AXCaKpCbWUBi4oiT7LebDa/z8oKti7M5+3rFkYeFJqAIiSKADH2nfq0wayDP50hVFA00Oygu6IhtjLydywmW+oEn/kq/4+9N4+P4zjvvL/Vx1y4AeIiSAK8AAIkeIsSD5EUJZE6LFla23EcX6+TzbHJJrGd7H7Wm/W+b66Ns7vOuYkdZ3P4iO3EieVDtiRbEklJ1C2KEkmA4H2DAAiAIIC5u+v9Y2aAwTndg54Lwu8P4DfT1dVP/bp6+unqp55ipHvmeqJDGC98ieDOX8K1esfYvsn1WOYSMCToHtC9c6vLQS5cXqTqAi2cF/bMxF11LUQbd8Gllyx3A3nk68gPfSE2ajhL/dGBy0DYcr1juHYCWvfkhT4Tziky9mYhng4yuYxEEjz0Feg5ObU9g+cwXz1H6K3v4Xrwt9BKa9KyQeh6rL6wZut6kJhEbt+A0SGEoqIsWoai+TKql7r8DoxzL9o88QLX0g0gxdxt0H0IJQyaMf69IhC6G6GpxFwPMeHYTvJbgXBK594pPNXRz5YVNdQVJyZT2rNZKErcEcusJtnj8YdoBZQJ3uXcj5HQSBnz3LLTthUVJdT6BD1+6yO8RQq011egiNycVye0WldXwR89XszLZ3t4/mQvo0mj4e11XvasXUzLotJpHGPBxVsjfP+Ni5zpn3QPOtFHtRce2ryMweH0YnN2NJVyyx+mozc44fu2Gg971y6mrXby/LbUWhW6cw8LDn5hQ6ZOkxk+9JXZnfskmEe+glm1FK1k0ZR6LKd6zEBaQCd4IaTJVKQReyV4/YS9fhAewLjRiauuZdb6jVCaYRoRf97rNkFD0yD0k7+A/tMp2jVI+Mk/Qn3/f0Mpqsp4mkxpjBA4dRjZ9TwEJk7SCy/djLvtftTqlRnRxVOzmlFv1ZTjzorme1Aljpz7XKfJPHLG/kI9c8Hhjut8eNsqS7ZN5bCQJtMqz51GD2xcyldftp7a+IGN9Sgi8YCTC5ud0arE7eLA2iXsb1tCKGoSMQ18uo46yzWcKuVsXwC+euQyS0vTc0mry7z83F2rGQlFGAgGQQgq3W6K3Vqa7Z3cZwsT8+Yl4ALk+P/4k6cx0g/X3p55l2kQ7kispDpeT3qcOe7vNE++aPPBnul5dOAKRG5hF+GLr6esX3G5bNcLgCsxEpl7faxwf+fh1M59AuYogVe/ld6xEiNxFsobgSECT/4B8th3pneyrxwl9Mwf4+84lBFdhAD9jg9b0wRA+PCuO+CcDWM6JX+f2JZZSCk52JmZV/8z4cXzQ2nH784f9yI7yJZWN0YC/PCdS3zjlTN867WzjAQj7GuutLTvjuVl7F1dl2ELU8NJrYQQeHSVEnfCuZ8eV4dHLaWcBdJOi1lZEktRW+zWWVZWzLLSYordelp1zScsjOAXMlKkyQyctvtKHjh7GGPrBxGaeyFNZg54NGgvFdkYRm6lXLVVKW8k9kxvc+bT4vV5r1uCS1Mixx5SLaL7XSKBIXRPmb1jWUyTaRgjRH765+DvTW3L0W8y6nbjXbndcY30ZRuJ3PXz8OrfpzDChf7QbyN8FQ7akLs0mcFolGDmnyOmIBQ18OjqFHtS8Zg0kkxqkjsuLJazyjOfJvPasJ9vv3yOC4OTMudcGAKgqVzj2q0o0+XVUYBH1tdwX2tDPOQjl+cgNylFn347vQUcrUIFNtRXWLbHGs/BD0YGsODgFzJShej02cvlnIA51I1euWwhRCcHXBHCTo6bcej6xPCH6epXFWi5B7qes1W1Z/Udea9bgkd7T0N40LZ8ka6XcK8/YOtYVkN0gudft+bcxyFf/TaiaQsKOK6Rb9VdGOXVhI4/A9eOTTqyBi17ca89gO4txslVgHMZomPkKGvJeKy1XZthIUTHKs+sRucGbvOnPznDbLh4K0p9kcI97Q2c6x5iNBTB59JYtbicbUur0NTkh7xcnoPs96fhUJRj1/1kEvvWVKGpimM2j2tV+Fhw8OcN5Ph/IWIfjbRcRaQRnliPHT5mA+ntnzGefNHmgz3Tc61iybQjQSlRscxS/d7WfQTsOPhtD6Lo3vgqu7nXJxU3htMMxRhJOOA2jpv4PEsZCXDmBXu2yAChS2/jWnlXRjTSq5rQ9/4ykeAIxs0LEAkhPSV4qlciNA1T0Zw/38T1GvteASXh+GQWPj2921yNF+5cWcMPT1h/OEtAB1xaeu2bH65F9pApvYbDEf4ihXOfQPeoyYlL/fzSnlbG+jvEef4g232rq28oo/XX+hQOrF2a0WMUMhYc/EJGqpVsXb706vWUpQz3mJEvhOjMiQtfOVS3Q99xW6fM3bzL0jkTxdWw7zPw/J+mrnTpVryb359zTexwU6R5C5OK7b5hJUQnOnAFggO2zYlefBOt+e6M6iVKqnF5Yys7mpoLGY0gM3ZukkN03KAKpOpCuovjefsSjlCyQ+QMVxTY3FDE0WujqWSfgLtW17K/rQGXS+XfjlpLVJDA3pZK23YmeMw9lGRSk9xxYbGcVZ65sJOXurptvU19t9tPz7Cf2hJvRuzJZ61m4oFQejH17XVebgwF6JslL8SyUpVfu68Nj548cTk9O6fTaj5gwcEvZKQI0VGXb8Xoseco4qtGL14Uyw++EKKTE65tPED0pzbO27Jt6J4SrIRUGCMDcPBvU9fZuIminb+AkGDmgSZWuV5ak04iUCiptt03LIXoBNIcwQr5Z6230PiEEB0ZRWg+pOoFxcV4roeYa5sJvnddA0evWZx4HcfOlbWAYOeKOp442m1r5srdLfVzsBkWQnSs8sxoZEqTg5192MWLXTf44NbljtuTz1rNxj3u9FzMcp+LX9zdyjs3Bjh0/BrnkuY/tNV42bOunrbqMkTGshJN7rOFiQUHf95Ajv+Pvxr3LN/C6KtfB2w8Rbfuj0fajNdji4/ZQHr7Z4wnX7T5YM/M3FXXgrH+MeS730t9vkrr8ez4uKV2hXrOEPnpn6SuE+DS2wQXv453xba80MRy+MmiJsK+ReC3F6rjbt5uScMJ3EKIjlB1W3aMQdHyRlNHOHG9kCAFsRCd5BR2mcWKihK2N5XyisVFrD6wZTFFcefEpSn8yj0r+OuD1uY0/eydS6j0udO2dX64FtlDJvQaDITwpzF34/SN9BdJyway3bdaqtNbFbqloQJFEWxaXMWmxZWAwDRlfJGuZGd8AbNhwcEvZKQK0VFV2P5JeOXvrNVXXI+7bQ9mfGGbhRCd3HHv+ofxu4rhzW/MfL7q1+PZ/QvgKcGML/Y0U53B3nOYVp37OIxXvo2xYhsyTzSxwhUJrNkPR79pvaGLNyBKalJqmE6IjihvsKX5GKqX542mzvDkEB0NRVFi62dNCM8ho/wj21Zimmd57fLwNIKP4/0barlnLKVhbN+22nJ+9Z4VfOng+Qk1T8bP3bmEHctr5mRn7HFIJm3Ljj7Z4cJiOas8M2EnoTRnZgcjiaCefNA6O1rNxkvdOuvrfbzbbX2irVfA+mmy4oyvkZYdreYDFhz8QkaKEB0Q+FbeSSAwhDz2r7PXVVqP9/7PIOTcFrZZCNFxjhe37MJYvZ3w+dcwLrwFoZHYKqA1Tbha9uIqio9spKjHDPkxn/njNDpYgMilo7ia7sgbTazwopadjF45Bn0dqZuol+Pd/nOINI5lJURHUVXCy7bA5bdsKe9t3pmWTfnKJ4TomBGE4kJIkKaZNNk25tpmiisKfHxHM3esvsWhE9c50TMxwHd7Uyl72+ppKC2etp622gq++MGNvHqplyOneugZNjCBRT7BjuZadiyvwTeWe3suNsNCiI5VnhmNirT0XKOisZCUXGudPa1S8Qc3LePd7lNYxSNbGyat4pwrrQofCw7+vIEc/z/pNXnR2nsJ16wgfOIZuP7OxN3clbDmXopa7kbo8UmKcvp6LPExG0hv/4zxST8YObfHGheaC++q7bBqO6aioZixcCs7mU4Cp19K6h/2EL18FNfybTnXwQ4XioL3vl8l8NLfw5WjMzeuqA7v/b+J6ilNr99bCNEB0FfvJWLHwV+yCbWocu7XYj5x4nohiYXo5O4Guqa6nDX3lBGMmgyFwqgCytxudHXy24SpcGkqu1fWsXtlffybeNvG+NwxP1yL7CETepV5XdT4BL1+e+d0Y6O1ha9yhVz0raWlRfzC3Y383YuXUpbdv6aK3SvrcOpaeq9jwcEvZKQK0UniWs0qlP1tyOE+ore6MaVE8xShVS5Fam7nMmgshOjkFY8t/PRcuj0M/P68aYsdLt1efHv+A5GBy0Q6DsLFN4FgrE01raht96E1bkYYUcx0tbW40JUoq4Ntn4DXv5Zab18Nnp2fSj+LVd7y3IfoTOYeTcGjeabZlht7Ejz2yCDzxh5nubBYzirPXNjJnrZ6vvPmdexgx6paR23IlVZSmly8NcLN4RBCQG2pl6VlRWnbsKmhis/sd/H9Ny5yfvKCYUClGx7atIS7mmqS9s+8JuGowasX+zjSdYOeYRNJ7I3cA9uWc2B94xQ7Cw154eC3tLT8f8D/Cyzt6uq6anPfZuB3gV1AFXAW+Arw111dXTla4iRLkEZsFDcahmgQIn4Qauz7aHharmou1OoVmKqKEg1DNIKMZ19Jta8lHh6NfQ7HnSmn6p0jl2EBWtSSRvOJS/8QROcw8UtVIRLIi7akw/XSWtSdH0W580Mg1PF+L1TMaHBu2kaisftFJJLyevA0biLkLka+/A0wZjgfSzbh2vFxFGSsjjzScc484kcaCZ0EUipIxY2MRkFxIqxl/nBpmkhikwrzwZ65ctOUGGbiO9PRY5imRMSP4bTddzZW8+y71xm0mJbrvuYKinQN03S2jU5xK1oZpuTw2escOtnLrUl++CI33NO+mJ3La2Lr0tm0YXl5MZ++fx09I36OXx0kEIqiq4KV9eWsqixBCDJyHmfiJ3pu8ZXDF5mMHr/kq4fO841D5/nV+9Zw56qaKWUKBTl38FtaWh4DfifNfTcALwClwBHgDeAe4C+Bu4CPOWRmXkKaJtKIIM0IMuSHSOyXyBQCIWVOOJEghAKYSDCNnNkxmRsRBTMSROaBRlk9H8ER5oTKBszgaF60Jd+4qbmRCpjBkKXrQa9ZgfLo7xDuPo156c2Y86/oUN6Aa9VdqN4yDMNESiPnbXOaRwOgmhIZDmIaBopHx4yEkXrihr6ABBLzO815MjxlGBIpFAwj2fF1qG4z/tiQAa00ofAbB9r4kx93MGzMXrZ1kYtw1ORvD3ciBDRUFLFtVS0VbpfzhqWJVFqFTYMvH+ycdoQd4GYIvvPmdTouD/Cpnc1JK/TaQ7XPy77mxBo9MadbSpAye78Fx3sGU4YMGcBfPnsKhODOldXZMcxh5NTBb2lp+VXgz9Kxo6WlRQBfI+bcf7yrq+sb8e+rgWeBj7a0tDzR1dX1bw6anFcQioJQdYSiI9xeELGLRVF1hBHOCUcBE4niKQLdkzM7JnNV96JoOiJg5IU9WeNibjdUz5o94CnKj7bkGVc8LpASxaNZvh4UI4ynaQPmyq0o8XKmqo/xfGmb01zzliCMKCIkUTQvQtVRdBdSEeNp8BcAgBrXQ5k3uggM00RVheNtUmNRchnTqsrr5nceWcdPTl7j0JnBKWshlOkxx7nzZpjOm+ND/e/eCPJUZz9ra9x8dMdqil06ucZsWkkp+YcXu2Z07pNxsjfIt14/yyd3tjhvZBYQjBiW5gMk8DfPd7FmcRll3vx5WLOKnDj4LS0ta4AvAg8BNwE3UGKzmvuB9cChhHMP0NXV1Rd/cHgJ+A1g3jr4CDWWS1pzgeGNvf1Exj7HY82zziVgSNA9oHtzZ8ckLlxepOoCLZwX9mSLC6FBdSv0ddrvXyt2ohQtwsyTtuQbF3r8ph3WCu56yDrXfQglDJoRe/BX3QhVRWjJufCTH0bfu1woStwRE3lhz9x5bOReUUCZ4F3O/RgJjWL/M9OGYo+Lf7dlOY9ubKSj9xa3RsOoikBKybffmD1G/2RviC88eYL/8r51lHqSHcTsn4/ZtDpzc5TOvtBMzZiCt675OTAaYHGJL2v2O8Vfu2xvAbOwYXL41A0e3bTM1n75gFyNEXyZmHP/U2ALYH8td3gg/n/KakBdXV1HgF5gV0tLi90Hh8KBnJgmU5FRFGnG0/bliJsRVEAxjNzaMYkLM4pihPLGnmxyrXWv/b5VtYKibR/OC/vzlQsznn61AK+HrHMjhDCNGDcjCGnEXjiOxSsnOx0LXOaBDc7zfLEjPa6pKuvrK9m9qp6NDYv4lxTOfQK3o/A3BxNpIvOjLZP5oZP2JhMDHO7ozhv77fAjp3qsNnEMhzpv2N4nH5ArB/8N4NGurq79XV1dl9OsY238/4kZtncRa19bmvUXBII3zjL8yj8xcvjLjLzyNUZPPocZHo1vlUxMXZlNTg6PPR1PvsjzwZ7scfeSdlhk43VqVRP1H/x8fBXW3Nuft1zE+1RBXg9Z5mM6SXKdJtNJmKbkxmiAC7eGuTEawHQgjjj5l2oBqZELrV4+3zMlXGc2XBqKcm5g9gXWsoGZtHrHxkJUCbx1cWhuxuQIPaP2J2z03g4mTQAuHOQkRKerq+s/OVBNIhFx9wzbE9/XzrC9oBG5cYpzX/0CwfNvTvheAsFj34EVO/Fs+3AsLEDGtmQlLd5Cmsz840LiOvBpwk99EQbOzt6xVt7N4gd+Gam5MUdnTru6wK2nyczH6yH7PP/SZM6FD4fCvNDVzfMdN0kObPApcE9bNbtX1yctemSv/tjjkEzalvv2OseFw/Vmf3VWKU2eP2l/RPfQyeusvDs5oCDb52B6raLprtork+vKh75ljSd/sgNTSpQCe/Ses4Pf0tLyT8TCbFLhia6urs/N9XhJKIr/n+nRM7FMYfEM2+cEl0ujujo30T+jZ9/g4t9/AjM0y1P3+SMEb12l7oO/g+bzABKpqGOrb2aKy7CO4XKjunSES8v48Szz+Eq2laXu/LAnB5yf+a8Md7zA7befguFJrynr2yjf8hDFy9aDNJBG6D2tlTVuAJKq4pn7ed5eD9nm8ZVsK0vdSN0FugfTXQQeH6iJ2OSYa5vv/GL3LX73e8cJT+Mp+E340Yk+Dnb28bsf2sLi6pK0jiURVFSXZqwtpik5erGPCz1DmCaUFrvYsaKWshJvRo5lmsRj8MWc6pqJx7Ryts6ZeDAYZTiKbVzqH82qnVa1Sjd7jQYZ7aOZ4jVFCr02R/Eri93U15XZ2icf4MQIfiNg5f1/feoitpA4QzP1TjHp/7xAdPgml77872d37hMYuMSNZ77Ekvd9Nq5S/DV5hrlIlj4Lx1vg1rhQdUrX7aO4/T6ifReJjvQjVR1XWQ16aU3sIUCaObdzvvGF62EST/xiyzFV8uj2PzvvGRjl8/96LGV4ht+Az//LW/zvj99JZaknb+w3pOQHb57nR29cwT8p9eM3XrjI1sZSPnb3GmoqvI4dN/GZPGi/EzwUSZEzcwaEIvnZ1xGCxgqdSxYy6CSjdXFsjDXX9tvle9Yv4Tuv2IsMv3/9Elvl8wVzdvC7urp2OWFIGkgk+PbOsD2xROHoDNvnhHA4ytBQIHVBhzFy+B8x/Les73DpbXqvXEArrcPUXCjRCCAzxgmHMYNBFK8GYS3jx7PKK8p9mKqLof5beWFPzrmrFiprMDUXoWgEbgfnpFXUjBLufAGuHYdQEDxuqG/H1bYbTXHlvr0Z4FXFOgjoH4kU3PWQbV5WVYFihBkcGsXUBYqqYuoKBHTGVhguAPzfg52WY6+DJvzjcyf5f3Y12zpGRXUJAslgn7Mx24Yp+ZtDnXT0zqz3m5du89al1/mtB5ppKnfm5bdpmkkj+M5O+6uoLkECg31zWMzPJsLR9Bx8nwsGsmjnZMym1c7mWi69ZmuNUe5aVZ1V3Z3C5royvmOjvBBwV1MVfQ5fj1ZRVubF5UrPVc/5QldzwHVgI1AHnJpme6oY/YKDNA38b/6L7f3Cpw7huuNnIBpGkbE88JnimJHYqI1hgMj88axyEQ/RUWQ0L+zJZ25HK2FGGX3nR3DyRxM73RDQ00H42D8Tbn2Aok2PokTJmzY6wRPhJ0oBXg/Z5ko8REeRZkwTxYWQIE0TlMSCOfkwvjczHwyEOdljb1DnzasjfCgUpchtZ7VeGM+iM3t5w5T4IxE0RcGjqfF5zdOX/+7Ri7M69+PHhj97+jS///h6Sty6RZuttSsGJ89NarsaibAAACAASURBVI2c5i5NY1Wli7MDFpe4jWNLU1VW7bSj1R3LFvH9N68yavHZpdIN7fWVs9Y5V55IsJXOqrmz8RK3i5+5Y7HlLEgfuWsF1aWe1AXzEIXs4J8glmqzDTiUvCG+CNYaYouRdWTdsgzBvN2Dedt+iid6zwKS8dflGeQkONk5nmWe+IHLF3vymVvTSgL+V74GF16d2ueS0fk0o/5+vHf/4vzSP/G5IK+HLHPieiEp1Cw6Ry/3p7Xf29f62bWiznL5VMpIKTl9c4jDHd28m5T9pEiFe9ZUs7O5Pu6Yj2M4FOHw2UHLNkSBF09381B7/uf+zkVP2r12MWdfvGhrn10tTkcp28dMWumqwm8+2Mr/fLKTVNMLPAJ+40AbinBe+YFAiJe6ujl8qn9s8rpPgb1rqtnRXEe5x5kFw3avrEMi+M4b12Yt9+FtjTy4vsGRY+YChezgPw38Z+Ax4K8nbdsBVAOHu7q6cp+byiHISJohQeFgPHvFQhad92QWnQxpFeg6ktq5T+DSGwSqmvG17c2LNjrBF7Lo2OGFn0Vn2G99IaBkjPjDto4VexySSdvGy0QNg388cppj16fOwRo14MmTfTx5so9f2bucdXUVY/seOW0/68tzJ/t4YO2S+MRYp/R0sq6YRtJWeWf4xvoKGsuucmnI2mzbe5srKPe4sm7nRD67VouLvfzX97XyT0fOcm6GePw11W4+umMVFV6343Y+f7qb7x6dGnDhN+HHHX38uKOPn93WwK4VtVP2TYfvWVnLuvoyXjh1g8OnByY82Dy4vpZH7lhJsaow19Xgc4mCcPBbWlpWAjrQ3dXVlUi+ehg4Cdzf0tLyi11dXX8bL1vNuMP/xawbm0EIb5qzuD2lsVfjCyE6CyE6DmklAY59314/fOeHiDU7EULJeRud4AshOu+tEB1dTS9+XFUVm8eC6UJ0pDT5+5dOTxi1nwlfPnSB/3ivwprqcgBOXrcxbyuOEHDDH2Rxsdem/TO3K4bpy0kJHX23OHziOuf7gkQkFGlwx4pK7l5TzyKfe5p9xZR6ssEVRfBr+9r4y+c6uHJ7did/e1Mp79+0fNa2Z4en1qqm2MdnDrTTMxrktTM99N0OIhRBbamHu1bVUOVzPssSCA6emd65n4xvv34NIQQ7l9c6ctwqn5fHNzfx+OblhCIGEolbU1lUV47HpTKcg3mWTqIgHHzgOWLZej4F/CNAV1eX2dLS8vPxbV9paWn5BWJx+XuBCuBvu7q6fpgTazMEtXgRekM7kWvHbe0nlm4EJLkI0TFGb2MGhjA1Dd1XhaK7smPHFJ74gcv2cQuRp9Yq3N0FEZtOg3Gb8PVO3A1tedBGB/hCiI51TlwvJIUaolNfVQzctL1fQ2VR6kJJmEmZo9cGLDn3CfzDwXP80Yc2owhBMGwxuHoSgmlmjLGLfn+Iv362kx7/xCnMt6Pw3OkBnjs9wJ6V5Xxg64opoSHZ6kn9gSAnrw0SipjousKa+jI+e6Cdw2e6ef7EDSb7+cvKNO5Zu5itSxfFR4FlliydGVa1qi3y8ujGJsauXSBT9g8GQ/zbW9ZX0v3Wa1fZsLiSYrez7qtbV5nY3sJHoTj406Krq+v1lpaWO4HfA+4B1gFngM8B/zeXtmUKvm0/y9AT9hx8d/OurIboGASJXDwKHYdh6NyYHRGA5dtR2w/gLm3IrB2T+EKIjrNaRW5es9b5JiHSfxV9SXvO2+gEz1SIjjHcR+hKB0SCoGroi5tRq1fkRZvT5deGI7x79jpDQ0FM1U11TQXrmry4CihEZ0N9BS4uYmdqZYkGrTVlto4VczFk0rbY/0MnrDtBAKMmnLwxQHt9JV6XGvvCJrxa4q2FU3pOdnQltwJhvvDDkwRS+I+Hz93CHz7NJ3asTnKYMx+ic2FwmCffukzXzckhWtdZXq7z0Oal/OEHNnFhcISB0RCqIqgv81FX7J1SV277cW7CmVLxF0/Zz4Py0tkbPLB2SQZtS9EZCwR54eB3dXU1pbu9q6urA/igwyblLTzrHmT0tW8SvX7S2g7r3o/mckOWQnRkeJTI4b+F/jPT23PhFYwLrxDc/BF8rXvyKuxkgdvQSqYXj4wZIlvhYoUWohPpv0TorR9A38S8AJG3IVK+HH3Dw7iXrMub9lvhfQMjPNN5nUu9QTQl5mCGcRG9MsJ33x5gW1sTD21ciaYmHP3k0cL84qqisL+9hieP92IV922ojzujdo4Fk0N0bgXDXLhlL085wKtn+mivr2LtkgrOD1q3G2KTG2uLffFPTmiYwMRt33j5bErnPoE3rozQerWfbUsXxesZ12ju9k3lb129yT+8dGlGey7civBXz5+nvc6LKSEUNfG5VNqbqljk9aCpds99JnlmtUqXv3Ta/uT1I129cQc/k1oVPpxNSruAjENoLio++ldodRbWFmu5B+/Gh+MfJBNDBpznUkoCh//vzM59Esyj3yJw7tWM2zTOJ/3QZu24hchTa6W401vFWXGX5kkbHeAJx81SeWYtF7zyLqGnvzDFuR/DrQtEDv8fRk8+lz/tT8Ev9Y7w9ZfPc+2mP96b4n+FwBQqUaHwXFc/f/V8B1HD/uhyLrC/dQmtNdZS5m1uKGbfKvuZU6ZzLwYC6T1QD4zE9tu1ujZFyam4d21NRjKlJKPPH+RUn711ECa/yciUhRduDc/q3Cfj+I0AJ3sCnO0P8W63n3965Qq/9Z23OXgmv7J055vbakqJP41Lf9BehtL3LPJiBH8B9qAWL6Ly57+OfPef6Tv4VRidFBdathxl/QE8jZsxNRdmYuGZDL+GD1w5Dn3TLUkwPYxXv4qxegdkIWxmIUTHWa1cyzcRfPOfLJ/rBPSmzdkLF8swdypEJzR4BeOFL1nSTx77Dv7SWnzLNuS8/bNxfyDEN165AiYIFEKmigsDIRQCwkNY+IgIN6ZQOdMf4l/eOMfP3bWKeEWJ1uYdVxT4lb2tfOfN87x0foiZcG9zBe/f1DT+vGPjWBKYHKKjpOmZxY4vKXLp3NtcwXOnraXKdJN4KBi3YTabrXMx4fPLXfYd4MtDUW6MBOIhMJkLO3nyTWvO/UwwgH97q5tRf5j3bWh01Lb0eP6F6Mz9gSNzWs0HLDj4BQrF7aPq/l+mbMv7uf7WswQGuhEyilaxGKViCUo0HAsRkEbsv1Azz999ymYrJKFTL+JbsTXj9smwAC0KEX92tChgbkUrRfXA4k1w/W3rp7u2HVV3Z79fZojLSDR2H4hEZi4fHo19DsdHKacpZ7zxhL3L5rVvIetXI/JIi8n8zdM9iGgIXTGIiaShY2AKFalohBUPUcUVe0gCXr54m4c3hChxu8Z+G8jY6/e5cYHkZ7au4IH2MEdO36Dz+hDBiIFbV2hfWsmOVbUUuzSQsRFKu/VL00QiMM3xfas8CV3soa7UM1bPIxsa6R8OcSzFRF0V+PQDq/Fq2gQbrNo/mZumxDAT35kTtnXfsj5pOBndt0ap8cXaJuLHSNe+6fhNf2CamPv08HRnPw2LitlQX+GIbenyTGk1V+4T4LfpT1e4YiskZ1IrKQvfyV9w8AsY0jQBE712NbJoEURiP0hGcBQR75ymEFnhZmgUBs/ab8Tpl5BL12fcPiOiYEaCyEg4q7oUIreqlbLuAczr7xC7aaeGsv6heaW/qbmRCpjB0IxliAQhFMBEgmlM1TpwG3rsTZon1E/4egeu6lV5o0UyjxgGJy/34DFNNGEiUVDNCJowMXChEUUixpz7BI6cucH+tvxfWCmBYt3FgbVLObA2YfO4ozDuRNlHIlrJTLqsvJrOuho3J3rtOZ3bW+uS6hF8ckczjWev89yJXkaMqeU3N/h4eFMjVR73hOPPBYYhkULBMJIdshjS9aGiUmKaMa0EOGZrAsfTSCs6G376zlXaaysslR2ORnjlzA1ePn2TxLSLOh/sbK3nzmXVuNJM15opreaKPWuqeKrTXhz+rjU1GW2HKQWmpOCd/AUHv4AhFAWh6ghFR7i9IGI3GEXVEUY4q1z60/xBDI0gXK6M26fqXhRNRwSMnGhkDFwjOtSNiUDzlaBXNaForqyfJye1clUvw9j/W0R+8ucwa24RDf2+30StacqbNjrBFY8LpETxaDOWQQETieIpAt0zpVzkykX71wxg9J5HNKyNHSMaxgwOYxoRFM2D4inKqS5D/bcZiapEFDe6iI3gRxQNXRiYqESFPsW5Bzh94zYPrEtLjnmFhP+mTJJoX3sDJ547b7meWi+sLJ88V0Zwb8ti9jUvprNviKs3R5CmxOvT2bKkkiJX8hsUpyAwTBNVFVPaVFXshl57MfgANcUeFCWmlWCqVnNFJGRtASuruHzboMfvp7549pSph8508923p65Wf8MfC/f57lvd/MreJlprrD0sJCNTWs0VO5vrbDv4O1fUZrQdioiFxRXyIlew4OAXNoQKigaaCwxvfCBVguZCohK5eYFo7yWIjIK7BH35RhRP2ViZROy7I9xtbeLZFGg6aL7M2JTEhcuLVF2ghTN+rORzELr0Jubxn8DQxbEmR4GoVgZrH8DbejdCc2XFnkxopdc0o3zwDwl1vggnfwIk36zd0LYfd+vdqN5yzDxqoxNc6HqsmWFt5vISMCToHtC9U8rJaJqOhBHGNA2CnS9Dx9NjaxJEAUoWw9oDeJu2IjQ967qElCARXIRQkSgISOIKBgqSqTfOiCHjq6YCE7a/t7hQlLgjNnG6bXN1OQdaq3jGgjOkAr98X+useq6rq4ivdjsdnGxXbOReUUCZ4JUJtrfU8cIscxmmQ5UblpUXI4QY08jpfjPisIMPcHnAT0Np8YzHfa7rOk9M49wnQwJfOnSR/7hPYU1N+bT1zMQzpdVcebnXzc/duYRvvnYVK/jUzmUUe/RJ3zprm6KIgnfuYcHBL2xIA4GJkCaKEYV4WsPgmSOY7z4JwxMnMEXe+gaRunW4Nz+GWrnM0bR4uq/CVn7oMVQ3ZiV1ZbbTZMpoiMBr34CzL07f7ugQvPPPBC69gu/+30ShJC/SGqajleIuQd30PsT6+4neuoERDaNqLrTyeqTqiqfVnB+pMZ1Ok6m6PUwTKZEaRpDgv/xXYJqVFoevw6v/QOD4j/Du/wxKUVVWdSlSBapi4oKx1JgJbioKiojFAk9GkVtj/GYr38McplvJFiTvW9+IS9f44bszO4IVbvi1+9uoKfLkQVsmtiuG8W1Ly4pZWqqlXA02GXvXLU5KPTpVo7ny0VCUQ2edDdEBiERnjhnv9Qd54u0bluv6m+fP878+tMlmGk7ntXKK71heiyLgG6/O7uR/aucytiytzoJtk/tsYSLPXtYsIH3Ebpgjb38P88jfTnHux3DjBKEf/wGh6x3j+zmQFk8oKqzeZ9tqveUeR+2YmU/6IcxwisDA0e/P7Nwn49Zl/M/+NdJMuHmZty1TWglFQ69cgquuGb1ySaxP5LwtGeQJJ8NSeabd5qpbRVq48BrTOvfJGO0l8PT/xgwnJjJmR5eyEjdlXle8B830dyraG6tmb897BLO5F0IIDrQt4Y8eb+eR9hpqfAIXUKRCW42XX96znN99bPOkRZbyGx/bvdpy2ZWVLnavrJvwndOu2MvnrTvadlDinTzqPI4XOuxlE4oAb161v6pyPrutdzXV8McfWM/7N9RS6R7/vsoDj2+q53/+uw1sWboodwYWIBZG8AsZIvbaWwoFU9XxdzwXe11vAcbzf0b4kd9FK6t3LEWe1n4/0TPPW7e/bDlqXXNW0nhmM02mEbwFHT+2rsPgOQJXjuNr3JRx2/JNq0LmTqTJFMXVUL0W+iwuXGcXgX6Cx57Ct+2DWdNFkbChsYqnT9yMpcZEjKfJBAxFMF2Izp2N1cQriuO9ySUw3Uq2ybzErXNg7RIOrF3C9Jh539xyMWVbQ7GX336gmT9/+jSzLeXVssjNL+1Zw/gc05hG46rN3T4pTZ4/OXuYTLpoqymf9rimlBw+ay19aTJe6uzmLlvXTP6lyZzMi3SN+1sbuL+1gZmRDXuSPxcuFhz8QkZSiI6IBuHo92ztHj75DK7tn3Ds9bzLV4Wx7RPI17+W+uBqCd59v4SIhjEGrhDqOgjnXmdssmbFKrTWPbgbN6FAQYXo+LssjNxPxsmfoCxrz7ht+aZVIXOnVrLVNj9K9JkMOfgAp59DbH4fIj4hNxsabVxeydGLg4wEYy5bqhCdxzbV49IUJrw5es9ymClEp7B5AlO3NZUX80cf2MjLF3s5fOI6/UnJgtbWetmzdjGt1aWIscnZiX2d1SgUlQw7H37P7pXluHV12uMGwpG03MnesbAmq22cb/0pk3xyny1MLDj48wShy8fAGLG30/mXMbd8EDwl8QdWCULMiXtXbCUgBPK1f2bihMtkaGAaBJ74PDDDr+ngWaIvnyX6ZgWuA5/GVVozR/uSL9q5t3NWftlGbvgEBs5iRkIoujuztqWpVXSgm+CFV2GkHxQVSutxN+9C8RTn0M4c88Tn2cqMaThzOVfNCuSOn8d4+e9T95NFLXCzK3W5CYgS6u7Es6Q9axoV6Qqf2rWCrx05y1AgHO9NyX/HsX9NFfe1LCZeyXse88O1sA+PrrJvdT37VtcRNSRhw8SjKygTnPqpcFKviHQ+96JHwP72pTNuT7fXp5PB8b3at96rWHDwCxlJITrRK+mNAIb6LqA3bRm7Qc/5Vb0RRW9Yh/jIVkJXO+HMi+AfAmnA8E0gBCQmBFtAeJDwD38XHv09tNKawgg78d9O61wYYT+4fDkPQUnWKnrrGuEXvwpDF6bYGzr+fUINm/Ds/Dj4KnNqcy64UyvZmqqOe8V2wkVVRF//12m1xrMIZctjmL1n03DwIToylPUVhD1lXj6xp5nXT/fyyqVhopEQgvEQnWWlKvetX8LmJVUw5uYkey3vTS6BVCE6hcuFpXKaKtBU1UK9zoad+DQVJ+EBPvvQGsrHsr5MPa5PT88Nqyq2ok8yz/8QnfzhyZ8LFwsOfiEjKUSH6Gh6dYRGZw0xsMvHQhKkwNe4EWVZO2bYj/8Hf0jMuU8HJuEjX8X1wG8VRtiJy8WswaQzQFV1lDzINpPQyrh5nvDTX2DWH7trbxP84WU8D38OxV2UM5tzwZ0K0UlwT81KzEc+hzlwldDVE8hwAHQXrrrVqLWrUaXJSJ/1POjJUFQlJ32r2CXYt66Oh7ev4HL3INcHQ0R9VdQ2LKOutJSEOxvDAmfsk2DcGc61Pc61KwYn63VWI1VRWFvr5WRPignsKeARcE/rIna31KdcnVlRYEdTGS9ftJcudPuaxGTj3Gg1v/nkPluYWHDw5wtcvrR2U3QPjr6eJ8EZ2+Z/49sQGphT8+g/Q/RWN0rlkjTtS75oHWjnbLxuFZzrs9c+rQyRN+EuAiPoJ/T0nxL/YnYE+gk+9yWKH/ptW8eSpsT0D2KYBprLG3tAyHnbbXCHQnQmc62iHq2iHlPRUMzYmy5TKCBNlLIGi+sGT4RWUZ9TvVyayuollVSXhjC8VUhver9X7wXMD9cie3Barz1rF3Oy55ytfT7/SCvDoSjBiEGRS2VZWQmqknCoU2P32nrbDv5dy6pTF5qEhb713sKCg1/ISArRURavxzxzyHYVat0qZ1/PTwpJMAO34eIbjjQ3eO5lPNUfTcu+bIbo6C33ETn3ir3GtR1ACg2ZYdusajV06ggzz6GYBoPnCPVfQl+0ImX9ZmiIYOeR2MJYMvbmKQxQ1gTr9uNt3IRQshtOMtcQHTM4SvDsa3C7B4iCbxF6y3ZUV7GlEB2r3LXyDoJvfN36eQHwLEJdtApTpAgnyih3oRhhTAFS1WCC85PsBC1wCbzXQ3Ssc+fDTlqrS2mr8dBhcYXdA61V1BZ5qZ2ySK3187ekxGd5ETOAT+1qxK1PNzchu1rNX578uXCx4OAXMpJCdLyN6xkVPpD+1Psl0HQnmu7DzESITjwkIXDGpqM7G4Z60g4nymaIjruigUhVM/SfttgwHU/L9vzJWmNECRx90vbpiXQ+j3tX06z1R290EX72T2G6ceihi3DkKwROLsN376+jeMvyIhRnthAdIxwg+MLX4PyRqXoc/y6R6lb0TY+huLykCtGxwhXVBc33wunnrJ+Y9Q+hYoDMnV6KEUKYJooEaRhIUyY5+UlvOhZ4/JNg3BnOtT3OtSsGJ+t1XiMhBP9+dwtfPnSK0zdnDyvds6qC961vdKRd71vfiCIET3XMnt/+kzuWsWVJIh98brWav3xyny1MLDj48wRCqCib3o959FuW9/GsPQBIMhmiI0d7nWpiDA5mhnGszRLM4DD+0y9Dz6nYg46ig8UHLtf+z8Sy0ZjRjNhmuy1hP/itjSRNQM+5WeuP9F8i8uwXU9dz6zL+Z/8M74OfA1XNC02m40ZwhO5/+30YmmWRmr5OIj85g3jgP6NVL3fk2N4tjxPouwCDFuLxG+/A27wLpJFbvRATfx8WMCPmh2uRPWRCL5em8h/3reWVS70cOnGd7tGJAxLNi9zcs7ae9vrE4mxz79NCCB5ev4wtK2t4sbObF88Ojg2D+BTY11bDjlW1lI5N2E3jGHO2cgGFhAUHv5AxaaErd+s9BPw34dRPU++67zMoFUsxcfiV/JSsIQ7ezEtrHckM40g749xQFPyv/DOcPZRGe5rQ7/44WsXSvAk7QYJh2AjNSUYoOGumlshLX7Ve19A1Ah2H8a3fnxeaTMe7f/zF2Z37MUQJP/3niI/9CTjQ/6Tbi+fAZwi++Hdw7djMh22+H++2DyA1V1YWk5udL4ToWOWxxyHJ/NRHWCxnlWcu7ERRYOfyGnYur+HGaICBkRCKIqgt9lDhdVuuxy6vK/Lwoa3L+dDW5UQME0WIeDw/KffNlVYgkVJyfnCE8z23iURNvG6NjUsrM6pV5njy58LFgoNfyJBGbNQ3GoZoEBEN4Nv4fvwl9XD8RxCcZhR2UTP6pkdRa1ZCxA9CjdUTDTvDw6Oxz+G4k+guday5rhXbIBJIyz4ZFqBFHW2zlBB6/stwszO18WWNULYIDAm+CvQV21AXLUOJhmP1OnkO5sgVd5ojRL7iGdsSGbwGw1ft1Xf8KWTrLkQeaDKZR/rOwY1TNhozSqjjBXyrtzlihyJNfHf/e8IjN4meOgQ9pyEYiJ2Dhnb0lt3oLi9Eo0iZ3jXjKI/4kUYEohFkJIzUjIUQnRm4NE0kAtOUc6pnOn5leJQ3z/QyHIygKQr1lT7uWl6DV9cy1i7TlBhm4jvT0WOYZmzRtJhWztqdzGu8Hmq8ngnHzUafUONvvUxz7rplUqvXLt/kmWNXuTlpbOjfjnbTWu3mztXVXOgZ4eZwEImkrszLXc111BV5HLPB6X4lnRyczBEWHPwChjRNpBFBmhFkyA+R2CqwrmXrUZasI3LzPEbfBTAi4PKhNLTjLq4EwAiOIuId2BTCMU4kCKEAJhJMA9eyDYQ7fjT3xpY3oXrL07bbiCiYkSAyrpETbQ6ceMaacw8wdAlW7sC1fAtqvJ5MnYO5cmkqsGgV3DxrrW0JNLQjw4Fp64ycTWMuhhwhfOMUrqoVOddkMjfefcZ+e04+jWza6KgdirsY7/qHgYcxhBjvW0I42tfnyqMBUE2JDBuYehjpLvybZ6ZgxOMyTAfXXLp4e4TvvHyeayOTKr08zBPHerh7RRmPbmhEH8s97xwMQyKFgmEkO6nWYEpJx81bvHWml8HREJqqsKSyiB0ttdR4fRhm/LHB+fWp5h0ypdUP3rnI82duzbi9sy9EZ9/EwZ2O3hDPn7nF8jKVj+1upsrtcdaoOcKUAlNS8E7+goNfwBCKglB1hKIj3F4QsadPRdVRjDDuhjbMZRtQjDAgMFUdEedKhjgKmEgUTxHoHhRdJ1yzFnrTW4gr3lJcOz6OcLnTtk/VvSiajggYzrQzHIDTh+w148xhlNbdGT8Hc+Wq7qV068PcfvrPbTXP3bJ75nMUtrnKchwyFJzTec8Up/eC/caE+kEIhO7KizZkk2veEoQRRURCKLqOVAQoLGAaqHFdFIf06ei9xZcPzd5fXzw/xIXe43z6QDsux518gWGaqKqw1aaO3lt87fAF/BN8LIOzA7c4dPYWzVUuPvvoZop9umNazWeosaRfjmp18Gz3rM59KlwYMvijH3byXx5eQ01R/jj5ipAoAoSw90Cab1hw8AsZQgVFA80FhjeemETGPsdjfbPOJbEwFN0DuheEhnvnxwg98bk021iE9ujn0Epq52SfcHmRqgu0sCPtDJ5/HbC4Gm8Cw9eJDHXjLl+a+/OUQitf83Zuv/4jGLA4it/yAGpZ/cz1q2n+1Oge0Dw512QKN9JbCEcKEPnYnkxz3YdQwqBJhO5GaCoxdyP5BrrAgdjADaAoc9dnIBBK6dwncHVE8q3XzvGpXS1zPu5EHhu5VxRQJniXM+/z9rV+/u7FS7Pae7o/zH/55qv88UfvimvlhK3zlyf6k1NaGabkyaM3mCuiwF/+5BS///imvDmPiiIK3rmHBQe/sJGUJlMxopAHaRanW7lT8VUQqm2HnuO2m+j70P8Al3fOKSSdTpMpB2zGk8dh9F5EKavP+XlKpZWKxHfvr+J/9v+kztaycjveOx6bVVtRthSJ/fUQXGV1OVmBNSX3lEw/xyUFVM2NkCYyGiRy5QSRS6/B6AhoGlQ14m7ejeItyZ92OsQX0mTa4eBUmswXTlmZBD6Ot66O8v5AmEqvKyPtimH2cjdHAymd+wRuheCLTx7jN/atddjW+cidvd6OXh9IZ8H2aTEUgXe6B9jUkE76T+v8pj/EhYERwuEoxV4XaxaVxdcTmE6rwseCgz9vIMf/5zKN4JgNTNimbXyQ6DM2HfzVe1B0Vyy2f872JV+0DrTTTPOnLZoY9c/xebKgleLyUvTAZxntOAynnpu6GnH5crS2fbibtiKFSuwBc/o6vau343/3u/a0qliFVlaTJ5pM4ss2w+nUqpWtHAAAIABJREFU2aomoGo1QtUIXXmXyMv/BNFJK1d2Hyd04klCjXfgu/Nj4Pblvp1OcUTss+J8jPd8g1OuRdQwOdhlfwXxl7q6eXRjo0NW2Mdhmw8lp3uDXL49wrLS4gxZNH/gpNvadWXQwdrg0MnuJAffWZy6OcQzb1/mTH94yra7V5Sxf/1SKjzuafYsbCw4+IWMSWkyMWIOVk7TCE5Jkxnbpta1ES1dCrevWG6ea82+WdMu5jRNprvM5smKo6g8K6vpOqaVquLZ9DBi3f1Ebp4n6r+NgkQrr0WpakKJRrC0+q63DJbeBVdeta7V+ocmaBUdvEL4/JswMgiagNIluFrvRlM9WddKa7+PqF0Hf92DjJ5/HfnKP8xe7tIb+Aeu4Xr0v6PleV+xzDU3CgJDiyLdxfFAYBlvcOJ/dnjUMAlEDdyqgktTbe2bDR57HJLMVZ8efxAD+zjdPQQb0z/u7FzMWi5qmBw6Y99xfOHkdT62fbXDts437myaTH/IqfH7GM4PhB2zLZk/d+oaTxybOZToxfNDvHp+iM8+1MLS0iISWs0HLDj4hYwCCdEBAUYY7z2/QuAHf4iVxZ/Etk/iKq0Bh8IznA7R8TRtJnji+7ZPmaeh1TEbMhmiM0UrDNRFTZhCGStvdwVkz46PEPzxRRi2ELfZfA++petQZJTI4DVCr3wLBs9NKRY+9i+EV+zAt/XDKJA1rVzeCpTWvYQ7D1k78WWN6EWlRA7/hbXyw9cJH/4Krj2/mJX2ZDxERxqg+2IP/YqL8Rm2MXc209w0Jceu93O4o5tzA+OjeDU+we7WOrY31eLW1azZMzsHJ0J0wtF03HsIGwnnxvl2pap3IBhOy7W61D/KXPWa/9xZfXxuHUhvLtJ0iJ13Z9v++qXeWZ37BCLAn/64i//+/nWUe91JdRU2FuaezxskjfZMCJXJFWfKNrW4Eu+jvwOlS2dph4624xfwtuxy2KbED5wzdWplNbCoZZZ2TINVu1H0RKaAfDlPmdcqwRWXD9+B/wQ1iXjZGbDuEYq2/gwIQfjmeUI//oNpnfsxnH8Z/1NfwIwkbjbZ0ap6zydQV2yZvS0ARTV49v0HIp3Ppy6bjKtHMUZvZa09GeUSQMSSAmT55jkSjvC/nn6Xv3/p0gTnHqDXL/nXt7r5/BPHuDY8mlW7ZkLy1TcXFLnSG7/zuXM37hdJMy1hJLqQJ9MKnLzyVjeUO1gbOB0gY0rJv75mPWIgDDzXcc1hK3KLhRH8QkYBhegkyomSatyP/z5GTyfRzoNw8wpggrcSVu/A27gZoemZDTtxqE5924eJ/Pj3LJ4sH+6N78uPcIkcaJXgiqcU3/7fJDzcQ/TkT6HnLISC4CuHZVtwrduNpniQSKLRAOFn/sSavMPdBJ//Cr4Dn86eVppO7QOf5vobz8LxH0Owb5JRblhzH1rzdkzdBRdettaWJAROvYhv62M57xNz5oqGIhSkYFJ4DhnloYjBnz59nJ6JuRanwG/CH//oFJ9/pI3qCen6smNnMpeAEyE61T4PZXpsAqMdtDdWzOm4s3Mxa7lSLT2XpNiTWKhrrvbNZ+5siM6Whkq+yeW0wsCmwx3LyxyzDSTHuwfw23zuO3RmkEc3GpPqKlwsOPiFjEIK0UkuZ4TRq5pg189PDPlI8AxkTXE6REeRBu6Kxcj7f5voT7/IrD8IaimeBz+L5i61HdaSNyE6Toe4lNTiufMjE8/7GI8dN3ziINi5ffSdxBy8gla+OCtaCVNBIPG17ESsvotI33kit3sR0kAtqkRb3IowIshwiOhAmiNDAxfIyyxCtkN0oiBNhFSQppk02TbmzmaKP9t5LaVzn4AJfPuVc/z6feuyYtvMHJwI0REC9q2r54m37U1a3bm8Ls6cb1eqeks8LpaWqly5bc9t3LKyhrnqNf+5s/poqsojm+r5ns3+NRN2t9U7ZhsIjl+yP8FcAmcHhqlbXGl733zEQojOvEHS6EU+vJIX5IkdCZ74gXO2flddC97H/ge07mfK87Jejlj/GJ4P/B5aWW2e6JA7rexwKSV0PIddBLsOWT5W5OY5Ro7+gJHXvon/jX8leOVdpDSs2yniOgmBEOCqWYG3eRe+1TtxL25FKNpYeWmkOSHNSMpMZMWmfOWJn6cswjQlz3fctLVP180Q/YFghiyyhuSrb67YubKWcpf18o+ur8Wj5zbT0d61i23vs315TQYsmX9wql8lcG9zPbtXzj1UZ3tTKYtLfA5YNI7RkM11auIYSXO/fMTCCH4howBDdHLFMxp2UrwI35YPYtzxYcz+i8hIGLzF6MU1CCEwNRdmNDLtvtKMvQ6U7xGtrHIjNAjG7Vm7/7S4cSZl5qXQtZMYr/8zjEwceYp2QVQphk2P411zN0Iwaz1SxJaGtHI9UJTmTdDtcyyTVE55DkJ0OvsGCc2k6yx4/WwvD7Yn5gll3s7JXAJOhOgAeDSFTx9o44tPdTCcwm/Zu6qC+1sXO3LcmblIWe6OpYt4oaObS0PWHK1P7lmOR8tdVqbC4c6G6IBECPjQ1uXUVfbw9LHr3E5jHGNDvY+f3bbSEXuSuUtL73HGo07uo4WLBQe/kFGoITrzNOwEE/TyxUByuNE0GgWHCZw+gjx1EMKJlHAarL4bz5o9KGWL579WKbgZTnMU1QjPGtIS6DyEfO0fZ97fHIG3vk5g6BJFd34EJUWIDshZyySuB7WomqinyvbiWFrT1oUQnTR5/8jUnNdWMDgaZtwRzYxts3NwaqErECwq8vLfHmnnpx3XONQ1MGX97aWlGvetb2DLkqqMtyuG2cspCvzafWv5q2dPpnTyP3jXUvavX8Zg30gG7Z4vPDN9WgjYvbKeu1fU0dl7i8t9I4SiBl6XxvrGSs70DHPoxNRQuYYSlb1r67mrMTYQ5rRty+vKeOuq/YnzTZWlSXUVNhYc/HmDpNELIeIfc8DHbCC3dkzhiR+43NoTvPIO5uG/mub8ReHMQYJnDkLrgxRtfhTxHtZKcRdNo5EFeEpmrDPcd3525z4ZZ18gULIYz7p7Z7Yz8dnC9SAUEQvjevtbdhqDu3HTjO0pKC5tNNshjC97bw8ivd0cQyYOX+TWeWzTct63vpHO3lsMBSNoisLSSh8NJYlrLQcnaQb4dI3P7G/npfM3ONRxg5uBibatq/Wyb30Dd65ZEvudXIAlZLJrCyFoqy2nrTZ5kragboWPu1fUcG3YT99wCIGkpsxLfZFvrIzTkFJSWWw/L8+Geh8lbt1xe3KFBQe/kLEQomOZ50PYSejq8Rmc+0nofIpRCb6tj79ntcJdAiXLYPhyar2SsWTTjCEt4XeeslWVeewHGOvvBzm9DnZCdBRFxb3uHkKnD8KohXUAAHZ9Eqm6sbSQWL7zHITo1Jd5Z1J2VtSUeZPqyrydk3nM5ZEZsUFTBe31FUxENtsoLJaL2bp3dT17VtXRPRJgKBBGVQT1xT5KPPpYOZmingWe4LnVqqHER8OEOPvMXGODgRB/9WwHN0aTt1nD/RuWkNBqPmDBwS9kSAPMaCwUIBqEiB+EGvs+Gs4ND4/GPidCLHJlxyQuwwK0qOMayYif6HA/IKGkCgVl2vIyHMQ49CXr5/bUU0SWrUOtXjFvtLLN2/aB1RH3ODwrtsX2n1SncbsPeo7bqgvpJ3TmVXxL101/TiPR2H0gErF0PQiXB/d9v07o2b+A0Z7Zj73xZ/A0bsz9OXCKR0OYQkEaGjIaBWXcQcPhkIEEbyorolyHWzbjgrctX4RpJm7y2Q+pkKaJROTUBie5aUoMM/GdmVZddUVe6oo8Y9+bZqwe05SI+DFy3c585+8FrYZCYf7g+yfTmnvzs9saWFbqwzRjGsk012TIJyw4+AUMaZpII4I0I8iQHyKxmFNTCES8c2abEwlCKICJBNPImR2TuRFRMCPB2ARYB+qM9l8kevpFuP7OhHMSrmtHbd6NWr18QvnwhTeIrZdnHZGOZxF3frTgtUqX64vXECmps7byLcDKexCqhgwHptQZ6T5lrY7JuHYCWdc8rZ2m5kYqYAZDlq8HVXXhuffXCZ59Fc68BJFbE49Xsw6tbS96ZSNGcDRvrp+5csM/gijSMSMmUs/WjVOwd10t33s7xcNUErY0+PCqOmYO100y4sfOpQ1OwjAkUigYRrJz71DdZvyxYZ5olUm8F7T6+ktnbDv35Tp8YHsj7TWVYw8/phSYkoJ38hcc/AKGUBSEqiMUHeH2gog9xSqqjjDCOeEoYCJRPEWge3Jmx2Su6l4UTUcEjDnVI6Ug/M6P4dTT05+UG8cxbhzHWHUv3i2PIkRcl4tv2T/BV99G7Pp5RHzUq9C0mjMXKqLtAPK1bxFbZ3AWLN+O+84PIczItHWm/0MtES7XtHUqHhdIieLRbF0PwuXGs+lhRPv9RAevEw2PoioqWlk9+MpQ8uSacZKrviJQNRRdQyoiawma966q48y1IU72pp60XeGCD21bEYsgyiHU+PEzaYeUkvODwxw6cZ2uGwGCgBfY2FjC7rWLaShOL7xpeggM00RVheNtUmNRcjk/Z4WA+a5Vnz/I6f4U94lJqHbDf3t0A0JMFEUREkUQn/xbuFhw8AsZQo0t/a65wPDG3n4iY5/j8dNZ5xIwJOge0L25s2MSFy4vUnWBFp5TPYFjT87s3Cfj7HMENBe+rY/H9h2xlz0lAdMIo7pLC1KrOel8+kXkm0+AOTy7QCXLUNbvx738DqTmhng60sl1Kr4y0hq4cpeC5pu2TqHHw0zCWlrXgxAaes1KVM2FkkijmuPrJHPcg6K7ESoITYOxkdzkG+g4jxqSqGni1pRJN9/py8/EFVR+cc8avv36OV69NHNfairX+JV7WimeMMHO3rGc4kJR4o6YyEj9o6EoXz54iguTYpcCwCuXhnnlUhcbF/v45M5mdFVx4LjxAQoFFCX9czkdT2g0PqE6N+esEPh81+rVM9bf1CXQF4L+YJiaosQD7bhWhe7cw4KDX9iQC2kys5n6UY7chBPft35+Tj2FufoulLJ6SDOjh2IYWU9Xmes0mf63vgudFh6i2h7Et/nxeDpSOesqwZ7a1fjT0F9btn5GHeykycy36yHb3EqazJFQmJfP9XKwo2dCzvY7lhZzT3sDy0qLJ5S3yjVV5WPbm9m/3s+Lp3o4dmmAkTB4NGiuK2Z3Wz0rK0tt1ZlZDk6myUzmgXCU//nUcfpTvNA4dt3PyPOd/Pq9a+NvFJxpVwxO6uW8Rv8/e28eJsdV3vt/TlX1PvsizaJ9JLU02mxtlmRtli3LCzaLWUIwOwQSLnCJQ0hI7g+4N9wbTOACWQiQAMkPQgIYDNh4tyQv8iJbkiXNSK19m0Wj2TUzvVad+0d3z/Rs3V093dPd4/4+j9Tf7ql6z3veOqf7rVPved+Zy2e2rdr7U0utfLXfyyxXdPNvrK3yHwUHf8ZAjrxmMy3esA5kV49xPHbSpibH63nO9FXxndqHc+MfQsls8Jkvna3Yi6akc7ZslSr3nnkxOeceoPkx/DVuHLXuhPIVmxMWboLzLydvfGc1lpqlIPWJ5Uff5+V8mGYu45v6WHs339t3fsK/Hbw8wMHLHrYuKuPd6xemnP5ylsvBfesWct+6hUQuXOQvCZSbZmTStXj48IWEzn0UZ7r8POtpYffy+gxqNHXMDFdsejCTbTWyedjkebk1/dOKgoOfzyikyUyapyX14+mXzF8jzwGMzR9EcW/H6Dhh7tzF25FW56RVcHPaVqm0i4E8+LApE+mHH8GoW5GUfOvquwmYcPCV9e9CxqtCbDJNZrbmQyg4ROD0q+DtBcWKUlGLbe6q6a2eHCdNZvPVnkmd+1i8cK4XXT/L+zYvjvlUzjgevvWQMX9Lj3xvMMiL5/swg6ePXeXWZXVjHkBORR+R5HHJ8kKazIKtwqhwWQkHmplDhcs2gUw5ydH5hYKDn88ohOhMa9gJIXM/jmH4ESEfjjmrGFSLQU8QUx4D+7KdccM/ctlWqXB/6wnQTdq45wxGbyta6eyE8q1F1Ri7P0/oqa8nFKusfS/Ouavjhv3keoiOvN6J99Av4fKhUX0zAK/ighW7ca3ajRBq1kJ0DMPgh/vOJX25X7rYz9rFfSyvLiPqCocxkzhMJUTHkBAMGVg1EbN/QfLKxU7MYtCAkx1944oXpdqvMNJpr9RsNFN4SNc5eKWLYxe6GPAFsVs0GmpKuLlhFkU265jjZ7atNrlreP6cud+PKjvMKXFOIHPsmM1PFBz8GQM58loI0ZmAx07axMcHuy/jP7kXrjRDcBDUqWSVUBCKivXWTxJ4MrFzCSDWvBOttDacajHHbZUuHrx6KiXrBjpOo5XOSqota81StLd8Bd+xR+DiwfHCqpejrbkT++wliXXO4RCdYO8Vgo88CEwSj2EMwrGHGbx2GtctfwyaLbO6yYnVONzajW+Sv02G/U1tLN9ZZu6kPEIqrkVIN3i9pYt9x1u43K8Pf760ysaOFXWsqimjqy+1GOX2Pm+Mg597mBmumHnsP9vOrw62oI/6NEhzh5ffHb3K1kVlvHPdQjR1xEIz2VbzS4uoK1JpHdATHxzBjhV1zITNtJOh4ODnMwohOknzZMNOQlIn8Mw/QfuYoki6yWo5UThnDYd5aLOWENrzlxhPfIO4aR/X3o+jcQdGjtsq7TxkLsVZFEYgMGn12om4UlaHc9vHCW15P6FLRzG83rAzOtgJRoDQpeMM9HVgb9gYznwziZxcDdExAgPxnftYtDcx+OrPsW/7SGZ1myRE52WP+cwXx9uHGAoEcVq1YTlhzAwuATMhOp2DPr71RDO9E0yfU51+Tu0/z5xilbkVrvEHJIFwilnzfenzBmi9PkQgGKLEaWNeiRNQU5I1OZ/ZYSeT8d8fu8jvm+I/kXnhXC9tvU18+tZGNDU852a6rd6/bTFfe8xDMlhYZmHbolmTyJQTnJF/KDj4+YxCiE5aw06koRN47JvQm3zIQCIoy24dFcJhr16I+IO/w3f+dfST+6GvBQiBrQKW3oxj6VaEo2zaw2PM2ioj3OokFag2B4o0TLerqXbU6kV4D/4cWo6Mk+s7+BNYvBPnurejaLa8CdHxel4gKec+irPPIde8BcVRnDHdJgvR6RxIbVW5d9jBj3lKMmM4JBui0+cL8LVHmvEm8EeuXNfpHOiPf9AkqCwaqSCbjP6ea708e7yVpquj46FLNNi1sobti2uxakpSshLzxDaaafxYW3dC5z6Ks90BfnPkIvetW8SbwVZzS4v43O1L+PsnTxOTiGscllRa+cTORjRVZWKZYtJz8wkFB3/GQI68FkJ0JuCxk3biYwbfeDStzj2o2BffNK4toVpwLN6EsXQrihH+GjIUbYTnga0ywW1zVuE//ohpK1vrGlNqN9TXTuCRvwUZJ4HmmX0MXTuD8/YHwF40Wk4OhuhIQyJPPmvahn7Pc1huuHvKOkgg0HmOgGcfdF4GaYCjDLFqB0Wr9wCjb+LEsNNhDsoM+QGeCGZ69tDB8wmd+yjMhkJB2EFYWZN8ONTjTZd55FjHhH/rD8HDR9p59cw1PrN7BUW29LgfM3ckTIwnj14xdfze0z3cvSYctvJmsFVDRTH/+741HDjXwd7jbfTFPHxfPsvOjhV1rJhVytjiVjMRBQc/n1EI0UmaJwo7kaEQND+d1suj3vZZsDqz3vd02ypTXK1aCEV1MNCavJFrbkC4KkyHM8mQj8CjXyeucx9F3xWG9v0L9rs/P3rM5GCIjrzeCf6e5O0XxZXjGDe+dUo66EOd+J/6R7g+xgEZ7KBv7yl69/8Edfunsd/4NiInU1Nqp8NrPvNF2XBBqlivdWZwCSQTonM9EOBQywCZxC3uiuHwjol0iOV7T7VO6tzHonVA5ztPHefP71wdkR1fbnw+88NOYnnHgJfzPebDRV+50MHbakvfNLZyWlRuc9dym7sWf1AnYBg4NNXEeIt9n78oOPj5jEKITtrCTgKXDwMpxtmPhVaCdssnsM9aBCmEjmSbZ7PQlbrx3ejPfitpU1vWvSUlPYfOvJK4Sm4srjWhd5xGK58zLCcXQ3T0gHlnGYCQd0rXWw504f/t34Q3704C3fDi3/cgSBX72rcBcPPyGo62nzel6vq5Rdgt0fj7mKckM4ZDMiE6r543nxWHGKmJUO0Q7Fk5L+asyXX2B0M8dKgtaR1aBwxevniNrYtq4spNzOPbaKbxCz2Tz694uNI5wHTZ6mLfIPubWjnVdp2ADk6b4IZ55WxbVkelwzZl+Wa5zaJhM33uyFzMZxQc/BmDmNWeQojOBDx20o4/Ru9LvPI0IexVYI2sZjor0dzbsM1ZiVStYIRypO/ptVUmua1+Bf6bPoD+yr8nNL3lls9gqZifkp2NU88kc3VHIXhiH7Yt94/IycEQHcVqN90vIGb/Q2rtep/7QVznXsb869//ddQFjVgqltI4q4xyK/SY2F+9Y0XdMO8c8nH4Yif9QwFsmkpdVRFrastRUyyGlQtIVvPeAX9K8m9fVsnLZ7tGhS6MRV2RwqdvX4ndMnZT7MR46YL57899Ta0xDn7qyN8rbR7BkJHaeXr4vEzaajAQ5Pt7T3B2zBOGIa/kaU83T3u62dZQxrvWpV6srgBzKDj4+YxCiE7SPGGITuLfsInRsBXnjeHYZUOzooSCyBzobyZtlWluW7KNQEkNocOPQGfzeJvXrsOy7m4sZXNSkm8EfdCf/GrjMK40jbJJLoboCFcVWMshYDJMp25Nytfb33MZuiffuyIJ597XI68SydDLv6X0rgcQQvBHt7qTznxxV2MVC8uKaOkf4KFXL3Cqc6yT24mNC9y+aha7G+tRRHTVMlab3OYSMF/oKnk4HRa+/LYbOHipk31NrbQOjDiNDeUWdq6sY01tRcQJS06Hw+fMP01oH5T0eH2UO2xJtTExD4foGNLgWFsPRy90MeAPYdUUFs4uYcvCWZGblFTl5xYvsafmshXbLURtlQndhoJBHnz0KF0J7jmfP9vLda+Hj25zI2IXQNKsT5QHdYPDV7ro7PeBlJQV21k3pxLbuBvX8eNqJqDg4OczpB5evQwFIOSD4BAINfx5KJAdHhgMvw9EsmNkS48xXAYEaKFJbSScJalNaWdxbth9Gm01FS5DAWQogG7oCM2GUC0THm+tnIe259PI3qsEOs4iQ0GExYa1binCVYESCoTPSUUHb2rZRAh6R9lEBkPh34FgMGfmgwgFYNktcPRXprpmWbop5eutH38qrmxJ2LkPomCgIIHBc4/j8n8SxeKgvtjJA3uW8A9PnCaef3DPqmp2L6/n5LVe/uGZyW8o/MDvjnVwvqOfj21zoygjxZ6Y4LG8Lxji5YvXOHK+i+veIFZNYdGsYrYtq6GmyBn33ExwaRhIBIYh4x5fUZLa05rqYhuqEGyaX82m+VXoBvh1HbuqjLKVMez3J9a5byi18MZeX4BSmzWpNibihiF56VQrP3zqzLi8UYdbBvnVoTZ2LSnj3jXzh/tmSIPmjj5augbRdR2X08bauZUU50FWpiWVpaSCGxZWYhgSgYyMq/Tq9vNXziZ07qM40jrE/rPtbF9Uk1YdYrk/ZPD7o5fYe6Z3XPv/8coVtiwo5t4bF+CcJNTPMGQkPWx+o+Dg5zGkYSD1INIIIv1DEAw/5zaEQEQG53Rzgj7wezGQYOhZ02Ms14MKRtCHnMRG1pqlcZ2LyaDVuieVma88ka1S4UbQh//sq3DmeQiEv3SDABWLYck27HXLEEIZd65qc2GfuxpdCNTI57pvcEr6KKSYPUFzjLKJodmQChg+f07NB9v8G/EffRxIYgMxwMItCEVL+XrTeSmueEl45d5AQY/mQQ8GCA20oZUuAmBucRFffftqDrV382JzG5f7dAyg1AKbGyrY7K6l1GKlc8gX17mPxfGrPh4+fIG33bho0mP2nmnlN0fG5uI3aBno4/lzfSyrtvGBLUtxDq8CZx6RaIoYB3tirJ9XxUOvm3sSZQeWVZWNki0Q2NXI05vUIkCwaJDKF6imKSm3CfDoGxf4zxfij79nT/fS0jPEx3cs57kz7Tz1xlXG7lR56PU21tQ4uGfDAqpsKYa5ZQAhaXCopYvnmlq5cj01Q1XbYX5JMboRdmOnYu+JMBgM8toVc3sD9h5r4+b5s0eiGNOIoVCIbz95nKtxvv4OXLhOc8sxPnfnCkqtlnF/N6TAkOS9k19w8PMYQlEQqgWhWBA2B4jw3aeiWhB6ICscBQwkit0FFnvW9BjLVYsDRbMgvPqExygWCzRshbMvJH8BFm1BK67MmT5Ol63M8lDHGYJP/T0TFvfqPgOvnMFXsRj7LZ9Asbsy3kdFD0D5IugxmRJ1wTqE1Toix24FKVHsWk7NB9VqQ7v7zwg9+nUY58qMQe0qHBvfg9SsKbdLgh9pMfxPokSCdEAgjCBKzL2WVVHYNLeaTXOrmWx1bv8JExmWgH1n+7hjdSiyUjcajx27xGMnuuKef/Kan288fow/v2slDst4RyATiCb6UBLch7oUjU3zi3n5YvKbxW9dUYWmpd+rWlRdTNugiU3rEcy22xP2czJ4OvsSOvcjxwb4m9++QW+cBw1vtHs59rsTPHDnUuYWp1aTI51oGxjiO4+fYnCKDvl7Ni9EUcLjSpB4XJnFK+fN77/o8sOl6wMsLCtKqy5SSn6w90Rc5z6K3iB89+lm/uLu1ZFQvhEoQqIIyPcqtwUHP58hVFA00KygO8LLZMjw+0g87bRzCegSLPZwFdBs6TGGC6sjvPFVC0x6jH3dO/BdaoJgEvHL1nLs69+ZU32cTlsly4P97ehPfSOxPbvP4Hvmu9ju/eK09FFZfivGAXMOvrZ6N2jOYTki6vAFtJybD1r1ItR3fAX/oV/BhZfHd0YthtX34GjcgRACOZV27RVwvSWu7QREgnOiK2ISzVUdExISPWpyHtQNnjvbF7edifDSuQ52L68fJbO3P/nRAAAgAElEQVT5al9C5z6KLj/85yvn+eh2d1J6TpULRYk4YiLh8fetW8jp9sSxzwALyyzsbpxjyubJ8p0r63nxwsnESsRg5+IyrMNPRsy3/fTR+GNuLOI591EYwLcfO8X/fPsqioZTsWb2ek/Erw34ePD3p9ATqxwXH902n2Wzy4GR8TSywTU9Ol/rS22z99U+Hw0VJQnldw76Od7agy+gY7WoLKsrpa7IOeHxZ7quc74veau1D0l++doF2vu8XPcFsagKC6uLeMeWpSyoTb7+Q66i4ODnM2QhTWY6Uz8qFie2e76I/+n/C/1xVgpds3Hs/hyqxYmR5X5ly1bJcv25Hyc/nnvP4T/2NEWNOzLeR8f8NQweq0/omA5j0WaszopRNsnFNJmjuKMYy80fIrTxPQQvHkYf6gNhwVJZh7V2GVLRwsfLqbWlLN2Mce1YEkaMOqwSa+1mVOfkK/UT8ZYB7/DtgRmcautj9/I5o2Q+c8ycc3i4dZA+X5BSe+rx4slzSLaSrcNq4fN3reKf957kQhwPdmWNg49sdcep3Dk1XlvkZFm1nZPXkq9MvHN5XcrtdQ158YzbYJ0eBIDnT7Vz56poitBMX+/x/CcHzkzJua9xCTYtns3KmorIJ8mNp1S4kWIYiz582sTyz/UM8OihS3jGjqlDbcwvs3DnDXNi+hc+d3+z+eQJL5yPXTTQuXK9j+fPHWTdgnI+tmMpxcObwPMPM7+U15sGIytjo9PzZYuTI3pEefQLLv7xqqsM191/hbb1j8Lx4bGoXIx288dwvPVLqK7SHOlX9myViAd7WsxXBm5+MjKSM9tHoajYbv8cuGYl1qlmFa6b3jdejojYKcfng2IrwrF4M0Wr78C5Zg+2uuXhR89pkm+bfwMorngWHAfnuneYOh7AF4hXfD7eeaNdpW6vPyXn8MCZ9pTaN4vY2ZcMimwWHtizis/uXsKaWidRd8QhYNP8Er5w1zI+uXM5Vi2z+wg+vH0pFUn6Qh/ZtoAqV+qx7ue7Mlvg69nmayk7rlNF+6CXs90ThDPGwdjx0j4oefiNdh74xWF+9fp5AiF9wuPSgXKXNaXzKoomP+/1y51884lT4537CC72BvnnfefZe3q0Q9/UklqdgAl1uNDD3/zmKIP+1L53cgE5sYLvdru/DHwJmOvxeJKuw+x2u+cC8YLwXvR4PFunqF7uopAmM2luJvWjIsG6YD3a4i2IoA8Z8iPtxai6Hj5Gs2KEgjnRr2zYKth1geCJZ6D1dHjMOVwwbyM29xZUZ9nw8f5zE4SGJIK/m0D3JSyVCzPeX1Fchf3uv8J37DE48SSRGLcRWMth5d04lm8dmWMxcnIxTWY2uLRYUbZ9CGP/Pya4uBKQ2OZsx9ZwU+Q9Ma/xuSPFja52a2xKPElLb2pOwOXOAczqnAqXgNk0mULAkspiluxYNnkHMqgzgMui8YW7VvOTl05zrH3ivR8lFrh/6yIaZ5eNO98MD6SYDz5ZeCV0DHqpKXKY1m2q/CWP+VXoyW5FDODZ0z00t/by1fdtxm6zpE3PKN+4eBaPJxnuFoUNWFYVzQg0Wub5nuv86MXk9lY89Hor5U4rN9RXAHKiXV5TQkuvlx89f5r/dtvyNEueHmTdwXe73W8D/irF02+MvB4FJnpGnFxy5XxFIUQn82EnSNBsGHooZ/qSLVsZ/kF8e78PXadGj8PrfdD0MP6mh2H5Hhzr3o4iJQya+9KPInjpDawV8+KGvqSNW6wUrX0b+o33Erx8FGOwD0OoWMuqsdQsRQo1EsYyviJxzofoTCN3zlmJd/NH0V/61zhXVmCbt4Oye/8HQonNTS6S4nNKirBgvt5049yKiKywnFCKK7MhySg5ZvVPnkOyITq5xl02C5/YuZxub4ADp9u51DlAUDcodVhYv3gWK2aXIUT8tKXJ8OIU88GbgTcUGd/TbMdr19MfetQ+KPnmI2/wxfvWp13nWS4HiyttnEk2TyZw64pqVEWZUObvD1820TP47esXIw6+SOn7IRFePXuNrk2LqCzKv1CdrDr4brf7T4BvTUGPqIP/oMfj+Wl6tMpXxKz2CDG8ujbtfFgHsqvHOB79wcwVfXKZj7eV4Rti6LGvweA14uLEE3j9g7g2vw9EihGATb9nsK8Nx/aPR8ZU5vsuVAv2eWsAMBQNxQg/lpXxzo2+z8v5kH7uWLSewKxFBE7uA89eYOTRtn3Oembf8n58FRsRKVaxVBXBrmWVPHHS3I3j5oWjw7DKnKmFFJQ5UjvPLFKzTm6hwmHjLavnA+G864YRzt6Srqwk7urU8sGbgWOCzEvTgUxd/6bWQc609FJpTU+/BgJBurx+BJJ718/jW0+cHvsMdEJU2WFJfRmvXelESsmsEgfzSlwIAV1ePyc6kt/HAdAxJDnbfZ2GihJW1rs4nMYwHQBDwv6T7bxj/fy0yp0OZGUEu93uZcA3gLuATsJPbIpTEBV18F9Pk2r5hUKITtI829VZ84lPZCvfq/+V2LmP4twLeGuXQUk98Fpy54zFlcN49/4Ljl2fRIgEYTBZ4jMxREdi4L92HqP5Kei8AIEAFFfCgpuwLt+GpljiylHK63FueDdy/TsI+QYQQS+Ks4zyhavBVYm/K/rjK2MudvJ8x7Janj3ZlfQq3d0rqrFr0ZXCsJz5pS6KVBgwuYtxfUPVKDnJ6myWS8B8Jdt84SLJ4+Jzq6ayvaGM586OL2SUDtiBWQ4b2bgGVcU2aEuyhoVJPHbkAvdvbEhZN5A0X+1lb1PrOEd8QanGxb7QqKPHwqWEHeZvP3l61OcVNrhlRS2p3v+duNxNQ0UxOxprOdxyJjUhcXClO703DdOFbK3g/zOwA3gK+BjwHKk7+APAqUQHzkgUQnQyH6LzJuRjbaVf74RLr5oamkbzMzh2fgLv8d+YOm8U2g4RunIU25yVWbfJRHymhejIgS68+74LvWPiX3v6oOccgcM/I7T2vTiX70hKpnCUotiLQLEgCCGlwYiDF/N0wwQvsVv53F1uvvl7D4m2vm1ZWModK+eMk6MIwS2Ns/jdMXP5u4sclinrnxyHfA3RSdSvMNIj9/ZVczl4vhdvBsLxd62oSlgBOVN8s7uWZ04lkao5BRw538f9G1PTzZAGvzh4nufPTXxTdaEvPCPnl2n0e0P0xETszCtRCRqStgGDwQkiebr98NChNspTfEg2FAh/5zRUlLCo3MK5nvQG6gT1zO75yBSylUXnIHCvx+O53ePxJLebYgzcbncFMI+wc/+nbrf7DbfbPeR2u1vdbvf33W53XToVzn3ErDRkKVtHrmQNmZhHfzBzRZ9c5qNt5T99ANPoOY8MDEHdDebPjUHw5LNx9MwyFxE7ZXg+hAY68bc24W9tJtTXnpH+6EM9eH/71fHO/RgYh37G4LEnTMoH0piRZF5JEX997wrWzZk4c0+FDd6zcQ7v3dgwaUjIzqW1zHJO/LfJ8LePnKR9MEHhsDQgdvYVMDnK7Fa+8u512JMw1uKK5D1HC7BtaW3qik0RNS6HKX3NwDeFafjrQxcmde5jcbE3xNr5FTz4jtX8r7et5Bv33UB1iZ22gcROck+Ku2SjG/CFEHzyluXUuNLr2qYa1pdtZGUF3+PxfD4NYqLhOWuBVcB+4AqwAfg4cI/b7d7p8Xhm7kbbQohO0rwQopO6regxVz00ikBfO9YdHyHw0JcgkOKK1NVmQkEfOMtzxj5RnskQHYmB9+IROPYE9IQfOetENpAV1SNW7sa+eBMyTf3xP/t9MJJLPSiP/hr/nBXYKuYnJ1+AFBbCezKiHkasp2GeVzltfHirm3cHghy50s2AN4hFU5hXVURDRfEYx368HJtF5b/vWcHXHz2etFNhAN97upn/794bx4QSTK0vY3nYZJJ02Sq3uEir3PrqYr794U38/AUPe0/1jIsBv6HOye7Vc5hT4uIH+09y/Gr8GzQB/Pc7llKcgWwzZvj9Ny/mb37XnPAplVmEb4bM69N+3cve08l/hz9zqpstS2czu8jB5f5BXr+S2RCXJfVlRHV2WjU+f8cqHjl6yZTO8bBpcXVa5Ew3puzgu93unwLrkjj01x6P5y+n2l4Mog5+E3CPx+M5H9HHBfwAeC/wU2B9GtschtWqUV2dSlRR+mAE/QgMKsudBIZCyEC4DLxU1OHwgenmMmBBt9pQrRaEVcuaHuN4JOykosSWG/rkMh9jK59NJLV5aixKHBquygpC7/0KVx/9e+g8nfikCVCs+bEW5dBYGuY6IKmMo1sq84GQpGPvD8Hz/MQGGWhBvvxj/G1HmH3nZ1GVqY1pf/dlhnrPmrom0vMUFXs+lVxbqoYRCdEpry4h6sJGJE2JlyOYU1+Z8rmN88p58UzyTsA1L3QGdJbOGV1gJ91cItJuqygPhUI8f6qdpw9f4nJ3AAOodilsW1nHnpXzcDnTW8wrdpPt6Oq86WmjyGXjI3vW8IHbDE639nA9GMSmqiypLsHptA0f/xfv2sjvDl/gkZcvMjjB/osNC0q4f+syqsqdae1/Kry8uoQH3+fkfz30Gj3m9pzGxY0LyyLjypw+v2tKOnv5MA5e7OL9Oxv51RspBWkkjWqnwubl9TAmO9PH6sr50G0hXj1/jbaeQaQhmVXmpHfQz38dSF6nuZUudqyZm7YN4tOJdKzgzwfcCY+CdD/z+r/AQ8B1j8fTGf3Q4/EMut3ujwHbgXVut3uTx+NJISF3HiI2C0oWuRj+csiuHjOVGwE/A2dfYeD0qxhDA6BacNQ0ULxmF5bS2rS3a3FVkkriNrUokrqsqJz6d/01Lf/+AAx2JjxvLMIpFXPH/pmeD10v/IzgZM59DIyLR7j65Hepu/PTU9Kv/9hek1cEjLOvoPs+iGovStwWEBuiE7VGdl0o8Ad1U859FE8dvYR7TkXW9U+FN1/u5msPHx23Sblj0OChV67w0CtX+ODORexZNTdt7Ubfk+G+aaoynBJVDv8fo4cQ3Lt2IffcMJ8jlzo513EdPRiitNjOzQ01FLusMedmTs9keU2Fi29/ZDsHz1/l8dcucjpS9MkCbF1WSW1VEf/xwkXM4M4b5yNS0Gd/k7n9KgD7mjr4wM5GDnjMf+ebwbu2LR52vsfqb9FUbl5SGzMeBMGQzoHmVi73Jn4+ogD/7a6VeencQxoc/GwVkvJ4PDpwfpK/Dbnd7meB9xN+upB2Bz8QCNHXl/l4zHioLHcgUejqGSLgD0DAB0gMzYoSLcQ0zZxAAMPnQ3FoENCypsdYXl7mxFCt9HX15oQ+qXLf8afhtZ8S8aCG4e3w4D36e6i5AfuOj6BY7GmzlT7/JnjjEXODUytlyDWHwYHgsHzKF6fk4F/XncgYOblyLSqLLCCgK45uZudDYKib0PEnk7aNce5V2s82Ya1alHJ//O2prbB1trdgqZifuC1FUFoUDtHpudafUluZQOtAaplKzrf3053BfpRXFyOQ9Fy7nla5p6718Z1nEj+p+bd95+jrHeK2ZenZxmYYRswKfnpjo8uri5FgelwtcNlZsHCkkm5wyE/3UPrzz6cD7lIX7lsbx31uGJJnnJe4OiQnOGs8GmudNNSVpTR2fSk8wvUZ0H2tP6Vzk8VdK6poLC+i2+Rc+dQty/nOU020JtgX8Onbl1PvsnEtzXPRDEpLHVhTTG2arU2204FoXXFnVrXIJMZk0VFkCEUakaweWeJGEBVQdD27eozhwgih6P6c0ScV7nv9N/DaTxjr3I9C+xF8j/0tBLxps5W1ZDZULDY1NEXjLlTJKPna8m3mx/jibaiKmhP2H8uFEck4lMb5EDr+tGkThU48M6X+IE3mi4xA6Mm2BYIQDGfRgdFru9nhqe771Y2xa9MZ0C3NMkO6wfeScO6jePhIO+2DvjT3K3eu/UzgiqLwmd0rKE+i/lJdkcoD90SjmlNpN1Wkfu4nb1lEffHE1asr7fCBzXO5a9V8UrFhkc3Cn9+5hj+4aQ61YzbkCuAtN9TyvY/fzLoFlSnrnwvIeiXbVOF2u79EeHPtVzwez0RVbBdGXs0Hj+Ul5Mjr8CPyLPBhHciuHuN47CTPBX3M8UC7B479Ormh0N/K0Ks/wbntj9JmK/umP8T3+/+ZXPslc3Eu3zVOvmX2EkKl9dDXkpwcwO6+JQX9p4lH36dzPpw1l44UgIsHYetHUu+PqwS6zTerOsqSbAvSmUUnXSi1pZYZo6Iosxk1puJOTYZDV7pMh9k919zKuzc0JD4wy8iEvfIFpQ4rf/mWNfzu8MUJM9wowM4l5bxlzXzsNguC1OZhjUuhfdDcUnzUcV5cYeVMt7n0OHNLVFbOLmfl3eVc7L3OySu9DAZC2DSFJfVlLKkoGfl6TRGaqrB14Wy2LpxFjy/A9UAQiyKocjioqSvHblW5nuUojakibx18YDXwDuAEMMrBd7vds4DbCSed2Dv9qk0TCll0kub5nkUncPwpc2PjwkGCN70Pi8WVFlspFXOx3PGXBB9/kHA+l0lQtgjrXZ9DKnbkGPlSs2Ld8ccEfvsVkioovuGDKOVzMXLA/hPxTGTRQU8t9EM3DKQltf6oDVvRLx8212DpAkRRVXLXRpD2LDrp4EVWjSWVVk53mXM+blo6O6O6hU0mSaetnj/Zjlk8d7aXd64zIhtj09XHdMoK20iaOn7mcadF5T0bF/HWG3UOXemid8CHQFBZYufGOZVY1JHNp6naavvyGn7+mrlsatsba8PnNtZyxuRegR2NdcM6zC8rYn5Z0SRHpseG5XYr5XbrJMflL/LCwXe73Q2E95a0eTyevsjH3yPs4D/gdrsf93g8L0aOLQJ+CJQA3/V4POa/2fIFUgcjFC6oE/JBcAiEGv48FMgODwyG3wciW/+zpccYLgMCtNCwjaTuJ9BxBsM7gLTYsZbVoTpKs67nRFzv74Srx00Pj+DxvVhW7pqyraKfW8rqEe/8GgHPC3ByHwRjVozKG1CW7cA2dw1SaJOORc1RDHd/gcCz3wfv5Bu3xIb3Y1u6KTfG9CRcBkPh34FgMH3zAQVSyVlk6BD0ptQf6+wleEURyOTSZAKwfFfy10a1InU/Ug9hGFHHNebpRhb59sZaTj+fvPOhATfUlkf6kRndpGEgEWm1VVuKhX+u+4MU27Qp6WAYMiasyZiSrLHcMCQi0ka6ZOYrt6qCTfOrx31uGGGbT8VWG+dV8cvXWpP+ZtKADXMrMQzJ6ppySrSL9CeZ89OlwNr6zM6xZMaVzMGnjmaRFw4+8AzhbD0fBn4M4PF4nnS73d8E/hR4zu12vwh0AtuAKuB54M+you00QRoGUg8ijSDSPwTB8EqUIQQiMjinmxP0gd+LgQRDz5oeY7keVDCCPvSBXvyn98Gp54hdifYDzFqBaNyFvWJeTugc5aGuC2MvfXLoOIMMbk3ZVnKC8YQQOJbcjFyyhZDfh6L7EFYH0uJAlRJCAQw9GFe+aivFcufnMa6dQT99ALojUXTOUpi3DvuCtQjNiu4bzAn7T8YNzYZUwPD50zYfKF8APedMXWaKZkPQjxEKpNwfNr8XDvwgufYql2KpWzbh+JiQ6xIj4MewS4wcKwi5clY5q2Zf5djV5HIRfmD7fAQio/2IFs3MBVuF5NSvma5LpFDQ9VjnPj3QjchtQw7YKtcxFVtpisqnbmvg759Obh/Hn+xejKYokbYEn96zjAcfPZnwua0CfPqOZShCyeo1NaTAkOS9k58vDv6E8Hg8D7jd7peBTxPOi68CZ4AHgW95PJ701ivOMQhFQagWhGJB2BwgwnefimpB6IGscBQwkCh2F1jsWdNjLFctjnBF0Me/MXkYREcTsqMJ79r34ly6Oes6j9h04o1GiQcICKvVdLuhoR5639iHfu5weAVascG85ViW7ESdtRChBxAINEcxSuRcw2S/VNWCpb4R6leEQ4Bi5Cg5YPNkuGK3gpQodi1t84GVe+D575q7zu5dCKttSv2xL1xLQHwc48UETv6s5Ti2fwxpcyYpPwSaFcVmQ6iCNCdRSQMEH966lH8/cIYjbfGz6nxwy1xuqKmADD++j0ZUpNNWVUWCKwPm9S6xWtKgh0A3DNQMXH81HCWXg+Mq9zBVWy2pKOZP9yzhX585Td8kq/ElGnz81iXMLy0idp7Mdtn5q3sa+Y8DZzg1SUjcojKN+7cuocppJ9NzLBEUIVEEeZseM4qccPA9Hs+CVP/u8Xh+AfwizSrlBSQKejC8YipVG0ILf4pmhWgV0unmEtAlWOxgcWRPjzFcD4Xo+vlXQU8i3dWhn+FzlWJt2JQT+qtFlSkVmsJVCZoz6bakEAzt/wFDVw6NlmME4MKrBC+8SrCqEfuuT6BY7Vm/ptnmwmIJ2yegpW0+2BZtwH/wl+C7luRFtuJYsgU025T7Y1+4Eb1mMf7m5+HEk0DMD3F1Y7h6bl0jQghkUjLtYBFI1YqwlaFoFhTFD6NWcbPPrYrGR7e78XT2s6+plePtIxvrLMAtyyrZ5q6h3GGLKyddXChKxBETaZN58/I6/utg8pvbAW6aV4xFG+sNpqJDeOV+fJrMqfctaqPwa3pkzlSeDlstKi/mb+67kRMdfRzwtNPRH37yNavEzs3LalhWXRrjFI8+t8pl5zO7V9I56OOl01fp6PchkVQX29m8ZDazihwZ67tZrigi7517yBEHvwBz0PuvMvTaL+g49BDG9RhHoGErNvdO1Ip5KFIHBIQC08oxgghA6DqI6W07Hr9+5HEIJZ/L1njlPxHzb0CRMuv6qxVzCVrLIDA+S0I8aIs3htMgJtGWNHT8T/49dHniC+1sxvfk3+Ha83kUyJnrmw0erdiqpHM+6CHsuz+D75H/AzJRnnaB5c7Po6oaSCMtfVPspajr7kXceCeGbwDd0NGsLhSLDUMo4eNlkjJlCKE5kaoDNGt4UzKCXIhXHsuFUFhWXcaynaUEdclgMIQmwGm1oIjp1hlG0mSmR+ZN86v5xcEWUwsFO1bWpVWHEaTTXrk5nuLxoG7Q5/MjhaDEYsFmUaYsMzmeHlsJIWicXUbj7PKU5FS57Nxzw4Jp6O9U+Ngxm58oOPh5Bu+x39P3678GfYLHXGdfwH/2BVh2B651b0UgKaTJFEhdZ+gNk/nFA70EWppx1C3Puv5CCMTyW5FvPJS8/s5qLLOXEs5xnritoaanEzv3UfReZvDIwzg3vjcnrm/WePR9mueDVlKF496/wvviv0HnqYmvQflC7Fvej1IxN7zRPu1jTkF1lCAUDSVV+VKAVEDRwjLjDqrcgUVVKFOtRC7utLefiRatmsJHti/kX547n9Txu90VzCuZLHNJbiFfxtXlvkH2N7fx8sXRIaJrap3csqqexRUlGddh+BtJSpqu9vBcczvNHd7hv62qdbK9sRZ3VexKfAH5ioKDn0fwNT1J3y+/QOQXdHKcfJxBBM71by+kyVQthK6eAn3QtL315v0Y9auyrj8SbMt34Dv1HHiTC91QN78fqVkxolVF46V7NHSk2QJLnmfRN7yHaDhGtu2TDZ6JNJlRLoqrsb/lixhdFwmcfgG6r4IwoLQa1b0dW+mcrPc/MddQFAUpIJwhSEb+EfNa4GN5+LYi/ba6oa6cD9+s86MX41cu3rO8kresnpe2dkdzkeRxyfL8SJP52PHLPHp84u/uN9qGeKPtNNsWlfKuDYtQRvnV6bdVv9/PPzzVPK6KqwSOtg1xtO0sC8ss/PEty3HatDTrkC889n3+Qv3yl7+cbR3yDR8CFui6gd+fZN6nNMDwD9H9ow+DnmS5ks4zKHPWoNqKUKQevnOXRsa50AMIXUdRVARMa9uTcb3zIsal18wbffAqwcvHkVYHWsnsrPZFUTTEwg3oLcfBHz+dobbtkzjqVyQtP9RyHOP8C6bNYziqsFXUZf36Zos7rSpCGvi8gYzNB83mxFrXiLb4JmyL1mOtX4lqL532PiMNQj2XkT0tSF8fwmJHFcQ/VwiEooFiw15cBIqGbyj65FEwsp5Y4LHc4QpvjM+ErepKXWxbXIXTAq1dgwQifowKbF1Uyge2NrBubnXk4VN62pUSpIw8iRRiSrLGcocrvC/CNxRMm8x086dPtvK7o5OnBI7iUo8fr99HY11FRvRxuGz4fEG+8svX6fDGd2B7fQbHr1xj06JqVEXNiD65zJ1FdjRVIeAPZf1Jht1uQQ3vvL9IJItksiis4OcJfMceRfqTjyEH8Hv24tzyIaIram/WEB2hTaHyZM95Qi98n9C1C7jWv53YsCfDdx1/92VE0ItiK0KdvZjhYj4Z6IvqLMN15xfxnn0Jo/lpGIr90VDAvQOr+1asxZWm5If6rqZkGtkfPS+71zdrPEMhOrnEjZAPb/N+pOdZ8PeMHgANW7EvvxWlfM7EcqQI/4sguz+T+YNM26nYbmHPirnsWTEX3TAwJJFiSHIaWk8/clnjPn+Ah48kX4pn35leNrsHqS92ZUSfn714iu4k1wjbBgweO3aZt964MCO6FJB5FBz8PIH3jd+aP+nsi+hbPzryY/0mDdFRK+cnUzc1PjxPMmgvwbnqdvzdl9CPPAItY6t/KuC+HduqWxFF1Rnpi2IB+7Kd6CtuR/a2IANDSIsdi7MCoVkwkgzLieUy1YTDBnlbGTgdPJMhOrnA9cFO/I98bXRBs1icfQHf2Rdg25/gnH/jBHIKITqp8LCbPT22UhWBOupvme6jSLPc3A7RefGU+Tqb+5paed+mxWnXxx8I8nRzpyldnvV0c/fqeWgx1XBT1UFKI2Y1PHeu0cQ89n3+ouDg5wn0vraUzhNDfeGUhog3bxYdmxO1YT362RTCdGLxxkN4NRvy9Z9OcoABnsfxn34B610PoJXWZK5fegClqAKojMluklomFa2ojJSCzVzlJJulZybyjGTRyRGOfwD/Y383uXMfi+f/iaD1c9hq3KPlGEGEYg2X55A6kuijfsnIumuBj+eQ7iw6ucGjSKfc3LbRS1olVd0AACAASURBVKfNOdQAL13o532b0t+vA2fMP6nVgTfae1lXXzlOZtugl+ea2zh8oRevDjYFlsx2smNlHUsqShACzvcOsL+pjdcuj0QfzCtR2bGynnX1lWiqINvXaPJxlf8oOPh5AiFSLHakxNx5v0lDdJBQvvYtdE7VwUfGce5jYAwQeORB1Hd8GdVRkvW+J+K2OWsIoRJb2TcZ2BvWD9slV/oyrXwGh+gMNT8Lvq6kx0Lw5Z9ifetXQImRUwjRSQkFO5lDNuzVNjDExc4BQlJS6rCwrLoUizr+N7pn4ppOCREIGVi19PasoydR2t2J0d3vhfqR9yHd4Ccvnea1K6P3gg0Z0Q3DZ5hbolFs12juGF8h+lK/zv9/4BIPa5f4zB3LqC3KTDhSAQUHP2+gVs5H720xeZaGtJdi6OZCNqbEczQkwTZ7CUW7PsnAs/9s0oapwou36RmcG96Z9b4n4ooFcO8Cz1PJd2/eBnBVmA4Hmkl8pobo6IoCTc8kPxYABjvwXzuHVr8iRmYhRCcVLoHpCtGZfi6SPC5ZPr0hOodbunjqjStc6h+9GKIAO5eUc/vKORTZLMPHm182CWM4IiaN+ksZ+94E5MhYNAyD7+07wYlr4x33WFzuD0F//OfC10Pwt4+c5H/c2xipXjte5+zxFG2VYygUeM4TONfdZ/6k5btQ9QCKDKFIIxJKkGFuBFEBRdenp70kuTBClDZuRdv2SbCVJzRdWuDZiwgFst73ZLhz9V3gqEquX1opjg3vzLrO2ebC0BFGKC/nQzyut54EvIlGwTiELrwyWqYRREg9JkQn9tF3gcfjM9NWuaKHeS4lPHz4Iv/6/MVxzj2Ea/U+e7qHr/7uGNcGfcPn1peaX0Mt1kAdfvKevr7MrkhtpbyixDEsZ9/Z9oTOvRnowE9eOEMmr93UeH6j4ODnCWzLdqGUzDZ1jmPpDkaHCUwnJ4ttT8TDk9Y+bw2u+74K6/5wvMHSjiCBq6dyoO+JuWK1Y7vrL6B0Tvwu2Sqx3/V5VHtJ1nXOOheRH4K8nA+Tc8M7uhBP0hjqHy2zEKKTEmaOezE9mA5bPeNp5WlP4pC16yH45mPNeIPh1etty2tMt3VLo7nf+WSxpcG8LiqwprYCACkle4+lthcwHs50B7hyfYDOIR+9vgCGMTNWz3MBhRCdPIFQLZS9+5t0//gjEEqc50rc9AFEcfX0P+bP0ZAEqagYqjWc9UVIbCt24X/9v0jtAWryCPkGUXPIDvG4KK7CcfdfY+0+Qd/rj8G1mCqqpfMRy2/FunQLiiExckTnQohO+rm0OiYayomhWsbILITopMIlUAjRSZZnPkTHH9RNpbq8HoIXTrezu7GeDXOr+MXLVzATir958awYPdLXF5tNZdfySp49kfzemp1LKyIbYSVnu6+nvKcgEf720ZEq6iqwc2k525fXUunIVuhO7Pv8RcHBzyNY566h4kM/pPfnf4rRH2dH/MYP4liwNpwNQ+rhV6FODw8Mht8HIo/xprPtOFwGBGghCA6BUBGqCkt2wmmTscYmIRQBQW/O2CERF3oA17xVOBetpbezGxn0I20uVGmAUMP7OXJAz1zgMhgK/w4E49gkR+dDPK6VVKeWVrZs3uixLgRSKkjFhqEHEVgjq3NhFzaMAh/LpWEgETPGVoYh0Y3oZ0Za2zAMiYi0kak+HDhvPvvM08evsstdiyLgT3Yv5ltPnUnqvI9unYdL0zJy7Q1D8t4tS3j9TBd9SUzwWQ64Y0X9sG1be8xXg08FOvDMqR6eOdXD/ZvnsHFuFdM9dg1jCnsWcggFBz/PYJ27hurPPo7v5NP4D/8SX9tZ8PvB5oKFG7Et3IC0OpDB8K22IQQiMlCngxP0gd+LgQRDn9a243E9qGAEfaPsYm24iUCGHXytuAbDN5gzdkiGR21FKIQQKkbQP/xll23dcokbmg2pgOHz5918iMeFaoMqN3SOrKolA9u81aPHuq6j2C0YwQC6LhECUi258GaCHrHRTLGVrkukUND1WOc+TbKNyG1DBm119ELyK95RDBrQMuilzulgQWkxn719Md9/8sykO1s04EM7FrCyuixjfdENsFotPHDXCv7pqWbahyZ3YOeXqvzRzkYsqjqsTygLDu9PXrqCRVVYU1OZEflXBga5dt2LEAqzSuzUOcNPLw0pMCR57+QXHPw8hNAsOFbeyZytbyXQd41rly8T8F+HwBAgMFQLQg8AAmWaOQoYSBS7Cyz2rOkxlqsWB4pmQXj14c8ViwV9x6fQ9/9jYqOXNUDvWXMXavYKtLJZWb0e6bJVruiWS1yxW0FKFLuWd/MhEddW7SG014SDv+hm1KLy0WNdcyBUC4rFiqoKhBKTtbeASRHNoDLdturx+nne086RSz0M+MBugaU1RWxvrGFeafEUJAt0w0BVRdr7pIaj5DJqq0F/SlVC8PpDKEVh3lBWzP951xqOtffy4sl22nvDYbZVxRY2LZnF2jlVkWJSmXMoo7Yqs1n5i7vX0NTew/7mNk51BYaPaZxlZ2djDe7qMsJFqUb0qS6yZUy3ePj3Fy7x9fvKY4ptTQ2GIXnhfDv7mtrpHLNfuMYJt6yq5+6aEhShMlKYKz9RcPDzGUIFRQPNCroj/PQTGX4fqTA67VwCugSLHSyO7OkxhgurA6laQQuM+tw2dw3+u/8H+oH/gJ5JHPjle7BtfDf+V34JJx9L+vJoa+4CzZ71vqfLVgU+mguLJXyhA9q0zgc9MICvaR90XYBQCFzFqAtvQpt/Y9r6Zq1fRWj1O+HoLxMP9NIFODb+IWiW0XIsdoRqQ6hq+AZCESjK2C2kBT6WC0WJOK3TYyvDkDz0+nn2nx1d1MwXhFcvD/Dq5TMsrrDy8R3LcNm0SeVMzsMr94oCyihPfOp9iNoo/JoemWO5w6rBoPmgNYdVHdVfBZUb6yu5sT7eanTmrnesrRQEa+orWRPRRUo5iTM78tmK2RXYuEjiHYDphQ680dbDhnmxoTqkxIO6wT/vO4Gnc+JetA/Bz15p4ULnEA+8be1UVc861C9/+cvZ1iHf8CFgga4b+FO8s08XnHYFIzDEUH8/MuBDGIHwcJYGitSzwoUeQOg6iqKGf6SypMdY7rCpCAwCXt+4Y1RHGbYlm1HmrSNkKYKSWVDVgFi8A9fWj2CrX4mQYJ3dQLDzEgx0JL446+7HOX9NTvQ9nbYq8BHutKoIaeDzBqZlPhDwMnTgRxgHfgTXPDBwFYauQV8L8sIr6E17oXQ2lpJZaemndXYDQUcltJxk0s3oizbhuOWPUVVlvBwhEIoGig17cREoGr6h6GphrPNa4LHc4bICYlpsJaXk3w6c5qWL8TMndXt1jlzs4KZF1ZGCTsm3JSVIKRBCRJzI9PXB4QqvKvuGgmm1SyzvHvRxptNckSgBvPPGeahKevubKVuFffv4coQAgxCnO1IrmDUVBPx+Ni6cNaluyXApJT98/hRNExTfGosrPT76Bn2snlOR9VV8u92CGn56cRH4sZlzCyv4MwZy5FWIyNss8GEdyK4e43jshJ/4GK2sFue6t6EY4Rs3Q9EQRmj4GKGouG75JIOHfwcnn5j4MljL0Ta+E+vCjRBzbu7YIT22KnAx8n4a5oMR8uF74kHoa5l43AGE+gnu/0eMzR/GtnhLWvrpcG9FLNqA7+Ih9IuHwpuGVRtUL8Kx9GZURwmGok081gtpMlPCdNrpxfMdvD6mIulk6PRKfvHaeT6wZWmGtTKHTNvrZncNj53oNHXOziXlWDIccpMKpmqrW5fVc/hCN60Dmc0+NxY9g4HEByXAue7rvNGW/M3JE0fbuGVZLXMqiqbcdrZQcPDzGUJFoiCFMpySD7Kcai9H0wKOSpM5FZmqin3Te2HNW/CdeQU6zoDuBUcRYv5G7LXLEEJkvb85YasZzqczTabvuX+J79zHQH/pRwRmLcJaXJOWfioSbA2bMNzbUaKVizUrIhRMkC61kCYzFS6B6UqT+cyx5MZUFK9eus596wO4rJYU2xUp6zoxz3yazDK7lQ1zizh4ObkbIYAdy2uZjutnjk/dVlZN4bO7V/CPzzZzqW/yCAaXAvOqbJzoSE9AjxilR2r672syn8P/2aY2PrBtienzcgUFBz+fIXUEBkIaKHoIZAgQEAqHDGSDYwTDi5W6DiI7esjgEIFLR9H7riKlRHWVYazcgmKXKOmykaZRtGwrxvLtw58bQglzmd1rMFUujBAKabTVDOXCCK/QKRmeD/pgN7S+buqrIXTsceyb78+ujYwgQrHGVLKNhnZIRtYSC3w8h5FKtplr62LfANfMFyzmpfPXuM1dn1K/wkhnf6ZnPL33piVcu36cC72JY/E/uXMhVc5oLYlcGE/ptZXLZuHP7ljF8au97G9qwxNT2bbGJdi5sp6Nc6uxaoLL/UPsb2rl4MX+KVWcqa9wTVn/w63m03y+dr6z4OAXkAuIubt9k4boyJDO4PHH4fjTwMiXjg60vfxjLI23YWm8A8Xmyq6Ncp5HfwxyRZ8c5dMUouM99Tymce5FjPXvRrFYs2ejQohOSpguO7X0pBZL3dGTwl1BBjEd9gqvXK/k4UPjNyNHUV+s8u4tDTSU525IR7pspQjB6poKVtdUYEgDX8jAqiiRolgj33tzS1zcv3kJ92+Ww5//24seU09DILWKwLHQU6yOe92XUkWQnEHBwc9nFEJ0hrmOxP/kt6B78oIiweanCZ55Hds9f4Eonp09G+U4L4To5FiITsfFyYZ0XASvt2OpXJhVGymqBSkEhRCd5HnYFcq8rYwUE66HDGMKuokkj0uWZz5EJ8otquBdGxZxzw06L13ooLVzkKBuUOKwsK6hmvllRUnJyR7PjK0UIXBa1KSP37WynoOXk0/BO9spWFReFCPLvJ6KiH2fPGyamvigHEbBwc9nFEJ0hvnQ3u/Hde6HEejB/8Q3cdz7JRQhsm6vXOSFEJ3cCtEJz2vzEKEAijSyYyOhAAoolnDhLAwKITrJcpiOEJ2KIjupoKLIlqJuUaSzP9M/nuwWjVuW1MGSXBgruW2rifjcUhdvu7GGhw+3kwga8IlblyFENN1oau0KIah1KbQNmrupXTRrKvUfso9C2ZEZg5i721GhAdniTFt7od5WaDk0uWnGYuAqvgvRmOZcsVcu8eiPQa7ok6M8eoOY6flgd5IKFFtxZvsflytIoSJVK1KoINRxbt6bCW2DQzzRfIVfH7rAY8cucfJa76RVMmNnXybhriolldJFGxpmJT5oGvFmHldmkSu2us1dz7s21Mc9psoh+OI9jcxyOeIelyx2rKg1fc5QIER7X26FpJlBYQU/n1EI0QEJPs8+06aTx5/BWLQp+/bKQV4I0UlfiI4x1EfgxHNw4VXwdgIalM2Bxl045q9FqFrC9pT5GzBa3og7nsfBVokorcNIoF/GuCKQNme4uJeiRRx/GflHzOvM5me7+vn1wQtc6B3zFKbpGqUWuOvGem5eNHvUuRKYjhAdVYFdy6tMpYBsKLcw22Wfgm4iyeOS5dMXopP/PLdstaNhNhvnVvLShQ5ePX2NPq+OpsCcCgfbG2tZVl2KiF08mGK7G+dV85uDLXhj/5QAZzoG+NKvDvOFu1fl5Wp+wcHPZxRCdML8SpN52/VfQIR8CNWSMyEfucILITrpCdHxNu9FvvpvYwZeAHrPwYFzeA/Ysd72KbQad9z2HPNvYPCANXxuslixBxWdrGV0khakbiCJhOoA0xF2kkv8UEs3P3z+ApOhLwg/e7WFjl4vb1+7IObc6bPVrY31vH6xi46h5LyeP9jSMIV2o0hnH94842km2sph1di1tJ5dS1PJymSOWzWFz961jAcfPYmZQJ1Bf4i/e+w4//td6yhzWk2cmX0UQnRmDOTIa06EMDB97flTe4Qmg/7M65aXPPpjkCv65CiPE6Iz5Hl+Aud+LHwEnv4GgY5TcdsTiorYfH8CWTEoqcXh3pKZPifNI+8jiB1RbwZc6huI69zH4plT3ew9PZKjezptZbeofG7PKuqK4m8mtAKfv9NNbXFq4WKZxJtpXE0Vb3ZbzSl28cV7GplTbG7zbL83yDNNrRnSKnMorODnMwohOmFusUHAfMo3aXViCDVnQj5yhRdCdKYWohPq78B47adJjUGA0JPfQX/vtxHq5GPR5t6OzzcEh/8zvjBXDbbbP4e0FWFEi1Jlw0YCpKqBEl41DH8kiRwY8zoz+eNHrmAGj7zexo6GGhRFTLutim0aX7hrNa9f7mRfUyuX+vXhv5VYYNeK2WxpmI3TosWVkxwXSR6XLM+tsJPc5gVbgaTGZWf9oiquvHEVM3j2RDtvXTsPTc2fdfGCg5/PkHq4RHwoACEfBIdAqOHPQ4Hs8MBg+H0gkod+OtqeswzOvWTOdsVzEUYQpJFde+UglwEBWig3xlOaueHrx3fmVRjsCH/vOyuwN6wDZ4VpmTIYCssIBkd9Hmh+1uREDuA79zKOhRvitudcuplgZT3B5meg9choEdZyWLYLx5ItCM2GEfRm19aqFRkMIDU9HI9vGEgEhhF1XHMnTCDdvN8f4GibuQUHP/B6Sxfr6iuzYisBrJ9byfq5VfiCOoPBIHaLilPTIg9lUtfHMGQkD7kAjLTqbRgSEWkj0zbKd16w1QhvutyNWfQNBWjtHWJeZe7WORiLgoOfx5CGgdSDSCOI9A9BMByjawiBiGRomG5O0Ad+LwYSDH1a2tYWbCZk1sFfvAWZA/bKRa4HFYygb0bZR/qH8B/9DVwaXxHWd/RXMHs11rVvRXWUJC3T0GxIBQyff/hzHQmn941rIxHkqeeQ9SsTj/WSGsTm+xHetxK6fhU9FERzFKGV1WMIBaSBDHizb3ddYlgCSFvELpGg1xRTr+cVPNf6UzrvdEsvN9ZWZt1WVlXFqiqAQEomzfaTLHRdIoWCrsc69+mBbkRuG94E42qqKNhqBN6gnvigic4LpHZetlBw8PMYQlEQqgWhWBA2B+Ga8AJFtSD0QFY4SjjjtWJ3gcU+LW1bZy0gNHsVXD2WnOHsldjcNxN9GJ5Ne+UiVy0OFM2C8OrT0i5Sx3f+EHKoF9BQSiqxzV2JotnSIz/gw//0d8AXJ1vI1aMEnryA7c4HUEpnJSVfsVtBShS7Nvy5CAUglaLsfZ0Iqy3pvikWC2pJJYZqQYn2M4fGEEpYR6kIUCD6VFvJn6fbKcOfovPgC4ZQcsRWhpQMBIKEDEmRRcM6pYI/At0wUFUxYZ8MKTnR0UNL1xAh3cBp11g7r4oSW+INjWo4Su5NMa6mily3VfeQn45BHxKodFqZ5crcfg+HRQVTW20j51nzq/BVwcHPZwg1nIJOs4LuiIxXGX4fiZ+edi4BXYLFHk6RN01tW3d/isCjD0LPufg200qx3flniP/X3puHyXFVh9tvVa+za0YaSaPRvvhKGsvWYhnvsiTvK8Hg8IvN9rAGAgkhcRIIiSHAx/KxfxDCZofgxAT8w2Bs8CrLG7aFZVv7lazN2jVaR5qtl6rvj6qWekbd01U93dOLzvs8M326u+rec0/dqjp969xzo43gxiiX1F5lKBvhGuxAGIKxotZlxfroe+03Z4x4W0AvEei4jprzrsUIBPOuy8Yi9sg3hnbuUyS76H/yu0Ru/4qn8o1QyNkvFjz9uZHnCKV1gp41fyAwdRGRlsll0Q+GJZsGRiiCEXQWtzJM03UuBk8hrT65LhIiH2rDIUyztLY62tvPM5v2sUIfIT2x59yxUa7smMCcsempC72W74zcmyaYad6lbcOKLft47PX9nBz0m+j/vnqA+RNquXXRVFrromllDawjZSPnNb825ysf7onxnN7H628epSdmEw3B7LYmlnS00VZf67mckZJLaatssm3Dmn1HeWrtHrYejZNOe4PJ0o4JXDil1V2UsnD1Gnlcp5trw0wYVX6TzIcicPfdd5dah0rjvcDUZNKivz+/FSYLRW3UxIr10NPVhR3rw7BiThe2LSfdYQlkIxnDSCYxTWdhm5Gq2zBMwtMXE7ds6NxBpl/nQbWE6BUfIFjTVFIblbtcEwlgYBHr7StaXcnuI/Q/8v/AgY1nHCeHJHRqEns14amLMAwjr7oS+zeT3PT7LHVkINGDXT+e8KhxOcuvDQcwbIu+3tjpz80AiTW/815fOp1bsLc8Q3znq1A/hmD96LLpE75lI4AdqIFgLRgBaurCgEFfT8xtrMHpG251yY2REE9s8jeBD+Da8ycwvqG2ZLZ6ZdchvvH4G2w73HvG1bOzO8GqHUfZc6SL8yeNTnPUc5fvhPk4q4kabuYpy4Z7n9vMU5uPEssSAbT/RJxnNnfSMamRUdHIGeWCQU2d83lfT3xEbAROqMv/rtrGz17cxbbDvXQnIGZDTwLePNbPs5sPsevQcc5tbyEYMIZVVyHlUthqKNmy4f6XtvKb1w9wtO/M+/WJmM2a3V3s7DzG/MljCPjoc0PJmw918cj6zjPqy8WN50+kY2Kz7/2GSzQaIuA81tsJ3Otn3zJ9WCP4xz79WhZpBBnxuo1AkPoFt1LzF98meMn74dyboeMmzAvvpO39/864qz+EGa0vIxuVq5x+QSx8+baVoP+xb0DvYXJy5A26n/1J3nXFNj2Zu45BWKcmyeYo33VW0j83jADMuMx3nQM4vpv4im/Tu/VFb3qUpey+d0nvUdVOfSTEwvY6X/vUmjBvvOM8lMJWa/cf4Z7n38y53Zp9Pfz0OT3suPzfvb6T1Xu6c25nA9/6vaarL5Z1m5G0lWXb/OTZTTy37diQ263d38M3H1tLLFFeAe/ldA7+9rUdvLAj93yVDQf7+M/nNw+7z6V4ap3/dJf10SDL8lgJt9RIiE4lI2kyM8p2qIbw9IsIY2MFw5iJOGZNraR+9CgXO01m35uvwsn9Q3btAex9nf7je4g0tvuvd88m7/WkOPoGSQIYxtCrwGZLkxmas4z41uf81zsI+48/pb9pHKEx00reJ3zLBmd1mszr5k9itY++d+sF7Y6pSmCrpGVx79PbPeu6Zl8Pr+09zIL20XnUa9ATi/PYJg8/7l3iwDN6LzedPyVDuSOb+nHFln2eMyTtOZHkN6/u4B2Lp4+Ibrnl8kmTebi3lye090w2r+3tZuuRLmaObhxWvSf6Y6zb7z+l9vzJLTTVVNYiVyAOfmVjy0q2XmVZnbV8bGVtWuG7qyfXPY558Z151Nvvuy4AM9mfc5XjbCvZBpvaiM+9ATY8klfd6cTXPkLkyr8seZ/wLdsh7GQS27JdJx+KsTqrZcGa/Uf5o97Pke5+DAzGNka4bE4bakzTqRSPhajLjzyhoZaPLJ3OD1bkmBME3NDRyqXTx6WVUxxbZZNf3XvE91mycsN+18H3U5fDC9sO+qwNVmw8zPXzJqeFaaTKHRkbgYFt2zy51sfABLBy6zFumW8RCQVGTM/s8sjZKpf8bB4hbCs37GPm5Y3DqvdAd5/vesFZzbYSEQe/akgb7TEM920J5FM6kHMf207St2uN4/AdehOsPicf+bTFRGZfiRltKKB+qQtcGdio7OXi2cpOWnB4C755cx1cnEe9gXpInvRfXyCI1XeCnjdehu79YNtQ20rNjAsJ1I3iVIiOTcZ66+bfTDcGbHjYf93p7HmdZG8XRl1zGfUPL7L73mWgi1cYNnce5ydPb6V70ATNvSd7eG3vVkZH4YPLFBMbS5O3+txxzdx1g+KhVTvZ2HmmYzG+zuC6BZO5YOIYim2roXh5i3+H+43D/Zzoj9PgIdPNYDbsPup7n35g38mejMdypOy1+XAXXfHc2w3m5V2HuHz6uMIrlAcj3beysWqb9yc4KV7d041t2+QzQTZF0rJzb5SBRDK//UqNOPiVTAWH6CSO7SH26LchNuhi39MJ6x+hf/0j9M+9mZqFN2FXQNhJNclFtVW//8ejACRO5KfP5Hmw3ecaCaNn0/3MT2DXn874qnftr2H8eYQufw92Uy0YWcJ4DJvohe8gMW0hiXWPw84XzyjLK/0HtxKeeXHZ9A9PskFRQ3Q2HDjG93OMjh/ugy8/ornr+nOY3FR/at/h1OtXntxYx8eWz+VIbz+v7z5Cf1+CUCjAjPGNTB01WCf71P+RDNE5cjK/Uc1jfTEaBmQM8lKvQU+eo6FODvLB5Y5c2MneI7nnDGTi4NFuRupYDi2XT4jO8Tx+KAH0JyyioXQH31+9zTXp/dU7zXWVF54D4uBXNhUaopM4sovYQ1/Eia4cgg0P0Wv1UnvB7WUfdlJNcjFtZQTyzCMcrM+qj33iEP17N2LF+jDDYcJtCrNxPKadJKKW0e/XwT+8Ew73Zv9+/xriv/xnEnf8G6Gm1jNCdNLlcEMroUW3wUV30PvCPbBrtf+293YNWUdZykUM0emOJXM69+n8f49u5ku3LUjLaJJfvcORW2qiLJ01weP2hbOVF9kJexn0GMQDA9N4emsXQG0kCCf81+fkIB9c7sjYCIw8sqY7OIPGI6dndrkcdHDkMJB92nR2Qu4CbPnWO7aulvF1Jvu7/R3NS2aNzUPb0iMOftWQNkJQ5iE6sZU/Iqdzn2LTE8Qmnkd03Kxh6jfoZlTyEIZylotnK8MMQMsMOLLV2/FPManjDH1induJvfY7OLj+1GYW0AcwZjbhBTcSHjuT/gnzYe9rPiobwrlP2+bAg19mwp1f8XQ+GKYB4fxCRaxX/oceM0T9ORdnrqMsZfe9y0AXLzOJpMXLuw7z+vZDnOiNEQ4Fmd5az6XnjGd0bfTUdi9s9Re/22PB6r2HuXDSGF/7lQovtvJCLJHkxZ2H2LT7KN39CWojQc6ZMIqLp7YSDZ2+9U8YVcueEyd8lz/6VOpKf8xpb2bzIX+x7GFIyy0/kELZKxejavMbxW3Kc79iMFK2ysXUMRE2H/I382N0FALm8FuwpGMCv3h5t+ft20bV0NE+atj1lgJx8CuZCgzRiR/eAcdzp2NLS9pdpAAAIABJREFUJ7HmCaxr5gxLPwnRKR9bGepK7D/6c/ADHVcP0Kdv+yqs536YfYdDm4g9von4W95DZPlH6X/ka7lj/8MtEPOe2YHuQ5zcthqrffGQ50P/8R3YbzwPO1/yXvZgVv2Mk4kYtR3LSt4/PMkGvkJ0nt16gF+u2jNolDTJG4f7eWzTYeZPqOXOi2cRDQVYscGfcwjwzPq9roM/sN5ylHPZKpds2xaPbtjD79YOjq2PsXZfDw+8spfr54zh+vMmYRoGl80ex6pd/hz8CybWEz01cdS7bmBwyYyx/OZ1f8dw6ezRrnM3uNyRCztx0pju9KG1w+Jp6f2ulH2rfEJ0rpjTxuZnd+CHK+aML4gOl0xt5eUtB9h+NPcgowm8/4pZw4r7LyWSB7+SGRSiY9oJTNtyH+eXSLbiBAAzmcy4XXzzSv/tPLgGeo8PSz/DSmAm+8vDRmUuF9tWtVMXQE2r9+M/7lwioyaeKiexd+PQzn36KfLSf5LYu4m6az6Bcf5tEGg8cyOzDjpuhmb/eY67XntsyPb2bVqJ/eQ3hufcp3j1fqzje0veP7zJOGF6luvtA/aAJ0On5cc37uEXZzj3A3ltbw9ff3QtPf2JvCY67j6WivvOrEO5ydlslUu2bZv/XbU9g3M/kN9vPMTPXngD27aZ3tLI+Dp/rsDSee15tMt5XxcOcZVq8VxXELhi9oQ86iusHA4GuHKmv4WO5o6N0lwTLZnOXuVYwqI7lsA6dRIWt97zJrTQ7OPBRhC4dHrKwR+eDgHT5GPL5jJr9NBPoALAJ6+by+wJlTl6DzKCX0WkjRCUc4jOcf8ZGwCS3Z2Y0bph6Jd+kpfYRmUvF9dWhhkgct2n6H/kK9CfI6NG81Rql3xwQDmJ1b8Zep9BJF/5NcaNd1F37lUkz72GxL6NJI8fxMIg1NBCuE1hByP0/NfHfZULOKvPnnozsL1921fB6vv9lzkEfXoltW/5izLqK9lk972LQWY2dx7nN697C7nZd9Li/pd9hna55Jvkbn93L5v3HycWTxCNhOgYP4rmIufDzmYrLzy//SDP5liEKcWfdp1gkt7H8tntfGjZbL740AZPkfi3nj+eKU3ugoF5csv8qXR29fG6h5zyf33dOTRFw1nrG469MtEdi/P8G/t5dccRTvQlCAVgxthGLp/bxvXnTeLVN49y3EMAeRB4x1um59xuJEm3VU8swR+3d7Jywz6OpEXLzB0b5cpz25nTmkozW3hMw+Cvrp3Llzz2uY9fMyvDE6P8iYYCfPyqDl7fd4Sn1+5ha9pofgi4afFEbl40jWCeWXfKBXHwK5kKDNEhzxMmCQSMQNmGnVSTPBK2MprGE735n+lb/QBseyHDEQ+AuoaaBTdBMHRq38SJ/XDI5+JVx7bTf3wfoVETsYNhQhM6CE2Ye2oRNDulG/llE7FME8MyBrTRthNYz9+bV3lDsnkFyYvvBLvM+7GBpxCdJ9bu8dV8L6ufZqIplJLSrz/Z5Y0Hj/HIq7syPMbfzdyxUW5aNLlomXmy2SqXbNs2j73uz56PrtnPUtXG2Loon75pDt9/fCOHhwiNvm1hG0vPafOt22mc/mAa8P4rFE/pvTy25gA9GR7fnDu+hj+7YCrj6muGqK9wYSe2bfPY+l08tK7zDF06d3bx4s4upo0K8ZHlih8/pYe0UwT45A2K1rpoQXQrjHzaVluPnOA7j23J6FxvONjHhqe2Mqc1ygeuUG4O/8LrM64uymdvnstPn9nMm8cz/wRvrYH3XZnKgpVvn8ssmwYsmNDCggkt9MQTnOyPEwqYNEXCtLaNIhoOcuK4l/lY5Ys4+JVMJWbRaRwFPsKcU4SizZi2VZaZYapNHjFbRWqpv/jdJBb/ObHtf8LqPgqGSaBxDNEpC7EDYWf7tOOe2LXBf+cBErvWEWmaMHTfDTRA0v9kw4BlnVFm3/ZX8DyR3C8njzpPs8qgr2SVPWTROdYXY8NB/z+q2upM9vnOgpGaYJv2ZCqL/PQb+/nVn7IvZ7/hYB8bfr+ZDy6ZxvltzZ7K9CefaSsv+75x5MSAkVgv9Fiw7sAxzhvfwrj6Gu5+60I2dh7nmfV72X20l3gSmmpM3jJrHBdPG0ttxkw23tvl4Lw3Dbhq9kSWqXbW7T/C3sM9JJIWNdEgi6a0MurUqP1Q5fqz0VDyg69u58nNQz9R3H4szn88qfm7G85l3d6jrNywj30nT/fFUWG4cu54Lp0xjppQsGC6FUZ2bLWrq4dvPpZ7LZKNnX18f8VGPnHVuWnzHwqr25i6KHddfz57urp5btM+DnQ514OWujAXz25jRnPDiNinNhSiNhQaZKvKRxz8qiHt120Zh+iEpl9KfMcqf00bPYtAXdMw9Us/aUtso7KXR9ZWZriW2lmXAGCZQUzLGc2xM2xvxfPLo2/HenPrM/V82Pqcr3ID0xZmLDOx/eW89PRGyqEoh76STXbfu2S6Xe4+nt9ofF00CN3+kuxdeo63+RWv7z0ypHOfzo9Wbueu60NMbmrwpUsu8nUtdnb6/3EKsLvzJOeNd2LiDcNg7thRzB3bBFkdo8JiGgbntbVwXtvoDPXlphCu2Nr9R3M69ymOxeAXL23jw0tmc9n08XTHEvTE40RDQepDQfc2WJ4OogH81zPeFxrceiTG89sPcMWM8bk3HgbtjXX8+YUzGPqHoeAXcfArmQoM0QlOmEPcb7aSc68bdriIhOhUia2CmVPl5SQYxcoR4hWas4y4Twe/8fzr6c1UZk8eq+d6pW40lmVRFscjo2yAGXAWqBsiRCee8J8LHZxc2JdMbeSFHV2etr/p3FZGRVMxOukO6kDZtm0eXLXDly6PrN7FR5bOyVpmPnImW3nZNx7Pz56xRNJ3XfnLhsftvMqFCdF5co33tIkAa/f1cKSnn5baCHXhAHXhwet7FNuO+dlq277j7D3pr5+sWLeHK2akVuItl7YUW05/X7mIg1/J2EmwEk54QaIP4j1gBJzPE7HSyLFu533MffQ+aDvDCGAueT/W41/z1sb2+UTa5wy7bXbMgGCiPGxU5nI52yo8blp+C6SMn+aUNUT5obrRxCct9L4Y1fjZRFun0tvTe2aZgSIlKJu8CCMZK5vjcaZsgWFiGxFs28ROWGAa2JaFjYFlpW6eBg2nnG5/NESC3H7BdOKJLazaPfRTgKvOaebqOe0D6nU4U956pItOnyG36w70cqSn32M4iTc5k6287FsTze92XhsJ+q7Lj2xZNslT2ZSsgtZhWTaGW0e+5Rzu7eeNI/6vKs/qvdx8/tSCtaXYsmXZrFi/y1cbATp7YdfxbtobasqmLcWWLcv5wV/piINfwdiWhZ2MY1tx7P4eiDsXKcswMNzOOdIy8T7o78XCBiuZcbtg4ziSl3+Y5LM/hKF+KZuNEI/T/+L9hGZcSLBhXN76JeMmVrwPuwxslEtOxPsw+k4AAahrchaHGkEdytlWgdpmaJgIJ3yMuEXHEGxow4715iw/uujt9J08AUdzPMZumMjY5R/ASvRh9fWfWeao8XDkDe86eiQ4/RKsvu6yOR5nyIEghGqxkgaWbQImWJB0o4qstND5yY31RACfYePMn9EKGPzFW2Yyf+Zxnl23j02DFs05v62GKzomMGNUA7Zt4OVevWGXtxCNwazdd5RLp4zLvaFHMtnKC+e1t/CrV/b5ru+8KaN91+WHZNLGNkySyXTnvkBlW+7PhmHovzvPiZR7jvYU1W6FJmnBwTzb2tndR1tdTYE1Kl8s28CyqXgnXxz8CsYwTYxACMMMYURqwHB+fZqBkDPKVwLZuZ/bziTAUDTrdsH2uVi3fYW+LatAPwn9GUJ2rC5nldKDEN/2DPGWGQQveRfB+mbf+gVCNZjBEEZvsuQ2yiQbZpDY7rUkN66EzoETSePTLyOsrsRsaR8RfcreVotuxX76e95PlEVvwwiHPZVvECFy3V/Tv+Fp2PTUmf0y0AhzlxLpWE6wvg5sGzMaPKOc8OylxLb5C/fJyaylhMfPwiqDY5BVDtZgBGowQvWY4eipCbapBxpm2oMNE4Plc0fzyIbDnk1QH4COU+n7DOaNbWbesma6YzGO9scxDIOWSIiaUKaQnKHpieWXTDMWSw5o13DJZCsvjIqGmTcuytoD3icuz2oJMbYmmnvDYWGQtCwCAaOgdgLHVgb+bTWAPJ24pGUXvD3FJGA68x3yIWgU/tiVM6bhZHqq1AWuUoiDX8kYATCDEAxDssade2c779346RGXbSBpQygKoZoh9zGDNUQX3oh53jUkuvYT++0XGHI878hWEr/7HOZNnyU4aqIv/YxwDXYgDMFY6W00SLatBP1P/wT2/ilzu7c95ziLF76X2nMuLbo+5WwrsIlMXUzfgnfCqx5yzJ/3dqKzLoFE3HP5RjBM7bxrseddTf+hN7GP7QHbSe0ZaZ2OYRhYwTBGyomMBc8oJ9gyidiYDji0PreOXjjnWmoWvw0Mw1/fwsI2A9gEHKe42McpFMUIRDFCYYxgCNwRW8M0XUcsfQI3LJszkRe3Hvac/eXOy6cROBX+dLqchmiEhmimhWsMz3JNOL/bYSQcOKNdw5Gz2cqL/NbF01j7u42edb/touluPcPTeWjZGbk3TTAHeInDryNlo+G0oaVu6AWPsjG6PjoCtiucbJoGk1obeD2PVLNtDbUFP3blLJumUfHOPUDg7rvvLrUOlcZ7ganJpEV/f77LpxSG2qiJFeuhp6sLO9aHYcWc7mk7aftKIRvJGEYyiWkGnJuUl32sBH1/+CrEvE2aS257jfDsKz2Xb9pJaiIBDCxivX0lt9HAtifpee7HsPuV3A3f8xpW3RjCze1F1a1cbZUuR1onYzdPwTq8G2IZJrTWthJ8yx3UnnNZ3nWZQKCumUjzBMItkwjUjyaAdWqb2nAAw7bo641lLMecPI/E1pchmXtE1Zj/DhgzHTr3MCC95ozLiFz8biIzLx5Q91B6g018v6b/T78g8dyPSb7+WxJrHyL+5hqStkG4qRXTMItzbAznaRRmBAJhMJy0ijV1zhOUvp5UrLPjmAUDBvOnjmbNzoP05ricvvvSySxoH31q3/RyCiEnTPjTDv9hOrddMIm6U08Mhq9PNlt5kevDQWa3N/DS1sM5n1381VUzmd7cUBCdh5JtG2zbcZgcp6lwddS4znlfTzzvcpoiYV7cso8+n3OUb1s8iZba8l+lNiXX1EUY1xDl0bXeskSlmNYc4qq5E0um/8n+OIf7Y8SSNtGAiWGYRa+3tj5KMGAS60+U3NGPRkOpQY2dwL1+9pUR/CrAtm1iB7eQOLwTkhZGfQvRttkYgQDlliYzkxzbsx5O+ljhNnGc7ud/SvTid2OGIh71Sz+BR8gWXtq+X8ObHpx7l+Qf78WevADCNUXUrTxtNViOTJpHaMoCkp1b6N+9AWL9EIoQnjCbwLhzCLjObtF0SL3Pso0ZbSB6w130PXsvHNKZD6hZh3nJndROmQ+AteBWjFgftp3EDte6bXDnt3jQyY7F6V75Qziw7sy6ju3Efuleulc/SPSav8ZsmVh4u9iG8zeIoW6RzTURPn3jfJ7bup+nN+zn6KD5jpdMbWTpvHba6mpxKyoKHWNH0RCEEz7GbWY0hxhbW9jY5OG6E9ObG/jcrefy1IY9rNxylMFh4lfMGMWyjnbG1A5ehKkyGa69DMNgSUcbD77qff7C2FqDGS2FTY86EoxrqWPW6AhbhlqlaxBLz20vokaZSVo2q3YdYuX6vezqOn1ChoDlc8ZwmWpLy4wlZEMc/ArGtg2OrvoNx57+Lzg+cAXDboIw5yrCC24maDoZHsohTWYmOb7xKf+N37Wavl2rMS56HzUzL67Y1I/xDU/6bLhN97ZVRDuWF023crVVNjkwZha1Y2aeWpkWbKxgGCslF6le23ACgIfcvmYU4Ss+gBXvJqFfgCM7IJGEaAPmrLcQmTwfO1QzQFcz5Oxv+2xD0jTpf/K7cDjHBOH4Mfoe/iLB275IuKalwHYJYpqm4+ObJu4X2MBQqR8jIZPlsyewTLWx/2QvJ/rjhIMB2upq0lbSzLxvoWTDgBsXtnP/ywOvpUNx/cLJBdcnl628yM01YW5bNI1b5k9h57GT9MaS1IQDTG6qIxwcGXtmlo0Cl1uYNJlLZo7j5S0H2HvS26zZOy6d4Y7sjqTtCmOrd182ky/8Zr2nye0XT2lkwYTmtLKKr2d3f5zvPrGB3SfOfKQSB/6w8RB/2HiIv1w6jY5xzUXSJ/195SIhOv55L5Q+RMe2khz55T9w9OmfQn+mBU4sOPQGyS0vE5yykEAoQrmG6MT/+N+QcdFsD+x+DSvaTKi5veLCTqx4L4kX7/Xf5v5ewjMuOqtDdIot2z1H6V3/OIlnfkp89a+Jr3ucxK412KEIwcZWDNvOGaKTfj4Ea5oITZpHZMZbCJ9zKcEZFxJuGoeJUTC9e9evgG0rPXYiC6tzF5EZFxbWdj5DdAbLhmHSEAkzujbCqGiE4IB4+zO3L7Q8ubmORCLG1kO5s428Y3E7F0wsfMjQcEJ0BssB06SlNsq4higttVEC5sjaEyj7EB0wCJgmC6eMYcu+wxzvH9rJ/+iyGajWUQXRfyTllK2IWVwwo4X1b3bSM4QLs/ycFt6+eJoTzjdCesYSNt98bB17POTq/9OOY5wzoZ6WU5PEC6ePhOgIJeXEY1+nZ/VDuTfsO0Tvk9+i7sbPQDBCaqStnEJ0/CfKG0jy5Z+RnNiBWdM4RF3pJ3AR2+9DtvvyXAyp50iRdSs/W42UbNs2Pa8/AuseHGhzKwaHNIlnNYlIC+GrPw5NM539C30+5Kk3Gx7P2XUGcEiT6DpIsHFs4fTJI0Sn3Lhl/lRGN9Xw8Cu76crgAI2rNXjrhVOZN955+lFoKslW5UCh7FUXCfK3157HK3sO8fS6vbx5/PTBjwDL5o7hsnPG05RxMndlkLLV6Joo/3LLAjZ2drFy/V62HewlbkNDGC6Y1sJlsycwusYNfx1Bntm6z9dCXPc9+wb/csuC05daYQAlcfCVUjXAp4DbgZk4vWgTzq+T72mtPWeXVUpdAPwrsBioB9YD39Za/3eB1S4bkl0H6HnxPu87dO2jZ/tqIrOvOHUjLqcQHcwGsPJbZj1F/6bnCCy8JWtdZRl2Egrn19jUysVF0q0sbTVCcs+rv4YNjwxt//4jxH73Ofrv+Crh5vbCnw95yPHD26HvUI6OcyZ9b7xE7aK3ljxEp9zkS6eN5ZKprWw4eIw39h6nL56kNhKkY1IL00/FXhenLZVmK3+yUeByCxOikyJgwoWTxnDhpDF0xxKc7I8TDpo0RcNpKSbLwY7Dt5VhGMwd28TcsU1kZmT7n23bPLVufxZdMtPZa7P1SBczRzcWWJ/095XLiDv4Sql6YAVwAXAEWAmEgYuA7wDLlVK3aa1z/oxTSl0NPAyYbjk9wHLgPqVUh9b6M8VpRWnpeeVXYPsLabE3rcCceZGbZcOARKwoMlbcGaxMJsHwuM+MRbDl6eEZZcMTmAtuyFqXYSUwsTHtRFHb70cOhmuJEwR8hnq1TMQsom7laKuRkBMHN+d27tPofOS7tL/zC0Mei3zOh3xk+0Snvz6UoucQpm0VTh8rjmGGMWywLQvMwKmq7FNPhmwGPCEqU9kwoGNcCx3jWoZVjn+58mzltV0OhSy3eDaqC4eoC4eKVv7Iy+Xdn3YeP0lXWgIxr6x6o9N18Attq8qnFEsXfAbHuX8SmKG1vl5rvRzoALYAtwIfyFWI+xTg5+7bq7XWy7XWNwPzgN3Ap5VSi4rRgFIT2/Ks/52ObceKp+JKbQaGDxRDxvM+UXWl//acQS92IjFEXekn7Ui0P7dsmEHIo+1hdWWRdSs/W42EHPM74fnoLvoPbi34+ZC3nA9GgXUYIkSnOm6ZxUds5Q+xlXfK2VbHumO5N8q0X09++50NlMLBf4/7+iGt9bHUh1rrHcDfu2/f6aGcdwFjgfu01ivSytkK/KP79hPD1rYMsXq95YsfTDIRwzKCWEYAKxAqjmyGSGJgmd73MUe1w4zLh28Xg6x1pYedFLX9PuXw7Cv9NbJ2HIG22UXVrVxtVUw5keyH3a/67nNH1z1V8PMhH9lomuBbdwDqxhdYnyD2GSE66aEBqR8iImeTq9dWYq9Sy+Vsq0Ce3uiptcaKoltlM6IhOm54zhZgl9Z6W4ZNNruvXu5W17mvD2b47iGctCzX+1ayAjDCtXntFzADRQ+7yDckoe7Cd9Ld3w27V+dpFRPTDGJkCTco17CTYP1okhe9j+SL93g5gkSXfYyAbWNJiE5BZfuY9xzY6SSPHCBSBiE6keYJxOvb4KS/dtTMulhCdMpOrlZbpShkudVmo2LK5W2rCU35+TUTW+pcqdC2qnxG1MHXWp8ElgyxyWL3dbeH4jrc1zNWdNFadyml9gKTlFLjtNYH/Gla3oSnLCSxf5O/nepaCdQ0OY9sDKcTm3ag4DJ2CEIWBAMQCHjfP2hTt+xjxLb+kfiqX0HsWMZmZEUtIxAMZy3fDIaxDQMzkH2bUsl16lJ6wlESz9wDZFn1tKmd6JV/RbhpbNH1KWdbFUs2jdOOqC8MMIfo53mfD3nIgXnXkfzjPd51n3A+ocYC9ycjhBEIgWlim6Y7io+7zL3hvpbemShnufpsZeOkyLQxTWOYZQ2UDdPEwHZtVep2lrdc7rZqra9FtdagO3OnqE3nsnMmnDpnCqWPYZqUOj1mISibNJlKqQjwz+7bBzzs0ua+Zhuy2gdMAsYBVeXg1y7+c3pe8pckKLzwNkItEzGSzqisGQgURSYRw47HMEIRCIZ87x9u+jOsjms5/sPbcU42b9Rf9H8INI7PWn64uR7bCBKwjhW1/fnK9QsnwbnX0bflGfo2PAXdh5ysK82TqTv/ekITzoVAcET0KXdbFUOOROrzStZa0zqJYHP282q454OvPnTh7XQd3Iy99fnciteOoeHGfyQQbSywPkEMM4QdCDlped0bZzTs3GrC4Tx/SJ1FVJutbNvGtsEwKLjTVOPaqFpsVUwqwVbXzZ+MfjzLqt8ZWDS5iXGj8hv5H4poyCQYqHwnf9gOvlLqPsDLZNZfa63/KUsZBvATYBaw0ZVzkXouk+3nXurzeg9l+SYcDtLaWqKlqlvnE1twPV2v/t7T5mbjOKbf+EECtaM4lX3HCBRNti0LwzSHVVb9+77Fnnv+2lP7xt78d7QuutJT+ROaW4ve/mHJag7c/NGy0KfsbVVgecfcJXRvWIkfWq+4k4bpM4t+PniV7Y/9kL2//DeO/fH+rDpHJnYw5QP/TqilvTj6YDmvGW6O7e3NZ3wmZKaabGXbdlGdpWqyVbEpZ1u1tzdzoDvO/S9kiuAeyKTRtfzjOxZTHw0VRRfDMBg7trEoZY8UhRjBnwIoD9u1ZfpQKRUAfgzcARwF3qa19jKYlgQMrXW2YV5j0GtVMfFd/y/bj+2nd/vQEwMD9aOZ/vGfEWoc436SfsiLLee/f8vit2ICu//r77ETWWbJGybjb72LMVd/xL15jGTbRK42uXXp+3w5+OGx02jsWOo470U+HzzLwXomvesrjL3mQxx+9j66Xn+UZPdxzHCU2mkLabniXdSrS12di6WPIAhCfrx32Wwa6yL859Ob6Y8nM26zeGYrd906n8baPNeSOUswbNt7GEShUUrVAf8D3IyTE/8arfUrHvc9CowCarTWZwQuK6VeAi4EFmitXyuc1jwNLInFEhw/7i9WrNCMbgqy/7df48jzv8DuH7QqqmESmb2Uhmv+jmDLpNIoWACSJw7Ru/oBel75FdZxJxrLrGuhZv6t1FzwDoItkz2Vk3ra0tk5vAW1zgbOVlvZtk3Xg5+l97VM8/YHEQgz/W/+h7oZF5x1dsqHs7VP5YPYyjtiK+9Umq16+hM8t/kAq7Yf4mRfgnDQZHprA8s62ph0amJtcSgnWzU11RB2wvZWAlf62bdkwy5KqXE4i1QtwplUe53Wer2PIvbiOPjjgR0Zvs8Vo1/xmOEaJrz9Xwhc9GH61v2exKHtYCUINIwlOu96Ak0ZH5pUFIGGMdQv+TD1Sz6MnYiDbTnxzIJQYAzDoPGWuyEQoveVX2bfLtrIqHd+i7oZF4yccoIgCGcRtZEg18xr55p57aVWpWIpiYOvlJoCrACmAWuBG7TWXjLnpLMOmOv+7RhUfiNOqs3OasugkwkzUkvtottKrUbRMYLFibUThBRGIEjTLf9K7aLb6Fl1P33rHsV2F4gLjJlG7QW3UzP/FsyabMu7C4IgCELpGXEHXynVAjyB49w/A9yitT6eR1F/AG4H3goMXl/+ZiCQ4XNBEISchNrPpan9CzTe+m/YsR6MQFh+YAqCIAgVQylWsv0+MBN4FScsJ6dzr5Sa7f6l50N6ADgIvFcpdUPattOBL+PkWPxGQTUXBOGswjAMzEidOPeCIAhCRTHSK9nOwRl1BzgB/EipjAl4OrXWn0x7v9F9XYozyTW1mNUHcRz93ymlVrplLgdqgc9ordcUvBGCIAiCIAiCUMaMdIjOEk6nrbxiiO12Ap8c4nsAtNa/VUotAf4FuMgtew3wDa119llygiAIgiAIglCljKiDr7X+AfCDPPbLmstea/0CcN1w9BIEQRAEQRCEaqEUMfiCIAiCIAiCIBQJcfAFQRAEQRAEoYoQB18QBEEQBEEQqghx8AVBEARBEAShihAHXxAEQRAEQRCqCHHwBUEQBEEQBKGKEAdfEARBEARBEKoIcfAFQRAEQRAEoYoQB18QBEEQBEEQqghx8AVBEARBEAShihAHXxAEQRAEQRCqCHHwBUEQBEEQBKGKEAdfEARBEARBEKoIcfAFQRAEQRAEoYoQB18QBEEQBEEQqghx8AVBEARBEAShihAHXxAEQRAEQRCqCHHwBUEQBEEQBKGKEAdfEARBEARBEKoIcfAFQRAEQRAEoYoBFZ3KAAAO9ElEQVQQB18QBEEQBEEQqghx8AVBEARBEAShihAHXxAEQRAEQRCqCMO27VLrUGnsBtotyyaRSJZUkXA4CEAsliipHpWA2Mo7YitviJ28I7byjtjKO2Ir74itvFNOtgoGA5imAbAHmOhnX3Hw/XMMaCq1EoIgCIIgCMJZwXFglJ8dgkVSpJrZDkwDTgJvlFgXQRAEQRAEoTqZCdTj+J6+kBF8QRAEQRAEQagiZJKtIAiCIAiCIFQR4uALgiAIgiAIQhUhDr4gCIIgCIIgVBHi4AuCIAiCIAhCFSEOviAIgiAIgiBUEeLgC4IgCIIgCEIVIQ6+IAiCIAiCIFQR4uALgiAIgiAIQhUhDr4gCIIgCIIgVBHi4AuCIAiCIAhCFSEOviAIgiAIgiBUEeLgC4IgCIIgCEIVIQ6+IAiCIAiCIFQR4uALgiAIgiAIQhUhDr4gCIIgCIIgVBHi4AuCIAiCIAhCFREstQLC0CilaoBPAbcDMwEb2ATcC3xPa235KOsC4F+BxUA9sB74ttb6vwusdlmglLobp72TtNa7few3CXhziE2e11pfNkz1yop8beXuew7wOeAyYDTwBvBD4Pt++mc5o5S6HfgkMBdIAi8An9dav+yjjMuBZ4bY5D6t9Z3DUnSEUUpdBXwaOA8IA68AX9ZaP+qjjKrvPzB8W52l16X3AvcAl2utn/Ox3wSc69nVQBuO3X4OfFVr3V8EVUtOPrZSSgWBk0AkyyZ7tNYTC6Nh6VBKBYC/BN4DzAECwDbgfuBrWus+j+VU1LVKHPwyRilVD6wALgCOACtxbgwXAd8BliulbtNaJz2UdTXwMM5Tm5VAD7AcuE8p1aG1/kxxWlEalFJvBfJt0wL3dQ2wNsP3Os9yy5Lh2EopdT6O09oIPA+sApYC38XppxXlsGYi7cfPCeApoBm4AbhWKXWL1vr3HotK9asXgO0Zvn9+mKqOKGkORT+OXQI4x/4PSqkPa61/6KGMqu8/UBhbcfZdly7G6Qd+95sI/BGYCLwKrAYuBT4PLFNKXaO1jhdS11KTr61wBiwiwFbgxQzfHxmOXuWA69z/BrgR58fMi0Ac5/ryeeBGpdQyrXVPjnIq7lolDn558xkc5/5J4O1a62MASqmpwGPArcAHgP8YqhD3KcDP3bdXa61XuJ/PAJ4GPq2U+r9a61eK0IYRRyn1UeBb5N+/UzfSr2qt7yuMVuXJcGyllDKAn+Fc8N6ltf65+3kr8ARwh1Lq11rrBwqo8oiilFqE49zvBC7VWu9xP78ReBC4Ryk1PdfNwSXVr+7SWleUMz8YpVQb8APgOHCZ1nqd+/linGP/baXUwyl7ZSmj6vsPFMZWLmfTdeltOE+p6/PY/fs4zv1ntdZfcMurwzlfrwI+AXy9MJqWnmHaKtWn7tFaf7FgSpUXH8Bx7tcAN6Rdw8cAvwUuBj4L/FO2Air1WiUx+OXNe9zXD6WcewCt9Q7g79237/RQzruAsTghACvSytkK/KP79hPD1rbEKKVmK6UeBr6HczM9kWdRqYteVfzgyUSBbHU1TrjB06kLHoDWuhP4qPu20vvVp9zXf013wLTWD+PcVMcBf+6xrAWABbxWSAVLxMdxRv6+mXJYAbTWq4CvAlHgQznKOBv6DxTGVnB2XJcmKqV+BjyA85TjgM/9FXATzoj0l1Kfa627gffjhNd9vGAKl5Dh2sql6vsU8F739W8GXcMP4YTtQG4/qiKvVeLglylueM4W4GWt9bYMm2x2Xyd4KO469/XBDN89hHPRu963kuXHD3BCJx4HFpH/48UFOI/yNufasIIphK2y9it3hPogcJlSqiFfJcuA63Dmvfw2w3e/dl9znjtKqTDO4/BNrrNR6Qx1TfFql7Oh/0BhbAVnx3XpCzgDUn/CCXvY5HP/awEDeGhwTLTW+k2ccJ0pSqm5BdC11AzXVnDawV9dKKXKkEM4tsk0X8qrH1WR1yoJ0SlTtNYngSVDbLLYffUyIbLDfV03+AutdZdSai8wSSk1TmudzyhAubAK+LrW+iEAZzDHH0qpFmAyzgXvb5VS7wJmAceA3wF3a633Fkzj0jFsWzFEv3LROE+O5gIv5VNBKXFDK5qB3Vrroxk2Sd1Q53ko7lwgBOxQSn0BuA2YCuzHGYH7QvpTunLGfVw9F+dpxMYMm2x2v+tQShlaaztLUVXdf6BwtjqLrkubcJ5c/1xrbeVxXcrVpzbh3DvnARvy0rB8GJat3L45H+cadItS6kM4E1D7cMJO7tZaV/y8Dq31zUN87dWPqshrlYzgVyBKqQjwz+5bLzFfbe7rvizfpz4fNxy9So3W+u9TDuswSI1oLMR5xHsQZ6JzEPgg8IrK0xsuJwpkq2rvV4VsX6pf3QD8DU4Gh+dwfkB8CnjJjeesBJpxQk4Oa61jg7/UWidwRs1qgaFGtKq9/0DhbHW2XJe+rLX+2TAykpwNfQooiK2m48SUj8eZx9eH06f6cEJWVimlLi2IsmWI+wPn8+7bXH5URfYrGcEfQZRS9+GEQ+Ti11rrjBM+3E75E5zRm42unIs697U3y/epz/OZpFMUCmGrPEndSNcDN2utt7v61AE/Av4PcB/O5OeyoIS2SvWrbBNMK7pfAY+4crb2pVKreWlfql+tBN7hxm6mJnrdj5PR6gc4I/vlTq7jDgOPfVee5ZRd/8mDQtmq4q5LJeJs6FOFItWn9gA3aa1fg1OpM7+MM/DwC6XUTK9pJCuML+FESRwAvpZj24rsV+LgjyxTAC+jLG2ZPnTTPf0YuAM4CrzNY07fJDDUo3Jj0Gs5MCxbDYNv4vyaP+FOwgGcSVpKqQ8AVwCLlFIXaa0zpRUrBaWyVWrkqFr7Va72pfDSvk/ipLbdp7U+NaFZa31IKfVunFCNP1NKtWmts40SlQte7OLl2Fdi//FLoWxVidelUnA29KlC8QBO2FcyPbxLa51QSt0FXIkzGPJWnEGIqkEp9XmcBCP9wO2pAZchqMh+JQ7+CDKcRUjckZr/AW7GmRB5jdba66SabmCUUiqa5Zd4NG27sqBUC7a4awpkylGO1rpHKfUUzsSmRWTOGzzilHBxm5Pua02W7yu6X7l5j6EA7XPzbmecHKm13quUWg1cjhOC8bBXHUtEruMO3mxTcf0nDwpiq0q8LpWIs6FPFQR3wG9Xlu8spdQjOP1pEVXi4LtPJ76Hk7WqD2eQdKjFB1NUZL+SGPwKQCk1DufR/s04k0Gu8JmzPvXrfHyW73PFlwmn2e++1pZUi/Kg2vtVKqXaSLSvkvpVF84Nb4x7wxyA+9kYoC/HxOFq7z9QOFvlopL6TzE5G/rUSFFVfcrNTPgQjnN/DLjWxyKFFdmvxMEvc5RSU3BW5VuEs3rhxVrr9T6LSc38PiM1mFKqESdFVGeFZ9ApCEqpf1VK/UoplS0zyjT31Uv2ompnqH5lALNxwsMqMluFGwpxEJiYJf3ZHPc106qiA1BKfUcp9Wul1Ngsm1RMv3JH/jbg5N4+J8MmCufekssuVd1/oHC2kuuSZ7L2KRfP52y1o5T6mFLqF0qpq7JsUjV9SinVjLOo53U4Ty0u9zhyn6Iir1Xi4Jcxbmq0J3BOtGdwOmU+J9sf3Ne3ZvjuZpybzyMZvjsbOQ9nouPtg79wnbNrcJa5XjH4+7OQofrVJUAr8Fx6zHkF8gec8yNTqrVUu72cO5e6259RjlLqXJwJb4epnAVnhjr2Xu1yNvQfKIyt5LrkjZStb1FKDfBvlFKTcc6znVrrsnLESsR0nP70nsFfKKWiwDvct4+NpFKFxl2DJBVutAG4JH3BOY9U5LVKHPzy5vvATOBV4Dqt9fFcO7grlM5WSqU/VnsAZyTyvUqpG9K2nY4zW94GvlFQzSsApdQM11ZNaR//h/v6qfQUYe7jvZ/ipBX7sdZ6P2cRWWy1Eierx9VKqQ+mbduK03eh8peE/3ec8+MrSqnUiBZKqRtxVkjchzM3hrTvMp2DqX71JaXU7LRtW4F7cH5EfDVTKsUy5R6cGNZ/UEqdykqklLoAuAsnq8T30z4/W/sPFMZWcl0ahFJqsmunManP3OxCf8B5MvL5tG3rcBJUBKiOPuWLTLbCycCXBO5QSt2Wtm0I+C5OQoLf+wwHLkc+j7MQ2C7gylyDpNV0rTJsO1eCCKEUKKXm4HQoA2f0PuNkGJzQmk+m7Zc6oEu11k+nfX4Lp5e0XgmcwEnNVwt8Rmt9alnvakEptQPnIjUp00md9v37tNb3pn3+deBvcWbOP4+Tp/pynFjZZ3F+bA2V9q7iGIatLgSexEkP9hJOrOKVOPm/f6S1/lBRFR8BlFJfwXHEenDa2oCTXi2O0xdWDNr+jHPQHU38BfB2IIbTj7qBpW55/wv8hTuZsiJQSn0UZ8JaHMcuBrAMJ3nDu9OXdD+b+w8UzFZn43XpaZxz7XKt9XNZvvuc1vrutM+n49hnPE5ohcYZZW0Dfg/c4q4/UFXkaatPAN/C6Y+rgDeBtwATcRbSWqK1PjgC6hcFNwpiN87k2NVkXmwOAK31ne4+O6iSa5WM4JcvSzidcukKnNSYmf7+zEthWuvfumU+hvOYcgmwBidFVNU598NBa/0pnEeXz+PY6jqckdq7gOXVdhMdDlrrl3FuCA/grM1wDbAT+AjwlyVUrWBorf8BZ7R+I3AVThzmwzjzYTyFRLiL0dyOY5c1OA7HcrfMDwLvrCTnHkBr/X2ckKMXcRzNxTiLd12d7rDmKKPq+w8UzFZyXfKA1nobcCFwL07oxI04aaX/CSdrStU59/mitf4OcDXwKM75dxPOQMYXgcWV7Ny7XMjpzDcLye5H3ZGroEq8VskIviAIgiAIgiBUETKCLwiCIAiCIAhVhDj4giAIgiAIglBFiIMvCIIgCIIgCFWEOPiCIAiCIAiCUEWIgy8IgiAIgiAIVYQ4+IIgCIIgCIJQRYiDLwiCIAiCIAhVhDj4giAIgiAIglBFiIMvCIIgCIIgCFWEOPiCIAiCIAiCUEWIgy8IgiAIgiAIVYQ4+IIgCIIgCIJQRYiDLwiCIAiCIAhVhDj4giAIgiAIglBFiIMvCIIgCIIgCFWEOPiCIAiCIAiCUEWIgy8IgiAIgiAIVcT/D/WmgFa0jHM2AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 360,
+       "width": 380
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, ax = plt.subplots(figsize=(6, 6))\n",
+    "\n",
+    "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
+    "plot_points(plt=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The acuracy on the  5  validation folds: [0.96 0.96 0.94 0.98 0.96]\n",
+      "The Average acuracy on the  5  validation folds: 0.96\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Applying K-fold cross-validation\n",
+    "# Here we pass the whole dataset, i.e. features and labels, instead of splitting it.\n",
+    "num_folds = 5\n",
+    "cross_validation = cross_val_score(\n",
+    "    model_scikit, features, labels, cv=num_folds, verbose=0)\n",
+    "\n",
+    "print(\"The acuracy on the \", num_folds, \" validation folds:\", cross_validation)\n",
+    "print(\"The Average acuracy on the \", num_folds, \" validation folds:\", np.mean(cross_validation))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "The code above took quiet long to finish even though we used only 5  CV folds and the neural network and data size are very small! This gives an indication of the enormous compute requirements of training production-grade deep neural networks.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hyperparameter optimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We know from chapter 6 that there are 2 types of parameters which need to be tuned for a machine learning model.\n",
+    "* Internal model parameters (weights) which can be learned for e.g. by gradient-descent\n",
+    "* Hyperparameters\n",
+    "\n",
+    "In the model created above we made some arbitrary choices such as the choice of the optimizer we used, optimizer's learning rate, number of hidden units and so on ...\n",
+    "\n",
+    "Now that we have the keras model wrapped as a scikit-learn model we can use the grid search functions we have seen in chapter 6."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "# Just to remember\n",
+    "model_scikit = KerasClassifier(\n",
+    "    build_fn=a_simple_NN, **{\"epochs\": num_epochs, \"verbose\": 0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8600000002980233 {'epochs': 100}\n"
+     ]
+    }
+   ],
+   "source": [
+    "HP_grid = {'epochs' : [30, 50, 100]}\n",
+    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid)\n",
+    "search.fit(features, labels)\n",
+    "print(search.best_score_, search.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/tarunchadha/anaconda3/envs/machine_learning_workshop/lib/python3.6/site-packages/sklearn/model_selection/_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
+      "  DeprecationWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8219999995231628 {'batch_size': 10, 'epochs': 15}\n"
+     ]
+    }
+   ],
+   "source": [
+    "HP_grid = {'epochs' : [10, 15, 30], \n",
+    "           'batch_size' : [10, 20, 30] }\n",
+    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid)\n",
+    "search.fit(features, labels)\n",
+    "print(search.best_score_, search.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A more general model for further Hyperparameter optimization\n",
+    "from keras import optimizers\n",
+    "\n",
+    "def a_simple_NN(activation='relu', num_hidden_neurons=[4, 4], learning_rate=0.01):\n",
+    "\n",
+    "    model = Sequential()\n",
+    "\n",
+    "    model.add(Dense(num_hidden_neurons[0],\n",
+    "                    input_shape=(2,), activation=activation))\n",
+    "\n",
+    "    model.add(Dense(num_hidden_neurons[1], activation=activation))\n",
+    "\n",
+    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
+    "\n",
+    "    model.compile(loss=\"binary_crossentropy\", optimizer=optimizers.rmsprop(\n",
+    "        lr=learning_rate), metrics=[\"accuracy\"])\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise section: \n",
+    "* Look at the model above and choose a couple of hyperparameters to optimize. \n",
+    "* **OPTIONAL:** What function from scikit-learn other than GridSearchCV can we use for hyperparameter optimization? Use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "Another library which you should definitely look at for doing hyperparameter optimization with keras models is the <a href=\"https://github.com/maxpumperla/hyperas\">Hyperas library</a> which is a wrapper around the <a href=\"https://github.com/hyperopt/hyperopt\">Hyperopt library</a>. \n",
+    "\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise section:  \n",
+    "* Create a neural network to classify the 2d points example from chapter 2 learned (Optional: As you create the model read a bit on the different keras commands we have used)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras import optimizers\n",
+    "from keras.wrappers.scikit_learn import KerasClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 309,
+       "width": 332
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "circle = pd.read_csv(\"data/circle.csv\")\n",
+    "# Using x and y coordinates as featues\n",
+    "features = circle.iloc[:, :-1]\n",
+    "# Convert boolean to integer values (True->1 and False->0)\n",
+    "labels = circle.iloc[:, -1].astype(int)\n",
+    "\n",
+    "colors = [[\"steelblue\", \"chocolate\"][i] for i in circle[\"label\"]]\n",
+    "plt.figure(figsize=(5, 5))\n",
+    "plt.xlim([-2, 2])\n",
+    "plt.ylim([-2, 2])\n",
+    "\n",
+    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\");\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Insert Code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The examples we saw above are really nice to show various features of the Keras library and to understand how we build and train a model. However, they are not the ideal problems one should solve using neural networks. They are too simple and can be solved easily by classical machine learning algorithms. \n",
+    "\n",
+    "Now we show examples where Neural Networks really shine over classical machine learning algorithms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Handwritten Digits Classification (multi-class classification)\n",
+    "**MNIST Dataset**\n",
+    "\n",
+    "MNIST datasets is a very common dataset used in machine learning. It is widely used to train and validate models.\n",
+    "\n",
+    "\n",
+    ">The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a >test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size->normalized and centered in a fixed-size image.\n",
+    ">It is a good database for people who want to try learning techniques and pattern recognition methods on real-world >data while spending minimal efforts on preprocessing and formatting.\n",
+    ">source: http://yann.lecun.com/exdb/mnist/\n",
+    "\n",
+    "This dataset consists of images of handwritten digits between 0-9 and their corresponsing labels. We want to train a neural network which is able to predict the correct digit on the image. \n",
+    "This is a multi-class classification problem. Unlike binary classification which we have seen till now we will classify data into 10 different classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading the dataset in keras\n",
+    "# Later you can explore and play with other datasets with come with Keras\n",
+    "from keras.datasets import mnist\n",
+    "\n",
+    "# Loading the train and test data\n",
+    "\n",
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(60000, 28, 28)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Looking at the dataset\n",
+    "print(X_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This digit is:  1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 251,
+       "width": 253
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
+    "i=np.random.randint(0,X_train.shape[0])\n",
+    "sns.set_style(\"white\")\n",
+    "plt.imshow(X_train[i], cmap=\"gray_r\") ;\n",
+    "sns.set(style=\"darkgrid\")\n",
+    "print(\"This digit is: \" , y_train[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 255\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Look at the data values for a couple of images\n",
+    "print(X_train[0].min(), X_train[1].max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data consists of values between 0-255 representing the **grayscale level**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(60000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The labels are the digit on the image\n",
+    "print(y_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scaling the data\n",
+    "# It is important to normalize the input data to (0-1) before providing it to a neural net\n",
+    "# We could use the previously introduced function from scikit-learn. However, here it is sufficient to\n",
+    "# just divide the input data by 255\n",
+    "X_train_norm = X_train/255.\n",
+    "X_test_norm = X_test/255.\n",
+    "\n",
+    "# Also we need to reshape the input data such that each sample is a vector and not a 2D matrix\n",
+    "X_train_prep = X_train_norm.reshape(X_train_norm.shape[0],28*28)\n",
+    "X_test_prep = X_test_norm.reshape(X_test_norm.shape[0],28*28)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "One-Hot encoding\n",
+    "\n",
+    "In multi-class classification problems the labels are provided to the neural network as something called **One-hot encodings**. The categorical labels (0-9 here) are converted to vectors.\n",
+    "\n",
+    "For the MNIST problem where the data has **10 categories** we will convert every label to a vector of length 10. \n",
+    "All the entries of this vector will be zero **except** for the index which is equal to the (integer) value of the label.\n",
+    "\n",
+    "For example:\n",
+    "if label is 4. The one-hot vector will look like **[0 0 0 0 1 0 0 0 0 0]**\n",
+    "\n",
+    "Fortunately, Keras has a built-in function to achieve this and we do not have to write a code for this ourselves.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(60000, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.utils.np_utils import to_categorical\n",
+    "\n",
+    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
+    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
+    "\n",
+    "print(y_train_onehot.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "60000/60000 [==============================] - 10s 172us/step - loss: 0.5587 - acc: 0.8518\n",
+      "Epoch 2/20\n",
+      "60000/60000 [==============================] - 8s 138us/step - loss: 0.2450 - acc: 0.9291 1s \n",
+      "Epoch 3/20\n",
+      "60000/60000 [==============================] - 9s 148us/step - loss: 0.1885 - acc: 0.9456\n",
+      "Epoch 4/20\n",
+      "60000/60000 [==============================] - 8s 139us/step - loss: 0.1569 - acc: 0.9539\n",
+      "Epoch 5/20\n",
+      "60000/60000 [==============================] - 9s 144us/step - loss: 0.1344 - acc: 0.9599\n",
+      "Epoch 6/20\n",
+      "60000/60000 [==============================] - 8s 136us/step - loss: 0.1165 - acc: 0.9659\n",
+      "Epoch 7/20\n",
+      "60000/60000 [==============================] - 8s 128us/step - loss: 0.1034 - acc: 0.9693\n",
+      "Epoch 8/20\n",
+      "60000/60000 [==============================] - 8s 126us/step - loss: 0.0929 - acc: 0.9728\n",
+      "Epoch 9/20\n",
+      "60000/60000 [==============================] - 7s 120us/step - loss: 0.0829 - acc: 0.9757\n",
+      "Epoch 10/20\n",
+      "60000/60000 [==============================] - 7s 118us/step - loss: 0.0765 - acc: 0.9771\n",
+      "Epoch 11/20\n",
+      "60000/60000 [==============================] - 7s 117us/step - loss: 0.0696 - acc: 0.9790\n",
+      "Epoch 12/20\n",
+      "60000/60000 [==============================] - 8s 136us/step - loss: 0.0635 - acc: 0.9813\n",
+      "Epoch 13/20\n",
+      "60000/60000 [==============================] - 8s 137us/step - loss: 0.0586 - acc: 0.9824\n",
+      "Epoch 14/20\n",
+      "60000/60000 [==============================] - 8s 126us/step - loss: 0.0538 - acc: 0.9836\n",
+      "Epoch 15/20\n",
+      "60000/60000 [==============================] - 8s 139us/step - loss: 0.0499 - acc: 0.9852\n",
+      "Epoch 16/20\n",
+      "60000/60000 [==============================] - 7s 125us/step - loss: 0.0457 - acc: 0.9867\n",
+      "Epoch 17/20\n",
+      "60000/60000 [==============================] - 7s 124us/step - loss: 0.0421 - acc: 0.9878\n",
+      "Epoch 18/20\n",
+      "60000/60000 [==============================] - 7s 120us/step - loss: 0.0396 - acc: 0.9888\n",
+      "Epoch 19/20\n",
+      "60000/60000 [==============================] - 7s 118us/step - loss: 0.0358 - acc: 0.9896\n",
+      "Epoch 20/20\n",
+      "60000/60000 [==============================] - 7s 119us/step - loss: 0.0342 - acc: 0.9903\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Building the keras model\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "\n",
+    "def mnist_model():\n",
+    "    model = Sequential()\n",
+    "\n",
+    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\"))\n",
+    "\n",
+    "    model.add(Dense(64, activation=\"relu\"))\n",
+    "\n",
+    "    model.add(Dense(10, activation=\"softmax\"))\n",
+    "\n",
+    "    model.compile(loss=\"categorical_crossentropy\",\n",
+    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
+    "    return model\n",
+    "\n",
+    "model = mnist_model()\n",
+    "\n",
+    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=20,\n",
+    "                      batch_size=512)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10000/10000 [==============================] - 2s 199us/step\n",
+      "The [loss, accuracy] on test dataset are:  [0.08791934847778175, 0.9742]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise section\n",
+    "* Reinitialize and run the model again with validation dataset, plot the accuracy as a function of epochs, play with number of epochs and observe what is happening."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "tags": [
+     "solution"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 60000 samples, validate on 10000 samples\n",
+      "Epoch 1/20\n",
+      "60000/60000 [==============================] - 10s 168us/step - loss: 0.5759 - acc: 0.8460 - val_loss: 0.2854 - val_acc: 0.9181\n",
+      "Epoch 2/20\n",
+      "60000/60000 [==============================] - 8s 132us/step - loss: 0.2497 - acc: 0.9281 - val_loss: 0.2891 - val_acc: 0.9099\n",
+      "Epoch 3/20\n",
+      "60000/60000 [==============================] - 8s 134us/step - loss: 0.1950 - acc: 0.9438 - val_loss: 0.1953 - val_acc: 0.9411\n",
+      "Epoch 4/20\n",
+      "60000/60000 [==============================] - 8s 129us/step - loss: 0.1627 - acc: 0.9529 - val_loss: 0.1701 - val_acc: 0.9491\n",
+      "Epoch 5/20\n",
+      "60000/60000 [==============================] - 8s 134us/step - loss: 0.1388 - acc: 0.9591 - val_loss: 0.1423 - val_acc: 0.9594\n",
+      "Epoch 6/20\n",
+      "60000/60000 [==============================] - 8s 129us/step - loss: 0.1207 - acc: 0.9637 - val_loss: 0.1170 - val_acc: 0.9653\n",
+      "Epoch 7/20\n",
+      "60000/60000 [==============================] - 9s 156us/step - loss: 0.1078 - acc: 0.9681 - val_loss: 0.1575 - val_acc: 0.9526\n",
+      "Epoch 8/20\n",
+      "60000/60000 [==============================] - 10s 159us/step - loss: 0.0965 - acc: 0.9713 - val_loss: 0.1124 - val_acc: 0.9679\n",
+      "Epoch 9/20\n",
+      "60000/60000 [==============================] - 11s 177us/step - loss: 0.0867 - acc: 0.9741 - val_loss: 0.1177 - val_acc: 0.9663\n",
+      "Epoch 10/20\n",
+      "60000/60000 [==============================] - 11s 188us/step - loss: 0.0791 - acc: 0.9760 - val_loss: 0.1363 - val_acc: 0.9569\n",
+      "Epoch 11/20\n",
+      "60000/60000 [==============================] - 10s 164us/step - loss: 0.0722 - acc: 0.9788 - val_loss: 0.1080 - val_acc: 0.9668\n",
+      "Epoch 12/20\n",
+      "60000/60000 [==============================] - 10s 161us/step - loss: 0.0663 - acc: 0.9806 - val_loss: 0.1158 - val_acc: 0.9639\n",
+      "Epoch 13/20\n",
+      "60000/60000 [==============================] - 8s 137us/step - loss: 0.0614 - acc: 0.9816 - val_loss: 0.0946 - val_acc: 0.9723\n",
+      "Epoch 14/20\n",
+      "60000/60000 [==============================] - 8s 137us/step - loss: 0.0566 - acc: 0.9830 - val_loss: 0.0927 - val_acc: 0.9719\n",
+      "Epoch 15/20\n",
+      "60000/60000 [==============================] - 9s 143us/step - loss: 0.0526 - acc: 0.9842 - val_loss: 0.1029 - val_acc: 0.9692\n",
+      "Epoch 16/20\n",
+      "60000/60000 [==============================] - 8s 138us/step - loss: 0.0479 - acc: 0.9858 - val_loss: 0.0969 - val_acc: 0.9703\n",
+      "Epoch 17/20\n",
+      "60000/60000 [==============================] - 8s 131us/step - loss: 0.0449 - acc: 0.9866 - val_loss: 0.1120 - val_acc: 0.9682\n",
+      "Epoch 18/20\n",
+      "60000/60000 [==============================] - 8s 131us/step - loss: 0.0416 - acc: 0.9876 - val_loss: 0.0958 - val_acc: 0.9719\n",
+      "Epoch 19/20\n",
+      "60000/60000 [==============================] - 8s 132us/step - loss: 0.0387 - acc: 0.9886 - val_loss: 0.0914 - val_acc: 0.9736\n",
+      "Epoch 20/20\n",
+      "60000/60000 [==============================] - 8s 137us/step - loss: 0.0362 - acc: 0.9894 - val_loss: 0.1186 - val_acc: 0.9651\n",
+      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 266,
+       "width": 395
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution:\n",
+    "num_epochs = 20\n",
+    "model = mnist_model()\n",
+    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
+    "                      batch_size=512, validation_data=(X_test_prep, y_test_onehot))\n",
+    "# Evaluating the model on test dataset\n",
+    "#print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))\n",
+    "history_model = model_run.history\n",
+    "print(\"The history has the following data: \", history_model.keys())\n",
+    "\n",
+    "# Plotting the training and validation accuracy during the training\n",
+    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
+    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
+    "plt.xlabel(\"epochs\") ;\n",
+    "plt.ylabel(\"accuracy\") ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What we see here is **overfitting**. After the first few epochs the training and validation datasets show a similar accuracy but thereafter the network starts to over fit to the training set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "Keep in mind that neural networks are quite prone to overfitting so always check for it.\n",
+    "</p>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding regularization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 60000 samples, validate on 10000 samples\n",
+      "Epoch 1/20\n",
+      "60000/60000 [==============================] - 10s 165us/step - loss: 1.5590 - acc: 0.8377 - val_loss: 0.9716 - val_acc: 0.8976\n",
+      "Epoch 2/20\n",
+      "60000/60000 [==============================] - 8s 138us/step - loss: 0.8379 - acc: 0.9038 - val_loss: 0.7613 - val_acc: 0.8989\n",
+      "Epoch 3/20\n",
+      "60000/60000 [==============================] - 8s 135us/step - loss: 0.6741 - acc: 0.9117 - val_loss: 0.6262 - val_acc: 0.9092\n",
+      "Epoch 4/20\n",
+      "60000/60000 [==============================] - 10s 159us/step - loss: 0.5959 - acc: 0.9180 - val_loss: 0.5582 - val_acc: 0.9232\n",
+      "Epoch 5/20\n",
+      "60000/60000 [==============================] - 9s 151us/step - loss: 0.5479 - acc: 0.9210 - val_loss: 0.5321 - val_acc: 0.9204\n",
+      "Epoch 6/20\n",
+      "60000/60000 [==============================] - 9s 150us/step - loss: 0.5141 - acc: 0.9247 - val_loss: 0.4847 - val_acc: 0.9319\n",
+      "Epoch 7/20\n",
+      "60000/60000 [==============================] - 10s 162us/step - loss: 0.4908 - acc: 0.9278 - val_loss: 0.4742 - val_acc: 0.9288\n",
+      "Epoch 8/20\n",
+      "60000/60000 [==============================] - 9s 143us/step - loss: 0.4715 - acc: 0.9310 - val_loss: 0.4672 - val_acc: 0.9318\n",
+      "Epoch 9/20\n",
+      "60000/60000 [==============================] - 8s 137us/step - loss: 0.4535 - acc: 0.9327 - val_loss: 0.4515 - val_acc: 0.9300\n",
+      "Epoch 10/20\n",
+      "60000/60000 [==============================] - 8s 129us/step - loss: 0.4401 - acc: 0.9343 - val_loss: 0.4353 - val_acc: 0.9337\n",
+      "Epoch 11/20\n",
+      "60000/60000 [==============================] - 8s 128us/step - loss: 0.4271 - acc: 0.9363 - val_loss: 0.4242 - val_acc: 0.9384\n",
+      "Epoch 12/20\n",
+      "60000/60000 [==============================] - 10s 161us/step - loss: 0.4171 - acc: 0.9380 - val_loss: 0.4094 - val_acc: 0.9371\n",
+      "Epoch 13/20\n",
+      "60000/60000 [==============================] - 11s 178us/step - loss: 0.4043 - acc: 0.9408 - val_loss: 0.4163 - val_acc: 0.9327\n",
+      "Epoch 14/20\n",
+      "60000/60000 [==============================] - 11s 184us/step - loss: 0.3955 - acc: 0.9414 - val_loss: 0.3897 - val_acc: 0.9418\n",
+      "Epoch 15/20\n",
+      "60000/60000 [==============================] - 9s 155us/step - loss: 0.3875 - acc: 0.9432 - val_loss: 0.3939 - val_acc: 0.9353\n",
+      "Epoch 16/20\n",
+      "60000/60000 [==============================] - 8s 139us/step - loss: 0.3796 - acc: 0.9440 - val_loss: 0.3698 - val_acc: 0.9474\n",
+      "Epoch 17/20\n",
+      "60000/60000 [==============================] - 9s 142us/step - loss: 0.3715 - acc: 0.9457 - val_loss: 0.3762 - val_acc: 0.9448\n",
+      "Epoch 18/20\n",
+      "60000/60000 [==============================] - 8s 137us/step - loss: 0.3637 - acc: 0.9476 - val_loss: 0.3578 - val_acc: 0.9502\n",
+      "Epoch 19/20\n",
+      "60000/60000 [==============================] - 10s 159us/step - loss: 0.3573 - acc: 0.9487 - val_loss: 0.3849 - val_acc: 0.9388\n",
+      "Epoch 20/20\n",
+      "60000/60000 [==============================] - 9s 156us/step - loss: 0.3512 - acc: 0.9500 - val_loss: 0.3953 - val_acc: 0.9338\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Adding l2 regularization\n",
+    "# Building the keras model\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.regularizers import l2\n",
+    "\n",
+    "def mnist_model():\n",
+    "    \n",
+    "    model = Sequential()\n",
+    "\n",
+    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\", \n",
+    "                   kernel_regularizer=l2(0.01)))\n",
+    "\n",
+    "    model.add(Dense(64, activation=\"relu\", \n",
+    "                   kernel_regularizer=l2(0.01)))\n",
+    "\n",
+    "    model.add(Dense(10, activation=\"softmax\"))\n",
+    "\n",
+    "    model.compile(loss=\"categorical_crossentropy\",\n",
+    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
+    "    return model\n",
+    "\n",
+    "model = mnist_model()\n",
+    "\n",
+    "num_epochs = 20\n",
+    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
+    "                      batch_size=512, validation_data=(X_test_prep, y_test_onehot))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 266,
+       "width": 395
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Evaluating the model on test dataset\n",
+    "history_model = model_run.history\n",
+    "print(\"The history has the following data: \", history_model.keys())\n",
+    "\n",
+    "# Plotting the training and validation accuracy during the training\n",
+    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
+    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
+    "plt.xlabel(\"epochs\") ;\n",
+    "plt.ylabel(\"accuracy\") ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-warning\">\n",
+    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
+    "Another way to add regularization and to make the network more robust is by applying Dropout. When we add dropout to a layer a specified percentage of units in that layer are switched off. \n",
+    "    \n",
+    "Both L2 regularization and Dropout make the model simpler and thus reducing overfitting.\n",
+    "</p>\n",
+    "</div>\n",
+    "\n",
+    "### Exercise section\n",
+    "* Add dropout instead of L2 regularization in the network above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Adding dropout is easy in keras\n",
+    "# We import a layer called Dropout and add as follows\n",
+    "# model.add(Dropout(0.2)) to randomly drop 20% of the hidden units\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "tags": [
+     "solution"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 60000 samples, validate on 10000 samples\n",
+      "Epoch 1/20\n",
+      "60000/60000 [==============================] - 10s 171us/step - loss: 0.6764 - acc: 0.8088 - val_loss: 0.2977 - val_acc: 0.9127\n",
+      "Epoch 2/20\n",
+      "60000/60000 [==============================] - 8s 140us/step - loss: 0.2915 - acc: 0.9158 - val_loss: 0.2254 - val_acc: 0.9330\n",
+      "Epoch 3/20\n",
+      "60000/60000 [==============================] - 9s 154us/step - loss: 0.2231 - acc: 0.9341 - val_loss: 0.1650 - val_acc: 0.9514\n",
+      "Epoch 4/20\n",
+      "60000/60000 [==============================] - 9s 148us/step - loss: 0.1859 - acc: 0.9456 - val_loss: 0.1690 - val_acc: 0.9485\n",
+      "Epoch 5/20\n",
+      "60000/60000 [==============================] - 10s 159us/step - loss: 0.1618 - acc: 0.9517 - val_loss: 0.1296 - val_acc: 0.9618\n",
+      "Epoch 6/20\n",
+      "60000/60000 [==============================] - 10s 163us/step - loss: 0.1470 - acc: 0.9554 - val_loss: 0.1240 - val_acc: 0.9606\n",
+      "Epoch 7/20\n",
+      "60000/60000 [==============================] - 10s 171us/step - loss: 0.1317 - acc: 0.9603 - val_loss: 0.1151 - val_acc: 0.9651\n",
+      "Epoch 8/20\n",
+      "60000/60000 [==============================] - 10s 162us/step - loss: 0.1228 - acc: 0.9627 - val_loss: 0.1084 - val_acc: 0.9658\n",
+      "Epoch 9/20\n",
+      "60000/60000 [==============================] - 10s 166us/step - loss: 0.1116 - acc: 0.9664 - val_loss: 0.1021 - val_acc: 0.9683\n",
+      "Epoch 10/20\n",
+      "60000/60000 [==============================] - 10s 158us/step - loss: 0.1054 - acc: 0.9670 - val_loss: 0.1027 - val_acc: 0.9685\n",
+      "Epoch 11/20\n",
+      "60000/60000 [==============================] - 11s 184us/step - loss: 0.0983 - acc: 0.9698 - val_loss: 0.0997 - val_acc: 0.9694\n",
+      "Epoch 12/20\n",
+      "60000/60000 [==============================] - 9s 153us/step - loss: 0.0933 - acc: 0.9714 - val_loss: 0.1050 - val_acc: 0.9684\n",
+      "Epoch 13/20\n",
+      "60000/60000 [==============================] - 10s 160us/step - loss: 0.0895 - acc: 0.9723 - val_loss: 0.0994 - val_acc: 0.9702\n",
+      "Epoch 14/20\n",
+      "60000/60000 [==============================] - 10s 172us/step - loss: 0.0847 - acc: 0.9747 - val_loss: 0.0906 - val_acc: 0.9722\n",
+      "Epoch 15/20\n",
+      "60000/60000 [==============================] - 10s 167us/step - loss: 0.0807 - acc: 0.9750 - val_loss: 0.0869 - val_acc: 0.9747\n",
+      "Epoch 16/20\n",
+      "60000/60000 [==============================] - 11s 186us/step - loss: 0.0774 - acc: 0.9757 - val_loss: 0.0894 - val_acc: 0.9728\n",
+      "Epoch 17/20\n",
+      "60000/60000 [==============================] - 11s 182us/step - loss: 0.0741 - acc: 0.9760 - val_loss: 0.0879 - val_acc: 0.9752\n",
+      "Epoch 18/20\n",
+      "60000/60000 [==============================] - 10s 164us/step - loss: 0.0714 - acc: 0.9771 - val_loss: 0.0876 - val_acc: 0.9742\n",
+      "Epoch 19/20\n",
+      "60000/60000 [==============================] - 9s 158us/step - loss: 0.0680 - acc: 0.9790 - val_loss: 0.0825 - val_acc: 0.9760\n",
+      "Epoch 20/20\n",
+      "60000/60000 [==============================] - 10s 160us/step - loss: 0.0671 - acc: 0.9784 - val_loss: 0.0795 - val_acc: 0.9776\n",
+      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 266,
+       "width": 401
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution\n",
+    "# Adding Dropout\n",
+    "# Building the keras model\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout\n",
+    "\n",
+    "def mnist_model():\n",
+    "    \n",
+    "    model = Sequential()\n",
+    "\n",
+    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\"))\n",
+    "              \n",
+    "    model.add(Dropout(0.15))\n",
+    "\n",
+    "    model.add(Dense(64, activation=\"relu\"))\n",
+    "    \n",
+    "    model.add(Dense(10, activation=\"softmax\"))\n",
+    "\n",
+    "    model.compile(loss=\"categorical_crossentropy\",\n",
+    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
+    "              \n",
+    "    return model\n",
+    "\n",
+    "model = mnist_model()\n",
+    "\n",
+    "num_epochs = 20\n",
+    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
+    "                      batch_size=512, validation_data=(X_test_prep, y_test_onehot))\n",
+    "\n",
+    "# Evaluating the model on test dataset\n",
+    "history_model = model_run.history\n",
+    "print(\"The history has the following data: \", history_model.keys())\n",
+    "\n",
+    "# Plotting the training and validation accuracy during the training\n",
+    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
+    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
+    "plt.xlabel(\"epochs\") ;\n",
+    "plt.ylabel(\"accuracy\") ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Network Architectures\n",
+    "\n",
+    "The neural networks which we have seen till now are the simplest kind of neural networks.\n",
+    "There exist more sophisticated network architectures especially designed for specific applications.\n",
+    "Some of them are as follows:\n",
+    "\n",
+    "###  Convolution Neural Networks (CNNs)\n",
+    "\n",
+    "These networks are used mostly for computer vision like tasks such as image classification and object detection. \n",
+    "One of the old CNN networks is shown below.\n",
+    "\n",
+    "<center>\n",
+    "<figure>\n",
+    "<img src=\"./images/neuralnets/CNN_lecun.png\" width=\"800\"/>\n",
+    "<figcaption>source: LeCun et al., Gradient-based learning applied to document recognition (1998).</figcaption>\n",
+    "</figure>\n",
+    "</center>\n",
+    "\n",
+    "CNNs consist of new type of layers such as convolution and pooling layers.\n",
+    "\n",
+    "###  Recurrent Neural Networks (RNNs)\n",
+    "\n",
+    "RNNs are used for problems such as time-series data, speech recognition and translation.\n",
+    "\n",
+    "### Generative adversarial networks (GANs)\n",
+    "\n",
+    "GANs consist of 2 parts, a generative network and a discriminative network. The generative network produces data which is then fed to the discriminative network which judges if the new data belongs to a specified dataset. Then via feedback loops the generative network becomes better and better at creating images similar to the dataset the discriminative network is judging against. At the same time the discriminative network get better and better at identifyig **fake** instances which are not from the reference dataset. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CNN in a bit more detail\n",
+    "\n",
+    "The standard CNN architecture can be seen as 2 parts:\n",
+    "\n",
+    "* Feature extraction\n",
+    "* Classification\n",
+    "\n",
+    "For the **classification** part we use the denly connected network as shown in the keras examples above.\n",
+    "\n",
+    "However, for the **feature extraction** part we use new types of layers called **convolution** layers\n",
+    "\n",
+    "### What is a Convolution?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1a5e0c6160>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 251,
+       "width": 253
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style(\"white\")\n",
+    "# Loading the train and test data\n",
+    "digit = np.genfromtxt(\"data/digit_4_14x14.csv\", delimiter=\",\").astype(np.int16) ;\n",
+    "plt.imshow(digit, \"gray_r\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This image in matrix form"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_astable(matrix, hw=0.15):\n",
+    "    matrix = plt.table(cellText=matrix, loc=(0,0), cellLoc='center') ;\n",
+    "    matrix.set_fontsize(14)\n",
+    "    cells=matrix.get_celld() ;\n",
+    "    for i in cells:\n",
+    "        cells[i].set_height(hw) ;\n",
+    "        cells[i].set_width(hw) ;\n",
+    "    plt.axis(\"off\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 471,
+       "width": 717
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_astable(digit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAHPCAYAAAC8+nn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAATzElEQVR4nO3cP0jVff/H8bcXgdJfEIJQycgxHAxqCQJrEIKCGoLIwIZo00xoc4nAGgqhWhr6A0FJFA3SZpPg2tBQLiWh/RErSINSOb/h5pJufnbdxO33fL3fPh5TeI7wGo4fnn6OnZpKpVIJAABI5q+yBwAAQBGELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSWlf2APJobGwsewIACUxOTpY9gSTc6AIAkJIbXVac38T51d83/V4X/MrrguV4Z5CV5kYXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6EIVvXz5Mnbt2hXHjx8vewpVNDY2Fr29vdHe3h6tra3R1tYWhw8fjitXrsT79+/Lnscq5byA/966sgfAWjEzMxN9fX2xsLBQ9hSqZHFxMfr7++Px48f/9vWfP3/G+Ph4jI+Px9DQUFy9ejXa29tLWslq5LyAleFGF6rg48eP0dXVFW/fvi17ClU0ODi4FLn79++P+/fvx9jYWDx79iz6+/tjy5YtMTc3Fz09PfHq1auS17JaOC9g5QhdKNjY2FgcPXo0xsfHy55CFU1PT8edO3ciIqKjoyNu3boVe/bsifr6+mhpaYnOzs549OhRbNiwIX78+BGDg4MlL2Y1cF7AyhK6UJB3795FX19fnD59OmZmZqK+vj4aGhrKnkWVjIyMxPz8fEREnDt3Lmpqav7fc5qbm+PYsWMRETE6Orr0fNYe5wUUQ+hCQQYGBmJ4eDgqlUrs27cvnjx5Ek1NTWXPoko+ffoUdXV1sWnTpti5c+dvn7d9+/aIiJifn48vX75Uax6rjPMCiuE/o0GBWlpaoqenJzo6OsqeQpV1d3dHd3d3zM7O/uPzJiYmIiKipqYmNm/eXI1prFLOC1h5QhcKcuHChWhubl72LWvWjo0bN/72sW/fvsXw8HBERLS2tkZdXV21ZrHKOC+gGEIXCrJjx46yJ7DKXbp0Kb5+/RoRESdPnix5DWVyXkAx/I0uQAlu3rwZT58+jYiIvXv3xpEjR0peBJCPG12AKrtx40Zcv349IiIaGhri2rVr8ddf7h0AVprQhT9QqVTi+/fvv328trY21q3zY8XyFhcX4+LFi/Hw4cOIiNi2bVvcvXs3tm7dWvIyiuC8gPL5CYM/MDk5GQcPHvzt4wMDA0ufiwq/mp2djZ6enhgdHY2If/1N5u3bt6OxsbHkZRTFeQHl814ZQMGmpqbixIkTS5Hb1tYWDx48ELkABXOjC3+gqakpXr9+XfYM/oe8efMmTp06FdPT0xERcejQobh8+XLU1taWvIyiOS+gfEIXoCBTU1PR1dW1FLlnzpyJvr4+n5UKUCVCF6AACwsL0dvbGx8+fIiIiPPnz8fZs2dLXgWwtghdgAIMDQ3FixcvIiLiwIED0dnZGXNzc//4PevXr3fbC7CChC5AAe7du7f07+fPn8fu3bv/4/eMjIxEU1NTkbMA1hSfugCwwj5//hwTExNlzwBY82oqlUql7BHk8PdHJU1OTpa8hNXE64LleF2wHK8LVpobXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKRUU6lUKmWPIIfGxsayJwCQwOTkZNkTSMKNLgAAKa0rewD5+E2cX/190+91wa+8LliOdwZZaW50AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC5U0cuXL2PXrl1x/PjxsqdQRWNjY9Hb2xvt7e3R2toabW1tcfjw4bhy5Uq8f/++7HmsUs4L+O+tK3sArBUzMzPR19cXCwsLZU+hShYXF6O/vz8eP378b1//+fNnjI+Px/j4eAwNDcXVq1ejvb29pJWsRs4LWBludKEKPn78GF1dXfH27duyp1BFg4ODS5G7f//+uH//foyNjcWzZ8+iv78/tmzZEnNzc9HT0xOvXr0qeS2rhfMCVo7QhYKNjY3F0aNHY3x8vOwpVNH09HTcuXMnIiI6Ojri1q1bsWfPnqivr4+Wlpbo7OyMR48exYYNG+LHjx8xODhY8mJWA+cFrCyhCwV59+5d9PX1xenTp2NmZibq6+ujoaGh7FlUycjISMzPz0dExLlz56Kmpub/Pae5uTmOHTsWERGjo6NLz2ftcV5AMYQuFGRgYCCGh4ejUqnEvn374smTJ9HU1FT2LKrk06dPUVdXF5s2bYqdO3f+9nnbt2+PiIj5+fn48uVLteaxyjgvoBj+MxoUqKWlJXp6eqKjo6PsKVRZd3d3dHd3x+zs7D8+b2JiIiIiampqYvPmzdWYxirlvICVJ3ShIBcuXIjm5uZl37Jm7di4ceNvH/v27VsMDw9HRERra2vU1dVVaxarjPMCiiF0oSA7duwoewKr3KVLl+Lr168REXHy5MmS11Am5wUUw9/oApTg5s2b8fTp04iI2Lt3bxw5cqTkRQD5uNEFqLIbN27E9evXIyKioaEhrl27Fn/95d4BYKUJXfgDlUolvn///tvHa2trY906P1Ysb3FxMS5evBgPHz6MiIht27bF3bt3Y+vWrSUvowjOCyifnzD4A5OTk3Hw4MHfPj4wMLD0uajwq9nZ2ejp6YnR0dGI+NffZN6+fTsaGxtLXkZRnBdQPu+VARRsamoqTpw4sRS5bW1t8eDBA5ELUDA3uvAHmpqa4vXr12XP4H/Imzdv4tSpUzE9PR0REYcOHYrLly9HbW1tycsomvMCyid0AQoyNTUVXV1dS5F75syZ6Ovr81mpAFUidAEKsLCwEL29vfHhw4eIiDh//nycPXu25FUAa4vQBSjA0NBQvHjxIiIiDhw4EJ2dnTE3N/eP37N+/Xq3vQArSOgCFODevXtL/37+/Hns3r37P37PyMhINDU1FTkLYE3xqQsAK+zz588xMTFR9gyANa+mUqlUyh5BDn9/VNLk5GTJS1hNvC5YjtcFy/G6YKW50QUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKNZVKpVL2CHJobGwsewIACUxOTpY9gSTc6AIAkNK6sgeQj9/E+dXfN/1eF/zK64LleGeQleZGFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISehCFb18+TJ27doVx48fL3sKVTQ2Nha9vb3R3t4era2t0dbWFocPH44rV67E+/fvy57HKuW8gP/eurIHwFoxMzMTfX19sbCwUPYUqmRxcTH6+/vj8ePH//b1nz9/xvj4eIyPj8fQ0FBcvXo12tvbS1rJauS8gJXhRheq4OPHj9HV1RVv374tewpVNDg4uBS5+/fvj/v378fY2Fg8e/Ys+vv7Y8uWLTE3Nxc9PT3x6tWrkteyWjgvYOUIXSjY2NhYHD16NMbHx8ueQhVNT0/HnTt3IiKio6Mjbt26FXv27In6+vpoaWmJzs7OePToUWzYsCF+/PgRg4ODJS9mNXBewMoSulCQd+/eRV9fX5w+fTpmZmaivr4+Ghoayp5FlYyMjMT8/HxERJw7dy5qamr+33Oam5vj2LFjERExOjq69HzWHucFFEPoQkEGBgZieHg4KpVK7Nu3L548eRJNTU1lz6JKPn36FHV1dbFp06bYuXPnb5+3ffv2iIiYn5+PL1++VGseq4zzAorhP6NBgVpaWqKnpyc6OjrKnkKVdXd3R3d3d8zOzv7j8yYmJiIioqamJjZv3lyNaaxSzgtYeUIXCnLhwoVobm5e9i1r1o6NGzf+9rFv377F8PBwRES0trZGXV1dtWaxyjgvoBhCFwqyY8eOsiewyl26dCm+fv0aEREnT54seQ1lcl5AMfyNLkAJbt68GU+fPo2IiL1798aRI0dKXgSQjxtdgCq7ceNGXL9+PSIiGhoa4tq1a/HXX+4dAFaa0IU/UKlU4vv37799vLa2Ntat82PF8hYXF+PixYvx8OHDiIjYtm1b3L17N7Zu3VryMorgvIDy+QmDPzA5ORkHDx787eMDAwNLn4sKv5qdnY2enp4YHR2NiH/9Tebt27ejsbGx5GUUxXkB5fNeGUDBpqam4sSJE0uR29bWFg8ePBC5AAVzowt/oKmpKV6/fl32DP6HvHnzJk6dOhXT09MREXHo0KG4fPly1NbWlryMojkvoHxCF6AgU1NT0dXVtRS5Z86cib6+Pp+VClAlQhegAAsLC9Hb2xsfPnyIiIjz58/H2bNnS14FsLYIXYACDA0NxYsXLyIi4sCBA9HZ2Rlzc3P/+D3r16932wuwgoQuQAHu3bu39O/nz5/H7t27/+P3jIyMRFNTU5GzANYUn7oAsMI+f/4cExMTZc8AWPNqKpVKpewR5PD3RyVNTk6WvITVxOuC5XhdsByvC1aaG10AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkVFOpVCpljyCHxsbGsicAkMDk5GTZE0jCjS4AACm50QUAICU3ugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJCS0AUAICWhCwBASkIXAICUhC4AACkJXQAAUhK6AACkJHQBAEhJ6AIAkJLQBQAgJaELAEBKQhcAgJSELgAAKQldAABSEroAAKQkdAEASEnoAgCQktAFACAloQsAQEpCFwCAlIQuAAApCV0AAFISugAApCR0AQBISegCAJDS/wHPCCRE+LcGRAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 231,
+       "width": 349
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Vertical edge detection\n",
+    "vertical_edge_kernel = np.array([[-1, 2, -1], [-1, 2, -1], [-1, 2, -1]])\n",
+    "plot_astable(vertical_edge_kernel, 0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 251,
+       "width": 253
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def convolution(matrix, kernel):\n",
+    "    # This function computes a convolution between a matrix and a kernel/filter without any padding\n",
+    "    width_kernel = kernel.shape[0]\n",
+    "    height_kernel = kernel.shape[1]\n",
+    "    convolution = np.zeros((matrix.shape[0] - width_kernel + 1,\n",
+    "                            matrix.shape[1] - height_kernel + 1))\n",
+    "    for i in range(matrix.shape[0] - width_kernel + 1):\n",
+    "        for j in range(matrix.shape[1] - height_kernel + 1):\n",
+    "            convolution[i, j] = np.sum(np.multiply(\n",
+    "                matrix[i:i+width_kernel, j:j+height_kernel], kernel))\n",
+    "    return convolution\n",
+    "\n",
+    "\n",
+    "vertical_detect = convolution(digit, vertical_edge_kernel)\n",
+    "plt.imshow(vertical_detect, cmap=\"gray_r\") ;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 231,
+       "width": 349
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Horizontal edge detection\n",
+    "horizontal_edge_kernel = np.array([[-1, -1, -1], [2, 2, 2], [-1, -1, -1]])\n",
+    "plot_astable(horizontal_edge_kernel, 0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 251,
+       "width": 253
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "horizontal_detect = convolution(digit, horizontal_edge_kernel)\n",
+    "plt.imshow(horizontal_detect, cmap=\"gray_r\") ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Maxpooling\n",
+    "Taking maximum in n x n sized sliding windows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def maxpool_2x2(matrix):\n",
+    "    out_dim = np.array([matrix.shape[0]/2, matrix.shape[1]/2]).astype(int)\n",
+    "    subsample = np.zeros((out_dim))\n",
+    "    for i in range(out_dim[0]):\n",
+    "        for j in range(out_dim[1]):\n",
+    "            subsample[i,j] = np.max(matrix[i*2:i*2+2, j*2:j*2+2])\n",
+    "    return subsample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 266,
+       "width": 247
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "subsampled_image = maxpool_2x2(vertical_detect)\n",
+    "plt.imshow(subsampled_image, cmap=\"gray_r\")\n",
+    "plt.title(\"Max Pooled vertical edge detection filter\") ;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 266,
+       "width": 252
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "subsampled_image = maxpool_2x2(horizontal_detect)\n",
+    "plt.imshow(subsampled_image, cmap=\"gray_r\") ;\n",
+    "plt.title(\"Max Pooled horizontal edge detection filter\") ;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's explore some more of such filters/kernels!!\n",
+    "\n",
+    "http://setosa.io/ev/image-kernels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CNN Examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this example we will work with a dataset called fashion-MNIST which is quite similar to the MNIST data above.\n",
+    "> Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.\n",
+    "source: https://github.com/zalandoresearch/fashion-mnist\n",
+    "\n",
+    "The 10 classes of this dataset are:\n",
+    "\n",
+    "| Label| Item |\n",
+    "| --- | --- |\n",
+    "| 0 |\tT-shirt/top |\n",
+    "| 1\t| Trouser |\n",
+    "|2|\tPullover|\n",
+    "|3|\tDress|\n",
+    "|4|\tCoat|\n",
+    "|5|\tSandal|\n",
+    "|6|\tShirt|\n",
+    "|7|\tSneaker|\n",
+    "|8|\tBag|\n",
+    "|9|\tAnkle boot|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loading the dataset in keras\n",
+    "# Later you can explore and play with other datasets with come with Keras\n",
+    "from keras.datasets import fashion_mnist\n",
+    "\n",
+    "# Loading the train and test data\n",
+    "\n",
+    "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
+    "\n",
+    "items =['T-shirt/top', 'Trouser', \n",
+    "        'Pullover', 'Dress', \n",
+    "        'Coat', 'Sandal', \n",
+    "        'Shirt', 'Sneaker',\n",
+    "        'Bag', 'Ankle boot']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This item is a:  Coat\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 251,
+       "width": 253
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "i=np.random.randint(0,X_train.shape[0])\n",
+    "plt.imshow(X_train[i], cmap=\"gray_r\") ; \n",
+    "print(\"This item is a: \" , items[y_train[i]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(60000, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Also we need to reshape the input data such that each sample is a 4D matrix of dimension\n",
+    "# (num_samples, width, height, channels). Even though these images are grayscale we need to add\n",
+    "# channel dimension as this is expected by the Conv function\n",
+    "X_train_prep = X_train.reshape(X_train.shape[0],28,28,1)/255.\n",
+    "X_test_prep = X_test.reshape(X_test.shape[0],28,28,1)/255.\n",
+    "\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "\n",
+    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
+    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
+    "\n",
+    "print(y_train_onehot.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d_1 (Conv2D)            (None, 26, 26, 6)         60        \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 6)         0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_2 (Conv2D)            (None, 11, 11, 16)        880       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 16)          0         \n",
+      "_________________________________________________________________\n",
+      "flatten_1 (Flatten)          (None, 400)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_151 (Dense)            (None, 120)               48120     \n",
+      "_________________________________________________________________\n",
+      "dense_152 (Dense)            (None, 84)                10164     \n",
+      "_________________________________________________________________\n",
+      "dense_153 (Dense)            (None, 10)                850       \n",
+      "=================================================================\n",
+      "Total params: 60,074\n",
+      "Trainable params: 60,074\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating a CNN similar to the one shown in the figure from LeCun paper\n",
+    "# In the original implementation Average pooling was used. However, we will use maxpooling as this \n",
+    "# is what us used in the more recent architectures and is found to be a better choice\n",
+    "# Convolution -> Pooling -> Convolution -> Pooling -> Flatten -> Dense -> Dense -> Output layer\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout, BatchNormalization\n",
+    "\n",
+    "def simple_CNN():\n",
+    "    \n",
+    "    model = Sequential()\n",
+    "    \n",
+    "    model.add(Conv2D(6, (3,3), input_shape=(28,28,1), activation='relu'))\n",
+    "    \n",
+    "    model.add(MaxPool2D((2,2)))\n",
+    "    \n",
+    "    model.add(Conv2D(16, (3,3), activation='relu'))\n",
+    "    \n",
+    "    model.add(MaxPool2D((2,2)))\n",
+    "    \n",
+    "    model.add(Flatten())\n",
+    "    \n",
+    "    model.add(Dense(120, activation='relu'))\n",
+    "    \n",
+    "    model.add(Dense(84, activation='relu'))\n",
+    "    \n",
+    "    model.add(Dense(10, activation='softmax'))\n",
+    "    \n",
+    "    model.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
+    "    \n",
+    "    return model\n",
+    "\n",
+    "model = simple_CNN()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 60000 samples, validate on 10000 samples\n",
+      "Epoch 1/10\n",
+      "60000/60000 [==============================] - 116s 2ms/step - loss: 0.5891 - acc: 0.7829 - val_loss: 0.4498 - val_acc: 0.8327\n",
+      "Epoch 2/10\n",
+      "60000/60000 [==============================] - 120s 2ms/step - loss: 0.3869 - acc: 0.8572 - val_loss: 0.4027 - val_acc: 0.8541\n",
+      "Epoch 3/10\n",
+      "60000/60000 [==============================] - 121s 2ms/step - loss: 0.3348 - acc: 0.8763 - val_loss: 0.3792 - val_acc: 0.8641\n",
+      "Epoch 4/10\n",
+      "60000/60000 [==============================] - 121s 2ms/step - loss: 0.3036 - acc: 0.8872 - val_loss: 0.3279 - val_acc: 0.8824\n",
+      "Epoch 5/10\n",
+      "60000/60000 [==============================] - 120s 2ms/step - loss: 0.2826 - acc: 0.8952 - val_loss: 0.3312 - val_acc: 0.8815\n",
+      "Epoch 6/10\n",
+      "60000/60000 [==============================] - 135s 2ms/step - loss: 0.2652 - acc: 0.9019 - val_loss: 0.3026 - val_acc: 0.8903\n",
+      "Epoch 7/10\n",
+      "60000/60000 [==============================] - 117s 2ms/step - loss: 0.2509 - acc: 0.9072 - val_loss: 0.3368 - val_acc: 0.8801\n",
+      "Epoch 8/10\n",
+      "60000/60000 [==============================] - 86s 1ms/step - loss: 0.2375 - acc: 0.9115 - val_loss: 0.2933 - val_acc: 0.8966\n",
+      "Epoch 9/10\n",
+      "60000/60000 [==============================] - 90s 2ms/step - loss: 0.2259 - acc: 0.9165 - val_loss: 0.3093 - val_acc: 0.8948\n",
+      "Epoch 10/10\n",
+      "60000/60000 [==============================] - 88s 1ms/step - loss: 0.2162 - acc: 0.9189 - val_loss: 0.2993 - val_acc: 0.8945\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_epochs = 5\n",
+    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs, \n",
+    "                      batch_size=64, validation_data=(X_test_prep, y_test_onehot))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise section\n",
+    "* Use the above model or improve it (change number of filters, add more layers etc. on the MNIST example and see if you can get a better accuracy than what we achieved with a vanilla neural network)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise section\n",
+    "* Explore the CIFAR10 (https://www.cs.toronto.edu/~kriz/cifar.html) dataset included with Keras and build+train a simple CNN to classify it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.datasets import cifar10\n",
+    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (C) 2019 ETH Zurich, SIS ID"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/08_neural_networks.ipynb b/08_neural_networks.ipynb
deleted file mode 100644
index 5702360de410dafa9809a1fa728cc312c666270a..0000000000000000000000000000000000000000
--- a/08_neural_networks.ipynb
+++ /dev/null
@@ -1,3626 +0,0 @@
-{
- "cells": [
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-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
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-     },
-     "execution_count": 8,
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-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# IGNORE THIS CELL WHICH CUSTOMIZES LAYOUT AND STYLING OF THE NOTEBOOK !\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib as mpl\n",
-    "import seaborn as sns\n",
-    "sns.set(style=\"darkgrid\")\n",
-    "mpl.rcParams['lines.linewidth'] = 3\n",
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina'\n",
-    "%config IPCompleter.greedy=True\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
-    "from IPython.core.display import HTML; HTML(open(\"custom.html\", \"r\").read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 8: Introduction to Neural Networks\n",
-    "\n",
-    "\n",
-    "\n",
-    "<img src=\"./images/3042en.jpg\" title=\"made at imgflip.com\" width=35%/>\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## History of Neural networks\n",
-    "\n",
-    "\n",
-    "1943 - Threshold Logic\n",
-    "\n",
-    "1940s - Hebbian Learning\n",
-    "\n",
-    "1958 - Perceptron\n",
-    "\n",
-    "1980s - Neocognitron\n",
-    "\n",
-    "1982 - Hopfield Network\n",
-    "\n",
-    "1989 - Convolutional neural network (CNN) kernels trained via backpropagation\n",
-    "\n",
-    "1997 - Long-short term memory (LSTM) model\n",
-    "\n",
-    "1998 - LeNet-5\n",
-    "\n",
-    "2014 - Gated Recurrent Units (GRU), Generative Adversarial Networks (GAN)\n",
-    "\n",
-    "2015 - ResNet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Why the boom now?\n",
-    "* Data\n",
-    "* Data\n",
-    "* Data\n",
-    "* Availability of GPUs\n",
-    "* Algorithmic developments which allow for efficient training and making networks networks\n",
-    "* Development of high-level libraries/APIs have made the field much more accessible than it was a decade ago"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Feed-Forward neural network\n",
-    "<center>\n",
-    "<figure>\n",
-    "<img src=\"./images/neuralnets/neural_net_ex.svg\" width=\"700\"/>\n",
-    "<figcaption>A 3 layer densely connected Neural Network (By convention the input layer is not counted).</figcaption>\n",
-    "</figure>\n",
-    "</center>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Building blocks\n",
-    "### Perceptron\n",
-    "\n",
-    "The smallest unit of a neural network is a **perceptron** like node.\n",
-    "\n",
-    "**What is a Perceptron?**\n",
-    "\n",
-    "It is a simple function which can have multiple inputs and has a single output.\n",
-    "\n",
-    "<center>\n",
-    "<figure>\n",
-    "<img src=\"./images/neuralnets/perceptron_ex.svg\" width=\"400\"/>\n",
-    "<figcaption>A simple perceptron with 3 inputs and 1 output.</figcaption>\n",
-    "</figure>\n",
-    "</center>\n",
-    "\n",
-    "\n",
-    "It works as follows: \n",
-    "\n",
-    "Step 1: A **weighted sum** of the inputs is calculated\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "weighted\\_sum = w_{1} x_{1} + w_{2} x_{2} + w_{3} x_{3} + ...\n",
-    "\\end{equation*}\n",
-    "\n",
-    "Step 2: A **step** activation function is applied\n",
-    "\n",
-    "$$\n",
-    "f = \\left\\{\n",
-    "        \\begin{array}{ll}\n",
-    "            0 & \\quad weighted\\_sum < threshold \\\\\n",
-    "            1 & \\quad weighted\\_sum \\geq threshold\n",
-    "        \\end{array}\n",
-    "    \\right.\n",
-    "$$\n",
-    "\n",
-    "You can see that this is also a linear classifier as the ones we introduced in script 02."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 281,
-       "width": 392
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting the step function\n",
-    "x = np.arange(-2,2.1,0.01)\n",
-    "y = np.zeros(len(x))\n",
-    "threshold = 0.\n",
-    "y[x>threshold] = 1.\n",
-    "step_plot = sns.lineplot(x, y).set_title('Step function') ;\n",
-    "plt.xlabel('weighted_sum') ;\n",
-    "plt.ylabel('f(weighted_sum)') ;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def perceptron(X, w, threshold=1):\n",
-    "    # This function computes sum(w_i*x_i) and\n",
-    "    # applies a perceptron activation\n",
-    "    linear_sum = np.dot(np.asarray(X).T, w)\n",
-    "    output = np.zeros(len(linear_sum), dtype=np.int8)\n",
-    "    output[linear_sum >= threshold] = 1\n",
-    "    return output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Boolean AND\n",
-    "\n",
-    "| x$_1$ | x$_2$ | output |\n",
-    "| --- | --- | --- |\n",
-    "| 0 | 0 | 0 |\n",
-    "| 1 | 0 | 0 |\n",
-    "| 0 | 1 | 0 |\n",
-    "| 1 | 1 | 1 |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Perceptron output for x1, x2 =  0 , 0  is  0\n",
-      "Perceptron output for x1, x2 =  1 , 0  is  0\n",
-      "Perceptron output for x1, x2 =  0 , 1  is  0\n",
-      "Perceptron output for x1, x2 =  1 , 1  is  1\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Calculating Boolean AND using a perceptron\n",
-    "threshold = 1.5\n",
-    "# (w1, w2)\n",
-    "w = [1, 1]\n",
-    "# (x1, x2) pairs\n",
-    "x1 = [0, 1, 0, 1]\n",
-    "x2 = [0, 0, 1, 1]\n",
-    "# Calling the perceptron function\n",
-    "output = perceptron([x1, x2], w, threshold)\n",
-    "for i in range(len(output)):\n",
-    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
-    "          \" is \", output[i])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this simple case we can rewrite our equation to $x_2 = ...... $ which describes a line in 2D:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def perceptron_DB(x1, x2, w, threshold):\n",
-    "    # Plotting the decision boundary of the perceptron\n",
-    "    plt.scatter(x1, x2, color=\"black\")\n",
-    "    plt.xlim(-1,2)\n",
-    "    plt.ylim(-1,2)\n",
-    "    # The decision boundary is a line given by\n",
-    "    # w_1*x_1+w_2*x_2-threshold=0\n",
-    "    x1 = np.arange(-3, 4)\n",
-    "    x2 = (threshold - x1*w[0])/w[1]\n",
-    "    sns.lineplot(x1, x2, **{\"color\": \"black\"})\n",
-    "    plt.xlabel(\"x$_1$\", fontsize=16)\n",
-    "    plt.ylabel(\"x$_2$\", fontsize=16)\n",
-    "    # Coloring the regions\n",
-    "    pts_tmp = np.arange(-2, 2.1, 0.02)\n",
-    "    points = np.array(np.meshgrid(pts_tmp, pts_tmp)).T.reshape(-1, 2)\n",
-    "    outputs = perceptron(points.T, w, threshold)\n",
-    "    plt.plot(points[:, 0][outputs == 0], points[:, 1][outputs == 0],\n",
-    "             \"o\",\n",
-    "             color=\"steelblue\",\n",
-    "             markersize=1,\n",
-    "             alpha=0.04,\n",
-    "             )\n",
-    "    plt.plot(points[:, 0][outputs == 1], points[:, 1][outputs == 1],\n",
-    "             \"o\",\n",
-    "             color=\"chocolate\",\n",
-    "             markersize=1,\n",
-    "             alpha=0.04,\n",
-    "             )\n",
-    "    plt.title(\"Blue color = 0 and Chocolate = 1\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 284,
-       "width": 406
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting the perceptron decision boundary\n",
-    "perceptron_DB(x1, x2, w, threshold)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercise section\n",
-    "* Compute a Boolean \"OR\" using a perceptron\n",
-    "\n",
-    "Hint: copy the code from the \"AND\" example and edit the weights and/or threshold"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Boolean OR\n",
-    "\n",
-    "| x$_1$ | x$_2$ | output |\n",
-    "| --- | --- | --- |\n",
-    "| 0 | 0 | 0 |\n",
-    "| 1 | 0 | 1 |\n",
-    "| 0 | 1 | 1 |\n",
-    "| 1 | 1 | 1 |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculating Boolean OR using a perceptron\n",
-    "# Enter code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true,
-    "tags": [
-     "solution"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Perceptron output for x1, x2 =  0 , 0  is  0\n",
-      "Perceptron output for x1, x2 =  1 , 0  is  1\n",
-      "Perceptron output for x1, x2 =  0 , 1  is  1\n",
-      "Perceptron output for x1, x2 =  1 , 1  is  1\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 284,
-       "width": 406
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "# Calculating Boolean OR using a perceptron\n",
-    "threshold=0.6\n",
-    "# (w1, w2)\n",
-    "w=[1,1]\n",
-    "# (x1, x2) pairs\n",
-    "x1 = [0, 1, 0, 1]\n",
-    "x2 = [0, 0, 1, 1]\n",
-    "output = perceptron([x1, x2], w, threshold)\n",
-    "for i in range(len(output)):\n",
-    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
-    "          \" is \", output[i])\n",
-    "perceptron_DB(x1, x2, w, threshold)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercise section\n",
-    "* Create a NAND gate using a perceptron\n",
-    "\n",
-    "#### Boolean NAND\n",
-    "\n",
-    "| x$_1$ | x$_2$ | output |\n",
-    "| --- | --- | --- |\n",
-    "| 0 | 0 | 1 |\n",
-    "| 1 | 0 | 1 |\n",
-    "| 0 | 1 | 1 |\n",
-    "| 1 | 1 | 0 |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculating Boolean NAND using a perceptron\n",
-    "# Enter code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "tags": [
-     "solution"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Perceptron output for x1, x2 =  0 , 0  is  1\n",
-      "Perceptron output for x1, x2 =  1 , 0  is  1\n",
-      "Perceptron output for x1, x2 =  0 , 1  is  1\n",
-      "Perceptron output for x1, x2 =  1 , 1  is  0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 284,
-       "width": 406
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "# Calculating Boolean NAND using a perceptron\n",
-    "import matplotlib.pyplot as plt\n",
-    "threshold=-1.5\n",
-    "# (w1, w2)\n",
-    "w=[-1,-1]\n",
-    "# (x1, x2) pairs\n",
-    "x1 = [0, 1, 0, 1]\n",
-    "x2 = [0, 0, 1, 1]\n",
-    "output = perceptron([x1, x2], w, threshold)\n",
-    "for i in range(len(output)):\n",
-    "    print(\"Perceptron output for x1, x2 = \", x1[i], \",\", x2[i],\n",
-    "          \" is \", output[i])\n",
-    "perceptron_DB(x1, x2, w, threshold)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In fact, a single perceptron can compute \"AND\", \"OR\" and \"NOT\" boolean functions.\n",
-    "\n",
-    "However, it cannot compute some other boolean functions such as \"XOR\".\n",
-    "\n",
-    "**WHAT CAN WE DO?**\n",
-    "\n",
-    "\n",
-    "Hint: Think about what is the significance of the NAND gate we have created above?\n",
-    "\n",
-    "Answer: We said a single perceptron can't compute a \"XOR\" function. We didn't say that about **multiple Perceptrons** put together."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**XOR function using multiple perceptrons**\n",
-    "\n",
-    "<center>\n",
-    "<figure>\n",
-    "<img src=\"./images/neuralnets/perceptron_XOR.svg\" width=\"400\"/>\n",
-    "<figcaption>Multiple perceptrons connected together to output a XOR function.</figcaption>\n",
-    "</figure>\n",
-    "</center>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multi-layer perceptrons\n",
-    "\n",
-    "The normal densely connected neural network is sometimes also called \"Multi-layer\" perceptron."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Learning\n",
-    "\n",
-    "We know that we can compute complicated functions by combining a number of perceptrons.\n",
-    "\n",
-    "In the perceptron examples we had set the model parameters (weights and threshold) by hand.\n",
-    "\n",
-    "This is something we definitely **DO NOT** want to do or even can do for big networks.\n",
-    "\n",
-    "We want some algorithm to set/learn the model parameters for us!\n",
-    "\n",
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>Threshold -> bias</strong>  \n",
-    "    \n",
-    "Before we go further we need to introduce one change. The threshold which we saw in the step activation function above is moved to the left side of the equation and is called **bias**.\n",
-    "\n",
-    "$$\n",
-    "f = \\left\\{\n",
-    "        \\begin{array}{ll}\n",
-    "            0 & \\quad weighted\\_sum + bias < 0 \\\\\n",
-    "            1 & \\quad weighted\\_sum + bias \\geq 0\n",
-    "        \\end{array}\n",
-    "       \\quad \\quad  \\mathrm{where}, bias = -threshold\n",
-    "    \\right.\n",
-    "$$\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order to algorithmically set/learn the weights and bias we need to choose an appropriate loss function for the problem at hand and solve an optimization problem.\n",
-    "We will explain below what this means.\n",
-    "\n",
-    "\n",
-    "### Loss function\n",
-    "\n",
-    "To learn using an algorithm we need to define a quantity/function which allows us to measure how close or far are the predictions of our network/setup from reality or the supplied labels. This is done by choosing a so-called \"Loss function\" (as in the case for other machine learning algorithms).\n",
-    "\n",
-    "Once we have this function, we need an algorithm to update the weights of the network such that this loss function decreases. \n",
-    "As one can already imagine the choice of an appropriate loss function is critical to the success of the model. \n",
-    "\n",
-    "Fortunately, for classification and regression (which cover a large variety of problems) these loss functions are well known. \n",
-    "\n",
-    "**Crossentropy** and **mean squared error** loss functions are often used for standard classification and regression problems, respectively.\n",
-    "\n",
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "    <i class=\"fa fa-info-circle\"></i>&nbsp; As we have seen before, <strong>mean squared error</strong> is defined as \n",
-    "\n",
-    "\n",
-    "$$\n",
-    "\\frac{1}{n} \\left((y_1 - \\hat{y}_1)^2 + (y_2 - \\hat{y}_2)^2 + ... + (y_n - \\hat{y}_n)^2 \\right)\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "</div>\n",
-    "\n",
-    "### Gradient based learning\n",
-    "\n",
-    "As mentioned above, once we have chosen a loss function, we want to solve an **optimization problem** which minimizes this loss by updating the parameters (weights and biases) of the network. This is how the learning takes in a NN, and the \"knowledge\" is stored as the weights and biases.\n",
-    "\n",
-    "The most popular optimization methods used in Neural Network training are **Gradient-descent (GD)** type methods, such as gradient-descent itself, RMSprop and Adam. \n",
-    "\n",
-    "**Gradient-descent** uses partial derivatives of the loss function with respect to the network weights and a learning rate to updates the weights such that the loss function decreases and after some iterations reaches its (Global) minimum value.\n",
-    "\n",
-    "First, the loss function and its derivative are computed at the output node, and this signal is propagated backwards, using the chain rule, in the network to compute the partial derivatives. Hence, this method is called **Backpropagation**.\n",
-    "\n",
-    "One way to perform a single GD pass is to compute the partial derivatives using **all the samples** in our data, computing average derivatives and using them to update the weights. This is called **Batch gradient descent**. However, in deep learning we mostly work with massive datasets and using batch gradient descent can make the training very slow!\n",
-    "\n",
-    "The other extreme is to randomly shuffle the dataset and advance a pass of GD with the gradients computed using only **one sample** at a time. This is called **Stochastic gradient descent**.\n",
-    "\n",
-    "<center>\n",
-    "<figure>\n",
-    "<img src=\"./images/stochastic-vs-batch-gradient-descent.png\" width=\"600\"/>\n",
-    "<figcaption>Source: <a href=\"https://wikidocs.net/3413\">https://wikidocs.net/3413</a></figcaption>\n",
-    "</figure>\n",
-    "</center>\n",
-    "\n",
-    "\n",
-    "In practice, an approach in-between these two is used. The entire dataset is divided into **m batches** and these are used one by one to compute the derivatives and apply GD. This technique is called **Mini-batch gradient descent**. \n",
-    "\n",
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "One pass through the entire training dataset is called 1 epoch of training.\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 720x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import numpy as np\n",
-    "\n",
-    "plt.figure(figsize=(10, 4)) ;\n",
-    "\n",
-    "pts=np.arange(-20,20, 0.1) ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Activation Functions\n",
-    "\n",
-    "In order to train the network we need to move away from Perceptron's **step** activation function because it can not be used for training using the gradient-descent and back-propagation algorithms among other drawbacks.\n",
-    "\n",
-    "Non-Linear functions such as:\n",
-    "\n",
-    "* Sigmoid\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "f(z) = \\frac{1}{1+e^{-z}} \\quad \\quad \\mathrm{where}, z = weighted\\_sum + bias\n",
-    "\\end{equation*}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 254,
-       "width": 376
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.lineplot(pts, 1/(1+np.exp(-pts))) ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* tanh\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "f(z) = \\frac{e^{z} - e^{-z}}{e^{z} + e^{-z}}\\quad \\quad \\mathrm{where}, z = weighted\\_sum + bias\n",
-    "\\end{equation*}\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 254,
-       "width": 389
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.lineplot(pts, np.tanh(pts*np.pi)) ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* **ReLU (Rectified linear unit)**\n",
-    "\n",
-    "\\begin{equation*}\n",
-    "f(z) = \\mathrm{max}(0,z)   \\quad \\quad \\mathrm{where}, z = weighted\\_sum + bias\n",
-    "\\end{equation*}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 254,
-       "width": 383
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pts_relu=[max(0,i) for i in pts];\n",
-    "plt.plot(pts, pts_relu) ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "are some of the commonly used as activation functions. Such non-linear activation functions allow the network to learn complex representations of data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "ReLU is very popular and is widely used nowadays. There also exist other variations of ReLU, e.g. \"leaky ReLU\".\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "Why don't we just use a simple linear activation function?\n",
-    "    \n",
-    "Linear activations are **NOT** used because it can be mathematically shown that if they are used then the output is just a linear function of the input. So we cannot learn interesting and complex functions by adding any number of hidden layers.\n",
-    "\n",
-    "The only exception when we do want to use a linear activation is for the output layer of a network when solving a regression problem.\n",
-    "\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercise section\n",
-    "\n",
-    "### Google Playground\n",
-    "\n",
-    "A great tool from Google to develop a feeling for the workings of neural networks.\n",
-    "\n",
-    "https://playground.tensorflow.org/\n",
-    "\n",
-    "<img src=\"./images/neuralnets/google_playground.png\"/>\n",
-    "\n",
-    "**Walkthrough by instructor**\n",
-    "\n",
-    "Some concepts to look at:\n",
-    "\n",
-    "* Simple vs Complex models (Effect of network size)\n",
-    "* Optimization results\n",
-    "* Effect of activation functions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Introduction to Keras"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### What is Keras?\n",
-    "\n",
-    "* It is a high level API to create and work with neural networks\n",
-    "* Supports multiple backends such as **TensorFlow** from Google, **Theano** (Although Theano is dead now) and **CNTK** (Microsoft Cognitive Toolkit)\n",
-    "* Very good for creating neural nets quickly and hides away a lot of tedious work\n",
-    "* Has been incorporated into official TensorFlow (which obviously only works with tensforflow) and as of TensorFlow 2.0 this will the main api to use it\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<center>\n",
-    "<figure>\n",
-    "<img src=\"./images/neuralnets/neural_net_keras_1.svg\" width=\"700\"/>\n",
-    "<figcaption>Building this model in Keras</figcaption>\n",
-    "</figure>\n",
-    "</center>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "dense_1 (Dense)              (None, 4)                 12        \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 4)                 20        \n",
-      "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 1)                 5         \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 1)                 0         \n",
-      "=================================================================\n",
-      "Total params: 37\n",
-      "Trainable params: 37\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Say hello to keras\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Activation\n",
-    "\n",
-    "# Creating a model\n",
-    "model = Sequential()\n",
-    "\n",
-    "# Adding layers to this model\n",
-    "# 1st Hidden layer\n",
-    "# A Dense/fully-connected layer which takes as input a \n",
-    "# feature array of shape (samples, num_features)\n",
-    "# Here input_shape = (2,) means that the layer expects an input with num_features = 2\n",
-    "# and the sample size could be anything\n",
-    "# The activation function for this layer is set to \"relu\"\n",
-    "model.add(Dense(units=4, input_shape=(2,), activation=\"relu\"))\n",
-    "\n",
-    "# 2nd Hidden layer\n",
-    "# This is also a fully-connected layer and we do not need to specify the\n",
-    "# shape of the input anymore (We need to do that only for the first layer)\n",
-    "# NOTE: Now we didn't add the activation seperately. Instead we just added it\n",
-    "# while calling Dense(). This and the way used for the first layer are Equivalent!\n",
-    "model.add(Dense(units=4, activation=\"relu\"))\n",
-    "\n",
-    "          \n",
-    "# The output layer\n",
-    "model.add(Dense(units=1))\n",
-    "model.add(Activation(\"sigmoid\"))\n",
-    "\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### XOR using neural networks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 320,
-       "width": 332
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Creating a network to solve the XOR problem\n",
-    "\n",
-    "# Loading and plotting the data\n",
-    "xor = pd.read_csv(\"data/xor.csv\")\n",
-    "\n",
-    "# Using x and y coordinates as featues\n",
-    "features = xor.iloc[:, :-1]\n",
-    "# Convert boolean to integer values (True->1 and False->0)\n",
-    "labels = (1-xor.iloc[:, -1].astype(int))\n",
-    "\n",
-    "colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
-    "plt.figure(figsize=(5, 5))\n",
-    "plt.xlim([-2, 2])\n",
-    "plt.ylim([-2, 2])\n",
-    "plt.title(\"Blue points are False\")\n",
-    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\") ;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Building a simple Keras model\n",
-    "\n",
-    "def a_simple_NN():\n",
-    "    \n",
-    "    model = Sequential()\n",
-    "\n",
-    "    model.add(Dense(4, input_shape = (2,), activation = \"relu\"))\n",
-    "\n",
-    "    model.add(Dense(4, activation = \"relu\"))\n",
-    "\n",
-    "    model.add(Dense(1, activation = \"sigmoid\"))\n",
-    "\n",
-    "    model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
-    "    \n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n",
-      "Train on 350 samples, validate on 150 samples\n",
-      "Epoch 1/300\n",
-      "350/350 [==============================] - 1s 2ms/step - loss: 0.7369 - acc: 0.4686 - val_loss: 0.7258 - val_acc: 0.4933\n",
-      "Epoch 2/300\n",
-      "350/350 [==============================] - 0s 71us/step - loss: 0.7279 - acc: 0.4657 - val_loss: 0.7189 - val_acc: 0.4867\n",
-      "Epoch 3/300\n",
-      "350/350 [==============================] - 0s 70us/step - loss: 0.7208 - acc: 0.4629 - val_loss: 0.7125 - val_acc: 0.4733\n",
-      "Epoch 4/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.7139 - acc: 0.4629 - val_loss: 0.7062 - val_acc: 0.4533\n",
-      "Epoch 5/300\n",
-      "350/350 [==============================] - 0s 71us/step - loss: 0.7072 - acc: 0.4629 - val_loss: 0.6995 - val_acc: 0.4600\n",
-      "Epoch 6/300\n",
-      "350/350 [==============================] - 0s 103us/step - loss: 0.7008 - acc: 0.4714 - val_loss: 0.6934 - val_acc: 0.4467\n",
-      "Epoch 7/300\n",
-      "350/350 [==============================] - 0s 109us/step - loss: 0.6948 - acc: 0.4657 - val_loss: 0.6875 - val_acc: 0.4533\n",
-      "Epoch 8/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.6894 - acc: 0.4800 - val_loss: 0.6823 - val_acc: 0.5333\n",
-      "Epoch 9/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.6844 - acc: 0.4971 - val_loss: 0.6775 - val_acc: 0.5467\n",
-      "Epoch 10/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.6797 - acc: 0.5257 - val_loss: 0.6729 - val_acc: 0.5467\n",
-      "Epoch 11/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.6755 - acc: 0.5429 - val_loss: 0.6690 - val_acc: 0.5267\n",
-      "Epoch 12/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.6718 - acc: 0.5571 - val_loss: 0.6656 - val_acc: 0.5400\n",
-      "Epoch 13/300\n",
-      "350/350 [==============================] - ETA: 0s - loss: 0.6703 - acc: 0.562 - 0s 92us/step - loss: 0.6685 - acc: 0.5686 - val_loss: 0.6628 - val_acc: 0.5400\n",
-      "Epoch 14/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.6654 - acc: 0.5743 - val_loss: 0.6598 - val_acc: 0.5400\n",
-      "Epoch 15/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.6625 - acc: 0.5600 - val_loss: 0.6570 - val_acc: 0.5333\n",
-      "Epoch 16/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.6595 - acc: 0.5514 - val_loss: 0.6543 - val_acc: 0.5267\n",
-      "Epoch 17/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.6568 - acc: 0.5714 - val_loss: 0.6518 - val_acc: 0.5467\n",
-      "Epoch 18/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.6541 - acc: 0.5686 - val_loss: 0.6491 - val_acc: 0.5533\n",
-      "Epoch 19/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.6513 - acc: 0.5629 - val_loss: 0.6465 - val_acc: 0.5467\n",
-      "Epoch 20/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.6485 - acc: 0.5886 - val_loss: 0.6440 - val_acc: 0.5733\n",
-      "Epoch 21/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.6460 - acc: 0.5971 - val_loss: 0.6416 - val_acc: 0.5800\n",
-      "Epoch 22/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.6435 - acc: 0.6086 - val_loss: 0.6393 - val_acc: 0.5867\n",
-      "Epoch 23/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.6410 - acc: 0.6314 - val_loss: 0.6368 - val_acc: 0.6000\n",
-      "Epoch 24/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.6383 - acc: 0.6486 - val_loss: 0.6342 - val_acc: 0.6200\n",
-      "Epoch 25/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.6354 - acc: 0.6571 - val_loss: 0.6314 - val_acc: 0.6267\n",
-      "Epoch 26/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.6324 - acc: 0.6857 - val_loss: 0.6283 - val_acc: 0.6267\n",
-      "Epoch 27/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.6294 - acc: 0.6857 - val_loss: 0.6255 - val_acc: 0.6333\n",
-      "Epoch 28/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.6266 - acc: 0.6914 - val_loss: 0.6227 - val_acc: 0.6533\n",
-      "Epoch 29/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.6237 - acc: 0.6943 - val_loss: 0.6201 - val_acc: 0.6600\n",
-      "Epoch 30/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.6209 - acc: 0.7000 - val_loss: 0.6174 - val_acc: 0.6800\n",
-      "Epoch 31/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.6181 - acc: 0.7057 - val_loss: 0.6147 - val_acc: 0.6800\n",
-      "Epoch 32/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.6152 - acc: 0.7057 - val_loss: 0.6118 - val_acc: 0.6867\n",
-      "Epoch 33/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.6123 - acc: 0.7057 - val_loss: 0.6092 - val_acc: 0.7000\n",
-      "Epoch 34/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.6094 - acc: 0.7114 - val_loss: 0.6065 - val_acc: 0.7067\n",
-      "Epoch 35/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.6066 - acc: 0.7143 - val_loss: 0.6039 - val_acc: 0.7067\n",
-      "Epoch 36/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.6037 - acc: 0.7229 - val_loss: 0.6013 - val_acc: 0.7067\n",
-      "Epoch 37/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.6007 - acc: 0.7257 - val_loss: 0.5985 - val_acc: 0.7067\n",
-      "Epoch 38/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.5976 - acc: 0.7286 - val_loss: 0.5957 - val_acc: 0.7067\n",
-      "Epoch 39/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.5943 - acc: 0.7286 - val_loss: 0.5928 - val_acc: 0.7133\n",
-      "Epoch 40/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.5910 - acc: 0.7314 - val_loss: 0.5897 - val_acc: 0.7133\n",
-      "Epoch 41/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.5876 - acc: 0.7400 - val_loss: 0.5868 - val_acc: 0.7200\n",
-      "Epoch 42/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.5844 - acc: 0.7429 - val_loss: 0.5839 - val_acc: 0.7200\n",
-      "Epoch 43/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.5814 - acc: 0.7514 - val_loss: 0.5812 - val_acc: 0.7200\n",
-      "Epoch 44/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.5784 - acc: 0.7514 - val_loss: 0.5785 - val_acc: 0.7200\n",
-      "Epoch 45/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.5754 - acc: 0.7571 - val_loss: 0.5759 - val_acc: 0.7200\n",
-      "Epoch 46/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.5725 - acc: 0.7657 - val_loss: 0.5732 - val_acc: 0.7333\n",
-      "Epoch 47/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.5695 - acc: 0.7743 - val_loss: 0.5704 - val_acc: 0.7333\n",
-      "Epoch 48/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.5666 - acc: 0.7771 - val_loss: 0.5677 - val_acc: 0.7333\n",
-      "Epoch 49/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.5638 - acc: 0.7800 - val_loss: 0.5651 - val_acc: 0.7333\n",
-      "Epoch 50/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.5610 - acc: 0.7800 - val_loss: 0.5625 - val_acc: 0.7467\n",
-      "Epoch 51/300\n",
-      "350/350 [==============================] - 0s 102us/step - loss: 0.5585 - acc: 0.7800 - val_loss: 0.5600 - val_acc: 0.7467\n",
-      "Epoch 52/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.5556 - acc: 0.7857 - val_loss: 0.5573 - val_acc: 0.7467\n",
-      "Epoch 53/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.5530 - acc: 0.7886 - val_loss: 0.5545 - val_acc: 0.7533\n",
-      "Epoch 54/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.5501 - acc: 0.7943 - val_loss: 0.5516 - val_acc: 0.7600\n",
-      "Epoch 55/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.5473 - acc: 0.7971 - val_loss: 0.5486 - val_acc: 0.7600\n",
-      "Epoch 56/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.5442 - acc: 0.7943 - val_loss: 0.5453 - val_acc: 0.7600\n",
-      "Epoch 57/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.5413 - acc: 0.8000 - val_loss: 0.5422 - val_acc: 0.7800\n",
-      "Epoch 58/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.5385 - acc: 0.8029 - val_loss: 0.5393 - val_acc: 0.7867\n",
-      "Epoch 59/300\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "350/350 [==============================] - 0s 90us/step - loss: 0.5355 - acc: 0.8086 - val_loss: 0.5361 - val_acc: 0.8067\n",
-      "Epoch 60/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.5325 - acc: 0.8086 - val_loss: 0.5328 - val_acc: 0.8067\n",
-      "Epoch 61/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.5295 - acc: 0.8143 - val_loss: 0.5297 - val_acc: 0.8133\n",
-      "Epoch 62/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.5264 - acc: 0.8143 - val_loss: 0.5264 - val_acc: 0.8200\n",
-      "Epoch 63/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.5234 - acc: 0.8171 - val_loss: 0.5232 - val_acc: 0.8200\n",
-      "Epoch 64/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.5203 - acc: 0.8200 - val_loss: 0.5200 - val_acc: 0.8400\n",
-      "Epoch 65/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.5172 - acc: 0.8229 - val_loss: 0.5167 - val_acc: 0.8400\n",
-      "Epoch 66/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.5142 - acc: 0.8229 - val_loss: 0.5136 - val_acc: 0.8467\n",
-      "Epoch 67/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.5110 - acc: 0.8229 - val_loss: 0.5101 - val_acc: 0.8533\n",
-      "Epoch 68/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.5078 - acc: 0.8286 - val_loss: 0.5068 - val_acc: 0.8600\n",
-      "Epoch 69/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.5046 - acc: 0.8343 - val_loss: 0.5035 - val_acc: 0.8600\n",
-      "Epoch 70/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.5013 - acc: 0.8371 - val_loss: 0.5000 - val_acc: 0.8600\n",
-      "Epoch 71/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.4978 - acc: 0.8400 - val_loss: 0.4963 - val_acc: 0.8600\n",
-      "Epoch 72/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.4944 - acc: 0.8457 - val_loss: 0.4925 - val_acc: 0.8667\n",
-      "Epoch 73/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.4908 - acc: 0.8514 - val_loss: 0.4886 - val_acc: 0.8667\n",
-      "Epoch 74/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.4871 - acc: 0.8600 - val_loss: 0.4847 - val_acc: 0.8667\n",
-      "Epoch 75/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.4834 - acc: 0.8571 - val_loss: 0.4805 - val_acc: 0.8667\n",
-      "Epoch 76/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.4797 - acc: 0.8571 - val_loss: 0.4766 - val_acc: 0.8733\n",
-      "Epoch 77/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.4761 - acc: 0.8600 - val_loss: 0.4728 - val_acc: 0.8733\n",
-      "Epoch 78/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.4724 - acc: 0.8657 - val_loss: 0.4691 - val_acc: 0.8867\n",
-      "Epoch 79/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.4688 - acc: 0.8714 - val_loss: 0.4650 - val_acc: 0.8867\n",
-      "Epoch 80/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.4648 - acc: 0.8714 - val_loss: 0.4612 - val_acc: 0.8867\n",
-      "Epoch 81/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.4612 - acc: 0.8771 - val_loss: 0.4573 - val_acc: 0.8867\n",
-      "Epoch 82/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.4574 - acc: 0.8771 - val_loss: 0.4534 - val_acc: 0.8867\n",
-      "Epoch 83/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.4536 - acc: 0.8829 - val_loss: 0.4498 - val_acc: 0.8867\n",
-      "Epoch 84/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.4497 - acc: 0.8857 - val_loss: 0.4458 - val_acc: 0.8867\n",
-      "Epoch 85/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.4456 - acc: 0.8886 - val_loss: 0.4416 - val_acc: 0.8933\n",
-      "Epoch 86/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.4414 - acc: 0.8943 - val_loss: 0.4376 - val_acc: 0.9000\n",
-      "Epoch 87/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.4374 - acc: 0.8943 - val_loss: 0.4340 - val_acc: 0.9000\n",
-      "Epoch 88/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.4337 - acc: 0.8971 - val_loss: 0.4305 - val_acc: 0.9067\n",
-      "Epoch 89/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.4298 - acc: 0.9029 - val_loss: 0.4268 - val_acc: 0.9200\n",
-      "Epoch 90/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.4259 - acc: 0.9000 - val_loss: 0.4228 - val_acc: 0.9333\n",
-      "Epoch 91/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.4220 - acc: 0.9029 - val_loss: 0.4189 - val_acc: 0.9267\n",
-      "Epoch 92/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.4183 - acc: 0.9114 - val_loss: 0.4153 - val_acc: 0.9333\n",
-      "Epoch 93/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.4143 - acc: 0.9171 - val_loss: 0.4116 - val_acc: 0.9267\n",
-      "Epoch 94/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.4106 - acc: 0.9171 - val_loss: 0.4079 - val_acc: 0.9267\n",
-      "Epoch 95/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.4066 - acc: 0.9171 - val_loss: 0.4041 - val_acc: 0.9267\n",
-      "Epoch 96/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.4027 - acc: 0.9200 - val_loss: 0.4002 - val_acc: 0.9267\n",
-      "Epoch 97/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.3988 - acc: 0.9229 - val_loss: 0.3965 - val_acc: 0.9333\n",
-      "Epoch 98/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.3951 - acc: 0.9229 - val_loss: 0.3929 - val_acc: 0.9267\n",
-      "Epoch 99/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.3914 - acc: 0.9229 - val_loss: 0.3890 - val_acc: 0.9267\n",
-      "Epoch 100/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.3877 - acc: 0.9257 - val_loss: 0.3858 - val_acc: 0.9267\n",
-      "Epoch 101/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.3843 - acc: 0.9286 - val_loss: 0.3824 - val_acc: 0.9267\n",
-      "Epoch 102/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.3808 - acc: 0.9286 - val_loss: 0.3791 - val_acc: 0.9267\n",
-      "Epoch 103/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.3775 - acc: 0.9314 - val_loss: 0.3759 - val_acc: 0.9333\n",
-      "Epoch 104/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.3745 - acc: 0.9343 - val_loss: 0.3732 - val_acc: 0.9333\n",
-      "Epoch 105/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.3714 - acc: 0.9343 - val_loss: 0.3704 - val_acc: 0.9333\n",
-      "Epoch 106/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.3683 - acc: 0.9371 - val_loss: 0.3672 - val_acc: 0.9333\n",
-      "Epoch 107/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.3650 - acc: 0.9400 - val_loss: 0.3642 - val_acc: 0.9333\n",
-      "Epoch 108/300\n",
-      "350/350 [==============================] - 0s 103us/step - loss: 0.3618 - acc: 0.9371 - val_loss: 0.3609 - val_acc: 0.9333\n",
-      "Epoch 109/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.3586 - acc: 0.9371 - val_loss: 0.3579 - val_acc: 0.9333\n",
-      "Epoch 110/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.3555 - acc: 0.9429 - val_loss: 0.3552 - val_acc: 0.9333\n",
-      "Epoch 111/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.3523 - acc: 0.9400 - val_loss: 0.3522 - val_acc: 0.9333\n",
-      "Epoch 112/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.3492 - acc: 0.9400 - val_loss: 0.3492 - val_acc: 0.9333\n",
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-      "Epoch 133/300\n",
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-      "Epoch 134/300\n",
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-      "Epoch 135/300\n",
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-      "Epoch 136/300\n",
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-      "Epoch 159/300\n",
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-      "Epoch 164/300\n",
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-      "Epoch 166/300\n",
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-      "Epoch 167/300\n",
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-      "Epoch 168/300\n",
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-      "Epoch 169/300\n",
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-      "Epoch 170/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.2215 - acc: 0.9629 - val_loss: 0.2305 - val_acc: 0.9533\n",
-      "Epoch 171/300\n",
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-      "Epoch 172/300\n",
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-      "Epoch 173/300\n",
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-      "Epoch 174/300\n",
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-      "Epoch 175/300\n",
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-      "Epoch 176/300\n",
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-      "Epoch 177/300\n",
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-      "Epoch 178/300\n",
-      "350/350 [==============================] - 0s 77us/step - loss: 0.2098 - acc: 0.9629 - val_loss: 0.2198 - val_acc: 0.9600\n",
-      "Epoch 179/300\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "Epoch 180/300\n",
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-      "Epoch 181/300\n",
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-      "Epoch 182/300\n",
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-      "Epoch 183/300\n",
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-      "Epoch 184/300\n",
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-      "Epoch 185/300\n",
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-      "Epoch 186/300\n",
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-      "Epoch 187/300\n",
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-      "Epoch 188/300\n",
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-      "Epoch 189/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1952 - acc: 0.9629 - val_loss: 0.2070 - val_acc: 0.9600\n",
-      "Epoch 190/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.1940 - acc: 0.9686 - val_loss: 0.2056 - val_acc: 0.9667\n",
-      "Epoch 191/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.1927 - acc: 0.9686 - val_loss: 0.2046 - val_acc: 0.9600\n",
-      "Epoch 192/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.1915 - acc: 0.9657 - val_loss: 0.2035 - val_acc: 0.9600\n",
-      "Epoch 193/300\n",
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-      "Epoch 194/300\n",
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-      "Epoch 195/300\n",
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-      "Epoch 196/300\n",
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-      "Epoch 197/300\n",
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-      "Epoch 198/300\n",
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-      "Epoch 199/300\n",
-      "350/350 [==============================] - 0s 65us/step - loss: 0.1833 - acc: 0.9600 - val_loss: 0.1962 - val_acc: 0.9600\n",
-      "Epoch 200/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.1822 - acc: 0.9629 - val_loss: 0.1951 - val_acc: 0.9667\n",
-      "Epoch 201/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1811 - acc: 0.9600 - val_loss: 0.1941 - val_acc: 0.9667\n",
-      "Epoch 202/300\n",
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-      "Epoch 203/300\n",
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-      "Epoch 204/300\n",
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-      "Epoch 205/300\n",
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-      "Epoch 206/300\n",
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-      "Epoch 207/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1750 - acc: 0.9600 - val_loss: 0.1886 - val_acc: 0.9600\n",
-      "Epoch 208/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.1739 - acc: 0.9629 - val_loss: 0.1876 - val_acc: 0.9667\n",
-      "Epoch 209/300\n",
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-      "Epoch 210/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.1720 - acc: 0.9629 - val_loss: 0.1856 - val_acc: 0.9667\n",
-      "Epoch 211/300\n",
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-      "Epoch 212/300\n",
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-      "Epoch 213/300\n",
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-      "Epoch 214/300\n",
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-      "Epoch 215/300\n",
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-      "Epoch 216/300\n",
-      "350/350 [==============================] - 0s 105us/step - loss: 0.1661 - acc: 0.9629 - val_loss: 0.1806 - val_acc: 0.9667\n",
-      "Epoch 217/300\n",
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-      "Epoch 218/300\n",
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-      "Epoch 219/300\n",
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-      "Epoch 220/300\n",
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-      "Epoch 221/300\n",
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-      "Epoch 222/300\n",
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-      "Epoch 223/300\n",
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-      "Epoch 224/300\n",
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-      "Epoch 225/300\n",
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-      "Epoch 226/300\n",
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-      "Epoch 227/300\n",
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-      "Epoch 228/300\n",
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-      "Epoch 229/300\n",
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-      "Epoch 230/300\n",
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-      "Epoch 231/300\n",
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-      "Epoch 232/300\n",
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-      "Epoch 233/300\n",
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-      "Epoch 234/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1509 - acc: 0.9629 - val_loss: 0.1665 - val_acc: 0.9667\n",
-      "Epoch 235/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.1502 - acc: 0.9657 - val_loss: 0.1658 - val_acc: 0.9667\n",
-      "Epoch 236/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.1493 - acc: 0.9629 - val_loss: 0.1650 - val_acc: 0.9667\n",
-      "Epoch 237/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.1484 - acc: 0.9629 - val_loss: 0.1642 - val_acc: 0.9667\n",
-      "Epoch 238/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1477 - acc: 0.9657 - val_loss: 0.1634 - val_acc: 0.9667\n",
-      "Epoch 239/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.1469 - acc: 0.9629 - val_loss: 0.1627 - val_acc: 0.9667\n",
-      "Epoch 240/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.1460 - acc: 0.9629 - val_loss: 0.1619 - val_acc: 0.9667\n",
-      "Epoch 241/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.1454 - acc: 0.9629 - val_loss: 0.1613 - val_acc: 0.9667\n",
-      "Epoch 242/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1445 - acc: 0.9629 - val_loss: 0.1608 - val_acc: 0.9667\n",
-      "Epoch 243/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.1438 - acc: 0.9657 - val_loss: 0.1601 - val_acc: 0.9667\n",
-      "Epoch 244/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1432 - acc: 0.9629 - val_loss: 0.1593 - val_acc: 0.9667\n",
-      "Epoch 245/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.1425 - acc: 0.9657 - val_loss: 0.1586 - val_acc: 0.9667\n",
-      "Epoch 246/300\n",
-      "350/350 [==============================] - 0s 103us/step - loss: 0.1416 - acc: 0.9629 - val_loss: 0.1579 - val_acc: 0.9667\n",
-      "Epoch 247/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1408 - acc: 0.9657 - val_loss: 0.1573 - val_acc: 0.9667\n",
-      "Epoch 248/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.1401 - acc: 0.9600 - val_loss: 0.1564 - val_acc: 0.9667\n",
-      "Epoch 249/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.1393 - acc: 0.9629 - val_loss: 0.1556 - val_acc: 0.9667\n",
-      "Epoch 250/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1386 - acc: 0.9629 - val_loss: 0.1551 - val_acc: 0.9667\n",
-      "Epoch 251/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.1380 - acc: 0.9657 - val_loss: 0.1544 - val_acc: 0.9667\n",
-      "Epoch 252/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.1372 - acc: 0.9629 - val_loss: 0.1537 - val_acc: 0.9667\n",
-      "Epoch 253/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.1366 - acc: 0.9686 - val_loss: 0.1532 - val_acc: 0.9667\n",
-      "Epoch 254/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.1359 - acc: 0.9657 - val_loss: 0.1526 - val_acc: 0.9667\n",
-      "Epoch 255/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.1354 - acc: 0.9657 - val_loss: 0.1521 - val_acc: 0.9667\n",
-      "Epoch 256/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.1345 - acc: 0.9629 - val_loss: 0.1516 - val_acc: 0.9667\n",
-      "Epoch 257/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.1339 - acc: 0.9657 - val_loss: 0.1511 - val_acc: 0.9667\n",
-      "Epoch 258/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1333 - acc: 0.9657 - val_loss: 0.1505 - val_acc: 0.9667\n",
-      "Epoch 259/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.1327 - acc: 0.9657 - val_loss: 0.1499 - val_acc: 0.9667\n",
-      "Epoch 260/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.1321 - acc: 0.9657 - val_loss: 0.1492 - val_acc: 0.9667\n",
-      "Epoch 261/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.1316 - acc: 0.9657 - val_loss: 0.1488 - val_acc: 0.9667\n",
-      "Epoch 262/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1309 - acc: 0.9600 - val_loss: 0.1483 - val_acc: 0.9667\n",
-      "Epoch 263/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1303 - acc: 0.9600 - val_loss: 0.1477 - val_acc: 0.9667\n",
-      "Epoch 264/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.1298 - acc: 0.9629 - val_loss: 0.1472 - val_acc: 0.9667\n",
-      "Epoch 265/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.1293 - acc: 0.9657 - val_loss: 0.1466 - val_acc: 0.9667\n",
-      "Epoch 266/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.1286 - acc: 0.9600 - val_loss: 0.1462 - val_acc: 0.9667\n",
-      "Epoch 267/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.1280 - acc: 0.9600 - val_loss: 0.1457 - val_acc: 0.9667\n",
-      "Epoch 268/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.1276 - acc: 0.9600 - val_loss: 0.1451 - val_acc: 0.9667\n",
-      "Epoch 269/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.1269 - acc: 0.9657 - val_loss: 0.1447 - val_acc: 0.9667\n",
-      "Epoch 270/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.1264 - acc: 0.9629 - val_loss: 0.1442 - val_acc: 0.9667\n",
-      "Epoch 271/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1259 - acc: 0.9657 - val_loss: 0.1436 - val_acc: 0.9667\n",
-      "Epoch 272/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1252 - acc: 0.9629 - val_loss: 0.1431 - val_acc: 0.9667\n",
-      "Epoch 273/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.1246 - acc: 0.9600 - val_loss: 0.1426 - val_acc: 0.9667\n",
-      "Epoch 274/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.1241 - acc: 0.9629 - val_loss: 0.1421 - val_acc: 0.9667\n",
-      "Epoch 275/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.1237 - acc: 0.9600 - val_loss: 0.1417 - val_acc: 0.9667\n",
-      "Epoch 276/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.1232 - acc: 0.9657 - val_loss: 0.1411 - val_acc: 0.9667\n",
-      "Epoch 277/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1225 - acc: 0.9657 - val_loss: 0.1406 - val_acc: 0.9667\n",
-      "Epoch 278/300\n",
-      "350/350 [==============================] - 0s 102us/step - loss: 0.1219 - acc: 0.9629 - val_loss: 0.1402 - val_acc: 0.9667\n",
-      "Epoch 279/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.1216 - acc: 0.9686 - val_loss: 0.1397 - val_acc: 0.9667\n",
-      "Epoch 280/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.1210 - acc: 0.9686 - val_loss: 0.1392 - val_acc: 0.9667\n",
-      "Epoch 281/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1205 - acc: 0.9629 - val_loss: 0.1388 - val_acc: 0.9667\n",
-      "Epoch 282/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.1201 - acc: 0.9657 - val_loss: 0.1383 - val_acc: 0.9667\n",
-      "Epoch 283/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1195 - acc: 0.9629 - val_loss: 0.1380 - val_acc: 0.9667\n",
-      "Epoch 284/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1191 - acc: 0.9629 - val_loss: 0.1375 - val_acc: 0.9667\n",
-      "Epoch 285/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1186 - acc: 0.9629 - val_loss: 0.1371 - val_acc: 0.9667\n",
-      "Epoch 286/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.1182 - acc: 0.9686 - val_loss: 0.1367 - val_acc: 0.9667\n",
-      "Epoch 287/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1178 - acc: 0.9686 - val_loss: 0.1364 - val_acc: 0.9667\n",
-      "Epoch 288/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.1174 - acc: 0.9686 - val_loss: 0.1360 - val_acc: 0.9667\n",
-      "Epoch 289/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.1170 - acc: 0.9686 - val_loss: 0.1357 - val_acc: 0.9667\n",
-      "Epoch 290/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.1163 - acc: 0.9657 - val_loss: 0.1353 - val_acc: 0.9667\n",
-      "Epoch 291/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1160 - acc: 0.9686 - val_loss: 0.1350 - val_acc: 0.9667\n",
-      "Epoch 292/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.1155 - acc: 0.9686 - val_loss: 0.1346 - val_acc: 0.9667\n",
-      "Epoch 293/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.1151 - acc: 0.9657 - val_loss: 0.1341 - val_acc: 0.9667\n",
-      "Epoch 294/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1147 - acc: 0.9686 - val_loss: 0.1337 - val_acc: 0.9667\n",
-      "Epoch 295/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.1141 - acc: 0.9686 - val_loss: 0.1332 - val_acc: 0.9667\n",
-      "Epoch 296/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.1137 - acc: 0.9657 - val_loss: 0.1329 - val_acc: 0.9667\n",
-      "Epoch 297/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.1133 - acc: 0.9657 - val_loss: 0.1326 - val_acc: 0.9667\n",
-      "Epoch 298/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1129 - acc: 0.9686 - val_loss: 0.1322 - val_acc: 0.9667\n",
-      "Epoch 299/300\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "350/350 [==============================] - 0s 91us/step - loss: 0.1127 - acc: 0.9686 - val_loss: 0.1319 - val_acc: 0.9667\n",
-      "Epoch 300/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.1121 - acc: 0.9686 - val_loss: 0.1315 - val_acc: 0.9667\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Instantiating the model\n",
-    "model = a_simple_NN()\n",
-    "\n",
-    "# Splitting the dataset into training (70%) and validation sets (30%)\n",
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    features, labels, test_size=0.3)\n",
-    "\n",
-    "# Setting the number of passes through the entire training set\n",
-    "num_epochs = 300\n",
-    "\n",
-    "# model.fit() is used to train the model\n",
-    "# We can pass validation data while training\n",
-    "model_run = model.fit(X_train, y_train, epochs=num_epochs,\n",
-    "                      validation_data=(X_test, y_test))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\"><p><i class=\"fa fa-info-circle\"></i>&nbsp;\n",
-    "    NOTE: We can pass \"verbose=0\" to model.fit() to suppress the printing of model output on the terminal/notebook.\n",
-    "</p></div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 270,
-       "width": 392
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting the loss and accuracy on the training and validation sets during the training\n",
-    "# This can be done by using Keras callback \"history\" which is applied by default\n",
-    "history_model = model_run.history\n",
-    "\n",
-    "print(\"The history has the following data: \", history_model.keys())\n",
-    "\n",
-    "# Plotting the training and validation accuracy during the training\n",
-    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
-    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
-    "plt.xlabel(\"epochs\") ;\n",
-    "plt.ylabel(\"accuracy\") ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "The plots such as above are essential for analyzing the behaviour and performance of the network and to tune it in the right direction. However, for the example above we don't expect to derive a lot of insight from this plot as the function we are trying to fit is quite simple and there is not too much noise. We will see the significance of these curves in a later example.\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Before we move on forward we see how to save and load a keras model\n",
-    "model.save(\"./data/my_first_NN.h5\")\n",
-    "\n",
-    "# Optional: See what is in the hdf5 file we just created above\n",
-    "\n",
-    "from keras.models import load_model\n",
-    "model = load_model(\"./data/my_first_NN.h5\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the training and validation in the example above we split our dataset into a 70-30 train-validation set. We know from previous chapters that to more robustly estimate the accuracy of our model we can use **K-fold cross-validation**.\n",
-    "This is even more important when we have small datasets and cannot afford to reserve a validation set!\n",
-    "\n",
-    "One way to do the cross-validation here would be to write our own function to do this. However, we also know that **scikit-learn** provides several handy functions to evaluate and tune the models. So the question is:\n",
-    "\n",
-    "\n",
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "    Can we somehow use the scikit-learn functions or the ones we wrote ourselves for scikit-learn models to evaluate and tune our Keras models?\n",
-    "\n",
-    "\n",
-    "The Answer is **YES !**\n",
-    "</p>\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "\n",
-    "We show how to do this in the following section."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using scikit-learn functions on keras models\n",
-    "\n",
-    "\n",
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "Keras offers 2 wrappers which allow its Sequential models to be used with scikit-learn. \n",
-    "\n",
-    "There are: **KerasClassifier** and **KerasRegressor**.\n",
-    "\n",
-    "For more information:\n",
-    "https://keras.io/scikit-learn-api/\n",
-    "</p>\n",
-    "</div>\n",
-    "\n",
-    "\n",
-    "\n",
-    "**Now lets see how this works!**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We wrap the Keras model we created above with KerasClassifier\n",
-    "from keras.wrappers.scikit_learn import KerasClassifier\n",
-    "from sklearn.model_selection import cross_val_score\n",
-    "# Wrapping Keras model\n",
-    "# NOTE: We pass verbose=0 to suppress the model output\n",
-    "num_epochs = 400\n",
-    "model_scikit = KerasClassifier(\n",
-    "    build_fn=a_simple_NN, epochs=num_epochs, verbose=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Let's reuse the function to visualize the decision boundary which we saw in chapter 2 with minimal change\n",
-    "\n",
-    "def list_flatten(list_of_list):\n",
-    "    flattened_list = [i for j in list_of_list for i in j]\n",
-    "    return flattened_list\n",
-    "\n",
-    "def plot_points(plt=plt, marker='o'):\n",
-    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
-    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
-    "\n",
-    "def train_and_plot_decision_surface(\n",
-    "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
-    "):\n",
-    "\n",
-    "    features_2d = np.array(features_2d)\n",
-    "    xmin, ymin = features_2d.min(axis=0)\n",
-    "    xmax, ymax = features_2d.max(axis=0)\n",
-    "\n",
-    "    x = np.linspace(xmin, xmax, N)\n",
-    "    y = np.linspace(ymin, ymax, N)\n",
-    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
-    "\n",
-    "    if preproc is not None:\n",
-    "        points_for_classifier = preproc.fit_transform(points)\n",
-    "        features_2d = preproc.fit_transform(features_2d)\n",
-    "    else:\n",
-    "        points_for_classifier = points\n",
-    "\n",
-    "    classifier.fit(features_2d, labels, verbose=0)\n",
-    "    predicted = classifier.predict(features_2d)\n",
-    "    \n",
-    "    if name == \"Neural Net\":\n",
-    "        predicted = list_flatten(predicted)\n",
-    "    \n",
-    "    \n",
-    "    if preproc is not None:\n",
-    "        name += \" (w/ preprocessing)\"\n",
-    "    print(name + \":\\t\", sum(predicted == labels), \"/\", len(labels), \"correct\")\n",
-    "    \n",
-    "    if name == \"Neural Net\":\n",
-    "        classes = np.array(list_flatten(classifier.predict(points_for_classifier)), dtype=bool)\n",
-    "    else:\n",
-    "        classes = np.array(classifier.predict(points_for_classifier), dtype=bool)\n",
-    "    plt.plot(\n",
-    "        points[~classes][:, 0],\n",
-    "        points[~classes][:, 1],\n",
-    "        \"o\",\n",
-    "        color=\"steelblue\",\n",
-    "        markersize=1,\n",
-    "        alpha=0.01,\n",
-    "    )\n",
-    "    plt.plot(\n",
-    "        points[classes][:, 0],\n",
-    "        points[classes][:, 1],\n",
-    "        \"o\",\n",
-    "        color=\"chocolate\",\n",
-    "        markersize=1,\n",
-    "        alpha=0.04,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Neural Net:\t 483 / 500 correct\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 363,
-       "width": 383
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_, ax = plt.subplots(figsize=(6, 6))\n",
-    "\n",
-    "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
-    "plot_points(plt=ax)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The acuracy on the  5  validation folds: [0.97 0.75 0.97 0.81 0.96]\n",
-      "The Average acuracy on the  5  validation folds: 0.892\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Applying K-fold cross-validation\n",
-    "# Here we pass the whole dataset, i.e. features and labels, instead of splitting it.\n",
-    "num_folds = 5\n",
-    "cross_validation = cross_val_score(\n",
-    "    model_scikit, features, labels, cv=num_folds, verbose=0)\n",
-    "\n",
-    "print(\"The acuracy on the \", num_folds, \" validation folds:\", cross_validation)\n",
-    "print(\"The Average acuracy on the \", num_folds, \" validation folds:\", np.mean(cross_validation))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "The code above took quiet long to finish even though we used only 5  CV folds and the neural network and data size are very small! This gives an indication of the enormous compute requirements of training production-grade deep neural networks.\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hyperparameter optimization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We know from chapter 6 that there are 2 types of parameters which need to be tuned for a machine learning model.\n",
-    "* Internal model parameters (weights) which can be learned for e.g. by gradient-descent\n",
-    "* Hyperparameters\n",
-    "\n",
-    "In the model created above we made some arbitrary choices such as the choice of the optimizer we used, optimizer's learning rate, number of hidden units and so on ...\n",
-    "\n",
-    "Now that we have the keras model wrapped as a scikit-learn model we can use the grid search functions we have seen in chapter 6."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import GridSearchCV\n",
-    "# Just to remember\n",
-    "model_scikit = KerasClassifier(\n",
-    "    build_fn=a_simple_NN, **{\"epochs\": num_epochs, \"verbose\": 0})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
-      "  DeprecationWarning)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8360000002384186 {'epochs': 100}\n"
-     ]
-    }
-   ],
-   "source": [
-    "HP_grid = {'epochs' : [30, 50, 100]}\n",
-    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid)\n",
-    "search.fit(features, labels)\n",
-    "print(search.best_score_, search.best_params_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
-      "  DeprecationWarning)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8759999972581863 {'batch_size': 10, 'epochs': 30}\n"
-     ]
-    }
-   ],
-   "source": [
-    "HP_grid = {'epochs' : [10, 15, 30], \n",
-    "           'batch_size' : [10, 20, 30] }\n",
-    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid)\n",
-    "search.fit(features, labels)\n",
-    "print(search.best_score_, search.best_params_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# A more general model for further Hyperparameter optimization\n",
-    "from keras import optimizers\n",
-    "\n",
-    "def a_simple_NN(activation='relu', num_hidden_neurons=[4, 4], learning_rate=0.01):\n",
-    "\n",
-    "    model = Sequential()\n",
-    "\n",
-    "    model.add(Dense(num_hidden_neurons[0],\n",
-    "                    input_shape=(2,), activation=activation))\n",
-    "\n",
-    "    model.add(Dense(num_hidden_neurons[1], activation=activation))\n",
-    "\n",
-    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
-    "\n",
-    "    model.compile(loss=\"binary_crossentropy\", optimizer=optimizers.rmsprop(\n",
-    "        lr=learning_rate), metrics=[\"accuracy\"])\n",
-    "\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercise section: \n",
-    "* Look at the model above and choose a couple of hyperparameters to optimize. \n",
-    "* **OPTIONAL:** What function from scikit-learn other than GridSearchCV can we use for hyperparameter optimization? Use it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "Another library which you should definitely look at for doing hyperparameter optimization with keras models is the <a href=\"https://github.com/maxpumperla/hyperas\">Hyperas library</a> which is a wrapper around the <a href=\"https://github.com/hyperopt/hyperopt\">Hyperopt library</a>. \n",
-    "\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercise section:  \n",
-    "* Create a neural network to classify the 2d points example from chapter 2 learned (Optional: As you create the model read a bit on the different keras commands we have used)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split, cross_val_score\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense\n",
-    "from keras import optimizers\n",
-    "from keras.wrappers.scikit_learn import KerasClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 309,
-       "width": 332
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "circle = pd.read_csv(\"data/circle.csv\")\n",
-    "# Using x and y coordinates as featues\n",
-    "features = circle.iloc[:, :-1]\n",
-    "# Convert boolean to integer values (True->1 and False->0)\n",
-    "labels = circle.iloc[:, -1].astype(int)\n",
-    "\n",
-    "colors = [[\"steelblue\", \"chocolate\"][i] for i in circle[\"label\"]]\n",
-    "plt.figure(figsize=(5, 5))\n",
-    "plt.xlim([-2, 2])\n",
-    "plt.ylim([-2, 2])\n",
-    "\n",
-    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\");\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Insert Code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The examples we saw above are really nice to show various features of the Keras library and to understand how we build and train a model. However, they are not the ideal problems one should solve using neural networks. They are too simple and can be solved easily by classical machine learning algorithms. \n",
-    "\n",
-    "Now we show examples where Neural Networks really shine over classical machine learning algorithms."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Handwritten Digits Classification (multi-class classification)\n",
-    "### MNIST Dataset\n",
-    "\n",
-    "MNIST datasets is a very common dataset used in machine learning. It is widely used to train and validate models.\n",
-    "\n",
-    "\n",
-    ">The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a >test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size->normalized and centered in a fixed-size image.\n",
-    ">It is a good database for people who want to try learning techniques and pattern recognition methods on real-world >data while spending minimal efforts on preprocessing and formatting.\n",
-    ">source: http://yann.lecun.com/exdb/mnist/\n",
-    "\n",
-    "This dataset consists of images of handwritten digits between 0-9 and their corresponsing labels. We want to train a neural network which is able to predict the correct digit on the image. \n",
-    "This is a multi-class classification problem. Unlike binary classification which we have seen till now we will classify data into 10 different classes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'keras'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-a17d0ae7aa82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Loading the dataset in keras\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Later you can explore and play with other datasets with come with Keras\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmnist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Loading the train and test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'keras'"
-     ]
-    }
-   ],
-   "source": [
-    "# Loading the dataset in keras\n",
-    "# Later you can explore and play with other datasets with come with Keras\n",
-    "from keras.datasets import mnist\n",
-    "\n",
-    "# Loading the train and test data\n",
-    "\n",
-    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(60000, 28, 28)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Looking at the dataset\n",
-    "print(X_train.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This digit is:  2\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 254,
-       "width": 256
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
-    "i=np.random.randint(0,X_train.shape[0])\n",
-    "sns.set_style(\"white\")\n",
-    "plt.imshow(X_train[i], cmap=\"gray_r\") ;\n",
-    "sns.set(style=\"darkgrid\")\n",
-    "print(\"This digit is: \" , y_train[i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 255\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Look at the data values for a couple of images\n",
-    "print(X_train[0].min(), X_train[1].max())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data consists of values between 0-255 representing the **grayscale level**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(60000,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The labels are the digit on the image\n",
-    "print(y_train.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Scaling the data\n",
-    "# It is important to normalize the input data to (0-1) before providing it to a neural net\n",
-    "# We could use the previously introduced function from scikit-learn. However, here it is sufficient to\n",
-    "# just divide the input data by 255\n",
-    "X_train_norm = X_train/255.\n",
-    "X_test_norm = X_test/255.\n",
-    "\n",
-    "# Also we need to reshape the input data such that each sample is a vector and not a 2D matrix\n",
-    "X_train_prep = X_train_norm.reshape(X_train_norm.shape[0],28*28)\n",
-    "X_test_prep = X_test_norm.reshape(X_test_norm.shape[0],28*28)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "One-Hot encoding\n",
-    "\n",
-    "In multi-class classification problems the labels are provided to the neural network as something called **One-hot encodings**. The categorical labels (0-9 here) are converted to vectors.\n",
-    "\n",
-    "For the MNIST problem where the data has **10 categories** we will convert every label to a vector of length 10. \n",
-    "All the entries of this vector will be zero **except** for the index which is equal to the (integer) value of the label.\n",
-    "\n",
-    "For example:\n",
-    "if label is 4. The one-hot vector will look like **[0 0 0 0 1 0 0 0 0 0]**\n",
-    "\n",
-    "Fortunately, Keras has a built-in function to achieve this and we do not have to write a code for this ourselves.\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(60000, 10)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from keras.utils.np_utils import to_categorical\n",
-    "\n",
-    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
-    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
-    "\n",
-    "print(y_train_onehot.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/20\n",
-      "60000/60000 [==============================] - 4s 67us/step - loss: 0.5984 - acc: 0.8378\n",
-      "Epoch 2/20\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.2611 - acc: 0.9254\n",
-      "Epoch 3/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2045 - acc: 0.9409\n",
-      "Epoch 4/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.1681 - acc: 0.9510\n",
-      "Epoch 5/20\n",
-      "60000/60000 [==============================] - 1s 25us/step - loss: 0.1439 - acc: 0.9579\n",
-      "Epoch 6/20\n",
-      "60000/60000 [==============================] - 2s 28us/step - loss: 0.1249 - acc: 0.9623\n",
-      "Epoch 7/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.1102 - acc: 0.9668\n",
-      "Epoch 8/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0983 - acc: 0.9709\n",
-      "Epoch 9/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0881 - acc: 0.9737\n",
-      "Epoch 10/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0795 - acc: 0.9764\n",
-      "Epoch 11/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.0720 - acc: 0.9789\n",
-      "Epoch 12/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0657 - acc: 0.9798\n",
-      "Epoch 13/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0602 - acc: 0.9818\n",
-      "Epoch 14/20\n",
-      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0552 - acc: 0.9835\n",
-      "Epoch 15/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.0513 - acc: 0.9850\n",
-      "Epoch 16/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0472 - acc: 0.9858\n",
-      "Epoch 17/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0435 - acc: 0.9872\n",
-      "Epoch 18/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0406 - acc: 0.9883\n",
-      "Epoch 19/20\n",
-      "60000/60000 [==============================] - 2s 29us/step - loss: 0.0369 - acc: 0.9895\n",
-      "Epoch 20/20\n",
-      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0345 - acc: 0.9901\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Building the keras model\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense\n",
-    "\n",
-    "def mnist_model():\n",
-    "    model = Sequential()\n",
-    "\n",
-    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\"))\n",
-    "\n",
-    "    model.add(Dense(64, activation=\"relu\"))\n",
-    "\n",
-    "    model.add(Dense(10, activation=\"softmax\"))\n",
-    "\n",
-    "    model.compile(loss=\"categorical_crossentropy\",\n",
-    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
-    "    return model\n",
-    "\n",
-    "model = mnist_model()\n",
-    "\n",
-    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=20,\n",
-    "                      batch_size=512)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000/10000 [==============================] - 2s 235us/step\n",
-      "The [loss, accuracy] on test dataset are:  [0.09651916160695255, 0.9708]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercise section\n",
-    "* Reinitialize and run the model again with validation dataset, plot the accuracy as a function of epochs, play with number of epochs and observe what is happening."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "tags": [
-     "solution"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/20\n",
-      "60000/60000 [==============================] - 4s 74us/step - loss: 0.5742 - acc: 0.8491 - val_loss: 0.3017 - val_acc: 0.9145\n",
-      "Epoch 2/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.2521 - acc: 0.9272 - val_loss: 0.2172 - val_acc: 0.9387\n",
-      "Epoch 3/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.1960 - acc: 0.9439 - val_loss: 0.1910 - val_acc: 0.9425\n",
-      "Epoch 4/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.1608 - acc: 0.9530 - val_loss: 0.1643 - val_acc: 0.9511\n",
-      "Epoch 5/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1381 - acc: 0.9599 - val_loss: 0.1601 - val_acc: 0.9494\n",
-      "Epoch 6/20\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.1197 - acc: 0.9650 - val_loss: 0.1434 - val_acc: 0.9561\n",
-      "Epoch 7/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1058 - acc: 0.9691 - val_loss: 0.1279 - val_acc: 0.9607\n",
-      "Epoch 8/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0953 - acc: 0.9720 - val_loss: 0.1181 - val_acc: 0.9636\n",
-      "Epoch 9/20\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0854 - acc: 0.9750 - val_loss: 0.1115 - val_acc: 0.9662\n",
-      "Epoch 10/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0764 - acc: 0.9771 - val_loss: 0.1144 - val_acc: 0.9656\n",
-      "Epoch 11/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0702 - acc: 0.9790 - val_loss: 0.1331 - val_acc: 0.9598\n",
-      "Epoch 12/20\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0644 - acc: 0.9810 - val_loss: 0.1063 - val_acc: 0.9672\n",
-      "Epoch 13/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0593 - acc: 0.9818 - val_loss: 0.1070 - val_acc: 0.9675\n",
-      "Epoch 14/20\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0549 - acc: 0.9836 - val_loss: 0.1022 - val_acc: 0.9690\n",
-      "Epoch 15/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0501 - acc: 0.9850 - val_loss: 0.1005 - val_acc: 0.9702\n",
-      "Epoch 16/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0461 - acc: 0.9865 - val_loss: 0.0884 - val_acc: 0.9738\n",
-      "Epoch 17/20\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0423 - acc: 0.9875 - val_loss: 0.0868 - val_acc: 0.9751\n",
-      "Epoch 18/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0385 - acc: 0.9888 - val_loss: 0.1024 - val_acc: 0.9705\n",
-      "Epoch 19/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0363 - acc: 0.9893 - val_loss: 0.0853 - val_acc: 0.9759\n",
-      "Epoch 20/20\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0335 - acc: 0.9903 - val_loss: 0.0857 - val_acc: 0.9738\n",
-      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 269,
-       "width": 398
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution:\n",
-    "num_epochs = 20\n",
-    "model = mnist_model()\n",
-    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
-    "                      batch_size=512, validation_data=(X_test_prep, y_test_onehot))\n",
-    "# Evaluating the model on test dataset\n",
-    "#print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))\n",
-    "history_model = model_run.history\n",
-    "print(\"The history has the following data: \", history_model.keys())\n",
-    "\n",
-    "# Plotting the training and validation accuracy during the training\n",
-    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
-    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
-    "plt.xlabel(\"epochs\") ;\n",
-    "plt.ylabel(\"accuracy\") ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What we see here is **overfitting**. After the first few epochs the training and validation datasets show a similar accuracy but thereafter the network starts to over fit to the training set."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "Keep in mind that neural networks are quite prone to overfitting so always check for it.\n",
-    "</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Adding regularization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/20\n",
-      "60000/60000 [==============================] - 4s 72us/step - loss: 1.5822 - acc: 0.8304 - val_loss: 0.9784 - val_acc: 0.8959\n",
-      "Epoch 2/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.8441 - acc: 0.9012 - val_loss: 0.7361 - val_acc: 0.9126\n",
-      "Epoch 3/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.6884 - acc: 0.9095 - val_loss: 0.6553 - val_acc: 0.9028\n",
-      "Epoch 4/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.6109 - acc: 0.9157 - val_loss: 0.6172 - val_acc: 0.8997\n",
-      "Epoch 5/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.5631 - acc: 0.9188 - val_loss: 0.5373 - val_acc: 0.9233\n",
-      "Epoch 6/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.5292 - acc: 0.9231 - val_loss: 0.5175 - val_acc: 0.9202\n",
-      "Epoch 7/20\n",
-      "60000/60000 [==============================] - 2s 28us/step - loss: 0.5025 - acc: 0.9265 - val_loss: 0.5395 - val_acc: 0.9064\n",
-      "Epoch 8/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.4843 - acc: 0.9281 - val_loss: 0.4763 - val_acc: 0.9272\n",
-      "Epoch 9/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.4665 - acc: 0.9302 - val_loss: 0.4438 - val_acc: 0.9352\n",
-      "Epoch 10/20\n",
-      "60000/60000 [==============================] - 2s 25us/step - loss: 0.4508 - acc: 0.9330 - val_loss: 0.4806 - val_acc: 0.9132\n",
-      "Epoch 11/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.4380 - acc: 0.9355 - val_loss: 0.4497 - val_acc: 0.9307\n",
-      "Epoch 12/20\n",
-      "60000/60000 [==============================] - 2s 25us/step - loss: 0.4252 - acc: 0.9368 - val_loss: 0.4623 - val_acc: 0.9202\n",
-      "Epoch 13/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.4156 - acc: 0.9378 - val_loss: 0.4554 - val_acc: 0.9185\n",
-      "Epoch 14/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.4054 - acc: 0.9401 - val_loss: 0.4432 - val_acc: 0.9271\n",
-      "Epoch 15/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.3959 - acc: 0.9414 - val_loss: 0.3848 - val_acc: 0.9445\n",
-      "Epoch 16/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3872 - acc: 0.9435 - val_loss: 0.3775 - val_acc: 0.9443\n",
-      "Epoch 17/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3804 - acc: 0.9441 - val_loss: 0.3817 - val_acc: 0.9418\n",
-      "Epoch 18/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3739 - acc: 0.9449 - val_loss: 0.4235 - val_acc: 0.9264\n",
-      "Epoch 19/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.3670 - acc: 0.9461 - val_loss: 0.3517 - val_acc: 0.9491\n",
-      "Epoch 20/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3603 - acc: 0.9474 - val_loss: 0.3961 - val_acc: 0.9333\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Adding l2 regularization\n",
-    "# Building the keras model\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense\n",
-    "from keras.regularizers import l2\n",
-    "\n",
-    "def mnist_model():\n",
-    "    \n",
-    "    model = Sequential()\n",
-    "\n",
-    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\", \n",
-    "                   kernel_regularizer=l2(0.01)))\n",
-    "\n",
-    "    model.add(Dense(64, activation=\"relu\", \n",
-    "                   kernel_regularizer=l2(0.01)))\n",
-    "\n",
-    "    model.add(Dense(10, activation=\"softmax\"))\n",
-    "\n",
-    "    model.compile(loss=\"categorical_crossentropy\",\n",
-    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
-    "    return model\n",
-    "\n",
-    "model = mnist_model()\n",
-    "\n",
-    "num_epochs = 20\n",
-    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
-    "                      batch_size=512, validation_data=(X_test_prep, y_test_onehot))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 269,
-       "width": 398
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Evaluating the model on test dataset\n",
-    "history_model = model_run.history\n",
-    "print(\"The history has the following data: \", history_model.keys())\n",
-    "\n",
-    "# Plotting the training and validation accuracy during the training\n",
-    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
-    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
-    "plt.xlabel(\"epochs\") ;\n",
-    "plt.ylabel(\"accuracy\") ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "<p><i class=\"fa fa-warning\"></i>&nbsp;\n",
-    "Another way to add regularization and to make the network more robust is by applying Dropout. When we add dropout to a layer a specified percentage of units in that layer are switched off. \n",
-    "    \n",
-    "Both L2 regularization and Dropout make the model simpler and thus reducing overfitting.\n",
-    "</p>\n",
-    "</div>\n",
-    "\n",
-    "### Exercise section\n",
-    "* Add dropout instead of L2 regularization in the network above"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Adding dropout is easy in keras\n",
-    "# We import a layer called Dropout and add as follows\n",
-    "# model.add(Dropout(0.2)) to randomly drop 20% of the hidden units\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "tags": [
-     "solution"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/20\n",
-      "60000/60000 [==============================] - 5s 84us/step - loss: 0.6608 - acc: 0.8141 - val_loss: 0.2953 - val_acc: 0.9151\n",
-      "Epoch 2/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.2884 - acc: 0.9153 - val_loss: 0.2283 - val_acc: 0.9298\n",
-      "Epoch 3/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.2261 - acc: 0.9339 - val_loss: 0.1740 - val_acc: 0.9480\n",
-      "Epoch 4/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.1863 - acc: 0.9450 - val_loss: 0.1466 - val_acc: 0.9557\n",
-      "Epoch 5/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.1627 - acc: 0.9511 - val_loss: 0.1273 - val_acc: 0.9611\n",
-      "Epoch 6/20\n",
-      "60000/60000 [==============================] - 2s 25us/step - loss: 0.1429 - acc: 0.9570 - val_loss: 0.1188 - val_acc: 0.9638\n",
-      "Epoch 7/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.1304 - acc: 0.9612 - val_loss: 0.1099 - val_acc: 0.9657\n",
-      "Epoch 8/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.1184 - acc: 0.9638 - val_loss: 0.1128 - val_acc: 0.9656\n",
-      "Epoch 9/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.1103 - acc: 0.9664 - val_loss: 0.0987 - val_acc: 0.9701\n",
-      "Epoch 10/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.1043 - acc: 0.9685 - val_loss: 0.1008 - val_acc: 0.9692\n",
-      "Epoch 11/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.0971 - acc: 0.9701 - val_loss: 0.0903 - val_acc: 0.9717\n",
-      "Epoch 12/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0920 - acc: 0.9721 - val_loss: 0.0937 - val_acc: 0.9710\n",
-      "Epoch 13/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0858 - acc: 0.9734 - val_loss: 0.0882 - val_acc: 0.9722\n",
-      "Epoch 14/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.0813 - acc: 0.9748 - val_loss: 0.0875 - val_acc: 0.9728\n",
-      "Epoch 15/20\n",
-      "60000/60000 [==============================] - 2s 25us/step - loss: 0.0791 - acc: 0.9749 - val_loss: 0.0872 - val_acc: 0.9735\n",
-      "Epoch 16/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.0774 - acc: 0.9757 - val_loss: 0.0905 - val_acc: 0.9739\n",
-      "Epoch 17/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0715 - acc: 0.9774 - val_loss: 0.0864 - val_acc: 0.9731\n",
-      "Epoch 18/20\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.0690 - acc: 0.9785 - val_loss: 0.0822 - val_acc: 0.9752\n",
-      "Epoch 19/20\n",
-      "60000/60000 [==============================] - 2s 25us/step - loss: 0.0675 - acc: 0.9784 - val_loss: 0.0837 - val_acc: 0.9739\n",
-      "Epoch 20/20\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.0662 - acc: 0.9791 - val_loss: 0.0819 - val_acc: 0.9735\n",
-      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
-     ]
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 269,
-       "width": 404
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "# Adding Dropout\n",
-    "# Building the keras model\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Dropout\n",
-    "\n",
-    "def mnist_model():\n",
-    "    \n",
-    "    model = Sequential()\n",
-    "\n",
-    "    model.add(Dense(64, input_shape=(28*28,), activation=\"relu\"))\n",
-    "              \n",
-    "    model.add(Dropout(0.15))\n",
-    "\n",
-    "    model.add(Dense(64, activation=\"relu\"))\n",
-    "    \n",
-    "    model.add(Dense(10, activation=\"softmax\"))\n",
-    "\n",
-    "    model.compile(loss=\"categorical_crossentropy\",\n",
-    "                  optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
-    "              \n",
-    "    return model\n",
-    "\n",
-    "model = mnist_model()\n",
-    "\n",
-    "num_epochs = 20\n",
-    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs,\n",
-    "                      batch_size=512, validation_data=(X_test_prep, y_test_onehot))\n",
-    "\n",
-    "# Evaluating the model on test dataset\n",
-    "history_model = model_run.history\n",
-    "print(\"The history has the following data: \", history_model.keys())\n",
-    "\n",
-    "# Plotting the training and validation accuracy during the training\n",
-    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"acc\"], color = \"blue\", label=\"Training set\") ;\n",
-    "sns.lineplot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], color = \"red\", label=\"Valdation set\") ;\n",
-    "plt.xlabel(\"epochs\") ;\n",
-    "plt.ylabel(\"accuracy\") ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Network Architecture\n",
-    "\n",
-    "The neural networks which we have seen till now are the simplest kind of neural networks.\n",
-    "There exist more sophisticated network architectures especially designed for specific applications.\n",
-    "Some of them are as follows:\n",
-    "\n",
-    "###  Convolution Neural Networks (CNNs)\n",
-    "\n",
-    "These networks are used mostly for computer vision like tasks such as image classification and object detection. \n",
-    "One of the old CNN networks is shown below.\n",
-    "\n",
-    "<center>\n",
-    "<figure>\n",
-    "<img src=\"./images/neuralnets/CNN_lecun.png\" width=\"800\"/>\n",
-    "<figcaption>source: LeCun et al., Gradient-based learning applied to document recognition (1998).</figcaption>\n",
-    "</figure>\n",
-    "</center>\n",
-    "\n",
-    "CNNs consist of new type of layers such as convolution and pooling layers.\n",
-    "\n",
-    "###  Recurrent Neural Networks (RNNs)\n",
-    "\n",
-    "RNNs are used for problems such as time-series data, speech recognition and translation.\n",
-    "\n",
-    "### Generative adversarial networks (GANs)\n",
-    "\n",
-    "GANs consist of 2 parts, a generative network and a discriminative network. The generative network produces data which is then fed to the discriminative network which judges if the new data belongs to a specified dataset. Then via feedback loops the generative network becomes better and better at creating images similar to the dataset the discriminative network is judging against. At the same time the discriminative network get better and better at identifyig **fake** instances which are not from the reference dataset. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## CNN in a bit more detail\n",
-    "\n",
-    "The standard CNN srchtecture can be seen as 2 parts:\n",
-    "\n",
-    "* Feature extraction\n",
-    "* Classification\n",
-    "\n",
-    "For the **classification** part we use the denly connected network as shown in the keras examples above.\n",
-    "\n",
-    "However, for the **feature extraction** part we use new types of layers called **convolution** layers\n",
-    "\n",
-    "### What is a Convolution?\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1a3afccef0>"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 254,
-       "width": 256
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "sns.set_style(\"white\")\n",
-    "# Loading the train and test data\n",
-    "digit = np.genfromtxt(\"data/digit_4_14x14.csv\", delimiter=\",\").astype(np.int16) ;\n",
-    "plt.imshow(digit, \"gray_r\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This image in matrix form"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_astable(matrix, hw=0.15):\n",
-    "    matrix = plt.table(cellText=matrix, loc=(0,0), cellLoc='center') ;\n",
-    "    matrix.set_fontsize(14)\n",
-    "    cells=matrix.get_celld() ;\n",
-    "    for i in cells:\n",
-    "        cells[i].set_height(hw) ;\n",
-    "        cells[i].set_width(hw) ;\n",
-    "    plt.axis(\"off\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 471,
-       "width": 717
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_astable(digit)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 255,
-       "width": 381
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Vertical edge detection\n",
-    "vertical_edge_kernel = np.array([[-1, 2, -1], [-1, 2, -1], [-1, 2, -1]])\n",
-    "plot_astable(vertical_edge_kernel, 0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 254,
-       "width": 256
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "def convolution(matrix, kernel):\n",
-    "    # This function computes a convolution between a matrix and a kernel/filter without any padding\n",
-    "    width_kernel = kernel.shape[0]\n",
-    "    height_kernel = kernel.shape[1]\n",
-    "    convolution = np.zeros((matrix.shape[0] - width_kernel + 1,\n",
-    "                            matrix.shape[1] - height_kernel + 1))\n",
-    "    for i in range(matrix.shape[0] - width_kernel + 1):\n",
-    "        for j in range(matrix.shape[1] - height_kernel + 1):\n",
-    "            convolution[i, j] = np.sum(np.multiply(\n",
-    "                matrix[i:i+width_kernel, j:j+height_kernel], kernel))\n",
-    "    return convolution\n",
-    "\n",
-    "\n",
-    "vertical_detect = convolution(digit, vertical_edge_kernel)\n",
-    "plt.imshow(vertical_detect, cmap=\"gray_r\") ;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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IJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQol9AAAIJfYBACCU2AcAgFBiHwAAQv0HhJtOZol3IBgAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 255,
-       "width": 381
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Horizontal edge detection\n",
-    "horizontal_edge_kernel = np.array([[-1, -1, -1], [2, 2, 2], [-1, -1, -1]])\n",
-    "plot_astable(horizontal_edge_kernel, 0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Ll0bv07lzZ0lSUVGRb7sAALDI1wiIjo5Wz549JUkFBQWN3uebb76RJCUmJvq2CwAAi3y/WNANN9wgScrOzm5w7PDhwzpx4oSSkpLUtWtXv6cBAGCK7xEwceJEBYNBbdy4UVlZWfW3l5SU6OGHH1YoFNLkyZMVFcUHHAIAcCn5frGgyy+/XAsXLtRDDz2kWbNm6dVXX1VSUpJ2796toqIipaen64477vB7FgAA5jj5KOHRo0crOTlZL730knJycnTo0CF17dpV06dPV0ZGRv2LBwEAwKXjJAIk6Sc/+YkyMzNdfXsAAMzjiXcAAIwiAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIxydsVAFxITEzVq1CjXMyRJp06dcj0hzLFjx1xPaKC2ttb1hDBz5sxxPSFMWlqa6wlhWrVq5XpCAydOnHA9IUyk7Tl9+rTrCXCMMwEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUUQAAABGEQEAABhFBAAAYBQRAACAUTGuB/gpFAqpsrLS9QxJUkpKiusJYX7+85+7ntBAdXW16wlhioqKXE8Is2/fPtcTwuTn57ue0EBcXJzrCWECgYDrCWGCwaDrCWHKyspcT2jA8zzXE+pdii2cCQAAwCgiAAAAo4gAAACMIgIAADCKCAAAwCgiAAAAo4gAAACMIgIAADCKCAAAwChnVwzctGmT3njjDf3zn/9UKBRScnKybr75Zv3mN79RdHS0q1kAAJjhJAKeeuoprV69WnFxcRo4cKCio6P16aefatGiRcrJydGLL74YcZfXBADgx8b3CMjLy9Mrr7yixMRErV+/XsnJyZKkgoIC3Xbbbfrggw+0ZcsWjRo1yu9pAACY4vtrArZv3y7P83TTTTfVB4AkdejQQZMmTZIk7dy50+9ZAACY43sE1J3mLygoaHCs7lPaEhISfN0EAIBFvkfAkCFDFAgElJ2drRUrVqiwsFBnz57VO++8ozVr1qh169YaN26c37MAADDH9wjo2bOnFixYoGbNmumZZ57R4MGDNXDgQM2fP19XX321NmzYoE6dOvk9CwAAc5xcJ6B///4aPHiwgsGg0tPTdd111+myyy7T3r17tX79enme52IWAACm+P7ugN27d2v69Om6/PLLlZWVpS5dukj69jUC99xzj9asWaMWLVro/vvv93saAACm+H4mYNGiRSorK9PChQvrA0D69t0BS5cuVUxMjF577TVVVFT4PQ0AAFN8jYDz588rNzdXLVu2VFpaWoPjXbt2VXJyssrLy3Xs2DE/pwEAYI6vEVBaWirP8y56WeC6Y9XV1X7NAgDAJF8joG3btkpISFBxcbFyc3MbHC8oKNBXX32l2NhY9ejRw89pAACY42sEREVFafz48ZKk+fPnh10wqLCwULNmzVJ1dbXGjRunyy67zM9pAACY4/u7A+677z7l5uYqJydHN954owYOHKhAIKA9e/bo7NmzuuaaazRnzhy/ZwEAYI7vERAfH69XXnlF69ev16ZNm/TZZ58pFAqpe/fuuvPOOzVt2jTFxcX5PQsAAHOcfJRwbGyspk6dqqlTp7r49gAAQI6uGAgAANwjAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIxycsVAV6qrq3XmzBnXMyRJp0+fdj0BP3BRUZHV8M2aNXM9oYH4+HjXEyJaTU2N6wlhAoGA6wkNRNKmS7Elsh5FAACAb4gAAACMIgIAADCKCAAAwCgiAAAAo4gAAACMIgIAADCKCAAAwCgiAAAAo4gAAACMIgIAADCKCAAAwCgiAAAAo4gAAACMIgIAADCKCAAAwCgiAAAAo4gAAACMIgIAADCKCAAAwCgiAAAAo4gAAACMIgIAADCKCAAAwCgiAAAAo4gAAACMIgIAADCKCAAAwCgiAAAAo4gAAACMIgIAADCKCAAAwCgiAAAAo4gAAACMIgIAADAqxvUAPwUCAUVFRUb3REdHu56A/5Lnea4nRLTi4mLXExo4evSo6wkRrVWrVq4nhOnevbvrCRHtUjwGRcZvRAAA4DsiAAAAo4gAAACMIgIAADCKCAAAwCgiAAAAo4gAAACMIgIAADCKCAAAwCgiAAAAoy5JBLz77rtKTU3Vp59+2ujxI0eO6MEHH9TQoUPVt29fjRkzRuvWrVMoFLoUcwAAQCOaPAJ27dqlBQsWXPD4wYMHNX78eL3//vvq3LmzhgwZovz8fC1YsECzZ89u6jkAAOACmvQDhLZs2aI//OEPKi8vb/S453maPXu2zp07p6eeekpjx46VJBUWFmratGnKysrSjTfeqFGjRjXlLAAA0IgmOROQn5+v2bNn695771UoFFK7du0avd+2bduUl5enQYMG1QeAJCUmJurRRx+VJK1du7YpJgEAgO/QJBHw3HPPadOmTerTp482bNigHj16NHq/rVu3SpJGjBjR4NiAAQPUtm1bffbZZzp37lxTzAIAABfRJBHQo0cPLV68WG+//bZSU1MveL9Dhw5JklJSUho9npycrFAopK+++qopZgEAgItoktcEzJw583vd79SpU5Kk9u3bN3q87vZvvvmmKWYBAICL8PU6ARUVFZKkZs2aNXq87vYLvbAQAAA0HV8jICrq228XCAQaPe55Xth/AgCAS8fXCAgGg5Kk8+fPN3q8srIy7H4AAODS8TUCkpKSJF34Of/Tp09LuvBrBgAAQNPxNQJ69+4t6d/vEvhPnufp8OHDio6OVs+ePf2cBQCASb5GwJAhQyRJH374YYNjn3/+uQoLCzVgwAC1aNHCz1kAAJjkawQMGjRIvXv31rZt2/TWW2/V315YWKjHH39ckpSRkeHnJAAAzGrSzw74LlFRUVq0aJGmTp2qRx55RO+8846SkpKUk5OjkpIS3XLLLfrFL37h5yQAAMzyNQIkKS0tTW+//bYyMzO1Y8cOffnll+rWrZsefPBBTZgwwe85AACYdUki4Ls+BKhXr17KzMy8FN8aAAB8T76+JgAAAEQOIgAAAKOIAAAAjCICAAAwiggAAMAoIgAAAKOIAAAAjCICAAAwiggAAMAo3y8b7JLnefI8z/UMSYqYHfj+AoGA6wkRrX379q4nNNC5c2fXEyJaVVWV6wlhIm2PBZwJAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjiAAAAIwiAgAAMIoIAADAKCIAAACjYlwP8MOxY8ckSfn5+Xr11Vcdr8EPVSAQcD0hokXizycSN0USz/NcTwgTCoVcT4ho+fn5kv79O60pmIiA8vJySVJlZWWT/vAAAPBb3e+0pmAiArp06aKvv/5awWBQ3bp1cz0HAID/2rFjx1ReXq4uXbo02dcMeJF2PggAAPiCFwYCAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQR8T9u3b9ftt9+ua6+9Vv3799eUKVO0detW17MiQm1trdatW6dx48apX79+SktL0y9/+UstW7ZMlZWVrudFnOLiYl1//fVKTU11PSWinDhxQvPmzdMNN9ygPn36aMiQIXrkkUd0+vRp19MixqZNm3TLLbfommuuUVpamsaOHavXX39dtbW1rqc58e677yo1NVWffvppo8ePHDmiBx98UEOHDlXfvn01ZswYrVu3TqFQyOelkSvgeZ7nekSke/fddzV37lzFxcUpPT1doVBIO3bsUHV1tZ544gndeuutric6U1tbq7vvvlsfffSRgsGg+vbtq5iYGO3Zs0dnz55V37599frrr6t58+aup0aM3/3ud9q8ebMkKS8vz/GayLB3715lZGSotLRUKSkpuuKKK7Rv3z7l5+friiuu0DvvvKPWrVu7nunUU089pdWrVysuLk4DBw5UdHS0Pv30U5WXl2vEiBF68cUXFQgEXM/0za5duzR9+nSVl5frjTfe0M9+9rOw4wcPHtTkyZN17tw59e/fX23bttWOHTt09uxZjRkzRkuWLHG0PMJ4uKiCggKvT58+3oABA7y8vLz62/fs2eP179/f++lPf+rl5+c7XOjWm2++6aWkpHhjxowJ+zmcOXPGu/XWW72UlBRvyZIlDhdGlqysLC8lJaX+H3heZWWlN3LkSC8lJcVbs2ZN/e3nz5/37r33Xi8lJcVbsGCBw4XuHTx40EtNTfXS09O9w4cP19+en5/vDR8+3EtJSfGys7MdLvTX3/72N69fv371f4927twZdjwUCnljxozxUlJSvI0bN9bffubMmfrbLf28LoanA77DunXrVFVVpWnTpiklJaX+9rS0NM2YMUOVlZXasGGDw4Vuvffee5KkefPmqUOHDvW3JyYm6rHHHpMkvf/++y6mRZyCggItWLBA/fr1U3R0tOs5EWPz5s06evSoxowZoylTptTfHh8fr7lz56pdu3Y6cuSIw4Xubd++XZ7n6aabblJycnL97R06dNCkSZMkSTt37nQ1zzf5+fmaPXu27r33XoVCIbVr167R+23btk15eXkaNGiQxo4dW397YmKiHn30UUnS2rVrfdkc6YiA71D3vP+IESMaHLvxxhslSZ988omvmyJJmzZt1KNHD6WlpTU41r17d0nSqVOnfF4VmebPn6/KykotXrzY9ZSIsmXLFklSRkZGg2OdOnXStm3btHr1ar9nRZS60/wFBQUNjhUVFUmSEhISfN3kwnPPPadNmzapT58+2rBhg3r06NHo/S72uD1gwAC1bdtWn332mc6dO3dJ9/4QxLgeEMk8z9OhQ4cUFRXV6B+27t27KyoqSocOHZLneaaej6vz8ssvX/DY3r17JUkdO3b0a07EWr9+vbZu3apHHnlE3bp1cz0nohw4cECxsbG68sordfLkSWVlZen48eNKSEjQyJEjGw1Ma4YMGaI//elPys7O1ooVKzR+/HjFxMRoy5YtWrNmjVq3bq1x48a5nnnJ9ejRQ4sXL9ZNN92kqKgL/zvsoUOHJCns7O1/Sk5O1pkzZ/TVV1+pb9++l2TrDwURcBElJSWqqqpSYmKi4uLiGhyPiYlRmzZtdObMGZWVlalFixYOVkYmz/OUmZkpSRo5cqTjNW4dP35cTz/9tNLT0zV58mTXcyJKVVWVTp48qY4dOyo7O1vz589XRUVF/fGVK1fqjjvu0OzZsx2udK9nz55asGCBFi5cqGeeeUbPPPNM/bF+/frpySefVKdOnRwu9MfMmTO/1/3qzj62b9++0eN1t3/zzTdNM+wHjKcDLqLuwehir2xv1qyZJKmsrMyXTT8US5cuVU5Ojtq1a6cZM2a4nuNMbW2tZs+erUAgoCeffNLk2aKLqTsdW1JSojlz5mjEiBHKzs7Wzp079eyzzyohIUGrV682/bqbOv3799fgwYMVDAaVnp6u6667Tpdddpn27t2r9evXy+ONXvXqHrvrHp//t7rby8vLfdsUqTgTcBEXO91Uh794DT3//PNasWKF4uLi9NxzzykxMdH1JGdWrVqlXbt26X/+53/UuXNn13MiTt11JCoqKnT99deHvW1r9OjRCgaD+u1vf6tly5bplltuMRtRu3fv1vTp03X55ZcrKytLXbp0kfTtawTuuecerVmzRi1atND999/veGlkqHvsvtCfl7rHbR6/ORNwUcFgUJIuesGbumO8D16qqanRH//4Ry1fvlzx8fF68cUXNXDgQNeznDl48KBeeOGuv6NEAAAEjklEQVQFDR06VBMmTHA9JyL959+b2267rcHxYcOGqUOHDiooKNCxY8f8nBZRFi1apLKyMi1cuLA+AKRv3x2wdOlSxcTE6LXXXgt7KsWyusfu8+fPN3q87nG77n6WcSbgIlq0aKFgMKiioiLV1NQoJib8x1VTU6OioiLFx8erVatWjlZGhrKyMt1///3aunWrWrVqpeXLl5sOAEl69tlnVV1drZqaGs2aNSvsWN0Vy+punzdvnskzJi1btlRsbKyqq6vDfrn9p86dO6ugoEBFRUX17zix5Pz588rNzVXLli0bfZFk165dlZycrC+//FLHjh3TlVde6WBlZElKStIXX3yhb775Rj179mxwvO4qlBd6zYAlRMBFBAIB9erVS7m5uTp69Kh69eoVdvzIkSMKhUIXfAWqFSUlJcrIyND+/fvVqVMnrVixwvzPRPr3843btm274H2ysrIkSQ888IDJCIiOjlbPnj118OBBFRQUNPoLrO7FWxZ/PpJUWloqz/Muem2JumPV1dV+zYpovXv31scff6xDhw7p2muvDTvmeZ4OHz5c/2fPOp4O+A5DhgyRJH3wwQcNjtXdNnToUF83RZKqqirNnDlT+/fvV69evfTnP/+ZAPiXtWvXKi8vr9F/6h606/73hf4t2IIbbrhBkpSdnd3g2OHDh3XixAklJSWpa9eufk+LCG3btlVCQoKKi4uVm5vb4HhBQYG++uorxcbGXvB989bUPW5/+OGHDY59/vnnKiws1IABA3hHl4iA73TzzTcrPj5eK1eu1L59++pv37t3r1atWqVmzZrVX7HLoszMTO3evVudOnXS2rVruSYA/msTJ05UMBjUxo0b68+MSN+eYXr44YcVCoU0efLk7/VC3R+jqKgojR8/XtK3F5z6zwsGFRYWatasWaqurta4ceN02WWXuZoZUQYNGqTevXtr27Zteuutt+pvLyws1OOPPy6p8YtTWcQHCH0Pb7zxhp544gnFxsYqPT1dnudpx44dqqmp0eLFi8MuS2lJcXGxhg4dqvPnz+vqq6++6L+F8GEd4a666irV1tbyAUL/snnzZj300EOqqanR1VdfraSkJO3evVtFRUVKT0/XqlWrFBsb63qmM5WVlZoxY4ZycnIUHx+vgQMHKhAI1H9Q1zXXXKNXX33V3AvdpkyZopycnEY/QCg3N1dTp05VeXm5+vbtq6SkJOXk5KikpES33HKLFixY4Gh1ZCECvqe///3vWrVqlQ4cOKC4uDilpqbqrrvu0uDBg11Pc+aTTz7RnXfe+b3uyy+7cERAQ1988YVeeukl5eTkqLy8XF27dtXYsWOVkZFhOgDqVFdXa/369dq0aZMOHz6sUCik7t2761e/+pWmTZvW6AXNfuwuFgHSt1cOzMzM1I4dO1RVVaVu3bpp4sSJmjBhAp/f8S9EAAAARtl8kg0AABABAABYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARhEBAAAYRQQAAGAUEQAAgFFEAAAARv0/6E1hjQPIblwAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 254,
-       "width": 256
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "horizontal_detect = convolution(digit, horizontal_edge_kernel)\n",
-    "plt.imshow(horizontal_detect, cmap=\"gray_r\") ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Maxpooling\n",
-    "Taking maximum in n x n sized sliding windows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "def maxpool_2x2(matrix):\n",
-    "    out_dim = np.array([matrix.shape[0]/2, matrix.shape[1]/2]).astype(int)\n",
-    "    subsample = np.zeros((out_dim))\n",
-    "    for i in range(out_dim[0]):\n",
-    "        for j in range(out_dim[1]):\n",
-    "            subsample[i,j] = np.max(matrix[i*2:i*2+2, j*2:j*2+2])\n",
-    "    return subsample"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 266,
-       "width": 250
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "subsampled_image = maxpool_2x2(vertical_detect)\n",
-    "plt.imshow(subsampled_image, cmap=\"gray_r\")\n",
-    "plt.title(\"Max Pooled vertical edge detection filter\") ;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 266,
-       "width": 252
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "subsampled_image = maxpool_2x2(horizontal_detect)\n",
-    "plt.imshow(subsampled_image, cmap=\"gray_r\") ;\n",
-    "plt.title(\"Max Pooled horizontal edge detection filter\") ;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Let's explore some more of such filters/kernels!!\n",
-    "\n",
-    "http://setosa.io/ev/image-kernels"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## CNN Examples"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For this example we will work with a dataset called fashion-MNIST which is quite similar to the MNIST data above.\n",
-    "> Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.\n",
-    "source: https://github.com/zalandoresearch/fashion-mnist\n",
-    "\n",
-    "The 10 classes of this dataset are:\n",
-    "\n",
-    "| Label| Item |\n",
-    "| --- | --- |\n",
-    "| 0 |\tT-shirt/top |\n",
-    "| 1\t| Trouser |\n",
-    "|2|\tPullover|\n",
-    "|3|\tDress|\n",
-    "|4|\tCoat|\n",
-    "|5|\tSandal|\n",
-    "|6|\tShirt|\n",
-    "|7|\tSneaker|\n",
-    "|8|\tBag|\n",
-    "|9|\tAnkle boot|"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Loading the dataset in keras\n",
-    "# Later you can explore and play with other datasets with come with Keras\n",
-    "from keras.datasets import fashion_mnist\n",
-    "\n",
-    "# Loading the train and test data\n",
-    "\n",
-    "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
-    "\n",
-    "items =['T-shirt/top', 'Trouser', \n",
-    "        'Pullover', 'Dress', \n",
-    "        'Coat', 'Sandal', \n",
-    "        'Shirt', 'Sneaker',\n",
-    "        'Bag', 'Ankle boot']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This item is a:  Trouser\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 254,
-       "width": 256
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "i=np.random.randint(0,X_train.shape[0])\n",
-    "plt.imshow(X_train[i], cmap=\"gray_r\") ; \n",
-    "print(\"This item is a: \" , items[y_train[i]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(60000, 10)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Also we need to reshape the input data such that each sample is a 4D matrix of dimension\n",
-    "# (num_samples, width, height, channels). Even though these images are grayscale we need to add\n",
-    "# channel dimension as this is expected by the Conv function\n",
-    "X_train_prep = X_train.reshape(X_train.shape[0],28,28,1)/255.\n",
-    "X_test_prep = X_test.reshape(X_test.shape[0],28,28,1)/255.\n",
-    "\n",
-    "from keras.utils.np_utils import to_categorical\n",
-    "\n",
-    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
-    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
-    "\n",
-    "print(y_train_onehot.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 26, 26, 6)         60        \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 6)         0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 11, 11, 16)        880       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 16)          0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 400)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_151 (Dense)            (None, 120)               48120     \n",
-      "_________________________________________________________________\n",
-      "dense_152 (Dense)            (None, 84)                10164     \n",
-      "_________________________________________________________________\n",
-      "dense_153 (Dense)            (None, 10)                850       \n",
-      "=================================================================\n",
-      "Total params: 60,074\n",
-      "Trainable params: 60,074\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Creating a CNN similar to the one shown in the figure from LeCun paper\n",
-    "# In the original implementation Average pooling was used. However, we will use maxpooling as this \n",
-    "# is what us used in the more recent architectures and is found to be a better choice\n",
-    "# Convolution -> Pooling -> Convolution -> Pooling -> Flatten -> Dense -> Dense -> Output layer\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout, BatchNormalization\n",
-    "\n",
-    "def simple_CNN():\n",
-    "    \n",
-    "    model = Sequential()\n",
-    "    \n",
-    "    model.add(Conv2D(6, (3,3), input_shape=(28,28,1), activation='relu'))\n",
-    "    \n",
-    "    model.add(MaxPool2D((2,2)))\n",
-    "    \n",
-    "    model.add(Conv2D(16, (3,3), activation='relu'))\n",
-    "    \n",
-    "    model.add(MaxPool2D((2,2)))\n",
-    "    \n",
-    "    model.add(Flatten())\n",
-    "    \n",
-    "    model.add(Dense(120, activation='relu'))\n",
-    "    \n",
-    "    model.add(Dense(84, activation='relu'))\n",
-    "    \n",
-    "    model.add(Dense(10, activation='softmax'))\n",
-    "    \n",
-    "    model.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
-    "    \n",
-    "    return model\n",
-    "\n",
-    "model = simple_CNN()\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/10\n",
-      "60000/60000 [==============================] - 39s 651us/step - loss: 0.5767 - acc: 0.7881 - val_loss: 0.4496 - val_acc: 0.8292\n",
-      "Epoch 2/10\n",
-      "60000/60000 [==============================] - 35s 580us/step - loss: 0.3875 - acc: 0.8581 - val_loss: 0.4103 - val_acc: 0.8446\n",
-      "Epoch 3/10\n",
-      "60000/60000 [==============================] - 34s 567us/step - loss: 0.3362 - acc: 0.8761 - val_loss: 0.3497 - val_acc: 0.8709\n",
-      "Epoch 4/10\n",
-      "60000/60000 [==============================] - 34s 568us/step - loss: 0.3062 - acc: 0.8872 - val_loss: 0.3450 - val_acc: 0.8758\n",
-      "Epoch 5/10\n",
-      "60000/60000 [==============================] - 34s 566us/step - loss: 0.2848 - acc: 0.8936 - val_loss: 0.3187 - val_acc: 0.8820\n",
-      "Epoch 6/10\n",
-      "60000/60000 [==============================] - 34s 569us/step - loss: 0.2670 - acc: 0.9015 - val_loss: 0.3577 - val_acc: 0.8684\n",
-      "Epoch 7/10\n",
-      "60000/60000 [==============================] - 35s 576us/step - loss: 0.2519 - acc: 0.9052 - val_loss: 0.3053 - val_acc: 0.8882\n",
-      "Epoch 8/10\n",
-      "60000/60000 [==============================] - 34s 571us/step - loss: 0.2390 - acc: 0.9113 - val_loss: 0.2977 - val_acc: 0.8928\n",
-      "Epoch 9/10\n",
-      "60000/60000 [==============================] - 34s 569us/step - loss: 0.2286 - acc: 0.9140 - val_loss: 0.3117 - val_acc: 0.8934\n",
-      "Epoch 10/10\n",
-      "60000/60000 [==============================] - 36s 600us/step - loss: 0.2183 - acc: 0.9194 - val_loss: 0.3118 - val_acc: 0.8940\n"
-     ]
-    }
-   ],
-   "source": [
-    "num_epochs = 10\n",
-    "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs, \n",
-    "                      batch_size=64, validation_data=(X_test_prep, y_test_onehot))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercise section\n",
-    "* Use the above model or improve it (change number of filters, add more layers etc. on the MNIST example and see if you can get a better accuracy than what we achieved with a vanilla neural network)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercise section\n",
-    "* Explore the CIFAR10 (https://www.cs.toronto.edu/~kriz/cifar.html) dataset included with Keras and build+train a simple CNN to classify it"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from keras.datasets import cifar10\n",
-    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright (C) 2019 ETH Zurich, SIS ID"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
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+    Copyright (C) 2019 Scientific IT Services of ETH Zurich,
+    <p>
+    Contributing Authors:
+    Dr. Tarun Chadha,
+    Dr. Franziska Oschmann,
+    Dr. Mikolaj Rybinski,
+    Dr. Uwe Schmitt.
+    </p<
+</footer>