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diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/194101046_ShivangiGarg.ipynb b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/194101046_ShivangiGarg.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Assignment 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "from mpl_toolkits.mplot3d import Axes3D  # needed to plot 3-D surfaces\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def warmUpExercise():\n",
+    "    \"\"\"\n",
+    "    Example function in Python which computes the identity matrix.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    A : array_like\n",
+    "        The 5x5 identity matrix.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Return the 5x5 identity matrix.\n",
+    "    \"\"\"    \n",
+    "    # ======== YOUR CODE HERE ======\n",
+    "    A = np.identity(5)   # modify this line\n",
+    "    \n",
+    "    # ==============================\n",
+    "    return A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0.],\n",
+       "       [0., 0., 1., 0., 0.],\n",
+       "       [0., 0., 0., 1., 0.],\n",
+       "       [0., 0., 0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "warmUpExercise()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear regression with one variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.loadtxt(os.path.join('Data', 'data.txt'), delimiter=',')\n",
+    "X, y = data[:, 0], data[:, 1]\n",
+    "m = y.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotData(x, y):\n",
+    "    \"\"\"\n",
+    "    Plots the data points x and y into a new figure. Plots the data \n",
+    "    points and gives the figure axes labels of population and profit.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    x : array_like\n",
+    "        Data point values for x-axis.\n",
+    "\n",
+    "    y : array_like\n",
+    "        Data point values for y-axis. Note x and y should have the same size.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Plot the training data into a figure using the \"figure\" and \"plot\"\n",
+    "    functions. Set the axes labels using the \"xlabel\" and \"ylabel\" functions.\n",
+    "    Assume the population and revenue data have been passed in as the x\n",
+    "    and y arguments of this function.    \n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    You can use the 'ro' option with plot to have the markers\n",
+    "    appear as red circles. Furthermore, you can make the markers larger by\n",
+    "    using plot(..., 'ro', ms=10), where `ms` refers to marker size. You \n",
+    "    can also set the marker edge color using the `mec` property.\n",
+    "    \"\"\"\n",
+    "    fig = pyplot.figure()  # open a new figure\n",
+    "    \n",
+    "    # ====================== YOUR CODE HERE ======================= \n",
+    "    \n",
+    "    pyplot.plot(x, y, 'ro', ms=10, mec='k')\n",
+    "    pyplot.ylabel('Profit in $10,000')\n",
+    "    pyplot.xlabel('Population of City in 10,000s')\n",
+    "    # ============================================================="
+   ]
+  },
+  {
+   "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": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotData(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add a column of ones to X. The numpy function stack joins arrays along a given axis. \n",
+    "# The first axis (axis=0) refers to rows (training examples) \n",
+    "# and second axis (axis=1) refers to columns (features).\n",
+    "X = np.stack([np.ones(m), X], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def computeCost(X, y, theta):\n",
+    "    \"\"\"\n",
+    "    Compute cost for linear regression. Computes the cost of using theta as the\n",
+    "    parameter for linear regression to fit the data points in X and y.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n+1), where m is the number of examples,\n",
+    "        and n is the number of features. We assume a vector of one's already \n",
+    "        appended to the features so we have n+1 columns.\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The values of the function at each data point. This is a vector of\n",
+    "        shape (m, ).\n",
+    "    \n",
+    "    theta : array_like\n",
+    "        The parameters for the regression function. This is a vector of \n",
+    "        shape (n+1, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The value of the regression cost function.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost of a particular choice of theta. \n",
+    "    You should set J to the cost.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "    \n",
+    "    # You need to return the following variables correctly\n",
+    "    J = 0\n",
+    "    \n",
+    "    # ====================== YOUR CODE HERE =====================\n",
+    "    \n",
+    "    H_X = np.dot(X,theta)\n",
+    "    J = 1/(2*m)*np.dot((H_X-y).T,(H_X-y))\n",
+    "    # ===========================================================\n",
+    "    return J"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With theta = [0, 0] \n",
+      "Cost computed = 32.07\n",
+      "Expected cost value (approximately) 32.07\n",
+      "\n",
+      "With theta = [-1, 2]\n",
+      "Cost computed = 54.24\n",
+      "Expected cost value (approximately) 54.24\n"
+     ]
+    }
+   ],
+   "source": [
+    "J = computeCost(X, y, theta=np.array([0.0, 0.0]))\n",
+    "print('With theta = [0, 0] \\nCost computed = %.2f' % J)\n",
+    "print('Expected cost value (approximately) 32.07\\n')\n",
+    "\n",
+    "# further testing of the cost function\n",
+    "J = computeCost(X, y, theta=np.array([-1, 2]))\n",
+    "print('With theta = [-1, 2]\\nCost computed = %.2f' % J)\n",
+    "print('Expected cost value (approximately) 54.24')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gradientDescent(X, y, theta, alpha, num_iters):\n",
+    "    \"\"\"\n",
+    "    Performs gradient descent to learn `theta`. Updates theta by taking `num_iters`\n",
+    "    gradient steps with learning rate `alpha`.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n+1).\n",
+    "    \n",
+    "    y : arra_like\n",
+    "        Value at given features. A vector of shape (m, ).\n",
+    "    \n",
+    "    theta : array_like\n",
+    "        Initial values for the linear regression parameters. \n",
+    "        A vector of shape (n+1, ).\n",
+    "    \n",
+    "    alpha : float\n",
+    "        The learning rate.\n",
+    "    \n",
+    "    num_iters : int\n",
+    "        The number of iterations for gradient descent. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    theta : array_like\n",
+    "        The learned linear regression parameters. A vector of shape (n+1, ).\n",
+    "    \n",
+    "    J_history : list\n",
+    "        A python list for the values of the cost function after each iteration.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Peform a single gradient step on the parameter vector theta.\n",
+    "\n",
+    "    While debugging, it can be useful to print out the values of \n",
+    "    the cost function (computeCost) and gradient here.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.shape[0]  # number of training examples\n",
+    "    \n",
+    "    # make a copy of theta, to avoid changing the original array, since numpy arrays\n",
+    "    # are passed by reference to functions\n",
+    "    theta = theta.copy()\n",
+    "    \n",
+    "    J_history = [] # Use a python list to save cost in every iteration\n",
+    "    \n",
+    "    for i in range(num_iters):\n",
+    "        # ==================== YOUR CODE HERE =================================\n",
+    "        H_X_y = np.dot(X,theta) - y\n",
+    "        theta[0] = theta[0]- (alpha/m)*(np.dot(X[:,0].T,H_X_y))\n",
+    "        theta[1] = theta[1]- (alpha/m)*(np.dot(X[:,1].T,H_X_y))\n",
+    "        # =====================================================================\n",
+    "        \n",
+    "        # save the cost J in every iteration\n",
+    "        J_history.append(computeCost(X, y, theta))\n",
+    "    \n",
+    "    return theta, J_history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Theta found by gradient descent: -3.6303, 1.1664\n",
+      "Expected theta values (approximately): [-3.6303, 1.1664]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize fitting parameters\n",
+    "theta = np.zeros(2)\n",
+    "\n",
+    "# some gradient descent settings\n",
+    "iterations = 1500\n",
+    "alpha = 0.01\n",
+    "\n",
+    "theta, J_history = gradientDescent(X ,y, theta, alpha, iterations)\n",
+    "print('Theta found by gradient descent: {:.4f}, {:.4f}'.format(*theta))\n",
+    "print('Expected theta values (approximately): [-3.6303, 1.1664]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With theta = [0, 0] \n",
+      "Cost computed = 32.07\n",
+      "Expected cost value (approximately) 32.07\n",
+      "\n",
+      "With theta = [-1, 2]\n",
+      "Cost computed = 54.24\n",
+      "Expected cost value (approximately) 54.24\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "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": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the linear fit\n",
+    "plotData(X[:, 1], y)\n",
+    "pyplot.plot(X[:, 1], np.dot(X, theta), '-')\n",
+    "pyplot.legend(['Training data', 'Linear regression']);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For population = 35,000, we predict a profit of 4519.77\n",
+      "\n",
+      "For population = 70,000, we predict a profit of 45342.45\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Predict values for population sizes of 35,000 and 70,000\n",
+    "predict1 = np.dot([1, 3.5], theta)\n",
+    "print('For population = 35,000, we predict a profit of {:.2f}\\n'.format(predict1*10000))\n",
+    "\n",
+    "predict2 = np.dot([1, 7], theta)\n",
+    "print('For population = 70,000, we predict a profit of {:.2f}\\n'.format(predict2*10000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# grid over which we will calculate J\n",
+    "theta0_vals = np.linspace(-10, 10, 100)\n",
+    "theta1_vals = np.linspace(-1, 4, 100)\n",
+    "\n",
+    "# initialize J_vals to a matrix of 0's\n",
+    "J_vals = np.zeros((theta0_vals.shape[0], theta1_vals.shape[0]))\n",
+    "\n",
+    "# Fill out J_vals\n",
+    "for i, theta0 in enumerate(theta0_vals):\n",
+    "    for j, theta1 in enumerate(theta1_vals):\n",
+    "        J_vals[i, j] = computeCost(X, y, [theta0, theta1])\n",
+    "        \n",
+    "# Because of the way meshgrids work in the surf command, we need to\n",
+    "# transpose J_vals before calling surf, or else the axes will be flipped\n",
+    "J_vals = J_vals.T\n",
+    "\n",
+    "# surface plot\n",
+    "fig = pyplot.figure(figsize=(12, 5))\n",
+    "ax = fig.add_subplot(121, projection='3d')\n",
+    "ax.plot_surface(theta0_vals, theta1_vals, J_vals, cmap='viridis')\n",
+    "pyplot.xlabel('theta0')\n",
+    "pyplot.ylabel('theta1')\n",
+    "pyplot.title('Surface')\n",
+    "\n",
+    "# contour plot\n",
+    "# Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100\n",
+    "ax = pyplot.subplot(122)\n",
+    "pyplot.contour(theta0_vals, theta1_vals, J_vals, linewidths=2, cmap='viridis', levels=np.logspace(-2, 3, 20))\n",
+    "pyplot.xlabel('theta0')\n",
+    "pyplot.ylabel('theta1')\n",
+    "pyplot.plot(theta[0], theta[1], 'ro', ms=10, lw=2)\n",
+    "pyplot.title('Contour, showing minimum')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear regression with multiple variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  X[:,0] X[:, 1]         y\n",
+      "--------------------------\n",
+      "    2104       3    399900\n",
+      "    1600       3    329900\n",
+      "    2400       3    369000\n",
+      "    1416       2    232000\n",
+      "    3000       4    539900\n",
+      "    1985       4    299900\n",
+      "    1534       3    314900\n",
+      "    1427       3    198999\n",
+      "    1380       3    212000\n",
+      "    1494       3    242500\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load data\n",
+    "data = np.loadtxt(os.path.join('Data', 'data2.txt'), delimiter=',')\n",
+    "X = data[:, :2]\n",
+    "y = data[:, 2]\n",
+    "m = y.size\n",
+    "\n",
+    "# print out some data points\n",
+    "print('{:>8s}{:>8s}{:>10s}'.format('X[:,0]', 'X[:, 1]', 'y'))\n",
+    "print('-'*26)\n",
+    "for i in range(10):\n",
+    "    print('{:8.0f}{:8.0f}{:10.0f}'.format(X[i, 0], X[i, 1], y[i]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def  featureNormalize(X):\n",
+    "    \"\"\"\n",
+    "    Normalizes the features in X. returns a normalized version of X where\n",
+    "    the mean value of each feature is 0 and the standard deviation\n",
+    "    is 1. This is often a good preprocessing step to do when working with\n",
+    "    learning algorithms.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x n).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    X_norm : array_like\n",
+    "        The normalized dataset of shape (m x n).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    First, for each feature dimension, compute the mean of the feature\n",
+    "    and subtract it from the dataset, storing the mean value in mu. \n",
+    "    Next, compute the  standard deviation of each feature and divide\n",
+    "    each feature by it's standard deviation, storing the standard deviation \n",
+    "    in sigma. \n",
+    "    \n",
+    "    Note that X is a matrix where each column is a feature and each row is\n",
+    "    an example. You needto perform the normalization separately for each feature. \n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    You might find the 'np.mean' and 'np.std' functions useful.\n",
+    "    \"\"\"\n",
+    "    # You need to set these values correctly\n",
+    "    X_norm = X.copy()\n",
+    "    mu = np.zeros(X.shape[1])\n",
+    "    sigma = np.zeros(X.shape[1])\n",
+    "\n",
+    "    # =========================== YOUR CODE HERE =====================\n",
+    "    for i in range(X.shape[1]):\n",
+    "        mu[i] = np.mean(X_norm[:,i])\n",
+    "        #print(mu)\n",
+    "        sigma[i] = np.std(X_norm[:,i])\n",
+    "        #print(sigma)\n",
+    "        X_norm[:,i] = (X_norm[:,i]-mu[i])/sigma[i]\n",
+    "        \n",
+    "    \n",
+    "    # ================================================================\n",
+    "    return X_norm, mu, sigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Computed mean: [2000.68085106    3.17021277]\n",
+      "Computed standard deviation: [7.86202619e+02 7.52842809e-01]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# call featureNormalize on the loaded data\n",
+    "X_norm, mu, sigma = featureNormalize(X)\n",
+    "\n",
+    "print('Computed mean:', mu)\n",
+    "print('Computed standard deviation:', sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add intercept term to X\n",
+    "X = np.concatenate([np.ones((m, 1)), X_norm], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def computeCostMulti(X, y, theta):\n",
+    "    \"\"\"\n",
+    "    Compute cost for linear regression with multiple variables.\n",
+    "    Computes the cost of using theta as the parameter for linear regression to fit the data points in X and y.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x n+1).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        A vector of shape (m, ) for the values at a given data point.\n",
+    "    \n",
+    "    theta : array_like\n",
+    "        The linear regression parameters. A vector of shape (n+1, )\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The value of the cost function. \n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost of a particular choice of theta. You should set J to the cost.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.shape[0] # number of training examples\n",
+    "    \n",
+    "    # You need to return the following variable correctly\n",
+    "    J = 0\n",
+    "    \n",
+    "    # ======================= YOUR CODE HERE ===========================\n",
+    "    error = np.dot(X, theta.T) - y\n",
+    "    J = 1/(2*m) * np.dot(error.T, error)\n",
+    "    # ==================================================================\n",
+    "    return J"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With theta = [0, 0, 0] \n",
+      "Cost computed = 65591548106.46\n",
+      "With theta = [-1, 2,3]\n",
+      "Cost computed = 65591512874.74\n"
+     ]
+    }
+   ],
+   "source": [
+    "J = computeCostMulti(X, y, theta=np.array([0.0, 0.0, 0.0]))\n",
+    "print('With theta = [0, 0, 0] \\nCost computed = %.2f' % J)\n",
+    "\n",
+    "# further testing of the cost function\n",
+    "J = computeCostMulti(X, y, theta=np.array([-1, 2,3]))\n",
+    "print('With theta = [-1, 2,3]\\nCost computed = %.2f' % J)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gradientDescentMulti(X, y, theta, alpha, num_iters):\n",
+    "    \"\"\"\n",
+    "    Performs gradient descent to learn theta.\n",
+    "    Updates theta by taking num_iters gradient steps with learning rate alpha.\n",
+    "        \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x n+1).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        A vector of shape (m, ) for the values at a given data point.\n",
+    "    \n",
+    "    theta : array_like\n",
+    "        The linear regression parameters. A vector of shape (n+1, )\n",
+    "    \n",
+    "    alpha : float\n",
+    "        The learning rate for gradient descent. \n",
+    "    \n",
+    "    num_iters : int\n",
+    "        The number of iterations to run gradient descent. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    theta : array_like\n",
+    "        The learned linear regression parameters. A vector of shape (n+1, ).\n",
+    "    \n",
+    "    J_history : list\n",
+    "        A python list for the values of the cost function after each iteration.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Peform a single gradient step on the parameter vector theta.\n",
+    "\n",
+    "    While debugging, it can be useful to print out the values of \n",
+    "    the cost function (computeCost) and gradient here.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.shape[0] # number of training examples\n",
+    "    \n",
+    "    # make a copy of theta, which will be updated by gradient descent\n",
+    "    theta = theta.copy()\n",
+    "    \n",
+    "    J_history = []\n",
+    "    \n",
+    "    for i in range(num_iters):\n",
+    "        # ======================= YOUR CODE HERE ==========================\n",
+    "        H_X_y = np.dot(X,theta) - y\n",
+    "        theta[0] = theta[0]- (alpha/m)*(np.dot(X[:,0].T,H_X_y))\n",
+    "        theta[1] = theta[1]- (alpha/m)*(np.dot(X[:,1].T,H_X_y))\n",
+    "        theta[2] = theta[2]- (alpha/m)*(np.dot(X[:,2].T,H_X_y))\n",
+    "        # =================================================================\n",
+    "        \n",
+    "        # save the cost J in every iteration\n",
+    "        J_history.append(computeCostMulti(X, y, theta))\n",
+    "    \n",
+    "    return theta, J_history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Theta found by gradient descent: 340412.5630, 109370.0567, -6500.6151\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialize fitting parameters\n",
+    "theta = np.zeros(3)\n",
+    "\n",
+    "# some gradient descent settings\n",
+    "iterations = 1500\n",
+    "alpha = 0.01\n",
+    "\n",
+    "theta, J_history = gradientDescentMulti(X ,y, theta, alpha, iterations)\n",
+    "print('Theta found by gradient descent: {:.4f}, {:.4f}, {:.4f}'.format(*theta))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f1345fc65c0>]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pyplot.plot(J_history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "theta computed from gradient descent: [340412.65957447 109447.79558639  -6578.3539709 ]\n",
+      "Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): $293081\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "Instructions\n",
+    "------------\n",
+    "We have provided you with the following starter code that runs\n",
+    "gradient descent with a particular learning rate (alpha). \n",
+    "\n",
+    "Your task is to first make sure that your functions - `computeCost`\n",
+    "and `gradientDescent` already work with  this starter code and\n",
+    "support multiple variables.\n",
+    "\n",
+    "After that, try running gradient descent with different values of\n",
+    "alpha and see which one gives you the best result.\n",
+    "\n",
+    "Finally, you should complete the code at the end to predict the price\n",
+    "of a 1650 sq-ft, 3 br house.\n",
+    "\n",
+    "Hint\n",
+    "----\n",
+    "At prediction, make sure you do the same feature normalization.\n",
+    "\"\"\"\n",
+    "# Choose some alpha value - change this\n",
+    "alpha = 0.1\n",
+    "num_iters = 400\n",
+    "\n",
+    "# init theta and run gradient descent\n",
+    "theta = np.zeros(3)\n",
+    "theta, J_history = gradientDescentMulti(X, y, theta, alpha, num_iters)\n",
+    "\n",
+    "# Plot the convergence graph\n",
+    "pyplot.plot(np.arange(len(J_history)), J_history, lw=2)\n",
+    "pyplot.xlabel('Number of iterations')\n",
+    "pyplot.ylabel('Cost J')\n",
+    "\n",
+    "# Display the gradient descent's result\n",
+    "print('theta computed from gradient descent: {:s}'.format(str(theta)))\n",
+    "\n",
+    "# Estimate the price of a 1650 sq-ft, 3 br house\n",
+    "# ======================= YOUR CODE HERE ===========================\n",
+    "# Recall that the first column of X is all-ones. \n",
+    "# Thus, it does not need to be normalized.\n",
+    "\n",
+    "price = np.dot([1,(1650-mu[0])/sigma[0],(3-mu[1])/sigma[1]], theta)   # You should change this\n",
+    "\n",
+    "# ===================================================================\n",
+    "\n",
+    "print('Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): ${:.0f}'.format(price))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Normal Equation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data\n",
+    "data = np.loadtxt(os.path.join('Data', 'data2.txt'), delimiter=',')\n",
+    "X = data[:, :2]\n",
+    "y = data[:, 2]\n",
+    "m = y.size\n",
+    "X = np.concatenate([np.ones((m, 1)), X], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalEqn(X, y):\n",
+    "    \"\"\"\n",
+    "    Computes the closed-form solution to linear regression using the normal equations.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset of shape (m x n+1).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The value at each data point. A vector of shape (m, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    theta : array_like\n",
+    "        Estimated linear regression parameters. A vector of shape (n+1, ).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Complete the code to compute the closed form solution to linear\n",
+    "    regression and put the result in theta.\n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    Look up the function `np.linalg.pinv` for computing matrix inverse.\n",
+    "    \"\"\"\n",
+    "    theta = np.zeros(X.shape[1])\n",
+    "    \n",
+    "    # ===================== YOUR CODE HERE ============================\n",
+    "    a = np.dot(X.T,X)\n",
+    "    b = np.dot(np.linalg.inv(a),X.T)\n",
+    "    theta = np.dot(b,y)\n",
+    "    # =================================================================\n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Theta computed from the normal equations: [89597.9095428    139.21067402 -8738.01911233]\n",
+      "Predicted price of a 1650 sq-ft, 3 br house (using normal equations): $293081\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate the parameters from the normal equation\n",
+    "theta = normalEqn(X, y);\n",
+    "\n",
+    "# Display normal equation's result\n",
+    "print('Theta computed from the normal equations: {:s}'.format(str(theta)));\n",
+    "\n",
+    "# Estimate the price of a 1650 sq-ft, 3 br house\n",
+    "# ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "price = np.dot([1,1650,3], theta) # You should change this\n",
+    "\n",
+    "# ============================================================\n",
+    "\n",
+    "print('Predicted price of a 1650 sq-ft, 3 br house (using normal equations): ${:.0f}'.format(price))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Assignment 3.ipynb b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Assignment 3.ipynb
new file mode 100644
index 000000000..ba4d2b83c
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/Exercise2/Assignment 3.ipynb	
@@ -0,0 +1,742 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data\n",
+    "# The first two columns contains the exam scores and the third column\n",
+    "# contains the label.\n",
+    "data = np.loadtxt(os.path.join('Data', 'ex2data1.txt'), delimiter=',')\n",
+    "X, y = data[:, 0:2], data[:, 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotData(X, y):\n",
+    "    \"\"\"\n",
+    "    Plots the data points X and y into a new figure. Plots the data \n",
+    "    points with * for the positive examples and o for the negative examples.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        An Mx2 matrix representing the dataset. \n",
+    "    \n",
+    "    y : array_like\n",
+    "        Label values for the dataset. A vector of size (M, ).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Plot the positive and negative examples on a 2D plot, using the\n",
+    "    option 'k*' for the positive examples and 'ko' for the negative examples.    \n",
+    "    \"\"\"\n",
+    "    # Create New Figure\n",
+    "    fig = pyplot.figure()\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    # Find Indices of Positive and Negative Examples\n",
+    "    pos = y == 1\n",
+    "    neg = y == 0\n",
+    "\n",
+    "    # Plot Examples\n",
+    "    pyplot.plot(X[pos, 0], X[pos, 1], 'k*', lw=2, ms=10)\n",
+    "    pyplot.plot(X[neg, 0], X[neg, 1], 'ko', mfc='y', ms=8, mec='k', mew=1)\n",
+    "    \n",
+    "    # ============================================================"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotData(X, y)\n",
+    "# add axes labels\n",
+    "pyplot.xlabel('Exam 1 score')\n",
+    "pyplot.ylabel('Exam 2 score')\n",
+    "pyplot.legend(['Admitted', 'Not admitted'])\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sigmoid(z):\n",
+    "    \"\"\"\n",
+    "    Compute sigmoid function given the input z.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    z : array_like\n",
+    "        The input to the sigmoid function. This can be a 1-D vector \n",
+    "        or a 2-D matrix. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    g : array_like\n",
+    "        The computed sigmoid function. g has the same shape as z, since\n",
+    "        the sigmoid is computed element-wise on z.\n",
+    "        \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the sigmoid of each value of z (z can be a matrix, vector or scalar).\n",
+    "    \"\"\"\n",
+    "    # convert input to a numpy array\n",
+    "    z = np.array(z)\n",
+    "    \n",
+    "    # You need to return the following variables correctly \n",
+    "    g = np.zeros(z.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    g = 1/(1+np.exp(-z))\n",
+    "\n",
+    "    # =============================================================\n",
+    "    return g"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "g( 0 ) =  0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test the implementation of sigmoid function here\n",
+    "z = 0\n",
+    "\n",
+    "g = sigmoid(z)\n",
+    "\n",
+    "print('g(', z, ') = ', g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the data matrix appropriately, and add ones for the intercept term\n",
+    "m, n = X.shape\n",
+    "\n",
+    "# Add intercept term to X\n",
+    "X = np.concatenate([np.ones((m, 1)), X], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def costFunction(theta, X, y):\n",
+    "    \"\"\"\n",
+    "    Compute cost and gradient for logistic regression. \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        The parameters for logistic regression. This a vector\n",
+    "        of shape (n+1, ).\n",
+    "    \n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n+1) where m is the total number\n",
+    "        of data points and n is the number of features. We assume the \n",
+    "        intercept has already been added to the input.\n",
+    "    \n",
+    "    y : arra_like\n",
+    "        Labels for the input. This is a vector of shape (m, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        A vector of shape (n+1, ) which is the gradient of the cost\n",
+    "        function with respect to theta, at the current values of theta.\n",
+    "        \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost of a particular choice of theta. You should set J to \n",
+    "    the cost. Compute the partial derivatives and set grad to the partial\n",
+    "    derivatives of the cost w.r.t. each parameter in theta.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    H_X = sigmoid(np.dot(X,theta))\n",
+    "    J = (-1/m)*(np.dot(y,np.log(H_X)) + np.dot((1-y),np.log(1-H_X)))\n",
+    "    grad = (1/m)*(np.dot(X.T,H_X-y))\n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at initial theta (zeros): 0.693\n",
+      "Expected cost (approx): 0.693\n",
+      "\n",
+      "Gradient at initial theta (zeros):\n",
+      "\t[-0.1000, -12.0092, -11.2628]\n",
+      "Expected gradients (approx):\n",
+      "\t[-0.1000, -12.0092, -11.2628]\n",
+      "\n",
+      "Cost at test theta: 0.218\n",
+      "Expected cost (approx): 0.218\n",
+      "\n",
+      "Gradient at test theta:\n",
+      "\t[0.043, 2.566, 2.647]\n",
+      "Expected gradients (approx):\n",
+      "\t[0.043, 2.566, 2.647]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize fitting parameters\n",
+    "initial_theta = np.zeros(n+1)\n",
+    "\n",
+    "cost, grad = costFunction(initial_theta, X, y)\n",
+    "\n",
+    "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx): 0.693\\n')\n",
+    "\n",
+    "print('Gradient at initial theta (zeros):')\n",
+    "print('\\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad))\n",
+    "print('Expected gradients (approx):\\n\\t[-0.1000, -12.0092, -11.2628]\\n')\n",
+    "\n",
+    "# Compute and display cost and gradient with non-zero theta\n",
+    "test_theta = np.array([-24, 0.2, 0.2])\n",
+    "cost, grad = costFunction(test_theta, X, y)\n",
+    "\n",
+    "print('Cost at test theta: {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx): 0.218\\n')\n",
+    "\n",
+    "print('Gradient at test theta:')\n",
+    "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*grad))\n",
+    "print('Expected gradients (approx):\\n\\t[0.043, 2.566, 2.647]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at theta found by optimize.minimize: 0.203\n",
+      "Expected cost (approx): 0.203\n",
+      "\n",
+      "theta:\n",
+      "\t[-25.161, 0.206, 0.201]\n",
+      "Expected theta (approx):\n",
+      "\t[-25.161, 0.206, 0.201]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set options for optimize.minimize\n",
+    "options= {'maxiter': 400}\n",
+    "\n",
+    "# see documention for scipy's optimize.minimize  for description about\n",
+    "# the different parameters\n",
+    "# The function returns an object `OptimizeResult`\n",
+    "# We use truncated Newton algorithm for optimization which is \n",
+    "# equivalent to MATLAB's fminunc\n",
+    "# See https://stackoverflow.com/questions/18801002/fminunc-alternate-in-numpy\n",
+    "res = optimize.minimize(costFunction,\n",
+    "                        initial_theta,\n",
+    "                        (X, y),\n",
+    "                        jac=True,\n",
+    "                        method='TNC',\n",
+    "                        options=options)\n",
+    "\n",
+    "# the fun property of `OptimizeResult` object returns\n",
+    "# the value of costFunction at optimized theta\n",
+    "cost = res.fun\n",
+    "\n",
+    "# the optimized theta is in the x property\n",
+    "theta = res.x\n",
+    "\n",
+    "# Print theta to screen\n",
+    "print('Cost at theta found by optimize.minimize: {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx): 0.203\\n');\n",
+    "\n",
+    "print('theta:')\n",
+    "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*theta))\n",
+    "print('Expected theta (approx):\\n\\t[-25.161, 0.206, 0.201]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Boundary\n",
+    "utils.plotDecisionBoundary(plotData, theta, X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(theta, X):\n",
+    "    \"\"\"\n",
+    "    Predict whether the label is 0 or 1 using learned logistic regression.\n",
+    "    Computes the predictions for X using a threshold at 0.5 \n",
+    "    (i.e., if sigmoid(theta.T*x) >= 0.5, predict 1)\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        Parameters for logistic regression. A vecotor of shape (n+1, ).\n",
+    "    \n",
+    "    X : array_like\n",
+    "        The data to use for computing predictions. The rows is the number \n",
+    "        of points to compute predictions, and columns is the number of\n",
+    "        features.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    p : array_like\n",
+    "        Predictions and 0 or 1 for each row in X. \n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Complete the following code to make predictions using your learned \n",
+    "    logistic regression parameters.You should set p to a vector of 0's and 1's    \n",
+    "    \"\"\"\n",
+    "    m = X.shape[0] # Number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly\n",
+    "    p = np.zeros(m)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    \n",
+    "    p = sigmoid(np.dot(X,theta))\n",
+    "    \n",
+    "    p = (p>=0.5).astype(int)\n",
+    "    # ============================================================\n",
+    "    return p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For a student with scores 45 and 85,we predict an admission probability of 0.776\n",
+      "Expected value: 0.775 +/- 0.002\n",
+      "\n",
+      "Train Accuracy: 89.00 %\n",
+      "Expected accuracy (approx): 89.00 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Predict probability for a student with score 45 on exam 1 \n",
+    "#  and score 85 on exam 2 \n",
+    "prob = sigmoid(np.dot([1, 45, 85], theta))\n",
+    "print('For a student with scores 45 and 85,'\n",
+    "      'we predict an admission probability of {:.3f}'.format(prob))\n",
+    "print('Expected value: 0.775 +/- 0.002\\n')\n",
+    "\n",
+    "# Compute accuracy on our training set\n",
+    "p = predict(theta, X)\n",
+    "print('Train Accuracy: {:.2f} %'.format(np.mean(p == y) * 100))\n",
+    "print('Expected accuracy (approx): 89.00 %')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Regularized logistic regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load Data\n",
+    "# The first two columns contains the X values and the third column\n",
+    "# contains the label (y).\n",
+    "data = np.loadtxt(os.path.join('Data', 'ex2data2.txt'), delimiter=',')\n",
+    "X = data[:, :2]\n",
+    "y = data[:, 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotData(X, y)\n",
+    "# Labels and Legend\n",
+    "pyplot.xlabel('Microchip Test 1')\n",
+    "pyplot.ylabel('Microchip Test 2')\n",
+    "\n",
+    "# Specified in plot order\n",
+    "pyplot.legend(['y = 1', 'y = 0'], loc='upper right')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note that mapFeature also adds a column of ones for us, so the intercept\n",
+    "# term is handled\n",
+    "X = utils.mapFeature(X[:, 0], X[:, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def costFunctionReg(theta, X, y, lambda_):\n",
+    "    \"\"\"\n",
+    "    Compute cost and gradient for logistic regression with regularization.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        Logistic regression parameters. A vector with shape (n, ). n is \n",
+    "        the number of features including any intercept. If we have mapped\n",
+    "        our initial features into polynomial features, then n is the total \n",
+    "        number of polynomial features. \n",
+    "    \n",
+    "    X : array_like\n",
+    "        The data set with shape (m x n). m is the number of examples, and\n",
+    "        n is the number of features (after feature mapping).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The data labels. A vector with shape (m, ).\n",
+    "    \n",
+    "    lambda_ : float\n",
+    "        The regularization parameter. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the regularized cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        A vector of shape (n, ) which is the gradient of the cost\n",
+    "        function with respect to theta, at the current values of theta.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost `J` of a particular choice of theta.\n",
+    "    Compute the partial derivatives and set `grad` to the partial\n",
+    "    derivatives of the cost w.r.t. each parameter in theta.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ===================== YOUR CODE HERE ======================\n",
+    "    \n",
+    "    H_X = sigmoid(np.dot(X,theta))\n",
+    "    regularized = (lambda_/(2*m))*(np.dot(theta.T,theta))\n",
+    "    \n",
+    "    J = (-1/m)*(np.dot(y,np.log(H_X)) + np.dot((1-y),np.log(1-H_X))) + regularized\n",
+    "    \n",
+    "    grad = (1/m)*(np.dot(X.T,H_X-y)) + (lambda_/m)*theta\n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at initial theta (zeros): 0.693\n",
+      "Expected cost (approx)       : 0.693\n",
+      "\n",
+      "Gradient at initial theta (zeros) - first five values only:\n",
+      "\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\n",
+      "Expected gradients (approx) - first five values only:\n",
+      "\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\n",
+      "\n",
+      "------------------------------\n",
+      "\n",
+      "Cost at test theta    : 3.21\n",
+      "Expected cost (approx): 3.16\n",
+      "\n",
+      "Gradient at initial theta (zeros) - first five values only:\n",
+      "\t[0.4308, 0.1614, 0.1948, 0.2269, 0.0922]\n",
+      "Expected gradients (approx) - first five values only:\n",
+      "\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize fitting parameters\n",
+    "initial_theta = np.zeros(X.shape[1])\n",
+    "\n",
+    "# Set regularization parameter lambda to 1\n",
+    "# DO NOT use `lambda` as a variable name in python\n",
+    "# because it is a python keyword\n",
+    "lambda_ = 1\n",
+    "\n",
+    "# Compute and display initial cost and gradient for regularized logistic\n",
+    "# regression\n",
+    "cost, grad = costFunctionReg(initial_theta, X, y, lambda_)\n",
+    "\n",
+    "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n",
+    "print('Expected cost (approx)       : 0.693\\n')\n",
+    "\n",
+    "print('Gradient at initial theta (zeros) - first five values only:')\n",
+    "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n",
+    "print('Expected gradients (approx) - first five values only:')\n",
+    "print('\\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\\n')\n",
+    "\n",
+    "\n",
+    "# Compute and display cost and gradient\n",
+    "# with all-ones theta and lambda = 10\n",
+    "test_theta = np.ones(X.shape[1])\n",
+    "cost, grad = costFunctionReg(test_theta, X, y, 10)\n",
+    "\n",
+    "print('------------------------------\\n')\n",
+    "print('Cost at test theta    : {:.2f}'.format(cost))\n",
+    "print('Expected cost (approx): 3.16\\n')\n",
+    "\n",
+    "print('Gradient at initial theta (zeros) - first five values only:')\n",
+    "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n",
+    "print('Expected gradients (approx) - first five values only:')\n",
+    "print('\\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Accuracy: 81.4 %\n",
+      "Expected accuracy (with lambda = 1): 83.1 % (approx)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Initialize fitting parameters\n",
+    "initial_theta = np.zeros(X.shape[1])\n",
+    "\n",
+    "# Set regularization parameter lambda to 1 (you should vary this)\n",
+    "lambda_ = 1\n",
+    "\n",
+    "# set options for optimize.minimize\n",
+    "options= {'maxiter': 100}\n",
+    "\n",
+    "res = optimize.minimize(costFunctionReg,\n",
+    "                        initial_theta,\n",
+    "                        (X, y, lambda_),\n",
+    "                        jac=True,\n",
+    "                        method='TNC',\n",
+    "                        options=options)\n",
+    "\n",
+    "# the fun property of OptimizeResult object returns\n",
+    "# the value of costFunction at optimized theta\n",
+    "cost = res.fun\n",
+    "\n",
+    "# the optimized theta is in the x property of the result\n",
+    "theta = res.x\n",
+    "\n",
+    "utils.plotDecisionBoundary(plotData, theta, X, y)\n",
+    "pyplot.xlabel('Microchip Test 1')\n",
+    "pyplot.ylabel('Microchip Test 2')\n",
+    "pyplot.legend(['y = 1', 'y = 0'])\n",
+    "pyplot.grid(False)\n",
+    "pyplot.title('lambda = %0.2f' % lambda_)\n",
+    "\n",
+    "# Compute accuracy on our training set\n",
+    "p = predict(theta, X)\n",
+    "\n",
+    "print('Train Accuracy: %.1f %%' % (np.mean(p == y) * 100))\n",
+    "print('Expected accuracy (with lambda = 1): 83.1 % (approx)\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/Assignment_4.ipynb b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/Assignment_4.ipynb
new file mode 100644
index 000000000..6e6015a56
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/Exercise3/Assignment_4.ipynb	
@@ -0,0 +1,643 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 20x20 Input Images of Digits\n",
+    "input_layer_size  = 400\n",
+    "\n",
+    "# 10 labels, from 1 to 10 (note that we have mapped \"0\" to label 10)\n",
+    "num_labels = 10\n",
+    "\n",
+    "#  training data stored in arrays X, y\n",
+    "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n",
+    "X, y = data['X'], data['y'].ravel()\n",
+    "\n",
+    "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+    "# This is an artifact due to the fact that this dataset was used in \n",
+    "# MATLAB where there is no index 0\n",
+    "y[y == 10] = 0\n",
+    "\n",
+    "m = y.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Randomly select 100 data points to display\n",
+    "rand_indices = np.random.choice(m, 100, replace=False)\n",
+    "sel = X[rand_indices, :]\n",
+    "\n",
+    "utils.displayData(sel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test values for the parameters theta\n",
+    "theta_t = np.array([-2, -1, 1, 2], dtype=float)\n",
+    "\n",
+    "# test values for the inputs\n",
+    "X_t = np.concatenate([np.ones((5, 1)), np.arange(1, 16).reshape(5, 3, order='F')/10.0], axis=1)\n",
+    "\n",
+    "# test values for the labels\n",
+    "y_t = np.array([1, 0, 1, 0, 1])\n",
+    "\n",
+    "# test value for the regularization parameter\n",
+    "lambda_t = 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def lrCostFunction(theta, X, y, lambda_):\n",
+    "    \"\"\"\n",
+    "    Computes the cost of using theta as the parameter for regularized\n",
+    "    logistic regression and the gradient of the cost w.r.t. to the parameters.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        Logistic regression parameters. A vector with shape (n, ). n is \n",
+    "        the number of features including any intercept.  \n",
+    "    \n",
+    "    X : array_like\n",
+    "        The data set with shape (m x n). m is the number of examples, and\n",
+    "        n is the number of features (including intercept).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The data labels. A vector with shape (m, ).\n",
+    "    \n",
+    "    lambda_ : float\n",
+    "        The regularization parameter. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the regularized cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        A vector of shape (n, ) which is the gradient of the cost\n",
+    "        function with respect to theta, at the current values of theta.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost of a particular choice of theta. You should set J to the cost.\n",
+    "    Compute the partial derivatives and set grad to the partial\n",
+    "    derivatives of the cost w.r.t. each parameter in theta\n",
+    "    \n",
+    "    Hint 1\n",
+    "    ------\n",
+    "    The computation of the cost function and gradients can be efficiently\n",
+    "    vectorized. For example, consider the computation\n",
+    "    \n",
+    "        sigmoid(X * theta)\n",
+    "    \n",
+    "    Each row of the resulting matrix will contain the value of the prediction\n",
+    "    for that example. You can make use of this to vectorize the cost function\n",
+    "    and gradient computations. \n",
+    "    \n",
+    "    Hint 2\n",
+    "    ------\n",
+    "    When computing the gradient of the regularized cost function, there are\n",
+    "    many possible vectorized solutions, but one solution looks like:\n",
+    "    \n",
+    "        grad = (unregularized gradient for logistic regression)\n",
+    "        temp = theta \n",
+    "        temp[0] = 0   # because we don't add anything for j = 0\n",
+    "        grad = grad + YOUR_CODE_HERE (using the temp variable)\n",
+    "    \n",
+    "    Hint 3\n",
+    "    ------\n",
+    "    We have provided the implementatation of the sigmoid function within \n",
+    "    the file `utils.py`. At the start of the notebook, we imported this file\n",
+    "    as a module. Thus to access the sigmoid function within that file, you can\n",
+    "    do the following: `utils.sigmoid(z)`.\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    #Initialize some useful values\n",
+    "    m = y.size\n",
+    "    \n",
+    "    # convert labels to ints if their type is bool\n",
+    "    if y.dtype == bool:\n",
+    "        y = y.astype(int)\n",
+    "    \n",
+    "    # You need to return the following variables correctly\n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "    \n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    H_X = utils.sigmoid(np.dot(X,theta))\n",
+    "    temp = theta\n",
+    "    temp[0] = 0;\n",
+    "    J = ((-1/m)*(np.dot(y,np.log(H_X)) + np.dot((1-y),np.log(1-H_X)))) + (lambda_/(2*m))*(np.dot(temp.T,temp))\n",
+    "    grad = (1/m)*(np.dot(X.T,(H_X-y))) \n",
+    "    grad = grad + (lambda_/m)*temp\n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost         : 3.085728\n",
+      "Expected cost: 2.534819\n",
+      "-----------------------\n",
+      "Gradients:\n",
+      " [0.355376, -0.491709, 0.885979, 1.663668]\n",
+      "Expected gradients:\n",
+      " [0.146561, -0.548558, 0.724722, 1.398003]\n"
+     ]
+    }
+   ],
+   "source": [
+    "J, grad = lrCostFunction(theta_t, X_t, y_t, lambda_t)\n",
+    "\n",
+    "print('Cost         : {:.6f}'.format(J))\n",
+    "print('Expected cost: 2.534819')\n",
+    "print('-----------------------')\n",
+    "print('Gradients:')\n",
+    "print(' [{:.6f}, {:.6f}, {:.6f}, {:.6f}]'.format(*grad))\n",
+    "print('Expected gradients:')\n",
+    "print(' [0.146561, -0.548558, 0.724722, 1.398003]');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def oneVsAll(X, y, num_labels, lambda_):\n",
+    "    \"\"\"\n",
+    "    Trains num_labels logistic regression classifiers and returns\n",
+    "    each of these classifiers in a matrix all_theta, where the i-th\n",
+    "    row of all_theta corresponds to the classifier for label i.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n). m is the number of \n",
+    "        data points, and n is the number of features. Note that we \n",
+    "        do not assume that the intercept term (or bias) is in X, however\n",
+    "        we provide the code below to add the bias term to X. \n",
+    "    \n",
+    "    y : array_like\n",
+    "        The data labels. A vector of shape (m, ).\n",
+    "    \n",
+    "    num_labels : int\n",
+    "        Number of possible labels.\n",
+    "    \n",
+    "    lambda_ : float\n",
+    "        The logistic regularization parameter.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    all_theta : array_like\n",
+    "        The trained parameters for logistic regression for each class.\n",
+    "        This is a matrix of shape (K x n+1) where K is number of classes\n",
+    "        (ie. `numlabels`) and n is number of features without the bias.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    You should complete the following code to train `num_labels`\n",
+    "    logistic regression classifiers with regularization parameter `lambda_`. \n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    You can use y == c to obtain a vector of 1's and 0's that tell you\n",
+    "    whether the ground truth is true/false for this class.\n",
+    "    \n",
+    "    Note\n",
+    "    ----\n",
+    "    For this assignment, we recommend using `scipy.optimize.minimize(method='CG')`\n",
+    "    to optimize the cost function. It is okay to use a for-loop \n",
+    "    (`for c in range(num_labels):`) to loop over the different classes.\n",
+    "    \n",
+    "    Example Code\n",
+    "    ------------\n",
+    "    \n",
+    "        # Set Initial theta\n",
+    "        initial_theta = np.zeros(n + 1)\n",
+    "      \n",
+    "        # Set options for minimize\n",
+    "        options = {'maxiter': 50}\n",
+    "    \n",
+    "        # Run minimize to obtain the optimal theta. This function will \n",
+    "        # return a class object where theta is in `res.x` and cost in `res.fun`\n",
+    "        res = optimize.minimize(lrCostFunction, \n",
+    "                                initial_theta, \n",
+    "                                (X, (y == c), lambda_), \n",
+    "                                jac=True, \n",
+    "                                method='TNC',\n",
+    "                                options=options) \n",
+    "    \"\"\"\n",
+    "    # Some useful variables\n",
+    "    m, n = X.shape\n",
+    "    \n",
+    "    # You need to return the following variables correctly \n",
+    "    all_theta = np.zeros((num_labels, n + 1))\n",
+    "\n",
+    "    # Add ones to the X data matrix\n",
+    "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "   \n",
+    "    initial_theta = np.zeros(n+1)\n",
+    "    options = {'maxiter': 50}\n",
+    "\n",
+    "    for c in range(num_labels):\n",
+    "        res = optimize.minimize(lrCostFunction, \n",
+    "                                initial_theta, \n",
+    "                                (X, (y == c), lambda_), \n",
+    "                                jac=True, \n",
+    "                                method='TNC',\n",
+    "                                options=options) \n",
+    "        all_theta[c,:] = res.x\n",
+    "    # ============================================================\n",
+    "    return all_theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lambda_ = 0.1\n",
+    "all_theta = oneVsAll(X, y, num_labels, lambda_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predictOneVsAll(all_theta, X):\n",
+    "    \"\"\"\n",
+    "    Return a vector of predictions for each example in the matrix X. \n",
+    "    Note that X contains the examples in rows. all_theta is a matrix where\n",
+    "    the i-th row is a trained logistic regression theta vector for the \n",
+    "    i-th class. You should set p to a vector of values from 0..K-1 \n",
+    "    (e.g., p = [0, 2, 0, 1] predicts classes 0, 2, 0, 1 for 4 examples) .\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    all_theta : array_like\n",
+    "        The trained parameters for logistic regression for each class.\n",
+    "        This is a matrix of shape (K x n+1) where K is number of classes\n",
+    "        and n is number of features without the bias.\n",
+    "    \n",
+    "    X : array_like\n",
+    "        Data points to predict their labels. This is a matrix of shape \n",
+    "        (m x n) where m is number of data points to predict, and n is number \n",
+    "        of features without the bias term. Note we add the bias term for X in \n",
+    "        this function. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    p : array_like\n",
+    "        The predictions for each data point in X. This is a vector of shape (m, ).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Complete the following code to make predictions using your learned logistic\n",
+    "    regression parameters (one-vs-all). You should set p to a vector of predictions\n",
+    "    (from 0 to num_labels-1).\n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    This code can be done all vectorized using the numpy argmax function.\n",
+    "    In particular, the argmax function returns the index of the max element,\n",
+    "    for more information see '?np.argmax' or search online. If your examples\n",
+    "    are in rows, then, you can use np.argmax(A, axis=1) to obtain the index \n",
+    "    of the max for each row.\n",
+    "    \"\"\"\n",
+    "    m = X.shape[0];\n",
+    "    num_labels = all_theta.shape[0]\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    p = np.zeros(m)\n",
+    "\n",
+    "    # Add ones to the X data matrix\n",
+    "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "   \n",
+    "    p = np.argmax(np.dot(X,all_theta.T),axis=1)\n",
+    "    \n",
+    "    # ============================================================\n",
+    "    return p\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Accuracy: 90.52%\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = predictOneVsAll(all_theta, X)\n",
+    "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NEURAL NETWORK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "#  training data stored in arrays X, y\n",
+    "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n",
+    "X, y = data['X'], data['y'].ravel()\n",
+    "\n",
+    "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+    "# This is an artifact due to the fact that this dataset was used in \n",
+    "# MATLAB where there is no index 0\n",
+    "y[y == 10] = 0\n",
+    "\n",
+    "# get number of examples in dataset\n",
+    "m = y.size\n",
+    "\n",
+    "# randomly permute examples, to be used for visualizing one \n",
+    "# picture at a time\n",
+    "indices = np.random.permutation(m)\n",
+    "\n",
+    "# Randomly select 100 data points to display\n",
+    "rand_indices = np.random.choice(m, 100, replace=False)\n",
+    "sel = X[rand_indices, :]\n",
+    "\n",
+    "utils.displayData(sel)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the parameters you will use for this exercise\n",
+    "input_layer_size  = 400  # 20x20 Input Images of Digits\n",
+    "hidden_layer_size = 25   # 25 hidden units\n",
+    "num_labels = 10          # 10 labels, from 0 to 9\n",
+    "\n",
+    "# Load the .mat file, which returns a dictionary \n",
+    "weights = loadmat(os.path.join('Data', 'ex3weights.mat'))\n",
+    "\n",
+    "# get the model weights from the dictionary\n",
+    "# Theta1 has size 25 x 401\n",
+    "# Theta2 has size 10 x 26\n",
+    "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n",
+    "\n",
+    "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n",
+    "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n",
+    "Theta2 = np.roll(Theta2, 1, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(Theta1, Theta2, X):\n",
+    "    \"\"\"\n",
+    "    Predict the label of an input given a trained neural network.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    Theta1 : array_like\n",
+    "        Weights for the first layer in the neural network.\n",
+    "        It has shape (2nd hidden layer size x input size)\n",
+    "    \n",
+    "    Theta2: array_like\n",
+    "        Weights for the second layer in the neural network. \n",
+    "        It has shape (output layer size x 2nd hidden layer size)\n",
+    "    \n",
+    "    X : array_like\n",
+    "        The image inputs having shape (number of examples x image dimensions).\n",
+    "    \n",
+    "    Return \n",
+    "    ------\n",
+    "    p : array_like\n",
+    "        Predictions vector containing the predicted label for each example.\n",
+    "        It has a length equal to the number of examples.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Complete the following code to make predictions using your learned neural\n",
+    "    network. You should set p to a vector containing labels \n",
+    "    between 0 to (num_labels-1).\n",
+    "     \n",
+    "    Hint\n",
+    "    ----\n",
+    "    This code can be done all vectorized using the numpy argmax function.\n",
+    "    In particular, the argmax function returns the index of the  max element,\n",
+    "    for more information see '?np.argmax' or search online. If your examples\n",
+    "    are in rows, then, you can use np.argmax(A, axis=1) to obtain the index\n",
+    "    of the max for each row.\n",
+    "    \n",
+    "    Note\n",
+    "    ----\n",
+    "    Remember, we have supplied the `sigmoid` function in the `utils.py` file. \n",
+    "    You can use this function by calling `utils.sigmoid(z)`, where you can \n",
+    "    replace `z` by the required input variable to sigmoid.\n",
+    "    \"\"\"\n",
+    "    # Make sure the input has two dimensions\n",
+    "    if X.ndim == 1:\n",
+    "        X = X[None]  # promote to 2-dimensions\n",
+    "    \n",
+    "    # useful variables\n",
+    "    m = X.shape[0]\n",
+    "    num_labels = Theta2.shape[0]\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    p = np.zeros(X.shape[0])\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "    layer1 = utils.sigmoid(np.dot(X,Theta1.T))\n",
+    "    layer1 = np.concatenate([np.ones((layer1.shape[0], 1)), layer1], axis=1)\n",
+    "    layer2 = utils.sigmoid(np.dot(layer1,Theta2.T))\n",
+    "    p = np.argmax(layer2,axis=1)\n",
+    "\n",
+    "    # =============================================================\n",
+    "    return p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Accuracy: 97.5%\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = predict(Theta1, Theta2, X)\n",
+    "print('Training Set Accuracy: {:.1f}%'.format(np.mean(pred == y) * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural Network Prediction: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if indices.size > 0:\n",
+    "    i, indices = indices[0], indices[1:]\n",
+    "    utils.displayData(X[i, :], figsize=(4, 4))\n",
+    "    pred = predict(Theta1, Theta2, X[i, :])\n",
+    "    print('Neural Network Prediction: {}'.format(*pred))\n",
+    "else:\n",
+    "    print('No more images to display!')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise4/Assignment_4.ipynb b/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise4/Assignment_4.ipynb
new file mode 100644
index 000000000..cb9635e31
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise4/Assignment_4.ipynb	
@@ -0,0 +1,788 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  training data stored in arrays X, y\n",
+    "data = loadmat(os.path.join('Data', 'ex4data1.mat'))\n",
+    "X, y = data['X'], data['y'].ravel()\n",
+    "\n",
+    "# set the zero digit to 0, rather than its mapped 10 in this dataset\n",
+    "# This is an artifact due to the fact that this dataset was used in \n",
+    "# MATLAB where there is no index 0\n",
+    "y[y == 10] = 0\n",
+    "\n",
+    "# Number of training examples\n",
+    "m = y.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 100 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Randomly select 100 data points to display\n",
+    "rand_indices = np.random.choice(m, 100, replace=False)\n",
+    "sel = X[rand_indices, :]\n",
+    "\n",
+    "utils.displayData(sel)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the parameters you will use for this exercise\n",
+    "input_layer_size  = 400  # 20x20 Input Images of Digits\n",
+    "hidden_layer_size = 25   # 25 hidden units\n",
+    "num_labels = 10          # 10 labels, from 0 to 9\n",
+    "\n",
+    "# Load the weights into variables Theta1 and Theta2\n",
+    "weights = loadmat(os.path.join('Data', 'ex4weights.mat'))\n",
+    "\n",
+    "# Theta1 has size 25 x 401\n",
+    "# Theta2 has size 10 x 26\n",
+    "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n",
+    "\n",
+    "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n",
+    "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n",
+    "Theta2 = np.roll(Theta2, 1, axis=0)\n",
+    "\n",
+    "# Unroll parameters \n",
+    "nn_params = np.concatenate([Theta1.ravel(), Theta2.ravel()])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sigmoidGradient(z):\n",
+    "    \"\"\"\n",
+    "    Computes the gradient of the sigmoid function evaluated at z. \n",
+    "    This should work regardless if z is a matrix or a vector. \n",
+    "    In particular, if z is a vector or matrix, you should return\n",
+    "    the gradient for each element.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    z : array_like\n",
+    "        A vector or matrix as input to the sigmoid function. \n",
+    "    \n",
+    "    Returns\n",
+    "    --------\n",
+    "    g : array_like\n",
+    "        Gradient of the sigmoid function. Has the same shape as z. \n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the gradient of the sigmoid function evaluated at\n",
+    "    each value of z (z can be a matrix, vector or scalar).\n",
+    "    \n",
+    "    Note\n",
+    "    ----\n",
+    "    We have provided an implementation of the sigmoid function \n",
+    "    in `utils.py` file accompanying this assignment.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    g = np.zeros(z.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    \n",
+    "    #g = utils.sigmoid(z)\n",
+    "    g = 1.0/(1.0 + np.exp(-z))\n",
+    "    g = g*(1-g)\n",
+    "    # =============================================================\n",
+    "    return g"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nnCostFunction(nn_params,\n",
+    "                   input_layer_size,\n",
+    "                   hidden_layer_size,\n",
+    "                   num_labels,\n",
+    "                   X, y, lambda_=0.0):\n",
+    "    \"\"\"\n",
+    "    Implements the neural network cost function and gradient for a two layer neural \n",
+    "    network which performs classification. \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    nn_params : array_like\n",
+    "        The parameters for the neural network which are \"unrolled\" into \n",
+    "        a vector. This needs to be converted back into the weight matrices Theta1\n",
+    "        and Theta2.\n",
+    "    \n",
+    "    input_layer_size : int\n",
+    "        Number of features for the input layer. \n",
+    "    \n",
+    "    hidden_layer_size : int\n",
+    "        Number of hidden units in the second layer.\n",
+    "    \n",
+    "    num_labels : int\n",
+    "        Total number of labels, or equivalently number of units in output layer. \n",
+    "    \n",
+    "    X : array_like\n",
+    "        Input dataset. A matrix of shape (m x input_layer_size).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        Dataset labels. A vector of shape (m,).\n",
+    "    \n",
+    "    lambda_ : float, optional\n",
+    "        Regularization parameter.\n",
+    " \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the cost function at the current weight values.\n",
+    "    \n",
+    "    grad : array_like\n",
+    "        An \"unrolled\" vector of the partial derivatives of the concatenatation of\n",
+    "        neural network weights Theta1 and Theta2.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    You should complete the code by working through the following parts.\n",
+    "    \n",
+    "    - Part 1: Feedforward the neural network and return the cost in the \n",
+    "              variable J. After implementing Part 1, you can verify that your\n",
+    "              cost function computation is correct by verifying the cost\n",
+    "              computed in the following cell.\n",
+    "    \n",
+    "    - Part 2: Implement the backpropagation algorithm to compute the gradients\n",
+    "              Theta1_grad and Theta2_grad. You should return the partial derivatives of\n",
+    "              the cost function with respect to Theta1 and Theta2 in Theta1_grad and\n",
+    "              Theta2_grad, respectively. After implementing Part 2, you can check\n",
+    "              that your implementation is correct by running checkNNGradients provided\n",
+    "              in the utils.py module.\n",
+    "    \n",
+    "              Note: The vector y passed into the function is a vector of labels\n",
+    "                    containing values from 0..K-1. You need to map this vector into a \n",
+    "                    binary vector of 1's and 0's to be used with the neural network\n",
+    "                    cost function.\n",
+    "     \n",
+    "              Hint: We recommend implementing backpropagation using a for-loop\n",
+    "                    over the training examples if you are implementing it for the \n",
+    "                    first time.\n",
+    "    \n",
+    "    - Part 3: Implement regularization with the cost function and gradients.\n",
+    "    \n",
+    "              Hint: You can implement this around the code for\n",
+    "                    backpropagation. That is, you can compute the gradients for\n",
+    "                    the regularization separately and then add them to Theta1_grad\n",
+    "                    and Theta2_grad from Part 2.\n",
+    "    \n",
+    "    Note \n",
+    "    ----\n",
+    "    We have provided an implementation for the sigmoid function in the file \n",
+    "    `utils.py` accompanying this assignment.\n",
+    "    \"\"\"\n",
+    "    # Reshape nn_params back into the parameters Theta1 and Theta2, the weight matrices\n",
+    "    # for our 2 layer neural network\n",
+    "    Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)],\n",
+    "                        (hidden_layer_size, (input_layer_size + 1)))\n",
+    "\n",
+    "    Theta2 = np.reshape(nn_params[(hidden_layer_size * (input_layer_size + 1)):],\n",
+    "                        (num_labels, (hidden_layer_size + 1)))\n",
+    "\n",
+    "    # Setup some useful variables\n",
+    "    m = y.size\n",
+    "         \n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    Theta1_grad = np.zeros(Theta1.shape)\n",
+    "    Theta2_grad = np.zeros(Theta2.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    from sklearn.preprocessing import LabelBinarizer\n",
+    "    encoder = LabelBinarizer()\n",
+    "    y = encoder.fit_transform(y)\n",
+    "    X = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "    layer1 = utils.sigmoid(np.dot(X,Theta1.T))\n",
+    "    layer1 = np.concatenate([np.ones((layer1.shape[0], 1)), layer1], axis=1)\n",
+    "    layer2 = utils.sigmoid(np.dot(layer1,Theta2.T))\n",
+    "    J = (-(1./m)*(np.sum((y*np.log(layer2)) + ((1-y)*np.log(1-layer2)))))\n",
+    "   \n",
+    "    reg = (lambda_/(2.*m))*((np.sum(np.sum(Theta1[:,1:]**2))) + (np.sum(np.sum(Theta2[:,1:]**2))))\n",
+    "    J = J + reg\n",
+    "    \n",
+    "    delta1 = np.zeros(Theta1.shape)\n",
+    "    delta2 = np.zeros(Theta2.shape)\n",
+    "    \n",
+    "    for i in range(m):\n",
+    "        act_inp = X[i]\n",
+    "        act_hidd = utils.sigmoid(np.dot(act_inp,Theta1.T))\n",
+    "        act_hidd = np.concatenate((np.array([1]),act_hidd))\n",
+    "        act_out = utils.sigmoid(np.dot(act_hidd,Theta2.T))\n",
+    "        d3 = act_out - y[i]\n",
+    "        d2 = (np.dot(d3,Theta2[:,1:]))*sigmoidGradient(np.dot(act_inp,Theta1.T))\n",
+    "        delta1 = delta1 + np.outer(d2,act_inp)\n",
+    "        delta2 = delta2 + np.outer(d3.T,act_hidd)\n",
+    "        \n",
+    "    Theta1_grad =  delta1/m\n",
+    "    Theta2_grad =  delta2/m  \n",
+    "    \n",
+    "    Theta1_grad_unregularized = np.copy(Theta1_grad)\n",
+    "    Theta2_grad_unregularized = np.copy(Theta2_grad)\n",
+    "    Theta1_grad += (float(lambda_)/m)*Theta1\n",
+    "    Theta2_grad += (float(lambda_)/m)*Theta2\n",
+    "    Theta1_grad[:,0] = Theta1_grad_unregularized[:,0]\n",
+    "    Theta2_grad[:,0] = Theta2_grad_unregularized[:,0]\n",
+    "    # ================================================================\n",
+    "    # Unroll gradients\n",
+    "    # grad = np.concatenate([Theta1_grad.ravel(order=order), Theta2_grad.ravel(order=order)])\n",
+    "    grad = np.concatenate([Theta1_grad.ravel(), Theta2_grad.ravel()])\n",
+    "\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at parameters (loaded from ex4weights): 0.287629 \n",
+      "The cost should be about                   : 0.287629.\n"
+     ]
+    }
+   ],
+   "source": [
+    "lambda_ = 0\n",
+    "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,\n",
+    "                   num_labels, X, y, lambda_)\n",
+    "print('Cost at parameters (loaded from ex4weights): %.6f ' % J)\n",
+    "print('The cost should be about                   : 0.287629.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at parameters (loaded from ex4weights): 0.383770\n",
+      "This value should be about                 : 0.383770.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Weight regularization parameter (we set this to 1 here).\n",
+    "lambda_ = 1\n",
+    "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size,\n",
+    "                      num_labels, X, y, lambda_)\n",
+    "\n",
+    "print('Cost at parameters (loaded from ex4weights): %.6f' % J)\n",
+    "print('This value should be about                 : 0.383770.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\n",
+      "  \n",
+      "[0.19661193 0.23500371 0.25       0.23500371 0.19661193]\n"
+     ]
+    }
+   ],
+   "source": [
+    "z = np.array([-1, -0.5, 0, 0.5, 1])\n",
+    "g = sigmoidGradient(z)\n",
+    "print('Sigmoid gradient evaluated at [-1 -0.5 0 0.5 1]:\\n  ')\n",
+    "print(g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def randInitializeWeights(L_in, L_out, epsilon_init=0.12):\n",
+    "    \"\"\"\n",
+    "    Randomly initialize the weights of a layer in a neural network.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    L_in : int\n",
+    "        Number of incomming connections.\n",
+    "    \n",
+    "    L_out : int\n",
+    "        Number of outgoing connections. \n",
+    "    \n",
+    "    epsilon_init : float, optional\n",
+    "        Range of values which the weight can take from a uniform \n",
+    "        distribution.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    W : array_like\n",
+    "        The weight initialiatized to random values.  Note that W should\n",
+    "        be set to a matrix of size(L_out, 1 + L_in) as\n",
+    "        the first column of W handles the \"bias\" terms.\n",
+    "        \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Initialize W randomly so that we break the symmetry while training\n",
+    "    the neural network. Note that the first column of W corresponds \n",
+    "    to the parameters for the bias unit.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    W = np.zeros((L_out, 1 + L_in))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    # Randomly initialize the weights to small values\n",
+    "    W = np.random.rand(L_out, 1 + L_in) * 2 * epsilon_init - epsilon_init\n",
+    "\n",
+    "    # ============================================================\n",
+    "    return W"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initializing Neural Network Parameters ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Initializing Neural Network Parameters ...')\n",
+    "\n",
+    "initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size)\n",
+    "initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels)\n",
+    "\n",
+    "# Unroll parameters\n",
+    "initial_nn_params = np.concatenate([initial_Theta1.ravel(), initial_Theta2.ravel()], axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-9.27825235e-03 -9.27825236e-03]\n",
+      " [-3.04978709e-06 -3.04978914e-06]\n",
+      " [-1.75060084e-04 -1.75060082e-04]\n",
+      " [-9.62660640e-05 -9.62660620e-05]\n",
+      " [ 8.89911959e-03  8.89911960e-03]\n",
+      " [ 1.42869450e-05  1.42869443e-05]\n",
+      " [ 2.33146358e-04  2.33146357e-04]\n",
+      " [ 1.17982666e-04  1.17982666e-04]\n",
+      " [-8.36010761e-03 -8.36010762e-03]\n",
+      " [-2.59383093e-05 -2.59383100e-05]\n",
+      " [-2.87468729e-04 -2.87468729e-04]\n",
+      " [-1.37149709e-04 -1.37149706e-04]\n",
+      " [ 7.62813550e-03  7.62813551e-03]\n",
+      " [ 3.69883257e-05  3.69883234e-05]\n",
+      " [ 3.35320351e-04  3.35320347e-04]\n",
+      " [ 1.53247082e-04  1.53247082e-04]\n",
+      " [-6.74798369e-03 -6.74798370e-03]\n",
+      " [-4.68759764e-05 -4.68759769e-05]\n",
+      " [-3.76215583e-04 -3.76215587e-04]\n",
+      " [-1.66560294e-04 -1.66560294e-04]\n",
+      " [ 3.14544970e-01  3.14544970e-01]\n",
+      " [ 1.64090819e-01  1.64090819e-01]\n",
+      " [ 1.64567932e-01  1.64567932e-01]\n",
+      " [ 1.58339334e-01  1.58339334e-01]\n",
+      " [ 1.51127527e-01  1.51127527e-01]\n",
+      " [ 1.49568335e-01  1.49568335e-01]\n",
+      " [ 1.11056588e-01  1.11056588e-01]\n",
+      " [ 5.75736494e-02  5.75736493e-02]\n",
+      " [ 5.77867378e-02  5.77867378e-02]\n",
+      " [ 5.59235296e-02  5.59235296e-02]\n",
+      " [ 5.36967009e-02  5.36967009e-02]\n",
+      " [ 5.31542052e-02  5.31542052e-02]\n",
+      " [ 9.74006970e-02  9.74006970e-02]\n",
+      " [ 5.04575855e-02  5.04575855e-02]\n",
+      " [ 5.07530173e-02  5.07530173e-02]\n",
+      " [ 4.91620841e-02  4.91620841e-02]\n",
+      " [ 4.71456249e-02  4.71456249e-02]\n",
+      " [ 4.65597186e-02  4.65597186e-02]]\n",
+      "The above two columns you get should be very similar.\n",
+      "(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "\n",
+      "If your backpropagation implementation is correct, then \n",
+      "the relative difference will be small (less than 1e-9). \n",
+      "Relative Difference: 2.33368e-11\n"
+     ]
+    }
+   ],
+   "source": [
+    "utils.checkNNGradients(nnCostFunction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-9.27825235e-03 -9.27825236e-03]\n",
+      " [-1.67679797e-02 -1.67679797e-02]\n",
+      " [-6.01744725e-02 -6.01744725e-02]\n",
+      " [-1.73704651e-02 -1.73704651e-02]\n",
+      " [ 8.89911959e-03  8.89911960e-03]\n",
+      " [ 3.94334829e-02  3.94334829e-02]\n",
+      " [-3.19612287e-02 -3.19612287e-02]\n",
+      " [-5.75658668e-02 -5.75658668e-02]\n",
+      " [-8.36010761e-03 -8.36010762e-03]\n",
+      " [ 5.93355565e-02  5.93355565e-02]\n",
+      " [ 2.49225535e-02  2.49225535e-02]\n",
+      " [-4.51963845e-02 -4.51963845e-02]\n",
+      " [ 7.62813550e-03  7.62813551e-03]\n",
+      " [ 2.47640974e-02  2.47640974e-02]\n",
+      " [ 5.97717617e-02  5.97717617e-02]\n",
+      " [ 9.14587966e-03  9.14587966e-03]\n",
+      " [-6.74798369e-03 -6.74798370e-03]\n",
+      " [-3.26881426e-02 -3.26881426e-02]\n",
+      " [ 3.86410548e-02  3.86410548e-02]\n",
+      " [ 5.46101547e-02  5.46101547e-02]\n",
+      " [ 3.14544970e-01  3.14544970e-01]\n",
+      " [ 1.18682669e-01  1.18682669e-01]\n",
+      " [ 2.03987128e-01  2.03987128e-01]\n",
+      " [ 1.25698067e-01  1.25698067e-01]\n",
+      " [ 1.76337550e-01  1.76337550e-01]\n",
+      " [ 1.32294136e-01  1.32294136e-01]\n",
+      " [ 1.11056588e-01  1.11056588e-01]\n",
+      " [ 3.81928689e-05  3.81928696e-05]\n",
+      " [ 1.17148233e-01  1.17148233e-01]\n",
+      " [-4.07588279e-03 -4.07588279e-03]\n",
+      " [ 1.13133142e-01  1.13133142e-01]\n",
+      " [-4.52964427e-03 -4.52964427e-03]\n",
+      " [ 9.74006970e-02  9.74006970e-02]\n",
+      " [ 3.36926556e-02  3.36926556e-02]\n",
+      " [ 7.54801264e-02  7.54801264e-02]\n",
+      " [ 1.69677090e-02  1.69677090e-02]\n",
+      " [ 8.61628953e-02  8.61628953e-02]\n",
+      " [ 1.50048382e-03  1.50048382e-03]]\n",
+      "The above two columns you get should be very similar.\n",
+      "(Left-Your Numerical Gradient, Right-Analytical Gradient)\n",
+      "\n",
+      "If your backpropagation implementation is correct, then \n",
+      "the relative difference will be small (less than 1e-9). \n",
+      "Relative Difference: 2.21026e-11\n",
+      "\n",
+      "\n",
+      "Cost at (fixed) debugging parameters (w/ lambda = 3.000000): 0.576051 \n",
+      "(for lambda = 3, this value should be about 0.576051)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "#  Check gradients by running checkNNGradients\n",
+    "lambda_ = 3\n",
+    "utils.checkNNGradients(nnCostFunction, lambda_)\n",
+    "\n",
+    "# Also output the costFunction debugging values\n",
+    "debug_J, _  = nnCostFunction(nn_params, input_layer_size,\n",
+    "                          hidden_layer_size, num_labels, X, y, lambda_)\n",
+    "\n",
+    "print('\\n\\nCost at (fixed) debugging parameters (w/ lambda = %f): %f ' % (lambda_, debug_J))\n",
+    "print('(for lambda = 3, this value should be about 0.576051)')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  After you have completed the assignment, change the maxiter to a larger\n",
+    "#  value to see how more training helps.\n",
+    "options= {'maxiter': 100}\n",
+    "\n",
+    "#  You should also try different values of lambda\n",
+    "lambda_ = 1\n",
+    "\n",
+    "# Create \"short hand\" for the cost function to be minimized\n",
+    "costFunction = lambda p: nnCostFunction(p, input_layer_size,\n",
+    "                                        hidden_layer_size,\n",
+    "                                        num_labels, X, y, lambda_)\n",
+    "\n",
+    "# Now, costFunction is a function that takes in only one argument\n",
+    "# (the neural network parameters)\n",
+    "res = optimize.minimize(costFunction,\n",
+    "                        initial_nn_params,\n",
+    "                        jac=True,\n",
+    "                        method='TNC',\n",
+    "                        options=options)\n",
+    "\n",
+    "# get the solution of the optimization\n",
+    "nn_params = res.x\n",
+    "        \n",
+    "# Obtain Theta1 and Theta2 back from nn_params\n",
+    "Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)],\n",
+    "                    (hidden_layer_size, (input_layer_size + 1)))\n",
+    "\n",
+    "Theta2 = np.reshape(nn_params[(hidden_layer_size * (input_layer_size + 1)):],\n",
+    "                    (num_labels, (hidden_layer_size + 1)))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Accuracy: 95.500000\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = utils.predict(Theta1, Theta2, X)\n",
+    "print('Training Set Accuracy: %f' % (np.mean(pred == y) * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 25 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "utils.displayData(Theta1[:, 1:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Optional "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  After you have completed the assignment, change the maxiter to a larger\n",
+    "#  value to see how more training helps.\n",
+    "options= {'maxiter': 500}\n",
+    "\n",
+    "#  You should also try different values of lambda\n",
+    "lambda_ = 0.5\n",
+    "\n",
+    "# Create \"short hand\" for the cost function to be minimized\n",
+    "costFunction = lambda p: nnCostFunction(p, input_layer_size,\n",
+    "                                        hidden_layer_size,\n",
+    "                                        num_labels, X, y, lambda_)\n",
+    "\n",
+    "# Now, costFunction is a function that takes in only one argument\n",
+    "# (the neural network parameters)\n",
+    "res = optimize.minimize(costFunction,\n",
+    "                        initial_nn_params,\n",
+    "                        jac=True,\n",
+    "                        method='TNC',\n",
+    "                        options=options)\n",
+    "\n",
+    "# get the solution of the optimization\n",
+    "nn_params = res.x\n",
+    "        \n",
+    "# Obtain Theta1 and Theta2 back from nn_params\n",
+    "Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)],\n",
+    "                    (hidden_layer_size, (input_layer_size + 1)))\n",
+    "\n",
+    "Theta2 = np.reshape(nn_params[(hidden_layer_size * (input_layer_size + 1)):],\n",
+    "                    (num_labels, (hidden_layer_size + 1)))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Learning Parameter = 0.5 steps = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Accuracy: 96.760000\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = utils.predict(Theta1, Theta2, X)\n",
+    "print('Training Set Accuracy: %f' % (np.mean(pred == y) * 100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Learning Parameter = 0.5 steps = 500"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Accuracy: 99.960000\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = utils.predict(Theta1, Theta2, X)\n",
+    "print('Training Set Accuracy: %f' % (np.mean(pred == y) * 100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise5/Assignment_5.ipynb b/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise5/Assignment_5.ipynb
new file mode 100644
index 000000000..e9edca269
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Exercise5/Assignment_5.ipynb	
@@ -0,0 +1,716 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load from ex5data1.mat, where all variables will be store in a dictionary\n",
+    "data = loadmat(os.path.join('Data', 'ex5data1.mat'))\n",
+    "\n",
+    "# Extract train, test, validation data from dictionary\n",
+    "# and also convert y's form 2-D matrix (MATLAB format) to a numpy vector\n",
+    "X, y = data['X'], data['y'][:, 0]\n",
+    "Xtest, ytest = data['Xtest'], data['ytest'][:, 0]\n",
+    "Xval, yval = data['Xval'], data['yval'][:, 0]\n",
+    "\n",
+    "# m = Number of examples\n",
+    "m = y.size\n",
+    "\n",
+    "# Plot training data\n",
+    "pyplot.plot(X, y, 'ro', ms=10, mec='k', mew=1)\n",
+    "pyplot.xlabel('Change in water level (x)')\n",
+    "pyplot.ylabel('Water flowing out of the dam (y)');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def linearRegCostFunction(X, y, theta, lambda_=0.0):\n",
+    "    \"\"\"\n",
+    "    Compute cost and gradient for regularized linear regression \n",
+    "    with multiple variables. Computes the cost of using theta as\n",
+    "    the parameter for linear regression to fit the data points in X and y. \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The dataset. Matrix with shape (m x n + 1) where m is the \n",
+    "        total number of examples, and n is the number of features \n",
+    "        before adding the bias term.\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The functions values at each datapoint. A vector of\n",
+    "        shape (m, ).\n",
+    "    \n",
+    "    theta : array_like\n",
+    "        The parameters for linear regression. A vector of shape (n+1,).\n",
+    "    \n",
+    "    lambda_ : float, optional\n",
+    "        The regularization parameter.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        The value of the cost function gradient w.r.t theta. \n",
+    "        A vector of shape (n+1, ).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost and gradient of regularized linear regression for\n",
+    "    a particular choice of theta.\n",
+    "    You should set J to the cost and grad to the gradient.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.size # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    H_X = np.dot(X,theta)\n",
+    "    J = ( 1./(2*m)) * np.sum(np.power( (H_X - y) , 2))\n",
+    "    reg = ( float(lambda_) / (2*m)) *np.sum(np.power(theta[1:],2))\n",
+    "    J = J + reg\n",
+    "\n",
+    "    grad = (1./m)*(np.dot(X.T,(H_X-y))) \n",
+    "    not_penalize = grad[0]\n",
+    "    grad = grad + (float(lambda_)/m)*theta\n",
+    "    grad[0] = not_penalize\n",
+    "\n",
+    "    # ============================================================\n",
+    "    return J, grad\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost at theta = [1, 1]:\t   303.993192 \n",
+      "This value should be about 303.993192)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "theta = np.array([1, 1])\n",
+    "J, _ = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n",
+    "\n",
+    "print('Cost at theta = [1, 1]:\\t   %f ' % J)\n",
+    "print('This value should be about 303.993192)\\n' % J)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gradient at theta = [1, 1]:  [-15.303016, 598.250744] \n",
+      " (this value should be about [-15.303016, 598.250744])\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "theta = np.array([1, 1])\n",
+    "J, grad = linearRegCostFunction(np.concatenate([np.ones((m, 1)), X], axis=1), y, theta, 1)\n",
+    "\n",
+    "print('Gradient at theta = [1, 1]:  [{:.6f}, {:.6f}] '.format(*grad))\n",
+    "print(' (this value should be about [-15.303016, 598.250744])\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# add a columns of ones for the y-intercept\n",
+    "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "theta = utils.trainLinearReg(linearRegCostFunction, X_aug, y, lambda_=0)\n",
+    "\n",
+    "#  Plot fit over the data\n",
+    "pyplot.plot(X, y, 'ro', ms=10, mec='k', mew=1.5)\n",
+    "pyplot.xlabel('Change in water level (x)')\n",
+    "pyplot.ylabel('Water flowing out of the dam (y)')\n",
+    "pyplot.plot(X, np.dot(X_aug, theta), '--', lw=2);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def learningCurve(X, y, Xval, yval, lambda_=0):\n",
+    "    \"\"\"\n",
+    "    Generates the train and cross validation set errors needed to plot a learning curve\n",
+    "    returns the train and cross validation set errors for a learning curve. \n",
+    "    \n",
+    "    In this function, you will compute the train and test errors for\n",
+    "    dataset sizes from 1 up to m. In practice, when working with larger\n",
+    "    datasets, you might want to do this in larger intervals.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The training dataset. Matrix with shape (m x n + 1) where m is the \n",
+    "        total number of examples, and n is the number of features \n",
+    "        before adding the bias term.\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The functions values at each training datapoint. A vector of\n",
+    "        shape (m, ).\n",
+    "    \n",
+    "    Xval : array_like\n",
+    "        The validation dataset. Matrix with shape (m_val x n + 1) where m is the \n",
+    "        total number of examples, and n is the number of features \n",
+    "        before adding the bias term.\n",
+    "    \n",
+    "    yval : array_like\n",
+    "        The functions values at each validation datapoint. A vector of\n",
+    "        shape (m_val, ).\n",
+    "    \n",
+    "    lambda_ : float, optional\n",
+    "        The regularization parameter.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    error_train : array_like\n",
+    "        A vector of shape m. error_train[i] contains the training error for\n",
+    "        i examples.\n",
+    "    error_val : array_like\n",
+    "        A vecotr of shape m. error_val[i] contains the validation error for\n",
+    "        i training examples.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Fill in this function to return training errors in error_train and the\n",
+    "    cross validation errors in error_val. i.e., error_train[i] and \n",
+    "    error_val[i] should give you the errors obtained after training on i examples.\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    - You should evaluate the training error on the first i training\n",
+    "      examples (i.e., X[:i, :] and y[:i]).\n",
+    "    \n",
+    "      For the cross-validation error, you should instead evaluate on\n",
+    "      the _entire_ cross validation set (Xval and yval).\n",
+    "    \n",
+    "    - If you are using your cost function (linearRegCostFunction) to compute\n",
+    "      the training and cross validation error, you should call the function with\n",
+    "      the lambda argument set to 0. Do note that you will still need to use\n",
+    "      lambda when running the training to obtain the theta parameters.\n",
+    "    \n",
+    "    Hint\n",
+    "    ----\n",
+    "    You can loop over the examples with the following:\n",
+    "     \n",
+    "           for i in range(1, m+1):\n",
+    "               # Compute train/cross validation errors using training examples \n",
+    "               # X[:i, :] and y[:i], storing the result in \n",
+    "               # error_train[i-1] and error_val[i-1]\n",
+    "               ....  \n",
+    "    \"\"\"\n",
+    "    # Number of training examples\n",
+    "    m = y.size\n",
+    "\n",
+    "    # You need to return these values correctly\n",
+    "    error_train = np.zeros(m)\n",
+    "    error_val   = np.zeros(m)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    \n",
+    "    for i in range(1,m+1):\n",
+    "        xtrain =  X[:i]\n",
+    "        ytrain = y[:i]\n",
+    "        theta = utils.trainLinearReg(linearRegCostFunction,xtrain,ytrain,lambda_=0)\n",
+    "        error_train[i-1] = linearRegCostFunction(xtrain,ytrain,theta,0)[0]\n",
+    "        error_val[i-1] = linearRegCostFunction(Xval,yval,theta,0)[0]\n",
+    "\n",
+    "    # =============================================================\n",
+    "    return error_train, error_val\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Training Examples\tTrain Error\tCross Validation Error\n",
+      "  \t1\t\t0.000000\t205.121096\n",
+      "  \t2\t\t0.000000\t110.302641\n",
+      "  \t3\t\t3.286595\t45.010231\n",
+      "  \t4\t\t2.842678\t48.368911\n",
+      "  \t5\t\t13.154049\t35.865165\n",
+      "  \t6\t\t19.443963\t33.829962\n",
+      "  \t7\t\t20.098522\t31.970986\n",
+      "  \t8\t\t18.172859\t30.862446\n",
+      "  \t9\t\t22.609405\t31.135998\n",
+      "  \t10\t\t23.261462\t28.936207\n",
+      "  \t11\t\t24.317250\t29.551432\n",
+      "  \t12\t\t22.373906\t29.433818\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_aug = np.concatenate([np.ones((m, 1)), X], axis=1)\n",
+    "Xval_aug = np.concatenate([np.ones((yval.size, 1)), Xval], axis=1)\n",
+    "error_train, error_val = learningCurve(X_aug, y, Xval_aug, yval, lambda_=0)\n",
+    "\n",
+    "pyplot.plot(np.arange(1, m+1), error_train, np.arange(1, m+1), error_val, lw=2)\n",
+    "pyplot.title('Learning curve for linear regression')\n",
+    "pyplot.legend(['Train', 'Cross Validation'])\n",
+    "pyplot.xlabel('Number of training examples')\n",
+    "pyplot.ylabel('Error')\n",
+    "pyplot.axis([0, 13, 0, 150])\n",
+    "\n",
+    "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
+    "for i in range(m):\n",
+    "    print('  \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def polyFeatures(X, p):\n",
+    "    \"\"\"\n",
+    "    Maps X (1D vector) into the p-th power.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        A data vector of size m, where m is the number of examples.\n",
+    "    \n",
+    "    p : int\n",
+    "        The polynomial power to map the features. \n",
+    "    \n",
+    "    Returns \n",
+    "    -------\n",
+    "    X_poly : array_like\n",
+    "        A matrix of shape (m x p) where p is the polynomial \n",
+    "        power and m is the number of examples. That is:\n",
+    "    \n",
+    "        X_poly[i, :] = [X[i], X[i]**2, X[i]**3 ...  X[i]**p]\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Given a vector X, return a matrix X_poly where the p-th column of\n",
+    "    X contains the values of X to the p-th power.\n",
+    "    \"\"\"\n",
+    "    # You need to return the following variables correctly.\n",
+    "    X_poly = np.zeros((X.shape[0], p))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    X_poly = X\n",
+    "    for i in range(1,p):\n",
+    "        X_poly = np.column_stack((X_poly, np.power(X,i+1))) \n",
+    "\n",
+    "    # ============================================================\n",
+    "    return X_poly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Normalized Training Example 1:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.        , -0.36214078, -0.75508669,  0.18222588, -0.70618991,\n",
+       "        0.30661792, -0.59087767,  0.3445158 , -0.50848117])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = 8\n",
+    "\n",
+    "# Map X onto Polynomial Features and Normalize\n",
+    "X_poly = polyFeatures(X, p)\n",
+    "X_poly, mu, sigma = utils.featureNormalize(X_poly)\n",
+    "X_poly = np.concatenate([np.ones((m, 1)), X_poly], axis=1)\n",
+    "\n",
+    "# Map X_poly_test and normalize (using mu and sigma)\n",
+    "X_poly_test = polyFeatures(Xtest, p)\n",
+    "X_poly_test -= mu\n",
+    "X_poly_test /= sigma\n",
+    "X_poly_test = np.concatenate([np.ones((ytest.size, 1)), X_poly_test], axis=1)\n",
+    "\n",
+    "# Map X_poly_val and normalize (using mu and sigma)\n",
+    "X_poly_val = polyFeatures(Xval, p)\n",
+    "X_poly_val -= mu\n",
+    "X_poly_val /= sigma\n",
+    "X_poly_val = np.concatenate([np.ones((yval.size, 1)), X_poly_val], axis=1)\n",
+    "\n",
+    "print('Normalized Training Example 1:')\n",
+    "X_poly[0, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial Regression (lambda = 100.000000)\n",
+      "\n",
+      "# Training Examples\tTrain Error\tCross Validation Error\n",
+      "  \t1\t\t0.000000\t160.721900\n",
+      "  \t2\t\t0.000000\t160.121511\n",
+      "  \t3\t\t0.000000\t59.071635\n",
+      "  \t4\t\t0.000000\t77.998004\n",
+      "  \t5\t\t0.000000\t6.449033\n",
+      "  \t6\t\t0.000000\t10.829905\n",
+      "  \t7\t\t0.000000\t27.917628\n",
+      "  \t8\t\t0.001442\t18.841672\n",
+      "  \t9\t\t0.000185\t31.270978\n",
+      "  \t10\t\t0.014306\t76.118843\n",
+      "  \t11\t\t0.032764\t38.041801\n",
+      "  \t12\t\t0.030051\t39.119110\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lambda_ = 100\n",
+    "theta = utils.trainLinearReg(linearRegCostFunction, X_poly, y,\n",
+    "                             lambda_=lambda_, maxiter=55)\n",
+    "\n",
+    "# Plot training data and fit\n",
+    "pyplot.plot(X, y, 'ro', ms=10, mew=1.5, mec='k')\n",
+    "\n",
+    "utils.plotFit(polyFeatures, np.min(X), np.max(X), mu, sigma, theta, p)\n",
+    "\n",
+    "pyplot.xlabel('Change in water level (x)')\n",
+    "pyplot.ylabel('Water flowing out of the dam (y)')\n",
+    "pyplot.title('Polynomial Regression Fit (lambda = %f)' % lambda_)\n",
+    "pyplot.ylim([-20, 50])\n",
+    "\n",
+    "pyplot.figure()\n",
+    "error_train, error_val = learningCurve(X_poly, y, X_poly_val, yval, lambda_)\n",
+    "pyplot.plot(np.arange(1, 1+m), error_train, np.arange(1, 1+m), error_val)\n",
+    "\n",
+    "pyplot.title('Polynomial Regression Learning Curve (lambda = %f)' % lambda_)\n",
+    "pyplot.xlabel('Number of training examples')\n",
+    "pyplot.ylabel('Error')\n",
+    "pyplot.axis([0, 13, 0, 100])\n",
+    "pyplot.legend(['Train', 'Cross Validation'])\n",
+    "\n",
+    "print('Polynomial Regression (lambda = %f)\\n' % lambda_)\n",
+    "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
+    "for i in range(m):\n",
+    "    print('  \\t%d\\t\\t%f\\t%f' % (i+1, error_train[i], error_val[i]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Optional"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def validationCurve(X, y, Xval, yval):\n",
+    "    \"\"\"\n",
+    "    Generate the train and validation errors needed to plot a validation\n",
+    "    curve that we can use to select lambda_.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        The training dataset. Matrix with shape (m x n) where m is the \n",
+    "        total number of training examples, and n is the number of features \n",
+    "        including any polynomial features.\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The functions values at each training datapoint. A vector of\n",
+    "        shape (m, ).\n",
+    "    \n",
+    "    Xval : array_like\n",
+    "        The validation dataset. Matrix with shape (m_val x n) where m is the \n",
+    "        total number of validation examples, and n is the number of features \n",
+    "        including any polynomial features.\n",
+    "    \n",
+    "    yval : array_like\n",
+    "        The functions values at each validation datapoint. A vector of\n",
+    "        shape (m_val, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    lambda_vec : list\n",
+    "        The values of the regularization parameters which were used in \n",
+    "        cross validation.\n",
+    "    \n",
+    "    error_train : list\n",
+    "        The training error computed at each value for the regularization\n",
+    "        parameter.\n",
+    "    \n",
+    "    error_val : list\n",
+    "        The validation error computed at each value for the regularization\n",
+    "        parameter.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Fill in this function to return training errors in `error_train` and\n",
+    "    the validation errors in `error_val`. The vector `lambda_vec` contains\n",
+    "    the different lambda parameters to use for each calculation of the\n",
+    "    errors, i.e, `error_train[i]`, and `error_val[i]` should give you the\n",
+    "    errors obtained after training with `lambda_ = lambda_vec[i]`.\n",
+    "\n",
+    "    Note\n",
+    "    ----\n",
+    "    You can loop over lambda_vec with the following:\n",
+    "    \n",
+    "          for i in range(len(lambda_vec))\n",
+    "              lambda = lambda_vec[i]\n",
+    "              # Compute train / val errors when training linear \n",
+    "              # regression with regularization parameter lambda_\n",
+    "              # You should store the result in error_train[i]\n",
+    "              # and error_val[i]\n",
+    "              ....\n",
+    "    \"\"\"\n",
+    "    # Selected values of lambda (you should not change this)\n",
+    "    lambda_vec = [0, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, 3, 10]\n",
+    "\n",
+    "    # You need to return these variables correctly.\n",
+    "    error_train = np.zeros(len(lambda_vec))\n",
+    "    error_val = np.zeros(len(lambda_vec))\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    for i in range(len(lambda_vec)):\n",
+    "        lambda_ = lambda_vec[i]\n",
+    "        theta = utils.trainLinearReg(linearRegCostFunction,X,y,lambda_)\n",
+    "        error_train[i] = linearRegCostFunction(X,y,theta,lambda_)[0]\n",
+    "        error_val[i] = linearRegCostFunction(Xval,yval,theta,lambda_)[0]\n",
+    "\n",
+    "\n",
+    "    # ============================================================\n",
+    "    return lambda_vec, error_train, error_val"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lambda\t\tTrain Error\tValidation Error\n",
+      " 0.000000\t0.030051\t39.119110\n",
+      " 0.001000\t0.174792\t9.879544\n",
+      " 0.003000\t0.249934\t16.322775\n",
+      " 0.010000\t0.385064\t17.006026\n",
+      " 0.030000\t0.669275\t13.050894\n",
+      " 0.100000\t1.443470\t8.149009\n",
+      " 0.300000\t3.101591\t5.882437\n",
+      " 1.000000\t7.268148\t7.227430\n",
+      " 3.000000\t15.867688\t10.089395\n",
+      " 10.000000\t33.372203\t19.819800\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lambda_vec, error_train, error_val = validationCurve(X_poly, y, X_poly_val, yval)\n",
+    "\n",
+    "pyplot.plot(lambda_vec, error_train, '-o', lambda_vec, error_val, '-o', lw=2)\n",
+    "pyplot.legend(['Train', 'Cross Validation'])\n",
+    "pyplot.xlabel('lambda')\n",
+    "pyplot.ylabel('Error')\n",
+    "pyplot.grid()\n",
+    "\n",
+    "print('lambda\\t\\tTrain Error\\tValidation Error')\n",
+    "for i in range(len(lambda_vec)):\n",
+    "    print(' %f\\t%f\\t%f' % (lambda_vec[i], error_train[i], error_val[i]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 6(May 3 -May 9)/Exercise6/Assignment_6.ipynb b/Phase 3 - 2020 (Summer)/Week 6(May 3 -May 9)/Exercise6/Assignment_6.ipynb
new file mode 100644
index 000000000..574ff0a5e
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 6(May 3 -May 9)/Exercise6/Assignment_6.ipynb	
@@ -0,0 +1,554 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Import regular expressions to process emails\n",
+    "import re\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# will be used to load MATLAB mat datafile format\n",
+    "from scipy.io import loadmat\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "# define the submission/grader object for this exercise\n",
+    "grader = utils.Grader()\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load from ex6data1\n",
+    "# You will have X, y as keys in the dict data\n",
+    "data = loadmat(os.path.join('Data', 'ex6data1.mat'))\n",
+    "X, y = data['X'], data['y'][:, 0]\n",
+    "\n",
+    "# Plot training data\n",
+    "utils.plotData(X, y)"
+   ]
+  },
+  {
+   "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": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# You should try to change the C value below and see how the decision\n",
+    "# boundary varies (e.g., try C = 1000)\n",
+    "C = 1\n",
+    "\n",
+    "model = utils.svmTrain(X, y, C, utils.linearKernel, 1e-3, 20)\n",
+    "utils.visualizeBoundaryLinear(X, y, model)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gaussianKernel(x1, x2, sigma):\n",
+    "    \"\"\"\n",
+    "    Computes the radial basis function\n",
+    "    Returns a radial basis function kernel between x1 and x2.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    x1 :  numpy ndarray\n",
+    "        A vector of size (n, ), representing the first datapoint.\n",
+    "    \n",
+    "    x2 : numpy ndarray\n",
+    "        A vector of size (n, ), representing the second datapoint.\n",
+    "    \n",
+    "    sigma : float\n",
+    "        The bandwidth parameter for the Gaussian kernel.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    sim : float\n",
+    "        The computed RBF between the two provided data points.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Fill in this function to return the similarity between `x1` and `x2`\n",
+    "    computed using a Gaussian kernel with bandwidth `sigma`.\n",
+    "    \"\"\"\n",
+    "    sim = 0\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "    sim = np.exp(-1*(np.sum((x1 - x2)**2)/(2*(sigma**2))))\n",
+    "\n",
+    "    # =============================================================\n",
+    "    return sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gaussian Kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = 2.00:\n",
+      "\t0.324652\n",
+      "(for sigma = 2, this value should be about 0.324652)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "x1 = np.array([1, 2, 1])\n",
+    "x2 = np.array([0, 4, -1])\n",
+    "sigma = 2\n",
+    "\n",
+    "sim = gaussianKernel(x1, x2, sigma)\n",
+    "\n",
+    "print('Gaussian Kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = %0.2f:'\n",
+    "      '\\n\\t%f\\n(for sigma = 2, this value should be about 0.324652)\\n' % (sigma, sim))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load from ex6data2\n",
+    "# You will have X, y as keys in the dict data\n",
+    "data = loadmat(os.path.join('Data', 'ex6data2.mat'))\n",
+    "X, y = data['X'], data['y'][:, 0]\n",
+    "\n",
+    "# Plot training data\n",
+    "utils.plotData(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tP5ApsaYsJ538cF9c+4zi5vUIcvNKUPUahebKCd77cD6PHt7j+NFtSExkyDu/KArvyj5kh/qKOwVBkGDn+Snhh5ZiXWNXcjZog+iZ8mjPApYvnisK65qk6O7BFt9NZGakiUbRyrGvqfHKulS6Aepd+Izw+T0LsJUrKVbfw2GcHitP2TWXvEv+oNOSc24v9l5zUfb04ljAIfLycjh2dC893EaS8ii+3l1EfrgvfQe9Lr5L321bMGvXD5ljS3LO+mEz9CNyQrcgmChQdX6BnEg/HLzno2jRjccbJ/P888OJuxqDpYkcdbX5Ii6SdcBp5r30qR2q7w569h3PtZij9fY3L2wLSjOLWheQmnP7Wfsun6W2nkQNGvoztDrHFKWSl5OF1tScjIOLafzuBoPrM4+uYs6MeYwc7VUrj/KiVK7GXcLScy41qVKzGjr8ZYOgmCVfn0KrckJ9Zje2wz6s9SMueXRDNIpCxQcatBazHh40mboJQSKlPD+T0OBfKC4pwax9f6IuhBhipK3ciL98lHkz9jJ4UF/WrF1LRPg+prz7Ab8d2M+aNT9x/vwZ9u7diYfnvzh6ZCdvvv4WAcfAxnsh2SG+RjBPuSYH7YMEA7y30eR1Yr+L7sWRfWoD8qadRaEv7zaCtMD1dWreFq4ePDy3x8AoatFtOFlBGzBv547mRmSVxhu4gW+/W49U4STusKrj85YjppN+2Af7cZ9VBU71GEVWyE9QXoqywwAyjn5Lkw+3khp9hCOHd6LsMIC4y4EcPRrKtyu/IaKWxblyF9G4eXfx/Ve6LebeSsXx5a/1MBkC9kPfQ9G8m0HEp7mrJ6Ghfsgad6DkUSIO4z8Xz1VfJKu/a0V3DxFOk3cbzr3zVbsDgLnT39Qn+6qjvxPGvcKp0FA6tG7C5WMrDQQ+6BWOutIKwLOvNTdo6A0ausHvSvy8UlO2q0VTtug9ho2b15NXqOO3fdvo2Xe8eK4yCMahmhZdncx7eZD8cyAb1y3BrF1/0auheWs30s4dQtlhAPlXTmDe+QWKk6+RcXQVqt5jsHTzNjCKqly9yAragKfXa1y+EklWkh5iyQ3eBIIEi37/Iv9aKA8kgoiRKt1fJv9aKGkg4v8yVXtee7OVGN4/59NpgMDI0f9iz+7NKNr2ZcWqpZi21uPC9l5zST/kY4C/2wx+F/W5XynNvF8n3uvh+RoRZ8JFzTDv0mEjzTvDfyWW7uOQObREHfYzyq5DKYwLxMTWGYllY7KC1tOydQ/KitPJEnToLJ3ICtyA95g3kCqcDHZY69b6GGihju8YYuWZgesRBET/9pQds0jZ+gnlGrXBsdEjBiJIpdh4LzB6l3V5EfUd9BbJN4KI/e1rvdF6TO0GUpWrF3mxgRQ/TsKxhseRvddcQzgtaAOvvfEh/v77qqJIgzYycMhEg++3vmRf8u6j2POrH7JG7Yk8F2mwgFSSWc/a0wpU8q/td33n/o4y4GnP1UcNGvozsjqv8pmN4NhWhD2qf2SVkZB2nnPITAhn87qlSJt0JPj4Bt4Y/zwZGWlcjQlEkJsjMbcV71EfX0U5Jji88jXa/GxKcjPErX+lV0NcdIiBIMnw/w7d/SsMGjCEsFA/ChLCsXTzQlKoxs21N+eDNiB37kxi4mW2bd3Ngf1+7Nq1HVs7B3JM7cg59ytmbfpQmnGPtNwscYEya9OH4sc3OB12jJkzZhh55aTsmIm2pIjdO9fjMF4Pj6Rsv03hrShSd8zCopcnQkEGXbq4EheyGYWVPh20RJONDh22L71HTTLv5Uli4kU+/+o7Tvr/TOSxlbUKrCaNnHgcsRMdQsXCFoDHSA+ycjSciwxG3tSFsrI8g+dd8t16erm6G75Ll5bYO/xAwOGtRNaihWYcXYUgkaCs7n7pNZe0A/8x2DHoF831OFbT7KtTXV5ESYmxHIu7RLmpuZGWnXliDRa9PLB09USQSPU7hIrdROU1Gf4rUfUZi+Ori8m/HEB2+M8AZKbdobgwF9MOPcg544fCxglbVVV64icl+1K5elFwPYKSxzeMFpCqazxR3zpXa6CWOL61/K7v3N9NBjRo6E9x3bPC40nXTXpnFsuXzMasjeGHmO6/El1ZCcp2fck6sRYLV0/UoVspT7mJWevefPDhOxQX5iNITTFr2ZPUXXOxfmEy6lMbkUhNkLftQdru+WBqhlk1OMBi2DROH/Yx+AhVrl5kn9qAUmHO6fCT2I9dREnaHbJPbcbB3pnomEsGXgo/rlvH4KFjGDbGHrnuMbt3bsBq4OvkXwsFQUBS4d9u1U+voaODzi49iY2/a+SVY+81j/RDy3AYX7XDULl5oz69g+KMZEpDtmBna0vrLiOZ9O4swoL9OX7UFwQBuzGL6lwAHyVF8uPqFdy/ewWd0gZLDPF/ZZfBPI4P0/uEV2DfpZnJnDhxDJ0gEZ83fdc8vvxyETdvJtLvxUlIFU6ifaC6D3V9vuCq3mPIidhFYdI5UtLviR5FzlNq+revx8SuubF7p5uXKJBrehFVx+9Fo/WueVh0H0FW0Eas+v+LwqSzFCadxaL7cMoL8yh+eJ3H22dg6epNVtB6VK6e5J7/Dc31CFSuHqAtx3bYR0REH8bGe4FBTvTqbdeW7KvmAqLqNZrs4C01PKN+wMLV28AzqrpN4s/83v6v1z0rbT2JGjT0P5BHbeleawsxr4tH+9a7+fcn75KyfRYqV0+yTm1CEAQcKxJepeycS9bJH5HIFDhUCJ+UHbMoL8jDcULF/19mkxX4IzJTBbZjKoNnPkWX8xib/OR64YDc0J8wkUjRlGsxa+uOokU3zFr2QNm2D+oneNus8vHF1LmTgYZeknYbq36GGnpC/GW6u7QUvXKSd8zCzlMv2KpDJkX34sg6tQkAp5e/MvCj79nVg55dZ3DubAgFNq0MhIT6+CpKi4sxq1gAzbsN4/bJdUhkcsyadCIzYDVOr/tg2dsby97e6LTlPLgeYWBctfeYo/eqeXFKlXG1WRfORR5F2XFghS1gIlcuXzTwoR7y4ktEnNpeJ+xl6eaN9vYFXhk5jN07t5K2/z841/CySfdfhaJ1b7T5WaJAzg75CZshU8mPPYkmMQJVz1FGXkQ18XurAa+TfWwl2eHbkDt3RJMYQaNJ3+kXskg/HMcuIiNgNSYW9mQHb8L6uYlY9h6DucsQUvcsJCtoE7ZDP8Ciy2Asugw2eC81vaUq32XKnvmYdhlO1qmNWPUzXECyg7cgyM1FyExzehuvvDyZqKgQUvadQ9ZlKJrwbSxd9p1ok3iab6++c8+iHPmzNfSG0P8/iGKiL7BwwSwKrFvx9TcL0Wq1tYaY10eZmemUFBVj2qQDOWf9kFrYYFYRWCNIpNh7zMLEqpHogSBIpKhcvRCksqprPD9F6dBcTJQlSKQoe4zG3MKSHb/sp3OzJqj9lxu3fexbykqKUb30PtaD3kKTdJbHP73HI9+PAHB4a7UopCo/6s8/Xyze/+brb1HyMEEMwxekMkwdqyJQK48NHPA8AI5OzmzatIM+HVqSfmCxUX8yAlYjUVjgOP4zg1D70GB/8ZqpH8yjcXkG6r0Lyb8aTPahJehKS/Qh6iOnoSsvJ+vkjwhSExzGLsRu5CfotOU8+nEiuVEH0WnLESRSzDr0R5N0lpQdsynNeljhgbFGfN7cqIPkxfjjMOELbEd8QnYJ+Cz9nIULZunTIYz8hIc5RWzcuAazGsbjBz9OJKdaW7Iuw9i905fC4iLshn9k9NyWfcZSdDcG62EfouwwUO9FNHYhFl2GYNX/dcozkyk7t4uly76jfcfu4n3Lln6HdcEjcSxyjq1gzqx5NHGwo/hRIjaD39WnMOjtjfP7W1C06IbK1RNdcT5Np+/Bsrde29YWZGOKFreevSiMPmzUv9rSOlS+y4ljPCm74Idbz16U/X4Gp9eWoWw/QJ/sq98r6Ipy6dWmGUTvY5nP91jb2lNUWIjHoH6Unfdj6bLvDGCsBvq/UwPk8gfwqC0g6MP33+JGUoLoLpe+ax6ffTafmzcTa63KU51HpSApzXoo4r2VXhzONcLPs0O2ILWwrRHZt8bgmsqglQOHjhITcx67WvyLLdy8ybt0hJxzv6HVqFG274cm6RxmbfuKWm2l73Sm/wrMFQoy1KWi+2TA/m9BZobE3BZBIsVu1HSj0HxLN28Cg7bx0qiJ7D9whC8v+lOgycO2FsOfpasX+ddOkRW8BQfveZTnZZIf7otzMxeGDHbnvQ/mkZ6n4OPp3/Cr3ybOndqISmVFeZMu1TTtWWQdXobVkPcMvExyQreQc9aPghuRKJp1QZMQhsO4zymIDyHtwGKcpxh6F6kjd6PsMLDeoC1Ft5EUZm+nLPsRKTtmoerlKcIYhUnn0FwP1+PEgevRQp1eNpZuXmgSwkj3W0Cz6XsMvFOyj3+LqdycT2YtFo2x1UPp+w56i7KCm4QG+9F30OvkaSA9Pb1O3NrS1RNNYgQPN0zG6dUlyGydyQ74geefG0pERGAdKQSqDLLV3VFhEB27Pk+LNvFEx+iN85ULiKljK33wV7t+xMRewmfVDm7ejBdTUkRERjLUe6YIY/3dYJAGyIV/FuRSW0DQrf3/MfDbVvYYybmgDSg7DjQK267JA6pwSPMeIyjLSK494OfEGmyGTMG88wsVkX1LaVLNbQ/0GtWrr71HeXEWGzetNcCoq5Olmzea66cpSU/G6RU9xFGa9RBTp9YU5qQa5FcpLyulrHEvg4pDGk0+Zu36k7prLk5vLMfUrplBfytzn1iYm1NelGqQ36R2I5kH+deCEaQyMgNWI9FkMbD/AELCQlB2GMBW3+9YuuJndCUZxFyKRNl+ADZFD5GVpxsEVzm9Ywjj5Ib+hKDTYj9mIaXp91BH7kLZcSAAhbcu1eqHb9FzNAWXj6POS8Fi2LRafaizTm3CYdwiFM26oI7YSdapjSKModOWk3H0e7KD9HYNZdsaxsijq1D1Houlm5dYXCIr0HBRKboXR3lpCabt3A3GvdIltHJO9ezqwcwZM4iNv1vrnMo49p0ei69ItaDqOYrs4C3ioi1v3ZuQkGN15tupNMg+vn0a/6jTmLTuTWzUQebNmMive3eIGSqr7+aqB3+l7JjFpjWfkXz/fr0pKZ7226srg2VN6LM+Hv8kyEX61VdfPdWFfzStWPX9V2069SM1XU3E+QTkcpnR7/rOPe11/wseQ158kcSoILJjTyFz7ozM1hmVqwcmVk5A1Udr/dxErJ+fSFp0IHdv3sW5WVsjHqkXj+uF2Im1WA+aRN4lf0oeJ2E/eqbITySdjoKEUEysnMg9vw/7UTOMrtGW64gPO8C5c6cxaz8AlZsXgiBQdC+OlN1z0SEgb9wOQSJFMJFR8ugGti+9jyBIECQm5F08gGXvMeRG7cfUoSXph31wHLsQlasnj6OOE3fxPP5H/LAf9xkqV080iZHkXwkQt/CVlLp3EYJURptWbdi3bydaMyvMWvVC5epZ1R+/+eh0WuSN2+v7IzOl6NYlJKZmlJWVcDspHscJX6Jy9SQv4TTnQvwJCDiMldc8VK4eZMYG497THYVQzt1z/lj0HGXQh8x9i1CYmCBt5YbKzQuFcyekFnbkXjiA5vppHMctqlWIKZp3pfD389jJtGQnnMG8x0iD849/mQU6HSUPEzBr1QvzTs/pCyxHHUDRvBul6fcovPgrAwZPpGvnjiScP4HmxlkEqYyMo6vo2K4TDy+HoEk6h2AiIytwPdYD30Tu3AnQQz6ZgeuwHfYBVv3+xeOo45w5dZyAgN+wHPIeVv1eqXdOpcecpDT7MdmhP6HqPYa8qANobkRSpk4hJ3I3NkPeozTlFrqSQjS3olA072YwTzL3LUKr1WJaMU/KkZAUtg/VgNcovHMFTXEJV2MucsT/N5QdBhrcm35wsT4Nb8VipWjejYcXT2I9aoYeShMklOskJIbspWvPF/9v315EOBvXLaXUqTORJ/dTLm1C8p3rrPD5jPLGLkSePECrdm6kZeQ8E3Lkj2grNV1NgP/ux1999dXm2uTqXybQN23a/NXUqVN5dP8Gu7Z/z1ivUbRv2xK5XEZ3l5Y0crSmkaO1wf+6flf+Ly9K5btVXzLkxRfp796NRo7W9fJ/2rae1I/+7lvib7MAACAASURBVN0Y4z2e5KQ44k/tRdXLw+BZU3Z9ikWPEVj3fxVBkKBFQvbVk8ycPt2IR1FOGtH+P6OwskfWpDOFv5+vU9CYNm5H/uVj5MUcrdPFTVtaiDouCNthH1J05zL5VwJEQSKYyCm+f5XCmxdAItWHhnt+iom1U8UOYS2WvceijtiBBB2amxdRtK4QwhIpps268ODicaxGTBM/TkEqoyg5zkigo9NRknGXjEd3kbfti83QDym4eoqCq0EgSMk4/gO60iLK8zIoiAtCMJGRHeKLvcdszLu8SG70EZQd+uttBhWCQR0fjk1FIYzKcb11eh+PHz/AasS0WhdARd4Dcu8nUXjtFDkXD1Ny8yzmSnNKkWLaqD1ZAasRTOSkH1wCOp0oxErVKWQmXcSmxsJadC+OgoRwlG3dKclMRpN0DlP75qQf9kHetAsF106RF3uST2fNw9rait/2+iJr3RvSblJw8wLenmO4FB2FrFVvih9eo/DWJawGvIGV+ziK7sWRtmsOmjsxKDv0p+juFSy6DQWpKQ+ijqPsMEA8phWkdc6pXb5r0DxIQNlhoF45GLOA3ItHKHl0HWWHARTfi8W82zDyog7gOP4zci8epODqKQSpKflB65g5fRaJZ46RczWUcgSyAzdgYtuUwlsXUTTrgra4gNSMVMxdBlNwLZTCpHMVc+xb+rk/z92rkWiSzqJo3rVWZSc/aB1T3v+UwYPcjb7XRo7W3LuTwLYtK/EcNQIHBwe6u7Tk0f0brFr+BTbe81G5epBzNQRZSRonjv9ascB7knM1BGdbBUMGD/o/yZH/Rgb8kdc96dzPvuufPYG+YuX3X5ULSlb4fEaxqjEHd2+mUZOOXI5/gFwuIyIinK+/mkt+sRJLS+snrlr7DxzhZ99VlDh0IjrMnw6d+3LmzGmj1frMhet/yup85kwEB/b/gs2oWjRpQULRnRjMuw6hOPkquYFrcRvwMrZ2TgY80jJyadqiI0rrTsi1am6fPYxZO3dDLXbnHHRQpcWamFJ0NxbbYR+K12TsWYC2QhhlHF6OonlXrPq/ikXXlyjLTSc30g/r597E3utTSjLuU5J6i5KHCdh7zEbRQr8opO5dhPWgSeSc/xVJaRE9e4+mrDCT3Ic30SSdQ9GsCzJbZ5TdRxh8nJkBq3Hwnms0BqaN25EfF0S3Tp0pV6eQnXgOmxHTEKQycqP2IzE1w6y1Kw7jP6f44XXyLh5CkCux7OWBzNYZqYUtedFHKbx5AXlTFyPBkBt1kOzwrZSXlWEzRu9iV3QvjvRDS1E074bUzBJZo3akR59EJ5VRmpeJWateSArUFBZqMHVqQ/6V4yiadyP34iG9YTghjPzYQIruxlKQEIrt8I9QtusrPmvqr1+QHxeE47hFqFw9KUgIx0RlR170ERzG6AVNwdVgZLbOXLsQxpnTgdh4z9dj10nn6dm5C2ciw7Dymoelmydldy5hJoWS3HR0ghR1wPdoy8txGP+5fldy+Tjq8B0U3YnBcbx+R5QfF4j69E5K71yk98B/Gc2pM2ciiLpwWuRREBdEWdYjyrIfiv3Ojwuk4GoQphbWSCwdKP39LL17uJJ84ThT3v+UFm26Uy5pQgtHc25H7EeCjpL87Ir7PSiID0Xm0ArN9dM4jJmPRGmFOvxnBG0Zj1IeIW/jTtnDeApuXcLS1VDZydy3iAnjJ5FTbMW9OwnG3+uZ00ZauEJuyrIlCylr7CIqFyZNOnP3nL+BclGukxAbsJPO3Qc1aOh/Nn311ZdfXboUiZn7BDSJZ1C07k3cmcO88cZbaIvTWLX8C8oau5B3P5oPpk6hsZNNnavWo/s3+Nl3FdZeFR/R9XCKs5M5dGCX0Wrt6ur2h6/O5UWprFr+BVZe8+rUpAuuBuu3tGd3MnPabPpUBE/U1lby3eucOP4rloOnUHTnMgXXToEgIfPEWpQte1AYH0xB4hkEE1Pyw3xxcnQk80owOokJuSfXMGvmp6JGZdquHwWxJym7cxFZ0y6Yd3oOq34vI3fuSHHyNXLP7cHBex62Q6ZiYl1NCFfAOUqXwdhpc/ngo0/56IP3yUx7TOKVC5TeicaiYidSKTjzrwVj/eJkzNv3E7fq6HTIGrUTIZSchAj8/A5x9eI5Hl4KwGbkDCzdvDBr1YuCuFPkRh2gNO02ynb90GUlk590HlP7FmSeWIOidW9KHidR+Pt5vZ90BRXdiyPz5I8o2/enLD8b68HvUpx8jfRDS5E3dSH3wgEseoxAIjVBkJnq4ZUJX+j9rq+FYmLXlJLUWzhWwEaFt6KQWTfBdthHFKfeovDGGVEbNu86hLyLh8g8+SNmLXtSXtFeyf0ECm9FoSsuwL5i11CcfI2C62GU52ZQqMmvWJy9KnY3Ljy4eBzL4Z9U7TAEKZLU6wx+7iWSzx3C3EKFrmkPUWgpmnej6PYlkX8lLFZ0OxoHBwemvjedHl1aGcwpn2WLKG3UWeQhb+ZC/uVj2I2cZsBDkprI229OIjZgJ0uWrOLNie/QteeLDB7kTiNHaxQKU8Z4jeC119/i2LHDaJ27iTCKvGlnkadZi+4VUJYthXcu4TD+c0wdW5B/PQIHrznGkKC2HHXSWbq5dGTr5pUG32tR1j0OHdhloIW3cDRnjNcI+vXtT3SoP+nRJ0WY06LnKCPNf/GSlfTo7tKgof/Z9NkXX31l0f9V0a1N5epJ5pVTXIu+wKGDFYK4lwcZMUFkPE5BqWpU56q1bMlCypt0ESetzLkzSeEHDT6WytVaZtHuD1+dd23/vkpbqK5JC4KoSSORkBu1H/OeniSeOSZqGnXhgjbe81G264t5l8HoSorIjdqPvedsVG7elNyKoq2THWmxoUx5/1M8x0wi+XYy2VdPMvk9Q43qzvnj9BkwARtFOckXjon4b1USpgX1LkIy++ZoMh5x9+ZdNBoNe/dsRdG2L2WFuVj0GEFx8lURXtBq1BTevIDEzIq8oLV06/ESxfcvkR0bjFaQkB24AblCSYGmjKDAw1iNmEaZOpX0Q0tRdhiAqVNrvcZboX0WXI/AxNJe1HhNHVpQEB+GfTXBUPkcldpm4fUwciP9KEgIw6FSc74WTO7535CYqcgK1Od2ser3il5ItuhGwbUQIwGnjtyFqVMbcs/uEQV9/uXjFMSHU3A9TMTyC+JDyTm7B8310yiad0dXXoLVwNf143JoKYoWPdCWajDvMQJNbCBldy6JAqjm7iY3cC1vvzubQq0DkyZNpmOnHkSH+pOXEIJp5a6k12ijHZG993zUt2O5f+cezs3aGcyptu26GPPoMdLQvnP8B8wUCn6/+Tu9n3uNDh1dxF3vhnVLsLZrTsy1B+Icbd+xG1GB+1FfDUbRvFvtPI+uwtS5M3LnTmQcWVkPbNietOhAos+ewqSVq4HGXds3XIm1FxZDb/cXiI06S9bVYCO7RvqeBbw84S2atur6zNji/tEa+tIN278qTbuLvFkXAw0k62qwwbZJJ5GSfO4Q0z/5pM5Vq1/f/pwJ2E/O1RBMmnRCZuuMeY0JVrlat2zZ8g9fncd6jSI61J/My0GU6wQyjn6LRY8RFdp1MEgkIh6s7DjQQNOoyc9AoxIEipOvoTm7E4tenph1GIggkaKVSMlPOsvi5VsZPKgvjZ1scW7WlpnTp9O5UwcjjSozI1Wv8Q//RByT9ENLq8a+YhFK3bsIna7cYBHKizqAee8xpEcf4VLUaWzG6A2imoRwih8kkHNurwgvaBLPIJSXorsXjY/PD/To1Z/3p7yLTFvMlWM/U67VIjTrweWwQ9iNXYCAYIA1F966hKJF96r5UE3YooOMIyuMBIPBc0ikyJt3o/j2JWyra7FSU7TJ0WhuXsRUZYtWq6UgPhR50861CqPMo9/SpJETqTGBmLXpY2DQK7gaiL3npyLvspxUih/EVyxCeohFHJexCyoWlBBKfz/Hex8txEJazM2IQyi7jzD4HjL3LWLW9Nl4eXmL86FNq+Z0cHEn6NA28m9dQtVrtME9KX4LsHnhbczb9wepKSmXjjBn9myDOdWmVXMEqRkXQg9TeDcWVQ1DcereRZi7vEhecjw4dyPvfgwfTJ3C4wdJ/Oy7ivImXUi+Gs74ca+I2n9O1mOOHz+Iia0zBddCUNUQpql7FyGYyNEW5qJJCDOCDWvOM60gQfcgjsYWJmReDqr3G67E2hs5WvP4we965a9WewlkJ0Xy7jvv1Lu7b9DQ/yBa/O3ar+y95lIQd4r8y8fElb7mS8wOWM3kqXMo1crrXLUKi6Fc2gRZSZqBFlpJ1VfrP2N1dnBwoLf7CwglGmKPb0XetDN2o6Zj0WUIJam3yT3/K+adByGzcybj8HJM2/XjethB/I8eQp2jYcP65Tg4tSYm5jIxl86iy8tCk3habzwNWM3LE97mUWwo6TGBIDUhL3QLb787i4SbWdy7k2Bka6jex+oaf3VBqGjejYK4UyKck3H0W5Rt3cm/fIziW1HoJCZkBW3EostLFEb9hiCVI23Zy2CbnRftj/2o6aJwQ5CiuRODmVzBkBGvE3E+ATOFnKxsNVEXwrHxrhBwiZGUZT0i9+JBcTHIu3wc08btKct8QF61+aDqMVLU4uXNu2DpPh5BEMiNOkjqvi8wa9WLssz7FFwLFgV0za131vEfMFeqmDFnKY/v3SBDrcbUsTX5l48ZCaMUvwXIzMx5fsBQdIKS9N+j0MSHkHv5OOadnsd6wOuYWDmJGHrRvdgqz44a8EN1jV/3+DoyM2fOnPY3WFgrSavVknjmmJGd58DBo9y5eRl7z9nGQgsoSAhFaumgN+YKAt1dXzKao6t85lNaXo69h7F9pyTrAQVxQThO0GPsGTFBxF08x65dvnoIs5eHkQfN11/OQmvTnJLUW9iNrN34XJJ+F22xBpl9M0oeJFB466IIG1oPmoQ6fDtFv58HqQn5Yb70GfgKk95+n8e34+v0UKrE2ivn9Qqfz+rMH2PauB1p0YGkP3pc7+6+QUP/g2jxt2u/kjfpiOZWFOi0FF4PR9XLw8CQlXtsBS9PeJuJb75W56r16P4Nvlv1JW1aNiEs5BiWwz8RBUClMaz6aq1QmP4pq3NjJ1v69e2Lc3MX7idG6bV1pJRcPsKcOQu5GhFARswJFM27UhB7ErRllFo35UpkACYtepJ44QRnwk+ia9oNK6GIlz09iD+1h8lT5zDxzddo3qwZJ4/sofDOFRo5ObJo0Zck372O76YVRraG2jBUmX0LMg4tQzCRk3NkKYLcAkX7/hQkhKO5dRG5iQRZYRZLl6yiRSMHYgN2MnzoCB5ejeSbb1bQw/UFrkUcJiVsJ6ZOrZE36YCq5yhxrAUTOdnBm5Hqyliy9Ft6du9i0IcSx04GmndetD/2HoZadO6F31D1exnNtWCK7sRUzYfDPpi17UPhrYtoEiMpy0kl98JvKNv3pzDpLAoLK7RIKIgLNMDWATL3LURXUoikpSs3Lhzj7r3bWLmPIz8uqFZhpENHceptUu8nMXfBMga/MJDAYweQN+9GwbVg0bCdftgHRfNulGTcg/IyNNfD69T4swO+Z8q773Pk0M/1CqCadp5K21D1ep417ym4Gkze5eOYoOX9jxcyeFBfo+8jMPAo9nXwUJ/+RR816lYPtl/DK2uv33by0+/X730VF0R5QTbagmwcxi5EamYpwobKdv3QCRKKfj+HIus233yzApeurmiL0/Hbva1OD6XsG5F0c+nIz74ruRZ3kbLG+p1ZcfJVUn/9goKo/QiCINpstILkibv7f5qG/lSh/4IgjBAE4YYgCDcFQTCKvBAEoYUgCMGCIMQJghAmCELTJ/HUFheSftgHU8dWlOWkYf3S++LHK3NoRWbAahTdRxN++oRRyHxM9AWWfD2NkwFH9KH1Zs7s3rkRlccc0GHAQ6fTYuHqQUp+Kb/ue3KF9v9fqgyDftN7NGUX/Fjm8z2Ojo3IzcnGcfznFUKkESZNOlGSchOH8Z9jO3IaaZoyTNoPxHbEJ6hLBQSJhAMHA2nXoRsx0Rf4bNEc7Md9TtNPdpJVWI6nxwtsWr9EH3o+4hPScwsYN3aYWM29kpYt/Q5Fxu+kH1yCzKEl2ac2Mu3DjzG7FULGYR8Uzbsi0ZaxZOn3/MfHF1e3vvzr1Un8x8eXWZ9+zoGDgfRydUednUFOjhqztn3I8F+FTqc1eF/ZwZsxM5Xx/sefGYVvL1v6HdZ590jZPlMMrXeeutEg+CQ71BdTKwc0Yb6YyOR6N8tqQSl2I6chs2tGWWYyeTH+OE74ErtR0zGxboR5WR4lqbewrSWUXtFjNIJ1Y8w6PcfDR4+wGvhGrRktK8nS1ROJiYznBg6qSN0wE7txn2E3ajroIPP4mqo+jZqOzK4ZSpcXMLFuQvohHyN+Gce/R2Eqx//YEaMgn/QtU8m/dNggLcDevfo5GhN9gbnzphvd82DdJHIuHhLvsegxHEEixalJU9q272rU/vc/rMC0otRcJY+UTZPFdh2851H8OMng3dhN/MEozcOkd6ZXjamZ0ig/fsqmyeRGHRD7ZdnbW0wTbNayR1XKgQq+Vr3HILVuTIf2Henl6i6myagefVudzHt58CBDzab1S8lSNkWhMKNRWTpp2z4h/eAS5I07oDIzw/ZxFOp9i8i/FowmfBufffYfI17/ZHpipKggCFJgHTAUeABcFAThiE6nqx6PugrYodPptguCMBhYBkysj2+Z+hFOry6piEh8QMG1MLG8mLypC6m79a53GYVagxqalSHygkMbfJZ/g8P4z5E3daEk457eB/bWxaqotJ2fknvxMFZ9xtaaye3PDPvt2PV5ho2xRyJ3ZMnX0wwiSe2955F+eLmBUFG5epNzVl/vUd51OLt2badj1+eJOJ/A6ZMbxWx2xcnXKMrJxKxNb0i/Ix4ryc1A2tZdzHNemVM6KTEWdXYmjhXjlJZ5n5CQMDEkXN7UhYzsB0REXjCo5Vj9uWpW4knZNZfM42sM3lfKzmRkpbmkZEtrTelQUlqOtqTIoB5oJWWeWIPN4Hcpzbiv96mviLB9/PP0GkmzZpN2YDF21QpxqFy9SAuqu2CFpZs3hUnnyApYq0+Ney201tSyBrU33bwJC/ejNDgYk9Z90Gm1pOyYhfWgt1CHb9dHk+ogZccszDs+h/rMbiRSGQ7jFhm1r3L1xDQplIlvzWD9uhVk+c1H3nU4OSE/8epr7xF++oSYUz4/zJepH8wXq0jJnLtSdDOKjN1zMeumLysnc2xD7vlfq5Jehfhi7/Up6rN+BlWEKse+eo52edfh5If70s11JCl3q3LZSwrVtG7VjuRailOoT6zm5YpKV7/uP1ytLutW0nd+irLHKNTBm3nt9fc5GXiQxwlhWLp5kxW4HrlzZ+OI2D5jsXStiog9H7TRKPtm5fU5J9eg7OmJhasHxffjKVKn41CRqC59zwI6NbXnzu3fxZTQ6bvmMdDFDRdBwskTv/Du+3NrTSfw33zn9Z37o6970rn66GlC//sAN3U63W0AQRD2AN5A9VY6A7MqfocCh57EVKKwqPpQR88yyvth0X04OWf9sBrwmpjVr3re5azgLZhVzyvt+SlpBxYbCsmeo/UwgNLSKJNbbeHBSYmxLP7yA0xMTJn8XlU2udqurS0st65sixJBh0XOXdTVQtKrC7VKDdVhzHyxFNfy5avp7tKSpMRYpBKw0zwkddsnlORlYdX/lYoUtRK9YL15Xpzo6n0LDXJK18zCZ+sxu9Z6j7XVcqz8refhXu/7qhzrukK4p0yeKi7ANUnVy5Pci4coy36MebXiEyo3b7JPbSIlIxl7j9kVqWarQuIrU83Km7rUW2HHoscI1OE7KMt6BIJA8aMb1dLGbkRqYUNe7Any40OwrCjgsGrVjyxZ8hXZty6iuXEWZYf+5JzdQ6O3vte7RB72waxNb9SRuxEEQR/2X8eCor59geyMO3z5zQ8kxUeyd+9OPvz3IiaM82Ly5Lf57ddd7Nq1HR8fff706nmBMv3mU5pxj6ygDVg/N1EfobvzU0rT75Idtg1L9/H6ohydnuPe1RN0d1lmMA+7uwzA3qGq3eXLVyORO9KlYzOx3SnvfsC2bT/VWulK2XN0RaWrCWxet4wyqSnHj+7m1dcm47fzR4rP/oKlyoqRI17i5Il9lKmzUUfsxGrAa+ReOCBmDq0sSJIfe5LCpHNYdB9OVuB6rCwtsbMUDGriylyGojm9jZkz57L/wD4e7YmkKC/LIP2z5YjpXD+20qAalLLHSM6c3seBg4EMHvr0dUn/iHD8/2Vb9ZGg0+nqv0AQJgAjdDrdlIr/EwF3nU7372rX7AYu6HS61YIgjAP2A/Y6nS6zBq/3gPcApCYyV0WjtliNnIHM1tmgzaJ7caQd+A+qXqPRXDnBBx8vpH3H7ny24H20jTtiO3IaZdmPyfBfCTrEvNI1eaj9fXDr/RwxMRd4d+os2nfsTsT5BJysS8VKOTaFj5k9dzk3k66yfs036AQJyvZ9MU2/y5ffrOZm0lWjayOjEnmub2dAv3o+17ezuHOoft3BQ0f1mlarPpik36Jd69bE37lrpAU93DQFqwGvYdFlCKmbp9C16yAmTpwkasaKtn2x1jwi9fEDpE5tKEm9Jaao1WnLsR1SleY1/2owZRf8GDZmNs/17Uxa6kPWrfWh1FQhps2tbZze+3ABKdkmRs8FkJb6kGWLZyO1boS955xaeaQf9sGi+zBKE8JY/u32WsemLvxYpy3n8faZtLCxoKi0jMxiLcruI8kP96Vrz2FciToGKnujpFkPfpxEt44upGdnGtzz8itTOBZwiFKZAnm34WQHbURmboPTlA1kndpM/tVTmLXqSWn6XWyH/5vS9HvkRh/BrLUbRbejMTGVM+LFYTxMySX6/GGx4Ebq7gUIpgqKH1QlXbu/9g2Ubd2xGzUdQZAYaZaCREr+tWDKzle9k5rjW/3/kq+nUWDTCtuR0xAECaVZD8k+4oPl4KkG77jwzHYkUjkFBWq9LeH3cwx88U26ubSsd77WbLtyN1Dfu0n9ZTYl6ckIMlPM2rpT+Pt5BHSYteuH5nd9Ajdl9i1y1FnIWvWm6E40VoPeIS9sCzITOYXFGjFBmE5bTl70UdRndkJ5GeadBmFT+Aj3QW8x0L0TYcH+nDxxkCnvzaZdh25oteVs3bqF2zfOYqZUkS/IsRk1s9Y5mHloKR/+exHtOnSrc3zr+/3fnvtftgUwberoaJ1O52b0svjjknPNAX4UBOFt4DTwECiveZFOp9sMbAZo1rylLlv9iJyjy7GfZLgFzw1cQ4+u3bh65Tivvv4BL4/XZ5zz8nqFfXu3kLlzNlaj59Bo4rdkBqytNXFVZd3FESO9DWoU1qyUo963kJ1bfYi5HI1OkOA4Xr99e+z7EZ/Ne5vSkhKsvRcgb+pC9p4FtVZUSYwLY+OmH7EZ9iHmnQZV4xkjHkv7eRpXrlzAdoxxpkNVL0/yY09i7vIi5r08SbkbSVlhCr6bVogwR6bfPMpKCyl7eF0UJqm7F2De+QUjvHOZz/dI5I76PrrUX0WnvlqO4m+Xlmi1K1n73ULSDyymSQ3BmnF0Fcr2/Si9dsogR3clj1qTRB1dharPOLEAgmVvb24FbuBk4FnWb9hARPg+li9fTVj4GaJ1WhyHfWg8bn3Gcv3SAY4cCTa4p2evPrj1fYGk+Eh27tyGVCJgPeLfCBIpJY8SMe/0HOadX0Adto3y/CyKY49i7eaF1L45JY8SUXR6jojw45jIFAZZFu1GTSetRtI1K/cJ5JzbS2lGMqpeHuSH+zJr1nz2H9j3xDzftWlklbVBq+/mHN82zFufH7aFKVM+ZIvvRhE2y1Q/pCj3Nr6b9hnM7SdVAFry9alaEnh9qy8/WAGNWLh6khW4AYexC9Fpy9HciBQLf5RkPQABMjLSqiC83XMpOvMz7039iM0/rTfI9lj5rnW6cgriQ7EZ8TE5v35mkOe+pnb9/MC+qDOSWLJ4JWvXriahlnlcmYRuwrja6+3W/P9P1dCfRqA/BJpV+9+04phIOp3uETAOQBAEC2C8TqdT18v0wX2QKXD0mGJ0Tt59FFfO+KFs349jxw/h5v4iN5OuVtSadEf3II7MQ8uwHvKeiOPWpMo0nzVrFNaslCPrMpzowPUo2/enKDkO06adKb4fT7kmF12b3mgrcGpBkGBagW1Xr/K+9afvKCrMx6xDf/KvnEBqbktBdjrRj26JdTql5rYV5d9q35arXD3Q3Igk75I/KlcP0q9H8OWXCwz6aTVyJiWHfLB9aaoRLFWZYjX7+Pe88vJkJHJHA9ytvio6ddVyrInbRZ67TElpGbYjjQWrZe+x5J3by/sfzSdVLTPC0Ce9M4sfV38lwhzZob5IFBbkXTxMQXyoHuYI8UVqYcusmR/TyXUcXy4eQ0jgAQ4f2vnEVLPV7wGIjb9LZNQNnKytKC4uwnZs1bbc3msu6QeXokmMRNmhP1lBG+jRvTcPrgeSnp6KssMAcs/t48OPFxJ16RrRF45QkvkA+9Eza01fnHPWD6uBbwAC2cGbadm6O42bd+ejaV0qtM0qLPdpMdPK2qDXasG0swN+4JV/TeW3A/sN7DJWI2cSd9gHq2pGRZlL3RWAKlPfOjbuSHxcKCWZD1D10OP0JoJAzpndaBIjUfUcSVbgBqQqB+TNupCyfaZYk7U6/FZdaJt1H0VR5C9s8d0o5uU3fnfeFCadJz/mODIXw+pLtdlvFG37Mmv2v8nOyqw1/bOiuwfHAg7h1vdFJBLJ/xTX/rth6BeBdoIgtEIvyF8FXq9+gSAI9kCWTqfTAguArU9iqgOc6kidaunmTf6VkxTdv4ZOqSLgsC+nT4eh6q8vQIxUgdTKSfQ2qC/NZ+iJXZy/eFHEtafN+IJtPy3n4do3UbmPJ/fCflHjfbx9Jg/XvQVaLQ5jF4hacN4lf0wdW5F1aiNeHmN4rm9nyotSeNlLpgAAIABJREFU2bxuCaVanXh/yo5ZpP32DYLUxECLzvBfJeJ/ULshzqLHcHLO6IWzsudIys/vxqE8g8fVKgw1mWyoqVXi7pWkdPUkKiqEd999B0C0FdS3pa6vlmN120C9PNy8KL99Hl1ZHs/17WnMw6UlPktykVhb6As2jJmP1MKOtN++Qd6kIzmRfjh4z6csN52EUxt57+PP6O7SktnT94kacuUz15ZqNiGo6p7qVNvOQH18FWXFxThO+FwsN5dw7RKCVCa+s0z1Q3RleSTfvohZu34AteZJzzi6CpWrJ1Z9xgEgNbMk84Kf2I/atM2n0ch+T4zhWNylWjFteeve/LpvC4v/s5LVP65GvXchsi7DyLt0GHvv+SIUUeturYKqp91NjI9A0bYvEpmCrKANyJ1dKE27ib3HHMoy7pMT6Yeqlwd5VwJI3T0f60FvkRO5xyD/fs00yZrT21BZqtDY9TB8dwGrsXT1Mpjz6tO/YKorM6iAVDkWMdEXxF2qTltO+oEzBruj6mTh6kHW75EkxZ/hX69Oqnd8n/Y9/Bk8/nINXafTlQmC8G/gJCAFtup0unhBEL4BLul0uiPAC8AyQRB06CGXj5/EV6IwN6qZaOnmjarCoq3VqDFr605p5n1OR8eh6DPeoJRZUXIc5jVcpzJPrMGi5ygx1zONOnHk6CHMOwwQvT8iIqPJyEhH0caNnLN7qyoCCRIcvOeRfnAJtkM/MNKCAcw7Pk9YeCjqQkvOn/ZDq7RG6dzJoOBvei1adFbQBngQL3oaZJ/aiNTcmrzYExRUq6RuauVI/rVgcoJ/4oOPF9K2fRe+X/UNaUd8DLbdUOUZUn1yW7r9P/bOMyCqa937v2kMvVfFrogFEFRsUZOYRLG3mKYmMZrEnIMlJhpbql2TGDUm1thrVLCAgtgRGyrNgoqi0qTDUAamvB82M8wwA5pz3vfmvfee9YVhZnaZvdZe+1nP8y/DyNxziV9WrdIjVpYsXWgyqdXN8T4PAfTv7iP1TgJ2dg6UFGQgcfJCYuuCujQfsYUl9l2G4lIDUSyIXsuwEeP02036eCbr1y03Moxo3jKAjCt/6g0jCqLWMmz4WLMRzvgPP2fL5pXk7pypz69bWlojbRpoZDdXGL4E+361fWbpH8LOnVvp8cp4rsXuJT9fSCfUbfbBoyhPjUWr1aB8nIzi7Ca6931Xv0Kpex2fF5GJRJUU5cxnw7oHOAwxXZVUpidSduc8Vi27sHDx9/Qb9E9Sru7nbtRarNu+ZGREUhi50uxqrS5i6dnOmfD4OmVKpVEQosp7YmRO4TZ8NmUpZyg8tQmvD1fVm+rUHbd5K1+ja18Usw6xhTXlqbGUp8bqEToSiYRRNSiauiu7JUsX6lephisD3bUQ7vdB+oe73N8YHVb3+r5oP/zVz/5/itCfWxT9f9VEYolW3qit4JloUAFXV5SiURToURs5u2YjdWpkBJHL2jodmWtTqnPTEYnFgrlxzT6KL/2JWGqBZfNAPQROl0/s0aENZ86eqnWa3zETdXkxUjtnfbRh2Axx0MqcB5TG7mb58tUsXbaIMqcW2HUbTUHkKkCLy0Dzxd3c8CXYtuuNfVEaQwYOZt/+Xcyd+z0HDh7kUtxpXu77Ctfi4/n66wWcO3OSY5FHePvdT5g08SOux1/mq6+mmY2MS66Go0g+iahSgXXnIfqHmCI5Bq7t45sF6wno0JynT9KZNfsLStRiZB1eN87xKlT15ngN8+n/zj7UlTnMmf05khZdcCzLwNpCwoPMbLTVVVi1Chbgq+8uIWPtB7w1fASffPa50bHjE1L5c9caLl2+yORPp9DWrw/t2jTim/lfcOnyRT795J+08WnHkqUL+enHWnTR/gPhHA7bxoIflnMw7DDnz0Ywf/4C3Nw8mDV7BgVKDY4DPzfbZyVHl7F4yc/cf5jFxnVLcRhiXnRNq1GTs2u2IEGbcJSpn4Xi1TTAKJoy/C31vQZIe7iH5o2W8f5HUGDTC+eQafoia2n0aqRNg1DcOW9kFNHS1cHIFUt3LvZdh1F6NRzXnKusW7edpNuP9ccaN24UBdbeRkXXkmPLsek7wajoWnxxN40/2agv2Evt3PT3go7rUXd1rAvMmri7EBo6g19+WY6rqxcJCVcQa7U41+TYS+OPUnr9CC4DpqDKfYhL9tUac46WRtfm6ZN0vv1uDhklldj2fJfi2D0gAlv//hSe3oSt/xuU3ohA5toUu8AQimPWs3z5aoI6dzO5vi/aD//KZ/+VxwLo27NDvUXRv08+d+nyb6uUFVRl3tazx2w6vkrRuW1Y+/RomEottUCRcAJJlYKOPr6kXzqC1NYJuXd7qu9dxNuzJbl3zhvpZ+vEfoy0u8VSlE+SsfBoRWn8EbM6F459x2PdpgdyLx+U968gQ4Jb4yBKHsdTknoZx/6hVOc/rZdG7vTy+9h3G0XejWg8XDz4cNKX3H/wgPBDOwSUwLMn9On/KaoqBfv2/oG8VXceJMQhllixbOn8BpmFioQT+LVugyr7Lnk3otGKJSjObOL9CYIsgKUcHO3XMHrYdSy0eaTEXOPbeVW81PMcA/vnIlXnkXLyGt/OryIwIAI3xz2gWY9WvU54rT2FVtQVB0dPtDJvXKylpJ0/QJdebxLcvQ9dggW5gzun9vLRx1/g5tnKiKaskzTWCa3lXj9B7tMHoNXW6JwM1qtQWjZuT1byRRPqe+yVuwwaNJQBg8bg6tGM85duYW1tSceAHti4dMTOxqJBydWLUYdo6vMq77//ESKpjV4mQq7KMysToaOYe7fw4+cfv0HUJOBfFl0zR+EuKc5j1/Zv2PnHMtb/tpawA2vJzfmdDr5nsbdX07WzmutnM8m/eRG11oLS6BVMnVzMhcjHyFt2N9KVeXolAtehtboyiIRzse8y1Ij6/jRXoz8HnVhX2e0z9YqE5R9frZca0GrVFJ/diuL2OdxHzmlwMs8NX4JVi0AKHt8l6sQRKm0b8zT1BlZteqDWqHDs+z4isQStqgrlk2Rs/V/HskUQz+KjSI6/xq6dG42EwHQCXMcPbKYy8w6e4380EqorvrgXx5ffR+bYiKLYXUhEMGDI+//ldPz/UP+B+V9//a37qHmCbGvNYBKJxGiqKim/fY6yO+caVHKzkslYsnQlH3z4MWPHTqAgt4DHcWH88MMyevYJ4b233yL+9NEGxX7yj6/GqnU3ym+frTFgrkMD16govXpYL72qlUh5HBfGpI9D+WTSRzxOTeR29G6UuQ/N08i1GspTztSYEgjb9u4Z/BxhfkH+NyX+DKKmxo4+ZuVob59n164w8rOyeRwXxvffL+O1fv1wcriNb/PpWFveRipV06GDhjGj1Xh5aQA1YnHNe2/WvicSqRHASbrXuaDZQ/OmXjTyepkePXrzzrvv4+wiROE6uQNBZrW7CU35t18XGqlgypt0pCI9EddB00wmIZfBM/6yxHH6w1tsWrfshSVXTeSJzWiq6CjmH334Ie6erUhPPPsvi655ujtSUpxH/MUwNq9bSHzcMa7EHiaoUwbTpsHkydCnD6SlaVm5Elq0gPbtYeAANVJ1EYkn4uncqYqICChXqBAVPaTs5jEkbq2Qe/lg13lInfviR2x9e2HZIsiI+j5+/AT9NdMJfikLH5sVCTMMYkDQ3S+/G4vExhHH3mPJC1uMvEkHIVCqGZdZ22dQduuM3smqPDUOdXkxmopivQpm+a0zaKsqUWbcJv/Emhqhs5PY+r+OsiCTnFtnoUmAiRBY1tN7nIw5gfPA6cgcvZA39sW+y1D97y6/dQZr396o713k48lf6YW7/ivp+P/tqP//L5rE0dNs5OnY+z3Eti5oq6vJO7zM5PP846sQWznQspUvgUHBwr4kEl59fTjz5v3AzyuX8SwnA+8mzVi3bhs+ng7mXeWP/oiFV1vKUk7VW2ix7zIMsYUlmRsno0iOoSD6d9q28WHhd1M4GXWM06eiqa4oqZ9G3mUoWpWSvKM/UXJqAxKJmOXLa/OCIrEE2zemkJz2UE951jFFRYjwUudRsPsrFEkxlBxdxujh7+KSfVVPbVac2cS8eT8g4g4fvX+JP/fY0anjMjTKAbRsPBtQ/As9U7dpePjwW7RVQ9FoHv2lLSd9OgsvdR5Fe+foaeWNJqw2pvzHrMe5/z9NqO/1tdQ7CYwbN4qnT9LZv3eTybWMvZlsdC1lHd7gdMwR/fbX4y+z4fclDVLMdTIRhjIORTHrkHu3x7HPeDzeWSzIHUT/jq1vb+RNOujP3/BYl+LOs3LpP7CQ/sm8eWU42MOKFfDJJ9C4MUgkwt9Jk2DhQli8GLKznQgM3I9Pm2ZoVVU0bgyrV0NUFGzaBMNDSigJn0/Fg2tG5513ZDkD+1vjqXz8XOr7/dRkzp07g03fCSaf2QYNoihmA8UGNH67LkNRF2SQt/NLrH17U5EaR+7OL1EkxfDs4AJEFpZY60g/YokQHDl66e8LkViCbcAAiuP2UXRhJ+6jv8YlZApalVIAHdSkRp0HhPI0r4j5X33E0yfpXI+/zJzZn2PTfQyFpzZRXZAhPEC2TKW6IEPQ7dFCfvhirG1scHB0aXhA/i9of1uEvvDn37616zRAL6dpaPelLn5GZfrNen00q3IfkfPkPoHB/fXLEHOORXt3b+X06eO4DjEV1keEoNDXuJ3gwNPMn+pnj8je/RVVBRkUnd+BVfNOSOxcqHhwjarMO9h2fI0H106hcvTm7PEDYOOEZfNAM9KgGgOfTgsU18LQajTQJAhZZRGSsnzyb0Q1qDD50cdf0n/gWzy6/4jCpBN8OGkGxUoHRo18E1FVOWnnD9D1pRG88coFLGVLyct9xvbteSxZksf69SWEh0NenjBh2Nv/a32UkQHbt8OSJbB+vYLwg7vIzTlJfqkrYon1c5eH15OfMmrUm2SlpZB+6Qg2nYxTWrl7vsLexpqyjLsm6SKzS9Y6HpJ+XYaRlxprdC3NaYy3btuTPzavoqi4nJ9/WoCsiR/23UbqVRuf7f8GrbICedOOes9MnXa+laUcV49maCQeKLJvUZhwEo1ISuX1MN5+5xOyU68Zpbu69ByNs6sHySm3WbYwlMWLlAwYoOHgQWjXDgYMMHelwd0dSktFFBS8TUBAb8aN+50FC1QMGCD0n1gs/O3SBQID4cjvccjb9EFiZScMZxFUPolnzcosZFp/4o9FM2GSaRqsPvVNXZN7+VB5L47yuxcF1c0aNcR2HV+mOOcBmuy7TPxkJoriSvITIpBKpWiklmgqFSiSTuqdrOzqKF4WRq5Eo6rCpt1LRjaCZYlRuA79Qm8IUnLzONJmQcSeOMiZM6dQ2ntRfucCcu+OlFw+QOnNSEFWuSa6VxVmosy8i6hpINdPH6Vth+4mHqL/m1Iuf1tRVCyz1Dq/8Zm+mFlyNQxEYqyaB+rFl+qKN7kOnYmq5JmgB+LugUQmY/Gin4i9cNqI2FOwZza+jZyIvx5fb/St1ajJ3vxPqkueYe3TC2X2PdQlz5Ba2aGqUGDt0xNl9j1UxbmILW0QS2WoFAU49RmHIvk0EmsHVCV5iC3kIJYKGN6Y9Ti9OpGypGi0GjV2gQMpiPoNMRpEdm64jZxHwYFvUSqKEMusQF2N5/ifjApzGWvfZ8yo8XzysYDPr684olFfRa38DImknMuXhehu0CAYOBA8PSE7GyIi4NgxWL9+LsOHf1RzBCUgN7gShv/Xvj527DATJnzOwIEas/ucPW8OPXq91+A5GhZFzUXEimvhuGRdpV2HLlw4F8m8eT+YFLR0r3XRmv2Qmfoit29jLxITroJ7K9RlRSYaMbkbJzE8ZCAHDu1H5N6aqgzBQ7Mq7SpyF29EXu0ovX4Em7YvoUq/jsTBE8uAAfUWeA3p8t99t5jAoGDUajWrVq/m/NkI5s37Qb/N6pU/YCHdx6RJgrDcyJFCpN3YuAZr1DIyYPp0e955ZzT5+duZOLG63u/+tk5CdNoA7F8ReAFajZqivdN5b/BDxrypBVqCbANisafR9axbFNUVXeUBA/UTrSI5hsKYjdjb2iCRSHhrzLts2rwOWctgGmnyjAqtarWar2Z9TsrDh+DSgurchybIl9yNk5j6WSgSuatA7y9T6UlTuqZTWXWrQd8U7ZtDQHMvYuNi9XpNWVunI2/cDufXPiZ72+eIZJZU5dzHffQ3NVouX/Lh6BG89fb4/7VF0f9bTNG/3GytrSmKWY/ExhG0oCrJw6plZ0qvHzXCHpdcOUTRhZ1Yt+1FbvgSVCV5yD3bkJlxC5u2LzH983+Qm5OJta9A4rFp/zL2A6ZyffsMrH2NYY25R1Zg301gJyqfpKBSFOgHQ9bW6dhYO1CpLKvFpW/7HMvmASifJCFvFgCZd7HrMgyxlQMFOjJSVioo8gQqNloUNyKwDRpIYfQ6CqLWInVugqrgKdaNfMk9sABVaSEyl6aoCp6YQM0AbLsMI+zQLoJ7vGpCkIiMOsehfau4cfUkJSVqHBygWze4eBEWLYIOHWqvr24p37MnTJ78E507j6BVqxZAOWBt0BOG/wuvHzx4yMSJs1mwQFPvPufOXcSva6/g3fwHHjx6qv+O4fk+j1ZuEzSYzD2xqJJuIZVZkl+s0gsp6Ygvgd1HAcYQNpFIjO0bU0gMX4JVt9F69cS6Te4/kD37d2Pf822KY/cgsXXGrtsoivKf4Ciu5sm1cKQO7th1H01pcSZ2Ygnll3Y3SAQyFF3Twexkdj56UtP5S7eQSgo4GbWXNWtqg6XiYuGh2FDz8ICCglJ27drHypX1T+YAQwerCZtwDFVuGtW5j1CWVWJhJWP7DhGtW2mJi0sjJqYfxcVgbWNJ1+6v8tLLI03EugxFwvJSY7EKCKEwZgOuw2ZRen4nfs292bR5nR4ZlrlnthEsNvVOAtevX8Ku51v19oNlwCB27NpBt77v84+p37Nr6yoT0lTh6U1Yte5m1L/xR5bhZkBYsu9SK2DnOnRmDWu3NvCz7jToP7DFvytC923XUfvx5Ol8NXMKGgtrXAd/LuhL5z8h98APiCysjKJ13aQrsXEUZGf18K3pWDSqfWpbtgjCqe/7VBdkkB/xix7mVBC9FtugIVRl3AKtFnVZIXLvDnoNjuqCDHIP/IBz/8+M4FsF0b8ZwcKkTo0EMSyd8uC2zwnpFcznX8zjcXoa//znJEpLS9BqtTj2flc/yHXnL5JIqM57rP9NhlAzqFk5bPuciW+NNoo0LsWdZ+F3oQweXG0UMS9cCH5+QoGtvrZ2LURGypgw4QOmTPmEVq3aGXxaCVgavZ46dRr5+VsajBA3bICqKpgyxR61aAViSS/AOJoYM2aokS6JLhq06jQYm6BanZOCqN+w8X0Jx9KHiCVS+vQdxIF9m5G06IJV/j2srCyZGvoFv6//lazSKj3RyhBWWu8qbOdMVIVZaNUqrH16oCrKwnlAKIWHfqCqrASr1t1RFWVi4/cG6it7OHwkRr/9X420tFotGRm/0MhtE/36aYiKEvLk8Nci9Pz8UqKitPptzbWLF+H772H4cBgypHY8bNgAly/DiBHCiq12ZSXh2DEZc75eidy2kV6s691x/2T0yKGo1WpGjnidEkU5rsNmYdW8U72QRuIFWKxu9aXjiDTUD0X75uDXojn3UhMpLi42echXF2Tw7M/vkMitcRliXpvp2YHvkdi74T5yntnPC8IXsXz5agKDgv/XRuh/Ww79+wWLvo2IPILT8Dk4v/aJPt8msXbALnAg2molJZcPGLnBWDbzR5EUg+tAA+ihxIKypCjsuw4XTBJid+PQ400kVvZ6P87C05uxsbaBqgoc+4cikshQPr2FRllubExg4CKvQw3IG7fHoecYYwilgUuPSGJBWuwR2gf0pbJKhNS6FWmpl7BoFUzFwxtYNjWw2GvmjyL5FG5DvjBCeRSe2YxIKtfXEERSCyO3ckVpOssXhrJokdokp7ppE0yf3nCevFEjOH5cg7d3ErNmbcXfvz1t2jQFVAhRucjo9fjxHxEaWtngPj094eefYf9+Jet/P0LYgXXk5maQVyJCLLEiJ7cIhdKaksfxQt65jvuSLu9cELUWx95jsWzeieyrEag823PjdBhOw2Yhc2tGXnwkas+OJMRGEzr9BxKvxuk9JM3Z6GXv/LIGVihcS1VhFsqM23pHnrLEaCofJ6HMfay3jVMkRlGefBKx1BIPr9YoyjV/ORdqa1OArXwCTvZnAC3h4QKCRXcN8/IgLQ06d67/mu7bJ6Nbt3e5fz+Vl16qqvf6Z2TAnDmwbJmQYtONh9JS2LwZli6FkBDjcdK5sxY/PxXffXMSR/e2+Ad05+VXh3Drfj5yuYxneSV08OtCYsINlJm3n+t7eut+vt5L12icG9SSjOpiiEk9s5eysjJENk7YdQoRzGdq9ltwYg0uQ2dRdv0oFfevmNjt5e+bi197f3Ie36M6PQGbOhDjvD2zCQh6g05BPf8DW/w72rx5c7+1aN3DaBAU/jkPlUqFvJEvlt7tQSxGkRBFxd3YWghjoCn00HXIDFRFOeQd/RHH3u8hb+wLCDBIeWNfxFI5HmIFfj4tuXcuDOeB07DvOhzbgP4on96m9MpBE6ebvD2zQaVEK5Y26EZTdPwXbG1tGDFsCD6tm/P40W2uxp1CWZCJSGaBtlJh5F9pH1T3obECu86DqXx4HcWNCKGIemYTCxYsJzCgIx4u57gQ8y3t2mnNFtTWrxeic3EDeCVra2Hi/+UXDR07qggNjWD06LdxdnZDmMwtEbJvwuvZs797oX3+8YcQEQrwOy1pD+5w5MAFenTvRZegAOztHflkkuApmhK9W+++NGzYKPKzskk6/gcWjdtj7dNTbyRs13kI5Q+uoC4tpOTSfv17xUmnoDyXhJuX9I42Iqmc0vgjVNy/DCIJ+cd+oqmXBznJF6lMuwZiCcWxu7H27aXPD8ubdKD0RiRuQ2sfqiKxlIq0a8hbduVx0tm/5EPp4eZAE88/ad5oIVJJSe34qTOBN24MK1cKqyl3d9PrmZICGzZYsnHjJsrLS0lMTCYoSGP6RYRCddu2wqRd9/3nF15VVCrEDB1q6gL2PEhj4Z/zmD5F8D1Nf3iLu7dvYKspp1Kloar4GWVJJxFJLMg/9iMySxsqcx4I70ktKIj6DaRyRIBV80DKkk8isXcje/sXlN06g2WTjijij6CuVJiFEKPVUpWRQkVFOY4hU01dyYDi1LNcvRzDkIEDcHNz0/+uzCd32bn1Z0YMHYhP6+bI5TLUlTn8tOIb+r3yCj27+f+PgS3+bRP6b79t/NZaq9RHbyVRqxk98n3uXjhKcVIMSGUUn9sOahXNPNzJuhGNXZDxpGuImc3e8QW2nQbg2PNtk2NZeLUh+1okqUlX0UjlWLUIQmJlj/JxMkWxu8z6Nao1aqqKsvH6YCXKp3fMEoee7ZqFuqoCmgQRe+IAKo2M31b/gFYsxbpNN1QFGSCRYeHazDzxaMeX2HYagFPfD7Dp+KqAb74ZQY+X36VTpxZYyz7F1fEYS5bA1Knmo/C6kaC5lpUFp07BW2/pbmpITq5gwIBXMBehr1nza4MRouE+J00yjALBz0/NN19H497Yn+tJT/QokbrR4NNcDcOHDSf15gVyrkXUGgmLJcLNXYdMptaKSTi+Q2/HpqN+W7bsSnX+YyofJyGRyijMz8XKpyfV+U9QPknG6eUJVD68ieLmcSybCggMe5OV2Aoce4/Dsc+4v+xDWVYWQxOPVSbXp1Wr5vz0UyUdO6pwdxeuT4sW8N13wvX39BQeillZQmS+YYOcHTu2EBwcgI+PN7Nm7dRvW7ctXGh+VdbQONE1T0/4dU0aY97MBHEwF6/eo6pao4/+Dh46Wr/vqVrLnQvHUGlkrFu7GLWXH/JqBY282lKSc48qRSGVjxJwdnbjlb79yUi7jadrE3KuH0Pi7I22UqF/QCsSoii5GoZIJMJ95Fxkbs1Q3Iw0EvkybDLPNuRcjUTi1gJFYrSAUGsimIvb+PVDU12pX83FnjioJ3idP3+WJYvmoPbqwNmje2nVNpiDh46wecMyqtx8uXYqDN+OPXmWV/yfCP3faRs2bvx20+Y9yDRKEiJ3sHDhCgYNHoJvxx40cbXm+tE/sLSwYODgt7h54zKOA6eZfWqX3TqNjV8/EImpfHRD7/lY+Oc8tJpaEk5VQRbKrHtYteisjw5ywxbV64so9/KhLOkk1bnpVKTGCnZndQe4VoOqNB+3UfMpTj7NjdjjVKu1+kFblnIamaMHlek3zROPRCIqH17Hxq8fIrEE67a90KTH06V9Mf1fWYNMKsjJNxSFv8hSfu9eaNYMggXYPh4eGlaseMCXX36FuQg9IyODxMSkeiNEc/vUNXd3UCjgWbY1vfv0azDq6NnNn+HDRnH92lWy7t2g4v6VeldC+cd+RO7dHoceYwRPz7BFuI2YjX3nIVTcjcXCozVVzx7iPmp+zXsXsfV7DbvAECR2bpTEh1ORFo99nZVY9s4v9YGAzj/zr/hQOtmngeYkYAjzFLNjRxEymYzoaCgrE+PpqaFNG/D1hchIEVu2wNatIs6csaW6uiWFhcX88cce1qxZS3l5FePGfcicOacpLRX6y3Dyj4/X8NlnpuPhRVdrGzfC++PvIBXtx6f1ALw8/PSR7JZNKxpkJxfcjObSqXBEts44vf4pivtX8XK0JCcnE+fhc3B+/VPK7pznpS6dWLjoJ1q09ue9t98ifP9W5K2Ca9OnTTpSaUAyywsTPFrtugw1ZuVSy8oVySwouRqOVlVdc48NRpFwAmXGLUou/Wm0mmvmbkPTxk4sWzwXp+EC4Snvxgmqi59y/kw4zjXvFSedpKmrNa/1e/k/Efq/05at+PnbNu174urRDJltG5o3b05ObhEXLt+hS5euvPbGSLybtGTXjt/qxczqTHK1VRXYdxmC4kYElU9uobi0V5+rLUyIQZmfoS+u6nKUG215AAAgAElEQVSmpfFHaiUGdIy3HV8A1A4gqQUll/bVGzXIvXwEBly1EpuuI6hIi8d5kIHxsdRCIE3U+9Bog+JGBBUPr2Pdtpc+1/jg/GHGjFbpv9dQFP4iS/nff4cZM4TIcPt2oThXWKhk8eJFfP/9Mn7+eSWZmU/w8fHG2dkJH59WzJq1td4I0XCf5s7J01PLr78+pGmbHkbRn7mo48KF80RG/InL0FmoS/PNrmTy980lZMAIirPTybsRheL2WQH/r5eH6EBp/GFcB00HLeSFL8HapyfFF3cjlsopOL0ZNCrchprjI4j1D1Xl4yR9jrhaI39upJWbl4Wd1QKk0mIuX4bZs4WUx9SpWiZPhr59VZSXi4iMhKgoSzZtUhMfb8fw4WPZunUV/fv35+DBQ/TokceUKUomT4aXXqoiMTGJtWtPsnz5EkpLXVmxIo0NG6o5d86Obt3GkJp6n969q02u/V9drWk0SrTqw9x76EtGtpjFC+fUMnvrmVQ1YgnlD65h1SKIsuQYHPqHkh531EhSQ60VG9WA3NzcCAjoxoVjO1DcOo9lUz8TrLpIKqf0WhhVaVfQiiTkHV2hZ+UqbkQiksooiFqLxNoRq1ZdjFZzpVfDcDW479RaMXdO7SX24jm03gG146SpH7di9hndo0gsuBK+Cf+gfv+J0P+dtm7d+m8nTZrU4NNI51jfULEFsZiSyweEoqjUgtKrYbzabxDXr8eyaMEyXGytiDu2EyufXkbRQcW9ODQVZZTfPltj5bYSTVUlakW+8J5YTOHJ9aAF5/7/MC66oTXQ8pBQfGkfTn3GYdNpoGlRtSaqrO/8RVILym5Gokm/jBopZWfX8t18JV5etddKqezM3bt5ZiNm3VJ+/nyBmOLlVbuU37tXmHhnz4aSEuGvry9MmwaffQb9+oGFBdy9W41YnMiCBbvw9+9McHB3/P3bExoaYRIh7tgh5M11k5e5JuTsq1m08AO8PNrUG3WoK3P0MggiRBTH7TW7ktFo1FSkX2fDhu0UZOeQ+eguDuJKChNP64t3doEDhbxq+BLk3h1QJBzn7THv8vBSBOWK4no11Q2DgtJzW/l82hcMHTrsuZGWn+893B0/RSotICNDuB4LF2JStO7RQ0tAgJaICBUqFVhZyfHz88PT05t33hnP999XMGCAxmiboCCh1jFnzjk2bvydxYuX8vXXcxg5ciTR0dHcvJnE/v1aE/LYi6zW9uwxXlmJRODq0h9Pj8706N6TC5EHKE46VSN1YDCp3qydVB17j8Wxz3gUNyLQVlfiOnw2UgcPYXzv/xrV/TgWLvqRTgEd9NetuDCb6OgIxE6NKEs+ZfLQzt3/DTOmfUn71i2J3b8Wi8btcRk4FVu/fqhKcim5uBvHl97Dsfc4yhJPokg8UUtiqlOXUkT/ysRPvhT8dDPuU3L5ABYeLZE3aotd0CB9/l0klQvm5pZypk+b/j8iQv/bqP8v0hYv+gnHskyK9s5BkRRD7sEfkEoklCYcJ2vbdD3kzcLSUv9a6tKEUzFHKbD25ocF83lzzFjeee9TKu7Fkb1tRq3r/Ceb8PpgJdY+vSg6vx2tVovHmG/xer/mvXPba6jPIrJ3zBSOH74Eq5ZdUcTt09Ori06uQyaTmZx7/rEfkVnboyrJJXvr9HrPv+zsJhZ8K+G9QalwbS2LvlcSGCjsw9KyLd26JTB37hYiI2WkpJi/Tra2IJXKsbR8i8mTZfTvD6GhAqzw11/B21sgHi1cCB9/bEo7X7QIbtxQExpawdixY3nwII2QkNe4cuUyrq4fMn26PQMGiJg+3Z7ISBlffy3g3+trOTk1UWL1m2g18fV+7+eVgia3VqNpEH5o13ko2YpqDh7YzauvDyf8yCm2bTtA+yaNKDm2HDBWxnQJmYLcxZuCgnwKCgtNZFefrhlH8dVDemq7baf+AsGo8zAOHNyHRlN/qgnA3WkXqKYBVQCEhQkQQUPMvmHr0EGAEY4cCStXlpKfv4Vhw0bj769scJuQkGpWrxY02CMjT9C5cxfKy/9kwwYN0dHCSsvCAv7xDwGqOHy4QPqqb5ykpMChQ0IAYNi06mi0Wi3eTZrxxawljB02iOJT65B7dxCkDt5djGXzQApPrsOx91jsu44QJAE6D6X0RgRarUZ//eVebXFycSWgUy2qzpDCX5V9H6dXP6Jus+06nJ9WLqVHj97Mnr+SJlYaivbOQVWUjVPf92kybR/2XUcgc26MQ693qcp5QOHhJSb7KY1ezbTpM2nT1p8PP5gE6mqsWnUl78gKo/OUubWg4OR6RBoV3/+w3PwFM2jX4y+z8LspPH2SbvTeuHGjeJaT0cCW/7Xtb025tGrXo8HlhU4Zr5m7DWnnDxD80hg8XWxIv5eE3LM15bcvYGFtj6utJTk3TmLT8RWqMm7jNnIudkGDeRYfRcKVi0RHheMyfDbqsgJKr4bpi6s6FEzJlYM4vfoR1m161L4XfwSnVyZg69efspRTVD5JwiF4FMrkE/j69aUw8w7V6TdRVVfhPORLk6hShAhNVioWqOjsF8DjyxF8/OksPFzdeJp2i0ZuTSi/d4Lv5pfQtasSR0dQVqjZskXIhR45IkckGkj79u1p1aol/v7+hIYeo7RUZJJTXbfOgr59exETc5qSEiUymRCBvfOOMGm/GPpBwCv7+uoKpr0oLCzm8uXLJCUlU1FRjZWVjJYtm1NYWEiPHvXzFwzz69m5tqQ99jbbz/1eeYU7V6KFomjrYOP86c4v0VKb/jKk46c/vMW8uVN5lHYPxxChtpGzb77Azg0agtTGEXkTP1LPHsSu9zgqH96kLPkkiMRGUac5ga0XKYraWK7B1roW0fKixci1a4UHaFCQhoAADTt2aBtMkQi1jnuMHDmY/v0HsXCh0gimWFuIFoqtXboIBKY//oCyMsyu1saPhy1boHdvw+PeA3UkWnEPLsVn4tPWj8DOvbgRe1JQZfTuiE273jh0fxN5Y2FZpkOYSW1dqMq6T3HcXtyGf1WTq44iLysHa3svzl+6xc6tP1NpJ1D463toy718KLt7kZPH/sTevROjR43h7o3zPLkSaVr32DYNAOdBpmgYXeE2K6eC/Xt/x2VEDXLq3iWUT+8YnOdgKu5dQlyt5PWBYxssip4/f9ZE0fPgoSMmUiPPK6z+V6Rc/jamqLWV3AgsX99r4f/BTJ82jYSUR6xYMgPbdr2NTXSPLcd99NcUntqEVetaWy77AVNJOSo4nqCFigfXzDLZ7IJH6D09dYxNu6BBghN9SR7WrbuhKsqkMuEoo0a+yYGD+5G16IryXhyu9bgu2XYeQtW9i4wbMcSEijxt6gcoy4ZhIRMmBUPq/urVOjKIksjIHQQH72HHjp2EhAzmypXzrF69nqlTd1FYqEAmA42mGpGomvv3TzN7tpaAAGFiPnxYEIH67DM4eRLWrGm4PwYOFKL61aurmT59P/37v8HYsR8SElLNypXVNeekIDLyPgcOqGncGMaMMd1PSooQJf76q/C/l7sTjRrV38+v9u3OokULOHMmgoLds5D7DUBxdhN9e/bm3IV9VN0T2Is6Ov6DR9ls+G0x0hZdESPYA1amJ6KpKMWqVVdyds3C490lWLg0we19AX1i064vBSfXUxC9Vh91otXWvPc7dh1e1Qtsyf37c/7sPqZPm2b2fAEUxVZGv/lFWaDFxbX/d+gg9HdYmBBh17dNQUEZq1atY8CAhqP5N96AuXOFB+nChRAXJ/RncTFIpTB0qNAnjRtDcbGYsDBNneM+hOqhDB+wDLGk+XO9aHUGK/JGvuQdXmY0UdsGDeVk1FY+nz4dgOFvrObDCW9j1aqrGfep4Xotf7vOQyg8uR6V4j6pyXl6rXfDVpmeiFarbdC5qOjBRa5fOmhk0aezyjM8T7vAQRScXMfdpHO8/e4H+n0Y9rm6MsfEgzgyfJPe0lHu3YGCGq9hnVNS3X00PLf9a5/V1/6/itDPnz/Lzz9+g5tHSz2548DBw/z260K9TrIuqsu7EYWFdwcj8oNlU39Krx0R8mt6saYQfW61weigJo+qw7DLvXxQJJzAqmUXXPp/RllSDBqJjMSrsbVP/dRLiK0dsGzcTi9vq9Fo9Plxjci4OKRfeZTH4OJwAqDB/KsulxoaepjRo4fQqpUHWq2MgwcPMWKEiBkzNHz6qZALLy0V0AutWgnReNeuEBAACxYI0Zo5VIRh06EfPv0U1q9XEhFxzCi/m5UlTD5RURrKyiAhAc6dE+HtDW5uuihQpM/Z6/Lr2Xk+9UboOgjjs2ILJn70CeLqSr3e+sjRY3m9/wjSH6RTmHiCDyZ+TmGRgt9/XaRHKJTfOoPy6W3B1k4n3Xr3Aoobkdh3Ha7/bSKRmNKYdQzsP5LiHKGwilhG5fUwAoMHo8q7S2FCDBqRWC8QptJa1hslycSHjCL0v1qM1DVPT2GSNXyv7jbnztmSlJTMlCkNw0gbNRIUGcvLhRpJcLCw39deEx7oP/5Ye36enlp+/92GceMaoVYbPGXQUlGRye37XZ8LYdRqVJQlnsS++ygTIa78iJXIJBI6Br5qVBQ9HbaZ8tQ4RBIZ+cdXY991OCWXD1B+7zIiiYTCU5uwDRjA7QuHuXIlzuykLWDP/Yzqarm7BbSWIYlJ+fAmVfnplN29WFuENSOfbRc4gPioP+stiurIU7oirM5XwSpwECVXDmHZLACsHEmI3IFU7sh3385EobTG3t7xf3dRVFcgE3n768kdWU9T2bJpBepGHfU6yb26BzB82CiSrsbx9GqEEflBYmWPIm43quI8KtMT9YYV9TIKwciooPDMZpBIjWBSZYkCC1Xu3Z6y2+eNqukiiYzis1uR2DijiP6V6VM/586FYxQnnUaNyIggZFjYcLS7C5ozwIumQ0QkJ2to08aXkJDhZgtphktv3XLa3R0qK2WkpUl4+WXNC004ffpAVJSMwYM1DBgg5JKNERy1BdXSUiGNsHUrnDtnj69vS0JD84yKpba2gXh5DnpusahTx5b06N7dRG+9cZPWTJ86lQ7tfY2L5EbolmlGfVL5ONFoQgdAq6U8PV5fWBW085fTKagnn0z8SA+fXbBgOa/169fg+UpF25BKSvW7zsuDhw8lBAW9WBpK1/QQwvfNb7NjB9y+raG4uPKFHsibNoFSaby/+o+rYsGCleTlhRntRyazx8tr8nMhjELAcxytqlofBAFk7/4KibqKxUt/MRr3rVo2w8vbl+Srpyi8FYtTv4+xDxqEfdfhAiv8igBsqLp5DGsbOyTNg0zAEFqtBvvuoylLikGReAKRWEp+xEospSI8JGU8i49CIxJTfvYPJn7yJY/S7lCSmyHAVYNM4arWbbpRlnyqwaLoiKEDiT99xMhXQeLUmIKT65F7d6T0+hGqUi/w4QcT2bxxFSqvDpQ+iefTSRMbJKf9j4Qt6iJ0I2ebzrW57507N+E49CvsggaTc+046Q/SadykNXt2beHU6WPYvTQWC3fjCk/RpYMAuA6uld4VZDpPokgQBkFexEpkIg3Kp7cpuxurr95LJVKUOWlU3IsDsVhQSpRbY9UiyCxLtSRqNU2b+1OWep4PJ82gWasA1OLanH+XnqPp4NfZ6CmrrNyPndVSRCLhfF8k/yrkUlMpLy+mceNE/URbt+ly4SkpxpjzY8fESCSSF8KVP3wo484dmD69mtJS+O03Iee6dKnpCkIn5RoXZ8Xly2fp06eYjIyrBpK7EB6WQG7OaRp7dyL5bpYexvivRCfm3HZMosPIX3AbNtMkopR5Cg4+eVnZyOx8GD9+AiKpDecv3dKTnwT4bIt6z8PKshq5+AtsrO4a7btxY1i1yoKOHdV/CeaZlSVEz2+bcuFqmKPw7bcaLlyAV155/gogJgYsLWsj/oaOe+6cHaGhQ3j27JD+/YwM2L6tgMUL1hF+KAyJRIVYKkPs2ITqZw/J3zcXrcYYoaVzSNI1rVaDbUUufV4dbSJjeyMlg7Fj3yf/WSaZty5h0ylEX7Oy7zKUomM/MnrUeJy9Aih5HE/BzWg0Igl5R3/EvutwKu5fouL+FWwCXqcs+RSVj5NAXc3H/5hbKzVds5rLKZLh08qb5KSrZuWztWgpvREJahWTJs9GpbU02+dubm507fYyWWkpPIo7gtTZW7/at+s8mLLEKOzkVly6fF7vzJV3PZq8rGyTOoxhtkFRruHAwcP88vN89u3dSXm1Hfb2jpw/f7bBKP+/RYT+268LUdo3ovzuRayad0LeujtPr0Zg2WmgflkjsnaiMOkEjdwd2LFjI9Zte1H56KZAyqmZHSvTEym/fc4E963TddFUVVB0bgtijZrlP67ltZB3KMpM5f65Q3w2eQqvhbxDRvodSgrzKXtwDSlafFs2Jz02jLKU00Y047zdX2JrY8OEidOYN3ce7du1xdPdEUtLC4YPHSBEmq4e+ierjXUpHVrNw8H2qH4yhxcng2zYUM39+w9eSGPFcBlvbQ2bN2t4+tSSjh2rzU44Z84IhbS0NIiP1wAasrKE6FGrFSaTujRzXTNcQdjaduPjjw/j6ys8pGodefJY+dM+Qt4IoEvn3n85OtHRtN8e8yZde7yBsvAxqWcPmtjHZe+ahePLH2Dj00MvJWFILjPn4POi5+HqdA2fZjOQWzwxuQatW7/Nq6/OZ8qUoyYwT0PoaF2Y5549Em7fFlNeLq13m65dobAQ7t0THqD1tb17BUJXcLDwgGnouHv3SqmubsmiRbtZt66K8HBIShLGgA5H/9ln0O9VLarc+9z8M5KyWxeYMX0mt8+Fk3PtOCKphb6YLHWsnSzlXj6UppzF28WKfq/2Jf3hLf7YuFxPx9coc9m9648G3aLeefcDPv14Is8e3yPh+A7Eti4oH93AbcQ8RBIZpVcO4hA8isr0BAYNeYtJH00wWc2lP7zFzm1rcB4+p94VRsXdWLp3DmZKaKhJnxvKBZSVPGP3rj+wChxEQfTvyJvW6OnXaLoXppzBaWDtyl0rlpiQ03QrHl22ISigA5vWLaJSDZVVVVSUZtKtiz9LF81BoayitOARn336iUmU/98iQk9/ksvT27FYNvFDcfM49t1HIXY0XtYoUk7j6ODOubPRuI/+WmBiJsVQlfOA0ph1aDQaii8fMMmv6XDf8kY+WHq3R2LrjOpxAtfi4ymvtmPEqHcYMGgMBYXFrF+3ggkTZ+BgZ8ejtLsMHPQmcRdPo1GrkTfx09OMlY+TKEk+jahJgJGovvnoUoqqagstGn2NSJRnch0OH5bRu/fz0yFnzthQUlLGyZNC5FafgUXdZbwuD7t9+2ZCQ49RXKzF01Ornzx++km4+YcOhc8/r02nZGbC/ftCxP888S8PDw1Ll94hPPwoP/xQbRLJ69NB357DrXEQpeXqF47QDY1LYk8cIDu3kvNnwvXoFsOm1Wooid2FqryEwtMbCXljBPl348i7EU1VQSYlsbsZOfoD0jIqzUZNOi/LulFSbv4T2jSZUmPLV9skEgc6dQqjSZOxtGnjQ9euXdm16xobN+azZYtQHHZ2hnnzTCfVlBTYuNGSQ4f2kZxczeLFd9iyRcupU8JKacaM2m0aN4ZffgF///rJY2vXQm4u3LoFR482fNxfftEQGFjIjBmVTJ4s6MLs3i2squr2XdcuENhJw6kYeOnVt7hy6SyVyKhMu4br4BkA5O6ZjVarwUKHSKqpHUnljkaGJFnPKti6aQVW3UYLgZpBgFSZnkjR5QOUVVaR/vAJ5eXl7Nv7B5bt+lD1NAVrn56UpcTg3P8fWLi1oCBmPdZtunHncjQdO71Cbn6J0bgR/GD9TSwc664wHl+OwC/wFaOxl/7wlh7Vcip8J0ePHMS625sUx+3HqnUwlekJlN+7jGVNDa8uDr4w8hcmTPpCT07T7U+XbciMPUTk4b0gkWHVqiuqwgxKS0uIOn4EjViCVauulGTep6CgVI8U+m8ToWc+ucvO7Wv0hUZFwnGUmalG8CJFYhQWbi2oVpbhZMjyEokpubQPB1sbLIqfohZJUBbloEiKFkSCIn7B8eX3Kbt1GkViNCKJhPyotWi1GvAOIO/eBS7HnaRVMy/WrlmGyNufJ8nnWLBgKZbWLuzZsRY14pqCm2BorHx6i+K4vXr/xJLkGJq62dDv1b4mUZ2tTT4dWs6ipOi0cRoiHAoKbAgJOYRKZfVcmv2ePRJSUlQMG6Zl+nTDyBe9F6W3t/DdusU3QcHvPUJDpzJ69GhSUipZtuweGzZUceSIsMz+8UdM4HBduwoTSFjYixVUt2ypZtgw0XPTQc+ybRg6ZOgLRcaGeVy7zoPJv3qEh7fj9HoudZvcywdlaizl969g4/sSyrw0Nqzfzt3EG6RdOYF1216UPL3FqJFj0CifseiHL7h/9yqamqjJv0Nbfl/zHRdjY1A36qjPhXq4aUGzrc7RpPTunYWVVUtARGTkWd555z169MhnxgyBmu/qKlw/jUYoWtaFmvbu/TLLl//I5csJiMVatmwRYI3BwbWs3iVLBDKQVArHjwsEMcN97dkjrMiUSpg37ytOnRLw6mvXHgZEdSJ/Kb/8ouHDD2HixNoazMGDAlKmoTqOokxEbrY106bN5ublc1RigdjRC0X0r/Tu9Qp3zx2i/P5VRBIJBdG/07FDB05Ehhl5u2Y9uI7Gpbnegaj0+hEqrh2kKj+DothdWDbpKIh85T8mPT2VUpE1yvSbNQzvIUb3nw4iWX43lqLMu7z37jtG48jNo6WRH2xJ1Go+nzqD9GvRRrn2jz7+Qu+H6+lu6lObfzMKDWIqH92sPead86jLiqh4cBW7QONVYuGf83hz9AeMG1srfvbHxuVUubfT136KLh1Aq1bppQoq7l1CXVGMSCyuLe6nxvHo1lVmfD7jv1eEbkQ3rlnC1BVmEkksUCQcx+uTDcgcBXyYjokpFoGoaWe0pYW80vsV0lKuUlVaiPJxIhI7F5z7f4bUzl0YPA+uIQIhwq9xoS8tK+f86RM1yAkhf3/rZjxhB7ejFkuwrhGNUj5JofzeRVTFOXrtdmF5JdUjWYzRGzm0afoB164WGBUUDY2B5849wLhx77J27akGafarVmmZOVPL6NH1Y5B9fIQb8+efhckgPBxSU+HsWRmbNv2Es7MTzs52DBjQi5kzZ/LNN7PIy8ujRYukBifh/ftfLH97+DDMmNHwSsPTE1avSqN94BsvFKHXHRvF8UeRNwswxqvv/qrG8k+IDrUSGcrMu3i8s4hn8VEkXo3j2tVYXEfNw67zEL3D/IH9m1GUVeA6QuAsZF+NJC4mjPJKJS4j5hjlQh2cHXF1NC4eisVymjefDqh48OAWISEjTIrVbdpA376CdvmaNbBtm4hz5+zw9OzNw4eP8PN7wNSpVXz2mVBY1qXe6hahJ08WXp8+XVv83LxZiMTv3xeK4O+8A6tWxTN69BCCgzsxevQAkpO1rFhxXy8bUF3dnMDAIiZONO7vF6njeHlqWb0qje5936Rrt5eNrBEjIg4hadYJy2b+lF45iNPLE8i6HY99/1AjOn7ZvcuoSp4JPJEaHZbGjvY8u39d/15ZYjSerk0Z+/5kzh7fh3XbXgYSD+0puXIQ18HTjeaG++cPEdjVeEzp7A91do1der1JcPc+dAl+2STXbjj2jCJ7sQR5U3/K71yoA4awoPzuBbOqkBq1ltS4SD0u3ZBvoUPmVdyLw7KZgSRBkw4o0xOMZbnFUtRPk+kY+MpfitD/VoOL9Zv38fRJOtOm/4NitRjXoTPNCtfnhi9BYuOErf8beiOI7N8noKlU4Dxynh4L2svPh+iTx3EbVWtIIba2p/LRTdxHf01BzEYs3FsYm1qEL8W530QjAf/C6N+wbtuL6ofXqNaA2NoeTVkRVjV4dJ3DUF1RfTA0O8jl6cOX+cc/BEiiOQxxSgp8/bU1y5cv5ssvZxMSUk1ISDUeHgLbMjJSRni4li5dtHzzjdp0BzVt3TphQh02zNjU4PBhOHFCzu7d2wgJGcyDB2msWvUzu3btp6BAgUymZdOmhk0Xli4VbvSGDDQ2bpSxa1c10dE0aMqgUkH/ASJOn09+IXH/p0/SmTX7C0rUYmzfCAUwMi0pPLUBiVaDRyNvilRS5P79KYheh9uIOfUaNBSe2ULp9aNI7FyQN26HiwGfITdsCc6vTTIxczhwcCvaqteMfotYbEWfPkIKberU0Odaxm3cKMPVdTyhodMIDu7G99+XG40JnQEGYHbM/PqrwAqdNKnhfnB1/ZCVK3/GnHGJm5snK1eWmvR3v34YmXGYa4Z9B8Z9Fn0qtl5rOagBEBxdhqW1LdUevkYcEnMGGqrLuzl8JIbLcReYO+8LRPYeuA6tz/TiOz6bPJUxb7//f8V04kV+y7MD39cwZusgqRDMPAr2zGZ8Hf6Jzqrv1qNH2A/6kvyIlWjVKlyHfGH2GPlhi1ixwrxZR0MGF3879d+7STNEIhGaqgpyw5eafK4jMNh3G0np9VpHdaugwWgsbLDwbo9ILMF+wFRibybjNuprA6fx/oICY40VnduwWVTnpZO9dXqtC/2Hq4y8Swuif8Oh91icQ6Ygd/WmdbMmqEue4TZiNi4hoaCF0mvCeRSf+IW33v5YP5kbNq3m0gtRwkNCqklMTGT//j+5fbsNH30kkEQ++ghu326DWCxl4sT6J3OAwYNBJjOl9U+eDAsXKhk79gM2b95KcHA38vO3s3Kl4IhTXf18Qsw77wjRfkN08shIKU5ONmRnN7yvnBxwcLBp+EsGTUdF79GhNSXHliNzbozHu4traNvrEIvFfDFzHtu2/UlAqxYUnVyHu6srlZf26PvXZdxKo/5V3DiGjW8v3EbOR1WQSc7OWbVjYcJq4++e3cT8+Quee567du0jJKRhy7iQkGp27drHqlWrCAmpNhkT/foJfq31jZmYGCEt9vxj7Kn384IChdn+dnDg3+o7d4/GrFu3jR4dWqb1gB0AACAASURBVFN0ZJnJ5zo6/j+mfI2XOo+ivXPq7Z/yc38w/sOpAHTr8RIRkefwsBKTe/AHk/3mHV1Bv35DGPO2Ke4z9U4C48aN+stU/ef9lvyjK7DwaIldDbKnMj2RnPUTUVwL10tJyP37s3fvDqPtEm5eIyHhCjZ9J9TIF7xHVV46uWGm8gV5ET9jb29vJJ/wou1vT7kIRdE8Mh8m4DpoulkCQ2n8UcrvXsRt8Bf6irrcy4eK22dQXDmEZcvOZt1V8o7+yGuvDyPn/i3Kbp9B3ro79t1GocxMNa9PvvNLZK5NBDGuGhJBzs3TOBukWBCJ9VAtrQaj5VVObhGXrl2ndZOlyKXbXhiSuGTJHfbv30ePHnn6/Gu/flBUVERSUhWBgdCkSf370C3DDfHHOinXTZugsFBFVNQxFi+uNkoJvAghRqGAyEhhuV9Xx3v3bhEbN1qxY8c6rK1tGzRlACHfa+v4Ok7ubV64KFqX3KJ8nExx7C6s23RHXV5C2r27aLAk/NAOrNr0wEarpLmXJ+lxR7Ct42qTv28uIf1H8PTOTSoexePYP5Tq/Kdmx0Le3tn4B75Op6CeFBRmm6RcRCIZzZpNAVR89ZUw6S9dWlsjqVuwrkUq3TWLVNKpZj54IBCD6n5uiIaqlek1Pl6bNrBnTxXvvTeC7777jvHjP2L27O9Ys+ZXMjIek5qaalal8UVEvXbvFmPn+BpO7m3MFv+fPLrDwQPb9eYjhk1Hx7d0aMvoUWPISkvh8eVjJiil/H1zGT1qPMVKB/2+L168wJnTEbiYSW+IRGIyky4apTd0RW3DYmxDVH1zY6+h3yISiai6f4ny1DgQSymJWk3z5v5kJ0ZT9eAKGrGE4pgNvP7GcH7+eTEKpTXZmen6oqhOyz/30ELEEimug0xlwbVASdrNf6ko+rdF6Drqv7oyh8T4SCMzWMNm32UYIokMC4+WFJ9YheJaOBWPbpC97XOsfHsjri7XCzQZttLo1Xz11dfMm/cN33y/kl5+PpQcW47ySQoVD66YFQhyCB6JqDiLwj2z9RGE+4fGUVvh6U24hEwBBJpxtcyS1JQLBHRojn87DRPGLMTe9irw4pTwoqIKvv++gokTq40i7IkTq/nxR+HGzWhA/0eInoTXGRkCumHiROHmX71aWM6PGGEa9emiwoZaRIRQLPv1V0HsKzS0dgVx5IiEoqJKxo//jJKSMo4elTQYyUdEwKRJ/ySgQ3N6d29PQIfmJq8N/1dX5hiZTBsLcIUisbYn51k2639bjMuIOTiHhFJYpeHm9Tgc+pnmJmyChnDn7g3mf/sTvfx8KAz7od6xYNt5GNkZSfi1a4pva1PxNaFZEhl5Frkc5HLhWkdFmYpm6frI2dmWgoIys2OicWMhb657aNZtuij68mVhvxYWpscLDQUrKxnBwX3Iz9/DypUKoqK0rFypID9/O1VVlRw7ZnrLv4ioV2SkhokTR5jtMw/Haj09vj46frFKhEpxH21VHkmJ17B7xfSa2wQN4cqVU/QK9tX3/6Z1gnRHffs1vP96d29vtI1zSCjFKlENVV8YR84hoWa3WbFkBi72ohf4LUOwdPWmtbszxO9j4kef8uhBPBYtu2KlKoVr+xg85C1ORB6gzKkFKTeOcjhsq16GACD32E8AuNXzu+w7D0Hi4En08T/N3h8Ntb89QjenwSzAi2op9CKZBYqbx+kU9AYlqefIu34cy6Z+lN08gVarxiHE9CmniwpatOnCobBjXDh3BMtOAyk4ub5eCQALrzYo71/CSaomP+msSYSXvXs2Eq0aiVMjs/R+jWo/9ja16oKGEXB9UZWFBcTGCpG8uebuLjwYbt82NZPQtd27oXlzATM+c6Zw4y9fXotcWbrU/EqhPi113bkuXAjx8fD0qYCi6NkTiosl/B/u3js+iur7/3/ubja9k4SOQKT3FqoiIISEEjqCWAFRERBUmogKCEFRI5EeVCAk9BIIofcaeq9RWiCk97Zlfn/cbC+J7/f789XH7/yT2dnJnbtzZ+6ce87rvF4pKRKDByuYMkWj5/C+e/ce9+7JOHBAQWGh3ARdERUloJYAG2Jj2Lr5N+7cuYeLu78FhNFmUlQm48Xm2abJpBpNKEw6j2+ooVJUK1NQlHwb39fHWVwnXXHRzUsXuXr5FGpVKf4DZ9hUyEm/dICirL20av6rSe0AgFLpT2lpT0JC+jJlipqUFPHSi4pCDz3s3x8WLRJJy4QEJe3bD+PBgwc21aBq1BBsiF27Wo5VerqgW1i/3jpNRJs2ApWUkKDhs89UDBpkScnr7CwRHS3RqpXpeOsomL/+WmDejVE0xnh270p+/PWkmoWHXhGIoAY5N/ev49ChvXj1n2biJOmk5JzrtNYnrf/4/Vd2xW3D8eX2FhXeMpnMJr2GrVJ9Y752LYb/MYfFJp49iaxWMyFZaOe3pF49wtvvTSJqVUQZCqsfebdP0qB2XY4d2yfQPWWJ9VZNWpD/PIn0y/uRZAqK7p/GtX4n+1oMCgc0T67/3yRFZTJZb+AXQAFESZIUbvZ9LWAN4F12zHRJkuz6fsZJUV3iS9mkJ/nHVjNlynTWRf9BaqEGj7b9yTq8Gkc3b5rXr8+1a+fx6jcNpxpNSFkzGcfqjajUyzJjp1Maf611M/bt34N3/+lkHV6NMqCOPhGmkzFzbxWqJwjKv3GIrP3L8LfCI5GTuA3nB0dwUDqTq1GgbNqT/KOrCQ+PoHWb9kjqlUiaX/THL1kCTk4ymjWT9ORboaHGSuxiYm/USEzAtiw5WXhlO3ZYfnfzJnz+ucCKL10qHuwqVUyTZ/aSXjpisNBQ0b9Hj8QLIDRUxOaNE6xxcQByFi3S2kzyzprlTFjYAOLjd5GZWYCrq3jR9O9vmrCNj1ewZ49QoXfxrFluUlTZpCf5R6Nw8/CmQOZkWxl+2zz8B32JDBkZ+5fgoC3FtfUA3Nv0peTJTdLjf0JTlIuDp79JUlR/L7Tui2fZZJB/4xBcWMqWDSUm53FyqkPLlvFMn/4z1679xuXLGqtjGx8vKmnlcoF0GTZsCHK5ApVqm80E6pIlAqI4zux9lJws9vXvL3Iltmz5ctBorBN+LVkCqanixaDrry4Bv2ePGN9KlSAzU0lhoQjN9OghPPjq1UGmmIzMYYzJGIFpIlHZpCeFx3/n08lT2bptE89yS3FqHkzhsd9xdnVDVbmRPimam7id7JPrcW3QGXXWcyq/GU72sbXkXdqNa/1OFD+6igNqlD7VcWoWTOaBZTgG1EWW+xxH32oom/Yyef6u3nxIJU8Z33w7k+d5pXj2nmT1HsneFc7Chb8gSRLTp39qQrxV29eLm7euoKhUC4+WIWQeXE63rt15+izZ5LeMHjeVuB1ryXStUW6Sl4ub2LwlgcWRkZw4tofewSHEbozBwacGHq37kHlgKVUCqvAiPU3sa9OHzP3L6N93AFO++OpvJUXLndBlMpkCuAf0BJ4C54ERkiTdMjpmJXBZkqRlMpmsMbBHkqTa1trTWa3a9aQvZonJ7/jpG6gLHnDk0C5ath/A4IH9mPfNBLK1SrRFOVTqPRGFRyUyd4bj1cMUhZBzOpbq46IofnSNnH2LcW3VD/c2ffUPZOb+ZbjUaY3fgOmos56TuuVbZI4ueLbpT9aR1fh0H03O2S3IHRzxaNtfT+Dv2W6gRZ91Gey6VetSt3ZV0d+gMAYPEgkSf5+NVKlkwCsnJ8NHHykADQsW2Ea6zJghSuxtoU3UahHmGDHC8iGMjxckXC9eiMk8IUEswY3b0iEobLWfnCw8v0OHhODBjz/a7uu0aQJVY6utqCgH/PzeYf78D9m+vV25KJ8ZM53oEvwpIb1e1e8/cfYWr3RoDJjeG2+/N4nnGXIe3dnPtQcPqPL+EpP2UlaMxlWpoAglpbnpuLzcHuf0+2hwoFir1e8rfnwN/+FzyEr4FUnSigf30Ep8uo+h4PoBQcPbKpSCY6b89AB1686jZs23kcnc8PWtiUZTyPz5tn/fzJlQXCxWSM+eObB7t3irfveddfbEo0fFC3XRIss2w8LES9seKik5WYRetm2z/M4YSbNjhxjvnBwRztFN3ACTJ3tw/Hh/UlLWm/z/8/R3Sc8eCpiO0Ymzt+gc1IDNsSs4c/oIb771Ee06dEer1bD453Ae/XWFEW9+SHquAzcu7SZbBfJqjfUqYk41mpCydgoypTOlLx4QMORrJK2G9J3fo1A64e7sSnZ2Km5Nu1OSfAcHmYzAqgE8efKXyfOn65NWq+HnRXNIy8vC/51fTH7Di5VjaNasK2+99TbffTuRAp86JhNy5s5wPLuNRpX2iLxLu3Bt0AXZg5N8O38lv/0WxaMHibz17kReZCtpUMeLP36LIKNEi0/oZJtolY8++ZJ6DZrr+/fdtxPJ966NMqA2+Zfi8eo8koLz262ed274apNrDTBxbJ//akLvCHwjSVJw2ecZAJIkLTA6ZgXwpyRJC8uO/1GSpE722tV56GAdRvT0yaNy37Rp2+bi7FMZlzYD9Z69sVeQsfdX5I4uKNDi7P8SsqqNyD2/A7mjC1p1Cc7eVXBrP5iCo7/Rq1cI8XviUFZrSMDQb/ReW96BSFxa9sWtteEloT4rYFXmfZfUS5E0ppPMd9/VwNf3qV3Y39KlwqubM8f698nJ8MkngjnP1kM4erRIgL79tqU3XlHI2+3bgTRqlGQXfrdyJahUtilfk5Nh8mRPUlOf8/77XSgpuWz3vKtWOfA8LZhvvjUgCuzByjZv3cnqFd/j2W+qxQoq/8JOXP48RsrzZCoN/FLvdTWoVoUrV87hO2CmgLOumwIlhbh1GEbO4ZW4uLjh7e1FtsYJx2Y9yTu0Chc3B76ZlWsymVer9hH16y9CBwN0dHRj6FD713XlStiyRYwJiEl++nQnHBxk9OmjMYGpxsWJFZtc7ohCoaV/fwgNVeu/f+stKgYNDRb3iblVFJ7Yu7eMGzfeJiVljcl3MsUEZA4fApZjpCl+wcwZU1DUbks1KZ0VK9Zx5fJ5pk//FGXdIKpp0/l44lyaN36JLZvXs3LlrzjX64BviBGEeOtcfIM/BgkhJhPYjsL7ZwHwaBVC/tX9uAQGUfL8Lp4KDdt3HLB6r1y6eE7veVu7R1wfnmLtmo08S35iAou1BbdcEP6zBXzQGhzRnGI4LWosg/qPYOzY0Sb/U5G5TbeK+LuwxYrwoVcHjAksngLmejXfAPtlMtkEwA14HSsmk8k+AD4A8PH15+rNh4B4s+rMeHv8pDniTbvre4s3bVZCBG+MHEdhYQFHDsXSoetIqtZqwccTm7L453D+3PsrMgdHXALbUfznBbxkpTy9sBO5o7Mo3/3zAt6uHhSdjWXMh9Oo37AFpYpa3Li0m8zY6Tg1Cyb/2GqGDhvDseN7SbtzAteWIeQeXoVS6cyBw6cIqFydE2dvce/OVbZuWsLC75It0CiXLr1gsaUovImFhYkJOTnZuvcVHy8m8/HjrU+karVIWFapYkieGbczYID4v06dbHuSCQkOwGOmTLEPv+vTR3iA9jm884Bidu26T4TpPU5ysql36OGhprQ0gYNHBuIfIHT3bN0P9+5cZfmS+TaTZG6t+/L8+iFkHgF6Hmxl02AuHlqJz+sfmPB1Fxz/HU3iRrr0eIfBA/uh1WqEB3ZuI+M+mUWXjjepXGm92Rk0iMm8EBChlPKghH36mHrLTZpAWJgWB4e+eHv7MnnyJjIz8/H1dWPkyOHcvPkRgYGVSUp6QWTkMiZP3khmZgG+vu64uRWTkqKy66EbJ8jNzdq9Ye3/fX3d9fxIxiZpliDJGiJXdCDp4VP9/q3b4vRJR6caTXi2YQbTpk7m6tVEk32//baKMWPG0bDZq0yfFciSyHAyY6frJ7VqY5ebJL6dajShNPUvJFUxeRfjCRjylT7U2rhJc67efGhxr9y7c5VVy8NxbT9UcM0Y1bYUP7pG7vVD5AO/LF5M954D6ND1HR7fPcANG3PMsKHvI3cKsHouEPfkpUtnheaCmTm36Et8wg7aduiGXC6v8NyWs28xzduEWD1vefa/QrmMAP6QJKkGEAqsk+mUIoxMkqSVkiS1lSSpbUBAgNWMufG2VJpOyrN7VjPi7m3CuHD+KJMmTiRu1yEGD+xHiya1adUskKz0h8gUSgIGfSk0Kr2rkpL2ArnSCf+BM6kUMhGFVxWyM54St+sQQweHoSl+weWzW1nw3ULeHtgP9blYxoz+kOPH9zB/3kKCO7+C6sx6ZJKEvFZLNsUupVmjWlT1ySdqxVzyvBswL9wJY/UyhcKT7Gx1hZAuKpXtGHl8vMETt2YvXggPfNEiMbG/9ZZYXi9ZYnhJzJghBBBWrRL71GrxNypKyezZrkRHryc7u+hvCzVY64uvrwfgbIHosIbQWLIE+vXTsvj7TyjKfWL3fojbsRbneh1MRBLSosaaYIA924Whzn5ORsw08m8cJutwFC4vdyD/yl69BFn+sdWEh/9scd+MGTNOfz9UCfC28usUiGIdV8BZ/xIt73qpzN6RISEq9uw5SETEYlJTU1Cr8zlz5iiSJKdDh24oFLXo0KEbkiTnzJmjqNX5pKam8P7777Jrl/Xz6GzXLuGJW7MePUR1qT1LSFAycuQIatQYj0LhYfatFtTj8Xb7koGhjfTjcuf6QYOYRFlNyK2HD/UeskyuwKlZMI8eJOr/p2f3znr0WfYuQ/1J1hEjkRq5Av+waciUzgQM+UrflkfbMG7fumL1XonbsRZZwMtkn4xFnZ9J6ta5pK4aQ+b+ZaRum4emIAt1aSHHj8aLcnpfjU3UjXubMBITD9OsUS2r5/pPUDgVmdvcWvfTI6z+LsqlIh56MmDsd9Yo22dso4HeAJIknZHJZM6AH5Bqq9HCohK7HrruTWsPCvVswyn9m9a4DZVGwrV+R72XVqnPZNLjvqdS99FGaiWh5B/7g6s3H+rPpazTjpmzpvHZ1IW0DMomavVylHWCmDlrGnXqd6GkpBifsqW8zgu5dvUkXv1FZWrKxods2foXw4ZKBAQMpUGD+fj6tiIlJb9cr8jTU3B1DBggJoFr1wRi4s8/DXBB4wSVscXHK5DLNXh7i/i2cWJu/HgxmbdvLybP334TqwG1WoavrzsjRw4iMXECgYF18PV1q1BfbXmAIDz9kSOHAsUm7SUnG3RNjVcJugKoV18tZsbMSXw6bQl3/syyuB8A3n5vit6rc2oWTM7hVbwx4gP279/O8xuH8SxLoDt5B0BuGpn7lxIwxFA1nHdhF0WXd9n0foy3A3yyqVzJ/NeZeuiimKqgQmNrbGIVk1/WFiQkHGTUqHcJCVEbqUPlkZDwB0FB0URH/0H9+oFkZ2exY4dAzdhaae3cKUJAS5ZYhueqVhUv9C5d7K/UEhM/wM2tDp06XeTOnemkpZkG5DMz91Jc+hf3Hwv4XasOg/UrW523bRx+0L1EO3QdqX/mAbbviNcr/+jMr/9UMvb8Qsq6z/Hr+5m++M+4rayDy/nok6+sjt+rXUNZv7ZsdV6nNUVJ56lSrQZ/XduPXOmk39ewRRc2b91pd8Vnb445cfYWx/ctR1GnrYmDkbNvMS6t+hqoA5oHs379Gho2e7XCc5tb676k3T5h9bzlWUVi6A6IpGgPxER+HhgpSdJNo2MSgI2SJP0hk8kaAYeA6pKdxsuLob/11mCTDLLuYrm17mcSz+bCJrZt328l676Ep9lF+PT5zGqMKmvnfMaN/5K6L1Vm5owpePabqo+5dmxSj6PHDptkv6XcNKQazSuQ0V7KrWt/EBDQHyhm0qTppKSsshtDX7VKwAK3b4dKlTzJyMhFqRRhFo0Gzp4VpEwuZcpnH39sWOrrUC7TpsFrr1m2ffOm8MxnzxbtHDjgTExMDCEhwQAkJd1m8eIVxMRsICMjD1dXQZVr7cUBIt6fkQFffWX9XLNnu5CYmEhgYF2TkviKxPFXrXJApRnKK6+PshlDv3w9iXs3T7FxYzQj3/qEIYP6M2rUIF4UYZJAT906l0rBH1sk0L3a9NPHUOVyuc14vbV8iHkMfdKkT0lP/42xY+3TMqjVpiEqkWfwIDU1haSkP61SARhfU+OYe7VqKlauNKCSzBPkQUFw4oQYP2OU0u7dYgU4ZMgb7Nmzk5AQtQXNREKCkujo3wkJ0YlBiN/54sU2bt9+y6xnfsidjumvW9OGNVm4YDYnLl23CCGkRY1l0scTqFqrhf762otzS1oNGQm/UPL8PtXHLDP5LnnFGBwUCt5/c5S+tF5T/IKfI77n7femsHLpXNLSMwgYLChBXqyfhib7GVqtFv+BIoeSEj0Vh/wX+AVU+Y/nGHNEjVOzYD26Jzommhy13AIF97+Y2+C/LP2XJEkNfALsA24DmyRJuimTyebIZDIds/1nwFiZTHYViAXetTeZV8QWzP+Jqpp0MmOnk3/9ELm7v2fIgJFUSjlP9qYvyb9xiMJjvzNrlmVJcEDl6nw4bgKlabZLa319fQms11SvPK9b4imbBnPg8EHcu43RL/Go0oiC/Fz8ip/ZLVsuOL6UWdNLcHWtrT/XxIkTiYuzX7QRHy/i25UqeXLmzCm8vV14/304eVLQoP76qwhPrFghYGuRkQKVEhWlZPp0Bzp0UFidzMGgNzlzJqhUvblw4aR+Mk9I2EdQ0CtkZPxBREQeBw6IcyiVpkUxxn3dt8+Rs2cVLFtmGrpZtcqhLHTzB4GBdct++8ckJCi5ebNipeuhoWoOHrAfU5DLFQx/4222bd9PvQbi2ocv+JkAFwWuXn4oPCqh9K1O9bHLrRaEubXuS1YpbN4UbfMcklSKpEmw31nE2O7d61ROQY5luEyENYaRlPQnw4YNo6SkkIkTTcNkOvP2BkkqYd68YsaMUREaKo5RqcSqLThYrLiyssRLOzFRhN7GjTOlgRg3Trz0d+zYjEYDmzapGD1a/P+kSe74+b1HYuI5QkIs01+urnUs9pnb1SsXOH78qNUQgkvLvmzdtgmtUTzy54jvTYptih9d49myd8lJ3Ebx42sUJV2wCkf2aN0PNXI2bFgHCG935owpZLrWYM3vETg4OBpW53IFlfpMRu7hry/ikckVeLQKRaFwYMH8n/AueEZm7DTyrx8ie1d4hecYENQUK1as5fWOneHiJhaE/0zvkDA+mxrOqLA+cGETYz+cTus2pilHa3Nbl/ZdyD+7iayNM8i/cYj8o6sZPmxkuVQF5vaPkXMZwxbNIVC6bVPY00eUyKoY4FGnDuHu4cnEKfP0ycnK3io2b1xNrbrtuJIYp6e+tcCTn99B3tnNNG76Ki2a1mNj7CqUXgG4tAkTMdfAID0JV975nWSfjMa1QRd8Cp9RtXI1rif9SeX3Ik3aTIt6i7eGZJGRAUeOuJKVVVSW6BrGzZt3OXv2FGFh1mGHM2bA9euCvEmSJB4+XMOxYxq7cL/PP4fhwwcSF7ePyMhCu5DE6GhRuq9SyfR9Cgvrz9ChI5gzp8gu5C4yUkwKCQkCcqfRSHTvrkGl0nD2rFjSC6y5gsWLf+L99wchYswAhSQknGbUqHfJyiqqIEJDxuD3vrd6P9i7V46fvsG5I1E8ff6Mqm//ZJIIS902F8fK9aj8xlwTpJI5JOzE2VsEv6amdtW5KBSFFv1zdp5DbOxDYmI2lSUq3ahfvx4XLlxm4EDo188wtrt3i8lcF+4yvq6zZ7vwww9z+OKL2fTsWUS/fpb4deMwmY6nx5atWiXCcmD7WF29QUiIqeeekOBQ5pn/UTaZF5qMH7iSl3eFixdNA/NqjQ+3/xIvReOkqLUQgh7uW+1lxowRAPvUF8ksiQxH5eiMU7Ngsg+tRCZpkFy90Rbk6GPm1tpKWTOZdo0b0apNZ33IxKlGE9LWT6NeZV9u3LqCg081/GzVKmydi59/AF9+/Svbtsdx6kg0ksyBSr6+dO39MV3aN+LooV3s27ud0WOnALB542padRhMaHBX/W++c/0gYz+cxt2/csq9R+/ducraNUuZ+OlsAipXN5nbunXvw9EjCTgFtkf54iaSBC8FtuXuzeMo6wShTP+Tr7/9BXkZh7U92GJFYuj/J6Yr/deZte1LF8+R/OQGLvU7kXjuEB9PnItUmsb5s0eRZHI01ZqyKXYpK1as496dq6xeIbztS+fi0EoQMNh2aW3h7RPcvX6EB7dO4PRye7SPLlrEXDP2LKbw7kk9VjZ97SRelMHfzE1Roy1r1x5g4ED45ZfCsgcmn4SEdVy8qECSHEhJUeuV2HVxzSVLIDtbeG2JiZ/SoUMn2rbVlEvq1b8/eHv7kpNjO5Gpe4j79BGQxipVJH2fwsLW0ratZPccvXsLCgFvb0/69AkGdhEebh0/ffOmhi++mEHXrp0JDDSoKoSE9CUxMZEWLZpVCGHh5e2mTwLpzDwRZO27jdG/8jT5Ma4NupCR8AuVR4ZT8vgGaTvDca3ficJ7p8mInY5zi976ZbDuf3V/K/uuIcB3k0W/ZDJnnj+fwPjx4YSEqIzi3PnExV3m2jVRTasbW3d38XLq1k1Uf6rVpmGNH35YxBdffMacOUV4e1tiwtu0EeLey5eL/ZGRFl0ysdBQAWtVqcQKy9zs5S/GjFHTsaOaUaOEhx4YWA0DSyNl204WbToosmjeuAoymTPffXvQwts2h/s6NQ/m0dlYWjQpQzs3qY2ffwT3bp4iOvp35DIJ30GCEdW5RhOTtsyL/zzahXHnwiaePEnSJ8llMjk+fSbzZ/wPeHYaTvaJ9aTtCKfa+6YXL33PzyjcfSh2cCNh52rOnTiM36Cv9KFVdf4DWjXrS6tmn9K95wADHLNOW25e3s20T9/iyuXznD0ei7JuEJtil/LxxLl271Fd8lRRu61+vrpy+TzJT26grNaQw4d24z/4K5sh38wNM7h38yTD33jb/o3Av6D035aG5IkTx0xUPlIv7ufc0b1s2x6DVpIRUMZv/fTEFmLXN2DOeAAAIABJREFUruTc6SP4DJiBR+u+5CbuwLVeB1Pe7OjPTUShZQoHCu6fw7eMDzvzzGZc63c04V7Ovxyv50EueXyD3Ct78bMirqDKek7B8WUs+l5jUZLdurWWZs3UHD3qwPPnDvTpI2fqVCEyULOmeMhXrXIiOvoPgoJaMGPGPNLSyif1qlYNFi26j4uLo9VS8uRk4eVZKxNv3VpLixZaoqMlu+Rc1arBsWMepKY+5MiRg9SsaZ87PTNTxYoVR3n99a5lSJdCQIavrwfXri0jKanUPgHUBgUe3q+Tnuf2t/RGF/88j9OnD9sVQii6dxplYQbSs1tWtV5fpGVTtdJ3KB1MISm+vr3w8VnFoEETrYpzt2snCrs2bBChsU8+EQVgr70miLYiIoS02/HjHrRvP4yoqOXExe2kevXL+PhorXLlP38Od+6Il8SDBxWTKIyKEhO6NTGSigmRw40bRfTu3RmQAWr9+ElSCU+fLrf8R806JFk98kob8Pz2GbKuHkSLnNz9kQwZ9A7Prh4h/fIBJLmC/KOradtpCL5+lfVjefLcHdq0acuZ04fRVhfUAS4vtSDv4i4Kru0XIjV7I/Hu+jZ5F3dTcP0gMgdH8o+u5p33p9C+Yw8LjVm5dzWhBWyH+Kr40RUcAzvw8M5VE0oAjSTnzuGNegUjE63jsjL+a+fPmGgdp17cz8MHD6le82W7ilu29JIL757GuVZz/bxTHlXBv54PHUyTUZu37iRux1pUpaXkedfFN2QiJY9vkL4nAm1RrigJfnydah+uovTJLdJ2LMAlsB2laX9R9d1fkMnk5JzdQn7iFhx9q+PSIlSUDFeuiyRJoNXi0TqUzP3L8H7lTX1FaM7ZLeSe24LSrxaVQizB/smrxuFUraGeS93ghYSgzX1Or8D9fDjOdnIsKkqJUjkYLy9PYmI2kJmZh6+vByNHvsGECR/ovVp//ypkZuaVWwDy+LFIMDo6KikoUOHtbYqCqUgSsrwiIV2RiVqdj79/ZSIi7CNgkpPhww/B0dGV6OhoQkK6ovP2tmxpyJgxT+yGkb780pllq7aTnqP9W7zWr73WFtf6nUx47q0lRTMPLOXosUs2E6GlhR1wUOSZ9KtduwvMnLmCjIw/7BZc6cIe5tcyOVnEqNPTX2DMSz5jRh5z5tivov38czGGFakOnTzZE0nSWh2j8iqFjdtITf0Lcx51EMiWGzfeRqstsPxnWXe08h/ZumUD69ev4dtvF9CqdRAXzp/hm29mopDL+PqbBSicK+sTmAvm/0RGrmSV4iHvaBRdOr/K8RNHcfTyx6XtQH3x37HjR/nmm/n6uPTl60kk7PyNU1duUOmtCJJXfoCmIJuAwbNsh2yip6LOSaHmBEOdga6IaMyH0xhSVnk6bFh/i0pSe/ztOjO+p8prQ5WZTOq2ecgdHG3qQRgXGYH9pOg/5qHPmTf/my3bNptoOcau/411a39F5VMXZ00RLuoCUs/tIO/yXiSNioAhXwl5pltHyTkZQ+HtE/gPnFG27xhSaRGSupScQysY9/EMAry9+PPEVpq17o224AWlWnCs1Yz8y/H495+KW+PXgLKk2eFV+IVNozTlPvmXE/Bo09ekvzmnNuCuySf/9okyL2Qx48dKPLl8kfS7d/nic/tqPZUra4mMfMShQ7sZNCgMSSri+vXbHDt2lvXrY0hOfkz9+nUoLCzk+vXLdlWCzp0T8e1+/dDT7ZpL0q1ZY52G1djMBaXNTacOP2hQH3788dcK6Zn+/jt8/72KCRPiGDKkN76+PoCa3NwoatbM5ttvLWl4Y2LkLF/hyMh3Z+LmVdWqF26u+ajT/Ex59og7t6+S/+w+RffO4FyrGZq8DEoeX8O9WU8ULp5lVMqL6Ny5B35V6tts39tjo4WHXr36O4wePe1vi3MbX5OVK0sJCmpBvXpVABkzZszDzQ0aN7bvNefmQmYmFBSIlYAti46G6tW70qVLW65du21BYVxRIfKVK0v5+utPMPfQQY2LSwA1akyguPgxBQXmMLq/SHrcCO9KrVG616N27dqcOHGMHxZ+hbxWK7wcNHR6NcyEwvbQjrUc2BeHf5W6yJUeaBTVcCWHB8e30Kp9P4aPGEPP3oNQaNXcObyRMeO+oF3HXmXt19HfD+b0yvlXEgwEbmWr87QN09FqtSar85InN0wEKqxR9+aXuJL7+CJZVw/qVwDmFN25+yNp23kovpUqW/XQy2tDlfqQkpsH8XHzIvfOCauUws1bvk7L1p0qRM71j8XQ09NScGvSTR+DOrRnLXG7d+DaoAvqrOfkK5Q08Pfm2dM/UXj44VS9kT5W5td/qoUH5t4imJzTsTgqZLwx4gOGDRGDNfnTT/Wwqqmff8SlKwlUeetHs6TZPJSuHhQ/vELJ01sEDJlt0V+vjkOolHKB17v3YsPGVYTPy6NlSwjpDT17VqzAJDMzn4SEY4waNcoiFpuQsI6goA388MMiZLI1xMdrrCa3dPFQc/6Q6tWFN96pk30aVvM+2SsSSkhQ0qlTZ9q27cLAgSJubwvjDgaMuhDuUBMZ+QcREToMsUyf5NuxoyzenCvDy8uNlm26siLqE6rXqKU/t60YunGu5OLpjRzdm41D7bbI886gykklZcOXoC7FJTBIH0tP2/UD7du157v5P9htv7TQ2pLIySblbUWupQ6HLmLUxwkMbISvrzuHDuWxZInl8cYWFiboAPbudeTVV0ttevJ794Jcfpxt2zYwdOhWOnY0FdCoaIWoUimRlPTCJAdiHE9XKJxp3HgNOTnnKCl5YvL/9epWQiavDRhixsawX0FhK+LCklZD2vZTuNXvZJIDi998Cpf6nUh5eo1mjWohl8v1sWxbOTbzZKz/wC/J2BNByrov8GglVufdXuvBvaSzpN09iWurPmQeXEnAwC9N+q+j7jWPh0+dNIqFC2ZzKv4Hi9L+vAORTJky3QSOad5H4zZOWKkKzTsQyZDBw9i6bbMJHt+4XykPT+mvR3n2j3no8xav/CZg2Jyy2Pg+Ll0+axIDdajRhCf3b+Lf/ws8WoZQcO0g+df24VyzKUrf6ni2MVXaztgbiV+/z5C7B1iITpw4e4szx+M5cmSvUA+/cRC3Zj0oeXxdJM3qtUeV+ifFyXeoPPRrG9S69Um7tJ9q/nJ+DL+if8ArKhTx/DkcPerGtm1brcZiW7fW0rSpipkzDzN27LusWXORzZtFbNTYI96xAxo2FGgFaxYQICBsDx9WTAv04EF44w3L727ehOXLlTx69Ih584otRKSN9UxfeUXsi4kRNL5BQWJFsmjRfb74YgKgJjn5N9TqTDw9xffDh8M7b3sxfNQasopq4uziYTdO/iItWy9c4BM2HaVfLdIvJujFxQsfnEOTn44MqWzVJkS9pdIinKo35PGlIzRp0dVCHb4iHvqKFRttUt4aX0tjcW6dbdworkm9empOn05jwIBQkpOTOXHicoXEt9euldGrV3dWrvyTggLTlY2O2nbmTPD2lvHkiZKpU79gwoR48vLQUxg/fixYNNvaEcDZuBFKS2Wkp5fQu3cPzD104+3k5CjU6mzTBrRHkOSvcObCc5YvCddT2JY8uUne3dM8Tn6Gd+hkwdOyfT4Kd198en5Ixq0zXE08za64WBPK2fTnKbh6VLF7P1ij3s7etRDXJj1Q+tUi7/w23Ju/zvOb52jU/HUe3jpJ8dPb+PefhvNLps+4jlrZOB5uLHahWwGY/OQyim6NohrOTo427197ghkl6clcPhJnU/hcR+NsfD3+lQIXcqWLvkw4o6iAgCFfm0jHFd45occSC8mmkZSm/kXa9vkWbelk6pxrNbcot23RpDaq3Dvs3LGegCFfC3EKCTL2LDYSS5iI3LMyjv4v2S0rd2oezMH9x1iyRMQle/QQfytXVrBpk/3FTkKCkpdeeomQELVdZEnz5qUsX/4bgwYpWLrUVMDg44+FZ9ynj/1rGxYmWBPLE6+Ij1egViuIilJaoQNwoVu31+jTR2O3v336iJeMrkpRh7kWK5IChIfnTJ0632B5u+WAahADep2geeMa5VJBGJf+Zx/9A9eGXfR4Y7++n6H0qW6CN3ZvEUzepV14tu0P7n5siVlst32lg3UPfeTIESQk2BK5ELZnj2XJvTFtQ9++ErGx20lIOMbEiZNxdLQu+5acjP7+Cg4GBweJI0eOMnGioWI4OFj8LS0Vx7Zvr5Of26ZHFfn5vcPkyZ707i0jMdGtXBnB+HgYPVoiJmabfsx0FAfm23XrfoOY3I0tu2wsT/PjogiqatJJXzORtB0LcKxSH8nZA6eaTcnY96u4qtUakrk3Eo/gCYLYymjcXFuGcOLYnnLvh59+jMS7QNSG6HDkkz+ZiH/qRdQPzuDVeQSqGwf55pv53L1xGJf6nan+0W84v9Rc/3znnTd9vo3pCf6OcIc1gZaKtFGcfBuX+p0s5h3jfrm2Mr0e9uwfC7moM5/qC3SqjzVk0HW6nuZhj/Tdi5DJZPj2tBQucG8VSs7pTbg2ehW5QmlSbguwK24Drg06G6gAQieRtnWuidCFZ9swMg8sI/X3CahVJUiFuYx480OOHd9L6rX9ZfuycHHU6LlIDOEHDdu2CQ962DDL36orqZakh3bJr5KT4dIlDeHhtsMpEyZULJRSVGQoWLIGjWvVCi5edCAubg1xcQeMSKLcGTlyGImJ79KhQwgREfbJukJDRdHKgQOi2lW3rBecLm7oytsDAnrj5XWe69dHk59/xbQRbTQvUop5kfkeIDC+331rwPiCCLUUFhYiz75LZux0vLu+Q86pDbyIma5PYhtD1IofXSPz0Er8B8wo4//oz9kDy22W+3u7H6BmFdOEqLBSJk4cR1BQtEUoQ2e6l9mcOQaIojGevHp1sV+lglGj3iQx8QQhId2JiztsUkFsDDM1vr/i4lQsXSrask+KJugEAgOrMWHCu0iSpMfMa7WiYtgaD7qun82bm1IS6CgOzLcDAvri6Xmea9fepbDwhtExEmjX4OBQSq/eQ1i+ZD7+ZSLuOhiwtijPpIqz+M9LFjQBOYdX8dEnX5ZL3gfQoes7ZfTKpgR9v/0WxaOzsYweNxWFc2UTegJj4r34hB1k3j+FU/Ng8o+upsOrI/TnXbtmqdXSfmOKbmXTXuzbu47uPQdY7WN5bfiHTSN12zxS1kzGo01/q/3KObSKD8fPNKFNsGX/GMrFy8tH0rr7WcSlnvz6Fu7NeuDT9V39vuJH10jd/h0Bg6wvSySthpS1U9AU5uD96ltk7l/K9Glf0zs0DIAff1zE7t0bcfCuZqEeXvzoGhn7l6DOTaNzpx4knj+BonI91Cn3+f33DaSmpjB9+qc41GiK9OQcixZZx27rUAm9ejkwbJjaakl1375vsH+/ZIJeMWYfzM6m3NL7vn3t85Hr2pwwQVQGzpkjvHVzgYm4ONi3z4nY2LUWpd66bYXCz6K/5qaja/3hB4Gd1jEL6lTuDTF0Q/v3708jOflX04bkPZErI/Ql4caUq1Jpmh4L7F3wjMYN6nH66k183/yRjIRISlOTqGZW6PVs6bugKkTmXQ2PNv3I3L+Mjz6cwPAR75qgEG7euUSjOvMRRdCm5uwcSFDQeeRyJxISdjNq1HuEhKgsSua3blXRtKmAKZpTG+vGSTcmoaFK/Pzeo3nzJowfP0nPe64TMbGPAhIeubWxN6YTSEjYx6hRb+rL+6tUgcGDRSXpmTPWKZh1fDu6NozHy3Lb8PnevSk8e2YGfpeH8sbIm+T7vGyCPEqP+16/kgYDHUOl3hPJOiKYEbO3fcOg/iNo07qpBRoGKoZ6snVc04Y12bJ5vQkSx5hKYtasuSicK9sU7rBG0a0Tuxg6OMxqnyoi/lFw9DfatOvCndtX+OqreRb9GjFqvEn7/5XAxf+VyRUOUsDwuVaqOLdTdO8MlUeGoyNsfP7HJAulobRdP+AZNNBUaejwauSOriCX4+0kZ/OWBDZvXMvyFb/i9FJLtMnX0Tp7U61sRaCj6nSp2xbF8xsUFxcJjuwzm8W+lJuUFhfj1X8apQ9O0LNuAh99aPs3rVrlwN279Xj8OLkMlujOyJEjmDBhAoGB1fD3r01ERJ7+gTT2yKyp3ZhXGYIQP/DyEvBAW6aDIw4YIMI09gQYjLlXzB9g8/5aM91EFRJigO2Jdl31SUCDifaTk1dy//5k04bkPblybYQFr07D6lW5fu2Cfl96zFRK0h7hN3i2njfbmqRg3vmdVEpJRCmXcfvuHUa8MYpxH4lz6h44SUpFUxyMXF5q8btq1PiUwMCZyGQ6pftikpKeERkZSUxMbNlqRsBOs7Iy0Wh2VgjWOGAATJzoDmgYPryImBgx/pmZgubBPteNdWgkwM8/O/D06cs8fPiErKwCPDwE5cPfgbGuWuWAv/87Ri/h8if0p0+X8ODBVJN2Ll92YcYsBVo3f+ROLlZhwDrpOa9Ow8XzVlad7Va/E4o7B8jLzcahTjv9S71VM0En8d9M6P/JcbqXgDF3kEaj0asPmb8E7LVh/CL5u238L/nQ/09M4V3FRhVnf4runiHzwAqK/7qIe6s+VOr3OZkJkaREf4FHy1AyD67A5/UPKLi2n6J7Z3Bv2ZusQ6vwHzAT55eaC8zxoZV8NO4d7t67iWuDLpSmPECtUhMQ9jFgmMx1vMspayajdXIg58xmw761U3Cs3xbnWs3J2T2X/jZ0P3UWGqrmwIEnpKY+KttjWkY9cuRQEhIEWZW96j1deMWaRzZihCjttse4FxcnvPgdO4RHby8GHhKiIjIygoiI7zFfYhv315bt2SPCN/HxwgOMinLQr0gCAytjWL7rrgeAZXs5uQWEL/zOwKsjk+PeayLXdobjZSRm4dIilJIT6+xO5iDim8kxxylJf4xbg86cOH2aoC5hJrzUbi5XqVvdfDJ3pF27E7i5vVzWX93ypJDAwGpERCwgIuIrDOMKSUm3CAqKtxuSiY8XY1m5MuTn5zN0qAPDhkHnzmKcjhwR1bz2LDTUkos+OVmsju7cURMWdocpU6wjkSrCib9tm5olSxpSXsjF+LO7eyPMLeJXLcrAjvgETyBz7xIbFZsRuLfobfK8vYiZQUnqnxSmvdBXbBvzqINlyMVWOOZ/eVzDZq/ydRlboi7sofSoz9fzBvytNnoN8NOzfP4nbVTE/rEJXa4U1IHFj66RvusHPIIG4VlWLeXeMpjM/cvw7fUR+Vf2UXj7GO6t+5C1bxnFJ9dQrWpVcq7vx615L3JORJN9Ilo/mYsY/HKUletw994tvZxV4d3TBBjphJrwLsvk+IVNs4ire7TpT87pWABK8ivGFW5IBOrMsD1x4mSCgjbQsaOKo0cpt7xfl3A0foAVCsHXMXOm8IqN+UN0ijcajZg8rlwxiDPbspAQNZMnbzbyyqz3117sWKUSoZfwcE9GjhxKYuKnVjx+4+thmWD08nTjpx8jBXudEQ2rMWdO8aNrZB5cgf+gL8vGr52VEvE++vvIpWUoJcfX4RsykexNM03Kp1s0qY1WfVQw4hqZi8tLuLk1Neuv/e3AwMZER69n0KBBDBxonQVRF0tPThYhsJAQNSD2jR8vQlUVhUYmJ4vt/ftFdapcbikbaO4YzJ4NL78Mn30GAweKF715H997D774YjZdu/bWE6zZ/v3is7d3d1q1OsC1a8PRaDIB+O7bEubMP0XyutsUZ+fiP+hLzM2jbX9yT2/EpV6QSW4rPe57k+fUqVkwj84ZUQZQMSqI/4vj/i3nsmf/aFI0//ohsg4sQ1m5LkX3z1B0/wzuLYLJ3L8U55da4tb4Ndwav8aLTbPJOrAcp5pN8NTm89n0H1g4byqpB5YL3Hr2M5xqiYcwY+9ivDoNp/DOCf2N8fyPSbg2MM0kSwVZlDwtMk2qmSVns46sxn+AwIYqHWWkpEgVUHsxJALNPZzAwGpER//OqFHvUlJSZJV3w9iseWR79ohilAEDxGT/4Yei8MTNTXjtq1aJBzwuDi5dEpA1e32uXBkyMnLx96+sJ5waOXIYEye+VzZR/V7G1a0iJESNRgOxsYKitbAQXFwUjBzZj1mzZhEYWAfDqsTAG258DYRZ99AzciW7Si7puxeBXE7OyfW4NnyFnDObUKU+xKNNfzIPLMet8avknIqh4NZRwY1etmqTyRUom/TSJ8pPn79KjYAf8PE8atEPJ6eqlO+hWv6ukJCuDBs2hAsXtrJ3r2TB16Mbg4QEJaWlKovJu6JYcTc3JZMnO5ORkYejo5iwq1Sx7xi0aiUm9Fq1RM2EWo1VTqHq1SE3V8XcuXPw8vIwISEbOXIgEydOKhtj02vg5dWQzp1v8+DBTJ49W02NGjBudAkzvsrEf5B1GLBnGTy58O5ptJlP8QmdgtK3OlXfNYy5NR71f8JD/zedqzz7xyb0Sj7eaC9uYsaMb1gTvZYctQyNmz+Z+5cx4o1RXLh0kZRNX0KVhpQ+u6MnyHqx7jNifv+B1NRHhn1l4gWe7cJwb92XonunqfLOz/oYvEcZgkWb9RTXFqHkH40SbqyLJ6rMZ6TtWEC1902TdMZQSADHqg3Zteu23di1oEUdjj2vTgcrq1ev2d8uVjFeulevLib1AwcEIsL8gdbxgsycab90/MULkYiNiMg3K3KKJTp6vb6/kZERfPxxDCUlRQwYYCykoSEhIZ6goP0W5f6Wv1/32YqH7lFEi0pCDzLl2T2rRRbe7QeTd3YTUn4meZfj8R84E1XaI3JOxeLWtDtFD84haSWcqjUk51Ssyaqt8PjvLAj/mWYNS2hcZ55VNkVf3+40brzeTv/t/67Zs78mKCieuXOL9IlOfRFVGWmXVqvF09OFlJQikzHp0UO8rO3FuOPjFbz/vkg0Dxw4CEfHffrxt2UCOSVyL199Jbx13arAmlWrpiYycjODByvNCt82EBS0tWyMdVz6z1i8eHFZTkFM/K+9Ju7LxUudcHq5swW6w5jz2zNoAKrT0XRs0YgTuxbi/46pVqOtwp1/o9f83xwnSVo8XM9Ru+Z5/X4fz3RqVC2w2Ebmgj37xyZ0Zxd3dPS5BujRLtr1fJcOr/YjqEsYRw/tIm6HoK7VL8v6fcGVrXP1uHUwVIl6tgvDs00/iu6e1k/wADmnYnCp2w7H9HsUnlyHq6sHqqqNcWn0Kuk7F+L7umVJpkfrfuRf3Ydbk27IZHI8Xp/EzuiPeOUV2ygXofbyLuV5eIGB1ahUyb3C6kDJyZZLdxCTha2wTXKyUJDXaIQknTnXi8527xYev26fYOBT0bGjSg+xCwysw4QJ77JuXYzVClXT4/cTGNjYyu83fFYoHC07LF3m4rkRfPnVI7tKLoW3j+PnBBlFalxqt8Sldkv9OD97cA6Fm6cFj7axNmR21gK8PUz7JZO50LTpGipV6gg48p946EDZCmwFo0aNo3nzUi5d0tC3rykEMT5eYseOYlauFEVZOqtIjHv7dg3x8T1JSrpNQsI+Vq8WAtT2HAPjPEpOjv1jk5NFQl0gbwyrKGv3xL17txg1apyZylK+Pm4/dmwJO+NP8WLjQ5RNwig4vppXuw3i9q1TZN4zwPH69htOwh7rVZLOLfoSHRNN+64GmNW/0Wv+b45zVD6iTrXZ1K6WbhL+qxGA/rPxdnn2L6LP7asv09ftb9XsU04c20v2o6smoRFruHXXJt30QHz3lsHknIrVP+gerftRePcUGldfgrt3ZujggUydOoFnO04RMNg677JHm74U3DxM+o4F+IVNx7FSDZTNwvj88x0MGmSJ5d27VxBSicmsfK9u5Mhh5SYc4+JECb9Ofs48QWqLWtUYPWNPkk4nwGCtBN1Qvr+SiIifWbz493KLoizL/c1/v/gcEDCSjIyDpKVtN/kmIvIuijr2vTrXVqEk71+Gv1mdQvGja2hlcmQKRyRJq0dDZR1ZjUuj10hMPMzo0e+JokczYFe9egupVKkPtmP+5W0bPoeE9GHz5q2EhfW3Wk/wwQdaOncWENejRw0qU8a6rzrOcvMYd4cOCuLiDiBJh+yKghub8T1S3rE7doj7urwxnjdvEXFxOy249M3j9pGRJZw5+xebti5lwdwSWrbKQitfy9Ytm9m4MZq+/YazL2Gr3cKd7KTTZYU7Bm6lf5uHrpDn0KSB4Tl2cnSlQaDK7jZoqep3DD/vckRe/6b9YxN6eZqiOmv3ygiuX9zFi9RnpO0Mt8Abp+36AUmtovDmEVSpf+HRKoSsw6vxDzO88T3a9BUTunsA+/Zup3vPAag1Eq5mFVrpuxfh0W6AgXe5bX+yD60kc8MMnJoHU3rrMG+9+wbZ6RtMYpC9enmQmHiEwMA6JCXdYvHi383ij4aYtM4mTnyPoKBYqwnH5GSh/XnqlPCwtdYZa616XBVBz8ycKdAVJ08aluDWLCRExeTJsURELCAmZlO5RUbi+I1liBmw5cnK5dCkyWKys9/n2rU30WpzAUMyTefV5R9bTfM2IaQ8NHh1mfuX4v3KKJMJQA8/DWxH0Z8XyD0fh1PlumX7gih+coNkmcSqlWMZ+95Zi34LgXt7MX+xnZT0F4sXLy1nbAvZuXMLYWEyuxPjgAECnXL/vsE5qFFD5EHi4sQknp9vGuMGDZMnb0SSBNVCSor4Li4OmxKHxvdIeWGdinCvh4So+OijrfTvb59Lv08f0a/x4yWGDS0RX0g7kWsOMXToTwx/I46BAweWq8lpXrjzb/LQZaio5v8rjeocRDJ6NOrXQv/Z1jaAnzUd8v/S/kUeuvVtgJ6v1Gfa1In49rC8Ez2DBpJzKhbnlzvgVKUuOadi8eowlPT4H/FoG6afnN1bBpO1fxkfT5xNiya1Gf3eWMK/n0tKdDIeLUPIOrIa79feI/fsFgpuHcOzbX8Kjv3G9wt/ISnpPuvXryE8PIJWrRRIqg0mMUhnZz8CAxuZFHRYEm+JmHT9+vX0ccesrCI+/xwGDJDRt6+kRy4sXWq36sSwAAAgAElEQVQsSmGbDMuax2UvDANif3CweNiU9qvZjaoPnStMUGUP5WP+2du7O5063eXkyaqAmNCWRZawddtfbNq6iYULf0HuFKDH8a5cGYlTjSZ4tBUrr+JH18jes4jSkhICBn2p14vMOSmUdIzFSiTPyuzbd4ax75n2xsWlHn5+A436Zb2/Qsj5PRukarp8g4gtx8RsLvfl17evINXSlfPn5AjN2Bo1RNjD2ktWrRbXV5LEC2HPHvF39GiRL7E25u7uhnukvLBOdnbFkDb5+SqbXEI6Cw2FTz914ZNPtEhSidE3+aD+AF/vMBb/spBv5yzkxcaZ+qKbyWWanNlJZ1E27akv3Pm3xdBdna8RWGOU+D3/E1Pi7f2qEQGXFgNVhmFbofAGNtts5V/voW/dFsfpo+upNMg6v7Fn2zAKbh2j6N4pNGl/4tVpBFmHV+HdbTQ5x/6gRMewtn8ZYQPe5EW2ks1bd7JqeTh+A8qSaqdj9XBF96bdyb0QR9bBFXTs2A2la1UaNqtKrwF+KJwr8+CvGwTWsPw9SUm3GTXqTavLUF38cciQIajVWlQq4WENHAgNGsDmzRLbtwtPvCIQNF3opUcPsRQ3ZmWsiJcVFiYmg/Bw+9WHArXjDhTj6+tWoZi/bZSP+WexLZOZLj8UChg2VGLY8N7IHJqyfc8lQOB4p88KZElkuL58O+fwKtzc3HGo1coAP+07xSoTZ+aBpcxbWGJyrjp1vqFWrfeRyeyjcpKS/mLUqHftjq0h31CZzMz8Ck2MubligtU5B7oiLRBjYl7R2bGjuL4ajUTnzgXMmSPuCT8/URVsTeJQozHcI7qwzowZIm8SFmY4Nj4ePb9M+ayMFZv4s7KK6dLlDrdvTyY9fY/J9+npO6leeSfLFsPWbXI2bb3P/LltaRXUjXy1D0+SLnHkkCjdf5Gt/D9HuSgU2bxUZS5NA++iLbtNmgZKaEsMnDWGz5LVOeA/NR+f7jRpEomDg/FF1aHFzLfhXzmhV9RD//rL3TgGtjcNjcT/hEfb/nrvW0DUolDnpZN1fK2JjmjxyTWoz8by009L9crbi8I/Q1k3COeXmuNSuyWOAXXI2x+JqkVf3Nv0xStoIApXT+5c2GTRL0mbbrJs0tnixSvKjTH366clN1dggXVe9y+/iEls0CDBkujra9+7Dg2F7dvFy6C01IHdu9V07mz4n/ISX2DgerGFddeZQO2MAJwrFPMvH+Vj/tkZhcIZH59uZGUdMT1MuxolCQwIiUBeRstqLFu2cWM0H33yJR3ateCbb2fwNPpzfPp8ZjXHknVgKR+NLaFVK7HPxaURLVrE4excDcu4uWV/KzK2hnzDAnx93UlJsV9hq0t4G5sO1TR+vCWfy549Anr4+uudePLkCefP39bH3Nu0EeGzlBRLOOKcOfDNN+jvkRo1QJIgI8Py2G7dLB0Ec0tIUJZN/KoKvNw9UCj8aNp0M1lZJ7h+fSharSlfjniBa8vCMqeQa4IZFLoIueJTJn/6KWBZKfm/9bwlmjU4BZpwhCdsMBGKk2x+NjYHB189qq6iplC48fLLP+PnF8zfy9/Ytn+dYpH54A0a2JvswiIUHn4iNFLmfWcf/QMHrwA82vQl65AQp8g5GYtrg854tgvTK5AsCP8ZuVOASfuVPGWigCWvFKdmwXp+heiYaHLUcpRNe+p1J3XKKPpyce0FJNU7Jr/F2bkO/fqlV7hMXsd3kpwsYp8LFoiHraLKMqNHi6W6m5uSjh07kpiYSN++GkJCVHzyiSg2qWg/zPukM0P5/jkCA+uSlHSboKBXmTOn0A6NgHm5v/VycfNtSSri2bP13L8/BevpfD90S86Cokq4ef6CXF5dPyYq1UPmz32TxFvuVHrLVCotLeotJo7NIjgYQEH9+oupWnU4Mj38q/zydn//KlbH1piHJydHeK7jxr1Pbm4BKtW2v61wpBtb8xWazm7ehFmznJEkGZJUxPz5BuK13bvRh2KMi8127RJj6+goPHJ7FAMV4ZOZPt2BXr2CcXHZb/f3CS6f94iIWKC/plptDnfvTuPFi3U2/89gPujgrSq1GqWDg8W2ve8qepxGk28VwlpRk8s9adYsGh8fY6pNW/dUxZ6H8r6Tydz+faX/FbWPJnzFhvVLSEq6R9bhKPwGzMDlpRY4VWvIiw0zyT6+To831uRl6uGLeQci+XTyVFq1DrJgKatR8yVWrFhbxqWwiQXhP9OqdRCVazTVe39jP5yun8wrYhVdZhvjynfsEA/f3/WuVSqBP09JUZGQcAaZTEF+fjcmTz5LYWGu3SQZmFK9mlcfGgjFHPjhh0Vl8f4NZGbm4+npzPTpCrp3l9kgIIs2KjypuMlkMqpXH4O//wCuXx9KXl6i2RHp+i03l1RQBVPzpWlI0gC0qgiuXVzF6bNOePWbatG2S8uBbItbz6BBLWnZcjNKZSVM6QjKN2tja4sZMSFhHbt3KwA5HTvanhh19QTGFhcn1KbsJxs1xMSo+O47A3vigAGC8fLwYcGfv2OHeFk4Ogou9vBwMbblUQzoQjIzZ4o8i3lIJj4e2raVOHToULm/T4ieTzDZL5c70ajRcqpX/4AbN4ZRWvrcekcAyNJvKY1mKaXZjGXru4oeZ494zr7JqFLlLerXj0Au/2ecYmv2r5/QAypXZ+XKdUStimTTplgKT0Xj4OGHo19Nan5ieNObV3a6tOzL1m2b6BXcT3/MpYvnCF/4HT/9GEmNmi/RvecAur7SkZ9+XsiC+T8hlysY/sbbDH/j7QpRVRrbf7LMNo93V7Ra0MtL3IjGMdzZs0+QmCgmwpYtW/Hqq9ZDBOaTiaH60MVE5/SHH5rwxRefmyUBi0hIcGDnTjh61IWCguKy483L/f8zc3T0o02bI6SmxnDr1njAkjRLmMSjR+FAOJcvw8zZTnj1s16R6NY6jBebz3D8xEDatav0H/XLfGztIYl04zF9uhOzZjnTq1cx/fsbJsbduwVU1LieAAw0CvMt6f7159yxAw4cUKFUiqpPE/WnsqTqSy/Bzz9bv4fGj4etW+07De3bw6xZoghp/34Bm9WFZESBmoabNzVMn+7IrFnO9OmjsWCfNLzcrd8Pnp6t6dTpAcaeZ1bWQa5fH2URkvl/ac7O9WnRYjMuLi+X7amo1/yf3/P/a/vHQi61ateTdIVFJ87e4pUOjS22zT8fP32Dx3cPcOPPvyzKwpNXjMGr8wjcmwrXU9JqyNwwg9c7dkbpUZ/K3ipWLQ9HWacdPkXP+WzqQrbv2M3Z47Eo6wThU/SM9l3f4dWOTe32w9X5BoE1ppmc29m5DrGx3cjIsB9jNl9m9+iBiRh0xRjxrDPurVypICDgXSIivue336IZP34SYWGmy29rLI5RUQ74+b1jRM7lSlLSXwQFvWKRBNSZgaXxhFm5P3a27X1neZxarebWrdFkZh62fTGAd0Y7kenWGd+QT43Eu3/ApeVA3FobmDgdr23h2eMr+vb/Tn8nTfqCjIy1jBkj+FcqMk5RUUocHAYQE7MZd3dDpahaLcZ9+HBT73fbNvHdvn2WXqM5K+e2beDkZBnrrkjIZMoUAYm15zRUhNEzKsoBB4eBeHt7EhOzsQzG6c7IkYPK2EWNKQIqdj9otQru3v2MFy9ibZ/4/8QUBAZ+R40aY5DJivhf3L//3XH2v5PJKv37Qi62kqL37lxlUfhnLJj/EzVqvgSgVwp/tWuonkrV3Nxb9yHr0CrUBdl4tRugVyA5cWwTI0bVsatxqNtXkQIGW0lRe0RWxrhylUp45j16mELKoGLVgtaW6iCW4pMmbSQiYjHvvz8GgAkTprBjhwaVyjqviGFp/CnGsL2/lwT8Wf9/Bqt4UtTWcQ4OzjRvvovi4geoVMJrkyQt9+9/Rl6eoUTagF1/grJJX4pP/sZP33/C8qh4Hm09j7zR6xSf/IPN2//zkn4xtrF07CiuSUXx2hMm7EYuN82LmNMBeHlBhw7iBQGWKzRrq4FBg8R9YpwMB/solj17RDxdJrOPWwfB01Mez5AgddtHaurzMmfAViwYKno/yOXONGoURd26X1Famml0TCmigtd82953FT/OxaUxDg4eZZ9lFe7v/+1x5X1n3f5VHvq9O1dZvmQ+zi93wKfomYkXLfN/mZLkWyZMbMYmaTU8XzMZdW4aSq/KeLTtR/7R1Yz9cDpr1yxFW7UhvmV86qrMZLJ3fY9HN1Oy/cKT6xg9dgqbN66mVYfBhAZ31bdfnofeocOFMqzyu3phAXNcubnI8s6dQs39668NbZl7Y8Ze3J491jnSwSA0odFklO0pJCnpBaNGjeXy5ctWk2U7d8KMGZ8xe/ZM/f+AK/7+LxERYR+iaBBDeMj/a88lNXWvUUjGkQYNlhMT+5AfI34nZu2vvPZaFzSaPCJ+WcePEStZv2YR3bq9/l/1Y86c+SxY8CNhYbB5s+nKypqp1YKPvFs3ASu0N4GuWiXUnrZvF567sedtazVg6z7Zs0dM2K1awY0bhlBM69Zw4QJ07y4mbHsc+RMmiByNvd/3+LHok6enu90Cun/Ok/3/67nse+j/GpSLpviFhbhBxyb1OHpMeNGZh6JwDKijV0ApfnSNjITFuLcONRG5yD4Zg0ImQymVMm/eIlq3aW+iGuLea4JVsv2c3QvpHTqUfXu2oKjTFp/CZ6xds/H/4+68w6Oqtvf/mZZGQhKSEHoxgiIg1YAigqhAICRUFQRUqqgJRZSACpciTdFApAqCGLqUQCB0EUTpCAgK1yAtCIGE9DaTOb8/ds7MnJkzkwn33u/1/vbz5Mmctk/bZ++113rfd1mA/uWhXNq2Fam4UlN/IyFhGWvXricjIwcPDyxZaeyLnOVowgQrBRxs/aUCq+zhIT7E115zPlVOS4Nhw6CwMF++K1JTbxMe3oaYmAIuXnTENTduDAkJjgkudDpftzIVde2q4Z13htmIM/kyYEA/YmPHWpAxCxYstQRVxUffn9jYWMLCavCvRP9NphwyM3dRpUr3Muvq34EgUN8vNfUq4eHhxMQUcvGiGAxXrCgfSfTWWyKJ87BhrtuALG87dapwp9nu6wr5JLeT/fvFe1XT60lLg3ffFXh0GU21erVQzLR3ycmDvE7nOivW8eNiQOjaVWmkyMF0W5LVv/M9/Gfq+F87l2uUy9/GQj+8Zwn5gfWdWtHGzDTSt8xAozdQuVU0mfuWEPjSSHKObxbrWkeTuXcxIb0/ojQ3g6IfV+FbuYolL2W78MdY+80Czv+RqtDYBri1cDB1q1Xjxq0bBPWahGetxtxbM4Euz7ZXUI7Ls9BFsY6mo0ePISNjvYNf3T7tnMEAYWEahg2TePJJa3ApKUmidWszR4+a6dfPtc922TIh1FRSklFGUZ/PypXriIgwubQO1Xzo7lroQ4dC374GS5oz8VGLBBejR4/miy/i6dq1mMhIybI9OVnD7t2erF27jIiI7g7PzHH5v29pPYwPffFiEVD84AMRZPzlF3XijxzTOH8eTp+G6tXh7Fmr5T14sHuzgS5dRHtS29a5M/TrJ2YJsp99zBicDvKffSY6ajU8ujt+evfiK/bL/8tW8/+Yha7RaLoC8xHpW5ZLkjTbbvsXwPNliz5AVUmSXCoV1K5TT9J6eFtyBgZV1vDBBzH8lZ5OcI/38a7fUrF/0fXz3P3uH3jXb4kx/RpotRhC6lJ0/TyVnuhI0dXToNXiVbc5xb8fQqvVYQhr45CXsrJN9hu5ZJ/cRu6xjQRFfYB33eaAcMFweiNbtu4F3LfQbUfTkJBQRceYliY+9NOnxQdja90kJ2vYtk2ipASCgvwYMKA/q1evISEhn7ffFse7miZPmgR6vS+JiasZOHAgERElJCebnLJA5SJcJ5VJT//Lcu1iIFrlMsC7aJEgp3z8seO2Q4dEYM21VerJ6dOnnCTC+HtZWvY4dHc6tffeE66U2rWt1np4uOis7fN5ZmUJK12SxHMF64Cfn19+ENMZl0DeNnSoaDs//ywQNt27u3YBff65gEDOmeN4fwsXCgPEFfnIikH/gr/bu/zfP9e/aKFrNBodcAV4CbgFnAT6S5J0ycn+MUALSZKGuKpXq9NJlZ7oZEGXVAss5asls9HXb01R6ilqxiQqmFe3Fg5GH1DNkmvUmJnmQPHOu3CAzH2L0Wq1BJdlG89cP5GqlQJIu/mrU1U3yVzK3bUTFaSkjG0zGfXuhzR4TOx/5NglQgOMbN64iLkzb1GrjPp79izMX+jLvpSDNGgQhu1oaptk+fhxkURZktQ/FLC1boQErXz8kiVw/77ScrO38lq00FKvXh+2b0+2oFPsUTRqRXadmEz3LddeHsrl0CGYO1dYqbawNnmqP2eOkDZw1WksWgSVKvVj5col/N9aSRWvQy1ZtjMftjxDMZkkFi0qcpo/Vt4/KUm4Ot58U7g57N/Xv4J8krdt3CgSoHTvrtTTd1bkAUinE9a9bXB15Ej3kpSPGmXg7Nmfy9IQVvw9pKbedSmEpi6UZpuE47/fbv4z53JtobvDVQ0H/pAk6aokSSXAeiDaxf79gXJxR7qA6lSJiCHbpOHG7/ssKJSgiNHoAquTe2qHYv+ANn2QHqTxYP1EjJlpFoq3heJ//TwPDi5H6+NPcJnui0arEyms/vwFwyPhCvmAe8uHk3Niq0JyN/eMOGfuvgRe7T+Cvr2jaNa4Hs0a1yM0wMiKpXPJDXiM6bM8MZuxYKAzfMJ5dWAMZrMH4sF7AV5l+idWpEJ4uPg43JGgFcf7cueO6CjPnhXTZFnMqUsX8b+kRKw/f94Ts1lLRIQVZSPj2l0VmaItrllce1hYIxITV/Hhh168846Gnj2tHfbgweJeoqNFx7B3r/DxeniIDuX4cRF0i4pyfd7oaNi8eZvivNY/Hye/1belpt5m9OgphITUQ6cLJiSkHqNHTyE19XYF61ffT34PtqVNG3H/tu9j6FAIDn6dEydO8MYbg0hJMbjcf+RI4WqJiYE1a0Tw0v48PXuKTvjiRfXnKPv0e/ZU35acLPSBPvlEDAq5ue6R1woKhF9/+3bhg5fbW0GBe8fn5xsJD3+OlJSfHJ5nec8+JeUnwsOfIyPjW+Lj89i7VyI+Po+MjG8JD+/MtGlfONm+vuycP6BsG3GEhDT6F9tGxdvNw+9X3jbnxZ0OvSZw02b5Vtk6h6LRaOoC9QFV8LBGoxmh0WhOaTSaU1qDt+hIO8dy/o9UiytEo9Xh17ybpXOVS6WWkej8q+FVkkP65ukOdd9PmU/gC8Oo9dYKRSef98MKWraNIiD/Npnr4si7cICsHbN55ql2ZB9dx501E8j79QAPDq4gqGssAF7NItmZso2zF65y7uI1Nm1OYsnCmVTu8QFVusZyJ68mc+ZqLISWKl1Hc/VePl/ExyNG0yKgiAEDepGSYrAoIJ49K6wzVyUiwsiSJSvQ6XwpKipi5kwhDjRxIsTHi44zIUHglRMShBU1b56exMSl7Nixg4wME717iw64pER8yGlpzs+XkqJnwIB+WMWpisr+itFooGlTa8e9cKEQiPL0hObNhZUmE5yGDxfnmjVLuAncVe1zPK/9suttKSnJhIeHk5Gxmvj43LKPO5eMjNWEh4eTkpJcgfrV9xPJsh0Rvrb5QF9+GbRaLdnZGbRt+zRffrmc774zMn26yBi0cKHSLdKxo4AQfvyxaBMdO+qoXDmAHTsczyFrtnz1lXiXJpPVfffee1BUBDt2aBTbFi2C998XBKRevajwIO/vL1yDJSUiNnPggLj2gAD3j582rYCBA98gNfU3RJD+EqNHxxASEopO50tISF1Gj44p215Qts9vZUJoBQwbZlS0sWHDjMTEFDJr1gyn28U5XyM19ZJN21hl0/E/bNuoeLt5+P3K2+a8VExNpvzyKvCdJEmq+TUkSVomSVJrSZJamx7csljaoW8mKDrhzH2L8Xm8PfeWDyf3ZJLFivZp0Y37GekEdXnboW6/lt3JO7cHSbIK7MgprAYNHMTqb9YzuFcPTMfXMWzoWxz9cT/BvSZR6fH2Il1ZdBxedcvU+VpFYjR4ceXijzRrXI/t21bj1aCtUPTT6vDtMoGfLtSwsBM1Wh26Ri8xL/4rbEfT2NjRpKQY2LtXfLQVofYLV4uRJk0ki+vC3sJ75x1ITtaRlLQd8KSgoIAqVURHv3evmBo3bSpcH8ePO55LxqHHxMg4dKtVM3DgSGbMKOKttyTFRyNrz8ya5ThQyGJfnp7uffQCe/3wlouAZb7JtGmFDBtmsvu4TUybVsjAgW+SmnrXoY7yLTfruWJjx5KU5NpK3r5dKEcWFSVZOo8VK8S7FPEN63tJSBBQRo0Gbt0Sx//4oweJiWvZt8/H4TyydX/njpgFdO4sXCK5uaKTnz0bzp2TGDrUaknn5Qlk1JUrAskiF1kT3VXZtUvAHnfuBD8/5bt093g5wCrzFVJSfqB165dIT1+psKrT01fSuvWzFku+PA7ExYtiNlLeLHf69AU2bcO+43fdNv5/t9DTgNo2y7XK1qmVV3HD3QLgZdBxT8XSvrfjUwxVHyH/7E5iR71DpetHydr4IXm/HlBNbiCXyq2jQULhqtHXacnnn8/m7l+30OkErX/QG7EsW74YTY0n8Kr7JJWfiiaoayy5+xIUg4fnk13YsEHoas+a+TkB+bfJ2jDJMggFDVqqGISKjq5i7WolGyMsrD6JiYmWaW5FrKM7d6zQxbw8gVVftkx8nAkJ0L+/AZ3Om02bNlG3bm0GDnyDefOEpazWAcuWoq0FN3myD4mJqwCByAkJqYtO50uLFi3p3Lmo3AQG27Y5bpNnIDt3ur5PIddajiB7OWXBgkUKF5PadUZEGElIWKxYn5Kyn9atnyI9fbnCchOdy1OkpOxR7B8W9gharZ5Jkxyt5K++grg48bznzoWRI60DIAiXyrx5wr3i7L18+KEXiYmr6NSpA4mJiUye7M3y5QbFeXbt0nP8uA6zWbz/HTsEgqZ2bYExX7xYnMfPT2z/4AMBiTUalUaEOy6cpCQ4cULMDGrUEINVRY7fudPqAoqIMJKYuJb+/fszY0aR4vnUrCme14wZRfTvP5jU1KusXbueiAjnwfgDB4SWvKsSEWFky5ZtD9U2/teLOx36SaCBRqOpr9FoPBCd9nb7nTQazeMIibSf3TlxQUEBVVQs7cpteqMBPIJq8fuV67R57nU6tXmarP1LHZIb3PpyENkntqj6wYuunyf34vcY0TFv7iTOXrjKps1JLFowjVKNHuPN86Qnvm9xwfSO6o906QCZ6yeS9+sBsg98Rf+B73Du4jUyciTadnidx2tWI3vHbIdrzt33GYsWzKBjx3Dsp0oRER0IDKxkyS7jjnXTpImwvmX3yr591mQXI0bA2297Exw8mBMn9hIR0YEFC74ot/FGRgqfqOy73b3bwIkTh4Fih2mpVmskMtJ1sLxbN3WYXGiomEVs316+Rdu7d0/+lano2rUbXX78ID7atWs3WI5JTf2N/v0HldO59Fe4AaCIvLxivvzSOkPq3Fk8x6QkQQqKiHC0Gt1JNtKjh4ZevXoQEfEMUERERAdOnNhLcPBgxo71o2tXDTEx3iQlSVSvXkrfvuWJdykHWnsXia0LZ9kyHNw048cLd9qSJYLJfP06itmJKxfQV1+J9bY6NUIXPY8uXYpdXnfnzsXMmDGtXJE7d2e5xcVGS9tISxMzHNkV2bu3WG7ZUtk2KtL2lO6jYEJCQh3cR39Ll4skSSbgXWAP8BuwUZKkixqNZppGo7ENfb0KrJfcBLbrA2uoW9qtRJUa/xoc+WEXzz3ThLFjxrB69XcEa4rI2jDJ0gn7V/Ii99QO7q4TnfCDA1+hM5eQuXexwKxrtXjXb0lJqZFd25azbOEnoNPjXb8lklZPWEggnBaZcYYPH8qUqfEM7tUDTm3krXcm0a9PtCUoWq1KKRfOn8L3eUe4gXfznsxf+I1DUFT+PXDgAFJSDG5ZN8nJwtcuB7Hsrbp580Cv1xATM8aSv3T1aoE3d1V69BDTfj8/eOEFPUOGvAl4MnDgSIdpqbuBM1vlSLnIrhRJ0jNxovpHP3Gi6BglSVvm4ni4qWjFsyh5MWPGp3TpUqIqz7BwofBp5+QU06xZW4ULpkqVSmi1YqCdMEGgeKKjhVurUiX1IPCBA+XHTCIjJTZv3qaY+oeFPUF8/ALS0+9w+fJJNBoNc+aUkpEhOmxXxX6glZOg2BbZhWM0wttvWwenvDxxP6NGCcNi/HgREDUYUMxOWrUSxsHp0yjcPCUlol5bJrOcEMPdILlaANq2uDvLlWcmx48rjSPbIP60aZCRITJyVaTtpaT8QHh4ZydBW1eB4L+HywVJknZJktRQkqQwSZI+KVs3WZKk7Tb7/EOSpDh36gPQGoQeddH186QtHUaOjbvDt3kXCv84xoBB73Lk2CWFldypzdOYjq+jbYcBvB07lWrBwUh5mTw4tAodZvr1eYP8X0WLDuk1iaCIWAyBNThw6CAmNJZ12sqh/P7bOfQGL+5nGTl38RpHT1xGa/BHb/Dij+tZnLt4zSEo6kzR7487Gbz3wXjS/rqL/cgaGzuSlBQ9WVnOrZtFi8RH07SpaPzlTxVFADY19TeyswvdzpAzYAD8+KOBmJgRTi37iriG7EtysobIyAg6d+5ESYlgJA4dKjqaUaPEYCVJAplTWrqlLDi1E6vl81uZ+0ctcGZ9pikpyRgMkpsoHjmLUhHffbfFoXOx/+j37YOlS02kp6+kSZOmaLWVKCoqZuZMDWfOWLVV5AE3J0d9AHTXmszLM9KqVbuyIJ3SIluwYL7lHblbn+1AK6eqszci5IDuzJng7a2jX79+HDwIb7yhnHl89ZXY97nnlPGbGTNEPZ07WwOm77zjCGdMSdFTWur+c6hTpyYbNzrH2b7wgjB61Io8KI8cKZZ79xYurTFjHI0jOYjv4YFN2yrfarbNTFotZpgAACAASURBVOYsKNu79yu8/vrr5Vjr/xtBUbeL6cEt8i4cIH3zNPzb9afgylGLpZ25dzGdOjxP0tZveKy+v8JKPnP6CIsWfU3TRnVYvfJzZn0yl2GDX8fPy5MRI97l8OFd+AcE4dPwaUsQM6j7WHSVq1oyGWm0OvxadMMsSeQH1mfjukU0bVTHAk3MD6zPxbPJNG1UxyEoCpTBHgeRd8rq7jE0jeLbxFXcvLkM+5FVhgFOnuzDhQsGJk8WH4sMBxs6FK5dE536pUvuIWHWrt0E+LBgwVJ8fNzrgH18YMMGbxIT1xAW1oi1azepWvYVCXzZFhFk9WD//u+pVOl7Vqywuot69RJWU5UqYjrfrZv8ARQycOBIUlNvl1k+z5GRsd6J5SPgaCJo+ybt2pV/ncosSl7k5xsVnYut+JX9Rz9ypMRnnwlrfOpUEaCWUSm2g6CzAdDdgdHHB8zmYl5+ebDDjGXt2q0W18HDDLQ1awo33XvvifZla0QsXSqs8ISEBFauXEVJiXLmsW+fSJbSsKGQF+jYUXTccgf+8cdw+LDrGWdKigGNxr3rNhigUaM/2Lu3lI0bxXp7d8mePULz5tAh5fHyoGwwWK990SIxM42PVwcFiGTdGhISHL9ZZ1azO8J1vXrBqVObHxq2+a9Y6P816r+vX4BUUFxMQPuBVH6qJ5K5lNzTyeSe2YFX/RYUXPoB70efxiPjT6ZMnc8fVy5YhLt8HqSScT8d7wZPO4h4GeqHo71zmcp+vtwvMlElcryqdkv65qkEtB+EX6seZK6fyBO1a3DmzDFV6n/63TQWJszG6OFlyWU54LX+/HR0PemFIRiaRJP/wyJmThNpzsLDT+Lj8whqZImEhMVlZIi8Mu2Tl3nwIIPS0p0MG2asIBnoBiEhjWjdOs9pFhq5LFoEV682YsOGZRbxJDXCDLjHhJwwQUxZbaUKdu7UYTKZmT3b0aUhH6eWw/SLL3T89lsNbt686ZJdKpOuFixYSUbGt0REGN2koe+13LOnZ5CCeVlR4k50tKwLbt3urI6K1N2xo3imffv2Y9Wqz1Ejpz0syWjRItEJN20qgrTZ2WIQkSQdCxbMZsiQIaSm/kmTJq2dPv+NG2HlStFZde9uJRotWyY6y169RJzGKianY88eDxITl/LKK0OIjDS5ZJcuWybiKsnJVo2jZs20/PabmchIMYjKrOqdO7Vs3WqmbVsdw4aVUloqZg6umNTOcucqReagPLJPSEgjt2QxYmLE7MC5DEL55/qPUf//E0Wr00tVX5nulLl5Z80H+Dz2LKY/fqZd04YcPnzIItx1Z/VYPGo0osqLIxxEvGR26POtmrBrZxLagBrUGGKn3fLlYHQB1aj2mpV1mrPzUyp1GKJgndpS/89eSLVkMxow6F369o7CZDKxcV0rvtui48MJ1pyVrVv/jK/vk4jpkTyi2v5WLtumd/v4Y6H98fPPjjobMhPTStf/E50umFWrJGJiyqeiX7x4wSKKlZKyh169ejuITMk6M7t3C4s6Ksoxc01ysgi2paeD0aghKMiXAQP68+BBNqWlrlOvLVok6vf1FfdUv76wAkGcR54uqxVBKR/MmjUbLVR8ZwzM5GRxHg8Pb/LyisoGz1dZsWIlUVHWzsXdtH8yhlxtwHU2ALozMNp2NMuWQXKygZycO8htw1Y+oqL1yes++siL6Oie7Nq1qyyJiXhfQrdctIfRo8eQnr6ckSOd9weffSbapNks2oa3t9V99s9/Wturnx+YTDq2b99Bp05teP31t9m8eYNLhvSECdC+vfgP8PnnOvbvN/Ppp5LLdHgeHp7k5eXTq5dr/XZnbFqrcZRXtsb1N+vMCLKvU9bWcS6DUP65nG37W4pz6XwqS7Vi1liUE7P3LMCnRQ98W0ValBOzj66jar+pqlK32T+to+bI5U6lcDP2fInW4KlIGC2X7JPbyD66FkNADYKj3le14NWo/2pJOMJqvoKPd57i+Natf8DXtwkVGYFTUn5i4MA3qFSpkMxMq5CTrdyuLOR04YLo2OLj/2GxGG7dci6nKnyO3mRn30KeKYSHt6d5c+F7ly0++87RbBY+8MOHBUPQw0Os8/PzYdCg/sTEjFJYHu6KesXEiE50wwYxhfb0FJhse8tX7dixY33JyMhXfFT2uT3lRBIdO4qYga0a4LZtZkymUkveTndnRPIH6mwAcDawLF8Ox46Jqb+9Prl9shFZd6Wo6KalbdgLvLmSENi1yxrktE0lmJi41EYIDUsbsKXOGwySWwqS774riEZ37giLOiVFDCL2ks7y4Bsf/w9SU+/SvPkzaDQl9Ojh2D537BBqkMuWWc/vXpINA8HBA1izZrPb7c5e7+Y/aaHLOXvVZabLP9f/lIXu5e0jeYaGYWj8Enk/rGDcuDg2b9lIWnYxXs26krlvCVVeGoVvk06K44qun+felul4+FelSs9J6u6UTVNAp6dqn4+dzwC+HU9J+lUMVWpSY+gixfZ7y4fTO6o/w4cPtaxzlsi6OL8dBn2W4ng1C90dKdmDB38gKqoHc+aUOrVKJk4U/vdKlSoxaNArZGXlW6xi+45NtuxLSvTUrz/EYiWMHh1HRsYqhcsiIKB862/8eHj55VeYPPl9m0TQWO7TXdlduXNcuFDU26IFrF/vvqspMNDXaUJud6zY8ePFeaKj3dc2kT9QV26PM2dEp3TtmjKnZ+/e8OmnwnJVm3HZ3l/nzmA2Z6A2e5PvRy05dd26dWnS5AmOHDnq1AqXS0pKMgMHvklEhNGilNm588OpOjpzZ9jOIuUZYf/+/alZs4TbtyVycoS/vkYNoa3+0UfKQaFXL/eSnY8d60dGRp7b7c7f39W3AeVZzfK3425mMuUM4D9vof/XgqJVq9ZUIFaq12nG27HTCKvZANOxdTzWoBGFp5Mcjsves4BXB4wkNCCErB1zVbd7evvh0/AZRRDz1uIhZJ/cZgli+rWKRKMzUOUlRxPAnvp/7uI1C9rG/rfRqAYXLMEekREe3r4M651rE+xbpUB5JCV9R8+eWpcBl65dhSZMQkI+GRmr2b49iaQkDRcvKqnocuCqY0crqkW+prVr1xERYVRgij/5xDHYZ39uEezZSHh4extEhvU+Ze0aV8U2aHfgANy+Lc7rvu5MpTIqvjopyR3sd69eOiRJyy+/aDCZlMQZtWIbAO7Z0+rntS3Hj4uYQsuWKILBrVqJmYdOJyx7V4iQu3fB19eALaohLCyUxMSVCrJRaKi4jq5dxSARGwtPPXWbQ4cO8c03X2Ey3Sc9/RLx8bPKOnNb/LQ6tf5hkU3OSGYCLpprOXdERAdOn97PSy8NobBQ7KPRwBNPiCC5vYUvI4ecYcjl5yAbR+5cu7e3ErpoMMDevSaefLIx9m3ZGfJERqyVBz2WiVWizfo6re//G5SLj48nY8eMYfuOA/Tp1YNmjevRomkYw4aN5B//mMn161fxe36ow3Fezbtx8EASf6VdVt1eqWUPAgIDqV56n/trBHEofcsM/J8dQOGVn7i7Ns6GdfqaqgXv2yqSEr2HhfpfWnSXw3uWEFRZQ7PG9Wjf9gkL8sZg0Ds0unr1Ihg9Oo7U1LsWRIZzmJMV5SFQJ66JMtHRWDrvYcNMzJhRhE6n4cMPPR3YhcuXGyxsUGFRi4i5LX5bxiTfuFE+xrl7d/jrL8mGNq0UOBowoL9qR2v7fAYNsuKVs7KsmHd3kDUyYiU2diwpKQbVj8od7Hf37qVotVrCw1/B07NSubR+W+ZjzZrQrJlgYsrQ0xs3REDuk08EokQNHqfTwdq1rq8rKQn69OmDPcIhIiKSEydOYDD0UeC+jUbxHJWIoTdVsP3W385QGg+LbAJ1kplA7+hV8fUjRw6nf3+D04ENROe7Z49zDPk774hleSbibICXS3KyIH/ZvpsRI0Rc4P33JzoR63JEngjE2ho+/NDTATW0bJmIAZhMQtIB5Dbb32l9/xUc+v9lufL7Oae65SAo/hnFGjwee84JJjySPDx58YXOvNGnJ+YT6wgKCsF0cT9VusTg89izZO5zZJ3eXTaUnJNW9UWvZhFs2JDImdPHmTRxHPkB9Zk6bRJms1lxvhMnjA6Nbv78gjLruz3jxr3vBgXZREJCQrksObDijO2JMCZTKb/91oDYWF+6dtUwdqwfwcFvcuLEcSIiXlTUYU/eqFkTCgvdxzhbsfDKYHNsbCwpKXoOHbJ24J06Cb+wCM7JGG/xvDw9sUAu3aOkS0RF9SAs7BGnFPmsLHfVBE1s376dxMSv2bp1C3FxepYsKZ/5ePGioMXL2YViYkRGoq5dXc8KoqOFxrir+9u715OPPvpQdXtY2CNUruxH374Gp1a+s/diW5xR6ytK6bctaiSzHTvAaDTSoEFrAgODGT16DKmpfwJyO1EfkOVzSZKWRYvU4aTyILloETRv3syt+lJS1K/dnWdmXxo2bACIfAC2yqdGo2jbc+eKOMehQwKHHxMT43bd/2r522QskoOMH00cqcj/WXT9PFkp8Xi3jFSkmss+KoKiRdfPk7F7Ab4tlKnoTMfWMX32CgAO//QrNy7v49erfxLy+nyMmWlk7JoPGvB9UsAQO3bsysGDu9AH18GvRQSZexfzWMMn+PPaHwr0zItPt8Pg15D2bZ/gXvpfzJ8znJkznUfix48Xja9lS8ftcpF9gZIkuRVwefttMV2VA2OOKcBWlVHJ1YIqBYwePYWMjG8VfkB30R5y0E1OjycQLlYdajn3Zs+eAsamFtSVp9cXLwpFwJ49hbXkLOC3Y4f4Cw/Xcf68R9n9vUhq6iUSElYpYKCFhUUsWWJ0G1b20UdeREVFsn79d7zwgghgyn7psDAxGMnQzJ07BQbaw8NLoXXu7rMbOhQ8PT3o1k1IK1gROXIWp2/KBl/1gFjFcr1eUq1DRmnIWkG28ZYnnoALF8Tzt4UmlpfP1j7gaAtrbdZMmclKfndq+XdtA7mtWrUkMPBouZr6O3dqOX/+BFeupKrWt327QGxNmqR+7cpnds3ps7ddts9gpVaWLBHCeZs2rfw3Z+b6mwZF7XOKykFG2/yfhsYvkXNwKWY0SDoPDIHV8W3WhYzdX6LRe+Dfth85J7YQ2Gko2ce+Q6P3oHLrKPIOrWD27HhathJvcNPmJFYsnauw+mXce/bxTdQICUKj1ZPpUwtD1XrkndmJf7sBFJ/drgplnDJjGc0a1yMhfjoG3SZGjFAVlwREo8vNtcKx1Iptfs7yAi7Llomp+dy5rrDXPpw4cdgmcKkMsMi5Rm0Dbe5gnKdOhZMnHRE44iM08Omnn/H+++85TYyhFkCbOxd++MF6P/YBPz8/YflMny4GRev9HVfNS+pOtiXboNWiRXDxoobff5cUQUG1AHOnTqJDf/fdIYoB8YUXYNUq0Xk4g5rK7/jy5fMkJCSU5WHNo0oVPwYMeJWYmBFO35f8uyK5XkXCEsc6QkKq8eabuSxb5mgQyAmmGzUSHbvRqMHHR0/t2iYmTZKcDiTLlonn2auXaJu7d6sjX5Tv7hFF/l0RyLU+i7Ztn3ca+JaLnIhj6NARxMd/oVpfVlYOK1YIEbPyn5l94FI9UGmfwcrZtY0e7cv9+9fLrQ8cQRP+/t7Ur1+Xa9dukJVVYIHdxsbG8uijTf9+HbozC/3IsUu0C3+MQwd2sG/3dxQVl+DdoC3FqScx6QxozCYwl+L9aBsK/zhOcM84NGi5u3k6Gsx4eHjR9rlX6dM7iiu/n2PNN1+Sm5dDkAp8EUTHnrl+Im2bNOH0mVMYPbyo3HW0Knoma8dsRoyayJ0Hetq3fYKPxvdl0cJCtyyzqVPLtxB+/vl7l5mCZMunUycYN875OWU4V3z85wCkpl5iwYKViuwuzzzzNIcPH6F7dxMREaZyyRnupJWLi9MRFQXDhzsf4OzxwGlpgm7u6Skw77aWobPOwRYSZ2/FlJdtyX5Qka1LcM/KFu9pV1mihcIygS2RRMKeAGM7K6lVS0Au09OvK67XWpxZZP9eC/3110eyefN35WLCZYKTDHG1f57ygLd3rzBYDAarS+Qf/3D+HK3vbq7Le9bp6riNXAkKUlrXck7dtWu3kpOT51YKv4pY6O5i0WXyX3n12c9WbtwQksjduilnufLsJTOz8J+SJDVUO+9/Lyjq7WkJLNoGGdu3fYIWTcPo0P5pzGaJ4N4fUiUiFq1/CJQUIhmLrRotQXUwpl/n3o5P0Wq1+Dz2LKVmM716RlJadJcVS+eSXViI56NtHLIV5dogXjyf7MLZM0fpP2AIUlYa2cnq6Jlx4+Lo2zvKcr15uUVu65ur6YfLRQ6cyAEXe9/wmTPC1fHeewIPfviwNdKvVoQ0wBZcCQn5+R1CkiA/vxNjx1ZmyBAN4F3mS9Y4+JI/+0yZKMG+NG4MklRKt27OO3NwDKCFhoLZrKFv31fZsUPPkCFKsajFix0HQlvpA8fAkSfPPtuB8eMdqe7O1ACzsysSlO1PWNgTZe/Jhy++0GM2i4HQma931izYuFGvkCB4GA1td4J/1iCceh1ara5cFFC3biAyT/qoPs+ffhLtUa8X6/btE3lPe/QQZDM5IKhWIiKMfP316rLsUnXKdOjj7HTJfcoV6QIr6iYzM89ynL18RGRk+VLOzgOX6u/I3WsTmcBc12cPmgBhOM2c6Si5LAe+NRrCnJ33v9ahFxQWu4QEzp7zCbr6ra16LD0+QOcbSNW+ky16LL7NuohApqmEkN4fEhQRi0eVGnzx2VTi4sZQuccHhPafSdHVU9wrk8rN2DaTZ55qR9bRtdz5djx5vx4g79AK2j/XlSVfzqCg2IhvR0f0jHeLSBLXJnL2wlXLNfr6ebnd6CIi1PXDRcBGb4EV2sunduki/H9PPmmFw8nuETnlm32R4VzlCQl98kkRhw8f4eefD2Iy3Sc7+wq//HIMb+++Dip6en35KJiKBFZBdA7z5oHBIPHttxsADZIE33wjUAkffKBuWdlD4mRol5yhxs/vAJ98IkhQtno5N2+qqwH6+7sXFLS+p4Ky93SYW7ceJTKyPDE1OHBAIibmDSoOU7P+dgcyZ3uNVqiiVep17doNbikf7ty5k5SUnQ7Pc9Qo4RtX63BGjRLrXRkvcno6ZXapVXZQ2AIGDOjnkL3JvuzaBW3bWmGBqamXytq7FZLZu7e6OJn6M3MPSugsg5Vt2bhRT+3aNSw5BoS87hgHITB7gTx3YLcBAWicnfdvaaE3a1yPz+clUL30Ppnr4mxyiCqTSjz4fgUavYdCiCug2zju5T6wJIT2CKqNf4c30OTehdMbiYx6VWQr6jkJn0bP8WD/Mpo2acquHevLyEgfqbpm/Fr1INuk4crFHy3X2LlLNDt3uph3YYV6RUYK94EarPDTT2ezYMFSS07Mtm0jkCQt69dvICDAm3nzhK/QmfVn//HI2Fd3hITkjDK2sKyVK1exdesGAgJ86NZNyP66I6lbubL7WGZZTCkgQAxUcoamXr1EJ6w2UCnvT2n9KLMXGWnZUtSzdauYEcybJ3RM7Iv8flzpfIv3ZBU1sxVdu3EjrdwOMjISPDw8LHLHD2uh24q8qUNUHa/RfoZmMrk36GZk5CqkleXn2aUL9OlTMU122yK/f3X4rhVyGRs7lp07XQ9eO3eCwaC3WNcLFqx0aO8Ve6/uWeiuYLMgdG/27jXRqNEfLnKeivpsocppaQKNUx7s1lWH/rfyoV/5/Ryrv1lE7JjJVA2tidlcyqxp75NRmE/N4UsUx9/6cjCedZri3+5VMlMSkKRSgruPK5fGb4+ikaUDSkqK8KzZiCAbdI2aHIHp2Do693zPgnL5fOYo5sxRhyXa+mtDQ63+PitT9BWefPJJ3n8/zuI/s/WXJSVJtG5tZsoUs2PlZUVNo6KilGhn/kNbMbHs7NxyfZFz5ohOvTxkQl6emLZXRJPEvo4aNYYqfOj2FHm1Yv+s1M5jHwz18ICRI4cSE/OGReBLDTXy7/Knuv4tlq3vZUNZTESIvNlfo5r/211EzqhRBqKiJAckh7vHDxkidOLtGbHONFXAMTby9deJvPPOaAtiyl42YcAAoR4qi1+5ijHI73X/fvFeg4P9VJ6Z/fN2/o5SUnYycOBIB1TNxo169u41uSEyd4SwsFBLrODUKWGc5eSIWbir9jRiBFy5Iql26n8blEtp0V0mTRyHrl5rakj3Wbr0WzZtWM3iJQuo2neKox7Lia3kHP8OfWANfJt1JnPvYgz+VakxTNnx31k8GC+DB4sWfU2t2nXZd/AoK7+aw530ewT1GI93fYEnlKGMkmTGr3kEOd9/xfjxk9i8ZSN38kwYmrxEwQ8rmTnrc3ReoRZUzrr137Fq2RQHFTp7mJ4cfBPJFsAZ2sS2XLwo3C2uNE7UIGMyyqVhw6cq0Nm4jvC3atWKhg1/dymeNW8efP89LgNu770nnkeNGq6FuJYuFddm/+HLUNBff72gQLnYilg5K7Y6Mjt2aEhOFnK4zoLVzoWVlKgRdxAPY8f6cfv2edU6HJfdpYSXoNcHO61DjabuDppp+XIDSUmowj8ron2zerXyO/D1dT1Q28sFAHz99TfExsag0ZRSUCCs+7ZthWUu8rCuJCJC5KSrGApITYzLftn1ttTU2w6Ipdq1a9Go0RWXkEZru5pFSEg9Jk7MZdo0YeB8/HH5A+aQIXD1qnqH7toR9B8ssg8dYPOW7Rw7vM6C9769fiKjRr7O5SsXVTtzgMqtoyi4dAippIisH1aj1XtQpfPbin2Krp+npLgYfe3mTJg4nvET5nDk6Gnu37+HV1hr7u/4jFoxiWg0WpGsesAsbi8cTOGP3/DM8wPL5AiacOjADvbs/pahIz9A5xXKkWOXLOfIyPeheasO/PLLD+zerYSs2TbcXbuEH7x+g0bMn7+EVi3quZU6Tp6+qlk0oCQaWXHoKwkLCy2jRLvu5BypyZY3pNjvzz9v8Mcf8Oyzzjvrw4dFsOzDD52LhJWWCnfKihXOrwmERTZypLDubOtISoJnn32WBQu+UKB2MjLcI2VlZUFMjDfFxcVIkoSvr/q+sm/1xAlb36rjsxH+VNeY5F27oEOHXH78sb7rC3yIotHUxNd/CXpDGMFVoGb1UMs1rl27jvh45YylZ0/Rlp55xvl7TEnRU1CgHvCXJQLKa1OyW2X4cHGuCRMEd+Kjj5wfq4yNiDJkyCt06NBUwTc4dUrMSD7/fBRhYaHIdHj323slyzH27dx2WQ0dNmDAy8TGvk1YWChhYTWIj59FfPzH2CKRxo1znT0sIsLI2LHriI//mAED+rFixUq6d5csYnHbt7ue5aplCpPL38KH/vuF/RgeCbf4wSt3Hc2V1Cv4PP6sAp1y0y6HqF/rKMw5d5GMRQ6qikXXz3MvaTZVe39IULfRZBaXkpK0wjJwBEWMRh9YXZFUWqPV4f/0y9SqXYde0d0pLbrL53M/oF+fXsyZ9w39+kQrZADk3y+/PJjbt72YPl1dq+PiRUGM+fmkB/e9H+X99wdxNz3HLap/9+5WVIiarsW8ecJaGjVKWFVZWUUMHjyM0aOn0K1b9wqgIpQ+w9TUu4weHWfx62dlFRAX5zyX5IQJAiferZu4RtvsNnJgtWNHPe+8MwKjUeNW51tYqKzjzh3QaDw4efKUA2rH3SQfVar4otHA3Llmpkxxnj3qo4+8HCQT1Hyr5flTXTEs/x1FktLIzerBg/uzuJdhUlyjWoo+d33KzpAcDyMRICNnWrVyPhsC8X70eskO/XJbkZJP6NTcIT5+gSJWIFBAL7vZ3stHGz1smjn30yLmIccKUlMli9+8Z0/KlaJw1aH/LVwuMpkoLSMLY6mZkF4fUnz7Mg8OLkcfWB2/5t3I3LeI5s3bcOHXM2grh+LXOoqCH1bi5+dHQXBDqkSMVvi+TaVmPOs+afGJO9M8l2V45SKZS8naOImm9etx5tRRdPVbU8N8n7djpyOV3LO4hQLyb3A//S76+k9RQ7pPh/Yd+XbVfHr0kBQyqUlJkLwTTGY9VXpNw7NWY7I2jOW1yDSWLilxG2srowfsySDLlgkqeu/eOrp1K1X44EXCCYnZs9UT9Fr9eScIC3sEeUqZkrKHgQNfU/j1+/QRHTWoKzo+/bTA2rtKMiCfq23bZ9xyU7z7Lmza5F4CjYULBRbaVRKF5csN/PZbAxo1+qfFDaFGIKpeXcNTT73CqlULcccNIqsXdu1aTLdupS4lcu3L2bMQ/6Unn0wtplYt5+vcK3q0WnFdWq2e6OgCFiwocdOnXFlBcHJG0HoYTXb5uJEjnaePA8GuzMkRbjlbwpqtW6U8co69MqX9tbkm3Ylld1yhat8NVMQFZ3Ut2buK7HkN9u3J1xdu3fqb+dDtg6JV/YstGYlK7qZSmv8Ar0daU3D5KFq9B156LSWmUjzD2mC4e5HiEhNDh48jIDCYVV/Hk1FsxqdZBHk/rODJVhHc/PMU9zLuowuo5jRYei9pNv5tX6bwl2RF8PPBoVXknkmmat/JSOZS7m+fS7Wqdcm4f4OAqDixLmkOXvVbExw5lvRVsRhz7uFR50lMN06j1ZopKTZjMEhoA2pSkpVNcK84vOs2B2TG6SIwaZg/v6jcl//WW8IKf7gECp4AREaaadnSyNGjovPKyRG+1IiITnz22VyLrrkzIol7TFIdp05piI7WqNK5ZV1ud6jTS5dq2LlTT0GBySazUzalpdtUj3PnWUye7I0kaUhIKPiXyDn2wTKjMY1duwaRmHjOYaCLjoYaNfVs2lTKilWezJhaQuvWWiSzxPwFpSSneOFZ8zFqe19mUYKRc+ckPppiQF+/HdVNP7Fwfgk6vXUiLZklNFrxLWswoXGKd3DXX64nOPj1MqKP9d5cEbSOHxfPuWvX8jXe5SJLAyckVGwgUAYRy8/6I+cVENLAJps2aJUeaNiwhktXipo0huvnZr0Gd9q2ffDXPpi7cKGQyPb0VGcej6dqLgAAIABJREFUT5sGly+rd+h/C5dLaICR5UvmENz7Iyo1eR5TdjohPeMI7jYaj6r18H70KQqLiwnqNYmgbrGYKwXRpWsv+vWJ5qVO7Vj9zXq6tGsPpzcyZ858Bg0azNq1Wxk+ZASaBzd5kDTb4fz3k+fh0+Bpik5+x9h3Ywm+e5LM9SKnad7ZnVR6vB2S2cz97Z/iHRbO3fQb+Pf4ACTEukfbUPTnKYpuXKAkN5Pg3h8R0nMi2qAwjBo/0HkS2HsmVQcvwhAsCFAgBpLcQwtp8ngxRUVGBg1SSoLal507dZSWCiyz/YfgDmY1MtJMr15R5OW9wIcfCjz5woVWeddKlY4o8nU6gzq6g9M+f96TpKTvCA5+k9GjfS34740bjUiSlt27D5GaetstN8Xevd6cPXtGMcXetWuPag5UsLoSJk0SZCR11ck1bifUlqfErqCEkuTJ7dvrOXq0Bf7+5xyki9+JeYWsom1s3jqWpcs98Qh7hinT/UF/minTO5Oc4onPY89Qkn6NtJx6fPZ5Dz6eUhn/HlOo0nU0dwvCWLR8KHqvc5a/S39ut/m9ATRPO70P97D1BmJixjjcmy1E0p6gdf68yFR0545whXXuLGZTsoqm2mzk7l0ICPBWhVwuWeJI+JKLGrTWFdxTVqYMDn6dsWMrlwnVVSY4+HVOnDgBGMp1pbjjCo2IMJWR2yrugrM+c3GcPWGsZ08Rj7LP4Sq7cWX5YbXyt7DQ48a/iaZ2U4K6jebON2MxVK2vcJXc3z6XwE7KjEQFP37LnHnfWOpTyyh05fdzfLVktgWTbltyTm4l5+h6Rr07iYaPN8dsLuXrr5dz/Y8T9IgewLat68jPz6Jqn4/xrNWYu2snog+sQWHqCUJ6xpWlwhuHKS8T70daKa733rZZVHlxhKpr586iVzFIeURFOaeJ24pXTZ7sjdkMX37pKDHgCkImT6llES0PD2jXTkTI7fe3zb3Ztm2EU7SILJ7VrZsS0aMUXnrGwUqyuoGs+0GxKuxLKTD2oiIwlZGRh78/vPiiY3IIuZw8KZACYE0y0bhxQz79dC6dOrX/t9DnoYCiomzOnetHYeFvDscbTYH8mTadYmN9ln+1mAtn91G17xRLm6ksFZLxIEOxTuPth64o1yHzltzOr/x+jk0bVtCibR+6dekACDDB7xf2M2ZsV55qvgKd1lEr25ngmf2sSdDlF9lZrb2Iju5DdHRf9HoTOTnqyTncdXcFBw8gJiZGkVdXr5fo1Alee801kqviWX/U4Z7lyUJMnuzNgweF5UIHlZo5ynM5gzTaC5S5mg25em9r1xrNZrOkenV/Cx96715dySooRO9fFf9nXiX76HrQQFCEuqZKTvJchr01gb69rYwO+4xCMgzSmQyvrOEyuFcPXnl1sEMdL7/cg7yA+gR1G+1yYMncvwSPkPour/de0mxCesah8wshM3EUn821WsC2PtysLNEBtWkDAQFWWFZk5KuqvnZnEDL7xuBq0JDL8uUG9PqerF27iUqVcPrxpqUJsk5yMphMmjJBpX7ExIyxCC6568cET2bM+ITNmzeTl2fEYAAPDwO9e0czefIUrlz5p4Mv39V9HD8ufPgREcIPaS8elpiYyO7dKeWKd9nCyuz95pIkcePGZ/z55z9UjtRwN7Mv1ap9XEadhxc6tcHz0TaKdnRvywyqdB7l0I7qjN1kqcm2nT9SN1TEbuq3JrDgNqu/2cAvZ08SFzcGwyPh1DDfZ8mSVVz+4yyNGgqn+82bO6gVuhhwzGQVEKBn4MDXiY0dQ1hYDVJSfmDgwIGKDEa2z+2559pTqdJBp89M9o+7gqs6+q7FM60Yjr8iWX8c93Mn25AryKZtsfrB/1I9lxqk0fY7sb9G+yxSoaFiJrRihYbUVKksd68QL5s/f9mvkiQ1Vbuuv4WFvmvPD1w4vYO76X+h8Qmg+hvzyUhJwHjvT6q/MV9x3N1lw+jbcwDF2uoWixwcLfTDe5aQH1hfIcObvWcB3i0i8WvVQ1Vm17aOb7/9ltPHt6MPqkNw97Hqqe42TyWg/SD8WvVwer1pS4fh364/vk1eIOf7RXR+ZA9vjRR6J8463u3bYdcuPfPnz2PIkN5O8xiqWegPG7TatUvUFR2t7Ayd5b5Uy8OYmvon0dF9uXr1GqWlQsPGx0ck/x040Ho+2Vrr0qWbU0s+OVl84Z984jygay+y5Y4PfdOmdfTr179cK00mfthafAUFdzl3rg/Fxdcdjisuqcaft6dz8McsRbvc9N1Wjv+4CfxCCO6hnr9WbkeVn7JCYeR2fvNOvgLSe2/NBJrWq8W5cyecyjqDaMsdnqlDnWqz8PP5xfFG8cHXfyHFRdWJiuxVjpiZJxqNhhkzilwIs3mg12stYm/OZm/2VrO7OTr/HRa6u+cSpCoeyode0WtyRuST5aDVyE//E/K5xoK/iIsbQ5Wek0DCYtXaW9d5p5IIunOSt2On06LpI5b19hZ6UGUN/5g6ySLDW3B4JWPGfkDi2kSyTVoMTV5ykNmV6zhz+jhxcWPwj/yA/IuHVDvqWwsHo69Sm2r9P1FY4Y4JqbeS+9MGKj/9KoUn17B8SWEFOiBh1SxYsFTVslALerkTCFNTPCzvWt57TwRpOndWz8OYkvIDL7/cH0kqtqgm2g5QtsmE09IgNrYSIDm15KdPh+Bg13hc2/twf9r/Jl26vOBgDVndEMKSj4jogtUqL+Xq1SncvCnuV4lC0fLX/de5+1d7vpg/l8FvjuOlTu0s5zx38RqNGtTg9deiuVckUWPYYsU13fxyEB7VHiW07xTFermdZ2fnkB/4iILZnJ08F9+Oypmi+cQ6ps5aoZr39uqfG6hXYy5q6csWLaqJh8fdcoN4eXnPc+TIj5ZnVloqEogfOUIZ4cebqKgotFozO3fusZHDtbVKH95qVid4QUUs9IrMBvz9vR8K5eLuNZWWFpCTcxRJksOYJYCHTR22y9bfWq0XgYGdnXbofytiUXmdOYiMRLfXH+Xrr79i2DBBNbSXDDhy7BKhAUays3MIf7I1R3/8lmFvTaB6nSdp85wOU/4ffH9gHW2f64/OK9RyHTJhaPacTzA8Eg5g8ZnbF7+nepHz8wYKr/3C/aTZDjh4uVRuFUXx7z9SdHobxfnWgJw7AU05k0psbAzh4Yk8/bSShKRGEjlwQFjarkq3biKYJXfo27aVn0+0d2/hEjIYIDnZxMKFjZE7iNTUS/Tv3x+NppjZs5X1yKJNzz1nZb2GhkJeXj79+hmcnvPsWdf3kZYGmZmCmbpli7iu558X651ZYLaEjhMnDpOQsJixY23p8705cSKmDE0hyER5eb/yyy/9MJnSLdc1abIn+vrtmPrJKcaMn8+mzcc4dngMhvrhLPxyNsEh89FqxYd65NglDu7dwl/pd6lq12kDVA7vTc7x7zCbTWi11s9Rbuc1g+phyL3N/bUfEBAhZorBg63GRdH182TuX0pwcAiHf/7Vst6W/HbkmB/PPb2GWlU/J8DvqOL8+/enldtexHP7yfLM3n57LcXFhfTsKRi9YuAuJCVlC8nJehsfMVitUHtyltgWGztStW3LxTXBC7tl58Q4KKgQ0e6bb5a7TMCRmLi0jKlc8Wu6d28nly4NR5KKnV/IQ5a/BcrFllj04PsVeD/6lIJQlLZ0GNkntirkbq//ccKS73PF0rmYqz3OxnWLaNqoDqEBRlYsnUtBlUe4ceMKsz5dSd/eUTRrXI/nnmlizWVatk5NGMzr/j+5t/UTpwNL5dZR6P1Dub9DIF6UCanfVBCgfFp0w8Ogp1IlDwtZw53cl7IMrjNZXYAWLXSMHw/LlulISxM+UmcoDpmY9O67onOW0TV797qXT/TYMfU8jAsWrKRmzRJ69ChfdXDbNvFhaDS4RBK4ug9Z2KtKFVnYS/yvUsW5AiUo0SthYY3KyCrXMZnyypA0n1uIRGazjsuXJ3PqVAeHzlxGoaQX1mbHliSLS6RKRAxGg5clF22zxvUw5vxO0rY1LhnPer9g7q77kHvLh5N3KknRzu/cusTY0eMovvsn97apoLV2xRP4wnCKDb6Y8v5wKnj35BOPUyV4GWgHuf2clc8tn7CwRsTEjEGv16gKxjmKbJUvduWsbbvKiVtRUTN5uSLywwItc4Tg4Ddt0DJyWscTZVmIXJOTwIvSUgmjMR+jMZ+iogecPduTixcH/0c6c/ibWOgt2vbh1zPJZK6Lw+fx9uQc28i9zFv4NOtG5r7FVHqiAzk/raPo8o9UatGNvEMraPtcfzZtTrKgWGTJgAkfjOXMmWME9ZpkWWdrzSstl0uKa7JdLikppdJjzyg6auGD745fqygLUzVz72J8Hlwlc10cnk27kHVgKR4GAx5XvufelZ/waR5huV6t6SY7d+5hxIjSCnxIIhAky+omJKxi7NiNCoGvjz/uwfbtWxk7dgt6fa4qNdvWX//ll0ofeXGxSHTsynKxlb21zh7iiY+fy9q1GzEaJSZNcn0/PXqImYGnp4GSEqPL+3dGMU9LE/dh7x6ypZk70wsR0Dkf3LHqLlzox4MHBxXHx3/pib6+YC9rNFp8O8dyZMdcBYrKs2kX1qz5hsebPgfAju3r8XmsnaId3U/+DL+nelG5tW07WsTLA99h796t3Pn1IH5lmbfaPtefKVPiMGt0BL/o6Eer3Dqa/Av7qNS0M3t2J9LpJeGHd9bOA/0CqRVqPd5dKr9Ml3dHrsK2bZRnNQNO2rbwH584oaT3/ysWekVnA2FhoWXU/lk2ddr6w52fy2TK47ff3iIjI8XxRP9CKc9D7paFrtFoumo0mssajeYPjUbj6H8Q+7ys0WguaTSaixqNppz85koLvVuXDqz+Zj2De/VA+nU3n32aQOfwFmQfXEaLFm3xvPMrn85dQKuGj5J9YBnvjZtIn95RbN+22kEy4NK1a5bsRBqtDs+mVmtetlZKi+7y2ez3eKy+v2W9LaW/WeN6xI6dTPXSDLI2TBIohKSZ+Bg0BNw8ZsWrf7+cAP8qfP7ZAgb36gGnN/J2zMfs3vsT69cnCWz8qY3Mnh1Pn95RDB8ew65dAqMqf0iuilVnRYz2Sgp0Hunp14mPX0CnTi8RH/856el3eOut4Q5WiG0nqJaEYd48kSHFmYa1fC3+/tZla5IJQXd2R15XHhRSUgwEBlZyef8vvKCemMAdV5Uz+dYdO6CoqMgpbdv2d37+7w7HfzK9N9VLMxSSziGvz1dIOuf9sIKpU2dZ2tgrrw6n9Ppp7qweR96FA6RvnkZEp04UnPiOO9++x4NDq8jcu5iWLdrSqmUTcrIyMRtLyNy3mHFj4wj0NZGfn+tU1tkQUgfj/Zvk7F/CsBHvqbZlW2u9dq0XkGwQb+4n9hB0efcw2kZVjLa9JWsrL9Gw4XOsWbOR/v1f5fLlk07p/f+Khf5/NRu4d+97jh5tWG5nXlrqDTwKPEpRcR3Lb/tl298abSuXdZbboWsE/mohEAE8AfTXaDRP2O3TAJgItJMkqTEwprx67YtOp+OVVwczffYKNBoNe/ftxqvB09zLSOe7zbvRarWcOnkErwZP892WDZjNZmbN/JzqpffJ2jDJ8oEFDYpXfGAFh1cy+M3RlvNc+f0ckyaOI9OnFt+sjMdsNrNh3Sree+8dcr1DmTptEmazmawH9ykqKiSyw9MYj60ByUxJaGP0Bj0vtH0G489rkMxmjNWeYPqMj+n38kC2bN1Lg8eetNxPp5d6smXrXkvQtWatOkyaHM+HH3pRrZq7mVRertBzVMuA7p6/3rmGNTjqc1iFlIT15ufn3gBlMEBiYiIDB77icvrbs6cIptoTNNxxVdlnRQJRz+7dMH58KQMHvkFq6lWnx5eU3MdofOCwvnbdKJYuXc0TtWuQs/NTQLSxv1aNFmqdO+fRp99Qrly+xHvvvc3N27c5eHAHSUkHaN2gLg/2LSbA35/+rw1h9meraBDiR+6ZHVR6rB03024wMW4s3m36Yi7MxqfBMyxcPJ+kbWscNI3Slg4j52QShdfOlpHcwpF0OsIaNOHM6eNMmjiO/ID6lrZsWzTaR/n92gpEB1ER8pGITmdmKkXQ1PSFtmyBjIwcV6+IlJQ9hIe3JyNjlZNkF3tcHv+wJSKii4orpXKZK+W4je/fvfLgwWESEhpSNyyItWuDOHw4mIsXB3PmTDGvD/V0nr1J+yaX/tyA1jMJrWcS/7y52PLbflmxzWO1y+spF+Wi0WieBv4hSVKXsuWJAJIkzbLZZy5wRZKk5eq1OBZnOUXtlRedwbQeqfEow4aNxGwuZe03Czj/RyqhbyqjO/YQxyu/n2PJwpkWd8y9NROoG+jD5SsX8XnsWUwP/kKvkWj+6CMcP/YDXo+2xedBKlkP7hMYPdFy7iAvH+7cvuIAG+v0Uk9VgpP973vpf5G4ehF/XT/jUjf5ww917Ni5jfbtnilb6x48yj5H4bvvCjdLeXCtd95xnlVJTTdchpKNHj2G/fsTad5ccoquSUuDGTPg5k09hYWlBAR4U1JSzHvvldKxo/o5x44VmhbR0SLfqKwp765865496pR0Z5AzScrn9u2N/POfEwBlKj2zpOXytZVcuphmaUNyAN87LBxT1m28Hm1DwbHvMJWW4NOwHUU3ziOZSugR+TK16zxikbYILLxN/YbPcvzweksburN6HOaSQsxFeQrimj6oNqXZ6RZZ5wcHvyLwheHkHN+CKTudqn0/tuxbrXIAd2//0yWcEeS22IgqlXdRs+oiFyQWR8ihLTHLGex2507BVdiyZYMdVLF8WQH53Tun+/97IIIV30+5bDLd47ffxrJ/f4olSF6j9CiLE4o5dw7FuoXzS9FqNZglCZOpBtfvfEiJsZbT/sF+2X5b7PDuDw9b1Gg0fYGukiQNK1seBLSRJOldm322AVeAdoAOMQDsdlWvPWzRSuiJUuDHnYlqmY6vY/uOAxaLRI1A9GDvYoqvHGH8hE/p/OKzvNyvG9mGKpQWZBMSPYHi25fJ3LuIqn0nW9igoMGYnkqIvO7bcUgaPdUGfYpGoyXv14M8OLCMwBdH4Nu4kzjPoVUUnEvhm282kZEjqULHbH/Ly4U5N5k5bQwR3YxEdncm6lSJZs02ExjYnopoN9tmQL9/P8ct5lvnzjBggEER1XdG4rGFkqWm/karVs9iNhcxa5bjACVrf0REiI7Z6r/XsWVLKW3b6hg2rFQhaLZrl5DibdrUCo/LzxeQzBUryh+chg4VOHhn5Ch7UojZXMLZsy+Rm3tKpcaqXLn+DwpyvSxtzRaNZWUSV6Pg8k827SkOU24memMuWp0HlXt8IMTZNk5CyrmHVOtJRTu/v2UGgXaEo+yf1lGl87vc2zoDrZcvwZHj8KrzJLdXxuJRVUl8u7d5OlW6vK04ntMbmTJjmUPba9a4HpIkIZU0sTwTmXyUk6NOGFuwYCkrV64iIsJIVJT7sFt7ISxnwl+2xR0tesdl9d+pqVfL5JY32cSe+hMbG6vQ1Hddn3X53r0dXLr0OmfOFFuC5LLoXttGf3L4qIdlXeb6CQzuFc0rrw5W7QNc9Q/OtnV4pvF/vENPBozAy0At4DDQVJKkLLu6RgAjAAKrhLSaOmcVoByBdu35gV/PJJNlhMpdnTAvN0+j1dM9afPUk3y1ZDY+bfqRf/EQwVEfWPaXseHej7RGunWRN4fGsOTLGZgl8Gn4DMaMm0jmUjxCH1HQ9tM3TyfI7qPIOrAMj+Da+DTvxoODyy0WWeiA2eSeTCLrx0QqPfYsgYV/0abD6zz3dBOH+3I2At9L/4uNG77mzvVfyM0txN9fcuiAAPSGF6lbbza1a9pqartnTbhDdz9zBqZM0QMa8vMFa9NgsJKCwJHR2r9/Xz76KI6wsFBSUn7i5ZcHIUklFhy6zHabPNk1izAuTo+HhyfZ2QXo9ZJLiYK4OB3R0RqXuOlly0Rn7kxDXo22nZNzljNnHKfbdzNeJf3Baxw59ruCrCYkKuoRFOGaSZz1YyIeXr5UfmG4Yn3eD6sICa3uup2XDRgPDq7AEFLP0nkXXT/PvR1z0Xr4oKsU4JShnLVjNiNGTeTOA70T60/i8Xq9MOjtO1YPKlWeS43qnalZPVQx42vZ0si0aUIKt1q18hNlBAcPID7+87I14llXTH7hGrZtuTyNcvvvwXrt9uQ1pfSBtbgSYbvNr7++RXa2gH6+PtSTzErtqBIxxsb4nEalDu84GJ/TZ6+okBX+n7TQ3XG5LAGOS5K0smz5ABAnSdJJZ/U6s9DPXbxGk8drM2fWZI7+8itBg+IVx936chD6wOpU9QSkUu5JlSi58wfeYeGUpKeiNxWir9uK/N+P2Exdx1KamYakMxBS5m65s3ocXvVbUnzzYrkyA5/MnMeypQu5fOWSwprXB9ag4PKPlnWy7O7Nm6nMmvm5xVo/c/o4s+d8wufzEqhVu67qPcu/JamArMxY/H1/Vnlqnjz55FaqVOkgXyHuWC6jR8e4VI87flwQeXr00BAZKSkIQTt3ClW9/futGi6OlHohb5qaepXp06exZcs2SkqMlJSAt7eGqCgYOdJ5O5OtMUmSyrXcvvhCz8GDMHu2eq5Ud7M82Vvo2dknOHv2ebs9Q9B6HgKUZLW/ckvQPxJO7rGNeIXUwT9CnUl8b9ssQnpNdNDplyn9vaK7O23ntgxjY2Ya6d9NRePhTeVWUTw4+BUBzw/lwfcr0WgkdL5B1BjypeL4u8uGMfbdWLpGRLu0/i7/sZsGtacCjj5vnT6c0Kpf8vTTHRUkm+PHYcoU92ZKolMWwRXZyl+y5CtMJvXZk1zU6P5qss6uZHYrLoMLzr6pe/eSuHhxMGA1JG7dgmkzPbmTXwe/LuOdDqpz5synRcvw/xML3R2Uy0mggUajqa/RaDyAV4HtdvtsAzoCaDSaYKAh4DzqhBW2eO7iNY4cu6T4vWVbMod+OEilDkMcjvNr01tMdwtKCfQPoiTtN0J6xhEUEYMGDY0efZyC337AO+wpC/olOGoCWv9QC/lHo9Xh1yqKgt+PEDpgFvqAGtzfPtfhXNl7FtCn3xAOH/mJy1cu4fN4O0udQd1GY7z3J1X7TrbUSbVG/HT0AJnetYibNJ7DP//Kps1JxMWNIT+gHnGTxnP2wlXVe5Z/n7+UzpptvUm9NRuz2eP/cXfeUVFc/99/baEtVaqKBTVW7FhjjBoTBQWsMYkxGhNN/dpjbClqEqPGJJbYe+8VFRV7iYqKithQVFSk92Up254/hh122F0kfp/n/PL87jmcs7vszM7MvXPncz+fdyl3RMXcvz8S667k5q+l70ePHm7TLT4pSVg2z5kjTLrlXdznzBF0W8aOFTDolrhjDYMGDcbT04cGDZpz6FAkw4d/xO3bVzEYnqFSORMaWnHQIKAitrJly9aXIigGDdIhlwtGDOVVFc2NNiqaaCIjlQwe/K54nXJyTnPzZh+L7xUWyyR9lJln5OsxM6lb/TWMcUf44utp1HRXkbb7J4ttMw79gUxpb8kcLh1TKdnKisd56zDUN49iNBqw8/Sn2id/octKIvvkKrz7TsG1+TtU6fYJRp0Wz7ct6bFOrULZtGUT1289koyv8uMt6oyBuIcbyMwJsdiHXhfN3LnjLGCK7dsLK6B/AruNjDwoFkHXrBHqIIsWCSs9a9yB8k5aCQl3GTLkQ9G02to4FIrdd8Vt/gnE8mX31N27n2E+mQNUrebK+G9n4+ngTk6E9fmjeVAIcgdfyXXfuXs/gwaFc/joGbEfdu85wKBB4USdvMDO3fv5bsrnRJ28IG5n2ibq5AWL3zFvL53QjUajDvgPcBS4C+wwGo23ZTLZTJlMZlLHOgpkymSyO8ApYKLRaMysaL/msEVzWJWJFGRNIREE5iVGA/a1m/MoNQ2fAd+XTdJt+pCY+IDJU+ehynxAyvpxIvql+qdLJOiX7FOr8QoZTfGz2xQmRFPlrU8tfsu5dRhHj+5i395NePb8Cl1WMqlbyiBr1T4ug6zlRe8VNdQ9Q0aRq5Px9F6UeC6eIaPJ1clE0kl54kd5glP9emEoHNZbHJPBoKWyMC1zuKMA17KULp01S2ZVmtfUAgOFCMoWCkKACerp0EFjVYr0nzi4VPa7BQVFREdHs3evpSvSzJkCAagy8qV6vZG4uFHcuNEbg6F8hOpIcvoYC7jrH3O/JTwslAMRJzBqc7h37xZgRJslxXy6tglHrnKj8MkNEQUDwpg6fWo/x/f/YVMJFMA1KBSjtpj0vbMwGvTIlfa4d3ofMJJ7fjPquJPknF4jBhSW24eJ483a+JKQjgLr4+M3z6oU7759V60+ZP8J7DYh4QVDhgy3OhmPHCkEFL/+KoXNlnfSsiXrbGqBgfD224UMGjS0lNSkegUZXNv3lNFouR87p70Uqd1JeRGPazfr80dK0i2aNa4lGUOrl8+loEodbl8/SLPGtdAXpXLp7FYKqtRh3ao5rFo+xypRsqBKHXZsXVLh+VSKWGQ0Gg8Dh8t99oPZayMwvvSvUs2cWGROfNiwfgmKOkEVEnpcWoWQe0HqNGTCAH/25RTiE/OZNv0vtqxfSFzEXHyGSXVYMo8spMpbn1KSkkDO+c02bwrn1qEk3zqBvU9tFM6eGLRFyOwdSdv9E/4jl4kPBu/wb8m5sAVnUwRfSjqJ3T8bd7NirV1gD5F0UhmCk6N9JvVrSY9Jq03j78vfonL+Eh8vucRDstwVlrwWyBtnWbRoEePG7RHJG4WFRUydWvGgNxGCbOWkTf83j5g6dtQyZMjHeHioSEkpqBTd2mg0VpqaXa9eddzdXazmYk0WaxVJxtarV52HD6eTkWEJ68lVd+B56jecufiYzkVPgDL0lYnen531CUuXzEdu54BD9cZkRi7Ab/BsZDIhRnJr0wfN/Quk7/0FVYNO4v8V3rVIOvcCmZ0jTnWkjOjMg/NwadsXtzZ9JMS1lM2TcG0VQt7lPXhoJTb1AAAgAElEQVSHTybvyh6yji9H1aCjZPv0iN9wb99fvE/smgrjrUdfb8n52Rp7tarKcS/nsZqdXWz1IWvCr1eUQxcm5f7/2DvXGt3fmj9q+da+PURE3CEwsBlarUDCqVwwkY914pIGg0HLw4ffWWV27t13gmVLFtt8KDu3DiX97jkWLFzIW+/0tYrgm/TtOBHBZzToSdp7QZTstkWUBPwsfqy0/WvEuUw5opWrVrN18zIUXrVwbRlC1vFlBDZqzN3794TPWoWQdWwpzk2749WjTLnJPGe4c/d+dmxdSm5urtWLnRu9B3VsFLq8dJwbdipXbPoNt/b9cSu9KdRxJ8g8thQZMlQNO6KJv4hP/2nIkEsgaw7+TSi5cxylR3WbRa68g3P5dfafFvk0W/kzo7EEde4HODtZklzAF1ePVQS1NIHDX020qLJO6T17WmK7X/Z/a5Zv1try5TIOHlRSUKBFpRLQMLb0zs3RDxXVBkyojSNHBPEoLy+TxdooEdlw584I0tK2SjeU90Nu9zNQJsM8e/YMsrIy8Ow7DYcagaSt/Q8leRkYjEb8SusnKZu+RZv1HPfX3xMnZHXcCXLObsT/i9WkbJqInVcNChOu4tN3MnLnKqRtmYxDlao4teiF+sxqxo39ltVrV5CrtxMnc/dOg5HJFeTHROAVPJqS1ARyzm/Cs+fXqK8fARm4NO9J9qnVOAa0pOjRNVyqBmDXtAeaM2uZ9esfKBz9KpWfNWjHgiFKcjkGDnRgwYJiq4zdyqJcOnToVmnLwd69TQ/dzaUCaVAZYS0ThLJnzzIklck2sfIyuMJvme6P3NwLxMa+h15vyUnIK2jLqDF5ZKlqomrSlZzTa3Ft04fCS1txbBGK0qcWOafXoWrcGcXdKPbsPfZSBF/yujGS4rcthF/m4flao9EiHwv8S6j/5lHCgQM7cKzfETvfAAGy9c6XPLq2H6++U9GmJ5J7YSuurUPR3D8v2Z8pZ5hXYGTpop8wyuQSdp15NO3Wpg8Fd87gUL0hhQ+jSV4/DregcLKilqB0dEFz5yyF8RdxadGTrKjlyBVKsZiqzUqiIO60xOgidcsU5Cp3FO7VsCvKJ8fKqiA7cj6D3v1Ekk8ztYokCM5d+oQ+PZ5Q3WcZMpk5SSSNvOy+FBVdx9GxBq9Kia6sYJE5S7Sy/w8J0RIZmciTJ8oK6dYREUZmztTSokUZjvnrry2hkuUjt9Gjh9Ou3Var+/b3FxxfTp504saNYxL5UdBQWPicrCzLJ1RymhcZOU+AsqhcGdAGQ14B9jWaUPzsNiXqHJzqd6Qk/bG4InNt1Qv1mTUY446TevcsLkFhZEUtx6ffVKGOEzqB9D0/S7SB3Lt8jObsOnSXt9Khy2CqB7Ri2o9/sWbNKm4fX45DjSa4teuLTCbH3rcOGftmYdBrRV0Y58ZdyL92kNy/t4pjMXn9OFQ6DbnnNvLp59+icPR7yfgyj9A1FhF6nz6tiIy8ZvHQNDebDg4WVmmWK6G11KvnZ0FGstZMLGJv76FER5vkYsui5orGqS05iHfeqcwqwryeIvyWXl/E/ftjSUvbafF9g8GOZ2ljOXTcl6HD3Vm2+BfS95xH1bAT2ceX8cEHn7N//zayC3JQNXid3PNb8fD0JurkBVHiJG3tKPRGIz79pkmK4a5t+pB9fBkZW5JEITbz/5uyEGB8bOt8/hXiXOY5vdFjf6C6IRNDwmV8352BS9O38Bm2AKeAlri17YNX8GjUscfwChkt2Z9rUBiZag0rlv6K3NULlZkOS170XtJ2z8TOJ4DMwwtAJsMpoBUlL+4LLLuSIrJOr0XlpOKtzp2xkxlQNehE7oWtKFyq4FS/Q1mBtfd4tOmPxRtTJlfg0qIn6uuHUPgHkpOTbjWf5hLUh+jok+gKUyxkB/w8tMybPcEqVbtzh0Bq1vwaucMZi33KZAaKilL5b2jKlXFKj4iQskTLt/IsUlMTblKNzfz9kiXCpPD990Ih05Sy+ewz4eacNUvQmLFFza6oNmBuOydMDqZcqD1Pnizl8uUWouiWqRmNcqpXe0ekz5uWx169xiB3ciNp8TARueLVawxyOyfyr0YIiodRSwnu2YsZP//F8IH90JxdiwwD6gubyuo4I5ZKAozCc+uZPWc+ByJO0KxxLebNnoCPh5IRIz5nw4ZdeBlyeb7oQ3Iv7SLv4FxkGFE1LGONFj+7TV70blxa98ahRqCQZvGsQWpaMsM/GcO7A/qIAmETxryPtuBFhfUbdzcV5dvw4d1sWqq1by8Uyw8eVDBmjIsVAatQQIWnp0ul8u3e3m6ldP+y/qqMsJYtJvSrWPBlZUVz4UIDq5N5fkFrFI7nqFP7Yzp3aIKXuxJ1fg6+A3/AK2Q0Kp/aZKQ9RltSgO+A73EO7AoyGVpfIR8e/E5nxo0Zjy4vHXu/10jfO4sXa0eL9ZXCS1sZNPA9ZLnJZB+Ybfn7UYsYP34yQL6t6/g/NqHbar5+/ixfvoGOga+J9GrzZsp/Y8RCna6kuAj7eu3x6f89uqwXpG6eRPbpdeSc3yRedKOumNQtU8iPiRA/kzu6oNSX8E7P/pw9exqXrp/i1rYP/p+vwnfAD+iyXpCy8RurxVBBvnQZ9tUaiEVRa/k0l6BQnmfkMGnSGInsQMy1y6xcNpssVQ2rVG1Tk8k8KS55SZjzCm306K9e6oG4b5/1ZXVSkgB33L1boHv37y+gYubMEV737AlKpZEjRyLZuXMX3t5DRbr1l1/acfu2jKVLrXtQBgYKkd+IEVRIzRao3Jcl+xa+P7T0+z0l379xoxdPnsyycqYB3E9chUwuTJZ/zp8r0Qny6TMJhcpdhCGaHuT5MRFkHlmIc+MunL9wFrlcwQeDP+bI0QscPxFN01o1rI7j3KMLGTvuW1q1bmd1DKSlpZCZmYGqXjvU0bv5ZdbvzJk9H+3jq6SsF3Rh0vfPxqPLMArjL5K6dQrp++dSmHAZVcPXWbVyHjqdju1b17F/3ybs67Zl2vffotPZxu9bawEB3mzatMmm/smiRSp27txCRkZqqb7QE+bP/9MMBgiDB79fSZXD923+35qkhanZkoMwX0WsWGHLZ3adeKzPni0iNnYABkNBuT05gXIZT5J/QiZzFT/9c/5clHXK0HRuIWO5cCMOj/DJovewAJIYTYpay5b1C5g6ZRxeZt7J9r51yIxcgNFoQFmrNdu3b0ZTrMXtLctlhVPLUHbv2VHhdfxXOBaVB9H7eWhtIgBM+W+jOosPBn/OmbNHyC4Bh+Y9yTu5Ao8qvqhlDngEj6HocQw5FzajatBRQgCxxqgrOLsWnU4nCnuZN6NBT2bkAkqSH1gYFCQv+4SmDRtz8+YVnBp0xDOkLB+fHTkfl6A+uASFUvzstiDH23/aP3aeMb2u5fcR7q5Zkt9v2fIgHh4d+W+ozrac0g8dEqLvt98WctG9epW5vB87JkTYvXuX5SyPHrX8zBaJo/LkEhfS0swdgv47Cvfp0z6A9KGZkvER6Tnvce7SXfFap6UmsXjRbLT2ji8l/hSnJpB3YStf/WcaqTn24j5OHtvD/v2b8ezxlcgqNqX+HOu0xj39NuF9PmTV8jmSMeDlqOLFszsYVVXw6f8d+UcX8XbHTtSoWYdVy+dRXJgPCiW+pQgvo0FP2t5ZFCfeeKl3aYNqvjQO6m99fFWdhbuLFBb32muzqVFjJAkJd1i0aJ2Zm46g9DlqlEkN0XY/VNbL0zrVv2yfJpLQ228XilIQqanw0UdUyIS2tE20fuwxMb3Iy5PiJzVF9Thy4iN2bNto4ed6+0YkCqUdeQY7qvSSchGS142xIJ5l7puFx9uf22AZV0cT/zcyudymt4LJNlP97M5zo9FY09q5/uuKojt372f18rkVeoGmbBhPuyaN+OWXuej1enbt3MzmzeuZPn0WLVq2YfKk8YLq4kfzhQt5eMFLyUOubq5ovBpKLOvyoxbh1DIUhXctMg78ZlUbPf/KfrxTr/D9dz8z86fvSM4vwaFZTzRn1zLg3U+Ijj5JilqHJjcDhxqBLy14lKdqm1+bwvzuONhL165Nm+7G2zuY/8bJRVC+e8TPP89k69adVinzSUmwZg1cuCBgkO3tkejQVNYCzkTiqGwxtoxc8mrnZf5ary/g3Dlfi9+R2V9EJnOzKE5fv5VA5P41nIuJxWfYQsk25sQfo0FPxtZv+bh/Xxo1e5MWgQFs37qOpcsWoWrYCV1OMn6DZ1P8NK6skJ79AnsFKAuz0VYNlBTKMnfNoESdhczeCYWLJ66tw9Bd2oRGrUZn0CO3c7C46Z8teB+nSniXZh9fxvzFuypdFH3ttd+pUeMLi+trIglt2bLNTO72fUaP/tzCN9QaKcjSJUpKCrLVlwkJdxk0aCjx8XdKnZIEuOry5f+k+Gk5NoxGI9HRbSgslAIQbsR+ydRpW237ueozcHf34m5ioiTfrc1KInXjBOy9/KnSa/xLJ/v0/XMw6opx8G8sMtdNc5Bji1BcgkLLQBqHbBdFFdOnT7d9Ff4ftrnz/pxer3FHUtNzOHfpDg4OdqSm5/Dn7z8iq9lc8P2UyQQ4145pGA1G7KvVRyZXIFPa8/TSYZq16kZaRh7efrWxc6lPQEAdzp8/R8SBrbgHj0bp7ofCyQ3npm9R/Pwu+Vf349pa6uSQuWMaAwcMpXvPAVw7FUHB3dMYkJMduYB3B37Mo0sHyYg5gm//aVYfMPbV6pN27RjFag2DPviCJw+fkH3rKMNHTiC32J0B/d9FVqIh4d4tXIyF5N8+g51/E+w8/VG1CEbpLiCQihJjyTu2iI8/ncCdh5ni9TC/NgpZJC4qKV46LW0HL1KzkMmb4ObqhEB+0CFENbKXvC577+npyqlTx6lT5y4LFhh47z1o1w7c3ITfcXODLl2gqMgOpbI+b76ZR3BwWaS7cSM0biykSaw1X1/Iz4e4uCKCg7vx11+LeeONEnH/1lpyMpw968LEiaNe+bxMrzMyjnLtWjfKk0MMRgW343uTkp4vudap6Tns2XuQ82cjxLFk3owGHflXDuDSMhi5QolRruRm5CbsXOqT+PgOSxcLy23XoDAKbp1Ac/88eZd34dt/Gq5BoaivH8ao8gB1Jh5KA6mX9pJ//TBKdz/U984jUyhwqteWktSHFMb/jU6nQ2/UI1PYWR2L9n51ybu8G83d8zjWaoadpz+urUMl4yvj4Dz69vuI5+kGq+PL0e4kjvbPJPv19HwLN7cWkusZGXmEkJA++PvHMGpUIV9+CW+8UUJs7C0mTVpP8+ZNqF+/lqQf6tcPYODAYOLijMyb95CVK7WcPu2MVhtAdnYua9du46+/FpCU9JQGDWrg6VnFal96ejrw9ts9WLNmI3Pn6hg1CrKz4dEjQY7AVtuxQ0n79oMJDu5mMTY0midcu9aFoqIEyTbXr8OU7+7hHj4Z19ahZMREEXvlIhs2rMCz71RcW4eScjWSZw9uYVc3iMyDf2DvVxe7KtVQOLmBXEHxg0uUJF7HuWUIRYmxpO74HqNeS0nKQzT3z+NYq7nQV61CcKrTmoLY46hjjyGTK8g5soB3B3xM/MVICu6ewSCToz69Gr22OGH69Ol/WTnNf2dR1CSJq751gpyI2Yz7z2i8U6+IOuSaM2v5dOR4C7KECbRfPm1iIg95vvO5xXE4tw4jOvok3bt25MeZ80Vd8y//M42RIz/FydHRwugidcUIC2eZc2cO06pZPUaM+JwDEScY2D+czh2a0KpZPcaNHctv8zexfft+OjVrYDWnaip41K3tZ6FlbdLWvvdwFFDfYtuSwmU8fPAORUVpvIpOtOn1li17K6V1ffv2fYvvVc6BqYzEIRRjKwZZmetwv+p5GQwybt4cTlzchxiN5T01XXj8/FeaB9a3GIfmRVHrTkN9kNs78mLVl6jjTpAVtYyub3alc4cmHNi3QZS8NbGK9blpElaxa1A4xYmxzJgxm3FjxqPPz8Te7zWyT6zAaDTi028qXiGjsfOohtzeEaNcidLFywJ7bpLSdazdAv8v16LLz7TOXD04j94hYYwdO/YfFUXBTnI9bZGEAIqLteh0hfTu/R5eXrUYM2YyCQmmor1Uzz8iYjc6nY6iovsYDBrAiFar5vjxtQQFvU1k5Bmb/Vyma67izz+VpKUJNZzKFz/L9peYuJLo6DZWjL/lzJvvi13dMkBEld7jiH34EK9+08R+dGrRC5T2FMSdxOm1dmQc/B2DQQga7HwD0Op1uHYbIZiB7/kZfUEODtUbI3dyh5xkSV/ZefrjN/hXjLnJFJ5fz2+/LWLkyE/5cUbpvFTqrUAFRdF/XYQeE/ecAQOEqPbRud206fQu7Tq8SZt2XYXoN/YoH48YT2qOnSSaOnfpDpvX/4muWqAkuk/b/A35t0/jayMvZVdViLDTXySTlAFBQW3o+laYGCW/1qAp96KPk3H9GEaZgrxji2je8m2Kn10l++YJ8ak57JPx6IyOknMp//rZk3vs2b0Rt56jLCI+g97IjagdHDq0D321QC4c3UOd+m3YszeCdavnUeLTmCunomjU/E90ek9cVVeQycz3oEarzcDHpyevGslOmfILX34pSNbaaioVrFsnKCGaf2/FCiq17cqVJfzww3iSk58zb95RWrUSovfy7fZtWLnSgVWrfrcZrVXmvNLT94vmzuYtK/dtEp7P5uSFLKv99esvU9FXbyoZSylbJ2M0GnCo1kBcKRYmXKXkxT1cmr5N4vWz2LnUp3u3btyLjiL75nFxNeYaJI2WM48swt7Vk4yUFDZtWoVXv2m4BoWhuX8BhbMHHp2HCMFCzUCKntzAq9cYXFv1ouBWFOrrkcgUdmQeWYRHl6EU3DlFQdwJihJj0WY8wSd8ouWKArh3/iCBLbpwIfreK0foM2bMxN//umR1dvmyUHxs3FhAvXz1FXTurLUSsQv7SEh4QPfuIUAJXboI23z5pbACzM+H+/f17Ny5l/fe64Onp6vVfq5fPwAfH1+WLj1G69YGevSA336DvDyhdqNSCSu8HTvsWLnSgU2bltOuXZBkH1ptJrGx/S3GRmFxAA+fzSdH40/e02uSfnRp1cti1WPUafF7d7rQf/fOob4eibJKNaEo2n8aGCFtz8/I5PLSFVoY+TEH0RdrLPpKJpODTIkiJ5mu3QeQlpHL+cv3xHlJrnQhMmJL8vTp01dY3jX/gxP68uUrpo8cOZKqvh44ONjRIjBAfN2yaV0c7GRcv3GN8PD+vN6+OdX8PCkoKCDxyX0+HjoMHx8fcRvTdv3Ce3HtVATp145iQI46ajEuzs7IarbEtU24WQpH0LK2qyqkcAwyOU8v7mPo0E8kx9EiMIB6dWrRt88AMpNTeHpxHz/99BstW7/O5yM+xc5QzM3ITfz882+83b271XMxvU58fIfVy+fiHj7J6oPFoC0kI+aIeGPn3jpJUVYi589G4BE+GdegUArunqGGlxOdOo1AhhKM0gKOStUAX99BCPQCGUIUUtFr6fu//lpQYRokKUnIVT59CuvXCzK3GRlC7vLkScEI+uUpFDf69x/EoEGDGTJEx4IFiG5Hpptw61ZYuBDmz19I9+7dbB5vZc4rPz+WjIwI6YHImuDstgY/Xy+b/dWxw+ucj9xN7q2T6I0yMg/9gb2TC0Wpjym4dRyZ0p6sY0twdPfGtcMgiq/sYejQ4Rw5uJEhH7zP0KGf8DQ+lvgze3FuKdVJSd0+DY8uQ3Ft15+nlw/iETJGiPhkcmQKewrunKYw/mJZ6qRliJg+1BfmU3DvHCXJ9/EJn4iqfkecm76F5mE0RY+v2fQudajWAPXdc+S8uE/v3uFWz9nB7gQYpRJMnp4huLl1EK/n0KGfMGpUodjPSUnCZP7LL0K6zc1NeKi7uUHr1gaaNtUxatRhBg58H09PT8CRceMmcedOLLNnW24TFATNm0NUlJ6MjDz69h1gtZ8TEp4yaND7/PJLMb16Qf36gjLonTsCmWjVKqEI2qLFIDZu3Fg6mUv3odcX8OyZlC8CYK86j6+PP25uHnw+8lOexsfy8Nw+VC2k+cSUbVNBJhdTYOb9V/TgMk712uLaJpyMfb9i1Gtxeq0drkFhFD+7TUHcSZsuVPbV6ov3eve3ukj6qKqvB+tWL/n3Tei2IvRzl+6Q+PgOc2d/R4lPY66djqBhkw6cP3+WZYtnofVrwoWju9ErquPoYC+J0H18fPDw8OTa38cxJsXy6WcTeaNLCH8f2kLWxe0AZB1bzKB3P+FJ9GHSrx9DprQnO2oZw0dM4G6CEK2dO3eGP3//ER+/uqg1BtIy8oi5GU9WxlMaN+1ITNxznBwdJLl78+OwFv28rDaQsX8OjrWaCQ8euQJl9SbEn9mLe/BocbAYkHMzchNNWnRBp7uLq7NUu9vZuQG+vqG8aoSelJRIbOxdWre2hE6aorBmzWDiRCGievNNIXc5f75QCE1Pr1we8/Lli/j7X+ejjwx07ixE46ab8ORJCAiAgAAlJSW+BAd3snm8lTkvtTqWjAypNZSmuCp3H7apsL8Ki0GvqE5tX2cendtNuzcG0axxfZ4/uoO7yp30m8dw8G+CY3EORY9v0DO4Hzt2bBBXVwajA3t2b7KZfy+IPY5bhwE4t5RGfJmRC4SzUGdSlBiLa6uyPFZRYixZRxfj9+50PN/+XNxOJpOTHbUUVYPXJYFLyuaJGEGyonh4di/OXk1fOUKfMuVnyUqs8rWTQrEvhw37hLAwAyGWemDiNmo17N9/n0GD+jBjxgyGDv2UKVNm8Ndfi0lKekpU1DHq1r0jWSm4uQl1n/feg2HDhHqPp2dbBg/ub3Ws6PV5PHu2WNo3RiW34kMqtarGCAZNNu5vDBavecbBeTRr+Q5NmzQmIeY0hQ8u4dSwE0VPbqDLSaHw4WU0Dy7iWLtFudXfFIxGvdhXBlnZvV6+tlNRhP4/xhQ15dBNzfQ6/t5NUdDKBOOK3L+as2dPi3oGOTumljqch0r2qS9KZc2KeSgD2lDdmEH/vqHs3L6BoqJ8VA07kXd5F/buvmSmPSYvJwuHBm+Qe34rjlX8MOry6NzhTTEPrwhow46tS1i+fCM3rl8RdDzqtmPH1iV8Nfonq8du7b1JPtdOIcO16AVZ26diF/gOeadW4urqivuzv0l/cAFV487kX9xB1tYXuAWPFf0qTc3EEpszZ4EgC6D3xGgBKVYgRQdU5nXZ+9Gjx9Cu3W4L5mVMjIA3L69pbm7MPHUq3LwpvDb/jomCf+wY5OfrqFJlG1ptCdOn68R9fP21pU5MUpKOceN2lroKWT/eyp2XJf5Z5ehos/8s+zKUcWPHimiQLp07MnnyWKlkcoNqHI3cLY7ZjA1jWL7kF7z6WY/A3Nr0QXPnDPlXI3BrW6bymHH4T2T6EozIkds54FnOFDr71GoLw2kTEssrbCKZh/4gZf14XIPCyIpaQu+QMI6f2oPm7hnhs2NL+fKLUTRq3sTqORu0qvKITspy6ML1FEhCZTT+EycE1URrzdT3UVFa8vJWs3nzdgYP/oDiYh29e1vfxtR694bdu7W0a/cmISFa5s83yeWqiYzcyK5dWlavrngfISHa0jFkQiiVHysOFtvIZDKrc5EtATXN/QtiP5rqFD3ChtMiMICpU79j4aJFnDtzmN9/X8yUSWMoSX+CXOUu8GS2TBZlG4x6PeobR9DcvyCwjk+vZvbs+eKxlB+Xttq/jvoviHO1ESnVbsFjOBchhTHaBfbg6JGNosM5WFrXmYRtrl45L+JwU7dMwaFmIOdiYiSpD/WtE2zevJ6W7XKs7sMcK/5i2xTWrFnJiBGfE3/vJhvWL2H02B/w9fMXj+OXGccZ+cUk7j/OJf7eTVYum41dnbY4aV7QrV0Hjh/biF6no8Q3EK0mibrV65F46wiffzWVq5dPE7t/toWdXnkpTi/3LKr7lL+qemyJDFl/LX1fr54fmzatlWDSExOF5XR4+MuNmR8+FORrTXj1xEThIdCrl4BPF27IAg4cEFQRy1P7zVuZaJL1401IeMzChQvYsmWvFaMDAcus06lJTl5nse+cvAKepT4BbNPgbf1v9pxfyghHNkTYijVqHF/rIJl4cyLn49Q6VCK8lXthq2RCdw0KI//idgx6A35Waj7e4d+SeXi+OGnnnlzJ+x98JnAx4sG9y1Byji8n+/hSmrfuQY+w4bwV8iG/zf6e1Khl9On7IY2ad/1H1H/Bt8YkJQuDB79LZGSZfk5urnUBLHN7usWLMZuM171UejcpSShyAmRnazh8GIqLyyC0I0Zo2bLlnwpvSe8Bo9FIUpJlkGswGLlVOi+Zz0VQyiE4/CeqoDCxH11a9hT70bVtP46d2IWDV9ky1c61AT/+3Jf4ezcpKSkGuRzvkNE41AiUyDYUpySQe3EHhoIsis5vpMObH6Bw9LOQCXlZ+1ejXGw5q2vOrhUdzk1/924dlzD73ILHcOfJE3zMkAUuLXqiuXcO166fkn1yNdqsJHF/Qz4cyoVTm1H4N7XYh+kJLZMrcGjWk8SH0WIkby5zaS6DuWPrEnzdiyXyuXl6OUUFGRj0OnxKZXbz9HLq1q7GgYgT1K9bnVuxV3HvbskSM0lxajXJzJs9wUp0DmUR+quhXEBFSEgo0dHReHsPY/RoF2bOBKUSQkMtf8289e4Nt27BwIHv4uz8vrjtrFnw+edSHfXwcCE18+OPZcbCixdL5VMFVUVXq8cbGXmGdu3eJDNzG/Pnq63I9p4RnddN7jLmTV3Y1aaUrAnhMm/2BByMyaIkg5+Hll+mf0WxJp+ih5fI3DQBbVYS+vxMDDI5ChcvQJCZ0BfmUZL6mNQtkwUxpb0/YyhSU3D7FKlbBaRW1vEVeAVL5Svc2vRB6V4Ve++akkkkZfkn5EXvQelRVVB0dHQmK2opH3z4OSNHfsr6ddvo3qY5eSdXM2XyDH7/fQkpSXfwcpMR1KIBU76bw++/LyYm5lnR/8cAACAASURBVAJebrL/CuUyevQ4CWPTmoyuubbKyJGWGvoVGYpfviys1hwcBN5DVJR13XQPj8rK97pSfpxrNClcvBhEYqIlvV5d2MIm4i5z3y+UqLPIvbiD5A3jUcedIPvkalxb9SZ53Ric6rUBF2/uXz9ggb5btnAmyOVijUMmV4hsdMdazXFrE46duy91A+pxOPIMA/qH25TXrqj9f0f9z49axNhx31K/oTR6GfnFJIsHgddH86UU/ailuLYKJX3/bOx8BMpt3rGF9Os3gDVrVuD4WnsMyXfJ3jbF5j40Z9fyVvdQ0VvSM2QUKWots2d9z9Qp44XJO1j4bP2a+WWrDbkClx6juXAjTlxtyOQK7AJ7cOpERIXeqCBIcWYW6Zn87WiyVDWY+XMkNlQC/utWr15d5s+fy5AhHzBwoB1qdeWiIa1Wxrp1y1i7drW4rTV/0a+/Fva3erVtowNzKnhCwiPGjBmLj09V5HIv+vfvz8yZGptGB4MHv8exY0OtSJ66gnI92Xm21/umfshy8mfL5mVkOvkzadIoli+ZhdqjHrnqfOzrtEVRnEfm7umk7fkZnTqL9ANzMRoN5P69FZfGnak67A9UDTqRc34TShcPPPt/R7Vh81E16ET26XXIlAIvpLx8hUubcPRZSWRsnoj61gmy9s9i/Kix+KTGkLHlW3LObqTkxT2cG3bi9OlIDAYDN29c5VjUEZzqv86GTWuZMnkcBR51RBmB+Hs3hXN6ibxEZVq9enUlUgDt2wtsYvNmS1vF1Hr0EAqW5Zv5g6C8mUp53fTu3QUmc0XNmpxAbu5loqPbU1JiCVNEMZHE5BniJ6a5aEif3nBtB3ptCTKlA6rX2mMsKSL7zAaMOi05f2/DzieArCOLcAkK486dG5I9/zl/LnqZTKLDU5QYy/PFQ8m9slfse9c24SQ8Tviv+udfWRR9Gbzv3vlDFkVRE9wx+dFtnlyMwMWsmASQsukbHOu0piDuBD59S1Ejt05gUNgRe+WCQBQICqP44SWqKPVk3jpjsQ8TCenI0f1lBU6zAqZbz1FiAVNvlFP8+Aae9kYR9qTPz6TgUQwuzd5B4eRWuhT/ExT2XLp0HoN/M8tCCUYcSglVRrmSwud38Xv/F9JjTqLUZ0lumv+2KFr+9dChXzBqVNE/QLC4MnHiZ4CMoUM/ZdSoIsk2FSEigoKEguuMGcJkv3mzA6tWLeby5b8lJBaApk2psKCWn2/g9m2hQGZqak0g8Yl/kZrharMQunvPAdatnod7+CQBgvYwGoWTK1mP7+Dd/ztcg8IoTIjGsXYLNBlJlGSnIlcqRQIQMiV23rUovn8O7aMruLTrT5U3P8KldRhKdz9kMjlGXQkFt0/i2qInBefXM7D/MF7cPEXG9SiMcgXq06vp2GUwTevX5tG53bR9Y5AA223flawXj0mIPorvwB9xDQoTiS6bN68uPeZQUq9EYlcnCM+eX5F27Rg3o/8m4sBWqvQRyDFp147x5OET/Gu+9srEInOSUGRkPDdvaiXw09mzYcwY2+PF318opDdvLoWsVrbAevs21KkjIK1eDntdLIE+pqZuIydHKnSnNzhy/8kaUtIbc+7SXcnYMIEfqlevxbWrf+MzQBgHBXfOoMt5gUKpxKd0bKhvHkMde4xmrd/Bv2Z9cR/du3Xj9q2r5CXFU3j/bxFyWqXbcDR3TlMQdwLkcrKOLUGp8iArM8cm+etlRdF/XcrlZY5FLkGh5OpkpUVRKbHIWJJhM2Xh2r4/RU9u4GhmTefVawxGvU7ieuTQLJik54niPooSY0XHGRMJKSzsXbSPrpC9bXKFaaGRX0xkw/ptdGrWgMxdMyQrA6PRQM7hechkcuQ1muHi7Ew1fbq4vEvb8zPubwymMP5vYeked4LsEyvx7j0emVyBfbPe7Nhdnv3736dczF+bHIRMZgblW1KSkCrp31/Q08jNzcfNrQ7Dhn1l1X3oZZFbYKAQvc2eDW+++SaJiWkMGfK5hMRy6hQvLaj16mWpze7q9qVVApH56/JpO0FZM1FKCGrZC/X1Q6U5VLlIAFK6VyX3760UPrqG0s0bfxeFzRVmty5vIXt4njlzFogpk56dOovEkYED+jBu7FgLFcZnzxJwNiMsVek9ziIl6NqmD0WPr4kpw+v374tEO/OU4aumXMqThDIz09i7d7tE8dJWXt3U/P2FWsuECUJtxSSaFRX1cnJar17Cqm7FChg+XBDeWrlSKry1fDl88w389tvvojJn2fFblg0VylACG7WucGwc2LcBp1JSlyCHPB4Hrxp49/+u7Nq36oWjk4rUF3clxMB33urEzp2HsFcoKMl4StbxZfj0nYxL0+64vz4YXW4aWceW4to6DLc3BnPuzOEKHc0qav9jE7otT1FrhYjUFSPIv1K2LLVr2oOjR/ZKfBJ37znA5MljcWw3QMyPm1pRYiwFcadQuHpT9OSG1Jpu+MJyaZkluHcegmOt5qIAk2kSVrXqxdO0bLZtWY4yoA367Bdk7Zfm4YoSY0nf+zPv9Ogn+kYej4qkRJ0lep9ihMzDCykpLqZK36l4howio1hGkyZteKt9RwrOrsfLy4eSW8fw7DkKVcM3yL2wFZ++U3Gs3bwU8bKC7yaXTymYiqIV+Y2avy57n5BwtzSt4YdC4YKPT21UKiUpKdZlSE2pE3t7IWUSFSXkPENDdezevR253GiR46wMm7RPH3ByAmfnk/TpE07z5sWSB8DLJgso09Y2bw8e2/awNb1u1WEAHgUvyNwyyaayZvap1bi2CiUraikepeNEJlfgWKsZGPTY+9alqCCP+IcPrHqFOrToxdnzZ9DpISNHy83bT9iz7yDHjuxFJleQmauTeE8uWzxL9Kj9aNg4ZEl3JePXMq24BKWdg03JXvWZ1bRs39fi/Hfu3s9Hw67w/HnZsV6/Dt3e/pMHD25TkWdtSMjrREefLVW8dEGpfHl+u3ZtYezk5wvplR49ykhBL+vbwkLhoT5okBBQlJRIrQh1OujRQ0Fs7DUrx2tZfMrMzrd6Pcx9PYcOH1967ceL17bqJ4stUrrakmKJf7Bpf3v2HUSn1+Pc6A2UHlVxqNlUgDlGzMWxdksULp4UPLhE/qlVfDDka5tj1AQksdX+dRH6q1L/7906jsz3NXIv7sTOpw7p+2eTtnIE2ceWkr5/Nva+dTHqtRiK1DTwcydj788Wx5RxcB72fvVwbRNe6to+y2wSNpJ1ZDEleRl49/8O56ZdKSwulqwGTA8Ap/odORa1Dx+3QlYs/gWDTCbCzSQG0/2nSSKn8+eOMG7sWOb+uZEdOyLo1KwBuYfmlRVPaguDJz9qEePHvkXLluXP4NUidFtFxlq1dBw4IJUhXblSgDHaKnp99pmAbLG3hy1bpEdX2ck4L0/Ih8+erSMmxiApllbWy7LMcEPB87T/0OC14AoLoS0CA+jVswsb1m/jjeYNK5Ruzrt2ALmTG0712gJCITTv6n4Url441GqKsTC/Aq/PcGSuvmid3NixdQlaTTIrFv9CYZGGQq/X2LF1CZ3aNZLIWJg8aq9eiqS4KA+5yp30/XOsHp+Dqxee9kZStk0jed0Y1HEnxRVmzuF5uLi4UrOqSiz2mktm5Li25qdfHTAYhMl86g8OpNo35P0hozAYzMeMdTq+QOtP5IsvRr5ULvfwYWjZEs6fFyZ3U3rG5DJk3ufl+1YuLwsMTLDXPXuEgGHPHuH9oEF6G16hlhG6VxVXqxIi5oAHL3clxUV5GEo01qUVIn7DzrcOco9qVAn+j8TPVV+Uysqlv+Ld/ztRRz/z8EJRcdErZBQKlTtoiwju2Zt3B/R55Qj9X5dDf1Xqf+KzdJ7fvYBPqfhRwc2j+Hu4kfYwRvxMHRuFh8qVlNQkqvQaZ0kUkEHJw8sUP7xM/p0zOAa0Eok+AszoAN69xwnyl3tnCUp4LUPEfHjanp9ROLvj+c4XaB5fJ/r0IfTI8OzxJUWPr6O+HmnB/gOpMJfW4CCpI1gjphj0Rh5eOkdY7zwJ/f9VcugJCXcICenHzJmFBAcbJHntli3Lcp2tWiGSgFauFHLYtqJtX18oKIDISGmOc//+yuXiT54UyCGmnOmdO2X58IyMlwsxbd8uTBKBzRrx8NkCjp6WW8hEvIo8A0YjBXdOoQrsRsmLeIqexuLS7G1St3+PTKHEKaAV6phDONXvgJsZwSd1+zSMhjLSiMzOnqKEa+TrZBzZuwWDXClSwpOjD/P00VOORu4uk7Ewq9M4teqN5t55vHtZjguMRopSH5GbnoxMBsoq/qhvCGMuPyaCEnUuRs/aXD8fib6qQIBKTi1k/ZrSukHrMDKu/01ifA5rNjjgHvYjLq3DSLl6BHtDPq93bENlai8NGjRi0qT1NG2qs5nfXroUnj0TAgET/f+rr4TXJrJanTpQo4Z0223bBL5DeemJpCQhBz97tpCOOXEC1OpiBg/uL8mh5+b+TU7OWck+M3Nq8vBJQyt1lFCxDrF582qcOwyi6MkNvEMt5w6jTIYu8znIlaAtRlm9sSjWJkqSiHNJE/KvReDdq4whjExB4ZPrJCc9tyAT/ZMc+r+W+t+xQwc+GDwMTy/BD7Ganyf+NV9j3JgxBDZpZEGHXbXid6hRVqh0rN2crDtn8QgZW0bLVdqTffuUKK5TvtlXa4Du8RWaB1QjMy2ZKooScuPOoKzeuFQRTWD1pe74HqO2CKeAVhTEHUfh5kP6np9EJ6SCuBN49R6H5v4F5E4uuLcbgFvbvhTcPkNB7DFcg6QYwIxtUxg/9hvCw/tUSibAvlp9smPPoNSVL4o2+cfU/xkzZuHvf0PCuDM1NzeoW1dwFMrNhYYNhRtu1y4YP77iiblqVWFCP3JEYOz5+RkoKhJuxuvXy248c/kAN7eyydg0gVetKkRs770nvDcV1Jo1s10MW7YMJkyQ41PtNL4+VS3GSnlJhrWrfiOsVzDZmS9eet0Lbp3AzrsWBnUmenU22rTH6LKT8C0tlmkeXEKb8YzC+Asgk5N5ZBHOgd3Ii96DJv5vdDmp5F7YSpVun+AaFE5hYixubcLIi96LY+0WyJ09yb51lIULl4syFiYdEUUVf7KOr7Aq42w6PnVsFIbCPORObuhyksXjyruyD5lBjy4/A98B34vyEi8SrqIICBIp6fn3o0lKdcQtWLgGxU/jyI07xbXoa0z8ZgKVkWDw9KxG8+YtGDUqgvx88PMziLIO27YJOe7XXoOsLGzS/00F8s6dy8bZ7duCJIS9PXTrVva5uZbMmDFlLGa5HKZM2UTz5kHUr18fcCQ39xI5Oacl102lakW1qv2o6uvB0sW/lGn4yBXY+Tfh+ZXDOLbsRe7FnTavvUO1BhTcOoHS3Q/NvbMYEmP4+ZffCAgIoF94Ly5F7SHlymEcTAFdq17oclJJ3zcLmdKB7BMrUBr1fPzxCNatXUT3bt14vX1zXjy7z+b1f9IvvBcNXgswUf/VttQW/3URevmnUUXRlPn31MUqCyGd8vK0mQd/x6FGE9w7DrIpzatHTur1kwT3n8j77w0m+dFtnl4+JOpxCPn4E2JEpb55DPXV/ZJqd0FsFLq8DLRpj3Gs3ZKCuOMYigrQ3DuDtxXhJINRQO7Uqd+GtIxcmzIBBoPB7DiV3D4Rw6CBenE/rxKhDx060gKNYt5q1IBGjYRJcv9+WL9eRnGxZYRUvqlUAixRJoMWLQbw11/PiIkpITFRQKmY33imiEwuF5bMEyaU3awqlSAJMGyY8N7NTYjcZsyw1IDZvl04zilToGFDB27F96xwTJ07d0YiJxF96TyyWs2EtIh5dG1GyUYuJz96D27t+lGUeBNjSSHevcyCBrmS4mdxuDR7m7zo3XiHTUBzeSchPfrwMO4KRS/uomrwOoUJV3DrMAB7r5pkHV+BQ42m5MdEUHTvLO06v0dA3Qa0bd+Vm9F/k3XrBM4tQ0jfNwuHmk0txoXRbFzoclMpSXmAU9026NXZeLz1KSXP7ghqgPXaYtRr8egyTBhDZkisjOi95N08jmOtFui1etzfGEzx01uk75uFY+2W+Dpp+frLj5DJ9JUaXwISph9xcYXMm/eIlSu1REQIE/mECYLAW3h4xWilvDxhEvf3L+vb778XSEbPnkHbthUjp9q0gaZNtYwadYCBA4ORy+8RHz8eo7FE8luZ2QE8TGxQ4TySFbXM4tqnbp8GxrK5A7mc/Cv7wKhH5eRMs1ZdiIl7jo+PD+07dufW1Uuk3zyKS6veYnrWoUZT8q/uQw70Dn+fHTs2iJIncoWKuXO+R18tkJP7N7Nz51Zy8wqJvX6pyvTpP860dt3+dQYX5U0GbP3P2veaNqrJnF9/4FzMLQuT5vRVIxn63mAOHokkTy/HLvAd1GdWM378ZHbv2UGKWodd03ckTun6olQLbHjSys9xqN5IYiSQtf9X3Lt/Jn4n+/Q60Y7OoUYgyWtHo8tNsSmcZDToydkxlSF9Qnnv/aFEnbzA9i1LSC3QYRf4Dpqzaxk77ls2bdlErk5eepwr+WWmmlatyvbj49OPwMBNpe8qZwRRWaOJnj2F6OiHH1SAjIULCyrl4t6zJ0RFqfjtt3lMnDihQteab74RilrmqRyTcca+fZb7Nxlu6HQy3NyMEkMOcELuIGjdWBsr5n1rovAHeLpz+84NFF41cW3Zi6yopTg37kLBvbPYVfHHtU1YKRohFPXNo/j0nSLWNYCyuks5lqf66n6cEk6SnpaOR5/JonuQnU8didl4yobxvNG8EX0HfSVKRkyePFZEsZibtbg070n28WV8O/E7Nm1ZT5pGj2NAq1JrxTKHIl1+Jhj0+PSbIrKlVQ07Ye9bh7yDc/nsy/9Q028Tk6cl49W/jFGtrFJdcmx5u6bx3X+GMGH8KCvjqHJGIz4+fqJLVc+eQv+9bAwNHy5E5AAajfDwBgUymYLZs0s4fVr4f0VG0KtWKZHJ/Pn00/LYcwGyqHTYgkzeUBwb1uYR07U36opxDQonK2opnj2+RH3zKIBgKH9sKU712lH4+Bqq+u3xN2bx1eifaNWsrqQvyzsWpWyaiFKdhsFoxD1sEg41AklbNxq9OhPPvlNL+3IchpIiDAXZyF080WY+l1mcDP+LIvSX550N3L9wGLVaQ/1aNUm8eICW7ULx8fEj5tpFWjdtwaPz+/n0s2/wqVpPkkuToARio9CrM9HcOSPmw8tLamafWomqUSch0pMryL9+qCwfb0OKVW8mvGVeR7h3cjsjPp9I7Xot0MvLxKJGj+lEp47xknN8lQi9skYTJ08Kk21+Pmg01cjOzqFNG9vbmFInX30lREnffHOE0FCD1dQOCBGZRgOZmVL8+LZtCu7elaPRKCVL98hIO86fd2D79o388IMvvXpdkRhy6PUK4iqI0CVSy6U56udXjlAlZAwKlQd5l3dj512b4icxfDD4C+7HXiY//iJGmYKSF/dQqlyp0v0zcRJ3rNWc9H2/UqXrx6jqd5Scm13V+iSd2ohDvXZiHtWxVnPU1w/hFVImviZT2HP/9G5UVRrz9PFd5s7+TgLfNZm1GEuKyLu8G4WTK7riEh7E38a+ThDqW1GoGpaNO8dazSlMiMa79zizXK2cvOjdaBMuM3DAR/TpvYRvp+Si9WiCJv4iTgEtUdXvgPr6IVxb9xZTQQYHd6IPrGfCuBH/aHzZEn9bu/blq7y4ODh7VojkJ0wQvt+tGyiVCh4+lBMVpeDBA/1L039+fgYWLMgV03amlqvuwMa9I7C3r/HSecR07Qsfx5AfcxD3Nwbj1qp3WX9E78apQUcK4y/i038qbkHhYj2koKBA0peSlZZcgWPNpuTHX6aK2UovN3ovDmY1PMdazdE8uIx373EUP4vj+29Gz7Byqv/OHLqtfGdF36tM3jkj5ihag5Hkpw9QNeyEJukWF88fR1+9KYacZ0z5fgHdu3a0zKXJZORF7yXz6F/4Dvgej67Dyb2wFc3Dy7i1lubDU7ZOwa1dP0qS7lMQdxyHGk1wbvwmBbeOUxB3XMirRi5Ari3C05BLbtwZ9MjQnFnLTz/PpVWLppI6QrNW3XirS3uq+nrg6GhP3/BgPhg8jHp1UsFwTvLbr5JDT0p6SmxsnFWFRVMzz2unpBg4fDiHhARo0eJleWzhRvP1hS1bjIwbZ3hp3t08X377Nqxa5cjevft49syJefMesHJlCWfPutG+/UcsWDAWGEdu7imLfeUVvE6dgCE2x5S51LK51rWdR1Uc/Bth7xNA0Y2DzJv3F6GhYdg7eXAt+iwyO0cw6PAKn4g+N420PT9j1GspfByDc4ueaO6cRuHmQ86u7zEajKJEszY7Gc29c2ji/y5zqSlXGM88sgg7lyrU8/fk9KkISnwbS5b42bu+w2AQRKHc2vZFZu9EwrndOLzWAc+eX+PcsBP5VyMoiD1W9hute1v8hnfYBJRuvmTfv0DtGk+JOAjavCwcazVDfeMIbh0GYOdZQ5IKKr5/ln27NhIQUO8fjS/z9w0a1GPSpA00barlzBlpHrx8S0oSBN/mzhVWbOVleZs103P6tB35+bpKpf/M03YA128O5seZj+nW5W1eb9+cqr4evHh2n7XLZ3H50kmrPBiZTI6qYScKE65g71MHB/9GyGRyHPwb4dYmHKe6QRQmRKNw8sCxRmOMciXZt46S+OS+pC9lSgfUV/ejib+EY82mVt2lim4fx9vBQNq1o2Z5d2G8qG8esTmh/+uo/6/adm5fbYFff/7XR1Jqbds+6PPT8RnwPZ7Bo8jRylA2eEOk6p8+cUDcn7mUgPrWCXIvbMG5kQA9LEm6BwY9Xj2+tDgO1zbhFD6Mxu3199GlPSF93yzRiUTVoBO5f2/Fzt6e9z/8ig0bdjGkT290l7Yy69c/aB1kQ6nq/2EbPform27qIEyqhw4JqYykJKGQ+ccfQi7TGqlj5Urh8ylTpMvpwsLK48fLu7K/9VYX5s//k7S0ZHS6DNLSkvnxx3Cysj5ApyuPYVSCcgbPUidV+FtpaSkUFxVS19tDhCiak8jyoxbx7qARtGrdjphrl1mxZBZGuRKnOq2R2TmgvnVKNC1wqtMafX4GGI0Y9Xqy981izFej8Eopg9oW3j0Ldo4YSwptQg6rvPUpLu0HcupEBL/O+oNq+gyytgp6MHkH5zLmq1E4J14gZ8c01HEnUJ9ezbcTvxPHaXHSPchLRZf5lPS9s2z+hmOt5ji3DiVFXcz0mUr0Rgd8B/6AV68xYDRYQOqQyVHIoGvXNyvuwJe0evXqlMoGqKhWTcaBA7a/u2+fMJFXLAinx8VF+Q9hrEJRftp3B8lS1WD92vkYDAZR8iG3sBBlnbaSeaS8PINrq15CvUOk7++z+j/16VUMHT7Goi81Z9bwzYTJKNWppO+xhE/nRy3ivfc/Z8OG3bRrGGD1O7ba/5qUi3kxw4CczIO/Y+ddC13mczNq7VI8On+Ic5OuIuql4NYx3Nr2RW+Uc+/kdpq16mYVPtksKBhdRgLpl/eTHxuFT78pNqrd9VHfPEZBzAFkcjlevceLtG/Tk9yInPiLkTRu2hGfqgGV1lQ3f63T3vy/oofu6elA8+ZBjBp1iKwsHdWrWy8yNm4swMIaNRIKWTVqlMEY586FDRuEtEzt2kJk3tjMJzgpSUC77NolTPjlkS2mlpwsaHycPu1C+/YfsmrVEtq1a4y1JXxKyiZyc/+WnL9O78q9J2tJSa9jQeE2HyumdFqJb2OS7l3FPXgU+tw0sUhVEHccpybdeXAxkoZNOjDzh/FoiorEQnjhg0uUJN9DJpPh00+QjNDEX6To0RXcXx+E8cVd+r77hQRqO3DQJzy8F0eJJgfv0PHWPUqvHaT4/nnavTGIgLoNadu+q8Sjtnzarc3rA2nX4U3atu8qpufs7O0o0Rls/4aZD6oeBeoH13Ay6ahXkAoqfHSV76b+p9JFUVvwxvr16zNwYD8eP85mz547FtR9E/wwIkIQe7M1VkBIpRw6pEChUFR6hWnC17uHfSfxCRXlE1qHknt2A8XxFzHKleQdW1QmzxBzDKNCSVbUMlyavk3emTVQUkTxi/sUJlwBuZzsk6vxDp1AVsRcnJxUqKo0pEbN2rRt35W4a1dJjTnEe4M/Q6505vKlM3hZ6SeD3kj8xUiUds5EROywgFhXFKH/ryuK7tq5mc2b1/Ofr8eyY9c2UtRaZNWboLl3Dq/g0WIRy1Rl9ukrFCnyDs5lxBeTGNg/vML9h4d1w1ijpVgUFZaxC3Fp1UuU1DQvitoqgmZtm8LQfmG89/7QVzpno34zRp00CnuVoqjpfULCIwYNepf4+Huim7q0yChQ/Bctsixk2focymRUg4MhLMwkoyoQSw4dkkrorlplh7f3cObP//Wlx3v//iiSk9dIf0w+DLndtxVeN/PilNGgJ2P/XBxqNqP4eRw+fcs+c27RE+OLOwztF8a+fbvIdQuQFMLTds0AmQzfAT9g5+kvCGmdWIHMaGDypB/oGSIdR+ULnOWb0aAnZcN4gl9vy9u9PvrH9wPAzt37WblsdoW/kbxhPAZNLh5vfkTBmSWMG1XMhi0OpBf54x0+SeJOD2ZkOXdPZkz84r8qipb/X2TkQQYPHkZwcDGhoUaJ3HJoaMVjBYRVYXCwDHd3J2bO1Ngstk+bJqTy/P1h2KcOZDl3wjNkrNiXeYd+w7nLJ+I1y4+Nouj8euzsHZkx41datW6HXq8Xtc3feL0zp8+eZsiHQ1mzZgWuvb9Bm55IfkwErq16k38jEr0mB1X9DlTRpDBuzHjmzJlBTk4O9vXa45H/mIyMDNzDrKeHjQa9pCha/jvJ68dSnPzAalH0X6eHXhlN6oq+16jZm/To641fzSZ8PaYJW9YvJPbhVfw/XyX5vvnyM3XFCAa++wkp2Uqrx2R6H3/vJsXFRRie3eb5wg9xaz+AvOg9VHnrU7LPbCTv4g7cOgxEff2QmJ6B0kJpnxZQBwAAIABJREFU5HxcgvrgEhQqGktv3ryeRs3efKVz/r+th/5/2HvvuCjO7v3/vUuTjkhRMTYsicaGBjUmMdFEBVE0miIxGh81Ro0odkzUR2PBEkWIvSQqgmLsBRsWFEVssWDHFlGaS6+77P7+GGbYZXcBjXm+T57P77xevhxmZqfcc8895z7nOtfl7l6byMi1eHp2IyjIMBIlM9Nw2ETkeimPNNBmzzMmjCG+bJmZEBVlSnz8txVer1KZxLVrw8jJide7juepJqRnPgKMt5vIZ65Rq0nfuwjLRp4U3D+PzNIOVXY6GcfXYdnIk9w/opBXs2HTpvWMmziP5aHzSd08AUefiQJlbmEulu6evIhahn0nPzKOLgfkWDbtxObwzbjUeRu5XG6QRx2EPpF1OASrNr2kPmHbrjenYiKwcGxr8Norui8Q+LvLn0NxcAnWbXtLzoZdu97knVoBF1cwb3YRbdpA2FZQFxeStmcBtYeE6BxTfE8Afg5ezoTx4kN+eb798tu8vN7l4sUzhIauxN8/gry8fH7+ufK+IjoOAj2uNd9//z0TJwbh6ys4Da6uwrZ9+wQP/6uvrHBzE849d1YRs+fF8jziuSQkU+PrYOl8hY+vkRfzK9+ODCQ5w1TSH4AybnOAzEJ71q1fJX08Leu3xtylAaml4RGXfj8KiKHNE5g0YRQauam07ukvA7Fq5Fnh+KBSFmHh3l5nnxeHQrD16AUVOOFVGtBlMlkPYBlCbfk6jUYTVG77N8AiQCzY/UWj0eiOoOXMmGJR+eWKtlW23+VL5wWyrl6T9c5v69GL3KuHsW7+kUS6VZESkaheYtPxS7LObceykSfZ53/Hqc9UZMihpBhLd0+yz0UyedKP7Ni1necRU7Fo0Z38mF/5/LN/ER9/nOTIc5i9/UmliiSV3fPfoVjk7t6MsLAtDBw4sFTgQim9HFFRZpibK0lO1vfE+/QRoIXl1YqqQsbl7Q3z5slITrYkLCyslEypvJcnLKel7SYhYRDCh0vX0jN7Uru2P25uZSXnhtptyc+hjB03krRdsbj0my7ASjcGoFEWojiyEpf+wrri1IfI5KY4WELXDzuieDGU1b/8RErkTDTF+RLkLCU8kLRdc5Eh11Exuptwhi++HCSde8nPoUwJnEhmqWKVDmS2tE9oQ2Zf5X3wHzdDgLuWniM/5lcmBkwmfOsaMh6cxOxtX/JOlQ3kAE5OPflppjdDR0ykxqfT9drVpo03WXG/Y67MYfvucIPPpeLlireJlAEajYYXLzbTvLlS7xqgTERl9+4ydauoKDN69vRi2bIQpkwRvPExY4Q8jDjDnDIFgoPz+egjod/WqQMrQ4tY9HNNYg8s0hnMQRCSGT9+Kj28euvNgKCsvefOEqhGFNHrcPadQsH9eDLPbEFWzQbUJcitHQWaj16TSNsdhOPHwyVP286zH7nxv6OoYHyQFeVQPfcJKZvGY+PRi4wT6wXaiQu7USmeGWwjqMKALpPJTIDlwCfAU+CCTCbbq9FoystobNNoNN9XdjzR/i4PXVwWlYKqIh9l29aHp+ExTBn/NZMCF+Li6iYdY/u29Qz/bgqbNq5A49xQqhYTcb6ZpzahykzWWveY2/ceMXrsbDZsWMfj8xEM+24KyRmmjPKfzcnofZyIjqhQkeT/hYcuLnt5dSY+PobQ0FACAnaiUOTi6GiDn9+nDBiQR1TUHkmtBspkxkpKhNh5794CwZarq0DYtXw5FVrPnrB/vylXrsSUKg3pq8uIdvfuWMoP5qoSWx48DeLY6Xze71BGAFJRXykuLsGqybuS6pCz7xTS9izApf/0speunS9ZsRFkmjny449TORcbDSZmyE1MqNZI8PCTN43HofNgMo6vx/GTEaCB5E3jsXrzfYOzrw6dB6PKuy88/85+1KrbilH+b3Myeh+HD21m6IjJmFRzfaX3AeDOwyxGj50tHW/4SH86dthG186P2LFTRuSOssFcLrelRYsw/vijkNFjv8Pp0+kG3xNRLm/Q57356KP2/NX+ZWxbeHgkwcGGB3PRvL2FAXv0aGHwjooyxcdHcDw+/BA+/FBfyhDg3j3dD8G1a3Ay5iIOvQP19rVs40NYeBiudVoQG39bZ5uOulPDdlyK24tl446k7pxDSXYaFnXeovj5fSzd3yElfAqufkGY13iD2v8qUx8rfHyNgos7GTEykKiDB3gcvZoBX42kSFaTUf6z2R6xmrhjq/Fo34uvvvqK/bvDOH5sNeb2zqABWW46GlWRwlgbVcVD9wTuazSCHLhMJtsK+ALlB/SXsr/TQ797+yqrV87Xm7Kk71+Mreen2JXiP21adyfrjCAfZdnam4zodZKOqOiRmzQQtEXH+E9n0YJJWIr0uzI5Tr0nk7Zzjk45sG3b3pw+FUnAuHEMGzaCVs3nA2XxzjYtxuloVL7qPf8dHrq4LHhNSwgOXqG1vpDExGd4eh6UNEe1ZcZWrQK1GiIi4LvvBC4XqBqyJT9fVeqZG78mAI0BmSazamG82aQRRUrj3lT55d6+nxOxZRXPNz7CufdkiXVTutNS5jzHbqNAoyEuehUamRyXvtMwsalB2q555N+Oxarpu2Sd3Uqtf4VS9OSGEGt2f4ecuEgWLQo1OPsqr1EK0KbFOLp80ucvvQ8ajQoz01TeamJJ67c/p9+nFtRxnY8gIQeff6bh888Edk5XVz+aNv0FudyCUf6eyOvposPyo3/BolVPrNqUhYL27P+dlSuq6nlX3UMX/zZEt1zeRBTU2rWmHDpkTlhYGIMGDXqpD4GYFHXobRjUYNu2F5mJ50qJtT4w2PaXL53n2qUonEtneMmbxvNWs+Yk3LpZtm7zRFK3TafOqN90fp91eBnjx0/FxaUmSX/ewLLJu8Sfj2aU/09oitO4fDEWyybvkpx0nVbNG9CmxUx8+gzkbkIs27aFsXBhCOO+H/LQ2L1WZUB3A7RZ758ChvB1/WQy2QfAXSBAo9H8WX4HmUz2LfAtQHVH57/FQz9+ZCd79mzBotZbFD+4gCJiKvLazci5vB9TRzeyz24jP+EEtu16oziyEnN75zKucd8pPDsbwZTJAVy+HCeJUj/bGsi+AweYNGUhy0Nnk7wxACdxIBi2UrqGwsfXyDq+lpHf/6Dneb+Mp/X/0kOvaD9399qS5uh77yk5eVLFvHm6IZXJk4V/CQmCx24oRKNtYhy07Loruw5du3P/OUVK0yq3odg/qn/yHXlXj5K64yfchq/S+W1aKXNeVtx25AWZWFvbUmznhiJ6nYCIyn0hhWZSwgN5cTBEp6oyTfGUmDNxmFrWfOUZ5svsZ2t1njdqLuTN+oWIVe11yvF2AZiY1KBVq+3Y2TUGNEAhe3eu5IuvxvPo9x+Qv/kxhbG/siJkLsuW/8bDHeeQv/UJhWd+Y/uuVVTtGb2ah+7oaE1ycm6lfcXMDPbs0RASMh8vr84v9SEACP7FAtMGnSrMZZi93Y0tWzbSrY+TznG08yEitFF07BL3LZR0FQBsPXzIPL1Z71qs2vRm/Ya1ZGakU903UBpfli6eRfKzu5J2cdqWKSwLCaHLJ32Ijb/D+x0+YGaLymGjryspug+I0Gg0RTKZbASwEehSfieNRrMGWAMCyuV1e+i3r51kz+4tWDXthCrjOeaOtakuK+Dp5X1YNX0PVcZzNLZONHa1J/liJKPG/MiWTaFkRK/DyXcqlvVbY2rnws0DiyRRAEAQBTgfQbeP59P1o0NMnTJe2Kdc/C3naChfDviW/p/25vKl88QcXkWfbqHUeaMeACWFKSwNXsj8eUskSsxXvee/00OvaD9Bc/Q0n38+CC+vmxXGxxs0gL17Bc4WYybIhH3xEteka00buSGT1weqllPZu2cLVk3eJet0BOqCTFz6z9Q7pl37Tym4cw7kprzX6X06dHifoAWzsGz8LhnRa8pEUmRyaniPJX3vQp1ZmlVrb2mWZug6DC2/yn5yeS7N3WeD5rzR9hHNze173N3nIpebop2jaNy4ORfOHSd42Qp+Dl7B77s38uGHH/PVV18TvGwZPwevZfuuLaXhlr/PQ/fz+1xHfNqQHTwoJD0//LCESZMC6dy5S5U/BCIOXUyKpmxLxqx595fKZYDQ9kOHDCNo4U8kb3qCU6+JksCNaIWPr5FxfC3OfafpXYtNWx9Sbh4H+9pSH7LrMZa0fbqiPlatvYz2oYqsKgN6EvCG1t91KEt+AqDRaF5o/bkOWFjZQV93DH3Hzr2cPrZRSkylhAdSYunA04dXJG6LlPBAzJ3q8TTpNj8Fred03E2+H/cTv20IJvNsOKZ2zgaz3rmn1tOhsx9XEx5x9/ZVLl+Ow/bdL3j+21jJUwcwqetBeNgK9u4Oo7CoAPOGnkydNpHevn78ui4YlaoI84btmTptIu07DzZ6L/+tHrpo7u6uPHnyhPHjy59f14YPF5AJH3xgODEqxkHj47+hMu8vNXUnKlWm3jHu3E+iSCmvtA3FnIpzvxlo1CXk34k1yq1j17Y3BXfOYe5Uj5MnjxN79gzO/YR+lbxRYNFMzniOU88ASQRDtMLH11AcW8W773apcJb2ss9cJiukjusSmjU8j7pU16RZA5XgaFdgZmZ1aN06DGvrFuhiw0XLx8TEignjh5ciWPKBQkxMYML4b5gwfpS0n669Xg/d338Inp4RUjivvIkFbiLKxctLSWhoMH5+fYmK2lrph6BrV2FZTIquWt+Uo0eqnssAYYyZ+cN+cnMyceozjbyE46Tt/Inaw3RneOn7F1OtgYckkvPiUAg2Hj5lod62vck8toa0LZOp7h1g8INgbKZfmVVlQL8ANJbJZA0QBvIvAT/tHWQyWS2NRvO89M/ewK3KDvq6Y+hzZx3DSpTn0vKctLHgNq26ozi6knlLV+rEN7t07iCQ8exbqEfqlXM0lPHjp1KrbiuJ+N723S8EpEspbM3VL4iiJzfITTiBRqMhJy9Xgii9iJjCqtCfUMtMpHWZkdNKJfTKaAP+KR66aFWZ6rZsKTDjTZkCvXrJ8PHR6KBmoqLMSpEtzYyeq7g4nevXPyUn54Le8YuKa9G0cXtkMoG9qaI2XBw0QYL0JW8MkPoK6ELCbEun3TatBbIluaUtDqVcKAC27XzJOL4OubklyZsnUPPrn6UPeuHja6TumoNlw7bcvvWHkRi64WVj29JSnxNzJIjoY6cl9IaIxT5/XhfR0aePCXXrWgIgk5lRp84Y6tWbhExWbLR99Zcr2vb3eejaCKsOHfJRqyEuTmBctLQUMOfaA72Xl5KAgO2cO3ccT88dVfoQiGZiAt+P/ooxY+ZUOZdx+dJ54mIiMG3QDnXBdTRoKLgfb9ALt32nL1mx4eRcO0bG8bVU7zKMvOtHyb99Gts23uSf+pWFC5exbdvWSmf65a+jMqu09F8jZKK+Bw4jDNSRGo0mQSaTzZbJZOIZ/WUyWYJMJrsK+APfVOnsr9HKSvUDjcqHKY6upGvXXrTx8NT57dU/LhITcxLbj4bqHdeytQ87dkaiVqtZGrxQB+lSw2sMGmURKZsnSmXgpnZOWDV9V1InsvcKQO5QG5d+ZdqDZs27cSJ633+kXf4uc3S0qVLJtYMDzJ4NBw6YEhBgS/fuMHKkGXv2QGZmAYMGDWbs2EkkJj7Q+31R0XPOnWttcDBH/hV3n6ySBvPKTCy/ztw2DYfOg1EpngtardejSd0xG/tOA8i/G0tKhFCqL7Iq1hn5q56EWzUzU4qSbmLZqL2kDysW4Fg17kjBg0tMnaofynlZizt7mJCFwykqPE379kKlZGamoKv66BH8+KOgrxkaChYWcvz9zbl3byG//z6Avn2VuLv/hItLbcaOncTx46dKJQZrYmLihLNzTcaOHUdiotH82n/cvLy6s2jRYqKjhXv95Rfh/lavForX7twRFLHOnxfi4gpFjg6dgKhpKtJQiPQRP/7oUGFIpjITaQEcek+lhtdY5KZmpO+cq8eqKZpdu96YOtQi99Q6nGo4oboZjXXLTyh58QTluS3Mm7+ExPt3uHgxFguPskFbpJ4wb/I+p2IOoVYbr341ZlWKoWs0moPAwXLrZmgtBwL6GKAK7HWHXETI1qqQf3PfQKLrxaEQrBq159Ifl7hy/YFU9FEZvNHaw4dnW2PZsGEtg4aMZ8HcCVi6e+ohXUxsqmPh9hZ27fujiAohZcsUangLhQvlYUvaIZxXvef/RMglMfEhISErCA+PRKHIw9HRGj+/z/H3H4Kf32dVinl27Sp46vn5KrZtW87AgSPw8lLh5aUsrQTMISpqE56eEYSF/YaX18fSdeTl/YFGU6B33LuPV1CkrFfaHqZ6bVP+79NxN3m/QzNGj50tFJqd/BXXwcEojq2RtEFt3u6KdbMPybm0n6zYCKyaf0RR0k00GrXAUghkHlpG82atufLHeRzeH0jujRMgk+klRZVpj9i48VcsbNxeKeQSdSSG7ZsncCH+NkOGCFJ+PXsKA5x29eTs2WXVk8OGqbGzK2D06FH062dKcLBKat91635jzZoNfPqpCcHBJVrt/huenmFa7V5xf9C11xtyEfvbhAnjDRYXffstdOok3O+cOQIfvqOjDcahttb4+X1BfPxIkpO9UZbrpncTn1FYbFmldy9owVyJJ0omk4OJqSQpCaUIuoNLsW3bq6yA6x1fVHERdO4+qhSquo33ugyi36e9OX5kJ7t3bkJezYbsC3uwbvYhRU9uCE6hmQV5Dy5RKDeVkqKvO+Tyt9jfAVu8fe0kd+7exKX/DMqbrUcv8u/EgqmFTtGH9lQchIeTczSUaq18dKo6H8dF8EmX+TRpGI7/2BF6SBeRL/nFwWBqeI0l+/wO0vcu1ImvQhlsqVbdVv/VSdGoqGMMHDgELy8lwcHi4JtLVNRmPD0jWLToZyZN2lqlqa6QlLJm4MARenzobm4wbJiKjh1VDBw4hPj487i71y69DgPet6wBbzbpXGHbGNumXWhW9GcC+bdP64TkxBfR7h1fNOoSUsIDybm4D7t3fAGwatOTP2K3Yl77LSnkpkx/TFHSLT3o6s1jq6occlGXnMXN5RCODmc4H5fC2SPnqV1bw8cfC4N5VSptQdh38WJo3ly3Y1y5oi5dX6JzjGHDlHTsqNRq94alW//+kEti4gNCQpYSHr5dGoDr1n2DTp0KK0y09+oFf/wB69fL8PMbgKgVqg+1LUv8JifrV8k3ca9d5WT6kp9D+fesaTyPmIpdj7E49/2BFweXkRI+VeClP74WTYmKvIQT5N+OxdbDm7xTG5g/fymJj5LZG3OaFSs28CJbQ0lhCnt2bkJuZoFlAw8KHlwkJXImRX/e0FlnWq81p08dfOmk6P8MOdeOnXvZGbnGKH+KKB8ms3cl4Vy0pNvX9aOPuB1/VCL1yoxaSjVzC9SpD8m/cxq1TE529GqQm+FauzEm5vaYWDbEUq3QUTIS+ZIzT6yn8P45itMeU8NLn5ddo4bbZw5QYlKbahbmr3zPr4ucy9C2xMR7eHn5GtQZ9fBQ8/bbKqZNO86iRUFMnHiM7Gw1tWoZJ/WKjDRDpapPx47pFfKh5+TAjRsF9OjRCZBRUJBISkqkzn7FSlsS7r1X5X5z+vQplv48k7y8YkKWzcO+9xRBYGD3PCwbt9ehp03eMgkNGh11ouz4Hdi1613ah5pQcO8cRcn3SmXcfMhLOFHq3QsfGbHewbfvQKxsa1X4LC0ti7A0HYe5yVosLR6S9PQOgYFPmTtXKFuvXVugKO7Rw/A7JLZZQgLcvi20dfl9N282vF77GC9eKBk7diWzZ89j6dJfePbsT5o0aYCjo8VL9Zuq7hcVdQgvr564uV1hzJhCRo6E994rJj09nehoQfawvJaoaDVrCveUng5xcZf55ZflJCU9qfB6//zzF9TqPJ3j3Hv0HknJ8iq9ewVFSOpRqRf2kp94gRreAchMzMiO34FaVYyJlR3OfX5AZmJK1qnf8GzfGQsrRx1FrBKT2qxdMQtliVqX6C31IXITUx2it6In1xn67WRUmmp611iRpuj/jIeunRSF0hdr3yLs23+KTSnhv5jomrtkhV5S9PftW9iwbjklJWpKajWn6MEF+nt1Z8+u9ZSUqLF4owWL5k9i3bot1HQs4cC1i9iVoxQo+jMBtUYDhXlG42s2bX3ITDz7X50UDQlZjZeXqkJPyctLxbVrCezZs5fevXtx6FAJOTllCToRjSAgWcyAPxk/Xr8wSNvERFdw8EKMKbSbm5tWud+ISWyT+u3Yvm09pg3eKeVwEQi58u+cRZn+J7ZtvMk4sR6rJh3Jit1Kwb04bFp1JyN6LcjkZF3YrYVQ6IUqZrNOoj3rbITkxafvX8yALwYyYlRAhdfoYBPNGzWnIRb+gC5VQlaWkBT8xaByZJmJRTMgxNLLW3S04fXa5usrVPV6e8P+/flcu7YRT8+thIX9ipeXNt//X/fQExOfMXDgEIMzte++Exg8y3O2aJurKxQWCuyeNWtqtGaNFV3vX/PQQYijP/vzJiXIsazzNorDoUIlqEsDUnfNxcKtGS8OhVDzqwWYWNlx/exmLl+MlWpZMiOn8eT2UfLz83SqlGv0DCB970KJVwrAto03eTG/8lk/X4PXVJH9z5BztenQjxuX90v8CIqjK+n6cS/u3D1H2p1YrFp7oTiykhZtPjZYcv/seTpFSmWZfNfmCezasY0iZbHOupHfDaagsBCnT3WFpsWkmKltDSzc3tJDUGjDlsze7sbhQ5vp8kmfV77nvzOGHh4eUWn1nTD4RhAcPJ/t239l4MARDBig0uF/WbfOtBTJ8is+Pl9WqQBEocgB8klO3s7t2/pMEjm58Oj5I6ByGGtcTIRUqPEiYgqax5dIu3tW4nBJDZtESUYSimOrkZlZUHDrNI6OTuTkKsg4vgG1qgg72+rkxG2n4E4sNq17kHF8Pc6+UwFRnWq9wNhZanaen3L63Fk833+gQ84lXqOpiYJ6tWbxRs37evemPfja2wuD+ssUzRjat6rHyM7WDuOUMG5cPgMHfkN8/OlSSobXE0MPCVmKl5cSBwdh0I6OLo/U0eds0TYRUy4O9rqhI2PXq4/tfJkY+t3bV1m9fA4lyHEuHaC1C8pcPv1BqhhN37+E4gcXsbWzR12ntTRw23Tz59qeIKp3G0nulSiSN02QMOzlYa9Zx9fSqctAozm2iux/xkMHmDx2oESfu3TpynK0l5EsWbLCIPGRUHASjlXTMtijU69JpO6cg4vvSK3qr14ojq3GrMYbejH3YmUJlo08paRocthkbFt7kRG9BgcDsKWhIyb/tRi6yg6NHkfV6/HQq1p9p1DkIhQb9SQ+Pr40KRVRGhO1xc/vM+Ljx+Hu3rAUFZNTparRS5f6kJNzycAejjxPN95u2sszA/ehxEQiSbL3CkCxcxYmZtaY2NRAJjfBuk1PCs/8Bkol5vXbYJ35kMkTpzFrdiBKZQE2b35ATfULbK2tuHz1DzJjNuPsO1WiYNZm7BRNu3Rcm5xLo9HgaL8HN+cpgH7Yydm5P1lZv0vt3rUrREVVrdJWLJoxtK+9/csdQyTBSkgQZmGhoWsIDl5auudf99DDw7czZIiS0aOF84SG6iZ6R48WEqDr1hke0LUx5domzhoNX+9f89AXB02gxMQCy4ZtdWDRabvm6eVOMo6tYtSY6XT0bK0TdzdzdMN1iPC1NrFyJHXHLNJ2B+kAJgDSDy7FuYYjfX17Vjg+GLP/mRj66bibWFazwMm1XqlgRH1S0jJJTc/maZqaQYP+xf37iSz9eSbOrg3JzVdzOu4mjx/eZP7caZjVakrRnzfIvxMraYXalZOFenEoFNvWPSh8cAnVw4uokZMRtYzP+n9D8rOHZCfdp/DPG9i06k5+QjSqJ9ep7uhIUfqfWLb8hIKE4/DsFv/6diIpmWavfM85OTE42MxFJtP1PGxsmuPs7MVfjXFWVWc0JsaWSZO+JzHxJiEhKwkP36qFhvkMf/8hpYk2FUlJSVy7dr1CIYLISDMaN3ajeXNd+aQrV2BSYA3snedx4WquTmxc+1lK7XT6FGdjj1HNvR1ZMZuo1sgTda6CnBvHqVavNXk3jmFi50zWoWCUKjU1+grxzPQL+zlycCcm9dqi0ZTg3G86z2J38vTxfayadgI02L/nh0xWOkBoNOT8cZCcuO2ATFJ/19aHPR13ExtrBTYW/8LR7iTlvUUzszq0bXsUN7fP+OWXtVK7u7kJYh9yObRti1ETxRsaNoTERP1909PhwYOKjxEeDvXrl2m5ilKA/v5qFi++y6RJ3/O6YuhTpwZx44aQ6O3RQ1darm1baNECFi0SvHZtyTjQlzYsb66uhq/3r8bQGzV+m9s3L5OddJ/8u+e0ZON05f0yDy1j1JjppGSa4ezsLMXdFdejpVxb4eNrpO2ai9zUDKee4/RzbEDG/cs8TUrH7Y1GBseH/xMx9Mq2acdTyxNwVWvcAdWjS5jb1qA4K4XUnXNw0+JoAcEbs2nZDWXCMUZ+/wMaVTbbtoUx8vsf6P9pb/71r2+I3LaZ335dg+p8BEuWrERu4aIjurF48XJJZu5VyLlkskLebrQUNMcobzKZJXXqBPA6PHQ/vwFERf1WISRRKNkfQFTUKQYO/AovL5VBNExY2Ba8vLrj7x9QWgloODYvxto3baoNJErrBTIla0wbtCEyYhWj/H8y+CzFdrp86TzrVy+UYpepYRNJ3TIZDTIdytuM/Yuws69OkYsQHit6cgNVXibOn/4o7aM4tBxljkKn0lgb9SIkRaN5+83m5KZc0Csdb9msLs7Vt1CzRgSGpv316s2gfv1JpbDIQp12d3MTRJFDQwW4XkVIohkzBLWoffv09zVGbax9jD17BI9YNDGMoz0LE/uGrr18/7KxMcPb2zAyCsoolXftErDkYvhuzx4Bk15e2lDbjF/vX4yhN69Pl867+PGHKcTFnUCxaw6uQ4XxQQzLdW9RAAAgAElEQVS7UZDJxInTdGh3L186L/GziPbisJAUMYphb9uL/FunuZcQQ0lhX5YGL2TQkPE6sXyQGWm9/6EY+svEU59tDdQj4FJsnQrZLyhSFRvWCvXoRdbZCAZ8OZzULHOJLGfHzr1Ebu3N8O+m0KzVh/T60gVXByXzg+bQpkM/oLMkuiHG7l/1Xhysp4BGP/bq5NSTt95agomJE68jhu7vPwJPzzCjkMSTJ2HPHjUmJptZtWqNQdxwWVzzK7Zvj2DPnr2o1RrGjBHQMO+/DwMGCFV7UVFlsXY3t/UoSslBy+TCfjBKYvRsayDLQkIws20C6GOGHX0m6vFR27TqTsaJ9WRlZ2Gluo0iYipFBbmY12os8VvX8B5L2u4gnLVyJTatuqM4tgpQY1uaaLdt58ut05uZv2iDTum4pW0RRXkfUbNGOlBGMyzGjIVZzFP8/e9IMd/y7e7tLbTDlCnCskhLrC3eUFQk4LK7doV+/WDiROjbVwhnuJY6f61aCV5t376CEpB4DFEJqKhId5AUQzBCCMwGfUpj3X4jzNB+1alX8PHpgVoNBw8e0pq19aWkREPPnvp9StuEGLqMkSNNyc1VYl6KXp03Dzw8jP/O+PX+tRg6CHH08+djsGz8LsVpD9Fo1FoMm54UJd1i85bNEu2usfoWmYkpVk06lhO3WIqVRxmG3dbDm+zj65k6dRxmDTxZ/ksQTs7LuH/3OmtXBSGvZmOU3Oj/hIc+d9YxCWsukuHoE3D1QHF0hVF+D9u2PuTdOsXhQ9v5cdZK6QscFxOBWUNPg7S7CVf2M2Xc18hLJclfZkZhaFtBTq7edTVtuopatb5GXxQCXtVDd3d/i7Cw3yQcum6i04S4uBI+/RQUigIcHSsWsGjZshhf3/74+sr45RelFC/dt0+IlVpYWDJkiJ8Ua792rUxWTpsZzxiJkUWL7pw+FcnMOX10MMNPwyZSvecEg4VdGdFr0WjUWDTugDz1Dh3ebsypc+coenoLqyYdSd+3mJqDftb7neLoSgZ8OZDTZ8+SmXhe8saHjphMmxbupaXjvWjRJBLUYdJvtWmGy2LGeRI6Y9GixVy9epHw8O1kZBQwcSL06SNQJXTrJuy/fr2MvXs1FBcL1bdduwpedXlvNScHLl8WNFzFZGOHDgK1sUqlLwAxY4bwQdA2MU598KBpKdbbuIceFXXY4Axt797fOXBAmGV07y7O2rZSUqLiyZOKY/qurlBcDPn5IndPIWPHTuXy5d/w8Kh81vi6PfTLl86zduV8nEqTn4YYNlPCp5KWkSnR7hqqb8k6HIL1mx+Q//gayZsnYdvGG8XRlUydOoPNW8JIvxuLZWtvso+vBY0ah94/ljqbgUTtWU9MzEkcek8l4/h6o21Qaen/f6vdvX2Vr7/ux9M/H0vrLl86z9df9yM1RYc7TIsWYJpEC1Dj6+BytAArsHBrrvMAklYPI/uCltq3R08UGRmcjN6rUw7s2GMMyblKguZNZ+2qIOx6TcaxxxgyimF7ZBh/p1laNqx8p1cwL6+PiY8/j5PTEAIC7OjRQ4a/vw0XL8pYvBiGDy/h/PkyL9KQJSXB5cslBAWppDCCiUkZRO3nn8HUVMaYMSNxd2+IUplBbu4N6fdzZxVRSxVL5rbJ0nNzHqxL55Af8yvTp5eNSHXeqMd3I76nIPkBabvm6l1T+v6fUauKcOn3IzW8x5IvtyD62CGUhfm49J9ODe+xoFGTc3Ffud8tZsCXAxkxMoAJk4MY6NsTLkYyb/4SmrzZStrPvc5EncFcW4Zv+HB02mDYMCWzZ+czevQoHj3aRHBwDkePCvteuwZDh0L37jKCguz45JPhWFlZs3kz7NwphFEMDYpffAGpqcI+0dHC/+PHC4P56NG660ePhkuXdJOMYhineXPYuVNFy5atjT7f48dP8fnnn6FSFRARoWTMGCHGDQLD5oIFsGZNWVJ22DAlP/8MQUFCuxgzwdO21Vnn7+9PVJQZCQmGfyMSvY0RMZyvaIbGlfnzZ6KUmZapEHmPpTglUUqIyuQm2LTqgbK4kG3bhGcvUk0oIgR6iez9C+nfxw+X7LtYmJpgWr0WiqMrGTXSnx5evkycEsQ3/frAxUiqV6+OReMy+hC7HmOJ/eMGdr0mC31fpv+BEu0fmRQ9ffqUDmC/QeN27Ny1T1Byd36LSyf30bRZB1LTs0hJy+Tyjae0aN6UizEHyH9wCZs2uqNQ8pZJmDrURPniT/LvxCIzMePFoVCsm39EzsXd5N87j8zElIzj67Fp1YO7cYe4cDEeVa3mQmGK3ATT2s24e2oX9j3K1NLV5ZJjVUl8VrTNutrvVLPQLYWvWfMLqlWrxess/BCXHR0d6dHjYyZNGseMGd/z7Fkadetek4qD1qwRXly5EbegKkUtYjFRq1ZpXLnSg5KSLGm7nR14dS8h4UoDHscdkxJLoqVtDeSz/oOp06CFTt9YMC8QTMxw6hlgIOmkQZn2GIcuQ5HLTalWtyUFj67i1HOcjsq9dkERADIZyTfO0qBxO2Lj79C2bTs+7NILuamNzjOq7bRcJ1m9eTM0ayanRw/DtIguLoIYiEympmtXoS1r1RK8+TZt4Nw5S86fP4WfX3+mTZtbYXuDEM5at043ofj8uTBIDxyou29CAqxcCePGQW4ubN0qDMgeHkJ4aNAgWLr0OP379ypXtKMiKuoA/fp9iY9PCQEBQj/44AMhARscLFAnt2lTVvgkJlxdXIQZwq1bZevK27ZtpnTo4EePHh+h3RdbtmzJmDEHyMmR4eqqlgrZIiPNWL3anPfff5eZM2cTGDhLp+AoN3dLlZKi5ceVEpPaPHl4i9gzx7Bo0JbMmI1YNvLEvEYdbNt46wImDgZjolHxzbCJ3LyvkJKij+4/IuP6YYYMn0BWkT39Pv0MubKAJ9fP0v79L3j/w+6kpGVy5vxtqU+92awNl07sI+/WSczcmmHm6IZVqx7S+bLjdzFjyvhZhtruHxdyERNi2oD9qD3riYs5LsVVFVsDdWBjd29fZc3y+SjVGlw+/lbvWuze6UPBxV1CUjQnHcWx1di28Sb3ygHMbKqjzE5FcVRYp0w4xtARkw3CkspTYOaeWs+CBcsMloC/zD2XhVwMEVGZ8zqTVhXtFx6+XQefXhkkripFLV5eSvz9t9Cnz0YDWx2Jivbjjz/C9Iq4AGza+hIff5yhQ4cAQjvNmfkdKjW49DNGnORL3s1TJK0aRs0v5xoOyZTDlhc+vkbujeMUm8qlKbUhbnuNJhNNsS6KR2iDikmWfHzKFHW0rTwUr6rQTxGCKNq+fcI67STjwYOm7NpVglqtYcgQQThCrRaYDZ2cygp7srPF889H7A+CZz4EubyEyEg4fLgMQ16ekkBbLUi0Xr2Evw3BEhMShJnB8uVtKd8XRS7+0NA1BARsRaHIwdHRhk6dOqHRnMTW9rSBxPxWAgPNaddO9zzlQy7lk+liIdCBaxclIYrnm8aTEhHIG9/rzrrT9y/G0tyMOfOW4NG2vQ7goaqqZXdvX2Vx0ATmz1tCq+adcHIOJmzDAi6XY/QEUGUlG+1QMk0FCtJ/p9Wt31gz6UdhABTJk8ovG9oWc3gVedUb4Ojlj0wmR6lIInPfQmw/KsMD516PRnU+gp+C1nP39lVWhMyG0tJaQy+5Rl1C2pZJOJiZk5eTRstW7bh06Tz/GhZA0tNHHD+2FycXd16kPeDrb/xJyTTj/Q7NUKtLWLp4Nmk5GXq0uylrhtGiRWe+/npQle6rKtvqun6Nva2unGDr1vtxcOiI4F1baW3R/tvYcuX7aRN0vXiRi709fPyx8PLu3g3m5sJLbMi6dhWQCSYmhreDEAro3l0Y+LQtLaMvMbFtWbU8SCfXoW0adQmKrYF83LETGTlybl8/RnFxIcUub1LDeywymVyoGI5ahl3b3hI1bu6NaLKj12Bq74LLN7pfnKTVw7DvNAB1fjaZsRFUc29H0aM/hMTX8ztYaYpp076PkDtp4En1gme07zyY3t2e4+YSilymWxzwV9oAhEE4IMCW1NRHjB07iRcvNjFsmErapp1otbcX4u316gmJVBAGyIkTwcvLizNnzugQV23aFEFoaH6FH4iy898ErIiKOsbnn3+Fj48KHx9dDPmBA2VkYWvXCnHwESP0702lgm7dhPCQoSStnx9s22ZZrkjIcP9NTLyJp2c3vcpT0RISDFee3nsSQmGxu/R+zZ3lrzeuKPYEYd91uM64knl6s76sXPxOuHGYr74eye+Rv9KmQz+8uwtUEDt27uX29WMM/24Kdx5mGXzP796+yqrl86jWqAPVC54xYfICQpbO5WHiFUGYJ/O5RNGdcWI9qlwFJbkKg3GXf5yH3qdb6Et5xouDJoC5pVQUIO6TfjAYu3a+0ktu1aYn+XER7Nt/HNCFFZb/mlYESxLN2qMXyY9iafFW3deYFP3PeugCJHFgOYIu3QKQNWsMQ+KSkqBaNQF5oU0J0KePYURFeXNxnc3e3Z9RrXEH/cSSRy+sPcqI044f3UxRcTFmDT1xKkjCtCSdP0vV0hXHVmNi7UD+3Vjy78YKpfrHViOXybDrov8lsmnjTdaZCFR5Cqybvkf+vXMC0uWNFiRvCqBJ/bo6iKnMyEDsTZbwhmuK3rFMTV2oXj2X5OSKB01jbQC6UDxt6GdurqFEq6AQdeiQ8IyePBGQMIGBU5kxY7rWUYUEemjohpcoILOSyvaDglSVkoWJnnmfPvr3lpIihIYMJWn1ZwaGiprKlkNCfq2UpsJQ5Wl5D708AZd2IRBUrEJk186X5JunWLN8HhZN3pXAEH9cucDZk1swd/dky8ZljJ0YJI0bS4MX8kFnb+bMDCErK0uaGbyImMJk/89RqtVacNmpKI6uJv/2aYEQTmE8AfGPi6EbA+yL9iLyB1q2/pjWHu+SkpZJo8Zvc+ViHAWpDym4dx5kJrw4FEr1DweTd/MEudeOIjMxIfv4Wt7p9BmOTq568fpZ/55MbpEVdnYOEhHYsqXTOXBgF9V9DXuPZjUbk375KOnPk7GyrfmPi6EnJt7Ey6uvQYIu7QKQb78V/s/JEQYVKythQJk5U/C+xo83HF8VyZciI02pW1etF0+9fsebRo1bSLFENXKyj4TSsvXHFP15kYyr0RJxmqqkhOq+gdh6+PDiajT2FmakPLlH8fM7OPWeRPHze1g26Yi5Uz0yT2+mRFmEkxESN4taTci9cRyrRu2p0X00BffikFvZU61OM6rVbcmf8Yd18iQlGjm3T57m8/66nnmdOv60arWVmzdvEhFxmw0bhI/fnj1CsY+bW1lxjFgcZCimrF3A5ehoS8uWTRk16iAnT5Ywf75+cc477wh0xT/9BCUl7mze/CvffONr8Jm/XAHZt8yaNR83tyuVEqwlJEDnzkIs38JC/962bBHyBAEBgpc+eLDwv6dnWZsYKxIq338HDRrOmDGFFd6DWCj1xRdl68rH0LUJuAyNK8nhk3H4cAjWTTpS+PgaKdt+AI1GKiaTmZpT8PQWrl/OJf3yUa5dOMfmzWtx7CMQbiVfOMCfDx+Tl5fPwqAfKXZ+k8sndpOvVGPRoB227QQYrFmd5uTej8e59ySpjyEzITtuGy6f/ohtWx+y435n5vQf/zdi6C/tGTevj5Pzcg7uXs/Js2fJOhshZaetm33I8xWDKIrdzMKFIXq0AFIBixYE8Y8rF3SUS8zrCFMoQ7S7Vm0q1gX8b/bQq+r5PHwoQN/WrIEdOwRMs4UFBrHp5b24zEwBahYSok/a1bJZfWTNG+LkHCwpni9YsEyvWMuxhjN5jo11oI0PdvyE6+ezpAG7JOcFWWcjcBuxjtwb0Vi6NKhQrcjuHV+yYiNKkU0+ZESvwcrd0+BsMD9mLfN+KtK5dnv792jUaD5RUYfZu/cQ3bpB7976Je6BgWBjo6+oo23loXheXj3p27cPhYWRNG9uOFzavDn072+Gk1NXunT5BGOQ1pcpIAMrvRyKIdP2zG1t9e9NLGQqLhbIxwzN2qCyoqay5ZcViRbNEGyxonHFtp0vmSc2gAYyT26gepeh5F49TF7CcUFw/uhqnPtOQyY3oXrPAG7sW4BjnzJnz8ajN7dPruNewhlpdleseCqQxF0/SurmCTj6CNwubsNXS+cV4bIO738tHUtuafzr9Y8rLKqKIEXardMSOTzArt0HiIs5QfXyIH+5CbbtP8f6cSwyc2d27NzL3FlCvOt07CXiYiKwav8ZuTdOkApMmRzA1avx0gNJ2TyeZyuGIDOzQFaUwxdfjuBA1G5Srx9BpSxCk5/NyDE/GtQFfJXCorquxVjojenFVFb48SoCBOHhkVV6eb/7TogRe3vD9OkCHM7ComJsurc3zJsnIzm5Ghs3rsbGZpDeftduPgLkeornYnuIxVpNG9jz24ZgHX3G2lriJuILYfN2VzTqEpx9p5C6cw7JGwOwbdsbxdGVmNs6knfzpBSSEQm4xPBdk0ZNebQnSGcKDpB9OBj/0QW0Lofsk8nkJCbeYuDAr5gzp9Doh23KFFAqBXiioZBMmebqt2gXjO3ff4Dg4IpzX9rkacaeeWUFZLrnz0ehyK3y4Llvn/BxHzNGWJeUJAzkBw+Cv7+ITRdCREOHCknZHj3KBnfjRUK69/KyItGilS8sqmxcsWvnS/6t02Se+lXHIUzfHUTG0VXUqOFMvpYusfPgEOm3QrhmHSbVrLHz8i/jfmntTdbZCFwGBJG6ZbIet4soaWjf6Uvs3hHGsuz4XZTkpBm913+ch14VQQptz1gs/jH2oEQ62xOHthAXc1AqEsrMzEbm0khHyCDh4UOd49h4CAOCVf02VM9/ytChQ7Cv7sialUFYNPTEOvMh/fr2en0x9Nz/nIdeFc+npEQYkLS98RMnypAthpJ2XbtCx46wf78pV67EU79+LU6f1j92y2b1JaWgytqmS+cOTJ0y3qA+Y/q+RXTs8CHpiiSSI3/A7O1PkBdk0qN7T2LPRjJqzHTu377AmSvXMW/SiazYCImAK2XNMPr1/Yxdu3dgbwBlY+Xhw659W+jWrUQLSmhO/frTmTmzcgpiHx8hDBUeDgqFgP4QE4R79woDoIWFhpCQ1fj7+0sCFC9LniaY/jOvqIBM0Hw1JSxsC+7ubwGFVUbZ2NrC/v0meHn14LffYliyJBdTUw2dOglyctpMiWI4bto04QMgzlyuXzdWJKS77Of3eZWVs7StvIdurBDIqo0PNqXQZNu2PmTFRugIojj1mYpiayD9fXvy8ME9Yg31wahlVO86DJu3yy5CdDQcu41CnZeBRibD8ePhOttFScPs+F3YvtOH4j9vknlmC3ILG6P3+o/z0AcNGS95ZFatvMg9tZ7PPh/GgajdKO7FYtGyO1nRa/lu9DSuJjwiaMFcIw+ql/QBoOZb7N2/G5f+M6RycjtTK4qTbuFcSrWaEh6IedNOoIHnv43Ftp0viqMrcHj/a8yc6/Fs70LGjhnBzVvXpARH2pYpRmWkXtZDt7WOpX6tZAMtqeR1eugiqsXMTKhSNJbMBIiIENZrD1oiXavh6sgy6bS8PCXu7rUpKdGXmIMyD70qbXP8yE4uXDyDY7dR0jYRfmjdoiu37l3myy+/YfNvIRSfDaNjZz8+9u5N3YYt2bAuGGVxHo59hBJ/kacFwLJ1T7Zuj8DJCMrG2sOX5G0n+X3HQz7/TIOjY1eaNQvF1NSV8PC+lc5wevUSPNhx42DxYhMOHTIjO7sQa2uBGmHtWpDLC8tJxb1bZa+0KmX7Xl7vlsq3rSQgILIUBWODn9/nxMd/UyrgLRyjKrKD+/aBSmXC9u3hkpzg2LEzefHC+O/Ej1txsVBUFRgIcrmcS5fEmYnh/puY+JDMTAU7dijp2LFivpvyIS3RQxdn5f36f8PevRHSuJJxdCXNW3fhdlwkuTdPYdvWh4zj67Hv8Bkpa4Zh2cZHqkGxaNmdzZvWoSopMRyu8ehJ7tXDWDf/SHJS0vctwtTelczYCDRFeTqsjeJgLlahJm8aT0rYZFSZybj0n1Fhpej/M9jim2+9rVmzIRLAIIJENEPbyihxDzJ9+hzaeHhy5XqiFGsdMHC0RA7/9M/HTAmcSHaJHLPmn5B7aj3jx09lx85InmUXY9GyOxlHV2HVtFOlUMiMU7+BugRLd08K7p3D1KkuDu99RfrehVi6v0PhgwvU6D0Zy3qtpd9wKZKdu45U6b4Mbbtx6zrN3BeBRp9O1sysFu3bX8bU1A79OKn238aWdf+Oitqv460Zg6SJ5uOj63GBIOb744/CoF1eOk00EUp348b1Ug/dSW8fmfl1ZDJ5pe12+9pJVq4KxappJx14l8ixocp4hkytokTxFIsm7+KQ8xClSsPQIcP5+ed5mDZ4h+K0h9T6Zpn0sommUZeQvHkiVs0+wP6dvtJs0LK1F9YevhIEkosruHJhFm+8MUZqTxMTG44c0VQJrujgYMWiRfOZNCmQ2bPzjbbZjBlWxMfHEBKymhcvKo59r1tnhpPTkFKUSFX7RkXbCklMfIanZ/sKr3HqVFP27NlLly6dpd85O9cnOLhizz4pSfi47dwJK1aAUtmDXbt2GL0mgXJAQGDVrq1kzRohlCdy2JTNMsxKceiZOueTmf3OlSvZAl9KQ09qq9NZseI3Zs78gXOx0ZjXaYazvJA5s+czYcJoFBkZ2LT2QplwjHEBkwkLDyNLJcfs7U/Ijl6NXG6Cg2+gUXhtSnggVk07SQ5DVvxOsmK3SlrENUrHnsLH10jd+RNWTd6VoLdKRRKpO36iRvdRVKvbkucbx1H0/J5B2OI/svTfxMSELp/0YeeuI7TxENLncrkJX3w5iJ27juiUYtd5ox4Tp5SWal+K5NuRgfTw8mXVqk183LETXIxk6pQZUpmusRJzxbHVUKLEuc9UaniNwdTRjZLUB6TtnFO6zh/T6nVQpj6WfpN7ar1OWfrLmkaTTdN6/zI4mNeqNYyOHcXB/K9bYuIDBg78htmz8/XK9IcPFwbn+fPLSrYTEiA/X188QeQYEZV3tC0pSfCUpk8XptetW3swatRXFZaBV2R3b19l1epQXPrPoIaXP2ggdfu/Sd0xW3pOAPLqtVGbW2LVrDNJz56R61CfBQt/wr63QMIlN6tGzsV9gme0bji5F7XoHtr6kB27VSrf9v9uMDWehaPY8j3pWyeSc2wpitQiGjWaip2dA4MHjyAx8UFpeKLi609JAWtrM+Ljz3P16lW8vCpmIfTyUhIauvI/VgZf3tzdGxIWFsaMGVasXWtKUpLwUUpKEipOp041Zdmyn7UGc8FeJvYOAglZbOxZo/sKfXWg1Fe9vYV+pVQKH4Xu3cW4fD/i48/TsaOF3jGuXL6hR92xaMG/uXjhNM79Z+DSfyYZxXD+/Fkitx/kuxHfI7t/hvlBS+nh5atD/2BjVx0z9/Y6UYCUNUPJit8p9SOb1t3JuVxGJ2HXzhczpzewatIJleIZKeECRUDqjtmYVq9NwYOLJG8cj1KRREnOC+RmFpjY1ABAXWR4Vgv/wMKil92vqseoqEgoafUwACzeeFv6kioVSaTtnINjt5E6XryIpngdhUXVzBNpXNe/XMvJadfuBDY2b/NXCobK71e+aMWQrVkjIFMcHQV2RLUafvmlQMfzSkoSXqb163U9d+0QjLe38YIUgIJCV+4/XQ/IKmy3HwNHoK7VFEcvw54MCM9E4OlpJvFvWNRpTvKWyVi/+QF27/hKsy95iZIvB3zLqZhDZBSDRcvu5J5cT7t33ufy5fNMGN+OLh8cJi5Ow5w5wgxFG71y4IAQ+5bJzPn44w+xtj5RBS96EMHBC3F2rkdwcMVhFKHIx4bU1MelIt7f4OWlMhL7Xo2Xl0hrWNW+UdG2suUNG8Lw9x+PTFZCfn4ZAZiZmSlnzpiVhoY+ln7n7PxWle5N9NBVKujRQ4ZKlW7wOioL4YCgluXkNJjg4IXExr6FUpmqs33AoDfIdWha5QJFMP7OHjx8ihuX95OpFMjiso6vxcrSijyVBlN7Z0nOUG5iil37z6RQb+6NaLJiI3Ds9j3p+xeBTIZD5yHkXT1MSYkSTUEOGrUKVMXCbDPzGa5+QTxZ1AeNuuR/o7DoVfnQKztGhZAlj17k/BFF4b04ktP/xMlnvICm0OJML18y/joKizTqAjTl+qyZmRM2Ntp1zH+9pB+qlULSygZzQwnN9u0F3u2RIwcTHz+OkJAQPdibm5sQC9X2yLQJqiqDMrrV8SYxaRitmjeQ9tMus3d1ULI4aAID/QYj06jIvx1L4bO7yGQynPv+gJsewmUF5rXepCQnXVddphRhYO7SgOwTa7GpVo3BQydL3PYiLDIoKJg2Hu+Ql90Pq2qHSEoSyKUWLtS/l549hUE1NraYPXuOYG4u4Lj/9S/9/IPI/x4fP46XU4nKo6IyeD+/AcTHj8HdvXalz/zlt1UjMfEBkyYFsmBBiYHZhIpu3VQMHDiE+PjzUhK3KvBI7cRlGTmX4euoCnzSy0tFQMB2Fi+eh0qlz1K6eOFkZv20/qWoO8D4OyuqpYkaCR3eacXMfwfy+HkyGSd/wwQ1X3z+L06e3E+yCHU8shJbDx/S9y0sg0FeO4x1q09QHFoOMhlyMws92buK7B+XFH3Z/apyjMogS7Ztfci9EU2Txs149OguaTvn6AzmoC9HVh4++SrXW838GY3r6l0OlXOeV7TN8H7a0+KKEpoyGXTv3hl399pGYW8ODrocL9rix4ZMhDJuDHuXXv1GczruJhqNME0WuezNGngSMH40L9JTMavVlKAFs7Fs3BEKr6DKTse6SUdeRC3D1S9IioW/OBSC9VsfUvjkKm4jyhQctD++GQeD+eLLb/F8tyun425KfVKbw/7mncu81eCudC/e3sI1a3/0MjMFGoSGDQXe7latymB5I0YINLLdupX3on8tHXgLXyLRaY34/N3dXQkOngNgNDQAAA0iSURBVF8KTRSfpbZHXfEzf7ltwrKoCVp5aCi4VOxbn+e9vJVPXEZFmeLn9xnG+nlVQzgKRQ6xsXXRaHTrBDQaGRcuJ5GdnY2diRWZ+xbiPHiZ1C+cek8m63AILdt6IbdwqXSc2rFzL3NLy/tnzlnD6bibnIv/g/TUVEryssDEDAdHZ54k55L8PAmzN1qgOLISi3qtyL9zRgcGmXNpP5mnNiEzMcVES59YlL1L37sQ0+q1jN73/++hVwBZ0i4xt3vHl9tHVmJuYYGjAQEMG4+eZEavRZ2fg227Xq+lsMiQhy7Y6yXdgmoSJA0q96YHDhxBfHx8Kexti8SHLU7927cX5NNGjBB+WxWSrp49YYz/NX6cUV+6f23IqZDtD8CsdlOKk+9L6KPnGwOwavIWjh9/q6coZOPRk6wz4Th/+oPOubQ/voYIvrRNIN2yQFOsey+VoXjEEJIIy5s4UYB31qhhh5/fl8THf1sKBxSsKvA7ocjnC17XM3+VY1TNO1YSELCd4GDBm9TuJz16KPH2Vunxt4hKROVnLoauo6rwSTs7jd5gDrYcPDqANStXYVK/Lfn3zuHUd5qELLF09+RF1DKs2/iQ/PiszgwbDBc5GtJEWLN8rkAGWIqcy4ycxoUz27Fo1IHi1IdYvfmeTiI0afUwqbgtL+E4Zs71sevwmaRP7NQzQBKUfr5xnNH7/j/toVcGWUq5fYzkG2WVYAD2vaYYkY7qTdHtMxRe2o3y/lk9+OR/u4cuQNI2UVSkqtSb1vbAvLw6Ex9/hNDQ3yTYm729FcXFRbz3noDDrrJ6fWaeTltpQ05lMjlOvaeQtmeBTujErp0vWWcjSjmpu5N1NkIa0O3a9ib/VgzK1McS8giEEJoII7Np68OzrbE6qkeiiddhZprGm/WFdVlZAithlUNIbsI+/fqZ4eTkR3DwEq12L5R+6+8/pJSnpbIin28w/PzL//33eOgv4x1rQw61+8mYMeFkZhZI8MyQECEBv25dWfxfnLkYuo6qwCcNYc8VWd04efp9Vi5fIEGLixVPybtxspxYRSBqjYb0ArVOgWJFRY7lldCwcsBKy7u26eZP+o6fUKU8ALkcZdojUsKnYtNSUM+q3mUo2Rd2k5dwHKu33if7bCSazCSsW3Yn48gKFHvm4zrkl4obnn8obPFl9jO27fKl85VCljRqFamFMtQFWSA30YMXvTgUgk2bntiV8jDk3ohGFRfBV18N1oNPvsr1atS30Cj767SbmZkLnTo9LP3r1eBnhvZLTHyAp6cnKlUBK1ZUrCgjJOfsSE19bvT42ko2+/cr9djuDB1zjL8Nu/efl+5fG3Jq022MDoUo6OJ10UDa7nlgYoZ9+/46zIpZsRE6IReNuoTksElYvfk+9p59S6GHkcycs8bwc9A8R1MsJPk+/VTwvB0djbNMQhnboEgIVcZaKEJf9J+DNhmafqLTjLCwMLy8Ouu1tfYxXhaq+rLHcHauWSUIYln/MHy8xMQHhIYGEx6+XSf+P2aMofj/y8Mn9RkWXZBbnODrr/uhsKqjkwxN37tQJ1wqghvsOw2AiwLsGPTf2cVBE/SOlX1gEdad/4WJTQ1eHFyGRlOCU8/xUt/VqEt4ERVKUfJ9bFt+Qs7lfdQorR5NXTuMjm3bcvXaH3zhNxJKcgjbvJ6CwkIdGoGKYIv/Jz10Q1/WaYETuXw5DufSKVLalil0atUa7twg09QRq45fkhW7VeeratOyG9mxERTfi8WylRe5J9fT4YMBvNlCKFXXjsn+t3vo7u61CQtbTc+eg17SAzN8fG2PTK3exN69SkbqR6okO3AAWrftrDeb6dB5ME/uHOVGaZxT27RDJ09XDkFTosKx63Byrx6WyvgVR1ZhUa8lT5cPwvadvtLH19ajJxnH1mBiZUfuyfUM/26q0eeg7aF37QpRUQL+viIrzwNeVrlp3LsW2iyG0NBQAgJ26lDdxseP1KKSxegxKl9+1W3aM7mqhIbEGLjh47m71yY4+N+lcXZxm6H4v/51CH3111KUjxIvL5XOx+/gQTmBgUU6Hx2lSsbt+48YNGQ8y0ODeBE+BXuvcVIYQzQBorwKW4+eOjNsqLjIUaSd0K4Ste/kR9qOWWTsCcKllDai6M8EaTagX8jWi3sPYpk+exWx8bdxdZBTVFysM5hXZv8nY+jaMXNjGqNWrb24cimS7b9HsWD+DE7HbMT16yXkXNovEXzlHAnlywHf4mBvwbZtYQQFBesRfP2V6/1PxtBBIH6qXt2a5OS8KiTnjKMQxGV392YEB4cwZsw4PD3b88EHxj2qgwflrFz7PW51hC+YNvH/gWsXDQpcaIdObNv2IvePw+Sfi8C6dU/UGsiKjcDWoyc5l/fz0YddiTkTier+WSxa9iDv1AZ6effiZEwEQUHBesIEopWPoffpI5CQvSwhVFnlZsXPwd39LYKDlxAcvKJ03ctoxVa07fUcQ6Dw3VpJaKh8DPz1X6+A8oknNDSYgABdLz86uhNZWYN1fm1maio82+b1cXIOJmrPBoNl+lmHQ/iocxeuXjvHd6OnSTNs0V6GdiLr0BJMzCyw6ypM5bRnlBXRkFSkS5pzNJSSYuMf039kYdFfNW29P2Mao2JR0NU/LhITcxLbj4ZKyVG3EeuoVrcllq19OH3mCJ99PpCdu47g0bZ9JWd+WauGqsSWMpFbGdWq1X/N59C1gQP9iIoyq3AfwQP7ssrHLCtIsWTdOjOdgpS1a4XpceC0j6TBXLTLl85LGq3G0EdoEBKh7XyRm1vw/7V3byFSlnEcx7+/2d3RXd12ld02atUtViGzC1Mi6cKiCAlakkASJETxwigCw6vIDl5YRrk3QYlCkGRZQUgnhVhZEJeydCWNZPO4au7B44K5p38XM9rM7ui8O7Tzzr77/8DAzPDMy3//88x/n3nmfZ9nwbx5VJ3/lf62fVQ8upSe377l2cXLeOOtjezevZcXFjfQ37KdDRs2sWbt66x/Z2uA962Yvv5KIPEVvrycQBcNpS4IlcjZkiDpKmi3ey+3bClh3bpStm3bdvOUxdGOpbFxIx0d5+jv76Gj4wSNjZuor6+nuDh1Na4Y6L8RStvR32lu3sOkhSuGHbNs7jO0nz3DV1//mHaB4q20HtxPa+vPw471z8lD9F2/njZIvNi0ldL69AL990cr0i5kK5nz1G33JX3lxZcZ6Oke+hXmptDm0CV1AiezNhxddRSVTIlXz0j7x9bbcXyQwYFTQC+ovnjq3bFYvHT4q83o6z49aP29Z4HUHQ6qgK7hLxgTJkjMrq0lVprhT752DdrbGTTjCDD0FIJs7pKIA1PNKJIYAC6YcT7zsfRAbOLkicUVNaDEFXL9V85TVFZJUVkFSAxcu8JAzwXi1XWJ+5c7+8AOATWgGrDjwNURxnlbEtMrK6mqrs6wnXxSVxeYQXV1oJyNxf4yQaKGwO9lTkYjL+U5fqZHdKy+rtOoJE7xHSl999K5QWJFMcWKiJXewcDVrsSkunRdRfGJsUkVsYHLnYNgbaT32aF9eYaZVWcKKLSCHmWS9pvZ/OwtxxfPS2ael8w8LyM3LqdcnHMuirygO+dcRHhBHx2bww6gQHleMvO8ZOZ5GSGfQ3fOuYjwEbpzzkWEF3TnnIsIL+g5krRI0p+S2iQNW0Rd0hpJRyQdkvSTpBlhxJlv2fKS0u45SSZp3JyWFiQ3kpYk+81hSZ/lO8YwBPgsTZfUJOlA8vP0dBhxjglm5rcR3oAi4C/gPiAOtAKzh7R5HChL3l8NfBF23IWQl2S7cqAZaAHmhx13oeQGmAkcAKYkH98ZdtwFkpfNwOrk/dnAibDjLtSbj9Bz8zDQZmbHzKwX+BxIW/TBzJrM7MYlui1AbZ5jDEPWvCStB94ldf3Y6AuSm1XAh2Z2EcDMOoi+IHkx4MbmuRXA2TzGN6Z4Qc/NPcDplMftyeduZSXww6hGVBiy5kXSQ8A0M/sun4EVgCB9ZhYwS9JeSS2SFuUtuvAEycubwDJJ7cD3wP+7+3WEhLba4nghaRkwH1iYrW3UKbE33AfA8pBDKVTFJKZdHiPxja5Z0oNmdinUqMK3FPjEzN6XtAD4VNIcMxsMO7BC4yP03JwBpqU8rk0+l0bSk8BrQIMN3wsrirLlpRyYA+yRdAJ4BNg5Tn4YDdJn2oGdZtZnZseBoyQKfJQFyctKYAeAme0jsZZuVV6iG2O8oOfmF2CmpHslxYHngZ2pDSTNBT4mUczHw1woZMmLmV02syozqzOzOhK/LTSY2f5wws2rrH0G+IbE6BxJVSSmYI7lM8gQBMnLKeAJAEn3kyjonXmNcozwgp4DM+sHXgJ2AX8AO8zssKS3JTUkm70HTAa+lHRQ0tBOGjkB8zIuBczNLqBb0hGgCVhrZt3hRJwfAfPyKrBKUiuwHVhuyVNeXDq/9N855yLCR+jOORcRXtCdcy4ivKA751xEeEF3zrmI8ILunHMR4QXdOeciwgu6c85FxL/Bx+U8D8/MPQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# SVM Parameters\n",
+    "C = 1\n",
+    "sigma = 0.1\n",
+    "\n",
+    "model= utils.svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n",
+    "utils.visualizeBoundary(X, y, model)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load from ex6data3\n",
+    "# You will have X, y, Xval, yval as keys in the dict data\n",
+    "data = loadmat(os.path.join('Data', 'ex6data3.mat'))\n",
+    "X, y, Xval, yval = data['X'], data['y'][:, 0], data['Xval'], data['yval'][:, 0]\n",
+    "\n",
+    "# Plot training data\n",
+    "utils.plotData(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dataset3Params(X, y, Xval, yval):\n",
+    "    \"\"\"\n",
+    "    Returns your choice of C and sigma for Part 3 of the exercise \n",
+    "    where you select the optimal (C, sigma) learning parameters to use for SVM\n",
+    "    with RBF kernel.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        (m x n) matrix of training data where m is number of training examples, and \n",
+    "        n is the number of features.\n",
+    "    \n",
+    "    y : array_like\n",
+    "        (m, ) vector of labels for ther training data.\n",
+    "    \n",
+    "    Xval : array_like\n",
+    "        (mv x n) matrix of validation data where mv is the number of validation examples\n",
+    "        and n is the number of features\n",
+    "    \n",
+    "    yval : array_like\n",
+    "        (mv, ) vector of labels for the validation data.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    C, sigma : float, float\n",
+    "        The best performing values for the regularization parameter C and \n",
+    "        RBF parameter sigma.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Fill in this function to return the optimal C and sigma learning \n",
+    "    parameters found using the cross validation set.\n",
+    "    You can use `svmPredict` to predict the labels on the cross\n",
+    "    validation set. For example, \n",
+    "    \n",
+    "        predictions = svmPredict(model, Xval)\n",
+    "\n",
+    "    will return the predictions on the cross validation set.\n",
+    "    \n",
+    "    Note\n",
+    "    ----\n",
+    "    You can compute the prediction error using \n",
+    "    \n",
+    "        np.mean(predictions != yval)\n",
+    "    \"\"\"\n",
+    "    # You need to return the following variables correctly.\n",
+    "    C = 1\n",
+    "    sigma = 0.3\n",
+    "    \n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    opt_val = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]\n",
+    "    predict = np.zeros((64,3))\n",
+    "    count=0\n",
+    "    for i in range(len(opt_val)):\n",
+    "        C = opt_val[i]\n",
+    "        for j in range(len(opt_val)):\n",
+    "            sigma = opt_val[j]\n",
+    "            model = utils.svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n",
+    "            predictions = utils.svmPredict(model,Xval)\n",
+    "            predict[count][0] = np.mean(predictions!=yval)\n",
+    "            predict[count][1] = C\n",
+    "            predict[count][2] = sigma\n",
+    "            \n",
+    "            count+=1\n",
+    "            \n",
+    "    row = np.argmin(predict[:,0])\n",
+    "    C = predict[row][1]\n",
+    "    sigma = predict[row][2]\n",
+    "    # ============================================================\n",
+    "    return C, sigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0 0.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Try different SVM Parameters here\n",
+    "C, sigma = dataset3Params(X, y, Xval, yval)\n",
+    "\n",
+    "# Train the SVM\n",
+    "# model = utils.svmTrain(X, y, C, lambda x1, x2: gaussianKernel(x1, x2, sigma))\n",
+    "model = utils.svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n",
+    "utils.visualizeBoundary(X, y, model)\n",
+    "print(C, sigma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EMAIL spam classifier "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def processEmail(email_contents, verbose=True):\n",
+    "    \"\"\"\n",
+    "    Preprocesses the body of an email and returns a list of indices \n",
+    "    of the words contained in the email.    \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    email_contents : str\n",
+    "        A string containing one email. \n",
+    "    \n",
+    "    verbose : bool\n",
+    "        If True, print the resulting email after processing.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    word_indices : list\n",
+    "        A list of integers containing the index of each word in the \n",
+    "        email which is also present in the vocabulary.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Fill in this function to add the index of word to word_indices \n",
+    "    if it is in the vocabulary. At this point of the code, you have \n",
+    "    a stemmed word from the email in the variable word.\n",
+    "    You should look up word in the vocabulary list (vocabList). \n",
+    "    If a match exists, you should add the index of the word to the word_indices\n",
+    "    list. Concretely, if word = 'action', then you should\n",
+    "    look up the vocabulary list to find where in vocabList\n",
+    "    'action' appears. For example, if vocabList[18] =\n",
+    "    'action', then, you should add 18 to the word_indices \n",
+    "    vector (e.g., word_indices.append(18)).\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    - vocabList[idx] returns a the word with index idx in the vocabulary list.\n",
+    "    \n",
+    "    - vocabList.index(word) return index of word `word` in the vocabulary list.\n",
+    "      (A ValueError exception is raised if the word does not exist.)\n",
+    "    \"\"\"\n",
+    "    # Load Vocabulary\n",
+    "    vocabList = utils.getVocabList()\n",
+    "    \n",
+    "    # Init return value\n",
+    "    word_indices = []\n",
+    "\n",
+    "    # ========================== Preprocess Email ===========================\n",
+    "    # Find the Headers ( \\n\\n and remove )\n",
+    "    # Uncomment the following lines if you are working with raw emails with the\n",
+    "    # full headers\n",
+    "    # hdrstart = email_contents.find(chr(10) + chr(10))\n",
+    "    # email_contents = email_contents[hdrstart:]\n",
+    "\n",
+    "    # Lower case\n",
+    "    email_contents = email_contents.lower()\n",
+    "    \n",
+    "    # Strip all HTML\n",
+    "    # Looks for any expression that starts with < and ends with > and replace\n",
+    "    # and does not have any < or > in the tag it with a space\n",
+    "    email_contents =re.compile('<[^<>]+>').sub(' ', email_contents)\n",
+    "\n",
+    "    # Handle Numbers\n",
+    "    # Look for one or more characters between 0-9\n",
+    "    email_contents = re.compile('[0-9]+').sub(' number ', email_contents)\n",
+    "\n",
+    "    # Handle URLS\n",
+    "    # Look for strings starting with http:// or https://\n",
+    "    email_contents = re.compile('(http|https)://[^\\s]*').sub(' httpaddr ', email_contents)\n",
+    "\n",
+    "    # Handle Email Addresses\n",
+    "    # Look for strings with @ in the middle\n",
+    "    email_contents = re.compile('[^\\s]+@[^\\s]+').sub(' emailaddr ', email_contents)\n",
+    "    \n",
+    "    # Handle $ sign\n",
+    "    email_contents = re.compile('[$]+').sub(' dollar ', email_contents)\n",
+    "    \n",
+    "    # get rid of any punctuation\n",
+    "    email_contents = re.split('[ @$/#.-:&*+=\\[\\]?!(){},''\">_<;%\\n\\r]', email_contents)\n",
+    "\n",
+    "    # remove any empty word string\n",
+    "    email_contents = [word for word in email_contents if len(word) > 0]\n",
+    "    \n",
+    "    # Stem the email contents word by word\n",
+    "    stemmer = utils.PorterStemmer()\n",
+    "    processed_email = []\n",
+    "    for word in email_contents:\n",
+    "        # Remove any remaining non alphanumeric characters in word\n",
+    "        word = re.compile('[^a-zA-Z0-9]').sub('', word).strip()\n",
+    "        word = stemmer.stem(word)\n",
+    "        processed_email.append(word)\n",
+    "\n",
+    "        if len(word) < 1:\n",
+    "            continue\n",
+    "\n",
+    "        # Look up the word in the dictionary and add to word_indices if found\n",
+    "        # ====================== YOUR CODE HERE ======================\n",
+    "\n",
+    "        if word in vocabList:\n",
+    "            word_indices.append(vocabList.index(word)+1)\n",
+    "\n",
+    "        # =============================================================\n",
+    "\n",
+    "    if verbose:\n",
+    "        print('----------------')\n",
+    "        print('Processed email:')\n",
+    "        print('----------------')\n",
+    "        print(' '.join(processed_email))\n",
+    "    return word_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------\n",
+      "Processed email:\n",
+      "----------------\n",
+      "best bui viagra gener onlin viagra number mg x number pill dollar number free pill reorder discount top sell number qualiti satisfact guarante we accept visa master echeck payment number satisfi custom httpaddr\n",
+      "-------------\n",
+      "Word Indices:\n",
+      "-------------\n",
+      "[176, 218, 707, 1174, 1120, 1120, 477, 1120, 681, 460, 1711, 1475, 1120, 1347, 739, 1819, 10, 1795, 1012, 1227, 1120, 388, 799]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  To use an SVM to classify emails into Spam v.s. Non-Spam, you first need\n",
+    "#  to convert each email into a vector of features. In this part, you will\n",
+    "#  implement the preprocessing steps for each email. You should\n",
+    "#  complete the code in processEmail.m to produce a word indices vector\n",
+    "#  for a given email.\n",
+    "\n",
+    "# Extract Features\n",
+    "with open(os.path.join('Data', 'emailSample1.txt')) as fid:\n",
+    "    file_contents = fid.read()\n",
+    "\n",
+    "word_indices  = processEmail(file_contents)\n",
+    "\n",
+    "#Print Stats\n",
+    "print('-------------')\n",
+    "print('Word Indices:')\n",
+    "print('-------------')\n",
+    "print(word_indices)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/README.md b/README.md
index d57e28b33..5aba70232 100644
--- a/README.md
+++ b/README.md
@@ -183,4 +183,4 @@ This repository contains the resources that are discussed during the weekly meet
 - [Aayush](https://github.com/aayushsynth)
 - [Satish Agrahari](https://github.com/Satish124816)
 - [Anurag Kumar](https://github.com/iamANU)
-- [Kartikay Goel](https://github.com/krtky123)
+- [Kartikay Goel](https://github.com/krtky123)