From c4af94b5abc0af4d332181bf13b3843f159472c6 Mon Sep 17 00:00:00 2001 From: Aishik13012002 <51096112+Aishik13012002@users.noreply.github.com> Date: Thu, 2 Apr 2020 19:05:52 +0530 Subject: [PATCH 01/14] Update README.md --- Phase 3 - 2020 (Summer)/README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/Phase 3 - 2020 (Summer)/README.md b/Phase 3 - 2020 (Summer)/README.md index 0036ceda5..6888542fc 100644 --- a/Phase 3 - 2020 (Summer)/README.md +++ b/Phase 3 - 2020 (Summer)/README.md @@ -1,3 +1,9 @@ +Aishik Rakshit +190122002 +CST +https://github.com/Aishik13012002 + + # IITG.ai - Resources include course/blog/tutorial/research paper links as well as details regarding graded assignments as well as prospective project ideas. 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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", + " h=sigmoid(X@all_theta.transpose())\n", + " for i in range(0,m):\n", + " k=np.max(h[i])\n", + " for j in range(1,num_labels):\n", + " if(h[i][j]==k):\n", + " p[i]=j\n", + " \n", + " # ============================================================\n", + " return p\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Set Accuracy: 83.42%\n" + ] + } + ], + "source": [ + "pred = predictOneVsAll(all_theta, X)\n", + "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# training data stored in arrays X, y\n", + "data = loadmat(r\"D:\\Github\\Learning-Content\\Phase 3 - 2020 (Summer)\\Week 4(Apr 19 - Apr 25)\\Exercise3\\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", + "displayData(sel)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "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(r'D:\\Github\\Learning-Content\\Phase 3 - 2020 (Summer)\\Week 4(Apr 19 - Apr 25)\\Exercise3\\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": 65, + "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", + " \n", + " a2 = sigmoid(X.dot(Theta1.T))\n", + " a2 = np.concatenate([np.ones((a2.shape[0], 1)), a2], axis=1)\n", + " \n", + " p = np.argmax(sigmoid(a2.dot(Theta2.T)), axis = 1)\n", + "\n", + " # =============================================================\n", + " return p" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Neural Network Prediction: 4\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if indices.size > 0:\n", + " i, indices = indices[0], indices[1:]\n", + " 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!')" + ] + }, + { + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 3c28a2abc141733020d1983a5004be2555402756 Mon Sep 17 00:00:00 2001 From: Aishik13012002 <51096112+Aishik13012002@users.noreply.github.com> Date: Sat, 25 Apr 2020 15:24:48 +0530 Subject: [PATCH 09/14] AIshik Rakshit 190122002 w04 From f549cfb5e3629ba7643b1ab0bf79a436cb699b6d Mon Sep 17 00:00:00 2001 From: Aishik-GIT Date: Wed, 29 Apr 2020 01:49:48 +0530 Subject: [PATCH 10/14] Create submission.cpython-38.pyc --- .../__pycache__/submission.cpython-38.pyc | Bin 0 -> 3494 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/__pycache__/submission.cpython-38.pyc diff --git a/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/__pycache__/submission.cpython-38.pyc b/Phase 3 - 2020 (Summer)/Week 4(Apr 19 - Apr 25)/__pycache__/submission.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3052f54ed3b81b8e7f80a6ef0b4b9064d2a1a819 GIT binary patch literal 3494 zcmai0&5s;M6|d^=>6sm`@-Q5_EgWhy*<<8 zsvg_hn&C2$;D*GR!!9}b@8HUTbDfYl-t{JkZq=(-->+W%-p9R_m5||k z^!G16|5ul>e^cZ5XQS~xiuxQxFu@Bp;xXsMTZJ{UW83g{;lvL5?81#*)K1}z{McWP z1#!!ag>m=+6Rz;SV8UBiaa;Hz_=3e9VQ;fu>nohWdR(`rvdG4RQe!oI5@~{4v)R%!MOXy_ZdK)OHttV$ivel-@FSkC z^a~XA1CSY?v4qVmP+SevGSse|1-0DDGqo?+HhaOhdVxNbLh_yQC>vLaDyBO+oTRc! 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The bias term is assumed to be concatenated.\n", + "\n", + " y : array_like\n", + " Function values at each datapoint. A vector of shape (m,).\n", + "\n", + " lambda_ : float, optional\n", + " The regularization parameter.\n", + "\n", + " maxiter : int, optional\n", + " Maximum number of iteration for the optimization algorithm.\n", + "\n", + " Returns\n", + " -------\n", + " theta : array_like\n", + " The parameters for linear regression. This is a vector of shape (n+1,).\n", + " \"\"\"\n", + " # Initialize Theta\n", + " initial_theta = np.zeros(X.shape[1])\n", + "\n", + " # Create \"short hand\" for the cost function to be minimized\n", + " costFunction = lambda t: linearRegCostFunction(X, y, t, lambda_)\n", + "\n", + " # Now, costFunction is a function that takes in only one argument\n", + " options = {'maxiter': maxiter}\n", + "\n", + " # Minimize using scipy\n", + " res = optimize.minimize(costFunction, initial_theta, jac=True, method='TNC', options=options)\n", + " return res.x\n", + "\n", + "\n", + "def featureNormalize(X):\n", + " \"\"\"\n", + " Normalizes the features in X returns a normalized version of X where the mean value of each\n", + " feature is 0 and the standard deviation is 1. This is often a good preprocessing step to do when\n", + " working with learning algorithms.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " An dataset which is a (m x n) matrix, where m is the number of examples,\n", + " and n is the number of dimensions for each example.\n", + "\n", + " Returns\n", + " -------\n", + " X_norm : array_like\n", + " The normalized input dataset.\n", + "\n", + " mu : array_like\n", + " A vector of size n corresponding to the mean for each dimension across all examples.\n", + "\n", + " sigma : array_like\n", + " A vector of size n corresponding to the standard deviations for each dimension across\n", + " all examples.\n", + " \"\"\"\n", + " mu = np.mean(X, axis=0)\n", + " X_norm = X - mu\n", + "\n", + " sigma = np.std(X_norm, axis=0, ddof=1)\n", + " X_norm /= sigma\n", + " return X_norm, mu, sigma\n", + "\n", + "\n", + "def plotFit(polyFeatures, min_x, max_x, mu, sigma, theta, p):\n", + " \"\"\"\n", + " Plots a learned polynomial regression fit over an existing figure.\n", + " Also works with linear regression.\n", + " Plots the learned polynomial fit with power p and feature normalization (mu, sigma).\n", + "\n", + " Parameters\n", + " ----------\n", + " polyFeatures : func\n", + " A function which generators polynomial features from a single feature.\n", + "\n", + " min_x : float\n", + " The minimum value for the feature.\n", + "\n", + " max_x : float\n", + " The maximum value for the feature.\n", + "\n", + " mu : float\n", + " The mean feature value over the training dataset.\n", + "\n", + " sigma : float\n", + " The feature standard deviation of the training dataset.\n", + "\n", + " theta : array_like\n", + " The parameters for the trained polynomial linear regression.\n", + "\n", + " p : int\n", + " The polynomial order.\n", + " \"\"\"\n", + " # We plot a range slightly bigger than the min and max values to get\n", + " # an idea of how the fit will vary outside the range of the data points\n", + " x = np.arange(min_x - 15, max_x + 25, 0.05).reshape(-1, 1)\n", + "\n", + " # Map the X values\n", + " X_poly = polyFeatures(x, p)\n", + " X_poly -= mu\n", + " X_poly /= sigma\n", + "\n", + " # Add ones\n", + " X_poly = np.concatenate([np.ones((x.shape[0], 1)), X_poly], axis=1)\n", + "\n", + " # Plot\n", + " pyplot.plot(x, np.dot(X_poly, theta), '--', lw=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OsrT4d2PLO3V3AP4qIp4BNucSlZmZ5SZLi///ArdLup7kUYqvAc6StAPwwxxjMzOzHIzZ4o+Ii4BXAt9Op1dFRH9E/C4iPlRtO0lfkfS4pDtHLfuEpF9Iuj2d3liPP8LMJp9SqcTSxYuZXSwyraOD2cUiSxcvplQqNTu0tpd1OGcHyVO3fg3sLek1Gbb5KnB4heXLI2L/dLo64/HNrIUMDAwwr7ub6f39rB4eZnMEq4eHmd7fz7zubgYGBpodYlvLMpzzbOAfgbuAP6WLA7ih1nYRcYOkudsYn5m1mFKpxDELF7Jq0yYOHrW8CzirXOaIcpkFCxeydmiIrq6uZoXZ1rK0+N8KvCQi3hQRR6TTgm045omShtKuoBdUW0lSn6RBSYMbNvgRv2at4rxzzuGEcnmLpD/awcDx5TLnL1/eyLBsFEVE7RWkAeAdE3nkYtrivyoi9kvnZwNPkPxi+CSwa0S8d6z99PT0xODg4HgPb2ZNMLtYZPXwMLXa8iVgfrHIo0891aiw2pKkdRHRs/XyLKN6NpGM6rmWUcM3I2LcFTsj4rFRAV0IXDXefZjZ5PbExo3sOcY6c9L1rDmyJP5V6bTNJO0aEY+ks28jeY6vmU0hM2fM4MExWvwPpetZc2Spx3/xRHYs6evAIcBMSQ8DHwcOkbQ/SVfPA8D7JrJvM5u8jjr6aC7q7+escrnqOv2FAkctWtTAqGy0qn38kq6MiHdKuoMkUW8hIrrzDm6E+/jNWkepVGJed/dzRvWMWAMs6Oz0qJ4GmEgf/8npv2/OJyQzm4q6urpYsXIlCxYu5PhymePLZeaQdO/0Fwr0FwqsWLnSSb+Jqg7nHNUX/zpgu4h4cPTUmPDMrBX19vaydmiIzX19zC8Wmd7Rwfxikc19fawdGqK3t7fZIba1LMM5/xl4FbAnsA64EbgxIm7PP7yEu3rMzMavWldPllo9H4uIw4D9gJuAD5F8AZiZWQvKUrLhfwPzgRnAbcAHSVr9ZmbWgrKM43878DTwPeC/gLUR8YdcozIzs9xk6eo5kOQC749Jn7ol6aa8AzMzs3xk6erZD3g18FqgB/g57uoxM2tZWbp6ziYpwfwF4JaIqH47npmZTXpZSja8SdJ2wIuBl0i6x8nfzKx1ZenqeS2wgqS2joA9JB0bETUfxGJmZpNTlq6ec4E3RMQ9AJJeDHwdOCjPwMzMLB9ZnsBVGEn6ABHxP0Ahv5DMzCxPWVr8g5IuAi5J59+N79w1M2tZWRL/B4AlwEkkffw3AF/MMygzM8tPllE9m0n6+c/NPxwzM8tb1cRf7QEsIxr5IBYzM6ufWi3+dwC/b1QgZmbWGLUS/2URcaCkSyLCD8c0M5siaiX+7SQdC7xS0tu3fjMivllrx5K+QvLYxscjYr902c7AFcBckhvC3hkRv5lY6GZmNhG1xvG/H5gH7AQcsdWU5Tm8XwUO32rZGcC1EbEPcG06b2ZmDVS1xR8RNwE3SRqMiIvGu+OIuEHS3K0WvwU4JH19MXA9cPp4921mZhOXpR7/uJN+DbNHHuKe/rtLtRUl9UkalDS4YcOGOoZgZtbespRsaIqIuCAieiKiZ9asWc0Ox8xsyqiZ+JXYo47He0zSrum+dwUer+O+zcwsg5qJPyIC+HYdj7cKODZ9fSzwnTru28zMMsjS1bNW0svHu2NJXwfWkDy85WFJxwGfAV4v6V6S5/d+Zrz7NTOzbZOlSNuhwPslPQD8jqRQW4xVsiEijqzy1uvGFaGZmdVVlsTfm3sUZmbWMFmGcz4I7AEclr7elGU7MzObnMZM4JI+TnKT1bJ0UQH4Wp5BmVn9lEolli5ezOxikWkdHcwuFlm6eDGlUqnZoVmTZGm5vw1YQNK/T0T8Etgxz6DMrD4GBgaY193N9P5+Vg8PszmC1cPDTO/vZ153NwMDA80O0ZogS+L/YzqsMwAk7ZBvSGZWD6VSiWMWLmTVpk2cVS7TRXJRrws4q1xm1aZNHLNwoVv+bShL4r9S0r8BO0k6Afgh0J9vWGa2rc475xxOKJc5uMr7BwPHl8ucv3x5I8OySUBJY36MlaTXA28gGcr5nxFxTd6BjdbT0xODg4ONPKRZy5tdLLJ6eJiuGuuUgPnFIo8+9VSjwrIGkrQuInq2Xj7mcE5JZ0fE6cA1FZaZ2ST1xMaN7DnGOnPS9ay9ZOnqeX2FZR7bbzbJzZwxgwfHWOehdD1rL1UTv6QPpA9cf4mkoVHT/cBQ40I0s4k46uijuahQqLlOf6HAUYv8ZNV2U7WPX9JfAC8APs2WT8oajohfNyC2P3Mfv9n4lUol5nV3s2rTpooXeNcACzo7WTs0RFdXrSsB1qqq9fFXbfFHxFMR8UBEHJnesft7kiGdMyTNyTFWM6uDrq4uVqxcyYLOTpYVCpSAMskF3WWFAgs6O1mxcqWTfhvKcufuEWk1zfuB/yJ5SLrv+jBrAb29vawdGmJzXx/zi0Wmd3Qwv1hkc18fa4eG6O315bp2NOZwTkk/AQ4DfhgRB0g6FDgyIvoaESC4q8fMbCLG3dUzSjkifgV0SOqIiOuA/eseoZmZNUSWssxPSpoB3ABcKulx4Ol8wzIzs7xkafG/heTC7lLg+yTXho7IMygzM8tP1Ra/pFOA/wZui4hn0sUXNyQqMzPLTa2unt2BzwMvlTQErCb5IljT6HH8ZmZWP1UTf0R8EEDSdkAP8ErgvcCFkp6MiH0netD0+b3DwDPA05WuOpuZWT6yXNydDhSBv0inXwJ31OHYh0bEE3XYj5mZjUOtPv4LgJeRtMxvJunqOTciftOg2MzMLAe1RvXMAZ4PPAr8AngYeLJOxw3gB5LWSap4I5ikPkmDkgY3bNhQp8OamVnNO3cliaTV/8p02g/4NckF3o9P+KDSX0XELyXtQlLn/58i4oZq6/vOXTOz8ZvQnbuRuBO4mqQ+z3+TPLLz5G0JJn1gOxHxOPAt4BXbsr96KJVKLF28mNnFItM6OphdLLJ08WI/j9Qazp9Fy1utevwnSbpc0s9J7tp9M3AP8HZg54keUNIOknYceU3ySMc7J7q/ehgYGGBedzfT+/tZPTzM5ghWDw8zvb+fed3dDAy4Jp01hj+L1gi16vGfSzp2PyIeqdsBpReRtPIhubh8WUR8qtY2eXb1uGa5TRb+LFq9TaQe/6kRsbKeST/d730R8bfp9LKxkn7ezjvnHE4olyv+jwZwMHB8ucz5y5c3MixrQ/4sWqOMWZZ5MsizxT+7WGT18DC12k8lYH6xyKNPPZVLDGbgz6LV37aUZW4547k49sTGjew5xv7mpOuZ5cmfRWuUKZf4x3txbOaMGTw4xj4fStdrFI/qaE+T8bNoU9OUSvylUoljFi5k1aZNnFUu00Vy9bgLOKtcZtWmTRyzcOEWCfSoo4/mokKh5n77CwWOWrQo19hHeFRH+5psn0WbwiJi0k8HHXRQZHHKBz4QywqFCKg6nVEoxNIlS/68zfr162NmZ2esrrL+aoiZnZ2xfv36TDFsi8kUizWe//tbvQGDUSGnTqkW/2Vf+xrHlcs11zm+XOaySy7583xXVxcrVq5kQWcnywoFSkCZ5CLaskKBBZ2drFi5siHD5zyqY0uN6PKaTN1qk+mzaFNcpW+DyTZlbfF3SFGu0doPiD9CTOvoeM6269evj6VLlsTsYjGmdXTE7GIxli5Z0tDW1S477hjrx4h/PcTsYrFhMTXL1VdfHTM7O2NZoRDrIcrp376sUIiZnZ1x9dVXt8QxJmIyfBZtaqBKi39KDeds9eFw0zo62BxRs1Z2GZje0cHTzzxTY63W1ogbmXyzlLWDthjO2eoXxzyqI9GILi93q1lbq/QzYLJNWbt6Wv3i2EQuTk9FjejycreatQPa4eJuq18cO/G007iwUGBNlffXkPxiWbJ0aeZ9TqaLl1k14kYm3yxl7WxKJX6A3t5e1g4Nsbmvj/nFItM7OphfLLK5r4+1Q0P09vY2O8Sq6v3F1ar3BDSiy8vdatbWKv0MmGxT1q6eqaIeozpaudurEV1e7lazdkCVrp6mJ/UsU7sl/npo5cTWiC+tVv5iNMuqWuKfcl09lpjIzWyTRSOu1bT69SCzbeHEP0W1+sXLRlyraeXrQWbbYkrdwGXPavWb2cxs27XFDVz2rFa/mc3M8tOUxC/pcEn3SFov6YxmxDDV5XFPgJlNDQ1P/JKmAecDvcC+wJGS9m10HFOdL16aWTXNaPG/AlgfyUPX/whcDrylCXFMeb54aWaVNPzirqSFwOERcXw6vwj4u4g4cav1+oA+gDlz5hz04INj3WdpZmajTaaLu6qw7DnfPhFxQUT0RETPrFmzGhCWmVl7aEbifxjYY9T87sAvmxCHmVlbakbivwXYR9JekrYD3gWsakIcZmZtqSk3cEl6I/A5YBrwlYj41Bjrb4AxiylOJjOBJ5odxCTlc1Odz011PjfV1To3e0bEc/rKW+LO3VYjabDSBRXzuanF56Y6n5vqJnJufOeumVmbceI3M2szTvz5uKDZAUxiPjfV+dxU53NT3bjPjfv4zczajFv8ZmZtxonfzKzNOPHnQNIHJYWkmem8JH0hLUM9JOnAZsfYaJI+K+ln6d//LUk7jXpvWXpu7pH0D82Ms1lcqvxZkvaQdJ2kuyXdJenkdPnOkq6RdG/67wuaHWuzSJom6TZJV6Xze0m6OT03V6Q3x1blxF9nkvYAXg88NGpxL7BPOvUBX2pCaM12DbBfRHQD/wMsA0hLcr8LeBlwOPDFtHR323Cp8ud4GjgtIv4amAcsSc/HGcC1EbEPcG06365OBu4eNX82sDw9N78Bjqu1sRN//S0HPsyWhefeAqxIH3y/FthJ0q5Nia5JIuIHEfF0OruWpEYTJOfm8ojYHBH3A+tJSne3E5cqHyUiHomIW9PXwyQJbjeSc3JxutrFwFubE2FzSdodeBPQn84LOAxYma4y5rlx4q8jSQuAX0TET7Z6azfg56PmH06Xtav3AgPpa58bn4OqJM0FDgBuBmZHxCOQfDkAuzQvsqb6HEnj8k/p/F8CT45qWI35+XlefrFNTZJ+CLywwlsfBT4CvKHSZhWWTblxtLXOTUR8J13noyQ/5S8d2azC+lPu3IzB56ACSTOAbwCnRMRvk4Zte5P0ZuDxiFgn6ZCRxRVWrfn5ceIfp4j4+0rLJf0NsBfwk/QDujtwq6RX0CalqKudmxGSjgXeDLwunr2BpC3OzRh8DrYiqUCS9C+NiG+mix+TtGtEPJJ2lT7evAibZj6wIC10uT1QJPkFsJOk56Wt/jE/P+7qqZOIuCMidomIuRExl+R/5gMj4lGSstPHpKN75gFPjfxkbReSDgdOBxZExKZRb60C3iXp+ZL2IrkA/uNmxNhELlU+StpnfRFwd0ScO+qtVcCx6etjge80OrZmi4hlEbF7mmPeBfwoIt4NXAcsTFcb89y4xd8YVwNvJLlwuQl4T3PDaYrzgOcD16S/iNZGxPsj4i5JVwI/JekCWhIRzzQxzoaLiKclnQj8J8+WKr+ryWE103xgEXCHpNvTZR8BPgNcKek4klFz72hSfJPR6cDlkv4FuI3ki7Mql2wwM2sz7uoxM2szTvxmZm3Gid/MrM048ZuZtRknfjOzNuPEb3Uh6YWSLpdUkvRTSVdLerGkQ0YqCDabpH+WVPMmszodZydJi+uwn+sl1fUB47X2KWmlpBfV2HY7STdI8jDwFufEb9ssveHmW8D1EdEVEfuSjLue3dzIthQRH4uIHzbgUDsB40r86c19Tfv/UdLLgGkRcV+1ddICctcC/9iwwCwXTvxWD4cC5Yj48siCiLg9Im5MZ2ekrcmfSbo0/aJA0sck3SLpTkkXjFp+vaSzJf1Y0v9IenW6vFPSlWlN/yvS+uM96XtvkLRG0q2S/iOt87IFSV+VtDB9/YCkM9P175D00grrXy2pO319m6SPpa8/Kel4STMkXTtqHyMVNT8DdEm6XdJn020+lP6tQ5LOTJfNVVJz/ovArWxZtmHrWJ7z90nqTW9+G1nnEEnfzXo+tvJu0rs9Je2ppK77TEkdkm6UNFKD6tvputbCnPitHvYD1tV4/wDgFJJa8y8iuTMT4LyIeHlE7AdMJ6njM+J5ERZvVdcAAANkSURBVPGKdLuPp8sWA79Ja/p/EjgIQMkDb/438PcRcSAwCJyaIe4n0vW/BHywwvs3AK+WVCS5q3gk7lcBNwJ/AN6W7uNQ4Jz0y+sMoBQR+0fEh9KkuQ9J+eX9gYMkvSbd10tISnYfEBEPVgqyxt93DTBP0g7pqv8IXDHB8zGf9L9hGsfZwJeB04CfRsQP0vXuBF4+xr5sknNfnTXCjyPiYYD0Fvy5wE3AoZI+DHQCOwN3Ad9NtxkpzLUuXR+ShPt5gIi4U9JQunweyZfKf6c/GrYD1mSIa/Qx3l7h/RuBk4D7ge8Br5fUCcyNiHuUFBI7K03ifyIphVupe+sN6XRbOj+D5IvgIeDB9BkNtVT8+9JSD98HjpC0kqRG+4eB11Zaf4xj7ApsGJmJiH5J7wDeT/JlNbL8GUl/lLRjWivfWpATv9XDXTxbIKqSzaNePwM8T9L2wBeBnoj4uaRPkFQb3HqbZ3j2c1qtLq+AayLiyHHGXekYo90C9AD3kbSuZwIn8Oyvm3cDs4CDIqIs6YGt/obR8X06Iv5ti4VJrfnfZYiz1t93BbAE+DVwS0QMp786xns+fj869vQLbuRhOTOA0Un++SS/dqxFuavH6uFHwPMlnTCyQNLLJb22xjYjSeaJtP+51hfHiJuAd6b73xf4m3T5WmC+pL3T9zolvXicf8NzpBczf54ecy3JL4APpv8C/AVJbfSypEOBPdPlw8COo3b1n8B7R/rZJe0maTwPEan1910PHEjyhXRFhvWruRvYe9T82STPTPgYcOHIQkl/CWyIiPI44rdJxonftllaW/9tJF0hJUl3AZ+gRk3wiHiSJKHcQXLB8JYMh/oiMCvt4jkdGCIpcb0B+F/A19P31gLPuVg7QTcCj6WlpG8kaQWPJP5LgR5JgySt/58BRMSvSLpZ7pT02bR//DJgjaQ7SB6RtyMZ1fr70kqmV5E8r/eqsdav4XvAIQDpF/bLgbMj4lLgj5JGKsoeSlJt1lqYq3Nay1DyUPJCRPxBUhfJ0MIXpy1z2waSppPUdJ9fqyy2pG8CyyLinoYFZ3XnPn5rJZ3AdelFVQEfcNKvj4j4vaSPk1ygfqjSOkoeEvNtJ/3W5xa/mVmbcR+/mVmbceI3M2szTvxmZm3Gid/MrM048ZuZtZn/D9cM/apxPVORAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "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(r'D:\\Github\\Learning-Content\\Phase 3 - 2020 (Summer)\\Week 5(Apr 26-May 02)\\Exercise5\\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@theta\n", + " J=(1/(2*m))*sum(np.square(h-y))\n", + " J=J+(lambda_/(2*m))*sum(np.square(theta))\n", + " \n", + " \n", + " grad=(1/m)*X.transpose()@(h-y)\n", + " grad=grad.transpose()\n", + "\n", + " # ============================================================\n", + " return J, grad" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at theta = [1, 1]:\t 304.034859 \n", + "This value should be about 303.993192)\n", + "\n" + ] + } + ], + "source": [ + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gradient at theta = [1, 1]: [-15.303016, 598.167411] \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": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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 = 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": 14, + "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", + " for i in range(1,m+1):\n", + " \n", + " xsub=X[:i,:]\n", + " ysub=y[:i]\n", + " \n", + " theta=trainLinearReg(linearRegCostFunction,xsub,ysub)\n", + " \n", + " \n", + " error_train[i-1]=linearRegCostFunction(xsub,ysub,theta)[0]\n", + " error_val[i-1]=linearRegCostFunction(Xval,yval,theta)[0]\n", + " \n", + " # =============================================================\n", + " return error_train, error_val" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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\t48.368910\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": [ + "
" + ] + }, + "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": 18, + "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", + " for i in range(p):\n", + " X_poly[:, i] = X[:, 0] ** (i + 1)\n", + "\n", + "\n", + " # ============================================================\n", + " return X_poly" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "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": 19, + "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 = 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": 22, + "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\t59.071638\n", + " \t4\t\t0.000000\t77.997856\n", + " \t5\t\t0.000000\t6.449009\n", + " \t6\t\t0.000000\t10.829585\n", + " \t7\t\t0.000000\t27.930121\n", + " \t8\t\t0.025083\t9.256265\n", + " \t9\t\t0.000249\t32.402637\n", + " \t10\t\t0.032541\t28.510531\n", + " \t11\t\t0.034697\t32.120191\n", + " \t12\t\t0.031890\t34.411499\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lambda_ = 100\n", + "theta = 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", + "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]))" + ] + }, + { + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Aishik Rakshit190122002 w05 ex 4.ipynb b/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Aishik Rakshit190122002 w05 ex 4.ipynb new file mode 100644 index 000000000..48ec40c40 --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 5(Apr 26-May 02)/Aishik Rakshit190122002 w05 ex 4.ipynb @@ -0,0 +1,851 @@ +{ + "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", + "\n", + "\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def displayData(X, example_width=None, figsize=(10, 10)):\n", + " \"\"\"\n", + " Displays 2D data stored in X in a nice grid.\n", + " \"\"\"\n", + " # Compute rows, cols\n", + " if X.ndim == 2:\n", + " m, n = X.shape\n", + " elif X.ndim == 1:\n", + " n = X.size\n", + " m = 1\n", + " X = X[None] # Promote to a 2 dimensional array\n", + " else:\n", + " raise IndexError('Input X should be 1 or 2 dimensional.')\n", + "\n", + " example_width = example_width or int(np.round(np.sqrt(n)))\n", + " example_height = n / example_width\n", + "\n", + " # Compute number of items to display\n", + " display_rows = int(np.floor(np.sqrt(m)))\n", + " display_cols = int(np.ceil(m / display_rows))\n", + "\n", + " fig, ax_array = pyplot.subplots(display_rows, display_cols, figsize=figsize)\n", + " fig.subplots_adjust(wspace=0.025, hspace=0.025)\n", + "\n", + " ax_array = [ax_array] if m == 1 else ax_array.ravel()\n", + "\n", + " for i, ax in enumerate(ax_array):\n", + " # Display Image\n", + " h = ax.imshow(X[i].reshape(example_width, example_width, order='F'),\n", + " cmap='Greys', extent=[0, 1, 0, 1])\n", + " ax.axis('off')\n", + "\n", + "\n", + "def predict(Theta1, Theta2, X):\n", + " \"\"\"\n", + " Predict the label of an input given a trained neural network\n", + " Outputs the predicted label of X given the trained weights of a neural\n", + " network(Theta1, Theta2)\n", + " \"\"\"\n", + " # Useful values\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(m)\n", + " h1 = sigmoid(np.dot(np.concatenate([np.ones((m, 1)), X], axis=1), Theta1.T))\n", + " h2 = sigmoid(np.dot(np.concatenate([np.ones((m, 1)), h1], axis=1), Theta2.T))\n", + " p = np.argmax(h2, axis=1)\n", + " return p\n", + "\n", + "\n", + "def debugInitializeWeights(fan_out, fan_in):\n", + " \"\"\"\n", + " Initialize the weights of a layer with fan_in incoming connections and fan_out outgoings\n", + " connections using a fixed strategy. This will help you later in debugging.\n", + "\n", + " Note that W should be set a matrix of size (1+fan_in, fan_out) as the first row of W handles\n", + " the \"bias\" terms.\n", + "\n", + " Parameters\n", + " ----------\n", + " fan_out : int\n", + " The number of outgoing connections.\n", + "\n", + " fan_in : int\n", + " The number of incoming connections.\n", + "\n", + " Returns\n", + " -------\n", + " W : array_like (1+fan_in, fan_out)\n", + " The initialized weights array given the dimensions.\n", + " \"\"\"\n", + " # Initialize W using \"sin\". This ensures that W is always of the same values and will be\n", + " # useful for debugging\n", + " W = np.sin(np.arange(1, 1 + (1+fan_in)*fan_out))/10.0\n", + " W = W.reshape(fan_out, 1+fan_in, order='F')\n", + " return W\n", + "\n", + "\n", + "def computeNumericalGradient(J, theta, e=1e-4):\n", + " \"\"\"\n", + " Computes the gradient using \"finite differences\" and gives us a numerical estimate of the\n", + " gradient.\n", + "\n", + " Parameters\n", + " ----------\n", + " J : func\n", + " The cost function which will be used to estimate its numerical gradient.\n", + "\n", + " theta : array_like\n", + " The one dimensional unrolled network parameters. The numerical gradient is computed at\n", + " those given parameters.\n", + "\n", + " e : float (optional)\n", + " The value to use for epsilon for computing the finite difference.\n", + "\n", + " Notes\n", + " -----\n", + " The following code implements numerical gradient checking, and\n", + " returns the numerical gradient. It sets `numgrad[i]` to (a numerical\n", + " approximation of) the partial derivative of J with respect to the\n", + " i-th input argument, evaluated at theta. (i.e., `numgrad[i]` should\n", + " be the (approximately) the partial derivative of J with respect\n", + " to theta[i].)\n", + " \"\"\"\n", + " numgrad = np.zeros(theta.shape)\n", + " perturb = np.diag(e * np.ones(theta.shape))\n", + " for i in range(theta.size):\n", + " loss1, _ = J(theta - perturb[:, i])\n", + " loss2, _ = J(theta + perturb[:, i])\n", + " numgrad[i] = (loss2 - loss1)/(2*e)\n", + " return numgrad\n", + "\n", + "\n", + "def checkNNGradients(nnCostFunction, lambda_=0):\n", + " \"\"\"\n", + " Creates a small neural network to check the backpropagation gradients. It will output the\n", + " analytical gradients produced by your backprop code and the numerical gradients\n", + " (computed using computeNumericalGradient). These two gradient computations should result in\n", + " very similar values.\n", + "\n", + " Parameters\n", + " ----------\n", + " nnCostFunction : func\n", + " A reference to the cost function implemented by the student.\n", + "\n", + " lambda_ : float (optional)\n", + " The regularization parameter value.\n", + " \"\"\"\n", + " input_layer_size = 3\n", + " hidden_layer_size = 5\n", + " num_labels = 3\n", + " m = 5\n", + "\n", + " # We generate some 'random' test data\n", + " Theta1 = debugInitializeWeights(hidden_layer_size, input_layer_size)\n", + " Theta2 = debugInitializeWeights(num_labels, hidden_layer_size)\n", + "\n", + " # Reusing debugInitializeWeights to generate X\n", + " X = debugInitializeWeights(m, input_layer_size - 1)\n", + " y = np.arange(1, 1+m) % num_labels\n", + " # print(y)\n", + " # Unroll parameters\n", + " nn_params = np.concatenate([Theta1.ravel(), Theta2.ravel()])\n", + "\n", + " # short hand for cost function\n", + " costFunc = lambda p: nnCostFunction(p, input_layer_size, hidden_layer_size,\n", + " num_labels, X, y, lambda_)\n", + " cost, grad = costFunc(nn_params)\n", + " numgrad = computeNumericalGradient(costFunc, nn_params)\n", + "\n", + " # Visually examine the two gradient computations.The two columns you get should be very similar.\n", + " print(np.stack([numgrad, grad], axis=1))\n", + " print('The above two columns you get should be very similar.')\n", + " print('(Left-Your Numerical Gradient, Right-Analytical Gradient)\\n')\n", + "\n", + " # Evaluate the norm of the difference between two the solutions. If you have a correct\n", + " # implementation, and assuming you used e = 0.0001 in computeNumericalGradient, then diff\n", + " # should be less than 1e-9.\n", + " diff = np.linalg.norm(numgrad - grad)/np.linalg.norm(numgrad + grad)\n", + "\n", + " print('If your backpropagation implementation is correct, then \\n'\n", + " 'the relative difference will be small (less than 1e-9). \\n'\n", + " 'Relative Difference: %g' % diff)\n", + "\n", + "\n", + "def sigmoid(z):\n", + " \"\"\"\n", + " Computes the sigmoid of z.\n", + " \"\"\"\n", + " return 1.0 / (1.0 + np.exp(-z))\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# training data stored in arrays X, y\n", + "data = loadmat(r'D:\\Github\\Learning-Content\\Phase 3 - 2020 (Summer)\\Week 5(Apr 26-May 02)\\Exercise4\\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\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "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", + "displayData(sel)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, ..., 9, 9, 9], dtype=uint8)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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(r'D:\\Github\\Learning-Content\\Phase 3 - 2020 (Summer)\\Week 5(Apr 26-May 02)\\Exercise4\\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()])\n", + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "y_=np.reshape(y,(m,1))\n", + "y_1=np.tile(y_,(1,num_labels))\n", + "y_2=np.tile(np.arange(0,num_labels),(m,1))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#y_matrix=np.zeros((5000,10))\n", + "#for i in range(0,m):\n", + " # for j in range(0,num_labels):\n", + " # y_matrix[i,j]=int(y_1[i,j]==y_2[(i,j)])\n", + "#np.shape(y_matrix) " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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(np.shape(z))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " g=sigmoid(z)*(1-sigmoid(z))\n", + "\n", + " # =============================================================\n", + " return g" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "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", + " a1=np.concatenate((np.ones((m,1)),X),axis=1)\n", + " z1=a1@Theta1.transpose()\n", + " a2=sigmoid(z1)\n", + " a2=np.concatenate((np.ones((np.shape(a2)[0],1)),a2),axis=1)\n", + " z2=a2@Theta2.transpose()\n", + " a3=sigmoid(z2)\n", + " \n", + " y_matrix = y.reshape(-1)\n", + " y_matrix = np.eye(num_labels)[y_matrix]\n", + " \n", + " temp1 = Theta1\n", + " temp2 = Theta2\n", + " reg_term = (lambda_ / (2 * m)) * (np.sum(np.square(temp1[:, 1:])) + np.sum(np.square(temp2[:, 1:])))\n", + " \n", + " J = (-1 / m) * np.sum((np.log(a3) * y_matrix) + np.log(1 - a3) * (1 - y_matrix)) + reg_term\n", + " del1=np.zeros(np.shape(Theta1));\n", + " del2=np.zeros(np.shape(Theta2));\n", + " del3=y_matrix-a3\n", + " \n", + " del2=del3@Theta2[:,1:]*sigmoidGradient(a1@Theta1.transpose())\n", + " \n", + " delta2=del3.transpose()@a2\n", + " delta1=del2.transpose()@a1\n", + " \n", + " Theta1_grad=(1/m)*delta1\n", + " Theta1_grad[:,1:]=Theta1_grad[:,1:]+(lambda_/m)*Theta1[:,1:]\n", + " Theta2_grad=(1/m)*delta2\n", + " Theta2_grad[:,1:]=Theta2_grad[:,1:]+(lambda_/m)*Theta2[:,1:]\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": 10, + "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": 11, + "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": 12, + "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", + "\n", + " W=np.random.rand(L_out,L_in+1)*2*epsilon_init - epsilon_init\n", + " # ============================================================\n", + " return W" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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": 14, + "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: 4.14102e+10\n" + ] + } + ], + "source": [ + "checkNNGradients(nnCostFunction)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-9.27825235e-03 9.27825236e-03]\n", + " [-1.67679797e-02 -1.67618801e-02]\n", + " [-6.01744725e-02 -5.98243523e-02]\n", + " [-1.73704651e-02 -1.71779329e-02]\n", + " [ 8.89911959e-03 -8.89911960e-03]\n", + " [ 3.94334829e-02 3.94049090e-02]\n", + " [-3.19612287e-02 -3.24275214e-02]\n", + " [-5.75658668e-02 -5.78018322e-02]\n", + " [-8.36010761e-03 8.36010762e-03]\n", + " [ 5.93355565e-02 5.93874331e-02]\n", + " [ 2.49225535e-02 2.54974909e-02]\n", + " [-4.51963845e-02 -4.49220851e-02]\n", + " [ 7.62813550e-03 -7.62813551e-03]\n", + " [ 2.47640974e-02 2.46901208e-02]\n", + " [ 5.97717617e-02 5.91011210e-02]\n", + " [ 9.14587966e-03 8.83938550e-03]\n", + " [-6.74798369e-03 6.74798370e-03]\n", + " [-3.26881426e-02 -3.25943907e-02]\n", + " [ 3.86410548e-02 3.93934860e-02]\n", + " [ 5.46101547e-02 5.49432753e-02]\n", + " [ 3.14544970e-01 -3.14544970e-01]\n", + " [ 1.18682669e-01 -2.09498969e-01]\n", + " [ 2.03987128e-01 -1.25148736e-01]\n", + " [ 1.25698067e-01 -1.90980601e-01]\n", + " [ 1.76337550e-01 -1.25917505e-01]\n", + " [ 1.32294136e-01 -1.66842534e-01]\n", + " [ 1.11056588e-01 -1.11056588e-01]\n", + " [ 3.81928689e-05 -1.15109106e-01]\n", + " [ 1.17148233e-01 1.57475695e-03]\n", + " [-4.07588279e-03 -1.15922942e-01]\n", + " [ 1.13133142e-01 5.73974043e-03]\n", + " [-4.52964427e-03 -1.10838055e-01]\n", + " [ 9.74006970e-02 -9.74006970e-02]\n", + " [ 3.36926556e-02 -6.72225154e-02]\n", + " [ 7.54801264e-02 -2.60259082e-02]\n", + " [ 1.69677090e-02 -8.13564592e-02]\n", + " [ 8.61628953e-02 -8.12835444e-03]\n", + " [ 1.50048382e-03 -9.16189534e-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: 2.23751\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": [ + "# Check gradients by running checkNNGradients\n", + "lambda_ = 3\n", + "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)')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "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)))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.00628234, -0.00146306, -0.05142674, ..., -0.09994837,\n", + " -0.11065824, -0.00457264],\n", + " [ 0.09763301, -0.00979571, 0.04509217, ..., -0.10976644,\n", + " 0.01176824, 0.09969519],\n", + " [ 0.09264358, -0.03911751, 0.04035854, ..., -0.05785748,\n", + " -0.05638749, 0.085673 ],\n", + " ...,\n", + " [ 0.08179291, 0.11364726, 0.11785947, ..., 0.01273425,\n", + " 0.01405875, 0.02204611],\n", + " [-0.09570627, -0.05675888, -0.1114769 , ..., 0.00722112,\n", + " -0.05075026, -0.04881935],\n", + " [ 0.05975326, -0.08410285, -0.01892549, ..., -0.00175921,\n", + " -0.11652668, 0.09348823]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Theta1[:, 1:])" + ] + }, + { + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 506a46fac5ae75d08b7dd1a4af9457995a4c17a9 Mon Sep 17 00:00:00 2001 From: Aishik13012002 <51096112+Aishik13012002@users.noreply.github.com> Date: Fri, 8 May 2020 16:45:32 +0530 Subject: [PATCH 12/14] week 6 Aishik Rakshit 190122002 --- .../Aishik_RAkshit_w06_190122002.ipynb | 2085 +++++++++++++++++ 1 file changed, 2085 insertions(+) create mode 100644 Phase 3 - 2020 (Summer)/Week 6(May 3 -May 9)/Aishik_RAkshit_w06_190122002.ipynb diff --git a/Phase 3 - 2020 (Summer)/Week 6(May 3 -May 9)/Aishik_RAkshit_w06_190122002.ipynb b/Phase 3 - 2020 (Summer)/Week 6(May 3 -May 9)/Aishik_RAkshit_w06_190122002.ipynb new file mode 100644 index 000000000..4bbb2eafc --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 6(May 3 -May 9)/Aishik_RAkshit_w06_190122002.ipynb @@ -0,0 +1,2085 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "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": "3.6.4" + }, + "colab": { + "name": "Aishik RAkshit w06 190122002.ipynb", + "provenance": [] + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "e08BDyVjPJzY", + "colab_type": "text" + }, + "source": [ + "# Programming Exercise 6:\n", + "# Support Vector Machines\n", + "\n", + "## Introduction\n", + "\n", + "In this exercise, you will be using support vector machines (SVMs) to build a spam classifier. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n", + "\n", + "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", + "\n", + "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4WOVqT6nPJzY", + "colab_type": "code", + "colab": {} + }, + "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", + "\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "G_zA4s7jP_Ou", + "colab_type": "code", + "colab": {} + }, + "source": [ + "\n", + "\n", + "\n", + "def plotData(X, y, grid=False):\n", + " \"\"\"\n", + " Plots the data points X and y into a new figure. Uses `+` for positive examples, and `o` for\n", + " negative examples. `X` is assumed to be a Mx2 matrix\n", + "\n", + " Parameters\n", + " ----------\n", + " X : numpy ndarray\n", + " X is assumed to be a Mx2 matrix.\n", + "\n", + " y : numpy ndarray\n", + " The data labels.\n", + "\n", + " grid : bool (Optional)\n", + " Specify whether or not to show the grid in the plot. It is False by default.\n", + "\n", + " Notes\n", + " -----\n", + " This was slightly modified such that it expects y=1 or y=0.\n", + " \"\"\"\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], 'X', mew=1, ms=10, mec='k')\n", + " pyplot.plot(X[neg, 0], X[neg, 1], 'o', mew=1, mfc='y', ms=10, mec='k')\n", + " pyplot.grid(grid)\n", + "\n", + "\n", + "def svmTrain(X, Y, C, kernelFunction, tol=1e-3, max_passes=5, args=()):\n", + " \"\"\"\n", + " Trains an SVM classifier using a simplified version of the SMO algorithm.\n", + "\n", + " Parameters\n", + " ---------\n", + " X : numpy ndarray\n", + " (m x n) Matrix of training examples. Each row is a training example, and the\n", + " jth column holds the jth feature.\n", + "\n", + " Y : numpy ndarray\n", + " (m, ) A vector (1-D numpy array) containing 1 for positive examples and 0 for negative examples.\n", + "\n", + " C : float\n", + " The standard SVM regularization parameter.\n", + "\n", + " kernelFunction : func\n", + " A function handle which computes the kernel. The function should accept two vectors as\n", + " inputs, and returns a scalar as output.\n", + "\n", + " tol : float, optional\n", + " Tolerance value used for determining equality of floating point numbers.\n", + "\n", + " max_passes : int, optional\n", + " Controls the number of iterations over the dataset (without changes to alpha)\n", + " before the algorithm quits.\n", + "\n", + " args : tuple\n", + " Extra arguments required for the kernel function, such as the sigma parameter for a\n", + " Gaussian kernel.\n", + "\n", + " Returns\n", + " -------\n", + " model :\n", + " The trained SVM model.\n", + "\n", + " Notes\n", + " -----\n", + " This is a simplified version of the SMO algorithm for training SVMs. In practice, if\n", + " you want to train an SVM classifier, we recommend using an optimized package such as:\n", + "\n", + " - LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/)\n", + " - SVMLight (http://svmlight.joachims.org/)\n", + " - scikit-learn (http://scikit-learn.org/stable/modules/svm.html) which contains python wrappers\n", + " for the LIBSVM library.\n", + " \"\"\"\n", + " # make sure data is signed int\n", + " Y = Y.astype(int)\n", + " # Dataset size parameters\n", + " m, n = X.shape\n", + "\n", + " passes = 0\n", + " E = np.zeros(m)\n", + " alphas = np.zeros(m)\n", + " b = 0\n", + "\n", + " # Map 0 to -1\n", + " Y[Y == 0] = -1\n", + "\n", + " # Pre-compute the Kernel Matrix since our dataset is small\n", + " # (in practice, optimized SVM packages that handle large datasets\n", + " # gracefully will **not** do this)\n", + "\n", + " # We have implemented the optimized vectorized version of the Kernels here so\n", + " # that the SVM training will run faster\n", + " if kernelFunction.__name__ == 'linearKernel':\n", + " # Vectorized computation for the linear kernel\n", + " # This is equivalent to computing the kernel on every pair of examples\n", + " K = np.dot(X, X.T)\n", + " elif kernelFunction.__name__ == 'gaussianKernel':\n", + " # vectorized RBF Kernel\n", + " # This is equivalent to computing the kernel on every pair of examples\n", + " X2 = np.sum(X**2, axis=1)\n", + " K = X2 + X2[:, None] - 2 * np.dot(X, X.T)\n", + "\n", + " if len(args) > 0:\n", + " K /= 2*args[0]**2\n", + "\n", + " K = np.exp(-K)\n", + " else:\n", + " K = np.zeros((m, m))\n", + " for i in range(m):\n", + " for j in range(i, m):\n", + " K[i, j] = kernelFunction(X[i, :], X[j, :])\n", + " K[j, i] = K[i, j]\n", + "\n", + " while passes < max_passes:\n", + " num_changed_alphas = 0\n", + " for i in range(m):\n", + " E[i] = b + np.sum(alphas * Y * K[:, i]) - Y[i]\n", + "\n", + " if (Y[i]*E[i] < -tol and alphas[i] < C) or (Y[i]*E[i] > tol and alphas[i] > 0):\n", + " # select the alpha_j randomly\n", + " j = np.random.choice(list(range(i)) + list(range(i+1, m)), size=1)[0]\n", + "\n", + " E[j] = b + np.sum(alphas * Y * K[:, j]) - Y[j]\n", + "\n", + " alpha_i_old = alphas[i]\n", + " alpha_j_old = alphas[j]\n", + "\n", + " if Y[i] == Y[j]:\n", + " L = max(0, alphas[j] + alphas[i] - C)\n", + " H = min(C, alphas[j] + alphas[i])\n", + " else:\n", + " L = max(0, alphas[j] - alphas[i])\n", + " H = min(C, C + alphas[j] - alphas[i])\n", + "\n", + " if L == H:\n", + " continue\n", + "\n", + " eta = 2 * K[i, j] - K[i, i] - K[j, j]\n", + "\n", + " # objective function positive definite, there will be a minimum along the direction\n", + " # of linear equality constrain, and eta will be greater than zero\n", + " # we are actually computing -eta here (so we skip of eta >= 0)\n", + " if eta >= 0:\n", + " continue\n", + "\n", + " alphas[j] -= Y[j] * (E[i] - E[j])/eta\n", + " alphas[j] = max(L, min(H, alphas[j]))\n", + "\n", + " if abs(alphas[j] - alpha_j_old) < tol:\n", + " alphas[j] = alpha_j_old\n", + " continue\n", + " alphas[i] += Y[i]*Y[j]*(alpha_j_old - alphas[j])\n", + "\n", + " b1 = b - E[i] - Y[i]*(alphas[i] - alpha_i_old) * K[i, j] \\\n", + " - Y[j] * (alphas[j] - alpha_j_old) * K[i, j]\n", + "\n", + " b2 = b - E[j] - Y[i]*(alphas[i] - alpha_i_old) * K[i, j] \\\n", + " - Y[j] * (alphas[j] - alpha_j_old) * K[j, j]\n", + "\n", + " if 0 < alphas[i] < C:\n", + " b = b1\n", + " elif 0 < alphas[j] < C:\n", + " b = b2\n", + " else:\n", + " b = (b1 + b2)/2\n", + "\n", + " num_changed_alphas += 1\n", + " if num_changed_alphas == 0:\n", + " passes += 1\n", + " else:\n", + " passes = 0\n", + "\n", + " idx = alphas > 0\n", + " model = {'X': X[idx, :],\n", + " 'y': Y[idx],\n", + " 'kernelFunction': kernelFunction,\n", + " 'b': b,\n", + " 'args': args,\n", + " 'alphas': alphas[idx],\n", + " 'w': np.dot(alphas * Y, X)}\n", + " return model\n", + "\n", + "\n", + "def svmPredict(model, X):\n", + " \"\"\"\n", + " Returns a vector of predictions using a trained SVM model.\n", + "\n", + " Parameters\n", + " ----------\n", + " model : dict\n", + " The parameters of the trained svm model, as returned by the function svmTrain\n", + "\n", + " X : array_like\n", + " A (m x n) matrix where each example is a row.\n", + "\n", + " Returns\n", + " -------\n", + " pred : array_like\n", + " A (m,) sized vector of predictions {0, 1} values.\n", + " \"\"\"\n", + " # check if we are getting a vector. If so, then assume we only need to do predictions\n", + " # for a single example\n", + " if X.ndim == 1:\n", + " X = X[np.newaxis, :]\n", + "\n", + " m = X.shape[0]\n", + " p = np.zeros(m)\n", + " pred = np.zeros(m)\n", + "\n", + " if model['kernelFunction'].__name__ == 'linearKernel':\n", + " # we can use the weights and bias directly if working with the linear kernel\n", + " p = np.dot(X, model['w']) + model['b']\n", + " elif model['kernelFunction'].__name__ == 'gaussianKernel':\n", + " # vectorized RBF Kernel\n", + " # This is equivalent to computing the kernel on every pair of examples\n", + " X1 = np.sum(X**2, 1)\n", + " X2 = np.sum(model['X']**2, 1)\n", + " K = X2 + X1[:, None] - 2 * np.dot(X, model['X'].T)\n", + "\n", + " if len(model['args']) > 0:\n", + " K /= 2*model['args'][0]**2\n", + "\n", + " K = np.exp(-K)\n", + " p = np.dot(K, model['alphas']*model['y']) + model['b']\n", + " else:\n", + " # other non-linear kernel\n", + " for i in range(m):\n", + " predictions = 0\n", + " for j in range(model['X'].shape[0]):\n", + " predictions += model['alphas'][j] * model['y'][j] \\\n", + " * model['kernelFunction'](X[i, :], model['X'][j, :])\n", + " p[i] = predictions\n", + "\n", + " pred[p >= 0] = 1\n", + " return pred\n", + "\n", + "\n", + "def linearKernel(x1, x2):\n", + " \"\"\"\n", + " Returns a linear kernel between x1 and x2.\n", + "\n", + " Parameters\n", + " ----------\n", + " x1 : numpy ndarray\n", + " A 1-D vector.\n", + "\n", + " x2 : numpy ndarray\n", + " A 1-D vector of same size as x1.\n", + "\n", + " Returns\n", + " -------\n", + " : float\n", + " The scalar amplitude.\n", + " \"\"\"\n", + " return np.dot(x1, x2)\n", + "\n", + "\n", + "def visualizeBoundaryLinear(X, y, model):\n", + " \"\"\"\n", + " Plots a linear decision boundary learned by the SVM.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " (m x 2) The training data with two features (to plot in a 2-D plane).\n", + "\n", + " y : array_like\n", + " (m, ) The data labels.\n", + "\n", + " model : dict\n", + " Dictionary of model variables learned by SVM.\n", + " \"\"\"\n", + " w, b = model['w'], model['b']\n", + " xp = np.linspace(min(X[:, 0]), max(X[:, 0]), 100)\n", + " yp = -(w[0] * xp + b)/w[1]\n", + "\n", + " plotData(X, y)\n", + " pyplot.plot(xp, yp, '-b')\n", + "\n", + "\n", + "def visualizeBoundary(X, y, model):\n", + " \"\"\"\n", + " Plots a non-linear decision boundary learned by the SVM and overlays the data on it.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " (m x 2) The training data with two features (to plot in a 2-D plane).\n", + "\n", + " y : array_like\n", + " (m, ) The data labels.\n", + "\n", + " model : dict\n", + " Dictionary of model variables learned by SVM.\n", + " \"\"\"\n", + " plotData(X, y)\n", + "\n", + " # make classification predictions over a grid of values\n", + " x1plot = np.linspace(min(X[:, 0]), max(X[:, 0]), 100)\n", + " x2plot = np.linspace(min(X[:, 1]), max(X[:, 1]), 100)\n", + " X1, X2 = np.meshgrid(x1plot, x2plot)\n", + "\n", + " vals = np.zeros(X1.shape)\n", + " for i in range(X1.shape[1]):\n", + " this_X = np.stack((X1[:, i], X2[:, i]), axis=1)\n", + " vals[:, i] = svmPredict(model, this_X)\n", + "\n", + " pyplot.contour(X1, X2, vals, colors='y', linewidths=2)\n", + " pyplot.pcolormesh(X1, X2, vals, cmap='YlGnBu', alpha=0.25, edgecolors='None', lw=0)\n", + " pyplot.grid(False)\n", + "\n", + "\n", + "def getVocabList():\n", + " \"\"\"\n", + " Reads the fixed vocabulary list in vocab.txt and returns a cell array of the words\n", + " % vocabList = GETVOCABLIST() reads the fixed vocabulary list in vocab.txt\n", + " % and returns a cell array of the words in vocabList.\n", + "\n", + " :return:\n", + " \"\"\"\n", + " vocabList = np.genfromtxt(join('vocab.txt'), dtype=object)\n", + " return list(vocabList[:, 1].astype(str))\n", + "\n", + "\n", + "class PorterStemmer:\n", + " \"\"\"\n", + " Porter Stemming Algorithm\n", + "\n", + " This is the Porter stemming algorithm, ported to Python from the\n", + " version coded up in ANSI C by the author. It may be be regarded\n", + " as canonical, in that it follows the algorithm presented in\n", + "\n", + " Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,\n", + " no. 3, pp 130-137,\n", + "\n", + " only differing from it at the points maked --DEPARTURE-- below.\n", + "\n", + " See also http://www.tartarus.org/~martin/PorterStemmer\n", + "\n", + " The algorithm as described in the paper could be exactly replicated\n", + " by adjusting the points of DEPARTURE, but this is barely necessary,\n", + " because (a) the points of DEPARTURE are definitely improvements, and\n", + " (b) no encoding of the Porter stemmer I have seen is anything like\n", + " as exact as this version, even with the points of DEPARTURE!\n", + "\n", + " Vivake Gupta (v@nano.com)\n", + "\n", + " Release 1: January 2001\n", + "\n", + " Further adjustments by Santiago Bruno (bananabruno@gmail.com)\n", + " to allow word input not restricted to one word per line, leading\n", + " to:\n", + "\n", + " release 2: July 2008\n", + " \"\"\"\n", + " def __init__(self):\n", + " \"\"\"\n", + " The main part of the stemming algorithm starts here.\n", + " b is a buffer holding a word to be stemmed. The letters are in b[k0],\n", + " b[k0+1] ... ending at b[k]. In fact k0 = 0 in this demo program. k is\n", + " readjusted downwards as the stemming progresses. Zero termination is\n", + " not in fact used in the algorithm.\n", + "\n", + " Note that only lower case sequences are stemmed. Forcing to lower case\n", + " should be done before stem(...) is called.\n", + " \"\"\"\n", + " self.b = \"\" # buffer for word to be stemmed\n", + " self.k = 0\n", + " self.k0 = 0\n", + " self.j = 0 # j is a general offset into the string\n", + "\n", + " def cons(self, i):\n", + " \"\"\"cons(i) is TRUE <=> b[i] is a consonant.\"\"\"\n", + " if self.b[i] in 'aeiou':\n", + " return 0\n", + " if self.b[i] == 'y':\n", + " if i == self.k0:\n", + " return 1\n", + " else:\n", + " return not self.cons(i - 1)\n", + " return 1\n", + "\n", + " def m(self):\n", + " \"\"\"\n", + " m() measures the number of consonant sequences between k0 and j.\n", + " if c is a consonant sequence and v a vowel sequence, and <..>\n", + " indicates arbitrary presence,\n", + "\n", + " gives 0\n", + " vc gives 1\n", + " vcvc gives 2\n", + " vcvcvc gives 3\n", + " ....\n", + " \"\"\"\n", + " n = 0\n", + " i = self.k0\n", + " while 1:\n", + " if i > self.j:\n", + " return n\n", + " if not self.cons(i):\n", + " break\n", + " i = i + 1\n", + " i = i + 1\n", + " while 1:\n", + " while 1:\n", + " if i > self.j:\n", + " return n\n", + " if self.cons(i):\n", + " break\n", + " i = i + 1\n", + " i = i + 1\n", + " n = n + 1\n", + " while 1:\n", + " if i > self.j:\n", + " return n\n", + " if not self.cons(i):\n", + " break\n", + " i = i + 1\n", + " i = i + 1\n", + "\n", + " def vowelinstem(self):\n", + " \"\"\"vowelinstem() is TRUE <=> k0,...j contains a vowel\"\"\"\n", + " for i in range(self.k0, self.j + 1):\n", + " if not self.cons(i):\n", + " return 1\n", + " return 0\n", + "\n", + " def doublec(self, j):\n", + " \"\"\" doublec(j) is TRUE <=> j,(j-1) contain a double consonant. \"\"\"\n", + " if j < (self.k0 + 1):\n", + " return 0\n", + " if self.b[j] != self.b[j-1]:\n", + " return 0\n", + " return self.cons(j)\n", + "\n", + " def cvc(self, i):\n", + " \"\"\"\n", + " cvc(i) is TRUE <=> i-2,i-1,i has the form consonant - vowel - consonant\n", + " and also if the second c is not w,x or y. this is used when trying to\n", + " restore an e at the end of a short e.g.\n", + "\n", + " cav(e), lov(e), hop(e), crim(e), but\n", + " snow, box, tray.\n", + " \"\"\"\n", + " if i < (self.k0 + 2) or not self.cons(i) or self.cons(i-1) or not self.cons(i-2):\n", + " return 0\n", + " ch = self.b[i]\n", + " if ch in 'wxy':\n", + " return 0\n", + " return 1\n", + "\n", + " def ends(self, s):\n", + " \"\"\"ends(s) is TRUE <=> k0,...k ends with the string s.\"\"\"\n", + " length = len(s)\n", + " if s[length - 1] != self.b[self.k]: # tiny speed-up\n", + " return 0\n", + " if length > (self.k - self.k0 + 1):\n", + " return 0\n", + " if self.b[self.k-length+1:self.k+1] != s:\n", + " return 0\n", + " self.j = self.k - length\n", + " return 1\n", + "\n", + " def setto(self, s):\n", + " \"\"\"setto(s) sets (j+1),...k to the characters in the string s, readjusting k.\"\"\"\n", + " length = len(s)\n", + " self.b = self.b[:self.j+1] + s + self.b[self.j+length+1:]\n", + " self.k = self.j + length\n", + "\n", + " def r(self, s):\n", + " \"\"\"r(s) is used further down.\"\"\"\n", + " if self.m() > 0:\n", + " self.setto(s)\n", + "\n", + " def step1ab(self):\n", + " \"\"\"step1ab() gets rid of plurals and -ed or -ing. e.g.\n", + "\n", + " caresses -> caress\n", + " ponies -> poni\n", + " ties -> ti\n", + " caress -> caress\n", + " cats -> cat\n", + "\n", + " feed -> feed\n", + " agreed -> agree\n", + " disabled -> disable\n", + "\n", + " matting -> mat\n", + " mating -> mate\n", + " meeting -> meet\n", + " milling -> mill\n", + " messing -> mess\n", + "\n", + " meetings -> meet\n", + " \"\"\"\n", + " if self.b[self.k] == 's':\n", + " if self.ends(\"sses\"):\n", + " self.k = self.k - 2\n", + " elif self.ends(\"ies\"):\n", + " self.setto(\"i\")\n", + " elif self.b[self.k - 1] != 's':\n", + " self.k = self.k - 1\n", + " if self.ends(\"eed\"):\n", + " if self.m() > 0:\n", + " self.k = self.k - 1\n", + " elif (self.ends(\"ed\") or self.ends(\"ing\")) and self.vowelinstem():\n", + " self.k = self.j\n", + " if self.ends(\"at\"):\n", + " self.setto(\"ate\")\n", + " elif self.ends(\"bl\"):\n", + " self.setto(\"ble\")\n", + " elif self.ends(\"iz\"):\n", + " self.setto(\"ize\")\n", + " elif self.doublec(self.k):\n", + " self.k = self.k - 1\n", + " ch = self.b[self.k]\n", + " if ch in 'lsz':\n", + " self.k += 1\n", + " elif self.m() == 1 and self.cvc(self.k):\n", + " self.setto(\"e\")\n", + "\n", + " def step1c(self):\n", + " \"\"\"step1c() turns terminal y to i when there is another vowel in the stem.\"\"\"\n", + " if self.ends(\"y\") and self.vowelinstem():\n", + " self.b = self.b[:self.k] + 'i' + self.b[self.k+1:]\n", + "\n", + " def step2(self):\n", + " \"\"\"step2() maps double suffices to single ones.\n", + " so -ization ( = -ize plus -ation) maps to -ize etc. note that the\n", + " string before the suffix must give m() > 0.\n", + " \"\"\"\n", + " if self.b[self.k - 1] == 'a':\n", + " if self.ends(\"ational\"): self.r(\"ate\")\n", + " elif self.ends(\"tional\"): self.r(\"tion\")\n", + " elif self.b[self.k - 1] == 'c':\n", + " if self.ends(\"enci\"): self.r(\"ence\")\n", + " elif self.ends(\"anci\"): self.r(\"ance\")\n", + " elif self.b[self.k - 1] == 'e':\n", + " if self.ends(\"izer\"): self.r(\"ize\")\n", + " elif self.b[self.k - 1] == 'l':\n", + " if self.ends(\"bli\"): self.r(\"ble\") # --DEPARTURE--\n", + " # To match the published algorithm, replace this phrase with\n", + " # if self.ends(\"abli\"): self.r(\"able\")\n", + " elif self.ends(\"alli\"): self.r(\"al\")\n", + " elif self.ends(\"entli\"): self.r(\"ent\")\n", + " elif self.ends(\"eli\"): self.r(\"e\")\n", + " elif self.ends(\"ousli\"): self.r(\"ous\")\n", + " elif self.b[self.k - 1] == 'o':\n", + " if self.ends(\"ization\"): self.r(\"ize\")\n", + " elif self.ends(\"ation\"): self.r(\"ate\")\n", + " elif self.ends(\"ator\"): self.r(\"ate\")\n", + " elif self.b[self.k - 1] == 's':\n", + " if self.ends(\"alism\"): self.r(\"al\")\n", + " elif self.ends(\"iveness\"): self.r(\"ive\")\n", + " elif self.ends(\"fulness\"): self.r(\"ful\")\n", + " elif self.ends(\"ousness\"): self.r(\"ous\")\n", + " elif self.b[self.k - 1] == 't':\n", + " if self.ends(\"aliti\"): self.r(\"al\")\n", + " elif self.ends(\"iviti\"): self.r(\"ive\")\n", + " elif self.ends(\"biliti\"): self.r(\"ble\")\n", + " elif self.b[self.k - 1] == 'g': # --DEPARTURE--\n", + " if self.ends(\"logi\"): self.r(\"log\")\n", + " # To match the published algorithm, delete this phrase\n", + "\n", + " def step3(self):\n", + " \"\"\"step3() dels with -ic-, -full, -ness etc. similar strategy to step2.\"\"\"\n", + " if self.b[self.k] == 'e':\n", + " if self.ends(\"icate\"): self.r(\"ic\")\n", + " elif self.ends(\"ative\"): self.r(\"\")\n", + " elif self.ends(\"alize\"): self.r(\"al\")\n", + " elif self.b[self.k] == 'i':\n", + " if self.ends(\"iciti\"): self.r(\"ic\")\n", + " elif self.b[self.k] == 'l':\n", + " if self.ends(\"ical\"): self.r(\"ic\")\n", + " elif self.ends(\"ful\"): self.r(\"\")\n", + " elif self.b[self.k] == 's':\n", + " if self.ends(\"ness\"): self.r(\"\")\n", + "\n", + " def step4(self):\n", + " \"\"\"step4() takes off -ant, -ence etc., in context vcvc.\"\"\"\n", + " if self.b[self.k - 1] == 'a':\n", + " if self.ends(\"al\"): pass\n", + " else: return\n", + " elif self.b[self.k - 1] == 'c':\n", + " if self.ends(\"ance\"): pass\n", + " elif self.ends(\"ence\"): pass\n", + " else: return\n", + " elif self.b[self.k - 1] == 'e':\n", + " if self.ends(\"er\"): pass\n", + " else: return\n", + " elif self.b[self.k - 1] == 'i':\n", + " if self.ends(\"ic\"): pass\n", + " else: return\n", + " elif self.b[self.k - 1] == 'l':\n", + " if self.ends(\"able\"): pass\n", + " elif self.ends(\"ible\"): pass\n", + " else: return\n", + " elif self.b[self.k - 1] == 'n':\n", + " if self.ends(\"ant\"): pass\n", + " elif self.ends(\"ement\"): pass\n", + " elif self.ends(\"ment\"): pass\n", + " elif self.ends(\"ent\"): pass\n", + " else: return\n", + " elif self.b[self.k - 1] == 'o':\n", + " if self.ends(\"ion\") and (self.b[self.j] == 's' or self.b[self.j] == 't'): pass\n", + " elif self.ends(\"ou\"): pass\n", + " # takes care of -ous\n", + " else: return\n", + " elif self.b[self.k - 1] == 's':\n", + " if self.ends(\"ism\"): pass\n", + " else: return\n", + " elif self.b[self.k - 1] == 't':\n", + " if self.ends(\"ate\"): pass\n", + " elif self.ends(\"iti\"): pass\n", + " else: return\n", + " elif self.b[self.k - 1] == 'u':\n", + " if self.ends(\"ous\"): pass\n", + " else: return\n", + " elif self.b[self.k - 1] == 'v':\n", + " if self.ends(\"ive\"): pass\n", + " else: return\n", + " elif self.b[self.k - 1] == 'z':\n", + " if self.ends(\"ize\"): pass\n", + " else: return\n", + " else:\n", + " return\n", + " if self.m() > 1:\n", + " self.k = self.j\n", + "\n", + " def step5(self):\n", + " \"\"\"step5() removes a final -e if m() > 1, and changes -ll to -l if\n", + " m() > 1.\n", + " \"\"\"\n", + " self.j = self.k\n", + " if self.b[self.k] == 'e':\n", + " a = self.m()\n", + " if a > 1 or (a == 1 and not self.cvc(self.k-1)):\n", + " self.k = self.k - 1\n", + " if self.b[self.k] == 'l' and self.doublec(self.k) and self.m() > 1:\n", + " self.k = self.k -1\n", + "\n", + " def stem(self, p, i=0, j=None):\n", + " \"\"\"In stem(p,i,j), p is a char pointer, and the string to be stemmed\n", + " is from p[i] to p[j] inclusive. Typically i is zero and j is the\n", + " offset to the last character of a string, (p[j+1] == '\\0'). The\n", + " stemmer adjusts the characters p[i] ... p[j] and returns the new\n", + " end-point of the string, k. Stemming never increases word length, so\n", + " i <= k <= j. To turn the stemmer into a module, declare 'stem' as\n", + " extern, and delete the remainder of this file.\n", + " \"\"\"\n", + " # copy the parameters into statics\n", + " self.b = p\n", + " self.k = j or len(p) - 1\n", + " self.k0 = i\n", + " if self.k <= self.k0 + 1:\n", + " return self.b # --DEPARTURE--\n", + "\n", + " # With this line, strings of length 1 or 2 don't go through the\n", + " # stemming process, although no mention is made of this in the\n", + " # published algorithm. Remove the line to match the published\n", + " # algorithm.\n", + "\n", + " self.step1ab()\n", + " self.step1c()\n", + " self.step2()\n", + " self.step3()\n", + " self.step4()\n", + " self.step5()\n", + " return self.b[self.k0:self.k+1]\n", + "\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Jwj0-AfHPJzb", + "colab_type": "text" + }, + "source": [ + "## Submission and Grading\n", + "\n", + "\n", + "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", + "\n", + "\n", + "| Section | Part | Submitted Function | Points |\n", + "| :- |:- |:- | :-: |\n", + "| 1 | [Gaussian Kernel](#section1) | [`gaussianKernel`](#gaussianKernel) | 25 |\n", + "| 2 | [Parameters (C, $\\sigma$) for Dataset 3](#section2)| [`dataset3Params`](#dataset3Params) | 25 |\n", + "| 3 | [Email Preprocessing](#section3) | [`processEmail`](#processEmail) | 25 |\n", + "| 4 | [Email Feature Extraction](#section4) | [`emailFeatures`](#emailFeatures) | 25 |\n", + "| | Total Points | |100 |\n", + "\n", + "\n", + "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "
\n", + "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CjI9RC-VPJzc", + "colab_type": "text" + }, + "source": [ + "## 1 Support Vector Machines\n", + "\n", + "In the first half of this exercise, you will be using support vector machines (SVMs) with various example 2D datasets. Experimenting with these datasets will help you gain an intuition of how SVMs work and how to use a Gaussian kernel with SVMs. In the next half of the exercise, you will be using support\n", + "vector machines to build a spam classifier." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wIMbMhUcPJzc", + "colab_type": "text" + }, + "source": [ + "### 1.1 Example Dataset 1\n", + "\n", + "We will begin by with a 2D example dataset which can be separated by a linear boundary. The following cell plots the training data, which should look like this:\n", + "\n", + "![Dataset 1 training data](Figures/dataset1.png)\n", + "\n", + "In this dataset, the positions of the positive examples (indicated with `x`) and the negative examples (indicated with `o`) suggest a natural separation indicated by the gap. However, notice that there is an outlier positive example `x` on the far left at about (0.1, 4.1). As part of this exercise, you will also see how this outlier affects the SVM decision boundary." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YDzxfDa5PJzd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "outputId": "55e00e4b-6ac0-416f-95d2-5754106a6c9d" + }, + "source": [ + "# Load from ex6data1\n", + "# You will have X, y as keys in the dict data\n", + "data = loadmat('ex6data1.mat')\n", + "X, y = data['X'], data['y'][:, 0]\n", + "\n", + "# Plot training data\n", + "plotData(X, y)" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q7MxHk0lPJzf", + "colab_type": "text" + }, + "source": [ + "In this part of the exercise, you will try using different values of the $C$ parameter with SVMs. Informally, the $C$ parameter is a positive value that controls the penalty for misclassified training examples. A large $C$ parameter tells the SVM to try to classify all the examples correctly. $C$ plays a role similar to $1/\\lambda$, where $\\lambda$ is the regularization parameter that we were using previously for logistic regression.\n", + "\n", + "\n", + "The following cell will run the SVM training (with $C=1$) using SVM software that we have included with the starter code (function `svmTrain` within the `utils` module of this exercise). When $C=1$, you should find that the SVM puts the decision boundary in the gap between the two datasets and *misclassifies* the data point on the far left, as shown in the figure (left) below.\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SVM Decision boundary for example dataset 1
C=1C=100
\n", + "\n", + "
\n", + "In order to minimize the dependency of this assignment on external libraries, we have included this implementation of an SVM learning algorithm in utils.svmTrain. However, this particular implementation is not very efficient (it was originally chosen to maximize compatibility between Octave/MATLAB for the first version of this assignment set). If you are training an SVM on a real problem, especially if you need to scale to a larger dataset, we strongly recommend instead using a highly optimized SVM toolbox such as [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/). The python machine learning library [scikit-learn](http://scikit-learn.org/stable/index.html) provides wrappers for the LIBSVM library.\n", + "
\n", + "
\n", + "
\n", + "**Implementation Note:** Most SVM software packages (including the function `utils.svmTrain`) automatically add the extra feature $x_0$ = 1 for you and automatically take care of learning the intercept term $\\theta_0$. So when passing your training data to the SVM software, there is no need to add this extra feature $x_0 = 1$ yourself. In particular, in python your code should be working with training examples $x \\in \\mathcal{R}^n$ (rather than $x \\in \\mathcal{R}^{n+1}$); for example, in the first example dataset $x \\in \\mathcal{R}^2$.\n", + "
\n", + "\n", + "Your task is to try different values of $C$ on this dataset. Specifically, you should change the value of $C$ in the next cell to $C = 100$ and run the SVM training again. When $C = 100$, you should find that the SVM now classifies every single example correctly, but has a decision boundary that does not\n", + "appear to be a natural fit for the data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4bYS2ZHEPJzg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 267 + }, + "outputId": "5bfe10c8-7bc0-49c1-93f9-7b04a9b94d1a" + }, + "source": [ + "# 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 = svmTrain(X, y, C, linearKernel, 1e-3, 20)\n", + "visualizeBoundaryLinear(X, y, model)" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BRPMkMxVPJzi", + "colab_type": "text" + }, + "source": [ + "\n", + "### 1.2 SVM with Gaussian Kernels\n", + "\n", + "In this part of the exercise, you will be using SVMs to do non-linear classification. In particular, you will be using SVMs with Gaussian kernels on datasets that are not linearly separable.\n", + "\n", + "#### 1.2.1 Gaussian Kernel\n", + "\n", + "To find non-linear decision boundaries with the SVM, we need to first implement a Gaussian kernel. You can think of the Gaussian kernel as a similarity function that measures the “distance” between a pair of examples,\n", + "($x^{(i)}$, $x^{(j)}$). The Gaussian kernel is also parameterized by a bandwidth parameter, $\\sigma$, which determines how fast the similarity metric decreases (to 0) as the examples are further apart.\n", + "You should now complete the code in `gaussianKernel` to compute the Gaussian kernel between two examples, ($x^{(i)}$, $x^{(j)}$). The Gaussian kernel function is defined as:\n", + "\n", + "$$ K_{\\text{gaussian}} \\left( x^{(i)}, x^{(j)} \\right) = \\exp \\left( - \\frac{\\left\\lvert\\left\\lvert x^{(i)} - x^{(j)}\\right\\lvert\\right\\lvert^2}{2\\sigma^2} \\right) = \\exp \\left( -\\frac{\\sum_{k=1}^n \\left( x_k^{(i)} - x_k^{(j)}\\right)^2}{2\\sigma^2} \\right)$$\n", + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "prJm7FaAPJzi", + "colab_type": "code", + "colab": {} + }, + "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(-np.sum((x1 - x2) ** 2) / (2 * (sigma ** 2)))\n", + "\n", + " # =============================================================\n", + " return sim" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z4ICyQvAPJzk", + "colab_type": "text" + }, + "source": [ + "Once you have completed the function `gaussianKernel` the following cell will test your kernel function on two provided examples and you should expect to see a value of 0.324652." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Rco6JcPsPJzl", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 84 + }, + "outputId": "93f94117-73fe-4113-c761-fd5b5ff18966" + }, + "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))" + ], + "execution_count": 6, + "outputs": [ + { + "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" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tKyxtab0PJzn", + "colab_type": "text" + }, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ny4bW73NPJzn", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x52mapoxPJzq", + "colab_type": "text" + }, + "source": [ + "### 1.2.2 Example Dataset 2\n", + "\n", + "The next part in this notebook will load and plot dataset 2, as shown in the figure below. \n", + "\n", + "![Dataset 2](Figures/dataset2.png)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CmkoTubhPJzq", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "outputId": "ed6cae4b-1648-4485-82ed-4a873cb83dfa" + }, + "source": [ + "# Load from ex6data2\n", + "# You will have X, y as keys in the dict data\n", + "data = loadmat('ex6data2.mat')\n", + "X, y = data['X'], data['y'][:, 0]\n", + "\n", + "# Plot training data\n", + "plotData(X, y)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GuxRvo-9PJzs", + "colab_type": "text" + }, + "source": [ + "From the figure, you can obserse that there is no linear decision boundary that separates the positive and negative examples for this dataset. However, by using the Gaussian kernel with the SVM, you will be able to learn a non-linear decision boundary that can perform reasonably well for the dataset. If you have correctly implemented the Gaussian kernel function, the following cell will proceed to train the SVM with the Gaussian kernel on this dataset.\n", + "\n", + "You should get a decision boundary as shown in the figure below, as computed by the SVM with a Gaussian kernel. The decision boundary is able to separate most of the positive and negative examples correctly and follows the contours of the dataset well.\n", + "\n", + "![Dataset 2 decision boundary](Figures/svm_dataset2.png)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vyr6wu00PJzs", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "outputId": "7d17cb99-75c8-4f93-c8f3-9e2dec3589b6" + }, + "source": [ + "# SVM Parameters\n", + "C = 1\n", + "sigma = 0.1\n", + "\n", + "model= svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n", + "visualizeBoundary(X, y, model)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TPzlZMk-PJzw", + "colab_type": "text" + }, + "source": [ + "\n", + "#### 1.2.3 Example Dataset 3\n", + "\n", + "In this part of the exercise, you will gain more practical skills on how to use a SVM with a Gaussian kernel. The next cell will load and display a third dataset, which should look like the figure below.\n", + "\n", + "![Dataset 3](Figures/dataset3.png)\n", + "\n", + "You will be using the SVM with the Gaussian kernel with this dataset. In the provided dataset, `ex6data3.mat`, you are given the variables `X`, `y`, `Xval`, `yval`. " + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "k-dU2W6ZPJzx", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "outputId": "0c84af5a-4170-444a-c03e-986c77eba4d9" + }, + "source": [ + "# Load from ex6data3\n", + "# You will have X, y, Xval, yval as keys in the dict data\n", + "data = loadmat( 'ex6data3.mat')\n", + "X, y, Xval, yval = data['X'], data['y'][:, 0], data['Xval'], data['yval'][:, 0]\n", + "\n", + "# Plot training data\n", + "plotData(X, y)" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xxo_ZddJPJzz", + "colab_type": "text" + }, + "source": [ + "Your task is to use the cross validation set `Xval`, `yval` to determine the best $C$ and $\\sigma$ parameter to use. You should write any additional code necessary to help you search over the parameters $C$ and $\\sigma$. For both $C$ and $\\sigma$, we suggest trying values in multiplicative steps (e.g., 0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30).\n", + "Note that you should try all possible pairs of values for $C$ and $\\sigma$ (e.g., $C = 0.3$ and $\\sigma = 0.1$). For example, if you try each of the 8 values listed above for $C$ and for $\\sigma^2$, you would end up training and evaluating (on the cross validation set) a total of $8^2 = 64$ different models. After you have determined the best $C$ and $\\sigma$ parameters to use, you should modify the code in `dataset3Params`, filling in the best parameters you found. For our best parameters, the SVM returned a decision boundary shown in the figure below. \n", + "\n", + "![](Figures/svm_dataset3_best.png)\n", + "\n", + "
\n", + "**Implementation Tip:** When implementing cross validation to select the best $C$ and $\\sigma$ parameter to use, you need to evaluate the error on the cross validation set. Recall that for classification, the error is defined as the fraction of the cross validation examples that were classified incorrectly. In `numpy`, you can compute this error using `np.mean(predictions != yval)`, where `predictions` is a vector containing all the predictions from the SVM, and `yval` are the true labels from the cross validation set. You can use the `utils.svmPredict` function to generate the predictions for the cross validation set.\n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8EH-QmDCPJz0", + "colab_type": "code", + "colab": {} + }, + "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", + " C_=np.array([0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30])\n", + " sigma_=np.array([ 0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30])\n", + " values=np.zeros((C_.size,sigma_.size))\n", + " min=1\n", + " for i in range (0,len(C_)):\n", + " for j in range(0,len(sigma_)):\n", + " model= svmTrain(X, y, C_[i], gaussianKernel, args=(sigma_[j],))\n", + " predictions = svmPredict(model, Xval)\n", + " pred_error = np.mean(predictions != yval)\n", + " if(pred_error" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wAy1IdhiPJz4", + "colab_type": "text" + }, + "source": [ + "One you have computed the values `C` and `sigma` in the cell above, we will submit those values for grading.\n", + "\n", + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NiPqCa_-PJz4", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zyzzvmcwPJz6", + "colab_type": "text" + }, + "source": [ + "\n", + "## 2 Spam Classification\n", + "\n", + "Many email services today provide spam filters that are able to classify emails into spam and non-spam email with high accuracy. In this part of the exercise, you will use SVMs to build your own spam filter.\n", + "\n", + "You will be training a classifier to classify whether a given email, $x$, is spam ($y = 1$) or non-spam ($y = 0$). In particular, you need to convert each email into a feature vector $x \\in \\mathbb{R}^n$ . The following parts of the exercise will walk you through how such a feature vector can be constructed from an email.\n", + "\n", + "The dataset included for this exercise is based on a a subset of the [SpamAssassin Public Corpus](http://spamassassin.apache.org/old/publiccorpus/). For the purpose of this exercise, you will only be using the body of the email (excluding the email headers)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ApXaCtn7PJz7", + "colab_type": "text" + }, + "source": [ + "### 2.1 Preprocessing Emails\n", + "\n", + "Before starting on a machine learning task, it is usually insightful to take a look at examples from the dataset. The figure below shows a sample email that contains a URL, an email address (at the end), numbers, and dollar\n", + "amounts.\n", + "\n", + "\n", + "\n", + "While many emails would contain similar types of entities (e.g., numbers, other URLs, or other email addresses), the specific entities (e.g., the specific URL or specific dollar amount) will be different in almost every\n", + "email. Therefore, one method often employed in processing emails is to “normalize” these values, so that all URLs are treated the same, all numbers are treated the same, etc. For example, we could replace each URL in the\n", + "email with the unique string “httpaddr” to indicate that a URL was present.\n", + "\n", + "This has the effect of letting the spam classifier make a classification decision based on whether any URL was present, rather than whether a specific URL was present. This typically improves the performance of a spam classifier, since spammers often randomize the URLs, and thus the odds of seeing any particular URL again in a new piece of spam is very small. \n", + "\n", + "In the function `processEmail` below, we have implemented the following email preprocessing and normalization steps:\n", + "\n", + "- **Lower-casing**: The entire email is converted into lower case, so that captialization is ignored (e.g., IndIcaTE is treated the same as Indicate).\n", + "\n", + "- **Stripping HTML**: All HTML tags are removed from the emails. Many emails often come with HTML formatting; we remove all the HTML tags, so that only the content remains.\n", + "\n", + "- **Normalizing URLs**: All URLs are replaced with the text “httpaddr”.\n", + "\n", + "- **Normalizing Email Addresses**: All email addresses are replaced with the text “emailaddr”.\n", + "\n", + "- **Normalizing Numbers**: All numbers are replaced with the text “number”.\n", + "\n", + "- **Normalizing Dollars**: All dollar signs ($) are replaced with the text “dollar”.\n", + "\n", + "- **Word Stemming**: Words are reduced to their stemmed form. For example, “discount”, “discounts”, “discounted” and “discounting” are all replaced with “discount”. Sometimes, the Stemmer actually strips off additional characters from the end, so “include”, “includes”, “included”, and “including” are all replaced with “includ”.\n", + "\n", + "- **Removal of non-words**: Non-words and punctuation have been removed. All white spaces (tabs, newlines, spaces) have all been trimmed to a single space character.\n", + "\n", + "The result of these preprocessing steps is shown in the figure below. \n", + "\n", + "\"email\n", + "\n", + "While preprocessing has left word fragments and non-words, this form turns out to be much easier to work with for performing feature extraction." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0qiLdrUxPJz7", + "colab_type": "text" + }, + "source": [ + "#### 2.1.1 Vocabulary List\n", + "\n", + "After preprocessing the emails, we have a list of words for each email. The next step is to choose which words we would like to use in our classifier and which we would want to leave out.\n", + "\n", + "For this exercise, we have chosen only the most frequently occuring words as our set of words considered (the vocabulary list). Since words that occur rarely in the training set are only in a few emails, they might cause the\n", + "model to overfit our training set. The complete vocabulary list is in the file `vocab.txt` (inside the `Data` directory for this exercise) and also shown in the figure below.\n", + "\n", + "\"Vocab\"\n", + "\n", + "Our vocabulary list was selected by choosing all words which occur at least a 100 times in the spam corpus,\n", + "resulting in a list of 1899 words. In practice, a vocabulary list with about 10,000 to 50,000 words is often used.\n", + "Given the vocabulary list, we can now map each word in the preprocessed emails into a list of word indices that contains the index of the word in the vocabulary dictionary. The figure below shows the mapping for the sample email. Specifically, in the sample email, the word “anyone” was first normalized to “anyon” and then mapped onto the index 86 in the vocabulary list.\n", + "\n", + "\"word\n", + "\n", + "Your task now is to complete the code in the function `processEmail` to perform this mapping. In the code, you are given a string `word` which is a single word from the processed email. You should look up the word in the vocabulary list `vocabList`. If the word exists in the list, you should add the index of the word into the `word_indices` variable. If the word does not exist, and is therefore not in the vocabulary, you can skip the word.\n", + "\n", + "
\n", + "**python tip**: In python, you can find the index of the first occurence of an item in `list` using the `index` attribute. In the provided code for `processEmail`, `vocabList` is a python list containing the words in the vocabulary. To find the index of a word, we can use `vocabList.index(word)` which would return a number indicating the index of the word within the list. If the word does not exist in the list, a `ValueError` exception is raised. In python, we can use the `try/except` statement to catch exceptions which we do not want to stop the program from running. You can think of the `try/except` statement to be the same as an `if/else` statement, but it asks for forgiveness rather than permission.\n", + "\n", + "An example would be:\n", + "
\n", + "\n", + "```\n", + "try:\n", + " do stuff here\n", + "except ValueError:\n", + " pass\n", + " # do nothing (forgive me) if a ValueError exception occured within the try statement\n", + "```\n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3lN_FRSmPJz7", + "colab_type": "code", + "colab": {} + }, + "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 = 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 = 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", + " for word in processed_email:\n", + " for idx,word_ in enumerate(vocablist):\n", + " if(word==word_):\n", + " word_indices.append(idx)\n", + "\n", + " \n", + " \n", + "\n", + " # =============================================================\n", + "\n", + " if verbose:\n", + " print('----------------')\n", + " print('Processed email:')\n", + " print('----------------')\n", + " print(' '.join(processed_email))\n", + " return word_indices" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SEe7pKy0PJz9", + "colab_type": "text" + }, + "source": [ + "Once you have implemented `processEmail`, the following cell will run your code on the email sample and you should see an output of the processed email and the indices list mapping." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BcdN-X5C-N-7", + "colab_type": "text" + }, + "source": [ + "# **The output doesnt show due to a function join in the get vocablist function in the utils.py file whic is not being recognised**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uSNA2wN1PJz-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 370 + }, + "outputId": "716f83f9-2f4b-493e-be70-070e9a696b67" + }, + "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('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) " + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mfile_contents\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mword_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocessEmail\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m#Print Stats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mprocessEmail\u001b[0;34m(email_contents, verbose)\u001b[0m\n\u001b[1;32m 39\u001b[0m \"\"\"\n\u001b[1;32m 40\u001b[0m \u001b[0;31m# Load Vocabulary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mvocabList\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetVocabList\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;31m# Init return value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mgetVocabList\u001b[0;34m()\u001b[0m\n\u001b[1;32m 322\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;32mreturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \"\"\"\n\u001b[0;32m--> 324\u001b[0;31m \u001b[0mvocabList\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenfromtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'vocab.txt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 325\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvocabList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'join' is not defined" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E0We5b5aPJ0A", + "colab_type": "text" + }, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9mGkwBwXPJ0A", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pr1-f-yQPJ0C", + "colab_type": "text" + }, + "source": [ + "\n", + "### 2.2 Extracting Features from Emails\n", + "\n", + "You will now implement the feature extraction that converts each email into a vector in $\\mathbb{R}^n$. For this exercise, you will be using n = # words in vocabulary list. Specifically, the feature $x_i \\in \\{0, 1\\}$ for an email corresponds to whether the $i^{th}$ word in the dictionary occurs in the email. That is, $x_i = 1$ if the $i^{th}$ word is in the email and $x_i = 0$ if the $i^{th}$ word is not present in the email.\n", + "\n", + "Thus, for a typical email, this feature would look like:\n", + "\n", + "$$ x = \\begin{bmatrix} \n", + "0 & \\dots & 1 & 0 & \\dots & 1 & 0 & \\dots & 0 \n", + "\\end{bmatrix}^T \\in \\mathbb{R}^n\n", + "$$\n", + "\n", + "You should now complete the code in the function `emailFeatures` to generate a feature vector for an email, given the `word_indices`.\n", + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5BwDhXGYPJ0C", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def emailFeatures(word_indices):\n", + " \"\"\"\n", + " Takes in a word_indices vector and produces a feature vector from the word indices. \n", + " \n", + " Parameters\n", + " ----------\n", + " word_indices : list\n", + " A list of word indices from the vocabulary list.\n", + " \n", + " Returns\n", + " -------\n", + " x : list \n", + " The computed feature vector.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to return a feature vector for the\n", + " given email (word_indices). To help make it easier to process \n", + " the emails, we have have already pre-processed each email and converted\n", + " each word in the email into an index in a fixed dictionary (of 1899 words).\n", + " The variable `word_indices` contains the list of indices of the words \n", + " which occur in one email.\n", + " \n", + " Concretely, if an email has the text:\n", + "\n", + " The quick brown fox jumped over the lazy dog.\n", + "\n", + " Then, the word_indices vector for this text might look like:\n", + " \n", + " 60 100 33 44 10 53 60 58 5\n", + "\n", + " where, we have mapped each word onto a number, for example:\n", + "\n", + " the -- 60\n", + " quick -- 100\n", + " ...\n", + "\n", + " Note\n", + " ----\n", + " The above numbers are just an example and are not the actual mappings.\n", + "\n", + " Your task is take one such `word_indices` vector and construct\n", + " a binary feature vector that indicates whether a particular\n", + " word occurs in the email. That is, x[i] = 1 when word i\n", + " is present in the email. Concretely, if the word 'the' (say,\n", + " index 60) appears in the email, then x[60] = 1. The feature\n", + " vector should look like:\n", + " x = [ 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 ... 0 0 0 1 0 ..]\n", + " \"\"\"\n", + " # Total number of words in the dictionary\n", + " n = 1899\n", + "\n", + " # You need to return the following variables correctly.\n", + " x = np.zeros(n)\n", + "\n", + " # ===================== YOUR CODE HERE ======================\n", + " for i in word_indices:\n", + " x[i]=1\n", + " \n", + " \n", + " # ===========================================================\n", + " \n", + " return x" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xXw4YmREPJ0E", + "colab_type": "text" + }, + "source": [ + "Once you have implemented `emailFeatures`, the next cell will run your code on the email sample. You should see that the feature vector had length 1899 and 45 non-zero entries." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yUzHGgqAPJ0E", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 229 + }, + "outputId": "56d212a7-a0ff-433b-8531-8888e68e4314" + }, + "source": [ + "# Extract Features\n", + "with open('emailSample1.txt') as fid:\n", + " file_contents = fid.read()\n", + "\n", + "word_indices = processEmail(file_contents)\n", + "features = emailFeatures(word_indices)\n", + "\n", + "# Print Stats\n", + "print('\\nLength of feature vector: %d' % len(features))\n", + "print('Number of non-zero entries: %d' % sum(features > 0))" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "error", + "ename": "FileNotFoundError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'emailSample1.txt'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mfile_contents\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mword_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocessEmail\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0memailFeatures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'emailSample1.txt'" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-3HEO3FEPJ0G", + "colab_type": "text" + }, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "x1LUlNSfPJ0G", + "colab_type": "code", + "colab": {} + }, + "source": [ + "grader[4] = emailFeatures\n", + "grader.grade()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x0U3IHg9PJ0I", + "colab_type": "text" + }, + "source": [ + "### 2.3 Training SVM for Spam Classification\n", + "\n", + "In the following section we will load a preprocessed training dataset that will be used to train a SVM classifier. The file `spamTrain.mat` (within the `Data` folder for this exercise) contains 4000 training examples of spam and non-spam email, while `spamTest.mat` contains 1000 test examples. Each\n", + "original email was processed using the `processEmail` and `emailFeatures` functions and converted into a vector $x^{(i)} \\in \\mathbb{R}^{1899}$.\n", + "\n", + "After loading the dataset, the next cell proceed to train a linear SVM to classify between spam ($y = 1$) and non-spam ($y = 0$) emails. Once the training completes, you should see that the classifier gets a training accuracy of about 99.8% and a test accuracy of about 98.5%." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MSVZH1CQPJ0I", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Load the Spam Email dataset\n", + "# You will have X, y in your environment\n", + "data = loadmat('spamTrain.mat')\n", + "X, y= data['X'].astype(float), data['y'][:, 0]\n", + "\n", + "print('Training Linear SVM (Spam Classification)')\n", + "print('This may take 1 to 2 minutes ...\\n')\n", + "\n", + "C = 0.1\n", + "model =svmTrain(X, y, C,linearKernel)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "d40tisb0PJ0K", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Compute the training accuracy\n", + "p = svmPredict(model, X)\n", + "\n", + "print('Training Accuracy: %.2f' % (np.mean(p == y) * 100))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ax8ahnzwPJ0M", + "colab_type": "text" + }, + "source": [ + "Execute the following cell to load the test set and compute the test accuracy." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XvoWkWWlPJ0M", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Load the test dataset\n", + "# You will have Xtest, ytest in your environment\n", + "data = loadmat('spamTest.mat'))\n", + "Xtest, ytest = data['Xtest'].astype(float), data['ytest'][:, 0]\n", + "\n", + "print('Evaluating the trained Linear SVM on a test set ...')\n", + "p = svmPredict(model, Xtest)\n", + "\n", + "print('Test Accuracy: %.2f' % (np.mean(p == ytest) * 100))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VzPW2DbEPJ0O", + "colab_type": "text" + }, + "source": [ + "### 2.4 Top Predictors for Spam\n", + "\n", + "To better understand how the spam classifier works, we can inspect the parameters to see which words the classifier thinks are the most predictive of spam. The next cell finds the parameters with the largest positive values in the classifier and displays the corresponding words similar to the ones shown in the figure below.\n", + "\n", + "
\n", + "our click remov guarante visit basenumb dollar pleas price will nbsp most lo ga hour\n", + "
\n", + "\n", + "Thus, if an email contains words such as “guarantee”, “remove”, “dollar”, and “price” (the top predictors shown in the figure), it is likely to be classified as spam.\n", + "\n", + "Since the model we are training is a linear SVM, we can inspect the weights learned by the model to understand better how it is determining whether an email is spam or not. The following code finds the words with the highest weights in the classifier. Informally, the classifier 'thinks' that these words are the most likely indicators of spam." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FVqaJiptPJ0P", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Sort the weights and obtin the vocabulary list\n", + "# NOTE some words have the same weights, \n", + "# so their order might be different than in the text above\n", + "idx = np.argsort(model['w'])\n", + "top_idx = idx[-15:][::-1]\n", + "vocabList = getVocabList()\n", + "\n", + "print('Top predictors of spam:')\n", + "print('%-15s %-15s' % ('word', 'weight'))\n", + "print('----' + ' '*12 + '------')\n", + "for word, w in zip(np.array(vocabList)[top_idx], model['w'][top_idx]):\n", + " print('%-15s %0.2f' % (word, w))\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aGvNBqncPJ0Q", + "colab_type": "text" + }, + "source": [ + "### 2.5 Optional (ungraded) exercise: Try your own emails\n", + "\n", + "Now that you have trained a spam classifier, you can start trying it out on your own emails. In the starter code, we have included two email examples (`emailSample1.txt` and `emailSample2.txt`) and two spam examples (`spamSample1.txt` and `spamSample2.txt`). The next cell runs the spam classifier over the first spam example and classifies it using the learned SVM. You should now try the other examples we have provided and see if the classifier gets them right. You can also try your own emails by replacing the examples (plain text files) with your own emails.\n", + "\n", + "*You do not need to submit any solutions for this optional (ungraded) exercise.*" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "X1rf_qR6PJ0Q", + "colab_type": "code", + "colab": {} + }, + "source": [ + "filename = 'emailSample1.txt'\n", + "\n", + "with open(filename) as fid:\n", + " file_contents = fid.read()\n", + "\n", + "word_indices = processEmail(file_contents, verbose=False)\n", + "x = emailFeatures(word_indices)\n", + "p = svmPredict(model, x)\n", + "\n", + "print('\\nProcessed %s\\nSpam Classification: %s' % (filename, 'spam' if p else 'not spam'))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YOcaeudMPJ0S", + "colab_type": "text" + }, + "source": [ + "### 2.6 Optional (ungraded) exercise: Build your own dataset\n", + "\n", + "In this exercise, we provided a preprocessed training set and test set. These datasets were created using the same functions (`processEmail` and `emailFeatures`) that you now have completed. For this optional (ungraded) exercise, you will build your own dataset using the original emails from the SpamAssassin Public Corpus.\n", + "\n", + "Your task in this optional (ungraded) exercise is to download the original\n", + "files from the public corpus and extract them. After extracting them, you should run the `processEmail` and `emailFeatures` functions on each email to extract a feature vector from each email. This will allow you to build a dataset `X`, `y` of examples. You should then randomly divide up the dataset into a training set, a cross validation set and a test set.\n", + "\n", + "While you are building your own dataset, we also encourage you to try building your own vocabulary list (by selecting the high frequency words that occur in the dataset) and adding any additional features that you think\n", + "might be useful. Finally, we also suggest trying to use highly optimized SVM toolboxes such as [`LIBSVM`](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) or [`scikit-learn`](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm).\n", + "\n", + "*You do not need to submit any solutions for this optional (ungraded) exercise.*" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "12pRsOxgPJ0S", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From fdae28c5e0739f98e98b3b8a02919233b68472e3 Mon Sep 17 00:00:00 2001 From: Aishik13012002 <51096112+Aishik13012002@users.noreply.github.com> Date: Mon, 11 May 2020 14:03:37 +0530 Subject: [PATCH 13/14] Aishik Rakshit 190122002 w07 --- .../Aishik Rakshit 190122002 w7.ipynb | 6157 +++++++++++++++++ 1 file changed, 6157 insertions(+) create mode 100644 Phase 3 - 2020 (Summer)/Week 7(May 10-May 16)/Aishik Rakshit 190122002 w7.ipynb diff --git a/Phase 3 - 2020 (Summer)/Week 7(May 10-May 16)/Aishik Rakshit 190122002 w7.ipynb b/Phase 3 - 2020 (Summer)/Week 7(May 10-May 16)/Aishik Rakshit 190122002 w7.ipynb new file mode 100644 index 000000000..f3687780c --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 7(May 10-May 16)/Aishik Rakshit 190122002 w7.ipynb @@ -0,0 +1,6157 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "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": "3.6.4" + }, + "colab": { + "name": "exercise7.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "oVCC7GRJgybh", + "colab_type": "text" + }, + "source": [ + "# Programming Exercise 7:\n", + "# K-means Clustering and Principal Component Analysis\n", + "\n", + "## Introduction\n", + "\n", + "In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics.\n", + "\n", + "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", + "\n", + "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kOISOllNgybi", + "colab_type": "code", + "colab": {} + }, + "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", + "from mpl_toolkits.mplot3d import Axes3D\n", + "import matplotlib as mpl\n", + "\n", + "from IPython.display import HTML, display, clear_output\n", + "\n", + "try:\n", + " pyplot.rcParams[\"animation.html\"] = \"jshtml\"\n", + "except ValueError:\n", + " pyplot.rcParams[\"animation.html\"] = \"html5\"\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", + "%load_ext autoreload \n", + "%autoreload 2\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" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r2reG38Egybl", + "colab_type": "text" + }, + "source": [ + "## Submission and Grading\n", + "\n", + "\n", + "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", + "\n", + "\n", + "| Section | Part | Submitted Function | Points |\n", + "| :- |:- |:- | :-: |\n", + "| 1 | [Find Closest Centroids](#section1) | [`findClosestCentroids`](#findClosestCentroids) | 30 |\n", + "| 2 | [Computed Centroid Means](#section2) | [`computeCentroids`](#computeCentroids) | 30 |\n", + "| 3 | [PCA](#section3) | [`pca`](#pca) | 20 |\n", + "| 4 | [Project Data](#section4) | [`projectData`](#projectData) | 10 |\n", + "| 5 | [Recover Data](#section5) | [`recoverData`](#recoverData) | 10 |\n", + "| | Total Points | |100 |\n", + "\n", + "\n", + "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "
\n", + "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0vxd0iVMgybl", + "colab_type": "text" + }, + "source": [ + "## 1 K-means Clustering\n", + "\n", + "In this exercise, you will implement K-means algorithm and use it for image compression. You will first start on an example 2D dataset that will help you gain an intuition of how the K-means algorithm works. After\n", + "that, you wil use the K-means algorithm for image compression by reducing the number of colors that occur in an image to only those that are most common in that image.\n", + "\n", + "### 1.1 Implementing K-means\n", + "\n", + "The K-means algorithm is a method to automatically cluster similar data examples together. Concretely, you are given a training set $\\{x^{(1)} , \\cdots, x^{(m)}\\}$ (where $x^{(i)} \\in \\mathbb{R}^n$), and want to group the data into a few cohesive “clusters”. The intuition behind K-means is an iterative procedure that starts by guessing the initial centroids, and then refines this guess by repeatedly assigning examples to their closest centroids and then recomputing the centroids based on the assignments.\n", + "\n", + "The K-means algorithm is as follows:\n", + "\n", + "```python\n", + "centroids = kMeansInitCentroids(X, K)\n", + "for i in range(iterations):\n", + " # Cluster assignment step: Assign each data point to the\n", + " # closest centroid. idx[i] corresponds to cˆ(i), the index\n", + " # of the centroid assigned to example i\n", + " idx = findClosestCentroids(X, centroids)\n", + " \n", + " # Move centroid step: Compute means based on centroid\n", + " # assignments\n", + " centroids = computeMeans(X, idx, K)\n", + "```\n", + "\n", + "The inner-loop of the algorithm repeatedly carries out two steps: (1) Assigning each training example $x^{(i)}$ to its closest centroid, and (2) Recomputing the mean of each centroid using the points assigned to it. The K-means algorithm will always converge to some final set of means for the centroids. Note that the converged solution may not always be ideal and depends on the initial setting of the centroids. Therefore, in practice the K-means algorithm is usually run a few times with different random initializations. One way to choose between these different solutions from different random initializations is to choose the one with the lowest cost function value (distortion). You will implement the two phases of the K-means algorithm separately\n", + "in the next sections." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5qCp6PO3gybm", + "colab_type": "text" + }, + "source": [ + "\n", + "#### 1.1.1 Finding closest centroids\n", + "\n", + "In the “cluster assignment” phase of the K-means algorithm, the algorithm assigns every training example $x^{(i)}$ to its closest centroid, given the current positions of centroids. Specifically, for every example $i$ we set\n", + "\n", + "$$c^{(i)} := j \\quad \\text{that minimizes} \\quad \\lvert\\rvert x^{(i)} - \\mu_j \\lvert\\rvert^2, $$\n", + "\n", + "where $c^{(i)}$ is the index of the centroid that is closest to $x^{(i)}$, and $\\mu_j$ is the position (value) of the $j^{th}$ centroid. Note that $c^{(i)}$ corresponds to `idx[i]` in the starter code.\n", + "\n", + "Your task is to complete the code in the function `findClosestCentroids`. This function takes the data matrix `X` and the locations of all centroids inside `centroids` and should output a one-dimensional array `idx` that holds the index (a value in $\\{1, ..., K\\}$, where $K$ is total number of centroids) of the closest centroid to every training example.\n", + "\n", + "You can implement this using a loop over every training example and every centroid.\n", + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "W9MYgaFhgybm", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def findClosestCentroids(X, centroids):\n", + " \"\"\"\n", + " Computes the centroid memberships for every example.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of size (m, n) where each row is a single example. \n", + " That is, we have m examples each of n dimensions.\n", + " \n", + " centroids : array_like\n", + " The k-means centroids of size (K, n). K is the number\n", + " of clusters, and n is the the data dimension.\n", + " \n", + " Returns\n", + " -------\n", + " idx : array_like\n", + " A vector of size (m, ) which holds the centroids assignment for each\n", + " example (row) in the dataset X.\n", + " \n", + " Instructions\n", + " ------------\n", + " Go over every example, find its closest centroid, and store\n", + " the index inside `idx` at the appropriate location.\n", + " Concretely, idx[i] should contain the index of the centroid\n", + " closest to example i. Hence, it should be a value in the \n", + " range 0..K-1\n", + "\n", + " Note\n", + " ----\n", + " You can use a for-loop over the examples to compute this.\n", + " \"\"\"\n", + " # Set K\n", + " K = centroids.shape[0]\n", + "\n", + " # You need to return the following variables correctly.\n", + " idx = np.zeros(X.shape[0], dtype=int)\n", + " \n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " m=X.shape[0]\n", + " \n", + " for i in range(0,m):\n", + " min=1000\n", + " for j in range(0,K):\n", + " dist=np.sum((X[i,:]-centroids[j,:])**2)\n", + " if (dist 729\u001b[0;31m \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m 474\u001b[0m \"\"\"\n\u001b[0;32m--> 475\u001b[0;31m \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 476\u001b[0m \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: ", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mgrader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfindClosestCentroids\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mgrader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrade\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/content/submission.py\u001b[0m in \u001b[0;36mgrade\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgrade\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\nSubmitting Solutions | Programming Exercise %s\\n'\u001b[0m \u001b[0;34m%\u001b[0m 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Specifically, for every centroid $k$ we set\n", + "\n", + "$$ \\mu_k := \\frac{1}{\\left| C_k\\right|} \\sum_{i \\in C_k} x^{(i)}$$\n", + "\n", + "where $C_k$ is the set of examples that are assigned to centroid $k$. Concretely, if two examples say $x^{(3)}$ and $x^{(5)}$ are assigned to centroid $k = 2$, then you should update $\\mu_2 = \\frac{1}{2} \\left( x^{(3)} + x^{(5)} \\right)$.\n", + "\n", + "You should now complete the code in the function `computeCentroids`. You can implement this function using a loop over the centroids. You can also use a loop over the examples; but if you can use a vectorized implementation that does not use such a loop, your code may run faster.\n", + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ekMev1nFgybu", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def computeCentroids(X, idx, K):\n", + " \"\"\"\n", + " Returns the new centroids by computing the means of the data points\n", + " assigned to each centroid.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The datset where each row is a single data point. That is, it \n", + " is a matrix of size (m, n) where there are m datapoints each\n", + " having n dimensions. \n", + " \n", + " idx : array_like \n", + " A vector (size m) of centroid assignments (i.e. each entry in range [0 ... K-1])\n", + " for each example.\n", + " \n", + " K : int\n", + " Number of clusters\n", + " \n", + " Returns\n", + " -------\n", + " centroids : array_like\n", + " A matrix of size (K, n) where each row is the mean of the data \n", + " points assigned to it.\n", + " \n", + " Instructions\n", + " ------------\n", + " Go over every centroid and compute mean of all points that\n", + " belong to it. Concretely, the row vector centroids[i, :]\n", + " should contain the mean of the data points assigned to\n", + " cluster i.\n", + "\n", + " Note:\n", + " -----\n", + " You can use a for-loop over the centroids to compute this.\n", + " \"\"\"\n", + " # Useful variables\n", + " m, n = X.shape\n", + " # You need to return the following variables correctly.\n", + " centroids = np.zeros((K, n))\n", + "\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " for i in range(0,K):\n", + " ci=X[idx==i]\n", + " centroids[i]=np.mean(ci,axis=0)\n", + " \n", + "\n", + " \n", + " \n", + " # =============================================================\n", + " return centroids" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3ZicJoSggybw", + "colab_type": "text" + }, + "source": [ + "Once you have completed the code in `computeCentroids`, the following cell will run your code and output the centroids after the first step of K-means." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8Bib76A0gybx", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 168 + }, + "outputId": "7bdb977e-6f92-4af0-a9d2-8bac43d930ca" + }, + "source": [ + "# Compute means based on the closest centroids found in the previous part.\n", + "centroids = computeCentroids(X, idx, K)\n", + "\n", + "print('Centroids computed after initial finding of closest centroids:')\n", + "print(centroids)\n", + "print('\\nThe centroids should be')\n", + "print(' [ 2.428301 3.157924 ]')\n", + "print(' [ 5.813503 2.633656 ]')\n", + "print(' [ 7.119387 3.616684 ]')" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Centroids computed after initial finding of closest centroids:\n", + "[[2.42830111 3.15792418]\n", + " [5.81350331 2.63365645]\n", + " [7.11938687 3.6166844 ]]\n", + "\n", + "The centroids should be\n", + " [ 2.428301 3.157924 ]\n", + " [ 5.813503 2.633656 ]\n", + " [ 7.119387 3.616684 ]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MqmnXJOvgyb0", + "colab_type": "text" + }, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wVUeEI7lgyb0", + "colab_type": "code", + "colab": {} + }, + "source": [ + "grader[2] = computeCentroids\n", + "grader.grade()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1_sjuPWtgyb2", + "colab_type": "text" + }, + "source": [ + "### 1.2 K-means on example dataset \n", + "\n", + "After you have completed the two functions (`findClosestCentroids` and `computeCentroids`), you have all the necessary pieces to run the K-means algorithm. The next cell will run the K-means algorithm on a toy 2D dataset to help you understand how K-means works. Your functions are called from inside the `runKmeans` function (in this assignment's `utils.py` module). We encourage you to take a look at the function to understand how it works. Notice that the code calls the two functions you implemented in a loop.\n", + "\n", + "When you run the next step, the K-means code will produce an animation that steps you through the progress of the algorithm at each iteration. At the end, your figure should look as the one displayed below.\n", + "\n", + "![](Figures/kmeans_result.png)" + ] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": false, + "id": "Fp6qu7Tfgyb2", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 655 + }, + "outputId": "a99978a6-e2d5-435c-b65b-777b7f32c1a9" + }, + "source": [ + "# Load an example dataset\n", + "data = loadmat('ex7data2.mat')\n", + "\n", + "# Settings for running K-Means\n", + "K = 3\n", + "max_iters = 10\n", + "\n", + "# For consistency, here we set centroids to specific values\n", + "# but in practice you want to generate them automatically, such as by\n", + "# settings them to be random examples (as can be seen in\n", + "# kMeansInitCentroids).\n", + "initial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\n", + "\n", + "\n", + "# Run K-Means algorithm. The 'true' at the end tells our function to plot\n", + "# the progress of K-Means\n", + "centroids, idx, anim = utils.runkMeans(X, initial_centroids,\n", + " findClosestCentroids, computeCentroids, max_iters, True)\n", + "anim" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xHsIYwgKgyb4", + "colab_type": "text" + }, + "source": [ + "### 1.3 Random initialization \n", + "\n", + "The initial assignments of centroids for the example dataset in the previous cell were designed so that you will see the same figure as that shown in the cell above. In practice, a\n", + "good strategy for initializing the centroids is to select random examples from the training set.\n", + "\n", + "In this part of the exercise, you should complete the function `kMeansInitCentroids` with the following code:\n", + "\n", + "```python\n", + "# Initialize the centroids to be random examples\n", + "\n", + "# Randomly reorder the indices of examples\n", + "randidx = np.random.permutation(X.shape[0])\n", + "# Take the first K examples as centroids\n", + "centroids = X[randidx[:K], :]\n", + "```\n", + "\n", + "The code above first randomly permutes the indices of the examples (using `permute` within the `numpy.random` module). Then, it selects the first $K$ examples based on the random permutation of the indices. This allows the examples to be selected at random without the risk of selecting the same example twice.\n", + "\n", + "*You do not need to make any submission for this part of the exercise*\n", + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gE3lUxW_gyb5", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def kMeansInitCentroids(X, K):\n", + " \"\"\"\n", + " This function initializes K centroids that are to be used in K-means on the dataset x.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like \n", + " The dataset of size (m x n).\n", + " \n", + " K : int\n", + " The number of clusters.\n", + " \n", + " Returns\n", + " -------\n", + " centroids : array_like\n", + " Centroids of the clusters. This is a matrix of size (K x n).\n", + " \n", + " Instructions\n", + " ------------\n", + " You should set centroids to randomly chosen examples from the dataset X.\n", + " \"\"\"\n", + " m, n = X.shape\n", + " \n", + " # You should return this values correctly\n", + " centroids = np.zeros((K, n))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " # Randomly reorder the indices of examples\n", + " randidx = np.random.permutation(X.shape[0])\n", + " # Take the first K examples as centroids\n", + " centroids = X[randidx[:K], :]\n", + "\n", + " \n", + " # =============================================================\n", + " return centroids" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rVE0nuE3gyb7", + "colab_type": "text" + }, + "source": [ + "### 1.4 Image compression with K-means\n", + "\n", + "In this exercise, you will apply K-means to image compression. We will use the image below as an example (property of Frank Wouters with permission to this class).\n", + "\n", + "![](Data/bird_small.png)\n", + "\n", + "In a straightforward 24-bit color representation of an image, each pixel is represented as three 8-bit unsigned integers (ranging from 0 to 255) that specify the red, green and blue intensity values. This encoding is often referred to as the RGB encoding. Our image contains thousands of colors, and in this part of the exercise, you will reduce the number of colors to 16 colors.\n", + "\n", + "By making this reduction, it is possible to represent (compress) the photo in an efficient way. Specifically, you only need to store the RGB values of the 16 selected colors, and for each pixel in the image you now need to only store the index of the color at that location (where only 4 bits are necessary to represent 16 possibilities).\n", + "\n", + "In this exercise, you will use the K-means algorithm to select the 16 colors that will be used to represent the compressed image. Concretely, you will treat every pixel in the original image as a data example and use the K-means algorithm to find the 16 colors that best group (cluster) the pixels in the 3-dimensional RGB space. Once you have computed the cluster centroids on the image, you will then use the 16 colors to replace the pixels in the original image.\n", + "\n", + "#### 1.4.1 K-means on pixels\n", + "\n", + "In python, images can be read in as follows:\n", + "\n", + "```python\n", + "# Load 128x128 color image (bird_small.png)\n", + "img = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n", + "\n", + "# We have already imported matplotlib as mpl at the beginning of this notebook.\n", + "```\n", + "This creates a three-dimensional matrix `A` whose first two indices identify a pixel position and whose last index represents red, green, or blue. For example, A[50, 33, 2] gives the blue intensity of the pixel at row 51 and column 34.\n", + "\n", + "The code in the following cell first loads the image, and then reshapes it to create an m x 3 matrix of pixel colors (where m = 16384 = 128 x 128), and calls your K-means function on it.\n", + "\n", + "After finding the top K = 16 colors to represent the image, you can now assign each pixel position to its closest centroid using the `findClosestCentroids` function. This allows you to represent the original image using the centroid assignments of each pixel. Notice that you have significantly reduced the number of bits that are required to describe the image. The original image required 24 bits for each one of the 128 x 128 pixel locations, resulting in total size of 128 x 128 x 24 = 393,216 bits. The new representation requires some overhead storage in form of a dictionary of 16 colors, each of which require 24 bits, but the image itself then only requires 4 bits per pixel location. The final number of bits used is therefore 16 x 24 + 128 x 128 x 4 = 65,920 bits, which corresponds to compressing the original image by about a factor of 6.\n", + "\n", + "Finally, you can view the effects of the compression by reconstructing the image based only on the centroid assignments. Specifically, you can replace each pixel location with the mean of the centroid assigned to it. The figure below shows the reconstruction we obtained. \n", + "\n", + "![](Figures/bird_compression.png)\n", + "\n", + "Even though the resulting image retains most of the characteristics of the original, we also see some compression artifacts.\n", + "\n", + "Run the following cell to compute the centroids and the centroid allocation of each pixel in the image." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ojwQcIqDgyb7", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 267 + }, + "outputId": "ee6496f5-4e1b-410c-98c5-b73781c469cd" + }, + "source": [ + "# ======= Experiment with these parameters ================\n", + "# You should try different values for those parameters\n", + "K = 16\n", + "max_iters = 10\n", + "\n", + "# Load an image of a bird\n", + "# Change the file name and path to experiment with your own images\n", + "A = mpl.image.imread('bird_small.png')\n", + "# ==========================================================\n", + "\n", + "# Divide by 255 so that all values are in the range 0 - 1\n", + "A /= 255\n", + "\n", + "# Reshape the image into an Nx3 matrix where N = number of pixels.\n", + "# Each row will contain the Red, Green and Blue pixel values\n", + "# This gives us our dataset matrix X that we will use K-Means on.\n", + "X = A.reshape(-1, 3)\n", + "\n", + "# When using K-Means, it is important to randomly initialize centroids\n", + "# You should complete the code in kMeansInitCentroids above before proceeding\n", + "initial_centroids = kMeansInitCentroids(X, K)\n", + "\n", + "# Run K-Means\n", + "centroids, idx = utils.runkMeans(X, initial_centroids,\n", + " findClosestCentroids,\n", + " computeCentroids,\n", + " max_iters)\n", + "\n", + "# We can now recover the image from the indices (idx) by mapping each pixel\n", + "# (specified by its index in idx) to the centroid value\n", + "# Reshape the recovered image into proper dimensions\n", + "X_recovered = centroids[idx, :].reshape(A.shape)\n", + "\n", + "# Display the original image, rescale back by 255\n", + "fig, ax = pyplot.subplots(1, 2, figsize=(8, 4))\n", + "ax[0].imshow(A*255)\n", + "ax[0].set_title('Original')\n", + "ax[0].grid(False)\n", + "\n", + "# Display compressed image, rescale back by 255\n", + "ax[1].imshow(X_recovered*255)\n", + "ax[1].set_title('Compressed, with %d colors' % K)\n", + "ax[1].grid(False)" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3eJc3yx5gyb-", + "colab_type": "text" + }, + "source": [ + "*You do not need to make any submissions for this part of the exercise.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o1ebUqX6gyb-", + "colab_type": "text" + }, + "source": [ + "### 1.5 Optional (ungraded) exercise: Use your own image\n", + "\n", + "In this exercise, modify the code we have supplied in the previous cell to run on one of your own images. Note that if your image is very large, then K-means can take a long time to run. Therefore, we recommend that you resize your images to\n", + "manageable sizes before running the code. You can also try to vary $K$ to see the effects on the compression." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nXG_6VzXgyb_", + "colab_type": "text" + }, + "source": [ + "## 2 Principal Component Analysis\n", + "\n", + "In this exercise, you will use principal component analysis (PCA) to perform dimensionality reduction. You will first experiment with an example 2D dataset to get intuition on how PCA works, and then use it on a bigger dataset of 5000 face image dataset.\n", + "\n", + "### 2.1 Example Dataset\n", + "\n", + "To help you understand how PCA works, you will first start with a 2D dataset which has one direction of large variation and one of smaller variation. The cell below will plot the training data, also shown in here:\n", + "\n", + "In this part of the exercise, you will visualize what happens when you use PCA to reduce the data from 2D to 1D. In practice, you might want to reduce data from 256 to 50 dimensions, say; but using lower dimensional data in this example allows us to visualize the algorithms better." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bxp7H8S7gyb_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 269 + }, + "outputId": "b0580a00-d51f-4618-bf5a-003ba1bb30b5" + }, + "source": [ + "# Load the dataset into the variable X \n", + "data = loadmat('ex7data1.mat')\n", + "X = data['X']\n", + "\n", + "# Visualize the example dataset\n", + "pyplot.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=1)\n", + "pyplot.axis([0.5, 6.5, 2, 8])\n", + "pyplot.gca().set_aspect('equal')\n", + "pyplot.grid(False)" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CPm3FsTogycD", + "colab_type": "text" + }, + "source": [ + "\n", + "### 2.2 Implementing PCA\n", + "\n", + "In this part of the exercise, you will implement PCA. PCA consists of two computational steps: \n", + "\n", + "1. Compute the covariance matrix of the data.\n", + "2. Use SVD (in python we use numpy's implementation `np.linalg.svd`) to compute the eigenvectors $U_1$, $U_2$, $\\dots$, $U_n$. These will correspond to the principal components of variation in the data.\n", + "\n", + "First, you should compute the covariance matrix of the data, which is given by:\n", + "\n", + "$$ \\Sigma = \\frac{1}{m} X^T X$$\n", + "\n", + "where $X$ is the data matrix with examples in rows, and $m$ is the number of examples. Note that $\\Sigma$ is a $n \\times n$ matrix and not the summation operator. \n", + "\n", + "After computing the covariance matrix, you can run SVD on it to compute the principal components. In python and `numpy` (or `scipy`), you can run SVD with the following command: `U, S, V = np.linalg.svd(Sigma)`, where `U` will contain the principal components and `S` will contain a diagonal matrix. Note that the `scipy` library also has a similar function to compute SVD `scipy.linalg.svd`. The functions in the two libraries use the same C-based library (LAPACK) for the SVD computation, but the `scipy` version provides more options and arguments to control SVD computation. In this exercise, we will stick with the `numpy` implementation of SVD.\n", + "\n", + "Complete the code in the following cell to implemente PCA.\n", + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "O-Mkz6yUgycD", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def pca(X):\n", + " \"\"\"\n", + " Run principal component analysis.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset to be used for computing PCA. It has dimensions (m x n)\n", + " where m is the number of examples (observations) and n is \n", + " the number of features.\n", + " \n", + " Returns\n", + " -------\n", + " U : array_like\n", + " The eigenvectors, representing the computed principal components\n", + " of X. U has dimensions (n x n) where each column is a single \n", + " principal component.\n", + " \n", + " S : array_like\n", + " A vector of size n, contaning the singular values for each\n", + " principal component. Note this is the diagonal of the matrix we \n", + " mentioned in class.\n", + " \n", + " Instructions\n", + " ------------\n", + " You should first compute the covariance matrix. Then, you\n", + " should use the \"svd\" function to compute the eigenvectors\n", + " and eigenvalues of the covariance matrix. \n", + "\n", + " Notes\n", + " -----\n", + " When computing the covariance matrix, remember to divide by m (the\n", + " number of examples).\n", + " \"\"\"\n", + " # Useful values\n", + " m, n = X.shape\n", + "\n", + " # You need to return the following variables correctly.\n", + " U = np.zeros(n)\n", + " S = np.zeros(n)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " sigma=(1/m)*(X.T)@X\n", + " U, S, V = np.linalg.svd(sigma)\n", + " \n", + " \n", + " # ============================================================\n", + " return U, S" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HaAXmrw1gycF", + "colab_type": "text" + }, + "source": [ + "Before using PCA, it is important to first normalize the data by subtracting the mean value of each feature from the dataset, and scaling each dimension so that they are in the same range.\n", + "\n", + "In the next cell, this normalization will be performed for you using the `utils.featureNormalize` function.\n", + "After normalizing the data, you can run PCA to compute the principal components. Your task is to complete the code in the function `pca` to compute the principal components of the dataset. \n", + "\n", + "Once you have completed the function `pca`, the following cell will run PCA on the example dataset and plot the corresponding principal components found similar to the figure below. \n", + "\n", + "![](Figures/pca_components.png)\n", + "\n", + "\n", + "The following cell will also output the top principal component (eigenvector) found, and you should expect to see an output of about `[-0.707 -0.707]`. (It is possible that `numpy` may instead output the negative of this, since $U_1$ and $-U_1$ are equally valid choices for the first principal component.)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cCCVzyuzgycF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 302 + }, + "outputId": "5b87bb8c-645d-4dcc-fe14-8605d7c2d8db" + }, + "source": [ + "# Before running PCA, it is important to first normalize X\n", + "X_norm, mu, sigma = utils.featureNormalize(X)\n", + "\n", + "# Run PCA\n", + "U, S = pca(X_norm)\n", + "\n", + "# Draw the eigenvectors centered at mean of data. These lines show the\n", + "# directions of maximum variations in the dataset.\n", + "fig, ax = pyplot.subplots()\n", + "ax.plot(X[:, 0], X[:, 1], 'bo', ms=10, mec='k', mew=0.25)\n", + "\n", + "for i in range(2):\n", + " ax.arrow(mu[0], mu[1], 1.5 * S[i]*U[0, i], 1.5 * S[i]*U[1, i],\n", + " head_width=0.25, head_length=0.2, fc='k', ec='k', lw=2, zorder=1000)\n", + "\n", + "ax.axis([0.5, 6.5, 2, 8])\n", + "ax.set_aspect('equal')\n", + "ax.grid(False)\n", + "\n", + "print('Top eigenvector: U[:, 0] = [{:.6f} {:.6f}]'.format(U[0, 0], U[1, 0]))\n", + "print(' (you should expect to see [-0.707107 -0.707107])')" + ], + "execution_count": 32, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Top eigenvector: U[:, 0] = [-0.707107 -0.707107]\n", + " (you should expect to see [-0.707107 -0.707107])\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GCx1-ZyzgycH", + "colab_type": "text" + }, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BpYyF79XgycH", + "colab_type": "code", + "colab": {} + }, + "source": [ + "grader[3] = pca\n", + "grader.grade()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sr5YOcXcgycJ", + "colab_type": "text" + }, + "source": [ + "### 2.3 Dimensionality Reduction with PCA\n", + "\n", + "After computing the principal components, you can use them to reduce the feature dimension of your dataset by projecting each example onto a lower dimensional space, $x^{(i)} \\rightarrow z^{(i)}$ (e.g., projecting the data from 2D to 1D). In this part of the exercise, you will use the eigenvectors returned by PCA and\n", + "project the example dataset into a 1-dimensional space. In practice, if you were using a learning algorithm such as linear regression or perhaps neural networks, you could now use the projected data instead of the original data. By using the projected data, you can train your model faster as there are less dimensions in the input.\n", + "\n", + "\n", + "\n", + "#### 2.3.1 Projecting the data onto the principal components\n", + "\n", + "You should now complete the code in the function `projectData`. Specifically, you are given a dataset `X`, the principal components `U`, and the desired number of dimensions to reduce to `K`. You should project each example in `X` onto the top `K` components in `U`. Note that the top `K` components in `U` are given by\n", + "the first `K` columns of `U`, that is `Ureduce = U[:, :K]`.\n", + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5pWgHq4ggycK", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def projectData(X, U, K):\n", + " \"\"\"\n", + " Computes the reduced data representation when projecting only \n", + " on to the top K eigenvectors.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The input dataset of shape (m x n). The dataset is assumed to be \n", + " normalized.\n", + " \n", + " U : array_like\n", + " The computed eigenvectors using PCA. This is a matrix of \n", + " shape (n x n). Each column in the matrix represents a single\n", + " eigenvector (or a single principal component).\n", + " \n", + " K : int\n", + " Number of dimensions to project onto. Must be smaller than n.\n", + " \n", + " Returns\n", + " -------\n", + " Z : array_like\n", + " The projects of the dataset onto the top K eigenvectors. \n", + " This will be a matrix of shape (m x k).\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the projection of the data using only the top K \n", + " eigenvectors in U (first K columns). \n", + " For the i-th example X[i,:], the projection on to the k-th \n", + " eigenvector is given as follows:\n", + " \n", + " x = X[i, :]\n", + " projection_k = np.dot(x, U[:, k])\n", + "\n", + " \"\"\"\n", + " # You need to return the following variables correctly.\n", + " Z = np.zeros((X.shape[0], K))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " Z = X@U[:, :K]\n", + " \n", + " # =============================================================\n", + " return Z" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8TEupZrgycL", + "colab_type": "text" + }, + "source": [ + "Once you have completed the code in `projectData`, the following cell will project the first example onto the first dimension and you should see a value of about 1.481 (or possibly -1.481, if you got $-U_1$ instead of $U_1$)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pFvraSG9gycM", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 50 + }, + "outputId": "324fb56d-38c3-4199-ca8a-b4018a479cab" + }, + "source": [ + "# Project the data onto K = 1 dimension\n", + "K = 1\n", + "Z = projectData(X_norm, U, K)\n", + "print('Projection of the first example: {:.6f}'.format(Z[0, 0]))\n", + "print('(this value should be about : 1.481274)')" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Projection of the first example: 1.481274\n", + "(this value should be about : 1.481274)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_70BjueqgycN", + "colab_type": "text" + }, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "F-EHrjFygycO", + "colab_type": "code", + "colab": {} + }, + "source": [ + "grader[4] = projectData\n", + "grader.grade()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "58_hd5MLgycP", + "colab_type": "text" + }, + "source": [ + "\n", + "#### 2.3.2 Reconstructing an approximation of the data\n", + "\n", + "After projecting the data onto the lower dimensional space, you can approximately recover the data by projecting them back onto the original high dimensional space. Your task is to complete the function `recoverData` to project each example in `Z` back onto the original space and return the recovered approximation in `Xrec`.\n", + "" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GfqqlyTwgycQ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def recoverData(Z, U, K):\n", + " \"\"\"\n", + " Recovers an approximation of the original data when using the \n", + " projected data.\n", + " \n", + " Parameters\n", + " ----------\n", + " Z : array_like\n", + " The reduced data after applying PCA. This is a matrix\n", + " of shape (m x K).\n", + " \n", + " U : array_like\n", + " The eigenvectors (principal components) computed by PCA.\n", + " This is a matrix of shape (n x n) where each column represents\n", + " a single eigenvector.\n", + " \n", + " K : int\n", + " The number of principal components retained\n", + " (should be less than n).\n", + " \n", + " Returns\n", + " -------\n", + " X_rec : array_like\n", + " The recovered data after transformation back to the original \n", + " dataset space. This is a matrix of shape (m x n), where m is \n", + " the number of examples and n is the dimensions (number of\n", + " features) of original datatset.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the approximation of the data by projecting back\n", + " onto the original space using the top K eigenvectors in U.\n", + " For the i-th example Z[i,:], the (approximate)\n", + " recovered data for dimension j is given as follows:\n", + "\n", + " v = Z[i, :]\n", + " recovered_j = np.dot(v, U[j, :K])\n", + "\n", + " Notice that U[j, :K] is a vector of size K.\n", + " \"\"\"\n", + " # You need to return the following variables correctly.\n", + " X_rec = np.zeros((Z.shape[0], U.shape[0]))\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + "\n", + " X_rec = Z@U[:, :K].T\n", + "\n", + " # =============================================================\n", + " return X_rec" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yltFgl0hgycR", + "colab_type": "text" + }, + "source": [ + "Once you have completed the code in `recoverData`, the following cell will recover an approximation of the first example and you should see a value of about `[-1.047 -1.047]`. The code will then plot the data in this reduced dimension space. This will show you what the data looks like when using only the corresponding eigenvectors to reconstruct it. An example of what you should get for PCA projection is shown in this figure: \n", + "\n", + "![](Figures/pca_reconstruction.png)\n", + "\n", + "In the figure above, the original data points are indicated with the blue circles, while the projected data points are indicated with the red circles. The projection effectively only retains the information in the direction given by $U_1$. The dotted lines show the distance from the data points in original space to the projected space. Those dotted lines represent the error measure due to PCA projection." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "msLabU3QgycS", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + }, + "outputId": "67597506-4b93-47f8-f219-475ea2356066" + }, + "source": [ + "X_rec = recoverData(Z, U, K)\n", + "print('Approximation of the first example: [{:.6f} {:.6f}]'.format(X_rec[0, 0], X_rec[0, 1]))\n", + "print(' (this value should be about [-1.047419 -1.047419])')\n", + "\n", + "# Plot the normalized dataset (returned from featureNormalize)\n", + "fig, ax = pyplot.subplots(figsize=(5, 5))\n", + "ax.plot(X_norm[:, 0], X_norm[:, 1], 'bo', ms=8, mec='b', mew=0.5)\n", + "ax.set_aspect('equal')\n", + "ax.grid(False)\n", + "pyplot.axis([-3, 2.75, -3, 2.75])\n", + "\n", + "# Draw lines connecting the projected points to the original points\n", + "ax.plot(X_rec[:, 0], X_rec[:, 1], 'ro', mec='r', mew=2, mfc='none')\n", + "for xnorm, xrec in zip(X_norm, X_rec):\n", + " ax.plot([xnorm[0], xrec[0]], [xnorm[1], xrec[1]], '--k', lw=1)" + ], + "execution_count": 36, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Approximation of the first example: [-1.047419 -1.047419]\n", + " (this value should be about [-1.047419 -1.047419])\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qsoCxgcEgycT", + "colab_type": "text" + }, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "cZ5ztAipgycU", + "colab_type": "code", + "colab": {} + }, + "source": [ + "grader[5] = recoverData\n", + "grader.grade()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w1SjlzbIgycW", + "colab_type": "text" + }, + "source": [ + "### 2.4 Face Image Dataset\n", + "\n", + "In this part of the exercise, you will run PCA on face images to see how it can be used in practice for dimension reduction. The dataset `ex7faces.mat` contains a dataset `X` of face images, each $32 \\times 32$ in grayscale. This dataset was based on a [cropped version](http://conradsanderson.id.au/lfwcrop/) of the [labeled faces in the wild](http://vis-www.cs.umass.edu/lfw/) dataset. Each row of `X` corresponds to one face image (a row vector of length 1024). \n", + "\n", + "The next cell will load and visualize the first 100 of these face images similar to what is shown in this figure:\n", + "\n", + "![Faces](Figures/faces.png)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CVrwFUKTgycW", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 466 + }, + "outputId": "b882bf97-7791-4768-bb1c-7f5140caf6e1" + }, + "source": [ + "# Load Face dataset\n", + "data = loadmat('ex7faces.mat')\n", + "X = data['X']\n", + "\n", + "# Display the first 100 faces in the dataset\n", + "utils.displayData(X[:100, :], figsize=(8, 8))" + ], + "execution_count": 37, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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9LIwhANJjfXFmEHfa7bYuLi4scke5PHQHrA40yf7eBIl5mJRaI87bowTUqXgeginWgkAIaBpnjgFttVomQ6wNxshnD0TAm5ub2tnZCQQNkgK1cAJJH8xJ1yQifo8c+NKBh/Sk69obpK/RaKR4PG6ZvpcbSZaNLpdLk1GQCtaRgW5PJhM1m019+umnevHihc7PzzWdTnV2dqZisWg/BE75fF7VajVQT0TW2XdP+FmtrngT5+fnGg6HGg6HOj8/19nZmRqNhpFeCL6j0aiePHlikHc2m5WkAMze6/V0cXGhTCZjicJwODQ7mM/nA4HseDxWOp22ffO11zCZy5dHfCkmk8kYtEnNEHnCtoRHLpez7G0ymRiqQTYmXSU67AcZHnCy1zXsi69dz+dzs1OsE68bjUZmi1OplLLZrIrFYgAp4nu9LfPrc5Oj9PaH95Pxl8tlW0Pm6pE0gnMyRtYV3WIufH4YFfHy+7bxVmdJxphIJDQcDtXtds1oFItFUwwm7VNajA4bUKvVVK1WrTgOTNHtdo1kgcPI5/Oq1Wqq1WoGfRANe8NO5Pvs2TObs68LSddYO69n4PxxfoPBwKJfXx8rlUqW0uN4e72eWq2WTk9PlUgkVCgUVK1WtbW1ZdHt8fGxms1mYF4ovIckJVn27YUdZQNW9g7XZ5wQQ7zxAxpFWKlNrVYrg5/CGbb/Xi9U4XquJME2xkiQoQBPA+9Q/4OtioB7Fma1WjXBJks7OzuzDD6RSGhra8sEnIyMZ/OBD+tAUOaVj2fGQABHe3iOdca5Y9xwKKxXsVi0Z2I+0hU60u121W63A1mtr0nxuchkv9/XYrHQo0ePVK/XtbW1Ze8DtcDokolI1wGQZ8DiGPjxga0nuXj5RxYJikEiYJ6DHEHaQ47Oz881mUy0sbERKCmQkXa7XT19+lQ/+clPLKi8vLzU8+fPJcmCy0KhYPDd+++/r/v37xsysF6vNZvNbE6SzElgF46Pj9VoNPT69Wt99tlnGo/HWq+vCIA7OzuW6YIaYNyr1aokGbqRTCbVarXUbDb18OFDq0keHR3ZHoME1Ot1g6aZF2uL/KGnrOlqdUWaIthKJBKmR2RAfI5H7Qi2ff0PKJVMr91umw6gB5lMRplMRltbWyoUCmY/PG8Am4EsY4sGg4E57p2dHSUSiQCqtL+/bwEEjsZ/FqhWPp8P6J7Xx7DDDOsqn5FMJlUqlSygIzjzNpCkBCb+1taWyuVyYH7InC9HYE9ns1nA3r1pvNVZdrtdvX792pTq7t275oyAKMgygBLIGjwsOxwO9cknn+jp06cWUcbjcR0fHxtjj7rCfD7X+fm5Li4u1Gg0DAY9Pz+3CAGBn81mpigMT5LBMfl6EgYX40u0NBwOdXFxoW63a4I4m82UzWZVqVS0u7urSqViGSPOcmdnxxacSGswGOj169f6j//4D+3s7ARqXp4qzWi1WsbAZAMRaBw8z0KAgaEmiPD0eE+C8JEjjg0okb97UgSRNpkIhtczNbe2tgJGm/rtYrGwVoPpdGr1BOojKCPfDSqBYOO4MDoQyYi0yX7G47FyuZzy+bwRVXAqKAPz8AMDQbbX6XQMTuJ3o9FIrVbLoFVTlHjcUIc7d+5oY2PDAgTo7ezNcDg0GAsiRqFQMONABkpW2Gw2LVv1cnF2dmZZ4GQy0evXr+3zWVOfZYbhV5xyPB43uDudTltgQhSO8Xj16pXBsqvVSrlczpwJrQhAZJ9//rl+9KMfablc6tGjR/rd3/1dSTKZikajOjg40G//9m9b/YwsACIfvIbDw0OdnZ3p1atX+vrXv679/X0LojxRg0Dr7OxMz58/149+9CNdXFwYihGNRq2cUa1W9fDhQ+3v72tra8uCXqD8crksSTo4ONCjR4+UTqfVbreVy+X07rvvqlarKRqNqtPpmGFFfnFivmUJXfRQPUE7gS+QrA984RVgRzOZjMHc/J6giFEul/Xq1Ss1m02zBejfer1WtVo1Z51IJPTZZ5+ZXUwkEup2u8pkMlqv13r69KkymYx2dnZUq9UMjh4MBlosFnr+/LnpCXs3n88DrWeZTEaSDFmkzl+r1QL6dxPS42u2/BsU4/DwUP/5n/+pH/zgB0omk9rd3VU6nTYSKc9OUIt+djodffbZZ8aN4LmZ0/7+vtkGXxoLoyTh8aWZJZlhPB7X3t6eORGEheypWCzqzp07lon46J2exxcvXli0nM/ndXJyolgspq2tLVtwBAPosVAoBIhBbBCQWC6X0/7+vs3ZCyNZgTeGXug8qYfnwkDzGcPhUBsbG8b28r1TwMfAaQh5Op1WsVjUw4cPVSqVDNYhggVWYxwfH1sLRqFQMAeG8cZBY2QhkGA86IXNZDLWK+jrOp4RidFinQkOiIwhnJCBoDQ+4sKpE633er1AXbbX66nf71sQQhCEYcCp4xA7nY5l0ARGvV7PnplnWS6XevnypXK5nGq1mg4ODgy+4nkglBD5+uEJXQR3qVTKakUEdkCYngiWSqXMGW1ubmp7e9uc5NHRkSEvOG7kAySGupiHprzckUVNJhP7++effx7o2RyNRrYu1PiBg4HBPCqCo8XZjsdja+vY3t622juojicDSbK6MqQzb9iAVIfDYQAl8QaPnmbqTbQB4HxYe48anZ2dBdqPyBR5ppOTE7169UovXrzQZDKx2iJ7SnA1Ho91eHhoTpEsJBaLKZ1OWzZcLBZVr9ctoG21WraO0WhUu7u7Aa5DtVoNIBReN0ATfMbCXEAdsKeJREJnZ2caDAaWNc7nc1WrVZVKJVUqFQsGo9FowIiXSiUL8tLpdKDmeHl5qXq9bqQx6qa+PDEej62uBwqCbkajUdPf1Wqlk5MTHR0dmd3muT0aFo/H7VkJsiGwfdm4qY6JXaaNsN1uq16vW+KSSCSMaCrJgipknGCV5AXELpvNqtvtar1eq1AoWGDudfJt462vwisDbwEzMtHlcqnBYGDO5eDg4AvQHsqXTqc1mUwCUS1EHmoIGA5wZCKCcKM72QOUeiAV6Qq6ov8TMoKkgEFkeKIRJCKgY0kGF5N9IpyFQkFbW1s6OjoKkIEwJKlUyqIqT4iqVqvWIOw3aDabqdvtBorQ1FHpnfLsTQz5fD5Xq9WybMG3NPh9QAhhVGLQqSsR1GQyGYMkgMK8MWTOiUQikPni+NfrtSaTiY6Pj63JHqjVQ0TUGSUZBA+kH4lE1Gg0rM7CM/BdJycnKhQKikajKhQK5ugJDCCXsc5+eHiMqNPXtdk/IluCLIwM+0iNcTabKZPJqNvtBgIalJjv83VfjI0UZJvznD6Ya7Vatk44U9+G5BvLcZgYTQ+d+XruxsaGSqWSDg4ONJ1O1ev1DJlBx6hFVqtVcyLU8NDL1WplzptAl+fwBBkcgQ8AcZa+dghiRGnB1/vZi1gsZgQP3l+pVKw0wtpvbm4ql8vd2MIAoWN7e9tsHOUCyDHecNdqtUCLSz6ft7a40WgUaMfy2aJ/XtCZxWKhjY0Ng19PT0+tTg4haDwem01drVZGgiJ4ZZ/y+XygrIKsXV5eqlAoWNmImib2wiNLOBkfDEgKtGQMBgPjILDm2HWSAhjh1LaRlTCy48fbapi+tk79kdrl1taW8R6wSz4oRud9UI7uULNttVqmM2FH6WUlPN7qLAuFQoDU44uhvi9vOp1alOGLx0SWi8XC2K1f+cpX9NWvflXJZFIvX75UJBJRNptVuVw2JUgkEtrd3TUl9Kky2RZO1TtESQYbQBzY39+3DPni4sKcHsbXw2KvXr3SYrEw2KxSqVjvEt95584dO53IG3oK89HoVU/oe++9p+3tbYNqotGo6vW6CZYf9+7d03A41IsXL3R0dGT1EIT97OxMrVbLmKZk2dFo1BSNiIoeOYx6NptVNBq1qKvZbKrVapmSDwYDgx+oTROV4RS98UfQgeZ7vZ46nY79rt1u67PPPlOj0dBkMlGhUNDdu3eN7cf8+v2+JpOJXrx4ob29PeVyOUlSu93W559/rvF4bNA8zgjItlqtqlarGRSPIcKBEFQxb+TVZ13pdFpbW1va2NiwU4EIsPL5vIbDoUFb0lXw4A8u2NnZsQi13+/r+9//vim5Xzcgsn6/b8gDDg7Z9XVX7ywhD+GwaXMA0iUrIVsiQEBn2VuQkHw+r4ODAz158kRPnjzRgwcPlMlkLEult5SgjayyUCgEglZIb8i6h9sIYoEVydRjsZjee+89VatVPX/+XL1eT/V6Xb/xG7+ho6MjNZtN5fN5bW9vG8xJC48nuq3Xa9Xrde3t7anRaOhb3/qW5vO5Pv/8c/3Lv/yL7t+/r0ePHunx48c6Pj42lMTbq2w2q/fee8/WWLoyrrQieZtHYE/wwZ6ypjgfn02GDyKh9lYul7W3t6ednR1LPKgNL5dLc/CQllhLTzBEFlkXShTsWzweN+cMQYjseLlcWukAPgrtamG2KGsDIuKZuwSbhULBPlu66rGkpQ+58eOmmmX47wSN6XRaDx8+NB2rVCpWCri8vLQ1Qk5xqltbW0okEtrZ2bFkr9/vG8qD3adXH3+FbIQDbD/e6ixLpZIajYYRNlBaFKder+vBgwcBNihQEsYNZ5lKpfTuu+/q61//ur7xjW9oPp/ro48+0vn5uTlWTyAgasDASNc0cmADsPTz83Ob897env397OxMFxcXVhD2J5qQSdGDs1gsTFF9wf309NRIHaTu1C0ePHig58+fm7P2WQjMWJQqmUxazYfXMsrlsmXWo9HInDZR+n//93+r2WxaVjiZTOxkjkwmY31hZAXL5dLqELlczkgnk8lER0dHRpjKZDIaDoc6OzszYwozEPIFBX5PPloulwbf0XeGMZtOpzo4OLBsv1gs6smTJ+YkiJpns5m1WGxtbdk+4DyIwr/xjW/YmvEdzG9zc1Or1Uq9Xs+YjDg7lNwPasGr1cqYjawZtW3eg9JLMlTgzp07evDgger1upEXIpGIHZVH1kMGSs2KdfFkFWQDVACZ8VHy/fv3LdgC6md+rKEPJCF3YaxxUhi3vb09y0hGo5FqtZq+/vWva71e69///d+trsncYbnTYgPUi47cuXNHg8EgsM7IIYY+m81qe3vbyHhHR0dW74UUF4vFVK1WlUqltLu7aw42nU5re3vbkKLnz5/b8W3UUnndL/zCLwRKEeVyWY8ePQpkipRvYrGYSqWS2TiemSDT134JrtBFuBiDwcCMNcgEqBuZM3YBu4j8tdtt9ft95XI5PXr0SAcHB4aQYBvm87mVp8KZZSaTUblcNruFHFPvxgaRQfsSBTK3Wq3UarXU6/X0la98xerEy+VS9+/f1+HhoVqtltk+ZMHzU/yBM5Bj0Lub9I81ZJ43EWo825jXZzIZ7e/vq1KpaHNzU+122xCvWCxmdgu7v1gs7Hebm5va3d01h8j6enuG7oBevGl86aEEXhg8VEZzeBinx3t7wgmCsrOzo/39fe3t7WmxWOidd94xZh3RiI8AeAAiwzBcAEHHZ2owaiE4sBhE8yxUKpVSuVy2gxTW67VqtZo5a96HEOfzeRMKDNf+/r663a4FCBgONgJKODDOxsZGoKXCCxGCCJuL5vLFYqGDgwOVSiVj7k6nU3GWLHA2BCLWmojXR73UiYCgJFlGkkwmVa1WlU6njRVM/RcIG8PcaDQMMmLPIdVgxPz3YbA8MsG5khCbiPKofQP1IUcYED4P4WePUSzeEyZRkTn615LZcdgAf8coklFls1nt7OxoZ2dHu7u7gcZ69q1UKtnpVl4eyfp8DxeRO2QqMlayVsaDBw/UbrcteygWi4FAEjINa43R9PUiXlssFgOHc6APtVpNP/dzP6eTkxNzrsgD6AQMdQKpxWJhtW72zD9vGPHJZrMWydM0Tr0xErk6pIPvY+18HR6EiXorelkoFAy6jsfjqtfrRmLiv15W+C/6wB5g4ySZYwKC94bVw60+MOb96IzfX+wfQZMvQyQSCdXr9UB5whtxWPjYPwbrTtsJn4+esQ/IhW89IzskeMvlcqrX66rVasY6v3fvniF42Hg+n+/0fc3oF74Bh+odvB/MxQd6Xk9BzGjPk67sOr3hHpmhFlkul22PKAGNx+NADy8EKt8OQ1LiCZ9vGm91lv5hqOkQbcLzY8MAACAASURBVN+E7fr6APAFwjmfz61pldrE/v6+Go2G1c1YZBQ8XEvyzhJYwUNtkiwjwlgAlfjsAUXi6CQv8P5EF78xKCyBAFFbs9k0xtxgMLBsB6PuISBfhPbpPlEoMAoCDYGD2g+tJGRjZEfz+dyyqU6nY3A2sLVntBaLReXzeYMjWEucKH1JoAHAlLQnSNL5+bk5SI84UA+r1WoBBw1hhgwb4Y/H4wabYGRTqZT29vasvoryeEeHPHhIhX0iEAqz2rzx47MwWJz0wTMAiZHplUolg862trYsE0QfeA4OfG42m7Y+RNsEZN4AQ0DY2tpStVq1QI+xv79v/X+Xl5fK5/OmzARB/ns84zGMxhSLxcCpJaxhLpezjPno6Mj2FHnEWcLSxCAB8aXT6S+wHj3bGc4DrTy0b1A6kGTfx3zJfkAPyFDIFGBdIsesKaQWIGC+H4dJsOJZ5qwTa+gzJX6wDwSg3uGxnjhLTyLzQaLPeghAgQ6ZE2gKcpzJZAItTYzpdBp4Tgw+NTielR9QDGyLPzmJQ/MJ1KLRqHZ2dgw1SiaTZqtwmARCOEzWDu6DZ6H+bwcwMn36PlAjAwRxKBaLqlQqRhpkf7H5yB7BC4Gxd4589v+6ZskpOZ7NyCYRHd4EVeDNgZxglbIZ1FIwPpubm2o2m7p7964ptE/Hbxo+mg6zHnGIuVxO5XLZ6MUcv+fhAzI8IDiyZ5wTz48R5VnpN5pOpzo8PNTh4aH6/X4guyY7Aof3PZ0efri8vDRDhHOhPSafz1tWx9+AeVarq145DAMkAQgAQLDn5+dqtVoaDAa6e/euHj58aA6YEzBQqFQqpcePH9tnQ54gepWuWopoIE+lUsaoox+P4Ahh5L3Qt2FKLhYL3b9/P2AQLi8vrS5B5ggRYbFYWO2P98C6Xa/XymQy1nJAEMHwn4+DITPEqLG/GAWMNc3z/Jv66nw+N2i/Wq0qGo2qXC7r2bNnOj09DQR9yBgGnFNsyuWytre3A/AzA+ZfNpvV2dlZ4Bg5PheDxO/C5C7KAKVSyYKw8CEhlUpFX/va17RarYys5V8HaiEFzyEulUqqVqt6//33bc5hR4JxBkrGKMHEBjImmOJmFAwyew3pqFwuG7oiXZ//vLGxoWq1GgiYPPLliTi0sLBenpEpBU97Qk/5L3VG5kMgh86Hg3dsJeuJPeSH7/PwNzICMx77xeh2u2ZLKd8gX9gm9MW3y+F0fBb78OFDq1tybGS9XrdyD+gStsAHml5vSGzQF39utNdBj/AwZ/wGtp7vymaz2tvbCyRY/lmw4dhJdGhzc1NbW1vWObBcLlUul7/A7GatSS4kvTEbln6Gg9Q9jEcTPpvPhrHZZBm0NFBParfb1usF4wys+c6dO9aszLmCCI7HuMHnWWgypY2NDbVaLZszm4cCkq2uVitzZmwykXFYuP3JG0BXFPnZ1Gg0auchcvQSRBeMlXTt1HEgwLieQYhCYCRQSOpvW1tbgUZz1pn5IgDj8VgnJycBhl+lUtFqtVKj0VCr1dJ0OtWDBw9sbr5eLMmgFm9EYNQx5/39/cC+7O7u2nOG2Z44MJx3Op3Wq1evjHhElkFTMe/DAPb7fcuagddgISKblUrFTvvwjFA/fBAjXR/Zh0KXSqWAIvv2BW+QcVjA7mSTEDi4uaLVauni4sJ6CXkuyg2cRcpRW8hk+JQafwQcpA+gXeprsBz9syIrrDuZK4gLgS7ZzMOHD60d5OLiwvSGLN3XmeLxuL797W9bb7GHYSHQEegQ0ZMF5XK5wE0ekKuwBwTl0WhUxWJRuVzOjD6wIX24QNYgTn6vPIufIInfcTMHaxx2Xr7mLF0fKgERkMyW/fSZJXPHvrBX2C/vVD3KgQyEkw3m6DOeRqNhMru3t6d0Om3H2pH5Qi4igyUJmc2ujr6Lx+MqFAp2/CClCEoQlUpFg8HA2OfYKdCAcP0RJ4Uu+hIJr8cusj5h/fRBIIGRP/QeWY/FYnZ4B7aZz0wkEqrVaiqXy2YjSJ54houLC/u9Rz9umpcfb3WWZBts2HA4tB4+4B1vSHhQHMJgMLC+sHv37gVYdBhEjjKit4ffEzETwfjvQAD7/b7BvQwfoaBkRJmbm5sGxTIQ6nDUQ7runR5CyDw82SmdTms4HJpRB9f3EAWZYbiwTX0B6JP6Ekxj1g3mItkqzgjnNp1OzZEBFZLtUfvwRovMA+gbgfGZPQLEPjBf/9ybm5uBTNLLBaw5YBkPe/g99hkR65pKpQyWQ+F9vyMGx9c8PfQUroV4gyhdGygPL/LD2vPsvrZKhMs8yBp8SxNsW38+pyTLqqm/UR/zFH+Gr2vTT0dgAbEjDAd7HfSHHGCA+A7/bBi6crkcKKOwF/P53DJO5sD9lt5gSrIDDTxZyme0njhE4Ofn4YlWGHccJb/n8/0hHD7TITAlQ+GH/YfU9ja5CP94JqxHIHx93Gd4fh89AQenCe/Dj7CT9vwNn7FCIpNk+gUHgACBZAEEBd3k2MF8Pm8HxHjegkflSqWSms2mBfe0o/DsIAWe4JnL5Wy//EHq2NE3MWDRfWwwz+vX368Tr+fzwsGLD2x5HTacHk7kw/sj/z3h8aU1SxaRyAkYEwVjoYkkfCblz+x78OBBwFkiQLlcTjs7O3ZwNkqAYLEZXhn8jyeNIJgYemj7ROHeeflF8bUsoCmiVd+rRZsCaxImHfEdwFdAnQg7AUZY+IHfUGgyMN+wTn8TBgRnQwbAv7m7juhOus4+cVrUShB4GJRE+ew9QuuNAH8LE2R4HeuPcqF0ZEEegkYJmDvkBj4bMgdrS4Hez8/XNNlv9tNH4+FgiP2GDINBYb9Ho5EFJhxvhux7hScShwXJHhKkABv6DIpnC5NDkAWGJ2aA3vAsPnvn79SPOFzDB4IeGpWuoC6ehf3wxs7XWGk54N8E0Thq72hwljyXd+7emCG7vnWGE1hwsqvVylq2WD+v395oo4t8L84B2B1ZWK/Xdqk533GTjPCsPJ+vN/J7Ai0P3fFe3secfAAa/h6MuUeucPi+HMIgu5NkB477U7d4r69lwmW4uLiwvaeHlqANWBOdhimMDYERDWpBwC7J9BMEJHyQOsPrzpuCEs9HYX+83Q4HwQRU7LG3xdhpnDzoHutD0MjaE4TcNL70UAJqFev12o7CSqVSunv3rh4/fmykGJiAGCyub4EO/ujRI5XLZctw2ERYdixuq9XSixcvVKvVVKlUDIrBABE50grBRjG8UgC5UAtBMNiEsPBKCixYt9vV8fGxQQQ8K3PgHEWuiuEYNgxbNpu1TaefCjjHZw0IB9EYzgzDQsM1WaE/61GS1QUxrhwLFYvF7LYWGLDdbteEA4cJtR3yBPuzXC51dHRkjoCs0NfgQBFoa8CI+MMYyAZwlhB3YCQDR1L3xIh6kopvJfL1Ihq/iahx+vz4NfZz5lkikYhB4Bjl8Xisly9fWt3xa1/7mnZ2dgK1JZw8a0Z/XL/ft/oizrJSqQQUnDkTQHlyiHfw/X4/EKDwzOgljgOjBQGMK6parVYAlYDJyMkltCpRUyaTJ1unNsvhFaAizAen4QcZNNkye8YasYboGZE+NUwMsQ9qkD1kGdmnrOIJXTh3ns2Ty3B4wKjSNVHqJiPNXnjWPfJNUO9vwWH4TIlslGDBG+ZwkI/D4PUEb8glg8PMCYoODw+trg6igaOQrgh5r1+/1vHxsR1kjzOkXciTrJbLpdmYXC5nLSQcqIDsoQe+FYxznal33rQmN9UpPYIHExe0BfvP8/Z6vcC+YZdpVaINxpMmCSQJQLiPlTo9mWm4zurHl8KwKAiQCZt0eHioyWQSiOqptXE2qnRd+6H1xAuDzxq3trb08OFDO3KKHkr6AcvlshlxBBvF9oYICACBZCFRbp+dElGxkUSpq9XK6hPQtGGFnZ2dBQT++PhYx8fHxiT0uLqHaDzEEt4QD3d75fRQpWfRQbGnmbzb7ZriYpzJ9DF8HPvV6/X09OlTnZ+fq16vW88VCosyeoVlPTiw2Q+CKOrHMCdR1MlkYrdwDIdDHR4e2vFqGG2cnyQ7No7sJpFIWJaEHCEDnojFesBmRoEZGHae0RtPSACcntJut/Xhhx9qtVpZDyhoA5/t7ylkjdCXZrOp6fTq7sadnR1VKpVAjx+yi9FGjsO1VoyNr0/7YARZZQ0oexC8JZNJHRwc6OHDh9rd3TUE4+LiQs1m06Jrom2y3VgsZm1LONlKpWJ9sR5B8lCYl1/k1e/ZarWy0gY6wPpDfpOuM8PLy0tjS5M9eNiVAMKvkQ88PfzqdWhnZ0d/8Ad/YK/3mWS4LYf5eCISuhJm0vryircnniPh66v8IM/Mmz0hSAgf0+khR0mBJAKn5fWTw1Mode3u7lpAylyQX4+SQXaE6wCCAgLEqVXMz9/py/zfNN5UG0Q3PKkwFotZtwFtVGGIFWidg1fu3LmjSqViwTjOkmzSl0uwH54wd9N4q7OEkShdN9yz4VyP44kQCBhRcyqVsmOKYMGR9sLw8tkJBn25XBoTNxKJBE6j8NAnUaCHNMky2HiEgOgM5+Ubp8NOjHn66H+1WpkRx1nOZjOdnp7ageGJRMIUH0Po5/k2WjJCS+2MZwzDfkSx/HA6DJcVFwoFUyICAl8LrdfrRvYZj8eBnsswPOThZwwfc2WfMJ7QsX39c7EIXi01GAzU6XQCTuPi4sKEHrjv+PjYHB1Kyf9L13cbhmE3v+43MRP93P1e47igy79+/VoXFxcWecPYRhZBBnx9ExYgGc+nn36qZrNpDsI76TAUxXqFM0s/f5ykD7w8TI5xIoCaTqeq1Wr23d1u17JmsrwwucW30fi6GRcfIPNhKMw/j8/SMN44AgJvHzijnzheTxTDWHtHhmMH7eH3Hv0hmPGIRBjaRaZ47021bvYC++GdM/r6JhYsz+ZlhN/79WGdvcNkL8M/DE+2Yp3QIen6jkrW5uLiwmBYrj7kaMzxeGx18/CartdrY7dzDjL1UfSX9fX1c9bybWQZv5b+dyAS3v7znKAfMGE9GiDJarLYfn84h0cevI9gv9+0j3586a0jOLJwvQfng5L5hlHIB76HjGwo7MQYwJrARMByHsYkoiaK8gVthl8c6jTMh+/3GSqOD0PkYQ8WFAzen6Maj8ctiqdOQzDhBZ51I8L1RoZBNIfAI2zhCJTX+L1AGal5kKmhUND2JVlAwskdOC4fQPh5+BoVjlC6JkWxVn6+6XTa2KpkO8DLnLQDU83fdO7hl7OzM1vnWq0WuFHBM/v8XPg+9iJM2mCdpeBRjXzvcDhUu91Ws9nU69evNRqNtL29bX2VGJTZbGZBE99PpAu0NpvN9Omnn9qBFZwvikEi68cg+bq4lw9PcgjXdQki/HMTVPpyyXp91V5zcnJiV2URWfusBl2g747aFNAp5Byew9fa/Ag7BfbNw8noGw6TdfQkmbCd8EEB64iOe52Zz+cGAfoMwvMYYHn+3u/9nrHqfWAYDmYwusxTuj57FEfPXvrnxC75Wi22C93yNViPTvhTn8JsZ2+/+Az/b7IkPgtW9uXlpd555x0dHBxYttbr9czueTvO93KYOyxaelu9gyYT9f29yOzPOrw9Ixj1/BZKHvQ+s+Y+mODIT2w8zz+fzwM2wxO1fBDGHr5pvPVpPv74Y73zzjvW7MuG4gy8QkynU7VaLXMywE65XE5bW1taLpd2wg11Hx5kOp3aUWEczYayo9CdTscyGNo9MC7eWXpo2EOtKASOPxydMTych1HjGKvwQQuXl5fWgC9dwWTUWOPxuJ0byzp5Rl7YkPvfE8nyjBjhsJNAiDivM5FI6OjoyBiX1HfI7iCscAblxcWFtT14QQ/Pi+/BENCKgxJ7ogo/3sGPx2Odnp5ar+dyubSLnWOxmPb39y0zHI1Gdut5PB7XL/zCL1i2PJ1OVSqVAj1WXmE8IcpDfX5guHB87Fu/31e73bZDMngt8uKvm+NCagy+b3eC8IGyttttHR4emuwS0PioF7QiDNHzLD6g8Vk4wQrPTQ8vmRMHYC8WC3322Wc6PDw0WJ6zlz0xhiCTlqhyuWxz4LYNH2h4J8vgNewB6+zhUgysJ6chP74uOBqNjGnNPHCmN2W33jgS8HKWqc8+nj59qg8++EB//dd/rW63a+xzj+J4yNRnnv40GJAw1huHg6P3yEuYRc+eEhB5R+nlwj8PgxORsEH+Dk/q+Hwel5GzFu+9955SqZShPXw2NgAHhY3n0P3T01Odn59bMMxrWTecNHvhSx7elvhgKjx80kDWDakPu0Li1Gg0TNYp78DtyOfzms1m1r41GAysdh9mjK/Xa3POBK1vGm91lh999JEZC6DUsGASOVLvwVDE43FNJhP99Kc/VbPZtIttua4qmUwaBj0ajXRycqJer2cbDcThCQxExtynSFTnDUw8Hg/cn1gqlUw4PQTkowlJ9l1Q/cfjsZ2nWSwWra7F91JPQbDCEYyHbxlEvGF2WziyRKA8/EPmS48lCojSdDodNZtNPX36VP1+X+Vy2WA46nCdTseuPEPxww3EGAYEE2PoDQ69oAQg8XjcIGEiQpz1bHZ15yjHtnEWKLI0nV7dJAJ8zToQQW5ubtrVb8PhUCcnJ8a2pT8KISdr8XUlv8bIALLJ65bLpV0FhCPc3t627Orjjz+2wIPMxdebqHVQf3v69KndhxmJRPT69WstFgvrc+Sz+Dxk2Neopes2KLIYD18jQ2QgnJE8nV5d/J3JZNRqtSyYnc1mX7hYGla1z1phLy+XS7tSijWD8EN2yfCGDyPka1Y+i+f1ID3IF3vqsxXWj+APJjfG1GdoBA9AkGH+AXKbTCb1/vvv69GjR5Kubr3hQH1fb/TfjUxycg4/nugCmY4gjj31wVs8Hg/8m+cNlzx8LRnn6Z0lsowzPj8/Dzh7n2WDisH94Po77BUENfSVz2Aefl39gSroNkfK4RM86/ttMOxNg7XkpCpuXKIXG37G69evA0du4gfo6d7Z2TGEB0cKR8InYNI1ETRc375pvNVZYsA8fOqhBLww0EaYxRaJRMxjU7OCiYUg4WQvLi40n1/fq+jx5HBW5hUkXOfJ5/PmdEej0ReIFfzXQ6VAOev12uo9q9UqUMtEmRASz/TzgildEwL8HKXgcVk+gvFwJ//mc1AShM/DRmR9wCnn5+f6+OOPdX5+rlKppHq9rmq1qk6no0ajoW63q7t371rgA+xFFkcm7ZuwvSB5puNNNStfQ2C/gFvX6+sTdvzBFtTYfN2M00Roji4Wi+YIe72eoQd8PmuBwvl6pl9j5oqD89ElbU6z2VWjfa1Ws6Di9PQ0ENDgZNgjYGJqn61Wy7KpSCRidxkul0sjP4UhbgJCn92z595Zsr4YUAImrnCTruD2YrEYIIfAhPV75D+bM2ohRfj7SOPx64MqPKTpZZdBluxrcuHXe3lB9jxj0TsgAjE+w8OE6Bfy6ANN6v3Ihq+jcXwhOunrV15GfKbD3mPEU6mUJpOJGWX0Bh3BOQLt+ZqYn5N3xshDOGAN8zK8zpFdYiN90IuD83J1enoacMow4XEmoC7UDZFrEiPup/V7HyYpsV5vgzRvsh/SdQtKNBo1XsXGxoZdr9Vut3V0dGT37ErXvabcxUurILrAXbmXl5eGSvmgxvuV/zUMi3DgLHFkkr6gAPRghheR6JfDCYbDobWjYPwhHfh+H85FxaiF4UB+h/NhFItFg1G5igaB9+ciYvR8hgDjCoiZ3xNhYrCJiHlmFNMLFeuA8OMwvWIzvLP2wUBYaT38huPnDspms6lXr17p6OjICvqtVsvOrz0/Pzflns/nZiDJsHwTOOQIf4uFzxTCRKVw7QpnSVDB3ubzedXrdSPDwLB88eKFrQFn1BYKBRUKBW1vb1sE7g0T6+HrTT4YCkPdXgmoY/uahe+FJNDAuXQ6HZufJJMbDBLtMKAGZMa8HmeGo/DyShb0JmcZi8UCET/77x0hJC9KF/ysVivTLY8CIX8EhBsbG9ra2tLu7q4dqH1ycmL7zx2xwG4eKsMxMtjrsDFEhgmIMbi+/snasrf+CEB03UOPnktxU2mDQDYc3IEYSdcEH4+ghD/Tlz6wVzApU6mU2RqCFZ7Vw658hy9fzGYzW0//nd5ZeofJwDbw/94hs2as0Wg0sja0xWIR2FfsACQufwEFWWQkErE+6mg0augh3++DPXxCGInyeuf18U1QLPLb7/d1fHwsSYactNttvX79WqvV9X2ooGjVatWY68jnxsaGtVNhK+gP/59mvm91lpFIxOjoOzs7BrnhNIkCqIv5qAljQepL9shi++J2IpGwOyKBeEihMUDAhzg86oFAwAxgOI7b89kHEYWvR2AwVquVYeC+58jj2JArqLtyBB6K7IXQs18RehTjJoPpFQaD4oWJjSXLQ1FzuZz1d9ZqNdXrdcViMSOqfP7558ZAhgHXaDSsL5J5At+RIWOM/fMwZ6J95o0j8RE4Dvbi4kJbW1uq1+tG445Go3ZHHW0VEIFisZid74rQU+/JZrO6c+eO3fzuISoPqb9JCVhTX38lSwEZyeVyevjwod2cAspAlM368D4cIQYLkow/kIIMAxjP98D5wClsPHCUvsYPo5WaE8ab2nMmk1E+n7fLBHzW0+/3zWmC+AAn3r17Vzs7O6aHBH+0cOHIOYeT4Q23JLtvFd1kXXD8niSGrvuj9Z49e6bz83OtViv9/M//vNWTQCCePn2qTqej+Xyu3/iN3whcVUVN2etduH49nU51enqqyWSi+/fvWwC4ubmpWq1mZRy+j33AJkBe63Q6Ro4hGGHvuVaLbJn3esgZFMtD+shtuB6N82QQzBNIoMM+0AIl4VaRSCRiLO1nz55ZBpxIJKwfFyY7pEVJevz4scrlcsDuwufwiYvXMYITDx0zbnKQYVlKpVI6ODjQ8+fPrYRAKx9BEkd5cnUeQTj23cP3oAA+EAmzi/2BKG8aX3qCT7/ft1MfyKwQHrK1aPTqAGmyRzYPYoYnLvgJrddri1Z5SCKiRqOhi4sLJRKJwMkzpM8o73g8tqOrJOnk5MSibLB3jD71NF8bwjFR6PVwHhAbjgOlxwFjJCEFNZtNzWazQGSIUHGgAMML2Hw+t83yBBmvWHzmdDq14+o4lLxer2t3d9eiXe7EfPr0qT788ENls1m7MJmbSSRZncI7TASSIAFmnVdYDw0heMzZG0mU5eHDh+acCRpQbE7kQblpuEcRcbqsOdDoaDQKsGhxOB4CCisxv2f9vaGRZHXSeDxuUTWZBdCpd47+s5Enz9jmc5kLkBYyQeBJdO6heOn6EG5eA0nLl0fIHBeLhWXjwNg4WoxvMpk0J0ndlvkRSCC3uVzOjk6EpTwcDi3g9HriR7PZDJxX7OvHtJ7wjP6YwNXqqjUrnU5bP+CzZ8/s2j5aFzhUo1gsKhqNGnrkCWp+jz3MhqPDjkiyIC0SiViPMMxfvo/P47lYc98elkqlLPhDr5iLr6kSrIGS+Lqtt5NhB+kHSQsJB68j8IJfsbGxYeUE9uDhw4dWdyf4a7fb1kONXHJowYMHD+zuT1ATviMej3+h35FBLfVnHbwXJ3dwcKCDgwML1iDocB55tVq14/q2tras3QknzZ5BMiWgwl75uSILbyI5Mr7UWRKxk5kRNWD4cFwwLHFO1CARHN+L6RcXOAvDTA2z2+1Kuj6Cy7PmcC4YU3+QuifV+Fqbh27IbFkwT9RA4BAmD6958gICQ+bF54ehLgwQhiNcH5GuhP8m3D+cAXtiALAY8CbBBMQpoGHub+OA73g8bjeHE40yV5TH18N8ZIsAerjYzw9h91AxWRZC6qNh1hBB5XNQSt+Lh3PymTxr72uPnl3ojQ3y5rNen3kALSUSCWtzwQh7feCz/WeG5dmXCfx7PFyMbBAQhZ+Tz0cefI2d4A9jC2nNZ4XIP+tCoMfzwBtgHmGHDiqUzWaNBMLfvTFCthmtVstOkGFtPczJs/iyA1E+J7YQJJAJI08cTkF9FX1nrfya+now38PhGX6dydAJhgl0sDEc3jCZTOyABmyBJAtIuHQa4lk4EAqvFYGl5zaEIWkvBz7juSkI9DIG1IhN4POSyauzuA8ODqy0NhwO1Wg0LLCl9oe9wEkxh3w+bxk39WaGh9bf5izflF16h8uNPJVKJRBwQUbkkHV/Nrb/bF/TlmR2jt+Fy17Izf/aWfLgKIl0HdXgLFEC6MzUqS4uLtTr9YxY4CNdf7INggdJBfbWen3FwCV6IMJHWWEqktExcDwIKifhkA1DR8ZZrtfrgCP3tSwgaG9YfQ8UhoqNDC+2j/qYm683MRC6m4gpkgKKQjGbEziA3VBynp9C9927dwPZfTR6dV8dGdL5+XmAHIHRxnFiXPy8bop4+R2ZA3XQYrFoz71YLAyeJDDiMAc+YzQa2V6ReeC4yV5ZM98H6J3lTWuIE2DPPM1dkp3Fu1qtTHbJ0MjOPKnK74tXcvYwDMWjN9I19ItM83msk1/TsLP1wR/BzGq1sptmkHHv1LwjZJ8IPtkX7yTZd8osuVzO1svX4b1jYpydnalarRpiFIaZw0GXD8Z4Fk6Curi4sOZ0dA8SB8xev16eVMV6emcJfI+eSDJiFHV6ao3YrMFgYGxS0BzpmrnJuhOUIotknD5I8twFghcyRO8QCJ78f71TZB99gMYe+0CHWj+vpfxx7949kyXY35RqdnZ2tL29bUfn+SvsYrGYXRuGDcO2MD+eh1ZCPzwHI8zH8LoINI+z5NjHWCxmJ0B5m4c+8X6Cm3B2zvd5mN6v5f8jZxmLxaxOOBqN7O40MjqcXSQSscO/fcZG20g0esVAI9MhOvC1I7ByjMj+/r62t7ftLEKyAWCMdDqt09NTnZyc6PT0NDBnFhBoz0ezCBQOE0FAqLrdrikPRyv5sxNvynaAIzz1nbXxxApYkb1ez6IzSXaKDX144ZYBH6XjQAg6qAdz4DdNpwAAIABJREFUQALRnO/L5L42nqVYLNp7iZRxpghLmATjIUIv3OFIHqOK0eYILO/Ajo+P1Wq1jCHKGkYiEY3HY5vH5uamdnZ2VKvVTDGoKQObQWbA6LLPYVjoe9/7nk5OTuwkk69+9at2LqZ0FVGfn59bm02xWDRGdbvdDjgeZMxT+1mH9XodOMydNSPA9NR8lBySxWwWPKqs3++bbFK75XUEiqx1pVKRdH2QObVVD9H5jDiRSFgNHYh3tVoFDj4g2+eYM/QHA3NTfecnP/mJ3Uy0vb0dWK/FYmF9k9Rf/fVKDL6bwApdyOfzlh2HCX7M3wckPtCYTqc6Pj62S9OZO6dK0VKxvb2tZPLqEPyDgwN98skn9jfO3KXG6nsM4U6wNh569cEFtoE1gaRHkIXMEsT7A1MYvh5IYMlzLpdLQ3Oi0agd5sK+erLYarVSNps1hCwej1vtGn30JS/2AX0D/QMJ8AH7cDi0a9DYU//jy3LeYbJvnDhWLpf1+eefGyeGfedMY3qDmR8Oz9eKCQaQe8hON9m0mwJtxlud5cHBgZ1e8vr1a2t8j0QixrIiCmXCHAsmSffv37djtsgQqBvAykSwiBaAcGgU5vt8hMSBz0dHRzo8PDSjIV03cqNwPrv07FWU2N80vlqtArWITCZjWRwOy18jRpSJMFHvwmh6JiKtM5Bn/BiPx1bTY8NoFaCOxpzX67VarZYp4mAw0KtXr8zJT6fTwBVhwJoIGXg/xXEP6QKX8z2+v4sggzX2GQFKyr9R7vV6beeQkolD0qC+7UkPfDYKihzs7e1ZLY7WI38llw+4PKTtxz/+4z+q3+9rtbo6RPnXfu3XTLFZo0gkYpD13t6eGRwOIuh2u8YSJEv1MKAnaFD7oT6FAaZhmtd5CJy6HIO+PZSfuiFGORqNWtCzWl3dWeozWpy2JFt/DDtGmnnApPXQXSJxffydv8KJoPWm8a1vfUsbGxv2PL79BQPJ3krBSxCk67OkYUwTaKGrHhYnsPZQGhwESYG95ZJzSE/UaxeLhTlM+vZwEhCagH+r1arZN+rX8DmonTIX9IUaMUEOOsh6eF30SBvPwfP6YAKZ87An88WJe4TA1ywhWLEu6/Xa+moTiYShAmTKsFKB/ynBeXKjRwok2QEfN50l7cdNtU7kEdInF1PP53ODiFlPUCLklgNtIID5NfGdEASO2C32CLl903irs6zVarboHHLso2eYsL6oinFIpVLWgB5mfPkoySs8t7l7qDYccTEwXCwWg2zPR6CeXUZkTKruDTWbjcJyXB+QFcrHovpIHUFkznyvr0+G+xcZPvu9qYbm4Qbef35+rlarpZcvX6rVagVqekRTGEFIDET0jUZDhUJB1WpVd+/e1f7+vtWFcfIYaU+QIAv0mVOYTMF6+Ge7uLiwGzmAzQlIfObhYTqyLz6TS6AxImE5Yv/8+/08jo+PNZ1O7eYEnpXMnxoO9ZBisWhZ/k3N3r42TsbOc/h6K44fI+ZLAMyZffIHJbB2HkL1LSA+Sl6v1wapE/WzJ5404iFU/s668x6yAupe+XzeDKpnP76JNfjgwQPrm+UsZuYKI9TLiIcp0T2fBfMev5/sAQED+4ITItjDIRKococjJ8IwIIuBKPmTXtizRCJhLU2sI6xQDDFscpAkX4f0ZQLmigPG+YT16E2w4Hw+D+iM31/P6+C7vbzhQLBBIBtcEP7OO+9oPp9bvTqXy+no6EidTscyUJ+0eAgWG9Hv9401+7OMsG3n3zDgScp2d3e1t7enZDJpwR8IZyqV0tnZmckdicN6fXVuM10C4blCHntTvdiPtzrLcrlsN4OTzpPye2MnyTBvXsODJhIJM/DhOxk9SaVYLJpT4oGAgxAuDNZyubQTYWKxWOBkEubDAuIkIpFIgNmJg+JzeR/OKR6/vtHen1CBsfC1XBbZX9/kI9wwOQP4gOGdEe/zLDNv8HC8FxcXevnypT788ENJsqylUqmoVqtZYZ6aK4o6mUzs3lDOeYQgBKTDHrAu1L/IiJj7m+qY3qgBh9PrCRzFkYUeyuU5fa2JYIu5J5NJlUqlQO3Qf6934F7wcYb+ZCAyCTINGITILRkq6ANrz/5K1/dCAs0R2CAvZODoBDId3nsCGv83345CJsP6ESWzH/1+324FQa4heZCpoYv8eAeDvBGxQ3Ci1lqpVALkpTcZlVqtJknWd8jnLpdLu8Day47v512tVoHTjXCyrBPOFyPPuvJ3X9YhY4xGo0YafOedd3Tnzh2rT0v6AnIwGo1MNheLhdUvJWl7e1sHBwf2N3/MHEgKpR8Yx5z4hQ75A/gl2QUIqVTKjLtH27zzZ3jokFKNR3l8WcIfzMB7CQ49ynfnzh09fvxYT548sSu5SGBILBqNhjqdjtkHf/wmwQGlBAie/9PhyxnILwz43d1dvfvuu3ZetEfuPPoCKcufkVypVMw+0PrmeQyscdim+PFWZ5lIJKzhud/va7FY2PFx9O+hQL1ez1LnjY0NFQoFqw3CpvOZkldkBI2obD6fB0hAKBvZHU3ACAPCzPD1PeoZ/qYBsqiwM/MRMA4TYw7mz+czXyI4bzwQGrK64XBoB1gPh0Pr0fICwvvYeNaWYIJMGaLBzs6O3n//fT1+/DjQFE/GBKzI9Uoe0gN+S6VSunfvXuCSV0kB50jW0+/31Wg0JEn1et3Om/QCzvDZOrUrjC0Hk7PG1LB8lsb6wYSEfIEz4exdMgu/7xiM8PBZp3Tl5Mh0IbH4liJuuQnDmBg5eumo8xUKhQAaEo/HzciAYGDEhsOhyRVBFJ/t9wEHgSFEHoD9gWSRqU6nY0YMYhxtOP5gEebn60WcWoT8UCdivgTLsNfD686o1+tWbjk+Pg4wtT1yg8w3m007ZpDj9Xzw6LN1gmPWDkidn62tLVtT6vjSdXB379493bt3L3AzDy00nOH76NGjAAJA8E9Aw3WBsIRxCDwP5EQCespNGHaC/Pl8boEt8C0GnECBbJP3M3ge3z6CfFEi8G0y/lDy1Wpl9deNjQ39+q//uorFoqrVqvL5vJXXcDoEYNjdRqMRIN+BsmETu92uXr16pVarFYDMkZNwgO3lyKMHwL0kARxQAK8F3eVQ/PV6bSeVnZ2d6fLy0vzR+++/r52dHQsc/MlkvjSB3XnTeKuzBM9FYZ89e6YHDx6oWq3q9PTUit1Ee7BWJZkx5MvZeF9sJvphs73R9MQbSZYB8HNwcKCzs7Mv1ACJ6DEwwDik7Ofn51aPDPegQXjxDaqeTcd8gEt924WHXTEW1Osg9HCrBbdvMFAWX8OF1YZhXC6Xgawom82qXq9rb2/PLs72hyN75iRGk32g55OLUj0Tlzpov9+3O+X4Hf2sDx48MNgQ6NI7qDAstre3F2gbCiuPrzGSWfq19BkfaIGPlpkje8z+hKNxlCIej6tUKqnRaFiNF9IH+wthhOeAvcsPEClG27Ow+T5vtHhe9gVSB2tLhO8b/nmtr+vyfQQP/pk5MMDX/vkcdATEBR3zZCpo+BhWjhtbLBbqdDpmwJnHTePp06d2BRyZnYd6yRgwpBADd3Z2rGUM3SVI4t+FQsH0hEzVk788oQ8j2Gw2FYlELBAAoeD7gX0nk4levXqlk5MT67P0yNN0OrW+z7t37xoRiPXzjHoP17NXOEpITjgo9gJZ8rqIDEEsYnQ6HYPH/XvoQuC4RemK5U1rCE5tOp1a4PDVr37Vgt5Op2O2ngSpUChoMBjo2bNn+vDDD+0Ag0KhoFKp9AWInxPESJQYntzjf+flyOtsNBq1xKBWq5l9Ozs70w9+8ANj8c7nc52enlqfvnTVdrK3t6dqtap6vW6HMrAnEBqZM7pA8PGm8aWXP3vHc3Z2pn6/r1qtpnv37umnP/2pZWQYKPvg/xuC8OSJcJ2CdJsITrpms5IF8dmeKh+NRnXnzh19+umnFqn5geOiV8rXnGazmb3eOzqMANEHxtUbOk/zRpF8FuZroUA7RM04SJ/BMjx5B4PO5+KUcDQoMtmmr4VRU/B1Wd8fKsmCAuAN2LC8Fio9QQiGB3KJJFNynt8Polef9fnePyAxz6j0tRVf9+EzPMklkUgE+iu9nHoni5x5mfDfQ7CEIaTm6JXKr5s/Xs5D+zhXD8nz3Z5Y5WWSf/MaMuZUKqVqtRrQP57BZ8zekPKsONpisWhECJwxJRIgdgIAnBZEHg7/wAlEIpEAPElvpoeAw4PAk9oeTotsiUAD50ILAEE3QbN0dXSlb20hm+Z3OFocGwQU9gI0CbKKlxXPxuQ9FxcXOj09VaVSCdQhpet2BG+YcfysYzQaDZwUQw2UGt5gMLAsO5/PGxHHIx6+/AHqwIlCjNFoFGB/YkuQzdVqZfyJdDptAQ/QNPDq9va2Zb6sKZ+L/YCYye980OVrv2S/XMBMZuht3NuCLP86X0Yhu6Rs0uv11G63A1yD0WhkXRPFYlG7u7u6e/du4LACnp8SBn6EYB0U83/tLMkc2MxGo6FXr16pXq/ra1/7mg4PDy0z816ZjILCrK9HkL0sl0uLPBA8vDtwI5+JQSECSqfTeu+99/SjH/1IJycnNzKYmBfFc4w7tSE2BDgPSJf5MiA6oBDeSBHN8SNdHz4/GAzUbDatXQH4gD6xcAbhnSWGxWcnZJk4lDCs6CFbTldCEYFRMCjUJv0h8NQAcO4YJSJ8slbp6iYXzptE6HwwhKPh3x5qn81marfbZjzIhH0A4iP2ra0tVSqVwGEEKAlK7KE771B88IZSI080tnNCFbLo54rsEzCw776mhlIjb55wQuRPUAH0E41GTQYxrrT07O/v25x975h/HvaMuiOZYb1et2MhQSt8/Yf/99ArkJq/fQbHyp56MoUvR9w0OKszErlqL/uv//qvgGPxzhskx6MKvv0M58zaQhzyNVDWw0Np7Fmn0zHHDePTBwqsK4H6cDjUy5cvVa1WlU6n7R5e3/rCTS5kwQRdntfga3e9Xs90B7geaBF74nXZo1mwbMOtZv4igbDNQz/p/cQG47APDw91//59vffee7pz546xr1kbAmlk7d69ezo/P1en01GtVrNr/TyUuV5fkQKbzaZOT08NFbupNHOTw0QmPX/BO0zuOUbPCApJSOLxuHZ3d40AdOfOHeOxUH/38D3wOb9DpuHSvGm81Vly1RAfMJ/P9eMf/1iJREK/+Zu/qV/6pV/SJ598omazqc3NTQ0GA4P4ULhsNmuKg0Bh+H3twC8k0EIsFrONISItFou6f/++3n//fb3zzjs6PDzU559/Hlh4SRZZSjKyC72eGGiObMIAEwnjtP2Zlf68WJSS3kZP2iHSQTAbjYY5nq2tLb3//vuq1+sBllu/39fW1pbi8bjBfx62xEn47Mg7P66ygREpBS+cxbgTsHCLh68Z4BRbrZYdKo5hpzaAHHAzBXAbToIgyWdZ3jHj0PP5/BeyJGTA9/ihJGQb1LD88YUELeEsD0W7SS54Nsgaz549UywWU6VSCZxRTFAnXWXHfG86nVa/3zclxxGFB3KObJAxJ5NJjUYjy1wTiYRqtZoODg509+5dez8ZBGuEA2OvPG2eU1d8GxDIA6ehIFPsPUEpr8EJUn6RZKdx4Zh9gOafk/Ho0SOTzfF4rLt37+r58+fqdru2/x6F4UxeHIyXeQ7/Z+2QZ09W8SgRhpDgOpVK6Vd/9Vft3NCtrS0dHByoXC5b29dyuTSZWywWevnypf2NfYUIR30Ye0YN0xO7qDPDyAQVgmRSLpetH5t1IkBHD5DR8/Nzu5bQk2UoEyBX3LXr2yR8tgkPgib/3/zN31S9Xlcmk7FbZZAH1gj7uLm5aTXDly9fBvZsMBiYfLdaLT1//tyORPRZuddBbAI25ya98YgVQRNs3UgkonK5bLVx9p9zpynTENxhs5ArWvxoB4rFYhZceo7CTeOtzpIN5ANisas7JT/77DP927/9m775zW8a7ZZsBCMNs5Voldd5Q3hTw61/MEkWXQF7ZTIZ67u7f/++dnZ2bqyX8TkIDtEhRA6UEGgxGr26y9LToYE4EUD+hmJzdiSwHPArTEAcCdEtjDOuB2JAQUfAMQyedu/ZsERFPhv1g5YalJE15kQdnBnG3BsCMh3+Jl0rHEYylUqpXq+r3W7rxYsX1hqEYQf+wxAB52KscZiQs/xcPBzvHb6HpTAuKIqPWgkspGAPn8/QYI/6Y706nY6dxoL8At/wHT76BanwDgDZ85AwTh6oy2dAzDGXy+ng4MBaeBhhqJl98eQXTsXCSfo6pGfmhjNl5IYsnVIFr+czvEP1PcThdWVAjPFzR8Y6nY4ODg4CASrOxNd/CZCxKQRetHP5S6M98gWJCB05ODiwQwg4uIG2IeaNzLAew+FQZ2dnyufzunPnjhngfD5vn4uzQh5xlv50KhAcYFcybgJu36pDECZd97z69gv0g4HOLBYLO3gChxiPxw0GZe1Xq5UFXr/4i7+oWq0W6HMnMJKub8iBdzEcDg11QZdZNw9dk336gCYcVDE8uuERKQbvxbmBgMXjcavJU+IDXvfcFOyx/wy+lx/0E7uIzL6NwfulJ/h4Uot0ZTibzaZ+8IMf6Fd+5VfMw1PnAoKEYYnBRwk8G4/hF9en6b7ATV2RA36TyaTdsnET6yoSuWrl2NnZsRqDbxzGUGCEfT3TZ0nhzJK1mM1mls3hmEj5fSYM9bxYLGpnZ0f7+/va2dkJtI7AvMTQsdmeaesNHsrjnUQYvkapqfv5WnCYZMMacwAze+JrQV6IYrGr4/Roq/FkGN6LsOLUGQQhKAFQmneOXi48NB1mjnqF9HCsV8bwYJ/pu0un06rX64YQTCaTgMPiPd4JEoWjaMiHh1p9HZrf8V5frgBhwZD7koJ/xjD0K133A/ug0wcF7CEGk4zOO2DPJ/CIBrKD/CE3/vu9w2GUy2VDAtLptBHq0um0kXW87BH4sfc8P8/sDZwPtpFzXxfG4EWjV8e6PXnyRPfu3bO6Fb2zPhAM8wUWi4V6vZ4dUhDOOjzhCPvE9/r6MrLhyVOege8/j30B8vaHT6BHfng2OnaHNWSurB3PN51O7X5YnA9rh7yCCgEbQ9Bin+lv9GWH4XCo09NTu0w8k8kEbNZNg3mF5Yi/+b8zLwJk4GzfvhZGlLw8+DXwcu7Jpdgrn4neNN7qLGkxwDjxIN1uV9/73vf0u7/7u3bf4PHxsS4uLqypF3INSuwFypMw+EwfaSNQGHAiJSCBUqmkSOSK4caN9jdtRj6f18///M/bnJ8+fWqL6wUGo+AjLEkBgfe1LDYGEoxXVKIyYLNKpWLXyWxvb2t/f1/lcjlwUku327VsMp1OazgcGu7Ps/G9fDbG66Y+q2g0amQfTi0iOwCSxtgDuZFV+miP2tpNEVehUDBIiSuVfE0NhZGue1d9Ez3y5IkWPI8n/aAwZO4opT+rlOGhG/YsPAjaIKptbm7q3r17Ojw8NGiNPkvWmswaWby8vLS2J+o81EFwgF7ew7VOH+QA/0Iq8XLBGhAkIbvIpe8J9nA6AZLPSGgB8QEYzp05UoLAmdDuBCHGz9vXUL2xe/jwoQWkIDV7e3vWe/eTn/zEngMj57NYX8MrlUrKZrMmg54R7VtsMHww7LPZrHZ3d/Vbv/VbqlQqZmz9az2a5Z0lOsyhH/V63WBtz573KIfva/SXC/veW8/B8PJK7Y+9BpXyTfXsK+Px48f68Y9/HCDgoTvoCvKGc+DgAa5aBMLl9cgs5Stq+Th7IG5QpPl8brcbnZ+fW3nBy8qbnOWbBs/q5QN7Bpu43W4HOCjSNWkx3GroERhfF5dkQQ823RP33jQib0qVb8ftuB2343bcjttxNf5nV0XfjttxO27H7bgd/z8cb4Vh//iP/3gNQaVWq+mb3/ymYfj0dUEs8Af+xmKxwAW/UhCn5r+cxNDpdHRycmLnvXL5K9TyXC6n1WplPTYnJydqNpsqFArWQ/U3f/M3EUn6/d///TWkIqACrv6inujrRxsbG9ra2lKhULCzH/1RZzwT84XlRV3GF6H5Tl+j4offQZSJxWL6h3/4h4gk/fu///uaI506nY5arZZevHih4+NjnZyc6Fvf+pbOzs70+vVrHR0d6Y/+6I907949uwCXNWeO3KJwfHysf/7nf9aHH35o0K1v9JZk60tdZb1e25psb29rsVgYkanVaumHP/xh5Nvf/vaaOoBnMVL/oBaZTqeNQAMUff/+fT1+/FiFQiEApXnYZr1e2/x/+MMf6vXr10Z4aDQagdtpwuQuIBWg/tevX0ck6S/+4i/WnU5Hq9XKmKHUpGkQByrj6jP2GogGyBuI1/cpehiUWhxwV5jmz7Vl5+fnev78udrttsFli8VCf/ZnfxZB/8rlsra2tuy8Wv/dwMWsHTqHfHK83OXlpbrdbqDOzbNDAoKQ5u9oHY1G6nQ6Oj4+1suXL22f0Q1fw//7v//7iCR1u911mFREOcDD1KyXP2uUsoBnUvv+VA7fh206HA7tph768cLH4yFb/rn523e+853I3/7t3679LT5+DYG3fVkiGo1af3I2mzW4Ex33RLib2jt8y4VnSVNaaDabev36tZ4/f65er2c340SjUf3TP/1TRJI++OCDNTU7f8Ez/dbheqHfD+p+zPXi4sIY2cDMlIRYD9/BgL1HXoFHsdu9Xi9gX7773e9GJGl7e3vNJfQPHz7UV77yFevp9fdS+qsH/fGQ6AZrRSmNfQu3pvjasIdbw7Xa6XSqs7Mz/ehHP9LHH3+sH/7wh/roo49uxI+/lODjf1A8DILvg6Q4DBkAQo5X3vCAjUrjM/fXwc5jcxGKZDJpiypdExQ8zowB4Hs7nY4Gg4GRUDDQ1Oo4QDmbzWo0Gqler1tdxH8OG0bNCSEkcMBZwohjU1EIjLgvOjM4YQNWFq0E8/lch4eH6vV6tuHMAdaaJ+TQdNxut9VoNCyo8HVVXyv0tSLqbigztHCYnp6m7RukEWAGdRt/J6CvJ1AzZH993cvXw/xBD5KscT6XywXYbP511F88CzUsyxhrarjsm2d8+9qHr2d554xc8HpeG661IieeqIKhojbUarUCZ/Iy+H/PmISly+dQn6VG5r/TG0kYoN7wYYz4jsFgYAdocIrT+fm5Go1G4M5Y5B5D7mUZFijf7fcIwpvvecOBMV+cgGcUI7esKc8EGQPDLsn+S2sXhCACBx8Ee5n19WH/478z7Og9fwGH79m1PvjjczD6sE1h5bO/3gki59QiGfToInvIDs/i2c5h4hFHbkJmQ8/RWd8H648kJYhkHv5QBN+HDkEoTObynAW/354A6Pks6DN+hnVE7v37vYP0MhXWBf6fvSJIqNVqevz4caAOetN4q7OEnsskWAAWiIXm7xwjlEqlLBLm/krfo8gP/YhQxGFjEm1w3uloNLLPKhQKms1mevnypSmmN9YcBwc5xTNS6eskyySK6/V6dtpOp9MJXDrtoxfpmiDABnqjj9Fi43ktThmikldESfrhD39op03Qz+dp0p9++qkx/xj0PnKDB0aJM0Lb7bZarZba7XZgDxEQ31yOAFNYJ+Mfj8fK5XL2DAQu3W73C6gBDjeXy2l/fz9wJiiRIv1lPgL3ZC8MOIYbshjXtREkbW9v2xpzLBcZhz95yK+Xb1EiOygUCuZEwtmWRwkwIBgDPs+fteuPb/TM2XBTO0YKmeB4MnTE36AjXd/wAiuyXC5bUAW7m7NtQSdYU3SVQMM7bSJzgrhGo6Gf/vSnev36tR16PxqN/i/2zqQ5rjQr/yeVSqWklFI5K1OpyfJUg102REU00RDAjgC+AQsi2LIkWMOOBV+DD8Cmt2yaaLoDmqK7ql2DZVuzlPMgpaYc/4vs39Fzb8muf8C2b4Siyrby5r3ve8bnPOe8TmrCWLKurJW2NbG2rAPB7uXlpa9F+Kgv3RtlHYeJZQQFkNnIcsng6QukeX04HNrZ2Zmvy9zcnLXbbbcFDAXRzFVbhfhO7BFOFma99uuCUoXbuZS4yP8zpIDRbTrGE8JNv9/34f3quFQmIA8x4IB3CZ8wBFkIedDWCv5MPzPOEnY+zHUSgsXFRev3+04SxPGQ1U8mE2fG6lmq6LYOTFEnztooCUuZ3DwDe4VeY0NJ0nCeJHGKgLCfONDb21v//cXFRXv06JGVSiV7/Pixve/6oLPkhHQ27eTkxJWP5m3OAoMFBTOJaCmdTju0Z3bnRK6vr61Wq3nUSkSr03A0qzQzz35oF0FQ1XDTpM1CFItFjxZGo5Ef5Ep/JWd0YoygZDOQGIgShWHDaIjGEbGpGBAMAxHbysqKNxITVHD9+7//e6D1Q8+vHAwGtre3F+gz/Y//+A93dJFIxAc7M7Ox0+l4+4oqMYYUQUcYiTZhQy4tLflJ6ThpGJassc4QTafTtra25qcylEolh+cIgngOHDqMUtYfI6NDHXq9XiCCnJubs3K5bIVCweG38/Nzq1QqPiCi3W5bpVJx6JhLjSKOnxMmFBnQk3CI9On7oycQp66GCmekLTrqNHWUG+xYnAMzeDUoMzM7OzsLZAk0lQM3MiCBEV+ffvqpG7Iw61iduE5mGgym012+++47e/36tTWbze+NNVS2L3BttVp1I6XTqPQMTQIQpvIADSP7yt48Pz+3o6Mj6/V6Ll9MksHxjMdjD050GhBwGv8F7eL7Li4u7Msvv7R4PG7b29su2zwvsqFsZYWQyRJZc7UN5XLZ78ewDc0kWW9G5b1+/dr7J2Ox6fmRTPXBJoLKkQnex+xmXYH0kSsm6YCYNZtNOzs7Cwx3UFRBbXk8Hvf+Vuw3ckPAtb297eibon1q+9FjHUOKDQf6HQ6Hvo+RSMQH4fMsCtkTRLIvWuIKt6tgL4DyI5HgCT1a7tHMGNZvuAc+sObv/RezQERM/2GpVPIaH86HzdfeJRaLyBOFQ5BrtZodHh5avV63drvtkTJnH/J3i4uLls1mPYpiBNby8rLhWsW/AAAgAElEQVS1Wq0AVGdmbkQRKE4Cp/5CvQsDxrgtIlugEu3pYYPMzBXy6urKv19bJRAgHKX2hrKeWuNlnTGmCCnGFEPCOw4GA/v222+9x/Sjjz6ytbU1j/qPjo48I9fITiE4hEXhSn60BxKDhaMkSmYGI1l8sVi0Uqlk2WzWstmsD33QcwK5xuOx9zhiIEACtAbSbrc9SwZxIHvnJAQdW0XjON8RhjRxkChht9sNIA5MbSECT6VSgb0gmFDoj+yUfjy+E4OJAUkmk4HpTsjKwsKC5XI5b7XCmXFxhqzCzt1uNwBjkbGfnp7aaDSyYrHo/YRAtmZ3tUF9jkgk4rNLLy8v3XDjBHUAAEaONSKQYS246FHlXfTMWAIIHTpQqVQ8gO10Ot6CQPsH5z0SaGgdkYZ0gr5wMBOPx91JENhri5WuLXqhjtfsrvkeXQHxGI2mw/3pKc9kMm7c1RiDynS7XR99SVZDy5CevkSA2el0bG5uzuF61p5nUsdBUMf3EDxywALOXVtKgE71MGtQFDJf9gmnwyD629tbS6VSjoTpYHqCfhAqvcj+G42GvyeyGY/HLZ1OWyqVCqwLZS2CbewWdUf0hT8DzzebTd9HTXgU6QFm5tCB95ULuT7oLNVTz8xMp99AOIjH4y7cQAh8Gc3F9OTg3TEk/f7dgcCQbsbjsZ/KUa1WrdFoWDQ6bdiORqM+C5AIIJlM+gkFuila0yFKhKxAtqLvx3/1XdkQnVxDpKJNwwi+Nquro2R+KuOVIJaE52sSYStMgiBohMdzclQWUfrS0pLNzExPXWBcnQ490MwNw0JQoE3dKCL/VQKIXgzrJnhhAIVm7sBNrI8qEGurwozCXlxcOIkHOEfhWhSZOhSBD9E8UDCZCJcaysvLS2s2m25UqeGQSWj9E3KPGhDus7Cw4E5dDV44A1e55/1xlvl83rLZrEfbehFsUo/UepzZ3VFX6NPx8bE7Mk620N+ZTCYexHBqA0aWoM7srqcPhAb5JlgBxVBihepfuHar0CSEu2azaYeHh3ZwcODQIbqhiFE0GvUMBH1W+Iz3wmkgu+ghk2tyuZxVq9Xv1Ss1cFTyDXqjNd3wQIzBYODoBf2y4boX9on3Ri+RU+qAyLfaB4IUmv/D9oLAg+cigK/X6153ZuSeDqNABlk71hQZ1XnXalvJ1kl6uB/On7XEKWnNUuvXCsOi0yCLS0tLHiDjkCkFKZlNEb7JZOJ+hB+GrU8m04PLGQWJTdMygJ409aHrg84SwYvH45bNZm37t9MwBoPpsSjHx8ceaRHpK8kFJSC644WpiTEYOR6PO0RxdnbmB4dSIwKuOD8/d2Ofy+Xs9PTU6ypcTOggygC3xyDyTigu9RTNDFk0zvBTcg01HCZZaDChmDkN45PJxEfJ3d7e2tbWlm+OrjHFfubIqrKFGXVXV1eeQdJwPRxOj1H64osvAlGVmTlrr9/ve+YYLv7z/JBv+HsNBHjm1dVVj9aWlpZsMBjY8fGx9Xo9azQaXs8mQyNS5DmY+cne1Wo1d+DUcnC0c3Nzlslk3CGcnJx4cDUajaxSqfgAcY6oYjyXCj9G08x86hJwO6O6iGy73W4A5iEbhkmMchLtEp2TSXJ6BwHT1dVVgNjCwAoIS8+ePbOvvvrKWq1WwMBcX1975FsqlXwSDdDZysqKIyCcmXpwcGBnZ2e2tLRkpVIpwErHecAqxiEtLi7a5uamT2hhbGU0Oh1vub+/79B+JDIdlJHL5VzftP6uhh+5JBCZTKZN9wcHB7b/24PLOeWjVCrZ1taW5XI519tms2nn5+fOH0gmkzYYDNwRghqBGpCJa/bJPsAcBw5ENtBR0LPb21vLZDL+negEQTJ1YfQD+4TjUpRIS06UYEBt2Ev0hMCMNQQpQQ+Ul6F1P04y0gy0Vqu5w2RcIPvJmDslUinUj21mgAWOG8d2dXUVGL7OfQmIGA9I0KQ2jv+S8FAzj8VizuA/OzszM7NKpWL5fN5yuZytr69bJpPxzzBQBh3t9Xp2fHxs9XrdExiCDcossPMVgeKdGVkYRqPC1w86S6KYubk5H23UaDRsd3fX9vb2HILt9/v20Ucf2dbWlk+AD9dKLi8v3WhoGwYLlslkbDKZOKSlRVoiRyA9shqNKHUB+Gw8Hre1tTUrFAo2HA7t8PDQlaPRaFilUnGISGuXZlPYQKeFcFRROFvCcCu7kiiLFgkEiHYXjXDr9XpgDq9G8kAzQCEYC4S13W7bL3/5S4fTWq2WrwNQkZJ3EBSyGO6tdUElQRDNa421UCiY2TQDqVarbnhvbm4cQue4qSdPnnjxHwgSgoFGzOos9VxCMmac5mAw8OH9ahwvLy+t0Wj4iRFmFghI2u22ZTKZAIGG7JeArtvtWiKRsHq9bpVKxWtFHGROkDQzM+PQFk4BAwe0nE6n3ZFdX19bMpm05eVln4lLcDI3N2fPnz+3WCxme3t7gUMBMNi5XM7P5SMoWFtbs2g0apVKxWFriE6Qv7rdrstNIpGw58+f2/X1tVUqFfv222/t4uLCHTGoCEbm8ePHdnBwYN99953967/+q52cnNjq6qo7Ej0NRoM7asUaWBCQUmMGkSoWi67HrIs632Qy6cEpugFag01ib9rttq8VtoOpQdFo1A83NjMPvJA7Mq94PG7lctmDfrJunJWWYhSmJbO6vLy0dDrtKANrfXR0ZKenp3Z1deUTvZAPkgbmOeNIFhcXLZ1OW6VScWiaC/uBLhEMXV5eWrVatbdv31q73Q4cPB+utbOGV1dXgXbAi4sLOz09dZtKnVYzyWq16jXG9fV17x5IJBJ+IhFtIXop2YnzeCmrkVBQEz87O7NKpeJ78vDhQysWiw7LI3e9Xs+q1aoP67+5ubHV1VV78OCB5fN5y2QyPgsX6DnMUtayWBjd0esHYVg2ApgU6OQ3v/mNGxSinFKp5NBIvV73F0N4MTCKl+MUyuWyzc/PO9TF5gAxMAoLQ0sEg5ByaXQG+QDDOxqNbG1tzSE+6qHaq8OpAkSAMCYhYmh9j2hKYRmlWQO7sbnACgsLC4EMQlmASrnG8SszjPUkEkLJFhcXrVwu287Ojr19+9ZqtZobMdi1SlLCUGj9i/sCVyCQ/B6yAOmH4IUMCCU3Mz/pgnXA8YfJQmQ3ROoEXrq+GpTMz89boVDwjIjnBIoLD6XXNSYogBxCvR1mJe9N5sVJB2trazY/P++ZKNmCMnDJFJW9yTsT9ZOFKgENmLFcLtvt7a0fsM3+EBFj0GAKHx8fWyQSsaOjIzs+Pra9vb0AI5tsgHfb2toK9ElCgqL+1uv1vAYLpD4ajVyudNQcAS7yqMjHT37yE9dHRlOyj+E2L5wOCNRoNPLsejQaBUhvGlxqDdfsLiAdDoeBU2p2d3etXq/7Onz00UcB4hlygUxTO7++vg7UMnEklAm0XUrHDXKSDPci20QPyYhqtZrt7+97jyPrkkqlrFgsum0DkYAkp+8frrviOOCAkBUuLCz40Hd+2EPWdn193WvnoCrYUdAn9pBn4Z5aN0ZHIUNpH7XZHckOJjhERmwZNXKOMKxUKtZut21vb88JSZScWEtlwE8m02Pgrq6u7PT01JGuTqfjzhsSEn8GicT+hWF6vT7oLNXRDYdDr4scHBzY8fGxdbvdgNOC4m9mzu7T/jqMJsIJJj+ZTDyKJquCeALWj2Dxb8C9s7OzgcxSCQWaVrNRLA7PgDNU6BFnSV0MNl948zFOvJ8+hzp31gfWHMZaL6X8Y9CA7HBWZH78zM7OBqDKjY0NW11d9ewMliWXtklAk8cJ8kNvJO+qrDIlcmCsIFtxEgDOSP8Og6w1UtYZo4vyK9ONddGa1/z8vBuUTCbjzp/sUIX9vvoU76GtG2QV1K0uLi4sEol45MvxaUDGzWYzMCsWR8M6md0ZD5wo+wuczb+zH9QBNfPVjH80Glmn0wmUC6LRqO3v79ve3p599dVXlkgk/Pgp5nhyFFG5XPb9isfj/tyxWMwDRDLkxcVFa7fb1u/3bWlpyZ48eWKlUikQ4GFwws7yiy++8PXM5/M2GAwCpDeN6sncWBP0Hb1iuDz6QGkDcgv2BEeOvdLWETPzvSIb0yxJ1xgZoFY3GAwCTHiCJC1f8F7wNHCiBELqPHDijUbDjo6OrNPpeGY1mUxsZWXF5ZpAmO/WhACHrwE2Q0NarZYNh0N3iAR5ynvArtEDTbardg97qOge64WugWYpg5+90jY69Bh0gK4DZFr78XnmbDbrtp8WOHrP2Ss4JLoe4/HYAwYSMmYMY+eTyaSTVOFf4CcUQQtfH3SWKBUPQdPyxcWFR+EwD3O5nFPZOZwznU77QmEcMHpaKL6+vnajgqNjc66vr30IOqxQDC7PoA4BQ6c1ODVq6oyBN3B09DaCjRMlqrNDaFE8+t4mk4l9+eWXTn0HZiZD0taS+4rfSsAhS4NVrAaWDFGZiPl83j799FN78eKFlctlH2wM65gMF0emRXKNMBcWFmxlZcWPNKIWxH7hLKnjoWjZbNaNRywWs88++8z3+ubmxtmr2qcbpukrtEX2iWwBwxJZAgP3+32HXrTfTjN1LowiZBcgdRwEkDODMgj2IpGI7ezs+ISfXq9nX3/9tbcYAYNpKwP1ZyDzBw8eeLbBelPT4Xl1wg0XUTzvWKlUHKkoFouWTCbt3bt39ubNG/v666/9xA+CMuBLvpvsGQPR7/cdTqNeSSBANJ5Op+3HP/6x3d7eWqvVcsiS/ldlKZqZ6xFZYq/Xc5Y0MLoGODo4YDKZ2Hfffec10WKx6OtFLymsZYJp3g/Z0P7XJ0+eWKfTsW6364YSdEhbrwg8sDetVsvheQIdJY0AMwK3UqPWNiOSBkUcKP00Gg07PT21SCTiUPZkMrFEImHFYtEPqq7Vak7i00B8eXnZZffq6sr7Y8miSqWSIxHX19e+t+gUzHX6zhcWFgKM/2Kx6HLJMHV0B8eJPSfLhKRHjVzX1OxuaDyli2azGSBXmd31WedyOe93XF5etrOzM9dJ7Kj271cqFTs8PHT2/MHBgQfoWh7BX3D82ubmpj158sSePn3qMvOh6weHEmhhNpFI2MOHDy2bzfo4ulwu57gwEKPCmkRWwBlakwOG5aBTolCiOD4fi02nLMC6pU7JoummcBCqKiAF8Wg0atVq1Sf6Y0zJqhKJhCWTyUB0tb6+7s3wwCz04JmZO5ZoNGqPHz+28fiu3aVQKPggAliFRFbqLEulkjt0nD0CqSeqaNRGMAA5Y2dnxwlYm5ub3i9HwMA+kF1iMGDlEWkqIUShMAIjMwtQzllXCAt8D3+Pg6B2AoypzwOkwj4qwximJhlDKpXyYAT4eXFx0aN+shUgcS6if+R5fX3dtre3A8xVFJ+62sOHD+3Jkye2ubnpBKVyuWzJZNJevXrlhA4yIJyQts8QYObzeYeAIJeR4VA7Go1GLmtcjUbDh39gPPiuRCJhOzs7PkYQQw6cSE2PQMjsLnDlkGbqStRUCQ4hO6E75+fnzmY2m/ZgYrRVlv/wD//QM9pWq2W7u7v27t0778NdX1/34A1GMTXVhw8f2h/8wR8EAgr6+WgtwIYo0x3jTNCJ7JDdklWQsQ4GA6/tsycgNbD8Ly4u7ODgwObn572WVqvVbHV11ba3t702SVJAPyfBvQbI2WzWa3kQH5kshq7Mzc3Z5uamJxgXFxeBZ9PryZMn7tTfvHljZubBAqQy7M1oNLJsNuvOWol7o9HICoWC9zfCbaAGzDAKbA12mKyVvwP2JFliPGO5XPZnVhY+8kJGSnkKJIo9nJ2dtVQq5WegkhUvLy97Jry8vGwvXryw8Xhs+/v7dnJy4twXMuVcLudoy8LCgvels1a5XM7l8H9ds+QBgZM4fqZQKLjSMSGD3i4yAOBbMgh+n78j+sLw0iLAdylLzcwCE1zI1nh5FSbN0Hge7SOanZ0NzJjkuYiUl5aW3CkRdWPMcSoIHVkI7CugIArdkBdwfkCzYUgTCjlOQes6rIlZsMkbZ0Q0mslknGJfLBZtdXXVarVagKJNFoWgz8zM+DsBJxEgKc1b66UYXP179pfPUTMDXpqfn3dCD/ujjv8+Z6m/r2QnhdiREaB9HDDPo9G4Rq9kKVr/hcE9GAycNLC9vR0gfCSTSdvY2LA/+ZM/sVKpZLVazbrdrs3NzbnRyWQyXqsH/iO6x2HrmmmGTdbHBcyMTJENUFuEXXxzc2OFQsFLFVrrVoiM2rWZeTbEes/M3J0zqggC6wY8qhAbgZ/CbWR7V1dXVqlU7OzsLEB8SqVSls1mA72K1MeYGMX9eUZsCnquzhI5Q3ZYVwJSegGTyaSdn58Hsnt0CmSFliOzuyZ6HPLNzY0nBmtra7a6uup6x1pQk9fRhApnava1uLjoAQiZM/utaA6XlhTINofDoaVSKVtdXXVbQK0Se4d8KVcBW06f78OHD70PfDwe29HR0fd6vZVNiwMlqyYIYZ/0d7lokQJ9oUa5srLiPeMEwuguKNDx8bE1Gg13pkrUiUSmAw1WV1c9+KLvk3UlsETPdQgDv4P8/K/ZsNoOgdKRQSAECDGQBAaIGoTSyJUJSYaGIYAAoXCVwizD4dCFGWhYD1Xl0hqXZlI4TIxCr9dzejSRnZ6iPjs76w4PlixZENEPG48RwnErTEqmxjriCNVZ8g5sHuxB3kP3gfVCOIEl1fin02nLZrOBmpE2W2ubA+/HvRWeIlBRx6jKi8KwTxgrshHNYBXmUGerTo/7QxLDiGLAQBq0DkSEzLOguPwuF/JHtK+1Ya1nmJmzB2Hf8dyQtBgEv7e3F5g6o+Qo1RfkFaPMc7IWyoJUZ5nNZt0ZEWET3DBMAYMCCQh57HQ6gaCC/ac0QLaGLoJSaIBMsAI8jdySxQHTqyyTXYGUKLIE1Mg0q+FwaMlk0mc3M64OWdFxidT82G/0AzlS2dC1JcAHdsbwk2kjywTtZJg4aa2dw+ZnkhKkRIIiZJWgi+dWnoWyMpEFnBdwKffAhmgLBs/L5wii2D+eg4SEujT6SFAPopTL5WxjY8MztU6nY5lMxhMJyjHoJ7YNewGUq+RAHLs6nnK57A4OR0jrWT6ft/X1dR90gkybmWerkUgkcJapknGoS45GI19DXVd0hgCBli0lPBJgfuj6wdYRBJHsEcE3M2+zoD5Gms3ioZiwH4kIgaCA/qg1MM2FFFmFDwp4PB73jEnHI3HhkMMvjgHAsWLAdWSdOlPgBDaf3yEDXVlZsbm5OR9UrvAwUN719XWg74j1AGLiIggYjaZ9idTCNOvAAQDNsPFAg5CQMP5Au9Tj1IHAOGQfNfMFhuEHY0dwYHY3rB4ni2Ky70S9rKcGUWSbBDMEWuwdECKZLcYDWQD6xCCYBad3YCx5Hy5g95mZGYdkGPfG3yOHwKFAkWQXrFUikbCNjQ1/plevXtnp6al1Oh2fyasBEC1D2oakxhC94dQXLq3ZwQXgvktLS5bNZv39x+OxZxbaoqP6QzBoZk4CUdlCrsJweDgjoT5Xr9cD2aqZ2d7enpPotra2bHV11czMSSSsBczceDzusHelUvF1UbKYwnfaxqGBEWuqRA+ydQxrOp32gfAEJapnsGkLhYLl83nXIS6+U0/JIAuid1HtI/KtU6BAm1KplJc5eH7mqSpRCJ1SewYKx/4yFpAADRurXAWed2lpyYrFogdctAPxLslk0srlss3MzPgadTodL3Eo/BqPx32mNbYc+xXuDX306JEVi0VLJBJeu4UFfXl5afV63fWX+4E4wZhmwhQwsNndAApQFrNpawt90GS/+IpoNOoZvCYA2s72vuuDzhKohA2FuYog4OGJynAKCimSFTabTV8g4BacDjjy4eGhj8HKZrPuMHEwmoXQkD8/Px+IxhF8fjRLY3ERSAwojlMNERi6GlwiRQQXh4pjIZMl24ZqjXAT1VBT4Eomk+6swxHOfVFlOp12+GBtbc3ZXDrNhrYXYBIYqqlUyra3t53RpqOwMEY6aQaEgIDBzDxa55l5FqVmaw8tjgyUwMzcaJZKJSsUCl73abfbtr+/7+vIGDclW2GMMIJap1KGbbiWrWP2dGYw74ZSNptNRxqokzGNCpj86urKFhYWbGdnx4rFopOqiJi1iZpAbTQaBSJ/HDtOWx2o2RS64u9AJSDtTCbTySSUQRYWFrwRnfYZGK7a44jxxJAQkDQaDTdSy8vLru9ag0Z/ZmZmPKuk1YDr7du39qMf/chJUfv7+1av1z2D0KxjaWnJBxLc3k6HudOjC2KCs+H7tO7GO/CuyIE6Sw1SvvnmGz8OTUsK6AIOgKwPndEaO0Eisk99Fha+/jtyjvzCHIX5TwsZyUa/3w8MPSGhwBlyMWwAtihZF8ESLOZMJmObm5uOOrHvBCJkmYocAOvi6JaWlpyLoXO7kUmSBgKp6+tr7/nld82mASH9wSAmvAMzmiGmsS5wOAaDgSMS+AuQTXSdGjryhAxgr4Bd0WuyXBI4fv9D2eUHnaUORUYQMUJzc3PuXDCSCBvOEkPLNA4cCg6D39dRYTixm5sbZ3SpcyOFhhKP8HHhcDDSGm3qhVPCuKuTI3pCAbl4T+2H1GyBDAAoh4ssCYNMDYGLuqZmXBqhhjMxRgBCsFJ4E8VGcePxuK89UDLTUMbjsTePo2QaFaK8ZMpkGAgYz6TtJgRI4bYC/SzjCiE+0ExO5sNUEogYCtuGZ49qvRR0QtnQXOwRRCD+DnnmXZlesra25pkcsKnC4tr+AdrA2isEpD21KCOyzHozQs0sWJuiRACiooGezvpUeWFvVIbROWSVtclkMmZmTkLCwRMkKqkCBIH103diOAR/JjOEx0BWrIYZx6dlkFKpZPl83vcSRjrvqyiQ7i3PpbqOfLLXo9HIzs7OfISgjoMkK2SIhAZ0ZEIq73p/AgUQgDCKog4Ucg/2E/3g+bFZGvihW8rL6PV6fioMk6kUfUHvsRFMHdLeYpIDM3O7zrrqUIFIJOKM/+Fw6HZAEQcNUhn4QIlL7a1ZcBQhY+yUG8F/QR5wiGqjkWnlL2BvcIjKUVBon31ShCv8877rB52lwltao8TTs6hKAtHaDJES5AMibRZEe5YQFlJzis66CECkLCxQgG5K+Jl1AfTPOB8cPNGXOkoWnSwrHr878w2jpHVbggDqnCgC0fh9ECEwZLhGiXFUyAGojGyOpmBdOyU4zc7OOjnj9vbW2WfsGwZMnSVR7u3trUfDEERQDtaI+g/OUoVXFfzi4sJhMzJcHcauBCOcDO9HLxXGg2dAfjTDBKYNF+pR/NFo5AGB7jlGn1F8oAs6MUaDFlU4ZIP7ay1WMxiUXCFmgq77IlpIIuGMhncjONL7KzKhtW7eBb1EN7S2xWxO6kTIMXUqhXWVOKY9gGQtGGqIUZA3cHzsHYxLWI5A0ThSZUYDkQHLkt2xftTjeS+ejYyo0Wh4MKeGnHvjMNE3dAynTQAX3i/eVxENZFCdp9ldzyfGH24AaIGSvpBb/Ty6xJxr+qyV8KT6gz1CrzmsgkSEOrDaI6B2atqQcUB2CCoItDWAIeDUnnrWWGWOYAjd0oAnTG7Cb2gZR20jchqWVbMp+kTHhHIvVJ/Ngsd3ve/6oLOkPcTsro0AA5tOpy2fzwfmnGpWhFMC1hqPxw63dLtdOz4+9rMjs9mszc3NObGCeZoKjfCCRAq6eNRFeE6Fa7S4Te1Jow4iQ2XbYew0RVeIjXsDFXA/jAEbrWQm4EqiSj3WiL9nDc2Chk77M82mQQyQBMqmRoJ1Yu3M7gzr0dGRQydA53wvUAsTMXCQYXzf7I5yH4/HPWvQNgSCG+rCGK+trS178uSJO616ve5wO7UrpjgNh0MrlUoOLxNtwkjmmVk/ICZkResPGEmMA9Ae69VsNm1vb89OTk58MokyhYHVlShG9hg+hkyJDjgGPb0hXFdFLjGaXO1221ZWVtwQaHCKfKj8QpZRaJcaNlk87V3j8dj3LBKJ2Lt376zRaARkLp1Ou45pHXswGPiYtvB0mQcPHjjEmEwmnZbPqEE90m88HnvvMzC1oi44TIKsubm7YR44BnRTiVKTyd00J4XAQYIoDaGjyAucA5xwJBJxBx6N3o1kQw+xK6BGSojj+fgzuqKOn6xQP6uoGE4E3eGinxA9Zb3m5uZ8HCHQK8M3GOH45s0bJwAqgx65vr29dQj36urKvv76a4vFYraxsWFbW1uWTCad88BAfLWHlJyQIS6CcZUh7CQ6gA7jQ9TmIIdmd/wTnBvrzH9JEJAznVuu99TMlOt/DcMyYBYvDsZNwZS2Crw9xW4eEiPG7MeZmRmvsSwuLlq9XrdarWa1Ws0noLAYkUjE+wQx4kSZYPQ4ON0UIhR+F8IIC6H1B4wnvVw0u2sqz6YSCRFlqhBAqjAzbxpn43GUXMBTm5ub/ndk2Sgo68kz6izZaDRqpVLJsz2eTyFR9gpjrZlmp9Ox3d1dZ+spHV+hFS6tMWngRBSvJBVljbIWjHBjaD6OqdlsWrVatXq9bhcXF5bNZi2RSFir1fJh+r1ezx48eGDRaNSzMIwVjgaHoAGKDilQWdYWFBwmxuvk5MTnzWq7Ao4CI4o8Ak2TjbJGyA01/bm5OWeI6tpiFLUGF4YXUXKyBkUFyDaQYUg8nOYD8sFazc7OWqFQ8B4zamKXl5c+iIL+OmSaBnkgZhyj1tU0QzOb9gAyh5Uh8ch0OJhjryB6NJtN29jY+J78qMNQxATYT2tOGtyyftT9aOHhWfgORUCwEWRJnU7HM3NIIlpaqlarvrcaZOOEYZkuLCz47F64FNQkeQ9l+nIPZi8rbIyzpVzFPuNkO52ODxRHJuCLRCIR+/GPf2yrq6uelBCgkqAwpABb8pvf/MbryaVSyQe9oE/h/ncN/tVO8n76gz3ROnS/3/epPTwbdlf3l98nqMAHEchqUKQwK6US7Bv7ponOfdcPnmep0A7Kqa0AGCqFTh4Ld8sAACAASURBVIj+cACzs7Pf68NkUg7DrIkUFOLpdrv+oiwQPzwH0J8KEj9hmEoXWQ0sQxEikUiA8cu76PvSBwbhgloEPyivQrn80I+3ubkZOGS0UCj4hAqtz1HvAQrUSIi1DmfOCCSCy/xPMlAMNwX3QqEQqM2GhQqB0khZSSMaCCgchVAT2ZIhKNSVTCb9eYGULi4uPIABSmJyE1ApTklhK7I93fuwXIT3CRnAqIzH40BrEGuIMVA0Q3u9iK6h4wNVaQ2L9g0lEWiwoWvIBZs6XEpQtiXrzVxQjjei9oSsh0sSZLj8YCCp89Xrdd9rAgyyAtYE1EQjc0YRUq7Q4JL3JpjFpuDsm81mgByIw1NjigwqpB2uU+JwNGvUui9yft+lMjUcDp0EN5lMHAEBSm02mz7LGMfHe2qgwncS7JFgEAiwhzo4ANSKo6s048ERaYCLDg8GAy8lXF1dWbfb9RM9GOjPfFvsAToAvArsT8IC5wH9Rc8ImFkz7L2WkbgIBng/HJMGrzw/B7o3Gg0nCnFwQHjv8Ds8D0GdojyaOWtQpXr0f4ZhtVbDpr6P/EKEob1MGg2iuCgD0TsMSF4YJcLQK6SoSqCF4vuMIgujEY3WBBE6NQLtdts2NjYcniDzAG7DUGIAyW7UEJPh4Th0kxiYzVFEXEBVQFRa88IAa+9mr9fzaFYZdThpJo50u10XON6bGk48HrdisejtB+zh+wre9zlLNYL8HoLP2ms0bWaulKwpNarxeOzQIQECe6/7x/FRGBxdd5VBVQYzCxhUjDD7A+uO2ggGnhpumNShzjIajfpgfuq/8XjcURGyOl07zTBpVWH99SJz02haITnkrN/vu0FkOhXyw+8SiBBkYFSQB2SOQItAlXfmhB/NIghEtd6Vz+e9zsc+8ww4Vd5BSxPj8dgPPFYWsZ5Dqk6fYAhZ451YJxwyOqMkm/ClcqKBIhkkNTicIaUK5rDyw3dpfY5AlVNxGA1Hr7fZ3XQpbCefp/6nMDe6qHYZR4r8K0ROrRbEYHZ21iqVik8AajabzqYfj8fe8wq5CadLWWQymXjwrmuH3IS5K1zqG5TAxHpjazmp5fT01Dsnrq+vrVwu+72VO8B6I4/X19d+wgtjE3O5XCDYUCcZ3vP3BVFmP+AsVUH1hlqk11qgDnoGN0bhWUDuq60JnFPH3Mbb21s/4BenCAOUOkckEnGnq3CbPjP/rxCHmTkTjNmX0J3fvXtnx8fHVi6XbXV11ZVLC+VHR0cuOFofNbuLwnVTNBLe3Ny0jz/+2DY2NgIKSh04TA7gd4B2gHs5RJbo8+rqytc3kUj4BJqTkxPb29vzmaLcy8z8kNidnZ1ApBoOLthPhVkR/Fgs5goA0xnjDkxEjxaGMRaL+WQdpg8pPE62jDHC8MNKxKEq0QylCxtyhQiRJbJY7W8DouK5QA3IfJi8AuQKhK3DqQ8ODiwanY6+474YHdjOXApBKbSrwYmZBZjJqnfoBJl2r9ezWq3mDgVYkfve3NzY4eGhG2pg3JOTE89GVWaJ+EElOp2On0oBWgBLejQaBUYycgAytTyYzcgzzGHIfxyjxekvekqHTpNSXdb6lbZpqCEm+9NsgqCHtTGbGlqFYdEDjD/yDEIyGo3ciJtN6/McAP769Wt3rOPxdG50vV53W/iLX/zClpeXbXt72zKZjA2HQ+t0Oh44KsGOwETRJq6wvnJNJhOf70tGdnV15TNtzabtgL/61a+sWq1apVKxXq9nT58+9V7ser1up6enbnM5cQfYGXtLsgACwB5pR4Q6Sw6Eh9hHIEdSARxfr9ft66+/ttvbW4fyz8/PXbaxySqnrAH8jJOTE/vpT39qsVjMtra27I//+I+dRwFax5oqSSxcuglfH3SWWm+LRCKOkSv7SFleg8HAo20K2DiqMHTIcAFYXfQrAQXog9P7pREYLx6PxwMGRhWLRVCDQ6EdRimbsLS0ZBsbGw5n9Xo9b1PgnS4uLqzRaPjAcQgLPG8YUgO2hkW2urrqPT4a4WJ0lE6OESDqwiGxRqzFzc2NffXVV54ZFQoFd1LUVBA2synT7enTp7azs2OFQsFH9d0HU2DA+a/CSWRWyAiRIc8KU449R5mJKGHljUYjHxGXyWS8H5f6mPaOmt2dWM+fYXiSJSpUqHUeUBKcB5nfzMyMQ33dbtdrNhChzKaHXb9588Z+9atf2evXr63f79uLFy/s8ePHViwW7enTp3ZycuLOZzKZ2OPHj30C1GAwcGiRddZ2FDV4GgHznJxOYWbe3wnagLOEkMU96IFkD6hj6YAMUAdgK+ryPKvqz3A4dLIR38FnWCd0FULM7e2tvXnzxkli7CMZU6VSsa+++sp7FDlUXDMkoH4tC/BnRXy0bKJBE3VtXRuCP+6liAPOCZkn69akgN5thixks1nvpxyPx06+aTQaVq1WA8ShnZ0dH0+3uLjoe0QQRBICIqclFrVxoE6snfYeYkshzHGEHnuDzkMGInAZDAbuWEH9MpmMLS0tOVLF4HtkhPF0IE8E1RqMmk0D0pOTk0C5CL1F1rvdrp2entqrV6+s3+9bqVRy2Bg5pfVHeSjD4dBrv9hzzl3mKMlPPvnEzzVmRCH7qnD+/3oogVlwrJmZueDrWCHqYUQJ/B7pOxkomRYKyUHB3W7XCQdEV0RGkUjElVHrRBjAMEtTjbzCk5BfqIMRoZAZgrcD0SmbUJ0E38s7a6EdJQwLNxEYjMD7it+6cTwbxgEHQSBhdjeEOJFI2Pb2tvV6PRuNppNnGo2GKzrPTDbCwajFYtGhMnWEYSepxhsHpYQLZXqamQcOHJIdjn75rMLU1LNRQAYIsAd8nr0m8OE7UX4MNIGWfi8GFHgSw8o+8j4EJW/fvjWzaURcKpWsWq36WDtaIZjGMjMzY6VSyY3T4eFh4MguDXw0mkVG3wcDqYxpbYlgYX5+3uE2hf4wIJwQMh6PPXtCvhXKwnixx1o+QddU7jSrYD252CN0BPnWU38IAA4ODqzT6ThzlgCUddJhH2YWeD+eH6dOBqoOU+VU3w3Hil5wb4WHVfYx/MCpSvqDTBKNRm1zc9Oy2az1ej07Pz+34+Nj63Q6NhpNWbg7OztWKpWcjawQJsx/zdLQEbVByDIoEoEAWRX2E8SJvlH602m74zNaf8ZR0NqlIzSRG56TGi2ypXJ938WAedBAnkmDLxjqnBRzfX1tiUTC+v2+tx4BwwLPI1tknTMzMz4ast/vW6PRsK+++soGg2nP9vZvB7IQbCLLCt+/7/qgs0QIMWRQt4FAlcaN4LL5CIwe5sznlLhxcXER6MFDgIgkotHpdAYiSRr+tZYWdj48O5uAUWDWJAukcJ2yKMk0iH4waPSCoRwaxWLAMNoq4MASHIYcfl5qsLwP8BlroT151NqYCxmPx+3ly5eBAjoTZ8hIELLFxUV7+PChbW9vO+yL4UTxwwZJDZMSNDRahpwDDARcGqbUc/EOQCJhIpQGICiymXnBXg26GhUyba1jcUHTpweUwIj3Uajr6urKdnd3PdJdX1/3FhmGZ6+vr3ttOxKJWKlUcmi7UqlYKpXy56X52uwObbjPUWKwudAr+uPIQnlunChtKxgPlSvWDdnXen34hBPN0Pv9vjPJVVeAvpVxqAQffh/INR6PO3pEozrw697eniUSCctkMj52jZYbRp3paEbeX+Fq5E5bMFRmwg4QZAMdVJvD9xDgcU+QpUgkYhcXFy5DGG5kGfIf9cZvv/3Wfv3rX9toNLJkMmmffPKJw5jsYzgQ1yBZkSnVIWb8koUju+gkmSXBJnAmDgd7zrS0RqPhwQVtbTCkWSvsNvfl+XRYAMHqfYE2aBf1e8ob4c8yLpQ6q66Byj+1+pubG0fOICwWCgV7+fKl27fDw0MbDqfnMcNCpu2RlhK1Ke+7frBmSbsIdPBOp+PRNwsGxEIx/OLiwo6Ojuz09NTrVWRCCHYsFrNsNuu1QY5ZurmZnn94fn7u9YxqtWpmFpiPydl6ZIkfulA07ZOKx+OeFWA4tWgMJKH1OuYeAnWoI2DzNOslOo/FYj54mWhNN0VTfyVOcS+ECOdByw7RN/NLUfRkMmkPHjxwog+GdnZ21msTQA5E32r0NLpVI8yea7RMYBEOQAi0tHUBA0XNjFrtzc2NvX371t69e+e1EiCmy8tLr2chY5oFstb8aIZADdLM/LDZaHTal0t0q5AdWdLMzIzVajVrt9t+lt6f/umfOoRGRArKwdhBHPUvf/lLOzw8tPF4yjKlD/h99UnN6nVEWLj2R0aOM9Lsm/fGeCucSm2YPmAlyFErarfbAZljvyeTiWeOBL6TycRbMu5zlhqorqys+EHSrVbLx5IB7WWzWdve3rZSqeSHNCA/6+vrdnJy4i0EWqsjuxiP7xjMODSVA9YE/aQUQxuUtgipo2R/6KsG4r+6urJ0Oh0IorkIHrEvjx498hnEt7e3dnR05MZ6ZmbGJ1aRESmvgjXXXmguTm+hpESdExIX4+Zub2/9ZBFahpShbXZX/1RuCM9F8EmCsrS0ZJ1OJ2D7dQIbDhsbEnbwlLfIHGnFSafTXvo4ODjwEgmlPHzC27dv3Y5yz36/b/Pz8/bkyRNvaSwWi34m5tbWlv3Lv/yLHR8fW7PZtLOzM+t2u1435sBt5Yu87/pBZ0lUQbF3dnbW+3UwLAju2dmZVSoVn9aDUSIy0xMZgH+UkXR9fW3dbtdqtZq3OMzNzXlPj9nd8ACILuHZsApXoCSwJsHxEcZ8Pu8ZgLbFcO4csDD3BerQCFQNHkVrzRAQsu3tbT/T8D4YFuFg3ZXxRrSIM1bogv9qFBeNRv0opGg06iw86iJk0jhM/g54XSFCjYJ5PtaQf5ubm3PIlekkOvSbCUJcOCeMP8aIWibPo5k0Coux4/svLi7caLFnOFkYnayJQvL0y+FUw7AbRmZtbc2ePXvmDfo8M06PvcMpImf7+/uBgRzKDlQkgf1T0g8XU1P4jMqzwqx8TqcXETFTeyoWi06cInhKp9M2mUx82AGyS42T/eX5CSYJupBHdZZhxjR2AmSqUCjYwsKCG1UcKkO+yZbJFk5PT71FAwiTjEJrTAr/oo84R+QCSDebzdrz58/9eXEg7KkyN3nGyWQ6YIVADw6H9p2yZsjHzMxMYKA59gcjXygU7MmTJ+40cELYB5jVZLF6YW9gcwO/wnBGLoE6NStnn9XWUOLSyVs8j2bpBMjIn2aXPDtoiHIGKHkpatRuty2dTrt9e/funZ2enjo6pgE2kGq327VqtWrZbNa2trbss88+s88//9zK5bLLEQnKo0ePbHFx0V6/fm0/+9nPvJaMrnCqEMfw3Yf66fWDNUs2BmcH6YV6JekxhoPsTaHI8fiOtcfCAqdx3d7eBk5tAALFQMLkw2gQETNphStMC9b/16xjOBxat9t12jSbHK7VEAxgzM3uoiY2+T46Ot8H3LCxseEGg+hfBQnDYmb+7zhLdS4KG/FcSmbC2BEgQBLBWaLkGFkyKoIQNXy8A/dUo4xgkd0qeUbPiiNbwjkr3Mq9eHbWgnYDNYrqwDWjUcgHAwkZgWzJ7I6khQzweeRJYd1odNoDWigUfF4pDgqHRK1IZYfIfGtry1sG2DfkB6OlDldr63qR9SAD7K+uVbhGxHcB/QPXMxxdHQxtO8BVPCtsR/Yf+YKoR+aqMsilSI0SV0ByKGMgQ5RqIOrVajU7ODiw09NTl1nWgoxO62PIOsEeTkAdjgZFuVzO8vm8DwXB2ZNdqlNRog3vOjs76yQc+hiRb4IKHTenBERIbQQAMzMztr297TB5GJ1S1mc4e9dEA8eEMwJBQTYJjsOkJoIv1hR5CQdyal/CRCPkiUCFPQkjO/r7lJh4/rm5OatWq35yjwY8ZK98ZnZ21rLZrH388cf2+PFje/z4sW1vb/v66aEE2LlMJuNI3+3trdVqNT/BJKw/+n7h6/+rZsmigcub3Q3fhYGlQoeRVIbk8fGxzczM+HR5qNNcOkUfrBoCxezsrFOstR+HWZY6G1YzViBUzbpwvuNxsAWFZ6YWybOFhZT3ollZo2sdEMC1sLBg+XzeHjx44AY7vEFkGvo51pQgRGFidThhFqAy/4bDoVWrVWu1WoF3UigT50MdirW6j1RBhMl34XAwiFrrYS2YOYrhA5JHvtSocX8ye4xAmHSEHGnPIPujjdjaEkH7BpkstXielzXEOAMPARENBgMnUek0HxSaZ4vH47axsRFg3GpLEcZEDZ5m7yobOmYvXNsMyzuGDLnhs2QmRP78mftrPVB1R6FUoHVYkmTeWvfV5yHQgWAGVM2JEew3RzPRfL+wsGCvX7+2b775xt69e2e9Xs9yuZytrq7a6uqqEznMLGD4WT8NlCORO4aj6kcul7P19fXAiExkAZlV5IU11zon06XIVGZmpuzQTCYTOBxgeXk5MLKz1+vZ6empmU3bG4DcKUPNzMz435EQYJPCyBF7pPZIxzdq4EuAx36y/ugSKATJCZfeX+VObZXqPHJIdq7OUj+PrJJh9no929/ft1arZYPBwAdxcGmdc2VlxT7++GP7oz/6I9vY2PCDJICHgcqRPQbeKEKDHoOaKs/k/5xZauZI2s3Lki0lEgmnfp+fn3vNEogABhICSMrPYiGcRMHb29uByJ3BBYPBwNrttkNc8Xg8QF3XjQwTZaLRqA9nR5lmZqaU9fX1de8lo32h1WoFsszRaGT7+/vW6XSsVqu50WaxFRohCn/48KE9e/bMnj596gbSLJj50ttJ9kKQogKpQkeBHaWG7g1kBoRNvU9JMzhOJTZtbGzY+vq6bW9vBzIwokOlsyMP0OfNLHAG4uzsrDc8U/NQR0lgoXuCw1clJ3NA8XA4rK2yr7VpejQaBU5S4aKmiGOEaUeNpt/v+2gtkAwGVVxeXloqlXKFJ+DCodCvy94Wi0V7/vy5dbtdP4RZIT0CTd6HteW5VJbZDw3EtM6s+wSKoW0EOIuLiwtfb4LLZrNp9XrdHRZtBdSHgMOYLQu8zvF57EmYyEGWg9EiiCiVSu4gT05O7Ne//rXt7u46R4Esjcz+5cuX9vLlSw/kkEOyWwiHPAPZMRf/RhaYy+XsxYsXtrW1ZfV63afCEPSRDcLYnEwmDmPigN++fWunp6fW7XYtGo3as2fP7NNPP7WHDx/6KEJt02BfYrGYNRoN++KLL+zdu3dmZoFzFinhqM5zVq5ZcPAD+4/s6HuDACKfIH3n5+cOh4cJZVpe0l55HDJZ82QyCbS2hcsK1HWVOKl2C3nHSW1tbfnZmr/4xS+s2+36XikXBNuCHMzOzjqnAH1vt9subwQlyB6s3ZWVFYvH45bP521ra8u2trYc2g+3Qt53/eBsWBYVzJ0F0qZsolUYhxyOu7Ky4vfQQQAQdcLZg8Kd9N3oEUY0qWOEiIZ0KLleGGY1TsyBxcHpQIRsNuvRSrFY9JoXAlev121mZsaZpMpqxHghdCjD8+fP7cmTJ14H0AyD682bN17f4zlxGCiARjz0e+L8whEca4bh0sklesQVZKDNzU0vdpsFJ2Jo1qPOkntwagSEAP6fejZN+ZCaiGQRTmoMfA/ZHtEy34cCw+Tj88o6huBAP5Y6no2NDScKEVCAWtDHi2JhJMmC6vW6jcdjJ6ZQ6+OIJGqHl5eX1uv17OjoyNbW1pyxCGNWIUEcDAZNIbTwxftjzMnm2RPQG0aTse4QhLi2trb87yBO3N5O5z+Xy2WLxWJ2fHxsb968sf3fnilKhkRtM5vN+vFu6JdCV/QNar80Bi8ajdrl5aU1m02r1Wp2e3trGxsbtri4aK1Wyx0zfbcfffSRr7XqcBgOJGBQwhprho0ZDof2/PlzW1tbs+FwaK9evbK1tbWA3VFeBXV41nI8no7f3N3ddU4Gx7ixH61Wy89MJXNhSEGv17Nf/OIX9l//9V/Oln769Knl83m3SZAPsYmnp6de0tJSk/bBapuM2d0BB2RZ19fXVq1WfYoW/4790uEBej/WjOwTElUmk/HyAKUY5A/bQVCjNUvKQmZTu1wsFm1nZ8e2trZsYWHB0um0k5IUwgVhJOAYDod2dnZm4/HY1tfXrVQq2fr6uu81RCoSulwuZ8+ePXPC5snJid3e3lqhUAgMtke2NPALXx90lkT0YXYhxpxsE4cJQ4xIEkOBwNHnQ7+jtjto/QWINEzcoP+u0Wh4ZkcthEuNg9n3MWiMrY5bI9tot9s+7QaKNLAsWRsGVJmFCk9ozSSfz3vGGiYEqbM8PDy09fV1W15edqONMSKS0qxCyTlaV9EoWeE26hbsFetpNp24oi0QGJ5wW42+JxkHMkJjND/UP4n+NSsAhiWKUwiQPxME8c5Es/wb38F/CSTIzO5zPGSGZI5MoAGqpMGcvUZ2YX1ySgOn7UQiEc/eyZrPz899rBg1Sw2AtPaLLGj2zv5wEaQQhLGeGDcicBwTQ73NpmPn1tbW/ISeWCxmm5ubDkfjrBinBiuZ0X/j8fRUErIlsztYWKFlZJSr2WwGWqq0RMFekzEnEglvU8jn897qlE6nLZPJ+DQkhRYV9g23KWBoWV/N4um9SyQSVqvV7KuvvrI///M/t5ubG9dpdA20I5FIuC0D8aHVgtr2d999Z61WywOKSGTaFw4U2263vWTz5ZdfWr1et8XFRXvw4IE9ePDAJ8uApKBfnOihbFQuHAHPxLtil2OxmOua1huBJdE9EAuFtBW2Bu0hgMQxqrM1s0DAoYGLOsuwXUbHaUmhVKAtO+w7iCCZ+M7Ojh9sTaCvpSI9jq5QKNjjx49tNBpZvV63XC5n1WrVB2yw5soneN/1QWep0SfpsQo/CxKLTUfWaQ8Rv8cPJyZw4ggQikJFOGWMNMOYtf+GUykODw+/t9m8dJgAgyASkeIw1ZlPJhM7OTmxarUaCBLoRePZaGuAzQVZAsHhO2Ox6bE2a2trtrKyEnCU/B4O5+TkxE5PTz2YUAOBE9TmWYSTiJ2IFMOvEFw8HnfjrVg+z8xxXShWuEalxAHeFbo233Fzc+MOkmfiWTA6ZLqwZzGwOjdWYXeIEopghGseECNgxKrjCdfSOPHezDzzxDjHYjHL5/OOoFxdXfmp69TZMODZbNYKhYKvMe1Oo9F0DBr9g9SPeM9wVhmuN+HcNbJVJIf1IRCanZ31/lac3NHRkbXbbZf/tbU1d+7ZbNZevnzpBwXs7+/7WMRKpeL1bej9OBcGaVD3UiKX1km5Tk9PAwEZcqDIBL2Vg8HAcrmcw/5wCEBZQJjQJw3Gh8OhB9ToA2uutcrhcDppB8Mai8WsXq/bf/7nf/oaczgy8kcphYZ4AuNIJBKA/BiRiQ3Cdi0vLzu8j7OEkLi9vW3lctmeP39uOzs7gR5O9IA2i0aj4URHvag/a40ZmVLIHkdKKwhIlJLJdP24H+tLcoBjRJbRPf6M7CpXAoer9k6TIa2zAsvq/HBIZwQAZJDr6+v2+eef28rKiq2urlqpVLJ0Ou29xoPB9Hi9zc1NR0YWFxdtNBq5j1JUChurtvV91wedZb1e95Fd9PUAo+loJaJfzfC0107JH2yCFp8V5lMIR0kdGMJms2lv3ryxVqtlhULB6vW6H5FkZgFDTAak+Lsq8nA4bT3QDIrvhZQQjUYdPoaUoM9LhMn34ShoF0EAUHqtSRCxJ5NJOz099RpoKpVywWL9gHVmZ2d9XBOEgEql4o6AviHNQLVuCVsM5cRZIfxkDUwBYZSaOllIDcgA701QhELTx6rtNyiDwpJKptBzMfldDB9OQUlnCr8pEcYsSM6iPGB218/I6QvLy8veZjMcDr2vD3g+mUza5eWl9/w2Gg2Hzsj0QCkKhYKlUinb29tzA9vtdgP7rgQravY8vwZ+QJMYQD7HOvP8GJSPPvrIzs7OnFX+zTffeN/zYDCw//mf//H9PTk5cflvt9tWq9W8OT2dTtujR48sm816uwzGZTAY+IxU9Enh7oODg0BNEb0K1+BZK+p10ejd9CUuYEMCXD3IGMhO22f4f4w1EHMqlbJnz57ZysqKtVot293dtV/+8pdmZj5BiExXkSEGJBDwM5KRMYEcwszF+3GCUZgwF4tNzyTlvE++dzKZBOwe81EJHhTmZP3QVdYHPUL2cUBzc3OWy+V8+Ee323WdBhLneMEw0Yn+cB1BSCmF58CWKrrDvVUulJw4Ho+tUqnY69evA10O+BcdmjAcDm1pacmePHli6+vrls/nPRDgNCKCSuzE7e2tIypmFlgXGOvYYu51H2cgfP1gZsmMUWAqirj0/vAlZ2dnXl8gCgkXS8lCzO4m86NMCDxZHVEoLzQYDNwIcEgthiccwfBfyAAK8WnUg5HV+p5+L//PQGwIK2RoCA4KQcaNw6N36vz8/Hu1nclk4kSLly9f2v7+vh0cHFgsFrMXL144dIbxp0ahgooA6ikZzWbThwYgOCg2jh44TZ+FHww4MKUOzUaQYJgBnZCd47h4PpyFDi4gA0UGQBkIUBjawD3M7gaKa9Sn6IEaCIgnfD8XiojzRg5w2NRiCPrIUoBtCTT0uDNgWoWRkQPIF2QV2pOMnLE/OhFF96XVankkjOIryYgADmNJvZhTUHq9nh0fH7tRPzs7Cwyz4HkwqslkMlC3Q2cnk4m36wyHdz2YyBzG1swcLQq312jQzJ4ReBM8qUE1MycBaiM9a6ZoCfaCQFd/Z2dnxx49euRDGU5OTuzdu3fu5HD+GGmQHS1/mE0dSSaTsc8//9yKxaIfhcb6Yg/UCGuZgSybkgK/r+QaMkr61ZH3cB2t2WxaNpv1YESfk30BdVlaWvJ+dx1+gE7Tg40tQwd4D+6tcP99vbQgYFdXV97qoQ6eYIm1uL6+tpOTEzs/P/fBDeiAmQX8y2QysTdv3vhMYfgGOgmIgIt3yGQyHsCDXLC/y8vLAbQKe4CTf9/1QWeZTqe9ltdqtRzb1xRdGYsInBbMeM3KvwAAIABJREFUdaEwYDyoKga/p+m90nkZlwRFmDpjGHJTZ6kOk/sQDWsEqjUW7sVzhOu0Ciei8PoeGDxILGQOyrQKF5LX19ft+PjYGo2GHRwcOHxGrSCwYb+9hxJE9N1oglYoFaIF9RclyKBA3EvrfnwOxp0e28QeYGi5j9Y8IXchG+xxuLaLAcHAEBjwnkpk0XXTvdT7qazpuvH3i4uLAfhzMpk4gYKMiPolz69ZErKitR6elb1Qo0TLhSIQimZo36rCrtTocRqQP7SORftHv993/YQ/QEYL+Yesn4xYmYZ66o06VJxbODhFB7g3V7juysW+E+SoIdYpNhq4wUkIk7gUwuM52BMNwCEalstlHz7CYfMEdWokz8/PvVUIREod/fz8vD148MAZ/8DIjGZrNBqOfMRiMR81iZxCkIKoooEO8k1ywrF6WsfkImjVlgqF+7EJ2n4HdE9gqnVQhsBzTwI21SH2jWfGVpARogcEXxrY68V9QG2QZZwiMpnNZj3Y1APWIfGBMrDODNYABVRHiYyjX+iyIoz3MejD1wed5bNnz+z4+Ni63a6dnZ35oipTigUfDofWarUCg5YxXOo42UyiKiIBfSHIE2Z3sCqQAXNk5+bmApg2l8IeZkFac9gxACOoMVaYQWuokUjEo1ldVH0v3RDmahIxsUkIlBr9YrHohKeDgwMrFov+b3yGdQzDjUCgCA4Kp0f98O8QKrQNgednLaBek4kisDoSDUVRpjNCqZkpsoLxIKhQ2MPMPJrn3vp7fI/WkVkLra8ij+qsFXHg/sBt3FP7ASknANnyPkr8QD55TzUoGCEUdTQaOcSqzF0cKc+IA9esG52i9QR4Wo+dwuDBHzAzNxaJRML7VTH0Okwb59fvTxvKM5mMvw/rHi5ZgBBQf8NpaTTO53hGJVBo7Ys/j8dj5w/onE6tT6uOQkDqdrsBFnGYHTyZTCeIPXz40DY3N720cXp66nVVM/Ogb3Z21trttsOOBFcYbwK4crnsrUbz8/P26NEjazQaVqlU7Msvv7SjoyNHXdbW1gKjKMvlssODOEvkmCCAWqXCgWG+g5Ld2H9FnLBDBH0EHAqlIneKiGjphzXXEg3fhUwot4SAQPVZSwrh7JjskvXHzpNobG9vO5qIPeE0onq97vJMpkgbEMQqnGm4qwA91vIe/AV06n3XB53lX/3VX9kXX3xhu7u7tr+/b+Px2FlJUMqB0YgOYEbqg/EAysIKp8/hHipgv36/78ww/h3MGep++MI5xONxZwySBeng33BmG75YULIySAQYBDVuCAo9WgsLC15zAGrSrEG/t1wu29OnT63f79urV6/sv//7v63T6djm5qZtbGy4AdDGctaJe1GjoLiNMvGeOBOMnjJc+Teyd5quOSexUqm4cdR3JdNR6AUDjGHFESCwOG+N8jTyJbqEuccJKkTJygDu9/veKsKJFsBys7OzgYOtmVFMBku2NplM/B4YMmRcSTrAPrC8w46T4EBJOgQ3ODx0QTMo2JOpVMrXkItRgewTjiUWi7lh5zsIXFjX5eXlQO8ne6YwuWbtOuIsDCmamWc61KS0PUAzS2BZ0COMrRpunMfs7PQgYmrGEGr47GAw8HNGcdBXV1c+rF5H8qksz89Pj836i7/4C/vss88cgj09PfXeTq5arebzfvk+au3IY1iP0IV+v+/DKx49emSffPKJ7e/vWzQ6PfJvbW3Ng6R4PG6PHz+2r7/+2o0+NTnQnHa7bfv7+3Z2duYyHGaRmpmf0qNBHHYAXQFB0pm5w+HQhyngqNAlsztkT4+vIyBTAhFOFHnTkgjD0cNsWO5vFuwZ5XMkXtRYHzx4YEtLSzYcDq1cLtvZ2ZknPLwzLYiFQsHW1tZ8Bm46nfbnxjGS7BAwYKdo9dKWrPddH3SWCwsLLmzZbNYb8hFeanl6ODJRiGaUOCqFVRE+IhjqDwjP+fm51et1q1ardnx8bFdXVx51AOUSzaiBwSgopIczAIYKsy75jBKSeG4iP36IBjUawuBg/InUcPAYCaCdcGYZjUbtyZMnNplMnKDBBBOzaeapVG6tvyEMCKDW0Ph3Ilcyh/tqSGTNZJRkktRk+G4z8/5Z9olMjKCpUqnY5eVl4Cg0oC2tK/A5nA+1aWX+IuTUl5Xsc3l5aZVKxXtOQTe0/sTFemNQWTMUpNvtekBDO4MSUhS6DmfCOBazuxJFPB535unCwkKAfKKBFgQUonPNhhOJhENV1WrVyQms42AwcFlAH8ymZDIMCUZ1MBgE6spaz1dWJfqh+wGJgqARw4YuKOyKbKg+g46wLwrJdrtdOzk5sclkYltbW87wJRCiHkggR+CMHdIWFiBA5vk+e/bM63Xj8di+/fZbnxTD/kGiwl50Oh2H8ZLJZCAz1tocqMH19bWXItLptD8HtkUDM4UINXND9w4PD72GqDVc5EzlQjkN6BI/4ZYMdbp8Llz3xraxp2oftbShGSzOSwMJDsRg/7m4H8+h8obeqC29vLz0oegPHjywZrPpz4bu4A9WVlZsaWnJWdTsp+ovNoMghaABcuTi4qL1+30/tOO+64PO8ubmxlslMFpMWQCzJ3q5vb1146g4MQuPoeaFWFQt+BKRXl9f2/HxsdVqNZ8yAnWdKftaSwzXsdgMhbxQchZP6wHKoMXpsLEqHJoxcG+FmxBWlIFh0Le3twFnyf1UkLLZrM8hpbk5EonY4eGhZzMIK/uhczIValAF1zVCIfh/zZy1PYcRddROtD5rZv6MOHBYaTr79ubmxqFADA0OStdYhxejrOyH7p3WQclmMZ5E+gRPsOmUnR2uhymUyqg1DEy/33cnR8ZFcER2qfcjOlf4kn1SI8R6894YUe2b00udLG1T/D1QWfhMQOSK4AJDiU5q6UEDynB9iT+z/gRHCschF3olk0l3Iip3Ku8Ko4MI3NzcWDKZ9Izn5uYmQEzT0yq4P2vOPYGTNzc37enTp954zr7QWnN9fe26AWkEA8xwFWyUPrsG0QThakc4LFmDMSWV8BxhqBrnQtuOBqb3XVrjVYemaA2OUu0OcgIiyHsREOFQdD25tyITfK/KF3JlZt9jz+u+q3ypbdXsllGm9Davrq5aKpUKoHdaglDIVUty7A2lA+SII+KoNdNjfH19HeisCF8/2DpSLBYtmUzaixcvbGFhwV69euUsJoqzc3PTU0DYHCIbHhjDDLsvPPycxWIsU6fTsTdv3vh33NzcBAgiCg/dd4WzQ4V1wjUThccgS6gyY+h4F70/hB8ckGab/X7fYSLNLMO1BbM7FieK/ubNG49cd3d3fWIKP8qSxdDqu2PgEBTeHYVQchNZAKxX4KB6vW6np6eBtg/WfGNjw6NwjBgZtDKGGRMXdi5kZkTBShZgvbReyXqRAVJTbbVa7ix5TpwkRX8uYDaNbK+vr63RaNjR0ZHT6xcXF63T6fheMdwhHNGa3Sk/BDQyNmqxKDORN78LixYF16xFISoMNsqOs6S+qfVOfg8jTA1MCUfAvMgI8kqWqIafCxninSBUmJnXY/XK5/NePtGSDDKqAdxwOPS+5uPjY++DQ36A1ykNMGOYZ2U9kY9YLGbb29v20Ucf2UcffWSJRCLgBE5OTgJzQc3uZoXynLTkkGmoLiGz9I7iMKn7mlngO2EYM/mJM2Z1VCRy3uv17OzszIMM9OW+iwCT4EcDHuwvwR8BIPeDT8G9gfBxFvQrElBqsNXv9915aoKgHITRaOR7F3aWmimzlhq0YkexP0CjuVwuMKWNzFoJaYqKYLeRZ+yGBlsnJyce8KfTabcl2goUviIfqtn97vrd9bvrd9fvrt9dv7t+ILP8sz/7s8mPf/xje/jwoZVKJW+GN7NA1KjsRaIRbVEgZScCURYWEQo4OpGapvLQ+7U/DiIH0c1f//VfR8zMfv7zn0/4Xp5BafZERWZ37SEaleifw3CmwokabSq8aXY30V+p92Z349h4j5cvX0bMzEql0uTRo0e2urrqkAIEqqWlJY9Oqe/pWZE0TvN8Cp3wDlqn1HoJmRA9Y2SDTG4CutBWlH/8x3+M/PM///OEtdB9ZP+AoqlnkZnw/oxBC9dEFFLWH42WmawDexK4jnWFJUek/fnnn0fMzNLp9IQakRIUkAMm1ZDtMYaM6BfZuQ/mHgwGvk5E6dpLSfSPbIVhcOpO3H9vby9iZra7uzupVCoe9d7c3PgELGqSQMPsN9kscq/wmFmwPUthVY3OqS2qHiiSo0xCsv9sNhsxM/uHf/iHCbZAs17qs8gLGYHeU+u177sUmdFAXwkcYfISWW644fyf/umfIqurqxNGPpbLZVtdXQ2MoBuPx65/6CMQ6Gg08hpkJBLx9h3qgkpkAsnQucUgIlprBnpuNpv2b//2bz6BJhaL2U9+8pOImdnf/d3fTcgaFxcX7erqymuiqVTKVlZWXB4g1Cncj34o8QcdR97VnoPIwDHAblDC0Hmu3333nX3zzTfO6P35z38eMTP70Y9+NNnd3bVIJGIvXrywv/mbv3H2c9iuaruK6gx/p2UDPoM+q0zwDuwjfkOz3rm5OT+Cj77qv/3bv70Xtvygs8xkMr5RGBkMSBirxgEq0UGn+5hZwHmEKbrg/GrEMWrK9uN7IDhQG+Li9zHgSr4IO0WMiBp+NYThOotukG6U3oN/UyhDYQp+P9zuAvSkA7D5PNAfPxjCcMEcA6QOH8jpPugamFrHkdF0zH5rXQXmMU4OhVOGJHCdwiDsicJ5CCd7pWuokLtChKrowNYwp1ECBnmPx+PvTZRCTlEe3S9VVJ5T69jA6MquY++ooWsrg7YxwLxTyJ/3ZP1V1vRSw8+aQPQhcFUmKOsItB8mUmgJQcsKOFYu1XMtV2hQo3Uz1RO9rz6P3kcDVQ3owiQSfe7w/e/7Hd3PcGBy36WnhLAO+t7U9wgm4BKMx9Nh/MgkNgiCE88CyQxDjIzz9xCYcFb6rLQO3dzcBMpWWg5AJvXsTAh1lEX4Pd1z1jEsC3Nzcy6/MNK73a7NzEwHSKRSqcDa833sm0LTujeffvqpH/rAOZLoh9YvlVeg7VroEWuJ/vIc/Ds2XUtoEHqoWbJv8/PTU3TYQ32O+64POkuONDG7O2CU6EQfBmOGAuCIWDQiAYwYLxw2klqngqAQ3hCUAAXE2HCp0eN32Bh9Tn6XSx2P3ke/T3/09+6LZu6r/agRUUHCWcLoVIFWA0uGpbVPor4whZsfJWGEjRNrguHjR9s7NJLDGEN8YZ8hnxAph5VF35390haicE1Y15f1JluizxD5QUb5PZxVmFRSLpcDSAXZLes/Gt31hJqZt41wbyW2IENkQxoscH+tMYcNugYFsG81OFSZ0f/XZ8BR6mCD8BABMjoNlpSRrmtMpokR0qBMAwWeWfsQNfDT/bsvwAyjNJrFqD7wmbAsaICqjkV/D9nWwRMayKmD5/2UIKK6RAAJQZGxlaPRyNrtts3MzHjWCSqkbTisKYiDohcEYZodqQ01swCjmCusP+oItfYP2U0JdNrfqfLEvhLEsmYXFxdWq9UsEon4pB0NoAhUQQcJusJ9zuvr684hiEanp7HwXNi3MKlS69KaeLBemhBoXzAEQPSRM25xlnBtqEtzXyUb3nf9YGYJPHp9fe2LNTMz4ywyzWxUsYhQcHJE02oQYE5qVqpZGIKvi6Q/GO2ws1TjS2GabJb5oGwCzedcqsQ6zUH7mfj3cOp/Hxygyq2RlzIfiX5QbJ4XZcBR0L+JQGuTPtkeLSzcNzyoXGFzdaTKpAMyNbOA8pNZktHd3t5as9m0vb09b3kBxmXvUSqUmvMQFR5H0ZW4ocEWxxyNRiNbXl62crls6+vr9uDBA9vZ2XE4cmZmxpLJpHW7XX93rr/8y7+0SqVitVrN22GI7in648h4DqBf5JZ91AEQOErNKBVtYL2VragZ6+LiouXzee8h1L5hDIRmYwqvKimDM175DIYY+I2gRx0t2QDvBzN1PB57OxisSqA99ACC0n1EFIXP+HdFFTTjI3AMZ9BcCsOFfxe5xrlgO5T5iCxidwjW2HvuDypDYDiZTHxIADqJESY4uLi4sIWFBZ+d/ejRI1tYWPAAVANIylY6vk/bHebn5z1rHY1Gfv4o5ESVZRADZAISIwkCcol9VBQQsqQGCEwVQqZBSHA2EPhYF53aBbRJfzIZOsQpLsZ/sje7u7u+9mR4ykhXBAN51iBfkx917hDhzs7OfOSj9gUr+W5hYcFHHUYiEe/3ft/1QWdZKpV8as9gMPBxbJ1Ox46Pj73/kYfguCxqazinRCLhNU8V/Mlk4th9t9t12ICIRdsAgENQUKKWMCyj0CFsQISXSEIVkSG8fA+GxewOutN6k94/rNg4IM22uRSyDjtLMkaz6WBnelJxkJrdc1CuGg817AQPGGSiawQfA0uPGsLJO6rDUGNHvYI15oSWd+/e2e7ursOfGuywDkSt8XjcqensJwOTw/UFsqXz83OrVqveQgBsQztNqVSyzc1NW11dtXK5bJ999pkHeaqsf//3f2/Hx8d2cHBgr169spubGyuXyzYej+2LL76wn/70p26YyAh4f7IUZI06FftLcMN7hh1meF3ZL4WyObCX9eGz/BcDD2ROUz77qY5Os1BkS+VDIXAuInDkamlpyS4uLlwvaMPAOM/O3o06vI8hbDZ1xufn52ZmgcBPs9twa0sYVcExaZCsJQ2FzWBn86O1cd6ZWiyjAVdXV93xc52fn9v5+bmzU9Eb5JG9xchTP45EIlatVv3MT2b04mg7nY4PUdd5vDqwRIc3xONxq9frFolEAkG+Qo3j8djq9XogoFS2biQScYYt65hIJCyXy1k6nbZYLOan74SnSGHv0um0tdttH8WHHo7HYz+AgFomx/0tLi4G9O/58+f25Zdf+rqenZ25DUS+1tbW/L90AIRLIipjOMl+v2+NRsPn6h4eHvrB2aBD2o7Gfg+H075sIPTxeByAu8PXDw4l0GiOh6ClQyGE8/Nzb/blM0SmqVTKPv74Y1d6Huz29tYbQ/f29jwrI5NFuBOJhBfflfhCRqIGBjhJh+KiVPwezmg0mh4oTeTCmYcazWpWBpSs8KVCBhhCDJZCzlpzJfrlwolgCMrlciC65RglnAhZEBCemXkjPRi8Qox8ptPp2N7eXmD4cyKR8CH5ZH9axyEAINswM3v37p3t7+8b5BMz8z1BwbQ9hYwKw4sB1CiRdSdi1loLB+ziWHFOnIjOFI6joyMbDAbuMLWWvby8bKlUyh2hjtSCmIDDwWnyHOwve6zZ/H2wTRgtQR+0/07rfhw3dHt7a3t7e36f+0oDDAvXiT2gDRhL5ItslRqnwlkYIc2Ox+OxO1iCSwx3GL7nc+wptSxFh3TsIe9zH9yKgV5aWgocDE4tN/wZvptsgoCw2Wx6nyJGkho3waBCkmbmxBgCRhwlATwTu8KcA/QTmJQAcnNzM0AsYjoUcCb2h1m8ZFTsoR4Xhg1SaNLMnNsQiUR8pi2Owcx8LCWfxw6i2+Px2B10KpUK2CO1jUr80Wleeo3HY58oBDLG+a+apS0sLFixWPQhM1oaY/9oB7u5ubGdnR1bXl52m6s2TbNJBqkwfL5Wq9nl5aWTO8l4Ff5m7TT7RVbDXBq9PugsWTScBmOmaNrVkwlisZg3hyNYnCBxfX1tuVzOSqWSCxrF41arZfV63Y6PjwPDmYfDu5mn9F6dn59bKpWyVCrlzFGtpakQg5sr1q0jmMhO2+22jcfTHi02jUVVNiFKihMn29L6ozp7rXciEAhXGM/nuyFD4XDA3fUsSv5udnbW50/S/A6kofVGFQrOWuTUBQ79hchF9pFMJn2Mlx5bhCDt7+/b4eGh95GqgLEurFe/3w8QDMIOQA0p6xOG9mEgqrNUGEbPdVxaWrJcLmfxeNxPdeG5gLaTyaQVi0Xb3Nx02STowAEAV6mRUsM9NzfnWe59l0byyIA6WiUuzc3N2erqqo1GI8/EWBOtkeMsMd6asTNiDP3BYQA/zc/POykICB14kCZ/mtnJIJEH/qzPgrPVcWnsGXrAnvCeug4aUOGgCfgoN2hWbnbHeNX+YK1FYWjNzB2kch64h+osyBcBwfn5uc+dVehY+2UVLdHgpNVq2fr6umfEyCYTh1qtVkB3Y7GYs7qVBU8phHuEYW4gUXRFHaWWurA1eqQeQR6HO+jsWPZcP0d2TA8ik7nUGWFzsLuQ77Q3cmlpyfL5vOVyOatUKoEB8xrcmZk7ONUDZcCip8gAAY7aRmUL53I5R1W0Tkz/JTKDXr3v+qCzJMrmwYi0cI5alM7lcjYcDl3ZOQAV44DnRuiURj0cDh2GQjiANrTR/e3bt2Y2NVTr6+u2trbmhpQLQwRMh7HWZtpWq+WD2TH4vIMe3JrP5125gJvDdSQEhGfXAd1a4+RSYo1vwm8jWoTKzAKQR61Wc4gJqIxo/vLy0kqlku/RzMyMQ0tkRTra6fj42KrVamCW6XA49JaLRCLhLR69Xs8KhYJj+azz27dvfVYoA76h5hPFqpKzJ/dlabwzyq8npJBtoozA/CqfCsv1+317+/at5XI5m5ubs0Kh4L97fn5utVrN2u22Q1WaGQJ/UWNVopUSTSC/cPyaEo7M7sgjSlJR50BgpnKhZCiVZYUtMdIYBCAjhRBxfmbmE7KoxyLXjMsbDod2fHzsk0wuLy8tn897NsD7697xPeEMEyOn74RtUAPH/yN31Lu4eFY1tOg/a8BnCLQ56QOHCT8CxwB8yr24H+tNKxZ73m63PRBhMhmlpUQiYfV63W3gwsKCZ7Y4GCDsbDbrRwpWKhWHb3kWAlUNKtBVLaew72rEtfaG/AMzYjvZH+SVi9KU1uHz+bzrZjwed57A8fGxnZycOAQLmzeZTHryk81mHZHh0uOzuNLptJXLZatWq7a/v287OzsuayCMvV7PB1TMz8/7RDNkQfkWWpfu9XpeS02n046QYNPwI5oEkQguLy9bu9325AW5uO/6oLPUQi+HkuJoDg8PrVqtWjwet5WVFSsUCi4Ug8HAEomE4+NkTjhZZoEyQWE0Gjl2TxZFAVbnLgIpXl1d2bt372xvb89PdOcicsCItFota7fbnpHMzMz4cVjD4dBqtZoVi0UrFov+3bVazRYWFnwKCLAZmSqwVbPZdFhNIQcMAQacjC+RSAQGY3Pp70H2abfbDrFgdOiv5B6tVsu++uorOz09tWKxaKVSyWZnpxMveM5KpWIHBwcu+Dc3NxaLxbyusrW15VN2IB9p9H56emrZbNYPlUahstmsZ9rAuhgm4HaEs9vtOtRGnYcJJ6lUygqFgpO9Tk9Pvf4SJs8g0Do2MBaLef8lMvv11187HPT7v//7ZjYlY1SrVWs2mzYzM+OzX8lOiOI1UjWbOqxMJuPGkxMO1tbWHP7SIIB31/IFdW8cM/JCdvn27Vur1Wrfm8aE0ScwxbASMLVaLZfvZrPp8DI6s7S05Fmdztrk1JWzszPrdrt2dHTk07MwIoyEA6Z/8OCBZTIZn75VLpc9GNWpJwQKYZb6fe8EBKbyxkHlENU45kpRLqZMVavVQGZJLY3gM5PJBEpJWrbAgeA8R6ORdbtd12ccJ4HcxcWFOw4CIyB0JVSRwS0vL9vDhw/tZz/7mScKZDRKCOS9Li4uPNtCd5EXnAlXeDLO/Py87w16pHXWSqUSYNqzXo1Gw2q1mvV6PdvY2HBWL/OWj46OvA45mUwnsL17986+/fZbW1hYsFwuZ7/3e79nn3zyif/O3NycOzJNCHZ3d63f77s8EegRuKyurnr7F/VHstu1tTXLZDKeASsUr4xnJXayD0yM01JCuMwCyRPY+n3XB52lQpYaKSPMmUzGcrmc5fN5K5VKjpUzXJgIbDgc2srKiqXTaSuVSpZKpazRaNjbt299xidEBS0cd7tdh1bUAWq0BVSggqMwYDweD0R8yWTSDWSr1bJUKmVra2tWLBa9r4ooCYaU0vTJ7piHiqEE4sUILi4uOmwKDNrtdgPkD11nRkkRMDC4GkgUQwrRivsSCeK4OUEBYhbBBcoSJn4o2QJoSaE2oDz2wMycKIVjJ8NkSICug5n5AAk+gzzNzs5aKpWy9fV1j9Lb7bYHN6AZBAd8XutPOAVIBQzaB/7iUoIIz1SpVAKMSZSVGcVcOECgaaAyUBKUHrnRmixZmMKY7A+OEcUnMuaiHskYRiXBtFotOzo6slqt5ojP3NycFYtFP52FE1twEpCEIIsAw/FnLYOQuUAGY98g7T169MiRHc1cwvVy7qnZJEYcOWF/dP2i0agHKZRjWBNkE51DD/P5vOsWbFMCXeRNyzTYCzJWUBGtoVFKgHE8mUxcH29ubpyYCKsV2JhAUDN1gmSCKtaWmun19bUf6QWJjf1WZj46TABKzXM8Hluj0XDolPu32213HPl83paWltwm8L5KmAJKLRaLnvAgU99++22A17C/v+/D6nO5nM3OzjozW5ndQMGpVMq2t7c9eFHSGtkfxBvmkLN3BE7h+iVog9oXJZRRe9cWRhAs9Jz7wsu47/qgs6TWgfLncjlXWmqShULBMpmMpdNpy+fzHrnMzs4GjGQkErF8Pu+zZnnIfr/vvTpE6YuLi1YsFj3FbjQavkF6ijvQh07mwBDhLPkeItVcLhdocSiVSlYuly2TydjV1ZWtrq56pELUQWQ/MzPjGDfYOJRmHCmsYAwIUSvKEoZgeTaFvFiLaDTqEzkQjtnZWa9v8X5Ac9DUgZtQAK3v3aeEOFGeEYPEs2umjGBRA1YiiNldoRyBZX/YK4goZD+FQsE2NjYsHp8OyN/f3w8ogDIbVaFVGci6qEeamRsjLmVx8ly3t7cO3WHYw72u7AtZJVExjg95JTPlu/ksP1ovUp3C+UNk0zor2aw2m3NvgkQieJ1ulEqlfBg1706diR96pv8fe2ey2+h2ne3FRlQv9o2otiRXuep0sQ/GIDedAAAgAElEQVSOYzgd8HuSQSaBkVEmAYLcRwa5h1xBps7AQCZBgATIxHAcwHByfKrTKfUUe4miSiIlkv9AeZbebxerykim3oBQjcjv281q39Vswg3AUewZCSgYVnjdwO30F83lcpEevJqYokiQxigVsQIy14Q6hRnZBz1zjWFCpyhL9t3MPKcBjwLjSg3C2dnZSHwPiE/vWtRYsMb0W62Wvx+jieeSrMOVVfA5cuHu7s6VgWZeK5yv0KrKDM2YpZYWA5GSCbLmuSYQhQaSA7SqORaaWAREDL9TngJ6lU6nrVAoeJlWLBaLlMWMRqOIsiRsgCd9eHgYibe/ffvW+QOeVjSpWCz6PmIsaP5DGPbhndCcepuaaEboinkrdByODyrL0ei+nRP3+62vr/tl0MPh0PL5vHt5/X7f1tbWbHFx0WtbEDxzc3NWqVRsa2vLqtWqpVIph8PU+xmPx5ZOp21zc9OePXvmTNXr9Wx2dtaD5DMzM558Ei4OooW5gC+xaOfm5mx7e9vS6bRVKhXb2NjwQP3t7a3lcjkXCAgwDk8VInPQ7i1cugzTbW1tWblcdqsF6BZPRgkJK1GFPHBOv993iDsej9uf/Mmf2Pr6us3NzdmrV6+c8CEU4ivUdkEYGv9DUZDcgPA6ODjwtHIy5TR70szce5ufn7fV1dWI9Y61z58qKCFYvPzV1VV79uyZPXv2zGZmZuzi4sL29vZsb28vEpvSpCpNAYfo8awGg4EVi0W/kV4hTeKd7HsymXTlTdo/hh0MxbkQEsjlclYqlfxMiI9ls1mP+2FMKhrD+1A8WK8wcyaTsWw2a7lczgU+Z4knGI/HI8jLeDy2arVq29vbPjc8GeDlVqvloY/19fVI7AbPbXt72w2MZrPpBm61Wo3c5oBgUbSh0WhYvV63ubk5+/GPfxwRVNAdXjpnB0zIZ0GcksmknZycuKEVlgpoKQm8wuXWnNPm5qajSpyDenIgAZrlDAJm9tAQH1riFhEUR61W86Q6DBOMNBKokGej0cgKhYJtbm56lmYymbTt7W2bTCZ2fn5ujUbD3zUajdzjA5UBsQjhQeoZMYqB8o+Pj61Wq1mlUrG7u/ubamq1mj179sw++eQT29nZMTOzzc1Nz2FoNptmFm2AQRY8qNz19bWl02n7/PPPHYUgMTAWi1m/33cPGvqOx+OR0pHFxUW/1D6Tybgch0ZqtZq32WQ/oPNerxfxAJEBxOgx+jA8SBjiz+vraw9hraysOBIFiqeZ3qEjo+ODypL4YqFQsPX1ddvd3bXT01Or1+tWr9cjcAqTU8u5Uql42vrv//7vW6VScascCy2bzVq/3/eLerH8jo+PLZ1O2+PHj61cLttkMnGrifgYQVw9aKwVBClCVpMj4vG4e8Lx+H2tJcKHZBWFmzX4TwCei3XBw1OplL169cr+7d/+zfb29qzZbNqjR49sY2PDre+VlRVLJBKRVmUIFo29KSza6XTsN7/5jQv2XC5nOzs79qMf/ciKxaL9x3/8hz1//txqtZoNBgOvc1IICosKGFwv511bW3Poj5gtsReFoREyShcK3xFXVCMJL5ACcDyc1dVV293dtSdPntinn35qa2trDhdWq1XL5/Oecq+1gWS4QiMIVAyWwWDgGdMwEAOvAs+d/Wcts7OzrmxSqZQnDQBhlkolW19ft7W1NVcgGHvlctm63a4/m33SRBgGQptzJ8aMktc7ODEueJe2j3z69KnHps3MPVsgOG5Oge7wLPjcaHR/vRfKn7ILjBxiRhjK7JHSQojeuFD5nyQKFZDsP8hCGLYwu0euiLsC7wJRawwYGUO2Pc/rdruRpDvmqp673sCBvNFbQO7u7mx7e9uy2ayVSiVrNBouhDE2+Nne3nY+Qb4Q4rm8vLSVlRX73ve+Z4lEwo23TCbjxipep0Lr0AgoGnumnvbTp0+d9jBMt7e3bXt722HHi4sL9yxLpZI3BeAsMcxZGzei1Ov1iMFTqVQ81BKLxWx9fT2SKZ1MJv2+Wz6ztLRk2WzWyuVyhCZQhs1m0zKZjLXbba/TpgQMB+373/++G37D4dDpmKYHiUTCkTCMY8JCp6ennhDZarXszZs37rQRdltbW3N+w+kz+z8k+GDBAVuAeUPcWleJcMfrIB6FQsQTMnuAXrXOh+xMrEgKimFwFAGMaGbe4DhMJODQtMsEClrjKyowr6+vHT4AqiSegUAgk4w4AYqWmNbGxoaVSiWv8/nOd77jcAGYu0Kh4T4TYNcYF9ANcQkUPBcea83lZDLxNSBYtBZOyzRoCvDd737XlpeXI2UL7AHGCAyLckKhYO3DbAgdVRLj8UNNFxnOlUrF4UJirjD+zMyMX/OEoQPci9IIPYS5uTlnHIVqdI+BrhCOrBHaxTPQq7P4Xi6Xc4MHYw9jAyFKcTYhAPZKSxegHepG+TcJVMBYDGAp9h7PjL3gfEFNiLMhdNbX1z3bU1vfMa9EIuGxNuYIz1AuANw/Go0sm83696FbaEsHBgFCGfgcC149RfgKulSaMzNHJUB3UHLstfIPEJ6GEaBV4oooXZRPInHfZJxktbu7O8/2JP6rTT1o4qFJZYQNeC50gUevbSCBISeTiW1sbETKL0BJFP6mOYQaXBgV8EWpVHLe4KdUKvk7gYc1J0P30uzhMgDkMh5aWBqmPWfZZ8rLgOoxZHCAzMzrNKFRYr4kySkdEXpCxhA3hfY5NzWGWOPc3JxdXV25g3Nzc2OtVsvDY8xf6Rg+DNGgcHxUWfJQLAe8Cc3yxKvgLrfJZOLQEOnqtFRC+NEdBAjm/PzcBRCETkwIy4usJWAshFtojXNwaoGYmTOUMqvZA3NDxHxGYylYuDAmwhgIJxaLWT6ft62tLfcwdnd3HTrVxAoEPoM4Eg0X8H55LnWQWGyTycTq9bo1Gg3PKGavNIGBP2FGIDRVDvl83oUF79RsXYQ1RMt8+X/2gsxd9omBkUItGXWyeA7j8dihWjIwgbcVUsGQ0tgEFq7OTxOAwsxShLBmWzJHGJx9wGMg8zebzUbaYaHkKTAnuQHFDY0xL02uQolAS5lMxmEhpQvWiGeEoYhXAcJyfX3tCR1AnsxLW4hpvA+Fzv/Bt3yWcyQ8omUuKuApcdABLWLkcZb8aIxMDWT1XNUD5rPh2SPEoXHCDMgteJSz11gYNIqBT6wShQbvcObsBwkpIbqADEHgajkaMUIcAvirUqn4OZE09urVK/e4NbNflSVt45CNtI/E20eOcRZmD5dYJJNJPw8+pzkCZLOjKCmdUUNHM0cx0DlDFB+OFaPT6Vi73fbfA+HGYjH3sAmRwA+8B0Wq+6sxbOV3wlAo/OFwaOvr6559v7Ky4tA6cXwME1Wg08YHlSWanizF8XjsQn9jY8PTfcn65AA1cAszYbFjwfR6PbeqM5mM5XK5CMOn02lPlSf2BrxIuQoWjsYtOWAOEYEcCh+FyrDoIahpFgYpxxAxSonNxvL/6quv7NNPP/UswPPzc7fYsRSBFhgowXw+7xAkhLywsOC1jtpVp9PpOHQC88EwrBGjBWh3aWkpUqvZ7/ft+PjYLzfWrDOEDDEsTUxC2JNgwA33vV7PlpeX/cZ31qjKgfkQM6RGbzweu2e6uLjoCAaxTPZThQBzVQuUeXOeTuj/Y32CCJAJSKySPdDYKN9ZW1vzMyBWooYVcCZxYs4DYYAhBm3BFwpXojhDxQNd4eGQbHNxcWFnZ2feiQnaZH+r1aqtrq76PBXS04xRFCZKoFKpWCwWs7W1NY+TX15e2vHxsTWbTffQ8vm8w3ta+8rQcgpixcCTGKooXXhYE29Yu4ZS9P/VcAiTsOBR9cxZt3bOQh6w9+QWUON8cnLiXg61k5lMJpIxTfkMSIg6F8SjafZPQT/7NRwOPWEsnU7b5eWl5fN5R7m63W4kKYzB+sg+x/tXmBrFAU2HSTPws8ZpMT7UwBsMBg4dI4/VGFae4mdmZsYTgBh7e3tOo9AM7VQ3NjYcWcLQRCZoWI816dmR+ImjRQUE6zAz++yzz5wW4QFQGuU/NSanjQ8qSyyFUJuzYLWigFKBK/RGc2J7CCbaP3FAwB6JRMKbBZydnblHlslk7NGjR84g/B8HrodH8S+CDBj35ubGu9YoHKUKF2LAAwM2BV7ByuK7JA/BjHg1eljApigwrETN1ERRZrNZD0QrJMWzmYdmFuoal5eXrVwuewB7PB7bzs6O3dzcePkBbcn4PlY21ht3++HpEP8iHsN89SxoHo1FWS6XPTMahsWSRZBA7DAHlh3NjYFVksmkZbNZPxMzcwKnvheYnhpfFIZeJwTkzx4Cn8NgxWLR9wJlR5xrfX3disWiw3ggKQgOBL/ZvSBTyDHMNgwVOn+qVxUOIM1areaGHmvnHeE55nI533PirmE8T41MUB/ONJ/P28bGhidZYdSgvEF1FKILh2YCo8hQ7Bg+OgfNzlZPCoWJsFRomu8DMRJj18/xDJVr/D/CGJmQTqftxYsXdnR0ZC9evPBsYYxzNdy/+93veoILGdua3HVzc+NGCHHw/f19N37MzFvgmd3H3uG9ubk5a7fbLptUWaphTN210pqiasgjRcdI0ARyRtFC9+xrIpGwy8tLL7mjYYnCoewrcphnkAilg56v5BLAU9AXumMwGETCDarI9H3wGbJDDQc9Z4wzDDhCEmqgKoLzvvFBZQkhqrs9TcPromCubrfrtW/AjMrUeFfAU/SQJbBO9xUsFd7J4mZmZjxgrHPRDhVsBgSzvLzswWigG5iEzYdwFIaFEGB+zcBTaIh4YbhvHBbzhrAZpEUzD5QB8AxeLT/K+AgYvO1SqeSGC3MEGhyNRpFUdiAbRQNI6NB5YvGFdbfAViACxHywMNkrhdex3pkj6fd4myhQIDsMEpQua8Jgqlar7oUOBgPb29vzxJwwiQq4ngw85pZM3t+8oIYema6k/wPzhfFYs4dLxFFG8EmoMBnTLFy+Mw0KwiCCoVE+KAboBsMWesdDITtarWalY/4Nb/F8DDAS+BS10Hq3kB6VJ8KcBpStJoDpniLoeHYoczTpBzmCUFTIlr3U+YVGC/JClfDS0pIncZE8BAJye3vrkHuxWLRqteq1vMCYxLRJtuJ9lMNR4tFoNHxfkTfAj9z3aGauoLSROggDPKuICMoCOlH5FovFPLdAM5QVAkUeQMtaI8vn2Vczixju8A4KVREHanKBPkejkaMkDOS18gO0pjpAoW+cN81hSCaTkXnBQxrKICyhSlLzMqaN30pZ6stgZk3kgDkoR6D+C2YGaoHICRqDjdOOiw2em7tvdaQCRpUYUBjv13RfPEBN0kHhLi0tReoFNXlJLS8Env6Ow4FwNDVdiQzBAMMrzq+wka6NTC4VtsvLy5bL5TwTmCxTlCAES0yPonmaIWAQoHyJWXIjjMLRECOelMLvZMeGnWrUA8BTUAWCwICBIFKsOFpqAX3jycOEus/qdSEINSuxWCx6zCcWi3lChQ4ELX8HGiROSm0imX8YUxh8mhijSgblBDKhyWWhUalGkzIqfw8/r99TuBK6JbsTo0DRCuhxaWnJYUAEE3PECISeECDMR4vd4Vn16hDO0/Za38O/1crXhJ2wrpV9hWfCpB1kCoKcZ4LYcDZqzGoylIYUSCTkO8So8Tbb7ba/I5VKWbVata2tLSuVSjY3N+e3WxCGwoM3M1eWoB2gLY1Gw05PT72+HJpeW1uzTCZjCwsL7i2y/5rZTas8ZBh7qBAm/ALNa2xSw02aWIMSUjic+amy5Hkad9bksuFw6HKcQYmZyi2eiXzEONFQl8pNzlmNHVAd9hAZo2eN3oB2oGsMWtVt0/iP8dFG6igcLY7WDDY2bjgc2tnZmZ2entpgMLDd3V3b3Nx0QczESUYhU4pU91qt5gqTVkpsmJl5XBTPldRohQcZeCQIexUyNFaAoDhstZ5IckB5sRczMzPuyWL9zM3NOaysSgxIQuO2eDQ0rmbk83kv4MWLyGQyVigUHFZVotS/N5tNT3iKxWIew0Sx9Xo9h/EgBAgMCBXi1AxX1sLc1fpkvXiW2pnELNr7Ew+O5hXc3cgNA8SIzs/PvWNLJpPxpC+Kq4mVwRhY+3t7e3Zzc3/dFiU1tVrNm/4zlDnVeCDWtLi4+I7VSl3W8vJyxECA+WAwoFVKZzhLvCgEvipFHQiF9yUYwOicHwyucSmFNDX+i0DUEgw+qzEo9VRUISrN6L8RvsxN16JzwsvhmSEihcGiBh6/A5FSIxM+xohC8BPeoURAS8PYE30PMkANUfIoeO+TJ098rnidjx49coSi0WjYt99+a3t7e9btdiPds2ZmZjxpkXDJ3d2d/fCHP7TRaGStVsu++eYbOz8/9zAVdZDcpIHBA68y+v2+KyLCTUonZPGzdspaqFnGs8PIoiTG7KF7EvSqtZL8P+eq+3d5eWntdtuNT2iDAU+rEjZ7uNN1OBza/Py8n03II2qIsiZ4EEOJPA0SFjVPAJoCadGM2JC/3jc+muCj3pfGBtU6HQwG1mq17MWLF17MOzs76wqNjSYGen19bYlEworFoh9YvV63VqvlTIoly0IpT0BponS0bRMbohus8w2zXVVoAAvqwNtTuAxBiLeFMCU7ke+Rsq1XidGc/OLiItKKDSIkSYGyChiEZt1m95YkzQ3oU7mwsOAK6ubmxuvP2AM9Ryw/2mAlEglPkUd5wAy06SMew34Sb8Z6C71ubXGGpchZ0pUHCF7jzjTRpiEC0OjBwYH3LaWoGANoMplYu922/f19e/XqlT19+tSt2ml30ylEyL/DxBtKO1AQnBvnircMLZHEgiGpCIXZg1eLEAaRUHhWUZoPzVeNO0oH4MVwL80e2oz1+31bWVlx4YK3g6EFnK5Z6sStVRmqQAkhV+bHnNgP9XZCeJ7LDK6vrz3jm33nfRgHyAQUHcYAHvr5+bk1m00bjUbe8Jz4+mg08mYpejYYc5yZerMYrel02r14vQHozZs3nr2azWbt6dOnbhAr4mN2L79qtZqHOkqlkl8+oefZbDadv6AbFDWjXq877EsGPXFK4HM1OnBQQNc6nY6dn5/b5eWl0yOKRENQGCzEaZGBnD0yVC98p7yGM2Fo6AGZxvlqGIOzSKUeLqHHcMcpU7RADTY14MLGH9CfGq5q/Cly9L7x0dIR1dzU4CjDYJEdHx9bu932ybx+/dpubx8uF9bkCKxZ0srxAJvN5jsuMRAJWbHaHYa/azIAG8ecVQDxPIWGiL0BN3CgWOQ6eCbrRkEQc9W5kHDAIQNRo+j1KiYyZjXFH2a7uLjwufCss7MzbyKdTCY9OxNBBdw8Go3s/Pw8co5czQNxtFotG4/ve/FSuE6au7YlU4gHj1Iz8NhL4jDMRz2eu7s7h+iVUcbjh/IRmJW6M7VkEWZApKwDxY5gXl1ddWSAESohVVaaiAYshZCh7672nkRhsm7NymWdZKYCo2m8D0RDf1i7hhRCxcSf+h1NysBDwtKHhzBmqGPV5AzNAej1eh7Pevv2rXeKIVs85P1pAwWm8TPmi0DkrLiHFMNDDRSz6IXUmtUOlIiRDF9AJ3hm0ArJYmEmqNlD32KlZ/YEQ1PLUTCaer2e359KHDORSPgtRrFYzC4uLjzL2uz+nslXr15ZuVy29fV1T5LkveR6oCyRK2YWaSlIWIM1q8GgMV6ldTwn0B5tqQnPwZ8bGxuR+Dm/o5mHKhpoVg01jfEzQlQF+tXwFXvM58KEI5QzxoUqSzXaFKVRRESfzRyUp/T/po2PwrAQPhoeKI6Nwv0+Pj624XDoxPHy5Utrt9velKBUKkUCzNyCAERwe3trnU7HYyVknepBkZaPsg3jZGyOHjTeIb/TzyFAuHEBIafWrAop9gIhhaDGuwBGUCuPecLAKCG91uj8/PydWA4BeNruETd7+/atHR4eWqfT8dZgtOBjrhAqlqp60trlJ5lMeqs4SjY0QUg9QBjKzCKCJUwsCJOQ1GJk7bSUQxCZmQs/zhjvUN+LUsUIo+WXWub8PbxPj6EQ4fssVLxs7QzTbDYjiUq6L2bmSgXLnpKAhYUF7+oTwqg6F2hMlaUOnSv7zpljoOntG2Sa4+nHYjEPSagxqB2agNSAvmkTRiw8LObW9aiHqkKUNXGOKK3r62vrdDp2fHzs0Gkul4vkAIBeUCvK+ZA9CdqgXgEKgRKVZDJpKysrlkqlPFkHGjGziPEOfwBHc64oJuieuZ+enlosFvNmIiTuYCwS88ZAGQzuL3Lv9XrO72oYn5+f+21DmhCHp8/QDlQYjnhuGh7g9xhrnI+2j8ObhH+BfxWaZP3QEVnQqugV8WA+Km/DmCC6hHUid/mO5rdoLbXmDIShH+VhDReEYYVpiI7O633jo43U2Thcc7K3gEXb7bZ1Oh1rNBr2ySefWKFQsHg8bmdnZ/b69Wsnps3NTYvH436/28nJia2vr3vTglwu50kf9Xrdi++JIRHnQ6hq4bwuEKaGacJgsFqhxA7Ozs4iwXi9gFVjNsRT1VPgsPGIIViIGsHFrQUaDGfMzc1FPBcsSyCh5eVlXzOtnBA49CQF1nj06JHt7u66x8l1OyRRLC4u2ubmpneNicfj3tzg7OzMfu/3fs8VHpCVWTTdXpkLAcNnWYPG4cbjsd8Z12w2bTKZWDab9ftD8RSAqIGdtHQHr0YhXs5Frcfz83PPjP1tBzSighfBiJd2fHwciaspo7M3tHDc3Ny0+fl5h++I2StEzXowRNVSZoTepCpSlDnPGA6HruSgBbJitWBfvQ7mSPIW1+/hYZyfn3uiGcofQcT5Qut4ProWjV1jeDDvq6srOzo6sqOjI4d8h8P7m1I0jqkdc1AQGJugIsSzMSpRaPV63cweOsJgiKpnSXz+9vahC5KZeWY/Bj7G0OLiotXrdavVanZycuKtArklo91uuzDGKOR73IcJ1AqMitLhhh2UEt42cLCePfSNsY5BMxqNXAap8sFLr9fr1u12bW5uzkqlkuXz+UiCErQKjeoVZ3d39y0/NcatBiTKivwWHRqzZ16a+4JBAdJHdzKNrTLUaEehahcoQlPsIfklylfwsRpn0O37xkfvs9TMJF6AoiQB4+rqyiqVij158sSTURKJhK2vr1u32/XYJQQyHA79lo+ZmfsC1mfPnnksqNVq2cnJieVyORe22oMWosaiDtttsTlqLYQxIiwsvUPt+PjYZmdn/eoxug7B8NwWjoIgKYU5KvykUJnGuPCsNc762Wef2cnJie8r+5dMJn3P1WOASIAwNE2a4l/NNqbhMwxA7BAPjUxQ+jPmcjlfN4IXK9jMIkYIyp/n6ec0HqEQGBnQeOSUAZH0w3NSqYdrjjS2gBJDiWopjJm5gtMaQOY7DY5V6Jyz5Bw0IUAVAenxNFAAPp+dnXVIDgRCG3Lgxagg03mpNa5zxfhj/9hjjBbKcLRBBcqCpDGFXDG+FNEolUreP5j7IuEP4qAgAhquUGhWFaQmgWjWNJcO7O/ve7Yu3b+4FIA2aLQBxEsmWQlPLBaLeZwTIw0Ds9freXP4t2/fuoeJd8F8MXQ1u3symThyg3K9u7uzw8NDOzo6ssvLS2+ofnR0ZIeHh96MA6gdIU/nqlQq5TKQJJ1qteoJN7RlY09JZkqn05FuONAf8yc+TUKhGo8gBlxZd3h46KGd29tb93Ix9ijNGQ6HPn/KXICoea+WYim/86P0qwl0/Ml7VFbzf1zCnUwmI6gFzhs8qvX80BzdgzAOp4UOFDpGBqgRNW18UFkiXBE0KiCHw6E3IYjFYm4dsjAyw7CosMyIM/I8mjxns9kIkV5dXXmshIUp0eJ1qnDmwEIMW2FC3sv8sbjJDOVqI9aYSCRc2fH/MCMEq1g5m60xJX5gaI2xmplbnuwnSgAFrI0BGKwfyBBhgyJiTcVi0W9ORzhqOj4EjoHBTRXsPUpJ91mZQIUgykWzz5R5EGQ8YzAYeJIBZRvh3vBZDd5raABrGAbCo+bMPjamJdYg8DlLrRlD6ZmZtwykuJp1gj7AG5rkFcLXup+qQHV+7CXfn+a54REiIJPJpO+pmXn5COdMUg30QM0qmc8oG80+x6PAAJhWHqP7GcaFzcybR3C3K0aG2QPPaPwYOsHbRICzf+wrBgjPgl7ZG+UX9VTC2BfeEnQIfUNXR0dHbuRjTPM8LUvRGDtysVQqeWJNq9WyWq3mXjHwtO4jtIAXyFAeA60AESEmjGxkb8iXAIrmPFFa7DdhM+Rev9+3Tqdjk8nEYXjWFnphIcqjPIXyVNrlObpujG/6u2oVBrTEmfMcZBjyAKPBzCKN8xWa1blMKymaNj56RRfWmlpjCCOSMvAO1cWGCBD86XQ6EkxHAGNZwjTEVbTPpVoN/ChspnEUXHkdMCFxCbJBubJLY1TNZtOD7RwiwqLf73vGHl1r9F0oS/Uo1aPCauOAGZqUhOLhsPG6yEgFUkPIKTQM4wBF0n2GJA88I4XjmD8QFplyw+HQW3mhHNhnFfJY+QgGPGJNPOD/wlgGiRLEelXY4zEiMKftM55v6FlCn2Frs4/ROUJTFRJ7Q4asZnNzhRN9YzlTsi8xCrkpQeO3inKosg6t4NDj1Llqiy9qi1ESxKK07SAeYSwWcy9GO8EQYwX60yxvNSB5r6JO4ZxVmGlSBlA7fA5qgMA2e4i5q8ICdVGvnLBHuN98L5VKucGAog+9jTD2haEBDTB3hHC73fbYfqlUsuvray8xAnVSBI6WkJlMxtbW1lxJoSzpR6woCOfMnEJlyRyRydo5iBpD/s3cmT9KS89UnYZSqWTpdNodpbu7+8ROrWsGvUA3MBT6nUYT0Cr0qPkfyETkydXVlWf1E29W5atnBsrBvGjUbvZQhRHyVejU/J+VJRlZKDYOkJICLCzqI4FmyeJUwmaj0fAU07IQgvV4Hygmsho5bE2swEPSgyFeMS2gjMUIrMTaECLFYtEuLi7cogLSNDO/Lf9R6u8AACAASURBVIPWT9ls1okJpsf6RaAC/+CNsqdh1yGSWhBE6plNJhNXXqwjHn+4cBkIB8HLvzEmKOOBQJivxmkg9GTy/gJdsmFBCcKsYwQhUDTMRJITcwTeUq8Uj5wf7oXk3Il5hMk/MAeCgHngmQJ1hl4hQxV8CMeqgsQwUGZMJu9v0FHkgeQYOiLpGYSxXaxkPQP1ujAsQqbWeSIcVYiTKcpASbLvJGs1Gg178+aNe7jw2ebmplWrVatWq7axseGKkrkgyOEHaNXMvOkI3irjfdY7HiK0NR6P/aYWRRNisZg/E6+Q76NE2UsEKfutpUwaCyM55fLy0jtiMZQXoVlgd5V3zGVxcdEeP35sm5ub3qqNnIyFhQVrtVqeVZxM3t9fWSwWrVgs2tLSkj1+/NjM7suvuKpqPB57qZgqt/F47AiPhpqAZ+EN1qCoAvzAOQDnmpnD3bR85Eov8jfM7mu/iVc/evQo0gWK8jKzh0sXlHY145TB/cUYzvl83usy4Qnom9Z38Bx6QpN8NJ4LHIyCv7q68iskb29v/Xq00NsOvVx+3jc+qCyxWDTBAgGrLcHM7ovfX7x44fCOWbTvKosmTkUTZhYJ9qylCsBYWlSNsNANDpWlChzif2yyNh7H81XlQsNy9R45KJIHEEbg5gydH7AnhEpqPkypxL+ysmIXFxcem+X3qmy0y4bWKAGZsSdAFsxZA+d8XmMzKEK8bnpgYtlDWHyOOagXxh6zThhG3w+TaGwIK1Lrn3gWnzd7KEDWhCuF0+kHnMlk/HMYHwxgIf2+0g70BL3rUC8KwwOPkUxIVYCKwkDbvEchSl1HKHCYsybTqBJGMehZYyVjnGSzWefder0eURI0vaA5OKUvmoFOGAW+4bxjsVikt67ulyp+RZCYM1Ag8mVa0l0I4xNXZD8w7KgBZQ+B4uAFaAC+VASKOWsd6GQSbZNI3I69HY/Htrm5aYVCwWZnZ32PyC7Hs0un0972jWvdSFbD6L64uLA3b95Yp9OxhYWFSLYr7wLqXVlZiSSssS/sM8qSPQ4NPdAkzTjVxJ7hcGjlctkVFXXO3HakfAhNqIIJDbxp5Svsq6IBGOPwB7wLkociZN6cM3JDO4eZWQTpHA6H7rARGsFQhDYUBURmYCxPGx9UljxELS/cZuJLWHG1Ws2+/vpr76FIZqAyOQKQrFpia8BbWAIqpHWBIXw1Ld6k8RHeyWDeMDAMyqGqUo3H45GuNyhLPEWNSSmzMU/mBuFgKWPd6KFwESoKU/eA76pyoHYShsSbG4/HEaFObZrC37FYzJUkxfZ4lzMzM16Izj7gbSlR6oBxdf2sAUGmMSLOEsbhfBU6VKFoFm27iOBVoaoeBYJtWmxYh75DPUt+VFGFMVF+z/+TdIXiCmlShRv/1vdrfEe/p3PQGkSN3+pn+C7vJ9aq+4FRiJJcXl6OtP5SaJjzAiZHOWsCR7jPfI95qSJFgWKsQo8YZ5xfuFcoAL4PTWgMnf3QEBFwMefFOtVYCWkEmtR/K71Rs6kZ2dABnWj4nCopRdK4vvDNmzcRRU5HImga9IL+sAzOmb3CQFJeDGkd+iDDlT+ZJ8+ikQK9qcP4KPuArNQwHUabno3SxfsQhE6nEzFQ8JZBF9WA0ZimNqRB1qkTBJqBk4IxGHqW/D1EnMLx0QQfmIgfmAZPA7e80Wh4B59EIuFwB0zQ7/cjheRnZ2eWz+ctl8t5+vTFxYWZWcTKUEUBoUB0KpwZMAbf0ew3CBHCUnde/w+LWGMDCMd4PB4pbNb4DDCgKnQOOpFIeKwLy5yBZa/XRyEggCdRWnQ9gcDpaMOgRyweciKR8Hgn8AmChBgMXTcymYzXymIYTbuii3PAwoeQsfgwSDT2pQylnpLGFqA5lKTGdtWgQeiHylEFo8ZGlG7M3o0LqoGCMtfuUTAYdIHABAnBgFJBobRB2EGzJPmsKj/1fHWeKGxVDgpt8xn19uFNYtHFYtEFLh6LoggqMNRDgf6oI9Twh/JcyH/MX41RFI9a8KFQDfdJhRpelO6NGhkoSzLHdW9isZjHnTlz5jsN7gYpQb6xn8wBwwH6gBdQ/iR8KbKF82H20GJOZRuoj+4F2cD6/7yX89Us9LBkIzSgWCPrA8Ei7MEeau4B+8381cBFJqqy5vc6FA3UTHIaqMBPGo56+/at6wTNwiV/hLaZk8nE7zVeXFy0XC7nipKmOfC1GoXqCCgNvW98tHRELVuzB28TZjo7O7N2u217e3u2v7/vHfmXl5d9UcQ3UXxMisB4oVDwW8MRILjT6lnxTgQtXq1CFCo42USUF0ocmFctMuABtWIVPlamh2lgerIDtYsPBKwwDlAz9WQMYgN08Gg0Gk6IpJxjjS8sLNjt7a29evXKut2uZbNZ++qrr9xaJN0eQY7yZ81YcwjUer3uUG6hUPC/o5QajYbHeFXQaKyN2AglMY1Gw2NMGxsbVigUIlY99WfJZNIzDZkPCg5CVjibeFIIZapyZW4qmFRohAO6gj6Ji0FjnNvZ2ZkbHWHihDJc6GmbWSS2Qrybe0nDNegIvWvtYoO3AR1j4bOH9CXl3EmW0lhwvV73a6DIbkWA6qXNd3d3HpoI5xIKGJQlPMQ6UNpcrcc5NZtN5zVuHVLIDhkE6qDvQ2lrEsjZ2ZnLDkqiMGRyuZzTL0Ozm5FJGDxzc3N2eXnpJVvLy8vW7Xbt7OwsAkkiF1V4JxIJv8ScJgbFYtHRHDokxWL3+Rqnp6eRTONMJmP/7//9P+ctpWUSExkoPE2w1D2hVASYWuFv9g9lg8fH/2MwI9cUqkYeqDOlMKsOzRaHj+lLfXR05J2PMLgx/gaDgXU6HW/0cHt763kxGHO5XM4THbm1hbXTKIK7WDEQVXFrXD0Mwej4rWBYhRt1QSjDwWDg8Y9Hjx5ZtVp1QqFbDzEOBEyn07Fut2utVsszw2gBRU9PhTrUi4RZQswaRkCp4T2pEkYhUYitUKBCsMlk0iEsBoTIe7BUyBhDiKoA5LlYSzDV8fGxP1eZnrmQSEJSAkojmUxatVq1ePy+c08mk7HPP/88EltA2cE0nAPxVZQcmXokDKkw1HiUWpLQhVrV/B+ZuLRFpL6sWq36/CBKunJcXl5G6qiIO1Da02w2vfHC7e2tNysA+lLPQmNeIaTCZ0MFxb+hMwwh9cbJEMZy18xX9WiYt+4PdIy3hqAKYzpK50rL6o1zrpyLeqj6/m63a9988411u13L5XJ+aQGGHQqehBISqOAjoEIEKPWJ8AbvxCANY1YMDFGN++fzec92H4/H9urVK+9Hu729bU+ePPE4VSJxf78t77u5uYko9Lu7h+vhLi4u7OXLl95AgLthtRSK+zTV8GPfOVuS/eABoFzmT8crlLAmHobhnbm5OZcz8/PzdnZ2FmlmwedJjDw/P/dSpNXVVb8eDeNC6UL3Wedn9gAlo3A0dAGfgnbQNAEZGUK6nB8DwzX8QZFpWZoO5oSc06YG+Xzems2m6xaF+jX0oK008UaRM0qvvAf+TqVSkY5FyPkwXPN/UpY6KSam7iueG8Jrbm7Otre3bXV11VZWVjzbEUvKzDw5qNFo2Pz8vNcd0TYPZtWCV3Xhgcqwoqd5lsBmbAiEROcQlBDZqxAGTKjKkoOenZ114lUlrR179J38G6WDEoCxm82mzxmC4DkIxOvraxd4QLFv37711PVcLudWpVrbCGGNGeENYBEDo5GYEMInvEtjjjxXoRmFKrHGwz3BW2Q+vAOBy7P5Hvu4uLjosBsWOQqfdngK76oyDD3J8Hfh5zUxK5FIeOE4cREYkgJ5hJAqPvWO2RsMxLAxNt9lr6cxahiXBEbUNaiAZM1kKbdaLReElUrFhRgCJp1Ou6DmQmCtx6Phwvb2tieocI6abBSGQRBuStPMGQMUmqpWq1av1937I8MYL1/vh7y4uHAPGSVDQ5FGo2H1et1L0bhmCxom/qbeOvNS+kdpwreaGAR6Qoct3o8s5LvsCYoOuanQdohS3d7eusIghEKiSjgU3ZsWo1R4H0ULfZOgCVqnUCrzVrpU74vnQQPsp9KrJonp0IYkGusFAVDjnOeHYQz1ANk7ytFQlpr5z3vwMjFu4D/VLUoP7xsfVZZYTGh7TbRZXl72Wzc44GKxaLlczltj9Xo9v8He7EHokWBQr9et0WhYt9t1RYIlpgkcCCLej/JD+epilYDU8kUYUGh7dnZmx8fHrhBWVlYsn8/75+nagnVdLBYjmXHxeDwCL6gyUSgYKIQ/2+22d1eBMJg788fib7VatrGx4QxMZxp67eKpctEv8Q0tAYDoeK5ew6SKQ61QzgIL2cwijML3WLNCV+PxQ2P0y8tLq9VqES8Q4qYmDIGigh/FhNeB8gYO02uIVJgpRKxDFaWumXMKk4Rodk88t1wu2+LiolupCg1r0wKepxmBlF5Avxojh77fN28d2WzWs501n0BjmdAhZ4vRt7W15RAjSTuFQsHOz8/t5OTEveibmxu/AQjPiKvV9Jz1nYrsqLeJga3rI1SBcZTJZGxlZcXbqIVxsfn5efeGCG2w/7VazU5PT61Wqzk0Wq1WLZ/Pe3YpSp6bOUKeUzqGXzgfZB/xMjxZUBqMfIUfdSgqA0+wdyFfjsdj78vKVXXMU/kMeiF8gWIKeRE+Bk1QRYlnqfXJ5DOMx+MIX2q9ue4ZPBcaRJT6hcoSo0Xj6MC+NHcAWtXnYXzp5efID2hIk6gwGrUGGaTg9vbW16kGkcbZ/9fKkqYAPADCn0wmVqlUbGtry1ZWVhzeJPnk6urKW2fRSQb8m8As/56fn7dqtWrj8dixaSwhDkKtMk2Xp7uI9llV6FYVFge7urrqsVIOAWbXGASKYzAY2NHRkdcl5XI5Nww4ZOqBiHliZHA4FxcXVq/X7fr62k5OTuzo6MgD12YPtZF8B4VJTOfg4MDr4FAgEApp3ChXFKYy6+Lione0IdtNlbuul3pZWvtp/EGtvpCggXOWl5ctk8nY2dmZ9Xo9Ozg4sMvLy0gXJxiWGCZ7MZlMXGCQTY2iwcIvl8vuUTMQVBgPocXLPJUO8AQRWGqtA8fxuXw+b0tLS76HjUbDLi8vPZuThCr13ICD8Jb0vkulUfhsWvxPhZOZOZrQ6XT89hg+pw3c6Rbz+vVrq9Vq9vOf/9y+/vprv64KQUqv0oODAzs4OHDoKpVK2VdffWV/8Ad/YOVy2QWgJhppcwJdi2aMh0IT48TMPKmE/qTNZtN+9atf2b/+67+6EU74gVaZt7e3kfIgeJL/f/TokZVKpcjFw8RIE4mEy7Nwj/k3+6LNSBKJhAt1siqZW6VS8Yb1xHkxsDSEpSEUaBHeZD+hldvbW2s0Gvbf//3f9uWXX77Xg0NecPYhrfAZvb8T+YtRdHl5aQcHB94dDI+T+tFWq2UvX760mZkZq1Qqtru7a3/0R3/k8kP3DK8RWlfUyMy8ST6oBFD7/Py8PXr0yJaWljynQ+FfeBKPFR0EUgaSBT+D4CgCik4JjeJQroV/D8cHlaUW7OJ9QXjEojRllwNotVq2v7/vgpaFmUVvrEgm728E4NmlUsnhLxSGBnoVSphMJpEGAIyrqytPitE4B4IFJl1eXrZSqeTeIlaGwhEaCJ+bm7Pz83MnELxN9Qg0nokCJrkGYqzVatZoNCIB+6WlJa9tQhDBPLOzs3ZycuJNE2h8bRZty6YCGCbEu8A6hGgQGuxHCAGzpyAInBf7DDTPM4DMsOqWlpZsfX3dPQBqEYHQNKNRYyJ4AGq5o7zK5bJVKhW/FBzrcdoaGPpvYmQan7y7u/NuMtAl56ZF2DMzM5FkNWpDNWGJS3zxrkmmCu+vRFggzEJPUpmVzzGSyaStrq7a1dWVff31134OfA46xUBCGdG8m7XS1F/pdmNjwzY2NqxUKtnGxob94Ac/8MsMoAeldfhR6d7MbHt72+8fxZhQvoB32StCOeVy2b73ve9ZrVaLlEcRV1taWnonnkZtIHtFrSgKkEQS5jqto5N+X41ORR1AEW5vH+omQZ0uLi58fpVKxd8/Gt3fqfmLX/zC+v1+xBubn593+cN7kQdXV1d2fn5uv/zlL+373/++XzatcKwmSMH3ICCadKPQ8u3tfR/Y58+fezLNaDRyBIXyp4WFBdvZ2XE+PTk5sevrazs7O7PLy0t7/Pixra2tuWentax4iThHShfIM+aEcsNoq1QqLqcwNBQp0R8NCyGvFVXi33jVyt/IeJ6D3JhGy+H4qGepkIEKObWIxuOHe980OUNLQTToDNas8Z7wZoRut+sWPxvLJiLAtSaHgaWJsuA7rAHYDC8ITwSBplAq68cTXVlZsZubG1taWorcZDDN08Jy0a4+QLDAm0pIXHacyWTs22+/NbOHFnS9Xs9arZbF43G//V0JRJ+llhQWF4wPA+lcwzgOwh8BofELGJoz0RiWQlnsJ/RAGzXt8ahxC43foFBBDUajkcetOAvmGCYicH6h0jQz29/fd0Wk8ReEkzKTxrFYJ94wyW4aXwWy4r0a61J6UshP49oMPsNQg4T9yWaznhoPLMnflSf1TIC0URh03yH5bWVlJaIsS6WSbW9vu2HFn2rUKN1p2GN7e9sbkxAfUpifdSlUDP9z8xBJHLoe0BcVmmR1q1cextUR0ECRIX2wh8xNBS70rgKcukcyKimt0k5SGAGsg7khE6APPHCVUdpGdG9vz5u1qxCHF5k3zzazSFIichd+7vV61mg03ENmbwijxeNxb8tnZp5ASHx7aWnJy2gwYtWDfvv2rb1588Ymk4kjHMQQUZKKFKqBjwGi60RuKRyLdwndK1qh+6NIEfPlWZyBxtY1xPS+8VHPUrOKyHKEyPUqIKAK0ndTqZQ1m00XMIPBwDcPa5HJsZEKpRHDgAE4cIiPA6KtHINrbrBKITwVyODVKHSsCtahDI6QQUHjqdKKj3ljNOg8SWCi7ovrsohzMGKxmBUKBRsO76/gYu/j8bitrKy4t471qAqQ7yNYmCMMCoEiuGFk/k9jHhCyZvaFwX4zcwNDY3/8qMVG/IOkmHg82owBJtJYkXZAwcPUd+g81NjifKDDUNH8/Oc/j8RwETC3t7ceQoC5NARgZpEOR9A/iVWcFXFztZz1fBnskRpV77NmQ+U5Ho+9804ymYz0g8UQZH4IgEwmY5lMxra2thxOnUweWiiSObq+vm4rKyvO71p2wpp0HghpNbTMzD799FOr1Wp2cXHhdKiKEvrQZ7LGubk529jYiAg2FAzGLHQCWoQRA7ytBovuMzwcCkOgTOYSesEkhcATlGghuEej+9tC+v2+vX79OhKr1sQupX0UZa1Wc4WpPIyM29vbsx/84Afv7D9olZ4569UEGpApZGSn03HECAeAc6dGsVgsOpJCuRQlF0Cp9GPmvYSbGo2GPX/+3D777DPb3d21nZ0dj+UrmoJBBH2ZmcO37CvyRfefhCDerTwEEglfY9zBzxgu6hQoHP4h+JXxW8Gwd3d3nriBCwxMBuTHbeGack2NEtCPTlIJ08wigsrsXsBocb7GJYklkpGmniWXwtKFYlqwGeZjvvpu6r4gtNDS56BYJ7EKCIwWSwj+RqPh8buXL1+6FxNaUMwLK47rfvr9vhUKBb8z7/Dw0NbW1iLWPF4FZ6NJWUCBZg+lCNNidxAdRKyWPXvMGUJkPEO9Q10L/9aEGDwVzkSz0xAqrEMzRzGOtHNU6J2pBRqe+T/+4z+6QFpcXPSEHZID0um0CypiJBorwqPgzNUKRlgg5PB6NASBJ4uXougA5wXdMfBWFQlIpVJWKBTs2bNn9vOf/9znHxqjoCqaGJPNZt1DIK6E4oIHUBBqyWMksF6QozCr18w8j0EFEOsM0QSMMxVg8XjcG2hrg/0wfAN9aYapZobzbgQzDUF4rxpCKDZioRjyKAYgZS1/Ar2BFrjbFyXGD2Eq9lb3Si81DpGZVCplq6urDi2rML+9vXXPlnNHcWnjFdYzGAwcXt3Z2YnE7qBFYFogY8IJP/7xjz2GD51hYMLnZO3v7e3Zz372M0ulUpbNZm1jY8P+5V/+xczMS3gUkcDpQHdQPtNut73LGzWotVrNzO4NKtAV6DxEcMymJ03CgxgL6u3iMGmyWjh+qwQfiBzYg3Rzat6YnEJXeGlY/MCjTBYIlUPj7whEvEklAIgGBiA7Uhc4Ozvrv9M71EKohYOmHETjExCbxprUylVhrJYsayAW2263rd1u29nZme3v71utVvPfqxK+u7uzs7MzOzw8tOPjY/dY8UbX19c99tJqtbywFqGjTJdMJt2D0CJ71oQiYmh8FaJBqaBItb2UztnMPC4EoSLQpsFGauGFRggCEKOM7yP0ldH4Pd9RGCZM1mAcHh5G0IObmxsrFotWKBRceWCVopTYAwqeEWr6O7Oo8FLvR2EjhfXYKwwXBLYqSqVT9eBAbz755BP7+uuvnY+AGkFteC88dHV1ZQcHB86zXNmlEDb7HSZ/QQ94UjxTrXlGPH5/pRyJfrS8VAUCP5GpCD9BF3gAGGl49kqneNMYYUqzSov6PY2bKR3rpd4h3WKcgZKwRjNzWsYwx5PW2KfGGvW9CstDB8ghnQPdgHToGUA7eJlaORCPP7QQJLylSUexWMyVscbgcYpIngFVoWEC64D2rq6urNPp2OnpqU0mE4dqu91uZM4Y9FoPSd7Ay5cv7fT01PMUNHkNmXlzc2PdbteazWYkbqp1wqALegcuZ68XLiBT+Bxz+V8rS2VumI9NwwoJg+oaA1S4RYkBLQ5RAauqB6PW5O3trQsrs4dbv2nbpgsk7tHr9Tx9XCFC9XhQNOpd6SGFcBnMpIIeQhiNRhGrLJFIWKfT8TKRk5MTT58Ooat2u22vX7+2b7/91g4PD83swfrWGGwsFvMMQIWi1ANmr1GMGr8KPRc1hMJYIgYGzIUhoN9lDZqIMm19PFvPVBWmKkvOhz1UAaufV8WPdRzGY1VZEieOx+NeW6tKnIJ/src1PMCehUoLIYdiVNiUuajnpd6z7pHunc4ZpaGeB55VuVy2QqHgF/MCy2JsKPTOHnGbA/C2oiR4mfCywn7KQ/qj8LjScrvd9rhlOp32zyk/sV+qcLXMBEGM4FUeZLC3KEB+VCFzviiTacoduaQ39rBnapgh/zg/9hfDFIOJd6oHqp50iITo/jCn0HBSBToYDCKepCZ4EaMDPodutP46TGAKvTGUmtIOawAlYX9J6uSycM6YXA3G4uKi0xrKintNj46O7MWLF9ZoNCIhJHTI/Py8pdPpyPv0Unqy5VVZapYsCZNam620pDz6v1aW+sV4PO6eGm44hb5YZ2wIQkatELVolKD4AVfXP9Ui1At1gYC581Ktt52dHfv666/t+vrayuWyW8MIV5SOWtIcDBAMm4e1yw9zRUCEFla73Y5c9Fuv1+309NSOj4/t6OhoaiKCmdne3p794he/sNevX1u/3/esOi7Lfvv2rXuTiUTCYWY8UC13Cd8RxiWVWZXoFSpi6P9pKzotIVLYk31ThIHn8HdVEApbqoDCeNHPq4LRdSmNch5qHDHUmtaWcCcnJ15fu7y87H9yvuqds18qgJmbzk8VYWhwsaZQgXIGOmeNC+r74/H7pJ3t7e3IPas3NzeO5mhCFwqfphzcjwq8trCw4PfR8o5QGTBHFZx6voxvvvnGDg8PPSEtRHVUSIX0qskvoEcom2nCHR5BAGu4AZmD4oA+QrgOjwUljWzSM8ArUVQGRAW5ZmZewsH/gSiwZyEMqXKIkInyB04EnjWDek1tWsCZsG9A8igoXQ/89fbtW09MAmbVxDygaRSywtDsKV27Go2GnZ6eumERhkFWV1cjiU3Hx8f2+vVrOzg4sFevXnmZDnus50OZ32QycdQR3tEkU2Q8ThChBnSJdnKD9kKj8kMjFnpJvxu/G78bvxu/G78bvxvR8UHP8kc/+tEEy4tY2Q9/+EP75JNPbHd31z1N3G4yP7kNfXl52dbW1mxtbc22t7ffubxVy1Jw22kyQA9GrDGsJN43GAy8cL7dbtvf/u3fxszM/uEf/mGi/SO1KTDWmMYutB+kwg9YiATl1XvA6lOrDu9YU9nVmry7eygIxjr+0z/905iZWSaTmTx9+tS+/PJL+4u/+Avb2dmJQNlaIB92CTF7sFCJYSgMFFr0YZxJ44yh1z8YDDzRCCjsr//6r2N///d/P9G4nVrKWKY0IaCAW2El0AGFdtUjVU9O4xF4RrQYw9PH8xqNRvZP//RP1mg0bGtry37yk5/Yn//5n8fMzP74j/948vnnn9vm5qYXrkMnZHBrPJq9B5rULGnOl89guesZMF/Wyn6SlMZQD4KEhr/5m7+JmZmlUqmJeiJ4MyREpNNpD0NoY4t4PG6VSsV2dnY8Mx1+w+Im+5FEJPaB3wO36fxoyPDq1Sv76U9/6o02xuOxdbvdmJlZoVCYhPH83d1d+/73v28/+clPXG5oTFxh3RAiAxZGxuAB8gMP4hVrmQDhA/6PsALe11/91V/FfvrTn044B+VlDRtoqzaF0fX8oBlqc80ekDlFcJQPVBZpfBHe2tvbs//8z/+0Tqdj8Xjcfvazn8XMzD7//PMJNde0G6UdI7eZaFiDphpkwKqHnEqlrNfrRUJUQOLwh+aJUCbDnkDPmj0Nnff7ffu7v/u72P/QwISbjTY2Nmxra8vPcjKZeJIo9aeVSiVyhZxWUWiHIQ0jcQZcfhFm8Gv4R8sOtd/v1dWV/eVf/uXU+pEPKssQtlhaWvJrtVgAvyf+sbi46Avk4M0ess40g07hGA6B5urNZjNyI/f8/Ly/Q9OQEfaMMK6lSm4aXMb7QzjR7KHNnkILYcIPzKKHpoynCgtFq5Cc2cPlwisrK5FkGeY0HA5dMIfwBs/lO6qgVWnrvBRNUGGsz9N4AfsL3KTQUfi9UJgovKjQZJiZqP/WfVTGu7y8tEaj4TdqINRQztCd3J+y5AAAIABJREFUClYdYVxL6+egMZRHOLdwXkA4Gn9TIUtiCtmUhBZQLioolXZ1T/XfwJSqnLUxAAkxwPK5XM4T1KAfjWHx/7oWTUpSY4h18o5MJuPXS4UJKTpfhdivr6+tVqt5c332eloMUmF0+ATlp5ApeQ4aRqDuEbpWYyyTybii4LmqfBGuamwqXymvqwJUGBlZoXPn2ZT6qIzRDOyLiwtPTqGeWEM9OtSoC2WZzpV3YRDBK8ggTXgKcxsITREao+4Z4xnFOC0HQXmZdaLo5ubmXFnBCzwHPrm4uLBCoWC5XM4KhYLnn6juCNfKueo5Ibu0HAm+0H3Ueb9v/FbKkgeQes5tBf1+PyIg0f7EgLAYSD5g0/hhUQiXbrdrtVrNjo+PvdcjB86mEqvDE8BSZGgcKcyO08NUAtMsvBDfxyLF49H4kx4YBKOMCkGxBlVmYSAZj4FCXwQYzE6Jira2Yl2hAtc1qnegsS8VyhAf3yF5gPPTxIv3jVARsq+0qws9dvYG4oWh2TfOA+OJJKlXr15Zq9XyLMVk8r6hQ7FYtLW1NTfONIvazCIMTexDk0doCkHrOgSGfl/jvhpDhBY4/36/b91u18uYms2m38ZCbSPvoU3eNKMlPCPKFKB75S+8VYQLFxkgEGmBqLRNYosarZpUg1LUPITx+L79HIk7nEE4b6U3srh/85vf2OXlpVUqFctms14Iz7M1Mxb+1ZR+4q14Mf1+37Nu8Qq63a7zMWum3IDbUzQhjeeRWEinLfaFRvTINpQgikKNXtYKUgVfKLKka4NOSb46OTmx8XjsTomeVYhGcGbIRGhVlT00jNGAUU6nMOZOdyTmSZ7H9fW1HR0dWa1Wc4MP4x1PjfXSyFzjiMo/2uUrHo97NUUul7O1tTXPQ6EC4Ntvv/UcgidPnth3v/vdyH6yL2FMWOUyqIB+ZjKZRPIQVDnGYrGpyB3jg8oSJmYDqH8aDofW6XS8Ofhkct+yKp/PR1xmCBKLjwmp50ayBRbh7OysFYtFhzNUQFG+QN9CiE3rLBGKWqaAIsDV1xq4sAmBJo+YPSSGwAyqfKkl1V6Lmk3K+jk0CDZUVolEwoW1JqmMRqPIfX66d2oFhwJbhbgmRcA8zFGRA+ZL0ozumWZM6j6zDk0MwToMmZc1wbisiXVrt49+v+/rq9Vq9utf/9r29vbs9evXtr+/7wIaCApGOz8/t2q16u+Y1jMYBueWGyAl4GOgou3tbSsWi97oG4XBOmB6TfxiHvv7+/by5UtrNBouzDUBIZ/PW7/fd28YxWNmkZ636mEBLwElXl9fez9NlMnq6qoLwV6v5+eGAABa06Yc0DaXCyBkudmGzlW5XM69Vfqivnjx4h1BHo7xeOxJIPv7+/b8+XO/fAGInm5YxWIxckUWtXaaCFiv163ZbFqj0bCDgwN7/fq1Z4gT4qAEaXNz0y91aLfb1u/3I+EWM7NOp+NGDHSKojg/P7dareaOAA3lEbShAXl7e+v3rfJvmg4ovAzkjixDro1GI9vd3XXP28zcQKHVJnQRlsSYPTTHIFtUuyhlMhkrlUq2s7NjT548cUONiyOYK/xBmOuf//mf7fT01G5v7284oT0o3jPJRJQjKa8o3Kty1sz8rttqtWqFQsF6vZ5fhYeMur29dbqZnZ31z4aXFkC/3D7DlX5A8JrohGwnDLGysuJ7p2jCtPFBZZlMJiMF0+l02m5vb10IAJWORvclF+Vy2VPa6eFpZg7fwKz8H0SDm48QpD1aaKWgdCBkLp5WQqJbDoIegkZo000Gpsnn8473Ly4uehmGCvYQGmMOHAz9QrG8NF4KhKWYv0KzEJJ67NzAAvw4Ozvr1jz3AapnrHh8CE0ApSjUbPYQ41GozCyaos45KXzNdzV+q7Gd4XBol5eX3txAuySpIUF/zM3NTRuNRraysuICgs8Ph0M7OTmxw8NDOz099bqtpaUlF+A0ymYNzIsYOkP3BgMrFruvMwO6JR5KFyhiJ2tra34pgMbZ1Sghs7Db7TpMHI/H/VJtMvNQygqrYjyE3mx4DmbRyw3CHr/Eci8uLlwh6V2hepYoXDyyer3uFxejHNrttl9Wnc/n/dorM/N4J/Q9bb5qACwvL1s2m7WTkxM7ODiwwWBgqVTKKpWKlUolK5fLdnt73xBd41PaXg1lSYb53t6e1et1LyGjxGxxcdGy2axf9TWZ3HcbazQa71wiwLkg49irwWDgHhcNQbipSC/KDvMcgE95HkocoxuFpn2GkQW000R5kgEfj9+XRygth2EV8iHwALVuHTmut3rQwB4a1J7cKFv4UOHbra2tSDhI61MVFZpMJhHjWhUXTgoISafTsUaj4RcEqBEA2sTa0CGcmToV7CvXMCJvQe3IPaCvL59VSPt9/Gf2EWWpMTrulhsO76+Z2t/fd6ucRJher+eWHck//OC6K46v3TVIglBXGGWp9YJYZvPz8w7FKKTZ7Xbd64EpVMGpVZRKpez8/Nyt51Kp5E3YFSJmLzg0vJjT01Nrt9uRwmkweb4DMauXF8KwQER0yGg2m24EdLtd7+qDJQ6j8qNEybM17qh7rzCill6w3lQqFZkjZ6tWXBgjwejhLDudjjMcFp/CrkD1uVzObyJhj2KxmCvbq6srh4E6nY5Dg8QyEN7UDwLpYqSFV7fpHpmZW5zEQ6gdpraVYmuU5GQyiShM9o3PagcneuHG43GH/4C71Xtn3QixafE/hlq+zIfzQCByFir8idHAS4lEwg1UkuRqtZojNNAGiXaXl5cu3MMOQJp7MG0QU87lclYul+3y8tIVD028ufYKr5hWkpROAZPSKYYfeF8hXxLiMIAVdqW4nb1krbrnxM6A583ME8vYLwxhbe2JIGbN5GiMxw9dgEA02Eu9TQcDod1uu7FCjBWD9310gaEO7A99aE2jevh0KYvH4+5hwt/I8X6/7yVqKG1udsE4wMAMbzbROCmD8+UMiHmDaJycnNj5+bnd3Nz4/rIGjA5QB2BkzRUIoecwzKbykBuEoCu8b1CX940PKksN4HMzBu2HXrx4Yblczj2xk5MTu7u7s0Kh4IXPMLF6hRSIjsdjJxjgAhak9VYcIIyA0lpeXrZKpWKDwcDOzs58zgcHBxHLpFAoRGIh3K0JI9RqNVtYWLBMJmO7u7u2u7vrwkAhQza+1WrZ2dmZnZ2duedCFlwsFnun5ZgyMo3bwwQfrhaam5uzXq9nb968sVevXtnx8bGdnJx403e6+ScSD5fkFgoFq1arls1m/XYCevpi+WJlaqIOSlCzBe/u7vwdMDwQITFqs2g8FE8GBUdGJ7EfvRkGhQlBzszMOPyCEic7r9vtWqPRsP/6r/+yw8ND99qfPXtmjx8/jtyXqrVoMzMz3mBAOw7pfqM4yNjFC9HEMeJXw+HQW3VxM0w6nXYBSyyt2+16k/xEImHb/3NhMoXQ2o2GWB/zBjLSWPQ0XtTroKgFVW8RDzsWi3lYAD5AANPBhIJwUCJ6MaP46Kt8cXFh3W7Xer2enZ+fu5GiHV7UKNH4PH9fWVmx1dVVe/z4scsCEn4wPDudjtMjFwpgJHO2rVbLlayZudCE1hDECFDNp5iZmXFFT0ye8zN7gEwPDg6s1Wp5/SpnRkIKAhZBDnQPfxKeAbYEfmWv+/1+pGUcqBzzAbG7ubmxjY2NSDIcIwxPYUjiBWL0k5zXarUi16YNBgPr9XqWSCS84TnKvtPpOE/2+31HBNLptJVKJfvkk098nTc393efEo7DkWItCsOen5/b/Py8IwfwRLfbtZOTE6vVau7lZbNZW11d9XhsLBazVqvl3mqlUonUlmMo08NWeQCjB+XOftPnttfr2evXr63X61kymbR0Ov1eHvyossRCWltbs1Kp5FfUzM/PO3R6c3Njm5ubfo3S4uKiMwExH6AfnqeHyCFzMSwQ68XFhXtBs7OzDoVhAbBwMGwOiMQEhUo4BC05YIOx8F++fGlXV1e2ubnpfRmJE00m9/cunp6eeqp1uVz2z8zMzLgn2G63I11igB1gVAQeo1qtulVKwBxBG4vF/JLqMPuOONba2prt7OzY1taWlUol3weEf9jooV6ve6cV0ACsVZge64yidZJSoAsUPlYtwsrMLJ/PW6lUcohqNBp5ic/JyYm1220bjUZ2eXlp9Xrd41YYHTASCT3D4dCy2axVq1Xb/p8roBDgGEIILjPzm1nUAtdm2JoUEY/HvcdxOp12j4RkDTxlzvTm5v4+UC4Vxugjhk+bRdCSer3uz8Gr1GQRjEjOa1o2Hu9B6CeTyUhiDYYNXgroDMYNNEfckRZmNNeGD7XVI+9AsREHpR0Z9Kx0zFDjEq8I3nn+/Lm9efPGWq2WGw3EjqHJp0+f2pMnT+w73/lOJKO+3W5HvGn2REslgNGbzaYdHx/7Xs/NzXkSGDdcmJkjIN1u1w4ODmx/f9+F7Wg0cicApIpzR3Cz99xeM5lMIu88Ozvzy6n7/b4lk0l/BmdPr+JisWi1Wi1ipONhA6fruLu7c29sfn7estmsVSoVe/TokRUKBZufn7fRaGRv3ryxs7Mz63a7Ll+RCbR8BE3rdDp2dHTk3cYwbGm68vXXX3t5SiqV8ngxewPfaK6CmbkihEYXFxet1+tZvV63X/3qV1apVKxYLNpkMnEkirBeuVx2XXR2duZXK5qZh8k0/4W1YfzpRQexWMxev35t6+vr/pxHjx5Zt9uNhDWmjQ8qS5hnaWnJL+gE/81kMm4ZIcS1zgdlBEFp4slwOPRSADBuoFpu2Hj9+rXV63W3hqgtIxYDdIY1zdAAM23vEDJ4EXhAKG7qvkhWaLfbbqUgOO/u7qzT6URgADBuCAeIBbi3XC5HeuKamQv15eVlnzOQHQxGwJw1o1jwAlFUsVjMMz5RioPBwAqFQoSZgauBrlqtlnW7XYfDsLzYA25WUUhCYT3+zTzi8bh3jjF7KNFAgGFxQtCxWMwVDpY5yjiXyznzm90Lerxnkm4ODw89MUKfh4DXzlEMDSmwVhI5gAZJUiBRRhOooI1er/fOXsDIMCV7f3Nz44qUs6fGkb3Rq+o0iSicu+45Ho7SPfTFvsNreBIgG/SEpTm4dsoBhmUOWOOTycTviY3FYp6liEeie0GcEP5AcQ+HQ2u3246YvH371uFpaBo0gJZnGJAY0xqSSaVSHudHKG9tbdnx8bErp5mZGSsWi+5l1Go1u7u788vbeTZ8hcFEXAsvGIMeIQ8yRR4GZ0lZDAYfUPbMzEwkzAFCxn243PpSqVTcayeRhXtSNYlRY3l0tAH5owqBpuR45JR7YPAhG9QDxtO6urryPQd+xhjDgCY5DdoljESryLCzWph1ylVi8NyzZ8/cIWk0GmZmrk8wGqALFK/SPjxBYhIK18w8Lq+Ji41Gww0LkCUa979vfLQ3LAk3q6ur7nGYmQs1Fq9BcoQHjAfjanxGa86IqeCN0k+VLCzc5pubG89gGo1GkRpAhmZKAXWYPTQkBp8HZwe2A0ZtNBquZPke6dJm5t3qtXE2JR0IeJiqXC67FXd3dxchKG2OrN4yiS+ZTMYFGAoJgY+iZK/7/b41Gg0XZNls1okYT4ozIQiOYsUj1NpDPVMIDMYyi94zp7EwsweBzhlgrZIMhSGB4cJlsVjQ6XTarq+vveY0n897Y4uNjQ1nIK4+Ui8L5Y6g0tIRnY8q8X6/b/V63b27lZUVPyu8U/abRC1N7tHsa+gfAwXlenJy4h4F6AZeBhdjazq7DoUSOR/ewe/ZdyC7MEMTQQ90D3LBZxE2WOl4fOFcyEAmxqNJbAyQJLy9u7s7F6o3NzfWaDRc2JXLZTMzj91xW4mWiMHHmmHJ7xBu8MfW1lYkhlwoFGxzc9PMHnrWktgEnVPgzq0z8CZ8D8SOYQ3qAfSHgYvQp5AeZXx5eeklEUDXGBLsHTReKpXcEADNwavWjGNt0EI5CGcxHo8jVyPifYISILPhF2gb/kBWAe2/ffvWs0uJvYJoYcwvLy/b7e2tJ4MpFM9QIxAHBZooFApWLBbdMKXONJPJWDabfad8DR5EFuFRQuuKkmh2OLKU8A29vD/99FPf61CfROj/vb8xcy+FjDW9yw2G1Sw+LSfAWuTANA7G4SOk2QwWYGYekyKpolqt2snJidXrdWfycrnsQoJBRhkWF4JmcXHRtre3rVAoOCTZ7/ft6dOnLrAnk/sSGE29JrWaAy8Wiw7vYvXh7QI33d7eXxJbLpc98yqRSNj6+noERmIQO4NR19fXXSmgiAuFgl1cXNjh4aFnyBK8Z4+BoBD6CHyy3GAgioJRUGq1DodDLxFCQQKDo4xUgeKl8f93d3de68b5//KXv7Rms2mDwcCv1ymXy/bo0SN7/PixxWIx967n5+etUCjYxsaGw6PazWNpacm++uorPyOyQLF88chRWAyMNQTezMyMe9f7+/ueYZ1MJq3dbtsXX3zhng9xVkVKYE6MPuons9msJZP3l5djORM7TafTtr29bQsLC/b8+XMPM+RyuQj8qwP6JZ0eKFkFD7QEvyhMD4+mUinfe5KaFFFRJYxXoOn2JGcgkLS+TgXh1taWVSoVR3hI7AAyJk6WyWTsyy+/tF//+teOMmxsbNjS0pJVq1WrVCqeiEFpj5m5gkRgq7JcWlqy1dVVz7ZeX1+3J0+eWCKR8HtkE4mEdyQzM1tfX/c9RzlqJmksFvOyE6BYzp7sTpREuVyOKEvOLZPJ2ObmpvV6PXcQNIyCcVwoFCyfz0dyLGjcoHFhjPtEImFXV1d+Bm/fvvV65G63G7lvGFlCEqLCukDC7LXW5JIZjmGAfAaR4pyur68tl8tFPHNFppSu2ZNcLmdzc3MewsChqVQqVqlUbGtry6rVqhu1YcYy8WHNKoZOyPYtFov26tUrD/0kk0nL5XLWbDat2WzaZDJxJAkj6X3jg8pybm4uEodEsWkWXJjhqR6kZuWpUIXYdPHLy8v2xRdfeCyl0WjYzs6ObyTxSWIgjUbDiTKTyfic19bWHD4j+4zYJFDn2tqaF21vbW05Qd7c3Hg8gXngCaHkUeLAWlrEvbKyYp999pkzGx4SECPeaJgNS4Yr1lypVLJcLmfr6+u2ublpz58/97jOd77zHbc+x+OxtVotZ3zOC6EIhAEkiMUF0SSTSdve3naP8/Ly0jNv1WolTq3QMZ6VmblQvLy8tGazafV63eeTz+ftD//wD90SJ3kKQq5Wqw754EVg2GCxo5QpqwEKMrs36Ih3ghKoEGXAmOoR4SnhsfZ6PY8hPXr0yOH2fr//TrJWmC1MDA9lmU6n3WhTesFSrlQq7qER1gDC0qEIArTDPDS5By8VYwwoj5pI6g1JoS8Wi/bll1/a0tKSFYtF29vbcwMID0NjwZVKxdbX172D18LCgndWUWX59OlT29zcdEON7HSE5I9//GObTCa+x8D3GDFLS0u2u7tr1Wo1go6guFToKwpCqARF/PjxY1dcMzMztra25vJDjXY8HHge7x7vDiVMPFpDDyhIFAF5F7xndnbW4dR8Pu8xehCaVCrlkLB6ohqWCOtCzcy9auQScd+rqyv79ttvPSsWj5kEwJWVFW+dB/Q6NzfnaAoJYXruGxsbfrFDKpWyR48eufdPKOPs7Mwmk4k1Gg1PVAwVsmYip1IpT6hLJpP2ySef+EUTqVTKnj17FrnQAMMfrxfEAVSHfYZPgN1R2qVSyarVqjey2drasnK57PRULBZdPk/LGWB8tM4S7+N9Fq+62hqXVC8zFoveh6cJCwotAkfyTopVwd813ohVG3Zc4Ps8F6GFl0QXDSAfTU9OJh9un9d0cyx6s2iHIA5QY4kYBVi3CG/6FfKj+6nF4bpffKZUKvm6Cbpz4wjeDN8B6tW4BsKKJKmFhQXL5/NuHABdcAMBdYe0NisWix5nC8+etbBu4lp4CwhsjY3F43G3QrWrBwqaZwEbcssCpTW8EwNDu8woBKueJQXvmsW4tLTke6oQUng5MgIQD16VF940+3F3d+cQEgYbkDVrxHtfXl72YnetMw35DNpEIWBkwgMKwyHoUILZbPadJh8kzWxubjod53K5SBYz3hznuL297QYEyqrZbPoZMkqlUqRwHI+FEMEXX3zh/15cXPQ6W+i+WCx67bOGdszMoUftYoUs0dpD3QNtYahlP/AWc+HstAyMOmFoTmFwBL4m/KBQiNVCoxqaIPlE27thmFOKAq0Q01TD1OzhukTCPSRhgczBAzMzMx7rx2PFYAdt0+4/0LvmEvB+5MLm5qbT2Xg89preWCzmxglnp2MwGLjCVzqcn593NIB5gaSBNoFqQP+E38hQRuYzp0Kh4DFLwmokp8ZiMadZ3gMCCI+9b3y0zhIFNi2tnQXzAghiWkYRB4KQAdLguyhXBCXZXSRJ0LRZG1NjiasHAVMggJeXlz1dmw0GUkQIAjVxqBwoa1Ji12xUYg4qKJk/ayAepd6pPt/soWNNmEUIQWcyGc96w6uE0PFK+A6/5+Dx1mDmTCYT+f9UKhVp96WxDK45o2sJe8Dc9fxhUOKOxIcRxJQTdTodSyaTno4ODMjQjE5iPJyZNj9HoGCIIAQxckgAYuTz+YgyIeZC1iExPOgHY2symbiQxvOCDhRZ4fecscaxtfwBw0qLpNkv7XrF/qIoEWqqLBFMSmcIARpYEEcjJq/nRowsnU5boVCwer3uGa/QMsjNzs6ObW5u+nO4LxaLnJFOp71Eg3Ij4DFiUECdID7qMdMgfpqVD01j8GhTBOBhzhPa0QYHmliFMMeQ1PPkOYQtFDbFgIB+NJkqTGyCV/R70DwKk/mjsJmH1tQq3fFM1mhmjogQ2y0Wi670tre3XcFxDhjnyHaUDM8EscNohgeQRXjBhHKQP9RiaqMHhmbc8z4z8yxtYpLQHDTAOZGgg6zRDFiULnKYvAAg5GQyaaurq27UUBa4uLgYQUc0pDFtfLQ37LRCTY2RqFLRZAhcZDabxbFAYFWIX+OcEJbWMLEBKEuC1ihQJSQlTuJhMJPCwmGSAtmVCnNp0oYmdsBUmjSElcjeYfWHnkg4UJbMh++DyafTaa/FJH7A3qOktXYTptWMNzN7JxEAT5s4Ch4iSmh1ddVr3oC/GfoOlC/zNntorB+LxXzv4/G4ew4IWZQ09AM0CTOhvBYWFrzsQYUXQlWTFEh7V9gY5aRwLclP6gUgXHkGMA9el9L/NKQAyJAyIWJ00C7eigpf0IB4PB7ZYzUcUXr6/6wJKA3rmUxFzpqkMhXixLGWlpZsbW3NSx/ChBSUZaFQcCFFHW86nbZcLhcp3VpcXPSf0Wjkxhhwczab9Tgv4QtFCsJ6Yg3vqFGGsqRcAfnB5QskcmEQo+AwOnQfeDYGPAp8YWEh4gVCLxhoNE2AdxVtgRZRaBirGxsbZmYepmGezI93mVkElVBDW40eaiPH47EtLi7aF198YVtbW97EhBt26HDDu1kLNKsODMlK7C3JdLHYfb0jRoLZvXG0ubkZcaqQd2r4wccoQGBZ3U/WpA4HRrAifnwWfYIegb/x0DXDe9pgTtpgY5qj559/72/soVM8k4G5+UHQmD14NVraoOUS6ror7s/vwjozLDr10nQuCEUlLrOHbg0Icj7P/NVKgzEQYJR5KLSolgabqdYfh4X1qpvNOyHID+0zGV08Kx6PeykNxehAdhAlyvTi4sL3gYQoNQKoq0PYaKLW9fW1C3MgZ4gIhsPQ0X3mvMlcg6g5XyxslCxWPUKGNQBrmZnDYHyXZB8KmqcF4JWG5ubmvJwJgc6AwTVxAi8JKF/rchEcKHtaf0Gn/A4hjTDUkgN+j4DAUleIGKWnMCADQYrFvr6+7jE5+raiDCkLgW7NzJWbzk0zMbWucmVlxRPmNCtWlQAIBPOEL6m/hW+1zEBhX01a4zPIC40HQ6O8l71TxIh3MQf2kJaR8FyYXarnpvJCaZrnLy8vu9fK5/i+eoI85+3bt45KhJ4VdKJZvRgF0CT7Cg9pdxyVQ8ydfaFdYCKRcGODpEE1CkA5Op1O5ByRtdAM+5bJZGx1ddVzHW5ubqzZbL4Do6ryhv5CZQn6Qc6H5rdA42rEwI98VqsCyGNANyCLtaIAua7JYewrxgJyA+dBEcRp44PKkg1gYxTCYKK6UerdqKeh0BmThimnxRMQHBr7VLyb94eby3tDlx+lhTWkv1N3Xj1h5q5BY+Y+zfpQCFWHeqPv+5yuQ71QLF5N/gnnhRKEILByOSeUkcZpFKaCYLQEAmZGoYWEFCIGzEnhFV3jNAEDJMRZ8iyeze/ItENpqbDU2LfOURU/A2tUPXEELWtAGGP58nlow+zdNnN8RpELaJ490fPVOD7ngGEEahIOhY4zmYyVy2VbW1vzDlpan8tQRCQWi0W8KtaBB6WIEHShdBqiAzzXzFwRMa6urrxxg3oYasDiOWhiF+emOQS8G3pUT4j5KaKgsTylsTBUpPyosL/CoYo2QJ/ILP0JeVWN7FAGYXAxJ1X+0BlKDL5B4KvMUb4hiQVFAi+zfoVsVQ6o8xMibRhyCvUPh8MIIsIzcHRAL1gHPMYABUAZD4dDl/3IMDWwzcwdLBCD8Xjs/xciWfAW+43sUnmBfGBvWIvKBfXgw/FRZakTCBUmRM1mq4BhIVoTpYXUIZFpCroyOkJZrRXepwqMoZBkKNT4u/5ojU7o/alyUchN56G/V+bl3SrEdd76rtBqVJgXyxjiDL+nHgDfVyYIlZd+XglKvWVloHBOZhZRqjBmuN/KgHp2MIbZw+XfKsx4Fv+nJTJaWxnuBQOFEyIO1AeGsB5oAB4gwlbnoowZKksVwtCeMr4aMrr3CjepoAyVpe4lAocewUCjGnfXpCNNllI0gb2nLAYEgxIZPHilVz0ThauIUzOAV4G2NPEGGmTNPEv3LlQg8CE0rWESRXq0NjBEg0IDXM8UY1x5mR8V3qxBk35oUs7AGNGzYJ0zMzO+Nxpn9XpLAAAgAElEQVQbR4kxP2iW94eeH2uCHohZo6B0Tbp+TRDT/eTvyA0QBzVeUMbTmlAoUkHijTo6DIwwnoXcVcOSvWBe7C2fVcNa91ZH6Oio/FMjib+roasZ1tPGb9UbVplciUGZCWVJ2QSEAyRQLBa96TgHRMxiPB7b8vJyZEHq6eifGsxXAc9QRmVzIX6FXNhAunOo1cGGq0ehikQ3mqECXxUqSphnTxsqUFgf8QpgKfXMlNH1GSoc+T+UE4Sjwo9MMpQlMaZpSh9mZi8wjGKxWOQqIAQ3zEPHEyxkrb+F6bV+D6HMXDhjLFsaKmuMQQ05aujC1lXEMFCm0KjGlFTQ6n4DgwHzqOGgClSNCPVyVCGHgh74jh9NSlJBavag4MIsWN5zd/fQp/bo6Mji8bhfK1WpVN5R2jrvN2/e2DfffGMLCwu2sbFhjx8/dug6FnvIQWAeoXfJYB3srcb7EEYIQaV79pIypdnZWfcmEGQoK6VDVcAoArMHQzeEvZXPzMzjqSHag7BWz3swGPiNQNCzlnawZj0P0BAyiLngWZNXoBdVRNAf8WOVHayb2lmNA19eXnoSEfNBLrCXahBoXJVz1OzaXq/nBloi8XCzDfJPeYg9UoONoWEA5AFKclqSldmD/KXGVRWyhp3Cwd5oOJB3qS4BMcFQRca8b3zUs4TA3gc/8jssLmA/BLwSkmLKEONkMvHNIKbAIrX4N4yFwjjc9sC4vr72lGP1TENhBbOrF6rxTJ5FXEshF4Z6ThwQexJCr1h16oExyEKkmw71neyJCnRVwjTLhpnIhONKqMlk4oX9MAuJGyrk1ehRi9/sgWA5A7MHj0vhI7MHaw54KJ/PW7Va9V602lQdpqIQmZFIJDzT18ys0Wh4NyWEL3CMMgT70u/37fz83MbjcSS+iYFnZu94BSF8Bnyn6MA0T5nvInTUyNH4pbaRUzrg89x0QluukL7YT0paoEcS0njm8fGx7e/v+w0Of/Znf2blctny+bzTlQpjrVPc3Ny0f//3f7ezszP75S9/aV9++aVtbm56kTkGi/LmNAPw4uLCE06SyWSkQQWXgGNQESZQw1c9coVtMRq5Auvm5sZOTk7s+PjYm3xnMpl3oEdoFZg7NFy1qYHCtyorUDLIOG2XSXxMPTidPx4bSpVkNowGPk+MU9dr9tDTWAfzRzmNx/cNWjSkoDCx0n2j0XCFPTs76y06VYnhxIxG91fn0doQ5wO+Yh6a6Y1RovAoQ+Wuylo1yBW1URmHfAWBxIDDOaPMhr1FOYahPTWKFMGbpuDD8UFlqYvRgGxonWE1KoSAsoCZybQjgI2A5GAGg4F3/ZiZmYkwsi5MD0gbhDM0wUDx6WmQqh6a/sAkbCCEgeWqsI8St+6XGhhqMalnzDg8PLTPPvvMjQAEG/uL4lALl3XTxQYi5hYBjI6whRrlIcxVLVoInM/rfLTXo8KydFPRuGEIw3J+ELd2MsFi1JirKuVGo2HHx8fusY1GI8/61KxerHisYfV+eS7zxsOE1lQ4MdT6Do1FFW6hxY6AUqUAzWOIIKARBsxfywUY0JjCmmSD4/He3d0XlO/t7dnh4aGdn59H4kHadF7pH97EmAUl6Ha7ngHMnaGUDul38TCUllutliWTSbu8vHS+x8NdXV2NeF8aX1OEgDVqVxZ4gIQzjK7T09NIA3M1kDgLaEMNXpVdH+JX/ZPzQmGS8AUkqgk70JAiPuwh3yXhSz1MNWagVWqBlTbhEzxKSj3IAFdHA57le5qFqtngmtjH5+kVG3rCDHhAIXXt08rQ82Ue4T6r/EXmYESqBx+iPv+fvTPpkSy9yvCJKefMyJgjp8qsudvdtps2NgaBhBD8AZb8DCS2bJBYsmCBWLHgLyAEGxYIkJFlG7urq91dlVWVY2TMQ84ZI4vwc/K9X2VVWV77Sqnqroq8ce/3nfE97zkfcgmy0u/3I21Dqtvqv8I9Vrm46/qgs9QXUGcVCpamxCokCqnSDzSZ3J6biACG2Un40DgrhEidpR6MSgaEw9RahsJQ+sy6cAo7h5vHvfQ9FfdXAlHojHUz1CGYmR0dHfnINn03jKEKA+xXekfNzE9ZQdC1jhKyjLU+pRF1SKrgHdW5YxR1/QaDQaS+FQZYClnxXoynMpvWEsmidb2pp1UqFXvx4oW3crCH9H1prYFB5swaVWapMhoxtKyTMiD1T2SR/cdZkUEgo6wPn5tMJpFaMw5A5UDXRMsKIerAd4EuMG2JLBrHUq/X7fXr19ZqtWw4HNrm5qZnEmRhGHKNwDHiW1tbnulcXV3Z3t6eXV5eWjab9QMMyJ50TcLAjzm7sCS73a7Xwba3t52tytppPVsdBAEjckdmQX00kUhYNpu1k5MTb3+iX46MDn3SIBW7ps5SIWW1axrAYH+wOToXm9YdpgWpraQmDNJBkMWRc7rPZMKsCeiBDi3R56f9hIEGjN5TG3WXQ8JWgOBgx6hXp1Ip31fOOyXwQ7c1iWE/dN3f5yw1gw99zLvWWp26JmvoG0gjZRxkUIfqhIFR6KTvQkr0+uBsWI0m9UUwhvw7DkkJCyhTLBZzA6bFbuqXQJ1kiBo9AIepQcPYYBw5V9LsdsK8Qh8qNGD46hh5dpyRCrAaFtoadHO06KzOhIUP1w4hUkNOIzjEAaJJsja+//Ly0hqNhh0cHHhUywnzrBuQmSq0ZlBzc3MOvXS7Xbu8vIxMy8BR4ZQUz78LNmEd1bEg6JPJ7XAAvrPT6Vi323Xm3fX1tR0eHvq5iolEwp4+fepw27Nnz+ynP/2ptdttn0fK98PSY02vrq6s1+v5qC+99DQL4DCVMxRenaRmkAQnk8nEs3eyDf4NmJj6LQEItVetu6t+oGdhZKuwHnsKfEqTNsEB5xCmUinL5XK2ublpP/vZz6xWq1mtVrOzszMrl8sRJ4exHY1G9umnn/rJF/F43A/d5gQHjsfTTAp9U1l+/fq1IwU4vrm5Oev1evbkyRPvb8VJsb781Ot1tx/0fBL4plIpP9oL+SN7brfbNjs766cjoc+auYcIgdoFDda4MMbsXb/f90B1OBz6KMH19XWfLw3iw/dqQDo/P+97UK1WI0MM9Lt5P07gUPvG31O3JvhE1nTesyJsyEy/3/fgGlgfZwSBjClnPGe/37fNzU178OCBQ/qsXZgEYDfCYBU7y/ewtiFCoYkJdhCkCMg7kUi4XeHkF7L10WgUYeUSaDB0xMzecrjqLH9jGFbp91ow1yiYLwrrima37LC7YC5Sa4X/9Aw1nFro6RXexeC3Wq3IZzAqOLLQSepCad0KIcDRMuy33++7Q+PdcApa6woXOoxaFDZR6FidMYJKtsZRSihFo9HwKJL1/fnPf26xWMwymYzd3EwPzx2Px264iQ6BxLT5ntmyHCSdTqddwTHQBCDU04h+77q0jsnvc1pBu922o6Mji8en56Ny0sAvfvEL29vbs1arZZlMxiP68/NzOz4+9iHs7D2HEjMcGUOmp8Vr/ybPbHabNeNktSVJI18UmvuMx2Nfw1Qq5afJcHitHuvEUWhklxpNq3IqPBhm73qF0CtoAQYQw1gsFv2MvnK57Gegsv4adILo0J5TKBRse3vbxuPpYHcz83mjGP+wwV8hZtU9aujoGG0FJycnfg4hz6II0cXFRaTnlAOqOc6v0+lYs9l04uDGxoZnOXNz04PTyayZH6pIE2upmVEYVPFsWsNinzisnsPYCSDgDfBO7C36bDZFLxYXFz0Y590V0SEAoawCUU3tFBelGD0aj+CUZ2ZtzcyPoXv9+rVdXl76AAiIPMDX+/v7VqlU3AZoojMcDu3evXs+cUeDDgI3etWx0VwkPRoMajIB4hiSR7l3tVr1Es7s7Kw1Gg1HHjiFinVjIAj6xpxj9OYu6DVEH+663uss78JvNapDqNkUXRyyGF6AxngeUiEdsO4QnrwL8kWIERayMS4gBBh5RBkoiEKMPKduEIGA1vXC7FAFgAxCgwkVBIVMlKykz4wgosi6YeooOdqIczYRiJub6cGsR0dHFovFfNgxgkJmcX197QaVqPLm5sa63a6TpJQIMx6PPSPk+3kmzST1vdlLjWrJfDqdjp2dndnm5qaPPhuPx/bo0SNbXFy0drsdqcFx7qYaeWa89vt9j3r5/sFg4M5yOBxGJk9h1BXiUuekcLT21vG+p6enVq1Wvda6tLTkQdTr16/9gFom1rDfd8E/KsdKblEjHuoaWSAyjrOktg0zkgOemXXLXGH2RFmhqVTKB0kjExh6Tu8BMicA1QBWa45cd6Eqo9HID0c4OzvzdUB+Ly4u7PT01J0heqKMTGpnZFnYE4g9DEZADtRYYqNCOWVvWWfV4dDu4PySyaSPcJyfn7dut+v6ARTM0A0cLFk0g9GxidwXnQv3X9tG1Fmi88oIZZ2VxMS7Izuqh2a3vccEIJDEGIzOEBSg84uLC6vVahaLxd6asqTfpegjFyQoRaV4l9DOq/wTZIDAdDodSyaTbosI/NHbcHoZ60n5hP8Ov/MuZCG8Ptg6wsMrPBXSsmGkKqTJg5M5zs3NRVhHaggQGq0pKmEEQVDoCghWiTAsHtHw7OysZ6vUuzDwbIQWtzFA/B3OOMya2QQ9YSB0HPrcCJA+f+gQcS7xeNy/SxWAyIvxY8q0BFbkvMh2u+3MYkgQQN5MfaGXbjKZeMDR7XYd3+e7ieqBUs1uiRNKftH6ndYu+/3pyeyNRsMnhzBCD4X77LPP7P79+54tnpyc+CSeyWTiQ8ATiYRtbW05/Hp8fGxnZ2fu3BKJROScOjUwnByi76bOSglJGDSNkHu9np2cnPgpGjiWy8tLOzo6irBW1VixVncZYJ5Bs96wjoWeYKxxWEBPlDl0Qgr9cPl83g3tZDKx4+PjSNM7TMeNjQ3v2RwOhz5ikhnMCimiI1qL1UuJUaHhr1arHpRhjAnotHWGwAzClgZqqlPtdtvK5bKjIsgk9iS0LyG6gHyGyJful34fZYB0Ou2Hz1erVTs+PvZAgEHyq6urrlsqX8xmBj42u00KQtvL+oeyjL1QJrEGBVqPVbgTaLvVavl7DwYDJ/RQ4waBwoYocaper0dafNTpK1oSBlFhHz3vSCBI9qy/g41Op9Ouq5RSYrGYjxoFaSMp0/5t5cCoLt2ViKm/u+v6YGap3h5DwsYBe7RaLbu8vPRh0NRoFLPGcWlvJQtCVDUejx3+CoUHqIkoF1gGJhgXsBowHs9CJI3g8r3quBcWFiKR/qtXr6xarUZqWGa3gUM6nXboRI2G1vI0oyRSxIDq55WhqVkAxBKOtGGEFeuVSCTsyZMnls1mrdls2osXL+zLL7+MtC7MzU2P7mJgNqPn4vG45XI5MzPrdrvW6XSsXq87MWUymUROWSc6v76+jkBGSp6JxW7nOI7H05NLdnd37eDgwK6vr+3TTz/10+CBiWdnZ/1IHgIslKTT6Xi9gaz6+vra2u22R8k6PxjyjpKazKbEE47P4jnVWWlmGRJZeMbNzU0/nYPT3FOplG1tbUWGQVP/JcsOlVCzWg2aQgfDOmjmgaFQVi1GQWtzk8nET1XQaJsWJQKPtbU1W1tb8yP4cGIwI6lT4kQJpkJHzhVmyVq7PDg4sEaj4b+r4yGXlpZsc3PTut2uvXnzxu1EInHbSkTmDgzMJCNOu2DwhGb1SvTj2VSXCTZChqeiCsjpYDDw8xtZF0784dxboOuLiwuH5uk151go7KHuFXYFBzeZTDwwCyHY0OmrMySICjMmbLcOojAzOzk5sVarFeGcPHz40NcqnU5bqVSytbU1m5+f91r2xcWFoxGssaKCoI5cuVzOksnbMXfKfued1S/w/PF43GcP8+/xeNzHO8JtwBbxo/KHrpNk8Z0KV2O/wjnoer3XWWpxXAVN63zAJaPRyPL5/Fv9NQgbcIVmWbqhZ2dnfjoCLEf+jaiH6JNIlFpc2IeEggI/YDjp00EwUBSc28zMjG1ubkbgUDJUMjqMKmxMhdlwMPpe/D/Oj03RRu7JZOJQjpm582e9yXBQCgrd1B3prRwMBra5uWmvX7/2QAXqv056UYXs929P9BgOh16L4/uok7Gu7CkGDweh5CvW9vz83A4PD+3//u///DDep0+fupFkLTR4wLiUSiXLZrMehdOPCCxMxlEsFm17e9vbjl69euVGRp1lpVKZCvyvYLQwGMMBKamL9y4Wiz6Mnz3R4e8KSSWTST+eazAYePCHHITZo8pACANhVKD5E0njiNmb4XDo6AB6oXVA4DcML7JHkFuv121vby9ygkin0/EDhGHMcrA2eo9R0qA4hDIJIpGtarXqPaP8G0Y8l8tZIpHwNjMgRYzezc10QD3rQAAIxEfpB/nDWLOmiiLwdzTHKwsaQ6sZnAatBN3j8djK5bIVi0WXF0oa1Kw5ZFvLJrR5MRBBoURF3AhqwvYHdFll5+bm9qgxdFKzNdaDszXJWmHQUovO5XJux2Ox6ZFpBIJmZuvr614XhUugiOFoNPKERnuGC4WCr/loNIrYI/ZDIVzsCM5Pj1RE/hUG1jJbGHQqOseaq86hk9zvXdcHCT5K8lEcOqzVYcTJDjCGZre1F4wq99SIQh2y0pQV0lJcHBiWQeMqOGqQgE+APWDzhZCy2TRL4MBRIiMMHu+GAdNGf94xrHHosys2z/pxaQ0CyEUbf7X9QpmIGDFgQ4W2er2eQywcTwXhCaHAiSs0DFFBHYaSX5ADvo9nDOstBEgHBwc2GAwsm83a4uKiE1V4Bu6JgKtBI6MiaDk7O7NKpeLHtY1GI/vkk0/s0aNHtry8bMPh0A4PD/1+urenp6cOU2ogo5/FWaJcOCCMHoYB8hUGSA0ahh3IFHnhOxTZUKUO4Wzkwuz2IAINklQGCUyazaYPu6bui9yAXKDDGCNkAsIH+8IxWnw/QSxyqffRDELXlPfnz5ubG2s2m1Yul/1d+W7uqyxO9iiEfFlLDUYx0spoZv3YW+QhrIvp8ypMj2wT1GmrBf+OjLLHnLai9TYdE0eAydB/1l8RD/Y+lK3wmXkX+gqxWRoMKIw8Hk8npZXLZS8vzc1ND2gHRaDmzfsQjLAWBG5kZSE8TvJEBseVTqedc4HsYYt0cpCZObqmNU72VZ2l6pAiW2pbeU7t/VS/RXmOz76LuGj2AWeJJ6ZephALL4vi4rE51FdJOxhlhWL5HSJ0ev/MzCNPTckVtoLYQIZBLY3Pav2JDSQ7IVIPnarCEFxay8RJKSFE4Rq9NDp6F4lDoRWEmc/yXTxrt9v1zCoWizn7i43WwQOFQsGGw6F1Oh27uLiwmZmZCCEIYUOhcAhAR+G4JyU/qVCFz69rCNxyeXlp7XbbSqWSlUolW1paskql4jR/neako6/CdYPg0Wq1bHd310kG2WzWtre3nSB0dnYWgV91L/X9WDu9wmCAz+IwIDlgwFZWVtyJU8vS2jXOHLKMGsKQ3BYafZVlPq9HTPEDGoD+MAjAzCLDOvge1lOdEJcaDcohsD459YQyizJL0Ue9j8LF+udgMIiwGAmMcSbU2zmHEMRBERENOi8uLrztTOFyjCEBBXVbRVTYC+TlroxEsx0QA82EdL80MCC7Zc+U12BmbkeAx0NnqUGIMqpV35BfnlGdI4iZZmwgeRzLRh83cDaBGKPtkA3kmjXnvWDzaguWmbmzBBXggmVPQIxtoR6qNpPPqNxqkqXrpXLIM/Nv3BNIn8RAbRXBi5Yy3nW911lSG4QkE2Z5RE7Ly8tOzMCIFotFh1W4NCs8OztzGLXdbvv9EbwQElMjSoTabred1syF40wmk7awsOALdHV1FWkK1uxKMyc2OR6PO/Slz6bGDYPDpml7AwGCRjYYWpSYSwVQC9HUGznAmoit0Wh4r+JkMok0Y8/Pz9v29ratr687S3ZpacmPKOLdaTug9gspysy8RkjNUWvKZua9TQqRhbBHIpGwfD5vf/Znf2Yff/yxjUYja7fb9uMf/9hevXply8vLtr6+bt/+9rc9Kma94/F4xFlp9txsNm04HNrGxoZ9/vnn9u1vf9u2trZsNBpFDi5eXFy0tbU1fyYicDKXMLsIHR19ojhcMm/qyKqojUbDEomEG0aM3Gg0chhToR/+H7lTeCqs/2GkCIrY58XFRdvZ2XEG6Zs3b+zw8NDLCcVi0e7fv2+rq6s+aPvm5sb1mZ466qscsA4Ra2lpyc+qzGazVi6XXX5YO4IsDVbD+ixyxDt2Oh3vCaU8YDYNZhqNhp2cnNjZ2ZmNx2M3pDwnbTIY71arZYPB7YxfMiGgWs0oydDCoESPF0NGNFAzM8/mKcPwWQ0GeFf0enZ21kcFas1Ms0UCRWwCtoRAE/KejoPkwpaqbeOeQOTcq9freR0Tch+OCvnA5gK1E3AAn6MzPP9dE5hw8iQ36njoQcXBXl9feyBG4ImdbLfbfl4x+qmJAexnDSywlawVeqXoKDVJfV/2StGXd10frFkSsWIkNPth4ajp0Rd3enpqtVotErFQ80FpaXZutVrW6XQ86iGCpWFda37UEFAsGJN6NBH1TgSaSJgIA6JAWPzGCfJuocPWOl0YibJpZrdFaLPbQ1bZMIwyaxfZiF9lcKwvQsI7lUolb7U4ODiwarXqzfAaXY/HYy+mm93OyiWihNYObEWECaEmk8m48FD706DFzHy+LI4Bg8C7JhIJj1DJTji1/f79+7a7u+t9rE+fPvW+KNZVB56fnp7aq1evnNH5B3/wBzYcDv3QWYzi2dmZ1Wo1by1Ip9O2s7Pj68uzcm+ic91rFJw1USo6PbbIB72E2vs1GAw8q9BAQhmXimSog9ZMgUsJSGbRYf1krPPz8z6EgLYZjOza2pozM/kdar6cWMGzNptNm5mZsVKpZGZmGxsbLkOsD8xOggHaEe4aPn0X4jKZTLwFh2eOx+M+rAL4eHV11QqFghM7CJTIODHO2Cacazw+PQaL1g6CdSWWaLDCvTXLAG1h7cniqbGF8KiiF+PxOGKLKGsoVKu8BRAelQX2SQ+ED5Eo7AOw4tLSUsRxqIxQs+ZeyLTC39gPM3PWPJ8jYwdWJXnA7mFfVZcUFtdn1mBT/UksFvNTqa6vr63Vatkvf/lLlzNKS9QsCfx5fjJTHCaOnHdAb3TwQghrQ/L5jTPL0WjkkSOLReSg0SIvgTHiQRAIFBNnR79Mo9Hw/imyCiVLoMhhrZKMlChAGUxArAg1zckKR+ilzCmNkGKxmBfNeQYyR8XG1QASwaih0Eycz4VwG/dEsclquA9ZFz/Q/zEQ2sMEDKJsP9YCyIRon/tqnxKUd30Pnh9BUhar1rF5VjIygijuDbyOASCIosYHKYXgAuPe6/VsNBr5wbGnp6cuK/wu5BEGx2cyGT+VHlnUuorCcTwPsqkBIXtMUAY6QVaFYeW54/G4z+dE9on0gah5nrBeCXqia8naawbDXujYMkgmOJSbmxs/KHp1ddWzMYIXng2ZQS/i8WnLwNramkfb2qepz0EZRHVKnb2uPe9zfX3t2aX2UkJK0roszoT7klHrWikJR3UmkUg4w1cD3PBSHUaHIMjgSMnatCaNfoXtbHrx/0zb4TBpnlGhV810lDsQ8gV4ZpUb/W+VF/aMpAOYkXJOPB53Zjvvhk1FtpgGhJ5jK4B7tb6rATvPwYVjp18XGeVZ1cGB7ii0qkEa6z03N+c2l7XGnsNAXlpa8jIUmbdOASNB4kefObw+eOrI+fm59Xo9rzOo8ScqIC0PFZ3eSzPzoddsHqm2RiwMA1ZyjzpLsspOpxM5SDV0ltqesbS05DUC0nwVUCUbaR2TDEOxcYUWiPbAz1Eg3ldrBlqfQZjCuYkoxvz8vJNz1MlS2ykUCh6J0S+FQ9KsjLVj3czMR0dhvIFAYEEScdKIjsPCOAPTomDaMKxOwMzeUnD2kJFf3JfsuNVqWaPRsFqtFjEoOKWFhQWfe4tcobS9Xs+q1aodHBxYr9fzsW0PHjyIyDLvgUMk+9PhDlrf0L0ajUbueGZmZmxvb8/XCSODPOfzeYcwqfNpnTckEaiyhvU/nlfr9/wwrYQTXui3RWfNosdnQSwhcwAeJGiiTzSdTls6nfbMcTKZOBSH8xgMBj66MCT4qFMKM0wYo41GwzPcbrfrI81gZmqwqtwJ7d2m35a9Bbbn3zGS6oyUO6B6yu9guIGHSQS0HqbBFQETgxVUr3FS8/PzjoTwPNwLeVCEqt/vRxxamFkir8gD66wZnXJFtDUCHcC+aE2QEhUwPax0WuS0xqj1XA1EFG7WvVdmNkFdJpOJ1FtxmDhqtcd36Q7BgJk5yoidoXthMpk4cU07CBRp0Nab39hZEsUzt3N9ff2tGiJCm81mrdfreapPywIPE7LxcDL80FyKMmhUy+92Oh2r1Wp2dHTk47tYYK7xeOyj01KplEfVCA71NjYAA6fEIV0wsiIdv4cjwGADczKhQx2FZgVKjQ6xcSK/lZUVn4+pERrCOplMLJ1Ou5C0220PVqhZhrASTmEyuW2SJiMJ94jjtAiEgNiAvvnvkBhF7QtYSusXZrdN9ZqpQYIhCGDiDEfycC+NiH/yk584q3lhYcGurq7s+PjYvvnmG9vd3bXhcGiffPKJff/737eHDx9G1hgonuPCWBcyRX4Gg+kkII48IhsnSkWGeBfIQ1DmGR5OX+v6+rorqiIRrBOZREiwIsKmZUZp71dXV56dc09KDCBClUrF5Qy5RK+0/ICsFAoFr+mwt+gnOkLAV6/X7fDw0Psm9dJMMpTxWGw6wvHk5MRHH7IXMzMzVq1WvS0IpEEzRhweTiicimNmLitMqtGaJpkE+qPkNerM2Aca6UO0Q5+NzBKYUPWdCVmnp6d+Msr9+/ffOohbnTXcAUbZhWvKRQAHM5WAGqiXbFCTDLJ4TQwoH+C0qAXybF9//bWZTQ9rKJVK9ujRIzs/P4+0ryhCoQNMtJbdarW8BHB9fW17e3tengEh4ji8iUcAACAASURBVD1WV1ftwYMH/jv9ft8/x55T1gOR4t0nk4mfvrS0tOR1USXHcZFMaHKlsHp4fTCzxEju7+/b/fv3PVLV6CyZTEbGImFk8eKaUSmzjZ43KMtEPkQnSvJgQk21WrV6ve7ZkSq32XTCxNdff23VatWWlpbs8ePHkRMTWBT9XXVMGGklUmgrS1jjwNgSDYVRlq4TWQLEKS6yaoRBmcAYAuADlIreLGpiGD8UCUcArq8GnwAAGJVg5OLiwur1ujtUdWq1Ws2++eYblwmFramBEtEpCYJaIUKojnsymXjvLbKSyWR8VBjBC5N9ms2mXV5eem11bm7O2u227e7u2u7urlWrVdve3rbPP//cPvroo4hT0oI/s27JIgkMiFS73a7XmjB44/HY2u22R9IMoScDJRCgTQc5Y2JKJpNxh6YRsMKIGDm9yDIajYb3e2IUCFi1TEAwimxi3BmiDTpCfY1IHv0jqCTAZE2A9iFSvXnzxg9zDzNJ/X4lxJmZP9Pp6akdHR1ZqVRyB97pdOzNmzdWqVT8HMzNzU2Xc+SGABr505YoBhSQtfAuSlLTEpJZtJld9ZfPo/Ozs7OeEGA7KBuB/GDrMNpknEyaQu+AenW/tA4LGYfniBjtX6FIBAVa/2M9cNzsH3O0T09PbWVlxe7fv2/37t2zlZWVyKkm2Dp+59WrV9ZoNKzRaLgjLJfLnhwoQ1h5C+guFwchDAbTUaSHh4del6a9Blmen5+3nZ0dS6fT3reqa764uOj7S4CKbJG84SSxfaGNVzng7xRev+v6oLOk1sUBxVCJcXjAh8B4pLf8O9FCWCMig0TIgCdUQZXowSBgqOVkWwgq197enlUqFYd5KpWKLyCKipHh+zHoPJOy5zRyVTgAA6yKpsrG5ig2zuaenp7a8fGx34c6GL+/tLQUmQmpDg9FQGjm5uYifaZ3KTs/PL9Sr1lj/ROlYy+Z7FOr1fx7tEbAKS8KTbPnYc0tXBdgH6aPKKrAKSJKBAPSAq5jkDnkkAcPHtj29rZPJuLSSU604cB6vb6+dtiH58GpIzM4Wd6z1+s55Af8iXPNZrORWbQEf1rv16BQYcawpg6shqFD1/g3zXgICJADkAhqv+pgVH7R4RDNUBgffcHYVavVt1AYlbm76oRqUGG0gy4MBgNrtVpWr9et0Wj44QgPHz506JvgB0iP9pjQBvFOOHmeRRExXQMtHSDDCotCoCEpYD1gqkIcgXTCOiAXZFSj0ch1nWwdB4KT4f/VjoQXLXjspcqByq1ZdFoU+kwrEEevAa+rDmOnaD/jM2bmekDwTaDOmqO7KssgBOxBp9OxVqtl+XzeisWiOzzulU6nIyQq/l77jDVZ4fmBW5X7onVvRUbD2qrC7XddH3SWRHLVatVPvFDoU6NIZXcpOYeNJNpOJqeTMNRZ8aA4FXp1gCRQpGazGTlBgUiN64svvvDB4ZeXl/bq1Suv6SkJ6erqyllRwAjg9ygOQwrUWZrd1pjYTP03NkehDtYJGLDRaHiWZnbby4RiZDIZh37IWolK9bOTySRyEgjrgWNQJ4kR41kwhigxxpx30DoxJ9IDa8FUvry8jDhLhFkJRKGzxPloPUKzHp6B+zNJBqOqp6MkEgmr1WoO3c3Nzdmnn35q6+vrzqTUNWZv9SivlZUV6/V6kaZx1hK5JHDBUVB3M7sll2mNibMNWX/2C8NlZpE6CQ47PO6Kfe33+55dK8KBcUVX2GfkV+VSMz4CM+111ehbnaYSREB7qtWqVSoVh09DWFn32uztJnren7Mo0UvY18jgeDy2jY2NyDi+RCLhcCKTfnCUNNPzDmS9qtvAl1xqTFlXLQ9BCuFHP6+OlcxTew6RNXVYSiCjvsz+kQRg23B84bWwsODsbBAReARAt4pu6bMuLCw4KsM+IAcEJFp6ePTokdttMl9FYrT2Px6PnZBH2YCLYI7r9PTUGo2Gs6L5bk0GNJHq9/tvweWUISD+hT9qe/kJHSaBsPqtd13vdZYo+dXVlZ2cnNgXX3xhZrdRq8KOGBYlvOhGE6ViJDSD0c8BJ0FJZ1TX119/bS9fvvQMgn4qjAbX7u6uC+RgMLAvvvjCoUoYfpx8wCxY3okeHSXHaG+XKgSRi9ZBgHc1UlUHe3U1PVT3m2++sWfPnkXWBvglFpsetUXE3Ov1IoxS1p0oKpVKRY4OMzOvbfHeCJZGq/xAvGLPgF9wcs+fP7cXL1746R9mZltbW3ZycuIRvtbicOgILO/O5AwVfqAqDBgOnDXHSREpzs7O2vr6upVKJXeyh4eHNhgMHMZmhiUOn4usCkO6v7/vQQi/y/4WCgXPAnh+zYQZZ5bJZCyfzzu0TqQNGxalZB3I9DDkZLbU1wgawovMsFKp2Hg8dj4AjmN2dtbJaIqAIN/IJ0YPWcARaB8u68x3Ar1B6KnVara/v+86pE7RLDoRR7PMsOY4GAy8rEK70scff2x/+Id/6DW2Xq9n29vb3q/I72odjgyPDIR1JZPmvdEBrdeyRwrBMaVHZ7qyJuh+iCbpO+t/s/9qnBUyJRHgz2Qy6fB/p9Nx9n2YXWpXQSKR8ICDAAnuh9m075yRdXw/zrLf71s2m/WRfNhzUBMSEUoPBDXYFHQJ2VdbSFmHi/GGBC0ERycnJ7a5uel7iF1GFxnHd3Nz458hQA1RNLVxmk1iR9TG8A4aTPDs77o+SPAhtb+5ubE3b95YOp2OZINAbndh/vrgmtVoZqMZCDAPxufi4sJarZYdHBzYycmJ1zwxcNr7xKWMJmpi+/v7ZmZeo9Hv0MhQoR6UUp83jJp1o1gvVUKFVi4vL61SqdhXX33lhzdz0ddIFBqLxRyKhYCkEDCOktYMhEyhU/YHJeU5CUYQFAgBvB/vTY/t8fGxO0/el/MP9dBqHDn9UpqpoTwQdaibam8lMCRDCICEgXNmZ2dteXnZ1tbWbHZ21o9r4jQRIsxcLhepq3PB5qaWcnFx4UGHOgwiZhADlEhrcFpHpuZD/xlwnxJH2A8z80BOT9fQw7Hf1RQ9Go0ipBWCO4wrjeY8GwQjGM+5XO4t4wt6QqM6rEGzW+b06empk5YODg6s2Ww6WcXs7pNS9M/w7zVQ6/f7PrhfT/OgPQHd1OCbjIVAmgwMA8ieY/h1EAV7wbvzPIrIwBuAFIKjRL6xc+qcwmwkdJ7oDhmbQp7sAY6eTC+Vuj3PMnSW+/v7buMUvsdBkZWZWQRCxR4oZNpoNKxSqURgW2SJd9XWMt6d9wN1CLP1sPbHd7P+4/H0fNijoyM/TB20kbYQJaBhzwjw1AZrUMa99e8JWNmnsANC7bvajPD6YGaJoeXl6vW6LS8vW6FQiBydpGksP+o8Mc4axWnxP3QstIg0m01rNBqRTDA8VUKFSZ2a2TQKa7Va3nBdLpcjdSiyQxVchTB0AXXTlH3F72sPo0bAwEZ7e3t2fHxs3W73raybSToMlEcotE+PmhiXwj+ss0J/IfwFrAakpgGMZv7cg+g+bNSFvcz6sm+pVMohJQr2PJPCjsgW60d2gJLMzMy404Smzng34E0QAowJtQoINqGx5ne0HaLRaLiD5VBi1hcjeVdmRLCCkR+NRh6BsyZ8Xp0K/6YZhfaXMtkkvFShr6+nZ0xStwkzIjXUyA+9lTgTrWvx+xhsdENrbegiLVvsYRg0hs9819/rWkwmE2u3296Sw/NT3iDwIuCj7ADqhKyCIij0BjSrWZgGtewzzpiMhT+VnIihRd+0XhZm0ne9s35WnQhBGfK5uLjox2UhJ2YW0VWz6bFgcCmULEbdX783rGMTsCKLBELqOCCS6TpoFqY2Emep9Uiz6DQj3k8zbH6XIRUMqADdYl/VjyiRK8wqVd/UIRPAqmPVRC3kEISBiV7vdZY4PW5wfX1tzWbTVlZW7Pz8PJIhhEKhmQMPjrDwArqRGGsiRmCnk5MTazQaPr0mHo9bu932FwxJBKGzBGePxWL2+vVrW1lZ8WcAftSFfFeWzKWRoWa1RKi8iwowo8SePXtm1WrVhsOhQ3Ws68nJie3v71utVrPNzU3P0paXl61er/vGA5EpeYa1JptT+Eefk1oD9UEQA4wJLFHeAWN+lwABdeJ0qJlBiur3+w5jsj5as9HaKzVT1hIIDQfEu2HIyGbJurV+vLa25jCORvxkIDhs+vvq9boVi0VvwTGbKjonLQAfA2HyjGRks7OzPjGHdYL4ozLDRd0zdJYKy4aXBnNK+GFfJ5OJD/7gnTEqBFKTySRSrwuNmRrQwWDggwK63a6vE7XjEF0JgzJ97vDSAHc8Hlur1bJsNmv5fN7MbmfaEgRqbQloWFs1gDRjsViEKWw2zS4hlmBbJpOJZ448j5LolDQYOsp3vbO+t75/uBYE0uo40VEcOISZ0WgUKa9owEo2zt4qJ0R7rNUp6PqrnsdiMWu32xFEC/1QpG00GnmSAgKj9T5sCcQjkBeVLyUzsT6M76zX677mTNlRX4HtZy/CoEN5HNhyDXDVD6mPwC7qz7uu2Ps86W+v316/vX57/fb67fXb6wOZ5d/8zd9MdLqLprCTycR7K4lwiRyU0WV2CwESiYRROhCm0pvj8bifxq2tAkSAFKVjsRjM1piZ2T/90z9NuCe1LCIWGHVmt5Cqwi98loiKZ9XMhuyIjIy1MLNIZjkcDj0q13mIZuZn8X3/+9+PmZn9xV/8xYSpKTrLk+hM++EWFxcjfaNkxlrL1eiNwjqR9S9+8Qv7l3/5F/vmm2/s4ODAmaFErul02g8FBhpRmOUf/uEfYn/7t3874TuhofP9jJhSqITnZw01mwfSIlsmwia7J5MCxr26uvL2AmbBqpwA1yGnf/7nfx4zM/uTP/mTCXK0uroamXo0Ho8tk8n4fThI2GyaoWWzWZuZmfGMMIS1lDHLBbR6fn7ubRBm0yiYWity0Wq1HE1pt9v2/PnzmJnZX/7lX06y2awtLy/7NBWVBXSP7AekBBYhUKM+Vwg9qQ6GfZjU0IHvmVBDZlKv163dblur1bJ//ud/jpmZ/cd//MeEz9NDi06zL4q6UBLgB5iQCJ8sJ5lMeqsKvw9agC7zHopahbwI/jsWi9k//uM/xv7+7/9+wr5nMhnr9/uRTBMEBWId54lqTVVRMWRYa35aKzczh9I7nY4jDUw1YsjBcDj06VjUIv/u7/4uZmb2V3/1V5Pl5WXvT2SgC89LOUh7sZFXZFAheIUfWR/07ezszA+ARz71MPjBYOAdA+gBtnE4HLpcxOPxt7IyLVFQN8/lcvbRRx/ZRx995IMFVlZWfH0p99y7d8+Z0hD+sCHz8/Ne2mo2m95aBndgMplYq9Wy4+Nje/Hihb158yaCAvzv//7vnfWDX6vPEkOKg1OqtjIG9TPKvOPCMMIKRFnYSBRfcWbuqa0lkE8wDpqS83woTQgRITCazmuajqHAWYbpOu8WGgGFZ9SBKtylKb827Cpsqz8wGpmjiOGIx+Nej0KxudRwYMwZB/fmzRv7t3/7N/vZz37mxJhSqeSHFY9GI2ce3tzc2MnJiTer61oCvfCO9GIhjAQFGnhoLUlZoXcRpkKW4Wg0ipBMXr9+bY1GwzqdjjP6VldXLZfLRYyZwrBKvArJWiH0zueotUMaQw/4DAaVWjp9tNDnOUUjhJh4J+SBoA4Z5aJ9Bgc4GAx8nXEaynBWGdO6In+G0JQSmMIL/dQaE/dH75BB/feNjQ1fC1jNwMw675S10DnHCiUOh0NnTfN9fI61Zy3ZDyV1qA6G76Xr0Gw23Q5Qh9fgPnR+WjJQ2TW7PVw6dEoE2DgQCEfoSljLN7tlTnMovDJL1c6EJZmwTqf8Ef4f/UXu1E4Dt1KnbrVaXgqBS8EUHmqOOhiAUoHa2nddaptJjorFogfrlPkI2lKp6XjH1dVVH8Gn9pk/CdTa7bbVajUnpcGnAOpnZCrPrOt/1/VBZxnWBrUAT+EXGnfY3K7RVUgC4qE0KwtrAxgvvgNDYWZ++gBRtj6vCrAaas0I9LvU4IQ1B41IEX7+1B91uGqglMiSSqVcqMMeJIR+OLyduDIajVwY4/HpSLnT01MbDAaWy+UsnU77+6vA6PtRZz48PLRXr1754ciZTMbHy2Fcac7XfVfjoq0gBC4oH3/ChNXMl2dTA69GLaxj6fdeXFxYu932toWXL186Q5c2AA0WRqORZTKZt4IIlQl9DpVPNYp6sTesAbJK1r+4uOhjzfr9vrXbbc98q9WqNZtNr8EpgxvDq3qiWSBEnnDqjxpHrdGFdfZQF1S2Q0eJDGhPIAQfDCFGnRqhvg9XPp93+Z6bm7PT01Nfc95PSX/oAdkMGS0ZAu9Jn506qLCXTmWSdVLHqTrC1ev1nDjGM4bBmgZUKg8EOryXssp1D3SMIs5RbQm/Q7LAM5NRMv5PL+wSfAMN/lWO1L4RpLBG6AjPy9r3ej0ndPX7/Uhgvrq66kd+YS86nY6T0zQAVWcZJk48G/8GikbGrFk7dXKQNfgEoAfhvUFBaPHCXsViMe/hD0c+4izf5+B/raEEbB5GEOLAzc2NzxJlbqhCKigTjc8YGOAjxrch+EAGGt0TPXU6nUhvI9EBxiqdTvvzqtPT+2lP5bsWRQXO7DYD4E8M212ZC8qsEab+kB1AtuDCQKFMrVbLhwBo4LG4uGiFQsEePnxojx8/tvv379uDBw+80M+a6XPUajXb3d21g4MDq9frVigU7KOPPrJCoWBra2tWKBRc4S4vL71VANYo368GBmOpMBhrRd+fGjY+B0SKLAD9KVmAjAZDcnJyYq9fv7ZqtWrVatWOjo48S8tms05uMTMf9o3cqbM0uw3IMFAonB7rw+9pwIZhQkaBaelLjMViVq/X7fT01A4ODuzly5d+ZiNkpFAZFS6jL1VJQmbTEWEYtGw2G3GYGDmVWe1x1ntp0KJ/p2gGzGcc5tXVlR0eHvpcaOBp1gz9XV5ejpwJmc/nI0xORVB4DpVVskjkA5gXOTabQtrD4bS3WiFV2Mqq0+wXzl/RqxBlMjOfPcraKtEnJPrxE0LBfOfp6amT14ACFYYdj8eRTBq5N7sda4mDAAK9a84qOjiZ3J7OAQNdm/eRgdAeInfoEWt/fX1tx8fHdnBw4MF6JpOxQqEQOXkFG4GdbzabVq/X7ejoyIfjh6gea65JjMqFyi+lCewa0DhlNJIDDX5UVxlYgaNcWVnxofsgVLqmejCHBn7h9V5niaKjGFpXvLm5PaSTTcXTm93CmVDAOaeSFBrGIkaCA0DVSGGUFbbh8zp0Oh6P2+bmpguDRsx3bZo2zPMZjCX30OwnpESH99caibYnNJtNz7iBEbiHsttQbmjxP/rRj6xarTr0og3RqVTKfvazn1m5XLatrS37/PPP7Qc/+IGVy2U/TBfHc3V1ZdVq1czM1tbWrFwu+x5QQ1xZWfHoDWNP5Li6uuonS+jz0t+mrQbsDQEPa6EOExhLBxpzFiRZhhqv6+tr++qrr+z58+f+DMVi0R49euTvQPTId+n0IlVI/l9hd61N0ZTNHmmvmbbEKJ0fuapWq/av//qv9tOf/tSePXtm3W7XyuWyFYtFK5VK9uTJE284pxUKg8lQDQIwHZ7N6REYBoX9zW6Z38ihUu1DpCYej0dOo6AUgMy9fPnS9vb2rNPp2Pn5uc9gZswewQkw2dOnT93hcwammXmtHviQvcFmkEHC6GUtyNa0dhmLxXzoBohKKpXy4O7o6MhljgBM1wqbpOsRll24QMs4Rg451nWkzKAZMvqOISZQoB6JraHmqcH7ZDKxfD4fYaOjtwSs/K5C3bCUeVccGTYNHkE6nbZSqWSlUiny3OhtPB737z09PbV2u20vXryw4XDotcJkcnqWa7fb9cyb1ivWjHauyWQ6YF+TEq4QwVOniR0qFApWLpctm83a7u6u6xo2i3UhQFMETe0o096QF56P4IdgntOv/vM//9O+/PJL72F+1/XBw58RJIwDA6h5EaBQhBZlIA2mmH11dWXLy8uOhzMdAweg1HVgBa0RlctlN25EIDgiFSQyALPb0wcwjEytwLEpRMWGUjvhXryjwh4KnZAdj8djj1B0jTSC13qN0qqJOIEc+/2+FQoFW19ft2QyaYVCwY8bAiZlSDDUayL+cLDCaDSKDE/XQefj8djp9ES+rA1ZNCQDDC3yoFALhhg4nlFwRIHqnHU6h9nUWdKwTABGvavb7dru7q41Go1I1A/MAqEA2eT+IULBXgJn637zowQNjBpDH2jzAULTutrMzIwdHx9bq9Wy8Xjssy53dnYsl8tZJpPxrJAgI6wxKmSmQRyBpmZQISR4F5+ACJsMmfcnkmbfT05O3LD85Cc/sb29vcjBBQSri4uLfh4tw+TpTyWI4+J4MHQTw87fa4tMt9v1mpiSjSDS6B6CYuGgtH1Egx11bsgrRhRbovVOs+ixW7qWZtEhJ8gfThn7om1A6A82kCMHabnQAAe9w+FyaR0T6F/tRafTcdlAxrALrC09yCBYDBannEVQjVwwoi4Wi3nmdnp66r3fSvRCF5eWliyTyUQCzHQ67RldaFs1cEulUq5b6+vrtrW1Zdvb27a2tmZra2tWqVQiQ9SZrobMK5EU+0ywXa/XI/NrQ9gctBMk6fHjx9ZoNNzuvOt6r7NUuAABoAcOFhRfOplMIo3qiUTCoxaMm7JhwZGJvogmgQIRcASV6IvxTLFYzO+vjdxKPuGZMUJ8L7Amzm4wGLgx4F1ZXDYYh4YgKGyD0B8fHzvOPxqNIoOVUXBqPSpIZGHcP5/PWy6X8/Xd2NiI/D/7wg+MuouLCz/FBSMDzIhgxONxV3DGmCHM7J3Z7ahDCCy8A5/VCJ0CvxoWlEKJYEyTAcZkv/mskgtarZYX59UhUIPVYRKTySRyIgEGPMweCJQwaCg/ctPpdNyY0vMG45vnQ0l1HF+tVrNYLGb5fN6nkMAQpoeW75yZmbFcLhcpMeiRZ+osCSTJyPl+DCnrhWGntsRQB+QPB0OGR111d3fXTk5OrFKp2LNnz6xWq0UCD+BEjiYjmKAsAuFCB2Von3S/3/cAkr/DkQKHtdvtCHEnmUxaOp2OZPZmU2fPSSTYEaB0xv3p4dz6u2oPWF/WW0/kUfhWa6WgZATpyCLGlYyH4Al9Zo0UeleZVTKWcjxwwgSo1PNUjgkQGGWHHWJGMfvd7XYjB2aDDvIOZMIgJwRzBO/Hx8eugwR8sVjMZ8yORiM/Z5YgR4dfcAEzax9lPp+3UqlkDx48sJ2dHZ/5vLy8bOVy2eH34XAYGTWo+08SY2ZOrmPGMEETGTuBh3JIksmkFYtFKxaLjoK86/q1apYKkenEFAbYsuhQ3OPxuB9JBIzDgyB4dzWsplIpH7BLsRyHuLe35yO5stmsffzxxw7faV1EoT8tgJvdnlyPY6CuSlGZjeJ9cOAYylar5U6f6JwI+uLiwvb29rxml81m7fHjx16vw4hozU7XmJaETCZjn3/+uRUKBY8G19fXLZfL+ZopPEw7w2g0PVCVs96UiELmPxwOrdfr+SQhalLx+JSJVi6X7d69ey6UNzc3trS05JAL66w1glgsZpVKxQ4PD71miIHQLJpIGKhldXXVW0zIYKGPVyoVq1QqdnR05IpC5re3txeB6mlVofWD++A8uYCgCCxYc4y6ziMma8zn87a9vW1//Md/bGtra5FAbzKZDmNoNpvW6/VsY2PDHj16ZMlk0uuWDNU4Ojqycrls29vb9ujRI9vY2IjUrHFmQEVcinwQ5GFI9eDkXq9n7XbbswxqNLTKLCwsuMyAXhwcHNiPf/xjOzg4sEqlYsfHx+7Ml5eXbXNz09uU0um0ra6uOmxHLRajRKuTmXktCFiPMzXJVinbcGQUAzfMbmvKyWTSAyxq0JDjqJWFXALqn3yW4EyJXAQsyhamPETtDuIKRDvWHJ3HhmGzkCv+nYB0PB47SsLzaqDMvTD+nMmIzOKI0WMdYjI7O2vFYtHK5bJtbGxEjhkE4SN47ff7VqvV7Obmxg/1xgFjIyHn8DxXV1fWbDY9kKpWq64TnHyjznLnV8dpYftZM01i8vm8O/h8Pm8bGxv24MED29jYsK2tLXf4ZlPy5ieffGKlUskzXiYW6XQvAhqy8tPTU2u1WtZutx2mHQwG1m63/TOpVMrnbWsAv7OzE6m133W911mSkhJJk6YjfPV6PVIAx8iSxc3MzPh8ymQyae122++5sLBg2WzWnUm1WnWDrCwmDDOR5mAwsFqtZp1OJxKJcCmrDwHkmXEWkFb4b7Jn+h+B8oiUIBZp4ZhjtngnlK9QKFihULDt7W178OCBO3yUCnarChLYfyqVskwmY6urq26kODcOJhcQEEqRyWTcYHe73QgpAQdA4IGz6ff79s0339iPfvQjOzg4sCdPntjW1pbFYjHb39+3ra0ty2Qy3ruIo9MWGFXor7/+2skhBCSKItAyABTF3NJf9Zra06dPvYVlNBr5EU1kaUpQ+elPfxqpXd6/f98NCk5S1y28MFq1Ws0ajYbX3ZFPhkInEgnL5/NOXNvY2PB3MDMrFotu6BYWFmxra8tlolarWblctouLC3v58qW1Wq1IoEh2Dbw5MzNjzWbTAwMujDClid3dXSd+cS94BYPBwAk3mUzG1tfX7bPPPrPt7W0rl8uuJ2QHEDgWFxdtY2PDHSzrRlbB+y8vL9vGxoZls1lbX1+PkLNCdil17vPzc8vlch6QVCoV293d9cA5Fot5Dx1kGQwq+sDZhUDA1K4Jnjh9ZjQaWbVajWTFBHrIj7LHWedHjx7532NYKRuR2Wnrix7rdnV15RkTNfz79+87wsZpODgP9JCAgGfRAEnJSBBalAhkZm4j6KHudDouyzCvuQf6APo1mUysUCh4QMEpIiQl1CeBzHGKEOza7XbkLElYutvb2xFui5lFbNxf//Vfe+bPMxHYsNe8P4hKoVDw5yJJ00AZmJ/v6Xa7aoz4XwAAIABJREFUfuzb0tKS26B2ux3pp4fMpmWryWQ6N+DevXtvO8JfXe91lgoVaITED5vOZkMlJrpQrHs8Hvs4KmpK6ggajYZVq9UI9EDWAXSF8BBl02upymp2e/wRmSMLBaSkkRxHflHkPj8/d5YjUAEZC0oJTAiejyCvrKxYNpv1Qd+vXr2K1DtxFiHBSN+TiElZyHt7e86uREHz+bzXNTU6bTQaflQR8Oji4qK/3/X1tZXLZXv06JFTvh8+fGgbGxuWyWScgamkKrNb6j9KCCTKPTBsmp3f3Nw4EQFFJVAgG8pkMrazs+PBFhBNNps1s9uMkEiQKBhWMDOKdbAEMG/YUqTrCrxEtpxKpbwuTqaE86C3DCju/Pzc5ufnXV6AenjHdDpt6+vrXp4oFou2vLxsq6urVigUIkYDw4EBDsloZua14F/84hdWqVT8SDQCMXQT2HtxcdGPH2PvQIKAlwuFgu3s7PhpJ9fX17axseEoxo9+9COr1WrewoEBm5+ft0wmY4lEIgJ3cRGMMKCi0WjYq1evPHtttVqeVTOIXrOabDbrfbPAlkCv2WzWMzK+V1maXArJA9Ehtzg19C+TyXhGFA6WgIdANkxGjXM3uz1eL5VKWT6ft4cPH/pwA1jcekyX8hewB5rlqD3DvoblBBw0EP7u7q7t7e358ACyPOSXVhzKGQSlBAI6Axg5x0bPzMxYoVDw/e73+75myARIHEkJlwYBv/d7v+dJFj/oI+vHpXVaiFNKTmN9sHn4J96DvcO5Usun7eTi4sK++uora7fbPqoS+FkJduH1a/VZwrrSlJ1jq7R2xUzHkJaOUIUUfWohQIXUQ8lklKmYzWYdvmTTiW5CejrPioHX2gvvhGGiXkAdBQo2xBadjAGEohtIRgmTkUDh/PzcIUOEgaw1NOQEHERYWk+jX+/o6Miq1aqdnJzYysqKPX361KOtUqnkAgPclUwmfZ8giACz4QyozTx58sRhEt6D/0ZplQAB5g+9nQwFJ8fa4QC1hsr3woY8Pj6OtAoAyUIegcQDBA+8lE6nvXEZ8hPrp3UovZQMoI5ydXXVT7vA0NICQGlBGbGgH7DAzcxhdBzK/Py8D+3f2tqypaUld5BEtDwf36nkMtU/oM/9/X3r9Xo2Ho99jZXdymfRzaOjI9efra0tlzXY6czRBcV59OiR7ezsWKlUsoODAzs+PrbRaOROKp/Pe1ZDZB86+LDdgnM41bFSCqEejpEl+1tdXXV9x3DTg0dgRrCOPcFWmJkHnZBYtM0A50oQiNxgN3T4OvIN7M1/K2zMusfjcVtdXfXDqs/Pz72Jn1nBSuAioIfdi/4rcQ55BtbVNSbbvLq6shcvXrhsxONxu7y89O4C1pBMnCCaAAuOCNk0ck+gt7Cw4MFas9m0dDptuVwuQqbSYApZCB3nvXv3IkkNDHx1mCF7W1nLyjkJa5XoHwiLOmLsGXsOV+Lm5sazcXRWWxDvuj54RBf1lGQyGREeyBLK8IQGDryim4sxxWlSKwAOYcGBY5TWDxyIoBHNkH7roimxRZ2yOjscOOPRYKOdnp5G6kREtkopbzQaDunyPUq2qVQqDmWcnJz4eywuLtr3vvc9+9a3vvVWbxoGTJmXTKF4+fKlTSYTz2Z//vOfW6lUcljw1atX3rxNwEKtQ1l1ZKMoT7FYtM3NTfvhD38YqQORSbGOFxcXvmYoLDArTh2jB3zGmL9ut2udTicyjiudTnuddzQaecZMk3AsFrNcLufkGBQBiO573/tehI1LEIRhwXCGBkYp5qADTAzBwMH+AyrkYFqcyMHBgUNrQOoo6dbWltc/er2eHR4e2urqqn388cdWKBT8eTgbFmPFM2JsFLpStiS6VSgU/Mits7Ozt9iPRODn5+d+cMDm5qYfdI6czM/PuzwQ8N2/f9+2trasUCj4OpuZn+8JdAYkDpNSn3l5edkH3FerVavVarawsGAPHjywZDLpfASMnuo0LWTlctkzcz0eDj0koIBdimzQB8pzk/GQYaDPOHQ+gw6qjoOKkMWvrKz4KShqtJHFdDptn332mX3yySeOWD1+/NjRHrgOOBRFW8j6zs7OIjZyZWXFkaa7iCcwhDlnGP168+aNtdttJ0OurKz4dwCTwy5lhN3FxYWXAdbX183slkvS7/etXC47eRAHjj4RyMKENbslAaqN43ewLQQSsIiVlUw2rnVgnBxrppk3ekLdH71aWFiwhw8f+nP1ej2X4UQi4SjF+5Adf4d3/otFmWLxeNxyuZwXv2OxmPcCws6CtbWwsOB1D+0ng8ABIQMDApP2wYMH1m63rd1u2/HxsRfe6/W6XV5eRth93DeEKebn5x3CI7M6ODiwVqtllUrF4QatATAO6fLy0vuS9MBleoBWV1etWq26sJC1oky/+7u/a81m0yNFptngVIiayS65+H16o87Pz/0or3q9bt/5zndsdXXVx8o9evTIPv74Y1tfX/fMsdfr2eXlpWcuOH0yIozjzMxMJJJNJBKRWgzRJQEEa4VxNzOPnCF3QDWHPKQwC8b04cOHtrOzY+Vy2dsscJbUl4HGV1ZW3IhRi9OIkzYB1p7MnQwEZEODKOA71juTyVgul7PV1VWbTCb23//9317jPTk5ibTmkAmDYgAt4/Cvr6/t1atXfj9OtmEEX7lc9gwCB0utJZyOoyjJwsJChMSys7PjU7MODw9taWnJ/uiP/sjW1tbs8PDQ/v3f/91RgbW1NWdVa1uBklt4R4JeiHSVSsWSyaR997vf9QhdIUxkjExa+9MgfxDwrK2tRZij8BEITGCM4uyy2azlcjmf0oKu4RQZzajtO2a3ARD2QXkU2lqCo1Ioj2wEI4t8/PCHP4xMERoMBlav163ZbFqr1bJut2tzc3NWKBRsc3PTfud3fse5BbFYzEqlkv3gBz9wgtPe3p4fh8U7gb6p86UUQTBPFsqlrP4XL15Yr9fz+vTMzIxVKhWbnZ21fD5vDx48sGw2G5Fh2vrMzCF4bDrHpdHORVlKHZMSICeTidtpgnrupc5Sg1cCbHVaQMAE7fA8sMEa6NJBgfySsPHD/pJwUPogu240Gra0tGTb29tWKBQiSKPa5fD6oLNUKj6pvcKrFO+1VUBhS4QAI7e0tOQDgIEDtMUA43N1dWX5fN4uLy+d0EPdkvvfNSlCHVg8HneGFFEuG4BT14iy2+1GTkfXrFFreCsrK1YqlXyCDBT69fV1W1tbc8HC8AGDUEPQ4cb6zHw3RBnYxnqK/e///u/bkydPfA4q6w4cikAAW7C+7AEkD75XC+Z8Dpwf8pXS281uZ76SucdiMWeYApkTuQKn0UNF9qwIBd+BUYL8gKHWqScogrL/tA4CRMy6q7JSlwDFIIs/Pj62s7Mzy2azTsUnoKMmiqEh2lWZAHon46QuSLQNUYkSAnV6eoDJ7GlH0QtHhUxjJM/Pz+273/2uffrpp7a9vW2bm5veckX/HeuvUCDPjiPRddH6p64nsoIx4u8hmCh0DIwF3AnDmAheexg100ulprNHofFD/OGZgeqUDKI1aGRCnw9kSh1uiDgooQaZ4Jgs5Ibs6vr6OgJhX11dOe9CiUbKwo/H45bP521pacmKxaIdHh56gqE1UmxgaH/IcsOJX+wJjl0nOj169Miy2aytra3Z+vq6126vr6+t1Wp5rRMZRPfMzANk4EqdLMRehHaWAI6Al3upXVYIGhkM94w9BbWkLILcY9+wQThvdc4EWHwnQTJBZCqVcmYwPIuLiwtHHZVgF17vdZbAHnoQLsqhswEVQtMCOtRtzUiAGYB1tbZFjw73Y4h3pVLxHiuc9+rqqi0vL3skEW4KkREbhjJSg6IGEo/HnWyi/XJEV9qmgXGG/oyzxrhjIFFOWjpCRi+Rtj6zOmNgWzICzaSKxaI9ePAgMjGH/kDuS4ZG9IehIGqmXsN7cR9qXgitZu1Ekyg2UCx1ZAKm1dVVK5VKDvvlcjlbWVmxYrHo48p4L4QYBaGGylqQ9ZANE8mivKyr1jb0vTSz5HeSyaQ3vBNQnJ+f+9oSaWpQNjc353UdYCCcJWtEdErPJxExToZ1UyfN3ytJQeVCvwdDyjMsLi7avXv3rFQqWbFYtMXFRfv888/dyPBZZGowGHj0r5ki64CxUp0kEOUzWqIwuy0faDTO2mK8tZaugRlwOaQjgmWy8ZWVlbcOSVeCmUKB4X6zL9SaeQ4+ow5biSCpVMrLPdyX4ARW5sLCgo/0G4/HPvADNIN7a4vS7OysFQoFR6xOT0/dYVJ2AnWje4AWB3pwVZZBhlKp6VQlauLoypMnT6xYLFo+n/cMHdsFHEwmFTpL+BuQXXR4A++ljol91hYb9ih0lmpLQv3UoIX30IEcWrZAt1h3/V0NmNF5kg9+jyBvZmbGW0XwFUq8DK8PEnz4Al002HVm5lEPcBWOCuXGCBJhKbWYRcOIawM7hjWdTtuDBw+s1Wq5A6TfMZ/Pe9SmF8I/MzNj5XLZ2VxkNSzceDz26fTdbtcqlUokmqJnEmHQiIcIVAvTNCKjgMDPZOjaw6nRrTbxUlTXrFFrfpqdA09UKhWbTCZeL4StaWZuMFF4anUoN9Et5IZkMmm9Xs9hRs3yEHbeCycFzEs/GAFBIpGwzc1NJ+vgVBHsmZkZy2QynpUx8YRIEydBjx9RombGoUMnYsdAc6FEGLvl5WWXXYr79Gpms1nPKJDPb775xsk1SqphL6lFYYj6/b6vI9ke76Uw83A4bfBX9jPXcDj00gY1M2Clp0+fWrlc9hGFCwsL9sknn0QybyAqzTrYazUKGBl0Tut86jQ1WNYgUJmMeuoLgRpBMFOZdBgGa8M7zs3NeS01PGmDvVZDrd+tz8o7aY1Ra43qLM1uAxnKTrwvfw9Swncj4/RPg5KQBdGmBix9dXVlO7+a6gSsSqak2T12CXQNe6qOB53O5/O2sLDg5BnNjLGzBJ0EcjgzdIPEhPUjAyXQ4L2RKc1qkSE4CGTSPLOiZzw/90FvkCkNNEk4yPaY9ISecVyjyjP/jc7QvYBN6PV6zl9hYAhjSbWsp/oXXu91lpx6YGZuLDWiJ0NDSMkuNXtpt9tupMkw1tbWbHZ21t68eeMYNzWdVGo6BimdTtvx8bFdX1+7gwAOAT7FuIeFZGXGaRuEQrcsoplFevPoOdK6ntkte02ZwSg63xFGJcrEIhJWUoeusxI1gLAw8OD7GBTqUJPJxIkfGNvz83NfX5xNu932QQnsE3sJSYQggCABdrAaE3WWZOblctmdmPbL8ewYTSI3yCVAkUw+oZ4FPKJKxj5wXzJMlE+VEcYqQRoXMoJB2d7etmq16lT7nZ0dN/TcCyidGakMzkA2uB+Gk+wgmUy6QyAYo3VEz9LUnxD25N5KNtN9pZUCqjtZDLKCLrI+mnlzby79t/AZuDBKOEs1dPr71J0wfmTiXIoc4PhwlsqYZ020DECwyvvzXMofUFlAJ/h94G6em+fRbFCHIOCkFIZkTUFDcB44b3XmOBwye8Z86mESWrNE78hiW62W99Fq4IdszczM2MbGhtc2kVm1LzCHGaAyHA4jzOz5+Xkfg4hj1XqwJjYhkkPGqzI2OzvrQbbKmCYQBCwEJMD+JGXoOnahVqvZ9fW1Q946+o7914EiDAkBHoc3gUPHpieTSecX6OCJd13vdZaXl5eOR/MlWiPECahgIyzJZNJZcYPBwBWb1DoURIbdIjBzc3NWLpdtfn7e+3uAQIHScJi6eESSClMqXIah5Z1oW8A5oUwhHIYSotDaJIuDXl5efqsWqUVxjAiOhQsyDeuNk8VpahRNJsdzUguF0k4NACO8uLhotVrNJ6YUi0UXav7d7PaYMIU7NRDRSBJDw+/Pzs46fH55eRmJlHlG9hpDwX10xjBQC9/DfmngogGJBi4YwLCFhgs0BHnjnQiiQB0wbKwvRhYnqnAuTnJ+ft5/Byha4Vn2/eLiwo0XAQ5rRRagCA6BqMKUwG1XV1ceLPJeyHssFoucHckzaz1XWx7IDjVA00zG7DZYxDFqvU1lnlMyOFqKo/SopZNJaTCFrChpj2xY9xSEB9lQOJfgMbw0Y9dWGyWbgEZAEBoOhxGjHa4Jeobeso6sPWvNUIThcOgMeSW1sB/qLAleaa2iz1NlGWjZzByd0RqglgjUVimBSFviQHHuQhCB71VnNHtXB8oaqY/QfVCd5eI+cCCQQWyDnhzCMA8l/PHe2gJ0fT0dkM7EsGKxaJ1OxwNprckT1IRcnLuu9zpLMg5qk0RDLJjiwLqYKM/FxYVHM4xrU5KCmbmiwKjiNHaNwBKJhDMQgQaKxeKd9SqiB41cNSPWyBIohM+EWLduqNltER5yEhdKi7ENSQTKelOiCBdrTGEdCJWNRPBZO6ADnnlpackHUmP4KI4vLi66cWU6CdEgcC//zkBovQ/RoRbhESwc6vLysgcoRP2spdZ6qcVCoNC5pgrPKATIOvE86sDDerKSAEJmKTUKhZrMboeVa+DHPZAlMmmtf4cwNAgK0BmDIWgJwLhCdtHsRglTaqx0XSlNsF/oIc+sSMt4PHYIk89oNoCRUdSAd9O6Dw6ffdDsTrMD1T8GpAOra3AKMqP3Vkc5HA4jbQKKmGBgFZlhL7EjOGI1pJrRhBCz2e3EL54FZ4ljVcY1es19gF9xNFrXxVaA0MAj4KxVDVyQDdAVerg7nY63VmlmScCFblCyUX1jXyG0cBFQYcdIXkKnp4QbhYHVNrLvobMM0T4utYkqc8iCwuoEIgxEZ9qVsmjJbCFeaXvg6empD1/I5/MeOCoUHUL77Pu7rl/riC6cAb2RZFcIDQKEcSXtZ/o7G0RNrV6vW6/Xc3YrxoomXoSIelWxWLR6ve79OPPz8z7D1MwiwsC8TwRQm5BVOWOxmPcfqSNj4XgOZYOZ3ZKecBi8s0IOCJXCrspUBWrhWl9f9+ZgZasqUQHD0Gq1nDG3tLRkuVzOyU80bAOPrqys+HzX09NTq1Qqtri4aKVSyckKzGlNJm/7MhVa0VmVKEev13NBw+ECM15fX1utVosML+bfqRdgfBV6Z32plyDEjBiEBMOYOS51mAg7bFEOHua+GEJqifRoFotFbxEgwmdaC6OzQANGo5G3h1DX5J7UVs7OzuxP//RPbWNjw5aWluz169fe50UGozAmQQ0OhWtra8tnp0KmUeRBnQ5OACOATCrkp6UC9ll76VjPWCzmzedkIFqP5fm1HMGlBECG5Ks+697hfJSFm8vlXHdxRNib4XAYGbCBTKgR1hq2Qpx8RksFZrcDSnDi6D7fo06A78Wp7e/vu4Nnb7hAJYD6Wbt8Ph8x2mRR/X7f2+Zo5To5OfGpPGHwzuxdBoFoeQm7rDVa1gZZ0ylS8/PzHsRBHtPAlnsiV3wPa4ZeEQCECQxyofKlWagGTioT9Awz3xi51dY7UIC5uTmfLGU2bQc8OzuzVqtlO78a0o5DZT9Uf1KpVATVuut6r7OkYAvEGnpfjUbIPNkciCZACPv7+xEGWywW81mT1Knq9bpvHm0nsVjM6dp66rU27OrGEBHyuxoxh1RynJdGNeGpIkTtClcRzQM9Ysj0OdSQ8N/h/3M9efLEnj175lk460wdSo0BURZTa66vr72mNhgMvLCNYszPz1uxWHQGGPUOjOPp6amtra25s6QFRRVeDbuZOVzFWlDXhjm9ubkZabPQkV7qlKjnEZhgkBFe3hc2LGxlonOUR/sHdbqOni6vRlGzYhwPgQ3wLcxryGNHR0cefGUymUjfXjwe97YjFPj58+c2HA7t008/te985zs+pg6IiAAH9AAkQKnrnBl5eXnpJA3NYPgu9imfz3tmfHZ25kEu70eWSW1aazmsCXtTq9Vc74rFovdNksHOzc1Fanaqf8jX1dWVw/QYUq0NxWIxb/4fDoe2ublp9+/ft5ubG6tUKtbr9SIsRWTfzFyftGWKurFmRRrkaAaLLCsvg/VRiBPIDsd5c3NjzWbTiXxbW1suUwrz0VObSCSsVCo5uY9AMyQa4uCArwkS74IFaQuCBBTW2Qg+cUCKkuihzbyXQsrn5+fuIAm0cIqgevF43IMZ7dGEdYtchAxe7KgifjjesFzFPbAHDAO5ubmxe/fuOf9iMJjOfuVkn7m56cET7BfDOpALzVzVzuHAf+OapVk0cqc9AMGiRsOGo8S0L2B8ibaV+ktWB/zHQtHHqMxZIF+MA8QJBEXhNv5dWyaAJth0Fgjl4rupFWBAeF9V7pA8gBHmnkAc2uyt5AE1JlxbW1v2y1/+0mnd3EuJLNpWooxizaSTySk1/OjoyOLxuDfQHx8fO9SNQ8f5M1CbgIHoGuOD8mtQQpQMFDgzM+PPy0kikCSAPxUOB3KiZQMhVeaiOkME2czcAGFoMIasKfuHk+dCidkPJhnpvyMT1KaBXhnVRs2Vw2ipw2s7gBomBmDoiDhKCdTMWR90SiNbhq2rUvMcBBTsCYELRpsSCqQNomp+h7auEMrGGUL+IhMeDAbOUtTsICQEKWSq/8/6q/OaTCZ+eonZLYrR6/Xs5OTEGo2GZ6jAmcqmVlSIwJagHj1V4hkGFL1ElnknZadixwgGsG3n5+d+7Bi/x/epgWc/YfhCtKMdhn1D7nkH9Eh5CdgbLmBX3knbMMjkFSLFmeNc+XetU2uJiuCPNdbSjtoHdCaE9DU54NJapUKwyJLWWxVZw8YREJtNoX4Y5IPBdCrR6empvws2XeFV/g3UwOw24NLk6K66N9d7nWWYKRFB8G8YS5SFmhAGA2G8vLz0kzFwYvT/EKHg9IBjHz9+bOVyORL9EM0Oh8PImZdaP1RnRCTBBihuzY9S/nkWhmKTWWvx/K6N53kggnDyAwZJ4UHuq8/MSebqRHk3hTKBsrUuCiHKzJxUhWEqFArW6/Xs6OjIWbthUV6HWBO9qyKrAWDvgdqurq78PhrFQ+3WvcPQIw/UZiBqwRacnZ2N9KdilDDyzElV9EGzFa03KXQV/r8yOhVCUniZkYT1et3MzGHN2dlZH0wB9LO/v++GT8lksEOJ0IlyNZBjz7UuzH5qHZX34F2oUWLUgOIh2LDnIAwws7WWyqQU5Az4bX5+3jONbrdro9HIyuVyZFrLXZwBs1vSBjKqz6byNBwO/YQLgoR2u23VatVnIau+6MHi7AVBHU6PcpA6PHWoGvDqpc6TZwdpUJSIeizB4unpqX8fwb/aP2DWi4sL63Q6rmMgBOy/OnW+Vx2gBnYcjBA6SwIYheM1a9QhMDgNglWFmQeD6cHVPBOBL3YHZ6P6g6PnO8OgKXRC6iDVcd7lVAmi2XOCLN5ds1vVP56Z9cHGaQ8we85n1MGH13udZViH00I9tRagLCISNYoceYXC0zsF0+vw8NBarZYlEgmvEc3Pz3tTO/As2ZvCdUqzVihICT7KPIS5iDFkszjphF4/jQrDlFwjapig2WzWbm5urNPpuHLDXCyVSv69RMj6w0UdNJVK+UQkdfKsK4pO1qpRumZWNNA3Go1IvZiMjpNR6IMFiiIqI2MnE0N5MQD8G/+NcdDRWUo2IWgB1qpUKn7GXC6Xs9Fo5KPPqDeyl/V63UajkZ+3+a1vfct723AIITmBTE0FH2XBoXF/oGCa0amJMzR8NBrZV1995TV6fvRek8kk0mfH4QDQ8Pf29rzXl9mjMIipTxG0qbNUMpbW3ZA9psaMx9Ojug4ODiIOiXWgBKHkPK0rQlShhgXcNjs7a61Wy46Pj+2LL76wZrPp7EIcLLrORRbFvTSQohGfn6urK6tWq24Inz9/bl9//bXDhW/evHH5vrm58ZOJYrFp2xrHuhHswgDW90Q3+FMdCbqHDVMCG/9GMIwOIo+83//8z/+447x37573DXc6HXv9+rWdnJw4aW5mZsY+++wz7/dTdIQAHTYxU7iweSoXuVzOTk5OInVAtVUKZV5cXDikzXMy6hHuBe9C6w2QP6TBYrHoDrTZbFqxWHR7pUGyBi5hcIrt0nqlojnsF++C/mCnE4lE5IAH7GcsFnMOAYRSHZupHAnuA4LBM7C3IeIXXh+sWarDDGEb9dZkZhgujC/F2c3NTT8SRfvggCCYB4mjYUQYToJ6lELCSsjg0kxD6fyJRMJ/B4r24eGhH2aKwgCXjcdj35jl5WU/9kmzJDYHmIhNuL6+tv39fatUKra6uupGA3gM1iTXq1evXJnIuojoyC50/XlP1pCs/OLiwtttGLOWTE7bbHSGLw4Vo0O0qYQkjCn7g8M0s4hwmVmkvYI9QpnMLIJAJBIJn3t7dnZmR0dH1mw2vTmaQ6/NLKI05XLZ3yefz9v19bWTanS8odY774JZNQvq9/seoQ6HQ2ebYsgI0rT20+9PzwKNxaZnYiaTSZ8TqoqIoab3azQaRWYb62kOHKNmZhEYlrm/auiVAYvsQ6ZrtVqOPFDLganLYc7cv9fr2bNnz/zYrJWVFfvWt77lAQOyNTs76zOIOYmE4fMhK9JsChFyMDVZrB6QjvwTqPR6Pdetq6sre/78uU/ZYoKN7mE+n/d91BossPH5+blDyewBOkPWpXKgWSjOic8iFxh55J1j++bn5+3LL790Is7Lly99sAOD5Km7xuPTUZNAggwGQa9AeDTD57k0wDObnqXKQeuKgKmdBl3odDpOjjSbOlr0l3VQZil2RMs/wPej0chqtZrVarVIPV+dJckLJS6uEJnjM5pZamarJRECOOwHJEb+n2QHOcBmEAjBdub7+D0ye4Wqf2M2LBnZuxhC6jzNzCMXZYXxYDhPJbnAciSTAmoF5yeT1fqBpv7qEPWZtAZDVIhQ6iBnDrNmoYbD6TQVJQjgtMh4xuOxj8drt9sRGIqaINEoTo+ISGFCjcZZt1Qq5cYVxaa2ppi+wi/as4nDULbsaDSKHI4di037kJSNRn1LM24+q2vKPoekAwIanW2Kw+F5UP7RaBQ52Pvi4sIajYb34DF7qcaqAAAgAElEQVSYgFpguVz24dowhdWwAS8hZ1qX0sxSHSf7qzUPIms9zgfl5/9Zj9PTUzs8PHSZJrrlwhCQIc3OzrrswORjjBnPcReRA4IX+xFC2hi2s7MzazabET3DgYLIDAYDh4wxpvV63c7Pz708AazPdylxKpFIOCOVOiPyHkLHOiYQ+VL0A+dAIILOczQbWW0ul4sgRwSPrAcZEXtDWUehdYUy2T+1ZzgyRZ2QH5wVe4Aur6ysOMns0aNHNjs7a7VazQ9HxolBKGM9FSnCHsD4DHkVigqYRQMS/i20c1w6EQuuCM9CgIKMQCQC2en3+3dyI3iPZDLpyBProaUm6t4Em+GF/KozVB3VerZ+Jsw8+Xv9fQJlvp991vqqlgi0dhzWS++63ussubkWZHXz1Bnh2EIh09RbmZqk1YpHj8djhySB8BACdZZmt6c0hM6S5+bFNTLUaBZDxGHQCDc1EXB9daRq0DmVnNYJJrNorQTHjABgtBRS492ArjqdTiSLI4jQd4L0AjzFZ8HnQ4INhAAcGlADnyUzU4cQwtXIQngp/GJmbhgZz6bkiUQi4c5tMpm44VN4nyiZdyiVSn4gMYfqck+el/cn2+JZ3iX4YRROhscYOgIXYGVklrUYDAbWarXcUZRKJWcAw0rGQAGvapCo9WN9DmSXC2dCgKCIDrV6PQptY2Mjktk2m02r1WquQ6VSyQMKAsZEYjqIm6O6aIJnXXFqsVjM+z4JOtkjXU8cNrVd9EENoNbvmQQFEvL48WO/32Aw8PF4QJ9kGLHY7UB8DCkBthpG1hOHzvOw9qyxmXnQQIKg0CH3pQ0KJODjjz+2crls9XrdvvzyS/vqq68cVqV+ij6hZ9rPyfdqnQ69QJY1UNV3QQ60pkoACulFbQWOmWBtbm56mDpkKtAR7DPBiGbnlA8IpFgndTQ4qrsCQN5X68ehbOt/ay1UnSx/D4uf7w5JZ3wW38Re8GeY1d7l4Lne6yw1YtQiLF+i006ur68dPtG+QBZe64csIlGAmUUixlgs5ufI0fuivwcsS31MlZWFhhqtBoljgXiuYrHowobhA1LEoeiGA6Hg/DOZjNdXtRcNOBpHTDQOYURJNGZmtVrNWwggGHERzauRRDCg3evp5vF43KFfamsc/UVGoKdAEKRwBBpCrBkD34fib29vO2385uYmYkzIpnq9ni0sLPhxaUCAOFPg7Z2dHSsWixFmKIHHwsKCn8cH0xNmJk6EPRsMBg4BY2iURMX+hUQDLoYPKORK5A1CQCBHLQSnyrg8jv85OjryWiZMb+QfKHFxcdHXSeG30Chq4KmZJTB0tVq1y8tLn2yCjKRSKR9W0Wq17L/+67+814zMZHNz0wdpdLtd29/fd+YhPdWnp6dWq9VsMpnYvXv3PKvCGWpfNPqnxp/PEPxiaEGU7t+/7+sRi8VsbW3NyTwMYjAzb07HmCs7kwB9ZWXFYTd+dP2QaQ3+m82mra2tvZV5Ypf0s4lEwhnB6AQZdKlUssePH1s2m7Xnz5/b69ev3cDD7n/48KG/r2aZvA8DMrT0pM+vNgFuAU6LZyRIp0eYIBjdrtVqVqlUHKY0mxLYQIDoOaaNCZiTNSYYwk5it7jYF/SHS59fs3WVebVvIXJIOQ09xi/gI7CZ2CzQOdAFgjRsD98ZPsu77IPZr3H4s76gYv3qsbVoi9HCQPCQx8fH1mw23fnMzMx4ewjkA0beqcHA8bHw6mjYPHXqyuQk4+DZmYxBhKoQCCyvTCbj/aJkthgzIksMOYVlIEacNwEF01Yw3GTTCg+ZTetHHAU1Oztr7XbbcrmcByNK5NE+QEZoUSMCftVBwwrZohA4HtYGViR9cRgk1lIjXbMpxNrr9SLZLzKhgQ1sSqVtKwoQj8d9wDx7DnEFhaDuAbNUyVoaTasz0Sidi5oGe3NzcxNpbObdFMLl+WFxsybJZNIKhYLXPJRUkM1m7fr6OjK2bDicznHluc/OzpxEQ69sGNiZmQcaYWsBWcLx8bEPDUmn0z5FiNo8/40jJjtkPBr1bQwGHAX0A4PN9Cyg5pubG1tfX/dTezQoOTk5cRgPJ0nbiho7gslCoRCpbyvfICwxhOujwQP7prIRBh6KivHf3W7X+xb1vuhLCNnhCEBHNDuan5+3b3/72+50aGfIZrNWLBYtk8n4uhJoaoaELaR+z1qE75LL5Wxtbc0dWbfb9fVV+J11wbYCj7daLZfRZDLpA0w4lD50GNhgbKqytwlYtQQyMzPz1rhJdUokWqy17jl2QwdVhLVE7sFzICsEGQTdyoYlwOZ9FHZV+/Ebw7BajwsfVDM6nCUOCfouTopGbFV6jC9KxdQZZVlpiqxtHDhMnKQKkhaRWSScPtGJRmEoQagQSqrQVg8OOya6UyXkvaifEKGyVlrwvouUBCx6cXHhTe+6XvzAvKSpXOu4l5eXfkA1UZcSqoC6ESSiLAyw1qFCfF+zBYQcwVdoQ5nEIAAEFGYWido10gdKg4QUttGwDhhEdQI8L7Ve/R4uFDusHxHJ8kyqOJqBkyUBcaOY/D7GmnF+qvyMekQfkHOtX4a1S6BwrRUTvA2HQ2u32zYejx1Oq1QqniHHYjGXMZ63WCza+vq6O2EmN3Fv3Qd6pHGglBj4HLKkgarZbd0+Fos5woQx1SB3ZmYm4jzCdyTIU6iX9VajpxA1BjUMlrQ+z314btopFPIM0RV1smq7FFpFP1ZWVmxnZ8fS6XSkJhtOPNNSC++ugb7ahLAEAmoA652JSrHYtA+UwejYS/YJh4x+EXgyXQuZVEY1f7LWEPNmZ2f9MHscH8Et9ludbpi5aQkGpi2ZMuiR1hrD+iZyq3A1F45RWbehDWVtlI+hMnTX9UFnGaap4YurEKqQK5TFywMxQPKgFkLGNZlM/FBlFVgyVjWELJhGGDybQsahQqGc6qxVqZQxpxRymFY03ZMdKgGB6RiTyW2RnmyGoECFVy+iqVQq5WujDk4DArIW1pX14fOTye3pE4PB9HR3ApXxeHrsWTqd9kk0GGSix7DmzPpoZsl+m72bPGF222bS799O8GCKj9YiNJsNjatmkWpwcBqsNZG51kNUlnkes1tDqwaQ3+H3iWxHo5HX4aiTqdEbjUZez+a7YFkCHWOI6BUme4OprM/EpdksRoX9vry89NNkJpNpC9Tr16/9nTCIiUTCYePt7W2fmsX9WV96FiE1EYiNRiPL5XLePsAadrvdt47GMzMn5ZDFAodpcEsQWSgUIs/BvTWTwBkRLPH36iBDVIH/1qBMv59g1izKfeC7kQNtJ+GeyvgHkuR7sWf5fN5KpZK/G8MAQIE0+1L7yV4QEIEQhJkeTppgWk8HwnZo2wtrTrtYOp32Z6aUABRMKxmZoY7mnEwmVqlUrNFoOHqFTPOu3Au94kIukWX+JBnh2EVq6Qot6x7yd9SntaSna0gQj1PUYJDPIE+692HpRq8PwrBabMdA4QzVcfDSkDSIFmhyLpX+n70z6Y0rvc7/UwPnsciaOIqipJZ6sLvhEY4zeWMgWzsIsssXyCrLwPt8ieyySr6BExg2YsA2gradTne7W1JL4lwkq4rFeazhv6j8Dp97TamN/Le+ACFxqKp73/eMz3nOeStqNpvx4GNjY6EUOAbODFxcXIzahRMhEAh3iGlsHOFicb1gTyaVNsLAIV4P5He+WZK0s7MTkYwzJoF4icJpWqcdhvcAQk2vs8/cBHbGQGOQvZ0D5wrlfn19Xbu7u8rlcvre976nUqkUtSjYmPR+vXr1KmqD77//fvRd4qgJQNxBt9vt6H/89re/HQ67VqsFPIdB8QyFPfHIjUDDgys+f3JyUvPz83EPbqDJ9GiDoU8wl8sFpEfLkWcPXCgEdS3+71k0TimbzcY+cHYq2Ti1X2d+7+7uhlHD4XKP6A+GnWABKBK4Nj26zFmHBBQe/NGTe3p6qv39fX3xxRdBNqMXbmZmRmNjY3GUE0MgstlskGf4fO8X/vzzz/X+++/r8ePHevz4sTqdjj799NPIgqljUg/3NcZREVSxHqxDr9eLwQ44BA8g2XNKFsgB98nascY+OAAHyfvByPUalRthep7JtNxZpklBHhjjlGu1WqK/D7gzn8/r5OQkzkD1hMJLR6ALIA84ZIIV5MZbR7A9PBs2iOfG3lxfX0eLGM8O69sdHXyLTqejnZ0dNZvN0H9GMbLPrDU12NnZ2eBmcBIOe+GO5/DwMEb8IQP4DGyiTz1jjKEHT+yPZ4ashbNyKXtIihYTz87ZO17vNtznSaevLz3P0qeESLc1QzJHvwloyjzAgwcP4pzIiYmJEMrLy0vVarWoQfFQKAfN9EwL8eifTWcBIAxw4VQQCuBMfwZvim42mwEde+8mggxT1adW+IXhvrq60vr6uhqNhkZGRrS0tKQ/+7M/09zcXDxHPp9/bZpPNjI8PKxGo5GAQdLRpWP6vV4vep/q9bpyuZxevXqVyDoQoNHRUa2srOjs7CyMGVAt69Vu9+eCMlnFFQ/hHxrqn41ZrVbjvtLrws8wXkTktKlIt6ctcJ/Ux3Z3dyOTZ/ITe+Lnh0p9J87fckg0gZ0bRQ7g9Ro02S736IbUn4dMioiXRnpG35HFIZcgJ2SBq6ur0b4DOoFxJuL2rJnLAzYY4ugfwQGjJIHG2E90CLb2wMCADg8Pw0hibP1sUaBd6vFvvfWWFhcXVSwWtbGxoWazGa0019fX2traCsiP60c/+pG+853v6OHDh3EcHGvMnvH5ZBE4k5GRkQgEMVzAypDkkKO7IGsvCZAZYoCl3z+TV1LUpGG5OjwnKTgOODsIX61WSxsbG9rY2IgWOJIAbI2XEpxoQ52eg+vpFJAUs2GRefTfL57TWcc4QEh01CQbjUYMJbi+vg4yGnbVSZYQmHCg9BwTNFK7f/DggRYWFrS6upoYkEIQRPCwu7sb97y2tqaVlZX4Po32EXD3ej1NTEzEZ91VryTQBbbF6ZJMwARmBOXy8nJ0BBAYu2PFthGwvu56o7OEjeUZHRvnqS0eGiPFhdDx0DDpKMC6s6QXkYgEw+ZFWgwKxoX3TgsTUQNChUBJt1Nbjo+Ptb+/nxgIAAMQIzY2NqZerxfHwwDloPyQO9g4WJAIoE/G8KkyruR+AUfzd17g9uwMajbwBQpF9MsJ7d1uN3pW+Z5MH2dJ3x8TkyDSMMqOYc4+WWd0dFT37t0LQsvm5mbUTh228ogW+SEo8UyT+/bmYupUZD9S30kvLy+HQvvoPxQPufSauqRoCPdIkp6ydC+ZdAsVYuD82Km9vT0NDw8HQY1WAp6n2Wyq0WgELOX6Q+Tvz58uZ3ARLSPzZJWdTr8daGxsLMga2WxWS0tLkSXAiMSwFIvFMBY4MO8r7Xb7vACmUjEchLojvAP2R7od5O3Q+09/+tN4z0KhkIABQaGQ76OjIzWbzfgbhlogCwwr5ys9OzkdoDmpiKzSbZTXBr1GioMCYfESxM3NTRDUpH6gyjAMHJDX/TxA9bo2MseRW8zDhU1MhkigQAbnMs2Fk0NPWFt3cFdXVxoe7h8R1mw2dXNze/g07GMfOMDzzs/PR3sW9cvDw0PV6/U4I3JxcTFIQWT+XicmMwZJlKRXr14FS5/AzGFyb5HDRmH/kG/2AQfKWuED2FcC6m73ltRJzd1b2VhXb0H8P9csgSeBDTwio97Hz9hcj+KazaZOT0/j9IJ79+5F8/HU1FRiviHtJd5QC8QG3T2dxaSjDn6HQSFqwSEQGbbb7YAkP/zww2h+v3//vu7duxcOqlAoxKxXKPYOm9DjeHPTp2s/ffo0goLV1VXNzc0lxlq5kXajGJth2TQQk9fF+EJIOc0FJ8vaIbwEAGQcrB/OBqfJ/szOzsa+Ehx88sknMYaOtZ6amtLbb7+tpaUl1et1nZ6eqtFoRNaBc0d2eDaEmdFb7D3G5+DgIBw9Bg9jB6R47949DQ0NRXTOPiCLQFZOEJP6ZBZKA8C3yBWsWCd44Ew4AmlnZ0ebm5vBNFxaWoqxeE+ePAkWI1kG70dG4XVHZKjT6URk7EGY3zPZVTabTYxC5AvFz2QyevjwYQxueP78eUCAvCekDHpgMbbA70CQfm4gduD8/FytVisCJqL/bDabiMY///xzLS4uanp6OuBbYFmcGTUxMh9kQVLUftkrrl6vF3AdDiRdWycw5bNAhjyYwkb4/5l/e3JyEtwJPodghr/1iUzYBc8UyewINmnxYLwnU8NarZbGx8dVrVbDFhDI4wDQ2XQtjVopdsjljPYZ7BD1bVC/kZERPX78WOVyWRMTE5qZmUmUeubm5mJPTk9Ptb29rWfPnsXgkPfffz/q3gQmTirr9XoxOcidJXIxPj6eYMK6HfSyDH/D3jqr2ksyBFZk09hrfBaMYT4LJ5quWf5/O8v0BkFsQHgwMv43Xkwnwpb6hf+TkxOVy+WYu0mk2e12Y5I8C4ZSUVNkPJR0W+zFOZMZSIoCOlAMR7k4SQeyy9HRkaanp2PRBwYG9JWvfEVTU1MaHh6Ofj/PYKg5ZLNZHRwcaGdnR61WS5ubmyoWi7p//344XiBYIj+cAgPjuTwDo28PGIaaFtOGBgYGVKlUwknjXIEVZ2dntbCwEIzeFy9ehLGlTkTLCwQMBC6TyQTpp1QqaWVlRYuLi/rpT3+qTz/9VF988YWkW8JToVBQuVzWy5cvo/YDacHrAewz9HafGgKzN5vNqlAoqFAohGJnMhlVKhVVq1UtLS1pdXVVDx8+TLAMR0dHY8BELpeLMV8wi7kmJiYSxCkUDCa2pJC18/PzkFOMDe1CwJ/Ly8uqVqsaHx/XwcFBGHjWenl5OaL4+/fvh7zy7ARR1GadVcsFCgP0VKvVYmZor9fT1tZWTJKq1WoqFov64IMPgvG6uroa+14oFKIfOF2nwqF4/dbZu0CjFxcXkWFUKpUY4FGv1+Oee72enj17pkKhoG984xtxAC+kM+rzh4eHARXyzNwTWTZwr9cSgQ/ReyeDOUPSYW2vP3rNS1JkrFKfuDY5ORmyh1OifIKNmJ+fj6DNexkZx8g1PDwcOg/Mil3N5frDLIrFYnAKNjY21Gg0IknA+Vxf98+65CJIwkljp3EInOjkCAC8DuRvbm5O5XJZS0tL0fMLD2F/f19ra2t6+fKl/ud//if6bt999129/fbbCQ4Ga0rCAyu7Vqsl/Mcvf/nLCAwWFhYCccFpERCgC9ha0CY/WYTslb0m8PHJUZ7IQa7zbBmOCOUSvnf+S/p6o7OkXxLn42QLHtKp1UTGYPYs6uXl7YHAR0dH2tvbC8HyxlKHVL2OxGK4ADmZxAX08vIyIqXr62sdHR1FpExkAoRAcRoB3dnZ0X/+539GMXthYSEMMhAxUeXNzY1qtVpMA7q4uNDi4mIcx0SE6tlKvV7X9fXt6DCuNNutVCqF86nX69EAT00BoTw9PVWxWIz5qNDW6ZXMZrMxSJhom9qMtz+wL71ev/Hcg4Nisaivfe1r0SvGnvDv4OCg5ubm9MUXX0S059Ajhg6jVS6X43Uw9DCMDv1x4dwx9EykmZyc1NTUVGQQGFnGjTnBSOrP02w2mwmSD4Qs4EcgGSJ7an6jo6NaXl5WJpMJSJYsEQiHutP09LSq1Wpke2SROKZOpxOTc/jeB9c7s5sIe3BwMOZ1QpphPBnBRibTP0rs+Pg4RrF52cCZgOyTG6put5tAD3AW1Kt2dnbiNZeXlzFQnPfzizmpv/71r/Xnf/7nCaYqQQqM4FKpFBkuWRKOCeOFEU2XeHyfqVUD8XmvIvfotWGHVSHnEFwT4BLoSYqMJh08kzUy7clHOfIe9OseHByoVCqpVCpFCxo1vmazqZ2dnWC2cq/oJrAtOoV9nZ2djeHyZHUgTyAwpVJJ77zzjorFomq1ml68eKGjoyO9ePFCKysrUUOnVxjnmslktLy8HCWbqampaJ9yvUOGIJ3BeXD9W19f1yeffBKIFvaHvSMz9TYf7AN92E7CIzjF0ZHEeG0cu+c9vL1eL9prXLb42f+Z4AMblQdypfAIjQckI+Ty4j7RAVChM7IcM2cjnKHoNUgnYJBNeIbr6TSGiCyMxfPazcOHDzU9PR0CDwuLqAhjB4uLe+l0OnF0EeQEslGgBq/VgePz975Ozu6VFHNdIbwwWswHtUPpHhkZCWdZKBS0uLiYIDmwjjgjJzwAH8OApU4LxAK8yykTFOEdjsch875kqL53GD+IRDTusxYuL3wO0DGBDgy5/f39IEmgVF4L4z34l4sj0/zzQBRwUM7WRd5QWEoHlBvGxsYC3udkea8hYazTMse6IZ84b4+QuaiHczlbECjTG/dBIGBSO4yHQ/YBCO7AkUPXW7Lqer2ujY2NRO3Is+B0SYEM9LPPPtM3vvGNQHI8cJT6CAUDyb3NCnnB+JFheonFyYUeGPF/11Mn/PA3rDXnRgLxLiwsxBQbdJZMnjoh8saedjqdOIYQmJ7yAUkGgQbZIGUO7OH+/n6MuuQeXT6cDeuOYWJiIkYsOt8DWaE9qVgshj4A9bZaLY2MjETttd1uB9ESeQfBIdnwVhNvsyMYwp5AjOQ6PT1VrVZTrVbTW2+9FXuEnjpCgF13vUY+2VOXE7JFt01OBuWzCGx5foJD4O/Dw8NAQu+6vpTg4w+U9uzUQ1AuVwivwcAWBMqRkqxaDBLFaTbEyUROL8dxE70xRk66hdLS9RY+l9cS9XNUTblcTvSWXV9fq9FoJIwxGQmCgqFEgGk+94EFKBOGjDVziJBN5d98Ph81trW1NTUajYBQgX/9BHCK+dPT05qfn7+zBsf65nK5gILPzs60trYWBCcULpPJRF0ThzU2Nqb33nvvzvvFcdCSQXCFsUAYvZbiRA0icgQ4l8tFbRuoDqLB9fV1ZHncK4FXr9cL+JC95nIY0uEjYB4MMDVFAhiH/tgDyCszMzNx3BERN71q3kPHniIPoBTu6DDeHkS1Wq2EvE5MTETmmz4bUlLAm4eHhwF1wfoG0UAu0B2+JzBiLQlQgAc///zzgFJ9XxzS5IJV+9FHH+mv/uqvIutLo0HAZRhd4HTP8OE1oP+UDVg7PjtdGwYxcIKRBx7YFoIOgrH5+flEbycoQCaTiYyN/cI2+AAQl2OQN5x6uVzW9PR0IF/Yp2azqd3d3cRcai/54Kz9Yv+mp6djni8JwcHBQZC0qPe7bUZWqZsTKKBL5XI5weBHrt1ROozN3vR6PTUajcjS/QCHTqejRqOh3d1dnZycBMuW5yBgYn1dHt0pg+qRnBHocMIOz+mBuDtKYHfXn7OzszhHdX19Xa+73ugs33//fa2trQUpBkPvUY8TCPgbjDdGgtmJFILTzLR8Ph/EAwwTUayUbECGtNPpdMJBLC4uJjYFJabGhKHGAPvCsRne0+jRktdFXFC5d56DiJfnIZMD5nRh9jpZ+gJ6gewzPj4efZH0EW5tbQVU2Gq1Yjze7OxsZMjcP/UnHNnV1ZU+++wzHRwcBHSEAhwfH+vzzz/X2tqaKpWKlpaWAlIG8uFZcQLtdlsbGxva398PSJp6IesA1AW5hpm87C3RLPDeRx991BfO/w0qnG5frVbD4KDcBCbtdjvYmxhbLg4/9kk0Ut/B1Ot1FYtFnZ6eBoQMy5HIHnnJZrNR28IZ7e/vx8xQPhsY12f0etsGjoA6rztVLurU7XY7GNx81et11ev1aF3q9fqn+Dx9+jQQj0qlEtkGGT+fj0N0HgAGC4e9sbGh58+f66OPPtLm5qYGB/uj2/zQAO49fZ2dnenZs2f62c9+psePH2t8fDxxjJlzDQh8M5lMMLFh33IShpPG0D8nDsEwRqZgq5OBtNvtCKCBDKXbM0N5P4LJbrc/vIP5p2RxPDdBnqQoe4yMjGhubi4c+vHxcQRhvV4v+BOOch0fH6vZbAas7ZDh2dmZ5ubmYoA9l2dlAwMDunfvXsDwBFIgPiABo6OjiXv2gN2JZWdnZ6rVahEcViqVBMnGEQvsCXvTbrdVr9eDQez9twRBp6enWl9fV7VaTSQ0/jzYY2c3e93ZSXwkMKAgOEwuEhUvNVCqIWN/+fKlPv30U21ubmpvb+9Ouyx9ibP8h3/4B/3bv/2bPvzwQ21tbUXE7bU4/nV2mddKXDGJHFAud4a+qEQ5OCl+5sIiKXB0nyQCBHJzc6Px8fE4jPXk5CR6D7k/Ird0yu6b66y4NAHDN1K6PfjW6evUXtggTlkoFovxPt6bxT0B3+DcJyYmonaJ8FIDgjiC8QUOxzn4+ZDAhJVKJaJw6Xa4w/b2tj777DNJ/drpkydP9Pbbb2t5eTmUi/W7uLjQ9va2/uVf/kUHBwdBFAHy86DCM4Kbm5tgFxcKhVhbnJMTyQgspqamNDMzE/UeUAia7F1+eBaYm6zx+Ph4OARqZmQcHIDM/VMq8LYGnh9jdH19HeQBp+KDbkjJI+o8QicA5LPp8fOaJUYHAhwkB2orQJU4393dXe3v70cN65vf/Kamp6c1MzMTQS0GCOaoZ2YeEF5fX2t3d1cvXrzQixcvQk9oTQK9cHTFdYi//e///u/IvNFJHDifSYAMBO4wKnLrGQw/R5bRNS6H6lwuXG9wPmSAY2NjWl5e1l/+5V/q1atXOj4+Dtg1zZHAYTCNiZp8qVSK9ydjA00j+OdZkSN6KsnGSERod5iZmUncL5ev0dDQUMCse3t7UR/tdPrTlzx7Q17dtvragUBhwwi0WDsyZdaWgJtnJVECvvU9gQjW6fRbnx4/fhzHxhHQeIsRsuDIjN8D7yspng92Lk7cpw+BcHEsHe1Jn3zyiV69eqVWq/V/77P81re+pQ8//FAvXryIFgUnCvi/HhU7tIqRYMC310CdsONTabzW54qCkCB0OCffbAq2sF2JpCD7FAqFgGt8Q/y5HPO+Kyjw2ikXsAH1TaAT6otku/TlFQqFeC39ekC2CEGr1dLZ2ZlmZmb08OFDLS4uKpfLBeX40VIAACAASURBVJOSHjGejxYOWGPUKhDSfD6v+/fvh7FOF9dBAeiRWltbi8geRw9DVOpnPh9//LH+67/+SzMzM5qamlI+nw8n5dk5cCJwNPs3NTUVtbl0TVxStLXQ2sIpJGSbBGBONklD/pICuQCKpZCP4cKgAZHyDMgazmVoaChqHV43c0ICr/HSgSt7GlLyTP0u6jrO0j/XMxb+7/X2zc1N3bt3L4YOAE+7o/SZq/5ZGH9nDOIcYFkTWPghwOmr1+tpe3tbu7u70RQ+PDycaDNzUgYZn8O0BFFOFONyQiHBpgeAXlv3IDy9tpIii3rnnXeCJ0DvpZNJuG/vc3S7gANym8aXZ7AESWSCBJrIcLfbDVmXFKMB71rjTCYTUCvO4OzsTJlMJroICEqQA09afE2QDQJJ7sftL4EIcslzMmACR+tcEshHvG5xcTHq1fRUIsfO95CSp+8438TtMLJExpuG7PmetcY/bG1taXNzM0pTabKaX290lsViMQ7fRbndubDovhkYXeAzFp6CsdcqvZfm+Pg4MdQA4Xaj4AV6n9PpBsaPn6IBmCwSBZicnExE+s7mcmHFyLsh9/t3NhdOzPsqqcUx8zCX658OMTs7G+OYJGllZSUxPAC4GLbtw4cP9fbbbwfMWq1WYy0uLy+1tbUVbONOpxOsT+qIL1++1MjIiBYWFvStb30rDCAQkws/lPLDw0M9f/5cOzs72t3d1dbWlm5ubvTOO++Eg93f39dPfvITvXjxIpwle0idDGdJtIjjZT1h90KCYE2BDEEHGNoMtE6U7EqCgTo5OQmWKFepVIqWpbGxsQSEMzAwEPVqHODx8XEoPEMbvHkZGQB+A6qDQQtE64QNNzzplh0PrkI587cHAAAX4hB8LYmgfVxerVbTy5cvNTExEW0wGDLvr3QOgNesQTiAbbvdbgR1vV5/ahTIhbOXuXjeer2u9fX1yNy4B+cNOCMeZApjTW3LgxPPLv3yOrMHt3xdXV3FOrtRRM6q1areeustvXr1SrVaLYZPsA4EKzhGZq062sb+uvP0WchAlzgZ6mcEVY5mzc3NaXJyMhxn+vLsEnSjUCgES7rd7g+jcGQJm+COh7XCrmIX0xk7eklARakD9IwhEgQ6jjhIt5PTTk5O9Mtf/jL0+dGjR2GHQHzSyQn7i2ylf+YXQTl2Dq4IX7lcTqenp9rb29MXX3yhWq2W6BV93ZW568P+eP3x+uP1x+uP1x+vP1631xszy/n5+d7U1FRkiGQPsCOptRFxACcNDAxEryJ1Hija0u0RPd5rQ1TmUSNQB/AmNS2yB4ekfvjDH2Yk6cmTJz1Yi/fu3dN3v/vd6A/y+0jj2ZKiPiEpEe0TLTocQYTvTDCvVXrLCc93fX0d0c7h4aF+9KMfZSTpX//1X3tklk5Ll27rNen7Iroiw0mzkHkta+71Q4edvb7j0ZfXdyiMHx8f6wc/+EHmt7/9bc/Z0Z6le13a0Qf/mc9iJKJjnam7stYQWPgbCC5E+d7zBdHDs9Mf/OAHGUn6+7//+97NzU2QrRhXBzTr68vJDDCboc07EzxNbffn5F5YF5ieZG5nZ2fa3NzUxsaGnj59GqeFsBc///nPM5L0T//0Tz3QBXoOv/71r+uDDz7Qd7/73YCT+UKeWXf+TwbnkK2k34MuWWP+z3Ohd2SxtAWQyR8fH+uv//qvM5L0j//4j73FxcVASoBu0WVvGgdx4PL7YD1cX/k/+vP06VNtbm7GEAp6ULlX6oTsW7PZTNibH//4x5kf//jHPTKao6MjvXz5MnH6hc92pUd7ZmZGMzMzWlhYCNaxpMgYQcI4yYV7cSSD50/3IUN+6na7MbgB/fzbv/3bjCT98z//cw+2OvVT4FMfX4es8vzp7NezYC63v2SeZNe5XC5q4N707xevwX7lcrmMJP3N3/xND72FyOTlga2trciGGalHT+vAwEBwFkA76dPnGSmt8eWkILfVyBGyhM1hfzqdjv7u7/7uzvTyjc4yfWEc00QY0lcgCgQMQ+pMTDbCjTyNpcCIwApsPoeSOuPQjYHXMTyN5jPSNGcgJ4Ta2V7pOiyLjEJRI0RQqLcBcSDAOOe08CCsDhEi4DwTyoGiOURA4JAugPv9OmnJGbtei+XefG3S8IYrA05AUqJ24/1WrBPPgWx4UMF+8XlumDOZTEB+7BVBDTAthhI2nJ+ZKCmx9g7Pczq8pMQ9U5vzQfq5XC7Yt5wgkx4biGNxQgrBYprc4/R81gl2dqlU0v7+fpQP3HD5lBIcFbVooGRvjaKR3IMUoGH22deeK+0o+Rmy6I7c/+97l34v1zcP9ly3CNb8Pdyop2u77ij39/djBBvQXzabjSHewG+UeyCtuPNE9zKZTKwvToe9dQIPpRz2cGRkJJIH9NcnfXnfK07UuRysL7pFPdjX19eJy3kfXtIi+CAYQEdZc7cD6T3jSgfnBET8HHIboybvujzwRi9JJFh/7GQmk4lBNewl6489dl1ptVpxbz4UwZ9VUqJ1z+1cOuHhmf2eX3e90Vn6m0lKeGfqS36TCBSZSLvdjqbyTCaTiBJ7vV7idIHd3d3EwOxisRiRxdBQ/4iYUqkUk1uoadzF4nNj7eQKjCv1tOPj44SjxPB51oTgQTluNBrxmuPj4xiYjSPDwNLmQL+T3wMZDhfH4JARMvHj7OwsyAYoWqlU0tzcXKyNZ4lpQ8CeuUC9bo+9UO7Oi/3PZrOhHCiPR8huXP316QiWLCVtELl/r2MjvNSeYYIyFtHvmc+jj5OonavZbMasX6+zO60eZiLkH5/5ubCwoEKhEBGt98B5dtTtdqNNxtm/1DkhuyGj5XI5iAZO8uDCYJycnCiXy8XkqbGxsTiWjWHqnHzhGa4PSkD+kCXuwZ2bP4dnovQ0subI/F2G11EiZ7gi+x5cuKNG1zxo47MJqBnv5+xrZODmpn92a6PRUKvVimEehUIhRgD650qK3mOyQvaUmjjsSshONzc30RLCXkLEgXPg4zR3d3cDVZCUQB2wd2NjY9Hv7etGskFQxJXeNxy896x6TRK9TNdq7wpW0utDr6kHqe+++67u3bunpaWlRCKQfg/qj9wzMtnr9QIBurq6Uq1WU6PRiN+TVTuxyn0A2amzdtPkKJdx7LvLp5Pt0nL7uutLM0ve5Pr6Oppf2Qh3AgiBRzwUgCFJAL0xh5BZnS6oTmnnoSXp6OgojoyamZkRUI83skqKIjOfB6OOxlXo8Ds7Ozo4OIg2hImJCa2ururx48cJRiPZzPb2tp4+fZo4koijkchIPOLhZI5Hjx6pXC5rZmYmWIPO2JMUDcIoPIp5dHQUrQA4zKGhIa2ursbYrEqlEgqPADic6kqTJj+Q1buQw86DgMH6km1JCsayT1py4hNEBYSd4ES6NU4uXzhQz+CRCQZcM+yAewEyYawWjsinvzh1HTnkPYAEaSMB1kXOT09P42/29/d1fHysSqUS5CyXd7Jf5JqB62QTQMUEUDABuS8CDmZqcvHeZ2dnajQaevvtt1Uul9Xr9fSTn/xEv/jFL1Sr1YKFmMlkEiUSSYlh3wQFBKuc/ZmOyr2k4oEY+o7jIxBwlIT1dCjdWZXex8x74zxoj/IGdZw1egHbEwIHI++63a52d3e1traWmDfLdKrJyUktLi6GLYNIxfzWer2utbW16LFEppEV1oTBCVdXV9GiUy6XNTs7G0xxDxCRS0oEjgQBbzp0ilwTCEC0cYeUJhZ62YGA1PUeEk6aTcyeeCbpRKp2u39c3/Pnz8OhZTIZbW5u6tvf/rZmZmY0MTGRKGFwAWsz9YtnwTEPDw9H4NFqtaJsx1pQEoPhS2dDvV7Xy5cvYxIYbWXYBOwGQ+RxjJ79+70g+5T3vN0sfb3RWXokSaTkrFCcipQ80gjhR8hZfISMUUvebIzwADER9XvGR3QFtMHC+tQTDC0RbS7XPx9zb29PGxsb2t7eVqPRCHwc1lYul4tmfow+EZ7XRt2wU6d0505GfXp6qq2trZhfCpuSKMchDIcncHIw9NhoBoQfHx9rfX1dtVpN4+Pjunfvnh48eBDtFMA7vofuHB1m9efDEMGqRemo6dEXiSz4+6G4rD9BEQbNR7+dnJwkBmaj7BgIntPr2+VyOYzmwcFB9BXyGTjvXC4XDdyOMPA+GJdsNhvzitvtdsxUhaHq47qQOZreGVYwMzOTiJRZP5AA2MZk4blcv7WI51tYWAhWdrFYDANPtiH1HV2r1Yq6CmMCX758qd/85jf6/PPPo57JPlIr98MACoVCBFcYfhAd5Nonszhkx6i/QqEQZQbag2A7u5FkIg2nDXkEzz2iO+yFDxXAQaYzJdbeh/CTPUi3E3lYAyYsIS/T09NxTJv35eZy/SEEtVotsntsCjNMvWzizfAMnnCEg2eSlKhLAiGDiBDMYeP4Gy/7vA4SdIfrwQyfS5AGz4BBJcji2NjYnQPHnfVPoPrJJ59obW0tgotSqRSylYbh2WvWy+esoisuK4645HK5OCybAJqafzbbHwSCbWw0GnFeq6NdzspFnwmsOEINZAYmvQcLdyFvfr3RWTqEKSkmorhQIHheO/Q0nMX0VpFOpxORvFOqqW06QcWFCUFLQ33pTIVNx4E1m02tr6/r+fPnMRwAWBEYLZ/Ph8H2rIvPxKlCe8fBS7fQSiaTicgGxSCDZo4rBsmdpQclmUwmYD3o4JwscH5+rmazGaezE70z3YdTO1gPd4zpLxdovg4PD+NYHSJehDzdYOwwqQ/lplgOjEwAwjrTbO11K98zshWCCgy0dNt72evd9m5KSqATaeiQi0CMRnL25OzsLLJADwAc6oLuzr1Sw/SgitIEMkNGDakBHUDRDw8PVSwWI1MdHx+Pk3m4isWi9vb2Yp1mZmaC3LaxsRHtLkx7oY8Nw8OAbHQKeBposdFohKNNtwkQNHIQNPJOhjk0NBT1q7QBxOB6v7GXb7zm7fpMkM0eSwqjxnsj9zyTlyMY3tHr9eJEHOA+yFwEc15aYpRiq9WKv/dAjnXhd2Qw3vaDDfD6oTsPHyBPIuD8Bv8cbKSva9opedbv0PjV1VWgMZSJWq1WyEq32w2iJqQZUAh3fjTs7+zs6OTkRNlsNnrlyej8ft3OONLil9sl55w41OzJFfwMZIDnPDo60sHBQZyugp1H5xqNhrrdbgyiYYgGcPe9e/eCfIpMg7akIWW/3ugsyRYx3gg/bFOHTDqdTnhxMkbOQex0OpFiS7d1RSJvjBa4NZ/r5+7l87dnPRLtIpzpe2bTBgYGdHZ2pu3tbb18+VJra2saGuqf6Qc8RYTH+15eXgaDkSjSz5j0ZnV3ls5Eu7q6UqvV0tOnT2PiCcfcSPq9cXdecxsYGIgIOG28MdaNRkOvXr2Kk9ovLy81Pz+v+fn5ONPQiSdOZMJoYSBevHgRRz2dnJxocnJSlUolIDHvG00rL+/jXzgDMkqm9XAsGPdAxkC9muxmbm5OpVIpHFva2HqgQgRKkMNkDu7P6zz0j2azWe3v72t/fz8x8g8HTiDjo/14L2bTTk1NaW5uLuSNPcQIQhphCLn3ELPHX3zxRWR8X/3qV2NcoRugJ0+eaHNzM+Z1rqysRICDPjHjl/MDmeDkA+09MsfIXlxcaHNzM0akATE6mQydY+bwvXv3tLy8rAcPHsRkLFAJLtCWqakpVatV5XK5CDg8a3anyj3hfDGY1GglRf2XDBzZmJmZUbVaVblc1q9//WvlcrnQ4Uwmo7m5OU1PT0edn+AVOQGxAqkAnmMN3WmBEOG0/axRL0XRe8rUIpA0IF6cPIcwo2O5XC7B68BW4oDcxt3lKJmo9fTp06hlU+/302mmp6dVLBZVLpf11ltvaXl5OcF47XZvj0wkKxseHg4dQVY5iQXbRfDsiA+XI5LYMtZrZGREp6enwZ52IhXvx964XhWLxbAbIGjn5+fa2tqKwfQc2k6P7cTEhB4/fhynGw0PD2tpaSm4COkAIOFbXvsb2xiiKW/QzWQycdI58IlDligkm7yyshIPNz4+HtNGWBAOusXRzs/PJwgZxWIx0n8iMaAhL8r6ww4MDKjRaAQp5+tf/7pWV1ej5cWbxoGDIImgoBiNfD4fA4ZRdCetsE6wd8vlsqrVajizWq2mmZmZ+Ft3PE5Ikm7P2PMDkYH7gBho/P3mN78ZrMDd3V0NDQ2pWq2GIJAh43iBH2u1mjY3N/Xs2TNJ0szMjN5991299957iXM8vfbC5WPiGMNGVAuzzWsFTnGnWI9MEbnOzc1peXlZi4uL8VkYLa/BkvE64QPYmgjUm9u5fvjDH+rk5ERbW1v64osvgqwwNDSkpaUlzc7OBrRXKpX0/e9/X9lsVkdHR/rNb36TaDNiaHMul4vxbRy67AzWsbExLS0t6fr69jxConPq51tbWzo5OYkDeb2RG1hwcnIyJjhRn2RiC9mtGyvug6lFQLHsqXTbIM6gdbJggld0n0xpfX1dT58+Vblc1vLysr73ve+FEXVnCaGNwBgIEF1LM0SRYwIUr8f5WbDoM5wGBiEwPJ5s8smTJ2GskT/qWsiU654fgXV4eBiIAxDm0dGRxsfHNT09rYWFhchMcbDME2ZcoWc5BM3YoGazGYz/gYHbkzLm5+fD+VA+IsBw2JrLuQE3Nzfa3t5Ws9mMY76wrQwiWV5ejkCz1+sFvInz8xqtQ8KNRkPb29sJEk8ul9Pq6qoWFhbiXp0kw30TIHAVCoXEHGOv2ZKIEBgTuPgs3u3t7UhecKqUbUCVNjc39fTpU/36178OPcnl+u0uPEM2m41DuHlekgzGib7ueqOzJHJHgD3igp6NooyPj8fxUEThLCTpNRna8HD/XEKUHrgWgc7n81peXg6209DQULARqWnQ/wlpg8uhumy2f+7f0NCQ5ubm9ODBg8jugAq87pbL5YLlCx7utT1/fuBGMtNMJhPPgZEZHR3V/Px8ZNuQOfg8LifdED1zesTR0ZEajUYYtd3dXZ2enmplZUWrq6taXV0NWIqeN/rN7lLe3d1dbWxsRI1mfX09ZuyOjo5qb29Pe3t7mpycjAkivA+ClFYeIkyUZnp6OlG7dljq6upKOzs7Gh8fDxJUPt8fpM/JIM6YZi/dUPh6ee2IOrpH6lzLy8v63e9+F/BwNpuNZ56dnY0AQeozVB8/fqyBgYGA56jvpesaji4w6WdqakqHh4dxbujZ2VnoULFY1MOHD7W1tZUYtXVwcBBBBdezZ890cXGRGBnJsHoMMLAkxBdkEH2DecjZrRDeGLvo5LR0nyZQISgOgcLV1ZWq1aoePXqk0dHR34vGcTLNZjOIGThmiHrA3owyJMMAnZFusyXQA3QSIpMffEz2TBB8fX2dkD/skmeA2AGCbVACsmDYrJx1WqlUEox/SWHbJCUySqBw521QQ6NuTfazuLiohYWFQDOwMfw/TcCSbk9XYsQl+14ul2P4OYFTOlslWMVhOnzMPksKVI5DzhmF6TrgtT4cL8GGE7/IGD1L9sAHrgckRrJAZI/gBNnFXkAMOjw81M7Ojp49exaD4J0lS6IFckAnxsnJiV69eqVerxdErdddb3SWeGYUHSiMTSJSICuDXUf0iPcn4sWQjYyMaGZmJh6cbBKDPDIyovn5+cRQgqWlpSBVAOMBAbsgUe/igggE7diViWfh2ah7QIIgMwPrR0CZ9cp7IwyufO12O4zn0NBQGAcnN7jAsd7ANhxI6604GCFGn6UZdjy/R6Q4FGpdtVpN29vbCRiS5+z1ehHBUZNz4gfGxB0lz+6DoonkgEaA0HgtcysHBwdVqVSCdu8Zp68L9+Z1Tn5OFuVOC0fsUSL7yFBolNRHMvIaRoRBYFtYWNDY2FgYRm92Z9/IkAj6kNWxsbEg++RyOVUqFa2ursa9+XmJbril/qg4ZB1DBvJB/Y978uyAe5IUQRuBwcDAQGT3zlLHkbDHOC+cEuUF4NqdnR2Vy+WEQeJCN7rdbkDXPBtZ3PV1fwh9oVAI5wzphLXFaVAX5p4I4jGmXp92NMTJgZlMJkEm8Xt2UpSXK87PzxMtBx4wks27s2YOL0zO09PTBGEQdAhdpP3l4OBAh4eHAYcSDDJ31sk77C0Gn/okthI4EdmArAYbFlvuXA2+x56wJuiMB7O0xXGffoFiAcF6nZXPYC+dx8CzkWnzO+SRBIsAgLIOzhJ05+TkRI1GQ5eXl0GOpB7p90JA2u1245AL+rvTBEm/vhSGdQo1hoaH5XBQahQLCwvhLDFqMKIwhpwJt7CwEDAXjsFPk8DwuBFDGIAPnKXKBZsNJ5HP5+NwYowc0R7ZGhkmNTUEhsicz0KBOBaLYAK4aGxsLEH2OD09jeDA8XUCEBd+6fYEe6Aa3ntsbCxqJGQqnDLw4sWLcHh3NQoT6UGP39zcjJ7WbDYbQ93z+bwODg60s7OjXq8XU1F6vV7MVHXD4YLH2mSz2Zh5W6/Xox+OA1XHx8f15MmToHoDybN+bticjJQmN7CGGDVqlgRcw8PDmpycTJxz+qtf/UrNZlPtdjt6eLmIeDGaAwMDqtVqIQeVSkVTU1OBKDQajUTLEkrmPZu9Xi8gJxAGh4RmZmbCyVFSABEI5fzfjAr9waHMzMwEScEhTTIWDHixWIxaMfOGMXAQS3gGAkj2EgeOEx4aGgrH5vVqh3bZH/gHICQEpMxIRVbm5+f19ttvB8mD4MXZpswvzmQyQTbyAQ/U59MZH4ZeUsL5ptEBnmt+fj4CRLL0VqulYrGodrudCKxYG4gv2CsOTicgaTabkSCwNjjc4eHhYLWTmXKGLcgNjoIMjAs7RE21Wq0m2k8gwKDva2trIU/j4+PxrARQnnxgwyYmJjQ7O6vFxUXNzc3FtKJyuayvfe1rmpubi0zSdZG1w9Zy4Ttcpl2XCayRQ8iA0u3pLFLfiQN7Q2j0DJXDIwh0cYjY1E6no1KppHK5HH6Ektfg4KDK5bJed30pwQdHiQNCuX04AFEZi3J1daWpqamAeGDVYcTGxsYiapucnNTx8bG2trYSNcvZ2dmYjkGm4FRrjBSbxOWKgEPnb4AQOM4KOJRoj1NJvB7Ae4+MjKhcLkc0B+N1b28vsupvfOMbEYGiwERLGGIgSY9gUGRqvrwf0AUElFKppAcPHkRdAaO1tLQUtcF0fRHHQ60FuBEhB9pC4N59990wzBg44HOu3d1dFQqF+LxWqxUwIU6XiBi5mJiYUKlU0gcffBBnR15eXgYTzqN9FIromX29i6xEZHxzcxMnzbPWbmA2NjZC/nq9XjBnyeYJxIhavVVoeno6QTLgX4ywB2ZAnrTJQGqpVquxRtns7bD85eXlYB1fXFwkztN79OiRnj17Fv3CoB5ARQSgZK6MV8tkMuFUZ2dnValUVCgUAi4nCJ2YmIhskedDt2jTobbKQPuZmRmVy+VwAGRAXA6jnp6eqtls6q233tLjx4/19a9/XT/72c+iBDM3NxdrQm11cHAwnkG65Q2gT2RbBLsEoD6QH4fJ3+AkgR6dpT82NhYHpgP1oYuXl5d6/PhxYqQiqFCn01G9Xo8EYHZ2Nvqp3XHjoOAxLC4uhkPg7EcSjQ8++CDx2QRgBBJulylBUZriWK56va6trS3t7u4GwXJ7e1vvvPOOlpaWAkZHjmibc7Qmk8loZWUldJ9EYGpqSouLi7EHoIrOIqULoNPpJAhdR0dHCT3myuVyAeWPjo7q7OwshmwwchB9dh9AYkVgIvVJfN/61rdi7732Cpt9aGgoCJIDAwPhY7g39yXp643O0pUA2IN0NZPJBDzjzdke3cEkI3pF6AYHBzU5OalCoRBRFvU0IIqpqalED4wXtdNkBn9Afs5rYAwSnezt7ens7Ez5fF5zc3NxGgNOhs0gKnFBkhSZrbe0EDmTTQM7uoIBOxIZpes8bCwGmufy+8KhAYmRCQChUBNEySAKEPCwhoxu416cDUxExrOkpw9Jiv3BubXb7Wh6Jxp24S+Xy3HaCkEQF3Ay7+U1ZHdG3g7iMua/o27ssDYXLRlAeUDt3W43shevrToLWUpOTfGaMzLqsg5jnPXy1givl3EBLXqzPJ8F8uGBBGxwEBGeCVlhTzFwfoC39/P5301OTsbvpOQkJ3SKEsT5+Xmc3Sglm9Gp4cOiheiUyfQPJx4bGwtUiRNgcCbeBoIsgBqwHx7IIv+sJ3vEeju7M41ScM/UvyTFOqOHtCZwTwQVkqJ1xYc+eG0X6Nz1jCCNeyiXy+p2+32PxWIxzrh1pMOHgnA5nAnc22g0tL+/HycQ4fTz+XyC3EWriHcaIO+sKQ5e6jsgWLygbun+V9dlh9z9d5B4XDdJKggwccDUudM9+rwf2Tx9sASrAwMDMbeWVqqzs7NItig9jI2NaWdnJ5AiMkwPru+6/iBn6Qbbjb9nINQMXDDZdAQWISXyhUFG3xesPFcSr8eQJeFE3Hi6svI5RPpAFs1mU5ubm9HzODc3lyCUeCMyETYZNf1SLDZwBREvNQy+R8mc5o0hTdd5eCZe586Ce3CyhbMUodjzPk5qwOk6qQHI3J8TZXYlYC2pFbD2kqItwckAGF9gJ35PFAgzE4eK0WMN3UC6nLxuz/lM/pZ/HW1wx4MS42x4bpwYz+qtStwX74kzYR8dLmbfeB50x5mNjtR4W8fQ0FA4eSdFHB8fR58q2Rr6gv6gh7yOtUX2mauMUXRnz1qQhYKwIEcuV0CfZNTeR5iuTZHB53K5cCaS1Gg0oq5H36tnXn58lNf3vcXEdcf5ARhn9MyDLq8tc/Ez1oDshuCXZ+Vv2BvkkM/xM2xZY+QDfXU54Iu1IUsEqXPWpjvJdDbmsCVEFcZBAk8jb0DxudztMYI+pJx7cwgYvS2XyxFEYB+x+47iwedwQpMHUaylrx3/4iw9oPDpUKB59AT7unvigF+hfAaMjx3ykl673Y5JXZ69Y/fvut7oLIl4EUYUCy/vURvCjcC6MJOSE5Vy9OODAwAAIABJREFUc9S7ut2ulpeXowZDI/fV1VUI8V1ZAxvs/TywN4FHRkdHA3qgxoJCo7A4FKASIklqeu12O3DvXq+n6elpra6uxvMQEOCURkdHVS6Xg7mHQnp2nMbznZCTNrhAXUSTGDQyZwSU+hgCAWGJv4NUhTH1kVIEJ94kjVChuE7+QPBYNxSXDBjYCPjE62Neb/HMFWVy1IDn94ACAwas7YYAaBgS1uLioqTb2aGsBRM9yFIoFbjx874u2N/MX/XAgqzDEQRY1zC3/dQRD0YwVhAy3AA9e/ZMzWYzAg1kDPmEzMTee0Bxc3MTjgl4E6eNY2WfMplM9B5D3MPApRmloBuUYCQl5hxTWmFfCVZ5Pli5TJEB4qPe5Nk4F/LgE8PIJpATghGyTIJbDzw9uPJarfcEzs7ORokAWXOSIkEORh55SqNww8PDIQOeAUJ4yuVyUVNGFqm/OZKDw3Ab5/YW1unQ0FDMzubfwcHBGBHHM93V9obMOF+AGuY777wTQdnExITm5+cjycFuso5HR0fhvKk7ciF/rAXr4nKIjaE2TtcFo/bYTxw2zhVWOAEOfsr9j/f0T05O6sGDBzo8PNTW1pa63W6CLPS660sn+Dhhxds/UHCED+FEgGHX8T4w+RAkCtG8jugK481MRx7+5qY/R5DPItpzAyopCDX0zoyPj4dDQxmJHLPZbFD2q9WqKpWKqtVqZLQoMu/PhBRqtmkBRMi8PumOJw1xchGRAU3gHHBoDk250rriYNgwqkRWvCf75gYTwfPsEqfmAQ9rzYVxIsobHx+PHrWLiwtVq9VE5kmNyOs4/IzAwSN9V2iHYtzIEY0ODg7GfVDrRX48sywUCkGEIVvEAKcHbHA/yIq3Ct3c3EQggHP0oM/3wxmAwKQ4V8+aPeNwo0vthiPmkD0cP8/NPvEvdW4GF/DZrjvU6FhLWMAYO2A8z6C4D4yUT7TiIgClvsioQgwjwROBiKQEEuKfyfOwzg7DeqbvsHYaZUKXnEjiFz23yLnX6Nh/6RY5YW29X9JrafRhsz5A37Djr66uVK/XYyQgpBlnvhIUeU+nO0v0kSB6YWEhMnLuj2wLW8fr8vl+vzhj6xzeZl+xEQRRk5OTYbcJcPgb9JMylwfynqXhuBxOTaNIDjmj+3BDeH5qzKVSKbJ2YFueEdnC4cORYR3hDEDsOj8/jyD/dSepSF/iLFlkV2YcmBNt/O+99oaC4lxGR0cDwiS68myDyBByhsMvGFsW1xc8DVEw1aNUKoXxm5qaiqG9tILk8/looh4bGwvWI1GKwzgsPJkZC8t9UevxuqSvE1GTOwYuhzLvguhwOp5tsS/cJ2vnEVx6XYh2yQT8NQ6N+p7fdZ/U+xBwghAyU8+yHfqTFCQSjCIBRNqI+ef5974GAwMDYdQIVLyGzJ5ICqgSg+SBBEaAbAgn5lG99yN68MB985xE2+myA/vqhhudcRKL6xTrQ7bhcD+ICvcFKYSeOoIAyEPei+r7wj17UIfRZ48wlI5YeDbrDsqNpNfueD6vp/P8Lnf+Ph5goTew2gcGBgKFQpbJeNLy47XXNCN2Z2cnHF8+n0/0tbLW3LvrIc+M83e2LA6ItWR/qNuhazgkZA3SIXtBkNFutxPZO0EAe4ldIUnxARqSIpggOCXzd2TEgxBfq16vF3Cn/43rJqM4qceScfu4O7fd7K/bQofNgf2ZFQsqQyAG58LbPQhkeF6Imp1OJ06JwWawbugIz5t+rvT1BzlLPL0bUK+98LCOU+Ms8fTp4b3U2sjCYJjiIGgd8ZqEPwxOwoVCuj3uplwux4L53E0wbgSHqTrAlCMjIwG3snFsLpmiC6s/O6/hC2eUrlGm79n/7xGWv94NsdfxMCROrEGhWS8npPgAYYQnnU36612ouU+vVeMsMeAYRNYGpWcfqX0RqfOsGCV3rOn1SgcBfD7r4EbH6+gh7P/r5MheeT43BB4skEHTy8hriFzTiu6v8yjajRABkdd72XfPqKR+K8H19XXiyDHWCPYq0T3sQYwqdWIMCrqYJsq5HCFzzktwEobLsaMXdzlLL8t4MMZ+OySaDpb4njILMi4pgdaw176erpNOUpKUsCX8fH9/P4IqbAOwKkiDB69An2Te7ijTz+a1X2SVfxnxxvuT0YNoACMS/LmzZI08AGGt/F59XXCYZMIuw26j3Fni5GlpcpTI7XB67i3wvA9Sx95wz25XXK+RR9qOcJbIETXXNEqS7taAKdtut+OkLIIfr1Gy1yBH6XVO2I/X/kbJ/j8cAlFwr3fLjMIIsRBOAkBoWAjeg8b0YrGoSqWi58+fxzioXq8/TYHo1xlWaZKH36fUn6c5NzcXMyEREu5ndXU1IDqfdgKNmAiJ7MQJFxgTF0iHTN3oeyCAMXY6s78va4qAs5EINGvH9wgx9040LCmgFYw7QsIasF7+nqyR1wM9WkXIuXgmdzQYL4hNXkhnvVEsJx14DddRAlcq7tnRCl9/no3XMizDBf/w8DDklftywgLvwf0wEAIojFoTgZ8TZsjwnHDmmZFn7XdlUV7v9HX+/ve/r93d3YDR0QV6PempZHITLTzFYlHLy8sqFArBLpduj3aCVOInOYDs+LACgiIPwlwWOMnCZdlZj25UMY4YQwy4B3JeB+z1bg8dxrkxyAAkCCdBAMa8Vc/8kRn/ciTIDw6g5k3g65kZqAW9yEdHR/Ea9LXX64XRxfnx3ryectPKykq8H7qBs3RHxaQZb8OAh+ABsjuggYGBKLVQ4vHn9zIEz4yOIYfs0/n5eWKU48HBQaKWDexLBgorlSEqbjs9+XJOgJeFCET29vZipi3r4sM1PLEiUIHTMjAwEHOXl5eXVSqVVK/XQ56QfYIc5MURh7uuL+2zdCgWIQMSQglwEhgPIl2mgzDx49WrV7q4uNDU1FQ4JBYeaMfp4PRO8QURg/txmIJrZWVFMzMz0ZbS6/WHDcDK29vbUz6fj9mBDOyVFMdTeZ2CCyEmO7q+vo7oBaXEwWAUnQSDkKeVVVIImGekDnm4Ac1kMjGgwJXRjZAbX5wZn4NAYeQwiA4Rp51WOjt1EoI/O5/jQ9QxqM6M5rBcV1SHo5wEw/uzbp7t8jpqNa5w6Shxd3dXKysroVQEHV4bJQtjfbgXv3eHrR328uHcfu/oA2MZiWxdn3z9PPBbXl4OmHloaChIapDlkAHqOujq9fW11tbWou7P/oGs4AAJKI+OjrS+vq5WqxWElUKhENE8BoYWkE6nP0INhMjX2WvLoBZeikBe05kmNmRoaChQJg5npwTC2pPheP200+loY2NDnc7twQeSouaMQ8IJslbUVj1A4LUeSPd6/SEHnC17dXUVze/05uZyueitpe+Ruc7cNzYBgmG3241GfgJc9hAiXDqzBGHyDNyDdNbXyybIONmf/z3vwbp4ttfr9eIc4Xw+r1arFcmBI0zAytibXq+XOBvSbWm6FECAgS5y+hFjESUlZMhLHh7YENgyIhH77GMd2VPWGXuBw/YyV/p6o7N0w8INcZP+PQsAJOaQGd8fHh5qc3NTp6enGhsb0+XlZURnDFGempqKB6nX65KSkF96g++qmdDXNzg4GE7PC9FESBgYBiF3u13V6/Wo+SCUtEkgmENDQ2GocPxsPFGbZ1wO1fmXG8U0HJcmAaWjTRweguMEIIcL74JQvWYgKVHj8Hod+8da53K3hz97xkdNyVsJEHx/Pl8XIkq/J//MdFbiUb7vBWuUNoLu3ELQ/9cwZzIZnZ6exjxKivsYHLJCHBLKynmkBIppI+FRc7fbjWBMum3B8gCGvUwjAF6zpB+YQIa9B3JtNBohrxhi7ochHzgUh6wxtlNTU5GxXV5eRgZK7cxrmawLziR9/BKXD3vwi71CL8iKfLwba+LHjfFMjmQAsbMm7DnwNPLlTiBtkxxF8RYK3ydkjD0CFsxmszFuzUk/znhGdzDCIGkuJ2REDKpAvtBjnEia4ANageF3fUaOWStH5/hduqSB/LjueUDjHI9erxd7z+AEuBvsL/bK7zktA54R87ygjq1WKya7YbsZvkEgwjpks9nESMWbm5tE4JjNZhO1ZJwqeuMkqv8vZ+k4sBuuu36G0Sb7c2MgKWi6zOGD0kzB+dvf/rYqlYouLi50cHCgZrMZ0QGQnkNV6WiIC4YtUBqbgzCx2Mxm5JBask2K1JlMJnB4qR84cGhop9MJhlav1wsGLkqAYPiG+pq58U+vabrwze9x8tRKcNxEeNyz1xTuqkGmo0+gGgw/f8+X14rcWXom6E4dOfD6WrrGyLowuCEd+Hh93NeMz/C6sD8j78vnOXXdGaVMBSFg8vo4sgK5ALIXryWr8kBCujVSfHESSzabjRoL94l+sAYYbgJHLpr4yUrcoHBWIfpFew7GnXmjPqItm83G0HWpf5AvwSjZF7KFzMIS9GktyAFBimcNZLM4f5dzZANjiePFyOEUCFII0LyOjOxi/Dy4lW6Nbpp8yNo7VC4p0VfL2knJY7BAk7a2tnR0dBQtBsCOBDUeBHLvwMRAlehiulaIU+LznLCVdpbIDvrlOokt9OenM8GRJ+4BR+n2xmvEuVwukCJkAtQFh+/lBXQujZ5Jt/bhrs4A9ICjA5mrC/qHvPgXOkHQiO7TwuI1c7cf6czUj5b8P8OwbhA8giNiQugQNM9iOp3boeNOaydaIU2Hsg8TlUV4+fKl9vf3dXl5GfMW3Uin75PLHSvpOFAgqT31nc3NzTACnU5HtVpN0i20nMv1Z69yesD9+/f13nvvRfbEtH8OUmWMHBtBLZKLiBiH4PfvzgIH4vCpnxPq0AnviyG7ax6mO0s3AplMJgHxUBdwo+8O1gMCDA/BBNklhpkszo/NwtATRBHheQbu2ZaTaxB8Z//5/fn9ojTehvGVr3wlcRgurGCMFo4Nw9xsNrW7uxvZmaSoeVxdXUV/4+DgYJz7hyPg+CzgxFKppHa7HRAcz8J++54xxYXPYz/4/uLiIk6sODs7CzIce8P7Xl/3ZxOjb+inG+DNzc2Y5uIDwGkCRy94P8YuTk9PJ2rw7ix3dnYCLvUpXMhXPp+PqT2lUkknJydx+gaOlvdkWAFyw9B5MvPz83MdHR1FDRbbQRCLI/aaZxpuq1QqIV8Ydw/O0L1Wq6X19fXQ6W63q/39fUn9qV4zMzO6vLyMmunQ0FC0K3FSUKvVilGgvA7nwjNgH8j0Kfl4XdgDcGypB8AEQDhDJ152u90Y/YZzZ+/RK9YMJ+9lDZADR3JINuAEEOyke0PZO7ojuB+gWLfNZIQ3Nzd69epVYjjB9vZ2jKTkZ/iITCajRqOh4+PjRACJjLJWyAlHqKGD3oaUvt7oLF+9ehWRF2+C4fUIDeOEQhGh8nfciNdmpqen9cEHH2h+fj5mROLZOY2AY3Kur6+jZuFYPcbaIxh3ABga2FTX19dx9FWj0dDz58/DwF5cXOjFixcx85QTQ6anp4NGD5SMY5ycnIzz1M7Pz6NPjfWiwRWjiGCn66xeiyMQwWhgnJ0R7FEqsBWvJ+DwoMIjRydluPHHgCBYKB+EHQRduiXT8Bpq215zdsgcGfDeSmonXvPmmX3N0nAe9+MO3x0lhj0t+BMTE9rc3Iy6NEQjSTo4ONDU1FQiW/7d734Xh1gPDAzEWZcDAwNaWlpSt9sNCvv5+bmazaYymf4EEc4TxWFtbGyoWCxqcnIyiFmeaWK404gDcsWzeO2NA3zRBfYcQ57P58NZU9d0CBR41dtmWGt3jvyNpOhVg6GLAfIshmx4cnIysScEecgJzo9zHqH3M6Cg1+uF0WWtqIOj+3AeCBik/lhDnpVmeq+jIr8EQJVKJcbDHR8f/17mAleC2ubp6ak+++yzOD2lWq1qcXFR8/PzOjs7Uy6XC7hwenpaDx48SARGjI4D2ZCSR955b6ETET24dpKlw6Zp4hgQqwfowJRk+QwTwM6z7gTmZGWHh4eq1+vqdrsqlUrhcLz3lvv3aUJul9H1gYH+yUTIGX/farUCrgZRyOVyun//fshjvV7Xxx9/HOgVduzhw4cxjIH3xE6n2d+sMXA/MoEOvu56o7NsNBrBGELRMX4YSCesAFGREiOg3gKBULBgRDX8PUafoix4Pxvh5BcMs0ddQJVAZThu7pHiMUdUFYvFYHcyQYPP6nQ6CZLR1dVVnOwBLInxODg4CMVCedPZIY4g7Sz5GUKFcqUFntc5JIYDckNHs74zWgkgGo1GOEnWmn+J3lgD9gPmG8/iBBwvmLtzTEOhLqhe201Dqv6+vM6NQNpBuiLyLGkHKykideoZwEppWByIfXd3N2o17XY7pkq5kyYwubi40P7+vnq9XvQ1Pnz4MHTkiy++0MHBQcg4cHQaRk7LBc8OvIbRJOr2dZKUiPopYXi9jzrW4OBgYsIOhhK4Lp/PR1bhfc8Y0UwmExCq7wPr7NwFJ1E53Mq9Hxwc6OTkJOpfTtjx8gBQHYEvTtPZ5h5QOtTJM3kPMM4V+eS9WSP2CrhweHhYlUpFnU6/rQG71Gw2Yy/Zi/39fTUajShzFAqFxNAFXktd25MAh6M9C04jUXfZD2SYdSOw9noeAQc2mfq816DdWbJOOEvkBZnCFqYJb3fV//we2WdsNGexIrusZbfbPwGFjNjRAewk/BfujeckyUiXewhCCTzRszc5SulLnCWngMAIw1gAz3CzRCyk7jhLohfwe5h90u0RQQgIeD0GFWUkonBCBZAsEJ0LDlED9+AQZqfTb1Ct1+uRNaysrMTkfqI9/p4Ih7qpG0ruZ3R0NM6UQ+loVM5ms1G85968qO5XmoTA/9PGxoXd4XHeG2cHc9EZZ61WS3t7e+EcfS2BL9ljLgwG0A4C7BefLen3nHg+n485lRhhj4QdKXC4hM9O/z6dlfEsaWfpBlxSZC8eRLkT4hna7dvTNnhu3gt5wjFyMszNzU20IIyMjGhlZSVQCaB8TkTB+cHY9Dqxw9qspRtR7pHMCJSA5zk4OAjyDo3nOEYyR17rE6ZwIuisOxCMrTsj/u/357LgaFMacsZRUusDwiYow2Zg1BxGvrm5SQzZpgUARML1hPtHd0dGRgLNwHlyoT9A6d4nzHq6/SLjJWiltYhMdH9/X7VaTVdXV6pUKup2u3EkFKgQEDIyiMPxEkSaLMmFnKaRI3eiDjViJyEhUZun3sjzYEt4TbvdDngf3RkeHg6ZI2HAhmSz2XB6bhNcV90h8VnuM7xejiNzWLndbmt6ejrx3s669ZKFIxEedLMOwPXpgO9115eOu/Moj5oCv3PlRwAcXyeagxRQrVbj9z6WDCUCogCm4EE6nU4wZz1TTSuHpMRZiQiJjzpqNBqq1+s6Pj7W1NSUisViQAmVSiWMKsw2NoBo1JV/cHAwMu6joyM9f/5cpVJJ09PTcVQPRydR7L/LwaOkDqFh+J2pxvP65nrGgHKsr69HlDU4OKiTk5OgtOdyuTAw7lj8y2ujZNI+kZ97Y238/87eRBF8aDsEE9bUhzzwLGkWsWdkPD+K579DATFwbsQ//fTTqBkhd0DJAwMDoXAQBqh5EUWvrKxEIAXMRuaysrKir371q+GE5ufnY41nZ2e1srKijz/+ODLblZUVdTqdcGYYASfESbfwPI4F50EAdHZ2FueG7u3t6dWrVxodHdXs7KzeeeedyHLTQQif5XN5fY6xGybpdn4q6IEzGdNGBgiUTNX1BfSC/xM0ku1CHELGmEONnFLW4F96IoHisBNAmOPj41peXtb09HScagEpxy8cJQEv8j00NBSZMnu1uLgYR6sBNSJTV1dXKpfLevfdd/XVr35V3e7tQeAcPu+N9oeHh6EbJBFexnIZ99GNyC1wdJpI5TwA/ha9Ozo6ivqun57knALqjTc3N+EsCRaANtn/dGaJrLj95MK+ZLPZqFGyFl6HRSa8VEBNt1gs6r333otsk6wSezwwMKD9/f0E34GkiyC+2WxG0DY4OJg4vONN1xudpRtUnBKKRuTomRKRCYaPAiuCTTRDRIpnR8EcgmBzyGycaMG9pCMGqT8DlMI5Y6y8ON1qtXR8fKybmxuVy+XE0UCSEudIUqPCAQBbSbe1JmeN5fP56K+6vr5WtVpNOBIE3b+X+nUWCt6si0PYCDrQwvT0dILhm4aeiEyBeRAUDJCPdYMIw/fpugfKjKMiq2AtqNl5ZolyM2OUYdF8DlCwM/cw0jh+1pg18RqmR6npbNyhXldWBjFLtxkiR3OxzmQglUolAZVeXl7GOlCPq1arceRYpVKJni4cvpNFFhYWdHR0pO3tbdVqNXU6nTi2DHlzHgAXsgtb1SPvfD4fsl4qlZTNZrW3t6d79+7pyZMn+pM/+ZMY8XdycqJXr15FFsE5k6xVu91WtVrV/Py8JCXIDhhBIn+Hj5E/v2dsAfvP/bNPvi4EoHwOBg79cPnAocLQ5+c7Ozs6ODhQq9XS/v6+tre3o9b67rvvhqMaHBxMzLt1Yo1nODiM4eHb8z65R/SAU3vgKFD7goxIXzdZ7djYWJCAcFSQSqiF+5xTTpvh/tJy4eQa7LHrAOgA+pQO8Bg9iq77sApsM4EK9oggtVqtxsEJBPI4OWBsz+788lIDbHP6S720k7aPlMNYO29Xwe/gnAnGSB4IOvg7d6C8t8P3b7q+1FmiAL4xFFfTUCzCzSa44YXOTrRwfX0drSP5fD6BNYPzz83NJQZ9cy/p+/OFZaJGJpMJ4fCFomYFW88nSZCBSrekBRYc4+tOCkFh5mqv1wsG78nJSZytBoyVdgJcFxcXwShjnXlvAhaMO+wtHBDH0fCeOET2BuiNLB3yEcYIkoQrF38Lc5FnZu3TkTnKzD3gJEEYeP+hoaGAUDAuZIHuLNMZC88g3Z5Kwr5zX+44XS65nFDEHvhnYiwwkg47czQQ60tDP6dnEG37PXKRyc/Pz0dNH6YpRsKH36dlg2fDYDmyALLB2aJnZ2cqlUqanZ1VoVDQ7OxslEaYq0kwBz2/Vqtpb28vmOJ+EgiZKbqQhrvdaPvzsgZkSWTCZCNet6JMQdTvdWHWHAObHhRxdXWl7e3tcJaNRkOHh4cBm1Kz9ToV2bHrN/86BArBB/kmU3YmtsPZo6OjMe2GY9Fg+vvJO/S2tttt1ev1qBWic8C76VKMB36sufMO3FliMwiAyRS73W4QqLw323XC68xkbgQRg4ODEag7p4IvgvK7oGNHw0ADgGsdIfQkxAdWsC/A9NgEEDSQLJ7F23jSwTQywf6m4eLXXX+ws/RaBBE+tTM+hAfAEHskgfEkQiPaAZMGWqE4TCaZzWbDKXjROG1MuYDNJEWvlhe4z87OlM1mo6bk8AHpOUbUM5lYMFMWDCd1ICLNRqMRhA6e3wkl6XsmQHCiAkLvhgmIsFarqVwua35+Xqurq4kewEwmE84SR4SAUmfAMBE8pJ0V+whJJO3kPVCiPcjbO7xniSk9CD0RNBRzPp9o2A0ye8xz+XM4pOPy52ubrvN432UulwsD5hkMcKDPwwTyBxLCGfHZyAT3kSYeDQ8Pa25uLqDNzz//XEdHR7HO3Es6q0fZcTxex5EUFPyRkREVi8VwROPj41H6IAOtVCoqlUpRJ9zd3Q24eWdnRy9evNDh4WGUEBYWFsJxEnx6MEMAl15n1sKdJd+zz5IS+4sxI1jl58iQBz84Ufqxae85PT0NRi1semwKiAwIyOuy4nQm7z93ApsHf5CjqO/hGEFwQCSQPZi+nU4n2MrojiMI/rn83mXZg+H0OmK7uLAv2FIv+bhOk6xQGiPQItgCVSFw80AEf4DdeZPTwVnyDO5X2FtQB9abQMchb7JMUEueH3312qfrFOU0fNqXZZQh23/IH3m664w2yBIYWnc8h4eHqtVqAcVANycSqFarun//fkKQWGjfNNh4s7OziYjLiRm+GNlsNmCXq6sr1Wq1RHQ2ONg/LXx2dlZjY2Ph2FA4z3S8foii8z5g8yw+8CyDDigse+0RIUpnENDWqctQQ0ApySAYrXVxcaH19XVtbm5qenpaf/EXfxFGH7gFiIRMktrg2tqa/v3f/z0afZnBCzTrQo/j9fqopBhMDOxCXxM1mvn5+UT0Ta0KQeZ5iByB43FMngE69Mjl8ArGAdnhczBmLhdkrLlcfzbtysqKRkZGEkcm0RfnNVpJiZYCMkQPnDxaJYjiC+NcKBT0+PFjdbvd3+sZRJH9OX0vQDzcEBFlw1b/0z/90yhjHB8f67e//W1E0sViMcoPlCWmpqa0urqqXq8XjfZkqj6OkDYUdBcnijFyJ+/1ZjdIGGd/Tow6rTzUrwjk4A44WSyfzycy1nw+H7Dn9PS0rq76g9CZkcx7ZjKZCIQd4t3d3Y36PJkUe7K/vx8BBEMI0BXkmnti353w4k4WGeR3OFOcAMxTR+cIarElXB7EpwNLfkYSQ6KAHB4fH+v4+Pj3EgL+jrV1YmepVFKxWAxbAezr+uvZIvfvNWh0stfrM3QdWvfj9JAHTxb8OQjOM5l+DzdwN89LQOp1d5/qRLkNopgPP0hn8OnrD8osHbIja8F5OmSGkOEsWRRINByZBQ17fn5ek5OTIWBuCI6Pj9VsNhNZbHqSiddRuRiMTcSxvb0dEUwmkwmIrdPpqNls6uXLl3E4dLVa1b179zQ/Px8nx7OhLDJO8ujoSC9fvtSzZ89Uq9WCHMDmVavVBKWc9UtnDwjSyclJZC80rsPIhQiDgL333ntxisqrV680ODiox48fa3l5WbOzs6EEKMnFxYWazaY2Nzf1q1/9SsfHx1FzSU8wQXGOjo50eHgY/Uves/jhhx+qWq1GzazZbAZhxWe9+vuBQgBJsyZAz45CODyUrmOQpXjmzGs9uyYj4nJHSe8VzqHT6ejg4ECNRiNIOM4iJaP0LNDZrPycvZSUkFM+m/ekp5OpULy31/Sk2wgcI+GoB3uGLnqNNd1GgYE6OTkJvT07O4ta3P3793V9G9l1AAAgAElEQVT//v2As6TbFhCMkBtcnJ8zVblYT37mtW320J18u91vyzk6OlK9Xo9BHw6b4UB4NmBEHDkG8ezsLA68hgOAIcdZpiHenZ2duHfnWwCTA0FeX19HgM1+OwnFHSD776xRgllKUBDuvDXE65M8M4mE2w2CVK8zcg+OuKAXXuJiKhV7g61wG3l+fh7lg+Xl5WgfZE2QNwIM9Nv9QTrA9UyY/mMPBnwvJUWp4Pnz57G2wNuFQkEjIyPR8lKr1RJjRz14Bbr1pAz2rc+t9pLR6643Ost0NO9OymuVCI7XzajjwSikuZ8MDYaWDxZ2OBAWFmPlgBGk2xqGbwAXBi6fz/9ehILAsViXl5daX1+X1FfypaUl3b9/X3NzcyoUCrq+vh0w4EQAJri02+0ICk5PT2Nqx/T0dOJ0ei5n1/oFnOlRGk7D60WS4jOkPqRzcnKizc3NWKtisRjTZRDevb09NRoN7e3t6ejoKCF0QMgYvXa73xqxv7+vZrOper2eGBUnSU+fPo266fT0tG5ubmJvCW5cWfjenQcy5c4Qw3hX3c7hJv87skzkAfgFOUrLs/8Oo0021Wq1dH5+roODA01PT4eRn52djbMSkU2MoE/P8VoPDFYmPV1cXEQQValUIhADpmNtvHVEunWYTl7wAQQ4Ug9MWAv+xrO6dM2N9XVHyHukoTrW3vfQ9UrqcwacEMJn+nxc4G6gM4JAYHGegz0HaRgeHk4cEE+dEBQFg0oW6PV+h3EdQt7Z2VGhUFCvdzsFh+cmeOGECx/VSAYGFM6eYJyRMWQV9IRgm1YMoGaXH4clkVe3I64j7A+y7MmLo1heOoI46YED2SLv5UEJ9o9gF1vMGERQHndUznHwZ3OZ5v/oArLkCCX3dnR0FMMLqA17fRybTLLEHt7c3CRG61Emoo6LbCCHng2nry91lr4p6bYHjLErCxFzqVSSdMsSBUcGk4f1NTs7q2KxqLm5uTjZHecLPZjCvxuItOPmAraV+tGJRzlkrzSYHx8fa2trS4uLi6pWq3ry5EmwFJ3Fm4ZuBgb6x3ktLi4Gkxd4t1wuJ7B2hMGznrtgRdYX1mE60sHpE3xks9kY7Ver1SIK4wR0oPJsNquNjY3ofwKChqmH8SXYOT8/14sXL7S9va3z83Ntb29raWlJExMTcb/Pnj2LaHNqakrT09NaXFzUyMhIwGave0avx/qact1VJ07Xa9JK6Cw3NzB3OUugUe4NSLZcLkd2CRubWt/jx49VLpcjkxwfH1e9Xo8pTaVSKWpPyCbZeb1e1+Hhofb393V1daVSqRTBDlAVcoKh5CL4AJaEjOXZNDBfJpOJwBLDh97hmNAtdwqsYbfbjTm4vKfXczxzJstE3wnopH7Q6Q3mwLTeFM4+oFec18kcXAyjT1dh3YvFYtgEgkUcK7VkjKPLnbdD8b0kbWxshJF2BMkDMoho1PAc+jw7O1OlUolxazCnYW96IAeUDsmLcg3751km/08Pr0jrE8Ehe+R9wcgAAQp9qdSgcR4+rJ0MnfejNogddwgcJryjK9goJ3JJt74CZMjbEN2Roes8N3wX5re2Wi01m80oS/FZZP4EZm5v3XbA06A+7k4cAtPrrj8IhnVjDhRLpknGhQFzZXSsnSiBSBGBIB2+vLyMaDDdq0mBl4UF4rjLwGxtbWlmZiYOCR0YuD3sOZ/PR0SCsmM4z8/P9dFHH2lvby8gomKxGNNwBgYGYh4h69Dp9OciPnz4UCsrKzHCCYHzTENSIiJ3g49RvLy8VLPZDCyetYNRi/KOj49HVjMxMaFHjx5Fn1ij0dDm5mYQQliH+fn5mEkJs43pMwjczc2Nnj59ql/84hc6PT3V3NycXr58GRR85IEIzovqGANmMkpKKK7Xoj37weiBCOCUfX1QOhTASWYuLwRioAFed4RchpHmnnO5XJxj+pWvfEWlUinWjZFyMzMzevToUTCQOZQZw18oFMJh4ySdmFOtVvXZZ59pfHxcDx8+VDabTZAY/Lk8MKhUKtrf34+6G8/HfWPEfA/Ozs6i9w9HxlogN8h3mjzmzeTslSMzvmYQ0tgHLg5c5349oyGDZn8xxARe1LxhYNbr9SBCUT/nPS4vL7W/v69Mpt/LCspB1uhkE/YBnbi+7k/ckqTNzU1ls9kEcxY9y+dvD1fwyTQelNFyUyqVol+XILJarSYIPtgph8bTwaozfgn4eF63yzgo50WQ3aURHQ8eICBhh3gOXofdJaPO5/Mql8tRpyag9yyQhMIzXA+gJCXGNvI7ly/qoyA38/PzwTTu9XpBBjw6OtLOzo62t7ejHWdhYSEClXq9nigLem0cWN37zkEmkB/g+7uuP4jgw0XxFcagZ3lAIa5Y0i0JwUdN5fP5MDzMYMVIes8QUBAK4sQOV3Dv5/n5z3+udrsdQ5qZrMK9gHe3220tLS1pYWEhhCqXy+nx48d3nm1H5JPNZuP3fk4cMAt1JSAKv1wRPeIZHBxMOBGm8BAkIDBEg2RHZB0oNsMPMPiQjwgOvCbAWqJ4sCT/4z/+I058IRhA6DxSnJubC2YcARD3R/TdbrfDcDqkwr2hyDwzCsP7ucI57O91arJT5M0zKTfiOCfgNAw+MA1kqHK5rG9+85taW1vTzU1/jibDwclcHE4HXk7XXTGuJycn6vV6WllZiYyDLBOkg2fy+5Wk58+fh1ElCyHzIbrHwBCIgXI4lIeDIAsAemZv3SDjjHEQyJ3Xf33dyeq4mAmbyWTi/dh3jL87MOn2fFSCD7IKyCXS7Yk16JsTz5xIBQKF4fd2EKDxw8PDqFXCqm2321HbQ8dGR0cTgTFyRhYMOnNzc6Pj42NNTk5qZmYmnmVnZyd0k3XmeV0v0R16NrkHYM+72LBe8nLHBzTsqI5D5ths7KjXg7kfbB6QLbaQ9Qexwgljr73Wy3pzwVRmr7HnfM7AwEDUhHkO/EYm0x8tib2bm5uLoGR6eloLCwsxEYkzW7PZfj8sSCPyxrQ1pha12/0e10ePHqlSqSQQtPT1RmfJw7gDJIJ3wg0CTGSRZr2hoEQK0O9nZmZiI0jpPbLi/RgphtA6NJV2lq9evdLExIRmZ2dD8cHeu91ubDDGcXZ2Nj4zk8lEnZXIzYkSGHOEHwHDsJP9eRvGXWuKUfULhWFt/Qvl4/Veg0U4nZiAIUnXOzAC0m2LDu99eHiozz77TC9evIjABmOGknEPTLshg2MsImtFBOewk9dZnKTiR1LhaNyhS8nT7h1+4t5c6Yhc0zUTWoYg6KD0QMYOaVUqFbVarQguiLKbzWaC2k8dh8DR5QKHg5OmBop+eEaAvKdl4vz8PBw568sz0AuIkwQaJdJ3eM7XlrVClllfsnPWGeSGdUXG0Hd0n0CHi0C60+nERC6HInH27CX37bYGuQbK4/8+FYhAzC/qha5rHsxeX18HLM49uxxhG1hnfoZMsR7YMAYQEKBgd1hfH7lJRs6zQRojIGeNvW7IvaQRB4JcdJLn5D2k2zNn+TuvVSMPaXljHbifTCaTmIWNTYBfQdDr9uj8/Fw7Ozva2tpKJCreQkSnAnuNk/bZro5KYi9AYRgbiKxj74CRJSVae8hM6XgA9WCtyuWyVlZWgpT5uutLj+jyBUe4HY51jJ3sC4GlJuG1Nh+bNDMzk2h+5jNRWpwQguqZFwSddES+t7en3/72t2HAFhYWNDc3F86MWZgIRrFYTEA3RF4sun8GdUKcO3AmRpJoGwOWrjOkC+9cQITdbnJYsLcf+Hu44uFsiPARPCd3eDZLxoViIpzb29v6zW9+o93dXd27dy+gwnTmIClmp46Pj2tmZkbNZjNgYOj7TlSSbjM+6nwESWT5DjO7Y8U5uoPkZ/47ns2JBm5gOJaI4MHliPfiOLmpqSktLCxEaxCGh6yEYAEFLhaLASdiOB1aRWk923YY1HvOXC6QM9ZTSrZXucy6Q3FIzR2GZ9bu1HEk3IdnpKw39+eZDHpDjQv5pL8RlrTfr98fP3Pjj17yXp6ZOZnKs3iv8TupxgNrSiOUKIDbICIWi8XIenDIwIvpEXkYaeTEa9mlUilRivF74bU8D7bPiVTuTAmCeE63F25D3K5w+Xu5w+TLgzN0CPtBNgcCSDmr2+0GwYapU2kUaG9vTx9//LGeP3+uRqMR94P8sl+Uf/xZ2W8CTUdSXA/pj3ffk81mY08ggoGeMBYSR0nZr9vtk/QePHigR48eRaDzuusPPs/ShZcxTeleNAw1gpyO7sl08vl8GK/Ly8vo50KgfPHdiQIV+TlvKD9Xp9OJ3kkUm2IwUJTDDS6M4Nm+oA6/eC0MuLNSqcT9+XM7uYffOezh1xdffKG33norzlhstVqJOgcGGEPgMCCEKkmJOgjrTZBC4AJkRMY6NDSkWq2m7e1tra+vB24PEQDSCM8l9TOetbW1yMJmZ2fVbDZ1eHio4+Njzc3NhSIMDQ1peXlZhUIhWjY8C063A6HcbuzSUbXvCTKWJpqknWWr1QoDgjwwm9MhUbJgjB41Ym8mx1h6MINhY2339/djn7wO6xmRZ8mOSnB95zvf0cHBgV68eBEMypmZmYAyMTwENk6uoFTg64nB9SjdkRkPVAjGcDZ8preCgCAcHR3FPTcajZhuND4+HsEF98Re8RkYTgJZRzCc/8Dne7aMPfF750I2qLGdnp7qww8/1MnJiebm5vStb31LkvTee+/p3Xff1dDQkNbW1sJGYGto4qclBviOFiMc7/j4eLyWwIPaN46AmjyBIs/kyYj3bxJkQAjiwlmxTkDDZHsE69hRZBRYE/1gLfk7b+bne4hKvd7/Y+/NfSPNrrv/U8UqrsXaN27NXqZnk0aQLAkayAaUODLwApMYTpz6f3BiO3doOHTkwHBkOJhIiQ3DsGUYljAazdY93Wyyi2TtK/fa3oDv5/D73K7uGfgX/uYCRC8k67nPveee5Xu+59y538nZ6/WcLETQdH5+bt1u1375y1/ap59+ar1eL6LjSqWSt7dLJpORlAxIjxK7gFMJyBS+TyaTVq1WveSK+4aRLZwV+oD3ej03jjh3ELH29/ftww8/tHK57O//uvGNBB+NjthAM3PPSb3aMJJSqw8lnEFOZDgcev5PjQpCgBIdDocRvBlDhCekc+Y58Xjcw23mTcSm0BvPRIHj9SkTFQ+XHCxeoTLtiOjU++Y9OBQIqHqKX375pW1ubtrOzo7t7u76DRXkH8iHLGqphkeoXrrmmrTZAM6J5jkR0rOzM4vH405oAqImSoAgxXr0ej07PT21VqtlOzs7trq6Gsmj0VYrlUp5LSGGkb1BZtRDVZgoJPJg7DUKYx1YW5VBjBXrzY32m5ublsvlXImSF0Y+MGgoGjPz3w2hU4wVin46nfpnYiCQtRB+5x2V2a3GcmNjwxqNhhMhjo+PI9FEWPRNhIZ8azoAFESjCSWsEGUq3B1G+fwukRTKTcknwN0oZ1r7kR6Yz+eu2NX5I0pVBxxZZx6ae1MYW88E8qDwKxcG12o1jyL29/fNzOz3f//3LRaLWa/Xs1ar5Y4hClfJZ+gbZV43Gg0bjUZOytM8sMLLGvnTEm86nboi13QPsjydTr2Hqt4NiTFfWVnxbkHss6IzOMgYYM4Hf2dtWUd0CXJP6gzjNpnclspB1qGRPEjTwcGBffnll3Z+fv5KNPzBBx94tKkwrpKXWHccDs6TRpzsCznJVCpls9ld9yeclBBmV6ccTsjOzo799Kc/9dr0ENkJxzfmLFUYWVR9qMKQioOj0NgcBIyFwoNHgDjgeshV4BEYvVoFjwj2pdmrNYyw4Hgui68MKX0/WkFpGzTWAoEkQb+8vOwHHe8unIcqdc396We3Wi07Pj629fV1e+uttyKeFAaO54VRNE6D5jg118jhRtFolIgSJ7dC/o09AFY8Ozuzfr/v9WRmd/T3er1uOzs7HjXiNAA9KvNOlQGD9VFHQpW2CjC/pwpd904VqMK2PIeWZ9xIATxHNx1dUwgGyDD7i6FAFvg9JUwoczvMEYXwMjKuil4H7cbi8bj1ej0nv2i0o46grkMIsylcpyQMPbPhfDjvGu0RFWmpEwMHBdgUOYnFYh5BhLXLihYo3Mbc9Z1Ye4WCWcvQmQEBazQadnR0ZLFYzLa2tmxnZ8eZ4bu7u3Z8fOzNy0MIGuRGIUPOmXaeQpY4c7CPtRQO5xFCo7JzacWnKN54PPYoSAfzQ/41HxkGOIuGnjXkVGUTvaqonkZlyNlwOLTj42Pr9/vWbrft+PjYW+UhTwxuaYGJrMEKeX1FWRQaVvlCDjB4SvpCblk33omghjMyn8+tVCrZ/fv3vYscsvSm8UZjSTiPAISKaDgcugJhkiH8ymGAFQnrS+Ed/l89GhhTKDhtIxceXASfF1bPiZAeb04jMQyRdj5BwYfGzuwu0cw7k8dSCFAV+6JkugoJYzgc2rNnzyyZTNqjR49sa2vLvSuUMApTPXJVlDgEQFqsDz+rUI8qIzw1vHpN3CNEdEHa29uzarXqHt9wOLTnz5/bw4cPPT+gLDYcD737D0OqOT1N9KMINBILvWEO0Hw+973Td1U4Rgdsw8Fg4HkP2JDFYtH3jznQ1GI0Gvn7KOwKykDug7nF43F3HjhHobFUZ0DzjxoNc40TDS+AO2ezmct9SJhBaZJ7Y7Dnei4VgVByFg6VGknOMJHR0tJSxOAyFNoDpoNfQCN9clHIB+uAktT6OfaeSJ89YK/12cgSjtFwOLSXL1/a06dP7cmTJ/bWW2950xEc22w2a0+ePLFut+vngHcClSFXSjkRewmhhLXXaCydTluhUPDIDEgRyBZUAAcTI6PIAm0JZ7NZhAgHmqFRq+6zGjv2XOVPjaV+P3RKeUcz872moxGf02q1rFaruXHSnKiev93dXe9SRi6Xc6w9wvUcq34yuyMUaimdlvKoscMRIZeJbIGwvP/++/bBBx/Yzs5OBPrXtQxH7Jus6Xfju/Hd+G58N74b/38fb4ws//zP/3wODq5N0/FEuUx3Op1G8G08QLwkvFm8kWTy9oojoBy8FYqTtf3dysqKXxzNzfOTyd19aMBUH330UczM7B//8R/nwFQ0FoAcg+eGx0WkozVQ5IeYNxTnEO/HgwW2wfOCxaU9B/V7XCPU7Xbt7/7u72JmZv/n//yfOSwuCChAOGtra7azs+ONx4GY8LCUrMH6UhiuESnrj6empCXynJo7IfLgMmy+9xd/8Rexn/zkJ/PHjx9buVz23C7PViRC2ZcalYXem+ZLlADCHqjXuyjC1J8Dsq/VavbJJ5/Yb37zm5iZ2RdffDGHmIJnamYuYzqH+Xzu3Vc0h8PPsP9KUtB3DNEFbQausJt2TOFrOBzan/3Zn8XMzDY2Nubkf8vlsv3xH/9xZJ1o9E00v7+/b3t7e5EaWNbM7K6LSlgkv7S05DeUAEvf3NxdfIDc0SYSIhzMyPPzc/urv/qrmJnZ3/zN38yPjo5sOByamdkf/MEf2IMHD7yxucKbek7hLWjZgkKwypbUvdb3u7m58UbhlAnAWVBmMM//0z/909iPf/zjOV2VtGyMdaerEPPk3+hBnQ9yqLqFd+LnOUdK/gPx6PV6dnZ2ZqPRyHq9nv3qV7+yWq1mq6ur9vjxY/vlL38ZMzPb39+fV6tV78ZFCR7zYg7JZDLS+o91JKLXnDRoBNUCGpVqfh6uiaIBIBFEh7QtXF5etj/5kz+JmZn99V//9RwYOpvNWi6Xi3RHg6wTwsiawmIftNwGOVEkhHXQdAORJj+j8oT+g0T3i1/8YiGO/UZjqZAAE1DoJJlMOsSiHTLM7trE8cIhZALMh+BwgziHmYMK9k9oroQJoA1VvpPJxAVHm48DTajhCIkVEHQwnBraI1AcNuAGVZ4KbdC9BkIN81c4jwHbElh3OBxGYL7xeGw7Ozt+KMwskveBhMO6orCB1dTYQ+xhrhiKMIeh+RBgdvaP5vcqtArvaK5On83/LToUQNOqTPl//o/f5XlKZkC+NBWgBBglEwHXaXpB6+LG47EbIeAeDitMaG2YzXwXvXP4PRRkCHECuWl+CoiXd1JFv76+7u29MAp0L7m+vrbd3V1/Lu+pua7Z7O5+SaB3dXy5rNvMPP/XarXcEKlDpdAxBkwhU72EnXdBuWIkMUa0vAyNjMLHoQOFXKIflJOgkLLKEYNzp44e66wpAjWinH3khj3CAVNHQGWdz0ZetdkIDrB+tvIUwp6lIbTKWdWLDNTgcfaBi/Ws6juxF5wX9hZdojWK6Ec1/Jr3VuhY882LzoWe/XAo5E4OOeQDaEkRhj/kPfD5yonR9dP01KLxjcaShdZcIpPBAGHFw441ajxe9+ci7wBjxWfzYjCkWDyt4wyfy8vr77GQKFi8TQ615i6VQMDGkl9QZYOiQ9ldXFz4O4RRhuZedM4YdxQKdXw8k9IDaOoYWoQbajdzQ1BUKBmaX9W90O9pFAdRA4VgZq90f9HfN7s7yBoB6giNieZU9f/1Z8P5auTJnxo5kH9e9HvkfBh4yCACGE51BM3MCRo8R40Nn827hCM0lqGyJ+esZ4icqipvVXCZTMYdqsFg4JFVLBZzg45y0rOM3KJYleym6wqZg5aUtN7jVhZkRfdXW0tibHE6dK/U+MNcTCRu28vBL6CHrsqHKkBFrFT+WW+MJSjPIiWt+c9QTlXxqi4KZWdRvlkdTj0PvDN7qI6QOpw4D6Axr2MJ8z7IAXvOz4UGSnUj+pugQjkZKqtEZqAI2oAcw675d32Wzjl0kkPHUR1hdSbZH56DruYzxuO7ixzm87mjgciKomUaGGADmDvff914o7HUUDWkJrPg2pyZWi+FytQ7DIfCspubm75xqVTKoSSiFwwZQqlEG43S9LOn06knzvlSBh4EDoSGqAEYGM+Pd93Y2IjAOvF43Jmi1BnCPEXYOSwckkXeS3jVELAaSuzFixfWbrctlUpZJpOxcrlslUrFqtWqt4hS2BdGq5lFmiabRWGiWCzmsDGCryzc6XQaYRrzDhhL9oTPZd0RTqJ/no9sqEDiKOnhMLsjHixyhvR3l5eXXUmGhkZbV+kBUe+aw4ZhUHIP9XLQ9lX2IazA+iX65Cv05kO51DVSqEjfASYyckvXHc4bERg9X6klPT8/t3q9boVCIQJX6YCAw/4SkaLYgQXpycm1dPyM9gdWeSaNAUKEowohiRs+VNbp0Xl2dmbHx8eWyWS8QDydTrtC1ghDIxHOFxAh+9npdPxCd+aAYcFhVBa0ktDQWTxX0Rg11NqOjnVmf5Enus1oORL7SJ9SugopYgY0rhGjmUUiK0WgICGpPsRgKrKkESzoHedMYVecL8pF2CdkW9E4/k8NpjrSyBxry7lTIpA6YOE5wTnVPre6F5ubm/5+lMGpnGnKBMdJjaUija8b31hnqVafzeQgoyyIoGDXIXwKIeFlKpsNWrQWyGvHfs0xqQeoSksjIbNbZUT9Tzwe9+4b2vKK31fviuePRiM3mrD+tPsKioODSX1Wo9Gwfr/vBw4YCvyeHEjI2kKgNQpKpVJWqVS8eJ5arF6vZycnJ/b06VMrFAq2vb1tNzc39r3vfc+VHRAueQcttNWDhTLAIGIkEDL2jnVUB4jWbQpFnZ2dueNALSxlOewjl3hvb2/7lW1hLatChurghJFo6DXzjkT8dGbRoYeQg4RB1OglkbhtHo0RoyaOf3c6Hf8/ngt8iYOlhp61ID8PeqCGWds8MnZ2drxMaX9/39LptOeIut1uJGIg74fyhp3I2qsSYn4YfPKX3OcJH4AogqgQB3JlZcWjbW1+r7K8vLwcueYMJii5KRT1aDTy98fAYfA7nY7FYrFXIFn2Tdft+vrams2mX7OGEVZkLB6Pe9oBpTge392lCPzMmuJQEyQQ/WOctfRMeRAa0ehl1qwZMghTHzSItdFcP7XO9Mg1u0P8OK+akkFeOC+LghV0A99nvamZ7PV63nQBiF9zkrCDYUZTNojzg24N0ywKzWv1xHw+j/QKxwhj6Or1urdOhOeSTqcjCKUijv1+P4LCINugNHBAWCvO4OtgYF/3N32TTeRleTkly+gGJhJ3jX8R4DA/p9ABhAFgGDxys7uSE/4v9I4Vhw6hRH4fWEk9cSI22q5pHofcjZl5hLi5uRnxZtUT5lAmEgnL5XKWTqcjFHwOK3PUIniNIE5OTqxUKrliIIql6LZSqTgc0ul0bDQa2Ww2s3a7bV9++aWtr687AYg90EgHQg/CroKMQVIIgjVHUOniw/dVeSl0pLKgTZYvLi5sOBzafD734nCaktO8WKMHLaVQz525snY4bJrX4p05mIwwmscRZF0VPqVxBfKK0eMwsp5E8eoFa+NtnkMNH9ETDhfzVRhYobyrqyvb3t72a8IwUnRSoVsQhpq7TPUWC20dqSkPzh17x+cq3EbpGF1s1GlFriErMVA+5NmQt/F4bCcnJ9bpdHxOGD7KtTKZjBWLxUhUp6gM60kUDkyOMUIuOAdK2FHHLiyEx7kkeqNLD80qVMb03Vkb7cijcyani/Nxenpqw+Ewki7JZrOWyWQiF76rrkRuNZfNGkAGUiQp5EbgUKqu1LQXDhyXvXc6HWu32xEZ5SzRRg6nCZgYfYJjrjpX56wRunIgQvhYEUczs0KhYOPx2ImG6AnlmNAQhaBN97fX61m/37dkMunrDWIxn88dXQt1Rji+9a0jmmdRUozCZCggVXSTycShQMXLze4KroFqksmkKxtu70AozcwVsEKKixS1zpl5amGxwlcYAmVVIljX19euDFU5oFCAd7S1mNnd5abtdjviZalCVC+m3+97NIQR39zcdDZeLpdz761UKlmj0fC6sEajYQcHB1YoFCyTyXhPXAYECxwH1oNoAdag1svhvY1GIzs8PIx4i3wmh5ADiGcMJEgUPZnctZdCwTWbTXc6GCgkYGBkiFpDFBiKkQiQZ+LxhgQCHWHeGvlgT1gf9ii63UwAACAASURBVFj3HbklguezcQyQF8hBWlsJy1ERCfafM8Sz1VienZ35Xav0N8ZTHgwGHgkS3UwmE7+oGkWpZ47zQ3rC7M4r1yiZfC+QpK4tZwQjhffPUGNJXhuWIS0ViRyYC9Hpzs6O3b9/3w3zInheZUPzkTxTc4Wcc41IQT3UMVR0AseOCFtzuMoDiMfjzlLmrOKIM2+QGe5grNVq1u/3PQ0AKxQjomkAPiPkBbA/ejYwFjhimUzGL8EmQlUDr8X7zBGCJU4YzgdoHw6NOuUqX6yJ5iVDuFT3XfUNUSTnh2AszL1i5GGu4gSqkSaoU9Zxr9ezy8tLRywVoeTzWPc3jW8Fw+IhxeNx95rxjDSXyQYjdGa3ERoeG+QIXgZSApsIHk5EqQyx8/PzCHNO2Yj6kjQG16QtJBiMELg+sCo/x4HjcxEYjY5R8vQ45LM4cESGZrcNx1XRIxxhDgKYjiiSDiPAKbTSWl1dtYcPH1q9XrdarWb1et263a598skn3pj+0aNHnu8h/6FN5/UwcrkzELJ23yBiGA6Hvs9qLDWynM/nHkXk8/mIguGZeHtcm3Zzc2O9Xs89RAweMoPB6vV69uzZM2s2m9btdn2f1ABw+wMNsYEFNSLRqBMZ5YuIHeSg0WjYb3/7Wydr0S8Xr1Rz28Bg9NFtNpv2/PnzSMTDlVwcfLq4ICuU96i3bWbWbDbtvffes83NTUun0zafz91wYYw0Cmy1Wt431MysWCz6fYood/YWmSA/RnSvDgaRA4pRoWSFyfXWEdCalZUVy2QyFovF7OTkxD7//HP7l3/5F6vX6/Z7v/d79vbbb1s6nbZut2unp6d2c3NjL/7ftWi7u7tWKpUi/U/RM0qIwQEDNeIdFBLV/Ve54nP1Dl11alDIGE3W2cy8PIIbiyj10j2dzWZ2dnbmECJGKJFIuNGB4Y6yV/Igfwfq1HsWNzY2rNPpuMPOReQ4qYVCwQqFghWLRdvZ2XF9gBMBksB64MxQMgRsDpckk8m43tVuN6yhwvtEeuH6K5dDHeIQyVNkSFNvNDTABnW7XS/l0TwmuWveBZllHTc2Nry/rfIFNI/6uvGNkSVCyWLigS8vLzsRhZcCVtW+sXgUZtF73PC2tOUTYX4+n7d8Pu9KiEOCoSaBTAJclSJeCQLHIuC11Go1PxQ4AFrbiRfF7xH24xwMh0PP51QqFY88NXpDICA/ra+v22AweIVMw3j8+LFHK7FYzNrtttXrdXcoksmkl59Uq1UXKt6LKIPfoRfr6uqqbW1tRW7LaLfb7pWq0kNI8PowtNVq1XMvXLnD3BWiDCM0PMtYLOb5NBiYRJ146kq7J2oBsma+a2trVi6XX4GXiDCJ+Hq9nhswXWPyc6AMWhbAXqGEC4WCE1xms5lHdciz2S00tLOz4zAyCvf6+tqePHliBwcHHjVByMrlcpbP5y2bzfrzLi4urF6v++FWY4n8gbJw5pD3/f19l0X6mGLMyHOn02l3tN566y1bWVnx+lng/GQyaa1W6xWCEucil8tZtVq1YrFos9nM65spr9GhTG2iGNIVyWTS9vf37f79+7a7u+vOM7DwfD63w8NDl8e9vb0Io5xBXWKn0/F5rq7eXlfW7XY9gu92u1av1z0KAbXhfJqZR+k4UESncBbK5bKzgakr1XNSq9U8+i6Xy1Yqldzhbrfbdnp66vA2A2f76OjI5VCdYghOOADsIQNEjMiK98AA9Xo9Ozo6slQqZXt7e/b48eNIeQ4dbTRQoTG+Oic49Rh/zqUie0pYRPctioa//vprN9ycSxxy9BBwdbvdttlsZsVi0de00Wh4MEP/an3m5uam65vwqsdY7Lb3b7vdtq+++spTIhhfLn5G779ufCsYFmiVMB9SyGAwcGO3vr5u+XzeFY56HkAiChMyWbxDcny9Xs9qtZo1m033MIvFoitbZVea3TEUdaC4MU7AKYVCwebzuX3xxRf28uVLu7i4sFKpZHt7e1YoFCJMr+XlZW/DBNSG13J2dmbn5+eWz+c9B0Hk+OWXX7riMzPL5/OR2zZQDipMXNbMgH0Zi91ed3R5eWmnp6fW6XTs008/tXQ6baVSyTKZjMOqfP7BwYG9fPnSD9zy8rLVajVrt9vekF0jTHKHODLcFUdUCfECz5iDpIO9gOij5Q5EUuybohL8nxpfjUKJhvEgUSjKTFTDDOQE7K9rijMAtBWyHYG5gTQfP37s9YRKYojH485IJn/DevB+OJXpdNp2d3dte3vbrwzK5XJ+3yM5IZAYzpcOFD/rSSSIoWFNgHZVeQGnYsBwPjWfSu5LWyuyDmbmDuy9e/dsd3fXr2y6d++en31dZ+QeCHdlZcWq1aorrfl8bpVKxR0erjdTaN0sWm6EIYvFYo5OII84XkT18CSIoo+OjqxWq1mtVrPJZOKKlduCtF4Qh0RzokoMRKkjNxhJjAMIFmUMkMM4b1dXV1YqlSyRuG1F+OWXX9psNrONjQ1HZCCyYNhwADh7Zndkrng8bul02rShyWw2s2636/JEUwNqoxOJhEOp6ElkR9EC/q3GTCsRVI9xX7BWNYQwLOfi4uLC0um0rw8Re7fbdfQIkhe2BoSCuWqdKMEUrR/5PTgSwNs4Ojib2qawXq/b9va25XK5//3lz1hqxaJ1gelEMx6PIw/CWzW7u8eQBD7Gcjgc+suxCRhRIgo8sPF47DR4kukhO1IHn0lXlFarFVE0w+HQms2mnZ2debNnFg8YNx6/bRCQTCY9GWxmkZzqbDbz62E4iEdHR/by5UvrdDqWSCTs4cOHls/nI2sTQhR4uwjE+fm5Q83Ly8se1dPXFANEroxSDjOzer3u80mlUjadTp1F22633bPDsBD5ocyLxaIrUK46M7tV2lqXp/k/PRhELSjfyWTidXN4/0T82gNU9xNoNGRhkhvWnCSRupJ6NIehh1UJZETm/B1oiHns7Ox4H1ON5JeWliyfz/v6s6f8Cdy3urpquVwuUgKlTgFnKOyIo7dLsO44Xxg+HBvmi6xo/2CFI4kQgFVDaj8yh3NiZu7AJpNJn9P19bUVi0WrVqt+Cw5QF4MIEodzdXXVqtWqcwT4TL6HXiCqmc1m3hABhwcnE4ODjri8vHRizGg0slqtZisrK1YsFp0cl0wm7erqys/7zc2NpVIp77OLPCJPlAopgY+c1mQycTic+ZjdpVVSqZTlcjnLZDKe5sFRZ1+2trZsdfX2hp6vv/7aFf3W1pa/E/K9vLzst7poZKqEonQ6bdVq1d+V9e12uw4h1+t1J1XhGBOZXV9fu1FifTV3yvkAsej3+643lKRETnN9fd33TCH9tbU1h3sJWjRPD2IHiQfZJ8Wg997iNCHHBGmcwcFgYIPBwJnY+hzSIbqHXFCgztqi8Y3GEg+KMHd9fT2Sy0HgyuWyw0KXl5d+sPGCiXRoUj6dTj1PdHFxYcnkbVd+opNut+tQ1Wg0snw+77AXsBnRlHqhvKxugCrcbDZru7u7Tm7Z29uzbDZr8Xjcut2ubW1tOYtwNBp5u6hisegGVYkwnU7HWXEPHz60X//61zYajezly5ee22u1WjadTm1/fz/CVtR1pnMJxieVSlk6nfYShp/97GfugQFfAzPs7u5aInHbquzw8NChhQcPHtje3p4dHh7axcWF/7m1teW1gYPBwJ0WojgUKU4JXj0GP6SFaxlKLBbzOQ6HQ2u3296yj0gXZALjrKxWBsJNngNDubGx4TkyPFyiejxyjfTCoRBS+BWPxyMtuNbW1iJRrkYUwP3sg9ndReGJRMK2t7ctn89bLBazo6MjXydgJGoQuRWj0Wi8AteZmcNHo9HIFR3wO2eP3Cve+ng8dqXFGu7t7Vm5XPbzQu4MY6vriQPFuYEwdHh4aAcHB7aysmKPHj2ycrls1Wo1Eg2DXpjdKnsc5EKh4KxuJR2RgsHBJu+v8qDRKxEfUZ+ZORmt2WzagwcPLJvNWqVSccJTPp93eLRWq/lac/YgEk4mE3v27JkNBgObTqdu3EFYVlZW7N133/XUB7nCzc1NKxaLVqlU7P79+76/OLObm5vOB3j77bddz7Xbbc99VqtVv0QdPZvNZt1pVOcaBi6RFVwMdEo6nbbhcGi9Xs8ODw89J2xmfp0eESBoGnoNkiWyuLS05Hc9Xl5eWq1Wc+eec4uBpyObEpQYjx49suvrazs+PvYzytxxxiglQ06U6MWaAs+fnp5ao9FwWFxzoXAw0Dmz2cyjbyB/UnCxWMyDJcoGXze+0VhikFgY2EQ3NzcO4aFY1DCtrq462QKlzwtzYKvVqk/04uLCPV888d3dXatUKlapVLwOByPCYQJuZWCggVPpDcvGbGxs2Icffmg//elP3TDjSaXTaatUKm7AgQDwdMfjsSeUYWulUim7urqyfr9vtVrNHj586NCTmflNHNCxgUK0fRXvD/2fZ2GoisWiw483Nzd2dHRkrVbLzs/P3ZigJIvFohUKBXvrrbfciObzedva2vLuKOvr6351Ek0NarWa/fa3v7Vnz575Ycrn835QUKbIhULh5P2AVvFg2+22H/JcLmfFYtHG47H3FKZEQ6No8jE8azqd2u9+9zsv0dnb27ONjQ2PhjBwzBdnJCxp0HyUyjdDoxjkiIiAvAjyr7fcaNkNxDQYhJT/bG1tRYguv/rVryybzfpZOD4+tm63G2HZsq7Q+kejUYTRzbOY33g8juTF2aP5/LbP7Q9+8APb29tzMkMul7N6ve4wIOdciVNEhygsrYlkb7WxgdltNJDL5ZyMRzROrkwjX84v64/DZ2busKLIiaK11hHnaWNjw3UFn48ipY4ym83adDp1ncNzicY4w5zRTCZj9+7ds9lsZoeHhzYajaxUKtkPfvAD5xJQ06z5TVJHGLObm9seu8xlMrm9m3c2m9kHH3xwq4T/H9KjV90hj7FYzLLZbCSXjf5jHqenp9br9Rxp0dKujY0Na7VaridAL9hbbcSgJB4i/ZWVFSuVSr7H+Xw+UmsJ3wC5X9TZy8ysUqlYrVazTqdjrVbLHj586IFIpVKxUqnkMst743Bg2CuVisuumdlgMHDIGU4Hv0fkmEwm7f333/frwYgyiWCvr6/9c9Ffrxvf+j5LhEohOKXWw1xF8SHEeD16Mep8PvccJl5Kq9VyDJxuH5pwJfmtd8TpHP2FEgl/1ubmZoSFqBEDkRAHFk8G+GQ6nfr8FUIjv5VIJJyOTIH6ysqKbW9ve04EWOXs7MwSiYTPPYwsge6AQPA8WVciSCKTTCbj0IiZeR3feDy2zc1NozE0TgNrUqlUfM25nBijR9Stwj6ZTKzT6fj74nWFxlKVdiqVsmKxGDFiZuYwrCpYIhaUJ0xCDiOHlvyp2e1VP5lMJpJX2djYcLYqCAgQEYO8FDKrzD3KClQxEwFpsTfyDXFFozSFC83uOtnwnswTkgwwPykB0BqgXbO7MgnyxgrBap4cBYDi5r0wjDiBmUzGIX4Mupl5KgIjiwODEueM4CiTo4JdqHNGweGY6BrzhXPF+2iqR1nBKDfNoxLJ48AwX/J4yvpOpVK2v7/v73p0dGSZTMbPjdldTSaQNTlU7aDDftOkBDmG3EeOFH2BU14ulx15Ik2iteU8kz1VciGOAaQ4dewURr24uLBGo+HrSDTIXsEyRVcq+RJdh3FiXxTiVaQGhAL5wCbg1PAu/I7OeT6fe6tODBsOFTnM0EnQM6dlTUrmUYSCMw8qQgBBb3HNyZNCAGJX1vfrxreus1RvkM1kkigjoj09MMrCwuMxMydAwERqNpuePNekOQvNZ6iHGob6zFNzmtTm4f1r/hWjzc+jNFCkKHU2HwFTo0o0bGbupdPXkhvElQXMOiyCHDGYurmaP+D5FDHzfYrTYY1ms1k/LOD65NAwKNSFEdHmcjl79OjRKxCrlpOw1sp0DZmv5DB4pt7/h8FXqr0eLDw79Vy1dGc2m7mnqdARDFO6ArGe4RrrwTWL5uT5vn6PLw4g80SJsxYccDW2ekkuxnR9fd3K5bIrNSVQaG5U58y7kLtHXnUNkG0ULecGtCafz1sul3MnDMcH0hSyCZyGEVMihTpV6uzS6IOhbGUGukLz26pH9ExiCEMnmEhTiUhE2UTCPJeIYXl52SqVip+/RCJhg8HA83pmFtElesuIRik4IOS1tHSNCF91E1/IJfoL0iCOH/ldLUVSY4m8m1kkemftYrGY6wx+T/Ov7AGoDQEChhonHPgdxwMnSg0YgxuNdI58Ducbw6OyjNEHmuZ8k/vXd8KmhHuKLCnSxbspi1cvnFenSh1a7eymOuZ/HVmCATNJvBde9Pz8POKFk6PRBSJaxItAaEm8E2msra3ZyclJJAGreSS9ZVyT0xxuhnpqLIgWs7M4euA0YiBnACRIpMVzMIZ4MQil0qHZOLwgpTarw6HrpM0OVFA0QgMSJpKghu/58+cO8XDoMLbK9EMBEg1zWzv1gyg/pXCfnp46iUn3Vkk2eKQcjkwm40xMuowgJxh7ov/QCSNKIBqLxWL2wQcf+IHM5/N+KKvVqsP8KBrkAqOqQ4lIRDZEoTgj7ItGVkok05y1RtekAzB85+fnTiRBQaysrHjNGwdUSStmFml7iAHGIXr69KnnYba3t61arbphAKbGqPT7fY9kt7a2/Eqkq6srR24gq6D8u92uw6apVCoScfGOnB9yahgXBuiAKj0dfKY6n3y+EqRwJjjbwNzK1lSYGOcaw0/ujXdJJpOWz+c9ZcPnEs1x1pBDTQPwHny+XodFNI6+UAPOBdAYU1JWsMsbjUZE9rVOk7w05yOfzy+UY84FnBDOHt/HYWIOOLjIMM40DovmQ9FvoFw8W/eYuaLDMLihjqN0C4hbAxUIUewza40OZC9xDgl+cGJU/y8tLXnNOfIZi8U8j67MauYNZ+b/U2SpQqw0dbNbD0OhLRYSg8Rk+JwQzlpaWvIcZiaTsUqlYt1u19vBIWzZbNZKpZIz8PCeEMjQE1CChBai6kHUaJModj6/LXylrdvq6qrXK6Iw1tbWLJfLOUuXwQYQBeDpY7j4GTw7rWtkzswDT1fXC8WjlH+IB9wdiPBxYGezWaQvJcYUZwCHAmWpbDZlql1fX0d65IbyoQeXuWGQyfERIZqZP0sjCjVIGFkMHXNhXTmU7D2frx4m3vLr8pPIpSpCns33iSCB5PHMkSGzO6KTFkKTA9fyJXKwPCuRSHhJyr179yyRSEQcOZ2POnI0SocZvbW15ZA8OUvuSx2NRvb48WO7d++eOxUYF6JNyqcuLy+dAAPTm/0OkQZQCM6umUVgTfbT7K4pBXuhuW3+zs/hFFCOxppzDtQ5Q16QB5xB5AoZIxJnXVXpKwkMJqtGb6y/Gh70hd7XqG0LzW6NE8aUCJPAYDqdumNarVa9t7M6tawHc6an7vb2ts8LucV4JxIJr0ksl8vuACp5UpEW8ppKnOJ99aYTdKs6h+Q+mad2UUI+cMrViSLwIqWAYwrHAQOs8qG6AcdkMpk4YQkkAH3LukC4isVi1mq13FAq8sm+zudz5yCEZNFwfGPOUkNXVWihoVIlooYM4dJcHcpCf4YmBBxejBM5T4yIlh6Enr/OBY+duXLIMD4qQHyuRpx6QPSwocCA7nhXnsP3w7yq5lyIBhih52ZmnjNBgaG8oaKrctbkP+9BIbu+E3Mj8U/5ARHffD53WBBPjpox8jK8D0OVmHqaHGaialVSwJJEBypvyBckKogw2o0D2IoctsJmfIZ6z8gEe6kRYSgX6m1STgGEqtCTRqScD+SZJs/IPPKOYsGY4qWTx6SQnhGiHgo9U94DujIej70IH1YfZC6UluZj+WyMu9ltvS9RsTocnAWIJbAWmYca+FDmQ2ea88K5Vfha0yUK17LW+v88B32gdaihw0TultyaKkTNXaOww7PIXJPJpOcjIZFoD1KcI4yHOr2cWUXXQHY4r+Rj+TcGIUzbKBsfo8x66LnUSJn3YK7Mj58jMtd3Vu6Crr860xgw1j780sHZooEGew4SpPKm6IPqc1i+mm5Tu0LQAKmKyx2ox1TbxGcrxK9BTDi+0Vjyp8Kx/Kl5q9fBFUQreBtsQOj547mHOQIiBgwaC6eHZpHypvCU/AyHAAFWwcGgkC9Rj0dxbTxuvlC8GCFV1giPChWEjPDnWDs9xMA6kDL4XQrUVfGqYkJ5YFiAznh3hatg2XHgEV7tXwpMxSFnzTg4ITQbGiLNRShZQ2VLv4fiIm+GEkOYMZZEnzgl7Cefr3Apc9a8oObP2CvWYD6fO9sRxw0YFZhNZVeRF4hSeMs4g6QcFO7V95zNZq/UWfK7muvX88jP8m6tVsu63a7d3Nx4E3EiMAwRMqPyicKGaIHDFO4nskAKhtSBojnqZKpM8xmau2TuKrMqV7pXi4wuv89e46DxHPZSn6+lNsxT+QygEyqr7DcOLBA1jhTGRB1hzrkaSxAzdb5AgZi/9lC+vr62tbW1Vxw/re9Et7LPuk6qY/R8qoMSQs/qUGmApOuhkRz6h3OKjlcoW88J8g9ypLpKdYn+riI0ijwq4sczzcxZudpchT65OMZq8GHOh0FMOL51b1ileOvCa86GDU8kEo6hw6ji+3gCGlr3+33ffF6Gf7O5JMU5RFDCw00Jcyzako+hAgU7DHgW8gzkl9Bg4rES+VxfX3uUxnN10xU+pF1fKAx46OqlMT8gTYx3LBbzvAsGDQUYi8Xc6yfngfHjHXO53ELYUxWDRlvkuoh8eCeclPD3UAxqEBXexOgplKSGSyN1dazI77EmGJlFThufr944UG7oqPAcUgi0UTs4OPCIPp1OR8oXQmWOEkARANWzN7lczmVAi6nNzCHUVqv1yjVBGj2jZFXRaUOMq6srOz09teXlZW/Fd3Fx4VcT0fEHBi7GGxp9vV63/f19K5VKdv/+fVeIKHJyc0Td7Xb7tdAxc1RoEUdNzx/7xeejO/gZlVNNQ4RnHblijsgmcsDPKgeDgULFMUHWVK5wWLQ+kTQHhg25o4MP661KHHIR84J/gM7AWNJMBcMAIUfnjF7gndCVMP0VKUOPKZmOvdX1RacoEoZ+4vsYV91H0A/t3c37MNTI8lzQjdls5qxh5YUg82dnZ14WdXNz404776d6gPkxZ/Qg54C7f7ExNzc3rhtV/y4abzSWejDNLPKyYaSJMdCXZRFoJFwsFr3DBXR2yhNqtZobKc3VqHeqYboq5el06pg+niMRJV4Lh1Wp9mwCtP10Ou3U6Ol0au122zeMw4wwwXyDqUjulQ3id3QT1KvVTeFgo0ivrq687u78/Nw9R95P756j7IDDUyqVnPKOYSASoIPG9va2e6scIM3/aJ4oFov5JcA4LMDRGiHxHnjU+n+sh0KhGMsQQmefFRLViFONlK4f8kgkvUjweTYKSqMSeqz2+317+fKlnZ2deR0xjhMOmt6lp168GhcaDiQSCfve977nZTzUtun7AZ82Go1IWzNtEq3vh0JVQ3N5eWm9Xs/K5bIlk0k3iLCdiWC0AxWkMTqewNzG82cNKVKnpya5feRZoSsUMd8Lz8Oi3HHo7Kgzw9lHBnR/tZ5waemuVAaDCfzM2eNZODhm5lGKmblzRqSkjj3/Jq+Jc033G4xD+M6kPPL5vEPCELDUueSZzBd9oKgYg8uNISACq2OstLcy5Wta1oexY29J2fD/mqsMYUmcGIVKcVxJKTBfRRwUkmedNC3E3hJZ0+ENuVVCFagPep15KkkL3cU7qVMAuU5TAqoHXze+0ViGcCEvqwpNlRWCdHh46JOl1RFRJJtWKBQc5uL76qWEeSb1HiCi4DkxOJDAEnhZwGLMD0Op3i/vjPfHopIzYKFR9gg/XpLmKVkvFBqHiP/T9eKzlVHJu1AnRPRDLq3f71u/3/cDDXwd1nJi5PD2tD8iwsr7sVYcMIw3UEav14usE+sbGkaUkUaYGBH2U5WgDvZZ4XSGQuAhDMqf6pSoguFz1AAjt7FYzNeUu+8ymYzfNoLhw0ngRgdlRSpZhbZzeMW0+NLDqBEh8GvYwYdoGPhZcz48j4iG3DWQErILdKoEC3J3moseDAZ+JlBAmlOiMYL2qtXzqXNG5llnhfxAIULZUNSBMxFC6+owao7pdU6XyhZyqXJiZu7gQ8JRhn3IjOSMaE4RY6akJRxzzhaNLFZWViJ34dKSjXko/Mo6sLeNRsPngQOEHiV9RXAR3pRC9MWceC/2jlIaImFFDZUIpWiIIj4YSioW2COVC3WCFIHDuLGG/Aw2gMCFCFtRNNU7fB77C/mPFAcpNNIoyn7ljIVppXB8K4IPQ3MzZhbxeBBYINPj42ObzW7bDOVyORcwDuDV1ZW3pgoJEIopK7QSQoRAg6rEEX4OPEYTJYUyQ0GpIoGij5CORiN/Fu3hlAADpRlFoPelqYetUZXmBBh4chwMrRVU4oKZeR4NxiMOgNYnai/WRCLhvQ+XlpY82gASR6iUCctt6RhvvFTWhndTiEuVnkKT6mBpbgZ5CQVUc1YcshBi0ShTIT2Fs/liaISqg//vdrvOJL26urL9/f1IPSsRFX9H9lGsrMf19bXfZwnE2Wg0vISInwNSAiVAuSmkqWcFA4dM06QAB06vxOv1el4yQF0tBBdVNii21dVVh7nYYyB3lB7oghb9q0yrjgiNpULY/Knyws+GUQ57vCjaJL1Af1hQkjDHprKn80U24DWosVReRpj/Y55Am5CeMFQ6b41ucKwwtuwFZR3T6dTRH9Aos1uHmf6wDAIMPhv9RHqKO2o1n4yh0Og5RA7N7tJcelbC/D7nEGY1ES35bCVc6Z6F0Loaz5ALAzEUpjhnQ2FpRSP0cwmuQBZw/vlc1gOHDIOMI/e68a06+IQRHp6MvixKu9/vW7PZtE8++cTp7Xt7e/bgwQOHm16+fGmHh4f2zjvv2Pvvv2/vv/++ra6uesNoBJrCYq15Y6NQip1Ox168eOFzlwB7lwAAIABJREFUpnMKm4wh5nANBgPrdDp+jyW3bJyfn9vp6alDrywyHjf9HQ8ODiIECe7ZTCaTbrQxWnowNVrloDBgbl1dXXlrKnJVKysrkV6NPIP3VGyffOr29raXvcRiMWs2m06UgiQEQwySABEk6w0cp3kT7vNDgekBUChNv0/kFsoTQsrhxcCSh+U5/KmerrL2wryXwoShsVTHjt8hmqZn6PX1tacLgEDX19ftwYMH7mD1ej0vu6BTEpD48fGxPX/+3B09DOrGxoaVSiV/HhC7khYgjDBw+LTuEIdU6f8o+Ww2a1dXV1av1+2rr76y9fV129nZ8RsVtAF5LHbbCtLM3HgyJ5S+MlFVYbK/vJ8qRo1acSQ1Dx9CynpGMEBEQLCv0TsYNhwmLnFG7imPYY95JuvFGSSCY75E6sPh0OUeZavQ8fLysrdXQ2HD1ib3TI9RYGEcuOvra0dn2GuYx8gzpCl1khchLKp3IQKi55AnZJ/14vyHjFMiehCLwWAQ4Qxo+QdkGn4XZ0xJdqS1tHZYz6nmpdl3HB3VIYo0gKLxuawvKZXV1VV3sszML7HWO4TpsnZ1dWWdTifyLlox8KbxRmMZegfq8aME2ASUHgy9bDbrkQ0wAuzFhw8fukLnVoa1tTVrNpvucauQElbrAVZoKJyz3mem+DzEBHKU2hhco0eEBa+m1WpZs9n0jUUBEF2Sv8S4cDCVjar0cIwwo1gs+iFEaHlf3kVLDubzeYS9xWYnEgnrdDpeA7u8vOwOCF94k9fX194oPpFIOFzD+p+dnVmv17N6ve6eaij87I1GmGqQkJlQhiaTSSRSViadmbnR4DNgxS76XIwka3x+fm6rq6t+tRsjREiWlpY8Ym40GtZuty2RuO0+RP5bFT/wNo25u92unZyc2FdffeWtGnEUDw4OXOGhhPf29mwymXhhPBAZ8Cs5UfVsNa8J+QdEAGXBc+AAnJ2def693+/b119/bbVazTY3Nz06HI9v6w8fPXrkpTfkUrUxNX8nn6Ss8ZDwx1AHhLwea645Pc4Qe845a7fblkqlIsX8KNVWqxXp+RqPx11nkK6AyBKSu5hbOP+bmxtfT8qxUKr8vEZKKH1QqF/96ld+djY2NiyXy3m5G05wt9u1RqNhz549s2QyaXt7e/bOO+/YysqK12tDGFSUxCzafYeh8HQILwOl6vfQUTTv0PMU5phxTEAtVAbJF/JZ8CEwvvP5XUlbyCzlfUICJvAoZ0W/x2eyJ6S65vO5O7akOojSY7HbHsyNRsOj6kql4sgZLGb0NGibOv+vG98qsmSEB4OX00Qv7daIDImywt6JYNxa4AxsRdI8zFnomM1m1m63/Q40huYsFbbSHAVzBq/mIOCp8z28S4VtFIIiKkOYma963eEBBcZVw5PJZJyCHXaqIJpTEgDvAnSDwYzH43Z8fGzJZNIjx9Fo5E3cNRIAZjYzZwyb3XplSl5AoeP1LpIDhVRD+Qn/jwiSHCGHj+hcyQ9094D2rc9TpcL782elUrGdnR3b29t7ZZ46N+D4s7MzVwDa2QYFj8JBPubzuRfPw5zFkyavp7lJPF2cE5AMnCYaeCtLljnyGXymklOQASJXbfO1v7/v3ZiA41AqRHPdbtejE4r3NWLn/TEQajB1L0MFoznqm5ubV5jfqphImRDlkHudz+euIzBMX331lT179sw/s1gsukJeX193CBlY0OyOpa/nUHOjyAVRocKwi2oPlQcxGo3s+PjYbwGCicxlzdyQw1WGw+HQdnZ2/Pu6rjjBrA3Or6Z09AyxzhqBKsKjDr12TdOSJd5tUbqHPVEGM/sAVAwPZDabRWrPWWcl2PE+oH1qhHGOQ9RIYVYl6uheA8k3m03/GTWumrvUOmn2H32qsv668UZjqR8SjtfBb/SbxMtDyZjdCjS3iMAy1No1NZBgzGFornNrNpt+dyRDYT9+DkHiEGoBK0YKaFkFj8VWGFcNoBYYh7k2nqUwDNg7MA4DBaqbxXOAmhSyxAvDG9Z6y9PTU5vNZp6zvLy8tEaj4UaVg4431e/3Pe+zuroaOQzkhCAlqPCHKEN4oMOkO4N5D4dD752LYkkmkx7pJZNJv1aHvTCziGIJlRyKvFQq2f7+vj18+NCfu8jIEN3NZjMrl8seFeC4qXcL0oBDg3KHDd3tdiNKREkMQEfsgaILKFLQDnIqrKsSkIgqNedHWoLyJb5oBsFVRhrBIz8wWjGWGFOcMs03ah4Sj1xz04v0g5KRVFZQzLwLjhp7jSPXarUcrWm32/bZZ5/Z06dPbT6fRxjfcAc0l8zaaP4aQ0lEY3ZHrgOOZW/j8XgkN8heoriBv5Eh9ufs7CzSSIU1GI9v7/x966237N69e5bL5fxyeWSNzw1zeaHsoi+VE4BxY415R868lmIBy/JvJVVpPtPsDtVB7ygMfXNz4xEoF06gq0N4nrOkhB6NoDlji8h7fI+xubnpkP18flcOhb1Sngnt9bRxCQ4Cn6v6JERTdbzRWA6Hw4WeLlGbJr/ZnHg87hEkB0DrloBitSE6CwgsirECBuMFUByE+41Gw05OTiIEHxLeurh6sJVlq8w3BFAXD3iKA8N8iChRnCgQjD+HlQiCw6LJe90UmKx6yDXnpREwESSMV20wDGRxenpqJycnLqSdTsejZKAfPHc6uMBkI+pDCRDxAImx38pE03nroV4EwcZiMc8fLC0t+S3osEK15g2yEk2iUQIob+BkzQFtbm56SzEd6nGrLNGHlrtZlQxAjlcjFeQmkbjt10k0cXBw4E0But2uKwGibp0nxrXZbFqj0fDLyWm2wFDYkptlkFH2QPttAqnHYjErlUruhHClEfNgrWD7AsWyrjhUikKoYxkqmXCPUf68szpSmkrRkgNgMoWpG42GPX361JrNprXbbecaYNy5DowSmNls5k4HsocThTHEwGkPZmQCh5h9DqNSomIMSC6Xsx/+8IcR8ommTQaDgW1tbdn9+/ctm806ixoEAEIMzrRGw8iskl8Y1P0yT9YXJAqnh4gPfYvO4EIFeCJEfcDizJ9zn06nLRaLOWLY7XZdtwG7FgoFv69Y9SsDB4sIUuveCWg0UlZ9gdPD95PJpFWrVT/T5+fnDsfPZjO/yB7iFTll5T4gF8ijpgZeN95oLJUtGpI5UJAhVBMmqDFesPLG49vyEeAypQejCDBcungcAJTP2dmZHR0d2dHRUQRS4eXxmogi8SaIqMyixe0hTVwdAZ2DMtswlhjCpaUl93jNLEJWYl14N2W36Xy004TSqVljhTyXlpa8bITDQLSosM7x8bFHJWZ3cNJkctu3k8N1dXXlNZmQQYCXoIkzX3VAFILGaCqch+Dr94E74/G4e+bUFfK8ROK2uYV6hCoXyn5FuQNxsT8MZfhh0PGGzczZsMCr3NyCAtOry5Bv0IhsNmsPHz70XObLly+t3W67cl5auu2KQ3PzVqvlhCJy4RjJMLfKe00mE+v3+y5/yIiWA6B8qSfu9/u+xhhSkAKMjqYQYGGH3rWiOkQpGnXqOuNg6B4pd0BJJlquxPvQ+u/m5sadN/LIcBeIGvg7dd2UPWiOVxvNK8JDeoMWm5qmQcbRScq8ZM+1dEOjVvaKn02n056C0sYlfLbmi4neFqWd1GACHbK2ilqRwmKvVKdiOPXWIQIczifrpA6EptMqlYqlUilHhrgomzrd/f19P+M658vLS5cBHG32S9E05SSEtgY5RIb4Ym6cFZxb3gudwXMw1BhI1h198rrxjZEl3msItygD0+zWOwjxbA4WB4iwHUVI4Tceenj4eEGNYhFcZapqBIMyBC5RGFfzLjrUEw3/rhECh1jnyELz2USDrAlz4Xf4UoXEz8fjd30OmQcen0JyCvHRBBr2LJCtGnigIJSICgqGU6NWJY4wJw51OMLoMVxXvkJiGAdFDS5UdBL1/FwoA/perDdfKCV+T+eph5Lolb0gz3FxcWHNZtOurq4sn8/7bTGKauC1sw9EFTCUiZY0x8v6as0q5RoKSak3jlwwx/Pz80ifZN1HogqVe6BYGlawnuplq6JUZEDl53V7uyjHE3rwvL8qbZUZ1SlqQEF0rq6uLJfLuVLXshJ+F0OgyhlIXiNLdFNYUqHQrTKoMUoYey1lAfHRHCBfurba8pPPU0hbnU1FEjj7CrszuDwap0jvyFzEJg/zxFqKQWpA9ZE6CKA3ikyxb+iLeDzuJUuQNfXMm5mdnp46kqMws55LnTNroo4Kv8N7q8PIXFkXhXNDxwNjrcQtlevXjTcay2636wdTISwWQg/45eWltxQCmmSB6ShCU27yh9ls1nZ2duzevXtWKpXM7E7INdGNsmAxYrFYJAekUSGepUZfLJjCQ3y2NhPHiJNHRdmpwdNoUQViUf5GDSRrp9FCOFBwRIgoZI0SMYYoDjxFrqNRR4NB6QketDohHHjmpoXeRNJA5gqd6FDFyFB4dhFUpz/H5+Kp07IrXDv1uDWyUeWgl4zrc9gfdXL00ABDAzsPh0NXckRuKDmtN8TQQsBBCbImeMKa7xwOh9bv9z3PxfuH0JXm9sxukZ58Pu8QLOdIG3ugYPRaJtZU9501U8RCo0GFY9UrZ/AZb8rxMBf+1Nyl2V3+GYPDHmJ86KilJTgaYeD4LkIvlIyk555mG2osF+WtSKtodMy7AN9ptMm7aZ9SolAiH1I5RIKweEHsCChUdy4i+NAajtZ6SuJSw8d66LvjLAOPc4Ue78AXhprcJPtNvhM9RqkNhrfdbnsXKXVenj175t2vwlRNGACwrjoflUHOksoi/xf+v9oQZFJJS6w1DlMIeet4o7E8PDyMECxUcalwX19f2+npqdVqtYjghpASRojNmM1m3qLt+9//vlWrVa/fxPtBmPTQxeNxv3svzJMhvKGiZM7Ly8sOIfR6PY8e0um0lUol29rasnK57HeurayseGcXiBrD4dA6nY49efLEDW4qlbJiseiHhMMdKglVTgxdS2A9HAEiW60HQ5EAB+tlzxwGZb9xRY3ZXQ5Hc09EWCgFFayNjQ2/4Zx54lEqk1GHenla76WkGAwdSpnfw1gBjVNTqz+rRpiDxvy2trYirFAGv48x0zU3MyuXy7azs2Pj8djee+89++KLLzwXUi6XbXt72/L5vLNG6/W6dTod63Q61uv1vKFBv9+PkGTW1tbs8ePH9ujRI8tms74GOCWq9MJUAGQTlEe73bZSqWTT6dRrAM/Pz5100el0bDQa2Xg8tvX1dfvggw+sVCp5Pg/FDWsd5Ymi1dKo0MHTPUI56s8ydH2RVZQ9jrU6wiobyKwaG41mkE91kHBg9bOIypBh5Fw/A7kk0sExUKYvaRsU6Xg8jvQlBQlRWJbf5Syb3TLM6cDDJRFEbJw30DdNwbDeoQKnc9fV1ZXFYjHPvyqZiHWcz295DJRacF0cn4lTQRMAenlrdEbq6urqyp0w6jshONKiE9Z8WKPZarWs0+lYPp/3YAXjhF7W6J1olvdnz4ikVYewfux1v9+34XDo31Ni59ramp8JAiOMf2hLwvFGY9lut61YLDqJQYWUSMzMfIPM7gySNvlFMEi8AiV1Oh0vp3jy5ImNx2NPzGrXEoSGRby8vIxcFK0vqF5HuCEoYNhRyWTSi2g7nY7XcUF6yOfz3niXqExZjOPx2NrttsVit91QstmsH5LQ6+YAcyjJmTA02lxZWXEhplAY2CQev+teRG0ZSkuhcZQP76nFxDR0RiFqxIhQMV+91QBvHGMwnU4jyANDvX+gx9DLDaMbjWzYK67agXHHXDU6UuWytrZm+Xw+wk5mqFLXZ4GYoExXV1ftwYMHlkwmrV6v+1o9f/7cnj596mtN9I9BW1tbc2Zxq9Xyms1SqWTvvvuu7ezs2PLy3f2HCkcrKzx08hRKIjeuho0i9H6/b61Wy6Hd2ey27hPFsLS0ZOVy2WUbB4nBZ4bpAdZK4VVdU007ID+LIDSMI5E4HaG0eTwMaM4p60qUQ1qA86QRsSINoC7krRQ10jwW+ipM4/BvdbD053VtaJxwc3NjjUbDO0BNp1PvLAQRJhaLea0jpT6a3+RzOAPkk0N0RtNaGAXWRwvx2Q9kBdRPG4+zL+hbs9tcPrLJhdU8T50QIG3yn5DktN8rg/QDncdwNlhvzrJySJB1yF4Y3Ha77WeXnCX355rd2q1Go+GRO84AFwxodyDOn5Ycvm680Vhyvx73SiocoLAgll2v1goT2uPx2EkdFHivr697rkdhJDw6zS3ynPPzc6vX69ZqtbzcQg84BiMUMvVS8IZYHLxdavzwwKDVaxIdmrJCbyHDb5GyRsDwdLTOkgOjEG2YS1FlCRkGYsPZ2ZllMhmPTlgTIlOgBjWeCnWrwtGIgKiZHCiHsFAoeHG71j/pF/LBnNV4YixRwmHUx1qRa0OwIVCFqIXZrZOkhB2NWFlj5CiUKXUCeW6hULD5/LboWkt+gLxwMG5ubhylgM4Ow7ZcLtv+/r5tb29bJpPxnCj1rewFiiUkRejfmScsRJiJYZ4f5X95eWmnp6c2HA6dPERnolwuZ8Vi0T1qcnp6jhYhNPxd4Ur2WPdhUSqA31dYEacXFCUej79C8w+jHBRxuGYKiZKPzmazke8vepdQqav8hkbY7I6pyrMGg4GvIfXMIDdA38p2nc1mTsThNhk15PyuMvFDJErnpQ4O66HzVyhSy674eY3qcQQ0v6yQJXMBciVXiqMH1KzPZ6A7tWNVWPvImiJD6H/+H2eAG2+Qp83NTddViUTC0ySkSoBp19bWnDWOfPHub2LBumy/6ZtETkRjlHbwAnpIKCvAQ6ARgTKTsP4IApefsvAcIA25wxxTv9+358+fW71edyOnLxq+eChoKKeVlRW/yFU9XAQ/Fot57Q6CBn1fKejLy8vePYWmyiEEGG44xpihNUPqWSKomlckgsA7JVrP5XKWz+etVCp5ITd7qN67wsDANaoAtM8lOQlli5mZvf322/b06VMbDAaROlWFyTX3o7T7kMSi3i0/izfIOpCPpaG8RtC8D3Ama6jQIevAYVdljvOCQmT/1tbWrFwuWzab9aiBzkbK6IbYwC0xiUTC7t27540RHjx4YNVq1SFmHFCzu7IqhSp1qHEyu6sN5XNCBAAyD+UQRG8o1pcvX1o6nbZ8Pu9zBCrTs6yOpRJp+D91bhTSNLMICzl0aNALjNFoZP1+3+r1ujO4iSSJZoBQFTJDp6hjxxpShJ7NZj1CWqTA1VjilCDHoaFRhxy5xJns9XoR55n94B2UhZpKpXy9iK7JiaPoYcVigBQFYXB+lpaWPLJnPeLxuF1cXLziBGjUxhqrk89eM1/0pO4x+0GDDSJE0h4447p3DK0t5tYQZZgrShASuZaXly2TyXgk22q1FvI+WHtuvEJfLS8vW7FYtK2tLdvf37dyuRzRWcrZ+F/DsGZ3zKt4PG7vvvuuR4thvolJIUwYFhQCYTUWXcPweDzu3i+wXZiTmkwmfqEnt8FzkLUMg8gKAeez+NnQ86XWjJ/HQOD1aIRI6yrIRRcXF174qvfcAXGGLDQ+g0J0Rq1Wi5TQaL9Z4LxsNuveNzk9oF8zs16vZ51Oxz777LNIzoN34iCQc2M+IXuPz1OjpkbPzOwXv/iFZbNZe/r0qT158iSSX1TZmM/nbtR1KHSHQtCuKhpFj0ajiBetRpLPwDh2u137z//8T1dAa2tr9uDBAzO7rZ9Uxag3d+jeq1fN/KgZRsbL5bJ78JPJxE5OTqxer7uzQeRWrVZte3vblpeX/Z7MFy9eWKPR8ChYyR/IpA6NcDiPOK+ZTMYNBZ2xWKednZ1X+nRSy9rv961Wq9nW1pYbFRQTBklboukZYN35zDAiJZpi/7TRx/X1taVSKW9FSEs+BuUFut/aiozPYKCQNWqiF/U777xjlUrFm6QgY4rcmEUbjKtcavQ8m81cjxHR4NjjRMXjcVfG1H6amUe4+XzeUqmUlyf1ej3r9Xq+frB1IXxhPJmDygWyjBOrqMpkMvGSG2B65rG5uWm5XM4v+caYKxSNQwgBCIRP54Rzc3FxYRsbG94MghwuOkORQVC7jY0NT+9pT9lEIuE5fK1/BKEsFAq2vb1t7777rv3oRz/yRivIB6k9JWbihFC+o2xl9ow1BD1UWxKOb2x3h/I+OTlxGIdFCz0XFK5uMKUdJKI13Nem3SwUn6UGDgg3m816wTdCEia/OQwq7MCQeINKGtJ8TDwe9wJtGHI4CkB/hPDq4YaUagSVA0A+iIik0+lEjGW323XWIgQkHBG9iuzm5sZrKvG08ZjwApvNph9AcsVquHu9nt+HR20mhBDQA7oGoUzIK1M8v7a2Zu+8847nXL766isnCim8zUFQCIx1J9rUui72nLwsRCaUMYZYYSiF4slt6AFkqDeO0lVGY4hOgAJohIsChMWKgl5bW7Otra1IBE2kCOTcarXs6OjInj596oc0ZPqFhlIHawiET82l1pzhXJEGKRaLLneQSdTpKZVKvkYqqxcXF85oVGSGOWLEIIupNw60a3ZXs0fkwjnAaU2lUra/v+8GotfrRRor6NrHYrd10q1Wy9swYlww5sVi0R49emT379+3hw8fOomD+WjJD84QDjgRneqPkKMxn88jze7j8biv4crKijdHKBQKViqVLJ/PO6pmduvQtlotV/KQZVhzzZuFEa+iVRT+oxtxhona2Wdt1rGycns9GOsX1qGji0kjwC0hN8m4uLhwHQifALkMYXz9N4ac9pn9ft+NJUiWOqxaYsJgruvr61YqldzBZu84I9qIgKHwdJjeI+plD143vjGyVINJJKU5BCZgFoVvNA+mUYDWI2nkqL+PYdLfo7s9nieeZmgswzycwlsoKQ4EBonDzM8rlKrvQc5Efw6lzJxRRFqozPNQcpAwGMCkCDU5LSI2hTRg3moOBUeDhDzvaha9N1GbBwObsFYIYaggze6uK8NYxmJ33Txg2mkDAy0IRoZ0hIJL9G92B4vxpXkTPktlYzKZRGBqDnaYo9L3CXOU/J9G2/pMHBVFUoieZ7NZBLZG4SlzGCVfr9et3W67zKjcvclQMtSAk/NivrrOPF/ro5m7MljpGsW7qaOrZ08hMtIIoeFlaIG9Quw4n7r2yWTS8vm83wCkDo4SOIAFIWqEBg29cO/ePXv06JHt7e05KxqHRuFiRQ74bGBclROdP3qCz8ORVX2IcwqvQEmORKF0vwGF4F2RMXgH6mSG8wKuZW7k5ubzeaQrGj9DjtHsVl8Q5SqCRkMLOvzALSFNhmxfXFxEgiWCIJV/5DScM++K80ZjDNJ1GgQpNItsItcYRE0FKHoHMqJnXR1SbIem3XAU35S7fKOxVGXHIcUAoGw1H4CA4QnpC+hLK+SjlGGFA4hKzW6NRqlUirAwOVw6T7M7aIb5cihYHA4xG6PlD7qYGEQUi+Lb/BvYNJwT3ndYM9rtdq3Vanm+i8G6YmwwyORUUUqxWMxvXadEgFsYaEyQz+e9xAEojVxBp9NxmDkWu6O2k8uAxKWEBhwlbtlgL4EjV1dXrdVq2WeffWa9Xi/iDLGmobemkb+SS0J4jAOmBecKxWq+hkOvkefrDBCRJMpYWdsqI2r0cBYhTQH/INMc/vPzcysUCu5xE4EdHx9bvV73vULmFd5cNM8wz0Y0PRqNvOH7dDp15cE7hTk6EAn9/zByphUk+87n8X8YnKurq0izeHVYMUQoMdUfKDcioVgsZplMxpERCDNKptL9ZZ2AnSkzg7jx3nvv2YMHD6xUKjkCxlxhgjI/ZEtJbyHXYFEOC9nBiFOnrb2JSdf0ej0/vxcXF35HLGuG/ON88G6kpxT+XnR+MLCkhciLIgNwEkajkQ0GAzs/P/d64rW1NUcKMZaUv1WrVY9Sj4+PIw4/+4F+wUiyBpSzaODA+yqSgl5RxjO/p/l49AdnEtlkDdDDIStby+NCmVe9wN5gLBedQ5ft135HhnrpKAo+FEW2KP+jhwnBQvDxyFDUWuuHwJEwNzP3PlkUvjTfaWYO9SLYet0V/zefzyOHJVRczAMFozk4DhUeNCUlapj5fYVfb25urFar2cnJiZfMMLSNXL/fj0QDSmJi3uRhiKJ4LuzCk5OTiMKEwQuDVVu5xWKxSNN1rnFibWFVnp6een5JmzPncjn7wz/8Q8tms/bZZ5/Zp59+atPp1HK5nMNMHBBkiYgRCJFuN+wthx3Z0IOD987PQv7Rw6YOD4McLAcOrxsITaFYjASfiVOH54m3zedfXV3Z6empw+RE3cDpjUbDnj9/br1ez0kuGGPWQz1dxiJjj4Lt9XoeuWjeWc+o1ulCkkDpIFvqaRPRaFTMuvBc1pHcDnulugJIXpnhqtz5XfLKlLSUy2WvW6V9Gk6JQq2qeOEL5HI5q1arnuMCxqXMC9nVKJB31Lw0UQ9OkEKDOLLqyOl5Jy/Hsz7//HNX9pwnDNXq6qrrNGWsayN8lQF1SNrttn+uOgFLS7flQcCbnOuDgwNvS8f1dXt7e3bv3j2rVqv+7hiqJ0+eOIRrFu3WBsMYeVPIlwh2kVGaz+eOOgHBk5/sdrtefoIeY52RZzWYer75u8Lx4bNVvyLr/H06nToEG3YyCse3MpYsmMISGsoSHSqMEkKz/F2ZTgw1sCqsCtU1Gg3Pr2k+iMQzo9VqWTqddsPMzwF7hHnORUpL56WQgCpTjTY1QiFKIbRHOfT7fTs+PvbmBvocBDsWizkBAyUDrKrQE8qbQ0bZCO8L3HZ5eWnNZtM92vl87nVerD8MXmV4EvUuLS15s4lms+nGmf6TzCOTydj3v/99W19fd4PNOqO4dM3VMeHf6rnqvuCMEUmiAPXg6z7wnqHQ4zlinLXbzWQyicgQhg55UbmllEhraVkLyEw4HjhTuu+aJwoN5Ju8Ws4Uv3N1dXsROGeJOlqFloDdUfZXV1eRiw0UZtVGE8hbeDZD9rmmMPRuAIZ/AAAgAElEQVQ8sb+smbKNMSpKrGFPiBCJBPUyePSJOuDIB3II34AzSAQOTIgR0c8ZjUYebUKi6/V6/jw1FBgPZFsNCp+pFzhfX19HUJEQXlTHBEOpzruuqQ6aEmDc6N2aTCatXC47Mx94E94IBMtOp2OJRMJvc+HSgfF4HEnVrK6uWrlcNrM79EsRIwyUooIYzxCGLZfLvk/A6Rixs7Mz63Q67sTjEIP6IZuqu8PWmzj+6ryFqTg9OziS4Z4p4heObx1ZqiFBeHkJNYZMVg1KODQ8Jlrk72rx+dnJZBK5AYUoFMOpC0c/Ww4+88S4q1f0pi+Fi1FAoeEP10Vxe+oyr6+v/U7JTqcTuZeQoYYnFot5cTvlNeqAALmZ3UHeChni9aLQNKrD41figMLGCAvOxXx+e+VXq9XyKIn3JOrAIObzeZtOp/bee+95yU3IQA3XjTkRsTOn1+U6kIPj42Mbj29vGHn33XdfyfEApYaeLSVBOFDIKhE7B57Dqv/PuxIRA7+Nx2MvH1LHjebw3DBCnkohXtbyTedk0bnhTIxGI3cKYTiydkqc4DyReyJCUnnWdMmirzClEDqVDM3hI6eKCigUq84oKIKmHVShIQPK5OazWU/kgP2jxAYFqExbzgdNJfhdHD7mqOvO7/E5Sm5iPVDEzB0Z0t/heTiqrI0682rUdZ/M7sqgWI/JZOKEmVarZYVCweFJzufKyorPHYeTtcN5J7dqZp6PVV2ijpA6iiEagkOk/69lh8xfg5nhcBgJujiruj5q/DTIUcg61MeaVtAzh64jrUBQEzaL0fGtjaVa6xBy1cQ5E1JrroOfx3PRwwiclEwm3WNDUCliNTNvGE7uQMdoNHqF6YWnQ5kCA+WnwkjegDIL9bAXwWK6WXjBWodJv8Tj42Nn8oaRFk0ZiEra7bZDG3igrKcqNfWMEADt1EMHHCAG9kghDD30RKOQda6uruzw8NDnw1pz4Mh1sY7lctl+/vOf2+npqb148cI6nY4n8BVOCmVCozcOrR5C3u3y8tLq9bp9+umndnFxYdls1n72s5/53agKw4RRGt1zUNIoXZQrTgQGh7XVA4tjhrHAwYjH764xisViTuSilo7cOxHMojUws1dkOZS3EHpC1pPJpHfa4ncwiCgBvX2DeYdEvVAJ4vCGihHFo8aUwXlC/jCyfD7nH2WpkRRnQhtx6/sq6hQaXnUi+FlukOEMgbigzM3uOA6cMVjuOGY6d/ZEo1xtLmJmjsxo1K4oG8aSHCFzBinR7jfq1OoIAwsQqXj8tvTr/v37zjBWY66QPQ4yCIQ6cRhyyFRA4YrE8GzlZqCjdF8Y5I+ZN04Exrrf70eMO2dB60dZC7UxYXTOz6jNYh7IBgFN2O6RSPt1I7bIAHw3vhvfje/Gd+O78d24G2+MLP/oj/5oDgX6+9//vu3v7/tVUBRFkyPQnCAQELdHmN119mGE0Y2G2WYW8RCvr6/9lgY8gZOTE/vyyy+t3W7bxcWFffzxxzEzs7/8y7+cq/c4GAw8oc59fdzSsbe35x4P3s3z58+9vyORRDqd9hrEbDYbgUtpvWR2i8u///77trKyYoPBwP7+7//enj9/7gl4ul3Qjeaf//mfY2ZmZ2dn8//4j/+wTz75xH7zm9/YD37wA3v8+LGVSiWHKpSRxvuFEBVeu5Km6Hqj5RAK7WjpCWvETQTdbteOjo7syZMndnp6avV63f793/899qMf/Wje6XRsOBzacDi0d955x3784x/bw4cPrVqtehEw3nOhUPCm70QkyvAlkkAGtFBaITz11tU71Eie7iCdTseOjo7sb//2b2NmZg8ePJjTwjCZTNpPfvIT+/nPf25vv/22N0nXMgLWGdIChdjkNyAehZBqCFvynhoJhZAk0R8twf7hH/4hZmaWyWTmvK/ZXUkLZwuYDM+baJLIBHYz6wh7m+gHYhwkB6IE5qPQPF44ZJX19XXb3t62YrFo+Xze/umf/ilmZvbee+/NiRaRP+ZHRKVEPyId5Q0QESiRg39rtAuExr9BchRSg5SlP8u69/v92Pb29lyha13j9fV1293dtd3dXatUKra9ve3yTd5fGb/K55jNZpHbZWBK9/t9Z6fD9mXNyVsqNIneTCQS9j//8z8xM7OPPvpoDoJFMxKeu7S0ZB999JFtb2975Hp8fOxkokqlYj/60Y/s/v37nkdU1AG5Db80taSRpf4ezFzYzGdnZy7LH3/88VxRDL17VeFdRjx+1zx9PB57GRBlOfl83r9Hy090WC6Xi0SiYUAIysTdsx9//LE3urm6urL//u//Xtgg9o3Gkr6Hatj0xZisQoCpVCrCgmQxQjhn0UtozhDlwwuT+OXfyhZTGCiEZxjz+dzhBKUvj8djP1QUzPJO2mGF/CG9SYFJYZiS71BYiTmGBI6lpSXvdMH/ZzIZK5VKVigUPOfL57KmvIdZtM5PBZz8r+4TEAoKU/ODKCaEi99NJpO2sbFhuVzOMpmMDQYDf5+Qbs87Y6RRgtrfkvdUUovC9HpQkA3NJYVyyDM1FYDsUCBeKBT8MxWaZU35gl2M3OCUQZ6ATcy/kZ0w76iKXZ0ahaDD3Kzm/9lTBkxshVbJSzNf5hFCU2GtKwPFixyy9qQ+1FCigDHEyBJzU8eZwb6EzFEl/fD+CpXp+ig3ItQJqjtYS11H8l2U0rDnyj9Q3aP5OGST3B7Mc2DB8/NzZ4UD16rs69Vys9nMjRU5btWLnAVkF4Y374ncI5Oq41g31jSRuO2Otrq6au+995798Ic/9JuT1tfXrdfreZepZrNpv/vd79zJvH//vpdcsV/wKpgj+lKdFR1vyhUuGuhGTdvhdLD+4X5oYMAeKvtW+8AOBoPI1XrIjco+Z0QveE8mk5HcajjeaCzpfcrQF9AF1QgQxh2GE0OjEah+mb1q/cPvaQ4JTzz8f4YeBmXUap6GeVOegpd3dnYWcQg4vBx+ivZ5LiQOykwwDMxd25hpVK2GlLGxsWGZTMY9Sz6D0gYtmib3ogcpjNJ5f5Q7RAYYd/oMzQkwV943nU5bNpu1fr/vQsX+EjFr7kBZnuqNLtrnMHIkP4JiQwnpIQyNlLKQ+SzWV68mC3Ml2gIL9ivG8Pr62htHkCPTdeUc8Cx14tgfPG0UoOat+NIcsEZUDC3Ax6CxP5p35f+U9AC5TY0oz11kLNWI6VrxfWSQf9PmEZSEoY4UZ0eRgkV5Uj3r4VC5DFEozeVrTo79hrnMc8g36+fiPIdz4V04L2bmCIZG97B4ta6Z52vNqZLBMLBhvladLSXqLVoXJTbx79XVVbt3755tb29boVDws1ssFh3p4VYc5pnNZt1AqEPDn5wx1l/lXeU4dNpCR0jRBS0hwSFY9F567jGGOHOgHZTEEXGen5+70eM+WnQIteSay4QJjx5QVnw43mgsr66u3AggiPP53A2MwlDxeNyJIXjJFMpzV2ShUHCBChV8aDiJ7oj4ms2mF4Zru6vwdhKtX2RT9SqvfD4faTpNOQrGEkYjUBHCAqRkZpGN6vV6EcHBQ6QPZlhSo4efAUGEzjwQl2AHKuVdI3kEZzgcRgr7WQ8ED4OJksOgmZnT9RUi1blSv0YrObNbZhtQKFASz0NuYNVRn8h7aB2cetkQi6C1qwFUZAKlp9CyyqAaVO1JC2QUj9/W5VKTN5vN/Eqfer3ujbF7vZ7LDVEqLQnxxFHE6pQxR21agKe8KAJU6A/lygiNpRJewpIKVdJmd4pez60+W38ndGCI2LWESAlQICMgLeqN4xgrnKmEO70eSlEPM3NYXpWsGj+cD31vdbTU0JNawMiHDqq+uzoL4fqFzTW4QQc5JDrhfllKwPSd0Bc450pSUtKX6jIlQCJfjOl0GtErMJwzmYzfCcz7LS3dtaVD5/3bv/2bvXz50vr9vqXTaXvw4EFkf1U2MYTID8/krEEqY+3VCVBZVhngxik9M2pMWQ+FzrWsjS5CvV7P7cJgMLBms2mtVsvTXbQfpGNRsVi0nZ0dh89Jo1F6t8gp0fFGY0nzXRS5mbkSXFpa8iu3oBkPBgM7Ojqydrttn3/+uXW7XVfQu7u79s4779j29rbXAqGUlXlINNTr9ez09NSazab/qV4YtZfc8s2gtimEM+LxW5ZVtVp1iGJ1ddVvDEC48T64r5AoC5bp5eWl35Rwenpqg8HA3+PXv/61e0q8gyoaBEhrTM3MIdyNjQ03HpVKxR4+fGiJRMKZleH1YIvqmVgblDRF2UBrZtHLmZeXl90b3d3dtXv37nnuEmUDFHl8fOyCz8/gEMAeJJcCtM09poVCwQ1vtVqN5Cs4DK1Wy549exah7pM7ViVNVKh3jvLudPNARlXBmN06Bzs7O7a/v2+xWMxOTk7s8PDQDg4O7PDw0K9/IwogB7Kzs+NN93k+Tgfrg0LE+cJxUu8VjxqngXXA8GqDDYXqzaLQbtgwQCEzhSEVoVADgbJDHlBSWlahkeTy8u19hRgfjEQ+n4/A3dpsRHPkikBhbDBIWoBO9KJKO0QWWANuVaHeUbsKmZmnETY3N21zc9O/twjZUeedmk0ug9/a2vLPicfjvv6wnmHF0iwAh+D6+tq63a5fRqyXFieTSb9gHoOhUCQyAiqkg7IabWGJgzMYDOzly5c2HA4tlUp5SQkG6q233rLJZGLtdtsbjtAXmsYAyprF8cDxUwNK/h/EQPeRvWRog35kiPwnv4MjgRNOD+16ve4ysLS05MZvMBhYvV63o6Mj17n8yfdIn3HX7d7enn3wwQe2tLRkjUbDPvvsM2fx9/t9+93vfmevG280ltxqgaAAY7AIg8HAC16n06l1u13/gvwxn992kMHoEA0C86G48cQRwGfPntmLFy/8lo94PG6FQsFzTNDxwyJS8g0sLIePPCNJdhaWgnuK0IFT8IDIF3LwiX6azaYNBgNXVkR3GsYrVMR8NJenP0dhOV46mDs3iTQaDb+gWq/G4lYDzRPq3ZtcmBr2PmQuqVTKer2eHRwcWKFQsEePHtn+/r4rCYwSkA4DwwRxZzKZ+FVjwFVKvKF9Gff9IfAMvfqs0+lEFCCKd2VlJZIfW1pasm6368l9hYVUTs3ucml0jNnc3PScB+SK9fV1v+yWCAKjAkGCNVQniFwW5AaMJc4C8D/oBIX3IBhE+8yTgcOmRCaMrxK62FNq6Yg8yKejMCjcZoRN6Lvdru+VwpexWMz3I5VKee/QbDZrhULBKpWKfyZ5OdIcCmFj7Bm8u5LBcHQ17xqmaIjUOp2ODQaDiFOCnKAcS6WS51ZR8Apph4hAIpGIRCE49+Tu0YHsCUaHdnc0nuDniHgxjjhYGkHj9LTbbWu3264L1QHS5gc8B+cMRxxHiUvLaWmnF2rH43En9lByxhkD+tWoXUlfpLIUDiXyUwQIJ0gH78LfQcjQF+1223U9AYlyBvhsbvVhjUGumK9Z9MYkLXnrdrvukOzu7tpkMnGHNJ1OR/pyLxpvNJZ4HCSK8ShYvHa7HSns5AU5eGwGiorDQBSquDheUq/Xs3q9bp9//rm9ePHCPwM2qibkETIdKvgcBjzo6XTqBB6zW8PH5bMsnBo0bctHBNHv970dF7WhunG8I7AKnidQwyJjiUGnVRqbf3l5aS9evLBPP/3UTk5OrNVq+a0BGOJUKhVp87W6uhpp5oxR11o7s7t+sAgUUAYKeT6fu0IETtcm7RxiVWgQkvB6mQMHCxgKeFAPDwcDiJ9cBO0L2Qtl/wI/hSxRhZHCNea6H4wJa81aLi0tuTOGR40ziAFdXV2NFJfTsguHido1FCgKWQlJRM04W+RP1Vji4NHNhlszNBqB1BWSg4Di+B4OH58/n8/dKBIlcXZ5jsL1OABAylweHRKpkCklN6FkkQtFbDY2Nuzq6sqbbzA/DJfCgOy7snNxDNXY4YgDEwOfK+SnjT10QA7L5/NWLpctn897ZEJ0xH6iL0B9QN2Yh84FfYLsMwfWdzKZeOcx9lr5E7q3ygDnDPAz/X7fjo6O/Hz3+31nenPPJ/1h0Wkha1tzrGGNLsZM1595sl/akIGhcm92V4+KYTw8PDQY9gRMOBWKLClCojA7xhdDjNyzHzqHk5MTS6fTzmvAQcTZet14o7FU+Iz83dnZmfX7fWs0GpE8IopEIQKFW8zM72Xk8POiHNxGo2EvX76058+f229/+1vrdDpmZm4IMBSdTscVVsh6DZP0ZuYGAMN5cnJiw+HQGo1G5OomoFDgNjaHzb24uLBms+keOJEC0cLe3p6VSiVXfHRuOTo6ssFg4N6ytnrSea+vr9vOzo43Rz84OLB//dd/tf/6r/9yQSIa4DBSDoF3vrq6GhFyPF29KUAFDycG5Vqr1RzKSCaT9uGHH1o+n7fr62sv2MX7xJAo6xECFOUn3HsHG/D8/Nwymcwrl/xS9kHHG71gdja7a97MXYB6j6iSkYrFYoQ4wbi5uXFiT6VScTnVhgwotEKhEGGFao/V+XzuyoM95HBrgbrmd4rFYkRxs1aqkIDAwj7H7XbboXeUPs4bZwsnA2gSp8XMnGDCmivpjHNLo21QGeQDmQHa5w7G0WhkxWLR7t+/b5lMxp0o5NjMIqVaEC+0C5DZncODYtPSF87IysqKG0giZfLbCgPiPAL1YTwwYPwsewB0jyxzLoD2K5WKbW1t+Z2hQINE6iAF+XzeZZur9pS9TjBhZn7OWKOQ+Ph/2XuT3kjP6/z71MSxijVPHJvdVEuy1JJt2ZbhRQx7+V8HWQTIyrsAQZBNkA8U5BNkEQQBEsQI4MRJPEqtobs5k8VizWRxrOFdML/D67mb3Qrybv0AREtkDc9z32e8znXOXS6XbWlpydeY9+ioSTPz+h0OCZm5urqy58+fO2MfJAUST6FQsPfff9+RAK0PIts4TR05SHbX6/VsZ2fHut2uIw1KIkwkEh70hFA3co5sUjNsNpv29ddf287Ojtdsmb7EPpGR4+zhmSBnpVLJTk9PIwGfZsS9Xs9qtZrPq8Y5zs7OWqFQsL29PWs2mx7Avul6q7NcW1vz0+JnZmY88zs+PrZXr17Z7u6uK1ixWLTNzU0f7cbJ8RgyBFanPeCIrq+vrd1u25dffmk7Ozu2t7dn19fXVq1WPRoejUY+AYcaELU4rdlRcEZ5+E76BhEwHCrR1+XlpWWzWTs+PnYnSAairMGlpSUn5Nzc3DgsTNsIrFMUl8yUSJSAQWEKVdT19XU7Pz+3/f19azab9u///u+RQ7RxLGrkGSuXTqetWq06wYQDhweDgZOiECCUeGlpyU9ooHcOYTw4OLC9vT2rVqtWLpft6dOnbmDM7mtoQMf0jx0eHr4GSbIfx8fHDoHPzMxYsVh059poNOzVq1d2eHgYgYxxqpDLzMznw15cXLjBLpfLdnt76wqljof5mWQzrVbL6fTb29seAJGhUJMDXdFMWFuKMOwoInKeTqdd1gioNFjB2BIs8FyaWeo+M9IOY4QOYax6vV6kJq2H/iIzZve9mhgmDIzCnmEZg4gc44eTD0kcZhbp7SSw4FLSC/VygqubmxsbDAaRMWsEltw3uo4NQPb11AjlKCwsLFixWLRisei1crIgJQ4p/LqysmLVatXS6bRdX1/b8fHxgyzRRCLh5DicNXqvLFp0EYQBGJhnwqlQ0qBHezAY2O3trTtYXUeyLgL8XC7ndm44HNq3v/1tq9frNjc3Z41Gw375y1866aXT6dizZ88iz2gWPeAbucBugJQMBgMPqqjng6IRvCHL9IlyKQELCB1+QLPZdFvBeFACVGQFZnssFnNm/mQy8QCo1+s5ekK2z2xh9no4HNr19bU9e/bM1zubzdrjx499ShmzcB+63uosV1ZWvE5IJsfioDCLi4tWrVbto48+sk8++cR2dnbss88+s+3tba+dZLNZPzoHNiowAY6O9o3pdGq5XM7K5bKzwShEE73jpFhIhSiI2JXmzIJqpkGkSHtMMpm0SqXikSZBANkvbDJqXWSZNB6T9aoSLS0tRZiqOE8d58SFEhJcIJhmd3C4jk4j6wPWu7y89FrR1taWn0PX6/Xs888/jygaygVMSgRNNqzKm0gk/OQXHLGZRU794Hknk4kX5RncThZAfRWFajab1m63bTgc2vr6uhvR6+vryNmWZDdAaKPRyJu6UVBlH9PCM5lMPADQ9QWqReGAta+uriyXy1kymXQmLKdbxGIxOzs784CROm8+n/e+OpAFIm1l8aH8Jycn/lx8FvLAQbdE1FwK27POajy1BqfnQrLWNzf3w9GB1pXtrS06mpnhkDGUt7e3npmiY48ePfKs6qGaIlkdJA3kQNmkylznMwgIFG5WJjCZAw6IZzEzr5GyLvAokBFeGxKnWBNKTgwC6ff7Lltas2NfstmsrayseOlF21IIJMimIN8QBOK0gXlpdQAGpm5OTa9QKPj9QrSZmZmxQqFgq6urbt9ub29tY2PDstmsZ0pXV1dWLpetVqtZLpfz/VEGsBKncLo4bYKZWCxm1WrVZUqREA2qQBKwX2bmdcfx+G4AxunpqTuz6XTqh9JTDiNgQk+wj6lUKsItIFOMx+8H22cyGc8e+d5isegICUjMdHp3pmutVrNGo+EIz5uutzrLYrEY2cTxeGzZbNbOz8+9lpXNZq1UKtnGxoY9efLErq+vbX9/3x+M8xcfP35s+Xze6184S5SdqJD6AJ4fBTk+PvboBkhNMxx/oOT9UG2+A4UOafGtVsvZYtPp1Gq1mk0mkwjlXuthrIUSPYAr+v2+vXz50iHHbDZrq6ur3oJAXQoYLbyI/tvtttd92EyFO4AAYZVhFMg+arWanyLebrft5OTEM3Ay8mw260aACSVM+hkOh85wnp2djRhZFFbrUQq9Yhw1u9YsHoHHUZOBAeVy6SADMjw+n1YdzYCU7s8ZgmRUXNQ3gMGA/oBJi8Widbtdn8gEC5w1wThBwoLswZoSrBBkaYN5LBZzh0FAhGOcmZmxcrnsTFQl4MzNzXlGRysWhpnvYP9pg9FWE3U4rBntSLwHVjUXOk1QAQqgx4Gl02l78uSJVSoVz5i5CBTITBjCj+NmzbUeqRkxjg2nF5JHlGQyHo+9hzCZTHrgo/IJooKh5/g0dWww4OFFsBeQFJXAw/1zrByBoh4xpbZIyYXA2DrdCaSpVCpZpVLxvVXy4+Xl5Ws9w+xjpVKx9fV1K5VK7rgIrggqceo4Ua3TKgrAXrCHBOeqX5zVitNi8L2SzTQY40KHQIMInthHHJuZWaFQiPBGQCtoYyRxQac7nY4lk0m3KwsLC+4UaRcrlUpmZi7PPDcs75mZGfcrb7re6iy18ZioFqYYkTWL0+/3bXt7246Ojvw0BDLEtbU1e++992x5edkjEKWWA5tsbm569Lm/v+8108XFRdve3raXL1/a2dlZhCBAPYOLegXMSWWJ0q/Id9BWQS2QGgJCwGYp2YcMhCyHNH8ymURozIzKwjjH43FnrSodnuc/Ozuz7e1t+6d/+if74Q9/6PW3crlsz58/d0M2OztrFxcXVq1WLZvNWrvd9ogISLhcLruDrdfr7nRbrZZnIUDH+XzeSqWSs56VVYghuLi4sPn5ea9NKf0fKIYIGkLO0tKSGwuyFgIOHHMsFnNBJROHJauMT6Dvm5sbazab3k5iZs5IxSChICiLygXrA6T56NEje/z4sR9p1Gg07OTkxDY2NpyUYXZ3fiC6ADO4UCg4S5Ho1+wukJifn3fjsLi4aGtra97ihJy3221rtVq2vb1tg8HA65qapbH/mmGxprQVKJGBLIT1BipFfmmlKBaLnolifEESFGKmnkVfKixYWskgpKiBYdQg5zoSNGjtifdMJhNrNpsRkovuH+/F4YS9lbFYzJ4+fWrlctmSyaT993//dwR+LhQK9vLlSw8W2+22lUol/3wzc1JePp93nSIT1wOosYcgAfxLYKeEMhwHv8cZKKJydXVlnU7HptOpZTIZq9Vq9v7779vy8rIzl5VUqZAmaEatVrP33nvPPvjgg9eChuFwaOPx2NbW1twZY7PJjvmXZyDIANLWWjj6A4OdIBn0D1RISWtaBqGHnb0DboYMSqJE8KosaYZLYMcJWAjoYTqTnEEEVDQJ25lKpfwEKxCETCZj6+vrfmLVm663OktlbmL00+m0VSoVW1tbs7W1NWu3206Tf/78uQ2HQ1tcXLTvfOc7lkwmrVar2cbGhj169MiWlpY8ogIuJfonYul2u97aQWTEKeDMGJ2fn7dnz575JlHv4Z5rtZpHahgBDCpCcHNz41mxGpyQTYkx5v0o6cLCgn3wwQeWyWTs6OjIxuOxLS8vey/m7e2tD2lYXFy0YrHoxiK8MHL0SP3oRz+yTCbjGc7777/v1GaybJ4hnU777MqVlRWPijEGZI35fN4ajYbDDdRxyYwvLi7cqYbGEIiJ6UwK1aLUamSBmpURTLBBlLi+vm71et2z6Hw+b/V63dnPwINa65hOp1atVl0xtK8Lp4DzDyN9ouK1tTV7/PixLS0tOXEBqJC65vLyskPXsVjMG5l5XvosMdza3gA8B1pBRPxHf/RHHuQtLS3Z+vq619GHw2GE0chVr9e97ICBYF1CA3d9fW31et2DBAgiOHJt0yBoU5Ys522a3RFFUqmUs6ELhYJtbGy4McaB8v964ZyAagkM9OSVx48fW6FQsKurK/v5z3/uHAGFyVl7ntXMrFwu2+PHjy2dTjuCMRwOLZm860nc2tpyiHJubs7q9bp973vf8z0+OTnx+6cNCqe6vLxsGxsbViqVHHkKCUHZbNbvE5vCpTYDO0FdeW5uzgezKIKAjaNk1Gq1vISAcyMI06OjUqmUbWxs2EcffWQ/+MEPnNsBXE1tdjKZ2LvvvuvOi/UnCOE1On1I5ysDaxOcFQoFb5NTpjdcCGQUREADv42NDQ9m4b5gcx49euTPSa2ZhAf5omsCJwkZCqhdh1Ag/9hq9jL/tSQAACAASURBVI4gZ2Njw/eQfSiXy6/B8+H1vz78WaNanCcNotTTwKBvbm5seXnZFhYWvCZWKpUikSEPz3/zmdlsNgKToBALCwtWq9XM7C5N/+CDD9yQhJklEJrOB0SQFxYWvL51fX1tCwsLEegKZdUGZV+sZHQMG9ERDviHP/yhnZycOHwBSxKl47OI/LkUaqJOCKNudXU1MoEIoaA+APRKdqjMVKjRQEzURxgegNPn8y4vL73mx14pcYHARmuKwDE4VL4PcgiwtDrW+fl571/j+8hyy+VyhIKvEGI8HrdqtfraHhB9ggpQ41C5yOVyVq/XfWg60Dy1C62Faa8ikA8oiPYsUkJQkoOZuaMhGMQg8QzUm9kfMvWw/k5NNJVKecZFsEndWgMJWKw4C1AM7eNUp65ZFuiG9kWa3TNWcejalqHQKBdHRZEtra2teQ2eYIv62ng8dofA+ihDVGF8WnuA05LJpGeusFRZV83ocOqlUslWV1e9BQpbAqpRLBatXC77YeLaI4luIlsEimQ8BNu6t+g3zjKbzTonArnBcamMKyMYx4rMcVH2Wl9f9z5SRarIakF+sKeso44qxVEiH9Q5qSWiY9QE+R2JyvHxse3t7TnrXtmwWm5CVlmr8/PzyEELKp+sN/elbUNqo7GXzK6OxWKRzJ+yFTVN7p1gkbXCDkLIetP1Vmepimj2+gBfmIVQuTHCKJhCtnNzc75JCqsoOxBGGgtEvQCCAYJHhAm8ogZGWVpad6ROEovdT6VAedU5ax2MDJjnV1o9Qp3JZNzAAlfBxGq32/by5Uund/NZYdSlDD2yR6YIlUolL7gzNFiL3el02inuGHUlGKDg2WzWcrmcR7DUUXk9TDaeW4OQsIdPa8SsBbAr+45iIPDKAAViLpVK7nRo/SiXy17o53s1wwjnDcNWBdpjjTW7MrvPXHGWOFOePySU8PlKeKFGCxlKAyyeg+gXKA+CBd+j603AyffTrsKltXNgaM1YwgvZ1HYA/h/npK0bIB7sM4Eka65QLEadgBM4T4cimN33q9JyRn0UwpAiDhcXF34WKYaVskfoLDGEEDsIxjDsoA1KOCJQm5+f9746zRJZYxjhhULBW490X0JniGzMzMxE2oXQBbUTyiIGLqbVQrMbRa0IAIGqyaa46vW6raysuN5j+LEl7BMBLrLK3zWzxEGj/+i9wpjAqQwvIPtOpVKR0hvsdg1iVS74IQiB2FQul92uKITNGhDAIWdkodgFHRXKd+OXYNZeX19HWOSKBiF3ZOdvut7qLJWmjZN7KAoC5ru6uvKNh/2JESN6VUhTi6woCJ+HIAKzXlxcuAKTUhMlqbIqfVjhPBYD2I7FpzbCM7KIRE6RxRJ2FpFwsViMkBRmZ2etWq3aZDJxZ84p4NwThlEFHKHMZDI2HA5tdXXV5ubuDoLWlg/IFplMxubm5mx1ddWWl5fN7J7Vp/UEagFm5kxeoAoMIMIJ1Mo9Qe/HuDE0QTMPFFPbI+LxeKS4rg3xsVjM60NM0iHQAL7UIQZAPhhLap70WDKmS9sc1Bhxra2t2TvvvGOPHz92+Jc1pIa0uLgYYSoqwQvnk0wmLZfLuTMkKs3lchGimjoaDcaQUdUDDWxClITMG2QCZiX3qDIE0oDRxTCgVzpNiZNWMLaQwpBPdIH3ZzIZr6tq7Z8Agmtra8sRDNq7QGgoLQCh0v+p66SnfKidQPcKhYIjFJA7sBMwxZXMRRDH8ARQAe6ZtiPqw5VKxT+bPcZZat2Y+6VsoHZMs0scOxlusVh0MgsGXBEXbCu2gj1Ux/P+++/bkydPfA4scoAMaGKgckIQiUxzbzgRykHsqyJHrEGxWLRPP/3U5ufnrdfr2dHRkf32t791eQEuhsSmthOm9OzsrG1sbLh/oMSntWTkB9ujgT3JF3tMuUK5J+l02vkWi4uLvtbYJkVzsC0hshNeb3WWRKIIDKm0ZloolFl0vqLZ68OdVbERCAQqlUr5VBQEiw0EMkVxzO7ZVWE0QE0MA6XvQdB5vTpGoFGNKNlEfT1Ky7MxVotaFI6be2i327a/vx85C1CdDeucSCQsl8vZs2fPPCO5vr52Ni0sS7IvhZd4Rmo/rIsW81GKXC7nkCvGkftVWA3Ihee8vLy0+fl5W19fd6YeBgJjTF8ikCyOR9GE0Wjkjk6jOQQc48F9AKUgbygIkStZJq/HuZLRca2urlq9XncG52Qy8QyTdhf2medW+SZjwOhr1kEfLExqDB9ZFpCsrjPfo4EW0BMX68xrs9lsRG4w3vygqwQb2kZidt+aMxwO/cw/zdBxfGQaZBdao8QREAgp8mJm9sknn0TIXkyw0oAB8h+OF/3DDrBG7DHZJmuay+Xs+vra8vm8t3eoY1GIGwY7n6OwvdndGbSVSsXHwkEAY++Vr4EtUyKMBgsEliEagO2BYIU9I7lQpIqgD6gSx6eB0be+9S0rlUrOxuY71Vbx3+i0lgDYW2QDe4T88DfNpOldLhQKfojE6empdbvdSGfE2dmZ92dq6wjPpqU2dBbkDHkDntU9YOTp3NzdBC5klEycbPfy8tKOjo68RZG91/56vpN91CTwbddbnaUqHw5Toz01IsAIWtzld1r/QmnYXIwxr8VokGqjSCgE/81mhoKE8mpDawglI1A4RmVsauEex6cwSehcMTSaeauDXl9ft8vLSzs8PPRn1/vRC+bk0dGRj1/S4jxOBVgJY6OkBvYiNPz6zGRiGFSt0WK4iTIJMHgva4wBwbAo1TsMMnhmFJJ10ho2z8f3kJ0xAFsDMIyRkkF0X5BVraWxZuwVdS6IJ8A3oA/A++ynMqppS9EaIe/HuGkgGMp2qAsazKksk8GpzqncapA4nU59mhYRPQ6dvyskju6EgaMGJPyeAFazFc2w1IFDCiPg0+HmIEMEFOg4BprPDrMtDZCArqfTqU/L4f0Ef6wVDhHZUOfL8+FMcXS6N+E6a/IAcqJBEEgVjE3WmPVRFjl2U0tR6BGvpVbNPXHl83mHJ29vbyM1PHRVyzGsLZ+hdi2UI9UfbDz7Abqh9fN6ve7IoeonwbjKZ0iMYs9oLeKeCXTYZwikmhGr/VQZzWQydnZ2Zs1m01nr7DkyCakOueG7+Jw3XW91ljwAi6H1FlU4fk/hGqPFhmAYNcIMX8NGhWQFfk8UBRTJ+xAyXTiEGkejdUKMl8LJ6rBDzFojc4wnz8E9wixUyANBq9VqPjlfIa4w2za7Z/L2+32HBvU+ze5rsvSrAlsSTaty6rqa3Z/uQsSoz6QQBM+I4QFaVAOqNTit0YROg+iagIv1VPKL7g/BGY4YIxbKi9a0+ByNFvkcLto+5ubmnHlMlgU0xEi6mZkZOzk5scFg4Bk9wYj2HOt9aDah0T2yxb88i8oi8hvKn9be0TV1xhp0EDmjI2YWcYZm5hkVn6EEIfSXz1MYmiBIZZ9nCp0lhgmmrdZACSwJSoDElOgCT4C91wCMYEVlD8eMIQwhU30eJeYg/0pw4fk0wAk5DAwk0QEa7KfWhCEX6dog1xqYqUyHGaJyO8I1Vh3ScoPC7tRMQ3gdG6C2U+UEHVL5RF5AMMhoV1dXrVgs+hzoROLuSDAmiHFpcoP88hlhTVgHR9DmxwAKRa0IWLmA3Tudjp2cnLgeMMeYtWRkpsomsq7ITni91VnSzKuCxgKyQSGsFNaz2FAo7ZohUlsyu58Kg+JoIVYjMDaTz0cJQ4FUIddMTg0zG6TZCUZdp1zgEDT7VHIGkKe2sPDdnMpweHgYWZ8w6qKuQx8cI6bYTJQLQgvQRyqV8r5W/p+oNBa7b4bmfjjVAMHgXjTzU2gHZ8e+cb/svUJnKJ8e9YOAUo9A6PleRSuo8zJjE+ZiCPezhtwH+6WDwEOW9KNHj6xcLtvs7KwfdUaP7fn5uaXTaXvvvfesXq9bLBazX/7yl352KsqtDftQ/hWKRjYJ7tQAqrFTZIX1wQHp66jnhYEp/ypphwxkOBx6nZvaH0ZEYXIMqgYtkL6o/fF6Daz4UQRFDZayfslqeDat/fKs2BZYmJA0cCQ6rozaGvK6sLDg7QxkKJrJolPAnzh8hWuVeAc/gvvmwgFCZNRzKcnogR9TqZTX0ulrxNEjrzh9zda50IUwwXiolqaZoaIUOlSCXlHKQHwfAQlsbOp7JBugThB2cG5KuJufvzt6kTW9urqyR48eeeYZzlklGGNPWQ/WAvuBPAyHQ+t2u/by5UvnN0DcSSQSTiA0M++9zmaz9tFHH9m//Mu/+Bm1T58+tVqt5kzshYUF75G/uLiIzPJ92/WNMKxOQoC0QISihlVhWKIwIgJgC8g4FKF182Kxe6o/zkf/phEWgoSB0LFmir8DUyjki+HjUuYn2DnGAowex0IkTxSuzh1Ywuz+xHEcAQQQ+ke1r1PvO6yVgcGb3WP+1B0p+LPZwIMoMUZMHRPOn2ZoIjjN8BBolPGhmkkIG6rzxBCwBhosIQvcE2tPEICzbDab7pwmk/sTOfjBiPAvpATqZGQaYS0tlbrrJ+x0OnZ6eur3hZE7ODjw80mRHwyQEk9wtBAhyEpDg8vfFEJlj8MyRRiQ8Fpta9JsVD/P7M6RcV8MiWZvMOo4MUVE+A5gfwZ1XF/fnUNKy5EaTfY1rJOZmTet83ugcA1SuY8Q+TC7g3FxQO1225EUrT2hP7lczpaXlz1reAgWx1lBFiEouLy8tGKxGCldEKDyGWTh2BE9wJlpW0dHR37e7NXVlbNtVV4gFz36n15zMmjWOCxl8XecpKIFZvdn9pJZ4SiwD+Px2O0MxwnyfrJdgmq6AWjRw6bxPSRMD9lOtQXsJ2gNdpSLliL2OixxaBBIYNJqtazdbttoNLLl5WXno3DKi8qRopHxeNwP5uj1eh4UctRkpVJxNA549vLyMoJePXT9r2qWRAk4S4wJxg6Dwv9D24cpxqKQMaqhJivE4eHtiXJUQXHG+j1qxFVJ2RBN13UYAgsMQ1D77OjfvL29jUyb4fVm5sac+hXN/Sgq308EyxBjhWm41JCw6Tw3ho415n3ATmdnZx7lKS6PAN7e3tqrV69coWBBUq/TyB0HprB2uKYohmb6CiWpwuOk9RxLhYtxGjgDstJUKuV7MTMzY6VSyQkoGjQBMaHkZFg4SxV8mKScnsDB3NS+6JkdDodWLBZ9hiZ1EGqdvLfb7fqeAO0xqIDTZjTzVPQizIw1yFS5YC4vRkEDQIXErq/vD7vFiEDgYR1Y77C+rBmiZhAHBwc+sYogV+cHh/CcyobC0IrwhNmoBpVqRMn4yY5hnxP9k8WZmX388cc+eUd1CFnju/gdBvvm5saJIqwtz6n1P+yF9h9CoOJ4qW63G6mB8VoSi6WlJZtMJraysuLGnL1UogpBCM6Sz0Mv1V5gQ3iNQqrAw9fXdwP2NaM7Pj72kZCQjSaTiXU6HR//SZA/NzfnSYDuJfusjpN9p4WKJIVL71W5JAQJBJXcNwM72u2222KQNmUgY4s0uCDwYZ1brZYTHzVQnJ2djRzNpcH1Q9f/KrM0i54dxheS2fHARPZEhRwMTaYYQjI4y/F4HCn28xlAn4pxa30ChdCHDJ0lDFuNQtjE6XTqMAXREPdAkKD9O1dXV24kU6mUU+nJbhgLpxCK2f1RPFo30EuVnNdTR9GaA8ZP748B9EA9RGV85+3trf3mN7/x1oj333/foTLgI2ogD0HTD0VaGiEi4PosOHiMRrfbdeagCq3uEZkv981gB4wAa6f3xbrrsHb9LJULshVg3m636wFbqVSydDrt81sTiYT32+nB0gzeaLVa3spEoEKtRV+nRgalDmtcoCChDJiZMwAV7kfmMcTM8Gw2m9ZoNMzs/tD2eDzua6NZpUbxfD8GCx0nA+fIr1gsFjlknH3B4eo6h9kv+xxG/8gc+zmd3s1yPT4+tsPDQ7u8vHToDYZkIpGw09NTOzw89NM33n33XVtfX4/UsYG6sRmxWMxn86Ij6Bo6r4x7deqKVum/mikrvKlrQbAOAodzxIaFNTJ1gqAj6IjaOBwo64eOkN1RlsCOohs7/3OqE+1bsVjM23larVZE57B5wPN8jmaTD9kGJfFwsW48t67RdDqNkMfI8AlKzcza7XYEFeC72E8SLIKUcrls6XTaoVz6ebXuTcAZBnJvut7qLPHWRH5qqPhgBBBBpyGfgcEU9TXK40EV5mDREBI2hqIu2SyLpcOZ9dIIHiEjYuP1CutyPifPQb8Zs26ZwBOL3Q3DfvXqlRfvnz17ZsViMeIg6vW694myVirMDwmZOs9YLGalUsmj1ng87lkKkEuv1/PPjsfjtr297QKUTqcjGS4Fd44FYy7saHR36srh4aG3TSjMzv6GjD/uUbMR1hrSA9Eo68gYLxSQ02VAB3D4jNfSLHp/f99ubm4cCgS2QlZQECV2hI6Ve+52u3Z4eGgHBwc++GFmZsbW19etUql4lEkfL/v+4sULOz8/d+OjRyTxo4zKxcVFazabLnMEgpoRhzViJVRxnZ6evubc1FFRj+50OnZ4eGjX19c+UP329jYyucfMfF/UWetPKnU/eSiTydjBwYEjAjjFcCoWDnN9fd1thsL2vEZ1gb/h0Njrfr9vX3/9tX3++ef26tUrG41GfmRWsVj0+vH29rZ98cUXPuSALEbrg+jKYDCIBJK69uglqAR1aK2Ps/YaaLAH/F2zr3g87kGHDvtmlivrq7A5DglolR8zc9JayHFQ3gY2RB0tCEs8HnenwZ4TAFxeXvpRiwcHB35YRS6X85YaRgsCX/KcagdCtEfvgwsfonvAOmJ3FMWkHEBr0C9+8YsIQrW0tOSENc30R6ORbW1t2erqqsXjdwPUf/3rX3tSQI8zydrCwkIkudCSXnh947g7rXeFDo3ZrWZ3WSjw03h8P3Py5ubGYRUi6Fgs5sdXJRJ3zc04png87jUviBLARgiNZp48JBdCrBEggsOFc0dByGiAXJVgAO4ej8dtZWXFbm9vPQJ7+fKlHR4e+n0x9B34Rp20DrgOCT58PkKoBADt8eK5qDVQrE6n0z7PEYFivendZJoKnwfEWa1WrVAo+ABsTjlBoDgVRKNFhTtQWGBzlImghqZxXruzs+N1HQzv8fGxtdttP6OyUqn4RCLga2ZWAsmyjhg6HAAoBmvFhVIRHIUTPfb3992InJ6eunJ3u13753/+Z9vb2/M114BrMrk7eglCDKPTOPcPg877kEfgKCXMYIC4er1eJBNTwxKPx92g0KNmZh58HBwceMCJU9IzLRcXF90QX1xc2OnpqRufhYUF+/DDDz3jOD09tUqlEjEkWuc0u4ND+X6FBXGWyBPrqhkluorjoG9uNBr5yS/IWLVatXfeeceWl5ft7OzMdZZ9JFgjoAqREv1O1huUCvnTmidsaLUL+lz6bBBFtJ9T4W+SBpIIgn+CPTgHBH5MI0LGde1xUGRlGiTyzFrjj8fv+sC///3ve2BEAMmh6+fn59ZqtfzgAAYo7O7uWiqVsmq16sEYgZ06S0V6GMau96xMdQ0+IBcRPA+HQ2ezMpP29PTUk5rRaGTHx8e+Jsx7XVtbs0KhYJubm1av1y2RuBvHCuGt0+l4jRqOR6lUsn6//2CAHV5vdZYYA4SbzAEDiUACt1E/Y4RULBbzQi8GBUHgNPB4/I5tyTBy6iIoHcJJZPgQlTgknyjkCgSjF88EvAkBABIAkBQ9OpppEbF3u113umb3Y/IgQ2n9B6VQYX6oZqnPwMSUUqlk7Xbb75somntOJpNerxmPxz7iTetiaoy1JsLUE6ZhMKmD+5xMJk4A0GgX2XiIVECAgMIMBgPfP2q+TDSimK/nlLL3yNfi4qK1220n7hBcscfqELWWxhqE8qywC7LLyQcEaDi62dlZH1v44sULH8mHM+ZZOYZKh01rDU7JCGb3hiNcVy0RmJkjBEqw0PdTK1NWOYaV/UV2ycAw4kBRZMqNRsM2Njb81BcyeZAManKs3Wg08mHbygLHAWtGrDAbcqywF04H9KFcLvtoRnSS9QFdSCaT3p7AaSiQ6BR6DQ3gQ7VhzYR07zRIUflRqJFnI5MErSDA12BHf/hM7Q3VEoJ+v5Z0eI/Kk6Ic2A+9Zz4D1EOh1GQy6bVhGNOMFC2VSnZ8fOykQQhMYckAOQZi5fVhZql2T2vWuv46ZQqnVi6X3RaA/GFfsLXM9WVKE3I1Nzdn1WrVdnZ2XF50uAaDW9Svvel6q7Mk0layBqwhoniiAX6fy+V8Qgu1t3g87lPgeS3TaVjI8/Nzj8rMLLL4WreCuURRVrNKFRQtpOvGajF4NBp59kYmrHXNYrEYKUab3Z8qwQB5IFK+l8ABAxBCBCiqChIKjPCYmR+Au7Ky4tCv1mCJaInS+CHq1DYfhIQggCgdRh/HTWWzWWs0Gr7vuk6QqlQuuA/2gMhSSVOXl5c+TcPsLsODvDGZTGw4HHr0TjMyzEfINUS9zKZk7xUGVeVTA6HKyr4SePF7iATAga1Wy8zMTyZRx6AGA4JNq9Vy0snV1ZUPjGBdcHqKNqgcKISnl9bQCJD4f6Aq6lYa3XO/3W7XMy5YmCFBB/Zxq9Xyk3Hi8bjXckEpmNeresSxV6enp5F7RkZGo1GkVh/WJ8m6lHCkjeSMx0OWksmkra2tWb1et3w+b5PJxA4PD21m5m4MHqQsMt5wbXDyarQ1o9dSCfKsTj2E9bWUpMQp7EjoVPS/Q3IKcotdUMKJwq7c13g89uBJnbdCxpo08BzsC99fKBRsOp36cHkGszM96uTkJEKQ0lKHyptCqOoww3sOAxGz+3YrfAmZPgFQLpezbDZrtVrNkxE9rGI6nfrB1pz7qc9YLpft6OjIbm/vhu5zgpM6yxDpe+h6q7PE0SnMBZGChdKJIRsbG07PNTPP1jjXkukJrVbL9vb2IofhPnr0yHK5nDtMMlUUVOFL2jp4rRpFYFk2AiYVyqHD3IEagLZwMkQdOG42GvbtwsKC11C63a5DkkTf6ix5Dawuzeq4cIDAtAh9Op22lZUVm52d9ftGWDlSCqXhM4HRzO6MEgxNrXtMJhPvGbu+vrZGo+HwIdE4CqfKQAYR1n1wQDgbjfpguGr7R6FQcEIVtQN605hViuxQc4U4w+kCEE3YK43C+VHjdnR05PtCdJ1IJGw4HNrvf/97r4+Px2M7ODjwIGN2dtY2NzddztXBoSObm5v+OxyRHq8EvAg0xh6wnzj/EAG5uLiIZDdkAQSXfKbW13i9Iipkz9RRIeCBCBCUoQvoSq1Wc1QECEwhVupu+/v7fs8QuZALhSK13gohij3EyYA2zczMeMDI/XByTD6fd7LR06dPfa9xjrQcaDAGSqX1UpVdMlhkHvnmvpQUqPekZR6ei0BA4XrsFwgJECN2RVu7CByAKNlvtRf8TeFkhYW1RMIeov9al43F7qeCjcdjlwGyrXq97pk+6GIY8JERE4DjyPSesWnYIdYR38L68HnsP0epYXfps2RGOM9G6U4RNp6RwA90k9GdBLPse2gzwuutzhJYDkPAZmLwyRYxgPl83jM0BJNoWvt6OHVCMyIUSgvEZvd9kDhqICgOCA2dJb04/MAIJcPDgLMZbBLZzUORpNYggU2m06lDtKwPJBXNQk9PT63ZbPoZfwiLQldsvtZDMKxk6ho0nJ6e+ncBrUBioB+Oe5xMJl4vpRUBRaDOCRs2mbwbOK2ELgIURt+xHhgajCByofVZMnacBgaLQfMEW+wTUTrPgmPhGDEm62iPlMJU7KtCUlwcH6ewHM8BYYdAhHpLLBbzXr5YLOb1YIwAsqLynkqlLJ/P+zAFDJqOzyMwYo/UgIeZDd9DgKTBQFhyQH712ZUnYHbvgBOJhK2vr3vUDrxJvdnM/Jitm5sb63a7dnJy4iSgmZn7sWeaEWtQR80cw0RAovqujhTILKzxaeZmZi4nlA9YI3UGBIPqRMiqQkgznFGMfQPJQC8nk4m3VvB3CCfYHtaG2jkZL1CxNuxPp/cjCNVm4LwJtkPYGFlRW6nQNoEc8hj+HX1BF/g9a87vZ2ZmbHl52dedoECTJZyVZoQgWXpgdfgdinDwd0VbHpqqpGxk7g+50T1UR45dIIHDDoR14BAFeOj6X8Gw+oGqnJrqU9ugN003X5UXIo0y6thYogo99kY3loc0swenX/D5WgtBqc3stYgmnFupEbHS2zViQ2C05qTvR3EIDiDdAN8hDArDhhEiP7AsM5mMQ4Dj8djbclC4i4sLJ8vEYjEXUgSMGocGJHqqx9LSksXjcXduZvc9omb3kDhKozKgzoIo2+w+wKBNhTUfjUbeCsDzsWYaOOma67rwefq9CmESJYZyQbaLDOh7QDiQU/ZH62+cuRfCcTwrhp1MGfk2M993VU4uPkvXW2Wfi30Ma2AaXBEpq54qZDadTr3uymQpzkGFuYm+kUHCguSoOMgg9A6qIeI+QwOumT5rpLrL7zHuOEsd96eBGwaZz9I95/DyXC4X6XVlb0P4FQOMPOF8yRD5XpxBs9n0UZSgSHwmc0nZn5mZGZ+2VSwWrV6vR9jjZKC6hxoYaJmDYQ8qMw/JkUK2ZLL039IZgHxqSxsyQ+mIPdFSnELb6tjIWnGc9KyHdlxlIJR19I33q+ODq6Ccj7AOzPODqOi9KJLBWj1kc/9/OUsejIVXmBBDxyKiRMwsRZAUJtNN1qkV/F5xcCUtcGnKTlYRCg+fqXU9s/uTPYASNLvTeqBCznw/sGCY6mtmQRSEMWazaXQnW+O7H6pZ8nk8Tzx+f8o6J9ljsDS6VbKPtpaQybJmRLtA67oOGAqGrD9UfNfAQi8EEWMGJMtnkn1j5PgbhoO6IOumhpRMhN8DvRCBkwmE9aSHnCW1LyJVnLgOjEfRtPY0Go0cZdG9UwhUZ56ydugJzFON+rlPgkTkQC8MmBoNXTfkXI0z9wM5hgsZoN6+urpqa2trXudRSBZdAO4ulUq2vr5u+zgfNAAAIABJREFU29vbTgiipxQChuqoro3CkMiDBtBqU8zu7QLPhV7BBQh1lKyVvYJoo4xkshDkU/cAtAF9UbKRmblTBIre2dnx2rQ6aQxyqMvNZtNZ0oPBwKrVqt+zIkg8g6JaBCMLCwuRNQ7XVi+CFZ611Wp56wQ2kGk4lLLCer/aIa39EbCp00KncaQ6wSd0llreCYNOrVdeXV05iY4AG5nnu1XHUqlUZFocZQjuJR6/O0oRlIuAns/SBOVt11udpTqjEDYisyPbGA6H9sUXX1in0/E5qeGZfhgpYBeFCsnWiByBDIF92IzxeOzRn0JW4T1rRM3vqVGdnJxYv9+PQGO8nvmfDCiHbYgDV8IPxI1qtWpra2s+8kydKb1CQDs0kStEoZc6ddaqXq9bu912JdWodjAYWDabdSVPp9O+VmYWYXCidNp/1+l0vOUHBVOhVriM9+A0NNuHwh+Px73dhAy21WpZp9Px2Z2QX8iEFbpT0sLs7N1ZdYVCwZaWlmxxcdHq9brXbIDclciBgoXKqIrN8AiiVd7P987NzdlPfvITJ5H8/ve/dwIPw6LDuq1OTdLSADJMSxEGQZ2IwrAaIGq2S1ao/btq5NTpqBFmHWZnZ215ednq9bpVq1VvyWA/QSqU4o/xnk6n9ujRI699InPpdPq11iZakkI2qJYyVDc1w+B++P6bmxs38uwvdW50l6yD9eIzOPOVnl5sFTBzGKyq3ikq0m63/RBh+llhnMO+pFWEqUlciUTC26IODg5sb2/Pnjx5EtkHgkANgrAfPCPZsn5uCKcq2zSVSvnRgP/2b/9mu7u7fvB5rVazTCbjM1ep7THasFQq+XpzqU7SCx0Se/i33W77MAt1lux3CGurA8Q/0JfKXlGq4bnRYy3LHRwc+EHP+B1kjzr88vKyLS8vux4irwQlfPebrm+c4MMNat8Shh9sfW5uziqVii0tLXnP1+Lioh+kCkataTv9REQUzWbTvvrqK8+g1tfXbXl52etx/X7fo858Pu+wRAhtaTrN4lPHoX5JxMFCKqOtUqk4s0ob8sfju/PlTk9P3WEdHBzY/v6+L/jHH38cmRTByRa0IRAEhBCFZiMKYWGEmEaRSt21VShUVK/XrVQqRQgvQC44Da0LKWuP70wkEpGhADwv9WigID2AVoMcojkcMtkaQUGj0bDj42MPGjD0ZvYaC+329tYbqGOxmO3u7lq1WvUB88vLy77/+l0EMeo8wt5aerguLi6sVqvZ/Py8DYfDiAPCaPL8Zhbp2xwMBtbpdCLoBeuOXEGUCklHyKdCwNzzQ4GfmUX6M+kTvb6+tsFgEPlM9CKE9ZCtxcVFOz09dTlGnsjagOpBJr744guLx+9bmDY3NyNsQ2rGGFK99Hl1fTSL5nlxTipPyJ+eCkOGo0F7Mpm0lZWVSDuPIknqZNElRT3MLDJHN5FIONTNmqrhTiaTzjLGKEPMUQIN+wv8zn1h/GmdYuQcuoAtYy3gWADtc6mz1ESD97bbbfv666/txYsX9tVXX1mlUrEf/vCH9umnn9rW1palUik7PDy03d1d297e9my52+3a6empDzAhECWARBZxUOg+NUtasKjr66kj+lzsHfuE48JWZTIZS6VSbi8ZcIPtbjabdnp66nC4MqBxyDB6KWNBIJxOpz48Hv+jKFDYXaHXNx7+rBsdKoZmiRzKubi46HBR2Aep00VgXMFW06xmZmbGayk05uL4yCiAVXDoek+aKaJgLGIul/PU3Oz+iB6MSqVSceNMtqC1LX4PFt7v91140+m0H1pK5qmwrNY7wwyC+0aoVMmXlpYsn89HTgxh7SFohLU9PkczxfF4HCHOaA1SM0f9fv6m9Rw17AROPKMSBGDO4WhZC9ab+1VZglXIPcOIHY/H7ugYbq89sLquWpPW+6RuOZlMnIkY1maBsei7RC40wOj1epFshbWjqRy5QnZwdlwqr4q6hJkle0TdnaAAp0lWMDs7a7lcLgLLKpzHvWn9WDNg/nt+ft6zXlplIP0wLxc0IJFIeNN+mIWEkDjrj+6zt/w/zh1ZUgauIkCsHQ5+PL7r0UUHcrlcJBvGZugPf2c/2Gf+xrNMJpNIAzt6UK/XbW5uzvsAtZQElM1/s+7UfovFottI9l2zQ10vgoXBYGCx2B0z+/33339NfrhX1m80uhvucHR0ZHt7e9ZsNv3EDU7xITgAacrn836QM+Q2Zc6rPqrdxYFhDweDgaMvJAV6qSzw/4qsqB0igzw/P/dsFaYt96qTu9gDAliCHM5XHY/Hfk/FYtEDVOSQPaN74KHrGzNLLk2Dw2gN44oRVOUB9hqNRlar1TxLIdNUggwtBplMxh4/fuyRGdGlWZSZioCFwsM9aRQbi901Zq+srHivH/U+zbIwCiyywov8nWw4nU7bYDCw29tby+fzDhdOp1Mf+UfUBOHnIWfJPeqPGpylpSWrVCoe+ekaosisJUw0Na6sGVETcAMRPc5OSRBaL+O9WgNj/4kMcTpA0cBINBIz5IE+RAgxyIZmREo2IOsn62N4AfergQMRrq4DF5D65eWl93FiHFAYoNrJ5K5/bzKZuLMA4uasPiUOIIMECzc3N87W1hqc6pJmBhifMLvEkTHDttPpuHEZje4mZlGmwBgqSoLczc/PW6lUslqt5qgHqA1yphB+u922//qv/7JGo+F/Pz8/t0Kh4AGw1qa11ooxVRnh+UKIUveagAeEYzgc2unpqfX7/Uh/n2b519fXdnR0ZJlMxgqFgm1tbVkul3O5Vv4C+6OBoJnZ4eGhG20ORUC/cZjAejMzM7aysmILCws+lYrggQt7RjCBnM/Pz9vy8rJniNyDEo4UcRiN7nplGc2YSqXsxz/+scuPXipP2MtWq2UnJycOQ4MYdDodh8/X1tbsnXfesY2NDTs8PLTDw0Pb2dmxg4MDHydKzRQZ0T54JeQwGYv5yvxN7YXW3ym56O+19o6OwcLmnshYFTWgXY6Wm0wmY8vLy97yoraj2+36AH3kExkESn/T9Y2zYVFoxYB5eLwzUR6RDa9HeaBsEx1gzMkmb25ubGFhwb797W87RIIToN+S4QRETkocUrhNHVFIMJidnfVz0c7Ozmx/f99arZY7YmanEqmk02nvl0PRUCiyZxq/s9ms1wPZ0K+++srvjYHiwGhqGFlDNg0okHWenZ21R48eOSypE2FwkuwTzkunaGg95OLiwlKp+8Z+NXYIqxIegFchdLCnqqCqgDgJnURCBIsBg5BEAEH7ARmO1t0wJJoxwtjDYQFXaXvQ5eWl9ft9X+MXL15Yt9u1ZDJptVrNJpOJw+EYdF23drvtdXUyO1obtra27D//8z8j3899zc3NeQ8s7VSKdqiB41Knqb/HecRiMT/oO5W6a9OqVCpWKBTcoGWzWV/vdDpt6+vrkUk8Kysrtry87HKFEcfAEmRQQvnjP/7jSFBMGwYXWaUafOQVZ82xdmb3NWN9re4pjeb7+/t+tBLQsAYIOGsCE3pvd3Z27De/+Y298847zvLV9jI9jo5nNTP7+uuvfW+4bwKxm5sbK5fLnmEmEgk/JoyggwZ3TRTU3lBD5ASXeDzusCGZk64rRvz29tYODw/tP/7jP2x/f9/6/b79xV/8ha8x/2qZBfmq1+u2sbHhjf3f//73rVKp2NXVle3u7noNkExK+RBPnjyxi4sL6/V6dnx87MGYZqQgIKylng+rjlKRHe4XXVCEgbWCXAiygcO/vr72WbW6ttpuxhALejNhd1N+2dnZsevrazs5OfFkCMQK35bP5+3Ro0f2puutzpI6IRlFCHdqZqkGnqxiMrnvtWNYQS6X89SZRcMotNttzw4wUGQDwK/UxWZmZiK0by4UzOweQlDlVMYZzg/ySa/Xc2Hq9/tWrVYjAwbA8MHOd3d3LRaLWTabtWq16ifDdzodOz4+jvRWQhDQOZ7hhQCyhtwntTJm6aqRCSEc6lNkZZoBER0rtKoMUCUvaeZGhsT3KLwIQsD4v/n5eatWq2ZmDn/oBCMlB/AZ2u5jZs6E5oeIkUOluQ/6HUEeMMbAtRAxzO4gdJSkVCp5Kw6KjaPF2XIiC+Qc/nZ1dWXlctkSiYRH31ozoq6FUSczZU0V5kNuNXAKnSWnQGDsNZDb2NiIvK/RaNh0ej/bdnNz02v7EHG4B+SU++dZyGRrtVpEJgm0CAqo/ys6xLMgVyG8huwq1KoZydHRkTtEhqdDRuPzCA5oy0Cerq6ubH9/P0LIYf2xF0ogwi70+307OTlxWab3GqON0UXmuX8N+qmNkamrLGvwH7YuMKBA9Z8yAY7t6OjIgwcuhR0hOGmGOjc3Z48fP7ZsNmtnZ2e2trbmB0KTAIG+7O3t2ZdffmnNZtPOzs4sl8vZ7u6uO9CFhQUv/eTzeX8ve0i2xmEIOEG1S/p8/E7hU2wKwSDBBAjIRx99FCkvYMdZC4JtnZetGWs2m7Xl5WXrdDrW6/Ws1WrZeDz2IEi7Hf7Pg9TPzs68JUDrNwgTN8NChDACmQ29L5qdqOLywMBmwIXav0NLCvRfIj8li4SKADSozhTFBdNWh4PSoWhM6+ciI7u+vjtvbXd31+LxuJ9SQW1hMBhYs9n0SRFEkEyBCR18KFBE7Dg/vmtvb88jIbLQEPICs1eWrzpHZSbiQMP90M9TGDGsOeDoUPJ+v2/NZjMyZ5eoG+XiX5ACDAcyRTSu9xHWDdXY4lCUDchovHCQ89LSkte79QxMEA7ejzxSQgC9QB7T6bStrq466xuHgSxpnTt0kLq2SngIa1a8nmAnk8lE9jWRSEQYkmbmrVypVMprSOgSU6C4r9nZWR9hp3wBhX7ZG+qKinao0VSDr3AaP8qqVkLK2dmZD+zA0AJ1c/IFBo3sgTo4WR7fOZlMbH5+3sefgQpoHSy8HzPzLIoWLdWReDzuSIy+Vx0ms5Vxlgol8izcA7ZISV0hwQ2EA2KckgTVRqi9Zd/YFzPzYRAMYUFfCLLN7uwjLG/QsNvbW4e/6Q1Pp9PeG01gTZAJOkNGqUiXJgXIt9YIucJaJSgEkGq9Xn8tiKSmGfZo81zoCNknSRpEJnQJRAC7sbOzY1tbW/bQ9Y3OEsiCm1HhVQHghjFqbAzHGnU6nYiBpg5FRK81RM6EhK00nU79aCkeDuXW6EQXkkUHFlIoz+y+Dy2dTrvwks2C8UOnZw0w1BiiFy9e2OrqqpndO1JGiRElY1D0c3BgXDgNzTQQ+vH47piqX/3qV7a7u+vrQASnjksFEaUmGlY2LA6MjI3vVTidz1TIVVl73K9m3GdnZ9ZoNHygOJEi8xdZO20VQaGQi/CQVoXbMTAYXIIWnCXrBdQdGiIiR0U0eJbw2XGUaihxLJlMxj755BM/B1WNJxkr9T0+FyOqhKKwVhkGdugPRpu9ZN24N4IMYDIMCTrVbDa9VsPrOYcVx6BToDQr4iJwQl6V2a5Bie4V66YoBg5sNBr5gdU447W1NavVahEEiftIJpM++kzRCchji4uL9s4773htmslUb7NRZhYZ9s8gdA3YaFXB9uFMQXsI+nQPuHc6BFgHhbU1EFHHx/2cnJzY0dGR91QrQqYZmwabXOPx2M//pP7O+0Aa+L5E4q5fnvnNmhnG43dHXD158iQyoJwSCAgOpQuSDm0/4iKJCRFJDfKn02kEomakHfA5P6CPEHYYd4euQVIDEUsk7uZe397eOu9jZmbGZZiEptPp2PPnz/9vzjKfz/vRTTTUchirRvwKmSm0R/bH4tIkjKDrokEwIEIjuppMJn5cEwxAalbAd1oLAuZU1hyZKMaK12MoyJ7N7owkkeL19bW1Wi03OjgHFBLjr3De/v6+vXr1yvb29jyjoklXiQp6qbPUe7y6urKTkxP7h3/4B/vss8/s4uLCvvOd71i9Xne2Ig5N12I6nboTJGjA4OgcXISYSJEAgTodcFdI0MAh8xx85+3trfV6Pdvf37fR6H7UGdkcZ23yGSil7oUykzUyJLDiSDWcHXVwghCeJTQgMzMzXr/DmWjggjwScdJaRKDBsHnY12tra07iYs04KNzsHn57yJhxqfyHSA1rhL7AEGfdtdzA/WP4FhcXPatRZ0h2SqSto+J0T/R+MU44AwJODCXHhHExd5fPU10L2xGSyaQ9ffrUSqWSj8rU/m0NjkLUQwlVIBnoOTrX7XY9iCMwJODjX/4ej8et2Ww6q1ab4tEN3kdJgKCVmnUsFnPniMOnfqatd+icBj5cHOK9s7PjB1wTFHDheBSWVIerqEYsFrNH/9Mjm0wmbX193VEG4G/lGKRSKfve977n+wWca2aR1iud/6poGTJLyYJLh6foPSpJDgdIrVTbw9B/yiskHjhkygn9ft8DHwK8y8tL39eVlRU7Pj52ch8yc3FxYScnJ/bVV1/Zz372s9d01ex/4Szj8bgz1JrNpt+UZhpsSpi54Cy0FwfhADbDSVEb1VoCEZIaCQws2SLCo5uiThzYBkgQeAZBpfapQjuZ3J1jCYmB2oqZ+QQIoh+tPwwGA/vyyy9td3fXLi8vrVarecM/9YkQykRg1FhOJnfnKB4dHdmvfvUr+/zzz21vb8/i8bjfF+ukpAkiKCU5hexknLBmPPQrAaVwP2pc1FipMirUjcM8OTlxeLRcLjsJAqiLaI/Ils8MHQx7QsYGZATiQPZEVqmwJsaYq1QqWblctoWFBWs0Gtbtdj2oUNiONYGWTpCggQOMWqDEFy9eRBiTyDFyp05ZnaHKnGZiXGQjo9HduZiQhbRWFe4tZDjqaRgcWprIkvh7uA9aGtF6s+43wSLBK6e0IMuaKeszoIu0uuDcyfi1vYUgD/1G59ChVCrlwziQJ2BCnuXq6u5kFoaHUPPWQI+1pCUhn89HavsKNVP3JAiZmbmbL01rC/rHSRkMACDj53lCkhP28OzszJ3k7u6ut40QvHOdn597LVUTA4KcWCzmDiwej9uzZ88cJYP0Mz8/7//m83lrNpte7w3Z58g/mTaQbZhRcilMq79D13hmJYgqWajX60WY5KwTe4FvYP806NYkgAwTRDAej3sAg8PFCa+vr9vW1pZ98skn9qbrrc6Svkg2GONEJkhEwGuIgMh2MEDckDpI/l/JJGw+0A51irm5udcazzWjDWFYMjPgNYwCCoyyoJA8C8aQRVTolM8kiwHvJjq/uLiwo6Mjn/BBFsfz4NTU0XCFUCNkp/39fXvx4oU1Gg1nAMOEJapEkYGbtNUCJ6DGTxtxEVigQ0hZWp9W1OChS//GmsL81Wwtk8l4FAfkyo9m1UqiQOGGw6ETCIC+iOg1etXapxpNM/MBGTgULS2wDmEdOZQvrXMRoMViMa/9kY0otI2S81p+/xDRSZ0WF7LKs2rWxueGe6StD+gZTlJrd8idlkbUGfOjNaawnnt2dhZhHeszalmBgAG5gqDDJCVKCNgHIEoNtDnWjexU61xaMtBAkfVg/9R+mN33beIwe72eM4/pxdW6LNksdV1eA7GJ+h7tLBo4aZaszoWgpNVq2fHxsR0dHVmr1fJ905nNZuZ10of0EzulssXvgCGVdU5/POtD/yKIGevMWurRWGofeTb2G3+huoTMo0f6ehIOHLGO4eP9/IsNVblR+wEzlkBK7T1yxwB8ArBSqWSVSuX/PpSAKI/IjROnmYsI7guDiE2YTqdeSwhZpAgKUbtmdSgg6TgNvDMzM04l54Hp9VKlNIs6SyA17pfX6n8jkBg13gekqOOQgF3Z5Fwu5yPXzs7O7OXLl9ZoNGw8Hvs0FZ4F2C8UFDPzTJG1ajabtru7a59//rm9fPnS2br0h2IwqHEAEevnYTTU6GhrhcLoWndggr/20IWsNlUAjRS5JxwIDFieD0eur0XgVWlQbPqs9LxIskwNvnQ/WUdVNDOz1dVVi8fj1uv1rNvtuhKxPpr1oZD6QyDIZxPMAc+yBmFQoeQLDQhDRxSuBZe+/uzs7LW+2oegPLP7Q8jJ/q6u7s6e1NIDuoWuUmMm4sfharY4Ho/dkMIYVhgWeeB5MJzsKZ8X1qGp+SpMTybEWqmtoTZNBoHdCGF8dEoNO8+v68v3dbtdS6fTNjc3Z/l83oNtdfLYOOTH7H48I3IOgVBZtOyjwrBm90eoHR4e2t7eno/jBN1h2AlXv9/32j4lEw1m0HucxO7uru8nLF7tx6WGTZnh4ODA2bEgQ2bm3BLNUrErWntXZ61ywe/4XmwuMkIdlKAQnSHpYZ2pnWpATLJDz2U2m40kBKwPzpJghIAgn8/b6urqW9mwsTdlDH+4/nD94frD9YfrD9cfrrvrrZnl3/7t306BSsg0lKmoWaFmZmb3szgVwlQPr1EIGQVwpRJhlHCgg9khORDZ/OVf/mXMzOyTTz6ZAhvASqM5nMNsqZtQwyKiYbgwcEqlUrF4PHpSA1HzZDKx7e1t29vbs06nY/1+PzJMGWZsoVDwthem2xBF/+xnP4uZmf2///f/prVazbLZrB+SHGY6YfYBbKaH64Y/D7FrNSslUlZI1Oy+5qDsTmqyf/3Xfx07OTmZKps2vIjUiSZ1+LKZebZD3QN4EGRCIUqFKZXMgGzQPgI02Ov1IiSbv/qrv4qZmX366adT7lehMc1GtUWFjAPYkVnGwHH070Gp14xwMpn4KSSJRMIZeEBMRLPIrvYrXl1d2fb2dszMbGtrawrk9u6779qf/umfOvlE63OgPBBJ2FuF97XmAxSm66T6C7qi8oc+Ifs3NzdOkhiNRvbtb387Zmb2d3/3d1OdU8y9shZE+9gDZdZrTVVlNIR11S6oLOiPlmGQj4uLCzs4OLDPP//c/vVf/9X+8R//MfZnf/ZnU0on8XjcPv30UyuVSpFjBDV7B9lCVqknAtsq7KdZtMqb/rD+8XjcZ6seHBzY8+fPbWdnx8+zXVxctL/5m7+JmZn9/d///ZSMCrhTSxn8HkYoNVbshiIbOm8YlI2DnpFNkELKGHQo6D7x/WdnZ448XFxc2J//+Z/HzMxqtdoUO5jP5+2jjz5yZjrv4f5hGqMv8EXy+bwtLy/bxx9/bFtbW35u7JMnTyJnC2ttGz0gk1SimsLh2grzox/96MG+vrc6S1J9zT51k/kCNoYvxgApSUPfx6WpOXUNTeuVsBHWGd50heQQoB8+H9atsgW5L+qB1CW0CRzlA14cDoe2u7vrtbmlpSVbX1/3c/4uLi6s1Wp5fUOL2kA1XMC5WofQvj/tY8OYUC+dTCZuiNRAarFfnZcKuUKoSvjhCiFBrbeGe6syonKg3xXuETCIyoveDz8qNyrkfD/PTrmAtdP3PWR41bjwvQobatAAmxnHoXuIQ9K1USib51XKPQ4zrPko5J3JZF4b7RUGQhqcKllHn0v1SB0L72N/tMVK369Omb1GBqmncgF76poqJKcGSoOfcE/C/VLZCfdQCR4q0xogAEtyCg+wZgjZatkoZLBr3RXHTsC0uLjorH2dMqRtM6pP4XOytjTja9M9csgFoxRdw8FozZsWOP0dtmQwGEQSE9URnKnKowazWn/WgJ7nAcYMbQesYNaKIFs7C2AQc+wbY/QgSfL+6+tr6/f7fl8MTcCeEMQhE3BvNBBWWVG4OLxvvd7qLImGtCal2Dh1ApwZN8CltQZuVG9Ki/dsgk5S0ddSo1TjGBa39YK1aGYeNcZiMSd6wNDU7IppKfQJ5vN5H0eF0jOd5+TkxM7Pz33M3dLSktVqNc9IcMg4SmpwkD/CepoygRFojLROv0FAiNK0rkvmpApIlhsaSP6OoFDHCcegsS8h200DCDWyZq9nACGxReunOAN6XHGaSvLQ/+Z+lagE+kGkr8MsVJZVflkHnkOZ2mR4OmeWz4J1yf9jMJWRaGaOEFAbJgCi5xjd4V5ZJ2XwMuBa/8ZzsCdqkLXmyPpr9K+/U/nT2o6uDfqDbPC9vBYdCtdUHdbNzY3X1hKJRKSvkGAPg8x6qeMKdVvvUWt+qqOgAciIyi5sSCYUKbICgka21Wg0IuPoFJ1BBlOpVMRZMpOUcXs6vjEM8vmdZtVXV1evBc5h/S+sqyFfmknhgOj/hiBF4z3yPp1ObXV11UlNuVzO9Z6AkB/6298UHGObsKnqeDY3NyNcCpzd1dWVHR4e2vX1tWWzWVtaWrJCoeAjC5PJu4EK3W7X9QAWOnal0+m4jT0/P3dmL9kps8Y5CAB/QqBFrzRr+Kbrrc6SBVDoRaOs4XDoG0SvIhGz9lfSE6lQLBAa8JkaZjXu3DyfyT0xYSSMDFBkIt9sNmuJRMLOz8+t2+3a7u6ura2t2fr6un344Ye+gbSJcKQLKf/+/r4vJA3CROEcKDqdTv3MOm0hgPyDc1MiiRpFmq1Zk0aj4bAfkXuv1/OsFhp7Op32c+poEdAJR2ogWVccDo6q0+k4ZJFM3s0YBRKm3cDs3mByn7yH9VZHqZmqMt4QcghLsEohF2G0NKuDeQdTLhysncvlfIB9NpuNRN3hUALWgnvhM2B5AzkTldIGsrCwYLlczteWbAIYCDRBoWacmkKCtFUhu3w+gxUwalwQK8hUMRy0RJiZG2oCNDXMfPdkMom0ZNCTSaBrdn8GKHKDnOHEQsahQrh6dButRzzzxcWFtdttlxkME6QSMjFaGJA7zcY0yAkzZAhuOAKyC8hhZ2dnToLZ3Ny0mZkZy+fztra25vaCoKNUKkWO1UKfVHZC2J8hJMhUpVKxR48e2dbWlkPdOgREHS+kMYXOYdIykJ8AQpEoHacI01zlj0lEBGIHBwc+JWkwGFi/37dsNuskGA226BNVMg4yogG0OhfkQp0ocqNrx+CDm5sbW11dtUQi4X389EcCk9LaQTAKg1WTB3wLM5LH47G3npyenlqj0bCjoyPb2dmx9fV1e/z4sb333nv23nvvWTabdRnjqDVF8B66vvGILmXCEaVppMLDEXXSwI+hQOmY2adCrr1sWrfRWg4XETPOh2g0zCzj8bgVmcpZAAAgAElEQVQflloul61SqXiEqwf49no9P2YHY0//FxkkpwTwnTSCM6WD50Vput2uK3Q8HvfoUudTKguNCwdD5oHhwkCSCTOaazAYuNDi8BQSh+4dKibBCzXDfr9v7XbbHR8ng4AaAJ3gfLXniedUo6l/47kwtBixfr/va4Z8afsIsArytbe3Z41Gw5uhcSaaCVDjaLfbVi6X/TM1AkfR9V5Zc+YCay2NWcYgBDhK9l4b02kRUvlFrvldIpFwwzs7OxvpdWVqSzh1iEAPo6UjyXA6h4eH/vfNzU2r1WpWKBQ8ilZ9U/iaSFwzMXWY3BvGCoeidTb2VfWUz8dGHB8f++zOTCbjOqTDLkCWzO7q2cVi0QqFQgSxQD50nemNJKDSrAe4NJVKeS2Q/kd1Pu1222KxmOVyOQ86kRVmz2rgqbW+s7Mz70FHrxiX2e/3I4Mt0Av0UycfoeuMyxuPx5HzOcNsZ39/3+r1ui0tLbn9UuRG0RHapMzuT8bh+MNwvB8OSFm9ZNrK4MXu8p3cA06Wz0LfzMznZBeLRatUKj6/Oha7O8mp0WhE7DfDLcbjsdtVfAYBCAEctpChKhwYjg24vLy03/zmN7a9vW1ffPGF/fjHP7Z6ve7ZNHv+NgjW7BucpUJ3CKdmjDgbhg/c3NxEmoBxMNPp/dE6GG42EgesQsjfyawQJiIBNuMhqj1GlD6nbDbr90u/JYJ+enrqWSqflcvlPOO5ubmJ9IGFlObr62vP+Dh3TbM3Tp/gDEYiuRCGVVgNSCkkPZDNEK0RXdEegCHEoalzCxWccwKV9o+CYDiJzqkDas1FYS0UBIVVZ4mw4wyI+FhfMial8c/OzrpjZDADBhfFJCOj9sHgbAxFoVB4DQbSujnKRT2y1+v5WDPmUdZqNVtaWvJpOApH6QBmHdygKAiGhefk9UTctF1BBoJopuUHCDs4I4Ik9ieRSDjcdHl5aZ1Ox9bW1mx5ednW1tbcMbBu7B2fwWACnYyCI8FIc/TXzc2N19/5UeKMrjP6zMkRZualAjJchY1BVMhYCE7RS63pKWGn1WpZq9VyxMXMvBShJ9sQXF9eXnqtkvUYj+9OWQGhwIlqQMUaau1vNBq5Ucdhgoqgz1dXV6/BsECFTD5ivyHW5HI5zzjRydDGHRwc+B5RzkL3NFgh+NYgkXXVpn/ldrAfOnxekaIQqiS45LOVY6C2gO+ZTqdemyTTzmQy3j6jcqW91FpL5bP1WWKxmJe6QKJARmZmZvxQatpulpeXPWCoVquRz3jT9Y3OEgNE0ywKTSTFJiAgZDdAChg3/tXaE9CXNh/z2RhUoqHwrD49tFmzNKaV4MCJ2OkRAnbrdDr2u9/9zm5ubqxSqfgcRQrLKDDn45ndOx2irLOzMzs6OvIgAoPN/XPqBYY+rO9waWSo66WGQuu0FxcXbli0WRu4CafDRaZGZkfj8Wh0NwqQDBKCAgxPoJzp9P7kD9aBzMLMIgYGmWGfqf0cHR350GzqokS1QCI8Z6PR8F7TV69eOTwONE5ESEa3vb1t7Xbbms2mnZ+f2+bmph8crkqtxBKCv/Pzcz/NJZ1OW6lUstXVVT8wV8kWamy0BqUQNLKiWSX7QpBE1MvnwqTUUoOZubMm4yP4wsGl02mfoEOPXrVatdXVVfv444/t8ePHDv9RBiCbbrVatre358HecDj0MzxxHkTdrDsoCbVKoPFer2ff+ta33GZQomk0GjaZTKxer1utVrPV1VU/71UNOpkh2TKOEaenmYvWupvNpsNyTO4CjmdyjpYxTk5OXAaQjWw2a5VKxQ0nOqhBlToJCCjAzwRXPMdoNHKG+sXFhQdTyDvw8OHhoX399dfW6XR8f6vVqm1ubvpxWJzeo4GvmdmXX35psVgs0oNK3RzHp2UtHYqCU9XB5zg8eANLS0v+e+w6e8Xv0SfVW4VjVS/MzLa2ttyOQ7DCxiwsLDji12w2bTQaOQIFk75QKLhtwn7QsbC2tuaOkHnRJycnHgxydCInN/385z93UmUmk7H19XVbXl527sObrrc6S6KM4XBoR0dHtru761E4DgIFx7nhyakNpNNpH2LLYbIoCw9IlAU8h0FgQzA20PGJwlBePd2aCGk8HkecGZklmwRUuru760QdHEatVvPPRPhw5vw/R3Qlk0l7/PixlUol++qrr3x9WA8ghNnZWavX604zDwkWyvbVAcFkTWThRJlAYqw/MBpZNRCERp+Xl5d2cnLisK3CPTMzMx4pawsJtS+NuMLskX1nXxHYTqdjr169si+//NIajYYfUabHtb377rtWrVbdsDH79eTkxL744gu7vLy0b33rW06iwoDz/AQ0FPaPjo4cBcDYhBcBExE8zhGCAaPYlK1rdn/weCaTieybNqkTwCjbFYXXrEgJQRhwMluubrfr2R2GDwPd7/ftyy+/dNo9dcbj42M3CgcHBz6cHCSEg3S/+uor293ddXlIJpMOzyUSCV9v9v/Vq1ee0RCQMbc0mUzaT3/6U19bMpl0Om1bW1tWq9Xc+RLAItPqBFgDAtHBYOBkF+SRlgQySeQNfcM53N7euh6AGCnRDP3++OOP3abwt7AuqQQWDtyOx+9GBp6cnERq21qD5H5IOAioO52OnZ6e+vqRzZN9AWUySi+8zs/PbXt724MM6uaaqWtgpIhgCHuD0BCQYJdhlSqszQ/ZLg5WURwt/+jFkBbKFpQ0ksmkPXnyxD755BPb2dmxdrtt29vbfsi9Jg06oo9gn/vA4SeTSWs0Gvbq1Suve87NzVmlUnEiJXYVWUK2ea4/+ZM/edBufOO4Ozak2Wzay5cvnZaLwmNQBoOBKw7Z3unpqc3Pz1s2m7Ver2elUsmjA4VyB4OBD7fFKJOthMaFXhkch0KCZlGG3Pn5uR0eHkZG5SlMiqLi7GOxmEcwnLDNEV0YACJOHCfwEhf/TVScz+cj2YpO9dB7xgkDp3GQqk5KgS0K5s9kI6I6FI0MB+VlUHKj0bB2u+3ZPN9pZk7EgGBBtqN1Oc0gNYIkQFHHjCBSY4MEZmYRR4ESEMBMp1MnwCgkTU0ilUq5saVeNZlMHMJhr8Kat9bayA4xULAjcQI4YYKkh5i/yq7VWo7KBxASkDl7HULaWvvVOs+vf/1r6/V6EZlKpVLW7/ft66+/tufPn7sMYwAgi/V6Pdvb23NmN0dgQYZ5+fKlQ7gQxnK5nNf519fXHZq6ubmxXq/np9WPx2NbWVmxdDrt71FZVgJcsVh0AgUHCjebTet0Os4LqNfrHkBhGKmb8kNmiYxwXVxc2OnpqR0dHVmj0bB6vW6VSsWhNQJ2rS0rWU17U1V2+G+CVq0bgxKQVRPAYaw1cOJ37CGZ8GQycdQKqLtQKFi5XHbnpVmiygVOtNFo2M3NjWfFfAa1cZwj2Tg6QVCOvJKI4Gx1LZStTTmBoI5ygiJjIcGMizUiUaCmTJb+gx/8wB4/fuxEJGq4IIQXFxeRMYKXl5f+rL1eLwJpgzxSxgOV4gQTYFqSJ61LKykpvN7qLKkHYRDY6NvbWyc6MEpOaw5EMYxoGw6HlkwmHb7hBBFlxvLZLCKQpfYr8X6iNoQpZD1SjyKKI0IBBgaiULagZhEIjzKA9ZkZdsAGcKQYJBAEhjP58vm8FYtFy+fzEcqyGhi9H609oCRKMSdbn52ddVJDCNUAXyMM7XbbIWJlqFLTGA6Hvt9KhcehaW0udPQELkBnWmjXficEMSRrscfAOkTqzATWfWCtcQYnJyeRyFjHnYURLjCR1t3m5+fdqGtmjGGBRMC+aCagzpJ1wlhqe4y2hrA3em9amggdAcEE2SVyfXh4aOfn526k4QoQdIxGI2u32258ILrQgN7tdp1MRhAAqoBjG41GkQCE03OQU+o9y8vLkXXGWKInyBlwfKvV8mPs5ufnHclR0gdZjuqq1oZ17Tn9ZHd31+V+cXHR+v2+Oy49MJ7gwMwimbKZ+d/G47HX47X9zez+4AZFetApzYZ4Dr4jDOJw4hASsROQV3RghQZ+xWLRg1DKTGTh19fXDuMqOUrtHLLI84BCgVpQe1SuB+/Tlhb0F10mONAaMxfBuw7wUH198uSJ1et130v2MJm8O0z91atXnr1XKhW7vLy0fD7viYAexg1Rk4uEgURNv5+ET7s63nS91VkyFWQymdjjx4+t0Wj4gaxra2veND0YDJyqSyZKHQTFTSQSPtm9Wq1as9n0SGY0GrnTgcwDhIKDInpCyIESQ1o1ys4RNGZmm5ubDvUdHh66QGPo2WRlcCFICDvKXygUrFQq2crKim1ubnoQMRwObWtry/sewdM5zox7Yz1CB89zFAoFS6VSViwWXztQlXoBawUMGxKCMPpE0Qrz4syBC4+OjpytzPQcqNwEOel02us1XBgwDBZrNR6PrdFo2PHxsbVarcgp6vTcUdzHmAKBKRwJHM55h5x+ANNzNBo52nF1deVn3y0tLVmpVHKEQC8MKTIFhJ/L5Ry6JfuCwUmUSg0bGAsISNcDo4OhYg8068TQK7wNnT/sCfzpT39qu7u7Zma2srJizWbTbm9vrd1u23A4tA8//NAymYwP8Qe6o5Wm3W7b8fGxB547OzvuyOfn572mrkjB/v6+7e3t2fn5uW1sbFi5XLZiseiZwerqqtXr9UjLjpZBYECzbvF43CcY7e3t2cLCgjvHwWDg9dDV1VW7urqKGDNtkwo/n+wVG8U5r8Vi0Wukr169svPzc8+05ufnfe+Au9ENAiwcgZk5iQcHkc/n/fcELaBck8nESqVShBAHqQs5J/ALM05kQdGr6XTq7UHAz1xbW1se1HEmKEhLo9Gwzc1N7yfH6SjqQ4AEakC2yL6l02lnxoI8gI6AxlALV5tiZs5vIIDkYr80c8Wmobs4tGKxGEH9BoOB3dzceABNhwOyNzMz43yIVqvliQR7jC5TH51Opx5sgDoyKeuhiWRcb3WW9XrdBWNtbc0++uij15qpgXXoOaNeR9RK5P7d737X3nnnHd+44XBoKysrDvctLS1FohicJpg6tQvFy5VSzgURhRScSIF7VnIFZCCMt0bRt7fRkxoQEo2YgNjYCHUcZndkI43qlGCi94yj5DuIMmHYKpOTz0FB6cnSmgdOiz3iM9fW1l5r1t/a2vJgZn5+3qrV6mv9hjw/94yjxyHgKKiZ/frXv/YCfaPRsM8//9wNFJT3paUlq1Qqnr1qTcXs7kit9957z8rlssNzMzMzHvSQ+W5ubnpNGxlhTbRHzuz+TEXWFxLQdDr1TAvolVYE5AWjh8IqwxiWNFmvGksMFMYKJ0K/GWQb+l0VBvrJT37iBKeVlRW7vr62ly9fOsT53e9+11KplJ2dndnKyord3t56P+hgMLBUKmUffPCBPXv2zFZWVuz73/++9wMzXGMymVg+n7ef/OQn9vHHH1ur1bLf/va39vLlS6tWq7a0tOSHWNPvtrS05ISfcNQfz4jusobsfT6fd9JNJpOxtbU1r2fR40zQBluboAz5x+hiF2KxmA8HoT4K+gISBOxOtkwgpeQ59plADllC/9AxnAoyiSyAquCQ9DMh4DAOc3Fx0fb3930P4vG4PXr0yL8DyJr2ldPTU19juBVkoEDbcD4InObn5+3JkyeeCEDS3N3d9Szs+PjYptOpZ13YT3gHx8fHniDww3NpxoacUy4JL3WgBOacp0m2CFOZTgK4LO122zKZjAdB8BrgWbDWZnfIwNzcnFWrVd+bX/ziF7a7u+vlllqt5oEqPA5sqDK7w+sbz7NkUVutlk9XmEwmtre35xkIjEKyHBYwm81asVi0Uqlkm5ub3joBbs7reFias5XuTOaHI1IWZdgeYHYX4fO51BXMzGnc1FoYd0cmB+SiDFuFe8jeMAYakfLfwHTa56VOUXuV9CLTI+skOmOWbK/XcwUkMuV+5ubm3GCYRU8C4f6JFovFokNwGmHzWTicQqHg95tOpz0TCGsdSvmn5vnZZ59Zs9n0gRDT6dQNwszMjPfQVSoVq1QqrmgKU7L3c3Nztry8HGGlItRkhNVq1feNA4SVbMSlTkzJBchYv9/3KBPGMad1QKWnkRtImj3RliJtV0AeYEmzF5x5iHHRhvWw/g08rH2T9AI/evTIzO7hWsoe6AGHXQNn0bt2c3NjtVrNqtWqzzQmE8e5PH361PuDcZZE8kyros/vTevMM+Fw0UvkiKxVyytk5Srv6C5Qptld4AO3AONOe5ZmCIq4qC6HkDj/TymBUgCcCUV2eB37paUXGKVqt7hAzGDSDodDn9AE8QSdgltAqQWiIHurKAXwJIGy2iACSOwbh583m007PT214+Nju7i48EAFjgkBLEE2JKJsNuvryHPosBLshsoE66zoFjJJMNXpdCKv5/lHo5FVq1UPApPJpHU6HZcTM3MEU3uvQcHG47E9ffrUdnZ2rNls2vHxsfeOa/IBl+T/nFmamTdAn5ycWCwWs1KpZKlUymFQSDo4QCIOBpejEGwAi4gxmZ2d9eZYFWxqPVr/4eGpD2odjWs6nXohOJ1ORwgmi4uLtrq66gPLYcBSS+DgU4UzNYPF2CEsYTEYB8kmau2C3wHJ6YVxx8ADWSjZxOxeSTRzCZvi+Q4zi7wWKBsDBOON5+B18XjcGWcIHUQE6iYP1dzoqWu32zYzM2OlUsmPOQJlSKVSLheVSsVfowYsdPa0ARHxsifALKwlEDk0fuqmXLp//Kh8Q6Di+bSUgOEGZtXWGZAXggHdOxwTBkzrWqAkZMA46lCmcP5AVcBRt7d3xwppgEfZAX0hg0e+dJ1WV1dta2vLjTXBEJAmARtrH9Z6tTVHkQr2DXnCOFJv1kCFwBr5I2iF3a2MTvZAHRZtT2Sw7I/WrPkJ/1/lgvvWujOGHWetk8awVYo6sffIGc+hFxwCstliseg6BQqF7YOQBfNXj0E7Pz/37N3svheUe9f1BUnR9g7knCBASUwKyyYSCQ9syfgJgHhWMmHsGjqh+4SN06Cc+ig8CrJzdbjIDqQzYOfBYOAB/WQycYgYewj6wFq/++67dn19bQcHB378GQE4+kBLyv+Z4PPZZ58503U4HNrvfvc7p9qvrKzY6empLS4uuhAlEglvni+Xyw6DEBEyHo8MZjAY+N+pUS4uLvriKaSBUUUQFZLVTaHeV6/X/TUIP8xHshQyP6j9Wpzmc5Xd9lAUipIgLOEPDhCDok6BS4kLGFn+TrRD/RJnoXAw8CVKozUoBBRDwrxFqPnMKdW6J32WkJgQOhQbmNvMfEIG0d2HH35oGxsbDs9dXV3Z1taWIxTj8djq9bqVy2VvU8DhQdxSkgGwiJI+eN54PO5RIhkP/YTq3M3sNfjM7H4iCJAPa8szEfXG43GPrOfm5uz8/NyfDUNK7UVraew/rEnqk7FYzHs7b29vrVwuR9aUCwNAcMAeESSy/0T49PdNJhM3kAsLC45oUP+rVqv2gx/8wHK5nDUaDW/UVjKMkjhisZhn7egMzx9eqgfIE4aXPsaH+oj5YU0x4KwrpRGypuvra8/2YdcTFKvjDZ0jVxjsabbG/uF4ze4HEaBjXOwLzjbUPQ2MKR8gV6AsuoeLi4vWaDTs4uLCptOpn+WqMOzz58/d2GNLaQ8xu6tvE0TTe8p9MXmMded8V9YSRBAbAOmLRAdHG9pDnp8907LTQ69Xwg+QOHwXSIgExLOzs9btdr1XdjgcWqVSsVgs5oxl6qnZbNbXCjvPWNB+v2+tVstevHhh/X7fUY+nT596n7MeZB5eb3WWCCdQRKPRsC+++MLOz8+tWq26Q8Sg6FxUzQaI4llsomyiVaIplJ16jg4AUAdDNsQGhrAmUALKiTByTyyi1jZVYRAs3hcWqrWGyP3wHoUbMP7/H3tv8hxpdpX/n0xlKpUacp40lKSq7rbd3WU7HEAQJoxXLCCC+K5YsmNPBAt27PknWMA/wAZWECwgcNiAAxvjarqqq1WaUlLOmUqNOf4W8ufk895WqX8BW98IRVdXKfO9773nnuE5zzmXdzdbMB9VMWq5B89TuIlIWaFavkNbQOFRMU8Ut0YARJmlUslZZBhhJVdpn1nmyLuwfjxjeXnZvvGNb1g8HvfrjYCiKHKmy8x8PndYJ5VKeckR6x+Px+3jjz/2iOH169eR9SIvOB6P3dBzoNrttkOG5Fy+//3vPwh6wFI0M69BOz8/t36/7ymDra0ti8VifjvIcDi0YrHo+VOo6sC05E94hioUoh+gXeZFhML+8jntDcteQiLh7+gnCzypMCDNQpAHzmYYTVEyUa1WbXNz0yEsZI1zB7RoZl+pF9V8uF5OrM/BaCp6wd+jKxRRSCaTkf7SpF/u7+9975EXDCI5QXXoQqSCsxcaMdYH+Q9Rk/A8PxaZguoo0xTjoGeR1BMoxmAw8EAhn8/b8+fPvaMUv08bR3VMvvjiC0cauOmI99eLonFY4R6QK2bParWaNypRdisOAqiL6mm+R+Hn6XTqdZm8G/l4hhKoVO/hgKBrONvc+ELa6PXr19bpdOz29taNejKZ9LM3Ho89TYLTRcrj5ubGqtWqN2jf29uLELAw8jSxf9940liWSiWHaMbjsR0eHlq73XZvBQyZg6/eNNGhWbS/Icoeo4kgoSgUqlKWGH+vBkmp1TqIDrT2CePI5xEALR95DKbRcg7NQWjuUuf1mFLWz/E9oQE2s8gh1ufyLARM15GDyt+Hnq0qB/WaMcz6ozAOjg6es0JtrLEOdXSIbhB8s0XZCNAsbF7eX43l2tqalUolLxiGRAWMR64B9IHyJdYcxfW+ZD0KV4uTNR8MHKkRoplFyov0u9gf5JV6WCV38AytfUMGkOHHIHpdH0VE+A4taeDZCicrqoJy4h1RJETiPEvPE+tMdx2MgLaBDGVBZZvn814aSbJe6piytxhk0ATkgz+zh8iVnsPwPOmZ03QHQz8bRqFhSkPPF//On0NkwCxqEMzMIzicLDOLyAkGBmcBh03PH44OZ47nJhIJNyLqpGPEeX9+lzpZEA5dH/QhhDaFJyG7QQjirCFLWh+ra6xOg5aPoSfYU8qbyJu2Wi0nOjJnbZ6C7BJEVSoVjxBDoia2QXV3r9dzBAPG82PjSWOZzWZ9M+k/enl56bVTwEEYSzYHWjWUayaLsCvEisBpjkkNDj8afeGNIoih4eEgkh/hv48lb4E7EA6lNjNU0EJIloOjf49yMrOIQ8D7q3HTeYf/z3vx3bzzY78Xes36Z/5fFZYy/EJSE3/mECupSIc+S9cNxa+RcCy2IH7hBSvZSVECPFfIGlyTxAHBGMfjcS9JQSFhhB4rz1FFCVrC7+vcIbOgFDjYRPKsA/tMBI8c0DWEdeXP9HNVOJG8JnsdGh6du+b2iLZ5D40kH4PjUUTMVfOvyhBnHcLIiLMKMrOxseEKTs9LaJgUpeF7VW7UyUMOMBTMV5WvOq6KSPBZncdjzqfOQ5+LkQ7PoZ579keNpfIE9MyGwYGiMhDfeFdSHoosAUWDRui7lUolN2REjtq4IlwnfSetKYTUgwzyOfQCAUd4IT3zJfggKkMOOYtqLFWuNYDRvDBkucFg4O87Go28VIpUCyVLrBVnUHP1ZuY2BnvB2QEVUz3BelQqFXvfeNJYQgIBqoOowARYaJQURInLy0trNBpWrVYjUAsToyxBi3qh8KqF1xyTGiploIaeLdECJAzt1KLeP5EFRAI8HiIJNkvhXjXwwD/quevc9LDgHGixu8JtDNbysWgRQSaa0EiEZ3AYQwWlTgfKUSMphaJ1vXmfxw4c647wqQfLe1MjRQkR+WyF483MvWP2jvzL9fW1Q2+wbSEboFj0c8ge6xgST0JCDi0VtWtRLPbAoIaFO5/PHXZFCSAzRNIYG/YC44s84ahhnNhT5hXCfjpndYbYA1jCetMFMJTZg4JpNpv+ORi5CqVR4sVnd3d3PTetRDLmSjSJgmPw/aEMK2KjjqQ6ROy9OioYCGUmQ77TpgUh4oTzqmul0YMaaaI4/p730LXWaFbXPkSfQudL54MS59+BG8ldlsvliENBwAH0SOMJLY0wM6vVajYYDGw6nXr+XHvWUjakvVfVCOOMUtAPsRF5Za/Je6oDABGGvaTXLbCp5sfVWVWOCevNe6OLwluFKC2hnzROZ7Vate3tbV8r9ojesJVKxTsaoUe077CZeRpjMpl4U5L/E8GH65s4lNC0i8WimZlPYDwee7ccyknu7u5sf3/fE8XKelJDgVDGYrGIch2NRs7AUpqwmbnhNrOvGAYWlxwZyoxITw0hc2ez8M7UiyRnhyerkXGv13ODkUqlPF+gjDw1IAr/KWSj7EqcisfYrWrYNIer+U09oCgxhcvJ0wE/KmEEQ49ix/NX712fR4Snz51MJhGCFrkWzfvQPJpoiTwBeUiS+fQNzuVyfhXZ2dmZPX/+PNK6TGHZMC+uc1Znjb3BUyaXq3WUfDdKgEiOUg3Nd2lkqV6s1vliJJgn+UCgrBA1UMOC8kGRowxhFOLocLsCbdAg0KEAUVq0SZxMHi40eP36te3u7kYcB7NoTbA6ALzTY+usEbwaKSWyqXHAGIVcAL5XeQsa0WkqAUWoyu6xyAq51QYb6kjpc8jxhyiJGi7yagQADPZWc6fsL/JBZKg5zsnkoYl4vV73cjxdXzOz3d1db+8Wi8W8pANdd3Nz45cT8DyMIaxnlV/q4mmtiQFT3cwZx6ipLtNm9urMq7FEBuPxuBtofY7+Hs4zDW9Y58vLS+v1era7u2tXV1eeIuKZ5ElZEyJRyEDsNb+raBS64bEgxuXmvf9iZu1227/s7u7O2a10eIBIcH9/70ylVqvlIbMmgFWBq0By+NmQ+Xzu/VFns5mz9FQp3t/fe8QT5hjZeOjECk8h7Pw/cwmxep0TG8DGI9DMiQhKjR+MTgygGj99nh5WVRTh7ym8QTROJMO/aR4AQcYxIT/CM4E66ECCQkVQNResuSXeUQCfYskAACAASURBVCESDpM6LDp/PHkOCzkIlInZg5fHHaPcHsL1S9fX15bL5dzwNptNT/DjAOlzkVWcoHCNWRcz+4qjAPqgB3A+n7sHyv/jSHC4VCEqlIVS0jo4lACfA3lRp02HwtS8j/6dkiYo4h4Ohz5PYDT16jECmUzG5/Pu3Tu7ubmxSqXibGXNN/IcdaAwlsDWzEmhOHJFwG6sFX+nconyVHRBfyfMcbNHyCD/FhpVNZih4Qz/HWcUpEnhR4WOFUbVKF3JVurEKpLwWJTKfCHd0dHpsXnDxeD9cc6RVSXsoYt4D+aiETB5y0Qi4SQ8ZJnAhL+DH4CeVLSM5yqiwGC/cT5YO85liMzoOQCe5Qq5er3u8nJ/f2+Hh4f+LrFYzB1a9CROo54j7f2sOjrkZuh40lgC5bAA9DGEjk7HHej3FxcX1m63I0KDMcWjQAmrglFIBmG8vLyMJG7xiNULCRWLmUXq4zSnoUZaDVeYN1PjiOHW9mds0tLSkhcMq0djtkjOKzSEkmZOauD1PTSy5HPaNQlIEyUPDIGxUygK6Ji2Znwvf0chMNCQ9ofUSAYPTtc+TOLr3qlh0QgoFov5lVLkLzFwdGtpNBreFLvRaNjNzY1tb29bLBbzYm0aY6A0NBIBkg0dGI0MII6QlwFW0qF5dTxPjL9GJUrOYd01X4iRIc8/Ho8j0JbCmxhTnXM4dzUKOiaTiTUaDe/ignFcXl52eEm9//l87qhNIpGwRqPh5waYbn19PWIwlYyBIxYyu/WsqbOlKRXN+7FevCPRDedXuQFKVDJb3A4C8sSa8ru6J3q+MQI7OzsR2BxDy98xbwZOo54vggbgUpxkzRXr2um+oVs16sYwaGMWRZHMLJK3V1g/dAbVWGpUzx5i6Eg3qDPAn5VXonCsGjw1Ogpb65yVnKb8lVCOOVMEBZTmsV7j8dhOT0/981dXV3Z8fGy7u7sON8diMTfq2uxC7RFzRP/rfr5vPGksP/vsM8e2YXGpQeDGkMFg4Mby8vLS8WP6ekL0od8o0ZAOIkG8ztvbW0/0qkfExvFfzVuYPTCbKI4HR+dzLJLmGHTzr6+vnXVFKQsKhO8BN4cJhpdCzkyLeoH3zKJlM0SlDPVu2UQM69XVld8/ieFSJcZasiZsNs/DG+Ogo4j0FoLBYODQFExVjK8eNjXwatARTJ6r+6jNA25vb/3mGiL0i4sLu7i4cBjr6OjIDg8P3WCSvyGapGwCGEkbFSSTSY/28LoZSsrg/5WFqLnpEJEAJidCpryBwwYcxo/CnWbRYnT2lmgS+SaH9thhVeWGvCrRbTwee2Ny3pPoHcOVSqX8pg8o/tS3jccPtZ5A9CcnJ5ZKpaxUKvneQ6hgj9UhUbhtPp/7GVCniTXXCFflfTqdenN8ImN0j7YnTCaTznIm76TRL4ZVUx+qbI+Ojrw4/eXLlw7F8xnVKewXvYFR1nz38vKy98W+vLx0vaBRlMLvzAE51Ppmbf6ghpb9VtkdDAZuGECCmBOBgCIbZos+u/weuWG9tUhrMjVy5ByxHpwJHA9lk3NmQv1OzlH3irVfXl72dyClN5k81E2Wy2WLx+NWLBYdvu31eg7PmpnrOTPzJibIJ/MH9lVnTp26/zMMe3Z2ZuVyOeIZ6oFnkhgoru+Zz+f27t077/1YLBbdsGqxsRoa9fqgQGt+CA8DAQg9Usbd3Z11Oh1rt9s2GAysUChEfk+NEYacf6N1H70Iy+VyxPtDQaFk9XAqnq+GDfhRIxcOEgMFqTAxRu3y8tLi8bjTxYmGeP58Prdut+sX266trVk+n3dljYeK4GMogV8UsoUYsr6+7mQcFB8t5jg0QGocHPXOMCwKDQ+HQ+t0Otbtdr1xebvdtp/85Cf27t07h4q++OILOz099UuJUaJ0XPrwww9dydMMAe8fwwwEo63jzKL0fz3UyBKer+ZlzBakKc2RcdBZDyJlctbI7nQ6dUXPv0PCINLT6CIkGISQ4mSyuMMP5+zm5sba7bYVCgVHO46Ojuz8/Ny+/PJLZxVrFNTpdCyZfGhYn8/nnWGIvJ6cnNhgMPCbMJBvkA+9zzbMvyPToE6KEKE/eB9Qmevrazs6OrJms+nypcQ7Rar4waFU8iGOqpb88N+rqys7ODiwzz//3KE8BjoEpACDo++kMoKc0F2n3W5bPB7325TIDyYSD3WxGMbJZOJnE6OPnPL95M3fBx0TkIDY0ILRzDydoZEqxMnZbObriNwghyAQ+Xze9xn+AnLJmiLXIVmQ5xGha2rGbMEBAUXBkVhaWnL0Tuvreb9arRbROVxfiC6G37C8vOw5yn6/73NQB151v3Ic9LnvG08aS42KgPrUA8MS0/IuTNAzgFUbjYZ7OHwey070igHgu/AK8OSVXasHjzGfzz0aw/gp3EtEwkIB7eAMKAFkNBpFiqlRxvydvieCzvdzkPQAg9ejtHXO/FehYLB81kYNr8JsHEZtYQdTeT5/YJLyvWaLriNmi7wjRpFrtZgTXjVRCu+q/+WQsJeQWvQZXHg7n8/9+hxgNBh75ALp65tKpWw4HHof2EqlYoVCwQkA2uzdbGHEOVQhhKZkCrPoXZKh04VxZJ8VijRbFFnzLAwhuVTWSp0snkFHK2XVsl7qROk6a35fo19Y1sDSnE/KE+LxuK8TOWCgN0pjVldXvfk4ipv90mb6fB9GTpUM4+bmxtbX1z1K4Cyxhir7+j3kcFkbUjugM6R/kGeY99rZC0XIfqvCns8fcv/chapkHF1rhRpxiDT/Rh9m/Xv+zJqB2qyurtrm5qYVCgVvID+dTj36oaOZRoLMXXUF/2Xc39+7HtIf5BI9wfNIJ6hTiByxn3TB0dSG5moVKg7XTPUwaxMaSoXR2WN1TJTgFuZXYZ1ri0jSYjj7s9nMzyApJc71ZDLxcxqm5dRIhihlOJ40lmEuB2qy4tVhKQQLq13/2ZB+vx+5Qw94C+9rfX3do8zb21uPIPFQGWGkFo7r62vr9XrexkwhDTxQJbCgZIkI9LsVa1f6M0pJhViVIgKAgguFT5+BMTJbQMsYdr2GhrVinTEotLuazWZ++zx7hFLgwCk5hggKyAPlphAm7893MF8MseY5FHZTo4SiJldJj1q6+xDhJBIJ++KLL7xl3e3trZ2dnVmlUvHG35DLkCFQCuaB84CjwQgNPOuth0ehWgyXrrkORRmSyaTn8ykIVyPIYM2m04cbNejkAwT7mFzrOjJH/ssZ5F21ZR4t9NRhgvxj9lB+UCqVnEkJexJFubKyYu12266urmw8HkeIdjhXjxnL6+vrSCcWonxdT2REvXpQkWw262xtvYuTvDr5KCIRvhcHBe4AjjCyqE43eidcX90nzq3WsGrOEllH5oD+SAFxJ+O3vvUtvxGG5xIckCJ5LD/3vqiS80RJnOZEMbjAnXwep0TfmwgQgwQsCxuaNVHYVGU41Geaq1emLYM5KuKD3VDdYRaNtJkH6RLOt6ZAksmkl35gs0BYOCMa6GluF0Op3IP3ja81lggOSpkHEBkiuBg5INSPPvrIPvjgAxdiYCiYbuDQeAx6TdbS0pL1+32PsG5ubqzX6/l9caEgqbCvrKzY9fW1NZtNOzs7s2Kx6MKihBTNQfBdGCYWG09bI8PHohX+iyDRHB4B1AP9mJcW5nXw9jV/i9Gjn6J6cERb6+vr1mw2PRrQbvzc0EBje4gSlNkQuRcKBet2u96Oq1wuO7SIEFOYz/PxFDmYOB/8PjccXF9f297enuVyOYeRfvjDH7rjBES7s7PjBv34+NhLcrLZrEP9HCyQBOaBc6YOiJm5Q6BRAp8ngtB30gg+zLOZWaRrEPsxGAzs5ubGLi4uHEbK5/O2tLTk98JeXV3Z4eGhnZyc2P39vV9XBAIRGljNE4EM6ByZM3lUFDtKg39bW1uz3d1dJ/cgY0B+y8vLkSg1n8/b7e2tdTod+/LLL211ddVJFLw/yIl64/V63a/OginKnLVGk3VnPolEwp1rbnyhTzQlMTh7qdTDLSlbW1uRHBvypmeR9WAuOILqNIV7q0pa89Uw/DG+RDd0lMlkMn53JzdcwAdAj5JCuLu7s1arZdPp1B3GtbU1R+o4u2GExnnCiaNuWXO07Cn7m8lkHDqnYQZnAscEx75er9vOzo4bF0XQNGXBuqnhZL78qCzzHhhz0kr9ft9arZZXP2gJF3PEuGmkzbNWVlYijQQ4t+hfODKPlZipU6QO+PvGk8aSYlBlX2JkUCQs4mTycN8dN6d/4xvfiPReVZhDozzFkRFU6gIvLy8dWuTw6E0TGjEyUIiUGWhNHIuvuQyMCcqdiAGISmFTFEqI0eM0dDodG41Gnt9j4zG+KN1w3goBKvSAF3pxceFKp1KpRNafg61XU/H7sF1RQvR9RVDNzPOq1DTG4w+3jtCM4vb2NtJ8wmxxQzxeNbR1BJUfjODFxYVNp1PLZrNWrVYjUGatVvOkfr/f94POZd8KH2tLLV03DD29HYmCNFnP/DnwMC35f5VRPfC69/rvyBW/c3Nz43dEnp+fu/IbDAaeH47HH3Kd5JToZoTjyTx1IKOgBopUcPBRdJpbR45Vlsvlsn9OSwzu7+99rynR0fq2+Xxu9Xo9El1q0wBVMCHkpygUXnxobFT+IUNxhhSiRH5D3gCf1egsPF+w90N9pE4Sc1aCDuklM/OAAIOv0CLGCQSEW5pAqpLJpG1vb3vEP5lMPOdsZt6PF7Kk3pgTDnLOnHsto8OJo0lBKvVwdR0MWzPz66hGo5E3akduut2uLS8ve3Cja8g6qnMfGlMz+8p5Qo4wgKF+aLVa9ubNG7u/v/ezApJAGkpb7nHrCsgBcsM7IC8EOHyvnhnlabDnT6GVZv8/rujioSSFOZBmFvGYEomEVSoV29raslqt5gcLJcUhQGngYeDBqNAiBIorawccBJWhm0IDhFgs5ixSrR/UzULIUIxKhdeDzeYvLS151MXc+F3myH19zEkPnv69Hma8JN4dJczvwdKlnpUm4xjgEG5UwdC8HGxH5sPhgkwE5KbF9PodSoJQY63OgEY7EJVubm4cauXyb95ZoTLge24RIQdB70agNIgBzM/MfC8wtqFXjpzyg1yyj+p4PBZZ6roSOWu5jOYweQeiN3JlvLeWRABHhznTcD+BXyGzMEfN+yg5gfdnnTAwCumSEgGFoPYWlmmxWHRHEkWJDlC2o54/XWc1hCo/GomojOlZRFHy/si5Mip1v3RoikB10GNNFB5zTtTRwGkEfcA4qQPFviMTXC4B/IqTWC6XbW1tzWWGOeGoTKdTZyyHOdXw/WDfU5zPGsBZUJnQyxL40Zw/devIPLKqsq/5P41kOec8T9fzsahY00PUwwMRw4KlAxE1wjQtub+/93IzRSj4e2Wgcy44Y+qk6TuEkfFT40ljiVcKZHd9fR3pxKC5rNXVVdvZ2bFarWbFYtFzgywaUCCRC0MnrIKv7EtyLlr/SFQQCrvWwHErBSQV9WY5iAjOZDLxa5TwNIi6MDR3d3d2cXER6USknhZRDXAgSlE9KfW2dQ1UuELlw9r3ej27vb31xhD0dsSzVWKLEpbMFhcc1+t163Q6EaKTGkzgcd5doQrmB/FJI3RyWaxXLBZzI3F/f2+5XM7zYnrYEF489nw+73AXkQNKmhwrEBWRrUIpQC58t66xHmqMFjkjzU+qV8qfGQp78hkgXqI39gYnzSxa7A5iQIqBvw8NvDpN7BfvqMZInUrWiTNFroc8GesCkoCiAg6Dlq8R/urqqissha8ey/ehnFRedH94F0WaNF+lRCoMJoYLmVOZRP7YB40YVUeQ39LyCZ4f5tYwlqurq84eB+XSmmQCAvJo2pEH2BtIkIu4KcdizuR+gdAppYKY+NgaLy09kA/paBPWF0LYAYonilUyHM4SLeLMzNmkMEnRgRo5qqFRnYqzGuqyxwZ7wZVcQMfMkWY4uVzOcrmc34k7HA69tE3PNHpWazj1XdVR48+6rvrnpwzmk8aSKHA6XbDuQmFcXn5oDLy9ve35DDwlkrfKtkLwgZ6IFskloKDi8Xgk74LnAXMO2CIM97VJ9/X1tZ2cnPhdfBw+ciKUvXBfp3ZtQalxmSgQCcKqxewcsE8++cRevnxp6XTa7u/v7erqKuJcsBkIiwqPesJE7Azup+t2u/b27Vs7Pz93okwqlYo0HiZ3icFaWVnxeyvxyCgh0ZaAHOjd3V2rVqsRgVTlaLYgcZiZG0ccEISVvQUuyuVyViwWff+USQ0hZ3l52ba2tjw5f3Z2ZkdHR9br9fz9rq6urN1uR0ghmlfWZhS6xmaL65jUC+b9dM8x1KqgtTGBHlAgbDNzeeHKKvZenZLb21tXcnjXiUTCqtWqw84Mom6cQ+QamVHjQCcTSDnT6dTZuRCPNAJhD1TOEomElctl29rasu3tbY+QgRfxzDnfWkfM4M5A8vba0AKlytqi1Ihkac7AedIaWHU2kUsGe0J+UWVRo0lyyloXqmdPoVjQknw+7yVyb968sdPTU4fYq9Vq5KIGLVuhbK5UKvlcIKNAWAKmR58Oh0P75S9/aW/evPGSqMd4GepsNRoNDyqWlpY8/UQ+slAo+Dlst9t2fHzsqARnNZ1Oe/53e3vbDg4OPPe+tLTkdd2sj6JqnGPWWB2YkJehxBotFeHareXlZSsUCn7/K2kLUga9Xs/1PeeLgI5nKVrD+YRBy+8oopRMJj2KD9uQhuNJY0mSXoVPoyKUMUXDnU7H+v2+K3tuKuFQcIjNovBHCHHhVWrkh/ASCYSEEh3q5ZycnNju7q6VSiUzM685w2De3NzY6emp3xivQk9tGgfixYsXEchIyRGrq6t+TxoePYotzEkxPwZQAv9G5MJaFAoFh7BJjk8mE09uQyLh3Uluo9DIpeIRUsIxnT5cxMwdlGtra1YsFj1a1dwge83QMhQlaZAL5VBdXV1FWq7RLEIPHKzHbrdr7969c8fs5ubGzs7OIsQWFBeGn/VABslNYYh1jTWCY501clPIUdEOVdCgEIqazOfzCPGIKA75wEljHldXV+6sUT7AGVG50BSAzpUonCgFeB2oPplMuqECxgL+08hHo+kwwisUCr6+GF2cWs4H51PPMoqOO01hViLjNMeHGGVmToi5urpysguyxDpqI3oleCnvYTabuXNA3aFCwNyHiKODHIelYArVrqys+IUQOHrU9ZktbhHJZrNWLBb9fSeTiTcrIIoaj8f24YcfWqlUciecd0wmk3Z8fGxffPGFnZ+ff8U50IGRI4ghBw65bTwe2/n5uXW7XUdz2u22NZtNOz4+tmw2a+Vy2S8z39zctEql4rB7uVz2BjKDwcCNikKymoLSc8B80V0MLc9AFtB5uVzOxuOxy2s+n7etrS2P+kmrID8XFxd2enrqpEWuwLu+vrbxeOzRJ3OACMXZ5tnIBxyM0MCH42sjS+jNar3VgyCySCaT3gAb2jzJeKLFVCoVCd8RUiap3n6n03GlGIs93NTOYQ+tv3qZeF0cJAqGS6WSXwwKXDKbzZwBS2Nis0XPUIwDCoPmCtp2Lp/P2/r6une519yXEqIUBlClZPbVRukKBeM44EUhqCTygYA4cLHYQxNhFBu5P4wl+UMMC4aY/UWJ6rribYMcUB5BxKR5Z4yQtp0DfhqNRg4xYQQwikS/5+fn/jmgb9ZGEQ7Nz5EnXl5ednYfURsDOWJdVWGqEVSvEzkjDaFwokaapAmY92QycSiZyAwYUyMFheIeI3SEv4sD81i+18z80gIlq0HoymQyEUd1Pp+7Q8a+8b18jjOqiIFC6Jr31fOnUTT5YNaZ/SO/hKO5urpqg8HA82UMnFZknPXm3xS2VaY8e65QYa/Xi+TuzBYNQYje+Zw6ROTbuZyZy8LH47EHBDjPhULB341z0u/37fz83J3Uu7s7q1arfqaIeg4PDyMQLPPCGQjlgvkPh0MPJNgv5kadK7XTsOGr1apXI3CpOakjrVNkPXD4mYuiM+po6FzV0Ct8G8oL64UNCFNV2A0cwcvLS0/v6IXqul+5XM5zylrNgIyrI6Ls2MecE8bXXv7MiwLhqKCxOSxEt9u1169fW7vdtvX1dfut3/otrz+Dcs3iQ5tXS05YfnNzY8fHx/bmzRvvMwu8yedZ6DD/x0KwgZeXl3Z2dma5XM42NzedBAJERqQai8Ws2Wy6p4cQz+dzzxGur6877ECetlQqWaFQ+EqdmlkURmOTWKvQWLJ5KO4wX4XBRCC0Xgx4h3lmMhn/PtqFYYT7/b4VCgUXUpiuqhxCxhuGhVyzOj2j0SiCAgBpkWOYTCYO5w+HQzs6OvLcaCwW8/ZVWmiuQl8ul81s4QH2ej03GKzTxsaG37pAZxm+j6EHlJwRAwWsRlJJN4+RAjDkOCHA2kSKsKJXVlY8MiZfS8TG+uIEAK3pvNRQUgur+Ug+n0gkbHt725UCiAvOEh2xVDEoEYzvIA+eSqW+kirAgVE5CZUfsgGDPZ/PR0o6yM8he9Pp1B1h0B6cITrcaN9iJaIQPeAocfEx0Q/7RYcgZAfdxTuoojdb9CbV9oKpVMr29/dtdXXVTk9P7fz83E5OTpzDAPKyv7/vpSSJRMLa7baNx2Pr9Xp2cnLiSpwrsEilTKdT+5//+R/vra37r3Njv1V3YCzhg6yurjqzmavl6M5Tq9WsUCh4xyatX9WzoboWvRWynEGTlGuBLIVcEpUVlXENCkBfeJ46RfwdBE51VCkPQW5YV9XxYZ6ctVNYWdf1sfG1lz8TIXCQiZxCthQU65ubG2u1WtZqtezTTz/1u8VKpZLt7+97tDYYDFxIlLwxHA4dcyf5rPAR432JZKBUFfyzszNbWlqyvb09K5VKkXB7eXnZryaCCcZhJ3LqdrsuQIVCwUqlkpXLZb8DUNdENwRPRiEVDnE4bzX8GEG+4+rqyuFjIkJgXpQBMA9CxtwVJllaWrJyuRzpQwq7Ur1vFCrCCAy3v79vZosDxJ4p3EG5Tb/ft5ubG1ccvV7PTk9PnSLOd+bzeatWq+4chNR+M/O8xnA4tHq97kSUwWBga2tr3iUllUpZv9/39Q8NpEb4ZuaHU/OKKD4gMoyP5jdns1mEPXp5eenIBJG2zh8vX5ECCG9Q6mFchtAxaxuLPXQJQingbHKeaAvGs0FlZrOZ9Xo9azQaXruMXKEoyQ3m83lnFcKOJgI1WzizOJeaJmGo0trY2LBSqeSRJbk1vhfkBdh4d3fXer2ey/B8PndoDgWoBtdswTZXAiHRm6IRrVbLoxeFWYlkOCOq0LUhPYobx/ib3/ymyyTvDBSPfCYSCfvss8/s6urKVlZW7P/9v/9nH374oZ8JswfUiuYbP/7xj51/8HVRjkLyo9HI++kiCyBdlCfh1AN5sm75fD5SAYAzpOgcJTzaMUv1v0ZlrCFrr+eP0hv2j3QMyIEaNp4HAY3gBX2xu7vrrUUhYGHQLy4ubGdnx3K5nBUKBYdfcWzRdSFPBqfqfeNJY8mX0YkDxalUeg7ydDq1Uqlk3/nOd2x7e9tarZa3YMtkMnZ/f28XFxd+8KipY8OBslig5eVle/HihcXjD0W/CuWEsETIZlNPAlJJt9u1H//4x05ioKUaCWyMD+2qUAAYqXQ6bcVi0S8f1nq+0MtStlXodanyY2BwVPhR1nwOZwTCQwgVYQjw1NgThRLj8bjnxRAQaubo3Qp7mKh4NnsoUN/c3LStrS0zM/unf/on297edsVB+y1khoMXj8ctm83aaDSyer1u7969s9FoZB999JFtb297o24+d319bWdnZ254OLjUVyJPEMWKxaLN53PPh5stiAQhPIjHrBE++0NUiTyrU8ihV6+Zw0lDByIgrW+kTAaFxXMgMrG27LMefIbmTIlclpeXnTxGhx6UHFEreXfaUOL8YBhBFcgH0teWz5lZ5GJgoG09c+o4qAPCsyDSmZl/FsOHU9Tr9TzqnM/nzsYEWtO0C3Wt6A8lX+HQsM+K5KB3fv7zn9vt7a2fHdZZIT8+85j8KOzN75PO4BwxL3XUaTWZyWTs5cuXEdIk+3V+fm4///nP7d27d97wG1lljqrjuBkGecKBwYmpVCr++8nkQ3cbIkmFZElHwK5m3+knTaCEvlBjiLFUPcY5ChvAmD3oe3LKEPFAYZBDhczNFkRKXXt14NHZapjJT5K31qoA5FfTL6qvrq6u7Pz83N43njSWymZFuJTNqknc2Wzmie69vT3rdruWSqWsXC77bQGak8TzU8OHJwy0pkWpIXNPN0iHYtIoBebbbDbt6OjINxhvBiVByQueMUQIIBOMg94qoXCBMrLCEf6dzluL/Pk3hBeBJI/J5uPRKbwRj8c9J4ngEiGjaMitaE4VD5lC9FCR024Ob/jf/u3f7NNPP7UXL15EmqtzkBR2hBkMmSWbzdr+/r6THDDoeOadTscp5eQAYUsCjQK7YdjokoThxPCEcBX/rxCOevDMBVklykUWMEysFfMkL8ecUqmUl8rQpBzPVi+9Vfg3hIqQGeRC3zuRSES6Q3Fu+L54PO6scPYGWI19IheokCDrhCLVrj7IED9h/pLB35G31NTC/f29d6YyM5dpFKHyGvQs69oR6Yb1y5y90ICDdB0cHHiTFW0Hp7mr0BngnXkHNazoMowGuUJNxUynU9vd3fXay52dnYiBQpbOzs7s888/96YT6iCFczR7MDwKM2PU7u/vLZ1ORyJ93R+FHVlbZQcrFIvRIz/IOyrHBAdBc5iaJ1bHr9lsmtniSjoNHNh/DQBw+pH30N4oSsf68PvsA+9PxKmchPD/cYAbjYa9bzxpLEnQ4yGNRiPvCqEwKEKUy+WcSAONmTZ2eC8cHMW61dgkkw/d/LnthMPSbrd9wRTqDA+rRnp44ySuO52OvXnzxp8LrRtCBNEABxgBUahKWZNhYlvnYLaIcpiLKhidt8JavjGJRQ9YGlNzyHg+UQr/xoG5Ja3wpAAAIABJREFUvr6OsP1Q0Nq1BwWjnXqm06l7nwgR8PmzZ8/88z/60Y/cGG1ubjrsrZ45gktLK+DN/f19++CDDzxfCeuu1WpZo9Gwer1usVgskrwnmQ9ko4oJSBTPmmgPiCeUDT2QGA019CG5BwPL+9GpCWYldZ2w7ah5LRaLViwWvRRjOp169K6evNmCJBfm39mjWGxBwIEqH4vFIvscRkiUdQCjUWITnjf2CPIQCoT9W1pacsOnjQI08tKoB+cRREDTD3Th0rPDdzB/JWWEpVw4MrpnigCoTuBnOBxao9Gwg4MDu729td3dXa/fYw1Cw6RRmzpTqqAVytUmHeSpccCfP3/uHAhKutBjs9nMjo+P7bPPPrNXr165TD5mLHU0m83IXmEkSXvc3Nz4Xk8mE4eBSZOowSTiU1gdJIHbbHq9npmZ191C2AtRMubE2uv5Oz4+9jUiWsRpwYEgykP+4Glgb3TPVb8ig1rrqqkl9CBnG71CDhYd02637ezszN43njSW3W7XczPn5+eOG6+urrpXqIaAQ7mxsWGFQsGT3LPZzBqNhof3WruGsr68vHQjmkwmrVqt+stMp1Pb2tqywWDgnoR6BSHcxmATEeLRaGQnJyeew/jOd77jORrgKQwlTRFQgOqJq5Fn43gWG65EIARRIQodEGJQ0Gtra35g+X+EH3jTzJz1hsJgLaC1M0czixh8pVDPZjOPmJnzYDCw+fzhsu8f/vCH9s1vftNvTTB7uCLoF7/4hd3e3lomk7FPP/000isVBY2jdH5+bjc3N7aysmLPnj3zEoBY7IE40ev1PIfAwUCgQziaPBvOFg2pYcByBRWKVWVBITSieeBBJdeQzyO6ZL+IkM/Pzx32xVBSkwhzmpaNHFgcToy4ylAymfxKTShzViNIQw2gLO4BBY3RXDNRsdki74QHz99pKkWfcXt7a2traxFSE0oNZwiIlTVgsJZm5kqX/q04R7w3BoNoABISDilOHXIF0UfTP6Fh0bybssonk4k1m02PsOknqt/12JoD+avzB2ynRlkjFG0osLKy4jfAUBbD7x4dHdk//uM/2s9+9jM7OTn5SiCgcwphWPQMxonmCehgNSjK7uff2Gfdd80lXl1d2eXlpR0dHfmlFrTQ293dtc3NTXcM+BxoIU49hsrM7Mc//nEkFwoczdDOXLCKlRODrHB2tdLAzBwBoW7bbOHEYSCRC9JccAQ6nY6dnJzYycnJ/95Y4pXQHBgoEiXIhgC3weyazWaey+EgAvUpxqy5KHISCAcvC6xFTlPhQy1uZwCd4MG1220vcCXUxtscDAb2m7/5m16HpmxFDqySPPB0QjiGoX+nHiReMdERkBODJL/mOCDhzOcPbC+tHdPPYxAeg36JnJR+rx6hCiI5O8o6VlZWbGtry/b29vzaHgRzaenhLsV3797Zv/zLv7hcYMgULpzP584YxOvDGOP4YPRgTvK7HBiUEAaUfYEdDNEGNi21c4VCwdcCskbIVtYIJ8xRsdYoXpQIJBzmoDKCc4jCgLRGicTW1pY9e/bMn4tsNBoNj1hVnjBWyA2GA6PR6XQcEibqViXEu4QMQqB49diRXVjTQNxaQK45XnXoGJoPR3YxEugT1ptzzVmhh62+O89UQpJCo2pg6FeLvJAPw8GnlhdHBn0RyoKeZd6Pv2e/zaJRbBiN8meVVfYO/fijH/3I/vM//9NOTk78/DE4F0+lnjQFNp0+MLKPj489naRGnPfmEgVSCeTnkUOiefa3Vqt5U5SlpSXveYvTUyqVXEbJL2O01AE8Pz+3er0eqTJgv0FK0K2KduCkMT/mhR1QPciaEYhxnjUvq2gAtcmU9QwGgyfbDD5pLMlnkZuZzWa+6HjSCBovzospIQBvAEWGgGp+iahCD6DCoQgqCgPvDthR56w/2nUIGE2Za0pU0XZVuvAIOf9VgVXYV2Ew3pFDo42rVdDNLJIPmEwmfo0SAkV/WyUyaNSs66XRAvuiSh0h1HwPa4NAak4L46CCqbDau3fv7OTkxJ/JnIgQ6Fgzm838HXkGXj+RTyqVsmq16s0rOBTUTNJsnWcQYaZSKc9r4qCl02nLZrNPyvZje6Y5QFVGoCCav9N8leatKIzHE+caLPYQqB/vlvQGUctjc1S4iTNBJIZ8hc3d9c9EzRhMjJnmNYFbcUBxEPjBQIcyrwod42Zm7hCzXur4KORMFIUR4/MofHX6lFQSciZYSxSlwn3k8mhEokiQKlo1lsjBY/pI10H1gMKzk8nEO/Uwl+n04W7f4+Nj+/nPf271et25Amocw7RMmOZ5DKKdTCZ+NSEyRiqNuUOMAYmBtcy7K9SfTqdtf3/fdnd33YlJJpPOtL6+vo7U3/P+RJkaxJD/1Mvf1cCij1TH6rvj0PPuimaEa6bIo/4wmHu/37dutxvhHzyG8DC+1ljSrJdQttlsWiKRcK8fQeewI0QKh5BPQ6FxcJXxSDSiSVv1LPDSUGxsBkqCAfTC919eXkagUPU67u/v7eDgwNli5EnDKO2xHIJC0OFB0UM2n8/dQ8eLC58xn889r4r3y7qRV1QoiKH4PeulRhBDgqHT6FgFk8gJjxBvbzwe23/9139ZLBbzdljMl+i90WjY27dv3Xki70QUjPxo9I7yQ1YwzOvr67a5uekeLwaZDj84Y0CgCqNCzLq/v/cbS7QHcUhG4EDx3jwvFou5B6v5MC2bMIvePYlTgoKp1WpWqVT8wuJSqWRbW1u+RtTrcibI506nU88PcahVvlh39g5mKTCuwu946LwDTiPGNYxaeGfWBCh7MBhYv993cgyG5X1DI8tYLBa5v5b9Zi2BylGCcAL4rML6vHNooFHwij7gcKAvUqmU1xODLKnDrg6sGsyQOMXvEC2G+U6zRc6Wwa1NMMzv7++tXq/bT3/6U/uP//gPJ/WEqJruYzh4f9Vp6J+rqytrtVoui/f399ZqtSJEQNYSNq92miLfTokcnATOzfPnz63b7XoXI+QCOWMPQmZ3Op22u7s76/V61ul0IrlFlRkl+7AuyA62QaN7bE04kAdSF9pYg/6z3W7X2u22R8EEb+8bscfC/F+PX49fj1+PX49fj1+PxXgysvyrv/qrOeQMvCkiSH4ooyDHph4h0AjJcJLlYes8YAu8f6w/jEu8Na0n+o3f+A17+fKlF2qvrq7GzMx+93d/d57NZi2TyVilUvH6Iorg8Y7JswL/aUkM4zHoAw9KYS7+He+OOWvOZzgc2snJib1588aOjo7s+PjY/vVf/zVmZvbnf/7nc3KP5Id0FItFv19udXXVyxJ4F50TkBueJHWxrBtsuG63a2dnZ3ZxcWG5XM4LfbPZrI3HYzs6OrKf/OQn9vd///e+f/F43H70ox/F/vIv/3LOeq2urvpckAOtNQN1CCn+SjNXD11hLWWGkr8i7xuy7jTnqHnjP/qjP4qZmb19+3auv6eypBEjQyF40AhykORIIdeUy2XPiQNZw8ilJk5zZHjdmkr4+OOP7aOPPrJvfetb9gd/8AcxM7M/+7M/m9M+LR6Pe04XREcjL4Xx+U7tQENko/sC9EQ/VxCk2eyhLpD2ge1220lNrJVCgWZm//3f/x0zM/u7v/u7Od+/tLS4sxMyCHNVti2RBHuskRz7aWb+OVAromFINYPBIIJWpVIpJ2W1221vMg/E/Ld/+7exv/mbv5l/8sknVqlUImkgInPVVUSj5HUvLy8jkS9RDhEakCVM7+PjY7+5aX193QqFgqeK6DDE5Q7k+EBmftVYImZm9hd/8RdzdC+XYXMWqQXXnJ+uHVEl5yDcR8g/vJPC3so1AaFYWlryKwRPT0/t4uLCb7Rpt9v213/91zEzsz/90z+dK2xOCkXTGVrKpKQyjeJZY72ykXfQc81nmB/cFyLu6XTqKaJUKuVpEjOzP/mTP3kUOnnSWJ6cnDisifAruYXJ0lKJfAwt11CGenj4jpAVilFh4VAISjrQPOLJyYltbW15rohxd3dnq6ur/tkwFxHCKvp3SqFGwPh3zUHyX8XqlXzBM2GiATNq6z/F3JWUARyi8Js+hz+jFHmeEhVUoML77mgQQNN7ygEgY8C8LRQK9uzZMz8cys4L5eCxNWXPde116GcVSgJWC79Tc0FK8FCGHd8bOjLMJZwHUDyQo+anFeJCwevnUBawTmGBQhAI802hI6C5FE05hGsU5sQ0p66KAjkL14P1DfOfs9ks0kcXJ4TPQohQRyl0cFWGdW207CnM9XO2QyiNd2JPGUBnwPXhmWNNdJ94R+auOS6VO7OHG33oLcxa8z1ao60kK31/Jd3FYjGHqWFaYqRbrZadnp5ap9Ox+XzuzHCYzeTZaf6C4xDup9mibSL6l7QJBlqHnlPOkOb0dO3NFiUWrCnfx/NgyeKUQKAi57y2tuZOjDqgGGieqURK1lsdXxxM0nMEXwQk6iyHkD3BFs9RMhnOlVZSAI+rjnlsPGksuYSTCSgjD2+bXBxGqlQqRVpc6aaxceQ/EQwzs06nY71ez2KxmBsWGGQcWg5XLBazTqdjjUbDlpeXvX+o2aLXo+buQs9IN04Vsw6UkuYqnlpIVfj6HQgcwoxA6b2bqiS1htPMIsQYVQKQJPR5KhxEQnoHKdHEu3fvHJvP5XLec5PP4uVVKhVLp9OeQ1QyyGN5HWQEBRmyMlknlQf9O30HjTzVgKJY+AxKiWehaN+X+8GRuLy8tMFg4Ief92avNH8FCoER5nf0gMHM1UOthokfGM9EluogEGky9Ht0DVh3jAHvrUoEJY9yIkJQ+QibzhMpwwnAIBNRqjJ5bG+ZV+g06n6p06wGDL2iPAA1oBoBofxDQo+iO4pohTkt1gX5R8+owVMyGmvGfumeaB5V14B1w1C2Wi3Pk+FELy0tWalU8sg7jOZ0zVWeFUHQfLCZeTcuok1ImNpgQ/dE95F8sjJ6+Sw6njaWGGp0PmdeS9R03ZE9LXfRfCTOJrIZBk2q/3hn5CCUE/b+MXnl93k/Iu3w9x8bTxpLFpeHhBAYd6ThmRcKBdvZ2bFnz56ZmbnHgyAgRNPp1Pr9vjUaDf+u4+Nju7i4MDPzJsuQJGiYjDeCUHz55Zc2nU69ZIVFoRk0hBIWEk8HRby0tKDgs0jhYVCv7rGDr0QDPq/sKxQHgstVWxRFI/xsYjKZ9MOjZSGsE8LMfXdQsYE1iIK1ZhLn4O7uzt69exfpqVqpVBzKgUxEtFMul21zc9Pb4OGshEw0HA8iXSJg9RhDUpNGG+qYMHcEnrngAEGaAWaCFIXRg/wTRrmsy/39wxVLBwcHdnFx4YcfQ4zsKTMTr1nXmfoy5gk5jdZ1KHHgUFXuwJ0oCi4Z1i5KOmciO/bQzFwRqSEN0Ro9swzmy/MgyemdrrqfCus+dg5CuBxlrIrwMcdFI3c9O+xzGB2rUwA5qtfrudOhXWxg8AO/6dlnvsiH3kAEQqBwL89gv8wWrfbQD8xPle5o9HDX5MnJiTWbTWs2m35VHnLe6XRsNps5wQbnFOdVDb6euUQi4Sxj9Nhs9lCb3O12XaZA+HDkaPupF0PAmuVstNtt33MqGGgqQg2mphRisZj1ej3XtwQ6yInqCdVNGEQ9XzwXRwKnhX3BGYfBrN+rBpJ54oDx/mEECjKgNbNhFK/jSWOphx6vOTw8yeTDdSj0B+XS0EQiYbVazYUymUx+pQkBHfYnk4nXQmIY6OhPToy7HDUv1u12LZFIWC6Xs5cvX5rZQ6PjWq1mpVLJowEMNMqNRcR4IaCwwNSzRfErXVzXRzeMaIXnoKRQuqPRyHMV3OjCv6vyxMDQVYeLTjU6p8HDYDDwg0E0HkJVnU7HD2y32/WcxmQysc8++8yWl5ctl8v5jQQog6WlJfv444/tyy+/9E4fZuaQhnrWKG/NL2F8iNoQTpQTXiKHij2C3h/2Np3P51YsFm1/f9/LfVQZq1EOI4rb21trtVp2cXFhb968sePj44jHrA3AzczLDVjPYrFopVLJI3G9VggWIrnpVqvllHRlk3N2QAXwqJPJpF9N1u/37Y//+I/NbNF9KYyezSxy+BOJRWcW/p89DKE3zl+9XreTk5OvdDtSxY8BCeFi8mOPDe3pigOrQyF8TR3gDJstUBp1ABRR4YaXs7Mzd+Rg1mqbP4wAP7RPRD7NFvCgRq/IYaPRsEajEXGkU6mUN50g3aMRLRHX7e2tnZ6e2sHBgUeUXHcIUsHZwNEzM7/yT5uXa8kZg9wujgE5dNid6BKcAeZ/d3fntZNctE5Ux953u11nih4cHDg73+xBvxaLRQ8yer2eXxKAHgG+VuY+BhE5Zf4qK8goOhq9y/eFZUX6fd1u15tegHIyB75XES+N4NF1/P37xpPGstVq+YsquQdoUaE4Ih0ernARm41QYu3xnqbTqWWz2Qhso+F1LBbzQ82BoPB2MBjY4eGhG0sEGVKJtlBCyBQ711xHLpfzdlh4XrwPsJbChxRv9/t9h4Wp+UR4mId60CSUdagS4UZ3rRtkczFAQIF0JiKKQZhR4hhxDNB8PvdG5ZeXl5FbYMi30dUomUzazs6OFQoF6/V6voZKhGBfFeKbzx/KRzBe5APJDeAt8qM5mH6/b6enp05PJ/eAZ1upVFwezMyRDaAt+tWORqNIgT+kHFqDPX/+3OdM1x/uU8S5YP4oBWrE1tfXbXt728zMiT00uWi329ZoNLz7lfa0NYvmzHEYhsNhRO4ZnIHpdOryr3kbokJgV2R2dXU10ghgPp9bNpuNRK/n5+fe0ECNCO8UQnVKxmCe70tNhKkNFDHzVUjMbFHozznRH94Tx4m8GXs1nU7dgIHYkDPkQml1hlDESibEYcPJgvB0eHgYqfmdzR6ahdzc3LiO0Rwmc6V5BVd5cZ7RRSA4fB9nW50FIsIwcmfO+m+dTsfJZZTFaCkQZwx0i9pfHG51MBOJhEeTer0XN5ns7u5aPp/3M9xqtazT6dhgMLBOp2PdbtdqtZpls1m/w9XMHNZH1+n5V92nkSK6liYlOBhKXsIpIHVAygD9g0OMTp5Op27HQBDUUXtqPGksgZcIrUlGAyuq58mDNbegeU4ODYdRYbpYLBZp9MyL6QIi9HiXeCMhnq/RFYdGG3DzvQpr4Xm2Wi27vr62arVq5XLZarVaxPAzeN7t7a01m01rNBqufHiXTCbjMLTmfHlnFSQUD2s0HA7duAG3sDaJRCICMxM9axekbDbrCgpFSz0fB5pLdjOZjDsV8XjcyuWyC6SZ+Q3q5+fnbqB0X/g+hE7zLSgmmJgoVzUeOF+MwWDgkDxt8IjKZ7OZw0YQyz777DNv4kAnIX5fIU2UVTwe94gUGcCzJx+KstPaS2SJ/ptEmEtLSz4X8lPdbjfCHNUmDOpMsoYcaOoMH5OL+fzh3kJl0Gprv6WlJZ9XNpu1u7u7SNuvVCpl3W7XmYrAzzhZGr0wTyXn6M3zauzDEUZzKCwcKd6FqGk2m3l0A4JCtE1kyrp3u103uLCOuWCeS4y5CQjjyvuFeTjOMHNCNyAP3W7Xms2mI1+c37W1Nbu6uvJ0AO+jcjYYDKzX61mz2Yzk+DDaOLI4iDi99N/V2szHHBIQCeB0HCkMDHKkuVyFGGF143wppLm0tOT/htFZXl72Fqaw8jk76+vrfq3YcDi0wWDgRk3rnDVXqrKhjoLySLR/sqbgNLerJL75fNFsn6YTnGfSI3qPMT+6fyAn7xtPGkugLrxVOj+gbIEVMACEvkwQCElbrGm+AAPLpDEM6u2tra1ZNpu12WxmFxcXDm/h3SG8jPX1dfd6wKI5gPl83jv1sCiDwcCazaa1Wi0v7Wi1Wra5uWmJRMIymYxDA0A8eJJEQa1Wy4tzt7a2rFar2c7OjpXL5QiOfnh46IYiFCQcjfv7hzslVfgV3oHZBQtNKc/JZDLSxYUDStQLTZpOJsPh0JsxxGKL+xI5jOl02iqVij179szvQzRbJPw1V0SUDmyC957NZu2TTz7xG+ShmeM4cO8eTk+v17PPP//cjfN0OnUWcblctu9+97v28uVLm81m9sUXX1i327WLiwvPY8Tjcb/lQ4v0Mbpra2uWz+e9fEOVNf1QiZQVlSACZa/G43Ekt4TD1Gg0/N01r6pNINhTjAZnIJPJ2He+853oAf2VMsGZxFBATuKMwQPgSjCII6zv1dWVywCyFRpwIhIlsqCsUFycG5SNIi38exjZYLBub2/938mHAZ+SK2avub8SJcg9t3opwObmpueX+YwiWbT0Yw4hocXMPOpAP2mejhs3tLwIQ6DGnT1aW1tzGL7ZbHpEyTns9Xo2Go0iDcS5powSr7OzMysUCra5uWn5fN4uLi6+4pjQjKLX61m9XveceyqVss3NTfvGN77hDhlIl8K5yDJokXYuW1pactLXbPZQQjQaLXqxHh0duROGI4kjC4p1eXlpGxsbkXVWEhJ6Dv2/urrqOg/HRkvoSL9hG9BP8AfQJezn+fm5NZtN3++1tTWrVCr+Pbwvcjkejx2VUS5JOJ40lltbWy5wt7e3jr8rHER4v76+bldXV1YoFLyX6NXVlUNBeGHKwuPQxWIP7FZ6iOIhxGIxzyvqhct3d3dOzuCHwXzxkvL5fKSJsOacgDD39vbs9vbWvcnRaOSbgGLRzjfkkC4vL71bDMSicrns3WOUwLK09HDxcrfb/QrhAcwfr5yIj8/prRqsD4xOoGmFyblGDCyftTIzj5aJnoBZ8/m8lctl29/f95ozIptMJuO5CLOHkiIiVe2ixKEh75tKpWxra8s+/fRTv1j47OzMms2md1ZSRmO327Xj42PrdrsO24A8AKv+8pe/9IidC8RLpZIbv1/84hcPgv2rQ8gAMsPAa/5jOp06xZ9cDUqG9dTcNlAsuR56Dc/nc3v27JnlcjmHkJWkwQ9eLhELd4VmMpmIgdeuTRgFaPvj8djfm9shcMRQANRlEgmghFBKQNuz2czLJ8iBaS5ZDQ6RiLKO1RtXiJbzTrSzsbHh7FPQIs3xT6fTCEOeNAgK/vr62hqNhjsDdLvScX19HelLinxipMI8qpLR4vG4k8SI2E5PTyOsVKJebWsIpwLHutFoOJSLY8+carWayw+GEmRH4cd0Om3b29uuEzUguLu7s5OTE79kmkuRS6WSffjhh/bBBx+4fF1cXNjR0ZEjHezZcDi0ZDJpl5eXflY5b6AynU7H6z85X/F43DY3N712HUIOqReccXgpDHWcQLWYoxJ+cLT7/b4/F3Lo9vZ2pEY+k8k46xbkAMeLMh39DtaJrkQEG9/+9rdtf38/gvY9Np40lgg1cOPR0VEE0iB5qpRnoiCEUllswA94bwpPao0lB3EyWTTJJhopFovuPWM8VMGYmYf0REdEHKPRyO9VY2huhd6kKCMiCo2amBeRyvr6uv8/RgB8XZsGwFRFSYe5KYWolPmF0BFRqCLDmNIQADgcrxSIhMjNbHHNTq1Wc8gWQ6YoAhETEJ4m6y8uLqzVanmBt65jIpFwB4N3PT8/t7W1NSfZUKqhHmO/33fChq4NEQ7En3q9bq9evXKms+abiEI0ImLgBLGfPJO5Ykhvbm6s1+vZZLK4bYJ1o4aYfLMqAHX+tFYtmUxGmJqKdGiuhv6xCukB8yaTSb/oOpPJOPwK4jIcDu3g4MBlw2xBDppMHuqfK5WK51H7/b7PRSn/KrdhXlXln/Xj7OucNQ+Ek4OTATszHo87hKjRLE6kpnGIFDljXOnGtW3FYtHPNqRA7hgtFouR99BbN5R3wDsB4WHoms2mOyGsayaTcYMJlyOReOjTu7W15UhXs9m009NTN97KJub7zs7OLJvN+ppx2xDRaa1Ws2Kx6CQxRrfbdbgfhIN34VyCriHPWq6EPM/n80gzdYIAbVCxurrqAU6r1bJ4PG61Ws0NTb1et16vF3ECccbfvn3rc+YOThCHVCoVieqRdbMosRQnh7OluUt+QF5IFQ6HQ9ve3rZcLudBDU5dOp12Y4nTVavVPLjQPuPheNJYxmIxF9BGoxG5rZzkNNg9uS+FKdU7BXZCwdCDVJmUWlyrCXO8smaz6QvLgqHgdCisyWEm5EZRaI0Sh7xWq1mtVnPYmO9ho4hElcXJwR8MBnZycuKbT2SDIcpkMhFloAomhFn4Tkg45BCATpR4gpCzhsA5GxsbViwWrdvtet0rZAiECkYfHXiInoA3zBZEJS12pqkB0R8HRI0LSq7X69nBwYFNp1Pb2Nhwb08ZhShV8tJ4hzhOmqyv1+vWaDRsfX3disWiIwchSYFIm4HhwAEaDAbW7XYdYtJr0MiHse9m5nkxzRMr8Qq5wwiwRxSaK9tWIU0Uby6Xs0KhEGHw4owCO1arVVtfX3d5gBQHnR+CGREm37+2tuZM72636/kxfofcH561Mt45t+rAaT1mPP54bXGYE1RHeDQa2fX1tTO0cVZYY8pwOGfqlMDc5QYjmnJjfE5OTjw65XOcbxxtZYyrMcUZvby89B6melUcBoEzC3+Dc16pVGx1ddXL4pB7HG9tSIJu4byxRpybfr9vu7u7VigU3HAzut2u9Xo9v5SZPUF2e72ey0w+n3cSEflIxmAwsIuLC2s2m96vmHQL8wWJY81jsZjl83l3xiG4cbbQJaBaDLghcFqU5KVOGwGClusoIZQAQUlgytDOZDJ+byryg6OBviRCBZ7N5XKWSCScnPW+8bUEn3a7bc1m087OzuzFixcRvJoDns1mLZvNRjaUTWdB1KPHCKoyyOVyjkUnEgk7ODiwePzhJm86zWjT2+3tbb9MVV9Q6cfc2IECa7Va9sUXX3juQBsusIjf/e53LZ/Pu6LVHANKWzcZZuHh4aEdHR25EcYgU3f64YcfesmGKhPmzPfClKN5OBsP26vX67kTgMDd3Ny4h5rL5ezZs2e2ublpuVzORqORM9a01CVkjDFvrU1NJBKRSBOl+OrVq0guoFar+efMzKnsKJ9//udiQ0/oAAAgAElEQVR/dkUxnz9cEv6tb33Lc8iwW6HMl8tlP3zQwIGJ0um03d7e2vr6uj8biAgPHqLP5uamr3GhUHClAnsZ54+8LF49xlPzU3j+2WzW4SKgy0qlYplMxr30u7s7d1bIj+ptKbHYQ8NpDO3W1pY9f/7cc9wMFDNnhjkhXzhG0+nUyuWyHR4eOpV/PB7b2tqavXjxwvb39y2fz/uNLr9qD+lQ3Pr6upXL5Qg5ClgaB2tnZ8e99Ha77Q3A30fyQaY5j0ChkKA6nY6dnZ1Zq9WKRO2Q68i5my2QnBCOLJfLtr297bfjvHr1yqE55GUwGHheNJ1Oe2kU51tJJRjKRqPh/AhQFaIuZWnO53NPvezs7Fi1WrVcLucGuNlsOgwYi8Vse3vb9vb2HFb83ve+53KJTOLUIc/lctnu7u6sXq/7ex8eHrqzurKy4tfTzWYz6/V69urVK4vFYlar1ez3f//37Qc/+IETpKhw4DP//u//7o7sdDq1n/3sZ9Zut61QKNj+r24cGQ6H9sEHH/hnd3Z23EnL5/MRIiL125rzNltczoyTosgjETDydXp66rKp3AzQLrqScS4xmJD/cBwIKHByII1xV20qlfIafdJBOFePjSeNJYlu7sojSWr24ClgJLHit7e3Lqij0cgL2VGSeHDAmre3tx7ltNtth6fIs5Akvr+/t3w+bz/96U+t0WjYq1evPCoJE7Ih4SiTydh4PPb6zNXVVa/FIUcBnLKxseFzVpqzMqf4L5AiME06nbZnz565EGvUTBSztbUVKYfROZuZE2/4XqDpTCbjBhySFU4AgoCBePbsmefqZrOZ7e3t+XcDZzabTSeWrK6uOsutUCh4vpGIIpFIeL4YYzgYDKxUKlk2m7WtrS1X8peXlw6FkTDPZrNef2q2iFSRAQSfGi3KVshVMw/WjMLttbU1j9Yx9qx1Op12Y8Wo1Wr+XkRlHHKuTJrNZlapVOzjjz+OMPE4tFqwnslk/CwAg2MQ+B3q3oDrlT0LMpFKpez3fu/37Hd+53fMzDznaraoBWStcNiIskBJUIRXV1eWTqetWq3a3t6e59SIiEulkss+JCjYjnd3dx5pk2rhfJdKJVtZWXEiFREohl3hbsqbyL+PRg/tFbU28vz83AkdyWTSy7UYypZF2aEHKIlZX193Jw1l+od/+IfW6XQ8Iga+pIQH44bscEaIQBuNhtXrdavX6168r5Dw/f29o1mwjz/66CN7/vy5bW5uOnGkWCzaixcv7Pvf/75tbm46elGtVq1YLEY6RPGeMEkp5cGR4zwrLwMHwGxRb0tAgh6i3ILoNZPJWLVatU8//dRWV1ft7du39vnnn9vbt29tMnm4piudTnsqRDkMNzc3Vq1W7fLy0o0KEDqOFFHsBx98YNls1nUiA0QFB1EJctPp1N68eWMnJyceDNRqNUcZQfuoO1cYfDabRUib6A6cGtjt7AGomyJz2qDjf20syTvBqNMcIMqCRP10OnWlQSkDVGUMjBoJQm4WsVgs+kTv7++tXC57OI9hwLBqjg9FpgeNZ+FxEHLTkYbDg4FnA4HTgGmYG8rKbOHlq5HCS4LYg5EB/sCo0zcxhF2BtVH2g8EgQncmqc36woQkOqG2EhgIwQGyJvoi97S5uRmBAnFSiMQ5xHi4NFrH+HCtz3Q6dTo/eeiLiwtXxpAOiIaUzcZ343UThYAuYLzNFrWxk8kkkq8gMsY5IPoiUtFaVmBa9hF2LjlFmJmgHNq8gn0myiR3guFibzjM1NbyXsvLyxFWNflajCXeLSgKI5fLOaSo8gxzlvwd5BDynoVCwUlayD9OLLINS52UxsbGhkOcIBeaH6IjCoiD5i91ME/e/fLy0nP5REJEMZDeyLEnEgmHDZljyKxFpiGx8fcw0dfW1jxPCAy+vr7usK2WZTB/Sji0LhYSHXXO8XjcqtWq90teXl62Dz/80La2tvw88O5EMLDJQUSIfJgzcDlyhlOKk7G0tOSM2w8++MDXuN/vR6BHHDMz8zwc+mhvb8/fEciYFBXOA4ZxY2PDtre37eDgwHkqL168iCAr1DJqScls9lDSRbMDzY0yMOroRpAqWgIeHBx46Uy1WvXUEU60yoGSHMmd6ztxzyvGMJ1O22AwcL2NQ6sBnMLx7xtPGkusuHZrx8PGO9LLm6lfQ8jIaWjtGAdKcxrj8UOrPCUPwb4kAiLPxYFRzx8P32wR1uuzUBIYNg4D+UcUEMYTYdZ6IA4Wf29mfnu4LjTeE0QavhvjpXC0b4KQf5g70CfCTT4QJQ0LLJVKOdxXKBTc42LdgVJhOnIoUXbknPjBmycqvru786ikUqmYmflhhCBAdI6xIZ9YqVSsVCpF8gdE7njqWpNnZhFEADYnh4P9Aj7n8Gn3j9AAMFhXDIEa2HQ67a3R8DwhCiFfms/l75BDzT1pZInRAKlAOWKAySniCYft7vDQkXmteyR/jmIiiqTOkigWGVDIivdm/UAvUOb6GX4XZYP8Irch3KbPMDPPtSN75XLZ0um0w3fVatXzXIlEwkuh2E/+i/ywf+ggPeegOcwBoiER+nQ69TtFkX81XtqwhDOIIl5dXbXt7W3b3t52wwN0Trka36f5NHKw5MS1JR8yh1NMAKK5S9Z4Z2cnsqY0LaD2nbGxsWG1Ws2dEtIAGChys5wpvfgil8u54YcfwflnjqwdcCZnPpPJeDOC8/NzN2wM9g7jlkwmI0xu0i35fN6Zqaw9AQLRM3pDbQB6OORzoA8gcKFHsBWhcfw/GUvCcwgheONaWItBYiPDJunKpFPhJt9CzaR6wignWGwscDab9YXjxbREAMOFssegYLBRZCgoPUhEKYrBY8xRSkQcUMc176deEyQVsyispP/PoKNPsVh0WJt5mpl/js/kcjnb3t6OdKIAjmI9iVZQbqPRyGtKya3wLNaNA6oELW5DL5VKXgMIzMOBn8/nPndynOVy2RUfudLJZOIEJdYBdMLMnJ1LNEC5wcbGRiQq4/cpYwDKod5TmZ0M7SCCl2r2wArM5/NmtrgeTQ8UipK1IrrkAKI4YUES0aixxCghyxqh4fzQ1ILnmj0ovlar5esUEn4qlYoTM5AjzhaKSssi1JMmz8+aZrNZ297e9nOI8bi5uXE4k/+yn5wplWVSIMpMRrbJm+KM4TCxHpwZ5I+1ZUDCCJEmHERavhHtb2xsRM4OZCdtNKJEE/1vLpezTz75xKFecmdXV1eOFuzs7Dhyop/F4cCBZG84kxhwHCbIdZRSAHWiHxOJRCT/jtxmMhkrl8uOWLEPyvpttVoOnat+oA6UNJWiFziZEK0gElFbmk6nPYd7dXUV6UMLOkaXMIaStVqtVoQ4ubq6aj/4wQ8cVcBhRofjkPO7OFFKPtQaTo042UvQEWBuUDYcXoXb3zeeNJYYR60tQgiYmLIfNTE9Ho8jn1eoTSfJn2GHoVwQGn4fg8gC9ft9V5oaWWoZAUYMBatlHMwZNhnzJ5+G8tVN1nwOMBUkC7NFk2qdu3aMYIPCgRCk02nb2tpyqAeFcX197cJBUT3rR52pshOBaDEmHMrpdGpffvmlNZtNh+uq1apHIargVJlhDD7++GMzMzeKCNbKyooVCgU3bDBkcahYUxSiRkoYdBRZuVz2Z0O2QY601EijefZF6+kUcmWNlc2JU0CkSjN5ojUUIh60dg4hJTGZTCJQH0rl9vbWz4vOg3fmLHDA/+Ef/sEdRnLMZl+9qQEEArlHgSPLdDJiTuwPjpJ65MyVyBJFq+gJnj9kLZwIXRtkRteZd15fX7cXL174MxSl4qwDO+Nww8LV0hR9JyBa9lGdd/aHfV1eXrbr6+uIzlLUhDXU6DGdTtvLly+tUqnYb//2bzvqQFMEcpWFQsH29vYi0a2WocRiMS/t4f+B6DVnpqjO9fV1pMOSQs/6HMoiisWibW9v287OjsOT2kgdx4ezioNL+gP9jVOEowIkPplMrF6vW6VScQIUNxahyyDoEbk1m007Pz+3u7u7SLlZtVqNED25X1i5APwbULbWnqKT0ePoNIX8gba1fIz3Q3fq73Nu0J2Qh943vtZYAiOaLaIPFLVCMmrgaKYMqYYcDsoFRUJ+jIOjXl7Yt5AF5Hl4DyHsw4urd8rhV6o/i8bBIz9LcSz/9phxYy4oexiuapx5V97NzHwT3+e98EzIDqpc1dBD1AG2o+s/+8RhIGJgb4iauZQYtiEwkjo0rLlCG+QAgWP5HSI59ltLP+7u7iKdOcwWxfYIq67p2tpapLge2DbMk+neat5O84ph9K7RFgdc0Y+QUMPeaq6Pg8rzeQZGlW4kwFPIhip0HcvLy3Z4eOgF69/+9rf939S5wnFjfsqmVTiYHxic/L7mdpBJDDH5HaJP5JcIB/hbozG+I4RhcT70R/eBNeOMgWyEaBDnhzPB2uv7qtOEniJqZL7quOIE6ZnGkSM3RiRMTpvP817kBOmPSpTD2urACaE1YafTcdgZyFXZwkCfvV7P+v3+V9CNUFcQ7VFJAHpC0w30DpE4OcqweT4GhnMOd2EymVi323Vnlf0jGFKDg5NCblgJjGYWcWCXlhZXk/E5olBSHKFM4XCE8qccEkXE1EZpUIZuwllgf4mSlTMQjieNJfh5qFwQ9vCgYCgVFkJ5admFGikNg/XAojA5cArlsunMQzF7DrrmVzigCBjPUeWoBkzfSw0mC675Pg4yXhTQhXqUDPUkw6Ebi6FRqjMOx3Q6tV6v5x4itxlobRpQOJAgsAaKic/jjOChq3JSRYzx4MBWKhVfR6ILPkeOgAgAujeHzyxauK5rgXEhWiSaJw+I7LCnKBmN9oGz4vF4hGSADCEjHHY9SJoPxODoj6IreLH8gDSsra25olPnLFxXlTfg12KxGOkioqQadTbMFlCyoj36DprHQebYD6I7vpNziByicNRoai5Pzylyq/PCuQNR4vf0TCoqwTO1/jlEodRpV0PO/iFT6iTzO8wX3cQ6IBcqV+TbgQGps+Tf6JWMwsVJwZlSSA/EDBnpdrs+P3KA4bmlXAxImXdTeVF0AfQKR206faghZg6cPdZpZWXFu0uxbxrREeWhq4DeCX4UpcLIaaoH4piiFayFks0o4SGi1n0haFFolHVVuFj/LdSlOP7qnClfQBEbnHIYye8bX9vBh0NH5KGKCu9PDZwSEszM8zKw7aDq60vitWluEUXHgimcqcQEJQuZLfJ7JPx1IREsPRzaMJuciNbb8EyUaJgXIj+iz1HPVQ28Rm+qyPEEGapoU6mUXV9fe8TGYaAuq9PpRHLJ0NAZytBdWVmx/f19Oz099QNjtiC5sDZqpLVBAQM6Oz903MBD58DpoValrBAK/8YP3q0aN2AU/p7fRfkyx5WVFe+CA7TEUK8Zw0UUowl/9kzhGtjfKmc0UaB2DcOVy+VsMBi4Itd918FhVUawOl1mZq9fv3YDreug8qsIC8S6WCwWYSyjLMgBZ7NZK5fL1mq1fB6ax0UmgLdxzNg/3gf51xF+h55t5sj6EtGyHig3zqmWFoAU4IhQl7u5uekwOE4WAzkDDQAqJK+NrANbagkCUYc66jQewEn97LPP7Pnz5543xThiPHCMgByvr6/t4ODAhsOhOwwwg2l8gNzxdzg5uq7IFuvQ6/UcZtzb2/OuZ5ShxWIx51fwd5TTQT6C4MP1bTQgYJ9wCoi4mY82rEdeiORVJ2qLO9aJnC41ogRWnEVkVysIWA910NU28bvarQt432zhkCPzGGsaPehNReF40liSy1LvmIHyZVGAhRD+m5sbO/xV43AW5OjoyNudUbyLQlR4lvwFz1NIEMHVLhwaneif2RA1sprTID/FoqmSBQbmnVgDoBfeUVmHGG5+FIZFaeBYqNcF2UKFgXcmOqSzDQl6kt7ciq7tzczMm903m013YPCa0um057G42QA2pSpr3kERBbMFYsBeEXmZWSSSpJxEPVsOCE6Jen2ao0GJoSAhGpETDRWq5qqVcci4ublxJcgzkaFYLObRK/JIdKRELWX4kUOjvRZ7CBStnrC+u8L+GErqcnFMGXt7e/bNb37TEomEOzihssDIjMdjr58lsmcevA8RCCxJiCGaz+L/gdvYR3WA1eFTeNbMPNepcqLrjNPCGcZBnUwmftMOa84NIkBmRJ+kESjJAabM5/PuAKkTkkwu7o/Um0jQFzh4zIuhuTFloL9+/dpOTk4sHo97ByHa5HEBAEasXq/7uaEelgjmu9/9rr8bkQ0pC4UVQyQKZzoej7tevbq6ckYzn0skEt6jGj00m828lEb1KWNjYyOSIsFoY6Dp6IOMUFeuaEQIdZtFb5HCqdPORbB7cQyQC+aA04k8cTYV5SMlo/n5WCxmu7u7Ef2N3lKCEOdbna1wPGksmYRCISgqNpBDi+fI5Ln8FGVZrVbt4ODATk9PbTAY2P6vGnar987E1UiaLUJthTcZKFmdL59TsgKHW4t51WPBg0HQaW6O0uZ7FcqjqJo5KZSJYkTolGgURsN8H7levGDWQCFejA0HHkcFZT2ZTBwiop0VkAuwXjabdeV0fX0dySeQW9Znq6CrYOJchBEXcAxrTMTEfqjsgBCEClTXBGWq0atC6ZoiwGnASVM5UaavwogocDVAfAYFjRxyaLWWkrXX9IPW5+kgItb1gZQEOsP43ve+Z1tbWy5rXETMXHWNer2enZ2deS6MdWFvQvhTo2daWmKwFQJnLXE8Ff4KDaXZguSGkwkx6TGnGA4CBhnnSdmiKH32mLZ7oELNZjNSD6rOhqZCmI+iY8i46hs9nwqj42RfXFzYu3fvvKMZ7Si5YaTZbPq5Gg6Hdn5+7uc0mVw0Lh+NRp77xynWq/mUpPJY6kYNKWgCThfroCkEHEWuomOdQv4A/0YjA9BDggscJIyi6jZNaTBHhqZ4GKwxzooabfQ1zwzhdp7BmVGYHdlVJzaXyzlRL6z7Vv0VIiU6njSWITbOJInyEDr9NwRtMpk4cYHFqtfrdnR05FcJlctln2AYWapRxsvVRC5/j6fCUOWEt8MBxuNkkTmcKBw8Pgz88vKy9z8MvWrYZ2aLiIP6Q35foSr1zvgeHQqDaaTLe/Bf8HyEyMw8p8Az8vl8hBgEew3DViwWPQLgkKIYgLMUJgnX1cwiBdWa+1VYj3krk4+DrYNoXSEVfWf1LomcFNrT3+Wd2FuGOk4YaNaaSFOdG410MfAYdCImjBasR9ZZZThcN3XaVMkoEYTxySefWDqdtuFw6FclAUli+IDd6/W6tdvtSHcT3QP2RZ/LO/JcIDIlhLCejymVx/JFqkyXlpY8F4UMsYYYQ5whnqkOMeui+0adK+/e6XS84J98tcqiGkwlWOk5DI2nOsd8BqN0fHxs7969s1arZc+ePbOjoyPvcYuBZM1wcCDT5PN5dx6m06mdnZ25MRuPx846xkHRHLEaS0WngGNBB5aXHzrp4DRRUoYTNBot7qicTqcR2JlzAFyKDiGdodUA6BM1UEDPiqjo+VN0AXnCQBIIYBQ1TaFtBnF61AZgLHUeIEzNZjOil+BtsHZqT5C3942v7eCDgDNRJWYAUyJkLOB0OrVSqeQQBR4TxchLS0teO8fhN1uUhSikpvkpbVrAwVWjpYeA+RMhouQ0+uWz9IRst9sRLxMvgzXQSIccTqfTsXa7bYlEwls94a0pQQL4CMF+LLLU6EgPs64Bxg+4JpFI+FVAs9nDrenb29tWq9WsWq06jMl30QqNAnSi/1gs5oomzA2z5rqu7J3uHwKH98f3TKdT7/IE1KkyhQJFVoCO+S6FWrniDM9X6ydVuStkilyg/DiUakxQSAo94TDgkZLH5XJlFBt1dhgaCAPQ9tXxwxFB0WB0JpOHhg2NRsPnTKSXSCS8q5DmNzEW7Xbbjo6OvCXi8vKyN7FWgh1sPz5DzRklIqPRyGG2TqcTyemqMeHcqDLXdVbURo0cDg7nYzweW7fb9Zaas9nMIdZ+v2/dbtcjSBicOIwoVNISlExQU8f6sL4ochxyZIN1R+doQKC54tHo4VKAn/3sZ/b69Wu7vLy0bDZrZ2dn9uWXX1qr1fIeuziZw+HQTk9Pfa24Uq5SqdjGxoa9ffvW5vO57xsyRaTN2oZOF636SDWwFqo3iNgwlkSqo9HIa+YxiooeAQtTypXJZKzdbkcCEmQZfch5M4saPT1//X4/QmiiIQPviAOkxotSmHq97nwEPqPNUwgKOONEx6TULi4ubDwee2crDCvPQuaegmDNvsZYKjuJF9KoCXhwMpl4n8rBYGCdTsem06l9/PHHngvjLrv9/X2//qXT6fhdlWHCFqq/Cg6HkaiGZ6uxxOCwUWqoOOhKSGo2m5GbBWgvRWIbAw5UqR4TnrjSvbe2tiybzUaS/ng1eJ3z+dw9HF1r5hUKPc8kr0bTBOanip8WcUQcyWTS85raYELzMVz1AzGFjj+sHUaUOakHZ7agjvOd2t2G/UHhoJzYEw7s3d1Ds/HDw0M7Pj52+LZUKtnu7q7n4XAUMDjKlmZuj0WWCs9zmBWCw/gD38BwJZdFQ4JYLOa9e5UYxm0PrVbL6vW6K7FwD1VuMUasU7/ft88//zwiF8DONDpnrzjwyIKZeQ9RZGwwGDi5bmdnx3q9njcyPz4+tkajEYlgQCVoL4YB0ygZOQQuDiNn9dBZW3WwOcvz+YJpSTQxGAzs4ODAnST692Lgz8/PPYdPThaZ4/tBoDSy1ogBB4556+UCKFr0nUa+19fXdnp6aq9evbJ2u+17CFkH499ut11HcTUWa0iEjq7JZDJ2fHxsnU7H7yZVciHjMSSGd2KeKt/q0HMW2Df0Ns/i9heci/v7e5dvan/n87k7p6w9xhGSGHNR+6CwqvIryIvyuxhBRZBwgJB3uimFXANNDeG08Y4Ea7rX2CY+z3rBX/lf5yz1kOgi6ARQTsPhMOINjcfjyF2JeJQ02G632zafz53JidesZCKepUpN815AsNopQolIGClYhArBkp8cDocR2FNLKvCAgIgQKH74Lgw8kQEKjC4azFkVmzaOVgxeIQGEQKFCDh2QFQdcISd9nkKFdBBRwgJ5CRQ/3wnUgRFhPREu5EPlgv9XUpB+nnccDAbOPJtMJn74aH11e3sbUVzqRGmPVYRbnTkMfMjUVEPJUKibKIjv1ogZOcDZ4SaeRCLhaMlwOPR30q40KA2NtFhX/S/P0NslGDgU+XzeHU1VRESfwKmsy2AwcOQBxcmlvhDtNB9Eqz3OA/kenIbQMVOngxGiECG8pYQ6SiOUoay5LQwLUZGSoJTcwT4ib2FeN1wnzdnBE2CemsNmntyZe35+bq1WyyaTh6vByuWyzWazrzgpwIJA6syNdeKccqkDewBrU6Hjx4YiI+iFkEBzf3/v76nOxGTyUDsJqjCfP9yRSgu98XhsBwcH1mg0PHpOp9PekUtTcJqDDP8cDoXnYe7qu2rKA4cVNEEJSrouIdTL0MoK0lMqj1zeoDYMJ+V/DcPy4mHOjr+D5UquD5iNSIQoJpvNRiKPRCLh5Bg8diBTDakZaqQRPMXrNbLkOXiW5B5RpmzI7e2tdTodP3jArEQ9HMR+v+8GHkapbiKHnTZ8lGTc3t56LZHZgiXIbQBAKcyZjdSfULFzQNhclAE5DxQ9xdQopmQy6RFzyHYFKtRWb5ro1ygXQdLm8+HBUAOpRAAONErk8vLSITS9LomcJrKzsbERkT1yd/9fe2fW03Z6vv/LGLND8AIkLAlZEEQzaatRRz2p1J70qO+hr7UnPaqqptGknZksxMNibIOx2Rfb/A7Q5+b6PkMS6f8/9SOhBLx8n+Ver3t5aLsGLXAWPNe9OObllujn4hXQhxsB19fXcfVVp9PR9vZ2wG6VSkWFQiG8NixX5xsgeYdhXdgxH6DQdD85p0qlomazmTEqURqFQiEyuAuFQvAFxhUwPYYt19R5/AhvmDl6NikeLnSQwteMVFmyNl8rRozHIDEIaKLBmi4uLqJLFr1y2ROHUjGQ0jmlxj77xlpAEKBbj2VLCu+91WqFAiGbeGlpSfPz81pcXIwyLugSyNgT8TyJsd/vh1zi3CuVyr3KMt1P31fW7NA0Z+9JMJ5gd3BwEIlIKLhms6nJyUl1u129e/dO9XpdnU5HDx8+jBgoKI4jIsgJDAx/psvw4eHhQMPI3ncZgWPjCWvckAN86/I/jWfzL/Tqse1yuRzzpsn+zc1NfJ979Pcp31jDZ19RNoMuFT69Xi/uvCOD6/r6Oqx/LC/SjEkvxo0vlUrh+RAjQoBiPbJYjyV4bJSuF8AiEBAWKni0rwWPhIAxfUHZaFcykuJGduINGAXAB8BcXG+FZ4kFSnG+lG2Y4EIR5eYeMwMGx0pymAbDJe19mCamcPHv1NRUBqI5PT3NwInSXX9UnunMgIXGZcO8DiEPDQ2F4eJwl8cvsXiBpREqvI+m7cD27B9nThwGQQK0lArF+zwcV5SsOVWWWPh4MbyfrMfr62uVy+WIzeRyOR0cHESCTbvdzpScuKEGDOkZvvCWx7h9zr6258+fq16vR/wXjwB0BE+AJvSsB14lvkSMBvpyzwSBcn19nREqvV4vOsUgUNhTh2LTmCXxQRAYjDzOemhoKFMKsrq6munCRIkDAwGKsevzzOdvm1KAYiE3MJIcXoUWiKWx/3wO2gIGbjabUaLBPapzc3Oam5sL2VWtVvXu3bu4DaZSqYScg+69nObs7CygTa7xc95xDzlVPI5QOKJD3BxDEmPe29txbR4wJ4oSWY0MIasaXqSlJR2qcEQmJiYyHXjwbL3O+ZtvvtG//vUvHR4ehpNEn16HUN2RIcyEosapwKkiDEPegBsPePoXFxcqFotxx2a73dZPP/0UPCMpFP/XxheVJdoahvZsNeAoGAuc2KEaIExnJseaPZaBIOLvJHWkn3EBwI0GLmAuLi5CwHqsjTgXghkrdnx8PAQrKegev+DvMCTyQZEAABx5SURBVNjs7GzE5UgkYH0EzfEwmRvWJJmB3W5XW1tbMWcX6h5rYe0e/CbLkH3DQPGgPQKBNGm8ue3tbe3t7YWwPjs70+7ubiQ/AY1AhGm6PftMrJF5+T7wfGA7t/JRCmRBT05OBuyNIKHnJRdBe9o7FjhnQ6Nsh+ZQrA79SXfGUKoY3UJ1Gob2mDc3zJDZOzY2FuUpnDcwraSoIwYm9md5LaPD7V7zxXPdUqco/vj4WAcHB5maMwQ78eLZ2dloRnF9fR3JJxcXtxeIv3nzJjxHzh+jB/6W7gSJw/7MJ008YR14aHge0LVDrdAG3jr8iWDk8w7R8hlqZj2R6+rqSvV6XVdXV6GA2BvkDOvz80WmMNyzI0P+4OBA9Xo9Ezoi7kbCFwmNk5OT+vTpk7a2ttTpdEJ593p3NzixZxTqS4pyBjfqPO6XGtD863E+6Nwz2D3RDSi7XC7HPZ9+3RWGBb2n8/m8SqWSlpeX4/o/T8bDkSHchNzAoPfw2F//+lednp7q7du3Ojk50ezsbCg95DG06vXgjjLiddJSj3Pk86z/7OxMh4eHGhq67U+8srISspzYa71eDwMCQ9z37r7xVRj2vngAkCneIgTpqfC8zzW2E4Gn/qNApLv0ZApv3TtxGMstSo//cUAITo97Sgom4tmOVTsOj+U7PHx3M0Sv18swYArt8V68Xofe3MrrdruZq5jS/fYYYCqQPLEmhZokRUAej4/vw5MELvIUcKBYBATn6PFHIFJJAUEzB+K5nBMKhfmh2Bl4435Onmk6MzMTDZa9+QTerQsLhA/Q2sjISAaaYXhszSFYR0yc3tIYk3u/QGgwWhovdqv/8vIyagNT2NfP0WOYftaEFBAI5XI5mudjWCKEof/Dw8OAbhE4oB/dblejo6MBc6Xr9BpZ9gPFnsKBvle+zwyPMXnega8ZesOL8tIh50toDKXN89kvhCQ04H2t3YDEmHJlmaIOnCE8w+XlNMjHUHYk6uTkRFtbW3F58vHxcSQ9Qg9eg5rL5cLQ4/nsNc/3uLSPVB5Qp8vn/QcF4DICmQBKw55hmAB7FwqFuGaPUBlOjRvvyEKnZ4fdJenbb7/VDz/8EJ4d6BbKlX3g72R193q9KKkBQie7FzmMpw2dtNvtMBBZC3SDcwHNuHJMDex0fDXBxwmKWBkxI5QHAtWtPBe6btnATMC1fiekHwAWOYcM4fAeoJhCoZC56y29pgnFDJNId238SIhwBoRw6MTC/7EOHfsnLsA+8R4I0UtVPMv3q1lX9yRveGCdPUUheDmKW7EkHUmKOMCPP/6oarWqXu+2xmphYSFgImBU9sMFCanz0p1n6dlkXgLiwhFvzekon8+Hx4sHSbYryWB4IC6sgeulO6OHs2RvuT6IH4Z7Ra7Y+HFhwr46dArq4UkteNKpgcN3uLeXQr6pd+tdUBgIIr63UChobm4uEoGur69DMdAMnxBDu93WzMyMSqWSZmZmQhBiBC4uLkZiFcYvrcvIOMVLZT3QoytAf405w+OuHDFC3ZPj2ZyNJ8OhSNP6ZJSPQ73sG2Vg0AA8wPdCV067bgS4sQPMiKIENQA+xABqt9tqNBra3t7WDz/8EJdcY7QwT87Zr9VLkQXuffW/35cs4wYOhjn8BF25vATRwtDzkBjoA/vU7/eDXsbHx7W0tBS3kOCJuszxuk3Ox/mV8eLFC/32t7/V/v6+fv7554z8Bi2CZy4vL0MvkKh2dnYWdOHhDQxtzhBaBjYGxUKueSzXkTo3LD43vqgs3TtjIQhpBB7W1dHRkfb29iLtHmZKD9+9oU6nEzfa05yYi3vBvYHuEEgwK/Gu1dVV/elPf4o5Hx4ehkCFELjLjc4qlLd8/PhRW1tbYZ3jlSGYsMqxJHHlYVAsIja+1+tFDRU3h9PpRbrLEkaIOvHDMDzDlaHDMf1+P+rlDg8PI87BtVkQOUq+Xq/HftJP8+XLl5qbmwtG+M9//hOdizxeyzqph3v79m0wqMcHKWVgH1PrzOnHY9sI+XK5HHPGCvSYNMoKIw3BNzMzE5+FdjwlPRU0aUKNK0no070ozgBr1yFEPApvo+fGAEL96uoqhDQCE4MTZeHZeM6s3gUIfqKhxN7enj5+/BglFktLS5FZOTExofX19YgLc4lvvV4PgYAyINnKb4vAkC0UCpmkL2jUcwjcgIFmWLefgXseCFrmwWuUDwFpo6A8oeTk5CSDNDnCQcyV+foVenim7sVLt03sHz58GPAuMDIdsA4PDzNddahRvrm5Lc35xz/+oZ9//lnb29sRz6ZUDpniJRrMDwXlxjVJhLwH2YKhwSA5EroFzsXwdOHPGSGn+X5il3Tzcl4mAbFYLEYCGzzgfOIJVdAnzgq0wuj3+3r58qU6nY7evHkTWd8YCdAFFQNuqF1dXUVzAeBa0ER0y9LSUuSNPHv2TJVKJRS4Z3m7R8u6CEmdnZ1loON0fLUpAZa7bxBxKuJInU5Hu7u7cSmzpPCo0pgG34MwpKD+6upKq6urGQuf52FlOuzJpaMPHz7U2tpafDdFzhwymz40NJS5Cw9rsdVqaWFhQQsLC/r2228D1ru5uck0GserRCHwvblcTrVaTe/evVO1WtX09LQePXqk5eXlDFzjsR+HBDlE97rd62AuroBOTk5Ur9dVq9WirotbRfDWPcnh8vJS5XJZa2trmpmZie+FYHK5XCRrpZmsIAb7+/va3NyUpIzSAPaUFAaGKz5PGsFzYe0en6CECMHmnUwccUAxe5IXkBvekyfvOM2l++yC3IUu608tf4+7IVDPz88j4chf55neHQcYi4xJknw8F8DnTCIVcDMCdnZ2Vi9evFC1Wg2+IKlOuu3vyeXgeML0zKQm+PDw8FdePt4GhhZ7hYUPTTicnMLdxKEQzpwp64Cm2F+/X/Ho6EifPn2Kmui1tTU9fvw4DGcM7E6no/39ff373//Wzs5OxJ6KxaK+++47zczMaGpqKmKI8LDDugwu0E5hWPdwUejUdg4N3faC/fDhg968eRP1iLOzs6pUKmFobG5uRuvMo6OjuMjbDTVqRZEJQISf89DZYz4HGoQXTT0iw50dR4sIGfFezufq6iq8Mpqme2IY/Ae07ZfHI1cwZhwhOzk5iXrf58+f69OnT78Kp3lrRS6D6PV6mpiYUKVSCTTu6Ogoc4bwPok89Mf1zmXIXtYCT6e04bScjq8qS7xIvhShPTw8HBqZ+KVb9R4IdssdYmGiQLWUnwCnOXTHQp2ILy8vtbq6quXl5SjEZyOZj5S9mgnh7dmr29vbmRgBGa5AJRTCE3vy+Z2fn0fvx9PTU01PT2t+fl7z8/NREAtsi9Ly+CXDGSiN+SDsUgGM4MR7xqPEu2SeZLLxUyqVMsJubGxMT548CQGDsYKX0e/3wzvBi/VYKgoTJQFk7bVsWMoowFT5e9zGL/n2ZAiUJUaKP5e54Dnw/c5QDrXxL4IijaWhWBFYnBdGApnH6fdId8KJOaOogJdOTk50cnKSCVO4EPPzh78wbDx2OT8/r1KpFAIAw4dsc26u9xggDfgp8nfIsN1uB9rhvJnuj3sU93kQzWYzEyPECwWmd4GK0sFQ29nZ0cnJiSYnJzU/P6+1tTU9f/48SjcwtohjHRwcqNVqBcxIdzBkj5eEYVi5hywpQiUoEB8+d4rc6VfLjTO7u7vBi1dXt7eAYLxtb29HYwL22GkMgwIjnh9JmXK4dI9BOaS7KwEdsvYQAUZeanBiKAG9sl+UbJHPgbLx20XIh0g9Xh+OJEA37OHjx4+D56F/zscRGT7neSOsmbMlW7ZSqUTCJnN3Z8t52g1U//99cXkfX1SWdG9xZckhAymywOHh4djk+4SHxx34O0W4bBQtq+hpyGYh/FCawAFPnz7VixcvMncA+m0cMJELRRRdoVCIRuNXV1chQIifccv70tJSWFUO8/V6PVWrVf3vf//TwcGBzs/P9erVK21sbGh+fj48LojxvmQfH07M4PG+XxAgBzw2NqZKpaKHDx8GQxcKhTAcUJbeJ5IzZT+GhoYiO25ubk7z8/NqNBoBBeMFU2wPnOEMknrv7nVLWeiDmBK04LFdGDDNYkYZAR/C5Gk8jH9ThZPu332/Y8yx/0CiMDKM6YlIJCA5nO4xVld+WMIO93imeRo/ZQA7S3eZgdKtIVEul/X48ePIxIUnESAIeMIH0D0eMYXp1ARWq9XIEOz1elGz6XvkhgRnIilTu1utVoMG/L2eiMHo9W7LXX755Rd9/PhRb9++1eLiol6+fKnV1VVtbGxoaWlJzWYzyjPa7bbq9XrM1UM3y8vL0ToOekDwUp4CH7oRxu/ew5g5g9g4GkNMeHNzU7VaLQw2yhVQUCSm8H3ITqdR9hfZgjJOh9O7e9oYjo50wEeps+IxQd8DYOF8Ph8yHAOFtfFMlLt7wZ6xCx+l8Dzvm56ezrTbhOb5fm9f6ZA/PEiGP04B8felpaWA3J3GUMjsv8tQDFbvS/z/nOCDe0+SBg/ltaGhobg1nNc88H98fBzxBogReM6DzN5PECbDenJrjxjL8fGx5ufn9d1330Vshvedn59rd3dXExMTWlhYCGVyc3MTVhHxrufPn6tWq2lnZ0enp6d6//59tAibmppSq9VSo9GQdAcHUB/VaDT097//Xe/evVM+n9fKyor+8Ic/aHFxMdMkgAPwDNlcLhcMzbpQACgiEhGArP3wh4eHM3FY4F7PHoVovSn8+Ph43AIP7InnmcvlVCqVIiUfxj4/P48WdCgGBAaKLA22ewlCCn9wH+H09LRmZma0srIS3rzD1g7VMXgWNImXzfl4TS3v98+6p+5/h26A5qEBV4ruQQITOtzjkDS1oyhcLHJXkKlC9d8Z0Mz19XV0CmIeo6OjevbsWZyNr53WbAh/PB6SYEjike6UxfDwcNSq+f2EeJdAur5n0IjT8ubmphYXF2OPXWjiIXgyDoX8CPHt7e3YY+r/iNFTY3lzc6NSqaS//OUvAfPm8/ngiUKhELkTrAf4Fv5jXbVaTZVKJbw7ZBLIxoMHDyQpGqtQckFrwg8fPoTy4WwcjoePXWFBA46E3GcsuaHiBip0hTzxn+Hh4UC18JoclWAOIAqdTkf1el03N3dtFUmq4YYUcjAwSkChpqenlc/n43m9Xi+QE86Mgcc9MjKi5eVlraysRC26K1j2hsQhzmFmZiYMAy7hINu1WCxmkim9jp5ELM7bjXccPfbx/wuGTQtLsdQ4RALDbjl6Eg5Be4jHLV3prhMMAhCmHRoaCtjT50JNVqfT0R//+EctLS1pdHRUzWZTjx8/DsLiuqKdnR09ePAgPCUOC2E6MTGhJ0+eKJ/PR9G2Q1MfP35Uo9EI4UBAGEY/Pj7Wo0ePVCqVtLKyokePHkWsB08IIiZ43ev1opkyA+sbJnXBTLyIBIWRkZHoI+nJVsR/SCNHOaKAIKy07R3CBaIlhgDR7O/va2trK6764txRllilCE6EgCdTYLXiDROrwUIlgcItSBc6GALcLsEecY7ATw7dpPE/F0L3WdsUnpPQMTU1lfHmWDcW6cjISNT5NpvN2DNPnuHcHH6/Tznyf3iMgUdKmIK7G1lPsVgMw+eXX34JCIsYsaf5cw7MmYxX1kUc1ZN5XIABGUKTCOZUkMOjZCQyHwRXWmYwNzenYrGojY0NbWxsaG9vL4w4+KFUKoVBTl2uy6HUi4aeuY4K79mTN6CNb775JkMHeFtAkv1+P2qYx8fHM/IQhcRelEollcvl2H+MJpefHqqAxtLkNOcnD2m5LHQ6cEXt2beSIpkFXkJONBqNTEN9zrXVasUzQY2mp6czxiN5BYTNQBaQccjHVHeQcbu8vKzf//73+uc//xkdr5gvXjI/8CloIHKNHzcIPRvfjWx4FoMMWNwhWZdpnxtfVJbULWG5pcrS4w8e1+F3h4U4DIgZCNaxerf8WQgEQVwQgQ38Cszlh0JT6729PT1+/DiT2MO8mW+pVAoh22g0MnENYk0wEIILAUgR/fz8/K9gAIeAPJvToUeGZ0B6/ZcrDI+1cBURc8ETg9ggapSLJ/+Q9ABRkVyApUqWMYq42Wyq0WhEGjzz5f8QegovsWcoeGjFy3mcMRAMMIRDiQhCZw6HMF053pcow5mnA4WLIYYgBubBQ3aP0OEb99LYM/6Odcv6Uo/Bfz4HG6fKEn6Bv8hanJyczGSN4t2CEHBm7PnIyEjQvHt5pNfzHXzWPUmPB2Pp+0CIdjodLSwsZAQeCJAL4wcPHgRSgCFHnMobU+D1uRGCUkCe+PVcV1e3/UVHRkbCeyA71PlpYmIijFkPD0i38UwMo1wuF+Ee4Mr19fXwjrvdrubn57W8vByePPWu7CF7BT07X7jSQQZ6UpkbUZy/7zk8lcYkkUXwVwozjo6OqlwuS1LkbKDEPVmPueDJOy+AapAdjjJKYVjkfLFY1Pr6uqrVaiQNOW+khqTrDQ+JeBJRmozl8oGzRwajOF0feFLf58YXlSU1SwhWT9SRlLFY/EB9oRCJK1KSEFLo1pMvYFi+B8iADh0bGxsaGxvLNCdnACNUq1W9ePEi5nrf5pAdipIFqmHTXDiQVg2clM/nI9ZHnJI9gqD9cIh/pVnCnpLNnjKwRl2pYEETC/a9HhkZCQEK1EoN49TUVECtrtwI4B8dHanRaGQSInZ2dqJPJHviwtTr5BBCrNPPGjjElSxwkMdfYHKEB3Ctt0BLSy/SOMnnMo7ZT+bJOvHCbm5ub4MplUqqVCohXPDQOMvh4eEotUAAIyhcSTrDujBIFeXnrNlGoxHZjrQU88QphJtf34VBRpY5NXM3NzehVFBAtHE8PT2N8hLO1XMM4FvfR2g1FcC9Xi/i/97mDR6GR6gPle6SbLyeGWUMrZMj4XFDFD7eBsoNI4bPI9RTmFNSJsbng99RQmQN8/cHDx7oN7/5jaampvTTTz/p4OBAz5490+rqahgrrVYrDAFgezeCCUt0u93w4jy+5krOadkhccImyBF4gtfgTZwaSdG9LJ/PR6mbo1YLCwuBwoGmIWc5c7xn0Lh+vx/7jsGdevKE4oaHh7W+vq7//ve/arVa6nQ6GdgY3gSNSBUlZ4ickO7K2dgXFGS/34+wCfyblkNhkLqDdt/4orLEyvNUW1duDlGxgQ5BcmCetcjrQ0ND0cWFBXoG4sjISGz2xcWFGo2Gms2mHj16pD//+c96+vRpJJ2kzEqc4tOnT/rxxx+1tramQqEQxArBE4z3i1JbrVbAZqnljPKBsMiqnZ6ezniVCEqvoQODR8n5nDlYhASeFcTtipfmxyjBmZmZUIQUTGMNolhYB/ODgYlvHR8fq9lsant7W61WK4jm/Pxcm5ubEWdkL7AspbvCcoc/uHYJwUi8wWkAwqXZt1uKnvCDd+yNJSD4tLQkDda7QQJcDXMgvGFwLN7x8XGVSqVQML1eL6BDPBSYPpfLhRHHLQ4IcI+/uIB2AXgfTOxC8fXr15qeno5G0OVyORMHy+Vy0R4wFTa1Wk2SVC6Xo+YPo5I9BHamsbbPB553xKjb7cbzgdCd1yVFU35uNikWi5qYmIj4uaTIEEchsE9OmzyT78frhfYJdyCHCK9wBp7BTmzbFQb8R7xNulUiOzs7wfsIZp8PTcDz+dtWcH/729+CFrvdrkqlUsD37AsxTm4tIWOfMBT0n3o1rtxdmdPmEfrkWSTlUN5ycXF3OTZeK94i34EBw55OT0+HwYP8wvvr9/taWFjIJPbwTN5L0pjHxZ3miI0/efJEr1690sXFhV6/fh2OEOfgoQAgcTfa6FpVKBRi7zHycrlcpnMSMsG9YGRX6mh9aXxRWf7ud7/Thw8f4nJghB8HxwayCE/TBSbyGFQaq3FP0j1LSWGdkSDRaDQ0Ozur9fV1ff/999HgGMvQCYzvury81Pv374MBC4VCXP7JcIVIIDsVXB47c4jQcXMYC+bGGiODDOJyYcigboo9xarjOe6FuAJGMOAtAnFCRKzTicXhJvosksi0u7sbCodkKgqpfd7M1TPwXJl5WYU3KUiz2lD4kjLp/g61cLZu+aXQNgrb4WEpC716lqrPHa+exCQEuVu4MCMeKElAtA4kecahNh8p3SMcoT/nA+eBDx8+qFwuB5rSarWiHIQGGmNjY5qdnVWxWMw0cQdSJ07Jfjvkjvfn3YNcMbh3DB9gKHgClwt5r5VtNBpx887ExERGmWHNp14T+4di83h+aii4x+RhFvgQ1ILfMVYd4VhdXZV0axxwv6d7k3jgGLIYy6A1nEWlUon3e2zs/PxczWZTtVpNe3t7kczEe0CaWAv7zN6wF05Xfo7dbjd4rN/vhxLGyAGNQKb4c3CEKAnzWL6HMjjzyclJHR8fZ84MxYghCY94OMv5DR4fGRnR4uKilpeX9fr162iswL4DqUMDfJbzp/kMZToe/uDHEwsdNXGdxD547PJz44vKcm1tLeqb8PK8SQEwKQfjcQ7HmdlcJpUG6Jm0KzGwZgT69fW1nj59qrW1Nc3NzanRaIQQY2NZuG/C/v6+9vb2ND4+rtnZ2SiKd3gDYoTRHeryBJW0HgrmgNhZn9cMsSdu1fqapdubFGDkm5ubuDDWa019eKwOheTKWlIYKlhWzAPi8Vgdl73SAcb/TltDny9wEUqFFH4XcB6P9rl6vIF/8frYe1677/MeB/T4MfCrG3Op5+ZCAEXr5wsjevyC72edeAncMEJDDXjA4e3UIEzhWKcFfx+DBJWJiYkomzg6OgqIXVK8XiqVVK1WY+8QyCSVIYh8XdR74p27sryP7jDeENweI3bagO6AYz0xCc+c4ehUamh43DuNn/J/5ytonB83VKUsfMnaQHwwrkk08/WCmHW73Xiv07Ab50CQdCCiGUG9Xo9r0fysUweEfSVpiHX6usm6doWGoQqNYrh1u7eXihOLd6MWeeEN6VMDqVC4vd4vlfnMxxEiUBrkjQ+exw9ZtNzgg7zAOXC+h0adHtvtdniXhCG4TBzjHU/TLzhIcyHc0UoNv3R8UVm+fPlStVpNp6enqtVqIRBSpYaCcsjGsWZXPBwEEIV/H8rCLVCy68bGxvT999/r1atXGhkZiaul0liDM1Uul9Pp6an29vYiLZpu9HQt4WAQkhAHr3tN0tTU1K+gvNRCccLxuE/qVfoeNpvNgIJRli5wPfAPEfE6cQ8sxNHR0egiQusrhy65VcXrW2u1WtzRCdO02+0oF0ktMSxDlBXvkfQreA5Bx9z7/X6GaV0gusJy78Ehza8pS4wOKRtfg95goDTI74kz7lWyT544BHRJLNPpzUMNMLcbiw4v3fc5Hzyf7jatVit6vRaLxdhTevyC/oyOjoZBBCrgcPXNzW3WOfvgmdVOmwhFh8dTw9DXCW2QhMWFyXSCIbTiCT5pvN376Ep3XpTHUaEH95AkZRo94Ml4/BsB6nN+//69yuWyut2ums2m2u12GCKO7rCXNB9wRI3z2d3d1eHhYWTCArcC06N0PT6LgsDrc9pOvW6GK0vOCbkA4gZdkL8BTXq3Jpd9ICieP8GzcQpcPvNcDzeRVObZuIw0dprP56PNKaiD05V72UC9eOqcPR7vyspKhH9IivPETubkZYueHMZ8PB5638h96cXBGIzBGIzBGIzBkH7duHUwBmMwBmMwBmMwMmOgLAdjMAZjMAZjML4yBspyMAZjMAZjMAbjK2OgLAdjMAZjMAZjML4yBspyMAZjMAZjMAbjK2OgLAdjMAZjMAZjML4y/g+sqaeGcCLfMAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lvngM3yFgycY", + "colab_type": "text" + }, + "source": [ + "#### 2.4.1 PCA on Faces\n", + "\n", + "To run PCA on the face dataset, we first normalize the dataset by subtracting the mean of each feature from the data matrix `X`. After running PCA, you will obtain the principal components of the dataset. Notice that each principal component in `U` (each column) is a vector of length $n$ (where for the face dataset, $n = 1024$). It turns out that we can visualize these principal components by reshaping each of them into a $32 \\times 32$ matrix that corresponds to the pixels in the original dataset. \n", + "\n", + "The following cell will first normalize the dataset for you and then run your PCA code. Then, the first 36 principal components (conveniently called eigenfaces) that describe the largest variations are displayed. If you want, you can also change the code to display more principal components to see how they capture more and more details." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "UoTugy79gycZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 466 + }, + "outputId": "0a37fac1-f7dc-4c1f-ab08-1b10aa24d20b" + }, + "source": [ + "# normalize X by subtracting the mean value from each feature\n", + "X_norm, mu, sigma = utils.featureNormalize(X)\n", + "\n", + "# Run PCA\n", + "U, S = pca(X_norm)\n", + "\n", + "# Visualize the top 36 eigenvectors found\n", + "utils.displayData(U[:, :36].T, figsize=(8, 8))" + ], + "execution_count": 38, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ryqL3Xc5gyca", + "colab_type": "text" + }, + "source": [ + "#### 2.4.2 Dimensionality Reduction\n", + "\n", + "Now that you have computed the principal components for the face dataset, you can use it to reduce the dimension of the face dataset. This allows you to use your learning algorithm with a smaller input size (e.g., 100 dimensions) instead of the original 1024 dimensions. This can help speed up your learning algorithm.\n", + "\n", + "The next cell will project the face dataset onto only the first 100 principal components. Concretely, each face image is now described by a vector $z^{(i)} \\in \\mathbb{R}^{100}$. To understand what is lost in the dimension reduction, you can recover the data using only the projected dataset." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "03nL1H7mgycb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "adbcbb87-e935-4bf1-eac3-370517b9e710" + }, + "source": [ + "# Project images to the eigen space using the top k eigenvectors \n", + "# If you are applying a machine learning algorithm \n", + "K = 100\n", + "Z = projectData(X_norm, U, K)\n", + "\n", + "print('The projected data Z has a shape of: ', Z.shape)" + ], + "execution_count": 39, + "outputs": [ + { + "output_type": "stream", + "text": [ + "The projected data Z has a shape of: (5000, 100)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s7dEWfWmgycc", + "colab_type": "text" + }, + "source": [ + "In the next cell, an approximate recovery of the data is performed and the original and projected face images\n", + "are displayed similar to what is shown here:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "\n", + "From the reconstruction, you can observe that the general structure and appearance of the face are kept while the fine details are lost. This is a remarkable reduction (more than 10x) in the dataset size that can help speed up your learning algorithm significantly. For example, if you were training a neural network to perform person recognition (given a face image, predict the identity of the person), you can use the dimension reduced input of only a 100 dimensions instead of the original pixels." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DzhpIobvgycc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 783 + }, + "outputId": "feae1516-adc9-4b1e-e316-84f2cea31b5d" + }, + "source": [ + "# Project images to the eigen space using the top K eigen vectors and \n", + "# visualize only using those K dimensions\n", + "# Compare to the original input, which is also displayed\n", + "K = 100\n", + "X_rec = recoverData(Z, U, K)\n", + "\n", + "# Display normalized data\n", + "utils.displayData(X_norm[:100, :], figsize=(6, 6))\n", + "pyplot.gcf().suptitle('Original faces')\n", + "\n", + "# Display reconstructed data from only k eigenfaces\n", + "utils.displayData(X_rec[:100, :], figsize=(6, 6))\n", + "pyplot.gcf().suptitle('Recovered faces')\n", + "pass" + ], + "execution_count": 40, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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SqeDkyZOYmZlBrVZDpVIRUENUyzXi9XoleJnNZqTTafh8Puzv7+PSpUu4fv06xuMxFhYW0G635Tnx3fDe1ODC7zebTVy+fBmbm5uYmZkRP0R/Y7Va0Wg0UKvV4Ha7ZX8SuNjtdkHZ/Hvqmvgwu6vDVR8EU4poNAoAglI8Hg+sVit6vZ6k4+PxWCIGHZrT6YSu63A6nUgmkygWi4IUVAdIdKNGzXq9Ls6BSMPlck0sCi6MRqMhaIroyuPxwO/3w+12o9VqwWazwefzodPpoFgsotfrQdd11Ot1NJtNAJDrD4VCct+JRAKBQEAWJlEQUQLTl263i1arJWibqbHL5ZJNwwXl9XoFFdM5MGDx91VqhkiQCM7r9cLv98PpdKLT6UiKVq1WJTVTKZ/BYCBBqlKpoF6vy/MeDoeSbtvtdgkGarADDtER1wb/htfrFYppd3cX1WoVgUBAfp8OiU6OG4vOmMGLz4DvguhlenoaLpdL3g+NiK1er6NUKqFWqwnd1Ww2kcvlkMlk0G63JWAStfd6Pfj9/onN2O12Ua1WZS3zf7lx6RTsdrtcA2k0OtCDgwO0Wi3E43HJBohgw+EwdF3H3t4eqtUqut0uxuMxfD4fAoEAIpEIvF6v0G2DwUCeaSaTwfT0NOLxuIAH3v9wOEQwGEQoFEKj0UCn04HX68X999+PWq2GZrMp6JROjO+Q+49UF5+HimT5M51OB+12W/YV02xaPp9HIpGAxWJBIBBAMBhEKpXC3t4ednZ2BGwNh0NMT09jNBqh1WrJ86/X6xPAgHt3OByi0+ngypUrWF1dxfvvvw+n04mlpSWk02m4XC4Bg+p9qVkw/RF92tTUFLa2tpDJZCRwd7tdFAoFyRDMZjMikQgSiQTC4bCsc4IOAhfuDdVvfZDd1eG6XC7Y7XZBM5FIRFClx+NBPB5HsVhEq9WSG2WUAG7DepXnJAqZm5vD/fffj2w2i/39fTSbTZTL5QkHQFPTSMJ2clp0BNzAo9FInA03J3+e6QSdIoCJxcKv93o91Go1eL1e+X46nUYikRAHQj5N5UDJJ1cqlYlNz0XMDeRwOOB0OuF2u4UrVO9vNBqJI2s0GhMIgs+eTtDpdKLX62F/fx/lchmNRkOcNlNFbg6r1QqXywWTyYROp4NyuYx8Pg+LxYJYLCYbme+J3C9TWTVdYsT3eDw4cuQIHnvsMQl4/X4f7XZbKA06FKa4DKBEBFw/wGFW1Wq1UKvV0Gg0xCl++tOfxpEjRyaugbRWoVBAPp9Ho9GQrIQZi8vlEmqA19Lr9WTT0HGxLhAKhZBKpSStJTL2+XySWXS73Yn16Xa70e/3kc1m8eqrr+LcuXPo9/t48MEHsbKygoWFBUlNSUs4nU7kcjlsbm7C7XYLNxsMBgVd8TleunQJ3/rWt7Czs4OZmRmcPXsWL7zwAiKRiKxPImTSfhaLBeFwGFarFdFoFDMzM4hGoxPvjUh9OByi3W5Lhkd+k6AgEokgGAwiGAyi0+mgUCggk8lIfUR9J06nU4IEg7fb7UY6ncbKygry+TzK5TIKhQIAIBwOIxqNCu9NB0aqgntrPB6jVCohl8vB6XTiqaeeQjKZRDgchtvtRrPZRK1Wk2fBoGG1WieCHjMov9+Phx9+GJ1OBzdu3JBsjBkdAVMgEEA0GsXs7CyCwaAALII0rnHWYejQP8zu6nDtdjui0SjK5TL29vYkKvBlmc1mWZh8iORiPR6PQHFN07C7u4tOp4NYLIaZmRmkUikcP34crVZrgi9zOBzodrt488038Wu/9msT12K1WmGz2YTm4E0DkAW/u7uLfD6PYrGIXC4HAFL4CYfDaDQaaDabCIVCOHr0KEKhEIrFIi5evIiDgwOJrIy8RGAqf8bCzmg0EhTGKErui/wXcJjucWMwzaTzY1CjoyWi4EJhyk3z+/1CH4zHY2SzWbTbbZhMJszOzkrazoJhNptFp9OBx+NBLBaT393Z2cHe3h5SqRRSqZQ4tmAwKJtP13XhchuNxkQwJeIhApiengZwmI7VajWUSiXh+AeDATRNQyAQwMHBAWq1mqB2FroYEBgoW60Wms2mpHUvvfQSHn/8cczPz8s1MMC0220Eg0F4vV5xkCwOcSPxmoFDNMjnT/rCarUiHo8jEonIhm00Gmi1WlLItNls4mjm5ubkOlgTuHjxIs6dO4dMJgNd1/GNb3wDL7/8shR9Tp06hbNnz8rvkmpigOWa63a72N7exurqKt5++20UCgUMBgPMzs4iHo9LRpdMJoV6IxW3u7uLnZ0dPP/884hGo7h8+bJ8fqVSwYkTJ+B0OhEKheDxeDAYDFCr1dDtdiVbIkXFLJLZIbMPFjpzuZzQVbTZ2Vk0m02USiXk8/kJZxyLxTA3N4eFhYUJvprAoF6vo9vtwm63o9vtolKp4IEHHkAgEEClUsFgMEAikRC6iJkQgzuzUb5rOlyn0ylUCX1Ir9dDLBbDwsKC7LloNAqTyYRsNotyuQyXy4XZ2VksLS1hfn4eXq9X6B9mcwSIPwydAHyEw61UKlhfX0e1WsWxY8dgtVolAkciEYRCIWiahnq9jlAohFAohP39fVy4cAG1Wg1TU1MIhUKSxs/Pz8PlcqHb7WJjYwPVahVms1mKK/yMbreLg4ODiWuhk+UC7/f7wsEyBSEtQSTFa5+bm8PKygqcTie63S7q9TrS6TSsViumpqaQSqXw+uuv4+LFi0gmk/B6vcJfMsJms1nE43FZHNVqFYVCQbhpprT9fh92u11SdFUhQBTFtLbf70shiM6J/Fi73Ua1WkWj0RA0zo3Fz+JCoyPtdDrI5XKCEsk1DwYD+P1+JJNJSSmr1ao8T3KPDocDmqZhdnYWo9EIW1tbov7gYqWjoBMdjUYoFouoVCpSTM1ms1LAII0xGo3w0EMPodPpYGNjA+12G9lsVqgfTdMQiUTEgfT7fRQKBXHQmUwG77//vhTu+E5MJpN8Vj6fl2yITpVoi8GDagpmSETApHEuXryIZrOJ8Xgs6IZKhXg8jqtXr+LFF1/EE088gc997nNyLd1uF4uLi/jVX/1VHBwcCD12cHCATCaD8XiMy5cvI5vN4rHHHkM6nZ7g+zVNQz6fx+rqKt566y1xJC6XCydPnsT09DSOHj2KxcVF2ScWi0UovlQqhfvuuw83b95EOBzGY489JgUpOp+pqSnZR+RHx+Ox0C0s+DkcDjQaDVQqFQEf5FX5M2o2S7oRAEKhELa2trC/vy9Zp9VqxZEjRxCNRjEajfDuu+9KphyJRGA2m/HGG29A13VxsKVSCZVKBTs7O7hw4QKKxaKk+gQvbrdbAns4HMbi4qKsTwBS72E2SZTrdrtRLpdx48YNXLx4EWazGTMzM4jFYhLMxuOxFHavX7+OXC6HUCgktY54PI5YLCaKLGYK9BcfZnd1uIyENpsNJ0+eFIlNsVjExsYGkskkTp8+PSF9CQQCCIVCeOmll7C4uIgTJ05ge3sbzzzzDILBIHRdl88xmUxIJpPyAi0Wi6gBjh49KtdRr9elMNJoNARZ8R83Fm+61WqJtGlvbw+9Xg+f+tSnYLPZ4HK5kM/nkclkEAqFhJOZm5vDZz7zGUGULpcLFotFENPVq1fhcrlw9OhR4Wu73S5KpRJarZbIt7rdLm7cuDHBr5GkZ3qn6zoKhQJarZZUS6n28Pl8iMVi6PV6ODg4EMkMjZuBDpmp+u7urtAJTqcT5XJZqq3tdhterxe1Wg3lchnpdFp+vtVqwePxoFqt4nvf+x6CwSAefvhh4XmbzSby+Tx2dnaQSCTkfujEQqGQOIZutytOkKk31SmU+Dz88MPw+XzY2trC7u6ucKNEPFarFeFwGA6HA/1+XxxntVrFcDgURQEAnD9/XgIbOT5KBy0WC6anpxGJRIS3ZnWb0q1EIoEjR47AarXizTfflIyH1880ktkGU83r169/YBWcHG6hUMCFCxdQLpcRCoWQSCTQ6/WEs19bWxPef319HfV6HXNzc7h8+TLW19clW6lWqwiHw8jlcnA4HPjud7+LaDQKj8eDXq8Hn88niD8ajWJpaQnxeBy5XE4AydmzZ+H3++FyuRCPxyWlV3l7cv+tVktqIFRrTE9P49KlS9jf3xcHOD8/j+XlZUG+Ki0XiUQwNTUlz42fNTc3h3Q6jevXr6Ner6NQKEDXdezu7iIWiyGTyUjAHgwG2NnZgcfjwerqKq5duyYSRSJTAqKDgwPJot1ut6xP8s8AJCtl7YV+plqtolqtIpFIIJ1Ow2KxCJXlcrkQDAbR6/UErUejUZGvZjIZzM7OimhAlZr+yA6XVTin0ykFB0JyyiZYmGJ6wBtMJpP43Oc+h0QigVKpJAuy3W4jEAjggQcekIVjt9tF98oKrlqZX11dxfb2NjweD44ePSpOm1GIFeFSqYTLly+jWq0KaT8ej7G+vo5sNosnn3wSfr8fzWZTpGjr6+swm82Ym5uD3W5HJpNBp9NBIBCQFwMAS0tLWFtbw+bmJmZnZ+WZ5PN5HBwciGO2Wq0oFAqiNOj3+4hGo0gmk/D5fAAOU+H9/X3kcjlomiaLnFwU07RWqyUoljYajdBoNIQ28fv9konk83nE43E888wzCAQCKJfLGI1GWFtbQzabxcrKCgaDAS5fvow33ngDx44dQyAQEDTqcDjE2dE5E+mw6q06XMqQpqamZB0Mh0P4fD4Ui0VBzpqmwev1ygams3n33Xdht9tlYbfbbeRyOfj9fnkuLFgRZTUaDXkWJpNJHDDRktvtFjkbUSCdf71eFyoqHo/j7NmzeO655zAej6V4Oj09jaWlJZFvRSIR+Xw61OFwiHg8LtdBmog63EKhgDNnzqDVauH8+fNIpVL4yZ/8SXzjG9+A1+tFNBpFPp+H1WqF1+sV7rtSqeDpp59GtVrFr//6r+OVV17BX//1X+Phhx/GF77wBVy+fFlUGyzMqppbOiPSc263G4FAALFYDGazWSiyVqslf58OpVAoSGAm6l9ZWcGDDz4oAZ1OOJvNIpvNYmFhQSSKNLvdjkAgIOspFAoJ0ia9NTMzg8FggFKphGKxiFQqhWAwOKFQKJfLokMeDAbw+Xyyf1wuF6amptDr9XDmzBmhqMijMlthIGYWQe6eSotTp05JPSYWi+Hg4AA3btwQIENd/MLCAubm5uB2u9HpdCQjLZfLopYid/sPcrgejweZTAYWiwXtdlvkEMlkElNTU4IsuZgBiNzr6NGjeOqpp6BpGt59913kcjksLi7KSyctQQ6LZL3dbkc2mxX+FYDQDBsbGzg4OEA4HJ7Ql4ZCIdlsLG6RDFebJNrtNpxOJ+x2OzY3N7G1tSXRr91uo1ariUSKqQHTJZLlxWIR7777LkwmE/x+P9544w0cHBygUqmgVCrhzJkz8Hq9KJfL8Hq9KBQKsNlsOH36NObn5yWqv/rqqygWi1hcXMTW1pakgnRQoVBIKvNqmkJ53tbWFkwmkzhFcrvRaBThcFjQYblcxurqKsLhMB599FGR4G1tbUHXdfzsz/4svF6vLExyXqRKcrmc8LRc0GzK4AZ0OBw4ODgQioDonCgmGo0ilUrB5/PB6XRKMbLf72N3dxeRSERSfGpXVZE6qZFQKDTBCabTadEzB4NB+Hw+yXSoaqESgEGWFfbp6Wl5PqdOncKnP/1pvPjii4JW6Iw6nY5cH3l2pq6qUZnjdruRSqXQaDRw8+ZNpFIpRKNRdLtdCepTU1PY3t4WLe3Kygref/99AJBC11tvvYWZmRk899xzCAQCaLfbOHv2rFAAlMqRk/f5fJIO2+12oQIYdFk4HY1GyOfz8rVOp4NsNivSSwAS7G/cuIFisYh2u42ZmRnJUIkOWYBVzW63IxQKTQS3Wq0m64tomLURq9Uq8qyzZ89ienoaDocDU1NTuH79ujRF8dmS62djDZ0oFRgABL3zPvhzatNGq9VCuVxGs9kUqoPFQD5X8tlUp7CRhHpdgrFeryeg706N9p12V4cLQF4seTjKNohmSBxzs+q6jnw+j+PHj+P48eMwmUx46KGH8M1vfhP333+/XHw+n0e1WhXdIj+LciW1cs0IRK5NbUxgYY8v32w2I5/Pi3OlsqDZbCKTySCZTIrM5NKlS0KOO53OCQ6G0rOKjWAAACAASURBVKpisShOxul0Ip1O4/jx41hbW0OtVsPKygqOHj0qFMGJEycEeaXTaaytrcnfcbvdGI1G2N3dBQAsLi6i0WgIDXL8+HE4nU5cuXIF3W5X0KvKZ1+/fh17e3vC7XIDz8/PS+ReX1+XDq1yuQwAE9KzYDCIkydPIpfLYW9vT2gBOi/Kq5h+srrPBdbv90XSRdqBioFyuYx+v49+vy/cZzqdxszMDPx+P3q9nmyaQCCAra0tXL58WXSlgUBAVAO9Xk+KHaFQCLFYbKIwct9992F1dRWdTgehUEioKqJhboputyvcXbvdRiKRENTb6XTQ7/fxyU9+EltbW9jZ2REKgakpA3Cv18Pq6ioajcYEwmUhRdWFd7tduN1uPPTQQ0IzkW9+/fXX5Z2wQBUOh7G2toYf/OAHmJubw8bGBnZ3d+H1eqWwxdoAtc1Wq1WyDv43kTgdkJod1Go1CYSU6VEd1O/3RaLId1oul0UvHIvFJANjBx6zFtXp0lmR+mLVn/+Y3agc9e7uLoLBII4fPy5BCYBou1W9PjXiVOFwXarNEgw2pCeo9lAz9N3dXbz++usYj8eYn59HtVoVzpqZHpt4crmccL3UzsfjcYTDYUHl1PyqzRd/b4fbaDRE2sSHNxqNsL29LWJ1GqMqHdsnP/lJ5PN52Gw2JBIJmM1mXLp0CadOnRIUwQhImE6ZBR0JjdGRUYfFD2o3B4OBtI4yAnOTRKNRmM1mQTg+nw/BYBBTU1N4++23kcvlREiucnWkA1TpksfjkTTf5/NhaWlJtHqsLhP9LSwswGQy4ebNmwgEApiamkI+n0c2m8XGxgamp6cFsZATp3zl6NGjov9sNBqSogGHOkdyz9vb29A0TTY0FQXsEGRXzvz8PNLptKB9Op1qtYqbN29K8a5arQrvXCqVUK1WRfYVCoUmNJ+tVguFQkF4un6/Lx2HfI7cqNQuezweKS7W63Ukk0nYbDZcvnwZN27cQKVSkXSYBY9EIoFIJILFxUWEw+GJQMyK/erqqhRPSbkwI6MzohEN09mFQiF0Oh0Eg0E8+eST+OY3vynggU0I1NASKcfjcdx///3ymQwOamszJVEU6lPfy2Ic6w8MAsPhEIlEQrTDBCJ+v18072rTETlKZoLqmmWRGjjkl1lfoHKHTpGpO4OUeg9qXYSKEa5PZqKhUEgK1bRisSiOuF6vS12B8tJ2u439/X1x8ES6p06dkqYejg9YWFiQghSLnZRgcc1xL7LRBoA4YVVDrHa7koNldjwYDES7bLPZ4Pf7JQvzeDwCMKvVqmQQJpMJ1WpV5IUsbKua+Q+yj+w0I6m+traG8XgMt9st8gkuSKYNuq5jdXUVuq4jHo8Lf+b3+3HmzBlcuHABs7OzwoURPfDhsX1wenpakJm6aMgF+v1+KYyxoqh2ZjGKlstlEWerjkLTNEG5BwcHCIVCggqoFyYnx4IVU2WTyYRcLodcLgeXywW/3y9yOJL31DyORiP4fD5JyY8cOYLz58/jxRdfRLlcxv33349ms4lCoSDNJaPRSHSPRMQzMzPyLFKpFCqVCobDIcLhsKAWddEHAgGk02mEQiG89957CAQCcLlcqFQqUsAiUtjc3BQZGwCR73U6HSQSCSwvL090J/E5siAG3NZGsyAIQCrCfLekLarVqqSqFosFR44cgc/nQyKRwJUrV1AqlaReEA6HJX27U/sKHPJlfr8fqVQKmUxmog9e1TIT7TcaDaTTaUFrACY44WPHjkkXE6vRKloaDof44he/CLPZPMFbUmrY7/dlT/DfwcGB8Ik+nw+pVEocN7WtLJ5S55lIJEQdUqlUpCGEwISyxvF4jCtXrgC43ZHHZwDc7rSk3npvbw/lclkKkq1WS7JJtdkhEAjIvudzZ5bDn3O73QJuVN4ym83KOjWZTDg4OECpVJrITIfDIWKxGJrNJnZ3dxGNRrGwsIBAIAC/3y9dXDMzM7h+/boU/Uqlkvx9ZhysC6jzONT1QRTMPc1Oz2q1KgoJgjEAsoYZDPjeXC4XYrGY+CzqkVkMZTHto+Rhd3W4w+EQXq8XlUoF+/v7sjjsdjvi8TjsdrssArvdjv39fayvr+NTn/qUIBU2HZw+fRrtdhsXL17EQw89hGAwKC+K5D51kUzzaTabTYjqXC6HeDwunCxROKObOlgjHA5LRbper6NcLiMcDkuU42YGIF1zjGZE35QZ2Ww2GbixtLQkEjj+f+ph4/G4zJWwWq146qmn5BrNZrOkIpFIRP6XnC+lWozqpG3YWgkcIhlqd+nE6IDJrc/MzMDtdmN3dxej0UjkcKReyHsxyJCbcjqdmJ2dFQ0rGyzurPpyoxFFEE0DkLSeFMvCwgJOnDghXTxEEnRYbHGmOH97exvFYlFmX/CaO52OtKfSSHV5PB7RU1cqFXl+/Ax2VZHLq1Qqsr6I0lm4XFxcFD6TBaVarSbo2ePxyDqh1et10WPm83m4XC6hLeicyF13u13k83l5Xnt7e8KTOxwOoewCgYDIH7l2mUURtdlsNqytrcle5TXS0fBrjUYDuVxOZFVut1sQnLp/+TvUJhOgEAhwDVPSWK/XZY3S1ME9U1NTmJ6elgDOZ2y327G9vY1Lly6hVqtheXlZlA1UB5Hf3t7ehslkwpkzZ6QoyXdLqZvX60Uul/s7jQ/AbbTLZ8TApc5v4ffpqyh7bDQaMn/FbDbD7/fL2id1UygUUCgUROf/D5qlwEIF2yzz+Twef/xxPPzww6hWq9jf35cUb3t7G1euXEE8HscjjzyCqampid74dDqNRqOBYrGIl156CeFwGKlUCslkUiQuFDLTAdLofFgEuJNeIJJmcYWo9MaNGyI3GY/Hks76/X7UajVks1lBEHQwRMPs9FHlP+Sw6aB6vR52dnakjXVubk5eKsXfZrMZZ86cgc/nQ6VSgc/nw7PPPotisShBAYCgTA7osFgs2N/fh67ruH79ulwDAwWLSXNzc4jH46ISIbJkas1C4JEjR5BMJoUHY/BUu+6cTidqtdpEhkDuk++ARlqIm4vUArn51dVVrK+v42d+5mdw4sQJSTOTyaQUaxwOB3Z2drC5uQmHw4F0Oo14PC50iiooZ9qmzkSo1Wpy/cPh4YQ59sWr0rRCoYBisYjd3V3Mz89jbm4OnPzmdDqFHqFsiJnc7Ows6vU6tre3BU3R0aobi2iUNIzZbJ6gD4h8qtUqyuWyZIQEDPl8HrVaTegv8v0MjkSa5Frp9EmncV3QETMj4ES6er2OYrEo90V9MVtT+axYKKQD5kwFlc9m5kcgQLqPRud88eJFAJBuzGg0KvuyUCjgvffew82bNzE1NYWpqSnhS5n9AsCRI0ewurqKzc1N7O3tiWyP74jDkK5cufJ3slwAEjS4nhmIOK1N13VBrWqn62AwkBkwlUpFNMRc/wQ84/FYskxSTir990H2kUUzbkaiqWvXrkkHCSUb3W4XOzs7cDqdMgmI1VMWDxwOB2ZmZkSQfe3aNfzgBz/AxsaGtFKSfrgzSqidQexkUpsBOE2KnVIOh0P64R0Oh6DpnZ0dGfN39epVrK6uCqJgOqcOpFDTAzoVVs/ZFqoORmGn09bWFg4ODhAMBpFOp7G0tCSth+FwGPfffz/eeOMNvPHGG1hZWZEOFnbYcKIW2ybZBgncTt9Z2BgOh5J+U861tbUFANja2kKj0cDCwoL08FNFUq1WpYlCHVlHrTMRDRsaKJQHIGmVw+GQYidbmLPZLG7evInvf//7SCaTOH78uGioWaAibaM2ldRqNdx3332yMdWBL9wEXG80DiyikkTTNAmaaoccCyKRSATPP/885ubmBL3u7+8LKmRhxmw2C7UxNTUFs9mMnZ0d2dB3rg11BgavOZfLSYbAAha5UAYqDvthcCPIUDW9lJwRtVNb3Wq1MBwO8dWvflXunX+HjoVadHU8JdEtgxH5aTaBMDCqxS4WXFX0zK5IZjnqXgVuj9lkG2wmk5HfobKm1WohFotJyzQdOdcGC3J7e3uYmZkR2o4ZBe+NSJuOmkice4oBnPdDSR0bG6inp06eHPX29jYuX76McrmMpaUlTE1NCYfd7XaFBg0EAhMNPnezuzpcEuUUpzO1ZzGFnMvBwYH0gz/44IMyP1S9APZl33fffRM6zmq1KkUKh8Mh0Uh9iYw4DodjYvwi02W3241YLCaoZTAYCC0RiURQKpWES4tEIqhUKtjb25vYoFzoKoJRER2ROgAZwsMxhEyzOEyGyoNIJILl5WUh2cn9cAbEd77zHZw7dw6Li4uia7ZarSKZojNh+gncLgAxbecm6vV6KBQKqFarsnBrtZpwY5x5QZ1vJpOR1LdQKKBer4sEhn+HUjlN06S9lc+G6To5QuqgV1dXsbGxgdFohCeeeAIPPvigZDcs0FD2xu6gdDqNP/zDP8TVq1fx6KOPTigqAEhnEAurNPL31LPy3VDBQeTLpgkWbK9cuYL9/X1ks1kpsDIImEwmLCwsCGVF+V8wGJSMhSm3avwMol02EqiDljjMh6qOSCQiVIvX65VCFTlB1gxIxZAKIjAYjUb4m7/5Gzz33HMCQOj8iTRZqOL8ZrvdjkgkIhyt3+8XUKKue6J9rjW1wYiom6hXdTLkh9V5DcDttl3qbw8ODvD444/j85//vCgoGPzZCDUajbC0tITz58+jUCjIMySNxRZjADKqlHtVpb/U9cK/MRgMZBgN5WLcz1S41Ot16RS02+3SsamqJAjECFZUIPhBdleHu7a2hvn5eYk+XFAApJc4n88LPxgMBrGysiLpP50hxdblchnFYlG6kRwOh4jAHQ4HksmkoDr1JbLIw0qnii6cTqc8eIqj6/U6nE4n5ubmJM0Kh8PysAqFAuLxuCAnr9eLQCAgAnT2jTM15/2SG2aEZ5MGK6tcbIFAAIuLi5ifn0cul5MmDFaRORpweXlZZD2M8vwb1Fqq7cXAoUSOPCjTbIrWnU4nfD4f+v0+Njc3pYjy6quvwmKxIJ1Oi8A/m82i3+/j9OnTcLvdgr7ZLEJnT6dH2ZO6KambBW4L76lt5CBvbp52uy0OhBPTWAxqt9uiTd3Z2UEqlZoIsNzYqmPlumD6y3SfSJBG3tNsNqNWq+G9997D97//fVy/fl3WCGkdpv2ZTEZaN9mEwdZptepNu3MuBguz3NjUkTJ7IsCw2WwIh8MYDg8nZ/FvqXyvytdSE8z0v9ls4k/+5E/we7/3eyKzYqsp51HQybhcLoTDYUxNTWF5eVkcM4tCbOThvqZzZUBXC1B8VtyLqlPj3ANSDkTBfC9cIy6XC08//TQCgYCMbWTLeqvVEm54fn4eZ86cwerqqnR1qtw8nyV5Xa5FPjfeJzl87iGqLCjvYzFyf39ftNu1Wk2em6ZpIsckIOC+YLBV6xwfZnd1uKurq8KVkshWh4OUy2V0u13EYjH4fD4Mh0O89dZbSKfTwkdRe1itVrG2toa9vT2REJFf8fl8yGazUjgDJuUVDodD5hRYLBZp62MLLvWjlLZwoSwuLmJ2dlYUD5wwX6lUJvrv6UCGw6GgGjWlAjAxA5W0Asl16jnJn/HnDw4ORHr24IMPIhgMolar4dKlSzKOz+PxYHZ2Fi6XS3hLIhlGTjX40DmyGENujyMn1eEbJ06ckL/JKjEnMlGfzLT/ypUr2Nvbk4jPyE1OXG3AoINhescFV61WUa/XYbfbcfz4cdjtdrz11lsIBoOC1JjuU2rYaDTw1ltvCcLY2dmBruuCyFSnRu5S/RrXA3XYdOrUrrIAF4vFkMvlhKt/6KGHxIkwtabDmJ2dleeazWbh8XgwMzMjnZF3qiW4Lti+TDqIxVym1ZTjsZECgKBnr9crpzKohWA6TBZh6XjZsfbHf/zHACBFoOHwcH4H0STRGwtXrLYzyyoWi+KwmNWwms+peywkqaMquf8oHROHcqueQl5cbdfVdV2KanNzczLhrdvtSrbk9Xrlb/D0B64tonDuP07k4/wSVQrHbIWOV+V1p6amcOnSJezu7ooKgzWpbDYrxWuLxYJUKoW5uTmMRoezRfb29oR+IShgE4nqIz7MfigOl6iC/fJMfSmCJpRmYaharcokI1UvyHZCpojk4IgymXrfKa9IpVIT49vUbiWVU6lWq1KMYEpDLSMfoKZpqNVqcDqdMqyC3Sjkbjm9ihuXm+pO7oqOkOQ90X8sFkO5XMa7776Ld955B7lcDhsbG1heXsaNGzdw5coVrKysIBwOYzQaYXZ2VgZIk3tkBf7OZ0HJFOkXUhkqr5vJZKRBI5lMirD72rVrImPhsSuRSAQ+nw/Hjh3D9evXUavVRF9JZ8bRgndSLES/auV3OBwinU7jzJkzcDqduHz5skj5VJ6aLbD5fF5kQ3QcRPm9Xm8CTTNjonGDMQWnyJ3IiwNQOPqQagSVmiBqI5Kbnp7G8ePHce3aNVy9ehX5fF4yIDY23IlkiMj4vIjguT9UKR7lWBz3yAIuefjd3V0ZJsOCpM/nQ7fblUIntaBOpxPPP/+8rEGm2gQfLLAyVWZLObsRWSTivRDJEuFSu8vMgmiWfLH6rGmkH7lWG42GFFhZIAyHw7Db7Xj33XdhNpvFxxw/fhzRaFQ+++bNm6hWq/Kec7mcoHvWHahsUovsqsPl/ycC5dhKh8OBt99+W4IJpXPj8eEM3kQiIT6Ma5bZJWlQAgIO9VFpsA+zuzpcRjnKsCiFoHiclXym1pyd2m63sbu7KxIOlXznWEOXyyVpUDgcFi1ss9kUnS2NFUFVoM1BIHw5bBmmODmZTE4UGohgGZF5XdSTklPmhuJ4Seoa6ZCZppMf4yLnAojFYsLjOp1OPPfcc7Db7fjBD36AN998E41GA3a7XQpMs7Ozcm+8PlXadCeaYlMBK7Xs5WZRo1QqYW5uDidPnkQikRCtcDgclk4YamaZflarVXi9Xpw4cQL5fF4CDx0f/y4XEzcV6Q5yzq1WC4FAAGfPnkUsFhPqg11rRCtqWyWnSdFhUUVBZ0uHRcSvLmj1WfDdUwLI9cAONg6PJz/HyjJTb6fTKQ0iLGxSmUMnkU6nP3BD1et1xGIxQe9M4TkYiQ6QjSg/+MEP8L3vfQ+f/exnBYXncjm88sorWF1dxWc/+1nMzs5KCysLOqQbAIgS5rXXXsPP/dzPYTgcitJjPB4jk8lgd3cXuq4L3Uancu7cORnG73Q6hT5hschms0mTBQBZj3R2zOTuRLcAJLskWOH+GI1GosRIpVIYj8f49re/ja2tLanv7O3tSVs6FUQnTpzA8vIypqenpTOOA3NYpCRny+xSdbiqs6X5/X4sLi7i9ddfl32Wy+VEH5xKpTA7Oyt7hmuGmR6z2Tu5XLXO86E+9W7fNJvNwseQIyHPFA6HMTs7KwuSTgqAaNdY1aeeF4BwI/v7+ygUCjhy5IgU3yg5sdvtWF9fl+sg38g0jRwji2ncQABkUxNFsVJN9MliE4/3yGQyEqm5aCORiKRVdHgsjjGdUdsmiUZ8Pp8ct8Kh3NQCJxIJvPzyywiFQnjwwQdhsVhw48YNGULOiitRhxoo7hz5Rp6b6FedR2G32/Hkk09KgZNIiI6MzpWBj/pOzhp1OBzY3t4WTSY79lTHzwVM58NN5XA4MD09LeMfyXUSee/u7gpCJ0fNz+Pvs0rNeyS65UhL1fg1lT5gGkzUPDU1Jc6WbcLk/TgchwU2blZuvDNnzuC1116TgeiULKqBCAAymYzMQOC6cblcUqT0+/3SenpwcAC/34+NjQ288sorSCQSEnz39/dlTkg2m5WOQnZ5ktPXdV3WCNNsIl/Scm63WwYkEU2nUins7u7Ks+JpDHNzc5KBkP9l4GCRisfOMMARFfN61LVJJ8j9TwRpt9uxvLwsa/HBBx8U500FFNE7tf4csMRzzah0obKHAUDTDsdb8h2q/4DbGRmzrOPHj2NxcXHiiB8qqaLRqDRgqN2m7KSk+oM8NtcMne7d7K7fVR0KCWqeuqppGlKplGyozc1NFItFKTRQE0u4TSkXJwQNBgOk02nMzc3J5mo2m/KCM5nMxLWQfyV3Sr0jHwSvkfNfa7Uatre3JTqz+khUTWqEbYQqsc/7Jk8EQKb084WS+qA0jJ1kfr9fBk5TSgcAzzzzjFTg2ZH29NNPy6AeFqlUmRYX051EvOrw+PO8zlOnTsFut4uWs91uI5PJoFQqyUbZ3t4WITcLfZyhC0AKFypSYeEJuM2dMpARxSWTSVgsFhHZk/dUkbGadhEpk5ZixsK+d1IVLEjy/6tGNMznx46vXq+HaDSKxcXFCR02AQCpIj4DOlMGB13XZdL/7u6udDuy7qA63N3dXRnmxKyQa4op/Xg8FpUGdemZTEaaHhiUefov181gMJBpaqS+LBYLstmsBBsAEhA4V4KqnYsXL6JWqyGZTCIWiwGAAJx4PC7H4RCocP0zyKtDYRiYGVT48yqqoxMieCLAMplMmLs1cYtrl3JFzj5eX19Hv9/H7OwsZmZmZCgVawQej0fapNVr4DrY39+X/ak6W74vrj2TyYREIoGTJ09ibW1NMnez2SyyUlXDy0yDGTBb/FWgpr6zu9ldHS6hPvlZRheS64lEQh64y+XC1atXZeoV+5SZBhKJUoeYTqcxPT0tVUrOT/D5fLh27ZoMjeEGJ2/DAR1qdKFInEqAXC6Hg4MDKT6Q/6Mul6MdqS0lH8sefPJXlKABEK0fR1OORiPZTMDhRDMuZJ4scfnyZVkERDiUWXEABjeBehwQqQEuHvVFcpFRB0tEouu66I23trYkgL3xxhvY2NiYkLBQz+hyuTA3NyfpK1NXdjnxYD7VQQLAhQsXcOXKFWxtbWF5eVkcZiKRwN7eHvL5vBQCGWhHo8OJ/ETiXMj8x6BCZEQdKn+XQZAoBrg96wO4fbwR+W+73Y75+XlZN6Qc+O5UuSILdIPBQM7a47H2zFAYgAkOVFtdXUUikcDMzIz04/MEDYIMzpNgdyM3K0+kZvs0+/dJ0XDTq9rfdruNzc1NORQAgKB6zne1Wq1YWVnB+fPnsb+/L0VLStGY+fR6PdFdkwPnO2fWQSkahw9xHbAgpfLqfBdqU4Wu64K8CVy4X4PBoCiHiHSPHj2KdDotTpIFWhaJNzY2AEDmXBBVcq/RqaqBUVUZsVC/uLgoJ3jQP3E6Gj9XlckRzJGz572qa0rViX+Q3dXhzs3NwWw2SzRlasEpVmorHhHt/v6+HPZHZ0V+ko6WRQ7OuyTU57zJjY2NiXZWphn8XFWjWqvV4PP5RMwNQCJ0KpWSM4/IIXHANF8KnTcLBES/Kr8IQDSddIAsJHBcm64fjl3c2NiQfnwemBcIBORgQHLQADA/P49Tp06JZI1zS1Xh+p0SOfLGwO1TTol67Ha7TPiisH9tbW1i4DQr30SlN27cwMLCAuLxuBxGSQfIziM6a/7dv/zLv0Qmk4HT6cQTTzwh0it2vz366KN44IEHEAqFpKNvfX1dTqxldsLqeKPREGdDNUw0GpWMhuuD8iEag6/VakW9Xke1WhXEzYr/9va2bAoOtGGTBhEoGzPa7bZoPUkh8UgpPgtmJyrF8sUvflEOaiTdxe48Tqgi0udz9/v9mJubk+DIfn2+TxYBWRvh547HY+maW1paEsSv67q0sTcaDVmXPLB1cXFRRP2c3aDyy1ToqO+Sz4C+4IEHHpD9zO8z8NMGg9vHphMRcp/w+tXWdDYfkIYDDjvMOKKRCiZql6kQUOWapBMI0u58P3S6qm6Yo1zX19dht9sxNzcn991sNkW2yAHx6rxrUmx8P3ymHFV6N7urw52enkapVBIZCpsemCJR70qHxqOv1QHedJwsXqjyHUo66Ag0TcP+/r50+KgPzGazCRqmQ+HhfeyHpzyMelyPxyMRlDwyOWA1raQTIxpXVQhqT/adBDoXGyc5tdttvPbaazh37hysVitmZmYwPz+PZDKJYDCIcrmMnZ0daJqGbDaLl156Ca+99ho+8YlP4Nlnn0UikRClBwsXd3Y1Me1nes40lgug1+the3tb2oG5uPiM+fNq2zJnA6dSKZw4cWKi4qp2HfE6iHJ4HHqhUBCqCDic4sVCHg8uBA7VBPv7+4L0yF9zE/BIIRac1NRR13WUSqWJGRssypACYfcf+enr16+LcoIBKRgMThQ21a5B8oL5fB65XE6KiidPnhR+mahOtSNHjmB7e1s2JduW6XwYVBhAnU6nZCXqMTcMBrquC7hgCt9sNnFwcACXy4WDgwM8++yzeOihh+Qd2u12FItFmcRGNMiTgE+fPo2VlRXJ5AhiqAvnNXMIDJEcny3PEmO7MPlKFWFyvQGQvcF7oIqFn0+qgvdGydpP/MRP4PTp08jlcggGg0gkEnjnnXfw1ltvyQkh5JtVcHJwcPCBmnk6W65f/k36g2AwCJvNhkceeQThcFhmPhBUOZ1OXL16VYDDeDzG8vLyRObBGhfByd3srg6XUZ1tn4uLi3jzzTfl/xcKhQm5jiqnolBe7cemlq/ZbMJkMomsYjQaSXrMEWrq3FOmN5Tn1Go1FAoF4ZfJL3IxUw3BjcyUgE5TlXqRLyWFwIcNQAZ08BrUpgcibA7rpp5yamoKx48fR71el24uproul0umPLEwNx6Psbi4KBuPjkQl49U0OpFISPGJC5uZAjc3K63z8/MIhUIy75fOhek577VSqchJw+SEiRz583cWzfhMmfWwoMBWymw2Kz9D/e9wOJyY90C+TN0kLEryJGcOilGRn2pq8Y0Dk8rlMkqlkmgq6XSoF1eLQmpAtVgsmJmZgcViwYULF5DJZFCtVqVjjM/8zqIZR11evXpVKJJcLodYLCYNBVevXpWaAo2Vf027feoHwQGVFTzian9/XzIEi8WCEydOyIGQAKThYnd3F4uLi6IcohPNZrN4+OGHpfOQnWgspFK6yRGa7XYbe3t7KJVKsp5UdQ73GvlfGh0Vs0weacU5FKux0wAAIABJREFUJurZe5qmiaKEg9Y519jv94uenEBPVTSoet1qtYq9vb2/szbU+ofqeLl/6VDL5TLee+896ehTnev29rYce+90OnHixAk5EEFVMBHI/IMaH4DDaM2K+pNPPonNzU2JPpQ4cVYsb4SOhKaedc9URC1Akb8cDAZYWlqa0LQCtx0/SW1Cf55mSjTUaDSEaCey4ueojRFE1oysHFPHg+q4sJg6AZANRwqEgmk6L541lUqlsLKygmq1imw2K1OzqCPmtQUCATn+hPpKOv9yuSyFhsFgMFFAPHr0qEisuLCJMCh1m5+fl+NI6OhUWoLyMCIbFqZsNps4dyJUNe3kAuMzcDqdSKVSOHfuHILBIKanpyWo7u/vC6rs9/syyYvomFQNAyFnGrPaTnQbi8Uk3edUMRrRBfXIfH5qKyzP+pqZmZEGADpvTdNEx8k6hNPplHPd1tbW5Pw7BnKifzUAXbt2DTs7O8L/k3LrdrsyDyAYDGJmZgaNRkNO9eWZWiwUcZJZLBbD4uIiOp0Odnd3hXfXNA2rq6syj4QdbQDkvLbt7W1sb29LMYpO6NVXX4XVasXzzz+PlZUVoVJUNMhgV6lUsL29jfF4jGQyiUQiIffL904AxaIYjRQfHQ+d5dramqyzq1evTtRiXnjhBTz++ONSbCWKvnHjhhzR9eKLLyKTycjJv8ySxuMxtra20Gq1RAnFrIgIl3QYUS6drd1ul7b39fV1vPzyy3I8F2lUi+Xw6KZTp05hfn5e5HQEN1x3DC5qIP4gu6vDJXlusViQyWSwvr6Oz372s/iLv/gL2TiqbIIpI3kaEv8ul0siH7WElH6ocxE8Hg/OnDmDq1evThTNyLfSUfMMLR6EqBa86FCIfJm68m9x4XKCEn9XlYmRi2TlmZtbdbBMZSkEZzeVOumKiEntDSeqJMpn4YJOOZfLybEo1KyqwScajUrjiNlslmIPAwclT8ChUHx7exu5XE4cL1EsUTHbPfm+6bDY7knqhM6dxg0XDocxNzeHmzdvyhEsFLnzusm1M71nGsr0nFQIKQIGZc5c5YIOBoNYXl6WayC/SwdKpQmddTKZlIo3ERFTdPKK1Pp6PB4ZlcnnSmRNXlOlZlTz+/145JFHcOrUKbz55psYjw9PoC6Xy9LGytMS+POj0QgLCwuCEIvFokxN83g8UgNR09S9vT0kEgk53YMbnmueNMS1a9dEDsfOt263i/feew+DwQAbGxsyHJ3PkQEvl8vJsVrHjh2Tk2lVCooOl9ym6nAJRCj94ruYm5uTGRDdbhe5XE4agB577DEpeHF2CE9auP/++7G3t4e5uTmUSiXkcjmkUilB2oVCAbu7uyJf5LPgc+H+VGsQ9A+UgdpsNpRKJdhsNhw9ehT7+/syu5gFPM7vLZfLE/I/rl0W7u/UJd9pd3W4dD6MFq+//jqefPJJPPXUU3j55ZdhtVpRqVQkTWbazwEl5ETVkWtcsOR/WLwAgIWFBRw7dgzpdFpeAHAbUbFoxaHDAHBwcDDh5ABIeylbQXkgHLlZznNgfz9nKFCMb7VaJ84AAyD9/jw1WO0uoWJCdfzNZhN7e3tyRDYRIxchTzPgc2MVuVgsiqMymUxSiKEFg0GEQiE5P4yVZNIWHA5CSoUIDoC0frJSToSjFrwY/FjIUNGcurnp6ADg0Ucfxfvvvw9N0ySNZrBQA6zFYpGxlBaLRZACu7DU/yaSorKE4xLT6bQ8C2Y9pJQYZKmICAaDophRkRnPRuP7IO3Fn+Us1H6/D7/fL1VsPos7ne7S0pIE0eXlZWln5ghPdiJyMhU37M7OjkyRo7MgYmSRiPK9YrGIcDiMF154AeFwWIbDq4ifwXd3dxfr6+vS8jw7O4tGo4FSqYTNzU0pHrPOQlUMVQ48ODESiUjRjkU1OjBmhWw0oum6LiCBz4k6ZK59zpU+e/YsvvCFLyCRSEjdgnTbsWPHJkY2plIpmQzGwwcajQauX78u80vUrBm4rRFmNgPcLpyRj2fXZiQSwUMPPYSpqSkZBcAJhJ1OR/TqBBQMKurJM+SG72YfiXABCNfBQs9P/dRPYX9/H9evXxcel3wtGyO4kBlFmToSKbL6SYfi8XgkCq6srOCtt96auBYWzogyqHgADgMDTwRl+sdWTfK+LNZxhkKxWBTEx00KHKKPmZkZnDhxAqdPn5YXRb6Wjp/3pEp9qGUcDAaizOj3+yI9YSOCw+FAKpUS3S0bFzjflc6c8iK1KOFwODA/P4+1tbWJlmR+BtNv8uw2mw1TU1Pw+/0yiIgUAdE65Tiq0yMyYoajFotUQXuj0UA0GsX8/DyuXr0qqIrvjAt0MBjIJudzYx8903wWCfk7TAnZ/3/q1ClZT1yf6rWzaszDBtnsQCepptHqsTgqH8f1S5TLnyfi5T8V7TNtV2VS5XIZR44cEckTC2pskqlWq9jc3BRO12w+PEWC4yDZVclup+FwiNOnTyOZTEoLsjrIh0GV97a9vY2lpSVRv5Cq4mhEStU4WpBOn8dBqddBCo7n3nGOCteruj69Xq+gc7WopRajOp0Obt68iS9/+cuYmppCrVaT+onZfDhkaGdnR3hecv3s3iRPv729jU6nI4O/PwhdqsGRCJf7hIU/3heHW6lBjJPwWFNRJ8txX7BmpHa7fZh95DHpwG35jdlsxiuvvILHH38cn/70p4WD4vHc1Ie6XC7p+OFFEYFqmiZqAaZqRJS80fn5+YnFxIcWDAZx5swZOTmC0ZAvkeiAwnNVRcCXz6IdF4T60EKhEBYXF3HkyBFRGDDaUT/r9Xrlb1EVQZTIk2cpk+Phl9S48ne8Xi8ikcjEwA0WZ9QmDiK2O6viyWRSioc0Vp3ZQMBnNhwO4XK5pD37Tl00AFl0ROZ09upcWjpJvg+mU9VqFaFQCJ/4xCdkkDYHevCAUW7wbrcrU9GIHujcGCRUsTmpDx7eSfTBjkYucP4DIM0v6rAdSsXG4/HEKEs6aAbdUqkk6Jeaabahk+dVK9+0lZUVmYMRjUaxvLyM/f19nD9/XjIKbmo6eGZMlL+5XC4ZYsP1Rvljo9HA448/jl/6pV/CzMyM3A9nCQC3gxsLRRyBGAgERDVCNE1tNA9b5awLdbC52WyemBrGlnCqSdhkcaejO3nyJN555x15/0Tp6pAnnpTL4jK7vXq9w5N1OQ+Eki2z2Sxnz3Hd5nI5Oe+PQVo19f+rWlw6Wu4pk+nwwNfr16+L4wQgwIjvnAV6UhdE4yz4q3Ti3Uy7W1XthRde0EulEnw+H86ePSvUAR0ZnTC7L9SmADUFG40Oz2a6efOmaAhv3LiBQCCA48ePy6SenZ0d9Ho9LC0tIZFI4A/+4A80APid3/kdnVGNRbnt7W3cvHlTOqLYOcMBO+Qs1fZftRdfRUJqWy/5Xj5st9uNr371q9rW1pY+HA6RzWZx48YNfPvb35bN9dprr6Fer+NLX/rSxAGQTPc7nQ4qlQr+9E//FC+//DKCweCEvIbNEg6HQ84je+CBB8RhjUYjfO1rX9MA4Dd/8zd1zvvl82BVWR3ak06n8fjjj+PZZ58VrlJtm+12u7hw4QLeeOMNbG5uYnV1VU4TpiwpFovJNZEW+ta3vqV9/etf13lAKKM/HSgXNO+dMw2YzjPzoC6Vz56ie1WSlkwmYTabsXXrSHd2lf30T/+0BgC/8Ru/oat6bgZCtTjC61Pbecvl8oSoneuZ6JOFL7v98EgXntNFCRGR8+///u9rt56XzhM+KpWKtN2yAMiBJ6R0aFR00PnxJAir9XAQNkEABzWRGwYgJ/2ORiM88cQT2te//nWdWQ7TZnLWTJ/H47HMgCX3yENRgdspOJ3Rnc+PdMve3h4uX74Mq9WKxcVFeL1e/NEf/ZEGAG+//bZO3bfb7ZaWfV6TWkzjnF0A0qhETbSmafKcef1U5LBmwe460hYejwfPP/+89vTTT+t2ux2zs7OYmpqCxWKRWoQ6XIiZCSk07g+1s/HOwjQBAX+PlNb777+P73znO8jlcjh//vyHVs4+UqXAiwUgk38oXLZYLJKK8SExIvCiqDHc2NiQqi8PTRwMBjILNxQKyVxXbhz1GkymwzmqxWJRpjixOYC0BA8jZFcX02ZubPUID6/XK/3vuq4Ld6k2J1BSBkCOAfJ6vZiZmcEzzzyD7373u7jvvvtw5MgRXLp0CcDtyVF0Lhx2fvXqVVy7dk2QMh2Sw+GQwwQ5vYxzOdnBpUqJWOBj9bxeryOfz0vveSgUksE7rVZLOGRuJDX1pKSm1+tJiyfTOACC8FgcJfrixmSwJdrlpqQDIwfM9JwbhsiTQUntOuPCVjMSZlJEx+q6YIZCx0IpFAtKbGKgw2Xqx+fBgKJydVSwqEVHcr+xWEzWP61YLEp6yufOd2YymSRTpM6XzR/kRkkL8ZoovFfvjQGpXq/LNDd+Po1pr+o4/3/23uS30fS64j6kSGqeKFIkRUrUPJVq7qpuVw/utl2eMsBJ7CAJkqyC/A/ZJtssklWAIEAWBoIAThaJATsIbNg9d7m6q2tQVWkqTaQoUaQoap5I8VvIv6uHslP9fQ6+XV6gYXepS3z5vs9zn3vPPedchBd8Z5cySXObrMylAQJnXaRBEvjg+VIdcNGAoypinQOvuU5tNM9ofNEDAL6AcURTk+wb1hJsFmBC3gmVGeuKKo3KjZ9RBXIYQARgjVzkwPP3OOA5PGtqanTjxg01Nzfr3XfffXk8fdkPGxoaDFehHKA7yUslzY/FYlVcRzKpSqViZQNyv3g8ro6ODgPpw+GwmY9vbm5qd3fXAjn34XazGZtN0251dVUrKys2XgazDlf6h68DPD7cydz5RARdyi5XAvr555+bemd3d1ednZ2KRqN6/vy5fD6fTdllDDQ2kel0Wmtra5qfn9fGxoY1cshEKd+QXLIo0+m0ZVSuc9Pa2poxGqD9TExMWJODSoO5VXw3FibCEBYTnsFQt9jkxWJR+Xze/CVgnLDACHbg5ARAnlFNzblZvSuaoGrgsIOmx/Nng8BegTAP79rFcBHfwGIASoHNQomOIxi9AndDAW/4/X4zn2YKM8ZHHBRgr7FYrGqf0BsgkDIdGbwa2IxqByoRDSCv98zvlrJ5a2vLDs2TkxMTcmSzWYM8CPI0j1m7blOPw5WDj/XvZmgkVLBqeI80Pff29ozlAuxDBQBc5Ep7wVmZJkIWzfpDnIG5FDEASTVMm/39fftZe3u7ZfxuLygajaq2tlZTU1NKp9NVTTO3V+GyojjYgJKgLiJO4eB2PSNceI3DioSA5mBDQ4Nu3rypnp6e3zzgQsY/OjpSOp2W13s2oqatrc1OaE4wTmUG89F1d/merrKEBUqZ0tTUpMHBQT1+/Nh+r92kw+dkKi0wRU1NjZXjbmntqpR46HQRcXTH0AUMhgcbCoXsoOA0/vzzz6uaiGR46XRap6enam1t1ezsrGUEeGqGw2G99957pieXZN1hyiCylNXVVStpkJO6lDA2D1nKyMiIxsfHde3aNXvRyDsJcGRIdM1dxRySRrIFKDB1dXUmC8Ymb3193WhNZIzg4S6vmrl2MFDA7vEzoNOLAAKxApuDqmJ7e9vKa9bMRde0XC5noo3a2lrblKVSScFg0HjC0WjU1gUTA2AVEAjd50u1wUblAEAI4X5fSdaAcxt8HKIue2JnZ0epVEpra2tmiwj0ReV40UgHtdr+/r6NUP/444/V0tJSJRyhSeo2pN2xPBzcQEE0N+mnEFiks+CNHJsZgF1dXTabj6QGRoLbNCOLxiOEKnR1dVX5fF6rq6taXl42hgPMBypIMsednR3lcjllMhljEYyNjam3t9cSKZp+mETRb3GTBvi59IASiYT9OxfQQCAQsIPetcEEz3ZjkgvFciBL0tjYmF52faE9IyVOb2+v3nrrLfX09Gh3d1fT09PmdAStBkyPzmGpVDKKCs0VRp2Dyw0ODtp4m5aWFsXjcS0sLFRluOA6mDTncrkq1yB8RsHEgCXcZhmNBiAIHhqnvHv6d3d3WwbGIoQojQ6fcouFvru7q5mZGY2NjenOnTs2ffaTTz4xbIoTFXMYsDn8eKVz96qOjg41NDT8igiktbW1aqT56OionfRkvyxiMK62tjbt7e0ZxspiZkFWKtVTd9vb2w1D4wBobGw0UQS4LM2nTCZjblTQZMDB2DzIdvl32A00GcmOw+Gwlajgvr29vVpZWTFWB9fW1pbBTzRMyXSls8DR0tKi3t5eEzW4gz9PT08t6J6eniocDmt4eNiaj2ykQqGgubm5Kum3u/mCwaAFFpeSSCKSyWQ0Pz+v2dlZq+BOTk4Uj8ftgDs+PlYkErHp1UdHR6pUKspkMkat9Pv9Wl1dVXd3twUX92AFg4QvyrPAY5n37fGcmfBTKZIRE3Tw4Tg5OVFnZ6f6+vpMgELluLS0ZBJlV5jDcyEOEORRjWEM4044gdEERk8FRWUlycQJPp/PLASgGJKgQCUFtkGQxfdmj/T29ppxFko/DkvgG9g+pdKZ9WsmkzF++sVpJG1tbUZhu9jg/v8UcAk0iURCr7/+uiKRiClZyBI5dZHmUkpTevDf9fX1KZfL6bPPPlMqlbIHu7m5qUQiYXOwBgcHtbCwYEFRkmWp4Cz4mJLtseGkc8zMNf8g6+Tf6XIDF8AqYLEtLS1pb2/Pus/8XtRoBEcwIp/PZ4YaU1NTqq+v19jYmLLZrD755JOqsiMWi9nipmTkpbvULhfmcDc3lofQlZCgwtWE6N/f36+9vT077IrFomZnZ+X3+20RgZn19fVpbW1Nk5OTNuxvenpaiUTCTG0IQJJM5ojs8ujobFw8huMoeQgsLjYO/BMMBk0OzWcgNyUAw18eGRnR8fGxPvvssypMjfvgmdTX11tVQVkIqZ13BMxF/wENf29vr65cuWIjiQhmDx8+VDab1dHRkVG03OYPawOMlK4+7IKnT5/qF7/4hVZXVzU8PKzXXnvNgsWzZ8/04MEDxeNxdXd3myKtqanJGnAkIjQ+A4GzcUHPnj2zz2afktUdHh6qv79fiUTC/CCoKAliHDRuVoycmz3R0dGhWCym1tbWqrFI8F7D4bAlPlzsF0QucJrX19eVTqfNK4Km9dHRkXmjSLLMmb0KlZDACxcX7jk+1MFgUMvLy3Yf7DEOCKptpLowkVxhEgkXFDrgD5IOrFep4nEaBMvm97zsemnApbyrr6/X/v6+PvnkEz148EBzc3M2Rvr27duqr6/X4OCgqVHy+bwZLbNQI5GIqWvoAHOKEwSKxaLNsXdxSzcTbWxs1OjoqGpqaqyxtLGxoampKQvAlMt0Sl1JLwwEghj0IU41lygPvirJeIcEb6hbdDD5X7//zNrx6dOndjDBEICcfXp6ZhxNs8kd1lhTU2McUtcKjysSiRi3N5VKGZ63tbWl7u5ufeMb3zCnLJpCYGfZbNY2KdaUVCW9vb3y+/367//+byOTF4tFFQoFdXZ2GnYonWWWzGPzeDw2cgTTcVdJx3RZIAUqhEKhoFAoZEGV95bP561cxtW/pqZGly9fVqlU0tOnT6s2d3Nzs/FXk8mkJiYmzLWOaobAdXBwoEwmY0NEs9msstmsisWiiQN478x5+9nPfqbp6WnDx6HYuUEGJgNJBokI1c/w8LC6u7sNv0f1RkWwsrJidp1NTU02Kn53d1c9PT0GPeRyOc3Pz2t3d1ehUMhgNd4p635gYMDYDHhNuER99gFYKerF09NT+4zT01MNDw9rcHDQqlKqFFgBnZ2dWlhYqBI+uPPPWB8PHz40hhLUNPY9yQ/TIPC9Xl5etr1INi+dwUixWKzqkK2vr7dGOfECZkxbW5tGRkaMjYTHSSqV0urqqlZXV9Xf329ezrW1tdrY2NDS0pK57A0ODur69evq7e1VNBq1g49+C8wY1v5vHHCBC05OTjQzM6OPPvpI7733nra2tjQ+Pq5SqaQvf/nLOjg40OzsrIaHh021grM8DazGxkbdunVL165dMwrG4eGhXrx4YXxAtPNNTU3KZDJ2H+BcnO5kt21tbVpeXlZ7e7sGBwctwGezWcOMGZuO6TlBjgvye3Nzs5U8zDtjMKZ0bqxM44hOuFs+ggcy5BGM9Uc/+pFmZmYsS4zH45YpERTdLJYDyVU4cRHgNjY2dHR0pMuXL+vq1at68eKFrly5omAwaI5byJ4lWYlFU4DvLMk6zi0tLTalwuv1amdnR+l02jxb3QUNjsm75plAZVtcXFQ0GtXbb79tNndM+VhbW7Mshm48sI7P57NGDM+fknNgYEBLS0tVa9Tl3O7u7trg08XFRU1NTcnv91szrVAoGLeTYM4ct+7ubpVKJUWjUQ0PDxv8c/PmTUWjUcNgyc7cIPMP//AP2tzcVE9Pj61DMsiGhgaz/UQ+jkBnd3e3qo8hyUQIfr9fN27csMN/f39fP/3pTzU3N6dCoaAbN25IkgVcMkLk1blcTl6v1yY9+Hw+m7BdqZx5JzNhBIoZPgq8i/39ff3gBz9QLpcz2XAsFtP4+LhxtwOBQBWLhvXFntnb29PCwoKy2axN9qZKRS5Ng/OVV15RMBjUgwcPrElPNkulgtcJ1SC8eEmGu5LFI/3lmTQ1NRkWvb6+rp///OeanJy0nguZK5/FlORisaj79+9rfn5esVjMqg32PpUCityXXV/oFgYTARyqtrbWZHa9vb1Kp9PmLt/S0mJZFFkOXXM2Le5VAO2U8el02kj6NKC4KE/Y7OB2dGTR1LvTeTnRIIdTooJzRaNRBQIBPX/+XJKqmBV8d1ccgKZfkhHF+V1QbyhHCNhvvvmmmpqaND8/r/n5eStrGhoarDHHRnFFI5zylORuwIXb2d/fb8bhwWBQd+/eNSzQJbVj8kN2guyX58a74rmPjY0pkUiYOxVDDt1Snu+NFBn2SnNzs21ankMymVRLS4vm5+f1/vvv6+nTp1pfX7eseXNzUzs7O8ZiuXz5spX7fI4rAHBLeYaBwimurT0b4RKJRDQ7O6sHDx6osbFRExMTNjQxl8spGo0a+8CljB0fnw0uBLPl73Z3dyuVSlnG09LSYo0tSfb/V1ZWtLOzo8HBQV27dk0ez5mJ++bmppW7hUJB9+/fN/ELrAU44MBjlUrFMG3W58jIiKanp21Tr6+vW4ZIpUCluLGxYRUb1DZcuGDmoEBbX19Xa2urpHMvaZ/Pp1QqpQcPHigQCGhoaMgywHg8XtUwZ8+zN+CIT09Pa3FxUSsrK+rv71cymbSsHyOjrq4u68ucnp7NFtve3jY4Cxkw0nyySDxb4Iq7ni6sld3dXeVyOT169Mjgv8bGRgu6jY2N2tnZUSaT0fj4uCUVXV1dyuVyRuV0LQ5IIBHkAEX19/dbIH7Z9YUTH9Cy19fXa2Jiwpow4+Pj6u/vN8Nmv99vemPKf1dVwsjpWCxmVBk2E9kcmwClkhtkXFkeGRGdc7Ig2AJgo/BuaXaRWYP7IkWuVCo2fYFgsbOzY6exJJsdlcvlJJ3r+KXzLjUsCLC4rq4ueTxno4g4KSk5AfGR0XJIwPFDdUX5xwUuhbouGAyaVp/GEJSsXC6nfD5v4+x5RsAFkPHJbhmQt7q6amNuwPe4X+4BfDkUCpkJi9tUZNwSXeSvfvWrlnUdHBxoeHhYAwMDGhgYUCKRUCAQUDqdtsyYQ9rleboTXKWzbGpxcdEy4Hg8XmVKf+nSJQvmNK8w+YGDjEn6/Py8HZ6utFg696Dw+XwmRHCHnN68edPK0AcPHmhyctIyZZRIGPFcuXLFjMNZa+CZfFdwZrBi7mN7e9syKqoXgh0HNhjy1taWpqam1NTUpM3NTaVSKcViMd28edOqK94vVpqMtwoGg4pEIhoZGdHQ0FBV959DngPyYhk9MjKira0tffLJJ1Wz8jo6OgwDBn4rlUqG60Lhi0QikqRHjx6ZYxnqTEzLWXuzs7PK5XK6c+eOuru7ja5HNUpPACFJMBg0+mkqlbJm/8bGhq1v9tCVK1dUU1NjCjQa6BwQZOU0dt3pzL9xwIX7ykIcHR21KN7a2mqO7VAn6Pa5CoxCoWCddzZ/V1eXGZOAMZGhXpzewMbihfPF+F8GQoIJEXDAjgmo7hSKzc1NraysWNmK6gmnIl4aRh6SrPymG012SHbK6UkXm0YiWBhjT0KhkIHslPXgla7mm8wLLJbr4ODATnK6tPBTg8GgYevgYAxTRAjgQgP4SXi9XsXjcWWzWSPu0xyhXKupqbFGJko6V+KIfWFbW5spiOLxuNEK29vb9d3vfldDQ0N6//33jbvd3d2tRCKhuro6MzGBAcEaghVBUOBqb2/X5uam0b9CoZBhejBbUHrl83mtr6/bge02dnHYwtOXRCGdThvu7DYvyUC5GMr44MEDzc7OKhKJaG5uTmNjY4bzh0IhZTIZffTRR+rq6qryq6irq7Osb3t722AEDGM4KNfX1+Xz+WyUEd+Tq6amxnoD7MlK5cyus6+vTw0NDUomkxobG1N9fb3W19erjJuo0njnLmcbCA8c2D0M3eQIXDYcDiuZTNrz5Xkhvd/Z2ZEky8oTiYS+/vWvKxqN6t1337VmryQzhAoGg2biHo/Hq6S3rudId3e3IpGIOYDRGO7r61M0GrV9VC6X9fjxY33++ee/Qo1rbW1VLBYzBgtq0ObmZjMFwpuDOOk+h//pemnARQnkSjEBiY+Pj1UqlSygwXOFSuP3+80DNRKJKBaLGSWJxhA0E4/HY3zdYrGorq6uKmd/SkoaVa50mPvE9Qs8hs1/UTHT0dFhTT0UWpxsHR0d1rTjJCUIurxLMmQeMEGMExjFGx6ura2tmpiY0Pz8vOLxuH23fD5vBxWdfYIclBpepvvZBGMMqcEjmUlWW1trZeXq6qoR1Tk0APyl8zlQfA6yY0phOvBuA49D0tX95/N5k1fzbJBZ824aGho0NjamfD6vx48f68mTJzY8kYOPjQ3rg+ALs4AeHV9fAAAgAElEQVTSV5JR99xuuCQj4tOMrK2t1dbWlqnIwPWgUJHxZ7NZI9dfHDfDesJRrqOjw+4Dr9dYLGZNI6oUOMbYEs7OziqVSikcDld5R5MMgOu7xk/wmDH52draMmy4t7fX3onP5zMqFE0leLI0hTs7O43Tzn6DyUPJTrY+NDSkjo4Oo6RJZ5gxBu8cPC6FE0MbSnB3ggTcbqTg9F4aGhoUj8eVSCTU2NioYDBoJb/Pd+ZNC8WR5l9tba1CoZBCoZBaWlrMb1qSBgcHFYlEjLKWz+e1tLRk75RmXEtLi90Hkv6LRkdUfOl0WoFAwAgA0WhUsVisyrwHZ7iXXS8NuJTyiADAG7EiI4Nws0vA+LW1Na2vr6u7u9sc48PhsO7du2cmyjjPs6BQllzMZFwJH2XJRWAe8URra6t14Qma0rm1IJsAfh6ZFObPbNpIJKJ4PF41+p3Mg6BH6UIgYooDXE7XqLi9vV2tra3GtwyFQkomk9ZIYZwJi4LfS/nE5frnusoiiOPwB2nw7e7u2vSHeDyu8fFxraysaHJyUpVKRZOTk1pdXa2aqeX+I8kOJDYd1JyOjg5TOrEWkHGDR/NnBPlisWiUnEKhYBsID1RJJu10M31XVsrV2tpqHNNCoaD19XX7TN4F/YeNjQ2b9+VWbqjc4Hl6PB5ragJlIIyh3CeocM3OzuorX/mKQqGQHjx4oCdPnhgOD+6LH0Vzc7NNAolGoyqXy1pZWTH+LgEbNgvSeAImVoeTk5OmPHP3gbsfoUdCfaMqBM7I5/NWnbjy99raWqXTaXk8HsMmyYRpXEGPhIXCVSgUtLKyolQqZXuSiqJSqegrX/mKTQaBSoUsn33c2dmp/v5+M2MHsiHjpRlIcgGFC2aTx+NRKBTS+Pi4MS/y+bwaGxttmvbJyYl5T3OYus/B6/Wqq6vLjPWhNdbX1xtMCZwAC4d48LLrpQEXriYBr66uzoB8xhfTLOGGV1dXdf/+fe3s7CiZTJpbPGN2wN26uroUCoUUDAZ1dHRk+A0lQ3d3d9W9nJycCIMQuKIudgR2S5bNlwffBSeFKwq+65qR8Pv43WSdkixDcbX6bAK6pQgFIpGI/TkQAHLH+fl5bW9va2RkxOg7XV1dFmzZXHA5gUC4MDuhuqDCgF8M/siJzujt/v5+dXZ2amxsTJ2dnVYSptNpbW5umn0iVBfpHBdkEVEWwj8sFApVDmnHx8eamZlROp3WtWvXbFMAW0Dgh0YWCoUs8EH5c63w6FK7/squqsntTnNYkv1RFeHzykYEo0TVBI6/srKipaUlow3CbGBTEWh5Lq5TGz8nI6IxtbGxocXFRVPyNTQ0aGhoSLdu3bIpu/hhwEPnOcER5qChSpuentbJyZkpOk0vghABi7UTCoWM+gd8R+lNp59mFEwAoCFESwQehDSHh4fGrvD7/SbQ4cJzl6kWqDWxB8UMvlgs6uHDh3r48KG6urpsXiElOtS4w8NDDQwMWIMeeAj4hefDHDdJdlDhFTEyMmJzyLAA5RA/OTmxSrhcLlclVcAjVLQ0zUhySLT+30AJXF8YcGmQUM4lEgn19PRYScVpymKem5tTuVzWzZs3tbi4qCdPnmh9fd2oPgcHB+rs7LRBfdgXomziC1Iq8QDB1YAy3MyWheNuFBYSUj0UXkARKI048Sl7WLxkA5SwvAgCAVgghxABYGBgwA4WSVY2spCpGObn5xUKhWw8Nhgw3xM4AdknF6e722g7OjqyZg6HysbGhm7fvq233npL6+vrev78uerq6vT5558rm81atXD16lXLxHCTAjLimbJw2dwcajxf7nNtbU0ff/yx3Q8ZlutdwDNFFYeUlu+OUg4oQ5LJay/SscAGKS/de8/n83YYANWwxqhcgsGg/ayurk4vXrxQNps1g3tMYvBhIFt3FVDSmXE+Q1ITiYSGhoZUV1dn6x6TJnevcKCXSqUquAoID+wa3ivPhiAEs4QKDgyRdQcjh4nMWCrSKGLGGFACdC6Sh6amJoNTEI64Q03pLbj7RjprKqMsIzDHYjH7B++TZ8+e6d69e0omk5YMUElgXTkyMqJ3331XH3/8sd555x2NjY1pbm5Oy8vLRg90ee0EXA4cPDbcxIlnAy7t9p9cLj2YLphtPp/X9va2BXYgVFf8xHN82fWFBuSoJ5qamhSPx03JxCkImZgXQJkJWb2zs1Ozs7N6//33DbCmNMMshQYGVBU6zu5FV56MlaDkOno1Nzerr6/PMhcyIHewJRgzv4/sgwOjpqbGZhM1Nzdb4Ccwg+vSiEPH7ff7NTAwYHgq6jHoaWTdVAT5fF6Tk5OGJbKheY68SA4Pd7HwDCjbycTIiBcWFszRKZPJ6Kc//akmJyeVz+c1PDxs0uqVlRW9/vrrtsEZAtrU1GSLEkaES7uB6UFmRzCYn59XPp9XMBg0Kg+L322iUsrRdAMzI7ijJLyo6nKzW+7DVQnyDFwjFhzowHK9Xq/a2to0PDxszatsNmvBBfc1Ah7lJHglVY17L+Pj4+Y+984771hpz6Yul8tVDmGXLl3S6empCoWCiTLI8MmgEOq4Rj40H7u6upTJZKoaZsB97sbHw6C5udlMxSuVilZXV7W0tKSjo6MqY6hSqaRYLGZwFAqwzc1N43XDoOD5LywsVAUZ6JO4vNH4TaVSevr0qTVyl5eXFQwG9Z3vfMeGcC4uLmp+fl5HR0eKx+OKRCLy+/368Y9/rJOTE/X29trASyAexB7hcLjKlMeVPAOz8e4ODw+VyWSUTqeNNsp/z/7m/UIZDAaDVhnwDFgHLovIxbN/3fWFbmF0Omm6kPmxGWhqsGmRtZKqX7lyRdeuXdPMzIw1hzKZjDl7kf256hRcstyNxYaDJUDwJYC1trYql8vp/v37Gh4etqYEhwbuXpD5T09PjavIYiP7hWZ0584dDQ8PS5J6fzlTCbWZJMugmELBhkUFBD2LMg8qDQD/o0ePlEwm1dfXZ6cr/7DZeD5cx8fHli3jXuQGSLAs8OKTkxPdvXtXY2NjtnnK5bIuXbqkV199VQ0NDZqcnFRjY6OZAfG5QCiU+a5/KAuOzYa7E1ARzwPsC1UQmfHU1JTK5bKSyaRJR13tP4FTkh08rEV3cUPX4Z7B34C3crmcSqWSIpGIqbfIhsAPEYow5JFSFcu/lpYW22xkQu47YbIDIoadnR2rArDrjEajqq+vVy6X0+LiYtX3RADC9yQpwPSGPQBWWKlUjIpJkHENlaRzyS/Cg46ODmsGrqysWMVBdcbzJyPEjyGRSCiXyxk8RNDyer3a2toyrJcL21bpDMLhEJyamlIqlbJm7Orqqurq6jQ1NaUf/vCHxnuGFXJ8fKz+/n5rdn744Yc2Jr5cLqu1tdWajXV1dYZRS7J75LDmMKJ5u7KyYu59JycnunXrlsGRxDbW7uHhodbW1mwNMSFDOofYLlpivux6acB1sz7SZ0oITsVisWi6YoxWMILgdPf7/Uomkzo9PdXa2pp9IQIPWnFeQjgcrjopeKEsJgIYHL1oNKpEIqGdnR09e/ZMm5ubun79unWACQ5wNgmGbkZEYODvjI6O6vbt2/YAa2trlUwm7aDxeDz2IlmgNBmZdkEjkYWJ7SOnNDpysnL3+3IflDdc3Cf3wfug/GQwJlxBIIh4PG6ZB5g2akA62jQ6KfOBU8DCef6UfrAHmpqaqvTwBKVAIGC8SkYgkREEg0E9e/bM4BuUTVQGBBCyRA7CiwIM6RzugREApW9vb8+MgsgeYcnQgEH6TNkJ7BMInE34XVlZ0fHxsTVjqTBww5JkVZnLbW5tbVUwGNTExISp77A1pWHE2iXDJeByL7x/V8HEweq6WfHnBEwuAgKwhs/ns8qir69P09PTmp6etqZtLBaz9fT++++rublZw8PD9rza29urDkLgCPczXZzd9a9gGi8NO7LCDz74QJ999pmKxaKuXbum9vZ2G59zcnJic+zwluBzCexUpG7TlcY6DXEao3t7e1pbW9MvfvELnZ6eqr+/X6lUyhwCgVsqlYpVce3t7Xr8+LH+/d//XaOjo3rrrbdM+EDVyl50M+v/6fpCLwVX3kvJurOzU+UU39raamA6ZTsLhJMwk8mY12gmkzH8LZlM2sZ89uyZUZjYEFyA1GDKlPw4LPHv4+PjOjg4UDqdtiF40F8KhYLhd9gOcgISbMHGEHWwuIAn+G5u6UaWXy6XzUD5ww8/NCUNah5oK7ykW7duKZlM2nPmYKMkRvjglq8nJyfG2Q0EzmwGESrkcjkLdpT6GKVkMhklEgndvHlTmUzGSOVk+jQFKAMZ6Q5kwoJlU9GdpVwHS0UxtLKyotnZWZPP/uhHP1KpdDaX686dO7p165a2trb04sULO9hpYrKZCIBer9ewWxc7BUt12SBwW+Gz0jAiWwR+4n3W1dXZQQlMxUHO+93Z2THaGp8JO0CS0Qjr6uq0uLhoEBv0qIWFBd27d08+n0/Xr183/2Gc1fgslGlkTG5TGPyX++cQInC5hzJJBh13kgMarjSpbty4oYaGBqVSKS0tLZkBUS6XU21trd544w1z6MNSkQqFgxIKqHufONrRlwBiwTmsXC5raGhI8XhcpVJJIyMj5s9A6Q8MQmP5008/tc+nkQk+DwxG4C8Wi0qn0/L5fNbc5z0WCgU9evTIIEGa1S5USIMTq4DGxkbNz8/r4cOHmpqa0ptvvqnx8fGqphnVleux8euuL/RSoMyrra21RgS0MLhq2J6tr69bV9Jtgi0tLZm3Jlzcubk508fDb+P3UiZevBcCD8oRII5yuVzloM+GYuOQMcCTJPvB25RFS4bR0tKivr6+qoVUKpWMi0rnnQ3JxmJxcuKSGS4uLiqfz9sG8fnOxkYzpBJVl+vexMu8qF4B6z0+PrZOOUoyDhAWAJlfW1ubNbE6OzsNn1tZWTEZJs+KJhKSYL4fpzjPVpI9t4vClGKxqHfffVcbGxuGa7/yyiuKRCLq6ekxjvKlS5eUyWT07NkzVSoVDQwMKBwOW0VCxkLmwJ9zcfAgDmDDYMHI33UzMoyBwOlpyLEhgVLY9Fybm5tmO8ln2Cb6ZRkKBzubzSqVShmr5v79+2ptbdXVq1fNjhGYgmzP5ZKT8bprjwqDjU2Vw/dkv/Azl0VDkoIUmOqpsbFRr7/+uik/P/30U3344YcaGhrS66+/roaGBrPkJHBS3ZFZAjty7ezsmMESDIJCoWCMk8PDQ7OldN/1xsaGJSXNzc3q6elRIpGwPkgkEtH8/LwJZogBQCxoBiRZgkMljf8taxhRCwZbfBdMsdgf+/v7SiQSSiaT+s53vqMf/OAHev/997WxsaG1tTU7PN0JM25C8OuulwZcFj1OR8vLy9ZVdXXfa2trevTokRYXFw27BYflAfX09BgnMJvN6l/+5V+UTqeVSqXM4Lizs9PGl7vBjtLBzXpOTk6MW0fZSLnHYqMLDq8XCAQLQbIISmwy6Hg8rt7eXlMASeeDCMnm+HMyh3K5rPb29irFGqUgY3kw1ZbObexo/PG8+b1uCelmOi4HlA42C5kG28nJiXVRAfwjkYjW19f1wx/+0Lr6fC4OTmxuMG0WryvOkGQZHEbjbHCPx2OcTcy69/b29M1vflNXrlwxwjplaGdnpwYGBrS2tqanT58a1u7isu5FMOIC+6cJy+SC7e1tWwcEYtaEz+czcQsYNQ1W5mORFQOXwIU9Pj62aR1uo8SV4EYiES0uLmpzc9PM8ovFoiKRiF555RVTw1UqFXV1dSmdTmtpacloXbBUJBmuiKCG90W2XqlUrDPPJALwdZc2WVNTY7S5SCRiewOYBsrnyMiIwQfPnz+3aoWxTa50Hf4twiUuJrLwvVkDBN1gMGisBEQRJEpATogRyuWycbWh2tFgdxu6rFESARgzxWJRa2trNhoJzBjqKdxtJigDn2CYMzo6qtHRUUUiEf3pn/6pWlpa9Pd///d6+vSp7eVr164ZX/fXJYoXry/0w62trVV7e7tGRkasPOF0LhaLmpyc1EcffaTa2lqNj49bZ5FMgvKDB7O+vq4XL15Y4MCjEz0yip+uri67B4ItHWlEC7lcTjU1Nerp6VE4HDYitCQLngRKSmK6vmxQTneyVr/fr4mJCUUiEWtk8RLhR5JBuh1Z7s2l6ZAZVioVxWIx9fb2mvKFkp+MlN/LxpHOxQRuwGWopLvAV1dX1dbWZmbuzHvj59lstooQzzOBTE6FQSlVLp9r9PHSgEYmnfNO8RagycEzh0g+PDysmzdvWnADAmGzgyVubm7q0aNHJlWGGUOmx/tj/XHReGLD0QTb3t6298Z9MR+O+Vh1dXXmR8DEaMrek5MTE/qcnp4a8wQ1oqSqjUWGSsCgpB4dHTWGzP7+vtrb2812c3FxUZKUTqe1srJifhRwbzk8gUhcOhjNO7/fb65hVGdcZOys4UKhYBMiGI2Tz+dN3k2woGm4ubmpXC6nQqGg7u5uvf3222ZaRWkPd9i9OMDoZeAMxt5jJhycWLjquIfRlGedkKWfnp4ZxCP9hk/vHuKIllD5bW1tKZvNqr29XYeHhyoWi/rwww8Nvqqvr7dxVSSMPT09+q3f+i3dvXtX8Xjc4NRQKKTf//3f18LCgr7//e9rfn6+6rNwiftfB1wCBs5P2WzWHK1wnuILl8tlZTIZTU5OGnmZuUYE6K2tLc3MzJizU0NDgxYXF630397eNoNqLvBNqEOUgaiuCoWCYa7gVCcnJ/Z3KFs8Ho/pwKGTkAGzQVtbW3Xr1i37Ti5dBBiBIOhmV7AGCDoEAoL50dGRwS5u19RVsNCkIrPjvl0gHrMfNjGfEQwGFQ6Hlc/ntba2ZhuJAMrkAPBKiObga5KMidDS0mLUKExkCoWC4epkJlDT6GBTUuFtcOXKFfNTIOvk+VAaNzU16fr16/b3CeQcZuDb9BDci+8G9/Zik41nWSqVTM7Z1NRkmD9dfuhV8Fylc/kzYgFgKb67G9yAIZgBRknc2dlp+wd5aaFQ0Oeff67/+I//MF/eiYmJKntAl5VDwgL5ngZvpVLRq6++qpGREdv4NEk5IHO53K9gknNzc8pkMlpdXTVVHUbc7e3txuLAi+Of//mfjQnQ2dlpEuRAIKB8Pv8rgZ6AQ4KDQU9NTY26urp+Ze1QMQJ9cRhK5wc78CCBmp/v7e2Z6pN9wLOg8UUl4PP59OTJE3vmwHvMIayvr9c777yjb33rW5qYmDAxDe+U94tZ/vr6uk1aBlaFyvmy6wvNawDcDw4OrOxmcmgwGNTIyIg6Ojr09OlTzc/P2wMmu0W2CYbJqezyK9fW1nR8fGya6dXV1SoerkuXItgEAgEb0cKmuX37tgkJZmdntbGxUdXFnZubs/KNDcj3pEN+69Ytm0sE7iOdKWiQYrplPtgRARRJby6XM+oaDloELHcIJAdEMpnUpUuXLAiQyRCQuTg0eA5g0MzLIiNAAEJJzLt0RRj4eUIV432Q/TO9g8OQn3d2dhqscHh4aCXgwsKCkeRramr04sUL1dScGXovLy/b5nKzxXK5rGg0qjfeeMPgJfcZE0TJNl32Cg1dpkVIso1MEAwGg1buwYfm98KgKBaLSqVSOjg4UHNzszY2NjQ3N2eULhzPgANcpzipepwMwSWZTFqp+vHHH9t63NjYsA3+2muv6dVXX7UDmcYRwZ6MHQgMeeze3p56e3v1xhtvaGNjwzBNqiw2Py5w+ARMTU1pZmZGPt+Zz+/Xv/5140wjPUYpt76+rvfee0+NjY2KRqOKx+OW0HCIMD7e5YlL5wbgUKtqa2tNzeVCHTAYECgAs8H3JQmiueuuXbB7LEHdKhDojwRtYmJCzc3N+vnPf24sFX5OI5Km6OrqqjKZjPb29rSxsWHGOfCNmSrC+hkbG1N3d7c18i5yxS9eXyh8IJBsbm5aJ5CJtkzubGtrs0GG2JzRuQU77e/vr+KaYlK9srKio6MjvXjxwkoc9OZcrhCALjafga1bKBSy8ufSpUsKh8PWFGN4HWUejSi67pyMHR0d+upXv1oFDRBklpaWjPUgnWcflNAEQkxxyLApnVxrSP6hbG5ra1MymTRpKyYYF+EEPhflEZsUySH6bjYsTSxkzGS4bHBoai7FiMPD4/GYmTqlHRlmLBaz78asNN41CxjcDzNtjIFKpZLy+bzJbhm509/fb41TNixwgCQLOm62T3aFdDwQCBi3kkwQ20+mNdDBHhgYsJ9XKmeG3FeuXJHf79d//dd/aWlpyfiz4K3ACQQ2LrxXkb6CUcPCODo60iuvvKJUKmW2lb29vRobG7MDWlJV1gdrBGmza0NaLpf12muvKRQK6Wc/+5lZDroS6YaGBvPpbW9v19LSkh49eqSDgwNdvnzZKg9ksuwzKswf//jH+ulPf6pYLKZbt25ZIkMA3dvbUyqVks/nM4c96XzCtms61NjYaIciNpnb29u2Zsg0oQJieMR3xmgG+h2NS8Q6kqoSAmAzv9+v7u5u3bp1yxpveDsDCzY3NxuXHQn47du3NTIyYsFzfn5era2t6u/v161bt3R4eKiPPvpI+XzeqHKu+fnLri80r3FVI2QUYLGxWEzBYNCGSYLbQhbv7Ow0rhrZIqWiJFMjkdW+ePHCSPgLCwu/srEIbgQdAgyuQYFAQDMzM+YjQIBjoXZ3dxs7gUwJOaTX69WlS5fMw9Ttivv9fi0vL5vtG+UKJxq4MRghXXjulQBH9gYvkqkUNCQIMgRcgtxF7il0FoQmZDVIaevq6myxUma65kNUCAQRdygkmz8UChl1j+zRhV0Qcuzt7Rl7pa+vT9LZAUZQYU1EIhEb0wMEwIaHEO9it8ineT9kSxcbNDxjOs2VytngRbLNpaUlHR8f69q1a7p+/bri8bhaWlpMHZZKpXR4eGgd8FQqpUwmY6pFPH5hRADpuJkMiiUwebi0NTU1SiQS8nq96u3t1SuvvGLuXQQpvjc4sHTGiACGwcqQqSelUkl37tzR5cuXValU9O677+pb3/qWuYS5jWXXpwQOfDab1fr6uj755BM1NTXZOoMqls/nTRo+MDCg7373u1WDJmFHrKysmFmQy+bgwOSdsCdQqh0fH9vkh/X1dW1sbBidkEoOCAn2Dgc13GWSHd4x8l63SQi8R1Ob59HR0WFBEo3A6OioBgcH9dprr5nRO8/O4/FoaGhIt2/fNkyXSvb58+dmeMM//yuWApQhTJxhJ8Cz5OQhg3OxE4IFNwRHt1QqWfeS/47gMzU1ZcFwamqqKuC6xHgeunTesYXwT4kiyYIx2R9kaDrOkuy+GxoaNDAwYAHBlZXi0bC4uGiLh0YOL9r1BDg8PBupDQ0Jn4BK5cwAhNOb0pRSl4wZOAEs2qVCYTno0oeg5kDIZiGBtRM0WbSIIujC19bWmtUj0AKb2+V+uoR2PBzIIJFl+3xnxvBuhxvcHMoc2DnDG5uamvTixYsq9R/PwG1MAkm5z4LDxn0HPOOWlhYlk0mz1/N6vWajSAWxtrZm3fC1tTWtra3J5/NpaGjI1jbUIbBLj8dTZUCezWZtrZA9sa4vUgVdpRJ8a9SSfr/fTFloDmGOjUNeKBTSrVu31NbWpkwmYxNLdnd3zQOBfgV0qJqaGoPfEP+gKmSdYw4P3OLxnA1whMtcU1Nj5kqHh4d6/vy5jo/PLDddIx8CHu/Z5ZjDTBkcHLQKFHgNEx+SOiiSsVhMbW1tZiLPs6WKICt2TZdYJ7xXxi7BbIDaenh4qNbWVg0NDam/v98OlHQ6bQkTVRx8dPoUfr/fWEmsCRqAv3HAJZBtbm4atsgNHx8f6/nz52YyLMmCBhvTJQGzACmxKQXI/jKZjBmHXCQQu7xU8ElKA7IySq+L3XIeFsqhXC5n2R9lCKcf7v+uYofPGx8f16NHjzQ7O2t8PTaXW87xvTgg1tfXq0jwbCYaZWRLsCDcBgDPy30WUJnYwJD6Oc2BSijTCEZkujRPoI9JMhWU6xXBom1oaFBdXV2V1ycNQv6MDjOOcSxmfAIYk1IqlaoUbK4ckiDglut8bzJdNhiXO36FNQKrhmdHlbW1taXl5WUVCgWrzIB14KVSLjP+CNMa3ORcaMXlRrviAsphsnTwSd4zBz0Bg6oEWhPBB/9XqiHcue7cuWMN4vfee88686jrSE5g7ICZRiIRffvb39bY2Jh2dnY0MzOjtbU1O8hoZHIoEbDy+bxisZja29ttLcJh5f25FVg+n1cymTS8k5ggnTVbo9GoBgYGFAqFzII1n88rnU7bZAkOZFeURFIGt1aS8anpI+Tz+bOg9suqkwA4NTVlhzjrHs75zs6O3nvvPW1ubtq8Mg4rqJVtbW02vYVnBZzpSq/duPY/XS8NuNevX9fCwoIWFxftho+Pj00RdHx8ZsfX3t5epTDj5KR0BFB2G1i47gQCZ0PoFhcXjd+Htr3qRn+J/SIwwPuVl8LGptHkNkfAazAQcelaPETwRzJ51D/SmTcunU0YFd3d3dbppnlFdk0A9Pv95tXJZnWDHqc4C5dM1FU8uQIHngMLBs4rG4ESnwVDZuKOtiZAkEEjcMCcm03H9wES4l1KMpyXIY9QmTjAyNhcWIDPJQMAInBpcEyruCh+oYwlG+dC8IL3LYcU1EEONuboEZwkmWyT7+raI5LF8L74Mw5DIAMut6IDWuBeCJbxeFyhUMjKUYIUlQTGPqlUyuZ6bW9vG0e1VCppbGxMd+7cscmxc3NzlohwD8Vi0TBTskU+7/r16+ru7lZLS4vW1tY0OzurDz74QC9evFBDQ4P6+vpMVp9MJm16B/AI/395eVlHR0e2r90KjL0GpY3DkP1PkDw+PrYACzXU5cX7/X5jRPAOWItAB64MPBqNGm2RHoTffz4inmqJ/Xfjxg1tbGzo2bNnWlpa0s7Ojt0bDcSenh4zYadSd383TUDpvBF+MW5dvF4acH/v935PH3zwgZ4+farT01MrH1tbW9xcdMgAACAASURBVK0JgUSRF4uRBP9Ow6S+vt7MtdnIBIlCoSCf72xiLyM8Lma4dCgpJzCvIGBCz3KbO5RLkOAJPGxoNjc6+8PDMys6NrZLd+no6FBfX5+ePHmi2dlZlUoldXV12e8GU0ZGSHe+trbWpqYSYCmR6WyzmYE74LUy4ttlKZBRcNC4f0Z25ZqykMGhNqO08ng8yufzWlxctCqAIEFnHKEAVQP+Aa6CicBPVkcWy9yvmpoay+JojEjng/4ufh/KSjYrz62lpUVtbW1V2RT0J0Yz8Ts4aIAlWJfwx+HbEkjJTNmsvBMOecQUPEuyezfIMDOPQ8Z9L+VyWYuLixoaGrKqhAMMjBaY7enTp5Z5IvP1eDzq7u7W9773PY2NjampqUnr6+t68uSJVZPFYrEqq4MTzLrkvdFsbWtr05tvvqnh4WE9f/5cwWBQvb29xnC4evWqPvzwQy0sLNiIIWT9y8vLlmDxPLgI9FSJKB4RSMB0KpfLWl9f1+rqqnFqoY/ynVhffC8OVel8WjOMI5dOR4B3JeEcCj6fz+bNNTY2KhKJKJ1Om58Hwouuri719vaqt7fXoBWyfjBkEoG9vT1ls1m1tbX97zDcQCCgb37zm4pEIvr000+tGcHAQgwjyIoIpC0tLdaQATI4ODhQsVg0s+t0Oq2ZmRk9fvxYx8fH6uzsNHd8FocbZCiR2GRADy51ja4+pyGZI6UvP3NxHyZD1NXVaXNzs6ox4i4kj8ejq1evanV1VdlsVs+fPzcyNpvP5X/yOZTHLj8Xcj2/F/wPeAQ+Mjiom9XBxnAPrnK5rOXlZQtomLYQLMjwyHAhjGMdSYZBZr69va1MJmO2fLu7uwoGg/b3XfoYDAEGevI5UJVOTk7sObBBgIII7NDEmIDhqu8CgTODeDasy9oAL8fbGK4qmY50PoOO9epmhDirkX0RjHmusFUQimAif3F9wmmm8UiCwKFdKpWUy+V079493b592/ib7hQK/KGhH3Fw8M5/53d+R3fu3DGmDIGZ/2Zra8ugCYZo8n1dSqDH49HKyooZCAWDQV27ds2eN5JmqjzuhUC++EuvCA5D9j4XWDTBlWoDaMptEJIhQ0ujgr7o2UEA5z5qamos9iBRhisrnQulyJTpudCfqFTO5MYDAwO6efOmCYXYP7B6GhoaDIpz+fdAP6y1hYUFswjFp/d/ul4acDOZjJLJpF5//XXFYjF9/PHH5vrj8/msw4h6x8U0p6amrDPNJqOMYMQLTSyXKkVzyc1kKLsJupz8bBBOW0ooMkyCiFvWutAEwYJsuVgs2gZxu6G8xMbGRiWTSTsY5ubmdHp6avgWL+Si4QrPxC1LwdYoXSlHYYSA/V4M/ENDQ1pcXNTGxoaNB3Fx5ObmZuPJ0piC4kWFUiqVFI1GjbHh9XqNK3x8fGy0NtecKBQKWYbL4iewYEgUCATM5lGS+V1IMswMjB+FDlAL98hzgOrmVhuu8k+S4cUXGypsSjizVBJkPQRPMn1KRN6R2+RxvQpIIFBwcYXDYYOzOOTdNYuI4PHjx9a1LxQKBhuALUNjhL9O0Hr77bf19ttvKxgMGoa/vLxsZbh0VjHw3GnqUemw7pEIkwRtbm5aYIaJwWRb4A3ezenpqXkQeL3eKgNud32y9oEV6urq7LAkgCEqgkMtnftdYzTlHrjuZ/D/kTSzp1i70rk1pVt1SrJ3VCgUlE6n1djYaPulVCpZc504Q6AF5pDOmDHr6+tGJpiZmdHJydksQNz3XnZ5XPzl/67/u/7v+r/r/67//66XZrh//dd/XXnnnXfMvLhcPtPY0xhyMStOEnBWShBwmFKpZDQVskGoSq7lIadLe3u7JiYmPJL03nvvVSiryVDJJC7a9blNOxfjJZMBf6TkkFT1u1zqEf/trVu3PDdv3qwkk0nrXmMsQslEiY40kSwd7BkGAt1pTl0w042NDcNN+VyyPr/fr7/6q7/ySNJ//ud/VsgMwdQoqbk3SPgwTDjpeS4uV5NSOxwO2/A+DE6AGjo6OtTZ2Ym5jOd3f/d3K3wunXSXAgjzA3iG5889wqj4dRM3/H5/FXuD0h5lWlNTk/71X//VI0npdLoyOzurlZUVgz9cEj1rAuockAMXWQxcTiAPl2pHlkpWTuPkl6W3R5L+6Z/+qcLUC3c9kx0DJVE1sGZdbF46NxEHEmONuPQtKjCqmfr6ev3lX/6l54/+6I8qZPzgjTTwKI/5fcja+e7QB9mDKMqge9JwgjlAI2pyclKDg4Nqb2/X3/zN33gk6W//9m8r0ll2ub6+br0MIEDgNDwY2Ec0Ind2dgyaYz3QXA0Gg9brcBtkKysrmpubU01Njb7//e97/uIv/qJy79499ff367d/+7eNi0v1RI/EjVNu5cW7IX6wz9kzUATJ/tvb29Xb22uwyPe+9z2P/ofrpQE3HA4bjkN5E41GrYkA3uFKVFlEbHSgBte9CokiC4AuqIt5ud0+V4MPYZnA6zo7sTgvdv5ZxJQlYL+u0QR/xguG4QAGS4PDhRr8fr8ZlaDRdzl50Efcz5BkG4KASnBFsYSRDrCDSzWhy+r3+23OEk1GynOYDnCM3ZKIgELHF1hGkpV/yWTSDGe2trZM4kj5x+KVzpU1BCYOFr4fi5TmG4E1m80aXg2di4OU0p6/R1+ATcjFoca9+P1nFpywPXDfYnMhDHAVSRxEdJhPT08t6DMzjn+H5kQSwcV34vmyjlz+MlASzUaYEARR/ruLf5/P47ByFVUuVMb9sb6AVKLRqOH8iF8QBuEYBwTFnocdALzAfnexcRSo6XS6iqrHe4fHS2Lj9XqrBs8SYN0xWezHxsZG8/lg4m4kErHEjrKfXgR9C77/tWvX9Pnnn6u9vd36BW6jl8MUjjryXZ4lh5u7X6TzpuPm5qZZGMD1Zu3/r1gK+FVC06qpqTFWgYup0HjC5ISb5TRra2urCmiNjY3271DECIqSzNWIy6V++f1+w44hI0vnc8vcxUvQ5EGwoN2Hz6YFYHc5teA3POzNzU2Td7a1tamtrU2hUEixWMwYCVCC2BB0XiUZ9sd3d4NtZ2enmpubDddjcYNRcnV2dqpcLuvFixd6+vSpJicnTbTgavw5EFw82cV6kQfTLX7+/LnC4bBmZmZ048YNjY2NWfc3Go1aYA8Gg7p7964WFxc1MzMj6Yw2t7GxYQ0jNiS4Ie+G74qDGQHPzUpdPi3fBVnmRdUd64+MFEtMgm02m9Xm5mZVJYPNIeuQgxjnKA43FHJYjUK7QlziYrjugc79c1/u4cahQnXibmoXq+S50LAplUra2tqyoYwkHBxK7kWliXJxdXW1KrOTznoz5XLZ1GTQKJG20kxmDbe1tamzs9NcvQiElUrlVwIxjUz2HNWX1+u1eEC1AwOHzDIajdr6RhKOQRESc6pJKrBkMqmWlpYqdhJJ0Pb2tiYnJ20PYvHIsFX2OSPaYUuwTukBQSelgnGNctzmNUnBy66XBlx36OPh4aGePXumqakpLS0t2WYlKyIrI3iEQiGjYJ2cnNjUh0gkoqamJqOa0BF0KTuul4F0XtqzIA4PDxUOh20hQ9OhDAOU5+FQarlluls+kLFR8rtwh7tZfT6ftra2jGIVjUatMSjJFtDR0ZFNSGXhkNWQafBCMSmhuXUx66XZwFUqlfT8+XPdu3dPjx49MpUTG9c9MIA72LQ4drW0tJi5DQsKZsLnn3+uH/3oR0adGR8f1507d5RIJGxj/eEf/qEWFxf12WefaW9vTwMDA7p//77u3btnpbR7DzSoCCy7u7vWBQeGoGRzv4d7aLhBkYtnWF9fb05TCAY4gDg4LwoWkAFL5xko906WhpMa9wA1CuN714ScC/EIBwnCE4IpZkvw2KnAeAYcNiQITFTO5XJGswN6gOkhyShzHHR7e3taWlqygwJVIIcW0lgajsh3acTmcjkr7YvFolVvrogIDr0L00CT9PnOzNWxIoVdxEHPs/R4POrq6lK5XNbS0pKi0aiamppsJFe5XFZLS4sZ/ywsLJjfNAlgJBKxhE+S0e9yuZxl842NjYrH4/YzBA4kADiPuaOydnd3tbq6ambkkqzK4T3BF87lcgqFQv+7DLehocGylsePH+vZs2dqbGy0gYR0yzEhkc5KzKtXr1bNet/f39f8/LxmZmZs87teAP39/RoZGVFPT4/i8bg8Ho+KxaISiYQkWVaLfJeNQofR6/Uqn8/b78O8gkzDDawsTk44lEAwBk5PT80LVpJt8EAgYFOGE4mEZbSFQkGrq6t2KKGTx7YN6hW/A67p9PS0FhcXVSwWq74HWVgikTDdtvsSHz58qA8++EBra2tqbGzU6OioZUd8R5czSBYPO+H09FTNzc2KRqP2ezmYwH5ZQPl8XnNzc9rc3NQf//EfG+4Gp3V0dFRtbW2amJiQ1+s1dgN8aKAdDtDT09MqGTILWDrvonu9XoOnCKjlclnhcFiXL1/Ws2fPzhevU4IjbDk9PbXsVJLBXWtrazaKCGjBvdwNhdUlvQv3v0URB67IxTPG55meA3ADzANoUS6fl4DEoQO3c2trS7lczsQ6MGo4RNw+BAeXdKYcXFtbMwiDd8bPS6WSSe0LhYIGBwdNCbi/v6/t7W0zDWddMhrc6/UqGAxWYcDsL+msiqPExzcChgyHHQZKLtMByTOQTjAYtD3JyCzkyMj0YRfQZ6D6rK+v1+DgoMmVOSyXl5ctm+/o6LDPRMHGe6LS2djYMIFWPB63dUF/gvixsrJiCY1r5PPrrpcGXHBLTDQqlYrC4bA1Jy5fvqxMJqOVlRWFQiHV1dVpbW3NsDCaZZXKmeFvb2+veQCgRa5UKmYMfO/ePfX19enSpUtqaWnRxMSE3Ut9fb094GAwaAFvdXXVTL2Pjs5miKHkam9vN5UJRhuoklx+LqRz6dzMxm3qsLm3trbU3t5uZhupVEqFQsE4eWDWly9fVnt7uxH7aVowthwcCOUTzlksQOnMnayzs9NKWK5Hjx6ZQ1sgELBsjqwd3wecx9yrsbGxSp5Lg4+Ny7tiwQEjPH78WIODg/r2t78t6UwZhKk5TmEEYQIkBxXNHUpHzI4IVpSuBGcCAocP5THVkxv8XBkrAdfr9VqwdPX0HMQzMzOqqamxWVkez5mPwOzsrPGq29vbq5zogGDAyXd2drS2tmY0OYIqpSVNP3c90ejBUpAMi4CD0TiCFVSDmKS7cmwmmbiKNTLPvb09raysGIH/4OBAXV1dCgaDyufz2t3dVU1NjVEPafoMDg5qeXlZjx49skaadMbvxegdDBm8FH9aVwRCI5eKET445TZwBZUpGS882EAgYEGNkepAjh0dHYpEIurt7VVra6sdKoFAQNFo1KhiHR0dGhwc1OTkpC5fvmwQJodALpfTysqKHYBugoKXy8nJifGUob3iywLWTK8G+1l6Py+7XhpwcX8vFovK5/MqFot6+PCh8vm8ent7NTExoXw+X7WggSFgJYANBQIBy1iZ44RlYFdXl7a3t5VOpzU1NaUXL17o1VdftftgEdfV1ZnuGmOYjz76SJKUSqX0+uuvW0MGzwMXHwsEAioUCpqbm5Pf7zcSOSc7nX3wM7AuFhJqtnK5rJWVFTO4iMfjlpVsb29rZmbGskgy3ePjY2WzWeXzeZu3hKJldHTUAH7uYWNjQysrK3Yiu8+is7NTGxsbmp6etoOisbFR4XDYpicDabimzZ2dncaIIHDRmAMGQKFFAwqhxXvvvafe3l69+eabFnC9Xq92dna0urpqrlTugFF8CCijCc5kPpKsLOMic6MjDkaZSqVMtspFqe9mG4FAQJlMRp988onW1tZs44AHor1PJBKKx+NGgod94/F4NDs7a1hpJBJRf3+/DdRMJBIKhUJKp9N2H3y+2+Qj6FLhuaZJDN5kndFMlmSm3XCmaWBhokOzSDrnhvMsEK3w55Tbfr/fMkQCApg3+HNdXZ2Gh4f15MmTKkYCgb2pqcl6EByWQF9AfZIMLgAKGBoaMqUWBw/qMpKaXC6nnZ0dO4hQfTLFe2hoSHt7e5qfn9fz58/V0dGhGzdu6NatW5a04LAmndkpwimmzwLbBSk7Np1UqzS7qfYw5kHp1tjYaFk0PH5gK4YHsGZedr004O7u7hq+xQ17vV699tpreuONN3T58mXLRmpra20eWXNzs/r7+3Xp0iVtbGwolUopnU4bPSYQCGhiYkKPHj3S5OSkSqWSGYRIMk9OLrJDSfb3I5GIhoeHtbm5qfv37+v69ev62te+ZlDEw4cPVVNTY3aKbMhMJmOsAk7jYDCorq4u1dTUaHl52Sz9wN54FpiwF4tFHR4eamhoSENDQ4rFYoZBYcqcy+U0NTWlwcFBjY6OmrBia2vLqEOuVwFlWSAQsMUNFuwyHQjeBNQrV65YRh4Oh1VTU6NcLqetrS3V1tZaEOrp6TEo6MGDB2ZfSdblksbZlM3NzWZnt7e3Z0HG7cgDj9DlpSHE7yO7gVROFg27BSjBDVyu4MBt7p2enioUCtmz8HrPvDdQ+vD/nz17ZuYqTU1NGh0dVbFYNBkzvhnYXOJ0RvB1JxGvrKwYnBUIBDQ0NKQbN25YYsE9gv1zcPM+wY2DwWCV+KVQKFgWzhpjLbjiD0ZbHR4emsjFlbjznDCqoWdAdgxEQ5LT19enTCZjcnosGsvlsgVGJOVAgvgmB4NBHRwcKBwOGzwETcy9EBSwpxcWFkzYUSqVbMbb6OioWlpabD2yP3hmExMTamtrU0dHhyqVip49e6Z4PK5EIqHHjx8rkUhodHTUPF0wr9ne3lZDQ4OuXr2qzc1NtbW1KRqNWgyor6+3KcW7u7saGhqSz+ez5jmH3NHRkYLBoOG2sJLI/jc3N+1Z0hv5dbi+e7004B4cHCgajWpsbEyXL1/WwsKCvN4zb8+GhrPxyj09PfYQeVCdnZ0aHR1VZ2entra2VCwWlclkzM/y1q1bunTpkg2lRKpYqVQMf3PLYbT0bnMFnf/du3eVTCY1ODhoQD1Nu1KppNbWVisVMJ5JJpPa29vT+vq61tbWrPsLltzX16dCoaDZ2dkqNyAWYKFQsGZfJpPRvXv3VFdXp7feekuvv/66Hjx4YJgmFoJkyARrrvn5eduoxWJRU1NTCofDNhjTnSXGxqpUKuru7raMTToPgpSbbNpK5cyZ/sqVK7py5Yq2t7e1sLBgHq5kX25ZyybCnnN8fNwOFd4HvE2eDaNToAtC16N0ZLpDKpUy2h8qMFd26vIvec/cG9MbuCqVM08NAmVNzZnjVl9fn27cuKF4PG7+zHxv1FU0pSqVikKhkK5fv26O/7u7u+rr67Oyn6YvzcVPPvlEra2tNhkEfBomCzDSRew6HA5rbm7OVICuMg3Ygu/LBu/q6pLP57MDjcAL1slhhQIO6IMDub29XZFIxJSAfX19NlaG6gbVYn9/v+LxuDKZjGWWxWJR09PTyufz5qQHXY5kzIUUYBjxcyqN/v5+bW5uanl5WaOjo/rKV76i09NTdXV1VU05xnGLzwkEAlpfX1dfX5/+5E/+RJKMmprNZk0hV1NTY5l2fX29BgYG1NLSovv370s6t5olRsFI2N/ft5l2br/Hpa0eHh4qk8mYBBtIkyYiGDDN9t844EpnGVUikdDw8LA6Ozu1vLxsAZQbwPKQCH/79m3j4Ennk21ZhBsbG/rJT34ij8ej4eFhA8aXlpbM7coNSmxIl7AvSYu/9PYcHx9XpVJRKpVSMBg0C0Q2YKVS0eLiohYXF3X37l3bAF/60pd07949/d3f/Z0+/PBDTUxM6PLly4pGozaWBSCeZh2NGr/fr6mpKU1OTtrGaGtr0ze+8Y0qJySX5gN+5PV61dXVZcGRRgrBlE1L2eniQuDEZK/uIuIEh9gNpevy5csaHx9XIpHQ+vq6wuGwvF6vcrmcZZktLS3GICGIMNAzk8kYlsVVU1Nj5ZzLp6YEBdPGlyIcDlu3mCweqg+/w+XY+v1+83b1+8/cx4AGuMjucFQrl8tKJpPq7u62DXNycqLnz59rdXXVvmdHR4dli1gRdnV12fibbDZrTZneX/r1QjmTzmWoXK5sHMcpNqRLV4Lriu6eHgGNVWhXGF9vb2/r2bNnJmJwuc4wW8iOOZyBLIaHh9Xf369cLqfDw0Pr6hcKBfX09BgWOz09rVQqpeXlZd25c0cjIyM2jSESiRgsBo67vr5uI+O53L3a399fxfkdGRnRl770JbW2tlqcYFQR5TkN7K2tLaVSKb148cJgRkzgPR6PJiYmTEjEoQbVDM8Nrmw2q+XlZdXV1en58+eanJzU6uqqCUFu3bplTUNXFOFSQmEdZLNZa1zOzc2ZQVMikVBvb6+am5vNtMeVOf+666UBl+CCdwKmwSsrKwaMg//A1YtGowqHw/J4PDYwslwuKxaLWfeTUhmIYWtry8o/Oo4Xbfhcs+5sNmvUHBpF8C85jbze83lexWJRGxsbBoz7/WcTHOrr620m0SuvvKK3335bqVRKkkzVAuWrpubc9YrFSsAIhUKqra3V/Py8/vEf/1EvXrwwT9P9/X3r3OZyOTNTgT7U19enkZERtbS02OlPEw480H2JYN9s8mKxqOXlZcumwV9bW1uNj0jQZvxPqVRSPB4371XgEyoC6HXguHAeCawcIJRwLu4FF5XF5xqWlMtlw+lonhK0OVApk2kmcS/4MP86/itlOs25ubk5HRwc2AjsXC4nj8ejV155xQIV8BhKt46OjiqS/MzMjFKplBYXF23QoktPc7FkPh+zFjr8kkyNCSzCniIjls4HtZbLZaNLAjPBEiLouMpGPpsLnwR+p8/nUywWM6oT3gUEbTr8OK55vV719/drdXXVmncYm9fV1SmRSGhiYkIPHz60jJtymyscDqtQKMjv9ysej1eNUScoEVxPTk7MjnJzc1P7+/vKZDIGB7HvqFJh2CD04XlSVdHEzOVyNpMuGAxqenrajOZLpZKtxZOTE2tE8l2hBxKEqeSgleJ6yOELdZBq4iIv+ldi6st+SNcT+ld7e7vNnQKToWSGU0mgrKmpUSaTMRiiu7tbOzs71tBhYB2b4uDgQJFIRIlEQrFYzB6edJ7BwI+EgsPmB/dyVWcXeY6bm5vGWmAB4G7153/+5+ZZS/bg9/uVSCQsO8EFq7m52fxjfT6fent7bZLn/Py8Hj9+bCNjXPcoymz+gQXw/PlzazBSgnP6k7W6F/Qc7rNQKBgGRwCE2UB3H7OOuro6K40bGhrU3d1dJSvmIGxubrYsWJIdKG5wRKq5ubmpxsbGKtoZMAEVQmtrq2WFPE++Ix1kqgdgBBY3GbNr1chFwOU5UMmkUinlcjlzlWNkeyQSUT6fVzwet4DL+uAgjkQiSiaTisVimpub08rKilKplB49eqSOjg7F43ENDg5WjQd3lYk+n8/WCM9akjVf3CyZzBAYgSYShx/iARg5bkMRPNitCqgk9vb29NFHH+nhw4cm2w6Hw6qvr9fi4qJ2dnZsMjNZNz0KRB7YlKJqDIVCikQiKhaL1p2nEeg+Cw5HGsC8Sw5wmBj0IcDHYXHQ6IQ3TIWDUILnREOYA9fj8SiZTEqSpqenlc1mrSkOo4fqz+v1amlpydgfYN1ky/QRIAQQg/b39+3gdd3SaNx6PJ6qw+fXXV8IKVCq0Q2PxWLWtCIQYrJN6YFaBL/LSuVsSB8YKQopv/9sImo8HrdZYP39/ZbZcG1sbFgGA41ocXFRq6uram1tldfrtVIOvXqxWFQ4HLZSjREvZI6uQxhZQrlcVm9vrz1QbN8k2Usjm6fxAxcR2hyyZbiEdEHr6+t1+fJlffrpp8a1Zez00tKSlUh0z2lQQAviguML5gcW5/IkUQCxeGhmwT+sq6vT9va2Wltb1dPTYw1ERBaJREI9PT0KBALWBCRbkc4O02AwaBuQoEXFgdcszAjwaChNwDwXHdnI7KTzsfRkhGTMF/mzVDLgl4FAQDdv3qwitHO4QJ+j9CTQ8Q65P0a3A8FA3m9oaDCSvSvMgRLmNrAoR3/dBFqyexrObvVGIIPHjGgE6IPfwXNzlX1ASF6vV7Ozs/roo4/U1NSkwcFBG4VDrwFXOCZbU3lR3q+trWlxcbFK/MReGhwctGyVLJMLehd0O9Yvfh7sLTJM1KnNzc1VKjwOk3Q6rdraWmt+g+0TPCXZZ/T09NjzGRwcNNiLhiUBFdyXAxyWEu8FfBwoBwk6CkbWkOvG5ja9X3a9NOCCxUky3IRMhsW1u7ur6elpLSwsaHh4WD09PWpubrYMzu/3a3V1VQsLCxofHzcX9TfffNNoOOCIdEkZZcxF2s6GwiZxcXFR2WxW0WjUUnnI+6enp9ZQISjw8GjmkZVTGtFE4ICATyxJPT092tjYsOfR2dmpZDJZ5WtaqVQsezo8PBs5MzQ0ZEELP4py+Ww0OKVSpVKxcpTGEA2Y09PTX+kC7+/vK5/Pa21tzXAxmlNAHT6fT8lkUl6v1wYHoh7z+/2mEEJU4PV6zbQazvSXv/xlNTY26mc/+5lBFdJZkGtvb9fAwIDp0P1+v4rFotHBECCgIHQVT5JMekrWBuxB6ciaozEIFOBCTWwu8FXoO+3t7QYFwTtlPTFPi4vDiEaNW/aiIsTUHeqjpF95JwRdcHTp3EAbeIBMGkye7AgRAaUuz4HKDLgCXJ8sUZLdj0vHCgQCevXVV+Xz+bS0tKT5+Xk1NDRofHxcb7/9tmKxmDWEmZLt8/k0Pz9vAourV6/q9PRU9+/ft4ZZpVLR1atXbeLv0dGRwuGw4vG4PYednR1FIhFLSuj4w92FfoV3CFO7KcmptsiswdPJaFmzVK/Mr4PpJEmjo6N2eNKMddWCZNxQ47gfGDokRChUyYKB+si+2bOuvPxiQnDx+sKAyy9zzZ656cPDs2mnz5490927d3X9+nX5/X7zdK1UKhoYGFB7e7symYzm5+cViUTU0NCgWhypqgAAIABJREFUnp6eKlUNBH7YDu5FtnJwcFDFM+zv7zfmA0IGAnM8Hq+iXsFLpDxOJpN69OiRNTdw3g+FQtaxzOfz1hzBQQtSeDweN5MXTls2FYormgyUUggWaHbw0smc6ezSLUW84B4++/v7JvbIZDIGhYATFotF+Xw+3blzR4ODgyoWi5qbm9Ph4aGVk6j7NjY2NDs7q1wuVzVuJpvN6qOPPtLJyYm+8Y1v6A/+4A+0sLBgVD0CB9kKyjAqDJgkzA4jqyNzaGpqMnMTmBLgk6w3nhn/H2jBzSBQmfH3CEjcH4cQ0BhsCqogAh5wB0Y1bBqv11tV0rtdbDfw09zknbuZE+sXJg9/n+Yn+DZlLIcVQRQnLHBkgj4Bn0SjUjkz7MGcvlQqaWJiQiMjIyYauHz5skKhkE2L+PTTT22wZUtLi+G44NB/9md/ptdee00ff/yxMpmMWltbTVXIsE/Wrbs+t7a2qkQ8Ho/HRDEESg5nHLb29/cVi8UMn4YBsLe3V+VNAdxCRp7JZOz3c/gEg0GDHVCfSrK9BSYNlk9TnmrB3Z/EqJaWFptWTVBubW016hsB+mJCcPF6acDlVOXELZVKRliGiPzZZ5+ppaVF4XBYuVzOcDr+29bWVlsoMzMzyuVyhleCzYBngSO6JizSuW0dJxVZhAuANzQ02AgfurlkHGSNmUzG6FngdIuLi/Z9Ojs7lUgkjMdIl146F18wCTgSiaimpsZwKlRc09PTpmjb2NjQycmJNQFZYNFo1IjtnPBo2bGnY+NRdnLRVKH8JlNkyitNx3A4bIGFw5H7gxs8NDRkWczCwoJJkbmvXC6ndDqtu3fvmjKLDURAcqlcLMJgMGi2e8hqaR4SdFpaWsytKpvNmmKIIOdaLLL4CaZcfDYbiUBL+Q1tia45gQLdOywCsGS3idPR0WHfg+/nquHc++BZACu4fhZQj5DAd3Z2GkVOOnf5Oj09tRlxwCT19fXK5/NaXl42VVV/f7/d90U/EBRwDO9k7Xd1dam1tVUHBwdaW1vTzMyMZmdnFY1Gdfv2bYME6S0EAgGl02m1tbWZ3J7vsbOzo3Q6bfuXNcPFNAsazEBlkowFQDOKuXPpdNq+B/TPQCBgE58xFydD5eBzx0/B8nGfqd/vt4SH9wXflqY4TVAOZ7B9MmKgBSA+7oXg6jYxLza4f+OAi/KFh358fDZI7rPPPtPx8bESiYT+7d/+TYVCQUNDQ4pGozo5OTEHqWQyaXzbubk5tbe3KxqN2oKECsOC5QvZTf6yUUAG6ZZiPDRJJtdrb283vBIwvrW11Xh7nFRgPsVi0Rp1Ho/HlF44TvG79/f3DeOldKckLxQKmp6etuYizvLY2xHAJWl5eVmHh4fq6OhQV1eX7t27p2KxqK997WsaHx/XkydPrBziu3K55SoBiWdGgyAajcrv99smZR4Y2nRkzolEwppa/O5isWgB7+joSPPz8/rJT36i4+NjxWIxuw/KbtevgSwN/T3PjzKRYMiBur6+bvhud3e3iUrIssCk3aDuYqcuI4LFTllIBgsLhjIVRRnqR6oMJlpsbm7q0aNHSqVS6urqskmubFA3yLnyXZ4tlQ7QFDLdZ8+eyefzGTcYhsbh4aGp7fh7BEsaqI2NjVr85SBXymD8P8iiEToARaGAamxstO9OaT83N2fcY4/Ho7W1NaNHUQHt7u7q/fff12uvvWYQzPHxsfL5vEmHm5qa9P+w92a/befXGfcjkRS1UAvFRSK1r5Zk2ZaX8azJTII2QYIUGLRNUaBF0Zug7U0vit7mqn9B74vc9aZokYu0QRpkmukkmSX2eMayLcnaqYXiIooUJWulyPeC+Rx9qUw8L97gvesPMMZjy9KP3+Usz3nOc6LRaA2kQM0Dfqsky36ov0jVQiwjt+rr69Xe3m6dnOiLhMNhZbNZeTwe+zuyGc4/+93V1VVjcCngAlny5+DL0PLYW6AunDb2xm34wviytzh1glKcwcuelxpcgG1JhtFBsVpZWVGhUNA3v/lNzczM6H/+53+0srKiw8ND48U+fvxYiURCk5OT6u3t1cDAgE5OTpROp03tC49HBMblcaM6OsMoxtAlJMkMNdVpLl1/f79djvb2dsViMZ2dnSkQCBhVhQo2fd5AGoiGwIWUZJV4uL2kyIFAQLlcTolEwrx+IpHQ8vKy9vf3NTk5aZXV3d1dLS4uWgFmaGhI4XBY7e3tWltb03//93/rq1/9qhUjiJKupilEWugelEol8+p0wiSTSaVSKfn9fl27dk2Dg4M6PT01URqoS2dnZwqFQmpoaFA2m7X9JoUDClldXa2JZMC9pEv6IIeTooikGsdL8QdGAI6ip6fH5CnJBCgekd653W3uQ7SDQYbDSgGEogwG3J0/x6WCPkc63NDQYI7i4OBAsVisRgEMR8d6YCT5PAQOQC1ra2tKp9OampoyGtPh4aFhk9QQOH8uX9Tn86m/v18vXrzQ7u6uVldXNTw8bHvmcoD53HScQQUDDoG6WCgUFI1Ga+5zuVy2IIJOvEQioYaGBo2OjhrBnzoH37OlpaWm+w9aH/sNJk53HFgstY9SqWS8/cPDQ9sr8Hj0jdF+gL3CmafugGaDpJriHHvuKtOB+RLYwcxgv8gKySI4KxhZ/o4AAAQAhsnLni/tNMOqwzWTZCyA27dv6w//8A+tX/mb3/ym1tfXtbW1pd3dXSteULn8xje+oVQqpU8//VSJREKxWMwuAMpQYK5EXTwdHR1mbMHIEMHZ3t7W2dmZstmsnjx5oo6ODt2+fVuDg4P2dRTSaFvs6ekx5gOcOxaTlmYAeak6Mn52dtY8GQt7enqq5eXlGq/IUEHSnu3tbUtX7t69a4VHigJNTU26du2alpaW9JOf/EQTExPG97sq2ELqTvQGLa+1tdW4wsArHBYiMPBishYiCZwUTgXoA6EODim0Mx4wVDfrICWk4YHvQ7EDOCEWi1lnG0wTCOVNTU0qFosWobhFIvdxYSYX+wQrRPOBn7mzs2OtnjRTHB8fa3Nz0+aNjYyMaGJiQs3NzZqdndXKyooFBZFIRIFAwPaZh7OL8eMyn5xUp0Cvr68rFotZxT+bzSqTyZizk6pMHDInuhHJcFpaWtTX16fj42PrGIMX78I7ZCYwRSRZh97Z2ZkeP36s+fl5vfbaa4rFYvr4448N8wXPpAqPCPinn34qj8ejgYEBa/OFA89wz66urpp9IfJ2W7XJ0li7bDar1dVVw9W9Xq/JJoKvojUxPz9vwQ5MKK/Xa3oHQIqsBZk5xhknXS6XzUhSWIY7i40imKKwRtYALMj3wFbg4Cnk/V5aChgFLDppDinK9evXjarV1tZm6frExISlS1yarq4u49Y2NjZaZd2l/GBQW1tba6IpMCsuNu9GlbJYLCocDmtqakqSrEBHhXp1dVXpdFqBQEA9PT06PDy0lAPxYUlGsQKDAv+RqhF+MBi09FSSUW04hFw+0iwKSrlcTj09Pbpx44bK5bIJArEWeOvR0VEtLCyYqAYp6VXckkMLPYzLIskKAhwsIqhcLmeC7T6fzw6i1+s1XJgWaqAjV/4OipX7cKFcw8i7YeThcII7w/FF95SIGjoQoueVyuX4JL6/G1Xz88GxERRx+ZTQf8DXDg4OlEwmNfib1vSLiwtr4kGCLxqNqqGhwc7J9va2wUrcA4p+v3WZfnM2uYhMXiaKoi2XM4LzBPZy9XcJFHBcDQ0NCofD1q0ILc91xpwN6gduwSibzer58+c2/tvj8djEaaA62AKRSEQTExPKZDJ6/PixFhYWrLjH/rp458jIiL0DP4/v534d9LPDw0OLbl+8eKGnT5/aGaDmQ8RbKBS0srJi9Ep0LoAEYBm5TpDaAQGL23oNBTCfz6u3t9c0PaCtsf5AF2gRo6vgZlust0sLvIrt/9YZedlfQtfi8oCNlUolDQwMmARhIpFQNps1bxwKhSzNwOhQkMCjQQonPYAGU19fb0bDfQ94pC52iNIQXxuLxRSLxUwb97PPPjP86ObNm2pvb1dvb68Vp8BuiCqIaInS6BCTLqGLs7Mzw8caGxsNNyb9xutjBJiDhnEDq+MzMDcKDwuNjFQZY+heKkkG3odCIRMRAWoB3wL0pwnD7V4j6oBOBP2MFJT3IrMgmnQfogiyDvdzudV42kIhmLtRGDAMD3PwpMtClEujcjEyNyNyPydyfrAbYDxEIhHlcjnNzs6axoLH49Hw8LAmJibMEPNzaL92fx0fH5tT4CHjcOlFwBl09YFDuopbFDPdKAknSsTvpqpuRyXwGutM5Zx0uqenx+4MkWlHR4dee+01BQIB7e7uKhQKyePxmFC41+tVf3+/daNNTk5qcXHRNEeQeSQQgProrgXRN8wP7MfZ2Zlx3yXZ78vlcg2MsL29rY2NDbuHbW1tunXrlr0rWrouxEPTEw/BDJj50tKSUebq6uqs2xUWEHfTPY8EQhcXF9bEEo/HFY1G7TxxBq820LzseanBJWXlEPH/NCwUCgX99Kc/1erqqhobGxWJRKz6zMUaGRlRV1eXeWZCdlICWhZZDDbBTSER7SB6AQPa2NjQ0dGRqRcRUUFxoh2PVlcq1dDbOIwuuO7zVSUmU6mUUbOkKtUERsH5+bkdBniflUrFogrSaJdJwM/is1ARJg3h4gF1uGI5Li7kbjZVYgyu22YJrYaDwM+BBscac/mJWrjs6DSgaOVyr93H5Yby/xTSML5cDH4eOrwwTYgKwBHdSFGqVeNyH86li/u7LAG387CxsVFdXV06OTnR1taWpfx9fX0Kh8Pq7Ow0CIWf39raqoGBAWuHBn+9Sm53YR/3966oCSk255iqOHAW55zagiRb09PTUzNi0MFwFjwYB869x+OxGXyNjVUB8JmZGYVCITPOjNDCYXC3ySi7urp0/fp1zc7Oqlgsanh42KJAeMN9fX01NE72AjEjDC4BiIt/U5/o7e211J0R7T6fz+o82AVodOydexfcCJc9BG9vbm5WKpUywwjbgM5XAjkCKuiViDFhd7a3t9XYeDn2nWyfs44de9nz/7poRmRHZFUqlfTrX/9a7733nrXlSlXJstnZWQP8Z2dnFY/HNTo6ahQgl9/mCnOwQRwg9z1IPwDBYQjQ7YVXkmTRGl5TkhUlwGc5tEQaPPBk9/b21N7ebpKEXV1dRhkigiJVBJOCSrW7u2spKxgazgj1NGhzVMwR9UCTAiN8tfLJxrpjg+DgEsUgVi1VnSbwAZ6cS0bV1+WMEoVxgd0uPL6GaBeDx+ED+4JvyxpjwCmulUolbW1tWfrGpZRkqR3ng4uK47gKr/Az2GcI6Bhc99+2tLQoHo8bbl9XVx3v0t/fb7gujheDiGBONps1R3H1fPK1/HvwbDKYk5MTdXd3W+ZEek52R1SLcBPfk5ScghFnwev1qru7uya9JaWFVgZ+TvcWcolEy240TNBCsRZNhlKppOvXryudTsvj8Rhui65uuVw2Fs7VPSGrkS6pXuw1ewW8UqlU7F0xZsAhOE+cFLAXjTp8DrewzJqSBXR3d2t1ddV6CQjmUFTDWSP6nkwmLeNsa2tTf3+/KpWKzRvkvcBzyRyvOsEvel5qcDkIfBO3C+bRo0d6/Pix1tfXdf/+fQ0ODqpYLCqRSFgUwOZ3dHSYIDgRFmInvPTp6amluRh3HrAzWlg5iCgKEaEyZ4pDhVECn/R6vXbIqbgSadNgQIqyv7+v+vp6w+/AHpPJpE16iMViVn2NRCJaX1/Xw4cP1dXVpTfeeKNGqBlOLBcSNgGMD5/PZ5KLRMxra2umL+weaJoEcCI7OztKJBJqbGzU7du3bZ1JUWF4EMVAvSJaYh1oVXRxdQ4WlCX3wbhipCjU4Pzc9yNSAFt1CeQYZxwBxbf29nYTIHKNOw/FEA77+fm5Uf6Iuvjsu7u7JnWJWhdBwdbWlnK5nHW1kblQeEP3ASOJgb26Dm4tIhqNKhKJKJ1OW2R8cnJi0wxIs+Fdg0NiLKnSn5ycGFuAVnBapt0Ij3sDzQ06IA6+u7vbJDf53sAkOEqobzs7O4pGoyZqg/oaBTta4L/5zW/WMFKkSwfPfWTfyHiJ0sFIJRmLwS2gXlxcWJZIJkiWw75zFsC8cT7uz21qatLMzIzm5uasjRn2BNxrgki0kMvlsk2wCYfD6u7uVlNTk81lzGaz5qBxPPz6vQwuqSgRCtHUxsaGDg4ONDIyotdff13vvvuuurq6dHBwYIIPYDObm5va3NzU8vKyUqmUbt26pb6+PqvkSpd98wDQtIrykJLABQ4Gg8YJfP78uYnK4DExsEANLD6GjwiR1mPAbiqTYLkYPUnmDOh42tvbUyaTUV9fn5LJpPx+v27duqXOzk5Fo1F95StfMZI1RSyAfsj/x8fHVuV1O+bcoqIrvu4aJd4DLiOTlXt6ejQ1NWWtlZVKVWM4m83WjBq5uLgwHYdnz55pd3dXTU1NFo1gyNwD5Bo7uIcu3uhSCGmXZawLBHmXV0smwvqDr1FAQ1KQv8M5u+eCCwsbBsOFAZaq2dkvf/lLpdNp3b1714wTlL/29nZTTQMy8vurU6GZ5gFMAmPn6rq4PE3SXXQqvF6v5ubmlMlkdOfOHd25c8eyDsSyUeWCL53NZpVMJjU7OytJmpycVGtrqzY2NgwCoUmBh4geQwPWS3EHtT6CF+h3fr/fWCiwAODlsn5QCre3t9XT02OSizhZ92GP2StgPSAUhHjcCB0VPAIYt1UbaVMU9tzA7+zsrEYlzV0HIIze3l7F43FTEZQuhxoQGUMRI+uiqOt2k/l8PhWLRZvY4sqVEjV/UTHVfV5qcEkTOdQY4FAopPv376u/v1/xeFz37t0zsvvy8rJ5CYzSxsaGnj17pp2dHa2vr5sHgftKlxmLTCrGQzqISAVKPqurq1paWtLKyopWV1fV1tZmLZBQRugqGxoaspSJ9IhiFhV0Im6w2r29PTO4FLHwxsfHxzV0H0Sb79y5o0qlop2dnZqvpaiDsccQkxpRGCSahyHBTC0eUkqMf0dHh0ZHRyXJLs3c3FwN1aujo0Onp6cmvej1VhWt4vG46uvrTWAa8e5IJGJYHlEbn539cItoQA9ulyD991DeSONJ5blMLs0GxgRYOMaDfSP65nEpUQwfxdlejdAxMrFYTG+99ZbBDqzD4uKiNjY2lEwm9fDhQ5MV/bM/+zNdv37dLjLn083A3M+AMXcjRrDgx48f1zgEGi5YF0k2Wml1dVUPHz7UwcGBCelIsmYZlyLJGoCTs/dwZmFIYES5czgVvofH4zEH4Yre050pyaiBjE1yWRKSbJ2vtkKT8eJMYdkQQMG0Ye0Ijvh6UnciXzfbcefOSTKZRdYoEAiov7/f5rWxZ/C8yV7AhqGlBgIB+95AQ4jeQN1zB5Je5Yh/0fOlGC6HncM8NjamkZERI04fHR3p888/N4HyaDRqtAy8+NHRkW7fvq2ZmRmtr68bQ+Fqhwa/pzjnbqLLoePijY+PG6bW0dFhBwYYoa6uqsn7k5/8RIVCQfF4XJFIRPF43GaAuVQV8EumISwtLdWIbGNYMIS0Sd6+fduwsHK5bIYEp0G0xb/b29szyhIREywNlLxcHVI3quOQE1nSWdbZ2am5uTnNzc2pWCzaZ+XAlkol7e7u2pqcn5/XpJl0kqF9UF9fXzMwz21bdGlanBP+S7ro9XqNutTS0mKRAU4T6h+GnSi+sbFRoVDI0jzeBUrPVVoYPxcd5p2dHaNUtbW1qVKpKBqN6t1339WzZ8/04Ycf6tmzZzZMFJz9s88+0/z8vEWzX/nKV/Tuu+8aV5jolfPpptHNzc0WLePQ3AivoaHBRJ0+/vhj7e7uqqurywpYdL9BwGd+IIIz0WjUOqSi0ail8S6k4BpNHoKOQCBgkEZHR4e1faMxzHlwI18iNrfwCy5+cXGhhYUF3bt3r6bzTrrU9uVOXy3sMULH6/WagNLx8bFWV1eVz+eNsRAIBJRMJvX06VN5vV7dvn1bX/va10xQCmdaKpWMVcF6IwpFZN/YWJ3XhpYuGRkYLnYKiAt4A94+705jDJAkzBHqDe6Z/F3Pl7IUPB6PpXwej0ejo6Pm8c/OzkzG7cMPP7TqpPsBKJBBMO7q6tLq6qpyuVxNsUW6jKhJFXggZkNXKhaLtpihUEhjY2Oanp5We3u7RQqlUsnoIfX19Uomk8ZwCAaDKhQKhuFhKADrobwsLS3ZJjIUcmVlxVouOzs7tbS0ZCke3UN45KOjI1PzB5zn+7tcXtJrPHI+n6+5RO5auAUa3p+KMnhlZ2en8YBJGzFCpJsnJyeanZ214sT169cVDodrSOGkXERSvA9wC84PXM7twHFFWRieSaoIRQpOZVNTk+2xy1ih4PW7sDH+jLNw+/ZtbW1t2TtQhAUqaW9vVzgc1tzcnNbW1pRKpexctLW1aXh4WLdv39b9+/f1ta99TZKsWcI1/JxrnqGhIT179sygLzIrF3Khc+zVV181cXMgI/r+wcij0ag6OzstuncNNyJKbjFUuoSaXE4ta0ixuLW1VX19fbq4uDDd46GhIYtWt7e39aMf/cjYOj6fzzSAoYIBPzx48EAzMzO6e/fub51PzjN3ERUv1oIC1QcffKDd3V27qwRwbW1tmpmZsaJ7LpfTxsaGent7df36dTs/sDdQC2T9YCbwHtAbx8fHDTbBQcLo4C7yuKp3OFg493SqYSPBo2FJvex5qcF10zroLOAYGBx6qnd2dpRKpZTJZKwoBNcRw42h6ujoMNk1vLWL6QCU86BrgL6o2wkSDoe1v7+vTCZj1BEqy+gj0HZZLBathZSLD5YHZkjbayKRsGiJSz08PGxRLZSiYrGoBw8e6JVXXrE1gwJEsYSIDo4nBgJuM+/i9ua7nteNpoguiW6gnIFfu7QVeIUYQL6+vb3dBFuQ3YORwmUDt7q4uLCIFe/9+PFjc5ZcfgRT+MX3vEro5zPR14/BwNm6Bxa+pduA4z4uxFCpVDQ6OmoKUZwpcMGGhqoA9bVr13Tt2jWLGDOZjI6OjtTb26tr165Z5OvKXJKqX4U/eG7cuGHTNHCG4MeI8ODUvV6vJiYmjNddLpetwAncBAaey+UsGnRrEdKl8XJ5uGCWbmpbX19vDj4UCtka7+3t6dmzZ/r4448N4jg+PjYWT6FQkMfjsSGhZGTBYNDkMBcWFvTqq6/W7ItraF16p0sJ3Nvb0+LiopaXl621m/pHMBg0pb/FxUWzEX19fdbezLksl6vi+f/6r/+q119/3WoXnCGcOqwbBIu4n0xIpoECfjhnGBjNFa2h8EpW5vLqWe+XPV/aaeZW8ok2mF7L4kCgZ5wJU0vhWHI5warYEGZv0YHEwSSV5EHjFhFw98PBBYTXenh4aPgnKSXv7fblE4Gl02mlUiljNHzyySd68eKF5ufnjVztHl5ma21sbOjs7Myim6WlJdOKIArCyFJEASKgKONGckT2XFQieoo4Vx+/32/deHhyDhkFBpyWx+Mx1gUXEi4uWDYCQpXKpT4thoV1Zt0fPHhgDs+VLgSOwECy1igruSLpHF4OP5kQhVPehc+G03EN3dWL3traqtu3b+tnP/uZ2traVCgUaiQXCRYoqNJe7GYfDDGEtcL78zPA+Nyf3d3dXcPlBEMFIiL6gc3h8XiseYI75qbu5XLZOgxJWXGynC3X4fJu3B3OO44CBT0ciftztre3a3BNCrsEWNwV1onfBwIBG0bgPjgG7AafHYofwVQ0GtV3v/tdywRd7Qson01NTfrGN75hdxP4AX7x2Vl1CvfPf/5zvffeexobG9M///M/W5DDmsDfDYVCevTokdZ/M0y2UCjowYMHSiaTJt5Op6HLI8dZENVSe6EOA12Qz/6y50s7zaBXUSkkuiXFJIVpaWlRe3u7hoeHDVA+OTkxY0lLLP/v8/lMnSgYDJqXB0dyDW5TU5P29vasag0JHLjDpcwAfPN1HEIus8sXpQHi/Pxc4+PjxrN9+vSptre3TQRZqjqflZUVJRIJdXZ2KpVKKZ1Oq6enR0NDQ9re3jagnQvOZUXDAO+KweRxAX5SfAwh6lo8pNwUzEh9uEBEUuDEGGvSU3QX3IIYUbl0WfHHmLvFM4xMJpOx9Lm9vV2RSETRaFSBQMAqyUTFFIdcHBTj4ff7a36uywIgfXejaP4tj9s0weV444039Pnnn1sbMlMMcApnZ1W1NASUwJgxlkApGBIgGKJyIver0TZZGG2/FNZcfjGXmGwL54gAkcupBbagOIqj5B35czeiwrHz7q5zpfjj8/msKEmrLPDLyclJDRbN5weWcfUK2DP2nIf9covt3GVScSBGJihcXFxYxyRUrXK5bDUE2vcZ7ArThhloMBsWFhbsPjU1NZmhraur09bWlj799FOryXC/OFe7u7tKJBI10zGg7Um1jRrBYNBG7XCXsS2/V+MDKRT0pYaGBpvlRXrIprL4HEwu3sHBgS3O3t6e6QhA7QgEAopEItZaVywWTVGKZ3p6Wp988on1jrt8w0qlYh6cNJr3otDnprqktqVSSTs7O1pdXbWJB8vLy1pcXNTc3Jx9Jg7e0tKS3nvvPZuN1tXVZcU1ZP2gVqHahXd08U7XcxLBYWD4Ggwc0b8b4ZJa43XxrC6xnGrwVYycAqjb/UPkyu9JlYhOKCDwrpIM08K57e3tWdSAM6aQQSchFxEmBikxHUKkx1ejWZfjiGHgcdeQf9va2qp79+7ppz/9qRVJoBGyBhRyV1dXlUwmDdcPh8OmSevCKGRJMGmI/Hjm5+eVTqd1cXFhXY4UgInuyPQoOr148UKZTMaCC+AlIBwKWjhrF25jXV1j6/P5dHx8bI6cIqPf76+J2nw+n51NqIRer9cidzSc3aiUvXLhFP6ce0UW4MpiEhhgzEnbgXzAkvf3961ZCHUxhHTAv2nmYU8ODw+VzWa1sbFhzAH2BIWx09NTLS4uan5+3rDS9Hk4AAAgAElEQVRgSXZnvF6vTQ5++vSpnU2XDsn5JaBkdBWC45xTuvO+zODWXfXU//f83/N/z/89//f8//O8NML9+te/Xrm4uNCzZ890cHCgO3fu6J133lFdXXUeUS6X0+bmpnZ2dvTixQuNjo7q29/+tu7evWvpi1s9xasBVdD8QLRLKgFO8/3vf79Okv7lX/6lQhQI7mkf4Deeje4kV/cSwjbRIN7KjSp4F77XVVpLY2OjvvOd79T19fVVwuGw/u7v/k537941vAYsic/H5yL15B3AqdyOLWTo3EiOz3h2dqadnR2Lmv76r/+6TpJ++MMfVsC9aOklGgEjpWJLV9f+/n7NvtLyDLWLd+Czu8T1vb09vf/++2pra9Nf/dVf6a233qq7ceNG5d69eyYejof3eDyWgrn4I5KGRBVEbawZ0AcRBipipKrIBQIh/f3f/32dJL3yyisVmCFMY4WLDQc8Ho8rkUjo4OBA9+/f19jYmEV/tF27o12Azyiy8VlKpZI+/fRT/e///q+t969+9as6SfrWt75VcbWUR0dH9f3vf1+xWMxwV/afdQZfpzLuYrBupsZeAjeB6XP+PB6PvvGNb9T9+7//ewUYj1/UNGii4S66WQM4viTLpMjs6Bjj76DyEX0vLS3p4cOHikQi+sEPflAnSX/+539eoa0dbJo1QKWLiJSxO3BwueOuiBK4MfAJ3a/YAb/fr87OTjvP3/ve9+q+/vWvVxCnGh8fN5pmQ0ODTTBGlAmsm/lkh4eHBuGhmYJgD3tDhunSZtGcOT8/19e+9rXfCeR+6dRejGQ4HNabb76poaEh26CTkxObzLu0tKT6+npTngdekGSdJaen1VlntMcB1odCIaONkbpd7VUnlSDNkS4rgmCCpD7uu5+fnxv+wqFzq/z8v1uFv0qtkaoKRJOTkxocHLT0gZ/ltpa6MAJkaQ6e2+YIHOC+L8UYUu1YLGZ4tPuZrn52F/8kHZVk+LvH4zHeM4aONQFX43IyZVaqGoNoNKr29nZls1kzQGB6bjGpvr7e2mJdARmcCtCGq9rEA4WHSrmLrdMdRxXehVdwmi5zA2rcycmJ+vv7TbTk/v37NvTTpemRsrot03BX2SdSXyZpHB4e1nT/uU5WqjqIVCplLcK8G+8sXUIbnEscKF8DBENQwJryc0i9WQ+MqVt4Zv3ZXxcPd40hKTQGD6gIB8FkB84L7A2wZe4x5xKjiXFkXQOBgLq7u9XR0WGFpouLCxt/vrm5aZMefL7qyHp+T9MQ59gtMF+lY+G00ST59NNPVSgUDBceHBy0ZijG/1BjcamGQA8uAwSIhLvkCulcbX3/ouelBpdLfXFxYR1VtOfxUr29vYpEIhobG7Nmh/Pzc1tsMCWMUKFQ0NramnZ2dixSWFtbs371UCj0hUA80SYHloICG+x2tbiHlMPFBSqVSoZ/gefC23TbOokEXF5vOBy26IvDe3x8bHrAGFsuilQtPOI9XcMA1gnW7OKcVHbBKN0D7XZ4YZhZG6Izl6aCEcbIUfgAh0NMfnNzU6urqzb1oa6uTpFIRNevX7d3d7UXMPwnJydKpVI6Pj5WOBy2Q8wBxjmy10TXRDkYGyYPUIzc399XIBBQPB5XPB63zMAtprpGjuieJx6P6+zsTOFwWMPDwybcQuRNVAOeTUbGZ/L5fNZPDybN2Jjt7e3fKnqyXx5PVULw0aNHyufzGhwctAIk684+UkDjHEF5PD2tzqjDCOdyOSvm1dVVBXeg9bmMn2KxaE0TEPRhFTDBGi1ojBYOl+4tAhEKhJzRcrlsmGm5XLbAg8/Lw+fBmRFBcufc6JkBBYxgwjnU1dUZZzqbzZqoDNkkd7qtrU2hUMjulFuQxgGlUil1d3fr7bff1sHBgTKZjPb39/XBBx9obm5ON2/e1M2bN2uYJARPZHzUiFwDD8UV/Q3OgHsuvuh5qcHlcHi9XkWjUYtA3La2aDRqpHIAdKIboiDC7/r66lC769eva2hoqKZTCKFfpB7dhw+PdBqkb6reNFxIqimMcXmImN3UZX9/3zpH3DSFQp8LQ0hV49bb22uRkVSNen0+n3XYsTn8In0mMnOr4BCuMbwuBOGO8HGLXlxUvpcbpdP9AlOjVCpZNHJycmJtpFwqopdSqWSt14lEwgogTU1NyufzWlxc1ODgoMrly64b9oAWZEZub21taX19Xd3d3ZqcnDRtVfYf1X54n0TeW1tbmp2d1ebmporFokUUdXV1NrFBkjUJuIYOIwd5H6fP+g0NDZlwNHuMEYJzi9HZ2dmxonBra6tisZhlX2ihxmIxmznH4zoBDDizv5aXl9XZ2WkdXvxMLrjbKMPQTlp76X6EXN/T02OND+Xy5Rh6SVa0perv8Xhszh4NRxSd+UWHJVlSpVIxRwd3Gc3pQqFgTQozMzM14+RpMOKuQhul6adUKpkwDK3f4XDYNFWk6oyzpqYmpVIpu7uFQkG/+tWvbDKJq5AGuwI2AsZcknGxMdxvvvmmBgcHtb+/r83NTe3u7lrXJTaAaeLcG5pxlpaWLNtCQ9dVoQPC4k79Xo0PYCZeb1XIlwuYSqXM4/T39+vWrVsaGhoyg0xECs3DTS+CwaBCoZB5eC4eFzKdTuvhw4daX1+399jd3TVuHfqhBwcH6ujoUGdnp3XM0JrKoT88PDQJNw75/v6+EomEtra2rIUWihULDiaIx5MuO83K5bLNCysUCtadJckoYa2trWagUJySZIRxF5tqb2+viYaprDO1wBXkZk/cFlu8/f7+vv07CPNwnuEqc7HoG5ek/f19mzmGJmxvb69aWlq0t7dn0Uq5XNXG4D1xEBcXF+rt7dXQ0JAkaX19XTs7O0Y1m5qashZq1pcLz7nw+XyKx+Pq6uoyGUtSOVJIsg03giC6ZY9wcjhL5pQdHh5aGsvaM8cMBwc0dn5+bipumUzGzlQul7MMDD4tD5kIl7C3t1eTk5NaXl7W559/rvr6eg0ODmpkZMT69Pk3tLnX1dWZ2BPdcpyV7u5u9fT0mLB9qVQyGiKV9+bmZsXjcXuncrnaMbi0tGRTeknZidqhVsIAaGioznJLp9NqbGzUyUl1RBBOG/4wAQVSl9CxOBsuRxhDzdl17Uoul7OAhexLUk2QMzQ0pHK5rHg8rvHxcdXV1RmdlGGZnHUCNe71yMiIcfdzuZw5M9TWYEVgozo7Ow03J+MlmAMHh/UBjQ3n6DZ7vez5UloYvcp9fX3a3NzUw4cP1dvbq1gspsXFRdXV1enVV1+tKUpBnSDNwoMT3XDx3BCdfx+Px3X9+nWlUil7j8ePHxs/MB6Pm6IVmKPP59POzo4ikYju3r1rGgsUx4jqUqmURWN+v9+MBBeRtJnLjfOQpJs3b6qnp0eFQkEPHz7U+++/r8XFRUUiEY2MjFjBrru7W7FYzC5IJpPR1taWYZtsqCQz/hS28vm8pWm5XM70IdwIwj2wHCxoNy4lBUPC14NDEnET/S4sLCiTyaixsVGvvfaaJiYm5PFUxWeI1OgqJKojSsGojYyMaHR0VPF43FJHuoKOj4+NSgfuiZOgC623t1dvvfWW+vr61NRUHQ2ezWat954x3leVqVy8nE5EpkG7QjOktmdnZybYgvZwLpezVJyCH9gmPNHOzk6TKSSFdAuRbnNNpVLR2NiY3n77bRtk+uTJE+uqwvjSycf48kwmoxcvXqitrU1jY2O2Pi69C6odkRsynuw3mRsBBWcDCOFquzFFJDj2UjXSXFlZUSaTsUyGgl9jY6P29vaUTCaNDnm18cGFvICnQqGQhoaGTEAql8spn88bJ5zMr6GhwXB2GnEmJycVDAY1OjqqmzdvqqGhQfl8Xqurq9re3q6JvMnId3Z27F4zFmtlZcVEqQYHBzU2NqZoNGpBGI1XjNw5ODhQW1ubbt68aRk4mTL83lgspomJCW1uburXv/61EonEb/Gzrz5fanCPjo40MjKi/v5+hcNhw42o+vX19am/v9/UtRipzKK5rXCRSETBYNAG6+3s7JghDofDVvzhIttLer0a/M34ExaxpaXFLpIkgzQWFhb04sULzczMWDeW1+u1ybOtra0aGRlRX1+fIpGIUqmUqY3R5svPAceTpP7+fsNxOjs7NTAwYAwN5NpcHGdoaMhUqTKZjHlKPKDf71cikbDR6oz6IZ2HaxwKhTQ4OFizJ0APRFlNTU0G7QCtbG5u6vnz54a5UR2WZBjr3Nyc1tfX5fV6NTMzo6mpKc3PzyuRSNjPIhrt7e01g06qy/4jKYgx6e7ult/vtwjr6dOnhqeinra3t2fQDU0v6+vrxtfm+8E2AQpwi21utZi9dgVLgGQk1RSDmpubrelBkubm5kxSlIYDfhZ6y/v7+/J4PFapd52g29zA3mWzWS0vL2tvb0+BQECnp6dKJpM2xPT+/fvq6empEY/n58L3xTlnMhmtra0pkUiY8e3s7NTo6KjdE/jts7OzWltbMybI1taW4em0fLM2OGHuJ0UpxM7b2tqsG/P4+Fj9/f2KRCKanZ21Nac+cXUtXEGkW7du6e7duxobG5PX69XS0pKtealUUj6fN7iPidN7e3vq7u42aOLs7EyJRMKadii+dnV1GeuDGgOdaF6v1zSJP/nkE926dUvxeFwbGxv2vW7dumWz68LhsKm38TnoQIQffXx8bHDRs2fPdO3aNY2Pj5sw1+/V+ADx+JVXXjFBlOnpaQu1qSBLqhFqIRIBJ+WyMTvs+fPn+vzzz7W4uCipWtXt7e21DwzEwMPwyb29PW1sbKilpUXj4+O6e/euCoWCHdDBwUHV1dXpyZMnmp+f1+joqKLRqMEAnZ2dunXrloLBoMrlsrq7u5XNZrW5uanm5mbdvXtXz58/VzKZtPcKBoOSLlttg8GgDg8PFY/HdffuXSWTSRtrDQsjEAhofn5exWJRvb29hiHipOjfT6VS2tzcNMYGERnfgzVEBF2qVfbHIFMwa2lpMW1VGk5cGUT2DLWohYUFtbe3a2xsTK+99pra2trsMhEhU2wKBoM1hoXWTSZwbG5uan19XWtra3r77betB53IY2dnR21tbfJ6L3WNiXB5ZyIzilper9eKklSe3YgKY+oWSzkPrhEgmpMui1U0OqCmBm5OFEyB8/j4WNvb2xYs0Jnm4rbBYNCcGUFDNpvVr3/9a+3t7WlqakqBQEDb29tmUKmYky3gKOrq6tTZ2Smparxee+01PXr0SD/72c8UCoU0PDxsMAGQliSjALa1tWloaEjDw8NqbW3V7Oysjo6O5PdXpzuk02nTS+As+P3V6RAtLS2Wel9cXBhmDm46PT2tcDisa9euqbm52dq6XedDlMm+DQ8Pa2RkROfn51pcXLS7eHR0ZNDE/v6+KacVi0VtbGyoVCoZ7rq6umpFQBo3OCMDAwMG0VBABFKUZO3H169f11/8xV8on88rlUopEAjo2rVr8nq9isfjllWNjIyYnZKqwUwymdTy8rIZ3Gw2a9OOf/nLX+qtt97StWvXzIG+7PlSgwt8QCoGhYeKOF1hfr/fPAVgNJdKkvXZLywsmPo/UdzAwIBmZ2d1dnam6elpUzTiaW9vr4lAPB6Pbty4of7+fi0vL2t3d1d/8Ad/YIZ5YmJCs7Ozdqm4+H19fYrH48ZxbG9vV3d3t1FVuru7bdxIX1+f9ZNLstlhLS0tluaEQiGtr69rZGRET58+VSqVshZjtHSPj4/16quvmlxiW1ubRf5gt9DQuIjM3kI5ydUPAK9yUyiwNnC5Dz/8UIlEwnBLotBsNmtGIRAIaGhoSA0NDUb1a2lp0RtvvGETVcHOOdzAOmDDpO0rKyt6+PChzs/Plcvl9Pz5c73++utmrDEIOIGtrS2LcKemphQOh030aGVlRZL03e9+Vz6fTz/+8Y/V2Nio4eHhLzS4brces7BckZyrHVJS1QBDOSoWi6YxwPSFo6OjmuGkkkxjAZUt9xkYGDA45ezszDienLk33nhDH3/8sXy+qih5Q0OD4vG4tY6TSWJ4I5GI4eMEBlNTU4rFYrpx44axgqjCS1JfX58GBgbsXhHdEyFzzphSXV9fr/b2dmv5pZ0bSU3ojETUdIRyp1z+Oup20mVgsr+/b7ohH330kbLZrA4PD422xpRvskLOWTKZVDKZVDgc1uHhoZLJpA4ODtTU1KSNjQ0r0uHkPJ7qbDYcg3RZWJYu9YN3dna0vLxsd7unp8eM6/b2tnVmAi+tr6+rqalJXV1dNuH33r17evbsmZ48eaJyuaxQKKT5+Xm9//778vl8Vvt42fNSg8s3AQ+log53kcsHPuT27/v9fh0cHJgaFZjknTt3VCgUTIJuZWVF2WzWIrvl5WWbT8/D2OSDg4OaS1EsFtXf32/f6+ysOh8Mjt7Ozo5Rn9BppW+d6RSBQEA3b940XKipqckYFBgwSZY2Hx8f2zht9AM2NzetMOT3+zU3N6f5+XmbdIDeAJgsB4J1u3btmrUhQm8iZWeOFI8rdkLRhTlqZArT09Pq7++31mcXCkI9KRAIaHR01ApATGalAYFpArBTgAKkS71RjN7+/r7NbaOYSEU+mUxalgEta29vzwoz7B3ttDdu3DDoplQqKRQKWcEVrJ0Hg+RiuUAgtJU2Nzdb9oHDY0/a2trU1dWl+/fvW6qLgaZN1ufz2eVEmJ0IlGd0dFTDw8Oqr6834aZAIKCvfvWr8vv9isViJmlKVkABFgPitq9CS/P7/TbWZmZmxqhb4+PjhrWzDzgH4D7ubygUMuiAaHpsbExdXV02AYK19Hg8tj4nJyfa3NyseWcMkjvBg0yAh2LZ+fm5CoWCPv/8cz1//txqBtFoVF1dXRofH7fM8OLiwoYZnJ6eKp1OWxG3XC4bRQyoiMzn+fPnevLkia5fv253UZI10Ljc5PHxcS0uLlqxLxKJWBGxra3N7gpzyyQZBMgQXHB7KGg9PT2SqhAdGPBVTPvq86UsBfATPLDLT4OGA/Z5eHho6Yt0WbjxeKqjmClsNTc3a2xsTP39/RofH9fy8rJisZj29/eN4eA+Ho/HZkSdnJxod3dXa2trymazCofDCoVC2t3dNTwpkUjUYGOkjfl8voYQTerEQW1sbLSuElJMItxkMqmpqSkz9IiytLW1aWtry1LjWCym3t5em1pKIQZuIyInLS0tRo9rb2/XycmJ0V5aWlqUSCQ0ODio69ev/xapm64XHEBzc7NV+InY8/m81tfXzflRYaWPHyFwootoNGo4cqVSsRQTZgNiQ5I0MjJiBVG306yxsVFjY2NWLIRlUalUlE6nLRWHGdHa2qq5uTlNT08b3WtoaEiHh4fa3Ny0kdiIdYMp8pDx8EBVa21ttUkXkozNwtpgKHjvO3fuKBQKaWpqShsbG8pms2YMI5GIJicnNT09bVX31dXVmvdg7XFWCMPcu3fPNF77+vqsqYMghgicrI1LTuMKZ5A5ZvDL4VbTgSfJpt6enp4aRIJqHL9n1qA75frqerKOcKZzuZz29vYskkYtj6GPNEi455OiI6k3xWWv16u+vj6NjY0Z/IXtgF9LkY/1ADrzer26e/euidlvbW1pdHTUsi/X2OH0YO+cnVWn1Fy7dk0XFxem8UshG+zX7/dbURBjS0fizs6O/vM//9PqFty/cDissbEx9fb2GmT3sudLi2aQjaGHAR7TMMDfIQcIhEClD+HlcrlsHEtSvtbWVlOwBzfBi7hVYBoDMEZsND+HiKpSqRgrwJV+5Bcao+VyWcFgUAMDA3YhMNYcbIwRG4/eLt5VuuTh3rp1S/l83qAV0tdCoWAXhGjZ9d541r29PSUSCevgCQQChn0xWsXdE5/PZ8YL0jfpYrlcts/f0NCg3t5ehUIhmypA5AFThOi/vr7eKvdgnm7nHRkORgHyNwWc3d1dpdNphUIheTwePX782Mj5Ozs72tjY0NTUlKW8ULgooHZ1dRlL5ejoyCridGNh/K9ebpo+GFBItxn4ZG9vr0FGFGbc5pCGhqqmcjQa1dTUlNLptA29hJJFkMC0ADIGHpgLRJc4WYSxX7x4YeNcSN1J+aHGsZYEMxg1jKXbUef1eq1zC2fsEvfhf7vrhONi/h8sADIugife1+/3a3h4WL29vTXz9Zqamsy481+XPeKK8GxubiqTyRhLoaurywKMra0t4z67cBNQBkVmCqxnZ2daW1uzqRiNjY0GhQEfudAbIkHZbLamJZnGBpdHTc2jo6PDlNy8Xq+18sJmoMgKpQ8b0dbWZsXIq8Hi1edLGx+4zKRu/GIjuUDwCfmweI6Tk5Ma+TuXR1ssFu3vqO7Sl+y2cHKQwLjAjzm0RJubm5va3t42fp6r0gWuTATHsDywWVJSDi+fF6Pj81UnMcDCODo60srKivb29tTU1KR4PG4E7GKxqO3tbasOYwD4TMAydXV1lirRMgrXlMPOprqXBwcE6dvVo5BklxsMF00LImOgGSYhuPOr4LFSPOrs7LQOPA4Tsn4YP7/fb+vCNGIMT6FQMMF2zotrlPr7+3V0dKSdnZ2aSjrwC4pWVPrdc0Ea3NLSYnS84eFhM4hM4pAuZRaPj4/t91wSnAmY/vT0tJ0ZeJ/Ii8I/J32VZEaTCBQIhKGEFBbJmGi55tK2trZaIQsqXqlUUjQarfk6KvMUSF3FMLfrDuPT0FCdTEwTAHix++vg4MDuOdgvUTIPlEdJFlgRjKCv6+4J6wZnlXZrMhfkWnHkxWLR6Hk05KBbfP36dRUKBdPGwFF3dHSoXL6cjegyWCh2wx2H8UIzB/eX1txkMmn2gCnNJyfVySyhUKiGN037PYHK2dmZ2RAXO/6dNvWlf/ubB/6s+wvKDpginR8YMbBbvFcsFjPiNhEOLAaiBQ4JG89DqgFljHTI7XVPp9MmpuxKwxHJ0EFClw6LxgF16UMuLxjnglaDVMtxZT0g6fv9fjP2TU1NNfDD3t6eGT7oSS4PERyaSJvNu+p8XGoYMA+XhM/MelHddzmGcKAxypD8Xc0J6Fq0OdKIwQV0Rc75fHQ/0fteKpVsSiyfh7MDHejg4MAiQTjb7AXZFONmwJZ53Ai8oaFB3d3d5qh4dy4YgQIiR0SZjY2N6ujosEyJTMFtoOAOUPCBB8sDnMaeuILeRGmcbbqwXIEkV2yeCLVSqRgvG4PrRsGSTAOCM8L64XyBBcDv9/b2bH6ZK87D5AK30La6uqp0Oq36+vqaDk0K0Jx7aHM8rDfNSFtbWzo9PdXW1pYVhYm4aaCBJ88aMXH6+PhYAwMDNVMc3HZhtD64Q5xPBkMCI2I/mIpMl6rf79fu7q4xaKQq/uueUbJjt6GJ9nCyCLJXbNHLni81uBQiOLzuQaQ3++DgwPRyoY+hNer3+y0a6u7utk3j8IE30Z5KZOG+OJcar4lnJIKFTM3FYhHYYAy0q0jE5nExWEgiIKJpt/2YIgXQAFGm2zbqHhwqtdDnKNwxNsTn89XMD6NQw7/hs7oHmoMFDYbohagQz49zwTDy/7yHi7XX1dUZgZzCKIeP77+/v28HjIkXL168MKYJUYHrLDH8RI+km6SBZDKuwabggnGEFUP046aCGEDpUnmNoiMMAFTXFhcXLXOIx+M155j08/Hjx/rggw90+/Zt3bhxw7RSucju+XdbjGEwSJei/RQn3bZu3rmnp8fGOREIwIHl9+wncA/ven5+bjoLFAilS/waXJ/vwxrDH2XGHZklzCP27/T0VMFgUOfn5zYJxbUDRIkwK6i7uI/P5zNVrnK5rJWVFdMa4f7jOJh6AU3u6OjI6gXoZ9MoAiWT5gNGePHz+a973rABGFjgkcbGRuMuNzRUZ8UBjRKkubATZwTqndutyb5frSl80fNSgwunkB/sCq3wYZjeAL2ENBdZs5aWFiFdR2sg4ToFKLyyy310MVw4fUS0eB+8CukUUQgROdEuC86muBgMlCzX8wIpuMYO6ggCPMlk0jp/WGQiO9psvV6vbt68adjl0dGRdRYdHh5aqy8RDcYbcWP+jbuxHAQaAohqwSKnp6e1tbWlVCplBHwoY0RSNHTADT0+PlYgELD5TvCpwQ4x7qSV8H7hrRKRc16IACgUgtejH0Fl2M1cIpGI6urqlEwmDQsH23RHsLjTL3BuHR0dNaOu4UjC/Pjoo4+Uy+X0j//4jxofH7dCDftP7/zNmzf1gx/8QB999JHeeust3bx5U11dXfY1ZBOuI5aqBau9vT1dXFzYZOJisWisFYwKAQMTMPg9KfDAwIAqlYrN+xobGzMHypmuq6szzi3t6hhc91zDSMEQu+sHtIEuAOeCAIQMgKjdVdSTaicEo+JVY1R+A414PB699tprunnzpkWb0Kna2tq0tLRkBWS3IIjBzWQyNZzhk5MTc0Iu9EHx2s1GWCvuNHff7XhFMMjV1WhsbLSo2M2O4OBLtZ2W2ELgnN8rwuWbuD3rbDgbyMa5Yhyrq6vWRcacr83NTUsRwDrBLl0SPAbLxRVJY9h4qCcUzzCoLCjRHMbTVccC73QP0VXmhfv5iW4++eQT/fEf/7G1xWazWTNCHHa6pCSZotO9e/dqiiRcgp2dHWNZQLEhsm5sbLTDctXIuHg6l4VokOgLwwdGd3x8bCpsPMAiW1tbevTokcESUGyIVqH5HR8f28HlMgEpcJhpS0b8hbWFwRGJROySw1EmhUcHAKgJB4ysnhtxuA+kfbRM19fXzVhvbGzo+fPnWl9f1+TkpFpbW7W6uqonT55Iumx5pvAxOjqq/v5+41aura0ZJoxTYw3cSGZzc9OKPvCIR0ZGzKiCFdNvz9jyfD4vr9errq4ulctVBa6zszO1t7fr5z//uRYWFnTz5k2LDnHi3EPOLA/Zj+skoV3xd2CuCPLE43GjPJIx0MHl8/msgOZymKXLySNgsTyuaBS4MQyO1tZWS+2pH7C+o6OjVrMgy+S+wDnHUTU0NNjdo82dyJh3BNp0s1W3k9UVwopGo+ru7rZ7QnJ+VHkAACAASURBVNNDW1ub6Ui4hTfX9nGvsX+/V2sv35SXlFRjBCC+u4aJTYBczgcdGBgwLAlPAn6DceCSI0/HgydyR+dcjb5Ri8rlcrYpjEzhexA1Imbiyqq5aRERp5sqUmhyUzfSiGKxqEQiYd0ot2/fNrWpoaEh4xa2t7drcHDQ8OHt7W0Vi0W1tbWpr6/P0iAiZ7iTrpFxMT5XEIaIPpFImAPgctFts7GxoeXlZfn9ft28eVPNzc0WAUKOb25uVm9vr60JBxOetSS7BLT4UvmnqIAoCJV9jAESnODH0WjU8LlIJKJSqWStmqTEHGLX6Lr7RFYALAUMksvllM1mdXR0ZK2XP/3pT/X06VMVCgXFYjGrO4DfzszMaGRkRK+99prm5uaMmgaHGscIrs1DSyfiM5VKRW+++abC4XBNIZashOJPJpOxHv2GhuqUWoKEcDis58+fa3V1VZOTk1ZDIMAgrXfJ/hTWgDVYL5w3QQ0NFBMTE5ZaozJWqVS0u7urSCSiGzdu6Ne//rWtNQ6H/d7f31cymazZk6OjI+sMrFQqNuIIJ4hIDhndnTt31NnZqb6+PitQ8TkvLi7MQd65c8fEhjgLbrrvFtoxzhReWX+yOzcKpbAvyeA0YCngUBQPc7mc2tvb1d7ebuvsngO38P67ni/FcDFebDibTaRL9EU3iqsSL8moNGwYITwFKSI2wnQMGApDvEM+n7d0gw/H96TS6/I9W1tbrTsnmUyaoZZk3FWoPEQ6boUVgwUJms+L1sLg4KDW19f14sULpdNpbWxsqK+vT729vWpra9N//dd/2UEbHh626IJJGaVSySK+J0+eaGVlRTdu3LCMAE/vGn0et8jAWoDHEpHu7+9ra2tLHR0dmpmZ0enpqebn55XL5RSJREz7dnl5uWaWE9xFLvDJyeUUYAw/RSqiHEkW0dJKTPqFNkQymVRPT49KparS1cHBgdG2oOYR9VxcXBik5GYkVw+0m7UUi0VbJyiG5XJZY2Njunv3rkZHRy36pAXY7/cbNNXd3a3R0VFNT09bQWxpaUmZTMZ46DhEzhoPLd1MrC6Xq+2rw8PDdn5LpZIJwmSzWYOmcrmcKXRtb29bsens7MyafXK5nLXwEtzgjNy0lzWhiMtZoTbBVBbaYVOplNU2KH4DEZJluewaNwNra2sz+MftyONn8z0oNJGFEsTRaFRfX28F9Uwmo2KxqGQyqUQiYXcO+uXAwIAFcdgZphu7TpB3px4CBIYdAt+n8M09CQQC2traMmYMHXE0OMAtB0bjPhDgXK23fNHzpZAClxtZO7xLsVhUOp22hXQ7LTo7O63I4VYQubSA3q6nAnA+OjpSPp+vSdlaW1tNAAaaFRN8qaYjrE2U60bdbnRBpEsBBG6nm3ZhaFzv5xbeEMfw+Xym6oRIRygUMv7ej370I21tbentt9+2NPLRo0fKZrO6d++eXnnlFYVCIdXX1+vjjz/WysqKAoGARYVg2fw/xoLD7xZ+wE7BxVdWVlQsFnXnzh0TjfnLv/xLpdNpG420vLxswiHlclmTk5OKRqNaXl42XdTm5mZr04ZNsr+/b6ky6wZ239jYaPoOiGE/ffpUH374oba2thSNRnV0dKRf/OIXunHjhjlQGjHYK84aRTIOt0vUh85ULlelMPm3OKzu7m6NjIxYJXtsbEyVSkULCwtGT5KkSCSia9euaXh42FrYY7GYTk9Ptba2VtMViLN2oSfOPRnc4eGhlpaWND09bZDJ+fm5stmsUqmUFhYW7LIXi0VrKri4qAr45HI5zczMGN7swikYivPzc9t/Lj11B7fWQfs9uhjj4+Pyer16/PixFhcXdXBwoMHBQY2Pj1tHGroowWCwpjgoybJan89n4kMu51eSURtdlghBAWcUbVrgRsTdz87OjN5J5nPv3j2dn59rfn5elUrF8HpJRusjopdkUyI4H7QAY3gx1nS4kQ3B+56cnKxhM4Dd09LtFtX4GWRKv3eEy2H3+Xxqa2uz5oK1tTXl83kD2EnLaGooFAoGursctubmZjvAvDheAwOzu7tb09rL5aYXm0PMh+YDX1xUZ9AvLi7q4uJCH374oZaWlgxXhCvpjichKgNz4j3o8nHXgOhcusSoOjo61NbWpnA4bB65Uqno7t27CoVC+uyzz/TDH/7QDgHtq5FIxA7j8PCw6urqtLCwYFKOdOocHR3VQAoUX4hcSLk5cEdHR3r69KkeP36sr371q2pvb7cCJY0F9fXV1uyJiQmjT5VKJQWDQdM6YA/oKgMTl6R0Om24KdEEThcjjTPb29uTx+PR7du3lc1mTRCb1uNsNqu+vr4aDAxjS/ZB4QNnbYfXe6ktDEMEUrtbOMWR0iXE2WO0y+DgoJHoSXWpdGMs2S9w6qsXi/SWrGltbU25XE4tLS3m0P1+v+7fv698Pq8HDx4Y5ENhzOfzqa+vT+FwWIODg9YkQdbiMia4U25H1lUGDp+fcz84OGgKZB0dHRobG1NDQ4Oy2ayWlpasgEebNmtKFks3IZEcFX7X4BJZsl9kQ/Dy3T8rlUrWkv706VOj4/l8Pg0ODiqbzRqDoKuryyAW2qtZV7Jc7mt3d7dlqtSXCFDIlLBL2BEcRzQa1cDAQE3GSxZEIbhcLtdQ3IDe6urqfsv5XH1eanD5QVJtUQRqU11dncbHx22z8ZAY36sV5rq6OnV1dSkWi9mBkWR0H1qD8TTuJuLJ4LqCe3EI+dXU1KRbt25JqkaIm5ubJm7NoQGToUKOp3Kr1lwG0iWv16tsNqv6+nqFQiGVy2UTYHGNEBSbYDCoW7duaXp6Wh999JEODg5MPEdSTZt0qVSyPvK1tTXjrgJfuFoKLl+YNcT4Hx0d6cmTJ/rggw80NTWlmZkZ2wPgAmAVr9erkZERjY+PS6qmS9ls1vZhbGxMt2/fVktLi1ZWVqxgI1WLRF6v1zpsiPAg5JPmn5+fa2xszIwcBPizszNz3hcX1VZL2otdw444uVsEdFM2WDHAGXxPIBucZqFQMHioUCgYG4M1XF5etnbOaDRq4ut7e3vGJIhEIlYgpSuLB0Pkir3s7+9rfX1dg460JkIqPp/PHCHC3hRmWltb1dPTY62zDQ0NJvqNQXGZOfwZDQMYM2oiLuea4tzFxYWGhoY0MjKis7OqhOLGxoaxRchKJZkz8Pv91gCBAaOpye26ozBGhAkWT8cqwQ8iP9xbsiSE1MmEOjs7FQwGVVdXpxs3bpg2sss2wuCSNfT29iqfz6tSqdTIoRLc0bSAg45EIuZccabYPqA2uLl1dXU1bdJ8PXfDbcP/ouelBhdVKCw4BHaUzTc2NhSJRDQxMWFVUQ46NCMOCNQZRJaZseTK4iGqXSwWf0vmjG40un4oLJHaczFpn8TDkQK5G+5izKRsUMhgRLCZpC4YNg4vCkUvXrzQ/Py8Ee+JdsEB+Tq4iH19fdYcQBWV8SP5fN6EfHhv2n95iDpIiThwDQ1V2ceFhQVNTk5qYmJCiURCuVzOIlFI7DgUChD5fF6ZTEaLi4tKJBLy+/1699139c477+j4+Fg7Ozu/1auOXinRNtgYwieMfPH5qor+iURCTU1NGhgYMDlKLhLOiwsPXxLaE3vLvrhPfX298SIpdIC1QfR//vy58vm8NTa4VL6rHGcMCsWTaDRqjBOM19XuP5evSTb44sULLS8v653fTLmGI31ycmIZBGeNlJeCEwMfwShd0Xre4epFZ2+AWDDE4L2Dv5mthqN3KZG8H/g7hpsuN84YDRIuVxo4z90PsHXYHwQ3brTp8/k0NTVl+9Ta2qrBwUG1trYqGAyqo6NDFxcXpusLfxvmAMEId5divCQLXmA3oK1BfchtpMC4npycWOMPRUckSNkfonSKyOwDwQYBxMueL2UpUDRiEzFa3d3dSqVS2tjYsPlEvb29FmJTJMrn81a9XVtbM44mbY/gURhcCg8cIOlSl7etrU3FYtEYAxhDqEZQb2ATsHkYVb7eLToRcdER5yrtuxgU2J6bujF2BwV8qs9LS0sG7re1tenNN9/UjRs3TFWsu7tbkozzt729rfX1dW1tbVnPtot1uz3iRIAu4V2StXx+97vf1e3bt/XixQu9/5upFAMDA7pz5459/vb2dvn9fpvVhDGDAvTOO+/o3r17isViWl1dteiW9ybCc7v9uPxuhRmVMAyCKwy9s7Oj1tZWtbe36/T01LqLKJ7yc7igZDzugcZxAmMR5d64cUNbW1t69uyZNjY25PVWRVMmJiZMevD4+FipVMoCiGAwqIODA21tben58+cKBAIKhULq7u42SUXOEZCT+x6u0yH6TaVSymaz6unpMeUrmAl0VNXV1am7u9voezA74PPW1dWZrgLrgiNyWRuIuLvVc9aKUfCsEf/mKuMDeAyHgrHGeLvRG1olbtsve4YzoqhOWzZO/+LiwqZ0Y+BDoZAZLQI6InV45ET0ra2t9hmJbnEekmxaMjWYcDhs1DRUApF7JXABuqAlmD9jFA+OjDoRTAbOJYEG9vJ3PS81uFQIof5g4bncnZ2dyufzevz4sba2tqylko4RhCeSyaS2trbk8/ksXcB4Em2VStVxKkgEUpWVqmkNs61aWlosGgT8plsL4juYH5fb7crBiBI1oQOBwSFCx0BzaIlI4UNCmwqHw3rjjTfU2NioBw8eGDYHt/HkpDorqbGx0QzLwMCAidcQ9VPk6e/vV2trq54/f25V6KsVcRe7hO7j9VanMkC1mp6e1szMjJ4/f66WlhaNjo7aRAQKimQUT58+lcfj0R/90R+ZuDoCNVRtka2UZER1UkGXnlcoFKyYglg3jkSSca2ZfODz+Yy14vP57L2IrNyW6qscXOmysAvuWyqVbObU9PS0cZ7r6upqZq/x5zjScrmsdDqtYDCo27dva2hoyLrFqKoT6WUymRpIgbPlRnder1e7u7uam5vTrVu31NjYqLW1NaXTafn9ft26dcucDCpVwFgo3O3u7qpSqSiVSikej5suNU6fDE66rHOQmrM3kPobGy+nRhOpup1/kizYwJjg3DkLroGn4OgqlkmXRhwYwuWGg33iWNBZ8Hq91iRF1ovBIwOEGQB02dTUZHoI0uVgU+lStpPOPTI5xu08evRIH374oRUGgSLX1tZsziCBWjqdNk2KYDBo2WUwGLT7xOd2Na5/1/Ol8oz5fN70L8FbwKowWERjTH3F6yDkTMrc39+v7u5u8wxcLojLNAMQqfCAj52cnBgcwWht3oOIWboUn6Eij4EhHQcrAsx3eYukeWw+F4ufEwwGbU1oziCqikQiisfjqlQqVviCZUD0AZ81mUxa3/bIyIgN4UQSD4N+enpaE9WxZvAZeS8uPocNTHByclKnp9VxR6lUSuvr66Z8Bt91f3/f2l0RAq9UKtYSWywWNTExoYmJCUmXuhI4N4oFtDzTrgqPsVwu65133lFDQ4NmZ2e1s7NjqlPLy8saGhqyiR9ANy7ljKKGG7VxyCkiejweqyHU11cVvSjM5fN55fN5w3y5JBhqN41Ewa6jo8MmjID5MkMsmUz+FqQAzucWYYvFohYWFqwwBEd3cHDQMHWpOskE8RRJpr+QyWRMiEmqZgl0XOI4XZ4vnwmjypmGweO2mRPl7e3t2X2ma4zR8hS4MTKcGShtwExuNurqorhMFvYFDBRGSiaTsdoCWSW2AwgOOIfCFgaYCNitS0gyR0m7OhO1JVlhl6icop+7lq7AUzqd1vl5ddzQ4OCg+vv7LVgiiJJqp4O/7HmpwcXz5vN5K3RB+m9uro7dLhQKuri4MGUmaEluLzmeH8V+6D4oMB0fHyudThtNhMKTe6AfP36sUqmk119/XS0tLdY+CQ64urpqRQIiUKq4pFtgdEQqdNm4eM7FxYXhT0So0qWRCYVC2t7eNmPLgTw9PVUoFFIsFtPOzo7xhEOhkImTYyyg1ZVKJRvDDZyyurqqVCql1tZWS/fdgZpAOsVisaZNkYiT/bl6wM/OzixKhd+J8fF4PDZQEmdzdHSk9fV1ra+vW4tmX1+fXW64slTPKaQeHBzo7OzMGlAwEOFw2CJaeNXgvPTsDw8Pa3Bw0HQRWHMgjKupPMUmt2LsDkN0M526ujolEgktLS0ZJkd7Lym6x+NRLBYzXip4L/oDGAiE7d33oI7gZlANDQ3a3NzU4uKiYbQtLS22xxQo3e9zdnb2W6R9eL67u7vmlCnucsFdp8teYyi5Z4VCwe4mNRciUO7GycmJVlZWdHFxoYmJCauFAOt4PB6bhg12fpWq53ZfgeHmcjn7rDQCUTCsr6+vkYGl0LW7u6tHjx7p4uJCfX19Gh4eNiaDW4jj7HNXgcqKxaLW19fV19dnNYLz83MFg0FNTU1pc3NTxWJRnZ2dZr/Q24VmKF1O7CbCpfED2+Rmey688kXPl8ozMnyxv7/f0jYOdywWUyQS0dHRkQ1SpGoJqwHgure3V11dXZben56emiTb/v6+NjY2rB0YLQaeZDKpjz76SH6/X6Ojo+rs7LTOFbfbikiOQoTf77fRxxRBcACSLI0Fd3aNNMaWBozOzk7zbKFQyDRbKTjgHUnVqPjTeUcBIRgMWnGJIkWxWLSpDYlEwnBP6HWffvqprQUelYtG0ZCooKGhwf7LgeYdgDpoyMBpMRDv7OxMPT09CgQCymazevLkiVKplF555RW9+eabZmSI4Nx+fXAvl2mBYHy5XNbc3Jz8fr82NjYMpioUClZo3N3d1cbGhrV/E6G5NB6yEddAnZ+fa3d31zBBMEIiYnRfObNuBxoRM+eNoIKCDGfgxYsXxo9eXl6uaXOWLrMft9Wad9/f39fc3Jz6+voMI/788881Pz8vSQY3EAkS8SED6fdXNXmHhoYsu/ld1COwfQwk70+ES8MFkEsgEKihBeI0OYPcRUk1co2ZTMayGqJ6HjIX1xEQbAFh5XI50xweGBjQn/zJnygSiViHKgXITCajBw8eaGFhwTrORkZGFI/HNTg4aD+bCJmfSbH+7OxMKysrpkkBp7ylpcVomzs7OzVFUgagEvD5fD4TsAdjppblOhdofW60/4U29WV/iWHa2tpSOp02nh4Gge4yOKaVSsVSIBYhGAya5+AyuAIv4LZbW1sqFAq2OS4u9Itf/MKM1meffaY333zTeuf39vYUDAatUsi8JYDtnp6emjSSseoYYRwDl5Q/I1KmtdGNOPr7+426Rmrf2tqqxsbqkLl8Pm9QBgU9jJJbmeYCgVu7+Cxp9OzsrLXdStXWW7A+t7jE58PxUH0lCiLK2NnZMXYDURNjceLxuKXhS0tLNoLkjTfesHlxkowhQoWZIg9js9EsxWGfn1dnnTEWpb293YTJT05ONDo6aj3q9fX1FkFRDXfbi92LD3TEmCFG4LB37vgYDMj5+bl9f4wkVEfwfhwzBpu9SCQS2tzctKzAvSdEwBRvcUo+n0/z8/O6d++eTk9PNTc3p3Q6rb29PdXV1WlyclKxWMyyQEYd7e7uGhuGu8W9wUBT1JJk3Z/AJnwG4DkgLt6dr+f8cqehTMHYwUjCqwcec5uHXOfT3t5uWgSce8RxiPLBisvlS/lK5tG5IjStra2anp7WyMiItU4T0UqX2Dn8fTJI6JYUajc2NtTf328OhwIhhhj6odfrtU47DK/LgsKwuhEtZ4s7/XsZXA7d7u6unj17ZhKMGAQ+MCA8hxBgG8I9BgAjsb+/bzhoLpfT3NycjYBm7pRL11lbWzPM97PPPlMkElF/f7+18tKhgkEC5D8/P1cmk7HNpp2XdIACIAeRxaIff35+Xk+fPrX3AAfs7u5Wb2+v8VFpacbAY9zAN7nEMCIw6kRtYEhE3KTIT5480fLycs3ljkQiduhwLKVSqSaFBD/kz1BhwjDQwgnvuaGhOhliYGDARu24LdLd3d2GD3OgYZhsbm6qq6vL5sM1NDRocHDQGkDcNHJ7e1vZbFbj4+OmVIaYN7PHwKV5ECLC2Li969IlYyKXyxlzBO4xI3owRNCK6KBy6VycCyhY6HnQLpxOp7W8vKyzs7MaMRfpkgrlFvooOl1cVKcOrK+vKxwO68aNG/rWt75lVLzh4WGDnGClhMNh+z2QBmkyRS1gLM4sGRb3kToF0TvNHGCuV9kKYLQU5ODG0xKMaHgqlTL1P7fL8Ysezjo1h3A4rFwup1gspmAwqJOTE3V1dSmbzRqdEkpnNps1Pjufp6+vzyAsjLm7F5wNpiFDw0ulUsZ0kC6ngMN7pjYETdNlxLifjUIcAQzrxRngfr3seanBBcsql6ualkQwKAlhcImm2HDSGX7vbuT5ebXPn3E6CwsL2t7eNtJxZ2enWltbaygrfHCM0ccff2wpeal02RdfLpetxZjQn0PKwaDS6XaPgHtRbDo+PtbKyop+9atf2TtA2gdXjcfjNu4ZaICUgs3j0EF1wUhi9PL5vOFtdOVxwV68eKH19XVJqvGagUBAsVjMuKbgzVx00my64q6qhNE0QgGFLkGmmXq9XhvHw/wqGCMcJrDUZDJpDR84HJwH7cKuVoWLg3s8HuMqMw4Io0O0C0QEswGjw8NlwMDm83nrdCoWi1ZFhgURiUQUCATU29trTouiC9ka74S4DNgnzR9uyyiPW8QiaiQyJzpfX19Xe3u7GZuxsTETcuesAyvl83nLgEh3uRNkCwQRbmBCdO1OmMaRYGB5T7cAxs/HaOM4yLYk1QQkXq/XjBjRJA9BCMEA5wbooqOjw/jZ7vo8f/5cn3zyie0lNDb+Dfg4HY2SzJni4LBBDQ0NBjf6fD4lk0k9efLEGCnAMuwlDhcbhM3ivHO/YE5wjt2COvS5L2LSuM+XRrgYzdPTUyUSCfX19Zl+JxECVVqqkhSveFFelkOYzWZVKBS0tLSkRCKhgYEBeb1ezc3N2QVyH6g/HJpUKqXPPvtMMzMzhquw4FCpIGq76TYGEViE4hleGMxrY2ND77//vnK5nHFPj46OND8/r7W1NYXDYbW2tioej2t9fV3ZbNboTeCa8CxxPkQjeF83wnQlEBk7RFHBzRw4+O3t7SqXy9rd3bUDC0WMr4UHTVSPEeDykjpR+AASwXDh5FpaWswQY+zopKIKjkYCMouNjdVhklKVi0qWwyXknIDxw0uVZLgg0BPZBqmoWzRzDzffq1AoWMGRVFCSpfngn1ebDlgz9gEd4f39fe3s7Ojg4MCMH0U29z1YLwya+7MkaWNjQ4ODg+YsM5mMOSVU8sC2afzA6ZFRtra2qlAoWFHU4/GYMyRSJ0InFb4aybrt4F9kaLkX7p9zd6CIuS2tFH95MpmMenp6auiMUMTg4GPQoM7BjEmn0xac0KQBa4F7QeGK6Jm7xbtJskCL+10qVWenbW5uanBwUD6fz5om3MwNfj0BGQ6J9+WdqYu4a4qBdiGvL3peanD/4R/+oaYggxwftA6q9W7bIcUK0kA2DlI4h6OxsVFvvfWW/vRP/9Q4bW1tbYpEIgoGgzWh+T/90z+ZN+T7sRFEAXSl4NEhPtMAQZEtn8/r5OSkJhrh8vBZotGovve976mpqUkjIyOSpG9/+9vy+Xz65JNP9Omnn5rGAZuWTqdrqGm8o9tLD6n8/Pxcn3/+uX74wx9qeXnZsF2v16tIJKKpqSlNT0/rb/7mb2y4JQ9rXV9fby2zGFQOBukkqTEi5RQN6P5iDdlXqEhonPb392toaEg//vGPzTF95zvf0fXr1619mtSTf0dn3KNHj4y54fF49N577xl2/B//8R+G9RLRE5VTLKLoB1wiXU5c4Hn99detvZjMxsVlwWy5YFx6SVYUcwtcZHSVSkW9vb02mBT2AxExeCbP3/7t3xoMQ4UbJ+GK7G9sbOjZs2eSZIpsR0dH6ujosFE15+fnVu+g+8tt6iGYwKgimUlVvbm5WcvLyzX4LL9nEjOwDRRFaE8YVbR6Se2z2awZLRg49fX1RiV0C5mTk5PmMOEKezwegyAGBgZM/hPnxh0Ck8aAn5ycKJlMand31+o93F2Ppyr5CseYX5L00UcfmTIftaLj42P927/9mxobG02oiEz06OhIuVxOPp/PFON6e3staoUEkEqllEqltLe3Zxj5/v6+lpaWVCgULKP4wQ9+8P/N4JKW40UuLqqjOeLxuGGTLvUEAJnohagFo8hmu733Xq/X/ksKiTfn4XsCX7hpIBE239MN9aH2cHHdlky8laQvjNL5/BRe+F5cSC7K4eGhtUGy4HSygV2Tgnq9Xm1uburBgwf64Q//H/bO7LnRMzvvD8B9BUiCAAiCIMC191WtXZqRRuMZjcd21SQTVznxjZ3b5DpXuU1VKv9BKpVKKhf2xXgcVyVjp6w441GkkbrVze6mutncARIEiI0kCG4gSOQC8zv9giNLE/vWX5WqW91NLN/3vuc95znP85yfanNzUyMjI+avcHp6qsHBQbW3t1tGRADhqlar9n04bSkVXX4x34Wsm0MOdQ50LRbw9va2Hj58qM8//1zZbFYtLS3a3t7W4eGhxsfHTQzB/eI+sqnIklGxSbLsyiWt1+t1BQIBy8yAl3Z3d/XixQvLUEOhkE2I4Oc5ULk49Nzym0OH7NdVKEkv8UoCL/eIZ87hxL9zucaUlK5sXWqYjR8fN6YR7Ozs2MY8Ozuz+8IUAeARjJ/c9QXPmwTEbW659CwugprU6HNQffL9yAKR35I4uQ0+cGYXg2ZaBpniwMCA8VE5zGgipVKpX+OeAiXxed3KgEOe90KZRZOc7HV3d9cGwmKR6PF4TCyFvJxD3efzWcAl2+R9XBbJ1NSUbt68aVl4JpNRrVbT2NiYzdbj4rCo1Wr2XPHlyGQy5sHMGiFD/rrrN574wCYmoKRSKSPQIwIgDSfjBD8kuNKEIVDy+kh3odFwk+DjuQR4HjQUNB4c4gcWDtmmuyi7u18ObZRki8fFX1isLS0t1hmXZMET5cvCwoKVu11dXQoGg4pGo7p8+bJ1+gm64pp9RQAAIABJREFUUmPjZzIZLS0tKZ/P69atW/rhD3+o2dlZM205ODhoGs5IgHchFspPXhPM1j08CHhk/dB9CEiU3i0tLUqlUpqfn9fS0pLW1tbU0dGh69evG8MCWpxricdhRbBGHkuDjcDOIXVwcGDOVz09PQqFQmpvb9f29ray2awymYwqlYrhlDAo8MzgIGN6KhczwUKhUBPLhAyQje6WjmDdboOLw/j4+NhUckyMgKmBmQwBzMXFR0ZG7F5Ae3QPPtgVTEAmQPn9fgu+IyMjxmcOBAKGV5Klkoxc5N6yFgqFgtHaOjs7zdCHz8Lndcditbe3q1gsmmeAJGv2QcvjdTAXGhwcNIYKhkeuX3O9XreAyIGD8bzruwCmj+E48MDR0ZGWlpa0tLQkSZqdndXly5cN+sI7+fT0VFtbW1paWrLDjos9QwAkKezr6zMJsauSpDmHQxoHAxURzW5w3nq9bpTK/f19g+B4za+7vjbg4lrlnv75fN7ehGACIwE8h9LEJQ739PRYt9Q1I2HhAHy73WFuIs0BSiM2ChaBZCDu1Fl+payUZM0DDhAyRxY92SM4mvsZuaHlclmffvqpNjc3rSHT19dn5Xgmk9Hly5d17do1O3Ao13Hbj8fjGh4eVigUss0B7WVjY8M+C8HXxdIoPWFHsPnBi7mP/B6akNuoYP6W1+vV3NycvvjiCx0fH2tiYkJ37tyRz+dToVAw1RjlN5sS7NPd0H6/X8FgUKFQSC0tLert7bWghg+sO7Ntc3NTf/Inf6Kf//znamlp0a1bt3T79m2Nj4/L6/VqdXVVm5ubllW5EBCXCw25353GEQGPDU1A4BAAzwQmW1tbsykPHAbZbNbUV7Ozs7py5YpaWxuCFi6UWxzAvCcwFng9DVKCbyAQ0Pn5uam9+P9cLmddewj9rD1JRvinWuQzuEwMgrbLKKLKgN5FoKjVGhJuHLwGBgbMgInXD4VCxlQ4ODhQOp0203hoYJJsEjOwFId0oVDQ6OiomYhLL/sRiClKpZJhrcFgUNevXzfMlv13enpqzxcYDQYC8YZn7B647e3t9gzj8bhWVlYMxqMnMTIyYvsJKI+Mn8rMnbJN9VEqlfTZZ59pYWHhHxZw6UYTeODB9fb22saiZM1ms9rY2DBFCV+YLIvA19LSMAh3S30UX5RYiAa4yODAfJgMQBBn03NakimCNZMV810A3+v1hkwznU4blsXGhNlAiUAmv7y8LK/Xq3v37hkNLBqNWjPBVb2cnZ01lTqQ6yORiAYGBppKczJFGloEp4uUGxYfVQPNpFKppNbWVoMkoNJw2oMlQs/q7OxUNpvV8vKyJBktjGYccs9AIGDZCQcvjA6v12vDQgOBgPGhEYj09PRocHDQAmdnZ6fy+bwpzHp6evRbv/VbSiQSev311xUOh9XX16dyuaxQKKRkMtnUMHMDp/TSUIgDGB08B76rCOKQJznAf2J/f18bGxvKZrOam5tTOp22yqK1tVXxXynfCoWClpeX1dPTo/ivfGW5mPZQrVa1s7Oj7e1tKz05zElQ0PyTUUH/Wl5eVqFQMKydYaMcHNJLBhC4PBxyghcBGMy6vb3deh904F3oLpVK2Sh0xDdIrPFPIdlhCotLH6MqdEVKcMYnJyfN6pEAXKvVVCwWFQwGjS9PVUzldnJyYhYAHR0dNrzV6/UaB3hoaMjmzMFmwAmOvUSVyl6/evWqpqenlUgkFIlEdHx8rPn5eZ2cnOjKlStNU7I5tJGmHx4emrIWhR/7Fd9kEq6L01n+vwKui+mQvfb29jZ1IZldhXHNxsaGuekzCcHn8xmboKWlxRo/8CdTqZRt1MnJSWuYcIEfk5GAQ0HZ8Xg8FqxpolQqFWWzWRMTAFX4fD61trbaaJmlpSWVSiUNDg5qamrKghjBye1g7+/vy+/366233tLo6Kj6+/sVjUY1OjpqeCUdVSgz4N58brr5BD422vn5ucLhsP23t7dnLAu3KUHJ09nZqZWVFT1+/NjocExdgKY1MjKiUCik/v5+I7GHw2HV63WtrKxocXHRFszJyYlWVlYsQBPMwuGwRkZGLJOXGrAFn4msKZfLqVAomMmQ1DBW//DDD+07Iel+8OCBarWafvu3f1uXL1/W7u6ulpeX9dlnn2l9fV2jo6O6efOmbt68aYf81taWcWC5XP4ogZQSESYMWTcbiHKypaVFmUxG6XRaDx48MLFBa2urBgYGFIvF7F7C685ms9rb21MulzP2iiRrzORyOaVSKXvurgcv1YIkg0FGRkYsqwSeA1Kp11/6HJMIULW50BCsDaZauIq13d3dJlc3qibw5FwuZ1NbyLQ5jPGUoFfj8XhsfllXV5cFaFzVuNrb2zU+Pq54PG6HH5/JnTuHBaPrucEhx4QSGlSFQkEtLS1WmSFamJqasowUto4kmzvW39+vYDComzdvamZmRoFAwPje3/rWtzQ9PW2ex66QiHvrKiqxWS2VSpZQojZta2uzZjcKwr9XwMWr1h0WSBeTE9RtWAEBwDGlm0v2AY+SEpuABt+uXC7riy++UDgcbnqI1WrVcBTIyt3d3Ya5YXpORxEeZ6lUauLAEsjJOMh4Y7GYrly5ong8bobiuIgR+ClZMUTGNwElEIGgo6NDwWBQnZ2dWl1dNZwRSIH3J7P4/PPPlU6ndeXKFfl8Pj18+FBjY2O2kDDA4WJR5vN5PX78WDs7O01c23K5rFQqZWogSRoYGNCbb76pS5cuaXZ21u51JBIxVkF/f7/+1//6X9rf39f3vvc9G7kCCyIUChlHFaimXq/bJmUT7ezsWFZWq9UsQ97a2rJSsFKpaHh4WLdv3zYM8dVXX1UymVSt1pgCkMlkTGACjrm2ttbUNHMboaurq1paWrImpTvrKhAIWBCPxWKSGjAXpkqINcjuotGo9vf3tbi4qGw2q/b2xqQIJgHAmeZCsVculxWNRlUul/Xo0SOtrKzY64+NjTUpsMCmfT6fxsbGbIIxwZ1GKj4aHLKsR1z5ONyuXbumQCBg7KFSqWSNODr2jMXK5/MmGNnd3VUikZDP51MymZQkq8jIEKFpsQ+R7iKEcu8F1oper9eEEvl83vYs0BNioEAgoJ6eHkuOgE1oiEYiEfX19Wlubs6MaPb39/X48WMNDQ1Z4kPPRZL+5b/8l6YSowHpQhCoX6PRqCqVih2Y9DVI4rDsBIIku4Xxsru7aw3m8/Nz2ydfd31twEVxAhgM9tPZ2WmlPAEKIw6CKpkEZenExIS+/PJLG7ECyZyH2tbWZgPzXCmr1Mi0aWAR8CjjW1palM/ntbKyYmNNyISRChMYWWBIOzs6OjQ2NqaxsTHzpaUM7OxsjBpx5bNsfL7X2dmZ5ufntbq6qnK5rM7OTl26dEnT09Pq6uoyPicBlwGOZBh37twx/Oftt9+2CgEqkEs85+Khr62tqVKpaGJiQpcvXzbsc2trS6urq5bd7O7u6vHjxzo8PLQhjt3d3RoaGtLdu3dN1XTp0iXzYn3jjTeMRkR2cpGvCn7/7NkzHRwcmB/yW2+9ZbO4oLmBybN+aHTgvxGLxXTnzh0zvQGGcbFrtPAuRsZz3tvb08cff6xCodDU3CJz29raMq700dGRgsGgKZhGR0fN57i1tVXXr1/XK6+8op/85CdaW1sz/1xKSpoo7kXfIhAIaGVlRf/7f/9vy/bZKwwilBojYBD4YJgfDoctezs5OTH7U4ZxsvZcuMwVYITDYVMz0iCqVCpKJpPa3NxUJpMxqAjoYW9vT4ODg/pn/+yf2TDTZDLZ5KYFPgqzhEY1AZG+iwWUXzUni8WiHjx4oMePH6ujozFI9dKlSxoaGmoy8HandKyuriqZTKq9vV2XLl1SJBLR5OSkPB6P0um0urq6FI/HDSKrVqsG/7hMiVdeecWqaXo2CH2AXEj8oJPRfKYpd37+0v/b5epKjaQqHo+rXC7rz/7sz7S5uSmv1/tr2f5XXd8404yyje4mDx+c8eTkxOwbMSx3CcJQtQjalEmUGtic8TOMv3EfIpgRLALXOR7e6eDgoJLJpAV8MmjKGoY4oumn4dLW1qb19XWlUimtrq4aE+HatWs27lySsSKwtyuXy5qbm7Mu9GeffaaBgQHNzMzo0aNHmpqa0tnZmQV9DqPd3V0b15LP53Xnzh3duHHD/IOj0aikBoxChu5CCq2trXYokJmS/eXzeeMD9/b2KhaL2QnM4MTLly+rq6tL4XDYGlg4fL311ltNGDEBDuYJzUeYHzRRL1++rHfeeUdjY2OqVCr6+OOPVSwWNTo6qvfff19TU1P6/PPPdXh4aK5w5XJZ7777rqLRqFKplAqFgiYmJnT37l21tbVpZ2fHhlxSHQAVcVG2p9NpVatVTU9PKxaLmeUngYmS/sGDB+rp6dH4+Lg1YMC9aThFo1H5fD5dv35dh4eHGhsbM6tLNiHublw0AZPJpFZXV5VIJAwOoRJwG43d3Y2pz5TXNGUIEDQ9z8/PNTo6arCN61wHnugyUqgohoeHtbGxYT4cfGf8YCnf29vb9b3vfU/f+973tLm5qddff91Uj0BKJBb1et38eWkqDQ0Nqb29vekAooeRSqX00UcfaWBgQOFwWIuLizbjbWpqyipgpPKbm5taWFhQJpMxg5rBwUF79q+99ppl1ARLKH2IhAiKMI8Iri7uzAGF7QB9Gr4nEOTp6al5ILe1tdk+jkQiZvjEz7nClX8QLYxGA80pMp1isWh4GQYbvb29ZoGG6w+8SbwzoW9ks1mtrq6adjkUClljjeDinihtbW2KxWJWzvz1X/+1ksmk0aeg+UBAZgIFTIZgMKiRkREVCoWmhhxDElOplG7evKmpqSl7GK7RhySjScFIoMS5c+eOJiYmVCgUjA5Gk2FjY8OYD/y6tbVluDaLHl0/jQhkrK7NJBdYNWV7qVTSl19+adQsMKTf/u3fNtEGjThwPVgj5XLZSk8+Cw0y8FaCBFxsSeaixmJsbW3V1taWfvaznxllsLW1VbOzsxoZGdHOzo4WFxdVqVTsPXd3d/WXf/mXeu+99yTJFHo0JCjxMpmMisWi/b0Lr7S1tRlMQ4d5YWFBg4OD+oM/+AOtra3pz/7sz9TW1malqSvKcb0taPitrq6aax1ewq40uaWlxeSpXKyNcDhseD6BdGdnR1tbW8pkMjo9PbXGbnd3t1EpCYJkt3BYWQduZk8gpIHG+gQfpVF6dHSkd999V8Fg0CrVlZUVPXz4UEtLS2pvb9d7772n999/3/oVkUhE0WhUPT095ltMMuTy8Q8ODsybAd8OLtSB9+/fVzqd1gcffKCBgQHNzc2Zl4NrQF+pVJRIJLSxsaFMJmN+JVIjySHbjEajVrpz8MNQ2NzctHspveThUmmToR4eHho/GvYLcYn/6F/AZKHyoecEXAiM1N3drYmJCdu3bjP17xVwwT0wlAFngb8ovVQ/Qe9yPRFaWlos8yTjBabw+Xz6xS9+oYODA5t0MDAwYPiWu6A7OhrTHNbX1/Xo0SPl83n5/X4bg0JTZGlpydRFBGiXCD88PKzJyUnTZPt8Pr377rsmMwRuAKdxS1iCC987FArZqJ9XXnlFU1NThhExn4xgC62ntbXVyNzcQ7J/fB8ot8hELvoHwB+WZMMPp6enFY1GjfHx2muvmTEO1pZ4obodbji60HgIdgRaV/ZIwAXDpaTc29vTX/zFX6hcLuv9999XT0+P/H6/+vr6dHBwoMePH9smBzeHhuXxeDQ7O6toNGrri82IPJPu/+7ubpO0F+4ttB8sHt944w1dv35dkUjEGqlgtl6v1zY1CkCYD8A1JAiudN0ltru8VqnhvQtNaHt7W1tbWyqVSjo6OlKhUDAvgc7OToVCIcO1oYohMyaTRlVGYECR546t5/nBUqAfAkRz5coVy5IRl0xPT2twcFDvvPOOwuGw2trajJK2u7urtbU1DQwM6IMPPtDrr7+un//852aUX61WNTExYdgnYo7PP/+8SQRSqVQUCoWsd7KysmLj2W/dumUUQyYs5PN5VSoVPXv2TMVi0XwrwuGwpqam7DtyOLr7kACaSqVsTbM+YVbBNpJeqiKBEmjkkwS4/sAuEwTmE+sNOirUP77TRX72/3fAdbumbDKI1a4GnEXrZsQ01WieuEGaAZLFYtGGxe3t7VnwSCQSvzaplqAzPDxsp+bQ0FATQB//lVYdkHtra8uafJyAlHRkxZlMRtls1k4wMhiXoM2CJkut1+vWEYa+RbMDCGJjY0OSzN+0vb3d7OGQEQ4ODjYFVjYRn5duuntxauOnEAqFNDw8bDhXKBSyMpgs8fCwMf00k8kolUpZo4yFF41GrRlEhx3hgqsRPz4+NsiDBg/YO4R9j8ej6elpaz6sra3po48+UjKZtJ9FKHN4eKjNzc0mJkwsFjPYBmiIxi0ZMBfZMBmf1MDvurq69Mknn9ioIdYt3w33KhRX9AikhjjGNYVxy1E4thw2XPQSYPLQ5ccjgj3Emu3u7lY4HDZGg+s5wu/ZT27iQacenJa+gvSygQiV0OW49/f3m0yYxInR6BgHnZ6eanl5WUNDQ1peXtY777yjf/pP/6l5N9DXINMHHiOoczH66t69ezbTLhAImKwYV7l8Pq+1tTVjL2QyGRM0ULJDH3MZSrwXPZFsNtsEJ3CRqEBhBPeVZE16r9erfD5vUmionawRKIlU+NVq1cyF8JSGgcS6cWmLX3V9o/CBYEtJwaIF44NkTCoNlcPj8ejatWvy+/3mIA8HsK2tTQMDA3rx4oWOj4+Nx+Ya/8KpY9GwcM/Pz5tMV9iUe3t7qlarmpyc1OnpqfL5vC1WiO1gzK6xDN+PLN5tGLjlPO5b4Gjt7e1NJyGSVq+3YeaCPJJGDkF+c3NTk5OT8vv9Vh4zNwxsGk0+99WFFKCZXbp0yUpsvhNeAJubm5Jkpy+G6ZVKRalUSmNjY9alxhKxt7dX4XDYnjnBjMOQrNv9HG1tbRY42tralEgk7L0kmXn94uKiYWJ4K9TrDevG0dFRm3xM9RIIBDQ5OWnev+4kZXddgJ8T7Dlo8JDIZrM6Pz+3wYDQC6WXWToHGgHV9cLgV9fUhjLe/RzggWD7tVpjPh5QiGui48pzyagpw4EiXNMiDnACMocVpS9rnCqJg4vGI2IQAjHvVSwWbR0ja2eWFx6ysF96enoMImFk1t7enlZXVy1D5SJrZ4gqgzDh5w8MDKhUKmltbU2bm5v279ra2iz4U61QCYJP06wm0XOnSLi0LhI0DmtJZnnqCmJKpZKWlpa0v7+vWCxmylD6SdyrXC6nyclJxeNxE38wR5HGtosbf931tQHXxblYdHQW3WmydPpSqZTy+bwmJyeN7oXO2NWJw/+8dOmSnj9/bg24YDCoRCLxaxMfaBCxwCgzIVOT/dCJZEMDc/D+g4OD1lSjrIlEIk0CAbryYFNsPBcSgCKGPp0MbXV11Rb28PCw1tfX7eHBetje3lYymVQgENDh4aEGBgbU399vVQBBkAd30UsBuITOdzabNYd+Fi1y49nZWYXDYZXLZSvfwZq4vxDyXWmwJONGcsDQFJBkVQ7wA2pEsMejoyNVKhWjIDFex1X8eDweUx/dvHlTLS0tNuEZcQDYNsHdteCTZPaLvb295vUAOR9aHFLyXC5njSvp5QRa7jWqLH7vSs9dCqPbbOXa3d01NsDe3p41PMH03KYMY4ZcC0oqHAQTZLYcFNLLBIRnQhPZXRe7u7tNEAzQHNRF9m8+n9fm5qa6u7vN9a+trc1ob4uLi/J6vXrjjTdMLVer1ZTL5awxeHx8rNXVVS0sLJiBjiR7RgyJhDlBsKQaBWLkM8DVp+8DhbNUKhkdjsMQ0RQsBVeuzd+7B6IFu19VDeC5zKaLRqMaHByUJKtcC4WCfD6fiRkWFhZM5EQDkUrJtSv4qvdt+gxf95d0w8lYuGGuoTiZG5y71tZWMwOZn5/X/v6+EomEkY6ZKw/hu7u7W9ls1jqs4EzuxQamgcHpD4ZCOc9n4sHjHo8ogcXHZnQpXi6+TDbhTgJlVllfX1+ToQjDNTc2NuTz+Wzy7s7OjpmAQ4p+9uyZke35t7iTDQ8Pm1sZpeFXCR/onHLITU5OamBgQM+ePVM+n9fZWWP67+TkpKamptTa2mqja1jI+/v7GhkZkd/vN+FJtVpVLBazwEslQ4nlslQ4yFBYdXZ2KhKJyOv1mkE1aq6RkRFbH6jsoEul02nNz8/r4OBA//yf/3PduXNH9+/fVz6fN3MSMh4afS6GG4vFtLKyYn/f0tKicDhs3XsOVqqx4+NjG8XD/YVxAeTEYYt3LeNVOHCBaNzAT/ZMkwUaGYHfVfvRACIod3V1mQmS1EheuH+sSWAMvCmoyIDzpJcZOs8O/wUkvvBls9ms7t+/b/uNvYQS8+nTpwZFXbp0yQ49nuHi4qKSyaSJhtLpdBNu2dracM7j3pEdc39Y4/QIAoGADYYdGhoyNz+YR9Dy4BSzHrlXNPJcjjIxoKWlpcnPl1+huB4dHdn7g593d3fbYX92dqaJiQm1tzdm06XTaUUiEY2OjpocWXo5iPKbGArSb8DDpXMOFoLdIKB4T0+PLcZqtapcLqfV1VXLevj3EIZzuZydqhDgR0ZGDAdkqKKrNHMzT35P9tLZ2WmBEXMayuzj42O76WC3UNn4fJxKPEhAcwI7D3diYkKrq6sWcDlhWfQYdMCn3N7e1tnZmSlUaAhOTExIetnkYLMjukDfzfe+iAlBpOfwo8MOp7dWa4wJZxz96WnD8L1QKGhyctIgivb2dlOgDQ4OWjYei8XsUCWAMAKFBUaGAb4HX5QgDK1obGxMvb29+tnPfqbe3l5NTU3ZJkS1NDQ0pJOTE33yySf60Y9+pO9+97uam5tTqVQyL4ORkRFTPbn3Y3p62u7/ycnLqbcEt+PjY42Pj6u7u9vMR9ypJcifue+s+Wq1qqdPn2pxcVGXLl0yzjeVCBUNFwEVKCAQCFiSAIvm7OxMm5ubeuONN/Taa6/p4cOHNi4HeAuLSYzIy+Wy8VTdwO31ei3YcBiXy2WrSlhXSPN3dnbU2toYRfPo0SO1trbq7bffVi6XM08ElGYDAwP63ve+Z+Y9kUikyewHiT/GNRfl56FQqKn8J5lx/XBp+sJmkl6yL4rFok05npmZMciSLJJDAzwdHxIXw6W5CewnqSn7dCXF9Xpd6XRa6+vrisViNlGlWCxqa2tLkUhEiUTCAjviK2ASV436Tfit9A0Bl5sJMH6RD8qJ62KrlUpFW1tbyuVyOjtrTNt0R5rA2UVNBvbIqItCofBrhi0sNh4UHF+wOkmKRCKGBadSqSZJI+9BU4oSpFQqqVgsGqGaRYGcz+U+Tk5O6i//8i/tNaDt1Go1+f1+Gw9C1//o6EhjY2NaWVnRixcvrKkyPz+va9eumfrF6/Waysg9YeEa8vm5wPHo5iM3xtMAjJfvjLro+vXrRkXjddiUiFhcZzAWJcEMziIXjSRJ5kgGPaqzs1PhcFizs7PWpY7H45qZmVEikdDt27eVTCb113/91zo8PLRNhbnPjRs3tLi4aA052AoXhQ+Y95ANskZggECTKpfLOjg4MO4opSsslK2tLcNevV6vDVl88uSJPv30U12+fFnT09Pq7u42zM7NcNkrrt+uq34kIYGbjI4/lUpZYnB2dqZkMmmHAAGSdU4y4AYKMFouEgg3U8d/wOv1GoNicnLS5Kr0IDo7OzUxMWHl/fz8vJLJpAk3+Hc09MCs3bXJ2snlchY7eAZAYTT+SHAwrT89PTVhhtTIXtfX163R7fV6zZ+Ye8LvMYhy1yfeEi6Hn2SNv6NiqtVqlrUCPaEmpBHOYc1rMxTT7bG4bKi/6/ragEsZQvDi9OcGSGrqYI6Pj6urq0tbW1vG1aU7vLGxoXw+r1wup0AgoLffftvKv5OTE9uoBwcHRt/hIiN17RTBUGETYIrCyYcqiAUDjYOZSUNDQ2ZJCKaUSCSMg8r3JqDT4eXBclpCE8KoBZwI6pDf77cx8O3t7ZqenpbP52syt5EasAmvQeZIZuQePjRxqtWqle24urnjYXp6ekxp5PF4dOnSJZ2cnNikgefPn9smPzw81MzMjO7cuaPt7W1tb283HZJfddjyLGhy8p783MzMjK5du6bt7W3Nzs6qra1No6OjisfjptPHcJ4OcF9fn9LptIaHhxWPx1WtVk32SpPIvRdYIZL5dnR0aGhoyJ7x3t6eNjY2DBaCCuT3+42DifcDBy0bkQNxfn7eDOcvX778a8o/Sfb8+/v7jXUC7guzJZfLqa2tTQ8fPjR1Xj6fN9gDvi7GUEBMwDc8BzBLl2MqqemQlV5mcQiN6vW6+S8Hg0H9x//4H3V2dqbr168bZzyVSulv/uZvrIr64Q9/aNkjsEm9XjdjHg429wqHw8rlckbThCpKb6JSqZjRTyQS0fXr1zU1NWUNUrr9Pp/PqFrwnr/88kvjwWJIBexycvJyvhtZtdtE41BnrcBUILbQ08AnAQ4wpvjDw8P23lSIrt0m9/riQXzx+kbzGjqsfAm3e8tCwa6OU+DKlStNEjkysa6uLnNod/Ej2AOueYTbEXdpadwwNlipVNLjx491dHRkm218fFwtLS1WhkYiETNNRyXn3siWlhal02lls1nTlbNgCTTpdFp+v1+lUqlJVgndBkyVm08WNDs7a5lVW1ubCoWCisWiQSFAKZKswdLe3t7kC+w+RLf5gTDC9RPmMMTicX19XR0dHZqfnzc5cDQatSyWwysYDBquvLS0pJ2dHQWDQWt8upQcMFIUbZRX3PNIJGIetejlOzs7zboPCbbf71c4HG7iosLsYPIHz4bGmduUqFar8vl8Ojg4sI4+n2d/f19bW1s2ip3S+uzsTIlEwoJVuVzWwsKCXrx4Ib/frzt37thGlBoTDLxerx48eKByuaxbt24ZrsoFvgytCMySwIuRE5Lnv/1wXyVHAAAgAElEQVTbv9W9e/ckySAH9P3Hx8d2cHEAVSoVw4TZ7GToHAAkFCQuBCCqQXDlb33rWwoGg/r5z3+uX/ziF3rx4oWNs0fxR2Dyer2Kx+PG8oEl0tLSoqGhIasaXaVZJBLR8vKyxYmLDcNsNmu8Xb4jc/o4ZPEvYKYe7/XkyRNls1lTSeJWxppwsWTiFdgvYgaSKZJJVxFLdQQezGw56SVcgCaA2IUCk6r4HxxwwT5dugxQQq1WM6kqQZEOq6tRJtslOJBF8EXAhCVZRucGXN6X9yCobW1tWfd9fHzcNms+n28ipw8MDNj8L2bc1+t1hcNhDQ8Pm7tWOp1WJpMxjJisnmtwcFC5XM5oU5y0dCj5HkiHeX8y15OThjs9kl+I/My8dyc0ALGwYLhccjYl487OjolHOjo6zPgkl8tpZ2fH/FMx9c7n8wqHw5qYmNCtW7c0Pj5uwaCnp0fRaFTJZNICFNklGC4wE9OKOzo6VCgUdHp6auY93HOyyq6uLh0cHOjFixfyeDwaHR3Vm2++aT4UZJqMzT48PNTExISWlpZsPZHhcbmYMn9+fn6u/f19pdNpbW5uGqebMr5YLOqtt95SJBKxf1sqlcxRzLX15FBua2vTrVu3VC6XlclkTE1mm+hXODoim0wm07QZyd7D4bDGxsbMxaq3t1ePHj1qMkShMUywhKbl+gIQaMFDJVmjib4JHgwkJycnJ5qdndXk5KRaW1v1r/7Vv9KHH36on/3sZ/rkk0+0v79viRPY/f7+vh3MYJU0qdkfSF65KOMvUqSg6VGBYg3KvsA9D0ojjTSkxjS4wLdDoZDRNFtbWxUKhZoELARXYhh/R4VK5S3JkpWLhwSJBU1s8Hu3f8L/0xNyK7Cvur5RacaNczv6Ho/HeG7YAsJ94+Sgy80HoHElyfAl0nMyVxaoq3nm4jVpZh0dHZk5NM0TbuDe3p6V8NBcOHnIumKxmDVQ4P/R8OJQcB/Uzs6Odas5cDgdgT84jBhASOCGyXB2dmZCj9PTU1P5sDkpkVDVuZxnrunpaRNTdHZ2Gj4INICkmcri8PBQfX19mpqaUjgcNvgH1gjetWTMUqPLDTa6s7PTRDZnE1PqElDR3ff391t5ViqVLMtkGGUoFFJHR2MS6+TkpDUN4Y9S7oEx4s/A53LXBc8GT1zUWaVSSalUSoODg9YlB/5AGh4Ohy0jHRsbM03+/Py8crmcjW1hsOm1a9fsZy5CGy6uTLbLZyNzv3v3rkZHR00JhngBLJBDXHo5lLJer9uEBfoFqKxQgVIdZbNZzczMWOZFs411WqvV7JDhQL97966uXbum//Jf/ov+9E//1HDZS5cu6ebNm3r11VcNxwQegzlAQw5xABeTXaAIUqEBe/GZYTlsbGyYX8T29rb6+vo0Ojpq8QLsmOp0cHDQcH2apPDIwX9dehi/EgyJXy63FwiF+ADDBU46sQkcmF9d86DfJNhKv0HA5YXhknJq8OChUlFuHB4ean193YBxMh5wTbrSYDs8DHAqt6vJxY1jYQILnJ2dmZExmR+loitDpeTm1KSUcY03oBa5RhV9fX0GKRweHhoRfHd3V4ODg5Zxs6Ax2sArIBAIGNHaJa7jAUq2i7oM5gcBh03rZrh+v1/JZNIyTJ4L9D0EFCxAnL3a2tpMUYbiKZVKGei/u7trwY97x/NnEfM5uCd8JxczgyjPd6URwgb1+/06Pz9XOp225zE8PKzz83Pt7Oxob2/PqFuVSsVYK/BT3WwKnioMGqmRwaz/arx8KBQyD1u+G1JPTNNrtYY37fn5uXkl8z40Os/PX06nBYN2WQpbW1v23Nra2qwZzMYMBoMmJ+czQr1C3QdkA2SEio/qiZ9jjfMZuR8IGQgu1WrV9hwHI2IbmmjAhR9++KHOzs7MO+Hu3buWGNAApvoYHBy0HgT33a06+K7pdFrFYlHd3d1m4cl7Yofp8tL7+vo0NjZmEn+UjcBnYKR4IrP+ObT6+/vNZdClztFrAKslkAKTkNS4a5hDkl+BL+lXXFS1uQeu+/uvur5xTDolrVsiSS/HJksvzV7oxHKCIoXL5/OGL6GbPz09tZIAwjY4HZixe0F1oQlBpgq0geqNRU7J7fV6LXMDRyW4s1BQnrEw4fey4CTZ65AJXXTixx+TwEmWmkwmtbS0ZBaAcI49Ho8GBwc1MjJiGSGfF7yJhpD7EMkeoDm5fGBOWe4P2nsandVq1fiFZOMEUsjgUJ44FOjQutAG3+1iU4LP5zYv2Oh4a6BnB+P0eDzm2n98fGxZOdzUvr6+pgaoCzUhXpHU1JRJp9M2aYKxJ/g4cODevHlTs7Ozkl4OOYVrSm9ge3vbMGnWVLFYtIyaKx6P6/Dw0A5/EgYardevX7cKhvsF3sehSdXnlsU0Tfls/B3Zrdu05FDjtbn3HH5ulolCEEaN3+/XH//xH1ugzufzhucODg5awsLeATYj4LqHIAKjVCplkFkul7P3YX/39fUpFovpxo0bllWSqZ6eniqbzdq0DJIiXP1u376tWCymoaEhY6+4bAH32bAWj46OlE6nTcHmBk334HKnObs9A4/HYw16niNucy5260rPv+r6RpYCnEsyFhYJFBk+OKY2lJTorC8yHTY2NkyF4oLeYLNgVV/1wQnEBCWyWX6eTcypRSbGTUulUjo/PzeaDZJMfA1CoZDRyyQ1OVPxmXgtOtpkJnx+tOPFYlFPnz6Vx+PRt7/9bfl8Pu3u7qq/v1/FYlHJZFIvXrxQLpczCbTX622SSZLxu9nUm2++qcXFRT158sToOiwAngkbrVqtWnbNCU8X/PLly4rH4014PMoe6HJtbW26dOmS+VpwCBJoCdT8nbtwoTdxb2lcPXv2zPD7Tz/91J43mRKHKAv84OCgyeXLDbjIWclmwNHIhvFIHh0dtekXiUTCcFQ2FLxtDrL19XXNz8/r9ddf1zvvvGM84e7ubsNnmTosvZxc3NfX1ySKQK5OBk0wJnjhL0tl5HKB2XccBvzqTk524SwOKzBjj8djrwcceHR0ZAdrMpnUxsaGVWI+n0/FYlH5fN6eIyU3lSpZ9+npqYrFYhNM515uVXdwcGD8fa+3MYC1u7vbBjAiQGLvnJycaH193ZgMPp9PoVBI09PTZupz584dg3dwU+OwkGRrhs8FvAakx55iz+LVcX5+bkpUZPFk1+gFXCEFDTjgVJq/X3d9bcDlxAU2AFcB9yDTkWSOQO4Yi6OjI21sbFjmRLZEEwAszO3sXSxbuQj0cC8JvJTSaLpdK0S09ijXyNLxIKjVanry5Im+/PJLzc7O6gc/+IFmZ2cty8VBiwXOyZzJZJqad1wuCM/J7PV6rXQnGOFCj2QZWgs+q2trayqVShoeHrYTl6urq8swQDJENh0CDLJZPj9d+56eHhNBrK6uamBgwBQ0ZMSuR4TP59P4+Lhh9nx+SkoglWq1ang8Byid9L6+Pp2cnGh+fl6Li4s6Pj42C0gybkk2Suf69esaGhrS+fm5BgcHLRsh6FykyFHpUNn4fD6Njo4apg1Ov7OzY9hmZ2enSqWSNjc3jSYWi8UsSPT29ioajZq95Pz8vLa2tuy7rK6uNkFe/+bf/Bv9/u//vqampqzjTfWHBy34NJN6GclDIgIVkHV0kZnDYeJ6gEgvk4mDgwMVCgWrBliLPT09BultbW1pcXFRH3/8se7fv6/NzU2D0uD9ghPjmTw6OqqJiQkFAgEzhoI6SNPYzXChSUKvbG9vzB3Dy6NUKml+fl7b29uKRqO6du2a9S64gBbGxsY0ODiow8NDLS0t6ezsTN/5znd0584dC3ZwvyuVikEKW1tbmpiYsEDIoQ4zym0Mcp9c/j19C9Y2gzPr9bpZD/AM6LUQc/5BGS5luSshdPE7lDV0LBl3jc6fzAKbM8oJ5l/lcjnj6rmNIzAV92KBoUZDqZJOpzU3N6f79+9rZmZG3/3udxWNRg2Ab2trs0YNI7qll9Z+//bf/luj+0Bu5xTf399vyurOz19as7EpyIqq1YYxdXd3t+bm5oynh7YeChRZKLOkKE87OzuNiN7T06ONjQ2tra1ZdsE1PDysH/3oR0qn0/rpT3+q9fV1w7xoiFGOUh7SDMGsg9ldMAfAdT/44ANFIhGj+BHQObkJIjRNaN6BNbI+gDD6+/sVi8X06NEjzc3NqVKpaGxsTPF4XHfv3lVPT492dnb05ZdfanNz07JccHd0624D0r0GBgYskIE1ejweOxDa29ttksazZ89smGUkErFARJOH7HR/f98wZGTQ8FS7uhrjtF3LP0l68OCBrXeyI5ILggUqQJIEgiwBk+qRwApFjoOeYA3Z3j1o2au5XE7Dw8NWVdZqNWNEVKtVpdNp5XI5+f1+ff/739fIyIgR/hnl4/f7lclktLi4qAcPHmhhYUG7u7v67ne/K6kh6MBTlkDmqkJJSqi+2tvbFQwGrcuPU93e3p6Ghob0e7/3e1YV09QcHx838/ZHjx7pb/7mb7S0tKRbt27plVdesbXGNAqSI77D0tKSRkZGLChSyZBxs9+A1FpbG8Y9VFCBQMCYEwcHBzb5xU1cqBpcbJgg/XXX1wZcFgCqK1dxxKnk2pKhrYcig3s+JSMLmq6ji9exeFzKBRebnYcGVc3j8SgYDCoYDKq7u9tMsW/fvq22tjZz/oLfKslGj6RSKc3Nzcnv9+tHP/qR7t69q1AoZAscri5dYLArml1AI5iGMDsLPJuGAAHM4/EomUw2YaN4xoJVuTZ2IyMjGh8f1//9v//XrB5Z0NDZlpaWzOKPgwpZbUtLw0AH+SYjdxiwVywWVa1WFY1GlUgk9O677+ratWvmys+EDBqUBwcHdi+oNFBwgaFTqqGoCofD1oBsbW2MOqKcR4lIhhD/lbXm2NiYarWaSTyHhobsfS4yV9jUJycnNjTS4/EolUppf39fyWRSoVBIP/jBD9Td3a3x8XG1t7fb4FPoamwksiyXbQM2XK02DLxjsZgdUlxeb2Pc/HvvvWfuVtC8VldXm/Bq13YULilJDPvIXftkqxd9AtiDLpUJu0KsOyXZYUJj+tVXX7XmGeu0ra1NV69eNftElGFYno6NjWlkZESlUklPnz41uh8NKdcPF8onmT1cV+hf0WjUGutIt+PxuImmtre3dXBwoFQqpQcPHujzzz/X3t6erly5oitXrtj+gclBg3ljY8O+8+LioiYnJ22yOEER4yYwY2C0iwwd7jOwFlUo2T+sBbcZTDZ9kc568fragOtGbVdS6Dr3nJycqLe318pJMLBcLqdsNtvUSXepTlxksy4eS/PK/RxADwgqGCZ548YNjY+Pa2pqSktLS3r27Jn29/cVCoXMeGJoaMiwmtPTUzNEr1YbpsqhUEi9vb2W9eKFAKYovez+trS0KBKJGIGb0w7cd3NzU36/39yxcE4D46PBhjqJTXd0dKTl5WVNTk4a3SUUCuntt9/W6uqq3QueQUdHh5nTuJNyEVOUy2X7/nAsweEmJiY0MjKi09PGuG4MtJ88eaJqtapXX33VjIY6Ojq0tbXVpMYZGRnRxsaGPVfYIyzCUqlk/sder1eJRMLGcvPZXGUYHGo663zPnZ0dg4/cMpsLbBAJbCQSMccxcG8UjqwjGpIXFXQkFfhyUHHhkIUMmIDk4uqtrY2JF59++qllgmCo+/v76uvr08zMjIaGhqw6Y/pFuVzW3t6eBc9arWbMCElWZbmNZXf/8NzBcMkifT6fBUKwXsaL49fw5MkTbW1tmRoPOXShUFA2m9U777yjmzdvWnB7+PCh5ufndX5+bgfKzs6OMSTYz+3t7TaKnGoFgVB3d7dGRkb0ne98RysrK/qrv/orzc/P6/Lly9bf4PA+PDxUPB43QRVwDbGB98KZjsx3e3tbL168MKjJFS2wF3kt9pqkJsUY3ssupMfPoa6FP44wyDX6+ruurw24lDQuKdjFLgGjeTNOHrqM2WzWwH3KIk4/MgGX5+b6Srr2jGRCNIIIqASrjo4OxWIxBQIB826tVCpaXV21oHl8fGwKka6uLnMhokSg69vS0phGgN8lXU5YA1Ija4jH43ry5EmTyfTGxoZlqvw6Pj7etEAIcmQG5XJZS0tLqtUaJjCrq6umTiOQf/vb37Z74bIrMGguFApNzwkeKu5cV69e1cLCgvb397WysqLt7W3dvHlTwWDQRu8sLi6qXC7r9u3b1nRBqkx3m++PKo0NTWMBfN9Vpnm9XiuDmYqLEg2YBxWYu4nIsgnmZBbuQewKAjCMQXxCgN7b21MqlVIikbAqrFKpWMVCl51MjIwGNseLFy/08OFDdXZ2KhgMGox0sTN/fHysX/7yl3rllVds37i4P43afD4vj+flZAk2NoGJCpB7xwEEbsifc7EvwYlzuZz6+vp07949nZ2d2bTkcDhsOG29XrfMPpfL6cWLFyoWi5bZHx8fKx6PKx6PG565tramubk5S6LwtaBzf/EKBoMGPyB6CIfDtqaPjo7MVY9+BlawQ0NDiv9q5tvm5mYTVswh7oorgKNcl0EMzZHj01eAnwxEw/4mIePZ0BfiWZDdEutYWy6HmkG0X3d9bcBNJBJaW1uzhckCQIDAB8HPlgdAZuSC93RYgQ8wvubvCLoEyOHh4aZFVS6XjeOJRLS3t9cyDU4zt+OJDJJFz4Zmg7g3GVoKN9JVzUnNGS60t0AgYBQkn8+n1dVVHR0daXd3V8ViUTMzMyoUCvaZwCEhjOOKBcVLapzO+/v7mp6eNlMaVEPu56/X62Z+4gY3uu5IezFkx2uho6NDy8vL+slPfmKcSJ/Pp0gkoqmpKcvAIPCDM6P+ktTkAUrWyN/BsqBbSyaHCxpChPX1dZuwCy8SAUZnZ6cpBekfnJ2dGT7NhSE4ElqoXOClHk9Dcvy3f/u3Oj9vOKG5vFJKRYI4z4f/X1pa0pMnTwwmi8Vixnl2Ay7rYmNjQ5988omuXr1qBzkDQ126FkE/l8spk8lY5cUhQfDHdxo+raQmdRPrgXvBz3d3dyudThu0h5IOOAN7VCw1qUBQgh0dHZkxPHtoa2tLm5ubTUyJarWqa9euNWG4fK6Ojsak3uPj4yaog70GJIWQBKigtbXVPBQ8Ho+5j5GRSi+hTu4FrBR3n4BpA9G5oh2SO37PunQVdWT1sBaoPsDfsSMgPmWzWW1sbDRl+191fW3A/Rf/4l/ov/23/6YXL15YuQG25SrOgBvc7NQ1tmCTgAe7Gn5+jn8HgO02SOgAUiphBoOsFJ4gGSpZ0snJiWEzLGpKDN4bpyuwMzDIarWq/v5+o/+45T/ZJNr3aDTaZEBM1r+8vGwBNRAIWDOBheb3+204HjSghYUF/c//+T8VDod15coVvfrqqwoGg03Pxev1amlpSf/u3/07y9hLpZLd7/b2duVyOd2/f187OzuamZnR6OiosRbAMhkfgrNXIpFQJBLR8PCwkffpunKCS7KDD04wzQ6gEeTDmJZAMWJY4sjIiAU6+KDu2Bj09UjKoTLRHORyD09EDTTOYHwkk0mbOPHhhx/auHJohTx/MlI29PHxsV68eKGFhQWDxVz/U3dzs/GPj4/14MEDzc7O6uys4UsM/EZjESzVNZYhgHC4cBC4DWVJTUIIl07H86lWq7pz547+6I/+SP/pP/0n47W6ak6/32/+E8PDw5qdnbVgvr+/r0KhoM3NTWuGer1eMyzH6Q+57dDQkBKJxK/hlsQFBizCamLaCN8R2iAMF54/MaC3t9cgKJcvjigBqhtUTL6H1+s1vrvX69WNGzeMncSzcFkRXDSawWO9Xq+tOSTfJJoYc+F7QpXwD5L2DgwMKBQKaXFx0RYa+AoBzAWTyYrwDiCAkjmWy2UL1ixusDQWBRvBxakoaQuFgg1+LJVK1ngC/yVw8VB4fwI8uCGZUmtrq9nRuYYVu7u71l2lTMETFby5u7tbP/nJT/T222/r6tWryufzRgs6ODhQMpm04XapVMrGP//iF79QNBrVhx9+aFaWfP7W1lZNTEzo8PBQq6urho+98847+ta3vmWLqVqt6k/+5E/07Nkz/c7v/I6NYAerxbyZYYzHx8caHh62TJQDpqOjw4YCJhIJU6DR6SbzwDaT4EujknErZD0ETUpVpjlT2vHzNC3oHEPdIjiTPZK512ovpxszdUJ6SSEkE2Pt1Ot1y0RoiqysrOizzz7Thx9+qEQiYWU1wY1DgU0KR5TGazweN3EPnXY3wACbIQ8Gl6b7TbAkMLa3t2tsbEyBQKDJ+AS80OU1U/7zHi4vl3/PQTU1NaW3335bH330kRYWFjQ2NmZVD1J8EheX+kSVFQqFNDAwYIecO0qH3go84StXrlh1dPEiKYGxREYPAwZojSY8hxasHPaz1+s1uAqmELzlWq1mcJprn8ge2djY0NlZw5GP5+dy012YAIiC7BXxA4IUsHb6BdwLYDr6BG7l81WX5zfR//7j9Y/XP17/eP3j9Q+/vjYcv/fee3VOfjJCV5/M6QjtA1tDwOTOzk75fD6bdkApCljNawFTuHPgOzo69N3vftcjSe+//369paUxPuXu3bsaGhpqMqrZ2NgwLBdMj+yHU4ryE/yGjAnsube31z4rTTCagn/8x3/s+dM//dM6s7jIRDgFwX7cxiLNQ74L/9bFq4E2YEaQHZINcapXKhX98Ic/9EjSgwcP6qjkwE8lNXVHEX3AkdzY2FC5XDbaFlaUrrwXCSjfh2wRPmk4HNa1a9f04x//2POf//N/rj9//lxPnz7V2dmZYrGYIpGI/TxwgasCAxaicSnJngFesoxE4j7t7e3pr/7qr/T8+XOj0vX19em//tf/6pGkX/7yl/W5uTltbW2Zyfrbb79tiqZCoWAOXmQq+EoAAbisGLdcBzcFq6a5hhjl+PhY09PTHkn6yU9+UseO0l0XcGUph2nW1Go1U4WVy2XT9/Mzq6urNul4ZGREiUTCqg6kr4VCwcaV//t//+89/+f//J96qVTSxsaGHj9+rPX1dS0vL5u1I2yQSCSiQCCggYEBY/3gdxGPx5uUbKgFETGwhgYGBux7kIF/8MEHHkn6D//hP9RpcAL/wD6iWiKjpiFFT0N6aQKDaALoobe314QQVITEDp5TZ2enxsbGPP/6X//rei6XM1tM2DCYMXV1ddn0EQRV+J64o7fQDbjeGmTdsK3A5V0m1+/+7u/+nYPNvpGHy42lwQJxn80FOZg3xcmLiZdQQnhYbioPhgldxnW2d1NzfA14uMAPDHgjeOPyJckCPuUl3XI2lMt/hK7FQ2SBsDgkGZ+YTQN0wX+u3ybvAQ6EMMSl07Gp+ewsNumlDzGfyeWf8hoQ5CH4A8PQ+HGVMDTqCKRYEVJqAg0VCgWl02kdHx+bsghGRiAQsHL+6dOnqtVqJgA5ODjQ8vKydZnxT2ABc6gRzLl/LuMCDO+i7PLevXvyeDxaWloyLjcXakG6ynjvgsVzuLkHmpsssHnAcXkeBF7XL4T1Tc/CrQx5pqwFSmQXgiPA0xwDVgPvhE+8tramBw8eaH9/XxMTEwqHw5KklZUVVatV9fb22lBIlzkDL/3w8ND8D+CXgm/zbCn1wf8Jvqenp0qn0yZp7+/v19DQUJOVKoIBSv2LKjGSBWKHK4oBDoEi2t7ebkkWa9VlYrAeaKxBAwRbdmFEng33gr1DP2B/f9+c4Do6OrSxsWGHYH9/v6anp3XlyhVFIhFbx7wXcYc9TrJ0kTb7m6AF3+iH66oxeKPe3l5btOAYBF4+JCcY2A04I5gKrkCbm5tGn4pGo8ZwuBhkuKlQhrAi3N3dNVoIIgcWEq/BqONsNmvz0jAXxvh5aGhIY2NjkmSd6lqtZk03GifwJJlesbOzo9PTU42MjCgcDjfxiWFqwJRgc/A92OQ0A7jnHC4sCBcvhJJCZ5VOPTgfiw7nehol6MWxuEMXfnh4qGfPnimXy1mm4Z7YNCcGBgbsYNja2jLBC+RzmoGbm5tKp9NKpVJWNYRCIeN/0i12M2pktm7lgB9EW1tjmqwk40a767O9vd3W1cTEhKLRqNbX1+3PyC5pLlFR8F04HLANpPrhfdyOOJgpr8vlZrUkE/QKXKm1ixOyeTs6OsyiEz+HSCRih11PT4+Wl5eVSqVUKpXU09OjW7duKZFI2HtLMjHH3t6e0Z+widzf3zdmAkF5f39fgUDAKH5QytLptMmbCXo0S8fHx41lxMF5Uf3HPSJQsg9rtZpVM+DDJAudnZ2WkHF5vV67f9Vq1WTJZ2dnNuX5okLVHXZLpUigXFpa0t7enj1rsnuk4ysrKyqVSrp3755VNRhOscbdtedW5zQFL0r9v+r6xiGSbGLSccB5ghJcSuAGmhcYj3Batra2GjVlaGjITlh+Znl5WUdHR5qamlJXV1fTqVkoFIxLSxOCIW+ffvqpachpGly6dMlOnnK5rKdPn+rRo0cmVWYTQSVaWlpSZ2enEomEXnnlFQPY3S4wDw9zHPjAuA9tb29rcnJSY2NjJuF1y6NarWYBhI1GBuCqW9gcBFEUc1wovsjKyCDZuLAE6BJDlxscHLTASzZJScfkUgQJbAav12uZxdHRkbElqBg8noZL2dDQkG0QsisaaG7GB/uEBV2vN8asb29vq1gs2qHZ3t6uWCymeDxuSjMmE2cyGbsX8B+LxaISiYTi8bju37+vTz/9VFtbW5JkrBkaYVC+cA+Dv4lqkiYa98iVlANFQPHiamtrs4OZzNo1d+FeuTAWlDc3yMAvp3Pf19dnyQr31pWKu8EOZsra2ppNaCboBwIBU3VyTw4ODmwIrMsiIqFixDk+KjSYCTCU3FSRXCQJ7Be3/IfvzMQU/ozDh8OYg5TDlAOHz3n37l0zp2FtoRZ0nwmwGOsZwQ9ObkjfNzc3DWoLhULGX4dlw3OEucF/fH6XYuoKYr7q+sYxk5wemGtQiiI3JNMiOLEoe3t7jV7l9/vNZyAWi2l0dPOcFvMAACAASURBVNTKcKlR/pdKJbsxLHAuNgZZz8cff6xHjx7ZjWJxjY2NqaWlxQIAGE53d7dhyMAW4MtnZ2dmIZlMJhWPxxWLxYwPSrDjQOHkDIVCCgaDOjk5MRw5mUxqZWVFMzMzZsICPs17ktHi2cCDJDtcXl62cUOIBNyACwYJl5Hvc35+bsY0lI0sENghLGoGdQ4NDWlyclLvvfeeJGltbU3Ly8va2NhQJpPR/v6+9vf37edcQyEC5tHRkRYWFrS+vq5KpdIEBXHPjo6ObJz6yMiIYYBIP3k9vITBzrPZrC5dumQyZ7ByLr/fr3q94WI3Pj6uXC6nn/70p8pmsxYUODjc6Rs+n8+yu+PjYysz3dKW6o7qhwCHm5qb7TDOh0OaCkF6ye3lgCWoUy2yB9jU5+fnCofDCoVCFsj6+vp0+fJlRSIRffnllyYcIdNknyaTSS0vL6ulpcXGJeE/vLu7axAcQguSDhgKsVjMfCI4dLxer2XbVExw1V28nYsEisMF21OgJ3jSfBZERy7uy+G8t7enL7/8Utvb2xoaGtLExISuX79uxvJ8d+BCV1l38YD0+Xxqa2sz4ymXejY7O6vt7W27R7FYrMmghkCeyWRUKpXMFyUSicjv9xtE5EJkf9f1jX64LoUC4Hp0dNQMKYAKKpWKEaOZYEuWSYl2dHSkZDJpVCL8MQlGgUDAmhLuyQ/m2tnZmAM2Pz+vL7/8Uj6fT1evXlUoFDICP+Tqzs5Oo3fdunWraXgksIXf7ze8J5VK6bPPPlM+nzfTDTJDHqrUPKEWMjYnXbFY1CeffKKnT5/aiRqPx5v+jdso8ng8yufzevbsmZ4/f65UKqXu7m5Fo1ENDw83KeC4WEDgTNz3TCZjqqBQKKSpqSlrsLS2tur58+f6+OOPVavV9NZbb+mdd97R9evXFQgETMDAQu7s7NTAwICeP3+u/f19wz3hoQKfEKC3t7fNvKe9veEtzLhpDgiCN40esHhKeQ4sfBMowefm5hSJRHT79m1FIhFduXLF7sXVq1f1xRdfKBKJ6MaNG8rn84rFYkokEpbpI/V1DwLEHP39/TbJFiqb2+iTZN8lHo/r1q1bZjB0UZ6OsxoJCckD5bWb8YHfcqAjhpAaYqNgMKjp6Wm9+eab+uijj8yL4+TkRO+++6452hWLxaZmMebxVBKsYZpKPp9PyWRSW1tbJkrCK4LGEoco/RD+PBaLGRxTLBYNjyYZcmMGiRdV3tramhYXF5VMJlUsFlUul20kU1dXl4LBoGKxmMLhsCUSPp9P29vbamlp0ezsrJn/3759W2NjYyaUoIx3kzXWPM+cphgccEk2eorDA5gBehd85dPTU+VyOc3Pz5uc//DwUF1dXeaO2NnZqWg0aonE3zvgclKdnp6a0QqTBDBvAecDVyyXyxodHVUkErHsjtdBGVarNZzzQ6GQbVSkvhi3uJkMJ3lHR4eZlPzWb/2W7t27Z6bPnHYA5RwS4C+ohPivpaVh1UcXnvnzlKPwVslA2ICUTJzS+/v78vl8KhQKKpfL6uvr0/e//30TPXi9Xvn9fss0wAKPjo60ubmpJ0+eaH5+Xh6PR2+88YZu3rxpWQFNR7dkg6TPlGKv19sk6QyFQubte35+bjzjv/iLv1ChUNDv/M7v6Dvf+Y7hce7o73A4rJOTE8PduTeIJIA+fu/3fk/ZbFZzc3Pme3z79m3F43FlMhlNTEzoxz/+sQ4PD/U//sf/MI5wuVy2rIb3Zbpta2urLl26ZI0bDrYXL17oiy++0N7ent544w1ze5Magz1bW1s1PT2tqakpszs8Pj62ctt1UOM5UGpSqdEMBYuj1Pd6G34O6+vrWlxc1IsXL/TBBx+ov7+/qXlH4oBcm8oC7jnBAeUZc8IITFhGBgIB3b171/oM+Xxe09PTZqNJ0gF/1cULDw8PbdLI4eGhNjY2zK40kUjo+vXrTVAAmCn8czJE5O40Z7PZrFpaWkxGTh9je3vbGuPuRQV4fn5uMwJxaSMzbGtra7Iy5H7Tu8C4nYyYHkx7e7vef/996/EAQdBwROUF+4DDDrgRvjjTJJBqP3/+3NzJgIOA2xYWFvTFF19YpRyJRCxj39ra0vr6us7Pzy0puIhp/38FXAjn3IharaZkMmkUlvHxcb366quqVCp6/vy5fD6fstmsjVDu6GhMQaCpAvTQ29urK1euGL4zNjZmPrN0zN1GEcbJyCcvX76sH/zgB+apSsZBmYavA8MD8/m8lVDgm2QWbHqpUW6+8cYbNlX2IkMAsH9/f9+sKPGWXV9fV7lc1j/5J/9Er732mllQYqRMNxRlCpuYoZbhcFiDg4N69OiRCoWC7ty5o2AwaJuSi2z3VxQYWxyvvPKKTRymcbG9va379+9rZGREH374od5//317DjRv+F50vDmhKQGRF4NpSY0s7PHjx9bEiUQi1lEfHx9XIpHQG2+8oVqtJqhKuVzOnM04HBEegImOj4/r4OBA9+/fVywW0927d/XLX/5S//2//3drrLnwyhdffKG2tjarWpLJpDY3Nw3+Oj09NeNpHOdGR0c1PDxs1B43s3PpW5T+qOmy2ayePn2qtrY2o565+4TBpJTkLS0tdsiwUePxuCKRiAYGBpTNZi1DRTpKAnDlyhVjtZDk+Hw+gzswTnHXJzABohbXRQ3oiaYd8nRJZg5EFUlTtlqtamdnR6lUSvPz83r27JllmmdnZyYpZ6+5e+Tk5ESZTMaktXfv3jVfDtYE1Rh4shu43YD77Nkzvf7660okEsrlcpawuEZTHBbsYxqfJDiDg4Pq7u5uihNM0qa6o3lfrVatybu2tqaf//zn2trasoPeVWpiefnixQubHH758uWvC6nf7BYmybqMBDey3XA4rJGREYMVjo9fzpOq1xtTcZG7cYKNjIyYXBblCwsrFos1Gf6iHEG7Tnk6NTVl7yc16EGUKgQIaGaM3sEdrFQqGd4LHxFeK5314eFhyyBcn1dKRTqb3d3dmpqaMoUXmu5PPvlExWLRTjtwMrLV3d1dLSwsaGVlxeCLvr4+ra2tGf0Hy8JwOGy0HPdzwNSoVCqKRCLq6urSo0ePlE6ntbi4qHq9rg8++ECBQEDZbNZGx0OjQi7pdtwxUCkWi1aKY9XX0dFhstiPPvrIDNLBNsmmmPX2+PFjtbW1mfk1TR0alzT1eMZkPPV6XWNjY+b9GgqFdOPGDdXrdQUCgabylXtL57u7u1uJRMKah2Q929vb1nyE2nR2dtY0IYGSFNgEXwcaQGNjYzboMpfLNQX+er1hsrK9vW0/T4W3u7urmZkZ3bhxw4JZvV63+4zpN4cPB3U4HDa8lCZyqVSS9PLQJVOTGpVdIBCwZAW4iWrB6/XK5/PJ7/erUCgYiwMPAjcZcecCDg4OamNjQ59++ql2dnZ09epVeb1ey+LBS7kw5alUKopGo9ak2tnZUT6f1/Lysp4/f26HGtJuKmQqm5aWxvggDqh4PK533nlHMzMzloHDZ8YQnbVB45I17SZW+HrAevJ4PCZlho8+PDxsjTgqVKBVPIXL5bKGhoYMb8cwKhqNfl1I/eamGRsczl1fX5+Gh4c1MDCgkZERrf9qHAZUK9feLhgMqlKpWHNkc3NT2WxWkUhEkUjE3IMODg6sfKfscRs0ZGKVSkXBYNBG47S2Noy8ycq2t7eVyWQ0Oztr3E+YFYFAwEqAg4MDPXz4UH6/Xx9++KFtquPjY+sG0513HxzMBHBp7g84H1kNwai3t9fwTUpXzKzPz88Vj8ebuuMdHR361re+ZffBNdHgWl5e1sjIiA4ODrS5ualoNKrW1lZtbGxofX3dRCpTU1O6d++ewuGwPvnkEwv0SG3hEeZyOWNK0FxZXV1VoVCwEnV2dlZXrlyxbGp1ddXoVHwvsv/BwUHVajWl02kLSmwEynawPzYrJHuaeeCPHGCvvvqq4cjJZNLuxfj4uOHelJP9/f1NLBSany4+zcHD4QOWiay7tbXVRs9LL8fcQ1vEC4OLPkQmk1E+n9fv/u7v6g//8A/153/+55IaATKTydghAimf9+vo6LBDG4cyfKJZl3xesGmqPjeri0ajNi/O6/UqGAxqfHxcg4ODBg1SAULLw5Uvm81aEgOThOnWNCxpWlHZSTIZuXsBT7W3t5vIhvVZrVa1uLioH//4x1pbW9PBwYFu3bplWDK+BeVyWW+++aaxnlgruVzOYhF89K2traamWalUauI7g7nCG8dQaX193Q5gTJVc34yDgwPzCUbuy0TuWCymdDqt5eVlBYNBO6DcuPVV1zfONEOpge+pJMscXOf6w8ND46UyiK+lpaWJv1mpVMx9icVC1uLqzC82JfgzFkw6ndba2poeP36sarWq73znO5qamjJzFkoisEGaB36/3wyDDw8PVSwWtb+/r3q9rpWVlSa6mitIIFi4mCpepyjX2MyU5jQ5zs7OTAVD95rXmZ6ebiKg00jCWYvs3J2TtLm5qdHRUR0fH2t7e9sYH7FYzLD1b3/725qenja+tCTzC6UM5HMeHh7aZ7rIliC4bW5u2oBGSeZTSzOQBmW5XDZMFyoc1QNDAclwOYTIjsBEKbE53Mm+aca4Zte8LpMfoD7x2hDlKWehubnafTY6xiYYyCCiIJGgq44JtmvYQvMvEAgYjvvnf/7nKpfL1ghiIwI5IEi4OCGaYEp2j4EPxjwHBwfmlsf3ZK8ODg5av6NardozYm/Bxd3a2rKgRRBiPbj7juprenpaksxJzp1pyK9cBMaDgwOtra3p+fPnev78ua0/qk78lPv7+81LhQOfNcX3X15e1snJiU2PwQ0O2AX/Y+4F95t7zuHF+HXuMcE9n89b0IYiCRxD9Yly8eTkROFwWAMDA+ro6FC5XFYqlTLY4qKRz8XrNwq4Jycn5iNJ1gAHji99dHRk3pepVMqoHAQuLN/a2hqzlehI89AIlKTxbkOgr6/PzETIFpaWlnR8fGwNGRY+htqcgMFgUNvb20qn06pWq5qdnbUBdq2trVY2j4+PW+nOxnM3PCUnhwJ/jmSUjKVWq5mDFZkQnxvrN0bFA8STORMQWJhYWLosBcjm0F6YQoDJOjhxqVRSZ2entra2lMlkjDsI5sXCYHihG6yYPVWv15u6ugcHB7p586Z1duv1usmh4VTTaGPj7+7uGgOB5hGlOnO9XNMjoBmCMrSq9vZ2k8Fyzc/Pa2dnx9Ypf++KTcgKGeDJSHZXDEDw8fl8qtUa0yboW4C/uraj/DyXx9PwDiZ7LhaLWlxctEGHrhiHchxHuVqtZhUe7w0tkIYsjS0YHPCcCVKSbC8cHx+rt7dX+XzepmVLMi41cBLrGEgCIyNwa/ykvV6vpqenFQqF7HvAJ3c55lysr1KpZL2TaDRq1dbh4aE2NzdVKBRsqgrJCs333d1dY3Bcv35dHo/HehwIZXivdDptRu58Vw5nKoOWlhaze63X6woGg0YbXVhYMIgAU6LW1lYzpAF+Yo3ACsI4a2ZmRs+ePdPp6akxKf7eARd+GzQdPGXJYmg8IK0Eq4TwjqUhpeng4KASiYRGR0ftptVqNeVyObMXZJChm13S1b527ZplA5OTk0okEhbo5ubmNDw8rJmZGY2MjCiTyRgPEO/K4+NjzczM2ESCSqWifD5v3euzszNrMCAnJrDy8CiLobJxWDD1k3+PpHZgYMBsCwHsh4aGbIPUarUmpRNND7IvVyXF6+7v75u4ZG5uThsbG7px44YZWEN5yefz6u7u1s7Ozq9ZPLLpuQ8cJicnjZlqV65cMZcxONVkP4ODg9adhX+IogwJtCSbMwXHkaYJAdFVB8EagM1CReD6EYAbchWLRSufYb+AsbsSaL4joor9/X3rYPM5CIg4acGogHtO34LP6l6IFNraGiNrBgYGDA+E0sXaQGbLGgL+4O8PDg5UqVSMdI8vLN+Fys0tq6WGdy9MBhpLVC7gocB3QGUwCmgMunakNOTm5ua0uLioq1ev6vbt22ptbW1Sm12sRkkOWlpaFAqFFIvFLLvOZDL2K5l+MBjU8PCw9RRwHzw+PlY2m9Xw8LBee+01C8rEJNYrFDe3ucy9BZunMYdgh72YyWS0tbVltMFEIqGRkRGDKhmX5dIwqSKIV8FgUPfu3VOtVrOe0Ndd38hSYEGA+/HgLjYvWMg9PT0aHx83YrDbtU2lUpaO4xVKQIfTCnHYBeLb2toswNbrdfn9foMUyELi8bh1eCORiDKZTFPWzLRUj8djlCnKMnwNCPh8RxQwXHwPPhuBmYyXQIOBC6elq1ahucMgw2q1apudkxkIgUPNveAT9/f3KxqNGhZK0KMpU683TJFpKNHMAu5wfSCQMO/u7poKCKI+XeWBgQELNASus7Mzm4wBdORKXwuFgrEoTk5OrORyJZncf9YbVRTNIj4/FZUbcPHjgOYkNaATjLRhlJRKJc3MzJhVIUGMTI5gfVG6zuHPpGhwdqTmXPC+GQ8Dvg/9j2BKhgu2eHZ2Zoeux9Pw8C0UCmYsH4/HLWt2syz3AGfNraysmCoNnwKYOEAolUpFmUxGtVpNQ0NDZjgFNgo3nkqJgx9qVj6fV73e8CaBIsYhYQHlV4EafwboahgJIY7BWJ/moGsgxLojkaMvgL81ATmdTqtQKNigTNbARQUfVSgUQdgFyWTS1goNaiiDg4ODlmR0dnZqZmbGRgyxBkk43OzbPXy+6vrGqb1sXjr8LshMs4nJAAQNLrKNjo4OvfXWW3r48KFWV1c1NjamSCSi/v5+K60IMIDd7uvcvn3bAjgn3OXLl3X37l1lMhkLUKenjYmssBxoOCCTJRBC6eBh0ATr6uqyjI4Hxg0kqLoqL6hmBGfXfwBMMBqNGsWKzMMNpARcMho2L2U5P8NFx7Svr08jIyOmMGttbVU0GlUwGFShULDGxtnZmS5fvmx4MPeJ7K6zs9M4trAEeO58r5aWFpvUKjXgBYzgqRDA+FkDbsYMP5bx6LA9XAMeyP8cChdNfcjK3HXxgx/8QE+ePJHf77dSe2VlRcViUbu7uyqVStrb29PY2Jhu375ts9jAJimjJdloJqlxoLieHgQBxAOuUYqkJkkwF01EWBQc3hzm9DA4/MBsg8GgQQOpVEqtra2Kx+MGfZC98YzYO8Vi0aosRCqHh4fW9a9UKlpbW9PCwoL5XA8PDxu0Q+VEwiFJgUBA3//+91WpVP4fe2/WG/d5nv9fQw73dfYZDjncRUmUJVmSbXmRkziLYbUFUiRIgaDpC+hpgb6EvoAeFehBUbTNQdAWaZClTZBf4jhepVg7JXEnZyFnhsOZ4U7OcOZ/QH9uPcM4dvr3aR/AsC1Sszzf57mX677u69bKyorpc0C7coulLKJ1MHMapsBjEUTH4Xq9JzoT1DLc7jFSeL/fr2KxqJWVFdOJoEUYp8iUC0nG0SWAoykEY82AUYr87e3txoZATQxVMgSDwuGwotGohoaGND8/r0qlYmwooCO3ffoPrc80uBw417ty+V2Bjvb2dhWLRUUiERUKBa2srOjRo0fmibg0FIXS6bTxH0mNoWh8Gng/NTVl4hyHh4c6c+aMwuGwnj59apV2ps2mUikrZoE1gYER9UFVIdriEPX19VlFl1TJbbmEBoMBJ22F9lYqlWwSBbxHFp03HGbgBTiVGDYMCobnNBeYSIs/i0Qi6u3ttQgnlUrZ+wUCAU1OTmpwcNA8O2Ikbm8/74szYu/BG3GC/Hk+n9f4+Ljh4Fx+yOI8dzICyP0oiJEe7+7u2pmgWOX2x8PFZo+7uroaRpiMjIxYZxitwLQHUxDFSC4uLlpaCAxBV9zg4KDheTAlXKdLMw7tp11dXRZN8awIQoCmcDY4T7/fL7/fr97eXnk8Hmud9ng81lTACByClI2NDc3Pz8vr9Ro8B/bK3rrn050D2N7ebk6ks7PTKGKcGUYGdXR0KJVKWSR6eHhoVD4yDKiBmUzG7gQFULe9mM9B8OHus+u4gAYQ+kGfBQiJ+wSUxLDWzc1No5LSmk8dpKPj2WBPzlxPT4/tFw4ZqKJQKFidAzUyCog4hJaWFqPu0Zzi9/t1/fp1O+M4b9CA05n/6fWZBre3t9doUIT5GDCqougWIJMIr/bhw4dW+WO+0s2bN/XVr35Vd+/e1dLSkr2Hq/3KRruV+WQyaRecSBsDR4WRqQILCwtGGwN6aG9vN8L722+/rZmZGRPJofOGvurTEagbZeEMKB4Q/VcqFSsyYQApApAuckjg8+FgwJj39/etw4yDxmF157uRdhFZ9/b2NhwoLkAgELBpxBgE6F8U6jBuRKu8LwvHBV8TziwEfZ5DoVCwS8UIbYqCXV1dNhHD6/UqHA4bbxcjUa1WjSrnMjyIIonoaFFlra+v27w1MNlSqaSFhQUdHBwoGo3ac56dnVU2m7WWczA8eN1ovlK5BgMkG4L6R6+/S/YHk+X5SrIzzTNy+aYU7IiqENBBVxi9AZ4FUApRP1Ghq05F5uDithgxqFj7+/u6evWqzp49a4VG1Pfc+XIMRuUednR0KBqNqre31yJ2Uny3boHBlWSddG5AQybHnSIIgunDM6YDDtycYjTTsOHt08Tj6jWwX0TgbqBBkdCtGWFQ0SFpaWkxTjUwB/REV7gLhwEkRVH5NAT4vzK4GArCc7wqNChEaSqVihW2XIpXoVCwwzI2Nqbnn39efX19Ojg4mXCayWQs1cLYcWDdlA11KaJlaDqZTMb4dERYq6urdvnz+bwNapyentbrr7+uzs5ObWxsaGlpyZSZYBVwUIj8XG/lFsMwmhhOF992WxOJcokiMdqIixDdlUolw3kRNoGdgKdmcWnd9LKjo8P0a2k75uBhXKDMkWJBQ0JEyE2JOZyku1xkIs4rV64ok8moXC4rEolYoQfYhgO8sLBg+grwsUnRwLLZB2QdwagxLGClRPywJiRZwwDPCdpYU1OTEomEYrFYg8APtQgXojg6OtLy8rIZlObmZqVSKYtOeR/OFVidCx8Q+fG8SKch0Z87d057e3sNTI1YLGbfH3YFmQB1gUAgYNg5BS63WI3DlE4aLdCrcDNRnBBt0IeHh3r8+LFaW1sNomMeGHSohw8fWnOE+5nAzD0ej9FCT0d07v+TsXR2dhrW7rIHKGYRyHBnwIaBbqrVqlKplLEuyLqamposa4A9xXcmikYLGtEeCnf0E9CivPyJpCcUw1KppFgsplAoZINHsVFuPQbaIIwMspQ/tD7T4C4vL1uY7xY88ORu1RBtAh42DzwSiehb3/qWLly4oJ6eHu3t7SkUCml0dNRI0WAyeGQXm5JknpeHRiEkk8konU7r4cOH9lnu3bunqakpw8MuXrxoVdJMJmOat5Cuo9Go4Ze0eELnIorlILm4LxVjvBpRB+wCDDiHmdfBmDCvjDQOrQiMM4fT1XNgL4hiJNnnAF8jZd3a2mpQTOKAAlm4hT+gBBwfQkNEmqROXO7+/n7dunXLIiyPx2Mjy2EF3L5928j+tLeeP3/eKH57e3vKZrNaXV3VwMCAhoeHlclktLCwYN06fC5SNi4TiyiLPanVahodHdW5c+fk9XptSnFvb69FRvBuW1tbDWohCqXZArqSJOOywjOH+eIWzWhecKlhTU1NVmQjA6L7EG1gGobQNYYbSqMOmcDW1pZFuGDAnEOcYDwe15MnT7S5uWkOAVhgd3dXkUhEGxsb+s///E+Vy2U9//zzunHjhlpaWrS8vKzl5WVjBPT19RkGDr+Y5yDJjJcbobKAnQhIOLNQ/lyoSToJYJBePT4+tr2FJcL7ffTRR9rf39fo6Kj29vY0PDxsQuySGn7fhcgGBwfN4aA/Qq2mvb1dly9ftvN769Yti+qr1aouXryokZERY/QEAgGLmoFMwOmBm75QhJvL5azTg0jUhRT4ovl8Xjs7O9boQOEJInE4HLbebIDxRCJh+CXauRgD0hEWaU4sFrORIBRs0Eh97bXXTGVLkhm8kZER653nwtZqJyN15ubmrPmCYpQ7rdZN2fh7XDQ2l/+nOsz3JhXf3d3V+Pi4VYMrlYqWl5c1MzNje8ZFgjZEFIsHd6MpsglwS6IMKrjb29uKxWLWkEKUAQZIxuFSutBn5bXdCBNiOc0YGBgMrGvEcDjr6+uan583FTeYKDMzM9re3pbf77eIT5Jee+016/j793//d83Pz5uBIV0kuuO5SmoQRCICj8fjBj3R3Vir1RSLxRSLxUywBtI/oiatra2GQaMBwgUOBAJW3IX1cvqZACGwdxgUnvfa2pq1nnNP3AJpLpczKKG1tdW+h1s0paEoHA7bOQPmAftE24BnTAPBwMCAZmdnLT0vFotaWlqywuWHH35oz3JyctIKbfDUCRzc4rAbRLDcQjufCbyzubnZKKaVSkX5fN4YE21tbUqn06aLvLe3ZwpqNFEEg0HLmoeHh003hAEI7t10oQruJxAXMAMyBXSX8Xm6urqMXhmPx1WpnOhvR6PRBnKAy//FPn4hg+tW8+FAQnUCt3UJ52x2c3OzYrGY6RZQQaVIhOoRs6EwVqdJ1CwqyXhVDDX4bSQSMY83ODhobalEdlTcufhcoqWlJf3ud79roJxVKhVFo1FjG/B5Dg4OTAOWAw28wO/gbTlsYMQPHjwwQ14oFJROp60LioNL5Rc4BvqbWwjhPdz0ErCfJo6NjQ15PB7FYjGVy2Vls1kdHBwoEAg0MDVwIO5n5TNQxKLxg0OEkZmZmbFCFhQpuLA0ppDWdXZ2Wmso3XFe78l0YnjVKDCFw2E999xz+n//7//p8ePHJwf0k8Khq1frXm5YDjS91Gon8oHLy8tWlY/FYnr99dcNXweDg34G4wQDyJ67XFdgGLIGN6ojasN4sb9kOzg9NKHdSbU4TleyMxgM2kWu1WpKJpPWUTc4OKjx8XErFLtqdtQCOJc8LxoagL3gpFOQSiQS+tM//VPLMltbW03VK5lMqlgs2mh7N6WmsYQOiXMvpQAAIABJREFUVBZwB8VDfgduO04cni2p/+DgoJ0Tl+vf09OjnZ0dC4zq9boxXujsw1lKaggY3GIgZxKH4A5SGBwc1NmzZ+X3+7W/v29a3p2dnVpaWrI9g0RAp6okC4Bc3v4fWp9rcN1IkwNARID34stx8QGUY7GYjo+PTbQEzGZzc9PoFIFAQNls1jipHD43jUZ9nj5xJknQLoqyFhKIAOxEK0SbVPf39/fl8/l06dIl9fT0KJ1OW1RC4Y9UnKgOEjRRBJAKUcD+/r7NCgMbRPCHEcscJg4RnpVL73J/4dVS6HJJ3QxdBMYAcgF7Oj4+1uTkpDo7OzU7O2venajRxf64lOB7khq+N1GD2ycOPZBIAgcBM4IIoKWlxd6XpoKzZ89qfHzcMFP+PtjjhQsXVCqV9N577xmMMzQ01NAizoKzy+eGujUyMqKxsTG99957yuVy+vrXv65vf/vb6u7utgkgRFRkCdlsVgMDA5qamjIBFxfO4Nnwvdx7wfkCEsAZkrFQ+Ozs7DQBeJwt2KU7CLFer2t5eVlPnz7V6uqqHjx4oNbWVr311ls6c+aMFYcxVNIz40eUSpcaUTCNOJOTk4b/b25uGothcHDQIBFEbkidkUccGBiwLjIKqjTWsE43afB8YA0RqFFwxUni/LiHQBkUpmKxmMbHxxUKhUwfpaOjQ4VCwRwgGC5n9vj4RNUMZTL3ebnC711dXZY1MIljYGDAZArQ/ganp6Hi+PhEB5miGWfys9ZnGlyXF0elGCCbF6bgAs3n+PhY+XxeS0tLCgQCRpXq6uqyLoxSqaR4PK5XX31VsVhM4XC4gcB/+mKROpBOuNGS3+/X5OSkpd5LS0vGP6xWq0bfIYoE36GdNx6Piwm0iURCtVrNUj8cjKSGSjgeHhigVqtpZmZGP//5z3X9+nWdO3fOosl6vW46s1zCvb09u1w7OztGAwoEAg2pIF1KsAQ4IGQK8G0PDw+tMwY8tKurS2fPnlUgENDy8rIKhYIdNGAFLjdRBx1nRG+k6nxn8DkiQaJPCqF0IYVCIfX19ampqckcDTKONJ0Ao/DdeObt7e1644031NzcrFu3bmlubs7OAYwYO7yfOAVgBj5rd3e3RkdHNTExoampKSvyTk1NyePx6OLFixoYGFAqldLq6qqSyaT+7d/+Tb/61a80OzureDyusbEx+Xw+i6JcLP10JEPhjuIhuDa/D1eds0tBkjTY7Zgrl8tKpVJ68uSJstmsdQ2eP39eo6OjqlQqBj1w/zjfBAREYzRHkH0QFFH88/v92tnZsWnaUOjgFY+MjMjr9do8MM4ghS6gLDcr5TyRsvNvAgeizpaWk1l1ZH5kWmhv0BCytbWlzc1NxeNxTUxMmPAO95TveNqgejwey4hwAG7hNBKJmMDO5uamdZHt7OxY8Q4dC5xjc3OzZfA4SppXpE9vVDq9Pnfig9frNc/BBqMG78rrra+vK5PJ2OHhz4eHh3X16lU7cHxphDuY8kDxySWCuxsYjUbV09NjjARaUC9duqRwOGxGngtOZRrvR6sjAiBer9coQsufaNnyoDhE7uZReKNSCS0L0nR3d7fefvtt/epXv1I8HtfNmzd1fHysYrFokEt7+8kMsr6+Ps3Pz+v73/++WlpaTIIxkUjY/kgnxofLyyoWi0qlUvJ4TpT05+bmFIvF9Pzzz0uSTVklcgmHw2ptbbVoTpIZe9drAw2gsgUFkGdB4YNzAfUmHA5rampKc3NzNnUC3QKidyJeOndcLJlomr2B53rlyhXVajXDEE8XY6VnDRFE+VS06RgEaiIzunfvno6PjxWJREw/olo9EcP/+te/rtHRUet+Qt+hpaVF2WxW/f39DbP73LMBQZ6zy0UH8iF7wSAjX5jP5804EkGD4Q4MDCgejzcYnFqtZnKLlUrFKH/Sydw/nCnvn06nTTmLIhz8ZmA6mhFcTJrzhnwiZw6NaOh7RLHuM8FwugVnNwOmVRpjhfPAIFI85J+2tjZNTExocnLSsFXqEa5MI11yrs3AEXd3d1s0T4QKzk/We3R0pFwup6amJgUCAfX19dkzgQlEMATE1dLSYjoMn8ZJ/lSb+lk/JAWleETFFS8NiO6C4pOTk4pGo9YmNzg4qMnJSTNKXIhsNqv79+8b5QKaEriZG0FAS2lvb9fCwoJ5GQjaqVTKRmBcunRJL730kq5cuWLvRepKk8SDBw/0i1/8Qh999JGOjo4Ui8U0NTVlFfpKpWLVY5eWRNGPpgeXBtTZ2amvfe1rGh4e1sLCgv77v/9bN2/e1Llz56zKDcOC9/d6vZqenrbUjAe2tbWlhw8fanJy0qherF/84hcaGxtTMBjUwsKC9vf3dfbsWXs+OBX2kCid6i+RAK2rpHsYB7Br6VkkR3sj9DQiHVJQDBhTIFx1Ji40QiAMJuTQEjVT5OP9+vv7TWQkHo+bAXejKbItnBTP2+v1Gn80EAgYXRHnUavVlM1mrcosSRcvXtQrr7xizTW0scJgocWYi+XqKfj9fpsbB4uBy848vVqtpo2NDWUyGet2k55NSCADRMuhtbXVVMHQXJVk/GUgApwzkxl4zY6Ok+GJa2trdj6J2H0+nyl/oXpFkEErPhEp4kbcTVJu7qA7KEBSAzRAFsU5olhFPSWbzapcLlvECPea+xIMBjU9PW2COjxnDDdpPdiy2zQkqSHKpiZB1182m9U777xj7KrBwUGbDFEoFLS2tqZwOGwGmWiXiJfGFLe4SoPEZ63P7TTj4ZJStra22mHmojQ3N2t8fFwTExOGe1UqFaPYZLNZTUxMaHR0VKFQyMSWES+HI8oXwkuyUOZJJpNGnoYy8+GHH5qy0Ztvvqlvf/vbpgFKqk613ePxWLpNhALhXJKxCLjYGDEW0R/i33h7LlpPT4+mp6e1vr5ugxUHBgYUCARs6N7c3JyJyaCETzQK5vfLX/5Sa2trevr0qQYGBhqeydtvv613333XvPyFCxdUKBQaFPWBOfC80rNR8zgUDqgbIeI4eQ4UOGu1mkXFkixrINrzeDyKx+Pa2dlRMpnU6uqqzXO7du2asQM6Ozu1trYmv9+vaDRqkTRY7Pz8vN555x3dvXtXN27c0LVr14w5wp67EQSXlKjS5eVykYG5iMhJqRGZdil4kixqAvqhk0hSQxecq/UBM4aCLN2A4INc2KGhIYXDYYXDYWUymYahh/39/RoZGdHOzo58Pp8ePHhg0BvQEtQ+nqXbkbm4uKjR0VF1d3erv7+/Qc2sUChoaWnJYLi2tjbDuYE0wuGwRZ18l729PWUyGRslBdxHncRNq1kU1XD4RIKS7AzA5pmcnLRCdLVateYdN4tGg6Ojo8OEqoALcCTQKbEZ2CQyTIx4c3NzQ/ZG1rG2tqZCoaC+vj7jjRNBj4+P2/6TiXg8HmudR3SH7+42KX3a+twI9/j42XgdDicYDZV7UhUiAA4qv0thyefzGW6JrixRH4Wo06m8JK2trWnkE3EaDCDpMpcfQvXbb79tnW3RaNRSakaDUJxBfBxRG7QC3OjapX1xueFcEjVTrKCY5fWeqA5NTU0Zi4LOJwQ7Ll26JOkZnYi9qlarunfvnn7+85/r3LlzWlxc1I0bNxqcD0T4g4MDw2ALhYLtjdtgQfZA+kx7bSAQMCzLjYi3t7et/dHtzJFkXT2SDKohwqI9dXt7Wzdv3tR//dd/GZ7b399vo1l8Pp+9VyAQMIiKQ3vp0iX5fD719/frS1/6kjwej2F2OAd3L4LBoNLptDFk3JZit73WlVukFZ12TpSyuKjsAdh6rfZMhYpzxoVnAU25bBNw00qlYgEFuHZXV5ei0aiSyaQJeeNAq9Vn43rOnz9vpP5yuWxnkjSaoEaScYThcUsnUMfGxoZyuZzm5uYaoIKxsTETld/e3lYoFLJMgsYU9rRUKml/f990JNhjjOlp+A97QdOMa3DZP/aVzI5zwZkAG/V6vRbd06EHXZLaDvCmy+2FkUBBmUwFVgL6CDhlJAmggg0PD8vj8SiTyUiScbpxxK7iIPdlYGDAoNQ/tP4oDJfIj8gWDBMchMNC+2dPT48VgAYHBw0ToUqLOhNpuxuVuRV+1ttvv21iv+BI7e3tGhkZMSPy8ssvq1QqqVar6dVXX20Yi0Ha2dzcbCLjx8cnupoIO3Px3A4n0lvpJPpFR5TUZ3d3V+3t7fa7OB/SKirxiUTCKq9cGgwVqWhzc7Pu37+vf/mXf7GDA0vB3YuWlha9/PLLWl9f1+bmpnVSoQsLDY9oi/TS7/ebMXExbQ77wcGBzeByMTi4sG6q5MIQzc0n/f1QyAKBgG7evKn3339fuVxOMzMzqlarGhoaMrUuIg3wW947l8vp4OBA169fVzAY1OLiou0TEajrjJeWlgyrc7t/cBZggFxiOo54ndbWVkuzOZsUUChIui3nbiTn8oGJgDB+pL3wyzmXYNpkRCMjIxoaGjLjgCZyPB63veV8Av+4RaJcLmdc5kqloo2NDUvx6QR1edTHx8c2YNTj8Wh1ddXkEWu1mp4+ffp7lDJEZcCHufcwbNyOUBZ766b2BAGSDI6glkNW69K6+LtgpxhOAh7GyJPxAnNIatB5wNHy/hMTExZNk7UxzSORSKhYLCoUCml4eFilUkmPHz+2/QYiY6wPwufSifN/4YUXGrpCP219bmsvOAwGkgozkRHpHJV/9GG7u7vl8/kUDocbRgin0+kGNS96uCkefBoXN51O6yc/+YmRpAcGBoyJEAwG5fF4rILLgYEjh3cjwoDTRycOdDQ8rNuiCatAkuGKtHG6qmakLHANe3p6LCUjXcRQwPAAz8bY7+zs6Ac/+IGWl5d17do1U9M6TTOBHB6JRJTP5422UqlUNDs7a5cbPDAejysSidi0C0jiGF83WwEHBdIhZZTU0C1H8Q2WgWvcM5mMAoGApqenDWOrVqu6c+eOksmkYXnxeNx69FtaTlTdGFeNPgTpP9i2ywmWZMwBHDUXDIPLJQNC4lzxMxbYPp+BqFSSGQbOBgbYTdmJwnK5nO3D8fGxUfw4CzQyuKJJdKQhAcn78Ky4W9Kztlnw1kePHlkKC3eWpo319XW1tLQYXovTwsCcOXPGCse0BTMKi7vA+e/r67PshqYh+MzsjbuX7nPCkPIseE3XiQJBuDYGuwPNsF6vK5PJGKaNvenq6lImk9Gvf/1ru/O8BufGDXYIEPiuUEBbWlqsQYQAgAIanWl+v99GUNGNiHO9ePGiLl682OCUP219rsHFuIKzkPrDFGATwfpQFwLvRSLw4ODAAHe++KeRxPGY7uDEWq2m9fV1MwaDg4P2cHgNjAZtv0SvGGFSif7+fg0NDWl8fNyiHSrp0D7AJvmZdDJm5ty5c9ZdglEFC+LzA0PQoIDRcSvWRPG5XM4oVgsLC1pYWLAhm8wew4Cw6HV/6623NDQ0pJmZGSOmg01dvnzZuoUkWfoPaZ+UGLiB5wh+y7OH63i66gwtCG9eLpftIlarVW1sbCiRSDRIE5JqQaxnfyGlu2JGGCyiNX7XTe8l6bnnnrPzVS6XG8R/XKzTlVkErwUPJULGUbk6s8BkbmWejjxXXKlcLiuRSKi/v9/adX0+n+GIbW1t1hmJyAx3gL1wI2YMFOk50SVtusfHx7pz544qlYr+7M/+TJJsUOXs7KwZL/DOWCymw8NDm7KAscDQgkXWajXbf+4KgvIYFgII1NSgw7GAutgzlltwpNjmwkT8N0UqhpMiUrO3t6d8Pm/nhddaX1/X22+/rZWVFXNMiNy4daGOjg5Vq89mkhUKBRsY6zYxjYyMWGsz9wFHXC6XlcvlGprBjo9Pppe/8MILDUXMP7Q+0+C69B2iByIh9zLzgME24GZWq1XTPEgmk+apqFJzcAHDaR+Fo8lyOXx4VCrqGElaOVmIsfBzLiEKRBDeSY2IlPiHbhSiotnZWYXDYY2Ojmptbc3S6lKppEgkYhEUVVGKTkQqkiztbm5utjE/OKH19XUrrLS1nUzIRUnMLdA0NTVpdXVVq6urunz5skEdwWBQfX19CgaDGh4etmo7BgPH40aA5XLZnBziQBgInjv4fFNTU0NjBFFbd3e3cT6h5UDux/EyfQOOKFEj9KZ8Pm+NI0AdkiwVdDuXXIOLAfJ6vTaZlyIKrbrgeZIsqyFqJJrjLPC5uFA4GvYA51EoFBoultspNjs7a5+DMwJERMdlX19fQ9GLZ8K94L65z55zWKlUlEwmlUwm9c1vftPmjb366qumfyDJaiWtra3GdPF6vZZd0hIPxObKnwJ9kIkcHh5adInRRV2OegwLOJDmJZemhpHE6ZNN8n4EdwQxwDKcvfX1dSuIbm9vK5vN6tGjR0qlUvYa0gnj5O7du9ra2rIzgv2A44s4kctEIcuemJgweIku2FQq1cBC4VmEw2G98cYbJmrkOplPW58rQE56jdGDOsKfkQ7QbUSFl6gTihVNC6TcXEBk/NzDiRAIy4206TKD/Iyx5cLgHTGw4EPgQBRg8vm80b64WHw/N6rEiG9vb+vOnTt68803NTY2ZikJUbWrV0pahWQbFDL2h4ieiidpOa2fRI8ej0ePHj2yCRoYoYODA3388cdGv4Pew7RhPDgXnxZsUisMJCNkOPQYOzqrOPAYDAwuF6BcLhtunM/nrbDA7xUKBeXzeStOcVnb2toUDAYNqgJbd3FSF4JxL60bCR4cHBhuub29rXQ6bS2oboMOWQdQiAsXEelj2KESYRjAMtvb2xUKhQw6cp07nzsQCOjo6EgrKyuGnRN1YYhcR8/ru9Kdrj4wZ8EtkM3MzOjjjz/W9PS0Ll++bEXk3t5ePX782PbeZc50dXVZkejg4NlsNDIi7gx8cDKxYDCoYDBoFXnaXTkjFCZPZ6Pu3nDvXCYIODb3zOU1E5CwB9Vq1SQAyK7L5bJu3bplsAl7xtkYHBzU9va2nj59at2ezI3jDmAcGSJLEZVz7cKM9XrdRP7dLL27u1vf+MY3ND4+bp/985bnj/ml/1v/t/5v/d/6v/XF12dGuP/6r/9aPzw8tI4VFL+Wl5eNzuKG8ngZvJzH49H09LTa29stwo1Go6bq1dHRoUgkoqmpKSUSCauCEnmdOXPGI0n//M//XAf8ptUSzQAXlCfiBo+jNdBNkd1WSFJsRHHApIBSyuWytra29P3vf9/zve99r442AL8jyZTRiBxcgRsipWg0qqamJkuniSSJ3sA3ifKBQsCq2tvb9bd/+7ceSfre975XHx8fN+oNe0VU4aZtbgcQntplbEjPqG9u+ypRB58DjPHevXv6/ve/73n48GHd5SOi9cA+okVAlZ/nQiRDdEWBjCIKMABFJShIcGyBoP7yL//SI0nXr1+v7+/vq7OzU9/5zne0vr6u7e1tPXnyxFqCx8bG9NJLLxkv2z2naBggjRmJRNTT06NsNqtarWZkeIp6FKsomH7nO9/xSNI//MM/1O/fv69SqaQrV65ofHxcBwcHBmkQZaMYx3nkHIHjEvWBqbNv5XLZOqVc1gwZyHe/+13PX//1X9ehSZXLZZPrJMKGeUB24qp2uTS/vr4+gxeAY4Bo0OuAofPgwQNtbW3pT/7kT/R3f/d3Hkl67bXX6mRqnB3OKRgt9DdgDgq31H2Y4AwDiMiYz0p2Uq/XbZx9JBKR3+/Xn//5n3v+/u//vk62QG2GuwUkyTMku0HbxBXqB17ks1Ash7dOxEsUL53UN1577bVnRZf/jcEFFiAFhADv8/m0vr5uY0LoWoKDS/hPwSIcDmtnZ0e5XM5UdoaGhiwF4xDClSWVYoEZ021GCk3hDHqaJGvrbGp6pmbmtpHS7URaCCbk0tbcdInCGoY+nU7bkL9q9UQz88KFC6anSjW/p6fHXr+trc0MI3tC8QHBFZgD/BwYBGPJ8vl8BqdQyOHyYriAVjCo7mvw3Vwsjd8HQ5Qau8w4fKSOvDYOA8OMHgOjsMEsYXggCO42D4DTunoKXCicNuk+0BOrpeVkwgRGpFarKRgM6rnnnlNPT49SqZRyuZxu376tS5cu2Xd3G2FqtZoZGeCCUCikaDRqcAxTJAqFgnGL3YUOCB2R+XxeuVyuoUsLvWZEycHcKR67zhDHxBmIxWLq7e01KqCblXJP6MQCGoIxA5yA8cG4bWxsNMghch75DmDhkPlbW1tNkHxra8vooDs7Ow1UKF7fDXL4DDh6t5kK+AdKJV1lzGJzcXagMP6e1+ttcOrumXf56G6Qw12APUMx7fDw0DQlCNYGBgbMsMN9d6ces0fcFe7BZ63PHZMOFkU0g9Hr6elRJBIx6gpYJAA0eCgGGs6hz+fTwMCAKf+AGbW0tGhvb882nYvvrlqtZoRjMFuibqhfXGQ4rKgwxeNxq0BDBWNEMgwK2keJKl3cCS4ymCzRwtLSknUHBYNBJRIJTU5OmlOioAUmh+PAA/O629vb9r3Btone3e4qcFI3MsH4ERm6l4XLTGSGQef7cVjx2Owzz9Olp3V3d9u5cOl7RGd4frjIcEvBRSnS9fb2moNyPweG0JW85DsRJbnFKrIQRtHDA4eKiEA9UWJfX5814vB6XDT+wchls1lls1mbNoviHRxZl+AOa8MdXTQ4OGhFTzDTfD6v9fV1raysaGBgQOfPnzdaFzQolz0hyZw+tDKweQyJi3ljxKrVqkXT0WjUdBQ4NxgNagsEFzx7MrRAIGAceuQgcSzoCKTT6Yairntv4XDDdHA7SOkudH+XAplLH8PIEt27dDDOhSQL8lgEEJxjjCwGFkfv0jdxINSlYEzAhHEZIwQhOFSCli+kpSA9a2fkgSBWTSMCCvNcauglkLX7+/vV1tamUChkYy2GhoY0ODjYMFID40kK4z44aGFUGvF4GB0MCAcVcYru7m51dnZa2k9PNULd6+vrRnDHgEJ5CQaDDSwFSaa1EAwGNTY2pq6uLuvCoaurUqkoEokoHo+b0LZ0Qo6HttTX12cNG52dnTauhPSTlIcKsPsQYVVIMqOxsbFh3VGusAZ6BO7cOIwchQYOGpfYhVTcdN7l5HJhcEZcCiI4ojJYK/z79CRV91kTKRO5EynDh8WhuAWZeDyuzc1NnTt3TsFgUBsbG7ZXbW1tGh8f1/j4uAmmc4nIflyogvPNZIZisajNzU2VSiXrInS7vk5HuRhs9joejxsFsLOz0+CpYrGo1dVVO4tkKcA5fEccbbFYNCU4eJ8uDOQaHApCPp/PomAUwHjmtM5yJ6GsMbWgra3NuNoMniyXy8rn8w18ViJRV/Rfemb4ydxgzrgjtFwYgXPM/6+trSmfz1v3KQv4pKury4a9trS0GAMBKIzXwyYQyUonY4joPOXvzs3N2YBRICdocQRB2DOgBcbRj4yMyO/3NxRlvxAtjPTeDZm5sPBtW1tbjeJEFAQnlvSeVKK1tVV9fX0m30fEhCC0i52cDs0xAFx8qvOBQEDDw8PGlTs6OlKhUDBuaDAYtHSLCIKOqBs3bpgzyeVymp+fN6NLKklle2FhwZwGXF4wvra2Nq2vr5tqP8IXfX19SiQSFuHR2kmrcTwetz2li429JhJCpd49eFTOqSbTsohICopU6XTa6GzRaNQkLDF2GFZ3ZArYnxt9kja5KRv4G5GCy7F0o2hev7293TIKont+FyMinXBaC4WCstmsTd7l80BnYxWLRb3xxhu6cuWKRSIPHjwwmllPT48uXLhgGh5QgjAIwCDb29vKZDINHYY0BQwODlq2IMnOtqsBS7RFk4HX61Uul9P7779vYuMtLS02Zvv11183niyRJ5kd55NskWjTTbtdlgjn8+joyNqi6/W6ksmkcU2BrujWkk4MMd8Xg9fWdjJLjUi8UChYtjg9Pa2xsTFzRNDPvF6vddixGAlEtkmqjs4umQxnnWeeTqe1/ImUKN+LzrhgMGiTIchccVZAOjwjDGZ7e7tlipFIRIlEwnjgMJRCoZB8Pp/u3r3b0NLPvSTrJPBMp9MqFouamprShQsX1N/fr0qlYoHnF6KFSTJaF0UgCiQMnMN77O3tWdTj9XpNjQgjQrTJBgM2SycYGOE78AUXmMOBwa9WqxocHNTQ0JBJ2EmyIX9ctM3NTa2trRk8wGGq1+sKh8OKxWLq6uqyA318fKzl5WXNz89rbW3NimiQ78vlsnFup6amNDQ0pM7OTp05c0aRSMTSrjt37mj5kxE67e3tCofDtnd4aZdriMrW7Oysksmk4X5E3xxYFqkLDgztV4wdz4AZXKurq9rc3LQBfHBA3cM6NzdnPEMXIw0Gg/L7/fZvnjUOEYPq8XjMAEgnylX37t2z9sm2tjb19/fb38fhRCIRbW5uanV11WCUTCbze0U1WlyJnljLy8t68cUXDWtjCghYO80GGC+fz2dYcSQSMYNFt97IyIgZhHq9btEwjRVra2tWh3DHpGO8GAz5u9/9Tv/xH/+hjY0Nfe9731M0GlW5XNaDBw+UyWR09epVO7eSjL7lGlOCF+6AS+Ln/Y+Pj43jTcYCzgt2XiwW9fjxY5M/7e3tNUlUoluifYwtePjIJwI3ExMTJjaEE2DEUl9fnwKBgH2Xzs5OG+66urpq4t6oBp49e9YGgUKrRLcCbYLm5mYNDQ1ZgR2lQbj3LvUNqMAtEkO7w1GRUZFtUauBh9vW1qZEIqFMJqP19XWFw2H19PRYZM7nooFqYmJC58+f1+HhoVKplAVmNA991vpcg4tgC0IegUDAJqNiCKgs44UlWfq6v7+vYrFohwPjiloYfFUwJdqC3ajHrerTire3t6e7d+/qRz/6kXWJ0C7a0tIiv99vGGpLS4uJqzDEcmRkROVy2TDD5uaTsUBEkGwwn2FsbMyaOkg5USs7Pj7W+fPnLSLs6OhQNpu1qBdnce3aNXm9XpOmy2azJklIG2ZLS4u1HDIxGQEN9pUIDPyN4hJ7TlZCSlcoFCzdBr8jWtze3lZra6sSiYQ5PRgcpFIME+RA003wfrSTAAAgAElEQVTkzknD07e0nExolk4EVYaGhqwBRjphdTDkE47n8vKyZmdn5fP5lEgkNDIyYpfM6/VqfX1dS0tLyufzDSkb2Gxra2tDO3BPT482NjaMiZDNZrWwsKBUKiW/36/d3V0TZ2caBgI3nE1J1okUj8c1NTWlhYUFvf/++1pbW2vALXGCpLXVatWyoOeff17BYFCpVMqcYjabtSgLqAQnjOPhPNTrdSturays6NGjR9ZFhV6IJGsggaMeDAbl8/ls6gPTC0ix19fXjTOP4cLQMp2js7NT+XzeAgFgi56eHiUSCfn9foNOWPDcpWdY/97enhYWFlQsFrW3t6eLFy/aOSUSJoAga2MGHdNeTjMMgIK4+27R9enTp7p69arJQVKvaW5utmJhrXYyXSSRSGhmZsZEcQ4OTsaoM8ON8wLs5fP5lMlk9Mtf/tKIAwcHB+rv71ckEmlo2PpfG1zCYx4YHTb37t2zyzQ9PW2FDQ6eOy4knU4bDkmH1Z07d/Tuu+/q4sWLunr1qvr7+xtG67BJRMCwEdAT/dGPfqQvf/nLCgQC+qd/+idtbW3pwoULunnzpukt9PT0KJPJGLTR2tpqMnN7e3uanZ21IsAPf/hDPXnyREdHR7p8+bLi8bi9Jwc6EokoFAoZ/BCNRjUxMaHDw0O9++67unXrlgKBgK5cuaJKpWJV79/+9reamZnR8PCwQRqVSkVnz541KtbZs2dtRFBHR4fW1tZ0//59JZNJq/azOMRElh6Px9oQObSIsXChkDZECOU0AR2Ip7e316JIHA4G3U1HKT7yvIAPcAaofQEfHB8fW6QpyYwSFeBSqaTW1la98sorGhkZUVdXl41LR+OA1k43la9Wq8rlckajovHD6/UqGAzq+PiZGj8FULdhg/3weDxKJpNaWVkxuAHqVHd3t8bGxoyN8uqrr2p9fb3hnhA9Iobz8ssva2xszCLK7u5uTUxMKBQKmZODLkUmRVABxjo4OKh8Pq+FhQXVajWNjY2ps7NTH3zwgX72s58pn89Lkl544QX9zd/8jRXUvF6vfD6fjcWRngnZY6RwDjS8ALdB6WRvr127Jo/Hox/+8Ieq1Wq6dOmS0um0UqmUOjo6lEgklMvl9JOf/MRajNGF8Pv9NlGlqanJJBCXlpbU1tYmn8+n0dFRi3ahc7pSmIeHh4apb29vWyQLQ4jzSpMQwV61ejLWPhqNWjZQrVaVyWS0uLhozlWSKXzhVJaXlw26vHTpkur1unK5nI0FwyCvrq7a56MR4/LlyxoZGfn/b3C5EBS3wABJDRlo9+jRI6sAk4YzHI6K3tOnT9XZeTKf/uHDh9aNw3C8sbExjY6OGkvALVYRjSWTSZstBn579epV5fN5TU5O6vDwUPPz87py5YqOjo60tLSk9vaTAXHgLxzqJ0+eKBQK6YUXXtA//uM/6je/+Y0kmbrS6OioCWyzF6FQSM3NJ/oC4+PjunDhgpqbm/Xmm29qc3PT0ovDw0NFo1HDworFor7+9a8rkUjo0aNHeu+997SxsaGpqSm7DIODg+rs7FQkErGUCW6vG02RWkoynBU4pFQqKZfLqbW1VVNTU2pubrYprm5qWq/XjWYDnYZnzfBCtwBKFOJyfXlGREUuTELBhOgGQRhed3Nz05gtTAgZHBzU4eGhPvzwQzNAVLdXVla0sLBgxSZWrXaicJXJZCwiAbrh+2KsmT21tram0dFRhcPhBpYC0wTAMumIpDj24x//WD/5yU80NTVlhTgWWqrQI/1+v7Vow0yhCEUaTzs39ElgEwovFFNnZmZ07tw5K9gi2FStVvXkyRN99NFHthdNTSettu+9956KxaJGRkbU3HwyZLG/v183b97U3Nycnj59Kp/PJ5/PZxhvOBxWNptt0Ml4/vnn1dLSopWVFfn9fp0/f16Li4taW1uzrAKNExZZCT8HKgmFQiqVSpqdndWdO3esqIzeL+wLosxMJmPaE4zWefDggRWesQnALgQfknT27Fk9efLEimz9/f1aW1vT0dFRgxZzrVZTsVi04ZTxeFyZTEa3b9/WSy+9pHq9ru3tbd26dUubm5v65je/aRnh4OCgBUJ9fX3KZrNKpVK/J8j+vzK4rq4AUQ6RZyKRUFtbmz7++GODFFBMp9+YVJfqNsYjFovJ7/crFovZpEwXnJZkUwMwEm1tbZZmwGNsaWnRX/zFX6hSqSiTyWhjY8OqkHNzc1pfX9fzzz9vhPrj42Otr6/bQb97967K5bKmp6e1trZmuCH98BCh+exNTU0aGBhQLBbT/v6+ksmkhoeHDU/mfXt7e7W7u2sq/Ihxu3QyIrb5+XkNDAzo4sWL+sEPfqDbt2+b4WXUi2tweRZEiNIJ1kpxyR0lsr+/r9XVVdMOxkh3d3eb15akX/3qV5JOBFD6+/uVzWYlyT4HcALRD2cA3Qj++zQNzYUwKBjCVgACQQyI4h8w009/+lOtrq4qFAqpXj8ZqpjP5xvS11qtpkwmo0wmo+npacOvMZp8PuoLHo9HPp9PV69e1dDQkFW3Hzx4oKmpKXMUzPeiuHdwcKBQKKRsNquZmRnVajVrtZZOtBTQxC2VSsZ5JTAg8g+Hw1ZQo5DKs0LnYGtry84rY+aPj0/EaqBEYlh4ppyLo6Mjzc7Oan9/X4ODgzp37py8Xq8++OADKxqBj/P+Pp9PQ0NDdp8ZqNre3q4PP/zQxG+kE5Hzjo4OXbx40SJ11N5YGByygGw2a/RQitGPHz82J0BmRNsw5wuYg72hTkH2TBMGARo4vXQCW6VSKf34xz/W5cuXzfmdPXvW6jaS7FkfHR3p/Pnz8ng8ikajymQyhtfTUr26uqqNjQ3F43Er7g8MDJjduXHjhsFEn7U+0+CS2gNuu8A2XTRorUajUYtwIVpzAcAi7969q0AgYMLhuVxOXq9XU1NTZpShF7mRDAUJRplUKhUbjYxhJ33r7e1VPp/X6uqqYXDoxZJWP3jwwMSuq9WqRkdH9a1vfUuLi4taWFjQ6OioYbFESpDFm5qarEFjcXHRJnZywYPBoA4PD3X//n3NzMyoqalJiURC3d3dNlIeIXK8by6XUyqVsrS+peVksjBe2k1TiERd+hDUo+7ubp0/f95+jl4vRsqlIOHcdnd3NTs7a4P1xsfHlc/ntbe3ZxEQhUwuOhQiogQiOKhMHGiqxehigCPzHdA3ZS7U6OiosSzgLtM5tr6+boaARZPN8vKyjo+P1dvbayPSpWdQFIyI7e1tS2V9Pp/m5+ftM3KJSfdx8u4UAi6o3+9voMhROQfacJ3OaVI8ewPbgd9hbDhGcGNjQ62tJ8MeoUm1t7drenpaHo9HMzMzKpVKFpQA5UWjUWtg2NzcVG9vry5evKju7m7duXPHcG7gJu4rkw5oLmEvU6mUBSI4P5cDTO2EhfElsKF5pFarKRwOm2JXvV7XwsKC1T3y+bx2dnbs7AIL9fb2KpPJWGQP3usydDhr1BhwOC4jBQomBpcCcnNzs00QlmTFRoIDqHrz8/NaXFw0J8ii+Eh96QuN2KH6jHF1eW7gKaFQyCJM0sZYLGYpGanlG2+8ofb2dj1+/LjhdxEDZyOlZ+NgXCPDQUd6bXt7Wzs7O+ZJoa9QkNrf3zdKGLQimixQGqJDBpGVUqlkURDNAxQAODRu6ry9vW3tyRj7o6Mjzc/PK5VKKZlMWvsfqSMYcrFYlN/vbxiV/sYbb+itt95SNpvV8vKystmsKpVKw0PkM5zWIeXCwTlligLTWWnjhB5EgeLo6Mhw57a2Ni0vL9tEArcbijSX5wGd6/RncmEllzKG8DwFO6JBMMGdnR2NjY3ZsEQYHXTtUXh1+dnABUtLS/rZz36mS5cu2SRYKsd0Sy4vL6tWq2lqasok+bjEcLzb29u1sbHRoEJHV5zblAFzw93700R+mmn4rm5TRLFYtC5N9hMaIgFHKBRqYAV4PB5T1AuFQnrxxRdVqVTM4DIrjssPZp3P522vMQjcBbBbPqPf77e9i8fjxtpYXFw06Op0G//BwUEDS8HF5Xd2djQ+Pq7BwUFrsGH6L40g+Xxera2tNnpdkkGYDDHA/hAps6AnSjIKmiTL9oaHhy2SB98nQAFKa2trMyYMDJSJiQk1Nzcbtx2KGDUBOMWSzFbs7u4ahPJZ63M7zSDXk5Yx4dPt4CgWi9YBgrybq4IEcXxwcFBLS0tmiEKhkBUT2tvbjarh9iZLMqoU6vzQiiD9U6Rqb29XKpVSJpNRrVbT0NCQzbhiiuv8/Lw1bFDMI/Ks1+sKBoNGGSP9l2SfjVQdnJE0l6gol8vpnXfeMSV+DjTEe8j0vBfRD11U7C197e3t7TZzjeUWNugewxGQIoEncrEgyYP1QbmC8lev15XNZtXZ2WndRSxoNe5ym0IopvE+XA6+kyvNCOke9gqTfuHCutAVESyC0243nCQr1B4fH+vJkyeG5V+4cEGhUEidnZ1KpVK6deuWNjY2dPPmTT333HPy+XwWsQ4MDOjhw4daXV01ylImk7EGF6LVra0thUIhnTlzxhog3Hvi8mYlWZswRU6X9UFreFNTk0XfwERo8+bzeZsUTCRJJ5zLBHIxQ5wcxgvYhgIqLCJobNzrTCZjBVvOYj6f19DQkKampmxGWrlcNhpZrXbSEh+LxRrOJ06F50OBzO1o4/ky87C7u9u6ybAb8G7BdF29D/fOsofg5ZxXzh40LbI07EtTU5Nh6fzegwcPdHR0pLNnz9rngaHT29urjz/+WF6vV/F43F4HiIP6jhsoftr6ozBcPhBRLpcPb41BheoFmRh+K4cSTip4FxQcRGGgw5z2EuCHpB1sOuA+fEo2iMgTjmWhUNDBwYHpIUAVg0vq9XpNS4H3ImKh2wVjiKGh7RG930KhYMUocCsqrxg+jJN7OEZGRgyPIoVDl7O/v18DAwMNB5oI0uVlwnfGKYKX8h0R3AH/hYaGE+zr6zO4CEpRT0+PGQv+LukbxpQIln+AYA4ODrSysqJ0Oq3m5uYGKUaXoSDJxsOn02l7PvCGGQfk9XqtVdZlCOAAwdTg3TIOnc/x8ccf68qVK4pEIhYB7uzsWAG2q6tLS0tLGhsb0wsvvKD5+Xml02m70F6vV2NjYzp37pzxyF0HRLRDzYIImcIid4iONn7u4rs8F4IchPTdNleXN4rT5YLjqMgMgEeYFs3YK/jUGFueYyKRsGysWq0aj5kIGmdAUwLFSbBPFk4YA7e5uWk8eo/HY4L3RMI0LXg8HuvkgovMmYIyimYtdEAyOIqcOCLObalUUldXlwqFgv2sv7/fqG84xZ2dHS0tLWl2dtbElCjCYmxjsZjVdqQTKKG/v98yCd7vC3WakVaB5bpKYJKsKNLaeqKUPjo6av3j1WrVhkYyloJDBvZCpTGVSlkzBe10rtFFNYnNIWJCWYwHQa/+5OSkQqGQVlZW7GHhlX0+n9GowJRcJSs4fnBwSVO4NDxQIBVEalBTwlDRJBKNRu370sdfLpfNS0qy7IALK8noSJFIpAE6cClhLqmbnwGfgDPRMoxhJrXDAOJwoNeAXfP8eR/SVJYLHxB51Ot1420+evRIkpRIJMwx8F1Jwbmc8XjceKJjY2Pq7++X3++398QR4tRYPp/PLqVbhFpYWDDtgcXFRY2MjOjFF19ULpfTo0eP1NXVZYyF3d1ddXV1aXNzU3Nzc7p+/brefPNNaxklbebv0Izi7gWXWZJFlZxZd79crQn32bE/7CXPxNVaBsLhrFEf4bwQEJHJ4FjogKNrDkYA9CacL40c0BnT6bRhzMAh3HeaI9rb243yyCJzGBoaslH0BF4EFpubmw0TSPge2BsiWqAxOMDcU/aLc+IOlHTvCd2dZNkzMzMmxBWNRg2W3NjYUD6fNzrdxsaGReU4MDJJGlgo4GPAuWOnM8HT63NpYe5GVKtV81BuGx0DAuv1umZmZpRMJjUwMGC43JUrV5RMJhtoHeB0pD4Ix3R0dGhvb0+7u7sW2WFsSUWhM4FjMnqDauP169fN6HO4KfKEQiHFYjEVi0XDdsDK+G58d0kNETopY6VyoroPz7SpqcmMOJHJ8fGx4vG4zpw5Y1GY27eP1oOrJ0DHD4Ifra2tRtVhYZQ9Ho81SfCggSN4Nm60jrODJE4BDZ0EIBSMAK+BgXfxO/c8SM9E0ff29rS2tmYR5ksvvaT+/n6DU3jGOAy3FVg66RZ88cUX7bOgOtXc3Ky1tTXDell0r9VqNavsA09gcIvFom7cuGGiJJD/4anW63WdOXNGCwsL2traUi6Xs9oD0xE2Nzf19OlTlctlY+C4BHf2g30mSmW/MCg4WJ4R+yA948rCb+Y9Dg8PrUnAbUzhdXHG7hwyDAANKhgNIDaKonwe2orb20/G6pw5c8ayNwwxMF93d7fxWI+OjizAYO3u7toMtNbWVism0SwEnQ4cnOYPUnsiTwIu6g7obBA0ELj19vZqaGiooYDGfeWe4JjJQFdXV20sFbUh9hqqF+cbCAZKK5xzAjngCe7jFzK4HAyX18gLc3F4oD/96U+tIh8IBLS/v6+ZmRnV63Vdv35d4XBYHo/HRq8QIYAhSifGhJRnc3PTDC6MBRxAtVq1QhBFl2g0ahgVWgIULkjb0+m0HW44wBxkIl0uymkshuYB1/jR1dPb22tGJZvNamBgQH6/X11dXZqYmFBbW5ui0ajJ/PE+tVrNaFDI9nk8Hq2trZkmK5E7y73cOAGMIYeKKIuDxzOE6kbxkNSSIqB7gYEj+DMcD8/JfS5U6NPptJ4+fart7W1dv35dU1NT2tra0r1791QoFBSNRnX27FkrxlEx39nZUSwW0/b2tuGwYKJENxsbG/ZMWfB8ibYgorMYDb+7u6uHDx9a9xf7WiqVzMhcuXJFc3Nz2traMkoQXXZgrpKsgHlatcwtqPIcuC9u4Q15QToaubicIyiWnDkKWzx3olv2n2cAVMFzx1nRUk9hlzFW165dU19fn2k9nD17VhMTE7ZnOLnf/OY3KhQKprKFoBLF3O3tbT148EBf/epXbc/pyCKQoLhNAY/pE+Fw2ASe+M7ULhABYi9xklD+wE2j0agSiYQV6jgXYLu09aMwBqsgEAgYT3llZcXU4JgWAZOou7tbm5ubGhwcVCQSUTKZtAIkAzj5jp/X1iv9EQYXb4IVB3vkYTY1NSmVSumdd95RtVrVV77yFfX392tzc1Pvv/++5ubmdP/+fX33u9/V8PCwyeUR7WHM4dNJUqlU0qNHj3T16lVJz5SQwK/ACQuFgnK5nOGAOzs7SqVSKpfLamtrs8q2z+dTuVw2gRKXngWly+/36/DwsAFfcrl9fr/fIijaCal253I5FYtFw6ASiYTxfoeGhnT27FnF43GjIRFpcUCJCI6PT6QjUdrK5/OGnbmX221AcNNSnOLpNlMoP0AJVLOJyNxolVSVhWOgm4r3JdLn3+Bgy8vLGh8f1/DwsNraTkS9X3nlFWtl5nnBgEGjAKP661//2g4xlWKM7mn2CgaLKAgDRNYB93J9fV3vvvuuYrGYvva1rykajVr/PtEUI2ZoHuC70bbMvmBcXQd0WmMCRwXODpRARoLiHu3QNIfgzMnakEDF+fH3cbLu+XRbXcHvXWzTrWlEo1GNjo6aHGG5XLaCL2csl8tpcXFRx8fHDQwT1wFgzN1nwr4TfVYqFetQpViHgNVpSIAiKY4WuIOsEOiKwrbP51NHR4e2trbsM/IZwLwJroiSsTVomlAIhNrHuaLBCXhwZ2fHBljS4ZbL5UyNz4VFPmv9UWPSKfKw4bAEwGvT6bQmJiaM4wmj4I033tCXvvQlG75IRZaWWzbGfZ9KpaKNjY2GajSRDEph9IIz6ZW2Rw41hZpoNGoRytHRkaLRqGFCeFiKaGCeGC8OMClGLBYzfAeDBJeUIh20F7pm3MIGGhJEvhROoJNAsEaUhBlSOAGWe5mBETCYZB1QXvh9DjOGcnt724w4h5gIyW0ecAs9iPa4C4gil8vp7t27ymQySiQSeu6559TS0mKdQNFoVAMDA0YDm5mZMW2B1dVVLX+iELW/v69sNqtr167pzJkz9hko7pzGkYmgcNwYH957bGxMFy5c0NramhXBfvvb3yoSiZhyGTJ/vb29RsXiHLi0LSAht9Ltnk9ghGq1aj9j7+AINzU1mdMZGxsz4fO2tjbDVXGEqVTKokE67jD2Ho/HeLSnHSU1F+oJFDZJl9va2pTNZvWzn/1Me3t7isfjGhwcVDabVSgU0s7Ojm7fvq3/+Z//UaVS0V/91V+pv79fy8vLxskmoCKSdcn+GCsKePv7+9ZpxmdG7Q+9B2BEGj/AornLFMOPjo6sOO/qP9CR6dIU6XgENnFrCLAYkEVta2vT8PCw9vf3rbU3l8tpaWlJnZ2devr0qWZnZ632Qps+ET/MnNPByqetzxWv4VKTdkMNgyvX3NysM2fOaGRkRB0dHYrFYiYec+XKFYXDYcOQiFJoG+UQgd3iUW7fvq1CoaDvfve7kmQFNrcTjcNJUYrKMXgL+qSjo6PGp8tkMsYaIDU8LX2IoPrR0VEDhsVhdDFD/p/xHEQy+Xze8KMHDx5YU8Hu7q6SyaR5cvDoVCqlRCKhvr4+68QCg9vc3Gzwmvw3F5zoAkNMNsKfHR8fa2ZmxgQ5gsGgKTn19fUpHo9bJw4XmOjeTfWuXbumS5cu2XvzXpKsvfOFF17QyCdjpt3X4flDJj88PFQymTSdV54vRi6fz2t8fNyoQ/l8Xtls1oSH3LOJkJH0jA8rnaT+IyMj1rbp8/mUz+f19OlTZbNZO8fSCXZ85coVdXZ2anFxUeVy2aIWIAAgA9gsbvGOyAZ4AEeAsSUapBhWLpe1urpqGUc2m9WtW7f0u9/9Tq2trZbaU3Dm9Tn/7CuFQs4uBoDn4+LIMG76+/uVTCb15MkTFYtFPXnyROFwWMPDw4a3r6ysyOfz6fXXX9dzzz2nTCZjhbW2tjZrKUfg5tMKiPV63aJZbAVTrGGtkA1wZ4G+yLqg1REU0e5LtIvAj9uIIKmh2Apbgo5I7gc1Hs4KRpesZmtrS0+fPrXIuaurSwMDAwqFQqaB6wYk0u8P0Py09ZkGd3t727w3ESgYCsAxwryBQEAdHR02+ZPfd6caNDU1WUsvF4XoFuNQLBa1srLSIH9HWgT1BT5iPB7X8fGxFd4A6sGb3PQG8jwKVblczlIOgH9YBHBAXV4lerNgNRg1iieku2ghcCDBsmdnZ3V4eKjV1VWr/Dc1NVlaV6/XFY1GrUqLsArNIe5yO5Y4QHhWoB8yh66uLh0cHDSo1yMlB0SDPgZRPYeHyjfZgh2aT4ptQC9dXV06d+6cAoGA4agYP4qjcCebm0+k97q6uvTw4UPjRfM9KH4BpeTzeS0vLxsE5Lb2crGh0QExNDU1GbZHO+3Q0JBxo/nOdCsiUO9+VpfrjCOCQsa+u88DA8jUEpoL4Igyp2t6etpEkLLZrB4/fqzl5WXDOOnwIu3mbGFoeQ9Ew90RUESGPMPt7W3LCgkO6vW6fD6fbty4YUaZAmOpVFIikdA3vvENi/jJFkOhkNbX161QWa1WzYm43X9wdqVnU1IItDjv8ItbWlpMwwDYCHuAAD2dimTYa2trymQyVmCcnJw0yVG+N+wMFO0whBRo+fySzJHwPIlei8WiFhcXrd28t7fXniH2ijPqsnS+EC2MdkIuFgUBvAuHkoo/6eXo6Kji8biWlpZUKBQ0OjpqBQEwM3fCAQeb6v3Dhw8bLhYGnv9maKPb3svDIw2laMCC0YAnhU8JfEEa6j4QIk7pWVssLacYf36XfSESp3ra1NRkcnocEh4U352JABhXFI4qlYqJSrMozBBBwOOEYUAk5kbA0WjUOuNqtZpR0tgPF+PigrhRHdVk16jzvji+Wq2mVCplLaeon9VqNdMmcLOZ7u5uE+/xeDxaXl42WGRra0vZbFaZTEbz8/NaWVkxh+cuLgkTJejrJ+1lcgf4O9SfRCJhEppQzag4RyIRgxWIsnAc8ITZAxZ8TgwYmQ4wApkcnNPBwUFjbSCec/HiRcsEiMbgrZOyElzQsHNwcGC4JfUIzgiKV1T/Ka7xHaAbcq7B86nGExXiUMPhsGlNU2jj/LkRLpV6eNBMR8CZNjU1WYttb2+v0bYwuFA8CXzYi76+Pk1MTCgWi2l+ft5Gxm9tbenSpUsNnaoUY4EVwL55pm5BE3txmkXiTpQgY3F50/zeaYU1V5Lg09ZnGtx8Pm/CFXxY+Ko8bLQ68a5QQBYXFy2tisVi+spXvqJYLGbYJVxR+KqkxsyQclN9r9drWCfVdXAqDnF3d7dmZ2ctNXU5vRg5LpfH4zEjTTrC5+CBuZECC8iCz8M+QFUiZUTDYXBwUJKsSEQqWCwWtbGxYV4YChQNDG6VmiYElkvHA4/is/HnGHdJVjyTZMYVT+2mnC5HF0iDFQgEGtJoLrL0bBYVSkx+v18LCws6Pj5WOBy29yyXy+aYPR6PFTogoQeDQaMFtbW1GcYGXMFlcMeu8P6tra3GgiC6pGNraWnJmiqCwaBaW1vNEbjji8jcMEIuXkuKSl0B4+Q+EwwQ+DlUK7epgd/DsAGjkf3x3kAFvCYYrNs9BQ4PFk80h+4DGR7RnVsAp0CLg4YWhTFEFAe2z87OjuHcBAt0kLnFJukEXoKjXiqVDCKD0eRmY9Vq1WiPpVJJhULBAgeE66H4tba2WnH56OjI2ok3Nja0srJiU78l6e7du7py5UoDJdLdS2pH7AfBD+eYO+TqiLiykdwDsHyKji6c94fWZxrcZDKpvr6+Tz0Ih4eHWlpaUiqVsg9+fHysra0tJZNJS+29Xq+lTa+//romJibsw4Njut05uVyuQenevVgcVoxDV1eXVlZWdO/ePevZnpycNEV2eLgzMzPGyVxeXtYHH3ygavVkKicCGdCL8GZupCg9E+VAnJlUjIIYUQC0E4pjdIz3AgEAACAASURBVLWxF25FFE/MYaf7iYfH3DMXo0LzgIfr8oTx3m4VlmeGU2tqajKjhVg0v+cacV6/q6tL8Xi8gepEiu1iyNJJk8P58+d1+fJlPXjwQJubmzYVgzQajQkwxPX1dYso+/r6dPHiRV26dEnt7e2am5uzy4bsoZu+0v2HBsLR0cmYGSKr5U8UxlCmunHjhsbHx3V4eGi8VETIiQ75LEQvRDBcWLf9mgVjwG3qgH7l0gw5t+6fd3V1NeCBLswDNkjKipHkTNKkw94BrRCBEmVSWAKioIgHBOeemebmZoO9wL8JBrgnVPDJdN1M0oUuyBzIZokaj46OGhgROJSDgwOTjcQZdXV1mWPGudJQhboZmRufAwohEToOAFYFAQN7yXvzb7JhhM/hEEvPArOuri5FIhHTjaHG9YWKZswuQu2JBwweQwoYDAYtTT06OplSCs+tp6dHKysr2t7e1qNHj0zwhmYDPiC8v+Xl5d8jELu4WrVatbHLpVJJFy5ckN/vVyaT0fb2ttbW1rSysqJbt25ZIaBQKDS0S7a2tmppaUmbm5saGBiwg8aD5VKgLsYF5OIhbMM4bBomwJowiKQ0LicQ+cpAIKCNjY2GgqCLeWK0kQd0jQy/w+cCOyJCBvtyoyJJZiTBs8AJIX3jNPme7u+4RSKMC+k22C9O6bnnnlMoFNKTJ090eHiop0+f6oMPPlClUrG27p6eHtNghZ41MDCg69evW+cZ78PzpkLtngtJ5jSQHjw8PLSZXIVCwRpRisWixsfH5fF4TDLT1SuAqkcK6mJ9GEvOwWkeLoEAPyfKhA3B5W9razMn6vF4LCKnuEgkjZF1DSgwEGcLJyDJXsetYWA8cLqcL4rara2tmp+ftzFM4LPZbFZNTU3WJMSAALBkAgxayN2FgJI7coYiMc+uWq1qbW2tIeoltUdvlroNI9rdYaJMW2lvbzfa3OlslSYL7iR/3tvbq0AgYBkv2fj9+/c1NzdnFEM0gru6ugzagrvd0dGhK1eu6NVXX7XXR4HtC9HCqtWqFhcXbQM4GBhc1LswDvDpmpqaLPxnBlChULA0cm1tzQZPEjF3dHTovffeUzqdbvCYkgyQBs6AGkNkGI1GdebMGZPwQzugublZc3Nz2t/ftx5wJqf6fD4TduYi4ekxgLVazQoA4HKuahEH5vDw0ISVE4mEIpGIDYnEAxJFYRg5UES0eFe8Ohgf6TXr3Llzevz4saXCLq4KHxZHQeTq0tioulcqFa2trWlxcVGBQMCI3vwdeL1wjV283e37J9LjUgOR9Pf36/Lly6pUKkqn07b3T548MWWsQqGgWq2m6elpxeNxXbp0SVNTUzo+PlY6ndbq6qp6e3vtUGPU3HMBFOD1noi2gPNyBuk0SiaTWlxc1MrKilGTEomEpqenNTIyYvAQxS8uD3i/K9WJ42dhCIlkOT9EQUiCJpNJSc9wVBwpNYqenh4bs8SfYfj7+vo0MjJiURnvw364BpdCKREX34dJF83NzTYEoFQqWQQoydTQ+vr6tL+/r6dPn1omBoPg4OBAQ0ND6uvrs7qOazNwWE1NTcbeoSmBn5P1IUfK9yB6Bt+FwgU9MplM2nSGvr4+uy9uwYqgDydCEOTylAmGGBeEJgri+ETGzc3NNr1if39f9XpdsVhM169ft+5a6LBfuPEBHGZpaUkjIyOGcQADsEG0dFJJpoBFxAWOSLFrY2PDvJ6LuWazWR0dHRmtiEUVHT4uEQQFvePjY9PZZeMB/jlMW1tbSqfTyuVy6u/vN1J5vV63llpSOlIlsCXpZD4XD433xgNTwWd8SFdXlw4PDzU3N6d79+6Zo+DytLe320Hc39+3DKJWqykSiRh+7DaDsK5evaqmpibdv3/fDAApKsaHZ+A6CChFKPrDIWbSMdQXIjOcwPb2tj766COLMBFfB8LgkBPpUhgF96IPvVQqmTH0eDxKpVJKp9NWhY5Gozp//rxRx9DAjcVi6uvrM5U4N6Li4hLdbG5uGuOAPUDib2hoSKVSyfrn9/b29OTJEy0vL2vkkxlqnGkMIpVoMh6MMsaVhWF0my729/fV19en8fFxE1Ah4JBkcAUOkNSWajtk+93dXQ0NDenll19WKBQyxgpQEoaS7isgLAzA/v6+afxSb8DZVqtVnTlzRlNTU0okEtbYNDk5Kb/fr+VPZs0tLCyoUChYM4LX6/1UCVNJ5uCPj5/N5evs7DQYDlU+MNlAINCAp1KsYxAm1ElqOrOzs8pms8YtRxIRuId7hExpqVSyINDVc3AdlM/nUyQS0YULF6yTDK0GOPL1et30kCmGcv5d3vUXKpqxAel02ugcpHFEQS6FB7qVpAbeIREP3gXgv7m5WeVyWRMTExr5pM2OKvbpFk4Xo+RhgnlCsCaVj0QiNpmTDaFyjQclfSOVhmrDmJWjoyOrYkuyPno4esACFDDA7w4PDxUIBDQ9Pa3m5mZLRcB8pROccGlpycYLcVAmJycVDAZNMxRu5cbGhu1FW1ubrl27pqOjI92+fdvwTelZais9gxqINrjsGGWKJz09PQYV0TrJc0UjYW1tzYzaV7/6VYt4OAdEgbwGB5m9bGo6UYqjkQON0UQiYRVpjDJZyfvvv2+FIPDv3zu8n+w5KTStuNKzLjQWNDGiPbqZarWaBgYGjBmSy+WMbkTBlT0l8uEcs9ymIAw0BpVo+vz58zZVmDSZiJqzury8rLW1NUvf9/f3FYlE9OKLL+ratWsKBoMNPFKKb9IJHYvsDSMF3XBjY8OMjdd7Mv49HA7b7D9U/s6cOWN37/Hjx3r48KHBPbA62Ev+HhEzC+1nMFKytPb2duN/V6tVcxxwdHkdGDl9fX0aGhpSa2urOaVsNmvi77Ck3KIYC/YHRT93IjJTnWEbYfypNTDOBwiG79TT06PDw0OzKVBIgT9pgHIDxU9bnxvhcojcLjMWaSQHEaMIi4BIA+/EYSSCLBaL6uzs1PT0tKrVqkWyKGexSGHhOOJVmaIAnkNFEVk96VlVm8uJRyOqw3HgBADns9ms0ul0A7fPFe2hRXNvb88i7ErlZD793bt3lUwmTVGIriKv16tSqaTl5WU9fvzYIAnaHrmIboq3vb2tVCple0Gh5ZVXXlGlUtHt27fNibitrhgFKEK0juKAyCwYV+/S1sCJoYO53F7pGS2MvSO9hZcN4R1cnAYSN8KSZHKUjEtiivKdO3dULBYVDodt3DVZwuk2UtI5vidnwYWAOFdkWmQmYJo4HFgScIbds8rvcQZcBwDkhpHBEHAPPJ6TMTXT09NGaVpfXzf9DYwMEzokWaR55coVGxPT0dFhHXl8H+4jVDuYCvw/UFWxWLTPQ6axvr6u1dVVra2tKZlMamZmRj6fT9lsVqurq2awu7u7G3QMCGjYezfadwvoMCYICIBaCoWC7t27J7/fr0uXLuny5csGXQHFTUxMqL293TBmsGAmUXs8HqvNQOlzo34+B23SaGbQPerCQ2h8c+6BHKD6ca9dRotrWMG1TzNoPm19boQL3ghfkosMzoo2Jt7CLRbRv89GYEz29/dNw5YOFNI2DKPLw+USU6jDkJzm27nUD9S4gDHcSiV4IpEW0IerUbC6umodSXz2Wq1mrbek8UQy8C65bPwuHSt892KxqEwmo6OjIytYNTU1GS2K1mGipNNqYRiQ3t5effnLX5Ykvffee+rv7zfxGw4DRtWN1IhyOZhExeDieHbaLjHGrqED0uF7gstRJMHhke6TzjJZFmL73NycpJMUHuJ9Op3WwsKCiZgQLbudSSw+OwaRaAjIh3ZcDCjfnVE09OlTWSd1BBOkVuG+p1vUdD8HKTJnlIu6u7trAklkMNls1jrt3CkYNGmALQ4ODmpwcNBSWLIAIAKXsUEhEsiH4MRloXCnaZpwR8TPzc3p5z//uXWr8XlghuDwoDwyOYI/ZyGRCmUTwRigo3q9rnv37hn/PRwOa2JiwmQ54Rq///77ltlRoA0EAopEIvadOOs06LCAL3nParVqWifNzc3mkFx6I3oZQCI4cLdugR0AaoKRAkX2C2O4HGaMLoaOwo/X620A9knhqHS7OBjFMS4kfNBarWZyjj09Pfa7Ltl/bW1NoVBIPT099ve5gGCEUiOpmfQWviO0D9IB96JQFALmyGazWlpa0s7OjqVssDHa29tNvxfNTOkkAoZ2sr+/b1oN7GO5XNbMzIwpltGZB6cQLikHuFo90YtIJpMN3hTjS4/9G2+8ob29Pd2/f98uNp+JrIMCDjxpt8CDoSSNyuVyCgQChunhKNz0nGiDKblgcGC30LJc2hmGxK2gI41Heytdhjs7O4azE2GfLtKxr7wvkRjt0BReiLS2t7eN4w39EF4uMNX29nZD5x3ZAN8fQ+tWxN3P4oryuFAbTgPqVTQa1fj4uBWNueAudY+7xfuRwlLEZcwLZ5+221gsZtkb8BAGB4gpl8spl8vZ3UaL2DVEpOnQo8g2IpGItbIDTbnPBIoW2cD6+rqi0aj9fQIeAqcnT55of39fiUTC5gGWSiVVq1UFg0GdO3fOGCjsBZky9xqHwt0fHBw0Y0w0nMvllM1mbTp0uVxWZ2enfRaXbsp5cxseyPz4mSR7ztxLjO9nrT8qwsUoud6SQ8KFxJi5HEFSEiIY5AxJUTiEpEiIy/AwWFRXOSBMAqBKDQ7jFqYA04E9wF5cWhQpMNxUCnlzc3N2IEntiZpQmtrd3W0wmuBjRFLwSzEaGGhGhhPB8voYe7zs5uamksmkKZ+xwIs4GD09PXrrrbdUrVb18OFDY0VgpN1IgKjT7abjOe7u7mpxcVHv/3/svdlv2/l1/v9w02Zt1EaK1EJtlix590xmcWYmmSxNUzRoUqRNgrZAA/QiAfoH9C73BQr0Jgia9i4octO0DZA2aZpvOzOJm7FnbI9tWda+cSdFkdolSuT3QvM6elNNnG+b3+8uH8DwLn34Xs7ynOc857/+S11dXZqamjJJTeAkN9pHcZ+iHOfD9fqk02QXRGQHBwfWAcaaJRIJoxNBOSJK5VKfx+pc4wQk1dDQoFKppFwuZ+yLcrms7u5uXbhwOgqdwhl7Sq2BH6SVpNA4Hs4cQQQP78i0BC4wtQCyQpcf3djYqJ6eHlt/zihr7RafOH+cHbe11E1zoRYGg0GFw2G7/M3NzQaZ0VIPWZ/7RxbIHa6vrzeRp1gsZg4bdSxYNi4vGJvBmfP5TkdQLS8vW2GY7+lmCRSy6CqrVqvWGr+7u2vcXzIZCovcI5wczseFQDwejzEucrmcQZIUj3lfivs4JQwtd5Kzjf3ASRPYwaIA5/5lj8fFX37z/Ob5zfOb5zfP/3/PcyPcz372s9X9/X0TJkGFnqo+88Xo1nI7V8rlskWu0llLqovruYwFWAKFQkGPHz9WpVLR3//933sk6Rvf+EYV3QMk5igMUSnN5/NKpVLa2Niwtsfm5mZFo1GFw2GNjo4qHA4bZzaTySgQCOi1117TCy+8oN3dXX3rW9/SvXv31NPTI6/Xa51Tf/M3f+PZ39+vvvfee/qHf/gHNTU1aXh42LBUIICTkxNLzUnTmDNGsQYcGZ4y0QHwBuI6UGiePn2qtbU1W4vf+73fqy4uLmp6eloDAwP61Kc+peHhYcsc3HQPz09EDMaMzgQcRrc1k5QJHVoKWX6/X/l8Xn/2Z3/m+eQnP1llBMvt27f1+c9/Xn19fXYuGO6Zz+eVzWZVKBRqZt9RRAUbhsfKe53PrFwo6+DgQH/5l3/pkaTf/u3friK3yd+5hVvOm9skAKRENEiRliLJeSzOxa/Zn4sXL2poaEjf+MY3PJL01a9+tYpmAVmUW1wDp3eVx1y2BDgxv6aQSXTFZ2eqr9tE09raqu9+97uey5cvV8FNGdYZDAY1NjamgYEBiw6JLIm2aUSBo721tWUsA1qroW/yvtDOWMf29na98847Hkn68z//8+ru7q4xLtbW1vT666/r93//982GFItF3bt3T3fu3NHJyYk+/vGP65VXXqlhtrCvrA1RPwV6oASicrry/uqv/srzk5/8pMp9ZO9J/8HzCTTd+YfpdFqDg4OamZlRe3u7FWE7OzvV29trY9qBWeh4SyQS+sd//EfF43HFYjH97d/+7S+dJPlcg0vaT+FFktEqCPHBD8FYWltbFYlEFAqFagodpGt8PfBWjBMFJrAqFxdyU1NA/4OD05lkkUhEgUBAkUhE0imWRRcTFzYUCikWixlOSqEnGAyaIpLP59Pg4KCePHliVW63Mu/z+RQOh2uKVsFg0Awuh9FN9cCNS6WSpVGVSqVmwgQFEvBE0lt6u6kw8wSDQcOSJZnR9Hq95mhOTk4ML4OcToqUTqeVSCTMOLupITikK5VJt5pL5UFgpL29XaOjozZ2JR6Pa25uToVCQel02nSGmd7sFikoTrH2nCfew8Wh3c5Dl73iqshxPvhB6sq/cVtQMcx8fQzc4eGhaRtwkXGUpJS0bLsKbjgHfi3JdG75LDhlnAMsCpwJZxt2ARoUGGIKZei3Is7De+BgMNCwcYBVqtWq0aR4cH7BYNAwchwH64IwP+l0sVi09Btc32VsuNAVd2x8fLxm3eBVd3V16V/+5V/01ltvqbm5WZOTk1acZ9/Ze5wYEAxOwYVUeIAqqfHgGCj+sUf0DzCiq1QqqVQqKZFI6NmzZ9re3tbc3Jzq6+sViUQ0NDSkcDisYDCoK1euqLW1VYVCQYlEQqFQSIVC4ddr7Q0EAuZFqRTSWw8mViwW9f777+vZs2daXFzU7u6uQqGQ9cQzVpqFg1taKBS0vLysmZkZJRKJGnpPPp+vOdBEHPz/xsZGm2Y7MjKipqYmbW5u2sRejBjAeH9/vx0csFw4qEtLS9azv729bfq9fH4Mrssp9Pl8unXrlpHt6bUmqoVYT+QOFYrIg+4nNCPq6uo0MTGhqakpq9bCLT4+Ptbi4mLN5QbfYphmoVCw7i3aF+lyC4fDZpQY0f3w4cMamhS6pu5kV6IRl394no86MDCgS5cuaW9vT48fP9bCwoIeP35spHqMbUdHhxldDAlnAAfGRQffxDGjJ4EQjItn8/UxlvX19TXapGCRZEHsq4td07Ho4ss4S5cKie5qU1OTCYe794S14eu6hSdoY9wn6Ihg1NRAYFBQW2DdXcpaX1+fFWNdBTUKe/CeOzs7NTY2pqmpKY2OjioYDJrBxjlub2/X1BlwBNw35DD5TDTtbGxs2Hq5wlP8HkUzKFcU7cB76Ux94YUX1NDQoKWlJRO6cSlnOAGXqijJlAmhJRK0sfcUPgnytre3rTOUMU2Mu5qfnzfaJGu0u7urubk5NTc3KxKJ1BSxDw4OlE6nawrBDx480ODgoD7/+c/rzp07zzOpzze4LS0tCofD6u7utoPg8/msWnp0dKRcLqdisWgeBBbB3Nyc6urqFA6Hjd6DN89kMnr48KHee+89FYtFS9EjkYg2Nzc1PT3937pXSA1dw0z0mEqltLCwoM3NTcViMdPLDYfDVhVubW1VPB437iMiwgwN9Hq9lpazwBh3DjRKSkdHRxoaGjKNgKWlJRtMR3suF4He72w2q3Q6bVEw3WYIhMDfvXjxol555RW99tpram1t1dbWll0wLrTf7zcFLxoqMKDHx2eaEVyGvr4+o+mwfhSPXKiHDARjv7+/b40FrIEkO2gjIyMKh8NaX1+3uVl+v19DQ0N2mbk0VNe5xMViUfF43IwoKR/pK3PPoJMxjsXVUoDx4EIf0tnUDgx7IBAwqhcZBpEUF5l3AEqhAEN6u7W1pWg0auwTlOC4J3t7e9rc3DQ9B74Xzr+1tdWiO5TaiNQQ03ELmBTQ3KCHhgWgIRqAeIjum5qaNDAwoOvXr+vq1auKxWJmyIAm3EkbkuzO8L2Y9OtqPRwcHGh9fV2zs7NWdIIGxuNyvilQUTzb2dlRX1+furq6TJToxo0b6u7uNl2RcrlsVE9Xt4KAj8ACg07RDOfFOrg8W7RCFhcXjV/M2B+cGTofFAlXV1fN0eP0yMLQYjk6OtJLL72k/v5++Xw+RSIRXb58+X9vcIEHWltb5fF4anBSOmFoSCgUCnbg+SD8HXjRycmJ0um0njx5ogcPHiiZTNrBj8fjRm2BysPDwpOe0V12cHCge/fuaX193S4IsnFceoxELpezyIuLE41GzdA3NjYqkUjom9/8ptbX1y39cbmOgUBAly5dUjqd1ubmpv75n/9Zd+7cUS6Xs89O1ZdNZxx3NpuV1+s1+hsMA3A0Ivq1tTV5vadztN544w2Njo5qbW2t5h1I25iLRXdaOp02buXR0ZFBLvCnS6WS4YBQko6PT+erEZXAE4ZeRtsj+yLJKGGjo6P2XhgxRimVy2X7fuhmkDaDg+3t7dmlBlucmJgwo4QBIirB+PAwQSOdTltLNdEk3HBJ1hHkjrTv6elRIBCwLIQUFc45jAT+fzwe19LSkukMuJMn2PdQKGQOj/UlUnPfn9Zhah1kVzgcOuxcXjuO0GXWtLe3m0whBgZMdWxsTFeuXLHZZTi+9vb2mh/z8/Py+XwmUkPqH4lEjJqJI+b+RiIRJZNJnZycKJVK2agrSVZDqVQqpjRIFkvLP1nU5OSktc8SxRJocUbIQhh5D32QMwz0QWuzJHMAfI1sNqsHDx5oeXnZbNPOzo7du+bmZg0ODiocDuvk5HSQ7ZUrV2owdu71+vq6/VuM9fj4uN555x2lUqlfr7U3Eomos7NT9fX1JgCzuLioZDKp+vrTkRSXLl3S0tKScrmc4VLgTmBlXISNjQ3NzMzo2bNn8vl8unLlivEf8/m8lpeXVVdXZ8U49zATGYL9IiFHpOj1ejU7O6vOzk7lcjkbWQM9xO/3G5EcXBdYhIizu7tbXV1d5ghceAFjNz4+rsPDQ92/f9/Sh5GREVUqFWuogDbS3Nys3t5eK2QRyRB9MU5me3vbNBRY80QioeXlZfX19dXMEiP6LpfLJiK9sbGhZDJpgjUHBwdaXV01bVKim83NTVFwOzg4sHExOBYwvKWlJWWzWWuvPDw8NB60JKPptLe3K5fLaX19XY8fP7bWRlLvYDBo5HmiRDDilpYWDQ0N/TfJRS6gq+kKzQfyOo9rJFF3kmSfxe0OognB5/MZTauxsVGVSqXmvTF21BmAUqD0gY+67B6iQIo3KGOR0QUCAZu4wQVua2uzqJHCnNuXz2gX7hCtvAQToVDIzoOkmjOHVKnH4zFNAlfSk88IJQ68lKYGBHBcHJTfo4Hb2dlpUSzZiySTRWxsbFRHR4cVkq9fv65Lly4plUrppz/9qY6OTidXXLlyxe4s2DHZATALsAEYO1CYW6je2dmxRgkw+N3dXVMDS6fTZkvQYyEYRGO3VCqZQ25vbzd9Ec5OKpWyYnMqldKtW7dMk6VYLGppaalmMsovep5rcIeHh2vmFlEI8Hg8mpqa0pe//GV9/OMf13e+8x09evRIktTT06ORkRE7dBRiAoGAMpmMSqWSjT4JBAJaW1vTgwcPFI/Hlc/na1T17SU/TCfw8MFgUL29vdaSB5G7t7dX4+PjFl3w3icnJ0Y2b2xsVDwe18LCghYXF22KcHd3tyKRiLa3t1Uul42ozcWikuvz+dTZ2al4PG4D9FZWVgxOCAaDloY2Nzfr1Vdf1cnJieLxuH7+859bpNfT06NwOKx0Oq3JyUldunTJIggaGIrFooaGhtTf329rQZ89vN69vT2trq7q+PjY0h/wvHK5rIWFBd26dUuXLl1SPp9XLpdTLpczpaVQKKSenh5rIcbQMKUUzvHY2JjGxsbsPcBUU6mU5ufnVa1Wde3aNeVyOc3NzVkkmMlkaiasEm1B3Kc+AK6WTqd1584d7e7uWkGTqbq0afJgPCqVimVjGFiPx2NjlWhC4CIS/VJIc3UX/P7TcTxNTU2WERHtBQIBi8pcHi5RLXKdCGljWImQ2BNXzMWd3kBBCyeJQAvBDgUdHKvbAk+rOZKK9fX1yufz5ojJSD0ej7q7uzU8PGxdni7nmM+ztrZmIi5+v98K4UTo3d3d1iHZ1dVVsxa0Mo+Pjxubp7OzU21tbVpYWFClUtEnP/nJmoKcJBNLIuuF2YJTGR8ftwyGiBPee7FYNB2XjY0NeTweFYtFpVIpg1lmZma0tbWl8fFxtbe3y+v1WicamSbrXqlU1NnZaWtzcnJi0q79/f1KJBLGHx4bG9Pw8LDeeuutmoatX/Q81+CCYbqYTk9Pjx4+fKiGhgatr6/r3XfftRZNlLOuXr2q+vp60yElDdrb29OlS5e0u7trgxPffPNNazRwIyAXFyIVCYVCpmcZCoVM3Wpzc9NmHRF9uB0psBWIuEgnDg4O9PDhQ83Ozqq+vl59fX1WSUeq0MWCEomEvv3tb+szn/mMQqGQIpGIZmZmjC5ULBY1Ojqq1dVVw0sDgVP9X7DYrq4uE+amyt/b26tIJKLe3l5rOUbSMZ1OWzQjyYp+HEq8+IULFxQOh5XNZg2bBTvf2tqyijXFsVAopPb2dsPeA4GAVlZWzOuTTZRKJRUKBVNeks6UqaCavfTSS5qamlJHR4fm5+c1MTFhONzq6qoVSsCewdElGYZLND4xMaGVlRX5fD6Nj49rYWFB77zzjuGmbhWYCjxQBc7Q7/eboaCyTeZEikpXFoWcbDZr70KU1dnZWTNdFufAzzwUgNG8aGhoUDQaNYy+Wq1aO7fb3YgBhgJIpB0IBHT9+nUFg0Hdv39fx8fH6uvr0+TkpO7du6fNzU2tra0Z/i/J3gto4Pj4WFD3gPTYe6I2jAnyieCTtPrmcjkrskmnAvMTExOWJbqKXzxACZcuXdL169cV+1ATm3Hk0WhUTU1N6uvrU2Njo3UBctegFPr9foMuaeZwu1oplMJwopDGfvh8Puvyw/GQIXR2dqqhocEYPDRblUolM7hE/gRQkhSNRo3VMTk5aUJHRMKXL1+uydb+xwbXFSXxVmTMfwAAIABJREFU+Xw2sbKnp0epVEqZTEYLCwvK5/M28XJkZESRSMSoUScnpyO/iQjq6+ttAgSdGUTDwWBQV69etUPI4/P5FIvFzCsz+oJohmoih5uNQ3qQr0fxZXR01CTq6uvrTS1/eXnZ0nrk7dhENnd6elpvvPGGYZowJXZ3dzU/P290qO7ubhPJYdoE/ft+v1+ZTEbT09P2ucPhsC5dumTRD5E+KlZUxSkUuIpfMBvgScNigGHQ0dFhlKNIJKKtrS1jeUDFkqSXX37Z9giOKheZw8YTDoc1ODioUCiko6MjJZNJpVIpa48lOqJKTUpM4ZOLhcgRc9c8Ho9ef/11K27BJsnlcpbiuwYXPI/iCVQqOMhE4ugWkJ6jskZ7NsVe8G8MSU9Pj/r7+41dw/u7hn9+fl4rKysaHh7WpUuXLFOQpImJCR0cHOjJkycWsRcKBXNAYLw4jlwuJ6/Xq+npabv8SFP29vZqbGxMy8vLWlpaspReOuMIwyDCmBONAisRxbq4MTALqbZLo4K5wjo/e/ZMuVxO0WjUJBrdWXNer1fj4+O6fv26yZ9yv13xb1cegJFOrC2yqMVi0Zxgd3e3jWmCp765uVlDGeWu9vf3W6a6tbWleDxudC4yJrrZiOyh2zFIgWyc9YWfTEDX0NCg0dFRO0+ws1xO+//Y4EIToc2QCxOJRIwSls/n1dXVZZNYSTs5dHD3SFN9Pp+i0ajK5bKNrd7f31c4HFYsFtPNmzdNEo6H6JRmCVdDF3UkoAOaDMDnXM4j7XgIb4RCIf3BH/yBsQySyaSePHli780IHdaCZ2VlxaLSSqVihaalpSUlEgn5fD6NjY1Z+sxnAPceHh7WwsKCpTtcwrm5OYtauNzt7e3a29szgwuQz8PMM1K8zc3NGgWwUCikkZER46P29fVZsZP0jYvV29trvfTgdNCZvF6vTVLu6enR5OSkurq6zKGiMJXNZu3SNjc3Gx8afQxwTJwJDqGxsdFobUTcrP/g4KBisZgk1RQlcL6Hh4fWXMEZZd9OTk4MV3Q5r2QKHo/HnBCt1JD+OU8HBwcGvaBj7GZgm5ubFl2CqxeLRTU3NyuZTCqTyRiFj4KXW6cAP8VIZ7NZzc3NGV/6woULZhQ6Ojo0NDSk0dFRtbW1aWBgQJIsbQ+FQnbxuUM0DGDUyCC513CuKVwhOEPQ09LSYkEUdDngmePj4xqhqXA4rPHxcVOAYw/YV+msvZeiIYVCGhy2t7eVyWS0v79vuPrCwoKkU37z9va2nj59amPlqS8AbXDnqFlwB3DErgAXDSo4awrCoVDI6j/UOFKplGZmZrS6uqpYLKZYLGZtwnt7e7ZXz3t+pcEF92ExWAA6zCgwwCtsampSPp83w8cmSrKLAThPMaGlpUWDg4NWwXe9lXQ2/BCupXQmrENER7XXVUWiAu0S+THM3d3dWl9f187OjnWjJZNJzc7OGuYnnc24ItpraGjQ2tqahoeHVSqVtLGxoXw+r3Q6rb29PQWDQeM+uuOX+f4YQ3iDpEzQiXZ3d5XP5013wu/322h59oTOMTSA4aISncHRlU6LUP39/VbgIvJGk2Jzc9M8N2uNzgF7QQEDgzA4OKhr165pYGDA0uNEIqG6ujpThGKt3EaXzs5OM3hAKugnEGUVi0WVy2UrvAEVwMhw+/aJUrlgGIPz+CqFH5c9g4GmLhGLxUw8BUOAg6cBoLm52Ypb7nsMDw9bkWxxcdGKZ6VSycYMBYNB49a2tLTYuQJ2grqGkcpkMspms+bAcZaSLCNiSKd0muV1dnaqq6vLoBZog0SBGJmjoyOjsPFnOGiMMsLvjJAKBALWcEMhkHXirEmnET00KZp7XMiH/ZBkdEbOHuesWCyaVgdnp1AoqK+vT5/+9Ke1sbGhlZUVxeNxK8xBU5POComVSsUmU7gMp729PTPOruA+2igtLS0aHR01XB3ta4qOMIFSqZTdSbLN8/Wn889zDS5VVA6Gq8rjFhwAnYle4Da63S9gqgDPzJKCk+dSL9xOI0kmku0KerhcRxaODjWiOYj7LBQwAEA9TQF4ucHBQd25c0eZTMYiELdoFggEdPPmTeVyOa2srGh6etqiNIx4d3e3YrGYtR3z7hgGIAGYG3D8OPDgpfwfV4hGkq2zdGZgwOeamposMkEBDZzW7YCCPQGjAfwOx0TjRV1dnfEe4eVKpykbGGW5XFZ/f7+mp6dtXJBbaeZzILeIPioUQIwo6nE4b/4MSIuGFkRQJFmbOeIybkMDEANBAzgh9QRGsPT29lonJfzulpYW+7+sOY6SFNwl+9+4cUPJZFLr6+vK5XKm/SvJcGCiSgq9NKjwfdF57e7uVltbm0kVYgzYF/YTMSAMbnd3t0XgLgzGOlHV393dtUm6rJF01izAeYQ3DEOENBpIDGrVeQODcDsRMDaB78O95WvwXq5oD52k4N0nJycaHh5WT0+P5ufnjcFD5E0LbjKZtM/S1NSk8fFxa0DiXrlt3QRwW1tbRusjC2psbFQ0GtXjx4/14MED9fX1GTQTDAZ1eHhoUzCOjo5qWB/Pe55rcElFALR5QWglLl2LH0QIQBFESGAfcA3diRBEqBguQH4eukqgm7kGFw+KF4WzKMk2kOjJ5TkSYUmqaSl99dVX9b3vfc823C2OeDwe3bx5U2+//bY++OADw7Voa75w4YJ5STw/Bw7jA9hfrVYtJcdwQuwG6MdgukYGzJmvhTH1+/01lxmnAv0F48hBQ1+0u7tb0WjULhXOCYPBHlLlxgD19vbq8PDQyPNE80AcqVRKm5ub6urqMsPa2tpqWhgUM4muyIQoULj6CXQs8Tl5aE1FctJVFAM+AuJAM6FcLlsLOvvjUqNw6mQNrqoVZx14gAfZQiIyInNabzFk3IOTkxOboAJO3draqp6eHgWDQRWLRXV2dtbgrEBcfD9XO0OSfS23hRh4iEyKlmxoXDhcnDZ74vf7tbm5aYaHegnBjbtnnA8eJpiQpbAnGFn2lbMFxdDn89mf095PBgNt8+TkRJlMRtvb27p27ZqGhobMgbhOkPOC6hxNOExqgQvs9/sNBovFYmZHsHcej0dDQ0PKZrP66U9/qps3b1oLPpov8OuvXLliBbnnPb9SS4HUgY3k4BHt8meuGAiXk2iClASDCq2JDaF4AIbCRvNgKMBH2BiMLZEIf44IDhN3IdETebmRDLJwvP/k5KTu3r1rC87nI9qKRqMaHBxUMpk0HJA0nEYKUmU6o8rlsm386uqqLly4YHKERD5UqDGUHEAcXs2mOd01Hs+ppi3REtAP+qdQi6jier1eJRIJ5fN542uSOrv96qSXrA+FDuk0bezp6bG25oWFBa2srKijo0Nf+tKX5PV69f3vf99oVQ0NDerv75fH41EmkzFjRMMBxS0uDOeES4oDdo2tJHOYRK4YZKJkUlYKZgQJ6PCCjVIQcwVctre31draamPvOWustwspkEpCLaJmQZTP2eUcAisQmABrlMtl4z8PDQ1pa2tLqVTKvh6fGUok30NSjR4E55XghX1EIpXPsb6+rpWVFVWrVfX392twcNBoXkT4bmBExMldYb9c5wN0gePjfQhEtra2LINkP8DiyUQIFEqlknWLcjY5927B6vz957NDkSQCBQJrbm62O3L//n2NjIwoGo2aI8KxMm/ws5/9rL75zW/qhz/8ockVoGWRSCQsCySoed7zXIPLhWehiRxI7/m9C3ajkOXiZdKZBgBeFbFm97Lz6729vZpqnysig1EHBuAduLiIvayurqqpqUnRaNToUW4rp8fjsdSlWq1aFE1bpNvd5B6kxsZGu/xoqmLsYh/qhh4eHqq/v98MOkLo8B6Pj09HvUNHIx3G0BBxg0+7hoZ0kqgAkjrGx+WbnodR2tvbFYlErI8ccSIEeEjfGRvEz6R+GJnbt2/bfCoE0qHy/OQnPzG8GlwrHA6rublZ8XjcIk5aq13HhZN1HR3rD1f3l51Rt+8eY023287OjlGAoEmR/tXX19sF5ILSJk6TQTQaNaEf3sU1uESx1A1guABDsRbsT1dXl7a3tzU7O6tKpWJZDAaqt7dXExMTqlar5pDZJ1qeOSMwYtCLADelpdzr9dbwVcvlstLptJ49e6bl5WWbwIF2RnNzs6ampvTCCy8YP3tnZ8caIggEXFqfa2SgLEpnAyW9Xq9lsLBZsCloV2AbiOIvXLhgcADYNp/v/IRizirdqdxLxHqADFwbRKsvWavX61U8HjeWCNkNcFilUtEHH3xg7KDBwUFrgnKDpV/L4HJ48e6u0AbRL33yGxsbxtN0IYHe3l6NjIzY4hYKBUudSYk5wES/boFGOlNjIiIA+HdTCNfYA2ccHByou7vbxGM2NzcNm2lvb7fLSCpJLzlVa5yJJHMGVC1DoZC9LxFnXV2dFb3m5+fV2dlpPFaYBz/4wQ/U2tqq27dva3h42HA/IhZoKqwLv+ZxFbJwAC4ks7u7a5oNfX191rkG1NDS0mKjyNfX1zU3N6dLly4ZHooBA9uF0yqpBrdEH4Ji2dTUlC5cuKB79+7ptdde00svvaRIJGJUOL4XF7yjo0MDAwMKh8NaXFw0HNlVFQOLJQ3FwfJsbW3Z5ceJcrm3t7eVSCS0tLRkHEkibBopaO1sb2+3SKynp0eZTEbvv/++8Zz39vbU29trTtPFTt3z6dLgCBLcVmFYIZVKRY8ePbLzSDtsY2OjZmZmlMvl9MUvflGXL182CIR5Z1x4oLL9/X3rmKSgQyDB5ccBn9cngenDWSuXyyarSfUfTJostVKpWIEXGO88hZO/43sEAgETlsrn8/au77zzjpaWljQ2NqbJyUkdHR1pYWHB6ifDw8MWjYObumeA9yaYYg+BzSjI7u/v2z7z2YvFolZXV1VXV6disWjiV/DQJRklUzrrMUgmk8aKwFYsLi7WZFjPe35lhIshYoMZc8OF5+XdgXi8zPHxcc2oGXAV1LRIw1gksCdgBncTWeTj42Or9qJaL8kMXWtrq8bGxtTa2qoHDx5Yk0Y+n9fMzIyam5v10ksvqbe311LNkZERixpd3AgmBGvB5R8bG1MikVBbW5vNZYMlgJbDnTt3NDc3p7a2NpVKJZMqfO2111RfX6/JyUnDQh89eqT19XWrkAL6Y/DdNmcq2kQ0HDQuGReGdcVwNjc3m4BOtVpVd3e39vf3lUgk5PV61dfXZ5AEDhEMFWPCnni9Xq2trenp06fKZDJqbW3V7/7u7yoSiWhqakqRSMQO5aNHj0wshOgWihkRcCwW07vvvqvNzU2jH7HmLl8TB83DhF7+Lc/e3p7i8bhWV1clyaJH8E721XVmRKGtra0aHh7W06dPjS/uavhyF1wjg1EAKgG2IGtzDTIz7u7du6fj42P19/dreHjYikYrKyvKZDL62c9+pq6urpo0njSbPXKzjoaGBh0eno5IYjIEd5Z2VIqwra2tCofDNZxmYBboZWNjY9b8gyYGn9Wty3AneciScDhuRonaIJxaVMOampqM0z8zM2MR9djYmNEFR0ZGrHGFPXMFboAR3P3gB8VkJs/A9OHO/OhHP1IqlbJ297a2NoMqNjc3NTk5qY997GMKhUL6wQ9+YBm8JKOPVSoVqyc87/mVI3bAteBEAmhzABjBzEhjDB8RCtHq8fGxHj9+bMpSGEr6rt3oluGKPC45301PONDl8uk4H6rvqI/V19ebfi8dL7Ozs/YjlUoZsR1viWcmheEiE+FWq1VLzUm5MJJ0OvX39+vSpUtKJBLK5XJ69uyZNjY2FI1G9YlPfMJETsCxxsfH1d/fb7xCsgf4qa6RATsDikDAg04ZWpVPTk704x//2ARs6uvrTY6uWq1aq25LS4tWVlZULBaNZuQyTMhWKCxJMk4oEQRNFrOzs2pubtbTp08NIvjud7+rxcVFTU1NWbW8Wq2aehQddru7u8pms1YUJNp3CzBeb+3gRD4754HoEcfi0ngQJOIzSTI4CfpgIpEwTvLt27cNo6a9Fs5vsViUx+PR9evXzeC4OCZFJfBwNxIrlUryer0GGTCuHL7xV77yFaXTafuaTU1NJjNKJudKO3IvoPm51fjzXHbW1u/3W3ETGIFgiGgcVhGMDTSdoTBKMsaE6+xcY8e/x+nQ5gwF7hOf+IRlbHDSDw8PNT8/b0LrlUpFz5490+TkpGlsQP0k4qYIjBF0YSW/32+NI67GQjweV3Nzsy5fvqxkMqlcLmfBYDqdVjAY1K1btzQ2NqaRkRHDttfX17W6uqrd3V1bu4sXLxr++2tFuER0YJ8Md2toaLCWvKOjIyu+NDc3K5/Pa3V11cLxZDKp5eVl6/nnYMEYIG2WZJQUZl3xuBEOVVGKSQh80K6KoQ4EAhobG1MsFrMoifbQdDqtjY0N6/PG05Gq4/kxZNLZbDCggxdffFHJZNJobnT1MH0XSlg6nbYGiIaGBrus4M6FQkHz8/OKxWKamprS2tpaTZGPSMW9zLRbUxF1xTuQEYxGoyoWi5qfn1cymazhPMLTxFCm02ktLS1ZSzNRNHQl2ASuUYGpAkMjl8uZIlQ8HjdNgnw+rwcPHmhvb8+mCvt8Pq2urhoveGpqyhxcoVCowZNdLjdRIo9L3icb29raMh44RmN3d9eGFQYCAVOK6+rqskzm2bNnWlpa0uuvv65YLKZwOGwFVKJxjMvGxkaNpoPL23ajXYTsMcAUOdva2myCL/Qir9erq1evamBgQDs7O3r06JFJGc7OzlpNhICA84HB5Wf2DfoaGRuO28UnXWgFjJOgggias0ABnPMIU+O8wYVni5NxGUBAkPw5Z6qhoUFjY2Nqa2vT+Pi4ksmkUdL+z//5P9YlSnGSorfL7HElNVkrzi01BaBGYKLh4WENDw9rYmJCr776qp0fMrFLly5ZQxFB0M2bN03mtbu7W9XqmQxsoVCogd3+xwaXyNPFcuG8ocwzPj5uEZvf77cW2729Pa2vr5tOgtfrNQUviN8bGxuWNmOAAO/dB+yKtIQ0hR7o1tZWDQ4O2obgQZFB5NKdnJxobGxM7e3tWl9fl8/n09bWlkVRdGplMhmLnjF2MBkoRLW1ten69ev6yU9+UoMfPnr0yLBgF/vm0jHeA3wQRavFxUVduXLFik9cTg46D4cbepJbeKDrB4N65coV4wqWSiXrTOro6DDY4uTkxChI+Xy+hvlAxONGUdJZFyEUoLq6OhuX0tHRoaWlJUmySM5tieSiDA8Pq7u72wojY2Nj1rVGQwdripE9DylQZOTsMTySIhMVbbi4RDiFQsHOHZHw9va2iewAl01MTGhjY8M0QdzCK5xPzoZbLZdqmSSuAP/U1JT29vaMBsbXg68bDofl9/s1PDxs2ZskLSwsWLRO+krGwN5AFwwGgxaI4PjdwAm8kUCE7jPgM74vlEfG8JBJBAIBU5oj6OHB0GFkaYwhQ3S50XDuOUOcSZx+LpdTT0+PGbbz3YJHR0cmzIMR5f2bmpoMtsSJcH6gwUUiEbv3rB13mztH1l0oFOT3n6qlXbx40dg7nEuKeL9W4wPpBOkdXpVe8Fgspv7+/pq0l29MOhMMBi1VZ6Gg3pCSwlw4OTnR+Pj4f2uPA1OlKknfO5Sa3t5eA/1ZcOlMjd5tc5RklXN0DkqlkrVsIk0YDAbNwXC5SWkxvAMDA+rt7bVCwNramhYXFxUIBKwJwm08ODg4MBENsOhUKqVCoaDOzk4VCgVFo9Ealgb4LA8UJddzU5WFntbe3m6HZ3h42L43mDlOgzXr6OhQIBCwLikgBB4KajxIUBLFtLS0aGFhwaI6mCLt7e0aHx9Xa2ur6Szwdehb9/l8FmWRPSCDSGTl0gZdvNCtlFPw4Gtwydl7t/AKwwT4ZWxszMbA8P5e76l28ZUrV/TOO+9oYWHBoBm3sMIeod0Btu5SwXCM4Kc4YFpGKSKxzwjrcIbr6+ut+Mq+uYVT6Uwgv7293Sh9uVzOWBi00nPvCFboTAPjRSOjs7PTdH09Ho8ZXTBRmBm5XK4mwiVIINLl/uHsXGhKqqV2uv+P9nO66wgoOAPcH4qD2BQev99vOt7u2SVjQl8E6AZWCf8eZoT7frT9joyMaG1trSZQ+1WRrb3X8/7STYtI5cGdqtWq4vG4CoWCScLt7+/XdKPQUebyFg8PD5XP5w1zJToD4EeI+3xl3uv1WgTr6oRixEgpwdtgMXBYMHwUATk4gUBAxWLR6C/xeNyMP6kJBxo8ih/BYFADAwMmuYjAMYwNcMpCoWCRFoI4GD6mZ/h8Pm1sbNgwRjBLsGoelxIGdoxOKD3gUOu2trYUDocNI6WdkyYNqEa7u7sKBoMWfbS3t1vaxuVwU8ednR0ziuwzn4EOKbDAarWqwcFBM9BuQZRIG0eAYUqlUlZxJiImsjnP+aSABAPDvdhuGu8S9PkcfX19poCFeI6bzXm9Xl28eNFGCCWTSYXDYfl8PjOIfD3XGYN7I+7CXmL8+TOcJhmY22ziYqqBQEATExN6+vSpTk5ODDajWUU646pjkKFdQgnLZrN69uxZzRhvAiGKbDji3t5eTU5OamBgwNbP6/VaQdBtVGhsbKyRD3WboFxmD19/bW1NP//5z5XP5+XxeEyqFbYE/489dDFhgjaaQIAgabXFYXFW4Ffz/5EaRbmPDJa7eb7hhbUjs04mkxZojY2NWQGOe8B5f97zKw2u28ILJ66urs5mMz158sSKTPT4wwPEE3NpWHg+yMnJiVXLDw4OzCNxmdz3wLO5BqBQKGhhYcHwJiLxtrY2hcNhw5mfPXtmOBtcv7q6Or3yyitqb2+3hTo6OlIikZAkA9cBw/FiLhXG6z1VRpqbmzPoAKMAh49IAPI1kAn84EDgVCd4ZWXFuorYQC6wG527PEui01AoZGIhDMZbXV218UOzs7OSZFoLROgNDQ0aHh5WX1+fotGoWltbtb29baprtFyDJbs0PJwbUTbpZi6XUzAY1Fe+8hV1dXXp7bffNloenEkc3vHxcQ3u7kYVzIfD+WCo3XOBgdjd3bW02W0cwZBj4DlHdXV1ikajGh8ft6YD1oeIifbok5MTjY6Oamdnx7QXwH7dPcEgwel2eeQYYAwXho5UGGOEtgEYJ3/u9/utrXdjY0N+v98gAJeyR0AgyYS60+m00um0SqWSNRyg8oYeivuewID5fF5Xr17V1NSUotGonUM3pcfADQ0N1XwNolw3c62vr9fS0pL+9V//Vevr67px44b6+vrU2tpqM82os6B2hqEEBwY/prGKwArKmeukJdXYLe6NJLt/kqwN2+/3W8aJIYeP7To+ApurV69aIAOk8mu39rqeG4yKajAyhxcvXlShULDhku3t7VZEIl3HyCYSCd27d89Et0dGRtTQ0KBkMqkLFy5oZGTEMLTzaYBLMWExoUEx7wk1+r6+Pk1NTVn/vMfjsTlOBwcHmpub03vvvafW1lZ94hOfMJ4qEXtra6sZFHAa0lKiHy4ZI0xowz06OtLVq1cViUSseIBgBgcyEDjV90V3k01aWloyj3x4eGh/76oxuQea4gWOqlgsamVlRSsrK5qZmTFPzaHgwpN+VSoVLSwsWPHg0qVLGhoaskkZQA4uvCGdVcR3d3c1Pj5ueDIVbFpt/X6/uru7Te2JoimYIVkILBFwdpybGxG7kZO7Fsw9K5VKyufzNRqwbW1tikQiBkmReTU1NSmXy8nj8ViKz57W19crm83qzp07qlQqGhoa0tTUlGKxmH0mokcejBzvSnRP1obxPK/KBf/TFYha+VAL+OLFi+rp6alhariQmcfjMQcmyYq+RIUEQRcuXFAoFDJnQDUeA5bP5w3TZdAnehvHx8fa2Ngw3jnFNIIe6FHuQE32h/MJFJBIJPSf//mfevToka5fv66vfe1runLliqrVqu7du6fp6WlNT08rHo9rY2NDkUjE7A5RL1ADgQkYM/Ciez4xrmRQpVLJ/q6np0cXLlzQ1taWcfe3trY0Pz+v1dVVE5ZivWF1wEro7u42njvYMVCVy676Rc+vNLhcUrdDg0iPjqnW1lZbaIoWKH8xohnQH/Wqq1evGj8WzJKKOIbePdCuoQP0fuGFF8zIoShPuheNRk3QHEgDRXhU57u7uzU4OKi2tjYTda5UKjbk0MXIiPTcdalUKsY9vXv3rokTY/hhIYCFgcexlhxaDg6fnegKvu15bp+rS+HxeEwmEkx7dHRU7e3tOjg4MHI/2qEYA+mMaUCUCKm7q6vLUjyi4qOjIzu0lcqpJCU8SnDr4+NjO/zvvvuurl+/bvs6MDCgx48f13QZBgIB0zdl3D2FNS4H0er5KJvHpWqx5ktLS9YC63ZhkaaiIeH3n45damtrM4iJUTqzs7NGwk+n05qYmLAi8nkcsqmpqaaqzx66LdFAaNAqYfDQCCTJaFB7e3t68uSJQqGQLl68aEMqpbPCITAbMEQ8HlelcjqlgOwMLJ7W74aGBqNJ8hk4NzTRkInEYjGjYUky2AbjDG6eSCSUSCR07do1M06u3ZBOIZJkMqmHDx9qbW3NRu3QgTg5Oalr165pfX3d2CLJZFL5fN6iSrICAjmofK4wPnsCd5mzSuBDoRSerdtxxwAEoBmyCoqeo6OjGhgYsPoR/Gz2gQzXHfj6i57/J4ML75MFhZ7lpumoHlE5hXrFgUCY+tVXX7UFoGJMNXVra8vaZF36D94Kz44DgPUwPT2tVCplzQhEyVTs8To0A9TVnU7d7OrqMs7vysqKnjx5YpEXlDeiO+AKcClJVoX+6Ec/qidPntjGo+OJoQI7ciMVDqYrAkL0R3pKxdSN6oiGYVSk02n19fXZ3DTakilmMtYG3QL2yCX+U4HG+FMoQfTD/RyStLq6qnK5XDPLjc8LFrm6umoSj/v7++rp6dHY2JjefvvtmuyBUTbIZLpOh/Wgks35c88nETVCLhD4wUExPIyLwhnTKeiKcoPfBgIB/eEf/qHhh+yr+z4X1pE8AAAgAElEQVRuIROqElANgQEFT1pLU6mUDb5cXV21zinuApzUhoYGpdNpLS8v66233tLVq1fV19en3t5e04BGsBwjs76+bsVqWl2bmppqtAhc9g74MvvPnQKSo+kCTZJcLmf3kDueSqVsHNJXv/rVmjvqQgEnJyeGezY2NurWrVvGzAFGIevY2NjQ4OCgSqWSFhYWrCgLPETghaoYXW8um4V3gNZJ5oGint/v1+rqqp4+faqtrS07E8AmwKYU11Fwk04hiLt375p2CPrHDQ2nc/RiH+o2/7LnuQYXIQkOEx8ID4f3o2f9+PhYwWBQ/f39RhTmQNAFMzc3Z9gdnh7NA/R1qQzyQF8hpWMx6+rqFAwGdfnyZWUyGT179kzr6+sWjY2NjalYLFqBggv6+PFjBQIBxT7UPsjn81ZQ6O7uVjwet2jOFU122Q/8Wblc1srKiiSZuLQbUVGtrlZPBdhdjinpuovxSWdjZ7hY7kOEynqsra2pubnZNFmJkGASuNEG6ZUk6zOHYoROLjANjRTsA3slnaavcK/5tzhgpv+m02mtr69bR9Pm5qY1ilBcpPGAyI/JAThx1puzR+GJh0YAuoigFbIOR0dHmp2d1eLionZ2dnTt2jWDOMhYTk5OlEwmLUre3983QX0yukqlYmuFY3Er85Jq1hYnyvtBy1tdXdXi4qKam5sVi8V05coVgzQoPDJ8kW6o999/X0+ePDGVtYGBAcMUXVwS0W5gJP4/TpY7mM/nTSAHXQYMpMt+IYsCYiHyg6Z4fHys5eVlG8Pj7gnn23UIzc3NevPNN7W9va1IJKK6ujrT6mCf7t+/b+PJ19bWjP3T3d2tzs5Oa13H+RcKhRrc221Sct+Bu4Yc7O7urjKZjLq6uvSpT33KImjohQRcNDmRRfH7yclJ3b9/X/F43Ar9QHu/FqRAOyqHCMPlGgY2i7SpVCppZWVFgUDA5AJJNfP5vHU0QWY+Pj7W0NCQeRE2wE3ZXINLtdstnoE3jYyMGKQBpre7u6udnR01Njbq4OBAuVxO9+/f18TEhDU9ZLNZffDBB2ZcYAa40RTRCwUi3uPp06f69re/bVACqQ40LLpniJx4XFqVG2G52BNQzfn/R/oLZjo/P28caCAIohKXfyqdGmwEyImIwIAxnNvb2zZl1xVJcTFEPD5RIfoWFFWIvvncGxsb6u/v15e//GWboLq3t2cz2Do6OozpAGZXV1dnUReX6DzPkcgdYSDpjD9OmzkZ2eLiouLxuObm5gyTZJgjhWBwVgw+55uzjCEn2JBkrbecD84nDj6ZTGpmZkaVSkWxWMzOO9g8GGFnZ6c5rkAgYFOx6cIDcnMbCLifR0enouJQAzEyBBxE8kdHR4rH4yqXywYpgdtGIhGNjo7WcF75NXrEgUDABmYS3Jw3Mqwd9oFiZjQa1cnJiVZXV1WpVIwZg3FnXDnt6KVSSZFIRDdu3DAFMyQrMbycR5fBQg2I7w0Hl+yVgiwCU7AeyPC4N62trbYXlUpFPT098vlOhWvi8bjW19cNMgWG/P73v6/Pfe5zv9SmPtfgtrW1mVgxU15dDFCqjcbAFemdPy+dBn5CNLK5uWmRFbxASaasz0PkSIqAoeKyk9I1NTVZwYc0enV1VZlMxlIcpp8CZRDxzs/PG/nZ7exyvaYbWUrS8vKyvve97+m9997Txz/+cYVCIS0sLFiKQ2GPLIFiGEUjl8BNZRzaGGvtRvqsN4YHrGl7e1tLS0uWfsF9Rtwb7EuSCa+QtdBUQsQLfMD6sNfwIyUZVuXz+WwiAVzdpqYmHR2djqhBm6Gvr0/t7e2qVquamJiQz3c6Uoh14YK6TQwYExy2S1x3H6JqhlriyHB2fr/faIFoKtAvHwgEDHJinV1nSjTnFut2d3fNofOwl1xwokyv12tBy40bN0zjA8lDLrLLcceokt24Oh1M5yDyB+fnnIJpwpPlnIIvk0lRSC0Wi/J6vZYVgMnD9eY+E0lj0DKZjBYXF5XP5+08u/eFLNR9Tz6f13uqRbK/v6/h4WE1NjYqn88rlUrV8K5ffvll60Dr6Ogw6hddZS4Ex/fFfsBJpybgOsPd3V1TEsSgAm1y5pGW3dzcVC6XU11dncl51tXVGbZ+9+5dk0EFvnr06NH/3uAyLsUdrc0BI9JjE6FLIANIlAiOValUzDDTUeTz+TQ0NGR8OKIat3AiyfAoeIVUikmd6FTBUJ2cnCiVSunChQva39837uGFCxeUzWaNIE8U9h//8R86OjpSb2+v0ul0zQF1Dw3RRSBwOuH2rbfe0sOHD7W5uant7W1bHzyw3++3IgbFFulskgaFrPr6+hrAHmaCG9ny8D4cXlKgtbU1a1dk0iwqUhhv6awQhUYGEdja2poVcIjgMHDnoykmFs/PzxurAx4sERpTWqVT6CUYDJoqWjab1dtvv20KTuCScGrZX+hgrD/dejxEwnt7e2aooNWxzkR02WzWhJRoNPB4PHYOiVK5vJIsssPJum2hrnAOBUPWiUvP/l+5cqWGF0xkiyFzAwC/329zycCm4YdWKhWDcDDGrA/nYGNjw3B77gzGsqGhwabm8v3q6k6HT0INpK2cs+g6fKLDubk5rXw44ZnRRzxoDJzno7pMgTfeeEOLi4sqlUpqaWmxAl1fX59mZmZUKpXU0NBgWSo881KpZNAlARz3AZiDfef7c0+gOSLuhGQrQQ5aGnQU8j3gGjc2NiqdTtdQ9Pr6+kz/o1wu66WXXtLt27f/2511n+caXMj5fr9f+XzegHOaGUhtoIZwSYiSXGoIxPO9vT1rNx0cHLRQHFAfwW43wiX6I+IjBcXDsUB0i5HGZjIZbWxs2EVGIIP241wup5/97GdaX1/X8PCwGWiXlsbla2hosBQXetv09LQZFqIF1I02NjasmMghpk2QS4vqWWNjo6X+QBOucI57eEmfpDOuMgYjkUjY12FwJHxnDihOq1qtmtgNqvfwUKnmUqRwm1mkU94laSVdgRTASD35P0QVCLSk02n19/dbhR2MlAiXCwQmej66PR/hYuy2t7drLgr/j7NDKprP5y36JlhAa4ApCnxNWowJANBc3t7etkIa++M2dPB9OQ887mgfDADFH853tVq1OWJ8n5aWFqsLAI1xznGkOMNyuWyDRMk6j46OTAipoaFB4XDY3o9zzXQE906jL80Z29nZ0fLysrE3iBJdWhjMIM6QC8GRdXzwwQfG3Ekmk+ZI/X6/NVHAaEDTgjsF59W9G0TU5wvtBAusAXuHUyDwIHNGU4J9RIeCNmyCI4/nVKvapYWVy2XFPhwq+bzH86s6I37z/Ob5zfOb5zfP/zfPcyPc7373u9XBwUGLGkiNpTNdUHA/wnK3BdgFzqWzSbqkW3gmogwiZCKeL33pSx5J+tM//dMqtCU8M9oDmUxGoVBI4XBYKx9O8pyenjZPXldXp/HxcQ0PD5tAyM7OjpaWlnTnzh3dv39ffX19JmM4OTlpRQ0wwS984QueL37xi1XSPLiVfG4wUpgTRGdQvPjs0llBAUyZwgUawuBYLu1od3dXX//61z2StL6+XmU9IWfjuYmaS6WSRUG5XM72SJJlEdJZhExRhXS9ru5sLhqUFzD8P/qjP/J8/etfr7rdRlSbXbyeKJO0/rw6HBENPFswMnB1ilLuv4O//J3vfMcjSZ/97Ger0Hp+53d+x3BDtx2UiBVclqzNnW9HsY1Ih4IvMAqqWqwPNLhYLOaRpL/7u7+rsqZer9ciPKYOuLgy3U9AVEAp3C2yQu4c0ZbL32Zv7927p3/6p3/Se++95/mLv/iL6tLSkra3t3Xx4kUrnjF5mayRiJ3olrMKVuvCNm72CE7P/jx48MAKT6FQSH/8x3/skaS33nqrWi6XrakAWJHPxBoR8VJgpLsQQfuxsbEaFTa6LqvVqqX4YOHuPL6vfOUrnq997WtVzhrTUJhQks/nlclkFI/Ha7SsOzo69OlPf1o3btwwYXHOptthSgZCYw0UOdbx5OREly9f/qXCCs81uL29vZae0/Hh9/ttPhKLCV2Gajy/l1STSmB4MEIYZjQYwMHcwhSGgcsM9Qw6zcDAgA1jZGGWlpbU3NxsqQ68VlKip0+fKh6Py+Px6E/+5E/U399v/eaSLOV21ZgovLipjPuO58VEuBiQzNk4DjsGGcjBPfwYZKgo57vu3O/vXky3UICRoVINfcZtu2Rv+Le8l9uqCt7sCoO4sIaLW3I++B5bW1uGk6O41dHRYU0xGB9wMea+uYUa3pvz4aZszc3NSqVSxmohPeb/uk4dg+cWglw2jCvABL+a9JG9Yh/4mjzurD1+8D3BoVkbHIdrbGi3Bo7DuLDG7jlzK/9U2Vl77lggELBZdqVSyeoorioXTTqdnZ3WGAF+6RbGKf7yM3cc2iO4Ng/FU2wDzTncXaY385m2t7dVKBRM5Byq5Obmpt15ilZ07J132BSHMc4+n89a9t0BqT09PRoYGLBiZjKZ1OLiorWzo5sMLxeoz52zSDGTdXLPllso/GXPcw0ui+oaQn4PFcjv91vE6s4Ac40VnS/uhaTK6F54FLncC83/x2tSqKNayPgeSeaVwAk7OjpMMampqUlra2ta+XBo3gsvvKC+vj4NDg4qEAhYAYmiGyNNwMgGBwdrIqNsNmvi1EQPHo/HAHW8IxoTRDQcCNbC7/fXqB65Y645pC7sg4F1H/BWih1ELFRcqTq7h/98EZAIC+qNJOO30qbKn/NZufwYI4w42QiCQBg21qOlpUW9vb02RLCxsdFGidN5SLMIWRP0P3eCcWdnp6anpyXJVLEwdtDjaBwhKyHrOjg4ML6p27nFJXbPNawPcFYmB/AQsbnBhtfrrcFmXeI9WCzRJnvCO/A1yaLgnbrZIQwLiPauaA1RO/oc3Ce38Af/uq2tTbFYTGNjYza7jagPA8K+uc6ura1NuVyuJrORzuo+PDR3+Hy+monNjGh69OiR8WmHhoZMbInvifOAMgprxM0ScU7cVehn0FQp0oNlt7W1aXh4WGNjY3rxxReVyWRq5Bg5t9STYEYwG29wcNBYMThlnPz5Tsjzz/PlyXXmObnkGF+6iKiy0itOMYHDy8vQYgsfzu24ks7GcZ+HIaSzKcF1dXVWES0UCgbQQyGBVpPNZm34HyN2fD6fQqGQPB6Purq6rEME0J4Ljefis2P8qMTv7u4ql8tZBxPkdgoq+XxeoVBInZ2dNcUmDrzLVNjZ2bHCHnQfRrxA0D9fJOJiY6xxUERKRC/ACW5aRWUdWMGtqEPOx1CjJwHTAaPLw+dhr7kM7mBOjF4sFrOInKwE8RTOAxEM6+kWdXAWkPl5SHGJPCTZ4ETWn7HWNDEQQcKVhpbGWrsz4tyvDZMGZ+MWw4A/iMjT6bSOjo6swwzBIoylqyR2nmfNnXHV3ZhCnUwmlclk1NDQoFu3bqm9vd2EY3DEtGUj+I4qFneMiSwbGxvWLDQ3N2cFW5wg54P7C50QQ8f4HYpWPHxmqIMUz6HlUbwGcggEApqcnDQmUjQa1dDQkJqbm+X1eo194vf7a4RiyKLILFk77giRO0wrtKeBImCKAOXF43HLrnBsBFLwdw8ODrS4uKh0Oq3Lly8rFovZmrD3RNm/7HmuwT06OjKDw8QAXghP6M6/gkbiEtYJ5znQRLV4blJG+ItEjOcNTUdHh/r6+kwsu1gsmni3u7CBQEAvvPCCEomELly4oEuXLpln7ujosLlNh4eH2traUjqdtopwMBi0Med8Ptdb08eN0aa6jQhIPp+37rzz6lpACbAFwPey2awymYzpSaDDcHJyop6eHmtS4HEhDdaUdIYDWK1WtbGxoc3NTUu5XEcCiVw6bQnFQPDO8KAzmYxyuZwGBgbU3d1thgFnybuUy2XToyXyYnSMGxWRGaGxAQRANd/j8VhrbkNDg7q7u42f7cJX7pnAGHBZgL7IZCYnJzU1NWX75VKLgLTcdmJEfWA38HeuroMrbMLngrL14MED7e7uampqypwd+8i/XV9ft+iblNSNKAuFgilokXWgbxAIBDQ9Pa1kMqmxsTHb02w2q+bmZo2OjioYDJp6nHQ28gemRaFQUDqdVjwe19ramqXNZFNgyzRu7O3tmfNubm42fJh76wZHiCFhoIkS0eQ9ODiw+khjY6NpmdA4RYbS0tKicDhcI36+tbVlMBfQA6wbFx5i4jat/9z3crms1tZWy36BXFz+eGNjo3WXtba21gyRRJd4enpaa2trevnll00ciTt2njd//vl/mtpLuyfRC2lqNps1Sg4bxYXr7OzU1taWtUy6I2xc/UowTheKgDvKg5CzK46C0fd4PJqenta1a9c0MDAgv9+vW7dumWfv7+83L40zODg4UCaTMcMB8ZnFpz0XnqJ05j2Jajl4cJKJWLLZbE3DgFs044DwPnz93t5etbW1GQGdr0vLtCvIjqHj8ruRiHSKP+fzec3NzSmZTJqDQ2uAqCGVSmltbc00PkdGRizNr6+v1/7+vh48eKDZ2VmVSiVduXLFsgsyHlLUra0t6+ajZx61J4/HUxMx0MTiyleSNREV0/bNZ3TTdfdhAKHf7zeDX61WTc1pfX1d2WxWqVRKH/nIRxSNRs14co6gt/G1E4mEKc/BUQ2Hw+rp6anhWWcyGYM3SDvn5+fl8Xj0qU99Sq+++qrC4bAZgePjY4ui4MsCIXDmgS2YbAy9jrlvwBwLCwsmCUqxrq2tTT09PWYI3VZxCrUI+mDISN9LpZLpT4DVolW9srKi+fl5JRIJi6AvXryo1tZWi67dgODx48eqVE5FnarVqkWaiMC4BUGCBPi9m5ubZogJPFhzJqU0NDRYBuUaOBfaGBgYsDZcpp+QgSaTSVNO466ifggNcnBw0Bp0sFe0do+MjKizs1PJZFL37t2zGgxjm9iPX/Y81+BSLFteXtbS0lKNqn61WjXhbfBACOzb29uamZlRuVw2zIouGfQnaYxgIYlw2tvbLeXkOTo6sgFviUTC8LNwOGyRA6NSWltbNTAwoPHxcSUSCSWTSdMVwIsxPPLWrVsWLdTX16tYLCqfz6ujo8P0Yd0IzU3BAfjBKt2U1K3IY1zgLDY1NdmIomKxaDAIGr5ELMAqYNs8RDw4Bgpt8Grn5+dNcQnZzPr6enV3d6uvr88Mi8/n0+Lionw+n15//XUbvoljKxaLikQiWltbUzqdtkIB7wB/kcLb8PCwhoaG1Nvba11o7C0cSKIUPoMrxEMUCw7GDxwf7+0eaPi0/B28yIWFBR0dHdlo+vX1dVUqFQ0MDGhwcFB+v1/FYlF3797V8vKydV1hTJqbm23GFmdufX3dqumJREJNTU26cuWKrQff7/bt23rllVds7BSKYODF3BGMDNmJJAsycEJugwwOE64qXF8M0sTEhBlwMHkiQxwyI5SQ8SQCd7vspDM8GOyYzi0cN3vX3t5uwRbPzs6OZmZmjF9MQRT2BoL7RPY0ybhYLwXnjY0NHR4eGk7qYrbncXLOmqSaGYIUucbHx004iMwY1ohrt5LJZE1TUTAYVDabVTabtXWIxWKamJiwgh+yoJlMRvX19froRz/6S23qrxyxk0qltLS0ZGOswVGr1ao2NzdrojkwKlLt4eFhi152d3fNCDQ1Namzs1N+v988D5eXQ3g+la9UTqexLi4uWnsmBzEYDKqxsdGgDaKSw8NDpdNpI29LssJPR0eHAeVMgxgZGVEoFDJhZEjgvAM93uvr69bShwe8cOGCLl68qFgspuXlZYNagDz8/lNt2FAopPfee0/r6+uSZNji3t6eFhYWLBJAGg5Pz+P2hzMUkSkLrBMG1tVG5XBB8SECwokQXWxtbenOnTtKpVJWNKTI5FLVpLPur97eXsViMQ0MDFinINkQUAA4N1mPJLs4yCru7OyYoQFX5TNBf3OFUv7t3/7NcGUoOv/+7/+u2dlZoxUxYptUMBKJaGNjw9TsfvrTn9rEAUSARkdHdePGDROUWVxc1Pe//32tra3p4sWLGh4e1tTUlL0HDmh0dFRDQ0M6Pj7W3bt39eTJE83Ozmp1dVW3b9/WZz7zGcU+HGqKkh7Op7Oz07I9j8ejRCKhn/zkJ3rw4IFu3rypW7duqVqtWm0CdT2cFjUS9pPsjX1obGy0+7u5uWkdaTRgSGdz+8AlKTSRNgeDQYODXLUuNyBob29XOp1WsVhUKBRSJpPR5OSkQqGQyXJiIKETuuyClpYWdXR0qKurq0aEhyYixHWYlEHwx92QZFQ+7gmMDAa8UkjEYSwsLEiSwR5NTU3mVGHTINvJSC+U3bhXTIZwC96/6PmVU3vb29s1MTFhLZC3bt1SV1eXtra2lEqltLy8rEwmY0IxxWJRfr9fN27c0Kuvvmp6p8lkUg0NDQqFQmptbbUFnZqasumx5XJZ8Xjc0k4er/dUijCVSqlSqehjH/uYOjs7lc1m1d7ebml0V1eXdX2huITnbGlpUTQa1eDgoCYnJyVJ+XxefX19CofDtin19fUmXE6Rxb1UpP0ssEsHcfm0wCulUskmjnK4aHUG34J7iNJ9IpFQa2urotFoDdwinQk74wB4L6bm4m1zuZzpXOCxgX/8/rNZZuDYeHTwU6bSAu90dXVZdElE6TIOwuGwRRznqTTAJ1x0sFYKKgw93NraqnlXN/KCT+yuxW/91m/pgw8+0ODgoGZnZxUIBJTNZvXKK68oGo3qhz/8oalE0Y59584dXbhwQT/+8Y8tg9jc3JQkm/f1rW99S3/913+t8fFxG+oYiUT0kY98xApxrkYxBV328+HDh5qenlZ3d7du3LihXC5nKlNUvyVpZmZG0mlkC0OHX9O+K0k9PT3q7+/X3bt3Va1WLRg4ryXgwlbUCxoaGowF0tfXZ6Is0WhUmUzGImK+Br+HTsj8NrITHPrR0ZGy2aw5Y57R0VFFo1ErzG1vbyubzery5csaHR01CiOMHxgB5+8IUS1R5N7eXs1IeaJhYAlJ9h4EefzalSUl6uVOo2THEAOXBULEiz4JnX/AJDB6gEy3t7dtksQvtanP+8uenh7DQW7dumWpMjAChQYwHwoTL774oqVuu7u76u3ttXQArwiGQwXSxZkAoHlIJ/DEDJ8kjSD1B8RPp9PGoSMqozrJpNTDw0NNTk5aEYQKNFEp0Rne0wXzSe8ymYwZzkrlVMKPlASqFK2/TH+lfbS3t9ewWKq5NGvwdba2tiwi4MFYUcU/Pj7W9PS0njx5ot3dXaVSKd27d09HR0fq7+83bJYUH6PFwYG7y/rt7+9rYGDAKrOkkFSZOah4diKe/f19yyYw7Lw/l7hSqZgIC/oTpMasH/RBjE9TU5NNCykUCjV9+7dv31ZjY6NFlQ8fPtSLL76oN954w9pz9/b2FIlETErvxo0blo3cuXNH2WxWb731liYmJvSFL3xBb775pkqlkp49e2ZG5+DgwOQakTV0DT+Ozev1KpPJqFgsKhgMmjD8yy+/bFxv9hnBpkKhYGt5eHioYDBoBndoaEgNDQ1mqK5du2ZRH1ANxgMWCGeK94OX7bJ8+vr67F1hDTCjzm1egdy/uLhookyDg4NmbBcXFxUOh2vmpDU2NlrGk81mlUwmVS6X9f7778vn82lqasrWsLGxUQ8ePFA2m63Rvg2Hw8bbzufzGh4e1vj4uEFVUObAc4FeeHdYHmh7LCwsGH5NVF8oFCxIoaiXy+WUz+fV1NRkcB/aKkjNPnz4UPfv31ckElFnZ6eOj49VKBRMzQ3N6P+1wU0mk7p7965GRkZ07do1vfvuu3rvvfeUSqW0ublpkUhXV5du3rypixcvqr293SqARCx4Wfqh6Ws/Pj6uGTWD/qVrcA8ODmyaaKFQ0LNnz9TZ2anh4WFFIhGFQiE7qNDFXPaEJEuviPCgH8HPdKueblHIrUazUS4JOh6PKx6P13CVkVWkX9yVsIP8f3h4qKWlJW1ubpphIaoeGBhQLBazgomLkcFn3dzc1MnJiRYXF/XWW2/p6OjIihkU3lxAv6Ghwah3lUpF6XTahukBY+AImRJRLpdNlZ9GAUk1OOvJyYm2tra0u7ureDxuGcXGxobW1tZM4zUUCqmjo8O0MGABQNFxo6ZKpWKZQkdHh4rFosEfpNCsZSgUssIjjoizcPXqVWtmITUmRfV6T8cjDQwMKBqNmr5BsVjUm2++qddff92ie1fMiHE1bgEP7jNZQiQSUbFYVEPD6XQJBiViJCqViuLxuOmTIODj8Xjs4nIHmEhBAcfFw8kSMDLg/WgsYIwZdUVRkmyss7PT+L2k5jCHXAGlzc1Nq23kcjn19vba3LOGhgalUilbC8T3qdPwzpVKRcvLyzaNpLGx0fjMmUxGH3zwgeLxuILBoAYHB3Xr1i3V1dUZXxY4Aqfd0tJi3a1Eni5bgjtDNI2BlU6hC2owoVBIU1NT2tzc1MrKip3rXC5ncBqwDDS8d999V3fv3jX4lICTTtH/tcG9d++eKfYsLCxYlxBUDTx9R0eHjVZubm621Kyurs7kElHj51JQIYZKhHE73/TAYWJQIgY8EAiYlySVy+VyNkoabNhNr2h7/UVdX3SzgTnyfV2vyc+wJTweTw1dis0lTUWRiQPgFppaW1sViUSMW0m7KOpikUjE6CnuegQCAfveBwcHmp2dVTQa1ejoqFWWe3p6TB4TQ+fihQihIx7NlAzoa3CsKZRw8HgPDiVUHoZVcvGam5u1tramTCZjkZrX61UymTQ+JGegp6fH2AU0TuRyOYOpKHzBkXbnu4FNUyNADGhjY8M6q0i/Y7GYPB6PFdoCgYAikYg+97nPaXR0VGtrazo5ORUjZ0jk1taW9vf3re2YjAKdYB4yEDfC7OnpscyB9cPwMJomn8/b/Cwio+XlZRPchibpcr6lM5aIC7uQKfBruNZIlpKtuY0v0N7g0+OQ9/f3rfOOqv7169clybBLBHaWlpZM31iSHj16ZHe5rjK+2EYAACAASURBVK7OoK3BwUHD6TnjLS0tGhoa0tramkZHR1Uuly06hgba0dFhDClXMIh1J2vjrvP53SYcJjewpwcHB2pra7OC/sbGhp4+fWocethBSE/m83krPPb29ur111/X6OioFhcXVSwW1dzcrIsXL9o9e97zXIMLTgjr4Ec/+pEmJyfV09Nj3g/RZqrhVGJp1XO7jA4ODoyWBNuBgwuZH16d+9AV4/f7bTonBR4O0sHBgY0wQR3e5b6CI0LUxvhgvCk2YHAxKi6pXZJRcygWNjY2WvRCVM3YdT6HWxggcq+rq7PoMRqNGlyCJim4o8sv5KKAAR4fH2tqaspaPEl/pTMHMTY2pq6uLpPT4z1efPFFSbJiKAWBSqVi3v3ChQtKJBJGkeJyw70ECigUCvrggw+Uz+c1MTGhaDSqXC6nrq4uw8ei0ahNCKAgBz2HMfIYBIxaqVSyqalgpC4/G2fppnGFQkHxeNyyJq/3dJYdl4aCC0YLLuy1a9cUCAQMS6UdFryeVlMcZiwWU3d3t6RaZ+ruM6wY4CUX76TtGdiC6bkwcNAWQQfg/PdxO/3c8+mqhhFAgF9S3HYphETn3BU0NDDGRIZU5y9dumQ4/PHxsVKpVE0WOD8/bxlpJBIxZ06wtb29rZWVFcOSqYtcu3bNZo1ReCVT5etRW4FDe3JyokQiYcHFeSqm275NHQdVQ3Djvb09zczMmJxrS0uLZWkHBweamprSycmp3Gs0GtXh4aGam5stIKLRiOjbDQh+0fNcgzs6Omof4L/+67/085//3CJNcA+oHblczniYHObd3V1tbGxIkrVtUnzDC3NI4d6hqesSiD0ej4XtFJGouAOgE73ye7ibRNR8Diho/D2bxM9u66wLyLsHXZIZbw456RwX1RXecFskbeE/rNDTKAFvFfgFbNTn89UcaP4vxoPoxOWVUsyAosVnouBGSjkxMWGTUn0+n8nTlctlS+25rBgw96GxJZPJKJPJqFQqKZvNWtrtYu/r6+s1lDY+P52KUu34IKrf4LDQkFwpQJdnCpRCoSOZTNbARcxxIyLG2NCqScGOIgrwDpMzYJnQNuquBWeYC0fQ4Iq/wLtFrJxiKlGYK1O5s7Nj2RLiNxhOAhRqDpwN92xxLsE2XZqdqynBz+yLy5jBGV28eNHWy22ewWCRyfL09fXZfqFl29PTY+k/xhFjSOsueiiwlOh2pKbDWUBHpb6+Xvl83jSc3XvrNhihZUGdhg5MCopra2tKJBJWzCfoILAC4kulUiYTQK0I+yfJ6jMjIyN63vNcg0u6SGi9tbVlFAm0Y+Gurq2taWRkxKI9FzPlgfpER5Ur+gxvjgPpHiCKWLR/ckCISJi35baT8jQ3NxtTAmoTbYPoQfC1aN8E5nDbWV2NBy4IBSzobWxUQ0OD8RdJKbno7iUkzSfVRJgDugktte4auoVKuNCuGEsulzO8EmMLBAGV6PDwUM+ePdPJyenAwHg8rnQ6rY6ODstc6urqbIy3z+dTZ2dnTQMG70ERlMNXqVSUyWQMyyfTIVMiosHgSmcOlSoyDtVNl4lazmNkLm5HA0exWFQ6nVa5XFZ/f7/pa/DeUPoaGhoMztnb29PNmzdNud/NgIB6wOvOU394BxcO8/l8BmtBZ6LYs7GxYdkfRmZvb89wc5wjRhdDDPPF5aSiH3seimN/XAcHZMTZh9olqeYech6Jant6eixA4rNns1mtrq5KOuMPS6c6BqiWYbSl045GqIicMQrRTU1NGhwctOGn4PcMRwW3BgYguMpkMnaH+GySrLDoBnYYZbS30Y9GW7u+vl7RaFThcNi0Ura2thSPxxUOhxUKhayYhva1x+MxbDwajaq/v//Xi3ApcO3s7Ki/v98iMIjDtH7SRnn79m21t7cbZQqD4xoTiOg0LfCCeGsq/O5FIhrg6xEFnX8QFnGFPLioHKxyuVxDg6qrq7NuMw4dpGoOviS7/C52dr4VFIPBOvE+7oXp/r/tnUls5Nd17r+qIovFmTWSxeI8NLspsge11FIsyZbkOIjhBFkEiIEgqyyDBMgmqyyzySKAd1kHATIhdow4k4E4smypNUst9kyyORdZVSRr5FhkseotqN/hrbLcxovf86ovIKgHdg33f+8ZvvOd70Sj9uCJ+Kkgu+I/7r40tvbidFxxbLd7j3S9ra3NxGB8Pp91YLkydQcHB7p8+bIePnyoVCqlmZkZew0wRPbPNTR8ZyIoLglRKJV76SKzqFQqFq34fOfjlhAAQUhFklWcKXLwuo0THzCIPA9qAKSNGI3V1VX5fD7rFoNryRQKLtjc3JxmZmbqlKmAucA6YRO4y9UAwSCTmfBvOU/wjnHo7CWOtlwuW6MMe+dOqJAuRpEDP7l74eKXlUrF4DP2hp/B2BIo8P58X5d6RTclmVW5XNbq6qqKxWIda0SSdZ+h20ETQqlUUjQatSibaJLWaYrYsALAW7EfPHe3cQqHxt6QUeLc3LoQBWGcFvxjdDWWl5c1ODhYJzpPZF8oFNTV1WVcXjJo9hrDLulnzkbjeqrBTSaTJhgxMTFhEm7pdNpGs2xsbGhjY0Ozs7OKx+P2BfHAwWDQUggEZBjZw0N1h03CFnB5uBh9Fy7g8OG9XQI4USQpotfrtagKDUzgDrwsbb9cDiAEjLBLsOYiEC2hYsZ7YAx7e3sNcmHoI7QsaFcbGxs6Pj42zA48CE5l44HGkbnFPC4JtDomJlcqFa2vr9eNvKbDLZPJaHFxUfv7+xodHZXf77c2WOADNyJmr9294DlEo1HF43H7OzcKwpEFAgFdunRJZ2dnBnmgV+FCKDw3/js8PLRpq42cZM4YzhCKn3QBuywtLSmfzysUCmlwcNAgAfA7Cr4tLS36r//6L62trenWrVs2Zr4xayIthQ0h1Resurq61NXVZeeDz8Fl7+zs1NbWlkXcOGUgESApKFI4H84394uMyaUtEhy40a1byHWLeycnJ6bS1dPTYx2GrnCN27HG+WpqajIxHTfzYHV1dWlyclLZbNayPBS7Ojs7DepyBWRg6jz33HPq7e2ty8jIflxNXbfTkWft/h6nB2zC58fJUUuCmiedG8q1tTWdnJxYc9PExIS12SMMRSEbWwU06UrTPm091eDev3/fcBAqngD/6XRamUzGxF8ODw+VTqfrJoYS4QAT0N0SDodN6IV+elJ7ST/zEBlKSXrmagdwoF1ZQzBUV/UL7BYi++HhoVpbW7W1tWWRIUYPg+8qOTUWRSQZXsjFRRUKfI2iChcPPJOeef6cqL+9vd0Uzbxer+2322nWCGvAFICTSQNHIBDQw4cPLYXf2tpSMpm01H5nZ0fpdNoKN8zAIn3DENBU4mJkpIMU8Jqbmy0KI5rHyJydndnnam5uNkpWuVw2TjOEdhyem2m4I3uCwWCdwcXZgj+6ztvj8SiZTCqTySgWi9k4IaQWS6WSVlZWrGpeq9V07do100nFYfIcuNTsuYurwxjh10Sx7jkGM8xms5qfnzcWSFdXlyYmJjQxMaH29naj65ECY0QwHkRo1WpVt2/f1uLiov76r/+6zuCyaD0HygAzhba5u7trBWOwZ4pWGCeeges8kJYkAnYLmbBtaMnmOVKjcR0LBrtYLJqRjcVi6u3tNegEeIOMwxW8JzInmyAggF2DYXZV4SiK0yJNBjc2NmZ8Wkk2boj7VCqVrMDO6/J8eTYY36etpxrcVCplBgr8CsPjjiHv7OzU48eP9cEHH+jXfu3XbCgeffOQg8FQIfjn83mbIYYh44C5kALG+uDgwNJQNptUIhqN2sjzZDKps7Mzg0FI9TnkpICk9+B4eE8MgcsIACPiYCOX19HRodHRUeuWozuI6aDBYNA62fx+v/XQg7UODAwoEomY7OP29rZVVKHeED1KsgPvFgTBdEnrEdHJ5/MaGxuTdN7VtLi4aFS0dDpt37dUKmlwcFBXrlxRe3u77TX6s7wXGK4b9bs4J3oSRPXSxZRghIaQ8jw5OVEkEjH8DCNGAYXpznQ7IYDi4oVcAIwDURm1g93dXU1NTSkcDuvdd9/Vhx9+qEwmY7ggz2Fra0ujo6MaHR21IuSjR49UKBR05cqVOuYKjTGuwSUyBNMH+sJo4Kzy+bw+++wzpVIp9ff3W6UeOU4aZVAJOzk5MQlDYCqw7uXlZb399tuan5+vuy9ALJxV7iqdXER+qOXx8xgZhjW6zh6oj9cji8pkMlaEY5G6t7a2KpFIqLm5WalUSnt7e5bBZDIZ1WrnIkOwJzwejyn2oaNC5Mj+AjO5U7zJKNlr9gJ4hGYtalAMFwDHJTiBzsq5J8jr6+tTNBpVOp02PJj3xIDzPm6U/b8yuIhOeDyeusiVN4MahYFg5HQ6ndbKyopVxHkIdK3RW0/hjQIUXrYxwm1ubjbvw+bwoPBsGKK2tjYrIJFiI0COoUYCkuiL7wmuhZGlA4jl8nUlaXx83ApNpG4Yn0gkosXFRZsskUgkrLX14ODAaGGkMJD819bWjNsHSd2NXNgbIBhSIkj6fr9fh4eH+vzzz43ry1RbxgcVCgXTx52fn1coFNLMzIwSiYQR38HHAoGA0XWAN1yD6+4NxhZDyLmgaER6BzzEyKLj42Pjtjammqurq4bFUkxz3xPMn8iaNJrzG4vFdHR0ZKO529vbdXBwoHv37mlvb0/Xr183/WK+n893PjHgwYMHqlQqevHFFw1XJMpyDW6xWLRuQSQxgaaILhkgGQ6HLZ1/9OiRWltbFY1GLRDY2trS4uKiZmZmzPjDm8bYer1eFQoFwzh5Fnxv9/7gAKiqU7QDd19aWtL9+/ctw7p27ZrGxsaUzWaVSCQUCoWMRcI9ok6BsXXPAZktkADdbfF43IrHdCJiJGGZEFhBi2TPXa0F8H4gQOkCTnEZG9gDghzujNv+DDTGdBg0YqCSuYECjhyjC0xDsIhDcvfiy9YvFK8pl8uWLuMRoY+g4uTz+fTKK69YNNDd3a2Ojg49efLEUmvwN5/PZ33cPHwwNjaKg8pqb29XoVDQ9va2GVQOGaE9wsREpt3d3fbg8cB4TIw0kAOpjNvTDQ/VhRQkWUpGZ1CxWNT+/r51z3DwQ6GQxsbGTGmISNTn8ykSiViH3OnpqfWdHxwcaGxsTJubm8rn85qYmKjTMJAu6GjgwGCLiHjXajWtrq4qlUppdHTUoppXXnnF8LfW1lZNTEyopaVFAwMD8vv9pqsA5cnv95tEIs0DLvuDPWV/iDhcPJW02uWMcjBpy9za2rLUnknMpHDLy8tKJpMKh8N2HtxzwV5zHrlQOHK+LyklBn18fFzDw8NWPCNFlmQQC45qYWFB0WhUwWBQXu/FpGE3klldXbX6RaFQUGtrq50PouOjoyO7C93d3cpms2b8kdHk71555RUFg0HVahe6CcAtnAGKta6RZX+BWUjDUcxCS5a9GRgYsMp7T0+P1WMIsDh3tLCDTcMawuC6kEI2mzVjx+ciAMGpBYNBZbNZpVIpo8DRrn9ycqKlpSUNDQ3VaSJgVOnUo8DsRppulO/xeOwOu0032AtYMLT/M/sNOIx6FXeWrIjWbLfGg8F1C+8/bz31bwuFQt3oaB46dCXpXHXrxRdf1K1bt+T1ei0yQhmKjhbSsKamJsNyoHVAGyIqaGQguBMMiBS5aK6XZHPb2trMAHIZaecjOoHOlEqlVC6XTZ4Q5Si8eCO/kVSFNGJubk75fF6Tk5NmgMDB6JJijhqk7Wg0atE+xjmVSml1dVXSuZ4nBrixWMfDBddknA/FjsPDQyWTSZvfhOen7xtdT+lc3pJJD0S2VNaJ5vb29ozk7fIcG6Efl37kOk1+z2eEDied431zc3NaWVmp4ySDqRaLRYXDYWsKwHDWHeAv3qtYLNr7NRp4il9Ert3d3erv71dTU5M1fZBOIlBORxrpO2NVgMDciwUtD6Uv4BH2CFU37gFqbhhl2DLAK3xH0l6gPKJJ1+Di8Nh/oqxAIFAnxOR2QGGQcLiM1oHHjWZCX1+fqtWqcbyB3YDgeKbuHUGsvHEiBoXPrq4uDQwMGC+7q6tLra2t1iIfDoeteBWPxw2Px8CRPRH4sfculdQ9pxQXySi4C0TfBFeoDeKYy+WyBYgEOKjv8WwrlYoVz8C7v4w9VXden/aXRFC12vkEAcB0Hk4oFNIbb7yh6elp0/BkYwYGBuo2nZSPPyPiJQrI5/MaGRmx9NA90AyEQ5OSQ82IHegl3d3dNrAQrA7aB22IsC6oeLa3t+vGjRuKRqNWVMCoNtKxgDtcis7e3p7m5+d17949hcNhmwNGs4BLqwHmKBaLWllZUTabNa5vsVjU2tqaqtVz8eZEIlFHeWLhyV1eJo4FnLBarZryv3QhYAIeBgwSiUQMIyVKAP7BCBDRuZmC6wR4fuydmxnwGnxeDqUkiypGvujY4uATReLcvN7zgX84ezfa5xnReELGxYXE8LmNNK5kJOIoUMN2dna0tramjY0NBQIBXb9+XeFw2KhktCA3ng1wTgwlWRROEf4xRUSXZsa55T6QCRElulkD7wMM5d6Txqo9RcZyuayVlRVrJGhra7NIl3Z7soqZmRl5PB4NDw9rZGRELS0tNo8PHLhSqejevXumCvhlewF+TsaFHCKRMfxd/iybzVqAQS1iY2NDTU1NphFNoYrvR3RKgRrDypkk5c/lcspms5YtMDiyWq2qr6/POvlon3ZFfAiM3IjabSpprDs1QqFftp5qcNkwgGeXrgT9g58htaYI4/V6jTANZsvi4rgdWrQLgtm4WAgR9t7enjY3N3Xz5k3793TrEJVwoUhHOjo6DFvzeDzWvkdV8ebNm7p165YV+ohSpAuuKZtK+yD/VpKNb5mbm1MqlVJT03lHEmpZpLEYor29Pd29e1cPHjwwpaZSqWQqS8xz4iKR5rBI5dg7LiaRD5zBy5cvW0rNQEainrGxMbW1tVnbJjzdo6MjS73A65jl9mVDE11+LfsBHIGCPp8LQwH/FvEQRtfDVMGwcVHoVqKg02hw2QP4oThSUsalpSUronZ2dioSiZhBwrgT1Xu9XhNJGhoasloBandwwcH8WcViUel02pojoJ3x/WKxmDFaKPpSZMTYAgHAC+f1uRM4H4IF9sWVGnSfDxFsLBZToVDQj3/8Y21ubqq7u1uJRMIMPhEjxV9kNl2aJAJCZInvvPOOjo6OLKBw94IMAwYEz39zc9OaGeCCLy4uSpJF5m1tbRobG9Pw8LAWFhaUy+Ws0Ea0CUREswpRrwu5uM7NHQ1GgYyuN7cJaWFhQZLMroCXAzVAY+RnoNYR3dKV1tgV2rieanC7urpsmgGGkAcbDoc1PDys9fV1I6jTgeFWWnkIbsGD1IgCFlEzOrFsmvsQoVOtrq5qZmZGHR0d1kKKBOKjR4+MGuQC5l1dXbp06ZLxJaG4QckBG2IjuYCNB5m0DGzY6/Wanm4kEtHy8rKq1aquXLmigYEBeTwe4zlubW2ZhyUFWl1dVaVS0eXLl9XX12dEdvYCj+oaGaIC9hJ+J5FmqVSyIhDdMkR5pVJJm5ubNuWANmCKCgy1xIhB1yIDcaNaN81zMTTSrUYMlwsAkZ9Dz8GFIugWYtxOx0qlYo0H7l7wmkA5bpcaTBpI6xgPyPg8YwqO8LFPTk6sYEXThVvkaYR5aP+FddDc3GzdbolEwgq3H374oebm5kyYBhYMEBvOwnWilUpFvb29isfjFsXDr3Z5ny5dkedRqVTU1tamy5cv6+TkRO+//74NLSV1pnBJIxPTdMvlsp5//nkNDw9LkhVxHz16pDt37tQ1b7gRLs/v+PhYuVzOYJ5yuayPP/5YW1tbVhxeX19XLBbT1NSUWltbNTg4aONxxsfHjXLa1NRktC4CCMZzYXBdCif7wl2m4Ht2dmYt3twZmrFKpZI2NjaUyWRsgjSOOxaLWfHM6/WadjfvBVTGd33aeurfJhIJE8MGg8IAUVCbn5/XJ598omg0qq9+9asWsbiEYCABOjf29/f13nvv6d1335XH49GLL76oYDBoAHWj1wwEAjo6OlJPT4/W1tb06NEj3bp1y7wWavTI+7W1tVnaXK2eC1xHo1Ftb2/r4OBAsVhMY2NjFrkAhbgNDxgJDjQPB+oP0AfRHB4Q2GB/f1/RaNRwK3AyUsFsNquRkRHDyI+Pj00LwpXcAwJgoReAZ+WZ+Hw+bW5u6uDgwMaJPHz40FLCUqmkbDZrgjC8RywWkySjzOAkQqGQ4vG4KSDR6y/VO0P3wPO8j4+PTdcWuANDhVEhWgMCIaUmqsB4IiRP1Ihz573hx8L7JHrlos7MzFjBy+v12vMgHZVUx/n0+/0aHBw0fFeS1SSI0BthHrD6XC5nMJbP57MGH2h9r7zyit577z0rFKP5CpmeCB5NC7/fr1gsJr/fXxdB7e7uWveca2QwCGSVcJ59vvPBAQMDA3r06JGWl5eVSqXM8cfjcaOlFQoFPXjwQAsLC+Y44a97vV795Cc/sckRLlTkLmwFynxIQR4cHGhlZUXJZFLRaFTf+ta3FIvFDKvFmTKhggyZII5sBFoYP+82/kiqc0JkP8ASBGLYGQrnLkOKjB02h893PkIKB+8GEAQctGn/UhguES5pI5uLF6MItba2plQqpeeff97mkU1MTFj1kaqxdI55rq+v6/DwUFtbWzbexq12uo0NkiwNbWpqMnHoqakp9fb2GlWjVqtpdnZW9+7dsxQa74bQcDQa1czMjMbGxoyG4m4a6RzRGbizdDH2nfSHSA4sC74q0S9YKFihW5Bqbj6X7xsdHdXh4aGl9Gdn59KEPDgKMSMjIz9zoCGEQ4viIuIcwZWr1arxXSnSuQ5GOsdTd3Z2tLS0ZPgZ0nnRaFS7u7sW+UmqwxXZG4wwkx3cbAKDgNHM5XImq+dOFOCy8GuML40SjY0nnA3p3OjhSIjGUODv7u426Cmfz5uYOYURtEzRoW1razNjyXPhsnPJG3F11NFisZhlNbVazQqCkkz4HFFuutkg+/v9fisIUzzjdeAvV6vnWrodHR1G92Lf3M9E27WbMYRCIX3lK1/R888/r2KxaA7v8PBQm5ub8vl8evjwoSqVir71rW9penraIsNAIKC1tTXdvn3b7gJNKm5whBPACDEkAIgK1hNZHRSs7u5ucypkNEArFM2hcwIxcN8a7QXwHxn0/v5+XcYFxg+mzDljmgewDayJdDptQlBu0MCdcIuXjUqHjesXyjNKsjeGuoWHODg40ODgoL797W9rcXFR6+vrVu1fXFxUc3Ozdnd3LYWTLnQRfD6fXn31VQ0MDNgGuNVt11O4VcmRkRGtrq7qv//7v1WpnGut8vCDwaAmJydNuer09FSRSEQ9PT3q7+83MeeWlhYbPuemAETWbiGAPSD640Fh6Gq1mo33AROkdVA695x4Uzizra2tpi5PQQ1hdRgTRF0jIyMaGBiwz3j79m0b5Q7OBu5ZqVTU19enYrGod999V4VCQa+88opmZmbMgOfzeS0tLdlkAvBtNBcoJvh8PiOiNxYK3QjXTaOIcIlupYs5WfBv3bHwFDwkWRTkwjoQ2kn73EIFz4szQwEINToYFhi/YDBotD8YMXTA0e1IFxpdiH6/34RmYK249QEWRUMMHLP2iHxXV1ctywsEAorFYkomk3au3foCWD+RJfgjzrpYLOrf//3fdXJyYgLiPAd3uY6LvSQFh/Y1PDxsz8/tootGo3rhhReM9UK79FtvvaWFhQWDQsBA3e4/MgcGEJAJ0nVJjQPaFdOeXSGd1tZWmwTtNmzw/BGfciNStxOS7jHqD0BEgUDAKHrcSZwD/573Yj9dTrX7vF3WCP+WIt3T1lMNLh8cI0Lag0E6OztTIpHQN77xDSPc0/HF5mAoicYwjiMjI5qcnLSmBi4WF7WxYwMP1dp6Pjf+0aNH+uijj+TzneszuFQsojz+g4PoRtGkJERrfE5oOqQNfA4Xs3HxIcZqUEnHwNCCSEEFwH9vb89oQfF43OAHWoxJIU9PT22ysMv2eO+993R4eKiXX37Zokj2Girc9va2crmcrly5omvXrpno9/7+vp48eaK1tTV5PB7l83kdHBxYxyCXHYF5NFspUnDoePZcXheX5dLCNKAiT6aRyWRUKpWMxkY019vbq8HBQQ0MDKhSuZh2W6vV6gTQ3aIEtQGMEcUdHBuYMq3XwWBQHo/HCnRc0KamJmNW4IhJhVOplD1TziXnmMXr0NZaq9VMUzgcDkuSYdS0p/f19VkgA3+XvQJiIaOAbVGtVrW+vq4PPvhAHo9HL730kvr6+uyZ8CyAb8AX+T0ZH3tDnYA70tbWZgJUiJZz5j/99FP9z//8j6QLrQKXIsmiO5L5hh6Px5pOpItsCicHHsv35PtDz1tZWVEqlTKeeV9fX11kzb7A9pHOecPAFLBAgDkwwE1NTYatsz9017k6JWRYLoOHZ+YGDTgPF/L6svVUg1sqlYwZAMDORcMzIHzhFgdWVlZMsCIYDNbJOGK4uVjQNVwV/UaMjOVSofr6+vT48WO9++67eumll2xOGKkn5GUq81TP2Rw2lAOF4eIQuQ6D93bJ5hxgHAC4Eh4Pgw230y0y8Zm4pKenpxoeHrbCSLFYVGdnp15//XUTW8ERHR8f68GDB4pEIhoaGjIqFAa3paXF+LxExqRmpVLJjBHdTnxWIj72DFYIUoVQ6NgLl6nR1NRkYuJEY7VazVJA8Nq1tTXt7e3VaSbQWQasAE+aLAGDQPbgZj5u1H12dqa2tjaNjo5aIwVRGxxjScbLJarDULEnMAAQKOF5NXY2ut1/ZClcwK6uLuVyOWO9cNlrtZqppWHgiaBc9gyfh8yAiBHx66amJmUyGW1vb1sruVv3ICORLoqqwBqcf9JujBQsFL4LRqparWppaUnf/e53lUwm67rM3LZqFp1tONxSqaRMJmNBBLgygzi9Xq/BPUBOiPhvb2/r8ePH2t/fj+SFxwAAIABJREFUt6BpdnbWhr3yXsALOIjV1VV7XuDp8ObdwIyiNP+R8RJQYViBQzDwZJPguXQDZrNZu18/bz3V4AJ87+zsGL4EDoKhwUhsbGwomUzaFwOLlGSbB+MB40MqyMOgi4T0zF1uSlwul41W9Mknn2hra0vf+MY3rAgFbgrgzWHkAPG5iF7ZYKJsPgOHXpIp/3OJKMgxooZLzyEE/8Uo8PBpF3QjGS4f+9bUdD6yfXx83CJvlt/vV7FY1Pvvvy+fz6dgMGipImk7BZVqtaqNjY26bCEUCtlrXLlyxQ4umsFdXV2KRqNqaWkxrJ0MgAKbW9zkQlOoAqNuamqy50hHIVCSKwVI9AAXORAImL5AV1dXnSgORobFsyTCIUoJBAJ68OCBUqnUz1CtfD5fnTFm6gBZG8YCkj/FQiI5tzjj3hMEU/b39w2bzOfzRk0EiuB5Qx8rFovmeNwxPpwb9EbIjCKRiHp7e7W0tGTTcDl7wAON0A9pt8vEcWsXOEgKlUTpwB3f+973dPv2bXk8HtNUcZ9hoxN097ypqUnJZFKdnZ02xaNUKmlhYcGq/+vr66bfAOeVzzg9Pa0XX3xR7e3tmp+fN1WvkS84+2SpbhG0UChYhlGpVOxOss9ACRhi3rMRKsNWca/gIXP+MfS5XM6mvPxStLBarWYFEDQjiUi8Xq9NaODvDw8PrUKNbKEkG/SGp0WtBz4uaR6cPWhC7sWSZJEyjQw87JWVFd25c0eDg4Oanp6uI+O78IdbmOGQuoeSgwOWSUTM6u7uNqMLnADIj9IW3UFuukpjBpEV35tIl58HX6Jh4d1339WtW7fU2dlphp8uolwup+XlZc3OztqFqlarpsLW399vF4dDKZ2rIg0ODtoAUCbH7u7u2sgi8Muenh7t7Ozo9PTUSOJ8J/aRdIzoopE/XKvVrIjpYuYc6NbWVg0PDyuRSKirq0u9vb3W1YWhohMO7J3F8+O7cWGDwaCmp6d1dnZmE2W5eC0tLdZwwlQJDDb0ObfJAdElsh8uqnsu3OIV1Eiq39ls1sZJodPhMjswkpVKxd4TWMvF171er3He+/v75fF4rOVWumj5Jgtkf4DFcBRudO9G9iwKiB6PR4VCQf/2b/+mH/7whzo7OzN8090HN5pm8Z3A3jGA8XhcuVxODx48MCydn+3t7bVWY87ByMiIhoeHjWFw8+ZNY3fk83mLdGGRcL9pdsrn83XBkAtBUZRzsyeyLjJGFmcD5TWgMAK/YrFor9cYKDYuTyNW+mw9W8/Ws/Vs/f9ZT41wf/SjH9VWV1cNtHfpP0Rmbs84WBnRJDO5CMupvJI2uW2qYEvPP/+8rl+/TrrtkaQ333yz1tLSYh1n4EgueRmcFiyQNNAtiuHB+HmXtO3y6Y6OjpTNZrW4uKitrS29/fbbnr/4i7+oEUFCpTk+PlZnZ6dF+h0dHQqHw/a6RH8Uk4h0i8WiYW/z8/Pa2dlRX1+fbt68qWg0qrOzM7311lv66U9/aq/7z//8zx5J+tM//dOadFH0I+JgT4nMSZdIFYm8XYgC2AU8mn3j9Vkujvpnf/ZnnnQ6XWOaLM0Je3t7loY3YvDs5/b2tuFcQ0ND8vv9xtRgPlZzc7NV6cH1SN9ef/11vfzyy7p165ZHkv7yL/+yxlganj00QemikEQ7N5kLaSXnjlFCFBElWbvx5uamiQ8BWQAJ/cu//ItHkv72b/+2RgeUCzm43FjSVqIxN4ICauDz8+/opnJrHZIsopLOo7m///u/9/znf/5n7erVq1Y8gg9ORkGUXi6Xje5GBgTjoaXlfE7Y3bt3bRhkPB5Xf3+/JBm2il4HU3QPDw/19ttveyTpO9/5Tq1arVp0W6udyzCGQiHbbyJLMFSKUewTBXg6NF1qG88HBsWTJ08MXiwUCvqrv/orz3e+850aMMzBwYEV/YE4wNvdYjnUPxqCsB8u9g6zAoVB4Lnj42NFo1ENDw+rvb1dv/M7v/NzJcOeanA3Njbs1xwiNgxMAxyxu7u7jtoFlgenjp7yTCajnp4eO7RcTvCS5eVljY+P2+GSLgB9UhmXqkFXDgYUrIYHx6UlpQBL5f3oVyetAIMCMuB7k+IFAgHrViLFoOIOBg2mSdMBhQGqsx0dHSoUCtaTHo/H1dPTYxOHu7u7deXKFf3oRz8yQrX7HFzWB3/m0ntcep3779xijItj8+duAYQ9cw0wRU3SYddYw2YAr3Y7yzgbVIApMIZCIZvAgWMEesFp49DZRzcjYx/YC5xMowgPPGeMOD9PUcR1FuwB2CZpOJgv59X9HLwXUACYOmeSi+t2OFHdJtDgbHNm2XfgJhyJC0G4MNno6KgCgUAdh5ngBpiO793W1ma1Du7V2dn5JA4aExYXF5XJZNTW1qbZ2VmDeWiEOTs7M8zePZ+wjqrVqk3zRlrTrae4gQA1F84sv6cHAEcEnHBwcKBoNKqRkRFjnlCMw0a549PdQm5jgxMw0sHBgTk77jAccp4vMCb3h3ZnAk8w3qetpxpcLg2XBBwOcRiM0ujoqPFGKSYBYpdKJTM0Ozs7Ngepu7tbvb29dSOG/X6/jo6OdO/ePb388st2wSFGQ/fisrsRKz3XFIHYJLyaS/1xx/C4HXS8P5FuKBSyLi/3EtEeSCRCpRTqCPQbaEZ0KNFmm8vlrCB16dIlzc7OWoRBJDQ0NKShoSEtLCzUDW90I3guJVgqB5p9wQDz2SioEAljNHhuVIK5gBQ5UFvjQFPRLRQKevz4sebn5y3q4nXAMCuVilGiIPHDNkgkEvZaFBq5ABDWccatra11lC/pAqPECXG5yGb4rjgLCnsYH6T5MLZulE8BxO2PZ1/ZAxb7yfuyVy7e6Rr5xsuNseFcg/mCFZJ10V7M+7sNKG6Did/vN/x7Z2fHioQYdiJ8zg+1g9XVVdNwPj6+GNqKtkW5XLaBiplMxgwumDHnEz4yI+g3Njas5kMGuL+/r7W1Nfl853Kl1IUODg5MQ5pnhIYFal6np6c2ABIdDoIe9oBonjE/OACcIOfTZSfQyks27o50qlar5qg4j9wx3p+s4n9tcClSQdugQNTU1KShoSFL5TjwnZ2dlibgAVzyNTqhLS0t1mnGpAQuK10+6LlK5wRm2iPdg458IQcXhSSiRaLD1tZW4xBTCYZ/h0PgwQBRSFIkErEx726kS7rEZ3MrwxgJ1IWoDJMSctAqlYrGx8fV29tr0xncKKWjo0PPPfecHj58WBdBuDCJG2US4fE83OoxhozL7Xp0vketdj6KZ3x83Jzg2dmZXX6v12uf4+joSMlkUh999JGWl5dtkget1exvW1ubTk/P9ViZHBIKhep0CSh6uNxGLgrpNIWU1dVV+f1+feMb35AkM0T5fN6iTvaFijTOCKPG+5KlbWxsaGtry8TWyXyIhl3qFAVe9pTFuaEgCtRDFudmfYw4Is3FQWOQOTs4AShj0PQ409xJjJ3X661rL4WGl8lk7DO3tLSYWA7OCcdG2+3q6qo1jzBeZmBgwAwpXPFwOGz74y4gAlg3xWLRDDjBDvAXOtjoZ0PhCgaDymQyymQyWltbMylIjKvf71c6ndbS0pK2trZsKgMFMkk2RJRslYjVzeBYLjsDyhrBF0JOsVhMPT09dnbYUzfAaZwY/mXrqQY3k8nYQ8e6Y2C8Xq95uHK5bMRkl7tGJMshhvoC5kgkASn/8PDQxmusrq6aweWBQIhG7o2UwOXEtbS0aGRkRP39/aYMD9QAnQse46NHj7SysmJqXfy9dB7NdHV1WYRLZxmRHSOESDndbiXSWDQJBgYGrMKO8DQ/k06nLRrBUAWDQVWrVc3MzFhHHQtMq/Ggw16A5uQuN1Xm8yM6xHtms1n19vYqFovZYS6VSuro6NCtW7fU3d1t/F6+O7oZjGXZ39+3aR/ZbNbG+HR2dlrL6qVLlxQOh62dlK5AIkqqvKSSUHJyuZza29vtM3BpiJa2t7ct6kcvllZN9zIy6mV7e9vwWQRhiIyIcojeXKF6sH53j4k2eQZu5Rt+tySLivgZgpNcLmdTkeEJA9Nx0ff29rS6uqp0Oq1sNmuf18WL+cxer1fb29taWlqqc6hkiG7GirbG1taW1tfXjcrmQhnJZNJa65PJpFpbW41RQLOHe9YQT0+n09rd3TWDxrw4SXX8a5/Pp66uLsNJa7WaNjc3JcngtomJCc3MzCgcDls0/vHHH+vevXva2trSiy++aOOk9vf37Rxx7l12FawDYBFJ5tTB+BHFOjs7V5lDJpJmERwsr+nivU9bTzW4jJuGvuRStkitIUiDb5Bauc0FHo+nbtolq1o9135FsxOPhrYuy1XPl2T4TW9vr3p6euT3n88revz4se7evav79+9rd3dXV65cMUyPaANceXNzU8ViUYlEQq+88oouX75sMnoIeLgULSJ1VJBoSYSLS+qzt7dno9H39vZMnIUDQJoeiURULpe1sbFhU3aJUkOhkOFeU1NTevDgge0FRomIEqiELiaPx6OZmRkbnJhMJlWtVi2zODk5V9R/8OCBSdRFIhElEgm98cYb+vrXv647d+7o3r17Wl9fNwMTCoVMzAUh8ImJibqWVuhkdLtBFaRg5WqvHh8f2/OmIwuDh4Gge4cZYbVaTbdu3ao7PzhJpsTyXkSwdAEy3hqlp62tLZscjVIXcAAYIgVA1MRco+xeLJ/PVzdVgiiVfwP+SpbHopusXC4rl8upra1N8Xhcg4OD6u3tVSgUsmiUlJ4AhT/nc0BrImCggQAYEGjHnVcGjry3t6cnT54Y1IMTwwb4/X7lcjlzWktLS5qenlZ/f7/S6XSd82cc06effmqvMTMzo5dfflmtra1mJ/g+iNIEAgHrGKVoBezo9Z7LZqZSKaM9kjkFAgGbmILBlS7qFGdnZ5YhU+gmG6DFF+jEzZQprFOMW1tbs/2NRqOKRqMWNEKBZUzP09ZTDS5iJ4eHh9rY2NDu7q6l1MjWeTzn42nQncVo8NBpbujs7LRBiRxCSQYzDA0NqaenR8lk0qrCLAoJzc3NSiQSikQiisfjSiQSFr1UKhXduHFDr776qubn55VOp+XxnPeeHx4e2lRY0muw066uLuOYwprw+XwaHBzUxsaGpY7gYOA8wWBQ7e3tlg5ycc7OzgyG4GJCwEfflY6ozs5OZTIZjYyMWLQ4OTmpSCRixopKKCuVSimbzZrBBR+Ox+Pm8F577TVNTU1peXlZjx490sbGhmUa4MeM2gGzPT4+tskUHJ7h4WFls1ndv39flcqFahkwwOnpqaXZ5XJZmUxGqVTKIBqfz6f+/v66lm1UmYBypqamNDQ0ZA0t4KhUo+mrj8fjmpiYsMq4dBG9kAHh3AYHB01uECxakjV1uNmVWwhEywEVL0mG90J2xxG4kAL/nu/Y3d2t7u5u9fX12cw21xk04rcIpRwdHWlqakrSeXa5u7srSaYNDPQiXUhKupxo0mI6CI+Pz6drEwRhsHnm1EXy+bwJUbltuleuXNHQ0JB2dnYsW6xWz1u+C4WCxsfHtbCwYBIA3JP79+9bF9zk5KTeeOMNy5LW19f16NGjuqIgOLrP57P5d/QA7Ozs2PidpqYmjY2NWQs3gQTQCAEB0NT29rbdeUSSYrGYmpubDQMmM47H41ashXUEvLC3t2cToNfX1+194UUzL3BycvJnzkXjeqrBxXPTz+zOiQdeYDQF5GR3lhSjORDepkGAiiEHYmVlRVNTUxoYGFBPT48pTrHAEtEOpcK6uLioJ0+e1FXUE4mEnnvuOfl8PhPTJoWnGQIjn8vldPfuXc3NzZkjIUWgkk4llyiBDZZk9DAOMgabPcIQ+f1+vfDCC6pUzrVNoXoNDw/rhRdeMHlJIlWG/UkXI+ZZ6+vrWl5e1tHRkeHDKC2hyfDkyRMFAgElk0mDBUiR5ubmtLq6Wlch54J//PHH+u53v2tCzRgjt19dkpaXl21fqtWqdcd1d3erWq2awyGSwthziagicybcKMclk0syY03zhRtNcUH6+voUDod1+fJlg6V8Pp/m5ubqhIxglYx8IYD08OFDi0zZI2Awt2XTdQJEhY2FIrf1ure3V/39/YZbYywxGjiscrms3d1d04+uVCr6/PPP5fV6NTs7q3K5rMePH2txcVGFQkGRSMRan93xSpKsa65QKKhQKCibzWp1dVXb29sGz0SjUQte0HAeHR2V3+/XBx98oM8++8ygDoKY9fV1ra6uamBgwOCOk5MTLS4uanZ2VoODg3r06JHtRTKZ1JMnTywr8nq9evLkiebn5y0bJfAqFArGLkBcitdYWVlRa2urhoaG1N7erh/96EcKBAL6yle+ovHxcf30pz/V/Py8mpubTWDpJz/5if7wD/9Q29vbZpD5vj7fueZKMBg0fJvgKRAIGCNpf3+/jsLKPYhGo3UNMeVyWdFo1GofyMu6nZD/1wb35OREyWRSa2trGh8fN2MUiURsvDcelGkD+/v76ujosIkHFAm4IETHyWTS8KdgMGiV7lAoZEacBY7n8/nMG+/s7GhxcdHG7FSr57oFIyMjeuGFFwwzQnmez0i/+8bGhj766COtrq4am6JYLGp8fFzXr183TVJgDJcmxHc7PDy0B7i5uWkRPphpR0eHRkZGNDY2plgsZpgYC/oMkTFjZ8AUSVFdI3Pv3j2dnp4aPzIWi1lqymiTf/zHf7SxMG1tbbp586Y5CYpqoVDIoBrU1OjQ6e/vVzAYtNc7OTkXcEbxCUggn8/bwZ2cnNTU1JR1JvLZJRmOy4BB4AnGIaFRPDg4qIWFBeNlsl8tLeezty5dulS3FzhR9g11fs5QT0+Pnjx5oq2tLcNIx8fHDZOLxWJWeGLSQ39/v3V4pdNpi1RJKxEnacQtpYtupv39fW1ubhrHOpVKaWtrq06ZzGXz7O7umlZzIBDQ2NiY7TFpLyOgksmk6WYwERmDWy6X7XPPz89rfX3d5E+JAJubm83wjYyMKJFIyOfz6fXXX9fe3p7N/ZqamtLk5KSOj4919epVxeNxbWxsaHNzU6enpzYhZXJysg5XJ7p16zzvvfee7t69q3A4rD/+4z/W7/7u72phYUHLy8s6PDzU3Nyc/u7v/k537tyRz+fTxx9/rL29Pf3e7/2eXnrpJaNONjc36/Lly8pkMgZ1lEolJRIJjY2NWfAADgtnlq4w6grJZNKydYwy3aNkEQQkwE2ubgQ1KOBKngE24WnrF4rXgHNR8OLAQznZ3NxUMBg02IEqOqkTU0knJydN9YfolkuMAf7ss8909epVYy6w2Eiqz9I53AFp3ev1Kp1OW7HpwYMHCoVChi9RJEAfgI2nyIGoTlPT+USJjo4OXb9+3f4tD5F+f0j8YJZUpJkwAOYINomRZmwLWBh0GTwqEQEGvVwu2/dkUdnlwpVKJX3++ec21joSiWhjY8MoKqOjo5qdnbUIissGrEEBwOv1ampqyqI89iwajZo8YSKRkCTz7HwGcO1PPvlEHo9HfX19Bg8Ui0XTQ+YCSLLoHw1fn8+n+/fv14mruzStN954Q6+++qp+/OMf2154vV77Wag96DMfHh4a3tfV1aUXX3zRaFZEX8Fg0KIXJiiD3Uvnafvw8LCampqsWYD+/MaiGRFxLpdTMpnUwcGBtre3LctqamrS1atXbX4WZ5UoP5PJ2GujoRAInM9VSyQSdQVXzuvZ2VkdV5TvTvReq9VMFCqVStmzunHjhulWwyaZnJzUV7/6VV2+fFk9PT2anJw0IXYKgB7PuYQkqm8nJyeanJzUysqK7UU2mzVcH8aRJONcZzIZ7e3tqbe310b9rK2t6Yc//KEGBwc1OzurpaUly4Ro3yU7TafT2tjYULFYVF9fn65evarJyUnbT0nWYOLy7/f39/XZZ59paWnJot1qtaqBgQFlMhnlcjlzoq7olcsLh/1Bsw4FPgr++Xy+Tlfi/9rgAgFAVQK7IE04OTmxCG95eVn5fN7mPbneIpfLKZVKqa+vT8fHx1pfXzf92FrtfKw3HgPGgBtBEDlStGttbVVvb68mJyetqMKfc4ndKHRkZETlctm0WGEc4K1gYEQikTqhC0B2jP3JyYnW1tas4t7cfD5Kpbu72wTIUf2PxWKWUjIuJRAI2Eyn7u5uTU9PG5UHLI90n4LH4OCgRZbS+YQGCl/Ly8uWxni9Xg0NDVkfPJkFc8pQooLV0dbWZvAGeHFHR4d1zlBEotjIJAKcD/xenAefA0PgNrS4DQvg3lR5KQBBlaLDByYEBhqsb3193fYiEomYcDZRSGtrq0WMGNvBwUEbhnp6emqpODgnf4f4OM8MbYatrS2rBXyZWhjfn7NNxMdkYK/Xa0wV6WKCCRKGTOPA8RwcHGhpaclGSHV1dRl0xeuB1xKAoMUA9ktaTVYxMzOj2dlZ7e7u6vr16/Z5MCQwJJiGEggETFTGZeN0dXVpdnbW4JuOjg4T0JGktbU1eb1ec7KFQkGJREIvvfSSOjs7NTY2puXlZdtvIMv29naNj4/rhRdeUC6X0507d/Sv//qvSiQSisfj6u3ttfuMQTw7OzMh89XVVSvSMTEcqGdtbU2Li4u6f/++QqGQrl27Zv8eMRywbaAFsotKpWKZIFDSzs6OBVwu5k0G/L82uBgGjAvcRIjS4HelUkmpVMouGAYar4SHlWS4iNs1A3mYQ8umsoiqkTmkuwQGAbgqqV17e7thOFRmAchRc3KnRbjtwjR6YMRJEZqbmxWPx1WtVrW8vGwYrSvS09zcrGvXrpmKEl59d3dXQ0ND9j3S6bQ+/fRTjXwhLo5AO8UTSPiIb8zOztpe9Pf3m2pWuVxWf3+/ZmZm1N7errGxMUuhWGCl4XDYnAfsEy4GjBOq3kROGDIOGhABB5FDCU5NhAZuhsFGKo8UmAIkUAgYK9EBRRRoVRD15+bm6lI2IjaCAAqBdLbh5KrVqnK5nOFyFMEqlYpBJtDB6KgiYtzd3dXGxoZJPjY3N9edTRbvTzYAjjs+Pm7OFGfCuaTwyWdJpVLa3Nw0Dd1cLqd0Om3tqeDIcNbBU3l/iqhkYa+99pqGhoZMhL9UKml0dFSjo6MmE0khkLtdLBZtoGRra6vxUP3++nE/e3t7FjnS+ovNgOIFhHh2dmY1mdu3b5viWTKZVDabNex4f3/fMgVGJm1vb2tnZ8da6MPhsKrV8xZ7iluFQkGrq6t2Dwk2ksmk2aGWlhY999xzisfjln3Cjlr9Ql0vEonU1S64h8gV9PT0WFa3t7endDpt/NtGQaOft55qcF2RZpfe4h4SyO5Mi6UQg+ckqsDYYcSISOnSgStHOuBGuDwEeuGlCyV2V+6R1kvXORBJkLpRsCOCcfmIzc3N1rzBe7qpY2dnpy5fvqxoNGrRLwcvGAyaGHQqlbIUkZZJYAZ4kB9++KEeP36soaEhjYyMWHrCd6TrJhAIWOVaktF+wNOj0ag5HgoSGFY3eob20t7erv7+fvl8Piuo0HDCBeagQamCw4n3hrhOFkHqD43GLUwyvbdSqRiFkENK9Lqzs6NoNGqYORg2mU1zc7P+4R/+QcfHx3rzzTfrzijsEC4QBUsoRvBKSQuJFNnn7e1tcxKDg4NWFMHhbW1tGfOiXC5bFuRGMm577uDgoBHtK5WKBRi0F2OwKMYBl+GkwM1pwZXqITV3uXRDghyaB379139dv//7v6+2tjZtbm7q8ePHVjCcnJyU3++31J8zAvxVqZyryCGTydnBQTFKnu/DuZekwcFBNTc3a2BgQAMDA9aNCPOFu7mysmKaBnC6Ua07OTlRf3+/jo+P9eGHH2pqakrr6+tG5fN6vYrH4xoZGdH+/r5SqZTS6bQZfjJCWELcabIHHCBKbIVCQX6/3yAm9pa7RKcaLBOMKxkZ+052+LT1VIOLij8PhQ9C9EcxC5Ac7JVQHk9SLBYtnZJk3UFuq50bRXFoWBgAPAjpF/KIcFbxZHR7uf3/GGVem/cCUnCFyxHPAE/jsLMHCFdQAKRAtb29bQWL7e1t4wGjNTs0NGRcxFKppJWVFROqhraGBgWGtVqtmkHlQLtUNDAqGk8ODw+1s7Nj5HiXtkWBgEIAhUjSe/rFwd+JCkm13QxCklX9OSNuGyyUMw4iI7WR6qSQFo1Gtby8bKLfPG8+K1X9ubk5+f1+a4yRZEaes4SToC2W5+5S36ABAZPAyz48PDRHSzszDS5QtlxmhxvN8AyIfKGAkQLTtYWGAk0ijeeeO+WOtG/UxcAgMk0B44JuAg0/k5OTJjKTzWbl9Xo1Njam559/3potCFgIQiTZzLf3339f165d0+XLl40PTEPM48ePjZkCddR9JjwzpppQx4F7m0qlVC6XNTk5qUePHqlQKFhRlcCEbtRHjx4Z/5WIl6xxa2vLgqf+/n4LxlwGB/gvcKLb7UiG1khB5TzAqMI2uMwZJDlp1cY+AWv8vPVUg+t22PCipJAYIJdGAY+uo6PDNCs5KI2XnV5poguMoJt6sjDwFM2oYLsREfxeXp/PCFTgThVw+9eJMuDf4gSAPHiIjW2TFGmgpXAJeLBMEy4UCspkMvrss88UDoetUDE7O6t/+qd/shZLOne8Xq9hqUQEbjTF4MFAIGBt1xwsVxfi9PTUWikxpkQpHCy+I90zkP5JXxErQXjcZViQuvPsmY9WrVbtwhClxWIxVSoVg6I2NzetwNrf32/4KJ+LwitGjcifKJy1sLBgl53n43YfctmKxaIGBwfV0dFhNKRgMKje3l67vHx2Xof2Z2hW1CsweK6RoU+f13CjWppN+Dm+E+eSPfL5zqf8npycT4wYGRkxiiRRE4FMW1ubKaDhpNhz6h/cWYziwMCARkdHlc1mdfv2bd24ccNmuKGly2ei6Pnuu++aEQ+Hw0bzpEgJbc7VtyBIoW4zMDCgq1evKhwOa2NjQw8fPlRra6suX75sHY+Mie/t7dXQ0JAODg4MIggEAjo+PrYWYzIHWpKdWr6GAAAeRElEQVSpJ7ifATEiYCqCEHjtZFAEdTg1YELolgQW7p3w+XwqFosWzGGs0Vb+pVgKtJFiCN1LQWQDdnpycqLl5WUjqq+srGhxcdEMSXNzsxljDiXGwe0Hp3OqEVIg5SKao0WSCiGesRF+oEWStkagBRdP470RwcFAQSeTZF04eDUKS0AZXFZgE7djCQEQcGyMJHQjJmlEIhG1t7cbfMOgSBfWcIUzKB5QZENikJQePJbPiZFl/yn+wIckSqKxA+eDUXa77XgdV6QGPjaOjuGNPp/PdAA6OzuVSCTMkBBBZ7PZOl0OetbBJd1CEWtkZEQ3b97U3bt3tbq6as+TZ0Irqd/vN+yOSJa22UqlouHhYaNVEVS40Y8bbbsdjyxgNorKZApEe21tbZYhIOhO4xAMDzKxYrGo9fV1DQ4OKhwOKxKJqK2tzXBuUl+fz2fiOyxSZ0nmcHFSiOd///vfVyQS0eDgoInYP3jwQJubmzZ+nah3d3dXb7/9tl599VXLSpGwdNucXaoenXlHR0e6f/++CVdxJ5iiTEfawMCA1tfXjb3j9XqVSCSsxoDzbWo6FylHInFjY8NqCTxz15lxP4HIYKNgd4LBoEFRTO2FuQIPva2tzbj2yJDC1OE8gPk2tnH/vPVUgytdiKVgBCl0nJycmHc9OztTKpXS+++/r7OzM42NjWlhYUF3795VS0uLbt68qVqtZl8IAw1UQAGOCLbxUAO885B5sBy2s7MzwzTBwMBkY7GYTQkF5ysWi8pkMkZJq1QqVkCSLopCRI7SBaSAd3Snv+7v75tT4fPykCgAMjED7Le1tVXXr1/XZ599ZiIfPHiitJOTEzN4LBwfKQwpDY7E4/FYYQF8GcfJ9ycScr8rHppUlsNLBgFeJV0MIHSpN9Vq1fYAAwVGh8Nsb283Rgf0JtgYtGq6jA0weVcjwE3ZfvM3f1OdnZ3a3d3Vw4cPDUM7PT017YFMJqPXX3/d1NjAconoMFLM3Ovu7tbOzo7tB6ljYxDgPhPOClkEkSjYOEU/qGVtbW0Kh8OmPQIPmSAAtgOTfqEmQW0kynY1HYjSpIuuMxxic3OzDg4O9Omnn+qDDz7QH/zBH2hxcVEfffSRUqmU8VIJCvb3962ovLW1pfn5eXm9XuO/F4tFm17hvq+kujuwt7enxcVF43onEgldunRJ/f396u7u1vb2tr3vxMSE+vr6VK1WFYlErEsP6idnwi0sE8x0d3dbAMF7M2PPLZy6WQnZMQFeIpFQIpEwgRy+B9DTycmJ1tfXtbe3Z0Gkyxghw/yltBT4wLwg0aibOoJrjI6O2hyhQCCgK1euyO/3a2pqytJ8aBd4RQpopJsUHjA47uF2GRIcvkKhYJsILrq9va1AIGDqYvSmd3Z22iGhDRR+MEwMLrvbQsnG8/61Ws2MjauEBjxCVO2OBB8YGFAoFNLR0ZF1ovT396u/v1+np6d6/PhxnTJWY9umu/h8vCfpDgeDC0kUAlMAlSVwztXVVYt6+W9ra0v5fN54tnCTcTQcJiIOKsmuDgHGGaNHitvV1aWuri5r2e3v7zeudCqVsuIdqaok62Zj/6vVqo3MkS4gLxoYSDFLpZLW19e1uLhougSwSRCxB0c+PT21MTjgonRsgd2CsYKFNxavcE5ANsBTcNclGb+Yy9toRDCuRLOc8Z2dnbqsy4Xd3AX2y33h7pJdbG9v6/bt21pbW9OdO3f0gx/8QO+9956CwaCJ3mPMjo+PlUql1NTUpNnZWc3Pz1tkzd3ECbrsFUnm7E9OTuzseDzn02/D4bDxf3FM09PTmp6eVnt7u1Eg4X/DsFhcXNT+/r5FvS43nuGbBFySDPIDNiQbpM2XPXJFmIaHhzUwMGDZcGdnZx2TJxgMKpvNan19XR6Pxzj1LgWyEWr6svXUv6Wi7BYL8PLgNKSkPp9PX/3qVxWLxdTa2qrx8XEb0V0qldTf32+Gm0IGURMXjKjOLVC5l43FZeZAlctlbW1t6fj4WO3t7dZqd3x8rP7+fg0PD6utrc2mEEMZIpWamJiwTitoQ3Q7IUZBpAH+28jCIBoibYZpMDU1ZZeE6BSd3fb2dt28eVPNzc1aWlqyAgstrLxH43d3Dw3q8+CBREzs397ennU5+f1+FQoFbW5uGhUmFAppdHTUUs5sNmuYKlgy1XAMLt9Fkp0PYAciYviugUBAsVjMDEkqlbJiF9gbRRSfz2fpGVEJzBgO8uPHj3/mnBKdIDAiyV6ju7tb8/PzWlhY0N7enqLRqCYnJ22oZTqdti5BcNu+vj5VKhVrUYa6iENt5FpyPyhuuXsjyebHdXV1KZlMamNjwwoy+/v7SiaTSqfTdo4wbjhQNyLn1zCIMD5AaERyZItkdA8fPtQnn3xid+34+Nh44lCfgLYoGuPw+vr69Mknn+jKlSvGNeXnsBEsnDDFc7BsPhPFJ/4OLi6ZHwXLcrlsGD0GGH47miAEGTg2zie0Qn5PZyXZA4I9MB56enoMZmFcPANMuUd0uRHMQJej7kK28UtpKVC9xkC5dKlsNmsiH0QmHR0dJtdHZJVKpax7CV4jHD1SJehXLmzg4kLuNE9wEoxuuVw2/A54gEvd3Nys3d1d+ze0I2IYh4aGlEql9ODBAxNIoVhHQeDatWuSZJSncrlc1+bHwyDdBbcjQjk5ObGoZnd3t04og9QbQZVsNlsXddKd5S5wTwwLbJCmpibLPKBGIeYMC4D9YXoytC2ijeHhYUsdeV8Xo3XPhftrt4CAY3ZbH5eXl1UoFLS8vKx0Oq2xsTGNj49rd3dXS0tLFkm79C1eg/cC2lpYWKjbD9LmsbExZTIZdXR0WKsvTTBEH0BZdCfCcz09vVBgI+qFukV07gYe7ueSLmQi+Q4YZzfahY+cy+WMzwmu29vbazxpqGGcHdJq9oaLjXHHKGJIgIVgPRwfH2t3d9eExScmJnTjxg1dvXpVKysrNhW4UCiora1NKysrddxzKFobGxtaWFgwvQO/32/nzt0LmBc8F5ffjuoZ2H6pVNLc3JwJ3VSrVa2ururs7Ey3b9/WnTt3jPf627/926ah4vf7jUYGVNgYmKCFAgzqwgrHx8dG9wPX5S663WXcb17j+PhY4+Pj8vl8ymaz1k1HkbaRvfJl6xdiuBwyjG+tVtP29rZhPWB3UDfAZnno6CqAiUHbIUqikuxGUYeHhwZVsKjM0vyAgcMrx+PxOk2CZDKplpYWa01ubW21Vl+wxtHRUfX29po0pMfjMboX3tGlOXHQXSOHp3Y5uzgfGA2uUj3GlhSTdO/k5MTwcRpI2HO38slB4r1pTuCwY4w5WJD+wbt5JihjgTvRNICMIV1NFLJcg8v3xPER9XH5EIKh8MUcLKAbSSays7a2ZhEsh51zxt5TrGzE9sH9vV6vZmZmdPfuXUmy6bA7OzvWhhkOh60ZhoiWicUuDs2fU3HmAhHJkD43po44KD4nGDaEeq/Xa8LeIyMjikQiam1tVTweNwhgZ2dH+Xxe6+vr1gYL/kk0CROFaAqjgOwid0m6mEHGXu/t7WlkZMRgtunpaT158kQPHz6Uz+czCVW0hYlk9/f3NTo6qrW1NZt4TIG1MdoHcpRkNgDNA7ImaGnSOcUQLu3y8rLi8bgFTXyXrq4u0w3h2YXDYRvxhQ2BzXHlyhUlk0l7RkBi3Jezs4tpzi4feWdnR2dnZ1b453PD1d/Z2VEkEtH4+Lh2dnZ0//59+znO/y/V2uuC8lR+6WIBXoA3W6lUjLNIJEzkR8RKygxHk7ZhKty8J4acBf6LKhYjP4iuh4aGjGfp0p4acZ1QKGSpDheJVGVpacnoHj09PapWz/U43T5xohgWjoOonKgWQYtwOGxGGiN6fHyshYUFi6AROmltbVU0GrVL79JUXCODESCC51LD9aRYQuEEUjqpOepiKHhhuInu/H6/qVK5BTf3Irv/d/nZvL7LgMDru7UAvvPa2lrd6BacoctakS5SeiQFWUTBlUrFlK+Wl5cNB6fRQTrHeTFu6XRaIyMjRpEi4uU8U5B1iewYF/bIPQcuBMalIwOC6bG7u6vd3d06mMqFS8jQUIHj77e3t+Xz+UzPGL4xESOFa9dB4bTRaF5fXzeJ0N7eXuuWi8ViikQi6uzs1Ntvv23NOtFo1AxlrVar+3O3UO3CKCyMHwvGg3QRvPDdMKSrX0yZgMaGk4JNcO3aNWPwICcpSQMDA1awRslNkn7jN35Djx490urqquH1PI9araa9vT3THpZkwlvJZFLFYtGCDDjau7u72tzcVDQa1fT0tKkWxuNxa1PmzP5SRTPXWmPodnd3ValUDOSnEk9aRt81D8Xv95vnpIXQ/WCE61xOKuYTExP23uBx4DQQjL1eb52IDbgNBSf6/6kG8/AhnbOp3d3dRiAnLWxra1NPT48p1BO5eDweM2x4Q1Ivr9drl4WLC7AOhS0YDCqZTGpubs4q6/39/QZloArFYa/VanVSlVTp3UNNUYBD5VLoEEymIIeqPmwEngfdYvBXuchoKYBx8lxJG13cCgdAAYboG+fAHsHjdDF99ou9xpG5F/ro6EjpdNq+NzQpoourV6/a39M+ir4A4kYUSREWunr1qrLZrE5PT00xLpfLyev1WprrYsgYfjcgIBrEiXOuXHobzgi4yW0fd+mFnFOeC0Us16Gx94yykS4yUX4OY8t3B0JDNzmfz+v+/fv6yU9+ou3tbSs4Q2ej0MU54NxRAOd7fhlLgayMIrXbTUfRC0dOEEbjArKQtNlOTU1pbGzMCmEU4yiiYUTJUCTp+vXrevPNN/U3f/M3dXvrUtkmJibk8/msIJdKpRQIBCxiBXJDhAihHLjc0CiBcTjHv5TBxfPyIanCcXCYjAlMwOFzMVk88dnZmQ2LC4VChvVJF1VvAOnT01M9//zz9jkgNWM4Ozs7TfAkk8lobm5OyWTSsDGX7oFBxahLFzOMwOsGBgbU1dWl0dFRExDhUrmtekSdROwYNS7I7u6uKaxJMu4nI1Pg9J2cnCgejysej+vq1asKBoP68Y9/bNgdlwbDtbi4aJ+BogmHyC2gYKgQRwFbJIpxYQSyChgP9MfjTEOhkPXru2eAyw1m5WKKLhcSh8Hvoc0glENBqLW1frosz46LiRGu1S6G/bGg11HIGR4e1ujoqObn59Xd3a2JiQmjdV2/ft0EjPL5vKla8f3pYIIji4gJF52olX1uxLTZazfYcLnrNLTQHru1tWVpNlAa0Vqjgef1oA3yfd2Cc6FQUH9/vwqFgvFgUbWjADw9Pa3Ozk4dHh7qrbfe0jvvvKNyuaxYLGbCR52dnSoUCjYeiYwVZ+tCdzwnly53fHxsn43CdkdHhw4Pz8eSo93B9wwEAhoeHrbMDuceDAY1NDRk+iCI/HAnXcYGQZ57Nl566SX99Kc/1ePHjy2zhEZ5dnZmUx52dnaUTCa1s7Nj2DITJ0a+kK9EnpKmB7eJB61v6ieNHO2fsalP+0usNSkqUdLZ2flUAyYi0MJLhOWmnEAJFIzC4bCF7JLsAhLRUJFknpkka4V01bC6u7vV0tJigP/u7q6uXbumb37zm0bFoWCCoAgYJfjo97//fX300Uf69re/renpaYsI2Tx4rO5euFxHt3uLyufGxoYZ3FKpZA0E29vbFp28/vrrGhgYMAyUg/fJJ5/UFQXpFlp19Ea51C4ehdHiApydnZmh4Hlx+F1ng4HBuPHcwNo7OzuNh+w2YGB8cZg+n69OKQnjyPdyPzdYPmOEMGxEZxSqiDbJZLhcbjRFCyyGLBAI6OrVq1pYWNDR0ZHxOTs6OnTp0iXD1Uulko33QbcApyRdRGLACjwj+NrueZBkbb+SfmZQIWcD+l4+nzca4I0bN2yKBfStZDKpd955RwsLCyqXz0XFv/a1r2lmZsaYLcVisY6pIsmK1aVSyaAW3hMDhujT48ePrfMRXeBoNKqHDx8qlUoZZMdZOD29GA3k9Z5PcGmsxbD29/etvgE7AWivVqvVcdsxxu4zBcaJx+O6dOmS2traLBCBfyvJsg+4uECb2JS+vj5NT0/r/v37xnYBKuKzIUjV3d1tGhpkEwgQQWtlL4GpqEu5xXOyjKetXyhATlGFg+P3+230hXTOuwOzA+PEi0gyvQEI7mtra1YI4JKRohA1vfjii3WKTNC5zs7OjHZCGn3lyhXzOpubm0omk7p06ZJ6e3v1yiuvaGBgQJubm3WedX19Xd/73ve0srKiN998U1//+tdNyAYaFJfMZQO4XSu0bHLxgFwQQwYDA8cNBM4HGtLFwneu1c7FNW7cuKFQKGScVCJr9EPtgX0RbVJJ5RC7MAeHk1ZMt40Rigx0MqJitBDAAomiiVa+jO7i0mH4XCcnJ4ZNuqIeQEInJyfWAAAEAz5MlH1ycq57ShpLugbE4J5PdGNJ5wYGBjQ7O6t33nnHIJOWlhZruqAZ5sqVK9blVSwWbWot9EIKZxSm3Mi9Marb2NjQ6Oho3bOhE4yVyWR09+5d3bt3T8PDw3rttdf05ptvqq+vTw8ePDBZRRgd7NnIFy2+bhG3VCqZIwJS4Dm6VKaWlhaFQiFls1nFYjE1NZ3LXj5+/FgrKyvq6urS3NycUZzcbJUzgIOMRqNGyUJjxb3zLDJa9oi95Dmwl8ADBFuBQMA+RzweV2dnpwnk1Go1oyvyLD0ejynPuTx97mpzc7Omp6ct48UB5fN5K+TT7g/sSUt2a+v5kMzh4WEL3ihCA5+SfXFGiPh/KYPbSHUAh/P5zmd+UZjBo9EQwMFE8s8tFLDRYCCkaBjkvr4+Xb9+3b4A/yaZTGpwcFDBYNCiDrzt5cuXtbGxoSdPnujOnTvKZrNqbW1VJpPR6Oiojo6OLD3OZDL6wQ9+oI8++kjDw8N6/fXXTcyCQwYfWLoQaoHuRS83n93FKsHYIE3DYyUFdfmtRPIUDxFa5qJ6POftuisrK3UUOXiTjXixi49hcNEI6OvrUyKRMI0KIBA3SqMoSYGMVJgiHdEMl5ufw3ASRTNVFm4jn1GSRdloKFNchQJ1cHCgXC5nXXzQ27jYjYeZ5+TS22i2oQhEQ8Tq6qouXbpkZxg2AntNVEjnIEMYq9WqRcYuNutqfXDOXKfjqq8xZYAho0tLS0YRa2trM6cK5em1114zXjXPNp/Pm2yjq1rFM4RT6has0Xim4eLg4EAbGxv65JNPND8/L+mC1ufi5mDs8GHJuMhyXJYM0WXjcpW0KETjUHkPDNjh4fn8sY2NDbW0tCiRSOjw8NCac9AlaW4+n1YRDoc1ODiooaGhutZ+AhOw40QiodHRUa2srNTRJomUa7Wa8W2bm5stAwZaYH9cu+YGXq6IF7awkbXRuH6hwSXKxdCAe5IG4uEo1mAcKC5gAOC4EYXhmSQZ4bxUKunll19WKBTSzs6OKUP5/X5tb28rmUzWtX+i2tPe3q7Lly9b1xCR8Onpqe7du2eHqVQqWcfRc889p+vXr2tkZKTugHHJMCZIvlHwIx0i7aCoAcEfB3N6emriPRhhDHEsFrM2Z17TxWzpOsvn8zZSheUWCMgQ2G+M8cnJiVHc4JFKF+PN+Vn+o5gG9YnvAOMCQ+caaC4dBoyK+uLiorLZrEKhkHK5nJ0RV+Q8l8tpY2PDooRisWhC1zRtkE01flY3mqJIyqRk9qa3t1d9fX16/Pix7VUwGLSx12QPtJU2NZ1rMLitpuC7LguDIg2FUhYdWuwzxqmp6aJ7sa+vT5cvX9aNGze0trZmHYE+n09jY2OW1ZDCg2e60FGhUDAmDK/P87969aoFLdQaYNv09fUpGAzq888/N2fjUtSGh4frRpoTsVWr5222jLNxMX2coQsR8N1xzAQhfH4mYvPvaIT5/PPPbaij1+u1TARDS4EXJ5ROp9Xc3KxIJGIZHdQuSfaMhoaG9LWvfc2UzTizFM5ZBBo4VQIXInPwdWBGzjKZnAup/FIGl95tNtKtRnMBgRv44m4FG3yXP+cBuIUUHkypVFJnZ6dmZmas88U+5BcE+pWVFfX29lrUSTR4enpqs6jm5uaUSqXME5Ly0hJMm+vAwIAmJibsMxAZAIeAL5E60vCAccTgugpq9HRzaCloBYNBwywZGAn1hgt5cHCgbDarZDJp+0drsPtASb/x0I1pPZQfRIKk8zSP+XKkb24kjrFlPzE4bhrrYrQu1YvnDITR0tKiGzduGAULus7R0ZENS6QIymgTDI279zh5/o+xdaPcbDZrqSUSgkSFw8PD1swABSkSidikVSIbhHuAo8h0XJF0MjjI8UAY7oKjCZZM5xy6wE1NTerp6bH95nvSAegGMUSdYOjuBIOenh5zqO65AM8mM6FoTOqMsltnZ6f+5E/+RP/xH/+ht956SxMTE3ruuee0u7urdDptMBU/D60qnU7bWebuEVG7RgZdZzoGcVhu9gieS5YDZY9usIGBAZs95zbwYBTz+bxxabEVrqoa7x8Oh3Xz5k19+umnSiaTZjxxajBRCCIPDg5MQIfAh+fMJGScAlEuWa9LeXva+oV6uLwQhpK0i4vGZaLIQ0HM4/FYWgh9AqNLayfiJBRHfuu3fkv9/f1WyHCXx+Opm/AL1YjU2+fzaWBgQD7fubA2UaKbAmJAe3p6FI/HLVV01a/w7FTRMVocGEmWzkPhQTuWAxeJRIznyHQFIhOXFsXr7+3tKZfLafULSToO8NbWljkte2BfRE2S7O+q1Wpd6o/2LAeMxgsuCI4DwwvrBGofBpf3IBJ1C0burzGYra2tGh4eNq6nC0dR+fV6vaa5urW1ZX/OoXerzwjeuJ19boS7vLxsURdTj4lQEomEmpqarNixvLyss7Mz0yQuFotGn9rc3LTRONVqta7gg4Fl78H93A5A0nUi7ebmZougMC6SbF+5J+wR7ayktQQ0KMeRfXE2XA48r41Rb2pq0ubmpiTZPvLndPGFQiH9+Z//uf7oj/5IJycn6u3tNfUuGkOWlpasIQiqJWcIx84Zcw1/V1eXpfbt7e3GzYdjjnoYUW5fX59xs9F0DoVCKhaL2tzcNJjp+PhYIyMj9rxhYXD+MIDumSXKff7551UqlaygSJs/d5qgBXvl9V50jXIPyLzIgN07hZ1p5Mx/2XqqwZ2amtL9+/ft4HCB3U4Xt8ODlMsFsIl+Mdhu1IWUYT6f18TEhG7dumUyaC72h4E7OjrS/Py8RWO9vb3m9ejKCgaDViDAKUDJcQ0NqRGVZAwNVc9GnJN2US6im6LwXqTgTBLlkJJqNjc329RhJmQwAmhnZ0erX8x2I51Fts+NIFzsGIMPJxPnBhYIJ5JIi4IOhoTP7k6IwChgZDlALvuES0/EAr6FseHwUjChi4fvyUUm/WSf3SYAFyOEs9m4F+vr6zZ9mIyG9w+FQua8e3p6lMvlbPIGzAbSQhw0hTvpIvvCYNGqjpFzAwI+Xy6XM8YIkRRnizvBJXcjP86cGzW6BWj310AG8KP5vMPDw/J4PNra2jK+Mw0gTFLu6urS6empZYgjIyPGbU+lUsrn88rlcvrwww+VTCaNhlWpVIxGRTeg9LMwGzYANgCtuND9arXzDrOTkxODuzh/ZDtg4BhQ7u7w8LDpX3R0dNi546xR1Me2HB8f212fnp7WRx99ZM+eTAlqF92qTU1N1oxDcT6dTps6mov9tre3W0s27Ba3Oejnraca3LGxMW1vb5vCFRa9kS7mXmAsvaQ6AQyMoqsGhtfu6urSN7/5TbW0tFijgXug+TWRx8rKirq7u7W7u2upJJtJ5IsxBpoIhUJmNFzeKF4LGhX4F0YA740WgaQ6wW6+M/QrsB9JdgHj8bhisZh5UtowXe9MdZxLt7S0ZJ1EblQHlo6DcGEOIiEX5kCzwi3s8excp+n+hxPitdB2wNuT9VCRpqCIQeTzUdzBqGxsbCiZTBoeRjTL5wA+aOxnd1/bNbjufmezWeXzeRuW2dzcrMnJST148EDhcLhOVS2fz9ed3ePjYytauZxbjD0k+J/XTcQlJvWmGQcM2A1U2EuieffcsG+uI3f1NHg2Lp7JZ5mfn1csFlM6nVY+nzfjiuEol8vq7Oy0zjXpPEN48uSJdnd3TRsFfJ3P0dPTo+7uboOguEM8v0aYB1402WJXV5eN4GIaNK3DZMOk92dn57PPYCTVaucKcrFYzOiD3CHeE8jIhVgIsvh8AwMDNlGFu4SjLxQKevLkiZ0J9n10dFSnp6c2q/Hy5ctW6ERcCGMP24fi/9OWp/HwPFvP1rP1bD1b/3/W00tqz9az9Ww9W8/W/7P1zOA+W8/Ws/Vs/YrWM4P7bD1bz9az9Stazwzus/VsPVvP1q9oPTO4z9az9Ww9W7+i9czgPlvP1rP1bP2K1v8BW9q0BWNEN+wAAAAASUVORK5CYII=\n", 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BGnm78XUbXJSFDUIxM5mMHeYQgm5ubnR5eWkRLDfmF4HD5B8im83aQSIVCyHYQ7E4eA8+N5vN2iZxr3iwVquler1ukZH3+DMzM5ZW+A3sdrsTESswCspERLmwsKBcLjeRJvkIYDAY6Pr6WsPh0Iylzw5Qeg7J4uKicrmcRd7SrWG6vr623/EyNzenUqmkSqViB7LX66nb7ZrD8Yd5eu3Zw+vrazWbTbVaLXU6Hd3c3CiZTGphYcGyFe6LiN4bxtFopEajoefPn+v09FTX19dm9Nrtth3Qdrtt64jS+ghkOByq2+2q1WopnU5PpIvolo8yvSPGSbL+nU5HjUZjIuJmHXEa3W53Ig3lz+FwaI7n+vpaIQQtLCyo1+tZhMOh7vf7E3uSyWTsvmu1mhqNhkajkXK5nJaXl7W6uqpisaiZmRm1Wi1Vq1VVq1XV63W12227Fvu6sbGhXC6nTqejvb09HRwc6ODgQOPxWFtbW0okEsrlclpaWlIul5Mk26dsNmtGGvgkmUxO7KMPVLwzvLm5mYD9+H8c98cZ4PF4rMXFRVsLnBNwF7AGeol+RFGkXq9n2ZmHKWdnZ23PZ2ZmLIhqNpu6vLxUt9u1KJXr+OwnhGBZ6uzsrDn9xcXFCQfBeeBZOLsEanzW7OyscrmcSqXSxBpzrqMomrAVL5OXGlwMaa/X+worT+qEZ8eL3dzcqNFoqNPp2GKQNmUyGa2srGh9fd3+D0NbrVa1v7+vk5MTw+IQvA8RJZEdaRERAGkAEQwHnijAp3PJZNIMSrFYtM28urpSv9834zw7OytJZoCKxaLK5bIKhYIpMen2YDAwZep2u5bSNhoNO8QofTabtUh1aWlJi4uLSqVSljKFEDQ3NydJEwd8cXFRpVJJCwsLiqJI7XZbFxcX6na7lgF4PAwhmsTTdzode17SWG8QcRqdTsciEe9YcCiZTEavvfaaOUB0od1u2xp4TG48Htu1waF9BNJqtXRxcWG4bwhBq6urev3117W+vj7hiL0xHA6Harfb5nTIhKbTQO4dfBRjiUHwRh3HdHl5qV6vp4uLC8vK/OEieq5Wq/qt3/otffTRRxoMBspmsyqXy9re3tY777yjBw8emN7yzAQ1uVxOW1tb2tra0vz8vJrNph4/fqzf+q3f0he/+EUdHR0pkUjo7bffVi6X0xtvvKFcLqeVlRVJ0sLCgkEyw+FQzWbTjMXc3JxFwwQNGCmenfUmwsMgp9NpFQoFZbNZO0dXV1e2DhhfvxZABpyxubm5idoNaws+77MTIB+MNjYEo1YoFNTv99VsNnV+fm6OhSje7zVGm2cfDAaWeWWzWVWrVYPSvO0ANuJMLC4uamVlRZubm6pUKpqbm7OaE1CUD9ReJp8Y4eK5SbE4IPl8XsvLyyoWi0omk7q4uNDh4aH29vZ0dHSkm5sbzc/PG8aYTqdVKpWs6LSwsCAKHjc3N3r99dd17949feELX9DR0ZFBDRwsHoSDj3JwfxSXiG7a7bbq9bouLi6UTCa1tramYrEo6TYiQRFSqZT6/b6Oj4+tMHJ1daXFxUVls1k74D6qpnjQbrfVbDYNl+Tgs/G9Xk+NRkO1Wk3D4dCU3RdnMKr8HgYTwN9jwf5goRwYZ64/XfwjbRqPx5ZaR1Gki4sLzczMqFKpKJfL2fOiNGQsnU5H9XpdURRpYWFB0q3xxqCsr6/bwaTgdX19bVEWGKp0G50PBgNdXFxoMBhYhA9+PRqNDO8lU7m8vNTe3p7q9bq+7du+Tdvb27YWl5eXojjCF86X6I617ff7ZkyJ5vP5vAqFgl0LOES6hc7q9bo57lQqpfPzc3300UdqNptaWlqy+yByrlarevz4sY6OjkwHDg4O9NFHH+n999/Xo0eP9Nprr6lSqUxEYz6qr1arOj091QcffKDd3V09ffpU9XpdklQsFi0yDCGoVCqpXC5LkpaWlsyZE5USzGQyGd3c3JghZL8XFhY0HA5tHTGcRJkEGMvLy1Y8JEI9PDzU2dnZxDnld1lz1pUzVywWlc/nJ/BbMjOgStJ+4CDOYAjBsuFer2dZJufbZ9LcBxkbzgInR5SfSCQse/d1B9YCHH1lZUXlclmVSkWVSkWZTEbdbnfCFqFbXxekQGQ5ncL4og9ebHZ2Vt1uV8+ePdPR0ZGazaYpdCKRsD/T6bTa7bZhhUS64D4rKysWUSAYFUmWBhKdLiwsWCR7eXmparVq0erTp0/1/PlzFYtFVSoVlUolgx6ur681Pz+v5eVli17wqJ1Ox5SEQ4sxabVaFhECn+DhSC9SqZSll7u7u7q8vJxQXGCIq6srNRoNyx4wlD7lJ3q1DbtLZYge8cBElqSFVHS73a4uLy+VTCZN4afxrcFgoEajoW63q0KhYBXu0Wikq6srXV5eWkFNeoEjE+FfXl7q+PjYrn91dWXMALDscrmspaUlpVIptVotg3uIfNCvBw8eKJlMqt1u6/j4WMfHxzo9PdXFxYXOzs4mIlycK06FgzsNG/CsPqNKJpMqlUp68OCB6vW6rfn5+bl6vZ7S6bQqlYp6vZ5SqZRFVsfHx9rf39fq6qrdRyqV0tzcnBYWFrS2tmaYI2tP5P7s2TP1ej01m00tLi7afQProOfPnz/X48ePdXJyok6no4WFBa2urmp9fV337t3T9va2NjY21O12dXh4KEnK5/NaXFy0jAwDBQQHTJTP5ydYItQLCHwkGStAkp0roj6P5xNBeyOD8yAKBs5ZXFw0o+ohADKeWq2mbrdr0FO321UURarX6xZ0kL2Mx2MzkGRxOHmE7IfCFmebINCTAXC0sJ9wIkS/l5eXevbsmWq1mjY2NlQoFMzY+qI1+vUyeanBxdtks1mLZqMoMsoLh5XCWTqdVr/fV61W0+Xlpebm5tTr9XR+fq7hcKjNzU2LBklTJVlEh7fJZrNqNpt2H6TpRHr8Hmk/BQnggG63q4uLCz1//lwffvih1tbW9D3f8z0ql8u6vr7WwcGBGZe1tTXzjuDEVHoXFxfN8I/HY3W7XZ2cnBjVhUgPXO7o6Eij0UjFYlEhBNVqNT19+tTWkOLMxcWFarWapc6kQz6KpyLOzyBcD8M8OztrqW69XtdgMFChUFAul9N4PFaj0dDx8bFSqZS2t7ctPfOFB49hFQoFbWxsmFKSOgLZSLIij08RSf0wop1Ox5wU6fvKyoodUDC5ZrOp8XisbDZrxi2Xy5lTJXIlavPwCrSvy8tLtVotgzkuLy8tgobxMjs7q36/r8vLS9VqNfv8N998U41GQ61Wa8IZI+h1Nps1/L9Wq02cExzQ9va2pZqNRsN0+OrqyjKk4XBoxkW6zSKvr691fHyso6MjSbKIaW5uTuPxWIVCQSsrK4bxrq2tqVKpqNVqWda2vLxsGCNRXLlc1srKiuHfwDucG/YQZ4PBX1hYsOzx+vpae3t7+uCDD3R6eqooipTL5bS2tqZ79+7ZNRHgOqDHlZUVra2tqVAomF5gxIEUut2ums2mRqORCoWCRqORjo+PTTeJVtl/gj2elWAOXSRyhZZGoMez45TOz8/N+XJmBoOBzs7O7AxSrOv3+zo7O1Oz2VSpVLLvb25uKp/PT1DdXiYvNbjSiwo1ITl/J9ItlUoT9Ckwr2w2q9dee02pVGqiiMECbWxs2EaNx2M1m01Vq1XzctNek825vLxUv9/X6empGW8fTbRaLTUaDVP48/Nzw5pXV1fVaDR0cHBgUWKj0bBq+urqqnlkIJTLy0tbh6urKw2HQzOGIQSdnp7q8ePH+uijj1Sv1yfAfqKpYrFoWDgpOkaaVBIDxcEsFouanZ21qBHhOUn3iUKJ9FKplB4+fGi/iyHOZDLGEW02m6rX6+r1eiqVSnZN1n40Ghlccn5+blVcjymDGafTaa2urlrx7uLiwtLeXq9naeH6+rq2traMPXJ+fq7nz5/r7OzMDsLFxYWazabm5+ct2ufa7PM0/Y17GQ6HmpubUy6X07179yxzCiGYYyF9hVa0sLCg+/fva2dnR+fn58pkMmbQl5aWtLa2prW1Na2srCiTyej4+NhgD+8EqWdsbGwoiiKdnZ1Z1JhKpdTr9cxoone9Xs8KsDi2y8tLK55tb29bFlOpVFQul82Igo9SDJNkRR0i2EQioXw+bxAWe8K6om+SdHFxoUajYcan2+1aZFgoFLS1taWLi4sJXSO78tEwxg74oFQqaXl5WeVyWYlEYqJGgpFnPyhuw86oVqsWvRcKBW1ublqGRNrPmhGlEuECq0m3QRl0VV8Y9NmopImiNZDT0tKS/f80owLMv9VqKZFIWNY6XVD9mgxuJpMxSAFPs7S0ZAYRiw75vdVqqdlsKooiPXjwQJ/+9Kc1Pz+vXq+ng4MDi2gGg4EuLy+Vy+UM1yQtYwF8wQe6DSn9+fm50dUwTkScLAi4jefTLSwsaDAYTGDOw+FQhULBopLRaGQei+IXArthZmZGzWZTR0dHevr0qQ4ODnR8fKxkMqnl5WWNRiODI1DcZDJp0enx8bEajYalavV6XZeXl4bFlkolLS0tWWHO41MQ/E9OTiyi5XnBtSqVikWMMBhCCMbMgIvY7/etgk4URPQA7Y+0F/I/9wCVjqyg2Wzq5OTE8HMgikwmo3K5rNXVVS0tLZkxBw/GiaFfCAWUfr9vhoZquddP9Iu1X19f1/Lysu0XUSbOSbpNvyuVinFc8/m8zs7OzKDMzs5qdXVVOzs7hnGTshLN+qIZewAOXigU9ODBAzNsw+FQCwsLZvyJHimMUk8YDodWoAF6Qh9gIxD4APXgjAmEKMZ6DikZJM6r0WhoOByaATs7O1O1WjXGQafTMViK9P3BgwcqlUqmO4PBYMLQ+j3xxVdJOjk5meDFAgVgeHHclUpFDx48MD0hMPLnDhiEqNfDkT5Tpc7C835c4IKRJntC12luoVAeRZEWFxe1tLSkbDZrmT20TeAsbNfL5KUGF0Ph6WEUgMBz2NBUKmUGZGVlRffv39ejR49UKBSMNiS9wEJrtZra7fZE8WqaUD4tLCh8RD4PPCeVShlVJJ/Pa21tTWdnZ5qdnbUUmqiJQgT45vX1tXnZ+fl55fP5CUyGawKhkLLkcjnt7OxobW3NlB7DORqN1Gw2TQmpBo9GIzOkzWbTaDooJJsJxcZjU1BXiJKAK8DRM5mMRWB0/AwGA+VyOUtfe72epdfVatWcC14aJaLaL73AziVZlAOUgQ5wGMHf8vm86cLa2poWFhYsiobUn8lkDKMlcvdQhGdG+Eq0dBuVcCBIH4megZmAC8BJqdhvbW3Z787Pz+v111/X+fm5vvSlL6lWqxlMRqqKvlMpJ5VnPdAPaHvZbFbtdlvSi24yHBbYIZHR9fW1FR3RA5gyQED8G2YKWaG/BswMSaZTFD/JjHwhi2tR/Jqfn7d0G/ydc0vUh5PzvPTpghW6gsMHS+XZuV/qHxR0YQLQMFIsFs1YAuPgiICm0FnfLcnaEgQCKXhqW7PZNFiOAi+G0zcpZTIZtVotHR4eGpxAwwlng8/xNLuvJi81uCgAnDdSU0+UBpgH64iiSMViUaurq4bdPHr0SMfHx2q1WhoMBiqXy7Y4KCL8N6hA3lv4iq7nTZIy0uHEzxGBrK2tqVar2WL6qjM4X71eN8WD8wk7IJFIqNFo2CbSvjkYDLSwsKDl5WWLAnBIVDf5XfBJvDrcVyrAROaLi4sGV3gO8Ozs7ITB9Y0fUMOAWoiaOAAUAHAAPtXHSDabTaXTaaMRQZuiSIIRQck5jPV6XbVaTdfX10bhIQLg84GeVldXVSqVrJovyfDVpaUltVotnZyc6OzszA4sTtMXGaerwLlcTjc3N2ZoYY6cnp6aoYXeRucS6eHa2prm5ubMqayurupTn/qUbm5u9PjxY3PK6Bf7Rz3DsxSazaal6OwNhxv2hddrngNdpuCGAyUaJKomm/ONHtlsdsJ40sTDvZIhYIjRhYuLC9M/spRGo6F+v28FIA9l+c/hjHj2APYA6fV6X+EY+Tcwgu/swlAtLCwYcweqZrlcNj0dDoemywRm08wlH+GSeZHlENCxjsCPvk3ZFy5xtqzH+fm5Li8v1Wg0LMijCMc1PY781eSlBpcbBFuj0l0qlcxYFItFpVIpXVxc6PT0VA1ENVsAACAASURBVJ1OR7lcTuVyWcvLy1pcXNTW1pbu37+v999/XwcHB0Zp8W2NKBMbNG1w8ZIsnG/L9S3CcHVZrHw+b5767OzMjBRNFr4JAcoXlVYOE4IhJPLw3FMUiQIRnhIjOj8/b4rZ7XaVSCQMm1tbW7OIAoNOhOVbZLkHz9HlkKGUfI/DgGHGaGGwSce8AUYZvef3n4XB9S2StEBDGURvgDLgT3Iw4QfzO8Vi0Qjw5+fnBjlhdFBuDJUXilD8CS+S6J/r5PN542FShFpeXrbMhqyoUqnoU5/6lHK5nI6Pj+16nuuJkS+VSnYfNC94wwNWCPRAyovh9c8y3W3I3mCg0CP0nG60EII5MN81idH29ELwdaL3bDY7QdvjfqnTwLoh7Uc8X9sbUgSn4b98s8R4PJ44o6PRyLj1QClkQsvLy+r3+2a80UccJ8GGb+v1QmaAccbQw6IAI+dzOIO+4zCKIsPGsRk8I/fPNViXl8knshTAUGhRlWR4FZvabrf17NkzPXnyRN1u16gTEKxzuZy2t7d1eHio/f19dTodq576OQJACwsLCxNFCTAWSRMHD+9HlxMAOWkxB53KMzACKWKr1bJuMd8iimLw7CgaG+MbHTBgFH7gOEL9IXKgSlsqlSYq6/CZ8/m83ScYGYbRKxPpGumLH6xB6oTSXl5eWjulhybgJ0J/IWqBBkOhlLXi+X2kLb2Y1dDpdGzfJBkufX5+rlKppIcPH5oCE534tmjud3193bqIOIi5XM4KQegCgtH2mKjnHcP5zGQyxsuWZFQpoibPGIBtQKTOdXwGNBgMlM/n7T58EdF3sLEf0ovZArAhfCs4/2b9JU3Q8oB3qNr74hnrgUHDGBA0+E4+BvmQifB7vvnAd6f5PfcOhGfjGj6aJcLlPPFzfj0wUjjIUqmk9fV1w9/pXINeKslsBZmf58tOc2BhVwEVeBtCYAR8MJ25+iiVUQSwqrLZrFFLifrB6H0g8TJ5qcEl6vTYCJvKYQV7e//99/X48WMtLi5OYEHQN1jU/f19HR4eqtFoGChNsYEKp8fuuC4YHZ1RPtWABgPmDJePVj4mR1H1RMGBAFAET3fy3UccGAp3RJKeR0r065sj4G/SXcfn4qDq9brRUYjqMTTSi5ZmHw1xCLhXrkXFmhRoNBqpWq1ORKrgUuCnRIOSzGDCVvCtwR475x5wZmCrRIo4tt3dXXW7XX3Xd32XdfbQcSe9qB5DAYS9AhWKaATDTJXZF838mo1GI+vTJ5Im4mg2m3ry5IlqtZq2trYMCur1eqpWq1aM4SACUQHzcF0Mrm8CYV0oYnFefEYApsh6o69EvD67wEhgNOj5Z21vbm4syvORHevCueDLN+gAP8AcQKenI1vqEKw/DBT2mkITv+v3hKzGfx76mkgkrLJPoRk8fXt727jaZErr6+va29tTo9Ew9g/FXeoR6ClnkXOD8/PnB4ePgcRZ+WfAeJO1np+f289SJyErB0by5/PrghRIT1g8HghqC16RDrMoirS5ual79+6pXC5b5Dg/P69yuaw33nhDtVrN2iAbjYY1RywvLxteOV0480aOf/sFIrL01Vw8OxQtOpbwaqRKwAAYSA4Dka53NigYka6PAMCzZ2dn7ZDC3wTzIzPAWJ2dnanb7Rrc4BsSwO68MksvCh2sPfftO+GiKDJa3MzMjJHi8b6k+RQGweeAMCCeg3+i0MAs/FwulzMuMsNJqtWq9vb29Pz5c3vuzc1No9KQekHDu76+VrVatYImLBh0DGM1rdg8B2sB/IATIFq7vr7W6empjo+PLXpdWVkxpgmRH11wMFUYDgP/FsgAkrs3uOgHaT1774fEoNceesAA3tzc6Pz83DIuHy2hF8A83lh46I0MkP3k532RFTilUqlofn7eim/oHNcjcKEaj9HlvnwnG1kuwp7hbDjT/Fy329X5+bmq1apubm60srKi7e1tLS0tGb2TtcR+wOP30NJ0OzD6IE0GCKw5PzfN1/X9AzheMkCK8EB2ZKJkLdMzPzyO/NXkpQb34uLCFI4IQ5Kl8KS/1WpVi4uL+vZv/3a99957euedd7Szs6Pl5WVL7zkUVLhJqTnY4GOQrj+uY2O6eMaGLi0tGR5aKBSs2eLi4sImLwEhoHhQP0gzaOTAKxM5wRX2mBRRLUpJJwspHkpDtE/KjJemWPS7v/u7RtjnmVBUDpRP1zgYrAVr6qu9QDwnJydqtVpGeIf7yzpQ4OTze72eUV54Jg4tys3BwmFA0YHudnJyomfPnml3d1fD4VDvvvuuHj58qGKxaOsN1Q0cn86zL3/5y4Yn4vh8iu1nHCC+kIOis35gnvV6XYeHh5a6DgYD7e/vW9QHZpfL5ZTJZCzCLZVKun//vra3t619naic4SoIdETW0RspTyUDvqIIRjrPocVBw4JgT/10Mw8febiJdJiojuenfgHEsrW1pUqlYp2h+XzePosCJfqFjrO+fCbnk+jcBwUeN0ZXfRTabDZVq9XU6/W0vr5uMyE4s54L3u/3jZcL7koGRJTp4SQfsHDPOBFJ5kBwXPQRwLcHygNi8/UACmqwqsiiwL+9o3qZvPS71WrVuqY8roOi+66dt99+W2+//bZWVlYsOiPChM96eXmpTCajjY0NI/23Wi1FUWSLzuZ5IJ503xsZYABS9s3NTZVKJUvr8ZZ0sJCioXzwfy8uLiyS81y96QUkhfetvESiQBhEemwqiu87x9g4pqLxrHhv8EJwzGkMFwXifkiHMIb040Onub6+1tHRke0fBwBIyI/DhBbnvfU0Fsc9gLOjoBRlzs/Pjd/76NEj5fN542aTqkIOZ3btzs6Ojo6OrHMMXqs0idliCBC+x715FgsHeH9/X7VazRzD48ePjU7lx3KCpYO7ozc4Hxp9MJweq/ODnNgrsEwcOJ9BpE+kmkrdDn+qVCrW4AHkQXs0Bh3d93AFwmdiCHyRDepbqVQyxgiNJPPz82ZEPeOAM8Oa+yImRg6jOL0nPBuZIuem3+/r4uJC7XZbc3Nz2t7eVqlU0ng8nsimWb9+v2/D16vVqkGZZHYYdwysv1+cNZChp9vxbDCkyNg4gzhBqJXQP0ejkQ3DAlpgLYjGvy6DC1WoUCgYOwAvCsXp8vJSS0tLZmTh6Uqy6u/Z2Zl2d3dtbgBTlDiwHjKgQ8tHuBh6DipKTFQJnYXKOFXPTCajQqGgnZ0dMwwsJlOpjo6OdHJyMlF8IO2VXgxqnk4H2USUHa8OL/Lm5nZqWrvdNjJ7Op3W4eGhnj17ZlQVWjWJmHx1GUPsIwgMAIaYKJRiE1ginXVXV1emNBT2ML5QvmiU8JmFT8e4lk/ZMDxEX2QKUOZee+01lctl62DCELK+XAOj0m639fz5c1WrVctUSEe5VwwJ4g8SGYF068CazaYODw91dHRkBxcjBS7MPUOCp7MMw3B+fq7T09OJqJzDOX2wyHIo4nF/vvmFnwHeIduRZBxk6iak676IhkxDFdKL9BbhDMGSoeOMz4a5QeUdg8teAxl4bq/Hsz3zwOunL0CRNWIvut2uwRgULQ8PD9VqtQzmYE+hctJF6IfQSzLHzRkBPkI8VYzPxEbNz8/b/u7v7xstDXzZt7uD15dKJaOZYaR9rYB7+LoxXF988hEOeAhYBx1ULOR4PDY8rNFo6OLiQjc3N5YugC0SVfrqI4U5u8m7tMx3C/koT7ql5mCowE2z2ay2tra0trY2wdk9OzvT/v6+tZ5irFlEX7SbJjJ7ripK6HEyCn/tdlvValXHx8dWDKEl+fj4WOPxWNvb20qn09ZZwwFhTXzk6q8P6ZzNR8FJf5LJpA07AWP1/EefBpXLZZXLZYUQ1Gw2zWhOR/J4ckm2f0T2fk2SyaThtrwKhs/EmKOYZD0MqKnVahMFGWAYKurTdCofcXnBoXK4l5eX9fDhQ2M70OnIPIvxeKylpSW9+eabunfvnnFx6cbjTwz29MHC6XheJp1R/jBiODg3RKzecUPIJzrDUBB9EsX6jA89JLJDb/k3EBAsDgbCAB/wPN5I4fh9xoeR9VjydI3BY79kjqlUSjc3N8bDZ0+r1ap2d3c1MzNjrd8MhuE1SR5+AarjOjhZnpWzwv/hPHB6vqU4nU7r/PxcBwcHarfbFtTxmZxL6K/SrVP0c5991je9Dl9NPnFaGIsGNOA3keJKv9+32ZS+SjwzM2NpNIaJhwZzWl5e1nA4tCgaT+jHvvFgnq7FNcC4UASoVUTX0xxUOpAY/YehpUDlO6pwHNKLNMVXZr2BpABSqVSsXRRjhEEBowIDpNPKPxO8WO7HFwtZi1QqZWmN51mCHS4tLWljY8OG0ABTALGQlsEnxglOH6Jpp8Na0LHljS6HlYaQ+fl5i0oYWAJ2x2eyX4zlo4EBrI6IkOf3DhbBCHPPREc4lKWlJa2urhpWiF6fn59bkW1+fl6rq6t6+PCh7t27J0mWjXj6E7zL6T0hkiM4wcBJLwxQFEX2hoLRaGQjL8m6OCcYQgysnzfC+fERpoelwIGZ4Iau+nSa9T49PbVOPRwF6+iLymSgnhvsITOvHzwvhUsidowhGVaxWFQ2mzUne3FxoS996UsTdsUXnMH6aaXFYZE1kDn57Ed6Ab+xB6zlYDCw2hSNMjjU5eVlG7izs7Nj3YiNRmOiVoBe+AI2+vwy+cQB5FT1uWnoEYxF9FGcrxRLshSBQgGYCEMyKCJR8WZ2A9xDv4kUurwS8HswDXyaQyRBhBBFkQ3WIZ2gQ8hXeMHhOATTmyhNvvLHtz+Wy2WjuCwuLurm5kbVatXmLTDmEKMOJojR84cPw+SLD35f2A+UDSXMZrM2cIUWTE9xIwpAUTmwUMeIrMH1PHkfAZMly8GxEYnSwcU7xPg7DTQYBg7W/Py8FXKgiHFQfO3AR4Osg8ewifKAj5gGB8cTB0VzhW9KAdOMott20dXVVXOAOGTWgwOHXF5eTlT2/eGeLrRyOInggNU4O1TJMSapVMqwbhpN/OH2kAIYPE05BBhclyhbkkF0nl/MdRGCCZw6z+wjc18b4HOJ8tE5DDMsnbW1NWPT0GxwcnJi2QbDalZWVizK9GM3CUY8ZgoEwD2wR+C73pBHUaTz83MtLy9b9kuAtrq6qs3NTW1sbFjRn2Bqmj7KPviMYDogmJZPnKUANsTFbm5urF0TRWGEH16UggN47dzcnOFk3kMwuMMPmoHg7d/4wEMQyXEd3+1FbzuRHEYLhSYK9Z1ZbA7Kzb1ixEjDpRcpA+kU16Kiubi4qO3tbT148EAPHz7U8vKyEomErq6u9KlPfUqtVmuiBRkaFbiWhwN8cZCowq+Fx1ZZG+6BwSx0EhExTsMF00aSJgRI/D5tnlai09PTr2Cs0NxBBEirMWuJMlJIIZqBVwmHGE4yEbzHjymWIhgBrgm1jJZYhgDRJOCLO+l02pgAOLx+v2/88JmZGSuU4QSneagIrwfCQNLMwz5JL9pNMYK+eEo7KawW9pYIP5VKWebGeUQPgAWmp6BBX6rVagazodvUPWgqQW/IKuCYovv8yb14ahb3iqCzrAc2AuoiwVoicTvNbHNzU8nk7Rxs9m1lZUUbGxvWeUYmnU6nLRDzVE4fUKDLPhqdpmlKsgBAklZWVibWjs5FhvyAc3N+Pb/a6xRO+WXyUoPLmDwOCMULSTalK4oi89a8z4mDROrAJlJdhOPIK585NEAYvGQP8RghHns4vJ0rSmeVr7CikESsHBQiGh/5Ehl7vicGmlSXw82hYVHh6yUSCSusDAYDPX36VJ///OdtTCS4WwjBOqsYoM79EuEMBoOv4PNNFwN8xRgnyEH3Ffrj42PrLmJNvCEF1sDpcT++v/zj/mTKGtQZ7hmDy32yxig7BSWMF3CD9OJdebTccnCJdLm+Xxfft49uXF5eGp4PToqTZVIakb7nuDJ34erqygwtjmI0evGmX9JlX9QFwmIPyL7YI4y0h2jQMR+VYajJNvxAcf/yRHQRrq8ke20Rsy3Y15OTE2tGwMkBaWGYfHME0axvZAHuwTFi4Kape+gOPwvNjsLS4uKiKpWK8vm86QmZGQaPM4XtoLbDa+sZ9eoZJL5zjHPpxUMaZCGMH2A9fUMXmS36IWnizHucW3pRYCbDepm81OASXRANQpZuNBoqFova2NjQo0ePtLy8rFwup6dPn9r0KA6+r3DCdyyXy9rY2LBiDVQRvP90aO4BaTAmFppWTirz0uRbWMF/vGJg4MGsUDb/+mQOoCeWe8OTSCTMsWDoms2mnj17pmfPnmlvb88mok3Ti5gmRdTBK3fAVD330Rsl9gQHRNaBweawnJ6e2tt0mXIETccXpZLJ29kYpN1QhijysP+e3iTdRriSjEKFAWAUIyn64uKiQSuNRsNGStbrdVtnP4+BQwZThesSTbF307qAssMxZkALXFMMr88mMHQ+02GAClG2j4xgORCBevHVa6r7RJNEhL4LsFwua2dnx6Av8Ee/rz4YoNBVr9fVaDTs+1AgpVujz8jPEIK9ywsKWy6Xm3jxaAjBZjID1bGXwDREc8B+RH+cT3TC45bs5XQA4LtAaW7hnFFUZw04G0ThFK0I6AjciLh5+QAG12cVZItEwNSI4B0vLy8rhKClpSUL0jyWTYZIJkLQxs/geKYZNF/Vpr7sm4wmw5svLCyo3W7r7OzMPCaDl0MI9nocNstTqjxozruB6Of3BGYOznSawuel02lLY1FQ3vCAwQLb9Z1TbDgLSBcZB4rKdbfbtYM5Go1MoVEGlBVMDH5jMpnU/v6+PvjgA73//vu6urpSNpvVxsbGBC7c6XT00UcfqVqt6vr6WpVKRd/93d9taZ2nQmFUfISbzWZtzB57kE6nrdDIAUWpVlZWTKk4xESOrLskg3EohvGzft25p3q9PmG4GbBCZEcjCukgHEuKPBw26HcUelhnFNtzQjkInh5FSsfBYm9gbODIqQ/w7D7dx6nwnKT6nkYHxdAzY7zhZ/39cJnr62uLIqlN+MHk/D+BABisx9m5JgXH8/Nz47BCnWOmA9De6empvdLJD10plUp666239ODBAxUKBY3HY2sOonhM1I0zHA6HNj+Ar4+jPXkmjee44hRoj8WJdTodi94JimhAwMgycnRubk5XV1fa3d3V6empRey85YHz7LNidMU7aQIX9AfDH0LQ1taWBT/oKfTKo6Mj7e7u2tD+VCplA+mpd2B8wXNfJi81uJB5wYfomoJLymKzqaS2RH0cUirSVNFzudyE8WPghi+SeGI30RO4JIeKdJHolkiPn2UyPpECVV5vrPl8FpnuI2Y3YHCJIBEq+slk0pQkkUgYD5fpagwKGY/HVh3mnW4zMzNaXV3V9va2FXV4wwRRnsempNuRhL74hPH0pO5EIqF79+7pnXfesUPn235Jwa+uruy1IRQ3UB6PzU0bmUajMUH25z75OQ6v72wDP0+lUlaMIPJiX6vVqhUX6fLDWWH8wSylFx1M7AfGFCfkOdUcNF9Q8jxXInkcP+wcSV+xDtMRbqFQsI5Jz6MlgOAeG42Gjo6OLPNBn/w98Iyk0BhrhvfD6MGpMWwd3Ts6OtLx8bG9JJIxjKTrq6urevPNNy2Nx1hxxnnPHkPxZ2ZmLBggc0Q/PWvBnwtJE3tBkfDq6kp7e3sGBfHapa2tLWMEwPDBGRGgfP7zn9f7779vI2IZo8pnEyBxb55F4Ivg3q6Nx2PrfHv+/PnEGjBZrVarqV6vq9vtam5uzuZeeyYWZ2a6qPtx8lKDC8f28vJSJycnevTokcrlsk5PT/XkyROdnJxYNdzjjr6bw+NWHFI2DmM5Ho8npujjEREq5bwqBK9/c3Njld5ut6ulpSXD6a6vr01hqUx6IjecXF4jg4KB8SUSCUv/USC8GJE7EQnp+r27GRLf+Z3faQUxHAPp4+bmpt555x31ej3Nz8/biwFzuZwxOCiiSC/eMoGQptdqNUt7SAExOsyW8LOKPcuDog0FRbjIYJYUL4h2iSK5J3r+KTL4QiKKTXcXxUH/SiAKKul02ubxjkYj1Wo1nZ6eWvHQ4+44chwgawNu5ivGEP1J4ymakSKin6yHTy+Z6sY9R1FkabnHY/3B2trasmyBjA3Dy3qQfW1sbCifz6vdbpsT8vftsW/PECDTxEjRdgzVjSj78PBQT58+tcJpPp+3Ae8nJyf2UlUzAC7IYc1xKtDTYDD4Nftq+umxafDsZDJpDgacfTgcWstuNpvVysqKWq2WZR/Ui2q1mr785S9rd3fXZqEsLS0ZHDUYDOxFqzhGj/sTgHBv7BPOoNfr6cmTJ/rggw/05MkTG8ZOlJ7NZrW0tGRvWyZr6/f7ExDQNE7/1eQTI9xMJjPx5tQ333xTOzs79iYBKFkUo8A6MLgYTgwteMdoNJqIekjbksmk8RoRNgCuLpsCzor3r1aryuVyWl5etnuj+krqhqH3vwc2hFEYDoc2n5MIgsX1VCloLY1Gw4B+or5isWhUHs+U4AANh0N7yR4Uu2azaS25rJMvBmBwKdAR8XiuMJHoaDTSxcWFnj59ahQ40l7PIIF3uLCwYDQ3Omng/PL8fj8wnMvLyxYxQDPi75ImonSPnU7DFePx2NZ/MBhYz7w/IGDdCM6On8H5eH4tqXWhULBUH6gBo0h7OAPzoTtWq1Ubes0B5X69bG1tmaGsVqvmtHle6GXAXbwVAkeFE5iefcC++iJfuVzW/fv3df/+fW1sbNi9QL28uLjQ7u6udnZ29PDhQ3ufV61W00cffWQZa6VSscAHB0wUitPwWSLGyzcz+IIkQtTp021fYEqn09b+z9tSaPEFK/aURZgFvgGG7I8CaaPRMIgQwWmQNfh7QJfRVaCMcrlsDUUU91dWVoxbz3pdXV3Z5/FZnu30MvnENz5wEbqz3nrrLd27d0/tdlt7e3uq1+uWunjcxne+EC35lkhwtvH4dqgGUUg2m1W/3/9Yg5vJZIzwTPTA4Tk7O7MoaHl5WZeXlyqXy5ZOg/d0Oh2jsZGK+DcDRFFk1XtvcD33znNTB4PbtybwptmZmRkb6wbfF1Dfp+rTjQ/tdlv7+/tGVeF+/XwHSfZupWKxaC+ClF4Q7DE+RL0UJnh9PMab+/WdQ1DCfAcekMX0oQEfh3qVSCTMCfOzvhJNAcYzDrzxgvTvoz2+R5RJuon4IpU3vNwfzpc3Tns+K46HlDmXy6lQKCidTluWwc9RpPFNHr5AsrGxYcW+VCplDpxCjo+8JE1kEnxhVLl/WCysAQbqwYMHevPNNy3iYo2J4Futlk5PT7W3t2fOkOr+ycmJQrjtKORMcC/oAM8KPkzQBY/d45Q4CK+fnsLJ+iJkE7y0FZ50sVg0ihqFPuiDOHOKocxuwTHxZg/PviHb8e22Hi4CYsRxFAoF3bt3z5qw0D/Wn8wD2A77xnmmdvF1G1w2Opm8fZNntVpVq9XSxsaGNjc37XXHJycnBrDPzMzYoWDBwaWINOES8vBEkxQZ/LBzDiyRga9cc6iJqgeD2/F6UNSWlpbMa2LUaHbwh5VDDa60uLhoowULhYLdAwfN033w8syd8LSsk5MT7e/vGwbk6THM/aRKXa1WdXZ2Zu3PnsriBa4qikmUx2EnLeTgMiZzZWXFDgeOEAdG5X26Y81Tszx2CV8UaiA4oy/wYTxRSt//zx7iVME+oTnhkDBujJKk+IWQ3nIdIkI/BwNHNO0wOJh84Yx9GzR77TFksDu/Lwy94d48dEFG5f/f09f8sHm46RgE1orC0sbGhl577TW98cYb2tnZsdfsSLJKPs9xeHiora0tm7aVz+d1fn5ur4zCGHlHBf+ZTIL3d2HgqLVgqP0IT28vOJesk8/APM4KTAF2uri4OPHcnAOgIfaVzI/i9nSRnWtgX8DqwZOBB7mWz7Q8FYzgw++D9KLxiIAE2/dxMzam5RNfsQOlBTI7w0XYFKrbWHowQ5Scm/OVegwdGAnUDzqBcrncxCayOX6iFtEfHDw4hFToO52ODcvhuhwCsBwUhIWmAIah9YcIheTvCAcZ/iPRAEaC97dBf/IpJWR80iJfZcUpcY8IJHLWzQ/M5udYb89pJOLwRgbFgtrDIfFK6A0vzw/Hk/2gkg1LgvUheprGtvxneRwa40oFH90j2pqekOULNkAKfAYGjCifl3LSdOKdD/ADNEBgEM/b5H54Lq8DnvLFDAd006+5Z12gO6TP/B3qGbqEgyYKu3//vtbX120QjR/o5J+DaJA9yufzFuR448oe+xrLNE98Gg6CBkcXqhccko8oPc4pvXhFE2vL/sEk8CMQe72ejo6OJoIWmEk4K3Sezwf689xzv184PtaBe/bsFM+dpy7iXyPF72GX/ID/l0l4GVH3h37ohyI4iHA1oUT4C3FhX1AgJYZnR3EKgBuvzgNRaPGL+NnPfjZI0k//9E9HvV7PGizOz891dnZmL4IkxfewBUqEQfT4jS8SeLoTCoOCMMLxF3/xF8PnPve5CM9OtXkwGFhBwUfgOBXwWhTp7OxMh4eHOj09NfoOkR0QxurqqnXZgLcNBgP93M/9XJCkP/tn/2zUarV0dnam4+Nj1et1S3MYLr29vW1db6RsZBfSCxgEvu7jx4/19OlTPXv2TKenp4ZRzc3NGcaLc/j1X//18DM/8zMR9CbWkGjUFOsuMvZYLZ1MwBXoCAbMOw0Oq+/4o6Hhx3/8x4Mk/eRP/mTEPnoc1w/+8QcIA0jUBSzi75+0ESwTQ0NnGpzzVCqln/3Znw2S1O12I1J55g4Dl2F8+FzfhOHhBN8phjH0XFSyOjIT31H4vd/7veFXfuVXIgyhF86l58PjDMhCpiET9sDDUoPBwOA3Corj8diyrV/+5V8OkvSP/tE/ioBzisWi8cs9Vsya8vwEF954Y6A5V358Ijam17sdCXp2dqaLiwv1ej396T/9p8MP//APRzhqZon4hg+e0zOVOPvTeuuDBV+T8v/n8e/r62v9fEBOrgAAIABJREFUhb/wF74qVeETGx98NHJzc2Mkeo9FguH6zhA2yxO3ebWOpyB5L8dBY8GnF9/jQ2yYf5cUC4q38eA7RgCFpdrvZ1uiXKTVPo2G8gaP0EdJYDooiDf0GADeGszIQOAD7oU1xBB5sN8Ln+dJ/GQDpJNEWLA/iNiJ8JjaRCWYVHpmZmYC24UlgBHw9CUOpMe8fEbjdQe81h8YnCAZi1d2npP1JVKeFqId9IYCiL8GxoKsh2gNZolvL4f9QmMGxUZqChQTp7E672B9ZOv5oNNZAs7E67EvQBPlE8X61JZn7PV6VkjCuHsamg88CJLQca7nqYQ4Qgpi0/eHk2A/KFT5YewYKs4CzwAFFN4vBooMjb30kSM6g+NBj/h832Ls4cHBYGD7Q+GNZ0MH+T2fbbBH6BS2wjM4/BdwyHTm9TL5xKIZD4fRoJrKw1GoYrgxN+lD+el5AR4/4aG9V/XpLg/O4uO5oG1RkPJdYnCGiT59NAtuSZpKLz9phY8+uLYk88TMZKCiTuRGVR2nw4aC7x4fH+v58+c6OjqyGbnlctmoJnhdipSeVjOdtoGJLi8va3193arw0PNo/MBg4pBgAwCB0JBQqVRULBb17rvvWipGZoJCEaWgFzgonyXgvPwsAQ6DN6T8mxTWv4XVzwzgd1hHb2ClF2+IRR+pFfgOPtaQbkQm+3PvUOhSqZSR3c/OznR6emosGtYfPcNBIkBV3APt3r6hASoV+8mh904dXBzHyxpjGDEI6CZRsNdPHAz3AjTBOmMsMITsE//HPvj1ojWcSBjYgv/zztBnjHDbqbPwah0mhNXrdYtMMbYYOJwa8xeIlovFovL5vDkdDKt3NMxtYT04Ex7C8d1k6KmvN2CLyOIoHrJv6CaNOnNzczZq4GXy+zK4FFOIBKCgkKrRg351dWVGzPMJoSBRgIKSA9ZJIwTFuWmMDMXAWOLll5aWLKpjZgAcOooJfkwe3pMD2O/fvoKFBfTNGnNzc3Z/kow8Drn86OjI8D9JFj16DJMoAeybZoi1tTVtbGxobW3N+K+8coiiI5g3xhcBOshkMnr77bd1//59PXz4UJVKxYbP0DlEhH11dWU98GQpDAfa3NzUgwcPTKGIqDA8THGi0IOS+WKdV0yMEetHdMmMDDICMFle/IhBJ5okvSazIF32esGMgOmsJJ/Pa3V1VVEUWeEokUhYo0ej0TCuMxEPkf5o9OJ9ZhRZMD7+XnyNAYiMaVjeaDHQmm48X+CkPT2VSlmRiop9IvHihYt0FWIgYJ5cXl7amfIDm3wwRHTJbGrwSA8F+iFBcH6r1eoEC8a/6JV6BDrgDS6BBxQ1giDgwGq1ai3eQDCsDQ6T4MMHczRIEQ3DYMAIkvlJLzI9+L6+KWU0ejFnGaPO52HT/DrD32+322bTOIcMCeINHezr/2uDy4NEUaTFxUVtbm7aFCwGPfhOMd8aiGHDqxBBQsBut9tWRadzhtmTzKf04qMqFBEvPjs7a3iWH14ynQKPxy/ew0YkQEpMagRDgYo4C0gEXa/Xja2B8SHVYdEx2n6zeOtBpVIxVgZtynx/OBxaZOPTUR/h+jmglUpFm5ubVokmol9ZWTFGBnxjCpq9Xs/eRAErg85ASRPpLYWTi4sL6+GXNJE6A69QWCXq8pVpDjEYPWPwWHNSXY/zcl9wjfnTrwX65ucRoPQ0VJDpFItFc544WB/BjkYjFQoF3b9/X5VKxV40eXl5qWfPnunJkyeqVqt2n14w/DTKQGPiVT5+4AyOiUgTXcnlctra2jJO9Hg8tpeAUk/AAXieKwbRGxTOG/AAzAje/AHGCozlC07dbteoVtDxfNs5EBPF4W63O7EWOH0P33BtmDhHR0dW1yE44AzSwMA+EhhNUwCHw6FyuZwFddgG9IIMhs5UnCvvOIQqSDaBrcD4Apv4oME3OeEcGVPA/32SvNTgoqDFYlHr6+tG/r28vLTOLOhHGFS8CkpJ1fbs7Ex7e3v68pe/rOfPn9vsg3v37hl2CBmcVBfxrXMsgO9mI30jRWTRSU2kW2ML3IDSUmjzOC/pNIRrfp/JXx7/hEpFIbDVaimfz9scT3AeXnMzHA5VqVS0urpq3SrD4dC8uKSJqjSYoo/qwJ1xhh9++KG9TYO+//X1dRUKBcPgiIiSyRfEfxovPOzBYTg+Pla32zXFJCIimuIAsBZkBhx+7oVDDGNkb29P1WpViURC29vbGg6H9o4oDCMvUCQVBp6CguijKSLLdDqt1dVV7ezs2BxTT3nyZHUPgRAJgXO//vrreuONN2z+xfHxsXX/EV3DnPCpI+3c6XTasrfz83MdHh6qVquZUSFDAyJjzZLJ5ETKPDs7a1mlr0UwQUySGWMMBYwBnAzP6otvi4uL5oTBH4kiidyprQBRwZggk+Bsk/qjSwjzqYGSMF68AYXsEN3x7dOcXRw671vj3FI/4h4lWbSayWTM+AMjeJogNQba9w8ODqytmOyQegO2Bf0Cq4bXjaHHGQJ/4ShfJp84vMbPMHjy5IlhkdVqVePxWFtbW3rzzTdVLpeNR8d0dCIguKXlclm5XE5vvfWW8WFRBowoi+8r3j4VQxHW1tYmCj4YF3swh93y+0AKRFCkLh4wB0Yh1cTpMLyCIpXHwaQXOGM6nbYNo5FDkvWlg9N5YJ60jvsg0kO8QhO1wPU9Ozsz4jfpLPAIRoeCHWlWt9tVqVSyqJioEsiAZ9nf39f5+blNAPOVWaAkfwiBcniLs59FC+H+8ePHNoj++vpa5XLZcDn0DCyYAuxwOFS5XDYHjxC9rKys6Du+4zv06U9/Wm+88YbRs+r1ug4ODgxCIUsiKoc5UywWtbm5qaWlJSsAnZ+f6/3339dv/uZvand3197VVyqVLOVHzs7OrOEDBxFFkTUO1Go1HR8fG42NNNdnDjhoHAJOYH5+XgcHBzbXllZYol2iOiI9shUCCtZ1ZmbGCl/+vV0UoTKZjEEOvl4yGo3sBY44CtqlaezxUa7Hn5vNpr03kAl66KHPLIEIgX5wcuw1Z5io2PPumaebzWYnZmj7cwxzh8YGRpfe3Nzo+PhYg8FA6+vrZvB5wSgY9cLCgjY3N/Xo0SO98847NvMEDjBn5uuGFDAKvAp9d3dXH3zwgVUXC4WCRRQM24A3B6VmPL6d4P7o0SPD62iJBTTH4/K6i+nGB6JbMEJSBXBaMF6MI0bPGwiaH1Au/k0qz6bzQkpfnZQ0Qf3AQBKZEWmEEMzbwrWNosgyA9IaMEBflARHJCLzvdp+LTyeKcm8NG2I9+/f1+bmprWngkUdHh6qWq3akI/hcKjV1VVrikilUjZIZ39/32hnrVbLIkWiCl84JVJC2cHRVldX9frrrxsXmXd1MRCEFJtOLJw1yp7P5ydaO4vFoorF4sSoStI7Wj/7/b6ePn2qy8tLow/yTjV/GOA+n56e6ujoSL1eT2+88YbRjMCWeY03mVCz2TSakQ8IeO26JMvUfJQOZgwFDGOEYeHeMRQYyq2tLb311lv64IMP9MEHH5iBJrrFIUky3WN4us88MfQYRj8HF13EuJF+AwtgnHq9nhV6t7e3tbS0ZA6hXq/bWiwsLFjWd3Z2pqdPn+rLX/6ywXCcB+AIdD6fz2t7e9vu3VNHJU1AIlyT7Ia6DnuCEfft0qw/rxMCLiJ7mZub0+bmptUS6GDtdDqanZ21wUp7e3s2twJjTtYCpPgy+cRX7BBFHh8f6+DgQFEUaW1tzYo/7733nu7fv2+YGwaBF8ChnJCpwX9JZTigRBxUh6eHlGBwiWRJDRjwjaKQ8nkeLt6cQw5+BlDO/wFl+AYCf7jZQN+L3e12jfzv2QkhBJutsL29rY2NDVN4/mSDiDRnZ2fNuYCJe0PHNZkBUC6X7XCXSiXDpbrdrhWH6vW6nj9/rtPTU1NwPDsOg4PCG445+KSM7JFv4fQVZTIMHAEOg3UolUpWrIDRgTPBCROFYYTB4UmvoUp5dgDG4uLiQh9++KH29/eVSqUmWmXZMwqQVJSjKFK1WtXh4aF6vZ69ZBL2yfLyshYWFvT6669reXlZGxsbNlhHmmx+oRDU6/XsZ5lgx8EG0yWKopCGoweS6/Vup9QBw9HtxZ+JRMImzvmgAjhheroY54RuKDJEGBzAKhSp0A/OiqeM4bAIWmAKeJiHtPzy8tJYOaPRyKbmgUd3Oh2r1dDKvL6+rlQqZUbe869hHaCHvAS0UqlYtI/wxgaCpKdPn5p+A9lgQ8Bqm82m1tfXDUYE7nr+/LmNWgVWIOIuFAp666239O6779psiq/L4FLM4C0Lr732mnUZMdD4jTfesLmSUHpQHF42SKGGF02ScqO44FWkIHgxhComQko7zeFjw6cVhGuCh2LoiQiAFnzniaQJbh8pFukzESuFG36eamsikbAhHbu7u9rY2NDFxYXBBqQiFM6AMPwgGIy7N/yefuRnh2KsSF9PT08NN2UkHpxpin9f/OIXDataXV21FJ3I0dO7pgtWwBDgW1StMb6rq6uqVCra2trSG2+8oWKxqPv37+v09FRra2vKZDJGa6M5g30cDCZf8shzs68I9LaTkxMzZsxbYG+BUeDXnp2dGUwGCf/g4EC/8zu/o8FgoNXVVT179sxSc3QAJgEFKP+SU/BvP/ylXC5rZWXF9hN+s6f8oWfMeAAPxYkzjInfIRL0TASct8+WhsOh6vW6njx5YoVBAgCyLKAp1iaRSFhLPkELmDHTxaDcwULytQ+/J6yRJC0tLWllZUX9ft8iffYCmI0sa2trS7Ozs6rVanr69Knq9brVQLhnXgnE7AVpch6vdJtlQEmr1+u2V2Qdy8vLSqfT5pRwdjA4SqWSms2m2Y5Wq2VcdlhaHrLAeXKGXyYvNbgUlzKZjDY3N80r+ogOINvzR6nkkg5zqD3hHBCbVK/ff/FCPIB/ZLoTxeOtGHQiVa5NxCi9ANH9ffAM/lXcRAj8jie45/N56+hCwSUZ9uWjEzw2g8nX1taMpgRk4XmV0LBwAlDxMLh+E7mWJMNRfaQOHadWq1kK1G63rcWX+Q3dbldHR0f6whe+MAEL+CYX31Dh6W6wEdhjHA8YbCKRsLetMgWq3W5rZ2dH3W7XCqsorYdfiPpwXp5f6tdd0sS9El3gtPL5vGU54N04I6akccja7baePXumfD5vjgw8m+o0tYZ0Om1OZVo/gSSSyds5yBikZDJpg3H8K7lhcPjolgIf18IgsO6eszoNN3njR82EVxbROUghdGlpyTIJnBVwlyTbEyAhshUCGk9D8zUGT4Ej45Vk9Y+TkxM1Gg0NBgO7Ri6X08bGhiqViubn57Wzs6Pd3V3LyHBwGHregUi249u0JRke3ul0DG7Y2trS5uamOXc6RqmBkKHzFpN+v2/BJTN4Nzc37eWW4OQ4MzK9T5KXGtyrqytL5fyQbTZ+PB5PNA1ILwpcPLyvgsLbhdyNRwBHJWynUu8Vms31qQMFCKI+okuiMxaFAgypBO2ivG0VA0mbIveMMZBu2QEUQsCFuBeqyKQcvCUWhYDlAZYM0wCWBXQholTffukPFOvpI3ZJNowZoB/COtzLfD5vxg8MC/pPo9HQ48eP1ev17PU6QCM4MgyuX3eiZelFlxeFQk8F5HOGw6HW19c1MzNj+CoKSl88kSeGyreDTkfYkqwwyF746AoDQWEPuIo5wtw7elmr1bS/v2/FRKrPZF+ktsz/9S20Hmsmpfa8TmoEGAvpRUTqubPoMPrlO6KkF00j0MwosHLuWCOKcMxb4HPQa4wQhUuonJwzoDUMENV33yLN/I/p4TVkqHCigQG9PeDeCoWCwUxQTufn57W1taV7d6+rRz9g+WAMYS6gBz5rXFlZUS6XM3hKkjUHeZ0F8uHcki3iiPv9vgUMflYL2SL2i6ADJtTL5KUGlwcCpwkhWNGK1j0fHUIS98M/JFlRRJJtpk+fvcGiCAIFSXrh2eH2EUVzkNlMlAQOLSkhESWHhWgaQ+ILcB7OoLouyZwNhxDuJ3w/7ntlZUU7OzvmZcGxl5eXjQpD9LG4uGjKzoGDVcChnBYKgvCgfWs0xQ/wbF+lpuiUzWatgEChC4PE74DnefI3hgS9ACcn4iPy9lQfP9OA6J7GDVIxokp4zkQvkPCnsxcfxfnBQuDHYJscptFoZMUz3t+F4Zqbu31zMdDC/v6+BQLAZOg6WQEO20e4pKHoCFkQ9ELPA/WpKNEiERxdb0RO6AWRLBGmL6p6ehrnDiiGe/dFXn9NHBmwCftI9IfDhFXjOfCMHQWrR3ifHC97ZH3ATu/du2c4Puu4uLio1dVVy8Ao3nJWCoWCwRDs7Wg0slc9AXVwXojifZMK58QXwomgKYqS4WKfcJqFQkEnJycTAQLUOLIs1vWT5KUGl95uUsYoigwbI6X0wyVQEg8tYMzgLlJ99ARnlBeFoxHCKxKVRnAyogIOIdEoRQHfMw1rgBfgobQQ9FFm7rfb7VokgjEj6sGRZDIZS518GgtjA3wT7BH2Aq9nBtuiq8o3khANcbi9QkOBkTRhHJm2hgPyzQMQ1+kCpDWTw+kpP+zpdOrucWTwMdJC/39kK1TGod1xcIFMwNUoZmCAm82m3Yc0CSd5/iXPj7MlusMJe0N5dnZmXVOe0cL+Q2hvtVra3d21qAxsldqEbzrwe+LfCuJ5vp4ni26hLwQonBWCGwqJvqPMZ4wUbMjcfIMJ90nxFUPiRxvyOThtzg9FTAwzBSc+y/8OxbiPS6HhnNPkkEwmbXylf3UOEA/wH4EB2Wa5XDYjl8vlbBwsLA3e83Zzc2NvAOdc+EBQehHNAmOy7rAQfBu1ZyxhJzDSUNX8voKbc51pTHtaPpEWRuSCEWDYCpEAbYEcDr+RPnXn4NE+yUP7FCuTydjEKwZ/SzJPiFGCzoHBBYKgJZNBF0RaRBdEuJ7fR2uf7yxBPJYMLOJ7yImwUBrmM5DmEkHgMT1eC6Vka2vLOsC8ocNjegK3JKsO41BwgFBTiHxCCNYp5mlMnhpHZIZj9dg3n0Vk6ZWMKMNH5UQ+vFUVhQfGaDabFvkRVRBZzc7OanV11Q4rjkZ6wQ5BB6ejCJ92+7kEGDLfDQl1x+sKk7iSyaRqtZparZaOj4+VTCYt+sbQEi1Np43wRynqURzzET6BBgefqNUHDnQj8jw4bI8ZEyETZLAnfAbnkD0maiNjIHhixgEGEOfsuyRhO5ARcp4xuL4ZCRkMBhZU1Go1Wxd0+fr6WmdnZ/roo4/U6XT09ttvGxRDsIZ+pFIp7e/vazgc6rXXXrN0H0NZrVYtwCAzQsenue2eCkp0TxROJAxjg0zCwzk0gbAH1GrYA+776za4HEQiAzaQyNanTGz8zMyM0ZX4OUax8VYAuoFYGLBN+H0eUsAggnfxRfToh9IQHXS7XVN+DhpefBp38qmH707hnVDSi6q8N2q0IoPNeY7w9fW1DfYmAuYAUDzY3d21rjKMfyaTmTjQeGQE58GGY9h8gZA0HMNJwwDP5xkFpFk+U0DZpRfMC9aOa/hiIZEVsAAOsFKpqFAo2N7zWRx4eJnAOJlMxiJUog3uGYPqI23Wh/TPc21xivw81emFhQWbY4GhZIjQ8+fP7a2wzWbT4AF/AP3ZQAqFwsTasSZgenDSh8PhxGAbT70CtgGDZr2hQxFNkk6TuXFP/vxRmPYGkX1HN4FUCoWCRqORUQtZQzjRFE9hotD6DdQ0LeyVzwSoNVAzePr0qfb29lSpVPTuu++q1+vp8ePHkmTPS5Ht+PhYe3t76nQ6un//vjkMP85ymqnk366CfrDu6KJvK/ZFb/aW9aeISd2CZhbODc/sqaIvk0+khfnw3HdfkbKRIhGpDoe3Pc6exhNFtyMBDw8PLR3wbZKkvUSP9Dgj/vARRZOegVMWi0WlUimLKjnA3DPVc0jOngwO1keEQNpOF5Qkiz49FYpNYZ0w9BhglBwF9waBVzBXKpWJIhPdPawlz4/wORhoIlKuU6vVVK1Wzav7lNN3EuGx2SegElgCRFJEOzgaDjWHyR9CIkkylXw+bxFNrVbTeDw2PB1e6ng8VrVa1crKik1Nw2lxAIkiPGUPRZdeQB6sB4cykUhY4bdSqUi6jVTW19dtQhq8UCAvz2H13Xs+MkVHEJwyRhdnRtrqX2uPA/l/2HuX3sbS6/p7kRJ1v1KkSOpWqnuVy922gxgwMgiCIBlklMDzTOJxkFEyytCjzDzM2PkSGQRBBgGSAEnbjrvt6uou3XkVRYm6i7f/QP5tLp6urvIb4531AQrdXS2R5zxnP3uvvfba+4Gi4vu9vRk7ZQ082PG8BB/uw+V7rBvFJeybvcH9QRtgKzQc0Y0IIsapESSgeHBMrqLhXbD3HAC0223t7e3p9evXOj8/18zMjN6+fRudfF6I5NmhJ7A3zpxL0my+FnTSUQD3vQOypXsum83q4cOHKpVKIzUb9jujZXd2djQ5Oamtra3omPVn5o8DuXddHxxeQ2HBp+tg9L4BeclsHI5e5gHGxsYi0iG0Rp6BZIVRbMnTWZPIgpfN1CkKFplMJqIyDiKZTnsRgIMVfZFAjxxDjcPFCbsW1zvBEGPT3kvxgDkC5XI5piRxfLhPMQLNkK541dovHCOpLkiO76HNlIosumMyAAo7OKJGoxFUCoUhnAKOztePdwlaYjOijCCg0ek2Pz8fcyZcfYEzRzv8+eefa319fYQTc1nVu/gxnCqBDHDgqE5SnMTrfDtggUIbXV/ezAHP7LQX9+XFKgrCIHgCG5/tk+AAD3DEODv2Azpp3rMrHHgXrAm1CWl0QBOfDzfsIIXMCAfMXGZXtdAo4dJG56BxtM5Zc0FlILn0FP7w8FBv375VtVqN4uAvf/nLQJuuIwZR8llnZ2fa398fmS/BXsR3uASt0WjEGFf29+3tbRwCUKlUdH19rWKxqIcPH+rJkydRAAZg8C77/X40ZJ2enurRo0daW1uLrkN36r8Twp2YmIiU150tL5cHubm50eHhYbxQHMbJyUk4PUkh0eClnp2daWVlZUSb63M7/SUSwXBKHgxI9/lslBLtdjsQIAbC7zrX5y2P6DDX1taicopB+ykF0Cw4HL4TFAxSajQaqtVqOjg4iLblsbHhsB7nbT219M4fR7h03SBbI+gxorLdbsdAnl6vF+J1znfzqWA4zqOjo3CypK9sdNAw71kaprXI1nDWFMw4EHNmZkYXFxdaWloaUTL0+/2QBtJ4wMmrpVIpDJ60GckZgY3LK/XYGOmsj+djZCCKEu4dXpOORVQbNMaQATnqwQa94w0qyU8ywCY4JQGNdbFYDHTk6+zInjQX+/IaQLLbyx0udQsKaEkbZV5BLpfT2Nj9qbQTExMxF4UpeiDdZOEYZ8Q+wuk7YOHvGF1IMD45OdH+/r7K5bKmp6f1/PlzbW5uxpqjYIHnhRLM5/NKpVLRmt7r9SII8Ow+ZEpSzG+gaQT02W63dXBwoLdv32p/fz/e0+bmZjhQgCEon3eaTqdVqVTiuB+GHMF9J7vdvu764DxcijIgCEeMILybm5uYRwuNQFonKSIqhSageq1WixkMpC5QFV4BxdmzsXgxfBYbzI2YIoenaqSELg3yPxjR8vKyNjc3VSwWv6I1hZ7AEOEk+/1+cLBUY5GiOV0AAmYOBYUZqqReoPHgxoWKgcII74IKPwWHdrs9krrCN4OccQR+arFvKmSAOLx+vx+BaWZmJp6ZzccaYg/lcjkoFtIwEAsXTprKMwjch0tjS04ncYEk+W7PuCjswCWCaLFbf6fYCHpeng/+kiYY9gSZFRf3Oz4+HjMjUJ5gK7OzsyoUCnr69KlKpZI6nY5qtVo0DkEjsGa8Xy/McO8UMX09CYLQSPCcoDRpqNXNZDLBW6+vr6tcLuvk5CRsaG5uLrIB7sODHQ0S7EXfq9wzgZ4Jb9A0krS2tqanT58qn8+PKFtA53Nzc1Ej4fDTy8tL7e3tRQcieww1Du+dPVKv17W4uBgOt9e7n2RYLpejE7PZbGp/f1+np6fa3d3VRx99FDUHn/A2MXF/vNL4+LjevHkT2evFxUXMkklOHfy664On9jqPhTP0lwcXBF/EZkUVwJwAuoFOT081MzMT6I8zs+BGIfUdyXhxzauoFMAwOAwDJyANESzOnECAUbuOl81TLBa1tram1dXVETSPM8P4vJvOpWEoONLpdGgK4a5xaC4rccIfJObcq0dOUnMcIc4aXgsEB2LECQ0Gg+hyIoVkdCCFBG84YP1xqoPBIFJsNjVZgxcQKNrwPBQemRsK6hgfH1e73Y6geXt7G8fX+9FHPA/r4nbhEikPFKA01s1VKcjBQPsgeNAu3wVica03AQiJHZe3hrv2FZkjDiiXy40cPc46OWeKk+azQHFkOgQCnoFiKfbP2mLb8LDIL2kfprtqYmIigAUzhHEmqIKQXLIX+Xecq/PZPLd3cQJyxsbGlMvltLW1Fe3XpPzVajXujRZtJISAK1ROV1dXI8d6JRsfkK6B3HmPUCgoYci0/+u//ktHR0f67LPPtLW1FeNV+cPzlctl7ezsxPwNV89ks9mRIubXXe91uKSAGI4bJO2gDLZhiIyk2IT0PU9OTobuFoNkk2BopHA40KQO1BEPkRfJDHwPKNjpBzYsAUEaTeUodvHvjOoj9eN3IP4xNrhOuGLoF1p3cVJo+jKZTAxxQZtMnzsbmvtNrr87XNc5g4AJCvCP0nDwN04SvSFBgs1EFsB7k4Y8IO+J+Rikf6To3Jf319O+PRgMIpAiI2TIvCMRnofju6EJZmdnI41mrb29m+fFsfO8BEY2PraE84LzR2SPo2XdJiYmRqSLrAH2h6TMawzYj9vrzMxM9OyTkkqKGbs4QPhjgg2pNI7atcLYTbfbjcAB8gYIgOZ4N05DULOACqLY7YOTaL2mSYd7Z14vDS98Z1KqB2VHkMGpglgZ2o2tcv804qC7nZ+fj2NQliCqAAAgAElEQVS52K/Iulhvp1RA4tgvhVn2P7RGrVaLLNFHV7I+c3NzWl1d1ebmpp4+farl5eXYa9VqVV988YWOj49VqVQ0MTGhfD4f3YmevX3d9V6HSzTFgNgM3mEEfId8pwODSfc8EJwaE4KcECdFvri4iMYGjxREObguHLintDRMYASgHddCOqJ1Xo40bWFhIRApnSrwlmdnZ4HWfQOAbJDHEYgoWODooTCItDwnawpygkdio7tjk0Zn47I2cN+gRegAaTjshjZmUBBtnC7i92YR0lbQM00t0rDNWbp3dHNzc1pZWQlJHtQRx7KA4tlQzo2i1fVgjOMgmPE7rkeVFIoOb1rBJly26MEaVYwXWVKpVAyBx8nyDnmv/C7o1gslHPntgAOeN51O6+TkRI1GIwqmOHjuD0AgDeWIFD0JYtgNKpjp6emYXMXvgQTJOMjI0ul0zEzAqUsKGR4FaEALv898D2x3cnIyZl4QfJO8pUtEneslWEEXYH/cO4NlaNigDuKt5QAZzyJcNplUkGBnvCPUMre3tyODeCjSQ7PAsaPYQR9+d3cXdkC26mqN3+Z6r8MlNYMiYOOMj48H3O71eiNnXnnq54UkENPq6mp0kfESUqlUzPpk+IcbNA9DSsgLZGPl83m9fPlS29vbMUcVATbFOYZOe3smz4KRZLNZbW5uanNzUwsLC7ER5ufno0LJ5xNFncqAS06lUrEenKCLJIVZwjjbXu9+/gCHQYJUXJ/pThY6gsCBNM7bQp3L86IM6Sfvlj+krxSr+DzXJ4JGpXsZFMj35uYmnNXq6mqk8pzRNT4+rnq9rlarpZ/97GdhpDhBkCio0RUn0FU+btEdrve9g/pZKxwn1AGbk3fj6JVnROA/OTkZ3zkYDGJzer+9b7Czs7MoHjmdwn1cXl7q888/16effqp6va5UKhXzNYrFYowNpGCMI+73+9FMwkkNt7f3Q1XoaMTxgWRBmNQ2KLzCpaZSKW1vb+tb3/qWnj17plKpFIgf50Jjwi9+8Qu9fftWd3d3yufzUVg6OzsbyRrd6eIIXWeMA4Wz39nZUblcDhoOIMBJCoAMD7QED6hKLwiy3rx77Bz+HnqPn6VuQfAkKFxcXMSsjUajoU8//TSC/tnZmVqtlvr9fvgwjgeDSkDZ8392uHS/IOciInrERyqCjo+CQrL4wY14bzxOiWEmzWZTJycnEW25EDCDglBCQMwXi0Xl8/mYgkRh6/z8PA6gI4VHcE+64ujOT8F1kTQOqtVqjTgEIqA7QT8TzI0FlIATIFXEIBj95u3PoEV3uBgNjsLF3aTI/A6pIhuabAK06cdWexeU6zZB84jJJcUMCNBnt9uNEwoIQvl8XqVSKeaVuqICZ+f8HGnZxMREfJ9Lo3C2vrlRcuBcMHrE6qTHCPzpQoSucIR0e3s7MrCc89fI2FwRk2xGgfIgg3FkhkPKZDKxJ1ZXV/XgwQMVi8V45zwLaJ1g462sdMutrKzE7FaomE6nE92DKDeg6iiGHRwcxAGRfAfIudVqSVLM4X379q1qtZoGg0EMHS8Wi/Fs0IlXV1cjAcZVFJ7+g7gpsEPbwMF62y22j12TeRHYPSA5lcA+IQt1p+zfMz4+HgAMoECTDnQGGRo0Rj6fV6vV0ubmZnS5Qhu9yyb+Tw4XiQbcD2oCyH0MuVQqSRpGegZhE7F9jBppJYJ4jstoNBohm3JRsTQqbCbKgVrgYaiMc2YWUVYaNg5QYXZOmUWamZlRPp+PGaYgZFISZF7NZnOk8cM5WrgsHCV0hTSU7fA8bFS0pJ4GMqSHoodXgYn+OFoX02OUpM0oL9iIUATS8BRWJFs4M45+4bmZYYFDloZzUeHf0VWvrq7GfULvUIQkVQN9MBcDeoVsiIJPMk10VQTXuxoAXFZFMJfuTyd++fKlFhcX4+BNp4fq9XpkJEdHR9GW6nJFR0r+TkDhODGQH2h/fn5er169ilNPmJTlWQjvFSqq3W6r1WoFtwsqX1xc1IMHD7SxsaGJiYkAQ6TJOO6JiYn4HdDo8fFxNB/967/+a3SXMezJ1UTSvTyOecWbm5uam5sLWoSjbtBU+16VhsokT7lBkuxBBg15S6//DrQMXZw++4S1wlGzRyWFk3Tlz9jYWFBGrFsmk4kjwh49eqRSqRRjRdlH6XQ67hE1DwGQQjN+JAmO3nW91+EuLCzo7u4uTg5AaIz8SxoObwDq45DZsFTKkepQZe/3+2q1WuHETk5OVK1WR+RZ/hIpPrjTxYFKiiM9eFHww3wOaNKdFYgIRIDxpVKpQMQ8B8WURqMRVWYcGEHF1wH06X8oSjCEA9UAHVagftAMf/zMKL94BjjW29vb2Mz5fH4Ead3c3AQn504KeRHrg9KE/+Z3e71eZB08Lzz92dlZfC+pGRVgl+ENBoNwumwkhqHTygkixLGyju/q8JKG0kQQPvSOI0aCOxwg2Y+3gXOESrPZjKoz/LhPhcLZelcUHZI4C+wdh0d3G3pdfpbiDfsIaZekkYYMn2qVy+XiFAqOQZIUxbZOpxOyLu6VCVwPHz4Mx8ZBsK1WK5yhF1/n5+f19OlTZbPZ0KPTzAHNgZzL3wnIFkfY7Xa/UqNgzi50JEU6l9FRFPP2fJeaAmYAOq5X9yFOvBPUFSsrK5Gdk2lhWwA5siYCCcVL9lSr1YpRn2Rh0DXJORvJ670ON5/Px4CHw8PDiKBsbja4t3kSUflDG613pmCUfpAfC8+G9qjpDpKXRyRxKRDOnA19d3cXL540me4uUiLX6sLn4PSY1CTdBx8GZoyN3XfUYaDwwX6fTiew+XDi8EGSYlA21VqiKL+fPF0ASoB1gR9ng7J2XkThD9IpkDlrx/PCLSdTZDhhKsIgPqgFUl3SMYo0oFbe2/X1dSgWKKCgcUylUmHAICzWlTX1QUmSYrPjXLAtnntpaSkQLLK2nZ0dtVqtSBfZJNijt0qzaVFD4EhZFy4Kvvydox1qGBRcKfSxzk6J8LkelP1swPn5eT179kwvXrzQzMyMqtWqdnZ2JN2jbByDz5qmm41Umkl8h4eHMc+W/egp8dLSUqgM2ENQNM4pAzLCofzmfUALEtShizi7DErEFTt+ggvZBLJOdLzsJQquoGV8D/fAviDjQW5G1j0zM6OTk5NoG261WiqXy8pms6HcQHaXzWYjQ2NNGXeAPWQymeB633e91+FypMjd3V0gBI5BxlDR1+JMiW6Q/1QCx8fHg5vjtEs6lFhMNi7te1w432SXmRdMMHKMlV5tXpz3muNk4FRBmczoPD4+jpeFU11cXAykXy6Xo7JNOzGIClRIhRRagr+Ha8vn85EykbqAnLwVk+KNrwXPSyDBOd3c3MQAaBALOlOKaiAGN0QOC6QtGkcHcpaGNAJOn4InmwTngTEiPXMKig3DWpASdrvdWCcQAwGbLMKlRu7ocMg8K7/LPUKZsBE94JNNUSwkI/KCEJ9LIZjClVMKZC/MJQCN4exB4ePjw1N1s9ls6M15frI82sA9AE1PT2t9fV3f/va3tb29rZOTE+3t7Wl/f1/SsHPQJWWu1WY/sSbFYjEOdiSrId0Hha6trUX2RaZLrQVq0E91cPscHx8faTjCLpaXl78y5Ak+FaAhDXl+Mi0vILPXyc6ctvN3AkVHbUVS3A8ZFs+/v78fJzswYgBhADUK9inP6QV33oEfcvqu64MOV1K8mL29vehLpzOGU0eJKkQ2EBARhd+RhvMQGCpMeo0TZlRh3KTxpV44Y9GT/w8uDYP39BlURxELw0CCgwidTc59LC8vx1HpnFJaKBSiYk0axufxUpeXl8Ox8/1sXtAjf0fXDZsddOu8kKNqEC50hPOurAu6YI5X4WwtuFqkQiBSrxDjkN1pS8NTEmhqoAWSzhyiP9QAztrVJ6ReFCebzWasvQcUjJwiqWsdcdxoab292+V03hbLxndKyLMkL9RhszhPNi1OkovPAa3h+KR7x0GhllnNXtjEpjjQ8vDwUAcHB6EEYD05Cv7b3/62FhcXoz21UqnEZmc/MJ8am+b+7u7uoki3uro6IpF0Z0XGNTExER2KnJjBPBCyQ/aVXwQpxgLQhEM2Q0DmJAeGw0Ol8d69ccKlV16vAHi5v/AskH0AVUFgRZHE+5SGThr6A/UGBTW0ymT43hFIzcfB0buu9zpcUr5ut6tarRYT8/HqcDlO1uNoES6TyrCJqeLTl1wul+OgNygANg0XlWIcSZJu4OddLsbiYmy+udhQ3W53pGMFbgtEQBrEWuRyOR0eHsYGIX0iZaPA4lpBinuuMSVQIWXCufpgZ3hGVwcknxlnA6qgkYE2Q94Lag4GA7HZyVJAba5p5b8dnXiDhKMp0A5rmk6nw0BRrfDfpP84OSrd8HcurSKIgkaSg0J4Drh9utNc3UHKDKXkzoX3xxqwkVxKx+aF38aR+ztJFnIcWEClVKvVqA0QpNvtthqNho6OjlSpVKLoe3JyosFgEKgql8vp5cuX+u53v6tSqaS7u7vYP8jCeGcM5uawQ9bDtbHuWLxtPklXwW2S7lcqlTgx2KV97BHug5rIzMxMqEMIoNBbvEfmLtAwA2p0NYmDIwAGw/TxT16gdoUEtsq+A61nMsOjfkC02A/rwr2yjtAqFNBOT0/D4fb7/d/d4eIoVlZWIuVpt9sRzdl0oFgWxDkO591Ansy5rFQqUREHsfmAEL8PHBrfy+UVUAyAy6O7NDyFF84ZFEDaRHpNqu996O64XJtHZMXxkXbjiDE0DNhTdV4eCDHJn+KAvDLPJvBCgRe5SC39Z3E+/tl+cqoLuPkc3gfZyuzs7MgEfRwiGQ1SGa828z5QYIDE2UA8Iw7SsyCyJZwOcidHYt5Mwn3x3O5kPMBCVRCsfJ298IK9uTP1oqtfSCNxZvy8p5sUEDkXLZ1OR+EKnTL0A45meXlZxWJRm5ub+vjjj/Xw4UOl0+ko8CEJ4wLFUhthPKFngMjzyGDYU2SC7rRAhtfX18F3tlqtrzRAObpEaULKjTPDBnG02Ab2Q40ESoF97B2lOHI/AMH1xn5x77wbqEqKi6BTEC+NU2RRZGMe5J1qIwhhhwTlJDhKXinX0H1zfXN9c31zfXP9/3e9F+H+5Cc/GRSLxTidAY4Poa9LYPDspMjQByBJnzQGWiCFkxRVZb4vlUrpO9/5TkqS/uVf/mXg0ReJh3e/ebGFnyGV8KIFfJ7zaHBZpFnIa9Bl/vEf/3HqL/7iLwYcNzI9PR3CfhoKPPXgj4v7QW9OtvsaILdBf3lzc38KAvMHfvSjH6Uk6fPPPx9A5TSbzdBIw2d6xRdZG2tDJkBmAseWzWaVy+ViwAz94RQ74T9/8/5SP/7xjwdv377VZ599pnK5HCk9SJ7nBFVJGskWnE6AznD+kWyJ9A7KB0rkn/7pn1KS9G//9m+DVqsV/B90lusiyTBcO+0ZAKgJW4JK4A/oHvrB09PV1dWUJP3Hf/zHgK7G6+vraB4BUbksLqniAZWzbqwZPKi3qmNDyPJom/6TP/mT1N/8zd8MaKKhwQLu2WkQsgJJI+tBdkk6zXrRMQbtQkHa5wdns1n95Cc/SUnS3/3d3w08S3KKwqk27ND3tdN9+AWn43z/kG0dHx/riy++0N7enu7u7vTpp5+m/vZv/3bQarWi9R5qA9RMtud/UPC4RI51wG85JQlNRRZG49Xs7Kx++MMffm2f7wcbH7xwhI6Rij96R19gHC5ENFIVn87k4nGcDYUnaVih9Ptwwpx/OnxnEzk/6SkmL4rPIhX1ijP8Kz/jGj0kMXBJbFQPAs7duSTNnRBpmL9E1uLi4iKKE7Va7StpqTScb4Hxo6Vkc0NTuCKE4hzGCw3kmkaKI86/4fhYP57PqQKcC07ONxbGyu95gYrioHcmuYTNCzcu0UlqPll36B66H9lUqFG8COoFM9/ITk0htue+uSeez6kraZhWwjkjh+ReACAuqRzZiFYMhkskyCG+J7BS1IWnT+5Zl1A6VeL/xGZxfF5rcceDwwWQoF+Fx022W7Ne/L4XrJ2ucJ00F+8Je0uurStzKKzxx3+eNWNv8Zk4UZ4F0AjgQq3hqheXmEHVoLaB9mHuCvb3vuu9Dtc7ujAauqBAJtKwqcA7uxwVsejcvMuVKNI4V0rRwi8KEhRB2DzOE/MnybOxYVBTgIjZcJlMJhYaNOB8nHTfWMGUJwoB3pXiL9m/G6efVBdgeO4A0AmvrKxofn5ezWYzDIML/qher2t3d1e7u7sql8uBrnzDYMS8Gzcu1oWB5v3+fc9/tVrV/v6+1tbWtLGxodXV1ah285lMfeLUYd6jaym5B9QFOCv4Y6R/FPlc8+kBCadOwHa+cGZmJjoGcbpwlOiSW61W6IFRbZCR8P2OahztMV6U5yUIJZ1tstuPZ2bjYlcUtU5OTkYKhdggv7ewsBCOlADF5xM8XduOLeE4sSdkSrS2+1l+rihi3bAV3hP3DW/KPTGsx1UdvhbsG3de7A1fL5fQ4WRZW8AOqgBakLGt+fl5lUqlQPqTk5PBqVK8RhvNse5kdGjAUc9QnKbdmKvX64VkrdVq6fj4WM1mM/YZ6P7i4iJQ94cmhr3X4bJQaFN3d3ejG8eREy8QJEv6RyMBshgaDhyOk7LRhYKB+QUhzTEubHqcpxsfmxRHzsZk03rhxqOgIyKclqdEJycn0VGFRpHhI4j9nSLwrheQMMguWbzzqj9OhWYAEDUX7Zlv377VmzdvdHh4GIUvbxPGafEZIy/9N0gKPS7TrFBIMAvh1atX+t73vqdXr15pfX09JGW5XC6yGwIQWkacPzI/3jVOBO0rz85mZ50c2XoVnKq7rwXIhu9BFkcREDRPoRYHhK35ySJsblAUciMcIZsRAb3fB0iPzM7Rq+uHCa5+aKgPeKE7EqqA4MrsDwqTPCPrJinWjntDN8vMDP44zYculvMAKSJKwywTW0yOjLy9vY1Tc92+WCOcHVQK/oSfAaH7hDIKpC6dw19go9g6wXZtbS1skc/3Ro1qtRradx+16F2lzHThEAWKd2SMtFd3u93QKCczddqw/fDbd10fHEA+GNwPSN7Z2dHnn38e4xWJ2KStjC7j6JhSqRRok7bSarUa3S0YNY55eXlZpVJJKysrXzkyw3kk73oikuOkqZA60uQeJAV9gFMG5YDQcdLJGb2S4iUgth4MBqGw4P4wKrgfnJojDiJlpVJRvV4PTWM6fX+YIAN0ONQOg+RqNBoxsb7dbiuTyejBgwfRDUVmQTbhqIiAICk0jgQtgiLjA9Eb12o1nZ2d6fvf/74eP34cgvDp6WkVCoVAC/V6PZAQY/DQarJJ3Ri90u1pPZuGIOocso8jlBTyKuRfOHCX86H15J68DRdniH6WjbmyshJT4/L5fEwPc7tPBjFSTldfOF3lto48DrrGFTOoZRgsdHx8HDMHnPriWT2wEwBB0Qz0RqY3PT0dMjxoKZwe94GjQWrF/6M9GgqIVlx+lovAhuNCmcB7xA5B9mQ2zFtZXl7W6uqqJicnI2iwP3h2AhjvGADBfydnILD/UQJx77QY9/v9r2iDWW/QPCNb8/l80AZ0qJEl8n3vuz6IcHGWHLzW6dyfO8UBbefn56rX66pUKvEyST2I6qenpxElkFzBd7DhDw8PVa1WQ+S9vr4e94HzxJlBOzSbzZHCG7AeRAmqZUNhGN4zzQaCayP1wgmDmviZsbH72b6vX79Wt9sNDpX2y8XFRa2vr+vhw4cqFAoxZIRNW6/XY7o9XS6O6vr9frxwZEHZbDbWglNMQfpMZgMZe0HQ3yN6RjINP5U4k8mMcIxXV1cjbde/+MUvwrEin2HAEC3UjUYjHCQHVYI8Sb2y2Ww4fYIFwcj5bFJIjBcUR0GHC4RLEGQYi7dz393dhYMDTfK7xWJRY2NjOj4+DooD+oDWX76TRhroqHdRXqTJPowa+ZXz6awlRVyoBNeCV6vVkfm5rA/vN8mLso7M+2BOB0VX3j9r73OnPR2uVCox8IcRnTQjSMP5y9QQTk9PR9JoslhQ++rqagyEkjTy/T6NzpsoKHBxJA+B/Pr6OiRk7IvZ2dmvzD0hsKH/ZX1xyuwxvg9NL/4IX8D3MuGtVCrFUH1pGGQJCmQW77t+q2lh9E8fHx/HyyZ9dZoApMLIxI2NjaAUSCspgtBKe3d3F/Nqm82mer2e6vX6yI17usAmhJep1+u6vr6OqjufizMgulItJrVwvo6XDseGw3PHBZK4ubmJ0y3odqE/HjQnSXt7ezo5OVEmk1GxWAwdIH3goJknT56oVCpFELu7uwsBPGvvBu0pIgimWq1GSoazAeVztEuhUIhjVBh4AroDQTnvSuqLCgJe9+nTp6HikIbaT6I7nBeBBCfNsSXMHGXjkgmAsEAbXvR0VORFCX6fFHx6elr1ej2O3ZYUzs+7laamprS1taVXr16NDPZpt9uqVCr6n//5nyh6FgqFmH+KZjuZdbi6IJPJhK4bFIed8RwEIgItU/cymYxOTk50dHSko6OjEe7WMyinWigWTUwMB5mjqZ2bmxsZKERazD2SHRLY0+m0arXayGBw1os/PHu9Xle5XB6Z7IbPGB8fj5Qd1QvgrN1uK51Ojwz5BiX69DyUINIQsTYaDTWbzRj7iTKABhI/BJSz0EqlUoASABb0EN1hjrJ5r2QQZAY4cc5SIxDxXvEVH5LZfhDhElHx8plMRhsbGyoUCup07g/CkxSHrlGt29ra0uPHj3V5eamjo6PYOLe3t7Fp6IppNpuBFJHTUCHmxVPsoOXRlQ/tdju4JtqIaQBwPgdJlxfOUB9wsTGvr69HqqXeLjo+fj8EZHFxMUY6ElF7vZ7evn2rTz/9NCYK4ZhJkXwYDU4AQv/y8nJkqMr5+fnIWjjni4HncjnlcrlIi/zoGtpzOTYEWofpXBziCHrztknGcUIrIbQHEZNa+7Adoj6zFUhLOc8LFO3yH38mkKVzojg0DNs3N87s7u5O7XZbX3zxhX7xi1+oXq8rm83q+fPnWl5ejvct3TvhjY0NPXjwQK1WK5wI4nqKJWRRZ2dnOjg4CO4+l8uNjBD14lWn0wmE6ScrzM7Ohl2BAJ2v9fbsq6srpdPpeK+Tk5Mxi+L8/HyEfmGDg8yQN+GMecflcjkQIk6EfTA5eT/Ef2pqKsakQrHhJMlS6Azr9XoBNJyzJgiC4vv9vnZ3d3V+fh62l8lk4qhxUGaj0Qgww+jXTCYTdr20tBQc7tXVVfycN1dASzAVjIx2ZWUlTii+vLyMphGC7fX1ddgqFCQ8s48Z9TkTqdT90U/Hx8cR5JLqqndd73W4pCmlUkmZTEbf+ta3tLa2ps3NTU1PT+v09FSfffZZVCyl++iyubmpjY0Nzc3NBdTnn7TlSYoKaqvVUqVSUbvdjgjpQyBwuGxMFiCVSsUR15Li6BNaH+FPMXTu0Qfm7O/vxwQw0ACppyNc1+AxJAaqhBkCFM+azaampqaiBVC6d6ztdjsOoOOkC1LNk5OTkNyQpoA6fS2IuuPj48rn85qbm1OpVNLY2FioBAh88GYzMzNaX1/X48ePNTk5qS+//FJv3ryJVPf6+jpSLr6DogRIz4sjLmNLpVJhbBRs4M/YjKwtyMBbbVlr1+vy71BDpL04WC4cT6/XCyfmowRBQDc3Nzo6OorvYiD79PR0zAJZXV2NEyuoRZCRMZCcFmbSRy5qAm5nyIY4fmhqaiqcsCMskJQfo+PSQQIfSJR37N/JPeD0XQ1EwMOWQWmTk5M6OTlRpVKJbJLaC/QgM5UZWAN1CE3G/bssjK5AnDIjARjkxDwHTsZm5OvPf/5z1et1ra+vq9VqxTwD5h48e/ZMxWJROzs76na7QW1RrIaqxG7InFCr4Kg5vLbTuR9jCbDChgj0dKSRscGJVyqVGF2ABJF1gEt+3/XBEx+YbLW5ualMJqOrq/vjzZkh+ubNG9VqNXW798NMNjc39fLlS5VKJXW73SgMEbV9JqmnYaQ33hbJBQpMp9MjhTLSnZWVFaXT6dBqQtrjNGmfpYCQyWRC83p4eBjifV4Ski+qmNI9j8hGqlar2tvbU7/fVzabjXZn0kNvRiD18IHbnU4nUi4X2eNcGebjDR1u0MVicUTNwdhIJCs4OdamUCiE1Iyi5vT0dAyvdh6Q1IrRcwQzR/vJ4ooXbuDGXeDOZ/LOKGyBLng+pGVIvbzFkiq+GzSyI5wOmki4foIeKE26R7f5fF7z8/OBcHAQUGNsNhQJPiVN0lfkP3wf75DiHo4TmwS98rxkBCgtUCVAz2BDAAk2NaiY35MUmd3MzEw0XxQKhSi+ui4euqPb7cZA8na7ra2tLa2srMT6gO7gXbFnP5PPVQiSAgWTVeKc0DXDoyKpZD5Ku91WrVbTwcGByuVyIFwULVNTU3r16pWePXs2EtQphqZSKeVyOUlDTT6FPvYHBchOpxN24JQIQZBM7/b2Vo1GI4KsT5Cbn5+PIhrUJc/3vuu9DpfIAcyGkD86Ooo+/Ovr6yhWZDL3E9QfPHigycnJoAkwAKJIt9uNNJbUDeczNjYWL5wLVOS6PKI3G927yEBtPscUgr7b7UYve6fT0ZMnTzQ/Px8pCmoHUlxeHvIiNMlERjZcp9MJ1ExUZ/14Yfw3M3FdiE909GCEoXmfOOnvYDAIdA91ASomi2AQOZy1qzyy2WygKhwPBc+ZmZmY/8m8XgoLODrQPk4KZO166vHx8TDsycnJ0Cu6jA7UAyKj8MTnewMKqM7tAtqL98zZeGQHaF2hK5JpOhVwilhnZ2c6OjrS27dv4+9JjymwIFHziwDnM6PZH97ZxPqja8emvKAFz9/r9WKgEbbC4ZIu3MeRws862gURs37sZ9C4F0i73W5U/1utltLpdOzt1dXVeCdXV1dxIgYKEi70rdAaHB3lYxl7vV7YMLpvQEq9Xle9Xg+OGSoL+gXdsBcBOUcQFQwqERAtCJ1TGrAzMjDHWggAACAASURBVLy5ublYIwIgdSPeEc+J08WnnJ2dKZ/Px9l0yZkO/58crndj4cxAZkQCIiXGxRf3er1ojeX3OSLbTwPFWJeXl8P5cEAbFzAfrg5k54U6ZDjdbjdSIqcTcLggCJwO381hk6Ar2iPdkHB4vHQ0ssjmqKoykIRCDhVyoiDpOfwgyA1jhScCYfl9eLeMC/enpqaiJZpmBHgt6AyKnUmHy7t2LTOInWHhGxsboZbAuWQymZGuNtYPegElBNViHJ+nuDhiaANp9MwzSZF5OO0hDRHu5ORkqGBQgcD1ZzKZQG3IgOBfeQ8U6orFYlASOzs7oWN1KsI72nyfONomENGmTeMJGRD8H9kIrcNU2yk4zs/Pj2xg1pz1x2lzOaeNZj3Z3kzQmZycHFEKsH7UQg4PD/Xll19qfHw8xnqCTjOZTKgqkp1VdC1CNeHoeV5peMLw+fm5dnZ2VK1WA1TROgy9SCaBg/NGmru7O9XrdZ2enqrX68UexTdhS2TM3mCEX/ACmMtDUdDgW7hv6gHYFioWb/J63/VBHS7Ih+o/joEpQ+12O0hoiiOuW+The71epLOcNQWnyo2T2rkuT1IsNNVsaXjApTttFodihWvxWFCXsiD9APESNV07i0MivSJiu4zq+vo6pEU8O2iIZgFSadJD35w4llQqFYcx0gRCESt5eQcb944TQ3XBM1JkoIrKc8Chs1Fxkqg9QBXMuIB7Ry4DegTZOj87MXE/+5Soj5FDK0lDvpbNKSmM3B0u/DhF0netAe+DIhAZF+gDVMj6s7EIWGzI9fX1OOeqWq3GCD7qBu58uFgDQARNCOiEfU2gYsi2+H132uhwe72ecrlc6IpdO47+2Hl1aXgEEXbOd/HOubfb29v4/2Rdp6enKhaLMXi70WioXq+rVqvF3qTLkueZmZkZsU+XrJE5kd1xf7x/Tj5hH7J/yZYonhIwyHAdXdZqNZ2cnMR7kYbgISm3Y02dY8dhooLxTjzoF3wglA7/D56ZQE4wfd/1XoeLRIKF8w4tqtGVSkWff/65Wq2Wtra2Rqp8IE44v36/r7W1NT169ChGERKh3XGQPnFBYpNqSkPeMdmei5Mn3QEd4wxwkHB1LDAbhe8nElKwIrVeXl7WyspK8Gc4Cuccz87O4mfh/6T76I+sCEPt9/vxAhcWFuL4a150ksMlCvuxODhUOsVSqVR0dkFn1Gq1OEMLOQsRmXvwziMyGQalOM3DGuGoQGw4Hd9YSVkWvy+NnlLgfKRzomzqd7XVEpDcfhYXF6N5hoYa0NBgcH9cDpIzLzixIZGxgepBinTgQbM4b5kcxAQCRJrFO4O+Yb0Jktjq1NRU7JXz83Pt7u6q0WgEB49D8RoH90HxBidMld2DLA0dS0tLccLB9fV1dNK1223l83ktLCxofX09inOcxgJKxjm6NJKLfU8Wxjvyll8vtnljDEjfxwQQSH2YDnwqPDKZIOuDTZJRUKyHEoB+5JRvUC4BE64eO1xYWAi6iuCLLWIPZKe/E8IFUbJIzodRUPnyyy9VqVQClZFqg3rYMBDtg8FAa2trEQF9Wg9GkoTmkNnulEG0/KFLBIMDCTkfiEESrXFGjg5wmjwzAQGHCx9FYYSIDlLDqYHKnPyHHuh0OlEll4ZDmKm8gm4p7LnOEWdLKyKVY0+tKFSkUqlACxx+6c0ROIZGozFSqIKvZPoWm4H75flYKxCTy8omJiZCe5tOp0M/7A55YmIiipUgW9AMxSbXgCY7EN3p4AxAtz4dCiSPMwTJQGNMTd0Pt6eoAqXDsdyzs7NfKQx6Ko+dUFDBFijuMk8WyolGFZy/t7Vj+7e3t1GcRQPNqbKgfW9fx5kT+ND5EvTz+bwePXoUKgCfp9vpdEIt1Gq1gs54/vy5crlc2BZBC2qAbjQyH+6DPYyjTQZ26iC8MwAHwRknzjHwrVYr0C2OHkfMPUFlYBdjY2NBoXjBkP2HGMCzEOwStRDUHBkG9BfcPhlc8rDa913vdbigxqThMKWqXC6HiL5QKCiTycSmZ3BIr9dToVCIqmW9XtfGxoZWVlaiHZFFAoWyGbm8Es1mxpFRZCJFJGXFKHEORDdpOAoS/Zw7AVABjo2NyoZgw/k590RzinXwwnCoPAucLigByRHpv4vkCRwECb9AYxQwOYUCTiqXy4Wkp9FoBNfshQci8+3tbZwMS288aItOLpw0Dg5n7WtFQYLghuOT7tuRkf0RVMkg2JysB+jCVQnekeUpm2snWTtsCsqJVs3r6+sozFK4weEhZWq32yqXy9rd3dXS0lI4OG8jJ3i69tTRMmoDSWET8MqXl5eBIKenp8NxsWlBdlApqGhQoJyenoYGng46fpbuPLh00ClZE6NEr6+vtb+/Hwdpuqzr7OxM+/v74dwWFhaCu/eTlZ0mJGhzUWuB98aZsre8UYDAAuiB3nNpmc9hwS8kkTN7nGwSO5idnY11AUnTtAN1RqEe5wqQo84D0OC+KC7izL0JxpH5110fbHzgASRFdbJSqcTZRr1eTxsbG7q7u9O///u/x6aXFK23PFi/3w8dHDfKpuUPkcY3Fk7IeRUnwJFwXF5eRmEKCRTIl0MbZ2dn4wXlcrmR5gCcBWkdGjw2BUJr7pM/VHtBnBSDQPiI+70FmHRMUhQWQV6uv/VUVRoOByEF5Ew4uDgkbZOTk1EVB62S8pAVkBLl8/mgJer1eqR/pHcEK1AdAQCH50GI++M9tdtt7e/vh5TQMxRQRDab1crKSqwHTsedMH8cQXgqCjjA0dJGe3JyopOTkxEEjdaWNHNxcVG5XE6rq6s6Pz8Pp3t4eKiHDx9qfX09MhLAR7KF050Jzgb97uHhoY6OjpROp0dOEgYRe0CF8+z1esGlMrDI6yLdbjfoKf89ggx1D9aJAmer1dLR0VEMjYK7BUzBoXIyxePHjwNMpVL3Jyuz58gOvAnEgQ9O1OV87Bn8CadXZ7PZuCe4erJKvhNnzvN48HRueHJyMvTNBGT2LQVdGhneFdhpOuLnoA8AKmQQOGjsDrT9vuuDRTOcLkJ3ZBbtdlvT09N6/vy5njx5okajof/8z//U6elppLNMoMpms3r06JHy+bxSqVQMOqHnHgcEhwJH6BuLRSMlwSHzoKenp6pUKhGZ4dvY4HBpOEJ+DjUFTpMNgwFTgMtms5HeuwYVnS+BhAEwkP6MN+SFg+hwFBQGvGUX9E/a5C+RtfL18oLc+vq6VlZW4j7hZSkWUbxis1O4QhpzcXGh4+PjcHi8DxA7k8Jwqrwrd76kilAJqDGSFWCkcCsrKzHsyEcpevMGduhrwe+TwfBOrq+vA3VzJAxOgs3DBpPuNaytViuolKmpqTjI0QMhDi7ZjMJ79XfT6/WiTZcjycmqms1mIEtSb7j6Wq2mo6MjDQYDzc3NqVgsqtlsand3V9VqdUR14DI5HBLUBrUFbJ0AfXR0pNPT0wAbKALYB6xbtVoN26ZVd2lpKYIJnC4Bngvg4hc2511xdHim0+noOMPWGA1aKBSCIiuXy7E/eXYAm1NKvE/8AjbkXXX4EH7Hi6Bw4YwMZaIcTSNzc3NRiMf5J23gfdcHES4vFydE2jwxMaFHjx7pD//wD7W1taVKpRJp2cXFharVarysYrGoBw8eaG1tLVoUDw4OAjVQwCLFYkG4SBO9kEJk8fvC6XY6HRWLRT18+FCzs7MxdYk0q1QqSRpW2zEib0/F2bGx4LL8oDi4KOYv8IJJqdh0REscLQic9QV5pNP351xdXFzEFCMcGxfPzfsBYWQy9zMbSqVSdDWByF2eg3Eisqc9kXeKsuHs7Ez1ej2cuQ8HAZ3ixDB+nC7vh6yEcX5JSodNjCNjM3i7r3P0nrpj5NgDa87vUhz15pBmsxmqC9AJwfPTTz/V8vKynj9/rlKppOvr65hpwefTJSXpKwjXbRE6qlarqVwux6AUdKJIxUBb6G2ZR1wul9Xv97W+vq6lpaXgXRH5023oQ43Yp/w7mRs1ErLLk5OT0OyCZClikXF4S3W5XI5iKWk69ZSpqamQ0vk6AAa4F4Jvkir0NmTex+zsrNbX1yNbYkqfZznYG34JpQ+ZNUVaaKOpqamwC1fy8LM++8IDBpk9GShdegzS8bkPfKZ33b3req/DJXqwubwzZmVlRY8fPw7t5/Lysv7gD/5A6XQ6IjuDIegAGh8f1+HhoVqtlhqNRkRxKufejeaRgkWmmutttqSwSKoymUxIzugY8nkJdB+5QJ8X4oJxryxL911McEooNkg9cG6kINwHDgz6hGhKp1C32w10ASqj84Zon0QL3J8XGuCGV1dXI4AQffkOL0QRKECDIECKJThGtKQUYjwgSgr6BdTKPXGfGC/ZBXpsFASoCQi4IA6vYLOJsYdk+i2Nnr4LZ+daTNJpZna02+0odBCs4f3Gx8dVKpXC1piDzOAmOPqk4+fyTJDuppubm1B5TE9Px8bGMdKWTKMQjqjdbkcau7y8HBQdDsAlg07D+FQsHCtUA04SOyXIM+NButeco99mXgqZiKM6Mp5CoRDPj8PBifMZOFY6Nvl7dLc4woWFhQhW7BFP/b3OAaJ0yoz3jb8ADOGA8ReDwSCUQlAjcPU4VKRnzF2Qhq3LaMkJnNgBQe/rrvc6XDd0RxsYwdjYmPb390dgOA5ga2srXioOkiNqWKibm5ugC+C0cAZ+JSUw0ugwGUmRnlN9xKDhMDmme3V1NQwNrhJ5DvfJd7rsi6q1GxU63U6nE9EfMp3mDdAb/KkXmTiqQxqewgu6BpVyH/HCrAJOwQKDAy3RuYVBeRGM6E+wIpPwbIa02QuS7nChEFyG58/hkj1JsVkljSBhFAM4fJwzdveuFA0nKg0REhsQ2Z+jT+wWThq+kncO8tzY2Ai+slAoxBrQIEBghbv0DMxVLgRPqDecOTyhz131QT44BApHrAuqAbTpNIkQQEF1OCKKxSAwCtj8fLFYjCK2D+Zvt9vRhemNHWhl9/f3dXd3N6LFRsWxvLw88n54bwQF9jW8OTZE4Rk6DzVNpVKJTkn2K3YhDaeHAWqo23DPoFokYbVaLbhpgh0/w5wXzySR5ZER8P+5b1p4CSi8f//3r7ve63D5QIycCEUK/L//+78xjcgXdWlpaUQ7iDNBfgKnygvgBQHRXWfIxd+T6hC9SSvZwO5svemB3/N5C3wHhgnPxN+7sNylYPCzzl86l8YfHBGfeXd3N4ImoF9cv4kRuaMFUfIzpOxJTtv5LRBAUj6Hk8FRdzqdMNpkwYP3QSEPR0ZqiDMGJbve0r/HC56gD+4XzpFAnCxKeVbj6+CbGz7O1SpeABkMBjFWb3FxMbSbqdR9//3jx4+1vb2t9fX1aEOWFCP++C4oI7cR7tEdQDo9elYWzh0bo9JO0GOdEekTONCZ8v3INHEuySHccNSLi4tR9GUfg2xLpZLm5+dH7sVPvvDGCvazdK+YAKRQsWfeghfNkjp5AITbBc6W6YMUCbG1fr8fTRbems5zcr/n5+fhOH0GMQ0P6MORgsFRc58MZE+n0yNHnvvcE5w0vD2Db/h8fAY0W9JvJa/3OlyfCcuHZjIZnZ+fh0oB/g/jmZy8H/VWKBRGznnn5aCvA2258/PKtG8ud/xsKIoEoDQimveYI5BmqDjRzU/FpafeCXk2BEhTUtw/WjxpGKUzmUzItPgcp1LQTkoa6Ugi/SXF8i4v1h9qgYt1cvqDtQfJU/HHyVNVBamx/hTRZmZmgi6Cm0PGh4NxTTKbxsXpcLRwxPxxdYAHIYKSUwe8I6Q7bB6Xi/nF73m1WhqOQ8QOeFdkJHwPGti1tbUABlBGUFJeaPWsaGQTjQ+nm3mlnfOySOubzWakyDy/F5IAIWR5nAwCKsfGCf4utOcz0cV6FxsAhyzN21txEvDnpNgECRw7+whUCrWQnCsBveOcKjbKsxIocdZOd7m6ATsHGPD38Og4Wya7sRaADCg8snGyChwymSYOn/dO1sHzsB44VSSQ+BrWzO3t6673OlzvNXZI3ev1VK1WVa1WR9IQrxpy6CILgPNhLiutcGjxMBoctF/QB7w4XgC9+3CZRGyXbnmbnzsJilWkbiA0eE+0rq7DZaydE/WuhURKdHNzMzIicGpqKjhgNnWz2VS5XA4pTjabjYExfAcO150NCBaUSQEM5720tKStrS1tb2+HDpGjxEF2GAkX33VxcRE8GoUlR4m8A0fKcIWeGjuC5T1QvHGO1W2KP1BTvEd/bpw5FxwmAZ3g7RyboxW+2zcTw11WV1ejBZiUEptwLSnKiKRskfUg9V9eXo4Csw8rggZA1uhggXZqeM7FxcXIILrdbsxWKBQKQVWxRjgESUHtQV3wPlBIkF4js+I+XNPsn8tkNZdD4nCd1mAPY1O8N9CggxmGyLgklJ9358t9ECDI6gAiFEG9oxN7JYjRTMRn8RmdTicKmBTJvZjJwHkaHJB4UtgGkBDcnU77uuu9DhdEwwLhvFKp+xMLXrx4oe3tbT1//lzb29uamprSxcWFjo6OosLLBC3mx3KmWSqVimqkb1IcmEcKOFI2GQ716uoq5CJjY8PZBbSCYpA4F0+3XTvHPyVFeyUbhRGGdIIlO+ngbFFbZLNZTU5O6smTJ3r69KkePHgQJ76S9jKE2RE681gxBFciuONhczsvCM2BPA6UWCgUIvWHWvHBPwj9EZbDURJ0cDJesHTjx3Gzvl6pdQ6cdmPQ6uLiYjwDFEK3243sAI0yRuybyA3a0RnSNzhT7Pf8/DxOULi5uYkN6ie4eh9+rVaL0zqmpqaCo3QZWyaT+QqvzjoxawR6A5uiGPfixQs9f/5cxWIxiqfsMYpEnMpB1kJlnHSftmucEuvDWoIC+Xfey/Hx8YizpSGGPUeQhAYhO8HZEuCxTefiubgn/g4KAOqDd7uwsBBB2OsoUBUErpmZmegCpBvSgz/7D0qMd8PPYRv5fD400AQ1bBA+lhoNNoXDpUkonU5HEZRxn649d7nq110fdLgYOml5On2vveNomB/84Af6zne+o8ePH2tubk5XV1ehPTw4OIi0iDPLbm5uVK/XIxXxaCQNkQypPPcBovTqN/D+6Oho5EgVIh6G7vzPzMxM6D4RVjt1kEqlAhVCmPuLxbHC74Dql5aW9PHHH0dQSjZeMP6N+5+fn9f29rY2NzdHeCkfcUhalOy6I4jQAYOzROr15Zdfqte7PyfOz2tCJzsYDALtIotiDODExHCwNKjJReaSYpOwwVljzx4IGiAWd85schCSNDzrCqeLA3KFSlKxwabDidMRBSqF6uE5Z2ZmtL29rZcvX0bHF+l7q9XSZ599pp/97Gfa29vT+Pi4Hj9+HIUlNhP/7vIf/s4lSxwIOjk5GTN6oQxA5LlcLpAZSppGo6HXr1/rV7/6lQ4PDyUpArbrTKGy/HJ+mcwUe6bo540+0pBj9boINurP8y7n7ty1vxPP+qB3yIDJHl1Vwc8RUKALO51OdNk1m80oCjK6kYBEqn96eqp8Pj8iTUNqxpl6SCWTWRoBE0DJehIEKULSaMReAd1K+op9vuv64DxcUgGqr9J9xR56gM6LnZ2dcASdzv3ZUxsbG3EK59bWlg4PD7W3txfktVe3MRYq33wXRuHtr0QTUCxpDZwgztDnNOCwl5aW9Pz58ygWgE5wJiAMFhaHS1SmiYGCBoUgNH8YLtP9j4+PI8KTOmYymUDf3tlGCg76dk7bL/6Oafku8QKBn56e6vb2NnSkfD9cJxyljyDEOXr3DUiaZ5MUaSn3D0rnsym4YDtwtnynpLAVKvfj48PB2t6F5TRCsnBGisu7yeVy2tvb093dXYj14c5Zk4ODA7VaraAFUAJUq9WQMmazWX388cd69OhRDHvBBnguR7hzc3MhbwKtkWbzTNAbtPmS7mJP8Lynp6dxgORgMFCxWNTW1lYgcKR1Tif4XsX58D6SlfN0Oh0D6F0zzRqBzDnOx9c6WZXnnSR9Brbi1IOrApDLQa1RsGJt8Q2chHJ9fa2lpSW9fPlSL1680MOHD6MQxv0wgZDz03w/TExMxCkfIGUAhQcT147783AvrIvXOzz4vGs9ktcHi2ZUsd34c7lc8JJXV1f65JNPossKxAOaYaGpbhYKhXA8SY7JpT1+7hecKRebjMMqJcXR3aRebDAMB6fi8hHvIiJieu87RoGhzszMxDCTVqsVnTKeEuP8SMHo9KL7jvTDFQz8N3papxM8rZaGqSc8b7zI8fEoanBSKhIknDnFAZeeUaVlXXAEoGGiOB1RknR0dBTzdXkfpPf+LkkTfbiLC9DhWnH2GDD/j+8D8b9LuSINgyGqmGq1qqurqwj2CNZfv36t3d1d/frXvw61iKQocr169SrkYU+ePFGhUIgMiClRrINTXktLS0FBgRZB2NzbxMTwqB7O5mq1WiO8Lr9TKBS0vLwcAMEHHd3c3ER242gM5QwOF/CCw4CX5J3QRUVgQFsO2MEhYScoDci6fE86sgNBercX7wpnWq/X9atf/Ur7+/uhLcaBucQKKmwwGGhjYyOKVThT9MVnZ2fa29vT5eWlvve97wVdxyAdaViDQRVxeXk5Mm8Y0MMaui2yBmh6yRQcKX9dQTV5ffCIHZwCUVVSVP4YfsEm8+lbVEtJuUmpUqlU6ONIsz11hb4ACUn3jtEdEQaPIgKJCAaK9nd5eXnkOBA6vvL5fIjhcT44IrrS2BTewkmkXFpa0tHR0YiekzSOtIz79xmdbBJv2CBCe+osaeTfHVG4dtj7v0EPOD2KbY50cIxJnfPU1FQYFIJwIr+jci6O0qagxntzaRf3yrpx3wQdSSEHdCmXDxlnnVgf6CcujJvfY6zk4eFhHN6JHT579kxLS0t6+PBhIBw4Psb0oWElaJOmuv4Y9OTBbmFhQZeXl5qZmQkePFlodPH+8vJypLtkEkkNKKM2SXl9DV0amExnoXkobjkq5TQPgi9ZkOtgqSsAsnDCY2NjsV/Gx++PX+d+HBHS+OF1GIpRZIJu0666wAHy3eiZb29vvzKTGUeLqoTOVtYau0I5g/9CzUGHHU4dP+XfD9UAHcPsEpQOfuHjktLF5PXBWQrOtxANmY+azWaVTqfjzDM3FBwr3CQSDDSb5+fnwYW5MVLMSQ5sgdfzCJLJZCLdg//04h7QHzRMQY0ZqURBCghXV1eRWnJqBPdBik/XD23MNG84n5lKpaJIBm2Bc0F77E6U9Jk0l1SMZ32XmNqzCJA9Uf/u7n4SPuP3WB9HXqBppoqRuvHZVK6ddwXRMkwEh84z8R3O90LdQAt5Zw5O1PWZ7txBxC4pSqazOLfp6emguqampuLUDRAqNkCK7zN2Sev5DlAOtsSa8DMEaS4q4YuLiyE5dP06gIF19PF/BC1sDE6dbkhHWgQlqCYvqOJUAT/JFlPuh5+jxZ4pZByF5XQdqhqyL+ocnJsG4vU9SXbnssTT09PQfbtyAD/BwQQUMcfGxqIeAYDD59BAQ4bASRVuV2Rq3rZM1sT0PgDG8fFx3Ktnk+xHfBT2S6ch79L1t++i/76yb9/3Px2NYRy8ZDRuoFzXrxHRQHGMOYQb5UhkjAZDgk7gnDMu54/cESFe5mdSqXuhM1GNP6BPCHwoDp/DwAsul8vRfszLkjTSO80sBO4TeQ2IiQ1BwKFA6MjEC0GOKki53bEkHS6RmGfGgd7e3g9+Pj4+jiKY86Hckw/ZBomQUrosKCl1IXoz7hC6yQ8IBF24ugFk74OqubzhBKfL5vZ1wFE4wuX54ZOZ+8qgoevra1Wr1TiZF2cAJQO6dInW7OxsBGbWz3WpOEW3T+SMFMWc+8TRIV9kP/h7Bo3hyF2i6FSDF3+94QGbcFrHZV68Px+GlCxwOvUADYXThjf29+LoGjDi75OOOP4Q7Pj3fD4fmSr70U9VwO+QbaEmGR8fj8arRqOhWq0W8lTWwmsVrhbhc7B7gB3O2jNOLi/24lTJ1B1YJP/76673/l+iZTLtc8MAyWFc3DBFEoZlN5tN1Wq1GCLDS0tqK1mAZHEEA/AKKJELaQZRiY3l8iWM1NETEQu9JAcynpycxGdwwfv0er0QkYNwifw4UDZxktvxaj8vhk3iG9A73JKVTzYPL9iLEj4xn3QMhwvqcCTAvfIuJY2sUxK9+Fq4+BtlBSkra8dz85nJKrCnu74eBEKCtjQcS+nvhJSbf1LFLhQKUYxB9QAY8CE6ODIc7/LyskqlUqS+HMkCNeF0QtLxE2RxEGRWzpnzfkGxPKPz+awZaAme1QOgc4zsBRwrBUw+Y3JyMqgmHCnPjp16I4E3v/DffD7vkQBD0McPYFM8I9+DH2BkJJkp9ANrQD3BAzwAiX1LtybHtnMMkktHsTnWjAyZzAapl88RccCYlPx5fwAZnctOAVAfohMkKfXbSBm+ub65vrm+ub65fvfrvQj3Bz/4wYAOHdK15eXl4CC9oEEqRjrmbY5ES1ACaTx9yo5G6Xi6vr7WP/zDP6Qk6ac//emAz6ExwCuxRHoikXepgDBBGqRujn69GONUCJHxhz/8YapQKAy++93v6k//9E/10UcfRUtikhrwQo8jfVCM86NEREf1pC6k+rSE/vVf/3VKkv7xH/9x4Jw4RRqiLsgUrpH74bkcsYKikjwgaZijYRDD3//936c++uijwebmpp48eaJsNhv3yTM4FeEpLevFz4AcPWthnShk8l5TqVRMZfvxj3+ckqSHDx8O+B3+HwUSvgvbY+CS2yM8Jeku9gvqm5ubi4p3tVrVp59+qk8++URv377V1dWVKpVKSpL+6q/+anB0dBQday9evNDLly+1vr4e0kkQGlygz+h13pD1cdqM9YBnxFYp/vzRH/1R6qc//eng7u5uZNiK26BnTs6LewHT3x3IDl49KWvsdu9Px97Z2VGr1dI///M/pyTpz//8zwfcm3+3c/2shaf8ZKjYBe8AH+JdrNgoKNrf3Y9+9KPUX/7lzvVjOQAAIABJREFUXw6oR0Ah+Dt32sg1za7zdyUKts994d+cSiVbvry81J/92Z99rVThgyoFXkwmc3/c9NraWjg+iOerq6tIY+HrcLgYOTfInNVarRYVSKrMpCVJDpO/oxjGIiQXJ+nQnPdLBghevFee4aeSDhmDREDNjIjktCqqujh2nEZSJoUzcucm6SuOGONzXogg491urDWGAFXjfKFTFO7sfB1RjvgMUtbIizvcr2tvuXdSTl9fNopz8KSzpIukijRAXFxcBEdPkZKmjHca8m82tNM2OCf+8P5dsoVTwZl4hxMblXeJ4yTgcUER+Om0vD+3H6dS/N2zPjg6qAGnvShceYqdlAz6vsGx8t/+Tw/Gfq/YcnLfeTcm0kredXIt2F9JasBrPvgPaXhALNwy4MHrDBTrnRriWX19+XdoI5d6JuefILdDfue0lwcCbNb3DM6ZfcZ6+rv4uuu9Dte5Jw5PzOfzofHs9/vRIYM2jc1GEYFKJ0itUqkEl+skOdwKgzHc4WKUOE1HRrz0d104O15+sgjBZ/LSGWKDM8NJSooquGsiu91uPKvfB4iP2QRJEh4jJDNwNQDP64UMl2RRHODZnE9y5+a8txsoxo/h9Hq9ENuXy+Xg2HF2uVwuntv5b66bm5uRBhEq0HTmEZykoZxPGg6Vv7291cnJifb393VwcBDzJWjjLBaLevLkSXTk+Vq4E+H/4TD9lA2CJ5sKVOPoB36b+oKjMuYSHB8fK5fL6eDg4Ct1B8AH7eu05l5cXISCAcfhmmRsm2yCPUWno6tHOA4pn89HUPBilaNFbPpdmY3vDw+CoFrPQsikADtTU1MhoWNNXZOMbXN5AYxZz2QeAAQ4e+yVzCuVuj8E9eTkZCQr8D2N6sSfj2YnFEJwr+hp6axstVoxy5eCGnJA1AdosEGvHARK4MKX8He/kywMVICDQyOLZAbUwdHeREwqeTgBHpRqPqjXU3JP9z2VZQEZUM2D+WAJhn7gcNDfcT+OsOiYg9aA2gA5eWWVaIix08OORpBFd+XC2NhYoDVpOB3MkR2INDl7FLkO94UTSxYQKbRR3eV72GD8cb2vdB/dkxOpWq2Wdnd39fbtW1UqlaguU3wE+ZAVSMO0n8EwCNgHg0EUFEulkvr9fqwLsiCv8l9cXKhWq+nNmzfx/TQPYMz1ej2kfLS4un2CFHm+JGXhmmSen/fIWvT7/ZHB8v5za2trKhaLoT3lgE4GM0nDkX7tdltTU1Mql8saDAZqNBpho6urq9H5RiEH+yE7xLkgTazVarHfOHfNqTfalaXhACJvVHBHhs040vZABLp2DTUFZTIPgnDSwfpeJaCTmVEMw1ak+6DMwZZQLuwb5HBIR2k0gFJJ6s9Bua7iADCsrKxoY2MjZup2Op0YJEQj1NnZWfiSbDYbemfAJu3mSERx+DhaLyImtbnvuj5IKXg1nKlTPkYvn8+rVCpFbzroCQ6M0X9ENbp3klxmp9PR8fGxjo6O4oRTLhwijpfvJvWguoww2qvsPIdznkjUQLPSkKJwHaz3q6O/nZiY0OnpqY6OjtRsNkM4v7m5qfX19Zg1CrXgY/54KT5AxbuDPO335/W5Ehg36BwDpTWUgTsuAid9ZwgKTujk5CROhe12u8rlctrY2AitIoEQFQWfCXqnwtzpdIJv82E6oAiffUzgAw0SmNbX1/Xw4cPgUweDQRxwSOXdaQ3eKw7c1Q5QMGQpLguklZigj+6bY8xxujMzM1pdXY2J/9PT07q5ufnaZhTWyU94lRSnTDQaDeXzeRWLxeiKhGbD0TSbTR0cHOjw8DDuxUcMkg1hUzy/pHg/yTnLOOGvQ7koEiSFLBFnQ9D38+igB6FfePYRp/KbTIJAxP71Roizs7MRH+EnpXDvqVQq3hX7HEmW25/TG5JCBriwsBCBDrUE9Oj8/Hxki+xxqE3u0YMYWSKZGxmUAwgPZl93fXAAOXIXFpBNgEOkfxxDfpc0A6fqiAxn1Ol0grzO5XJRNAO6SwrK4uzsLOgNPgNjPjo6irPTiGignX6/H5QGCAqU6aP23Mn5IkuKWb5XV1c6ODjQf//3f2tvby82J0PXQUQLCwvqdDpqNBq6vLwMhAfq5AhserRZD14gInQvbPgmAWV7ZxMXm4WpbHQ34fAymYza7XZMcAMNMBWJYhEIB4QEkgQNplIplUolPXv2TBsbGzEBC4PFKaCbxHGCmhhByO8WCgWtrq5GJ+L+/r5ev36tSqUStFOSL4R2QQ/MHGbn9wns2KFvGhxivV6PwAjaQ4vdaDQiaJAJubpneno6Jsk9ffpU3/nOd+KoopOTEzWbTR0dHYVT397e1oMHD7S0tKSxsbHQjruzJzt0CRhaVWzfAxBcL2L+ZrMZdux0kheHko4KUT8zNmh/Rg6JvhZZF11b7nC9QIujxfHlcrng4VlHgodTQHDPi4uLevTokdbW1rS9va1Hjx6FPrpcLuvt27c6ODgIKpO1uLi4GGlK2NvbU7VajdEDMzMzYWtkmGjlXQqXTqeDS8aXAJh8tCx1rCQv/q7rvQ6XF8Km4qhwUFc6ndbCwkLMcaWrjCjZ6XTC0XiRqNvtqtlsqtVqRZQBgXgk5IJb63bvT05lcv3k5OTI+D8flAzUp3jGgjE1CBH12dmZarVaHPYIL5ssFBGJMTomOxF8CEDZbFZbW1tx3htUyPr6ehgzn3F0dBRzahFhu6GQ1tJNJw0pD6KvpEhZ5+bmos2xUqmo0WjEoBkvWvEdrAsFokwmE+sB4vcCk2cMg8H9KQoPHz7U6uqq1tfX45QPJvS3221VKhVRwb+4uAhdKIEml8tFezZjFOHxm82mGo1GHNntulPWAu01w2v8JGgyBVAZ6TvvM5/PB59KhxgIlXTz/Pw8WroZdwndwsVpyePj43r06JGWlpbCEVEP4DRnWlwLhUJU/GlLn5qairmrmcz98e23t7dRYPaOOfhPn7Hg9AFFUJw4gZZ2dAqFyZoG90q7K+vsOlwffIOTTO5V3s3m5qY++ugjff/739fGxoYkxRCro6OjaNLBiV1dXYXNrq6uxnNSK6CAiP15QQz7PD8/D7QMD42P4L5QXS0sLIyoaaAzeL+AMugZ7sF1zGRvgNL/s8NFmfDixQu9evVK2Wx2BE25o2Sx6TmmdRdHxwzKTud+HuyXX36pnZ0dtdvtIKzpEsIpc+EUkIoQpaksE3Ug4nEKdFiBWhhw8uDBg6i0cmw0TsS5GRaVDcqmLpVKevTokSYnJ0OE/ebNmzgx+Pb2VkdHR1peXtbs7KxKpZJKpZJ6vV446uPjY1Wr1aBpTk5OwhBAM8zvZH15LtaHCLyysqJisahsNhvrD4Ug3aMI1gcjgf+jqLW+vq6JiQmVy2Xt7e3FuEecgHNUcGbT0/eHfe7v7+vNmzfBzz979kzPnj2LIDU3N6ezs7Mwek51JkWt1+tqNBo6ODhQo9HQ1NSU1tbW1O/3Va1WdXd3F0fDeNGMDcTGB5GzAaRh1dzbg8fGxuI0XO6JzjQvNPJPeNelpaUozLlyhDGM/D+KfiA9MjucFjUEPsOLtC5XWllZGWkTJiB7I4TbRSqViuloExMTWllZGamew1VfXl5qfPz+wFCGMdGoQ+aB/UvD0aQ+F3hlZSWyAL+8jjMzM6NSqaTNzc2RExYYpwrQ6HQ6wW+TtVIjubq60q9//etQMvT7/aD3yBCSqggaVjjNI5fLaWtrS4PBIIBVsVjU2tqaJicnA8HTIAKoBKSAwi8vL7X7m0MGADuTk5Pa3NzU48eP48ig910f7DRjaAQnrMKr8ILgZ+GTQJgQ1zg9UlZpSPC3Wi2Vy+XgkY6Pj7WxsaGZmZmRSjhIwDmgq6uriPScUIDB393dqVKphLGSrk9OTkbBhOg3PT2tfD4fPDUpPy8QY8U419fXw4lDgZyenmphYUGHh4eRMl1cXIQx4hQkRYWUAzVx6gQKig1ePfaX6HwlhYZyuaxarRZoqVKpjJD7q6urERgdsS8vLwdFUygURvrZSYXpIkqeGUU6WS6Xtbu7q3q9Ho7+93//93V5eamtra2QD5JyoVip1WqamJjQ1tbWSLADFVKoY62heLxo5hX+4+PjmFnAIHc+w7l60saVlRXl83ldXl5qaWkpMjNSYTIyMiev4DNNjQukxLuGygFZ5/P5sN2xsbG4h6mpqZDtkQnRuck7ASiUSqUIBu5gcLB8Ls4Ivp29CW/KONXx8fEoNkPHjI+PhyNj72Arfpw9++3q6kq7u7tfkXCSSdzc3Gh3dzfOAXPuGUSJf3jw4EEg1nq9rk6no7m5ueBdz8/PtbOzo4uLi1BrFIvFOL4Kykwanua8sLAQ0wRpdWemN4qliYmJCIDI+giWfJ6jVm9fR/ecTqcjc/5Q4ey9Dterf56OevsoUafTuT8gEgkHqALOiZcFKsUYVlZWwoHiQJDocPlBlCwYTtjTXGRGoEh3XrwEKvNehaQIR1pDIPGeeVAvRTEMp91uq9/v6+HDh5Eak3pQbCFN5PMoHs7OzgYiW1pa0mAwGElLSHvZVNy/i9hJ/3DMNB8QHDOZTBz3wya4uroKjSMccK1WiyJEKpWKAtvd3V2sDc6aTSoNizXS0PFwtA/pOKgbxwWyAQGTOaH9xbn1er1YO29Y8I0NbYCOk+9zjTJIjU1BAw+n9f7e7/1eOA+O4xkMBpEllEolPXjwIFC3SwP5XIoyvV4vJHQEPQ+g0n0LbbFYDGUPAYfnY48hbYJXd6QOkPCCF9kLa+LvmyNhJIUGlWFROCDUINlsNoZsO+UCBeQnIzhNw57m705PT/XJJ5/o/Px8ZEDVysrKCH8t3Q9Emp6ejoE1gKelpSV1Op0YsUkg4FnJ+Hx9nVd2u5cUdBfHFEkKbpc9Twbgw6SSjRYER+6LPeGZz7uuDxbNkJnQ1ACpjTPFieA0QROgMoflkiI6Li4uKp/PRxNEo9EYkR45peACdHcuLLIjaUmh82UGJpEUxwuCPjs7C8lLUpAPp8UCUuDj5cJlUWgDHfJdbEhQ9fT0dFRf0U+C3tiE/JNNAF+dzWZjLdhoZBTIZEBNnj7SXz4/Px9UDid3EAAZ18fzsakxKDYiSBWD83VaW1uL8514t9hEp9NRtVpVrVYLkTnrTofY9vZ2PBtzCJx380lv7uhcIkeRCRTvjRvZbDYkQltbW4GAQaHPnj2TJD158kS1Wk21Wi2GKy0vL2t9fV1bW1taXl4OftCRNmfd8Z2utyWwU89AariyshL8qXOsOEw0vD6VC2UAhS4kbNLouWrsHfhQQJAHEegK9tPMzMyI7IrMi/3B+4Qq5PmgKJL2eXt7G4fMDgaDaBpaXV0NlQa+pd1uq9lsBvp23T1KgHQ6/ZWUnb8n4+C+XJXUbDYjI6Dw6SDIaSRsn4MF/OgeBwJOa05OTmppaUnZbHYkg/y667caz0iEAMmw2dn4LDy8B/panyzEhsfg2Ahwpmx8aTiZi2tqamrkuBocOgvJYkgaqQizCZwquLi4CHkJCgycsUdnjIe0ChoARImD5755RheiX15ejvDOODbvnsLoXJoCX8zGcITL+oKe4FK99dkzAdQVaHqpOoPqoWB8PQmspKmzs7OhEeZ9UNihIcZtBS5VuufT3r59q9PT00BuDIZvt9va2dlRqVSKNHFiYiL4Mvg0vseHmEjDwT8EP54Ve1hcXNTm5qa2trZi+DQBnyLaxMSEstmsXrx4oadPnwY9wQAjjsJZXFyMNcApcSUVOtgWWQg2CG1F4CDVpyrvztrtCGDhTSM45qRMzmc8s4f4vvHx4TQ01hbVAT/PPkilUuHInP+Gg5cUKgw/LIDfJwujhgO6ZS/SRHB4eKi7uzsVCoVQNbhfgcc9PT0NB+fdjNQI3DYIWEj65ubmIlNggh8FcLhh1oo1BAjROesBDwfNfqXQ+jsXzTBcUK0P6gX9JY0GlMiiEx3gtqrV6shRy45icHbewutGzUKDMnHsRMPT01Pt7++rUqmEcUijJ0ZQLeeMJT+njHQdjtRlHkzfQipEZAdloG+lOIDTAbVwOQfLWVc4GSruqBN4ke5kMHzn8SiggCwIjKBNaUhF8C6p3lMZZo1ZU4yJTMKbUfg+77bxTeazVhuNRnDbDG5mbbrdrvb29rS+vq5isTgiX0KjypqA3pP6V+Riy8vL2tjY0Pb2djRJeNMCgZJgBldK0GRzLy0t6dWrVyPFSafM4MWThUz+EHTRE/s9897JGKh3oDmVhicRk7HQKYfjJYPhPbHBuUf+SUBGw8peZs8iCcRWvEjIe2B9fC8BGsgq4T/DoZgCZnZ2NrJgnCZgIJVKRZMHUizf+2TXZIigYILS/Px8ZAmzs7PBQUsaobvYg9gTz8IeZeKfF1fxZXd3dxGkWHOCKp/FeyDzcrngu64PysJ8cyGZIn1B4O8CbByMp71wLpzagJaXGyeFdaNLavsg2jEcd+RwqVT+Ly8vowGC3wetgp68D5yNwqL7BvFmBAoPFMqc6wH1ITXzqM4GI1JidBTi0ul0zA4A4fogGr94bpwchsozgrbp8OKZkQyBfqh48+zeZcW98+zQDThm3hPjGfk7H4aDrpRNTQpOEAO5cOQMiM075EBZNAmw2f0CMeZyOa2trelb3/qWtre3tba2pmw2q263G0cCUYhxVATKdaVCoVCI4+3ZtIwOdIUGlzchAD682w0bZOP7ABjWgvfs4ID0FXvhZ/kut1X+H0gVxOZKoV6vF3p2OG5sEuTJPTAAJ9k6j9PH4dJB6j4D2Rk1nouLi/AX8NLYFdmq7w9oEfS1FNK/+OKL6LzD0XrnJBd8KuvGO2C9WG/4Wr4HuyWTQZfN/qUO486WvcBn/04I11MlXpx/iXdZSMPTU2dmZmJzghzHxsZGjsMhMoKKcYgugeFCY+ith7x0b4tkDgDwn5eAIYLurq6uAoXjsLwqzu/wB0PDkKlogviJekRy+En0myAq9JbI5paXl4NjTaoUeFZPJVlPDz6gAX4G+oUz1Ej5QCWS4jNxvt7Jx2Zho/k8gmTTAe/EVRg+rY2A7GJx1zZTTHIu3p+F4E3xwoexhAH/RibF/ARXVNCk8tlnn+ns7Cw6ETnymuyA7yBDmp6e1vb2torFYnCNoDkvHnNRbMQxU3hFEoZ+lffq+lAQtjs36iHYOTaJDXL5WnpWhp263cBNoi1Gu+wBzbMW5z+9gYln8frOu94J0jEcFkCCAiAT2ECNFNRxyBQve71enNE3Nzener0eiiPmU2BH3Af7iTWmGxPbx1f5uwQQEhihWygwExSSBX1s3BHy+64PysK8JRIn5RuWlJL0F3lWr9eLnycFh/ien58f4UIdpVEscSKeaMPLx2D953EoUA84U6IwBugpE6mFo0YcWBK5E1hARHxHv39/Qq/zz+gAffQfJ0RMT0/HwZbHx8cj7dDeYMCGTmqSuS8PUhi5d1CNjY2F7I7NyR82AGoL7xyDnyTIsgmhQvguECq8Gu8FvhADXVpaCicI8iCId7vd4NZyuVwgK2gm3gmOJJn58P95dgahIKZvtVr69a9/rS+++ELLy8t6/vx5nDRQrVYlDYfoEEBev36ter2u1dVVPX78WMViMTY1vCeZCNfZ2ZkmJyfVarXUbrcjwEr6yiAdR5RwyqVSKc7iGwwGqlQqOj8/j5+DTvGCKQUw1xvz/xzpOs2Ew6WpAAkgn+NdVlAe8LNkcbxLHHOS/gO4eOrNd9CiPDExoXq9HnSaO2L2Dc785OREd3d3UehcWFiIZ/DDD1yZgPNO+irfz6hgsFsKs6lUKlQL/f59h+rNzU3w2bw/nt8pBL+Hr7s+qFIgovnL9RfvEYIXCJrEEXNz9NzTlUYhDoQKvCft57q6uoqONZArzoCHdnkMsh5P10grWDAMAs4tiSo93ZMUvCfP5bw0PetUWFmLpaWlGOwDmpXuZURnZ2dRZARlgG5xVgQDeDY2gqMaUstMJhP85Pj4uE5OTkLm5MUksgXWqFar6e3bt3GKKU0O6BDHx8dHqtmSIpJTySfysylRMYyNjWl7ezu4sJWVleBL6/W6Wq2W1tbWtL6+rhcvXuju7k77+/sj81zJdqA13MC9RVW6D8x0DV5fX6tcLmtnZyfkaa1WS5988km0rfZ6vQAKY2NjoeKoVqs6ODjQ3t6eNjY2lM/nwyGwEd3JVKvVaHjgjC2Xd4H4cU4uD0MWBxpGgsXz9vv9oMcc6Trv65vdC9q8IwpDyLzm5uaCN6fY5LMdJicnAxDAu0rDjIRAB1hK8paAMBQ3UDg0WVCwRZ63ubkZNNDNzU1o8tnP5+fnsf5w9tgj9ueZtrenJyk5qIF0Oh3OlP3K+X5IIGlffhdF4+DNs8zfiVLw1C75Re6MeBCH9RMTEyEGB/4js2DRPF0iEsODuZOhuODSF9I2vttTQ5wW1WUGXCAJ4yW5KoHPxYl70YR74KXyMzc3N2q1WvHi4E9BkDMzMzHch+gO58jQGNC/Fx15LpfkxQuzoMCGc05ramoqunnOz88DbdGqenZ2psHgfpJVv9/X4eGhXr9+rXa7rUKhoO3t7REufXx8PIKhozpHUSBWihnopt1BUKCkXXV9fT26qWizRFB+cHAwciQ2GY+nctIQ4fJ+GPjS7XajA/D6+jpE8b/85S91enoasi/uC0qC5pxSqaT9/X3t7u6q2WyGyJ4ZHZ7WS4qDGPlzdXUVYy29cOv2Ax3D2Vx0d1ErYC94+yyOAvt0hOV0nPP52LCjv62tLZVKpQAxfAfZBD9H5R1ED9XkWUGSusAuvbA4NjYWVM7U1FQcaLC2tqaZmRltb2/ryZMnKhaLYU/Ly8uRtR4fH2tsbGyEgiND873AWoBGncbx4EerMK35i4uLkUEDwMjyJicnI8DB9Tu16c+eRLzvuj6IcJ3b5AM9nYA057+73fvZCEzO92r74uJiOGy0djhLHoDN5QiXv2ODQVk4J+Y/B1+8ubmphYWFWKyzs7MRsTaR3SM3ygmkHxggvJo3UDC3tNVqRYUbLrBSqejg4CB4qGw2q8vLy5j5yjlMFE8cvcF5E3iSDtcNm7SMQHN5eRmj/ebn56MFGeSFJKfZbOrm5kb1ej0kWpJGWoBB+sw+oNiBE3EjhyoAzbNRjo+Ptb+/r3Q6rVarpcXFxRhCT2ca3DsSLNqKcYq8r6Sjw8lTZKEY67MaNjY29PHHH6tYLKper6vZbEb/PU4vm81G4W17e1ulUkmS9Ktf/SqO33a5k9Mr0r3D7ff7MRDo6upK6+vrEURxgN1uV/V6XeVyOdAXtgJiYw/B7bpKIFlYS6o2kj+XVNuMj48HkmMNSOndgcIV46DI6rxA7NlS8vvZS3DwZMneRHV3dxcBKp1OB7qkyHlwcBAzkefm5nR+fq5SqRTvyh2tK4skjcjE+ONzDyiOUYM6Pz/XyclJrCtBCF+DWiSZ3eIjoS0+pMGVfosB5KTkLj1CeuKpHimwpFAjoFmEbyWNdr2ccyFs7neJqan8OpolDad40Ov1ojKODpPLjZW0hdQXBOGoAKTEIsJnXVxcBD8Msd5utwO5Ly4u6vj4WLVaTQcHBzo4ONDPf/7zmB7GEJKlpaUoBIBq7u6Gpx5QRQXlukE7N8WapNPpEJvv7u7q7OxM+Xw+inb8c2FhIWY4UCFmDqikGMcHauVdI9yXhoUC59RxtjQZ4IBOT0+jJfOLL74IhUm73Y6NyLQukMnCwkIgXC9oJg0arg5ngQSJ3wVFPX36VBsbGyoUClEcOTo6Cu51fX1dT5480cuXL6Ow1mg0YrAQ81wJwEn9K4EM3Sdom/vhPWUy98OBaDvvdrvB+3qGt7CwoAcPHsT0NZwXdooTxRZ8n7jjccQF4qTGQiega7BxYO6cGJzkew+AgiY+eQ+uHXYZm++/s7Mzffnl/2PvTZobS4/r7wMQ4FQcMJMAySJr1NR2ywp74YV33tkrbzx8By+989fwyguHI7xw+FvYXlmypLYUraFr4oSBxERwngD8F9QvcXC7miW/infnG8HorioSvPe5+WSePHkyn3fqdDr68ssv9fbt22ibR7lCzYeDPUHkkr6mnuB3S5o6kQL7cFoU/pziqd83GSh26LQChXx3tjwbAfl3anwgjZYmtIL3RPuCk2awqPw882dZJKcWuGkQHmJo+p65HMHisFlI0JhHoNFoFAOxKdqA0j1V8M4eL76B2vk8aaJThMf1IgYvr1QqaX19PRQCnCjKMc6kQ6SnpFR0xVBB9lNWk+oAN2RXVLDJDw8PYzAMiGVmZia4ZCrTiPtBOwy/Juj5s7Nx/f168cELX6VSSfl8PgqEuVwuHDDFG1JlH/qMcyaVXVlZibGFOCucHRdOAVvw7Gk8HodDxsbm5uZULpcD/ZIZFIvF6Ej01lHWCl4TXtrXngv7Q+fdarX0/PnzyOS8Zbzf78c8YAZc+5owZrRWq0VTAogWzj55ucpHms5C0ul0OE/2H86CTjR+v89oBhFjR2RTtNADBAjW0jSXnFwb7BlkTFGMrODg4CB42nQ6HU01oHJXYRCEkqBNUuhmCTIgYrh0KBJavdnPOGNvqIDPxW4BQa7e4cJOH7sedbik1URoxOsejVOpVFThXdCNZnUwGOjk5ESj0SiGddzd3UW10uUlIATQARfRjALK6elp3I8LjnF8OEJJQerDqZJC+SwG1xZSUGKje4HKC3H8bho4KJARkSuVShzjjA70yZMnWl9f18bGRlSmM5lMvExODYBPgkJxJ8NLTvLfyHg4JYA5DRz1gmPDwdBgsby8PPWOUJMg+KajzsXdvHuMns/C4c7OzobgvFarRQrHc8AN45A5yYBuIhAzwZSNCtrm8pZmNhmOgE61drutL7/8UgcHBxqNRur3+zHuEJqKFth3794FomPgCrNQ6f+HI/dUHp4TB8CAeirsLqfCiQBacFYzMzMxNhQZFIUsimnUNzzgelB0ROmIlCxpNuCmAAAgAElEQVSQfQrHjkOhSAcg4D07n8l6k2rj6PiccCi/Qb/sO5c18jP8mVkSmUwmVCwEYR86jpLHU31swRVF2IYHANacNfH7wnZd38/zokRifyVBFnbMZ5Ft/M4Ol+jKh3laQ+Qul8sqFotR7YXbRHSMvs61liwEyJY+baJ/UuAOd0kqz1hH52VAEURwfjfaUF4aKAXU5YVAEDz36EiBy8X23sRBOzHDjQuFgp4/fx498QQEn37myBnHAip1ftrfCQHEi2vImtA9UnW/u7tTt9sNaY6/P5Cgp4o4MVAmnDiBSpqkhRg0To9UjJQynU6H6gHjRoEhTQoOHxtcz2c6X59Elq7hZOIXgQCOuNfrxehIMiRaN9lAFE1x3GxqHAHjL1dWVmI/fEyeRoC4v7+PDAL0inyQNB5HAx/q1FW/3w+KCD7SgQG/0+WDXkTl83AgyUIXts2Xn37gRSL2OIVg+H3u82ONETSouDPyYp5L2pCmUTTlOKJ8Ph9ZMF/YDPeT7AHwYOwUIfbFffgUNF8b7BaQ5c/E97NHCLagYedwfyeHS3rJJnf9KY4TOdLm5qZqtVpo6EBNvV5P7XY7HDAFNchvkBXHw5BmJeUVQH7SCqRjl5eXgRSd5GdBXMTvxH3y+XB+cKkYrG8s/o7PYoA1xSqGKpOisqEYH0fAAs3iIAk2HNbn957NZqfQPuvihUYMzQ/tZLPAx8F/sRlJm6nkokJ48uRJVNg5tUBSyHj4fTgS1gI0QBMAyBhDThZ64CBBXaBt9JM4FIYWsTmS9onRe5EE++JEgIuLi5i8tr29HeuGU+Ud0O56fn6upaUlVSoVbW5uqlQqqVgsamZmJnh8L2SSqkoP06h6vV4UYghMqVQqpumhX6ZgyztnbeBXWSdX5HgRG/CAPSQdBiBpZmYmBu44XxtOIDM57opgjTwKWSIFXt6n/523OTMYCifE7/JhRIuLi9F2PTv7MCBobW0tMk8CF8/rz8wedZkm64ZjhIbBKcP/0pTkx1ARKJFE0ibstAp7hLVyn+R7EF/w2PVbndqLE2JzeZ/39fW1ut1u8DqcE4XRcL4Si8SNEm34wuEwWs8jBZDeI7Ck6FZDSuIO2Q3SHa5/3/n5+dQoP6gNuk1SqVTcs38WiGNhYUGbm5taWFiIwMIJtF4YxKCddwYRIlGhau7fT/Bwh+tNAW4UpHY4S4zTn5vv51kxQCRkGDA87NzcXKRtyKUwLvSdbCpvooCOWV1dVS6Xm+pwcgkcgXtlZWWqwQQHSFCAWwftcjlS8ffNSMWnT5+q0+lEZ9XTp0+DYwcRsZEvLi7UaDTUarV0c3OjQqEQQZPfQTcZG5aL50AGOBgMQu7lahSekyKk862sIX+mks/vIzhRRPMvnAy272k9NMfy8nKcyIHNnZ2dTVERHojY3+xTNLy8f2wr2VZLvQTHjr155sY64djIqhwMgRgd5PDO8BnuZJ3ioRsManM0GgVNgqNtNBpRrCTQoN/1YiyBkcl6PirT0TaZkhcQP3Z9UhYG8qRAhmaR4g7IYDweR+UbCRUbBSTiI/Yc2eGU6cAi5eBizgCODoOCEmCIDKmQI3JeKE4+qaMDCbLZQSQYMHQBkY7pWThB5G+lUkkbGxuh/SQYQZfA08I/eWuxC8vZeGxiPoPLCxI4W56VDYme9OrqakoG5Qjb1RgUKEDKRPrhcBhIi5N42QxIoFyqRSGVOcAbGxva2NjQ5uZmoDtkdCCNbrer4+PjODaJEY7I6chckvwZ9+GzF/hiY+zs7ESK75vLP4MUG7UCyhGKdp1OJ+YwwLEni7r+DijKMexob29POzs7wY2urKyEPXtXl9NDOAukfMPhUMvLy6pWq7Evknbs6S0OB0eKg0dpw9AWZke42oXnYBYHQY4AkaQZ/SQQaeJwJUU2zM9jtyhU3CGzDjg5qC6me0mTLJf96lSDO18+mxGs5+fnoaH2LjTfQw4aQKqO0peWlgKRO0VGIZQA9LGCpl+fdLhM+Tk5OVGpVJoqZqDVBHni+YnMLqGamZkJQ4bn4bNJ5ThXCpqCizSMCiqOAwTq1f1erxeteaSYGKYbgk+upwLqjQ6pVEoXFxdqt9vxcqVJoQbkD7fpVAtpIM4NnppONF40zpfiBd1AqBSQOHGyrTQ9pIRRjyAD57cxiIWFhSgC8rm8MzaQj6eEP81ms9FplE6nVa1Wtba2Jmki02PTkhW4VI5jiygG4QyR7TWbTbVaLbXbbc3Ozmpra0snJyfBlXoHGcE1OSgFSohORDYIDR+kr8PhME6ZQKHA+2ZDwQXTJMF6YsdovAkWyYEtZGDZbFblcjnohHq9rjdv3oTdQbWRmbmDkBTvAwcpTcZh4lB4V6BX7BX6jCDCPAnoj36/r1arFUEYMJSkH+gQ88BEWu01DXyBI1x+nkwtk8lEEEEfzdHn7FGKgx5csXHWh8yFY9WRfnlNwqlEfAMBnSOa6AfI5XJBO9D9Sr2BNfbaircKQ2Pg//gzvu6x61GHi0yG6jcnB+DxeQFwf0Q6okWyIw0+BrUB6Pbk5ETHx8dxXDeVVC42PcOf0ZOSQoEAMWK4S9IAHA/RESN19Mvi4iRoPz04OAhDkh40xoVCIYTbiLNpKQa1EWAwSOdOcT5OUeCAoRVYv5OTk2hz5J2wMalyY3QgGNJfOt6Q9hAMQITcL8gTJMuEKk45LhaL2traUqVSifcLJdNqtdTr9aJzjCDHUSlsdHSb/X4/Wmuds8YWKHC44Xvrt3OnnuH4kfZQJcwzYFDKwcFBBO9kuyZpLmkyzgE6BMfl4/y44AzhC+Hsu91uHH2OmmV/fz+aKfjd2Cq2gtIHKoLNTZBEwkVQ5nNcmUCwRytPcfb6+lqrq6tfK6a5moHPpUZCMMGhQO+wBz2NJivB3lhPaUJF8f4pdm5sbKhWqwWFA0eMyodTVaSJFNABQrLFGIcLwKnX69rc3Iw1hB5DoeLBhqCF7bkvwWHzPS4CwA8ma0/J65MDyHEsOFzXsXIzrsH1GZmkIbxIH1pCSup8F2kvUhAuqAr4n7W1tXjJyTZdCjikb+gO+T54afhBHC4RkkhFV9j+/r4kTTlH5CwcdofUimgODUBBEPTi0hGfe+CyFq/knp+fx7pwJaV5tEkS5HyOBFVk59BIKbnHbrcbCJqjQjKZTBxTdHd3F1V6F8GDWBiJubOzEyoVDJsBRd1uN4yYNmskYVArnvmA3Pl+kD8TpLhAtKyDb0aCCUGHbILPco05Y/6cooEaw+nwLNBXnjqSPeHUZmZmQm5H+/dgMIiB8rzb4XA4NX/CR0bSyozN47TguKVJjUWajEWkUOsOmu/3QiRzLVh/D8hkVhx3w1qh9XYUy3vgShbkqHVAHUAR4LSRSr5+/TpOMr67u1O/348pXejDqXu48sipJpydS8XOz88jm2II/ezsrEqlUtwHAQ0/ha9wTS5ZD8/HfgL0ubT0setRh8vLOjs70/HxsfL5fKRdnqbjjEhj3eElZWUYMmd+EcUQXz958mQK2kua6tqhF5vFcWPCyIhwrlskcsMnk6bNzs7GgpEGjUYP06T29vZimDnPx2bKZrNqNBrRNACHWy6XVavVQoMJR8YmdXkUSALOG+PGeECDySYQUjRQLsgSYyPQeUcMRZLBYBBoEaVEJpMJSQ4dcZwknM1mVa1W47A+HEwmk4mNgUHDZ7PxJYWDxy54N0zcZy1ccE8whbrA4XW73SmHyzP7yQWSouADSkRhQdGGVmKyIU5zpqjEcBScva8XDi6p9cxkMtE9hpNhbgTOK5fL6enTp/rWt74V74VN7agUdO0zil2u5cVCL7ThdHESXvjh7xYWFmKmBRkpz+m6+2KxGOCB+0evSw0Gu3YHy350pIctUDDmCCaAV6vVCudHowHvBzDjBXsoOKabkf6TLXFfUKLHx8fa3d2N7k4oAsAFDtYLkfzZKY5khu6SNL7nd0K4GL4vDBuF6OONAdI0se3pEeiCTpyTkxO1223V63W1Wq2A9tAFnqZQVINsv7+/1+rqanwvqgJPj1gIUB8b3UX1OGdeDpGeinWz2VSv14tNjMNOp9PRWXV0dBQGm81OzvRig8L/eQrqxR5JUTykMEka7WeucXH/OBRQlRcuMBioFFJ0DMtRFSgKIwaZcgru2tqa1tbWoiVUUrxf+NNOp6NmsxlOC5kNziK5CV3O5Vwz9gSd4v32fDl3ihOCawVhkAKTZfCs1WpVc3NzyufzU8URUlum3cG503aL/XlxxO2TTSs9ZHjQBAyAp7Ds0+Ood3C/3iHHRmfTg66S6NYdrv8cawoSJDNjVjWI052HZ6PYuvSAqPn9d3cPA9wZ5UmAcVTnlBl7yzWq3jbMRDJoSw4gJUi4BtgLVRT0eH7Wj7XwLJHC9/7+fmShDragQl1V4SjdA4sX5lBteaHMeeRvuh51uGwA+LderxfoLpVKTQ298A4gh/Y+c4E0j41wfHwcsh2kG8kjqKWvD44BHVcqlRhdR/pMqoHzdI0jKDiZjvBFUIB7c46R56SIwuZcXFyccq4ezaFP4JnYsI5iWRucLS+YNDk5KMW77njBpHsu0YJTZLPBudPtx5riwFw1gfMn3aNLjWfhGbkXNky73Q6lBLNdfVNiSy5Zkyb8IwYNMqEg6rpJL0ogm/N350UUMhaczfz8fBTFPODhlLLZbHQ8YpsoEpyCQC3DhaObnZ2N98Dv5flRblDFJ/NijXB6OArqCawxtsyG5s9u34Ac1hOnQnbhxSiCPvaGnZFhAFB8LQElaKN5r65UcbTLffLuKZCRQbA2PosCJQFO1g9+xFmT4rMe/Jff7esE4INLX1hYiNm67kDJPpMNTrwjAgn+zYu6fI5L/b7petTh/sM//EMYA97fH+r8/Dw0nKSObHqXfHjqRMdKqVTSZ599FmioUCioVqvF8eH+Ev/iL/4iWv948SA8EIgjN6K3c6fcDz+Lw0Myw6LxPbVaLXrhJenVq1eam5vTT3/6U/3kJz+ZeqalpaWogielKh6BUWU0Gg3t7u7q4OAgtLtwm0tLS1pbW9Pm5qZev36tP/zDP5ySyFGYqdVq4dB5+WwuRznS5FgeCmiVSmWK03Y5V6fTUTqd1vr6enD0BwcH4bz//M//XH/2Z38W68RGvbu701dffaUvv/wy1hxn7/yXK1tIoUmfCQJoktnc0FOVSmXKoL///e+rWCxqe3s7eH0cEM7WkYs7ATYTfyZ9Ho0eOvW2tramVBzeoYb9c/3d3/2drq6u1G631e12gwoii0M5s7u7q5/97GexV3BY7uBc9eOnV7guHdkWIw8l6Re/+EW0li8tLenk5GRKw4p9+OwJzyZcpuaNOEk6hcDgygDPwHhHfqgi9BdNDi9fvgybIch4yg7Nxp768OHD1CECZE48CyeAo2z6z//8z5gMB2X25MkT/fCHPwwH7s0OZMvQFHDgSS4cDtvHcLI2Dsz+7d/+7Rt96m81vMadqKfhzhuBWijaEGFdcgJ/hCaRzejpOp/rG8TlVr45SKtwrFR1oRVce8t9eFR3/SEXBiJN6BE2MEaC8JpFpgecfnuiM2kgG9ZPKn3//r3q9Xp0u/DCeemeNXjUJcq64TsHRUEKQ3FEyrq6JA0+/ejoKIT/nU4nihScvVapVKYcP+vn78w72gg8jhz83YAcUAG4s0L3Svs0Ei+vIEvT6MmVHdgrNkAg4n6c304iMuycQMSgcl8z179KCsQMt81nUqBimJGDEKeDWCNQNieneIrtBUJAB++A98G+48/+rKyP7xHuydE6gco5SUfVfu+eXvvP8/z+PlxT6yeOeIaHr+FZ3a74bDJEaErWEODBunO/3Du+CnoJx8o7Q9YKuvYi5szMTFB8HwvcyULhY9dvNZ7RESFOlXkHRC+KR0RuECc3ztQfKuE+tBoOhYXld+Eo3ND8vCckYn4uvadESZkIDgeuEh6XtA4E6ukS9+BIjkIfcie4283NTT19+lRbW1uhJ4Xm4D5Go1F0PT179kzFYlG1Wi1UGUztotjjbaOshd+PNCli8btIuVgPl6VhGOhhDw4Owvnj6LyQ4228rAWpE/YAZcG78NkISWkglAe/v9FoqNFoRCELpQnBmM2XlH5Jk83twRqnA3UE9+fo3yvbbBjeLdwthcBut6ubm5t4F6yxa09nZ2ejIOcbG86VLIj1IegwNwQnMzs7G/OECdyAGUegoD0v0jiw8PVz7t4dGWoL3o8XwCUFYICzRSaGjLPdbgeX67wl9+iNFuPxOJQ0c3Nz0QDFewOIgPwpjs/NPUx3cw4XRM2gK/ag04ceMAAtUFhkDrQRO/0oKe4PP4VEFNoFvwOt4s0wtDQ/dj3qcHGmIF0WHWfLpuPvXQ4mKQTGoD8eAjkWEd3VDSwqBz2yWERn2kbz+bzW19dVLBaVz+eDo0SZANTHmEFd6AdZNKrCHMtC5Z+NhAHCe56cnOjdu3dqNBqhwaVPG5Thjo0CIDKo58+f69mzZyqVStra2gr94d3dnVqtlvb39+OYGedo/Z2QUkmKAML6et+90z9uhJeXD6flNhoNvXv3TvV6XdfX10FnoNOEc3YaQJrwh6nUw/H2nGrB6Q3wbK6LpaEDLStV6NFoFHQBonQaS+Dt2fDuuLkPRzCgFzaSbyiQvjtoFBsU/sg89vf3dXBwoH6/r+FwGFLAzc3NeE6XLXKxHwiEAAX4aIp03so8MzMTNQzeNbUD1tFVKC4fvL6+jmAO/YLKh59lvgbFUwIdRxwRZJ33JWDQcYi6iDPyMpnMVDdpEhBQ84GOZB+7g6eQKyk+HwctTc6aA+0zVEjSlHKF9mlsW1IgVJpFFhYWlM/nYwoZfgjABdoHsQIEARyASCge/n00mii49vb2dHR0NLVXP3b9VsNraI9zmQpj+FAwgD6RwRBROPmUiAticwE5erxcLqfhcBiFqHw+Hy9Keohc8L2VSiUcLY0SvFgq1hg5iwjioRJ/eHgYY/pw5nQCueZOmjQaENk5lBA064Joj8ZsPtBLoVCIajWVVrpvOp1OzPulwk0A4SJ1xXAzmUwU6+7u7mKDuLbYBegzMzMxBHt/f19nZ2ehy+SecKyMtfPuQmk6PatUKtr5zSkJ/LtzkAyjPzs7i8HSpOXwbjSTcNYdCAxHhUP0c9X4PTjRmZmZCOxJe3OOFPUDyoF6va56va4PHz7ow4cP2t3dnXK0pJWpVCq4Tagf3yeui0bri74cDpfNyP0gs0OrixyM9JYTUkDJOEzsAbqCd4Vt4cBcMuXFSNQvzP9wdQyAiM4rAiD36TUdbC556gPvhvfJZ+DUsSWUGnNzc7q6ugoqy9cIVRN/7zWK2dlZVSqV2AMEMeR4yL+q1WoUfyl4ei2I+5Umaiv2Gl2UHsS98YHJZqnUw4Ae7wr92PXJ8YwYDogLLeHm5qYKhYJmZmYiNX337p3evn0bzvTy8jJeIlOIQInHx8checLBFItF7ezsqFgsKpVKaWtrK+7F08R0Oq2joyPV6/Wo5LpMbHZ2dgq1wive398HV9hsNiOaQk0QwUhxQMXShLP0qVM/+MEP9Pnnn2trayumpEHWJ7k/adJnnk6n1Wq19MUXX4T0jEhMCoiBJgdigCYuLi50fHwcwQ1D8QEbNH3wWQyl8S451nQ8HgePC1rkkD/UGMk+fjZho9HQ4eFhDJyhEkwHEbpqMhDokkqlEtIuOtO63W6klEyRmp+fV7vd1sHBwZQsjI3DKRoEREcroL5yuRzHPSGIPz4+1ocPH7S3txebHRkcQdzfiaSo1LveEmBCakmjECMaCeKuYABxLS4uRjs2jsv5U6gjVyqQQSbnXxQKBY3H45gbAT0CHeGSTLrnvG2ZVnVH1EincPY+iAqn7oCAfcppIzQdQQs5tUQxlCwzlUpNOfPb29uYX0wTDnZdqVT09OlTlUql2NtotKHoXr9+rc3NTT1//jxOzyCLAKGjfoEeIXhzzzzj3NzcFNqFd8cWCO5JhVXyetThulyF6DwcPgwuOTo60v39fVQG8/m8NjY21O/3tbu7G5pZogOntRJZ3dmBUk5PT3VwcBAdQr6xUqnJPFNQ2/z8fOiBQUEXFxdRrKNDpVKpqFKp6Pp6cqorQ76ZXYtjIqoxZtALaKQjVMafPn0aG8Z5XxwTpxCDqq6vr0NZQPHszZs3cZDdxsaGZmdnowMNA/Y0mlS/2+2q0WhEFRakOB6P48gaAhnZw+Liora3t8OoXB4nPUz/lxTBj9mkICfeCTza2dmZ3r59G+n+/Px8fK8XkdhcyHPa7baWlpb06tUrlUqlqe43ZsAy9Syfz0c6TFeiX/wdmx5kg7yNppTvfve7+vzzz/X69WsVCoWYQ4ATKJVKccICUiIc5XA41MuXL5XP5wMl+jthihjcZqPRUL1eD/ToWlrpAaWVSiVVq9UoAPEZ/DtpMdVxr6eQrXgaTacczpvMDr5zMBgEJ01GB71Gx1+v19Ph4eEU+pudnVW1WtXS0lKgY78Hislc3Hs2m40sAuCVSqW+RnuRujPWEduoVCqxF3O5XAzzIQjxnqH9/GSZV69exe/a2tqKQEvWyZqhOuj3+1M1BK/9OE8uTRAwwBF6h6aQ36nxwXWB/HK6w1KpVERV0KsX0uj8Qd6TzWanYD4CfqbP397eRvvk7e3tlMMl2iI7q1Qqev36dXChw+FQP/rRj/TDH/4wTmSVFBGPima329Xh4eHUsBYCCOkp9wyv5VpR0CMddL/+9a/15s2biJILCwuBCvke0Bcvg5ZCOpqePHmier0eaTXcG+kU3T++FtAf2WxWxWJR1WpV1WpVi4uLcTw66P76+lqtVitQOceKdLvdqSaF1dXVkNeVSqXYwDgmUIc06ezCoSwvL2t7e1s7vznqmg2VSqWmUFS73dbe3p7evXunYrGo73znOzF3d2VlRVtbW7q+vp6iFiRFY0tSHUAGRsbCO2OgNrxaLpfT0dFRHJT5/e9/f4obxLkXCgWVy2UtLy/r7OxMX331ld68eRPBhUwolUpNjWfEwdPV1Gw2o1ni/v4+gjTvcmVlRTs7OyqXy2q322o2m5FJeLHZZwskES82yYUDdvVIr9eLY546nY4ODg7U7XYDGJTLZZVKJb18+TICSaPRmApaIHpSZlJ7wI7rU/29EHA4kZggjjMGkV5cXEQBiiIqaBQEnE6ng/65ubmZygBdqcJ9bG1tTcnmKAaiQCBQ3NzcBIianZ3V7u6uLi4uQo0AjZRsmSZDAnhS/EzqxD92fbK11zubiJ5MXGLqEedBMU6On4FLpOmBn4dzxHm4Ppbik6eOvqD0QWcyGe3v7+vDhw/q9Xr693//d/3qV78KJwX5T4cRHNHh4aHu7+9j8DVRHSKfhcZpeqEI54tulbVA71er1XR/fx8SuOvr6zh5wCvRpFsYR6fTiXUAFYPmXeMnTZ+UixoApzozMxOcISS/pBgET8W4UCjoxYsXuri4mKIKqMoyC5YmFyRtBDICSCqVig37rW99S+VyeYo24h1T6Ycjff/+vdrttj7//HN973vfC/S8trYWwQZbIXjTIONFCVJRn6hWrVa1tbUVNoT4fzgchgOAJ6frbHl5WRsbGzEikiE9L1680He/+12dnJxMKWfc6WMbpKNzc3PBR1cqlWhrRuw/Ho9VrVb14sULzc3NRU0gnU5HsYvMg4IXWdHl5eQgy2RxhoIz2YQ0qbi7SqNYLIYKZm1tTd/+9rf1+7//+7E/vMkDrT2nXWcyGV1eXobiolAoBG/NhTaeI4uYBgevWqlUpoqn5+fnWlhYiGPp9/f3I3iQdc7MzCiXy2ltbS0yGfTo6PEplEsTegXu3SkZ9hm1AXyKN3ABkvA9CwsLEaQAh9QXfCa3y/K+6fpk0cw7lS4uLqaMxw2fokE2m42TAYgoiML5L6PrvAMFEhrZFAUzaSLc50F//OMfRxpAZZPPpGqKzo4iEn/mOOb19fVwECARjMVHKLrWMNkpRgYAp7S2thY8MrKf3d3dqWLP/f29SqWSyuVyFLE4yBEE7xO1kOxwecpDhxeDW+CyVlZWtLGxEUUXioQnJydx3A78H5uU6jgTvThXK5/Px/tlXdhgzAa+vr7WF198ERtpPH5o/X727Jkkqdlsqtlsand3V/V6PSiP//7v/1a1WtXz58/jnsiW0ul0ONNOpxNHv3tFnDXNZh9OHibAwtlCI/n3wfniQFdXVzUajYKnJEDSwg5VQ0s5gScZBKnqb29va3NzU9ID3XF0dBT2haKkWq1qc3MzKA+K0mQRdDMOBoMoAGIDFCT5s2tOJcXeoTB7f38fg4pcuH919XCq8WeffaatrS21223l83m9ePEiVDOsfa/XCzDCc9ZqNS0vL8eAJi4cEDQGfDy8KAVUgNjBwYHS6bTq9bq++uor7e/vK5fLqdfraXFxMcAZmQiFekAFWSQoU1KgY/dNFOgBUNAqaK4JKjh4PzCBdSX4U5+iEYYgmJRwfuz6JKXg3SlAeZ+eBMqASKbDxHuVnXcBuTCTk5vms/P5fEyD5wIdkpa6AYMCisVitIFSmMLZwMtxgB8biU1eLpcjTafggKTG01o4RtB4NpuNo0FcrD43Nxf9261WS9IDAqHogVEhLfIBJ5zrRlXeGxe4SHEINmwy1q1cLmtlZSW4Yo4tOj4+VqPRiPSWVN0lMATA8XiswWAQxuSDWwiWd3cP56Xt7e3FWMilpSUVCoVAZmdnZzo8PNSXX36pbrcba3d6eqqf//znyufzarVaqlar2tjYiDPURqPRVPs3g92THVek36wR8kI66rwxgGCMqobUOZvNhhqESrxX9l1xgfNNOn6q8pIigBF8QF9zc3MRyFZXV2PgPukpFM7S0lJow8kYXfblzt616tK0ogTAgLLBlR+8y2q1GoVE5kA8ffpUlUolCrOAJThTZjJQk3HKi//3NupcLheZB6NCb25uQstOZkZ9BXUHraoD9XcAACAASURBVNHIzNBD40Bdw+tD4Xk/7nc8g/UMjXvh2flZ9jqgc2lpaaojzlVA7Bkvsn/T9clOM5yWC9GRI8GB4jBJBVyK5ZsBforoTJoEz+uRyS9Po+EtmfAE/wNfheEOh0MNBoOphggfigFHSopBOoCsBh2gRzmE8Y6WaexA1weCqtfr2tvb08nJSTgfHM3+/r4KhYLW19fDkfsx6TSRQI34xTrlcrlQeKysrKhWq6lUKgWihy/0LiGO/6EN9uLiIhwu8hwPUE4HeaEInTLCb3hKWkvX19eDW9/b29NPf/pTffXVVyHdAlXs7+/riy++0NXVlQ4ODlQul7W+vh4UFUeJE3y8+QK7QOXAOnldAR5aUqAueF+6DgnmUEYoSaB2KMq4QiDZncW/eaMMhWY+n2DPXGFsy3Xu5+fn0f7O7FXvouJ9AFLI5NirPhfDj+OBgmCI03A4VLfb1cnJSQQbnDto+7PPPtPq6mpkjxQXQYyAA2yDC0otl8uFo4SPJpvkve7v7+vq6korKyuh0sDxDwaDOP2DAijZCoVoFBLI7rgP9jOFagKn/xdunO+BWsB3uGYdHpt3zz26//Bmk8euT6oUMCgiPpV7ZBHIioD6rq0FFYDu4KVAWCyaNOkPdznOxxwNv49B2cB/DAFJWa/XC4rDnSxGAYnubbHenUXgADn4CyKKeYRkk+FYkd3wcpGLgBrq9XoUD0khkeaAJjEOrwJz36w9mxR+jbWQFBwsTgeSv9frKZ/P6/7+XkdHRyH3cy5dmqBCNlayZZNnW1lZ0dra2tRpq8yAffPmjd6+favj4+MoYuDYUaVQYOx2u9rf349KNlkNNYPkTARUEBRfQSJwoWRZcJHeBeXrRH0CR4Utuo4ZZ0MR2TcWf4/TQv3AJqY4h0yKIIEz5XeQ8vOMdKbhMMkQcRCu5aVDEaCAHBEbp/FmPB5H4Zu9QKGadP/w8FCdTkfr6+vRYISD6Xa7gUyZqOfjQ0F/+XxeT5480fn5eSBLwBVyLxQncKRepOX70LcTKMhEoUloqCE7Y186uk02y/A+0Uv7UCJvOcZWQL/4geSa4eNovnrs+uQAcie42fwuT2GijzTpAHFH7UaKFtS7SJx7WV5eDoGyH7GDCB1nCzfM4oAAOL2XDUaKAApn0IXPGuWzcFpUZ+FseRaQAKiRdOb29jYcCMZBhR7USIEunU5HNbPX64XeU5o+AdS1ihiQG7Sk0A0zt9VTIEnRyIA2mHvHSFkr9KjQQZ4uk4om+SzSMozU5UugJ6gm0lEKhrxXdKmkhlBIpPMEFVJzgpnPcyCjSKVSkWWApninOG2XEXnqyLt2eoXgjOMCcKCrRj7IheOk3x4k7HQEAdx/jvdKWzaOmp9HAQLKo3gJ+HHlCIHJgQ/fBzAZjSandHQ6neCueaeZTCYKsHt7e7EXsWf2VaPR0MHBger1uprN5tSMYuwThcGTJ0+mJqH5YJrhcBj3xh7zoE8wwh4BTvgT7AeAh23gc/gM6AP/fC98gcqho7zgCqA7PT2N0ZC+P8kOCe6/E6XgLXoUGbxTBJIYhAC0x7gdgmP8IBDQ7srKSsD++fn5aC11ATELCHeJppGFQQ5EOsa8A2nSFkw6yoJhwDhDyHI2hbdlsrkxYtqLSTnpjkun05HSO43AkStEeU9JkJnwAtmE30S+g8SgNBYWFmIADuu+vLyszc1NVSqVmOjmRRYXdzN4hTSbjc+FQ/IGDN/oPrgI/hu0AWJZXl7WcDiMo3r4fiRFyIZyuVykhqyzBz3aqMN4f6Pf9hkSPhwJXSp2SJEP54zTQm/r2mHacClaYXtoz5ONDzhIHC7Ohf9iRwRYNj82C21G6k+dhFMK2GMENfYhgY9gSlCng/Li4iJGU1KQPTg4iIyD4OJa4Xa7rQ8fPmhnZycQ59nZmZrNpt69e6df//rXUfxEG8uFM6OQCJAgeOMAcbZIxdhDs7OTU6xdBw+QYN/zORTCPIPF2TqylfS17+HzfS+40gPfxH3j2HHMBDQ4fIDkY9cnEW4ysmIgRCb+vd1uxwwAUmIE3j7IZWlpSbVaLYY+w42Mx+OQ1DjvizFJkyjscwFYLNKnubk5HR8fT/Wuu0PDGXqHjc9V4EW4IkFSVEzZXEh5KLrAczM7gQ3kImoMGyfGi+TvHRk7unWES4qGIfP/PgSoWCzGablsLFqF6RrzITcEARwd6bxzdtwn6+hI2LliuHDS4eXl5WjOePXqlb73ve9pcXFRxWJRe3t7oXlMp9Mhl6PI56cy4GQ8EFM0o5hFtuUOBrtivUDAuVxO6fTDXFrnQakBsCZwwY6Y+Z1cvAecjXORPhRpdnZWz58/jyIV2Y40aVOGfoM7vb29/VrFHZvywMi/k96T7mJr9P632231+/2o/INcqcMUi8XYb71eL+SL/X4/qLBmsxlqDuovXO5wCArYqDs7bJxTZG5vb2MU5XA4jCYSslD2Eg6Xve/jE5PODn+BzfLlzSTcz9XVVWSk7G/shX2Bj/Fpfs7T46wfuz554gMOwtv5WFj4S9JIBOjoLtF01uv1EPyvrKzo5uZGW1tbIbrHIfJnOqa4XO7FA7kTxVDZOEQiJFqkNtLktNFMJhPVTYwO4wBJ+PMuLS0FeoEHikXMTCb8Ly8v6/T0NAajOF8EPQO3TGACjWEgIGPSKXe4aFHhDEEgBB5612leoOOsUCgE6oAOwdBwaiBOnJTTQW5IHsQIlhQSkQ1tbGyoVCqF07i+vtYf/MEfRNNBrVbTzs6O2u12rD0/m+yAc8mTO1xUAZziisOF2iHVz+fzU0qVYrEYMyNoskmn0yFx4t36TFhUJaBdpzYc9bh9gnoZmL+8vKybm5toYf7Vr36lRqMROm1+L5kSfC1rnkRtAAj+3Zt0nMfE+ZIaQ3Ug/aJQhO3BSYL8mXXgmlMcOZ/FxSxrnC1ZAik6NsZe5xQGpJo+i4Csi+/3Ij3cPfdNWzAXGRYOmb+TJhreJ0+exJp4YHIKihb3m5ubqdGNgE3WBZXWp65POlyMyAtERGGc4MzMjIrF4lR1nwo4aBXjOTs7izTTh0nw4j6mDsBJYVQ4CB6QVIz7WVhY0MbGRhybsr29HdIvimkYIK2KpIo++MO1p3CVyGOcf+Pf6EICCbM5KcR4n7oPiQGhsklJSQlmnrJ5lZqozLqDXOEtW62WWq1WFJ3u7u5iSDpSrsvLy9DM4ry9AMGzOlrwAiJOAG6TM912dnZUKpW0t7enWq2mdDqtZ8+eqVqtBv0hKbTCOA4MGgcJesExulwQ+ZS3p3p2BPLOZieHA3pnEVkG9oBTA2kxR4T1TmYTXLxPBt04h4jD7Xa7mp+f18nJiX784x+r0WjoV7/6VcgjCeS0pHIMEPw174B9Ca3EurH+FH0JAJICZdNmvLKyEnI+pJvdbldv3rzRwcGBRqNRZKGnp6cBilwpxF70grCk6NKD6kFFhJ1AAQKItre3AyRICoqMd41vwAfhh5yT9WI3l68XDtkbH6B2UALd3NyEc8aPcD8EHIAHPoo/U+T7ba5PHpMOevQISvQkrV5ZWQnxOi26oDcoB9IqNidOBWNhUaRJ6sSFswb1QBWwiExxury8DAUDiKRQKMS8BD631WppNBppdXU1ChWgV6KypCmH67IyPocUAqdKxCUtr1arMV+Agl2hUAjOOZmWkiLPz89PDQfxQgtc6d3dXfCQ0DxEftALDvbo6Ejv379Xp9PR8vKyqtVqBMFMJqPXr1/r5cuXMfUoWZ3HqBxxOrpySVU+n49uLbS9OANJUdjh86BgCCR3d3dBNfF7sSMKhVz8Pw6XdwQVBMd3d3cXw09KpVK0YZPW0/ZLBoAAfmlpKY5a4h3RCeaUFw6d9Nh5SqRWMzMzwf36LBECJSk9xVcv5Lo0ic2OQ/IJbj7KEQcEPQF6o2327u5Ou7u7oW9utVp6//69Go1GBHD2AnaFA4ULZ+CM2zGNRY7WpUlRDzUO9nJ7exsTBUHvx8fHQXlwL9iSK0a4LzJEfie2g//CRn0vEzypr7ikD94WqoAgC83E72V9sTUPSt90/VYOd3FxcUp9IE0KWWxsClLwZx7dj46O1O12Q1fox5b74vBiKFpxuWrAnaHLZUCrNFOQ9uHMIeGhPNggOA2c7u3tbXSKkf5ImoqgOFwv2lAxh46ASwX5Um2mkwvUhJoCfTLEvSNev+bn59XpdMIQvAsPQ7y9vVWn04nN7yJwZg8g24F+IGCA/HG6oAnsgctVFa4AQAf75MmTSJ29wHN6ejpFAfAOSe0IHsiYvGjhDkaanGnmzSEUeHkG7u/o6CgkT3t7e1GMotBF+ojtQTvNzs5OHQEE+vGNhdOhJRr+D0pBejh6ZmtrKySR0qQ2wV5wHacrNyg6SZNDRL2YJ03a8EFkrA33nUo9tKtvb2+rVCqFCuQnP/mJfvnLX6rVaoVunSYU1hs0R1GSZ0Sd4BK5paWlcD4Uc70NHnDEf29ubtTpdIKyY//DPxN4WEucGkGe/Q3FyXphD96kQB0AP+PZHDUAfgZAiD04f3x9fR2BzWWnHyvUJa9POlw2v09+d7kX0bzdbkdvPovEBiBNQlMIV+eaROcEHd1iZK5JTRZziJZsLkYWzszMqNVqTSEzIj1DVjy1YLgJDpffI01IdzYDjtZTDMh2pCrSJDBJk9TG74XnQDsKh0ZEdlG5JK2vr4cShEo+9APGCoLjeOmVlRV9+9vf1ubmZmwc5hfgdFlbAiARHuPHEDFo1h16CXRYKBSCXkJ2AypwVFsoFAIBHh8fRwei9IBcmTHM5qbLyNNGAgr3RrMEhSiolcFgECMzr6+vo1+frkjfsAzbQfvJJkJpANp2h3t3dxfPvbq6Gic941BxdBsbG8rn85G9nJycBNrEgZDRYL9wxd5w5CJ79pl/BvWEVCoVyJ2Mq1QqaXV1Vdvb21P7CxqOWSXb29tBz7AWvA+6Il2Wx8V4U+iqdDodfyZgsSf6/X7IFpGzYW8Ef2wb5YUHWPyMUxzsT5xgstjLWmFTTkMSeAGX7GFXIHBPyWIhf/87OVwWEwkXPAY374bKMGOXreA8fJ4qf0cFG7LbN2WSD3FHRXGH/7LpmavpA3aSQnFSA1IiIjii+36/H1SBN0CwFiyqS1WIpqAJNjyFItJBOCWMzw8SpEGCwqLrmv2lStL29na06fK5BB42KO+GZoWZmRmtr69/jXsjmJJeg0i4R19rnA8bFIeLPhmESIaD04A75d15cdGLU6A8lCN3d3ch31pZWYlZrX7RZu7FPW+ThU+n1555AlAYKFawA9L3jx1Nzzp7yy7X6elpzNGg8YO1gttlRitoi3/3zC6p9PDMR1LQDs5JsyZe0MIpwYHSTMRYSuoltVpNqVRKhUJBL1++nBoliV347+Mzer1eZGPJAvLKykoUGqm7kJ1ioxwjBKCTFMUvP+QxlUqFZBEnD2CDZ3cqj/Uj+EAzkA3jaKE88Q9k8iBmgnhSSunf4yqepIzyseuTw2u8EwzBt3OFzEogpffZpKAeIiMvjBQWJ0ZkgzNxzSef47wLG4D7womz+WlJvLm5mUK3LmgHRXqL4nA4DG0wBuXVaI9uIG2cElyOn1JAKucREYkdaJT7u7i4CI6S6VBwkh41nz17FpV/imakqGwW0kqcHo4Ph0uRx4+jyWazkTr7ES8uw/NCIU4Dh+ABzSkO1g+drZ9C69XmYrE4tVmwBTY+duAGTdZBYEulUnEEFJ+FxIx7KRaLqlQqMTKRIDcej+MUkKOjo5iBwdhGMhHUJv5Ojo6OApHjcKUHygPngt4UB8Vmd0eLrhv+lAwqnU4HKiZY8h5xuNgxa0v6j5Pn+whQBD9ajT/77LMACt6c4EUuwBNI3GsuXIz57Pf7U9Qg9y0p1gnbcx0wqBlQArqF+5UmaqZcLheFbp5LmjTPuFMEEHn7sjeiQG8SRNxfYNPeteeUhUs/fS0+dn1yeA03RDWeRfRCCppOnBWDbShaoKnkvwi7ffwhUYjN7Jo6UBbOlqKLNybgFOAqifhsWPhODIqLY9Zvbm5iKHS1WtX19XWMlmMxnSdEuE3aj3MHsbIGpCiDwWCqyOSIHSTt+laq+DhdrufPn0fbcLfbjd+JdMb1tU5lsEHZiCADDIwJZWwyRvt5ayWGxTBqR7wgkdPT0yiYeaskn93pdLS3txcbjfdBcIBXc1UAn+96X54LRQzBZzgcRlFxNBrp+Pg4htLTgcax2kjn2CjJd5LJZIKPbrfbUWD1WbiStL+/r3K5rGfPngU1RGBnw2JHc3NzWl1dnVLVzM7OBoXjKTLrg/SIfwdlEUSlB80sDUPYKk4pnX6YvNZqtb7WQu7pN6m07zfoCOYEI5vk+9i7XDQ2ke2mUqnoNmOfMBuDQEfByhG72xbyPxD+xsaGnj59Gs7f6U3pQU7Gz/i/+X9dheNI1R2xF4ORlzK/5ZsUEb8zhwunkpTpeIGGCmkul9PW1taU9+eFuMQJJ+WdPaRRICZ/IO8ycQ0k0a/T6XxtZB6Oz1GUOw4QzfHxsS4vL7W2tqaXL19qc3NTxWJRnU4nIp4vKmviXBtcKg0UpMc4T0eWGBUOkVTKkZE0KR4mZTd0a6FfxVlAB/A7vTPG01bOTuPzCR5kGhg+HK+/C4yJghJ8HKkb6Hg0GqlQKARP2Wq1YpyeN2g470XRDZTPWrkaJFkB5nvYdKB5gh3rIE0G7SD6xybRaPOeFxcXYzYuXWnQEaPRaEp6xbW/v69SqRSZG5TQaDSKNUD6xloBCLArBxBwi163oPZAYE4Wm5l5TJBDy4uy5+TkRHt7e3r79q12d3dj9i2txPDUIEOCTbVa1c7OTnwmYz45bRi74kLihTwM2R4gCcffbDYDMaO99mKgt8nSjryysqJ8Pq9araZyuRx7D0QKODw9PZ1qquLf2S+AjSQ9CZ3lSB8/x7NwTE/ST0oT7fVj16MOlwfG6bI5qPBxo8heVldXY1whD+WVZTYDBQw/94uURfo6qnN+xjWG4/E45nUiJdna2tLz58+njleGW2QozuXlpd6/f6+f/vSn+tGPfqR0Oh1DoTc3N+N+Xc7EhsEZYKgul3KJWDabDR7QkTmtxEyy8pQtWVDDYbrDzefz2tzcjAEioFEKOhSXaG4AkdGMgXyOlHE8HmttbU1bW1tRRYffZu1wdmwwNgcG7xQBqI3iCUoM0l3WgDWCioFmIQDh1Ph/nICnbFTjM5lMqBKQ6AwGg0ipCXgEtmKxOHWEEJuIYL61tRVOmPPJOCkBp+ki92azqQ8fPmhrayta0+GDaWo4ODgIlYi3yjtVQXZBAOR+kvSB84hcnGDr2Q3Bc2FhITjUVCoVw7yRsXkx2o8J4igqggDHOjWbzSl5X3LOxvLyskqlUjQxzM/Px1FJ9/f3MbR/dXVVn3/+ub7//e/r9evXMe+WghpStcPDw5j/sbGxEU1TDAoCNOD4Qfte+E7SBKwnaBXAAc2D8smpP+R62IBrp7F9nyvxsetRhwsSo7jDBsP7077rtAApHTwN6SuolM9yTZ1zg+6cuFyi5ChrZubhOBgQFBsUIf+rV6+0s7MThsVntlotZTKZGMh9dXWl169fa3t7O2QtoD2XipBmr66uTnXekRby4kh/WXxSE4KIPy+ojWf3iIyTcB6ZebM8k4+gxEg4bTWbzUbxrt1uRyaA42F4Dse1J4/XBplgXNxrPp8PJICxwz3zLLzz29vbqU4r2p9BNqSb7XZ76llYc7cJbIwLhIe6YDgcRsGNIhs0EdQI6JrCqafvXhwhXfXDIL2Zxa9ms6lsNquNjY2QF6F+oSDq2RvzAkjpKfBQ6+D5PZty6ZtnW1xIMDkCnMDEGiwuLmprayvoDDTh0BCsIccgffXVVzo4OFAmkwkVC8f0tFotXV5ehkPzzAOQQpchjUVogHmHqJm8jRynd3l5qWazqV/+8pf62c9+pt3dXc3OzsYwHe6HNQBZ4nBbrVbYGpIyB0fsU/dj+CJvesJRU5zzTCipdAA0JOmm5PWow8WbA9dxlD71nsoiL5lIyIagCADf5E4FI0qm27wILlJ2Hg4OmXZVSTEfYWZmRru7uzo7O9P+/n4gGSfD0aJeXl5qfX1d6fRDFxRHqxDRXdwOv1ooFKaOvnFpF/eFJIugkES23As/D6pxPor1AWVywQn6abwEmpubm0gTOYgRx5nL5aLlEvoHo/TiBfpPHyLuWY0kra2txbHn/JukEKCPRg/975yjtre3FyjBq90c7kcQRqLF+sC1+d85snRkRWBBd8pxT0+ePFGtVlM+n4+AwPdSVKPI5+gcez84ONDBwYF6vZ4ymczUCEYuzlOrVCoRtOh8w4kvLy9HsQ5dLcVaD94uB0RahYN3xQzf6wU3xg2S5ZydnYUskoLh6uqq+v2+ut2uPnz4EGM0KXQh1ZqZmYmTQ2jX5eTk5InWTilgi/l8XpVKZaozEpvc2NjQaDTSxcWFms2mhsOhjo+PY8+TCfR6PY1GI1UqFRUKBW1sbESHJvUh1gIELz04XGoVFDzJIn3ADI6efe6O24Ob2593mmIzvEsKtI9dn3S4RGXvn3a9qRuwI13n93jh8HdAeSA6EJ90nL/nok2YcYRsFowYRMLEJ14CwntHn04B3N3dhfOpVCpaXHw4gZcjSJyrIzAUCoUpp00QAlU5TwQPipqDnn3QBEfI0C5NAYPPdM0lF7w36+iV0bu7u9Bdog3FcZH+kQIOBoN4RlcDgOB5D7Ozs9E0wuYuFosxZB1+0jOW09PTOHY8l8vp2bNnkfb7EBjXUfO74HKhMECe3KejSxoucPKsKWgP1EugLxaLUxkbBWCCH2hufn4+VDcguqurq2glTqaNFOnevHkTx+cQZL1w5lJDL/BiB37ihM9QSPK2yTSZPYcaxouP/X4/5G9kFKTKtBfv7u7GSSpo5jmUlPdycXGhRqOho6MjDYfDAALJyjz7C+UJ41c5u4z6gE9V45QWT9f5f+Rj6MVR37im1/lZSdFGDbLHPqBpvEBNgHDZH+COYEGNhGejPuDBkbkTPgvif+1wc7lcyCh86LFXFnlQ/s6r+fwZp0G66A8pfZ3jRIvHdXl5qbm5uUAXrmFlkj4by/lllzRhlB4ReQEcGDkcDqP4QJECg8Zpg8IQRiM3AXGgxkBEzTAVBp+4s/cpbFSjcfDIdkD/XLS/grAwPlC2Dy+/vb2dUhDgZHFULsdZX1/X5uZmIF/4Tyq3GLWk2JD1ej1mU5BeoUQg7eWsOXhJxnhKmuIq3R6ct2Uzu/6bCxDABvK08v7+Pg6MBMlwZDZpIYjH5W8U7Zj7Svsrv8fREhebk1Sce6crCwULiJpB3s1mU51OJ46HYVMDLihO8kUGgX14IZHN78EPJNjr9aLCXigUVKlUQn3AUeIcCJvMgJBPDgaDmMLHnkMJkGwAwHZzuZyKxWIEFIBULpeLoMX7Yf/wXuCvvRsV2WdSsubaf+lB1318fKxWqxUUBNJFAjp+AaDhDpcuOs86kw1ZOFwK98fHx0GhPHY96nCJihgJsxNmZ2dDneByIxYU7gTn6uJm0KlHexbYJU1JgyZNXlhYmDpgj4WjGAY3hgP22Qiul6NJwBsN4HQ5Np2X5EbAPfIsSHaurq6iiWIwGKjZbE4VDVyK5ZOw/J64L+6Nv3Mng4QNx4qToTqK4+NzcPQMIAEdOfcM1QHS58ytxcXFKKwRqKTJabDr6+tx5hgBBFUC90DUB8njHDzI+nOjeIHicMlOsgmEAgcpMN9PYLy/f5ibgSSMvnlOpoCX87kFVOKbzaYODg7UbDYje3Ok5DUG1rPb7Wp3dzfWBuoEqovvhXoDyeI4QF3YZHI2gsuRCDJeAyBgeTGTQhA0Qjb70HL+/Plz1Wo1vX79OibQuUQPu0JVcHx8PHVuHWn+3d3d1+otOF1maxB0GYUJ3ZTJZAKYeKs/mZLvWwCEB3ccoQ8fwi5QIK2vr4c/8nXx34WvgJOGBnIdvPO6vh84aQMhgJ9+8bHrUYdL8SOXy0XK4y/SnS7fj+P92OBmOlmI+O70fFIYP+MvkagD5zcYDKY4X6qxcKugPnf6btBEYVImWnvp52ahaeJwjjmTyahSqUS1FU6OF8VGcBTLsHOUG8niC5s96XzYoFxwbH5kjCsIQFLn5+fBYxHA0IC6HI13XCwW9ezZs5DFMWjFeXPnLUGDTLXi/TrVQlZCwZE190Iq98aa8R5BHC4ndDpImhzX7vwwQRy7wFba7bY2NjYiHWYmLrIw7I2gW6/X44w8fz/8v59I4vcDcoVOovEAhQP2QWBYXl6eomzY8GQ3ySzNn9OLZjhmbAbE6PbQ6XQi02SNOB8QJwZ6RLYJD4yUEH4a5JhsPXd968zMTAQ1d+KgaNaSBiF3tm7T/JsHK7Ibgq4DExzn2dlZzPRN0gau8cbHuAMnW0h+eWMO4Mrblj8lC0sltY3/d/3f9X/X/13/d/3/cz2KcP/kT/5k7EgT5Al8J4XzzpykyN+n8XsRDsQFCoPcJooMh0P96Z/+aUqS/uiP/mhcLpf14sULPX36NCRPICB4FxC4w39p+owjR9YoB7yTBPkQUqp+v6+//du/Tf3TP/3TmHQO9OD36koOF0YTrYmmoBtpuqPJeUtpIokCAf71X/91SpL+9V//dQyv5sM3KKJRdANZ+yCRZMHTNYdoCOGm0+m01tbW9J3vfCd67q+vr/X3f//3qb/6q78a+7E0uVwuChTeCZWkcBytgeKxi2QxDbTM8dmdTidGLH7xxRcpSfqP//iP8X/913/pf/7nfzQcDvXy5Uu9evVqirODYkIL7IJ2vx8Kti4V495Byz4JbW5uTuVyOSVJ//zP/zzm+6hnuFYXtOYoD5vFhuF12+12cLvcr8vKQOZwrIuLi/qXf/mXVKfTGTcaDf34xz/Wz3/+8yhoguZQh8DN++Ael3Z5PQa7IjOD0yXToxCe1uUO6AAAIABJREFUTqf12WefpSTpH//xH8c+f5fPSjYg8Mw+CxiaxNVN3C/P7ffPO+Iebm5u9Md//Mepv/mbvxnf3k7GQULNgV4lRXZLw0qxWIwGDM9mfRynN3z5Xvfi7v39vf7yL/9yknr8bxyuk8TcRFIXiQFRGMHQMGZSI9JlvnjgQqEQbZaI1JNdRVTvXdmAM5Omj97x5gwuHA3/RgeKd4m5pIu0i7GUHmxcusOXqyyk6VkDOH8+w+/bJSfehSRNH0Pi3KWnzXw+huFqDKgUTzH5L2vGpDaenxSN4g3pHhuA+yageuXc0y1+p28gChQ4OgIuX159pwg1Ho+Dzzw/Pw8qiwuq6fb2YTh5pVJRtVqdOnjx6uoqAgM8Mo4AyRt1BxyLa36hUygy8c4diMBZ8sX7dAfs+mzWWJrw0NwngaXX603NJOG9zs7OTtEZTjWgQ2ZYD7Mx4OsJqG7PPIdzt+wVp9+g2NjHpNTuC7Db5L6UJhJTbBIKD1DjWn+4cklfGyhPMw0dbNiOd0Rik+wlHDxtu95Rt7S0pPX1dW1tbWlzc1OFQiECGc/tTSofU4jw/3RPPnZ9cloYBo6j8eqhd2t4AQNDcaLbmwmWlpbi/CpHYaVSKYZ1Oz/FJkUYvrCwEJwTg1Y8InNvGM/9/X1oDdlAkqa6nnK5nMrlsnZ2drS1tTXFQ0uKeQwQ5egXcUigZThTVyTg2N2pUNgBMbAxXSqVRL2SplAZG4Jndv7Lu5JAWK4k4TOZ7QlX6et6e3urWq2mp0+fxrAh6QFRIGVzhYA3Q7AZfbITIzoJBnTanZycTM2jSBZiXdzuDpe1pXuqUqnEcS48B9w/nD6bA4fiMiSfHMb6gFywLZy8c8k4ZGzV1TDOy/P+ENBns9kIAuwVfgakhbPgazgcam5uLmY5u+N3BMezuJTTM1ACruvdCbpo0Nkf8NwOsrxZwZ2Mq5nIbLFZL55TbEMlgm46yQnf398HRwoHncvlPgrunMPlHh1w8C6QEPKZZBadTkdbW1txWgxrTm0Jv5Is6tLw4LWWb7oedbigFF6YHz3uiMCr4CwYD4QQHy3oyspKnMDA5ru+vtbx8XFspmKxONXOisNFMnZ9fa1Go6H9/X31+32NRqOI/HxxOu/p6Wm8SF4qzh+nh2GVSqWYBfDs2bMwCEnhFHGat7e3ISFDzkXDAJV+nDbaQSIxgcrbMBFOE4Wz2exUpOUCFfPS2TRQAjgXnBNG4AUZNgIIF6fU6/XivCactD8fa8Gkfq+e44goPCIlY0YuhQt+rxcqkUZdX1+HogClBMUnT+e4sEs6mxYWFtRqteJUWgqgIBHPGMhIuBfXZ5J9kbq6Fp3N5eMbuSeoCy/uJaWRfPlsj7u7uwj6oC5sFioEhIVyAFCTpC3S6XTIsZymo6iDJpjWbadQoKkAD35yCg7N6QGyW6fQKBoxQD6Xy4WumgYBhljxOUxRY0+w3+/v7+PwSj6zXC5HmzDPwOe7rGw8Hk+dY4hvIfAXCoV4l3RDsi95745m+RzWnHfIfiK7+NT1yVN7oQtIwTBKabrTR9LUpsRBeRdSoVDQ+vq6yuVynP/lnVrIukhhuTwNvri40IcPH/TFF1/ow4cPur5+OHqnVCqpVquFkwMZZTKZr53mAD0BL+OGQ1qXz+dVKBSCvmCT8iJA6RgQzvLs7CwiqTRJvzFsur7QVJ6fn6vZbKper6terwfSJNXhxXLB54GAEGI7Zwsni0NK6m69RRnHPR6Po1d+fX1dh4eHISDHUF2TTOB1bTSO1lueyQj4eVA9vfCOOAmKZCODwUC1Wi1UDQws4QLlwMFfX19rb29Pb968iTGLgASe19cffi6bzUbKyfeCcEajUXw/n0Frq98HDo2BOE4/gTLZC6Ak56x9BjB/BvWTAaKvRcpHsMAu0PAyM6RSqcRMXN4TskJpWh4pTZxlNpsNvpo2cZAq98ZMWaep+EzUD4AO3itfBFjXUPNzUBjX19dBr0AZ5XI51Wq1qZb9ubmH88aQnrF3yOgIDNIEuIGSJYVG2DtHAXc4VIAB4E1S1C0KhULonZOKoo9dvxWHC4qCWKa9jsXGKXsEpWOHtBENZCaTCYlZsrgGguVnuTzS39/fa3d3V4PBIJwSA0mYaF8sFkOwDbocj8daX19XqVTS5uZm3AvdWUiAePFu3G5I3KvzqnRutVotdbvd4M34LzwrP0ufPvKjw8PDmFrGBTWDFIcLfu309FS9Xi9+J3K9ZBs1KNdT4/n5eVUqlcg0isWixuOxjo6Ovlb84vLUE0Q4Gj0cu43WFpphdXVVGxsbMaDHNdE4R7IbNgQt4YwQJOVfW1vT8+fPtbOzE4crchGcoYPQfdLplcvl4jQJrylQPCLogqTgEkGoZG6VSkXPnj3Ty5cvYyxgEmmz2XO5nDY3N4PaAERApzmy5c9IBjkpw3lLL7DhgKUJP+qNDyBgsiTADxJEiomNRiNQus/edamW3yPDaFZXV5VOp3V+fh4AAV09F2Apm30Y8MSEMnTpPmiGYAmqJKAtLCxE8RAkPzc3p0KhoM3NTe3s7ETxXNLXTgzhZHDGcjpX7Z2vNHDgsMlOCLqpVCramff29uKEZZpaqtVqzCABjPk++dj1qMP1Deq9+66xy2QyIXjHYSwtLcVw52w2q36/P4XI4F1WVlaivxx9Kj32jnAZwdjpdCLqbmxsqFKpRJTjczGsxcXFiDocbjkzMxNHoUA7MPWq0+lMbU5Qn3N5pC5oFb1DyBUDFFfOzs5iveBx6ZbpdDra39+P1Hc0GkXfPx1epLzu+C4uLiK93dvbizmtzrPh5EnfXbeIHhTHQJGJjXZxcaHj4+PYpAQeL4DwX5AOqK5cLmtra0svXrzQq1evNDs7G8/ZarWiWOon57qdzc7ORqPA2dlZTIwC3b169Wrq1F5SOMaCMsvAGyDS6XR0KoJafBg4fKIPMorNkcmo0Wjo7du3ev/+vZrNpn7wgx+oXC5P8ZZkEtBNDhDQs/5G1RDT2EC+rG86nVY+n9f6+ro2NjYCObEGbmM8H4U2SUEL0XhC0weIjgALEoayIHvAwaZSqeAue71eDDzCBkDPPjfDL5QhAI/Dw0M1Go0YdrO2thYNNaBKMoFMJhP727vIRqNRzD5xPa00GTIPapYUA/zpImWeghffKIihpfX5KKzZ1dWVms1mZEySAtBBBdbrde3v72tm5mGQ1tbW1mMu9dPTwoh2d3d3arVa4VQoUjCp3weDzM7OTk3Sd/KdgSX5fF47Oztx9AubA4TsBu3OrVarxRe/A8NjA/ISQQe8NBBHr9fT0tKSqtWqXr9+rUqlEgGFkYe8HO7Dhdb1el3NZjMO3oP39cINaSbRHKd/f/8wovLDhw9qNBpqtVo6OTkJY3z58mUgOadvuDg+RnpALpubm4HcZ2Zm1Ol0dHh4qOFwGBEXvtQn5pNNEDDhq72gQiHPq85c8G+s487OjqrVqtbX12Pk3t3dnd69e6cnT54EQkN2BkXFYGu6l4bDYSD1brern/3sZ2o2m7H2jvaZ4wAFwEbmpGKaRCgAwSuCbnGGnJbA5zONDaqBwhZjCwEhXKTZ2AHBVpoEaqgRlBf8zGAwiG4+lBabm5th22dnZ1pcXIyh3aBJAAQBApvHiZNxgmIXFh7OLHPFjzfZjMeT6VmMlYQimp9/OAPw5cuXcQgldn1/f/+1taDh5+7uLgbgALA4quvdu3eq1+vh3JNKHFcr3dzchC1C7dDVSVZ0dHQUgI/Me2ZmJkZRolpxJY6kGKzDsBzeLUCRzlN83NraWgQupqsRlBiq/tj1SYdLtPDOJgZJ0HdOmkufPA62XC7r8vIy0LBPyyoWi8Hn8nuQtvAyuTAc107C8WSz2ejzZkAG1AfqBNABRa5U6mEYRqPR0PX1tTY2NoLUJ1UkuuLwQBRsTqItU7WIplSGXRVwdnYWQeXq6krdbjfmNXgf+cXFhQ4PDzU/Px/InJ/jury8jIwjn88Ht8yZbAcHB2q32yoWi1PoxYudKCloR+Xss9vbWx0dHcUpCThbpyVYCwIp3WbpdFqDwUCpVEqHh4ehleY5FhcXY5A3iBO0Cw/nlA7yv0wmo3q9HgOMPoYs2UyoUHK5XBTsyDIADswAcC0onDHZCum1S8hANFdXD6fNehrNkJj9/f0Y4wi4wNH7LA0q55xoDZobj8fq9/sxBY93Vy6XI0D40HCXHKIg4sBKMgaophcvXmhtbS0oALI3AhWoj3XyzkJmUlBvqFarMVSGbJALRwSdBw/qz0qtgM5ReOZ+vx/AC8Tog6rIxqDnKFzj9HCi1DMIoD7UnTVhTjHOGbvDbuG8/USZ4fBhTi9ZHSAOOg17+//scLmIiDhSBjg7wX92dqZ6va5GoxHcGGkmUQTVAptdemhVBdERKb2tj98P4gQlNRqNSNfZBDc3N0ER4KRIh0B4OJPl5WX1ej2dnZ1FXzdppw80Ie1FpcFmg8RnM5NiUYklQlMUdO2upNiA6+vr+r3f+70pCRgyOww/Kf1hZN9oNJpK+2jNnJ9/GPjMqEA4RIpts7MP81iPjo6USqWmCn5v3rzRL3/5y3CO3oPOO+Pd4ABdH0ka7vIdinoU01jL+/v7KRQOp06rJ+kdz0GRKXmB6EBxOGw4bNC4j51kIhxVahAOKeb79+8DRIxGDyMCX758qWq1GlymXxx5dHp6qkKhENwedgI/60Wner2uo6Oj4A3Jlk5PT7W+vh7zH1xTCuJzykJ6cLgLCwva3NyMLAuVAvwiP++aWM4FGwwGSqfToTqamZlRuVyeks05LUiGQkDhAjQRRNrttt6/f693797FSRHY6NLSkjY3N2Mfu6OuVCr69re/HaqkpaUl5fP5UB4RMJkI6MoN3itFPbJypyOgkwg8MzMzQeksLy/H5xPEvJmGgEXwoyiNA37setThgm7YXBgMqRxj6TjFYW9vT71eL47jBm32+/14eE55oGIKp1oul4MTBu1SIOHFUm0lqrkoHUPI5/NaW1uLjch9sHmYBMVLPzs7m5LKUOCBq0I7iDGQeiKDwiFfX18HSoQ/RPLj4ndeNPc9Ozsbz05KT9T17+UC7VOMZKYqWuBSqRQV5qWlpeC4yDRQC4DUeY5+vz/V5dRut3V9fa1cLhfpkgvcQTcYKsGHjYl0js/GZtiQcGau02V0oDeBUDFmXZwzxPjpKGLiG/pb9LBsHG8MQR3Bu+LfCZJ+1Ar7YGVlRScnJzESlCubnQw1SqfT4ZC9Us69cl/oP09OTqJQxrwDuEQCKFyjZ1icmAKiggemoO1zFVDfMFeC30smyEm7ZAWpVCqc1/LycswT9gDu3LOvBTwwNnN0dKSDg4MoKrKXsRPeLXs5nU7HvTGfutPpRDDmHD9QKEGOoMpeJGMZj8dRVAcdY/MAAWidxcXFOHWCzkzoTWwEAEGNh05AgloyEP+vHK6nLHSqkOrjHFKpVET3ZrM51V2FlhRFANCdxScSETFd1zY/P6/t7W1JCpTAIJDLy8uIbKSAnAYMWibN5AWD1EC8OE7S6WQnFM6EBcQgiaRsIjabi6NB6mhVvSMLA6VZgLVjQ3uF2rtZ/J34AA10jKByvobDYSDXbrcbnK7rqsk4yBL6/X44czZdNpsNTs/pFWgFeFHvfuKzWKvDw8MY9I3jgVv3oSQ+XIRBR2RVvHeXILEJcChQNkjToAJwqtACZBmgRkYJemME/Cn2Dh0AivGNtbi4GB2TSMtcXuQ0AvYGOmLIEDJGzmMDaSI5wom02+0IMAAHnIyjLd6TqxtAxSD5ra2tqfPGvHkok8no6OhIZ2dnkWEycIh9yjM5IGC/kflks1mtra2pWCxOBR0ACj4EHwOQ63a7cby8pDiaCApMUgRXfA3vlUwSTjWdTgdCTmasKG0kRSaSyWRCtcB7JnPleaEbKCT6/OvHrk+e2uvdTwjncRZsEGQVl5eXU5Ii5+MkRTEKThDnMx6Pw1jS6XRQA1zwvdVqNSq8OD0q+eVyWc+fP9erV69UKBQiVcBoPTLPzc2p1WqF4wdJeTNHcoxiUgRNqg9q8i4Wigl8Ln/PxgdxY7AYJ8+Ew4YG8M0NVUKAwHGBGlFZsLbw164wweHhjGl9RW/oE7jc2FyqxH07B8bvpFEAXhXZGqjAW1z957xAiTMl8LGGfiGxm5+f1+npadAqFHzg26AbeC9+UgZryqkG0Bjee8/fubrBHb9z6uwPZE7evOKO0OdaII/Dlk5PT/X+/ftwtjjw09NTHR0dBVdNYUySBoOBer2eTk5OpoIuzo1AxPehrwVk+N6en59XuVxWr9fT/v6+Op2Oms2mtre3Va1WQ0fPPneHS8EzWTD3AIHmGAkY74H3y7Oen59rfX1dtVot5i7Tyci+ZV1d++60EwGHfcDvYr8wpwMhAO+QLIKgz1qy9tg8752gieLnm65HHS4bwgtIpJW+8R0Rejusp/ukRSBIKuQ4Bxw6G9AXrVwuq1KphBSM9KBWqwU1wL+vra1F5R1nwAZis9CA0ev1whGQMqFWoGjDc5IqkTbTOcdnk7p6V4rPHyC19BkGbqw4FOecWF9P5X32AEECBIOD513wWWhg3clgQGQZBCNSXkkRUCjogQ5wyLyzy8vLmMaP0c3NzU2la6CQZIqN48dB8wyumvDWZHe6HIWDYzo5OZmSCHW7XXW7XaVSKb169WoK6WLbTvXAs2Of7gjS6fQU7ZXs/kMDTNXfT0QAteNQyVKgLdgbpNpIwXgu7BanRXGXLENSBBg6pkDWvFNHyVdXV6rVahFAKABDRVAMlR6c3+Hhofb29sJWcaik2F5AdOBCTcPthKwBupFOPqcpeBcUrtgD3qqPDwLA+VoQqNlfUDVeUzk9PdXBwYH29/d1fX2ttbW1QPCgWCjIYrEY3LL0oBRiT9FNB7L3AuLHrt+qtZcbgKtgUT3lwYgoWg0GA3U6ndiwbHZeDhwOUdIRpLekSlK1WlW1WtXGxoZ2dnbimGQ2M0iWkyLYIMhvQJKk1KlUSuVyWZubm/HzCPLpc0cakkRVGJtX7D3FxmBA3pICLUIvgEj5OacvcAigDQp5XF4IARmBYnhPUA44ZdIpWmSd2+R3c+8YMsoLnhXHKGmqoYNNCRrnGXEo0Br39/dRrHGk586IrMlbb7GNpJOTFBIjuLTT01Pt7e1pb29PBwcH6nQ6Go1G2tnZiZZvd3jYtHdIcq+0jfpsDlJGD1ySAsmx8aTpGRYoBdgvLr1iU7O3WKN+v69msxnPiS1iW8kL9IfSgxkAvJ/hcNI+ns1mY1gUgd1rDgTHnZ2dQKi9Xi8oEb54NtcuS5OTNFzxAKUBIoT6I4izPtgtgAHun4DBegN+8D9esGLPeFClYI4++Pj4WEdHR1PHPvGuuQeyh42NDdVqtSjW8xlkRvgV7PSx65MqhSRCc56HCjNcFKkgEhkWZDQaaXl5OaK9D5yhOAXH6jIXLvhZimv0UhPRkYDQvuuCbJAjGxtNH/zyyspKRC8KV44qk2oJumhw4i7PAfnBGXua7I6NDc+mx5G4GgSjIzBxgXa8ak8Qc/6Oe6W5pFgshrPn32k2QSoHAnO+k7/zjYVjx2Hx7K4BpTAEGiiVSjGwiNZLlx6BxnG42BmBCOTl7wMUyXvt9XpqNpva29tTvV7X3d1dZC6zs7PB2WE7jUZDV1dXMTCJdSb1xun77AHeoztc53m96QL7o80WKRXrw/vh6Bsyw5WVlZA2wX1jT549ouZgvzj955ysKyPu7u5UKpXC2UFtSBMOFW6SkajMqKDFNpVKTTUuOML1QI+9SpPaA7Ir2p95JzQrYRNI4lAD0KVGnYT9ie3hlCVFpoAvIaMk6+UA1IuLC2UymZjZgTqD2gzBAB76yZMnqlarOj4+DrUSNordf+r6rWRhvAwWkzmVSLlmZmaCjGdCPAM4MATOzOJgQ6qrRG1epG82LpAn6bRr/y4vL1Wv19Vut5VKpaJDh5QAJ0R3FmgKQ4VzIsJDaWDUbkweVZ0vIoJSKKGABo/FujlC84IjwQB0A9FPauoOF+UBtAep1v39fbwbjISuPbSuPrUNQ2RIi1ecQWA+oIZiozRBTJ76wRFC4XDo4M3NTaTiHG+DDXhhhTrAcDiMyWSuTiF9dEeHE+SdgRrJWorFonZ2dlQsFtVoNFSv18OW4ENJW5FSlUqlaJrAJgg6bPakPA2ZHpt0PB6HXaE1JsPgOe7uHgbWQMfQuLKyshIBxPW2ZFy8D9epS5P02lvRsT3ACNJJ7h07gA5JPlOv19PGxoa2t7dVLpdDjsXvx54cHNFuDbrnvhg8g92hEELpQ/chNNPi4qJWV1c1Go1C5cIpzOjT/X6xJUlRN3Jfwr+DlAE3FNIZouSFVWgZABR9BIuLi0HNsHd5V8n3krx+K4frPAmkuo9KzGQykb7xRQqBI7i9vVU+n9erV6/U7Xb1i1/8YkohgLF4Ws5FGgcqRPBcr9f19u1bvX37VsfHx0EFQCN4wYgTS5G24UxQQMC/4VRdbYEBezHKkTOkPAEDRJZsy8UZk56T+qGl9JZDnAtkPBcOl3QNDo5o644TaZg0rXjAEHFKOFOoGUlR0SYI0TXHfUIjsMlolCCFZM6DUxjeJMM7prEAhHp+fh6DgVhL3oc0mSnMWvB8rpqBiysWi6rVaiE1AoVAX9A0QUAGhd7fPxxAeXp6GmjJ7zsJCOgAY54zlAydVWx+gilodWlpSf1+P+gI7INCz8nJyZQkCURPAPQirs9EwNbIwrh3shTmA5AKQ8XRCce76ff7SqfT2vlNF+Hq6moMreEcP+yYC1vGIUsKeo/AxZyJ4XAYxUUHFXDpbl8EDJQxrBXFa18L6iwOjABfNEAQvAaDgQ4PD9VqtWLYfTqdjmE4ZEOXl5ehnQfUwcGDcJPqlY/60kf/lW/KTIY/sNE93cLISRNYRKJ6LpfT8+fP9b3vfS/Olke3i3GBVEBRfuM4UVKB6+tr7e/v6/3793r79q12d3djIhULD9fqk/q5P4oS8Iq0lA6Hw0CUbEpeIijLOVovPMHjEZxIX3xgC8gNWQ+BgQCF7tOdJl19vhYYmn8fnK80Qea0OxPxHT1jqIuLi6FHpICI88JQ+WItCFisWSaTmZpCdXV1pePj42ie+PDhg548eaJnz54Fz3h//zAwBvQG/YLwH2dI0PFgwQV/NhqNpgYfwScjR0SLCdrCPih4rK+vq1AoSHpoF87lctGJhOPwdlkyGC6KTp51cEKCSxn5eQ/KFJ9AzewpNOhsaAIioAIKjnWBHmKvuLrF6xtnZ2fa39/XycmJjo+P9dlnnymfz4dT5rNwKOzNnZ0d5XK5uJePVf4lhSP1gJb0GZKieMaoRD7XAwNZ8NHRUex7lAEAP/ao71X2Iu3pAAQKj7e3t+E8eX8nJycR9GkGur6+VqFQ0NzcnDY2NqYOSMA/sBe8KP6oL33sH71ji7TbN7bzkYxIpJ0OTpQB1tVqNfheimvz8/OR4rpKAePiIjXv9/vBMe3v78fwl9FoFM4dSI+TIYUF4VKcYzqYay2Hw2HoMX14DmvhtAr/7zplrvF4HJuLFI7iIdV0JvJToPCCF0EDNJDU9hHR+V4QpBfvUqmU+v2+JIVhkpb6+vJ7vLeee8XRUSBx/tTlci5RoiqMxI3UGM5sMBhMtSSTXeRyOY3H42hR5r5xRDyb2wWto/B3jMvzVBadMdwxn0WFGVTLjAxoDVJ7bJzNzD34xnLdJ9V55xl9hsRwOAxqAaTm2R0D7clcXAaF+gAUTbYkaSozciUIQRWVhSTt7+8H3UPzERw2SNfbn5vNZrTW8k74fLcJ9olLBr0+wRcKEvyGF/hYT+Y4JJssWOfhcBioXtLXAAHPzOfd39/H5/Fu5ufntba2plwuN+WUfUpfqVQKyeDZ2VnosdlD/A7oyt+paEbU8Ao6RgeKo62XriEcLZwr3Cn8Lqjh9PR0Ks3FiL3azwW/RFvgYDBQvV5Xt9uV9DDBB+SMIyK95Rgf5Dy8DC9M9Pv9QC1wPegDfUFJ73BKyUo168WGoEjIMcroWZNHp/C7+UxSQUfqXNfX17HRnKdLOiKoE5wFE5FAL66nhjvEOXnRAONKFoKci0SLnU6nAw2TqoLS2RA0Yayvr8cXswJSqVToLem5573xLpJKBdDT/Px88IA4OLrw5uYezgLLZDKhiwVtDYcPE5+Oj491eHioi4uLsF9v7/ZiGRV1LoKeS+pcw0v6DzLiM/wUDbhWClY4cZwu6hnsK6mE8ZSanyVY037LFL/FxcWQj/H97BMajFCAYDdMkCPAJbM/Li/YsSe82Hd5ealOpxNUBJmpI1J3rChpmI3iPLY3WVAv4nkoShIYWFtkh3wPw5fIIqHH2KPMwZidnQ0NMfNleEZoQfbSY9cnW3sxFDy7X9wA4/NYBLhL+vXb7XbAfrhGNoqnS6AJfjcX5Dm/D30lhrK5ualXr17FKQ9O6OPQ3HnQwZLJZGIOLU0AcJaSpoh5Ng0GTiTD+EmDWSvWAJSHA4CXo62RTQ3nR7oEz5R0pp1OJ4ZtO2fKPZFyee+3p8XJgAZq4nngonHKTvGwQQk8pK9etJMmVWJvCgFVUCCpVqt6+vTplFwNQ06n0zHS7+LiItQwSfSQlNZBKbD5oCuWl5djalSpVIo1JtgifqfXHz4WSsJRJSqV5MAWVwdgqx6UWD8PkgSQ5L4CzJyenkbzBqiTzwTFk4nhVMg2qAcQYEmhGcnIvA845kqlEnJLuN2zszM1m81It9vtdsxdgLdmH3Ml5ZI4XcAUxS+yDIIbmYGk0OhyvxRW4YIpnLGvHeT4WvBuvJEHEOh2TGETDhu7gi7kM2mv5lm8uOmn2jx2PepwZ2ZmwnG5rIlUgU0EcoTnGgwGU4Q1jpaNTDV4fX090AbyFZcGcbEh0QFCfJOKkKJRjDo5OQlyn+dw2RdpHLwfm4WUmD8iJ6csAAAgAElEQVS7/McbCwgGbD4QPBpHZCbwtnSWcfyOC7W9OQPaw/mrZMqGlAnniaGORpPTFEjTMUY3DKc4eHdQLcvLyzH5iEArTZyta0wJLBi0p/GZTCbGC8JnYtCsByMVQTcYP4GZ94jzQMWSRFPJ4uzKykogQiRZhUIhhq0zh/fi4kKdTkd3d3dRHIIfRYXDiRu8J9dq+n2QFpNOO6oi6BCAqUWAXgmc2B8ifYIAskeoArIX11pjnwR25lRQtcex4hwJIDQMIU2DXgGcuOaYjkRakhlWlMz0pOkmIfY+wZLgiC2wT1wzT2Dm/eJQUS7goEG7+JakxJCARrrvlCiZI/uZvc+egKZypYMX5Qia7Csvmj92PepwcXQ4Up+RSmUVJMMQCZAgaJeiFSiD1lkKU2x+7+5g43LxUuETiWY0WJAKQpCjyyUwkBaen58HImSG7mAwkKSp1NUjJf+P7COVSkVKT7AADeKkVlZW9PTp05hVy6aVFIU/OGjmjfKybm9v1W63p7qv/GIYNY6JNBrEub6+PrVp2HSOaLlnb0RgbSWFo3PpG+mkNHG4ICcvomC8qBacZvBqOW2VOCPQOM6CwOuSMC+6cF83NzfB7ZOBVKvVGETC2WgUlXACjJIENCwtLYVsinVk0PRgMIhgm3S2koIDZIOzSeH0sPvhcKhut6uDg4Op00FcqcPv8IIpew3HxLvyjkScCk6cY+wpnA6HQ9VqtXBYOA6czvX1dYw49CaN/8femfRGmh7X+uTAeUwmyWQm52L1pNKAlmVtrD9gAwZswHdjwAsD9s57Q3/AgFdeeGNvDHijpRde+B/IA9SW21Cr1TWwODMHJnPgmEnmcBf0EzyZXc26sO5SH1AoqZrM/L73izfixIkT8fK87E0P4L63/PJATTYFPwqy5WfIVuFIef84c7I1jtCi6OtrLT0WcrlwmDhKH46DfQ1nf/C2ZCK+3x1Zs2astTfLeJPSu64nHS6dH/BRksIJuNibGwfVwqctLS0FT0crIach0J/daDQGCPh2ux3RzTcWEY7uH5BSs9mMYd7ZbFY7Ozv69NNPY8KRS8pIkX75y1/qv/7rv/T69Wvd3d1paWkptKKgbYyMC0TB86HnHR19GLbu7YYgZ+RAXlji9Irj42OdnJxE2kekhp9utVrRlOEX52lhEKQ8CMqpjrueEMeHxIyN7dkJhu5twiAV0C0SLHe4BGE2HXKqarUaRUuq45eXlwONI9iQa1h7vV6cvOHpJumjB2K4TtdzUoxaXV3V9vZ2VOBxPm5rvV4vtMrPnz8PlAjIoNDCtDEv/AzrxHESbFCeD+TOnORyuaxarRZFROgXT+8p0lSrVc3NzandbsdRP9AuAApHVNAo7CPoN87jQgb47NmzyBikx1kivE+yJrIHLxi64xxWHnARJEGM7A2nvsiWoCQdrEkKW/WjoOBxqQW5cggfhD25pNNpStaoVqvp6OhI6XRamUxGy8vLIaOEFnX1yOjoaMwAdxvE6bK/aIn+putJh+tyMHgYT32IQAy7AMHw5XCQbiAeOXEIVI5JP4ZfIOMHSUdISZF4QOzTYIFigojOxiR1pErKZ3phDYeOw/HCAmkraRtpKIUIuLFOp6M3b97o8PAwIjwbgYiKQ5mbm4uNdHFxoePjY+3t7SmZTGptbe1r76RYLAbKB+mAiGZmZmJqFqjZ01aeCefl6RuOlp8f3iA4At9UpKcEIEmRAo+Ojmp5eVmffPKJPvroo4EDOb0A2263oyLdbDajnZWsyPvth4tmrKtP3aLoCGrEPuDqU6nUQGeRywA7nYdhO7R9ViqVqD+QNbzLwUBjkFZSPQe9EgQSiUQUeNPpdPCBbG6G8yeTDweDwjVD96DuoPAKwpQGB7bgWCgAVavVmFW9v7+vDz/8MCgfBgBxVJX0qNNmYD9I3HXITksNFzJdyeQ1Gs9WeKek9K5z5m++A5US9wGVSG0HP4INujySzxsGCclkMqYNYkOVSiUcPVI6P02EvcJnI6ekJkNjz1PXe880Y3N6gQJkxRlCnqbgjJhzUKvV4g9FEAh556EcwrtCQVKc2wWCxoCnpqZCWsJDX19f68svv1SxWBxodnBSfWlpST/60Y9CuI2MyZELBSkupjqlUql46aRMdKDMzs7GEBGOz0Gjy++CIJCbcH9sjFevXuno6CjOdeJeMGo+k26b+/t71Wq1UFXc3NyoWCyGgcMV82x8PxIpkBcI1B2uFwFBmWxoV2sQ1Tudjubn52OyWCaT0cbGhr7//e9ra2tLi4uLA44TpwAi3tvb0xdffBG0jx+nwsYdtk8CN5zi3NxcSNM4R42TgCmisZmgXHq93sAxOvDx9N6ji3Xlhl+gaORsyWQy3o+L4aEqUqmHwd/cE8GLIEm2CF3X7/eDiiMoUN13lIozw7Fgn9BYR0dHevnypVZWVpTL5QZqDdAuOGGaE6gX4Fwdubqy52uOxQIBz8fPkgFDLQDIhk+dhpokIPqQcfYHa0wGyfoPF+Bd8sjhsxsbG1FopK6CVhdfxR7MZDLhu5LJZExd49/QcyO9+6brvQ6XyEsk4eImqdxR1aWaihRkdnZWi4uL0c9PJw1ckacb3uXi33VychJiZXq/cRaJRCKKby4rIU3mBff7/YG5sSBVEA1pJvcDtYAxedGMaMpL5F7hf6iWe+HCDc9nFvT7/QhMb9++1a9+9as4WwlnCa8oKfhK540vLi7iGUHeToFAgzDJC2Q0MjISaAn9Kzws381zIUuSHh0x6IRiEhwi6zU6OqpGo6GXL1+q0WgE7QEKJQCwbtlsVuvr64HMKWKxaYY39rtoEVB0o9GIlJ4NxHs6Pz8fmDfrMjoCMM0p/p2upnGqh1SXAg8FNugkQAgZwejow0AYT40dPSG1dC039wo3y/ryTNBG2BqOBRrA0Rjze3kPnmW41Iu9QHHLETjvb3gtvMrvigXpUYvLfociAmV6MRzKArTJGlJMHhkZCTBDkRxnPDo6OjClDIDgdCFOF6DHs7rzxcY9+yEQnJ+fq1qtRi1ndXVV6+vrWlxc1FPXeymFXq8XrY28MBcYYxgg2uXl5RCgM2zGq8Ok066TBdK7eN853Eqlona7rYWFhYhokO9EQJ+y7wuNc8aoWHg2PhV2nhWnz4vhPnzjOQeIXpCKOOibghK/68+FogOaghMs9vf3Y+iK80+Q+WzGZDIZ6SX3cHNzE+iRVA10yrwLRyUYF5pouFfPMqBwSL1dyM/mwcGMjIxEcO12uzFYnkMd4aRdJUK6iEgdNQwbm3SVtRt2umQNcL+sq2chPDt/+6bnGeG5vYDCOnhLqjsQBwS0W0OL8Bl8J8e+swZeqHP6g+BBgdKVJdLDWED07Nw76wEYwIm5ogSblTTwHZlMJo7RwXl62y4yLJ+ixfqy13BAXJ7ie/HMwQzBEdDF2lCMd8TvE+6Q24H4aQqBn8fhYhfsNS/+8d3ck9MoXsDlvvF1NGwRIAjMoNu1tTWtrq4OtOG/06c+9R+dayP15AXD/8H7sRC+semWISKzyVERYCRsfByZvxxJ0ZXFWWTD2juvWPoAE+9+4r+Pj49rbm4uhijzLM4vDWsLMSQMHKPib3TIfA/RGqmQFzP4Lr6Pgs7JyYmOj491eXkZKAijYaNirHymp7f8HEh2+Kww1ohggHNhaEytVguD9TSRYez+XZxqANqHjwa5jY+PRxX94uIiWr1TqdRA4wXoHg0z7wsHjxNmow8HYtYQJwJSGt7kFKYI0DSWeGD2FmbvVpQ0cD/cg6M6dJx+3DYInWeArmF6GhcBw7MznACbH504w9wJujQhSIND9JEhsjY8L5lDs9mMdniKxU5fuJYbrtXBCAGZdRrOAtgbvGfWDWBDZse7pUiJ/0BRwj2xl+BNu91uSBh7vYdmGgAf7x5KY7hhiXcNt4+TxV+5Zt9BFSf3UhfgvqemprSysqL19XXlcrmv0U3D15MO12UX8EfwJF6Z9pmkNCfU6/WImAyEcJmI6zqJqLwANjIXG7LZbMYoOx8Iwot2rhONnOtcIcRpJSX99E3Gs71r4VybyqaDWgBR4STpQ3cNMioCDIlnZ2g26RJEPYbu80a9cISREMy4d9JTUvV42enH+bncF80Y/l5ZP4yRgg9OBufnA0HQLYPc4MXRZHvlnOfCeWHAjqi80uyO0d+T2xFZGPfGhgNtDdsJgZNgT+HNR/yBPtmQoLHhIhG2xYaFmvBuNNYcfhbnSP2Be3O1C2l5r/fQKQi/DH/LmvC+KGLTcckeZQ87b469nZ2dBYfLbAky04WFhRjyDqrmPh1EeNXeQRmIlXX0f4PWGy5yEoAACqBfsgcyJ2Rk9/f3ocTg3fF50H29Xi9sigAFdUA2BL3pygRs01G8U2IMJ19dXY2GkWEZ5/D1XkrB/8ZBeUpGGgMHy+JIisgwjOp8MhEOxdPo4ZSNtIrPJVUGAXi6NzY2Flyyb2TnMEF/oA8KF0Ri527521PRd1EXpCugBJ7FyXqXtaXT6UCYPlIQ/tfnlbrDxYmylnwWAQoHQ6aAg0ajzH2x+Yfbtz0wsjHQB3t2w3uiWIFOW1I0X/CdoAP+NwbtG440GOqCexgulnkg9rWdmpr6WoAFRbFBWWPeK5mIp9FQaGxWKCvshd/ze3Ib4/37e+M7uRcvdpFV8WwEdOdeLy8vQ69KECfLw+G6nAo5IZmKyx3T6cf5EmQG3AfcLjJJ2qFByzhxp9SgXrgc4frzsXfY284Dc1+uiuJ9geoJNjS1EDjgpr2tmu/lmQE8OHHfs77PvZ2a98xeYt8TvLFV5GQEkOFgPHwl3lVh/M31m+s312+u31z//68nEe7f//3f95koVC6XdXJyomKxGHyST/MB6YJWHNYjRaKdjz+zs7PK5XLa2NjQ6upqFNtGRx9Gn21vbyck6R//8R/7RCzvzwfBkNr6QA04FlDMsBYQHgqUCkLns71S/Hd/93eJP/7jP+4TbRuNRnSWkMKBuOGtpMfUxu+HiM7Pw7H5vaMKcI78r//6rxOS9Ed/9Ed956SlR1TLv5E6gkT4fv4GoXE/3ngAh0WxjZSOFOwnP/lJ4osvvuhLg2iGDMRPzPU19y4qTx35bkeP/PGRlS4h/JM/+ZOEJH366af9yclJ5fN5LSwsKJFIxAQ4GhFAIuik4Zp5N3w/lAADpqEgkEDyTKlUKuisP/iDP0hI0o9//OM+mlsyGZ4HlOzdSvCTXshjPb3YS0o7LJfy6v39/b3+9E//NPHjH/+4zzsm3UaQP6yXdTrAuWgyHYqZKI7g3RkoVKlUojg4MzOjhYUF/c3f/E1Ckn7/93+/T2fbyMjj8fEgwOHvdA4bxO10I4iamoMrCpLJpKampgKRT05O6v/8n/+T+Iu/+Iv+zMxM8NPsBefesU0vlkHzsTbcn/9xRO4yQTLcy8tL/dEf/dE3wtz/p1N7eXDSdca4URSj5Q9H406HBSXFw/EgXHa5iacVw+mBXzww6SwpoafHXkzjfnxz8xL9+YYNgRcjDU6EomIpKXi6qakpZbPZmJlJdx4pCdyjOwAvig07fC9SOL3ikipPj73YhxG4Q8TYcGr8rncLeTDjvXm32ruKI17k5H/zXnBUBBg2HmvgXKqncPx/HCIBwgOIpODe2DA8j28cfo7PRN873EXkff28GwKJ0zFw0c6N8+/YJbbP++f94DhpeGEvedEXuyPYQu24uod1gr7wfeEUGM6dQS/S4FQy3pEHM9Qmy8vLyufzMUSm13sYFk/xHJADz8wFkPF1nZub+1r3IxTAu2zXuwrRJfMu3OE6PeB1F5RCBFd4WPYB74X7hU6h4Oh0BGoJPht7dUDpChOvp7zreu8Acq+c4lDYxF7Z9V56dxRoQL2jZWlpSfl8PiRkFLDgwNzwJIUOjoXjhTv68U4wEBqIk7+HZzLQaoth87Ko/g7LkNhcVKEh8lFRSAoind550CGFRZ8j4dVZXp47FnfGXCAVAhgG6BOiQEB+QoBzgxQLcKqgWYzMOXQ2JhvFP8PVGvDP4+PjUZVHC+tzXdmAXixyw3ceGYQLyuTdczGQBDQzMjKi5eXlAc6Uz2AWBjwvmlg2Lj8nKdpM/URjdK/UAbzrzu3cD2dknQg8rCGnT/T7/RgX6QgPQEB246cqE5hx8sNriBKFzsBMJjNwFBHol/MAPYgxs4F2WrhgX4NOpxPZAIoB36s4SWxweXlZy8vLgUDhtxn4wud6PcgzHjha1pDvcG4dgAH/S0AnUPBsXKho/Fw2ZJwUEPnfqDTorvXCqre189y/tsP1tIgH9JTeiWuXduEEcYBoNPP5vNbW1mIGJZ9HuukFMC4qiQjSfTCNk95e8XQDQKpEOyxpZ7VajXZBfp97xcl7+oLzJmJ7mnx1daWTk5NwQGtra9GdRYqYTCYjZQXx4aQwLkTcNGWQnrmxYJRogNHxokXs9/sDU7IIlI5ynDrgfkjfHJ1jxER26RHtYxs4YpfX+JpTWedzsSc63tAP42ScJvJmApAqFwUuRgt2u90oeLAGICM6h7yxwzuTpMez2jjPi0CK9pRW40KhMGBjZAjLy8va2NgIx+LUDsAA5++0AdkjZ2gBCGq1miqVSigbXJrGejllNJz2ske5B94N7xkb4/d5n61WS8ViMY4QJzvD/lFEDBfEWEtQIVPI6GzDBlH1cLoHsi4CKvfg79/lYuwrsoxhRQ77kvVH/QKNCSWEHPPo6Ej1el31el29Xi8oH4phvA9sqNfrxec5FcG6PHW998QHUCVpBxt0bGwsoptX+UkfhyMn2lDGwNHL7WkQm2MY/fDvOCIQzcLCQjhsNHo4fjb22NjDUSpoEx2psLggBSrDpGzO5SDjYXPTE08zRqvVinOQOp2HE2vpLnIOL5PJaHFxUblcTrlcLloWGWrCgXleIXVUh4OGUsF5IaIfGRkZ6KxCjQC6BPkTrLxKD0/oVXfn0pwz9g0OcgJFssFJe+luY7oVg3D8zLWbmxudn5+rWCyqVqsNjNdztOsXAYy0t1Kp6PDwMDqA0ul0tJ2T3pLhYJ/YGYEFh06G4NI1AgZKCy7QFPUHRkvCc46MPIzshEJDQZNIJAKFMmgJG+10OsFB4mzGxsZCHsn5byAqngXbH1bfYPOsBWCJfQ7a7HQejz7iHfCucJje/UYdgAt6B04Tx4otzc7ORpcb56vxfsmUPWNzimNlZSW6WEH5aG9dmkYg4N4Y64gCY3l5WTMzM7q9vY125zdv3ujo6GhgWh2gEQoklUoFDZTJZEJbTraLHv+p60mHywvE2ZJyeErIJk4mk3F8MMgNZ8xnOXnNpoVHkzSAioeRDAaUyWS0vb2t58+fxxg+GgiQkXCAYaVSCT6HjSBJuVxOL168GNDi0qBxcHCgg4MDnZ6eDowo7Ha7URCZnZ3V6uqqtv7ncD2cBrNMGb0HOsFRI+WBI6VNc29vT2/fvtXp6Wm0D0qPVArtstLjFCUcUafT0fPnzyNtTKcfR+2VSqXQQ6NhRNDOe7i8vFSlUtHp6anOz8+j24uJW7lcbmBKGO+SdU2lUpESkoK78bHZ4dv4/xQFaa0+PT3V8fFxnOIBp8p0OWgK39x8DlI/poE5hUPgcsoKCgG0QtcVDnZ8/OEAymw2GzMUqtWq3rx5o729vaA3uJD69XoPk85KpZLevn2r4+PjmNGxtLSkjY0NffDBB8rn8xHo3eF6d9jq6upAgQlgQjDhFAbuA0eNMySQEES9sAotgn2xTnyugwQK2wx3Ittst9vxDB58oMVoRLi+vlaxWAynubGxoWfPnmlzc1PLy8thW9gNzg1qpFAo6KOPPtLOzo4WFhbU7/dj+hmNC/5s0iPthjSOTPr29laHh4c6OTmJd5ZIPAwUYv8zP2NlZWXgebLZbGTI+KlqtSpJkTHTnffU9d5OM0dBLIprQHk4DAckBEKgs4V20GQyGfRAPp9XoVCISOEcnafRGB3cTrvd1sHBgd6+fRtG0mq1lMlklMvlAmE3m83g1fr9h2lDqVQqaI1sNqt+vx/HuviwFg8EkiL1SyYfBu+8evVKr1+/DqoB5A8SYfMdHh5qeno6Ukyc8unp6QBnh+OmFfbk5ESlUulrabRXnIng6XQ65g0zfrHRaKhUKun09DTQM3woqJQC4Pj4uLa2trS2tjbAm/KupMeuJy5vEwaxZjIZ3d3dDVSyKVaAQECtCMgrlYpevXoVk9w4aBRbarVaqlQq2tvbi+DvmzuVejgeaG1tLc6vazabA+MLe71eDM7mHgjSFN4mJiaCrqJPH26az93c3NTd3Z1KpdKAfZJWY/tkYDQLSYp5IvPz82FbDkK4nBPnPUMv1Ot1HR8f6/Xr1/rqq6/UaDQ0MzMjSYEQnf4ATMA/8j74WdJh1hHHBW2GWoi95qoPnmF4BiwgDVRLoYx9CnpMJpP6+OOPlc/nlUgkVKlUVKvVQvfLmu3s7GhjYyOOafLPYF8M66JdDUSgJxD6gZSFQkE7OzsxcAa/Uq/Xtb29rXw+r5GRkQABktRsNrW7u6ujo6OYDIhvgHJ66npvpxn8BSkyqRgpCqMJKUJgfEQVUn26jk5PTzU2Nqbl5WVtb29re3tba2tryuVy8SKHK58gqvv7+wFuqdvthig8nU7r+fPn+vjjj1UoFOLolOnpaa2vr+v+/l4nJyeSHjq5EolEGNJPf/rTGIkIL8P3sWFwQC6E9gBASy6caa/XU7Va1cuXLzU3N6eVlRWNjIzEETtMFsrn83r27Jm+/e1va3Z2Vu12W2/evNHPfvYzffbZZ6rX618T+3sW0Ov1YqA5huZieu6fTQYqhiqCcuGUCKgKuLXLy8t4Hzhcd9q++eDvWq2WSqVSHFvjTgzKCHQBUmm328pms9rc3NTGxoYmJibUarXiGHG4VQKA9Ni8MTo6GuM4M5lMrHG/3w+gcHZ2Fs7M0d3BwUHYN5kUQQ5ZlB9CipMcVs4kEokYtYntUE/wTjQcKIOCQN8EosvLy8gMJycntbKyEnNADg4O9POf/1w///nPtb+/L0na2NiQpHBEFIBQFbD/QHsMfGJYDUXsbDarcrmsRqOhTCajlZWV2IulUikOW8XpwnOSOXDBBcPRQx9ymnEy+TAA/PT0dKBIxbl9oG2XIh4fH+sXv/iFTk9P4/zBQqEQE98oPnNRV2IPUF85OTnR2dlZrD2jTdvtdhxbtbu7q2QyqR/84AehpDk7O9PBwUHUGxqNhorFoi4uLiKLTCaTWl9f10cfffSUS30/wmURksnHkXOgXu+aYvOTdjN4ms6YcrkcTiuRSMSZZO12OwhrhpQPF74oZkkKnpYjULrdbozFW1paipQaJOnFhUqlovv7ey0uLoZzODg40JdffqlXr16FI52dnVWhUIhjod2YSA+3t7e1ubmpmZkZ3dzc6OjoSI1GIxw2L5MRljjqYrGoer2u8/NzLS4uxoQwjnKfn58PQ4OHGr6gA6AE4AtxXnd3d5FhgLYdacI7NRoNdbvdOFXXFQugHtoVHYXxHilcehCA3woD+x/EMj4+HnQPTp+0k8++vr7WwcGBzs7OIijAmZ6cnER1nwuFSKPRiAwKeRHvF0rBW343NzfDzt6+fatisThQzATZwWfyDjm9BPqGC6dJoRKnJj2eVgJ3nUwmgzYiGNXr9QiABG72kgMAaLxyuazDw8M4+0vSQFs96+Z6UVJ6WlgpGo6NjWltbS343ZOTk5CEIdfc2NgIeRz7jWPk0Yz7HgFlM5EL6g/7Pzg4iOyNIEBK7gCBYjC2UywWVa1Wg8L0wTsoHCTFQBuQ7+3tbfDSi4uLymQy6nQ6ymazUV/Adur1uq6uriL4XF9f6+XLl3r58qWWlpb04YcfxjxjBuqjQR9WFL3req/DJcVG6gKq5MNBu8NyJKJWu/0w5ev4+DiGnlD08XFpVEyp/jkXwmLC99AkwUxZEAOzDChgXV1dhXNNJB56o8/Pz4PLHRkZ0dramn77t39b8/PzOjs7C6fM4YVOxsNhb2xsaHt7W8vLyxEFR0dHVSwW47mo+FLUIHWkOOSB58svvwzk5u2NpIhwe9LjpDKcFvdFrz9pDgbA2rHhKfCcn5+rVCqp2WyGkXQ6nXjXOBeOmXEdLikUm8K1tM1mU6OjoxEYJQXPKikcrE+Z63Q6wStKD9Ph6vX6ABrhjDpHU8ibSBNxCi6BoqDBf1tcXNTHH38cw9rn5+d1cHCgTqcTyBt6BA0nfC9BbnhTuWKDhotUKhVD111cz8/5bAcyQPjWdrsd3LEXcKBIXDPqtoEKgjGL8/PzWlxcjCE0UGvQZ16NX1tbiyDC/ZK5wddDQeTz+Rjij2aVC7QJb3x9fa29vb1QB+3u7qpSqSiXywUKXVhY0OnpaVB7rBlKnLm5OWWzWSWTyRjWj11Cg2DfkoLCbDQasS+Wl5e1uroaKgVUDQQfGmPGx8d1dnYWA9vJ9lgb1hX7QfnBCSe/1okPHiEx8MvLy3ggquNEfPR/dF/hcFdXV9VoNLS7uxvOblgqxQR+abAizn1A4DuqRqrBH34PVCQ98mugm9vbWxWLRWUyGU1PT6vbfTjcsVAoDOg/eSnwYC54vr+/V6lUiheRTCZDEyo9pDCVSkXFYjGaPFzWBPpig4+Ojg5IZM7Pz6OTDcQ0vLn5LgIdxSWcFlXnycnJKAR5Rw3UAedJ8Xkum2LAuzdjsLFJjXFEBB34OXg7VwLwN0iOk5Kr1aoWFhZiXi48davVisEkaD49EPPfj46OdHBwEBpYEGa//zjcBvQDleO2ygBxpnBJiswA+2PN3pVGezWbtfT3M6zRZN24v36/HycGQ3uwqUdGRgZmKuCANjc3NT4+rlwuJ+lRLsh9TUxMBI8MsGFPkJW4xJBjwgEqUBEgPgIRg5Wo/kv62mS9drutWq0WlA5/k4WxFmRW7BfO8cOXDM/D8AwEegZKAdAhKQI9P7+wsKD19fWBQ12hLtXtVnkAACAASURBVAAmSBTn5uaCJjg6OoqgB71D0wwZCMW0XC6nVCql5eXld3jSx+u9sjBvACDSOq9JRCdq4/Hn5uZCw7a+vh5Gsbe3F/wMMhQQAdKR4Uofnz07O/tONMF/5wWBhkBzpHcYY6lUiqBAQYWXxr+DyjxNQeZEyskBit5YAcI4PDyM42LYvMjaEOB3Op0Y9AJf6ANrMDwMm/uQHgMJf/j/OAa0kFSWKVLxN5pGipUENX6XzYWzp+gnPepVpceZoSD2YTpIUqwxh+0lEolwII1GQ/v/Mwe42Wwqk8mE2J7N6p08vrn5uWKxqLdv3+r29la5XC4CErwlvwOag09kkzJoh0BBtdy7vrw46Kdf+BrgoKlsw9Fy3+wnggibFiR4fn4eCBFkBhLj/c7MzARSwwlg2ygdXCpGEMHxewcWVCAU1sTEw2narvnFWXvDEbI9sonhbjdSegILfD3NBSggrq6uVCwWNTk5GRQFDtTfG/foVBtB3xstHIVyf5JiH7k2nM8iSPGOKAJ2u92Q/1Wr1aBPUObw3Z7RSwrA803Xex0ulVxvPyXlhUuEPyPy+DxWSWF09F2Xy+WIsKT6OFKMdlgWRjEI4/ZUhjQbzZ+POfSuMZwMqSwXDgeHjPOnmCEpjJi+e9cQO11wdXUV55LV6/W4TwzG2xTR9sFLsqHy+fwAH+5Djfl90Cq/h4MlIGJYOBJfT+mRHllcXAytqffQ+zHvzgmy3gQ9rwZzf0xcAmlwn6BulBk4KdJmSbFmvFOcG/bnDpfNXa/XQzlAeo7hO//LGXcTExOxjmzKdDodRwANz4Tg58ja3jVk2htGCDpkgc4t93q9aKggIOBk2Uc3NzeqVqtB1aEYWFhYULfb1fT09NdOxGCN6fRDi419uJaXyWoU53he0mqCOmge2g4UT4brHVZcUGK8P4Is8xho26/X6+GAh7M2nD9gB3uhGAYlxe9zPNBw8Q5KhGOS4NrZFy4ZxV5TqVQU/6GZ7u/vtbe3F74FRQyNFGR7wy3f77reO0vBO7wc4TKHgA3t4mrOKyKNJdWCg6IiSGRBeE00xEFxeZuwb25QN7IXFgSHwx9+lrGQzWYznBwvlWju+mAq2b4eIFOclHNvoNtSqRQOwNEQUZUN6JuRNQRdSo/pqPPIKCVcCgO/R6QnpfZgxsbDGAmAS0tLsclJ22ZnZ4PPwhl4Fw3qleHmFJAF+uJh6RHcIekiRk/6j1MY7m6jHdYVCpIGOtSQK4LC0NQWi0UVi0WNjY3p+fPngeCgKfj8Vqs10EVG6yu6ZOwB5zUcwNgTcNQEQe96SiQScTAjduC/y2diF/CHXh8BVdEE4qia73JE6jw/g/enpqa+VrSGNuCesAcQPfUIBlfBp9NVx0UNYHp6OvY8gYuAw3tkRCv7GlsmQyDgELiwRWgbMjSQJ3sdH0G2Wy6XY/+B7ilIQrlBbfHMrCOZ59nZmcrlcjhmTnhwymZYufKu60mHS9RGrznc5somcYPEw+OUoAJ4UURseCAMCwqANBKUyn245IlNCi0BgX95eRnf56kxTh2HRpWTAt7ExESgeTd8noMLh4Kzhl8CVdFmC6JCHke1l5fF78Ndg9xZS1JPd6Rc7jRd0O4dXrwH7/iiwuw6Tzbh+Ph48MvwlBi73wMO1zsDndeFu0MzKim4eTgzNiUbjc8ky/BBJ7xjUnyX5PE5HrRxBOVyOTY2qojvfve7Wl9fVyqVUrlcVrlcVrVajbS/Xq9HEXVtbS3uo16vh5yO56SpYvidsLbD9+nUHBI4fo81xo77/X7QWihuSPF5VpwMgIP1cw07AdyBUC6X0+rqajjOs7MzSVI2m9X19XXIAlOpVCgGsBVoLgIGdY7hZ2WIkz8n2Qx+BOfOzzNEH1slw/Fsl4DPd6KCgIcHvUuPFJA3f1Sr1SgUjo+PR6cpcsW3b9+qVqtJepznDPigSYNuNM8Y0Ni7Tv+p6706XBYLg/cF4GWw4N1uV41GQ0dHR7q6ugqujMh7f38fGwBNG2iJdJDKJCJ1Pte5Qb8vuC+0cAsLCyGm5oXyHbx8j0SuKiBt9mq0GxOfA4qiIwYqAYQL1+u63ImJiUBp8JdEXDhaggAcL0USv3DOvAd3tvDJ3q/O2vLvbHJH0xQPeWZ+39fA01feB8EJvpLTdym8cM4ciAT0SuGDwtzU1FQcw8TPUan2oSXD6gCQMmvhagJE8nd3d1pbW4uhNq9fvw4aolgsBjeXTCZVKpV0fHwcHCmfiyPA+Q8HQenx6CNvdfUsxDlwLhwD9QJ4flQGrAG/540urB9r41O/QMw4XgpHKysrURQiwPnYVBwuKg9mc1BIBlwAGnw4jD8TunPvgiMY0kaO7a2srAzIKLF3npPi7u3t7cCkPX4fDTm+hj3tjSNkUOzXXq8XlGCj0dDx8bGOj48jI0XRwWCt9fV1ffjhh/HcntXe3d3F9w4X+991vdfh4jCpOvsFh9lqtXR0dKRaraa9vb3Qk2IwtEiS9kuP0+2p4HtbHMUnLifIHWl45bPX6ymbzWpxcTF4LBDpzc1NUAG++Hyud3zheKTBk2KdbuBsKjaCD7WQFCdOMKwHMTxIEv0eXKyf08a9kX7D8w5vUgIenCFRFwTvKBTFBVpTd3ZO90CRkFFwD5Li9/lM6RHhI8xHZ3tzc6Pp6Wnl83mNj49Huk+RinfomRFUhA8S4rldR+sIgnQWhIGN4hhwCv1+X2/evFGlUokqNel6Op1WoVAImQ/8aS6XC2eLDRHknQ6QFDZMlkTx0REP7wgbSCQSEVgmJia0tramTufhQFFqH3D4XjQEELgSRNLAOYEEYt4bahcKO6lUShsbG1pbW9P8/Lx2dnb0rW99S+vr60omk6pWq0qlUvrFL36hZrMZwRV7YH98E6JDIcF+8RkNUAWLi4taWVmJIM59UswEZU5NTUXD1O3trTKZTGQf1IQYiUrwhZtlz5JJOZWAf/LZJa67Z3ANxThGGkBvsPcd/DgP/U3Xkw7Xq9ogQyI10YY0aX9/X81mMyIpBYh0Oq3l5WXt7OzEAsIfEQ2RiXDzoCUuDMvRHWiGXnX4qVQqNTC8hFSXPxiB6yl98YiAbHKMi2o9ztV5PJwUKQ0jGre2tvTs2bNoHczlclpeXo6iCYiTjQXfRQGQIpMXA6hqg2pBowQkl8ewwakK+zg7vguHzrMgg/FCJFwdG5afBYlfX1+HrAn+dHNzU5ubm5EBUAuo1+tRMGUqFS2Xnc7D0B+aZkipkf8NF81wdJ7x8IdnBjn+9Kc/1cXFhXZ2drSzsxPFQDTANLp0u93o0ScIUCQCYHgdgXXGTtl4fk+OiEdHR6OV3ZU8FKMvLi7UaDQiY/Kjyfl+bNWLzKBAghKOjbSYVJxAxL9lMpk4VTuZTKrZbMYcEt4pTt7VKdyDB0Yur0MAcnBS6G4/+ugjbW9vxzAcb8GnQEk9iPXhvUALMEaRAqnvZ1AwwcmdLfLSZPLhxN0PPvhA5+fnA1I2n1UBBQUlgVJBGpwL7bTZN13v1eESbRyZjI+Ph5Sp13voU765uYmJPKQuiOcLhYK2t7ejhfDnP/95HPcM34hTxdlVKpW4DzcqXiLOKJlMxvAPT41ZeJ4DmQ1OibTWjRbnDML0z/LGD1JWDA4HIj0eColRr6+vR7Elk8kETcAfBu4QxEC4ZAPDqahLwFgbR+FUjnEKcGasNYYIsg1DsHXp9XqRync6neDFFxcXw8h4D8PNBRMTE8rn81pdXY2qfjr9MNvh7du3qlQqgXhqtZrevn2rV69eqVQqqd1u6/DwMET/6JPZ+LxLrmGHywZjY2cyGeXzed3fPxw0eHh4qP/+7/9WsViM4I9UjMBPcwyfA3VEYMdBfFPvPsGPIIYd4aBJyV1l0m4/nKTA0CS4xKurq0CDZGasgdNbXAAEB0eAFPh1aDg/kJEJeIAhWmhRAThXDEAANTqydBtlP3Q6nYHsb3x8XIVCQR9++KF2dnaUzWYDYAFcCJbo5lF0UDydmJiI0Y+uXGIteG6K9l4f8fkOl5eXMYiKIEhBk6FS6XRaJycn2t/fV7/fH9B6I6sjs/m1KQUenkodRjc/Px/VTq/20wGysrISXOra2ppWVlYCwSUSCRWLxSiOgUioQMLzMvdAepwiz8ORpvT7/SDL4Qe9mQDFAw6GTcHmZcE8/SZ1gpLg+Ujter1etMSSWvoRJGyEs7OzQEmVSkUTExO6uLjQ8fGxqtVqHGgIKrq7u4uWQxwurY9u0BguToVWTZ5nWKZzf38f6g1QlAcJuHQn/fm++/v7UHcwwIUN72jLHT4tyolEYkDIDq8nKTr6mPdLtkEhp1wuB6pmMhQyJHe4TjGxJmRlFDs2NzdDUrW2tqZXr15FtyFgAFsn0DBjAvvgGd1O3OGyT1h31pffhXfE9srlsu7u7lSr1TQyMqJ6va69vT29fv06kBS0XLPZ1MzMTKw3KAq0ivSLtQBx+d/39w+Hd/Iu+v2H4+l3d3dVrVY1NjYWqTl7bGJiQisrK5qfn4+fd9QM/8s6uX0SEEDGrC0OPJvNRuF8bW1NiURCtVotahyADFD73d1d6GiRX5L1wPuzh6RHhQUAwjlW6jUXFxfxDqGmpqamlM/nQ8vMfNxmsxk+hn0APUdQRc3yazlcjJyIAlIEEaCNA5qToiwsLGhjY0NbW1taX18PYfnl5aXm5uZUKBSCPHdNJmkuEN43kv+NUSSTyeBqMDA4UVIm75K7vr4OVE4V1iuxzmNSfeWlgIbgT/l+nDpGT1WeYszt7a12d3fDkdbr9WjYgC+THosuyWQyNJToAN2g/edAGV7191TTiyagaYTwRGi/f3fUPGMikYgignOAODqCG9IYestpi6SoAdpYXFyMKnSz2Yz0ENR0e3urWq0WKTASM8YG+kUgRjnigZaKPLNP19bW9OGHH2p3d1d7e3uRQeHY4JGHpY4EIM8svJAlKezOFSFsPCrrqHJwJDgYugvL5bKur6/DhjY2NlQoFAZmNjgoIauguMwaQpM47YO90ViE48jn8yF95JlxvHRdIb2iK5C6A0CM98qF43GtPigfWgtfglyMFH98fDyKp64YGh0dVSaTifugCNdsNoOP96I6jpg96WMvHZWmUqnItsiQFhYWAllz/5OTk1paWgrtOsoIgpc3ZAwXRoevJx0ugzR807oMhwLDMKfpBajb21stLi4Gwk2n08rlcrGZ2Dg4uGazqUqlEpIV6bF1kBfN/4azoUiCA8Tg4H1BeaAqnK1rbdlMoFoQLgtIpCQdoeACh4pCgPOjvDmCijnFo7GxsUjZKaLx3cnk43ATIvLwLAWiOugXB/+1l5tOBwqZnp6OSMz7pJjmEjCcDsEH7pYUnLV3lDwzMxMFPtqeCXBsFqQ7pOlsNu5xbm4ukCNUxcXFRQxKGa6GYzfdbjcCKbQRRRHebbv9MCybVtetrS2dn58PtCWz6VzuSP2C9N+73YYdLjbgcwqg4WZnZ4MmgZfEyaKUGB0dDbTnR9MgcZQeKS/0vu5YsDe4bn6efeGFNlQL3hUFpQWY4t4J3rx7l7TRbOBZhssTnSpE3obNpVKp8CF0YjoViFoCX4MUDEqn03kYfoT9uA4XpQc25ZmP06Jks2SS+Bq0x+z5ZDIZgc5/DtrGbWeYXvnannzqPzYajYFoTxRBFkHfPzIW6bEo4FXEXC6nQqGgpaWlqMhz2gGGw2ZnCLA7XOckeYmkKuhfiaC+GVxuRqrCszhKcV0jz8mLh1LA8WFg7iBxjnDWOBjGx3EPTDCjf5zNSXCgeu18mXfscR8gHQIFqgOMnftzWQypjys+SHWJ+jh/fh6donNW0mCxxNEuyJwTD8hgXC0C0h8+VRZnDCIeGXmcpUBKxzvlIjNCrkVBkGJbtVoNfe0w1VQoFHR/fz8g5PdGEucDCSzQU8NUBr+H0/XsC7slTae2sbS0pPX19YGz37Ap0Bc1BtJoKB4vJGKfU1NTwfnidJkvQmDz+8dZ+lQ50Lg36qDZbbfbAVB8hrBr76XBArdrxAn0rmwA6cPxgoRZS+6JzwHIoMigXuK8rySVy2UtLS3FKRq8O4II4Ip94//G3mI/8w7Yv2TmrAHo/l3Fw3ddTzpc0i5GmLFI3mxAAYCFRovpG4nhEZubm1pdXdXc3NzARnSnw5QoVyngPLy/mntxBDQyMqKZmRltbm7GoAkQJuLnYrGo4+Nj1ev1cNJecHEHBq/I5vaiFEJ7XoQ7JnSNnsq40U9MTERHEs4HVIcTQd7ltIikyDB48aBgXra3rIIMnGsdrqZiaKyDC89BuH4yhvTIYYIckP+xIf2/ofP1zAANox9FT22AGcmSYlTj7e3tQKsyFy2/cN6sOyj77du34UiZE7C6uhotvmxgOrcoYJKqk756AQbE4/fh/5s1dgWLi+6xCzILUlQ6sZxewyHV6/WggrBT9iHBeWZmRldXV2EfZBzJ5ONwKAagEyShGNzREACwHfYOWRpcLA4Mms0vlxrizEDZOGA0+ygDcNI+QwJqg2AP3UhAIdOj2Iydl0olLSwsKJvNan5+Pu7ZOV0cJJLG8/PzoBFA3l7Ex17J5rPZrPL5fEyV8+z3qetJh4sejwsH0el0YrgIRDh8jrc/ArFxHDgKEKHPaJAUqJlNyOX8GYsFl4SxQ1GUSqV4cJwEVdBKpaLj42OdnJyE5AZKgKiLUWPw3oABAuZ52My8DDbQ8vLyAKXgAngGPrN+XpH2Ily32x04BoSLs5i88UJSFAbpi+dy7aTLV0BtpPPoDl0DTAqIQ3WVCGvlgcRF4wwE4v5BvWdnZ6rVaqpWq4FAb29vo8j1rW99SwsLC6H0AKWxnr65Jycn42copHB80M3NjU5OTnRwcBDHPhUKBW1sbERR13n+VCoVlWrQvtsY6SpAwfeFc7vYCWt9cXGhg4ODENYjzQMhAiI8YCGL4/QPwIfP2SVoemGOOQ/YB0BmZGQkZiJgkxTSisViFKZwiCA8d8jugNmTPlhn+OIzcMrj4w8zbclw/JiiYW0va0LrOSNWOfYKpOmDc/x9sNfhg6HJoPMovjJ/meOtcLoUmUHcTl/2+w/H7bx48SImhfmQLN9777qedLgXFxfhiHACOEgWfGxsLDgqTodlQIgbEzfNyDc/KBBdG5wdgzK43BnyNy/SHQXOBIRMmkJ0BlGhtnBx9bs0jszolTSQFvHckORUcff396MYgtRkmOvDuDBsigwYArKsTqcT9IWvBcO9CTA4XTYgf/h3kDKIiHvo9XohRJ+ZmYmOOHg/uD7QKf/OWniR0d8xKI5Ouaurqzh9lqN+CDZI5ZhV+sknn+jZs2eSFFplun8I6L65Keim0+kYGwpyBXFAj6Bt3d/fj4YDThzgYEE2NrphODvQrRclXaXgVMawHUHBcdwRNs/lqJnvgo/2z8/lcsG5elcbjmZmZiYQGOgcpHl7exvHxKCdd+SKs/MMDi4bu2q32wMnSvg+GKZXuD/2vx+/hMOk24yjlED27G0O3YSWQxN9cXExUF8hUDnNQ2aK/aFu4L+z30CqlUolvoMCLfvvXQEFe4HvBmR6kf2bricdLtwYG5+FBIU5ryopuB6ORQHxYkjJZDLQJkfHgFyA8XyGPyQ8qEebRCIREZ8hGDhv0hJoChCPc19UP+lZxwlyocVjJgDphes+QSReROT0ioODg4Hzq9Aj0ofvLc9wRXCqLqPB8XMRYNhMZAzwqiBS1o3P4Oe9RRPHivIC1IHBUYnt9XrBc0qPXYKsy93dXawDmwapDQ0oMzMzSqUexP7cAxt9fHw8zrebmJgIB12tVpVIJAYyqOGGA+yMqnW9Xo8qMjI0CrIEIr6XDckpuD6Tmd/BTlgnnJAHUudqWZNWqxWOk8lriOuxbxwyawFVwKkNExMPhyh+8MEHEUjcprFBSZFdgGQnJyfjWUFu+/v7kYLTAJDP5weOWFpYWIjvIVienp4GheVIEf7X3wlr5s0cFCzb7XbQelAGIHPpcVgRgR3qEj4fOgFnz7sfLtyxHwk+0GrsX4J4LpeLQwyWlpain8Bt1rMqgvrY2JhWV1e1tLQUjhvqYbgbd/h6uqQmBUdJf/VwoYkqMakfm1lSGD5Di9mEbGwiFCkLaJTKo79Ed7q8HE8RSJtwXj4PACdCmyK/DzJzjobISeBwh8s9ghzhqJlABs9GWy+oBAlMrVYL3puuIPji+fn5gUo4yAw0wAUqAxGQZqG6APF4xHdHC11ACu2FFDIQqBLWFW00jt8DD06VdBkkhHNESsY7mZiYiIyD+8ao7+7udHZ2puPjY+3u7qrRaETFfvi4Iy5v5iBg8/1sDGwjlUoFqgPpsxnJUrABbAs+lIAmDXK2kmJvIMtCgkegWF9fj40IVUDXUyr1MC8CdQ6HfzIchvkGzEr21muf7oUT5SDSqamp+NxaraZmsxk2z1ljNAytra1paWkpKBZSY8APQIEiKLIyWnGd5nGNMGifrrNms6lqtRonnmDLno0AOnh/rnP1d42NY6PQOPw7dunzH0DjKIEIvASZ9fX1AS26qyS8OQNg5/vUqZKnricdLpuN6Eul1/WrGLBPBnJjRVaEbnc4HXCROE6YF88FTIdX9VQRB4BjxXGwqTAADIfNw8ISlbw1ks1HWis9jo3jv4Miecb5+fnYvLT2rq2thXOkqgnZz/dyPxgLLzuRSEQ3kPNC8M7eOnlychLP7hIi/ua4EZopQM+kr2xanCXG7SJxvov34E0A/H+KMd6ZhNFzDwym9qDDGV4U0/b390MuRfEDWZt3VsHtg5LgtT1wu8SNd44yg/sFVRLcSIdJ211+iGP11NEHA+FAeeaZmRktLy/He5c0cN4W9uRNNmhOs9msnj17pq2tLWUymbBXnsUbQbwAyGxb5liACj2gowDp9/uqVCoh20NKh92xN8h6fK4AgdIRLjbNXkaSRxZydnamy8vLyGIKhUK0vGcymThzDNUQWQvHKbH2BGtHmPgDV1Wx9+nmvLu7izoIwQKg4u+cNXA+niBB8AWAgeqx66euJx3u1NRUvCxeGIaHI8OQvC0PDRvcFo4WR0j1FANgg/b7/XDMLoWSHjt9XHgNygPJwlvynaR+OGg2KZsSJ83f3vVFdZgFdJ4Uo+Jz4alcJ0lDA90otFNyki2bz4t+dDjBmXoLpr8TtJlsHIzSkSUcK0EANECgZI0IYlSo0aYyNxQlCBmF9OhkWX+chk/18uFBODP+hp6gMj06Ohr/nZbKi4uLGPXH0TvDDhe7hGsGgeNwWddhzSq2CUKTHtuUCRRI50DyFIzIvLwaTQD0bA1bb7fb0eWGLZZKpXCErCOBYGdnJ4LI3NycVldXtb6+Hm3V2BfrhpP2SVrDfC5BxO8ZxHpycjLgUKTHbM4lntgbR4EjfXQqhXXEpkCGkqLN3SVdDAc/OzvT3NycFhcXBygKbz6CQoSPhaN2x+pdr87rUk/o9R4HT/kJEPgh7Bp/4/JQV97w72T3kgaKeU9d73W4EMw+gZ2NCmpjcUnRke94VZOHIDq7PIPoPjY2FifvetHAK7Jwyd7NIw2O0YNX9iKbS50wKpw/m41FRyYyLLjnPiiooLyAFyNtBSFTIfbNx3pgEDhIT829kwgZFRdpFd9NCkplmucDvRHFSTk9E/F1u7m5icqsV3K9mgy/6evsSBcHO9zAAKp2zpz2SZ6JjYhyQVKcPEFm4HIrSbExhwMe6817Zg0debGOoBvsiToB/853gMa5nFbg50Bf3pJ6c3MTBWVGINbr9Tim3AMXz4TjJv0HkYM6oRPIQFkD2qGxD4Ia0sNhzr/T6USgg+Lh+aEz2GtkqYuLi+FwXbs+vC5knfDRY2NjWl5eDilbqVRSMpmM+dGJxEMnaqlUCjvzDICCrAcZLxLj5CVF0PFiFt9LrYQsZHZ2NugwR7s8iyuYeEeuYMIuKMg7OHrX9aTDhZjGOWCURP5erxdIFETFzQ3LSFhAIqVr19i0FEYwYi4WmJ/zlMrTRiKRb0zQr3fA8AdkR9cTFWpaLr2zBIfiaaanqTgFKBfnYiluEGDgpthYroWkgsxGcCcqPVa+kUrl83mtrKyoWq0OpKmSBkT6bEZPmZzWGBacQ5ngoEBh3APIwzW+nhrzt0uXcIw4B4qX3onH2Va0gCK38YDLxZp5EYd3BV+JBhe74fn9eUAvLk3iv4NYvTrv2Z4/O4EEEIKNgUihRvL5/EARis9w2+R9eDOEz0f2opvvLe+2I0NFPgc69kwER8T+ge9mJgb03vz8fKT+cJ4ecP2dgHqhVdCc0/CBfpaslnPpkMGxH1y1AbL1QhzP45mppFgDl3Px3gn+bu/YPMGOC1/Fc2LTZHBw3GT4/y+UQuJ9MobfXL+5fnP95vrN9f/nehLh/uhHP+oj2cnlcqGJA7mBMl3ETQFhOLWQHiMPKav0ILWoVqtR0CmXy9rb21OtVtO//Mu/JCTpL//yL/tIaohokkLj6C2laGV96ItrblFNzM7ODsxBaLVaOj4+1hdffKHj42NNTExoZ2dH29vb+qd/+qfE559/3n/9+rV+9rOfhZZzZWUlullAPMyXqFQqoTkl/QaRM+pweXk5imCOGIiWpVJJb9680fn5eazF7/7u7/Z/9atf6ejoSFNTU/r+97+v733vezGVjVTPaR0XloOKQID+PkinqAzTrEBFt91u68///M8T09PT/enpaf3gBz/Q7/3e7+nTTz8NOZsPVUGAXi6Xo5vOER33Q3oPB0kG4BQQnPzt7a3+6q/+KiFJv/M7v9OH56aoA2p1/s2bZnh2Mhbn7ECyjn4dcdIS/OzZM62trelv//ZvE5L0h3/4h304Sa958M5ds0zGwvd5pgDSggaCanA+v91ua3JyUrlcLnjUf/7nf058+umnfWgw6DE6oUBpUINzc3PKZDIDdRJkjSBv5gAzrY1CLwgTipGM94svvkhI0p/92Z/1F0a4mQAAIABJREFUT09PdXR0FBKq9fV1ra2thUrJ5VPQLN5izP16tuz7G7QOwoUeabVa+slPfpJ4+/Ztn8zZuV3qDCgOnObjc10rzxCrdDodiJ99iz9kONXnn3+uL7/8Uq1WS//wD//wjRNsnnS4pF50U8AlwTHCU5Hq4eigBuCS6Gait5k0gkplpVKJohtFIudCqC6yqZDUkFow+tAlGt3u47EwKCUWFhaiUOKKCd9QaANdQSAp0hokNSsrK9rY2BgQqZOuuCCctUK/jPyJ+2R9/G+ceDqdHjgbiuCBM+HZaFEkIKHF9C66dDo9wFtD9UDreAeUdwuyAZwuarVaMYRkbGxMFxcX2t3d1fHxccxzRVrG51DcdGdEIPRBL14QcR6T1N55VIIHdjDMJzstgPPzYqorEJzH5X/jJD1dpVXZVTRwqQROeHl33NwXGxyqgYDC/WLPaEtxuNgH1XXnKKXHUzGgc3zPOU0HDQNdNqw88k4ynOr8/Hzw8RQuXX/vF3poJg2ur6/rO9/5jp4/f67l5eWQ79F1SDEL+/C6Bu+cd+j36Tp0b3Hmfbh0bGZmJuoz1Cr4HHhY+HEHGs1mc6Dox9xg/s7lchG04I1/rcYHjGdubk65XC66snCAtAYytb/RaIRRMutyZ2dHq6ur2traipMPOMBvd3dXpVJJzWYzFA60fA6f+OByJU53QP/IvXCAITIqjCKTycSR6b1eLz7fpVQUt7xZwKVnIGOmLG1tbYXmEYTOS7u4uIj/DR9br9ejoIOToyDCcSOcBMoZXJOTk1HscGPCeYDUpYc27HK5rGQyGagBwT3rdHt7q7Ozszh3zMf2MaeUY4pA7d5E4kUDNsjt7W28/93dXe3u7uro6EilUilUE5wPxbQuNgPcbaVSGeCs0XiTCRQKBS0sLMSmC+M1jo0gC3oE0YHeXTLHO8b5+uUySEkD+lBJEbjn5+cH7oMCHI6CJhskeAS0mZmZAW4XlMZ940DhA0FwrCPOyE8dkB4n5lGgymQyWl1d1bNnz5TL5aKbEuTmfD/ZDcEHDteDJCgbu6YG4RJASbEPCcq8ZwpWOKnr6+uYfYz8yrMTuGDWH47dZwB7/QQNPP/usj9agAFE3sbs60/NqVqtxhB2+HfGDQCgWAsKiwC2YXXV8PVelQJDGnK5nMbGxgJyc5NII87OzmLQyOjoaExj397ejk6WZDKp4+Njff755/r88891fHw80PU1OTkZztPJZ9ceOhIjatH5hvqBn8c5gcRITbzyTnFmbGws6AAiq6slEO5ns9n4zmKxqKOjIx0fH6tSqcQL4l5BorQzN5vN2IAY09TU1EAVnc452l2Hi2agNWYW4HA5mqXVaml09GHgdqFQ0MrKSrw7hn/s7e2pXC7H8Btmn4KkyQAYMgRd4M7Jq/nT09ORMi4tLWljY0N7e3s6PDwM/e/4+MMQczZ5u92OJhCeH5RHcHO9NMOB3MmwviAVNrZXtEHIjgZx3DgZkKGnmK6LZgNDwdAwEJso/XhIKiiKwI8dIXnywjDyKP9eNjQpbzqdjlSW73XqBwTm6JNA9ezZM7148ULPnz9XLpeLFngoLygDnoGuONbIaRhHfj4oHEqPC4dMlgIVhRphc3MzzlLDdkdGRqKwRuGOdYJiubt7OOqm2WyGn6FwTMBwHa7bKh1rxWJRp6enqlQqAyev8LxkbQwc8mIzGRiKD7JnbAPf877rSYdLlMzlcmq1Wnr9+rX29vbixAbpwRky9QcnxsZF3sPRN19++aU+++wzffbZZ9rd3Q1ZGSkrToHIzsVGdN4XkTy8k3eiYJiIs+kmY4ZCNpvVzMxM9PHTWlypVEJ+c35+Hkidi3tgMMre3p729vbiWHTQKIGBM5cIDvy+92hjKBxpAp83MzOjFy9eaGdnZ4BScN0pFI3LVjy9ItgsLi6G0Z6cnOjly5cx0Ht8fHwgnSRwTExMRKsnrausP9w93XRQML3ew0mz6EY//PDDCDSSAqGBJqSHQAaCZaQlz4R+Eo2wO00u510Z3oKDxq6wFd88UER3d3fRhUUw90YQAgaZCSkquljWA5rGEZ8jXumBohsfH4/spdVqRaZB0wWOhxGVfgQ41XueM51+HJTtIIOgt7W1Fdw+ygKoNmzNJ26B/MkuyAwBMT4AigyG6X5crjaBnkLL3ev1wvHmcrmwHTroyM5oM4Y2BNB99dVXevPmTZz8zR7j53wfsB7c48HBgV69eqXj42M1m83gZqkdzM3NRU0FG3WdN9QjtuKNT1CRyAGfup50uKS6k5OTgYx2d3d1fX0dcg10f6TOpAM8MJxnt9tVqVRStVrV7OysXrx4oZubG52dnUXUoWjA8TNcpIikRBQmiJ44YEnhWBhOgjyEF0G3yPT0dMzqlRQDxaEGjo+PAx1KGkg7/A88sPSInHgJID5QDxQG4/hcXA3Hen5+HkGBEws4BoZn8BkCOCYKGrTnghbGx8e1tramfD6vVCqlUqmkvb09TUxMKJvNDsjWEolE9Pvf3t4GlbSxsaF8Ph9tupwCu7CwoG63q729PX322Wfa39/X7e2tJiYeTm3liOmVlRV1Op2gKfz9jo4+TMfCmbg8B9RImppKpQaGZntKDP+Nc/DuQ4of0FGsXyaTCeRGww5O3fl40BL8qkvQcHZQANiac9X+MyAzuH8cFFIjahTwzQzOh1udnp7W+vq6nj9/HkeMS4p3PTc3F626Y2NjqtfrarVaA1IruGGXU/oMEkAL+5tGm/v7+ziF+vDwUNVqVScnJwMzSKBFmNPw7NkzbWxshFTu5uZG5XI50HwulwuKjTUm88BxM0P64uJiYF97ByqjBdirkuK7KpWKTk9PYxZDOp0e6JQjg5yeng76xWee4IBpXCKLAoETvPEfT11POtyVlZWoxEvS+vp6oDMOCkwkEjo9PdW//du/6T//8z91f3+v+fn5SNVJV3nQsbGx4JWazaa++OKLINDpyiGKcwH3QVqJREIrKysh7i6Xy9G9BRokevESiJqgsXq9rpcvX+o//uM/IrV1LS8D1IlkDK5ABQF3TCqIw/eGhkwmo0KhoFQqFZO94HngtBhL+PHHH8dR3ZD35+fnwT1zOTq8v7+PY55rtVrQCgjjOVPsxYsXUehhchTtmVAQjEO8vr7W6elpzM84OztTt9sdQPtwsrSOVioV9ft9raysBNWwurqqxcXFKNSR6mKYtVot3hEGThGFFlpXMgyjGDYnQQc7m5iYGFAeMIDFjxGn+Ds7O6tOp6NyuRxrRkpLtpTL5QaC/9LSUnwPF2ADSgiboPGFPcOmLZfLocmmOEvaS6BDUUMzABwip5aQ/kNtkFqvrKxodXU1QNLh4eHA9Dd0tIAk6gpkLYClUqmk169fx3eRTbEHsYW7u7uBAncqlYrMBzqDtvdms6mjo6M4T4zMZXp6OtaBtJziGp+NDpg9SYMSNAH1EkkRwBqNho6OjlQul1Wv1wNMzczMxFzukZGR4LS94L+2tha0E89+e3urw8NDjY2NReABcMKJv6uQ6NeTDpcXD/8ESri7u4t0udvtBtdLtNra2lI6nY7+b16sC4d5YZ4KAuPd0WFMY2NjA4aIlOT+/j5OnaCllslPU1NTkeqNjIxoe3tb09PT2t/f1+Hhob766iv9+7//u371q19FlZmRayAY7zSiyANi7ff7geBHR0fjhcPhkmqk02lls1m1Wq0YOE0HzsjIiHK5nD744AMtLCzEFHtSGDqLuChYQR/gyFOp1MCaUIQAEeNwKfwxowAOlqIE6gAfzsF7AoWsra3FCR75fF4LCwv6rd/6LSWTyahSo1jByKF8KJhIGpjpQFWdHn8cDJ/FjALn9lHOeBGMf6NQUqlUAsF49xvcNWgTJ8C6UxjzYUGsMxOmuCjmgHzo1qMNlj3jtA2bE8rEG2zYO6urqxHUqPwfHR3FekIhSIrhNmtra/GdIETszGswZKCsnXf/cbhApVJRsVhUs9mMTIvuP5QjBDr3GdlsVh9//LG++93van19PQp2d3d32tjYiGyFe6e4lUgkosYCVck7RXlAZkgGREMIRWlJsefPzs5UKpWiXuFzMqgTSA/O3bv+GNnJPAp8TiqV0ocffhiqJ3h0kDuB63/tcEntaAulEFQul4OPgThPpVJx/PFHH30Um+b6+lrHx8fxuzMzM6rX6zo8PIxTWm9vb6NnHkTn1Wh4no2NjRgIA5KiKstmyuVywYdSRQVhZbNZFQqFQBq1Wk25XC6GYjM+DgnXsIYPwhxnRtEKVFipVIJm8PSMluX7+/uvoWbG5uHsvboOp+drgTPFCc7OziqXy4XjooAErz03Nxf3gbqiVqvFKRxw6O5cSbO73W6MOWTGhSTt7OyoUCjo+fPn+ta3vqVEIhF6W9YWjjidTseMC6RNnEVF8PApb91uN47Optefws6wQfvcCW+7dlmXc4qSAoEi+aPgVy6XA5lCWVGoYx4vDooZqH7hyKQHNIbag4yN1JbC0/X1dQQ13k+32w2HWq/XNTU1FYd3zs3NBdBpNBoh+QK4IMFkJjVrA83C8HHoCtZnWF0BlQFgIChB1eGQ7+/vo6jqwYcC+fr6evCwyP5Yc04HZt1weki8KJq32+3IDLAxD54UwjjWHnDEO6YYOzIyEtPm+v2+5ufnI5i12+1QSVF8J+MCsHQ6D6NanfPFGQ+3L/9aDtdbKklHksmHc+zhT0GPDB7/4Q9/qLW1Ne3u7sYRJ5zCIEnZbFZjY2M6Pz+PQhnQnsjjWlNJAxwdf0MfeK+zOxbuz+VMt7e3Ghsb08bGRjiUdDqtQqGgUqkUQYIXDLKQFJvZT73FKVKVJa30WZ+gRSr0pJLwhlSNE4lEOFw4UpyUa09RhiSTD8NOGF2Ilhj0yH9nw5JCb25uRn8/QaDX68WmJk1HEkQwo+IvSVtbWzGwe2FhIXTTp6enevv2rUqlkiTFDFc2J3w6QQVnyZqDInGsqVQq1oQCq2c+HKcDMkUPy4nPnU4njmDiPfg8DgpIFG/Y7NBTCPfhP9lkqFjcPnFgqBkSiUQEnn6/H3LH+/v7oCOgahhIRPH29PRUBwcHqtfrwaV6qsogGLIL1oJiMaNQoQhwtIAesgWQpdsVdgJISacfT7hAj+/jFOHQuUj9AUVM+sKRewblKguygNvb2ygkYifYI9kf74jiXbfbjSKfNNheTBBC3YFjhRJEg4uNw9MT4PAx7C3URNSnkslkACMKcU9dT/5XClQcAzMyMqK5uTnNzMwMaNE6nU4USl68eBHFGd9Q3W43hsEQzUlheSCXdw0L3P0F89KQZcD7wC+BovhcNiOcEChlcXExItn8/Hyc1sCxI6AR6bHn2gsxzLflyBh4T4pxODqMmEKKd8bgIO/u7uIQQJ6dYOSbe7gbisYBVBik8iCaQqEQx9SjLNjc3IzmjaurKxWLxQEtIZsWJIcCA8pkY2NjYIoXzplsaG5ubkDHPDMzM8CvsrFxnsytoGrO5xFMyQpcSoTjwcFhR6ST3hXFEdeSBqr6/A5ZCAiI5hscoqs02FBun4AN3ku73ValUolg7+oRqt7cD5pa6DIQ1unpaXR5UdCCultcXIyjgrB9Aiazp3HS2APPRlGVZhwoPnTBUA8gc/hf0C+NQ96I4k6Gd8S7xWZQgHAvFOFQGmEXPspTUtRTAC/QCgxVB7h4IwYSzpmZmdj7ZFRQMRxJD/cKrcnzM8KR4E9HKFK+ZDIZ9CIa8uGi7ruuJx0u3UtEfhaSyMni4elBPefn51GRTyaTUZ1FUgHvA/EPce8jC4cnMwHtcT6eNvJCaQklartTJi3nLHs2/MLCQnwmkZtJTlAIvERQLVGbTYEBU/0EafACeenwXkRXb3cl+g93vg0P3nbBOr+HYgTaxC8kcKwjEX9paUmFQiEcmp/SgcZ3YWEhnEWtVouU2Y8PYtQmqTEcMhV7bxZhYA3OGUfpjsm7utiYvEdsjwt1AwoPCjE8L/eFbblsCwqAYpJLDxcXFyNQI93zSXi8B19jimRsUjal/ywD+ZF08e5wwDhVTrtGfC89joDMZDLK5XJRlGSf+FHhFHxxuHCNFPfooOL5veIOmsXpEGxYS2+HpyDqF4Vy/zwoMN47jg6fQPec73lXAI2MjMShsL1eL7IO1BS8M947Q5IIruwnQAXgg0AxOjoanC72xHuh4wxES4EP7p0W9vv7+/j9p673IlzSxXq9HgckcswLUQxHOTk5GdEdnofNxx+gO5GQF+e94iwSl2+Y29vbeJncIykECIk//Dc/4BJnhYQGlIOzZWHZxCBQN+RisRipDYJ9eDIfmI220dsWMVYiMsUEUAbIjtSVz+LyLhycBqgUx8yGx/nwXBgXGkwMdXx8XOVyWff398EF0p1G0AERSQ+VetdmelUeRAViYCyhz3YgTUTTzKZyyRLvzp0BKI3Lg5gjLW/dbbVaqtfrkR4TvPkcHKqkSFld6kMKDdoG9blOnPQ5nU4P6Hb5fNYEB0amhAPjc3mvS0tLYUsoaKDboIigCdgvOCH2gf9h37CXob94jpGRx1nD0CaoP0DgUEB8Pmh+eDoWTgenDYDwrjT+MBtaeggYAAj+oN4gm2YtuSeKgwz89/fvtRDuVxo8fbvb7UZrsVNH7DGet1arqVQqRdba6XSiGAw4w/a9IeZd15MOF8OkiACJzcvmBfOykGi9efMmTsaFk+z3+8FzwFWxyVgoIhqLxEUqxIuiMOJaVD6fIgkbls1P5OK5eMkYK07o+vo6xPc4HDY3FVpkKS7eJ9ouLS2F/Iz0h5cNssWhcC+kwDyrpHAyqCO4oF3YzM7LsZkR+vMzrkPGSVer1UBM+XxeExMT4Sh8psXV1ZXOz88HtLOgbm86wUao6r9LL4kT5PgZ/66bm5vgeUHxODkv6Dm3707KnRcomSPP6dpyJ+sFYQJat9vV+fm5yuVyqE8ILu7whwehe2FpcnJSjUYjnoOAAXLle3infB5BjRQaFDw5ORlOjdoAiNuzAYICIAlFERkm6BZHwYB9dKPT09NaWlqKP/l8foDzdIQPUnzX3ABQNs9J1gJo4p3TkHB3dxeUEb6B/eTzO9izIyMjQXdkMplAx6y3pIH9RgGQbBhni6wMnp59ygB+vmt2dlb39/d69eqVTk5OtL29PXBIqiN0z8C/6XrS4bKoyF1wfJDGICmiMMJ9dLE423w+H8779PQ00gVPldlgGKjrLZ2f4SW6hAc+FKQFDcBG8BmYIEOfLoUDvbm5GSjOoQ10Y6JqjcF7KkrK6JGVrhYQD4bDenkWAMfnEhicDxeoLpFIDCAw1pAq8sXFRXCxUCHtdjvmN9ze3ur4+Fijo6Pa2dkJGZWfX8Wa0EHH97Ch6NKC04ayoEhIwGCdkUzBm4LksTG4NDYL8h/eG/bHBaolTQWpkuZxmsDl5WV8J++adcMZkqJeXV1FuzYtrFA10FPSY1bHO2Eok7eXgmJZc4qCgBOcIKdb4JQymYw2Njbi/QIGCCzsReyePYJqp9FoDDQgDdtapVKJjlEUJBSYlpaWtLW1pbu7O+VyuQEKBjAkDdYSfB/zLiRF8wD2z+/g2Nin1AL8mbrd7gBF4mtB9kSwx5lCIcAtM4KAd8Iaegdao9GIeQhkJ55lst9ubm50fHysdrutjY2NsDWnVvnz1PVeh4thUyzyP91uV2dnZyoWiwMvdHR0NNKinZ0dffvb39bExIQWFxf1r//6rxFpKbZRhcbBk1K4QYMySEtxsKArDJmGgbu7u+gcYuPizNnEvGxQA+k0CG14c8OpOtcDJeGGMDY2FoZ/dnYWUZPnggOnKINR+rg5V18MO1w4KI4wB9mggUXatrq6qoWFhShedjqd0BWy+Y+OjtRqtbS6uhpOl3fpgcy7prj4Tq9o++ActKf8Luk9NAQa5FQqpdPT0wH1AlVrnMkw0pEUhyViq9w3QZ/TI9LpdKSDSJOoN4Cu2DiFQkFv377V7u5uHFsEMkMxMawHlhR2BoJzh8uGpIDMvVEdJ4VnTefn59VqtZTP52PYD04A4IAj4T5cBcD+oJAKVQWQQY5Zq9VCOcMa85lXV1cqFAqRdvO5ZBH+LtzJQJ9JgxPM8B+AJRQkBAOc3du3b3VwcKBerxcUJrUW6gbYKL0B6HcBR54JD2c22AqyMUaP9no9HRwcBOAAbdP6D70DSGHyGX7CfctT13vHM3p0oCoIamMYCt0bSJhIk9F44mi4GVARFVmXhBCtnHxmA1IAYUF6vV4sEDwZi+pNCtLjiRQYVKfTiTOj4KWpULJwfs8UexjTBoIDIUA5ZLNZTUxM6PT0VKVSSaVSaWDkHdwxPBzZAxkDMhsn9T2NBu1Dk0BTgGTK5bJqtZrGx8dVqVS0u7sblX3ODEO+xpozkGRtbU2pVCo2Kxtb0tdSbzYzWmJHi5x1Va1WdXR0FDQIwbDRaAQFQUWdIgc0Ed1gFDkIQu5waQrhHUG/IHin0OetwwRFpEukgjiVfr8f3ODZ2dkA9wfvTSrK1el0Is0H6ZK2+rsjgNGqCn9IsOd3er2eKpVKFDR51875QwV4URcum3mvNAOlUqmwObIMpzwoKqMfnp+fj9nKgKNGo6G7u7sBaR/P5oDA7Rg0ix3Rzce7oIhaKpV0dHSk8/NznZ6e6vz8PPawjz8kuMLRotIBPRMIHBTyjOwjbAitbyKRUKPRGCiA8zsjIyNaXl7Wd77zHX3yyScaGRnR7u5u0HGLi4uRMbmS4anrvRwukYmFQ2nQ7/fjpjlobnp6Wo1GY4AbwsAkaXd3V69fv1ar1YoCFNpYbxggxeDCmIjgRCR30l5lXF5ejv/mY+780DoMwKkBjMiLGBgVeuPJyYdjbQqFgo6OjgZ4OhAySKpWq8UmAlkWCoUY1tJqPQw9J6VkzUGvbDA3aFJHkA80AO3KHLF9f3+vr776Si9fvozD8giaBEMQMtXiVqulxcXFQGegETrm4I7hIH2NoTmQ75RKJX3xxRd68+bNgJKh13uYFAafD9eLM6LaC5eH4+dePOvwU5j5GRwuvDOZBGgKnpgAxRxlmhGmpx+OZd/a2go65fT0NNYwnU7HWnFxD9BivBfoJu4P3S9dld744HMaKAhxn+w3CnOgMzI66THzoeuJAzuhnrLZrBYWFoIjpiOMACE9Ni1w/wAZKEO+z5UVOFn3GSiUCADYP/QAe2xnZ0effPJJADfeOTM6oHmwORQUOFDWBVBAMHalj/SoVoFy4dBVBv0wiQyfgASRBokXL15oc3NTq6ursa/RMSP99MLb/9rhYvA4Q08N4UppiKCYxEakEFKpVPTy5cugH+idBvGAOEkB5ufng1bgIuUD8SBIJkXBWOFecUagYXRyZ2dnUVhCDI88pdFoDHQtYSgYI0iWzqutra1Ix0llcCS0Hq+urgZfRPq8vb2tfD4fnTNwTThapEIuVndUR+T1RgQKRGdnZ7q5uYl1oOBFgdIr/vxNQbNWq+nw8DBE5Kw/KMIHraPf9PSQTZ9KpdRoNLS/v6+joyNVKpUYPMIGgBaCUnEJmnOjfB8pm28q6RHROk/IWsDtYa+k75OTkzF3gtSaNSaQzc3N6dmzZ6FRxjlfXV1FEcodP87X5YgABM/MJEVn2MrKShRl6NXHzr1IBddJKo3Tw+Hynd4myzrBW0ODoWogEMDZAhZwblASkiJroRLv0ir2mAMCfsYVK6BzDgRFI89wpLm5OV1fX8dsbfwEDp+mA0fHwz7CC1bv2i8gevY6zTiMinR1Vb/f1+zsrNbW1rSxsaFCoRCa9IODA3311VfxPmi0wgf8Wo0PpLxuVGwyHBB6Uyr4/G8fGEFVl58HCVLkIdVlA0L4czlPkkgkgrslXcIZY5AMhkEq0uk8tJtCvk9PT+vs7EytVitSE4wMHvT29jaCjKRA7Ix03NnZ0eHhYXCmzNJNJBKB2pHZQGfwwhm2Adqmcgwt4eP4hl8g6NtnAvM+ut2HVs2VlRXNz89HQOS/0YBB0wNcbrVajcYUppVRJadSDoLFuEEp8H7wuHzGycmJ7u7uND09HQGatJSNQZskp9pSOca2WH/esVetJQ3IjEBgoBtoF1JoqBRJKpVKqlQqKpVK0cBBOg2Xu7q6Gnw61AX3Q7Dh8m4pSQMyQ94liJEOJvTrICmoFZAy+wxHdH5+HsHFeUl3tNi6UzJkknD2fvzR+fl50GPQKwQe3mu/3w+H5AOaKGL5wZa8E9aAYOP6YvYu3Ct7ZWJiIgbG+BoQDJlQCJ2A42f9hw9wZD2csuEoLqgEVB/r6+saHR3V1tZWDPInmOHsQe5LS0uRsY+OjkbA9NkoT13vPbUX58XNgxqppnLsNYuODpUz6PlZ19OxYa+vr1WpVCKKk0K7KoH7IG1jA+K8WHiQBwuZyWQiPcWASI2Jvrwg+EEQOT3vFBt4qWz6ubk5bW1taXt7OzYDkioiqLcugoLPz891eHg4UPgDrfMcIEl//uECIpuZgTT39/cRxRkJSWBYX1+P7IGIzLQvlBik82wWF7+7rI7gy/161ZchM3C08Pq5XE7r6+sxDIUhIxRWV1ZW4l5waEz3arfbUYzkHfpacLEpWq1WZAlQMqT5gAevkjcajQhyPlsVyWM+nw8b9Q5AuEAuWjpdB4r43iWG/GFP+UhJshpv7sDOl5eXdXBwoGKxGEN2sE3shJSWzIxAw2c1m81wtHDH0A1zc3PxvdQYOI4KG2CYCzQQLfjUA7h4Rm+W4HNRApTL5Zhk1263Va/Xox7BvbF+2KHvB3SwZEPDAdU746RBGSDvx4HW5eVlFIxRIlFQ63a7URNhvRcXF9XpdKLJg7Zwfx/fdD3pcEFStI66HIsFRYJyfX2tk5OT4FIxdulRU0o1lM1B1CCNh1cc7ihiwdhsVH8p2jQajUjPMUQWjvQYQyQAwE2yQUFpFJDgc7z7De6SqvfOzo7evHmjw8PDuA+aJQhWtO/SKkoQbaj9AAAgAElEQVRBBcRIyoPUDIPgO4cvAsfo6Gg4CZC9c85EW2gJ+DQ28bCBkhEQlNzxsBlApmQX/P9+vx+DiuDBUSvwmcy0Zbwg/Nfi4mLwlVSej4+PY5iQ85esFRc2htPnnnAUcP+ZTCZGR4K4cIZzc3PK5/MxiAiUx7tyOoVnw7G5AyAAucPjHuFXycIIHgReHAjNEVBCNODg7FEckNJDDbmTAU0jyaN2Ua/X48QEshECE3QglFM2mw25FJPluC/sHxnbcFXesx7XffPeS6VSjIz0BhMQORQm8yVcAQRVSO2AehJT5QBQrL/bPu+CsQQgXwY1AQKhKKGnaBbx9wbthz+DXoBCeup6mnD4n5ukUYHIQNUbEpqNixiY1Bri2lP++/v7aJfz9I/IilRnGOGxcDhTHBSfh+YPHSuL4FwvL7/f70chgXm/FCNwHK6xlRTcEU4ULndjYyO0xUwdQttLceTq6iqQNs7VJ6PBYzMcA86VNR52vDjkZDIZKIlNenx8rDdv3sT7cmdF0CJj4X5ov93Y2IgJT1AgOHa0yzgYAiUbgjZHutfW19e1tLQUDRIuZcJWms2mxsfHY1j5+Pi4VlZWNDs7q9evX6tSqQyswbDDxbiR8JBNsNF4VvhuKvWAApAtcqn7+/tIuUlRUWCA8tkD7nCH3w9cNKkz6+91Ae4PzlNSUCPVajXGoqLgAUhQYE6lUlFclB4or/n5+a91U9JQgW2TibGHsEGcB4AI6ZQ3KOGQ0TBTtPSuu2EkSTZRLBbjoFGaHbLZbNgVBXjWh3Um4BEcAAvn5+fhcLElz1ixd1/3kZGR6HzEF0gPJ9Pg6LEhOl85eZsi58rKSowm5TtYg18b4cKl8FJJWzzisDBLS0sxIJiUB27l4uJC9Xpdp6enKhaLkUIw9IPUA0kKyMQN2ivAkgKdOK0wOTmpjY0Nra+vRycTBgfKhF9EToMECM6QGQouB5IeC3egORwng9jd6HK5XMzIBXUTCChOMHOXFPPs7Ez7+/vx3aR8LiTHoAlmODwGzSQSDxOqDg4OVCqV4nsddeCIcWJTU1PBnTF2kcYIikSSBoTqBC9QEh1JtMVCWayvr8dGPz09DekN2cPs7Gw4NZwSiMvfrbd0OoLAaYH0obAo8KAqICg0Go3ofnNeEUTMzzKz9ubmJuyUqjyb19Novyf0tq1WK5pNcBrMHHFOG+eOU8XWcrmcarVaZF5kZjwLFX8QLm3mgCGoNUnhDFgDeF4kXk53gLilx3Zbf16CHjQOxUd/ftePU0A/PDzUL3/5S9VqNa2uruqjjz7S9773vZhDgTQMx0nNCAkoPoduQD/XDG4aTpfAga+A0sNWXCfPAHNOnEayeHl5GTKxVCql+fl5bW5uKp1+OGPO0Sx71kcOfNP1XkoBdCk96g2JjFTuSHNxwpDaGDFRCUObmZmJdlIQJykt1VR3uKSMvHwWnw4RUt9EIqHFxUV98MEH2tnZicHIoMzz83Pt7+/rl7/8pQ4PDzU1NaWtrS1ls9mIZD45nkiL8XEfjJbEyYMSMpmM0ul0pKc4U36WFJmCBQWVdDod3VxeccfBuMPlOXHkNBAwVKNQKOjm5iYCF9kCSIDgBPqgkAln5y3JPlnJNxbZChkACEh6PG0WXaK/G6r9bCJ4VA41pABLowHon+ADTcCFk6BLCgcOXdVqtSIwc4aWH06IVA8Hz0yBYrEYG9g5bHdOwx2I2KlzqiAvUnvmXdRqtVBxgNSwOZAf90qwgQahqASPim00Go2QXDlChA5pt9vKZrMDxwBhl9gdgdmzP9q4yezgcUnj4ex9rwLUvOMLW2J/PHv2TD/84Q+1vr6uXq8XDrdYLKpcLoeenCIXJ0/gS3yMKooG1gJ7JOBQ+ASk3NzcxL3v7+8HLYg0DKCDP1peXg5gRYGR90Aw7PV64f/+1w4XVOnIwrWh3BgVbg53I/VyXdz09LS2/meOKg0GFJio2MIbOl/L7xN1hyM8zokIf3R0FJpJOGivlDLLl+n1i4uLur+/j4E0flQKm11ScDZwOsxzcGE1L5o1AmH/X/bOnDfONLv+pxayuLO4VLFYXCVR3ZqexTNeAAPGfASHBpxM6LEDw7AjAxM6cGYDjgz4Gxhw5GAiOzXgWY1RT7eglkRRXGvnvtX2D/j/XZ73bTVloOGsH4BotUSy3vdZ7j333HPvA+Xhh9R7DdBcBq2hdH9FEOEeg0wt6IHNhgNEboSMhivPHaEzZ4RPyIfoOIaxIJnGBvWQzdfGlQSXl5dqtVra3t4Oaodr6RG2Y7yhk0qlkur1etwqgAEjWvIWf2nOkEE1EMiR0BSueG1tTc+ePdPq6mrivi72XTabDT0oYe7Z2VnwohhbaCynFFgTnCcHH2306empnj9/HsAFB0aVHg6FdRsMBjo7Owtgwa29XJBJroPErKSYX/Y6PC+5DGgFAAqUlysriKb4GSRsJycnIaXCLlxdXcWNEG5wCeMx5uxpQNbl5WX0h9je3g7JG/eO7e/vRzTU6XQiEuK9JQVwSWu6mV+nEyRF0g4wxcWXtFPd2NjQ4uJirCFOenFxMSoSJycn1ev1QjpGlEdU5ef1ofGgwQWGsyA+mVh2dIEsvCMCz1jiHWmKwkGGsCasY5Jcj4kn80kF1eFtJyYmQh/aaDS0s7MTncVQIHiIAApFN0rnIvhGPLQnsEiWcE8WfybzS0iNsQdlebUUGxoJncuMnOinf2jaazKvhK7cKUZSkiKC4XAYnYzgdzEGHkI6eqOHAtVMGGTWyRvigJpANOibqRx6+fJllKm6SB+9sGd1x8bGdHp6GnwrKAI5oDs9N7iuqshms9E4G96asA+x+tv/f8llqVQKg+uNjThA2exd6S+0lCthJMXfMeA3vWTbo5qzszPt7u5GyTU0BeXv8Mjwr96vFuUJ5wMOlbaJnAdu0cAY85xELIASHAotIIkqvF8t/K6kREKK5KBftEofaIbnW8ifSIqG+kS4t7e32tnZiYQziJ/LHjFqi4uLcZZcCpbP5wP9gtD9rHr+CBoEEIXemnmHfiDJTGTPXDqAZL9CQWILKRj5WjpcyHk2sG9kPshDBpIi3oGHDUcPWbLnHHJQDm3hOOyOIDC0bHImZHJyMiG05oBks9kovQQhYRSRUBFKZrNZ1Wo17e/vq9lsBk9GuO7KCGRVhIUk/2q1WqIbPIiUA8RzuBQo3XSG5AgLzwHjMDNwfB7C12o15fP3d8jBz0Gj0BUftMYh5gp0NrRL4Qi9vMyXw43BxehSfZTP37e0BEWB0Pj/lZWVuBSRpBLPk8vlEr0biKSgrdIZYA6Vh9DFYjEcHUkess4oH05PTxMIl6QjHC1c8sTERKIm342v70+iMp7Tz0qv14tCGP5teno6JHE4Xs/Se+IWxJU2zBxsnoOqOiqfcOQ0ZGJcX19rf39fNzc3gXIxvvPz8/FcDhigRLwcHETK7RgMIj5QNucBZ9/tdhP6ZOgpVBDYAZr9zMzMJFqUci5B6+74PAqGvpDuIzIHB66T5hlubm7iYgEkppw19go/71JKwBRn+X3qIsYH++FizDyJRJaWyUNKQ4iLsN81uH7g0bxxaNlUJF8IBxmObL1kD/kYE0eCDKoCXpYNjIMYGxuL6qZMJqPd3V29e/cuQhjQ4+3tbaK66ubmJjbocDgMzgkOzlUSvmmk+wogNgFzAv3A4vJ8UA6E1wy+j+e6urqKO+YoHCEbOz09HXe4keAi447mFD0yISnv4QbYN6mkOEwYb1AbKPrs7EyHh4dRQsoNqRiEUqmkk5OTRGbaC1i8NNa5Rad4OHxe8YWzoQcAzwX3ixKGK1eYX+eYcSwYTw4jn4OT8IPOGcA54pwxztTcs49ZW+R3TvdABXmEA7JnbzrH7s8FqAD9srY4QL/lhJAamgCD5tVcLnnifYhgjo6O4sqg99kNtwuucED1AG0Bj44ahqQpVAMGFxURdAyKINbMBzYEHbLngKR7nTRnjzwFJeFUIg4Gg6AhabqPnhuK0IubMPQPKRU+2C2MBydTCDrlwSUlMu+Li4tBG9DBxzPlhP3wfiAsSGgm0CeRQ++G3jcKiJew0KtpCPVBn7lcLmQmPMvbt2+DMMezS/clmtJ9NZHztBcXFzo6Ooqa9XQyCcNJ02JCHxwYSB5j4iE2hj29eG6cOaAe7iOrco7Y0QQHgfCu2Wzq4uJCzWYz+jCA8OFOkeA5d+uhGkUYVEv5Ne+1Wi0MNgcGxOja7tiQ+fuOTqwDf0536MKgsVcI63HoPD9zjdwHlEhYCJJ2g4YRlJKFDpwHV6Ww/1xsj9HB6E9PT4cxd5oN0b7TEERgqCi8kxh7kHdiwM2fnZ3F1UOZTCZxpTpKlvHxu6uXABQUydDEifXGEBG6X11dqdlsRmKL66o88uA56KfAuWSfkAzmnEE/0D8Cg0VSjN8B3cV9ZkQAzCkOyeeJ84ZdYB/5XGMXzs/Pg1ZE5cK+JqnMvEL7eAQMsmX9vmp8sHkNxo3DAaT3SZMUXAeG08MvMqHAda6kkO4lNfBMGF33nK6JxYugWfTQQFIkTTxpR+LMhe8sIk2Eb27u+/Ny8HguXzwytjge9LfwZ9ANHGYcAgudLm30zDsbh1DPi0cYziOCPrzdpf8evDt/9rVBStPpdNRqtdRqtRKIk7UrFAqhFmBN3FFIimw4cjBvdI1GmaQDnwuPTwiKkcSwOSfHe3FAGewtR+8YP+fdeF/4fu7MIkLCgUItON/nvCGI6n37k7VxvTiGFcTM+/PeniMB5fMcvIM3iXFNdroxPYYApwKNQPQyMjISbTMpMun3+5F8pmqKvUpPCi5WJdHJXnHJXnp/4tyYR8AWgAm0DMWHY8CBuHNjrknyumyTPYjzdDUVn0kUwN7hi9/NOmDoiTqgLGdnZ8MRoapiz3W73YQMjHnDQb5vZNIH+pvxzfhmfDO+Gf8340GE+5//+Z9DPK+Ho5ICpSB5Arm5nChd8gi8d34MdIN0Sbrn5v7sz/4sI0k//OEPh/CveDbpPlSE7HYBvRP1cEaO+Ph5T3BMTU1pZWUlOgTRqvDHP/5x5i//8i+HeGs+E+Tt3CKeFTSS5gDd28Mrgmgh8PHAeNJer6e//uu/zkjS9vb2EJE8z45nBVkSBoJM+H9fD+meD+ZZ4A69bwAIBtrnr/7qrzJ/+7d/O0wjTb/GJX13ls8Vvwu+zhNXLsb3cJbwfHFxUZVKRX//93+fkaQ//dM/HRJFrKysROcnj2icc0XDzft69R1cMfPJM9L8BWQIFXV2dqbNzc2MJP3DP/zDEL6ekJKIjXJjR2NENemOb35WJEVE4qiZPAl5h7OzM/3rv/5r5kc/+tGQPgdw1NAizL3TNJ7U9AIIzqTTRZ4Qrdfr2t3dDRUQofbf/M3fZCTppz/96VBSIEFuVkC+SPKNfUFiD1Ts55fP5hlR7MAxs368R7fb1V/8xV9k/uRP/mTIGlM9RktGImzoKbqpLS4uRsUjkQ6Xwfo9jqw/85iOJq+urvTDH/7wK7NmDxpcT9C4NjadhYXTcgKbjeQhIguPjo4NiBHzjKEfaA4ML8ah8Uy2N12mmILvZ/HY7PCROAHeh++DwyP5gJHwUMc3SNqYIhuBV0pzTFAJGB1PoGAo4Pf4PIaHRv77CaldJ43BgX/z1nx8fz6fD90ylBBUhtMW/hweFcFb8e/+vtAuXiiQyWQSDVIoseXdyYzDTWPIOXQeRjutJCkSd+QFMH5wioSNXljjYSY/wztDQYyMjETDdKd/4hCZaoTfw+Dz2B9eJswB5kx57winFAjNJUX2HJ6eDl78bt4NpwA4QEnE+sMrU2Kept3cMUDV8LlQHmlqSbpX8zjt54CMf0fB4+oX3sN5dHTS3r7SE5TMsc87Zx96yZ8LWSYUmqssaEWJ4ofLCegHQtKQxDK/i7PjSqqvGg8aXL8cz8lwPrTf74cX8sli07iMyA8IJb9MRDabjcYYnpFlwB+zEUiMkKQhGwp/S7KMBBCTz4S6/hKj48bSO99jZByJXF5eqt1uhz4RA+/qBP7rlVbSlxMseEwUAc5DUqX3vg3tlTzMFSQ/aIY+ABxovphDbwXJPLsUkDn3A8eGdokeKMKvOuL5mQOMFXxvpVKJIgT4WyrP4FtxtBzUQqEQ1YOSwjCDLt0Zs488IYZjxUk44mZO/D1Zd+fZ08oIfh6nki4NJ/EFOnPjwefx8y5xY54xxPTYwCDCu1Juzbu6pBGFEM/kz4Vqgao2VC0e9XAuQPuoifr9fnC76bmg6gqOczgcBgDBaLE3QbOALPY5QARngGSMlqGunEDG6NIsQIknsaempgKlSoqeL7VaTZeXl+HssXvw3fl8Xq1WK4CLzz9RgquRvlbzGg+TCOk9rOcDZ2ZmEiEthQqEJ/wODgSGD+/IJKJG4IAySKZx+OljCTqkLwELTtOJTqeTkGsxOV6pg9zJQy2auXBzA3+P10ZzyybG+fCMJJoIRzm4IKjhcBjJOpAsKhCST+VyOYyLz4UbDzd6oCWqepDZMfd4ZNAFzobKG1BI+vMoWaYdH//OYRoM7ltQEoUgk2G4AiOXu+vetbm5qY2NjdCN4iz5OVAYTURAa45wMcw4O5eykagDCRIWE+Z7o5R8Ph/7AUTnahD2JyoAEka+Py8uLqKJO6oD1AYkE+fm5jQyct/zGCNGZMdcIfGiZwB3s+EMQXr8DkmJxjqOpCk+6fV6kSQmQUX7QadQMKruHHAWOKaFhQUtLy8n1otB4xtAFJWSntDjGZhbnKADKcJ5Gg75jRvQEKB9zne63wM2BrDIO87OzurRo0dRCQjNICnOLNJPL4hin6HiQN/Ns39IoSD9Lwwum4kHwphSo03WHukIxllSgq9lUQkRQFsgGfpgsrheXeWIjywuPTxp4kzVDoeu3W5HxYo/j4fMnU5Hu7u7UUmVyWS0sLCgJ0+ehKHxDDEbKY1cMcJuGEBmGBk/uP1+PzgleulSVIGGlPDPDyPzj0HE8WAMnc92KoB3R2kBt0bIjuH3EAl53WBw1/yZa7Ole6NPyN3pdELRAKLjzjYQPMim2+2GBGh5eTnaS9L3gPenTSZ66qOjowT/zDs5lQKtxCEYHx+PpkqVSiXejdAPLSVf3ESA08dosc+c+3VJFhWMNEDJZDLR1JxKLow9uQY3RjhHEBvRgmvHAStOz7geG+SJDJHP9eIZnBrls1RzsW+QTIJ2kYbRw5ZBKevs7GzobBkgbgqLAC5QEGdnZ6rX62q32xE5oBfOZrOBurmKqlwuJ0q8kWfSWCddZi0p6BIu/aTcnP3R6/UCMS8tLWlubi5yUThpr8zE2KNwQRaHFLJcLqtUKoVG96HxweY1fABXjcDToq1ksllMLoj0Kh/Xy7lGjrJBNhYljysrK1paWornSMN0Dgce5eLiIvSDxWJRmUxG5+fn2t/fT9R6Y7hubm4inKBJBlzO0tJSyLkWFxdjQ2OkJycnE9Ijr1JBZkXduVMsLBz6V6gOFgwaA70mv5Pfz8BR5fN33dVKpZLy+fvewI1GI64oIbkGJyXdIUeSnY1GQyRaHj16FFeJ4Ixevnypd+/e6fDwMJCPdH/FDjwY1VT0lvWDAvLwYhSKO4gkSDbQWSvtYNnwNDn3fYHhYl94G0JQbqVS0dbWltbX1+PWAW+IThgMWiaJSdJqZGQkUCr72REu83lzcxM9G54+fRpSIlCZV+1R9UazJkAJNEAmk4mEDe9Phyvua+PqIklh1HDaaLDZOxSdsE7z8/NxnY3nI0gU5vP56Dz3+vVrnZ6eRtUgkZ9XVPpcOGfNWWTdiHRop4nTwMHNzc1pY2Mj7hCj6x4aYGiIXC6n+fn52Fs4BkmR5CSJSnvIRqMRe7DVagVQg264vLyMwhlu6/Uruq6urqLoA/nXzMxMtGx0e/FV44OFD8fHx3r37p1evnypZrMZ3vHm5uZL4YdX2cANsbmazWY8PJwcHo+fwegQXvvB8uzm8fFx6AcZhD50hlpeXg7ECxc5MzMTdfUXFxdRU49xQ8jtt5vyHISZPANhiPfhJRHGZsM48WfmiDCHd/J6eUJs70+R9uDoJwlj8LzQKa9fv45+BV5lBTc2OTkZiHFiYkKbm5t6+vRpXJJ3eHgYyIW5p/kJw3WICwsLWllZ0drampaXl2Ne4cVA1KAslAfQTJlMJmgadxQgddYxk8kkih+8pyuJC3SXrCdrjZEslUrq9Xra29vTZ599pu3t7UCcpVIpVCpPnz5VNptVp9OJ63harVZwp54YY7+ur6/r2bNn+p3f+Z3gV3EwhMs0kgG9cwcdaBIE3G63dXBwoMFgEO0Mq9WqlpaWwugWCoUoYYdvZB6IuphLIhi0ttJ9q0++l94jhUIh9hNniwo5kCv63nSO4fz8PPIiOIipqam4ysjVBVw6C8dM83OKPobDYXTmI3lIrxBUM5IiWgP5U51WrVa1sLAQt2Tw81Aq2AwiJJJtIGRsAx3f8vm8NjY29Pjx40DcUKhHR0c6OTlJ5BjeNz6oUmAxuNqZ8FdShDsgJRIb1D5z0LmmhHJOqAVK5zjAhHscNgbeHzScz+djUUA5uVwuUF6pVIo+m3CveGdIdv6ehQDJSAqPRzaWzUnml2obGp04XzUxMRFGBUN+e3sbVSpEDfTlhYfa2dkJeoBL/2iN5/QKkrHr62u12+0II+G7er2eXr58GQcCFI3x87Jh0DPZ2pGRER0cHOj169dBdWBUPbMP+uQ+rEqlEmgEeRdUAqExlBMJx8FgoGazGSJzumeBAF3tQDQ1NjaWMPoYKiKH09NTvX37Vq1WK5wEexV+EmfXaDT07t07/frXv1ar1dLq6qp+8IMfqFqtBt8HZUE4eXJykqhW9AGSLpfL0R3s+fPnevXqlfL5vL797W/rBz/4gR49eqTZ2VldX1/HNexUB8JxttttvXnzRj/72c/04sULzc3N6fd+7/f0ySef6MmTJ5FoBKliZNzwkWDs9XoRiXCmQOmu0OAsQPkRdksKp0ioXSgU1G63wx44IPCrczCmlHY7fUG+JpfL6ezsTPv7+wFuiNoADblcLpzN5ORk4vJQDDE2grXw3hNw8VtbWxFBAgChIEGxTsux7iT5s9msPv744+gffX5+rlevXsUNJfD7D40HDS6hzPj4uFZWVvT06dPgSm9vb6MrF11/6PG6urqqzc1Nra+va3x8PJrHkNAhkeBt73gx+GHnhfgel5GQAcfoUqaK16RuGyRJWJ3JZOIaZgwx/BK9IRYWFqIU1SfQeejV1dXIYJOEQ/fXaDQSybHLy8vo2+AaW+/TgHHCGPuV1p6YcMTiaA4e/M2bN7EJHLFAf1xfX0cyBETIZzebzeDSmfORkbu+DHTPku4MLoePg0ASM5/PR1IEmoWDI0m1Wk0HBweRyCJ7zaaHo6TslGohR78MLp4cHR2N3rPwkoTyudzdzQguHQSBsUe4oQRO+fr67vr6fr8fITJnAb7cEa503/Taw3DuISsWiwFEmKfhcKjt7e2o38e4gbBmZ2dVrVbjPrXLy0vt7u6q1+sF9YNhlZSgb6iSAjWjAOJMA4SI/qAeeAf2ysnJSfRFIXrlZ0i6NRqNRGUVhhS5Hg4P9OeUmnSvjMF402rUE2GcTxKO0AicbZA09Aq2A2XB2NhYKGLoob2zsxO0jqSInAAgjUYjgA0U1PX1ddg9UDE6dxL0adVGenxQFjY/Px/E8kcffaRKpRLt1OAx4H3m5ua0tbWljz/+WEtLS/Fvz549i54FILqLi4vImJO1JSQAjTBIHvFnDj0ogY00OTmpk5OTOCwkfBzJep9ceNpMJhOHle5KPCMLwsaZmLi7FdeVAmwIb2coKUJpwkS4Tni0brcbhtp1hRgRDIM7HzK0GOmdnZ3og+Ad7NGPwoWz+Wm/CI8GWkNxwnpgAIfDoebm5uI6bQbhGetyfHwcAncaROM8cIAzMzOq1+t6/fp1RAaSokTapUQgUdASiRbns+llzOaH0+b/KcCB98PQDYdDTU1NqVwu6/Hjxwlah6y4Iz4MDKoY5svHcDhM3KkFj/29731PxWJRn3zySeLuNL9ZAKAC5UBv1pGRES0vL6vb7UYSDIOV5k9BeXw+sjD+jY5t3nYRFQXrhjQPh+drmslkArVOTk7q9PRUOzs7ajQaiQjM1UwjIyNRDry0tBRU4/LycnSz4xYOko6Xl5cBuGj0jaPnXHD2MbhEaHC4JNNohO+FQbu7u0EJ8r2enPSEOrkSErCXl5d6+fJlSNpwGkQm0EYPjQ+qFOD8kFocHx/r1atX+u1vfxv9MG9vb7W6uqqPPvpI3/rWt7S2tpZ46NnZWT179kxjY2Ph1bmmBr4IeQzG1g+WdN+kxhtjeCaZpBkhFNlZjDhJkZGRkYQIm9CRfyNDDyp34+kSEFcmoM4gROIL783nQWcgQcrn88EBYSQI95GH+eWS/hx8NrcMoz2mrywhFQgUiRDv7UUVXoGGFBBEBnfnG4kDzUHkmnjn4ziwJDpKpZJub28jw5vP57W3txeGRLovovAKwsFgEIabfcLg+m6as8DHYaSJUHDuhM5ERtVqVVtbW9H9jGbTa2trkaXGSV1dXUUYjwFg4CjZH4T80n00MD8/H/uC9oa1Wi2SQSB+SSF/GhsbC0PlckmnDlzs71WP3gcDmg6KBDphfHw8Es8kuHw/gyhZc87g9fXdFUT7+/uq1+sJegVjlM1mQ6KHNC2fz8et3iTUm81mRLpEG9vb29EqkltlMKCXl5cB8NiD6Lc7nU48BxEfoMeNJ2eACAvDzZyxRlCe4+PjUXWK0sQBGnNJT9yHxgdvfGDDdTqdMHDdbje6weOxq9WqVldXVS6XNTExEdpBGmnMzMxofX09pEdcly3dX7tNeJmuPiHDT+a42+3GJoavRQvcaDRic3JfGaWq9G0lDMeoIo4n5CdpBQ8q3Yf9LIw3MHFpknehAiFzCLs1vwgAACAASURBVJCwFAqFQDFcsXJ5eRnPSkKBCqz3ialxSGNjY1peXg51heuCx8bG4hryfr8fNwxQNnt7exuiboy4fxYOgPfnfb0ogYQCjdgp04RP73a7EQY2m824/QFBeaPRiEIKDJJn6zFIfvsCY3Z2NhKbhPzMM9EG1VvMPcYMhEuDdgwKel4+C6ABR88lhK4HxmmzF+Cwacxye3sbkjMUJUQmToWNjY2p2+0G/QTnzFnAOHihBWqJXq+X0LQCHjCmrgEnuev0Fvvh5OQkmjFBl7Eu3vSJxvdk7BlejMMeyGQygaTR1HpxBbTP9PR06NPr9Xr828nJSUR7FxcXCcqR7yeqwl6Aavkzc+dnVFLsEfTDzC3n/+bmJmgkKiTZTzhyEvIwAQ+NBw0uyQ46xQ8GAy0tLalUKqlcLku6rwrByoNWMKhkiNkgGEI2HsYWo0So7l4cTgoJFSiFkljCk0ajoVqtFskxSQknwaFl4yE3cdKfv+fgYmR4Ljw2zwhFwMZl0xHOEiKD3DgYJOSmpqYSVXEoFdiYXuXE4CDCJ3ulEmHa9fXdLRjlcjmy1oSjrj2l78Ht7a1mZ2eDvwMpTkxMhEaS9+NdUJyQ6IHPAtWTaAFdo+e+uLiIaKnT6YQuG7TvMip+ptvtxpww0ChzOIh+qNl3lIzOlMgH9Efm3NUcPAuZfRwqhipdCekJNpeMcagdsXOhKoAEtItBJ1RGLsbe478YUz4Hw+9ltq4X5myBWNO9E6BToGBYJzh6chueVCaHgATS92c634CjOTo6SpTXcz5JfJEo5u95HwwnToD3xqmhImm325FvIRJw1YFXdrJ/oUjq9XpcaYS9gBrENuBsmF/vKe330H0tlQIkNghXUiRckG+gg202m4HImDC/C6tQKIRMBiPHJICkeSEmmkE4gK6RQ0kGm16WyNUWFxdDaA4KGAwGgVYxHF4Dzd+zGBhmBs/I4fEw3P/sB4EF4/c7L+lyM7xmJpNJlB+7PpHhJaxeUAFKcaOOAsAPgvOkbGBucoXawOCmuV4G7whvia6RjDWOBMcxMjKSaF7C1/n5uU5OTrS8vByHDdSHwSVEhd4isuIQSApU7z0DMLwgW6+wA+G4oXRjBFLEmBG9YMBwbj4fjkKl+2onDBvr7ZVu5+fniSZDnhNAmQEK5Xf7evN7pPuog/dxh4CRhkqBSsCYgvy8NwjUyOjoaCA53z/MOaDM1wR1iAMQ72XbbDbDmQM+SqVS0E9eDYpRo9iE6JQz1m63w055Doh3YP69Sha66fT0VM1mM3TN2DM/c34uyP3A5xPNzMzMhEroa5X2ws2BHGgo4h7x/Pw8dJ+ffPJJJKEw1GS9yehxvQeb2CuwSLKxyRkcVAw9hhRkQ6IGlLawsBBVcm5w2+12kONOU4yMjCTQG2G/G0YOFv/Fy7to3J0Ehpl396QGYTNoDy//vtsd2DD+DG5sMV44N1Ci90iQkv1lQTzwhNI98Y8D4nudO2RNMAhIuFgfvpcKHqIgLheFt4M2YvOPjo5qaWkpqqV2dnbCGJEo4fbUxcXFeCfej+dNvx/PQ4UdjgzOjn0BDULEQMk3+4fqMww3B55xe3uboIMIxTGQ/Jd5hCYBxLjhA2Hi4PgZN+CoAJzy4jk9pIeLx4DwbtlsNlQfyPZwlMwN8ws3yfnnnOEMvT+DpOjahZH3wh1HnZS+E32wR+HiV1ZW4lYWQBwKApK5yDQJ/XkOqBlPIuIkBoNBGH+oiNPT06Bw2KP+/V5hyjktFotBzzCvH+Jvpf+FwWXyMRgYOUmq1+t69+6dfvWrX4VGbW5uLl6WQ4a3ub291dHRkU5PTxO9BuDf4HFAigwOn2eNvWmMpNCbUsbJIoGM2AQsDpuOTlSORPhz2uBy8NjYGClvRenlvm5I4ejY/BQtUKEEP+RluhweR5duVDDq19fXieoqBOTj4+Px87yLI3KQmfNpJCwJgUH7hNQ+DzwLxgRUUiqVtLq6qvHx8SjhJGnm8j4SdIPBQHNzc1pdXQ1nvrOzo3q9Hpu6VCpF+zwGTmBsbCwkhc674hh7vV6oLzyZxTMDHggrccYkYSnQYC/g7BkYOOYSxQPGMR3NcUU3jXco4EAl4p/jhpz/gr6R3fEMDOY5rZtOSwg5CzhtjKG3QZTuDRgZ/TS15mifZvWofKAvvEyaa5agB0kOS4rkFGvNOkj3Tddx9FBkbqOk+/Cf+ZfuIwsiK8AOBR/sG+aPHAiOCOdFwhu+F3UPNutrlfYeHByEl3K4TQKm1WrpZz/7mX7729/q+9//viqViqampiIpgnDfpVxv3rzRmzdv1Ov1VC6X40DBqU1M3N++y8ATEV7yu9hUHCb0i/B0cL9kiCcmJoIP8kw6yI4NTaKKTS7dt/HDYFFlws0FnuEkBEGXSpYTDaZvCjR8hHQ4BldvpJEvXpdKPe/fQGa7WCwm2htC8nvzEMIpbj8AXYCgvJesb2gMLkaLJMbY2Jjm5+ej8c719bWy2azm5+ejUxTSJ5A6Ifzk5KTW19cjLGPuiahAO64QYC+wJ8kjoP0GeTui8laU7DM/jDRk8bZ70n3fAk80MTigzI2Xq5JgYT/yXG5UqtVqFD845SEpnDjIyzXjFItwHjwPwrxwfjkrXv4MZUJlVb/f1/z8vJaXl1WpVMJAY7TSzZ6I6jwC4xm9ClJS9JbwDmBcqYPsbW1tLfYfCgU3pp4XIUfge9Of098Z++NADpCFBh01Q7vdjr2APcGB0n/BDT7OhGjNc0/vGw8a3FqtFocJPgcPdnx8rNevX+vTTz/V6elp6Eq/+OKL4GowHEwUhQ/SXZjpCSQ2NYvpi3h+fq5araZmsxkoSFICNfB3l5eXajQaiQw/C0B9NEke7piH78O4YST5/T5AZ4QcZPhpyDIyMhJtEdGpIlvyAwjfyqZh0Tk8PIOHZFKS1khvekooQZPei5TL8Wir12g0tL+/r3a7HRueyASP7V2x0ogJvaJzhui2i8VioEJ0sVAQ4+PjkcmFU6Sqb3T0rsn448eP43OPj4+Vz+cjZ5CWp5EAZf+A6kl88U6DwSCuUkLdQPjO4SFpRREFYTiG10NkXxPXnkr3LR1JrjE//F4y30+ePFGpVIqG98wBztYTkBgifieFKkdHR7EmvkcJ1flsHKdHZ3C+oE9Jkajl53gvjChGN5vNBt+czjF4Ih2+lGQsuRE09KBNKhqJ8KAFOSegTX4f4IezAm0nKVA168q8YVNw4OSV6MnAHHC+iYCQ+ZFIdNULw2mVh8aDBpe7wagOI0wiSfXpp5/qzZs3KpVKGg6HevHihXZ2dgJas2Go2KFqp1qtJnp1uiQDj/E+g3t4eKitra0Ij1hgwmDkKlTJsBDoByniuL291cHBQaBV7+NKcoWiDgy50wn8PzzV+Pi4yuWylpaWlMlkAj1wWEAWoGqUBIVCQZubm8rlctFE5OTkJMJIDK4Psqx+CMhIU8HX7/dVr9fV7/eDJ+M9SVzW6/W4dZUyYjYshsuNm0uymAcOuUcGZP0pyaSnBaiKpGAul1OpVApHfnt7q/39/WgtWalUoi4fLWuaJ3ufQcjn87GOoCNKgjF2IEbmBO4QHpn9TbgL4sXYupGXFHsHXjqdGGW9er1e9Eg4OTlJhNqoErrdbkRgksJwc7hxeqenpzo6OtLu7m78Pd+HQyCHQJUW9AW/A0kf6+nZeXrHsu7QP6yH5wHSBpekKQYTp8nIZDKR6EI9ASdKZEF0DCXJ3FIdyXySI4GuY72ZC3cc7BmKKYrFoh49eqRPPvkkke9BLsrZYE/7PHtSHkSMA3poPGhwKZ8jkw6/2m63tb+/r93dXfX7/Qj3PJHmNABZ3eFwqNnZWX300UeS7jhi6f6ueacSnLeEXzs6OlKj0UjIoFzZAEqhcYqkSLQhkULmhDfl+aQvo0eMOZvEDQ2LDG0BAiN8pXMa+kASezw7iQEOwujoqGq1WvCx3qnIK7wcYUHYk1giekDfOj09HeH95ORkXLsiKcL9wWAQNADPgTPzRA/vKylkc54AAWEXCoU4SKAD+lpMTEyEGJ5EWCaTibnKZrNBh9AfgnCdjexUE8MTlhhC1gluFL4c5ItcicO+uLgY/DAHjvLOfD4fqN8PP4Okm0v/0vsSRQiHGp4VmofEFRV+SA4BCx5pDQYDnZ2dqVarqV6vS1JEhmnOkr0NkKAoBqdM0xWMMr0xlpeXw+jCWXpkyXyDAv3ZfL8Ui8XIk8BVU8kJiiQKJtEIrw69iDEjce7lvh6V8a5OfbrMlH0yGAxCZQGfTC8Kok96I4DModdossM5xrGhEvlaKgXCIBYLOH11daVWq6Ver6e1tTV997vf1dbWllZXV9XtdiNUR9RPOEaIVi6XNTIyEv1qCTFIIjiHxSAEqdVqEeYxuZIiuQM3BF9K0QWhiuv2nOIg1GKjgILcMxOaMBegEJ4F3sdRhFdcjY6OqlgsqlKpRGs4Kus4rFAtLm/yw+3aTBQBoAw4z3q9HmGrG5RqtRrNxMvlcvBVlBqjjSY5QahM31Y+242JOyWcgSszQCJQR6OjoyET2t/fV7PZjFabGEZP9HkY6Vwpa4bzYb5Yr1wuF3MDOpufn0/oq6mkYp6Gw2FotSWFggR+27PWHk7SNwJjDP/N8/G9UE+rq6uBZqenp6OABHSMIyc0dxkglIL3lWVNmBOe34GODxBouVyO4hy4ZegM1t9lhJOTk4Ho0AundeIYoNvb20CR19fXmpmZiW5sXohDExmeg0Q7wIMowCPZ4XAYyULWZ2xsLCIazgN7hb93VRNgiHdknzOwQxhh1tDnAwCGUfc98VXjQYPLgfEQCYNTKBS0srKiSqWSaGoDL0Y4nq7IGR0djauHFxcXdXBwoE6nE5wjh8bDWbKk9JtlYxDWEwZg0MlG8/xoHjG28LYYEQ4nIQuHEG45MWH5+xZ3JGW8vNC1yLwz5LwbE3gyPDpXeqQzrm5gpPvsOn+PJEVSzNHV1VW8F60HCQsXFhZCljczMxMhtms+O52OOp1OJLl8nngnKSk1cxkTaAN0gGTM9cn1el1v3ryJBiLp8tx8Ph/FFPw7B97XwtGLG1zCXfYHThJnzbxDZ0C1NBqN+FwcHRIsDIsjSUmR8eYZKJrwQgEQGlQKYSrnBIUHzs1pEnhV30enp6eBwqQv37EGyPEWjUj4oO+mp6e1vr4eiW44XYw8EirnqIkUAEpUjTHQgx8fHycuCaBtqid0e71eApjB446NjUUimY5o7HOcK4YfB+MUDpELdAIKkqurKx0eHkZjnWKxGL+Hs8vPeSELTpPzARfOGeD/nXL8qvGgwZ2enk7UoXOw5ufn9fjx40BpGxsb0YCm3+9HU16y8DwYhpRNBaJi8uFm0iHU5ORkhG0UUqBGQKpGcghURBtHjBDXnxA+5fN33cPoLE+PUzK7JLtcvM2zIR1CXnN0dKROpxNhIOEJP+PSGZDa8fFx3BAK4gPd4Djw1OlGPjg+6B43OCBFKIZ8Ph9ohI2BvIWQ1lGfC9Tz+XxU8nnyzjcVRpT1Aslg2FykD8JgDQlheabx8fEwgHC+RFLj4+OxdgwiDubC55t3oYoPJ4fRZQ5JprVarVBs8Bk4Xn6/Vw368DwEa4mTYQAoQNr0A+Cd+X9P/lI5xbkBsSL4x0hJ947ZQ32XcaLZBfHj2LLZbDRXB7VxRllbwmlPdtVqtUiM+nt2u91wqHNzc4nkIdEKCdy9vT0dHR0lruVxJZAj9ZGRkURJuQNABwDsSfYHYFG6vyQByg4+mHVBE47B5dlxfOx7zn+az+fvHxof7BbGIvALs9msisWiHj9+rGq1Gs0o6HFAmED/SEInD0s4VNRMdzqdREs2kBGDFoWunfVN1ev1VKvVwnCzCfFIbBhHj6gqCoWC5ubmopuSG1ZPDJGAwKNyMGiQ7O0IB4NBokk1h4D54boSNgvNd1wyA08E8mHQsNqrncjIDgZ3vQJWVlYCocOXoVt2owDVQTYftQYFInB4GFM2sodQbtwwaK57BY1TXTgYDMK4etc1nB3cHp35a7Wacrm72wowngwcEfOMgWBuLi8vtbe3F63zQHVwxERJJNToNQH6RyLF/kOr6nMh3dfze5KKBvcYVPhZfo7DDK/JPGPYyd5nMpmoGCSkJqnnFWBwmRhB8gSE/81mUzs7O1FUQWk8Uju+AAEjIyMBTFxH3mw29fbtWzUajYgS0giXcm/u5pMUfPjBwYH6/X5IEskleJTFfob3JioCdH3IqLlsy0N9L/TxhkzI6+DfJYUDRC+Mrh2NPOcIW0Oi/2slzUiSQZwzARy06+vr2Gh4qlarFYfYDweLQ2b46OhIX3zxRWRZaYjNgzvvRKchUEKn01G1Wg0eExRC5r3f70coMjs7GxpHNhIIEh6LhJ+kaODiqEm6ry4D7UPUVyqV6CTV7d419dnc3NTq6moYSi9nhf/GmNAkHMMK+mHDpQ3u+fl5ohwaZMBzOuLDcNE0HeTaaDTC0XFweT84XO8twNynES6bCycFSgBRwz0SgXgzcr6HsBVaB86Mtn5nZ2dRgcYcMohwLi8vA8mTdef5cAAYmIWFhZD54NiZT8Jv118TicGvsv7O12EcQaSocujtSqR0cnKid+/eRZtJDj3P4kUCoLexsbE4h8ViMXFVEVERn+kacVQ2qFRIxLHWg8Eg9ixKDSILpIIU5KysrMRnHx0d6c2bN6Fdh57xAY3XarVi/huNhvb29iQpUCpFMswRyBqlAnNze3sbxpnoxGkS1pszzO/PZrOxJuwFWkM6tYRBBzSRkMPpEFViP4hSyW+5gicd/aTHgwaXggFCbCp60Lru7Oyo1WpFAmJhYSGaVW9sbIRn9E5OrVZLr169iq/r6+vQqDqy9UWEU6Ib1d7eXvRnpUKJg89CTExMRCcxavqpEsGw+b1PhBYUMHDImEBoAbwn6L1UKqlQKGh1dTUhcSFc9BJhjD2Z8kajkQiTM5lMoCGMBAktBndL8XzOMYEu2GToNbnBGI6cuvPx8XEtLy8HGqQKkPcABbrEiuf0pISL9ElwSfcqB5wzzhSeH66UeSfhOTY2FiWckhJJUt8XOBYOq/N6hM6bm5vxvM6be+TG98/NzYWRyuVyUSQCVePyqbQsDITMenCA4aXZq1NTU3r37p3q9XoYd/IOdL/jZgPWmCw4DWP29/fVaDQiMScpziBJbRpvj46OBm0zHN71NkZxgKifvXNychKRIgngR48exd5uNpt6/fp1XMHEz7uRYV4uLi60t7cXjhugBGIvlUp6/PhxqCHgqdl3OJnz8/NAxawTewiqyPMk0l0USJ8GjLInf2mChUH1G09wYl7Rd3p6GpQpZ5iIx4Ek5++h8aDBpSKITQSvATdydnamnZ2daN34ne98RxsbG/r+97+vra2tuHQPI1Kv1/U///M/AcfxNBhD53t8Q7Nhs9lsIMPXr1/HJi6VStrc3IxDWKvVIvMLz0rjZC6XI/wtFAqJZinQCa5nZEPz3BgfQqHFxUVNTU1FcoTN5dItiiBA2ktLS1pYWIjvJSIAHRFZIDdjtFqtKIMmnMEQgBabzWb8PEiKw5gWjHNAd3d3I9kxHA4DdUj3SZ+0M8Szu+7z7OxM09PToTMGoRAOEuLi4GjhybP5DbaFQiFKtYvF4peSVU7vTExMJJQMHoWwd3gXdNIeIeRyd30UMHZ+qOjpAL+JIWSAEjFU/B0VfazRzMyMnjx5EhJAkptUMPmV6iScoRXQctdqtUDJUEiSEkgPh8a6I/RHIQHFx/f63pbus/yLi4va2NiI+9m2t7f1+eefq9VqBS3nCVyeA9R4eHioXC6npaWlQInT09Pa2toKY0shFfkF185KitutWQMAIK0/fU96sQxombVA4+uJf7qhER0gZ/PeDnymc7ycG86E738XCbxvfPDW3tHR0eiRSRYP3SkolsxnuVzW2NiYLi8vVavVggznhZEsZTIZVSqVMAjwJZ5Z9JANMppMY6PR0OvXrxPyHUT0H3/8sRYXF8P7+/NynbHfTsvmYwMSZrh+ko3ExkAmwuf797DonsSAMyVsf98twnwexg0eEf6Isbe3F1y6i7sdGbNhMMxUBrEWJK5AmMfHxzo4OAjjAK9ISOZcMe/kaI8DS7Ky3W7H3VPZbDaSFZRCU79Og2iMDaoX6V56NjIyEkk4CmkYcL8YXLhkjDuHw6sXvQyc4YgYXg8+HvrD9bhw0j74bE+sUTINnwq6QxZF4hCDy3PxeUikeAey7NyMgCIkfWZZI/YpYS9yxzQFwFwz3+Q1MKjc//ab3/xGn3/+uc7OzhK6c49+4MGhszCk5ERwbMgUkVfCoeLgPMznivN8Ph/IHU7eDR77lP4d2AzmwJO/qCyYQySCAEx4bqIPlEmOZFlr7BXU0kPjg4UPbAw4IUriuAbblQeEQTwwPBCJJBYGFYMnWEAVhDeOIECSIAxJkXhyvsyvh+ZnSJIQqpHIwfB4Ms09oEtKGGl6ARSE0XFex/WaZNy9wALEQ/WedH81iKQIBV2QL931tyCrDefpbfVwCN1uNxwaf+fSuP39/biWx5MFFAq4woF3TZf5wpORpHFEmL4vCqkUxhYHRNXP5uamNjY2wrlwKCmZRq7mhg76h7UjCQoXC0rGIGOAMWCOpEBOrCn0i/drxkH6nuFn+XuiFKqpSPrx86B8DjbzRUIPI+JqA5xpp9OJzD7GGoSLwWTvknjiOfl7jA/GOF3I40YGTe7BwYFevHih58+fa39/X9lsNopo0oUoRGrQKhQKcIlkr9cLqmEwGKhSqYSyB7rr9vY2Ss+huaQ7ipMyXOaL9/c9zJVOyOzS1BFnH2cP7QEH7PMxMjIS68d6cdYARxhgkqcPjQcNLrefkuVl4fDUExMTEQJRD88Bo5UjMhuqrXhR3ygkZ9LhaXp4bXu73db29nbAeDYZRtcTDt6UJR3Ssjmo2GJhnOvjOfkvzoSMOIeUzczv9tJm5o5NjXTMQxTCQngmboH1hi3NZjNC5eXl5dAmgmx9Q3i5KQcbZUKz2Qzht2s93XlKSnBavAefBw3AxgRRSPfFACRQ4Go5hNK9prlYLGp+fj5xd9rMzEz0KJ2eng5k4wiCUJBIjPVOHwSMAlpkN7yEkqw36BX+Np1IY794GE2SlUPIOnolVL/fD4fuXdI8gevIG36QeSVvsr+/H4eaM8PeYu/gSFy14d8DGACRk6Clmg79Nrztq1ev9Pz5c71+/Vo3NzeqVCqhI+b9GNB4nAWiJz4D5Hp0dKTPP/883h0uGFtBQQxJ7GKxmCgk8WQjOQJsBk3rucaL50NG6ckwoj53RpxhIk1PLHriFIDV7XbV6XRCMPDQ+KDBpfk44aGXkILeuOOHahF6TMK/eFctjBqaVXhAiG9PrDDgJjEIGPazszMdHBxE6OVXjMP7koyDjmCiyWhzYNIEOxvUM/JsZDg810CysHhJnABfKA5mZ2ej3JVKKN5PuncqyFHS4S9Cd9Ax4aqrKDyRxtx45hc50OnpafwMBgRERdY+za1JSpRlsm4kPJwWwdh7dQ97JJPJRP8GEBXPBcWUyWTCMSEz9DJSUA0HEQOOgwFVw4njiNKyItaI38ke7nQ6kQhDl8o6pav/mDMOIZzfcDgMznMwGHypTSdr4jx7t9tNCPp7vd6XkmUgd/ZbGqmzB9yg+JcXzKBrxdGRcLq8vNTh4aFevnypFy9e6OTkJPIgSDK94EBSgBiiD4wfSWR4e78WPX3WeBd02vPz86pUKtF1DoDA/vOCBM4eVFm6+xp7yPW2/h4enXrBij+X7z8v5PEex181PnjjA9liPCUbD8QIXUBpImFUu92O1od4PBBGoVBIGFmXMknJ2njpnhBnYuDaEJxzkd38/HyE4Bg4FgXnwOJAwrPobiBc+uUbwheXZ/Jn9Yosz3SSbKRElL4UoEaojcFgEJV43J2VNg68B6WJ8FJudOC4OGwYZdA1yRmoGIpGEOuTnXUuPV3JBDJkTjA6uVwu0AYGzvlTwvxcLhdzQejnDtJvveAgeaKQNSNTjeHl573zl0cAyKMYbqj83XESJLaYe/aHI1wHCl5i7IlE1on15KD7mrr0jGfj52u1WqBbkovpCMyNCnOCI3bD4lSYI2TWEPBRr9f19u1bvXnzRq1WK8Fz83nurFgT16ai5feiAowYe8OloOQuaBrDlV4LCwtxZkCpbh88pwKgOj09DQQN5cX88vOsia+Hq5H8WUH/REKsNTkuuOuHRiZttb8Z34xvxjfjm/F/Mx5EuP/8z/88JAEDAsKTuMdg4GHxBnyPo0DPqlND7p2ElpeXtbq6SnerjCR9/PHHQ8osPfsICQ6d4KGZe3yQiWv58OZeuw/tAI3SaDTU6XT0b//2b5mf/OQnQ2q/uXqIuSDEIdsMkiTR5/PlHDLzxM/TWKbb7erly5f6xS9+od/+9rfqdrs6PDzMSNLf/d3fDT1RQLjDPKb7IrgCw2kBkAYcFCg0rWkENdLc5kc/+lFmMBgMCbvp9IUihVCW8BtaqdPpJGglj2Ck5J1xCNfh+gkpV1dXtbS0pD/+4z/OSNJPfvKTIajLEQ98qkcgzAPP5xw9xRoUZZDYch6Y8k9vbPTTn/40I0n/+I//OARdOc/N+vqzuCqG/cgXPKJ/uf6bfeMFOPl8Xv/+7/+e+dnPfjaELiLByRryLsy5F6qA2oj6kFA2Go1EUx6QMfsG9NtoNNTr9fTrX/86I0n/8i//MpQUF4WurKxoYWEhwaO6bXDtvHR/rQ8RgVd8OvIkcUmSDnXKj3/848w//dM/DemN4fpzV9e4XSO6IMJwW5bW0IOGvQcGPZ2pYPzzP//zr2yo8KDB5SG8SshF+v4wPjnOb/nksOExuJ4ZRceKTMSb17A4bHTCRgy1E+ioHTwkcL2eJyn4ggNFhyopRPcMNgyUhnO2hGHpTeWaXuax0+lELwEoAAT/GbyqGQAAIABJREFUcLqoQegXke4fwKbxEAonQsKLsN0TR37IWV8SOekGNZ4xZt74NzY/4VS9Xg9KCQfEfIyPj8c+4gse1dUCGEUcEmtHx6jp6emgORhelcUzsV8IU3FyrIvzvswdBpeQEB42zec7D5wGGn5QCVm90IL38UowlxXxGe87N64RdXmiD+ad+fBnBEy4I3N9PUYHjTttG09PT0NOReUm9ArAx5U1fJZTbHD7zD/P7kU7/NmBGu/nPCkN8T1nkKZ3+BwP+Xk+7BVzhdqBBKc7R881ua7b1UvvYwfSFOCX/v2hf2Qjnp2dqdPpqN1uJ649pyoEA0atMYtJwgRtXjqrnDa6JEjQ5CIRck4Prg8kiQH2P/NcbFg8F5wSnbkwcBRJ4Gl5RinZIxejLymUGt4UG0/pqIVNRklvrVYL44QaAc4RlM9VQOhP3fizMUls8DxsBFQgFJSQ4GQtSFrhDNxb47g4VH6tuSc1yHBTbUjFIb2FiRxA+blcLpwaGWLQIocGhAmKc7SJY6CvLsOjGNA6aMwdIOvl84Dx4UB7uaYbJgwAztHXgOHGlj3ikVNaG4szS7c/ZA1xjjgfd1YeGRA9SUlZm88nz+/PCkplzlCv7O3taW9vL0qqh8Nh7AkcI0aW8/s+TS/PThEB1ag4/LTDxYAyz56IOjg40NHRUSgOuMmX6jTKoX2NiQbcsbkiCOfpCV/UOnw+e5QzBtgDfSM9JdnogPOh8UEdLuj28vIyKit84fgzC+v1zCgBQA48nKMShNNkBSHNx8bGtLGxIUlhaL1BNpU2TLSXBCLaBlkgXeLfMOzeW4DQg4Xg59zT+ed52Oohoc8Pg9+DMcfz02Qb2Rq/gzaKZOzda/Z6vTBiaQThqMjRGU6KuaYWH6IfTXC5XFa5XI4uTzx7GlEj96vVaoGEPMTDoKGxpbyatfcEoe8zKrNIhkJBUKqN404PDijyRU9SEvE46qMKjFaW3tjH6Q72te9zT675YA8QYrrj8mhQuo8e6L/gkibOze3tbSB7UDpgB9mZOxw+I5PJhAE9Pj5OKB48sYnhoyhlf39fe3t7oYIg6qTdKPKry8vLxOe5U5Puu6JRCcb/s6bsC5pecdcflODNzY0ajYZarZZ2d3f19u3buCmGklySZxTwUAvAnkXWR6Kf5yfi8PVlT7jeGgfIPnJQiMF1qhQn61HgV40HDa5XPZXLZa2trYVXcS2mlGyV52EcNd6gUoTG/DyoksXpdDo6OjpK9JQlXObQkD32DLSH51ST8TuQGdHX4erqSrVaTQcHB9EfE2dAaSmfizEDsXuZKtUuvJ/LTfheZClsUpAT78HhY4P64QSheqtKkEL6gsRisRi3nGLU4EuZH3jXnZ0dffHFFzo4OIgbJZaXl/Xs2bPYlLe3t6HVzWazmp2dDZoHiVe3e9esZ25uTvl8PlDv3t5eZNT9rjbPLoPAbm9vE82gcUrQI8wzc+7DQ8dWq6WDgwMdHx/HIfCbOOiaRhhLcxXv4MVIGzP+nfAyHcLilIi0OHigV5A9RoHohOEcIhK0paWlAAeUsnLLSqPRCJmm0xEgNzcc6csicQgUAAAEcHYUq3gRCPSROxl0ujgaBhFxu90O9Njr9RLRy2AwiP3uFF+pVArjTAS0ubkZXdvQnaMPfvPmjZrNpgaDu1tLMP6g2+FwGBGjn032Dl+OvnFGPqfYH9Yv3dyq3+8nnNlD44McrqSEdXftZTZ7f++9ewte2vk8QngvPnBUzKE5OTnR4eFhQnvqnaskBZKlCxiCag4uG51rr/n+YrEYyJZ+uZubmyG8R5BOpQuJBOaCTYyn5lCywBwkdz5+mwJJORINoHLmmM9AfwradIPbbrcD3RP6Tk9Pa2VlRVtbW5qbm9NHH32kqakpXV9fx+Wbfpnkq1ev9OLFi5CV0WzoW9/6ltbX13V7e6tarabnz59rZ2dHuVxOq6urqlarkhSHGOfGO7jekpJQ0DahOhuaa25wHMPhMLoxccgvLy+jggzKyveFdB/eswa02qNwYHx8PIpIqJL0g3lxcRHJJ4ygh+qSYn+yr/h+hifgOMCgUNeW8rOu52Ztodymp6ejRzMFBoPBQM1mM1qgkgTygYH0JG23e3c/WrPZDKkaToiyXRwy0jP6INMz1qNCdN9e7ZeeC9ag3W5rfHw8buNdXFyMsmiaNtH5bHR0NO6+GxsbU71ej2elsGpubi7uwsPW0Oc6l8tFu1VJUcUIGvWCD3caOEIAIHQYDjNN6SDJS9tDgBlg56HxoMHlAelSRAiaz981pl5cXNTq6qrm5+cTITU8Lp6CK5J7vZ5arVYURfCQLD6ogDCf4bwJ1W3Pnj3Txx9/rLm5OY2MjOjk5ESNRkOHh4dqNptqt9vB/VDFVCwWdXNzo8XFxSjz5b8ufG+323H7ApVNrnKYnZ2NJFM2m01UUIGYMplMGCKSbJ79L5fLevLkSZTvzs3NaXl5WaVSSYuLi2Gw4HgZu7u7gQ5IIMzMzKhWq8WdaNPT09FBDc9MOIzo/PT0VBMTE1pdXdXCwoKazaY+//xznZ+fq1Ao6OTkRGdnZ9rd3Q3U4RwuEQG9NtgfhI4gdBJ6bFCMxs3NTTRmQdw+NTUVJZLwwRitubm5WCsGRpH9CCU1Pz+f6IEwGAxCBO/5APjcXC6XuMqG340RxXh6si1NKbDXcYD07fBKJSg2T5bhWDqdThgOKtp2d3e1u7sbDdIBFDgTSYn/cvYAGyS/Wq1WtFoEjeVy9zfjFgqFAAKtViuq/7w6k30Nd0nOhabmDCqu2F+VSkXf+973Aqlms9k4p+/evdP29na0C93Y2Ihn2dvbCxuxu7sbemsc78XFhQ4ODtRqteKKd6dsyFE4EAS4kYMAFKID91wQ/DM9QMgrUA8wNjb2pWuyyGE9NB40uHhqbgg9OzuLSq6FhQWtr69reXlZkhIlkNlsNkh4Ei4kp8iI0vSatovj4+NhsPr9fqIHrIfl/Pfo6Chx/xdtBeFSSFSdnJxoYWEhwkkWA8O+vb0dkz8yMhKHmsQURtMPL8YWo0eBCA0xWBS/iRWnU6lUVCqVtLq6qo8++ijmEh7LrzmnIbQrNg4ODlSv16NqaX19Xaurq5H8OT8/jxso2GwUnUhKIB76zL58+VJffPGFbm5utLa2FkiZKisoDK+sk+4cMtfSnJ2dBT1wcnISoTEJM+/PQC6ApAwUUKFQ0KtXr4LmYa7h7srlcqKvBI6d3h6Li4uBZkhSwmNiJIrFosbGxsIpDAaDkJ7RZJo1JKLyPr6ubmA4csrn81paWtInn3yi+fn54CSbzWY06+FsYcgODw+1u7sbiJJ+yTSB2t/f1/HxcRTugNy9tBeDcHNzE4asVqt9qcoM5Eu12ejoqK6uriJRjUNaW1tTtVoNtMulmqwjHfAWFxfjVhdJQdW1Wi0VCgUdHR3p17/+tX7zm99ENIX0kc+CHqDZ++7urnZ2dtTr9eI2X0rDiapcSsaaY+ycA+c96cNAefjR0VFEVxS3QH9gl6DzpqamwoC7vBDASFIxLZN93/ggwsVAFgoFlUolra+vB2KsVCoqFAqhS+WaGXodHB4e6vT0NOA897rDN2IwCZkwGHBsDHg6QhnuJSP5A/oi0UQ7v0ajoUajodnZ2TAyJHlopvzq1Svt7OxE96WPP/5Yv/u7v6vNzc3glt3gglIIN+Cr/WprSZFkoJetUypnZ2fRuwGjSOhCyCIp0bqPgT6YxBpdjAjRnz9/HgYZVL2wsKDvfOc7iQox1oEwbmRkRJ1OJ/4OOgiqhf+yLzyDD0Ig0w/lAuoiivEsPpsXThQjTBIHjhJeHnWJ95VAAUK5N6XCIOj5+fnYK/n8Xb/bcrms4fDu5mkM1uzsrFZXV6OPg8uIJEUSCu6Uz2awbtAGFxcXevnyZUQIgAPQN++QyWSi0frh4aFmZma0vLwczhAEydyC/nH8RBiSoikQNzuwz3n2bDYbDjabzUYbSirBNjY2dHBwEOdlc3NTT5480cTERKIZPLQQjpRyWwZOrtFoRE9cSUELfe9739Mf/MEfBPrnmXZ2diLCOzg40PX1tTY3N/XRRx9FMq3VaoWzoek/Z8lL4DmjUCzkckj2NptNHR0d6fr6OlHJiM2BOoHSI5KkFQFJVs6T663f1wPGxwcNrh9Oyuskhefu9XrRQu3w8FCjo6OqVCqRWCFUgsPE6MKnsVHg4GhW4do+NjGogpCMLDQqCM/oY1Dr9bpKpZIODw+jWxYXJBKGwGUNh0M1Gg29efMmUKwb0dvbW7XbbbVarQgvSJrBUXoCAQNDSA2PdXBwEIk2wljvVlSv1wOdj4yMJMJo0Dlh6hdffKHPPvssIgYQI2Wz2WxWlUpFjx8/jgIL1A9bW1taWFgIeQ8RQqFwd9dTrVYLtE/4JN3fgiApJDE4J96Hxi8chnQ2PZfLJdrywRmiu/X6dugLR3SSEhQNYbJ3JkOJISmQ0cLCQvRlYD5HR0cjY+6Hx7WrjqCcm+V3sD/oG8s+abVa0XinWq0mtMjZbDaoG24uBnAwj9VqVVNTU2o2m6GmoajAkzQgtbdv30aPam/BibH38neMfyaT0crKSkRc4+PjkbTD0S0uLsZzc06hKby/hUv5QLuzs7N69OhRlOkS4eGof/WrX+nt27d69OiRisViAKxisagf/OAHEdVARfLfiYkJXV5eBlJ2bt7L1Y+Pj/Xpp59GdIuj5+fYd9AtaOB5XyKp8/PzSDaSm3CdPSDsofHBa9KlezEvh5pafbztxcVFeFWacZ+fnwefNzZ2dxcSh5MNis7XM6CgmnQWF+NPz4S0vpNuZLe3d20k4V8x5iR6mFQE3Zubm6pUKnEQkJ/kcrlAFRgZvJeL4Tl86a5fIDZPZLBxkJ/xLiA4yHu4OqIDN7gYaRJ7Lor3QgCcCGjc+wNziODfmXfWHGdHsg+VAs9PMQPcFr8fYToI05OiaUE484XTxtBCOzHHKGJIJjqyHBkZiYMFxwefjjwtn8/HVUpQPGTBeVfWFccA5cA6YkjZP2mtJSE698HhkHlPIgT6YxDBDYfDmDNAQ6PRUKlUikjR15fmMuw5aALWz4s4SMpya0s2m408wdramlZWViJqgfJyh0pUxVoAHLzggbXwNWEuAT/j4+OqVqtaX19XuVzW0tJSaIXJl7x+/Vq7u7tx5Xs+n9fJyYlevHih7373u3GVPAaNpDeJR+gVPjtdsIX8L62WgnrETgwGg6gW4+c4x67X5tl9b5MM/loGN72pXD/p1VNHR0fa3d3V+fm5Zmdn4wBCF5Cc4GYIID6bA6Po3JTzlq6RBKGh68xkMvFZINZcLhfhDwYc9NDtdhOloKAJ0JOL1tPNt0lA0XCFRBTomIPIxsBgEYJjdMfHx1Wv1yOcdX754uIi7oQDyXgYXS6XQ6MK8iPUpuHH2NhY8KvwsCRakC9xUFF5gJB4dgwZSgruAWNQAUeCy7Wk/l4YGg42ewJniYNhLjGeUA/IzeBRPUOOWB7DyhyTCMJRQTnQg9X5V/YmMjeudqEggC/n6EmwpAfzPDIyErc2uGge5wuiJZzljMDnUvBDggdZnJdhexEG34sxmZ2dDX6WZB/zUK1W9eTJEy0vLwcdhdEiWqRC0G9MYO1wsCQB05I6CggqlUpCRgXdAVIsl8vK5/P61a9+pV/+8peRfGYPnZ6e6vnz51paWorGN0SoJLLYA940yeeE6Jv8DPkn16Rzlk5OTkIyB1Ah0QrIwUG7btsrCjHKD40HDS7ohlCZjQjyQipxeHioer2eyNJxgAhVWTjX+nkZJQUBXrbL8E0HUc3hweBRMQX6o+8DG344HMYEcxi9pFRK1k2DbhgYN7LIDJ6Zv6P59tnZWSSNMGLwX5lMRo1GIxKRXDUiKW5MwDgWCoVEoog7yBCDk9hDfgb6pOctelTCNEJ6PPvFxUXQCswzIaHLqrxRumfEXQuLMcCYInAnWeYVTtI9DcCakBVnztjAUD2NRiPhfFA+0GeZz0UiRTEJw5Ey+9uLQZAo4dyJkqiyhAvFafnecD00Sh4vcXaninH3slHpzlFTjQh/LylRCedqGeZdunc+8/PzsQboYPn8yclJVSoVVSqVMLbMCdGRc9E4W84NRSk4H6JOL364vLyMCwa4oovvh+Lgq9fr6ec//7n29/djX/se7XQ6+vTTTwNU0OYVYw9FiUFnLrzQg2QgUXO/34/oBceBusadGGACZ47N8eSjJ+GxL1+Lw2VTMaHIKdgwcDkkpLiEj4eGD3OJGOGUox02P4jGrzqR7ss2WQyoCZepsPgYS9AuE8RBY6Ni2F04znMTHnqVkVezEVJ5NRM/45lTR0pQLGh92+223rx5o7dv32ptbS16jHp1GJpWv0SyUqlE1RW0inR3QwLPQQjV7/eDh2MNcEwcjk6nE3I7Dino35sKucg+Hc7yuzEIhMNw/+6IvVIKw0ik5GWaGGY+k1uG6QcsKforYOjpQ+vRBg7By1M5kKAj1ALn5+fhnHA80FGuwnG9OXuX3+dKDK9GAlB4BaDziYSpHsq6ftQNtFfMMV84G5JRzIEDERK30n3U6EU0ODiim0KhoGq1GgYL+hAHRLSYjkZB+fD/hOtUgHHGKK7p9/uJCAC1Dga+VquFfA7jCOrF+aAuYS74OyJcEvJw5NgzbBhz6v/GfKC04iJJV6Ww7yR9fYNLyO4CfxdeOwJB0oLhIXHhBoiKDze2DssxZu8TuGN0qbq5urrS1NRUbERJkXgi1HGvTbgJMkZywp/dMPuEcnDd8DIwGl7FVa/X1Wg0dH19HbrHXu/uSujFxcW45Tefz8dlmHt7e1pZWQn+20txMVwMEh6EbmxcnotMNh6bXqC8P16fyIIbWLklg3CSeeMQ4MElfekwQxfAiWFISZDynIRbHv7OzMyE5ImkH3wc+815bZoLSQrtLZSGc9YYbPYH6gQOINHK1NRU8LPn5+fhnDicNE1Jy+LS8h9+L3OAgcLw8m8UeTC8iMj1ui4/hNsmUct+9ao3AAAacS+8cJqM6kaS1Ohv/aIAjCDIFG4XaqXRaKjdbofBdoRLtIoRcl0q78n8ExWhJacPtCRtbW3FezpwYv55ftYiTfHgHAEXFN9gd0jiA4iYJ+8Lg2YcB4PeG6fqER+AI12Qkh4fNLi+KSQFQoPDw/t2u924JHIwGISImrpsZCwLCwtxgPDkLjhm87ph82y1lCz1JQzwBAdC6ouLi8jUe0IG3q/X64UA3nlYFtfRnm9s3jdd0kiVU7PZjEQEi49Q2iU9kiI6cHkJGwz+yLPAODVQunNHIFuIfr48uTcyMhLcMjw14niMvSc2ydST4GFwELzSBifhxphkHr/PezRwLTh9LAjVHfU60oRv87kAQXEIeX9438FgoLm5udg3yPXm5+dVLpfVaDRi/3mVGAfa8wM8t/+XucAp+V4F1eLoQcd8FpVX8NxXV1chl8NxwBdjxHg/Qn/m1cX9fDbnFoPAdTcXFxehWz47O9PR0VFI10g+t1qtkNStr69ramoqECaAh2d6H497cXERaBYJFQ6fq6Occ8f5IROrVCpRUYnihlyDrw2afo/2sAk4WNaENeBc93q9UCzNzc1FdR+yV+d5vdUjemCAEWuBg39oPGhwnTQHhdJZB28MJ8KhRauGIJ6wv1QqKZfLaX5+PrKgnU4nDCATgfHzxAahvKNWNhXGhsMFf4mR43bQarWq8/PzRCOTRqMR+j2QnPNqLgViIjHqhJggGoy5h1B4ZHi7o6Oj2BBUeXFjBpv06uoqnARJOu8WhkFCGUGpJc6PElmSCDQHASlR/YUx73a7oRU9Pj4OztVLFN2gSfe0CweIZ8KY+tqho4UH46DgbEGP7XY7obYghGQu2W8usqdUG0R/cXERBoHMMwlYTzbRe7hUKkV/EA+ZXdWCMcEJ4mDS8j8OuJRs5ckXP9Pv9yNi4Q6xhYUFVSqVMPasC1edQ+1MT08n8h/p/YnjY63ZHySP3r59q08//VSnp6daXV3VysqKzs7OtLOzo/39/Uiygvpw4peXl1pcXAzdMM+ZVmtIdyCNn/EzyfOUy2U9fvxY09PTuri40Pr6umZmZvT48eOQM2YymUiWoWWnpwRI1bXe2Avm3VUUAAPnX0nW0gZgfHw8ejWQ4GVu6VKHk8EZuuHm89J2633jf4Vw8bCSNDU1FciDzc5BGR0djYmBJ8Nb4TkQp3NIvRTWE1XpQ+uCeUJHUAWhkCOMlZUVZTIZVatVbW1taW1tLcKDTqejWq2m3d3dEMRTRMDvTIeMTCYhLMUWaaMj3UtiWAgOI0mYiYkJlctl/eEf/qFKpZJqtVoYQX6nGx8Py+BhnSZw+R4GBqdA5y+X0PgFiW4oeSeMOZsV9A3CJVzFABHBeGLVw0W+h/vGEK2zSTEWIOlsNhvfD5/oqJUBdzg7O6tWq6XhcBg9NTqdTvy+8/NzNZvN4PKy2Wx0OCOqIWFF6SyfS9gKavIEjK+J5wncIbInmA9vVOPJL+YalASvDBBgbaE84Kv5/fwcRoAcBWelXq/rxYsX+vzzz8OY7+zs6Pj4ONAtGX2n17gU8fLyMpF8ZT955Il94P2cz0QlsLq6qkqlElWd3/3ud9Xv97W1taXHjx9rbW1Nc3NzajQa6vf7oaNGmuiRqtsHlx6y39GFuyqHqIvcUz6fV6VS0dOnT7W0tPSlnh0kG71cnPXFmUhKKEgeGg8aXAwAyMl5WDpBEVaurKxoZWVFlUoloYdE/0f5KguFDm50dDQhlvZwKfGgpnWDfCdxBy/F98FzFotFVatVLS0taWFhIdE6Dt6KyiakKmgxPdnBgqeJcng2FhzDgaqA5xsbGwveG6e0sLCgpaUlFYtFbW9vR49cOE9P8BDOsrAudeNZoBioEqNaC84T5ISqpNVqRbhGeI2RIYkAP828g/KdfsEQcMBAPwjDOdzOt/G9HsIjoYJewUijNIBv81t7mSdkcVQ6Oh3S6/WiQuz4+DjmhwtIqdknQcd6uiIAx8a7egMc1tmTdZ5EIyxlHnB07B24bRCaGweSaCTjiPQkfUl+5Mlp54UpUPrss8/05s0bZTIZVSoVjY6OqlaraW9vLwF6HFwRBUl3DYtAquxHDK7PBfQNtgMQxb73PtSUzqKWYY+Q+P3ss89Ur9dVLpcTxVJQR+xrIkEMP599c3MT54lEO06I8zE+Pq5yuRwl9+RZJicnQ6kA3VatVhNdzthrrK+LAL5qPGhwWQDfdEw0hpjNRheilZUVzc3NJaRCbAhKYDHcPDSHmIPK9/tzYNhBs9AAGAm4TCpxKMljUxwfH8chOzo6ilC+2+3GdfC3t7fRjCLNx+D12Zg8M//G+0GXOK9FXT/Z19nZ2ZhPDrAnqUhyEOb6cxBij46OJgwCiUt00JLigs1erxcNcSj1pNSSxBbGzjPybF7PrDOc3mGeqRDsdrthlDgYrl8EoXQ6He3t7cXV7yTgCBuZH0kJXpeBQaP5Dd3PmEPQvx9m6IXT09MwOOhrJSX0uhhQlDEembmRSX+vG13fwwySinC5kmLfgaa9+o+9yc84GnZAwOFH5tXtdkMN8/LlS7VaLa2srOj3f//3NTk5qaOjo6AS4P+hEW5vb6PJD9EHRp4oLG1spfuoA5TojoHv5Zmvr6/19u1b7e7uan9/X9fX19rb21Ov19N//dd/6ec//7kkhUqG7lxQRlAK0DzsURoRQQFwvnBmLt10apKkGBWh2Bb2GpJQDKzLwxycPTQeNLjOEblsgkMFH8cGGwwG0ZMgl8sFdwliGhsbSzTJwLDAw2JcIPp9cPCd84PTYSN6KAM/A5pFDsOhIwNZKBQilEL/Ojc3F+jINZtwgGw26b65Nw6Gg8sm4HlAG6AcjCUhHTe7widigNPhE2ENn88zEoKzIVxITxMUTwRSqk2yAkNNKAVPRVLg9vY2MsHphCa/EyE/KEW6L8PmPZCikZRxRQcd3TiUHraz7/wAY5joJEbNP86UxA2HCUWNVytJin0k3fdS5T0xjux/j3QYHDhQpUveMMCcHfY2PSyku6hlaWkpnhcHAZWDw3M+mJ9ziRmAgJ/p9/va398PdDsY3PWNffr0qba2ttTv93V0dBQGeW9vL0rx+/1+Ym/zGURxGC+MLwOH5FQTxhGHSdEHjqTdbqter+vdu3f6j//4Dw2Hd70urq+vo6kR+5LomIo7V4awnpTm0qbTczI4WxAuVYXNZjP05ryfN0BiftG9o4v2SPh9nHZ6fLDSzMNovKeLsD10ur6+jv4F/AycFYs4Ojoaf4fnYUOTkU6HbBwY5EwYLD4DrpOQAR6TLwyKJ4Mgv1lswkoyn86dSclECCFMr9eLyi6cCgccjSfPwiKCtAmLcEjD4VBzc3OxSX2Du8GFPuDgYghBS14hRdaajQ3an56e1uLiYnTfcg0kEjYpeTElCPWr9odLcEC2bjTZJ8PhMMIy9JSuuXXlBagtbbD9symEWVxcDK6W+SV0Z/+AmElALSwshFrGEz04f0esjmY818BzYHg8M8654O/JQ2CMPCylUQ03LrD+GFDm17lunJCkRBSCsb24uNCrV6/06tUrtdvt6JblCNpvUYEj55z73EtKOFxXvrjBJfqFmuFn2Mc0vCeCIpmNYTs4OAj6YX19PXrpsnd8zaFgQNL8znK5rPn5eR0eHoaywxsmsb44FBJo7969ix4iUHZwsyS60TTDA0PHpJVVXzU+2ICczQaf5HXzg8EgodkkmZT29m4IXWOKEXb0wsulxdTZbDay3R7yYbhcegICJwzhc96HCNCLcogI8TFobCZCWukObZO8AKmAAvg9PvkYrUwmEwfc74gjZJWkpaWlONQYGJdCeTIKGsOjDg4emVevPAIdeJHDcDiMzDQoECcGkmDzYTzZE47AOGyIzdkznoD0RJFLAUHHOEM2L9EBe4RkFuPq6irmf3Z2VktLS6GiZ28oAAAOHElEQVTVbTabsQc95MQJUZk3PT0dN9UeHx+HgQWlYygdwaTDaJ7PdZmORomMvLiAFpaOVrkp4fj4OCGTwyjgLFCNeETFuuOE0cw2m82g8UhYQzOdnJxob29PBwcHevfuXTRN4l3d0XNuPCnnSJ7B3DHPDlpwFNiLbDarYrGojY0Nzc7ORlNySsnn5+e1urqqzc3NoMRomgOIYy97P5aVlRXt7u7q1atXOj8/TyibcDKoQnCCUA2orUi2AVIAWXQSg3t3sCB9uU9yejxocDlkTLYLs0lauSwFhMDPuZfL5XIhh8FzuwQMg8uEeHUV/NvMzEwYDBA3V7pQbowkSrq/NRTeCwTl2WMyv16HDn3h3CloBPTrCg4vy4UjJUEGr4paACNDJ30OymAwiMoy5hPD7M3Y0wfNtZgcFDKtCwsLoX7gkICQkFhBubgyAXQ2NzcXSRAXuLs3d/0hP8umhAvmADpiIzzDgfNc9KlFD8qhwLA7sry4uAhVDA1ikBbRBQq0RZQBp8ttFIAHwnjpXissKXGQPRnldIcne5kb0DC6WZwH/CNAgfUn4mJdvKEO+5ckIproNOXHPBOB4pyQVa2srGh8fFytViv6wRItIh3kOTEmzlE6GAJxujqAOfJmSex59ig/3+v1Qt8KFVAsFoMOQBf7+PFjlctlTUxM6Pr6OpDw5eVlJN79/aU7g7uxsaHPP/9cjUYjkvsutaT/Ng2vAAZEp46kScp6YxtKk92OpYHW+8YHKQUQiW8ipxJAHk46gzTx9J7thJT3BACJAbL0CI8Z3kgFROMvxt1cdIV69OiRqtVqhG1sCDY0yYJms6mlpSUtLi5qbW0tOtITgvuGxoF4AgMjAioESXPFCJsdzpUWgWRdMWBsJMItnuHm5iZa/DGYL9BROukIb0zLTLL6JMc8yz4yMhJRA3w8qJ0G06xbmqNyXpJDTjIP6Z4nhuDTMVpU9cDxcag5fNxwgLSHje2DK31wKDg3mpRcXl5GFyyMDUadMmFXDlBKynriSJx7ZU87qnN1hqM3UDotBSm2yefzkSTDCaCioDABZQVZc/Yi+5G959pTQIjTgKOjd+0GKXgZDAbqdDph7Hk3uH8vgcVIeh6Bpjx8Hg7IB47m9vY2bizJ5XJR2AD/Kim04b1eL/hR+iejbsrlcpHkfPv2bZSje2LRnfH8/Lw2NjZUrVa1vb2t8/Pz+F4QuUc8gAISt4XC/eWnKBdmZ2ejohJg5YDCI4CHxgfbM7oEwjWIoEdCGFAe38MG5bDhfVhUwh/pPkxGE7u8vKxyuRzPwWG4vr6OwglEyZlMJjrq397edXnf2trSH/3RH+np06exQfr9u5t+3759q1/84hexWarVqlZXV7WxsRG6T8I9koFsOvg3jBoOiENKL99qtapyuRybbHZ2Nq5cKZVKgWT56nbven92Op0w9MwTIaavicuUXAvJJj4/P48LMmmPCdqgoQzZeEIj7rRCCE4jHOcwGe58GSA0DEOv1wuDy/z7s0JnoDn1KrHr6+voCiXpvfSEdHd7Bc4BJEgDGfYmVNTKykp0qMLAEY1gENvttmq1Wji5VqsV+zi9rx3VUWbtiS3mSLpLdG5vb+vg4CB6jmxsbEQFFQkaSXGlOLkA6b7owsEN/8ZnOPKGF4b6IlLrdrtR4MJ5Ssv32MuAHCKX0dHRKEvHiTK/bmRcQ8yzk8wlJIdGghqgAAS6kCTY4eGhvvjii4j0mCMP7aEVia6lO3CxsbGhra0tvXz5MnIFJM2c8gEEjY2NRdku5b3FYjFap0LleLcyIu900vSh8UGD6xk+JtdJc4yKpDgUHuJ6Fg9PhPFmY2CAZ2ZmtLS0pOXl5USHLEKPdrsdBpTDXK1W9fTpUx0dHWlvb0/b29v67//+b52fn+vt27cxmf1+X51OR+/evdOrV690enoaN1hUq9XogQut4dwOG8mTfIR8GANQQb/fj8bO09PTYQhIHpyenoasjQ1M4i2fzwcyJtyjcsrXhM1C+M58gqhYh1wuF6HQ0tJStLXE20tfbuuHI4EnxwGAhjjkLo3jiw0I/5zJZMK5wLPR9wEE6HJBDjkdqa6vr+Mwvm+AdPgMELurNViTbDari4uLhOTPE4ysJ0oRrt92A+tffrBQ5jB3/AznABoLp8+hrdVqiXaa0A3j4+N6/PhxVGjCX7o6yNeDz+CckbgjN0ECDZ7a9cdESs6zUqDjfVAoDKKHLobaf1a6v+kb1O8aWPY1Jf8k9ChacXUSTgAaEMNdKpUiiiLRzjnxXh1LS0t6+vSpNjc3VavVIhHoBRlEGNBnnFtQLs7ZC2+oasQZcR6cbntofLDwAc7WS+M8YeLcKKiFwWKzGfr9L985BsGfzWa1uLgY96T5tTIs0v7+vhYWFrSwsBBGYHx8XKurq3ry5El45hcvXujo6Ei//OUvAw1LSckPv4cKONQTaEVJuPE+Nzc3kexDs5rWpjpPSqhDeSO/E0/KBZyEusfHx3EoSAKenZ1Fco2BceUg8HlONUiKvq7X13cX5dGk5OzsLKIT0KDfU4exRJPJQUhrcd3gEr1IimobHC9oiqQrsidKvzudThhXDhDriEPnsKYRLu0USX6BGFFe9Pv9CM3RGlOrTxUZXDGRDUaPa9sR7fuzQPswvN8D+92fd2pqKm4zAB1ls9l4JsBHoVCIJJKvNdQde9P7ZTAflNTzmTwD74rh4BxD8UDVYTi5HJLzx/oXi0WtrKxoYWEhzi/USlq+h4OCR0Z+RbKQf6eQZXt7W/V6PXJCJDSnpqa0tLQU5b+APBy4y8qgn3iG6elpPXnyRJ988klofTG0OAUHhk6nOMolagB8uJLDAYj/+aHxwdJer+pIe3dkXyAceE1egu9zlCspeC04LZpPVCoVra+va2FhIbGI6FUPDw9VLpcTteIkE6rVqk5PT2PhQHouX2Fz5/P5uPuMiwNJ8iDzcHWDdF81RnUMG4qDwXMQ1vp14YTVZMe5WwvHQnNmNicLSi9W76XA57Fp4S5BVDwHh4cKN4wZSgyMNt+Ht4c7Bh1CF0ETMFhz1tbDWX4nBgDD7WiWiAWDC4qAZiAk9mdNG/3T09MwBrShxACTkceAk5Bh/iWFUUddU6/X47YGr3qSkvpSkC8D0OANZzxBCNIHsbpw3jlvT6Dx2fwdCh7QudMy0n3fE7hZwn5oK/YLmf9vf/vbqtfr4ZABEjwn+YN2ux0VWcjKOBP8jPPZkkJ1wzvRlMfpJFQlkuJKLqgybiThHMGdskeYF0/8uqLi+vo6rgkC5XLVkecdiEhwonzRkhSHCAXVarXi6iJUWm5g3eZ91XjQ4Dq5jDidjQePycHzP2Nc3TBzKMlOkhzhEJdKJVUqFS0tLUUoxCAcbDQacbupc2WFQkHLy8vBpdETgIlzpQIos1wuxxUzIHnnegjRGSw4v4eQlfdyL4fshBJBuF80g3hP6S4s5uK/3d3d6DQGBZLeTMwHm4ZNL90nODk4zK87BhwVVUQYKQwjPRjgzRyx4Hj5bHeioCoPo1l/qI5MJhMKCSoG2cA8I9EBRpsy0PeFrziier0eiAiFQqlUimblnU5Hr1690nB4pwGem5sLDpHIiSb6zDdzC/LkME5OTn4JxeDMoCdokch+8nUDlRL9wTv6XPqcgcbYu8wvTpVzQjTmdBeZeIwx70H4zHlkrxPG0+im2+3GvXokXJFd4hAJvRn8mbXyZCyfxz6DFqR7GPua96cwA1oL0IJtIUJhXzI3OOJCoaD19XU9e/Ysbkb29QCU4dRYb5fAQsdwjxp/jw1I7/OvZXDdqIEGkHnxoQxv6MCG8o3GhLBYvIwjFIxtmppgEeGfHD1yMIvFotbW1qJbEbpFDAscH8k2EA+cnnOwTKK/H1lQb+4B6sMosbkwPunSZRIT9NbM5/9fe+fPojAQRPE5OK3SWAYEq3z/7yPBRhRMViQqtrni+A3PgNpIqveag8NI3J3/82b3Nx6PR5RS4ng8xuFwSN7gOI5JmdHIEmOEg6OWp++KcEPDUyFWegy1NRQfZ8jhL7BJEGg9qYrvnQoZQxJKJeJYv77v89rvrusyFWRAQTvJeh23Rrukyvo+DK+QCjNJV9d1rFarvN15HP8J7tTp2HcONCKKVGML7Q/jQjSnkfY03SQT0giVaAjd0L/sje4PQQ4GgUgUo4p+qo6gm1CdNKPTSJ3fhjPj/W+3W3RdF/v9Prbbbex2uxiGIRusOAvkC+ete6JrhC5ycwL/J+tYLBaZGVA7ppxFuSAisnYLRx3Z49mpDjCqzWFZTdNE27ZZMtImPqUjWD1ab0bfVS5UJnS/lMn1Dh+bZtqRxVNhtDAuqnga6amwTUcOSTOhgpAS4tlUoDEONFRKKdn80XSeiZTr9ZrfTYeR1BTPx4QNSgd3FkEm0uI9lFeJI6KRgYIiuESo9/s9mwwokTallstlduRLKXG5XJ6oXRhBNWisLdEAaabWOZU6pA1OnlPutDIOWAeUhIiZA5pRgCnfUPmpEc9TiSrMpKhMgmmGQFqnGYA2+DDEuhaaWlKm4MjGqqqiruvYbDZ5qDbKhiOJiIxMcRLaIMPwIyvKBtGAgvWjLoxxYf01jWU/+K08P1VYyjqsJ84VvjSfRR5wghgAZFlLPTg/1oyMA0ra+XzOs6T7vs9D7JW7zn5p41uBjhEJq9EiWEG+WX8u3iTowYjSBF2v109riAxqSq92ij4NxpF+yel0ymcx7gycMCijeqSBDvxhRu+V6aD4ZHB/XnWADcMwjO/ifcHBMAzD+BpscA3DMGaCDa5hGMZMsME1DMOYCTa4hmEYM8EG1zAMYyb8AZvJ8JvLB5PcAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wh0xYXKwgyce", + "colab_type": "text" + }, + "source": [ + "### 2.5 Optional (ungraded) exercise: PCA for visualization\n", + "\n", + "In the earlier K-means image compression exercise, you used the K-means algorithm in the 3-dimensional RGB space. We reduced each pixel of the RGB image to be represented by 16 clusters. In the next cell, we have provided code to visualize the final pixel assignments in this 3D space. Each data point is colored according to the cluster it has been assigned to. You can drag your mouse on the figure to rotate and inspect this data in 3 dimensions." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OYApW1ISgycf", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# this allows to have interactive plot to rotate the 3-D plot\n", + "# The double identical statement is on purpose\n", + "# see: https://stackoverflow.com/questions/43545050/using-matplotlib-notebook-after-matplotlib-inline-in-jupyter-notebook-doesnt\n", + "%matplotlib notebook\n", + "%matplotlib notebook\n", + "from matplotlib import pyplot\n", + "\n", + "\n", + "A = mpl.image.imread(os.path.join('Data', 'bird_small.png'))\n", + "A /= 255\n", + "X = A.reshape(-1, 3)\n", + "\n", + "# perform the K-means clustering again here\n", + "K = 16\n", + "max_iters = 10\n", + "initial_centroids = kMeansInitCentroids(X, K)\n", + "centroids, idx = utils.runkMeans(X, initial_centroids,\n", + " findClosestCentroids,\n", + " computeCentroids, max_iters)\n", + "\n", + "# Sample 1000 random indexes (since working with all the data is\n", + "# too expensive. If you have a fast computer, you may increase this.\n", + "sel = np.random.choice(X.shape[0], size=1000)\n", + "\n", + "fig = pyplot.figure(figsize=(6, 6))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "\n", + "ax.scatter(X[sel, 0], X[sel, 1], X[sel, 2], cmap='rainbow', c=idx[sel], s=8**2)\n", + "ax.set_title('Pixel dataset plotted in 3D.\\nColor shows centroid memberships')\n", + "pass" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E4yWw5f7gycg", + "colab_type": "text" + }, + "source": [ + "It turns out that visualizing datasets in 3 dimensions or greater can be cumbersome. Therefore, it is often desirable to only display the data in 2D even at the cost of losing some information. In practice, PCA is often used to reduce the dimensionality of data for visualization purposes. \n", + "\n", + "In the next cell,we will apply your implementation of PCA to the 3-dimensional data to reduce it to 2 dimensions and visualize the result in a 2D scatter plot. The PCA projection can be thought of as a rotation that selects the view that maximizes the spread of the data, which often corresponds to the “best” view." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "r6baoDlIgycg", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Subtract the mean to use PCA\n", + "X_norm, mu, sigma = utils.featureNormalize(X)\n", + "\n", + "# PCA and project the data to 2D\n", + "U, S = pca(X_norm)\n", + "Z = projectData(X_norm, U, 2)\n", + "\n", + "# Reset matplotlib to non-interactive\n", + "%matplotlib inline\n", + "\n", + "fig = pyplot.figure(figsize=(6, 6))\n", + "ax = fig.add_subplot(111)\n", + "\n", + "ax.scatter(Z[sel, 0], Z[sel, 1], cmap='rainbow', c=idx[sel], s=64)\n", + "ax.set_title('Pixel dataset plotted in 2D, using PCA for dimensionality reduction')\n", + "ax.grid(False)\n", + "pass" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 7c7ab19e00a467b6d6ac744fa107e2d882799553 Mon Sep 17 00:00:00 2001 From: Aishik Rakshit Date: Sun, 19 Jul 2020 09:43:38 +0530 Subject: [PATCH 14/14] Aishik Rakshit 190122002 w08 --- .../Week 8/exercise8.ipynb | 1346 +++++++++++++++++ 1 file changed, 1346 insertions(+) create mode 100644 Phase 3 - 2020 (Summer)/Week 8/exercise8.ipynb diff --git a/Phase 3 - 2020 (Summer)/Week 8/exercise8.ipynb b/Phase 3 - 2020 (Summer)/Week 8/exercise8.ipynb new file mode 100644 index 000000000..d3ed240ba --- /dev/null +++ b/Phase 3 - 2020 (Summer)/Week 8/exercise8.ipynb @@ -0,0 +1,1346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Programming Exercise 8:\n", + "# Anomaly Detection and Recommender Systems\n", + "\n", + "\n", + "## Introduction \n", + "\n", + "In this exercise, you will implement the anomaly detection algorithm and\n", + "apply it to detect failing servers on a network. In the second part, you will\n", + "use collaborative filtering to build a recommender system for movies. Before\n", + "starting on the programming exercise, we strongly recommend watching the\n", + "video lectures and completing the review questions for the associated topics.\n", + "\n", + "All the information you need for solving this assignment is in this notebook, and all the code you will be implementing will take place within this notebook. The assignment can be promptly submitted to the coursera grader directly from this notebook (code and instructions are included below).\n", + "\n", + "Before we begin with the exercises, we need to import all libraries required for this programming exercise. Throughout the course, we will be using [`numpy`](http://www.numpy.org/) for all arrays and matrix operations, [`matplotlib`](https://matplotlib.org/) for plotting, and [`scipy`](https://docs.scipy.org/doc/scipy/reference/) for scientific and numerical computation functions and tools. You can find instructions on how to install required libraries in the README file in the [github repository](https://github.com/dibgerge/ml-coursera-python-assignments)." + ] + }, + { + "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", + "import matplotlib as mpl\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": "markdown", + "metadata": {}, + "source": [ + "## Submission and Grading\n", + "\n", + "\n", + "After completing each part of the assignment, be sure to submit your solutions to the grader. The following is a breakdown of how each part of this exercise is scored.\n", + "\n", + "\n", + "| Section | Part | Submitted Function | Points |\n", + "| :- |:- |:- | :-: |\n", + "| 1 | [Estimate Gaussian Parameters](#section1) | [`estimateGaussian`](#estimateGaussian) | 15 |\n", + "| 2 | [Select Threshold](#section2) | [`selectThreshold`](#selectThreshold) | 15 |\n", + "| 3 | [Collaborative Filtering Cost](#section3) | [`cofiCostFunc`](#cofiCostFunc) | 20 |\n", + "| 4 | [Collaborative Filtering Gradient](#section4) | [`cofiCostFunc`](#cofiCostFunc) | 30 |\n", + "| 5 | [Regularized Cost](#section5) | [`cofiCostFunc`](#cofiCostFunc) | 10 |\n", + "| 6 | [Gradient with regularization](#section6) | [`cofiCostFunc`](#cofiCostFunc) | 10 |\n", + "| | Total Points | |100 |\n", + "\n", + "\n", + "\n", + "You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.\n", + "\n", + "
\n", + "At the end of each section in this notebook, we have a cell which contains code for submitting the solutions thus far to the grader. Execute the cell to see your score up to the current section. For all your work to be submitted properly, you must execute those cells at least once.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 Anomaly Detection \n", + "\n", + "In this exercise, you will implement an anomaly detection algorithm to detect anomalous behavior in server computers. The features measure the throughput (mb/s) and latency (ms) of response of each server. While your servers were operating, you collected $m = 307$ examples of how they were behaving, and thus have an unlabeled dataset $\\{x^{(1)}, \\dots, x^{(m)}\\}$. You suspect that the vast majority of these examples are “normal” (non-anomalous) examples of the servers operating normally, but there might also be some examples of servers acting anomalously within this dataset.\n", + "\n", + "You will use a Gaussian model to detect anomalous examples in your dataset. You will first start on a 2D dataset that will allow you to visualize what the algorithm is doing. On that dataset you will fit a Gaussian distribution and then find values that have very low probability and hence can be considered anomalies. After that, you will apply the anomaly detection algorithm to a larger dataset with many dimensions.\n", + "\n", + "We start this exercise by using a small dataset that is easy to visualize. Our example case consists of 2 network server statistics across several machines: the latency and throughput of each machine. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# The following command loads the dataset.\n", + "data = loadmat(os.path.join('Data', 'ex8data1.mat'))\n", + "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n", + "\n", + "# Visualize the example dataset\n", + "pyplot.plot(X[:, 0], X[:, 1], 'bx', mew=2, mec='k', ms=6)\n", + "pyplot.axis([0, 30, 0, 30])\n", + "pyplot.xlabel('Latency (ms)')\n", + "pyplot.ylabel('Throughput (mb/s)')\n", + "pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Gaussian distribution\n", + "\n", + "To perform anomaly detection, you will first need to fit a model to the data's distribution. Given a training set $\\{x^{(1)}, \\dots, x^{(m)} \\}$ (where $x^{(i)} \\in \\mathbb{R}^n$ ), you want to estimate the Gaussian distribution for each of the features $x_i$ . For each feature $i = 1 \\dots n$, you need to find parameters $\\mu_i$ and $\\sigma_i^2$ that fit the data in the $i^{th}$ dimension $\\{ x_i^{(1)}, \\dots, x_i^{(m)} \\}$ (the $i^{th}$ dimension of each example).\n", + "\n", + "The Gaussian distribution is given by\n", + "\n", + "$$ p\\left( x; \\mu, \\sigma^2 \\right) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}} e^{-\\frac{\\left(x-\\mu\\right)^2}{2\\sigma^2}},$$\n", + "where $\\mu$ is the mean and $\\sigma^2$ is the variance.\n", + "\n", + "\n", + "### 1.2 Estimating parameters for a Gaussian \n", + "\n", + "You can estimate the parameters $\\left( \\mu_i, \\sigma_i^2 \\right)$, of the $i^{th}$ feature by using the following equations. To estimate the mean, you will use: \n", + "\n", + "$$ \\mu_i = \\frac{1}{m} \\sum_{j=1}^m x_i^{(j)},$$\n", + "\n", + "and for the variance you will use:\n", + "\n", + "$$ \\sigma_i^2 = \\frac{1}{m} \\sum_{j=1}^m \\left( x_i^{(j)} - \\mu_i \\right)^2.$$\n", + "\n", + "Your task is to complete the code in the function `estimateGaussian`. This function takes as input the data matrix `X` and should output an n-dimension vector `mu` that holds the mean for each of the $n$ features and another n-dimension vector `sigma2` that holds the variances of each of the features. You can implement this\n", + "using a for-loop over every feature and every training example (though a vectorized implementation might be more efficient; feel free to use a vectorized implementation if you prefer). \n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def estimateGaussian(X):\n", + " \"\"\"\n", + " This function estimates the parameters of a Gaussian distribution\n", + " using a provided dataset.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of shape (m x n) with each n-dimensional \n", + " data point in one row, and each total of m data points.\n", + " \n", + " Returns\n", + " -------\n", + " mu : array_like \n", + " A vector of shape (n,) containing the means of each dimension.\n", + " \n", + " sigma2 : array_like\n", + " A vector of shape (n,) containing the computed\n", + " variances of each dimension.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the mean of the data and the variances\n", + " In particular, mu[i] should contain the mean of\n", + " the data for the i-th feature and sigma2[i]\n", + " should contain variance of the i-th feature.\n", + " \"\"\"\n", + " # Useful variables\n", + " m, n = X.shape\n", + "\n", + " # You should return these values correctly\n", + " mu = np.zeros(n)\n", + " sigma2 = np.zeros(n)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " mu = (1/m)*np.sum(X,axis = 0)\n", + " sigma2 = (1/m)*np.sum((X - mu)**2,axis = 0)\n", + "\n", + " \n", + " # =============================================================\n", + " return mu, sigma2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have completed the code in `estimateGaussian`, the next cell will visualize the contours of the fitted Gaussian distribution. You should get a plot similar to the figure below.\n", + "\n", + "![](Figures/gaussian_fit.png)\n", + "\n", + "From your plot, you can see that most of the examples are in the region with the highest probability, while\n", + "the anomalous examples are in the regions with lower probabilities.\n", + "\n", + "To do the visualization of the Gaussian fit, we first estimate the parameters of our assumed Gaussian distribution, then compute the probabilities for each of the points and then visualize both the overall distribution and where each of the points falls in terms of that distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Estimate my and sigma2\n", + "mu, sigma2 = estimateGaussian(X)\n", + "\n", + "# Returns the density of the multivariate normal at each data point (row) \n", + "# of X\n", + "p = utils.multivariateGaussian(X, mu, sigma2)\n", + "\n", + "# Visualize the fit\n", + "utils.visualizeFit(X, mu, sigma2)\n", + "pyplot.xlabel('Latency (ms)')\n", + "pyplot.ylabel('Throughput (mb/s)')\n", + "pyplot.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n", + "\n", + "Use token from last successful submission ()? (Y/n): \n", + "You used an invalid email or your token may have expired. Please make sure you have entered all fields correctly. Try generating a new token if the issue still persists.\n" + ] + } + ], + "source": [ + "grader[1] = estimateGaussian\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 1.3 Selecting the threshold, $\\varepsilon$\n", + "\n", + "Now that you have estimated the Gaussian parameters, you can investigate which examples have a very high probability given this distribution and which examples have a very low probability. The low probability examples are more likely to be the anomalies in our dataset. One way to determine which examples are anomalies is to select a threshold based on a cross validation set. In this part of the exercise, you will implement an algorithm to select the threshold $\\varepsilon$ using the $F_1$ score on a cross validation set.\n", + "\n", + "\n", + "You should now complete the code for the function `selectThreshold`. For this, we will use a cross validation set $\\{ (x_{cv}^{(1)}, y_{cv}^{(1)}), \\dots, (x_{cv}^{(m_{cv})}, y_{cv}^{(m_{cv})})\\}$, where the label $y = 1$ corresponds to an anomalous example, and $y = 0$ corresponds to a normal example. For each cross validation example, we will compute $p\\left( x_{cv}^{(i)}\\right)$. The vector of all of these probabilities $p\\left( x_{cv}^{(1)}\\right), \\dots, p\\left( x_{cv}^{(m_{cv})}\\right)$ is passed to `selectThreshold` in the vector `pval`. The corresponding labels $y_{cv}^{(1)} , \\dots , y_{cv}^{(m_{cv})}$ are passed to the same function in the vector `yval`.\n", + "\n", + "The function `selectThreshold` should return two values; the first is the selected threshold $\\varepsilon$. If an example $x$ has a low probability $p(x) < \\varepsilon$, then it is considered to be an anomaly. The function should also return the $F_1$ score, which tells you how well you are doing on finding the ground truth\n", + "anomalies given a certain threshold. For many different values of $\\varepsilon$, you will compute the resulting $F_1$ score by computing how many examples the current threshold classifies correctly and incorrectly.\n", + "\n", + "The $F_1$ score is computed using precision ($prec$) and recall ($rec$):\n", + "\n", + "$$ F_1 = \\frac{2 \\cdot prec \\cdot rec}{prec + rec}, $$\n", + "\n", + "You compute precision and recall by: \n", + "\n", + "$$ prec = \\frac{tp}{tp + fp} $$ \n", + "\n", + "$$ rec = \\frac{tp}{tp + fn} $$\n", + "\n", + "where: \n", + "\n", + "- $tp$ is the number of true positives: the ground truth label says it’s an anomaly and our algorithm correctly classified it as an anomaly.\n", + "\n", + "- $fp$ is the number of false positives: the ground truth label says it’s not an anomaly, but our algorithm incorrectly classified it as an anomaly.\n", + "- $fn$ is the number of false negatives: the ground truth label says it’s an anomaly, but our algorithm incorrectly classified it as not being anomalous.\n", + "\n", + "In the provided code `selectThreshold`, there is already a loop that will try many different values of $\\varepsilon$ and select the best $\\varepsilon$ based on the $F_1$ score. You should now complete the code in `selectThreshold`. You can implement the computation of the $F_1$ score using a for-loop over all the cross\n", + "validation examples (to compute the values $tp$, $fp$, $fn$). You should see a value for `epsilon` of about 8.99e-05.\n", + "\n", + "
\n", + "**Implementation Note:** In order to compute $tp$, $fp$ and $fn$, you may be able to use a vectorized implementation rather than loop over all the examples. This can be implemented by numpy's equality test\n", + "between a vector and a single number. If you have several binary values in an n-dimensional binary vector $v \\in \\{0, 1\\}^n$, you can find out how many values in this vector are 0 by using: np.sum(v == 0). You can also\n", + "apply a logical and operator to such binary vectors. For instance, let `cvPredictions` be a binary vector of size equal to the number of cross validation set, where the $i^{th}$ element is 1 if your algorithm considers\n", + "$x_{cv}^{(i)}$ an anomaly, and 0 otherwise. You can then, for example, compute the number of false positives using: `fp = np.sum((cvPredictions == 1) & (yval == 0))`.\n", + "
\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def selectThreshold(yval, pval):\n", + " \"\"\"\n", + " Find the best threshold (epsilon) to use for selecting outliers based\n", + " on the results from a validation set and the ground truth.\n", + " \n", + " Parameters\n", + " ----------\n", + " yval : array_like\n", + " The ground truth labels of shape (m, ).\n", + " \n", + " pval : array_like\n", + " The precomputed vector of probabilities based on mu and sigma2 parameters. It's shape is also (m, ).\n", + " \n", + " Returns\n", + " -------\n", + " bestEpsilon : array_like\n", + " A vector of shape (n,) corresponding to the threshold value.\n", + " \n", + " bestF1 : float\n", + " The value for the best F1 score.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the F1 score of choosing epsilon as the threshold and place the\n", + " value in F1. The code at the end of the loop will compare the\n", + " F1 score for this choice of epsilon and set it to be the best epsilon if\n", + " it is better than the current choice of epsilon.\n", + " \n", + " Notes\n", + " -----\n", + " You can use predictions = (pval < epsilon) to get a binary vector\n", + " of 0's and 1's of the outlier predictions\n", + " \"\"\"\n", + " bestEpsilon = 0\n", + " bestF1 = 0\n", + " F1 = 0\n", + " from sklearn.metrics import confusion_matrix\n", + " \n", + " for epsilon in np.linspace(1.01*min(pval), max(pval), 1000):\n", + " # ====================== YOUR CODE HERE =======================\n", + " pred = (pval bestF1:\n", + " bestF1 = F1\n", + " bestEpsilon = epsilon\n", + "\n", + " return bestEpsilon, bestF1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you have completed the code in `selectThreshold`, the next cell will run your anomaly detection code and circle the anomalies in the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best epsilon found using cross-validation: 9.00e-05\n", + "Best F1 on Cross Validation Set: 0.875000\n", + " (you should see a value epsilon of about 8.99e-05)\n", + " (you should see a Best F1 value of 0.875000)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pval = utils.multivariateGaussian(Xval, mu, sigma2)\n", + "\n", + "epsilon, F1 = selectThreshold(yval, pval)\n", + "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n", + "print('Best F1 on Cross Validation Set: %f' % F1)\n", + "print(' (you should see a value epsilon of about 8.99e-05)')\n", + "print(' (you should see a Best F1 value of 0.875000)')\n", + "\n", + "# Find the outliers in the training set and plot the\n", + "outliers = p < epsilon\n", + "\n", + "# Visualize the fit\n", + "utils.visualizeFit(X, mu, sigma2)\n", + "pyplot.xlabel('Latency (ms)')\n", + "pyplot.ylabel('Throughput (mb/s)')\n", + "pyplot.tight_layout()\n", + "\n", + "# Draw a red circle around those outliers\n", + "pyplot.plot(X[outliers, 0], X[outliers, 1], 'ro', ms=10, mfc='None', mew=2)\n", + "pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n", + "\n", + "Use token from last successful submission ()? (Y/n): y\n", + "You used an invalid email or your token may have expired. Please make sure you have entered all fields correctly. Try generating a new token if the issue still persists.\n" + ] + } + ], + "source": [ + "grader[2] = selectThreshold\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 High dimensional dataset\n", + "\n", + "The next cell will run the anomaly detection algorithm you implemented on a more realistic and much harder dataset. In this dataset, each example is described by 11 features, capturing many more properties of your compute servers, but only some features indicate whether a point is an outlier. The script will use your code to estimate the Gaussian parameters ($\\mu_i$ and $\\sigma_i^2$), evaluate the probabilities for both the training data `X` from which you estimated the Gaussian parameters, and do so for the the cross-validation set `Xval`. Finally, it will use `selectThreshold` to find the best threshold $\\varepsilon$. You should see a value epsilon of about 1.38e-18, and 117 anomalies found." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best epsilon found using cross-validation: 1.38e-18\n", + "Best F1 on Cross Validation Set : 0.615385\n", + "\n", + " (you should see a value epsilon of about 1.38e-18)\n", + " (you should see a Best F1 value of 0.615385)\n", + "\n", + "# Outliers found: 117\n" + ] + } + ], + "source": [ + "# Loads the second dataset. You should now have the\n", + "# variables X, Xval, yval in your environment\n", + "data = loadmat(os.path.join('Data', 'ex8data2.mat'))\n", + "X, Xval, yval = data['X'], data['Xval'], data['yval'][:, 0]\n", + "\n", + "# Apply the same steps to the larger dataset\n", + "mu, sigma2 = estimateGaussian(X)\n", + "\n", + "# Training set \n", + "p = utils.multivariateGaussian(X, mu, sigma2)\n", + "\n", + "# Cross-validation set\n", + "pval = utils.multivariateGaussian(Xval, mu, sigma2)\n", + "\n", + "# Find the best threshold\n", + "epsilon, F1 = selectThreshold(yval, pval)\n", + "\n", + "print('Best epsilon found using cross-validation: %.2e' % epsilon)\n", + "print('Best F1 on Cross Validation Set : %f\\n' % F1)\n", + "print(' (you should see a value epsilon of about 1.38e-18)')\n", + "print(' (you should see a Best F1 value of 0.615385)')\n", + "print('\\n# Outliers found: %d' % np.sum(p < epsilon))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 Recommender Systems\n", + "\n", + "In this part of the exercise, you will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings ([MovieLens 100k Dataset](https://grouplens.org/datasets/movielens/) from GroupLens Research). This dataset consists of ratings on a scale of 1 to 5. The dataset has $n_u = 943$ users, and $n_m = 1682$ movies. \n", + "\n", + "In the next parts of this exercise, you will implement the function `cofiCostFunc` that computes the collaborative filtering objective function and gradient. After implementing the cost function and gradient, you will use `scipy.optimize.minimize` to learn the parameters for collaborative filtering.\n", + "\n", + "### 2.1 Movie ratings dataset\n", + "\n", + "The next cell will load the dataset `ex8_movies.mat`, providing the variables `Y` and `R`.\n", + "The matrix `Y` (a `num_movies` $\\times$ `num_users` matrix) stores the ratings $y^{(i,j)}$ (from 1 to 5). The matrix `R` is an binary-valued indicator matrix, where $R(i, j) = 1$ if user $j$ gave a rating to movie $i$, and $R(i, j) = 0$ otherwise. The objective of collaborative filtering is to predict movie ratings for the movies that users have not yet rated, that is, the entries with $R(i, j) = 0$. This will allow us to recommend the movies with the highest predicted ratings to the user.\n", + "\n", + "To help you understand the matrix `Y`, the following cell will compute the average movie rating for the first movie (Toy Story) and print its average rating." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average rating for movie 1 (Toy Story): 3.878319 / 5\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load data\n", + "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n", + "Y, R = data['Y'], data['R']\n", + "\n", + "# Y is a 1682x943 matrix, containing ratings (1-5) of \n", + "# 1682 movies on 943 users\n", + "\n", + "# R is a 1682x943 matrix, where R(i,j) = 1 \n", + "# if and only if user j gave a rating to movie i\n", + "\n", + "# From the matrix, we can compute statistics like average rating.\n", + "print('Average rating for movie 1 (Toy Story): %f / 5' %\n", + " np.mean(Y[0, R[0, :] == 1]))\n", + "\n", + "# We can \"visualize\" the ratings matrix by plotting it with imshow\n", + "pyplot.figure(figsize=(8, 8))\n", + "pyplot.imshow(Y)\n", + "pyplot.ylabel('Movies')\n", + "pyplot.xlabel('Users')\n", + "pyplot.grid(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Throughout this part of the exercise, you will also be working with the matrices, `X` and `Theta`:\n", + "\n", + "$$ \\text{X} = \n", + "\\begin{bmatrix}\n", + "- \\left(x^{(1)}\\right)^T - \\\\\n", + "- \\left(x^{(2)}\\right)^T - \\\\\n", + "\\vdots \\\\\n", + "- \\left(x^{(n_m)}\\right)^T - \\\\\n", + "\\end{bmatrix}, \\quad\n", + "\\text{Theta} = \n", + "\\begin{bmatrix}\n", + "- \\left(\\theta^{(1)}\\right)^T - \\\\\n", + "- \\left(\\theta^{(2)}\\right)^T - \\\\\n", + "\\vdots \\\\\n", + "- \\left(\\theta^{(n_u)}\\right)^T - \\\\\n", + "\\end{bmatrix}.\n", + "$$\n", + "\n", + "The $i^{th}$ row of `X` corresponds to the feature vector $x^{(i)}$ for the $i^{th}$ movie, and the $j^{th}$ row of `Theta` corresponds to one parameter vector $\\theta^{(j)}$, for the $j^{th}$ user. Both $x^{(i)}$ and $\\theta^{(j)}$ are n-dimensional vectors. For the purposes of this exercise, you will use $n = 100$, and therefore, $x^{(i)} \\in \\mathbb{R}^{100}$ and $\\theta^{(j)} \\in \\mathbb{R}^{100}$. Correspondingly, `X` is a $n_m \\times 100$ matrix and `Theta` is a $n_u \\times 100$ matrix.\n", + "\n", + "\n", + "### 2.2 Collaborative filtering learning algorithm\n", + "\n", + "Now, you will start implementing the collaborative filtering learning algorithm. You will start by implementing the cost function (without regularization).\n", + "\n", + "The collaborative filtering algorithm in the setting of movie recommendations considers a set of n-dimensional parameter vectors $x^{(1)}, \\dots, x^{(n_m)}$ and $\\theta^{(1)} , \\dots, \\theta^{(n_u)}$, where the model predicts the rating for movie $i$ by user $j$ as $y^{(i,j)} = \\left( \\theta^{(j)} \\right)^T x^{(i)}$. Given a dataset that consists of a set of ratings produced by some users on some movies, you wish to learn the parameter vectors $x^{(1)}, \\dots, x^{(n_m)}, \\theta^{(1)}, \\dots, \\theta^{(n_u)}$ that produce the best fit (minimizes the squared error).\n", + "\n", + "You will complete the code in `cofiCostFunc` to compute the cost function and gradient for collaborative filtering. Note that the parameters to the function (i.e., the values that you are trying to learn) are `X` and `Theta`. In order to use an off-the-shelf minimizer such as `scipy`'s `minimize` function, the cost function has been set up to unroll the parameters into a single vector called `params`. You had previously used the same vector unrolling method in the neural networks programming exercise.\n", + "\n", + "#### 2.2.1 Collaborative filtering cost function\n", + "\n", + "The collaborative filtering cost function (without regularization) is given by\n", + "\n", + "$$\n", + "J(x^{(1)}, \\dots, x^{(n_m)}, \\theta^{(1)}, \\dots,\\theta^{(n_u)}) = \\frac{1}{2} \\sum_{(i,j):r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)} - y^{(i,j)} \\right)^2\n", + "$$\n", + "\n", + "You should now modify the function `cofiCostFunc` to return this cost in the variable `J`. Note that you should be accumulating the cost for user $j$ and movie $i$ only if `R[i,j] = 1`.\n", + "\n", + "
\n", + "**Implementation Note**: We strongly encourage you to use a vectorized implementation to compute $J$, since it will later by called many times by `scipy`'s optimization package. As usual, it might be easiest to first write a non-vectorized implementation (to make sure you have the right answer), and the modify it to become a vectorized implementation (checking that the vectorization steps do not change your algorithm’s output). To come up with a vectorized implementation, the following tip might be helpful: You can use the $R$ matrix to set selected entries to 0. For example, `R * M` will do an element-wise multiplication between `M`\n", + "and `R`; since `R` only has elements with values either 0 or 1, this has the effect of setting the elements of M to 0 only when the corresponding value in R is 0. Hence, `np.sum( R * M)` is the sum of all the elements of `M` for which the corresponding element in `R` equals 1.\n", + "
\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def cofiCostFunc(params, Y, R, num_users, num_movies,\n", + " num_features, lambda_=0.0):\n", + " \"\"\"\n", + " Collaborative filtering cost function.\n", + " \n", + " Parameters\n", + " ----------\n", + " params : array_like\n", + " The parameters which will be optimized. This is a one\n", + " dimensional vector of shape (num_movies x num_users, 1). It is the \n", + " concatenation of the feature vectors X and parameters Theta.\n", + " \n", + " Y : array_like\n", + " A matrix of shape (num_movies x num_users) of user ratings of movies.\n", + " \n", + " R : array_like\n", + " A (num_movies x num_users) matrix, where R[i, j] = 1 if the \n", + " i-th movie was rated by the j-th user.\n", + " \n", + " num_users : int\n", + " Total number of users.\n", + " \n", + " num_movies : int\n", + " Total number of movies.\n", + " \n", + " num_features : int\n", + " Number of features to learn.\n", + " \n", + " lambda_ : float, optional\n", + " The regularization coefficient.\n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The value of the cost function at the given params.\n", + " \n", + " grad : array_like\n", + " The gradient vector of the cost function at the given params.\n", + " grad has a shape (num_movies x num_users, 1)\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost function and gradient for collaborative filtering.\n", + " Concretely, you should first implement the cost function (without\n", + " regularization) and make sure it is matches our costs. After that,\n", + " you should implement thegradient and use the checkCostFunction routine \n", + " to check that the gradient is correct. Finally, you should implement\n", + " regularization.\n", + " \n", + " Notes\n", + " -----\n", + " - The input params will be unraveled into the two matrices:\n", + " X : (num_movies x num_features) matrix of movie features\n", + " Theta : (num_users x num_features) matrix of user features\n", + "\n", + " - You should set the following variables correctly:\n", + "\n", + " X_grad : (num_movies x num_features) matrix, containing the \n", + " partial derivatives w.r.t. to each element of X\n", + " Theta_grad : (num_users x num_features) matrix, containing the \n", + " partial derivatives w.r.t. to each element of Theta\n", + "\n", + " - The returned gradient will be the concatenation of the raveled \n", + " gradients X_grad and Theta_grad.\n", + " \"\"\"\n", + " # Unfold the U and W matrices from params\n", + " X = params[:num_movies*num_features].reshape(num_movies, num_features)\n", + " Theta = params[num_movies*num_features:].reshape(num_users, num_features)\n", + "\n", + " # You need to return the following values correctly\n", + " J = 0\n", + " X_grad = np.zeros(X.shape)\n", + " Theta_grad = np.zeros(Theta.shape)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " J = (1/2)*np.sum(np.square((X@(Theta.T)-Y)*R))+(lambda_/2)*np.sum(np.square(X))+(lambda_ /2)*np.sum(np.square(Theta))\n", + " \n", + " \n", + " for i in range(R.shape[0]): \n", + " idx = np.where(R[i, :] == 1)[0]\n", + " Theta_temp = Theta[idx, :]\n", + " Y_temp = Y[i, idx]\n", + " X_grad[i, :] = ((X[i,:]@Theta_temp.T-Y_temp)@Theta_temp)+lambda_*X[i, :]\n", + " \n", + " for j in range(R.shape[1]): \n", + " idx = np.where(R[:, j] == 1)[0]\n", + " X_temp = X[idx, :]\n", + " Y_temp = Y[idx, j]\n", + " Theta_grad[j, :] = ((X_temp@Theta[j, :] - Y_temp)@X_temp) + lambda_ * Theta[j, :]\n", + " \n", + " # =============================================================\n", + " \n", + " grad = np.concatenate([X_grad.ravel(), Theta_grad.ravel()])\n", + " return J, grad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After you have completed the function, the next cell will run your cost function. To help you debug your cost function, we have included set of weights that we trained on that. You should expect to see an output of 22.22." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at loaded parameters: 22.22 \n", + "(this value should be about 22.22)\n" + ] + } + ], + "source": [ + "# Load pre-trained weights (X, Theta, num_users, num_movies, num_features)\n", + "data = loadmat(os.path.join('Data', 'ex8_movieParams.mat'))\n", + "X, Theta, num_users, num_movies, num_features = data['X'],\\\n", + " data['Theta'], data['num_users'], data['num_movies'], data['num_features']\n", + "\n", + "# Reduce the data set size so that this runs faster\n", + "num_users = 4\n", + "num_movies = 5\n", + "num_features = 3\n", + "\n", + "X = X[:num_movies, :num_features]\n", + "Theta = Theta[:num_users, :num_features]\n", + "Y = Y[:num_movies, 0:num_users]\n", + "R = R[:num_movies, 0:num_users]\n", + "\n", + "# Evaluate cost function\n", + "J, _ = cofiCostFunc(np.concatenate([X.ravel(), Theta.ravel()]),\n", + " Y, R, num_users, num_movies, num_features)\n", + " \n", + "print('Cost at loaded parameters: %.2f \\n(this value should be about 22.22)' % J)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n", + "\n", + "Use token from last successful submission ()? (Y/n): y\n", + "You used an invalid email or your token may have expired. Please make sure you have entered all fields correctly. Try generating a new token if the issue still persists.\n" + ] + } + ], + "source": [ + "grader[3] = cofiCostFunc\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 2.2.2 Collaborative filtering gradient\n", + "\n", + "Now you should implement the gradient (without regularization). Specifically, you should complete the code in `cofiCostFunc` to return the variables `X_grad` and `Theta_grad`. Note that `X_grad` should be a matrix of the same size as `X` and similarly, `Theta_grad` is a matrix of the same size as\n", + "`Theta`. The gradients of the cost function is given by:\n", + "\n", + "$$ \\frac{\\partial J}{\\partial x_k^{(i)}} = \\sum_{j:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)} - y^{(i,j)} \\right) \\theta_k^{(j)} $$\n", + "\n", + "$$ \\frac{\\partial J}{\\partial \\theta_k^{(j)}} = \\sum_{i:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)}- y^{(i,j)} \\right) x_k^{(j)} $$\n", + "\n", + "Note that the function returns the gradient for both sets of variables by unrolling them into a single vector. After you have completed the code to compute the gradients, the next cell run a gradient check\n", + "(available in `utils.checkCostFunction`) to numerically check the implementation of your gradients (this is similar to the numerical check that you used in the neural networks exercise. If your implementation is correct, you should find that the analytical and numerical gradients match up closely.\n", + "\n", + "
\n", + "**Implementation Note:** You can get full credit for this assignment without using a vectorized implementation, but your code will run much more slowly (a small number of hours), and so we recommend that you try to vectorize your implementation. To get started, you can implement the gradient with a for-loop over movies\n", + "(for computing $\\frac{\\partial J}{\\partial x^{(i)}_k}$) and a for-loop over users (for computing $\\frac{\\partial J}{\\theta_k^{(j)}}$). When you first implement the gradient, you might start with an unvectorized version, by implementing another inner for-loop that computes each element in the summation. After you have completed the gradient computation this way, you should try to vectorize your implementation (vectorize the inner for-loops), so that you are left with only two for-loops (one for looping over movies to compute $\\frac{\\partial J}{\\partial x_k^{(i)}}$ for each movie, and one for looping over users to compute $\\frac{\\partial J}{\\partial \\theta_k^{(j)}}$ for each user).\n", + "
\n", + "\n", + "
\n", + "**Implementation Tip:** To perform the vectorization, you might find this helpful: You should come up with a way to compute all the derivatives associated with $x_1^{(i)} , x_2^{(i)}, \\dots , x_n^{(i)}$ (i.e., the derivative terms associated with the feature vector $x^{(i)}$) at the same time. Let us define the derivatives for the feature vector of the $i^{th}$ movie as:\n", + "\n", + "$$ \\left(X_{\\text{grad}} \\left(i, :\\right)\\right)^T = \n", + "\\begin{bmatrix}\n", + "\\frac{\\partial J}{\\partial x_1^{(i)}} \\\\\n", + "\\frac{\\partial J}{\\partial x_2^{(i)}} \\\\\n", + "\\vdots \\\\\n", + "\\frac{\\partial J}{\\partial x_n^{(i)}}\n", + "\\end{bmatrix} = \\quad\n", + "\\sum_{j:r(i,j)=1} \\left( \\left( \\theta^{(j)} \\right)^T x^{(i)} - y^{(i,j)} \\right) \\theta^{(j)}\n", + "$$\n", + "\n", + "To vectorize the above expression, you can start by indexing into `Theta` and `Y` to select only the elements of interests (that is, those with `r[i, j] = 1`). Intuitively, when you consider the features for the $i^{th}$ movie, you only need to be concerned about the users who had given ratings to the movie, and this allows you to remove all the other users from `Theta` and `Y`.

\n", + "\n", + "\n", + "Concretely, you can set `idx = np.where(R[i, :] == 1)[0]` to be a list of all the users that have rated movie $i$. This will allow you to create the temporary matrices `Theta_temp = Theta[idx, :]` and `Y_temp = Y[i, idx]` that index into `Theta` and `Y` to give you only the set of users which have rated the $i^{th}$ movie. This will allow you to write the derivatives as:
\n", + "\n", + "`X_grad[i, :] = np.dot(np.dot(X[i, :], Theta_temp.T) - Y_temp, Theta_temp)`\n", + "\n", + "

\n", + "Note that the vectorized computation above returns a row-vector instead. After you have vectorized the computations of the derivatives with respect to $x^{(i)}$, you should use a similar method to vectorize the derivatives with respect to $θ^{(j)}$ as well.\n", + "
\n", + "\n", + "[Click here to go back to the function `cofiCostFunc` to update it](#cofiCostFunc). \n", + "\n", + " Do not forget to re-execute the cell containg the function `cofiCostFunc` so that it is updated with your implementation of the gradient computation." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.21854479 0.21854479]\n", + " [-1.28974424 -1.28974424]\n", + " [ 0.56573717 0.56573717]\n", + " [-2.10545279 -2.10545279]\n", + " [ 3.97454101 3.97454101]\n", + " [-5.96911993 -5.96911993]\n", + " [ 1.5654019 1.5654019 ]\n", + " [-0.46637769 -0.46637769]\n", + " [ 0.73479282 0.73479282]\n", + " [-3.92027864 -3.92027864]\n", + " [ 4.78158178 4.78158178]\n", + " [-8.43848669 -8.43848669]\n", + " [-1.69221538 -1.69221538]\n", + " [ 1.45120264 1.45120264]\n", + " [ 0.14192897 0.14192897]\n", + " [ 1.34316793 1.34316793]\n", + " [-6.75175685 -6.75175685]\n", + " [ 1.19370711 1.19370711]\n", + " [-3.18881009 -3.18881009]\n", + " [ 2.88213511 2.88213511]\n", + " [ 0.79078929 0.79078929]\n", + " [-0.54483812 -0.54483812]\n", + " [-2.24008985 -2.24008985]\n", + " [ 1.53833331 1.53833331]\n", + " [ 0.22935881 0.22935881]\n", + " [-0.9965765 -0.9965765 ]\n", + " [ 0.57725344 0.57725344]]\n", + "\n", + "The above two columns you get should be very similar.(Left-Your Numerical Gradient, Right-Analytical Gradient)\n", + "If your cost function implementation is correct, then the relative difference will be small (less than 1e-9).\n", + "\n", + "Relative Difference: 1.37803e-12\n" + ] + } + ], + "source": [ + "# Check gradients by running checkcostFunction\n", + "utils.checkCostFunction(cofiCostFunc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions*" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise anomaly-detection-and-recommender-systems\n", + "\n", + "Use token from last successful submission ()? (Y/n): y\n", + "You used an invalid email or your token may have expired. Please make sure you have entered all fields correctly. Try generating a new token if the issue still persists.\n" + ] + } + ], + "source": [ + "grader[4] = cofiCostFunc\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 2.2.3 Regularized cost function\n", + "\n", + "The cost function for collaborative filtering with regularization is given by\n", + "\n", + "$$ J(x^{(1)}, \\dots, x^{(n_m)}, \\theta^{(1)}, \\dots, \\theta^{(n_u)}) = \\frac{1}{2} \\sum_{(i,j):r(i,j)=1} \\left( \\left( \\theta^{(j)} \\right)^T x^{(i)} - y^{(i,j)} \\right)^2 + \\left( \\frac{\\lambda}{2} \\sum_{j=1}^{n_u} \\sum_{k=1}^{n} \\left( \\theta_k^{(j)} \\right)^2 \\right) + \\left( \\frac{\\lambda}{2} \\sum_{i=1}^{n_m} \\sum_{k=1}^n \\left(x_k^{(i)} \\right)^2 \\right) $$\n", + "\n", + "You should now add regularization to your original computations of the cost function, $J$. After you are done, the next cell will run your regularized cost function, and you should expect to see a cost of about 31.34.\n", + "\n", + "[Click here to go back to the function `cofiCostFunc` to update it](#cofiCostFunc)\n", + " Do not forget to re-execute the cell containing the function `cofiCostFunc` so that it is updated with your implementation of regularized cost function." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at loaded parameters (lambda = 1.5): 31.34\n", + " (this value should be about 31.34)\n" + ] + } + ], + "source": [ + "# Evaluate cost function\n", + "J, _ = cofiCostFunc(np.concatenate([X.ravel(), Theta.ravel()]),\n", + " Y, R, num_users, num_movies, num_features, 1.5)\n", + " \n", + "print('Cost at loaded parameters (lambda = 1.5): %.2f' % J)\n", + "print(' (this value should be about 31.34)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[5] = cofiCostFunc\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 2.2.4 Regularized gradient\n", + "\n", + "Now that you have implemented the regularized cost function, you should proceed to implement regularization for the gradient. You should add to your implementation in `cofiCostFunc` to return the regularized gradient\n", + "by adding the contributions from the regularization terms. Note that the gradients for the regularized cost function is given by:\n", + "\n", + "$$ \\frac{\\partial J}{\\partial x_k^{(i)}} = \\sum_{j:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)} - y^{(i,j)} \\right) \\theta_k^{(j)} + \\lambda x_k^{(i)} $$\n", + "\n", + "$$ \\frac{\\partial J}{\\partial \\theta_k^{(j)}} = \\sum_{i:r(i,j)=1} \\left( \\left(\\theta^{(j)}\\right)^T x^{(i)}- y^{(i,j)} \\right) x_k^{(j)} + \\lambda \\theta_k^{(j)} $$\n", + "\n", + "This means that you just need to add $\\lambda x^{(i)}$ to the `X_grad[i,:]` variable described earlier, and add $\\lambda \\theta^{(j)}$ to the `Theta_grad[j, :]` variable described earlier.\n", + "\n", + "[Click here to go back to the function `cofiCostFunc` to update it](#cofiCostFunc)\n", + " Do not forget to re-execute the cell containing the function `cofiCostFunc` so that it is updated with your implementation of the gradient for the regularized cost function.\n", + "\n", + "After you have completed the code to compute the gradients, the following cell will run another gradient check (`utils.checkCostFunction`) to numerically check the implementation of your gradients." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 3.50890756 3.50890756]\n", + " [-2.88348521 -2.88348521]\n", + " [-2.63942248 -2.63942248]\n", + " [ 0.05440719 0.05440719]\n", + " [-2.72595071 -2.72595071]\n", + " [-7.18596012 -7.18596012]\n", + " [-2.79751098 -2.79751098]\n", + " [ 2.13565548 2.13565548]\n", + " [-3.54251445 -3.54251445]\n", + " [ 2.41385255 2.41385255]\n", + " [-0.46899046 -0.46899046]\n", + " [ 1.79433369 1.79433369]\n", + " [-0.14173411 -0.14173411]\n", + " [-2.25267548 -2.25267548]\n", + " [ 1.74546065 1.74546065]\n", + " [ 1.8129789 1.8129789 ]\n", + " [ 0.03079782 0.03079782]\n", + " [ 5.69546225 5.69546225]\n", + " [-1.43580364 -1.43580364]\n", + " [ 0.75009875 0.75009875]\n", + " [-1.58970763 -1.58970763]\n", + " [-3.32501488 -3.32501488]\n", + " [ 1.84757803 1.84757803]\n", + " [ 2.95052666 2.95052666]\n", + " [ 2.3218307 2.3218307 ]\n", + " [-1.24133207 -1.24133207]\n", + " [ 0.8911853 0.8911853 ]]\n", + "\n", + "The above two columns you get should be very similar.(Left-Your Numerical Gradient, Right-Analytical Gradient)\n", + "If your cost function implementation is correct, then the relative difference will be small (less than 1e-9).\n", + "\n", + "Relative Difference: 1.64678e-12\n" + ] + } + ], + "source": [ + "# Check gradients by running checkCostFunction\n", + "utils.checkCostFunction(cofiCostFunc, 1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You should now submit your solutions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grader[6] = cofiCostFunc\n", + "grader.grade()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Learning movie recommendations \n", + "\n", + "After you have finished implementing the collaborative filtering cost function and gradient, you can now start training your algorithm to make movie recommendations for yourself. In the next cell, you can enter your own movie preferences, so that later when the algorithm runs, you can get your own movie recommendations! We have filled out some values according to our own preferences, but you should change this according to your own tastes. The list of all movies and their number in the dataset can be found listed in the file `Data/movie_idx.txt`." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New user ratings:\n", + "-----------------\n", + "Rated 4 stars: Toy Story (1995)\n", + "Rated 3 stars: Twelve Monkeys (1995)\n", + "Rated 5 stars: Usual Suspects, The (1995)\n", + "Rated 4 stars: Outbreak (1995)\n", + "Rated 5 stars: Shawshank Redemption, The (1994)\n", + "Rated 3 stars: While You Were Sleeping (1995)\n", + "Rated 5 stars: Forrest Gump (1994)\n", + "Rated 2 stars: Silence of the Lambs, The (1991)\n", + "Rated 4 stars: Alien (1979)\n", + "Rated 5 stars: Die Hard 2 (1990)\n", + "Rated 5 stars: Sphere (1998)\n" + ] + } + ], + "source": [ + "# Before we will train the collaborative filtering model, we will first\n", + "# add ratings that correspond to a new user that we just observed. This\n", + "# part of the code will also allow you to put in your own ratings for the\n", + "# movies in our dataset!\n", + "movieList = utils.loadMovieList()\n", + "n_m = len(movieList)\n", + "\n", + "# Initialize my ratings\n", + "my_ratings = np.zeros(n_m)\n", + "\n", + "# Check the file movie_idx.txt for id of each movie in our dataset\n", + "# For example, Toy Story (1995) has ID 1, so to rate it \"4\", you can set\n", + "# Note that the index here is ID-1, since we start index from 0.\n", + "my_ratings[0] = 4\n", + "\n", + "# Or suppose did not enjoy Silence of the Lambs (1991), you can set\n", + "my_ratings[97] = 2\n", + "\n", + "# We have selected a few movies we liked / did not like and the ratings we\n", + "# gave are as follows:\n", + "my_ratings[6] = 3\n", + "my_ratings[11]= 5\n", + "my_ratings[53] = 4\n", + "my_ratings[63] = 5\n", + "my_ratings[65] = 3\n", + "my_ratings[68] = 5\n", + "my_ratings[182] = 4\n", + "my_ratings[225] = 5\n", + "my_ratings[354] = 5\n", + "\n", + "print('New user ratings:')\n", + "print('-----------------')\n", + "for i in range(len(my_ratings)):\n", + " if my_ratings[i] > 0:\n", + " print('Rated %d stars: %s' % (my_ratings[i], movieList[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.3.1 Recommendations\n", + "\n", + "After the additional ratings have been added to the dataset, the script\n", + "will proceed to train the collaborative filtering model. This will learn the\n", + "parameters X and Theta. To predict the rating of movie i for user j, you need to compute (θ (j) ) T x (i) . The next part of the script computes the ratings for\n", + "all the movies and users and displays the movies that it recommends (Figure\n", + "4), according to ratings that were entered earlier in the script. Note that\n", + "you might obtain a different set of the predictions due to different random\n", + "initializations." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recommender system learning completed.\n" + ] + } + ], + "source": [ + "# Now, you will train the collaborative filtering model on a movie rating \n", + "# dataset of 1682 movies and 943 users\n", + "\n", + "# Load data\n", + "data = loadmat(os.path.join('Data', 'ex8_movies.mat'))\n", + "Y, R = data['Y'], data['R']\n", + "\n", + "# Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by \n", + "# 943 users\n", + "\n", + "# R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a\n", + "# rating to movie i\n", + "\n", + "# Add our own ratings to the data matrix\n", + "Y = np.hstack([my_ratings[:, None], Y])\n", + "R = np.hstack([(my_ratings > 0)[:, None], R])\n", + "\n", + "# Normalize Ratings\n", + "Ynorm, Ymean = utils.normalizeRatings(Y, R)\n", + "\n", + "# Useful Values\n", + "num_movies, num_users = Y.shape\n", + "num_features = 10\n", + "\n", + "# Set Initial Parameters (Theta, X)\n", + "X = np.random.randn(num_movies, num_features)\n", + "Theta = np.random.randn(num_users, num_features)\n", + "\n", + "initial_parameters = np.concatenate([X.ravel(), Theta.ravel()])\n", + "\n", + "# Set options for scipy.optimize.minimize\n", + "options = {'maxiter': 100}\n", + "\n", + "# Set Regularization\n", + "lambda_ = 10\n", + "res = optimize.minimize(lambda x: cofiCostFunc(x, Ynorm, R, num_users,\n", + " num_movies, num_features, lambda_),\n", + " initial_parameters,\n", + " method='TNC',\n", + " jac=True,\n", + " options=options)\n", + "theta = res.x\n", + "\n", + "# Unfold the returned theta back into U and W\n", + "X = theta[:num_movies*num_features].reshape(num_movies, num_features)\n", + "Theta = theta[num_movies*num_features:].reshape(num_users, num_features)\n", + "\n", + "print('Recommender system learning completed.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After training the model, you can now make recommendations by computing the predictions matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top recommendations for you:\n", + "----------------------------\n", + "Predicting rating 5.0 for movie Great Day in Harlem, A (1994)\n", + "Predicting rating 5.0 for movie Aiqing wansui (1994)\n", + "Predicting rating 5.0 for movie Saint of Fort Washington, The (1993)\n", + "Predicting rating 5.0 for movie Prefontaine (1997)\n", + "Predicting rating 5.0 for movie Star Kid (1997)\n", + "Predicting rating 5.0 for movie They Made Me a Criminal (1939)\n", + "Predicting rating 5.0 for movie Santa with Muscles (1996)\n", + "Predicting rating 5.0 for movie Someone Else's America (1995)\n", + "Predicting rating 5.0 for movie Marlene Dietrich: Shadow and Light (1996)\n", + "Predicting rating 5.0 for movie Entertaining Angels: The Dorothy Day Story (1996)\n", + "\n", + "Original ratings provided:\n", + "--------------------------\n", + "Rated 4 for Toy Story (1995)\n", + "Rated 3 for Twelve Monkeys (1995)\n", + "Rated 5 for Usual Suspects, The (1995)\n", + "Rated 4 for Outbreak (1995)\n", + "Rated 5 for Shawshank Redemption, The (1994)\n", + "Rated 3 for While You Were Sleeping (1995)\n", + "Rated 5 for Forrest Gump (1994)\n", + "Rated 2 for Silence of the Lambs, The (1991)\n", + "Rated 4 for Alien (1979)\n", + "Rated 5 for Die Hard 2 (1990)\n", + "Rated 5 for Sphere (1998)\n" + ] + } + ], + "source": [ + "p = np.dot(X, Theta.T)\n", + "my_predictions = p[:, 0] + Ymean\n", + "\n", + "movieList = utils.loadMovieList()\n", + "\n", + "ix = np.argsort(my_predictions)[::-1]\n", + "\n", + "print('Top recommendations for you:')\n", + "print('----------------------------')\n", + "for i in range(10):\n", + " j = ix[i]\n", + " print('Predicting rating %.1f for movie %s' % (my_predictions[j], movieList[j]))\n", + "\n", + "print('\\nOriginal ratings provided:')\n", + "print('--------------------------')\n", + "for i in range(len(my_ratings)):\n", + " if my_ratings[i] > 0:\n", + " print('Rated %d for %s' % (my_ratings[i], movieList[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}