{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# This mounts your Google Drive to the Colab VM.\n", "from google.colab import drive\n", "drive.mount('/content/drive')\n", "\n", "# TODO: Enter the foldername in your Drive where you have saved the unzipped\n", "# assignment folder, e.g. 'cs231n/assignments/assignment2/'\n", "FOLDERNAME = None\n", "assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n", "\n", "# Now that we've mounted your Drive, this ensures that\n", "# the Python interpreter of the Colab VM can load\n", "# python files from within it.\n", "import sys\n", "sys.path.append('/content/drive/My Drive/{}'.format(FOLDERNAME))\n", "\n", "# This downloads the CIFAR-10 dataset to your Drive\n", "# if it doesn't already exist.\n", "%cd /content/drive/My\\ Drive/$FOLDERNAME/cs231n/datasets/\n", "!bash get_datasets.sh\n", "%cd /content/drive/My\\ Drive/$FOLDERNAME" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "pdf-title" ] }, "source": [ "# Batch Normalization\n", "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization, proposed by [1] in 2015.\n", "\n", "To understand the goal of batch normalization, it is important to first recognize that machine learning methods tend to perform better with input data consisting of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features. This will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance, since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n", "\n", "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, they propose to insert into the network layers that normalize batches. At training time, such a layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n", "\n", "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n", "\n", "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n", "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "pdf-ignore" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=========== You can safely ignore the message below if you are NOT working on ConvolutionalNetworks.ipynb ===========\n", "\tYou will need to compile a Cython extension for a portion of this assignment.\n", "\tThe instructions to do this will be given in a section of the notebook below.\n" ] } ], "source": [ "# Setup cell.\n", "import time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from cs231n.classifiers.fc_net import *\n", "from cs231n.data_utils import get_CIFAR10_data\n", "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n", "from cs231n.solver import Solver\n", "\n", "%matplotlib inline\n", "plt.rcParams[\"figure.figsize\"] = (10.0, 8.0) # Set default size of plots.\n", "plt.rcParams[\"image.interpolation\"] = \"nearest\"\n", "plt.rcParams[\"image.cmap\"] = \"gray\"\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "def rel_error(x, y):\n", " \"\"\"Returns relative error.\"\"\"\n", " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n", "\n", "def print_mean_std(x,axis=0):\n", " print(f\" means: {x.mean(axis=axis)}\")\n", " print(f\" stds: {x.std(axis=axis)}\\n\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [ "pdf-ignore" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_train: (49000, 3, 32, 32)\n", "y_train: (49000,)\n", "X_val: (1000, 3, 32, 32)\n", "y_val: (1000,)\n", "X_test: (1000, 3, 32, 32)\n", "y_test: (1000,)\n" ] } ], "source": [ "# Load the (preprocessed) CIFAR-10 data.\n", "data = get_CIFAR10_data()\n", "for k, v in list(data.items()):\n", " print(f\"{k}: {v.shape}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization: Forward Pass\n", "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n", "\n", "Referencing the paper linked to above in [1] may be helpful!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Before batch normalization:\n", " means: [ -2.3814598 -13.18038246 1.91780462]\n", " stds: [27.18502186 34.21455511 37.68611762]\n", "\n", "After batch normalization (gamma=1, beta=0)\n", " means: [ 5.55111512e-17 3.44169138e-17 -5.27355937e-18]\n", " stds: [0.99999999 1. 1. ]\n", "\n", "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n", " means: [11. 12. 13.]\n", " stds: [0.99999999 1.99999999 2.99999999]\n", "\n" ] } ], "source": [ "# Check the training-time forward pass by checking means and variances\n", "# of features both before and after batch normalization \n", "\n", "# Simulate the forward pass for a two-layer network.\n", "np.random.seed(231)\n", "N, D1, D2, D3 = 200, 50, 60, 3\n", "X = np.random.randn(N, D1)\n", "W1 = np.random.randn(D1, D2)\n", "W2 = np.random.randn(D2, D3)\n", "a = np.maximum(0, X.dot(W1)).dot(W2)\n", "\n", "print('Before batch normalization:')\n", "print_mean_std(a,axis=0)\n", "\n", "gamma = np.ones((D3,))\n", "beta = np.zeros((D3,))\n", "\n", "# Means should be close to zero and stds close to one.\n", "print('After batch normalization (gamma=1, beta=0)')\n", "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n", "print_mean_std(a_norm,axis=0)\n", "\n", "gamma = np.asarray([1.0, 2.0, 3.0])\n", "beta = np.asarray([11.0, 12.0, 13.0])\n", "\n", "# Now means should be close to beta and stds close to gamma.\n", "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n", "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n", "print_mean_std(a_norm,axis=0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "After batch normalization (test-time):\n", " means: [-0.03927354 -0.04349152 -0.10452688]\n", " stds: [1.01531428 1.01238373 0.97819988]\n", "\n" ] } ], "source": [ "# Check the test-time forward pass by running the training-time\n", "# forward pass many times to warm up the running averages, and then\n", "# checking the means and variances of activations after a test-time\n", "# forward pass.\n", "\n", "np.random.seed(231)\n", "N, D1, D2, D3 = 200, 50, 60, 3\n", "W1 = np.random.randn(D1, D2)\n", "W2 = np.random.randn(D2, D3)\n", "\n", "bn_param = {'mode': 'train'}\n", "gamma = np.ones(D3)\n", "beta = np.zeros(D3)\n", "\n", "for t in range(50):\n", " X = np.random.randn(N, D1)\n", " a = np.maximum(0, X.dot(W1)).dot(W2)\n", " batchnorm_forward(a, gamma, beta, bn_param)\n", "\n", "bn_param['mode'] = 'test'\n", "X = np.random.randn(N, D1)\n", "a = np.maximum(0, X.dot(W1)).dot(W2)\n", "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n", "\n", "# Means should be close to zero and stds close to one, but will be\n", "# noisier than training-time forward passes.\n", "print('After batch normalization (test-time):')\n", "print_mean_std(a_norm,axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization: Backward Pass\n", "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n", "\n", "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n", "\n", "Once you have finished, run the following to numerically check your backward pass." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dx error: 1.7029258328157158e-09\n", "dgamma error: 7.420414216247087e-13\n", "dbeta error: 2.8795057655839487e-12\n" ] } ], "source": [ "# Gradient check batchnorm backward pass.\n", "np.random.seed(231)\n", "N, D = 4, 5\n", "x = 5 * np.random.randn(N, D) + 12\n", "gamma = np.random.randn(D)\n", "beta = np.random.randn(D)\n", "dout = np.random.randn(N, D)\n", "\n", "bn_param = {'mode': 'train'}\n", "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n", "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n", "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n", "\n", "dx_num = eval_numerical_gradient_array(fx, x, dout)\n", "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n", "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n", "\n", "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n", "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n", "\n", "# You should expect to see relative errors between 1e-13 and 1e-8.\n", "print('dx error: ', rel_error(dx_num, dx))\n", "print('dgamma error: ', rel_error(da_num, dgamma))\n", "print('dbeta error: ', rel_error(db_num, dbeta))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization: Alternative Backward Pass\n", "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n", "\n", "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too! \n", "\n", "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n", "\n", "we first calculate the mean $\\mu$ and variance $v$.\n", "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$ and normalized data $Y$.\n", "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n", "\n", "\\begin{align}\n", "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k & v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n", "& \\sigma=\\sqrt{v+\\epsilon} & y_i=\\frac{x_i-\\mu}{\\sigma}\n", "\\end{align}" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "pdf-ignore" ] }, "source": [ "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n", "\n", "The unknown/hard part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n", "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n", "$\\frac{\\partial \\sigma}{\\partial v}$, \n", "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n", "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n", "\n", "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n", "\n", "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n", "\n", "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dx difference: 2.6722610095440874e-13\n", "dgamma difference: 0.0\n", "dbeta difference: 0.0\n", "speedup: 2.00x\n" ] } ], "source": [ "np.random.seed(231)\n", "N, D = 100, 500\n", "x = 5 * np.random.randn(N, D) + 12\n", "gamma = np.random.randn(D)\n", "beta = np.random.randn(D)\n", "dout = np.random.randn(N, D)\n", "\n", "bn_param = {'mode': 'train'}\n", "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n", "\n", "t1 = time.time()\n", "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n", "t2 = time.time()\n", "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n", "t3 = time.time()\n", "\n", "print('dx difference: ', rel_error(dx1, dx2))\n", "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n", "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n", "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fully Connected Networks with Batch Normalization\n", "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n", "\n", "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n", "\n", "**Hint:** You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running check with reg = 0\n", "Initial loss: 2.2611955101340957\n", "W1 relative error: 1.10e-04\n", "W2 relative error: 5.65e-06\n", "W3 relative error: 4.14e-10\n", "b1 relative error: 2.22e-08\n", "b2 relative error: 5.55e-09\n", "b3 relative error: 1.02e-10\n", "beta1 relative error: 7.33e-09\n", "beta2 relative error: 1.17e-09\n", "gamma1 relative error: 7.47e-09\n", "gamma2 relative error: 3.35e-09\n", "\n", "Running check with reg = 3.14\n", "Initial loss: 6.996533220108303\n", "W1 relative error: 1.98e-06\n", "W2 relative error: 2.28e-06\n", "W3 relative error: 1.11e-08\n", "b1 relative error: 5.55e-09\n", "b2 relative error: 2.22e-08\n", "b3 relative error: 2.10e-10\n", "beta1 relative error: 6.32e-09\n", "beta2 relative error: 3.48e-09\n", "gamma1 relative error: 6.27e-09\n", "gamma2 relative error: 4.14e-09\n" ] } ], "source": [ "np.random.seed(231)\n", "N, D, H1, H2, C = 2, 15, 20, 30, 10\n", "X = np.random.randn(N, D)\n", "y = np.random.randint(C, size=(N,))\n", "\n", "# You should expect losses between 1e-4~1e-10 for W, \n", "# losses between 1e-08~1e-10 for b,\n", "# and losses between 1e-08~1e-09 for beta and gammas.\n", "for reg in [0, 3.14]:\n", " print('Running check with reg = ', reg)\n", " model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n", " reg=reg, weight_scale=5e-2, dtype=np.float64,\n", " normalization='batchnorm')\n", "\n", " loss, grads = model.loss(X, y)\n", " print('Initial loss: ', loss)\n", "\n", " for name in sorted(grads):\n", " f = lambda _: model.loss(X, y)[0]\n", " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n", " print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n", " if reg == 0: print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization for Deep Networks\n", "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solver with batch norm:\n", "(Iteration 1 / 200) loss: 2.340974\n", "(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.115000\n", "(Epoch 1 / 10) train acc: 0.314000; val_acc: 0.266000\n", "(Iteration 21 / 200) loss: 2.039365\n", "(Epoch 2 / 10) train acc: 0.386000; val_acc: 0.279000\n", "(Iteration 41 / 200) loss: 2.041103\n", "(Epoch 3 / 10) train acc: 0.497000; val_acc: 0.309000\n", "(Iteration 61 / 200) loss: 1.753903\n", "(Epoch 4 / 10) train acc: 0.533000; val_acc: 0.309000\n", "(Iteration 81 / 200) loss: 1.246584\n", "(Epoch 5 / 10) train acc: 0.574000; val_acc: 0.313000\n", "(Iteration 101 / 200) loss: 1.320590\n", "(Epoch 6 / 10) train acc: 0.633000; val_acc: 0.339000\n", "(Iteration 121 / 200) loss: 1.159473\n", "(Epoch 7 / 10) train acc: 0.683000; val_acc: 0.325000\n", "(Iteration 141 / 200) loss: 1.151109\n", "(Epoch 8 / 10) train acc: 0.780000; val_acc: 0.337000\n", "(Iteration 161 / 200) loss: 0.627576\n", "(Epoch 9 / 10) train acc: 0.806000; val_acc: 0.332000\n", "(Iteration 181 / 200) loss: 0.879936\n", "(Epoch 10 / 10) train acc: 0.777000; val_acc: 0.322000\n", "\n", "Solver without batch norm:\n", "(Iteration 1 / 200) loss: 2.302332\n", "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n", "(Epoch 1 / 10) train acc: 0.283000; val_acc: 0.250000\n", "(Iteration 21 / 200) loss: 2.041970\n", "(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.277000\n", "(Iteration 41 / 200) loss: 1.900473\n", "(Epoch 3 / 10) train acc: 0.373000; val_acc: 0.282000\n", "(Iteration 61 / 200) loss: 1.713156\n", "(Epoch 4 / 10) train acc: 0.390000; val_acc: 0.310000\n", "(Iteration 81 / 200) loss: 1.662209\n", "(Epoch 5 / 10) train acc: 0.434000; val_acc: 0.300000\n", "(Iteration 101 / 200) loss: 1.696060\n", "(Epoch 6 / 10) train acc: 0.535000; val_acc: 0.345000\n", "(Iteration 121 / 200) loss: 1.557987\n", "(Epoch 7 / 10) train acc: 0.530000; val_acc: 0.304000\n", "(Iteration 141 / 200) loss: 1.432189\n", "(Epoch 8 / 10) train acc: 0.628000; val_acc: 0.339000\n", "(Iteration 161 / 200) loss: 1.033932\n", "(Epoch 9 / 10) train acc: 0.656000; val_acc: 0.337000\n", "(Iteration 181 / 200) loss: 0.908564\n", "(Epoch 10 / 10) train acc: 0.714000; val_acc: 0.323000\n" ] } ], "source": [ "np.random.seed(231)\n", "\n", "# Try training a very deep net with batchnorm.\n", "hidden_dims = [100, 100, 100, 100, 100]\n", "\n", "num_train = 1000\n", "small_data = {\n", " 'X_train': data['X_train'][:num_train],\n", " 'y_train': data['y_train'][:num_train],\n", " 'X_val': data['X_val'],\n", " 'y_val': data['y_val'],\n", "}\n", "\n", "weight_scale = 2e-2\n", "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n", "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n", "\n", "print('Solver with batch norm:')\n", "bn_solver = Solver(bn_model, small_data,\n", " num_epochs=10, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': 1e-3,\n", " },\n", " verbose=True,print_every=20)\n", "bn_solver.train()\n", "\n", "print('\\nSolver without batch norm:')\n", "solver = Solver(model, small_data,\n", " num_epochs=10, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': 1e-3,\n", " },\n", " verbose=True, print_every=20)\n", "solver.train()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [ "pdf-ignore-input" ] }, "outputs": [ { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n", " \"\"\"utility function for plotting training history\"\"\"\n", " plt.title(title)\n", " plt.xlabel(label)\n", " bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n", " bl_plot = plot_fn(baseline)\n", " num_bn = len(bn_plots)\n", " for i in range(num_bn):\n", " label='with_norm'\n", " if labels is not None:\n", " label += str(labels[i])\n", " plt.plot(bn_plots[i], bn_marker, label=label)\n", " label='baseline'\n", " if labels is not None:\n", " label += str(labels[0])\n", " plt.plot(bl_plot, bl_marker, label=label)\n", " plt.legend(loc='lower center', ncol=num_bn+1) \n", "\n", " \n", "plt.subplot(3, 1, 1)\n", "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n", " lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n", "plt.subplot(3, 1, 2)\n", "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n", " lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n", "plt.subplot(3, 1, 3)\n", "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n", " lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n", "\n", "plt.gcf().set_size_inches(15, 15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization and Initialization\n", "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n", "\n", "The first cell will train eight-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [ "pdf-ignore-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running weight scale 1 / 20\n", "Running weight scale 2 / 20\n", "Running weight scale 3 / 20\n", "Running weight scale 4 / 20\n", "Running weight scale 5 / 20\n", "Running weight scale 6 / 20\n", "Running weight scale 7 / 20\n", "Running weight scale 8 / 20\n", "Running weight scale 9 / 20\n", "Running weight scale 10 / 20\n", "Running weight scale 11 / 20\n", "Running weight scale 12 / 20\n", "Running weight scale 13 / 20\n", "Running weight scale 14 / 20\n", "Running weight scale 15 / 20\n", "Running weight scale 16 / 20\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\360Downloads\\cs231n\\assignment2\\cs231n\\layers.py:148: RuntimeWarning: divide by zero encountered in log\n", " loss = np.sum(-np.log(p[np.arange(num_train), y])) / num_train\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running weight scale 17 / 20\n", "Running weight scale 18 / 20\n", "Running weight scale 19 / 20\n", "Running weight scale 20 / 20\n" ] } ], "source": [ "np.random.seed(231)\n", "\n", "# Try training a very deep net with batchnorm.\n", "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n", "num_train = 1000\n", "small_data = {\n", " 'X_train': data['X_train'][:num_train],\n", " 'y_train': data['y_train'][:num_train],\n", " 'X_val': data['X_val'],\n", " 'y_val': data['y_val'],\n", "}\n", "\n", "bn_solvers_ws = {}\n", "solvers_ws = {}\n", "weight_scales = np.logspace(-4, 0, num=20)\n", "for i, weight_scale in enumerate(weight_scales):\n", " print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n", " bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n", " model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n", "\n", " bn_solver = Solver(bn_model, small_data,\n", " num_epochs=10, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': 1e-3,\n", " },\n", " verbose=False, print_every=200)\n", " bn_solver.train()\n", " bn_solvers_ws[weight_scale] = bn_solver\n", "\n", " solver = Solver(model, small_data,\n", " num_epochs=10, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': 1e-3,\n", " },\n", " verbose=False, print_every=200)\n", " solver.train()\n", " solvers_ws[weight_scale] = solver" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [ "pdf-ignore-input" ] }, "outputs": [ { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot results of weight scale experiment.\n", "best_train_accs, bn_best_train_accs = [], []\n", "best_val_accs, bn_best_val_accs = [], []\n", "final_train_loss, bn_final_train_loss = [], []\n", "\n", "for ws in weight_scales:\n", " best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n", " bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n", " \n", " best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n", " bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n", " \n", " final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n", " bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n", " \n", "plt.subplot(3, 1, 1)\n", "plt.title('Best val accuracy vs. weight initialization scale')\n", "plt.xlabel('Weight initialization scale')\n", "plt.ylabel('Best val accuracy')\n", "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n", "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n", "plt.legend(ncol=2, loc='lower right')\n", "\n", "plt.subplot(3, 1, 2)\n", "plt.title('Best train accuracy vs. weight initialization scale')\n", "plt.xlabel('Weight initialization scale')\n", "plt.ylabel('Best training accuracy')\n", "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n", "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n", "plt.legend()\n", "\n", "plt.subplot(3, 1, 3)\n", "plt.title('Final training loss vs. weight initialization scale')\n", "plt.xlabel('Weight initialization scale')\n", "plt.ylabel('Final training loss')\n", "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n", "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n", "plt.legend()\n", "plt.gca().set_ylim(1.0, 3.5)\n", "\n", "plt.gcf().set_size_inches(15, 15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "pdf-inline" ] }, "source": [ "## Inline Question 1:\n", "Describe the results of this experiment. How does the weight initialization scale affect models with/without batch normalization differently, and why?\n", "\n", "## Answer:\n", "The result show that with batch normalization model could be:\n", "\n", "1. much more robust to bad initialization(even with a bad initialization can get good performance)\n", "2. much more robust to avoid overfitting(one some scale without bn the model the a bigger gap between val and train)\n", "3. prevent vanishing gradient and gradient explosion problem\n", "\n", "I think the reason is that batch normalization prevent the input values of the layers to be too small or too big, so during backpropagation the network can get a better gradient.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization and Batch Size\n", "We will now run a small experiment to study the interaction of batch normalization and batch size.\n", "\n", "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [ "pdf-ignore-input" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No normalization: batch size = 5\n", "Normalization: batch size = 5\n", "Normalization: batch size = 10\n", "Normalization: batch size = 50\n" ] } ], "source": [ "def run_batchsize_experiments(normalization_mode):\n", " np.random.seed(231)\n", " \n", " # Try training a very deep net with batchnorm.\n", " hidden_dims = [100, 100, 100, 100, 100]\n", " num_train = 1000\n", " small_data = {\n", " 'X_train': data['X_train'][:num_train],\n", " 'y_train': data['y_train'][:num_train],\n", " 'X_val': data['X_val'],\n", " 'y_val': data['y_val'],\n", " }\n", " n_epochs=10\n", " weight_scale = 2e-2\n", " batch_sizes = [5,10,50]\n", " lr = 10**(-3.5)\n", " solver_bsize = batch_sizes[0]\n", "\n", " print('No normalization: batch size = ',solver_bsize)\n", " model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n", " solver = Solver(model, small_data,\n", " num_epochs=n_epochs, batch_size=solver_bsize,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': lr,\n", " },\n", " verbose=False)\n", " solver.train()\n", " \n", " bn_solvers = []\n", " for i in range(len(batch_sizes)):\n", " b_size=batch_sizes[i]\n", " print('Normalization: batch size = ',b_size)\n", " bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n", " bn_solver = Solver(bn_model, small_data,\n", " num_epochs=n_epochs, batch_size=b_size,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': lr,\n", " },\n", " verbose=False)\n", " bn_solver.train()\n", " bn_solvers.append(bn_solver)\n", " \n", " return bn_solvers, solver, batch_sizes\n", "\n", "batch_sizes = [5,10,50]\n", "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": "
", "image/png": 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uWVFdEUvfXYrH72FayrQ2Aa0lsNV56oKPb1ngevbw2cGA1lKSPzoiOlyXJV3QMgIiIiIiIv2A3/pp9jXT7G+m2ddMk6+JJl8THp8neLvZF9jnbwoe85NVP2mzRlpXWha4binF336Ba+k/tIyAiIiIiAwoxzIksCe0hKdgSPK33m4TnI5if5OvCY/f0+njmnxNbQJa6P7Qio095cHTHjzqBa5lYFCAExEREZF+pbMhgfe8cw8byzeSl5bXIQR12kt1jPu9fu8RWnVkkSaSqIio4E90RDTREdG4Xe7g7Xh3/GH3h+4L/na13dZ+/zXLrqGkvqRDezLiM7ho7EXdvi7pXxTgRERERKTfqG6q5v4V97epeAjQ7G/mD5v/AJs7f1xLeIqOiMYd0S4QuaKC4elw+7sKSYfb3xLAoiKiwraW2e1zbm8TeAFiImJYMntJWNojvUsBTkRERETCqryhnFf3vMryvctZWbwSn/V1epzB8Pxlz3cawCJcEX3c6v6jZWhpXw45lfBRgBMRERGRPldYW8jyPct5de+rrC1di8UyZsgYbph2A//c8U/KG8o7PGZE/AjGJo8NQ2v7v8VjFyuwnSAU4ERERESkT+ys3smre17lv3v+y+YKZyzkpKGTuGXmLZw7+lzGJY/DGMOEoRM0JFCkCwpwIiIiItIrrLV8WPEhy/cuZ/me5eys3glAXloed8y5g3NGn8OoIaM6PE5DAkW61q0AZ4y5AHgEiACetNY+0G7/aOB3QHLgmLustS915zlFREREpP/yWz8FZQUs37Oc5XuXU1hbiMu4mDt8Lp+Y9AkWjV7E8PjhRzyPhgSKdO64A5wxJgJ4DDgX2A+sNMa8YK3dFHLY/wHPWGt/boyZCrwEZHejvSIiIiLSz3j9XvJL8lm+Zzmv7X2NsoYyIl2RnJxxMjfn3cyZo85kWMywcDdTZFDoTg/cfGC7tXYngDHmaeAyIDTAWWBI4HYScKAbzyciIiIi/USTr4n3D7zP8r3LeX3f61Q3VRMbGcupWaeyaPQiTh95OolRieFupsig050AlwXsC7m/H1jQ7pilwCvGmC8B8cA5nZ3IGHMzcDPA6NGju9EkEREREekt9Z563ip8i1f3vMqbhW9S56kj0Z3IGaPO4JzR57AwayGxkbHhbqbIoNbbRUyuBn5rrf2xMeZk4A/GmOnWWn/oQdbaJ4AnAObOnWt7uU0iIiIicpSqm6r53/7/sXzPct498C5NviaGRg/lguwLOGfMOSwYsQB3hDvczRQ5YXQnwBUCoWWDRga2hboJuADAWvueMSYGSAVKu/G8IiIiItKLyhvKeW3va7y691VWFK3Aa72kx6XzsQkf45wx5zArfRaRLhUzFwmH7vzLWwlMMMbk4AS3q4Br2h2zF1gE/NYYMwWIAcq68ZwiIiIi0guKaouC5f7XlK7BYhmVOIrrp13PuaPPZVrqNFzGFe5mipzwjjvAWWu9xphbgf/gLBHwlLV2ozHmPiDfWvsC8BXgV8aY23EKmtxgrdUQSREREZF+YHf17mBo23hwIwAThk7g8zM+zzljzmFC8gSMMWFupYiEMv0tT82dO9fm5+eHuxkiIiIig461lq2VW/nvnv/y6t5X2V61HYDc1FwWjV7EOWPOYcyQMWFupYgYY1ZZa+d2tk+Dl0VEREQGMb/1s758Pa/ueZXle5ezr2YfLuNidvps7pp/F4tGL2JE/IhwN1NEjpICnIiIiMgg4/V7WV2ymuV7l/Pq3lcprS8l0hXJgowFfHr6pzlr1FmkxKaEu5kichwU4EREREQGgWZfM+8Xvc+re1/l9b2vU9lUSUxEDKdkncKi0Ys4Y9QZDIkaEu5mikg3KcCJiIiIDFD1nnreOfAOy/cs5839b1LrqSXeHc8ZI8/gnDHncErmKcS548LdTBHpQQpwIiIiIgPIoeZD/G/f/3h176u8U/gOjb5GkqOTOXfMuZwz5hxOyjiJqIiocDdTRHqJApyIiIhIP3ew4SCv73ud5XuX80HRB3j9XtJj07l8/OWcO+ZcZg+frYW1RU4Q+pcuIiIi0g8V1xXz6t5XWb5nOatLV+O3fkYmjOS6KddxzphzyE3N1cLaIicgBTgRERGRfmLvob3BNdrWl68HYHzyeD6b+1nOHXMuE4dO1MLaIic4BTgRERGRPrBs5zIeWf0IxXXFjIgfwZLZS7go5yK2VW1j+Z7lLN+7nG2V2wCYljKNJbOXsGj0InKScsLcchHpT4y1NtxtaGPu3Lk2Pz8/3M0QERER6THLdi5j6btLafQ1BrdFmAiSopKoaKrAYJiVPotzxpzDotGLyEzIDGNrRSTcjDGrrLVzO9unHjgRERGRXtTgbeCHK3/YJrwB+KyPOm8d3zrpW5w9+mxSY1PD1EIRGUgU4ERERER6SKO3kS2VW9h0cBMbyzeyqWITO6t24rO+To9v9jVz5aQr+7iVIjKQKcCJiIiIHIdGbyNbK7c6Ye3gRjYd3MSOqh3BsDY0eihTU6dy5sgzeXbrs1Q2VXY4x4j4EX3dbBEZ4BTgRERERI6gydfE1oq2YW171fa2YS1lKmeMPINpKdOYmjKVEfEjghUjxyWP6zAHLiYihiWzl4TlekRk4FKAExEREQnR5GtiW+W24BDITQc3sb1yO17rBSA5OpmpKVM5feTpTE2ZytSUqWTEZxy2vP/isYsBOlShbNkuInK0VIVSRERETljNvmYnrAV61TYd3MS2ym3BsJYUnRTsUZuaMpVpKdOOGNZERLpLVShFRETkhNdpWKvahtffGtamDpvKp6Z9immpTmjLjM9UWBORfkUBTkRERAYdj8/D1qqtwaC2sXxjm7A2JGoIU1Om8qmpnwr2rmUlZCmsiUi/pwAnIiIiA5rH52Fb1bY2BUa2VW7D4/cAkBiVyNSUqXxy6ieDYW1kwkiFNREZkBTgREREZMDw+Dxsr9reZhjk1sqtHcLadVOvC85ZU1gTkcGkWwHOGHMB8AgQATxprX2g3f6HgbMCd+OAdGttcneeU0RERE4MHr+HHVU7nGqQgbC2pXJLa1hzB8LalOuYmjqVacOmMTJRYU1EBrfjDnDGmAjgMeBcYD+w0hjzgrV2U8sx1trbQ47/EjCrG20VERGRQaolrIXOWdtauZVmfzPghLUpKVOcsBYYBjkqcZTCmoiccLrTAzcf2G6t3QlgjHkauAzY1MXxVwPf7sbziYiIyCDg8XvYWbWzzZy1LRVbgmEtwZ3A1JSpXDPlmtZhkIkjcRlXmFsuIhJ+3QlwWcC+kPv7gQWdHWiMGQPkAK91sf9m4GaA0aNHd6NJIiIi0leW7Vx2xIWpvX5vsGdt48GNbD64mS2VW2jyNQEQ745naspUrp58dbB0/6jEUQprIiJd6KsiJlcBz1prfZ3ttNY+ATwBzkLefdQmEREROU7Ldi5j6btLafQ1AlBUV8TSd5dSVFdESkyKMxSywulZCw1rU4ZN4ROTPhFcHHv0kNEKayIix6A7Aa4QGBVyf2RgW2euAr7YjecSERGRfuSR1Y8Ew1uLRl8jj6x+BIC4yDimpDhhrWXO2pghYxTWRES6qTsBbiUwwRiTgxPcrgKuaX+QMWYyMBR4rxvPJSIiImFW1VhFfkk+HxR9QFFdUZfHvXD5CwprIiK95LgDnLXWa4y5FfgPzjICT1lrNxpj7gPyrbUvBA69CnjaWquhkSIiIgNIbXMtq0tX80HRB6woXsGWii1YLLGRsURHRAeHRobKiM8gJyknDK0VETkxdGsOnLX2JeCldtvuaXd/aXeeQ0RERPpGo7eRtWVrWVG0gg+KP2Bj+UZ81keUK4pZ6bO4ddatzB8xn2mp03hl9ytt5sABxETEsGT2kjBegYjI4NdXRUxERESkn/H4PKwvX88HxR+womgF68rW4fF7iDSRTE+dzk25N7FgxAJmpM8gOiK6zWNbqk0eqQqliIj0LNPfRjbOnTvX5ufnh7sZIiIig47P7+PDig+DgW116WoavA0YDFNSpjB/xHzmj5jP7OGziXfHh7u5IiInLGPMKmvt3M72qQdORERkkPJbP9urtgeHRK4qXkWNpwaA8cnj+cj4jzA/Yz5zh88lKTopzK0VEelDBc/Aq/dB9X5IGgmL7oG8K8PdqqOiACciIjJIWGvZW7M3WHRkZfFKKhorABidOJrzss9jQcYC5o2YR2psaphbKyISJgXPwIu3gafBuV+9z7kPAyLEKcCJiIgMYEW1RXxQ/AEri1fyQdEHlNSXAJAel86pWacGh0VmJGSEuaUiIv3Eq/e2hrcWnganR04BTkRERHpSeUN5MKytKF7Bvpp9AAyLGca8EfOYP2I+CzIWMDpxNMaYMLdWRKSfqN4PO16HHa85t7s6ZgBQgBMREenHqpuqyS/OZ0XxClYUr2B71XYAEt2JzB0xl2unXMv8EfMZnzxegU1EpEVTLex5xwlsO16H8i3O9oQR4I4DTz2eBheF7w5l5MJKImP9zly4AUABTkREpB+p89SxumQ1K4pX8EHRB3xY8WFw8ezZ6bO5ZNwlLBixgMnDJhPhigh3c0VE+ge/H4rWOoFt5xuw933weyAyFrJPgdmfhHFnQ/oUWP83ePE2yvPdNJRFUbYhgYyFHqeQyQCgACciIhJGTb4m1pWuC5b231C+Aa/14na5mZk+ky/M/ALzR8wnNzUXd4Q73M0VEek/qve39rDtfAManKJNjMiDk7/gBLZRJ4E7Bl91Nc27d9P03j9p3nWAxg3zqdu+zTnN7gTSvv5VIgfA/DdQgBMREelTHr+HjeUbg3PY1paupdnfTISJYFrqNG6cfiPzM+YzM20mMZEx4W6uiEj/0VQLu98O9LK9DuVbne2JGTDxAuyY02mOmkBzSTXNu3bR9MZ/ad79JM27duGrqGg9T0QErthYMAasxboiKXu9kIzTw3NZx0oLeYuIiPQin9/HlsotrWuxlawKLp49edhkp0pkxnxmp88mISoh3M0VEek//L7AsMjXnZ99H2B9HryeOJrjZtAckUNzQyLNJYdo2r0Lz/5C8PmCD49ISSEqJ5uo7Gyic3KIyskhKjsHV0w0Oy68CNvUFDzWREczfvl/iUxLC8OFdqSFvEVERPqItZYdVTuCQyJXlqykptlZPHts0lguG3cZCzIWMHf4XJJjksPbWBGR/qZqH+x8Hf/m/9K87m2ay+tpOhRJsyeN5oaJNJc14G9oBPYAezDR0USNGUPM5CkMufBCJ6hlZxOVk0PEkCGdPkXR0nuxfn+bbdbvp+zxn5Px7f4/D04BTkREpBusteyv2R8MbB8UfxBcPHtkwkjOG3NesLx/Wlz/+MuuiEh/YH0+PLu30bziZZrXvUPz9i00ldXRfCgSb0MEEBP4gcjMYURn55B0WmtAi87JJjIjA+NyHdPzNqxdCx5P240eDw1r1vTIdfU2BTgREZFjVFxXHKwSubJ4JUV1RQCkx6azMHNhcFhkVkJWmFsqIhJ+vqoqmnbtonn3Hpp37qD5w3U079hGc0k11tc6ncsV7SIqazTxMycTNWkGUWMDwx5Hj3bmrPWQsc//o8fOFQ4KcCIiIgHLdi7jkdWPUFxXzIj4ESyZvYTFYxdzsOEgK0tWsqLIWYttz6E9ACRHJzNvxDxumn4T8zPmkz0kW2uxicgJyTY307xvn1M8ZNcumnftpnn3bqeASGVl64HGEpXgIyrRS/zMoURNnEb0rDOImn8REcMz9N/Qo6AiJiIiIjjhbem7S2n0NQa3RZgIUmJSKG0oBSDBncDc4XOZnzGf+SPmM2HoBFzm2IbuiIgMVNZavKVlNO/aRfNuJ6Q1BX579u931mILiEhOJHqYm6iYaqKiKohK9BI1IoWomWdgJp4DY8+E+NTwXUw/pyImIiIih1HZWMmDKx5sE94AfNZHVVMVS2YvYcGIBUxJmUKkS//rFOkTBc/Aq/c5a30ljXQWWR4g63QNdP66Opp2t/SgOb1oTmjbjb++PniciYkhKjubmClTGHLaLKKjKonybiOqoYCIyAOBRbRPddZjG3c2pE1ySvdLt+j/QiIicsIprS9lVcmq4M/2qu1dHuvxe/hM7mf6sHUiQsEz8OJt4Glw7lfvc+6DQtxheEpLKbzjK4x8+KEjlsO3Ph+eAweC4Sw4R23XLrwlJa0HGoM7I4OonBySZs9uLcufEk1kzXrMrtdh5z+gsQoagIwZMO7WwCLaCyAyulev+USkACciIoOatZb9tfvbBLZ9NfsAiHfHMzN9JovHLuaPm/7IwcaDHR4/In5EXzdZ5MTl8ziLM7/89dbw1sLTAP+6HSr3QEIaJAyH+HTndnw6uLXwffnjP6dh1ao25fC9lZWtvWi7d9O82wlrnj17sSGVGF1DhhCVk038SScFQlqggMiY0bhiYqDxUOsi2qsfgoOBP3wlZsLki2HcWRoW2Ue6FeCMMRcAjwARwJPW2gc6OeZKYClggXXW2mu685wiIiKHY61lZ/VOVpWsIr8kn1Ulqyitd+awJUcnMzt9NldNuoo5I+Ywaeik4JDIjPiMDnPgYiJiWDJ7SViuQ2TQa6qBko1QVADFBVC8Hko3g6+p68c018Lr3+18X3RSa5jrLOAlDB+UYc/6fHjLy2nYuJGqZ58Fa6n6619pLCjAU1iIr6qq9eDISKJGjyYqO5uEM84IWdw6m4hhw9oWEPH74MAa+OBRZxHt/SvA7wV3nDMsct5nYOxZGhYZBscd4IwxEcBjwLnAfmClMeYFa+2mkGMmAHcDp1hrK40x6d1tsIiISCif38eWyi3B3rXVJaupbHIqnqXFpjF3+FzmDJ/DnOFzGJs8tsuiI4vHLgbotAqliHRTTUkgpBUEAtt6qNiJ8/d9IC4FRuTBgs85v1/5P6gt7niepFFwaz7UlUFdKdSWQW1J6+26UqgthZJNsPMNaKzuvD3RQyAhveuAl5Deuj/MYc9XW4e36ACeoiI8B4qc30UH8LbcLikBr7ftg/x+PKWlJJ53XiCgjSE6Jwf3yJGYyMN8/a/cAztfd3rZdv7PGRaJcYZFLrzN6WXTsMiw604P3Hxgu7V2J4Ax5mngMmBTyDGfBR6z1lYCWGtLu/F8IiIieHweNh7cGOxdW1u6llpPLeAsnH36yNOZM3wOc4fPZWTiyGMqSb147GIFNpHu8PudYFa8zglpxeudwFYX8hVwaDaMyIUZVzu/M/IgMaNdL45tOwcOwB3rFDJxx0DyKOfnSLxNTqBrE/BKQm6XOWGv7o3Dh734tK4DXkJ6YH+608ZjYH0+vGVlgWB2AG9oSDvghDb/oUNtHxQRgXv4cCIzM4idPZshGRm4EhMoe/SnbRan9h86RNqXbj38XLjGQ7D7LaeHbcdrULHD2T4kC6Zc7PSwaVhkv9OdAJcF7Au5vx9Y0O6YiQDGmHdwhlkutdb+u/2JjDE3AzcDjB49uhtNEhGRwabB20BBWUGwh62grCA4zHFc0jguyrmIOcPnMHv4bM1XE+lLnkYo3RQIaoFeteIN4Klz9rvckD4ZJpzrBLUReTBiOsQkHfncLYVKuluFMjL62MJeXVm7gFfaNgCWbobaN44i7DnBzhcxFE9THN76CDy14KluxlNRi6esCm9xidN75vO1OYVryBDcmZm4MzOJmzMHd2YGkRkZuDMyndtpaZiIiDaPKVp6b4emWL+/zVw4oHVY5I7XnJ99K8D6WodFzv+sU3wkdaKGRfZjvV3EJBKYAJwJjATeNMbkWmurQg+y1j4BPAHOOnC93CYREenHapprWFO6JhjYNh7ciNfvxWVcTBo6iSsmXsHc4XOZNXwWw2KGhbu5IieG+orWHrWWsFa2xfnyDxCV6IS02de3hrW0Sd0bapd3Zd9WnIyMdoJi0sgjH+ttwlYX4927Fc+ebXj278FTeADPvnK8ZVV4KovwVO/B39zuccbijvPhjvMRm+hiyIxY3KnDcKen4M7IIDJrFBGpWYEAOPyoe/Ya1q5t0/sGgMdDw5o1ULm7tYdt1/8C4TMwLPKUJYFqkfM1LHIA6U6AKwRC/5wxMrAt1H7gA2utB9hljNmKE+hWduN5RURkEKlorGB1yepgYNtSuQW/9RPpimR6ynQ+NfVTzBk+h5npM0mMSgx3c0UGN2udkv0t89RaAlt1yKCrxEwnpE1eHAhruZCcDa7Btai9r6am86GNLXPQSko79J5FJCURmZmJOzeDuIwM3JkZuNPTiBwagzvBRWS0B9NQFujVKwvp4SuCsnWwr6rzxkQlhgzbTOsQ8MYuWQDvvAvekLUsXREQ64FHZjj3h2TBlEucwJZzJsSn9MbLJn2gOwFuJTDBGJODE9yuAtpXmHweuBr4jTEmFWdI5c5uPKeIiAxwxXXFbUr676x2/rcQExHDjLQZfD7v88wZPofctFxiI49tPomIHIOWkv0t89RaetYaq5z9xgUpE5yiFfM+48xVG57rzAMb4KzXi7ekpDWQBYKap6goWBzEX1vb9kFuN+4RI3BnZBA/bz6RmRm4Q4Y2ukeMwBUf372GBYdxloYM52wX9so+hF1vtr5PXfH7nEqfFzwYGBY5QcMiB4njDnDWWq8x5lbgPzjz256y1m40xtwH5FtrXwjsO88YswnwAXdaazsusiMiIoOStZZ9NfvalPQvrHUGayS4E5iVPotLx13KnOFzmJYyDXeEO8wtFhmkmmqhZEMgrK3rWLI/MgaGT4NpHwkUFpkB6VMgqpuBpJuOZWHqFtZa/DU1bQqBtO9B85aUOAVXQkQkJzuhbPRo4hYscMJZIKRFZmQSmZrSYe5ZjzvGYZzBYPerswlW9Gx/zEmf7/FmSngZa/vXlLO5c+fa/Pz8cDdDRESOg9/62VG1o00PW1lDGQBDo4cGy/nPGT6HiUMnEuHq5S9DIiei0JL9Lb1roSX7Y4c5vWkjWn5yIWU8RPR2aYRjV7T0Xqr++leSr7oqWIzDejx4Sko7L60fuO+vq2tzHuN2BwqBZASDWWT73rO4uHBcYs94eHrbYa4tkkbB7Rv6vj3SbcaYVdbauZ3t63//UkVEZMDw+r1sqdgS7F1bXbqa6ianOlt6XDrzRswLlvTPSco5ppL+InIEwZL97cJapyX7r2oNa0My+/1QOuv3U796dYeFqb3l5XhLS525eiEihg3DnZFBVHY2cSef7ASzkB60iJQUzCCbo9fGonu6XnZBBh0FOBEROWrNvmY2lG8I9q6tKV1DvbcegFGJozhr1FnBwJaVkKXAJtJTjliyPxLSprQt2T98GsQmh7XZR8tbVkZDQQENBetpXF9Aw/oN+GtqWg/w+/GUl5OwcGHbHrTMTKf3LPYEny/bU8suyICgIZQiItKlek8968rWtVmDrTlQF3t88vjgcMjZ6bMZHj88zK0V6ecKnjm6L9j1Fc58tWAlyILOS/a3LII9IhfSJg+YMvD+ujoaNm6kMRDYGtavx1tU5OyMiCB60kSix0/g0EsvgdcbfJyJjmb88v8e9Vw4kYFMQyhFROSoVDdVs7Z0bTCwbTq4Ca911mCbPGwyn5j8iWBgGxozNNzNFRk4Cp5pO8Step9zv/4gJI9uG9balOzPcHrTJl3UGtYGUMl+6/XStG2bE9QK1tFYsJ6mHTuCBUTco0YRN2sWMZ/6JLF5ecRMmYIrNtZZmLpdD36nC1OLnIAU4EREBrFlO5fxyOpHKK4rZkT8CJbMXsLisYuD+8sbytuswba1cisWS6QrktzUXG6YfoOzBlvaTBKiEsJ4JSIDkN/n9KbVlcF/vtF2fhI49/99V+COccq8t5TsbxkGOYBK9ltr8RQWtvasFRTQuGkTttFZmywiOZmYvFwSzzuP2LxcYnJziRw2rNNzHXZhapETnIZQiogMUst2LmPpu0tp9LUu7BodEc2l4y7Fb/2sKlnF7kO7gcAabOkzgvPXclNziYmMCVPLRfqp0EBWXw51gZ/g7TKnR63ldkMlnZZ2b++m5TB8athL9h8rb2UljRs2OEEtMBTSV1EBgImKImbqVGJn5BGTm0dsXi7uUaM0L1bkKGkIpYjICeiR1Y+0CW8ATb4m/rb1byS6E5k1fBYfmfAR5gyfw9RhU7UGm5x4fF5oqAgJYWVQdzDkdnkgkAVudxnIDMQOhfg0iE+FtEmQfYpzPy4V4lPg5a8752kvaRSMmtfbV9pt/qYmmjZvDhYaaVhfgGfPXmenMUSNG0vCGWcEAlsuMRMnYtz6b4pIb1CAExEZJErrS1lftp515etYX7aeorqiTo8zGN666i2twSaDT2ggC/aSHWzbY3ZcgWwyZAdux6U6v4O305xjj7SGmt83YMq8W7+f5l272sxba9yyJVhQJHL4cGLzckn+2BXOvLXp04hI0BBrkb6iACciMgA1eBvYdHAT68vWU1BeQEFZASX1JQBEuiKZNHQScZFxwRL/oUbEj1B4k4HhaAJZ6BDGwwWyuGGt4St9SkgQS4O4lJDbqUcXyI5VPy7z7ikpdUr3t8xb27ABf20tAK74eGJyc0m58UZi8nKJzcvDPVwVZ0XCSQFORKSf81s/u6t3U1BeEAxs2yq34QuUFM9KyGJ2+mxy03LJTc1lSsoUoiOiO50DFxMRw5LZS8J1KTLYHG1Z/BbBQFbWbu5YF0MYG6o4ciBLO3IgixsG/eGPFnlXhj2w+WrrnHlr6wPz1goK8JY4f/whMpKYSZMYcsnFxAbmrUWNHTu4F8AWGYAU4ERE+pmKxoo2PWsbyzdS43EWtE1wJzAtdRqfnv5p8tLymJ46ndTY1E7P01Jt8nBVKEWOW2dl8f/5Rdj+qlMWP7THLBjIKrs4WWeB7LTWIYxxKSG3+1Eg6+esxxMo4d+6QHbT9h0QKGDnHj2auHnzghUhY6ZMwRWj4kUi/Z2qUIqIhFGzr5nNFZvbBLbC2kIAXMbFhOQJ5KXlkZuaS15aHjlJObiM/houYVJXDkXrnLXK/vdgx7L4QYFAFlrEI3g7td1cssAcMgWybrHW4tm/P1ARMhDYNm3CNjUBEDF0aHAIpDNvbTqRQ7WWo0h/pSqUIiL9gLWWfTX7gkMh15evZ3PFZrx+pzBAelw6M9Jm8IlJnyA3NZepKVOJc8eFudVyQrIWDhU6Ya2owAlsReucbUdk4J6DCmS9zFtZSeP69W0KjfiqqgAw0dHETJvG0KuucqpC5uXhzspSCX+RQUIBTkSkl1Q3VbOhfEObwFbVVAVAbGQsU1Omcv3U68lLdXrYhserMICEgd8PFTuhaG0gqAXCWkNF4AADqRNhzEJnYemMGc4i07883Rk22V7SSIW3HuZvbKRx0+Y2hUY8+wKvvTFEjx9PwqKznXlrM/KIHj9eJfxFBjEFOBGRHuDxe9hauTUY1ArKCoKLZBsMY5PGcuaoM8lLyyMvNY9xyeOIdOk/wdLHfB4o+7Btz1rxemh2Kg7icjsLSk9e7AS1jBkwfFrnC0wvumfAlMXvTzylpRTe8RVGPvwQkWlpHfZbn4/mnTuDa601FqyncevW1hL+GRnE5uYy9BNXEpObR8y0aUQkDKwFwEWke/TtQUTkGFlrKaoratOztungJpp8zlyTYTHDyEvN49Jxl5Kblsu0lGkkRiWGudVywmmuh5KNbXvWSjeBr9nZ746HEdNh5jWtPWtpkyEy6ujO34/L4vdn5Y//nIZVqyh7/OdkfPsePCUlbeetbdiAv64OAFdCArF5uaTcdFOw0Ig7PT3MVyAi4aYiJiIiR1DnqWND+YZgz9r68vWUN5QDEOWKYkrKlGDPWm5aLpnxmZprIn2rodLpSQvtWSvfCtbv7I8d2hrSWn6GjdVQxz7kO3SIhg0b2Pe5z4PHAy4XEUOH4jt40DnA7SZm0iSnwEig2EhUdrZK+IucoFTERETkKPn8PrZXbWd9eetQyB1VO7CBtajGDBnDSRknBQPbxKETcUdoron0oZri1nlqxYHAVrWndX9iJmTkwdTLWkNb0kjQHxV6jbUWX3k5ngMHWn8KD7S537IwdpDfjyshgdTPf57YvFyiJ0/GFR0dngsQkQFFAU5ETmil9aXBEv7ry9ezsXwj9d56AJKik5ieOp3zxpwXXCQ7KTopzC2WE4a1ULm7bWGR4gKoLWk9ZthYyJwFc25wQtuIGZDQcV6VdI/1ePCUlOI5UNgmlHlbglpREba5uc1jXEOG4M7MxD1yJHHz5+MaMoSDTzwRnMsG4C0uZsgF53c6F05EpCsKcCJywmjwNrDp4KY2ga24rhiASFckk4ZO4rLxlwXXXBudOFpDIaVv+LxwcFvbkv3FBdBY7ew3Ec78tHFnB6pA5jnz12L0B4We4G9owFNU1KHXLBjUSkqcap0hItJScWdmEj11CgnnLHLCWmYm7sws3FmZRCQktDm+aOm9HXpBrd8fnAsnInK0uhXgjDEXAI8AEcCT1toH2u2/Afgh0LJwzM+stU925zlFRI6G3/rZfWi3M2ctUGhka+VWfNYHQFZCFjPTZpI31SnhPyVlCtERGr4kfcDT6BQTCe1ZK9kI3kA1x8gYp/LjtI8G5qvlQfpUp8KjHDNrLf5Dh7oc2ug5cABfRUXbB0VE4B4+HHdmJvHz5+POygwJaJlEZmQc83DHhrVrnblvoTweGtas6d4FisgJ57iLmBhjIoCtwLnAfmAlcLW1dlPIMTcAc621tx7teVXEREQOZ9nOZTyy+hGK64oZET+CJbOXsHjsYioaK1p71srWs6F8AzWeGgAS3AlMS51GXmoeeWl5TE+dTmpsapivRE4ITTWB4iIhPWtlH0Jg8XaihwTmqeW19qylToQIDZA5Wtbvx1te7gxn7GIOWktVxxYmJgZ3RkZrKGsf0NLTMZF6D0QkfHqriMl8YLu1dmfgSZ4GLgM2HfZRIiLHadnOZSx9dymNvkYAiuqK+Mbb3+DBFQ9S2VQJgMu4mJA8gfNzzg8GtpykHFxGldykl9WVtw59bOlZq9gJgQI4xKc5IW3Cea09a8nZoCqDh+XMPysJCWTt56EVYdv1bAXnn40aRdyCBR2CWsSwYRoeLSIDVncCXBawL+T+fmBBJ8d9zBhzOk5v3e3W2n3tDzDG3AzcDDB69OhuNElEBqOSuhLWla3jO+99JxjeWvitnwZvA3fMuYPc1Fympkwlzh0XppbKgFfwzJHXNbMWDhW2LdlftM7Z1iJptBPQZlzV2rOWOEKVIDvhb2g47PBGb2lpl/PPYqZOxX3OOUecfyYiMpj09viAF4G/WGubjDGfA34HnN3+IGvtE8AT4Ayh7OU2iUg/5vF52FyxmXVl64I/LYVGutLka+LG6Tf2UQtl0Cp4Bl68DTyBuWjV+5z7NSWQlNka2IrWQUPLnCkDqRNgzMLWoZAj8iBuWNguo695SkspvOMrjHz4oQ7VFK21+KurD1te31dZ2faEkZGt888WLOiR+WciIoNJdwJcITAq5P5IWouVAGCtPRhy90ngB914PhEZhMrqy9qEtY3lG2n2O+W4M+IzmJk2kxlTZzAjbQZf+d9XKKor6nCOEfEj+rrZMtg018Mr32oNby08DfDf/3Nuu9yQPgUmL27tVRs+DaJP7N6eskcepWHVKgq/fhfxJ5/UocS+v76+zfEmJiYYxmKmTeswBy0yPR0ToQXGRUS60p0AtxKYYIzJwQluVwHXhB5gjMmw1rZ827oU2NyN5xORAc7j97C1Yitry9Y6ga10HQfqDgDgdrmZmjKVqyZfxYw0J7ANjx/e5vFLZi9pMwcOICYihiWzl/TpdcgAYy00VDo9alX7Qn7vbb1ff/Dw5/jcm5A2BSKj+qbN/Zivpob6/HzqV6yk7p13aNq6FYD6d9+l/t13cSUlOWFs9BjiTjq5Te+ZOyuTiKFDNf9MRKQbjjvAWWu9xphbgf/gLCPwlLV2ozHmPiDfWvsCcJsx5lLAC1QAN/RAm0VkgDjYcJB1ZeucwFa6jk0HNwXDV3pcOjPSZnDNlGuYmT6TKcOmEBVx+C/Hi8cuBui0CqWcwPx+qCsNCWV72wW1fdBc2/YxkbHOHLfkUU5vWvIoeO/xkKGRIZICx5ygQgNb/YoVNG7eDH4/JioKV1KSU4TF74fISJIuv5zM734n3E0WERnUjnsZgd6iZQREBiav38vWyq2twyFL17G/dj/gLJI9ZdgUp2ctfQYz02Zq2KMcPZ/HKRDSVe9Z9X7wNbd9TEySU0gkeZQTwNr8Hg3xqR0LirSfAwfO2muXPNqxkMkg5jt0iPpVqzoNbLEzZxI3fz5x8+fhzsxk5+KLsU1Nwcea6GjGL/9vh7lwIiJybHprGQEROYFVNla2mbu2oXwDDYGFiFNjU5mZNpNPTPoEM9JnMDVlqhbJlq411zkhrH0wa/ldUwS2bRVCEoa39oxNvhiSR7cNajFDjr0dLSHtSFUoB5kjBbbUL3yBuPnziJ0xo03xkKKl92LbVYe0fj9lj/+cjG/f09eXISJywlCAE5Ej8vl9bK/aHgxra0vXsrdmLwCRJpJJwybxkfEfCfawZcZnao6LOI5n/pkrEoZkOj1lOae360EbDUOywB3TO+3Nu/LECGz5q6hfsaI1sFl7xMDWXsPatdBu/TU8HhrWrOndCxAROcFpCKWIdFDdVB0MagVlBawvX0+916kkNyxmWLDIyIy0GUxLnUZsZGyYWyxh4/dDbUkgmO3tJKh1Mf+sw9DGkB60xAxwqQphTzlSYGsZEnmkwCYiIn1HQyhFpEt+62dH1Y5goZF1ZevYfWg3ABEmgolDJ3LpuEuZke4EtpEJI9W7diLxNjvzz7rqQTtU2Mn8s2QniA0bC2PP6BjU4lK0oHUvOmIP2xe/qMAmIjKAKcCJnGAONR+ioKwgWGhkffl6aj1OD0lydDIz02Zy2fjLnN61lGnEuePC3GLploJnDj+nq7kuJJh10oNWUwS0G6mRMMIJZJmzYOqlHXvQohP79BJPdApsIiLH7vk1hfzwP1s4UNVAZnIsd54/ictnZYW7WUdFQyhFBjG/9bOrelebuWs7q3cC4DIuJiRPCM5bm5E2g9GJo9W7NpgUPAMv3AbekKqKrkgYPh2wTkBrXzbfFenMMWtfFKTld9JIiFQICCdfdbVTdOSDFdStXEHT5g9bA9usWcTNn0f8/PnE5OUpsImIdOL5NYXc/ff1NHh8wW2x7gju/2huvwlxGkIpcoKoba6loLy1d62gvICa5hoAkqKTyEvN46Kci5iZPpPpqdOJd8eHucXSbT4vHNoPlbs7/hSt61i90e+Fkg0w9kzInN1aVr8loCWO0PyzfuZIgS311i8qsImIHCWvz88DL3/YJrwBNHh8/PA/W/pNgDscBTiRAcpay65Du4Lz1taVrWNH1Q4sFoNhXPI4zhtzHjPTZzIjbQbZQ7LVuzYQtVRx7CygVe52hkbakP8JudxO79nQMR3DWwu/D657rrdbLsdJgU1E5NhYa6lt8lJa00RZ6E9tE6WHnN8t2yrqmvB3MQDxQFVD5zv6GQU4kQGizlPH+vL1wcBWUF5AdVM1AInuRPLS8zgv+zxmpM0gNzWXxCjNQxowvM3OnLPKXSHhbE/r78D7HBSXCkOzYeQ8yP24c7vlZ0hmaw/aw9Od87aXNLIXL0aOlQKbiEjnmr1+DtY5wat9ECuraaK0pjG4rdHT8Y+WkS5DWmI0aYnRZCXHMHNUEmkJ0fzuvT1UN3g6HJ+ZPDCqaivAiYTZsp3LeGT1IxTXFTMifgRLZi/hopyL2FuzNzgUcl3ZOrZVbcMf6FEZlzSORaMXBUv55yTl4DKuMF+JdMlaqCt3AlnVnpCgFghphwrb9pZFRDs9aEOzYfRJreEseYyz/WiLhCy6B168DTwhf1F0xzrbJWy6DGzR0U7REQU2ERnErLVUN3ja9JI5YaxjOKus7xiyAJLj3KQlOMFszuihwZCWlhhNWkIM6UOiSUuIJinWjcvVcfTR2LSETufA3Xn+pF677p6kIiYiYbRs5zKWvruURl9jcJvLuIhxxVDvc9ZdS3AnkJuaGyw0kpuaS1J0UriaLF3xNDpVHLsa6uipa3t8wohAMBvTtgdtaLazz9VDgfxIVSil1/mqq6nPz6d+xQrqVqyk6cOQwNa+6EhUVLibKyJyXBo9PsprOwax0GGM5YFtzb6OvWVRkS7SgyHM+Z2eGNMmnKUnRpOSEEV0ZPfnavf3KpSHK2KiACcSRuf+7VyK64s7bI+NjOVr877GjLQZjE0aS4SKSoSftc6C1Z0GtD1Qc6Dt8ZGxHYNZsCdtNERpeYbByldV5fSwKbCJyADn91sq65vbDF3srKesrKaJQ43eDo83BobFRbXtIQsNZgmt24bERGqufghVoRTpZzw+D3/b+rdOwxtAo7eRKyZe0cetEprrQuae7Q4Md9zdGtJCy/FjnPlmQ7Nh3FntAtoYSEjXYtUniCMFttQv3arAJiK97lh6lBqafW3CV3AYY7t5ZuW1TXg7qfgR644gfYjTIzZpRCKnjk/tGM4SoxkWH4U7QlM8epoCnEgfstbyyp5XeHT1o+yt2UuUK4pmf3OH40bEjwhD604Afr+zMHVXwxzrStseH5UAQ3MgZTyMP6dtSEsaBe6YPm2+9A8KbCJ9o78PcQsnn9/i8flp9vnxeP28WHCA+1/6kCavMzSxsKqBO59dx383FZOWGOOEspBwVtvUsbfMZSA1pEdsSkZiSC9ZTHAIY1piNPHRihDhpFdfpI/kF+fz8KqHKSgvYHzyeB5b9BiHmg5x73v3tpkDFxMRw5LZS8LY0n7uSHO6Gg+16zkL+anaC76QwGxcMGSkMw9t4vkhAS3H+R03TL1oJyBPaSmFd3yFkQ8/RGRamhPY8vOpW7GC+hUradqyRYFNpJe1X2i5sKqBu/++HqDHQ5y1Fp/f4vXbYCDy+FoDktfXNix5fBaPv+vjWo+1eP2tt0P3tT3W4vH6A8fawHlbfmyb285j/V2WwQ/l8VmWrS8mMSYyGMSmZQ7pMK8sLSGa9CHRDI2LIqKTgh/S/2gOnEgv21G1g5+s+glv7H+D9Lh0bp15K5eOuzQ4r62zKpSLxy4Oc6v7qYJnOlZVdEVCxkzAOiGt/mDbx0QnwbDszueiDRkJkfrCLW0V3v0NDj3/PFETJ2CMq21gmz2L+PnziZs/n5jcXAU2kR7U6PFR3eChsr6Z6578gPLajiNUEqMjuWbB6DbBprkl6BxjCGq57fH56a2vwy4D7ggXUREu3JEu3BEGd4Qr8NN6OyrCRWTo/cjOj3OONUSG7IuKdHHPPzd2+vwG2PWAvlMMRCpiIhIGpfWlPL72cf6x/R/ERcZxU+5NXDvlWmIjB8YaI2HVXB/oNdsFFbtay+7vfAP8HYd94IqA7NM7r+gYO7QPGy4DjfV6adq6lfq1a2lct476/FV4CguD+2PnzCbh1FMV2ESOQbPXT1V9M1UNHqrqnUBWHfjdsq2qvrl1XyC0dbaOV2eiI9sGokiXi6jIowtEzrFHDkTOsa23g/siXIFjTeC8nYWs1tt91aN1ygOvUdjJItRZybG8c9fZfdIG6VkqYiLSh2qba3lqw1P8YdMf8Fov10y+hpvzbmZojIJEkLVOT1loOGu5XbELatsVd2npRessvIEzt+2Tz/dyo2Uw8JSW0rBuHY3r1tGwdh0NGzdiG5wvPRGpqZjoaIiIAJ8P3G6iJ04i9ZZbwtxqkfDw+PxU1Xuobmimsr5t8KoKbAsGs3pPMIjVN/u6PGeky5AcF0VynJuhcW5GDo0jN8tNcpw7uD05Nopvv7Ch0x44BZLO3Xn+pAG9rpkcGwU4kR7i8Xl4Zusz/HLdL6lsquTCnAv50qwvMSpxVLibFh4+Lxza3zGcVe6Cit3QXNP2+MRMGJYD4xc5c9CG5bT+jh3qzEV7eDpU7+v4XEkj++KKZIDxNzfTtGkTDevWOT9r1+E5EFjuwe0mZuoUkj9+BbEzZhA7YybG7WbHeec54Q3A46H6738n7Qu3EJmWFr4LEekmr89PdYMn0PvV0vPl3G4JXVUtAa2hmco6J4x1VuiiRYTLkBzbGrwykmKYkjEkGMyS4qJIjnUztCWUBY6Lj4o4qlLxHp9fgeQYtMwLVNGXE4MCnEg3WWv5z57/8OjqR9lXs4/5I+Zzx5w7mJY6LdxN632dDXVs+V21t22PWUSUU15/aDaMPrltSBs6BtxHMbR00T0d58C5Y53tckKz1uI9cKBNWGvctAnr8QAQmZFB7MwZDP3k9cTOmEHM1Km4oqPbnKNo6b1Yf9shXNbvp+zxn5PxbX3GpPu6W1XR57ccamgdihja+9UyZLGzYFbTyfpcLVwGkgJBKynOTXpiDBPTE0mKCw1fbcNYUpybxOjeXbNLgeTYXT4rS6/PCaJbAc4YcwHwCBABPGmtfaCL4z4GPAvMs9ZqgpsMGiuLV/LwqodZX76eCUMn8Piixzk169TBsxBl+6GO7X/XlrQ9vmWo44g8mHpZ25A2JNOZq9YdLdUmD1eFUk4I/vp6GjdubBPYvGVlAJiYGGKmTwuGtdgZM3APH37EczasXQuBwBfk8dCwZk0vXIGcaDqrqvj15wrYU1FH3shkqgPBqzJkKGJoMKuq93Co0dNlsQ0TCGJOr1gUw+KjGJsaHzJcMRC+YtsGs8ToSFz9tPKgAolI5467iIkxJgLYCpwL7AdWAldbaze1Oy4RWAZEAbceKcCpiIkMBNsrt/OT1T/hf/v/x/C44dw661YuGXtJsLLkgNIy1LFNONt95KGOQ3MC1R1DQlrcsDBcgAx21lo8e/YEw1r92rU0bdkaHOroHjM6GNRiZ8wkZtJEjNsd5lbLicxaS1ltE/sq6tlbUc/egw384n872gwHPJwhMZEkx0UFhyIOjXOCWfB2SK9Yy3GJMW6VgBcZRHqriMl8YLu1dmfgSZ4GLgM2tTvuO8CDwJ3deC6RfqGkroTH1z3O89ufJz4yni/P/jLXTrmWmMh+vqBzc13IXLTdRzfUcVgOjF7Ydi5a8hgtXi29zldTQ+P69cGw1riuAF9VFQCu+Hhi8nJJ+exngqEtcpj+cCB9r6HZx77KevYeDIS0ivpgYNtXWd+moqIxHLZM/XO3LAwEsyiSYhXEROTwuhPgsoDQagL7gQWhBxhjZgOjrLXLjDFdBjhjzM3AzQCjR4/uRpNEekdNcw2/2fAb/rDpD/isj2unXMvNuTeTHJMc7qY5rIW68q7no7Uf6hiT5ISyjBkw9fKQuWjZPTPUUeQoWb+f5h07qF+7Nlgdsmn7juC33ajx40hYdDaxM2cSO2MG0ePGYSL0+ZTe5/dbSmoagwFtX0U9+yobgmGtrKapzfHxURGMGhZHTmo8Z0xMY3RKHKOGxTF6WBxZybEs+vH/uizzPmeMqhSLyNHrtSImxhgX8BBww5GOtdY+ATwBzhDK3mqTyLFqqSz5i3W/oKqpiotyLuJLs77EyMQerHpY8MzRzenqdKhjYJhj5e6OQx2HZDmhbMK5IcMcszXUUcLKW1kZHArZuG4dDQXr8dfWAuBKSiJ2Rh6JF17o9K7l5hIxZEiYWyyDWW2Tt7XXrKK1J21vRT37Kxpo9rX2orkMZCTFMnpYHGdPSm8T0EYPi2NonPuw859V5l1Eekp3AlwhEFoffWRgW4tEYDrwRuA/aCOAF4wxl6qQifR3fuvnld2v8MjqR9hfu58FGQu4fc7tTEvp4cqSBc+0rapYvQ9euBUOrHF6wkLDWvW+dkMdowMLV+dA9imtPWga6ij9hPV6adyypc26a8179jg7IyKInjSRIZdcHBwKGZWdPXgKAEm/4PNbiqob2gW01vsVdW3XGUuMiWRMShyTRyRy7tThjBraGtAyk2OJinQdd1tUVVFEekp3iphE4hQxWYQT3FYC11hrN3Zx/BvAV1XERPq7FUUreGjVQ2w8uJGJQydyx5w7WJi5sOe/WPp98NBkqC3t+piY5LZz0Fp60IblOMVEXMf/ZUKkpx1pkezYmTNai41Mn44rLi7MLZbBoLrB06H3bF/gZ39lA15/6/ecSJcha2gso4a27T1r+UmKU/EbEekfeqWIibXWa4y5FfgPzjICT1lrNxpj7gPyrbUvHO+5RcJhW+U2Hl71MG8VvsWI+BF895TvcvHYi3u2smR1Iex4DXa8CjvfgIbKLg408PVdzgLWIv1Q+0Wy69euxXugyNnZySLZ7qxM9a7JcfH4/ByoamBfRUOHYiF7K+qpbmi79MPQODejh8UxPSuJi3IzguFs1LA4MpJiiIzQH75EZGDr1hw4a+1LwEvttnW62qm19szuPJdIbymuK+axtY/xwo4XiI+M5445d3D15Kt7prKkpwH2vAPbA6Gt7ENne8IImHQRbPk3NBzs+LikkQpv0m+0XyS7fu1amjZtbl0kOzPDCWqf/GSXi2SLdMVaS1W9p0MPWsvtA1UNhHSiERXhYuTQWEYNi2PmqORgOHN+x5IYo140ERnceq2IiUh/V9Ncw6/X/5o/bv4jfuvn+inX89m8z5IUnXT8J7UWSjc7YW37q7DnXfA1OfPVxiyEWdfBuLMhfapTV7r9HDgAd6xTyEQkTEIXyW6pDukrKwfaLZI9cyaxeTNwD08Pc4t7zvNrCjVH6Rgc7evV5PVRWNl+LpozH21/RT01Td42x6cmRDN6WCxzxwxl1KysNsMdhw+JUZl9ETmhKcDJCafZ18xft/yVJwqeoKqpiovHXsyts24lK+E4v6TVVwSGRQZ+agLDyNImw7zPwPiznfXUojqZ79NSbfJoqlCKHCdPaSmFd3yFkQ8/RGRaWpt9oYtkt4S19otkx598stPDNnMmMRMH7yLZz68pbFMlsLCqgbv/vh5AIa4Tnb1eX3+ugPw9FaQnxrSp7lh0qLHNOmjRka5gKFuQM6xNQBs1LJa4KH09ERHpynEXMektKmIivcVv/fx71795dM2jFNYWclLGSdw+53ampkw9thP5PLB/pdPDtuM1p2Ik1ik4Mu4sGLfI+Z3Ug0sNiHRD0dJ7qfrrX0m+6irS77idxvXrW9ddW7sOX3U10LpIdsuaa7EzZhA5dHAP5fX4/Ow5WM+2khq+9lwBNY3eDse4DAwfoqqu7ZUcamwztLG94UOi2wxvDP1JTYjGpV40EZEu9UoRE5GB5IOiD3ho1UNsOriJSUMn8ctzfsnCrIVHf4KKXYFhka/BrjedNddMBIycB2d9wxkWmTlLC2BLv+FvaMBz4ACNGzdR9eyzYC1VTz9N1V/+Ejwmavw4Es49JxjWBvMi2U5Qq2NbSS1bS2rZWlrD9pJadpbX4vEd/g+ZfgunTUjto5YOHM/k7+90uwE2f+cCYtyD87MkIhJuCnAyqG2t3MrDqx7m7cK3yYjP4Punfp/FYxfjMkeoQtZUA7veckLbjtegYqezPXk05F7hBLac0yE2udevQaQzLQHNU1gY/GkuLMRT6GzzHeykOA4QnZtL+peXDNpFso8mqBkDo4bGMXF4AmdNTmfi8AQmDk/k5t/nc6C6scM5s5Jj+cEVM/r6Uvq9d7YfpLCqocP2zORYhTcRkV6kACeDUnFdMT9b8zNe2PECCVEJfGXOV7h6ytVER3RRGc/vh+J1gWGRr8O+951Fs93xkHMaLLjFCW0p45xvfyK9zF9fHwxozcGQ1hrYfBUVbY43bjfuzEzcWVnEnH027qwsXImJlD7wQLBaJNbSvHUrMRMnDvjw1hLUtpbUOmGttIZtJTXsKq/rNKidPSWdCelOUBuXlkBsVMeA8bULJreZ0wUQ647gzvMn9dl1DSR3nj9Jr5eISBgowMmgcqj5EL9e/2v+tPlP+K2fT037FJ/J/UznlSVrikOKj7wO9U6VPUbkwcIvOYFt1AKIVDl06XndCmiLFuHOymrzE5mWimm3sHvR0ntpPzjQ+v2UPf5zMr49MCqdhga1rSU1bCutPWxQWzRl+BGDWldaCpWoCuXR0eslIhIeCnAyKDT7mnn6w6d5Yv0THGo6FKwsmZmQ2XqQpxH2vhcYFvk6lGxwtsenw/hznMA27ixIGDwl0SV8jiugBcJYzJQpwbB2uIB2JA1r14Kn7SLHeDw0rFnTzavreUcb1EYPi2NCuhPUJg5PYEL6sQe1w7l8VpYCyDHQ6yUi0vcU4GRA81s/L+96mZ+u+SmFtYUszFzI7XNuZ/Kwyc6abGVbWqtF7n4bvA0QEQWjT4Jz7nVC2/DpcIxfjEW6HdCC4cwJapGpxx7QjmTs8//o0fP1BI/Pz+7yOraVBoJaSS3bSrsKaom9FtREREQGKgU4GbDeL3qfh/IfYnPFZiYPm8wvz/0lC4dOgZ1vwNuPORUjDwWqpKVMgDmfcgJb9qkQFR/Wtkv/d8wBLSqqdYhjHwW0/qwlqG0NBLRtgZ61XeV1eP0dg9o5U4YzQUFNRETkiBTgZMDZUrGFh1c/zDuF75AZn8H3p9zE4tp6XC/dA4WrwPohOgnGngGnfxXGL3KqR4qEUEDrGcca1M6d2hrUxqcnqFqhiIjIMVKAkwGjqLaIn639GS/ueJHEiCi+GpnJVds3E73hAzAuyJoDp9/pLKSdNQci9PEerDylpRTe8RVGPvwQkWlpnR7jr6vDc+BAp+HMU1iIr7KyzfEKaIfX7G07R217aedBbcywOMYHgtrE4U5IU1ATERHpOfqGK/1edW0Rv37v+/zpwJtg/dxQfYibqqtJSmiAqZcF1mQ7A+KGhbup0kfKH/85DatWUXz//SRdeumxB7SpUxXQutDs9bM7uI5aTbBXrbOgNmG4gpqIiEhfM9a2LzIdXnPnzrX5+fnhboaEk7VQvJ6mba/w9K4XeMJ3kBqX4ZL6Jm5NnErG+POdYZGpE7Um2wnC+v00bdtOff5Kat9+m7rX3+hwjImKaltaP1jF8cQOaM+vKey0zPuxBrWW0vwThicwLk1BTUREpDcZY1ZZa+d2uk8BTvqF2rLgmmz+Ha/xErX8dGgyB9yRnBKdzu1TbmTS1CvAHRPulkofsF4vjZs3U78yn/r8fOpXrcJfXQ2AiY3FNjY6QT8igoSzzybjnm8RkZJyQga0rjR5fTyTv4/v/mszTV5/cLvLQGpCNBV1zcGg5mqZozY8MVjxUUFNREQkfA4X4DSEUsLD2wz73ndC2/ZXobgAgPeS03h4RAqb/TFMSRrP0vlf4+TMk8PcWOlt/qYmGtevd8Laynwa1qzBX18PQNSYMSSeew5xc+cSlTOWvZ/8pBPeAHw+6t58E2DQhDef31Lb5KWm0RP47aW20cuhdvdrGj3UBO63P7am0Uuzz9/p+f0WDjV4+NwZY4NDHxXUREREBg4FOOl5Bc/Aq/dB9X5IGgmL7oHcj8PBHYFetldh11vgqQNXJIw6iQ9P+SIPN+3i3YMbyIobygOz7uHCnAtxmcHxpVza8tfVUb92LfX5+TSszKehoADb3AxA9MSJJF1+OXHz5hI7Zw7u9NaF1YuW3ov1tw0m1u+n7PGfk/Hte/r0Gtqz1tLg8QXClrc1hAUCVU2H+55A+Gp7bF2z74jP5TKQGOMmITqSxBjnJz0xhrGpzu2EmEiGxLj54X+2dPr4Jq+fO8+f3NMvgYiIiPQBBTjpWQXPwIu3gafBuV+9D/7xeXj569AQKMs+bCzMvBrGLeJA+nh+tvG3/Gvnv0iMSuTOuXdy1eSriIqICt81SI/zVVdTv2q108OWn0/jxo3g80FEBDFTpzL02mudwDZrFpFDh3Z5noa1a8HjabvR46FhzZputc/j83fe0xUSstrf73isF5//yEPS46IinJAVHUlijJvEmEgyk2OC90NDWcv+0GMTYyKJdUdgjmL+558/2EthVUOH7ZnJscf1OomIiEj4KcBJz3r1vtbw1sL6nG2Lf+yU+B+WQ3VTNU+uf5I/r/wWADdOv5Gbcm9iSNSQMDRaepq3rIz6VauCc9iatm4FazFuNzEz8kj57GeImzuP2JkziUg4+kXVC779M+7++3oaPK29VDFuF1+/YDLekhpnWGEnvVqhPWKt+1qHH4bOEeuKO8K0C1SRjBwax5BAj1dL4GofwIL3o93ER0cQGdF3vcp3nj+pw+sV647gzvMn9VkbREREpGcpwEnPqt7X+XZvI8z7DE2+Jv6y4bf8av2vqGmu4dJxl3LrrFsZET+ib9spPcpTWBjsXatfmU/z7t2AU3AkbtZMEr90K3Fz5xKbl4crpvNCND6/pbK+mcq6ZipafgL3D9Y5v1/eUNwhbDV6/Nz74qYu22YMTohq6eGKiWRYfBRjUuJJiI50Alh0y9DDQC9XyLEtgW0gzhG7fFYWQKdVKEVERGRg6laAM8ZcADwCRABPWmsfaLf/88AXAR9QC9xsre36m5YMXH4fvP69rncnjWTZjhf56ZqfUlRXxKlZp/Ll2V9m0jD1BAw01lqad+2mPn9lMLR5DxQB4EpMJG7OHJKu+BhmxmxqR4+jotm2hrAVB6iob6aitjWctQS16gYPXRXFTYh2Qtfhesp+ds2sYI9Xa6+Ymzh3BC7XibvcxOWzshTYREREBpHjXkbAGBMBbAXOBfYDK4GrQwOaMWaItfZQ4PalwBestRcc7rxaRmAAaqiE5z4D25fDmFNZVrmBR4bEURwZwQivjwsbmnh3+Fg+bChhyrApfGXuV1iQsSDcrZajZP1+ajd9SMV7H1Cfn49/3RpcVc4i2U2JSZRkT2FP1iQ2p49lS+xwDjZ4qKzzdFkF0R1hGBoXxbB452dofBQp8VFttg0L3E9JiCI5zk10pNP7dcoDr3U6pysrOZZ37jq7914EERERkT7UW8sIzAe2W2t3Bp7kaeAyIBjgWsJbQDzQvxadk+4r3gB/vRaqC+Hin7BsWDpL3/4WjdYpNFHkjuQpdyTJ/iYePO1BLsi5QJUlw8xay6EGr9MTFugBqwz0glXUNVN5qJ6ondsYtmMjI/d+yNjiHSQE5jWWxiazPmUsG7IXsT5lLIUJaSTFtQQuN5nx0UyPd3cZyobFR5EQHXlUBTg6ozldIiIicqLrToDLAkInPO0HOnSrGGO+CNwBRAGd/oncGHMzcDPA6NGju9Ek6VMbnoN/3goxSXDjSzBqPo88e14wvIWKiYzhorEXhaGR/d/zawq7NUep0eOjsr6Zg7XNVHYRylp/PFTVty7gDOD2eZhcuZfp5TvJq9jFRRW7ifY6Jf2rUjIombWQ3ZPzsHkzSRgzijlxUZyX4ISz5Dg37j4syqE5XSIiInKi6/UiJtbax4DHjDHXAP8HfKqTY54AngBnCGVvt0m6yeeF5d+G934Go0+Gj/8OEocDUFRX1OlDSupK+rKFA8bzawrb9CgVVjVw198LKDnUyKzRQ50g1i6UHWy3rb6LdcOMgaFxTs9YSnw0OanxzBkTRVqEj9FFOxi+ezNDtm7AvW0zxuMBY4ieOJG4cz9O3Ly5xM2ZQ2RaWl++HEdFc7pERETkRNadAFcIjAq5PzKwrStPAz/vxvNJf1B3EJ69AXa9CfM+C+d/HyKjqPfU86P8H3X5sCHuNOqbnXWy/H7wWevcDvz2+S3Wdr7d32YbgWNtu2Pp5NijOS9tHuP3t5yXw7TB4reEHBt63nbna7ndxTVvL61t0xsGTlXF+1/+sMNrGBcV0WZ+2Pi0BIa2mzMWOlQxKdZNhMvgq6qifvVq6le+5azBtmlT6xps06YRd/31xM2dS9zsWUQkJ/fwB0ZEREREelJ3AtxKYIIxJgcnuF0FXBN6gDFmgrV2W+DuYmAbMnAdWAt/vQ5qS+Gyx2HWtQCsK1vHN976Bvtq9hHROBVv1DaMq3UYpfW7Kd59FlPv+U+YGn5sIlyGCGNwuQj8NiHbAr8NnW93GSJCHucyredyu1zO/ZBjPyyu6bIdf7hpfrCQx9C4qKMuY+8pLaXhzbcoC12DDTBRUcTm5ZFy82edwDZzJq74o1+DTURERETC77gDnLXWa4y5FfgPzjICT1lrNxpj7gPyrbUvALcaY84BPEAlnQyflAFi7V/gX1+GuFT49L8hazYev4dfrvslv1r/K4bHDeenZz3Bpx4/SOSQNUSn/QfjrsJ6kmkqOx/voVncfeFkXMGw4wQlV7sA1LLNZeh0e2iwinAZTJtARIdA1dX52oavtufrS4erqnjahCMPX7TW4ik8ECzp37Ayn+Y9ewAwcXHEzZrFkIsuJG7uXGJyc3FFR/f4NYiIiIhI3+nWHDhr7UvAS+223RNye0l3zi/9gM8D//kmrPglZJ8GH/8txKeyq3oXd791NxsPbuTScZdyUebnuetpp4PVe2gW3kOz2pwmKzmWz50xLgwX0L8da1VFZw22XdQHetfq8/PxFgXWYEtKIm7OHJI/8Qni5s0lZsoUTGSvT3MVERERkT6kb3fStdpSeOZTsPddOPlWOOderCuCpz/8Cw/lP0R0ZDQ/Pv3HbN8zlk89uZ7hQ2JYsmgCT7y5U2Xej1JLMY4n/rGCG159kt8s+gyf+8j84Hbr89G0dWubwOarqAAgIjXVKTbymZuImzuP6AnjMS4t0SAiIiIymCnASef258Nfr3cW6f7ok5D3cUrrS7nnnXt458A7nJJ1Ckvy/o/vvrCfd7ZvYXFeBt//SC5JsW5yUuNV5v0YXD4riwX/LKCqYhdPmXUkmxwOPvmyE9pWr8Zf48yTc2dlkXDaaU5omzsX95gxfT7kU0RERETCy1jbv6r2z5071+bn54e7GSe2Vb+Dl74KiRlw1Z9gRC6v7H6F+96/jyZvE1+d+1VS7Rnc+ex6Gpp93HvpND4+d6TCRAjr8+FvaMQ21ONvbMRf34BtbMDf0NDhtqe8jIonf+1UhgwRNXasU2wkUNLfnZkZpqsRERERkb5kjFllrZ3b2T71wEkrbxO8/DVY9VsYdzZ87NfURLp54O1v8sKOF5ieMp2lC7/L0+808pt3VjElYwg/vXoW49MTOpzKU1pK4R1fYeTDD/XLtcQ6DVght/0N9djg7UDg6uS2v7EBG7zdiK2vd45pbj6+hrlcxJ98MpkPPkBkamrPXrSIiIiIDHgKcOI4VATPXA/7V8Kpt8PZ32Jl6Wq++fY3Ka0v5ZYZt7Ao4xpu/8N6NhUd4sZTsvn6BZO7LG1f/vjPaVi1irLHf07Gt+/p9JjDaROwGho63PY31GODtwOBq9PbDYHj2t4+5oBlDCY2FldsLK6YGFxxsZjYOFwxMUSkpbXui43FxMbgio3rcNsVGxM4Lg5XbAyu2Fh89fXsvuLj2KYm53n8furz86Gf9YyLiIiISP+gACew5z145pPQXAdX/p7mSRfy09U/4Xcbf8eoxFH87oLfsWXPMC7/2fvERkXw60/NZdGU4V2ezlNURNVzz4G1VP3tb0QMGQIu4/RUNTaGJ2DFxeKKCeyPi8V0eTsQuOKc85i4QBCLju6VIaLlS+/F+v1ttlm//7iDr4iIiIgMbgpwJzJrYeWT8O+7IHkMfOoFtkS6uHvZVWyr3MaVE6/k5twlfOeF7fyroICF41J4+BMzGT4kpstT1r71NoVf+Qp4Agt5e70c/OUv2wasYG+UE46CASsQuJzwFQhcYQ5Yva1h7drW16qFx0PDmjVhaY+IiIiI9G8KcCcqTwP86w5Y92eYeAH+y3/O73e+wKNrHmVI1BAeW/QYCf5cPvZYPkXVjXztgkl87vRxRLg6D0lNO3ZQ8oMfUPe/NzvsM9HRjPvvK7jT03v7qgacsc//I9xNEBEREZEBRItGnYiq9sFTFzjh7Yy7OHDxj/nMW1/lx6t+zGlZp/G3S55j/bZMPv6L9wD42+dP5gtnju80vHkrKyn+znfZeellNKxaTczMmeB2tznG+v2U//wXfXFlIiIiIiKDmnrgTjS73oS/3QA+D/YTf+ZfUX6+/6+PY7F855TvcFLa+Sz50zre3XGQS2Zk8r2PTGdIjLvDaWxzM5V/+Qtljz2Ov7aW5Cs/TtqXvsTemz6jIYEiIiIiIr1EAe5EYS289xj89x5IGU/VR3/BfVv/yH/3/JfZ6bP53qnfY8t+Nxc+8haNHj8/vCKPK+Z0XNvNWkvt629Q+uCDNO/ZQ/zChaTf9XViJk4ENCRQRERERKQ3KcCdCJrr4YUvwYZnYcolvLPgU3zr7a9S2VTJl2d/masmXs8P/7ON3767m6kZQ/jpNbMYl9ZxbbfGLVsoeeAB6t97n6icHEb+4ucknHHGgCweIiIiIiIyECnADXYVu+Cv10HJRhrO+gYPRXt5+n93MD55PI+f8ziR3iyu+MUHbC46xKdPyeHrF04iOrLt2m7e8nLKHnmUqueew5WYyPBvfpOhV30C4+44tFJERERERHqPAtxgtv1VePbTgGXjZQ9x1+5/sPvQbq6fej23zbqNf64pZekL7xAXFcFvbpjHWZPbVon0NzVR8fvfc/AXv8Tf1MTQ664l7QtfICI5OSyXIyIiIiJyolOAG4yshbcfhlfvw5s+hSdnX8ov1/+UlNgUfnXer5iSPIevPLOeZQVFnDI+hYevnEl6yNpu1lpq/vMKpT/6EZ79+0k480zSv/Y1osfmhPGiREREREREAW6waaqBf34RNv2TPVMX8404PwVb/sxFORfxjQXfYHuxj4seeYuSQ418/YLJfO70sbhClgdo2LCRkgfupyF/FdETJjD6qV8Tv3BhGC9IRERERERaKMANJgd3wNPXYMu38uyCa/lhxUoia9z84PQfcN6YC/j5G9t5ePk2MpNj+NvnT2bW6KHBh3pKSil7+GGqn3+eiGHDGLF0KclXfAwTqY+IiIiIiEh/oW/ng8WWf8PfP0u5283S2Rfwv9K3OCnjJL5zynfAm8y1T77P+zsruDSwtltiYG03f0MDB596ioNP/hq8XlI+cxMpn/scEYmJYb4gERERERFpTwFuoPP74c0fwBv382rWFO5NiKC+eht3zb+Lqydfzauby7jz2Tdp9vr50cdn8LHZWRhjsH4/h5Yto/THD+EtLibxvPNIv/OrRI0aFe4rEhERERGRLijADWSN1fD3z1G37d88OHEu//CUMiVhCvefdj9Z8dnc+8ImfvfeHqZnDeHRq2YxNrC2W/2aNZTc/wCNBQXETJ1K1g9/QNy8eWG+GBEREREROZJuBThjzAXAI0AE8KS19oF2++8APgN4gTLg09baPd15Tgko/RD+ei2r6w/wjQnTKPKW89ncz3LLjFvYXd7I5b99hw+La/jMqTnceYGztpunsJDSHz/EoZdeIjItjYzvf5+kyy/DuFzhvhoRERERETkKxx3gjDERwGPAucB+YKUx5gVr7aaQw9YAc6219caYW4AfAJ/oToMF2PQCnudv4fHkRJ4akUZm9BB+e87PmJk2k6dX7uPeFzcSHxXJb26cx1mT0vHV1lH6q19R8dvfApD6hVtIuekmXPHx4b0OERERERE5Jt3pgZsPbLfW7gQwxjwNXAYEA5y19vWQ498HruvG84nfB699l+0f/JS7M0fxocvLxyZ8lDvn3YnXE8UX/7yal9YXc9qEVH585QzS4txUPfccpT/5Cb6ycoZcfDHpd9yOOzMz3FciIiIiIiLHoTsBLgvYF3J/P7DgMMffBLzc2Q5jzM3AzQCjR4/uRpMGsfoK/M/exJ/LV/LwyCwSohN5ZOFSzh59Nvm7K1jy9AeUHGrk7gsn89nTxtKQv5JdDzxA06bNxM6YwfCf/pTYmTPDfRUiIiIiItINfVLExBhzHTAXOKOz/dbaJ4AnAObOnWv7ok0DSvEGip+5hv+LbuSDlKGcMfI0li5cytDoFB59dRs/Wb6VUcPieO6WhUyxhziwZAk1//0vkRkZZP7oRwxZfBHGmCM/j4iIiIiI9GvdCXCFQGjN+ZGBbW0YY84BvgmcYa1t6sbznZjWP8tL//0K3x2aiNc9hKXz7+ajEz5K8aFGrvn9+3ywq4LLZ2Zy76IxNP7mV+z8wx/A7SZtyW0Mu/FGXDEx4b4CERERERHpId0JcCuBCcaYHJzgdhVwTegBxphZwC+BC6y1pd14rhOPz0v1K3fzvZ3P8XLKEPKGTeH+M37E6CGjeWVjMV97rgCP189DH5vOWTveo+SyW/FVVpL0kY+QtmQJ7uHp4b4CERERERHpYccd4Ky1XmPMrcB/cJYReMpau9EYcx+Qb619AfghkAD8LTCEb6+19tIeaPfgVneQ95/9BN/0FlKRkMCXZtzCp/M+i9dn+NbzG/jD+3vIzUri4XFNuO67leJt24mbO5f0u+8idtq0cLdeRERERER6SbfmwFlrXwJearftnpDb53Tn/Ceixn0f8MhLN/HHGEN2fDqPnvM401KnsbWkhi/9eQ1bSmq4Y1IUl733R+of+x/uUaPIevQREs89V/PcREREREQGuT4pYiJHZ/O7D3H3xl+xIyaSa0ady5dP/x4xETH8+YO93PevjYygiee8+cT9+J80xsSQfudXGXr99biiosLddBERERER6QMKcP2Az9PIb/55LY/VbmGYO5pfnno/C8ddSFV9M7c/t5r/ri9kSW0B5+W/iK2tJfnjHyftti8RmZIS7qaLiIiIiEgfUoALs33Fa/jmvz/DGtPMebFZ3HPJn0mKS2HFrgq+/JfVjNm+lmd2/Ju4kkLiFp5M+tfvImbSxHA3W0REREREwkABLkystTy/4mEe2PQUEVjuz/kYi0+/F7+FR5Zv4/m//4+vfriMKQc+JCo7m/SfP07CmWdqnpuIiIiIyAlMAS4MKhoruPelm3itZjvzfIbvLfopGTlncaCqgW/+5k2m/edpfrbnAyITE0n7xt0MvfpqjNsd7maLiIiIiEiYKcD1sTf3vMq3/ncnNf5mvupK4/qr/4YrPpV/r9nL2w88xhc3vUKc38Ow664j7YtfICI5OdxNFhERERGRfkIBro/Ue+r50bv38rfdLzGxqZlfjb6Yief9gAav5fff+RUT/vE7rq0/iGvhqWT/391Ejx0b7iaLiIiIiEg/owDXB9aVreMbr9/BvvoSbqxt5NYzf0DU9I/y4f9WsG3pdzm9aBvVI0aT8ZMHSD791HA3V0RERERE+ikFuF7k8Xv45bpf8quCJxjh9fBUYzRzr/wXzXYob33mNoa9vZz06HhqbvkKC754AyZSb4eIiIiIiHRNiaGX7Krexd1vfp2NFZu5tKaWu4bOJf6jP2Pf75+h4slfk+T1smL+hSz+wd2kj0gNd3NFRERERGQAUIDrYdZant7yNA/l/5gYbzMPlZZxzrwlHKqZxuZLr8RVVkp+Zi7RX1zCpz6yEJdLywKIiIiIiMjRUYDrQaX1pdzzzj28c+AdTm3ycl9lHQlTvs3un79BY8Hv2ZmUxYuLb+dLt3+C3JFJ4W6uDHIej4f9+/fT2NgY7qaIiMgJIiYmhpEjR+LW8kcivUYBroe8svsV7nvvPpo8dfzfwUo+0jScssLT2f2bR6mJS+JXs64k6bLLefgjucRH62WX3rd//34SExPJzs7WAvAiItLrrLUcPHiQ/fv3k5OTE+7miAxaShLdVNNcw/0f3M+LO19kuiue7+3cR8KBXHZ+UInP/z7PTz2Xf0w+h//7+Bwun5UV7ubKCaSxsVHhTURE+owxhpSUFMrKysLdFJFBTQGuG1YWr+Sbb3+T0voSbml284m39lCxZQwHq4vZNeMUvj38TDInZvPc1bMYkxIf7ubKCUjhTURE+pL+vyPS+xTgjkOzr5mfrvkpv9v4O0bHpPKHgloS3ouk9GAyTBnLw2dexCsmnc+fMY47zp1IVKQr3E0WEREREZFBQAHuCJbtXMYjqx+huK6YEfEj+PjEj/Py7pfZVrmNGz05fOyP66nfF4N3eBo7b76RrxxMJzEumj9cOYPTJqSFu/kiIiIyAOzevZuLL76YDRs29Pi533jjDX70ox/xr3/9ixdeeIFNmzZx1113Hde5srOzSUxMJCIigsjISPLz83u4tSJyJOoaOoxlO5ex9N2lFNUVYbEU1RXx6JpHqSwr5LdvJHLRQ1tpKI4j4bOf5aFP3s8XS9M5aXwaLy85TeFNBpzn1xRyygOvkXPXMk554DWeX1PYq8930UUXUVVVRVVVFY8//nhw+xtvvMHFF1/cq899PG644QZycnKYOXMmM2fOZO3ateFpSMEz8PB0WJrs/C54ptefcqC9Vz/72c8YP348xhjKy8uD26213HbbbYwfP568vDxWr14dlvYt27mM8549j7zf5XHes+exbOeyXn2+gfb+dfVvrb+8fy08paXsvu56vANsvtell1563OGtxeuvv87atWsV3kTCRAHuMB5Z/QiNvkaSay1L/+hl6CE/56zx8+Bjh4h7r5IhC6dQ/evnuLJ5Bst3VvOti6fy1KfmkZoQHe6mixyT59cUcvff11NY1YAFCqsauPvv63s1xL300kskJyd3+FLZl7xe7zEd/8Mf/pC1a9eydu1aZs6c2TuNOpyCZ+DF26B6H2Cd3y/e1ushbqC9V6eccgrLly9nzJgxbba//PLLbNu2jW3btvHEE09wyy239HQzj6izPwwufXdpr4a4gfb+Qef/1vrD+xeq/PGf07BqFWWP/7zHzun1ern22muZMmUKV1xxBfX19dx3333MmzeP6dOnc/PNN2OtBeDRRx9l6tSp5OXlcdVVVwFQV1fHpz/9aebPn8+sWbP45z//2eE5fvvb33LrrbcCTli+7bbbWLhwIWPHjuXZZ58NHvfDH/6QefPmkZeXx7e//e0eu0YR6b5uDaE0xlwAPAJEAE9aax9ot/904CdAHnCVtfbZDifpx4rrigD42Nt+Ju+DH/3aT2IjbB4J53//bn7pWsDP/rGd7JR4/vGFU5iepbXdpH+698WNbDpwqMv9a/ZW0ezzt9nW4PHxtWcL+MuKvZ0+ZmrmEL59ybQuz/nDH/6Q6OhobrvtNm6//XbWrVvHa6+9xmuvvcavf/1r3nnnHfLz87nrrrvYsWMHM2fO5Nxzz2Xx4sXU1tZyxRVXsGHDBubMmcMf//jHLifGZ2dn86lPfYoXX3wRj8fD3/72NyZPnkxFRQWf/vSn2blzJ3FxcTzxxBPk5eWxdOlSduzYwc6dOxk9ejSTJk1i165d7Ny5k7179/Lwww/z/vvv8/LLL5OVlcWLL77Yd+sZvXwXFK/vev/+leBrarvN0wD/vBVW/a7zx4zIhQsf6HxfwGB7r2bNmtXp8//zn//kk5/8JMYYTjrpJKqqqigqKiIjI+Owr8+xeHDFg3xY8WGX+wvKCmj2N7fZ1uhr5J537uHZrZ3/L3LysMl8ff7XuzznYHv/utIX7x9A8fe/T9Pmrt9DANvcTENBAVhL1dNP07R5M+YwbY+eMpkR3/jGEZ97y5Yt/PrXv+aUU07h05/+NI8//ji33nor99xzDwDXX389//rXv7jkkkt44IEH2LVrF9HR0VRVVQHwve99j7PPPpunnnqKqqoq5s+fzznnnHPY5ywqKuLtt9/mww8/5NJLL+WKK67glVdeYdu2baxYsQJrLZdeeilvvvkmp59+OsYYzjvvPIwxfO5zn+Pmm28+4nWJSM867h44Y0wE8BhwITAVuNoYM7XdYXuBG4A/H+/zhNMInyW51nL2OosLSGiEn19o+MXVLj65azyPvradj80eyYtfOlXhTQa09uHtSNuPxmmnncZbb70FQH5+PrW1tXg8Ht566y1OP/304HEPPPAA48aNY+3atfzwhz8EYM2aNfzkJz9h06ZN7Ny5k3feeeewz5Wamsrq1au55ZZb+NGPfgTAt7/9bWbNmkVBQQHf//73+eQnPxk8ftOmTSxfvpy//OUvAOzYsYPXXnuNF154geuuu46zzjqL9evXExsby7JlrT0j3/zmN8nLy+P222+nqaldkOoL7cPbkbYfpcH4XnWmsLCQUaNGBe+PHDmSwsLeHSrcXvvwdqTtR2Mwvn+d/VvrD+9fi+YDB9re76F2jBo1ilNOOQWA6667jrfffpvXX3+dBQsWkJuby2uvvcbGjRsByMvL49prr+WPf/wjkZHO3+NfeeUVHnjgAWbOnMmZZ55JY2Mje/d2/ke4Fpdffjkul4upU6dSUlISPM8rr7zCrFmzmD17Nh9++CHbtm0D4O2332b16tW8/PLLPPbYY7z55ps9cu0icvS60wM3H9hurd0JYIx5GrgM2NRygLV2d2Df8X8LDKMlByvYtSoJAn+M9EbAhGLLuZlV3F1bwyNXzeSymVrbTfq/w/WUAZzywGsUVjV02J6VHMtfP3fycT3nnDlzWLVqFYcOHSI6OprZs2eTn5/PW2+9xaOPPsr999/f5WPnz5/PyJEjAZg5cya7d+/m1FNP7fL4j370o8Hn/Pvf/w44XzKee+45AM4++2wOHjzIoUNOL+Sll15KbGxs8PEXXnghbreb3NxcfD4fF1xwAQC5ubns3r0bgPvvv58RI0bQ3NzMzTffzIMPPhj8q3iPOUJPGQ9PDwyfbCdpFNx4/EPwBtt7FU6H6ykDOO/Z8ygKjO4IlRGfwW8u+M1xPedge//65N/aYRypp8xTWsqOc8+DwFBGrMV/6BBZD/2YyLTuzX9v3/tpjOELX/gC+fn5jBo1iqVLl9LY2AjAsmXLePPNN3nxxRf53ve+x/r167HW8txzzzFp0qQ252kJZp2Jjm6d9tEyPNNay913383nPve5DsdnZTnfe9LT0/nIRz7CihUr2vyhQER6X3fmwGUBod8k9ge2HTNjzM3GmHxjTH5/WvzxtNohnLXe4vY5990+OLvAMudgHC/ddprCmwwad54/iVh3RJttse4I7jx/UhePODK3201OTg6//e1vWbhwIaeddhqvv/4627dvZ8qUKYd9bOgXioiIiCPOn2k5/miOBYiPb7suY8vjXS4Xbrc7+CXK5XIFz5eRkYExhujoaG688UZWrFhxxOfpcYvuAXds223uWGd7Nwy296orWVlZ7NvX+r+t/fv3B7+M9pUls5cQExHTZltMRAxLZi857nMOtvevq39r/eH9A2fum/W3/bu09ft7ZC7c3r17ee+99wD485//HAzTqamp1NbWBueo+f1+9u3bx1lnncWDDz5IdXU1tbW1nH/++fz0pz8NBrE1a9YcVzvOP/98nnrqKWprawGn97O0tJS6ujpqamoAZ77dK6+8wvTp07t1zSJy7PpFERNr7RPW2rnW2rlp3fzrVU96Y/V4XLbtNpeF/E2TGZ0SF55GifSCy2dlcf9Hc8lKjsXg9Lzd/9FcLp/VvS9Hp512Gj/60Y84/fTTOe200/jFL37BrFmz2vyVOTExMfiFoCeddtpp/OlPfwKcanupqakMGTLkuM9XVOT0mlhref7558PzpSXvSrjkUafHDeP8vuRRZ3s3Dab3qiuXXnopv//977HW8v7775OUlNTj86eOZPHYxSxduJSM+AwMhoz4DJYuXMrisYu7dd7B9P519W+tP7x/AA1r14LH03ajx0PDcYalUJMmTeKxxx5jypQpVFZWcsstt/DZz36W6dOnc/755zNv3jwAfD4f1113Hbm5ucyaNYvbbruN5ORkvvWtb+HxeMjLy2PatGl861vfOq52nHfeeVxzzTWcfPLJ5ObmcsUVV1BTU0NJSQmnnnoqM2bMYP78+SxevDjYiyoifac7QygLgVEh90cGtg0arpIG8LebzO03uIo7DjUTGegun5XV7cDW3mmnncb3vvc9Tj75ZOLj44mJieG0005rc0xKSgqnnHIK06dP58ILL2Tx4u59kW2xdOlSPv3pT5OXl0dcXBy/+10XRT6O0rXXXktZWRnWWmbOnMkvfvGLHmnnMcu7skcCW3uD6b169NFH+cEPfkBxcTF5eXlcdNFFPPnkk1x00UW89NJLjB8/nri4OH7zm+Mbsthdi8cu7nZga28wvX9d/VvrL+/f2Of/0Svnzc7O5sMPOxZP+e53v8t3v/vdDtvffvvtDttiY2P55S9/2WH7mWeeyZlnngk4lSdvuOEGwKlIGaqlxw1gyZIlLFnSsWd43bp1h7sMEekDpqWb/ZgfaEwksBVYhBPcVgLXWGs3dnLsb4F/HU0Vyrlz59r+sq7I4eYFvXPX2WFokcjR27x58xGHT4mIiPQ0/f9HpPuMMaustXM723fcQyittV7gVuA/wGbgGWvtRmPMfcaYSwNPPM8Ysx/4OPBLY0yHcNef9ca8IBERERERkePVrXXgrLUvAS+123ZPyO2VOEMrB6SW4WQ//M8WDlQ1kJkcy53nT+rxYWYicmQf+chH2LVrV5ttDz74IOeff36YWiRd0Xs1sOn9ExHp3457CGVv6U9DKEUGss2bNzN58uQuF+UVERHpadZaPvzwQw2hFOmmXhlCKSL9W0xMDAcPHqS//ZFGREQGJ2stBw8eJCYm5sgHi8hx69YQShHpv0aOHMn+/fvpT2sriojI4BYTExNcHF5EeocCnMgg1bK4r4iIiIgMHhpCKSIiIiIiMkAowImIiIiIiAwQCnAiIiIiIiIDRL9bRsAYUwbsCXc7OpEKlIe7ETKo6TMmvUmfL+lN+nxJb9LnS3pTf/18jbHWpnW2o98FuP7KGJPf1VoMIj1BnzHpTfp8SW/S50t6kz5f0psG4udLQyhFREREREQGCAU4ERERERGRAUIB7ug9Ee4GyKCnz5j0Jn2+pDfp8yW9SZ8v6U0D7vOlOXAiIiIiIiIDhHrgREREREREBggFOBERERERkQFCAe4oGGMuMMZsMcZsN8bcFe72yOBhjBlljHndGLPJGLPRGLMk3G2SwccYE2GMWWOM+Ve42yKDjzEm2RjzrDHmQ2PMZmPMyeFukwwexpjbA/9/3GCM+YsxJibcbZKByxjzlDGm1BizIWTbMGPMf40x2wK/h4azjUdDAe4IjDERwGPAhcBU4GpjzNTwtkoGES/wFWvtVOAk4Iv6fEkvWAJsDncjZNB6BPi3tXYyMAN91qSHGGOygNuAudba6UAEcFV4WyUD3G+BC9ptuwt41Vo7AXg1cL9fU4A7svnAdmvtTmttM/A0cFmY2ySDhLW2yFq7OnC7BueLT1Z4WyWDiTFmJLAYeDLcbZHBxxiTBJwO/BrAWttsra0Ka6NksIkEYo0xkUAccCDM7ZEBzFr7JlDRbvNlwO8Ct38HXN6XbToeCnBHlgXsC7m/H33Bll5gjMkGZgEfhLkpMrj8BPga4A9zO2RwygHKgN8Ehuk+aYyJD3ejZHCw1hYCPwL2AkVAtbX2lfC2Sgah4dbaosDtYmB4OBtzNBTgRPoBY0wC8BzwZWvtoXC3RwYHY8zFQKm1dlW42yKDViQwG/i5tXYWUMcAGH4kA0NgLtJlOH8oyATijTHXhbdVMphZZ321fr/GmgLckRUCo0LujwxsE+kRxhg3Tnj7k7X27+FujwwqpwCXGmN24wz/PtsY88fwNkkGmf3Afmtty8iBZ3ECnUhPOAfYZa0ts9Z6gL8DC8PcJhl8SowxGQCB36Vhbs8RKcAd2UpggjEmxxgThTN59oUwt0kGCWOMwZk7stla+1C42yODi7X2bmvtSGttNs5/u16z1uqv19JjrLXFwD5jzKTApkXApjA2SQaXvcBJxpi4wP8vF6EiOdLzXgA+Fbj9KeCfYWzLUYkMdwP6O2ut1xhzK/AfnOpHT1lrN4a5WTJ4nAJcD6w3xqwNbPuGtfal8DVJROSYfAn4U+CPnDuBG8PcHhkkrLUfGGOeBVbjVG1eAzwR3lbJQGaM+QtwJpBqjNkPfBt4AHjGGHMTsAe4MnwtPDrGGeopIiIiIiIi/Z2GUIqIiIiIiAwQCnAiIiIiIiIDhAKciIiIiIjIAKEAJyIiIiIiMkAowImIiIiIiAwQCnAiIjJoGWN8xpi1IT939eC5s40xG3rqfCIiIkdD68CJiMhg1mCtnRnuRoiIiPQU9cCJiMgJxxiz2xjzA2PMemPMCmPM+MD2bGPMa8aYAmPMq8aY0YHtw40x/zDGrAv8LAycKsIY8ytjzEZjzCvGmNiwXZSIiJwQFOBERGQwi203hPITIfuqrbW5wM+AnwS2/RT4nbU2D/gT8Ghg+6PA/6y1M4DZwMbA9gnAY9baaUAV8LFevRoRETnhGWttuNsgIiLSK4wxtdbahE627wbOttbuNMa4gWJrbYoxphzIsNZ6AtuLrLWpxpgyYKS1tinkHNnAf621EwL3vw64rbXf7YNLExGRE5R64ERE5ERlu7h9LJpCbvvQ3HIREellCnAiInKi+kTI7/cCt98FrgrcvhZ4K3D7VeAWAGNMhDEmqa8aKSIiEkp/KRQRkcEs1hizNuT+v621LUsJDDXGFOD0ol0d2PYl4DfGmDuBMuDGwPYlwBPGmJtwetpuAYp6u/EiIiLtaQ6ciIiccAJz4OZaa8vD3RYREZFjoSGUIiIiIiIiA4R64ERERERERAYI9cCJiIiIiIgMEApwIiIiIiIiA4QCnIhILzHGWGPM+MDtXxhjvnU0xx7H81xrjHnleNspDmPMO8aYWWF43qXGmD/29fP2FmPMbmPMOYHb3zDGPNkLz3HYf0/HcJ5LjDF/7Yk2iYj0FQU4EZEuGGP+bYy5r5Ptlxljio0xR70Ui7X289ba7/RAm7IDYS/43NbaP1lrz+vuuU9kxphLgBpr7ZrA/aXGGI8xpjbws9kY87FjON8bxpjP9FJbrTFmvTHGFbLtu8aY3/bG83WHtfb71tpuvQ7GmBuMMW+3O2+P/Huy1r4ITDPG5HX3XCIifUUBTkSka78DrjPGmHbbrwf+ZK31hqFNJ4xjCcg94PPAH9pt+6u1NsFamwB8GfijMWZ4H7bpcDJpXXD8uPXxa9xf/QW4OdyNEBE5WgpwIiJdex5IAU5r2WCMGQpcDPzeGDPfGPOeMabKGFNkjPmZMSaqsxMZY35rjPluyP07A485YIz5dLtjFxtj1hhjDhlj9hljlobsfjPwuyrQM3Ry+x4KY8xCY8xKY0x14PfCkH1vGGO+ExguWGOMecUYk9pFm4caY/5ljCkzxlQGbo8M2T/MGPObwDVUGmOeD9l3mTFmbeAadhhjLghsDw6vC9wPDh8M6V28yRizF3gtsP1vgR7PamPMm8aYaSGPjzXG/NgYsyew/+3AtmXGmC+1u54CY8xHOrnOKOBs4H+dvQ4A1tr/ADXAuCO9NsaY7+F8Zn4WeI9+Ftg+zRjzX2NMhTGmxBjzjZCniDLG/D7wnmw0xsztqi0BPwDu7SqAGWMuDZynKvCeTwnZt9sY83XjLGJeZ4wZH3jdbwx83iqNMZ83xswLvGZVLdcQePw4Y8xrxpiDxphyY8yfjDHJXbQj9P1teT1afrwtn21jzF2Bz0mNMWZTy/sUaPcvgJMDj6kKbG//7+mzxpjtgdf2BWNMZsg+G7iebYFrecyYNn+UeQNYfITXW0Sk31CAExHpgrW2AXgG+GTI5iuBD6216wAfcDuQCpwMLAK+cKTzBsLMV4FzgQnAOe0OqQs8ZzLOF8tbjDGXB/adHvidHOgdeq/duYcBy4BHccLnQ8AyY0xKyGHXADcC6UBUoC2dcQG/AcYAo4EG4Gch+/8AxAHTAud6ONCG+cDvgTsD13A6sLuL5+jMGcAU4PzA/ZdxXqd0YDXwp5BjfwTMARYCw4CvAX4CvactBxljZgBZOK9NexMAv7V2f2eNMY7FOK/VpsDmLl8ba+03gbeAWwPv0a3GmERgOfBvnN6z8cCrIU9zKfD0/7N33/FRVfn/x19nSnpvJCSE0EOvgjQFpNp7Q1232Ou6q6u/XRXbWle/YlvL2ta+dkWULgJK7xA6pJPe25Tz++NOJj0ESDJJ+Dwfjzxmcu+dO2dKYN5zzvkcjOfrW+o+z435EigCrm+kvf0xepXuBiKBH4DvVN0vF67CeG+FANU9yeNcz8UVwP8Bf8d4bw4GLldKnVl9F8CTrscxEOgBzDtGe9Fa316rR3MSkA9849p9ACP0BgOPYPR2xmitd2P0jv7qum1II493mqs9lwMxwBGM57K2c4HTgGGu42bV2rcbSFBKBR3rMQghREcgAU4IIZr3HnCpUsrH9ft1rm1orTdqrX/TWtu11oeB1zHCx7FcDryjtd6htS6l3odfrfUKrfV2rbVTa70N48N4S84LxofyfVrr/7ra9TGQBJxX65h3tNZ7awXUEY2dSGudq7X+QmtdprUuBp6obodSKgaYA9ystc7XWtu01tU9WH8E3tZaL3Y9hjStdVIL2w8wT2td6mofWuu3tdbFWutKjOdquFIqWBlzwP4A3OW6D4fWeo3ruG+B/kqpfq5zXosxJLKqkfsLwehdq+9yV49Piet8/9RaFxzruWnCuUCm1vpfWusK1+NZW2v/Kq31D1prB0YwHn6M50gDDwIPqoa9vlcAC1zPvw0j5PpihNxq87XWKdXPsctjrrYtwvgS4WOtdZbWOg0jkI50Pfb9rnNXaq2zMb4kaOn7E6VUJEbv9h3Vcw611v/TWqe73i+fAvuAsS085VyM99sm12v/AEaPXUKtY57SWhdorZOB5dR9z1e/9iEtfQxCCOFJEuCEEKIZWutVQA5woVKqD8aHyo/A6OlwDZ3LVEoVAf/E6I07lu5ASq3fj9TeqZQap5Ra7hqeV4jRA9GS81af+0i9bUcwep+qZda6XgYENHYipZSfUup11/DEIozhmyFKKTNGr0ue1jq/kZv2wOhROVHu50YpZVZKPeUaXldETU9ehOvHp7H70lpXAJ9izGE0YfQ41Z/jVi0fCGxk+2da6xCttT/G0MnrlFI3udrV3HPTmGM9J/VfE5+mhkdW01r/AKQCN9XbVec9oLV2Yjyntd8DKTR0tNb18kZ+DwBQSnVTSn2ilEpzPfYPaOH7UyllBT4HPtJaf1Jr+3XKGHJb4ArNQ1p6Tho+3hIgl5a/56tf+4IW3p8QQniUBDghhDi29zF63q4BftJaV3+wfQ2jd6uf1joI+H8Yw8uOJQPjA321+Hr7P8Lo8emhtQ7GmANUfV59jHOnYwzrqy0eSGtBu+r7CzAAGOd6fNXDNxVGAAhrYu5TCq65Yo0oxRh2WS26kWNqP8argQswhvIFAwm12pADVDRzX+9h9M6cBZTVH25ay36MkZKxTezH1cO6kJqezOaem/qPAYznpHdT5z8Jf8d439V+Tuu8B1zzvXpQ9z1wrPdRc/7puv1Q12O/hpa97wFewhj6+Y9a7esJvAncDoS7hknu4ATf80opf4zhwy19zw8EDmuti1p4vBBCeJQEOCGEOLb3MQLEDbiGT7oEYnwYLVFKJQK3tPB8nwHXK6UGKaX8gIfr7Q/E6N2qcM0nu7rWvmyMOV5NhYEfMIYOXq2UsiilrgAGAd+3sG3121GOUTAlrHY7tdYZGIHmVWUU9LAqpapDzH+A3yulzlJKmZRSsa7nB2ALcKXr+DHApS1oQyVGj4ofRnioboMTeBt4XinV3dVbN14p5e3a/yvGc/Uvmu59wzWscgnNDANURoGS2cDOYz03Lkep+xp9D8Qope5WSnkrpQKVUuOO8diPSWu9AiPs/K7W5s+Ac1zPvxUjbFYCa072/lwCMYaVFrpC770tuZGr9/JMYK7rtavmjxHSsl3H/R6jB67aUSCukaGi1T7GeL+NcL32/wTWukJ3S5yJ8V4WQohOQQKcEEIcg+uD4BqMD5rf1tr1V4xwVYzRg9CiBYG11gsxikQsw+j9WVbvkFuBR5VSxcBDGB/Iq29bhjHfarVruNnp9c6dizHf6i8Yoec+4FytdU5L2lbP/2HMncoBfsMowFHbtYANoxcyC6NoBlrrdRhFUl4ACjGqO1b3kDyI0WOWj1Gs4qNjtOF9jOFxaRgFRH6rt/+vwHZgPZAHPE3d/9veB4ZiDPNrzuuux1PbFcpVMdF1/tWuNsOxn5sXMeZO5iul5rvmyc3A6MHLxJjjNfUYbWqpf2AUcAFAa70Ho1fsJVf7zgPOa2L+34l4BBiF8douwCio0hJXYYTadFVTifL/aa13YYTsXzHC2lCM57raMozgnKmUavA+1lovwXhffYHRu92H41ti4SqM118IIToFpfXJjKIQQgghOi6l1HXAjVrrSS04djVG5cjNbd8y0REoYwH3a7XWl3u6LUII0VIS4IQQQnRJruGpy4BXtdbve7o9QgghRGuQIZRCCCG6HKXULIw5VUc59jBNIYQQotOQHjghhBBCCCGE6CSkB04IIYQQQgghOolmFwn1hIiICJ2QkODpZgghhBBCCCGER2zcuDFHax3Z2L4OF+ASEhLYsGGDp5shhBBCCCGEEB6hlDrS1D4ZQimEEEIIIYQQnYQEOCGEEEIIIYToJCTACSGEEEIIIUQnIQFOCCGEEEIIIToJCXBCCCGEEEII0UlIgBNCCCGEEEKITkICnBBCCCGE6HAWHFzAzM9nMuy9Ycz8fCYLDi7wdJOE6BA63DpwQgghhBDi1Lbg4ALmrZlHhaMCgIzSDOatmQfAOb3P8WDLhPA86YETQgghhBAdyoubXnSHt2oVjgpe3PSih1okRMchAU4IIYQQQnQYu3N3k1Ga0ei+jNIMtNbt3CIhOhYJcEIIIYQQwqO01qxKW8WfFv2Jy7+/HIVq8tjf/fg7fk3/VYKcOGXJHDghhBBCCOERNoeNhYcX8u7Od9mXv48o3yjuGX0PgV6BPL3u6TrDKH3MPsxKmMWvGb9y4+IbGRk1kluG38LpMaejVNOBT4iuRgKcEEIIIYRoV8VVxXy+93M+2P0BWWVZ9A3py+MTH+fsXmdjNVsB8LX48uKmF8kszSTaP5q7Rt3FOb3PocpRxVf7vuLN7W9KkBOnJNXRup/HjBmjN2zY4OlmCCGEEEKIVpZZmskHuz7g832fU2orZVz0OK4fcj0Tu0887vBVO8gdLTvKiMgR3DLiFsbHjJcgJzo9pdRGrfWYRvdJgBNCCCGEEG1pT94e3tv5HgsPLUSjmZkwk+sHX8+g8EEnfW4JcqIrkgAnhBBCCCHaldaa3zJ+492d77ImfQ2+Fl8u6XcJ1wy6htiA2Fa/vypHFV/v/5o3t79JZmmmBDnRqUmAE0IIIYQQ7cLmtPHT4Z94b+d7JOUlEeEbwdyBc7ms/2UEewe3+f3XD3LDI4dz6/BbGd9dgpzoPCTACSGEEEKINlVqK+WLvV/wwe4PyCjNoHdwb64ffD3n9D4HL7NXu7dHgpzozE46wCmlZgMvAmbgLa31U/X23wzcBjiAEuBGrfUupVQCsBvY4zr0N631zc3dlwQ4IYQQQojOI6ssiw93f8j/9vyPYlsxY7qN4frB1zM5bjIm5fklh+sHuWGRw7h1+K1M6D5BgpzosE4qwCmlzMBeYAaQCqwHrtJa76p1TJDWush1/XzgVq31bFeA+15rPaSljZUAJ4QQQgjR8e3P38+7O99lwaEFOLWT6fHTuX7w9QyNHOrppjVKgpzoTJoLcC1ZB24ssF9rfdB1sk+ACwB3gKsOby7+QMcalymEEEIIIU6a1pr1met5d+e7/JL2Cz5mHy7rfxnXDrqWHoE9PN28ZnmZvbh8wOVc1Pcivj7wNW9ue5Obl9wsQU50Oi0JcLFASq3fU4Fx9Q9SSt0G3AN4AdNq7eqllNoMFAH/0Fr/0shtbwRuBIiPj29x44UQQgghRNuzO+0sObKEd3a+w67cXYT5hHHbiNu4csCVhPiEeLp5x8VqtnJZ/8u4sM+FdYNcxDBuGXHLCa1JJ0R7askQykuB2VrrP7l+vxYYp7W+vYnjrwZmaa1/p5TyBgK01rlKqdHA18Dgej12dcgQSiGEEEKIjqHMVsZX+7/iv7v+S1pJGglBCVw3+DrO630ePhYfTzevVdgcNneQyyjNkCAnOoSTHUKZBtTuE49zbWvKJ8BrAFrrSqDSdX2jUuoA0B+QhCaEEEII0UHllOfw0e6P+HTPpxRVFTEyaiT3nnYvU3tM7RCFSVpT7R65bw58w5vb3uSWJbcwNGIotwy/hUmxkyTIiQ6lJQFuPdBPKdULI7hdCVxd+wClVD+t9T7Xr+cA+1zbI4E8rbVDKdUb6AccbK3GCyGEEEKI1nOw8CDv73yfbw98i91pZ1r8NK4ffD0jokZ4umltzmq2cmn/S7mgzwXuIHfr0lslyIkO55gBTmttV0rdDvyEsYzA21rrnUqpR4ENWutvgduVUtMBG5AP/M518zOAR5VSNsAJ3Ky1zmuLByKEEEKI9rXg4AJe3PQimaWZRPtHc9eouzin9zmebpY4TlprNmVt4t0d77IidQXeZm8u6nsR1w2+jp5BPT3dvHZXO8h9e+Bb3tj2hgQ50aHIQt5CCCGEOG4LDi5g3pp5VDgq3Nt8zD7MmzBPQlwn4XA6WJq8lPd2vse2nG2EeIdwVeJVXDHgCsJ9wz3dvA7D5rC5g1x6aTpDwodwy4hbmBw7WYJcJ9bRv4A66YW825MEOCGEEKLjO+uzs8gqz2qwPco3iiWXLZEPth1Yub2cb/Z/w/u73ielOIUegT24btB1XND3Anwtvp5uXodVHeTe3P4maSVpEuQ6sc7wBZQEOCGEEEKclAp7BRuObmBV2ipWp63mcNHhJo8N8wljUPgghkQMYUj4EAZHDCbCN6L9GisalVeRx8dJH/NJ0icUVBYwLGIY1w+5nmk9pmE2mT3dvE7D5rTx3YHveGPbGxLkOqHc8lwu+uYi8ivzG+yL8Y9h0aWLPNCqhiTACSGEEOK4aK05XHTYHdg2HN1ApaMSb7M3p0WfxrbsbRRVNVwVKMgriKk9prIzdycHCw/i1E4Aov2jGRw+mCERQxgcPpjBEYMJ8gpq74d1SjpSdIT3d77PNwe+odJRyZQeU7h+8PWMiholgeMk1A9yg8MHc+uIWyXIdSBaa9JK0tiUtYlNRzex8ejGZr98Uii2/W5b+zWwGRLghBBCCHFMpbZS1masZXXaalanryatxFg1qFdwLyZ2n8ik2EmM7jYaH4tPi4YgldnK2J23mx05O9iZs5MduTtIKU5xHx8fGM/giMEMCR/CkIghJIYl4mf1a98H3YVtydrCuzvfZVnyMiwmC+f3OZ/rBl9H7+Denm5alyJBruNwaicHCg64w9rGrI1klRlDvQO9AhkZNZJRUaP4767/kluR2+D20gN3giTACSGEEO1Da83e/L2sTl/N6rTVbMrahN1px8/ix7iYcUyKncSE7hOIC4xr9PYnUgSgsLKQnbk7jUCXs4OduTs5WnYUAJMy0Tu4t3vo5ZCIIfQP7Y/VbG31x95VObWT5SnLeXfHu2zJ3kKQVxBXDLiCqwdeLcNY25jNaeP7A9/z+rbX3UHuluG3cEbcGe0W5GxZWaTd8xfiXngeS2Rku9ynJ9mcNnbl7mLTUaOHbVPWJvfIgCjfKEZ1G2X8RI2iX2g/9xqGMgeulUmAE0IIIdpOYWUhv2X8ZvSypa12FyLpH9qfibETmdR9EiOjRrZraMouy2ZnrhHoduQavXUFlQUAWE1W+of2dw+9HBIxhN7BvWXOVj0V9gq+PfAt7+96nyNFR4gNiOXaQddyUd+LpFezndUPcoPCB3Hr8FvbJchlzHuEgk8/JeTKK4l5+KE2vS9PKLOVsS1nm7uHbVv2NncI6xnUk1FRRmAbHTWauMC4Zp9vqULZiiTACSGEEK3HqZ3szt3NqrRVrEpbxbacbTi1k0CvQMbHjHf3snXz7+bpprpprUkvTa8z9HJX7i5KbaUA+Fp8GRg2sM7wyx6BPU7J4WoFFQV8sucTPk76mLyKPAaFD+L3g3/P9J7TsZiOudyvaEOtGeS004mjoAB7djb2rGzjst6PLTMTe3q6cQOlCLnyCgImn4Hf6FGYg4Pb4BG2vYKKAvf8tU1Zm9iduxu7tqNQDAgbwOhuo92hrav1MEuAE0IIIU4heRV5rElfw+q01axJX0NeRR4Ag8MHMyl2EpNiJzEkYkin+oDv1E4OFx12D73ckbuDPXl7qHRUAsb8luoeuurKl938unXZUJdSnML7O9/n6/1fU+GoYHLsZH4/5PeM6Tamyz7mzqo6yL2x7Q1SS1IZFD6IW4bfwplxZ4LDgT03t8lQ5v7JyQG7vcG5TYGBWCIjsURGYsvMxJaSAk4nVL8HtAal8O7XD78xo/EdPRq/MWOwdus4X9jUllmaycajG909bAcKDwBGT/zQiKHu4ZAjokYQ6BXo4da2LQlwQgghRBdmd9rZkbODX9J+YXXaanbl7kKjCfMJY0L3CUyMncj4mPFdbnFmm9PGgYIDRqDLMXrp9uXvw66ND7oRvhHuipfVoS7MJ8zDrT4527O38+7Od1mSvASTMnFu73P53aDf0Te0r6ebJmpxVlY26C2rysrk0MHNZBzZhW9hORFlFvxLHahGPoubw8LcwazBT5TrMiICk6+xbp8tK4sDM2aiKyvd51De3nR/9hmqDhygbMNGyjdvxllWBoA1Lg6/0aPxHWMEOq+EhHYP/lprDhUeYmPWRvcctvRSowfR3+rPiMgRRg9bt1EMiRiCt9m7XdvnaRLghBBCiC4mqyyL1WmrWZW2il8zfqW4qhiTMjE8cri7YuTA8IHuSfunigp7BXvy97gD3Y6cHRwqPITG+LzT3b+7EehcPXWDwgcR4BXg4VY3z6md/JL6C+/sfIeNRzcSaA3ksgGXMXfgXKL8ojzdvFOG1hpnaemxe8uys3EWNVxiA7MZS0QE5sgIcv2cbNcppHqX4hMVw6Th5zNi4FSsUVFYwsNR1uObg5ox7xEKvvgCbLaajVYrIZde6p4Lp+12KnYnUbZxA+UbN1K2YSOOfGMtNHN4OH6jR7t76XwSE1Hm1p1nanfa2ZO3x+hhy9rE5qzN7tEBYT5hdYZD9g/t36lGCLQFCXBCCCFEJ2dz2NictZlV6ca6bHvz9wJGpbWJsROZGDuR02NOJ9i7c851aUslVSU1yxm4iqVUL5EAkBCUYAQ6V6GUxLBEfCw+HmyxocpRxfcHv+e9ne9xsPAg0f7RXDvwWi7pfwn+Vn9PN6/L0Fob88taEMx0eXmD2ytv72P3lkVGYg4NRZlqvlCxOW0sOLiA17e+TmpJKgPDBnLL8FuY0mPKcfeGHbzwIiqTkhps905MpPfXXzX5uKsOHaJsQ02gs6UZfxcmf398R47Eb8xo/EaPxmfYMEzex9cDVmGvYHvOdveQyK3ZWymzGz2AsQGx7sA2uttoegb1lKG/9UiAE0IIITqhtJI0dy/b2oy1lNnLsJgsjIoaZVSMjJ1Ev5B+8sHnBORX5LvDXPWyBtnl2QCYlZm+IX2NQOcaftk3tC9WU/tU5iysLOSzPZ/xUdJH5JTnMCB0ANcPuZ5ZCbParQ0dxcmUxdd2O/bcPFf4yqoXxnLqzi+r3XPlYvL3bzaQVf+YgoJO6m+wOsi9se0NUopTTirInSxbRgZlGza6e+kq9+0HQFmt+AwbVtNLN3Ik5sC6c9CKqorYkrXFHdh25O7A7jSGM/cL7ecOa6OiRnWookkdlQQ4IYQQohOosFew8ehGVqWtYnX6ag4VHgKMYX/VxUfGxoyV3pc2crT0qHsZg+pwV72mlJfJi8SwxDrDL3sG9WzV5QzSStL4YNcHfLHvC8rt5UzoPoHrB1/P6TGnn7IhvbGy+Mb8spxGQlndcObIyzMKetRjDgk5ZiizREZi8mvf5RfsTrvRI7ftdXeQu3n4zUztMdVjr789P5/yzZuNULdhAxW7dhnFVEwmzP36kD8gmt1xsDz8KJvtxlBli7IwKGIQo6OM+Wsjo0bKyIATIAFOCCGE6IC01hwpOsLqdKOXbUPmBiocFXiZvDgt+jQmxU5iYuxEEoLav8CAMF6f1OJUd6irXs6g3G4Mo/Oz+DEofFCdnrrYgNjjfq125e7i3R3vsujIIhSKOb3m8LvBv2NA2IC2eFgdlnY6sefkYD+ahf1oJhX79pPz8svgcIBJYY2Px5FfgLOwsOGNTSYs4eHHDGWWiAiUl1f7P7jj0BGDnNaa5OJkNh/5jZTfluLYvJ3uBwrpn6bxdhXHLIsJwTJiGDETpxEydjzWHqfm0h6tRQKcEEII0UGU2cpYl7nOvS5b9VyshKAEd2Ab3W00vhZfD7dUNMbhdHCo8FCdnrqkvCRsTmMIXoh3SIPKl9WFRuovHDyr5yx25+1mbeZa/K3+XNrvUq4ZdA3R/tGefIhtQtts2LOysLnCmS3zKPbMTGxZR7FnHsV2NBN7VnajpfKrWeNiCTjjjEaDmTksrNWLbnha/SCXGJbILcNvaZcg53A62Fewzz0cclPWJnLKcwDjPT4yaqQxHDJsGAmZTqo2baVswwbKNm1yB2xLZKS7yqXfmDF49+tXZw6gp329OY1nf9pDekE53UN8uXfWAC4cGevpZrlJgBNCCCFaYttnsPRRKEyF4Dg46yEYdvlJnVJrzf6C/cawyLTVbMzaiN1px9fiy7iYcUzqPokJsRPoEdijlR6EaG82h429BXvrDL08UHAAh3YARqGZcN/wOkscVAu0BnLDsBu4tP+lnXZdK2dFBfajR41Q1kQ4c+TkGmuS1aJ8fbF264YlOhprtygs3aKxRHfDGh0NXl6k3Xobuqqq5nhvb/ouWXzcc+E6O7vTzg+HfuD1ra+TXJxMYlgiNw+/mWk9prVakKtyVLEjZwebsoz117ZkbaHEVgJAtH90nYIjvYJ7NVndVjudVO7f7y6KUrZxI/bMTABMQUH4jRxphLrRY/AdMthjvaFfb07jgS+3U25zuLf5Ws08efHQDhPiJMAJIYQQx7LtM/juTrDVqjJn9YXz5h93iCuqKuK39N/cQyOzyrIAYyL/pO5GL9vIqJF4mTv2UC5x4srt5STlJbmLpPx46Ed3oKstxj+GRZcu8kALj01rjbOkpF44yzRCmSuc2TMzcTQypNEUFOQOZ5ZuUVhrhTNLt25Yu3VrtvhHS8rin2paM8iV2kprCo5kbWJ79naqnEZY7h3c271g9uhuo+ke0P2E26y1xpaWTtmG9e5QV3XImNurvL3xHT68ZoHxESMw+bfd/F6bw8m+oyXsSC/kkW93UlrV8O8xNsSX1fdPa7M2HA8JcEIIIcSxvDAEClMabg+IhltWg28oNFGwwqmd7M7bzeq01axOW83W7K04tINAayCndz+dSbGTmNB9QpccGidaZth7w9xr0dWmUGz73bZ2b4/WGkd+viucZda6rDvEsXrh59rM4eHHDmcnWQDkRMrinyrsTjsLDy3k9W2vc6ToCANCB3DL8Fsos5fx0uaX3EN07xp1F+f0PgeA3PJcNmUZi2VvPLqRPfl7cGonZmVmYNhAI7C5QluoT2jbtj83l7KNG92BrmL3bqPYjNmMz8CBNQuMjx6NJSzshO6jwuZgT2YxO9IL2ZFWxM70QpIyi6myNyxqU5sCDj11zgndZ2uTACeEEEI0prIEjqyGA8tg7b+bP1aZwC8C/CPBP4J8vxDWWDSrncWsrsggz7W+0aDgvkyMm8zk+KkMjRja5RejPZky76eSmZ/PJKM0o8H2tuiB0w4H9pzcuj1mR13hLDMT29Gj2I8erTM8ETAKgURFNRPOorFERWLq4EVAThX1g5xC1fmSwGqyMiJyBNnl2RwuOgyAt9mbYZHD3Atmj4gcgZ+1fatt1ucoKaF88xZj6YINGynfts393vTq08e9dIHf6NFYYxsObyyttLM7o4gdaYXsSDcu92eVYHcaz0WQj4UhscEMiQ1mcPcghsQGc+1/1pJeUNHgXNIDd4IkwAkhhGgzTgekb4GDy+DAckhZB04bWHwBzQJvEy+GhpBpMRNtd3BXfgHnaF84834cJZlsLzzI6rIUVtnz2YkNrSDU4WBCeQUTy8qZUF5BeHXZcqs/+LsCX0BUzfXGfvzCmuzd6+gaK/MuGlpwcAHz1szDp7Ccu7928MKFZiqDfZk3YZ67l6QldFUVtqzs5sNZdrZRubEWZbW65po13XNmCQ9HWbr2Fw5dkd1pZ+pnUymoLGiwT6GYHDfZPYdtcPhgrOaOvZags6qKih07aq1HtwlniTEfzxwdTVniUFJi+7MxuCe/VAVwMLfMPb0y3N/LFdaCGNLdCG1xob4NhpjKHLhWJgFOCCFEq8o/AgeXG71sB3+GigJje/Qw6DMN+kyFHqezYPUTzDv0FRWmmv/ovZ2a8yJGUxwUya/pv1JUVYRJmRgWMcy9kPbAwATM5flQml3vJwdKsmquV29vZB6U0bsX7u7dMy4bCX0Brksvz64Dp7WmMimJgi++JP/DD6j+0l/5+hq9M15WlNWKslRfWupeWi1gPcYxXlZwb6t3jOscytrIMdZax9Q6p/v+au9r56qFC1Y8SPq/P2fCFlg9AmJvvpRzpjzm3u8sK3P3jlWHM3vW0ZqiIEeP4sjNbXBe5eeHNToaa3Q3LFHd6g5ndF2aQ0OlpHsX1tGG6LaG3JJKdqYXsT0lj6NbdqK2b6F78h6G5B4irLIYgHLfAIr6DcZn5CjizpxA9zHDWtxDLFUoW5EEOCGEECeloggO/2IEtgPLIe+AsT0oFnpPNQJb7yngH4FTOym1lVJcVczcH+a6y2TXF+kbycTYiUyMncj4mPEnviit02kEyNpBr6R+8KsVACuLGj+P1c8V7qLqhb5Gevv8wluld09rTeXefRT9uJDihT9SdfiwsUMpo7qgyYR3v374jR6NttnQdrtx6b5eBXY7uqrWvgbH2KD+7235OcVkqhvyvGoHwRMMnk0dk7UV56bPyNrkC1qB0gT1rMIRMhh7qcJ29CjOooavtzk42Ogxi+6GtU44cwW2bt0wBQRIODvFtecQ3damtSaruNIYAplWxI70QnamFZJeWDPEMT7MjyGxQQzuHsyQ7kEkOgvx3rXdWLpg40ZsycmA8WWG7/Bh+I0eYxRHGT4ck2/nXJJFApwQQoguy2Yrpzh5DcUHl1KU/CtFOUkUm6DI6kNReC+KQuIo8g+nyKQoriqmqKqIosoiim3FFFcV49THmtSu2HrdVs98QLZV1A10pdlQmlW3R692CGysdw/VsHevuSGdXv5GKHOp3LePooU/UvTjj1QdPAgmE37jxuI/cRI5L/4f2lZTFl95Wei7dFmrzoXTDkfdkFdlA3vdkOe+rKoJi9puN8KgOyQ2DJTuY2z1z1OFtlehqyqNEFlVhbZXb69/HrtxHrsdbXe4f44dPDWYNT5hJizDz3KFs+pQVl1Wv1un/fAp2lf1EN0KR03o8TH7HPcQ3bamtSY1v5yd6TVhbUdaETkllYDxT0/vCH9jGGT3YAbHBjE4Jphgv+aHfdqysuosXVC5Z4/xN2ix4Dt4sHvpAr9RIzGHhNS5XUedwysBTgghRIeltabCUWGEquqAVVVUJ2xVb3NvL8uhqDyXYnsZZTQfwLxMXgR5BxHoFUiQVxBBXrWuewe5t72w8QXyK/Mb3L4zfIMN1Ordy3GFvFqhr7GhnE317ll8qayKoCjVl+J9NiqzK40MOCCWoEkjCZw6GUtsbzIefYyCFVvBWSvYmjQh08cSM/99V5sc4LCBowqcduOy0d9txlzE2r+3yzGu7c5axzpsTQTh46Odrh+t0E6FrczEkcWR6FrPlzJr+p57FEvPgRA5ACITIbK/cRnWByxSLES0XP2F4mtXofQEp1NzOLeUHelF7EwrdIe1wnJjaQizSdEvKsAV1oziIgNjgvD3Pvl5mI6iIso3bzZ66DZspHzHDveSFN79+rkDXcnKlRR9912HnMMrAU4IIU5R7fUfulM7KbGVNB7C6gWw+tuLq4qxOW3Nnt/f4keQyUqgw0FQRQlBVWUEOp0EWQMICulFYORAgmJGEhzYvUFQ87H4tOgxdJZvsFuNrQLKctw9eJX7kyheuYGitXuozDDCnV+shcCeNoK65WLxrlux8OCPEVQWNAwY3iE2ep9bbIShY/RunjSzl/FjsriuW10/XmCy1vu9jY4xWWvaYbY0eZuMq8dTsEc3DLz9bMRcPhqyk6AgmZoJhWYI7wMRrkAXOcD4Ce8HXp6tGihEfXaHkwPZpa5KkIXsdJXur15rzctsIjEm0BgC6SowMiA6EB9r+8xDdVZUUL5tm7uXrnzz5jpLZCgvL/ouXdKheuGaC3BSakgIIbqo+oEkozSDeWvmATQaSGxO2zEDWFO9YiVVJY1OoK9mVmZ3sKq+jPGPadgrVt0jZvYlKO8wQambCDi8GsvhzUYY8AqEXmfAsKlGAZKw3nWG+52M6uekI32D3aasPlQVOChauI6iH390r7nlO3o03f4wm8CZM7F2izKO1RrK8+v04PXmd02fe9xdtYJNS8JPSwJTvYBkMrfaa98eysujwZlZd6NTUV4ZD3M/M36vKoPcfZC9x/WTZFzuWVirV1BBaE+IGFCr126AEfR8gtr1MYlTU6XdYSyIXatXbXdGEZWuNdZ8rWYGdQ/i0tFxDHYNhezXLQCr2eSxNpt8fPAfOxb/sWMB0HY7qff8hZKlS8HhQGtN9quvdbheuKZID5wQolNZsOJBXjz4FZkmiHbCXb0vqlPFrSvRWuPQDuxOOzanrclLm8OGXduNy1r75v06r9Gy0r4WX8ZFj2vQI1ZuL2+2PT5mnzpDD5saklh7e/U2P4tf83PItIacfTXVIg+vgqoSoxcidnRNtcjY0cYHe3HCqlJSKPrxR4oX/kjFrl0A+I4YQdCc2QTOmoU1uoWLjTe18HlwD/jzjlZscRey7TNY+igUpkJwHJz1EAy7/Ni3s1cZxXiqA131T+4+o6ezWlBswx67yERjmQohTkB5lYPdma4hkK45a3uPFmNzGPkh0NvC4Fol+4fEBtErIgCzqWN/uWLLyuLAjJnoykr3NuXtTd8liztML5wMoRRCdAkLVjzYoMy7j1Mzr1fzIa46CNmctprg00ToqROMmglNtY+pfY5jHdvovtptcV1W72uuV+tkJIYl1g1e9XrA6s8RC/QKxNvs3bqNKM2FQytc1SJXQFGqsT20V01gS5gMviGte7+noKrUNIp/+pGihT9SscMIVz7DhxE0ew5Bs2Zi7d79+E+67TP47k6w1Qr+Vl84b37LQok4eQ47FBxxBbskyN5rXObsBVvN8DD8I+v12LlCXkC3TtWLKdpWcYWNXelFdeas7c8qwbUeNqF+VveC2ENcQyF7hPph6uBhrTEZ8x6h4Isv3PPiALBaCbn00g7TCycBTgjReTlsxryQvINMX3k3Ry0Nh2BYtCYuMA4bNBme2pJJmbCarFhNViwmS6OX7utmKxZV97K52zV7+/r7XNerf79lyS1kl2c3aK/HinLYKyH5N1cv23LI2Apo8AmGXme6yvtPhbBe7d82l46+LtDxsKWnU/TTIooWLqRim7EOlM+QIa6ettl4xbXC4zrRHiXRtpxO4wuR2sMwq38qC2uO8wmuF+xc4S4oDkyeG+4mTszx/PuVX1rFzvTqKpCF7Ewv4lBOqXt/tyBvVxXImgIjMcE+XWa5ioMXXuQeNl6bd2Iivb/+ygMtakgCnBCiY7NXGost5x00hgnlHXT/5BelsczXi8X+fqz29Wn822KtmVVahgWF1eqH1TsAi1cQVp8QLL6hWH3DsPhFYPUJPqEg1FyAsigL5lZYY6steLwoh9aQtbtmWOSRNUavgMkCcWNretm6j2yVdcpO1teb03jgy+2U22oqEPpazTx58dBOE+JsmZkU//QTRQt/pHzLFgB8Bg0icM5sgmbPxqtHD882UHiW1lBytGGoy04yCtpUs/obQa7+PLvQhHb9W+1KX6i0teb+/ZrQN5ydaUV15qylFdT0nMeF+rp71AbHBjO4exBRgS0r/tSpdfAvoCTACSE8z1YO+Ycht25AI++Qax5Nzb9F2b4hLA2PYYm3iQ3OEhxoYn0iKCjLprSRoRoxDieLRvzNCIEFR4z7yT9ilFKvzeoHIT2NDyGhPRte9w5owyfAM9q9rHRJFhxcUbOIdomraEN4v1rDIieBd2DbteEETXxqWZ0PNdViQ3xZff80D7SoZWxHs4zQ9uOPlG/aBID3wIEEzZ5N0OxZePXs6eEWik6hNBdy9tQdipm9B4rTa44xe0NEv4bz7NpgyYOu8IVKU7TWOJwah9Zojfu606lxun53Vh/jdB3j+t2pa/Y5nTXbb/rvBnJKqhrcl0nhHgIJxhprtXvVBncPIsTvFFyuohMMAZcAJ4RoH1WlRiCr04t2yLgsSqt7rG+YUUHQ9ZMZEM5iWzZL8neyOWcHGk1CUAIzes5gRs8ZJIYl8sPPDx3fHLiqUmP4Zf1gV329qqTu8X4RjQe70ATj2zkpntGQrRySf62Zx3Z0u7HdNwx6T6kZFhnScXt+iitsrN6fy80fbGzymNmDo+kR5kuPMD/jJ9SPuFDfdiuBXZ89O5uiRYsoXvgjZRs3gtZ49+/vHh7p3dtzw1BF0zplj1JFoVFgqE6vXfNLHjgjBmAL64cttA825YPN6cTm0NjsTuxOJ1V2jd3pxOZwbXc4sTs0VQ6n+/q873ZSUNZw+HuQj4UbJvc2Ao821hqrCT8ah5Oa8KN1TVhybXfWCkLV24150rUCkisYOZsJVk5ds80IWdXnoO4x7nO4trtCW3t66NxBrjXWAgn0kf/HgE5RhEkCnBCi9VQUNexBq75eUq9Etn9knZBW89MLfENJKU5hyZElLD6ymO05xgf/fqH9jNAWP4M+IX0ajLdvtSqUWkNZHhQcbhjs8o8Y/7A77TXHK5MxLyS0pyvYJdQNeQFRp0YxAKcTju6oNSzyV3BUGmXd4083AlufaRA9vMPOodFasyujiBV7svl5bzabjuRjd2oUNFoyxttiIi7Ul9T8cneZ7GrdgrzpEepXK9gZIS8+zI9uQT6tWonNnpND8eLFFC38kbL1643Q1q8vgbON4ZHeffq02n2J1nciPUoOp3aFHCPU2BxObE4jCNUJP02EovoBqe7vTqrqXbdX38ZZHbaM/VW1rlff3mwvJ8aeSg9HMj0dySToNHqTSjyZWJTxd+LUihQdyT4dy37Xzz5nLAd0d0ponbXsTMpYENqklPuyepvZpFBKYa7eZwKzch1jMrabTKrBOcxKoWqdo/Y569yPSWFW1JyrepvrflT1+dy3w3UuVat91LSv9v3UaV/NbWuOqz6Hsf0vn20lt7RhD1xHH0HQrioKIWWdMZx/1fNNHKRgXkF7tqpJJx3glFKzgRcBM/CW1vqpevtvBm4DHEAJcKPWepdr3wPAH1377tRa/9TcfUmAE6IDKM9vGM6qf0rrFcUIiK4bzMJ6G9/EhvZqdE2ig4UH3aEtKc+YQDwofJC7p61nUAcZ7uWwG0OH8l2hruBI3eslR+seb/WDkPimh2h2wCGDLVaUURPYDq6oeQ9EDqwZFtlzAnj5e7SZzSkoq2Llvhx+3pPNyn3ZZBcbpaMHdw/izP6RnNk/ktT8Mv7x9c4mP2A7nZqckkqS88pIyS8jJa/cuJ5XRmp+OemF5XW+WbeaFbEhdXvt4sP8jJ68UD9C/KzHLAhgz8ujePESihYupGzdOnA68erdm6A5cwiaPQvvfv3a5PkSJ6esyk5WUSVZxZVkFVeQVVTJ84v3UlJpb3Cs2aSIDvKhyhWkaocvZxt/x+5lNmE1KyxmE1bX9bqXJiz1tllMJrwsda9bTDW391F2IqrSiKw8RGT5YULLDhFaepCgssOYaxWUqvCNpiy4LxUhfakK7YctbAD2sH6YA8Kxmk1c+cavjCtZyn2Wz+iuckjXETxjv5wNgdP5+b6p7pDVVYpqnKyuPOT0hBUfheQ1xheNyWvg6E5jPVGTxfhS1tEw8HaZHjillBnYC8wAUoH1wFXVAc11TJDWush1/XzgVq31bKXUIOBjYCzQHVgC9NdaO2iCBDgh2kF171P9cFb9U55X9/ig2LoBLayPcRmacMx5Y1pr9ubvZUnyEhYfXsyBwgMADI8czoyeM5jeczqxAZ3wP5eqMmMIUf1gV329qrju8b5hTc+9C+7R6vNHTkpVqfENZfU8tuzdxnb/SGM4ZJ+pxvDIoBMoPd9OHE7NttQCft5r9LJtTSnAqSHEz8rkfpFM6R/J5P4RDSbqn8wQtyq7k/SC8joBL8V9vYz8esPBAr0txLl67eKrQ16YLz3MVYRsXEP5okWUrl0LDgdePXsSePYcgmbPwbt/P/nQ6gFaa4rK7UYgqxXMjOuVZBVVkO263lhQa84lo+LqBiGLwmqqCVBetYKUV2Ohql7oqhPATAovi3FptbhuX6t3qt0cz5IHkYkcLbERmr0eL1XzkbFce7Fj9OOcdv5N7dfuTqRTDtFtLVpD/qGasHbkV2M6BxhfsMadZnzRGD8e4sZA0oKuPQdOKTUemKe1nuX6/QEArfWTTRx/FXCd1npO/WOVUj+5zvVrU/cnAU6IVqK10VPSVEirqFVKGmWEiNo9aNW9aqEJxj9qx3XXml25u1h8ZDGLjywmuTgZkzIxKmoU03tOZ3r8dLr5d2vVh9uhaG30YuYfbrz3riAFai9toExGSK4d7EITXL/3bPu1mpxOyNhSU94/Za3xzaTZ2/gPr3pYZNTgDjssEiC7uJKVrsD2y75s8stsKAXD40KYMsDoZRsWF+KxBWaLK2xGqHMFOiPcGYEvLzOH0SnbOCNtKyOy92HRTo4GRrA7cRzZoycRMDCRuHB/oxcv3I/oVh6eeSpzOjW5pVXuYJZdVFkT0mpfL66kqt4QWjB6OaKCvIkK9CYq0IfIQG/X7z7GNtf1c1/6hfSCiga3P+WHuDW15EHqehod1OwdBBf9G2KGG/9uypcZpyanE7J21g1s1dM4fEONoBY/HnpOhJhhjc9h78pVKJVSlwKztdZ/cv1+LTBOa317veNuA+4BvIBpWut9SqmXgd+01h+4jvkPsFBr/Xm9294I3AgQHx8/+siRIyfwMIU4BWkNxZlNhLRDdXuBlMkY4le7B80d0nqC5eQWaXZqJ9uyt7H4yGKWHFlCemk6ZmVmbPRYpveczrT4aUT4RpzkA+4inA4oSm+k9+6w8Xv9uYQWX+O1qx/sqq83MlTVran/oApSag2L/Lmm17XbUOgzxQhs8eOPO7y3J5vDyebkAn7em8WKPdnsTC8CICLAmzP6RzBlQBST+0YQ6t+BejdrcRQVUbx0GUU/LqR0zRqw2XFGdyd/7BnsHzSOnX7RpBSUk5JXTkZheZ3hdFazonuI0XMXF2r03MW7hmn2CPMjtAXDM7u6KruTnJKa3rHqEJZdp+esgpySKhyNjFUM8rEQFeQKYYHe7uuRrqBWHdoCvC0teq5liNtxmhdC47NSa/ELN4Jc7Z/QXhLquiJ7FaRvrglrKb/VfBEdFAc9qwPbBGP5iw78ZWNLtUuAq3X81cAsrfXvWhrgapMeOHHKOdY3QE6nMRerfkDLPWgMF6g99MRkMT7UN1Y4pA2G6TmcDjZlbWLxkcUsPbKUrPIsrCYr47uPZ3r8dKb2mEqIT0ir3ucpwVbeSPXMwzWBr7Ko7vG+oY0Hu6xdsPyJukNETBbwDYdS1xy+gOiaeWy9pxjFWDqw9IJyVu7NZsWebFbvz6G40o7ZpBgdH8qZrl62QTFBmDpo75SjuJiSZcsoWvgjJatXg82GtXt31zptc/AZMrjRMFBld5JRWF4z765eL15eveIFAd4W4moVVKldXCUu1A9fL8+vu3eiyqscjfeQua5XD2Os/5yA8bk+3N+LyMDawaxhb1lkoHebVBg9pYe4Ha+mqgQGxcFl7xqjBjK2Gj9Zu2tGNXgHGz0utUNdeN8OsdakOA6VxUbBkeRfjcCWtgHsrh7siP41Ya3nBOMLzi6ovYdQmoB8rXWwDKEU4hgaW4fEbIVeU4zL6p40R2Wt/V7GN4z1C4dUhzSzpU2bbHPaWJ+xnsXJi1mWvIy8ijy8zd5Mip3E9J7TOTPuTAK9OnHBjo6uenhmY8si5B8xgp+z7lwrW7mJtDWhxE3Ix+LrBIuP8UVBn2nGWk4d+NvqSruDDYfz+XlvNiv2ZLH3qLH0Q0ywj3tY5IS+EQR14NLYjpJSSpYvp+jHHylduRJts2GJiTHWaZszG5+hQ0+6t6yk0l4n0NVcLyM5r4wKW92hfxEB3sRXL4vgKq4S5yquEhPsg8Xcsm+vWyuQaK0pqrA36B2rc901vLG4kfllFpNy9Yx5G+Gs1pDG2sEsPMALawsfm/Cw41mny15phLjqQJex1aiWW/2B3+oH0UPrhrrIRFkapiMpzakJa8lrIGMbaIcxeihmOMRPqOll8z81RvOcbICzYBQxOQtIwyhicrXWemetY/pprfe5rp8HPKy1HqOUGgx8RE0Rk6VAPyliIgRQXgDzRzYsGFItalDDgBbW2xjz387fJFY5qvg1/VcWH1nM8pTlFFUV4Wvx5cy4M5neczqTYyfjZ22dktDiJDkdUJxhhLl3zwYgY0MQBfv9CelbSsyYIjpSmeTGJOeWuYdFrjmQS7nNgZfZxNheYUbFyAGR9IsK6NBDBJ2lpRSvWEHxjz9S8vNKdFUVlm7dCJo9i6A5c/AZNgzVTkN8tNbklFTV6bUzqmca8/HSC+oOz7SYjOGZ1cMy41zDMqt78sL8vVBKtWhIoNOpySurqtNTll1vSGN1UKu/RAOAj9XUaO9Y7SGNUYHehPp5ddheV3ESTmaOksNuFEipHeoyt9Ws/2n2gm6D64a6qMFg9Wn+vKJ1FCQbxbKOrDGCW85eY7vFB2LH1IS1HmM7dxXnk9AaywicDfwfxjICb2utn1BKPQps0Fp/q5R6EZgO2IB84PbqgKeU+jvwB8AO3K21XtjcfUmAE11aUQbsWWBUPzq0su46Y3V4/gN2ub2c1WmrWXxkMStTV1JiKyHQGsiUHlOY3nM6E7pPwMci/9F1ZPpfgyk/eJQjyyPAqVAmTdwZuXh3j8J0zzpM/n4dIgSVVzn47VAuP7vWZTuUUwpAfJifu5dtfJ9w/Lzatnf5ZDnLyihZuZKiHxZS8vPP6MpKLJGRxjptc2bjO2JEu4W242FzOMkoqHD31tUurpKaV9ZgbSk/LzPxYX4czimlorHQZTHRPzqQrKJKckoqsTcyvyzQx1LTQ1a7tyyo7hyzwBbOLxOiRZxOY2RL7eGXGVuhosDYr8wQNbBuqOs25JjVlsUxOJ2Qs6cmrB351ShcA8aQ1/jTXYFtAnQfcdJz8rsKWchbCE/K2QdJ38Pu740x3GAUERl4Lmz5GEqzGt7GQ+uQlNpKWZm6ksVHFrMqbRXl9nJCvEOYFj+N6fHTOT3mdKwy5KTD0VVVVCUnU7n/AJUHD1C1/wCVBw5QdfAA2tZMOXOzGXNgIKbgIMyBQZiDgmquBwdhCqp33fVjbA9EWU4sUGmtOZBd4l5Ie+2hPKrsTnysJsb3DufM/pFMGRBFQkTHXVeumrO8nJKVv1D040JKVvyMLi/HHBFB0MyZBJ09B99RozpkaDsepZX2OssiJOeVkZpfxpLdjfzb5XJG/8iaOWZ1esuMHrTOPAdPdDFaG8PQM7bVCnVbaq15qiCiX91QFz0MfEM82OgOzmEznsfqwJb8qzH0H4x519Vhred4Y7SRzE9slAQ4IdqT1pC+yehl2/298a0TQPeRkHgOJJ4HkQOMeUfHM8a/jRRVFbEiZQWLjyxmTdoaqpxVhPuEG+X+e05nTLcxWEwdu+fjVOGsqKDq0KGGQS05Gew1Qc0aG4tX3z5YY7pT8L/PwFGrl8RiJvKee1Ba4ygqxlFUiLOwCEdx/etFYLM10ooaJn//huEvKLgmFAYFYw4KxBQURKWPP1sLHKw5WsWSlHKOlBjt7RsV4ApskZyWENYmhSNam7OigpJffqF44Y8Ur1iBLivDHBZG4KyZBM2eg9+Y0Shzx38cJ2viU8tIKyhvsP2UL4svOr/qCs/1e+qK0mqOCU2oVwFzxCkzN6uBqjJj2YfkX43Qlrq+psBaWG/X+muuwCZVQlusuQAnn8qEaA0OGxxZbQS2PT8Y/8grMyRMhNP+BIlnG+P366sOae28Dkl+RT7LkpexOHkxazPWYnfa6ebXjcsHXM70ntMZETkCs3wj5jGOklKqDh6gcv8B92XlgQPYUlONDxYAZjNePXrg1bcPgTNm4N2nN159+uDdqxcmP2M+Ysa8R4xvNmsHOGXClpJKzMMPNdsGrTW6vNwIc4WFOIuKagJf7eu1wp/tSDIVrvCny8oanLM7cKnrx2m1Yg4KwhoSgnmdEfhymwh/1deNkBiMyd+/TYfV2bKySLvnL8S98DyWyEgAnJWVlK5ebVSPXLoUZ1kZ5tBQgs87j6A5s/EbM+aEeyQ7q3tnDWh0Dty9swZ4sFVCtAKlICjG+Bkwp2Z7STZkbq0b6nZ9U7M/KLbhsgaBMV0vsJTlQfJvNSX9M7a4poQoiB4CI6+t6WUL7MJrvnqQ9MAJcaKqSo01tHZ/D3t/NMbQW3yh71mQeC70nwV+YZ5upVt2WTZLk5ey5MgSNhzdgEM7iA2IZWbPmUzvOZ0hEUMwqc491KuzcRQUUHmgYVCzZ9ZaB85qxTshAa++ffDu09cd1LwSEjB5Nb8sxMELL6IyKanBdu/ERHp//VVrPxwKyqpYtT+Hn/dks2p3BmX5hQTYyhkapBgb6cXwEBPxVgeqtKSZIFiEs6ioJqg2xmRyBT0j8BlDPOtdrw589cKfOSAAZW1+GHDGvEco+PRTgi+/jMApUyj+8UeKly7DWVKCOTiYwJkzCJw9G/9x40650FaflMUXp7zyfMjcXjfU5ezDvYadf2TDUBfSs3OFusK0mt615F+NJWrAKATTfVRNOf8eY8En2LNt7UJkCKUQraUsD/YsNIZHHlgG9nJjDa7+c4zhkX2mgVfHqcaYWZrpXlh7c9ZmNJqEoARm9JzBjJ4zSAxLlAIBbUxrjSMnp9Gg5sjNdR+nfHzw7t3bCGq9++Ddt48R1Hr06LAhwenUbE8rdJf435JSgFNDsK+Vyf0ijIqR/SOJCjq+Yjfa6cRZWoqjsAhnUWHjPX9Nhb/CQvSxhn76+dWEv+pg5wp/mM3kvf/fOkNSTcHBBE4/i6DZc/A/fdwxA6AQ4hRXWWIsY1A71GXtNsriA/iEuNaqG1Ez/DKsd8dYfFpryN1vjCqqLulfkGzs8wo0Qlp171rsaKna2YYkwAlxMgpSjMCW9L3x7ZN2GAuJJp5jFCKJn9Dma68dj5TiFJYcWcLiI4vZnrMdgH6h/ZgRb4S2PiF9JLS1Aa019oyMRoOas6hm4W1TQADeffrUC2p9sXaP6RTFLnJKKvlln7GQ9i/7csgrrUIpGBYX4g5sI3qEYPZQSXetNbqysib8NTsE1Ah8tcOfs/bQT5MJ/8mT6fHSfNQxejuFEKJZtgrI2llvrbqd4HBVefUKMIqj1O6pi+jf9p8vHHY4ur0mrCX/VlPAxS/CCGs9Jxol/bsN6VCfd7o6CXBCHA+tjW/KkhZA0nfGP7IAkQNrQlvMiA41/OFg4UF3aEvKM4bMDQofxIyeM5geP52E4ATPNrAL0Q4HttRUI6gdqF3x8WCdD//m0NBGg5olKrJTBWi7w8nmlAJ3if/taYUARAR4cUY/Y022yf0iCfPv/AHHlpXFgRkz0ZWV7m3K25u+Sxa758IJIUSrcdggO6neWnXbawqAWHyM0FRnrbqBTZfZb8m6ebZySNtYE9hS1kNVsbEvpKer4Mh44zK8b4f6rHOqkQAnxLE4nUbVpKTvjOCWd9DYHjfWFdrOg/A+nm1jLVpr9ubvZUnyEhYfXsyBwgMADI8czoyeMzgr/iziAhspmiJarMnS/IcOoatq1sWyREW5w5l3n95GaOvTB0tYx5n/eLwyCstZudcIbL/sy6G4wo7ZpBgVH+Iu8T8oJqjLLZycMe8RCr74om71TauVkEsvPWbRFyGEaBVOhzGEMaNesZRK10gOk7XeWnUjjAXJk75vvKr17KcgsHtNwZH0TTW9flGDasJa/HgIlvmrHYkEOCEaY6+EQ7+4QtsPxnpsJiv0OsPoZRtwNgRGe7qVblprduXuYvGRxSw+spjk4mRMysSoqFFGyf/46XTzl2pPx+t4S/N71wtq5sBAD7a+dVTaHWw8nM/PrtCWlGl8Gxsd5ONeSHtC3wiCfbv23K/2LvoihBAt4nRCweG6gS59C5TnGfuVyfhxNrPup8liLGdUHdh6jOtQhdZEQxLghKhWUQT7Fxu9bPsWG99oeQVA3+lGL1u/GR2qgpJTO9mWvc1diCS9NB2zMjM2eizTe05nWvw0InxP0XVnjlOLSvObTHjFxzcoJFK7NH9n01SVwJS8MlbszebnPdmsOZBDWZUDq1kxtleYay5bFP27BXSq4Z5CCHHK0NpYsqg60P38dNPHXvctxI0BL//2a584aRLgxKmtJMtYm23393DoZ2PogF+EsTZb4rnQ60yPVlFacHABL256kczSTKL9o7ljxB1EB0Sz+Mhilh5ZSlZ5FhaThQndJzA9fjpTe0wlxCfEY+31tK83p/HGV+u4fulbvHPWn7jporF1ypYfd2n+2hUfW1CavzP5enNag3W6zCZFmJ+V7BJjCE2PMF+m9I/izP6RjO8Tjr+3TFAXQohO54UhUJjScHtwD/jzjvZvjzhpEuDEqSfvkDEefPf3kLIW0Mbk3IHnGaGtx1hjgWMPW3BwAfPWzKPCUdFgn7fZm0mxk5jeczpnxp1JoFfnH6p3sqoDyR/Wf8bZh39jbfRAdsQkcmmEje4FmVQdOIAjL6/mBj4+WBJ6YerVG3NCL1RCL+jZCx3THW224HBqnFrjcBo/WoPD9btTa5xOjUNrnE5clzXHG5cYxzVzDuMS97mMY4zbOmrfR/X1euesObdrSYI67at7Dvf9u37fklxAVe1FvF28LSbun5PImf0j6RXRtotiCyGEaAfbPmt8Dtx58xsWMhGdQnMBTr5qFV2D1pC5zRgauft7o1QvQPRQmPKAUYik2+AOV03pxU0vNhreQrxD+OmSn/Czds5hey2htabS7qSw3Ob+Kapz3V5zvcK4PLjnCFfvXcE5h39FAeMzdzE+cxelFh82BXYjObAvKTFRJAd240hgN7L9QtDVi5NnABkafj0IHPTgI6/LpIxeMZNSmE0Ks1Io17bq7dX7TCYwK4Wp+njXdbNru6p1DpOJRsMbQJXdye8n9mrnRyqEEKLNVIe0Y1WhFF2CBDjReTnskPKbEdiSFkBhsjGJN348zHrSGCIZmuDpVjZpc9ZmMkozGt1XWFnYKcKb06kpqbJTWFYTtIqaCWA1Ic1OUbmtyYBRzd/LTLCPhaFFKVyStJKBe9Zj1Q6cgALsysTyuJE8P+pK7pk5AKtJ0VcpBpioF3xcoUbhvm421QpKqtYxrtvW3m78XrO99rnNTd1X9fHukOW6/3rnbksTn1pGWkF5g+3dQ3zb9H6FEEJ4wLDLJbCdIiTAic7FVg4HVxihbe9CKMsFszf0mQpn3gcD5oB/xy7qkVWWxQsbX+D7g99jUiacumGIifZvv+qXNoezbs9Xhb1O2CpqNIQZxxRX2HA2MwrbpCDI10qw6yfIx0r3YF+CfK0E+VrqbHdfd10GYKfsp5/I/+ADKnbtwhQQwPI+4xh/cB1erkpbFu3kjLRtLBx/CXee1a+dnrHO495ZAxrMgfO1mrl31gAPtkoIIYQQJ0MCnOj4yvNh7yJjTtv+pWArBe9g6D/LGBrZdzp4B3i6lcdU5ajiv7v+y+vbXsfutHPD0BuIC4jj8d/+iU3XLBxsVd7cNequFp9Xa02FrZmhiPWCV/1AVlblaPb8XhaTO1wF+1qJDPCmb2RAnbAV1CCEGeEswNty3POrbGlp5L/3CYf/9zmOggK8+vYh+uGHCD7/fHL/9gjqYN3EaMLJg/m/ARcd1/2cCqqLuzRWhVIIIYQQnZMEONExFaUbwyKTvofDq4y1TQKiYfiVxhptPSeBpfNUC1yZupKn1z1NcnEyU3tM5d4x99IjqAdfb06jIuNiVNhClLUAbQuhPHc2O/f2wVSa0WTPV/3hijZH88WIArwttYKWhZ7hfnUCWHATPWJBvlZ8rG1f7EVrTdnateR98AEly5YDEHjWNELnXoPfuLHuEBiduo9KZ93AaXU6iE7Z2+Zt7KwuHBkrgU0IIYToQqQKpeg4svcagS3pe0jbaGwL72tUjRx4HnQfBSaTZ9t4nA4XHuaZ9c/wS9ovJAQl8Lexf2NS7CT3/tOeWEJ2cWUzZzCYTYogH0udYYZBjQ4/bBjCAn0sWMwd83lzlpZS8M035H/4EVUHDmAODSXksssIvfIKrN27e7p5QgghhBAeIVUoRcfkdEL6Zkj6zuhty3H1onQfZVROSjwXIjvnXJ1SWymvb3ud/+76L95mb/465q9cnXg1VrOV0ko7321N56N1yU2GNwX8cNdkd0jz9zJ3qVLvlYcOkf/RxxR+9RXOkhJ8Bg8m5sknCTp7DiZvb083TwghhBCiw5IAJ9qXw2YMiUz6HpJ+gOJ0UGZImARjb4QBZ0Nw5x3upbXm+4Pf88LGF8guz+aCPhdw9+i7ifCNYGd6IR+tTeabLemUVNrp3y2AYF8LheX2BufpHuLLwJggDzyCtqMdDkpWriT/w48oXbUKrFaCZs8mbO7V+Awf3qUCqhBCCCFEW5EAJ1rfts/qrkNy5t/AJ8joZdv7I1QUgtUP+p4FiQ8bxUh8Qz3d6pO2M3cnT659kq3ZWxkSPoT/m/p/9A0exPdbM/hw3Wq2phTgbTFxzrAY5o6LZ1R8KN9sSe/yVQIdBQUUfPkV+R9/jC0lBUtUFBF33kHoZZdhiYz0dPOEEEIIIToVmQMnWte2z+C7O41y//X5hhll/hPPhd5TwKvjr3PWErnluby0+SW+3PcloT6h3D3qbvr7T+WTdal8vTmN4ko7faMCuHpsPBePiiXEr27xla83p3XJKoEVe/aQ/8EHFH73PbqiAt8xowmbO5fA6dNRVqunmyeEEEII0WHJHDjRfpY+2nh484+Ee5LA3HXecjanjU+TPuXVLa9Sbi/nqgHXEKfO5/3FuWxOXo2XxcQ5Q2O4elw8Y3qGNjlEsCtVCdQ2G8VLl5L3wQeUb9iI8vEh+LxzCZ07F5/ERE83TwghhBCi0+s6n6ZFx1CY2vj20pwuFd5+y/iNp9c9zf6C/QwLP42oqiv46EcorjhAn0h//nHOQC4ZFUeof+dZ6uBk2HNyyP/sMwo++RR7VhbWuDii7ruPkIsvwhwS4unmCSGEEEJ0GV3nE7XoGPwjoTSr4fbguPZvSxtIK0njXxv+xeIjiwmxRtO98hZWr4rHy+xkztBorh4bz9heYadEQQ6tNRVbt5L34UcU/fgj2Gz4T5xI9CPzCDjjDJS57dePE0IIIYQ41UiAE61Ha/AObhjgrL7GsgCdWLm9nHd2vMN/tv8Hh1NB3mxSsibSOzyEv58dzyWj4wg7RXrbnJWVFP2wkPwPP6Rixw5M/v6EXnkloVddhXfvXp5unhBCCCFElyYBTrSefYshbx+MuBoO/VJThfKsh2DY5Z5u3QnRWvPDwZ94cu2zFNqysBUOx5F7NrMTB3L1+fGc3vvU6G0DsKWnk//JpxT873848vPx6tOHbg89SPD5F2AO8Pd084QQQgghTgkS4ETrcDph6SMQ2gvOmw/mzl9lcOmBrfzztyfJsu/EURFNaPmdXDdyGpeOjiM84NRYbFprTdnadeR/+CHFS5cCEDBtKmFz5+J3+umnTHgVQgghhOgoJMCJ1rHjczi6Ay75T6cOb5V2B19t2cerW18jz7wCnN70s17HPdOuY2KfKEymUyOwOEtLKfzuO/I//JDKffsxh4QQ/sc/EnrlFVhju0bFTCGEEEKIzkgCnDh59ipY9jhED4XBF3u6NSfkQHYJH609zOd7v8Ae/AMmczlDA2fz+JS/0Ce8m6eb126qDh8m76OPKPzyK5wlJfgMGkTMP/9J0NlzMPn4eLp5QgghhBCnPAlw4uRteg8KjsDcL8Bk8nRrWqzS7uCnnUf5aO0R1mdswif6W0zh6QwIGsYTkx9kYMSpsW6ZdjopWbmS/A8/ovSXX8BiIWjWLEKvmYvviBEyTFIIIYQQogORACdOTmUJ/Pw0JEyGvmd5ujUtciinlE/WJfO/jankV2YTGrcIv4SNRPpGcd9pzzIrYdYpEVochYUUfPkV+R99hC0lBUtkJBF33E7IZZdhjYrydPOEEEIIIUQjJMCJk/Pba1CaDVd+DB049FTZnSzalclHa5NZcyAXs9nBwAGbUep7wMmNQ27kj0P+iJ/Vz9NNbXMVe/aS/+GHFH73Hbq8HN/Ro4n6890ETp+O8jo1lkIQQgghhOisWhTglFKzgRcBM/CW1vqpevvvAf4E2IFs4A9a6yOufQ5gu+vQZK31+a3UduFppbmwZj4kngs9TvN0axp1JLeUj9el8PnGFHJKqogN8eWyyUVsL/8vR0pTmBo3lXtPu5cegT083dQ2pW02ipcuI//DDylbvx7l7U3QeecSNncuPgMHerp5QgghhBCihY4Z4JRSZuAVYAaQCqxXSn2rtd5V67DNwBitdZlS6hbgGeAK175yrfWI1m226BBWPQ9VJTDtQU+3pA6bw8niXUf5aG0yq/bnYDYpzkqMYvowxbLst/gxfRUJQQn8e/q/mRg70dPNbVP23FwK/vc/8j/+BPvRo1hjY4m6915CLrkYc0iIp5snhBBCCCGOU0t64MYC+7XWBwGUUp8AFwDuAKe1Xl7r+N+Aa1qzkaIDKkiBdW/C8KshqmMU+0jJK+Pjdcl8tiGVnJJKugf7cM+M/pwzPJRvDr/Pk9v/i7fZm7+O+StXJ16NtRMvd3As5du2kffBBxQv/BFts+E/YQLRDz9MwJlnoMxmTzdPCCGEEEKcoJYEuFggpdbvqcC4Zo7/I7Cw1u8+SqkNGMMrn9Jaf13/BkqpG4EbAeLj41vQJOFxP7tG0U6536PNsDmcLN19lA9dvW0KmJbYjbnj4pnUL5wfD//An5Y9T055Dhf2vZC7Rt1FhG+ER9vcVpyVlRQtXEj+hx9RsX07Jn9/Qq64gtCrr8K7d29PN08IIYQQQrSCVi1iopS6BhgDnFlrc0+tdZpSqjewTCm1XWt9oPbttNZvAG8AjBkzRrdmm0QbyEqCLR/B6bdCiGfmjqXklfHp+hQ+25BCVnElMcE+3HVWP644rQcxwb7szNnJ73+6h63ZWxkaMZT5U+czNHKoR9ra1mwZGeR/8ikF//sfjrw8vHr3ptuD/yD4ggswBwR4unlCCCGEEKIVtSTApQG1P6XHubbVoZSaDvwdOFNrXVm9XWud5ro8qJRaAYwEDtS/vehElj0GVn+YdE+73q3d4WRpUhYfrU1m5b5sFDB1QBRXjY1nyoBILGYTueW5zFvzNF/u+5JQn1Aem/gY5/c5H5PqPOvTtYTWmrJ168n/8EOKly4FrQmYOpWwuVfjN378KbEMghBCCCHEqaglAW490E8p1QsjuF0JXF37AKXUSOB1YLbWOqvW9lCgTGtdqZSKACZiFDgRnVXqBkj6Hqb+HfzD2+Uu0wrK+XRdMp9uSOFoUSXdgry5Y5rR2xYb4guAzWnjg10f8eqWVym3l3PdoOu4afhNBHoFtksb24uzrIzCb78j/8MPqdy3D3NwMOG/v56QK6/CKy7W080TQgghhBBt7JgBTmttV0rdDvyEsYzA21rrnUqpR4ENWutvgWeBAOB/rm/+q5cLGAi8rpRyAiaMOXC7Gr0j0fFpDUvmgX+kMXyyDdkdTpbvyeajtUdYsTcbgCn9I3nsgnimJUZhMdf0qP2W8RtPrX2KA4UHmNB9An8b+zd6B3etOV9VR46Q/9HHFHz5Jc7iYrwHDiTmiccJOuccTD4+nm6eEEIIIYRoJy2aA6e1/gH4od62h2pdn97E7dYAXXPi0anowFI4/AvMeRa822ZuVUZhOZ+sM+a2ZRRWEBXoze1T+3LFaT2IC627yHZaSRrPrX+OJclLiAuIY/7U+UzpMaXLDB/UTielq1aR98EHlK78BSwWgmbOJPSaa/AdOaLLPE4hhBBCCNFyrVrERHRhTicseQRCesLo61v11A6n5ue9xty2ZUlZaOCMfpE8fN5gzhoYhdVcd/5aub2ct3e8zTs73sGkTNw58k6uG3wd3mbvVm1Xe7JlZZF2z1+Ie+F5lLc3BV9+Sf5HH2NLTsYcGUHE7bcTcvllWKOiPN1UIYQQQgjhQRLgRMvs/BIyt8HFb4LFq1VOmVlYwafrU/h0fTLphRVEBnpzy5Q+XHlaPD3C/Bocr7Vm0ZFFPLfhOTJLM5nTaw73jL6HaP/oVmmPJ+W8+hrlGzdy5PrfY0tPR5eX4ztqFJF33UnQjBkor9Z5zoUQQgghROcmAU4cm8MGyx6HbkNgyKUndyqnZuW+bHdvm8OpmdwvggfPHcT0Qd0a9LZV25u/l6fWPcX6zPUMCB3Ak5OeZEz0mJNqiyfpqioqDx6kIimJ8o2bKPj8c9CaqgMHCDznbML/8Ad8Bw/2dDOFEEIIIUQHIwFOHNum9yH/EFz9GZiOXY7/681pPPvTHtILyuke4su9swYwvk84n61P4ZP1KaQVlBMR4MWNZ/TmqtPiiQ9v2NtWrbCykFe2vMKnez4l0CuQB09/kEv6XYLZZG7NR9im7Pn5VCYlUZG0x3WZROXBg2CzGQeYTEaBGACrFXNQsIQ3IYQQQgjRKKV1x1o3e8yYMXrDhg2eboaoVlUK80dCWB/4/Q9wjMIZX29O44Evt1Nuc7i3mZSRTzQwqW8EV42NZ8agbnhZmg6DDqeDL/d/yfxN8ymqKuLy/pdz+8jbCfYObq1H1uq0w0HVkSPusFaxJ4nKpD3Yjx51H2OJjMQ7MRGfxES8EwdgiYoi5U83oCvdSyeivL3pu2QxlshITzwMIYQQQgjhYUqpjVrrRoebSQ+caN7af0PJUbj8/WOGN4Bnf9pTJ7wBODUEeFv4/o5JJET4H/Mcm7M28+TaJ9mdt5sx3cZw/9j7GRA24IQfQltwlJRSuXcPFbt3U5m0h4o9e6jcuxddUWEcYLHg3bs3fuPG4jMgEZ+BiXgnJmIJC6tznox5j6CdzjrbtNNJ9quvEfPwQwghhBBCCFGbBDjRtLI8WPUi9J8D8ae36CbpBeWNbi+ttB8zvB0tPcrzG5/nh0M/0M2vG8+e8SyzEmZ5tFy+1hpbWjqVe1xDH129a7aUFPcx5uBgvBMTCb3icrwHJOKTOACvvn0xtaDwSPmWLTVDKavZbJRv3tzKj0QIIYQQQnQFEuBE01a9AJVFcFbLe4LC/L3ILa1qsL17iG+Tt6lyVPH+rvd5Y9sbOJwObhx2I38c8kf8rE3PjWsLzooKKvftd4W1PVQk7aZyz16cxcXGAUrhFR+Pz+DBhFxyMd4DBuCTmIglOvqEQ2bvr79qxUcghBBCCCG6OglwonGFabDuDRh+JXQb1KKbZBZWUGFzoDDmu1XztZq5d1bDIZBaa1amruTp9U+TUpzCtB7T+Otpf6VHYI/WeQzNsGdnU1FdUCRpDxVJSVQdOmSsdwcoPz98+vcn6Jyz8UkciE/iALz79cPkf+whoEIIIYQQQrQVCXCicT8/DdoJUx5o0eF2h5M7P96MBu6fk8j7vx6pU4XywpGxdY4/VHiIZ9Y/w6q0VfQK7sXr019nQuyEVn8Y2maj8tChulUg9+zBkZvrPsbSPQafAYkEzpzhDmvWHj1QLai4KYQQQgghRHuSACcaytkHmz+AsTdCaM8W3eRfi/ey7nAeL145ggtGxHLTmX0aPa6kqoQ3tr3Bf3f/Fx+zD/eOuZerBl6F1WQ96WY7CgtdIW23uwpk1b79aNccM2W14t2vHwFnnmn0qA1IxGdAf8whISd930IIIYQQQrQHCXCioWWPgdUXJv+lZYcnHeW1FQe4amw8F4yIbfQYp3by/cHveWHjC+SW53Jh3wu5c9SdRPhGHHfztNOJLTm5plT/bqNXzZ6R4T7GHB6Oz4AB+F93rVGyf8AAvHv1QllPPigKIYQQQgjhKRLgRF1pG2HXN8bQyYBjr0OWVlDOPZ9tZWBMEA+fZ8yVW3BwAS9uepHM0kyi/aO5pN8lrExbybbsbQyLGMZL015iSMSQFjXHWVpKxd69VO7ZUzNfbe9edFmZcYDZjFevBPxGjTJK9buqQMoaakIIIYQQoiuSACfqWvII+IXD+NuOeWiV3cntH23C7tC8OncUPlYzCw4uYN6aeVQ4jPXQMkozeHnLy/hb/Hl84uOc1+c8TKrh3DKtNfbMTCp2J7mrQFYmJVGVnGysAg6YAgPxGTCAkIsvNoZAJg7Eu19fTN7erfscCCGEEEII0UFJgBM1DiyHQz/D7KfAO/CYhz/zYxKbkwt45epR9HKt8fbiphepcFQQUqK5+2sHL1xopjBAEegVyAV9LwDAWVVF1f79NaX6XQthOwsL3ee2xsfjM2AAQeef5xoCmYg1trtH14QTQgghhBDC0yTACYPTCUvmQXA8jPnDMQ9ftDOTt1Yd4rrxPTlnWIx7e2ZpJgCXrHKSmApXrXCwerCJhKx00tbdR2VSEpWHDoHdDoDy8cG7f3+CZs3CO9FYV827f3/MAQFt8jCFEEIIIYTozCTACcPubyBjC1z4b7A0PyQxJa+Mv/5vK0Njg/n7OQPr7PO3+hOSXsSMLRqThmnbYdp2Y221sm7r8E4cQMDUqe75al4941Fmc1s9KiGEEEIIIboUCXACHDZY+hhEDYJhlzd7aKXdwW0fbUIDr84dhbelJny9veNtyiuKefYTJ8q1krdDwZa+ZsIffYg5I5s/txBCCCGEEKJ5EuCEseZb3gG46hMwNd8b9uQPSWxLLeTf14ymR5ife/snSZ/wwsYXeHx1d8JLUtzbzRpGHjExIG5qmzVfCCGEEEKIU0XDcoDi1FJVBj8/DT3GQf/ZzR76w/YM3l1zmD9O6sXsIdHu7d/s/4Yn1j7Bzan96b86BUx131YmDdmvvtYmzRdCCCGEEOJUIgHuVLfuDSjOgOnzoJkKj4dzSrnv822M6BHC32YnurcvOryIh9Y8xAWVg5j2yT6Un59REKU2m43yzZvb6AEIIYQQQghx6pAhlKey8nxY9Tz0mwU9JzR5WIXNwa0fbsJsUrx89Ui8LEbuX5m6kr/98jfGeyVy3VsZmLp1I+F/n2EJDW2vRyCEEEIIIcQpRQLcqWz1i1BRBGc91Oxhj32/i10ZRfznd2OICzXmva3PXM89K+4hMaAP936isRWXEP/JmxLehBBCCCGEaEMyhPJUVZQBv/0bhl4G0UOaPOybLWl8uDaZm87szVkDuwGwLXsbty+9nTj/WJ5c15uqrdvp/uQ/8RkwoL1aL4QQQgghxClJAtyp6uenwWmHqf+vyUMOZJfw/77czmkJofx1phHO9uTt4eYlNxPuG878wrMp/+o7wm+6iaDZzRdAEUIIIYQQQpw8CXCnotwDsOl9GPN7COvV6CHlVQ5u+3AT3lYzL101CqvZxKHCQ9y4+Eb8LH68FnEHpc+9TMCZZxJ5153t/ACEEEIIIYQ4NckcuFPRssfB4gNn3NvkIQ9/u4M9R4t59/djiQ72Ia0kjRsW3QDAG8OeoPL39+DVowfdn3sWZZLvAYQQQgghhGgP8sn7VJO+GXZ+CeNvg4CoRg/5YmMqn21I5fapfTmzfyRZZVn86ac/UWYv4/XJL8EDT6Orqoh75RXMgYHt/ACEEEIIIYQ4dUkP3Klm6aPgGwYTbm90996jxfzj6x2c3juMu6f3J78inxsX3UheRR5vzniDwKffp2h3EnGvvYp378aHXwohhBBCCCHahvTAnUoO/gwHlsHkv4BPcIPdpZV2bv1wE/7eFuZfOZIyewk3Lb6J1JJUXj7rZWK/20DRggVE3nUXgVOmtH/7hRBCCCGEOMVJD9ypQmtY+ggExcFpf2pkt+bBr3dwILuED/84jgBfJzctvpV9BfuYP3U+A/dVkvKv5wmcPZvwm270wAMQQgghhBBCSIA7Vez+DtI2wgWvgNWnwe7PNqTw5eY0/jy9P6N7BXLb0tvYlrON5858jnGOeA795XK8+/Wj+z+fQCnlgQcghBBCCCGEaNEQSqXUbKXUHqXUfqXU/Y3sv0cptUsptU0ptVQp1bPWvt8ppfa5fn7Xmo0XLeSww7LHIGIADLuywe7dGUU89M1OJvWN4KYpPfnrir+yNmMtj054lGnhE0i57TaUyUTcKy9j8vPzwAMQQgghhBBCQAt64JRSZuAVYAaQCqxXSn2rtd5V67DNwBitdZlS6hbgGeAKpVQY8DAwBtDARtdt81v7gYhmbP0IcvbCFR+Cue5LXlJp57YPNxHsa+VfVwzlodX/YEXqCv4+7u+c3/s8Uu+8k6pDh4n/z1t4xcV56AEIIYQQQgghoGU9cGOB/Vrrg1rrKuAT4ILaB2itl2uty1y//gZUf9KfBSzWWue5QttiYHbrNF20iK0cVjwFsWMg8Zw6u7TWPPDldg7nljL/yhG8tv0ZFh5eyN2j7ubKxCvJefU1SpYspdvf7sP/9NM99ACEEEIIIYQQ1VoS4GKBlFq/p7q2NeWPwMLjua1S6kal1Aal1Ibs7OwWNEm02Lo3oSgNps+DenPXPlibzHdb07lnRn9+zv0PX+z7ghuH3cgfh/6R4iVLyHn5ZYIvvJDQa6/1TNuFEEIIIYQQdbTqMgJKqWswhks+ezy301q/obUeo7UeExkZ2ZpNOrWVF8Av/4K+06HX5Dq7dqQV8th3u5gyIBJnyI98sPsDrhl4DbePuJ3KfftIv+9v+AwdSvQj86RoiRBCCCGEEB1ESwJcGtCj1u9xrm11KKWmA38HztdaVx7PbUUbWfMSVBTAWQ/V2VxUYePWDzcRHuDFmOFbeXP7G1zc72LuO+0+nEVFpNx+O8rPj7iX5mPy9vZM24UQQgghhBANtCTArQf6KaV6KaW8gCuBb2sfoJQaCbyOEd6yau36CZiplApVSoUCM13bRFsrzoTfXoUhl0DMcPdmrTX3/W8b6QXlXHTmYV7fPp85CXN46PSHwOkk7Z6/YEvPIG7+fKzR0R58AEIIIYQQQoj6jlmFUmttV0rdjhG8zMDbWuudSqlHgQ1a628xhkwGAP9zDbdL1lqfr7XOU0o9hhECAR7VWue1ySMRda18FhxVMPXvdTa/u+YwP+7M5OLJGfx334tMiZvCE5OfwGwyc/TZZyldvZroRx/Bb9RIDzVcCCGEEEII0ZQWLeSttf4B+KHetodqXZ/ezG3fBt4+0QaKE5B3EDa+C6N+B+F93Ju3pBTwzx92M2pgCktzX2NczDiem/IcVpOVwu++J+8/bxNy1ZWEXn6559ouhBBCCCGEaFKLApzoZJY9AWYvOPM+96aCsipu+3AToREHOaT+w7CIYcyfOh9vszflO3eS8Y9/4DtmNNEPPODBhgshhBBCCCGa06pVKEUHkLENdnwOp98CgcYcNq01f/3fNnLsO7FHvEO/0H68Mv0V/Kx+2HNzSb39DsxhYcS9+CLKy8vDD0AIIYQQQgjRFOmB62qWPgo+ITDhTvemt345xLJD6wnp/V96BMbx7xn/JsgrCG2zkXbX3Tjy8uj54YdYwsM9124hhBBCCCHEMUkPXFdyeBXsXwyT/wK+IQBsPJLH08uXE9zrPbr5h/PmzDcJ8wkD4OiTT1G2YQMxjz+O75DBHmy4EEIIIYQQoiUkwHUVWsOSeRDYHcbeAEBeaRW3fvojfj3+Q6ivP2/NeosovygACj7/nPyPPiLsD38g+LxzPdhwIYQQQgghREtJgOsq9vwAqethyv1g9cXp1Nz22RJKw14l0MfMW7PeJDYgFoCyzZvJeORR/CdOJOov93i44UIIIYQQQoiWkjlwXYHTYcx9C+8HI+YC8NzS9WyzP42vj523Z79H7+DeANiOHiX1zjuxRkcT+6/nUGazJ1suhBBCCCGEOA4S4LqCrZ9AdhJc/j6YLSzZc4D3Dj2A1buM/8x6iwFhAwBwVlaSesedOEvLiP/PfzCHhHi23UIIIYQQQojjIgGus7NVwIonofsoGHg+h/NzuWfl7Zi88pk/9VWGRw0HjKUEMuc9QsW2bcS+NB+f/v093HAhhBBCCCHE8ZIA19lt+A8UpsAFr1BSVcYVX/8JpzWDB0Y9xRnxp7sPy//vBxR+9RURt95K0IwZHmywEEIIIYQQ4kRJEZPOrKIIVj4HvadS2fN0LvnqRkrVAS7tcR9zh812H1b6228cffppAs46i4jbb/Ngg4UQQgghhBAnQwJcZ/bry1Ceh23a3/nDD3eSXrmNIV438PC0q9yHVKWmknb3n/FKSKD700+hTPKSCyGEEEII0VnJp/nOqiQL1ryMY9CF/GXPJ2zLW0NgyWW8fektKKUAcJaVkXr7HWinkx6vvIw5IMDDjRZCCCGEEEKcDJkD11mtfA5tr+CR8DCWpy7CkXM2713zZ/y8jJdUa03GP/5B5d699Hj933glJHi2vUIIIYQQQoiTJj1wnVH+YfSGt3lmwOl8lbqEypypPDblDvp1C3QfkvvmWxT9sJCoe/5MwOTJHmysEEIIIYQQorVIgOuMlv+TV0KC+KAyhaq8CVwQ/wcuGR3n3l3y889kv/ACQeecQ9gf/+jBhgohhBBCCCFakwS4ziZzB+8cWcjrwf6okrEkqKt49IKh7t2Vhw6R9td78R6YSMzjj7nnwwkhhBBCCCE6Pwlwncwni//M82EhhNpH4Dh6Ca/OHYOvlxkAR0kJqbfdjrJY6PHSS5h8fT3cWiGEEEIIIURrkiImnci3a5/nCWcmQ5zd+HXfZbx45XD6RBqVJbXTSfq991F15Ajxb7+NNTbWw60VQgghhBBCtDbpgeskFh9exIO732FspYPNe29g7rheXDCiJqRlv/QSJcuX0+2BB/AfN9aDLRVCCCGEEEK0FQlwncAvqb9w38r7GFZZSWzGLPrEdOfBcwe59xf9tIjc1/5N8CUXEzr3ag+2VAghhBBCCNGWZAhlB7c+cz1/XvFn+to1/8g2c4VzOl9fPQofqzHvrWLPXtIfeADf4cOJfvhhKVoihBBCCCFEFyY9cB3Y9uzt3L70dmItgbyemsz8skv556WjSIjwB8BRUEDqbbdh9vcndv58TF5eHm6xEEIIIYQQoi1JD1wHtSdvDzcvuZlQn1DmH0omzR5P1LjLOXtoDADabiftnnuwHz1Kzw/+i7VblIdbLIQQQgghhGhr0gPXAR0qPMSNi2/Ex+LDvwIn0bMkjf+F/In7z6mZ95b13L8oXfMr0fPm4Tt8uAdbK4QQQgghhGgvEuA6mLSSNG5YdAMAr056nthf32AtQ7jhd3/A22LMeyv85hvy3n2X0GuuIeSSiz3ZXCGEEEIIIUQ7kgDXgWSXZXPDohsos5fxxow3SPvmPUJ0Ifqsh+kRbsx7K9++g4wHH8Jv3Di6/e0+D7dYCCGEEEII0Z5kDlwHkV+Rzw2LbiCnPIc3Z77JkQM2JmR8yO6wqZw+eSYA9pwcUu+4A0tEBLH/9wLKavVwq4UQQgghhBDtSQJcB1BcVcxNi28itSSVV896lSDVl63f/wlfVUXfK58CQFdVkXrnXTgKCkj4+CMsoaEebrUQQgghhBCivUmA87AyWxm3Lb2Nffn7eHHaiwyLGM1NL33Nm2oRFUOuwr9bIgCZT/yT8k2biH3+X/gMHOjhVgshhBBCCCE8QQKcB1U6Krlr+V1szd7KM2c8wxlxZ/DAl9s5L/9dzFYTXjP+DkD+J59S8OmnhN/wJ4LOPtvDrRZCCCGEEEJ4ihQx8RCb08Zff/4rv2X8xqMTHmVWwiy+3pzGxvWrucT8C+bTb4LgWMo2biTz8cfxP2MykXff7elmCyGEEEIIITyoRQFOKTVbKbVHKbVfKXV/I/vPUEptUkrZlVKX1tvnUEptcf1821oN78wcTgd/X/V3VqSs4P+N+39c0PcC9meV8P++2s4TQV+BdyBMugdbZiapd96FV2wssc89hzKbPd10IYQQQgghhAcdcwilUsoMvALMAFKB9Uqpb7XWu2odlgxcD/y1kVOUa61HnHxTuwatNY/99hgLDy3krlF3cVXiVZRXObjtw02Ms+zjtMrfYNqDOE1+pN5+E7qigrj338McFOTppgshhBBCCCE8rCVz4MYC+7XWBwGUUp8AFwDuAKe1Puza52yDNnYZWmueWf8MX+z7ghuG3sCfhv4JgIe+2cHerCI+i/sGyruhx91M5kMPU7FjB3GvvoJ3nz4ebrkQQgghhBCiI2jJEMpYIKXW76mubS3lo5TaoJT6TSl1YWMHKKVudB2zITs7+zhO3bm8suUVPtj9AXMHzuWOkXcA8L8NKfxvYyrPj8giOHs9nHkfeR9/TuE33xJx5x0ETpvm4VYLIYQQQgghOor2KGLSU2s9Brga+D+lVIPuJK31G1rrMVrrMZGRke3QpPb3zo53eH3b61zU9yLuO+0+lFLsySzmwW92MKFXKBfmvQWhvSitHEDWM88SOGMGETff7OlmCyGEEEIIITqQlgS4NKBHrd/jXNtaRGud5ro8CKwARh5H+7qET5M+5fmNzzM7YTYPj38YkzJRWmnn1g83EuBt5bWRh1BHd1I1+FbS/nIv3n360P2pJ1EmKRIqhBBCCCGEqNGShLAe6KeU6qWU8gKuBFpUTVIpFaqU8nZdjwAmUmvu3KnguwPf8fjaxzkz7kz+OfmfmE1mtNb8/avtHMop5aXLBxH86zM4w4aQ+uL3aCDulZcx+ft7uulCCCGEEEKIDuaYAU5rbQduB34CdgOfaa13KqUeVUqdD6CUOk0plQpcBryulNrpuvlAYINSaiuwHHiqXvXKLm3xkcX8Y/U/GBc9jn9N+RdWkxWAT9an8PWWdP48vT/jCxag8w6TviWeygMHiHvhebzi4z3cciGEEEIIIURHpLTWnm5DHWPGjNEbNmzwdDNO2i+pv3Dn8jsZHD6YN2a8gZ/VD4Cd6YVc9OoaxvUK4725gzC9NIqcvZFk/5JP1N/+Rvjvr/dsw4UQQgghhBAepZTa6Koj0kBLlhEQx2l95nr+vOLP9A3py6vTX3WHt+IKG7d9uIlQPyv/d8UITGvnU7y3kOxfLASdfx5h1//Owy0XQgghhBBCdGQS4FrZ9uzt3L70dmIDYnl9xusEeRkLcGutuf/L7aTkl/PxDacTrkqo/OEl0tdG4jN4IDGPPopSysOtF0IIIYQQQnRkUuawFe3J28PNS24m1CeUN2a8QZhPmHvfB78dYcG2DP46cwBje4XhWPQUqcu8Uf6BxL38EiYfHw+2XAghhBBCCNEZSA9cKzlceJgbF9+Ij8WHt2a+RTf/bu5921MLeez73UwdEMlNZ/RG5x4m7aUvqSr1puf7L2ONifFgy4UQQgghhBCdhfTAtYK0kjT+tOhPALw5803iAuPc+wrLbdz60UYiArx4/vIRmEyK7H/cTGm6F9F/vQO/MY3OTRRCCCGEEEKIBqQH7iRll2Vzw6IbKLOX8fast+kd3Nu9T2vNfZ9vJaOggs9uHk+ovxdFn/yH3OVHCBmfQOgfbvFgy4UQQgghhBCdjfTAnYT8inxuWHQDOeU5vDb9NRLDEuvsf3v1YX7aeZT75yQyKj6UiqQk0p94Ht9IO9EvvOOhVgshhBBCCCE6K+mBO0HFVcXctPgmUktSefWsVxkeObzO/s3J+Tz5w25mDOrGHyf1wp6fT+pNN2C22Ii7dy4qJNpDLRdCCCGEEEJ0VtIDdwLKbGXctvQ29uXv4/kpzzM2Zmyd/QVlVdz+0Waig3147tLhYLeTdvefsefkEjdDY5n1Vw+1XAghhBBCCNGZSYA7TpWOSu5efjdbs7fy1BlPcUbcGXX2O52av3y2laziCl65ehTBflaOPvMsZWvXEjMmD99L7gXvAA+1XgghhBBCCNGZyRDKY1hwcAEvbnqRzNJMov2jCfEOYXfebh6b+BizEmY1OP7NXw6yNCmLR84fzPAeIRR88SX5//0vYSN9CB4ZCaOvb/8HIYQQQgghhOgSJMA1Y8HBBcxbM48KRwUAGaUZZJRmcH7v87mw74UNjt9wOI9nftrDOUNjuG58T8q3biVz3jz8h/Yhqt8vMPUNsHi186MQQgghhBBCdBUS4Jrx4qYX3eGttvVH1zfYlltSye0fbSYu1JcnLxmKPTub1DvuxNItiu5jUlBBQ2DoZe3RbCGEEEIIIUQXJXPgmpFZmtmi7U6n5s+fbSWvrIpXrh5FgEmTduddOIqLibtlOpbyw3DWQ2CSp1sIIYQQQghx4iRRNCPav/FS//W3v/bzAVbuzebh8wYxuHsQRx97jPItW+j+2MP4HHob4sdDv5nt0WQhhBBCCCFEFyYBrhl3jboLH7NPnW0+Zh/uGnWX+/dfD+Tyr0V7OH94d64eG0/+xx9T8L/PCb/5JoKC90PJUZg+D5Rq59YLIYQQQgghuhqZA9eMc3qfA1CnCuVdo+5yb88uruTOTzaTEOHPPy8eSvmGDRz955METJlC5J+ugZdGQf85EH+6Jx+GEEIIIYQQoouQAHcM5/Q+xx3YanM4NXd/upniChv//eNYvHOzOHTX3Xj16EH3Z59BrXkGKouMuW9CCCGEEEII0QokwJ2g+Uv3sXp/Ls9cMoz+wVYOz70dXVVF3CuvYHYWwbo3YPiV0G2Qp5sqhBBCCCGE6CIkwJ2AVftymL9sH5eMiuPS0bFk3HsflbuT6PHv1/Du3Qu+vRO0E6Y84OmmCiGEEEIIIboQKWJynI4WVXD3p5vpGxnAYxcOJv+ddyhasIDIu+8m4MwzIWcfbP4AxvwRQnt6urlCCCGEEEKILkR64I6D3eHkjo83U1rp4OMbRuFc+xtZ/3qewDmzCb/xBuOgZY+B1Rcm/8WzjRVCCCGEEEJ0ORLgjuHrzWk8+9Me0gvK8fe2UFJp54UrhtOzIo9Df/kL3v370/2JJ1BKQdpG2PUNnHk/BER6uulCCCGEEEKILkYCXDO+3pzGA19up9zmAKCk0o7ZpDCVl5Ny259RJhNxL7+Myc/PuMGSR8AvHCbc7sFWCyGEEEIIIboqCXDNePanPe7wVs3pcFAy70Gq0g4T/5+38IqLNXYcWA6HfobZT4F3oAdaK4QQQgghhOjqpIhJM9ILyhtsu2rPEkYlb6Xb3+7D/3TXAt1aw5J5EBwPY/7Qvo0UQgghhBBCnDIkwDWje4gvAKEVRTz9y6tMP7yOa5MWsbrPOEKvvbbmwF1fQ8YWmPr/wOLtkbYKIYQQQgghuj4JcM24d9YAfK1mrk5azJDcg9y59XP2hsUT+dDDRtESAIcNlj4GkQNh2OWebbAQQgghhBCiS5M5cM24cGQs5vxc4r9cjwlQ2onXX/8fc8b1qjlo8weQdwCu/BhMZo+1VQghhBBCCNH1SYA7hjErv6IADYCyWBix/We4+ExjZ1UZ/Pw09BgHA+Z4sJVCNGSz2UhNTaWiosLTTRFCCHGK8PHxIS4uDqvV6ummCNFlSYBrhi0ri8KvvgKHqxKl3U7hl18SeestWCIjYd0bUJwBl74N1UMqheggUlNTCQwMJCEhoWbIrxBCCNFGtNbk5uaSmppKr169jn0DIcQJkTlwzch59TW001lnm3Y6yX71NSjPh1XPQ7+Z0HOCh1ooRNMqKioIDw+X8CaEEKJdKKUIDw+XkR9CtDEJcM0o37IFbLa6G202yjdvhtUvQkURnPWwR9omREtIeBNCCNGe5P8dIdpei4ZQKqVmAy8CZuAtrfVT9fafAfwfMAy4Umv9ea19vwP+4fr1ca31e63Q7nbR++uvGt9RlAHzR8LQyyB6SPs2SgghhBBCCHHKOmYPnFLKDLwCzAEGAVcppQbVOywZuB74qN5tw4CHgXHAWOBhpVToyTfbw35+Gpx2Y903IbqIrzenMfGpZfS6fwETn1rG15vTPN0k0Zhtn8ELQ2BeiHG57TNPt0gcpwUHFzDz85kMe28YMz+fyYKDCzzdJHECbFlZHL7mWuzZ2a1yvsOHDzNkSNt8KbxixQrOPfdcAL799lueeuqpY9yiaQkJCQwdOpQRI0YwZsyY1mqiEOI4tGQI5Vhgv9b6oNa6CvgEuKD2AVrrw1rrbYCz3m1nAYu11nla63xgMTC7FdrtObkHYNP7MOb3ECYTdEXX8PXmNB74cjtpBeVoIK2gnAe+3N6mIe7ss8+moKCAgoICXn31Vff22h80OpLrr7+eXr16MWLECEaMGMGWLVvavxHbPoPv7oTCFEAbl9/d2eYhrrO9Vi+//DJ9+/ZFKUVOTo57u9aaO++8k759+zJs2DA2bdrU7m1bcHAB89bMI6M0A40mozSDeWvmtWmI62yvX1N/ax3h9ast59XXKN+40ZgX34mcf/753H///Sd1juXLl7NlyxY2bNjQSq0SQhyPlgyhjAVSav2eitGj1hKN3Ta2/kFKqRuBGwHi4+NbeGoPWfY4WHzgjHs93RIhWuyR73ayK72oyf2bkwuoctT9/qXc5uC+z7fx8brkRm8zqHsQD583+ITb9MMPPwDGt86vvvoqt9566wmf60TZ7XYslpYX43322We59NJL265BC++HzO1N709dD47Kutts5fDN7bCxidHp0UNhzol/2w6d77WaOHEi5557LlOmTKmzfeHChezbt499+/axdu1abrnlFtauXduq7Xx63dMk5SU1uX9b9jaqnFV1tlU4Knho9UN8vvfzRm+TGJbI38b+7YTb1NleP2j8b609Xj+AzH/+k8rdTb+GALqqivJt20BrCj75hMrdu1HNlM33HphI9P879qgdu93O3Llz2bRpE4MHD+b999/nueee47vvvqO8vJwJEybw+uuvo5Ri/vz5/Pvf/8ZisTBo0CA++eQTSktLueOOO9ixYwc2m4158+ZxwQV1vnPn3XffZcOGDbz88stcf/31BAUFsWHDBjIzM3nmmWfcz/uzzz7LZ599RmVlJRdddBGPPPJIC549IUR76BBFTLTWb2itx2itx0RGRnq6OU1L3ww7v4Txt0JAlKdbI0SrqR/ejrW9JZ599lnmz58PwJ///GemTZsGwLJly5g7dy4JCQnk5ORw//33c+DAAUaMGMG99xpfjJSUlHDppZeSmJjI3Llz0Vo3eT8JCQk8/PDDjBo1iqFDh5KUZHzwysvL48ILL2TYsGGcfvrpbNu2DYB58+Zx7bXXMnHiRK699lrmzZvH7373OyZPnkzPnj358ssvue+++xg6dCizZ8/GVr+QkSfVD2/H2t5CXe21GjlyJAkJCQ3u/5tvvuG6665DKcXpp59OQUEBGRkZJ/XcHa/64e1Y21uiq71+TekIr1+1qvT0ur+ntc5ohT179nDrrbeye/dugoKCePXVV7n99ttZv349O3bsoLy8nO+//x6Ap556is2bN7Nt2zb+/e9/A/DEE08wbdo01q1bx/Lly7n33nspLS1t9j4zMjJYtWoV33//vbtnbtGiRezbt49169axZcsWNm7cyMqVKwGjSMnMmTMZPXo0b7zxRqs8biHE8WnJ12FpQI9av8e5trVEGjCl3m1XtPC2Hc/SR8E3DCbc4emWCHFcjtVTNvGpZaQVlDfYHhviy6c3jT+h+5w8eTL/+te/uPPOO9mwYQOVlZXYbDZ++eUXzjjjDFavXg0YH0J27NjhHia1YsUKNm/ezM6dO+nevTsTJ05k9erVTJo0qcn7ioiIYNOmTbz66qs899xzvPXWWzz88MOMHDmSr7/+mmXLlnHddde572PXrl2sWrUKX19f5s2bx4EDB1i+fDm7du1i/PjxfPHFFzzzzDNcdNFFLFiwgAsvvBCAv//97zz66KOcddZZPPXUU3h7e5/Qc9OkY/WUvTDENXyynuAe8PsTH4LXFV+rxqSlpdGjR81/Z3FxcaSlpRETE3PCz119x+opm/n5TDJKG4aOGP8Y3pn9zgndZ1d8/Rr7W2uP1w84Zk+ZLSuLAzNmQnXY1RpnURGxz//LWCP2JPTo0YOJEycCcM011zB//nx69erFM888Q1lZGXl5eQwePJjzzjuPYcOGMXfuXC688EL387Zo0SK+/fZbnnvuOcBYTiY5ufFRFNUuvPBCTCYTgwYN4ujRo+7zLFq0iJEjRwJG0N+3bx9nnHEGq1atIjY2lqysLGbMmEFiYiJnnHHGST1uIcTxaUkP3Hqgn1Kql1LKC7gS+LaF5/8JmKmUCnUVL5np2tZ51C4YcGAZ9DkLfII93SohWtW9swbgazXX2eZrNXPvrAEnfM7Ro0ezceNGioqK8Pb2Zvz48WzYsIFffvmFyZMnN3vbsWPHEhcXh8lkYsSIERw+fLjZ4y+++GL3fVYfu2rVKq699loApk2bRm5uLkVFxjDS888/H19fX/ft58yZg9VqZejQoTgcDmbPNqbqDh061H2+J598kqSkJNavX09eXh5PP/308T4lJ++sh8DqW3eb1dfYfhK62mvVkd016i58zD51tvmYfbhr1F0nfM6u9vp1iL+1ZjS7RuxJql+CXynFrbfeyueff8727du54YYb3GusLViwgNtuu41NmzZx2mmnYbfb0VrzxRdfsGXLFrZs2UJycjIDBw5s9j5rfxFV3QOrteaBBx5wn2f//v388Y9/BCA21pgJExUVxUUXXcS6detO+nELIY7PMQOc1toO3I4RvHYDn2mtdyqlHlVKnQ+glDpNKZUKXAa8rpTa6bptHvAYRghcDzzq2tY51C8YAJD0vVR9E13OhSNjefLiocSG+KIwet6evHgoF45sMGW1xaxWK7169eLdd99lwoQJTJ48meXLMljDVwAACeRJREFUl7N///7j+kBhNpux2+0tOr4lxwL4+/s3enuTyYTVanV/iDKZTO7zxcTEoJTC29ub3//+95750DLscjhvvtHjhjIuz5tvbD8JXe21akpsbCwpKTU9mKmpqe4Po+3lnN7nMG/CPGL8Y1AoYvxjmDdhHuf0PueEz9nVXr+m/tY6wusHx1gj9iQlJyfz66+/AvDRRx+5e0MjIiIoKSnh88+NeZJOp5OUlBSmTp3K008/TWFhISUlJcyaNYuXXnrJHcQ2n2CbZs2axdtvv01JSQlg9F5nZWVRWlpKcXExAKWlpSxatKjNKmcKIZrWohnFWusfgB/qbXuo1vX1GMMjG7vt28DbJ9FGz1n6qFEgoDZ7ubH9JD8wCdHRXDgy9qQCW2MmT57Mc889x9tvv83QoUO55557GD16dJ1vmQMDA90fCFr7vj/88EMefPBBVqxYQUREBEFBQSd8voyMDGJiYtBa8/XXX3vuQ8uwy9vk35+u9Fo15fzzz+fll1/myiuvZO3atQQHB7f68LuWOKf3OScV2BrTlV6/pv7WOsrr1+Qasa1gwIABvPLKK/zhD39g0KBB3HLLLeTn5zNkyBCio6M57bTTAHA4HFxzzTUUFha6q3OGhITw4IMPcvfddzNs2DCcTie9evVyz5k7HjNnzmT37t2MH28MoQ8ICOCDDz6gpKSEiy66CDAKrlx99dXuXlQhRPtpeUmoU1Fh6vFtF0LUMXnyZJ544gnGjx+Pv78/Pj4+DYZ0hYeHM3HiRIYMGcKcOXM455zW+WA7b948/vCHPzBs2DD8/Px4770mqjS20Ny5c8nOzkZrzYgRI9xFA7qKrvRazZ8/n2eeeYbMzEyGDRvG2WefzVtvvcXZZ5/NDz/8QN++ffHz8+Odd05szllH1JVev6b+1rry6wdGkZjqwjC1Pf744zz++OMNtq9atarBNl9fX15//fUG26dMmeKuynr99ddz/fXXA0ZFytqqe9wA7rrrLu66q+HQ3q1btzb3MIQQ7UA1V3HKE8aMGaM7zLoizRUM+POO9m+PEMdh9+7dxxw+JYQQQrQ2+f9HiJOnlNqotR7T2L4OsYxAh9VGBQOEEEIIIYQQ4kTIEMrmVM8zWfqoMWwyOM4IbzL/TYh2d9FFF3Ho0KE6255++mlmzZrloRaJpshr1bnJ6yeEEB2bDKEUoovavXs3iYmJDcpSCyGEEG1Fa01SUpIMoRTiJMkQSiFOQT4+PuTm5tLRvqQRQgjRNWmtyc3NxcfH59gHCyFOmAyhFKKLiouLIzU1lezsbE83RQghxCnCx8eHuLhGV5YSQrQSCXBCdFHVi/sKIYQQQoiuQ4ZQCiGEEEIIIUQnIQFOCCGEEEIIIToJCXBCCCGEEEII0Ul0uGUElFLZwBFPt6MREUCOpxshujR5j4m2JO8v0Zbk/SXakry/RFvqqO+vnlrryMZ2dLgA11EppTY0tRaDEK1B3mOiLcn7S7QleX+JtiTvL9GWOuP7S4ZQCiGEEEIIIUQnIQFOCCGEEEIIIToJCXAt94anGyC6PHmPibYk7y/RluT9JdqSvL9EW+p07y+ZAyeEEEIIIYQQnYT0wAkhhBBCCCFEJyEBTgghhBBCCCE6CQlwLaCUmq2U2qOU2q+Uut/T7RFdh1Kqh1JquVJql1Jqp1LqLk+3SXQ9SimzUmqzUup7T7dFdD1KqRCl1OdKqSSl1G6l1HhPt0l0HUqpP7v+f9yhlPpYKeXj6TaJzksp9bZSKksptaPWtjCl1GKl1D7XZagn29gSEuCOQSllBl4B5gCDgKuUUoM82yrRhdiBv2itBwGnA7fJ+0u0gbuA3Z5uhOiyXgR+1FonAsOR95poJUqp2P/f3v2FWlbWYRz/PsxMMCpIJAzmGEdo6KJ/KhKhEKFdCEUTBGlUiHglZHVTWjfedBEhIZoEZtpAQxFm5EWUMUIFhUU2aVM3MU46dkZHQjMJ/z5e7CVshuLMxDmzzlrn+4HNftfvwOLZN/vs33rfdy3gc8Albd8FbAOuHjeVJu67wJUn1G4CDrTdAxwYjjc1G7i1vQ/4W9vDbV8CfgDsHTmTZqLtatuHh/HzLH74nDduKs1Jkt3Ah4G7xs6i+UlyNvAB4DsAbV9q++yooTQ324GdSbYDZwD/GDmPJqztr4B/nlDeC+wbxvuAj53OTP8PG7i1nQc8sXR8FH9gawMkWQEuAh4aOYrm5VbgS8BrI+fQPF0AHAfuGZbp3pXkzLFDaR7aPgncAjwOrALPtX1g3FSaoV1tV4fxMWDXmGFOhg2ctAkkOQv4EfCFtv8aO4/mIclHgKfb/mHsLJqt7cDFwLfaXgS8wASWH2kahr1Ie1lcKHgrcGaST4+bSnPWxfPVNv0z1mzg1vYkcP7S8e6hJq2LJDtYNG/72943dh7NymXAR5McYbH8+/Ik3xs3kmbmKHC07RsrB+5l0dBJ6+FDwGNtj7d9GbgPuHTkTJqfp5KcCzC8Pz1ynjXZwK3t98CeJBckeROLzbP3j5xJM5EkLPaO/LXtN8bOo3lp++W2u9uusPjuerCtV6+1btoeA55I8o6hdAXwlxEjaV4eB96f5Izh/+UVeJMcrb/7gWuG8TXAT0bMclK2jx1gs2v7SpLPAj9ncfeju9seGjmW5uMy4DPAo0kODrWvtP3peJEk6ZTcAOwfLnIeBq4dOY9mou1DSe4FHmZx1+Y/AneOm0pTluT7wAeBc5IcBW4Gvgb8MMl1wN+BT4yX8ORksdRTkiRJkrTZuYRSkiRJkibCBk6SJEmSJsIGTpIkSZImwgZOkiRJkibCBk6SJEmSJsIGTpI0W0leTXJw6XXTOp57Jcmf1+t8kiSdDJ8DJ0mas/+0vXDsEJIkrRdn4CRJW06SI0m+nuTRJL9L8vahvpLkwSSPJDmQ5G1DfVeSHyf50/C6dDjVtiTfTnIoyQNJdo72oSRJW4INnCRpznaesITyqqW/Pdf23cA3gVuH2u3AvrbvAfYDtw3124Bftn0vcDFwaKjvAe5o+07gWeDjG/ppJElbXtqOnUGSpA2R5N9tz/ov9SPA5W0PJ9kBHGv7liTPAOe2fXmor7Y9J8lxYHfbF5fOsQL8ou2e4fhGYEfbr56GjyZJ2qKcgZMkbVX9H+NT8eLS+FXcWy5J2mA2cJKkreqqpfffDuPfAFcP408Bvx7GB4DrAZJsS3L26QopSdIyrxRKkuZsZ5KDS8c/a/vGowTenOQRFrNonxxqNwD3JPkicBy4dqh/HrgzyXUsZtquB1Y3OrwkSSdyD5wkacsZ9sBd0vaZsbNIknQqXEIpSZIkSRPhDJwkSZIkTYQzcJIkSZI0ETZwkiRJkjQRNnCSJEmSNBE2cJIkSZI0ETZwkiRJkjQRrwNll47G2/dcjwAAAABJRU5ErkJggg==\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.subplot(2, 1, 1)\n", "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n", " lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", "plt.subplot(2, 1, 2)\n", "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n", " lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", "\n", "plt.gcf().set_size_inches(15, 10)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "pdf-inline" ] }, "source": [ "## Inline Question 2:\n", "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n", "\n", "## Answer:\n", "训练过程中,batch size 越大越稳定,在训练集的精度越高,而在验证集中不一定 batch size 越大越好,因为小 batch size 相当于引入了更多的随机噪音,起着正则化的作用,因此使得训练集的精度低但保证了验证集的精度;batch size 越大,均值方差的估计越稳定(越能反映样本整体情况)。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Layer Normalization\n", "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n", "\n", "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n", "\n", "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "pdf-inline" ] }, "source": [ "## Inline Question 3:\n", "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n", "\n", "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n", "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1. \n", "3. Subtracting the mean image of the dataset from each image in the dataset.\n", "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n", "\n", "## Answer:\n", "当我们考虑:均值= 0,beta参数= 0(此时我们有gammax/std,其中std=sqrt(sum(x^2)))和gamma=x/std),第2项类似于层标准化。\n", "当我们考虑批量大小=数据集的大小,gamma参数=标准偏差和beta参数= 0时,3类似于批标准化。因此,批标准化的结果将是std(x-mean)/std + 0 = x-mean。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Layer Normalization: Implementation\n", "\n", "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n", "\n", "Here's what you need to do:\n", "\n", "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_forward`. \n", "\n", "Run the cell below to check your results.\n", "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n", "\n", "Run the second cell below to check your results.\n", "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n", "\n", "Run the third cell below to run the batch size experiment on layer normalization." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Before layer normalization:\n", " means: [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n", " stds: [10.07429373 28.39478981 35.28360729 4.01831507]\n", "\n", "After layer normalization (gamma=1, beta=0)\n", " means: [ 4.81096644e-16 -7.40148683e-17 2.22044605e-16 -5.92118946e-16]\n", " stds: [0.99999995 0.99999999 1. 0.99999969]\n", "\n", "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n", " means: [5. 5. 5. 5.]\n", " stds: [2.99999985 2.99999998 2.99999999 2.99999907]\n", "\n" ] } ], "source": [ "# Check the training-time forward pass by checking means and variances\n", "# of features both before and after layer normalization.\n", "\n", "# Simulate the forward pass for a two-layer network.\n", "np.random.seed(231)\n", "N, D1, D2, D3 =4, 50, 60, 3\n", "X = np.random.randn(N, D1)\n", "W1 = np.random.randn(D1, D2)\n", "W2 = np.random.randn(D2, D3)\n", "a = np.maximum(0, X.dot(W1)).dot(W2)\n", "\n", "print('Before layer normalization:')\n", "print_mean_std(a,axis=1)\n", "\n", "gamma = np.ones(D3)\n", "beta = np.zeros(D3)\n", "\n", "# Means should be close to zero and stds close to one.\n", "print('After layer normalization (gamma=1, beta=0)')\n", "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n", "print_mean_std(a_norm,axis=1)\n", "\n", "gamma = np.asarray([3.0,3.0,3.0])\n", "beta = np.asarray([5.0,5.0,5.0])\n", "\n", "# Now means should be close to beta and stds close to gamma.\n", "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n", "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n", "print_mean_std(a_norm,axis=1)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dx error: 1.433615146847572e-09\n", "dgamma error: 4.519489546032799e-12\n", "dbeta error: 2.276445013433725e-12\n" ] } ], "source": [ "# Gradient check batchnorm backward pass.\n", "np.random.seed(231)\n", "N, D = 4, 5\n", "x = 5 * np.random.randn(N, D) + 12\n", "gamma = np.random.randn(D)\n", "beta = np.random.randn(D)\n", "dout = np.random.randn(N, D)\n", "\n", "ln_param = {}\n", "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n", "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n", "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n", "\n", "dx_num = eval_numerical_gradient_array(fx, x, dout)\n", "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n", "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n", "\n", "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n", "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n", "\n", "# You should expect to see relative errors between 1e-12 and 1e-8.\n", "print('dx error: ', rel_error(dx_num, dx))\n", "print('dgamma error: ', rel_error(da_num, dgamma))\n", "print('dbeta error: ', rel_error(db_num, dbeta))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Layer Normalization and Batch Size\n", "\n", "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No normalization: batch size = 5\n", "Normalization: batch size = 5\n", "Normalization: batch size = 10\n", "Normalization: batch size = 50\n" ] }, { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n", "\n", "plt.subplot(2, 1, 1)\n", "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n", " lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", "plt.subplot(2, 1, 2)\n", "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n", " lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n", "\n", "plt.gcf().set_size_inches(15, 10)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "pdf-inline" ], "pycharm": { "name": "#%% md\n" } }, "source": [ "## Inline Question 4:\n", "When is layer normalization likely to not work well, and why?\n", "\n", "1. Using it in a very deep network\n", "2. Having a very small dimension of features\n", "3. Having a high regularization term\n", "\n", "\n", "## Answer:\n", "1.[不正确]在前面的例子中,网络有五层,可以认为是一个深层网络。因此,在深度网络中使用层标准化是正确的。\n", "2.[正确]特征的小维度会影响层标准化的性能。这个问题非常类似于小批量批量标准化的问题,因为在层标准化中,我们是根据隐含单元的数量来计算统计数据的,这些隐含单元代表了网络正在学习的特征。因此,隐藏尺寸越小,层标准化中使用的统计量噪声越大。\n", "3.[正确]有一个高的正则化项会影响层标准化的性能。一般来说,当正则项非常高时,模型学习非常简单的函数(欠拟合)" ] }, { "cell_type": "code", "execution_count": 16, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No normalization: hidden_sizes = 5\n", "Normalization: hidden sizes = 5\n", "Normalization: hidden sizes = 10\n", "Normalization: hidden sizes = 30\n", "Normalization: hidden sizes = 50\n", "Normalization: hidden sizes = 70\n", "Normalization: hidden sizes = 100\n" ] } ], "source": [ "# 层标准化:改变隐藏层的大小值\n", "def run_hiddensize_experiments(normalization_mode):\n", " np.random.seed(231)\n", " # Try training network with different hidden sizes\n", " hidden_size = [5,10,30,50,70,100]\n", " solver_hidden_dims = [10, 10, 10, 10]\n", " num_train = 1000\n", " small_data = {\n", " 'X_train': data['X_train'][:num_train],\n", " 'y_train': data['y_train'][:num_train],\n", " 'X_val': data['X_val'],\n", " 'y_val': data['y_val'],\n", " }\n", " n_epochs=10\n", " weight_scale = 2e-2\n", " lr = 10**(-3.5)\n", "\n", " print('No normalization: hidden_sizes = ', hidden_size[0])\n", " model = FullyConnectedNet(solver_hidden_dims, weight_scale=weight_scale, normalization=None)\n", " solver = Solver(model, small_data,\n", " num_epochs=n_epochs, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': lr,\n", " },\n", " verbose=False)\n", " solver.train()\n", "\n", " bn_solvers = []\n", " for i in range(len(hidden_size)):\n", " print('Normalization: hidden sizes = ', hidden_size[i])\n", " hidden_dims = [ hidden_size[i] for j in range(4)]\n", " bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n", " bn_solver = Solver(bn_model, small_data,\n", " num_epochs=n_epochs, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': lr,\n", " },\n", " verbose=False)\n", " bn_solver.train()\n", " bn_solvers.append(bn_solver)\n", "\n", " return bn_solvers, solver, hidden_size\n", "\n", "# Run model\n", "ln_solvers_hsize, solver_hsize, hidden_size = run_hiddensize_experiments('layernorm')\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 26, "outputs": [ { "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot results\n", "def plot_training_history2(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.',labels=None, label_prefix='-'):\n", " \"\"\"utility function for plotting training history\"\"\"\n", " plt.title(title)\n", " plt.xlabel(label)\n", " bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n", " bl_plot = plot_fn(baseline)\n", " num_bn = len(bn_plots)\n", " for i in range(num_bn):\n", " label=label_prefix\n", " if labels is not None:\n", " label += str(labels[i])\n", " plt.plot(bn_plots[i], bn_marker, label=label)\n", " label='baseline'\n", " if labels is not None:\n", " label += str(labels[0])\n", " plt.plot(bl_plot, bl_marker, label=label)\n", " plt.legend(loc='lower center', ncol=num_bn+1, bbox_to_anchor=(0.5, -0.3))\n", "\n", "plt.subplot(2, 1, 1)\n", "plot_training_history2('Training accuracy (Layer Normalization)','Epoch', solver_hsize, ln_solvers_hsize, \\\n", " lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=hidden_size, label_prefix='hsize')\n", "plt.subplots_adjust(hspace = 0.5)\n", "plt.subplot(2, 1, 2)\n", "plot_training_history2('Validation accuracy (Layer Normalization)','Epoch', solver_hsize, ln_solvers_hsize, \\\n", " lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=hidden_size, label_prefix='hsize')\n", "\n", "plt.gcf().set_size_inches(15, 10)\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 27, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No normalization: regularization = 0.0001\n", "Normalization: regularization = 0.0001\n", "Normalization: regularization = 0.01\n", "Normalization: regularization = 1.0\n", "Normalization: regularization = 100.0\n", "Normalization: regularization = 10000.0\n" ] }, { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 层标准化:改变正则化值\n", "def run_regularization_experiments(normalization_mode):\n", " np.random.seed(231)\n", " # Try training a very deep net with batchnorm\n", " hidden_dims = [100, 100, 100, 100, 100]\n", " num_train = 1000\n", " small_data = {\n", " 'X_train': data['X_train'][:num_train],\n", " 'y_train': data['y_train'][:num_train],\n", " 'X_val': data['X_val'],\n", " 'y_val': data['y_val'],\n", " }\n", " n_epochs=10\n", " weight_scale = 2e-2\n", " lr = 10**(-3.5)\n", " regularization = np.logspace(-4, 4, num=5)\n", "\n", " print('No normalization: regularization = ', regularization[0])\n", " model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None, reg=regularization[0])\n", " solver = Solver(model, small_data,\n", " num_epochs=n_epochs, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': lr,\n", " },\n", " verbose=False)\n", " solver.train()\n", "\n", " bn_solvers = []\n", " for i, reg in enumerate(regularization):\n", " print('Normalization: regularization = ', reg)\n", " bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode, reg=reg)\n", " bn_solver = Solver(bn_model, small_data,\n", " num_epochs=n_epochs, batch_size=50,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': lr,\n", " },\n", " verbose=False)\n", " bn_solver.train()\n", " bn_solvers.append(bn_solver)\n", "\n", " return bn_solvers, solver, regularization\n", "\n", "# Run model\n", "ln_solvers_reg, solver_reg, regularization = run_regularization_experiments('layernorm')\n", "\n", "plt.subplot(2, 1, 1)\n", "plot_training_history2('Training accuracy (Layer Normalization)','Epoch', solver_reg, ln_solvers_reg, \\\n", " lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=regularization, \\\n", " label_prefix='reg')\n", "plt.subplots_adjust(hspace = 0.5)\n", "plt.subplot(2, 1, 2)\n", "plot_training_history2('Validation accuracy (Layer Normalization)','Epoch', solver_reg, ln_solvers_reg, \\\n", " lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=regularization, \\\n", " label_prefix='reg')\n", "\n", "plt.gcf().set_size_inches(15, 10)\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 2 }