{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "

Table of Contents

\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# code for loading the format for the notebook\n", "import os\n", "\n", "# path : store the current path to convert back to it later\n", "path = os.getcwd()\n", "os.chdir(os.path.join('..', '..', 'notebook_format'))\n", "\n", "from formats import load_style\n", "load_style(css_style = 'custom2.css', plot_style = False)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2018-09-17 21:36:47 \n", "\n", "CPython 3.6.4\n", "IPython 6.4.0\n", "\n", "numpy 1.14.1\n", "pandas 0.23.0\n", "matplotlib 2.2.2\n", "sklearn 0.19.1\n" ] } ], "source": [ "os.chdir(path)\n", "\n", "# 1. magic for inline plot\n", "# 2. magic to print version\n", "# 3. magic so that the notebook will reload external python modules\n", "# 4. magic to enable retina (high resolution) plots\n", "# https://gist.github.com/minrk/3301035\n", "%matplotlib inline\n", "%load_ext watermark\n", "%load_ext autoreload\n", "%autoreload 2\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.model_selection import train_test_split, GridSearchCV\n", "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n", "\n", "%watermark -d -t -v -p numpy,pandas,matplotlib,sklearn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradient Boosting Machine (GBM)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just like [Random Forest and Extra Trees](http://nbviewer.jupyter.org/github/ethen8181/machine-learning/blob/master/trees/random_forest.ipynb), Gradient Boosting Machine is also a type of Ensemble Tree method, the only difference is it is stemmed from the boosting framework. The idea of boosting is to add one weak classifier to the ensemble at a time, and this newly added weak classifier is trained to improve upon the already trained ensemble. Meaning it will pay higher attention to examples which are misclassified or have higher errors and focus on mitigating those errors. Boosting is a general framework that can be applied to any sort of weak learner, although Decision Tree is by far the most commonly used ones due to them having the flexibility to be weak learners by simply restricting their depth and they are quite fast to train.\n", "\n", "Suppose we are given some dataset $(x_1, y_1), (x_2, y_2), ...,(x_n, y_n)$, and the task is to fit a model $F(x)$ to minimize square loss. After training the model, we discovered the model is good but not perfect.\n", "There are some mistakes: $F(x_1) = 0.8$, while $y_1 = 0.9$, and $F(x_2) = 1.4$ while $y_2 = 1.3$ .... Now the question is, how can we improve this model without changing anything from $F(x)$?\n", "\n", "How about we simply add an additional model (e.g. regression tree) $h$ to the already existing $F$, so the new prediction becomes $F(x) + h(x)$. In other words, we wish to improve upon the existing model so that $F(x_1) + h(x_1) = y_1, F(x_2) + h(x_2) = y_2 ...$ or equivalent we wish to find a new model $h$ such that $h(x_1) = y_1 - F(x_1), h(x_2) = y_2 - F(x_2) ...$. The idea is all well and good, but the bad news is probably no model $h$ (e.g. regression tree) will be able to do this perfectly. Fortunately, the good news is, some $h$ might be able to do this approximately.\n", "\n", "The idea is, we fit the model $h$ to the data using $y_1 - F(x_1), y_2 - F(x_2)$ as the response variable. And the intuition for this is: the $y_i - F(x_i)$s are the residuals. These are the areas that the existing\n", "model $F$ cannot do well, so now the role of $h$ is to compensate the shortcoming of existing model $F$. And if the model after adding the new model $h$, $F + h$ is still unsatisfactory, we will just add another new one.\n", "\n", "To make sure we're actually learning the residuals, we'll employ the idea of gradient descent. Say our goal is to minimize $J$, an overall loss function additively calculated from all observations with regard to $F$, a classifier with some parameters. More formally, we're given the formula:\n", "\n", "$$J(y, F) = \\sum_i^n L\\big(y_i, F(x_i)\\big)$$\n", "\n", "Where:\n", "\n", "- $L$ is a cost/loss function comparing the response variable's value and the prediction of the model for each observation\n", "\n", "Instead of trying to solve it directly, gradient descent is an iterative technique that allows us to approach the solution of an optimization problem. At each step of the algorithm, it will perform the following operations:\n", "\n", "$$F_b(x_i) = F_{b-1}(x_i) - \\eta \\times \\nabla L\\big(y_i, F(x_i)\\big)$$\n", "\n", "Where:\n", "\n", "- $F_b$ is the version of classifier at step/iteration $b$\n", "- $\\eta$ is the learning rate which controls the size of the learning process\n", "- $\\nabla$ is the gradient i.e. the first order partial derivative of the cost function with respect to the classifier\n", "- The formula above actually refers to stochastic gradient descent as we are only computing the function for a single observation, $x_i$\n", "\n", "For example, say we're given, sum of squares errors, a well-known quality indicator for regression model as our loss function. So now our loss function $L\\big(y_i, F(x_i)\\big)$ is defined as: $\\frac{1}{2} \\big( y_i - F(x_i) \\big)^2$ (the 1/2 is simply to make the notation cleaner later). Taking the gradient of this loss function we get:\n", "\n", "$$\\frac{ \\partial L\\big(y_i, F(x_i)\\big) }{ \\partial F(x_i) } = \\frac{ \\partial \\frac{1}{2} \\big( y_i - F(x_i) \\big)^2 }{ \\partial F(x_i) } = F(x_i) - y_i$$\n", "\n", "Tying this back to our original problem, we wish to update our function $F$ at iteration $b$ with a new model $h$:\n", "\n", "\\begin{align}\n", "F_b(x_i) &= F_{b-1}(x_i) + h(x_i) \\nonumber \\\\\n", " &= F_{b-1}(x_i) + y_i - F_{b-1}(x_i) \\nonumber \\\\\n", " &= F_{b-1}(x_i) - 1 \\times \\frac{ \\partial L\\big(y_i, F_{b-1}(x_i)\\big) }{ \\partial F_{b-1}(x_i) }\n", " \\nonumber \\\\\n", "\\end{align}\n", "\n", "As we can see, the formula above is 99% the same as as the gradient descent formula, $F_b(x_i) = F_{b-1}(x_i) - \\eta \\times \\nabla L\\big(y_i, F(x_i)\\big)$. The only difference is that the learning rate $\\eta$ is 1. Thus, we now have an iterative process constructing the additive model that minimizes our loss function (residuals)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In practice though, Gradient Boosting Machine is more prone to overfitting, since the week learner is tasked with optimally fitting the gradient. This means that boosting will select the optimal learner at each stage of the algorithm, although this strategy generates an optimal solution at the current stage, it has the drawbacks of not finding the optimal global model as well as overfitting the training data. A remedy for greediness is to constrain the learning process by setting the learning rate $\\eta$ (also known as shrinkage). In the above algorithm, instead of directly adding the predicted value for a sample to next iteration's predicted value, so that only a fraction of the current predicted value is added to the previous iteration's predicted value. This parameter can take values between 0 and 1 and becomes another tuning parameter for the model. Small values of the learning parameter such as 0.1 tends to work better, but the value of the parameter is inversely proportional to the computation time required to find an optimal model, because more iterations is required.\n", "\n", "To sum it all up, the process of training a GBM for regression is:\n", "\n", "1. Initialize a predicted value for each observation (e.g. the original response or the average response or a value that minimizes the loss function). This will be our initial \"residuals\", $r$. It can be called the residuals because we're dealing with a regression task, but this quantity is more often referred to as the negative gradient, this terminology makes the $- \\nabla \\times L\\big(y_i, F(x_i) \\big)$ part generalizes to any loss function we might wish to employ. In short, GBM is fitting to the gradient of the loss function\n", "2. For step = 1 to $B$ (number of iterations that we specify) do:\n", " - Fit a regression tree $F_b$ to the training data $(X, r)$, where we use the residuals as the response variable\n", " - Update model $F$ by adding a shrunken version of the newly fitted regression tree. Translating it to code, this means we append the new tree to the array of trees we've already stored: \n", " $F(X) = F(X) + \\eta F_{b}(X)$\n", " - Update each observation's residual by adding the predicted value to it:\n", " $r_{b + 1} = r_b - \\eta F_b(X)$\n", "3. In the end, our final output boosted model becomes $F(x) = \\sum_{b = 1}^B \\eta F_b(x)$, where we sum the values that each individual tree gives (times the learning rate)\n", "\n", "To hit the notion home, let's conside an example using made up numbers. Suppose we have 5 observations, with responses 10, 20, 30, 40, 50. The first tree is built and gives predictions of 12, 18, 27, 39, 54 (these predictions are made up numbers). If our learning rate $\\eta$ = 0.1, all trees will have their predictions scaled down by $\\eta$, so the first tree will instead \"predict\" 1.2, 1.8, 2.7, 3.9, 5.4. The response variable passed to the next tree will then have values 8.8, 18.2, 27.3, 36.1, 44.6 (the difference between the prediction that was scaled down by the prediction and the true response). The second round then uses these response values to build another tree - and again the predictions are scaled down by the learning rate $\\eta$. So tree 2 predicts say, 7, 18, 25, 40, 40, which, once scaled, become 0.7, 1.8, 2.5, 4.0, 4.0. As before, the third tree will be passed the difference between these values and the previous tree's response variable (so 8.1, 16.4, 24.8, 32.1. 40.6). And we keep iterating this process until we finished training all the trees (a parameter that we specify), in the end, the sum of the predictions from all trees will give the final prediction." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we will use the [Wine Quality Data Set](https://archive.ics.uci.edu/ml/datasets/Wine+Quality) to test our implementation. This [link](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv) should download the .csv file. The task is to predict the quality of the wine (a scale of 1 ~ 10) given some of its features." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dimension of the dataset: (4898, 12)\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
8578.20.400.4813.700.04259.0169.00.998603.100.529.45
23257.00.350.171.100.0497.0119.00.992973.130.369.76
2797.00.300.494.700.03617.0105.00.991603.260.6812.47
16877.30.260.3317.850.04941.5195.01.000003.060.449.17
5316.40.180.484.000.18664.0150.00.994503.060.409.35
\n", "
" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n", "857 8.2 0.40 0.48 13.70 0.042 \n", "2325 7.0 0.35 0.17 1.10 0.049 \n", "279 7.0 0.30 0.49 4.70 0.036 \n", "1687 7.3 0.26 0.33 17.85 0.049 \n", "531 6.4 0.18 0.48 4.00 0.186 \n", "\n", " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n", "857 59.0 169.0 0.99860 3.10 0.52 \n", "2325 7.0 119.0 0.99297 3.13 0.36 \n", "279 17.0 105.0 0.99160 3.26 0.68 \n", "1687 41.5 195.0 1.00000 3.06 0.44 \n", "531 64.0 150.0 0.99450 3.06 0.40 \n", "\n", " alcohol quality \n", "857 9.4 5 \n", "2325 9.7 6 \n", "279 12.4 7 \n", "1687 9.1 7 \n", "531 9.3 5 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# read in the data and shuffle the row order for model stability\n", "np.random.seed(4321)\n", "wine_path = os.path.join('..', 'winequality-white.csv')\n", "wine = pd.read_csv(wine_path, sep = ';')\n", "wine = wine.sample(frac = 1)\n", "\n", "# train/test split the features and response column\n", "y = wine['quality'].values\n", "X = wine.drop('quality', axis = 1).values\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1234)\n", "\n", "print('dimension of the dataset: ', wine.shape)\n", "wine.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class GBMReg:\n", " \"\"\"\n", " Regression gradient boosting machine using scikit learn's \n", " decision tree as the base tree\n", " \n", " Parameters\n", " ----------\n", " n_estimators: int\n", " number of trees to train\n", " \n", " learning_rate: float\n", " learning rate, some calls it shrinkage, \n", " shrinks the contribution of each tree\n", " to prevent overfitting\n", " \n", " max_depth: int\n", " controls how deep to grow the tree;\n", " this is more of a decision tree parameter,\n", " it is tune here to make later comparison fair\n", " \n", " all the other parameters for a decision tree like\n", " max_features or min_sample_split also applies to GBM, \n", " it is just not used here as that is more\n", " related to a single decision tree\n", " \"\"\"\n", "\n", " def __init__(self, n_estimators, learning_rate, max_depth):\n", " self.max_depth = max_depth\n", " self.n_estimators = n_estimators\n", " self.learning_rate = learning_rate\n", "\n", " def fit(self, X, y):\n", " self.estimators = []\n", " \n", " # simply use the response as the original residuals\n", " # and covert it to float type to prevent error warning\n", " # that it's converting from int to float\n", " residual = y.astype(np.float)\n", " for i in range(self.n_estimators):\n", " tree = DecisionTreeRegressor(max_depth = self.max_depth)\n", " tree.fit(X, residual) \n", " y_pred = tree.predict(X)\n", " self.estimators.append(tree) \n", " residual -= self.learning_rate * y_pred\n", " \n", " return self\n", " \n", " def predict(self, X):\n", " y_pred = np.zeros(X.shape[0])\n", " for tree in self.estimators:\n", " y_pred += self.learning_rate * tree.predict(X)\n", " \n", " return y_pred" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tree: 0.5477007211969738\n", "gbm: 0.42077294261208087\n", "gbm library: 0.42210698230175164\n" ] } ], "source": [ "# compare the results between a single decision tree,\n", "# gradient boosting, the lower the mean square\n", "# error, the better\n", "tree = DecisionTreeRegressor(max_depth = 6)\n", "tree.fit(X_train, y_train) \n", "tree_y_pred = tree.predict(X_test)\n", "print('tree: ', mean_squared_error(y_test, tree_y_pred))\n", "\n", "# library to confirm result\n", "gbm_reg = GBMReg(n_estimators = 100, learning_rate = 0.1, max_depth = 6)\n", "gbm_reg.fit(X_train, y_train)\n", "gbm_reg_y_pred = gbm_reg.predict(X_test)\n", "print('gbm: ', mean_squared_error(y_test, gbm_reg_y_pred))\n", "\n", "# gradient boosting for 100 trees and learning rate of 0.1\n", "gbm = GradientBoostingRegressor(n_estimators = 100, learning_rate = 0.1, max_depth = 6)\n", "gbm.fit(X_train, y_train)\n", "gbm_y_pred = gbm.predict(X_test)\n", "print('gbm library: ', mean_squared_error(y_test, gbm_y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly, Gradient Boosting has some similarities to Random Forests and Extra Trees: the final prediction is based on an ensemble of models, and trees are used as the base learner, so all the tuning parameters for the tree model also controls the variability of Gradient Boosting. And for interpretability we can also access the feature importance attribute." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def viz_importance(model, feature_names, n_features):\n", " \"\"\"Visualize the relative importance of predictors\"\"\"\n", " # sort the importance in decreasing order\n", " importances = model.feature_importances_\n", " idx = np.argsort(importances)[-n_features:]\n", " names = feature_names[idx]\n", " scores = importances[idx]\n", " \n", " y_pos = np.arange(1, n_features + 1)\n", " plt.barh(y_pos, scores, color = 'lightskyblue', align = 'center')\n", " plt.yticks(y_pos, names)\n", " plt.xlabel('Importance')\n", " plt.title('Feature Importance Plot')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 392, "width": 582 } }, "output_type": "display_data" } ], "source": [ "# change default figure and font size\n", "plt.rcParams['figure.figsize'] = 8, 6 \n", "plt.rcParams['font.size'] = 12\n", "\n", "viz_importance(gbm, wine.columns[:-1], X.shape[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But the way the ensembles are constructed differs substantially between each model. In Random Forests and Extra Trees, all trees are created independently and each tree contributes equally to the final model. The trees in Gradient Boosting, however, are dependent on past trees and contribute unequally to the final model. Despite these differences, Random Forests, Extra Trees and Gradient Boosting all offer competitive predictive performance (Gradient Boosting often wins when carefully tuned). As for computation time, Gradient Boosting is often greater than for Random Forests, Extra Trees, since the two former models' procedure can be easily parallel processed given that their individual trees are created independently." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gradient Boosting Machine can also be extended to handle classification tasks, as we'll soon see, even in the classification context, the underlying algorithm is still a regression tree. To adapt the algorithm to a classification process, we start by defining a new loss function, cross entropy (also known as multinomial deviance), denoted as:\n", "\n", "$$L\\big(y_i, F(x_i)\\big) = -\\sum_k ^ K y_k(x_i) \\log p_k(x_i)$$\n", "\n", "The notation above says:\n", "\n", "- We have a total of $K$ output class (categorical response variable) that ranges from $1, ..., K$\n", "- $y_k(x_i)$ is a dummy indicator of the response variable that takes the value of 1 if the $i_{th}$ observation belongs to class $k$ and 0 otherwise\n", "- $p_k(x_i)$ is the predicted probability of the $i_{th}$ observation belonging to class $k$\n", "\n", "So the next question is how do we get $p_k(x_i)$?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Softmax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Softmax function takes an $N$-dimensional vector of arbitrary real values and produces another $N$-dimensional vector with real values in the range (0, 1) that add up to 1. The function's formula can be written as:\n", "\n", "$$p_i = \\frac{e^{o_i}}{\\sum_k^K e^{o_k}}$$\n", "\n", "For example, in the following code chunk, we see that how the softmax function transforms a 3-element vector 1.0, 2.0, 3.0 into probabilities that sums up to 1, while still preserving the relative size of the original elements." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.09003057, 0.24472847, 0.66524096])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def compute_softmax(x):\n", " \"\"\"compute the softmax of vector\"\"\"\n", " exp_x = np.exp(x)\n", " softmax = exp_x / np.sum(exp_x)\n", " return softmax\n", "\n", "# this can be interpreted as the probability\n", "# of belonging to the three classes\n", "compute_softmax([1, 2, 3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we wish to compute the derivative of this function with respect to the input $o_i$ so we can use it later when computing the derivative of the loss function. To be explicit we wish to find:\n", "\n", "$$\\frac{\\partial p_i}{\\partial o_j} = \\frac{\\partial \\frac{e^{o_i}}{\\sum_{k=1}^{N}e^{o_k}}}{\\partial o_j}$$\n", "\n", "For any arbitrary output $i$ and input $j$. To do so, We'll be using the quotient rule of derivatives. The rule tells us that for a function $f(x) = \\frac{g(x)}{h(x)}$:\n", "\n", "$$f'(x) = \\frac{g'(x)h(x) - h'(x)g(x)}{[h(x)]^2}$$\n", "\n", "In our case, we have:\n", "\n", "$$\n", "\\begin{align*}\n", "g &= e^{o_i} \\nonumber \\\\\n", "h &= \\sum_{k=1}^{K}e^{o_k} \\nonumber\n", "\\end{align*}\n", "$$\n", "\n", "It's important to notice that no matter which $o_j$ we compute the derivative of $h$ for the output will always be $e^{o_j}$. However, this is not the case for $g$. It's derivative will be $e^{o_j}$ only if $i = j$, because only then will it have the term $e^{o_j}$. Otherwise, the derivative is simply 0 (because it's simply taking the derivative of a constant).\n", "\n", "So going back to using our quotient rule, we start with the $i = j$ case. In the following derivation we'll use the $\\Sigma$ (Sigma) sign to represent $\\sum_{k=1}^{K}e^{o_k}$ for simplicity and to prevent cluttering up the notation.\n", "\n", "$$\n", "\\begin{align*}\n", "\\frac{\\partial \\frac{e^{o_i}}{\\sum_{k = 1}^{N} e^{o_k}}}{\\partial o_j} \n", "&= \\frac{e^{o_i}\\Sigma-e^{o_j}e^{o_i}}{\\Sigma^2} \\nonumber \\\\\n", "&= \\frac{e^{o_i}}{\\Sigma}\\frac{\\Sigma - e^{o_j}}{\\Sigma} \\nonumber \\\\\n", "&= p_i(1 - p_j) \\nonumber \\\\\n", "&= p_i(1 - p_i) \\nonumber\n", "\\end{align*}\n", "$$\n", "\n", "The reason we can perform the operation in the last line is because we're considering the scenario where $i = j$. Similarly we can do the case where $i \\neq j$.\n", "\n", "$$\n", "\\begin{align*}\n", "\\frac{\\partial \\frac{e^{o_i}}{\\sum_{k = 1}^{N} e^{o_k}}}{\\partial o_j} \n", "&= \\frac{0-e^{o_j}e^{o_i}}{\\Sigma^2} \\nonumber \\\\\n", "&= -\\frac{e^{o_j}}{\\Sigma}\\frac{e^{o_i}}{\\Sigma} \\nonumber \\\\\n", "&= -p_j p_i \\nonumber \\\\\n", "&= -p_i p_j \\nonumber\n", "\\end{align*}\n", "$$\n", "\n", "Just to sum it up, we now have:\n", "\n", "$$\\frac{\\partial p_i}{\\partial o_j} = p_i(1 - p_i),\\quad i = j$$\n", "\n", "$$\\frac{\\partial p_i}{\\partial o_j} = -p_i p_j,\\quad i \\neq j$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can tie this back to the original loss function $-\\sum_k^K y_k \\log p_k$ and compute its negative gradient.\n", "\n", "$$\n", "\\begin{align}\n", "\\frac{\\partial L}{\\partial o_i} \n", "&= -\\sum_k y_k\\frac{\\partial \\log p_k}{\\partial o_i} \\nonumber \\\\\n", "&= -\\sum_k y_k\\frac{1}{p_k}\\frac{\\partial p_k}{\\partial o_i} \\nonumber \\\\\n", "&= -y_i(1-p_i) - \\sum_{k \\neq i}y_k\\frac{1}{p_k}(-p_kp_i) \\nonumber \\\\\n", "&= -y_i(1 - p_i) + \\sum_{k \\neq i}y_k(p_i) \\nonumber \\\\\n", "&= -y_i + y_i p_i + \\sum_{k \\neq i}y_k(p_i) \\nonumber \\\\\n", "&= p_i\\left(\\sum_ky_k\\right) - y_i \\nonumber \\\\\n", "&= p_i - y_i \\nonumber\n", "\\end{align}\n", "$$\n", "\n", "Remember $\\sum_ky_k=1$ (as $y$ is a vector with only one non-zero element, which is $1$ when the indicating the observation belongs to the $k_{th}$ class. \n", "\n", "After a long journey, we now see, for every class $k$, the gradient is the difference between the associated dummy variable and the predicted probability of belonging to that class. This is essentially the \"residuals\" from the classification gradient boosting. Given this, we can now implement the algorithm, the overall process of training a regression tree has still not changed, only now we must deal with the dummy variables, $y_k$ and fit a regression tree on the negative gradient for each dummy variable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the dataset, we'll still use the Wine Quality Data Set that was used for the regression task, except we now treat the quality of the wine (a scale of 1 ~ 10) as categorical instead of numeric." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import GradientBoostingClassifier" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "class GBMClass:\n", " \"\"\"\n", " Classification gradient boosting machine using scikit learn's \n", " decision tree as the base tree\n", " \n", " Parameters\n", " ----------\n", " n_estimators: int\n", " number of trees to train\n", " \n", " learning_rate: float\n", " learning rate, some calls it shrinkage, \n", " shrinks the contribution of each tree\n", " to prevent overfitting\n", " \n", " max_depth: int\n", " controls how deep to grow the tree;\n", " this is more of a decision tree parameter,\n", " it is tune here to make later comparison fair\n", " \n", " all the other parameters for a decision tree like\n", " max_features or min_sample_split also applies to GBM, \n", " it is just not used here as that is more\n", " related to a single decision tree\n", " \"\"\"\n", "\n", " def __init__(self, n_estimators, learning_rate, max_depth):\n", " self.max_depth = max_depth\n", " self.n_estimators = n_estimators\n", " self.learning_rate = learning_rate\n", "\n", " def fit(self, X, y):\n", " # encode labels with value between 0 and n_classes - 1,\n", " # so we can easily one-hot encode them\n", " self.le = LabelEncoder()\n", " labels = self.le.fit_transform(y)\n", " Y = self._to_categorical(labels)\n", " del labels\n", " \n", " # the predicted probability starts out with \n", " # a value that's uniform over all classes;\n", " # then we compute the residuals (negative gradient),\n", " # which is the difference between the predicted\n", " # probability and the class label\n", " y_proba = np.full(Y.shape, 1 / Y.shape[1]) \n", " residuals = Y - y_proba\n", " \n", " # train a base decision tree on the residuals\n", " # for every single class, hence we end up with\n", " # n_estimators * n_classes base tree models\n", " self.estimators = []\n", " for i in range(self.n_estimators):\n", " for j in range(self.n_classes): \n", " tree = DecisionTreeRegressor(max_depth = self.max_depth)\n", " tree.fit(X, residuals[:, j]) \n", " y_pred = tree.predict(X)\n", " self.estimators.append(tree) \n", " residuals[:, j] -= self.learning_rate * y_pred\n", " \n", " return self\n", " \n", " def _to_categorical(self, y):\n", " \"\"\"one hot encode class vector y\"\"\"\n", " self.n_classes = np.amax(y) + 1\n", " Y = np.zeros((y.shape[0], self.n_classes))\n", " for i in range(y.shape[0]):\n", " Y[i, y[i]] = 1.0\n", "\n", " return Y\n", " \n", " def predict(self, X):\n", " # after predicting the class remember to\n", " # transform it back to the actual class label\n", " y_prob = self.predict_proba(X)\n", " y_pred = np.argmax(y_prob, axis = 1)\n", " y_pred = self.le.inverse_transform(y_pred)\n", " return y_pred\n", " \n", " def predict_proba(self, X):\n", " # add up raw score for every class and convert\n", " # it to probability using softmax\n", " y_raw = np.zeros((X.shape[0], self.n_classes))\n", "\n", " # obtain the tree for each class and add up the prediction\n", " for c in range(self.n_classes):\n", " class_tree = self.estimators[c::self.n_classes]\n", " for tree in class_tree:\n", " y_raw[:, c] += self.learning_rate * tree.predict(X)\n", " \n", " y_proba = self._compute_softmax(y_raw)\n", " return y_proba\n", " \n", " def _compute_softmax(self, z):\n", " \"\"\"\n", " compute the softmax of matrix z in a numerically stable way,\n", " by substracting each row with the max of each row. For more\n", " information refer to the following link:\n", " https://nolanbconaway.github.io/blog/2017/softmax-numpy\n", " \"\"\"\n", " shift_z = z - np.amax(z, axis = 1, keepdims = 1)\n", " exp_z = np.exp(shift_z)\n", " softmax = exp_z / np.sum(exp_z, axis = 1, keepdims = 1)\n", " return softmax " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tree: 0.5438775510204081\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mingyuliu/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n", " if diff:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "gbm: 0.6010204081632653\n", "gbm library: 0.6163265306122448\n" ] } ], "source": [ "# compare the results between a single decision tree,\n", "# gradient boosting, the higher the accuracy, the better\n", "tree = DecisionTreeClassifier(max_depth = 6)\n", "tree.fit(X_train, y_train) \n", "tree_y_pred = tree.predict(X_test)\n", "print('tree: ', accuracy_score(y_test, tree_y_pred))\n", "\n", "# gradient boosting for 150 trees and learning rate of 0.2\n", "# unlike random forest, gradient boosting's base tree can be shallower\n", "# meaning that there depth can be smaller\n", "gbm_class = GBMClass(n_estimators = 150, learning_rate = 0.2, max_depth = 3)\n", "gbm_class.fit(X_train, y_train)\n", "gbm_class_y_pred = gbm_class.predict(X_test)\n", "print('gbm: ', accuracy_score(y_test, gbm_class_y_pred))\n", "\n", "# library to confirm results are comparable\n", "gbm = GradientBoostingClassifier(n_estimators = 150, learning_rate = 0.2, max_depth = 3)\n", "gbm.fit(X_train, y_train)\n", "gbm_y_pred = gbm.predict(X_test)\n", "print('gbm library: ', accuracy_score(y_test, gbm_y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Understanding Model Complexity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following section, we generate a Sinoide function + random gaussian noise, with 80 training samples (blue points) and 20 test samples (red points)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def ground_truth(x):\n", " \"\"\"Ground truth -- function to approximate\"\"\"\n", " return x * np.sin(x) + np.sin(2 * x)\n", "\n", "\n", "def gen_data(low, high, n_samples):\n", " \"\"\"generate training and testing data from the ground truth function\"\"\"\n", " np.random.seed(15)\n", " X = np.random.uniform(low, high, size = n_samples)\n", " \n", " # generate the response from the ground truth function and add\n", " # some random noise to it\n", " y = ground_truth(X) + np.random.normal(scale = 2, size = n_samples)\n", " X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size = 0.2, random_state = 3)\n", "\n", " return X_train, X_test, y_train, y_test\n", "\n", "\n", "def plot_data(x_plot, X_train, X_test, y_train, y_test):\n", " \"\"\"plot training and testing data\"\"\"\n", " s = 20\n", " alpha = 0.4\n", " plt.plot(x_plot, ground_truth(x_plot), alpha = alpha, label = 'ground truth')\n", " plt.scatter(X_train, y_train, s = s, alpha = alpha)\n", " plt.scatter(X_test, y_test, s = s, alpha = alpha, color = 'red')\n", " plt.xlim(( 0, 10 ))\n", " plt.ylabel('y')\n", " plt.xlabel('x')\n", " plt.legend(loc = 'upper left')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 377, "width": 520 } }, "output_type": "display_data" } ], "source": [ "low = 0\n", "high = 10\n", "x_plot = np.linspace(low, high, 500)\n", "X_train, X_test, y_train, y_test = gen_data(low = low, high = high, n_samples = 100)\n", "plot_data(x_plot, X_train, X_test, y_train, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall that in a single regression tree, we can use the `max_depth` parameter to control how deep to grow the tree and the deeper the tree the more variance can be explained." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 377, "width": 520 } }, "output_type": "display_data" } ], "source": [ "# when using scikit-learn, the training data has to be\n", "# a 2d-array even if it only has 1 features\n", "tree1 = DecisionTreeRegressor(max_depth = 1)\n", "tree1.fit(X_train[:, np.newaxis], y_train)\n", "tree2 = DecisionTreeRegressor(max_depth = 3)\n", "tree2.fit(X_train[:, np.newaxis], y_train)\n", "\n", "plt.plot(x_plot, tree1.predict(x_plot[:, np.newaxis]),\n", " label = 'RT max_depth=1', color = 'g', alpha = 0.9, linewidth = 2)\n", "plt.plot(x_plot, tree2.predict(x_plot[:, np.newaxis]),\n", " label = 'RT max_depth=3', color = 'g', alpha = 0.7, linewidth = 1)\n", "plot_data(x_plot, X_train, X_test, y_train, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot above shows that the decision boundaries made by decision trees are always perpendicular to $x$ and $y$ axis (due to the fact that they consists of nested if-else statements). Let's see what happens when we use gradient boosting without tuning the parameters (by specifying a fix `max_depth`)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 377, "width": 520 } }, "output_type": "display_data" } ], "source": [ "gbm = GradientBoostingRegressor(n_estimators = 300, max_depth = 6, learning_rate = 0.1)\n", "gbm.fit(X_train[:, np.newaxis], y_train)\n", "\n", "plt.plot(x_plot, gbm.predict(x_plot[:, np.newaxis]),\n", " label = 'GBM max_depth=6', color = 'r', alpha = 0.9, linewidth = 2)\n", "plot_data(x_plot, X_train, X_test, y_train, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hopefully, it should be clear that compared with decision trees, gradient boosting machine is far more susceptible to overfitting the training data, hence it is common to tune parameters including `max_depth`, `max_features`, `min_samples_leaf`, `subsample` (explained below) to reduce the overfitting phenomenon from occurring.\n", "\n", "The parameter `subsample` (technically called *stochastic gradient boosting*) borrows some idea from bagging techniques. What it does is: while iterating through each individual tree building process, it randomly select a fraction of the training data. Then the residuals and models in the remaining steps of the current iteration are based only on that sample of data. It turns out that this simple modification improved the predictive accuracy of boosting while also reducing the required computational resources (of course, this is based on the fact that you have enough observations to subsample).\n", "\n", "The following section tunes the commonly tuned parameter and find the best one and draws the decision boundary. The resulting plot should be self-explanatory." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best hyperparameters: {'max_depth': 4, 'min_samples_leaf': 5, 'subsample': 1}\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABBEAAALyCAYAAACM+0x9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xl83FW9//H3mT2Zyd4kTfdCFwqllCKKoFC52CsqslVRUMSN+9PfBVy4V5DFcoULCCgIer0/EVBANlmqIpSCVkRUShegpbQpbVLSJUmzzySzf39/TDLNZJ0kk2XS1/PxmEfyne855/uZadPm+5nPOcdYliUAAAAAAIDB2MY7AAAAAAAAkB1IIgAAAAAAgLSQRAAAAAAAAGkhiQAAAAAAANJCEgEAAAAAAKSFJAIAAAAAAEgLSQQAAAAAAJAWkggAAAAAACAtJBEAAAAAAEBaSCIAAAAAAIC0kEQAAAAAAABpIYkAAAAAAADSQhIBAAAAAACkhSQCAAAAAABIC0kEAAAAAACQFsd4B4DRYYzZLSlfUtU4hwIAAAAAyLw5kloty5o7lhcliTB55efk5BQvWrSoeLwDAQAAAABk1rZt29TR0THm1yWJMHlVLVq0qHjDhg3jHQcAAAAAIMNOOOEEbdy4sWqsr8uaCAAAAAAAIC0kEQAAAAAAQFpIIgAAAAAAgLSQRAAAAAAAAGkhiQAAAAAAANJCEgEAAAAAAKSFJAIAAAAAAEgLSQQAAAAAAJAWkggAAAAAACAtjvEOABNLMBhUa2ur2traFIlEZFnWeIcEoBtjjJxOp/Ly8pSfny+PxzPeIQEAAOAwQhIBSX6/XzU1NSQOgAnMsiyFw2E1NDSosbFRM2bMkM/nG++wAAAAcJggiQBJiQqErgRCfn6+ioqK5PF4ZLMx4wWYSOLxuILBoJqamtTa2qqamhrNnTtXbrd7vEMDAADAYYAkAiRJra2tyQTCtGnTZIwZ75AA9MFmsyk3N1c5OTmSEj+7LS0tKisrG+fIAAAAcDjgY2ZIktra2iRJRUVFJBCALGCMUVFRkaRDP78AAADAaCOJAElSJBKRJBZpA7JI189r188vAAAAMNpIIkCSkospsgYCkD26qoZYDBUAAABjhTtGAMhSTD0CAADAWCOJAAAAAAAA0kISAQAAAAAApIUkAgAAAAAASAtJBAAYAWOMjDGqqqoa71AAAACAUUcSAciwYDCoX/ziFzr//PM1d+5c+Xw+ud1uTZ06Vaeffrquu+46vfnmm/3277op7flwu92aPXu2Pv3pT2vt2rX99l+1alVKv40bNw4Y7xtvvJHSftWqVUN6vVVVVVq1apXuvPPOIfUDAAAAkH1IIgAZ9Pvf/15z587VpZdeqqeeekpVVVWKxWLyer2qr6/Xn//8Z91444067rjj9K//+q86ePBgv2Pl5+ervLw8+ZCkPXv26Le//a1WrFih//iP/0grpl//+tcDnv/Vr36V/gvsQ1VVlW644QaSCAAAAMBhgCQCkCG//OUvdc455+jAgQNauHCh7rvvPu3bt08dHR1qbGxUOBzWhg0b9IMf/EDTpk3TCy+8oJqamn7Hu+uuu3TgwIHkIxgMauvWrfr4xz8uSbr99tv117/+td/+M2bMkM1m0yOPPKJoNNpnm1gspt/85jcyxmjmzJkjewMAAAAATHokEYAM2Lhxo77xjW8oHo/r7LPP1ubNm/WlL31JFRUVyTZ2u13Lli3Ttddeq927d+vaa6+V0+lM+xrGGB199NF64oknVFhYKClR+dCf6dOna/ny5aqrq9OaNWv6bLNmzRrV1tbq1FNP1axZs9KOBQAAAMDhiSQCkAHXXnutwuGwZs+erYceekgej2fA9i6XSz/4wQ90zDHHDPlaubm5OvLIIyVJgUBgwLYXX3yxpP6nNHQ939VuqObMmaOPfOQjkqTq6upe6zg88MADybaDLUBYVVWVbNPT8uXLk+N1dHRo1apVWrhwoXJyclRWVqbPfvazqqysHDDW+vp6XX311Tr22GPl8/nk9Xq1ePFiXXPNNWpsbOy3Xzwe1913363jjjtOOTk5Ki0t1VlnnaW///3vg79BAAAAwCTjGO8AgGz33nvv6bnnnpMkXXHFFfL5fKN6vY6ODr377ruSpHnz5g3Y9vzzz9c3vvEN/e53v1NLS4sKCgqS51pbW7V69Wrl5ORo5cqVuu+++4YcS2lpqVpbW9XU1CSbzabS0tKU8zk5OUMecyCtra065ZRTtGnTJrndbtlsNtXX1+uxxx7T2rVr9dprryUTLN298sorOvvss5PJApfLJZvNpq1bt2rr1q168MEHtXbtWi1cuDClXzQa1cqVK7V69WpJksPhUDQa1R/+8Ac9//zzeuyxxzL6+gAAAICJjkoEYIT+8pe/JL//xCc+MarX2r59uy644AI1NzeruLhYX/ziFwds7/P5dO655yoYDOrxxx9POff4448rGAzq7LPPVn5+/rDiWb9+vZ566ilJ0syZM1PWcDhw4IAuuOCCYY3bn+9///tqamrS888/r0AgIL/fr5dfflkzZsxQY2Ojrr766l59qqurddZZZ6mxsVFf//rXVVlZqY6ODgUCAb311ltasWKF3nvvPZ133nmKxWIpfW+99VatXr1aNptNt912m1paWtTU1KRdu3bpjDPO0Je//OWMvj4AADA+AqGoNu1p0urNe/X4+ve0evNebdrTpECo73WlgMMZlQhI37Rp4x1BZu3bl5Fhtm3bJknyeDyaP39+RsaUElUNV111VfK4ublZoVBIbrdb5513nm6++WYVFxcPOs7FF1+shx9+WA8++KC+9rWvJZ8f6VSG8RAKhbR27dqUCowPf/jDuvPOO7Vy5Ur97ne/UzgclsvlSp6/5ppr1NzcrKuuuko333xzyniLFy/W73//e5144ol688039fTTT2vlypWSElNFbr31VknSddddpyuvvDLZb+7cuXrmmWe0bNkytbS0jOZLBgAAo6y2Nah12+tU09Shen9I4UhcLqdNO+v82lHbpuULy1SeP/BUVeBwQiUCMEJdJfKFhYV9zueXpJtuuklTp07t9bjiiiv6Hbe1tVW1tbXJRygUkiSFw2G1tLSooaEhrfjOOOMMTZs2Ta+88op2794tSdq9e7deeeUVTZ06VStWrBjKyx1XK1eu7HMKx6c+9SkZYxQKhbRz587k8+3t7XriiSdks9n07W9/u88xXS5XMnGwdu3a5PMvvPCC2tra5Ha79a1vfatXP7fbnZJYAAAA2ScQimrd9jpt2duqtmBUc0u8On5WkeaWeNUWjGrL3lat215HRQLQDUkEYAy0tbWlJAS6HgN9in3//ffLsqzko62tTZs2bdIll1yil156SaeffnrKTW9/bDabLrroIlmWpQcffFCS9OCDD8qyLF144YWy2+0Ze52j7cQTT+zzeafTqbKyMklSU1NT8vkNGzYoHA7Lsiwde+yxfSZypk6dqttvv11SYn2LLhs3bpQkLV26NGUtie5OO+20jLwuAAAwPnbUtqmmqUN2m9H8Mp/yPE7ZbUZ5Hqfml/lktxnVNHWoss4/3qECEwZJBGCEuqYUNDc3y7KsPtvccsstKQmBiy66aMjX8fl8Wrp0qe677z599rOfVTAY1GWXXdZrHn9fuqYsPPTQQynJhGyayiBJeXl5/Z7r2hEjEokkn9u/f78kybKsPpM4XY/W1lZJicqFLvX19ZKkaQNM45k+ffrwXwwAABh3exrbVe8PqaLA06ui1BijigKP6v0hVTcMvCMWcDhhTQSkL0NrCEw2ixYtkiQFg0FVVlZqwYIFo37NSy65RI8++qi2b9+uN954Q8uWLRuw/eLFi3X88cdr06ZN+tGPfqSdO3dqyZIlOu6440Y91vEUj8clSQUFBWpubh7naAAAwEQTisQVjsSV6+r7tijX5VA4ElcoEh/jyICJi0oEYIS6l7Q/++yzY3LNWbNmJb/ftWtXWn2+8IUvSFJyB4Ou47HSNW0iGAz2eX40FigsLy+XlFhfYqjjd21XuW+A5NlA5wAAwMTndtrkctrUHu57zYP2cFQup01uJ7dNQBd+GoARmjlzps4880xJ0l133SW/f/TnzO3duzf5vdPpTKvPhRdeKIfDoUgkIrvdPqwpFX2x2RL/jPQ3laNLYWGhJKmmpqbP8+vXr89IPN29733vk8PhkGVZev7554fUt6u6Y/PmzcnpDj11394TAABkn1nFuSr1ubW/JdjrdxnLsrS/JahSn1uzS7zjFCEw8ZBEADLgxhtvlMvlUnV1tT7/+c/3+2l7pjz++OPJ748//vi0+pSXl+uOO+7Qd77zHd1+++2qqKjISCz5+fmSBq8kOPbYYyVJq1ev7nUuFArpzjvvzEg83eXl5en888+XJF1//fVqa2vrt200Gk1JAK1YsUL5+fkKhUK66667erUPh8O64447Mh4zAAAYOwvK8zSjKEexuKXKOr/aghHF4pbaghFV1vkVi1uaUZSj+WW+8Q4VmDBIIgAZsGzZMv3sZz+TzWbT6tWrkwsgdi3sJyWy2e+++65uu+02rVmzZljXqa2t1fe+9z3de++9kqSzzz47ZWrDYC6//HLdfvvt+uY3vzms6/dl/vz5cjqdamlp0ZNPPtlvu8985jOSpF/84he6//77k1tWbt26VR//+MdHbWrALbfcouLiYu3YsUMnn3yynn/++eTii5ZlqbKyUj/60Y901FFH6fXXX0/283q9+s///E9J0g033KAf/ehH6ujokCRVVVXp3HPPTdnNAQAAZB+v26HlC8u0eHq+8jwO7W4IaNOeJu1uCCjP49Di6flavrBMXjdLyQFd+GkAMuQrX/mKysrKdOmll2r79u36yle+Iimxa4DP51NbW1vyxlmSzjzzzOT6BH254oordNVVVyWPA4FAyifly5Yt0y9/+ctReCVD4/V69bnPfU6//vWvtXLlShUUFCSnLtx+++1auXKlJOmrX/2qfvWrX+mf//ynvvzlL+vSSy9Vbm6uWltbVVxcrPvvv1/nnHNOxuObM2eOnn/+eZ1zzjnasmWLzjzzTDmdTuXn56utrU3hcDjZtueqzN/97ne1fv16rV69Wt/5znf03e9+Vz6fT83NzXI4HHrssceSlQ4AACA7led79Mkl01RZ51d1Q0ChSFxup02zS7yaX+YjgQD0wE8EkEFnnXWWdu3apYceekjPPfecNm7cqPr6+uSN8sKFC3XKKafooosu0tFHHz3gWK2trSlz8R0Oh0pLS7VkyRJ95jOf0Ze+9KW010MYbT//+c81ffp0Pf3006qqqlJ1dbUkpSQ9nE6n1q5dqx/84Ad64okntG/fPnm9Xp133nn6/ve/P6rxnXjiiXrnnXf0P//zP1q9erW2bdum5uZm5eXlacmSJTr55JN1/vnn69RTT03p53A49OSTT+pnP/uZ7r33Xu3YsUN2u12f+MQn9L3vfU8nn3zyqMYNAADGhtft0NKZhVo6s3C8QwEmPDPYYmjITsaYDcuWLVu2YcOGtNpv27ZN0qHtCgFkB352AQAADk8nnHCCNm7cuNGyrBPG8rqsiQAAAAAAANJCEgEAAAAAAKRl0iYRjDF5xphPGWN+YIx5zhhz0BhjdT6OSqO/zRhzqTHm78aYZmNMmzFmkzHmP4wxrgzE92ljzJ+MMQ3GmHZjzDZjzI3GmLyRjg0AAAAAwGiYzAsr/oukp4fT0RjjlPSMpI93PhWWFJO0tPPxaWPM6ZZl+fsZYrDx/5+kr3UeRiUFJR0l6RpJnzPGfNiyrNHZ7w4AAAAAgGGatJUIneok/VHSDZIuHUK/G5VIIAQlXSIpV5JX0lmSGiWdKOl/hxOQMebrSiQQ4pL+Q5LPsqw8SadIqpZ0hKTHhzM2AAAAAACjaTInEX5vWVa5ZVmfsCxrlaS16XQyxkyVdEXn4Xcty/qVZVkxK+EPkr7cee5zxpglQwnIGOOWtKrz8C7Lsm63LCskSZZlvSrpXEmWpFOMMWcNZWwAAAAAAEbbpE0iWJYVG2bX8yW5JbVI+n99jLta0g5JRtKFQxz7DEllSiQK7uhj7E2SXuw8vGiIYwMAAAAAMKombRJhBD7S+fVly7KC/bR5ofPr6cMce4tlWXv7abNmmGMDAAAAADCqSCL0dnTn160DtHm78+siY4wZpbFLjTFThjA2AAAAAACjajLvzjBcFZ1fB9odoeucr/PRNgpjd7U/ONCAxpgN/ZwadBtLAAAAAACGgkqE3rydXzsGaNPe7XvfBBkbAAAAAIBRRSVClrMs64S+nu+sUFg2xuEAAAAAACYxKhF6C3R+zRmgTW637/0TZGwAAAAAAEYVSYTeutYkmDZAm65zfsuy0l0PYahjS9L+IYwNAAAAAMCoIonQW9fuCMcM0KZrl4Vtozh2vWVZAy6qCAAAAADAWCKJ0NufO79+2Bjj6afNRzu/vjTMsY8xxlT002bFMMcGAAAAAGBUkUTo7SlJIUmFkr7a86Qx5ixJCyVZkh4Z4tgvSapT4n3/Th9jHyfpjM7Dh4c4NgAAAAAAo2pSJxGMMVO6HpKKup0q7H7OGJN8HyzLOiDprs7DHxpjvmCMsXeO93FJ93eee8SyrDf7uOYDxhjLGFPV85xlWSFJqzoPv2WM+Y4xxt3Z74OSnlbiz+RvlmX9YQQvHcgac+bMkTFG69atG+9QRtXh8joBAAAwuU32LR7r+3n+7z2O50qq6nZ8raTFkj4u6deSfmGMienQzgnrJf2f4QRkWdb/GGOOl/Q1SbdLutkYE5Lk62yyS9JnhjM2cDh54IEHVFVVpXPOOUdLly4dtziqqqr0wAMPqLCwUN/85jfHLQ4AAABgLEzqSoThsiwrIuksJRIF/1BieoMlabOk70r60BB3Zeg5/qWSLlBijQS/EsmcdyTdJGmpZVn7BugOQIkkwg033KDNmzePaxxVVVW64YYbdOedd45rHAAAAMBYmNSVCJZlmRH0jUv6387HUPpdIumSNNo9Lunx4cQGAAAAAMB4oBIBAAAAAACkhSQCkCGxWEx33nmnlixZopycHJWWluqTn/yk/va3v0mSjDEyxqiqqiql3yWXXCJjjFatWqVQKKSbbrpJS5YsUV5enowxam5uTmn/5z//Weedd56mTp0ql8ulqVOn6txzz9Wf/vSnPuOqqqpKXrs/69atkzFGc+bM6XWu+4KAjY2N+va3v625c+fK7XZr+vTp+trXvqb9+/cP+N48/PDDOumkk+Tz+VRcXKzTTz9dzz777IB9+vPAAw/IGKO//OUvkqQvfelLydfX8zV0tV2+fHkyjtNOO00lJSUyxuiZZ56RlPpn0J/ly5fLGKMHHngg5b35yEc+Ikmqrq5OiaNn2+6G+z4CAAAA421ST2cAxkokEtHZZ5+t5557TpLkcDgUjUb17LPPas2aNXr00UcHHSMYDOrUU0/Va6+9JqfTqdzc3F5trr32Wt10002SEkmJgoIC1dXV6ZlnntEzzzyjq666SjfffHNmX1ynmpoaXXLJJaqurlZubq6MMdq3b5/uvfdevfjii9q4caOKiop69fv3f/93/fSnP5Uk2Ww2OZ1OrVu3Tn/+859111139Wo/mJycHJWXl6uxsVGRSET5+fnKyclJni8tLe2z3+WXX667775bNptNBQUFstlGnkMtLS1Va2urmpqaZLPZel27e1xdhvs+AgAAABMBlQhABtx444167rnnZLfbdeeddyZvLKuqqvSxj31MX/3qVwcd46c//al27NihRx99VH6/X83NzaqqqpLX65UkPfroo8kEwr//+7+rrq5OTU1Nqq+v12WXXSZJuuWWW/TQQw+Nymu87LLLVFRUpFdffVWBQEB+v1+rV69WYWGhqqqq+kxePPzww8kEwpVXXqmGhgY1NTVp//79uvjii3XllVeqvr6/TVT6dsEFF+jAgQM6+eSTJUl33XWXDhw4kHysX7++V58NGzbonnvu0Q033KCGhgY1NjaqqakpOcZwrV+/Xk899ZQkaebMmSlxHDhwQBdccEGvPsN5HwEAAICJgkoEDMlv/rlnvEPImAs/MCsj47S1temOO+6QJP3Xf/2XrrjiiuS52bNn66mnntKJJ57Ya1pCT36/X2vWrNGKFStS+kuSZVm67rrrJEmf/exndffddyfblJSU6Cc/+YkOHjyoRx55RNddd50uvPDCjHzS3p3b7daLL76okpISSYlqi0996lO69tprdeWVV+q3v/2tfvjDHybbW5al73//+5KkL37xi7rtttuS58rLy/XAAw9o3759evHFFzMaZ1/8fr+uvvpqXX/99cnn8vPzlZ+fP+rX7mmo7yMAAAAwkVCJAIzQCy+8oEAgII/Ho8svv7zXeafTqW9/+9uDjrNkyZKUBEJ3mzdv1s6dOyUlpjT0peuGvaqqSq+99lq64aft0ksvTd74dnfOOedIknbv3q1AIJB8fvPmzXr33XclSVdffXWvfsYYfe9738t4nH2x2+1p/RmMhaG+jwAAAMBEQhIBGKFNmzZJkpYuXSqfz9dnmw9/+MODjvPBD36w33MbN26UlJiDf8wxx/TZZuHChZo+fXpK+0w68cQT+3y+65qSUqotumIoLy/XwoUL++x78skny+EY/YKoefPmacqUKaN+nXQM9X0EAAAAJhKmM2BIMjUFYDI5ePCgJKmioqLfNtOmTRt0nP4WBJSUXDeg+41mX2bMmKG9e/cOeZ2BdOTl5fX5vMfjSX4fiUSS33fFMNBrd7vdmjJlig4cOJChKPs20Hs71ob6PgIAAAATCZUIwARht9sHbRMMBscgksknnfcWAAAAwOCoRABGqKtMfv/+/f22GehcOro+SX/vvfcGbFdTU5PSXlLKdIFgMJjyiXeXlpaWEcXXl64Y9u3b12+bcDicrOQYT13v0UBJmtF4jwAAwDhoaZHq6obWJydHmj5dMmZ0YgKyCEkEYISOP/54SYmFBP1+f5/rIvz1r38d0TWWLVsmSQoEAnrttdf0/ve/v1ebHTt2aO/evSntJamwsDD5fU1NjebNm9erb1/bIo5UVwy1tbXasWOHFixY0KvNq6++qmg0Oqzxu3afsCxr+EF26nqPupIwPQUCAW3btm3U4wAAAKPsJz+Rbr1VGs7/2+9/v/Tww1Ln9tvA4YrpDMAIrVixQl6vV8FgUD/96U97nY9Go/rxj388omssXbo0efP/3//93322WbVqlSRpzpw5KUkGn8+nOXPmSJJWr17dq19DQ4PuvffeEcXXl+4x33rrrb3OW5alW265Zdjjd23PmIlFCI899lhJiZ02+qpG+PGPf6xQKDRgHFQqAAAwwXV0SD/60fASCJL02mvSSy9lNiYgC5FEAEYoLy9P3/rWtyQltl+8++671dHRIUnas2ePVq5cqd27d4/oGsYY3XjjjZISiYDLLrtMDQ0NkhJJgMsvv1yPPPKIJOnGG29Mfjre5TOf+Uzy3O9+97vkp///+Mc/dMYZZygcDo8ovv5i7kps3Hffffrud7+bvOGvra3Vl7/8Zf3pT39Sbm7usMbv2qXiqaeeGvEN/FlnnaWcnBzV19fr4osvVl1niWNLS4tuuukmrVq1SgUFBX32nT9/vpxOp1paWvTkk0+OKA4AADCKGhulkf7OU1ubmViALEYSAciA6667TitWrFA0GtXll1+u/Px8FRUVafbs2frjH/+o++67L9nW7XYP6xoXXHCBrrnmGknSPffco7KyMhUXF6usrEx33323JOmqq67SRRdd1KvvVVddpSOOOELNzc06++yz5fP55PP59MEPflCNjY36yU9+MqyYBnPRRRfp//7f/ytJ+uEPf6gpU6aouLhYFRUVeuCBB3T77bcPe+eEL3zhC3K5XHrllVc0ZcoUTZ8+XXPmzNGHPvShIY9VXFycrIp44oknVF5erqKiIhUXF+vaa6/V9ddfr6VLl/bZ1+v16nOf+5wkaeXKlSosLNScOXM0Z84c/fa3vx3WawMAAKOgZwLB6ZTmzRv40fNDhGFOwwQmE5IIQAa4XC49++yzuuOOO7R48WLZ7XY5HA6dddZZevnll/WRj3wk2bb7GgVDdeONN+qll17S2WefrSlTpsjv96ukpESf+tSn9OKLL+rmm2/us19RUZFeffVVXXrppZo2bZri8bhKSkp02WWXaePGjZoxY8awYxrMPffco4ceekgf+MAH5Ha7ZVmWTjvtNP3hD3/Q5ZdfPuxxjzrqKK1du1Yf+9jHVFBQoAMHDqi6urrfdQ0Gc/nll+uxxx7TSSedpNzcXMXjcZ1yyil6+umndf311w/Y9+c//7muvvpqHXXUUQqFQqqurlZ1dbX8fv+wYgEAAKOg55TFuXOll18e+PH5z6f2icXGLl5ggjIsBjY5GWM2LFu2bNmGDRvSat+1aNyiRYtGM6zD1ksvvaQzzjhDs2fPVlVV1XiHg0mEn10AANL0xhvSmWceOl68WHrhhYH73HqrdNddh47/8z+lb35zdOIDhuiEE07Qxo0bN1qWdcJYXpdKBGAM3HbbbZKkj370o+McCQAAwGGq53SGdKaYOnpsZkclAkASAciEWCymlStX6vnnn09Z5G/r1q1auXKl1qxZI6fTOaLyfQAAAIxAz52W0kki2O2px6yJAMgxeBMAg7EsS08++WRydf78/HxFo1G1t7dLkmw2m+65557kVoIAAAAYYz2TCC7X4H16ViKQRABIIgCZYLfb9bOf/Uxr1qzRW2+9pbq6OsViMc2ePVunnnqqvvnNb2rZsmXjHSYAAMDhazjTGXpWIjCdASCJAGSCMUZf//rX9fWvf328QwEAAEBfhjOdgUoEoBfWRAAAAAAw+WViOgOVCABJBAAAAACHgUxMZ6ASASCJAADZyrKs8Q4BAIDswcKKQEaQRICkxJx+KbFVIYDsEI/HJR36+QUAAAPoWYng8Qzeh4UVgV5IIkCS5On8R7S1tXWcIwGQrq6fV086vwQBAHC4oxIByAh2Z4AkqaioSB0dHaqtrVU0GlVeXp5cLpeMMXzKCUwQlmXJsiyFw2G1tbXp4MGDkhI/vwAAYBAkEYCMIIkASVJ+fr6CwaAaGxt18ODB5M23OCQ2AAAgAElEQVQJgImtuLhY+fn54x0GAAATXyYWVmQ6A0ASAQnGGJWXl8vr9aq1tVXt7e2KRqMs3AZMMMYYORwO5ebmKj8/Xz6fb7xDAgAgO/SsREgniUAlAtALSQSk8Pl83JQAAABg8hnOdAYqEYBeWFgRAAAAwOQ3nOkMVCIAvZBEAAAAADD5kUQAMoIkAgAAAIDJLxhMPWY6AzAsJBEAAAAATH5UIgAZQRIBAAAAwOTXM4lAJQIwLCQRAAAAAEx+w9nisWcSgUoEgCQCAAAAgMPAcJIITGcAeiGJAAAAAGDyYzoDkBEkEQAAAABMflQiABlBEgEAAADA5JeJJAKVCABJBAAAAACHAaYzABlBEgEAAADA5NezEiGdJALTGYBeSCIAAAAAmPx6ViJ4PIP3oRIB6IUkAgAAAIDJLR6XIpHU55zOwftRiQD0QhIBAAAAwOTWswrB6ZSMGbwfSQSgF5IIAAAAACa34UxlkJjOAPSBJAIAAACAyW04iypKVCIAfSCJAAAAAGBy65lEcLvT60clAtALSQQAAAAAk1vP6QzpViL0TCJEo5JlZSYmIEuRRAAAAAAwuQ23EsFmSzy6i8czExOQpUgiAAAAAJjchptEkJjSAPRAEgEAAADA5Dbc6QwSiysCPZBEAAAAADC59UwiDKUSgSQCkIIkAgAAAIDJLRhMPWY6AzBsJBEAAAAATG5MZwAyhiQCAAAAgMltJEkEKhGAFI7BmwAAAADA6LAsSx2RmNrDMYWiccVilixZcthtcjsSD4/TLqd9BJ9/9tydweNJvy+VCEAKkggAAAAAxkw4Gle9P6T6tpDqWoNqbo8oGrcG7efzOFTidako16XSPLem+FwyxqR30Z5JBKYzAMNGEgEAAADAqIrG4trb3KFdBwM60BKUNXjOoBd/MCp/MKrqhnZJUo7LphlFuZpRlKPyPI9stgESCkxnADKGJAIAAACAUREIRfX2/lZVHQwoEus/c+By2ORz2+V22OWwGxkZRWJxhaKJKQ4d4Zh6Fit0hOOqrPWrstYvr9uu+WV5OrLMK7fD3vsCTGcAMoYkAgAAAICMCoSi2rqvVbvq/b1u/iWp2OtUaZ5HZXlulea55XH2cePfTSxuqbk9rKb2sOrbwtrX3KFQNN7tejFtfq9ZW/a2aG6pV4unFSjH1W3MkUxnoBIBSEESAQAAAEBGRGJxvVnTosratl7JA5/HoSOmeDVnilc+99BuQ+w2oxKfWyU+t+aVJRZjrG8L6b2mdlUdbE8mFKJxS5W1fu2uD+ioijwtqshPLMjIFo9AxpBEAAAAADBiNU3t2lDdpEAo9ZP6sjy3Fk8v0NSCIUwhGIQxRmX5HpXle3TcjEJVN7Zr+4E2NbdHJCWSCVv2tqqy1q/jZhZqXs9KBLc7/YuRRABSkEQAAAAAMGzBSEyvVzVpT2N7yvOleW4tmVGg8vzMJQ/64rDbdGSpT0eW+rS/pUOb9zSrqTOZEIrG9druRu0JefV+h1u+aGcyYShJBKYzAClIIvTBGDOU9WKXW5b1lyGMfYmk+wdpFrAsyzeEGAAAAIAxd9Af0t92HkypPnA7bDphdpHmTPGOeTwVBTmautij3QcDemtvSzKuAxGjP855n447uFsLmvfJMJ0BGDaSCH2rHeR8vqQcSWFJW4Z5jYikxn7OBYY5JgAAADAmth9o06Y9TSlrHxxR6tXSmYWDLpTYXSAU1Y7aNu1pbFcoEpfbadOs4lwtKM+Td4hrJ0iJqQ5HlPo0qzhXb+1t0TsH2mRFo4oamzaUHqn9uUU6yelW2vURPZMIVCLgMEcSoQ+WZU0d6LwxZrOk4yT9wbKshmFe5lXLspYPsy8AAAAwLmJxS//c1aCqhkPTF5x2ow8eWaIZRblDGqu2Nah12+tU09Shen9I4UhcLqdNO+v82lHbpuULy4Y9HcJht+n4WUWaWZyrf0Y61NL5/D5vsdb4PTrFH9IUXxrTGnpOZ6ASAYc523gHkG2MMUuVSCBI0q/GMxYAAABgLIWjca3bXpeSQCj2OnXmsRVDTiAEQlGt216nLXtb1RaMam6JV8fPKtLcEq/aglFt2duqddvrFAiN7KZ9is+tj/mrtKip5tC1jV0vvl2rHbVtgw/AdAYgBUmEofti59c6SX8cz0AAAACAsdIejurFbbWqbT2008G8Mp8+evTUIW/ZKEk7attU09Qhu81ofplPeR6n7DajPI9T88t8stuMapo6VFnnH3Hs9lBIxx/crVP3bZUzHpMcTsUt6fWqJm2obpRlDbAkGgsrAilIIgyBMcYh6cLOw99YlkUaEgAAAJNeazCitW/XJrdQlKTjZhbo/XOLZbeZYY25p7Fd9f6QKgo8MiZ1DGOMKgo8qveHVN2QgeXCwmFJ0oxAo86s3qDi3ENJj+0H/Hq58qCisXjffalEAFKQRBiaMyWVdX4/0qkMxxhjthpjOowxbcaYLcaYHxtj5o5wXAAAACBj2oIRvbStNrnTgc1IJx1RrGOmFYxo3FAkrnAkrlxX31UMuS6HwpG4QpF+bu6HojOJIEm+aEgfnZOnWcWHpl/sberQi9tq1RHuo8qASgQgBQsrDs0lnV/fsCxr8wjHmiKpRFKTErs9HNP5+DdjzFcty/pNOoMYYzb0c+qoEcYHAACAw1wgFNWf3qlTRzhxI++wGX1o/hRNK8wZ8dhup00up03t4ajyPM5e59vDUbmcNrmdGfjcMxhMObS73TplXol8NQ69va9VktQYiGjttlr9y1FlqbtCsLAikIIkQpqMMcWSPtl5OJIqhH2Svi/pSUmVlmWFjTFuSf8i6TZJR0v6lTGmxrKsl0cSMwAAADBcXWsgdFUg2G3SaQtLh71bQk+zc20qe/J/NWX7W/I6jNR9SoNlqT0ck91mVOJzS54R3rbs3Jl67HLJGKOlMwvlc9u1vqpJliX5g4nX/C+Lyg+t88B0BiAFSYT0fU6SS1JU0sPDHcSyrBckvdDjuZCkPxpj/ibpdUnzJN0i6eQ0xjuhr+c7KxSWDTdOAAAAHL6CkZhe2laXMoXh1AWZSyBI0tGP36/YC79VJGbJSLLZjIyRLEuKxy35lNg60u20DzbU0LkPbe04ryxPboddf9t5UHFLCoRievHtWp2+qEz5HmfvSoR4BqZXAFmMNRHS17Urw3OWZdWNxgUsy2qR9N+dhycZY6aMxnUAAACA/sTilv5aeVBtwcQn7jYjfXhBqSoKRj6FoTvXljflctjktCeSB7G4pUgsrljckjGJBILLYcv8DYvdLpWUpDw1szhXpy4olb3zYu3hmF7aVqvWYIRKBKAHkghpMMYsknRi5+FIF1QczD+7LiuJRRYBAAAwZizL0j92Nai+7dA2jqfMm6LpGVgDoZdQSHaTqDRwO+xy2W3Jh9thl9tpl90MbeeHuKRIzFIwEldHJKZgJK5IzFJK7cC//ZuUl9er77TCHJ22oEyOzt0mOsJx/fmdOgXsrtSGJBFwmGM6Q3ou6fzaKOn34xgHAAAAMGrerGlRdUN78njZ7ELN7LaLQUZFEttF2iTZ7EbOn9wpHTX8tcEb/CG9XtWo2taQmjrCikTjcjpsKspxqTzfrffNKVbJkbOkqVP7HWNqgUfLjyrVunfqFY1bCoRi+pNtij5qd8oT69zekt0ZcJgjiTAIY4xd0uc7Dx+xLCs8UPsM+EC376tG+VoAAACAJGlXvV9bO3cqkKT55T4dNTV/9C4YCqUeH320dOyxwxoqEIrqxTf3aYs3V/Y8o4oCj4pdDrWHo9rVElRl3FKzK1+fLJoi7yBjleV59OEFU/SX7fWKW1KbzaU/TT9W/1LzptzxKJUIOOwxnWFwZ0ia1vn9iKYyGDNwPZYxJl/SVZ2Hr1mWVT+S6wEAAADpaAyEtb6qMXlcUejRCbOKRveinZUISc7e2zyma0dtm2qaOmS3Gc0v8ynP45TdZpTncWp+mU92m1FNU4cq6/xpjVdRkKNT5k1JbBhht6nZ7dXL045RzBgqEXDYI4kwuK4FFd+2LGv9YI2NMXOMMVbn45Iep2cbY/5hjPmKMWZWtz4uY8zHJP1N0gIlpnNdnaH4AQAAgH6FojH9tbJesc6FAwpznfrQvCmy2Ya2HsGQhXsU+HbbMWGo9jS2q94fUkWBRz0/tzMmUZlQ7w+puiGQ9pgzi3P1gbnFki1xy1Sfk69/lC+UFaESAYc3pjMMoLMy4JzOw0wtqPiBzoeMMUFJAUn5krpSr+2S/o9lWX/K0PUAAACAPlmWpb+/25DcytFpN/rQ/Cly2sfgs8aeSYQRVCKEInGFI3Hluvq+vcl1ORSOxBWKDG17xiNKfQq5QtrUeVydVypv0KGlw44UyH5UIgzsM5JylKgMeCgD49VKulzS45K2K5EwKOj8+rqkWyUdbVnWgxm4FgAAADCgrftata85mDw+6YgS5XuGfzM/JD2TCC5X3+3S4Hba5HLa1B7uu0qgPRyVy2mT2zn0259F7qgWNO9LHr8ddmpnmtMigMmISoQBWJZ1r6R7h9inSontGfs61yHp7s4HAAAAMG5qW4N6s6YlebyoIm/0dmLoSwaTCLOKc7Wzzq/9LUH53I6UKQ2WZWl/S1ClPrdmlwy2rGIfHA6dUP+u/E6P9nmLpXhc66sa5XM7NLXAM+yYgWxFJQIAAABwmAlFY/r7uw3J47I8t46bUTi2QWQwibCgPE8zinIUi1uqrPOrLRhRLG6pLRhRZZ1fsbilGUU5ml/mG/rgDoeMpFP2v6PikF+Kx2VZ0is7D6otGBm0OzDZkEQAAAAADjMbqprUHk6sg+B22HTKWCyk2FMGd2fwuh1avrBMi6fnK8/j0O6GgDbtadLuhoDyPA4tnp6v5QvL5HUPoxDbbk+EZ8V02t6tyoknpkyEo3H9tfKgorGhrbMAZDumMwAAAACHkaqDAVU1tCeP3z+3WDku+9gGEYulbpVojOQY2a1Jeb5Hn1wyTZV1flU3BBSKxOV22jS7xKv5Zb7hJRCklLhyYmF9ON6gF40Ut6Tm9oj+satRH5o/ZUSxA9mEJAIAAABwmAiEolpf1Zg8PqLUO7brIHTpqwrBjLwSwut2aOnMQi2dmcGpGfbUBMuUeEgnzi3WP3cl3sc9je3auq9Fx0wryNw1gQmM6QwAAADAYcCyLP1jV4MiMUuS5PM4dMLsovEJpud6CG73+MSRjp4VEtGojiz1aUH5ofUV3qxpUW1rUMDhgCQCAAAAcBh4t96v2taQpMSH/h88okRO+zjdDvRMIoxgPYRR10cSQZKWzSpSaV4i+WFZ0qvvHlQwEuvZG5h0SCIAAAAAk1x7OKpNe5qTx4sq8pM3wOMigzszjLoe0xm61nKw2Yw+NG+K3I7ELVVHOK6/7Twoy7LGOkJgTJFEAAAAACa516uaktMY8jwOLZ6WP74BZVMSoZ9KBEnKcdl18ryS5HFta0hb9raOVWTAuCCJAAAAAExiexraVdPUkTz+wNxiOcZrGkOXbEoi9FOJ0KWiIEeLpx9Kyry1l/URMLmRRAAAAAAmqVA0pterD+3GMK/Mp7J8zzhG1Kmv3RkmqgEqEbocO71A5fmHpoewPgImM5IIAAAAwCT1xnstCkbikqQcly2zWx+ORCiUepxluzP0ZIzRyUemro/QfStNYDIhiQAAAABMQg3+kHbW+ZPH75tdLJdjgvz6n02VCINMZ+iS47LrA0cUJ4/fa+zQu/X+PtsC2WyC/CsCAAAAIFMsy9Lr1U3J42mFHs0szh3HiHrouSZCllcidJlRlKv55b7k8YaqJrUFI/22B7IRSQQAAABgknm3PqAGf+JG3WakE2YXjXNEPfRMIkyCSoQux88sVH5OIvEQjVt69d0GxeNs+4jJgyQCAAAAMImEojG98V5z8vjoafnK80ywm/Rs2p1hCJUIkuSw23TykVNkM4njBn9Yb+9n20dMHo7BmwAAAADIFm/VtCgUTSym6HXbdXRF/iA9xkHPNREmURJBkoq9Lh07o0BvvNciSdqyt0UzinJUmJv+6wyEotpR26Y9je0KReJyO22aVZyrBeV58rq5jcP44W8fAAAAMEk0t4dV2W0xxWWziuSwT8Di4567M0zkJMIQpzN0OboiXzVNHWrwhxW3pH/satCKo6fK1lWiMIDa1qDWba9TTVOH6v0hhSNxuZw27azza0dtm5YvLFP5RNiqE4elCfgvCgAAAIDh2PRes6zO6fcVBRNsMcXusml3hmFUIkiJbR9POqJEXTmcxkAkrWkNgVBU67bXacveVrUFo5pb4tXxs4o0t8SrtmBUW/a2at32OgVC6cUBZBpJBAAAAGAS2N/Sof3NQUmSMdLxswrHOaIB9KxEyKbdGdKsRJCkghynlsw49OewZW+LmgLhAXpIO2rbVNPUIbvNaH6ZT3kep+w2ozyPU/PLfLLbjGqaOlIqToCxRBIBAAAAyHKWZWnTnkOLKR4xxTuk+fdjLpsqEXpOZ0izEqHLUVPzVJqXSJJ0TWsYaLeGPY3tqveHVFHgkTGpUx+MMaoo8KjeH1J1Q2BIcQCZQhIBAAAAyHK7DgbU3J64MXfYTMqn3xPSJN6doSdjjD5wRLEcnWshNLVHtL22rd/2oUhc4Uhcua6+l6/LdTkUjsQVisSHFAeQKSQRAAAAgCwWjcX1Zs2hKoRFFfnKcdkH6DEBZFMSwdbjlmkI0xm65HucWjy9IHn8Vk2L/P2saeB22uRy2tQe7vt8ezgql9Mmt5NbOYwP/uYBAAAAWeydA23qCCc+lc5x2bSoIm+cI0pDNiURRliJ0OWoqXkqyk1M24jGLa3f3dhnu1nFuSr1ubW/JSjLSp32YFmW9rcEVepza3aJd1hxACNFEgEAAADIUsFITNu6rfh/7PTCibmlY08910TIpiTCMCoRJMlmMzpxbnHyeH9LUFUHe69rsKA8TzOKchSLW6qs86stGFEsbqktGFFlnV+xuKUZRTmaX+YbVhzASPU90QYAAADAhLdtf6siscSn1fk5Dh1ZmiWfTvfcnWEiJxF6Lqw4zCSCJE3xubVwqk/bDyR2VthQ3aSpBR55nIeu4XU7tHxhmSSppqlDuxsCCkficjltKvW5NaMoR8sXlsnr5lYO44O/eQAAAEAW6gjHVFl7aJu/JdMLe63mP2Fl0+4MGZrO0GXJjEK919ih9nBMoWhcm99r1klHlKS0Kc/36JNLpqmyzq/qhoBCkbjcTptml3g1v8xHAgHjir99AAAAQBbauq9F0c6tAotynZpZnDPOEQ1BzzUR3O7xiSMdGaxEkCSn3ab3zSnSyzsOSpJ21Qc0d4pX5fmelHZet0NLZxZq6cwJvtMGDjtZMGEKAAAAQHeBUFQ767pVIczMoioEqXcS4TCqRJCkGUW5mlWcmzx+bXejYnFrgB7AxEESAQAAAMgyb+1tUdc95xSfS9MLs6gKQTosd2fo6YTZRXLaE4mftmA0ZYFMYCIjiQAAAABkkdZgRLu7rep/XDaWu2dTEiHD0xm65LjsKX92b+9rVSCUmQQFMJpIIgAAAABZZOveVlmdVQhTC9y95tJnhWxKIth63DLF44lHBswv86koNzGVIxq3tGlPc0bGBUYTSQQAAAAgS7QFI6pqOFSFsHh6wThGMwLZtDuDMb2nNGSoGsEYoxPmFCWP9zS260BLMCNjA6OFJAIAAACQJd7ed6gKoTzfrbK8LKxCkKRQKPV4Iu/OII3alAZJKsvzaM6UQ4ssvl7dqDiLLGICI4kAAAAAZIFAKJqyFsKx2VqFIGVXJYI0aosrdjl+ZpEcnYsstnZEtb22LaPjA5nkGLwJAAAAgEwKhKLaUdumPY3tCkXicjttmlWcqwXlefK6+/4V/e39rckdGUrz3CrLxrUQuvRcE+EwrkSQEossHju9ILkmwlt7WzSnxKscl32QnsDYI4kAAAAAjKHa1qDWba9TTVOH6v0hhSNxuZw27azza0dtm5YvLOu1WGJ7OKp36/zJ46yuQpB6JxEO80oESVpYnqd36/1q7YgqGrO06b0mnXzklIxfBxgppjMAAAAAYyQQimrd9jpt2duqtmBUc0u8On5WkeaWeNUWjGrL3lat217Xa6u/bd2qEEp8Lk0tyOIqBCm7dmeQxiSJYLMZvW92cfK46mC76tpYZBETD0kEAAAAYIzsqG1TTVOH7Daj+WU+5XmcstuM8jxOzS/zyW4zqmnqUGW3qoNgJKZ36ybJWghdeq6JMNGTCKM8naHL1AKPZhbnJI83VjfLslhkERMLSQQAAABgjOxpbFe9P6SKAo+MMSnnjDGqKPCo3h9SdbdtHHfUtinaWYZQ7HVqWmGOsl7P3RkmehJhDCoRuiybVSR7511aYyCs6ob2UbsWMBysiQAAAACMkVAkrnAkrlxX37+G57ocCkfiCkXikqRILK4dtYeqEhZV5I9JnKMu23ZnyHQlwoED0n33Jb724JW00Fagt02eJOkNxTQjdkAO9VGRMGuW9NWvSoWFI4sHGAKSCAAAAMAYcTttcjltag9HlefpfePcHo7K5bTJ7Ux8FL2rPqBwNJFQ8HkcmlmUO6bxjpps250hk5UIHR3S+edLu3f32+Rom13vznm/QnaHApLeaajS4sb3+m78+uvSo48OPx5giJjOAAAAAIyRWcW5KvW5tb8l2Guuu2VZ2t8SVKnPrdklXsXjlt450Jo8v2hqnmw203PI7GNZvSsRet6kTzSZTCL89KcDJhAkyRWP6biGQ23eLpqpDns/Uz5eeUWKx4cfDzBEE/ynFQAAAJg8FpTnaUdtm7bsbVVlnV8VBR7luhxqD0e1vyWoWNzSjKIczS/zqbqxXYFQomze7bBp7hTvOEefIX1NZbBN8M82e05n+OEPpaKioY9jWdLTT6fV9IiWWm0vnK4WV66iNrveKpmt99dV9m4YjyemV0z09xCTBkkEAAAAYIx43Q4tX1gmSapp6tDuhoDCkbhcTptKfW7NKMrR8oVl8rod2rajPtlv4dQ8OeyT5Cax51SGib4egtS7EmHNmsyMW1oqXXONZHpXmNgkHR82WteS+HN/V9KCopgKHZKuvDI1GROLZcf7iEmBJAIAAAAwhsrzPfrkkmmqrPOruiGgUCQut9Om2SVezS/zyet2aG9zh5rbEzeJDpvR/HLfOEedQT2TCBN9ZwZJyssbnXGvvVb69Kf7PT1NUsU7dYnpL5I2FXj0kaPKpO99LzWJMIq7RQA9kUQAAAAAxpjX7dDSmYVaOrPvVfW3d1sL4cgyn9wOe5/tslLP6QzZkET43OcSaw9k0kc/mlhgcRDHzyrUgS0HZFnS/pag9jV3aFqmd4sAhoAkAgAAADCBNLeHdaAlJClR5b5w6ih9Cj5eQqHU42xIIpx7rrRwofTmm4l1DUZq6lTpQx9Kax2DwlyXjiz1aWddYqvPTXuaVeFwKGUCBJUIGEMkEQAAAIAJZNv+tuT3M4ty5XNPsl/Z+1pYMRscfXTiMQ6WzChQVUNA0Zillo6IdueX64jGxkMNSCJgDE2S1VkAAACA7NcRjqm6IZA8nnRVCFLvNRHc7vGJI4t4nHYtmpqfPH6raKZi3RdjZDoDxhBJBAAAAGCCqKxrU7yzWr7E51Jp3iS8wc7G3RkmgKMq8uR2JG7fAs4cVRZMO3SSSgSMIZIIAAAAwAQQi1uqrPUnj4+ajFUIUnbuzjABOO02LZ5ekDiw2bS1eJbCts4FFqlEwBgiiQAAAABMALsPBhSKxiVJXrddM4tyxzmiUUISYdjmlfnkddslu00hu0PvFM1InKASAWNokq3SAgAAAGSnHbWHFlRcUJ4nm80M0DpVIBTVjto27WlsVygSl9tp06ziXC0oz5N3oi3MSBJh2Ow2oyUzCvX3zl0d3imaoQXN++QhiYAxNMH+RQEAAAAOP7WtQTW3J3YtcNiMjiz1Danvuu11qmnqUL0/pHAkLpfTpp11fu2obdPyhWUqz/eMVuhD13N3BpIIQzKnJFfb4mE1S4qaxLSGE5jOgDFEEgHAhJVVn6oAADAC3asQ5pZ65XKkN+s4EIpq3fY6bdnbKrvNaG6JV7kuh9rDUe1vCWrL3lZJ0ieXTJs4/3eGQqnHJBGGxBij40IH9ZfOmemVBRVa0BHWJF1BAxPQBPmXBABSZd2nKgAADFN7OKqapo7k8YKy9G8Hd9S2qaapQ3ab0fwyn0zntn95Hqd8bocq6/yqaepQZZ1fS2cWZjz2YelZicDuDEM23QqqtCOq+px8xY3RW/VBnTzeQeGwwcKKACac7p+qtAWjmlvi1fGzijS3xKu2YFRb9rZq3fY6BULM/wMAZL+ddX5Znds6lue7VZCb/k31nsZ21ftDqijwJBMIXYwxqijwqN4fUnVDIJMhj0zPNRHck3Aby9Fmt2vpwV3Jw6rWsJrbwwN0ADKHSgQAE05WfqoCAMAwxOOWdtYd2tZx/hCqECQpFIkrHIkr19X3r/W5LofCkbhCkfiI4hyuvqYmLt3fpJnq9mkmlQhD53CoNNim6YFG7fUWS7G43qxp0akLSsc7MhwGSCIAmHC6PlWZW+Lt91OV3Q0BVTcESCIAALLae03tCnbe4Oe4bJpRlDOk/m6nTS6nTe3hqPI8vW/G28NRuZw2uZ1jX4Dc39TEnD0NKovE5HLYZDeGNRGGw26XJB13cHciiRCPq6apQ42BsIq9vJ8YXSQRAEw4E/1TFQAAMmVHbWoVwlC2dZSkWcW52lnn1/6WoHxuR0ry3bIs7W8JqtTn/v/s3Xd4ZHl54Pvv71RWVSm2YqvVuXtmemaYQDIDeAjrhzAXbGA95jpcg30d1mtj38U2vnvXu46w631293rtB7wsF2xfG5zB2GA8hLkwZJjA9DTTrQ6SupVbqXI8v/vHKZWqjqpKVVJV6aj0fvrR0zqqdBTqhPe8geMDwaatc1WxGHzhC7C+Tjqb58bNdVxrSU4quL/Lg9flIpPP0/iFAIcAACAASURBVHvxKbJ5DZj4PC4MCSI0zm0dI/VmEkzEbjNjWtMZvnNrnYfPD+3nmolDQIIIQgjHcfJVFSGEEKJZ1uIZlqPWpAJD0dBYx03nhsNcWYxycTbC5FKM0R5/2XSGvKkZ7wtwdqjx525ILgdvfjN897sAGHnN3bk8FzS4XNsDI3kgb2ryeS1BhN1wb53G3bMyzYxpNdWYW09xO5bmSEj6TIjWkSCCEMJxHHVVRQghhGgWreFTn4JvfhNMkytmGLQ1aeiYShP4h0jDTxkE3pjOcfdKnEgyRyKTI29qXIbiXq8b79gwx/7VO1s/3vGpp4oBBLACBKYGV5XMCsNQVhDB1HgCjZVwCIrlDAA9mQQnvHmmCsvP3trgVXdINoJoHQkiCCEcxzFXVYQQQohm+uhH4d3vBiBjuJg+9VJQVlbd2ZvPQKrxIAJACLgTyOetk3KNRqFwGQqXS2E89UV47DFQjZVKNGRmpmxRF/7ZexttUsq6j2koeOUrW7dencpdfhp3dyDPtLLiVPMbKZaiKYbCMgpbtIYEEYQQjhP0uYv1fLfWktxYiRebMQ2GfIz3BXj4/FDrr6oIIYQQzfSZzxQ/vd49Qq4QQOhNxxnaZQBhkwEYLoWnQukAly7B7CyMj+/pNWqamytbjJ4+z3dHTtMT8OB1bS8/zORNVnLgeeMbeeX997duvTqVLYjQrfKcGAhy47Y1yvM7Nzd47V0SRBCtIUfgFSilfhz48A53i2utd3UZVCnVDfwK8FbgOJAEngber7X+6908pxCdZrjbzyP3jjG5FGN6JV4cC3V8IMjZoZAEEIQQQhw8MauJogYme0eLXz63MVflAU20sdHWIELq+3+Abz3wBqKpXNm4ZrBKEyeXYoT9bl5z53Dr1qmT2YII5HLcfbSb6ZU4poalaJqFjRQjPRJIEM0nR+G1ZYHVKrfFd/OESqlx4IvAycKXYkA38Grg1Uqp92ut/9VunluIThP0ubnvWK+McRRCCNEZUikAFrr6iHoC8IpX4Onr5UQ4Cc2uNPjwh2Fqams5Gm3yC9jYgghD508x3heQ0sRWKemJAEA+T9jv4eSRINeWC9kIt9YZ6RnZh5UTnU6CCLV9RWv9cLOeTFkh2L/GCiBMAT+stf6KUsoP/DzwPuBnlVJPaa0/2KzXFUIIIYQQDlAIIlzdPLF78IWceuAc7uP9zX+tL35xX4MI/uPHePiMlCa2TIVMBIC7j/Zw47aVjXA7lmFuPclYrzSuFM0l79r2ejPwEsAEfkBr/TSA1joF/J5Sagz4ReA3lVJ/rLXO7N+qCiGEEEKIpkqlSLk8zAYHrGWPhzOD4da8Vtj2vG0OIjA2JqWJrVQliBD0uTkzFOLKolU68+zshgQRRNPJO7e9frjw/2c3Awg2/xl4FzCCVd7wT+1aMSGEEEII0WKpFNe7hzEL/QGO9ATo6fK05rXaGURIJmFtbWvZ7YbBQUBKE1umQjnDpgtjPVxbjpE3YSWWYX4jyWiPBBJE82xvlSpa6VWF/z9T6Uat9SzwXGHx1W1ZIyGEEEII0R6pFNdKatTPjHS37rXsQYRCU8eWWFgoXx4e3n6SK5qrSiYCQMDr4tTgVq+Ji7N7m/whhJ0EEWq7oJR6TimVVEpFlVIXlVL/VSl1cueHllNKDQGF3LVioKCSS4X/72r0NYQQQgghhHMt4rUaKgIeM8/ESE/rXixka1jYykyE2dny5bGx1r2WsNiDCCWZCAB3jXZjFJp1LkfTLEZSbVoxcRhIOUNtR7BO/NewJihcKHz8tFLqJ7XWf97Ac42WfF5rjs/mbaM17lOklPp2lZvuqOfxQgghhBCiDbTmmr+vuHgiuoQ70MIU83aWM9j7IYzWdRgr9sKe6VGSiQBWGcmpwRBXl6wMlIuzGwx3y7hH0RySiVDZHPDvgbsBv9Z6AAgBb8TKFAgAf6yUemUDzxks+TxZ436Jwv8y70YIIYQQokOkk2luBo8Ul0/Hl7dfTW6m/QwiHD3autcSlhrlDJvuGuum0H6DxUiapahkI4jmkEyECrTW/wz8s+1raeBTSqkvA98CzmCNZHxZ+9ewbL0erPT1QobCA21eHSGEEEIIUcHU3Br5whldfzpGv8rv8Ig9amcQYX6+fFnKGVqvRmPFTSGfm5NHglxfjgPw3GyEoTskG0HsnWQiNEhrvQH8bmHxpUqpI7XuXyJe8nmt3LWuwv8t7H4jhBBCCCHa6erCRvHzMxvz4G/xyVw7GytWGO8oWqyOTASACyXZCPMbKW7H0i1eMXEYSCbC7ny98L8CTgK363hM6dZ1DHi2yv02t7rzVW4XQgghhBAHyHI0zUYhldytTSaiyzDW4r4Be2ms+N3vwhe/CNlsffd/ztYzXHoitN4OjRU3hf0ejg90MXXbqpi+OLvBw+eHWr12osNJEKFNtNbLSqnbWM0aL1BlzCNbUxkuVbldCCGEEEIcINeWY8UT8onoMl4z3/5MhEidY/6eegq+//vrDyBUIpkIrbdDY8VSF8Z6ikGEufUUK7E0AyFfK9dOdDgpZ9idl5R8PtXA475Q+P9fVLpRKXUUK8AA8LnGV0sIIYQQQjhJJmcys5KAnHVSfmajkGzq1HKGv//7vQUQvF44Um+1r9i1OssZAHoCVjbCpotzdQaUhKhCggg2Sm1WDVW9vRt4T2HxG1rr5QaefnMk5PcppV5Q4fb/A6tEYp6tgIMQQgghhDigplfi5EwN2Sy96ThHUoWygnYHEeotZ7D3N2jUm94EhpxitFwdjRVLXRjrLn4+u5ZkPZFpxVqJQ0LKGbY7rpT6GPBB4DGt9QyAUsoLvBr4PeAcYAK/Zn+wUkoXPv0NrfV/sN38Cax+Ci8B/k4p9b9qrb+mlPIB/xr4xcL9/r3WWt7ZQgghhBAH3LVCZ3xyOU5HFrZuaHUQwe+3TjQ3Ty7TaSvDwOOp/biFhfLlt7yl/h4Hx4/DD/5g4+sqGtdAJgJAb5eX8b4At9asSfOX5iK87IxkjIjdkSBCZS8pfKCUSmFNVugGNre6CeBntNafb+RJtdZaKfU24ItYDRm/qpSKAX62fhcf0Fp/cO/fghBCCCGE2E/riQyrceu6kJHNciKytHVjq4MISlnNFTe2pkIQi0FfX+3HLS2VL7/rXXD2bPPXr4PE0zmuLEaZWU2Qzpr4PAYT/V2cGw4T9LXodKvBIAJY2QibQYTp1QT3pnOEWrV+oqPJX812i8AvAC8HXgAMAj1YgYRJrF4F79daT+/mybXWt5RS9wG/CrwFOAFEgacLz/tXe/0GhOg4f/VX8Ed/BOvr1e9z7hy8730wMdG+9RJCCCFquH57a8L3uJHBZ5ac6LU6iABWSUNpECESqR1E0BoWF8u/NjzcmnXrEIuRFI9fXuLWWpLlWJpM1sTrMbi6FOPKYpSHzw8x3N2C33WD5QwAAyEfIz0+FjbSaA3fnY/wohP9zV830fEkiGCjtU4C/73wsZvH1+ypULhPBPi3hQ8hRC3z89ZVkJ3MzcGv/ip89KOtXychhBBiB6apmSoJIpw00uV3aFcQodROzRWjUUiltpb9/u3PIYri6RyPX17i4mwEl6E4ORCky+smkckxv5Hi4qzVwPCRe8ean5Gwi0wEgLtGe1jYsLJNri/HuOdoD36Pa4dHCVFOgghCCGe7fLn++z7/fOvWo8PsS+qlEEIcIvORFKmsCUDAazCaT5bfYT+CCDs1V6yUhVC75/ihdmUxyq21JC5DcXYoxGZ/9rDfQ8jnZnIpxq21JJNLMe471tvcF99FJgLASI+f/qCX1XiGvAnPL0Sbv26i48mRohDC2eqMrAN170APu31LvRRCiEPkxvJWFsKJgSBGOlV+h4MQRBgaau76dJiZ1QTLsTQnB4LYB7wppRjt8XNjJc70Srz5J+q7zEQAqzfClyZvAzC5GOWu0W68bpmoIeonfy1CCGczzfLll78cvvlN6+Nznyu/bS9zrQ+J0tTLaCrHyYEg90/0cXIgSDSV4+JshMcvLxFPNxC8EUIIUSaVzXNrLVFcPnUkZE1HKOXztX5FQqHy5Z3KGexNFaUfQk3prEkma9LlrXxdtsvrJpM1SWfNirfvyR6CCON9AboD1uOzec3kUp3jP4UokCCCEMLZ7DvFUAiOHrU+jh2rfV+xjT31Muz34DIUYb+Hs0MhXIYqpl4KIYTYnZnVBGZh6Hd/0EtPl6e81wDsTyZCJFL7/tJUsSE+j4HXY5DIVD7+SGRyeD0GPk8LTrl2Wc4AVpbEnaPdxeXLC1Fy+RYEOkTHkiCCEMLZ7IGB0sj7HqLwh9Vm6uVoj79q6uVyLM30SrzKMwghhNjJ9ZJShtODQesTexAhEGj9inR3ly/vlIkgQYSGTPR3MRjyMb+RQmtddpvWmvmNFIMhH8cHgs1/8T0eA1lNIK1ARCprcuO27PdF/aQnghDC2ezlDEZJ7FOCCA3b19RLIYQ4BNYTGVbjGQAMBRMDXdYN+5GJYC9n2GVPBGnGW9m54TBXFqNcnI0wuRRjtMdfNp0hb2rG+wKcHQrt/GSN2kMmAoBhKO4YDfPktDU++9J8hNODIQxDGmmKnR3ed70Q4mColYlQaQeqtXSSrqE09TLs92y7vaWpl0IIcQhcL7miO97Xhc9d2FclD8B0hgo9EaQZb3VBn5uHz1uBlltrSW6sxIs/n8GQj/G+AA+fH2pNoKUJF1LODIZ4bjZCOmcST+eZWU1w4kgLsiZEx5EgghDC2eyR9dKdplLWcumOM5cDz/aTY2GZ6O/i6lKM+Y0UIZ+7rKSh5amXQgjR4UxTM1USRDg1WLItdUImQoPlDIne/mIzXpehCinwW1faL85aPRYeuXfs0GYkDHf7eeTeMSaXYkyvxIuZGscHgpwdCrXu59KEIILbZXB+JMx3bm0AVjaCBBFEPQ7nu10IcXDYgwiG7Qq5BBEasq+pl0II0eHmIylShXKwgNdgpPQK/X5MZ9jjiMerRqisGe9m4Dns9xDyuZlcihWb8TZ9hOEBEvS5ue9Yb3t/BnssZ9h0djjEpbkIOVOznsgyu57kaG8b+nWIA03yVYUQzlYrEwG2BwxkzGNNm6mXdx/tJux3c2MlzlMza9xYiRP2u7n7aHfrUi+FEKLD3ShpqHhiIFheX74fmQj2xoq1ggjxuPWxyePhRs4jzXidqkl9oXxuF2eGty4cPDe7sZe1EoeEHCUKIZzNvlO0R97ty9JccUf7lnophBAdLJ3Lc2stUVw+dcSW0eWEcobZWfjMZyrfd3m5fHloiHROSzNep7IHEXaZiQBwx0iYKwtRTA23YxmWoimGwoezz4WojxwpCiGczb5TtAcN7JkIEkSoy76kXgohRAebWUlgFqb89Qe99HTZ9k/7EUSwlzNMTcE73lHfY4eHpRmvkzXxIkqX183JI0GuFTJpnp+PShBB1CTveCGEs+0URJAxj0IIIRxgamUrC+FkpeZ09iBCoA115/YgQiOGhpjo72Iw5GN+I4XWuuxmaca7z5p8/HPHyFbpy621JJGUlIeK6iQTQQjhbDv1RLAvS08EIYQQbRZNZVmOWo0TlYLjA13b77QfIx4HB+HsWZicbPyx3/u90ozXyZrUWHFTT5eHsV4/c+tWsOvyQpQXnejf03PuRjyd48pilJnVRLHccqK/i3PDYSm3dBD5TQghnK3RTIQ97kSFEEKIRk2XZCGM9vjxe1zb77Qf0xmUgj/9U3j/+2F+vr7HuFzw0EPwYz9GUCkePj8EWFenb6zEyWRNvB6DwZCP8b6ANOPdLy24iHLnaHcxiHB9OcY9R3sq/y23yGIkxeOXl7i1lmQ5li7+rV1dinFlMcrD54cY7pYyCyeQd7wQwtl2aqwo0xmEEELss6mV8qkMFe1HTwSAiQl473t3/XBpxutQLbiIMtztpz/oYTWeJW/C1aUYdx/t2fPz1iOezvH45SUuzkZwGYqTA8GyrJeLsxEAHrl3TP7mHEB+A0IIZzNtHZ/tO02ZziCEEGIfrcTSRJLWvsftUoz3Vel1sF9BhCaQZrwO1KLjnztGuvnKtRXAKmm4YySM29X6NnpXFqPcWkviMhRnh0LFkaJhv4eQz83kUoxba0kml2Lyd+gA0lhRCOFs9p2iYdtsyXQGIYQQ+6g0C+FYX1flEy6tD3QQQThQixpLT/R3EfRZAYp0zixrGNpKM6sJlmNpRnv8xQDCJqUUoz1+lmNppkveb2L/SCaCEMLZ7DtF+05TgghNI82MhBCiMaapy/ohnDhSoaEiWKV2pdMNPJ7tV5KFaESTGytuMgzFueEwT82sA/D8QoTTg8FtJ/bNls6aZLImXd7KxxtdXjeZrEk6a1a8XbSXHBUKIZzNXs5g32nal6Unwq5IMyMhhGjcQiRFqnBSE/AajFTbTkoWgmi2Fo64Pj0Y4uLsBtm8JpLMMbueZLyvSoCsSXweA6/HIJHJEfZ7tt2eyOTwegx8HkmkdwL5LQghnK3RxoqSidCw0mZG0VSOkwNB7p/o4+RAkGgqx8XZCI9fXiKelp+tEEKUmrq9lVp9fKDG1Vp7EKEdkxlEZ2vhdCqv2+BMydjO5+ejTXvuaib6uxgM+ZjfSKFLs3YArTXzGykGQz6OV2tcKtpKgghCCGez7xTtO80WRuIPC3szo7Dfg8tQhP0ezg6FcBmq2MxICCGEJZs3ubWWLC5XncoAkokgmq9SY0XbyfdenB8JYxRiYkvRNCuxdO0H7NG54TDjfQHypmZyKUY0lSVvaqKpLJNLMfKmZrwvwNmS4IbYPxJEEEI4mz2IYG+s2II5yYeNNDMSQojG3VpLkjOtk7aegIf+oLf6nSWIIJrNMLYfE9lLQPegy+tmYmCrhOH5hdZmIwR9bh4+P8TdR7sJ+93cWInz1MwaN1bihP1u7j7azcPnh6RHk0PIb0EI4Ww7NVZsYTrfYSHNjIQQonGlpQxVGypukiCCaAWXqzxwkM83tWHnnSPdTN22GofOrCZ4QTpHqIUn8cPdfh65d4zJpRjTK/Fik+fjA0HODoUkgOAg8psQQjibPSiwU08EyURomDQzEkKIxiQzeRYiW4GBmqUMIEEE0Rpud/lxTy4H3hoZMQ3qC3oZ6fGxsJFGa7i8EOHB4/1Ne/5Kgj439x3r5b5jvS19HbE3EkQQQjjbTkGESjWBoiET/V1cXYoxv5Ei5HOXlTRIMyMhhNhuejVeLD8fCvvKrpBWGpd75/QyZyipI96nIIKM8u0wbegLdcdINwsbywBcW4pz99EefG4ZT3rYydZCCOFsOzVWlOkMe3ZuOMyVxSgXZyNMLsUY7fHT5XWTyOSY30hJMyMhhLCZXkkUPy8tZag2Ljd7dYFj2Txet4FLqX0JIsgo3w5kv5DSgpLOsd4AvV0e1hNZcqbm2lKcu8a6m/464mCRIIIQwtl2GvEo0xn2bLOZEViNwm6sxIsHl4MhH+N9AWlmJIQQBdFUlpVYBgBDwbF+K4hQOi7XZShODgSLAdlsLEk2rwETn8eF0eYgQq11m99IcXE2AsAj947Jtv4gadMx0PmRMF+/vgrA5FKUO0bCGEaVcabiUJCthBDC2eydhncKIth7IsRi8NxzcO2aVZPq98Pp03DhAoTkyvomaWYkhBD1mVndykIY6fEXU7vt43I3S8PCfg/jAYUC8qYmn9dtDyLUWreQz83kUqw4yldq0Q+QNmQigNXz4+mZddI5k3g6z621ZNnkBnH4yFGhEMLZdspEsJczlO5A5+bg05+G6WlYWIB0Gnw+eP55uHgRXv96GBtrzXofQNLMSAghdlZWylDSK2ZzXO7JgeC2cbnubAbDUFYQwdR42hxEqLVum6N8b6zEmV6Jyz7gIGlTJoLLUJwdDhUzVi4vRiWIcMhJEEEI4Ww79USolokQi1kBhCeftAIP585BMAjxOMzMwMqKdb9HH5WMBCGEEHXZSGRZT1j7GZcBR/sCxdvs43KDczc591cfIbCyRNfSAkqBLvxrd08EGeXbodpY0nl2KMyluQimhuVompVYmoGQr2WvJ5xN5nUJIZxtp+kM1Xagzz1nZSC4XFbpQne39Xl3t7Xsclm3X7rUunUXQgjRUaZX48XPj/Z24XFtHUqXjstFa77nN36J44/9PUNPfo3QrSm0BlX41+4gQtm6VSCjfA+oNpUzAAS8Lib6t7IPLi9GW/ZawvlkSyGEcLbdNla8ds0qYZiYAFvqJkrBsWPW7VevNnd9hRBCdKypklKG47Z07on+LgZDPuY3UrijG4Rmp8tuN02NoazUcI4cacv6Vlo3vTmbskBG+R5gbW4ufX4kXPx8ZiVBMtO6oIVwNgkiCCGczd5YcacRj5vlDKmU1QMhWOWAKBSybk+lmrOeQgghOtpqPEMsZZ2kuV1WH4FS54bDjPcFyJua6fl1Ns/VtYZ8XqOxAgjG+Bi86U1tXffSdZtcihFNZcmbmmgqy+RSTEb5HlRtzEQAGAj5OBLyAmBqa1KDOJykJ4IQwtnsUXXDFvusFoX3+60mivG4VcJgF4tZt+/DrG4hhBAHz9TKVinDsb4u3K7y/VHpuNy15Bp50+p/oFBkwj08+57f4gVnhgk89BLoam9TOhnl26H2Ycz1HSPdPHH1NgCTizEujPVY2TXiUJEthRDC2ew7xJ0aK27e//RpawrDzIzVA6G0pEFruHkTRkbgzJnmr7MQQoiOorVmpkYpw6bNcblTZgSvyygGEVx93bz4nW/b15N0GeXbgfYhiDDeFyDocxFP50nnTKZX4pwalAyWw0a2FkIIZ7OXM9TbE+HCBWuM48qK1WTx2DGrhCEWswII+TwcPw533dW6dRdCCNERlmNpEoX6b5/bYKS7ehZb0OfmwnAISpsUdvnAASfpMsq3w7S5nAHAMBRnh8I8fXMdgMsLUQkiHEL7vzUTQohadmqsWK0nQigEr3+99fn0NExOWj0QfD4rA+H4cet2Ge8ohBBiB9MlWQjH+rswdkrf3mnfJUQz7EMmAsDpoSAXZzfImZq1RJalSIqhGoG13Yqnc1xZjDKzmihmzkz0d3FuOCyZM/tMfvpCCGezR9XtO8xaUfixMXj0UWuM49WrVhNFv98qYbjrLgkgCCGE2JFp1lfKUGanUjwhmsH+d9WGTAQAn9vFycEgk4sxAJ5fiDY9iLAYSfH45SVurSVZjqWLPTyuLsW4shjl4fNDDLcgcCHqI1s0IYSz2XeI9WYibAqF4MUvtj6EEEKIBi1GU6RzVmldwGswFPbt/KCd9l1CNIP976pNmQhgTfzYDCLcWksSTWUJ+z07PKo+8XSOxy8vcXE2gstQnBwI0uV1k8jkmN9IcXE2AsAj945JRsI+kRGPQghn2+lAbJ9S+YQQQhwOpaUME/1BlKqjE70EEUQ77OMxUE/AUzbm9EohoNAMVxaj3FpL4jIUZ4dChP0eXIYi7PdwdiiEy1DcWksyudS81xSNkSCCEMLZdqorlSCCEEKIFsmbmpurDZYygJQziPbYh8aKpc6PhIufX1+Okc2bNe5dv5nVBMuxNKM9/m1BO6UUoz1+lmNppkvGror2kiCCEMLZGi1nkCCCEEKIJplbT5LNawCCPhdHQnWUMoBkIoj22OcLKaM9fsJ+ax2yec315eac1KezJpmsSZe3cvCty+smkzVJZ5sTtBCNk7Co6CjSxbUD7dRY0b5s74kghBBC7NJMWRZCsP4HSiaCaId9aqy4SSnF+ZEw35paA2ByKVqWnbBbPo+B12OQyOQq9llIZHJ4PQY+j1wP3y+yRRMdQ7q4digpZxBCCLEPcnmT2bVkcflEvaUMsHMAXIhm2MfGiptOHgny9M11cnlNJJljYSPFSI/teDubhccfh9nZup7zBespVkMTTLuPE/K5y0oatNbMb6QYDPkaC+yJppItmugI0sW1g5m2VDUJIgghhGiD2fUkOdMqZegJeOjt8tb/4J0C4EI0gwOOgTwug9ODQS4vWE0OryxGtwcRfumX4G//tu7nnAAezcPH/vVvM/mClzDa4y87rs+bmvG+AGeHZFT3fpEcENERpItrB9vpQGynEY9CCCHELpROZai7oeImKWcQ7bDPjRU3nRnaKmGYXU8ST5f8/SeT8IlPNPR8BuAzNK949ouE/W5urMR5amaNGytxwn43dx/t5uHzQ3JhcB/JT150hM0uricHto9e2uziemMlzvRKnPuO9e7TWopd2Skl1CE7UCGEEJ0jkzOZ39gqZZhoNIhgz6Iz5LqdaAEHZCLA1rjH+Y0UWsPkUmzreDsS2dWxmUspTvhMXnPnMNMr8WKvs+MDQc4OhSSAsM/kpy86gnRx7WD2HY/9QEwyEYQQQjTZ3HqSzWl1fV0euis0d6tJMhFEOzgkiABwZijE/EYKgGtLMe452oPLUJBIlN8xHIa3vKXyk9y6BZ/7XHHRg+a+Y71yAdCBZIsmOoJ0ce1gOx2IOWgHKoQQojOUTmU41t9gFgJIEEG0h4OyMcf7AgR9LuLpPOmcyfRKnFODoe1BhPFxeO97Kz/J44+XBRHkwpBzyRZNdISJ/i6uLsWY30i1rIurjI/cJ/aUUAkiCCGEaKE9lzLA9pM5aawoWsFBx0BKKc4MhXjm5gYAVxZjlYMIXTXeT/s8slLUT858REc4NxzmymKUi7MRJpdiTe/iKuMj95F9h2gvZ3DQDlQIIcTBt+dSBpBMBNEeDspEADg9GOLi7AZ5E1bjGW7H0hyxBxECgepP4ICRlaI+skUTHSHoc/Pw+SEAbq0lubESL57oD4Z8jPcFdt3FVcZH7iPTBK3LvyY9EYQQQrTQnksZQDIRRHs47EKK3+Nioj/IjdtxwJqeti2I0EgmggQRHEvOeETHGO7288i9Y0wuxZraxdU+PnKzVCLs9xDyuZlcihXHR0rjlyardBBmm74hOxwhhBDN0pRSBpBMBNEeDjwGOjccKgYRZlYSPBBLUJarK+UMHUG2aOJgVwDFZQAAIABJREFUSyTggx+EGzcACAL3FT6Khofhh34ITp7c1UvI+Mh9VM+VHAfuQIUQQhxMTSllAMlEEO3hsHIGgIGQj4GQl5VYBlPDtbU0F0rvIJkIHUGCCOJg+zf/Bj7xiZ3v9/GPwxe+UHvDVYWMj9xH9RyE2csZZIcjhBBil5pSygASRBDtYT/pdkhJ57nhMF+NrQBwNZrjThQGhfJUyUToCDLvThxsn/98ffe7edMaG7MLpeMjK5HxkS1k33lUSge1H5g5ZAcqhBDiYGlaKQNIOYNoDwdmIoA1Nc3nto6L46kcs8H+rRtrBRGkseKBIVs0cXBlsxCN1n//L38Z3vCGhl+mHeMj7WScZIFkIgghhGiTppUygAQRRHs4NP3fZVjjHp+bi0Amw5XeoxyLW5kJ9WQimEA+r4lsJPjcN28e3uNgB5Pfgji41tbKl8Nh+M3f3Fq+ehX+8A+3lp94Ylcv0+rxkXYyTrKEfWcoPRGEEEK0SNNKGUDKGUR7ODj9/8xQiEvzEXQmw2JXDxueAD3Z5I6ZCHmtyeRM8qYmnkjztesrh/c42MEkiCAOrvX18uXBQXj00a3laBQ+8IGtDerkJCwuWo0WG9DK8ZF2Mk7SxrT1mah0Jceh9YBCCCEOjs1ShnQuz1IkhaHg0lxk91dAJRNBtIOD0/+DPjfjfQFuZjIATPaO8cLlazWDCIk8qJxJNq9RQEBp7p/oO7zHwQ4mP31xcNkzEfr6ypfDYbjvPvj2t7e+9pWvwA/8QMMv1arxkXYyTtLGvjM0KvSdcHAUXgghxMEwt55kLZHlykKUZDbPWjK7t0xAexBcMhFEKzg8G/PccJibWSuIcL17mBfcnsJTI4hwbS3FSdMKILhcChcmLkMd3uNgB5Mggji47JkI9iACwEMPlQcRfu/36pvmUEHF8ZGbBgfhR38U7r13V8+9ScZJ2tRzJcflAqVAF7r+mqb1USngIIQQQlRwZTHKlYUos+tJhsK+vWcC1lOOJ8ReOfxCynC3n+5UnAiQM1xMhwc5UyOIcCuS4bi2eioAGCXvo0N5HOxgEkQQB5c9E6G3wsbk5S+H3//9reWpKeujFT79aat5Y0/Prp9Cxkna1Hslx+0uL2PI5cDrbd16CSGE6BiZnMkzN9dZS2QwlOK+Y70ECvvhXV8BlXIG0Q57LGdoRyPvM7Elnix8frV3tGYQIaUVGl28kKZsQZFDdxzsYLJFq0ApNQG8BXgN8AJgGMgA14FPA/+31np+F8/748CHd7hbXGvdnA59nW6ncgaAF74QfD5Ip4udXvOmtjZQKFyGwuVSzZl1urpqjZF885t3/RSl4yTDFbpCH7pxkvVeyZEgwnZf/Sp87GMQidR3/64ueNvb4FWvau16CSGEw8ytJ7kdSxNN5zjWFygGEDbt6gqoNFYU7VCjnGGnAEG7Gnmf3JjnGd1FXilWfSFuKy9HqtzX6/OgUGhtJZkatvfRoTsOdjAJItgopY4BU0BpLnkEK5v93sLHTyml3qq1/sIuXyYLrFa5Lb7L5zx86slE8Pvhl3+Z/G/9VrHTq6kpBhEME1ymwus2cNnKB3blO9/ZUxBhP8ZJOpr9IKzalRyPB5Jbs72dVhPYdnNz8EM/1HiTyU98Aj73OTh/vjXrJYQQDjSzmiCb1+TzmpHuQMX7NHwFVDIRRDtUKWfYKUDw4hP9fGNqtS2NvH3xGBPRGDe6rcbmkxl31SDC0cEwhgLT1LhcCpXfeh8dyuNgB5Mt2naboeJ/BD4CfE5rvaaU8mJlJvwhcBL4uFLqvNZ6YRev8RWt9cPNWNlDrZ6eCED8J36Kzx99AatPXsRQiv6gB5/bRTqXZzWexdSaEwNdvOTUAH5Pg1cKnnqqvFzi2Wcb/CbKtXucpOPZgwjV+hzYr/Ac9gkNTz65u5+BacI3viFBBCHEobE5lcHjsjIT/VWucDZ8BbTeILgQe1EhE6GeSV/TKwnypm5PI+9EgrMbG8Ugwkxa8UAuj8+9/Zj77FgfhqGKQT2MPHlTH97jYAeTLdp2a8D9WutnSr+otc4An1ZKvQF4CugGfhr4jfavogDqDiJcWYxyJXCE6IMv5+xQiI2Sq/uG1lxbinHb76bnzuHGN5Tnz5cHEb7zHYo5WLvQznGSB0K9V3I8ttKPw56JUBintCuJxM73EUKIDjG3niRvQn/QSySVYy2R5UjIt/dMQGmsKNrB/neVz9c16evqcgytNS883t/6Rt6JBEdSUfrTMVZ9IfIeLzdux7ljpHvbXYNdPvJuAyhkD2dzPDW9itfrOpzHwQ4mvwEbrfUG8EyN259XSn0NeBh4sF3rJSqopycCLZ54MDFhjZKMRq3lSARu3rS+vkvtGid5INRbU+rwEUdtZ//+X/Yy+MmfrHzfv/kb+Md/3FpOpVq3XkII4TAzq1bgdKjbj8dtsBbPNicTUDIRRDtUOP6p57j30nwEber2NPIuXJw4sz7PN4bPgtfL1aVYxSAChoHL5cKnVLGP2fec6MPr9xzO42AHk9/C7qwU/pew8n6qpycCLZ54YBhwzz3wla8UGzd++xOPM/PSV+2pw23Q5+a+Y70yvma3QYTDXs5gDyJMTMDrXlf5vpcuSRBhH7SjI7YQorZs3iplAPC5Xbz5BUf51vRqczIBpbGiaIcKQYR6jnu11iiD1jfyNs3iccXx6DJPDp4i5/EQSeZYjKQqN250uTBME8Ol8LgU//KBMWmW7UBypNIgpZQbeKiweHGXT3NBKfUccArIAdPAY8Dva61v7H0tD4k6MxFaPvHgnnvIf/nLxcaN61//Nl8burclHW4PnXqv5Dh8TnLb2YMI9nKPUn7b32WHBRGceLLero7YQoja5tdT5AvXD3q7PJweCjHS429OJqCUM4h2qFDOUM9xb2+XNQWh5Y28S5pee3SeU6k1rijreHtyMVZ5XycTtw4ECSI07ueAEcAE/niXz3EEGMDqv9ANXCh8/LRS6ie11n9e7xMppb5d5aY7drlu+67ug357T4QqmQitnniQuuMudM4km9co4BX/9DGCZ0/z3Ve8jrlYtqkdbg+deg/C7CfJhz0ToZE02g4OIjjxZL2ehlcg2wsh2mGzlAHgWJ81u75pmYAynUG0g/3v6qmnePXP/CAPJDLkTQh4jPI+XVqTzJq8XllfzuU1Sim8boVLKXLK4PqpC1z9lz/D+OjQ3hsY2vosnclFuFL4/NZagmQmT8BrO7aTEtUDQbZoDVBK3Qu8t7D4B1rrSw0+xRzw74G/ASa11hmllA9r6sPvAXcBf6yUuqW1/mKz1vsgqfugP50uH+nndkOo8oau1RMPro+cZMK0Aggul7WhfvAPfofuuRkuvuPnm9vh9rAxbSUm1YII9q8f9h2OPYhyCIMITj1Zr6fhlWwvhGi9vKmZW986jjjWX3m04+5fQMoZRBvY/65SKYIzN3Bn88WLW4ahUMrq+22amgDgcSk8LoNsfvv48/unrxE+Oszo6//d3vePtiBCr0cxGPaxHE1jari2HOPuoz21v6fDnl3qUBJEqJNSahT4OBAAvg38aqPPobX+Z+CfbV9LA59SSn0Z+BZwBngf8LI6n7Nic8dChsIDja7jfoqnc1z8i39k9NOf4ngug3ugn7nXPsL8yKntB/2VshCqTERo9cSDydAQQ94AgWz5ydfIN5/guXf+QnM73B42u81EOOxBhEaugHVoEMGpJ+stbfQqhKjb/EaSnKkBCPvd9HY1OV1aMhFEOwwPb/uSAXhLJhzkTV0MEBgK3IbC6zZwKYVhuIoNDDfv4zIUdy9cx92MTD37xKeuLs4OhViOpgEriHBhrLt8fyiZCAeCbNHqoJTqxzr5PwlMAm/UWjf1SFtrvaGU+l3g/wFeqpQ6orW+3czXcLqZ/+/rvPj//DnIm8Ur+me/8I989v1/SWhooPygP7Za/uAq/RA2tXLiQdpUfPm1b+Vf/NOflX3dnbKucDS1w+1hs9ueCId9h9NILW6HBhGcerLe0kavQoi63VwtzULoav4LyHQG0Q7j4/BjPwZ/8idlX3Yphc9TOUDgcik2u4AZUGxgWGbddpy9WxWCCMf6u/BNr5HOmcTTeeY2UhztLckEkuzSA0G2aDtQSvUAnwHuBmaA12qtF1v0cl/ffFmsgMWhCiJkv/A4Om/iMrY2ZJ54lPEvPca1N7+9/KA/WV8/hFKtmnjg8xh87c0/hu/O87zyv/568etGYaPXtA63Ha5SL4wL8+ucguLOTqYz1EkaKzr2ZL3ljV6FEDsyTc1saSlDX5NLGWB7EMGQ97Rokfe9D979blgtP/E3Ch81jgC2zM/D29++tbyyUv2+jSgtPQbo6sJlKE4NBvnuvDUefXIxWh5EkAtDB4IEEWpQSgWBTwEvBBawAggz+7tWnUutrVuRUttVw8Fnvsm1N7+9/KC/zskM7TDR38XV7gBPn7iHV5Z83cik62vcaJowO1u95svjgbGxquUanaBaLwxzaoWj2Xwx7a7ucobDXj9XrRY3FoPnnoNr16xggd8P8bhVKLn599UhQQSnnqy3utGrEGJni9EUmZwVQAz6XAyEfM1/ESlnEO105Ij1sVujo+XLq63LRAA4MxQqBhHm1lPE0jlCm1nBMnHrQJAtWhVKqQDwSazeBCtYAYTJFr/sS0o+n2rxazmOPxlFocrOZwCOPPskKpcjkdNbB/32ngj7GETYbNx4ZcVNPq+LDWzIZplcitVu3HjzJjz6KExN1X6Ru+6Cv/gLGBhoxbewr2o1wEulc2TzGjDxeVwY9ZYzHPZMhEqNFefm4NOfhulpWFiwmpP6fFYAIZm0AgqG0TFBBKeerLe60asQYmelpQzjrchCAGmsKA6WUMi6ILN5/JBKWQGArj2W+lQJIoT9HkZ7/MxvWMccV0v7E0k5w4EgQYQKlFJe4G+BVwHrwPdprZ/b43MqrbWucXs38J7C4je01st7eb2DqC+bxFBWmqGrpDbLnYzTO3mJy/0ntw76HZSJsNm40chmUIpi7ZnOZAj73bUbN374wzsHEAAuXYKPfQx+7ueavv77rVYDvKNhDwrrZ2oFaKpcNZbUt3L2g9d83gogPPmktXM+dw6CQSsL4emntw4aAoGOCSI49WS91Y1ehThs6h4LXaC15tZayWjHVvRDAMlEEAeLUtDfD4slFdsrK80PIgS2gnZnhkLFIML15Rj3Hu3BMJQc0x0QskWzUUq5gD8HXgdEgddrrZ+s87EngBuFxXdorT9ScvNxpdTHgA8Cj22WRRQCFq/GGvF4DjCBX9vzN3IA9WaTZA1FNq/LruhrDeqJJ8g/cmLroN8eRKijJ0IrDXf7ecMDx/G6XSUNbDSvOT/I2ZHu6icEzz9f/4t85zvNWVmHqdUAz8jnMQxV7C7skcaK9bF//0tL1nvG5YILF7ZSfbq74Y47rOV83npchwQRnHyy3spGr0IcJnWPhS6xHE2TKvRC8Re2By0hQQRx0AwMbA8iHDu2t+eskokAcLQ3QMBrkMyYpLIms+tJK6gn5QwHgmzRtnsIeGvhcw/wcfuJTYmbWusXNfDcLyl8oJRKAXGgm62eJwngZ7TWn290pTuBOxpBVRlJ8+LP/CX3P/Mleru8+NyG1QCm1D5mImwK+j3g9+IpSSW/bzQEVRq7AXDrVvny0aNb9f2ZjJWCvmmy1dU0+6NWAzylTSuQVPhXd0+Ew17OYD943WyQdO7c9t4aXq9VxtBhQQRw9sl6qxq9CnFY1CqF2zYWuuS9frMkC2G8r2tb8LpppJxBHDT2ktlm9EWoEUQwDMXpwVDxvXp1KVY5iHDYLww5lAQRtivNl/YXPqpp5Gh7EfgF4OXAC4BBoAcrkDAJfA54v9Z6uqG17SSRSNlImpxpkjfB1OBNxAhMXcUwDMyS0TRF+5yJUOT1lp/AptPW1yrRensQ4bHHtr6X9XWrF8Kma9esDWmHXc2o1QBP5XJWJkrhX93TGQ77Dsf+/Zum9XcZrFD/7/FYQYRczvqb7KAgAsjJuhCdqlYpXMjnLh8LXfL+v7VWOtqxRf0QQDIRxMFjDyI0Y0JDjSACwKmSIML8RqHBYjuO6So1mj592srWDElPonrIFs1Ga/041ojF3Tx2qtpjtdZJ4L8XPkQlhWaJBqANyHaFMSLWhiWvNWYe8mYel6m2uvVvmpjYhxWuoJEr4qur5SdswSD09Gwt9/bC0JCVir75XNPT1kbuINMaPvlJePZZ0JoXrScZuB0nkzMZCFoBl1xXkNmXvxbyeUxTYyis0Z9SzlAf+/fv81mBgnjcKmEo5fFYQQalrI8OCyIIITpTrVI4pVT5WOhCEGElliaetjIEvG6D4XCt60R7JJkI4qDp7y9fbkMQIeRzlzVYvL4c4177e6XZ5QzVGk0//zxcvAivf701FU3UJEEE4QxaQyFgYAKZnMnf/m+/wiMffC+BdAKlrKkNpqnLu/UDvPGNcO+9+7jyJXy22spMpvp97VkI4+PbU83PndsKIgBcuXLwgwjvfz/89m8XF0eAvmyebF6joNgL49Tf/L98++E3oQG3oaxmm5KJUB/79z86av0tzsyU90QA62dqmlaQwe22dqb2ESlCCOEwtUrhgPKx0AU3S7IQjvYGrCZurSJBBHHQ7JCJ0GgTU2DHIAKUN1i8thzjbre7POO4mcd0sVj1RtMzM1vf86OPSkbCDiSIIJwhFrNOZIB8XpP2+Jh64CEe++hnCM/dLN5NA9MrcYI+Ny89NcCd58etPgJOYc9EaCSIUOn7OHsWnnhia3ly0oqQHmSf/GTZooF1RcjeC8MVi3Lq+SfxuKzMEwOkJ0K97DvckyetDIOVFSt979gxa+cYi1ljRlUhQLMZjHnoIbh9u77XMgx48EH4/d/vyBGkQghnqlUKB5DI5LbGQhfMrJZOZWhhKQNIOYM4eGr0RNhNE1OgriDC0d4Afo9BKmuSzJjM+boZL71DMzMRnnvOykCo1Gj6woWt2y9dghe/uHmv24FkiyacoZCFANY4v3gwyGiPH+3zEDl5tuyuntEsl1fi9PcPcKeTAgjQWBBhdrZ8eXx8+33Oln/vXLmyu/VykuXt00tLe2GUNtQcNpNbGSdQPYjQ6tS3g8Z+8BoOw6teZX0+PW0FozbT90ZGrIBCafZBPWNHS33hC/Bf/gv8zu/sedWbaVdXTYQQB8JEfxdXl2LMb6QI+dxlJQ1aa+Y3UltjoYH1RIZYyto2ul2K0Z4WBxEkE0EcNFUyEXbbxBSoK4hgGIpTgyEuzRUaLPr7y4MIzcxEuHbNKmGo1GhaKesiy+QkXL0qQYQdyFGUcIaNjeKnGk0yEGooRdEx7OUMta6IVypnsDt3DrBKPPJ5zdq3n+Xxb9482CdD9m6/v/Ir4PFgAMZjj+H5xje2bksny+8rmQj1se9wXS6rvu/RR63o+tWrW42EzpyBj3+8/syDahw2PWTXV02EEAfCueEwVxajXJyNMLkUY7THX3Zikzf11lho4OZqeSmDq5WlDCCZCOLgqdITYbdNTIG6gggApweDxSDCvDdM3O0jmEtbNzYziJBKWRdRKjWaBuuiSjot/aHqIFs04QwlQQSFIh0MN5Si6Bi2k9lkLMHzM2uVr4TWE0Q4e5a81mRyVqq/6/o1rj3xJG6Pwe2gl9m+IC/63vsZ7quyMXSaRKJ8w+zxwLvetRUNjkahNIgQjZY/vsMaK7bsSrn9+9/8uwyFrMi6PboeaMIVOfuBwj7a01UTIcSBEPS5efj8EGBNXLixEi8GCwdDPsb7Ajx8fqj4Hi8d7Xisr/KJTFNJEEEcNFUyEWZWE3ifeYq3f/6v6V7bnk36StMkmcnT2+WFsO1i2rVr5ctVgghhv4eRHh8LG2m0YXC9e5h7VmesG5t5TOf3Wxf8KjWaBqvM0+ez7idqki2acIaSIILLUNDTXXeKoqOUjHPMa80Tl+b4zlqo4pXQ75+aoWxTWyGIEA/3kguEUOsbKMCfz/Ku3/2pYpNJDeQHB0n8zV/Rdfdd2x7vOPYshIGB8nQy+8msvRykgxortvRKeaVMhFqq7SxPnIDPfKbybZcvw5vetLXsoCDCnq6aCCHaowkj1oa7/Txy7xiTSzGmV+LFYOzxgSBnh0LFAEIklWU9YWWouQwY7W3DCYKUM4iDpkpPhPx6hLf+wa/Tm9io8CBLNm/idRng2eHvvEoQAeDMYJiFjTQYBtd6Rrl7dcYaedfMY7rTp60pDJUaTWtt9YkaGbGyNEVNEkQQzlAaRHApPP195E1dV4qioxSCCJsTJqbm1on25SpeCX3d1DQe2Kr3rxBEuLIUIzxynOH171jTCQqsPniKfF7jub1M7A/eT9cHDsD0UPu4IHvqXI2dC1B/OYPDgwgtv1JeLROhmmpBhN5eq59CJUeOlC87KIiwm9FvQog2auKItaDPzX3Hemu+l2+WNFQc7QngcbUhk1GCCOKg6e21DjC1tpYjEchmGXvm64Si6+jC9Cw7ra0sYlV5yn254eGqN433BfC5DdKGQcLtZS7Yz9H4anP7XF24YG1jqjWazufh+HG46wBcmNtnEkQQzlASRDCAsYlh7j7aXVeKoqMUggibDQI9+VzFK6FTM0u4NjbIu10YLmWd5A0NbXu6mdUEnjP3MHrl2YovZxiKvKlJ35pr3ffUTJUyEUrtlFZfbyaCw3sitPxKeaMHr9WCCLWuBtp/Vw4KIlQa/Racm2H8S5/FnYiT9fpZO3E/6WN9+7iWQhxS+zBirbQfwnhfixsqbrJvh6WcQTidy2UFEtbWtr62usqpZ76KoawM2NILWptMU2Modu4z8s53br8AUcIwFCcHgzxvWMcs17pHrCBCMy8MhUJbU84qNZo+fty6XcY77ki2aMIZSqYzAISGj9SVoug4m0EEU2NqGPQrNpQisLxIz/WtyQqjC/OY2rqfx6WsKy7G9isj6azJV177g0zoFIOXnkYVxmC6UkkCtxetgDEaUun2fH97tVMmwk5BhA7pidDyK+X2IMpOB6/VggiV6gU32bNGHBREsI9+826s8fAv/TieuNVjQ2s46fLw5Lm/BI7t78oKcdg0e8Sa1laj4mSy4s3xrMnqdeu9byg42p2AtSZkIgwM1B5r22hZmRBOMDBQHkS4fZsjX/8yaUORzWvyec3Xf/7/InnmHKlsntuxNHlTc2YoxCvPDeKp0hSdwUEYHd3x5c8MhXi+8F6ZDQ2QcHvpavbErVqNpu+6SwIIdXLomZg4dNbXy5e7u+tKUXScQtq4Lvzr0nkCX/8SL/mdX0aZ5RvBbOE+QOWmilgnQyoU5Ms/+e6yJpMDF5/iFe/5qWIKmTvn7CvvRfZMhEbLGSoEWoADF0SodKW81J4nkDR6BaxaEKFaKQNs/10lk+VjIveRffTb0NPfKAYQwLpq4tMZzl9+Ch552T6uqRCHUDNHrMXj8Pa3w7e+VfUuN3uPwuApAIYTa/hmL+71O9jyEz8Bv/VblW+TxoriIBoYsN57mz77WYy1VbxuAzBJ+oN8/sJDpLQLb9BgcNzKEL7//BBdTZh41O33MGRkWQI0cL17hLtbcUxXrdG0qFtTi8KUUq9t5vOJQ2TD1qylp2d/1mOvCpkIm7Vh2VSa05/8i20BhG31Y0ePVny6if4uBkM+5jdS6M0aNSC/2XuhkEIWpMlR2lbZazlDtYOwAzbisfRKeSV7nkDS6MFrtZ97rSCCy1XWSBStHTMS6dxwmPG+QLGvilqYB6xVzOet0J3LUAx7HDgmVohO18wRa5/+dM0AAsDN0Fb69LHYHkfZ2n3oQ7C4WPk26YkgDiL7xZ0PfAAAl1L4PC7Mhx/mJedGeOmpAb7n1ACvuXOYR+4da+rI5DOurezaaz0j6KyzLwwdVs0Oi/6zUmoK+GPgI1rr6SY/v+hUtnKGgxhEyORMYt4gkfAgEeUlotx8MwIbrj68xx8g63KTM1zklYFpWlds3YaBT5m4LrwKz7PzuA2F3+MqfBi4DUXY72YpkubKYpSx3gBdXjdRbRRPhtyGIqAPyAa2VeUM9oOzZqe+NZn9SnnTJ5A0q5yhVhABrGyE0gkaiURzxkXukX30W3J+kWzeRKEwlPWe8boNKpR2CiFarZkj1qamat6cdHlZDmy9xnhspca9d2l+fnuzOCtiWf41CSKIg8B+ccfWs+zI97+RN99X+cJXsxwzsnjzOTIuN3G3j4W0ZudCCNFuzQ4iJIATwK8D/04p9QXgQ8Dfaa0PSNG22Bf2cgaHBxHi6Ryr8Qwr8Qxr8QzryQzJjAneMRi5Aw3k8pq1vMEiXoK+cLHh7crIOHmXh4DXRV/Yj77jDrj3PkhUvnpuamt0znIsxdXlGArFWCzHg6E++jMJgiqHK5up+FjH2amcYacT0GrlDAcsE+HccJgri1EuzkZaM4Gk0XIGn6/y13eqC+zqKn/vJhK1a4TbqHT0W7dO4HUZKBQuQ+FyKSsNz+FlL0J0pGaOWFu2zawfGio7fpgN9EPYOsEfysbwnzqx9/W/ebM8SyJd4fC2UgDBAaVeQuxoYqL6bS4XvOpVLV8Fl8fNycgil/usYMXVpJIgggM1O4gwDPwQ8A7gZcBrgFcDG0qpPwc+rLX+dpNfU3QCh2ciJDI5FjZSLEbSLEVTxNNVrnQXrjQorHTpfr8Lt5lHa42pNQrF1YffQNfQEc6NhHH5dxi9h1Ufds94D0vRNKuxNNm8xuMK89Xj92EYEMxlGAq4GFyMMhj20RPwbGvW5xj2TAT7CedOPRE6pLGi/Up50yeQtKOxImxPR3ZQc0XYGv2GTlSeXW1KOYMQbdfMEWu3beUJv/mb8KY3FRdvPr8EG9YJ/7HjfTCyQ3ZVPR59FL70pa3lSkEE+7alWgBcCKd561vhIx+xxrDavfvd2y/+tILbzZnIQjGIMJtRpLJ5/JX242LfNDWIoLWOY2UefEgpdRZ4J/CjwBjws8DPKqUuAv+OowDXAAAgAElEQVQT+DOt9WrVJxMdLZ7OcWUxysxqgnTW5Pvmlwnm9dYVQgcEEVZiaWbXk8yuJVmrkiVQylAQ8ii6YysEc2kCuTQvPpLDu/AcvtUVXGYOj2ly30O/y8nzx4sniHlTkzNN8qYmkzNJZU3SuTzJbJ5YOkc8nSeWzuJzuzjWVzjJjnmKHU0Sbi9T3hBTU1Y3Xa/bYCjsY6zXz2hPwFmTLPaaiVDviMdPfWrHOtn9Ngy8zdQks3nSOZN0uIepH/4Jul70+r1PIGnWiMd6yhlKOSyIUGS/WrnJ4cEmITpSM0es2YMIJePj0rk8i5GtjIGmjXa0Z25VCiJIU0VxUI2OwhNPWAG+0r/tkyfrmq7QFC4XPZkER1IRbvu7MU2TG7fj3Dm6w4UN0VYt26pprSeBX1NK/VvgdVgBhUeAe4D/BvwnpdTfAx8GPqNLu8aJjrYYSfH45SVurSVZjqXJZE1es75BOpfHZRZqlXe6AtoiG4ksUytxplbi1bMNsGqqB0Je+oJeBoLW/yGvG+NzMZi/tHVHVwbit8EsjJ4yoO/cKAS9tmfcObqayZlsJLOsJzKs31aspSKs+0LklFF2wJLJmdxaS3JrLQms0dvlYbTHz9HeAEdCPoyd5vi20k6ZCM0KIsTjcONGY+u2D1xAqPDB0ixj7/s1+JH/BfYa+LEfwNrLPewkiCCEaKdmjVizBxEGB4ufzq4lMQtHlgMhb/MC6vYgQqUGkBJEEAeZ3w8PPrh/r194v5zaWOS2vxvyJteWYxJEcJiWb9W01ibwKeBTSqkB4IeB/x24ALyt8DGrlPoQ8Eda64VWr5PYP/F0jscvL3FxNoLLUJwcCBI0NP5cplD7r9GGQnv87LKlXMMyOZOplTjXlmJVMw4MBUPdPoa7/Qx3++nv8lY+GffaggOZzPYDjHqaRVXgdRsMhn0Mhn3Q54Obz2CiWPcFWQoPsNwfYCmSJp0rT6NcT2RZT2T57nwUn9vgWH8XE/1dDIXbHFDI57f3vujrK1/ebTlDu6LjrZbJwOXLe995N3oA28lBhGx2ewbMJilnEGL/NGPEWo1MhJtryeLnxSy+ZrAHETIVehJJU0Uhdq/wfjkeXebJodPkzDyRZI7bsTRHQlV6OIm2a3do9ARwHqu8QcPmfDvGsZox/qpS6j9qrX+jzesl2uTKYpRba0lchuLsUAilFL71VZQCl0uRz2sS/iC3luNWLXMLrcTSXFmMcXM1Qc7cngjjcSmO9gUY7+1ipMdfmJG7A3sQIR63GkVtcrubc0WicGXZQNOfjtGfjnHH6QEwDDaSWRY2UsytJ1mKpsiXnCelcyZXl2JcXYoVAwrHB6yAQsv7KGxslP8suru3XyGv1uBvU7UDsfvvh0cegX/4h72toxM040TcHkTYbTlDPY0VSzkxiLC6Wv53V0oyEYQ4uNJpiEa3ll2uYilkNm+ysFESROhv4tQYKWcQorUK7xePzjMRXeZ6IeB/bSkmQQQHaflWTSk1CPwIVrPFC5tfBp7G6o3wt1gNGH8GeAj4daVUUmv9n1q9bqL9bs2t8D3/7T9w/srTuPOFnWzJAb5hKOKBINMrrQkiaK25tZbk+YUoy9HtO363YQUOJvq7GOsN4Gr0Sr39pLhkNA6w6yyEbZSyAhalV0AyGfD76Ql46Al4OD8SJpc3WYymmVtPcmstYU2QKCgNKAR9Lk4eCXLiSJDuOpo9VmPvdeHzGEz0d3FuOExwp/GOm99XIADJ5PbboHY5w//4H1baeulB5UHwnvdY9YebYrG9P2ej5QzVykh2Kis6CEGEpaXqt+13ECESqb4ObvfOP38hDjP7PuXIkWIDw7n1ZDGA3tvlIbyH/do29v14vdMZhBD1KQm6ndpYKAYRplcTPHC8D49LGpU6QUuCCEopA3gjVh+ENxReRwER4KPAB7XWT5Y85M+AP1NK/QTwQeCnAAkidKCxv/sLznzts1U3AEpBwh8knW1umrFpaqZW4jw3FyGa2n7Q3tfl4cxQiOMDwfoyDqqxX6Gwn9A2K4iw+VoVggil3C6Do70BjvYGeOHxPpZjaW6uJphZLQ8oxNN5Ls5GuDgb4UjIy6nBEMcHuhraUFfqdeH1GFxdinFlMcpro/OUdUCoNgqwq6vxIMKmwcGymtgDwb6+rQgiHOZMhFpBhP0qZ1hfhx//cfjGN2rf78EHrS7ZDhmbKYSj2HudlLxPbq5u7UMm+ptYygDSE0GIVis5ZhlKReg2M0SwRqffXE1wanCX469FUzV1q6aUugsr4+BHgCG2yhW+ghUc+EutdZWzA9Baf0gp9R+B481cL+EcvTevo1BoXXlkstawPjKOz9OcKKPWmqmVBM/ObhCzBQ8MBRMD1lXypqVH2a/42kdXNjuIUBqkSKVqXrlUSjEU9jMU9vPAhBVQmF5JML2SIFPSR+F2LMPt2CpPzqxx8kiQM4Mh+rY1gixXqddFl9dNIpNjfiPFxdkIo1PXeTnFoRLVxwTVaq7YiVdz7Cfq8fjen7PRA9hqZST2EY529t+VE4MI9prpUvuVifDRj+4cQAD49rfhT/8UfvEXW79OQhw0Vfoh5PImc+tbh5pNm8qwqZ5yBslEEGL3bMcsp8w4Txc+v7YclyCCQzQ7NHqRrV4Ht4E/Af6n1vr5Bp4jBvTteC9xIHW7NIayMgNcru1RhNUjo3z3B9/BAwN7a6uotWZ6JcHFuQ0iyfITBY9LcXY4zLnhEF3eJr8F7D0RWh1EKFXpQKYKe0Bhbj3J9dtx5te3ulnn8prJxRiTizEGQl7ODoWY6O/CXSE7YXJqkdvTc4RSeU4NBlHpHKQhAPT7NNeX46RvTJPPa4zN37sEESytCCLYD2B301gxFNr5533QMxH2K4hw61b9952dbd16CHGQVQkizG+kin2Own43vV21g+ANs+/nJYggRHPZjllO5uM88/+z9+7BcaXped/vnL5fcW/cAYIkSA7J4czszM7eV6OVdqWxZyNbkrUVq+QqV6XishNZUuKsy07iOJajuMpyymW7Skm54iqVI8VyVLYjKx5pa1c7e9PuzmhnZndIDkmQBHEHutFo9P16zskfB+huHHQDDfQVwPtjschGd58+6O5zzvc93/s8r2IuNEaSeRK5YlPWW6E1tKO+6quYWQf/wTCM2lH3R/MpOh/4KHSIfjuUVIWiZqBpBt//5f+B6CdfI1MssZkskHH7uD3Vx3zo9CrjViLHD5Zi7Fo6LThsCs+NB7k2GmjOsnAUnRQRanWCOAU2VWF60Mv0oJdcUeNZNM3jcOqA+BJNFYimdnh3eZe5YR/XRv2mx1TX4W//ba7/69/hcqGETVXqVphouoFmU3Hsiwj1SrSPEhHOY0modSLerJ3BME4+gK0nIhzHWRcRumVnsH4+Hk/lM8jnD76P1scKgmBizUTYs4atxCrHz3SrrQwgwYqC0G4sx4tHKzDZ79lrXQ5POxC+LhxPq89qc4ZhLDWzAcMwZNnlHOPQSqh2FdDRdIMlw8m9XR2nw87IkI/LAx5eux46VT/nZK7Ie8u75ZNM+TVtCjfGglwfa6N4UH6xDtoZGgl3OukmHTZujAW5MRYknMixEDa7V+xXJxRKOg83kzzcTDI54OFG5Bmjv/M7oGkYGHU7PCgKGHt/ytSrRDiqzeN5XM1pdSVCrcHrcZ03an0vGwn1OwsigtU3XU23KhGswsDf//vwS79k/v/3fg9+7dcq90kbSkGojfXYHh5G1w3WDrR2bLGVAU4XrCgigiA0jvV4KZW4POIrj+8Xt1PcmezrbJty4RAtPas1KyAIF4BCAZui4HLY0DSD6zNDBC4P4XKozA75mA/5TywgFEo6d9fjPNpMUt2p0a4qXB8LcGM8gMveocmntTqg3cGK1bRARKgmFHQTCrrJFTWeRMwuDul8ZWC0FsuydnedgZmPcCmywtju5pFZF8renzK3b9d+4RbaGY7sFnEKoaotWEWEZisRThqqCLW/l4HA8c87CyLCUZUI3VrlP2qVUrUInVKJIAi1qWFn2ErmKGrmQMDnsjHUjnZwp6lEOI8CuCC0C+vxomlM9HnwOFWyBZ1sQWc9nmVqoA2VRkLD9MgoWrgwFE2LgQqoNoVPPzcBH50+9eaebad5dzlGztLNYW7YxwvTfa3PPDgOq4hQtDh6jpogN/taLRYR9nE7bNya6OPmeJCNeI6HW0k2dvfSqHM5Yi4f2xM3sA1fYi4ZZq6YxK0f/L0LJR1VUXC4bNAXgJ//efj0p2u/YIvsDMd1i3jteojRYAtFndNiDS9sRyXCcdR6z8+LneGoSoRuTdCPsptYB09SiSAItbGKCENDB7oytG2CIXYGQWgvNSoRVFVhbtjP/XWzwvdpJC0iQpeRs5rQWay+fetEuEFS+RLvLO6wET/YWmnY7+QjswOt67ZwUo77fdpZiXDKTIRGURSFiX4PE/0e4tkiDzeTPMvnKGEunhYdLt781M/w+NXXmBvyMTvkw8BgI55D0w1uTwZ5484EHFcBcJSdwbpKW4dGukUAvHFnovsVCa0WEU5TRit2hs5y1GdUYwVGEIQaWEQEY3iY1QN5CG2wMoB0ZxCEdlPnOjg37CuLCGu7WbIFDY9Tjq1uISKC0FmaFBF03eDBZpK7a/Fy+jKA12njpZl+Zpvs6tA03RQR2lSJUIs+j4NX5wa5o2/xZPsZj/onSNuc+II+XHaVh+Ek9zcSBDx2roz4uT0ZbDzrogWVCI+2kqzGsthUhfmQv5zVEHA78LvsLIRTrMayLIRT3Q/nabWdwVr9cloRoZFKBKsA0msiQi53OJekml4JVqweMFn9QFKJIAiHSOdLKOtbqEXdzONB4e24QtxexGW34XGqjLRrMeE0mQgiIghC49SoRABz7DkScBFJ5jEMWNxOc3OigQUPoS2IiCB0FusE5wQiwk66wPefRolZui5cH/NzZ6ofR43Wgx2nmyJCLlf7cW3EnUpwK7bCc7FVlgPD3B3/czAeZCeVp6gZOGwKbocNp10lU9DQdeP4IJwWZCIs72SIpPLMDfkOhT0qisJ4n5vFaJqlaA8k/Fon4s2KCK2qRDgPmQjWcmcr3apEOMovLZUIgnAkW4kcb324yeuRbdAqIsIfrhUp2eJcGwswPzpQN+i3aazX+VrXXrEzCMLpqSMiAFwZ8RFJmsLd0+2UiAhdRM5qQmexViJYuxnUQNcN7m8kuLsWPxCc2O81V8O7Zl2oxXG/zxm2M9Rkr/RexeBSMsKlKRevfuoSDzaSB7pkZAs6330S5Ycru1wbDXA15K/fKaMF3RnyRZ1CUa+bieF12ikUdfLFHljlta74NzsRP83gtZb4ZRU3amH9rLLZ2o/rFsftj9gZBKE30HX43d+Fd981k3jrUNR0diNpZuNplD2ftKIo5D0+NrIahZK5yPCFm6Pt21exMwhCe7EeL1XX6ulBL3+2FKOkGSSyJSLJPCOBHpoHXCBERBA6ywntDPFske89jRJNVZ5nU+H2ZB/PjQV7r71LJysROhSseCTW7hN+P6GAm1DATTJX5MFmksVIumw9yRQ03l/Z5e56nCsjfm6M1eiS0AI7g8uh4nSoZAolAu7Dwk6mUMLpUHE5eqB6pdWVCKcREWplTTRSJdTrlQjHHRO9aGeQYEXhIvJbvwX/y/9y/OM0g8mSxoQBNlvl+h8OTTHocxJO5Enkiuxmi0dspEkaEfClEkEQTs8RlQgOm8rsoJcnEXMR60kkJSJCl5CzmtBZGrQzGIbBo60U76/E0KrG0MN+Jx+/MkSwxsSwJ+ikiNCIL7PdWEWEqhL4gNvBRy8N8vxkX7kjwn4XjZJm8HAzyaOtJDODXm6MBSqtuFpgZ5gZ9PI4nGIjnsPvsh8oazUMM+xxxO/qfoYGtF9EOOkKmGGY23j/ffhX/8r8nl25ArduHa6asH5WZ01E6EU7g7R4FC4i3/hGQw/TdAPdAJtlAWFx/AoKCn0eB3lNY2Unw0dmBtqxp6fLRBARQRAa55iKvCshf1lEWI5meHl2oDcszRcMOasJnaUBO0O2oPHdp9tsxisXZlWB56fMNoNt8zm2guPsDNYVjGbohUoE64S3Rhif22EzK0fGgyxup3m4mSS+t0pkGLAUzbAUzRAKuLgxHmDS7aHuJ9zghPjaaIBHW0nuriVYCKcY73Mf6M6g6QZTAx7mQw2EB7Yb62p+LmdeME9b/mqdoDZgGSqj65XXf/LEvO1ywYMHcPcuvP46TEzU3/dMxvxQe+UYtR4TNtvBwYjYGQShN2gw08fY+2MdB/zwYz8BsGeTM9prVWskj6hZMVcQLjJHVCIADPtdBD12EtkSJd1geSfDlZEeGM9dMEREEDrLMXaG9d0s330SJV+qDAD6vQ4+cXmIAd/p2kF2lONEgqNW2Zt9rV6oRDgi0d+mKlwN+bky4mMjnuPDjQRbico+h5N5wsk8wZyfG31jXEqEsRuWgWCDAzGfy85r10MArMayLEbTFIo6ToeZ2D014Gm8W0S7UVWzGqG6tWM63ViLxVqcdvBqGOZguFg0RYDxcfj4x819WV6GaNR83Je+VPmc7XZTpNivMNJ18xhvpVjWDNZjwus9+J3tFTtDdfWB2BmEi4j1e/4rvwKXLh162N1nOyyEk4T8btwO81hZu3SdDfswAJquE/Q62mtVa+TaK3YG4YySzpd4tJVkeSdDvqjjcqjMDHq5NlrDftourIsfNcT0KyN+3lveBeBJOCUiQheQs5rQWeqICLpu8P7qLg82Dk5Kb04EeX6y71DpYs9y0YIVj7Az1ENRFCb6PUz0e9hJF3iwkWB5J1MOzUyoDt4OzfPDoTmu7a4xv7uOW98bkJ1gIDYadPPGnQkWwimWounyxXB2yMd8yN8bAsI+fn/7RIRG37NSybxQK4r5nP5+c0IbDJpWhnv3YGkJ7t+HV1+tPM/rhXi8cjuT6R0RwXpMWEWEXrEzVH9GYmcQLiLWY+KnfgpefPHQw+zLMTYehHmUK5Xb967GMrCbw8CgqBmMBtzttao1UgVoFUVq5c4IQo+xlcjx1sMwq7EskVS+vPiyb0l97XqI0WALx7H1OCJYcZ+5YR8/XNlFN2A7VSCeLdLn6VGr8zmlh0bRwrnHMA5nIjgcJHNFvvM4yk66MuD3OFU+eWW4MyerVtLNFo89amc4ikGfk09eHeaFfImHW0mehFMUHeZ7mLfZ+WBolvuDM8wltrgeW6XvhCWhPpedF6f7u9/G8TistoBmchFOa2d4+WX41rcq1QW3blXuUxSYnoaFBXj8+HgRYaBNXuSTUqsSoRqxMwhCb9BghkAtq1o0VSBX1Ihni4wGXe23qkklgnAOSedLvPUwzN21BDZVYW7Id8AGenctAcAbdybavwhzjJ0BTKvsRL+n3AnsaSTFS+3KQRFqImc1oXPUuKgux3J8bzFKSau0dJrod/Pxy0PlUsUzxXEDhfMkIuj6wdVzaKwtYA18LjsfmRng9kQfT3dXeVjKk7abv5+mKDzuG+Nx3xiTSwmec/oJnTVx6Tis4ov1fT0Jp7UzfOELpkjgdMLnP3/4u+r3m98xq/+3lzs0HCci9Iqd4ajuDCIiCBeBBlsiWq1qD7fMDkA2m0LQbbZ9brtVrZFgRRERhDPGoy2zNbdNVcpVPmCGZPtddhbCKVZjWRbCqfYvzDR4HbwS8pdFhMXtNC9M9fde17ZzjJzVhM5RVVqso/D+6DwPHm+Xf6Yq8OJMPzfGTlnG3QsoykGPuJXzJCLUEhCaDI9y2lVujAW4tvgOy4FhHgxMseOqTLDXUkXWPgwz6HPy3HiA6QHv+bhgWEWEVlYiNDp4HRuDn/gJuHattpUilTK/c9bv8FkWEXrFznBUdwbJRBAuAg2KCHDQqvadxxEwwGFTuDEe5GdenGz/Kqm1uqtYNI/T6mP3BL+PIPQCyzsZIqk8c0O+Q8GliqIw3udmMZpmKZpuv4jQQCUCwHjQjcepki3o5Io6G4kck/0tzB4TjkREBKFz7E2sszYn3x5/jsjAaPkuv9vOp68OM3gWwhOPw+XqjIjQ7e4MTVoZ6uLxoGJwKRnhUjJC2NPH/YEp1n2D5UHaTrrAdx5H8bl2uT4W4MqI/2y397FWcLSyEqFREeHKFbMLw/KyaWWoHkQYBqysmELD1asHn9fLIkKtTIRqurXKfxI7g4gIwkXghBVU+1a1zXiOAa8pqH/iylBnsm4UxbzOV19zC4WD13epRBDOGPmiTqGo43XW/q56nXYKRb29nU/2aVBEUFWFS0M+PtzLU1uMpEVE6CByVhM6Rz5P2NPHd8ZukLU7wWF+/SYHPHzi8tBea6ZzwFEe9FaKCI2UVLaTU4QqNoRlohfKxgll48QdHh6EAjxLFND2rmHpvMa7S7t8sBrnasjP9bFA3QtgT9MLIsKtW2Ybx2jUDFGcnjaFoVTKFBA0DWZn4ebNg8+zTsyb2fdWY7VeWN/nXhERxM4gXHSsYlkDK/fpfKmcpaQqdHbyYBUR8vmD1+QGMx4EoVdwOVScDpVMoUTAfXgcmymUcDrU9nY+2ecE18HLw/6yiLAay5AvabjsUvnTCeSsJnSMDzcSvD91h/30A0W1cWe6j5vjwUOlU2eao8IVz1MlwgnaO56IOm0w+4pZPnZ5iBdQebSVZGErVW4FWtQMPtxI8nAzycyQl+fGgmejJeg+rbQznHbw6vfD66+b/19aMvMR8nlzsDw2ZgoIr79+eF8bqEToWsuo82BnEBFBuAicIstlJVY514z2uTu7EOF2QyJRuZ3LQV9f5fZps2kEoUvMDHp5HE6xEc/hd9kPjMsNw2AjnmPE72pv55N9GqxEAOjzOhj0OdlJF9ANWI5mmB9t0aKWcCQiIghtp6jpfP/pDstrlUmnSyvxqcQzxib6jnjmGeUoEaHOBPlUdLvFYxvtDHWx2XCrKnem+rk5HuRZNM2HG0mSOfMCoxvwbDvDs+0MY30ubowFGe9z975IZV0hb0ZEsFppTrICNjEBX/qS2cbx8WNzYOx2mxaGmzdrf8bWfc9mD9zsasuo4+wMvRKseFSLR7EzCBeBU4ifKzuVc830gPeIR7aB40R8yUQQzhi1Op9Ud2fQdKP9nU/2aaDFYzWXR3zlqqQnkbSICB1CRAShrSRzRb75aJt4tlg+CQzlknxm4z7ey5e6u3PtolN2hm4HK1onuq2yMzidpufUMA7+XFUPTLDsNpWrITMPYW03y4cbSSLJynuwGc+zGY/gd9u5OuLn8oivdzt+WCfnzeQKNFtG6/ebLRyr2zgehXVi/vu/b9ohgEJJZzucJJTIM6YouPt8RD71OdbGrnemZVSvViKInUEQDmL9nlvFNAvZglY+3ysKTA102Ad93PVXRAThjGHtfLIYTZdF/xG/2Tq17Z1P9jlBJQKYVRTvLsXQDTMzK54p0udtsL21cGpERBDaxlYix7cWtinslZyjaVzbXecjkaeoGI33rz9rWAcX1YiIcDyKYk72rN76OoMwRVGYGvAyNeBlO5XnwUaSlVimrEGkciXeX9nlR6u7TA96uRryt2/l+7S0shKh04Fe1on5d75j/gUUzWC2pDFjgM1mVoPo//H3+Po/+9f4p+fa3zKqV0WEo0qdRUQQLiInFD9Xq6wMoYCr8wLxcZWAEqwonEGqO58sRdNl++HskI/5kL8zAgIcPl6OuQ66HTamBrws75jnhafbKV6aGWjX3gl7yFlNaAsLW0l+sKcKghl69OqgyuXIk8qDjir7P8tclEqEdmUiAHg86Ok0mmag6QYGBrrq4NFy7Egf/bDfxafnXaTyJR5uJnkaSVHUzC+hbsBSNMNSNEPQY+dqyM/csK83AnjOsogwUP9CrekGugG2qjacaqnAyA/fITlzuf0to44TEXrFznCUiCB2BuEicMIMgeo8hKlOWxng8LXcGuIqIoJwRtnvfNL2No5HcUI7A8DciK8sIjyLpnlhqv98tADvYc5JHL7QK+i6wTvPdnjnWUVA8DhVfvLmKJddlsHweRURjvq9jqpSaPZ1ui0itKoSASg6XeSLGvmSRkHTKWg6WQ3+5EGYP/zROluJ3JHP97vsvDw7wF98aZKPXx5kyH/wvUpkS7y7tMt/eG+NP32yzVYih2G1T3QSqwDTyu4M7S6j/emfPiyC7GHs/bFGUtj2vqttbxl1XCZCr9gZjspEkEoE4SJwgvL/XFFjK1G53k0PdqGlm9gZBKF9WBfjGrhWjwfduPc6R2QLOpvHjBOF5hFpVGgZuaLGdx5vH7i4D/ocfPbaiNl2zxr4dl7tDPVEBJeLQ7OpZui1YMUWiQjpfIntoQkGni6hYK5iK4pCcmKKZK50Ih+93aZyecTP5RE/sXSBx5EUi9tpSnvVCZpeCWL0uWzMDfuYG/bVbG/UVtopIrT7OLtyBb76VfjGNw4NpB+uxHgSSfPig3cYv/uDqnvM97/tLaPEziAIZ4MTTLrXdrNlu9qQ39mdtr7HiQhSiSAIp+cU10FVVbg07OPBXrvHxe00E51s+3oBkbOa0BJ2MwW+8ShCOl850GeHvHxsbhC7bW+CYJ3kdrISIZUyw96ePKkkzl+5ArdutbYMH+r/Xq20MtTanrWcst1YKxHqrEaflEdbSd7/S3+N1zY2Gd14BkB6fIp7/9XfZj7kP7WPfsDn5KO+QV6c7mcpmuFxOFVO8wVI5zXuriW4u5ZgJOBibtjHzKC3M23DrJPbVtoZOrECNjsLf+WvHPqxsRzj2YMwff9X/ICIoGh6Z1pGWY8JsTMIQm9ygkyElZ2KlaHjXRn2ke4MgtA+aokIhnHsQtzlKhFhNZYhX9J6w7J6ThERQWialZ0M330aLa/uAtyZ6uP2pKV9Y7dEhPV1ePNNWFqCzU3zYu9ywYMHcPcuvP662dquVdRb+W21iHBO7QzLOxkeDk5R+Oe/S2C/0mDvwqFA0z56h03lasjP1ZCfnZAG704AACAASURBVHSBp5EUz6KZSgAoEEnmiSTz/GBph8l+L7NDXib6PQe8/S2lnZUIXVwB228ZpaOgaQaqqqAoUCgUWQin2t8y6jg7Q4MDk5YjdgZBOEiD3RkKJZ3NeEUc7IqVAQ5fz0VEEITWoSjmMVN9HGnaseOZfq+TQZ+DnXQRTYflaEbaPbYRERGEpri7FudHq/HybbtN4ROXh5gerLE6YLUzdEJESKVMAeHdd80T0rVr5op5Og3LyxCNmo/70pdaV5FQ7/fytHiw0207g3Wi2yIRIV/UKRR1s0S1xuSulT76QZ+TQd8gL80MsL6b5el2mo3dbDnPQ9NNUWN5J4PdpjA9YAoKY0F3awN79r57OqBpBomtHb72zgouh8rMoPfIMMlDdNrOcAT7LaM2+j0oCuWQzJ1UjoDb3v6WUdaBvdttTk6qV/cbGJi0nKNEBLEzCBcNwzhccVNn0r1edX4e8Do6bz3bR+wMgtBeTiEiAMwN+9lJxwB4up0WEaGNyFlNOBUlTeftxR2eRStlhT6XjR+7NkK/t84k2jrJ7cTk5t49swLBZjOtC/uT0mDQvL1///378OqrrXnNTtkZeq0SoUUijMuh4nSoZAqlmgPEdvjobarC9KCX6UEvuaLGs2iaxUiaWKYifJU0g8XtNIvbaVx2lelBLzODXkIBV/OCgs+HZhgUSjqabqCurzP/N/8LDKeDrY9+ike/8Jd57cZoY60pu2FnOILRoJv+yQFUu60sIlwa9DD13Gj7W0ZZzzkul/l+VE9YumEXsH5G1auuIiIIF41aVQh1qoOquzLUXKzoFBKsKAjtxW4/eA0vlRoKJ58d8vLeshnuHk0ViGeL9HnOaQZblxERQTgxmUKJbz6KsJOuTLBCARefnh8+uldzNyoRnjwxLQzXrh0elCgKTE/DwgI8fnz2RAS7/eCqqqaZJ9lOrXi0yc4wM+jlcTjFRjyH32VHqfrcOuGjdzts3BgLcmMsSDxTZGknzbNohlSuMvHLl3Qeh1M8Dqdw2lUm+z1MDXgY73NXMkBOQNruQi3pFDUDBXDqJa4/fh/DgPkP3uYPnH7e+os/01CYZC+ugLmcdrApOGzmZ3l7LACdaB9lzUTYFxGqz0WlUmfzWXQdrJ1AqkUEaxm3ZCII550GJ9wlTWdjt9rK0MMiQg+ehwXhTGE9ZhoU1N0OGxP9HlZjWcAMWOxqu8pzjLR4FE7EdirPH9/bPCAgXA35+dyN0NECAhy+yHZi4J7Lma9bL/TP7zfvb2UoYadEBEXprqXBGv7XokqEa6MBpgY8aLrBQjhFMldE0w2SuQ756Kvo8zq4M9XPf/bCBD91a5Qb4wG8zoPf80JJZ3E7zbcWtvl3767xrYUIi9vpAxkLx/EopZNzesxuFDalrHeZtkCF6YUPymGSx9KLg9duTYxr5bCccmDSMmpZGaoFTglWFC4aDYYqbsRzlPa8DEGPvburiyIiCEJ7sV4LT9BN6fJIZcz/bDvd3Rbe5xg5qwkNs7id5u3FKNremFZR4JXZgcb9Rt1o8eh2mxf7dNq0MFhJpcz7WznB71SwIpiTomy2cjufPxwe1y7aZGfY99EDrMayLEbTFIo6TofKiN/Vfh99HYb8Lob8Ll6a7ieSyrOyk2FlJ0umUBkAl3SDlZ0sKztZVMUs5Z8c8DDR78F/xP4ux/Osff7n+fE//Nc17x8wCkRS+cbCJHtx8NqtibF1YL9fiVBNt0UEq8DS7f0ThE7ToAWruivDTDerEOD47khiZxCE5rCOXaxziCOY6PPgsqvkSzqZgsZmIsd4n7R7bDU9MLoUeh3DMHh/ZZcPNyqTRqdd5TPzw415tPfphp3hyhWzC8Py8sFMBDBLildWYGwMrl5t3WvW82y1Q0RwuyFeCbY8dSXCaVRaayVCi+wMYE6+37gcZO07j0g8fICRyaF43QRv3WDy8iv4TvK9azGKohAKuAkF3Lw8C9FUnpVYltVYhkS2MhjWDXPlbCOeA2IEPXYm+j1M9nsY8R/MUcgXdb73079I8Yt/AX9kg+F773H93/yf5fvdhVzjYZK9OHjtViVCIyLCCVY3WsJxIo/YGYSLRgOhippusLZbEcy71tpxn+MyiXpRzBWEs0QTgrqqKlwa9vFw05y3LEbSIiK0ATmrCUdSKOn86ZNt1qt8iH0eB5+9NnzyVORu2Blu3TLbOEajZoji9LS5Yp5KmQKCppk97m/ebN1rdrISwSpYnNSWUSrBl78M//7fNxfM6HC09vNcX8f35ptcs7blzIYhstT6tpxNsF+h8OJ0P/FskZWdDKuxLDvpg4JOIlsikU3yYCOJ3aYw3udmvM8UFVwOFafTRnhonOzUDIp+8GKppFKNh0l2o+LnOKx5JN0UEbptZ7D+7seJCFKJIJx3GqhE2EzkKO61kfa77Qz4OphjUovj7AwNdpsQBKEO1mvjCQX/y1UiwkrMbOPttIuLv5WIiCDUJZkr8o1HkQOrq5MDHj5xeeh0B2I3Jjd+vznhBLMLw8JCZUI6NmYKCK+/3rr2jtB5O0M1J61E+NrX4N/8m+b3w++vm6Z9YrrRlrNF9Hkc9E32cXuyj3S+xNpulvXdLFuJXNkGBGanh33bA8BupoCuGzyJpHh+sg/NfXCVTUulGg+TbNBf3FF6qRKh25P0ozozQG3rh2G07vgShF6jgXNWtZVheqAHVhSPyyPqsS45gnDmaFLwH/A5GfA6iGWK5XbdVzuQpXWR6IHRpdCLbMZzfPvx9oFwuJsTQV6Y6juQln8irCJCA61aWsLEhDnhvH/f7MKQy5kT+qtXzQqEVk9E6/1enjYMfI5bDTmOd95pzX600g7SjbacbcDnsnNtNMC10QAlTWcrmWdjN8vabpZ0/uDF0OO0UdLMct213SzPFRxM9Y0RSu3gLuRxZLONh0n2YhltN3z+htFYsGKv2RkUxfxbbTHSdZmECOeXY3JCdN0oJ61Dl7sy7HNcJkIvnocF4SzRAuvh3IiP2NIuAE8jKRERWoyc1c4xu9ki//adFVwOlZlBL9dGAw2F0T3cTPLucqw8hrWp8LG5IS4NN9lSzzrB7WSZtd9vTjg7Menspp3hpCLC/fvN78P8PPz6rze/nX260ZazzdhtZhvIyX4PrwDxbJH1vSqFSDKPy27j2piZKRHLFHiUNvheaB4lpNBfSDPoUrk10YfnuA4ocFis64XBazcqEUqlg69js5nvhfX96HTmQCNlzjbbwQGTiAjCeeaYSoRwMl9e0PA6bQx128oAx197ezGbRhDOEtax9ClEhEtDPt5f3kU3YDtVIJErEjypFVuoSw+MLoV2kc6X+N7TKE6HyuNwikdbSV67HqobhqjpBm8v7rC4nS7/zONU+ez8CEP+FlQN9KJXux10qsUjNC8ifPjhwdtf/zpcv97cPjVLN9pydpg+j4M+j4PnxoMUSjpbiRzru1kGvA6eRTPEbRr2vYl32hMg6/Oyvhrn/kaCsaCb8T43E/2e2qJgLw5erWJQJ9ot1apCgMOCRrcrEWp9PrUsF+f1fCkIx5yzVmJ7VoZ8juloGOXDb1QqCq9cMSvUOm1tkxaPgtBeWlDB6HbYGO/3sLZXybQYSfPCcR2uhIaRs9o5xmVXeWlmgEyhxEY8x921BABv3Jk4NPnIFjS+uRAhmqoMvIf8Tj47P4LH2aJJiHVQ3yk7Q6epJyK04/dtRkSIxWBrq3Lb4YDLlw89LJ0v8WgryfJOhnxRP3Fly4npRlvOLuK0q0wPepke9PKxy0PsZgpshOOs//MEEU8QXQEK5uda0syyXrO01+z4MN7nYaLf7BZhU5XeFOu6YWeolYcAvW9nAGnzKFwsjhARDMMw8xDiu3D3LtOxJ7C1Usk2evDADE/udNjucd0ZejGbRhDOEi26Vl8e9pVFhGfRNHeasWULB5Cz2jlGVRRsqkLA7cDvsrMQTrEay7IQTh3oNR9J5vn24wjZQqXMdm7Yx6tzg+akpFVYRYRemNy0g3qTW28bfJzNBCtaqxDm5w99JluJHG89DLMayxJJ5SkU9YYrW05NN9py9hD9Xif9s8M8t3mPog5b3j42fIOs2yFtuYbud3x4uJnEriqEgi7G8TLu8BAs7nmIe6ESoRt2hnoiQq3gwk7SqJ2hGhERhPPMEdU5kVSeXCoDd+/iXlpkxIj0Rtiu9Tp/XCVCL5yHBeEs0aJOSpP9Hlx2lXxJJ53XCCfzrR+3XlBERLggKIrZUm4xmmYpmi6LCE8iKd5Z3EE39h8HH5kZ4PqeP7ulWFdIO9HisRu8/LI5YLCe8D760da/1nEDmaOwigjPPXfgZjpf4q2HYe6uJbCpCnNDPrxOe0OVLU3RjbacvYaigM+HI5FgKr3DVHoHLgdIePxs7OZYj2cJWzs+6AbruznWlX649Ar+Yo6J9A7jJRujmo7d1sXWRr0sIvRiJUK3ulkIQjc4ou3pyk4G1jcgGmVKyaPc7JGw3ePaK/eirUwQzhItularqsLMkJeFrRQAi9tpERFahIgIFwiv006hqJMv6ui6wXsrMR5upsr3O+0qn5kfbt/BVc+jfN64dAl++7fN1omplNmV4Wd/Fl54ofWvZX0PT5ITcIyI8GgryWosi01VmA/5y+Vfx1W2NE032nL2Ij4fJBKV2+k0wYEBgmMOro+ZHR/CyTwb8Swb8VylFatuDl5TDjeP+id4lLCh/mDVrFLo8zA14CHQ6WChXhIRWrS6cWoamVxIJYJwkTii7elqLAuRMCQSTE8N9k7Y7nEtHkVEEITmaKH1cG7YVxYRVnYyvDI70N2FlXOCiAhHoCjKGPB3gDeASSAOvA38U8MwvtbEdoPAl4GfA2aBLPA+8FuGYfx+s/tdj0yhhNOhoqrw9YdhthKVQXa/18Fnr43gb4fHfZ+LYmcA+NznzL/t5riBzFE8eHDwtkVEWN7JEEnlmRvyHfKP1atsaRmdbsvZi1h/x1TqwE27TWWi38NEv9k6NJkrshnPsV5IsmXolJS9C6SqohuwGc+zGc/z3vIuA16HmcMw4KXP24HjsBsiQj3RstsTdBERBOEgdfIDtlN5sx1usYSzmGc0MFj7+d0I25VgRUFoLy28Dg77XQTcdpK5EsW9dtqzQ012nBNERKiHoih3gD8BhvZ+lACGMQWFP68oyt81DOMfnWK7U8A3gbm9H6WAIPA54HOKovyWYRh/o9n9t2IYBhvxHH6nnbXdHIVSJR19ZtDLxy4P4jiBKneqsL2LYmfoJNaBzK//OvyTf9LYcyORg7ctIkK+qFMo6nidtT/P6sqWttDJtpy9iLU7RTpd+3F7BNwOAm4H86kVtCd/yrY7yLpvkA2XjV3LY2OZIrFMnB+txgm47XuCgqc1XVhq0QuVCPvWn7NgZ+h2boMgdJI6wtrKzl5XBoedSbWImumhsF0REQShvdjt6ICmGWi6wffurhMPrp062Htu2MePVuOAaWkQEaF55KxWA0VRPMAfYAoI7wG/ZBjGvb0Kgr8H/LfAbyiK8q5hGF85wXYV4PcxBYRnwC8ahvGniqK4gV8G/hHw1xVFec8wjH/Z7O+hG+aBt+9hj6byOO0qkwOe8mPuTPVxe7LvRNs9ddjeRbEzdBLrQCaZNP+elL4+GB09uGmHitOhkimUapa/71e2uBxSEtYWrJUIx4gIZUolbIbBaDbOaDbOS9MeMi9NsL6bY203y2Y8eyBLIZkrcX89wf31BH63nUtDXmaHfPR5Wlih0Asiwv755izYGSQTQbhI1BMR9hLVGQkxE/b1VtiuVbCQTARBaClZQ4GihqYb6AY8WI3x7Gn01MHes0PesoiwEc+RK2q4HXJcNoOICLX5a5g2gxTwRcMw1gAMw0gAf0tRlCvAXwD+V6BhEQH4GeBjgA78RcMw3t/bbg74x4qiTAC/CvwDRVF+2zCME9SmHyZf0nlvOYbdrlAs6ThsKvOjAVx2Gw6bwieuDDE1cLKOAU2F7V0kO0OnmJpqzXY+8pFDXtOZQS+PwymzgsVlP2Bp2K9sGfG7RM1tF9ZKBIudoS41VsC8TjtXQ36uhvwUNZ313SwrO1nWd7OU9EpVUipX4u5agrtrCQZ9Di4N+5gd9DXf5rWXWjxaJ+i9KCKInUG4SNToZBBLF0jlzJ/bpyYZS43Dexu9E7ZrXQTZ3oYvfrFy+9mzg/dLJYIgNEw6X2IrVWBYM1DAzOYa8qAM+U4d7B1wOxgJuIgk8xgGLEUz7QmRv0DIWa02v7j37+/uCwgW/jGmiPARRVGuG4bx8ITb/eq+gGDhN4FfAcYw7Q1/dIJ9PoTPZeeFqX4Wo2k8PhuhgAuX3caA18Gn54dPFa7WVNieVCK0np/7OfiDP0D//vfLJV8GBgpme0+bTeHYOoFQCL785UM/vjYa4NFWkrtrCRbCKcb73AcEI003mBrwMB+6APkE3eC0IsIxk1SHTWV2yMfskI+SprMRz7ESy7AWy1LUKoLCTrrITnqX95Z3mez3cCXkZzzoRj1N21drGJph1H5cK2m0EqEX7QzdFjoEoZPU6M6wEsuUb06N9WO7/Doo9E7YrrUKUNPgBz+o/3gREQShYR5tJbGVYASw2czxg13Xmg72nhv2EkmaY4PF7bSICE0iZzULiqIEgJf3bv5xnYd9DzNksQ/4CaBREeHHj9quYRhriqLcA27TAhHB67Dhcdm4Nlo5SK6M+Hi5iVTSpsL2JBOh9QSDbP32/8133llgaztBNF0o20uGfE7G+9x86uowI4EjSr5GRg5PWjBFqNeuhwAzIXsxmi5ve8TvYmrAw2vXQ61t7yhUOK2dwXqcHVHxY7epZh7CoJeSprO+m+NZNM36brbc9tUwzM9/NZbF67RxecTH5RH/yUJYeyFYcb/8WOwMgtBb1OjOsLKTLd+cHvTC4HBvhe263RAINGYfVBQYGGj/PgnCOWF5J8OoxoFFC2Wv81Qzwd7Tg17+7FkM3YCddIF4ttha6+YFQ0b/h3kOU+8GuFfrAYZh6IqiPAReBRqqn1MUJUQlpLHmdve4jykiNF2Xl8qXKO2tLNpUeOXSIFdGmrvQNhW2JyJCyynbS5IKNk8/42MHqwXWcwb5mMIb00OnmuyPBt28cWeChXCKpWi6HKI5O+RjPuQXAaGdnFZEqJN0fhx2m8rMkJeZIS/5ksbKTpbF7XRZtQfIFLSy3WFywMONsUBjnsRurKxbPcpiZxCE3sTy/Y47PMSz5njBrpoTBqC3wnZtNviVX4F/+A+Pf+xf/atm7pAgCA2RL+oUVduBIkal6jxx2mBvl93G5ICnLFI+207zQqu7i10gZAZwmPGq/68f8bj9+8aPeEzbt6soSr36uRv7//G77Xzm6jADvuYn7U2F7VnLiyUToWmaspc0iM9l58Xp/ta3cRSOxmvJK2nUzmAV604R6OWy28oZCvFskaeRFIvbaXJVF+y1WJa1WJZ+r4NrowEuDXnrVzh1o9tAPfvUWbAziIggXCQs3+8VZ6UDw3i/u3f7uf+Nv2FaCldX6z8mFDIzHARBaBiXQ0Wx2zGMihtS1SvniWaCvS8N+SoiQjTNnam+Q5XVQmOIiHCYaiNytu6jYN+w1+jSfru2eyTTgx4+NjeE096ai3BTYXtSidBymrKXCL1NE90ZDtCkWNfncfDSzAAvTPWztpstH//77GaKvL24ww9Xdpkf9XN9zAxvPYB19b8bmQj7lQjWSXunrQJWQaCGlUhaPAoXCss5a9VZsWBOnzD8ueOMjh7qbCQIQnPMDHopuZ3oulHORFD2zhPNBntP9ntw2lUKJZ10XiOSzBM6QZcHoYKICGccwzBervVzRVF+4HXaPvKZ+ZGWvl5TYXsSrNhymrKXCL2NVURoUbDiaVFVpZyfEM8WWdhK8jSSLnd3yJd07q4leLCZZD7k57nxYKV9klXl70YlQj07Qy9WInTbciEInaTq+510uNmxmQN6VYGJfk+9ZwmCcE65NhpgyefGADTNQFUVjFKJZK7YdLC3qirMDnlZ2DLHVIvbaRERTomICIepXu7zAPVSc/bl8QZH9oe2W4+Tbrcu7eh/2lTYnrR4bDlN2UuE3sbanaENwYqnpc/j4JVLg9yZ6udJxOzZnM6bE4GSZvDhRpJHW0muhvzcGAvi60Z5fr1MhG7bGRrJrBA7g3CRqPp+r/qGyiLaWJ+7ZVWUgiCcHXwuOzMjAWw2BU03O4/FHz8jFfqAWa+T0aCLVynhe9rguMjCpWyJhU2z8Hs5rPBydgv7aTpP9QrW8U6HEBHhMNV5BRPU77wwsffvxim3+0GLtttxTh22J3aGltOUvUTobU4rIrSpEqEWTrvKc+NBro8GWN7JcG89UQ5E03R4uJliYSvFtbjBTdWOW9+bsHczE6HbVgEJVhSEg1R9v5cDlW5B04M9bmUQBKFt+H1udIet3L78x7/2//C5r/1+4+3Lj2AE8F/6KCmHmyKwvvEhM6ntFu15F4hEuvKyIiIc5gFgYHZouEUNEUFRFBW4vnfzfiMbNQwjoijKNjC8t9167SP3uzI0tN1u4XPZmQ/5MQyD5Z0M+aLOUjSNYRhcGw0cFhI07eBAWFHaOrnpNul8iUdbyfJ743KozAx6a783TdCUvUTobU5rZ2ikXL7FqKrCpWEfs0NeVmNZ7q3H2UmbYoJuwIO0wZO5V7m5s8L13TXsnZi418tEsJ53um1nkBaPwkVn75jI2J1E3QFQVRTF9C4LgnBBsdtRAdWm4LC1vkpgLrHFB0OzADwNjp5tEaFLiIhgwTCMpKIofwZ8FPg88O9qPOxjwH6/nq+dYPNfB/7S3nb/N+udiqJMYgoMJ91ue0ml4N49ePKk3Js5OjbFW7YRnuVVIql82dLwOGyWNr92PXSw9VutEutzmoa6lcjx1sMwq7FsY+9NEzRlLxF6G2slwmlFhA7ahhSlkpuwvpvlg7U40VQBFIWiauOHw5d41D/B87YAl3XjQA/olmMVEdx7x5xVVOn0Kr9VEBA7g3DR2TsmVvzD5m1FZTToaoslUxCEM8Lzz7d185eS4bKIsOkdIFddLSk0hMwsavO7mCLCLyqK8g8Mw7BaC/7W3r8/MAyjnt2h3nb/EvAFRVFeMAzjh5b7/xvMCogNTMGh+6yvw5tvwtISbG5CPk/R7mBD8aG6BuGVzzJ3eebA6vfdtQQAb9yZqExe64WcnTPS+RJvPQxzdy2BTVWYG/Id/940yantJUJvY61EyGRqP85KIyvdHWCi38NEv4eVnQzvrzwth8tk7U7edod4dG+TVy4NEAq0KdDIKiLUszN0eoLeyOfT7X0UhE6yd0yURQSb2vtdGQRBaC9f/CIsLsIf/dHhOUQLCADD/UG2HV50YCnwMa5noy1/nY6QTDa+0NRCZHZRm/8D+FVgFvhDRVF+yTCM+4qiBID/EfjZvcf9XesTFUXZ7132PxuG8fctd/+/wPcxKxn+vaIof9kwjO8piuIC/uu91wT4nwzDaP0Rc1JSKVNAePddc1B77Rr4fGytRsjfW2CyEGa8z8PypTFKqoOA24HfZWchnGI1lmUhnKq0FexA2Fsv8GgryWosi01VmA/5yxkFR743LcDnsvPidL+0cTxPtKoSoQN2hqOYHvQyOeXm6dYCHwzNkrU7QTfYzRT56v0wc8M+Xprpb/2q41m2M3Q7t0EQOommkbM5iHj2CjxVlSkREQThYqOq8Ku/av5tE3NbSbafxQBY9Dm5fnusba/VVl5+2ZyrdRiJva2BYRhZ4GeAKPAR4J6iKHFgF/jvMDMT/o5hGF854XYN4OeBRWAO+K6iKEnMTgy/ifl5/O+GYfzLVv0uTXHvnlmBYLPBrVsQDILNxrbqYmn0Eh6PE9/mGn2LC+WnKIrCeJ+bSCrPUrQqCO6CdGZY3skQSeUZ73MfCDmEI94bQajFGcpEOA7VZuNqYpMvPnuHO9El7FUlg4vbaf7jD9d5uJlE140jtnJC6gUrngU7g2QiCBcJTWPVP8T+0T+ilvA4xcogCEJ7mR70su+q3EkXysHQQmOIiFCHPavBbeCfAU8BF6ao8P8BnzcM4x+dcrurwIvAb2CGONox20h+HfgFwzD+evN73yKePDEtDDMzB/ILSppBSYfC2CTunQj+1WcHnuZ12ikUdfLFqoHvBbEz5Is6haKO11l74lbzvRGEWng8B3ND8vnjV811HQzLRNw6Ie0Ge/tgN3Ru7yzz5+OPmR6shKYVNYMfLMX4yv1NdjMtKsKql4lgfT/EziAI3UXTWPKHyjdnbOJLFgSh/bgdNiaqAlyfbcsC30no/hJVD2MYxibwK3t/G33OsUlhhmEkgP9+72/vksuZA3FLWbXdpmC3KWScbgYKeWyFg4P1TKGE06HiclQN1i+IncHlUHE6VDKFEgH34d+x5nsjCLVQFPPYq65AyGTMiqB61KpC6IUAU8vE3Vcq8Jn5ETbiWf7sWYxkztzvnXSRP7q7ye3JPm6OB5sLXqyXiWBd+e9FO0O3hQ5B6CC5gkbY21e+PWOX1UBBEDrD3LCP1VgWgGfRNHem+g5VEgu1kZmMUB+326wYsPSnH/Q58bvtZHcTaA4nmrNSVWAYBhvxHCN+F7NDVeJDvdLic8bMoJcRv4uNeA7DsiJc970RhHqcNBehB60MQN3y/PE+D3/u+XFemO7DtvcQ3YAfrcb543ubxNKnr0ooZXMUNYNcUSdb1PjmUoL3lmPkrZe9btsZpMWjcMFZLaoVK0M2gccuQ1NBEDrDRL8H5945J53XiCTzxzxD2EfO1EJ9rlyBsTFYXj5QIj0acDPgcRCIbLDm6WMrNIWmGyRzRRbCKTTdYGrAw3yoytN9QUSEa6MBpgY8aLrBQjhFMlc8/r0RhHpYRYT0MaV2vSoiHFGeb1MVbk308dO3xxn2V84LsUyRP763yQer8RNnJWwlcoS34+RLGgVNp6DpvB/J8icPwtzfTKJVC3zdtjNIo2KtHAAAIABJREFUi0fhgrOUrwxFZ1KRrnWUEQTh4mFTFWYGK0Gui2JpaBgREYT63LoFs7PmAPbePYjHQdNwZVLcjK0y7LGTH5/m/cAE7y3HWIymCbjt3J4M8tr10MHWghfEzuBz2Xnteojbk0ECbjuL0fTx740g1OOk4Yo90t7xEA2srPd5HHz+5igvzfQfqEr4YC3OVz/cIp1vzHaw32Y1m8xiGOYAwWFTGRsOksyVWE+VKJR0ynvQbTuDiAjCBSZX1AiXKqXDM8nt3hE/BUG4EFwarogIyzsZtFaGPJ9j5Ewt1Mfvh9dfN/+/tAQLC6bP2OXCNzbGzIsvkH/5M7zsCJIv6rgcKrNDPuZD/sOT5Hr+5HPIaNDNG3cmWAinWIqmj39vBKEeVhHhX/wLszqoHtnswdu9ItZZ/YXW8MfywxSeGw8yOeDhe0+ibKfMCqbtVIH/9MEGH788xPTg0a3f9tusfqJUwGarvK7bbx5/2O1ouoGmGag2pfNWAevr1Qq+lBaPwgVhNZbB2Buwj2QTeLRCb4TBCoJwYQgF3PhcNtJ5jaJmsL6bPXasIYiIIBzHxAR86Utw/z48fmyGLbrdcPUqrps3ue33c7uR7VgrEc6xiABmRcKL0/28ON3f7V0RzjJWO8Obb57s+b1aiXDMynrQbVYl3N9I8KPVOIZhdnD41sI286N+Xprux26rUd3wT/8pk//2P/CziTQD0c2DdzscKIpCwOtEN0DTDRw2pTcrESRYUbggLEUzYJgi2UwqYv5QKhEEQegwl4Z83FtPAOZ5SUSE45EztXA8fj+8+qr597RcEDuDILSUwcHmnh8ItGY/muUUK+uKYmYlhAJu/vTJNum8OZFe2EoRSeb51NVh+jxV55E334Tf/E0CRQ2XpmOziAy6wxQuHS4nxt4foPMTdOvriZ1BuKDkihrhZL58PphJbpt39Ir4KQjChaFaRFjbzVAo6eXARaE28u4IneEC2RkEoWX83M81N6D+hV9o3b40QxPdBkYCLl6/Pc70YKWX8+5e6OJqLFN54HvvAaDs/al2TOhOF4WA2Rozb1QeA3Q/WFHsDMIFZTWWMY9TXa9YGUBEBEEQOk6f18GA11yY0HQOji+EmkglwnlmcxN+4ze6vRcmDx8evC0igiAcz6c/DV/5Crz99sknu9evwyc/2Z79OinWiXKdTIR6OO0qn5kf4XE4yQ+WYmg6lDSDbz7a5s5UH7cmgigRsxTapiqoOui6YWYiKCof/uX/Et3pwjAMYjkNVTEfB3TeztBIJYLYGYQLwFJ0b5Cu6xUrA4iIIAhCV7g07CO2vAvAs2iayyPSSe0oREQ4z4TDZhBbLyIigiA0xnPPmX/PMi2aFF8NBRjyufjmQqRsb/jRapxYpsDHt6M4AJtNwaYr/Kf//G+ydvMl/DMTOAYGyOSKbMRz3FJt2FSlErrYbTtDrQmT2BmEc07ZygCgGxUrA0gmgiAIXWF2yMt7eyLCViJPtqDhcYqoWQ85UwvdwRoYJwhCV0nnSzzaSrK8kyl3FJkZ9HJtNNB8R5Em7AxWBnxOfurWGN95vM1WwpyErOxkSRSDfNbhJlDM4bSrBD76IsXRKzxK5Sksx3A6VEb8LiaH/TjtasXL1207g4gIwgWkbGUARvRsxcoA0p1BEISu4HXaGQ262ErkMQxY2klzYyzY7d3qWUREEDqPzQZvvNHtvRAEYY+tRI63HoZZjWWJpPIUijpOh8rjcIpHW0leux5iNOg+/Qu0UEQAcDts/Pj1EO+txHi4mQIgni3xx9Mv8dn1e4RyCT79iZuEXAOH2qzeiA5jq2452Yt2BslEEM45yzsVv/GMZvEeSyWCIAhdYnbIV16geLadERHhCORMfZ4ZG4Mvf7nbe3EQu930eT//fLf3RBAEzAqEtx6GubuWwKYqzA358DrtZAolNuI57q6ZacVv3Jk4fUVCi0UEc5MKL88OMuB18s7iDlo6RcFm5+tTd/jE5gNmJkZ50es93GbV4zp4uxftDG14vwShV8gVtfIgHWBGSx18gGQiCILQJaYHPfzZM9AN2EkXSOSKBN3SUa4WIiKcZ0Ih+OVf7vZeCILQwzzaSrIay2JTFeZDfpS9VfqA24HfZWchnGI1lmUhnDo8IW+UNk6KL4/4CRazfLOQI2dzoCkK3559gY8kStyo1ea526GFYmcQLjgHrAwBFx7N0gJaRARBELqEy25jot/DaiwLwNJ2huen+rq8V72JGM8EQRAuMMs7GSKpPON97rKAsI+iKIz3uYmk8ixF06d/kTavrA9nE3xh+T2CBfOiTyDAu0u7/GAphmHtBGEtle5FO0O3hQ5BaCMHrAyD3saENUEQhA5xaaiS27bYzNjnnCMigiAIwgUmX9QpFHW8ztqFaV6nnUJRJ19sYuLf7klxJIK/lOcnV95nJJsAv9mW6eFmkm8/3kbTq4QE66Rd7AyC0DGsVobpQc/h77dkIgiC0EUm+t3Y9zo4pXIltlP5Y55xMRERQRAE4QLjcqg4HSqZQu0V+UyhhNOh4nI0cblod1Dgttkezq2X+PG1D5iuyoBc2cny1sMwRW3vNbu9ym9ddW0kWFEqEYRzgtXK4HXaDx8T0p1BEIQuYrepTA9U/JBNVWKeY+RMLQiCcIGZGfQy4nexEc8dKv03DIONeI4Rv4vZoSbaslpsElgtBs2yXekxbzd0Pt1ncH0sUP7ZViLP1x+EKZT03rMzSCaCcIE4ZGWAxoQ1QRCEDjI3XBnzLEUz6HqLxy3nABERBEEQLjDXRgNMDXjQdIOFcIpkroimGyRzRRbCKTTdYGrAw3zIf/oXaXd5fpWIAKAMD/Py7AAvTFfCkLZTBf7kwRY5pcvtE08jIoidQTgH1LQywOHvt2QiCILQZUaDLjxOc+ySK+psJXNd3qPeQ0QEQRCEC4zPZee16yFuTwYJuO0sRtO8txxjMZom4LZzezLIa9dDp2/vCO1fWbeICIyMAHBroo+XZwfKP95JF/laRCNrc1Ye2+lKhEZWXbttuRCENlDTygASrCgIQs+hKAozg5VqhGfbmSMefTGRmjFBEIQLzmjQzRt3JlgIp1iKpskXdVwOldkhH/Mhf3MCArS/EiESOXh7eLj83+tjAWyqwtuLOwDENYWvTt/hc6sf4CvlOz9Bb2TVVewMwjmkehBetjJAY9U5giAIHebSkJeHm0kAVmIZPqoNYLfJ+vs+IiIIgiAI+Fx2Xpzu58Xp/tZv3CoiNJGJkM6XeLSVZHknUxY7fmxlgyBVpXVDQweeczXkx64qfPdpFENVSTo8fHXqBT6/+j7ebgcrip1BuABkCiXCSdPKoCgiIgiC0PsM+V0E3HaSuRIlzWB9N8fMkPf4J14QREQQBEEQ2os1WPGUE/etRI63HoZZjWWJpPIUijpOh8ora5u4ihpOu4pNUcp2hmouDfuwqQrfWVlBB9IOF1+busNPajqeU+3NKRE7g3ABqQ5UNL3GVULBORQRaomdM4Nero0Gmq/sEgShY1wa8vHBWhyAZ9G0iAhVyJlMEARBaC8tWFlP50u89TDM3bUENlVhbsiH12knUyjh2o1R1AxAx+WwoVbZGaqZHvTymSkf3zIMdEUh6fDwtcAQP1nUcDs6NHGxTphqtbMTO4NwzliKVlsZLJ1ezll3hnpi5+NwikdbSV67HmI06D5+Q4IgdJ3ZYW9ZRFjfzZIvabjsZ1/obAVi7BAEQRDai7USAU5saXi0lWQ1lsWmKsyH/ATcDmyqQp+q4y1kUABNNyihQH99S8Zk0MWnNj5kf48SioM/eRAmV+zQRN0qCNSaMImIIJwjkrki0VQBAFWp6sqwzznqzlAtdiZzJeaGfLw0M8DckI9krsTdtQRvPQyTznc40FUQhFMRdDsY9JlhzLoBKzsSsLjP2ZZ7BUEQhLOBqh6cLGha/RXHDz+Ee/cOCA2lZzuMbSW5E3DhdlSe50gn9zavoOkG2UA/zlqr+/vYbEyno3xy4wF/On4DQ9fZzRR562GYz90YxWlvs7Z+GjtDExkSRyEl10InqK5CGOtzH17FO0fdGaxip7InoAbcDvwuOwvhFKuxLAvhVHvyZwRBaDlzwz520qYQ+mw7w9VQoMt71BvIKEFoGzJAFQShjM12UESoNzH+vd+DX/u1Qz++XdS4puk46iQjKwoYGOSC/fQdtR97k/bZVAR9U+G7e9aHnXSRrz8M87kbobqv0RJ6xM4gJddCp6jOQ7g05Dv8gEaqc84IyzsZIqk8c0O+soCwj6IojPe5WYymWYqmRUQQhDPCzKCXd5djGAaEk3nS+ZLMYxARQWgTMkAVBOEAVktDvVyE3/md2k/f+2MY9d0RCgrFwdp5CGWqJu1zyTBa7Blv792Opgp8e2GbH7s2gqrWeJFW0AN2hqPyJTbiOe6uJQB4486EDJSEpohniuxmigDYVYXJgRoxpo0Ia2eEfFGnUNTxOmsfN16nnUJRJ1+UjiuCcFbwOG2MBd1sxHOAWV11cyLY5b3qPmf3TC30LOIJFAThENaJQT0RIZGo+WObqqAqoOu1Kxh03TDv/4mfOHo/LJP2q4ktXrk0UL69Ec/xvadRjDZZCHqhO0O9fImA28F8yI9NVcol14LQDEs76fL/J/o9tat8zlElgsuh4nSoZAq1xzeZQgmnQ8XlkOG3IJwlZqu6MixF00c88uJwds/UQs8inkBBEA7R6MTY+vMvfAGCQdB01rfTRNMFVMDrsuNQVYq6TiZfQlNU1I++zPN/868fvR/WCYqmcW00QL6oV7VxyuBy2Hh5dqDGBpqkkXZ2jQoup0RKroVO8awqD2G2Xmu0c5SJMDPo5XE4xUY8h99lP3B8GYbBRjzHiN/FbC1bhyAIPcvUgBebuoOmQyxTJJ4t0udxdHu3uoqICELLkQGqIAiHaLTNo3WS/ff+Hly+jAPoS+R4r4ZNasTvYmrAw2vXQ/jcx1zU64gZz0/1kStpLGyZq+8PN5N4HLbWlyw2IiK02c4gJddCJ4im8qRypkBgtylM9NewMsC56s5wbTTAo60kd9cSLIRTjPe5D1iFNN1gasDDfMjf7V0VBOEEOO0qE/0eVnayACxHMzw/dWQC07lHRASh5cgAVRCEQ1iDDOrZBY6YZI8G3bxxZ4KFcIqlaLoc2Do75GM+5G/Mv2+tRKhaBX1ldoBcUSsPEt5f2cXtULk80sIBfw9kIlSXXAdqiC5Sci20gqWqQMXpAS+2ejkj56gSweey89r1EACrsSyL0XRtsVOyRgThzDE76CuPD55F0yIidHsHhPOHDFAFQTjEae0MlgmFz2Xnxen+01cxWSftVaugiqLwySvDfL0YJpzMA/D9xR3cDlv9VdST0siEqdGqjVMiJddCuzEMg+VGrAxwrjIRoEVipyAIJyOVMltDP3kCuRy43XDlCty6Bf7WLARM9Lux2xRKmkEyV2InXWDQ52zJts8iciYTWo4MUAVBOMRp7QytXpW0ihmWSb1NVfjstRG+9uEWsUwRw4BvP97mCzdH6fe2YLBwmkyEFlciSMm10G4iqTyZgvm9ddlVxo7qxnSOujPs07TYKQhC46yvw5tvwtISbG5CPg8uFzx4AHfvwuuvw8RE0y9jt6lMDXh4tm0KpEvRtIgIgtBKZIAqCMIhGg0LbLeIUCNY0YrTrvLa9RBfub9JOq9R0gy+8SjCF26O4XE2uT89kInQjpLrdL7Eo60kyzuZ8srrzKCXa6MBWXm9gCxVVSFMD3qPbpl6zioRBEHoIKmUKSC8+6557bx2DXw+SKdheRmiUfNxX/pSSyoSZod8ZRFheSfDi9P9h/LfLgpyphZajngCBUE4hFVEqJeJ0G5/dIMTdI/Txo9dG+Er97coaQbpvMY3HkX4yedC2Gu1qWuURlo8ttnOAK0tud5K5HirRuDl43CKR1tJXrseYvSolWjhXKHrB60Ml46yMkD7hUNBEM4v9+6ZFQg2m2ld2J/QB4Pm7f3779+HV19t+uXGg26cdpVCSSed14ik8oQCF/P6JrM4oS2IJ1AQhANYlfp6q+vWCXOrVyWtExTrpL6Kfq+Tz8wP89bDCIYBO+kC330a5dNXh0+/8tADdoZ9WlFync6XeOthmLtrCWyqwtyQ70Dl2d21BABv3JmQ8/4FYSuZI18yj2OPU2Uk4Dr6CSIiCIJwWp48MS0M164dHmcoCkxPw8ICPH7cEhFBVRWmBzw8iaQBs0uDiAiC0GLEEyhcKDoQ6nOmOW0mQqv90apqDiyqKyF0ve7rjPd5eGV2gHeexQBY2cny/souL80MnO71e8DO0EoebSVZjWWxqQrzIX9ZXAm4HfhddhbCKVZjWRbCKbkWXBD2S30BZgb/f/buPLjR/Dzs/Pf34iQOHuDN5tEX+5xp9fQcmpFGo5YlyxprbFW0VR5HduSKXU5qnUptnK1yxWXH68RJqlT2xo6dijZSrWOnvGtXNltrpyyPY1ujicbSzGhmei72RbIPXs0m2AAJEvfxvvvHC5LgS5AEQAAEwOfTxWqCePniJUAC+D3vc+wc9bxDC01nEELUWTJp9kDw7tJnzeczr08mq3aTx3u8W0GEcJwro117l2y1KAkiCCHEQdWpqU9TK7UnQj0WFDbb9tvJ5fYMVoz3+1lLZrn9cB2Am4vr+N0OTpfb18UwWi6IMBuOsxxNcaJ752JRKcVgh5t7oRgzoZgEEY6AnG4wv1LiVIYNtc4+EkK0LrfbfL8Vi5klDFbRqHm9u3rZAn1+F21OjURaJ5nRCa6nGOg4etkIzd8CVwghDlNhU5/VVTOl7tlnzf9XV82vv/KKud1RVmoQoR4LijJKGjZcGe1kqHPrTcI798ME18s8s2H92Wy2nemXUHr/iAaQyuikMzoeZ/HHyeO0k87opDLV7+sgGs+D1QSZnPn76nPb6fHtU8oAO//+WmA6gxCiTk6dgoEBs4mi9bXSMGBuzrz+9Omq3aRSitHAVoD0fihWtX03E3mmFkKIg7A29WlvNz/faOpjs2019TnKKs1EqMWCooQJDVZKKT55uocujwMA3YC/nXpEPL1/AGLX29ntZ2uiTASXQ8Pp0Ha9H+LpLE6HhsshbzeOgsKpDIVvsnel6zvf+EsQQQhRqosXYWzMfJ28fh0iEfPzSMS8nMuZ11+4UNWbLRxTPxeOk9MbN9hfK/JMLYQQB7HR1Gd0dPemPg8fmk19jrJSggjFFhS1KmcoVOIi3WHTeOFMLy67+bMkMzrfnXxU+puHUiYzQN0aK1bDaMBDr8/FYiSJYXnsDMNgMZKk1+fa9oZLtKZ0VmdhdSuIcKKUx7xYec8RHZcmhKiAz2eWjF65Ap2dZhPFN980/+/sNL/+4otV703V43PhdZnvJTI5g8VIoqr7bwZSeCaEEAdRh6Y+sVSWyaV1ZsPxzUknowEPZ/r9zdPxvpSFcbEz9bVYUFRQzrDB67Lz/HgPr94Kbk5s+MG9MM+d6t7/m0sNIlRzxGMiAb/xG/DOO/vvJxCAX/gFuHq15N2f6fczubTOxMIaU8Eogx3ubdMZcrrBcFcb4+X2jxBNZ24lTi7/K9blcdCRz9rZU7ESHyGEKMfQELz8spnxOT291dz69GkzA6FGza3Hur3ceGBOIJoNxRnuKiH7qoU0ybtPIYRoUDVu6rO0luS120HmVxIsR1OkMzpOh8Z0MMrk0jpXz/bR394EDX1KWRjXa0FRQTlDof52N1dGu3h3xpzYcO9RjIDXydkB/97faP35dkvbrmYmwr/9t/AHf1D69m+9BR99VPx3uQivy87Vs30AzK8kuBeKbf6O9vpcDHe1cfVsX/MEu0TF7j/aqgs+3lNi5olMZhBCVIPPZ45wrMIYx1KNBTybQYT5lQTZnI7ddnSS/OVVXQghDuLUKXMKw+ysWZtXeOb8gE19Yqksr90OMrGwhk1TnOj2bjvLO7Fgvni9dGmo8Rdp1oyCYs0C6zUvvgqL9LMDflbiae7mxzxdm12h0+PYO6BTaSbCQYIIH3xQ3vaZDExOwlNPlfwt/e1uXro0xFQwykwotpktM9btZbzP1/i/m+LAYqksS2spwPxTP15q+Yr1d1smMwghmkSX10l7m521RJasbrCwmjhSpXvybC2EEAdx8aI5xjEUMpv4jIyYEfFo1AwgHKCpz+TSOvMrCWyaYrzPtzlCz+924HPZmQpGmV9JMBWMNv74vFJ6ItTrrOQBMxE2PH08QCSRIRRNY+QbLX7hsYHdF82lLpiqGURIpcr/nnS67G/xuuxcHuls/N9DUROF3ckH2t20OUv82y212agQQjSg491ePpyPAGZj2aMURJBnayGEOIgaNvWZDcdZjqYY7HBvBhA2KKUY7HCzHE0x0wzjhUo5+39Y5Qxl9EQoZNMUnxrvoc1p/myprM7rU3s0Wqx0OsNBeiJYAwK/93vw13+9/ePKle3bZDKV3544ku4/2mqoONZdRl1wqdk5QgjRgEYLnu8erCZIZ4/OOGN5thZCiIOqUVOfVEYnndHxOIs/VXucdtIZnVSmCV60SlkY1ysToYo9BzxOO8+f7uXbN5fQ840Wr82u8PTxwM6ND2M6gzWIcO6cmT1TqKtr7+8R1RONmhlLd+5sPU+cOmU+JjVq/lVrK7E0kYQZeLJripFSRjtukMaKQogm1u52EPA6CMcy6IbZYPZUb3M+l5dLgghCCFENNWjq43JoOB0a8XQWv3tnp/N4OovToeFyNEFSWSnlDPWqj65SOcOGXr+LJwoaLU4tRen1uXY2lyu150Mtyxmczp3bOCy/W5KJUBsPHsArr8DMjDn2NZUym67eumWWRL34ohmQbDL3CjKhhrvacJTTWEwaKwohmtxYt5dwbBUwpzRIEEEIIcShGg14mA5GWYwk8bns20oaDMNgMZKk1+dqjho8a2PFUqYz1Ko+umChogO35sJMxRcOND7z7ICf5fUUs2EzrfsH98J0eZzbx9xVGkSoZjlDsSCC9WsSRKi+aNQMIFy7Zj6+Z86YY2FjMbMpayhkbvfyy02VkWAYxrZyqrFSpzJsqFczVSGEqJHRgIf3Zs0gwsO1JMlMDrej9Z/LJIhwhLXE7HkhWtiZfj+TS+tMLKwxFYwy2OHeNp0hpxsMd7Ux3tcEi45GKmfI7zdnGKSzOm9PB7kZ8VQ0PrPweTSeyjG5tEabw0Zfu5vXp5f5kYsDW2dmG6GcweXauY31OCSIUH3Xr5sZCDbb9iku7e3m5Y3rb9yo64iyg1paS5FIm3/LLrvGYLnjZmU6gxCiyXlddnr9LpbXU+ZQrnCc8f59Rj63AHm2PqJaZva8EC3M67Jz9WwfYM4gvheKbf6t9vpcDHe1cfVsX3ME/RqsnEEH0lmdTM7gU//h32D71d/CGB0pa3xmsedRFMyFEyytpTgz4Oetu2GeH+8p/vNJOcPRceeOWcJw5szOrBylzKkuU1NmT5UmCiLce7SVhXC8x4OmqT22LkIyEYQQLWCs28Pyuvl6ez8kQQTRolpq9rwQLa6/3c1Ll4aYCkaZCcU2s4bGur2M9/ma52+0lLPr9Rr3ZrORyxnkdAMFDDy4x2d/85f5zu/+UcnjM/d6Hr39cJ2F1QRgnp3t9bs4O+A/nBGPpWQiWAML0lix+pJJM6Dj3SXd3+czr08m63tcB5DN6cytbE1lOF5JWZX0RBBCtIDRgId3Z1YwDFheTxFLZZvn/VmFWvunE0W11Ox5IY4Ar8vO5ZHO5v57tAYEjCJjEOuVidDdTU430A1zTCNAx71J3OFHJLt7N8dn3gvFmAnFit7vez2PPjnWRSKTYyWeJrie4r3ZFXr9LgKlLphKua9KVUpPBMlEqD232wzgxGJmCYNVNGpe726eDMCF1QTZnPm76Xfb6fYVCVDtR6YzCCFagNthY6DdzWLEDATPhuOcHyzyXN9CmqClt6i2lpo9L4RoDpWUM9RqQfGzP0vObsfA2JZZ3n5/avPz/cZn7vc8emm4g3ROJxxNoRvwvelHZDIlBhGqlYmQy23/XqWKB2YkiFB7p07BwIDZRNEaFDIMmJszrz99+nCOrwKFpQwnym2ouEEyEYQQLWK0e2u87VFYQ0kQ4QhqqdnzQojmUEkQoVblDJ/+NN/9v18h0jOwbT3XcW8riLDf+Mz9nkd9LgcBjxM9v//1ZJa3g5b+BLVurFgsC8Fajw8SRKiHixdhbMx8LK9fh0jE/DwSMS/ncub1Fy4c9pGWJJnJbZ5xA7MeuCLSWFEI0SJGujxstIUJxzKsJVv7tVSCCEdQ4ez5Yppq9rwQojlU0hOhhguKgVPD3Pixv4uub0UROu5OAqWNzyzledTrtpu9EPLur2W46+/f2qjWIx5L6YcAMuKxHnw+ePFFuHIFOjvNJopvvmn+39lpfv3FF5tmvOO9R7HNAFyv34Xf7dj7G3YjjRWFEC3CadcY6mzbvDwbiu+xdfOTkO8R1FKz54UQzcG6OChW51/H1OYz/X4ePXYRA8jlDDRN0XF3kvVkpqTxmaU+jz51PEAinTNTv3Wdd/pP05Ncoz2TKL0nQqWZCKVMZoCdmQjSWLE2hobg5ZfNMY7T02YTRbfbLGG4cKFpAgiwvZThZO8B3itIEEEI0ULGuj3Mr5iNle+HYjx2rOOQj6h2JIhwBLXU7HkhRHOwptEXO7texyZrXpedj/3wczhsipxuTmpwzd5nbjFMV6B93/GZ5TyPOu0aoViKNT1HVml8b/A8n597D1utpzOU0lQRpJyhnnw+c4RjE41xtArH0qzGzd8Ru6YYDVRYygDSE0EI0VKOdbZh1xRZ3WAtkWU1nqbTs8trb5OTIMIR1FKz54UQzaGScoYaLyj6hvvInTyOfn+WnG5gYPBZtUrX+fF9x2eW+zz6yVM9/NV7BjlgxeXl/Z6TPFnrxooSRBA1cHc5uvn5SMCDw3aA0keZziCEaCF2m8axrjZm8qUM90NxLksQQbSSlpl6dPuWAAAgAElEQVQ9L4RoDqU0VrSelaxDkzXbxYvY5uZw2MxMic9klmDACxj7Lqb722y8dL6XqWCU2VCMVFbHZdcY3XwetW3uo8upeMKd5Z38997uHKLf4We42I5Lua9KYS1n2K0nggQRRIlyusH9gjrfA5UygGQiCCFazmjAsxlEmA3Hm3s89x5kpXiEtcTseSFEcyilWaD1a7WazlDo4kX4y7/cuvxLv2R+lMgLXM5/7OcM8HDwAvO+bgDedPfzYiq7M2hb70wE69elJ4LYxcJKgnTW/Dv1umz0+XcJTJVKpjMIIVrMUGcbDpsikzOIJrOEY2kC3tbLRpD2+0IIIWqvlJ4Ih3FWss4j9T6+NIk3a2YIpDUb378TwrA2mSwWPKkkG0HKGUSV3X20Vcpwsse3raFoReo11lUIIerEpimOdW1NaZgJxfbYunnJs7UQQojaKyVF/zDOSj75ZF3Pfrr0LM8t3kIB9PSyvJ7ixuLazg2rkY1QajlDtUY8ZjK1+RANIZHOsRhJbl4+3nOAhoobJBNBCNGCCifczYZbc9SjPFsLIYSovUYtZ+jrg9/4Dfjt34ZwuPa3B/QZKR4/2ceHL7wAwEfzEQY72ranO2ra9gVWJZkIpY54tC7cyl24Ly3Bz/88vPPO/ttW4tw5+MY3zFGIDS6WyjK5tM5sOL7Za2g04OFMv7/pew3dexTbnMza53fhdzv2/oZSSE8EIUQLGmx3b5Y0xFI5HkVT9PgOWP7VYJr7Fa0GlFK9wJeBzwFXgGNADpgFvg38jmEY0xXu+yrwnRI27TUM41EltyGEEA2pQRsrAvAzP2N+1NEF3WDh5hKhaBrdgO/fecQXLg5g3+h0b7NtX8xXkolQaU+EcoMIX/967QIIALduwde+Bt/8Zu1uowqW1pK8djvI/EqC5Whqc1rHdDDK5NI6V8/20d/uPuzDrNi9R1spuQduqLhBpjMIIVqQpimGuzybz5uz4bgEEY6AB2y/X6KAEziX//g5pdTPGobxxwe4DR1Y3ud6IcR+olG4fh3u3IFkEtxuOHXKbJbn8x320YlClZQztPCCQtMUnzjVzSsTD8nmzHnS782t8vTxgLlBNcoZrEGEUqczlNtY8d698ravxMxM7W/jAGKpLK/dDjKxsIZNU5zo9uJx2omnsyxGkkwsmCUrL10aasqMhFA0RSRhBpfsmmIkUIVSBpBMBCFEyxrrLggihOI8MdJ58D4yDaT5Xslqzw58F/g/gb8yDOOhUsoGPAv8e8wm3P9ZKXXdMIwPK7yNOcMwjlflaIU4qh48gFdeMRcXDx+aqdsul3nWcmICXnwRhoYO+yjFhkqCCC3eZM3vdvDkWBdv3TXLKKaWogx1tnGss23nYsrafLEUpZYzHLSxojXoYLMd/LHT9e2/D5VOqKiTyaV15lcS2DTFeN9Ww0G/24HPZWcqGGV+JcFUMNqUE5HuFmQhjAQ8OGxV+tuUnghCiBY10O7GaddIZ3Xi6RyPoml6DzrRpoHIs/VOnzYM47uFXzAMIwd8Tyn1eWAC6AN+Efj7h3B8Qoho1AwgXLtmLljOnAGvF2IxmJ2FUMjc7uWXJSOhUTRqY8VDdqrXx4PVBHPhBABv3Q3xo48P4rbeX7UsZ6h2EOGP/xief768fVhNTsLVq1uXrWesG8xsOM5yNMWJbu+OM01KKQY73NwLxZgJxZouiJDTDe4XBBFOVauUAY5c4FAIcXRommKkq407yxslDbGWCiLIs7WFNYBguW4Z+Iv8xSfrc0RCiB2uXzczEGw2s3Shvd38vL3dvGyzmdffuHHYRyo2lLIoPkLlDIWePh6gzWneP8mMzlv3wqXdX/uptJzhoEGE3W6nHNYAUoMHEVIZnXRGx+MsHvjyOO2kMzqpTPNVK86vxMnkzEwYn9te3TfBRzBwKIQ4OqxTGnaMdG5iEkQoX/4UJ0fj3a0QjejOHbOEYXQUrPVlSsHIiHn9dEU9UEUtSE+EXbkdNp492b15eWElwXT7wPaNqjHisdTGiuX2RLDejjUoUYlq9ISoI5dDw+nQiKeLBzvi6SxOh4bL0Xxvu6aD0c3PT/bszLQ4EOmJIIRoYX1+Fy67+byfSOssr6f2+Y7m0XyvZofv0/n/Jw6wj16l1DWlVCz/MamU+oZS6vFqHKAQLS+ZNBcu3l3San0+8/pksvj1ov6sQYRi0fgjGkQAGOxo4+zAVunNtcAYEUfb1gaVjHisVzmDdfsjmIkwGvDQ2ebgw/lV3p8N8879MB/MrTAXjpHMmM0Ve32ubWelmsF6MsPSmvmmV6kqTmXYcIT/5oUQrU/TFKPdW41oZ8PxQzya6pK8sTIopb4EPJW/+J8OsCsP8ASwAniB8fzHzyql/plhGL9VxjG9u8tV5w5wfEI0NrfbXKjEYmYJg1U0al7vbt5xai2nkhGPNV5QxFJZJpfWmQ3HSWV0XA6N0YCHM/3+Q+mg/7HhTh5GzC74WZudNwbO8fm599EwalvOcNARj6VmPJTDGkRo8EyETo+D5fUUs+EEyUwOl93MTHCvmL/DQ51uzg/6Ge9rrh4tG7W8AEOdbbuWa1RMgghCiBY3GvAwtWRmdM2G4zw51tUSUxokE6FESqljwDfyF/+bYRh/WcFuVoHfxAxEtBmGEcAMKHwa+D5micRvKqW+UoVDFqJ1nToFAwNmE0XrGW3DgLk58/rTpw/n+MROpdT413Fm/NJakj//8AGv3gryxt0Qb94N8cbdEK/eCvLnHz5gaa3+WSx2m8YnTnWjKUBphN0+rgdGzCurUc6wWxDBumA/aE+EagQRrI99A2cixFJZfnDPnLDhd9vN9FWHjXTGTF1dT2YxDHjmeKCpxjvqusHd5a1ShtO1CIBITwQhRIvr87twO7b6HgVbpKShJZ6tlVK/Bvxahd/+NcMwfmWf/fuAP8WcyjAD/FwlN2QYxvvA+5av5YDvKqU+A7wKfBL4mlLqTwzD2Dd/1TCMog0e8xkKVyo5TiEa3sWL5hjHUMhssjgyYpYwRKNmACGXg7ExuHDhsI9UbLAuCg+xJ0IsleW120EmFtawaYoT3V48TjvxtJl2PrGwBsBLl4bqvujr8jq5NNzJ+/mgy/XAKMdiYQKNXM5QiyBCE5UzbIx39LsdXBxqZ3k9TSiWJpvTsWuKWDpHj89FJNm4P0MxC6sJkvlGkB6njcH2GmR2yXQGIUSLU0oxGvAwmc9GmAnF6a/F82mdtcqztYZ5Fr/Sj10ppdzAn2FmDywDP2IYxqNq/wCGYaSBf56/OIxZ7iCEKMbngxdfhCtXoLMTpqbgzTfN/zs7za+/+KKMd2wkpfREsC4Ua3RWcmPRZ9MU430+/G4HNk3hdzsY7/Nh0xTzKwmmChrK1dP5QT+9WTONXFeKNwbOkstUsAAtdXF/0MaKtZjO0ESNFTfGOw52uHE77IwEPFwe6eSp4wEuj3ZxabiDSDLDTCi2/84ayLaGir1eNK0G6bd1+psXQojDVNgXYS4cR9ebf0pDSzxbG4bx68CvV3u/Sikn8F+BH8IsRfi8YRi3q307Bd4q+PwksFu/AyHE0BC8/LI5xnF62myi6HabJQwXLkgAodFY6/9KKWeo0VnJjUXfie6dneaVUgx2uLkXijETinF5pLMmx7AXpRTPRhd4xegkqzQiTg8fLMW5cqbMHZVaznDQTIR69EQo95jqqBXHO0ZTZlbOhlO9NXo+rWMJkxBCHJZen4s2p0YirZPK6iytJxnsaNv/GxtYSwQRakEpZQf+GPgiEAV+NF+OIIRoFD4fPPOM+SEaWyWNFWt0VrIZFn1+cjyxfJe3+8y+HrfCSYbXk/T5y0iBrDQToZwFu2HUp5yhgTMRCsc7+t07x1s21XjHtTV45RXuLEQgbQadBm1ZvG8nanN7b765/bIEEYQQLWijpOH2w3yDxVBcggitSCmlAX8IfBlIAD9uGMYbdbjpjxd8fq8OtyeEEPXRQD0RmmLRp2mMRxaZ93Wz6OkCXefNu2FefGwAh63E4yp1cV/srL9h7MweKSab3V6aomnVCf4Ua6xY6jHV2WjAw3QwymIkic9l35bdYhhG84x3NAz4yZ9Ef/8D7p54Buzm78vpxZsQrXoVZ3FSziCEaFGjAe9mEGFuJcHTulGbMrE6aYKweH0p89X/G8BXgDTwZcMwvlPFfe92nQP4l/mLi8C1atymEEI0hFIyEepUzjAa8NDrc7EYSWJYejM0zKIvv4j++NIkDj0Huk40meX9udXS91FqrwJNq3waQi2yEDaOyfqSWUlzyTo40+9nuKuNnG4wFYyynsyQ0w3WkxmmglFyusFwV1vjj3e8fx/ef58H3i4S+QCCO5fhWDRUv2OQsbxCiBbV43PidZmvtemszsNDmAJVTRJE2Om3MacvZIGfKHeUo1LKyH/8epGrJ5RS/1gpNb4RUFBK2ZRSzwPfBp7Pb/fLpUxmEEKIplHKgrBO5QxNsejLL+o92TRPBach34RpainKYqTE1PJyehVU2hfBul21ggjQNCUNXpedq2f7eOxYO363nXuhGO/NrnAvFMPvtvPYsXaunu1r/PGOkQgA0x2Dm186GXmIRp0agLlccPVqfW5LCCHqTCnFSGCrweJMKH6IR3NwDf6KVl9KqVHgf8lfNID/qJT6j7ttbxjGQJk3cQH43fznKaXUOtAObLzrygK/ahjGH5a5XyGEaGwNVM6wsegDmF9JcC8UI53RcTo0en0uhrvaDn/RV/Czn1gPMufRmM9ffvNuiB99fBCXfZ/7p5wsAafTbE66odQgQi2aKm6w27cfRzZb3f1XUX+7m5cuDTEVjDITipHK6LgcGmPdXsb7fI0fQABIpYjZXSx6A+bljg5O/fBlUHU4p+F2mxN1xsdrf1tCCHFIRgMebi2uAzC/EienB7A1aUlDE7yq1VVhZoYD6K/y/v8h8EngSaAP6MLsuXAb+B/A1w3DuFHl2xRCiMNXSjlDnYIIcDiLvlgqy+TSOrPh+ObtjQY8nOn377w9y/31TMDGsl0jldVJpHXevb/CJ0737H2D5YxerHQaQq3KGWDnMZVaYnFIvC47l0c6D2WiR1Ukk9xt79/MOxgI+PD/6i8f6iEJIUQr6fG58LpsxFI5MjmDh2tJjnU2Z4NFCSIUMAzjPnCgcJBhGLt+v2EY38DstyCEEEeLNYhQLDW9jkEEqO+ib2ktyWu3g8yvJFiOpjYzH6aDUSaX1rl6to/+9oJ6cMv95UbnmX43r//tdVgOcj+TZSQAI+dPwsWLxUeaJi31lrUoZ6hlEMH6+DdoOUOr0BOJbaUMp7TmrtcVQohGNBrwcDOfjTATikkQQQghhNiVNYhgFKmzrnMQoV5iqSyv3Q4ysbCGTVOc6PbicdqJp7MsRpJMLKwB8NKloa2MBOvPvrTEyI0bnLgX5l4kDZksP7Br9N65hXtiwkwFHxra/j3lljPs9b27KSfboVxNlonQ7ObX0tsaKo44Sg/alJVlI4QQR9hYt3cziDC/kiCnG01Z0iDP7EIIIWqvwcoZ6mlyaZ35lQQ2TTHe59scAeh3O/C57EwFo8yvJJgKRreyIgrvL8OAN96AaJQnbXaW+i8Sd7pJpdP84NECL1zLD/N5+eXtGQnlLPArzUSoZU8EyUSoq8nVrd+X05FFNNdwSd9XdpaNEEIcYQGvE5/bTjSZJZszeLCa2NZwsVlIEEEIIUTtNWA5Q73MhuMsR1Oc6PZinfSrlGKww829UIyZUGwriFD4s2ezsLgInZ04L17g4zmN78Q1cLuZP3aKu/MJTs7MwI0b8MwzW99XzgLfel0jlDM0UybC974Hv//7mxMODsRmg099Cn7hF2o25tRqNZ4mGDfvX80wGF9dLGncYkVZNkIIccSNBTxcf2A+P86G4xJEEEIIIYoqJROhTiMe6y2V0UlndDzO4j+Px2knndFJZQruE2sQIRSCS5dAKQbtOuPOLFNpOyh4t+ck/XPv4Z2e3h5EKGeBX63GikexnGFlBb7yldLvs1K8/jq0t8NXv1q9fe5hcim6ef8Ox0K05dIlBREqyrIRQogjbqx7K4iwsJIgm9Ox2+oTNK6W5jpaIYQQzcmaVVCsJ4I1sFCns7C15nJoOB0a8XTxRXA8ncXp0HA5Cn7ewvvLMMzFute7+aUnXGl8mnkfZlxu3qIDI2FphHeQnghSzlC6jz6qbgBhw9tvV3+fRaSzOvcfxTZ/hjMrC+YVJQSENrJsBjvcu2bZLEdTzIRiVT9uIYRoVp0eJ+1tZqA8qxs8WG2+Rrat8Q5NCCFEY7MsMI5SOcNowEOvz8ViJIlhCZ4YhsFiJEmvz8VY91aQYFsARSmySmN+fpn351Z4536YifkVRpIr5HI6pFI8dPmZVpZ0yIP0RCi1saJ18VzLcoZaLNSrwToFo1oSidrs1+LuoyhZ3YBMls5UjL6keXaslEyEirJshBBCMFpQwjAbjh/ikVSmNXJFhRBCNLYjXM5wpt/P5NI6EwtrTAWjDHa4t9WN53SD4a42xvsKmiIWBFB0m40FXDy6PsVM/3GyOthtCp87hc2tk4oncbW3855vkIFkBr/bYd6/1kW3NVCw13WNUM7QLJkI1myM556Df/pPy9/P++/Dv/7XW5fjtX9TaRiGWcoAkM1wZvXB1pVt+48dK8yy8bt3/n4VzbIRQgjBWMC72TfmwWqCTE7H0UQlDa3xDk0IIURjsy4IiwURWrScweuyc/VsH2COc7oXim12sO/1uRjuauPq2b7tjefy95cOpNB4YPdiS2cZfzRLeuAYcaebxOoaY9OT3OwZIzsyDAODvHk3zOfO96GKBRD2uj8bcTpDs/REsN4HQ0PwyU+Wvx/r41OHTISHa0miSfN+dWTSHF8Pbl1ZQkBoNOBhOhhlMZLE57JvK2nYNctGCCEEHR4HHW0OIolMvqQh0VTPlRJEEEIIUXtHOBMBoL/dzUuXhpgKRpkJxUhldFwOjbFuL+N9vp2d6/P3Vy5nkDMgePK8OSbv4QLtC/fpSqfIOZws+Dro6vJzf2ycQZeb5fUUNxfXueCzHMB+C8JqZSLsle1QrmYNIlSajeGxlKPUIYhw++H65ucn06vYjYK/yxLKGSrKshFCCAGYDRY/nDen+syE4hJEEEIIIbYpJYjQoj0RNnhddi6PdJbWpT7/s+d0A92AsdA88e7zRI+NsjZ2ElsmTc7pItw3zAf+Ifp6Apvf+uH8KseGnHQU7m+/DAHr9aX2RJByhuoFEazlAzUuZ1hPZrY18zqTXNm+QQlBhIqybIQQQgAwEtgKIixGmqukQZ7VhRBC1J61seIRDCKUJf+zG/l/Z/7mz+Bv/gyA2z/xs9z86v8MmEGG6OwKF3wuOj0OwrEMugFv3A3zeRQa+UaO+wURGrGcwXpMrZ6JYA0i1DgTYSoY3fx8sNONPxndvkGJP0fZWTZCCCEA6Ghz0OVxsBLPkNPNcY/He5ojG6E5Qh1CCCGaWyk9ESSIsCW/GFf5f4VDHU7+xX/d/HyjcV2b08ZzJ3vYOIERjia5HhjZ+qb9FoTWxX+pC/ZyxkiW66hlIljLGWqYiZDN6dxd3hq7eKbfv/PnKCETYcNGls2XLh/jJ54e4UuXj3F5pFMCCEIIsY+RgikNM000pUGCCEIIIWrPWs5whEY8VuRTnwLApik0Bbq+FUVwRNdQ2cyOxnUdHgeXhvOlEtks1wOjhF35WvT9FvfW/gONUM5w1Hoi1DET4d6jGOmsGcjzue0Mdbh3jqosI4gghBCiMmPdW0GExdXE5nNzo5MgghBCiNqzBhEKT61vkCDClh//cfj61+EnX+bB575I1u4glzM277ZUeJWpYHRH47pzA356/S7I5tCV4o2Bs+SUKr8nQiOUMzRrEKHS+8C6aE8mi2fsHJBhGNwsaKh4bsBvTlWQIIIQQtSd3+0g4DXL93QDFlZr31S3GiSIIIQQovaksWL5vvQlHP/ud+j45tfRe3tRyuyBkMnpLC0E8bvtPHasfVvjOqUUz54MYNfNBXfE6eHD7uPlT2eoNBNByhkqX3xrWvFAQpUtrCa2xjraFCc26m8liCCEEIeisKRhtoSShlgqy3uzK/zZ+wusxNNobe1dtTy+YqRYTQghRO1VEkRooRGPB9Hf7ibX343+aImcbjZafCZgp+t8f9HGdX63gyf88Hb+8s2uYY612enb60Yqbaxo3U7KGQ52H7S1bV/MJxI7eyUc0K3FrSyE8X7/VidwCSIIIcShGA14+GAuP6UhX9LgtBc/17+0luS120HmVxIsR1PEUjk0Z5u/nscLkokghBCiHirpiWD9niPM1tmBw6ZwOzTaHDY+M9S2Z+O6cVeOwfjWyL4320fI5vZIjW/EcoZmzUQ4aBChUJX7IoSiKYLr5vFqCs70+7autAYRqhkQEkIIsatSSxpiqSyv3Q4ysbDGejLLiW4vHqcNI5upftraPuQdmhBCiNqTcoaD8VtOMqyvF99uQzrNM0tTOHTzPo062nh/bnX37a1n/UsNItSynKFZMhGqeR/UeELD7YJeCKMBDx5nwX1crbIMIYQQZSulpGFyaZ35lQQ2TTHe58PvNgMPhlGDBjr7kCCCEEKI2iulsaJ1kSjlDFs6OrZfXlvbe/tUCm82xZPBO+Zlu43JpSgPI7ucrKg0E0GmM1S3DKCGmQixVHbb+LBzg+3bN5BMBCGEODSjgf2nNMyG4yxHUwx2uM2GuIdIgghCCCFqr5RyBmsgXcoZtlgzEUoIIgCcXF9iOBraXJC/dS9UfHxUpY0VrWevrfs5CGsmSqMGEZqknOH20vpm7K6/3UXAawkcWYMI1mMRQghRM6WUNKQyOumMvj2L7JDIOzQhhBC1V0o5g2Qi7K7dctZ4vyBCQRDgmeAUrnzzvFgqx7szKzu3ty7+S12wSzlDdYMINSpnSGVzTAejm5fPDliCUtns9sCepsnfnxBC1Nl+JQ0uh4bToRFPH/7roQQRhBBC1J71rLL0RChPuT0RCha27lyGpx1bb0buPYoxv2J5c1KtEY+1LGdo1MaK1Qyk1CgTYWopSjZnpiF0tDk41mm5nWKlDIecKiuEEEfNfiUNowEPvT4Xi5EkRrGy0DqSMLMQQojaKyUTQcoZdmftiRCJEEtlmVxaZzYcJ5XRcTk0RgMezvT78VoWtqNOnePdHu6HzODBD+6F6fG5cDvygZpKRzzWczpDo2YiVLMngjUToQpBhGxO39ZQ8cJQ+85aWhnvKIQQh26jpCEcy2yWNJzo8W5ef6bfz+TSOhMLa0wFowx2mM/VStPqftZFgghCCCFqz7pokXKG8lgyERKhVf7yzSlO/Pa/4cr19yCnoxQ4NEXKruHKpra/wDudPHm8i6X1JIm0TjKj8879FZ4f79m8fptKMxGqGUSotMSi3mrZE6EK5Qx3H8VI5c9meV02xgKenRvJZAYhhGgIIwEP4VgEMEsaCoMIXpedq2f7AJhfSXAvFCOezqFsjrp3wpXTPEIIIWpPRjweTEFPBB1YfrCM7/e/yfh3/zuBlWW610N0RUJ4QsvYl5bIrEbYdg+7XLjsNp450b35pdlwnJlQzLxQaSaCdbtqljNYH/9GLWeoZjZGlcsZdN3g5uJW/4zzg+1oWpEyBZnMIIQQDWG/kob+djcvXRris+f7ee5kN16XDT2d2KfGsfokiCCEEKL2pCfCwRQEEXI5g1xkjaF7t7DZ1GaSh1KYl4GcbpDLFdRLDg8DcKyzjVO9W2c13r6/QiKdq3zEYy3LGZqlsWI1+0JUuZxhJhwnljL/rlx2jZMFZ7S2sT6OMplBCCEORSlTGrwuO5dHOvnS5WN0eZzoibUiHZNrS4IIQgghak8yEQ6mMIigG2jra3SvhYpuqmkK3TC3A+C55+DHfmzz+itjXXhd5n2bzuq8dS+0c8FeahChluUMzZqJcJBSgCqWMxiGwY0HW1kIZwf82G27vO2zBiuknEEIIQ7NflMaGoEUnAohhKi9UnoiWBeJ0hNhS0FPBAMDZzyGx5K9+Orv/V9kfB3kdIOJhVWujHXx4x8/Cd3d27Zz2DSePdnNt28GAXiwmmQ6beN04UaNEERolkyEapYCVLGcYX4lQSRhPo52m2K837f7xlLOIIQQDWM04OGDObMvwkZJg9PeWOf+G+tohBBCtKZKyhlkOsOWgkwEhcIbjWCPRTe/ZtgdrB0fJ9HbT6ijm2T/AMbQ0I4Awob+djdnB7YWldfWYN1RcPa51MaK1WwqaNUMQQTD2HlfWftLlMNazlBhJoJhGEwsRDYvj/f5cNn3yOyRxopCCNEwSilpOGxymkcIIUTtWQMCxVLTpZxhd263uTjNZLBpCk03m+bZbGaGR6K7D5TCMAwWI0l6fS7Gunepf8/72HAni5Eka4ksWWXjjYGzfG7uQzSMxshEaIZyBuv95HAcLPhVpUyE+ZUEK/F8FoKmOD/Yvvc3yIhHIYSouj1HMbv2XobvNaWhEUgQQQghRO1V0hNByhm2KGWWNITD2GwKm67I5MzmiZqmiHf3sp7MsBhJktMNhrvaGO/bI30dsNs0PnGqh7+6/hDdbuORu53rgREeD89WHkQ4yFn4HQfYBJkI1V58VyGIsCMLod+H27FPQE7KGYQQoqqW1pK8djvI/EqC5WiKdEbH6dCYDkaZXFrn6tk++tt3f81o9JKGxjkSIYQQrct6Vtkwdm4j5Qx7y5c0aIDTruHIT2bI6QZzrg7uhWL43XYeO9bO1bN9+57lAAh4nTw+3AGa+fhMdI+x7PaXFkTIZrcHg5SqbuCnGTIRql3OUYVyhrKzEEDKGYQQoopiqSyv3Q4ysbDGejLLiW4vT4x2caLby3oyy8TCGq/dDhJL7R4cb/SSBnmHJoQQovasjRWtC0LD2JmdIOUM2xX0RbAphcthw2W34bRpdJwc5bmT3Xz2fD8vXRra8+yG1YXBdvrazcWvAbwxcI5Mpoc2aVYAACAASURBVISz/sVKGayP80FUOjGinqpdzlGFTITCLITTpWQhgJQzCCFEFU0urTO/ksCmKcb7fPjdDmyawu92MN7nw6Yp5lcSTAWje+6nkac0SBBBCCFE7e1XzlAsC6GaC9JW0L79jLIGOGwKt0PjsafO8aXLx7g80llSBkIhpRTPjXbg0M3HIOpw845ncP9vtC6gq50C3wzlDNXORDhgEGEuHN+WhXChlCyEYrcjQQQhhKjYbDjOcjTFYIcbZXkvo5RisMPNcjTFTCi2535GC4IIGyUNjUIKToUQQtTefkEEyULYX8GYxx0GS1j078Hra+Pp4BTfHzgHwD13J8dCcUa7Pbt/Uy2bKkJp5QzRKFy/DnfumGfT3W44dQouXgTf3j0hqqLaZQAHKGew9kIoOQsBpJxBCCGqKJXRSWd0PM7iS22P0046o5PK7B0U2ChpCMcymyUNjdJgUYIIQggham+/EY/Ws8wSRNipfY+zygMDFe82lspy52GUM+ElBl2dzLb3QyrD69PL/B3/sV3fBNU8iLBfJsKDB/DKKzAzAw8fmgthlwtu3YKJCXjxRRgaqu4xWVnLAA6xnGEmVGEWAkhjRSGEqCKXQ8Pp0Iins/jdOxsOx9NZnA4Nl2P/ooBGndIgQQQhhBC1Zy1N2K+cQYIIO+0VRKhwsbzRPTo4v8rxnM7jD27z0NVB1O7gg/sruO02vnzl2I50TKD+QYTC35Fo1AwgXLtm/q6cOQNeL8RiMDsLoZC53csv1zYjodolHRUGEXTd4MOCLISzA/7SsxBAeiIIIUQVjQY8TAejLEaS+Fz2ba+h5Yxi3thXI05pOPwjEEII0fqknOHgdgsiaBr09pa9u8Lu0ZGcwqYpPMrguYc3sadT5N57j+9NBbk2u1J8B9XuB2C1VybC9etmBoLNZpYutLebn7e3m5dtNvP6Gzeqe0xWDTKd4c5ylGjSvH+cdq20iQyFpJxBCCGq5ky/n+GuNnK6wVQwynoyQ043WE9mmApGSx7FDI07pUEyEYQQQtSelDMc3G49EXp7KxqtWNg9+uRg52aySF9yjQsrcxjf/QvuJ6O82uXheLeXbp9lgXyY5Qx37pglDGfO7MxyUQpGRmBqCqan4Zlnqntchaq9+N6YcLExAjWTMX/uPR7fbE5n4sFWFsKFwfbyz1JJJoIQQlSN12Xn6tk+wBy7ey8UI53RcTo0en0uhrvaSh7FDI1Z0iBBBCGEELVnzUSwli9IOcP+OjqKf73CUoaN7tEnur1gt5Nzt2FLmmc4Li7P8KCti+gH7/D62SusJ7O8dGmQi0MdW296DrOxYjJpLuC9u7yR8vnM662L42qrdk8EpcxshFhBx+5ksnhJRr6p5O2JeyQiBjjstA30ceb8lfJvV4IIQghRVf3tbl66NMRUMMpMKEYqo+NyaIx1exnv85U1SakRSxokiCCEEKL2rEGEjTOtGySIsL/dMhEqnMxg7R49+9mXOPGt/wfDgJye46m566za3GTmFrihFJFEhk+c6ubq2T76292HW87gdpu3F4sVL/OIRs3ra70YrsWYy7a27UGEeHxnECHfVDJ1f5YbK3bI6uCw83hwCvvadPlNJSWIIIQQVed12bk80snlkc4D7acRpzRITwQhhBC1J40VD263TIQKgwiF3aMBPvwH/yvf/ZXfIqvr5HTwZhI8uXib8dUH9HhdBNdSfG86xGu3g8RS2Z0LaMfODtQHslcmwqlT5kSK2dmdASnDgLk58/rTp6t7TFa1CKTs11yxoKnkxLpOZmAQTp7AP9DDychDs9nkK6+Y25VKpjMIIURDGwls9cyZDZc+/rdWJIgghBCi9vbriWANIlRQ49/yLl8uftb9s5+taHejAQ+9PheLkSSGYWDYbHxw9mn+y5f/UX4LxUj0ER+/9x5uh42+dhePoimmg1GmgtHD7Ylw8SKMjZm/N9evQyRifh6JmJdzOfP6Cxeqe0xWtWhIaG2uaA0i5JtKrtlcTA2fMW9T07jcaUertKmkNFYUQoiGNloQRNgoaThM8i5NCCFE7ZXbE8G6vTDr///0T+GP/ghWV80z/5//PFy9WtHuzvT7mVxaZ2JhjalglMEON8vRFJNjl/ifMACFpsEPv/dt7l79IuvKht3u5sa8zvnBdi7XIpW/0F5BBJ/PTNkHc8E8NWUuhF0uMwNhbMy8vpbjHaH6PRFgZyaCdUJDvqnke8efQMfM8Omz6Yw4ckCFTSWlnEEIIRpao5U0SBBBCCFE7ZXbE0EyEYo7dw7+1b+qyq6KdY++uxwl7O4i2D/K4PIcdk1D5TJ89Xd/ib8ee5KcZqPr2EluH/t7h9tYEcya/5dfNs+4T0+bC2G32yxhuHCh9gEEqF1PhELWTIRkkocZjQX71pvHK+6C46ikqWS5QYR8U0fu3Nm630+dMjNE6nG/CyHEEdRIUxrkXZoQQojaswYRZMRjQ7B2j1YG3A/FWLzyLKN/vUD+RDft6QRPLk3x1sA5hubusPzG97h7up2ThTurdTlDJrNzG5/PPNteyzGOe6lFTwRrOYMlE0F3ubnm6TcDGG43JxxZAraCv6dKmkqWU86Qb+rIzIw5ZnMjA+TWLZiYKL+poxBCiJIUm9JwWCSIIIQQovb2K2ewBhWknKFuCrtHjwY8vHoryPXnv8Az3/kzVG4ruHN8bYlFdyeznf0Mfv813r7TR6fLRyCVb+BX63IG6+9MI6hHY8U/+RN4993Ni3cXwqyG0rD4CLvfx+XoXTDy941hwNKSuQ+Hw2w8WYpHj7Zf3u3nKGjqiM0GZ86YZTaxmHlboZC53csvS0aCEEJUWbGShsMiQQQhhBC1t18mgpQzNITNPgnHTvL//sK/4Kl3XqUtGSOrG3Td/IjLD6dY8/iJ233kFhd5ffACX5i9hkvP1r6cwZqt0gjqEUR45RXzA0hqdj4Yewp0A3I5LgTv0haeNf++dN38MAwzgHDjxs6pKKXaLRMh39QRm80sXdjYf3u7eXnj+hs3Di87RAghWpi1pOGwyLs0IYQQtSflDE1hW58Ez6f4z489Qzqj43RoPPfBd/n8f/gNri7d5K/HrpDVbMQcLr4/cI6rDyZQgUB1D2avxoqNohZBhN1GeQIf9JwgZXeAYeDNpDgXnjMDcNmsuaDXNPN+c7srDyBompldUEy+qSNnzuzcv6qwqaMQQoiSWUsarC2m6kXyRYUQQtTefkEE62UJIhyajT4Jnz3fz3Mnu3n2ZDfPnexm7Od+Ctvf+2m6skmeW5rc3H7R28XE8cfgS1+q7oHs11ixEdQiiPDFLxbNxFl2+7nTMWBeUIonV2ewu5xmBsjGh9ttZjIcpBzoC1/Y/edIJs2febcgQyVNHYUQQpRso6QBzKS0TO5w+iJIJoIQQojak0yEplLYJ2Gb//234Kd/ipEHDzi/DjdjgN3OR2NjdHf2U9V2eg7H9stHJRPh4x+Hb30LXn99s5mkbsDbSS8Y5t/FMVuWYdcnDn5bVsPD8NJLu1/vdps/YyxmljBYVdLUUQghRFkKSxrSEkQQQgjRsoqNeDSMrZRo61lmCSI0JqXgyhW4coWP6Qbh20GW1syF9PfvhPjCYwP4XFV6a3FUMxEAHn/c/Mi7vbjG6uwqAHZN8dSlQajW/VyOU6fMKQyzs9t7IoD59zw3BwMD5phNIYQQNVFY0pA5pAkNUs4ghBCi9pTaWUNdWMgn5QxNR9MUnzzdg8dpPlbprM53J5erl1p5VHsiWMRSWT6aj2xevnisHe9hBBDADByMjZkBnevXIRIxP49EzMu5nHn9hQuHc3xCCHEEFJY0HFJLBAkiCCGEqJO9xjxKOUNTcjtsPD/eg5aPD63GM7xxJ4RRjU5PEkQA4Af3w2R18/7saHNwfqBIGUG9+Hzw4otmNkpnp9lE8c03zf87O82vv/iijHcUQogaGwl4DvX2pZxBCCFEfWja9sBB4UJTRjw2rR6fi6eOB/jBvTAA8ysJPpiP7OynUK5mLGeo8pjLO8tRFle3mhQ+faILTatw6kK1DA3Byy+bYxynp80mim63WcJw4YIEEIQQog4KSxoOg7xLE0IIUR97NVe0LhAP0l1e1N3pPh9ryQy3FtcBuPFgjXa3nZO9B1hQNmMmQhUbCsbTWa7NrGxePjvgo8/fIA0LfT5zhKOMcRRCiENRWNJwGORdmhBCiPrYq5xBGis2vSdGOhnq3Frk/uBemOD6AUb9NWMmQhXLGd66GyaTM7N1fG47Hxs+YGaHEEKIlnKYJQ2SiSCEEKI+9spEsJ5llnKGbWKpLJNL68yG46QyOi6HxmjAw5l+/+E12bNQSvGJUz38zc0lVuMZdANen3zEj1Q6scEaRMhmt0/0aAQ1CiJMB6MsRrYCMM+eDGC3yXkfIYQQW453e/G5D+c9QGO88xBCCNH6io153GCdziDlDJuW1pK8djvI/EqC5WiKdEbH6dCYDkaZXFrn6tk++tsbI83dadd44Uwv/33iIamsTio/seFz5/tx2st8TDXN/Cj83dD1Q81SsQZzfiQSw5MzsNmUmdpZhZ4I68kM12YLyxj8jVPGIIQQomF4XXachxRgliCCEEKI+iinnEEyEQBz0fra7SATC2vYNMWJbi8ep514OstiJMnEwhoAL10aapiMBJ/LzqfO9PDqzSC6YU5seH1qmatn+7CV2xTQZtuZsXJIQYRiwZyr0Ti2bA6brnDaNWwH7Img6wbfmw6RzZcx+N12PjbcUY3DF0IIIapGTvUIIYSoj3LKGaQnAgCTS+vMrySwaYrxPh9+twObpvC7HYz3+bBpivmVBFPB6GEf6jZ9fjcfP9m9eXlpLVXZ6McGaa5YGMxZT2Y50e3lidEu3HoWw4BMziCd1YlxsN/b9+dXCcfSAGgKPnGqW8oYhBBCNJzGOG0hhBCi9e0VRLCWM0gQAYDZcJzlaIoT3V6UpReAUorBDjf3QjFmQrGDj1SsshM9XuLp7OYIqtlwnLZZjSfHAqXvxBpEqGdzxVQK/vRPYW6O1ZU4A8EoXVmdHp8L8g+FPZsGmyKXM8jpBtOraS5XmDjwYDWxOd0C4PJoJ92+6jVqFEIIIapFgghCCCHqwxoYkEyEfaUyOumMjsdZ/OXa47STzuikMnrR6w/bxaEOkpkctx+amRK3H0ZxO2xcHCpxpV2suWK9/OIvmkEEoDuj48/p2DRVtK+jpinSysbMSoLLY+XfVCKd4407oc3LQ51uzg20V3rkQgghRE1JjpyFUuqqUsoo4aPnALfhVEr9klLqfaVUVCm1qpR6Qyn1D5T1VJMQQrQK69NbYRBBRjwW5XJoOB0a8XTxxXM8ncXp0HA5Gvfl/MpoF6MFY6g+mItw++H6Ht9R4LDKGXQdvvWtzYtG/t9ur9BKQcrdVlEwJ6cbvD61TCprfm+bU+PZglIQIYQQotFIJsLudGB5n+vLppRqB14Fnsx/KQ60Ac/mP35MKfV3DMM4nMJPIYSoFSlnKNtowLM57s/nsm8raTAMg8VIkl6fi7Fu7yEe5d6UUjx3qptUNsfSmjkS8d2ZFew2xale397ffFjlDKkUZDKbF1X+324TJg0Dbj59taJgztv3wzyKmn0QlIJPnOrB7ZDffyGEEI1Lggi7mzMM43gN9vtNzABCGPgZ4FuYGSE/DfwfwEvAvwB+pQa3LYQQh0caK5btTL+fyaV1JhbWmApGGexwb5vOkNMNhrvaGO/bZzFeRdYxhy6HxmjAw5l+/64TImya4oUzvXznVnBzwfzW3TA2pTjes0cA5LAyERKJbRdVm5trX3iZVL4nQmEcwQCmvL08eOFz/FCZwZzbD9e5uxzbvHx5pLPicZ2VPC5CCCFEJeRVpY6UUk8AP5G/+PcNw/jz/Oc54A+VUp3A7wC/qJT6d4ZhBA/jOIUQoiasgQEZ8bgvr8vO1bN9AMyvJLgXipHO6DgdGr0+F8NdbVw921e3RWKxMYdOh8Z0MMrk0jpXz/btugh22DSunu3j1VtLhGPmWf437pp9AHYNJBxWJoIliGDvCbD08/9oc9RmsWDOYz3+soI5DyNJrs2ubF4+0ePl/GBlfRAO8rgIIYQQ5ZJ3afX1lfz/tw3D+G9Frv8GZhZCB/BlzMwEIYRoDdZMhMJxf9bFoXXbI6y/3c1Ll4aYCkaZCcU2zzKPdXsZ7/PVLYBQOObQpilOdHu3LaQnFtYAeOnS0K7H5LSbgYRv3wwSSWQwDPj+nRA5wyhe2nBYjRUtQQSbx1PVYE4knuFvpx9t/gl0+5w8c6KMqRUFqvG4CCGEEOWQV5P6+kz+/78qdqVhGAml1OuYJQ0/hAQRhBCtRBorVszrsnN5pPNQxzhOLq0zv5LApinG+3yb/Rn8bgc+l52pYJT5lQRTweiex+l22Pihc328essMJIBZ2qDrBuP9/u0bN0g5A21tVQvmRFNZXr29RLqgkeIL473YtMr6KlfrcRFCCCFKJad6dterlLqmlIrlPyaVUt9QSj1eyc7yUxfO5S9e32PTG/n/L1RyO0II0bCknKGpzYbjLEdTDHa4sQ4SUspM8V+OppgJxXbZw5Y2p43Pnu+jy+PY/Nrb91e48WBt+4Z7/c7UUpEgAmwFc750+Rg/8fQIX7p8jMsjnSUHEJKZHN+5FSSRNgMIdpvihfFe2pyVB82q+bgIIYQQpZB3abvzAE8AK4AXGM9//KxS6p8ZhvFbZe6vPb8fgAd7bLdx3WApO1VKvbvLVed2+boQQhyOvRorSjlDw0tldNIZHY+z+FsHj9NOOqOXPObQ7bDxQ+f7eO32MqF8s8X351ZJZLJcGe0yF8QNlIlwUOmszmu3g6wnzZ9BU/DCeC/dPteB9lvtx0UIIYTYj7xL22kV+E3gKaDNMIwAZkDh08D3ARvwm0qpr+y+i6IKu0Yldt3KHPkIUL9W20IIUQ/l9ESQTISG43JoOB0a8XTxhXw8ncXp0Moac+iy2/jM2T76/FsL6dsPo7w+9YhsTj+8IEIyuf3yAYMIGwGEjYaSSsEnT/cw0HHwZoe1eFyEEEKIvbTEK4pS6teUUtkKP/514b4Mw3jfMIxfMgzjXcMwkvmv5QzD+C5mT4Pv5Tf9mlLq0O8/wzCeLPYB3DrsYxNCiG326okgIx4b3mjAQ6/PxWIkiVEYAAIMw2AxkqTX52KszDGHTrvGZ871MRrwbH5tfiXBt28FSdqd2zduwkyEZCbHt28ubY62BHj6eICRgp/3IGr1uAghhBC7OfRFcJVomBkClX6UxDCMNPDP8xeHMcsdSlVYjLjXu5GNdxXRMvYthBCNb6/6dt2Sai3lDA3nTL+f4a42crrBVDDKejJDTjdYT2aYCkbJ6QbDXW1ljTncYNMUnzzdzbnBrcaKoWiaVzpP88hd0GyxyYII8XSWv7m5xEo8s/m1J8e6OF3BfbSbWj4uQgghRDEtkS9qGMavA79ep5t7q+Dzk8BuPQms1jADCV5gaI/tNq5bLP/QhBCigZXTE0HKGRqO12Wv6phDK6UUV0a78DrtvDuzAkDC5uBvhj/Gk8t3GI8sHnpjxXJE4hlemwwSS5nHrBQ8cyJQfJTlAdT6cRFCCCGs5BWlTgzDMJRSNzF7LVzcY9ONqQw39thGCCGaz15BBClnaArVGnO4l7MDftrb7HxvOkRas6Erxdt9p1l2t/NUOoNz/10c3AGDCHPhOG/cDZHNmeUFmoJPnOphtLs6JQxW9XhchBBCiA3yqlK+jxd8fq/M7/0OZhDhh4tdqZRyA5/KX/x2+YcmhBANzBoY+KmfAmd+Sbi+vve2omFsjDm8PNJZs9sY7GjjC48N8LcqQzj/tfvtfSwvZnh2LUl/+8EbEu6pwiCCYRhMLKzx0UJk82t2TfHJ8R6OdR58wsNe6vG4CCGEENA6PRGqRlmHLG+/zgH8y/zFReBambv/4/z/55RSLxW5/ueBDszpDf9fmfsWQojGZn16jURgedn8sHbDlyDCkedz2flcNsiJtaXNr8XSOt++GeTdmRVzekOtVBBEiKezvDa5vC2A4HXZ+PzF/poHEIQQQoh6kiDCThNKqX+slBrfCCgopWxKqecxswOez2/3y4Zh7HgHo5S6r5QylFJ/YL3OMIz3gP+Sv/gHSqkfLdj/V4Gv5a/7bcMwgtX9sYQQ4pAN7dUO5gDbipZlt9t4bmmS5xdv4sxlQTf7C9x+uM63PlpkNhTfZw8VKjOIcHc5yrc+XGRxdSsYNtDh4kcuDtDpqUsBhhBCCFE3Us6w0wXgd/Ofp5RS60A7bJZhZoFfNQzjDyvc/88Dp4AngW8ppeKYEyI2hmT/OfC/VbhvIYRoXP/kn8C1azA/v/s2SsEXvwgvvFC/4xKNK5+RMhp9RG9ijTftX93sOhxL5fjb6Uf0B108OdZV3cV6iUGEtWSGazMrPFjdnklzbtDP5eFONG3X5EYhhBCiaUkQYad/CHwSc5HfB3RhlhfcBv4H8HXDMCpuemgYxppS6hPALwJ/FzgNpID3gP8EfNOwDnoWQohWcO4cvPUWhEI7RzpuaGsDv7/4deLoKZjS0ZZL8xl/hjsnA7w/u0oqa/4OLa2l+IuPHjLc1cbFoXa6fa7d9la6fYII8XSWiYU17ixHKXzF9rntPHsiQF+tezYIIYQQh0iCCBaGYXwD+MYBvv94CdukMUsXvrbftkII0VKUgp6ewz4K0Sysoz5zOU71+hjp8vDRwiqTS1uL+PmVBPMrCQY73Jzu83Gss63yTIBdggjryQyTS+tMB6NYWzKcHfDxseFO7DapFBVCCNHaJIgghBBCiMZkbbCZHwXqtGs8ORbgdK+fD+ZXmV/ZWvQvRpIsRpI47Rpj3R7GAh66fS5s5QQUCoIISc3OQs7J/ZtLLK2ldmza3+7iYyOd9FQjA0IIIYRoAhJEEEIIIURjsmYi5IMIGzo8Dl4408tqPM2NB2vMhOObmQnprM7UUpSppSg2DXp8Lvr8bjraHHhdNrwuO27HVpDCMAxSWZ14Okck5yTcc5Lltg7Cbh+saWAJIAS8Dj420slgh0xeEEIIcbRIEEEIIYQQjcmaiZDLFd2s0+PkE6d7eDyZ4e5yjPuhGLHU1rY53eydYM0kUAps+dGjWb2guYGjF7oyW5edjs1PhzrdnB3wM9DuZo+p0EIIIUTLkiCCEEIIIRrTPpkIVn63mR1wabiD5fUU90NxHq4liSaLf59hQLZYL+NMevNTBfR0tDE82slYtwePU946CSGEONrklVAIIYQQjanMIMIGpRR97e7NKQnxdJbgWopQLEU0lSOWyhJNZcnmtgcQnHaNNocN/1qQzvA83cl1ehJruM71wWB7VX4kIYQQotlJEEEIIYQQjWmXxorlMgxYiaeZX0mQyui4HBrjfT5O9/pwO20owKaprfKE+QmIRLZ20CZ9D4QQQogNEkQQQgghRGOqMBOh0NJaktduB5lfSbAcTZHO6DgdGtPBKJNL61w920d/PmNhUzK5/bIEEYQQQohNEkQQQgghRGMqsbHibmKpLK/dDjKxsIZNU5zo9uJx2omnsyxGkkwsrAHw0qUhvK78WyJdh5RllKPr/2/v3oPsvOv7jr+/u1rtSruSdYmuWBcjS7LsGIxtnAzQxnYCQVzcXKbjFnKxaQqdEopnmhYwSYDQhoubIThpCaTYhCTAtIUkUGNI7eBQCnjAsg2SLFsWtuSLLrYsS9qVdiXtfvvH86y1Wu/l0e6ePXuO3q+ZM+c85/c753w1c2b1nM/zu7h9oyRJg1rqXYAkSdKI2trOPD7LkQiP7D/Kk4eO09oSrF/axbyONlpbgnkdbaxf2kVrS/DkoePsPNB9+kUjjUJwFwZJkl5giCBJkmam4dMZznIkwp7njvFMdx8rznvxdowRwYrzOnimu4/dB3tONxw/fuabdAyb6iBJ0jnO6QySJGlmGj6d4e674eDByi/ftO8oCw73snR++xkhwsnOeTz+uuvoX/VSTpwcoO/kwOkXDQ8RXA9BkqQzGCJIkqSZafhIhIcfLm4VrT45wPL+gXLnhWFtd32NL3/qy8xua6G9bcjATEMESZLG5HQGSZI0M82bN6mXt7YELQEDA/mitraeo8z6wQ9Y0tXOmsWdpxsMESRJGpMhgiRJmpmuuQaWL5/wy1tbg9aWIIH+/iTLLCGzOO58dj/nL5zD+qVdp19kiCBJ0picziBJkmamJUvgrrvgO9+BY8fO+uUtQN+xE2zfe4Tnek6w7N7/y4b7v0NQjFBY33+EZRuXnt7eEQwRJEkahyGCJEmauRYtguuum/DLFwAv7zvFzgPdnJo3m9k/+i5BMUJh08BR2uYP231hpC0eJUnSC5zOIEmSmlpn+ywuW7WAK191KXPaWuloa6GtNWjbv+/FnR2JIEnSmAwRJEnSueElLznz+KmnXtzHEEGSpDE5nUGSJNVWdzds2wa7dhXTBTo6YN06uOQS6Ooa//VTZeXKM4/37oX+fmhtPf3c8BChY9h0B0mSznGGCJIkqXaefhruvBN274Z9+6CvD9rbYccO2LoVNm9+8Y/7Wpk7FxYsgOefL45PnYJnn4Vly073cSSCJEljMkSQJEm10d1dBAhbthRX+zdsgM5O6OmBPXvg4MGi3/XXT9+IhJUrT4cIAE8+aYggSdJZcE0ESZJUG9u2FSMQWluLqQvz5xeP588vjltbi/bt26evpvHWRTBEkCRpTIYIkiSpNnbtKqYwrF4NEWe2RcCqVUX7o49OX03nn3/m8dNPn3lsiCBJ0pgMESRJUm309hZrIHR2jtze1VW09/ZOX03D118YbyTC3Lm1rUeSpAZjiCBJkmqjo6NYRLGnZ+T27u6ifTp3QBg+nWH4SIThgYYjESRJOoMLK0qSpNpYt67YhWHPnmINhKFTGjLhiSdg+XK48MLpq2l4iPDYY0WNg5555sx2QwRJks5giCBJkmrjkkuKbRwPHiwWWVy1qpjC0N1dVhlvaAAAFQpJREFUBAj9/bBmDVx88fTVNHw6w44dcO21o/c3RJAk6QyGCJIkqTa6umDz5uLx7t2wc2exBkJ7ezECYc2aon26tneEYjvHlhYYGKjWfzqnWkiS1AAMESRJUu2sXAnXX19s4/joo8WaAx0dxRSGiy+e3gABYNYsuPxy+OEPx+/b1gYbN9a+JkmSGoghgiRJqq2uLrjqquI2E9x6K3zoQ/D446P3WbQI3vlOmDdv2sqSJKkRGCJIkqRzy9q1cPvt9a5CkqSG5BaPkiRJkiSpEkMESZIkSZJUiSGCJEmSJEmqxBBBkiRJkiRVYoggSZIkSZIqMUSQJEmSJEmVGCJIkiRJkqRKDBEkSZIkSVIls+pdgCRJ0ozQ3Q3btsGuXdDbCx0dsG4dXHIJdHXVuzpJkmYEQwRJkqSnn4Y774Tdu2HfPujrg/Z22LEDtm6FzZth5cp6VylJUt0ZIkiSpHNbd3cRIGzZAq2tsGEDdHZCTw/s2QMHDxb9rr/eEQmSpHOeayJIkqRz27ZtxQiE1tZi6sL8+cXj+fOL49bWon379npXKklS3TkSQZIkNa6pWMdg165iCsOGDRBxZlsErFoFO3fCo4/CVVdN/b9BkqQGYoggSZIa01StY9DbW7y2s3Pk9q6uor23d2rrlySpARkiSJKkxjOV6xh0dBThQ09PMYVhpM9qby/6SZJ0jnNNBEmS1Himch2Ddetg+fIifMg8sy0TnniiaL/wwtr8WyRJaiCGCJIkqfEMrmOwevXo6xjs21esYzCeSy6BNWugv78IJw4fLh4fPlwc9/cX7RdfXJt/iyRJDcTpDJIkqfFM5ToGXV3F+glQjF7YufP0+grLlxcBwubNbu8oSRKGCJIkqRFN9ToGK1cW6yds316MXhjc6eHCC4sRCAYIkiQBhgiSJKkRrVtX7MKwZ08xHWHolIaJrmPQ1VVs4eg2jpIkjco1ESRJUuNxHQNJkurCkQiSJKnxuI6BJEl1YYggSZIa0yTWMejpO8Uj+4+y57lj9J0coL2thdWL5rJh2Tw62z09kiRpNP4vKUmSGtcE1jHYf6SXex4+wJOHjvNMdx8nTg4wu62FRw9088j+o1y9cSnL5ldckFGSpHOMIYIkqea86quZoqfvFPc8fICtTx2htSW4YHEnc2fP4tiJU+w93MvWp44A8KaXrfS7KUnSCPzfUZJUU1711UzyyP6jPHnoOK0twfqlXUS5q8O8jja62mex80A3Tx46zs4D3Vy2akGdq5UkaeZxdwZJUs0Mvep7tPcUFyzu5BWrF3LB4k6O9p5i61NHuOfhA/T0nap3qTpH7HnuGM9097HivI4XAoRBEcGK8zp4pruP3Qd76lShJEkzmyGCJKlmhl/1ndfRRmtLMK+jjfVLu2htiReu+krToe/kACdODjB39siDMefOnsWJkwP0nRyY5sokSWoMhgiSpJrxqq9mmva2Fma3tXDsxMijX46dOMXsthba2zxFkiRpJP4PKUmqGa/6aqZZvWguS7ra2Xu4l8w8oy0z2Xu4lyVd7axZ3FmnCiVJmtkMESRJNeNVX800G5bN4/yFc+gfSHYe6OZo70n6B5KjvSfZeaCb/oHk/IVzWL+0q96lSpI0I7k7gySpZlYvmsujB7rZe7iXrvZZZ0xp8Kqv6qGzfRZXb1wKwJOHjvPYwZ4XdgxZ0tXO+QvncPXGpW7vKEnSKPwfUpJUMxuWzeOR/UfZ+tQRdh7oZsV5HcydPYtjJ06x93CvV31VF8vmd/Cml61k54Fudh/soe/kAO1tLaxZ3Mn6pV0GCJIkjcH/JYeJiMeBNRW735CZf3EW73018K0KXZdk5rNV31eSZiqv+mqm6myfxWWrFnDZqgX1LkWSpIbiWduLPQN0jNHeCQxeMtsywc8YKD9nrHZJagpe9ZUkSWoenrkNk5mvHKs9Iv4OuA7Ykpk/nuDHPJGZayf4WklqOF71lSRJag4uh30WImIJsLk8rDyNQZIkSZKkZmCIcHbeArQBJ4Ev1LkWSZIkSZKmlSHC2fnN8v4OFz6UJEmSJJ1rDBEqiohLgVeUh5OdyrAkIrZERE95eyQiPlN+hiRJkiRJM5ILK1Z3Q3n/LHDHJN9rLkUgcYhit4f15e1tEfHezPwvVd8oIu4bpemiSdYoSZIkSdIZHIlQQUTMAt5aHn4hM09O8K2eB24BrgTmZOYiikDh54DvAq3ALRHxlkmWLEmSJEnSlGuKkQgR8fvA70/w5R/LzPeP0+cXgWXl4wlPZcjMB4AHhj3XD3w7Iq4B/gF4NfCxiPhSZg5UeM8rRnq+HKFw+URrlSRJkiRpuGYZidBCcRV/orfxDC6o+OPM3DKllZcy8wTwe+Xh+Zxef0GSJEmSpBmhKUKEzPxgZsYEb+8d670jYiFwXXk42QUVx3PvkMcvrfFnSZIkSZJ0VpoiRKixfwG0A6eAv65zLZIkSZIk1Y0hwvgGpzJ8MzP31fizfmbI48dq/FmSJEmSJJ0VQ4QxRMRGTv+wn/RUhoiIMdragD8oD/cCNVl7QZIkSZKkiTJEGNvgKIRDwFervCAisrx9cITmrRHxrohYPxgoRERrRLwGuBt4TdnvfVV2ZpAkSZIkaTo1xRaPtRARLcCvl4dfysy+KXjbi4Fby8d9EXEUmA/MLp87BfxuZtZ6AUdJkiRJks6aIcLorqXYahGmbleGdwCvBq4AlgILgePAw8A/Ap/KzO1T9FmSJEmSJE0pQ4RRZOZdwKhrGIzxulFfk5mfAT4zmbokSZIkSaoX10SQJEmSJEmVGCJIkiRJkqRKDBEkSZIkSVIlhgiSJEmSJKkSQwRJkiRJklSJIYIkSZIkSarEEEGSJEmSJFViiCBJkiRJkioxRJAkSZIkSZUYIkiSJEmSpEoiM+tdg2ogIg7OmTNn0aZNm+pdiiRJkiRpij300EMcP378ucxcPJ2fa4jQpCKiD2gFHqx3LdIkXVTe76hrFdLk+D1Ws/C7rGbg91jN4uVAf2a2T+eHzprOD9O02gqQmVfUuxBpMiLiPvC7rMbm91jNwu+ymoHfYzWLwe/ydHNNBEmSJEmSVIkhgiRJkiRJqsQQQZIkSZIkVWKIIEmSJEmSKjFEkCRJkiRJlbjFoyRJkiRJqsSRCJIkSZIkqRJDBEmSJEmSVIkhgiRJkiRJqsQQQZIkSZIkVWKIIEmSJEmSKjFEkCRJkiRJlRgiSJIkSZKkSgwRmkxELI+IT0bErojojYj9EfG1iPj5etcmVRERqyPipvJ7uyci+iLiaEQ8GBEfjYgV9a5RmoiI6IqIJyIiy9sN9a5JOhsRsTEi/iQiHo6Inog4HBEPRcRtEfFz9a5PGktEtETEjRFxV0Q8ExEnI+L5iLg3It4fEfPqXaMUEfMi4rqI+HBE3BkRzw45b7iowutbIuLtEfG98vt9NCLuj4j/EBGzp6zOzJyq91KdRcTLgH8AFpdPHQG6KMKiBG7OzI/WqTxpXBGxCtgNxJCnjwCdQGt5fAj41cz81jSXJ01KRPwx8O4hT92YmZ+rUznSWYmIfwfcAgyehHYDs4CO8vizmflb9ahNGk9EzAW+Blw75OnDwHxOn3PsBq7NzJ9Mc3nSCyLil4C/GaV5U2buGOO1bcDfAm8onzoB9ANzyuMfUHzHuydbpyMRmkREzAG+ShEg3A/8dGaeBywE/ojiD+QfRsTr6lelNK7BoOAO4J8Di8rv8VyKP4iPUXyn/zYiltenROnsRcTlwG8D99a7FulsRcQ7gE9ShAYfA9Zk5rzMnAOsAH4D+G4dS5TG83sUAUIC7wMWZOYCihDsXwLPA2uA/163CqXTDgBfBz4EvP0sXvefKM6Xe4EbKM6fO4E3A88BrwQ+PRUFOhKhSUTETcAnKK4MXJSZTw1r/xvgl4AtmXlFHUqUxhUR5wFrM/PBUdovogjJOoAPZuaHprM+aSIiooUiPHgFxX/gW8omRyJoxouItcA2ipPRt2fmn9e1IGkCImI3sBq4LTP/1QjtNwC3l4eLMvPQNJYnvSAiWjOzf8jxWoqLaDDGSITy4trjQDvw7sy8dVj7P6MYpZDAZZn5o8nU6UiE5vHW8v4LwwOE0i3l/eURsXGaapLOSmYeHi1AKNt3AN8vDw3D1CjeBVwJfCoz7693MdJZejdFgHCvAYIa2LLyfrS/wfcNeTy3xrVIoxoaIJylX6UIEA4Dnxnhff8OeIRidPpbJlxgyRChCZQLwQz+oPrmKN2+T/GlAnCRRTWyg+V965i9pBkgIl4CfBjYD/xuncuRJmLwZPOLda1CmpzHy/tXjNI+eB69f5SLcdJMd015/+3M7B2lz9+X99eO0l6ZIUJz2MTpRWG2jdQhMweAh8vDi6ejKGmqRcQs4NXl4dZ61iJV9CfAPOB3MvPweJ2lmSQi1gFLy8P7I+Jny51zDkbE8YjYERG3RMTSsd5HmgEGR9HcGBHvLadPEhGzI+J6iinBCfxOvQqUJmnw992IvwVL28v7TRERY/QblyFCcxi65d3TY/QbbHOLPDWqdwLLgQHgL+pcizSmiHgz8MvAPZn5V/WuR5qA9UMeXw18B3gT0Ebxg2sjxY+uByLikmmvTqruj4H/SnHR7SPA8xHxPHAc+BKwA7jOv9VqYIO/76r8FuwqbxNmiNAcOoc8Pj5Gv2Pl/aS+NFI9lFuYfqQ8/NPM3D5Wf6meIqIT+FPgJEX4JTWiBUMef4BiPu3PZuZ8inOJN1CsIr4C+HI5Wkyaccp55jcB/x44VT59Hqd/C80DltShNGmqDP4erPJbEAwRJDW7iFhBsaLsHIrFj95T34qkcf0BxUrgnzDwUgMbep6YwC9n5r1QTJPMzDuBt5XtG4Ffmeb6pErKlev/H8W2538NvJziR9R6ii0fXwrcFhEfGfVNJL3AEKE59Ax5PGeMfoOrzXbXsBZpSkXEIoqFYC4AdgJvHGPBGKnuIuIyihXtn6AIE6RGNfR84RuZ+fDwDpl5B8UIBXDhZs1cnweuAj6bmTdk5o8ysyczH83MjwLvKPv9R6fmqEEN/h6s8lsQJvl70BChOQyd+7JyjH6DbXtrWIs0ZcqFj74J/DSwB/iFzNxf36qkcX2SYveQ9wMREV1Db0P6tZfPuZ2YZqqh5xcvChBGaFtVw1qkCYmIi4HXloefGKlPZv4lxe5PLcCbp6k0aSoN/r2u8luwOzOPTubDDBGaww6KYYYAI6anEdFCMdQQTq/MKc1Y5ZzyrwNXAvsoAoQ99a1KqmRNef954OgIt0F/Vh77N1kz1XaKhWyryvG7SNNu05DHj43R7yfl/dralSLVzOC5xFgjaQZ3cHhosh9miNAEyiTph+Xha0fp9jMUC8gA3F3zoqRJiIg5wNeAV1FcGfiFzNxZ36ok6dySmceA75WHG8foOtj2eE0LkiZmaBC2eox+gwHwpK7QSnXyrfL+n0RExyh9Bn8nTvq3oCFC8/hCef/WchG64Qb3vb1vpDmN0kwREbOBrwDXAM8Dr8vMsfa8lWaUzFybmTHabUjXG8vn1tarVqmCz5f3r4+IFwUJEfFGYEN5+PVpq0qq7sEhj//1SB3KLXmXlof31rwiaep9Beij2FXnt4Y3lt/xjRQjxr442Q8zRGgenwZ2U2xR87/L+V9ExLyI+DinV0y+uU71SeOKiFaKQOz1FFcCNmfmlvpWJUnntNsohsm2Al+JiKugmCYZEa8HPlv2+z6GCJqBMvMnFAs0A9wUER+JiKUA5bo0NwCfK9sfB7463TVKQ0XETw3egIVDmhYMbSunqwOQmfso1mQC+HhE/Hp5Xk1EvAG4vWz7Ymb+aNI1Zjp9rVlExMsphqcsLp86QrF9TQtF6nRzuQKtNCNFxD8F/rE87AUOj9H9icx8Ze2rkqZWRAz+x3tjZn6unrVIVUTES4F7OL1w4lGKUGFwUdDtFKPGnpr+6qTxlaN07+bM9RGOUlx8G7Sf4uLF/dNZmzTckPOE8VyQmY8PeV0bxZbobyif6gP6Of23+gfAz092UUVwJEJTycwHKVaxv5VicZh2ivnkdwCvNUBQAxj6N6kDWDbGbcm0VydJ56DySu6lwH+mCAxmUVyc2AK8D7jKAEEzWWbuBa4AbgK+DTxH8cPqCMX3+MPApQYIamSZeZJid5F/QzE6rI/ib/UDwHuA10xFgACORJAkSZIkSRU5EkGSJEmSJFViiCBJkiRJkioxRJAkSZIkSZUYIkiSJEmSpEoMESRJkiRJUiWGCJIkSZIkqRJDBEmSJEmSVIkhgiRJkiRJqsQQQZIkSZIkVWKIIEmSJEmSKjFEkCRJkiRJlRgiSJIkSZKkSgwRJEmSJElSJYYIkiRJkiSpEkMESZIkSZJUiSGCJEmSJEmqxBBBkiRJkiRVYoggSZIaTkT8YURkRDwbEctHaI+I+EbZ576IaKtHnZIkNRtDBEmS1Ig+ANwPLAZuG6H9ncAvAseBX8vMk9NYmyRJTcsQQZIkNZwyFHgrRUiwOSL+7WBbRGwEPl4eviczH6pDiZIkNaXIzHrXIEmSNCER8S7gVuAYcDmwC/gecCXw98Dr05MdSZKmjCGCJElqWBERwJ0UUxd+CPwf4H3Ac8Clmfl0HcuTJKnpGCJIkqSGFhErgB9TrI8w6PrM/B91KkmSpKblmgiSJKmhZeZe4OYhT/1PAwRJkmrDEEGSJDW0iGgFfnPIU5dFRGe96pEkqZkZIkiSpEb3XuBVwGHgCWA98Ed1rUiSpCblmgiSJKlhRcTlwPeBNuA3gCeBu4EA3piZX69jeZIkNR1HIkiSpIYUEXOAv6IIEP5XZv5lZn4L+ETZ5bMR8VN1K1CSpCZkiCBJkhrVx4BNwF7gHUOevxnYBiwHPl2HuiRJalqGCJIkqeFExOuA3y4P35aZzw22ZWYf8GvASeBXIuKG6a9QkqTmZIggSZIaSkQsBG6nWPfgv2XmN4b3ycwHgA+Uh5+MiLXTVqAkSU3MhRUlSZIkSVIljkSQJEmSJEmVGCJIkiRJkqRKDBEkSZIkSVIlhgiSJEmSJKkSQwRJkiRJklSJIYIkSZIkSarEEEGSJEmSJFViiCBJkiRJkioxRJAkSZIkSZUYIkiSJEmSpEoMESRJkiRJUiWGCJIkSZIkqRJDBEmSJEmSVIkhgiRJkiRJqsQQQZIkSZIkVWKIIEmSJEmSKjFEkCRJkiRJlRgiSJIkSZKkSv4/eVg0+lSaXwsAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 377, "width": 520 } }, "output_type": "display_data" } ], "source": [ "param_grid = {\n", " 'max_depth': [4, 6],\n", " 'min_samples_leaf': [3, 5, 8],\n", " 'subsample': [0.9, 1]\n", " # 'max_features': [1.0, 0.3, 0.1] # not possible in this example (there's only 1)\n", "}\n", "gs_gbm = GridSearchCV(gbm, param_grid, scoring = 'neg_mean_squared_error', n_jobs = 4)\n", "gs_gbm.fit(X_train[:, np.newaxis], y_train)\n", "print('Best hyperparameters: %r' % gs_gbm.best_params_)\n", "\n", "plt.plot(x_plot, gs_gbm.predict(x_plot[:, np.newaxis]),\n", " label = 'GBM tuned', color = 'r', alpha = 0.9, linewidth = 2)\n", "plot_data(x_plot, X_train, X_test, y_train, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Reference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- [Slide: Gradent boosting](http://eric.univ-lyon2.fr/~ricco/cours/slides/en/gradient_boosting.pdf)\n", "- [Slide: A gentle introduction to gradient boosting](http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/slides/gradient_boosting.pdf)\n", "- [Blog: The Softmax function and its derivative](http://eli.thegreenplace.net/2016/the-softmax-function-and-its-derivative/)\n", "- [Notebook: Regression Trees and Rule-Based Models](http://nbviewer.jupyter.org/github/leig/Applied-Predictive-Modeling-with-Python/blob/master/notebooks/Chapter%208.ipynb)\n", "- [Notebook: Gradient Boosted Regression Trees in scikit-learn](http://nbviewer.jupyter.org/github/pprett/pydata-gbrt-tutorial/blob/master/gbrt-tutorial.ipynb)\n", "- [StackExchange: Derivative of Softmax loss function](http://math.stackexchange.com/questions/945871/derivative-of-softmax-loss-function)\n", "- [StackExchange: How are individual trees added together in boosted regression tree?](http://stats.stackexchange.com/questions/135378/how-are-individual-trees-added-together-in-boosted-regression-tree)\n", "- [Stackoverflow: How to access weighting of indiviual decision trees in xgboost?](https://stackoverflow.com/questions/32950607/how-to-access-weighting-of-indiviual-decision-trees-in-xgboost/34331573#34331573)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" }, "toc": { "base_numbering": 1, "nav_menu": { "height": "160px", "width": "252px" }, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": "block", "toc_window_display": true }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }