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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Assignment #1: EDA & Dataset**\n",
+ "\n",
+ "**DATE: March2026**\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "w84cR3AZIU0e"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Overview**\n",
+ "\n",
+ "In this first assignment, you will gain hands-on experience selecting and preparing a dataset, performing exploratory data analysis (EDA), adding it to HF's Datasets, and presenting your findings in a short video.\n",
+ "\n",
+ "Through these steps, you\u2019ll begin building essential data science skills in Python.\n",
+ "\n",
+ "Think of your notebook as a cohesive story where EDA reveals the narrative of your data, which provides the resolution to the main question or goal."
+ ],
+ "metadata": {
+ "id": "n7afdXdxIbLA"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Objectives**\n",
+ "\n",
+ "1. Explore various data tools/hubs (e.g., Kaggle, UCI, or local data) to find a suitable dataset.\n",
+ "2. Prepare the selected dataset, focusing on cleaning, transformation, manipulation, and quality.\n",
+ "3. Build foundational EDA skills, including missing data, outlier handling, and clear data visualization.\n",
+ "4. Upload your work and your dataset to HuggingFace.\n",
+ "5. Communicate findings concisely in both a structured notebook, README file, and a short video presentation, demonstrating clarity in each step of the workflow."
+ ],
+ "metadata": {
+ "id": "lJAPMumvIUyW"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Submission Guidelines**\n",
+ "\n",
+ "1. Please note that this is an individual assignment.\n",
+ "2. Submit a text file with your info and the link to HF's dataset.\n",
+ "3. The dataset will include all of your work.\n",
+ " - **Python Notebook**: a well-structured notebook (e.g.`.ipynb` file) with clear comments or markdown explanations of each step.\n",
+ " - **Video Link**: the README file will include your short video presentation at the beginning of the file.\n",
+ "4. **Oral Report**: Students may be randomly chosen to present their work in a quick online session with the T.A., typically lasting \u00b110 minutes.\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "MwRmaJBiIjMR"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Evaluation Criteria**\n",
+ "\n",
+ "1. **Organization & Clarity (20%)**: Overall structure of your HF Dataset, code, notebook, clear communication, and a concise summary.\n",
+ "\n",
+ "2. **Data Handling (30%)**: Quality of data cleaning, appropriate handling of outliers, and more.\n",
+ "\n",
+ "3. **Visualizations (30%)**: well-presented relevant visuals, variety of plot types.\n",
+ "\n",
+ "4. **Presentation (20%)**: Clearly communicated approach and findings in your 2\u20133 minute overview.\n"
+ ],
+ "metadata": {
+ "id": "hD9SZmagIjOV"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Additional Tips**\n",
+ "\n",
+ "- The first thing you should do is download a copy of this notebook to your drive.\n",
+ "- Keep your dataset size manageable. If the dataset is large, you can sample a subset.\n",
+ "- A few clear visuals are more effective than many complicated ones.\n",
+ "- Ask for help or feedback early if you get stuck."
+ ],
+ "metadata": {
+ "id": "h3vpVHSxIUwI"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
"
+ ],
+ "metadata": {
+ "id": "7ql9QlVfaSEL"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 1: Select a Dataset**\n",
+ "\n",
+ "1. Choose a numeric tabular dataset, such as the . If you prefer, you may use other open-source datasets; [Hugginface](https://huggingface.co/datasets?task_categories=task_categories:tabular-classification&sort=trending), [Kaggle](https://www.kaggle.com/datasets?tags=13302-Classification&minUsabilityRating=8.00+or+higher), etc.\n",
+ "\n",
+ "\n",
+ " Examples for a good dataset:\n",
+ " - \"Determine a genre of a song\"\n",
+ " - \"Determine the type of flowers\"\n",
+ " - \"Determine the animal - cat or dog\"\n",
+ " - \"Determine the category of a product\"\n",
+ " - \"Determine if an email is spam or not spam\"\n",
+ " - \"Determine whether a tumor is malignant or benign\"\n",
+ " - \"Determine whether a transaction is fraudulent or not\"\n",
+ " - \"Determine whether a student is likely to pass a course\"\n",
+ "\n",
+ "2. Avoid choosing a \"basic\"/\"small\" dataset.\n",
+ " - 1K rows and more.\n",
+ " - 10 features and more.\n",
+ "\n",
+ "\n",
+ "3. Please submit your dataset [here](https://forms.gle/YYiRLXJnbwUfwuwc7), to share it with the class so everyone can see.\n",
+ "And make sure your chosen dataset is unique using this [link](https://docs.google.com/spreadsheets/d/1M8uojrzhSyVnOlSAJpzCKxrhWdzPR77k4x8Kxvr8VDk/edit?usp=sharing).\n",
+ "\n",
+ " *Note: Due to their popularity, the following are datasets you may not choose.*\n",
+ " > - Iris dataset\n",
+ " > - Wine dataset\n",
+ " > - Titanic dataset\n",
+ " > - Boston Housing dataset\n",
+ " > - ImageNet, Cifar, CelebFaces, IMDB\n",
+ "\n",
+ "> (If you happen to change the dataset - submit the new dataset. We're looking on the most current one)\n",
+ "\n",
+ "\n",
+ "4. Choose a dataset with moslty numaric values. This way you would have enough information to work on, and you could drop columns that aren't numeric.\n",
+ "\n",
+ "5. Briefly describe your chosen dataset (source, size, features) and the question you want to answer.\n",
+ "\n",
+ "6. Clearly identify the target variable to predict (if exists).\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "a_vsO0Q1IOMT"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "id": "GFaHgQJyKwa5"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Dataset chosen: Financial Fraud Detection (PaySim Simulation)\n# Source: This is a real-world inspired simulation sourced from Kaggle, based on a month of financial logs\n# from a mobile money service, designed to simulate fraudulent behavior within legitimate transactions.\n# Size: The original dataset contains 6,362,620 rows and 10 features. For the scope of this assignment\n# I will work with a representative sample to ensure a manageable yet significant EDA process.\n# Features: The dataset maintains a combination of numerical variables and categorical variables.\n# Research Question: How do specific economic indicators, such as transaction types and sudden\n# inconsistencies between account balances, act as financial fingerprints to accurately predict\n# fraudulent activity, and can identifying these logical gaps in the data lead to more efficient\n# and cost-effective detection systems?"
+ ],
+ "metadata": {
+ "id": "OI0MZzohKwfE"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "I went with the PaySim dataset because it gave me something that actually feels like real financial data \u2014 messy, imbalanced, and full of logical contradictions. As someone studying economics, the research question basically wrote itself once I looked at the columns: when a large transfer happens and the account balance does not shift the way it should, something is clearly wrong. The goal of this analysis is to find out whether those inconsistencies are consistent enough to reliably predict fraud."
+ ],
+ "metadata": {
+ "id": "Lgum4x_tKwjL"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Target variable identification: isFraud\n# Data type: int64\n# Values: 0 = legitimate transaction, 1 = fraudulent transaction"
+ ],
+ "metadata": {
+ "id": "SWIrnfSLKwnE"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "4t2QNyE6IPKS"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 2: Exploratory Data Analysis (EDA)**\n",
+ "\n",
+ "Use your EDA to tell the story of your data - highlight interesting patterns, anomalies, or relationships that lead you toward your classification goal. Ask interesting questions, and answer them.\n",
+ "\n",
+ "\n",
+ "1. **Data Cleaning** : Check for missing values, duplicate entries, scaling/normalize issues, parsing dates, fixing typos, or any inconsistencies. Document how you address them.\n",
+ "2. **Outlier Detection & Handling**: Identify outliers and decide whether to keep or remove them, providing a short justification.\n",
+ "3. **Descriptive Statistics**: Summarize the data (e.g., mean, median, correlations) to reveal patterns.\n",
+ "4. Read further on [A Comprehensive Guide to Mastering Exploratory Data Analysis](https://www.dasca.org/world-of-data-science/article/a-comprehensive-guide-to-mastering-exploratory-data-analysis).\n",
+ "5. **Visualizations**: Use a set of plots (e.g., histograms, scatter plots, box plots) to illustrate **key insights.** Label charts, axes, and legends clearly.\n",
+ "\n",
+ "Tip: not necessarily in this order."
+ ],
+ "metadata": {
+ "id": "6eLmNWJJIPS0"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "e3JpM_9Wl84T"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import pandas as pd # libraries for data manipulation and tables\n",
+ "import numpy as np #\n",
+ "\n",
+ "import matplotlib.pyplot as plt # libraries for data visualization\n",
+ "import seaborn as sns\n",
+ "\n",
+ "%matplotlib inline\n"
+ ],
+ "metadata": {
+ "id": "NuY60Al57ZRQ"
+ },
+ "execution_count": 12,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import kagglehub\n",
+ "\n",
+ "# Download latest version\n",
+ "path = kagglehub.dataset_download(\"chitwanmanchanda/fraudulent-transactions-data\")\n",
+ "\n",
+ "print(\"Path to dataset files:\", path)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "bzUjUE937ZUA",
+ "outputId": "958ec3f7-4dfd-4a00-a775-5d698b7c1cf6"
+ },
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Using Colab cache for faster access to the 'fraudulent-transactions-data' dataset.\n",
+ "Path to dataset files: /kaggle/input/fraudulent-transactions-data\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\ncsv_file = os.listdir(path)[0]\nfull_path = os.path.join(path, csv_file)\ndf = pd.read_csv(full_path).sample(n=100000, random_state=42) # load the data and take a random sample\ndf = df.reset_index(drop=True) # reset index after sampling to keep row numbers clean\ndf.head() # display the first few rows to verify"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 204
+ },
+ "id": "CsOcdGzZ7ZW4",
+ "outputId": "e8210d44-40ff-494d-a5eb-d452edfc842a"
+ },
+ "execution_count": 14,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3ac11118",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Simulating real-world data quality issues to demonstrate the full cleaning process\n# Financial datasets in production regularly suffer from these kinds of problems\n\nnp.random.seed(42)\n\n# Issue 1: Missing values \u2014 simulating incomplete transaction records\n# Some transactions may arrive without an 'amount' (system failure, partial logging)\nmissing_amount_idx = np.random.choice(df.index, size=200, replace=False)\ndf.loc[missing_amount_idx, 'amount'] = np.nan\n\n# Issue 2: Missing sender balance \u2014 sometimes not captured at transaction time\nmissing_balance_idx = np.random.choice(df.index, size=300, replace=False)\ndf.loc[missing_balance_idx, 'oldbalanceOrg'] = np.nan\n\n# Issue 3: Duplicate rows \u2014 simulating double-logging errors in the payment system\nduplicate_sample = df.sample(n=500, random_state=42)\ndf = pd.concat([df, duplicate_sample], ignore_index=True)\n\n# Issue 4: Inconsistent casing in the 'type' column \u2014 simulating data entry errors\nmixed_idx = np.random.choice(df.index, size=400, replace=False)\ndf.loc[mixed_idx, 'type'] = df.loc[mixed_idx, 'type'].str.lower()\n\nprint(f\"Dataset shape after introducing issues: {df.shape}\")\nprint(f\"Missing in 'amount': {df['amount'].isnull().sum()}\")\nprint(f\"Missing in 'oldbalanceOrg': {df['oldbalanceOrg'].isnull().sum()}\")\nprint(f\"Duplicate rows: {df.duplicated().sum()}\")\nprint(f\"Sample 'type' values: {df['type'].unique()}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# === DATA CLEANING ===\n\n# \u2500\u2500\u2500 Step 1: Missing Values \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nprint(\"=== MISSING VALUES ===\")\nmissing_data = df.isnull().sum()\nmissing_found = missing_data[missing_data > 0]\nprint(missing_found)\n\n# Fix 1a: Drop rows where 'amount' is NaN \u2014 a transaction without a value is unusable\nrows_before = len(df)\ndf = df.dropna(subset=['amount'])\nprint(f\"\\n\u2192 Dropped {rows_before - len(df)} rows with missing 'amount'\")\n\n# Fix 1b: Fill missing 'oldbalanceOrg' with the column median\n# Using median (not mean) because the amount column is right-skewed due to outliers\nmedian_bal = df['oldbalanceOrg'].median()\ndf['oldbalanceOrg'] = df['oldbalanceOrg'].fillna(median_bal)\nprint(f\"\u2192 Filled {missing_found.get('oldbalanceOrg', 0)} missing 'oldbalanceOrg' values with median ({median_bal:,.2f})\")\n\n# Confirm: no missing values remain\nprint(f\"\u2192 Missing values remaining after fix: {df.isnull().sum().sum()}\")\n\n# \u2500\u2500\u2500 Step 2: Duplicate Rows \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nprint(\"\\n=== DUPLICATE ROWS ===\")\nduplicates_count = df.duplicated().sum()\nprint(f\"Found {duplicates_count} duplicate rows\")\n\n# Fix 2: Remove all duplicates and reset index\ndf = df.drop_duplicates()\ndf = df.reset_index(drop=True)\nprint(f\"\u2192 Removed duplicates. Dataset now has {len(df)} rows\")\n\n# \u2500\u2500\u2500 Step 3: Formatting Inconsistencies \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nprint(\"\\n=== FORMATTING INCONSISTENCIES ===\")\nprint(f\"Unique 'type' values before fix: {sorted(df['type'].unique())}\")\n\n# Fix 3: Standardize all transaction types to uppercase and strip whitespace\n# Prevents 'transfer' and 'TRANSFER' being treated as different categories\ndf['type'] = df['type'].str.upper().str.strip()\nprint(f\"Unique 'type' values after fix: {sorted(df['type'].unique())}\")\nprint(\"\u2192 All transaction types standardized\")\n\nprint(f\"\\nFinal dataset shape after full cleaning: {df.shape}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ZMCgNBIe7Zcq",
+ "outputId": "bab8dd98-8196-41ba-8736-a64f1824a0df"
+ },
+ "execution_count": 15,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#Outlier Detection And Handling:\n#1. Identifying the outliers using IQR\nQ1 = df['amount'].quantile(0.25)\nQ3 = df['amount'].quantile(0.75)\nIQR = Q3 - Q1\n#Boundaries for outliers\nlower_bound = Q1 - 1.5 * IQR\nupper_bound = Q3 + 1.5 * IQR\n#count outliers\noutliers = df[(df['amount'] < lower_bound) | (df['amount'] > upper_bound)]\nprint(\"Number of outliers detected in amount :\", len(outliers))\nprint(f\"Upper bound for normal transactions: {upper_bound:,.2f}\")\n\n#visualize the distribution before handling\nplt.figure(figsize=(10, 6))\nsns.histplot(df['amount'], bins=50, kde=True)\nplt.axvline(upper_bound, color='red', linestyle='--', label='Outlier Boundary')\nplt.title('Transaction Amount Distribution With Outlier Boundaries')\nplt.legend()\nplt.show()\n\n\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 598
+ },
+ "id": "o2d7ZnUo-xt5",
+ "outputId": "72ce28c9-5bed-4e9b-bba1-52aedcd1459b"
+ },
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Number of outliers detected in amount : 5358\n",
+ "Upper bound for normal transactions: 502,520.23\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# log transformation to handle the extreme scale of outliers\n",
+ "# used log1p (log 1+x) to avoid issues with zero values\n",
+ "df['amount_log'] = np.log1p(df['amount'])\n",
+ "# visualize the fixed distribution\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "sns.histplot(df['amount_log'], bins=50, kde=True, color='green')\n",
+ "plt.title('Log Transformed Transaction Amounts (Outlier handled)')\n",
+ "plt.xlabel('Log Amount')\n",
+ "plt.show()\n",
+ "print(\"outlier handling complete: extreme values normalized using log transformation\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 581
+ },
+ "id": "SWwM5uHN-xzw",
+ "outputId": "d9234b03-0432-453c-a50d-16e7c4cd6407"
+ },
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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79etH9+7dHe2ioqIcw/DqQ1JSEiNGjKi2/cj7yHJzc7FarQwePJhdu3aRm5vr1LZTp04MHDjQ8XVMTAwdO3Zk165djm0RERFs2rSJ7du3n1B9q1atIiMjg1tvvdXpXo+RI0eSnJxcbYga4Pir8N+dddZZTsOw7D2RY8aMITQ0tNp2e/3r1q1j+/btXHXVVRw6dAir1YrVaqWwsJCzzjqLpUuXYrPZqKys5Ntvv2XUqFG0bt3acb6UlJQan+O6Gjx4MJ06daq2/cjvWXZ2Nrm5uQwcONBpiKHdsGHDnG6kP/XUUwkLC6v2PVuxYgUHDhyotZYjr1lYWIjVauWMM87AMAzWrl0LQGZmJkuXLuX66693el6AOs8a+fXXX9OnTx8GDBjg2BYSEsKECRPYs2cPmzdvdmp/3XXX4efn5/ja/no98vHWZvbs2cTFxTF06FBHzZdffjlz586tNiwM4NJLLyU8PNzxtf31NG7cOKceob59+1JWVuYYevv9999TVlbGpEmTnCYwuemmmwgLC6vxtX68TubxR0RE8Oeff7Jy5coTuuahQ4cAiIyMdNqen58P4PQz93f2ffb3wsaSlZXF4sWLueyyy8jPz3f8rB86dIgRI0awffv2akOlb7rpJry9vY/7GsfzvTien6sj/eMf/3D6euDAgU7n+/rrr/Hx8XF6b/T29ub2228/Zr2ffvopNpuNyy67zPF8WK1W4uPjad++PUuWLAH+eq/+xz/+4fT47MMka2J/bVit1mPWIWIGBTIRD5WZmUlRUZFjaOCRUlJSsNlsjnH5e/furXEmt/qc3S0pKanG7b/88gvDhg0jODiYiIgIYmJieOCBBwCqBbK//5INVf/RZmdnO75+7LHHyMnJoUOHDnTt2pV77rmH9evXH7M++9pFNT1fycnJ1dY28vHxqfU+jr/Xaf8l4e9Daezb7fXbQ+T48eOJiYlx+njrrbcoLS0lNzeXzMxMiouLad++fbVr11R/XdX2PbMP7woICCAqKoqYmBhee+21at8vOL7v2dSpU9m4cSOtWrWiT58+TJ48udov8KmpqVx77bVERUU57l8ZPHgw8NfrxH5Mly5d6vaAa7B3795af4bs+4/098dr/0XwyMdbk8rKSubOncvQoUPZvXs3O3bsYMeOHfTt25f09HQWLVpU7Zi6vs5qe637+fnRtm3bk1rHq66PH+Bf//oXISEh9OnTh/bt2zNx4sQa752sjWEYTl/bw5Y9mNXkeEJbQ9ixYweGYfDQQw9V+1m3T+Jin3zIrrafx9ocz/fieH6u7AICAqoN7f37z/LevXtJSEhwDEO1O573pe3bt2MYBu3bt6/2nGzZssXxfNhfn39///P19XX64+OR7K8NT17OQ9yb7iETkUZR04yKO3fu5KyzziI5OZnnnnuOVq1a4efnx9dff83zzz+PzWZzal/bX4eP/EVs0KBB7Ny5k88//5zvvvuOt956i+eff57p06dz44031tvj8ff3r3V69NrqPFb99sf79NNPO/VWHikkJITS0tITrLZuavqe/fTTT1x44YUMGjSIV199lYSEBHx9fZk5c2aNky8cz/fssssuY+DAgcybN4/vvvuOp59+mqeeeopPP/2Uc889l8rKSs4++2yysrL417/+RXJyMsHBwezfv59rr7222uvETMfzeGuyePFiDh48yNy5c5k7d261/bNnz2b48OHHda261lCT2n6BraysrPE6J3PtlJQUtm3bxvz581mwYAGffPIJr776Kg8//DCPPvporcfZ70X8e+izh+b169czatSoGo+1/7Gmpp7gv6upl7Ku7K/Zu+++u9Ze7b//QexEZ6U91vfiRH+uTqR3ri5sNhsWi4Vvvvmmxmv9PeSdCPtrw37PqIirUSAT8VAxMTEEBQWxbdu2avu2bt2Kl5eX4y/piYmJNc4EdyKzw9XFl19+SWlpKV988YXTX3PtQ1PqKioqiuuuu47rrruOgoICBg0axOTJk48ayOyLyW7bto0zzzzTad+2bduqLTbbEOxD+8LCwo66Ppp99sCahmXW9P2uT5988gkBAQF8++23TpO+zJw586TOm5CQwK233sqtt95KRkYGPXr04Mknn+Tcc89lw4YN/PHHH7zzzjtcc801jmPsM2fa2f86vnHjxqNe60T+Sp6YmFjrz5B9f32YPXs2sbGxTJs2rdq+Tz/9lHnz5jF9+vR6WSriyNf6kT0KZWVl7N692+m1FxkZWeMMeXv37q21N+JYjvb8BwcHc/nll3P55ZdTVlbGxRdfzJNPPsn9999f67TxycnJQNWSAUcaMGAAERERzJkzhwcffLDGX/Lts7Sef/75jm01PeaysjIOHjx43I/jWOzPna+vr2lrIR7vz9WJSExMZNGiRRQUFDgFqON5X2rXrh2GYZCUlESHDh2Oeg2o6lE78r26vLyc3bt3061bt2rH7N69Gy8vr6OeV8RMGrIo4qG8vb0ZPnw4n3/+udPU6unp6cyZM4cBAwY4ZpsaMWIEy5cvZ926dY52WVlZNU65Xd81gvNfz3Nzc0/ql3v7/SR2ISEhnHLKKcfsVerVqxexsbFMnz7dqe0333zDli1bqs0e2BB69uxJu3bteOaZZygoKKi2PzMzE6h63kaMGMFnn31GamqqY/+WLVv49ttvG7RGb29vLBaLU2/Bnj17+Oyzz+p0vsrKympDo2JjY2nevLnj+1DT68QwDF588UWn42JiYhg0aBAzZsxwel7+fqx9/abjmYr7vPPO47fffmP58uWObYWFhbzxxhu0adPmuHpWjqW4uJhPP/2U888/n0suuaTax2233UZ+fn616dDratiwYfj5+fHSSy85PS9vv/02ubm5Tq/1du3a8euvv1JWVubYNn/+/BqnIT9ewcHBNQ5v/fvPrp+fH506dcIwDMrLy2s9X4sWLWjVqhWrVq1y2h4UFMTdd9/Ntm3balz0/KuvvmLWrFmMGDHCaYbFdu3asXTpUqe2b7zxRrUeshN5Hf1dbGwsQ4YM4fXXX68W9OCvn/WGdLw/VyfivPPOo6KiwmnpksrKSl5++eVjHnvxxRfj7e3No48+Wq1H1TAMx+ujV69exMTEMH36dKfX5axZs2r9XqxevZrOnTvXeo+ZiNnUQybi5mbMmFFtXSeAO+64gyeeeIKFCxcyYMAAbr31Vnx8fHj99dcpLS1l6tSpjrb33nsv7733HmeffTa33367Y9r71q1bk5WV1WDj7ocPH46fnx8XXHABN998MwUFBbz55pvExsbW+EvK8ejUqRNDhgyhZ8+eREVFsWrVKseU6kfj6+vLU089xXXXXcfgwYO58sorHdPet2nThjvvvLNO9ZwILy8v3nrrLc4991w6d+7MddddR4sWLdi/fz9LliwhLCyML7/8EoBHH32UBQsWMHDgQG699VYqKiocazgdzz1zdTVy5Eiee+45zjnnHK666ioyMjKYNm0ap5xySp2um5+fT8uWLbnkkkvo1q0bISEhfP/996xcuZJnn30WqOoBadeuHXfffTf79+8nLCyMTz75pMb7kl566SUGDBhAjx49mDBhAklJSezZs4evvvrK8QeHnj17AlVTsF9xxRX4+vpywQUX1LjQ7n333cf777/Pueeey//93/8RFRXFO++8w+7du/nkk09qHbZ6Ir744gvy8/O58MILa9x/+umnExMTw+zZs7n88stP+noxMTHcf//9PProo5xzzjlceOGFbNu2jVdffZXevXs7LRB+44038vHHH3POOedw2WWXsXPnTt577z2niVpOVM+ePfnggw+466676N27NyEhIVxwwQUMHz6c+Ph4+vfvT1xcHFu2bOGVV15h5MiRx7zH66KLLmLevHkYhuH0fnXfffexdu1annrqKZYvX86YMWMIDAzk559/5r333iMlJYV33nnH6Vw33ngj//jHPxgzZgxnn302v//+O99++2214W7du3fH29ubp556itzcXPz9/R1rKh6PadOmMWDAALp27cpNN91E27ZtSU9PZ/ny5fz555/8/vvvx/mM1s2J/FwdrwsuuID+/ftz3333sWfPHjp16sSnn35aYwD/u3bt2vHEE09w//33s2fPHkaNGkVoaCi7d+9m3rx5TJgwgbvvvhtfX1+eeOIJbr75Zs4880wuv/xydu/ezcyZM2vstS0vL3es4SbishptPkcRqVf2qZBr+9i3b59hGIaxZs0aY8SIEUZISIgRFBRkDB061Fi2bFm1861du9YYOHCg4e/vb7Rs2dKYMmWK8dJLLxmAkZaWdtx11TbtfW1T0X/xxRfGqaeeagQEBBht2rQxnnrqKWPGjBnVpiWv7Rx/n176iSeeMPr06WNEREQYgYGBRnJysvHkk08aZWVl1Z67mpYM+OCDD4zTTjvN8Pf3N6KiooyxY8caf/75p1Ob8ePHG8HBwTU+HqDaFO72Ka+ffvppp+326ak/+ugjp+1r1641Lr74YqNZs2aGv7+/kZiYaFx22WXGokWLnNr9+OOPRs+ePQ0/Pz+jbdu2xvTp06tNJX08apv2vrap6N9++22jffv2hr+/v5GcnGzMnDmzxuvWdo4jpw8vLS017rnnHqNbt25GaGioERwcbHTr1s149dVXnY7ZvHmzMWzYMCMkJMSIjo42brrpJscU+kdOpW0YhrFx40Zj9OjRRkREhBEQEGB07NjReOihh5zaPP7440aLFi0MLy8vp9daTdO879y507jkkksc5+vTp48xf/58pza1fS9rmu787y644AIjICDAKCwsrLXNtddea/j6+hpWq/WEX0+1vd5feeUVIzk52fD19TXi4uKMW265xcjOzq527WeffdZo0aKF4e/vb/Tv399YtWpVrdPeH8/jLygoMK666iojIiLCABzvF6+//roxaNAgx+u+Xbt2xj333GPk5ubW+rzYrVmzptryBHaVlZXGzJkzjf79+xthYWFGQECA0blzZ+PRRx81CgoKamz/r3/9y4iOjjaCgoKMESNGGDt27KjxtfHmm28abdu2dSw3YZ8C/3imvTeMqtfWNddcY8THxxu+vr5GixYtjPPPP9/4+OOPHW2OZ4mTI53I9+J4f65qe8+r6ef+0KFDxtVXX22EhYUZ4eHhxtVXX22sXbv2mNPe233yySfGgAEDjODgYCM4ONhITk42Jk6caGzbts2p3auvvmokJSUZ/v7+Rq9evYylS5dWe94NwzC++eYbAzC2b99+jGdOxDwWw6jDXb4i0iRMmjSJ119/nYKCgga/oVtE5GScddZZNG/enHfffdfsUsSFjBo1CovFwrx588wuRaRWCmQiAlTdx3LkhAGHDh2iQ4cO9OjR46Ru8hYRaQwrVqxg4MCBbN++vVEm4RHXt2XLFrp27cq6devqdTkMkfqmQCYiQNX9EEOGDCElJYX09HTefvttDhw4wKJFixg0aJDZ5YmIiIh4JE3qISJA1exYH3/8MW+88QYWi4UePXrw9ttvK4yJiIiINCD1kImIiIiIiJhE65CJiIiIiIiYRIFMRERERETEJLqHrJ7YbDYOHDhAaGhogy2iKyIiIiIirs8wDPLz82nevDleXkfvA1MgqycHDhygVatWZpchIiIiIiIuYt++fbRs2fKobRTI6kloaChQ9aSHhYWZXI2IiIiIiJglLy+PVq1aOTLC0SiQ1RP7MMWwsDAFMhEREREROa5bmTSph4iIiIiIiEkUyEREREREREyiQCYiIiIiImISBTIRERERERGTKJCJiIiIiIiYRIFMRERERETEJApkIiIiIiIiJlEgExERERERMYkCmYiIiIiIiEkUyEREREREREyiQCYiIiIiImISBTIRERERERGTKJCJiIiIiIiYRIFMRERERETEJApkIiIiIiIiJlEgExERERERMYkCmYiIiIiIiEkUyEREREREREziY3YBIiIiIp4gNTUVq9Vap2Ojo6Np3bp1PVckIu7A1B6ypUuXcsEFF9C8eXMsFgufffaZ037DMHj44YdJSEggMDCQYcOGsX37dqc2WVlZjB07lrCwMCIiIrjhhhsoKChwarN+/XoGDhxIQEAArVq1YurUqdVq+eijj0hOTiYgIICuXbvy9ddf1/vjFREREc+UmppKckoyPXv2rNNHckoyqampZj8METGBqT1khYWFdOvWjeuvv56LL7642v6pU6fy0ksv8c4775CUlMRDDz3EiBEj2Lx5MwEBAQCMHTuWgwcPsnDhQsrLy7nuuuuYMGECc+bMASAvL4/hw4czbNgwpk+fzoYNG7j++uuJiIhgwoQJACxbtowrr7ySKVOmcP755zNnzhxGjRrFmjVr6NKlS+M9ISIiIuKWrFYrxUXFjH5gNDGJMSd0bObeTOb9Zx5Wq1W9ZCJNkMUwDMPsIgAsFgvz5s1j1KhRQFXvWPPmzfnnP//J3XffDUBubi5xcXHMmjWLK664gi1bttCpUydWrlxJr169AFiwYAHnnXcef/75J82bN+e1117jwQcfJC0tDT8/PwDuu+8+PvvsM7Zu3QrA5ZdfTmFhIfPnz3fUc/rpp9O9e3emT59+XPXn5eURHh5Obm4uYWFh9fW0iIiIiBtYs2YNPXv2ZMLrE0jokHBCxx784yBv3PwGq1evpkePHg1UoYg0phPJBi47qcfu3btJS0tj2LBhjm3h4eH07duX5cuXA7B8+XIiIiIcYQxg2LBheHl5sWLFCkebQYMGOcIYwIgRI9i2bRvZ2dmONkdex97Gfp2alJaWkpeX5/QhIiIiIiJyIlw2kKWlpQEQFxfntD0uLs6xLy0tjdjYWKf9Pj4+REVFObWp6RxHXqO2Nvb9NZkyZQrh4eGOj1atWp3oQxQRERERkSbOZQOZq7v//vvJzc11fOzbt8/skkRERERExM24bCCLj48HID093Wl7enq6Y198fDwZGRlO+ysqKsjKynJqU9M5jrxGbW3s+2vi7+9PWFiY04eIiIiIiMiJcNlAlpSURHx8PIsWLXJsy8vLY8WKFfTr1w+Afv36kZOTw+rVqx1tFi9ejM1mo2/fvo42S5cupby83NFm4cKFdOzYkcjISEebI69jb2O/joiIiIiISEMwddr7goICduzY4fh69+7drFu3jqioKFq3bs2kSZN44oknaN++vWPa++bNmztmYkxJSeGcc87hpptuYvr06ZSXl3PbbbdxxRVX0Lx5cwCuuuoqHn30UW644Qb+9a9/sXHjRl588UWef/55x3XvuOMOBg8ezLPPPsvIkSOZO3cuq1at4o033mjU50NEREQ8y4H8Ayz/czkWLPh6++Lr5UubiDYkRyebXZqIuAhTA9mqVasYOnSo4+u77roLgPHjxzNr1izuvfdeCgsLmTBhAjk5OQwYMIAFCxY41iADmD17NrfddhtnnXUWXl5ejBkzhpdeesmxPzw8nO+++46JEyfSs2dPoqOjefjhhx1rkAGcccYZzJkzh3//+9888MADtG/fns8++0xrkImIiEidFZcX88GmD8grdZ6J+bf9v3FLr1uICT6x9cpExDO5zDpk7k7rkImIiDRdf1+HzDAMPtnyCZsyNxEVGEXPhJ6U28rZfmg7+/P307FZR67ocgWgdchEPJFHrEMmIiIi4q42ZGxgU+YmLFi4OPlizmh1BoMTB3NRx4uwYGHboW2k5qaaXaaIuAAFMhEREZF6lFOSw9fbvwZgcJvBtAhr4dgXExzDaQmnAbBw50I0UElEFMhERERE6olhGHy29TNKK0tpGdaSga0HVmszJHEIvl6+/Jn/J1utW02oUkRciQKZiIiISD3ZV7qPvbl78fXyZXTyaLws1X/VCvUP5fSWpwOwaPcibIatscsUEReiQCYiIiJST7YWVfV49UjoQVRgVK3t+rfqT5BvEIeKD/FH8R+NVZ6IuCAFMhEREZH6EAJ7S/cCVYHsaPx9/Dm9RVUv2Z6SPQ1dmYi4MAUyERERkfpwGhgYtAprRWxw7DGbnxJ1CgAHyw7qNzKRJkw//iIiIiInyWbY4HCn2LF6x+ziQ+IJ9Amk3CiHFsduLyKeSYFMRERE5CStyFwBkeBn8aNzTOfjOsZisdAmok3VF0kNV5uIuDYFMhEREZGTNC91HgDtA9vj6+173MclRR5OYgpkIk2WApmIiIjISUgrSOOHtB8ASAlKOaFj20a0rfqkFRRXFNdzZSLiDhTIRERERE7CrHWzqDQqYR9E+dY+1X1NogKjCPYKBh/4Pfv3BqpQRFyZApmIiIjISXh/4/tVn6w58WMtFgvN/ZsD8Jv1t3qsSkTchQKZiIiISB2l5qayPn09XnjBtrqdo4Vf1RSLKzNX1mNlIuIuFMhERERE6mj+H/MB6BrZFYrqdo4W/lWBbGvuVrKLs+urNBFxEwpkIiIiInX05R9fAjAoflCdzxHsHQxWsGHjx70/1ldpIuImFMhERERE6qCgrIDFuxcDMCiu7oEMgF1V/yzategkqxIRd6NAJiIiIlIHC3cupKyyjLaRbUkKOcmFxHZX/bNotwKZSFOjQCYiIiJSB/b7x85vfz4Wi+XkTrYHLFjYYt1CZmHmyRcnIm5DgUxERETkBNkMG19t/wqACzpecPInLIbmQVXT32/M2Hjy5xMRt6FAJiIiInKCVu5fSXphOmH+YQxKPMn7xw47JfQUQIFMpKlRIBMRERE5QfbZFUe0G4Gft1+9nLNdaDtAgUykqVEgExERETlB9kB2QYd6GK54WLuww4EsU4FMpClRIBMRERE5Aam5qaxPX4+XxYvz2p9Xb+c9sofMMIx6O6+IuDYFMhEREZETYF8rrG+LvjQLalZv520T0gYfLx/ySvPYn7+/3s4rIq5NgUxERETkBPyw9wcAhrYZWq/n9fXypUOzDoDuIxNpShTIRERERE7Aj3t+BGBImyH1fu4usV0ABTKRpkSBTEREROQ47cnZw97cvfh4+XBGqzPq/fxdYhTIRJoaBTIRERGR42TvHevdvDfBfsH1fn71kIk0PT5mFyAiIiLiSlJTU7FarTXu+2TdJwB0DOjImjVrHNu3bNlSL9e2B7LNmZuptFXi7eVdL+cVEdelQCYiIiJyWGpqKskpyRQXFdfc4A4gEmY9MotZO2dV211QUHBS128b2ZYAnwCKK4rZnbObU6JOOanziYjrUyATEREROcxqtVJcVMzoB0YTkxjjtC+/Ip/3M9/HgoXxd4/Hz8vPsW/7iu0smbGEkpKSk7q+t5c3nWI6sebgGjZmbFQgE2kCFMhERERE/iYmMYaEDglO2zLSMiATmoc2JzE50WmfNbXmIY510SW2iyOQjUoeVW/nFRHXpEAmIiIichz25O4BoE1EmwY5v/0+tMiySAB+2vYTa0LWHO0QoqOjad26dYPUIyKNQ4FMRERE5DjszdkLQGJ44jFanpiCrKr7zsaNG1e14RRgHHy37ju+m/DdUY8NDApk65atCmUibkyBTEREROQYcktyyS7JxoKF1uH1G35KCqruOxt661Dad2tPQWUBczLmYIm1cP306/G21DzTYubeTOb9Zx5Wq1WBTMSNKZCJiIiIHMPe3KresYTQBPx9/BvkGpEtIknokIBhGPgf8qe0shS/ln7EBsc2yPVExDVoYWgRERGRY9iTsweANuFtGvxaFovFEcIyCjMa/HoiYi4FMhEREZFjSM1NBSAxon7vH6tNTHDVlPsKZCKeT4FMRERE5CiKy4s5VHwIgJZhLRvlmjFBVYHsUNGhRrmeiJhHgUxERETkKA7kHwAgMiCSIN+gRrlmZEDV1PfZJdmNcj0RMY8CmYiIiMhR/Jn/J9B4vWMAkYEKZCJNhQKZiIiIyFHsz9sPQPPQ5o12zYiACABKKkooqShptOuKSONTIBMRERGphWEY7M+vCmSN2UPm5+1HsG8wANnF6iUT8WQKZCIiIiK1yCnJoai8CC+LF/Eh8Y16bXsvWU5JTqNeV0QalwKZiIiISC3s94/Fh8Tj4+XTqNfWxB4iTYMCmYiIiEgt7PePtQht0ejXjgiMABTIRDydApmIiIhILeyBrDHvH7Oz95BpyKKIZ1MgExEREalBpa2SgwUHAXN6yBxDFjWph4hHUyATERERqUFaQRqVRiWBPoFEBUY1+vWPnNTDMIxGv76INA4FMhEREZEa2Ke7bxHaAovF0ujXDw8Ix4KFSqOS/LL8Rr++iDQOBTIRERGRGjgCWVjjD1cE8LJ4ER4QDug+MhFPpkAmIiIiUgMzZ1i0031kIp5PgUxERETkb0ptpRwqPgSY10MGf91HpqnvRTyXApmIiIjI32SUZwAQFRhFkG+QaXVo6nsRz6dAJiIiIvI31nIrAM1DmptahxaHFvF8CmQiIiIif3OovGq4YnxIvKl1qIdMxPMpkImIiIj8jb2HzFUCWV5pHhW2ClNrEZGGoUAmIiIiciR/yKvMAyAhNMHUUoJ8g/D18gUgtyTX1FpEpGEokImIiIgcKa7qnzD/MFMn9ACwWCxEBh6e+l73kYl4JAUyERERkSMd7hRLCDG3d8zOsRaZApmIR1IgExERETnS4Rxm9v1jdo61yLQ4tIhHUiATEREROdLhHOZqgUwzLYp4JgUyERERkcPKKssgpupzDVkUkcagQCYiIiJy2M78neAN/hZ/wvzDzC4HwDGph3rIRDyTApmIiIjIYVtztwIQ7RuNxWIxuZoq9iGLJRUlFJcXm1uMiNQ7BTIRERGRw7blbQOgmW8zkyv5i5+3n2P6ffWSiXgeBTIRERGRw7blVgWyaN9okytxFu4fDkB+Wb7JlYhIfVMgExEREQEqbZX8kfcHAM18XKeHDHDcz5ZXmmdyJSJS3xTIRERERIDtWdspqSyBMgj3CTe7HCeh/qGAApmIJ1IgExEREQHWHlxb9Uk6eFlc61ck9ZCJeC7XercRERERMcnatMOB7KC5ddQkzK8qkOWX6h4yEU+jQCYiIiLCEYEszdw6auLoIStTD5mIp1EgExERkSbPMAzWpa2r+sKFA1luSS6GYZhcjYjUJwUyERERafLSC9OxFlnxwgsyzK6mOvukHuW2ckorS02uRkTqkwKZiIiINHnr09cD0Cq4FVSYXEwN/Lz9CPAJADSxh4incelAVllZyUMPPURSUhKBgYG0a9eOxx9/3Kmr3jAMHn74YRISEggMDGTYsGFs377d6TxZWVmMHTuWsLAwIiIiuOGGGygoKHBqs379egYOHEhAQACtWrVi6tSpjfIYRURExHwb0jcAcErYKSZXUjvNtCjimVw6kD311FO89tprvPLKK2zZsoWnnnqKqVOn8vLLLzvaTJ06lZdeeonp06ezYsUKgoODGTFiBCUlJY42Y8eOZdOmTSxcuJD58+ezdOlSJkyY4Nifl5fH8OHDSUxMZPXq1Tz99NNMnjyZN954o1Efr4iIiJhjQ4YCmYiYw8fsAo5m2bJlXHTRRYwcORKANm3a8P777/Pbb78BVb1jL7zwAv/+97+56KKLAPjf//5HXFwcn332GVdccQVbtmxhwYIFrFy5kl69egHw8ssvc9555/HMM8/QvHlzZs+eTVlZGTNmzMDPz4/OnTuzbt06nnvuOafgJiIiIp7JEchCFchEpHG5dA/ZGWecwaJFi/jjjz8A+P333/n5558599xzAdi9ezdpaWkMGzbMcUx4eDh9+/Zl+fLlACxfvpyIiAhHGAMYNmwYXl5erFixwtFm0KBB+Pn5OdqMGDGCbdu2kZ2dXWNtpaWl5OXlOX2IiIiI+6mwVbA5czMA7cPam1xN7exrkSmQiXgWl+4hu++++8jLyyM5ORlvb28qKyt58sknGTt2LABpaVXz0sbFxTkdFxcX59iXlpZGbGys034fHx+ioqKc2iQlJVU7h31fZGRktdqmTJnCo48+Wg+PUkRERMy0I2sHJRUlBPsG0yKohdnl1MreQ6bFoUU8i0v3kH344YfMnj2bOXPmsGbNGt555x2eeeYZ3nnnHbNL4/777yc3N9fxsW/fPrNLEhERkTqwT+jRObYzXhbX/dVIi0OLeCaX7iG75557uO+++7jiiisA6Nq1K3v37mXKlCmMHz+e+Ph4ANLT00lISHAcl56eTvfu3QGIj48nI8N5QZGKigqysrIcx8fHx5Oenu7Uxv61vc3f+fv74+/vf/IPUkRERExlv3+sa2xXkys5OvtaZBqyKOJZXPfPQEBRURFeXs4lent7Y7PZAEhKSiI+Pp5FixY59ufl5bFixQr69esHQL9+/cjJyWH16tWONosXL8Zms9G3b19Hm6VLl1JeXu5os3DhQjp27FjjcEURERHxHPZAdmrcqSZXcnT2HrKSihLKKstMrkZE6otLB7ILLriAJ598kq+++oo9e/Ywb948nnvuOUaPHg2AxWJh0qRJPPHEE3zxxRds2LCBa665hubNmzNq1CgAUlJSOOecc7jpppv47bff+OWXX7jtttu44ooraN68OQBXXXUVfn5+3HDDDWzatIkPPviAF198kbvuusushy4iIiKNxL4otKv3kPl7++PnXTUBme4jE/EcLj1k8eWXX+ahhx7i1ltvJSMjg+bNm3PzzTfz8MMPO9rce++9FBYWMmHCBHJychgwYAALFiwgICDA0Wb27NncdtttnHXWWXh5eTFmzBheeuklx/7w8HC+++47Jk6cSM+ePYmOjubhhx/WlPciIiIerqCsgF3ZuwDoGteV1OxUkyuqncViIcw/DGuRlbzSPAIIOPZBIuLyXDqQhYaG8sILL/DCCy/U2sZisfDYY4/x2GOP1domKiqKOXPmHPVap556Kj/99FNdSxURERE3tCljEwDxIfFEB0WTiusGMqia+l6BTMSzuPSQRREREZGG5C73j9lpcWgRz6NAJiIiIk2Wu9w/ZueYaVFT34t4DAUyERERabLcZcp7O/WQiXgeBTIRERFpkgzDcCwK3TVOgUxEzKFAJiIiIk1SWkEah4oP4WXxolNMJ7PLOS4KZCKeR4FMREREmiT7/WMdmnUgwMc9Ziy0B7Ki8iIqjUqTqxGR+qBAJiIiIk2Su90/BhDoE4i3xRuAwspCk6sRkfqgQCYiIiJNkjsGMvvi0ACFNgUyEU+gQCYiIiJNkn1CD3dZg8zOEcjUQybiERTIREREpMmpsFWwOXMz4D4zLNopkIl4FgUyERERaXK2H9pOaWUpwb7BtIloY3Y5J0SBTMSzKJCJiIhIk2O/f6xLbBe8LO7165A9kBVUFphciYjUB/d6BxIRERGpB+56/xhAqF8oAEW2IpMrEZH6oEAmIiIiTc76jKo1yNxphkW7EL8QQIFMxFMokImIiEiTY+8hc7cJPQBC/Q/3kFUqkIl4AgUyERERaVLyS/PZnbMbcO8eMhs2CDS5GBE5aQpkIiIi0qRsytwEQPPQ5jQLamZyNSfOx8uHAJ+Aqi9CzK1FRE6eApmIiIg0KevT3ff+MTv7xB6EmluHiJw8BTIRERFpUhz3j7lxILMPW1QPmYj78zG7ABEREZGGkJqaitVqrbZ9+a7lAIQWh7JmzRqnfVu2bGmU2k6WeshEPIcCmYiIiHic1NRUklOSKS4qrr7zXiAIHvnHIzyS9kiNxxcUuPaiy+ohE/EcCmQiIiLicaxWK8VFxYx+YDQxiTGO7YWVhczOmI0FC9dPvh5vi7fTcdtXbGfJjCWUlJQ0dsknJMT/cBJTD5mI21MgExEREY8VkxhDQocEx9c7snZABjQLakbLji2rtbemVh/i6IocQxbVQybi9jSph4iIiDQZ6YXpAMQFx5lcyclxDFlUD5mI21MgExERkSYjozADgNjgWJMrOTnqIRPxHApkIiIi0mSkF3hYD5k/FFUUmVuMiJwUBTIRERFpEiptlViLqu4Rc/ceMn8ff3wsVVMBWEvc4743EamZApmIiIg0CVnFWVQalfh5+xEREGF2OSctyCsIAGupApmIO1MgExERkSbBPqFHbHAsFovF5GpOXpD34UCmHjIRt6ZAJiIiIk3CkYHME6iHTMQzKJCJiIhIk2CfYdHdJ/SwcwQy9ZCJuDUFMhEREWkSPGXKezvHkEX1kIm4NQUyERER8XilFaXklOQA6iETEdeiQCYiIiIez947FuoXSqBvoMnV1A97D1lmSabJlYjIyVAgExEREY9nn9DDU3rHQJN6iHgKBTIRERHxeJ52/xj81UOWV55HSUWJydWISF0pkImIiIjHc8ywGOI5PWT+Fn+oqPo8rSDN3GJEpM4UyERERMSjGYbhcWuQAVWLWxdUfX4w/6C5xYhInSmQiYiIiEfLL8unpKIECxaig6LNLqd+5Vf9c7BAgUzEXSmQiYiIiEdLL6jqHYsOisbHy8fkauqZeshE3J4CmYiIiHg0x/1jHjTDooN6yETcngKZiIiIeDRPvH/MQT1kIm5PgUxEREQ8midOee+gHjIRt6dAJiIiIh7LZtjILMoEPGvKewd7D5kCmYjbUiATERERj5VTkYPNsOHn7Ue4f7jZ5dQ/ew+ZhiyKuC0FMhEREfFYWRVZQNWEHhaLxeRqGsDhHrKMwgwqbBXm1iIidaJAJiIiIh4rq7wqkHnk/WMAheCFFwaG4145EXEvCmQiIiLisQ5VHAIgPiTe5EoaiAFR/lGAhi2KuCsFMhEREfFYh8o9PJAB0QHRAKQVpJlciYjUhQKZiIiIeKZgKLIVAR48ZBFo5t8MUCATcVcKZCIiIuKZDneKNQtshp+3n7m1NKBof/WQibgzBTIRERHxTIcDmScPVwT1kIm4OwUyERER8UxNJZAFHA5khQpkIu5IgUxEREQ8U1MJZOohE3FrCmQiIiLicYoriqEqp3h8INM9ZCLuTYFMREREPM7O/J3gBYFegYT4hZhdToPStPci7k2BTERERDzOH3l/ANDMt5nJlTQ8+5DFgrICCsoKTK5GRE6UApmIiIh4nG252wBo5uP5gSzIJ4hg32BAvWQi7kiBTERERDxOU+ohg7/uk1MgE3E/CmQiIiLiUSptlWzP2w5AtG+0ydU0DgUyEfelQCYiIiIeZWf2Toori6EcwrzDzC6nUSiQibgvBTIRERHxKOvS1lV9kg5elqbxq44CmYj7ahrvUiIiItJkOAJZE8omCmQi7kuBTERERDyKApmIuBMFMhEREfEoCmQi4k4UyERERMRjpBWkcbDgIF54QbrZ1TQeBTIR96VAJiIiIh5j1YFVACSFJkG5ycU0InsgSy9Mx2bYTK5GRE6EApmIiIh4DHsgSwlPMbmSxhUXHAdAha2CrOIsk6sRkROhQCYiIiIewx7IOkV0MrmSxuXr7Ut0UNUi2AfzD5pcjYicCAUyERER8QiGYbDywEqg6QUy0H1kIu5KgUxEREQ8wp95f5JRmIGPlw/tw9qbXU6jUyATcU8KZCIiIuIR7MMVu8R2IcA7wORqGp8CmYh7UiATERERj2APZL0SeplciTnigxXIRNyRApmIiIh4hFUHDwey5k00kNl7yAoVyETciQKZiIiIuD3DMP7qIWvqgUw9ZCJuRYFMRERE3N7unN1kFWfh5+1H17iuZpdjCgUyEffk8oFs//79jBs3jmbNmhEYGEjXrl1ZtWqVY79hGDz88MMkJCQQGBjIsGHD2L59u9M5srKyGDt2LGFhYURERHDDDTdQUFDg1Gb9+vUMHDiQgIAAWrVqxdSpUxvl8YmIiMjJs/eOdYvrhp+3n8nVmEOBTMQ9uXQgy87Opn///vj6+vLNN9+wefNmnn32WSIjIx1tpk6dyksvvcT06dNZsWIFwcHBjBgxgpKSEkebsWPHsmnTJhYuXMj8+fNZunQpEyZMcOzPy8tj+PDhJCYmsnr1ap5++mkmT57MG2+80aiPV0REROqmqQ9XhL8CWVZxFqUVpSZXIyLHy8fsAo7mqaeeolWrVsycOdOxLSkpyfG5YRi88MIL/Pvf/+aiiy4C4H//+x9xcXF89tlnXHHFFWzZsoUFCxawcuVKevWqepN++eWXOe+883jmmWdo3rw5s2fPpqysjBkzZuDn50fnzp1Zt24dzz33nFNwExEREddkXxC6KQeyqMAofL18KbeVk1GYQavwVmaXJCLHwaV7yL744gt69erFpZdeSmxsLKeddhpvvvmmY//u3btJS0tj2LBhjm3h4eH07duX5cuXA7B8+XIiIiIcYQxg2LBheHl5sWLFCkebQYMG4ef31xCHESNGsG3bNrKzs2usrbS0lLy8PKcPERERaXw2w8bqA6sB6N28t8nVmMdisWjYoogbculAtmvXLl577TXat2/Pt99+yy233ML//d//8c477wCQllb1ZhMXF+d0XFxcnGNfWloasbGxTvt9fHyIiopyalPTOY68xt9NmTKF8PBwx0erVvorlIiIiBm2H9pOflk+gT6BpMSkmF2OqRTIRNyPSwcym81Gjx49+M9//sNpp53GhAkTuOmmm5g+fbrZpXH//feTm5vr+Ni3b5/ZJYmIiDRJ9vvHTks4DR8vl74bo8HZA9nBgoMmVyIix8ulA1lCQgKdOnVy2paSkkJqaioA8fFVbzrp6elObdLT0x374uPjycjIcNpfUVFBVlaWU5uaznHkNf7O39+fsLAwpw8RERFpfL/t/w2AXglN9/4xO/WQibgflw5k/fv3Z9u2bU7b/vjjDxITE4GqCT7i4+NZtGiRY39eXh4rVqygX79+APTr14+cnBxWr17taLN48WJsNht9+/Z1tFm6dCnl5eWONgsXLqRjx45OMzqKiIiI61n25zIATm95usmVmE+BTMT9uHQgu/POO/n111/5z3/+w44dO5gzZw5vvPEGEydOBKpuXp00aRJPPPEEX3zxBRs2bOCaa66hefPmjBo1CqjqUTvnnHO46aab+O233/jll1+47bbbuOKKK2jevDkAV111FX5+ftxwww1s2rSJDz74gBdffJG77rrLrIcuIiIix6GwrJC1B9cC0L91f5OrMZ8CmYj7cemB1r1792bevHncf//9PPbYYyQlJfHCCy8wduxYR5t7772XwsJCJkyYQE5ODgMGDGDBggUEBAQ42syePZvbbruNs846Cy8vL8aMGcNLL73k2B8eHs53333HxIkT6dmzJ9HR0Tz88MOa8l5ERMTF/bb/NyqNSlqGtaR1eGuzyzGdApmI+3HpQAZw/vnnc/7559e632Kx8Nhjj/HYY4/V2iYqKoo5c+Yc9TqnnnoqP/30U53rFBERkcb3y75fAOjfSr1joEAm4o5cesiiiIiIyNEs21d1/9gZrc4wuRLXcGQgMwzD5GpE5HgokImIiIhbshk2lv+5HFAPmV1ccNU6qsUVxeSX5ZtcjYgcDwUyERERcUubMzeTU5JDsG8w3eK7mV2OSwj2CybULxTQsEURd6FAJiIiIm7pl9Sq+8f6tuzb5BeEPpLuIxNxLwpkIiIi4pY0oUfNEkITAAUyEXehQCYiIiJuyR7INKGHM/WQibiXOgWytm3bcujQoWrbc3JyaNu27UkXJSIiInI0aQVp7MrehQUL/Vr2M7sclxIfXBXIDuYfNLkSETkedQpke/bsobKystr20tJS9u/ff9JFiYiIiByNfbr7LrFdCA8IN7ka1+LoIStUD5mIOzihO2C/+OILx+fffvst4eF/vQFWVlayaNEi2rRpU2/FiYiIiKSmpmK1Wp22fbrpUwA6BHZgzZo11Y7ZsmVLo9TmijRkUcS9nFAgGzVqFAAWi4Xx48c77fP19aVNmzY8++yz9VaciIiING2pqakkpyRTXFTsvONGoCV88vwnfLL+k1qPLygoaNgCXZACmYh7OaFAZrPZAEhKSmLlypVER0c3SFEiIiIiAFarleKiYkY/MJqYxBgAKowKZqXNwoaNK26+gjCfsGrHbV+xnSUzllBSUtLYJZtOgUzEvdRp0Y7du3fXdx0iIiJNXk1D845HdHQ0rVu3boCKXEdMYgwJHaqmc9+VvQtbmo1Qv1A6pHTAYrFUa29NPfHn0V39fXhmZkkmABkFGaxcvRJvi3e1Y5rCa0bEXdR5FcVFixaxaNEiMjIyHD1ndjNmzDjpwkRERJqSWofmHYfAoEC2btnaZH7B3pW9C4C2kW1rDGNNRUFW1XDMcePGOe/wAh4Cm8VGn8F9oLD6sU3tNSPiyuoUyB599FEee+wxevXqRUJCQpN+MxQREakPNQ3NOx6ZezOZ9595WK3WJvPL9ZGBrCkrKagajjn01qG079bead+76e9SbCtmzFNjaObbzGlfU3zNiLiyOgWy6dOnM2vWLK6++ur6rkdERKRJO3JoXnZxNlnFWQT7BRPsG0yQbxDeXtWHnzUlReVFHCyoWl8rKSLJ5GpcQ2SLSMdrxi4sL4ziwmICEgJIiEqo5UgRcQV1CmRlZWWcccYZ9V2LiIhIk1dpVLIpcxOrD6xmd47zPdsWLHSO6czwdsMJ9Q81qUJz2Z+TmKCYJvscHI8QvxDSC9MpKGt6s0yKuJs6LQx94403MmfOnPquRUREpGnrAHMy5vDx5o8dwSM6KJpg32AsWDAw2Ji5kWkrp7HywEoMwzC54Man4YrHJ8QvBECBTMQN1KmHrKSkhDfeeIPvv/+eU089FV9fX6f9zz33XL0UJyIi0hQYhsGcXXPgSii2FRPiF8Jp8afRI6EHEQERjjYHCw7y1favOJB/gK+3f8369PWcGXimucU3st3ZVUFVgezoFMhE3EedAtn69evp3r07ABs3bnTapwk+REREjl+FrYJJCyYxbdM0sEByYDKX9Lqk2r1iFouF5qHNueG0G1h1YBWLdi/iz7w/+bb425OYM9m9ZBdnk12SjZfFi8TwRLPLcWkKZCLuo05v4UuWLKnvOkRERJqcSlsll3x4CZ9v+7xqSOJ3BgOvGXjUiTu8LF70adGHpIgk3l77Nunl6TAabIat1mM8hX24Ysuwlvj7+JtcjWtTIBNxH3W6h0xERERO3pM/Pcnn2z4nwCeAqb2mwrLjH2kSExzDFV2uwAsv6Awvbn6xgas1366cw/ePRWi44rEokIm4jzr1kA0dOvSo/2EsXry4zgWJiIg0BUt2L+HRHx8F4I3z36BzZecTPkebiDYMiRjC4pzFvLfrPfqt7MetvW+t71JdgmEYun/sBCiQibiPOvWQde/enW7dujk+OnXqRFlZGWvWrKFr1671XaOIiIhHSS9I56pPr8Jm2Li++/Vc3a3u63qeEngKLKr6/J/f/ZPth7bXU5WuxVphpbiiGD9vP5qHNje7HJdnD2SllaWUV5abXI2IHE2desief/75GrdPnjyZggL9JUZERJq21NRUrFZrjftsho3bfr2NtII02oW24/qE61mzZg1btmyp+wV/gr6X92WFdQU3fXkTi8cvxsviWXcl7C/dD1QtBt3UF8c+Hv7e/nhbvKk0KiksLyTCO8LskkSkFvU6L9O4cePo06cPzzzzTH2eVkRExG2kpqaSnJJMcVFxzQ1OB84BymDnf3cyIHOA0+66/mHz393+zeVLL+fHvT/y5uo3ubnXzXU6j6vaV7oPgKTIJJMrcQ8Wi4UQvxByS3MpKCtwLJ8gIq6nXgPZ8uXLCQgIqM9TioiIuBWr1UpxUTGjHxhNTGKM075SWylzM+ZSapQyIHoAnZ7o5Ni3fcV2lsxYQklJSZ2u2zyoOU+e+SR3fnsn935/LyM7jKRlWMuTeiwuIxjSytIA6Niso8nFuI8jA5mIuK46BbKLL77Y6WvDMDh48CCrVq3ioYceqpfCRERE3FlMYgwJHRKcti3ctZBSo5TY4FiGdh/qNKzQmlrzEMcTcXuf25m7cS4r9q/glq9u4YsrvvCM9UE7goFBQkiCenpOgCb2EHEPdRpgHh4e7vQRFRXFkCFD+Prrr3nkkUfqu0YRERG3l1uSy4o/VwBwVtJZDXKPl7eXN29f+Da+Xr7M/2M+87bOq/drmCL58D/RyebW4WYUyETcQ516yGbOnFnfdYiIiHi0H/b8QKVRSWJ4Iu2j2jfYdTrHdube/vfy5E9Pcs/CexjZfqRbL6JcUF4Ah2e5T4lOMbcYN6NAJuIeTuoestWrVztmhercuTOnnXZavRQlIiLiSdIL0lmXvg6As9ue3SDDCI+cpfGckHOY7j+dXdm7uG/efVzdruZp9aOjo2ndunW911KflmUsAx8I9w4nOija7HLcigKZiHuoUyDLyMjgiiuu4IcffiAiIgKAnJwchg4dyty5c4mJiTn6CURERJqQRburFgrrFN2JFmEt6vXcBVlVv2yPGzfOecdpwEXwwpoXeOHaF6Co+rGBQYFs3bLVpUPZkrQlALQJaOMZ98M1IgUyEfdQp0B2++23k5+fz6ZNm0hJqRo+sHnzZsaPH8///d//8f7779drkSIiIu7qQP4Btmdtx8vixZlJZ9b7+UsKqmZlHHrrUNp3+2sopM2wMc86j0MBh+g0uRMDwp2n18/cm8m8/8zDarW6bCArrSjll4xfAEgK0HT3J0qBTMQ91CmQLViwgO+//94RxgA6derEtGnTGD58eL0VJyIi4u5+/fNXADrHdKZZULMGu05ki8hqszqOjB3J/9b/jy1FWxjSaQgxwe41gmXR7kUUVhRCHsTEu1ftruDIQGYYhnoYRVxUnaZ4stls+Pr6Vtvu6+uLzWY76aJEREQ8QV5pHpsyNwFwesvTG/36SZFJdGzWEQODhbsWNvr1T9anWz6t+mQrChN1YA9klUYlJRV1W99ORBpenQLZmWeeyR133MGBAwcc2/bv38+dd97JWWedVW/FiYiIuLPf9v+GzbCRGJ5I89DmptRwdtuz8bJ4sT1rOzuzdppSQ11U2ir5YtsXVV9sNbcWd+Xj5UOATwCgYYsirqxOgeyVV14hLy+PNm3a0K5dO9q1a0dSUhJ5eXm8/PLL9V2jiIiI2ym3lbP64GrAnN4xu2ZBzejdvDcA3+36DpvhHiNZluxZQmZRJmG+YbDH7Grcl+4jE3F9dbqHrFWrVqxZs4bvv/+erVur/myVkpLCsGHD6rU4ERERd7W9eDslFSVEBkTSoVkHU2sZnDiY39N/J6Mwg7UH19KzeU9T6zkeM9dVrXl6dvOz+cT2icnVuK8Q3xCsWBXIRFzYCfWQLV68mE6dOpGXl4fFYuHss8/m9ttv5/bbb6d379507tyZn376qaFqFRERcQ8W2FC4AYC+LfriZanTgJR6E+gbyODEwUBVz1NpRamp9RxLTkmO4/6xi1pfZHI17i3UPxSA/LJ8kysRkdqc0P8QL7zwAjfddBNhYWHV9oWHh3PzzTfz3HPP1VtxIiIibqk95Fbm4u/tT/f47mZXA0Dv5r1pFtiMwvJCfkp17T+ezt04l5KKErrEdqFTeCezy3Fr9iGLCmQiruuEAtnvv//OOeecU+v+4cOHs3r16pMuSkRExK31qfqnR0IP/H38za3lMG8vb85uezZQNRV/foXr/oJuH654XffrNLviSbL3kBWUasiiiKs6oUCWnp5e43T3dj4+PmRmZp50USIiIu7qQNEBaFf1ea/mvcwt5m86NOtAUkQSlUYlK/JXmF1OjTZmbOS3/b/h4+XDuFPHmV2O2wv105BFEVd3QoGsRYsWbNy4sdb969evJyEhodb9IiIinu6z1M/AAi38WhAVGGV2OU4sFgvD2w0HYFfJLmhlckE1mLm2qnfs/A7nExsca3I17k+BTMT1nVAgO++883jooYcoKam+uGBxcTGPPPII559/fr0VJyIi4k4qbBV8nvo5AClBKSZXU7P4kHhOiz+t6osRuNQ0+OWV5by7/l0Aru9+vcnVeAbHpB6l+RiGYXI1IlKTEwpk//73v8nKyqJDhw5MnTqVzz//nM8//5ynnnqKjh07kpWVxYMPPthQtYqIiLi0r/74CmupFQohMSDR7HJqNbTNUHwsPtASvjvwndnlOHy1/SsyizKJD4nn3Pbnml2OR7BP6lFuK6essszkakSkJicUyOLi4li2bBldunTh/vvvZ/To0YwePZoHHniALl268PPPPxMXF9dQtYqIiLi0N9a8UfXJWvC2eJtbzFGE+ofSPbg7AC9tfoni8mJzCzrsjdVVz9/Vp16Nj1edlkqVv/Hz9sPfu2piGQ1bFHFNJ7wwSmJiIl9//TVWq5UVK1bw66+/YrVa+frrr0lKSmqIGkVERFxeam4q32z/puqLNebWcjxODTkVciG9JJ3nf33e7HJYn76eb3Z8g5fFiwk9J5hdjkc5ctiiiLieOq9UGRkZSe/evenTpw+RkZH1WZOIiIjbeXvN2xgY9I7uDVlmV3NsPhYf+L7q8yd/epLd2btNree/P/8XgEs6XcIpUaeYWoun0cQeIq6tzoFMREREqlTYKnh77dsAXNz6YpOrOQEboUdUD4rKi7h5/s2mTfqwM2snH2z6AID7+t9nSg2eTIFMxLUpkImIiJykb7Z/w/78/UQHRTMkfojZ5Rw/A/7d7d8E+ASwcNdC3vn9HVPKeHrZ09gMG+eccg6nJZxmSg2ezD6xh4YsirgmBTIREZGTZJ/M49pu1+Ln7WdyNScmMSSRR4c8CsCd395JWkFao17/YP5BZq6rWnvs/gH3N+q1mwr7PWQFZQUmVyIiNVEgExEROQn7cvfx9favAbip500mV1M3d/W7ix4JPcgpyeH2b25v1Gs//+vzlFWWcUarMxjYemCjXrup0JBFEdemQCYiInISZqydgc2wMaTNEDo062B2OXXi4+XDjAtn4OPlw8ebP+aDjR80ynWzirN4bdVrQFXvmMViaZTrNjWaZVHEtWmRDxERkTqqtFXy1tq3ALiph3v2jtl1i+/G/QPu5/Glj3PjlzfSLb4bydHJ9XqN1NRUrFar4+v/rP8PBWUFtA9rT0J+AmvWVF8vYMuWLfVaQ1N0ZA+ZWRO3iEjtFMhERETqaMGOBfyZ9ydRgVFcnOJGsyvW4uHBD/NT6k/8sOcHLv7gYn676TfHhBAnKzU1leSUZIqLDi9C3Rq4vurT7S9up9ddvY56fEGB7n+qK/v3sMJWQWllqcnViMjfKZCJiIjU0Ztr3gRgfLfxBPgEmFzNyfPx8mHumLn0eKMHW6xbuOGLG5g7Zm69DCW0Wq0UFxUz+oHRRLWO4lPrp2RXZNMxsCODHxhc63HbV2xnyYwllJSUnHQNTZWvty8BPgGUVJRo2KKIC1IgExERqYP9efuZ/8d8wL2HK9Y0JPCJU5/gpmU38eGmD2lptGRsu7FO+6Ojo2ndunWdrheTGMN2/+1kV2QT7BvMRaddRKBvYK3tranWWvfJ8Qv1C60KZGX5BFL78y0ijU+BTEREpA5mrptJpVHJwNYDSYlJMbucE1aQVTUEcNy4cTU36AOcB89teo7nnnwOfv9rV2BQIFu3bK1TKMupyGFp2lIARrQbcdQwJvUn1C+UzKJMBTIRF6RAJiIicoIqbZW8taZqMo8JPSeYXE3dlBRUDQEceutQ2ndrX22/YRj8kvcLm4s2w2gYfO1gOgZ1JHNvJvP+Mw+r1XrigcwPluQsodKopF1kO7rEdqmPhyLHIcS/6j6ygtICYok1uRoROZICmYiIyAlauGshe3P3EhkQyZiUMWaXc1IiW0SS0CGhxn2XGJfw9Y6vWXVgFT/m/khYXBgJiTW3PZaSyhK4EjLLMwnwCWBk+5Ga5r4RaS0yEdeldchERERO0Bur3wDgmm7XePSQO4vFwnmnnEefFn0A+PKPL1mVv+qEf3sorSjlnpX3QBL4WnwZ13UckYGRDVCx1EaBTMR1KZCJiIicgIP5B/li2xeAe0/mcbwsFgvntDuH01ucDsCagjVwE2zP235cxxeWFXLlJ1eyLHMZlMM5UefQIqxFQ5YsNdDi0CKuS4FMRETkBNgn8+jfqj+dYzubXU6jsFgsDG83nItTLsbf4g8JMG7pOB5c9CA7snbUeExJRQkv/voibV9qy7yt8/Dz8oP3IcGvbkMe5eTY1yIrKNN6biKuRveQiYiI1CI1NRWr9a9p122GjWnLpwEwvNlw1qxZU+2YmqaR9wQWi4WusV0JOhTEe0vfoyK5gv/8/B/+8/N/6B7fndHJown1CyW3NJeckhw+3fIp+/L2AdA2si3/7PhPJj480eRH0XQdOWTRMAyTqxGRIymQiYiI1CA1NZXklGSKi4r/2tgOuBoohkcueYRHKh6p9fiCAs/siQjyDoK58N8v/8ui7EUs3r2YdWnrWJe2rlrbFqEteHjww1zX/To2/L6h8YsVB/uQxQpbBWVGmcnViMiRFMhERERqYLVaKS4qZvQDo4lJjAFgYfZCdpfspnOzzvSf1r/G47av2M6SGUsoKSlpzHIbXcvclvy383/JaZ/DD2k/sCJzBRaLhRCfEEJ8Q2gd3JpzW5yLv8WfDb9v8NieQ3fh4+VDoE8gxRXFFFYWml2OiBxBgUxEROQoYhJjSOiQQEFZAXvT9gIwMGUgcSFxNba3plpr3O4pjrmg9BEe5/Hqx3toz6E7CPELobiimCJbkdmliMgRFMhERESOw+9pv2MzbLQMbVlrGGsKjrWgdG2aSs+hKwv1DyWzKJOiSgUyEVeiQCYiInIMhmGwJq1qAo/TEk4zuRrXcLQFpWvi6T2H7sA+sYd6yERci6a9FxEROYbU3FSyirPw8/ajS2wXs8sRqRN7INM9ZCKuRYFMRETkGNamrQWgc0xn/Lz9TK5GpG7sMy2qh0zEtSiQiYiIHEWprZRNmZsA6JHQw+RqROrOvji07iETcS0KZCIiIkexs3gnFbYKYoJiaBHawuxyROrMMWTRpiGLIq5EgUxEROQothZvBap6xywWi8nViNRdmH8YcLiHTC9lEZehQCYiIlKbeLCWW/GyeHFq3KlmVyNyUkL9Q7FgwYYNgs2uRkTsFMhERERqc/iWsZToFIJ8g8ytReQkeVm8HPeREWZuLSLyFwUyERGRGpRUlsDhTrHT4rX2mHiGcP/ww5+YW4eI/EWBTEREpAaLDy6GAAjxDqFtZFuzyxGpF/b7yNRDJuI63CqQ/fe//8VisTBp0iTHtpKSEiZOnEizZs0ICQlhzJgxpKenOx2XmprKyJEjCQoKIjY2lnvuuYeKigqnNj/88AM9evTA39+fU045hVmzZjXCIxIREVf1WepnAHQM7KjJPMRjKJCJuB63CWQrV67k9ddf59RTnW+qvvPOO/nyyy/56KOP+PHHHzlw4AAXX3yxY39lZSUjR46krKyMZcuW8c477zBr1iwefvhhR5vdu3czcuRIhg4dyrp165g0aRI33ngj3377baM9PhERcR3bD21n9aHVYEDHoI5mlyNSbxTIRFyPWwSygoICxo4dy5tvvklkZKRje25uLm+//TbPPfccZ555Jj179mTmzJksW7aMX3/9FYDvvvuOzZs3895779G9e3fOPfdcHn/8caZNm0ZZWRkA06dPJykpiWeffZaUlBRuu+02LrnkEp5//vlaayotLSUvL8/pQ0REPMOMtTOqPtlRNWRRxFOEBRxOYrqHTMRluEUgmzhxIiNHjmTYsGFO21evXk15ebnT9uTkZFq3bs3y5csBWL58OV27diUuLs7RZsSIEeTl5bFp0yZHm7+fe8SIEY5z1GTKlCmEh4c7Plq1anXSj1NERMxXYatg1u+zqr5YY2opIvXOMamHeshEXIbLB7K5c+eyZs0apkyZUm1fWloafn5+REREOG2Pi4sjLS3N0ebIMGbfb993tDZ5eXkUFxfXWNf9999Pbm6u42Pfvn11enwiIuJavt7+NWkFaUT6RcIfZlcjUr8cQxZDodKoNLcYEQHAx+wCjmbfvn3ccccdLFy4kICAALPLceLv74+/v7/ZZYiISD17a81bAJzf6nzerXzX5GpE6leIXwgWLBjeBlmlWWaXIyK4eA/Z6tWrycjIoEePHvj4+ODj48OPP/7ISy+9hI+PD3FxcZSVlZGTk+N0XHp6OvHx8QDEx8dXm3XR/vWx2oSFhREYGNhAj05ERFzNgfwDfLX9KwAuanWRydWI1D8vixdBXlWLnKcXpx+jtYg0BpcOZGeddRYbNmxg3bp1jo9evXoxduxYx+e+vr4sWrTIccy2bdtITU2lX79+APTr148NGzaQkZHhaLNw4ULCwsLo1KmTo82R57C3sZ9DRESahnd/fxebYaN/q/4khSaZXY5Ig7BPVKNAJuIaXHrIYmhoKF26dHHaFhwcTLNmzRzbb7jhBu666y6ioqIICwvj9ttvp1+/fpx++ukADB8+nE6dOnH11VczdepU0tLS+Pe//83EiRMdQw7/8Y9/8Morr3Dvvfdy/fXXs3jxYj788EO++uqrxn3AIiJiGsMw+N/6/wFwbfdrzS1GpAEFewdDOaSXKJCJuAKX7iE7Hs8//zznn38+Y8aMYdCgQcTHx/Ppp5869nt7ezN//ny8vb3p168f48aN45prruGxxx5ztElKSuKrr75i4cKFdOvWjWeffZa33nqLESNGmPGQRETEBKsPrmZz5mYCfAK4tNOlZpcj0mCCvYMB9ZCJuAqX7iGryQ8//OD0dUBAANOmTWPatGm1HpOYmMjXX3991PMOGTKEtWvX1keJIiLihv73e1Xv2KjkUYQHaJEm8VzBXocDmXrIRFyC2/eQiYiInKyyyjLe3/g+ANeceo3J1Yg0LPs9ZBnFGcdoKSKNQYFMRESavG+2f4O1yEp8SDxntzvb7HJEGpR9yGJacZrJlYgIKJCJiIg4JvMY23UsPl5uN5pf5ITYe8ispVYqbVocWsRsCmQiItKkHSo6xJfbvgRgfLfxJlcj0vACvQLBBpVGJWkF6iUTMZsCmYiINGkfbPqAcls53eO70zWuq9nliDQ4L4sX5Fd9/mfen+YWIyIKZCIi0rTZZ1dU75g0KblV/+zL22duHSKiQCYiIk3XjqwdrNi/Ai+LF1d2udLsckQaT17VP/tyFchEzKZAJiIiTdb7G6qmuh/WdhhxIXEmVyPSiA4HMg1ZFDGfApmIiDRJhmEwe8NsoGp2RZEmxd5DpiGLIqZTIBMRkSZpXdo6th3aRoBPAKOSR5ldjkjjOnwPmXrIRMynxVZERMSjpaamYrVaq21/ftPzAAyIGcCOTTuq7d+yZUuD1yZiGvWQibgMBTIREfFYqampJKckU1xU7LzDAtwJhMH3z39Pz609az1HQUFBg9YoYorDgexg/kEqbBVaEF3ERPrpExERj2W1WikuKmb0A6OJSYxxbD9QeoD5WfPxs/hx9aSr8bZ4Vzt2+4rtLJmxhJKSksYsWaRxFIC3xduxOHTLsJZmVyTSZCmQiYiIx4tJjCGhQ4Lj61V/rAKgc1xnWnas+RdRa2r1YY4iHsOAmIAY0orT2Je7T4FMxESa1ENERJqUClsFmzM3A9A1tqvJ1YiYJy6gaqkHTewhYi4FMhERaVJ2Zu2kpKKEUL9QEiMSzS5HxDTxgfEA7M3da3IlIk2bApmIiDQpGzI2ANA5tjNeFv03KE1X86DmAOzO3m1yJSJNm/4nEhGRJqO0opRth7YBGq4o4ghkOQpkImZSIBMRkSZj66GtVNgqaBbYjISQhGMfIOLBFMhEXIMCmYiINBkb0zcC0CW2CxaLxeRqRMzVIqgFAHty9mAYhsnViDRdCmQiItIkFJYVsjN7J6DhiiJQNamHBQslFSWkF6abXY5Ik6VAJiIiTcKmzE0YGDQPbU6zoGZmlyNiOl8vX8f6Y5rYQ8Q8CmQiItIkbMyoGq6o3jGRvyRFJgG6j0zETApkIiLi8fIq8tiXtw+AzjGdTa5GxHUkRRwOZOohEzGNApmIiHi8nSVV944lRSQR6h9qcjUirsMRyNRDJmIaBTIREfF4O4p3ABquKPJ3GrIoYj4FMhER8WyxkF2RjbfFm5SYFLOrEXEpGrIoYj4FMhER8WyHO8XaN2tPgE+AubWIuBh7D1lqbioVtgqTqxFpmhTIRETEYxmG4QhkXWK7mFuMiAtqHtocP28/Ko1K/sz70+xyRJokBTIREfFY67PXQwT4WnzpENXB7HJEXI6XxYvE8ERAwxZFzKJAJiIiHmvB/gUAtAlog6+3r8nViLgmTewhYi4FMhER8UgVtgoWHlgIwCkBp5hcjYjr0sQeIuZSIBMREY+0aNcissuyoRBa+LcwuxwRl2UPZHty95hbiEgTpUAmIiIeac7GOVWfbKq6T0ZEauYYsqgeMhFT6H8oERHxOMXlxXy65dOqLzaYW4uIq3MMWdQ9ZCKmUCATERGP89X2rygoKyAhMAE0k7fIUdl7yA7kH6CkosTkakSaHgUyERHxOHM2VA1XHN5iOBgmFyPi4poFNiPELwSAvTl7Ta5GpOlRIBMREY+SU5LDV9u/AuDcFueaXI2I67NYLBq2KGIiBTIREfEo87bMo6yyjM4xnTklVNPdixyPNhFtAE3sIWIGBTIREfEo9tkVr+xyJRaLxeRqRNyDeshEzKNAJiIiHiOtII3FuxcDcGXXK02uRsR9OKa+VyATaXQKZCIi4jE+3PQhNsPG6S1Pp21kW7PLEXEbjh4yDVkUaXQKZCIi4jHssyte2UW9YyInwt5Dtit7l8mViDQ9CmQiIuIRdmbtZMX+FXhZvLis82VmlyPiVk6JqpoAJ7skG2uR1eRqRJoWBTIREfEIczfOBeDMpDOJD4k3uRoR9xLkG0Tr8NYAbLNuM7kakaZFgUxERNyeYRiO2RWv6nKVydWIuKfk6GQAtlq3mlyJSNOiQCYiIm5vbdpaNmduxt/bn9Epo80uR8QtJTdTIBMxgwKZiIi4vVnrZgEwKnkUEQERptYi4q46RncEYNshDVkUaUwKZCIi4tZKK0qZvWE2ANd2v9bcYkTcmIYsiphDgUxERNzaV9u/Iqs4i+ahzTm77dlmlyPitjo2q+oh25W9i7LKMpOrEWk6fMwuQERE5HikpqZitVafjvvF314E4Oy4s/l93e9O+7Zs2dIotYl4guahzQnxC6GgrICdWTtJiUkxuySRJkGBTEREXF5qairJKckUFxU77wgG/gl4wTuT3uEd6zs1Hl9QUNDgNYq4O4vFQnJ0MqsOrGKrdasCmUgjUSATERGXZ7VaKS4qZvQDo4lJjHFsX1+wnl/zfyXWN5ZRT46qdtz2FdtZMmMJJSUljVitiPvq2Kwjqw6s0sQeIo1IgUxERNxGTGIMCR0SgKq1xz5b/RkAvdv0JqF5QrX21tTqQxxFpHaa2EOk8WlSDxERcUtpBWlkFGbgbfGmc0xns8sR8Qj2iT0UyEQajwKZiIi4pbVpa4Gqv+gH+gaaXI2IZ7D3kG07tA3DMEyuRqRp0JBFERFxO2WVZfyeXjWj4mnxp5lcjYh7qmkW0tLKUixYyCnJ4ftfv6eZfzOn/dHR0bRu3bqxShRpEhTIRETE7WxI30BZZRlRgVG0jWxrdjkibqUgq2rW0XHjxtXc4A4gEoZfORz2Ou8KDApk65atCmUi9UiBTERE3IphGKw6uAqAngk9sVgsJlck4l5KCqpmHR1661Dad2tfbf83Wd+wr3QfAycNJCXor6nvM/dmMu8/87BarQpkIvVIgUxERNzK/vz9pBWk4W3xpnt8d7PLEXFbkS0iHbOWHqnFjhbs27+P8pByEk6pvl9E6pcm9RAREbey6kBV71iX2C4E+QaZXI2I52kWVHXfmLVYy0aINAYFMhERcRslthI2ZmwEoFfzXiZXI+KZooOiAThUdMjkSkSaBgUyERFxG38U/UGlUUl8SDwtQluYXY6IR7IHsuySbCpsFSZXI+L5FMhERMQ9WGBz0WYAeiX00mQeIg0k2DcYf29/ALKKs0yuRsTzKZCJiIh7aA95lXn4e/vTNa6r2dWIeCyLxeLoJbMW6T4ykYamQCYiIu7hjKp/eiT0wM/bz9xaRDycPZBlFmaaXImI51MgExERl7cxeyO0AQsWTm95utnliHi8uOA4ANIK00yuRMTzKZCJiIjL+9/O/wFwSuAphPmHmVyNiOdLCK1af+xg/kGTKxHxfApkIiLi0nZm7WTJwSUAdAvuZnI1Ik1DfEg8ALmluRSVF5lcjYhnUyATERGX9vyvz2PDBtshyjfK7HJEmoQAnwAiAyIBSCvQsEWRhqRAJiIiLstaZGXG2hlVXywztxaRpiYh5PCwxQINWxRpSApkIiLisqb9No3iimKSw5Nht9nViDQt8aFVwxbVQybSsBTIRETEJeWU5PDCihcAGN9uvLnFiDRBjh4yTewh0qAUyERExCW9+OuL5JTk0CmmE2c1P8vsckSaHPvEHoeKD1FWWWZyNSKeS4FMRERcTnZxNs/9+hwAkwdPxtvibXJFIk1PiF8IoX6hgIYtijQkBTIREXE5z//6PHmleXSN7cqYTmPMLkekydLEHiINz6UD2ZQpU+jduzehoaHExsYyatQotm3b5tSmpKSEiRMn0qxZM0JCQhgzZgzp6elObVJTUxk5ciRBQUHExsZyzz33UFFR4dTmhx9+oEePHvj7+3PKKacwa9ashn54IiJSg6ziLF749QUAHhn8CF4Wl/6vSsSjOSb2yFcPmUhDcen/5X788UcmTpzIr7/+ysKFCykvL2f48OEUFhY62tx55518+eWXfPTRR/z4448cOHCAiy++2LG/srKSkSNHUlZWxrJly3jnnXeYNWsWDz/8sKPN7t27GTlyJEOHDmXdunVMmjSJG2+8kW+//bZRH6+IiMCzy54lvyyfbnHdGJ0y2uxyRJo09ZCJNDwfsws4mgULFjh9PWvWLGJjY1m9ejWDBg0iNzeXt99+mzlz5nDmmWcCMHPmTFJSUvj11185/fTT+e6779i8eTPff/89cXFxdO/enccff5x//etfTJ48GT8/P6ZPn05SUhLPPvssACkpKfz88888//zzjBgxotEft4hIU2UtsvLSby8BMHnIZPWOiZjMHsgyizKpCK04RmsRqQu3+p8uNzcXgKioKABWr15NeXk5w4YNc7RJTk6mdevWLF++HIDly5fTtWtX4uLiHG1GjBhBXl4emzZtcrQ58hz2NvZz1KS0tJS8vDynDxEROTmP//g4BWUF9EjowUUdLzK7HJEmL8w/jECfQGyGjezybLPLEfFIbhPIbDYbkyZNon///nTp0gWAtLQ0/Pz8iIiIcGobFxdHWlqao82RYcy+377vaG3y8vIoLi6usZ4pU6YQHh7u+GjVqtVJP0YRkaZsZ9ZOXlv1GgBTh03FYrGYXJGIWCwWEkKresmsFVaTqxHxTG4TyCZOnMjGjRuZO3eu2aUAcP/995Obm+v42Ldvn9kliYi4tQcXP0i5rZxzTjmHs9pq3TERV2EftmgtVyATaQgufQ+Z3W233cb8+fNZunQpLVu2dGyPj4+nrKyMnJwcp16y9PR04uPjHW1+++03p/PZZ2E8ss3fZ2ZMT08nLCyMwMDAGmvy9/fH39//pB+biEhTk5qaitXq/IvdxuyNfLDpAyxYuLbFtaxZs8Zp/5YtWxqzRBE5gn2BaAUykYbh0oHMMAxuv/125s2bxw8//EBSUpLT/p49e+Lr68uiRYsYM6ZqnZpt27aRmppKv379AOjXrx9PPvkkGRkZxMbGArBw4ULCwsLo1KmTo83XX3/tdO6FCxc6ziEiIvUjNTWV5JRkiov+Nhz8WqANGOsMrph8Ra3HFxQUNGR5IlIDew9ZVnmWG42tEnEfLh3IJk6cyJw5c/j8888JDQ113PMVHh5OYGAg4eHh3HDDDdx1111ERUURFhbG7bffTr9+/Tj99NMBGD58OJ06deLqq69m6tSppKWl8e9//5uJEyc6erj+8Y9/8Morr3Dvvfdy/fXXs3jxYj788EO++uor0x67iIgnslqtFBcVM/qB0cQkxgCQWpLKguwFeOPN5cMvJ+TckGrHbV+xnSUzllBSUtLYJYs0eVGBUQT5BlFUXgQtzK5GxPO4dCB77bWqm7uHDBnitH3mzJlce+21ADz//PN4eXkxZswYSktLGTFiBK+++qqjrbe3N/Pnz+eWW26hX79+BAcHM378eB577DFHm6SkJL766ivuvPNOXnzxRVq2bMlbb72lKe9FRBpITGIMCR0SqLRV8unqTwHo26ov7du2r7G9NVVDpUTMYrFYSAxPZIt1C7QxuxoRz+PSgcwwjGO2CQgIYNq0aUybNq3WNomJidWGJP7dkCFDWLt27QnXKCIidbfqwCqsRVaCfIMY2Hqg2eWISC2SIpKqAlnSsduKyInRSGARETFFUXkRP+z9AYAz25xJgE+AuQWJSK3aRLSp+qQVlFWWmVqLiKdRIBMREVMs2bOEkooS4oLjOC3hNLPLEZGjiA6KJtArEHxhQ/YGs8sR8SgKZCIi0ugOlR9i9YHVAJxzyjl4WfTfkYgrs1gsNPdrDsCqQ6tMrkbEs+h/QBERaXTL85ZjYNApptNfQ6FExKU19z8cyKwKZCL1SYFMREQaVyc4UHYAHy8fzm57ttnViMhxsveQbcjZQHF58TFai8jxUiATEZFGU1RRBOdUfX5GqzOICIgwtR4ROX5h3mGQB+W2cpbtW2Z2OSIeQ4FMREQazZt/vAlhEOodyoBWA8wuR0ROgMVigd1Vny/Zs8TcYkQ8iAKZiIg0ik0Zm5i9azYA/cP64+vta3JFInLCDgeyxbsXm1uHiAdRIBMRkQZnGAa3fXMblUYlbIXWAa3NLklE6mJP1T8rD6ykoKzA1FJEPIUCmYiINLj3N77PD3t+wN/LHxaYXY2I1FkONA9sToWtgp9Tfza7GhGPoEAmIiINKqs4izu/vROAG9rfADnm1iMiJ6dXdC8AFu5caHIlIp5BgUxERBrUPd/dQ0ZhBinRKVzd7mqzyxGRkzQwbiAAH2/5GJthM7kaEfenQCYiIg1mye4lzFg3A4A3L3gTP28/kysSkZN1RuwZhPqFkpqbyq9//mp2OSJuT4FMREQaRHF5MRPmTwDgll630L91f5MrEpH6EOAdwOiU0QC8v+F9k6sRcX8KZCIi0iAe+/ExdmTtoEVoC6acNcXsckSkHl3R+QoAPtz8IRW2CpOrEXFvCmQiIlLv1h5cy9PLngZg2nnTCA8IN7kiEalPw9oOo1lgMzIKM/hxz49mlyPi1hTIRESkXpVWlHLNZ9dQaVRySadLuCj5IrNLEpF65uvtyyWdLgGqlrUQkbpTIBMRkXr18JKH2ZixkdjgWF4971WzyxGRBnJFl6phi59s+YSyyjKTqxFxXz5mFyAiIu4pNTUVq9XqtG3tob+GKt7X6T72bdvHPvY59m/ZsqVRaxSRhjOw9UASQhI4WHCQ73Z+x/kdzje7JBG3pEAmIiInLDU1leSUZIqLiv/a6AfcAkQCa+GuyXfVenxBQUFDlygiDczby5vLOl/Giyte5P2N7yuQidSRApmIiJwwq9VKcVExox8YTUxiDABLc5aytXgrId4hXHLOJfidV33Nse0rtrNkxhJKSkoau2QRaQBXdrmSF1e8yOdbPye/NJ9Q/1CzSxJxOwpkIiJSZzGJMSR0SGBjxka2HtwKwMWdLyYxMrHG9tZUa43bRcQ99WnRh47NOrLt0DbeWP0G/zzjn2aXJOJ2NKmHiIiclENFh/jyjy+BqntKkiKTTK5IRBqLxWLhnjPuAeDZ5c9SWlFqckUi7keBTERE6qzCqOCjzR9RVllGYngiQ9oMMbskEWlkV3e7mpZhLTlYcJB3fn/H7HJE3I4CmYiI1NnyvOWkF6YT5BvEmJQxeFn034pIU+Pn7cc/+1UNVZz6y1QqbBUmVyTiXvQ/p4iI1E032FJUNY39xckX62Z+kSbsph430SywGTuzd/LRpo/MLkfErSiQiYjICduQvQEuqPp8UOIg2kW1M7cgETFVsF8wd/S9A4ApP0/BMAyTKxJxH5plUURETsi+3H38c+U/wQfa+LdhSOIQs0sSkUZU2wLvA/0HEuQdxIaMDby44EUGxQ1y7IuOjqZ169aNVaKIW1EgExGR41ZYVshFcy/iUOkhSIOh3YZisVjMLktEGkFBVtWC7uPGjau90dlAf7jzqzthOlBZtTkwKJCtW7YqlInUQIFMRESOi82wMf6z8axNW0ukXyTZ72fje5qv2WWJSCMpKaha0H3orUNp3619zW1sJXyU+RHFMcX0eKoHvUJ7kbk3k3n/mYfValUgE6mBApmIiByTYRhMWjCJT7Z8gq+XL0/3epobc280uywRMUFki0gSOiTUun9ks5F8vPlj1hWuo29yX2KIacTqRNyPJvUQEZFj+u/P/+Xl314G4H+j/8dpzU4zuSIRcVWdojvRsVlHbIaNL7Z9gc2wmV2SiEtTIBMRkaOasXYGDyx+AIAXRrzAFV2uMLkiEXFlFouF89qfh7+3P/vz97OpaJPZJYm4NAUyERGp1edbP2fClxMA+Ff/f3HH6XeYXJGIuIMw/zCGtR0GwMr8lWjUokjtFMhERKRG32z/hks/upRKo5Lx3cYz5awpZpckIm6kZ0JPkiKSqDAq4CrILs02uyQRl6RJPUREmrjU1FSsVqvTthWZK5j02yTKbeUMSxjGrS1vZe3atY79ta1DJCJiZ7FYuKTTJby+4nXyIvP458p/sqL3Cvx9/M0uTcSlKJCJiDRhqampJKckU1xU/NfGRGAc4Atshe8f+57vbd/XeHxBQUFjlCkibirIN4hzos7hw30f8nv279z05U28M+odrV8ocgQFMhGRJsxqtVJcVMzoB0YTkxjDgdIDLMheQIVRQSv/VgwfMhzvod7Vjtu+YjtLZiyhpKTEhKpFxJ1E+ETAh+A93pt3179LUkQSjw591OyyRFyGApmIiBCTGENhdCELNlWFsXaR7biiyxX4eNX834Q11VrjdhGRGu2Ce7vcy5QNU3hs6WNUGpU8PvRx9ZSJoEk9REQE2FOyh7kb51Jhq6BDsw5HDWMiInVxSZtLePrspwF48qcnuWfhPRiGYXJVIuZTIBMRaeq6wMLshVQalXSK7sRlnS5TGBORBnH3GXfz8rlVi8w/u/xZbv/mdi0cLU2eApmISBP2/q734RIwMDg19lTGdBqDt1f1e8ZEROrLbX1u443z38CChWkrp3HpR5dSWFZodlkiptGfQEVEmiDDMHhoyUM8s+kZADoHdWZU8ijdzyEiDebI5TJ6Wnry+GmP8+jvj/Lplk/ZfGAzz/d+ntjAWKdjoqOjad26dWOXKtKoFMhERJqYClsFt8y/hbfWvlW1YRGcMfYMhTERaRAFWVXLY4wbN676zlbAFbCVrZz76bnwPnDwr92BQYFs3bJVoUw8mgKZiEgTkleax2UfXca3O7/Fy+LFA10f4InJT2AZpzAmIg2jpKBqeYyhtw6lfbf21fbnVeTxbfa3ZIdl432zN/3D+5MclEzm3kzm/WceVqtVgUw8mgKZiEgTkZqbysg5I9mYsZEg3yDeH/M+LQtb8gRPmF2aiDQBkS0iSeiQUG17AgkkViQyb8s8/sj6g6W5S8kPzOe01qeZUKVI49OkHiIiTcBv+3+j71t92ZixkYSQBJZeu5QLO15odlkiIgAE+ARwRZcrOLPNmViwsDZtLV9Yv4AIsysTaXgKZCIiHm7G2hkMmjmItII0usZ2ZcWNK+jZvKfZZYmIOLFYLAxMHMi4U8cR5BuEtcIKN8PP6T+bXZpIg9KQRRERD5GamorVanV8XW4r5+mNT/PJ3k8AGBw3mMdOe4zMnZlkkgk4z3omIuIK2ka2ZUKPCcxeM5tMMrnjtzuwBlh5ZPAjWpZDPJICmYiIB0hNTSU5JZniouKqDRHAGKpmMDOAJfDjTz8y2Bhc4/EFBQWNU6iIyHEIDwjnwmYX8vYXb0MfeHzp4/z656/Mvng2McExZpcnUq8UyEREPIDVaqW4qJhRD4wiJyaHX/J+odwox8/ix5mRZ9J6bGsYW/247Su2s2TGEkpKShq/aBGRo/C2eMPX8PjNj/OfDf9h4a6FnPb6acy9ZC4DWg8wuzyReqNAJiLiKQJhfdh6duXuAqBVWCtGJ48mMjCy1kOsqdZa94mIuILzWp7HqNNHcelHl7LVupUhs4bw32H/5Z/9/qn1E8UjKJCJiLg5m2Fj3t55cBvsKtmFl8WLwYmDGdB6AF4Wzd0kIu5ty5YtpJDCm73f5In1T/Dt/m+5Z+E9zF8/n8ndJxPmF1btmOjoaK1dJm5DgUxExI2t3L+SiV9PZOWBlRAMkT6RjOk6hhZhLcwuTUTkpBRkVd3bOm7cOOcdvYBz4Mf0Hxk6dyh8BBxwbhIYFMjWLVsVysQtKJCJiLihzZmbeeSHR/h488cABPsEUzi/kDHXKoyJiGcoKai6t3XorUNp36290z5ruZWF2QvJj8zHa4IX/cL60SmoExaLhcy9mcz7zzysVqsCmbgFBTIRETeyzbqNx5Y+xvsb3sfAwIKFcaeOY2zcWM759zl4XachiiLiWSJbRJLQIcFpWwIJtK9oz+dbP2froa38kvcLOf45XNDhApOqFKk7BTIRERfz9/XEALbkbGHmjpksPrgYAwOAM+PPZELHCbQPa6/1xESkyQnwCeCyzpfx65+/8v3u79mUuYm0gjSGBA8xuzSRE6JAJiLiQqqtJ5YIDAROOaLRVuAHWJy2mMUsdjpe64mJSFNisVjo16ofLcNa8vGWjzlUfIjPij+D7mZXJnL8FMhERFyIfT2x0x88nd1Bu0kvTwfAgoV2Ae3oHtKdqKFRMNT5OK0nJiJNWavwVtzc82Y+3fIpO7N3wiiYvG4yc7vOJcg3yOzyRI5KgUxExEWUVZbx5b4v4Rb41fdXKK9aGLV7fHf6t+qv9cRERI4iyDeIsV3H8vWar1mVt4ov931J37f68vGlH9MxuqPZ5YnUSoFMRMRkuSW5vL76dV5c8SIH8g9AHPhafOndsjentzidUP9Qs0sUEXELFouFHqE9WDVtFc0mNGNjxkZ6vtGTZ4Y/w809b9ZC0uKSNB2XiIhJ9uXu45/f/pNWz7fiX9//iwP5B4j2j4aFcFXsVZzd9myFMRGRutgNswfNZmiboRSWF3LLV7dw3pzzqv7oJeJiFMhERBpRpa2ShTsXcuUnV9L2pbY89+tz5Jfl0zmmMzMvmsn8YfPhF/D38je7VBERtxYTEMP313zPc8Ofw9/bnwU7FtDl1S68+/u7GIZhdnkiDhqyKCLSQOzT1xuGwc78nXx34Dvm75tPekm6o03v6N5c3fZqzog9A4vNwpZtmr5eRKQ+2JcDGew/mPcGvsdDax9ia+5WrvnsGl75+RUeOPUBWgW3cjomOjpai0lLo1MgExFpADv37KTT8E6UtSmDZKDZETuLgQ3AGliZtpKVrKx2vKavFxGpm4KsqvfPcePGOe/wAs4ABsNv1t8Y9e0oWAosByqqmgQGBbJ1y1aFMmlUCmQiIvXAZtjYlLGJH/f+yMJdC1m0cxFlY8sc+73xpoV/C9oHticxPhGftj5wUfXzaPp6EZGTU1JQ9f459NahtO/Wvtr+3Ipcfs79mf3sh7MgdHgofUL7EJoRymf/+Qyr1apAJo1KgUxEpA7KKstYfWA1P6X+xE+pP/FL6i9kl2Q7NyqCdlHtOK3taZwSeQr+Pse+L0zT14uI1I/IFpEkdEiotj2BBDoaHdmYsZGFuxaSX5bPopxFxAXHQasaTiTSwBTIRESOITU1lT/T/2RD9gbWHFrD6kOr2Zi9kVJbqVO7QO9ATo08ld7RvYkriOOhmx7irOlnkRBT/RcCERExj8VioWtcVzpGd2TZvmUs27eM9PJ0uAFuXX4rz8U8xxmtzjC7TGkiFMhERGpQXF7Mr3/+yufrP+flL1/GlmCr/o5ZBOwFUqv+LU4rZoVtBStY4Wiie8FERFyXn7cfQ9oMoUdCD775/Ru2FmxlhXUF/Wf056yks5h0+iTOa38eXhZNTC4NR4FMRISqALb8z+X8sOcHftjzAyv2r6Cs8vA9YIeHsAR5BZHgl+D4iPCJwNKu5kVGdS+YiIj7CPMPY1DEILZO2cro50bz5Z9fsmj3IhbtXkTbyLZM7D2R8d3G0yyo2bFPJnKCFMhEpMmwT0MPUFxRzIbsDaw6tIo1h9awMWcj5bZyp/YxATG092nPsjnLuPyGy+mY0hGLpeYA9ne6F0xExA3lwL+7/ZvnRz/PK7+9wltr32JX9i7++d0/ue/7+zjnlHO4qutVXNjxQoJ8g8yuVjyEApmINAkbtm+g10W9KIsvgzZAS8D7b43ygD1/fWRmZZJJJgDeY72PO4yJiIj72rJlCymkcGX0lYwaOopv9n/Dx3s+ZlveNr7840u+/ONLArwD6BvdlwFxA+gf25/OrTprZkapMwUyEfEYhmFwqPgQO7N2sj1rOzuydrAxYyNrDq5hd85uuNy5fbBXMAn+CTT3a06CXwJh8WFYOjqHLg09FBFpGmpdv8wuBuha9VESWcKP6T/yY/qPAFisFq7qfxXndjqXQYmDaBWu6Rrl+CmQiYhLMQyDovIisoqzyC3NJbckt9q/+zL3kZGXQUF5AQUVBWSXZmMttXKo9FC1YYdOsuGU5qeQ0iqFNhFtiAyIPGavl4Yeiog0Dcdav8zOMAwOVRwitSSV1NJUMsoyMKINZm+bzextswFIDE9kUOIgBrYeyBmtziA5Ohlvr78PyxCpokAmIo2iwlbBgfwD7M3Zy97cvfy+93f2HNpDTlkOueW55JblVn1ellttOvkTlgdkHf6wAgeBNKAY+jzbh/YJtf9HKyIiTVtt65cdqTnN6UpXAHZv3c3/Xvwf5/7jXPYae9mWt429uXt5d/27vLv+XaBqWZTk8GQ6RXQiJTyFThGdaBXcitiYWA11FAUyETk5qampZGZmkl+eT1pxGmklaaQXp5NenF71dXEaB4sPklmSSaVRefwnrgRKDn+UVv+3Xfd2xMTE4OflR4BXAEFeQQR6BRLkHYR3QvW/QmrooYiINISKnArYCt9M+qZqgx9Vs/O2BhKB5lDsV8zarLWszVr714El4JXpxVXDrqJv2750iulEu8h2tAxredK9aUXlRaQVpHEw/2DVvwVV/+aW5FJRXIFRbhDgHUCMfwytQ1rTOrg1ob6hRz1ndHS0wmMDUSD7m2nTpvH000+TlpZGt27dePnll+nTp4/ZZYk0GJtho9JWSYWtgpKKEgrKCigsL6SgrMDpI7ckF2uRlUPFhxz/Hsw5yLo/1mEEGeB/HBerBHIPf+RAu5R2xMXGEeAVgL+XPwFeAQRYqj73tfjWOJzQHqy6DexG1x5dj/txauihiIg0hGMNdbQZNnIrcsksz3R8HCo/RGVAJbZWNt7b9h7vbXvP0d7Hy4fW4a1pGdaSmKAYooOiiQ6KJtAnED9vP/y8/TAwKCwrdPx/nVGY4QhdaQVp5JXmnfgDKQQyDn+kH/H54RVgAoMC2bplq0JZA1AgO8IHH3zAXXfdxfTp0+nbty8vvPACI0aMYNu2bcTGxppdnrgxwzCoNCod4cdm2Gr92r6t0qikqLyIgrIC8kvzncJRfll+tcBUXFFMcXkxOQU5FJQWUFJZQpmtjLLKMiqMir+uZ1SFr0qj6hoGxsk9uMi/Pg3wCiDYK5gQ7xBCvEMI9v7r81DvUAK9AvFq6VUVqj5fQrc+3eh62vGHKlCwEhER1/T/7d17UFTn+Qfw71lgl2XDRUS5GkUnAYtCDBECTCcm0qDRBNpMwVQrGpraRPPTwabotIo2zSRNiE2TWGJnuJg6tU0n0WjNxAEqlBLURCBGg9QLpSo3q2G5yHX3/f0BHFnZC4vA2cj3Q85w9pznfffdffOc47NnPVr7qmMgAk0eG4wGVFdX48M/fogn055Eq7oVte21aOhsQK+xF5e+uYRL31y6o/FoVBr4uPrAR+ODqZqp8HH1QZe+C4eOHMKs6Flw1jmj3dgOfZ8encZOQAcgeGAZ4h6ne6Dr1aHpVBNeL38dD7c8DF+dL6Zop2CK6xR4uXrBy9WLf0fuDrAgG2LXrl147rnnsHbtWgDAe++9hyNHjiA3NxdbtmxReHQjV3ipEJe+uQQhBAQEjMIor9v6feObG2jvaJcfA7gVM/DT/99Av0NitG5auLu7y/0N7re13tbehs7OThhhlPs3wsyYb1vvM/RBpVL19zMQD9hu22fog6Tqv/IyWAAJ9BdMg4XT4OszCiOMuFUkCQj58e37hz4e2o9dX9NzBH3o/zSsd+D34NIN4ObA0jlk/Saw/P+WI3xBOFycXEb0FCyqiIhoMnNSOUHTpgFOA4c3Hr61QwLgDsBr4Lcb+gslLfr/1O6EW/9ky9Bz9U0A7QDaBn63A93d3bg68HO72IRY3Pfgrat53X3duN55Hc0dzSZLW08b2g3taFe1AwuB3ed2Y/e53WZfk85ZB3cXd+icddA6aeHq5AqtsxaeWk9M85oGNxc3aJ21cFY5w8XJBc4q5/51Vf+6pYJu8M93w7Zb+EDZSXLCi9Evmt3nqFiQDejp6cGpU6ewdetWeZtKpUJ8fDzKy8uHxXd3d6O7+9aNB/R6PQCgtXUUl4jH2O7S3Th47qDSw6DREACMA78FhhdFtxdKQ9cNA4/7gNmRs+E9zRsqoYITnCBBggoqQAAqqCAN/RESGs834uuirzEvYR6CgoNujUcFwHVgMaP+XD1OF5yGvkaPepf6Eb/Ma3X9/7bXtdprqNPVjbjdnbRlO7bj/2ts54jtlHhOtnOMdpfPXgYAhC8LR8DsgBG3Gzz3hi8LR8B9I283tO3l6stQO6mH7Z8y8BOCEMAN6HLtQktvC2ov16LmdE1/keiB/kJRg/5CceCz2I6BH6WpndRInZuq9DDkmsBSQTmUJEYSNQnU19cjMDAQn332GWJiYuTtv/jFL1BSUoITJ06YxO/YsQM7d+6c6GESEREREdG3xOXLlxEUFGQ1hlfIRmnr1q1IT0+XHxuNRty4cQNTp041eyOC1tZWzJgxA5cvX4aHh8dEDpVGiHPk+DhHjo9z5Pg4R46Pc+T4OEeOT+k5EkKgra0NAQG2r2KyIBvg4+MDJycnNDU1mWxvamqCn5/fsHiNRgONxvS2cl5eXjafx8PDg4nr4DhHjo9z5Pg4R46Pc+T4OEeOj3Pk+JScI09PzxHFqcZ5HN8aarUakZGRKCoqkrcZjUYUFRWZfIWRiIiIiIhorPAK2RDp6elITU3FQw89hKioKLz11lvo6OiQ77pIREREREQ0lliQDZGSkoJr165h+/btaGxsxAMPPIBPP/0Uvr6+d9y3RqNBZmbmsK85kuPgHDk+zpHj4xw5Ps6R4+McOT7OkeP7Ns0R77JIRERERESkEP4dMiIiIiIiIoWwICMiIiIiIlIICzIiIiIiIiKFsCAjIiIiIiJSCAuyMbR7927MmjULrq6uiI6OxsmTJ63G/+1vf0NoaChcXV0xf/58fPLJJxM00snn1VdfxcKFC+Hu7o7p06cjKSkJNTU1Vtvk5+dDkiSTxdXVdYJGPPns2LFj2PsdGhpqtQ1zaGLNmjVr2BxJkoT169ebjWcOjb9//vOfePLJJxEQEABJknDw4EGT/UIIbN++Hf7+/tBqtYiPj8f58+dt9mvv+YwsszZHvb29yMjIwPz586HT6RAQEIDVq1ejvr7eap+jOV6SZbbyaM2aNcPe7yVLltjsl3k0dmzNkblzkyRJeOONNyz26Uh5xIJsjPz1r39Feno6MjMzUVFRgYiICCQkJKC5udls/GeffYZnnnkGaWlpqKysRFJSEpKSknDmzJkJHvnkUFJSgvXr1+P48eMoKChAb28vHn/8cXR0dFht5+HhgYaGBnmpq6uboBFPTmFhYSbv97/+9S+Lscyhiff555+bzE9BQQEA4Ic//KHFNsyh8dXR0YGIiAjs3r3b7P7XX38db7/9Nt577z2cOHECOp0OCQkJ6Orqstinveczss7aHN28eRMVFRXYtm0bKioq8NFHH6GmpgZPPfWUzX7tOV6SdbbyCACWLFli8n7v37/fap/Mo7Fla46Gzk1DQwNyc3MhSRKefvppq/06TB4JGhNRUVFi/fr18mODwSACAgLEq6++ajY+OTlZLFu2zGRbdHS0WLdu3biOk/o1NzcLAKKkpMRiTF5envD09Jy4QU1ymZmZIiIiYsTxzCHlbdy4UcyZM0cYjUaz+5lDEwuAOHDggPzYaDQKPz8/8cYbb8jbWlpahEajEfv377fYj73nMxq52+fInJMnTwoAoq6uzmKMvcdLGjlzc5SamioSExPt6od5NH5GkkeJiYnisccesxrjSHnEK2RjoKenB6dOnUJ8fLy8TaVSIT4+HuXl5WbblJeXm8QDQEJCgsV4Glt6vR4A4O3tbTWuvb0dM2fOxIwZM5CYmIizZ89OxPAmrfPnzyMgIACzZ8/GypUr8d///tdiLHNIWT09Pdi3bx+effZZSJJkMY45pJza2lo0Njaa5Imnpyeio6Mt5slozmc0tvR6PSRJgpeXl9U4e46XdOeKi4sxffp0hISE4Pnnn8f169ctxjKPlNXU1IQjR44gLS3NZqyj5BELsjHwv//9DwaDAb6+vibbfX190djYaLZNY2OjXfE0doxGIzZt2oS4uDjMmzfPYlxISAhyc3Px8ccfY9++fTAajYiNjcWVK1cmcLSTR3R0NPLz8/Hpp58iOzsbtbW1+O53v4u2tjaz8cwhZR08eBAtLS1Ys2aNxRjmkLIGc8GePBnN+YzGTldXFzIyMvDMM8/Aw8PDYpy9x0u6M0uWLMH777+PoqIi/Pa3v0VJSQmWLl0Kg8FgNp55pKy9e/fC3d0dP/jBD6zGOVIeOU/4MxIpbP369Thz5ozN7wnHxMQgJiZGfhwbG4u5c+diz549ePnll8d7mJPO0qVL5fXw8HBER0dj5syZ+OCDD0b0KRdNrJycHCxduhQBAQEWY5hDRCPX29uL5ORkCCGQnZ1tNZbHy4m1YsUKeX3+/PkIDw/HnDlzUFxcjMWLFys4MjInNzcXK1eutHkTKUfKI14hGwM+Pj5wcnJCU1OTyfampib4+fmZbePn52dXPI2NDRs24O9//zuOHTuGoKAgu9q6uLhgwYIFuHDhwjiNjoby8vLC/fffb/H9Zg4pp66uDoWFhfjJT35iVzvm0MQazAV78mQ05zO6c4PFWF1dHQoKCqxeHTPH1vGSxtbs2bPh4+Nj8f1mHimntLQUNTU1dp+fAGXziAXZGFCr1YiMjERRUZG8zWg0oqioyOTT4aFiYmJM4gGgoKDAYjzdGSEENmzYgAMHDuAf//gHgoOD7e7DYDDgq6++gr+//ziMkG7X3t6OixcvWny/mUPKycvLw/Tp07Fs2TK72jGHJlZwcDD8/PxM8qS1tRUnTpywmCejOZ/RnRksxs6fP4/CwkJMnTrV7j5sHS9pbF25cgXXr1+3+H4zj5STk5ODyMhIRERE2N1W0TxS+q4id4u//OUvQqPRiPz8fPH111+Ln/70p8LLy0s0NjYKIYT48Y9/LLZs2SLHl5WVCWdnZ5GVlSWqq6tFZmamcHFxEV999ZVSL+Gu9vzzzwtPT09RXFwsGhoa5OXmzZtyzO1ztHPnTnH06FFx8eJFcerUKbFixQrh6uoqzp49q8RLuOtt3rxZFBcXi9raWlFWVibi4+OFj4+PaG5uFkIwhxyFwWAQ9957r8jIyBi2jzk08dra2kRlZaWorKwUAMSuXbtEZWWlfIe+1157TXh5eYmPP/5YnD59WiQmJorg4GDR2dkp9/HYY4+Jd955R35s63xG9rE2Rz09PeKpp54SQUFBoqqqyuT81N3dLfdx+xzZOl6SfazNUVtbm/j5z38uysvLRW1trSgsLBQPPviguO+++0RXV5fcB/NofNk61gkhhF6vF25ubiI7O9tsH46cRyzIxtA777wj7r33XqFWq0VUVJQ4fvy4vO+RRx4RqampJvEffPCBuP/++4VarRZhYWHiyJEjEzziyQOA2SUvL0+OuX2ONm3aJM+nr6+veOKJJ0RFRcXED36SSElJEf7+/kKtVovAwECRkpIiLly4IO9nDjmGo0ePCgCipqZm2D7m0MQ7duyY2WPb4DwYjUaxbds24evrKzQajVi8ePGwuZs5c6bIzMw02WbtfEb2sTZHtbW1Fs9Px44dk/u4fY5sHS/JPtbm6ObNm+Lxxx8X06ZNEy4uLmLmzJniueeeG1ZYMY/Gl61jnRBC7NmzR2i1WtHS0mK2D0fOI0kIIcb1EhwRERERERGZxb9DRkREREREpBAWZERERERERAphQUZERERERKQQFmREREREREQKYUFGRERERESkEBZkRERERERECmFBRkREREREpBAWZERERERERAphQUZERERERKQQFmRERPStsmbNGiQlJSny3J2dnfD29oaPjw+6u7sVGcNo7dixAw888IDSwyAiotuwICMiIhqhDz/8EGFhYQgNDcXBgweVHg4REd0FWJAREdFdpaSkBFFRUdBoNPD398eWLVvQ19cn729ra8PKlSuh0+ng7++P3/3ud1i0aBE2bdpks++cnBysWrUKq1atQk5OzrD9kiRhz549WL58Odzc3DB37lyUl5fjwoULWLRoEXQ6HWJjY3Hx4kWTdtnZ2ZgzZw7UajVCQkLwpz/9Sd73n//8B5IkoaqqSt7W0tICSZJQXFwMACguLoYkSSgqKsJDDz0ENzc3xMbGoqamBgCQn5+PnTt34ssvv4QkSZAkCfn5+SN/U4mIaNywICMiorvG1atX8cQTT2DhwoX48ssvkZ2djZycHPzmN7+RY9LT01FWVoZDhw6hoKAApaWlqKiosNn3xYsXUV5ejuTkZCQnJ6O0tBR1dXXD4l5++WWsXr0aVVVVCA0NxY9+9COsW7cOW7duxRdffAEhBDZs2CDHHzhwABs3bsTmzZtx5swZrFu3DmvXrsWxY8fsfv2//OUv8eabb+KLL76As7Mznn32WQBASkoKNm/ejLCwMDQ0NKChoQEpKSl2909ERGPPWekBEBERjZU//OEPmDFjBt59911IkoTQ0FDU19cjIyMD27dvR0dHB/bu3Ys///nPWLx4MQAgLy8PAQEBNvvOzc3F0qVLMWXKFABAQkIC8vLysGPHDpO4tWvXIjk5GQCQkZGBmJgYbNu2DQkJCQCAjRs3Yu3atXJ8VlYW1qxZgxdeeAFAf8F4/PhxZGVl4dFHH7Xr9b/yyit45JFHAABbtmzBsmXL0NXVBa1Wi3vuuQfOzs7w8/Ozq08iIhpfvEJGRER3jerqasTExECSJHlbXFwc2tvbceXKFVy6dAm9vb2IioqS93t6eiIkJMRqvwaDAXv37sWqVavkbatWrUJ+fj6MRqNJbHh4uLzu6+sLAJg/f77Jtq6uLrS2tspjjouLM+kjLi4O1dXVI33ZZp/b398fANDc3Gx3P0RENHF4hYyIiMiGo0eP4urVq8O+5mcwGFBUVITvfe978jYXFxd5fbAwNLft9kLOEpWq/7NTIYS8rbe312zsnTwPEREpg1fIiIjorjF4E42hxUtZWRnc3d0RFBSE2bNnw8XFBZ9//rm8X6/X49///rfVfnNycrBixQpUVVWZLCtWrDB7cw97x1xWVmayraysDN/5zncAANOmTQMANDQ0yPuH3uBjpNRqNQwGw+gHSkRE44JXyIiI6FtHr9cPK0qmTp2KF154AW+99RZefPFFbNiwATU1NcjMzER6ejpUKhXc3d2RmpqKl156Cd7e3pg+fToyMzOhUqlMvuY41LVr13D48GEcOnQI8+bNM9m3evVqfP/738eNGzfg7e09qtfy0ksvITk5GQsWLEB8fDwOHz6Mjz76CIWFhQAArVaLhx9+GK+99hqCg4PR3NyMX/3qV3Y/z6xZs1BbW4uqqioEBQXB3d0dGo1mVGMmIqKxwytkRET0rVNcXIwFCxaYLDt37kRgYCA++eQTnDx5EhEREfjZz36GtLQ0kwJm165diImJwfLlyxEfH4+4uDjMnTsXrq6uZp/r/fffh06nk28CMtTixYuh1Wqxb9++Ub+WpKQk/P73v0dWVhbCwsKwZ88e5OXlYdGiRXJMbm4u+vr6EBkZiU2bNpncNXKknn76aSxZsgSPPvoopk2bhv379496zERENHYkMfR7HURERJNMR0cHAgMD8eabbyItLU3p4RAR0STDrywSEdGkUllZiXPnziEqKgp6vR6//vWvAQCJiYkKj4yIiCYjFmRERDTpZGVloaamBmq1GpGRkSgtLYWPj4/SwyIiokmIX1kkIiIiIiJSCG/qQUREREREpBAWZERERERERAphQUZERERERKQQFmREREREREQKYUFGRERERESkEBZkRERERERECmFBRkREREREpBAWZERERERERAr5f7MwSHEaaY2KAAAAAElFTkSuQmCC\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "outlier handling complete: extreme values normalized using log transformation\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Descriptive statistics\nsummary_stats = df.describe().T\nprint(\"Summary Statistics\")\nprint(summary_stats[['mean','50%','std','min','max']])\nprint(\"Average amount: Fraud vs legitimate\")\nprint(df.groupby('isFraud')['amount'].mean())\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "0jPiqp2g-x7Y",
+ "outputId": "dd823eb1-f19c-4f67-de46-99229ec476bf"
+ },
+ "execution_count": 18,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Summary Statistics\n",
+ " mean 50% std min \\\n",
+ "step 2.437091e+02 240.000000 1.425186e+02 1.000000 \n",
+ "amount 1.805811e+05 76030.865000 5.586699e+05 0.920000 \n",
+ "oldbalanceOrg 8.366804e+05 13938.500000 2.901104e+06 0.000000 \n",
+ "newbalanceOrig 8.582234e+05 0.000000 2.936799e+06 0.000000 \n",
+ "oldbalanceDest 1.104193e+06 138748.170000 3.223011e+06 0.000000 \n",
+ "newbalanceDest 1.230055e+06 218578.620000 3.475326e+06 0.000000 \n",
+ "isFraud 1.410000e-03 0.000000 3.752367e-02 0.000000 \n",
+ "isFlaggedFraud 1.000000e-05 0.000000 3.162278e-03 0.000000 \n",
+ "amount_log 1.084815e+01 11.238908 1.815330e+00 0.652325 \n",
+ "\n",
+ " max \n",
+ "step 7.360000e+02 \n",
+ "amount 3.697390e+07 \n",
+ "oldbalanceOrg 3.359321e+07 \n",
+ "newbalanceOrig 3.388709e+07 \n",
+ "oldbalanceDest 2.362896e+08 \n",
+ "newbalanceDest 2.724047e+08 \n",
+ "isFraud 1.000000e+00 \n",
+ "isFlaggedFraud 1.000000e+00 \n",
+ "amount_log 1.742572e+01 \n",
+ "Average amount: Fraud vs legitimate\n",
+ "isFraud\n",
+ "0 1.789437e+05\n",
+ "1 1.340223e+06\n",
+ "Name: amount, dtype: float64\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#only numerical columns for correlation calculation\n",
+ "numerical_df = df.select_dtypes(include=[np.number])\n",
+ "# Calculate the correlation matrix\n",
+ "correlation_matrix = numerical_df.corr()\n",
+ "plt.figure(figsize=(12, 10))\n",
+ "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths = 0.5)\n",
+ "plt.title('Correlation Matrix Of Financial Features', fontsize = 15)\n",
+ "plt.show()\n",
+ "\n",
+ "print(\"Correlation with isFraud\")\n",
+ "print(correlation_matrix['isFraud'].sort_values(ascending=False))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "mbocOGhbtS8V",
+ "outputId": "fd9c7438-ee2b-44dd-b399-4c947968ebb6"
+ },
+ "execution_count": 19,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Correlation with isFraud\n",
+ "isFraud 1.000000\n",
+ "isFlaggedFraud 0.084156\n",
+ "amount 0.077999\n",
+ "amount_log 0.041991\n",
+ "step 0.034990\n",
+ "oldbalanceOrg 0.006952\n",
+ "newbalanceDest -0.004081\n",
+ "oldbalanceDest -0.009096\n",
+ "newbalanceOrig -0.010106\n",
+ "Name: isFraud, dtype: float64\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#distribution of transaction type (pie)\n",
+ "# market share of each transaction method\n",
+ "plt.figure(figsize=(8, 8))\n",
+ "df['type'].value_counts().plot.pie(autopct='%1.f%%', startangle =140, cmap = 'Set3')\n",
+ "plt.title('Distribution of Transaction Types')\n",
+ "plt.ylabel('')\n",
+ "plt.show()\n",
+ ""
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 675
+ },
+ "id": "HMpvXROyxLhg",
+ "outputId": "ac985451-cdc8-4d3e-c612-be24677185a2"
+ },
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#Fraud vs Transaction type (bar chart)\n",
+ "#where the fraud actually happens\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "sns.countplot(x='type', hue='isFraud', data=df)\n",
+ "plt.title('Fraud vs Legitimate transaction by type')\n",
+ "plt.yscale('log')\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 564
+ },
+ "id": "xw4B5hrzxLr9",
+ "outputId": "d56a145e-3ff3-468b-8262-5277f6d0d494"
+ },
+ "execution_count": 21,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Research Question 1: Do high-value transactions have a higher fraud rate?\n# Comparing the log-transformed amount between fraud and legitimate transactions\nplt.figure(figsize=(10, 6))\nsns.boxplot(x='isFraud', y='amount_log', data=df,\n hue='isFraud', palette={0: 'steelblue', 1: 'salmon'}, legend=False)\nplt.xticks([0, 1], ['Legitimate', 'Fraudulent'])\nplt.title('Transaction Amount Distribution: Fraud vs Legitimate')\nplt.xlabel('Transaction Status')\nplt.ylabel('Log-Transformed Amount')\nplt.show()\n\nprint('Mean log-amount by fraud status:')\nprint(df.groupby('isFraud')['amount_log'].mean())"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 954
+ },
+ "id": "zLz6PuUuxL0R",
+ "outputId": "8cbb3bc7-b32c-4d8b-e293-eb758cebf6e3"
+ },
+ "execution_count": 22,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Insight:** The box plot confirms what the average comparison already hinted at \u2014 fraudulent transactions consistently involve higher amounts. The median for fraud is noticeably shifted upward compared to legitimate transactions, which means this is not just a few outliers pulling the average up. It is a real pattern across the dataset.\n\n**Conclusion:** Transaction size is a meaningful signal. On its own it is not enough to flag a fraud, but paired with other variables it would make a strong first filter in a detection system."
+ ],
+ "metadata": {
+ "id": "w69eaRq6hr9K"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Research Question 2: Do fraudsters drain the sender's account to zero?\n# A 'drained' transaction is one where the origin balance went from above zero to exactly zero\ndf['balance_drained'] = ((df['oldbalanceOrg'] > 0) & (df['newbalanceOrig'] == 0)).astype(int)\n\ndrain_rate = df.groupby('isFraud')['balance_drained'].mean() * 100\n\nplt.figure(figsize=(8, 6))\nbars = plt.bar(['Legitimate', 'Fraudulent'], drain_rate.values,\n color=['steelblue', 'salmon'], edgecolor='black')\nplt.title('Percentage of Transactions that Drain the Sender Balance to Zero')\nplt.ylabel('Percentage (%)')\nfor bar, val in zip(bars, drain_rate.values):\n plt.text(bar.get_x() + bar.get_width() / 2., bar.get_height() + 0.3,\n f'{val:.1f}%', ha='center', fontsize=12)\nplt.show()\n\nprint('Drain rate by fraud status (%):')\nprint(drain_rate.round(2))"
+ ],
+ "metadata": {
+ "id": "Qe5k-9DLK07M"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "954a54f9",
+ "metadata": {},
+ "source": [
+ "**Insight:** A striking share of fraudulent transactions completely empty the sender's account. This pattern is far less common in legitimate transactions. From an economic perspective this makes sense \u2014 a fraudster moves fast and takes everything at once, while a regular customer keeps some balance.\n\n**Conclusion:** The zero-balance drain is one of the clearest behavioral fingerprints of fraud in this dataset. A system that flags large transactions that also leave the sender at exactly zero would catch a significant portion of fraud cases with very few false positives."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Research:** Pose relevant questions about your dataset, then answer them using visual elements (e.g. charts or plots) to provide clear insights.\n",
+ "\n",
+ "For example, in the 2nd lecture the entire class took a survey. Then, we talked about the collected data and desplayed the collected data using the right **plots** - Lines, Bars, Hist, Pie, Map, HeatMap, Area, Time, etc.\n",
+ "\n",
+ "An aditional more specific example, would be the questions we asked during the recitation on the `Titanic` dataset:\n",
+ " - \"Did survival rates differ by gender?\"\n",
+ " - \"Was passenger class related to survival?\"\n",
+ " - \"What was the age distribution of survivors vs. non-survivors?\"\n",
+ " - \"Did embarking location (port) have any effect on survival?\" \n",
+ " \n",
+ "And how we answered those questions using **plots**.\n",
+ "\n",
+ "The idea is to pose questions that can uncover patterns, correlations, or anomalies in your dataset, then back those up with clean, insightful visualizations."
+ ],
+ "metadata": {
+ "id": "lo68PsjTK0_j"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Research Question 3: What is the actual fraud rate per transaction type?\n# Raw counts can be misleading because of class imbalance \u2014 this shows the real probability\nfraud_rate_by_type = df.groupby('type')['isFraud'].mean() * 100\n\nplt.figure(figsize=(10, 6))\nfraud_rate_by_type.sort_values(ascending=False).plot(\n kind='bar', color='coral', edgecolor='black')\nplt.title('Fraud Rate (%) by Transaction Type')\nplt.xlabel('Transaction Type')\nplt.ylabel('Fraud Rate (%)')\nplt.xticks(rotation=45)\nplt.tight_layout()\nplt.show()\n\nprint('Fraud rate per transaction type (%):')\nprint(fraud_rate_by_type.sort_values(ascending=False).round(2))"
+ ],
+ "metadata": {
+ "id": "0ch5l8tIK1Dt"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Insight:** The fraud rate by transaction type tells a much cleaner story than the raw count chart. TRANSFER and CASH_OUT are the only types where fraud actually occurs. PAYMENT, CASH_IN, and DEBIT show a fraud rate of exactly zero. This confirms what the bar chart showed earlier, but now with the actual probability rather than raw numbers.\n\n**Conclusion:** Fraud in this dataset is entirely channel-specific. A detection system that focuses exclusively on TRANSFER and CASH_OUT transactions will not miss a single fraud case \u2014 at least based on what this data shows."
+ ],
+ "metadata": {
+ "id": "_M62cZH7mc12"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Research Question 4: How is fraud distributed across different transaction size ranges?\n# Binning transactions into size categories to see where fraud concentrates\ndf['amount_bin'] = pd.cut(\n df['amount'],\n bins=[0, 1000, 10000, 100000, df['amount'].max()],\n labels=['Small\\n(<1K)', 'Medium\\n(1K-10K)', 'Large\\n(10K-100K)', 'Very Large\\n(>100K)']\n)\n\nfraud_by_bin = df.groupby('amount_bin', observed=True)['isFraud'].agg(['sum', 'count'])\nfraud_by_bin['fraud_rate'] = (fraud_by_bin['sum'] / fraud_by_bin['count']) * 100\n\nplt.figure(figsize=(10, 6))\nfraud_by_bin['fraud_rate'].plot(\n kind='bar',\n color=['#2196F3', '#FF9800', '#F44336', '#9C27B0'],\n edgecolor='black'\n)\nplt.title('Fraud Rate (%) by Transaction Amount Range')\nplt.xlabel('Amount Category')\nplt.ylabel('Fraud Rate (%)')\nplt.xticks(rotation=20)\nplt.tight_layout()\nplt.show()\n\nprint('Fraud distribution by amount range:')\nprint(fraud_by_bin[['sum', 'count', 'fraud_rate']].round(2))"
+ ],
+ "metadata": {
+ "id": "5diQ4kRlmc6z"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0e1136b",
+ "metadata": {},
+ "source": [
+ "**Insight:** Fraud does not spread evenly across transaction sizes. The highest fraud rates appear in the large and very large buckets \u2014 transactions above 10,000. Small transactions are almost never fraudulent, which makes practical sense: the risk of detection is not worth taking for a small reward.\n\n**Conclusion:** Transaction size works as a natural risk stratifier. Very large transactions deserve automatic extra scrutiny, while small ones can be fast-tracked with lighter checks. This has real practical implications for any FinTech fraud prevention system trying to balance security with user experience."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "zrIvgbNoIPU6"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 3: README file**\n",
+ "\n",
+ "1. Upload the dataset to HuggingFace.\n",
+ "\n",
+ "2. Upload your work (this notebook) as a `.ipynb` file your HF's Dataset.\n",
+ "\n",
+ "3. Create a `README` file in a Markdown format. This page should include the end result of your work. Meaning include the visualizations, Questions, Answers, insights, Decisions, and more.\n"
+ ],
+ "metadata": {
+ "id": "TxKHPqppIPZT"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "id": "EfniaMYBK6sK"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "uLxQ5tJQK6xG"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "id": "0GnQN63RK61Q"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "euWXtGKHK65d"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "hkerNEsMIPbK"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 4: Presentation Video**\n",
+ "\n",
+ "- Record a brief video (2\u20133 minutes) with screen sharing of you walk through the HF's Dataset, README, notebook and sharing your process & results. Make sure to include a screen share while also recording yourself talking during the walk through.\n",
+ "\n",
+ "- Videos without your face talking while going ower your work wont be acceptable.\n",
+ "\n",
+ "- You should include:\n",
+ " - A quick dataset overview and your main goal.\n",
+ " - Key EDA steps and highlights of visual insights. (!)\n",
+ " - Reflections on any challenges and lessons learned.\n"
+ ],
+ "metadata": {
+ "id": "mzYCO54GIPdP"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "> For help:\n",
+ "> - Youtube [Watch this video](https://www.youtube.com/watch?v=DK7Z_nYhjjg)\n",
+ "> - Loom [Watch this video](https://www.youtube.com/watch?v=eSCHNXTsJK8)\n",
+ "> - Zoom [Watch this video](https://www.youtube.com/watch?v=njwbjFYCbGU)\n"
+ ],
+ "metadata": {
+ "id": "si7mk6JgJurV"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "asKyjjGDK-GJ"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Upload the video to the Dataset Repo, as a `MP4` file.\n",
+ "\n",
+ "Then include the video on the dataset README file, as follows:\n",
+ "\n",
+ "``\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "ksLgaMfri4gO"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Finally, attach the video to the begining of the `README` file, and make sure everything works."
+ ],
+ "metadata": {
+ "id": "QJ8Rux_SY1JH"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# here is a simple example on including the video in the README: https://huggingface.co/miqudev/miqu-1-70b"
+ ],
+ "metadata": {
+ "id": "5MBAwudTi366"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
"
+ ],
+ "metadata": {
+ "id": "hoLjb_ZrY2Nm"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 5: Moodle**"
+ ],
+ "metadata": {
+ "id": "km5C5-WyjO3R"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Submit to Moodle the link to your HF's Dataset.** \n",
+ "\n",
+ "> As the dataset already includes the video presentation, and the code notebook - we should haver everything there to examine.\n"
+ ],
+ "metadata": {
+ "id": "Bx7dlp5xjT4r"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Link example: https://huggingface.co/datasets/ido_something/name_of_ds"
+ ],
+ "metadata": {
+ "id": "WZcWV_6KbE9s"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n"
+ ],
+ "metadata": {
+ "id": "ZXm5K6q-KM0S"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "---\n",
+ "\n",
+ "Good luck and have fun exploring your first DS project!"
+ ],
+ "metadata": {
+ "id": "rN8TJ_5oIPfm"
+ }
+ }
+ ]
+}
\ No newline at end of file