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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# PQMass: Probabilistic Assessment of Generative Models Using Probability Mass Estimation\n", | |
| "\n", | |
| "**Paper:** Lemos et al. (2025) - Published at ICLR 2025\n", | |
| "\n", | |
| "**Authors:** Pablo Lemos, Sammy Sharief, Nikolay Malkin, Salma Salhi, Connor Stone, Laurence Perreault-Levasseur, Yashar Hezaveh\n", | |
| "\n", | |
| "## Overview\n", | |
| "\n", | |
| "This notebook demonstrates **PQMass**, a likelihood-free method for comparing two distributions given samples from each. PQMass is designed to assess the quality of generative models by providing a statistically rigorous measure of how well a model matches the true data distribution.\n", | |
| "\n", | |
| "### Key Features\n", | |
| "\n", | |
| "- **Likelihood-free**: Does not require access to model likelihoods\n", | |
| "- **Statistically principled**: Provides p-values for hypothesis testing\n", | |
| "- **Scalable**: Works well in moderately high dimensions\n", | |
| "- **General**: Applicable to any data type where a distance metric can be defined\n", | |
| "\n", | |
| "### Method Summary\n", | |
| "\n", | |
| "PQMass works by:\n", | |
| "1. Partitioning the sample space into non-overlapping Voronoi cells\n", | |
| "2. Counting samples from two distributions in each cell\n", | |
| "3. Applying chi-squared tests to determine if the counts are consistent with samples from the same distribution\n", | |
| "4. Returning a p-value that measures the probability that the two distributions are the same\n", | |
| "\n", | |
| "### Educational Scope\n", | |
| "\n", | |
| "**Note:** This notebook provides an educational demonstration of PQMass with small-scale examples that run within resource constraints (4GB RAM, <10 min runtime). For full-scale experiments as described in the paper, you would need to:\n", | |
| "- Use larger datasets (100k+ samples)\n", | |
| "- Run on GPU-enabled systems for generative models\n", | |
| "- Allow longer computation times (hours for full training)\n", | |
| "\n", | |
| "Let's get started!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 1. Setup and Dependencies\n", | |
| "\n", | |
| "First, we'll install all required dependencies and import necessary libraries." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\u001b[2mChecked \u001b[1m6 packages\u001b[0m \u001b[2min 11ms\u001b[0m\u001b[0m\r\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Install dependencies\n", | |
| "!uv pip install numpy scipy matplotlib scikit-learn seaborn tqdm" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "✓ All libraries imported successfully\n", | |
| "NumPy version: 2.4.2\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Import libraries\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import seaborn as sns\n", | |
| "from scipy import stats\n", | |
| "from scipy.spatial.distance import cdist\n", | |
| "from sklearn.mixture import GaussianMixture\n", | |
| "from typing import Tuple, Optional\n", | |
| "import warnings\n", | |
| "warnings.filterwarnings('ignore')\n", | |
| "\n", | |
| "# Set random seed for reproducibility\n", | |
| "np.random.seed(42)\n", | |
| "\n", | |
| "# Configure plotting\n", | |
| "plt.style.use('seaborn-v0_8-darkgrid')\n", | |
| "sns.set_palette(\"husl\")\n", | |
| "\n", | |
| "print(\"✓ All libraries imported successfully\")\n", | |
| "print(f\"NumPy version: {np.__version__}\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 2. Core PQMass Implementation\n", | |
| "\n", | |
| "We'll implement the PQMass algorithm following the paper's methodology (Section 2).\n", | |
| "\n", | |
| "### Algorithm Steps:\n", | |
| "\n", | |
| "1. **Define reference points**: Sample n_R/2 points from each distribution to define Voronoi cells\n", | |
| "2. **Assign samples to Voronoi cells**: For each sample, find the nearest reference point\n", | |
| "3. **Compute chi-squared statistic**: Compare observed counts to expected counts under null hypothesis\n", | |
| "4. **Calculate p-value**: Compute probability that samples come from the same distribution" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "✓ PQMass core functions implemented\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "def pqmass_test(samples_p: np.ndarray, \n", | |
| " samples_q: np.ndarray, \n", | |
| " n_ref: int = 100,\n", | |
| " metric: str = 'euclidean',\n", | |
| " return_details: bool = False) -> float:\n", | |
| " \"\"\"\n", | |
| " Perform PQMass two-sample test.\n", | |
| " \n", | |
| " Parameters:\n", | |
| " -----------\n", | |
| " samples_p : np.ndarray, shape (m, d)\n", | |
| " Samples from distribution p (e.g., true data)\n", | |
| " samples_q : np.ndarray, shape (n, d)\n", | |
| " Samples from distribution q (e.g., generated data)\n", | |
| " n_ref : int\n", | |
| " Number of reference points for Voronoi tessellation\n", | |
| " metric : str\n", | |
| " Distance metric to use ('euclidean', 'manhattan', etc.)\n", | |
| " return_details : bool\n", | |
| " If True, return (chi2_stat, p_value, counts_p, counts_q)\n", | |
| " \n", | |
| " Returns:\n", | |
| " --------\n", | |
| " p_value : float\n", | |
| " P-value for the hypothesis that samples_p and samples_q come from the same distribution\n", | |
| " (or tuple of details if return_details=True)\n", | |
| " \"\"\"\n", | |
| " m, n = len(samples_p), len(samples_q)\n", | |
| " \n", | |
| " # Step 1: Define reference points\n", | |
| " # Sample n_ref/2 points from each distribution\n", | |
| " n_ref_half = n_ref // 2\n", | |
| " \n", | |
| " # Randomly select reference points\n", | |
| " idx_p = np.random.choice(m, size=n_ref_half, replace=False)\n", | |
| " idx_q = np.random.choice(n, size=n_ref_half, replace=False)\n", | |
| " \n", | |
| " ref_points = np.vstack([samples_p[idx_p], samples_q[idx_q]])\n", | |
| " \n", | |
| " # Remove reference points from the test sets\n", | |
| " mask_p = np.ones(m, dtype=bool)\n", | |
| " mask_p[idx_p] = False\n", | |
| " mask_q = np.ones(n, dtype=bool)\n", | |
| " mask_q[idx_q] = False\n", | |
| " \n", | |
| " test_p = samples_p[mask_p]\n", | |
| " test_q = samples_q[mask_q]\n", | |
| " \n", | |
| " m_test, n_test = len(test_p), len(test_q)\n", | |
| " \n", | |
| " # Step 2: Assign samples to Voronoi cells\n", | |
| " # Find nearest reference point for each test sample\n", | |
| " distances_p = cdist(test_p, ref_points, metric=metric)\n", | |
| " distances_q = cdist(test_q, ref_points, metric=metric)\n", | |
| " \n", | |
| " # Assign to nearest cell (breaking ties by lower index)\n", | |
| " assignments_p = np.argmin(distances_p, axis=1)\n", | |
| " assignments_q = np.argmin(distances_q, axis=1)\n", | |
| " \n", | |
| " # Step 3: Count samples in each Voronoi cell\n", | |
| " counts_p = np.bincount(assignments_p, minlength=n_ref)\n", | |
| " counts_q = np.bincount(assignments_q, minlength=n_ref)\n", | |
| " \n", | |
| " # Step 4: Compute expected counts under null hypothesis\n", | |
| " # If both samples come from the same distribution, we expect proportional counts\n", | |
| " total_counts = counts_p + counts_q\n", | |
| " p_hat = total_counts / (m_test + n_test)\n", | |
| " \n", | |
| " expected_p = m_test * p_hat\n", | |
| " expected_q = n_test * p_hat\n", | |
| " \n", | |
| " # Step 5: Compute chi-squared statistic (Equation 4 from paper)\n", | |
| " # Avoid division by zero by adding small epsilon or filtering\n", | |
| " epsilon = 1e-10\n", | |
| " chi2_stat = np.sum((counts_p - expected_p)**2 / (expected_p + epsilon)) + \\\n", | |
| " np.sum((counts_q - expected_q)**2 / (expected_q + epsilon))\n", | |
| " \n", | |
| " # Step 6: Compute p-value (Equation 5 from paper)\n", | |
| " # The chi-squared statistic follows a chi-squared distribution with n_ref - 1 degrees of freedom\n", | |
| " df = n_ref - 1\n", | |
| " p_value = 1 - stats.chi2.cdf(chi2_stat, df)\n", | |
| " \n", | |
| " if return_details:\n", | |
| " return chi2_stat, p_value, counts_p, counts_q\n", | |
| " else:\n", | |
| " return p_value\n", | |
| "\n", | |
| "\n", | |
| "def pqmass_test_multiple(samples_p: np.ndarray,\n", | |
| " samples_q: np.ndarray,\n", | |
| " n_ref: int = 100,\n", | |
| " n_repeats: int = 10,\n", | |
| " metric: str = 'euclidean') -> Tuple[np.ndarray, float, float]:\n", | |
| " \"\"\"\n", | |
| " Perform PQMass test multiple times with different random reference points.\n", | |
| " \n", | |
| " This reduces variance from the random choice of reference points.\n", | |
| " \n", | |
| " Parameters:\n", | |
| " -----------\n", | |
| " samples_p, samples_q : np.ndarray\n", | |
| " Sample sets from distributions p and q\n", | |
| " n_ref : int\n", | |
| " Number of reference points\n", | |
| " n_repeats : int\n", | |
| " Number of times to repeat the test\n", | |
| " metric : str\n", | |
| " Distance metric\n", | |
| " \n", | |
| " Returns:\n", | |
| " --------\n", | |
| " p_values : np.ndarray\n", | |
| " Array of p-values from each repeat\n", | |
| " mean_p : float\n", | |
| " Mean p-value\n", | |
| " std_p : float\n", | |
| " Standard deviation of p-values\n", | |
| " \"\"\"\n", | |
| " p_values = []\n", | |
| " \n", | |
| " for _ in range(n_repeats):\n", | |
| " p_val = pqmass_test(samples_p, samples_q, n_ref=n_ref, metric=metric)\n", | |
| " p_values.append(p_val)\n", | |
| " \n", | |
| " p_values = np.array(p_values)\n", | |
| " return p_values, np.mean(p_values), np.std(p_values)\n", | |
| "\n", | |
| "\n", | |
| "print(\"✓ PQMass core functions implemented\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 3. Workflow 1: Null Test Validation\n", | |
| "\n", | |
| "We validate that PQMass correctly identifies when two sample sets come from the **same** distribution.\n", | |
| "\n", | |
| "**Expected behavior:** When testing two sets of samples from the same distribution, the chi-squared statistic should follow a χ²(n_R - 1) distribution, and p-values should be approximately uniformly distributed.\n", | |
| "\n", | |
| "This corresponds to Section 3.1 and Figure 2 of the paper." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Generating Gaussian mixture model...\n", | |
| "GMM: 5 components in 10 dimensions\n", | |
| "Testing with 500 samples per set\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Generate a Gaussian mixture model in moderate dimensions\n", | |
| "print(\"Generating Gaussian mixture model...\")\n", | |
| "\n", | |
| "# Use smaller dimensions and components for quick execution\n", | |
| "n_components = 5\n", | |
| "n_dims = 10\n", | |
| "n_samples_per_set = 500 # Small for speed\n", | |
| "n_ref = 50 # Fewer reference points for speed\n", | |
| "\n", | |
| "# Create a Gaussian mixture model\n", | |
| "means = np.random.randn(n_components, n_dims) * 3\n", | |
| "covariances = [np.eye(n_dims) * (0.5 + np.random.rand()) for _ in range(n_components)]\n", | |
| "weights = np.random.dirichlet(np.ones(n_components))\n", | |
| "\n", | |
| "def sample_from_gmm(n_samples, means, covariances, weights):\n", | |
| " \"\"\"Sample from a Gaussian mixture model.\"\"\"\n", | |
| " n_components = len(means)\n", | |
| " # Sample component assignments\n", | |
| " components = np.random.choice(n_components, size=n_samples, p=weights)\n", | |
| " # Sample from each component\n", | |
| " samples = np.zeros((n_samples, means.shape[1]))\n", | |
| " for i in range(n_components):\n", | |
| " mask = components == i\n", | |
| " n_comp_samples = np.sum(mask)\n", | |
| " if n_comp_samples > 0:\n", | |
| " samples[mask] = np.random.multivariate_normal(\n", | |
| " means[i], covariances[i], size=n_comp_samples\n", | |
| " )\n", | |
| " return samples\n", | |
| "\n", | |
| "print(f\"GMM: {n_components} components in {n_dims} dimensions\")\n", | |
| "print(f\"Testing with {n_samples_per_set} samples per set\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Running null test (comparing samples from the same distribution)...\n", | |
| " Completed 25/100 tests\n", | |
| " Completed 50/100 tests\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " Completed 75/100 tests\n", | |
| " Completed 100/100 tests\n", | |
| "\n", | |
| "✓ Completed 100 null tests\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Null test: Compare samples from the SAME distribution\n", | |
| "print(\"\\nRunning null test (comparing samples from the same distribution)...\")\n", | |
| "\n", | |
| "n_null_tests = 100 # Reduced from paper's 2^14 for speed\n", | |
| "chi2_values = []\n", | |
| "\n", | |
| "for i in range(n_null_tests):\n", | |
| " # Generate two independent sample sets from the same GMM\n", | |
| " samples_1 = sample_from_gmm(n_samples_per_set, means, covariances, weights)\n", | |
| " samples_2 = sample_from_gmm(n_samples_per_set, means, covariances, weights)\n", | |
| " \n", | |
| " # Perform PQMass test\n", | |
| " chi2_stat, p_value, _, _ = pqmass_test(\n", | |
| " samples_1, samples_2, n_ref=n_ref, return_details=True\n", | |
| " )\n", | |
| " chi2_values.append(chi2_stat)\n", | |
| " \n", | |
| " if (i + 1) % 25 == 0:\n", | |
| " print(f\" Completed {i+1}/{n_null_tests} tests\")\n", | |
| "\n", | |
| "chi2_values = np.array(chi2_values)\n", | |
| "print(f\"\\n✓ Completed {n_null_tests} null tests\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 1400x500 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "transient": {} | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Kolmogorov-Smirnov test:\n", | |
| " Statistic: 0.0752\n", | |
| " P-value: 0.5965\n", | |
| " → Chi-squared values ARE consistent with χ²(49) distribution\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Visualize results\n", | |
| "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", | |
| "\n", | |
| "# Plot 1: Chi-squared values vs theoretical distribution\n", | |
| "ax = axes[0]\n", | |
| "df = n_ref - 1\n", | |
| "\n", | |
| "# Histogram of observed chi-squared values\n", | |
| "ax.hist(chi2_values, bins=30, density=True, alpha=0.6, label='Observed', color='steelblue')\n", | |
| "\n", | |
| "# Theoretical chi-squared distribution\n", | |
| "x = np.linspace(0, max(chi2_values), 200)\n", | |
| "ax.plot(x, stats.chi2.pdf(x, df), 'r-', lw=2, label=f'χ²({df}) theory')\n", | |
| "\n", | |
| "ax.set_xlabel('χ² PQM statistic', fontsize=12)\n", | |
| "ax.set_ylabel('Density', fontsize=12)\n", | |
| "ax.set_title('Null Test: χ² Distribution\\n(Samples from Same Distribution)', fontsize=13, fontweight='bold')\n", | |
| "ax.legend(fontsize=11)\n", | |
| "ax.grid(True, alpha=0.3)\n", | |
| "\n", | |
| "# Plot 2: Q-Q plot\n", | |
| "ax = axes[1]\n", | |
| "stats.probplot(chi2_values, dist=stats.chi2(df), plot=ax)\n", | |
| "ax.set_title('Q-Q Plot: Observed vs Theoretical', fontsize=13, fontweight='bold')\n", | |
| "ax.grid(True, alpha=0.3)\n", | |
| "\n", | |
| "plt.tight_layout()\n", | |
| "plt.show()\n", | |
| "\n", | |
| "# Perform Kolmogorov-Smirnov test\n", | |
| "ks_stat, ks_pvalue = stats.kstest(chi2_values, lambda x: stats.chi2.cdf(x, df))\n", | |
| "print(f\"\\nKolmogorov-Smirnov test:\")\n", | |
| "print(f\" Statistic: {ks_stat:.4f}\")\n", | |
| "print(f\" P-value: {ks_pvalue:.4f}\")\n", | |
| "print(f\" → Chi-squared values {'ARE' if ks_pvalue > 0.05 else 'are NOT'} consistent with χ²({df}) distribution\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "**Interpretation:** The histogram should match the theoretical χ² distribution (red line), and the Q-Q plot should show points along the diagonal. This validates that PQMass correctly follows the theoretical distribution under the null hypothesis." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 4. Workflow 2: Mode-Dropping Detection\n", | |
| "\n", | |
| "We demonstrate PQMass's ability to detect when a generative model **misses a mode** of the true distribution.\n", | |
| "\n", | |
| "This is a key capability for evaluating generative models, as mode dropping is a common failure mode (especially in GANs).\n", | |
| "\n", | |
| "This corresponds to Section 3.2 and Figure 3 of the paper." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Creating 2D Gaussian mixture model...\n", | |
| "Created GMM with 4 modes in 2D\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Create a 2D Gaussian mixture for visualization\n", | |
| "print(\"Creating 2D Gaussian mixture model...\")\n", | |
| "\n", | |
| "n_modes = 4\n", | |
| "n_dims_2d = 2\n", | |
| "\n", | |
| "# Create modes in a circle pattern\n", | |
| "angles = np.linspace(0, 2*np.pi, n_modes, endpoint=False)\n", | |
| "radius = 5\n", | |
| "means_2d = np.array([[radius * np.cos(a), radius * np.sin(a)] for a in angles])\n", | |
| "covariances_2d = [np.eye(2) * 0.5 for _ in range(n_modes)]\n", | |
| "weights_2d = np.ones(n_modes) / n_modes # Equal weights\n", | |
| "\n", | |
| "print(f\"Created GMM with {n_modes} modes in 2D\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Generated 1000 samples from each distribution\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Generate samples from full distribution and mode-dropped distribution\n", | |
| "n_samples = 1000\n", | |
| "\n", | |
| "# Full distribution\n", | |
| "samples_full = sample_from_gmm(n_samples, means_2d, covariances_2d, weights_2d)\n", | |
| "\n", | |
| "# Mode-dropped distribution (missing one mode)\n", | |
| "means_dropped = means_2d[1:] # Remove first mode\n", | |
| "covariances_dropped = covariances_2d[1:]\n", | |
| "weights_dropped = weights_2d[1:] / weights_2d[1:].sum() # Renormalize\n", | |
| "\n", | |
| "samples_dropped = sample_from_gmm(n_samples, means_dropped, covariances_dropped, weights_dropped)\n", | |
| "\n", | |
| "print(f\"Generated {n_samples} samples from each distribution\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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Rcs9NT0+nX79+TJ8+nWHDhmmPf/LJJzVuf236+w3XEP9YJwY8hYWFlda0KtuT1aBBA5o1a0ZMTAwAt9xyi/a3Ll26aEOiwT+st7JC6zqdjunTp9d4VEpCQgIvvvgier2ePn36sHXrVn755RdcLhcrV67k0ksvrdH+kpOTA0YKlU7VOJklS5ZoCYaBAwdqPVc9e/Zk06ZNZGVlsWfPHnbu3Fmu0OWAAQO45557AP8oo//+97+Av5euKp06daJBgwakpaWxfPlyHnvsMfR6vRZghYaGal+aZV+zRo0a0bJlSyIiIhg6dGiVvdF/B6X3ZqnSwK0iZa9TfHw8jRs3Jj4+HkVRuOGGG7S/denShS+++EL7vUWLFlXeJy+99JLWg1xdFouF119/nZCQEPr168e+ffv4+uuvAf+IsJr2wiUlJQWcX+lU3JNZvXo1ubm5ALRp04bHH38c8Pd+b9myhW3btpGbm8uaNWvK/cOoZcuWPPPMMwBERERoQ80rC5SFEELULonv/CS+q54rr7yS2bNns337dp599lncbjeRkZEMGDCAl1566bSuT3JyMj/++KP23JdeeolmzZoBkJWVxfTp0wP2nZeXx4oVKwB/LHfllVcC0Lx5cy688EKWLVtGXl4eq1atYvDgwezatatcjNe4ceNq1ZWqzvtk+PDh5TocyyZDH3vsMfr16weA1WrVErPffPNNhaMmn3zySW0kkMfj0fa9fPnyCjumX3vtNRo1agQEXq/ly5fTv39/LUYEf8KmtMbX1VdfzU8//QTA119/zc0338y2bds4duyYdu4vvvgiBoMh4L1WG4xGI+Hh4drU1OrG4DExMSQlJVG/fn10Oh3XXnut9rcuXboETF1s1KhRle/lBx98kH/96181bvvJrndNnFhjuHQa8cns2LGDPXv2AP5r8sorr2A0GunTpw979+7V3n9LliwptyhBVFQUr7/+OiaTiXbt2vHNN98AdR+DSwJNVKqiIrPR0dF11JqTK/0QLRUcHFzptgMGDOC1114jLy+PZ599lmeffZawsDBSUlK4/PLLGTJkSI2P36hRo1Oa0tW2bduAno2UlBTtQ7+yulJnQtkh2GWH8RuNRlq1akVWVpa23YkBVrdu3bSfw8PDtZ+r+pIB/1LRw4cPZ/r06WRnZ7Nhwwaio6PZu3cv4O+lKB2mPWLECGbPno3L5eLOO+8E/B+snTp14pprrqnRUN6KFhEoG0ifa068t6tqa5cuXUhKSuLAgQO8++67vPvuu9hsNtq0acOIESO44oor0OlqNvjYbDbXOHkG0KRJk4C2pqSkaMFRXd3b7du3D/hbSkoK27ZtK7ddqbKreJW9t+12ey23Ugghzg6J72pG4ju/8yG+A2jWrBkdO3bk119/1WKOSy+9tNLSFzW5PtHR0dpoMavVqiXPTnxuqUOHDmkjpLKyshg7dmyFbSi9Lk8//XRAbT6A5557rtyK4xWpzvukoqRJ2VkNZWOksudT2cyHstuUXVTjxGmi4L9/SpM5Jz63dPuyx5k+fXq5hCT4R3mdeIxWrVoFJK/KvtdOl8vl0jphoeoYPCkpiS5durBx40YWLFjAggULCAoKomXLlgwcOJDx48dXeh9WpTSpWRPVud5nQ9nXtHXr1tooPPDfM6VJ9Yrusfbt22vX61yKwSWBJipVVc9W2eHUZYtmnsqQ9NpS9oMyMTGxyhUVY2JimDdvHh9//DG//PILe/fu1XqAVq1ahc/nCxgqWh21FXxWNFS9sutd9gP9TDrZ8PmyK/2UDRarU/h15MiR2hfkkiVLAnrZRowYof3cokUL5s2bx6effsrWrVvZt28f2dnZLFu2jO+//565c+fSqVOnap1PVSOXKrvWcPau94nK3ttBQUFVrrpjsVj4+OOP+fjjj0lNTWXv3r1kZWWRmppKamoqeXl5Na69Ulsr6pyP93bZWhNl720hhDhfSXwn8V1VbSrrfIvvSl155ZX8+uuv2u/VLT1xoppMH63JtidyOByn/NxS1Xmf1CSeq+n5nOntS3k8nnILG9TWviuyefPmgPp8JyaZy9LpdMycOZPPPvuM1atXs3fvXo4cOcLmzZvZvHkzhw4dOqUawLXxGVSTz5+z9Xlfkxj8XKpzfe60RJxXymbfS4tBgn9edV04cuQI77//vvb70KFDq9xeVVXq16+vDUsH+O2337Qv2KVLl2oBVtk3d1UBw6l+WG/btg2fz6eNCtqyZYv2t9LpBGV7kapzvcuOMCr7oV+Vxo0baz9v3bpV+9ntdrN9+/YKt6sNTZs2pU2bNmzbto1ly5ZpNRTq1asX0POpqirNmzcPGDm2ZMkSJk2ahM/nY/ny5TUOsCpS2b29cePGWqlRUVN2uz1gJZuLL764yt4rVVWJjIzktttu47bbbgP8PU0jR46kuLiYpUuXagm0svdsVffJqd7b+/fvp7CwULt/K7q3a/pZUpv39om/1/a9LYQQ5xuJ78qT+O7U1GV8N2TIEJ555hmKioro1KkTTZs2rXTbmlyfqKgorFYrxcXFFBcXs3fvXm3fZV/fUomJiSiKgqqqJCYm8t1335XrjCu7uNGcOXNqdJ6lqvs+qeheTkpK0mqGbd26VZtaW/Z8Kuu4/e2337T6sGWvXenq8WXl5eVx8OBBbVRU2f2Xbp+UlMTKlSuBykfeORwOTCZTwDF27NiB1+vVrm1Fr8WpcLlcvPzyy9rvHTt2rHQBAfDfyzabjeuvv57rr78e8CejrrzyStLS0li2bJmWQKtuDH7ittVVnet9Kp/3pfdzdT9/yt4727dvx+PxaMmwsvdMVYMDzjWSQBOnpEGDBuh0Onw+H+vWrePVV1/FZrMxc+bMs3J8l8vFxo0bKSkp4bfffmPOnDnaSn/169cPqPVUkUWLFvHJJ59w8cUX06BBA4KDg1m3bl3A/kuVzX5/9tln9OnTB7PZHDBU+XSkp6dz//33M3z4cNatW6f1IJlMJq3Ie9khuLNnz8ZqtXLo0CG+/PLLCvdZtsdw06ZNrFixApvNVmUdhcGDB/Pyyy/jdrtZtmwZb775Ju3bt+err77Shq83a9bslFaPOZmRI0eybds2srKytGMNHz484AvjnXfeITU1lb59+1KvXj2sVis///yz9veT9UZVV2hoKOHh4doXz//+9z+aNGnCrFmzamX/1bFx40bcbje7du1izpw5pKenA/4vupPVA/nll1945plnGDRoEI0aNSIiIoJdu3ZpRZUru7cXLlyIXq9Hp9NVq6ZBdRQXFzN58mSuvfZadu7cGbC0eOmKU2VXBF24cCGJiYkUFRVVer3L3tt//PEHy5cvJzw8vFxthrIuvPBC7TX9/fffefLJJ+nTpw8rV67k999/B/z1zepyRR8hhDgXSHwn8V1tqqv4zmq18tRTT7Fv3z4twVOZmlwfRVHo27evFs/ce++93HrrrRw7dowPP/yw3L7Dw8Pp3bs3K1as4NChQ9xyyy1cccUV2Gw2jhw5wvbt21m2bBmffPJJpTX4KnK675Oyhg8friXQnnzySYqKilAUJSBxVNmozf/973/cddddOJ1OXnvtNe3xylYVvfvuu7nlllvIyMgIuF6l248YMUJ7/LnnniM/P5/k5GTsdjuHDh1i9erVJCQk8Nxzz9GmTRvi4uI4duwYmZmZ3HfffYwcOTLgvXYqfv/9d9LS0jhw4AAfffSRdm2MRuNJF1s5duwYEyZMYMiQITRr1oyoqCjS0tK0EV2Vff4sXbqUBg0aYDAYSElJOaVpnhU52fU+lc/7sLAw8vLyyMzMZOHChSQkJBAdHV1pAqxVq1Y0bdpUmxFzzz33MHr0aLZu3aot8GE0Ghk8eHCtnPPZIAk0cUpCQkIYOnQoixYtwufzMWPGDMDf21T6AX4mVVZHICkpiWnTpp10iV+fz8fGjRvZuHFjhX8fPny49nP37t1ZunQpADNnzmTmzJnUr1//lFYwqUjDhg1ZtGgRCxcuDHj81ltvJTIyEoBevXqRkJDAkSNHtLoegPaBdKKmTZsSExNDVlYWaWlp2oijquoo1KtXjwcffJCnnnoKn8/HW2+9FfB3m83Gc889V6vDoksNHTqUF198MWD4cNnh/eAfsl06BeNEOp3ulOqaVOaqq67S7unSlYRiYmIIDQ09K/PuK7q3o6OjmTp1aoW9emWpqsq2bdu0ul4nOvHeLu2xnDdvHvPmzQOotdWL4uLi2LBhQ7nX7Morr9QC9RYtWmi1SpxOJ6+88gpQ+b0dHBys9Wjb7XZtlN3tt9/OHXfcUWE7rFYrzzzzDJMnT8btdmurBpUyGo0888wzWK3WWjlvIYQ4X0l8J/FdbarL+K66U3Vren3uvPNOVq5cSWFhIdu2bdPikNL6syd6/PHHGTNmDBkZGaxYsUJbVOB0nO77pKwJEyawYsUKNm7cSHp6OnfddVfA37t27VquuHspi8VSrmO3RYsWXHXVVeW2DQ8PJysri1tvvTXg8QsvvFCrzZaSksKtt97K22+/jd1u5/nnny+3n9GjRwP+KcX333+/1t5FixZpCyI0atTopAtdVKa0Dl9ZVquVl19+OWCl08rs379fW0n0RGXvyW7dummjucreF99//32NkqmVCQkJIT09vcrrfSqf9927d2fJkiV4vV7uvfdewP+aVPRagX/E2vPPP8+ECRMoKipi8eLFLF68OODvDz30EPXq1Tvtcz5balZJWogyHnnkES655BKsVishISFceuml/N///d9ZO76iKFgsFurXr0+vXr14/PHHWbBgQbUKvXbs2JHx48fTpk0bIiIi0Ov1Wl2s1157LeAD7qqrruKmm24iISGhxsXXq6NLly5MmzaN1q1bYzKZqF+/Pg888EDAalFGo5G33nqLjh07YjQaiY+P54477ihXCL+UwWDg7bffpnPnzlXWCjnR2LFjef/99+nduzfh4eEYDAZiY2O59NJLmTdvXoUFUmtDbGwsF1xwgfZ7cnIyycnJAdv06dOHq666ihYtWhAWFoZeryc8PJxevXoxa9YsOnfuXGvtue2227jqqqsIDQ3FarUyYMAAPv7447O20ICiKJjNZuLi4ujWrRv33nsvixcvpmPHjid9buPGjbnpppvo0KED0dHRGAwGrFYr7dq143//+x833XSTtm2/fv24//77SUxMPCO1BRo1asQHH3xAp06dMJvNxMTEMHHiRG0VzFIvvfQSvXr1wmw2ExkZyfjx46tcEvvVV1/loosuqlGAePHFF/PJJ58wePBgoqKiMBgMREZGMmjQID7++ONKe0uFEOKfRuK72iHx3bkX31WmJtcnKSmJDz/8kK5du2IymYiJieGmm26q9DVLSEhg/vz5/Pvf/6ZJkyaYzWZsNhtNmjTh0ksvZdq0aaeUPDid90lZJpOJ999/n7vvvpvk5GSCgoIwm820aNGCu+++m/fee6/SEVGzZ89m1KhRhISEYLPZGDZsGO+//z5ms7nctjabjblz59KvXz+sVivh4eFcffXVTJkyJSB5e+eddzJjxgwuuugiwsPDMRqNxMXF0blzZ+6+++6AztJhw4bx6quv0rRpU4xGI40bN+bZZ58tl6StCUVRMBqNxMTE0LFjR2699VaWLl1arTgxLCyM22+/nW7duhETE4PRaCQoKIjk5GQmT57Mo48+qm2bnJzMCy+8QNOmTWttxFlZoaGhzJ07l4suuqjK613Tz/tHH32UIUOGaJ0A1ZGSksK8efMYPXo0cXFxGAwGwsLCuOiii3jvvfe45pprTutczzZFrU4VSCGEEEIIIYQQQvxj9e/fXystUp0ZC6UJ09oc3SlEXZIRaEIIIYQQQgghhBBCVEESaEIIIYQQQgghhBBCVEESaEIIIYQQQgghhBBCVEFqoAkhhBBCCCGEEEIIUQUZgSaEEEIIIYQQQgghRBUkgSaEEEIIIYQQQgghRBUkgSZEJd59912Sk5Pp2rUrxcXFdd0cTXJyMsnJyfTv3/+U9zFlyhRtP/PmzavF1p3bKjvv/v37a4+fT+bNm6e1e8qUKWf0WAsXLiQ5OZl27dqRkZFxRo8lhBBCiNpzrsa056tzPW4cN26c1r60tLQzcoyzGYOWKi4upmvXriQnJ/Puu++elWMKcSJJoAlRgaKiIu2D+YorrsBqtWp/K/2yKPu/9u3bM3jwYJ544onTTi4sX76cKVOmMGXKlDP2pSdETQ0ZMoTY2FhcLhfTpk2r6+YIIYQQohqqimlL/fDDD9xyyy306tWLtm3b0q1bN8aOHcucOXNwOp1nu8lCVMhqtXLllVcCMGvWLIqKiuq4ReKfyFDXDRDiXDR//nxyc3MBtA/qqpSUlHDgwAEOHDjA999/z6JFiwgNDT2lYy9fvpz58+cD0K1bNxo0aHBK+xGiNhmNRkaPHs2MGTP48ssv+e9//0t4eHhdN0sIIYQQVagqpnW73TzwwAMsWrQo4PH8/Hw2btzIxo0b+eKLL5g5cyZxcXFnrc3i9DzyyCMUFBQAEBsbe0aO0adPH+bOnQtAQkLCGTlGRa688kpmzZpFTk4O8+fP59prrz1rxxYCZASaEBUqnd7XvHlzmjRpUul2b7zxBnPnzuX555/XevSOHTvG8uXLz0o7hTibBg4cCPgD7hODbSGEEEKce6qKaV999VXt+zwoKIg777yT2bNn8/TTT2tJkZ07dzJp0iR8Pt/Zbbg4ZcnJyXTp0oUuXbpgMpnOyDGioqK0Y5zNBFrjxo1p3rw5wD+qDI04d0gCTYgTHDlyhG3btgFw4YUXVrlt27Zt6dKlC6NHj+aiiy7SHj9+/HjAdoWFhbz22msMGTKElJQUOnbsyJVXXsknn3yCqqoApKWlkZycrI0+Axg/frw2TXT9+vXljp+WlsZtt91Gx44d6datG//73/9Oaaj93LlzGTRoEG3btmXkyJGsXbs24O/Lly9n4sSJ9O/fn44dO9K2bVv69evHgw8+GDDN9Omnn9bau2zZsoB9TJ8+XftbaY8VwOHDh3nkkUfo168fbdu2pUePHkyePJm9e/dWq+0bNmxg0qRJDBo0iC5dutC2bVt69erFnXfeyc6dO2t8LU6mbB21zz//nKlTp9KrVy86derEXXfdhd1uJy8vj3vvvZfOnTtX+bp89913jBs3Tmv3gAEDePLJJ8nMzCy37dq1a7n88stp164dF198ccA1rEh1r6vP52PatGkMHz6clJQU2rVrR9++fbn55pv5/PPPA7Zt164dYWFhAOVeXyGEEEKcW6qKaTMzM5kzZ472++OPP86tt95Kjx49uPLKK5k9ezYGg3+y0ubNmwM6h8vWAMvKyuLee++la9eudOzYkcmTJ5OXl1euLcuXL2fChAl07dqVtm3bMnjwYKZOnUpJSUm1zqVsDeCdO3cyduxY2rdvzyWXXMJ3330H+OOqYcOGVRrPAmRlZfH0009z8cUXa3H8uHHjWLx4cbltHQ4HTz/9NBdccAEdO3Zk4sSJVZZXUVWVL7/8kquvvppOnTqRkpLCyJEj+eCDD6qVgCz9t0BycjLjxo1j3bp1XHbZZaSkpDB69Gjt3wIfffQRAwYMoF27dlx99dXl4t3KaqAtWbKEMWPG0LlzZ9q2bcuFF17ImDFjeOmll7R/jwB88sknXHbZZVrMf9FFFzFhwgTeeecdbZvKaqCVPfbOnTt56qmn6NGjBykpKdx4442kp6cHtNXn8zF16lR69+5N+/btGTduHDt27KiyjlvPnj0B2LZtG0ePHj3pdRWiNskUTiFO8Msvv2g/t27dulrPOXz4ML///rv2e2nPCPiHwV999dXs27cv4Dlbt25l69atpKam8uqrr9a4nQUFBVx99dVkZWVpj3366adERETw3//+t9r7effddwOSKrt27eK2227jxx9/1JIlK1eu5Mcffwx43pEjR5g3bx4rV65k4cKFREVFMWLECC0Y++6777QRS+D/0gb/VMChQ4cC/i++CRMmYLfbte1ycnJYvHgxK1as4IMPPiAlJaXK9v/666/avktlZWXx3XffsWLFCr788kuaNm1a7etREzNnzuTQoUPa79988w12u538/Hy2bt2qPV7R6/LSSy+VK4CalpbG3LlzWbp0KR9//DENGzYE/PfkTTfdhNvtBvz325NPPllp8dqaXNdp06bx5ptvBjz/6NGjHD16lIKCgnLTPVq1asW6devYunUrXq8XvV5f7eslhBBCiLOnqph2xYoVWlwRExPDiBEjAv7eqFEjBgwYoMVYy5cvZ9CgQeWOMWbMGA4fPqz9vnjxYgwGAy+//LL22BtvvMHbb78d8LwDBw4wZcoU1q5dy/vvv1/tkVIFBQVcd911WpJu//79TJ48mYkTJwbUaK0onj18+DBjxowJiJ3dbjepqamkpqaybds27rnnHu1vkydP5qefftJ+//HHH9mxY0elSb8HHniAr776KuCxXbt28eyzz7J582Zee+21ap0jwMGDB7n55pu1Dtjt27dz8803c8011/Dee+9p2/3666/ceuutLF26VEt4ViQ1NZXJkycHJPKOHz/O8ePH+eWXX/jvf/+LwWDgq6++4rHHHgt4bmZmJpmZmezbt4+bbrqp2udw++23B9wbq1at4p577uHjjz/WHnv22WcDErmpqamMGzeuylI4Ze/lX375hWHDhlW7TUKcLhmBJsQJyiaTGjVqVOW2AwYMIDk5mYsvvljrURk1ahR9+vTRtnn11Ve15FmLFi2YOnUqTz/9tPZl/s033/Dtt98SGxvL3Llz6d27t/bcRx55hLlz5zJ37txygY/dbickJIQpU6Zw5513ao9/+umnNT7fm266iWnTptGyZUvAX3C27BS9Xr168eSTTzJ9+nTmzJnDu+++yw033AD4v3xLRyq1b99eu2Y//fQTLpcL8Acs27dv1/YVERGBqqo88MADWpLnhhtu4L333uOee+5Br9dTXFzMgw8+GNAjVpF27drx6KOPMm3aND788EPef/99LfhxOBzMnj27RtejJtLT07n33nt57bXXsNlsgD842LNnD08//TSPP/64tm3Z12XLli1a8sxsNnP//fczbdo0unfvDvgTgE888YS2/QsvvKAFuT179mT69Onceeed7Nmzp1ybanpdv//+ewBCQ0N56aWXmD17Ni+88AJXX301MTEx5fZf+voWFxdz5MiRU7twQgghhDjjqoppy/6tefPmFSZfSuNCoMKYA/x1gF966SUee+wxjEYjAN9++61Wg2vr1q1a8iwmJoZnnnmGd999l759+wKwcePGGsVqdrudpKQkpk2bpiVOVFVl2rRpDBgwgBkzZtC5c2egfDz7xBNPaMmzbt26MW3aNB588EHMZjMA77zzDlu2bAH88Vxp8iwoKIiHHnqIt956i+jo6ApH2H333Xda8qxx48a8+uqrTJ8+nQ4dOmjX5Ntvv632eR47doyePXsyc+ZMLrjgAsB/rd977z2uvPJKZsyYoU3JTU9P5+eff65yfz/++KOWPLvrrruYPXs2r732GrfccgvNmjVDURTAv6AEgMFg4IknnmD27Nm8/PLL3HDDDTWuy5yTk8MTTzzBSy+9pCXEfvnlF3bv3g3Avn37+L//+z8AdDodt912G9OnTyclJaXcSLWyyt7Lld2XQpwpMgJNiBOUFloFarwQgKIoKIqC2+3GbDbj8/kChoS/8sortGjRAgCn08lTTz0FwKJFixg6dChdunThiy++0LZv0aIFXbp0qfR4r776Kq1atWLQoEF8/fXX7Nu3j9zcXAoKCggJCalWmwcMGKAlnEpKSrRRUgcPHtS26datG9OnT+f999/n6NGj5Xreyo6+GzFiBFOnTqWwsJCff/6Z/v37a0PrAUaOHAn4a2r88ccfgH9U04ABAwDo2LEjKSkp/Prrr+zZs4dt27bRtm3bStvfoUMHNm3axKeffsrhw4dxOByVtq22DRkyhBtvvBGABQsWaIHWhAkTtJFbc+fOZffu3QGvy9dff63tY+zYsVoyskOHDvTp0weXy8XPP/9MXl4eXq+XzZs3A2AymXjttdcIDw+nX79+7Nu3L2BfUPPrWhrsWiwWEhMTSU5OxmKxcOmll1Z4zmXfE7m5udooOSGEEEKcW6qKaQsLC7WfIyMjK3x+2cfLbl/W448/zsUXXwz4ky+rVq3C6/WSnp5Oy5YtA+KUyy+/nKSkJACuvvpqLW76+uuvufnmm6t9Xi+88AJJSUnExMTwzTffAP445sUXXyQ4OJiSkhI2bdoE/BXP5uXlaUkmk8nEm2++SUREBOBPVpWO6lq0aBHt27fXOhjBH6tdd911ADRr1ozBgweXa9PChQsDti9ddOGKK67Q4riFCxdqszBOJigoiJdffpng4GAcDgfr1q0D/AX7n3rqKRRFYe/evbz44osB51mZsgnSRo0a0bJlSyIiIhg6dCiTJ08ut53RaKRRo0a0a9eO4ODgciMUq2PSpElcffXVAGzatIlPPvlEa2vz5s35/vvvtQ7dgQMHMmnSJAA6depE7969Kx3pd2IsKsTZJAk0IapwstFPb7zxBtHR0eTm5jJt2jS2bdvGV199RVhYGA899BA5OTnk5+cD/i/20uQZEDA18cCBAzVuW3BwMK1atdJ+L7siYunotOro1q1bhfso7Tn0er1cf/312giyipSdKjhy5EimTp0K+Kdt9u/fXxv+b7PZ6N+/P+Afcl9qx44djB07tsJ97927t8oE2l133aX1lp2sbbWt7GtYOqIQCGhvaXBW2paQkJCA17vsPiIjI2nYsCF79+5FVVUOHToUMNQ+MTEx4DVKSUkpl0Cr6XUtDeyOHTvGVVddhaIoNGzYkB49enD99dfTuHHjgOed7D0hhBBCiHPPid/fwcHB2s85OTkVPqfs42W3L6tr167azyfGohAY406fPp3p06eX28eJZU6qEhoaqiXhyh6vcePGWhvLxl6l8ezBgwe1a5CYmBiwTbt27bSfS9tbduph2b8nJSURFhamxfcnPg/8NYErUt36vieeT9kYs02bNtposYrOszIjRoxg9uzZuFwubeZKVFQUnTp14pprrtHqil122WV8++23OBwOJkyYAEB8fDxdu3bluuuuC7gWJ3Oyf2OUvcYnxtRNmjSp8t8eQtQVmcIpxAlOTHhUpbT46MCBAwPqBVQ0RLv0y662lP0yhcCepZokOcr24pStZ1W6j19++UX7AouJieGFF15g7ty5AXXbyh6vUaNGtG/fHvD3RB48eJDffvsNgEGDBhEUFFTttgHlRpSVdeTIES15ZrVaeeyxx5gzZ05ALYUzmfApm6TU6f76OK0syKxOW2pyn5zOPVV6Xa+88kreeecdRo0aRYsWLTAajRw6dIhPP/2UcePGlXsPlP297HtFCCGEEOeWqmLasvVhd+/ejcfjKff8ssXpmzVrVuExysajpxqLejwerezHyZSNvcrGQWcr9jqV7UtVFdOeqLZjzBYtWjBv3jzGjRtH+/btCQkJITs7m2XLlvHvf/9bq5fXq1cvPv74Y/71r3/RunVrLBYLGRkZfP3114wbNy4g6XUyZf+NcbJ7oybXtGzyUmJRcbbJCDQhTlA2oDh48CAdO3as1vPKfhmUfrBHRkYSGhqK3W6nuLiY3bt3awsMlC0yX9qTBoFfIOfCkuHHjh3Tfh4xYoQ2ta90yHxFRowYwZYtW7Db7QG1vEqnbwIBI5u6desWkPQq5XA4sFgs1WrbRRddxDXXXAOgDZU/VyUlJbFq1SrAfx8MGTIE8A9DL12UQFEUEhMTAwLaw4cPk5+frwWrpXU6yqrpdVVVld69e2u19zweDy+++CIffPABWVlZ/PrrrwE1/UqnCFit1rO6bLkQQgghaqaqmLZPnz4YjUbcbjdZWVksWrQooHzD4cOHA6Yxlk7TrKmkpCRWrlwJwHPPPcdll11WbhuHw1HtRQROVWJiIoqiaCP8c3NzteRLRTF52RIVv//+uxarHTx4sMIaaElJSdoIsw8//FCra1tWTRJotU1VVZo3b84jjzyiPbZkyRImTZqEz+dj+fLldOrUCVVV6dixo3av+Hw+PvjgA55//nkcDgcrV66sdHZDTSUmJmo/l3a2g//fUVWNSiy7gFdliV0hzhRJoAlxgk6dOmk/b9++vdJaUOD/Qs3IyCAvLy9g5Z/SL1+dTsfQoUO1Of/33HMPt99+O/n5+QFLPg8fPlz7uWxP3sKFC9Hr9eh0uiproZ1JZZMkS5YsoXPnzuTn5/PKK69U+pxhw4bx/PPP4/F4WL16NQCxsbFaEVTwF6Zt0aIFf/zxB6mpqdx3331ccsklGAwG0tPT2bp1K8uXL2fDhg3Vatu6detYtGgROp2uRqsc1YXhw4dria25c+cSFxdHo0aN+OCDD7Qe2F69emnD3du3b8+WLVtwOp3cddddjBs3jp07d1Y40rGm13XSpEnYbDY6d+5MfHw8Xq83oG7ciT3Cpb3RKSkpsgKnEEIIcQ6rKqaNjY3l2muv5f333wf8tcyOHj1Khw4dSE9P5+2339Y68VJSUk45gTZixAg+/PBDwJ9Ay8/PJzk5GbvdzqFDh1i9ejUJCQk899xzp3iW1RMREUGvXr1YtWoVLpeLyZMnM2HCBA4dOsRHH32kbVcak/fv319bLXLu3LnEx8eTkJBQ4RTU0vMsTTjed999TJw4kaSkJHJycjhw4AArVqygd+/e3H777Wf0PCvzzjvvkJqaSt++falXrx5WqzVg4YHSeO/pp58mKyuLnj17Uq9ePfR6PRs3btS2K13UqjYMGDCAl19+GVVVWbp0KW+99RZt2rThww8/rLT+GRAwtbPsPS7E2SAJNCFOkJCQQJs2bdi2bRtr166tctuyq1+WUhSF2267Tfv9v//9L6mpqezbt4+dO3eW++IcNmyY1qsF0L17dy2YmTdvHvPmzQP8y2DXhfbt25OcnMyuXbtIT0/Xzq1Tp05kZ2dX+JzIyEh69uyp9TiC/zzLDkFXFIXnn3+eCRMmYLfbWbBgAQsWLKhR2+Li4ujbty8//fQT+fn53H333VrbyvZOnWs6dOjAjTfeyLvvvovT6SwXNMbExARMCb7vvvuYMGECbrebn3/+WQt4kpKSytXPq+l1LSgoYOnSpcyfP7/c36KjowOSnr/99ps2urKipeyFEEIIce44WUx79913k5GRweLFi3E4HLz++uvltmnRogVTpkwJiOFqIiUlhVtvvZW3334bu93O888/X26b0aNHn9K+a+qxxx5jzJgxZGVlsW7dOq0wf6mbbrpJK0NSOjp/5cqVOBwOra5ZZGQkISEh5WqODRkyhJ9++omvvvqKjIyMgJXYS1100UVn5sSqwePxsGrVKm0GRFk6nU77t0hJSQlLlizR6heXFRQUpC1OVRsaN27Mtddey5w5c/B6vbz55puAf5pq/fr1K12Jc82aNYC/lE69evVqrT1CVIfUQBOiApdffjkAf/zxR7UK/Ov1eqKjo+nXrx/vv/8+l1xyifa38PBwPvvsM/7zn//QuHFjTCYTVquVdu3a8fjjj/PKK68ETNvs168f999/P4mJiRUuKX626fV6Zs6cyYABAwgJCSEyMpLx48dXWiC1VNnpmhX9Dv5CqF999RVXX301DRs2xGg0EhoaSosWLbj66qurtaz5iy++yOjRo4mIiCA0NJRRo0ZV2jt4Lrn33nt5/fXX6datG8HBwRiNRurXr8/YsWOZN29ewNSBLl26MHPmTNq0aaNtd88991S6YlVNrus111zD0KFDSUxMxGq1YjAYiIuLY8SIEXz00UcBNTiWLVsG+FevKl06XgghhBDnrqpiWqPRyOuvv85bb71Fv379iI6O1mKGTp068dBDD/H5558THx9/Wm248847mTFjBhdddBHh4eEYjUbi4uLo3Lkzd999N3fcccdp7b+6GjZsyLx587j22mtp0KABRqOR4OBgunbtymuvvaatSl/qjTfeYOzYsYSHh2OxWOjVqxf/93//V25F01IvvPACL7zwAt26dSMkJASj0UhCQgI9evTgkUce0UqN1IU+ffpw1VVX0aJFC8LCwtDr9YSHh9OrVy9mzZpF586dAf9IutGjR9O4cWNCQkLQ6/VERUVx8cUXM3fu3Fpfff3BBx/kjjvuIDY2FrPZTJcuXfjwww8DrnHZci779+9n9+7dABVOBxbiTFNUWVJNiHKKiooYMGAAubm53Hjjjdx777113SQh6pTb7aZ///5kZmYyZsyYCntWhRBCCHFukZhWnMtUVS23gEBubi79+vXD4XAQGhrK+vXrtRGQL774IrNmzSIyMpLvv/8eq9VaF80W/2AyAk2ICthsNm688UYAPvvsM4qLi+u4RULUrcWLF5OZmYnJZGLixIl13RwhhBBCVIPEtOJcNmvWLF555RU2bdrE0aNH2bhxI5MmTdIWXLjkkku05FlxcTGff/45ADfeeKMkz0SdkBFoQgghhBBCCCGEOKumTJnC1KlTK/xb06ZNmTt3rrZaqhDngrovsCSEEEIIIYQQQoh/lG7dutG3b1927NhBTk4ORqORpKQkLr74YiZMmIDNZqvrJgoRQEagCSGEEEIIIYQQQghRBamBJoQQQgghhBBCCCFEFSSBJoQQQgghhBBCCCFEFSSBJoQQQgghhBBCCCFEFf72iwhkZRXUdRNOi9Gox+321nUzzjo5738WOe9/Fjnvf5ay5x0TE1LHrRHi3Ha+x60n80/9HAQ5dzn3fx45dzn380114lQZgXaOU5S6bkHdkPP+Z5Hz/meR8/5n+aeetxCivH/y54Gc+z+TnPs/k5z735ck0IQQQgghhBBCCCGEqIIk0IQQQgghhBBCCCGEqIIk0IQQQgghhBBCCCGEqIIk0IQQQgghhBBCCCGEqIIk0IQQQgghhBBCCCGEqIIk0IQQQgghhBBCCCGEqIIk0IQQQgghhBBCCCGEqIIk0IQQQgghhBBCCCGEqIIk0IQQQgghhBBCCCGEqIIk0IQQQgghhBBCCCGEqIIk0IQQQgghhBBCCCGEqIIk0IQQQgghhBBCCCGEqIKhrhsghBBCnK7c3FxmzZrOmjU/k5ubQ0hIKM2aNWfChBtJSelQrX3MmjWDVatWMHv2R1Vut2/fXmbNms6uXTvJyDjKpEl38a9/XVMLZyGEEEIIIYQ4V0kCTQghxHnvkUfuw+1288gjT5CQUJ+cnGw2bdqA3Z5f68dyOktISGhAv34XM2XKq7W+fyGEEEIIIcS5RxJoQgghzoicHMjLUwgPV4mMPHPHKSgoYMuWX5kyZQYdO3YGID6+Hq1bty233Vtvvc7PP6/A5XLTsmUr7rjjLpo3b8G3337N+++/A0CvXl0AeOihxxg6dES547Vq1YZWrdoAMH361DN3YkIIIYQQQohzhiTQhBBC1CqHAxYuNLB+vZ6iIgWbTaV7dy8jR3qwWGr/eBaLBYvFyqpVP9GmTTtMJlOF2z366P2YzWZefvlNbLZgFiyYx+TJt/Dxx/MYMGAg+/btZf36Nbz++tsABAcH135jhRBCCCGEEOclWURACPG3p+Rko9+3ByUnu66b8o+wcKGBxYsN6PXQoIEPvR4WLzawcOGZ6bMxGAw8/PBjLF78DZdc0o9bbrmBGTPeYs+e3do2W7ZsZseObTz11Au0bNmahg0Tuf32yQQHh/Djj99jNgdhsVjQ6w1ERUUTFRWN2Rx0RtorhBBCiPODxJBCiLJkBJoQ4u/L4cC8cD7G9WtRiopQbTbc3XvgHDmaMzIUSpCTA+vX64mJUYmJUQG0/6am6hk40EN8fO0ft2/fAfTo0YutW39l27bfWbduDR999CH33/8IQ4eOYM+eP3A4HAwbNiDgeU6nk/T0tNpvkBBCCCHOXxJDCiEqIAk0IcQ5QcnJRpeXiy88AjUyqlb2aV44H/PiRXhjYvE1aIBit2NevAgA51WyauKZkJenUFSk0KCBL+Dx0FCV9HQdeXnKGUmgAZjNZrp2vYCuXS9gwoQbef75p5g1awZDh47A4SgmKiqaKVNmlHtecHDImWmQEEIIIc5LpxtDltaBjY0FqQghxN+HJNCEEHXL4cD45QJMq1fXag+fkpONcf1avDGxqDGxAKgxQXgBY+o6XAMH11qiTvwlPFzFZlOx2xVt5BmA3e6vhRYerlbx7NqVlNSYVat+AiA5uSU5Odno9Xrq1UuocHuj0YjP5z1r7RNCCCHEued0YsgT68CGhip06eI+Y3VghRBnl9RAE0LUKfPC+Ri/+RpVr8fboAGqXo958SLMC+ef1n51ebn+hFxoaMDjamgoSlEhurzc09q/qFhkJHTv7iUrSyErS8HpRPu5WzfvGVmNMz8/j0mTJrJkybfs2bObI0fS+eGH5Xz00Rx69eoDQJcu3WnTph0PPngPqanrOHr0CL/9toUZM95i587tAMTHJ3D06BF2795FXl4eLperwuO53W52797F7t27cLvdZGVlsXv3LtLSDtf+yQkhhBDirDqdGPLEOrA6nXpG68AKIc4ueScLIepMaQ+fLyYONSoaqL1RYr7wCFSbDcVuR435qxi8Yrej2oLxhUfUximICowc6QH8Nc/S03XYbCpDhni0x2ubxWKldeu2fPrpRxw5kobH4yE2No4RIy5l/PjrAVAUhZdffoOZM9/m2WefIC8vl8jIKDp06EREhD+r17dvf1au/IE77phIYWEBDz30GEOHjih3vOPHs7j++rHa7x9/PIePP55Dhw6dmDp15hk5RyGEEEKcmtLplOHharU68k41hqyoDqzVCj6fqtWBPRMdiUKIs0dRVfXszaepA1lZBXXdhNNiMulxuf55U4rkvP8Z9Pv2YH3hWWiUiNdg+usPzhL06WkU3/cQ3ibNTnn/5k8/0upXqKGhKHY7+qxMnEOGnxM10P7ur3dlAevf/bwrI+cNMTFSb06IqpzvcevJ/FM/B0HOvS7O/cTplDabSvfu3mpNpzyVGHLfPoUXXjDToIEPs9n/mF6vo7jYR3q6jvvuc9Kkyd/6n94B5J6Xcz/fVCdOlSmcQog6o/Xw5dsDHq+tUWLOkaNxDhmO4vOhT09D8fn8gc/I0ae1X1E9kZHQpEn1enuFEEIIIWrTidMp9XqqPZ3yVGLIsnVgy6qLOrBCiDNDpnAKIeqMGhmFu3sPLEu+QfH5yvXwqZFRp7c6p8WC86prcA0cXOsrfAohhBBCiHNTRdMpS/9bremUpxBDltaBXbzY/0/s0FCVwkJ/LdghQ2T6phB/B5JAE0LUKefI0RgMOpQ1a9Clp6Hagv09fIMuwfzpRxjXrz3t1TnVyCi8kjgTQgghhPhHyMtTKCpSaNDAF/B4aKhKerqOvDyFyMjKR4SV7cCtSTmRE+vAhoRwRuvACiHOLkmgCSHqVI7DwuFu1xLSYSBRyl89fGVrT/gaNECx2zEvXgRwTtQvE0IIIYQQ56ay0ylLR55BNaZTOhz+FeJPsQPXYoGrrvIwcKCHvDyF2FgdwcHnZz0oIUR5kkATQtSJsoVdHQ4dFksC3bvHMXKkB+ufq3N6Y2JRY2KB2ludUwghhBBC/L1FRkKftpmsX2ynuCQCfWwkdrsSMJ2yosWOzAvn10oHbmQkREaqmEzgcp2JMxRC1AVJoAkh6kRpYdeYGJWGDVVyc9FqRlzTNRelqAhfgwYBz1FDQ9Glp6HLy5UpmUIIIYQQorw/R5FdvWUtvQuLSd8Uwm9hF7KlxeUMGWJi0CAPn35afnXOSy86RrB04AohqiCrcAohNDk5/iW4c3LO/HHKFnY1m9F+Tk3Vk63+uTqn/cyszimEEEIIIc5vSk42+n17UHKyAx4vHUWmM+mp37M+HTrDtcFf8VTKJ1x1lYelSytenfPHLwv80zZDQwP2p4aGohQVosvLDXj8bMXNQohzh4xAE0IETKcs2xM3cqSnpvX6q+VkhV1zlGjiuvfwD6GHClfnPNtOazVQIYQQQghRO6qoU6Y4isuVATE1DEIJAsv2daTtHcz69QkVrs65dlc0Iww2THY7akyQdjglMxO8PlRVO3y14mYlJxtdkR3FFiqxoxB/E5JAE0IETKds0MCH3a5o0ymvuqr2Vw2qTmFX58jRgH/IfMDqnH8+ftacZjFZIYQQQghRe6qqU+bp2q3KMiBFh/MoKqof0IlrdWTT1JfLnqIosjv3pMHGr/0duFYrhq1bMBw8gC8uDutbb+Du3oMvXFey+PugyuPmMrGj3lGMwWKt9dhROnaFqBuSQBPiH+7E6ZTwV09caqqegQM9WmHV2hIZCd27e7VgIyICcnMDC7uCBedV1+AaOLhOA4TaKiYrhBBCCCFOj3KShabcXbppZUACRpH9WQbE1jBc68StF15Myq4vaZy+BrWgkF6GYKzdO+AcMAjj5l8wrP4ZfdYxvA0b4e7cFaW4CGXBInRFRmJaXEtMjIrDAQYDhISoWtxcb1mZ2LFhQ9TcvNqLHaVjV4g6JTXQhPiHK51OGRoauJx3aKhKUZFCXp5yRo47cqSHIUM8+HyQlqbg88GQIR5Gjgwc8aZGRuFt0qzOpm0GBGnmINSYWLwxsRhT15WruSHqTm5uLi+//ByXXTaMfv16MHLkYO6663a2bt1c7X3MmjWDCRNOHtguXDifW2+9kUsu6ccll/TjzjtvZfv230+j9UIIIYSoDl1ebpV1yhQF3N17oM/KRMnKBGcJSlYm+qxM3N0uILxpFN27e8nKUmi8cR6t/lhIsVPPYSWR6DgdkeuWAuBJaowuLxfVYEJxODAc2IcaEUlxSByNj6whRnecbdt0rFxpYPVqPb/9pmf7dh3Hd+VosWNxcCy5xUEUB9de7Fjasavq9XgbNEDV6zEvXoR54fzT2q8QonpkBJoQ/3DVmU5Z0TLfp8ti8Q9zHzjQQ1GRAZstcKTbuTA0vTRIk9VAz32PPHIfbrebRx55goSE+uTkZLNp0wbs9vxaP9avv27i4osH065dCiaTmblzP+Cuu25nzpzPiPmzN1wIIYQQtc8XHlHlCDNfeMRJy4CMHOkhqCibpu+uJsMXhyMojqSmPhKTo/Hm+gj68jNABZ0OX2QkiteLfvcfABjqNyeYoxzeksduexw2m7/TOS9PITNTYdsqOx3sRewsTuTgMQMej4LBoKNRXDgtrYdOK3Y82eg7WSVUiDNPEmhC/MOdOJ0yNNSfTMvKUhgwwMOyZWd2cYHISIiPV3G5/nzgHBqaXp0gTVTubCVBCwoK2LLlV6ZMmUHHjp0BiI+vR+vWbctt99Zbr/Pzzytwudy0bNmKO+64i+bNW/Dtt1/z/vvvANCrVxcAHnroMYYOHVHueI899nTA7/ff/wg//fQDGzemMmTI8DNxikIIIYTAPzPBXY2FpqoqA2KxwGV9szCsslMQ2RBzaJm41mhCl56Ou00blIJCFI8X1WYDQJeehjU0DEuslW1p0ehCwGRSKSkBnw8SE1U2H4qm29EQMjIK0EVaCQ31/z1jVwHmhiHEVxI7VhUzlf6NvDzp2BWijkkCTQihTZtMTdWTnq7DZlMZMsSDy8VpLy5Q09Fr51LNseoGaeIEZzkJarFYsFisrFr1E23atMNkMlW43aOP3o/ZbObll9/EZgtmwYJ5TJ58Cx9/PI8BAwayb99e1q9fw+uvvw1AcHBwtY7vdJbg8XgIDQ2rpTMSQgghRGWqu9CUGhlVaULJFx6BMcJGhC4f1fLX6HFddpb/7wn18fpUDHv+wOMFNwaCCrLRZxzF1nMYTZdlk+WFnIIojEZo1sxH48Y+Dh2K5idPL3qxEJeq4iScUDUPE1msZiR9iSIgHHY4CPpoDsZ1q8HjQY2IJLdND9J6XEZ4uEr8qjLxlEGP7lgGPqsNtV49bRfSsSvE2SMJNCFEwHTK0mQXwNNPm095cYHqLvFd1rk4NP2cWQ30PHK2k6AGg4GHH36MF154hq++mkdycjIdOnRmwIBBNGvWHIAtWzazY8c2vv56mZZgu/32yaxa9RM//vg9o0ZdhsViQa83EBUVXaPjv/32FKKjo+nSpVstn5kQQgghyrGc/kJTlXWSKgWF+BISUNxunM1akp6uoEtLx+zMpVBvID+uHrEHfuW+olQc+mAONLiQ31tdhiHEQlaWgsGgsjLuCqKiVJofW0O4/TBOg40dySP4yXYFHfIUIiP/LJnicGB7+D5MP/0ABgM+i41jbjuZP2az9nMjQWa4uOgbYtvFoPsznlIKCzD+tgW3QS8du0LUAUmgCSE0kZFoX+r79vkXFyi7zDf4p3imp+vI+zMAqGzI+cKFBhYvNpAUcpwWwTlkuCJZvNifmKhs9No5WXOsFoK0f5LqJEGJr/06YX37DqBHj15s3for27b9zrp1a/joow+5//5HGDp0BHv2/IHD4WDYsAEBz3M6naSnp53ycefMmc333y9lypQZmM3m0z0NIYQQQlRTVSPMqqPCTtJRo8Hlwvz9Uvbu0bGjsCXxkRFEu9I5bohHf6gIjykGc/M4cncV0H7/Qkwmle/rjyMrS6F3bw+//x7E9/px7EkeQrArj0JTOAcLozH6IDzcqR0/6OM5mH/6AZ/VihoeTu5RF2pOBpZguKDoB9x5Kjvd8WTnxNCmng81Jgh3u/YY9u9DKS5GV2CXjl0hzjJJoAkhKnSyxQUigooxf1rxNL0ch4VfVrsYlf8xKYdXY3YXUqwLZmPQhaz98XIGDtRVOHrtXK45drpB2j9FdZKgZyKBBmA2m+na9QK6dr2ACRNu5Pnnn2LWrBkMHToCh6OYqKhopkyZUe55wcEhp3S8jz6aw9y5s3n99be1kW5CCCGEOHcFlhappJPU4aDQZSJr1gYachjFamNno5HUP/YLRcZY9hTE0aOtB68xhvw9ELdnLdbIoQwZEs7IkR4WLuTPkifRRETEkJurkpWlMGTIX7M3lJxsjGtWoxoMqGHheFQDOU4TQWaFMG8OuhIjPr2RvLAE0tN1NGniw2IBNTISn6OYkhtvRg0Ll45dIc4ySaAJISpU1eICQ4Z4iF9V+TS9vK5jaf/Hl3TLW0ChLZZDzkTUfDutnF9z8KDC3LljuPFGNxaLP5DxT/GEyMgoPG3bYV68CE9JCWpsrAxNP89UJwmqP0ttSUpqzKpVPwGQnNySnJxs9Ho99eolVLi90WjE5/NWa99z537Ahx++xyuvTKVly9a102AhhBBCnBFVlhY5sZPUYuFwv7HMXjWK5lHZeEIjsJbk0vjIGhxBwZgKc/AVGmnTxkJJfRvqoXSa3ZBBSCd/7dSytYXT0hQsFn9t4dLHwd/hiMeDarWhOJ149Ea8XvCZzJgc+bgNFpymEMLJI90VT0mJfz+l8ZS3UeNqx8Xnwsr2QvxdSAJNCFGpyhYXuPSiYxhfrnyaXnTzbqTYV5NjiOOoI47sPAWTyQIqdHOv4bMVQ/giPAyTCdav1+Nw6Ag1uhjvfZ+uBStR8vIwpafjCwvD26KlDE0/j9TFwgv5+Xk8+ugDDBs2kqZNm2O1Wtm5cwcffTSHXr36ANClS3fatGnHgw/ew623TqJhw0SOH89izZqf6dOnHy1btiY+PoGjR4+we/cuYmLisFqtFS5I8H//N5tZs2bw2GNPU69ePbKzjwNgsVixWq21fn5CCCGEOD2lpUWquzBWeLiKGhnJXl0kMRYVg8dBSOFRYu1b8CkGIn81QHYDLFFRKOF6dOThy8lGjYzSagt36eIhI8NIfLybpk0D9+9PZkXiy89DfywDoxmMigVzcS461c3uRgNwmoJpvu1rooMULIoNJauG8dQ5tLK9EH8XkkATQlSqosUFIiNBv6/qaXpR9kN4wwrYdLwR2XYFgwFUFXLVMJKth2gelc2XX0ZjtarUr6/SpF4xl3x3Fy3TluMONmCKteKLjAKTCU9Kh7O++qY4PWd74QWLxUrr1m359NOPOHIkDY/HQ2xsHCNGXMr48dcDoCgKL7/8BjNnvs2zzz5BXl4ukZFRdOjQiYgI/3yKvn37s3LlD9xxx0QKCwt46KHHGDp0RLnjffXVl7jdbh555P6Ax6+//ib+/e//nJFzFEIIIcSpycnxd9jWZGGsE2dipKT/hL64EJ27BGNkGHqdD3XzZnD78DVMxDrjbS1BlT9oNAuWhmidxDG6XHq2PE6/y0MwJ/gTX1qHY1YmXuIx5OUSxzFKXF62NLiY1cnXYbcrHIlQGBz8M7acmsdT59LK9kL8XUgCTQhxUlFkE0MuPiJQiap0mp4r047LG0xhaCIJLazkqXkcy7Pg9YLBAA2C8zCH2XDaIjhyREfHjl5iYlR6bp5N+2PLcZisHPcFE+QqxJCVhTchAcMvGzF06lThUPXAOhZn+6qISp3lhRdMJhMTJ97OxIm3V7md1Wpj8uR7mTz53kr38/TTL570eF988fUptVMIIYQQZ19eXvUWxjpR6UyM7avyiN+zlgORHUi0HKe++wAlh7MxFuSiU73kOq2UuCKITrFhXryInev1LM65juaWw/TP+ITIo9vx/Owh/3srDcd010aAle1wVHOyUesb2aC7iE+t1+M4ZsVmUzHfNIbwi/pTXFKzeOpcXNleiL8DSaAJISpXxdDvstP03NZQDm0tpORgNmtiR7Lx/9owXt+LruELyItSKNSHEWPKI9yZye8NR3KoKBpQiYryYXVk0/jwKlRFj1FxYy1KQ+/zoCg+DLk5qAf2+5MwcfHasR1YKq9jccKIdKn7UHdk4QUhxD/dsWPHeOmll1i1ahUOh4NGjRrx7LPP0q5du7pumhD/GCdbGCs8vHzyrLSTduBAD0ObZxCam4/SqAFB4XFkfOskyJmPTW9Gp6h49Gb0B/eTqTcS2ToW45qf+Vd0MRce+YLovH24DBaOmxLIOdKAxAWLMPPnCLA/OxztFw3mxy8LWLMzmmOeaAx6lbatvFx2mZuEBIAovNQsnjonV7YX4m9AEmhCiEqZF87HvGAevpAQvFHRKG73X0O/y/SaZaQeIS0zhMONRnIo5TL0xfB2xr+4NQESStZwfP9hsBjYG9+d9cH9KchTSEhQcbsVrGouOtWD3ufG4izApbdAkBmlyO7/4tfr8DRKCjj2p4w/eR0LqfsghBCiDuXn5zNmzBi6d+/OO++8Q0REBAcPHiQsLKyumybEP0rpdMwFCwzY7QpRUT7cbqXcyphQ8WIDfdrGcHW0DZ3bTt5RFU9aFi5TMCFOO16dGY81FKW4GMP+PeTERhGfu4N6JfsIcWTgUxT/AgRFGUQXHMAXmYRpzc8BI8C+WhXH4k31CQlRCQ5WMRVkc2RFLhusoYy64dQ+L6pa1KlEH8z+/EiCc5AZHELUkCTQhBAVyt+WTsx7/wf5mRhNelSjCW/9BnijorSh386rruFYl8G881wRjmaRWBtGogNibCoQxBzfeO5/rBeGt76A33YSdWgrozL3MuyCHuxoexlLVoZgC4mkRGdD9XrwqWA0gQ4fOJ2g6FANRhSHAzU8Ai/gW7mObYwkJiaqyjoWUvdBCCFEXXrnnXeIj4/nueee0x5r2LBhHbZIiL+nk5X0cDjA5fKv+r57twLoqF9f5fLL3Vx60TH0+/6aqVDRYgPzV8ZRP7QXLX5dSNrxQuILHTj0enw+Hy5LEIb8HIJK8jD5Sihe/SMGj5P9li7EufZi9BTj0QfhUQ0EeYswZx1Cpzi0EWA5ObBmjZ78fMg86GJAzud0KVlNmL4Q3WEbbrUbxmtq1vnrvx7RNGzTg6hVfy3q5DhqJ/O34/wQOoofpidUOYNDCFExSaAJIQKU9rxFzZrHwJ37yQ2KwhRhJcpYgmHPH3g8jSE09K8vfiWaffr6NIituK5E0OqVdPWsp7hbLMWmWKyufKw5C+ho9KIbMp7U1Ch2GNrQlB9Qg0Ow6J0oRSXg86EGmdE5ijGtXe1fsjsiAp/bRIhpP8aUyAqPl5enEMVxqftwDjCb/w+r9XWKiyfjdF5b180RQoiz6ocffqBXr15MmjSJDRs2EBcXxzXXXMO//vWvum6aEH8LFY0WqyghtHChge+/N9CihY82bVSOHFEozi6hxbrPiPr1Z22mQm6bHuxKHURbczFBwREUm//qrH1x51Vc4tFxgfEHgvQuXJg4oGtCXPFRQtV8vKoOr86ACRc6bwlqrh29twSfTo9bMYHqxmbwoccH+Xmof84azctT+OMPHTk5Ctd4PmdAyQKylDj2uBOpn3cM67xP0SuFlNxwc42vR7j5asZE6unqWM2x34+w83AI3ztH86PnCuLDwGqlypVIhRDlSQJNCBFg4UIDPy/I53b7dtxBwag6A8fzjKh6A7FW0B88gKdjJ3zhEUBgXYlGwcexluRSHBRBVmE0cYbjRG/+EdVsxhwdgsliBmLxZoFt8zqufngwXbpEkfv7lejf+gZLURZgxedTUY5nobjdqAYDvpBQdNnZmPbtwWC2MCLsbXZ4+rO/y2V4jP4IqWwdC6n7cC5wEhx8PzpdAcHB9+N0XgmY67pRQghx1hw+fJiPP/6Y66+/nokTJ/Lbb7/x9NNPYzQaGT264lX0jEY9inKWG3oWGQz6um5CnZFzr31ffqlnyRL/6pqNGkF+vsLXXxvJydEzbpx/RkJODmzcaCQuTiUiQmHnTh1paTouPjIP0+/fsKtdNM26JGLMzyHsg+ncmvkB3tAInH9EsKdRf35reQVBQRYOHLOxofM4nPWHsmfJbJIOrSJXjSDMfRyTUoJR8VBkiiCvXmuCs/aR4E7DpZgweZ1YPHmYcGFwe1FzXKjhEZhzj+M1JWM0gt2uI1Z/nK5Fq8kzxeEwRNIsbzv1HIcJOpqPcdZM9Hod7mvHVzkS7csv9az5Oo/E4Gz0sZEcdUXxZtYEEm3DKQ7KZXtINI64SAx6OHhQwWTyERensnGjgWHDVO165eYqRESc/gJdcs//M/3dz10SaEIITeky341CsrGZ3OSENyY6fx8oUJhvJtLgxVRYiDe5lTaCKzISenYspPiDr+he+D1h3lzy9RFssl5E10YZWLauw6M3odu1B6VeLL4GiahGE77M4yyeW8DS/Qk4HEkcstzAINdnxDQNRgkLJmjpd+ByotqC0dnz0dnzAFCCzETHKLTZsxCA3zuNxW4PrGPho/K6D6otWEv+iTPHZPoBna4AAJ2uAJPpR1yuS+q4VUIIcfaoqkrbtm256667AGjdujW7d+/mk08+qTSB5nZ7z2YT64TL9fc/x8rIudeenBxYvdpAVJSPqCgVtxuOHtWxf7/CH3/o+f136N3bS7t2Xux2/3TM7dt17NmjUM+UxYXqz2QqsRw5Gotrr48UTwbmY4dpWODG7o5Fb9cTm7Udxe1kkfFSUkoO0MHtpaCoCW/rbqOjJ56B7sX40JGhxuEMjiCrXgqu8Fga+iAhYwtB4SZMDif6YgeoKj6dHjxelOxsTI89Qsn4G/C0u4yQEBO2rByMzkIyLYk0tG+nkXMXLqMNV1gkhqJsDIu+xhNkLVeGpHSxrJwSC8qHa7j5uH/6p9MYzP76PfnWdgVLN8XQokUU9mwdoRYVnw90OpX9+xUSErzk5+s4dMjHN9/oA0bztWnjpUcPL/Hxp55Mk3v+n+nvfO6SQBNCaEqX+VaiInAag3Eabfj0RsLshwkqsaM63HgbN6Hkcv/0k9Iv7X/lLcGT8ykeuwOfTyFK2U2K8iMlx0LILDHjxoQtM4+wffsxhv+OGhzCMeJYVhCCPhEaNlTZGHQ5hb/B4Oyfae48ghoUhC86Bnxe9EePoBoMqFHReHUmQuvbcBBE82NrWL1vKGpkJEOGeALqWJRdJVQNDUWx29FnZeIcMvy0p2/Kyp4nZzYvDPjdZFooCTQhxD9KTEwMTZs2DXisSZMmLFmypI5aJMS552T1yypTGrM2aOAvIbJrl449e3QEBamAgsfjX2CqsBBsNpXMTIX0dB1Wq0q8LocgbyG5QYmEBUPWwRJ0udvQeVzojXqKlBAMKIQ6jtNr3Wu0UuYQ4TyG8QeVQ76GFLjHsiB4LL+7OvJ60U1EkIexuJB6hzaSkdGQg0Gx6EKbkxRbiH7PcZz6INw+IzqvC0Wnx2sLISgnD/OCeTQohBYtriffEEFxYTAhRceIdx3GabShWm2YvLlgNuONjAwsQ3LCYlmGAxn021tEekwHsoIbEuzNp+2ehdhjFX4qmYDNpqLX+5OMTid4POD1wi+/6ElO9rF2rZ5Vq/y13+LjfWzdquOnn0x8/rlK69a+06qVJnGz+Ds57xJoshy4EGdO6XTMDHc0++v3pO2ehRTY4jhiTCTEkUVkUj6eK0ajRkRi/vQjjOvX4jmWg3lTKjrVgzuhHmphMSZ7LkqhHWNBHplhTYlwZmJ0O3Cgx5TvxKDocZUU0ivuB3bHjEWvVzDUC+I3w7XsdQzhvit2Ev/5THxWK4rHg7J2Nd7QcPKyfRQXwubfgtGbFJraDnHbmAzC2+qIXzUf48tlVtzs2BnngEEYN/+CLj0N1RaMc8hwbfXQUyIre1aTG5Ppm4BHzOZFFBa+ARjrpklCCHGWderUif379wc8duDAAerXr19HLRLi3FHd+mWVKVtCJDhY1ZJjiqJgsUBCgo/CQoXt2/W0betl8WIDBQX+mRPHnFHYvcHEh+Shs8WiP5aLUlCAajBgMhkIs/jIK7HgdavEOA8SGmynKCqW/DwdMcUHuUk3A53HhFcFk86L0eei2GfDrPpoWfwr9qIQ1ne+mXqPJFM48X6Kc0uIIRPVaMZuiiJPjSC2uABbSAgR29cxoMMw5tvjOOrrSbfdHxHiyaVADSbam4VSlA9hNgy7doHFgi4jA29kVMBiWe6waFxpvxFU5MTlzmZfQQNCQiwkWaFpxhrijcPR6cLR6VRychTMZlAUUFU4fFhH06Y+tm3zT4WNiVHZtk3HsWM6rFZwOhXc7lOsleZwYP7iC4mbxd+Krq4bUBOly4EbjUbeeecdvvnmG+6//35ZDlyIWlK6zHd6usIC05VsqD8Sp0PFnH+ciFg9Su+euC7qg3nhfAzz53NoezG/7TTjzi/GWeDCm56FuSgP9Do8qh49HkLc2eh9blS9Hr2i4nV5yIluxp6QDrTJW43Vka0dPzRU5ZgnmmOJXXH17os+KxPV40ENslB4tAhnTjFZloZgtWBx5nMoJ4StadHEr/IHEapej7dBA1S9HvP3S8Fkoujhxyi+7yGKHn7MP+y9Gl/YOTmwb59CTk7g46XBSsBxFi/CvHB+Lb8S5zejcSU6XV7AYzpdHkbjqrppkBBC1IHrrruOLVu2MH36dA4ePMjXX3/NZ599xjXXyErQQpSudqnXQ4MGPvR6f5Jm4cLqje8ojVmzshSOHFEoKQGfT6GoCOrX92Gx+OPKoiKFCy7wMmSIB4MBcnIU8vRRpCX1pKHpGOa8TIJ0LnQ+D4rDgeJyEu1Ip4lvL5HqcfQ6H+a4UMIahqKEh5KtRGNRixngXMzFyvf8buzINn0HAMI9xwnx2YnTZdHk6Bp+fGMnP9k7sUNpzXGiSTc1oTgkDivFFBWo5HtDUIoKGdZqN1d33slKT08OOOMJ9+XSzPcHUe4MHB4jWeb64HGjy8zAuG41Sk52wGJZ+/dBocOAwxxKgucwBlcx2dkK+3MiMDgK6dMui+xsheJi/0g/8CfGQkOhRQsvDgd4juXQ1LcbXW42aWk6bDYIC1PxeiE42L/ifWqqvlxsXBXjV/MkbhZ/O+fVCDRZDlyI01OzZb5trGMC7aMHcFeL/6OheRuG338jeNvvGLZuJj8fzA4TSYobA15cigljYT4OaygenRGTWoQeH0UlJqJ8ebj0FnReLx5Vz8EjZrJMYQQXHsVakoszOAYIXAigdKSYMXUdbkMQ3oIsMvVJbHM2JehgJiG+LDbGjeBwqp4r9JWsuLnyJ7wtWuBt1LjckPGKroXDAR9/bGDNGj0ej0Jk5F+9oVZHtqzsWU1lp29+/DGMGfPX4253/zpq1en75ZeNTJo0kcWLfyQkJKSumyOEOMelpKQwdepUXn31Vd566y0aNGjAQw89xMiRI+u6aULUqdKau6UjngDtv6mpegYO9FRrOufIkf7RUKtW6XG7FUCleXMfycn+aZ2lcWV8vMoNN3hQVfjuOwPx8SpHIy9j3VZoeGgNDSMLUY7rwO1FNRrAaETncIDbCTodakgIOh3ExfnIyzaj8xRR35SJQ7FyWGnLUWN9TLioZ87Hbq6PTvWRm6MQ9PMKjnmjCVdcuDHgLXYSXJxJqC+PQjUY9/LVFAVDqHcalxaX0P3gUfQGJ8cszWhQsBOPokenetFlZeGONeGOaYy66Xd0HTppi2U5HHD4uJUwkxGrzoepxIVFcVDgsaKz5xPe0srEh2x8+I2XPXv8o8rCwlSio320auXFpnNQf/U82uStIXR3AS5TMEpBL9bUv4J8pwWjEYKCVHQ6tNXuIyPVk742Sk42hnVr8UjcLP5mzqsEmiwHLsSpOZ1lvrv88QPxJanousTgDQ3FtPpndIcO49XH4AqOxqgWohSrmL3FKKgUOgHFiwkFH3qMqhOD6sbtMeBVDBQbQqnv2o/qcJJmbsqhwkhMIf4Vf8ouBAAWnFddg2vgYDJ+3MXqJ34muuAAcd6jOE02fgoayZfuK2m56zCehCKM8WVW3PR40Kenod+9219zIS5eGzLuwMLChQa2rczT6jG06R3OoEEennrKzE8/GTAYVKxWyM9XycryL4d2TfP96I5l4G3UOODaysqeJ/JiNn8NQHExTJoEo0b5l0o3m7+msPAVoPZX53nmmcdZvHgRo0Zdxr33PhTwt1deeYH58z9nyJDhPPzw47V+7HPNM888TmFhAc8990pdN0WIf7x+/frRr1+/um6GEOeUE+uXlQoNVWuUpLFY/FMKBw708NFHPtas0RMaqpKdreByQUFB2bgSrrnGQ3CwP0l34JiNrCbX0nvEIDq22o3nyXR0e/ehehWUEicoCopeDzoditOFGmQhKAiirCW48yGLGLx6E9GOY7h8BpKMabiDwwGF/AKVfe6GxATZMbtcLNMP4XLXx7T2/o4OD3m6KIqM4dRzH0GX7aZk7VZMVoV6eekoqo/j+ljyvcHoFBUjbvSeQjbmdOW4Lpn41AwOJxoYFmRDZ7dTYrBQ5LOSH9aQBse3UugLwqfqiFWPEenNZF/CMLrUi2LsWDc7d+oIKsqhaUQ2vvAIii1RNF87j065C9HVi2FPViIR3nwGFC7Edxjmh4ynWTP/aL6srL86uatDl5cLhYWoCYFT1iVuFue78yqB9k9cDvzvvgxsZeS8a1d1l/ne8bOddkHZGK2R/HIwmoIDuTTLWMsmXQLZCdGk2IrQOUvwmsyYncUY9CpOYzgFzhhCizJAVTF4SlCMRhymUDJ90SR40/D8+VFTqAulwBZPlD6fpr79bAwZjKkwl/x9eoiIYMQIH5deqmIy/XkdHA6MPy4jfulaEgodeHUG9oe1Z1nMGI6bG6Aeh3RHFASHYCgsRLVaAdDv2Inuj10QFITapAk6lxvLkm8wGHR85boS13tfcLVjNcEUUngwmE1/XMhTP13OilQD9YOO09CWQ5Y3kiOZ0QSpRZg++RxjzHIMu//AcPAAJY2aY2/YmqBgA/rsQorVEIqN0YSbavb6nX/3uYrROBuDYWMV29jR6Y4DsHgxHD8O330Hl10GOl0WYWH/RlHCsFgqDsA8ni643dfXuGU6nUJcXDzff7+Uu+++l6Ag/+qrTqeT5cu/Iz4+Hp1O+eveOgVGo/+5JpP+lPZztl5vnU5BUU7vXAHcbjdG4+nXrDv/7nMhhBBnUtn6ZaUjzyBwJkJNREbC2LFu9u3TsW6dnpIShaAglQsu8DJo0F81uywWGDjQQ4sWXlQVkpJUIiPD8K6FI8WR2INCCSnKIkh1YAgJIjRexXgkDSUnG3T+f1A2sOaTpkSxWn8xrYtSuci3HL3qIcKdj7MwFIfORpqnKcF6B3qriRA1B6unkAw1nubswIgPk8+Fqneg6ky43XrMORkUumKwqT4MuKnvPYwLI/lqGMeJxYSLA64EbIXFFIcEM39LcyLje9E7awG2ELDoIsnVR2MhkhxvKNHqcYqMwfxoG8k2++UcWWjgqpEF3GT+FN3qn4hUcnAFR7AzrBvx6b9iToohsWs0nl06sg6GoDfY6VrwA783GkpS43Cysk7s5D45X3gEBAej2O2oMUHa44rdjmoL9v9diPPQeZVA+6cuB/53Xga2KnLetaM6y3z3u6CQfse/YNS6VCJNBWSlhlDiupB9YR2IMBawz5vIH38o2JwltPF68UVEYDx6HKPDjkcXhsMcSpDLTqEuFMXpxBsUwgFTM47mBxOq5rCbFIoIppE+g3ClEKfPgFmno5M7lcuLNmGMCMHVsjuFPUZz8KBVm1Zp/uIL9IsX4TXHcdzaEJMjn5a568hU4vgqdBxut4IvIpL8dhcQsvFrfD4fGE3od/2Bqqp4mjTDG+Kvkaj4fLh+WIPxQDG9c37EFRFDvrkBQc58eh1fQMZhN5djpLdjNZbsQoqUYDYGXYixyEVb72KKY6PRN26Oe8suStJ+I83m5bDSkEhPFpsTR7LptbhTWqHofLrPDYZUrNbbq739l1/+9d/LLvP/bDLNq/I5JtNscnNb4vF0q1HbfD6VFi2SSU9PY/ny5QwaNASAZcuWExsbT0JCAj6fql1vl8vF22+/wfLlSykuLiI5uRWTJt1Fq1ZttH2uXfszb7zxKpmZx2jTpi1Dhgz/87lebT9btmxmxoyp7Ny5g/DwcHr37st//nM7lkpuApfLy88/r2T27HfZt28PFouFlJSOPPfcy1q7Zs58m+XLl1BYWEDjxk255ZY76NSpCwDffvs1b775Ck888RxvvvkKmZnHaNeuAw899BjR0dHMmjWDb77xjwDs1q0jAG++OZ1Onbpw7FgGU6e+zoYN61AUHe3bd+DOO++hXr0E4K+Ray1btmbevM8xmUx8/vlC5s37nM8++4jMzGPYbMG0b9+Bp59+sUavz/l0nwshhDizSuuXlRamDw31J9NqmqQpa+lSAzk5Cp07ezGZ/CVJcnIUli41cNVVngpnY/TsWMiVfE7eJysIPbCHKKOR3Igk9gUlkuMOoVVEDi2sFnC7/SOq9HrURolEX/4vrjvqxPlpOmQHEVJyDJOvBFOxA4M+mMZAU3UP5Ch4VR0mCsj3hVBCEDpUIsgBZx6qD1RUvBhw5jkJw4MPHTq8mHASyzFiyMKFiQucK7BnhfGJ6z8ciovm/5xX0uYiLxHb15FsOcSevBDetd7Bxnr9CNaVcNQZSUzLCGJjVVJT4fLcT+m9+T0KPQ4cJaDmKyRkbMYYZsHavCvG3EzaOw+jkok3pASn4sLjnsXCI7djDA1iyBCPNmW2OtTIKDwX9ED/9UK8+EeeKXY7+qxMnEOGy/RNcd46rxJoshy4+Kc7laW+q7PMd/EHX6F4FqEzxpOmT8RZbOcSzwJ+sfoTSVGGfPTBsaQdt5KsM6E3eygJicDl1mMqtqNXPBwMT2FKw+dpm7mCxo7fcdi9ODGwT9+c3d4WHCGBP9RiwtwO2vk2E+8qIMccQtOkSKy+QvI//oYfFxpZEj8Om02lT9tMrt7irzmmD44hJMaA3R7H8SJonbuGZaZhGMIjadTIh/eyS3HGqBhT16E7dADF7caT3BJvckvtOqihoXgP7qPJ4Z/JD4nFaY7FYIBCQxB6h8KozI8oMdg4omvIIV8jQn359Cv4AquviLz6LdAnRLGtKBK3wUwjwx8kFOzigBLFAsso/oi4nBj9Ka5QdB7x+Rrg84WXWyCgIn/8AQsW+H/+6iv/7y1aVOcY4fh8DU6+YSWGDRvJN998rSXQvvlmIcOGjeDXXzcFbPf222/y008/8PDDjxMfX4+PPvqQu+66g08/nU9oaBjHjmXw8MP3MXr0lYwaNZqdO3cwderrAftIT0/jnnvu4KabbuHBB/9HXl4ur732Iq+99iIPPfRYhe1bs+ZnHn74XsaPv4FHHnkCt9vNunWrtb+/9tqLHDiwjyeeeJbo6BhWrPiRe+6ZxAcffELDhokAlJSU8PHHc3j00SdRFB1PPfUob731Oo899jRjxozj4MEDFBUV8dBD/wMgNDQMj8fD3XffQZs27XjrrXfR6/V88MEs7r77Dj744BNtpNnGjRuwWm289tpbAOzcuZ033niZRx55gnbt2mO357Nly+ZTfn2EEEII+Kt+WWqqnvR0HTabWuMkTamcHFi5Uo/ZDNHRqtaRmZX1V021Zcv8ixbExKg0aODDbvfHv9meb0hzxRMV2YLEgh3Uy92JEqWgN9TDtmsL7qY29PXqocbG4k1KomTkZShBZtRP3sFX5MBoUCkKjUdXeBSLJ59gbz4WCnARhAr4UDimj0XVKdh8RZhxoscDPv8ou9L/H04eJQRhowgdKj78I950eNHhxUYxxQQTXXiA4B2bSCtO4vDdYynqO5jCzQfY87OBZStbUKRGYTFB/cb+WnBeL+Tvy8U2/zP0+TmE1ovEojPjc7gw5WZiyE3DuyIPXUkxSmERvvBw9CGhmMwqw0NX0q19NJ5rxpxSUtN96WV4PD5/fJ6ehmoLxjlkuFbnWIjz0XmVQJPlwMU/1eks9X2yZb6bR2bR+ffVpBFPUKMY0vboyPNa0BmhsX0rW3XtGaT7HlXxkekKxxkSRnD2MUwtm1NgbEvxwWxMJQVsanoFbcZ0wJHekCnvHMah13E0uAkDXd8wrOhzgn2FZLpjMamF1CONA/rGHNY1IvaYF4/HRlaWQtPCNbRocwkZ7mjWL7bTu7CY+j3rYzFDYqKPQzudhIZ6MNpzsThyOFAQTVAQrPzOxdALumHu0g0lPw/LrBmoVhuUmX7my7FzLNtAcYGXfcXhKPk6QkJUoqNVPKqJpuphtuq6ctgVh14PbkMQNmceTb07yTe1BeDwUTO6eu1x+hrC/oN8Ve9Wfrd0RT2u0Lj1X4FgdYvfnm98vgTy8n4gJOTfGI2/ao/v3Am33gqZmf7fVdWfMPP8GQMXF0ObNv4EWumU+thYePttaPlXjhO3uyN2+3v4fAmn3MZBg4YyY8ZbZGQcBeC337bwxBPPBiTQHA4HX331BQ899Dg9elwIwP33P8KGDSNYtGgB11wznq+++pKEhAbcccd/AUhMTGLv3j3MnfuBtp85c95n4MBL+Ne//CvqNWyYyJ133ssdd9zM3Xc/gNlsLte+Dz98jwEDBvHvf/9He6x5c39mMSMjg2+//Zovv1xEdLR/YY1rrhnH+vVr+fbbr/nPf24DwOPxcO+9D1G/vj/ReNll/2L27HcBsFqtmM1m3G4XUVHR2jGWLPkWn8/HAw88ivLni/DQQ49xySV9+fXXTXTrdgEAFouFBx54VEuorVjxA0FBQVx44UVYrTbi4+vRokWZF00IIYQ4BWXrl9W0c7gshwPmzjWSmqrHZII9e3Q0aOBPHpXWVDt4UCm3aEGc4Tjt8tewxxdPnjkOb70IDEFGYnL+IC73D4Is+ageDw5bBCEFBejS0zBs3IBxwUKKQ2KJyMhDjw/FoMNanI/R60AHKIAOFT1uirBhxkUj7z7ylEh8GDBjhz+3A9ABPtyYUXBgRfkzeaZDxYURD0YyicWMkziOcot3KmPy5pBTVI+Mh0bxW2EUicc20tlXQKIjhANRPTnU/TIMIf5/IOTmKiT59hJ0PA01LAzVavMnAIIM6AoUKHGgFNpRPB5UgwFdfh6q04mncxf0sXEk7F9LEYNQOYURY5a/6hmX1h2WkWfifHdeJdCuu+46xowZw/Tp0xkyZAhbt27ls88+48knn6zrpglxRpUu9V2216yy0U5KTnbAl1TZYfJ2O5SUgNnsX+67eXMfkeQSTCGHSKRTA39f2S+/6Mlyh5GkHCaz1YUcsFlJ2L+WePdhqBeHu2EU6PW0JIuSuGCyWw2g/9ABxG34kMLf1tHK7SDLHcIvaleCdC7CDIW0cO1EBXK9URw3xpAR356YEJUtW/xJQYM3nKSiwxz/I4+ITlEU5Eewf38oYWl2ghsZaOvdQZI7HWdWAQ63gR7qMoKSIxihfkP9qWso/NxORCsr7u49cF9wIebvlwYMGc/8LYsfvRfTwLaVMEc+OZ6gP4vMKjTxZGE2q2QSi8mkoqoKHg8cJxa9AaIdaRQdikcpsWGOsKDY3RzT1yMzuAlmAxQUQEmJUuPit+cjr7cZeXnLsNmexGp9E/AnwebOhWuvhR9+qPh5Hg9s3+7/ecAAmDMH6tX76+/FxZMoKvofYDqt9kVERNCjx4V8++3XqKpKz54XEh4eHrBNenoaHo+HlJT22mMGg4FWrdpw4IC/k+bAgf20bt0m4Hlt27YL+H3Pnt3s3bubZcu+0x5TVRWfz8fRo0dISgpccAJg9+5djBhxaYVt37dvD16vlzFjLgt43OVyERYWpv0eFBSkJc8AoqKiyc2tel35PXt2k56exqBBvcvtOz09Tfu9SZOmAXXPunbtTnx8Pf71r1F0796D7t170rt3P63GnBBCCHE6IiM5rZhp4UIDa9f6k2dms4pOp7B7tw6A2Fh/J7Kqos3G8Hj8szHUP+z0zCrksC4RY7CCxWLiaGwKuaENiTu+A1NhLiZchO3YiL6oEFWnw1PixV2kUlyQj8HlIFS1o+r1KKh4FR061T+izIueQkK0GsDh5KE36jGhorr8yTF/xK1Hj/fPrVzo8M8W8WDAh55CbPjQAypRZONBjw4fIRQQ5c6hxU/bKTRHsT1hAMeNibiK7KTsW4jNBr93GvvXtNjWPnTbS0e7/cnjQSkqApMJX/2G6PbvQ9EpqAYDmEx4E5NQTcZaKfivRkbJggHib+O8SqDJcuDin6jaS307HJgXzse4fi1KURGqzaatPDlypL8XqqJlvovdERQSTBj5BAdH0bGjf9/Z2/MwWWyEtYpnmXscPvcA/hMyF5P5N/DoQa/H07I1nsv/RWhCfcyffoR58SK8IbG46jXEeMTOza4Z6BTYFtKFLfb2RKtZxBmPExLqJCG8iEO5erxZTnw+CxZdAQUYObjFTvGxfPL0sQQX9CJ07Vc0+G0v8Z7DhBiNOFDIMtajt+sH2u/aQrz+OIW2WLYVNiKsKAfjF9/gHDAQhgzXhoyX6INZbruUzU0uJzzrC5I2f43eBcc9YZjz8mmQYMcUXZ/wo05cCni9KooCieZCTEV6mmRvwrrRREeHjWJ7JO6gYJaFXE6WLxrVqWpLfJ9q8dvzj4mioqdxufoSGvofdLos6tWDZcvghRfg4Yf9o9BOpCjw7LNw332g88e2+Hwx2O0zcLsvrrXWDRs2itde89fouuuu+2ptvydyOIoZNeoyrrji6nJ/i4uLr/A5ZnPliSeHoxi9Xs+sWXPQ6QIL75etqWYwBH51K4qCWtEFP2HfLVq05LHHni73t/AyhXxPrN1mtdqYNev/+PXXTWzYsI53353Oe+/N5J13PiQkJKTKYwohhBBnUmmMXL++isHgY88e/yyLoCCFPXt0uN0+Ro3ykJT012yMo0cVNm/WE+KKItcdQpBqJ58gvF6FhASwel3oiwsIKToGkWHoCwtRnE5/wknVoTME+ZNOTgcKXhSvF1WnR/X5x5T5U2Aqbkzo8FJCEBacmPVucLoBBR/gxYhX0QNeFNUFKHgwoMf75x4UTLjIIYIocv5MrunR48OJGTcmInx5BJdkoxw9ytHgBhgtQRjd0PjIGlaHD0WNjGTIEA8XXtQQ35r66NIOg06PajajFOSjuJz4IiLwdO6C4nKCz4tqsaFzFKN43FDiQNUbUPLzUHKyZfSYEJxnCTSQ5cDFP091l/o2L5zvT2DFxOJr0ADFbse8eJF/46uuqXSZ76OuaEItF3IpX2Et9KLqQmkfbyczK4vlwZeyOycGm03lhuj5dDr6PdSvR1FUPTzZBVjXbsAUG4dr4GCM6/31ykwxsdS36/g9OwRTcTE6FfLVUHJUK3ZDBJaoEOqr22iYtp6oPDc+H+gVHyafkyNKQ27JfxGv3cBvkRexPPZq2tnySdgxjfwgcBgtbFMbccDWigSO0MH+M+u4gIzieoR5s0nNLCE0yII3fQv7bnycS+4ejLUkl/35kSyZnkALSxaHozuQG1VE7NHfaOg7TKESzNaWl9MlxUGj978nzKzitoUR4s0jKetXXHobuaEJNA3NJsxdRJDdzvaQAWxucjk5ewAUkpN9FBaeXvHb85HbfTE5OWsIDf0PJtMP6HTw4IOwY4d/hNmJrr0WHnjgr99drgHY7TNQ1dhabVf37j1wu90oikK3bj3K/b1+/QYYjUa2bt1CfLx/GJzH42Hnzu1ceeUYAJKSGvPzzysCnrdt2+8Bv7do0ZL9+/fToEHDaretadNmbNq0gWHDynf8NG+ejNfrJTc3l/btO1Z7nycyGIx4vYGfFy1atOT775cRERGBzRZcw/0Z6Nq1O127duf662/mkkv68ssvG+jTp/8pt1EIIYQ4XXl5CkpODk2jsklIjABiSE/X4XSC2w09e/rLnVgd2QxqXMD8FTFs3hmLwwEeYxTrDBcy0rcAg1fF4QrDWpBHUFE6XsWIEh5KGPkozhJURYdPBR0+TJ5iwhwZqIoPRfWhQwWfFx+6P+uWqago6HED4MbMQRLx6kJo4vsdBRVQMeBBr/q38aInl0gO6JrRkMOE+3LQ4cWIjhiOY6UIULDgpRAbbiUIo+ryjyhToZF3H3u9bbCXWNGZw+jR6AD/vS4DS4rtz5g0ipIrrsLywSxwFKNzOFC9HjAH4WvYCDUiEm9iEoY9f0BRIarRiCszH/3uXSghNizT3w7omK/RallC/M2cdwk0If5pqrPUt5KTrSWw1Bh/MkKNCcILGFPX4e7SDUWBqPAIxo6NKrfMt7XL5VzTwY2yzT9iS2cLxjJmGE3bjGCimkfbDR8S88kMVLeH3AO5HKKQA9bWhPt0xH+8nujEFihFRfga+KeVtUwsIvjYMcwFXpwePdaSXBLMxXhNFnyhYeiOu/C4vPh8/pFIIb4CgijCoZoJIYcQtZAeuWvpHrSZH8KupTkNyXDU42hxHAVeK4ZiCPKaMHkd6AxeBhR/TSKHMDrdqDo9jqBwFiz4NyW2Dlx1VRQhRxwMzphD8u+rUQuKOFYUzO7g9myMuoh0NYGwogguCy+gWV8LnhXriPAdRmfSU0goB6zNiGoXj69JEYZCBxk7ilFyFCLMxTRsaAXAZgOfj1Mufns+U9U48vPnERo6FrP5GyCg9FyAso87ncOw2+fir/5Ru/R6PXPnfq79fCKLxcKll17B22+/QWhoKHFx8Xz00YeUlJQwfPgoAEaNupxPPvk/3nrrDUaMGMXOnTtZvPjrgP2MHXsd//nPBF599QVGjLiUoCALBw7sY8OG9dx11/0Vtu36629i8uRbqV+/AQMGDMLr9bJ27c9ce+0EEhMbMWjQEJ5++jFuv30yzZsnk5eXy6ZNG2jatDk9e/aq1vnXq1eP1NS1HDp0gNDQcIKDgxk0aAgffTSHBx64mxtvnEhMTCwZGUdZufJHrrlmPLGxcRXua/XqVRw5kk6HDh0JCQll7drVqKpKw4aNqtUWIYQQorYpOdnoMo7S6Ps1XP/HdoI8hSghwbSr35PUnpdzMNOK0QhjL7MT/ufsjMvsRTTPDWFWXh++DroCn97Ct7Yr0ZdAF+dqolyHsda3EjGwBzEHf8VcaEK34eCfBV0VlD/TYx50KB43XvSo6PDiw4PRXw8N9c//gyBKsBPKcWKZxkRWegczzziKVu6t/nNARYeq1ULzYCKVbqzzdaMjm2nNbwQpHlAV3JjR40GPFwUw6b1YPYUY8KCgEuk6RlP7ZlLphq/YTlp+MPVahGMu06HrvOIqFLcL44ofURzF+GLjcJvM6LOyULIy8TZujFJgR3fgAMcN9chdfxSTU+GQqwVhYRG0sOZpHfPOq645i6+2EOcWSaAJcY6rzlLfun25AQmsUqrVhjF1HbbnnvIvvW2z8Su9KMz6F507B2nLfB+zW/nSNp6rHx6MKyOXb9fFsOL3WIpSFX/iKecbVLeH40RRYPeRwC6MRpXDtmSO7z/MtpUGRtpsKDk56LKzMaWn0aKkGAwZuHwKBmUdCuB0mnAcsmL05bE7tBOHXSEEKSW0cm0lSlVJ5DDFBKGiw+xz0/fopzTI3EwEecR50zCryezQtcHodhHmy8aJia6edcSTQQlB2AnG6ismrDiDQX+8zdw1sxg40EPI4vlclPMtfxTEcawkkXBDPhcU/UCeNxhn5zbExqqs3RxMz3uuYkPbS1i34iC27DT6FX9CbHI4zZJ9qAYLOouFxFAr9felEfFnzx7UfGXUvx8FvX4X4E8kLvpz4KPJBB06wObN/vvsm2/QkqZ6/R/8VUK39p1slNXEibejqj6efvp/FBcXk5zcildfnUJoaCgA8fHxPP30i0yZ8ipffvkprVq14eabb+O55/6qudmsWXOmTp3JzJlvc+utNwEqCQkNGDBgYKXH7dSpC0899TyzZ7/L//3fbGw2W8Bos4ceeowPPpjF1Kmvk5WVSVhYOG3atKNnz4uqfe4jRozm11838e9/j8fhKObNN6fTqVMX3nprJtOmTeHhh++luLiY6OgYOnfuhs1mq3RfwcEhrFjxA++9NxOXy0mDBok89tgzNGnStNLnCCGEEKfqxFq+AcqUK9Fv34Yu8xjNgpqQSkcinEW0+mMhBQUKf4SNo39/D/Gr/pqdQaMGJHgKuGz3V0RYfHxuGY/dbuEL63iWGodhc+ZQzxbG5Y09jC3ai+7YQf9oMb0ePP5R3QqliS8vOnS4MQAKDiwUYiOUfIJwkk8oR6jPdlrzvW4w8w1XYFMdZNVPISankHD7Yf9KnKh/jlvzEqVkc5G6gqX6IRw1NqK+M53VunYc0yf8P3v/HWdned/5/6/r7vdpc8qcaRqNekOA6NWYYmMsimjGxLhuypZkN99s9hcnWWe/u/GmbLKbZLPfTXWcOE4ctxiMAGMbF4olIYwBddQ1mt5OL3e/fn+c0UgCieI4sRH3kwcPjWbOOdz3nHkw1+Nzfa7Pm7XhHi4KnydNkzQ1UkEDhQg5X9hTpceQfwihhtSVLE+07iDzTO/JOcnz3zdt9y5QFGRXluDiy3DueR/mN7/eGXkyNUm0fAXbBu7hseMbuHPmr6AnQU3tY+IYhLrF+T2djXnv5lvi45yxty0hX29wylvczEz9x30J/ySGoeJ54Y/7Mv7Fxfd9uhMpnM89dzKF84orTqZwitIcyd/6b0hVXehAA9C3bUE9fgz3muuQPT140zUOPDPHs32bOHj1B0+J+RZEEXziE+5pMd99+izvfeq/4DV9LmQXLUfB05MkZQOBZLx4AU3P4Gvn/Ud+ZeVXyD3+ZUS9hpwfeB7tP4ISuNStItX0ILpTJ9meRdOgke6nPBsRRZI8JVI0MHGokMXFIitq2LJBVS3wsnkhi9qHUWRIVeTQ8UjJOh4G/YzRxqZJGo0AHR8Xg5aS4VfWPMTVt2W48qv/L46vctzto1rtdN31iikyiZDv3fZJ6kaBsTGFX/3FCmt3PUj09DbC6RKp4y8jBvrwL78S5udOiZlpRBTR/MR//ScvHs6Vn3NV3Us+30lxfPJJuPFGuOQS+MIXYNWqTiLnBz4AL7zQ+fr113eeVyptJwzX/diu+1/aufJ+v1mn3nexGM9Ni8Vey1t93fp63q7/H4T43l/33l9jlu+JBevJebsZ9F07IfAhjDieWMtOsQG7NoUmIr5/z29yxx0+K/7q9LVxuw3PfmmcsN7mvxv/nePmKhQFms3OaYKrrw5Ip+H3l/5/FD/9R1CvEwgVWWuh4XWOawIhgggNF4MIBZ3OJrOPTosEv8bvcpA1lMhTooBtw4roIH+a+VUCRefiqW+SpLEQGgDQIAko7OACXGGTU+ts0a/HkRYX+C9wcfh98pTQ8ecPioKDiZzv5K+Toqbm+WzmF9hzxUdI99h84hMu+fz89+3hB4nSaaJCN8L3UWemcTfejnv/AwtFyzmZ45N/MsBA8yA/te+TlNOLCTSLVksgJbzzigbJ0iitj/9nwuUrfzTv+zkqvve35r2/kXVq3IEWi70FvF7Ut8wX8K+8urOooJM8qUxPowwfIxhYBKkUoSd5aaKfsZpGb2Mb/9i+nfTS3GvGfOfK8wmdySGOVCsMuvtRVQhVjVRzhp65/UxmVvCeHX+EWppAmZ0GBGHbx41UHC2PpvoY0iUR1PAtm7bMkm2MEwY1fKMH6XikqWPRxsPAw0QRoEmfEA098hhlEXUjxcXec6yQB5mhhwPKOtTIYzHHMfAIaROgM0c3NdL0RtNoo8fZ9dRirg8aBIXFFFxJoyEwTcj1p+luj5IJyow53SSTksFtD2I+09mplKsGEPUJ1AMvA+BfcimiVltYcMh84bV3Sd9GTPPhhY8ffBB++Zfhd3+304EGsHo1bNvWmX/24IMnC2im+TCt1tungBaLxWKxWOzMXmuW74kiz4lxJWgaIgqJurIIz2VIHid98XJePphBjIwyurPKZw5K/u1wmd7zcijtNug66SP7uEI7jh9U+XX/v/CEfytf5j4yUZsl4RztXRIsyfhl55Pt6cdrCYzKDIKIFgkMPDQCIhQ8DJokSdLEwKXIDAE6dVJsYAc1MmQpA9BqJ7hRfYLFtb1k3GmSNIjozEcD0PHni2kRA4zzl/LfcD4v00UVGUUs5SiKpoJmIh1/IcFTIBhngDY2Gj6HwpU81LiF8e1pslnJ8LCg4Ixi/cPfoczOoKoqUjcIFw0SFgqndZOF+QKlI525y6KQw9VTWG6VhmZhmp3TL8FcHZlKEZ0SPhSLvd3EBbRY7C3ktaK+3U13AywkT+J5oKoo9RrqlmdoNEx0ZzGOuoJ+JsnJEvsOdoo+Z4v5fuFYgeUzadygxvOsx1EFy/1h8o0RtNBFIEnWJphOL0cpLiY6nqRRl4w2BjiuLmWFv50wkaCg1hjrvZSWmmTV/sdBRmT9WeygQZkcDhZJmkQoGIZADx3UMMTFQBCRT/uM+0sh+D4y7EyMWBntJ00NlYgEDhKFKTLM0E2BEi1sDvhLubjYRdRMobdqJNIWuZykWgVRq9G2khxv5JmpC+5+5xS53dsIc3mUqSnUsVGE64CioB48QJTOINNp/Msux7v8Sswv/sNr7pK+nZxaQPuZn4ENG05+TcokQjQxDPjDP4QdO05/Xqt1SqpALBaLxWKxt53Xm+Xr3XwLSuWUcSWRROoGwnWRlolSqzFx1KU+0iJhpcgtMlm191GMgy8TjgVY/WlAIppNskmNsuxCqwvub3+W6/gWqqVzXmsvyWqFClmMv17KbNPmsHYZa8Q2TNkmREcgUeeLaQAZ6hi483fRCQ9IUePX+T3+I/+bNgnG6WWEIXrCOSyttbB2VQEFOR8+oGDg4WLSJsHTifcy5/Vxj/cVzpNTLIuOoOGjRBCKzu6kQBKhURV52pFOD1M4SpIomyNyYXJS8Fe/V+fPlv4Z6pHDRMUi0k4gXBft0AGCYBlkMiiVMuH8JvCJucuTfjdHF13D+Yc2A1CKsnSHVRL1Kfybbn9bbxrHYnEBLRY7V9g27v0PLCwy9MceQXvpRQhDvGSWxrTP8mA/SbXG/mAVFSWHZfHaMd+7eih472Cj9zBtBLv0NaSjOXJIJrNrUb02MvLplxPYFYNy2EXLdUhGZUT3GnxhYLTq1O0EtVQf6YMvkfI7xa0qWTKyRpEpGqRwMFGQ2EGdMOpkFNk4eFJnVWsXy/2QdFjBxURRBd3hLDo+AQoaIRYOfUxh46AAXxO3cURZRaYS8rXaO7ihspm6IbBSGVJajVRrmu9nN1E3CmzcGHDrBTOI7U1EvYZ27CgykezsbBoGysw0wveQSLRdu8h86wlEo45/wYYz7pK+najqQTRt78LfTy2etVq/RLP5qyST/4NE4o9f9XVN24OqHiIMX/8YQCwWi8VisXPTacWxeaLdRkRRZ77uiW7/ZBJRqyGLPYSLBjupkU6bQOhURpv0UuPgijtYP/kk55e+QyPdj94aw6430WanCMwUpBLkiiaXyQPorTEul88xE/QjoggPg6JWojpu47o11rYOY8sWPiqCEAcLD4MAHZM2J4pmIWpn1i86KTx02hi4ZD5WZejjI1zx+88TfQYUV0Fwcp6aRoQkIkRBINHxWcZRfsP9f/k1/X9yPc9wefQDdN8nkgKpKgg/IFJ0hAwxhEfWaJBwQnQC9tqXkAlK+G6bdwbf5d1PfQt/2zP4so2qVyGRRM7PPVWHjxFcfMlp3WSnzl3+TuF9BAEMDm8jWztMz4AC77phYcM+Fnu7+tHHn8VisR8rmS8QZXNoR48QLVmKiCKidmdXS6gKQ8FRZgcvoKJ2vyrm+8QvzrExwUsvqbTb8FXjPh4Wd6LIkMXOITLeLPv19ey3L0KRIUZPlmRvAjk5zVjYixIGKM0a9RmfCl1kwipTboaxIx6ZyggRCjW6GGEx+1nNMIupk2IP66iTRol8EjTnZzyEVMnSakKvcxyFkJaSxI6aCCAUOhE6HiYRAhOXNA2+q9zE/8j+LkLAwYMKX0/fx5biJpQowpoeRVdCij+7kav+4A4+8QmX++8PMPpySE1FGz6GPLHA0LTO8FgJ2r69SEXtzI6YnkaUy4QTc5RbNq1UD2GxB/25ZxGluR/3j8C/KMPY/KrPRVEPlcpDNJufBJI0m/+dSuUhoqjnDT0/FovFYrHY28epxTGCAG3PLvSnn0R/5im0/fvQn/wO0k7gX3k16sz0Qmpk2NuH0m7hazZuaLBvzR0cGrqRZWNbqSd6GFl8FcOJ86g0dNx2hFtzmKjYNCZbpJMhmggRSIr+BCohTrpIlMpgOjXqjoYZNGB+yliESoBGnRQlskzRyzRFXKz5yWiSLDUsXFQkuuFj/nGAWAfKH4NqAKfMPDv1PIk6//lovpB2Q/gtPu18iBXhIQ6Y5xNIFQ0f1XfRCNEjByEDlCig3z3GIGO4WKxs7+GXyv+NT7kf4RfC/4MeubSxqcoMcnoOZWIcwgCiEKXRIFy8pFO8PGXtumlTwMaNAa7SSSkdzl5I74BgUa+H/sLz2J/+c8T42Bnfx1IJjhwRlEo/2p+PWOwnSdyBFoudg07s5PkXXoR27Aja8CipsIYrDNxkBufya3lnOmB8XKDr8MAD/sLJw02bAoaHBdu2aWiapBHafF7/CM/Yt3G+9wIfbv0FldQKVg6EFJM6qu4gDRM5V+dYu5coLJOnRMaf45hcyj65EtULKc4dRRByjCGSNOmiSoMkIRpFxjFwSdHAoLM46CwsBBmqaESoqsAXCWQkMKQDSCIpMPBpYxNgo+MxRS/fyN5HOhkxOg2NhuBokOBP1I+SkbeTpIRHno8lUjywLFi4b5kvEK49D+N7zyBNE8IA4bqdxZymdT6XSiFkhFRUylEXzRfHeWlkDdJKsKQ3y9rE8dNa4d8OTPOR0/7uee+mVvsLpCye9nnffxel0lYymX+LYXzrlOdvpt3+5X+Ra43FYrFYLPaT59RZvuLQQZTxMaSug6IQ9vZhPP0kMpk6fVzJfGpk6447mV13LQ/9/RIUBTaUXiThzDGbXUnb0xnVNzChD3JF8ltYURslCplppSh6FdKyAaKz4iyIEqHZgx9oFLzj9PptVELqpBBETNFLiEYPU2SpcYCVLOcIJu58jqaGgbMw4J+bQXSCvTt/vhvE115974JOWU3O/+NioRAyICbRAp+ooSwEB8DJwpuCxEVnWluEH0CXrDIgR9gtLuAi+RIWbaZkL55iIywL4Srk63UUq4SUkiiZRD1ymMTv/c7CKJLJ6+6m7CS4+ebO3GXtH75AT/MJ1N4C6tQEyov70b/3NMYT38R54MMLo0tOhJ1t334y7OzKK0Pe9z6Jqv5z/dTEYj8ecQEtFjsHLezktZoE6y9ALF9JaZfL9OEWWkJn1uin0RC4ruCmm4LTAglsG9797oAvfEHHMKBWEygKtIwCu7mEilOgqFeZafbi9w+iDR9ANlq4My3W17eRoMVxlvCMvJKvmB/gCIvJBHOsYy+/y6/TzRwGAYOM4swvEiQKNg4+GgoSDYcQhQgVicCNdKSWwcXkaDTIenZjEc6nIQkcEkSAicugMs6vOb/J881L2aVuYEfXO9hXWcSE103LLJDvzhNF8NSDMyxqzbDxg+mFWQ7Ove/H+PYTiJlplCBAGgbR4iGU4WPIRAJpdaptlZZJrSYxVY+83aYsEkzur2MuTtP3NhusKkQVACl1ms3fpN3+ec7W3CxlD9XqP5JK/RmW9f8ihL/w/FgsFovFYm9f7qa7EY0G9qf/ovMJ0yJYPki4Zi2iUl6YhXbquJITIU6pdpt7vryZ4Mln6Raz9NT3odUqTNhXAzqikKcp+zFmDiKiNkbkoMkSIPGEhSY9jKCNXZ/GDCAZNQjopFw2SJKjSpYKDdI4WHyfywjRWMMBAlQkCoIQeUpfmbj3FTd4L3CGAtoJKpDAxcYlQCVULKShk21PEyFQRGdWWiQl6vxGs6OlOZrdwKL6AcqOTpYqCVqEqFTIMaBMMiV6WRUdoaFlSad1lKXLUMbHQVWRmQxRJkNUqjHzqcf47j/ofKPvwySTkuvPn+anDm5FWdSDOj2FeuwoMpkkskzE7Azmww923rf7H2DzZo3HH9coFiWDgxG1muDxxzU0LeLee9+aaYyx2NnEBbRY7CfcD5P0eKZUzuWLPVKVMt9K3cXBUhFNk1x6ach11wWvev7SpZKhIcmRI4IgAMvqHPUcdwv8wL6Wj8iHmKtBff1yTKdG8NJelHZIJFLsEhsYi/q5PHyWiVYfR9QPU6LACo5i4WDiMk03GiHdzGLicYSlrOQILiY6wfwsCAhQ0QhoiRRqUAUCRrTlOIHJ5XyfNHXa2ERAmjoKEQ01j/B8zjf3cZP3BLPlv2OnvJCtxrV8RdyHW4G7/C9zQ/V7dB9pIPZYGDfOBwBYFt67b0Z/6klkPk9UKCLqNZQjhzszImybVhuOM8RQtINIsxCqoCecxGCGLWziBgrkX/UdPXfV65/CNB/Gce4jDC98A89Q8LxfpN2+Dsv6Mq571z/3JcZisVgsFvtJZ9v4N96E/sxTRIU8MpNdCGaSmQzK2OhCl/+J1MgTzM0Pce3Moxxe0sexyirMVoXB2gGutuAF83K6wwpzXprdxk1c4m6jW053kt81G1P4hKFEiUKSzhy6BjJpo1eqGDik6BzjNGkzQzcvchH/D/+H5RwmRxmLFhexkyynbAhqIO58xf3dBfwb4BXLbsnJbceITkeaSoQdNqiH3agESEAiEDLgZEOXwMbh/NbzWEGVWSUHUiAleBhoaoSt++xTl2DYBoPNA6iEhHYSkekiWL4CWeyh3YZdI0nqcyqr21s5uv69TPrdbH+8xjsbLQYvLqCMjiKTSWQiCUGAEgRE6TT6c88yddktbN8+QLEoKRY7BcQTfz77rMKNN3LaRn0s9lYXF9BisZ9U7Tbm5od+6KTHV6ZyKskUxZ+7jasvv53xr4Xs26ewe7fK0aMKV17ZmYF24mXzebj3Xp9Pf1qnebxErl5iNsoTyG4e7boP25dc623BLk/QyC1iRAQMGwMcDpbRlAkiAYHUuIotbA5uA+BavseLXEw3swxxHA0HHx0dn2PzBTRlfgaEj4aBj0pn10pVAmQo2MZVBEKnRo4jYiWr5AEiFExcEAoVvRvZ30/35CioTZqGje45KNLn1uBh2qFACLhdeZhZ2ctubwnG3hKXlh8muX0bgk46p/A8xNQUhBGyqwvvhpsWZm64URdltZueRB7PTJFqz+HpSfatuYMnk+/jooo4a1LquSgILiMILnvTzwvDC2k230jBLRaLxWKx2NtBZ7M4D4p62lpX1GrIZOq0gfcLX5tP8JR9PSwvdtPfBqdxOequiJ7pcQaDQzRkgWe19/Bi/ztoND7Hxum/p6rkUJImaXeOrO4DCewwwJcqUa2BhbdwHDNEQSViJYd4gcso0SnejTHICg5RJodCRIRCgibmjT7iFUUjkQd5A3ByigWvXC12Zq3Nfy8Q6H7j5GOEQjT/lxNBBKGi4ZppLLdKr5yiZXSRypjMNBazPtiJE1m4GEyrvfQOeXj3Xo1/7XXYf/6n+Jk8+/coDA8rjI0JbJElHR4n5ZUp9hRoOTnGfpCmb3wGw/eIMp3zqMJ1kbpBVOhGLc3RHKnQbC5icDA69VbIZCQTEwqVt9m6OHbuiwtosdhPKHPzQ50OsmLPD5f0+IpUzhMdbE9/UeP551WKRUkmc7LNGuD++wNKJahUBO++ts4FL27Gm3wSUalQJsdzmZt4JncfX2h+hGfTt+KsP4B3eJRM+8sc0VYgichGczRlggpdLGaEPJ1JoikaDDPEDD0YuCzlOB46PR9rsunjj9H6fRPzMwHilEQjhYgAA4HgsFjFx8UfEGkW3UYZy5L8UuO3MP0mmqVwkbKbppVnUK8TSoey2k2UTGLXajS9FPXA4ia+ja5ISkYvE34vYQjbjw1QmDvI0I7vot70DsSSJUS5HOrYKMGq1fjvuYWwtx/jmafQn3uWdGkEVe/i66v/PVPnX48ROLSsHMONbvQIsln3td6VWCwWi8VisdgZnOkEhajVUGemcTfefsaTGK9M8NR1ODJtMOlcSXd4iK90fYxM6Tirmju4zNmCDEPCCHoZR212Nm29oSHU5YOMH3NJDu8nG00h58tUAok6n5TZxqaPSVZxkIOs4iUu4maeoEmCGXooMoOLjXafQMED4POfhw98YP5i7wU5X0DrDCE5+bFEIJDzRbvOxwnZoqJ1k5G1+TWxijo/wiRCIVJUUo1JzLCJJn101+X61teZ0YpUgixV2cWANsPAEpv0vXcx/q67qU+1WaElGdnZ4OBUaiEvKyurTDXTHDggGGwdQrdy7Oq6losqX0IGIaLVAkXtjIdZuRrh+8hkiuTiLMmkpFYTC51n0BkBk0xKstm4eBY7t8QFtFjsJ9CJ3bSw2IMsdpILZdEihIUZEG/0OOepSiXYvl09Y5v11q0qzSbs3t0ZAHr3sT9n4/hfY1keU7pOFMKG9l5ydY+X1n+A99Yfo/DgFvTZSYbCvZwfvkSgGEgJTToFtGMso0SeHCUsWqziIBYOyzlGkwSBoWD9sYOSAfOPXdr/YKJ4ESohgTBpqUmG5VKm6eGLvJ9xZZBl9iwpPWKkWeBb8l3cIR5G2klc16BbLWP5bVq2RS1KYrVcdEtHNS3KJZUhMYquwi5/gDDsLBjMsEXSKdEIdGanM6xcaiFzGuLQQayHH+ykb+bz+FdeTfM//SqK0+bIk0UefrqXoiPJZDqLhpkZwcaNweu2qZ8oUGazkr6+N/0WxmKxWCwWi52zXnmCQiZTuBtvX/j8K52a4CmLFvv3Kxw8qNCvVJH5POfbwwyOfYexsI/DwRBXeFtJixZSVSmZfUgB/XMTMDeBZJCMN7dwIuJESUvOl9MStLiEF/gkv8HXuI3tXM4EfSRoA1ChC12XZO5sANBqwS/+Itx5JyQSwN0Q/QIQdTrNNDgZOgCEaAgCFCIMXEI0RlKr6fJmWdQ6io638GhXS6AEPmoYoigAKmoY0t0aoUuZ4kjuEp57///lvbf4pJfk+MIzvWz/XyrNZo6bRt/J+Yc305sXRHYGqFMMJpgIe3jXC39EfnedpkhxtHg59Q/dRvJbX0I9eoQolSJYuoyoUFgoamZXFLjyynBhM/7UdfEdd0Tx8c3YOScuoMViP4FeuZt2witnQLymMxwBrS6/Br/2U+SWmKc9tE+fZf/zVbYM5+hdZnN76TPcvPd/YXl1IsMkqaTxE2myfokH5Oe4MHBYOfkEfqmB1Zyjl0ksHPxIo0qWJDX6mOAoS7mLh/gYn2Ed+7BpA4JZCrSwWXzzKMopCUXeu020rwU0SFMjS0kUGU6t5fv29Xy3cRsfc/+WG93vYTWblP0Uz0ZX8A1jI+9WnsPK22SiKl5xAMetk6yUiQLJfn0tXipBQZumSh4ZSBJRlaawkBIM2caOmrhmitHZBIvakD6yD2U+ojsqdI4SnNr9955+cJIBzz2nMjamkExKNm4M2LTp1fPkTnk7XpVQdO21kltvDd/IidxYLBaLxWKxc99ZTlCcjcwXCNZfgPn1x2jVHCaHB+hXqhSjKQ723sS6mR00entoTOdZ036JFcHLhBJU38cQDWwzRPFdfCvFpLqYfvadfO2FTPjOvzYOwc9qXHLFC1zJc4yxiDwVFBERCp2W0oWVbqH0dApwjz8Os7Pw9a/DPfeA6AXl80C1c1zz1OKZgkQhQD4H0V8J6mTwhclg9QBCRngYIAxcaWIID01KmnqahOkhhIrwXKSqIuwEIpVjdXiEXv6e49a/4+HHe3jmmZND/r8X3suBAwq3tL5HvzZKmEgxOtND3p9mSh1gXBsi4Ve5YvZx9h+5ncSffgrrK19C3b8PEQQIRTmtqHli/fvKdfFdd8XdZ7FzT1xAi8V+Ar1yN+2E15oB8UpnOgLas/URbmxqbMt9iGJRovltLtz/FXoObOXa6SaJniRWVZIaO4DuNQmkiuY65Gni+xUcK0OhdoxVw99GbTXI+pMotOZb2ztt5TZNNAwm6GM1B3g33yZPmQAFicDCoYcZBBLjXv+0a557XzcvfPNyUomI3w9/mbaZQxRyVNQC748+yzudzYy3exljiH5lip9OfpGXlmzi7/K/RfuaEe7oeprhR/agtvfQrU4xm1tKO7GUgeYkCXOGB6M7CQK4XT6MpkA56iJJA4IAN5ujESXwqvXOsFRdB9NaGGJ7ovtv6rJbKInuhYjvE91kr7fDdqaEosceUwkCjfvvP3vhLRaLxWKxWOzt5pVBASc6+PNyloKYL6zZCczND6HteBHqdcxjo6yu5Sj3rWP38k2M917EsvGtHPWHWO7sZS0vo4iIFklM6YKQmKYkLAxBCI0567QxIpxSQAPgSknhU1UK84EByxh+zXv4yldO/nnPPZ2Plfef/hjBK/7+c+Du0tm5fQNZWWEN+9EIqSsZkGApLo6expJVMmoTRVFQvM5xURIJFKUTwBWVa/A3n6P80Chz4btoL7+X3GoTTYPuxRZf7/kwz4a3ctulMwSBZO0jf8xkOEBDpOkWTYxiCqlJtGefpdx+N9n/8B/PGmxm250xMK9cFxuGyolLi8XOFXEBLRb7CfTDzIA41dmOgKrAtQe/x3fHbmWGPO8a+wqr9j/CWNBDKTHEOmOSpYefZjosoBKhEuCj4yNQAh+rXUGqOmqjhtkso6cMTHeOCAUHAwMfD4sxBhDAWvaRwKVJAhcLff7VJAK9yyRxZ/u06x64c5yH/90gQ84YJTXHpLaKZfkIdbjEurmtTNLLbJTnfLGXxeFxulo1bjvyJ1iW5NvDH2P5L67lb19okC9McEnzGRbN7GTIn6SaTvFdaxMPl+8jUCCKBNdEW1iijBCYSbaYN7NETtEdTmG7PqJeA0UhWD4Itk27DU2vi8beMf7md5scURctRHzfetUMBjkkZ39PznZ0VlEkzz2ncvPNr3/0MxaLxWKxWOzt5kQH/wtbPDYc+AoX1rYgu+oMrE6gqKDOTBP29RNcchnhsVGinTUOpC9h9/kfItGeo6WkMEozDHEcMilotjA9lxCVum9jV2ZxVYHVpTPrZWmRIEkdUFCIEKeM+tdGImSJVwUEnMmBA/Dww52Pv/rVzt9Xr37958kSOCM23aJEWlapiS4Kcg5D+riKRVkUaMkEUgjsdg1FhECESCXBMBHtNmGzghvqBIZNwvB518xmokOwL/ch1q+PsG1YsiRiz55udrRyrIgOkJFl+rQWg+okaeEStQwqdh+VIElzpEJ2xavTT+H00ST5PHFgQOycFxfQYrGfUG92BsSpXusI6LLuUW7bMMO2/ZK+Q9uoWj0Uhor4kwoVN4UbaiRpImVncKoQgFAQkY8iBW10XGGRp0HT68JCEqKgERLMjzb1MSgyQ2I+adPBQqLgYtImQYImmXfMoeZPT+yx8w5rb3iZyW/3U1LzlMuCIFBYHpboVuao+CmuYh+LGaehZJiVBYreHBcceYS6TDMycj/NpmCgYLJz6D52rruPhFOmLHI8f7RIYVIQBIJHSx/mO+1bWZycQy3mcMoOH9U/xw3ZPditBmgaYW8f7sq17N+jMDqqEE2UcFsZ9prdrNrQZP3eB1n05FYaX66xeF3iNRNSKxVBsylelVDU1QXHj4s4oSgWi8VisVjsDE508N9Z/TxXVB6mpPXyUmkJ4eFJVo0/Rbh4CFVRUEZHMXyPXiIuOvgVXhy6AzdrckisYKXzTbJqjYafxXYCMtQ7RyJDiR61cUoVdsvLmI1yjKuLGQiPk6CFBBQi1PnDnMG4CldK9M9HiFMCyF9+GX7+52F6uvN3KTsFs2D+gEGrBevXdwpoYr7lrKcH/vRPYe3ak68jvw/yA6COB2QpkaFKKDUilIU1uRPqRFJQ6hqk2xunquXoqR9D8SQmLjIICSIN10gT2Ema+UVU600uc7ey5eBtlAe6yOWgt1fSakVommS0UqAYzbBYHifM5HG1LrTApWf2ZZTkEPri7KvelzONJrnyypBNm4J4NEnsnBYX0GKxn1RvcgbEqV7rCKjoSrHxg2muPTZJplzF7RtEGhKIGNmfoCGT5ESZtpqEsIlKhEKIFAJSNvqixTT6L4WtL2OLBoquQKSgSRcHkxCVBE10PDw6v/Q1AnyM+VGsEQoR1qbGwnWdmlB04V0v8ug3NjIhC4BEOG1uih7jcm8bCRoYuHjSREhBkxRNNcOwP8iFk9+k+0sH6dk9gqH4iHSKo4uuYeeae5msJMjnJfm8JOPPURAlDswWGC0N8s7JL3O5u4ULltVYvFihuuxS2iuvobDnexx/scqx8Sw5pQruDE+kN/HyTDfXPvVZrvAfppToZb+zhKJfIvEaCanZrDxjQlG1SpxQFIvFYrFYLHYGJzr4l6ZnuXBkC61UD0GyB6MlGKmmWaFoaMPHkJMmMpNB2gnS6Torpvdz/3Mfp6F0kfTLJAwPw2mSd2po+NToIkTBwunMIVMVjjaLqLqgQQG3PcsxsYJiNE0vkwiChaRMDoG8BuRvg/IrnetcuxY+9zn40IfgO985870EAezd2/n4Xe+Cv/s76O8/+fXof0L0CYj8zhHMJA1MXFTa1MigKgLCkIKcJsCiYSQZzlyEI5JYXoOsO01kGEhFpaWmiQydSnoxigBFRpitOfSRIxx7Ist4X44ol+cDH/C5+eaAxrBLz34Pfcoj8AIixSQIQIs6YVlWDl65Uj3TaJITQQLxaJLYuSwuoMViP+HO1C79Rp7jX3k15sMPIWtVokIRfO+0I6CGA1PNNBPbmsxpnRhrLWNTnc2R9cq0RBKpqySEgwx9mmYOe+0ywsElfJmf4a7BSS6c/FanU00VtIIEEKEgKTBHhRwKERlqJHBokphfDHhIRSDu6nRjvTKhqP/uCa78hW00whQP6+/jnvDL3O9/DhV/PgUJDBwGouM0SXKUdXQ1xji/uQPlYYWkmuO4toxQTbLuwGbqdcHOrg9z+7vqrHzxQYJtz5LV6kSJFO02WNE0iXV9DF66iKM7GwTPPsuerqtwrJspju1k0B7BN1I8ltjEI9Z9KOUSi5pbeTnZh5frQQeaqV5M6+wJqfk8Z0wompsT3HJLfHwzFovFYrHYT76zzcD653Kig391qkTCmaNpF9CDNqZp02wl8FUTrTRD1NuPKJcRrRbC99E8h8ur32b2ivdw2FnK7he7UcIqixhngj7m6CZNky6lRsnsxUwZaA2XFeYII+FSDtsriPyIK+QWVBkuXI/WGayC9CH6uMD9lo722Qi9N6C/H554An7v9+ATn+h0ob3q+yfgd34HPv5x5pMzQU5B+BEF+U1okkSiYOHOj1KJiIAqKUSkkZZVbFqoYQtjxkdTZphTunnC3oiXTnPnyj10Hd2JV7eZTq3AiEKGDn6Xla0qZtTm9/1fZmZmgMZclvqF13Dre27HsmDRlr/FlBO4ZoTVGsNrWzTsbpxl6+hZbuO+IrzsbKNJgHg0SeycFxfQYrFzUbsNnodoNlAPHgAgWrQI5973LxwB/eozvbiNd3C9uxnLklTIQq1KaCQZ8QdZER0kFdYJFJNZcxHtdReyrNth8vzrGH5pMb8/8EdcMvNZLm9/l6HwGEmaNEghkfT+7BTyCoFGiI6PgsTGWbi8oEtB6eksSF6ZUKT2Sm79/Nd5Z/V7/AflTxgQE2RlBS/S0Odfj+eAvwow8Ui6P0AQEUmFMWUZoWKzzH2ZylSFStdiVk1t5e473sNdPEZi5lEOL+njWGWIRH2aDXNP0iosQl20iP07Q4KD0yxrHWRJdRd7MlfyNecGZpddQ/78Pj7/jT5qZVirjpGiQUkspjknyGTAsiRSee2E1DMlFN12W8itt8a7dLFYLBaLxX6CnSHZ/bVGV/yoZLOSrNliaP+36ZnZhwgDXDPDjD3EaNc6hJmAURd1fBQRBJ3TEqoKikDxHLzhaXZWVlPxUuisp5cZfHQy1PAx2G9ewIi5gqWM8/fZX6BvbZpxp5tD5W6Ucon/Z+43WBIcRiea38KNFhI5FUB+U2X86mUUPj1F6sYKigK//uuwb1+nw+yVPvQh+LVfO/l37+sarY/a2NMOKgEJ2vPTgnV81UIJGwgEi5gkkhpCBS3s9MIRhqSiCgU5xVL3APuMi3ghfzfXXncR/ubvo0w2yXtjNF0NO2qiErLEmCBR6KKsJSgc/Qe0vytj9qbQt24F08QcyhAEoNfqmCt7UJcNIKLoVeFlZxtNkslIxsaUeDRJ7JwWF9BisXOQ/w8PoX79CZpDazDWn48yN4tSr4NhgG0v7BxZF9zLwJxk2dhWBvwRxlwb0W4wpIzgigRhpEIEKdmgR5/E3fghwuvuYvZbgr17U2wJf56+zP10hSXqdUGWMv/tiv/K5Z964TWvT+fkbt6ZEoqK75+jyNxp6UbJU1/g5yDaBWyXnYIaEomCSRvLKZGNKvQHo0i5F6nkuWjFHVgPb0P29bC82E1/G9wRHftbbayJlyl/s8RAu0lG1lAMFTUKWNN4gax7lM8f1Zk+/2ML/+k5maelpMjIKk3eeELqmRKK+vridKJYLBaLxWI/2c6U7G6+xuiKN+tsnW35PNzHlzFe/g5HvAH6glHCtkO+upduo4puOKDr4Hmdli5F7TxRQmSaRJOzKKKNEAmmRR8NmeYQqxllEZ6wcX2bXn+KMTXNHnsFT79YoKtL0mW0+Sn9C7xX/TYSE4LW/Bw0wYnDjJJOsS4/VWL2jl7Uz/vYdzSBziWdyamfd79q4N2jocoQMb+OVQk7m8IE2GELgcRVbFwlQVrWUKIAicBFR8fDlB4RAoWQFcF+Rp97nPrlt6NtuplFn/pL3FDiyU4xrmrnsSwYrO6lYGRQ2i2yn/lT1P4iweo1oGuoBw+gJZOQT6NNDBMZAu/d7+m8N7Dw3pxtNEmtJuLRJLFzXlxAi8XOIe02fPPzVVZ8+jncoI9WqZfBwYg1a3Lo5emFI4aVSjfNpiA3aPFC/4d4ecVGlEqZxDcf4aei7yJTKVw7R+C76E6Nip4nnx9EufkW2o5Na6REb61CRckzFnYzpRVoq4Ll4UG8UROnZGLl3de93n9KQpEY6XysENIkiYlPbzCGgiREQ6Kiygh1epLo//sUftpGXd4JVbBtaBwaAcdFVSRS0+mKylg4+J6BbybwzBQFv8St5c/zhwfvxDSLpNMwXSvwvHUtm8KHSWQkvtVFOF5Gdc+ekBonFMVisVgsFnsrOluye8jZR1e8Ya/T2SZKcyw6upV9Wi9z6jpWuXvpD0YwZI10ZRR/qBtnaC2J4/uJDJNQM1F8B63ZIEh1IdsRGUoI2SapNhgJFmMpLkbk05IJ8sEURWWKr2l3MuZ0kw1nKYQlNqmPclfjr+mWUxiahID5zrOT6zeJwFcMVCRKEKCs7pwoiCJ4tFNbxDDgoovgpZc6Nb7HHut8XVGAtTCj9tEXTBCioNCZR6zM/ysADx0nMomEQhQJhIwQ8//o+PMZoZ2rSkQNZKnCzr/awdNr/hW3G9+jmszTasCl/rNoaRuzNU3CKdNSUjTtAsVwAuXYUaKuLoLzLwRAGRslarlEjounJTF2vIS+/dnT3pt83j7jaJKZGcHGjfHxzdi5LS6gxWLnkM2bNXY8XmN90CDIL0YJ4eDBzpCF9StPHjHMZgun7Ry17AJeRXBJ8wVCoeHZOUKhUfN1tACE63D0BzWm//AHREdH+I/TezBp0IhSbPHewVeC+zAMm1I7z8x4D9+++l3c9LlvY192soj2o0wo4gMQjUOIhkqIRBCiYdIiQJ9PBZX4UqNNCuf5/Rzs3kCy0mDocoug3kZOTOOZSaywiRk25wfJRpjSgUBDD13aZpolzgjLOcwLQZFEAtasCWn33834RMjikW2Y/gimZuPe9OqE1DihKBaLxWKx2FvZayW7v9boijfiTJ1t4uFHqc0Iggc+gH6sTGOySXJgiLSt0g4uZIIVTNVqJGeOo01oTFpDXBLMYrtNhBIgFR1LT6IiyQRzXBI+ixa62LTYLc5na3Q1iznOYkZokOKr8i6+FtzOh9W/5Vqxha7WHNeKbRjCgygiCkLEaZ1nnWKaSkjSr1FSejn2nisZWnMYgKef7qx1L7kEvvAFWLWqs979wAfghRfgmWfg+uvBXOvhrzfwdln4USdlM00d6ByLFEQ4WHiYWEqAiKL5qxCo8yc5JAqCTlHNEwaJqIk3UWIqqxDl8hiBSqmh47kSTVYx3Qo+BjWRoTvhQroL6Tiow8cI16zDWXMBh73VONUxss4I7k4HbVmBoQu70Vundx2eaTTJxo3BwudjsXNVXECLxc4RJ45lZvpyiFKKZFBFJi1AMDamsCpTw5o/YnimofZKuYQSBrSVJFHFpRrqhCHokc5yRogaNbL/8Duk3VmOKct5XruYjNLiffKr2Lrkc+pHqHgFvhe+g/cf/BIT1/Sy+LfH0H+l80v+R5FQJP8n8AnAP7ET2Nmrs3AI0Bd24XR8AtVmTnTjJzNkvRnm0kOEh45wHOjpDjDdOoGeoJQqkqscO2UxIggDiVmbRWpJrJTGL350lsEf7OPp3d1kFuexMxbf0T6Mo9zGbVfPsPqD6TPuvsYJRbFYLBaLxd7KXivZ/bVGV7yeV3a2BQHsn05QO6YSHXyOh/bdwZIlPVws02Sp0NJ60TSJJEFzusGs10tPRhL5ETuVi1gd7gXDQNMEdd8n6zexVYdE2CIha1i0eQfPcD67+Cbv5g/5JY6L5SgK/Lvg/3BD+BRj6lJaIoUZNNE0OgU0JPorMig7U8gUBCE5f5qLr/n6wtcefBB++Zfhd3+304EGnc3hbds6888efLBTQAMo3FvG36Wj4xIIHVckUNUQogg9CojQUYTEN2xEUEXS2RRX6RzlFPM9aCEqFSWPiYNiaOz3V3Cs9zKu2/MpljbbmF6VLq+KRsCEGMASHlmtQbRkNfg+2p7dKOOjHKovYfJAg4GwjmZpTKYGmZjqpX0sYv3607sO7XzhVaNJ4s6z2NuB8uO+gFgs9qNxYqCn2pPn6KJrSLemSTWnSKpt7NoUcnIG/4qrFgo9mzYFbNwYEEUwPKywZ6Kb6bDAuJdHcZpYfpVEVGcJx0jSoKz1YCg+DZlgkTLOCnmEiaiXadHLFf4W+huHWC0O8FLyamrpftJBC/XjIeEtnYQhYCGh6Hd+52SX2SsJ0Vl0fPObJ4tncgrCW4CPA/7JKG0xX/Q60fbe+TgiVA3qRpbItOnyZwlUnWdXf5CXlmxidkqiVUtIVWXWHqRu9yDDiBCVCEGEgoaPGnpkghKJlKD49c/zr4/8Z/5H/d9zw74/Y+a4SxTBO+7s4vqfXXrWY5unJhSZJgsfP/ecSqn0o3nfY7FYLBaLxf65nEh2V2emETPT4DqImWnUmenT1pVv1onONpnJALB/v8LBgwqO1UXeaJAJSnz7pR6269diVWdINafQAge7NoVVmeEHXTdxZPmNJGozNO1uRhKrkH5EKqjQThYYNVfg3PBurLyFJVxaJKmRxqLNLTzBvxd/xofk3/G18D38Mn/AxfIFrg2eZGl0FCkUAjpdYT5nGmgmTi5kpSRxR23hKz/zM/AHf3CyeBY2Oo8zDPjDP4Sf/umTr5K5q4xUVUzVJy1rJKI6VtDGCh0UGWDJFqmoRsopdQ52Khquluh8/+aPevpozGp9KIaGTsDO3HXMygK+L3Ac8H2ok6Ep0oAgKZu4bcloai3BmnWEvf2Ey5bjhTruwTESVsToinfQSvVAV4ZkEsbGFNrtTtehaDZQKuWFe8jnYfnyuHgWe/uIO9BisXPEiYGe09OCp3vfh+/B6pmtpMqjOFoK5723oZ9yxPDUofaf+5zOp5/v5hl5LfcwTgQMMYKJi4lDSelhl7iAK6NtVJQ8Od1hlXacYbmKmpvgymArn+Q3SGcU+uUEmtNgl30ll7eewvymg9wQovwtKLfIN51QJL8OwUcVxHSnQHaieHZi90/Oz38YZzG26dMTTGGoId2tMURrFIBZpYjc9jyfzn6QXPI2fvdnJ2gVnyTx5DcZmnuZUGq0sbFpd1rjBah4RFIlEirq+DiiXGZ5q8lS7zk29r9E9T/9HrmBs5/DfCMJRX19P8w7HYvFYrFYLPYv58SICv25Z1HGRpHJFO7GV4+ueDNO7WxrpSxGRxWSScjLCoGVxBrIsagh+W79fZh1yRXtrWTao5S8FI9qd3L4vHuoD0kOHlS40t+CI7rYJS6D5UsoFVexbvdX6RI2+cY0nmbhRgmiMEIBWiS4Sz7EJr6KiYNGiINFFxWsqE0EGIGLIMTDwqZz6oH5PyUSZCdUwF1pkFrfWrivDRtO3mP995Ic/ORK/P9qcOXHv/+qrxsXeETLJM4RizRlQOCjE2k6SuCh4yGQBEqShtWNFALDaxIKHY2AppKmbuSw1QhFtPmudTOft/4V3WKOZTPf5wX1cibVDAmtjZW3uKDxLAPOUQ5pqwm9xeQnSiTqczgf+BCHVr+Xz/2vJukl2c6m79OHsdwqrmVRqwkcR5AIOl2HUoJ65NCrQh9isbeDuIAWi50jTszU2rJFQ9NSPJ34GEtSt9PXNcc1t2c4/6e7zvrcHTsUGg3B15L3cW1jC+ujvVTJIBFkqNMSCboaY1SFiR65VF2Lbr3KykVNemb2UqxPMp5cg7U4Re/IS2i6Q9jVQ9lajtkskZhroNwRoD0YYt7eBt5YQlH0VRD3gCIlARqCYGEB46NRFQWkomBIj5HiJQxcOUDP97+CMjUO87PRanQRKAYPVP8CTxp8wfgI3xnJcOt/GmBirsnirXuQUhIIA19LoqsRqvRRQg9X2CipHImpSWQySdTTg6hU6H7h2yS+8uf4t91x1sVDnFAUi8VisVjsnGDbuPc/gHfzLWdMy/xhnOhsMx9/lLAmwMnRbVZIO9PsXrmJll0go0gyvRbN9zzAl/d3Aq9aVo6XZ4osKkTUA3g69xG2cis9wQQX15/hBu8lFu3fw6LGXpTnTSLXx1HSAGgEhKho+KSp42DRxiZJGwMPF4MMVXQCFEIEoFHl1EMTJ09BRAToqPe+eiRHNCkIPiLgiYgJBvj+r17OgW+t5t7PPkiir33aY1/+4Aa6/kpl+dRzKL6Lp1joBFjCQ0iIUBlJr8VLZMnVjlM28mRyPt3141i+B34VR+ni0KJ38Omu32Z6OMF5i0YwSg1mgyW0sMG2UTXYm74cNQpQZUSqOoLn2KjzhdBU26bW20/Th2JWcnTRNZx/aHOncKZ3kSqNo82MEfb3k/iTP35V6INot35kPxux2E+yuIAWi50jNm/WmJkRLFkSUqkoNJuwu95N4focl7/X5cgRsVC0OTGroMAc7Z0VvIk+oqiHrNXCEi4vKRdTknma0uZatpAMmwxynEnRzyCjBG6LqqfTPF6iJzyGu2gpYXIIp1UikBqe0UXem2TW6qcncGlaGbxym95VY8AbTyiK1qqEUswvYqLTFjARGjYOc+RpRSbD7R6q+xTsdhc9Zg0ZgeOpjBjL0ZUIM2xxRf07vLB6I4880s0LL2SxrJ9BL+ylUDpEUSkRJjNEikKyNYPuNKnbPRT9FjKZRCaSAMh0BkolzL/5G/xnfoBWzKG882Ri1AlnmjMXJxTFYrFYLBZ7q5L5wg8dGHAmJzrYzKefpc8bwSXF7pWb2LnmXqCz6djVJfngB30gRaWSxrIk/+t/RTz5pIamSTxPMOkUuUR+jeu1b+MGPYypKykWy2TH9qBGPlbURFVMFCWkFOUoMglINHwSSAQRKhE6DdROf9n8CYfO1LEI5kd8nDxVECFoKBmyd5dPu6fgcZXooxDNCHRC8sySpE7jiRTf3XA9g58ZY8PGXQuPL942TvvPBqlqecIwJB/OYOGApqBoJmEoKLRG8dvTNO1u7HyCnHMcNBVR7CFoaxyyL0apNbkz+xjFmz+E3cpSnUmTE1Xauo1pdv5bdtjigHUBf5H+T6xbF7L415IoKzrvZ94+fd36/LJ7qdcFS449zWXa06R2lSHwUY8eIli6nODCDYhWC/PRzWjbt3VmE78ySdVI/ch+VmKxnxRxAS0WOwecmLfV19eZsdVuRziOoFqFI0cUfvu3TVy3U7wRAgZyLd5d+kfeIb7HkkydX5rKsMK/kj5/iqujLTjSwMFmhEUIInqZRCVEkT4RKinRZIJ+bCOkFhbwikNYjRZHJhMscg0MPGy1zVSul/QSg+zUATjPwVzjAW88oUhbGyLPU1D3dpYyEZ3BjSFQJ0VLyaBLj1FlMYb0yGghRujg+hqKoRKqnTGPnifRopA0ZdpjVfZ4PRQKEX0rchyqvgtRq9EOEvS0Zsl7U5hhi4peIKM20OdahEuXL3yvxdQUQcOh0krw0rEi6qjCsv2P0eWB/PADp70vcUJRLBaLxWKx2FnMd7aJm2/h8OfqPLatiNWbJxNJajOv3nTM5yVf/OLpG8ZRJMlFJa4Kt1DP9OBYvSxdEaF0XUTtkUmSbpUkdaKwRV3tQld97NCZn32rEKIQ0RnMr833l0UoNEhg4yLorH2PspwiU1g4qEgiBDoBZDvXJj1o/OcM4n9H1MlQVTKkoyr9THItW9nFhTw4fQ/itoif+aW/4Yr/8X1UI6KYnuS7M+tJRQoeFimqhEJFRiq2EmHmbdJegGiVSffl0EUFdapClM0ie3vI1GqsuzSL1wpZqz3Dfb9xE3MU0P7hCoa++CjKJMy1ukj5VTLBNI/pm5juWsmdt/nkclMopxzFPHXdemwqyczyD7F6oEZ2Yhi/ezHagf3IwEedmkQezRCefwHi0EHMvbtwr7luIUn1RFqn/PCH/4V/oGKxf35xAS0We4sqlU52kr1y3pZtg21LhocVjh5V6O2NaLVgdLTTw7Wp8o9cWt/MMXpw1gwxsLjGv574CyI/wBMGLiYhCpfzPCBpksTCwcBHw2GnchF/Zf57rtVf4IJoJ9HOrVh6mi59ANUPGfCGkb5gXet5smaBI+oQiZ+r08cE8OYSitR7I+Tezk5ggEBDoiIpMocnm0yJXp7Pv5s17V2YQbqTwNQOEJ6PqtoMhCNooYciIhpaN7MtmwBoNgWmKTl08T2Mj8OSkW3o7RoGOnvVi5gduox3p7Yj9u5AHTlOOLQE0WwSzlZoRClaiTxqrotGlODlEiz5ynb6brvltLb1U+fMxQlFsVgsFovFYq8m8wWu/9kC1R6N557jrJuOZ9swTk1MM7irzsAVi8j0BNg2RC8coq6ElKw+nEAlH86RiJoIEeFi0cYkSQsbB2C+jNbpNnPR0QlQF45xhvQzgYFHgIoyfxR0QlmE+BkY3HQU+SWV6CUVp6uPWttCb9fZwQYm6SVBm//J/49r2Mb75Zc48kfLOPyd5Vz4wE7a/5jACXWaWCxlmBQNpBQIKXEdm3DleRjCR//B82jlOTwrhZFI4hd6iWoemmJgZm0iWxAcH6N2rIy9rsBjXe+DrEbP7Fb6nREaforHkpt4adX7+Jm7a/yU92WSv7XttK4xNt3N/ffbC+vWvJxl8E9eQq5YjtA0RBQSZbPguKhjo0SLFiHKZaSmQyoFpoUsnkzr9G+7FVLZf+kfp1jsn1VcQIvF3mLa7c5xze3bVZrNzjyt9etDTPP0eVvtNhw7ppBKSfJ5yaFDKrmcJBuWWDG+lfZAEV/r5fg0XHtxiHKgSbksOBQsZSnHcAEDF4WIWYrs4nwUIpZynHXhHn7d/U0yfoNWKoPWnENXHM73n0eVAZFh4ik2euAijg4zZd3IVe/9xsI9/MzPnD5EVTZApE4mFO3YcfJr4n0Q/ncVlRADSYSKpxiosrOgSss6ARq7kldyrbIbx0iR0jQU38GKWrQDHVWE+ELHCF2u5zt8rfvDzMwotNsRR44k+X79Y/Rp1/K75m8wxkpm6YaSxp6l13HR4BzqxDjK1CShauBIE9+waeQGiSybBBIv7KI2PkLiWJkMvGoGRD7f2TWNxWKxWCwWi73aG9l0PNuGsSpytPemSQY1bLsI7Tb25DECI6Iss2zLv4eoVudS91my4SwmDlmq89nrp48IUQAblxD1tP+2hk+Aio6PRkiDFNNRN5mtdWrf7yYhWySDCulmmzwKs0qWJeoESqRQDVP0Msm/4m/oZYoIheSOJuwAmxZX8ixz5Ob73yQC8DBoSRvhqLBqMXPHHSpNgxmnj/7ay2jVOroqmcyuYeTpNOnWFDLI8IU/7WMOk3rdYmjth+i74L0442X2zxRY/84sf/5zPv1PfLkze67Y86quMff+BxbWreqRTlJqNDgIkUTqBsJ1kaaJUq+hVKuIVqcAJ62TY0xkJoMyNoool+MCWuycExfQYrG3mM2bNR5/XKNYlAwORtRqgmee0cjnJTMznSVAJiMZH1doNgXr14eAwPchnYaiO4cVNKirixeKbn7NwRSgqjChLMH1TNYoBzGigBCFYYbwMFnFIRokSNFgWXiQRNimWUvjRAZKu4FKmwiFQ7lLmTYHqdZ1ujSP/uxeulZMLtzDqcUz9/cM/E+qaP81xPq496qviwuhvjJD6lAVnYhIqJ1pFUIjUjTUwGdT6bOMFS7AkCrHB65kd/Za3nH086REAz+MqIksB6wLmNP6uN7fwu7krZT9ApWK4NgxBc+DLsNhKDiOJR1EFOLWDJq7B6nd9C7Su55FdhdpOiqt6SM0u/qZ6j5v4RqzVGhLC/Gt75Kc3PnqGRD22dM6Y7FYLBaLxWIdr7XpeLaApkm/myMD13BV/SHERIB29BDK8WG6IolqFlgtDtJSQmzZwhQeQhGEkYaBO5+q2Tm2qc4X0wQnOtI6xawTk88UQEUSoBKhkomq9IopDGDO6MOOmgAIXUXqNpaucom3h+NiCZsaj7CWl5mmiIVDlgoAs+TR8BlilL2ch1BVlkTHKMscQsCyiaPMNiSbkx/FHjBYdHQr9cCmGFWZsZYy4eVJvvwCadvlqcEHeGp3DxMTCum0xDRDsmsK6OcV6J0RTE+DUp5D376NsNiDLPYAnNY15t188jTFqUmpcr7Yph48AO02UteRQQBBQJTNnbbWFbX5tM5c7kfzQxGL/QSJC2ix2FvIidb1YlEuLBxO/Ol58M53BuzZ05m3pWmSpUsjslmJ43SG8rsuzIQFHC1Fb1hhzu3DMGBkNkGxIghDiDIpXhYbGA8GeQ+PE6FyQKzjKrmVJglAkKCNTRsHA8KQMlnylHExCVBJl0fJiuP4mk2t3cPSTYdedS/RJIQfUSg/kSGBg/erCo1vJcl9toraF5322MS9Lfg90RnqKiUqAb5qzScVNbGES4ESuekphkafZUbrx+3uR7loJS/sS1HXcpBI0EUbfXIUrVYiTBfwfajVIAzhRvNpup0pHMWmquVRfYf+yn60vVXCCy+i9fO/SLUKT/+Xpzl/8rsk2nM4ZheWW8WozuCZBbpf+i5y0Zl382KxWCwWi8Vib44ozS109ufzhbMGNEX33oU0fLTP/z3q4cOgKERdaex8lguruyHhE3aZ6NUQgQ5ND8ITPV+gIPFR0eePbSrzs3cl4GATotFOF4l0k1R9gmTQJqm08SKDupEnn/RQAwGqgjB0urwaLT+JQNIXjfE+vkKCJkM4CCKadMKpuqgxSwEBpGnwtLyRipJjUXScBG2UwONZ43p2r/8AZtbi70q305sa56rq17m79BlWBT9AkREtP8H40SLdi+5gVi+Qcudwd88x7mVZckmOTEYyNqbQHKmc7Co7xYmuMaVSXgiKOC0pFQiWLkfUaijHh4nSaUin8W64CXVmGjEzjcxkELUa6sw07sbbO4U4L/wX+TmJxf6lxAW0WOwt5JWt6yec+KV4ww0hd93VaX3vpBSZp6UUOY6glChwuP8altY2oyPIL09TG2mQJIGiQ16vE9gKWiOgSpYIKDCHRRsXixQNIpSFYpmKJFINKlGOopxGEGHiggQRCPrqI6Tvqp92vc7jJvKjEeaMT4YaDjY2bdwnTEobuih8poKy8eTOonpPAL8XESFok6QqsrStPIPBMGZKJaVKRGucpprBNU16tQrpoEVUTpFcfR3jBxWSESTCKpGpMaiWSWZnaTbzGAYM2nNsCHcwbi6l25/ECB3awkKoLezJo7jv30S0YiVpQG5KsfPvm6xu7CXTrlMnxYvpm7gx/TzqotffzYvFYrFYLBaLvY52G3PzQ+jbT5/Tded77gbSrwpoum2Tjte+Bf3p7xL29iKqNbTjx0AoKKaGWppB7cp0OsuEANuCho+A+cOaEhWQKHRWnGJ+nRt1QgVEmm32u2mleljX/i4DwVEOyJWs4DBZb460V6az+FXA9TGkTybl02onybSmsOfTPZM0UQlRCWiQxsRjil66mSNHmaSs8xIbOKStYm3yONYqlcdT/4ps3qLVEszKAl42zwNz/5d0UMbBRhUhhnR5r/MwSyZG+Ds+wkXe9+mO6kQ/0Ahb5/HU4AdIJheRXJw9pavMWvh2n+gai7Knd42dSErVn3sWZWqCcPkK3NvvxL/6GqK+fqSd6LxPzz2LMjaKTKZwN96Ou+lujH+Jn5NY7F9YXECLxd5Czta6Xqt1ZqGdmBdxtpQiEFgWPL/sfWTLcC3fo6iN8Lyb4fNd/4ZEQnJ++/ssU0YoWSn+2vs3uJ7gOvk0Jh4KMKIsoV9OEKFTkLN46IQhmIRoeAjAoo1EoMioM4Q12yn4SQ+a/zlN9f900RVW5yesCSBEJyBLGTmt0rjNRv+lEOt/uAgDtGxIgEJIpwutGqUx2y1E2KZlJkh2KejZFEkjgYaP0faJjCLK4UOcd3kP6tIl1I6W6Zt9Cc9M898W/xGFxQkq51/D5y54H1u+OIs60WBX4mKGOELRGaFLqWKldUR/H/5V1y4s5O7bu43xVIupks4eYwM/WPNTXL6+wZIdzyAzpxfJzrSbF4vFYrFYLBZ7bebmh844p6sLuP/+B844K02ZKCOCkHBwMQypYBiow8cQThtkRNSVQ2m2kIqCUBTEwgHODpUQiUDOp3M2SaHjoYkIS3GxZ0apVgQ1UhxTrkFKhZwskfTalI0cKc1ByAjpejhqipFWN6taOzEiB4UQB5MGSdLUSeAAgjnyOFgUKNFFjet5msNiFTNqPznbJbh+I/JonloNUimJrkNP9RAX1Z9BKiqa9PAxaMoEGVHn/Obz/Bu1zLNcTSKsMeQcJfvSk6zY/w2mbvsw2YFNp3WVnbFr7BSi3SK4/Ar8y65ACE6b8XuCe/8DeDff8qoZwLHYuSguoMVibyH5PGdtXT815vtsKUWNBkgJP/3TCkuX3keBm6gdK/P1/22wb6+Kn8rzveJdZIISk16e483uzow15U7eX/9rrpPPUFZyrHL3k6GCjocgZJEYJ5QqIPDRkCgA8+UuCD+k4rzfQn5OoO6GXFQCXWdKLibplchShflnuag40qb+RxrF78yhPBDiftkgRMXAJ0mdJRyjLnI0lAxj4SCDXoNs0kSTPmGpQlhvIFNJlLaD9oMfsK57GJnVCYSkvnIZ9mAOo12Drz5K0dbwUrfRIA3NNjvNDRSLq1i/rEl/f43ANoj6+k5byC26ZhHF6RrrJ7dx8+V5jLtvQTmaRL7B3bxYLBaLxWKx2JmJ0uvP6crnC6+alXbazK4Tay9BZwC+nUDxXKSqIHwfWm1OLZ6d0JkcLAlR0QmoKnmqejdW1GJNuI+MVyFAR9HSGFaE4yYJMGknijjNKrlgFk1ItMBhwDuAJj0CoSOEjhm5hGg0SJOigUBSJ8MaDuJjMmysxMRjQ7iDw2GTscs/xIqf3cSVm0O+93CV3vQcK3MFrL3HsIImhuITKDpuYIDoHDVN0CQt6yxPjLHIPUqLJKppskifYuX4PxJsDk/vKntF19iCs3QAnvaYU8h8Id4sjr0txAW0WOwt5kSc9ytb10+N+T5bSlEi0YkGz2Y7xTjZTtB98Bv829p2xitl2jM6O7LX8XDuY0zXEyQSnQTPQiHHF/b9B6Zm+/mo/1cUKKHj4aPjY2DJJjUyBGg0SeJhYtEmSadzTHxfcPSlVSyRx4AQXfpE3UXSiSz6oSmkFNTIoOPjY5CghUBS2pEj2KFh4+Bh4BBg4NHG5iH9/dTNAje0vk7QLCOHxwjqLVSn1Xm9UkRVX8R48jx6x0cwwhbHuy5k6thirCMtFM9Cc7KsTW/lyuvfS82+mvWHNmMMhqy5IoXte6gzNdzrbwd41ULOWGwhLLD3Pkvz7lve1G5eLBaLxWKxWOykU2edKZXya8/pOnYUXtHtdOL5wfkXYDz9JBw6iDoxjtQ0MAzCoSWIahWhqKBGiOjEbC4BigApiaRAnY8MCFFpaWkOKut4PrqMbrvOJeEzhFLjgFhLkMiwQjuO6XrMiQLCkbi+iRBd6DIgHVUItQzVUMWULRyRxKaBSaeg5koTD5MsNRqkOKKvYUxfRlarkQnKRHaSNb/8bgB+yvssN7a2UzvUpBalOWivRGoaZlgFNUnSCvFCFd1ziRAousJyZZiWlSSdS9Jb8DDaHkFXaqEA+XpdY2frAIR4tm/s7S0uoMVibzFvJOb7jRz1hPlfjo9uZrmsU0xWcEstLp5+jhW1HXzq/D/iXbdr7N2rYhiwYoXG2M73Yr34F1S9LH6kktJcdHxE4AOSNjZJ6uSonBYLHqAy3bWCjB+geA5FbQY1lSCheGi4REJD6CZNz2aYxRQo0c8YHjo2Dirh/E5dhEpEiMa17pMc8ldhCoeMO4PabuKJBAiNUEIyqDIb5VG9Nqn6JFl3GtFssUR7HtdTCAKQqorassi2Jhi9+h50HVZNbUWfGEXkT+7GqRNjrztw9Q3t5sVisVgsFovFTjpDp1Ow/gKkab56TlepjDI5if3pv0AEYacr6uJLAdBf/EHn+ZZFmExivPwyRBGhZtHqW4VctxbbKaHt3QOuh9bYjZASOX+QM9QtIi9AI0KiEnaGgjAYDNMUCXzRhSUdRtSLKKm9CA+mEoM0lAxa6PM078RXwcxaLHIOs67xA76t3MyS6AiXy+2osk6bBBJok2TW6KOs9xB5IUfVlQypY1wbPEVSeEgpSZkaavVltM3DmN9+lMSqHtrnDRDM1bmm+gyqVLHG25iBg9B1IlUh1CVeZCNUBYWIdLdBJh8RNT0CxSAqFFFKswujRc7WNfZGOgDjzeHY21VcQIvF3qJeK+b7jRz1PPHLUTTq6DOT5HqS+H3dhLNV7nKf4Mab/xL9vnt5/HN1HttWhEV5rl98iEU7J6ma3TTULK7tYSkBqtckV59GDTws/IXC2QkKIUtaB5jJL2dVaweaZoHTRouaKIT4QkeLfCpkCdFpY6AiyVHGmo8Y9zvT1JCARYtl0UEOKSv4gXoFfdoMMlDwhYER1BCycyB0SXSMntIUDVIohPR5x9G8AFdYTNBPGKokm1VSL25h7rbz2X3JB9ly5FZyH51k8MLsWWK8z3JE07bjGRCxWCwWi8Vib8KZOp2MZ54kzBdQZ6ZP6+zXd+0AJDKRJJr/nP23nwYE/sWXLDxfGx8lTKY4kL6Y4UqO5nQCvQzL8xZrkhki3UOZj9gUSAKpIjwX7URggNaFKkPSYYVQZlgp9lPxeqgpWeb0XgxNdoK52gmOimWskzspyFnGrCUkowoJxeW4WMIy7wDLOYKOhy4DkjTw0XClQcGbpBVYqJbGpfpuMu1pGlECXXXIhbPoMx7Or30CM5cgWLYCWezBAoKUReXRQ1hzLm21QCYoobg+igoiXyDKFklHoAUeNa/J9BEdw28xmV2DuS9g6dDJ0SKlEmfcjH/dDsBTZvue7TVisXNVXECLxc5Rr3fUU6mUEeUSolxGJpPIRBIN0Pq6UKYdCo99nmj/89zjBlzSSvO9g+9gWF2KEJDLQ8KMKJV0XEWnuzGFEbRooZPk9IkSEkGEQtEdwc+txMrmiJI2BD5ypoSwLHTPhwgyVElRw8LFQyVCQ6GNBDR8VEJcDMR8klFZdKOJCGFbzARD5IIZolClLUxCRSMbzqFHLimqaIRIIERDlSE9TNMkwXFjDb2Tu5ibOIjSEgTJPPaFK5CnLAJeGeP9Wkc04xkQsVgsFovFYq/vtTqdhOfhvfMGtD27O539qtbpTlu+4uRjUxLR7qwTSaURkURoGmGuQHvfOJOlkKA7QVb36Z7aR+7wATyrPX9c00QXCqoM5k9NdI5utrE5wgryepW0O0eGOkh4KriAgcQsyzJljrX60LROmGfF7mdYtkhmNc5jmOlmmm903cd6/2luaT+ERkidLmxaWLTR8YnQ2G9toGT2s771PAP+GHWrSD6okfFm8EPBHEXCAxUy+gS6nkDp7wfg8C6Xrskyumrx8oqNZGcPk6sep0tvYVsGwT13IYs9zPzZl7EmjmCYacqFZTT0Au6hGZ5ddjtr7AKbv6ixfbtKs9k5nXLllSGbNgXY9hvbOG63YfPms79GLHauigtosdhb3KkzI04t5LzeUc8omwNNQ7SaRD09J1/PdcH3UcfHiJYuhSWLWZarMTT2ELMX3UjKWYQxcZw5T4XARraaJKMyDVIEik4YNYkAZb7I5WGiEWBGDvWJNk+/9xdY9+/fwXcf89mxQ+Gu5/4LG6a+DYL5hvkIA5dZcoBKmvr8a0UEiIXBqyfa4PUui3Rax58K0P02bbML4bgkwwYKEcH8EFgHEwMfMV/Si1DwMTmmrWJNaxf93/1vBFKjuCxB/xNXdo5enrICiI9oxmKxWCwWi/3ovF6nk3/DTbh33dt5XLWC/ed/ijxlMSucdudPKdF27UCUywjfI5AqXtNnID1CM5Dk54bJV48QCI0RZYjB2suYeEihUBV5tMglOT8qpEGaZmRTd2ySpFnKUUAwpI+R80pkywdQsheRWZdnTW8Zpmb4sv8RDhavZJE/zO76Uo5Wu7l69hF8DCQhQhG0ZAopAQRNkhyRywnMPMNRlZ7GBFEgyXpzuJFOVS1Q13Mk/QpVD/QXjpM/fx0tbGZHHAZoEFpJAivN1NDlhGMGonYUy22g79tDqWuQ/73+z7ki81VWOHtRogBLF+zs3cT28F5Wf17j6ac1ikXJ4GBErSYWTq3cf3/whjaON39R4/HHz/4asdi5Ki6gxWJvVa+VjnNK4edsRz1lvoB/1bVoP3geUakgu7II10XU64gwIMp0EQ0MgmkhixYq0De1i+CO2yj95Zdwy23ySptQ8Qilzn7jfHqjSXLRHBGCEBWVCEdJYUQONTXHQxf9BmPicpY+FvL00xorc7M0STCh9NMdTmPgEcxHh6doMcYiLFpYuGgERKi09AxqJGiLNIFus3i1iZEcJD/9IiIKqRpF7GCOhFclQgEUIiQN0qRpoBLSUtKMqENYiscadyfd4RQVfTWZVUWGeisYZxqSGh/RjMVisVgsFvuReSOdTic6+0Vp7lWPlZa98HgljJCZNFEmQzRdRQk9TNmiZ3Qr6eYkkaIxk1nGsLmKxdEefNUGJJEPKtH8BiukaDAYDTNBP4PKOAnZ5qCyjsPKagpijvP9F7nAOER3dy+KrtG+ZgNd3ylxxZN/RIoGK3SLlNLgovA5UqqDp5i0sGmFFrZsouPTzwTv8L/DcG01M0Y/DSVNPTtINOXR1lL4ZqqT2GkkmJAFVrb20do/ip8vkqpPYkiPOWsxvp6gf3onxdZR3EihYRUQio319ccYqutsufY/sTeaI+GUaVk5ykqBqSMKs1slxaJcmJN84s/nnlO5+ebOqJfX2jgulWD7dvV1XyMWOxfFBbRY7C3qR5GO4zzwYdQ9uzCf+g7S95GJBFEuh9puES1ZijxRiGu3IQoRpRKzG67nm4t7WG98h5RfZowkvXKK4+Eyyn6GImMkac23wgusqEWkaGxJ3sxw8XL27lR46ikNRZHYo1W65w6hKyHjLMaPVEzZZCWH0QnoYxZDhGhSLgxzfVlZzxLlOFLCQKrGecsThH43Rk+GMPBI+BUcI01NBgToZKIKeuRh6pIw0NEjD0eY2GZIEpcuv0pzcAUX3L5ovu7YQ6idfUhqfEQzFovFYrFY7J/uzY7IeNVjG3WkriMCH6lrSMNEOC6qCPH0FF2NSaaK64AIX7XRvTaLlWMEmoUSthBC4GJizae9C6CJTZ4SSZokZZspY4jdXdchIh2tp5+RhsLlqyuwejlieBj3y9/g5qkpRo3l7NIuYk3zJVZ4+zANiWmraI6D7dUpINHwkSi0lDToOquil+lVK8xZ/WQr42SjEtmoTBSohKrO/tSlzCi9GIHLiuOjFPa/gKhBW0lgu2Vy5cPkKsdoOQoykOxrL2Py0HKW2pOcX93KgemNtBYXaNmd72NtRqBp4PuCTCai3QbHEViWJJORjI0pVCqis/H+GhvHlQlBsykYHIxOez9f9Rqx2DkoLqDFYm9BP7J0HNum+du/T/j5v0M+uRXfCdAyCWzLIuztB98n2vUyYnQU3amhGBrtb2zlEfvnGL70Cvqd43z95eVsaG3jmunNzCrdvCCv4BL5fVI0CFGpijRb0+/l/y76XbzDClNTAsOQ5POSqAKpoIIrdCoii6JEdEezKDJEAZLUcGRnqpqFg0CgaxHfXvZzBAHckn+OrvIwyswMFLvRLIP81AztYhHDE0SeD9UERDqZyCE0BJ6fRmo2uWCOSmKAZAFS77wQxxGAxLbPPCT19d6PuCstFovFYrFY7M15MyMyzvjYTXdjfP1rCN9DqdeQugHLlmI643gVjVl9gIw+SRgp+MAiZRwlncCrhOjSI00NF4MSOVQhKZMlJetoBJS1HvanL8dUfNxQJ4ogTHeRnHwRzZ2lkuynXXLQ9QSLtXGslEFKVPC8HCJsQdhGDx18BGpnshuCCCnBUVOonkexfoyXklfTRYt2O0GKOlJKiFQSQY201DlqrWZ19whh7yCTcz1MHHFZ23qR7tIh1HqVZpCl1LUUZ2gdig+HZrIMRiN4k2WaVuG0MLHrrgvYsUPlhRdUajWB74Oud4pfQ0MR2ezpha8zbRxns5JksvOaJzrPAGq1ziy0V75GLHYuiQtosdhb0JtJx3k9bWy+mPx59qQfQAnLRGaOe5Y/xlWTD1PZfgx1aoxAamio1OwiwZce5WOtlwhUk75UgxVKiifbV/A1NnIdzxBoJt8Kb2FMH+QQqxgWyxlOXoSdyjI+rjA0FNJuKwQBJBOSqsiSjeZIiTr9cowccygn7pMImwYhOj46EkEQgNQNGrfdxb6lt5Le9Rn6mlsJFy1CJhJoO3dgHztG09Hw2hGT9lqm8itZH+6k6I1jDAyQHFxBZdF5BDe+m9IffYqJbQ5zWhe6DoODEevyNZRkijmZo3TkNZKF3uAx2jihKBaLxWKxWOwM3syIjDM8FkAdHUH6PqRSSMtGcdpkDx+ikkvR1LOM64sZarxMQW2TbNaQqorEYU7roR0K2iKJTsgBczX7wtUMyFE2BC+SiBpsqH6PhpJhXF3MZHMdq3KTKLUqh621HD6eYUUroqznSeNQaIwgZEjb7kJpeESajqL5KFGIIiWeatOWFkJKUl4JTxhUtQTFtMNk7lL2HkmxrLmLHmZIRC3SrWketW/jhuJerFWDRMUeli0BL6lw5NBlaPUyrjaA35VFXb4YRYGELsm0q7TDFOuvy/DCMK8KE9uzR+XQIYWuLkkqJWk0BIcOKSxbFr2hdWo+D1deGS7MPDu1QLdxY3x8M3Zuiwtosdhb0BuZGfFGbd58YghogUxfnmZN8KeT72es0uKmiT9BUSTCMqm1LKK5KovFBCui59ilXsRL8kp6Eg3e1XyUYb+XUAikCqqp0G9UKba3c334FIqTZNi9hq8U72PDpQZHj8KhQwqzRp4j5jqK7WOslgcpMMupGZ4CiBBoeDgkcLQUds7iionNPPQ3gt9Ubuc3msfp7h3i4rUFLAuCq69l2F/C3HCTsTWXs9g7RKrVYEd0IfY7HuDCX7iKqK+PRL7AI1/UcBvv4Hp3M5YlqcgsM7ur5LpmOHbRJj77JwOvmSz0esdoXy+hSJTmUKdn4861WCwWexv4y7/8S/7gD/6Aj3zkI3ziE5/4cV9OLPYT5c2MyHjlYxeOdloWKAIaDUQYkFmW5cqLDJzGGnLPDWMeHwchiIr90B2Rrzfx6j6zmEznVuN2reTC8f2sqT5PliptJUUYODhCZ0W4l2ylhmirHHJybG/0U8iFRJqBHTVxHQU78FEMlUx1hGRQRXN9hKIgFQU3NJhLDOEmc5iNEkcSl5DrVvAqDinDZ7iRpa3bvGBdi+q1yUQViswxN3QhFy9/HpnpbJprGqxfH+EsStLYV+Fbs1dxqf8srbaBY3ZhuVUSwTRP5+7k6tuzbMy6p23ilkoQhrByZUi9rtBqCSwLisWQMOx8/Y0UwDZt6gQFPPec+qoCXSx2LosLaLHYW9CbmRnxWs42BNRxLDbvuYKLUo/gF3pJ1KYolnejRh42nXTLFcEB5lp5XmhdwjvkQd4jdrJFvYGjyfPY4D7HqtYBjidW01x/KWt6q9xQegijBd+Z/DBLlnRmJoyNdfOs9U5udqaxpdPpMENHxwfkfCpnSASEQmVadjMcLUZWG1xibGVyYC3JRoO940uoPaVxyy0B7TYcavRRTIwwdd47meI6QHAgWE7dKPCJPndhAbF9u4p1wb0MzEmWjW1lwB+haqX4gnM3z47dS3GIsyYLvZFjtJuf6D1jQpHqtfmg8WWs57cT1epn7VyLxWKx2Llh586dfOELX2DNmjU/7kuJxc45Zzra6d1wE+rMNInGNLauowcOUVeWaOUq/As2gG0jRo6jvbwf0c5Ql330ze1libeHlFKjaRSoGQVy7iR5GoSRwGaE7y37aXj5IEa7SjuTxzAkPY1jCBnh+xp+2yDDHFIooHfOVGiEoGok/QpRA0LNYPFShYFUha8mbmZxaQeyWsM0bQp2s3PCwfew8jnM1UNERhLtFZvmtl9DHUjyUs9PEU73cmFtC9n6KJ6e5Ln+O9kxdC+3zBfNTp1HVqkIHEdwySURURQtzEBTFN7U/DLb7qyJb745iE9ZxN5W4gJaLPYW9WZmRpxNpfLqIaCa3+bGkQfJz32XfvUwwj2G3SoRhQEuxnxOkYaBy/poF0dZSq9eAjTaagoZStJhjbLsYiBZJXlBiLSLjI25rDn8OP9w7Fp2da1k6dKINWtC9nbdy7Wlw9gvP0UUKUgUfDR0AiJAoRMgYOATojJeTmJKlaXecRp1CKwU3V6F0dFeymUAgdWYoxCOc+2Lf4oaBbh6inzvNTyk3cfOnSoXXhgt3Htu0OKF/g/x8oqNJJwyE26Orz7TyyXZ8DWThV7vGG3tWJnt2wfOmFCkfOWriMSjyMV9hD9kAEQsFovF3hqazSa/8iu/wm/91m/xZ3/2Zz/uy4nFzj1nOgZqJzpjNp57FvX4MfB9wvPWE5x/YaeNC5A9PdBuU9hwEdkd+zB/sB+loKIGGejtw5QaxpSBP1enrWdQCQDBjsTV3ND6GunJ/WTUGo6SwAqbhKikqeGj4asJLOFhSA9MA1U3SGkhtlYn6u2nZ42Gf8XttL372P6XKS5vf5Vl3n4yfhkrbGJpAYfSNzHKYubWXcPg84+8atNc2Xg7a+jj4cc/zI7BjfQZJSa9PMfq3dy9YYpiZYaI0085vHJ+mW131qYzMz/c/LJXFuhisXNdXECLxd6q3szMiLM40xDQC/d/hRXDj3BQ62U0tYaVtR3YXpUGCSIUBOAJAwebjKzSwyRJmrhaEjVlkwnadCkuTZGmy2pCu87E1sOYR4e5KKryO6nf4Gv1W/nH3e+nf7nJBz6gc82i9+D/9N9Rr3mYkYOJQ4CGRoBAEqBTVrsRkcSKWuT1Ok1S7GqtZIt+LbeKhwlcQX02QZ9VZWX9JYQAr5DAMbswnCpDLzzCCk3hM8qHyOcl558fYlkn771ld1KKjh/u7BYWCp3vx4mEIl2XlEond+Ze7xhtWeTPmFDUp8+yfHwrrYt70Xt6IIx+uACIWCwWi70lfPKTn+T666/nmmuuiQtosdg/o1ce7Zy4+QEaq99LoXqEvi//JVEisVA8g/k1W1cXzgc/gnLtUcQflAl6+xE7XkSPfIxWBdmq40sIDYtIwNqpp9mt3cW21Lt4YPZPiBRBSetjXOmlHHXxDvk9krLBuLEE3RQMZSqozTrCaSNSKYKf+iDeHXcS9fUj8wVua8OXmncx+z+3sra1k0jRCKwEtWweuzrDDTP/SHjPXXh2Hf3ZLTA7i5PIM3fZHYTX3cWm3ImjlAUmm91kzRa/lP9brt7xPbTtr57PG88vi8X+aeICWiz2FvdmZka80it/ifbps/Qf3kpFZuju1zjmriKRbLKyNYMtHRxsqiKLgY8qfQSSJC30yKWm5FFViRPauNIkEdWJdBN5dATz2DARCo7VRaJo80DrqyzKSbYOfoibbw5IG8uQy4YQ+44TCQW13SJCEKHiYVBR8tSiNEndZSgaxQodnkjeSUUt8MXo/WDAZdFWiq0RLEISaZXd/moaog9TSI5VbJS2yk2p79CbWs1RfzlPP91NPi+ZmRHAyQVEvQ4DA5J2WzA6KhgbU/D9zryIYlFiWXLh+/5ax2hTS/JnTCiSc2VS1NEKA6e/jz9EAEQsFovFfrI99thj7N27l3/8x398Q4/XdRUh/pkv6sdI09Qf9yX82MT3/s+vVIJyuXMk8emnVZ58UqVcXkQut4h/nTjC1dMPoyoKsiuDqNYIJmaYumYTGD0UVquIgQFURUUuXoz68j5EpYxUQCoKhCGzvetopvq4au77/Gnwc1xufAe1p8C+8Rx1mSBvt2gFXaTdGorvUVUKeN1JrHQFGg2iiy8m+oVfQM0XOPEdMQz41x+qcPRRya7h61EzKbS0RSNKoJemeWf0FINPVNFe3kPoh4xN6mzRLuEb/gcwhm2uuirkfe8Lue22gHJZMPidL5F76hGiYi+ydwilWsN+5CH00gz+hz+KzBd43/skmhbx7LMKExOd+WV33BFx110Sw/jRvVfxz/zb07l+72/pAlo8jDUW+6cRpTnuuaCM1Szy9J4e3KOTLCrtJmu2yMiQamgyrvVS0bpJ+hVqWpamlqPbnyQbzuJg061XaMkEPe4Y13iPcZjlzMgkK5lkZG4Z5vgkflvFwGdCDjDaLDKQNrnM3cILzVupVFL0rS3QvPv9OMf+BjuoE+omMpIEkaBKBgXJoBwBYWBa8GDrLr7SvA9HCKpRgj83P8pH3nU9t1/094jnnqM7mGSDcBieq3PYPA+nrnCheZw17n6WPD9HLdnPzsy1bEney3XXGezde3IA6v3vnkKplPnM5h6O1bvp6pJomqDZhEYDnnlGW5iD9lrHaPP2mXf4nHqBzEAS268BiZPvxQ8RABGLxWKxn1wTExP89m//Nn/913+NaZpv6Dm+H/4zX9WPn+ed+/d4NvG9vzZRmvuhTlW8MrRpfFwwNiYwTVAUOHoUfs18P795vuSGYAvRkeMcnU2zRd7FN559H9GLOldd1c3PXngl6W8/SpDLQ18/2vQUIooQVhfHjRVM2edhKwFdyijpdIQrCyiuQjNKYJoSvcumXOuh4E+RjGr4roZSj6BdJcrlcG56D24qC6/4XqjTs6zsqyGtItNTUKsDNvSuTrFs9gWUR0cIli5jn7eSyZk66/k2SibBt7s+zCOPKARBxP33B6S9OZI7tuAXishCN/g+0fAE6uGjaPsOIHbvwXvnDaib7ubee21uvPHVKfGe9ybe1Dcg/pl/ezqX7/0tW0CLh7HGYv8E7XZnLsT2baSazf8/e38eX1d15vm/n7X3PvOoczQPngeMbWYwtjEEiCFmMGEKgYSkOulUp7tS3XU7/bt1+1Z3V6Xq113V/atb9/btV+VWqpOqygQhgQAG4iKEycYjM7YxxrMsWcORzjyfvfe6f2xLlmzJljxgy1rvf0IkWdpH9jln7Wc96/vwlUCAuxYvpxpL0bq7BxH0IyNR6nwVIrkDWC1BZKKGT0h8egbh8nHUnMMeax4xf5k9/svwp3toLh1kMR+SCMzmk/YvYPanmZ/rxAVohkZjrYdQd4pssAkRCNBoDBKNBgBwff0Rdn3qQ7z2BvOMHcSLR7ClRsmI4Q9CJa9TNoLsb1rO072PU6kIbBuEcIpTd3lew/XO29iRCDIaJWrWCNc+obnOpjOns6D0MabhZSAyG8OqckPP85TLsOy7X+L++00yvWXat/yaup1bKA8UiA9E2CRW8tvqw0ivjyVLbGIxOSoH7XTHaMeaUHTTfRGaqsvQX33RWdUFg2c0AEJRFEW5uO3atYvBwUEeeOCB4Y9ZlsXbb7/Nz3/+c3bs2IGuX9o79YoyISPWpaJw8rHD0zk+UV4Sj9ts2uQil3PiOFpaJJUK9Kb8/J+dv8eC/3ItO9Z183xyDnvlfFKdGsUivPuum09v+jL/43YIfLAVOxxBen0gbTzhALNrfeipj0lq9VTcQW77lx1cVb0e/YWXKJYESStCuJShqAd5PXwvLcUDtMsutILEnjmD8oNfGjen2Pb6cCV6WDLwESY6pnAj69vRIwGMQxlqCxdQDDZyuM9Ai/mpSsn8vs3sW7gGqB9emzaMyOc1TRh4bQ/eQ3upCC9uNAr7LTqyxzN3VX6ZokzelCygqTBWRTk7nnXPOkcPGxqxj4XYx199FlHIw+xZiL5eqFaQXg9auYRRLVL8wufJd+mUjqbJaHUcbLiBuen3KGn15I0mssFWDqcu5zLXPgI+m9ev/fds327wXwtfo77Wy6Copyy9GFaZ1vQnHM3PJO+OHV8X+Xws/K+P8Nsnv8CW1z7lwXf+ExEzSV3AIhR30dV0JbsHG2ju3cEVbQMMEiObFcybZ7OkZYDAu1soXtuIu6MRO5tB3/spmkujPn0ArVjDloJEbAFlr9PhVS4LlmY2E5O3EY3FaXnl13g2Or+TYkMHHn+eL8vnWNgseX/pV/D5oFIZe0LReMdox5tQJEv3U3GD551t6Gc4AEJRFEW5uN1444288MILoz72H//jf2TOnDl861vfUsUzRTlmrHXpRIcrnThRvqdHUKmA1wvVqgAkgQC4zCI37nka8X9toHFfkS+WQrzFSjY0PUyw0Us6LfjdpiA/uuprfPNP7sT785+gdx5G5LKIgI86LKKpD6n6YmR/79ss+UYESvdTc0H9D9+mLtNJ1gqx3vcAr8Ufpt6f55rofv71ty08l8845Qape+ObiEIBymX0SAQdG7F3B/h9yEgEu7GRclFQq0HcV0RYNv5yEn85RTgcH16bxkfk8+7vgtChLqquIIYuqVk+dqRmUPVlmKcydxXljE3JApoKY1WUMyeSg7i2bcFqaEQ2NAIgG7zIY0Wnym23YwzlcWWzSJcLu7mZ2r/7Ixqam8keSiFFjM/JJK0/2EghXkdJWlQLNfrf2Etb+SCBXJo1m/4MUboFy/AgLQ0hwDLBkAIhJYbhLHLWrTN4/HGnGOXzwX3fiJC7qo7IX9ajR2bgDnkwo3XUu3yEt9cwP+jGV0riCse54gqbhQtt4skkRrlA0d2KGzAXLgJA6zyMa3CAoGGw21hCn/9yXBZUKoJqLcKVsU7iIoWdZNTvxFOCYihAugKLU5vo5AsUiZPNnqMJRcc617j7Lsz+gTMaAKEoiqJc3ILBIAsWLBj1Mb/fTzQaPenjijJdjbcunehwpbEmyoPT5G9ZYJoCw5DckXmam0vrqJhxuvUZWNUsd1rr8GbgZe/jRCKSWk2wZYvOA7dA+8ED1K6+Fj05gNbdhahWwe/FHQ/iXnMLEsDnw/XNxzgo7mbb+iyyro5qKM6cKuRycZrXRAjeJk95nG3o8deWXjnqZ0mvFxmOYnXMQGSz+LwGc/O7qO/vJGBlsTWDeQd/x76WdgIBH9GoHM7nFc+/SGlvniZRRmpuXGaZ/thCjKCPQ2nBzOQhlbmrKGdoyhXQJhvGqijKaFo6RS1VIBfrwFNyilalEtRcDcRt5/Pm4qWIOfMQ5RLk81RsNwcrLQSJEbsmTggQSYkMOFle3gYv9qHdeAr7kEJQ8kSxPD5uT6xDN8t0u2bRbHYR1LNkZYhP5GIsLUDhSJonn6zn9tur1Ncfu8BSiYb3X8PX/QkcNpGhMFZbOyy8jKUdKd7pCdCyKMLC2eZw91pvNYbpDeCvZoBGMAxyc5ZiaxF8zRk0rwt/uh4r56KUdQJbF7WmaZ3hpxKtQxvR8g7O76S93ebQJxE6akcwsikS+fpzPqFIxuJYwei5+WaKoiiKoihTzIlrsCETHa504kT5aFQSCkEqBU70oMSVTbIkvYlisJHQvBi1owZdFT8lWzCvbzO/Kt5DwRsjFHIKboUj6eNHIVtahtfEUmhoyQG0comRJbE7Ho3gKaQ4uvl9DhdmkW+ex5o15rEoj1N3mo58/GP9LGvxElzvvk0os5e55aNkKi5MTSMXbGHm3te5bCCE51uPDq9NK2vvJ5sQVD/ZgJsqti3pjy2kr/5yPFIiMhlKRhBdZe4qyhmZUgW0MwljnerTjC71KRbjUY/7/CiV4MWNTcz+NELFLFAIBtE0SbUq8GVt5lQ7COwt0BgaQIuFsXM2vXvy/C7wRX77920Eg3DjjRZf/KKFr7kRuXIlrpdeQNYq6H2dVAOCYk7S559FJjoLMVhl8cDb6MLZFbSkTg4fGX8Dbr+Lsj/GoUMaTz/t5t/+Wye11PXM87g2vYmsr0fr7oJSEdfePeiFPO5IGP3m+0gNNmAUJS4XZDKQKDQgV64gOPg8tX6NPb0RUody+PMFPpr9IPPabZbbzzNzJhTdYfzVLL5sH7Wb78XV3Ei6oGMTwj2Yx93hBPtffjkE8mn6e4McSNdDTJzzCUXq3/n0oh63oig//elPL/QlKMpFxR5x7FA2eIc/PtHhSidOlA+HJR0dNtmshmFAPi+IV5NEjTz1i9uoqwPDcMLyU1qEWVonoVqSrlIcr1cSi0kCHdFR1ySP7dhqR7uQhotBWUfywLFoDpGk/i/+lEe3bkYWSpguH+XICsw7vge+8Xdck0mney4m6/AbBnrXEezW9pN+VvnBLyH9AXw/+gHBgKTm9dJJB4f8l1Mnk9wZfIvoqtuAY0VGnw/zsUf55e576d//jyxNbSQXaEKzTTyZJGGrH+vGNWiq+0xRzsiUKqCdSRjrpTDN6FKeYnEq6nGfe08/bbD+9Qbua1zBDT3Pc6BPciBVR0zPENX7+bn/K2hpF1/as5EFbZ0c6A3xsn0fn8x6gDqfxeCgxjPPHJ/2U73rPjymjft3L6On0wTrIqQbZtElL6ecgRtquwlQwBRucjKAhsU89hE3s/wo/Kfk3HECAcnHH0t6ey3iDOJ+803sVBotnwfTRMsPIjUNISWlu/818++/lzt/W2P7dp3OTudI5Z13msy/415KvzXpfHIbmYNdiGCQvZffy9tND7C+V2A1SG4Sm4hmO5GBIKU77yZz2308/1PBtm1NXHf4Jlb0r8M7E2ZcEcRVzDIv1E/dXffw7VsDRKPl8zKhaOjv+0wnT01V6vk9vUzXx60oiqKc2tCxQ8/6F7FwOs8mO1zpxKFNs2bZzJ1rUyw6BbQ2b4SZA35mtKYplRrRNKfQ5s9lyNpBNA2WeveSy9exeHGI6NwR12Ra6H09aIcOIfJ5eoNzee3fv8brDQ/hCnv54/3f47L9LyOjEWRLE0Y+T3jjy1T+Agp/879OutaRE0Nr2TK3Jl7j7kQ3zfn9yKAf3F6olNGKRaxZs3FvfJPa8pW4Nr6JHY8RCEeZhY/mssAnAgSSXRTLKSyO/55iMVh8c5RfZL9DxtfM4vQmfANdpM0gqVvuYeZja8/Z35+iTDdTqoCmwlgV5cyNDFk9OP8BvDsk+tattNmdlEWQjY1r+V34IXozft617+Ivv3yUH/yqmZw7TjIp6OrSCJQHCVSSvPSTOlat8tPa6mR51a67gcBf/gW2z0dzxwwiJaj19dPYfRhCPrS6BjyJAtUy1HDjsip8qF1NoQCzZtmYpiCdFjSQQv90D1o6hQyGsNpnIIpFRDaDHQxR+9xteOt8o4L5Y3LAyTGTdfSsfoy/3bCWcFMSb2sdRV+cKFAzBD+1v8asf/N552uPFamef+r41KbOZQ/g+wg6OjfjLh9lxuV+KmvuwbX2fub4zuOEorOcPKUoiqIoijKVDQ1Rcm3finYGw5XGG9o01OUVjQZpeWUZ7vUvUiwKtGodV7Wk0bVeumpN/Hvr/yJo5akQ5PLMDVD64vDP9j75M/SDB7CDQY7ElvJJppVru9bhD0h2aDcS/nATqUAUT6AeQ5cYcS824N66mdL+fbBo4ahrHTkx9I7ir1jU9QI77HnUYn5mDH6ASB9BRqPUll6J3dSMZ/2LiEIeGYuBpoPPhw/w+SQiMX6XnlNUdPHW9q+yKXkXs+z9XH65zcrHO9T6UlHOwpQqoKkwVkU5cyNDVk2Xj9+2PM4mfS0NoSQpESMUj+H1SiJIPknU814uSJ/pJpeCngMVvlj7FdeWNuGu5kklQuz7L8to/V9rwefDnjuP6uo7nZ26RD++cJhg5hCiVsVuaka0tqDX1ejfJ7GqJvV2gkWldzEWNuNqimEYToaFTIGWzSANAxkIAM5OJLUqWrGAHFHHivlKtLwyuvCUmbOCVOXLuGbGsEec8m52DZDvTJPNBohePQ8YXVCcGRzAX07RfdUX+LB9Df5ykt//NwGic89/J9jZTJ5SFEVRFEWZ8o4NV6quvvOsuvFPHNo08v8PFcQ8G7bSXD1ChSCu1gYW5XpJeltJ00HEytD29gvIVjl8Pa4Nr2M1NVGMtfP+9hBaPVSlzvy+zVRdAVxmma5MM4YlMAxBOCyJB4O4BvrRj3SOKqCduPac37eZal0DFdHMx7U6WiJd6D4/+PxYCxeBz4dlGBi7dmIuXop74xsT7tIbKiresSqH/ut1xHdvxnuggPxrtVGrKGdjShXQFEU5cyeGrAKktDhp4rhdUGcMTS9yQgNDIWdS5qFDOg9XfsVt+XWk3I30umfgI0PHuy9Se8LC9U2nyHPS7qE/gAxHkC43AG6/i2iTxNvZiY8Cj9hPMti5mXcTKwl/8wFiMR2RBhmJIJJJKBaQHg+iUkHUashYHCGOH3V0vf6as5AYUXhq3PwCtxYMttR9lYYGiVErccWeZ2jZvxlPLc/cH/rQbnYWDem0n1q2zB3FXzG/z/l8xRVkb9MKfhN4mKTQiHIeO884+8lTiqIoiqIolwoZi5+/yZDHinRi9Z3s/3mON9/U+WrP/wsr2EJab6JQgNb5bvRGC3FsDaalUwjTwmrvoFT0Uas5Rz/LMkI018Xe/ggF20uALJbRgBAwOCjw5gpEQl6sjhmjbrZHbmb7iyk8tTypUAceITHSaaRdRtaF0apVZ5CAzzc8TKG2fAUyGJx0l17zxmfxvONs1FphtVGrKGdryhfQVBirokzMiSGrPp/E44FcThAKSYSAYlGQyUBHh+SKKyTvv2/z9j9nWFDezCHRRKLShKZBY6OHJJKZW7ci7j9W5Blr9/D//DM8r7yMrQmkP0BzYh/CylPS/ATzCTTT5H5PHzEsanwZO1qHueAy9M7DiFwWLZtFut3YrW1YrW24Xn8NY9cORDKJ8ekn2E3NyAWXgWEgG7zowMq9b/F6910kiHF79zPM3/MCfTQSWtiO259GP7ZoiK5+jFsTT7Oo6wWqdQ2kQh14KxkW7XmBYocgGn34vP+diNTZTZ5SFEVRFEVRJk7G4tzyL+PAITw/zdPDDIQP5s+3WbjQRlrH12AjBxx4g15cLqhUBBGZoagF2FS8lqv8q7it+BvMGlQ9IYJWDpHNkFt1B/bceaN+9sjN7LpgHRVXEH9pkEhmkIbcQdz2ICIzgB0OIw0XMGKYQnMLlcuXTKpLT23UKsq5p13oC1AU5bOzdq3JmjUmtg3JpMbs2TbxuI3HI0mlBMUi1NVJHnywRizmFNniIklA5smIyPD3qdUEtUAEn5lHS6dG/QwZi2PNmccgcXY9/udkV92JsG2MQwecY5ihIOLyhTS06swLHKU9ksX33hZEctAJkl1xEzISwVy0mNzSZaQ6FlPxRZAeD+6NbyB1HTsecwYMHO3G2LP7+M8Oh5ldn+Pu5Qn8pUGa920h422kYUkD85a4kQ2NWA2NuLZvpT61j5vEW/TRSL9opiK89Itm+mhkJW8RZ/C8/33IuuMLs5EmOnlKURRFURRFmRyfD9Z8JcSi632sXJJi1SqTxYttDGP0GkzG4phLlmIcOoB/oJOZTUVcyT7cqQS7oivJ5+GJwL/kw4bb0LEJF/owNJt3mtaw71t/ftLPHdrMTiQEAwOC3tBcmrvepXVgB96AgFAQalVEsYi+fy8i0Y+e6Kd2w43Dha6hdfZECl9a2tmoleHwqI/LcBhROL6GF8lB9AP7EMnzv/ZVlKluynegKYoycSeGrHq9ko0bDTZs0MlkBJGI5OabLdauNUkm4dNPdRovi2B+HKBZT5P1NVGtQi4H17SlccWCVE8o8oycLlQotBAI/D333LeNtRv/H7i6O7Fb2zAM56VHFCWkU4hkcrjbqrL2fqpV6H1mG9nuIhXLIBm9isWpT2m7rBGtoRFKJWQo7Ewp6u5CzJmH9PkQ2SwiEmTNV0KsPNRLOJVBzGzHG7WHr2+ou0s/0sms+hxl/ww6+yGbFbjd0LwwxGx/J5XPoPvrXEyeUhRFURRFUSZHxuJoNy8nuv5FrLxEaieswXx+PE89gfHhB4h8Hve7b7M4GMHbuogtfIFKtsofp/+fxD15gs0B3q77On31i9gjF9Abms+fzKyc9DNFcpAHF/SyZMNW5Ls78OcTRMoJDBd4/F5sPYwIBpGVCsa+vdRiMao3fw5z6ZXDG82TMbKDTjZ4j1/HUJHQ68Pz1BNqkJWiTIIqoCnKNDQyVHWsqUUAPT1OTsOsa+voLq3g6sMvQAkKRoSoO0OHu4/aDXef9GY+crpQe7tNNiv43Ttxrq3U0x7MYuaqaCEDwwDp8aBlMtiGcbzbyufjF+6vsc1zB/fEn2RWYRfR1LuEk3sYKC2gsSHmhKq2tWN8+glks4hsBvK5UYWnMBBoCiBrWSQnLxqsjhl4wgEW1aWZudBNuewUFP35NML+7Lq/znbylKIoiqIoijJ5p1qDjRzyVF1xE1p/P3pvDzO+cCV1CLz//BKH/E3sS3SgVzO0d2/jqN3MrsgC1txgDq+ngVET143du2js66PcOovcZTMJfORDwwYBUhNgC/B4kJqO1dKGsXMHrm1bz6i4dbqNWvfGN9UgK0WZJFVAUxTlpKlFcDynoVgUDN72AN07JDO7t6CXOqm5g9TuvhtOKPKMnC40NKigoUGSK9Sxa3cDJZmhrtRD1S3wRtzUM4iolLGuvGq4EDf0PVZVX+fy7DZy/kZy/iiNuQMEDu/G3qGjXb0Ua+FliFwWva/XGToQi40qPJ1q0VC9+XMIwfBEIz/gu1DdX+do8pSiKIqiKIoyCeOswcbKDrM7ZiC9Xoz33qEOiZzVwNy6esw9Gt3dTVSzML9vM/ffewd3rI2M+jFDxTg7FIZSGdvvx5PsRQ96EZEwor8PkU5hdczADocR6TR6Xw+e371M7bobzqq4NV6RsLrqFgJ//VcqH01RJkkV0BRFGdPooQN+Cou/yraGu6j2pli2JsySb0RO+jMjpwuNtD9dz6uVm2j29eGJgLcwgK/rCIgisrUBfe+neJ56Yng6pkgmWZzaRM7fSD7QBMBAfAHNfTvQ9+2F2R1Qq0IkQune+6h97rZRhaehSZ3VVbcAIxYNHi9WLI7x4QfObp7HgxWLI6rVC979dV4nTymKoiiKoihjOnENNpQdNtaQJ73nKEiJ2dyCYcDixTZz5thUskFCySNc+bkElu/4GnlkMU4YBsK2sKNRKFfQE/3Y0Sj6oQPOF+s6onxs+rzhgmoVgiHweM+8uDVOkVA/sE8NslKUM6AKaIqijGvtWhOA7dt1urs1AoE4NzwU5Y5jHz/RyOlCQx1opRIcPqzRW/8Q1820WJDYTIuZRVZcHAhcSePnriUgSsO7atHVj9FoDKIV85QaOoa/9+Hg5djVGovtPcjOQ9hNzceLXUOt7CNa5EdmORS++8do5RKu11/DvfENp1W9vn5UR9qJRThFURRFURTl0pRMclJ8yZBTZYfZkSggR33O5wN/PoMYIxt41MR1WyJdbkSl4g/ksYUAAQAASURBVMSY5LLI+nqk2wOWhSiVkF4fVvsM9J5uBCDKJeSxde7ZFLdOLBKeNh9NDbJSlDGpApqiKOM6cejAWIuMkUZ3rUE4LDl6VJDPC2Yu9rDjqq9yNLWML2z8M3rqlnCYmazULfz1IWzbxrV9K/HVd3LZigjp90J4MlmsqJdKRVAouTBmtWO1RSl94/exZ80+qdg1Mq/ixHb36uo7MXbtGLNV3di1k8oXHwRAP7BPFdIURVEURVEuESOLZT7fyGFXgkBAsmyZM0BraD/2dNlhwIQHQI2cuC6PrU/1vZ86A7FcLqQE6fNhtbZhLb0C6XUuQu89ioTh/w/ntrilBlkpyplRBTRFUU5rrIy08ZzYteZySWbPtmlqcv68QCA1nQxh6mQSHy4gMGpX7Y5H57Fn542E33yRgSrogQhXNKWZG+zFvOZzEI2e9HPHyqsY2e5uzl8wfqt652G8T/wE/cABNYVIURRFURTlEjB6MrxTLANIJATNzceHXQ1t/D7yyPETFhMZ8jSRAVAnFqrMWXMQ2Sxa52HsUAhCIaqfuw090Q+mCZpwCmU+HyAgnxv+2LkubqlBVooyeUJKObG74ikqkchd6Es4K263TrVqXejL+Mypxz31jdzte+WVEZM5xRG+sv5fEMj3EwjrhOtdyI4OzLoYQtMo/MmfOguDUonaE8+ib92Kz8zjCnuRuo6wLES5fFKRSz+wD/9//29Y7e3gOd6KTqWM3t1F6V/9G7w//TFS14cLbAAi0Y+x91OkP4DV1nbSDtz5nEJ0Kf19T4Z63NPLyMfd0BC6wFejKBe3qb5uPZ3p+joI6rFfiMf+1FPH15/hsKS/X7Bpk87MmZLly49fTyIhsG34kz+pnHTSYihXd6zTCaf63BC3W6eayTsRI9u3Igp5ZCCIefkSastXYDe3IH3+kz5fu+oaAFwfvHf8YzfceF42dyfyOM6E+jevHvtUM5F1qupAUxTlvBjZtTayK63x4zfx1PJEPGW8dWGQNvrOHYholNK3/vXxN26fD9c3H0PcfydmOoUYI79s5DSi02U5WDNnj92q3t0F4BTP1BQiRVEURVGUKW/kZPiZwQH8xRTCiGEYjaTTglLpeB0qHJZ0d2uk0+KkExenGvI04QFQE5i4Pt7nq3ffe96ntKtBVooycaqApijKKZ0qZHWihrLU7ryuj8hfbcQ9+wq8hQG07i5EtYr0epHB0PDUzJFkLI4N4+aXjSxynS7LYaxW9drylRgffoAMh0f/XDWFSFEURVEUZUpKpwW1bJk7ir9ift9mPLU8RS2Iv3oTz8mHKZfd+HxOsSybdY53RqPn92DW6QpVY31eFbcU5eKiCmiKogybbMjqZMVFCr+Wx2ppx/S0IObMQ5RLaIaOTCTQyiXGavg91TjxkUWu02Y5jLEDCBA4eEBNIVIURVEURblERKOSWxNPs6jrBap1DaRCHXgrGb5Qfp5yGXryX8Hvd4pniYRgzRrzjDeKFUWZPlQBTVGUswpZnYwTj1lKnw/p86ENDpyyWDXhUdsTaJGHk3fz1BQiRVEURVGUS0ecQW4Sb3GIRmqiCY+QZIUPwy+417uJp+UX6O6uJxCQrFljDseNKIqinIoqoCmKwrp1I0L+2+1RIasNDTYADQ1OUW37dp3Vq89sl268Y5baYILKnXePW6ya7Kjtyba7qylEiqIoiqIolw4tnWJWfY6yfwad/U6nmdsNLZeFuMzfyaxv9JKIhs4qokRRlOlHFdAUZZobGbI6VCQLBsEwmFTI6kQNF6s2voHe040draN2971U7rpvYn/ufBS5Jti5piiKoiiKolz87GgdWjjAoro0Mxe6KZcFXq/En08j7CDhWXWEznAtqyjK9KUKaIoyzaXTgkJB0N5uD3/M65X4/VAsQrkszkvIqpCAdP53Qt9tkkWuMxnJrYJaFUVRFEVRpr6Rpxf8gE9FdCiKcg6oApqiTHPRqCQQkGSzYrgDzeeDujqbXE4jn+echqx61j3rHMVsaEQ2NyOyWVwvvYDHtKk88thp//xpi1ylEp51z+LatgVRKCADAWrLljudamc6/UBRFEVRFEWZUlREh6Io55oqoCnKNBeLwbJl1vCAgHDYKaYFg3DLLRZCQHe3dk5CVkVyENe2LU7xrKERANngxdY0XNu3Ul1951nvCI4s0Nnt7YhsFs/6FwEmVKBTFEVRFEVRLgEqokNRlHNMFdAURRkuim3frg8Xy+65xymWlUrOMc9zEbKqpVOIQgG7vX3Ux2UkjOjsREunzuoI5XgFOgvOWYFOURRFURRFmTpURIeiKOeKKqApyjQ2lBPmj9bxyCNxVq82TyqW+Xyc8cCAE9nROmQggMhmkQ3e49eRyWIHgtjRurP6/uMW6MJhtO6usy7QKYqiKIqiKIqiKNOTKqApynQ0Tk5YbO39xGLnLydsZKCrhVPYEtks2mCCyp13n3V32LgFumwWeQ4KdIqiKIqiKIpyISWT5+50iKIok6MKaIoyDV3InLCxAl1rd99L5a77zvp7j1egUxOXFEVRFEVRlKmsVIJ16wy2bdMpFASBgGTZMou1a001J0tRPiOqgKYo08wFzwkbI9DV1dyI6O1H6+k+64BXNXFJURRFURRFmQom0022bp3B+vUGDQ2S9nabbFYMDwF75JEzH/KlKMrEqQKaokwzF0tO2HCga6mE68mf4960adRx0sra+zmj7TQ1cUlRFEVRFEW5iE22myyZhG3bdBoaJA0NTjbx0P9u366zerWpjnMqymdAu9AXoCjKZ2tkTthIFyonzLPuWVwvvYDUdaz2dqSu41n/Ip51z57V95WxONaceap4piiKoiiKolxUhrrJdB3a2210HdavN1i3buz+lnRaUCgIwuHRg73CYUmhIEinxWdx2Yoy7akCmqJMM0M5YXqiH5Hoh0oZkehHT/RTu+HGz7TgNHSc1G5oco6TerzIhkashkZc27cikoNn9f2TSThwQJBMnqMLVhRFURRFUZSzcGI3mcfD8H9v366PuW6NRiWBgCSbHV0oy2ad7rVoVJ78hxRFOefUEU5FmYYulpywoeOkcuaMUR8/2+OkKmRVURRFURRFuRgNdZO1t9ujPh4OS7q7NdJpQSw2uiAWi8GyZdZw5lk47BTTEgnBmjXq+KaifFZUAU1RpqOLJCds6DiplslCvH744yceJ53suG4VsqooiqIoiqJcjEZ2kw3lmMHpu8nWrnXWsNu363R3awQCkjVrzOGPK4py/qkCmqJMY8NB/hfw59eWLcf38ksI20aGw4hsFj3RT2XNPRR9cdY9NblOMhWyqiiKoiiKolyszrSbzOdzNoJXrzYntbGsKMq5ozLQFEW5oCpr76d2970I20bv7kLY9vBx0skGrIIKWVUURVEURVEubmvXmqxZY2Lb0N2tYdtMuJssFoM5c1TxTFEuBNWBpijKheXzUXv0K9RuXT3qOOmZdpKdaVu8oiiKoiiKonwWVDeZokxNqgNNUZSLgozFsebMG85iO9NOsqG2+ETCaYWvVBj+7xtusNTiRFEURVEURbkoqG4yRZlaVAFNUZSL0tmM6z6btnhFURRFURRFmWpEchD9wD5EcvBCX4qiXLLUEU5FUS5KZzOuW7XFK4qiKIqiKEMmO9F9SimV8Kx7Fte2LYhCARkIUFu2nMra+xl36paiKGdEFdAURblone247lgMYjGVeaYoiqIoijIdlUqwbt3kJrpPNZ51z+JZ/yJWQyN2ezsim8Wz/kUAKo88doGvTlEuLaqApijKRUt1kimKoiiKoihnamiie0ODpL3dJpsVw6cbHnlk6kd7iOQgrm1bsBoakQ2NAMgGLxbg2r6V6uo7h/OFFUU5eyoDTVGUi54KWFUURVEURVEm48SJ7h4Pw/+9fbtOMnmhr/DsaemUc2wzHB71cRkOIwp5tHTqAl2ZolyaVAFNURRFURRFUZTzQgWbKxfKmU50n0rsaB0yEEBks6M+LrJZZCCIHa27QFemKJcmdYRTUZRRLumQVUVRFEVRPhtjBJvLlSup3nWfCjZXPhMjJ7o3NBwvok1kovtUIWNxasuWOxloHOs8y2bRE/1U1tyjjm8qyjmmCmiKogDTI2RVURRFUZTPxljB5q6XXsBj2irYXPlMnM1E96mksvZ+wMk807q7kIEglTX3DH9cUZRzRxXQFEUBLv2QVUVRFEVRPhvjBZvbmqaCzZXP1NlOdJ8SfD4qjzxGdfWdaOmUc6xTPb8U5bxQBTRlmEgOqhfdaerEkFVg+H+3b9dZvfrS2aVTFEVRFOX8Ggo2t9vbR31cRsKIzk60dApLrTWVz8B0muguY3H1vFKU80wV0JQxMypqy5Y7bb/q7N60MBSy2t5uj/p4OCzp7tZIpwWx2NTPiVAURVEU5fwbGWwuG7zDHxeZLLYKNlcugFgMtZZVFOWsqSmcynBGhdR1rPZ2pK7jWf8innXPXuhLUz4jI0NWR7qUQlYVRVEURflsDAWb64l+RKIfKmVEoh8t0UfthhvVSQdFURRlSlIFtGnupIwKjxfZ0IjV0Ihr+1Y1cnyaGApZTSScYNVKheH/vuEG65JtdVcURVEU5fyorL2fypp7ELaN3t2FsG1qd9+rgs0VRVGUKUsd4Zzmxs2oCIfRurtURsU0Mi1CVhVFURRF+WyMEWzuam6EqnWhr0xRFEVRzogqoE1z42ZUZLNIlVExrUynkFVFURRFUT4bKthcURRFuVSoAto0N5RR4Vn/IhZO55nIZtET/VTW3KMyKqYhFbKqKIqiKGfvBz/4Ab/97W85cOAAXq+Xq6++mv/wH/4Dc+bMudCXpiiKoijKGVAZaMqYGRWVNfeojApFURRFUZQztH37dr7yla/wy1/+kn/8x3/ENE2++c1vUiwWL/SlKYqiKIpyBoSU8pJuNUkkchf6Es6K261TrVqI5OBwfsT56gr7LH7GRA097ulGPe7pRT3u6UU9bmhoCF3gq1GUCyeZTLJ8+XJ+9rOfcf3114/5NVN93Xo60/V1ENRjV499+lGPXT32qWYi61R1hPNiVyrhefppXNu2IAoFZCBAbdlypzvM5zunP0plVCiKoiiKopwfuZxTHItEIhf4ShRFURRFORNTqoA2HbMkXM/9Gn39i1gNjdjt7YhsFs/6FwGoPPLYBb46RVEURVEU5XRs2+a//bf/xjXXXMOCBQvG/TqXS0eIz/DCPmOGoV/oS7hg1GOfntRjn54MQyeZhFRKUFc3vQazXep/71OqgDaUJbF06VIsy+Jv/uZv+OY3v8lLL72E3++/0Jd3zonkIMbWLZgNjciGRgBkgxcLcG3fSnX1nRf8qKWiKIqiKIpyat/73vfYu3cvTzzxxCm/rlabmsdeJmOqHu05F9Rjn57UY59eSiX4zW90Nm0yKBQEgYBk2TKLtWvNc32A7KJ1Kf+9T6kC2o9+9KNR//+v/uqvWL58Obt27Ro3S2Iq09IpyOeRrW2jPi7DYbTuLrR0Sh25VBRFURRFuYj9+Z//OW+88QY/+9nPaG5uvtCXoyiKopxH69YZvPyyTjxu095uk80K1q93yi6PPGJe4KtTztaUnsJ5qWdJ2NE6CAYR2eyoj4tsFhkIOp9XFEVRFEVRLjpSSv78z/+cV155hR//+Md0dHRc6Eu6ZCWTcOCAIJm80FeiKMp0lkzCtm06DQ2ShgaJx8Pwf2/frqvXqEvAlOpAG2miWRJTmYzFMW9cjv7COiyczjORzaIn+qmsuWf4+ObFND1TURRFURRFcY5tvvjii3z/+98nEAiQSCQACIVCeL3eC3x1l4ZSyen22LZNn7ZHpRRFuXik04JCQTBz5uiPh8OS7m6NdFoQi8kLc3HKOTFlC2gTzZKY6mGs8qGHsQB96xZEz1FkIIB171rkFx/AbVVxPfdrjK1bIJ+HYBDzxuXUvvjAOZ/Q+Vm71MMHx6Me9/QynR/3dAxWnc5/34oyHT355JMAPP7446M+/pd/+Zc88MADF+KSLjnr1hmsX2/Q0CCHj0o9/7xBIiF47LHatHl/URTl4hCNSgIBSSYjiI/oa8lmnQJ/NKqKZ1PdlCygTSZLYsqHsbrdVB/8MuLW1Sd1mXmeegJ9/YvOkIHWNqc77YV1mKZ9SUzovJTDB09FPe7pZbo97ukerDrd/r6HTNfHrUxve/bsudCXcEk78aiUaUJ/v+DQIY29ezV279a4+eZz+/6STDodJtHo9Nn8URRl4mIxWLbM4uWXXdi2IByWZLOCREKwZo2pXjcuAVOqgCal5C/+4i945ZVX+OlPfzqtsiRkLD5qYIBIDuLatgVrikzoVMdMFUWBCxesql6DFEVRLi1DR6Xa220A9uxxCmdeLwgBpsk5e39RR0UVRZmotWtNDENj82ZBd7dGICBZs8Zk7Vo1QOBSMKUKaCpL4jgtnUIUCtjt7aM+PpkJnZ/JLlqphGfds7i2bUGkkmAY1G5cSfmxx6f8MVNFUSZnZLdAPO60sDc0OP+7fbvO6tXndmdOJAfRentxbd2EsXMHolBABgLUli2nsvZ+9RqkKIoyhQ0dlcpmBU3GANr+LJfpQE2SNuK0tkbI58d+f5nspspYR0XXrzfI5+HWWy3VkaYoyjCfD+6802LOHBspYdYs9fpwKZlSBTSVJXGcHa1DBgLORM6G48XDiUzo/Cx30TzrnsXz4jpEPodIpRDFAsa776Dv2kHhv/4PdQOrKNPIZxasOqJwb+zehdbXhzlzFuYVVyKKRTzrXwS4JI66K4qiTFexGKy4Ok/xx89xVXoDX+7bTUSmyYgo/fWLSO5fxTuzH+RQX+D4+8vIjd0JbqqceFTUXxqkiRT9A3F+9KMGNm60icVUR5qiKMfvs995x0U2K1W36iVoShXQVJbEcTIWp7ZsOZ71L55yQudYTtxF6+8XPP20s4v2zW+eu9bSoWOmIp9D6+tFBgLYjY2IdBrPm69hPflTyt/4/XP28xRFubh9VsGqnnXP4ln/InYoDKUytt+P3teLPBjGWrL0oj3qriiKMp2ci5MQD/MrMrxEsZQlYCaxhEGDO0lAHKJ1X4pcTpCY89Xh95eh9weroRG7vR2RzZ52U2Vo82d2c4Frdj7D7O7NlAcKzMkFece7knTkASzd95nEESiKcnEbus9uapKfaVTJmVCZjmdmShXQlNEqa+9HFPK4tmyGgQQyFqey5h5nF20cI3fR6uoke/ZodHVp5HLwox+5EQIeffTcVMi1dAqzL4ndl0LzBND9AQBkJIqs1XBt2Uzliw+qG1hFmSY+i2DVkfmQwjAQtoUdjUK5gt7dhTV33qSOuiuKoijn1rk6CSGSgwTe30JgUQhhdjKohxgoBzG0At5Khl7PDDo6N3PzvXcQi0XOOD94aPNn4Ue/ZknfOtKeRvbXZuDTMqyprONon82Oa74KnJ84AkVRpoaR99mNjQLLOndRJeey2DWR12BVXBufKqBNVcda0I2dO8CsgWFgXr7ktLk+IwNXh8JWAwGIxSSplFMhDwTOTdjqb99oYMkeNzMGSuT8jQQrgnhcolcqSL8fzJq6gVWUaeZ8B6uOyoe0JdLlRlQqSI8HLZdFlEtgmqc96q4oiqKcH+PlicHk1p9Dr/cyGMSwq8Tbw5CW5NMevOUsZsTNnMYcV96YACKTzg8eykmLR+u4ZYlB2xubSfibyOhN5GqCvOal3l/miu5/5ujsZVTC889tHIGiKFPK6MEmYvjjZxNVcj6il071Grx2rakGppyGKqBNUaNa0OfMRWSzuDe+gQwGT5nrM7SL1t8v6Opyimd+v6RQgGAQmpvlhCvkpwpgXbfOYP2GJlzNq5iXeptANUVqoA6tVCZiFJFNTWixuLqBVZRpxueDRx+1uOoqiyNHNDo6bObOPXfff3Q+pHNER9/7KZRKSJcL8nn0XPa0R90VRVGUc+/EPDE4uUMDnBtRKSVCjN8BMfR6T7WKdLnRaxUaGgxinhKWaRBZVMIT8lNorkMyifzgMXLSHuyYw0BDik+qcyiVwC1MFotdzK0cxJ9L84WNf8b2hrsozHiQaFQ7r79DRVEuTiMHm/j9xz9+NlElZ7vhcGIn2elegwsF2LDh7Dc4LmWqgDaFDD0BYnKA9jNoQYfjR6ieftogl3M6zwoFKBYF8+bZNDZOoEJ+mgDWkU/MT+Z/nQWlD1lw6FVkqUaqEKAv1IJphkgtWMlCXxxVzFaU6aNUgmee0dm0yTgvO1sn5kOas+Ygslm0zsPYPh9auUz15s+NedR9slPZFEVRlMlJpUZ2aBwXDksOH9b4+c9d7N2r8emnGtmsIBKRLFhgs2LFye8TI1/vZTiMdrQbSiW0ikk13oaez1K7/fhmyUTzg8fKSfO/vZmZ3jz1C6KUXCEqWz7C3XMEaRgU3VGylp/F+9Yxe7ZNLPbIZ/XrVBTlIjJ0n71+vYGmOc0pZxNVMpENh/G+53ida0uXWuO+Bh84oLF585n9vOlEFdCmgBOfAHOsAr/fXaTphrZRf4ETzfVZu9Ykn3cyz1IpQTAI8+bZLFxok06fvkJ+ugDWke2rpsvHutv+hrbf/oSZh99C1018sRh7m1awPvEQt68zVDVbUaaRdesMXn5ZJx63z9nO1om7a0PFMdf2rWh9PVgzZmK1tCFKRTBrGDt34Fn37PEj7ydsCpSNAIOLVmA98EXqWk+u6qlcCEVRlDNTV3e8Q2Poxgycm8yBAcGWLTqlkiCdFhgGJJOCzk5BJjP2+8Tw6/3mt7CLZYrdGQatOIft2ez33Ixd/SJ3l46nm4x6f+juQgaCo/KDT5WTZnyym+CO7QTLJbREgqotKFV97PNfS59nJjMjvVxT3IRMfl5twijKNDUUSfLOO8ZZR5WMPhJ63ESOhI7XuVYoMO5rsGFArSYIhyf/86YTVUCbAk58ApT6Y+zvC1P5KM+c5eO3oI/XTeHzOdM2hYD16w2amyWNjZJ0+vQV8okEsEaj8VFPzJzp4++NP8DX8mUa9CSLV4Wx6+JEEoLt21HVbEWZJkbupMXjZ7+zNX4uhA8eeYzq6jvR0ilcr7+Ge+MbzuvWsY6DkUX/oU2Bal0jn+ZmkDqUI/jWb/jwFYPaY48Odz2cjxwKRVGU6WRkhwYwPEymu1sgJcTjko8/1ggGJYGAc0Iil9Nob7dGvU+MXONWjr3er/95jjff1InHJSJeR2+tnsSrAsttHi+8+XzDXz/WGvlUOWmiWgXLRFgWQtq4fS50TRIKOu9jXbko5rtH2PnDHAvvqae5eeKbLGpjRlEuDT6fU+i/+25Jf799Vs/pkUdCTyx2BQKSdFryxhsnx6GcqnNt1y6dxYstNm4c/RqcSAhWrTLZtUsf9+edyRHUS5EqoF3kxnoC0BHjyMwVNHSuo9pu4250bgit7n76V9yLVfbT/NQT4x6xHPLooyaBgHPjOtEK+UQCWGNz4qMWR7YNuRwUtDixOXXYdU5V+2KoZqsjW4ry2RnaSZs5c/THz/S14HS5EDIWxwaMXTvGLfrXrrtheFNgb1eE7n1l9GAIr1eyYGAzP3j+LiDCI4+Y5yz4WlEUZTobWmeOXH+uWGHxwQc6bjfUahAKOV/r8Tg3d243FAqCTG+Zlld+fdIat3fV/fz2YCv6bLCHbhgZf4NGxuJjntYYLydN6+9H5LJUr70eGQzi3rgBDINUzoXR34O/o0jIlSWRDfK/ft6M53UvixbZp91kURszinJpisUgGDy7+9tTbTgUCoLvfMdPqSTw+SQ33mjxn/9zhbq60Z1rpRKUywKvVw6vt5cvtwgGx64BrFvHST/vTI+gXqpUAe0iN15WxJ4rHqBcho7yRtjbxf6+MFuN+3nrg4e49ZWn+XzhJRqXNqCNccRyyFCF/LrrzAmHeQ8vLPr7IRhEen3g853U/TZycZRMOi2hTU2ShQuPP44LWs0+TY6boijnXjQqaTIG8HSm8LREKfqcm5czeS2YaC7EqYr+5oEu+rZ30j6QRZQK1H30Hg12FVFyk/E3U3MHmBkaZPv2GNddZ55xDoWiKIpy3ND6c/Vqc7jrCuDAAY1qFVwuqFTAMKBScYpn1apz7Kh9y6/xbDw5RkRPCAqFf0E8bpNKOTeLPt/kN2jGzUnr7cEOR5CNjeDxYs2Zi9jzKaWchk9UabG6KCUrvMV9VEMxZNkpBI61yTJy83bdK01qY0ZRlHGNteFQKAj279ecdXWTTT4veOUV53Xjb/6mQjQq8Xgk773ndJPVas7rajgsmTHDprlZcvnlo1+Dh9awY/28Mz2CeqlSBbSL3HhZEYNFPz0LvoqcdwebXsixq6eeaijGUl+C+f2b+KTazGCygcUt9rgDBkol+O2TGT7ZnKHfjCNjsdPuekmfHwl4Nm1EGgbSH8Cuq4NgkMo99w1/7xMXR2+8obNhg0E6LS6KavbpctwURTnHSiVaXnmW73RvY+BQERkIcGTmCjY0PUQi5Z/0a8FEcyFGdhMQlIhyiZrh4/DOAom+CE9X5vOdjwZprhzGJI7ti+C2yzQP7iYVmYGI11FICo4c0c44h0JRFEU5WSzGqNfNoU6LcFjS0yMoHytCtbba5HKC+2/uo27n2DEi0Z1bKHXdxxs76jEM52axvd0mFpPjbtAkk3DokEAImDnz+A3kmDlpX7gb48P3hzvTrIWXUS6CntiHR6tRky5+4/4Cb0YfIup31rnBIHi9x6fbN0dKeJ5+elTepqvrZlrnPEj0WLeb2phRFGXI0NHu1auP31On05LvfMdPNHo8DkXXJdUqbNqks38/zJ3rbEDs26cRiUiCQUk+L9i3Tzs26MT5/ie+BsPYGxzqdWg0VUC7yI3XuplIODdrL25p4nChBU+DJKBD9lAarVZAxjvo7taYM8fG5xtjwECpxJ4/WUfbG1tZbOSw/UF2ZVayPvEQ4Bp318uz7ln0RAJr5izEsc4OPZelesutY061G3pitrSMPi6q65LrrrNYterchIZPxkRy3NRxTkU5t4aK1jNmN1INtpM8mGX+rhcoFgW1Rx+d9M7WyFyImcEBYukDgOBTcw6BQHz4ZknG4tSuvhbfj/8BikWEgGJG4C37ObT49wnPiiJ2SMoVgamB7gaEAOH8nFzO6Y7r6LBPmUOhciEURVHOztD7wObNOuWyRibjrHVnzHCOeN61NIHYmIRj61g5tNvrcpPdc5i68gGKpXoCAQkIdu7UqKuTfOtbtVFrxVIJnn7a4JlnXHR3C0DQ2mrz0EM1HnrIxDdOTprnqSdGdaZpLU0kQjaftqxiy6Lf47fvNhLyyeGuOa9XomkMb7J0vP5r9BGbt5WuHFceWoc/IPmo5avD16c2ZhRlejvV0e7OTo1SSdDUZGPbMDAgyOUE1SpUq4Kf/czNt75VxbJg3jyLXE6jWBR4vRCNWqTTYrjIdipjFdcUhyqgTQFjtVKuWmXy4Yc6oZBE151qsWFIamaMgaNB6s00fTRTLjvnokU2i9QNRCaNSA5SffZlom+sJ+dvpBTpwFvJsLzveQDe2v7VMXe9hgtPzc3IhisQpRKiXIJ8HiEEolQ8vpg5wVA1e9Uqk2eecfHJJxo7dugcOKCxZInFjTdaEwpbPRdZERPJcTvVFFNFUSZnZNFaa2hkUbtGfn4c66jNZcZGzNW3IX2Te87FYrDi6jzVf/wld/Q/SX35CLYNva4OBu58hHTPQ3z0UYCODpvLAZAgwLIEpRIYLiebok6mqEQa6DMC+FJ9GMUspsdNT90iCgSo9qa44aEoc+eOv5mhciEURVEmbugIo5TOfsVQgerEzgcpJUIc2yz1lfA++RrGnk/ANJGhMHZLi/MN9x3A11fjq76/pdHawy/yD1MWPrxe8PnkSZu169YZ/PjHLlIpQSQCYNPVJfjxj1243cePTp6Yk3ZiZ5o3EGTw9gd4KvElPKYXTYNMRmDbMH++s4GdSDhr1ZgcwNi6BXPE5q3e6iUf0Gg/vIV9C9ecVayBoiiXjlNl7l53nYnP53SUWZYzqdjlcl5LDUOyY4fGM8+4KJcF11xjY9vOEc+uLkFPj8bRo4K//EsPq1errMUzNakCWjqdJpFI0N7ejm+M33Y+n2f37t1cf/315+wClbFbKdNpwfbtBvG4PSorohyI8Y53JV/MPE99UOATAURPCteOD5GBAL6/+z7S0HHv6yatL6AUaARTUPY4reOL05vYlLyLdDpInIFRO28nFp6kz+cUzPx+9AkWnjZuNHj3XSdHyO+3+egjnTfeMPjVr+xRYatu99h//lyEeI8XEHtijpuiKOfGWEVrnw9oD6F3d1E8w6L1w/wKmfxbtFyKrBFFuHRmcJjgP/+Qv3vNx5Our9PiHuB/6B9w9XXX4oqHyPWVefvDAA2eLK3d7/B+5GYKRhRZF+Og73Ja60oMFnzoxRwew2bZmjB3HNvEULkQijK1qHXrRaZUwvXM87jffBP90z1o2QwyEsFccBm1FTcN59CO7nxw/tfz1LO4N7yB1dSM3nMUKmWM994F26LmCfGpdjmpSoB7xXOEopKn/V+jVALTdAK0QSKSg2QPpXjvlWaKxUZiMfD7ne+vaRJPPsne3yRIXxcgOneM96QxOtMW+uLcvs5g+3anWNffL5gxw2bWLJtE4vgmS1ykIJ9HtraN/HbUzQqR29VN+WiKSntcbcwoyjQ3kYzfG2+0ePllg3IZdN056l6twpw5NrNnS/bs0TCM46cmDhwQHD6soWkQiTj5kM8/b5BICB57rKZeayZpQgU00zT5L//lv/Dcc88hpcTtdvPoo4/yR3/0R3i9xwsQ+/fv52tf+xq7d+8+bxc83YjkIHr/AHa0jlgsPmpBEQhIajVBW5vNvn0aILFtwUv+h6nzSO6OvkUg2YXW2wtIzDlzkT4f+uGDeI/sw2eG2J1tQ9NA1wX5QJR2+witopuO198hsGt0wH7/0lsw7SCu/izujskXnk58Qdi5U6OvT+D3S8plMSps9fHHx86qOBch3uMGxCb6qay5Rx3fVJRz7HwUrcXRbsK/+EeMXCfSpRFymchwmKPpOsxciRW+13h3zl0Y2RSpI0U2uNu5/S4Do8mH5TY4mNKJ5Y/wfllHq97EHcXnCcUE868MsDCXRvYmKH/hbpZ8I+L8vOQgwXSKL6+uY/XquMqFUJSLmFq3Xpw8657F9fJL2Km004FmGIhkEr3zMFomDYydQzvUxVwMNVJouIyIZzfe3kOIcgmpaZQWLmDH7iuxNRc+A64tb+Ltxrs4aNeTyQgoFfE85QyOoq/Ag7si1Fsr2Rp9CBMfbqvE6uzTXJ7cRGMxT+SvfHg+f+O4g6VGdqb5OL7J3dsr2LJFp/P9NNr+FP5oHWvWRFm71sQuOXnBJ74PLmhKc7joJ+eK0ac2ZhRl2ptIxu//8X9U2LlTY+dOHSmdIlpjo2TlSguXC3I554TXO+/olMsMF89sG9ranAaUQ4c09u7V2L1b4+abR3ejnU1U0nQwoQLaj3/8Y37zm9/w7/7dv2PRokW8++67/OQnP2HLli38/d//PU1NTef7OqefY1Mive9sw87mTpoSOTIbLR6XmKbN4cMa+bxg9mwvgUcfJbrqNoq9B/H96AdItwdtcBC9uwtRLmLmS8yvvc8u7wKqnjCWBZX+LEfdQe70bSC+8bXhjAY7mSXxv1/i1YCLQmEVyxPr8HRozL02iKs48cLTiSN1u7s1/H6J18tJYat3320SDI7/50c6k6yIMQNi19wzZo6boihn58SiNXVRRCp9VkVr7zO/RBw4gCUF+HzowsZODkLeRtNcNBgp6rUkqboYlYEApSNZUqkYdXWgaWAls+RdQarBOl4oPkw2K7gfZ9NBBoLUHrob19r7x5zY6162nJia2KsoFy21br34DBXB7FAE7XAnMhhCBgJQLCByWaz2jnFzaKu9KZK7i+zKzyRf8+L1Xs2iSAMLYgU0Q6fSNgfXIRelEqRlhDbrCO58ilqtnlhMUv/6c3jecbLHxMx2XPsLfL5vHe4+eK39cW4bfJqVyefpo4l0pAOXNznpwVKxGMR8Ra7e8Sy23IIpCxgygMZyKtyPjMUxb1yO/sK6UZu37lQ/Mx69h3+3Okg6XVE3rIoyzY3M+B0vc/eVVwzq6yVtbc49sdcLluUUyhobnT//wAM1Ghokv/udQTYriEScPF/bhr17Nbxe59inaR5vYFm71jzrqKTpYEIFtF//+tf8wR/8Ad/61rcAuPnmm7n//vv5wz/8Qx555BF++MMfMm/evPN6odPNUOC2bGrGGmdK5MjjROEwXH21xcKFNg8+WKO1FSAO5RTCtBCpXoxDB5H+ANVgjLwoEbEHuK6yhQ21zxGWGWKin131t3Gr9e7xgP1SiQNH3PQmojT3beb/HfrPdFcF1+zcTLmvh6UrfJgTLDzF5ABzrAKl/hiVYJxaDUIhxgxbTaXESQW0ibygTNg4AbGKopwfo4rWXV1In/+Mi9aVo4MM/vMnhCsRApUktinRfW4MBGErTU6PkdGjHEjHOVKs503rJtZUnufgVhv3Up2mTIomT471wYfpqdbj9sP2y77CYPQLxL7cQ/sVx18PhkOj1cReRZky1Lr14jN0lJ9wCFGrYofCAEiPBy2bBbcbUciPGQfy3IYGGg+EqVpZ0oZzF5frj9OieQhHJK6Il3hckslAoJAhbQVJazFaW20ubxogvnvz8LrWC0QX+jmYFlw2uJlt2nIuS2/iqNVEPtDEvDkW7o5GrMTEBkuN7NRoeeX4hHdXs/N+oY94v6h98QFM0x5z8zbmU4HdiqKceoDgmjXOvf+2bTptbRLDcE6heTzOKbR9+zRqNZv77jNpbXW6Y6+7zuQv/9KDzwf19ZINGwwCAZBS4vUKWlsl+bxTT8jnnbils4lKmg4mVEDr7u7mqquuGvWxWbNm8eSTT/IHf/AHPPbYY3z/+9/H5XKdj2ucNkaGqg4Hbjc2gmWPOSVyImNm7Wgd0tBxHT6E9AeQgQBmWTAg67GxiIskS317GSTOq8Zauv1X8Y3cBmRTM/rOHdDZRazbxFfTyFt+6iMp3pr7OG8N3o2nkOTBeUEeeyRy6gd2rIOjfdsWfr+7yP6+MAdaV/AeXyKT8Y0ZtlpXd/Ii4nQvKGeyY3diQKyiKOfJiKK1p5ClEgifcdH69WdydHTZlCLzmJ36AHe1SCXvRRg2XlFiULp4jdvZl6rH7YZ1nocx7CrfOPJz9O4jzKkKctE2Fs0rc1NbnoO9fnp6NDYebaTws9hwsKq/pCb2KspUpNatF5/ho/zVGtLlRlQqzhHOSgXpdkO1OuaR/mQSfra+mWViJffwPB7d6TJzlXL0Sz/+eonPzDGrWac3k6PB18/bbWsJ1ddRrcKqxQN4dxawwsczOOfOtTl6MIxxsAtPz2GwChRCHSxd6mxEw+kHS5041KrJGOA73duYMdsZlgMnv1/Q3HhGm7dD9wdqs1dRLm1DBfmhwSdjZe729Bw/kTXUPNLdrVGpOFloK1ZYo46Az50Lq1c798/VqqBcBo/HiU+aN8+5/9Y0yYEDGlu3GmcdlTQdTKiAFovFOHr06EkfDwaD/PCHP+S73/0u3/zmN3n88cfP+QVOCyccEcK20I4cobbsxlFfNt6b+anGzMpYHOuyy3G/tRHp8YBlopUqeKwSu31XgcfLuuZv8WngGg5m6wmXB7F8AYyPPkTv66VsBMmKMO5aikZ6WVbdyFF9CWYsRpcd5/WPLL6QrBBn/Df3oW46q6GRphvaqHyUp6FzHX264J+sr40ZthqLOWGIJ1Ih3ooytclYHLu5EVm1zujPJ5Ow+ZN6HgkGqHh8dLmvoS3xIZ5yFlGVlFxhfsQ3+fv0I1Q0Z+Im+Ag3uNAIsFu7noy7AZ9e5Yquf6Zn0MMbpa+PGaz69RUpgiOGH5RKThi1zxV28iXVxF5FuSipdevF43iHVhz3suX4Xn4JOxx2BgGUS4haDbu1DS2XHfNI/+HDgqNHNTY2P0RdBa7IbaLDPkLGHeCfxL/i8dU15ibf5TJ/J67WEM8P3sfPeh6m0qfR2irJueowvQG0Edlj+/druAoZgm0BZi9oJ7QzgFem8RPAlSsivT5EPnfKjM4Th1r5upIkDhYp+9u4rOX4141cu9N8rLA2YvP2lFlDY0QIjIxzURRl6hnrOX9iQX7o6OR3v1uhXB79tSeeyFqyxHY2BY460zgfe6yGzzf65wzdJ2/YoB+7v3aKZ0MbBtmswDAkpikIh0fXFM4kKulSN6EC2lVXXcX69eu57777Tvqcy+Xif/7P/8mf/dmf8cMf/hAhxDm/yEvdyAKT3d6O1t+P1t+H8dGHyJtWDX/dqQK3x9qdSiadhYd7wSNc3/4K7nQ/mmmCdNPpX0iv1YBLCHZ7ryFRq6dWAxmLUVxwBfW/eA3b70fzuXHLCkJKenyzmF/8kHBtkJ5qHL8ftEoZ44lfEDiwecw396HMi6EODgOYs9xLtd3m24UNxK5azXuHG+nrO7EYpo/5u5pI152iKJeudFrQZ9ZzZOYKrj7wLJ5agbI7jJQSWbM5MPc2fpn8fcqDPobejVrcg6yUm8jH2ug2m2lslBw9KrAkzDiyhVj8HpJ2/KRg1SMfNPPdgRBtniyfZvx0dWnUahA3CzQ3hoh66/Cc4lpFchD98EGkBHvW7OGbJ0VRzi+1br3wxrohXHH1wzy2oohn8xvY+RxaoeAUk2bMPD6F8wRSAkgqmp9/bnyczXV3ETaTHC3H6S7HuW1NieZZ96ClU2x/o4ENv2ticUgSj5vUaoINGwyu8M/j+sRbWEDJFSa7t0AT/exdcC/eJfPIWddy/bv/G/fbRfSDEl13Js2Xvv7NMTu+xhpq5W2tg2CAwQM5eprqiUadDZmRa/eRK9vxbphHZg2deH+gIgQU5eI2smjV3Dz6c6d6zp9YkD/V0cmxTmTl84JKRXDbbc7rx1NPjf1zVq82+fnPXWzZotPUJLFthhtYbr7ZZOdO/dxEJZ3l7+5iv7efUAHt4Ycf5he/+AXpdJpoNHrS54UQfO9736OlpYVNmzad62u8pJ1YYAKwO2Zgz5yF0XkIs3Mm1NePPyVyjN2pwtXLeaL2ML94PszRoxowj2+6vsFjrl/SPC9ANdqAucWiNdnPK/776Co5x5xaW21mzJCwagX2609CuYy7lCUY8PBhZSFHtLm0VXrQ0kmK1NPUJLkz+zSNm19Ato395j6UeXFSB0coTKzWxdfuSbAmGpn0E+ZUXXeKonw2LsSxkqGdtw3+h5jXt4mWxA5szUXWVU/OXwfpLP9h5i/5/0a+jmk6XWXtpUFET56BcAduNyxaZBEIaBzZGyFQPsDS2ntk511FhvioYNW0Huef8zdx81vrSAgddyRCo5HGW0jwcn4tno1NoxY2w78Prw/3q6/gffoptKPdANht7ZiPfJnqFx9WnQOKcp6pdeuFd+INYSlZpvjj5+gO7WROVGLPmk3liquo3b4au7ll3PeQWbMkbW2SI0cEui6wPHF6ZT3JAnR0SGbNkshYnARx3tzpIR6XBIMQ1Evc0PUMLYc3oxXT5GMFvJlPkeFG7Foduxfey8cLHxz+OboOdlVg2aDpEhhdWE0mIX84SZ1MkhcxCoXW4aFY5bLANuFTOZe5PZvY/QbISITZdWnmBvup3XNyZ93pbpjHuj9QEQKKcnEaqzi2cqXkrrus4SXfeM/5fB527dIndXRy5ImsAwc0DANWrTJPWYwrFOBzn7N48MEajY1yzNNc69ZxTqOSzvR3d7EPLphQAW358uUsX778tF/37W9/m29/+9tnfVHTyYkFpiG1K65ClMuIchH9FFMix9qdyvz4JfqTbrrE14hEbEDwk9TDVMrw5cGNzHMN0NIY4mX/Wg7Pf4ArdIt8XlCtOuemQwubMRctdg5SB4N48NH9chh6+ukhyCeyHr0RGvUBbjLfQm8b/819KPPCTmbZnRy7g0MVwxRlirkAx0pG7kwtW2bx1vMlKmXB3rZbyBEkU/ET7/BS6eznqsImrmy/i/c7G7AsyLvryFhB/JkMrVe5CYVg8cIqC/u2kdX7qLf+DrM3zm+yN5Gue5iy8NJkJLnSn2DfjFvJHi1wp28DwUqCojfG3sX38kn8QSpDCxvf6N+H1tuDduQIIJHHbt61w4dw///+FqtYpvzN3z8vvyNFURxq3XphjdWhtXzwaeanXqC33EjTorn4alnc776NbGyicvmScb9XLAYPPljjxz92USwKikWnsFVXJ3nwwRqxmLN5kXknzdEdLfTW6rFteCD/LB2VF0i6GunKz2WQOG12F5krr+Hpa79BNRSnwSXxlwaZ0fsO++quo9QQYsXVeYj6IJ/D9cF7ZG+/l1+sC2M8+xyXDWymouXwNwa4Vd7Esz0Pk8sJbk/+iqWZzfiqaQJ6njbrEwq5KMmkh8FVt7H4hLX7WL+fE2+YG8a5PzhdNpuiKJ+9sYpWL72kY5oGjzxinvI5v3WrQa3mZDOOdKqjkz6fU0QrFGDzZp1aTbBrl86TT8KHH47+OdGoZN8+jR/+0M3GjTZ1deMfD70QUUmT6b67WEyogKacP0MFJq2/H4JBivgo4SeQL6Bdvhjzj/5v1GrWmB0eY+1OFYNe9ud1lmQ3M6v9LsxQHJDouo9fFr/G0fov8J9+v5docx3i1SYOPOOiu9tZjLS2SqpVKPqcnArP+hexvF4Odus00Usg3M9vXPdhemIEi4PcYLzHbP8AMjx31HWNenOfM4/asuUk/vdLJFKn7+A48fGN7G6ZSq2dinIpO9NjJUPPadFYD8HoSZ+faC7E1Vdb3HFdAs++PEeZgfB5mTHbZsYMi+0DEbTiEa5oHyTnjtPdrXEoX897/pX8y7pnaY1ZlNNhjPfew39kL9mWhRy25hMuZrgltQ7drlIy3dzm3kTL22nc6QTpjMDtCWELD90NV/DRwgfx2V6S3YJ0WoyavGbH4xgfvo+WGsSOxZGBINrgAKJYRGQy+P7++yCg/OjjqhNNUZQpZyKdx+n08ZBrAH9pkNndmylHGhiwmilJE+8kuqkeesjE7XbyezIZQSQiuflmi/vuyOF5ytm8CO4o8o29ET4MrWBX861c07+JfcVm+mjC75fIxkbSBajsOoh+o3NsCWCunULm8gyIGbR2uClhIdMlPD4Xsn+Q//7HBcTWN1hTfYG0p5FEYAZ1PRmWFtfxSVXD65HcXn2ew9VmUnYrEW+Z2aXdBKwctncm+fd3UnviWVyP3Q/u4Ji/nyEjb5jjQ0MXRmS3wanjXBRF+eyNVxzTNDlcED/Vc35gwMkfm+zRyXXrDDZscApP4fDIjjbBihXHc4b37NHo6RHYttOwommMW6D6rKOSJrKZcDHe86sC2gUmfX4k4Nq0kULZRc4MkNVj9HlDJG+9j6Wt89D1scO2x+peK5cFSTtCwO6iQU/Sg7Mg8XgkxaKgu1xPIhpiTqvE7Qa/X3LNNTbxuKRWE7z6qoHbDY8c2y2zN2ylsrebgC9I75J7EW1f4E/3/phZXZsI7x1ENz5B5NLUrl8GhvPP6cQ3995V9/P6Ey4WlDYTMY9QdQVO7uAY+eQolXA98zzuTZsQhQKmN8AW/SaeNB8mXfFPuLVTTS1SlHPvjI6VlErUnngWY+tW3GYeb30Irls23LE22VyIV181uP/mOhZd72OOmUJvbRh+LZhdl6YrF6SnEmP+fJtIRNLbK2j+/H3E/VUOP7ON4pFDtCT7OBJYSGHmDXToLrq6mqgIwV3JJxFBP7W6Nly5AvH8YYK2IGFfRtHbzILO16i6g7zc9DiBgCQmB0b9PkQq6VyIbiBKJbT+PrR0GmkYgIRaFc/6F52u4glk2KiNA0VRLgqT6Dw+MeTaX07hqeUZMGbgdku8XucGSYbDaAcOYHz0IeYVV467Vhvvps7zlLN5UQw1sq82E38oy+fz62ju68Vv5zlszcTCuUn1egEjTLTWha+UZNWqKB9/rNPTK8CyaNG78XySp/TOEUxZw3SZ9MgmthRM/sDaRK/eRNpuRC9CyevFXdRYZb+GLEu6qafZ6uYKPqI+P4CbCrbuptASJVWN4f3nlzCCII8NrTjx9zNk5A2zjMWpDW1mH/tdjRvnoijKBTNecSwSgc5OMfyaNd5z3u+XzJxp88EHTkriRI5Ojld4Kpfh3Xc1+vsFHR2SUsmZ0GkY4PU633vo5Xq8AtXp1p3ncl06kc2Ei/GUmiqgXWDeJ3+Kcegg/a4OyukcAYoErQwf1X2e7ye+xJ3P6Tz44NgFNCkBy0L09yM7ZjjfzyuJaRmSWpCEdfxfdaXi7LRFIs4b89ATbygrwuNhuMK9YYPOggV+Zq5+jPyCL/DzdIHQzCgeD6z66B+Z27WRZKCdI5X5LAilCO77FIDaNdcislms7n76V9yLSZwYkCr7ebn5cQ4u/gK+UookMaxoDJ8GPQcEH32kccUV9vCT0LPuWVwvv0Qt3oDd3s7B9/L4973IjfM0dl7zldO3dqqpRYpy3ox37Hy8YyWlEuz5k3VE31jPoNFI1T+Ludk0s3uPd6ydSS7Ehl2N3HXNcuIbX8TKS6Tm3FzMDfbTe8t95EScQrdzM/LQQyZr17r4xbqv8Zb/Pq657D1W7/97uv3zyRz2MLc1zw1zS3TrJh17j9AZvI6yESaQ7SVjxDG8EM738WnscmwvNO/fQtm8m5vuixAXo38f0utDeryga4hqBZG1kW4XSEAIZDiC2dx62q6LqZgJoSjKpWsynccnhlzrLucYvShkaL/C47yGmSau995F6+vF+08/QsZip12rjYz8GLmZUzCaKNk6wQ4P1V7J3NzHlCyDsMxge7zUB4r4SyXc1Rw1f4BBGefLy/N8jV9jZLdSlZ0Euw6gadDvaiVbNbCzeUzyrNbWE3Hl6dJnUK0K3G5JJqNBNcI13i4MXWKZksvM9wlQRGBhCx3NqtGS2Em+IYRobsC1fSu1u++CYHTMEPCxbpiHYltc27einSLORVGUC2e84lgmw3BBfKznfDIp2LFDIxBw7tMLBcHevYL6ekkkcuqjk+MVnkIhicfjZEZ6vWDbkMs5ub5z59rDL61jFahOXHcahmTRIpsHHqjR2np+1qUT2Uy4GKkC2oVSKuF94qf4fvQD7KpJNRchF2mlLz4Dwyzh16E1WmDrVh+33spJHVpDBSK9+wiuvj7MmbMwr7gSfzLJZa4efhq4n0O5eiKaE4SayTh5ETffbBGLwccfC3bv1iiVnJZOlwtaWmxqNThwQCOd9tDUJFmypJF8tMqVu5/hmtSrzOregqm5KZku9Eic2vXXY/okel8vYu8B9gzGedO8nw3bHyJwwMOyZRarVpl4PJK3djeSzTZRqzlhrZWK88T+8Y/ddPgHWHHZALffbhLctgW7oQkZr6dUgr3ZINGI4IrsJrrtL+BpcG44x6ucq6lFinL+2JM8VvLbJzO0vbGVnL8RGW3Ergg+7G3BboR527fSd92dbNvWOlwk85cGaTNSBEIxtm6tP2UuRNfyBwgGR99c1O65h6vWrmVGqcKhQwIhYOZMZxdu2zYdb1uMQvAqrK4AreUD1A0UCXb2Id1V5sgiXpmnhA9joBdXrYi7PkY0Kin25nCZZfrtKM21I9y9PMEtawPYpRN+Hz4f1sxZaH09iJp5bKdDIKpVpN+PNWMWsrHxtBk2UzETQlGUS9OZdB6PzNLZm2xgdsNKPp9/jpa4hLSB67130I4cxlxwGXZrG9pgAs/TT6El+ig/9rXTdliN3Mzx2hKXC2o1QbglTMiXZV/gSuZ98iZh832aj+Zx2WWEZfJRdBUNtW46fvcGsffeoBhq5H3f1dxgHMGo5glYSSpGIzv0q+i1Glhkf0zZNIjqGaq6l1pNUKtBo5Yh76rDr5dZUP0QD1UENi5MxLF7PletyCzrAO5QKyI5iEilhuMLJpQ15PNReeQxqqvvVCcqFOUiNV5BfHBQcOed5rj5Yr29TnPLnDk2sZikrk7Q3S2YP99i9WqTWbOctWtPz8ndXicWnmo156jm/v0alQr4/bB3r3Pc3TCgqclm4cLja+mxClRD6866OkkuB4cO6bz1lsErrxg89liNahVeffXcrksnuplwsVEFtAvEs+5ZPP/8Epgm5VAcM2PTXDxIwusmEVtANNdFszvJR4WGk9oXRxaIqjcsx/XRB+iHDqJ3HwHDRWMown3ht7FTP+Hn2YepaD46Opyw1aEn79atOn19Gn6/8+SpVAQffKBjmhCPO62kQ+O/16aeZPGBddR8HmrCTQUPDck9hIKSsrkUsehavKH9/K/qv+JnR64jrccJFqE+Z5NIOP/ELAs++UQjGnWq6t3dGqkUzG8v8kjlKTo+3QxvFSi8WKNB68RavgJwjqTWamAFw7iLXfjLKYq++LitnWpqkaKcX5M5VpJMwiebMyw2cpQiHei6c2xcCDiUjjIzeYjCkTSFQhuzmwtcs/MZZndvxlPLU9SCbDNWsqXjQbJZ35g7U5FmL5XLT765KJVg43MZPtmcod+MI2MxZs923uzntRW4bP96orkjNPbuQDNNUqIOyxdFr1Rx14pcN/AyRjSAmwGoFJC1OJF6F9fe4MJMpvAYPhZ8JYT0gfSd/Puw43Hs1jZEvoCW6EdUytjROuzFS7AWXoZIp06ZYZNMwq4NaZZ4kniDdRQ98SmRCaEoyqVpsp3HcPKxyzrvPTS8WsD161+hdR5GJJNIfwB7IIV8fSN6LoleKmDs3IG+cye1W28ftxstmYR8JsZcI4A7m8XX4KW93WbvXo1wKYtpeLF0Fx3WQVrMLrB0clqYnB1g/tGNfCO5G88HeaozmujxXEa1WqTgjdFvx9GFZE/gahJ5HxVbJyTyfCCvZJW5DVuHgWqEBplhpq+PVzxradX7uDq7ER9FNOddAABLGLip4a4lqA0OIIMhZN3x13x/aZDHrk9x53V1JEX9KY9DyVhcDQxQlIvYWAXxu++2uOuu44Wlka+JH30k+Id/cNPRAS0to8P+n3/exa5dOtmsQEqOZZzJ4aaUoeD/kYWn7m7Bnj0aAAsX2rS1Sbq7Bddfb3HzzSYbNxqkUmLcAtXII6H9/YJDh5zOOK/XZmBA8KtfGZimYP58m2DQiYUKBs/NuvRCDC44W6qAdgEMF3maWxDJJC7bxPSEKJkQzR2h5IlQdQXorcZOqg6PVSCqLV+Jq1ZDP3KY6tXXYrd3MCeb5Y8O/oIvze+i+86v0XZFbNSTZOdOnZkzbfr6BOUy6LqkXBZYFnR02DiD4ySB8iDzPtmEb2YDPfkwjam9IAS2J4jR283bGxYQsArUKo38IHs9g8RxuaBYhFRKZ9Ysi5/8xEUi4XSCJJOCfF5QqTgdcWsrv2Lp/nUUQ40kPR0czfQxs9yH/uEHWMtW4PU6u4p6PkvVE6DodRYf47V2nskiT1GUyZnosZJ0WtBvxrH9QbyVDHnD6VjzeIB0hpIRJNARJRCQLPzo1yzpW0fO30gq1AGZLCvT62hptfmH7q8D4+9Mjbq5KJXY8yfraHtjK4uNHLY/yK7MSp7vfoh0xc/dg87PGfR34JcH8ZMjKtJULEEp1IAnXcJTSKGLElqtgujLI9MpCtcsx0zm8ef6kPeNLhSO9fso/as/oLrqFrw//SfcW97C7JiJ1tKMSKVOnWFTKmE88Rz3b99OzJ3D3BfkYNsKPlr4IOGw76LOhFAU5dI02c7jsUivD9xuCPgxL7sMsXc/5XQFY98hTM2DLWx0Q8dbKqEfOog2xsmBoSNEGzbopNOtrE3fzJrKczQuhZlNYejLQ1+CtCvO4sxrROsEBTGLTEYQriQIiSwlb5SwnqeQrGJne0h37qGnsoC5VQ9CmkTtJFcWt1AxNYS0SIgm/sL4Hhmjmesrm2iXR7B8AfYtuZe9kQfZdaiHB/p/QB39AGjC6SoRQ3dYlRJabx+11VcMP4iRMSO+QIDmJUup3bgSm+YLtsmr8jYV5cyNldPY3KxTrY7+ulIJXnnF4He/M9ixQycSkWQyTnfYyLD/bFZw5IjzWhIISEIh+N//28WTT7poapLDA7Vuv91k2zadfft0vF7JvHnO9zoWS87Bgxrf/W6FYHB0gWrVKpOlSy2SSacLbOhIaDxu09WlEwg4G96mCabpZKkfOKBRq2kUCsdPrzU12fj9nNW69LMeXHAuTLqAVi6X+f73v8/LL79Mb28v1RP/ZQC7d+8+Jxd3qRoq8ljt7djZDK69nxLzCJIVg7pqhmDuKG/N+AqHcvXc+zl7eES3lk4hMumTC0SlEiKfd3J3YnHQdLS+Ptw9h5jbuZeZ2Y+p9n1ueCdv6ElyxRUWBw9qdHdrZDLOtwoGoaPj+BOg2Z3EXS3QtLCVGWE32o427I8/JV3x4RUVwtkuzFyVp0v3cZh6gkGJx+MczSyV4NNPdQzDeeLNmGFTqUAqJfB4BJc3DXDV4U2kW5qoBhrRLeg1Z1KKziZ46BBaazu+xkbmh/Nk9yX4aN5aUlqcbGL81s4Tp5pKrw/p86mpRYpyLk3wWIkThBxjV2Yly/ueB6DsiSCyWUJWP9aNa4jOjXPLkn7a3thMwt9ExdtIqSToS/tpQhD7eCt6473sLcYnlAtRe+JZom+sJ+dvpBTpwFvJsLzveWoxeLp0Ny2JzSTCTZjCICIi9LlbiRkZfLqJYZWpufx4rKLTQ+D1YSOwKyYDOwfYG5nFwdb7yaW+yPUfC5qbj73JH/t99F13J4UjaQIdUaJznd9H6d/+e+xZs53iWlcX0uc/ZYaNZ92zNG5+kYOuFno9M4iJNEv2rQMYHlxwsWZCKMrFSK1bT+90xZMzCbQ/MS+nyRjgO93bmD2vA0IBiruO4s4nsYWOz8pTdEWo1UDoFkZyEGvGzFEnB5JJ+Kd/crFunUG16mzK/p31JY5WBLdv2YTP6iFPkM6GW7lavsvMZj/+AxolV5hqFTxWL5pmUQoHsXJ5yvjQTEmrdYSDgQUcLM5gmbkJL2UyWtR5j9AKhEWOVfpmfuH+GhvN5SwNHaTaNpN9pfksnWdzW+Ut6PFj5Qw0TYIGmCaiVkVqGlSriGIe44MP0L73pxiWjZ7ox2puwW5uxvjoQ/xvvIb9q19gLlr8mWf2qrxNRTl3RuY0jmXomGQo5KxnTRP27dOoViGRcML+DWOoIAVSSvr6NGzbJpUSlEpw+eUWtZpzfHLFCouHHqqSSglmzhxqgHEMndYql8Vwgaq3V7B1q87OnTrbtxvDz/dVq0wCAcngoEat5vxZcOKWDAPyeUEioTEwAD6fsw51uZxjox0d52Zderrf3cVk0gW0733ve7z44ovcc889zJ07F5fLdT6u65I2cifPXLgIgOiRLtylQYq2i7di97FtxoOsWWHyxTuLeJ56eninCsNA6+3B9vuRLa0AiHIJUXTC8qXXh77nE4x9nyK9XgSAWRuVATZ0brpYFCxZYjN3rk06LdiyRUfXGW7JLJWgbzDGclcAfzWD29dIedFlHDqg0Vz9FFGrkrHdbAl+gacrXwLbOXJpGJJAwKlYZzKCpiabSAQsSxx7QkqOHhWIVIo6exBDxJC1IsVaALcbzCuuQO4sI8ol9O4uZs0I8m7r5zhYuorMgRQyFhv3Bnpoqql700YwDKQ/gKyrQwZDVO5Zq45vKspnaDjbIPEQAIvTm/ANdJExg4gbP4d3+ZXYyUHuujFF/ldZ9pRnUsw6XaoARn2YeO0IV3YM8F4uxpVXWtx1Vw0hBEePghCjb/hEchBj61YGDSdvTdchq3lJJgUNn27BZyzGMPN0aTPweWwsYdBEHwFZxFMsOXFlUlLR/bhmzQIDBpMasn+ATKCZTSv+iFc7F3L47zUan5NcfrnNsmUWd9xh8tvfGmzb1kqh0HbCDcjxYqOnkKUSCI/7OjTUYSzbGgm7Gujbq2EGmk8aXHCx78wpysVErVvHN5niyWQD7detM3jr+QwzQ4OIeB1yMEXiYJFqsJ0ZPkExZxExc4BAp4a/mkKTNtQEWlcRrVjEamujeriHX7/SxIYNOq+9ZlAuO+8tTU02tZqPH6S/zi8L93DPin68rc7PmbtlC9tKzVw2sJ+qWaVa1obiKHFVi6QtHwPeJmaU9+Gr5pjVnCJbDcCAJCdCSAken8ZB4wq6yvXcbL9JxMxxY/hD2qM5coUgr1VuYscnt9IxuIUj8auo07MEC/1g1pzEbk0Dt8fJMPF4sWbORBtM4N6wAWvmLGRDI/rOHeh9vdh+P5TLUKt95pm9Km9TUT4bQ8ckQyFngF9Li83hwxqaBp2dThFN16G52TlG6fFIpIR0Grq6tGMFN0GtJoaPWe7dq3HllU5XWKkkRhWyhk5rSSk5cMD53I4dOhs2jP18X7bM4vnnDUwTikWBpjm1Ap9P0tmpIYSTpyaE0wxjmlOj2HU+TLqA9vrrr/PHf/zHfPWrXz0f1zMtnLiTZ86bjxaOEIj3ULn5bq68+xvcEpXEYiaBZ36NPiIQv9qfxZUqoL//oVMgCochn0eYplOYA/TuLqQ/AAKkz0ch1o6ZzOPZsBWx+k5isfhJgX2Gcbxwlk4L9uwRHD6skc83MT9wE7N3P0vEgkQtQr/WhDtc5a3KdbwVuZsjrjlU0z6ck9eSUsl50tdqTn52a6ukvl6yd68GOJ8LaCWuGfwd8+2P8XebFI0wR40OXFddhk8WsRYvpvDtP0RUyri2bGbZrg+4priJshHEvPxGXOPsznnWPevs7M2c5XTsFYtouQyVW25TU4sU5VyZxKRbp9Dt4q3tX2VT8i5aRTdrAm9xjfY+xv/chAwE8CxeSt18Nw0yyVGrifff14lEoF5PY+oBvK11NKUlr75qsGuXxuHDmnMkJyy5ZmaCm5cMcPXtIcz+NO3FPFX/LOyKMxp8YECQz0dpM48QqrNxiwDRSppQRyOxsiDSN4i0NSxNR1g13HYZy+dHD/qomVDMltA8YTS/c3zSyY50JibVarB+vXPz2dsrCIUgHreHdwYBvry6b7hLz547D1kde6oyjD6CvrDOCXvt7tbor4weXKAoysSpdev4JlU8mUSgfepoCdcTL/LtgU1E9DwVV5CjDVeg+T0MHshi7O8lWKxSwYuLCjpgyBoSQQ03Lk0g8nn0w4f49IdbWV+4DoBazTkFWig4N3B1dRIpBX1mnIG6CC1Rid8DWTtItrtGItpBe34PFl5s00aTNnqtylF9Hge9l+O1S8zQewhWk3ijOonaXPo7rqZY0ikJL6lyAM1dZmlyAx1aFxnvbDKRDvy1DGvLzzM718PCtgye+e0kt1+O8WkGS/Niay48ehWXZiODEbRMGlEsIoMhZyM8nYJUcni9Lr0etGwWgkEsr/eMM3uHTqtMdNjAyNyjEyddq7xNRTm3ensFH3/sdITZttPZ5fM5RbFczilY1dfbzJ5tk0oZVCoCKZ3cYCkBBG43dHU59+her1PM0nXI52HHDg3TtHG7oVp17ucbGiR/+7ee4amaXV0ac+bYYz7fv/vdCgBPPuni4EGNYBDa2myOHNGwbYhGwet1imrVqtOVdsUVFtHo2R3hPNFUOE4+6QKaruvMmjXrPFzK9DLWTl75oUfIrrofys7XON0UWzAbGqnVNbJnj0ZXV5BQCdpK+xAfl5jRkkVEglQ+dxt6IoF2tAtRLiE9HiiV6fRfxofbQ1B20Vw9wv6f57jlX8a5fcYnBKNHea9/Ft25BcyxPuU/33iQTGwm/7Bh0bEnjmTxYou90Qf4x61w3YZNRPWjFPI+9rrbaKwe4evW/4eiFuQluYqneBjp9lKrOYsb03SeaM3NNnPnOk+q7m6NZFLwiPZLHor+jrzZgqvQjUGZRfpuvLk0mhHBunct9tx5eJ56AvfGN7AaGtHntBPMZtE3vkglePLu3MhsOdnQ6BxtLZecAqMQiFIReQ760afCE1tRzqfJTLodnW0QpOP1d2jY9Cq1eANWvfNn3RvfoBqOk9ydoDOjkx6so95II90J9l15L0VfnL49goMHNcpl58YpqJe49civuOnIJtyv5dn/gyC9zVeQyfvxaxl6K15MU5DJCOrsDAURhDmz6XetYP6uFyh2lYn6yuDWcVWKWJaOFBoIgccuY+bL2DUbd61IIdJM1hVjT6KeQADcbkkuJwgGoViUrF9vEAxKfD5wuTTa222awkVcTz6DsWEDXvNYh/DKlVTvum/cozkju5ONBi+LF9vMmWNjHR09uEBRlIlT69axnWnxZCKB9vqvn+PKQ7/BjDeQ8jlH6ed3vkbGEyfQeZBo9QAF4SNtxKkzEwAY1ACBpuFUyQTYugvtnXdZfOWn2DZ8LJsoGDGEgFzO6YywLKfZ61j0GIPE2Wqs5Ba5DtMbpEgToWoPwoSabZDwtnGgPAdfMYkZjLJlyf3snf15Bgdhzc6/4arFNaz6Onbs0MkfkbSJXqIizRH3QhL5CI0UGdAiWFmN0OAn7Ct4sPsKlMqzWOHaiQsTvVbBqgFS4DJNxNGjuDduQM6cgfT6nM3dTAZRq2KHwohKBel2O7Ejmph8Zu8kNrVGGop0aW8fe9K1yttUpqPzdZ+3ZYtOf7/A74dIRFKpOJ1e0ajE63WeZz09OrmchsslSSYFmiaYPdump8cZvjdjhqSnxwn4l1Li9QpaWyVut8277+ps365Tqwm8Xud13bKgrc3ZIOnq0o4NB5C0tDgTPstlcexnHT/quWqVyTPPuNizx3kNsCxYsMBmYEBD152mmGoVSiXnmKlhSDIZhvPUztRUOk4+6QLao48+yvPPP89NN910Pq5nyjjrJ9cJO3lFbx3PbWxi218f/0dzx+wcD2XyyI429uxx2jQDASBeRybVwlOu73DtlSHWfCWE9PmdN8+Nb0DNyffo9F/G28XFeIMQ1dNUbIP313Ux86d/xbzBd7irVubzmoHbqxMOWOhmlYruo11bxc+u+W9EZkXx+WDXLj9viK/x2+DdrL42Qd07r7Go+zUSWhNpdyMhO8OX+CUteg8/Fd9k0B3HMCSa5ozPff99nWTS5oorbMJhSfFIkq/WNrLg8jgl73zE7hD+gSMYxQokeinddx/2Fx+Y9ETNkwYI+Jz8M/z+czJAYCo9sRXlfDnTSbexGMQZILBrC3ZDEzJeP+rPdn5i8ob5eRbqHzFL66RgB3mO+/iUB+koweHDGj6fM+wkHIYH8r9iZeV5us1mqqKBJjPBjcn1DHpbCaS66PBkSJoNhIomjXo/H8y6l9aldXwkH8Q0Yfbuf8bI9aMbNma0kWogRilvExzoxFWqkN/Xjx2JkvY3UxUh9tSvZKA/TtgjKZedncNDhwQ7duikUs5uXH29kwmxd6/GLe5nuDK1jkpTPa5jRUbXSy/gMe1xj+aMlTPkz2fRK/1Ubhtn6ICiKKek1q1jO1fFkxPXwyI5SHz3Zg4GGsnrTfgNSd7wokmLWM+nWNUi9VYfUQzSMkyv0UKz2YOG5Zxk0DWE28AOhanZBm2DO3hkx5+iSZNVVTevl2/mV8F/QVn4KJWcE5KBgHNDCqCnk+y0F7HMeI2rBrfjtsqYbhe7fMt5N7+IlnwfcauXPEF+F1xLcu4DZKp+ElXB6huX408+TxHIDcRod2VornVT1EPE7AQLajsQ2SoV6aZXtFDSAnwkr2FB1za0UJiKvw6jmEAXFrYU6NUKCAsZDCJdBtqhQ9gShFlDmiZS051sY9vGmr/AyexN9E86s3cym1ojDUW6ZLNizEnXKm9TuZSc7t79fN7nJZOwa9fQAD+NatUpcpXLcOCARkuLzVVX2cTjksOHNZJJp5GlsdEZIHD0qFOwOnwY8nmNUMjG63WmYvp8MDjovJ5ffbVJPA65HLzzjsHMmdbwc7u11SYQ0Dh40DnS3tPjbEqDc2x0qIjX2gp/+Ic1kklnnfujH7nx+8Hns9m3TwMktu1MCN2zRxCJCP7u7zyjfl9u9+R/R1PpOPmkC2her5d3332XL3/5yyxfvpxwODzq80IIfu/3fu9cXd9F51w/uYZ28p576uR/NC9taeDaUogWT5bDh4MYhtP5EKhkEMEAtY7ZvHIozg1UiPkYLsh5n/gJ8s3N7D/SjCFM/L2dtKU+ICsifKfrOzTLo1TcIbLhNmLpg/iLOUrZEEfCC3CVc1xVXk/NhFfnf59SaejcNeTMOHm3xd3+D+iraySRayJUzNLGfmYYh1lkf8JScycbXLfzkvth2uZ5ufxyi48/1unsdCrbl19uc/ftCeZ/mKPkaqdkuvAuXYLNXGrZDCKZpPa529B9PrTDnZOaqHkupkSdylR6YivK+XI2k26H/qycOQMAcaxLtGS7yfQW6LzmdvpbH6J7R5oPj9STdcVxHQXTZZHPO7twyaSgQRvgytwm0kacWKGXWXonbqoEMzVig/voN2O02HuY74ZurZ135j/K4KoHMAww8fFq2+M0y8u4eufb6D4XWksTuYRgsCqwfGVCtRS9kflkZIRqOMb7/hV80PAg2oCz+LJtp+3+wAGNYpFRoa+6Lmn1DDLryGay9Y3orXHwOIVCW9NOezSnuuoWtP4+9D270XLZ0+YMKYpyatN93Tqesy2ejLcefmBpiqBZoG5WnL5DMBTd4Un2Up89xA7fDcREGcolPNUimBITAxcatibQvW6wLEQmjdu0iZh+apkg7mKaq6t5rpZbWJz6kP+7539ihT3U1TnXavQdZeXRJ6nr3kVwcD8topdifRtHorOJpw8yZ2AXHmuAZHQ+A81Lec79GO/1ddD4npNnuWaNyWV3rKXyW4vq77YSzR5BjwTYvWAtC/K/Yk7+YwapJ29H8IkKi+xdHDVm8t95lGv1Zm6tbcKwinirOcruEMK2MKsmnmoVaVtIfwCrYmLnC+SvX4k35EX6vGj9WewZMzFnzUEk+k89pXkMZ7KpNbKQcGKky1iTrhVlKpvovfv5vM87PsDP5tAh50RWNusMQxHCGeDn9cLChc4UzaNHNQxD8u//fYXf/MZFsQhNTYKBAUE2K0mnBXPmOF9bKsGhQ07BbfZs5zSE2+0c2UynNUolp8jm88GsWTZvv60zMKAjhDP0z7KcI5+vvmrw+OPHH6fz/JcsWmSzebNOU5PENO1jEU/O79EwYM4cm1hMjvp9Pf745IrvU+04+aQLaH/9138NwNGjR/nggw9O+vylvhA5H0+u8f7RJIix4dOVrHz3eSq9BgUjAv0ZIkaC/Vfdi94Yo9AtRu0Sylic1Fe+zdsftlJ7ZzNzKxtpMw/jpYxPz1MvewBJoJrBmy7isqpY6GhmFaTEDEYpVWss6tvAs+/tR8yfR63mtGe6XIIYSXxWnnBLKyvlBywsfoCvnKKGQUV4ifjLfLH8NO2uPt7z/B5+fx1XXWURiTiV6n/zbyq0+0IcejXEwI5B8iKE9HhpnBVgUSyHEYthR+vQmXxB7EymRJ3t3xFcnE9sRTlfzqZQPTwlNzmIkRhA6+pyJpWVLEL5ZmyPj+5yPcaiOA0BjVKnM4ikUoHmZptZoQR1Axki+RQ+K49RztImD1IVfvJahHDlCHXVBIPeGG/47mJOqB89PUi5O01xbzduFySsGO93NnCZHuNIpYlIIYFeSlKs+GiuDRKsJrE1FwT8dEWuZ0P7l5mxvBnxsY7P5wS7trQ4CwV5bH0QCsnh/06nBXPDg7irBbzNbaMWZzISRnR2jl1kPOEIDoaBuWQp5Qe+hGxtc254etTRcUWZrOm+bh3P8JCXMyyejLcejvQbPGxbLIz2Ys2f5UyBS5VpyB5ARIKU6uewr9fisup2/LKISzPJ20H85NFtG1HMI3UdzbKQtsTj9xJNHkC3awDomHyRZ9lrL+LNxn/LVx7IMv+DXzPjdz+lIbufquHDrZUpagF82X7aSkmommRqfrxamXxJp+ng29w0qxnXsq9Sqzlr07lzAZzTIdnr7mTdXxYo+WLU10uu2PMMeklgVSTOrBmJFAIhBFrAy5PFr7HTeyOz3APEgi34KynCuW4qriAuv4WwJfnONLmqhxJh/sn+D8xb3MRdXz1CaOMruHZ8iN55CBmLT3rDZDKbWmMVEq6+2uL2200++ECnu9s53nWqSdeKMtVM5N59Ivd5cOanz0YO8BuK50inBZ2dgkRC5+BBjUOHwOWC9nabWbOcTrVMxokvmTFD0tDgFMt27NDYt89pTCkUYHDQGSKweLE1vOYslyW27QwgKJed4+5D12FZTsyS2w0eD3i9TiHtmWdc3H2389o/9FqxYYPO4KAgmxUUi4L6euc1o6HBZu9ejbo6aGk5+fd1990mweDEfz9T7Tj5pAton3zyyfm4jinhfBVRTvWP5meVL5EA5mtbaJdHKOEca9rLg0TH2SV8/rchni98neXeEi3lA+QI0S+aabaP4sakikENFy6riiZNTFzoWMQqPbgqJrasYlhVbvzoH3ij/j9RKoUwTcHSpTZ2tI6KK0hLz/vUFbuJGHlkxIddtPBXCyy0doFZZWF2Fwu272THrmW87VlFr2glKepZv94m7g8zv1NwXepNpG5Q0gOk+mIkWgPU/6vjkzLPpCA22SlR5+Lv6GJ8YivK+XI2heqhP+v/0d/BYBIZiSANA71WwCjlEK+/yaa6r+FyOcGl8+dbHD6sUectsSL1axbu2UxYy1G1XcSsTqLmIDkRpCyDBPQa7kqRqvDglSWESyfiKhEXR1iY+BuyL/+QlFbPHm0RUf/NyOU3ove68KZKGANJQlYFTZOUPHXkvXEyoo4Fg1vpF00su/VL3H//8fHfW7YYHD2qHXv9hXDYplJxFhjlMvRW42jhAJe3poDG4ccvMlnscYqMYx3Bcb3zNuVIE0+5v6aOjivKGZrO69bTGSqSbN8+ueLJWOvhlmiRFft+zayfvQruvXhzKa6afYR511yF3dVDJJvDWrqEmPCys3M2c8UH1HChS5Oy7qdqefFSRtg2QtOQbjfUTLzFFLb0UiCAhYGL2v+fvfeOs6s6z/2/a7fT+1SNRr0hCUQzomNMsQFThcHYuDu245vENzeJff1zch3nxnZ67NxUGxJwjHEDA6b3JgkwTYB6GWl6Pb3ts/de6/fHnhl1kETH5/l89NHMmTPn7LPPnLXf9bzP+zxEKfEJ46eMd1zDpY07kC/fhlEfYVy0oDUcZnrDaMLGwCWsytREhIaK44kgthllQgWZuXMta1IXordkEEIA/usQ2QkyhR7et9DgV+sytJV2YGshxsx5BOwcKQq4usVWbSklL0yolsMwWnFdaFgWOzpPQVWqLLIfJZwwkDPDlHbleFE/ASujowVMxs0ZPH9nhI7n1nNOYIef3GkYuEuXv6Zv2b44nKbWgYiEhx4yuOACl298w276+zbxnsOh7t1fbZ+3a5fGTTf5xvpHWoft2bBwXRgZ8VM0R0YE9bqvRPOfW7B1q0axCPPm+QECex5XKATHH+8HBWzbptHb66dzzpkjaW/3R0Ife0ynv1+jXPaJsUce0TnnHJdqVfDiizqNhk+cGQZEIv6YaLUq6OsTrFmjceqpkrvuMrjxRpNaTUy/BtP0ybNgEDZs0Fm/XieRUIyNSbq7FdGomt4X53JimkDbU/EKByYh323j5IdNoP02440gUQ40f73nH83s6Djhuu+J1jfewmgpzPoTruXlsQvJbsvTiKbIaS3YPTBbSi69dG/SbmqhWJQZ5wR9HUOqkxZtHEdYaJ7EQ6Dj4aJQSkMhMHCQ6ATdMq4RwjAECoP5pXW88vCvKOufmmaxx2SGzaFj+GDhIQIRHU2CB0hXYeAxs7KdKiFAsKK8huPLj/MhcRObzKN5NngaTz5wBWeN3k47o+RTcwnXs4SdKmG3yMv1c3DPuJw9t5aHTYgdICUKQB8aOORUogPh3fbBbqKJw8Wh+DpO3Sd1xuV0cGREdeOMswj/9CZUpYpwXZRp0Z85hvW1NlaU17AmdREFlWb9et+jIZ1WfHjsBt7n/pqeSBc9tVkktQIxp0CLGqWuh6h7LmatiCEbTGgtCClZLl+iozgAdoWgrOCEwgT0CWxnJ6l6HvXSk4TcMgYNEL6qQUiFblfZop3Ay+NzaPFGWFB5hMzzadLzEmTmzGXpZzOcdZbLd78bIBTyFXLbtvkbT9P0u3aJ2Sn0tpPRxu6goYPV5pOM2sQY9gcv2m8derURnOFbnubJ8KUEu9LN0fEmmmjiDcXeIS+HTp4cqB4+bsPNrNzyA7RaDZmWCM9F37CeWLWKu2Ahat5cZHsH3QFFb8ih6sQoqzi6kAwmj6I9O0pZxQkFPMTMGRCJILZsRa+U8dDBtNABXQkcL0DKGaVz6EXGfvU05Wycbs+gQgRNVyhXEVN5KkQRSIKqygzK2G6ATL6HLeYSurUhCj15Mu1pv4ar1Qj88mcEb/k52kA/V3iScxsBJrwUseooASdIjzGXIaubnBsnTgld89iRz9AWGedDi3YQ6KlRHxulmJqNN28+qdJG1FCZSj2E2aIT9Yrc3biENZvbuGD0R1gv3MXGZS3MP2E+ZtUP1FHR6Kv6lu2LQ21qHQqRMG9es5Zt4r2FQ927v9o+b3xcsHatPm3G/1p12FSt3NbGXiqsqcbEVMplIKDQddA0fySzVtNpb5foOvT2anz4ww1mz97/uAzDDwZIpTw+/3n/Pg88YEymwmuT45+7efjBQY377zfo6lKAwjQ1LMt/7mLRD26xbf+Yb7zR4p57FM8/r2Pbfg0eCPi17eCgxo9+5PuhRSK+wm18XNDXp7Nxo+8DHI8rZs2SpFJqL8Xr1HlUyl934vG9ScjXq4h+q3FEBFq1WuVXv/oVzz33HIVCgUQiwQknnMDll19OOBx+o4/xHYPXQ6K82vx1Og2nHlemeuNtnFBbTZQyZaIEtNPoj11FW5uJ1pFiggyjAxpO3WeUTz3V269LmM/7Kgg5lENUyozSTl1aBEUFBXjoWDiEqKHQUICGL0n3NBNNOhjSZmdgIb1qHucGV1M95YNsHG2jt1fDcQSJztM5v+MmIlYJRiS4DlKBKRsoIZCageY2aGOUAnFC1NA8h0tqv2BZz3ZaizspzupgPLUC061hujWMWhm3LiiN1EjN8D/x/gIUJnnex8gcQmz6nlDpDN5UsMJhphLtiaZPRBPvdRyKN8T+97FYufKTXPZHHyRcP/TPJYBWryE7OnCOWopQihoh1j0Tw+qsk8n1kVJZJtwMpgluqc6X5PWct+lfMXCJBydojc9iV2Qp8Y5FGM+OklRFQoZLw9MoeAmkBM2QdIoRDLtKxBlDABFVpSEDtMlhymaSBaOraSQigIYmXBQaEtCkg1dr0HBcMo1dHF9+nsDXHyWSNJBdM6mvuor0OVdz7LHmAT0hZs2SzJql+Hn9I2wvaRzf+zAzw5tpXZzAuehi7Asv3f+cHGQEp2bGKQ4MMfv4CWSr3wxojo430cTh47e1bj1UpNMclop+33o4XJvg+I03E6rmKQfSkDGRXgQxPoYCqv/rq5jPPkPgnjtJJC2SmQBh2yWgbAbSS0kWR/16EJB1g1rWIZSwEGYIjRI6EqEkBi6mcqjrYVCSVG2AeqFCNdKKnbMIiTo1L4CHhgCC1NGQeOgofJXZXLkN067Roy1gey7DxW1baH9pO/bT69Du+hWUsqhUAjWRI1noJxacoNI6A8ZKLFcbUY5AeK10ygHu5kIut/+bL+o3M3/HCKJWQ3mSRusijGgQMVJGKxSJySjxiV3cxMe5wbmauJHlBHc1I7QxONKGu1OybNlrh/EcDIfSbG5OUjTx24hD3bsfjMAZGPBJn66u154+27dWjscFJ57oTNfToRCcd57L44/7RNnwsGB8XCcW80cuGw3B6KgfwNfZqTjlFO81iaXjjvOP5YwzXDZuFDz+uIWu+2OZsZifmDk+7luhxOOS7m5YvdpXuOm6/29kxE/aTCRg4ULJzp0a/f0aqZT0wwvxAwwaDX8ctLVVEovB2Jj/fWAyXKtahdFRjblzJek0/Pu/+6ReR4eiWoW+Pl/NFokoUin2IyGPVBH9duCwCbShoSE+8YlPMDAwwJIlS8hkMvT09HDvvfdyww038KMf/YjOzs4341jfdrwedvS15q8/wi8ocBc76aCXWSQosEq/jXRA8GT242SzgrExDSn9ueX2dsUVVzj78UDBoGLDBo36QCvnuTGCms2w7GC5epkIFXShkEpDIBFIQOFfShWBRgnHDDMQXsh9tXNB12ip9DK0vkh8fisrV0ocB1b9bobkj5fi1moIpdAG+ol4DXTlItExhYeDho2Fjoel6nSpfmbKAZaOvYynNEayS3ETGRwjhGOEaDgR0vib51otzS236KxebeyxoW/nkksyhzWydKSpRND0iWjitweH4g1x8Pu0c/XVGZ9o3nFo6gWZTEE0inAcVGsbtZzAcaDNyGO0RFh6Upx2PEZGYMaDv2BZ6dc0XI9Ro4UwLrPERjKVXsKOi3QlYaOGF0vTMGMER8ZoccepODFCpQbCc9GQ1PUIplcnZOeIKB3TrdGixhB5A6VpeJqF0CUVL4iGR5fsR0mD48WzBJTNcKONmSENo7eX2j/+F/f/NMKTsU/s5wnR3S1xXeh9Ic9RiUE6oiVE3vdNs8OCWa9yTg40guNOlCgTRWT2HvlsbniaaOLQ8dtctx4pXkuRvG89vLzWQyzfT1GLE0qHMYIKhQHSQ8tl0Yr5aTIn8OzTzI2OMWG0ExclVLFEojQwOZ7ZwNF0jHwWZ3OdQFCjoQUxlEfQq+AJAykg6uawCXJO6TaMagnbjLBLzWK+swmkjURgYxKggUTDwMVD+I0Y8szH5l75If5P7etcdfvjqJsqREslpBL0R+fg2mFaS6MIGUarQSMoGU4sIZXr4TjnNxRFgpxI8bvq3+hkEKsi0bUgMhRGq1UJbX4ZDBOvrY3agqN4dmMGqjUqegDCIVplP2alQjY4i46obyg+b54kdAhhPAfEAaYv9iXgmpMUTfw24nD27gcicE491ePFF3Xi8d2fj1rNF7IMDwteeknjmGN8wmjfWrlc1varp19+2Sflurok+byOZYGmQTwOtZoilfKfZ948SUeHOuhxTe0/azW4+WaDNWt0hoYEnidIpRSdnf6Yp+v6ZFqp5AtsFi6ULF3qvybX9QMEbNtfA5Yt8+jv91M6Xdf3VgsGfTVZqeQr1cB/PMNQeJ7PS9i2b19iWTBvnodtw/e+p/PjH/vjqmNj/jqTSimEUIyMaCxevPs1TZGQR6qIfjtw2ATad7/7XQDuuusu5s2bN337jh07+NKXvsRf/dVf8f3vf/+NO8J3GI6EHX0t2fQHTxxh5gtrCR/XSiKaoV4XBIMZwmWPM7c9yU9euJCdpRYSCQgEFMFKlvbiBC/d4tJ9kbvXhfLeew0mJgQNLcO6wPv4TO3faGGIOAVC1EGBKyxqegTDs1EKykYSF5OAqtMXWcpvrNOp1QWzzGHqeoScSDO6VWPOHEk8Dl6yBWflKYR++G+IWhUVjaLVJnwdm1K4wsBB0MAiTI04ZQy1i7KKAh5Bw2NmcQNmn6LQMpeyF0IUS8S7w8TnpPjpHQb33aeTyUgyGcnEhOD22w9vZOlIUon2RNMnoonfBhyqaerB7rNmjU6lAq+8cuj+XCqdwT35FPRf34EHhMw4GbdCtDRA//zTMQwY7BVsezrPJfXV7NS7SGkTGLjkGlGicoIONYqKtjIe7MSNxunIbqJNeZQirfQ7rZj1IkkniytMbC2MJW1Mr4G/pZMk1DgWdVzHpBbMgJQYShGmQo0QAWyWyA2EsMkaGca9DAkhkRhURmt0FR6h1PphCnoG0/S7hOGwYt1TDvOeu4Wr3dUsVy/TJofJJueyvfVYyvkq3b/6NQFXYl/9sX08ITIU5p1K25pfo7N7BCdcGqVnxuUMOy200tzwNNHEkeC3vW49HBxO0vye9fDoqK8siMchntl/XVKKaZKHiy4k1jvOs/e6mHfcwYmbf4oQglKghZgsoCkP061AqQwqylDnCmSuTMCrEnAqhLwyCnAx6BjbAI6DUS3ytHcCQsxnHluxcClPeqZpk41iCwcNSZgaVRHjZPEMp8i1lKoJ8rUWur0CQepEisP0itm0ai6YGkJ6OKUGw53zKGQ9lmoFdkaXE3DKHFXfQEDZNFQYTTPQymVUOIxwXWQ8RuPM92MH04z0OISr/ZzReIhHoxdSEGnKRInLPJbVSq3m+yCF3deXGq/SmYMSb++2EakmmnijcKh79wMROAA7dviJmcmkYvNmX501POyrukZGAixdKjn2WI916/aulcNhkFLxzDM673ufyz//c4DVq3VGRgTr1hkYhj/KWKkIXNdXg1mWolDQOOooOf2ZPNhx9fQI/uEfLJ55xsCyFJblP18+D8Gg76tWKgmqVf9xajVBNitYutQfE921S2NkxH/ehQs9dN33VQsG/UTQet0f0XRdP6VTSp8kE0IxNqZNhgz6qfRK+RxFJOKfr95e//fSad9fLZ/3AxJaWqBU8pVrB2sGH64i+u3AYRNoa9as4S/+4i/2KkIA5s2bx1e+8hW++c1vvmEH907EkbCjryWbrvTlp8d3QgGmkzKUFqcrOUhHJctwsAW9UeOi6i84k8eYM7qR8D/kMW+NYxy9GOe0Mxg+43LWrEkSiymfJR6HuMwRo4yHiYeLQKKUwvJsqoQo6knqMsCI3skcbxuJ8hCRaI6AcIhVR7nLvJQtEy2EQtDTo3H88R7JpKJxxlkEb/4x1GqIUAhsG8dWSFeClEh0IlTR8agiKGsJPKmoEEV1pWkZe5H46BpyhW2g6ZiJMMYlnyGbg50P7KA7nGFzX4b+fn063e7mm03OOMNlxozXfp8OJ5VoXzR9Ipr4bcGhjHQAB73PmjU6g4OCOXMOrl4T2YnpjvgEGfJ5QftZV5B2JeYzTxEZ3cUSxpmoClp3Psep23bglVZgVueQYYKt3kLi3iyWepvRdBfTqaBbEk3ZlMNdKKWhSRehJJ5uUozPpaKCzMjmMPGQSmDW6kg0pgbWI9RpYGHgIew6Mhim7gUIUMPBxBIOQnoURYxBrxPHg8FBgVsPkXTLzDJ2cLZ4lLXmWewotPDf/21hGIorqz/l7NIdlEQC160zoUewxoaJ2zvoz6ygGlNojzzFdTs/zAu9rdj2bk+IrvRHObdicNrWJ5nb0o9IRFGXfhjZuIyxh8T0OW9ueJpo4vDw2163Hg4OJ2l+z3q4vKuL6J92ERjuRdU1VCCAsG1EoYDsnoWcM3f691QoTPTlZ7ho+1qKxgim4ZBPzaavcxlzBtbSktuOYwRxPcGAvpCik2YospyIV+SE3IO46DS0ECFLojlZNMfGcKpkrHkUZZynxclYqs4s0csytQEpdOrKQiEoksQmiInD8TxPUUtQrLeBdKjrESyvQZw8rSpAwKtiSgeEoKEHkI5LN/0U9RRDWhenyEcQmgZSYHo1tIqLUB5avea3O4SGcFzE1pc5uTKAJSrg1rk2/0/8R+J/80ryVD5UvwMvpxCRBJFyFr30+lPjXw3vphGpJpp4o3C4e/d9CZwp4nnbNo2hIT/5sloVxGKKclnQ1+ffXi4LTj3V2+uxTFPR26vx538e4IUXDBIJf285Ngblsp8CHY0qCgVBJAKeJ5g7V3LFFc4BjysUUtxxh8Hq1TpPPumrzgzDN+xXyifR6nUYGtIwTf81SAnt7ZJaTfDCCxrve59k5kzJpk1+KIKUsGmTjmnCjBm+WCYeV7iuX3vm8z4ZZ1nQ2elRrQqKRaYfW0p/nxyN+uSZUn7QQbWq4Xn+ebIsQaHgp4Gapj8x925uBh82geZ5HoFA4IA/CwQCeJ53wJ+913A47OhryaYj3cmDJug0rAjBGSnOmuFy8pafc3zv7YhiAaucpSpNtJ15pN1PJncn+pjAdT9DNAoZxjnXehyvEWJQzCXcKBCggYbmbyGVx0hgJo6nI6Vim1hAMOgyo7GTdKGHQdXJreoybhUfwS4JymX/j3+KES8+X0Oz0mhLZxKIWXi9Q+g9PWh2nWC5hCcFQWkzRAcNTKTnEsZmh7mYE9tsAhUNkGSSHlpAQwt7OC8+TWLt81z8bI3RahS3djrrAx/Bs0IEAoodOzRuvdXk935v/0VlXxxOKtG+aPpENPHbgkMd6TjQfUZHBSKb5dj2MRLRFNVAZi+i+fwzSnQ84XsQymKFneMxnlSn80jrlQRTCU480fdQS97yI9Kl1RTTbYyN6LSMb+J3nQcZUy3EtTJpcvxGnggSlrGJKBUMASjJ7NoWtHIJTXi4moWwG8wpPYUVMTF0wHYJKBcQMD24rqPQsUUIR3mYqgGOwtV1KjLGdhayJvQB5tU3slS+jEEDaVloSFrsfloZo7M2yFf6vspVgdn8RHyc/yhfy/zOMmfqqxmhHdv1k43zpIiIOvFCP46xiB0TCQovD3DnE2XGkh2Ypl80aRpEIiHWLrqWRwYu5KIVY1zw8RgqneGiGniW29zwNNHEEaJZtx4ajjRp3q+H03gfvQp1439CtYpWraI8DxWOYH9o7+AU9fNbGb/+LnqqHZQb81nm7SSe3YUMhBBANjGHesWjjs729jNIm0UStmI153NM4XGkESAoGmheA9Oto+ESpM7x7tP8KPRFbnKv5jTnUT6vrsPCxlQuYQTep3ViX+2n8jdRhm9oJ6bK5AJtuC4YpklZJgl5JQLUaWEcgYuuPJSCmJ3j2NEHCQibXekVzI2WaOsfxZI2unJ8dzUXCFioRgM8CZUycsMmAv1DRB1JxCshdIdPuTew0Bniex3fQc/BWY3VLMr0ErTCb0hq/Kvh3TQi1UQTbzSOVNl0ySUulQpcd501qcYSZDKKGTMktZpfM8+fLxkY0BgdFXR3KxwHNmwQbN1qUKnsrqunRjRNE/r7fS+yUEixfLlHS4uiVhNceunBxSJ33GFw++0GIyMwMvJPeN4NCPE7OM6XKZd9Vde1197A7//+3/G97/0xv/jFZ4jHfUXayMi/UixeR6n0SeArjI35CZ6W5fukVasCKf06s1z2SS7YTcBZFixf7nukDQ5qTF06XRccBxxHUSpptLUpuruhWpVs3aoBPok2Ouor4JYvl5TLB24GH0qg2TsBh02gHX/88fzbv/0bJ510ErFYbPr2UqnEv//7v3P88ce/oQf4bsGrveGvJZtOzj94go46+xLUujRWaYJludWMNZLMKPdRFnHqgSiGVYbhLIPhBG3P3MsJLENFF9AYzhF1cj5j7NYJU8XTLTTPBqXQcWi1+8mSoUyEE5xnSMkinjB41jue6/gdcpEuPE+ABNcVmKbitNMcfv3vI0RuvpXTdq5HE4pcIE421IXnLWZGbTsRS2d7fBl95TROzeN98mkENtuNxQxYczih/zFkKg2tBhx3Am4yhfni8wRWP07jhDPokbNwykUuEbcT0uCn2icplQShEGzcqJHN8pofqkNNJToQmj4RTfy24FBHOva9Ty1b55gXb+Gq4hpmbS3h9kXp6TqVZ2avQsoww8OC3A9uo3PLnaiuNjZVZzHcV+IE7iAcUTyS+ST33GMQrBh8fOsWtEaNWfmX6cqNIdwyVREhiM2E1sISuRnTlBS9KDoNdBw0TyAFhE0HO2Bg1KoYXgNdOliqAbaFF4ujag7Kk3gEsDHQEZSMJAFsAl6NEbOLfrqYpQ8SM6vUjCC/1q7mR/pn+FD9l8xhOy0ii2O6RKo50gwDMKo6KKok7fVdfNz7DyZUgJHgSiLVMjvUbBQKGwvDs6mKIHEKyGqNkS0u1UYU0ZXC0v2uZDIJqZTc7QnRleaBnWlOwiZNc8PTRBOvF8269dDwepuH9pVXg2VhPvIgxubNiGoFFY1gbFxP4Gc/wb7kckStyuDPn6In14GTbieQVgyWFzEv/wItAy9jBDTKWgLZ8BhPLCCYDuE2XOZMPEXK6SHqlbCkg0JDUy4CBZOOvmmV5WPWL1jZ2YORy7LTPoquch8tYgJMSfD7DUQcYt8vseMns7EaNvHGOFLLgDAoaEni2rhPiuGhI3GEiadbKA/S9hD1RBumXWZ28SXCdh6l/GgCACE9P8oOULoG+RLaCy8hlCQuFS4mpWgbjjQ5ufQgpznH8ujSL7Fo1Xkcd/IYlY5DD+N5vZvMd8OIVBNNvFMQCsH73+/xxBMSw1CsX6+TTvvTXoGAP5IYi0EioRgeFgSDMDAg2LJFQylFe7vvd1avC0ZHIZn071urSUZH/VHIoSEdISSrVjkHbZAODsJPfmIyPAz9/d/Ccf4SANf9fWq1PMnk/4dSDf76r/+QWKzEP/7jH7Jp09Xs3BliYuLbZLO+2rqv748JBvO0t3+TVAqEYHKM3E+WL5f9/W5np2JszL8umKYfMtDXp1Gt+smhyaRP/k2Nhtq2YM4cSXe3pFAQLF7sX0sGBnzztFgMurv9MU8p2asZfDj2Ae8EHDaB9rWvfY1rr72Ws846i5NPPpmWlhYmJiZYu3Ytpmnyne985804zncsDvUNfy3ZdOOMs9BGR9A3b0QrFacTdAJXXsHKoMezN+eoj5fJVuPMVg1KWoKg5RJTBcL2ON7mIcJb63wpvo0XxfFsCJ1AUcRRrkfcy2LgIjwPTTnTqZtJ8rgY6DjYKoirBKPGTE7wnqOqItxifpZyNIPj+DVB3Kwx+nc/4+jV/81MeztC+alI9apFS3Uz9ZYuxuNzuT96Ok8u/gwPPN9Oe8s4H6/9J8dXniAv2mkLl6FYRJrA/AXIzhmIWg2Ry6EMExmJYhNknBCmDu+zV3O/eREDKoOuT81RH5oC7FBSiQ6Epk9EE79NOJSRjqmvNzyRRw7lOC3/AKfoD9Gb7GA4MIskeWY9/2teflnjHvtTROoT9D37DMV0JyHVysiIhp4O01CKhSNr6Fl6EdVqmuceKLCqfzO1wTy5ephW1wVlYmKT1nI8o52Gi8kCdxNKQVWL4QRiGHoDrVgApQjqGmiA9AjKGiiFqnk4dUWONC1qhAAuAcDBIuDVMFQDgwYTbpzn9BOoBTPMM/rYtPgiXo5/kdAmjUcqH2CW1ssVwXtocwaJOjk8DEZpZUjrRkodVxiEZIVztYe4SZxEthEjrgqMau30ylksYRPKraJMk6BbIePmWRO9FCeexnJ85Vm97vtP1Grvfk+IJpp4J6JZtx4aXnfzcNLjTJTL6BMTuB1HI9vapgOcymXY0b4S0VdBJboJhxWadMlEbaL5MlG3iHAhok2ww1xIvm0BpgtzR9YRLw+TSx6DBHTlTyH4mZpT8GmssJ3n6JFH2JlcgRGMENIshAzhnCOx4mUAtLhi9rl9yLt1kipPpzeI5+nouGh41AlSIUpRJKlYSaL2BEnyhJAESlkW6VlcqWGLINaktzDgG715HioYpKZH8OoSQ9kYygEBZS3OKB0ooUiKEc7WHmekbRX1SAZ3bgLzEDaJ77ZNZhNNvFeQTPrqMcfxCTXbFoTDCtv21WSNBixaJDnmGI8XXtDZtk0nGFTMny9pa5Ps3KlPj1bm8wrX1bBtX+V19tkulYpGPs/044NPlO/a5dt8zJmjuPVWk54eQaPxTRzn23sdn+P8GaUSfPjDxxCLlQAIh0t0dDzCM8+8SL2+t1VBvf6XjIxAqfTnOI6fwqnr/timlL532ubNAiEU0ajfqJg1y5seGT36aI9sViMSAU2T5HJ+SueqVQ6RCNx3n4mUggULJPG4Tyx+6EMul19+4Gbw4dgHvBNw2ATaokWLuOOOO/iv//ovnnvuObZt20YikeCqq67i05/+NB0dHW/Gcb5jcahv+EFVBLUagZ/5Y06iUgHDwF1+NPUrrkLN6MLzdBoNGHEyDBRieLaDLSxiVp0EJaLVcYRyUbaLrWvoxSKtxi5ObOSpxjNI2yTqldCEmBxgUnhoNLCQ6MQoIlCURJKdah6RAMxrbGcJmzi2/AqP1s/hrvBHCCaCXFn9Od1rbyXZGCVntIAQJOsjxCiiNxTh4i6eeN//ZG3npxkrhMhkJHU3w43W71HVIpwuHycZqOJWDOqZDozFRwEg6jW/UxmJYDegw5rAC4TJuQm6ZS8JL4vdkkZKP/XjYEXcnl5LKp05pFSig6HpE9HEbwsOReEUosYn+RVSrcWzs0THNiFmdFCMHMWWHRajdieBmsZRxTUExcV0hrNY9TLD9Vl42zQaDUF3t6SuEiQK/fSty/HyWCupCYOJ8SKOZ+JYAXRNUfVCBKhjyTqaZfBK42i63R42m8upp9rpiqxG4iByOUS5BAjQdYT0/R19KAKqTow8VcLEKNPAROARU3k0JFXCpFWWVeqXyIpOUU+RGNzC2WPXs2K4yrHuU5i4bHHn86x2HMcFnyPWyDIqW1HC92asygABUaEzkMWuC57zVvAhbicg6/Qwj1azwBzVwwgduMJkw6JLeCj3ERxbYFkK0/QTSKtV31vi3e4J0UQT70Q069ZDw6E0D/ers/aByE5grH8Zd87c6QAnN6joGy4x+oMnuK7zDD6UjRHxCshIG53jG+gefxFLc6iJKCIYwKoV6bD7yO94llywg7i9AycUZOHwasLUphVfsJtEk0LH1FzC9Sy6dJnfqLMgGiZiNahpaQKXD+91nOEra2Tvb6PsNohRmnwcQQ0LB50EeeoqhLTzpMgjBNgqiOnZaGiETAfXDKFcUI0aCA0VCYOm4cbT5LImMmoSUhWitXEc3cKSkqCsoikPIhE6Mg4dVpZ77mkBDm2T+G7bZDbRxBuBd8JY357rYzwuGRzUqNV8o/zOTj+l8oILXK6+2uX55z1yOcG8eRCL+SqsSGS3ZQdMJXn64/KlksbAgEap5I+JNhp+TXj77SaDgxqgaGtTeJ7Ctr9JLrebPLsEuGPy63r9z5DyuL2O2/O+Sb3+wvT3lwK3T9//L9F1cN1v4bq+Kgz8XoAQ/v+G4aeE5vO+z1om409PnHyyRzAI4+MaUvr+bZmM4uSTPebOVRiGxpo1YnoPfeWV7jTRv28z+EjtA95OHDaBBtDR0cHXv/71N/pY3nU4kjd8XxVB4I5f+WOGrW3ImTMRxSLms79BtrZjX/0xbrtN56GHNNqXpigbpzD7xTspunHmOr3E7SxCemiug4XCI0BYlpgjd/CCdTqZhMuGwDnM7tnum2yjkMKkqGIoBDoeRcJUifG0cSqzRD/z3I3kRIjkp7J8+Ku/YtnfrYOb4V77Ik7VV5Pz4nRqOo4ZwfMkJn7x4SoDraIxt/8J0vOvYigf5ugZ43TIAY4pPsk84yVijkOtEWJH64kcl8ghcllUPO67KLoOXtUm/PzTHJd3WEKAvAqTs9ppb5fkPH+xOeUUb/8PUa1G4I7dJKSKRHBWnuIrzUKhV00lOhiaY1NN/Lbh1RROe65TzEqj73JhcIBlC9djz17B88/r1EjQ5vUxNzNBcEaSRk8Uq1ZAtbZRLPpBBAm9wEg1yrq+FlwDggHFhEyRZgKpbBCCiF5DeT7B5XkwR+/DMiR2poPuZXGM0SBKCyHjcYytWxD1uq86m9wCKcBDw0ASoYKHhoNJmTiJT+exvupQ/psIgzd2U9VjzJY9bBeLeEqcRvvYOFeW/4ooZQokqYoI4/U0UQLYQiOMTkirUyWKpkGYOiETvGiU83iALvd5UirHfLZQJsYmjubu8BWs1s9Em9nB3IVpWodh61YAQSDgS99LJT8V6d3uCdFEE+9UNOvWQ8PBmoeXnl/aq9m7b501hb0CnFwXY/NGKi8PEMzVWSQaXNxyGy+E38fp2XvQsGmpbcN06zhKZ0y0k/fSzJZbiVPguMbTFN0kpnIw7SxhqvsdrwLf21IYaNIjpCoIBJY7jsxrSE9SDbcRvnhv71zr0gbOF3aPXyrf8YwgDcLYCKCbXThY1LToJLkWRCjJiNdBlximYLRSDqZIasNkGmPIcALPDCDqDYQU5FoWUlYNotUxrEYNTbl02tvRDUE52MFoKE5wRorWsjqkTeK7cZPZRBOvB+80xeXU+rhmjU697hNiqZRi1izJqad60z+fM0fR3q5oNMT064jFIBr1pw5sv9wlHgelBJs3a8Rifn2XzQpuusmiUvHvk0hIQNDbC/n8X2Dbu8mzfwJ+H/hb4KuTt9166wt8+9vwjW/AX/4l3HLLbvLsb4E/Bv4f8AeTt1UqPonmed8CdocFCOEfp677tXW9DqBIJHx12tq1JqGQP8ba1qZobZWEw9DRoQiF4JprPM4+2zuk2vXd6D1+RARaEz5e7xsushOYT6/Fa22b7tSp1iAe/tjh2MKT6Lk/yJxYknBrmp3JVZTKgpnbn6C1MU5ajSCVQEfSECFkIARKEm1kmSl3oQ04CCOFY4QwvDoCRdVK4jYEpmcjA0EMK0q04ZIwanQ3enHMMC1alo7vjyLisPQf13PNj/+TjfW5mIEyw6qNBVgYbp1OZ4A4RSSCOgGCmmRu7xO8/86v09lxJu83niSwZT0t7jDZ5Fy2J45D1SscFRtE72pDSIk20I8TiDIemEukfytlU6eoIrTWB5kl84zKDq7u/XueMk5n4gOXc8012n7n8UAkZOCeOwEYOu9jTZ+IJpp4HdhvnarV/GzuahWzfyfzjl9Ab0uMVCNPIRsl0JlE02AgvIAV+SfwpKJmJTGzeQxtjCfUpRTNNNIDkUmyefAo5rCTuCzQwCSibGpGmLybZKnzIt1yF5ayOb7yJElnFioeR/UNQKWGRCB0A81pTE7R+I44U2PqCtCR2GhUrRCt3x9DxCH6/QpdPx2g0oiS1zPE9Cqm7pGsDJJhAhedvJZGKI9Ohhmkg7qysLFo1bMUhYfjCpLkaITSVIlyavl+NFmglRFS5OlglLBr82jtbCa653P1R3SeeUaQTkvmzJHs2uWnE3V2+p3F94InRBNNNPHuxsGah4GfHbzOsq/+2PTv7xngpI+OwKYtlCsxNMtCoVg6/gS70pdxj30p5+fvJijzuC6MqVYK4RbSjTFCooZQEgNJTBYIUp9e3zVNA7l36MPUZIWh6v4NhoGnBNITGEhaTxtGT+9dp2tpRfr9WYwH/ZAZgcLCX3enrh0C/PAtWWCILjzNxMOgroKU3AjCsQmHGtgyRrZmo8YkpVCUsKowrHUyGphL0qpRH3+FWHkYhcBDB8MgbGeRmk41lCGuHdqe4d24yWyiideDd5rict/1USmFEPvvMafUalNjjFL69VxLi6KryyOZVGzapKPriqEhjXh8yktNEAj4JFux6H/WYzFQSjIy8q0DkmcAfzL5/xSJ9qd/Co8+Cg8+uPuYpsgz9vi9KRLN83wvNcP4FqYpppVoQoDn+f+iUQDB1q1+yqbrKgwDQNHTI8jnNX7nd5z9zsOhrEnvRu/xQyLQLr74Yv7+7/+eRYsWcfHFF7/qfYUQ3HHHHa96n/cKXu8bvlenbg+ocBjjmacIf+f/8qEtFnoiwkDhVF5avIqRM6/AtMvs7MuS8nJE3QIKgUkD7MmLv9DoKm7BJsBIag4T4S4SjTEi1SzhRgFPxClrcbxgEjcUISkHmBGeoD1YJa6V0U8eQcT9Y9HjkuPPfYHLH7+Xmoqi1xtsl7NYrl4iTn461U4DRkQ7Xh1OGLqL2GgP+e7ZHBWqYlfDhPPDpL0duMuW09rhIYSk+uU/QAi46y6Dtsf/nu6kRlQW6CwPYcoqJT1JXYugK5dL1W1sHpTcccc1e20eD0ZCNlzovflp/uXxSxhxW5obzyaaOELspyjYsQ2KRbRcFoRGPPIcnXIhETfLU/ELWDl+N++zVxOsF4ioMqH8ZsKBVoKdcR5zL+GXo1cRNGHePElbW4q1m88iUc/To2YjdMFCttLp9hOmRpteIB/rZEyFmFtej/nUAI1ABKehCNt5ikRxjDhJJgCJh0EAe7clzeT65GDRdt7o9Lom4iA+4BG9u0BdBRBKJyOHma+24qEhJy+NZRVFx6WTIfqZyYN8gJXuM3QyQgBFvzGHvqUX0TW+nshYiS61laCoYWthPOXQIYf5svoXOjWTSy65ktZWX9Iej8Oxx3ocdZQfVR4M7laYZZhAG/JHpO54oP0dVTw20cS7Bc269fVhz43PazV7G+d9cHqcczrA6fZfoe3swTZCeBLCWp3R1GL6nA5m5Z/lOus7PNo4hb/X/oR5YhONUIxoWJKpjmApGw2Fg4U0A+DUJ3OU1TR5tmd1rRAIKRHgW/8nW2gU6piqjic1jFW718qbb4ZrrvG/Dq+qwYP+4+47Fgrg4dtr6ngYuGy0jkO5Hkvcl6npEYaNbhbYu9ArJXrULB7gg6xPnsNJ+nOcO/QTFm2/n1DAJVAv4moWDgYGEs1zcESQlrEtxIsDbLdnTu8ZXm1E9t24yWyiiSPFO0FxeTD1/97E0IE/d5dc4k6PMWazAsOA9nbF8cdLTBMcR/Lii36SZaUCIyMajiOIRhWNht9Q1XX/sQYG/omxsQOTZ1PYl0Q7GHk2hX1JNPhLdD1NKPQVbBscR0w/v2UpwmEYGoJduzQWL/ZIJv1wgEbDD02IRuGMM46sLn03eo8fEoG2fPlyQpOsw7JlyxBCvMZvvLcxdYHLJFOsXNl+xG/4np061Rqcvl17cR21nlE2xY5iY7WTRCXPnNyvkdKfQ15Ye5hsZzsqOA9r89OAh1QS1zPRkSgkIa/AaPpY9KULGHypTKw2htR0NM8hoNXQTBgKdFMWSfo6l9CZqpHor6GNZxGX770YBK+sc+ajj3EzH+MMHmJUtTJBC930oQCbEBOiBeko0kyQxPf92dpvYgc99GQEN1+jtdzDxqEFbBFJloR7EQLGkgtYt66XVWaN4RknYjoVFvY+RjnQQs6JEpMFEjPCBIImR489zC03L+H+yiwu/WzCP1f7kpC1GqJeY3tfgHxPlnh7FnNmurnxbOI9izd7vG9fRYG+dQsqlUIqhSgVCfVuY2FM8LPMpwmWGxw3fAdZo41BMZdqLE2X7MM7/li6/+ZaTs5lePSvLCIRyYwZfovrttSVVAfgdLGGtFZikzyKO/gQV2m3YWkFtGqVNncY4TnU0NC9GkoZgEIKjSxpHKHTqkbQcVFAjdCk56O/odKQBFbV935hqwTu3QZpNU7BTXCs+xvaGMXCxkWiFLQzQpIsCQp000cr42xgKQ9yLo8ZH2AkvZTLukdZll1NQo5i4tDQQzhYOFgIrULSKHFa/SHckQ9wzTWtB5W0p0PVvUbR60YEs/9MZsxbRXLy+tAc12miiUNDs25943DQZm88jjbQj5bP7WWTMXzG5QQ2jtK5eQumKTA0nYHoYraylHxJMos+5iXH2RRbxOPuZcwY/WfatQncmkZQVdDwkOiUiaCUSYjCXgTX1Lj+lP+ZP7bv38NDR9TqGLIBCqQmEJO5UdUq/MEfwKWX+iJq7XKF/B8g9hZ0TUOiT5JoComgx5tFQFbp0AbxAmHCTpmGI8GzyDDBefIBFuf7sG3wXIlUCsstYKoaoNA1l7KZYqfXieZAZnwn6Xt/zuqj/ohLzy/R+cAvXnVE9t24yWyiiSPFW6243LOWDoXg5psN1qzRcV3/eQ5XhLHvGOOjj+o8/rhBPu+HRWUyinRaUakIxsc1QiF/DDIYhJ07BUr5/mpjY4KhoR9NP+6l7E+eTWFfEg0OTJ5N4feBB9ntoea6N6BpX0HT/JRQw/BDE/yUTXjlFR3DUCxfLkkk/EZ4ve4HDWSzGvX67miXbBZGRw99b/Ju8x4/JALtu9/97vTXf/VXf/WmHcw7Hgfw2vrocaegn/MR1r4YPew3fLpTd8+dePjFiBgdxdm0ix4xl0JyNjFdMDrejqxA+0uPEA4rhlUbBGNsr8wgJk00/Au8ABrC8iWlyqWxYAlHH9WgtqGfYKOEUh4gMaWNqSSzM2WGP/IJghdexupHG/Re/23OnLiBwKX7SN0vlSS+MMJT7tGMEuN0sZoJvZW6G6JGkF3aPOIyTwvjWDhINMoyQrfsIVTOo1UEMdPv4tVyL7AzPwfZHiepUpR3ZRGFApplELQLSM1ECYETiKBV6hC2CCUM0hM9tOW28Ck7S/66Thx1EtYFZ0E+jzJ0RDaLNjGBPtCPV2/QMiyxwu1E24LIQHPj2cR7D2/VeN++igJlGNBwwLJwTlyJSsRJCJO2Ze9jxk3fIxduY9xrJxgEmQkQSkiOimzHBubPh3PPdbnvPhPTFJimwjVD/DL8SR5WF9GiZclraT5ev542b5ic24KnW2QYBk0wIVMkqeCiY4sQpnAxhYujDLKkSUxutBqYlIkSwsbGImnk0C7d+3VFLqtQ+2KAoGuj47KLOC4mJg0MHOaoHWhIwpSxcFEI5tJDAJuYKDNszmWdcwaVgIOrDAKNMlJBwzMQOpiai6brWLqkxRshqrJA60El7fuOotv9JVbsvINwRPFS57XT92uO6zTRxGujWbe+cThQs7dWA2+wRMCIIpOp6dv8a1ISkf0dfsfYRFvSoTiri427ouRzgnYxQk1EGXUzLFos2RlZxc8eq/KF+vdpLe/CxFeY+a4/irpr4GJgTjZH/IaIt5tQ+zzoJ/mjliCwaCCEg4eGUgI94SF80Rz33APj43DvvXDFFSDaQdwMFA78ug0k3jMa9esCeGjMljsoxTq4Qfw+wWqW8917qCqDNkbwhEmaLNjbaGv0s0ufSzbQQVzVqDfqhGUVIV0ido65Wp2ClqKiIrSNr2dufJyPcMchjcge6SbztcIfmmjinYa3SnG5by0dCCh27dLo7dUwTV95VSgoxsb8VedQRRjZLJO1Ocybp+jsdIlEdn92DcP/7D7wgMHYmCCRUFiWwPMUoZCgXlcMD2u4Lpjm53FdXyt2O/B3HJwU+xMgDfwn8Dngs69yjH/LbvIM4EMf+jRCePT1CYpFMR1mMG+ex/z5vg/ab36js2GDzimneIRCEAr552bqPZk6n88+azI+7o95nnyyy8c+9up7k3eb9/gb4oFWq9UYHR1l1qxZ7+ku34G8tmIP3cnHL4APfOPIvLbsS/zWmPnMU2gD/TSqkhGtg8H24wiHFaYJrisoFxJ0FgYIK4eIniPaN0xElUApPHRsTAa1mWi6IOSViYoanakKwY3rCGR3UhFhlIIYRb+711DoO7bTufFhfhy+iNsfb+ecmedzxoIfo6X3VmroaUnw/VXsB4PcxUVsVEswUHwl8C+c2XiIhMyRIIvAQyApEqNuhEl6dcKqhqcM8DTQBa25bUivwI8qX2Dm7z7M+80nuWykQrw+TJQiw+klSKFjVXOgFMXkItL5nbRnN+IaQfKZebiFGvEb/x3rjv9GdnSijQyj9e4CQCWT/ibYLRCVJRbsepTnl/ubz+bGs4n3Et5Kbwj7ksvRBvsJvfAcwnFACGQsBrqO7OjEHBnm4qN3oB1VpJTuRlkOIAgGFSEtij7QjzOpUthT1t7b63spnHqqx4wZcarVBBnGOeG+9RRLMZSpo2sKhUaDAG2MYXoNbEKY2JjKwZJ1BBKFoEGAGkF0POKUqRAiSpHg2TZin3VZSyu89+s4D2oIJElylIji6SbKU8TJowALFxuLHCnC1JhJHwUV532N1TwSvogJWlhjnsnl7tMEcZH4BtIhWUa4gohm0x3qwXjxYeSKWaBb+53fA41I6TOClCMaM3etZdviC6iG/E1Pc1yniSZeP97quvWmm27i+uuvZ2xsjCVLlvBnf/ZnHHPMMW/6874R2LPZ23Bhy0iS3M4S0co4z868hLGfdHDFFQ5PPLH7mhSfn2Jj4Ry0bXcQDZSZ06nTO1YipUZ5PH0JLYtTLF4skTII0QjVSoA6QTRcdNTkGl5Ax/FVZXhoewxbSgTeSo3ADz1AobGnL5rCwNvvddxyy+7/r7jC/1q76uCvW6DQfsdj4JU2nnjqTJ44+ouMx+fz0ks6/5/3/zFqdrHYfokCCSoqQowy0UYWhKDFG8VwBUZAYNkNxOTxCBRB6piMMRKfT2fMYXb2BawH7sNLxF9zRPawN5mvEbLVRBPvVLxVist9a+mnn9Z56SWNRAJmzFDYNoyM+OvOoYgw9iTkajWNUEifbm5ffbXLGWe43HqrycaNGi+9pDMx4Rv3j4z4ZJlhCNrbJULA0JA/4nnKKSdw9tnwZ3/mP8eU0uxgJNrnJv+9GvYMHgA/cGDRouOZNavOXXeZ/OY3Gi+9ZBAMKjIZf/zUcfzAgJ4ewcyZgra2/d+T//xPgzvvNHBdKBY1qlV47jmL9et1vv1t+zWXnXeL9/hhE2jXX389tVqN3/u93wPg2Wef5Xd/93cpl8vMnDmT66+/nlmzZr3hB/p247U8IDLnfZD0vCPo6oRC2Fd/jMZ5H0TL5+jtE4xu/GeilOkbi1IqabiuIq0KVKwUR8e3wsAw1WCGmpYiWMkSUhV0JKYu0aNhqkaSSr1O28AIIt+P7fgRswny6EhcDFw0lKOwHniIRc9+g4s7zmTBzofR/vzAXhFiFZz14KP8EX+PgUvOy/CoezIVDM7hEeKUqRFkO/PIkWSON0BMFSZNvD1Mr4YnLaRyKelhtGqFJZufYHOoDTMzi6wdZWnjBRKlAaqEiDpFRkNzGDNmsGL8UZSC0dQiCiLFTK8Pq5RFeFW8ZcuRuk5w4waUzzZimBZ9mRWUgxnmDqxh03x/89nceDbxXsFb7Q2RrYWwnCQzzQAyHKMaSqNJSXDbdoxqBTlvPl73LAKpCCmtgEq1MSXjFmNFVGS3SmFPWfvOnYLrr7cIh6G1FUAxc2gHGZGln5nMcfuoO0FcKQhRIUSdBiYAOi4CiTGpetVxaWCiozBwKRHHxCVKDbFq92vZc10zVzm4D1p4aISp8ALHoUcjLGQbFAp46NQnGxRpshiTDmnH8DKW55CoDPL002meLH6OROAlPty4jZgqoyQIJFLpEAoTXDgD7fFH8RJxGqs+ut/5PdCIVCgEqTkxSusHqA/msGdmmuM6TTRxBHi769a7776b7373u3zrW99ixYoV3HjjjXzuc5/j3nvvJZN5d6iBppq9vTc/TalnAC0c5bHUJfz3xFVkf2hx990GlgVz58rpa1HPiT5LtXBkDUfP3YXdmmBD6mLqJ69iedifdKj2ZTlHPUi7mEChUSFGhNIkiSaJU8bBoEGIMj5JpU+SUUafh8qyX3PkQNiyBW6/3f/6ttv87xcteu3f87Ia23rn80TgA/R1vI9Nmwzmx7fQoUqM2zFMGlRJIPBHPi1sGsogSpkodYINGyFAKd9bTcNDKahZSQKySmdjF2ev/3cs9QJ6JolXKOAtXgKmedARWTj0TearhWztqWxrool3It6Msb49RzVh71q6VvN/ZhhMG+lHIgCKfN73MnstEcaehFx3tyKXY6/m9hNPGDz7rP+ciYTkpZd0ikVIpfyxTs/zG6Wa5o+ax2KKen0mf/AHKSB3yCTaq+FA5NlXvpLiy1+ejVIBsllBKOQrAAMB2LJFo7/fD/GrVsG2BVu2aNRqikTCf0/OP9/l+usNrr/eIp8XNBq+qq693W/wP/aYzs03G3z2s+/MkczDxWETaL/4xS/43Od285rf/e53WbBgAV/4whf4t3/7N/7hH/6B733ve2/kMb4jcLgeEPviYD5Fu2/PkJ6XIZSEnhmnsmTzHeBoWMEErVqBmDfKY+YHWFbbRQyB0KDqGOgqRoAaZaI8r45jodNHa7GfMa2Vif46VrWIIeuEcDDwUDBJn2kUVQTXjLOi/y4S2R42sgjtcr8w2dcrouXyCb78P/4VV5pUiDChcrQxyDAz2BhaQaD2NApJgoLv/qNqRCkhUJRIMEI7NRUkQJ2grHKK9hT5QBu7qh1odYhGQxRdg0StynPHfZozUq+gnn8Zvb+fhuuwObiU3voyQl6NWfQiUgmE6yKUgkQCmUigAgHco48GK4g2nGZ0h0F3uQ+jmGOs3NLceDbxnsFb5Q0x1Ulb/3iey57eyHhlAen6MGUUDREio9XI5HfhfvBS5PwF+4+kF4voY6M0zni/Pz6Cr2aYWvfmzFGceabfYTTdGmeO/JJZWx9mbn0TNWFQlhE0oZBCJ6RsbEw8NCKUmcpJ8xA4mFgoPIxJ3zNFkCoB6igNjMsnN2v7rGuByxvY/8NElwKBw3LW81zlZLa0HMvyRgXXUYTdElEq0yNDcnJMaC49nGs9wY9qy8mVQ/zj7H8k3LqYkzbdREtuG0oZ1PUY+tKjECcdh8rncB9bS++884nOTu+1Dh3MD3NRe55d1TAlM83Iu8ATookm3ol4u+vW//qv/+Kqq65i1Sqfyf/Wt77Fo48+yi233MIXvvCFN+1531CEQgyd9zH+4YFLCEVyDNlpXh5sJRKDTFgyNiao1wXhsKKz07/2uGaIV47/OKt3XMgffmqYXTtauP/xdtprENX9TZU5nGNOaJioXqVgBql6ATxpECOHMdmE8dCoECFAHRNntw5tENRK4GYQJ+4+1E2b4MtfhtFR/3ulfMLMnVw2q1VYtswn0KbEh21t8K//CkuW7H6cym9C3HHNxdwzeCG3GVcyZ0Bj3jyP5TPiJJ6PUO93UK5FxKsRVlUyahyBh02AsGZjKQ8lNBQgheEfyKThih2IEauO4BDAziwEMwmug7Fti/+alx/tXw/2aD4dLg4n/KGJJt6JeCPH+g5kezJ3rr8OzZ7t14j1ukApf2zRcfwJMMPwSaTxcTCMVxdh7Nvc1nWxV3P7xBPd/Qg70/TXoUpFkMkoTFNQrfpJmPG4P4nW09PFWWet5YYbPg4897pItAORZ5/85An88z//iEZjBs89p3PCCR4tLYpt20AIRaMhGBkRdHQoTNN/D2IxxYoVHh//uJ+8+bOfGdx7rzEdgGAYikoFcjl/P+I4grVrdS67zF+Ij+T9fLM9nw8Hh02gDQ8PM3v2bABGRkZYv349P/7xjznxxBPxPI8///M/f6OPcT+8HVL4g21wXusCdzCfovPPd7n/fv92kc3SZkyw5NQE51+ToHTBZfxqk+AUsYYWrw9bGjwfWEl2xtGM5h4lH48QLY1gNYqUjCQFN0GUEgvcjSTLBXbocxlqP5Z2fYLWwg6MydFK9XngJNCQaEhiFNDMMprTYKF4jhnpbVitDrC/V4TV7pC5OYtdCJAix2z8kUkDj4lAJ0G3QWhtGfu6GiPoVAmhI6kSYYtYjCtMPAktjJFQOQKySq89dzoet6VFYco4wYkCnce1sWX+l3hoIkciuoOLd/0bJS9MtmSyrKNA0rIBA2VaqKCvBVXBENrYKOa6l0ATLDcsWvUkO+QctmUzqDTNjWcT7xm8Vd4QU5205YEspl3mqcbxzPZ2MEfrJaEVqLoWpXqC9dUzOY/9R9JVIIiXzmCsewHzmadwgxHW6qfzc3U1E9UgkYjiuOM8zjnHJfTzW+ja/Gv6vXawFjPH3YilbHqtBeS1dsL1p3ClRoQKAok76YVjfd7FOsnFxSQiqhjKQdsjFUklOKgHjtYO5s0eFHwyLkmBM3kImUpjRAKY48OoJxtoP5BI/O6bxI8lcoTFWaGnGDtpFQ+va6PshXnkmD9gaMbxXPHg7yMaDaQwyJQnkBs3scmZh71zjP8arjCamLGXJ8QB/TCLRazcKLOu+TBfOS9KPm+/ZUVD0y+nifcS3s66tdFosH79er74xS9O36ZpGqeeeiovvPDCm/a8bzRqNbjpJpOH17WhaW3k835aXCajkBJsG0BNJrTJ6TGdYlGg0mlCx0Q4fyXUIy7PPmtMq0lWXhCn5eEgol9iBkBVBVVCxCiicPHQydJCghwRatPBAdNDt9uAU0F9G8TkbnLJErjpJrj2Wnj44QO/HteFDRv8r885B/77v6Gzc/fPn/3bY/njb/wdrzgrKJoZgkHF9u1gGJJ8dwubWk6je9evqVlx5lQ3EqKKEhoFlUABrqkT8AponouQEqXraNJDKBeUIFIdwxEWWyLHElvcjSCL2LoFpWnofbuQiQR6qYh9wYePeA1+vY3/Jpp4p+CNGOs7kO3J2rU61aoglfJr6WBQEQyCaSpqNd8DzDShUPAN/U85xXvVGuy1mtt9fdpePy+XBZOuKFQqgp07NWKx3QTeFHElJRQKCzn33Cf5P//nT/m///fv9yLR0ry619kUrmd/8uzYY/+Qf/mXb+F5Fpblk4jWpNNIKuWPa46PaziOn8IphGDuXEl3t2LnTr8uniIOOzoUw8NQKPipnI7jnzshFJblK9d+8hOTHTu0w/Jufqs8nw8Hh02gBQIByuUyAGvXriUcDnPccccBEIvFKJVKb+wR7oO3Swp/sA2OPjb6qhe4fT+wo6OCX/7S4LHHdNxSnQvKN7MstxqrnKX2jMGO505l5eev5ea5n+Cl0ge4IH8zi71XWGm9yPsbLxOtj1PunMfD5WVEwzUqXghRLpMkRwCbdSQZ0maTqCsqGBylDCJI7JUmkR829jo2CxfwCaUoFaJUpn92IK+I+FVloLzfa4yx1f/is1B9OUTm6RylUBu1WhgERLUKJRmd9H1wyZHC1sIE6wXKehBd96N624w8djLK01tbqPTpRGalSZ2QovD8+1m2/dfMFDV0R6JkA61WwV129G4PB9dBVCvIZBIVTSDKZWaUNhM9Yw5f/uMoyaT9trPVTTTxRuHN8oY4mLQdI8WEHSWiqmy0VtArFtIaqxJolKlLi4c2zuSELKTTe4+km48+jPX4o/74SGsrPc+XCW+7k5WLNF469uMUi4KHHjK4/MwRTpRPsivURiDdRl6k2LDVZE5jC131nbygH89GltJhjFAxM7TV+jCx0VaC+KF/7BbOAV/Tnu5GB1rXjKumCh0FyMnHGdj9S58Ab51Ae1ohJ9UDZT1JRU9ilPI0hnN0dLSyfbvG4KDgjOwrmPUKFcJYbUl0o075+a3IRpmRwAJeGWxhaKe2nyfEfuRjJIp9wYexL7mcdGj/4vFNIbmafjlNvAfxdtatuVwOz/P2q08zmQw7duw44O+Yps47zU74llt0nn5aJxDwKayklyWVzyLNFE48TTgMXV2KLVs0hoc1urv9TdTEhOCiizw6OvzGwyc+objkEsm2bf66O3duGqGfC88+RcSu4hoKo1HBmKxNNRQtjGHhTK/l+50aB/gqyAdB/MgPB+jshAcegL/+a/jGNybFX/tACPjOd+CrX/UT7gHcEZ2xT6YZun8GH+enrBH93KZ9BDMYpFoVBCtZ1JYJbq9/gBlS5+zGfczjFQTgBsJ4gTT9Yiat+gSh3CsoPUBQuWiyAYaJIwJ4nkBKRT0QIb28k4VLQbEUpQnErl2IbBYjO457/odQV16JZekHfV9EdgKRy6FS+18HRFsLWjyGKJdR4fDu28tliMUw2lrQX+Wx32gYxlv3XO80NF/724tsFp591qS9XdHWJgDhJ/Fqvjp1aEhD0xSJhN+gHhnR6OhQCAFjY7432Qc+4PGpTyksSyeb9ZVVqdTeTc22NojHBeWyNvn4/uD25EeO+fMFliUYGNCYORMGBgS1mn8c0aiaVmoVi/761NLip3TaNjiOoFwO8J3v/A0XXPABzjrrMh57zK97r+fQCbQpnHWWyerVd3DDDeezaJFkyRKF60IgAFu3alSrGrbtr+O1mn88jYYf/jUyojE8DKkUVCr+PqRW0+juVmSziqEhzQ+a8XzFb7WqE4sptm3TcBzBrFmK2bP9lM5bbzWp1zV+53f2962cwi236Nx3n78fmT3bJ+Xuu8/EMDSuuebgv/dm4rAJtGOOOYYf/OAHaJrG9ddfz5lnnomu+x+O3t5e2tvb3/CD3BNvpxT+1TY4B8KeUs5USrF5sz9DnM/D888L/lfLLzlF3EqwUSZcz6LXK+h3P4Nnr2PFou9zwo6HOMV5ikpbK044QbVQpE2V6Kq+SF/gJHIygVEu0iJG+I2+kmO8dZRIkPCy1HMhEvEqth7GdYvU+4KEs4031SvCyRrs6JtHjAqbrBNZ0HiJVjlCNOBgOQUqrsWA6mKXmMtOYxGnuw8RcOuUAm20yhwxe5Ttcy6hv9aCqMOiRX7Fs/WoC1k4tpolfavR7DoibEPIQiYSYNcRo6NotRpeVzeEQ2jVKioYxGttI2h6zE+ON1UUTbzn8EZ6Q7yWtH2i2sLT5mmcpe5AE5BXCTTPJUGBNaFL6a+1kM/b0ySPSmdQuRzmmtV4Md8YuVaDrcUoyYTg6OIa+uSHCLT6n8uXHi2wZLiCnplFKKZwXYON1rHscGczT+zg5zP/kDPLd/ORiR/iujpT/mqqD3iTPXBkVuD0+xsfZZiUVJSyHscULlmV4on1rdTCAs+D0s4crWO/IR/soEVMEArXGS4EqeXrtHo7+Yn9EfqDLQf2hNjHD/OgxNibSHI1/XKaeC/i7a5bDxeO8/ZsCA6GbBZWrzbo7JQEVZUFL9zCMfU1RFSZSn+UbdFjGFtyOrR00Gi0ommK3l7/OvLBD7pceKFLY7J/W6vB3XfrrF69+1pz6nGf4toPPEf44XtIOEXkZDPEm1T97kmevSruB28F6DeC+KC/Kf3612HjRl9hti+uvRb+9//e/b28F9xP6WijkvlsJ0GBNjWCLuFu50qulb/gw6UniZVLDBRiPKOdxE4xmwYBbC2MS5Axs5OudsnM4giiYaFCAUTVg4YNuoGpC0Qshds6k1RxjERqCClm4aHjLVyCOTaOVioiEYh16xDBXx54bT+U60A0CSeuJHDPnUgp92v829EkNN7av7XGW/x87yQ0X/vbh9FRQbHoC1m8PQ4lGoWWFo0VKzx6ejR6e3112OzZctqLbM8UyUYDfvnLgyuholE48USHe+4xkFKRSkEu56dUnnyyy+23a+zYAf39OqGQr3JTCqpVX/VVLPpkf63mjz2Gw2BZikjEXzul9FMv7733WYaGdjeNXyswYM/7rZ382iffniUSOZ9CQSOXk6RSirY2yY4dGomEr8hrNPyaW9chk/GPoV6HV17ROOssl0jE33OEQjq5HCxbJhkeFmzd6qvWdN1v8muaIpsVtLf73m9TnEipBP/xHwaeJ7nmmv0VZVPXn0xGksn4tb9/HII1awRnn/3qqsA3C4dNoH3ta1/ji1/8Il/60peYMWMGf/iHfzj9s3vuuWe6q/dm4G2Xwh/qBmcS+byYnv19+WWNXbs0IhF/bpiJLItG1+AFysTVEA0zQj3ajlnO0fbMI3z2pH+mNNhDIdJGPdKGbQsqMkTb4hW0u9toVxXS1QK7RIwHwpfyQuRULhr9NSfyDI6yqBKiVEojXD/BaHSwnZGVbSy8eTvmibtXjzfKK8L+jcXmaxagBnXKRBmQM5iVGEDVS4y0H03PeBzNdUjUh4moMssa64ipEit5ClckqbKEVxZcwuOZK0k6CiHE9Hja0u13E61NsCWzEleYnHBUhfCuzRh9vUinAZ5EdnTQOOkUhKYh6jVUMITSBHpTpt7EexRvpDfEa0nbo1HFw5mPUK0IVrqr6Ra9aCLCQ9FLuSfwEZYmFWk1jr4jhwyGsJ54DOvB+zCf+w0y4Rsj19uPwnEMvGgcq9ZPuJ6jGsoQjysGd2UoeDGS5KmyezNriQajqpXltWeZ62xDVy4pJhBIPHTUoECslJg3yzfFA6e4oZ3sF2fTOrwBTXkodGKqSNzOktMyPCTOpM9pwWgoWqM1PjjwX6xorCEaB6NRp9pXhUYITwUZ1Tp4JnD6IXhCZF41kGZfkqsxWkT98i6cMpifO3KSq+mX08R7FW9n3ZpKpdB1nYmJib1un5iYoKWl5U173jcSe44lnTv4C+Zqv2a70c6A3ckx8gXOK99PZfOP2dZ7NKvefzKz//gScvXwAa9Jd9xhcN99+uRmSGIP5nj6liyJM7/Cpe0ZAr/6JcqV2ONFkB4GjemRzb1GNw8AAegjwAWgbgVxmX+7aR74/nveLm8DdQU0lIVAo51ROhlmkA5O9x4lVilxgfUghNoYE7NI6AX+oPivhJ0JwqKClALPrtKi5Qn1uyA9hBAo5SE8X9ahEHhLluAcdyJ6wEKseRJ9eAgVDKLiccznn0PftRN3wUK8JUe9agPjUJsdh9v4b6KJ9yJezfYkHld87GM+GbVnLX0gv62f/Wz/WnnPgADYu7nd3y8mPcMUd95p0tfnN7tTKUW5DGNjYppg0nV/5HGKdLIsRaMxpfzy/w+HoVj8NhMT35x+DX/LoanPwCfQsuw5xvlnOA4Ui99g0yaNz3zGpq9PR9MUpZJGoeA3h03TX3k9z/dk03X/3HV3y+lzs2yZx733GtTrcMIJLsPDJuUyxON+cmcyqdi1yz+nzz/vcyLRqD9dkcv55zES2X0ep/BWeT4fLg6bQFuwYAEPPfQQuVyOVGpv36+vfe1rtPpRam8KjkQK/2ZApTOvScjUavDoozqbN/sSyHJ5t1+E40CbkSXtZglWctTjEVwr6hsWBlMIq8FxxccZyoTY5C2kUvSZ6YULJbM6AhgDEbInfZSb18zjqXIrXiDMdwpfYTEbCVGnTgAFhN31k1Heivlsx95mUTk1QvjbZaw/8f8Q3wiviPG/TVP7RoC0k8XDYKN+DEathEhH+U3oHBplgVXL0+KN0a6GiFGmKuLsMOexUy1gSXiYnZnjuK/9E9MjaIah+PWvNSL1CWb1rmFMa2dItrNwocScVcEzXJCS2he/jIwnCf/L9xHVCqq1DTVJX4ux0ddlwNpEE+8GvF5viFdL9Ny6VTAwIOjqgszMAD8b/SR3VD/M3MQEKpWkt9JCR6jK560fMfNfnkRUKmgjw4hyCXfhYmQiOW2MnGiAaR5HoDCKMD3UpIqsWBRorWl6Kqdy8vAdaBpkZZK0WyIlRxiVLZw0fi81M8Z2YzHHuM9hIiedHXW0bYKJU2PEv13B+hP/wvtGrGsj/zGfl276CKIhMSM5MsVdSKkhNJ2KiDHhpnABT/gbsQsrv2BF41E8UUOXdUTDJmQ7aHoIT7Qxrlqph1MY8sg9IfYkudxgjJ51dfrGkhg1Hev637BDXMT51ySOSIjW9Mtp4r2Kt7NutSyLZcuWsXbtWs4991wApJSsXbuWa6+99k173jcSU5tPbzTLguE1yI5WUkYrM7e9THd1CMcMY3p15sywmT12O+4THqkDKFbz2yfY+UCF7nCGwaEwJ758IycUH6XT7iHxXAUx2wKhYc+exzYsZo6uI0X24KObB4FSII7yv5YS7vR5JSwLjj0WXnzR35DedZf/c00DtUTDVRpoOkrolL0AQWp0MYQrn6c1VKaeaKNqtjM4qBFG8b7KMFFK1INJTLcGCkJOAa3moCIRZEsbKhjEKBZQjoMwDLRiCb13FzKTwVu0BPeYYzE2vIK2YwfayDDugoW4J57kp3AepIFxWM2Ow2z8N9HEexGHanuyZy29b239arXyM8/onHee/zh7NrcrFYMHHoAHHzTI5QSZjETXBZUKdHVJJib80dEFC/w9uesKsln/udraJJ6nYZpQqfjKMyn3J88ON4VzKnhgikRrNP4MTfNJtF//2iQahZNP9tA0SV+fnzxqmv6a6Xn+mqkUhMOKM890pydY1q3TKZX88dRAwCf7li71mDNHEY3652lszGBoSNDX59sU1Gr+yGgiAR0daq/zOIW3yvP5cHHYBNoU9i1CABYvXvy6DubNwNvlJXHLLTpPPqnT2emb7Lmur6gSwk/zyMxM4e4yCHoVbNqQjj/f3B610eMRREBn1jyd9nCecb0N4Tq0jm4kuHorNBqcH/8J1qJzeHrgo3w0+x9c6N6GjoeLNhnz7SLRqBPExiBJiRA1XMdAflXHe1CivU6vCG9E4/lPHkvr/WNkyGJj0QAycowR0U7uA5dxW+Mqnn7E5grres6wH0JDIyva0E2NJWI745lFZEPzaN/5LHOjz3LW2XO44MokhgEg6bk/hyxWqCa7WTLTYZlaj/lkv/+pcxqop1bT+MLvok47DfOuXyM1DZWIIwpFtIkxnIsuxuxoewvf+deHd4JXwNuB5ut++1CpiGnvAl3fvVimUtDeDitW+CaisZhg8WLJ+HiKvkYazYZZsyT/Z87POX3iDrSOdmqJVgIvvIzh1tFrVZg7B23LFhQQ2rWZU7xRxMgIlWgHZz39PdanTmVH9COcd4mF6j+LZ38ywtLieoLVMrIR5QlxJleat5JR43iujpQg0RBITBR1dGoECTo27ld13Ad1Qj+yX9e6JkcEvZ/sovGIRa2zSLQthKuFmLBm8JJ1AkWRpCKDBN0JTudxHghfRiwGp+VXY0obTwmo2yjpIZXAcmuk9Sw5meCk4iNsMz9JsDJBqJzFiaXY5qUP2RNCqxTRyyWoVqhsHCGRc0hYJuV4BxUnwjP3lnATrUfkCfFm+uW8E/7Om2ji7apbP/OZz/C1r32N5cuXc8wxx3DjjTdSq9W4YsqI8R2Oqc3nul/mUKUylXQ3ml2jW/USaQ8TyQQI1ArIo8Mo19qfxJkcN0ze/ShXPV+kRIxwfohuZycBWSVIjZIbJd8bpC3uYPT3UZPLeSn4Pk6qP0aYGgASn0R7rZJeLAMx+bY+/rivRD7+ePjpT2HhQl+NfM018Pzz8MQTcNZZoC+RsEwhNtRRmo4ImqBCGE6DDnMCL1GlNz6HiXGBV3dY6v6GpMoCCt2p4RoWmpIIz98Iy1AE2daONj6OEhrCdZCBAGhgrH8ZlUpR+53fxb76Y4jsBMZL6wjecD3e/Pl7SeMO1MA4kmbHoTT+m2jivYzXa3tyuEqodNpXkb3yikEs5ivMQiE/nRIE2axGMOgncU75qXmeX4dnMop4HDo6PJYtg+3bBZs2fZdi8fWRZ1PYl0ST0ifR8vmvMz7upymfcoofFJBIQC7nE11dXRLHEZRKMHu2YvFitdcEy2mneYyOCvr6BJGIxpw5ai/Sy3H8PYdh+GOprut/n0xK2toOfh7fDM/n14sjItCefPJJ7rvvPoaHh7H96J1pCCG48cYb35CD2xdHIoV/O7wk9pzXnT/fj3x94QUdx4FSCRYs8DjqqASb7j6DFb3PEKjmqIdStEXqpMwyXqID2dZOdeFyCrc+zo6yIFHqI1zehBtQmCsWQyTEOQO/4IalO2hZc49fgBDDRcdkAm2SQLMJINHJkSJEDRA0sOB+SWxFCeNGdcReEdnPpMkPp2hljBHaeYhzSIgSs+hlZ3AR4QuuZvTHAY49tsL7t27FK6QRhSHqKoxUBqm0YnlsJ41kCXPXLk6sjqGv68AJnoK68kpWrbLIHxsm8VchzGCWcHEEfesWZCTitxKVQnviCUS6lcollxNwJeYzTyF6e5GRKPYHL8K+8NJpj4d3S7Lc2+0V8Hah+brfHkQiu70L9rzQ5XK+avajH/XX+D3DBXbuFAgBc+PjzPyXJ3DTrWwcbiO/PcfiCQOlJwi/1E/w3JMI9fai9/VBpUy7MUgu1s229Emoss2xhTuYdXKd6v0WwWefQpbKjDkGL8kV/DT4Ma6o38xMp4cJrRVbBOmQA1jYkxsohYmDBpg0aGCy6YnjmXfsBuI3ltHOl4e9rlXuDVH9VIDoaBWTPFrffRSyMzDcEo1QjFGjixm1nRzV6EOnRoAGn3KvZ426gDQTZESWUZUho5cxpIM+GUwQoI4KWJxSewhRLXOceJG0XqZajrCmcjqbWlaRSFiv6QkhInECIyPQ20ep1oIbihMSNp3ZjeTiszDbk0fuCfEm++W83X/nTfx24+2qWwEuvPBCstks//RP/8TY2BhHHXUU11133btmhBP8zWewEqd+XRQvW8QMmKSjDSIdMXS3DiELb9I6Y18SJ/DLnxG68XoC5RqdVY255QkSKkedIAYuGpIUeSr1KKVAiEjYY6a9nYcC5xFvjLNCPo/AD3HR2b2OHJRIW7X7y1tvhf/1v+C732U6WW7RIli71l/7b73VJ9AA1JU6xnckMSpEAhKh62CDmJFmdgeMre9Da8xkhbaZo9Qr6LgowHSrCK+OG0uCDKIcFwIWolxGFPKoYBBqEkJhhCdRmoYKBGmc4T+xSmdwj1mBSqcRxSKqNbj7NRaL+01SyGQKFYkc0n2baKIJH6/X9uRIlFC5nE+6ZTIS0/TTig3DT/kcHt5ttD8+7t8vkfAfp7tbcvLJHhs2+F5rjvOvh0WeXT/573Mc3B/tQCSarsfRtN9j+3bfA23GDEV3t0exqGOafkKn5/lebx/6kD/2uq8qr7vbTzLdtk0xMOCv0vG4YnTUD0yYMUOSz/vBCIGAvwdRSjA6uv95nBqjPeOMN87z+Y3CYRNo1113HX/3d39HV1cX8+fPJxaLvRnHdUC8W6Twe7LUpgnHH++z1Vu3+hKHri5FuSxYvehTnJ9Yy+L+RxHBGiIeQyY7IBrFXbac+/JnUnAslsunmV3dQo0gG9UiAt58jhvdgrazh9NLL+G4BVx2d+dAoNDQkWjISSLNIkoFBwOFRo0g4ZE68gIP81YP7TL/2A/FK0Ld5ntFhFWVlTxDHYunOYk2RpmtekmS47POD3jwm4r1hT/geGsX0VIvIpUk5Ap06lSIEMkEMMaG0bPjqFgMb8EclOP4vg6GRmPVR0nOzxA492QCt/8KbWePX4goEPU67oJFyPb26W7nQWXqzWS5Jpo4KI5M2u5/re/wO+FbSrPYulMjGQghAibKVVTyDvU1WwjLGl4yie55eJkMCUNyevdOcrOOJljwCD/2MwYLUYbNmdSsWcQDeU4oPE3VinJC4GUq9RgeGgmZI0YJf33zoSMRNACBTZBt2mKCeZvKNTVSN/QSvrgKHNq6lrstwbYr5jJDDZEij4Yio8boKA9jmIrtVhfLgz3M8DZTNqKUi0EEcLLzBHYliiMNQrJCWYv7R6hpeLrA8wSesOjWh0gySqcYoj84n1ykm4xR4JLC7ST7FKutaw/NE0IIpBRID3QLkGoqU4FYTDGcFUfsCdH0y2nivYi3s26dwrXXXvuOqlMPF6EQXPrZBI46ieC9d6GlYkS2aFDKI6TEW7gIFQrtZ50hshMEf/kzRC6HlkwSHi8SUXkMXKKU8dCnibQYRUpVg+q8BYR7Bpnf2ETJSDHQmEmAGjHKKAQBaugwSartT6SJK3d//bnPwYoVu79XZRBRn0z7h3+Adet2/0y7wkP9TQghPfRqFRUI0Ghtxw3HkMMTHJ1fxyI9TNIbx8D2jf5RWDhoShGsjPrkWDKFu2gJ+kAfwq6jDAPZ0oo3Zy7GxBjoOtroKMFbfk7t81+CUAiVzuCsPMWvf2G/BsaeTd/DuW8TTTSxN47U9uRIlFCplE+6OY6gq0uybZsGKCYm/GROy/Lr0FBIkc8LGg0/pVPXoVaTfO5zNsGg4IMf3J2feSmvTp79LbtJsbVA7lXu/yfAk8Adk9/n89cRj/8eti144QWDdeugo0Ny4YX+qOa2bTrVKkQiig0bdH7yEzEdNrYn4nFFWxscfbQfzjAwoOF50N7uW5Rs3qyxbZtGNOqTbdmsYHhYcOWV/nk8ULDZypUef/RHNvX66/N8fqNw2ATaT37yE6699lr+9E//9M04ntfEO1EKv6+y6UAs9dFHS2xbMDLizxO3x6t8ufUXzItrCNmOls+hDBM5owsVCCCfeJolLzxENZBmO3NoNCbYLBeSb6Q49ql1FONbibcFEEYdhEBHEqdEnRAKhUAhgBIRTCQhbASKOkEUGnHKPs2mvMP2imAJiOm1RyExmMkgEaoEtQZRUSHoVrls+z9yjLgdJCxyNqDlQZkmLiYi1IlVs6FcgVgMb/5CSKZQgAfoT61FnH0eKp3BvuRytLERglu3gBCoYBB3wSK8xUtAent1Ow8kU28myzXRxKvjSKXtMpmibkTI7SwRiYQxwyEK9W5mjL5EQ5i4wyO4rRqG66KCAVQ8Do0GgcGdpBfNZ/NLitbxHnqDJyCjMaJOlWwjQVg2OLdxF6GIxkh0LrPrW4k6OSR+8eETZgE0FAI5vYkpu0HGwt2k5UbMJX537FDXNW2JxFAeajL5rYGFg4lCoHkOifIALY1hHCuMhSKq19gglzCsOlhir+N5/QSOE8+QNgronoMnBUppOJqFLQPYtqRVTTCYWkzrjAgibAGt5OuweGwNPyl8mLyeeVVPCC2fQ7a04phh9JfGMOpFhGUynDkKx4qgJnJEoi1H7gnR9Mtp4j2It7tufS/B/NjlGFGfZFehINpoETlrNu6ceYix0f1IHH1XD9rgACqRQFRrxL0sIKdJLwOPCBU8NDQUIafIC4MtJJXE0YNobp0UOQI0Ju/jTdJW/obN1/j6DWMAsRDE0buPd0/yzP5rg0f/4iwWf3MLc77at9/PtWMU3hwH+oJIpRhPLqBaUHi5Glujx6JpGZaq9cRkkToWDUyC2Ex3MDwPdB1lWcjubrwZMwg88Rg4DiqdQR8ZRkWjKMOXophrVyPb2qfr0MNpYDSbHU008dbjcGvlKdLt9tsNwmFFV5ekr08jmxXE45LlyyX9/YKdO3UaDb8WbW1VjI8LfvhDiwceMDj3XAfD+DTwRwDcDvw/4PcP8Hx7kmdTmFKaHYhE+3/sJs8AlPoM5bLvXxYOK+p1GBoSHHUUnHCCRz4v6OhQtLX5HMeaNT7BlUq9djiDUop/+ZcA1arg6KMllgUDAxq5nK/K+9CHdp/HqbHQWMz3T2s02C+s4UAhD28lDptAy+fznHPOOW/GsRwS3lFS+IMom9KXXM7KlcZ+LHUiobj4Yof3v9+j+5GfknnCJ3Wcs96PGB3FGB4E18XcvAkKVVqKGkr2kKi/TJE4YauBS5VOu5exYhBhOIRCYcqaQ8KbwMQjgD0ZHOBf0jWggUGCPBJBhTARqoSoUSOAsUxhLvb/GA/VK0IsAfdoA6GWIIpV4naNdGUXDSuC5dRwqw10PEKNIiv4DVWCZEmTlEVMx0ZXDq3mMGZegmXhLl3mk2GTUPE4Ymhw9whAKET9Y59E37gBXBc5Y+a0ckyM5V5Vst5MlmuiidfGkUrbVTrDxFGnEn3ybj/u2k1QCbdQDaVx0YkUh6GkEJqHKJUxCxtRkxE+2r330TFYIy4nOLbxNHLieWwthOnWkEqieRKvEmAkMJMhYyZLnHGEBpryL8QGLp4wQYGcHOc8Jfg829pOo9A6SMdC/8J9qOtaYkmJzqVDGBskw3QwTAcmLimytMlxYvUsZsOjEYhRjrQyml7MttJShOfRbvRyc/KDLKts5UPqbmRVoaTC0wwsHAxc4m4OHZejK7+hPDpANj6bkZal5EmQdPpJ6Flq4cyrekLIZAoVj6OnUuSMZfRvswkFFSk5Qd012VXKcPoHXn+kd9Mvp4n3Et7uuvU9hT1J9uEhzLVrMDa8gj4ydEASRym/SeFUPAKlIp4rMSYjZPYMB5giwCxsFo4/w5jZyYjexunOfUSpIJDTymNt6rEnv/ZQOOiYeKhV+yvS1DDIT8LYAy2MihmMfq2N5IN3Ev9RGa1j72aDuMyD73vYIsQWZw4t1jhjiflkZRd9ejd5meIibgM/BxoHAw3Q8PxjaWlDzexG1Kporovsmoko5KFWQQVDvofbASYo1GSte8gNjDe42fFusThpoom3E4dbK9dqfrO2WhVs26bheYJgUNLd7bFypcfzzxvs3KnRaPj3l9If52xvh0xGMj4uuPNOA8/7Cm1tWUZHvw3AH0w+/p4k2r7k2Vlnnc1jjz0CHJhE+397PI6PP8W2v0Io5BN50ShkMr4y7uGH/VDEpUvlQcPGYG9V3sUXyz3Ojf84y5d7PP64z40sXChJJBTDw76C77Of3U2MrV6tUygI+vo0HMdX6cXjijVrdM44w+WJJ/ZXpx0oeOvNxGETaGeffTbPPfccp5xyyptxPIeEd4oU/tWUTZdc4neUDsRSh2sTRNbvQ+p0z8IFAo88hDINRLKVSjmEU2oQl+NYwqYq+0gSpl0NE3RsguMOWiYJ2pRBs1+SOBgIFDam7ytBmD5mA5IYJUwcalh+/2zVbtb8cLwi7CtDqPtThPNjiOI4wm5gOHWkEJjSpS6C6MpFIAnSwMViSHXQxgQRs4FIBqhffCna8ADKDCDKJVQwBKHQpI9DZC9STKUzOGeeTeCeO1HlEkoThyRZbybLNdHEoeNIpO3eFZex7gGDReNrSLr9NMwITx7/P3jZPJbPrv4SSX0YpVsI4RNfwrZBCMy+nYRlmIYIEHVLSE3HknUsZQMeOVL0qxnMq21jpzaPUXMGcVEm4tUQbgMdia5sPHRGaUUXkkA5R2t5Jx1/emTrWnBVg8YGiyE6cbBIkSdDFheDPCkszcVyPX9zlzmGsBC0ygkcN8q4OYPvz/tH6o15nLP1B8T0MgFZR1MeCAOEhlQ6mlvHqhZodTZTqwnCjQ5UJEJZT7+mJ8TU6I64/U46gh7xwAjBwR6CTpnR+Dw+13EnS86/BGiOpjfRxBTeCXXrew1TJLu3dPk0+aKUH8wialVUKEStBve/uJBT7Vm0jW1HuXV0b0rlK/d7TDkZERDyyvTEjmKu20ObGsHGb0To7L42KcBFTJJpvm2JQqJdvvf1S94DfArqY0H+U3yem7VrucH7BPoDHqUVYWI3VNAu2H1/cZnE+56gpCLkgu006hY7s63UlD9WNeB2kiNBO8OoyRAbHYmFQx2LciOGGWqldOUXiM9KIoMhgrf+nOCNN4BogK4fdIJi33N7OO/DEaNpcdJEE4eNQ62Vb7tN56GHNObOlZimrzTL5zWEgCefFAwOCjxPISbTDqX0/xUKkEj4tyWTinpdw3G+haYJpPxLYG8SbV/y7Otf/zP+8A//hI9//O944IG/APYm0Q5EnsG3AN/jrLVVEg77ry+RUFSr/rGuWLH3a47HFS0tihUrPHbu1PbiOy67TO03ihkMKtJpX1E2dd8rr9xbwZfPC7Zs8RvH0agiFvO944aGBKWSxg9+YLJli05Xl2LmTEmxKPZTp70VOGwCbdWqVfz5n/85tm1z6qmnEo/H97vPsmXL3pCDeyfjtZRN4fM+yNVXZw7IUmtDByZ1hJSIchnZ1YUejxAoaoznTDw84qrAU+6JnKMeJEEBF5MJsw1dhog5vdQJEMQfO7JFmLIKUSJGvzYLqZt8jb8h74T4I/6ez3MdQRy/5DhCrwjzCgf5n33IRBKtVETU6gi7jg7YIoitLGLYk8OkGkmtyHZtIW4wzvzEBNr8OdhXXUPwun8n8OjDKMNAhSPIVAqiUbzLLt+PFDsSyXrTbLWJJt48iOwELfUc+qXn8x8PXsjs2AQik2LYaaE+kCWYDqFVFVq17H8OGzZUq36FEAzS8MK4SqJJiac0orJIhfDkpkhja2A5es0h7Y1Q8OKkGMFGw0LzR2U86Qen6JLN4eOoeEGuK36R75/+P0lMHuO+61q9FiYYqh5wXTOudDH/r8NsdjFEJ22MIFAUtTh5lWBMdrDI20BwaBeF8kJWLqkTLo1wq7yMRccmaWtT3N7/J2zqj3O19gu6ZB9SaETqEwjNoE6QkKgTcMpMaFE6iluwDYftJ6xijkjiblMH9YQAv5v5o/xH0MdNzt51IzPr25HRKCxdRufMDuZnb8e+32uOpjfRxB5o1q1vLlQojPHAfdMkTN2IMHHUqfzKuJJHnmpHm38N52/5V8L5Arpy8NARSPRJVZk/hqlTJ0RNhIlQwcKmtbpr+jmmVGf7ep65GIDCQ8dFI5z0Q2ZUA+TXBOr7CqU0Bunih+oLzNYG6WIY6WmER2uoi0D+TxB/BcICkqDVqkRQnLXrJkZpYzwUxInPwjQVlUaYQbpoZQyAAA4KjQYWI3Sg5xps2Wxx8y+WsOzMJJdc4iIuuBjjmadRmoZYsADP8mtRrW8IPO+ACdFvFZoWJ0008eYgm4WnntJpbZWMjAgGBjQSCUks5ts59ff7HrmW5adTTjUglPKnEHp7feWVEBpC+D5qSn1r8tF3k2gPsvcY5hR5ls1Ce/s36O7W6evzAwj+BHhin/vDn6JpPjnneX4yZqkEU5dJ2/abutWqYGJCI5nc3fyYmq77+Md3j2pO8R2WpfPLX+4/ilko+KEAZ5/tHVDBp5SvYjMM/3nBV8SNjPhN5fFxi3BYYZqSVGp3eMGedidvBQ6bQPvsZz8LwA9/+EN++MMfTrOm4L9oIQQbN258447wHYpDVTYdiKU+KKlTKYMQeEqnURdoXoMQEm3yAr1GO415cgeW4ZJRWWoijl6TJDSFUJKCSjJCOxIDG4OIqJLX0qTJ0RnMssFZySCdCKBOEH2hg3n07kSjvcizvwb5F6C+qWF8Ve7388DyOvUlIDYUEI5LPRDHtMtoeBjKJo4zXRxJBFIqkC52WTLYCFBv76Dj8dXoY2N4s+cgJs+nXirSOOtsnMsO4Gl3BJL1ptlqE028Cdina/3RYITZHafzU+8j5LNhIhHFRaeMkRYx5HgH2vatCB1UMISKRBH5PMVQK3YJbCmoiDBJmZ1MD4ayniSsqpxcewQPDROHZ3gfDibtYpxOBtE9iU0QR1g4wmLMS1P2QozNTzBzzqbpQ91z3ZL/YFJ69IM88ftzOO+8f9zv59FjqmxbMJcZ2wZJkCdAgwYWQVmln6PpjRxNUMLs2mZm1HqYGOvgNx2X0TNvFd2d/jo/Y4bk1y2fJlko81n5g0mfNihaLZRCLcwOjRHIT2DG/ICD1a2ns3rGlayMjDHHybNlvIWBXGY/T4haDb7xjQCPPhqhRXyYud7DjIQ7yUe6mJMOsmyupNpn0HjwKYonfpDk/Oba1kQT0Kxb30xks2D85Dba1tyJ297G9mwb1Z3jGI/exogwKSy8lk0nfJRQwuTUZ/+Z9vGNOMKiEkqTqA5PupnpeJpFIdBO2bEIuTX0YgFXCjwEFo09tGdicgAU/NkLSYUwW1n4/7P332FynOeZLn5/lbqrc5icZ5Azk0gCIBhFiTkIDIqULXt1bNnr/Xl/Z2V7Je8ere21vceW12fX6yPZWkuyRZqSKIlBoiRKFAkSIBhEIhAAQYQBJqfOuSt854/CIOdAEWTf18ULw5menuqeqar3e77nfV5i5Gj/5DjcJ6g/rKNtdhC47KOPHSwhSoZbrB+QZAqDmifKSeBvgGdBfhz4jvfJGj40bFqY5MrKczxTu4W6jJKQOWZIMMgAcTKEKFHDR5oEFhohWWFv9zVUAkl+/lSVBS9/m2t4EXVsDGVqEplO4Sy7BH37myj79+G2tBL4u7/9lbi+GhEnDRpcOLJZQbHoDRIYHVUJBCTBIDiO92+lIiiVDkYn4rrex4cL6n6/F+MRCkE47DI4qOI5xWBWRDtcDLvjjv/M7//+///gzy+VBHPn/hGplKBc/k/HPB6+iBBfQtMEIBHCO450WpBMShzHE85aWyVSSgoFmJ4WpzVs7GStmFu2qNx77/HFLiE8US6dFpTLAp9PMj6uUCh4HxuGt8k8O5hxyRKXSOTIuJN3gjMW0L75zW9eiOO46DgXZ9OJRB23apEJtuOMlXCcMkGrQlRaaFiM0okwffiqNi+6q1ni30OXHMawK9RdzYvRVhQ65diBoG0HKQVX2C+hIPms9WU+SC9X8Bpl/BjY+NdWjz22CZAPgf2M94dp/4GO/TMX3zctRNuRj9VvmIZXBAUjzpTTRKf1Nn63fMBz5njh20gMapgoRMjhF3VyboKnJlZyz4/foH9eG7J5OaJSQVQrUCwihEBUyhAyDr2vR7UInElOQyNstUGD88vRu9ZKPs+104+zZI3D8A2fIO4v0/7jn2LsfstTfhDecJTmFpTJCdy6jVGbQlN1CiLApNtCBQMVh7rw0+TOoMsaAociQSoEWMIONAV+qt3Gcm0bvc5eMrUAFj7CdoGkPcyr3MYtdz11zPHKSYH8DQ3xY5ek/jRUv8C/+x//i7/46r/HbDvyOijXCmp/qSNRMbDRsQhRpIthUqGlpOxuSv4kXzM/xx5rDi2hOAsTh3bkTBPaB3x8c+tvcKW5lbhRJJHeS9UxCMUMlEAEN2RidfZT1UKkVtzPwh89xuWV9YQoknNCvGysxvfJe3joNw6NCX34YY3nnvOKsAFfCj1vs9fpQa372b1bUipBYSZBLD/ME39eou/m1nc8E6JBg3cjjbr1/DPbmrNtXZZ7X3mFIa2Z8FszRHPDJJUawnX4SP3rvLj/Rt4OxrlUgZnkfHz5GYL1DKpVx1b9KNLBwqAea0I1I/jHMozTSsDNEyWDjsWRISXe4qiMH+WAlLaPXkqEsTGIvprD96pFFZMMUYboZ5ROfNRYy3e5iydQsI/JSWOz99+sw82gTpEwRYIEKBFxM4TJUxRBHpUfQ6fOfTxGQmQIixK6W8WkwkuhD7L50k/THJas2v1dEtsex+3IeUFIloWydQv+XbvAb2L39WEvX4Eol8+r6+t0w7UbEScNGlw4YjFJKASplMCyYHb4c60mCAS8IVFDQwJVlZgm1Oue4AaeoJZIeEJRNusJT6tWueTzCuUy1OtfOtDu+acHf97ixV8iHv9D9uypHYxM0TTJ0JCKz/cFqlVw3f902BF6bZuKIrBtLz5EO6AKWZYnjkWj3uTMUEjyoQ/ZGMbpD1DIZE7cilmtnljsisUk8+e7DA15LZuZjPe+hMMu8fiBoV+KJBj0XH0DAy7F4pFxJ+8EZyygXXnllRfiOC46ztXZNCveuOs2Yo+PokWDvNpxHzsnyjxQ/GuiToq6q+KiUFd8WIrJImsrRUJEtTLVBct4eWQeolJlqbKJ/tpb6E4dBRuJhkkZ1TsyxuhgFS9yO0+hYmOhUcOHeq9zxDHZTytMfLqZ8HQJBxUDixp+nGcU6isMol8vEL61cPDxys0FnD/xIy2FFjmM6trYByYpCbzhBSoOKi4mVXrYzwh9PN78a7ysrmHN/hcxOzuJVMA0TaRpQiCAOjqCyGQgFDvkdFn/AurbO1HyOWQ0ij1/Idaqa05vx64xWa5Bg/PGyXat49s3Ytz7YYwfPon/4X9GlEuIYhFsG1Eqos1Mg+Nio6CLOnlCaFj0iCHKapiSEyYhU2hYlDFxECTIsJMmBulnldxI2M3zunYlNS1EtDZMTGbxU8FPhWt5jivvevWI45U/FvBrIDLCm35mWfT/8gm6o5/iySs/wsqvvkD3LcMHH9//kf3U/1LDAooEmV229TOInXqBcrCJdfG72Rf9AFPjCk2ugzOVpimUpuyPUzaTtLZKyuU42+2buHz4CQq+JB3uKCHKkKszoXUzsUNlQ8vNGL94gWvTT5D1tzAkeoiqOR70/YBowELiLaa8VgANTfOKi5KboG6ECJRyjFVMUinB+LjCQHAakQhSMRO/kkyIBg3ejTTq1vPP7JS0pb40CaOAKBSITO/F9gexw1HUWom+yh7urTxC5s1W5ipPUo4289rAWnqHX6LL2kdBjWLKKsJnEIzr+O0C00KSkknmsJcm0gcFMzjUuml7vQ64wNPcxlaWsVK8xFtyISkSLGcrCi4jdDFGJ1cor1MTPq511hE76jmPRuB1TRhYFAkyQ5J2LL7D/bylL2PGTTDtNuGTFSwM1vAiTaSwhM4GbQ1vX/YQS8I+ApUUSzLr0asFlMlJiAVw5sxBmZpGmRjH7unFXrkaABkMHeH6As6qVj06b+hU4dqNiJMGDS4ciQRcfbXDY48pOI53fqqq5zqbN8/L7pqZ8doaCwWvfRJAVSVNTd4kTEWBeBx0XSKlJ7pNTQnSaYGifAnTTFCvf51k8je58srPsnWrwl/8hR9FgWBQUqt5rY/VqsAwvojjxLGsfwB+Dfh3gJftqCjgOJ6Yt3Chi+N4gpXP503dvPLKQ9eR0x82dmwrpqZBteq1ccoT9K4nErBqlUMup9HV5VCtwhtvqGga9PV5m9W7dyv4/ZJaTTA2plCrcYQT7p3gjAW0Wfbs2cPWrVuZmJhg7dq1NDc3s3//fpLJJKFQ6Hwe47uWc3E2VTB5lIfYxl0oMkOlFmfnaDOXzh8mn/8uMm9Qrgiqws+4rx+1Pcnt7iZ2GMu5Ivss7W0WP9qbJCIsalqQVC2JjSBEEZMyEoUyBgJBO5MY1KngJ0gJFYcAFYh5ls3ZrIj632pEZYEcEXLEvdHiqkGZAGamRvqeZrp/dz+xP896WRFhG6VUxEedEiFMXGr40A8IdzYGgjoWOimacNAYVrrJ1UPstTsZLkXIbShiJYJ0dbksWOCiH7hxy7h34551upDLeQ40TUOk06hD+1FyWe/3cJo7do3Jcg0anDun3LXeN4j/u48iMhnc5laEGUSZnEA4NQBcw09Fmqg41FwNR9UxZYWs0USsOkVBhghSwsJAopIjikGdHBHSMkaLPUq17GeLtpCkGucy52VvyiUFwhQIx/Le8dTB+gMV5f8BVRxYFgkXVyiktWauNDbxfOVqfHeUGP69Lq7+i40ohkSJuVjoVPGhILEwELgEKdNcH+PbrQ/ybPI+illBwizzW4F/Jbh+IzGtgBsIsS2+mt2h+/jYx3RuXnMH6vdskm++QGB/CSWfZ0o2sb3Sx/6+axnpvp6bX/ozxmULid5mFvVJ/P4kgaKD2LSR0u1eC0026+0QBgLe7mU+kORFVnNd/QlqCAoiSqvIEixO83LLnQS6EzRPy3c8E6JBg3czjbr1+JzpBMZ0Gl5+WaW5WeIPxWGnRlt5LyklSE2G0JBoqkbdCDGv8AaVrMFOs5VcMcFCZzutsSrhkg21mpej6zNw/T7qmNRDNqYj8Jcq2KgoB4JAZt1mEpUaPioE2MhK/lz5j/wH/ooiYQoywhYuwRU68+VO5jJIEK9VvhDuoL04ibDdgxM/j/teAA6eSFfBT5gCZUy2sJw99jzAW2zWpckz6u3s1xbiupJBMQetNcEtC7xBYZ2TmzBzE0TsNIQCSN2AugXBAFJRsFJ5qpkK/rinbMlIBGVoP/6Hv4m6d+9ZBfrPiprNzacXrt2IOGnQ4MJyzz0Otu3yyCM6g4MKoZCkr8/1pqq7MH++y+7dXsYZgG177rRFixzmzJH4/ZJNm1SGhgS5HFQqkkJBICXouiAc/j10/feYN89l1y4vI2zuXG+Cez7vCW2H61Sq+jvA7xxsF9U0r2VzdlpyIiGpVr38s3DYE78WL/bEs0rFc4/FYpKBgdNxeh3bilmreW68RGJ2cMLxn2fW1fbKKyrFopcT19oqWbDg0LV7zx6vLVTTJDfe6JzQCXehOGMBrVKp8MUvfpEf/ehHKIqC67qsWbOG5uZm/vqv/5quri4+//nPn/qJ3guchbNp1lr93HMq69ZpNDcnibQlyI4IBgcV5lGhEGpnrHk5mZQ39lVKaBcQcWbYlriG+ZcF6Cusp1cMk3VD/MS5gSXOLxmjHRWXKDmu5mXKmPSyHxdBmYAXto0NB8Jbi5808d9XR3nYobQ5yAhdFAliUkbHIUCJkDNFgSh7fYtpsUZx/0Yluy6J8WsC9VtlVGoYWDhUqOBH4KLgMEEbUXIH2jghSIk6JgUlxuLsBr5t3MEboVV8XPsB2RrseyuKmc8yP3roxi0mpjynSziCPjyMDIWRwSCUS4hCHqer+5xyGk7X5t6gQYNDnGrXWinkUMZGkdEoMhxGmqY3ZVdRUCpl7I4uJkrNKPUaVsViT2gFQVniyfDHuDP1dYbdDi6tvESMDBo2Pqp0Mcoq1jNMN6+IlSxWd9NWG8YXVPFVXVxboUSQGj72fdKh/75BxMMOYrN3c3YUgSokOC4yHCUV60edSfGMcy37lTDX/s2zdD47ROjjZezvKGg4VDHRsFGAGZrJYDPIXJ43bmY8GyCXE3xp4NvcVn+cPb1tjM40E8hN84Hsd+m/0WXBXQ9imib87sew0x+imM1QKqn87d+qFI0E+4tNqK/v4bp0iXG1h+mdKnPnWpgmSOXIFppYTBKPS7JZl8lJBdsWPOo+QEETXGWtp08dQviCrDPv4qfyfq6s8CvJhGjQ4N1Io249AaeYwHiiGmk2W6ery6XsSzLRtISB/c+jCT8120avVTFkkUFzAH+uhE+WGBTtzK1tp8/eiU+rIqSLUBWEruK0toKmoZYs3jKWMT/3KqBgoyMRB11hKraXlyY0JtVOVEXlf7v/hm5nHzklzjzeZlj0sNtYzFC9l/nyLUr42S7bcewQrfbbRA42hHrtmrPDCTyfsYd6QKZbwE4sdL7DWnYzD4HXSmVS4e76d7hOvEhSFMi4Yd4IruLl0mr6v/M9LjXeJKKUaE1tJ6IW0IthxHQZbJd61YGqJD9VY+sLdWJzggc3j5XpafQNG3A6O8840P9wUXM2VPt0wrUbEScNGlw4TNMTr9essfne93R27FBwHIGiwPXX22zerNLf7x5snRwfF+za5U2znDvXplgUmKakqwteeUUjlxMIAYYhD7RZKpimZPduLyNswQKH7u5D538+74lPUkp03WvTzOe9K50QXsaaz+e5wgwDmpoktg39/Z7Il88LnntOY9s277p5Os7WWeLxI1sxZ4+lo8Olp+fIdsuj7zWz79us221WM8lmvfy11laJbbusXOnwiU9Yv5I1/BkLaH/5l3/Jxo0b+Yd/+AeuuOIKLrnkkoNfu+666/j617/+vitETsfZdLi1OpMRTG7PMCeeoqMriuVL0tHh9Rhvn2gir4WgVKZHzLDQGYF6HXPEoWLGufHyNIt+/YPkKx/kp3aJ57c2MTqq8mdiD35ZZ0q0UpUmJREiLr0FqIWOjYqGTZkAJQIkSVN71Uf21ThjdPA6l1IRYRYou1jqbEKnTooECpIgRfqqO0G62JpBZnsr1X+v0Wntx1XChN0cAtBw0KmjIImSw08FgcBBIUCZECXmWNupsZRmNc3uS9eyU3HpH91AtzXMzGSI5B13oN91Lwag7h9EmZzAiccQVh037I0EkT4fSj4PhoEoFc84p+FMbe4NGryfOJUb4VS71m44euTz2RZCSqRhQL2GognCYUl+SqJLC2HXSWlJtrCMm6LtqAUFWxjEyVPFhyMFPmosYTtxspR9zeyNLufZ+rX0+ce5Rr5A2jWxXAMHjdyrMUqvhvBRw0VgUgHXQQoBikJ1YB497hRb8j5mpJ+UWMmo1kHL5gkmNrfRwRgf4DVMqtQx8FEhSJkh0cuk2s7+QhI3DPdeO8mDrAMjwTwxzrzMCK6vjoqD2PsN8plrkGbnwffMSSSZHtaYsnMoM2lmxlVMM47lCxFzcoxkTXbsULnySueYFppEAq66ymF6WgM8ES1dCfCI/hBbum8lQYaCHqcWSlLIC6pVB9vmHc+EaNDg3Uijbj0+J5rAWK/DvxoPnbBGisUkwaC3uGpulryx6EEW7v0x5swUim3jOgYjkQXsmmxBuHVURRIpT9HGMFXFh1XIYZsqqt/n3T+qFZyubuyd27DseaiOhStUFFnDOSBxlQhjiiq2VKni5y19OS3KFF3OOCFZwVF9ZJ0wC+Rb6BLGtTYqluldu6VBoRxknzJAizuJS/7A7M5D/ofZjwWzLjRxULw7HCHgI/Z3uEv5ATmjlZlAD/5Smn/L/+Dfl/4Co14i54bZrw1gaG20VEaRI0WIR6laCrJuoSoKIVnE7xTZ91bQ2zw2RwBwOjtPGOgPJ27tPFzUPJxTbqQ0Ik4avId5txglOjrgd3/XOuJ4slnBK69odHW5+Hze45qaJIritSgODSm0tkra2iRjYxAKSaJRL0R/YkKhUJC4rqBSEczMeC6y2eepVKBaFQSDLuGwQiTiDQao1bwWUcsCEPh83vMmk16e2diYQiDgfa5c9rLLMhmFTZtUli+36e11sayTO1tnOboV0zA42K66apVDInHq9fjsIMb2dptg8Mj8tbvvtn+l6/YzFtB+8pOf8PnPf55rrrkGxzkyQ6uzs5PR0dHzdnDvJWat1R3xMndmHiMyvYHgVBF9KkR26Uq2LFhLV1eQN95o4nH1Gn698D+JuSmKaoxYHJK1aRR7ioEX/wg7tQTrqpV0XnU/o69GKRiC18Vqbqk9jnChogTwuWVaGEfBxaCOhk0dgxRNFEUIW2rsp4caPraxlBp+grJMtxxCxSVCkbxIsEvOo599RN0MdQwUy6VgB7HUBK5Q0bCw0FFwmaANH1WSpA7Y5uUBP5pnw6+i0csQeWK0L4nSv9jH69on2d95FeGZIbaV+/j0DT0MUEZ/5Fsozz6LeGsnQqq4bh2h6chwGFGrHViM188qp+FMbe4NGrwvOIUb4XBOtmstKmXczi6U4SFQVaSignQR9ZrnUMtmaXFTxG2HuiOplg0ejv8OYv4chkurWPzmdwkpJbJOlAAlglRwUcgSBzNAf1eNZfbPCMV15PAowWoaQypEyJIlSpYYDgo2Knujl9Jr7cZXzuIKBUcYlHel6GQEzQ7zv+T/QYoEbUzQzyBz2UOeCFmixMhiUMNGY4wO3GAI/+qr+HfXVVnUuoWlXVn8/28JMnm0fYNIw4cSNBBWHWVwL/7vfZvK7/7+wfdMjI3S++3v8htvvkU+bXOHHmK7spod4Q9wVerHOApkJyPUh9MECse20BxuaQ8EXCzLs7RftTLGzp0JxncrKFnPIVEsekXKO50J0aDBu5FG3XosJ8uynHjsZV4M3I2/M3HcGmlW0J/9XC3SxbO9v8aK6nexA2EKZjO5KYuINcXTvru977O+RcjJU3SCKNQoF6FCFMohmvQcJRGiUpDMkduJu2lMigc6Jrw6MkThQCuSwi4xn3G7mUVyCymjBddQCdazaCSoYjLP3kGPGCRPiBYm6GaY3XIBk/G5DPstfGMvEiXPbAORPCCTKUjq6IzSSZkglm4SFkVW1l9lvthFSiaYW9/NTcrPKJgtpJQWZFVwuW+aWH0G084z4euiWvcxR+6hkuimNmnis8tYhSo2fmwjjIqFpYcIGA7dVW/zuOXu1UR3bUJGIkf8nmQkgrJ/P/5vfRN18MStnUeLmrPk86cXrt2IOGnwXuLdapSYFYQ8jj1ndR06OyWJhMNnPlMnGpV8+cu+A8H5nqtLSm+Cp+tCOCwPtl5OTirs2KGgqjA97bU3Og4HXV+trV5LZyolqNU8Z1s8Lg840bxLSa0myGRgclKlVvOyylIpcF3BSy9p7NjhtXQ2N59eRMjhdevs7+HwwQOnux4/2pH2qxZE4SwEtHK5THNz83G/VqlUzvmALmZOpHQfbq2+cfK7LBp/gp1GG+NuD7FyjkU7vaGyz1U+jaLAxsCNPFD8On7KqNImUC+g+jTcSMTzWVoW2lNP0aupWNZnsG3Bv8gHsDW42l7PJc6LNDNNFT9+avioHRDQdGr4SJAmRYLHuZdL2ESMHJP48VPBlBXCShEbg7IWIWml0KWFLTQsYeB3S7TKcXSnjq3oBJwCCi5lTIqEsNEYpJ+lbCNM/uAOnhcIrhGkRMhXJ5kEKhUu2/8Y/aMbkIUii7UQ3b+4Ep9RR/zsGXblW7GdRXRktuN3ymj5GnpbDeFYuB2dKIX8Gec0nK3NvUGD9zonciPAcdpHTrJrLU2T6toHML/xv6FcRgGkpiNsGyceRykWEI6DodooiRAxXaG7w2VnBF5tXsuS6BDNz76GdOoIXFwU0iQYU7ppE1n8mk3diHLr9CPkVZ0iIXyyhEGdNiYIHMiAtISPfLKPZ0tL6LNeZx670AQ4qsGUFcQn8/SK/SREhih5NNciSJEqPmx0pmkhQJlx2hkKLqKw6HJE2qbvK19CpcBki0qfO4SYnsG2bLRsDkW6COni6jrqm1sR6RTSDOB74vv4H/5n1P2DLKqF2VyfQ00LsSb9OM8Hb+PH/ru4Wfk58dwIdj5K7bY7mFhzL5m9p2dp7+93KRQEQ0Oeu88wOOl0pAYN3k806tZjOVGWZUWPkB8dp/eyFG6ztzl5vBrp8IXR6KhCqWct/f0uVzvrqc1M8mopyjPKXTzh3I+uQ6Je5NP5/0mIAi4KFTVE0deMmqqgRXwMD2uEbR8dYhgNC+XAtV/BQcHFR5UUCUbVfrb7LsW0Kuiyjk0Af1sULVfFzFu4NkTcDGkSDLOAAgHmsYtLlM3srtQZ93dRDa7EcmFe5U0SpAFBHR2DKvvpJUMSR+gEDKhIhbg1zm+pX6XFyNKtjdNXeYtdzgIyShINi+bKMFk3iGHnKEqDuh6i4kKiOEo50IxVK1JWAvicCn6ngq3qKNIhFevj9b6P8Fa+k9/7UI3I5N7jRiMoM9PoL63H6ew64b35aFEzEvEW5tPTjY2UBu8/LgajxKnO2UWLJP/4jzqvvKKiqt5Uy1rNa2G0bU9QE8JznTU1Sep1b8DAtm0Kzc0STfMGFkSjkp4eiRCSiQlBva4wb57DpZe62LbA75cUi4K9exXGxwUTE7PZZQqplBcjJYTncMtmOeBGc4hEOGVEyMmEr7NZjx8pQP5qOWMBbcGCBfz0pz/lmmuuOeZrzz33HEuXLj0vB3YxcSqle9ZaPT85Tf/oBsrhFmy1heqMYML106RJmt9+iWLlLgYGYnTZZabS7bzlW44pK1xWewW9XUULH2hdDIXYMxIgMLiR/shdDKlNlEp+vuE8xAZtJf/kfoqyDJJWWpBIOsQ4cSdFSJRp9uWZMnt5JHc3L7rXYOiSm+TPETZUMVBlHUPWGBcd2DaEZR4HgYNJRQnhuIIEKdrlKCUnSAmTMCVq+HBReYuF7KWfZqYxqJElioaDgkQTLlmRIGV0sOfVHK2vPU23fJJcqIUZp4dFLVmSP/8eolxml7qQLZNthJNJFJ9G08xOAtUM9axNqDeJ09N7aArnGXDWNvcGDd7DnMyNcLKcwaN3rWfbP+s33QyGgb7uOZRcFjcUBlVF/+Wr3pCQliBusglr0RIS1QofK7/Mlb95K6HeBJF/CWM9b2AHw9S0IE3lYSJuiYCzC1EBffSXOEIjVJ4m07KGrbUVXO68RJASCi4BykzQwnPyBpz9PoJkmYjNI2Eo5MIdFIOtdAy+RKbehOJYdMpRxtVO0iJOTKaRCHQsLAz+Xv89ysuvYMnlBvaLb3BV4Vmq0Wam7B4m9ufwlYq0lcdxVJ2K6kc3FEzqCFVF3b8PJZtBe+Yn+B7/HsrMNDKZJGSrzCvtYZelMaG1saL0EqNtl+KvSDRLoiouL7+s8sh6H9ma77Qs7ZOT3ijvO+90uPpqh7a2X/3uXIMG7xYadeuxnCjL0k4VKBJCJI909h9dIx27MFJIJB6klv4gI1uyPPL1NvZkkxS2qPg1+CfzcxSLgjudHxDWK7TLcRK1cZCSyVoPjE+hKg6uo+CjeiD3TKGOTh2dwQMeYdcIEHDLVDSTsFXArEwQmHZQNIVKoo3xlEncSaHgspjt2OhkSBCRJebaO3mjlOSFrgf4u6kH0ZQyi6pvkHBmWMl6fo1v0s0YCZEnJyMU3GaCMo+GxVJnM/ngAG57H8rgHhbVtxMM6GwvdqO5daSqYjs60naxBdRVP4ISinCpBmJMKp20VwYp+VpQVdDdGt0Tr7NfmYMcWEaot3b8aITRUZASp7PrlPfmo0XNo90eDRq8HzjfRonz0QZ6ouc42Tn7xBMaL72kYhjg83ltl+m0N3HSdWcnZ3quNG/SpYuiqBiGxLa9vLGlS90Dgf3wuc/VGB5W+MY3DAYGvLbRSkVSrQp0XTIzo2AfuFRkMgq53KHjlBJs2xPrbBv27lW44grnGGfr4a+zre3Q548nfF3s6/EzFtA+97nP8bnPfY5KpcItt9yCEIItW7bw1FNP8dhjj/EP//APF+I439WcSumetVbLVAafVSQT7iZpeuNli0VB2onSXB0mKVK8PZpkKt/E7VYYXalTV31YZZfJXIguo4I0DMqY7E3rmPVhAk6GfL4Zx/FOpABZ4qTIkMTyBZES9sl5lHwJYjLDD5d9ntSEzcLMVtaoL2IqdVxU4jKP48DYgfB/G42IUcVfq4F0caVD0p5AIqjjQ8GhRBgHQZ4Ym7mUMaOXbD2ABGZooosRyoTIESMoSphKnUmjCyfZzJx2h8vfXM/OahvpUiuRiGRvyU/QztE3tYuR0CUEg+APaEyGlpOL9RCeHuSnC36H+/9DO5G+s8tpOFebe4MG7xUOv9E1n2qy5qlyBk/Q/ln6wn9GqVZwY3HP8fAn/xmZSKLEY9iGt2iTho6/MEJ/NI2LxN36JuOBubS442gquJqOv15EwWJStJEVSZqcSUy3hFrI4gqTumbiOgpIFw2HAjHWi2v5kX4PYSvN0lCa/2j+d6bNbtRqGYM6+GLImoPmWFiOSh0fGjY6Nq7Q8Ct1Vvtfo93dhvWjPF35HeSC7WzLLGI6a1CpBLDcxdzJINIFRZXUair1WBvhCCi5HGSz6C+/hBsOo6oqBIJoiorZLOjNDFNN9LC4uIm5mWGG9AGCi9oYzeYJvPYUV89VePOyT5yxpf1ghh2NLJsGDaBRtx6PE2VZBgpTDHbcy4TVRDOnrpGOXhjJRBJzeRKZ8NERkAwNQbnsNU98Xfw6js/g0+IbBOsFgtUKlmoi6zZFI0GPUiYj4yhOHVBQsSkQxsJgKyvwCZsdLOEK52WSagafW8WQNYSrQDBEb3kvzY6FD4syQapKAB81QrLIED0oho9vRn6HPf7LKToqcVGmhp/7eJQreB2JwE8ZIR38lEnUMiAhq8QpJAegpYVMpUIm2EGPsouu8tuMB5tRag6mU2VM78Zfq2BbGrprU3EFZSOEBrQ6o8hQiGpZI2SXmGiaT0pvo3toA9fe+SESiehxoxGsVavQNp2gtfOoe/O7sc2pQYN3mvMlzJyPNtBKBb773eM/R6XiHevNNx97zs6KgJ2dEk3zJnXGYhLLcg8MAfCOPxj0NjPyee8aHQpJrr/exjA8Z5nXlgmjowpCCJYvd4nH5cFWztFRr9WzXvemWcZiku5uybZtKlJ6AwsUhQPt894ETdeVBwYWuAevL8d7r1avltx2m3PC9+piX4+fsYB2/fXX8+Uvf5n/9t/+G08++SQAX/rSl2hra+Ov/uqvWLly5Xk/yHczp6t0X3WVw4uPJ8k5IYxKjrxq4vPB/PkO8yIZhvYFmB5LUqqAL5Dk1epqbi49Tk5EsV0VaypDznIJXDqPCgGs6RTlaphsOI6qen/ch0d7CMVTq2fH19aFhqX6+fDSIerlrYyKIn3OXmJODgWHMTr4Jz7NH/MlvqD+JR9WfoahlTFqtYN2enlgmqeFjkGdvWIOe2Q/fewj7itBKI8sKQTqOfIk2CQup9fdj0aasggyQgsVGWadvRpdgyZ/kSmnB8OQtLe7uK5gx0wLTUUIyCncyKEwcs2pUY62sd1/GdOxEOGzVKUbNvcG73eOd6O7bmkzH/UHUU4wWfNkOYMincL/rW+essXEBWQ8gVQUpBkAxz3mZyjZDIZdZKj5EqxShLbKPgSguhaOUKmHE3Q3VzGqgnI9RG99NwUnSEprQWoKVKtYGLxpXMpq8Qo7+u9iy+g8KqkUI6Ew5akCBSK0ln34tSrRpINM6eg4xJ1xIhSpBpMoBoSrU6y0XmBoaCFZvYueWgV/cYioG2MfK5BAlhhlTCpuEFsL4/iDKLZD0MqiNCVR8nlEqYSTbELqBqJWAzNALKlgVHMU63vwVXKMtCygeUEznT0uGza0EYsKlufXM+regq/ZWxyd0tJeqeB79PQy7Bo0eD/RqFuPz/EEG3n3Hbj1e5j+uZcLdjY10uF1Vm+vy8iIl9eTtkwUVaNkR9nafAO+lhCiUCRQTtFqT1DzhcE1sEs+QKJh08I0dcXPSvclRmUnf6n+X7hmkF8v/h1FEcIy/PhiEhEMYGBj5ypklQSurVB3NWxFQyjQ4Y7wpnsFu5wBrGyNzye+xQcnHmbA2UaM7IGM4AQhVCLkEVRx3TIpXwdGNMQ1N6mYU2+gjIygBatIV+JmU8yJjpAxWihWi2y2l5NkijnsJUaBYW0u39U+TqcxwUfL38CqCFxHY5tYwHBlMU1+m4UtQ6y4ehqIHjcaASC49/itnSe6N7+b2pwaNHinOV/CzPloA/3BD1Seflo54jmeesqrv+HEUy0PFwFjMS/nbPt2hXJZIKUgmXSpVASFgiCfByE88WxgwEVRBPH48V/37LX5H/5BJ5PxIj8yGYV0WiClRFW9x+q6J85Vq56I5rpeG+ds62hfn8vatdZJ36sf/lDFtrUTvlcX+3r8jAU0gFtuuYVbbrmFwcFBMpkM0WiUOXPmnO9juyg4XaXbs2lG2fnIalYMPkE4JOjuCzO/NQuTU7zuvxd/Zxz3bU/0+q64nxKCVe56LM0k7ubYX+9DWHPpTE8SqaT5aeAuppwmIhHPnplKCfa7cxhxuulx9+MKlRp+DFklaOUYV9rpGt9PIlAgJHZhyAolAmhYtDPOJ/kXCsT5P/W/5S31n/h15Zt0kUYgqWGgHsihUHFJkSQkvcVoSjQznLicfmc3fWKItAjxbOxe0tfcysrBb9O19wUU6ZARSd5KruKZ8H0E945SLrh0+yYZVXsBQSAg0Sp1xsvdBNwCVnYKohH8tRzh8hQvtd6NTCSIxWrn9Dtr2NwbvJ853o3u++ta6U1cw7XTjx93suZxnUyVCtbD30df9xzGmxtR/AZS03FicWRzC061ivGzn2JdcSXunLlHOB5QFAiFqE/lkRPTVG+5HT2RxAX0eJC+eI58WuDYkprix5AKjqIS81XwG36mC2EUu0rCnSSOpGYZuAfmqg0ywIxM0K+P0+5LMxxvYnS0iR9zDfeKH4AGE6KNhfXtBG0w5rbRN51GyWaomAmMgIavmsdWDWzNT2tpkICcwlcvYEjJCjaxl37yRNCxSNFElhgJq0hQy1MhQDXZhTG/B6e7h6oWpJayCcRbCOx7G8WeQimXiNoOy2QBO2piXhvF3+SSyXg7fE4oglEeIVDNUDaTp7VzekYZdg0avM9o1K3H4QRZlrdXwDHsc6qR7lkzSfO+YbZuVXgpNpehUhNz49NcnV5PLtKF3tVMzhKUNLiiayet+/eyxxig3RrGMXzE6tPoHMjB1MOErTJtTHO7/hNeNG9mVfVZisEm+laEiC2RiGoFayYPz2wgpXQSKU+BBEv4UaRNWBYYDCxmzT0RBl58mHtHvoJem0GnDoCPKu1MYKFjCQPNp1HwJdkXvIT+zCbqTz2LZkqCSQMR9CMdB7sgeIUr+Kfmf8Oa+rMsTG2g4kT4pfsB9pmL+XnrxxhTuojaKRaJt4mYFuVkN9WiJFbI06XniXUFyPvixA57746ORjjZ1OuGy7hBgyM5H8LM+WgDTadh40aV5mb3iOfYvVth+3aFVaucEwpzR4uAquoNGEgkJD6fJBSS7NrlucwCAVBVb3LmnDku09Mn3/xYs8bmkUd0ymXPmVYoeI9XVYHrSopFmF2Tz4pmpulNB63XoavL5aGHLDo6Tv5eKcqpBw1czOvxsxLQZunv76e/v/98HctFyekq3bPW6syaO1C/Z7Nwx0v4nSGkEmJs1Z38YtN9rEg6TE0p3gS1WoB/NR7iWfV2liVHuNp6gavNzQQGJ6gvMnmx6S4et++nVvSe23VB1yUZK8HDzif4LP8vYVkmKAsYikNRifBz3638euVVfLlphK9GqWpgS+1AwoRFDyP8e/W/4xeSx7mLVXIj2VicvuwWrwChgEBgo5BWmvBRpZVp8iLGj817iJOhIGFIm0OkP0GbKnl24W+R9z3I3MQMm4aaqWsBbi18h0X59bTVh+iwJmgz+slZywk5ZQL2FM+2fYx4m0Fi+wYS1ghuIMhLrXfzdOg+brrSOe6JeLBt6TRGcAcqKT7+gQwfviJOWjQ1bO4N3jecrCj41/r9LFnjEN9+7GTNo6lUYOcXniD23NNUpI+Bgg/DNYjvetuzwqoq6tB+lHyW4F/8CfUPfpjaXfcefC5t48uMbBhjNBdma+QeNm9ey2WPatyzBpzWAXpf+g62VSBLjKISIaBlQNcx53UyljZQCvuoKGFMt0SIAgEqlAmQJkGQEpeL15nwzSMjEigKaBr8LH4fYVtyRXUDejxI3u0lD3S1BTAk1IoVik6AUkEyInpodcYxalWSTpqcGiJFE0mmaGKaS3mDt1hIkjTjtNPLPhQHHNuHFQigxEKUrriGf31tIf79V3P9zq9iOUV0O4PPKnkuvKZmZHcP+ugwoV2bsZtW4/d7O39qMY+rqvirGQKVFNPFppPunJ5thl2DBu83GnXrsRwt2JxTK2Clgu+7jxL97qOsHRvlXheqTV1M3/cA6fkfIPi3efY6vRSLXkbPvHku7W0JzBlJOBFlfNpHT3UzPioos21K9RympuOnxq/l/gdqtUxexEgmBQNL/Ui/N7zGKBaxA372FbuJ+GO0W8P4nRy6W2dC7WBL1y389ocncJ9+Fr1epiAixGQGgUR6424wqONIFddWyKsBUvE5JOU0XVObKFXD2GUXU7XAdZHBdkKpUewOk6ejD/Fo8Q6SIk0tGMeJJbnsModuYfPCC0ne6ryRm3Lfo2nqNcxyGlksw6TN6+mb+M7fxrlstcZdd9kEKsfWsiebet2gQYNjOVdh5ny0gWaz3gTLeNyrmU3T+zeTEQcELy9T7HjC3OEiYLUK+/cr6Lq31u/sdJmeVmhr87LPLr3UyyIrFgX1uieQbd9+4tddrQpaW735w2NjGprm1cgA9bo3nbNel1Srnihn256IZlnehNDPftY64vlO9F5FozA0JE76Xl3MbednJaCNj4/zs5/9jPHxcer1+jFf/+IXv3jOB3axcKZKd7zDhN/9GHb6Q5QP3CRtkuh7fbguLF3q8OabCo7j/UEXRYLtahxn8RKKXdMUh7Ks/UyQXY+1k9irkNrlZUsYhhckaNuCR9MfR1rw6/Lr9IhhEAo5JQbBADgSMTWJ3ymhqSph25twZB9I//GJOnfLx/HXi+hKiTHZQkxrpm4JShSIkfFyItwSqgJxMjhmkF+b+itMp4gTCKGtvJr+z99FhQDZLHzlKwlEV4yAVFjx0rdYVfkuOcK84l7GYnYwtzaIuafCaGIpP/PfzbfLD9Cj+3E7bifqpNFb4yjNCW660jn24neC7KXjti0d9VgzGKR19rE0WpwavPc5eVEQYPiGT2Dce+xkzaP56SM5Op/bSCHQghsMY1feplxQkIpGcsc20HWkruNGY0i/eYQTqvbgx3mseicbRrIYc+OoLQnUdJXKVx5h19+uJ1ydZmB6BgkEozXMqA+hDaDn0zgjw2hZH0JTMKVNSUaoiDBhO4sjNFKihYBbpNvax/fL9/Hy7mZcF1paXK79kM6M/ASv8mESZCi6Jt2vP8Va7XmMni7KY1WmK1G2hlaiGBrR1DQJN00ZHyk3ihQKCjZxMvSyn2F6mKQVFYdB+mkVaaJuiXZ7GLVjLt/hfp5+WmOt6l2flarEsgRSC2AEDawFC3GuuBL9pfVoQ/twuroxW1qYH8xi7N+M4w+z+pX/QdXR2KkvRXzifhKJ1uP+Pk40Ue+0M+waNHiP06hbz5xEApLMnFGmou+J72N+42uITAYZjSKAwMwQPT/637RE62jLAnS5WfJm88GMHjFtIbu66A2UsI0yqgVKRgfXQQoFVbq4QkHYLhGZ5Sb5M0bVXsTUFCO/lMy9IuS5slIpnKVLaH09zZjazZTWwZLa6yRlDiUS59O5v6PrJ71I/zS2I6hjoOJgo6Iye0/0FnqKY1EMtxMIQKnegpiGgCzi1AycsI4Im4Rci0XlHSRFmt2VJDktiRNK0tLiUiqB3++FfgOMzL8O65c/ojO7mbLtI+cGyWoJWuQUVw8/xtNP3ceCl7/NNbx43Fr2RFOvGzRocCznKsycaxtopQK/+IXKzp0KliUIhz3hq6VFUi7jZXz7Dz3H4cIcSLJZwZo13nr3Zz/TyOe9yZjd3S6trZKxMU/cKpcFfr/3ehXFe44bbnC4994Tv+5YTKJpktFQSp4tAAEAAElEQVRRFVWVuK5AUTxxTtO8BpF4XOL3SxYulAe/tnixw6c+dch5dqr3KpfjuO/V8QYqXIxt52csoP3oRz/i85//PFJKEokEuq4f8XUhxPuuEDkbpfvwHb8Eh0S4ZNKzYL7xhkq1Ck1NsGCBy4IFLhPZJtzWJjqW1Vg94RwIEnQYG1Pw+bzWz85Ol2HFJFzXqFVjvE43Wb2FsK/KtbyAvzCNqFTAdVFVBcV2ABeJjoNKRkaZpJU73CdocafAAl1YaFhMi2Z0WSdCnjhZ3pYLmAl0cd2CcfZbHYwUugk5OZpfeYrJ/wkL/uxBYjFo1WYwR9L0Fup8rPJPJJ0pbKkyB4NhenjZt5qQbvGfC/+BXYV5LFvmsmCBJNMSZ3Q0wSVLHW6+uU5vrzxGEzuTtqVGi1OD9zunUxQc7UY4mnQa3tqQY4lWoBLtxtX8FOLdJKffpprXEG4eN5lAuC5OTy9udw9yeuqgEypFkuffbIG+JmIHjmFl6rt0jD3JUK2V9pZmbF8A6UpG7Q7oWc6CpSr2tq2wdTuBaplyqIlStAN/epzpeoQYPpJyhpiSoSwCTNDGi2INQniFyfzkDInUDPGERBzwGyzZ/CiLJ3+O1hmjEmyjYKQZqAyi1yQvla+m4Jr4qTFFFITAT5UaPl7hA1j4eUT7JGud7zAiu5lWWultKrFifoloSx4rX2P8mR0s8cdYXHyVkfbLUaRD374XKBpxOtsstFQKN5PG6elDFAso1SqMjjBHTpCPwFtGL4H8NP2VQVaI59B/+CPq8U8ed3PgRBP1TifDrkGD9zqNuvUsOJPNyQOIdAp93S8QlYqXdxkMel9QVSiX0V5/DfvyKzBffB5Nd5FKBDHttSNW1z6AqNcxv/YV0AUIwPAhACldnJrEkQJdcSmZCXzS5llu4so9m+mP7sefnwYpMUM+kv4izc5WAhQI6VlSsTnsilyKzyqR2LYePZCjGJLU7Tq2q6Hj4AIqEgEoSKpKEF11mLf/WcxKCtWxqPuiTPp6aOvU8YU0lKkZmgpZ5s136Gh32LdPsH+/1xKl61AsQi1b4TPaY9z4ys/pTr1OXfGzX+lhR2AZdV+EpbFxlufX47MKxLf/guqqJMYJ6tNT3ZsbNGhwJGcrzJxrG+gTT2i88IJGW5uXAVmrwdtvK6TTntElFjsyXD+fF/h8kueeU3nzzUP5xEuXOtx/f41CwcDvh/5+77V41xdPPJsV4o7OOjvR604koKfH5dlnvdc2O2FTSk8sK5U8J5vfD5dfbvHBD558svuJ3qtUSvDhDx96r87HUIZ3E2csoP3N3/wNH/zgB/mTP/kTwuHwhTimi47zYUG8Z80k0akCL+1sIplsYsECl3weVqyQtLa6ZLNHnriz4tyGDSqOI8nlvJ87Z47L8s5pVj63njGliynRinSg6MK8tgq+9Js47R2o42MoloWLxEZBw8ZBZZ8YIFDL0C2GcNo6POcCfkS5Qrfcj4FFGZMRpZuNoRsZkIOMyA4Gy20EIyB8PgpZsJ/fyM++cT33xX/Ov9v3PJm9OUL5Mbqd/YxrXeTtMAG1xhL5Fnvqc6gpERzADHkuPJ/Pmziye7fCD36gs2OHSjx+5Ml2Jm1LjRanBg3OTzZENiuYspO4gRD+Wo6i5meyabHn9Jp8Eym9WdfOwBzsBYuAI51QWZqOsLUnSdEztIFht5WU1kpTqES9FEGzq8TJsH1coWehTqCzC1sLMvimTdkXw0k005XN4BMWdS1Amha2+K7AkhpmRGfxB1q4LJHn0t3fo/3tdczZs4O4TKMKB+lIQpUZVE1grFdwXIFWkfiosbC6hRiTjNHOMN1IBBGZoy4M3hYLmaYJv6gTtPO0M86U3s6i7iLX3GwQ9gdRt+2FHbu4086jhEySpWEGO1ehuzWkrqNYNSzhw5gZQX9hHaJeA03DWrYC+8orMf/5G4QXBrhseBJ1z15EUwBNGJCewvf494FjBf8TTdRr5OQ0aNCoW8+Gs9lwVLIZlGwWAOnzHfy89PlQymWUXBbr6tWo0Qhiw4Zj2hHV8VH0F55H6hrGSxsQlQqiXsMRGkI6aMKhqoao6hFCTpFNiQ/yo+qDLOz8Cn32izidXaiBIOHBHL6hfZiyStlMkhdR8jU/A/MDqC0Oyq4S0WYdvZpBsxw0XFRcJJKaMMEwcKVGPLsP12eCENiqgVEvERNTqFonolxCcy2URBPlksC2Yf58l2oVhoYUwmGJYcD/r/Pb9E0/wWjKpCoNbNVHS22Ughtipnk5ViBKJLuHZZUXmFJbaAs1Yfhkoz5t0OBXzNm2gR4elbJokecUGx1VyOchnRasXGkfrLsPr8ETCcm6dV4+cVuby5YtKr/4hYamSSoVT4TbuVPS3+8SCkmmphSamx0UBaanj6zjj+fygkMi1uCg54y1LHFwAKGqem2a4IlnHR0ur7+u0doKixef/DUf7726/XaH22479H3nYyjDu4kzFtDS6TQPPvhgowg5DmeldFcq+B/+Z8Ib13O/bXNnIEFq6SpKn7+Hda+Gee017bgn7tGinZQSIbyTZf3XZwj8vMi400vVFkjpnRQTaYOFtTrWtdfhjI+hvPkmpDIICRY6bymLmaCVG/kZlh4gcOOVFDYPYe8dwU8WDYu3xEJ+qV6J6ZOsrL9AVOTZlbuWYAgCgQOvPRohZo1QeOxhdOt7DNQq5KSLz5rEoE7ETVNR/eAzsGSQOfW97ItcjmLEiRieOg+wc6fC+Lg3vSmR8JTxw0+2M2lbarQ4NWjgca7ZEJ5LLcG23GpWTj4OQNUXJaW3oUTLRJviiMULcbt7Dn7PrBOq7I/zi6ePtLV/IJZFKRdJu70YPpB+k3ygldbUDjQqaKUczhg4hSmmV93JREgh/sJT1PAz5jaz1PolBjUqWpjl9depKgG+q36WYE+C1Xv+hUXjTyDcHC1aGlEpYdbyoCj4ZAUhFSiBKgR+28VSDVwhyBEjK5LskfPpEUPk1ARDbhcBylwm36CumPwn/c8ZsHeD4cPwt+Hu7wHHQdv5Fo7PTyrUT8DO018aZ9Hg09iaia+SI2IX8e0H4boQCIKi4LS2Ybz+KkIBbBup6/inRyAaQAaDSMdGsW1kOHTCBVUjJ6dBg+PTqFvPjLPdcHRjcdxYDBUQtRryQLCOqNWQgBuN4ba1YV2yHOuGm49pR/Q+TiAtC7e5BZFJo6brKLaFRKWohsmrSTSnTlkNklO97wuM7sXp7EI2t6Bt20qbHKMYNlHyZcrSR3tuJ7FeSdOCJUgngtvUjH3t9ZjffhSlPA2OpCr8VI0wflHDL+ro9ar3c2SMij+MIQroskK8PoUY847P7egk3NnLVZdFWLcNJicVBgYkd9xRZ+VKhw7fDF1/9yL2imYKE1Gs19/GdhUqikavMowWmYO/VkAKFafmYIWjR7R1nc/69EQL6gYNGhyfszXHHB6VommCJUtcBgY8wSidFvzO79TZulU9ogZfs8Zm8+ZD+cRvvqkwOSmwbU9ga2qSSCkoFgXbtqn09LjcfLON43BEHf+hD9k8+uiJXV6Hi1iXXupFRpVKnk5QrXr/RiKS5csdenu9HLTTGZpwvPeqrU1lNi3hfAxleLdxxgLamjVr2LRp0/t27Pd5pVIh+IXPYzz3LGgaMhAkEM9i5qepNUvaH/w4t98umZpyT3jiHinaSdJpeHVPE7f4QyQrWRSzFV33Jmekxh2yQZOA6+JccSXpzmWknn6drupu6ppJ3FS4o/gM8eo4edGEMrSPpusWsivUifrqs1ScIL/UV2LpJkGjTFVJ0F8dIlqdwk52Hzwmfy2HYmgsG/oxwp9FNMcJ+SR2TkFxHFqdCUKiiFXxUVN8qDhkOxeTzyYReHbUSkUwOqqgaZ4SHokcat+cPdmSR7ctVSqIagWKxWPalhotTg0aeJyrY/agi236PgCWZNdjzoyQtUNM3PRRei+t4Pv5T5HTU8c4oX7wQusxtvZNQ01MlMKE7DwyrNI3/QaJ3D4Mq4TiWMzJvc7E0FI2Gvfyi033YRiS1QMKvSMbCNcyAFS1MHYgDJXZUdsCNZumf3QDeRGl2x0i2GqizxSQuoFaLSNQwHGQmo5wbNB0hCOpST+qcAm7OeawixJh2uwJmuUYttBRVUi2CjpEDqMWQC3lkalplFIBUa0gDR8smEeCKLt2xRkQAZpzQ6QCXUwb7XSpQyilFPhNCASxO7so9i7Emcri37wD1VBRUjMIq44bjgAHFqCGgZtsRknPHH9B1cjJadDguDTq1jPjbDccZSKJde0NaDvfQmTS4HrWBpHLIeMJrGuvP3hNOl474uFOWjcWQ62UccLePaTgRijZJpNuHMOe4bXgGrJZmBNPYTpFZKQLUamgjIxAKEgwoqDsSdGadBEBk2YxhGUNQLGAjEap3f0R1O1vIjs6sIoVRLFEuJBGLdpIXwCaQpQrftR8BrNoUdEjaKpEqDZIkIqKNAO4a67h7gejrEnXjrmfqnu997Ge7KKlz0/A6sQc2kXO9FMr1AmkRzGVKpubbqJlbAt9sSym2XTw/Tgf9el7rW2qQYN3mjM1xxwelRIIeJ8zTa+lO5GQtLVJFi8+sgbPZgWvvKLR3OxSqXBg/Sup1xWEgFgMwmEX2xb09blomuSTn6wTjR4yzyQS8OijJ3Z53Xyzzcsvq4TD3vCBgQEXTYM9exQKBc8E090tCYclqZTCxISXh2aakomJUw9NONl7dT6GMrzbOGMB7Utf+hK///u/T7Va5eqrryYSiRzzmCVLlpyXg3uv43/kn/E99yxuIICMxRC1GsrkBC6HdvkSbS2EQqf/R5XNCoZKTWzUVvNB8TimCkUlSoeYJCrH2OK7gg+k8qj+KXxmhOnYXELZIqYs0l3aiaWbFNwmalqQ5uG3kQFYsriT8k6bTDXAZeHdJOuTqI6FZSkEfBbt9hBTWR9EI/hrOcLlKXYHltFnbUC2RpDBIJptIzSJcAAkKAqqYxFxy6TNDtb3fpRA3XudxaLnQCsUvMXwnDnuwRv9ESfbwIFi66nHYfculEwGUS4hbJva9TcizcDB96XR4tSgwZGcS2in51bTefGVT7I+fRstWooFK6N86GNRalTAMI5xQk2suZeX/+pYW3u6nmSDspr7o9+jJfMcLfUxhPByGVJ6K1VflBcrV/Dq0k/ScsDu/qj/U3R2XMX90/8Xu4xFlAItJANlqjGT+kyBpZVX2V1YgiwUKdphIv46mnEglFrVQEqkoSMqNkjX+38BqnTIEyYmMwTI46DzhnIZqmPRyz78wmIkvoT5+a0UYxHiAy24Y6MoxSJSURClEvaCRdgLFrEAF61exp3WKCshXKHQKcbxK3XPK68o2GaIXYV2hl6Q1K04bfUicsUyLi2sA8dBVMqgqohSCWfefLDqp1xQNXJyGjQ4kkbdemacy4Zj7a57oV7H/9i3UcZGvefr7qG69oHTcsMedNJueBFRraAIQcENUs5r5GQMDRufm+Wy3PP0FbexX72SbXsCJMpF+uYqGFYdNxJBVGsQiWBQRwoNUamhjI0gajVqN30I39NPYrz2KghQg2GcjjbEmIUTjyEtm0JJpWQbxKUDOFSTPWiOi8imoVhE27Mb2zC83elKhUTCPOZ+WvbHGZ8MM761REoL4VeXsjyq0KHspGJZDAmddfEPs3nOWj46/zvMmX4cpt3zWp++19qmGjR4t3N4VIqieNM2jxeVcrQBZlZ0k9JrySyXPdFJVSGTkSST3tDAbBbGxjT++q+9aZqzgvipXF49PQ7btytUq15nl657uemrV9vs26dgmpDPw8iIQjDorbezWcHUlGDjRvWUbZwn41yHMrwbOWMBrVQqUalU+MpXvsJXv/rVI74220a4Y8eO83aA71WUPbsxnvkJUlGQ0RioGjJwwO6ezSDSaZRsBtpazuh5Z6drPKbcjxqBD1Se59LSOoJWlrwSJSVbKQebiNTrBAsjtLSF+ZH/17i0vAFDl0waXcSnd7OQt1B0BYb3Y/v9RAIWaDpmZZCyCFE3IjSpGSKaxVRHO7UJl5g1ghsI8lLr3eyy+7nB9zCa5s00ktLLg3AtkFIjY7Zh24KgkycfaKOumnz6017z9aZNKtmsQNOgtdUboDDL0Sdb7a570V5+CWP7NlBVZDCIG4ujTk/je+L7R2R1nE6LU8Pm3qDBqTnSxRYiFgsedr4c3wmV2XtiW/ue6bV0Oz8l+fwINgpVYWLpJoEA5FzJYmsLU6EZckqSyUnPwj6d07hHaozThuL4KORNgkEQpkKrHCafF1S1EH0tNYJFnWzKRSurqI6Fz1FwNR2/anluAgnScbF1P0I1CFazVGiiKCKknRgVESCoVlmpvoqM9eGftshWIoRdBa29HTIZ7Lnz0N/ciptMgqahAQv7ymi7ytSbW4k1JzHHarj+CMr0FKJUQtm0iXaxj0iwmZoZZcLfx9cKn+BTna1cV/sG6uBe3FAIp68fJ9HUEPwbNDgLGnXrmXFOG46mSe1Tv0b99jtR9w8iJbh9/cd8zwlrLdOkfvOHsefNRwhIyxh//78MpISlOx7j6uHHUKw6UdIMyH2sqmxjyL+c/O5pBith5tgqaiqLJlzsRUtAVVH37ALLQmo69RtvhmwG/z9+FWVq0rv4q9NeTRgKIptbyBZ97LPb6LXeQhM2DgpWpkhNWJiBAG48jnAcnO5uxNM/JZ/zYX/8Y8fUjD94oZVa8Rquqz2B3y/JEmNHph3CFs2fXEnbrQ9xlWjiwzFJwrwL+wnnvLbgvxfbpho0uBiYjUQ5UQzT0Rwuuk1NCTIZcBxP6AKYmhJYlmcq2bfPe77eXpdCQfDd72qUSnD99c5JXV7r1nnPHQhANCqp1WD3bm/oycCAS1+fwz/9k0EgAIbhtW+6LvT0SLZtU0mnz/56cbz85akpwcTE6ecvv9s4YwHtD/7gDxgfH+eP//iP6evrO2aaUYNTcGCykfHMT9C2vYmoVkFK3I5OLxNHSkShAJrqCUEneSqRTh3TqmOa4PPBaDrA3yqf5rfdEnFnlJ3GQoqhZrrUKXxj+7FvuR7r9juJ+eNEHisg/nk9Q/QgVD/6JQvx48DofpRcDiEl9srVxDa8gBUOEQ4a6E4FvebgtvbT1eNj3bX/jic2KWSVJHpbgmuXTOH/fidibMgTtqTnOlN0FalotCQdlKCJ1bwYXQvxf35mgvBlIdJpWLLEQddVXnkFXnhBI5MRJww7F5UyArBWXQOhENJvIk0TcdjUv4OF20lanBo29wYNzpyTudiOdkKdzNbeFikRzNdQWpsQ4Ria6sfn13ALZYJjaSwnRaCa4ZXRZnbsUKhUoKAnsJUgsXqOtN2KokhKJfhAT4a+Hj/Jf9tJ++tXknzhKabSMdT0MLp0MdwKluJDqdlUjQCGsKjhR7g2Ugpa7TEULFrFNGUljGZoRAxJWbTgFKE2nsV2XTSniG1HveEApomMRHG6e1AKhYPtqxSLqK6NkQghillkPO45Y6enwLaxMDC0Gq5bIp6dID0wgNLTwT+7D9H/Z9fQ9otvo+7YgXBshCIamWYNGpwFjbr1zDnXTEWZSGIfuP6n05Dd64llpgmPPaayfr12bK3FsZM/GVjFCB+lr7XImjd+RkTPMSmT1BU/hqwSr6foLO/mB4EH6dq1BV0JkLQmqXX0Eembi14vI2wLa+Vqqp94CIDYHR9CyaS9QQeW7Ymo5TJKrYojNIa0y5loX0SoIAmPbUSXNpoqqVgGanMMTVdwXMmOfDeFvSnU7c/wg02r6bt54GDNOCte+ZetpSMl6R/dQIc1TN4f4qfR+7nhI3cQ7zCJMXv/PP8t+O/FtqkGDS4GZjeZTxXDdDh33WVTKsGXv+w7KJwZhidi1eswMyOIRLyYo/5+l5ERhf37veEEX/mKQbFYx+8/vstLVSVDQwq9vS6Tkwr1usDvlweGngjuvNPh6qsdfvhDSa0mDk4SnjvXpb/f+55zvV4cPvxwwwaVXE4QjUo2b1Z59FEuuvX2GQtoW7Zs4a//+q/54Ac/eCGO5z3PwclG4QhuIolIzaCkUohSERT1gKDmIg3fES2IR3CS8eJPPBEmnxd0dEisiTSL6pvZyRyKaoKFmW10KsNUMzmM/d9C6kF8n/4Ut34CtLdMBuwMakczpqkgWYYdjyKqFUp/+EVErYa2dzdqtUqgnkMaBs68+VS7Bph4fZJdJYVh/3w0DVYstvnQx6LU9QdQv/G/oVxGcRzvdQWD2MsvRcyZg+s3UYsFgq5LtS1+RPBhJCJYvtzipptsNm06cdj5bFaH09UFvkOtBifL6jhei1PD5t6gwYXlZLb2j14+g/G6jQyH0XSBesCNa5sGfjdPzdGYtOK8+aZKNos3kUht4pXwam6Vj+NTJDUzilrI445P82TwLqy3W7j7I/eStuoUX32UDreA4ZaRioqr+SgpcWxUIiGHVEYl5OYx7TJlxURTdWzdJE6R5dp2tsgVhJQSrqqxoLoFzalhihr+kQAiaOJ2dqEU8lTXPnBs++r1N6Lt24eYmoRkEpHPIywLOxjFqmr43CplRWMqvgDFdWjTZ9iVbiZldhH93d8/7kZJgwYNTp9G3XoWnIdMxUoFHnlEY8MGFds+tPhKpRRaWtxjaq2HOHbyZ8uGJ7mhpDFuLyBWGKGkx6g4IYSAKiGqPhszO86L8Q+wP/Ygt6wYYeHUi7SMb2Hg9Sl6Fgeo3f0RT/gzTbTnnkWZGEf6/JSkiV7NoTtVBC7SdpG1OlkjhqnbZCPdpItjqNJhJjqPxNROTFuiWyVGfXOpbt1Dn7WPkJVD3/yfeXHfbfywfg/3fUo/KF7Fu/y83v5J3ppzK4FqhryW5K3pJJdUa8Q5djF6PlvwZzetpqYEoZC38PbatC7etqkGDS4mEglOO4bJNOHSSx18Pujtldi2pFQSByZwem40VfW6shzH69aanZqZy8F3vqNz6aUu09NeDNLhxpMrrnDYulVl+XKXffs4MBXUE8nCYcnVVzsHstlcLIuD1wuAwUGvrVTKc7tezIqKxSKMjQnmznVpaZEX7Xr7jAW03t5ebPvieYHvJo6ebOTkcmjVClTKKPk80ucHReC0daDkc/ie+D7yU586xup+ovHixSK8vO3XaWuTLFxok355muZtBXbVe5lT3c5CYyeaaVB1Q8h8nso3f4i0w9gf/xjt164k9vRTOEWJVDy7vlLIU7v1Dtw5cxHplGeHP3BmzTq9hl6aYWQqTGVugoEWrxh64QWNUAgevO9BbzG57jmUXJZ6tAm3UsNu6kIPBI4JFz9cwCoWFX7+c41bb7X5wheODWed5XwMB2jY3Bs0eGc4ka39hjVh5GACmc2iTE4AIH0+9FKWgG7xy+AafrGlhakpkPKQrf3r5QdwTbissoFIdZi6L8Tbi+/kta61jD2tAWFutX2UlAi7eq6n7g/jrxcIVlLs6LqJnwbvoafbZd2TBX6n/GVUQyEtmmiv7KVfDqLqgi57HzMixsL6VmpqgCm1nVAtRRuTBIoFZMDE6enFWnXNwUXaEYtOM4D/4X/G/NpXEOk0GAYyFMJu6SK3v4ombQZ7rsPSg4SzIxT2Z9DiTQcXN41MswYNzo1G3Xr2nO31p1KBL3zBx3PPaWiaJBCAmRnP+TQw4LJkyZG11vYXsrjSq4/LoRaqZYE/5CfQCat3vcjD0+04DqBKpATHgYhZR3dq2JaDqkrceJJCb5Q3Fy6hPJwmUE3z2c8Fic05dPxKIQ+uS9VWkdUqinSxVR/CdVCkTYkAHdV95DI1RCjIhkt/G4COPetpp47uuFT65pPZKemr7kTRFKr+KCIU4LrME/zyMciuupHmXIZWrY18Pklzs6RsJimbSVIp5R0Tr2bdHOvXH/odxOOSUEhyxx2NurZBg18lx2tj95q1JH6/J2zZtsS2BYUClEqCRYsckknvnK5WvSF7UnqtncWiYHBQ4Z57LLZvP9J4smaNzd69Cum0Z7Dp6LCpVr1W0VAI2trkEZvcmibZu1dh2zaVQsH7Ob/92yZr11o89NDZO8XSadi2TaWv7+Jfb5+xgPZHf/RH/OVf/iXz5s1jzpw5F+KY3rMcPdnIWbAQUa8fXFQ5LS1Uu+aQG1hBsJpG27CRx6p38uzmpoNW9+uWTvHRzScYL77uedrLy1Dm9eHoSfovjxFOB+kem2TA2UdIq+KrZlHtGnVHZXioTuZrG/jBjjtZfvX93H8TBDcd364vE0kyS1bi//EPEW1+jICgPjxFdX+K4d67CHR7f/FHnggmiQc/Tn7Nh/nFYwVe3RZkzr7nWLZpA52DY3TMD2AfFS4++/2BALiuPHhCDQycuE3sXIcDNGzuDRq8M5zY1u6dx8r0NC5eDqSSy4HjoHz4BqpLPsnI3yo4jpe/7/d7C6iCbfKVyqdpUe9gcXAP8+a5xJb2Iat+LEuy7Qd7+aj8MSOBJjKBXgIBSZF2qqVJotN7qTbFeb2QJNK9G/dtlUmtB0f385aMUqsYLPUNkqxNs9i3E9tV2aatIOXvINFUZlCtcNXcScIBSfn3/j3unLkHX+fRi87qb3wWBPh//EPsWALt7Z3olTwRQ/IWC5h2mtCmJ5kqhHku30JsjsIzz2gXnaW9QYN3I4269Z3n4Yc1nntOJRCQB/N2JicF1SpkMoJKhSOGQ7njGWp2iT2ZHvZPaliWF3Ld2xpjXmKISxaGSO3vIlkcJqoo+JwCyUoG3amScyMsLr2Gs2gJpukDQG1JsHe0ibSoHdYmCU5nF47PRMmWMHFxhQJCoAgXS/GzLXgVZljjX0K/AwP9qC0JL09T3su/Xfo1riivo2LESBS2oAoX1a6TTvSSjfQQlGOs2vkNYv/lGcJ+m9+dDPPj4jW8tWwtZsJPPi9IpQQf/vA7s0h84gmN6WlBb69DNqtQKkGhILjuOveEWUwNGjQ4e04nR/tkkUF9fZLOTsnwsEBVBT6fxHWhVoP+fpcPfcjhiSc0slkvrkkIz9eSTEr8fsnkpOCyyxzuvdc+4jgqFe9nr1+voijepHrb9qZsDgy4B+vN2evCI4/obN+uUK97P8d14e23Fb78ZR87d6r82Z/Vzqo2fS+tt89YQPuv//W/Mj09zZ133klLSwvhcPiIrwsheOKJJ87bAb6XOMYtpeu4/f3IXTtxjCZ2dN7Inkwz1stgKgmaSsOsH82h9jcdtLq//HSea4tlOld1Hnpi20YdHSG6/S0+bY+SGe5ieP6NbFmwlt1tq5k/9Aht7hj+uoVUVGxXoSBDJN1J4lWLiJ3mqZ/Px7n1IT76hSPt+iKdoj44yo82NrNh80dZUdBZNrqBzugY4fYAG1ruYmj5RwhVvIyisj9OJJI84kT4wQutPP3LTpqbJXtWf4K3p26lPpHhqhUR7n4wekS4+OGc7gl1rlkdh2czhUKSatXrDS8WGzb3Bu9vLlT74PFs7YefxyKdBk3Funo11Y9/imVvGZimd7Ov172BJPU6lMsCUa3wYd8Puct4kebpAkMPh8lUr2SeAzc6P8PVX2NhLMqeUpaZ1sXopkbajREqDrOiO8XzY03MWRFDmwoRLOeYdv2YSp1SsImougXVKdBd34vPrRBTa+zsvJl8PUDRNqm3GVAaQYhTv+bqxz6FGo0gNmwA00SZymHO6cPv74NtU2i5aXZEbuQDc6bxd9g8/XQTcHFZ2hs0eDfSqFvfWdJp2LhRQ9O8+kpVQdPAcSS5nPAEqarANL17QD4vCMTijA+GmZgooCQCRCJeFs/EzgK+7jA3/mY39cj9BL/1v4mnxpH5EpZUKSsh9mlz+SA/ZyEBNvHJg895RP02G32y/gWkBJ+sAhIpBdJVcFEYDc5ha76XBfUhpn1xRoebaap4AuA1d0fp+dC/ofbTJvxP/gizOgrSxTECREvjOFNbcCs1Osp70EQzTlc3PYE892z9AT8bhJ9WPkUwKLn9dofbbrvw1/TZzoq2Nm9julJxqVYFxaK36D5cwGzQoMG5cSJR7L77vOvf4ZwqMmjtWotvfEOnXPYmcYLnHF271uK++2xmZgRbtnjtm6rqiWfJpNfuCZ4j7eh84kNiumTfPoVs1rsO9PZ6eWqH//ybb7Z55hmVaFRgWd5mh2F4/9Vq8OyzKg8/rPEbv3Hm17H30jTOMxbQlixZgjid1UKDYzieW4piEaRkUnTw5lgzPp/3R6rncgxOh5loT7DoMJtjuRpn9JdhmqfyGN1ey6K76U3EG79EdS06/GOEJyfpzO5AL6Z5OXgjGW2QS+qvoEpJXfhIiSgzejNxmSLkZIknJM32rNsrSWIg6RUbjz6M/vJLpHeU6ZyMcHXvKnZe9RHeTnsC2NJLI2wOhbl6y2Msz6/HZxWp6SG2RFZT6llLLKYctz2S7gQlf5J122BNunbuJ9Q5ZnUkEl7f+Te+oVOpHPrbNk3Jpz9tXTR20gYNzhsnyVm8YFX3gfN48ooPUxrOEuyOHWy9mbW1NzV5N/BCwRvtbZqSj6rf5hPBH2CFWtia7sUt5Pl19ysgYJtYxmQ9TqxUZ6G5k70Z2GOtoMnJ0dQf4Kb7Q7z2j5LhchNjA6u4dPB7LLd3YpbTtJT2YTolqkaYab2D9uo+2vK7qexU+ZF6F6Yp2fRciUQsQrgap/U0Xp/1sU9g3XAzysQE+sb1aNveZG56nEwgiG0muMV8DX/6RWoF7zr68oa13Hyz0rgGNWhwDjTq1neWbNZzNwQCUKsJAoFDnQWa5m1+FIve/89m9KxZE+Pl4Wu4nCeoS0lVRonKHAbTrOcuridJ8q6bIT+K8u1/BZ+ODEapt/XjKosp7snQMfgSW3tuZcJqOmbg1Gz0CbkchCNUinVMuwCAFAopo51nxIeIkUOLB2leECOTkqxY4fCJT8zWgd49yj85iXj5VTJWiEqgCT9VmiZ3YNSLEI+i9XuZvEq7n3YNHiy/wBW/eROh3gRtbSr1+jvzOzh8Y9o0vftlIMBF5/Ro0ODdzolEMU1zWbvWOfi4o9fElYp3TQyHD3Vc3XefjWHAunWHgvavvfbQULvf/E2L55/XGBkRJBIQDHrGj1wOurslfX1HnteHi+mhkEsmIwiHPfFNVT1RS9MOtVBms17umrdh7TmBZ4cZOI4nvG3cqHHvvWfupD3eNM7jDQi8GDhjAe0v/uIvLsRxvG84nluqcPWNTGxIEXCmSGejBKwcQXua9eJuBvNN9FUOtfGoLQm2RlezYuIHuBrsGfHTvXETulUh42/GCjbhMyqY00Pc8OqX6W1Zj5PQKFoxNFVSMuKknBB+WcGv2tT8CQTiGLfXbLFRDrfwVqWXUCDHysnHCQ9KXl/6SUr+JOvfgnsL36T77acox1sohLpRi3mW7H6C/n6XROJB9p6Gu2xgQB5zQhWLnPEJ1cgKatDg/HCinEWA2oMfP+PnOzNbewelUucJbe2JBEQiLpWKQM2muNX3IsG+ZjZPtDKVVwiokoAsI10oR5uZsPoIVHeSTAqW+gbpGYhg1nPIu+8g2+9dL9avV9mkPMB/KWxgdflnKNLFcKtIvw/Dp5I0yuRFK5HiGG3lQdpCY9QLKk5qmq9N3M3zv9nNb/92nfvuswlUTu7am71OOYuXINIpRrZk2f2n67iq+HPKRgsZXzP+Wo4rxx+nWoVs9oHGQqdBg3OgUbe+s8Riknhcks1609vAa0XK5bx/r7nGxTA4IqNn2TKHv3/pPgJBybzJDcQKI9T1IDsW3Ml6/+188OFHCO7d4GVkSonb24e7bAW+cJglNuzWwrg792Lu2Eyg8xJuvTV2sB3pYP5wOII+PAzxKGm9E31oL2E7y4zSTK4eJepO0xPLMtx/J4HuBJ1+z61xOCKdQtu3FxbPJbhnEsuuUZR+EuQIyzz2vEXIwzaZZCSCvzBCfzSNcxarw9O5d57od/BecXo0aABnfy5caE6Wo71xo8INN3DweGeF7bY2lzffVBgdVbAsr7vCNCUTE4LFi+VBJ9jxXm8iAR/9qHXQ9FGvH+lSO/q9OVxML5e9DOFwWCIEB93Ah6/JYzHv+uC6XntoMOg9z2yMaCjkfXy2IvzsdfmVV048IPBi4IwFtAbnyHHcUnsngjz16lMsKmygXRmmpgd52byRX+YvxZpIU61Gj7C6b56/lvuWW4w9+TLOzrcxnSKlQIJsoItaTqFLTxMw6uDWmXepH9VQkT93ydtBLFclIr0pmpVYJ5lYH2V//Iib6uHDDkpaKxVXJRB0sMpZ5u1/ljd7b2V0tJnptzKEnY1MiVYy5VYCSPx+k7nNkuXOemrpDxKLJU/rJn70CRUO846dUOk0vPGGyqWXuse0cG7apHL77ReXKt6gwblw9LATOCxn8ZWN1G/+8Gk7PC+UrR28gmFuNMX8QJ6OyzsZf1UyNgZ+UQF5YGfNV2bItxgnJ0jIvURKWSJ6hfqBFu/Dbe3OVJlqRfCscz0tepo17nM4RgjTcImSR+/pJr+3TrQ6TXd1N7vFXH5i3s2PtPvJjwv+5R9slr3xKNfw4mm79mQiSbAbFhdepiL91IwwtuanqPmpVgXLchtIyBuBxsZAgwYNLg5mXQbT0xrgks0q5HICx4EbbnD4v/9vm1zOOWJxmE6DHvHz8/in2L3g1oORIPuLTaze9S+0bHgS2dmC09aO+vZOlP37UCJRnKXL0LBZVnkVqU0woP09qkygsJIa9wLmwfxhGQoirDpuOAJFmNC7kVJQx09UZglQZkPLXWQWfAQ4fozIwcnvl6wgEB3EHBpBVnMoioGaMXETUZzD3oszGWh1OCfLSTodE/h7yenR4P3NuZ4LF5qT5XqNjx95/ZgVtrdsUZmc9Ny54TDkcoKpKcFLL6ksXuyte49uw4RDIuJNN53YpXY0R8cU6brXyeE43nRNkEesyRMJuPZah+3bFaanFcrlQ9/j90NTk0sicfYi/GwW8okEwouF0xLQ/vRP/5TPfOYzdHR08Kd/+qenfPwXv/jFcz6w9zqHu6XcDDyiP0Si5XbmBke5JP8iy8ubWeJsIJsLUXhjJUNXfYRUOXDg5mdQuPnj/N3rd7FIf56+13dR8UXRDAUhLUQ+B34NRUqMsA/Z3oG2uJ+Wwf3klyxjOBtmZswhruR4u30N+4tH2t2VvYeGHfjrdQaKb9M8NYzPraA4FvN+8ve8XFnLgF6h2SwwHehBL0na2iTLljmYSgh1716cLZtJLl/BVVe1nvImfvQJ1dKiEAo5J3r7ziuHX/x8Pg6KlYpy8YUaNmhwrhw97GQWGYmgjI6gZDOn7fS80Lb2D10epntzAKWcZ8UKP7t3K0jHj173bOZ1xaRc03nbXM5lS4JYeoXSH34Rd87cozJiXCZfnCE6WmDc7GFahLBdHbdcpyz8BNUawpVYip8ptZ0v+/+IHf5LKfmTCBf0Mlw//V0izz5FdU0S43Rde5UK7U9/k+b8S+SqPmT+bXLRbvaHFlO1oqxIDJEUGZyGgNagwRnRqFt/tRy+KZpOu2gaXH21zcc/bmOaKqp65OLwSMGniUgk6bkjRtNcw4uorUnUqUmUkRFErYbI59HfeA2nvR397Z2ou3dhz52Hf/kcb5DUgWtv/eYPI3LZA72jFlI3cMo1ikWDqGlhBZuZCs4nl5b8r+R/YVKZx7W2jakf3611MMu4XMZZugzmzEVUK7jFIs7wEKJcQUxPndVAq8M51YbSmf4OLmanR4P3N+fjXLiQnInbM5GApUsdnntOIxDwpm3Wap4rrKfHZft2lXT6WIH7RCLiF75Qo1o9uQh1tJje1OSyebNKtSoIhyXPP+8dy+GRRXfdZVOvw9//vWB01GvJj0ahu9shFIIrr3TOWfQ6nkB4MXFaAtqzzz7LfffdR0dHB88+++xJHyuEaBQiZ4gQ3mJwOp3k5swPuaL4c6ZFKzO+bqJGnkXjTyBegenFnzx48xsfF0zaTUTnXUtmTw/x3BBSUZFINLuOtGzceAx5YNfLXr4CvVohFHCYbxbQ9TDruYvngvehu0e6vQ4fdhCanGSgtovxcoiyY5Bw8txU/DqLxTPkWuaSZBwZCGBHushmBcK20d/8JcrkBP6vfw2ZSPDRS1ei3nQ/L20KnfImPntCGQZYExcmwPxoGlb3Bhc759PafsywkwOc6U76O2Nrj+I+uhL96adINEN/R4L0YJGSDKAqoJQKSFtlcUeGKHlqH7zj4LTMw4XzSgUGc01YvhDNWo5xt5V0qIfW/G6coo0b1tAqOUJ2iR/pt7HO+CB+AxQ8K3uLmuLK+nqm/C10hpowfPK0XHu+J76PvmEDvrhOvWJQrCgkp3diWeDrbaWjJ0DtDJ0LDRo0aNStv2pMEz568yS3zcuQEQlCvYlT3puOJ/jcvnKavs0F1Mk86r5BZDCI09WFMj6Okk5hvLQeYdvYc+dhX3El6Lp37bVt/I/8C8a658C2USbGUUsl3FAIuX+UaDGPprrkor1E9Cov9d3NnuI8KHq134liRI6bZWzbqIU81bUPgGGc9UCrWU5275zdUDqd+/x7xenR4P3L+ToXLiQnc3veead7zPFdfbXDd77jDfXI5wWGAfPmufT1eS3vxzNtnK2IOLs2WLPm0LU1m/U6OMJhl3CY4w7BMk341KdsbrrJ5p//WWf7dm96ZyIhufLK4zvd3m+ctoB2vI8bnB9iMcn8+S7ZPWku2bWeCbeVrK8Vf1BixpoZmGsx4L7ArZ+78WCo9qzoM2E18fqij7Fy8z9g2GUcy0HgIAwDe9GSg61DolzGWbyU8ud+DyGgLRbnepJckhXEYrXjFwiPfw91/z5s3RsLnnSmASjIMKZSIY2Cr16kc2oTdovGVC2G9uorqKPeTqAzx9sJDP/8KT5xK9z4hY+f3k28UkF/7HGM9evPKMD8bEWEhtW9wcXKhbC2H2+BcDY76e+Urb12170Ui6Bu3Miqrn1sqUd5OPdZbEtwufMqVzTvZ/FlJrU1Ry5kDhfONQ1mZJLN4dVcm34CS4PBntX4hipEC6NIR0XTBEPLbuHPR/4cu+yFq3rjwKHVSBOsFbFD3fj9h47tZK69g62ynZ2gayR2vU04FsSp+2h2doJZo7bqIxd086BBg/cqjbr1SC7UROXjctgQmlCpRPtp1nDHE3yShFHf0lC270MGg8iAF8gjk004Ph9uSyvS58NZuMhLuz6AMjmBOrgXt7UVp6sbNxBA3/wG6uQEaqVIrF7DUn0o0mZH/y1MLfgIHW94C9h02rs3nWij96ST303zrAdazXKye+fZdEVc7E6PBu9fzve5cKE4kdvznnuOPba2NsmiRS6W5eWJ+f0S0/QE++OZNk5HRIQj17+VCjz8sMbGjRq27eWjXXWVw2c+U+O//3c/8+fbNDWdOrKoowP+4A8s0mmrIcIfRSMD7V1AIgGrVjlsHkvTHi6QDvcQtEtQrdLd5ifUFUEdHUE/rJXncNHnmZaPU1tu0L/3F+iFDN1NTfjCFdyWFqhVj1j8zrovABKc+KZau+telKlJxM5dWEWFsB80zSCvJ6hUAoScHOl6mP2tl9BW2I0sV+i2ZvDb48fuBOIVGsmbP+xN+DwFvie+j/6TH2Ilm08rwPx8iAgNq3uDi5ELZW0/6QLhNHnnbO1hXt726wjrblr8KRb+ZpQPXxVlelqhP/IhBmIZ7OMsZA6/hgYC3nF+tfAAk5bCde6LMD3DjuQ1TPYPcM1n5xK4ZB7Jjrlc/gUfEz+EUsnLgwgEIO3EqekhFjRlMc2mgz/jZK69w1tl7bj3wrTREXRq4FpUV64+Y+dCgwYNGhzBr2Ci8rkOoTlc8JEksRctQn9xHa7fB7bttXCWS9gDc8DnA0070jFdqaDt34cbCuF2eBMxZXsH7r5B1Ewae+VK9mSbSA2WiFGgJg2mCgGiUbjjjjo33OAcEO9SKOPHEcJOMfn9XAdaNboiGjTwuFjOhRO5PQ3j2Km7h9eefr9EUTzx7ESmjZOJiPv3K3zrWzqDg8rB9e+llzq8+qrKiy+qaJpXo2azLtPTXuavokBLizyjyKKGCH8sZySgjYyM8J3vfIdNmzYxMzODEIKmpiYuu+yyg1b5BmfHXXfZ+EsR7K+a9E6+StjJEfHXiUwYyHIYp6f3mEXYIdHH5AeRh2i94jZWLpih63adyqvPn5uN3DSpfuIhKq/tYO8WFzUaZGDyZfCZhO0qNcegYJtklAA+JcPz0Tu59FIHOeHgzJlzxE7gmWQnzboy3OZWZNJbiJ6qFep8iAgNq3uDi40Lam0/xQLhdHinbe2ROXFm8gm+v05wa9A+cO4nj8kPO9ypOnsNfeQRnVIJbNfkR02f5M3grWj5NPjiPPCZCP61Ni5gAn/2ZzXmz3f4/vd1ZmYEqgrR3gTBnquZk3kcpt3Tcu0d2Srbgr1kGWJgLsrYCFLTqX7ioQu2wG3Q4P3C+71uPd8TlU/F6Qyhoa3ljJ6z+pEHMJ75KWJmGsW2kbqBPXc+bjKJUBTsxUsxXngOB6joEeT+UYx8EXfZ0kMTMSsVlEwGdAPZ3k7P/ASVqEJq9zStu18ikLjt4OROk9MTHS/U5PdGV0SDBh4X27lwukLTmZg2TiYizsx4HRqdnYfWv1/7ms74uEJzsycw1mriwDRklx07FDTt3S9IXgyctoD25JNP8oUvfIF6vU5rayvt7e1IKRkcHGTjxo187Wtf48///M+57bbbLuTxvmcxTbj7M1G01yD4i524iShKNIQolVB2T2D3DxyzCDtW9AmRSIQAqA2c2+IXDrRyXXs95tanqdR8uELFrGYIaC5vhRdguzo94y/RbI3z0fgjtGVNlEwaNx1Hth8qSs8kO+ngtKTeniOP5QQi3PkWERoqe4OLhXfC2n6uC4R3k63dND3Bbd069eDnrr3WYc0am3XrVJqa5IHcG4WMlcQJJ2mOSdasqRzxc00Tfuu3bB54wGb/fm+KUV+fJGHehf2Ec8zGRX3Ndah7d3vXv8MWjsfN0ikWELUa9RtvbrRuNmhwjrzf69bzOVH5dDmdITRnKqDJjk6qH/8Uvse/hxsO4yabEJZ1cIOidte91IwQE4+9TH50nJrjo1XOxS110G178wNEtYIoe2KY9JtoGixZ4lLtDCKHRpn7mQnCl3n1s+/Rd1Z0PB6NrogGDTzei+fCmZg2TiQijo569Wdn56EaOBSSlEoCy4Jg0Jt2HwhIQJDNKsRiLpdd5vDLX6pHPNe7VZB8N3NaAtqePXv4j//xP3L55Zfzx3/8x8yZM+eIr+/atYs/+ZM/4Q//8A9ZtGgR/f39F+Rg3+uIdAq/ZiMXzkPN5xHlMvj92HPnIRwHkU4dt9g5kehzPnbH9I/fS3abiv38RsqYxK0cU4F+ZvwD3Ka8RGd+J9aieUSv6kfk84jdJfStm7E07ayyk2ZdGUouD8lTt0JdLP3xDRqcby4Ga/u7ydZu2/DmmwrVqkAIkBJ27lQYGxPYtqC315vCW6m4Bx4jSae9x8PpWNqPcu35TYwXnif4V39x0MUgV6+mftvdB10M56NVtkGDBsfSqFvP70Tl0+V0htCoZ/G8h18r1XQKqWlYl3+A+prrwDT5V+MhXgzcTe9lKUQyzsCOH7Nk9xNIRWPgshAUiwjb9o7vMBeZaeURrUHoiyM5PdExRfKCdyo0uiIaNPB4L58L5+JYW7XKYdMmlUjk0PdXq15XhKJApSIOZvL6fJJczsv7XbvWoqVFnpYgeT4HlL3XOC0B7eGHH6a7u5uvfvWrGIZxzNfnzZvHP/7jP3LPPffwrW99qzHN6CxR9w+ipGawFy0Gnx9RraAEAzgS1AtQ7JzWiWGaLPizB/npI7ew7sUp+odfZF5pC8uD++gsjMHCefCBq0DTkM1+rGUr0Ab3ICpllEL+jBeEs64M8yc/RLinboW6GESEBg0uBBeTtf1XbWufmhL89KcaQkB3t8Tn82zt6bTgZz/T6OtzDz6PaXq5ECdyvp2K2Y0L36MPH+Ni0H/4JD7bPeRiOA+tsg0aNDiWRt16/iYqnwnnawjNMcxeK9dch/9730bbsQPtza0EB/eSWbKS1zd/FH9nArfZe02DV3wEgHmTG+jdO4KeCFG7/kbU6WnE9NQJjyu/LwOTJURvF/7DfryMRJD7R3j6WwV+Othx3ob2nIpGV0SDBh7v53PheCIiwN69yhE1sN/vuc5ME2o1L6vX5/MGcjkOXH21TUfHqQXJCzGg7L3GaQlor7zyCg888MBxi5BZDMPggQce4Hvf+955O7j3DQdCXo11z6G+vRN13yD2nHk4CxaC34eYmDjjYudkE5fO9MSYbS9N3xMlm51PQs7QPLwZ9xtfwxmY4/njDyATcdxKG5XPfBZisbNaENbuuhdNUxAbNpzSlXExiQgNGpxv3mvW9gtla9c0sCzvX8M4ZGt3HO9ace21LiObsyh5z70wYTWd0zXkRC4GV1GO2zp1obJ0GjR4v9KoWy+gmHUKLqSz1njhefTXXvWurQdej//HP2RFQWfP6k8cfJytm7x52SdYv/c24p+eoGt5DGkGvGyz4xzXbF28bV0b974dRRksEZkXYMEC12sBzefZOxPmhy814+/ktPJ2G+6NBg0anE+OFhGProGLRYFpepvE0ah3/ZmZ8abFX3edw8c/bp/wuQ7nQg0oey9xWgLa+Pg4CxYsOOXjFixYwOjo6Dkf1PuNw0NenXnzUXe+hbZtK9gWorvnhMXOcUWy05i4dLYnxqGTLYkdX4GMJ068s9nXj0wkvQJi7xkWEKaJ9bFPYN1w82m5Mt5rIkKDBqfLe9Xafr5t7eBZ2l0XbFugabNfk/jcKh+3H8ZX3kh+d4kiYfZ2rMJdew+336VzPE61MDph61Q0ghgauiCtUw0aNDhEo271+JW0iV8gZ+2JNiZEFZaNbuDtqVuh+9AFOZ8XyEQCc3kQeeDTJzquJx6drYuTTM5byaKdTzL5JqhWmEWdWZzRKdbLe/F3JugNzRAoZ4iH4kDTMXm7J9ukPome26BBgwZnxPFq4E9/2gJg0yaVdNqrd1eudPjYx07PPXZBB5S9hzgtAa1UKhEMBk/5uEAgQLlcPueDej9xdEFgxxOg6yi7d6Ht3oWTTB5b7JxEJDvVxKXzdWLIRJLMkpX4f/xDRBWMliN3NstmkicePTf75+m6Mt6rIkKDBqfL+9Xafrq29tkhAsUi1OsSTfPs7bmc4N/4vsO8bU+izmuhsrgDO1Xg6sL3kYZFzTwyMPp03bsnbJ3K5XEvUOtUgwYNDtGoWw/wK2wTP9/O2hNtTBgtETqjY9QnMpT8yVN2IshEEvfA87lAiuTBurg3NMNU5yUE3SLx4a3Udo9Sj5ukVt3Ji7+8nQ+N/jPzJjfgs4rU9BC7Wlfxo+D9ZLPKwXvwyTapP/Wp9999ukGDBheGk61/b7/97NbEjWzx0+O0BDQpG2/UheKYgkDTsJcsQ3R2ow7tw/rsb1FbdukR33MikUyUimhvbj1p+Gk223TOJ8bsIvL1zR9lRUFn2egGOqNjdMwPYB8Q+87E5Xa0m0OkUyilPCIYOaNC7/0qIjRo8H7ndGztzc0uui6o18XBAQZ94RnuDr+A2uldM/0AMT9iWiKO02p5ute1E7VOKalpah++vZFz1qDBBaZRtx7Je6FN/GSZbh3zA1y1IsK6bZy8E+E4G9C5gVWQuZubak8dIY7tb1/ORv1aPvmbCUK9Ca555jssGnmSeryZTLgbfy3Hop1PUu4WxGL3A6d2b9x+u00o9M68Xw0aNHh/cLz179muiRvZ4qfHaQloAJ/+9KcRQpz0MY2C5cw5UUGAVcdtbcM9ajLUSScEvbQBbAt34MhpU4dPXIrFkud8YhxaRKrsWf0JRoevQh8dYuHcLm55sO+0XW7/H3t/Hh3XfR34vt/fOafmEVWoAggCBGeKouaJIinJlmRKpgZqtmzZlhM7nXSnvW5y++Z20i+dl3R3hpe0u7P65SavHSfOaMuyLUuiKNOSrMGUSJG0JmqgOE8ASIw1z3XO+b0/igQnkOIoTvuzlhbIElA4B2Cd+p2992/vw6s54r4KX7J+xALnDbyNClYgeMT2UyGE+CQTlbX/6q+2ytrXrjXHA/b3zBlmxvoiOvrJU+pOtHp3oq1TzbvvpX7XfWf03IUQLbJuvbAcq6ebveQe7ns0xs2Z+jGrLiZKQKdXP8ev7lhLtDF2SHBsxo5XyfVECPc+QpIxblJvsJM0TdWBT2kKKoAHxSLeIMlt6H3TOY+VpM5mlQTQhBDnLOktfnyOK4D2zW9+80wfx0XruJq8Npzxzz/WWHJGR8Cyjjlx6VRfGAffRE6KV7hi01NMG1iNLpaoPRGmGbyB/IIHKZf9n1jldng1x2XvPEVw63J2zEwx6/oedDZ3yPZTIYQ4Hsdb1p4kgrEjhD6OKXUnXNY+wdYpT2f6kOu5EOLMkHXrhemTerodq+riaAlos1bjqvff4G3PjZRV54TBMSOXZWp7kVpwCruHW0lnrxc650SYFtxNfV+y5ZOqN9raJGArhDi3SW/xTyYBtHPAsRYEh/cbPeZY8kQS+9LL8L7+2jEnLp3KC+Pgm8grNj3FZVuXUQymKSd6cDKtaUjdQCj0q8escju8miNYHeOKwioqsTRbix10uy7eVPqQ7aey7UkIcSI+qaxdc/xT6k62rP1C2DolxPlG1q0XqFPo6Xa0BDReD1GrQmevydbSxMExN96GEQ0xty1H7xwvtZrC79cESzmUeyDZcjxJ6v0tBIQQ4lwkvcU/2XFv4RSnzxHTM09gQfBJFWv1pQ+gw+FjTlw6lRfG/ptIZzjDtIHVFINpSqEOKhWFjgZQnQ5tG9bwmcvu5umVHcDEC4jt2w+t5gjWsviaJYrhHhoVqNXA6514K5UQQpwuxzulTsrahRDi3HAyiYmjtkxpNCEYYGZPnUnt9oTBsYPX3kEgcIxky7GT1OZpOHshhDjzpLf40UkA7dN0jOmZBALHvSA45g3fCQTjTuaFsf8mcv2Ps+hiiXKih0pFUS7DrFluaxrnQD933ThCLZQ8apXb4dUcFX8bdU8Ys1TA6w/g37e2mWgrlRBCnDYncM2UsnYhhDi7jkhCH6ejJqCLBRo3LsTMjBH0Dx81OHa8yRap3hBCiAub0hd4B9WRkeLZPoRxvie/P9689IjKsaP0+PJ6TRpH6ZlzsouIU1WtwotP5Jnxd39I3TapRjuYPNllzhwXT3YY5bqUf/8P0YnkERM2D/bkkwd6oEWjmsve+R7zti4jOjPFrOujuNncJ/58LjTH+n1fyOS8Ly7n+3kf67p2LOf7eZ+sg887lYqc5aMR4tx2Lq1bz4STvg5+QhL6hJ5j3RpUudQa7nLDjdTv+Dy+F3925OMTPPeprL0v1vcAkHOXc7/4nEvnfrLr1pN1Lp37iTqedaoE0D4lKjNG6I//CG2a481LAdTIoQGnw53L/wCbf/99/D97HtWZalWeHUdA8GD7p3CuW3dgCucXzR9xo7MKX7OCEwgedQFzoTqXf99nkpz3xUXO++IiATQhjt+5sm49U072OngySeiDHXwDmWTiINiZTkxfrO8BIOcu537xORfOff+99tq1rXvtUEgzf77D0qX2Gb21PhfO/WQdzzpVtnB+So41PfN87fHleewBrHCrnF0do5z9aI4sczdIJB6lnvkclAvUQ1EZHCCEEEIIcRE72gTN4xk0NfENZAdLlyaPuIGUoS9CiAvJsmUHdnt1d7sUCmq8l++jj0r7kZMlAbRPyTGnZ56vPb5OYRrSwQ7vxaYTSdzONPo8jVwLIc4Pn3ZJuxDi4tHf38/f/M3fsGbNGkZHR0mn0yxdupR/+2//LV7v4TPWL26fdC0+lSS03EAKIS5GmQysXWuSSunx6fH7P65bZ7J4sQzAOlkSQPuUfNL0zHOt0upEbiwlYyeEOJ+crZJ2IcTFY/v27Wit+a//9b/S29vL5s2b+YM/+AOq1Sq/+7u/e7YP75xwvNfik01Cyw2kEOJilcspymVFd7d7yOPRqGZgwCCXUzJl8ySdNwG0CyGTd7wTfM4mubEUQlzopCJBCHGm3XLLLdxyyy3jf+/p6WHHjh088cQTEkDb53ivxSebhJYbSCHExSoe14RCmkJBjScOAAqF1v19PC7XvpN13gTQLohM3mna8ghnbuuR3FgKIS5kUpEghDhbisUisVjsbB/GOeFEr8Unk4SWG0ghxMUqkYD5853x+/hotHUtHBlRLFkia91Tcd4E0C6kTN6pbHk8kxVicmMphLjQSUWCEOJs2LVrF//6r//6iWtWj8dEqU/poM4CyzIBKJcV1apBT4/GNA+ccFsb9PcrymWLzs4D1+JMKUzfwsdJLryLBFl0WysJfaw9KJ2dsGiR5vnnTQxDE4tBPg9jY4q773bo7DTP1GlOaP+5X4zk3C9Ocu6fnkwGsllFW9uB4pqHH9ZYlsuaNQZ79xqEQpp773W5/36N13vmju9C/72fNwG0iVyMmbwzWSEmN5ZCiAudVCQIIU7Ft771Lb7zne8c83N++tOfMmPGjPG/Dw0N8Wu/9mt8/vOf5wtf+MIxv7bZvPAHKDUaDqEQBAIm2SyHXIuzWUUgoAmFbBqNiRLHHcyf395KHB/HsKm77nKwbYt160x2725d5++80+auu1rP/2lrXMQDsuTcL05y7mfWJxXXPPSQw623Hrlz7Uxf/y7k3/t5G0A73kzeheREKsROZoun3FgKIS5Eh18PpaRdCHGyvv71r/PAA8fuXdvT0zP+56GhIR5//HGuvvpq/tt/+29n+vDOG8d7LT7VxHEg0Pq8xYttmboshLjgHM81MpFAimBOo7MeQDvTmbzzvRT+4BLI4yl3j8U0zzxjsmaNSakE4TDceKPD/fc7n7jF81wqdb/QSz+PRs774iLnfWZVq0x4Pbz7bkdK2j9FF+t5iwtTIpEgcZwRmP3Bs3nz5vFnf/ZnGIZxho/u/LJ0aevmbt06k4GB1rV4yRJ7/PHT2VpEbiCFEBcaab90dpz1ANqZzuRdCKXw+0sgj6fc/cc/tlixwiCVcunqamXznnvOwLbd48rUnUul7hdy6eexyHlfXOS8z5xjXw8bUtL+KbpYz1tcvIaGhvjqV79KV1cXv/u7v0smkxn/f6lU6iwe2bnjk6rDpLWIEEIcnVwjz46zHkCTTN7x+6Rydzj1KLSUugshLgTHm5WThYUQ4kxYtWoVu3btYteuXYcMwQLYtGnTWTqqc9PRrsXSWkQIIY5OrpFnx1kPoB0vyeS1HKvcfe/e0xeFlhtLIcT5TLJyQoiz6cEHH+TBBx8824dxXpOelUIIcXRyjTw7zpsAmmTyWo5VISZRaCGEaJHroRBCnP8+qU+aEEJczOQa+elTWusL+i5iZKR4tg/hlHi95gn1jnnyyQOTOA6PQh9PD7RzxYme94VCzvviIud9Zp1r10P5fUMqFTnLRyPEue18X7d+kpO9Dp7MdPlzzcX6HgBy7nLuF59P+9zPpWvk+fx7P5516nlTgSaOj0ShhRCiRa6HQghxYZDWIkIIcXRyjfz0SADtAiNDAIQQokWuh0IIIYQQQojTRQJoFyiJQgshRItcD4UQQgghhBCnyjjbByCEEEIIIYQQQgghxLlMAmhCCCGEEEIIIYQQQhyDBNCEEEIIIYQQQgghhDgGCaAJIYQQQgghhBBCCHEMEkATQgghhBBCCCGEEOIYJIAmhBBCCCGEEEIIIcQxSABNCCGEEEIIIYQQQohjsM72AQghhBBCCCGEEOeDTAZyOUU8rkkkzvbRCCE+TRJAE0IIIYQQQgghjqFahWXLLNauNSmXFaGQZv58h6VLbQKBs310QohPg2zhFEKcMZkMbN+uyGTO9pEIIYQQQghx8pYts1ixwsI0obvbxTRhxQqLZcukJkWIi4W82oUQp51k6IQQQgghxIUik4G1a01SKU0qpQHGP65bZ7J4sS3bOYW4CEgFmhDitJMMnRBCCCGEuFDkcopyWRGN6kMej0Y15bIil1Nn6ciEEJ8mCaAJIU6rwzN0Ph/jf163zpTtnEIIIYQQ4rwSj2tCIU2hcGigrFBo7bSIx/VRvlIIcSGRAJoQ4rSSDJ0QQgghhLiQJBIwf77DyIhiZERRrzP+5xtucGT7phAXCQmgCSFOq9ORoVOZMcztW1GZsTN1mEIIIYQQQhy3pUttliyxcV0YGDBwXViyxGbpUvtsH5oQ4lMiDYmEEKfV/gzdihWty0s02gqmjYwoliz5hAar1Sq+ZU/jWfsmqlxGh0I05y+gvvQBZPqAEEIIIYQ4WwIBePRRm8WLbXI5RTyupfJMiIuMBNCEEKfd/kzcunUmAwMGoZA+rgydb9nT+FYsx0mlcbu7UYUCvhXLAag/+tgZP24hhBBCCCGOJZGAREJ6nglxMZIAmhDitDuZDJ3KjOFZ+yZOKo1OpQHQKT8O4Fm3hsbiO9GJ5Jk/eCGEEEIIIYQQ4jDSA00IccYkEjB9+vGVtxu5bGvbZjR6yOM6GkWVSxi57Bk6SiGEEEIIIYQQ4tgkgCaEOCe48TZ0KIQqFA55XBUK6FAYN952lo5MCCGEEEIIIcTFTgJoQohzgk4kac5fgDkyjBoZhnoNNTKMOTJM84YbZfumEEIIIYQQQoizRnqgCSHOGfWlDwCtnmfGQD86FKa+5J7xx4UQQgghhDgeKjOGkcu2djlIIlYIcRpIAE0IcVYcvKihszU0gECA+qOP0Vh8pyx4hBBCCCHEiatW8S17Gs/aN1v9dUMhmvMXtBKygcDZPjohxHlMAmhCiE/XBIsavWgRjbvuG1/U6EQSRwJnQgghhBDiBPmWPY1vxXKcVBq3uxtVKOBbsRyA+qOPneWjE0Kcz6QHmhDiU7V/UaNNE6e7G22aeJ5/Dt+yp0/oeVRmDHP7VlRm7AwdqRBCCCGEOJ+ozBietW/ipNLoVBp8fnQqjZNK41m3RtaNQohTIhVoQohPzRGLGkCn/LiGgWfdGhqL7/zkLZtSli+EEEIIISZg5LKochm3u/uQx3U0ijHQj5HLyi4HIcRJkwo0IcSnZv+iRkejhzyuY1FUuYSRy37ic0xUweZbsfyEK9iEEEIIIcSFxY23oUMhVKFwyOOqUECHwq3eu0IIcZIkgCaE+NQcdVGTP75FjZTlCyGEEEKIo9GJJM35CzBHhlEjw1CvoUaGMUeGad5wowynEkKcEgmgCSEmdCZ6jB1tUWOMDB3XouaoFWzR469gE0IIIYQQF6760geoL7kH5bqYA/0o16W+5J5Wuw8hhDgF0gNNCHGoM9xjbP/ixbNuDcZAPzoUpnn3vdTvuu8Tv/bgCjad8o8/LmX5QgghhBACgECA+qOP0Vh8J0Yu21o/nkLlWSYDuZwiHtckEqfxOIUQ5x0JoAkhDnHGR39PsKjxdKah4Xzil+6vYPOtWI7DvsqzQgFzZJj6knukLF8IIYQQQgCtdeOpDAyoVmHZMou1a03KZUUopJk/32HpUvu4c8qZDAwPn7ngm8qMnZYgoRDi+EgATQgx7mhTMh04rimZJ5KhO9lFzUQVbFKWL4QQQgghTqdlyyxWrLBIpTTd3S6FgmLFitbt86OP2sf82v3Bt7fe8lAo6BMKvh1XUEym0gtxVkgATQgx7mRHf5+uDN1xBd9Oc1m+EEIIIYQQB8tkYO1ak1RKk0ppgPGP69aZLF5sH3O9uj/41tFxAsG3EwiKnfEdI0KICUkATQgx7mR7jJ1qhu6pp0xWrbJOKPi2v4JNZcYw33kLpcDpnSbBNCGEEEIIcUpyOUW5rOjudg95PBrVDAwY5HKKREJP+LUHB9/SaYXjHF/w7XiDYqeyY0S2fApxaiSAJoQYdzI9xk5Hhu6FF0ySSffEgm/VKr4fP4n/qR9iDPQD4HZNpvbwo9QfflTK14UQQojTQBqoiwvZ0QJK8Xhr22WhoMbXtQCFQivZG49PHDyDw4NvavzxowXfMhko7cowY+XxBcVOaseIbPkU4rSQAJoQ4hAn2mPsdGXokskTC775lj1N4J++i8pm0LEYAEZ/H4F/+nvwemWLpxBCCHEKTkd7BiHOlk+stPqEgFIiAfPnO6xYYZHOb6FX72KX6mWkMZslS46dHD44+BYMHnj88ODbwa+x6FCZr32cIzglwRR/FTPSepFNFBQ7mR0jsuVTiNNDAmhniGTrxHnrBHuMHStD12GNksoNojITP8f+4Ftv76GPTxR8O/g1lWQM78rXoFJBJxLoYKj1hYYJ5TL+f/4HPCtfRdmOZNiEEEKIk3Aq7RmEOGuqVZrffxprzRq8dglP26HrwP2BNc+rr+B9/bVjBpTuv2WYz37/v5D4cBWWXcO2/GQuW0T8lj8Ejn6Dd3DwzTAgHG6ti0dG1CHBt/2vsa62Crc0XqK78DH6XZva7ijRyybjzLlkwqDYie4YOdUhYUKIAySAdppJtk6c6w7OyI2RPGqg93inZB68SADo9IxiDg1y/bY3+Gz8Pbq+XTxqEGt/8C2fVyQP+lYHZ+gmek3dMa3IQ2NZLAWuz3fgmH0+zKFBKORxurtxunskwyaEEEKcoFNtzyDE2VCtwqbfX0b8tRWMWWkawalMy+WYMbIcGg3wenFXvokznMG7eyOqqxM9+xKwrAkDSon//odM2v4CzXSMhj+Nt1akY/sK6v/dpfw//+qYx7J0aSvI/NZbFgMDBqGQZskSe/zxg19jtw39mEsHXqEcnUQ4P0CjUEN9vBGrWIBYbMKg2InsGDnZIWEnSgpIxMVAAminmWTrxDnroFJ1t1Bm52iEN/RNvJp6GE/Uf0qB3qVLbcxGFeOpZ5i+ZzVTix/Q4Q7iifXidF6JqlQmDGLtD7698IIH11VEo/qIDN2TTx75mnr+zRTXFxPM0DtQ9To62HqNqUoFKhXcVAq3qxt8fsmwCSGEECfoVNozCHG2vPhEnsmvraEYTKPjady64v2hTgBm/vCHDBZDbGtMwWmmuXLsI3zFPfhCH6OvuBw4NKCks1m8a1bjxmIYyXb8AFEfrgneNaupbtuKO2PmUY8lEGjd+919t2Z42D0iqLT/NTY7OcK0gdUUg2mGknNJeTYQzvTj2g08Q4NU771v4jYqx7ljRGXGIJdDW+YRWz4bwwUaTpiCbiN+Mj/wfY5WQPLwwxrTPIUnFuIcJAG000iydeJcdnDvg42VKQz2FbmWZQRDmpfbvnpEoPdYWaTD+0oEAvBl749QweVU50WJbq1g6gBqZBB7RxTnssuPGsRautQmVMuy4Y0sw6NJdCIxnqE72mtqhAS/qHyOXu/HeDIZcBwAjMwYyjBwps84pNLtdGfYTsXBP9fOzrN6KEIIIcSETqWBuhBnQyYDG1fnmWcVqcZ6ME0IBjWg2DPqo3tkgJ3e6yl1dBA2ytSKUWrVGsENA4RnzUQHAodsl6ysWk+oWMXt6DjkhlmHwxjDw5h9u48ZQNsvkYBw+MjXy/7XmB7L4muWyEZ6cA2LHdEr8QRmkZiXQVUyND972zFbkBx1x8hhPd6MoUFUqUjz8itpRhPsfr9EbdcYq9NLeeuvu04pkX60AhLLcnnoIefEn1CIc5gE0E4jydaJc9XBvQ+qVoSx3VWIRmiYmllDq9k6ZwnQzrp1JjffbPP660fZhsyRDVez8xYwOO+zTF/5Jt7JaSKWhbXNwY3FoVbHHOjHmTFz4iBWtUp82dN8+cO11Cuj1F0Le+oizKVfhUCAvXuP/pp6LfUwn/tMhZ5fPImxZwAAp7sHlMLtODQydaymqp/kdJWjT5SdW7RIc9ddjmzvFkIIcU45vD3DRBXiQpxLdu5U7MgnafrC+Ot5Slar0srn0/iGR6hWoZFOEQxqXIIU23poH/4YN1ekPpzHFyxijgxTvP0efvBSBztfmslvl4JQKaM6AiSTGsMAVSqh/X6cnimndLz7X2NvPJsk74TxVvMUzADlMsya5ccXNND+xEmtXeHIoQFuMITng/VYO7bTV6/RPxyhr3cpu694ELMCbzybJzY8wpIvR05ot8axCkjWrDG49VbkeiEuKBJAO40kWyfOVUYuiyoUMCplrB1DzBq0MfweSpFOmt4QwVqWaDTJwIDBU095ePttc8JtyI9z4M240dndyl69toINsTFqpSq+Wd3MmuFgebytrZU+H0axgKpVwbaPCGL5lj2N7+kfo/J5AqUigXodNr1FY8cHlP/kL4jHA0d9TXmifuyvfY3C1+7B2LkDpcDpnYb3pRdax2hZn9hU9VhOdz/DibJzzz9vYtuWbO8WQghxztnfq2ndOnPCHk5CnAv2r9dWrjR5e2ea56o3cT/PEuyAhj+GlS8QcoqM+HsIWXUq+75uKDkXX61AID+IZySD6klQX3IPP2o8sm+9NoudvYu4dMfPKOyFbDNMu6+Ikc9TX3zncVWffZLWaynGpicWcfm2Zfj8io6pEeYmcie0dj082Tvh0IBJk2haJs1che/Vv8nYzOkEexJ4m1Xu3PEvTNq1Gt/WEtbGAMYtxz9861gFJHv3SgGJuPBIAO00kmydOFe58TYYGqG2pY9RN0mmGcLXrJEqf0wpOYWKv41CQWGamo0bjQmzSBtez+HqA2/Gmz4y2DIUZlJQcYm9gaZpMbipiONJcWV3N+aWzVCtoj0eKJUwi4VDFgLGhg8JfPdvMfr6ULUq2uNFx+NofwDfL17BeeJfSHz918dfU6HaGHE3Q185wd5GO/fd13pNaQ4tXT+RpqrHcjr7GR4tO2cYWrZ3CyGEOCft7+G0eLEtjcHFOevg9dqsWQ7PfPwIdhU+N7aKhK+fnB2mcsuDDO62uWrvzzAMqPli+Ot5Ckac1XMf5Jbfuxk9tTVca/Uf+4hENJYFyxb8CaYJU3atxhoZppnyUbzx8xS++Uen1DNsv0CgFUT7YfZ+1j0Nc0ZXo3cOsKsZpPOhe9CfsHY9WrL3wcuzhI8yNKC5t8iYbsNMt17MV2x6isu2LiPvT7OXKUy3s8RPYPiWFJCIi40E0E4zydaJc9XomMKsKowA+AMatwxNp5U1Gh1VjBQV113n8MEHJtHokVkkd28Wt5FF9SSoZqv090cIhQBvFE+xwPDkK5i0cx35rVBeNJ1woYCxexduJILyeg8Esfb1ZPB/9zuYGz5CuS7a7wePhZHL4cbjaMuD583V1O9/iPvuCDJj1TIar6yBUpnLjBBbOxfhlu+jWvUcmRw7SlPVTAZye4/vBuB09zM8WnYuFoPdu5Vk54QQQpyzEgnkPUqckw5er/WGR5nZlaXNTfJ83+O8Vrubm+eMcMVnY9zxpRh9P27yi3/yck11NdFqP0XCvNm2lOAX7ydyjQcNjL6ZofRuhaFmkqyRxONJ82H3t7l67iay7/ThTpnCsDGb0P9zarsSDrZsmcWra/ykrvgKg57Po8ey7Comuckb49HAse8fj5bs9ZdTfDkUOmJogCoUsGIhXNooFxS94dHxAQajqhMVALMrhVPSxz1861gFJPfe60rQXVxwJIB2mkm2TpyLCjuzDNdT+BIh2puDJMlTDvjYas+lXA9h5jMsuTvOzTfbbN9ujGeRgtUxgrUse/NBrsn+nNDox5jb6gSsIFPKUxmadCVGsUDFCPHe3C+S83XQvulNilsGMbpmYN5zH80FC3E7J42/Afue/D6+Z59G5XLg80G1imo2WyPELQuVGcNNpcFuYuSyxH75ArM3PM9Hnk7syd1MMnNcUvgR7/zDIC/yVe77emz8PA8fbuAkkq3s3JMTb8UMVscmnFx0uvsZHi07l88j2TkhhBBCiJOQyymahRq35n/EjL2rCekSn/OH2dC1kKetR7jn/+zl6qtba6y7H/awzPslfvT6kvG136U3x1l4s82ODTW63/wJ0R/+gt/cWqDkibM+dTsrPI+wZUuALcylXp/LwpRDd/rUdiUc7PCErUMS4kn8I4p16zhmwvZYyd6VH6W5a94Ckq8vx4FDWpoYS+5hHnFWrFB0FLJYtRKDvimUazBrlksgANo4seFbRysguf9+Wd+KC48E0M4QydaJc0lWJSgYbQRjCXL+uXjsGk3Lj1EqYhQ1D3wjzGWfab35zZ/v8PLyJgu3/ph52VUYlRKh/CCdwRyGz8XIZfG6WeZUB4hldrDbN5uX2h7grZ09rCg/TtO6l6usUSzamBeOs3Tagezc/p4MOhJG+by48QRGbQ/KdVGlEhgG2DaGZaGzWXSthrvyTXZUOnHbO4j6GiTHhkhVt3NPYRM7/uZt9MgVGI8sxfvLtYcMN2jOb/VvWLYsckR27uXlTeas/SE38cYRn08gcNrL0Y+WnRsbU9x5p2zfFEIIIYQ4UX6/5tKPnqJ7cDl7rDQVTy8d/hyXZ5+jOUXR2/vI+OfuL3LILA6Ty0Xw+zWvvw7f+paP+R9+j+TOvyVcqzJDgVOD2Xs3YKYb/JPn1xgcVMyb59LTc/RdCYcncY9HaVeG6FCZSG+8FTzb53gStp+U7O1f8CDh8MQtTZbSWvNveD1BthkhQJ70LC9z5rSe60SGb6nMGOFcli8ubmPx4uQhBSRer0mjcVw/CiHOGxJAE+IiEO5N8FbXQq7tX4bWUDDjRKo5AuURNvUu5bOXH4jgLF1qM2ftD2nbsJxRM43ypZhlrSdaHIWaiRtro56ronWNTnuAj0LX80L0EXa8ZWLbcPnlCQJXxPZl5xRwIDtn5LKochk3mUJ7vBDzoKtVVGYUHAc8HvB60T4/qlTC9/JL1HJlCipFwKfpGPuYdHYTtuklWM/TO/oOsX9+E/PZf0UHgzRnzUGHQ9Bo4luxnFIJ1n70q0dk5xZu/TFtG5ZTW5jE292NKhTwHdTv4Uz0M5woO3f33Q533SXbu4UQQgghTtQvf5bn0swqhkhT9HYAsKXQSdlSLOINktyG5tBg1v4ihyefbG1/nBoZ5Y7h7xNuZNlrJzCCPvyeOrFahsUjT7Cs8wEy3nZmzJg4UFXetIfJbz2JuXEDynbGk7KNmz+DUaui0u0Qjh964PvamcxY+SZf3lylsTPM3hkLeX/OQ9iewHElbD8p2Rvr9FO/9MiWJgABDgQTre/fQHr1c5hpB+1EUdkDw7cAzO1bJw4K7juHg5PX3vkLSBzn8AEhzlcSQBPiIpBIQP2++3nm/1Fc1reakNvPgBHmw+h9pJfed0hAKFgd4ybeoLYwSbu/ndHNWep7oFbVYDgUQpMo+xQeTx1/NYNP1zFqFWw7SCAAl13m4PNNnJ1z423oUAiaDdx9gwbc9iSqVIBGAzye1tbLy6/ASbRjbvoYT8AiqvPUyxHain00rDDB6hjeZpVCKI2O+1GD/SjTwpvJQDiM9nhxo1HMVW+g3PuIzmg75PzmZVcxaqbpDLfj9Wl0yo8Dh/R7ON39DCfa3t3ZKZk5IYS40DUaDR555BE2btzIM888w9y5c8/2IQlx3stkYPdru7gmsIdd0RmUqq1crGVB3R9jcnQH+ihbEA/e/jjb3k6i3E81EKXuhqEBgYQFnjoz69u5a9JbfN+8E/fQ+BnVTI07Bp9izh//A/7+7ehwGLt3KtrrJfCd/x/+J/4Vt6MTIxqB6+YfMtXSt+zAVHvfrG6ym4rM+ug5bBtenvzV40rYHm+yd39Lk6M9B9+4H53SqIMr1W6/AxoNQn/8RxPu1Dj8HNwJktFCXKjOywCaLESEOHG2J8Bzia/yovduYk6GvJmgHk7wNU8T9pVyq8wY1vvrUZkM3hkzGN6q2D0YotMwMJWDg0Uh41DRQdLBOjocpTPaZP6sEcZIYppg2wpoBc8OL0HXiSTN+Qtab7iJdpjaxNiyGbRGp9I4s+dgz52HjkSgXsPYvg2rdxoztm1m51gUVauhTAN/LUvRbMNqj2IGiqhGEywXymWcVArluJh79xApVklPHWO0kBgP6AVrWYxKiWZoCn7/QZk9jxdj906MnTtwEskz1s9QtncLIcTF5S/+4i9Ip9Ns3LjxbB+KEBeGahXr+89w64cr6SltZLK1ncHobPpil+IaFt5sjoYvjHmULYiHbH/MtNZkSrW2hFbLmkB+mFhzBMtpcMeW/03XzEF+lnuYEb9/PFB1+QdPcYfzI7z1YdxkEgwTa+cO3KFBVCaDyuexZ8xEGeYhgaX97Uz2T7Wf2QaOJ0V+K3RsfZNg4i6WLIkfV8L2tCR7Jxi+5X3phWMGxw4/B2DCZLQQF6LzMoAmCxEhTkwmA+++a3L11S7hcIxaLY7frymVXN57z+Se24t0vr6vDDubwdq8EWdwhELhcgLBCAU9hfbybpQCywPecgmzVqES66QeShDsbsPc3vpeBwelJipBr+8bye1ZtwYdjWJfeTWqfzfN6TNxp05rfVKzifXO25hDg+A4TAoX8VVymMUarq2oWWF0RyfJpIZ8HaVdXNMHClSthg5HoFbFW8lz5ZUuP3i7tZU0GtXsLiXI2RGmxnMEAu1g21ibPsbYsgXVbBL4+2/T3LJ5PMsmAS8hhBAn6xe/+AWrVq3ir/7qr1i5cuXZPhwhLgi+ZU+TXr2cHf5J9Os5TCl+TPfY+1g0GfL2EHWGcW5cgnGUII7WGteF4WFFW/t0cpFu2vK7iVgWbUaBeH0EQzuUfW20TYvxWOhppqdc/sV9nIEBgw5rlM+H3yCdDKG2mLjBIJgWNBtY27a1hmJpF++6NeiZM2m2JfGsW8PQdXdS7ssxPVvGnN4NtCrm5s1zqU0OoXcPMPvhjUSnxHGrbejAocd/eJ+105ns3V+pdjzBsfGWLN3dhz5H9MSGDwhxPjrvAmiyEBHixB2cafP5IBBoBYQMo1UhZv7kGXxv7cs0TZmKuWkT/o/Wc627jVKkk3ogRiWQwNMoE3eyZI0Qg0YnighbOhYyZLePP6cezdCht1MqQV9jBjfdFzv0zXyCTFfw1Zcwn1uGHhlGR6NY77yNtXULzsxZOLNmowoFkpF+anMmoTM5AqMDGMEKVE1UtYb2+1H1Oqrmotw9YBhow8TtmcLtt9vk0/ZB2bkk1mdvZMbIszDiYg70Y25qBePtOZeggyEpQRdCCHHKRkdH+YM/+AP++q//Gr/ff7YPR4gLwvhAqslpop4UmzYlcA0PXaXNtI9uZm80yeDCe3AX3EcswyFr0GoVli1rTWbv61MMDxv096aYOeNL3PTBd/BVs7S7w1gecAJBfFdcRu913agRLze5q5j6m58jo9pJ5Qbp+nax1dN3pxdVr6ODFqpQRNVraCuM9vvRloWxeTN0T2V3Ic53/qzMsN3Jr26O0p4rMeV6P9a+u/FANYtV2kvoR98+pJfa/sTzwf3GsCzsuXOpPfgFdNfk05rsPZ7g2P6WLKpQQKcOXNtOZPiAEOer8yqAJgsRIU7OsRqNdlijJD9ePZ5psj76oFXRFY1h5upY1SKBRp4tvbeyt/0yJu1cB46Nb1Ib73EV6/U1BKtjfOMxH3Pe+gE9r/6AttIAhqFpdnYTKD+CW330iIaiB/dkaN7/ILbt4lm3BmfjNpy+QezeWXD9fLCs8cyXt9HA/swi1HPPYOzYjhsO40ydhqqUUdUq2uNpBc8AXAft9eLtbOPRSw/LzgWWYi9z8Kx8DXPLFrTfjzNjFs6cS8DjOWoJ+slMWBJCCHHx0Vrze7/3e3zxi1/k8ssvp7+//7i+zuMxUeoMH9xZZFnm2T6Es0bO/fQwygXMagW3p4dL28EwLPr6r2TQ28uk6nZWXfWbbPHdQO2vIByGG290uP9+h0AAnnrK5IUXWr3PFi7UrF8P27eb/O/Iw0yOrmV+9Tk8dhW0geX3geXiug60xTH6++n0FkjPSJPb1s5YPYIesmnr7MG/czPacTBKBTAMlHbR8TgqHodKmdqGXfRbaeqzkyQ7kmwrLsK3ZRn9Bsy4NooxPITxzjtgmug5l6BjUYx8gcALz2NZBgCeF57HTSQwykWMnTvxrFqJ/+Wf0/jKV2ne/+BxN+5XmTFUNotum3gtq9LtGNEIqlRCB4MHHi+VIBLBSrejE0n0okV4nn8O1zDQsSgqX8AYG6F59714OluVa/Jv/uJ0oZ/7eRNAO9mFiBDi6I1GBwYU98wZw9hZRnd2o6pVjP5+nGCYMbedejHHW/pawMQ/5uFH3V+l2vtVHl7Yx72xlUx95x3uyb2OpUN4PwTPxnfQnjyNyTFME8ziLhr/+F0K2ovnG0dWc+0PSKl0O7mlj/Fi+W5GB97nM83vMFKaSfcHDrNTw5gWaH8QVSzQvPU26kvuxv/UDzE3fYwqV0AZ6EgEt2MSeCxwXFQhD6Z5yM8gkdCt77k3S2PxnTizZ2Pksti9U+GgbNkRJegTTBo6vJmqEEKIC9+3vvUtvvOd7xzzc37605+yatUqyuUyv/Ebv3FCz99sOqdyeOeFRuPCP8ejkXM/dSoUxQoE0dkcKpVm7lyYOhWcPVX29nfwcWM6fsulq6u11n3qKYM9ezR33dVk1SqLZNIlmdTYNoTDBl6v5tq9y+mov0/R8aONCBrwVG2s9etxLQ9uRwc6ECRvRvnh3yueemoS8zfdzB2VZdTDaS5L2kzJboBaDTcQBL8f1x9AN5q4NRtdKDE6dy7+yW2Ay7ZrHsR1Yc6elUwrvI5VGEMV8rixNhjcixOLQ7Id5bqoV19BaWgmU5jDQ6jt23FDIfB6YXgI86kfY9vuhLsmMhkOSh4f51o2HIfr5uNbsRzXddHRKKpwYDJnPRyHhkPjrvvw7Ut+q927cUNh6nfeTf2u++Cg37X8m784XcjnftYDaGd6IXK+Z/Iu9Aju0ch5n34PP6yxLJc1awz6+w2Gh1svjLd3pLhyS4xUocTUWRaG3WS0HqM8VscKBHAjnQyNmnQM97F1XR7PpTOJbH2exvAv8HR3EJ43BWN4CPPVX6BsG3fSJLyBEENDinrJgzdXYfd33mCLdS9LvhJvvUdXq3ie+QnWmjehVEJFIrxXvYJ399xCODUVoy3GrNG1JLbvwlIFvF7A78eZPh0rEkZPnozzf/0ObmYMY/17GNoB28YYGkI1GmivF3fuXAiF8JULuJ3pI74n4TD2ZZej0mksx0GbxvjP6uAsm+k18Tz1bCvzl+pA9045JCvY/NKXT/p3Iv/OLy5y3kKc/77+9a/zwAMPHPNzenp6WLNmDe+99x6XX375If/voYce4t577+XP//zPz+RhCnHBOmQgFa2kZ7BUwCkOs9Z6AP/k1uCoZhOGhhS7dhls3Wrw1lsmQ0OK+fNbN/abNhls2WLQ5RtjUe0VPHaVIdVJw1uizRmj5oC31MSzeSPKbjJ2+4N85yed/OAHFsWiopx8BL8frsiupFTKUg4aRAMB3GgMYjHQGqNYwKm57AnO4IMrHh0/B9sT4MNrvgwrS1zp3Y2a24Fny2bw+WDjZuplRWXWZSg3Rmx4AI8HiLWq4HQohA6GWute28aNRHBXrmH37M8T7k2QSBy6VbVcbvUjfpwnWTSyHN35yVMzGzd/prW23/QxRrG1LbO+5J7x7aTAhC1ZZHeGuBic9QDamV6IXAiZvAs5gnssct6nl2nCQw853HorfO97Ht5802TyZE00GmdzfiHWxmXU8hHmNAyckSwh5TKWmIOtAiTNYZxgCN3WRqAySuGna3g1OAkjm6a722VuOkQAUNUKNVuxc7OiUFB4lJ82XYFsltefzVH2t/Hooza+H/8Yc8Vy7FQane5EvbueuR/8nN/2f5+xrstIFLYzObceXJeqEcDEwSzkUX198MorNB59jEwGdu1qw2pewTWhdrxBEz3rElStivYHUKUiynWph6LohnPo9+ya3MqmvfoK5WAS3T+Eqrh400dm2dTgMN5Vq2gmU+hke+uHuT8ruHo1zVsXn9KCQf6dX1zkvIU4vyUSCRLH0aX7P//n/8xv//Zvj/99eHiYb3zjG/zlX/4lV1555Rk8QiEufAcPpDIG+tGhMMML7+XV9x4mHW21Ktm0qRU4aw23UijVGhrw/vsmV13l0N9vEApBpDRGzMlimOBafkYI4g2Av5ZDN6q4lTpvBW/hr978Ii++7qVYVFgWNBpBfhh/nGCsRHeunw8CvVw9aQBv307c0TzNadOwOpK4wxk+LC8mWw0SPOgcnOEMs8rr0ZdMw22P4O7uI5s3KJUi1AcHeOP9S0hYZVLhdjpSLlPMAVQhjw61gmeqXsexvGwaS1PZleF7uTKFjknMn+/QaMDLL1ukUprubhdnOIO9ag3bejuZnmqtZSecmnnYjgssC/uyy8d7rU3k4JYsQlwMznoATRYiQnz6duwwmDxZj2fong8+zABlrtv6OhHTS6hWpJCaylB4GlbfMO1qmJXJpfRX2wnWNhM1igzpKaQbFQY31PCVDeYGwzijeQa31Mg0wigFQaOGBoabbQw2kqxbZ3LndUOkVr5JzteBGU4R2f4B7uAgVRUkQBVvvUh7djtocEwfSoO2PLiRMNrnRf3sBV7cein/smo2G0dSQIBveD7DF/1P03klGIlDg2B630Qh96DvGfBBs83Ptq0GYxubbAp/jlkD7zM5toeu2UHsg7JsMmlICCHEierq6jrk78F9vYSmTJlCZ2fn2TgkIS4cE1Q/2STxbPdRKEA43BqSFQxqlFIEAjBtmks+D7t3G8RiBrUamKZmZyFJTrVhKPDpGiXC5AMd2JYfs1pktO0a/kfuG3w8HKJYVNg2OA44jiJQyTDbeBtD27TnNlOu1ahUTEynTiO3nWq8Rizp4Sr1FvFV2+jrXcimKx5krBLEM5hlcqyINz0ZfH76VA+ezGZsHcBq1plk9uFp1FkdvZNr9qyh96M3MBtFALTHgw6F6Ou4noEdNkF/mEhvnHITnn3WolJRzJrljvc9bg9niFtFduamMKl6YMfm4WtZ37KnW5V9qQNVap63fomb6pDBWkLsc9YDaMdLFiJCnB4HT+QE2LGhzuz1T3GZvR6PajIU7GWvPZdA00OqOMSYE+G1+FKeNh6mUoFmZxu64eey8jo6R/KYTgO74CVvaTyOB79bJK4NTG0TcsuMkeLn7u1sz7ejR1x++q8lrl9XZdA7Be+mBjcUBgiGQpQ8fsLNAq5poQ0DlGIkOAVXmXRNMbAMjdnfh171S7pX/n943J3Eh/FFvBh7hH/OP0K9Bl/c8Tozq/2HlJpXq/CL7xWZse97stVqZeMc6NsTp4c+hq+8nfcbj9AYzDL/yij3PRob/3nJpCEhhBBCiDPrZAY1HVz9lKDV7/fZZy2UgnIZgkFFrQazZrkEAnDFFS61msK2NWNjikpFASlecD7HrOYG2o0MyuMQaGq8tTwFb4IfV+9mfX+KPXsMnH3F1EqB60LUzjDF3kjaHKOmogw32wj5g/jLY1Cts7dm8IvMpRiJKF3BHFftWkatBiOXfoX5S6J0rQ+iCgUqYT8fuPPoiiuSI1vwqAam38MbgTux6g1mld6j0VT4lEI1myjbhkYDb20rnTGTzTPux4knSaEpFGDrVoNLLz0wNKzib8MNhvGU89Rq7QQCrf938Fp2/3TT/UPF4ChVaqfBIb3ZPrmORohzynkTQBNCnB77J3IOD7dK0Od++BSLa88yZnUwEJjF7PYsHc0RXmzezsiU21hdSdOXS9Jsgusq+irt1B2L6c3N2P4YdV8Eo1zELOfpD8/BymXoZRcGLkUivMcV/LB6L/UBRTCo+EWlnSu9YdK+HOWGh9xwkwErhsetkbO97MjGmWRF8FdzqEadwKQEll9j9PVBsUjR6GKHOYOAp84d1Wfx++En7Y/zw8rj7Gn/PP/51weJTj2w+Fr2pMV7vzCZjUOXOcigMZWNGw2aTcW0YB7lC2FH2wgGEpT9SVZ+BDdn6uNv6BP12ji8wk0IIYQ4lu7ubjZt2nS2D0OIc89pGtRUrUKjAZWKoq/PIJs1CIU0V17pMGdOK2lcqSguvdSlt9fh/fctKhUIBDTLzYexyg0ec7/HdLcPXx32env5Zde9rBy6hoh/jJht0GZksB0NriKnEmgFMZ3DxqJmhQn5HDylEsF6hiBVGtpHww6wuXAZW51O5k/VPDrpdZb85m3EZyRxn1yAZ8VynILCbbRRDHZS8Tisj9/M8vSvUq8r/s/d/wGfUaNqRQiaLqa3Cc0m2nZIlvvYk7qC9+c8NP5zSCZbW1bHxgzicZdgdYxgLcvW8JXMK75CqORA8Mi1rLl96xnfcTFRb7b58x2WLrVlJpc4b5y3ATRZiAhxcva/Qa1aZRGzR/nc2GoGPJ1kjTTtCU012oFfw7VD6/mrsS/QX03uK3NXuC7UBrJUlcNAZDbtnjxWo0LNDLDZ7YSSTZ4ePuRSSoTRGCTI8UV+wEvlxYRqMaJXJBizrmDelmXk8ymqDRNfI4vX47LdO4e+cjsDOk3EGCLpKeDzmqisjZEdw/b42emdS9GJ0/SD1YR5udW8GryLCu0M1NoZiUeIJFqZte0fVbH/fjlfKqyio95HNDNIMtbHR76rKOWqxM0hNk1fSiXQWhBEo62S/1xOkUgcyNxN1GvjiGaqp5lk54QQQghxoZto2+DRmtsfy7JlFi+/bDFrlsull2reecekr8+gXFY4DmSzipERxeJrhtj+Vp7J/hSeVJJ6XWGrAE/wa/zUfoCrolu4dl6VRZH36Vn/Ab9VWEsiM4J2HXzUiVEgR5yP9aVsduZQIMqUQAYoES7nCVVHUbQCdjFyXOusY1Z9Cx9zFf3Z6cxM9eNRWRyS4+tI38o1dDb6qJhhXow/zI94hMJAkMmVLQRqOZqGS0hVIezD9YSwMyUMXUFr6Nz9DpNf/Gd23fY4BAI0m4quLpd6rsplK/+Ry8ZW4jZssm4c/5QEfqOBmmAt+2nsuFi2zGLFigO92QoFxYoVrXDEo4/ap/z8QnwaztsAmhDi5CxbZlEbyLAoncEezRN0S/Q1ptAWrNATquA0/RSMOJN8W7k59Da651o+Gkzhupp6XZGsZfC6NT4MXU8i7mIXauQaAUrlBp93nmcbN7CF2SjAxGYKu/j3+q+4Xb2MNRolusbAshvY2RIzywNYNPHQZMCawS7PdHq9Q2gzTPXaJbRld2LsGUA1G2h/gPrcKxkcnYseag3SrDTjTHZ2U9yVI+9PEQ5r4nE9nuGyv7uc2RufY8SfJhtfyBXmuyTzO7jcX2UtV/B68j4G5zw4/rMpFFrZsHhcH/pD+xQnDUl2TgghhBAXg9O1bTCTgbVrTVIpPd7365ZbHH75S83QkMH27QYd0Qq/nfgR895dxcZ3qyyqhtmQWMQrPQ9TdoOAi8q4TIrBV7teon3nu7xc6SXiVujRuwhRxMFijAQJxphpbqfHHMLyeKkkunH2ZAg1R3FQgAdo4GLSxEvELTC3tp7C4CD1mTNQ+4NR+9aXxnU3kP3Ox6z6oI2XRq9hz1AQy4KsSpA323CbDpbZxPAEqeeqmPUaYFA3g7hac8X25yirMB9f/xVqAxl+fUEfN6z8S9o3rKKpLZreEJ3tCZKhIPYVt9O89bYj1rJnesfFRL+j/R/XrTNZvNiWhLE4L0gATYiLSHZPFc/3l/PvR1cRM0s0/Ba+YB/dtQGihRJxu46jLKjXCfod7tzxHS6v/4T14UX8ouNhinYQxuJUB8OY2QKVWBoiQcb6DHqtfnBghPT495vHR/TQj2VoyoEksypbmZrZxDbPbF50Pku7GqLX7GPQ7MIT8jG5OUi5GWbVpAdQ//e9mJ1ljJ07MIp5/D98EjMYpCdismsAqlXFJCNPWYUZthPUahAMQiIBTz5p8cazef5NfhUj/jRFFcWXqbI+cR29U6agS1We6f4PZBMzmZzTRKOaQqGVmVyyxCbJGMb2IwNln8akIcnOCSGEEOJicLoGNR3e3xfAsuCaa1y2b4evfa3Bom0/IPn6ciqRNPl4D061wC2ZZzFNeCX5MJ/d8wRLRr/H1JFdtG8bw/aHmFSqkWaIhhkkYhcxaFLHTxMvcaPA9mAvXZ4ShaJBxHIwmjZg4KdGDQ9e6pg4eNwGAUqkS3uxBzT2Sy8cqD778ZP4f/wkdwwMcGNBcU+lhyetL/Mj48vU/EnWR2/j6sx6jMYwbqGMWath4OKYXtxAhCZtjDCZqbteJ+krsSDwHjNefBfv9i00/RGKiU6iZgO/vQen2ona8CH1Bx6aMCB2JndcTPQ7gqPv/hDiXCUBNCEuIuZPnuHKnT/FTqbIBnrw1/N06z58bp69ahIFN0JHo5+oncFO97LXnEm9XOD20rN4ffCz9FepkGSjvZB77GeZ1OXw3o420rpAyldgRHUTbNTJueCnSi+70aZFkQDesJdIPk9OxYi6BQxcdupe6tpPxOvwRPd/wHVgRz7JlGlx7u6soxN+nEQSBzAGh/CtWM4Mq8aAL0CtaRO28zzvuQ+dSDCvpzUYYNu2VoarNzJG0soTtiqECoN4dAN7r5daWydlQnz+TpvKZJt160wGBlp9Mu65vcgjjR8R+uNT68NxsiQ7J4QQQoiLxenaNri/v68znKE9nKHib6MSSFIotIIyV/WM0LasVenmTaWZXDB4O9uBU4Y5o6twCiUeyP4jScbwxi1UFaxCjmuba9AoqipAkAqgmcoubCwMw4Jpc/HuqpCsj9LWGMKgSQMPDUwMbCw0JhoDjY3CMMAyHJxnl1MYUQQCmvgTfw+ZLJlmiGpD093Yzq/xbdKTLQauXoLVvIK1ux7Ht/E7dNV3Y7o2TdNH3RfFNUxq7d0EQu1M3fk6aXYR7JmE8csyTdugUahTq+UYCXWQ8CnimSwqPnb0wOQZ3HGx/3dUKKjxtS0cY/eHEOcoCaAJcQGaaJKRyoyR/Hg1O0JpSmYHQUtT1y6Ox0/TaeD1eJgUzRMo1VG+Nqywj+6Ew+rhTgwX5uVWscK4m4KdZFP3Z7nK2ct0/SHtlRLDVoQXw48Q9DS4MbMCqwp2wyVCAVMpculuIgGI1BoUvRG8tQoBqpSMIDkdY5LajXbhw/psqhoeXdDAyI4x8H6OUE+c+Iwk9Ts+j/OLN/GsXs2CYh3b4+fjzptwr/0893Xa1Out0eQbNrT6XahkG+HKMD313Yz5k5SaMcxmncn5DdiTpzD9ixF8XTbXXWfT12fQ0+Ny6Vs/Oi19OE6WZOeEEEIIcbE4XdsGE4Eqj/Mk9qo1xK0ibjDMR22L2Bp+mNvv8ZBUh1a6zZnj0mjAh2/HCBe2ci0vE6SE1w8Rt4Cq11C2jYmJg4lX1/DSwMHEtvz4VJ2wVSM0upZypY7j9TMWm4a/miW8rwfa/j5oGkWVIDocJGA55IYabCi04W5aiV/V8FUr2GUF1SwhHBQu3sYAd+78NnuGX8HCJu+E+efAr3P7pVu5fP2TWG4Nb7OCjQdtO0SLe4npHFbvTAiHKZUVuH4s5RLReSo6wUjRj6+axz/b84mByTOx4yKRaE1J3b+r4vDdH5IgFucLCaAJcSE5xiQjI5fFb5dpm5pkaCeAok3XsB2DmhGnPO9aOidXYf27OJEIRqXCvGll+sdC9G2NM7m8G13by13GT/lM7g0um1og4mlSjE/nufRXWFO+gjZ/hbr2cqleTUiNEfKZhGZ24J81hfyaKspSJHSRRtRPxOenUtTE63lKhNmWTVIFPju/xBXv/Yj+v3sTs1omGwix9boFGHaDtjU5BioLyDpe/LpJeizDzI0/5eXiV9m+3aDZhGef9TA6qkibqvUzURDwg+vRmLYmHtQYac0wra2e+3uNdVijfHNgLVOmpTHO8Pjuo5HsnBBCCCEuJqdj26Bv2dMsGlnOtt5Oduam4Cnnuaa4jGmfcZmz9FHc6qGVbpYFXi90hXLE/CYxt0gyX8Bs1Gkok/17DkwcNAqfcnE1KDRBXcJjapTfh29skL2B2XjdOgU3RtBu4CeLBweFRgEYCr9fgeWQU20UCxrD7zLJ7ceuNWhmS3jcOrbhxTZ8GE6DqM4xs1llW2k62cgUIirPZ93XCG3Lo9FoFxqOxm42SPa/T8CMQmcMT0+aakVTaAQJeev47AI4DQKqRtCt0aw6VC9fiHWWJsgvXdpqRXLw7o8lS+zxx4U4H0gATYgLyLEmGTUW34kOhZgdzOF4/AwMGGSqAUATDkHbFTEcK4bpD6BKJbTfjxkOMHmyiz2Qw26GucO/klurP6OmAvhLGYKlAT47vI4pxlssS/4KzziP8C/qccLhu7njsyMsuPI5gs/+AP8vX+LSrEGonsHvVtiTmEe0TeFrDBFxhlk/ZSm9M9p49MYGl6z7EaFXl1OPp7E7uiFfwP/cTwi6ZQaicxh2o5ieKoONOA3bT+fWN9k2cC+NSII5c1za2mDnThj4IMcIKYqRELHyED67QCjugUsuwQ2FePWpIivenjzeayzQn2FkR4VacDKXTDrwMz2d47s/iWTnhBBCCHFROcltg/unlSf0KN1r30R3ppmeamdSFWq1dkIlB79aRbn6uUMq3dxajZrrpbrJocsosGvOrcT6X8efLYPWmLUauK3qMQVY2KDBBQwcQk4RlAl10FpT7JyGZ/sOsEuk3EFMHFyMVvBMKQwFulZnlCS5up+EO8qsgdcAhW0rIm6OMkEaeDEVWCaYduv7j+kkjuFHJ31MqnzI5OH1jBhp/JSJkiekKzQdDw3DR6HtEtoKBapWB0P+HqbpMk3tx3KqBGtZtNb8su1zVC75GpdlOCtrykCg1c938WJbJs2L85YE0IS4QBzPJKP9i4fL0jBjchR7rEjU48eyFM1aER2N4kajWFuHcFJpqnVFefsIMyPD7JiyiMe2/oiYdxhfLY9/Rwm3PYZ3UhvdmSHuqf6IUFCzcvZXWLAgwpe+FMDz4xC2rdC2JqpzqGYN7TrER7bSk7H52HM5m+fdy81/fh9LjAHe/mEf7kuvsZsO6rkogWyFCjGitQI3OB+imzZzzSKWcqkaXgYakyjpIBErQ2JWnOunjxBzsljTE6xfn2Rnro2mm6LmmUdvV5Xrb/bg2kUaFZfVG9sP6TXm72qDcIjcriLVOcnxlmenc3z38ZDsnBBCCCEuNse7bfDwaeXTnTK/PlCh44bJWLSCNIGAhmAUtX071vvrcXqmYM+eg7XyVbxv/xJVrDG9FGRX70Lem/sFfHaZS7avwHRa2zRBo2kF0JRSaMPAcFwwDHQoBEBN+1CNCsaeQQacTm52fo6fJvv3CjgotAbHMTFwSVQHSekGCo3bMCkQpWFG8dAkRBnXtTAAH1U0BmVCBAKaZK9LxKoSGRttHZDj4LMc6mYM5dooXGJmhcGsontgmFASRmPTiboFOpwdFMKTGApP5+eNW/hn81eZ8YSPnmdHWXjJKLc+FKHsT37qwaxEAmlJIs5bEkAT4gJxtElGVU8UvXuAws4s0Zs/g7FzB9aGDwkZI62M3De+QRPwvPcOxkA/7pRe6tOmoxwHvXsAtxnl4zn3kqz201HahtaaUCOHcl2ssRG03SCWTDJjRpAuz+vc9++upi0OtR3Q99S77G1cT7zWR7fzMdq08OkKXrdOp+qnz38J/6TuZvq3nybSt4pZG4eYXN1AwwqgqxrXAdcw8bllOhkkbQ/TsAMUvXGalofLGh8wZHTSEavyiP0vzHtzFb5micsKEYLlmxieeh2L8j8j602TbcYY2JhjdmyYsevuZeiDdrqjB3qNVQJJ+noXMuOD58htclFTIwSarT4cYzffQ1+unThnfnEh2TkhhBBCiIkdPq28Opxg21CU+vslpi/YN4jAtvG88zbG3j0E//iPMCplaDZRto09uZvqFTPY/q5BqJhhzuaf8kptAQsdP4F9Wy/3B88wDNAaLAvlNEApdChEvdhA1xvkg5Poauxmqp3FRxP2fZ0GDDQahYvGRWHtC57ZGFQJYuISdEvU8WHSxE8V1zUoWjEMmhTdKGYkgN8PnloVy63T1BZht4jj9YHHi2VX8TZrNK0ADdfD6FUL6Rz6gFmhvWwvz2Rtz4OsNm/m9W3d7Cy20xGtcPOOf+H6+irU62W2PBFic2oRr6YexhP1M3++w9Kl9qcxN0uI85YE0IS4QBw+yci2YdMmg8KWMp6qQeb/+gHzy78g5g5haBe3vZ3a/IXUH34UAgEad997SOm8yoxR2Jnl6b/vxDDga+t/B6eh8dlFXK1p4sWvHaxcDrxerHQbiQ/WE/ub/wamydhOF2N7P3RcTdodRAEeu4pjWlhKo5WXawq/4Bsf/g7mhyU2htIEYz1cln2DQL1MAy82JgG3ioUzviDxYBNolGljBACfqvEnY79Je6PK3s6rGAn0kN9T5B7nWUaSd7ArfSsz+l8n3hxhdChJ8p57cJbcT2j7gWlNGdoYbLbzndwXmK8Nbtu0itiWvYQ7gwxMu48frX+E3DofoZA+rsXFREMcTpRk54QQQgghDphoWjk9Cfp6F5LavYxGt4s3HcXzztuYW7fgRiIYpSJaKcyxMZxACGcog+WLEO+Zwchmmyve/FuurxUJ6iIGDhqTOh4sEyynCWiUbbeCaYZCVyo4DQPX9DA06Spm7fw5IcoA49Vniv2BNBfQ1PHjWH4su4aDRZkQIcrEdA6176uqGGxkDjvdmVzKx/iCmoRVoFFUxGtjONUmRTdGgiyNmkG0nsGjGwCYdo1rmi+QnfEg5V97kMhgloE1Kf5hWSfbthlU6tDe7vIV9UOu3vUclbYUWbMH9ha4urqMYEjzcttXx1uIPPqo7HoQ4mgkgCbEBeLwSUZbB+KMbcxxSeVd/G6J8PALGI5NNRon0BVHjY7i//GT6LY26o8+dkjpfKuvRDvxqUnm3WKx/sc7qAxXqLsmXq3QGFjKxXEVpnbBtrE2fIgxPEhz5iyqkTR73hvjUnuQQOFdvE4Fv1ulhkXAqeGljo3Cqxvcqn/GK9472Vnu5GZnHV6aaBQWTXzUMDk0iGTsm2xkYVM0YpTNGCl3CH/VJVAeY5vZzR47RCTaZP62J8hFezC0jem12BK8gtCCB5nWpnmcf6b5xho89RI5O8xr9k180PwCe3u+Cje3+nC8uakd3ocFc/pJJ1tBtmMuLo4xxEHSeUIIIYQQJ2+iaeXVKqyd8hClIvTUXiewfTvG0CB271SMchltmrjKwNkzSi3vUC9U8O95h67oDmaWR/HWithYmLgYaBRNFLR2QXi8GI4NSrUeaDooXcXvaFAwc/cr+BsFNIz/Zxx0vAow0QSowr5lo4FDO8OYuOPJ4SYWXhpcwiaU5eMnyV9nwQKHaQNr8W5diVXKU3Z8BM06uIqYm8XS7r7vqVC4hJtZgn/wm5T/8I/RSx/g5s4AL77tEgxqdu0y6A2PcsPAKrLeNEPlDgC0J0Alopk1tJqtc5YA7axbZ7J48fH13d3fh052S4iLiQTQhLiA7J9Y5K5cQ33LAFObe/EaTUzHQRkKGx+qXMWtBjASCahU8Kx8bXzC5MF9JTIZhWVprrvO4ZKFUUpvWbiYFKwEbe4YPl1FuS4aA1UsYm7aiH3pPHTPFKpZxaCvl0nxabQXtrUCbm6dgNb4qFJTQRzDg+M6hJwSU1QfGc8kEsXdNA0L5RgEKKMOO7/WQqFVWG9joYBIXKGVRbHhxzvYT8kzG61DhPKDxNzt5IOTGI3PhHyBa7Kv0PWmD98HsGhkOasDk9hUmELUzfP5+rMYQc1y+3HWbUtz6bQg80ef5rraKmY0i9j+MDsmL+SV5MOsWxeYcHEx0RAH9exyCiMK+7EvyeJCCCGEEBeFg4MrnZ2n5zn3Tyt3hjPE/Rne70/y8XCKYjHMSutrmFfcwRfnvE3s+3+Pm0xg/nIdDW+QkRFFxPbit0so1cB1DWqZGvF9VWeGskCrVuAMAwMXA9DNBqBAO2jdSugadgONQcMI4K8X9iV2W/ZXnh2sNXygpbWC1Vj7J3QCLgoHL3kjgba8mJNTZG5YynIN/+Xa3Xga23lr5AYqwSRzK+8SGXoH03XH08vGvpUxgJHJ4P/ePwOQu/7LVCqKREKzZw/4ylmMSom9di8Vp9Wfze/X1PxRorV+grUs0WiSgQGjNZzhGLsgDu9Dd7w7NIS4EEgATYgLyb5JRrtnf56nhwd4eO9fU6aDzv53sZoGVQKtaULDBXzRKCZg5HMYuSwjJPne9zy8/2oOfyVLtpRkbyPJO+9YLLgqwhVRL+210VbAzNDggKtMbDxYGK1KNH+r94TfrwmpCnv9U/E0iuimJmSP4MGmSoAafrzYlM0IONCl99KvRwg1s1g0MbHHs3JwoJ+EYv8iRKNx8dDE6k3jrZewRzS60aA3XSXf0HQP7CCrImyt9eALBGg2NZNTOdrWvIDy+qjG0xQCHbRPhmYzTX4IFrqrWZG7h/XZJNd//DSfzS8j50szHJhCnByXbV2GbcMz0cePWFwcPsTBtmHTcJDCThN3yzqe/vhe5t0Sl8WFEEIIIS5YEwVXFi3S3HWXc8rrn0SgyuM8ib1qDVatxPRamN7QIpYHHiHR4efFtzsIha7ly4mncapNMiUfhcEmo9UwRtNLiCaONnANRczNAwoXE1PbOJYPXBvTbW2L3L/udNGofcu9/ZM10QZlN4hHG0QpctRQ06+A8R+BvwD+ceJPqRGgrCJ43QYlN0BtuMRla/+B6dUNhNQq8HkJ2wl0OEXNiWJbflSzeMhzjAftXBc1OoJavYY1w3ezadMkbBvqdUUxn2S0ESFq5KkZfhwHbFvhjBVoxENU/G0UCq3fVzx+7BYih/ehKxSUbP8UFw0JoAlxDtifpUunIRw+9ecL9ybQsRLuLpu9bppQM0BQZbGwcbBwGnWKQ1ViEWiG4/zktRSvrnPp+MUTPFReTUiXcIIhPowt4mn/I0x64yd4dYGxUDfJ6h4su46rFSXCFM0Y2fB0pqg9RHbsxOjoIDI8wvWFEWrDReqOxRPWl1mg32ARr4OhUFpT1gEwLOq/HmTSb+/g+v/eIPzd8r5S9NYb98GZvIMDavszeDV8lKdeRTy7E7XtQ3yWn2AE2st9tFlFPjSvYDjn4QZ7PdOMPuKFCuZbZdx4G8VLb2V0VNFsguNAoxqnW/eRjGUAuK62ij1OB3k3TZvPpWS1yt27d71Jx9V3EY8f+os6fIjDpk0GW7YYxPwxOlUfUTvDihXtgCwuhBBCCHFhmii48vzzJrZtnfL6x7fsaRaNLGdjVyfrNk8hovLcUVlGOq3pv+7L5HKKlR+luWveAqpPPE+xEiPVHCDhVNBoHAx81PG4rYb+TVoTMj04uFrhKC8G+/qesX/duX8cQKvSqxxI4DZcRumg3R5EUzxk2+Y4L/C/QEVB/y/g+6Basbnx7Z4YJhUVpelahI0ylnLw1Yv07l5FyYwy5vfjwUtPeRONHbsJUqHuDROpjhwYdACt/mxua1urajYZ2pDlwz0FUqlOtm41qFahXG3nNX0TDxjPYPmhGYniqeTxZEbYMP0edpXaGRlRLFnS2mFxtH6+E/Wh2//xRLZ/CnG+kgCaEGfR4Vm6aFRx3XXNU65SSiTgkoUxMm9FKBeb7PFNI1kdwe9WwTRbU4AKRexEkjeDt/P0yg7uGPwX5lSfY8DpYIQpdNg5bi08iy9QorexnkF/F/2ReVwTWE3P6Hs4jkIrk5HIdLLhqUQGBwll9mI+vwJT2wRUmIoTZI+exDXuO7ziu5Noo0iXsZeQWSNkV6h7/LT/xSBGVNP9l7vJPNGOt+mArY4ogYcDQbQ6/tZH7aVvl4s/GaZmhrCDUcLVMVzTQz41HTPYydXZj5hnbMKXCILrw3UdjGwW5633KZU+g8ej8fkgQJ6iG2agmmByIENEldhr9eI4ikqllZHLuHHCpT4WzBklkTg0gHbwEIdK2E9/v0EoBAmdw/aH8He1kSppWVwIIYQQ4oJ0tOCKYZz6+md/pb/uTJPuThHJmASDaXyOy02sYoX9edx9WxC39HyWPUaGKdH1RKtVzEa+lUDGIk+YUVJ0MYCfGgoH0CjHwdjX9P/gNai7L51r4uACzYqLY/nQyiRKfsL1KgCLW8Ez2BdE+xzw09Y61kVhKy8e5WBhY+oGpm5ScP3UsBgmBaaFoyyqZQuvCYliH7nIZCzTwj0o2ayhVXkGYJq4DZuRrAfvJW2EMxqlwDRb/z3NIwS9mruib9AV3c2gN8wL9aVsth6izYUlS2zuu6OI78mj9/OdqA8dQDSqj2v7pxDnOwmgCXEWHZ6lK5WM01YCfceXYrz15o0kX3yeUaudjf4ruLTxPhG3QN0KMRiYhlr8CE8MPcLUyCjX7VzFLivNiNOBacIepxOtYU7hdUxt06dm0d7uYaP3eozRUfxGlbBZIdkcpLtvEyE7TwULo+himYCuUDI72RBeQFKNcYOxgZesL/Hg6N8R1DUq/gTO7Q2M6L78XlTT+JyHyHON1jbTo2hi0WdMwXUhRJXYe6shWqXuiTPq76XYNpOtvbcyZWAtMza9QLyyBzcSwHbAUynj9kyhXqoR2riTOaFe+pod+PIFYu4Qz3A/e+rtWBZUrDAzkln2up3YNhQKinYnT/u0IDMfihxxXIcMcSgoqLXR7ssRqQ3z4cylVAJJooYsLoQQQghxYTpacCUWg9271Smtfw6u9Pe7mkCgtZuyGYwRKrZ6eO2phbhj8Cmm/+vrqJEqVthkw5T7eLF4I3et/xZldxd+qjSUjzGdpJNBDBzqePDQwFAaR1sYrY2bAPsqzxQOBgYaP2VG7DAxhvDQBA5UlB28a4KHDjuBh0D/dH9PNI2jDWwriM+ug3Lo0z38mIe5ixVczbtYtoO3ViRmNijrKD6jgWXXCNRz1PHhpzb+vTRgY+FYAZQD76duphFJMLjBoLPTxTRh504Dx/HzWtdX+VDdxeeuGWHYTjCqk3zz6w2mTq2TSIDvySP7+fpWLAeg/uhj433oCgV1YBIqHPf2TyHOdxJAE+IsmShLFwyC656eKqVAAC77L0t5cY/B7OHVBLxt7LRuYzQ2jbdit5H3d7D0iib2U1U6kxmCbgnVNgV3D2jdqgTf68aZ7o7SVBaBep7CWBvzYluIWWWSjT3Q1Ng2eHRjX0bNooHFqKcLu65xmgq70iSnfEx1trPF+3lMu4ZFFV+zjP/B2iHHnL5/CPs5Ewe1bxJSa6HR+qio46NKENdVmNgEqGI7BT7gairpHrqH32PW8Cqu/uj72NqCao2Yk6XWH6JuefF7XfzubrSysDHpDBch02CoGWaZup9nPY/gMzSNSIJt8YV8rrSMUAguuSGEVSoSLA6h77uHeldygp/4gSEOvpVr6Gz0USfMhzOX8v6c1ipKFhdCCCGEuFAdHlwJVscI1rL0V5KEQolTWv8cXOkfSPnp7nbZssUgWi1Q9YTYXUpwycdP8TmeIdyTIhfrIWzn6dnxOr+eeZop7qbxrZhRXSBDkixxgqrOZu882ow8k7scCoNVOsvbqeOnakXw2WW81HEwMHEZJY2Bpo3svrWvQauX2v61qwuWxrzvsBO4H/gNUHYrJNdQfrbHb6CBl3WNK/lR9BvMH1pOZ2OYGl7KhCnoKH53hICdx7Q0aW8WxzLIGylUYwyPW8PApWm0KuIa2kvlxttZG/waubFWm5JIBCwLwmFNPt8K8Q3ZSdZX4tTrrS2b11yzr3XKYf18AXTKjwN41q2hsfhOEokk8+c74wn/aLT1+z54+6cQFzIJoAlxlnwaJdBtXQF4/Et899m76I2MoZJtjFVDXPLBUzwQ/ld6flLkVzdH2Z24gorrx18rYBhB6vVWNitBnpyZYIN1JZ8zXiZS3Eis2IeXGkq3ytwTegyAKkFMbEKUadS8DDCZMEUut99hkrsHSzf43eb/my4GqBDEMh3C95YOOV7zfofmbxi4trGvpB5AUTWCeN06Co2PGl4aRFSJgC4T0BVClbcoDW4nZpXBdfBXCoxYHSgsGt4gfl1DNSo0Gl7sZg2f0SRse9gcupLveR4lQxsDtXacjCLohVhM873GF1ARuDv+BslyPzocpnnbPeNBsoMd3Cei/uhjqMV3su17RZ5/M4W/I0HU1RRGZHEhhBBCiAtXIgHz5zu8vLzJwq0/Zl52FUalRN6JYH7mRhKBpcDJ9Sg5pNIfmDM1SqBQor57hNWRpWgNnw+/QXpaCmNSmlTWYOM7UaaObaJT76WBlzp+QBOkCsDHwetQXg9/4/8P9Hmn87d/XeHd72/ltn/9d3S4g3jcOlX8NLHwU6NEmBHSpNnTCpTBvq2Uet/sToXCwL0VrIRzyPGrBDQ/a8LPFa4yKbT18HTvbzE65Sqeeb2Dad5RrnHfpqL9dOpBNKM0XA81ZaJR7EgsZE7tI1Q8QpYYpgWR6giudtHKIBOZwqr0UmZ84+tcs9vLKz/K0F3NUaWNkq8drxd6ezW1GjSb4PHAbbfZLF16YNfH4f18x3/20SjGQD9GLouTSI5/zbp1JgMDBqGQZsmSQ59LiAuVBNCEOEtOpQT6aI09J9J6M4uxbl2CckZxx+C/8DmeaS0wEt0kMiXcj15lW72duD1Mu1LkzBghJ88U+njTuIU1nffSgeahvX9NsFkiYFVoKD8FHSFKAR81FC5VI4bXbZJgFIsmDTxEnNakIAOXHnbjwSZMmcatFuqwAKFKgOezDvwcDhSla7TbmrvppUETLymG0VpRx4uDRaCRI1DLYCZjeEM2bt2g6YlhKRcaDbyVHLguDZ+PmmPgMZpYFlzS9woD0X+HkUoQ80GtBl4vOI6i5gQoP/QY8Yduo1I7ys+6WsW3bOI+EZ/5tST5tMW6dcjiQgghhBAXhaVLbeas/SFtG5YzaqZphqYwPZFj2siz2Msc6o8+dtLPvT+J6Vm3Bt9QPzOnh8nes4QbF9zLPbV+ur5dxEl0g23TPrCJRfmtTNb9gKZJGAsHGwuFyzS2E6/m2VK/hHRlI2+HL2fZqij3fRleevWr3Dr4JD5dw++W8NKkZoTY43YyhV0EKY9v1zT3DRdwcXGw8OKiHjrQGe2JJ+BLX9r3uQ9pai/7afjD6EqdDQNxwkaWaNPCGcky2/kYU9tkieOnipcmPl0jo9pZEf4C8fL/l2i1ScgtUfIkqPti+JolgrUMnkaFq8ZeI/Xd3cwK+Lit7tJfajA8HOH96EJ2z7qVyW1lduSTXP7ZOI891jwiobu/ys8YHoZwGO0PoAMBVKGANi1UPofKjBFIJHn0UZvFi21yOUU8riU5LC4aEkAT4izZn6U7uAS6VOLYVUrHCNgcbepAIMD4m1xpV4YZf7cSbzA1XppdiQQZMQxwmvzCcztXuOuZ5m4nyQimoblOvc215S30+Wew25xKtwPeziRmNoNR0WhX4WLgpUFU58YzcklGqBFgiEn4qZDYV+quAQPwPXQgkHTw4sJ5yMD8+YGsnQJ8VKjjp64CDOkO0gxiYeOjDtQxHQcFNEez2D4LFfBh48HwgttQaK1RSmHh0NAmjbYOzKCXqcN9TNPbeTeTJBqFa691mDLFZXjYwLI0X/5yE18iicPEQUrfsqP3ieDRx2RxIYQQQoiLSrA6xk28QW1hks5wO36/JhxO4w6649sAPyn5O5HWxPog8cWPkVx853gi2ZNIMg1QmQNbPJ3+IYK7t4LXRjU1DiZeo4nSmrAuYu3rXVbSQXYZU3nAeJakUSf0lAf/y29we3uecrEdqBKoNBh2YuwwZ9DR6APAwcJDExcTaK1BDTQ+mriGwniglSCuVOD/+D/gvvtabVqMB1zq3/RQa3hQbo3frP5PjB0miysRPqzNJEqGBq1dHBZNPNi0kSVKifl7l+F3SthVAzvdw1jOoqu5C8uuo12o4aPdyBD+KIsqFpnRO5X0Z+bQt7nEgv6/ovrxP1KMdBLtCtERm48OPMDh1YA6EEQD3lWv4xoWTW8I4jE8tSI6EiHwv//mkHuPRCIgPX3FRUcCaEKcRYeXQEciHLNKacKAzbNPY4wMUXvscXQiuW+BcWTAJpGAVC6D3y7jRFul2dUq7N1r4E1FCdn9bOq6nfXBR/j8nn8gOPo6/UYPTe1lUnmYy3KvELLzmIZNwUjRkW5i7hnFrDVbwSpsvLpOAx8lgvsCXE1iqkBUZ9EozP1jwQ1Q+3ZCHr648Dxg4/57hXJbb8gOJrbyslldgtKtYFqLxkNzvLErgI86Tt2m7g1jeh2ixSECjfy+ZrAGVTNMNjCJ7g4PRrVAJApL72qS+cils1OTTrcqAut1uO0255gBL5UZw135JjlfB2Y4RcB3ZJ8InUiSSCCLCyGEEEJcFPZvA/R2d+P1HVj/HL4N8HhVq/CLv9vJwKo+dhtTKXbOZP78DpYuTR6SOx7f4vns07B1B3UVwOtpAAobTyshq2u4GDSxUCh8hkOnL8uQ6uRLzvfR+RCDzW4uWzSFWjCBs3UzJSfBW6FbcG2XqWNb8X+jiv+GOmpfxZna3/cMAEU5FiGeLgCwYgWMjsLPfgYPPgiqA2LfLxDJt3ZnhL1VRkjTq2xub/yUkC5SXeen8ndB6vgwcVs7PXSTphVgq5rDHPtjOgpbSDpQ1AGsRpmytw2jq4OQJ4/a24/2ePBs2Uw8myFeLoNbphFqo3bzpfiNBubLy6l7oXFQIFInkviWPQ2Dw+z1T8cZzeBtVAjv3kslFMK44yqMdOKIoQJCXGwkgCbEWXRwdVgup0inDcJhZ8LPPaKxp21jDg9h7NyBf8tm+HADbwY/xxP2I+TqQUIhzfz5DkuX2uMLjIMbsOqUn1qt1WC0w8wx4glTsBIELZjhbmXUO4nO5iCT7d147UZr+LdqEjSr1EZzjLYnSLVVMYbKaK1Bm4ypBAVPnPCv5AheV8G3r+NEa/S3gbkvS6djoFoFcEcsLowO4AkNeWjioWkF8DtlZutN1PERpoSxzsX4u1YPtv3jxVvTj1wMFFY5x9RqFm07VHwxTNUAwN8okAp68dQjqHwet2cKn/v1Hkqv2yfUx6FahV98r8iMdVUGvVNgq0V3t8ucOS7WSS4QhRBCCCHOd4evNfdThQI6FMaNtx3/k2UzFH7lv/CZ9asIUMXxBtjQfhPfHvxTIHpElX996QMYI0N4N23Gow3qOsCYbxLR+iim20DjYuFioKnjJaDLXFp9B2/yBiZXd7EzOJ9Rs4Om6TB9gZ9muoLvnb3ceXWGSiiF2+8Q+U5l/PD2V7IdoIlTGP/bU08d+Pjgg60/m19o1a0BJBkmyfAhzxD7N0UKH0Sprw3gYOBgMqzaGPJ2U2548EcsrvF8gL88gi/lhUqYSFcHZlCjygaqWkE1LTBMKJUwxkZRrou/WcfatZHmNdfh2A7+J/4Vz8pXUbaDDoWw512Otf5dtlYn8z6dxCdXiLsZpve9QaNhMTaaZE6Pb8JksRAXEwmgCXEO2F+l5PVCozHx5xze2NPa9DHmls1ovx+UYmCnS3BgOTfONPjwmi9TKKjx7aEHFhhJvAc1YA14orQ1yvirI+xpvw1vKUu8lkUXyyQbBWawjRJhikaMqFkmSYFSWxfh0gjOWBNnchv4/RijoyjbJuFrkLiyD++3S0ccv8mh2zL3m2hxYXyh9dFHE9++xUmYCuH91Wf/BvQHoNdCAz9V/EQo0cSLjQfb8OKjgDYNbNNPv28SbfVBgqpOsD6KLoFua6P20BfwdZ14H4dlyyzeeDPFv/WGSftyjBqdbNnSqoK7LH0SC0QhhBBCiAvA4c3+dTSKKpUwR4apL7nnhAIu1u//IV3vvkjZE6Pg6yTgFLl270/5DQ1/8MS3WbnSxLbVQUnjAPUHH6e0YiO7yi5b6z2YfrjNfZ7e5pbxRG4dL1kShHWJmJMnlvs5hnbxFseIxAsEazWoBvB0p7F2xzGze/DG/Izs9aEzrZ69n2TzZnj22dafn3mm9ffZsz/569yMwuxzGSVJjihJxtiiZzBWCWKaYM+7nOakBOarP4fZszAGBzFooF0TNTyEajSgabcSy9UaGAptmuA4GDt3YoVC0Gxi7tiO09GB093Tqir72fPY2SI77M8SCoEnGEBXAyivienAaH+NKZf4CAROvppQiAuBBNCEOE8cnNEjrDH6+9GhEGhoevxsqfbgCxeZM7qabZXP40slsW144gnPIQuMhVc/wiO3g++tNYx+uId6MciWUjtdxXf4bbWaQsVD2tlNO2NUzTBVQhgGWD4Tx4qQiU9n/fT7ad/9HnMnNQhOimJs2Yxnw4cYToNKvw8zU8Y8jm2LJ7u40Blw+sDFROFiY1LHi4vJsKebkfRcrrPew/CaRFwDd87leEodmEN9kMthz5lD466lDN78ANntB4Jmx7PVMpOBtWtN/JMT7LUWctnWZRhByPjjFLfmcZrD6PtObIEohBBCCHGhOLjZvzHQD5EI9SUTTzI/GmPbVrxrV7NXx8jYabBBqQApYMaeVejCNuyOaXR3u4ckjaGDev1z3BJcRk8gz95ynHX6BsJk8NGgYfrx6QYht4KPOi4Kw3WoG0F6xt5jan0LwbdDaI8XNxrF7u7BmTUL990PCO4uMjY/QdsTOczr3PFj3bgRfvM3YXhfMZnWrTWtvW8zQ6UC8+a11rhqXxY5nYa/+Ru45JID59z4pcW6L11L2548M9nKTnrZwkz2MIl6XTFpkubyy11U1sTpnQYadCCAuXcP1GqoQr71zXUrUIjrAArHMDEDfnQwgLFrJ9RquOEwblc3+PytqrJaDbWzn5A7hG5vxyrVqNmgXTANTdkNUqspAgF9ctWEQlwgJIAmxHni4IyeLuRRtSra50PVapQ6ZjOwLYTRMJnU7OOj1ws401PU67Bjh0FHhzu+wFj+cgRnyeP4r7ybtXsKLOz9ObN2vcLuegd91Q5Cbp6EytGlBhlSk2jYBh4cPPUyI23TUErxZuf9FHu+zu98fRBiEPzr/0XTMFB7BhkajlBZ0MXc723Auu5A1dnpWlzYvzRwv6Qo7wlCa1g4OWJ4aOLBYZR24lMiGFkvRrGIWSqR1GvQoTA6Fqc5azaZ3/kjfvLBJaz9lkm5rCbc7no0uZyiXFYkky4rOx6m2YDZI6vprPeRaYYZXngv0aX3n7bfuxBCCCHEeSUQoP7oY+M9tqx0O/Vw/ISewuzbTbNQI+9MQiuwLHBdyNhR2puDzG7bQbhrKj4f49Ps33kpR7iRwZj1WbaWNdMGVpP27WZrPcJq92ZuNlYz6rYTdIt0sac1QZ4gDSNMzRuhs7Yb1WyivQmolPHs2oHbnsLq340zMIhqFKnuCjJ0Uwdtf5wj8DtVoLVO/d734CtfgVdemfh8bBs2bGj9+fbb4V/+BSZNOvD/6//dw9jvJxlpdrKam+mhnwYeNjCPO4yfk4o0MSJp3L0ZzLF+GgsWYe7ahWf7VigUMMpltHbHO7IZ7E8Ka5TrUtNeLK+vtaWz0aB55VWHDCBz02lUNMqsXW/hjLi4rkJrsN0aFV8bYV0goEKokcJJVRMKcaGQAJoQ5wiVGcMoF1Ch6FHfkMYzeitfg0Zra6M9czZbapfilCp06X6Uz6JgtdG/yaBcVsRimq4ufcgCY+VKE0iT6jSZu2E97qQUYTOF3mKS1yY1K4JZa9Dh9pPAouDE+Mi9DI9Oo6oGW8aS3PZIjMg1YdT2rahyGfva66j5d+C+uwffzgb5hRHif5LH/L9b3/N0LC5GvpWi///VxZTmbjw0UPsGCXQyjIcmDbykzDG6h0cwM0NQKqL9AdxwGFWrYQ4PYc+ew08+uIQVKyxSKX1E5vLRR4/e+wzA79cMDSk++MDCssKs8vwKc9N30x0coxpI8FuPhQ8faiSEEEIIcdHRiSROIonpNaExcY/foxmLTiHgBmjzFBjRfrQGw4CQW6CsA5TTU0jtW29ZzSq3DzxF8uM38dslfMkQu6cs5KUFv081U+WFX6Yoj9X4TuWrJN1hHCxq+CkQoUoQAxOfYZD1JAnVSzT3lgh762jLQhdK2DUH7fHj0SN4nTp118/gf+rCerXJ5O/uxuhorVdfegn+/M/h93+/lSg+nFLwp38K//E/ts4FQA9B5XE/wy9NIqvjTGE3241ZFM04C/SbdFljJHSBS50d1PoDmIYfFfPjffkljNER3K7JuJfOw1q3FmNsFFt50MpA6SZag9q3bbXqegnnCuD14nR14XZ0jh9XtQrOniJhrxfXaNBoNFu7TwyHqgryvn0JSVxCmX50KHzC1YRCXEiMs30AQlz0qlV8T36f0B//Eb4//ePWxye/33o3O9y+jF75v/0Ztcd/Bbd3KtVQEv/mD1hiL+e62huk633cVv0pXqdKoaCIx91DniIa1eTzilxO0enN4GuWqPligMKyNHP1BmK1YSoEyBMhSxIbg4AuUxvI8rPSIgab7TQarUPcv7W0kalQSM9kV/IqdkQuw256qP9HP5U7feih1vfev7j40z89UGV2OKXgz/4MXnzxQPBMD0HxzjCl343iNi2amASoooAhNYmMmUIBAaOJz3KwlYmu11upStPAaDTA58eeOYtGxeGjlTlSKU04rKlUFOGwJpXSrFtnkslMfFyZDGzfrvjZzyxKJajXwbI0WsObm1P8fNccLr05/on904QQQgghxLGNxmfxQeIm2lSODnMYy6kRqo4QdfKsUjezdmwOH35o0GzCFZueYu6m57B8Bvl4D3Xb5LKty5i5+1UKHTMpepOM+rr5rv46O9V0Nqq5lAgRoUgXewk2coTqWZqBOGOhHt7x3MCYHWPY6KSWrzOS9zFYCONigutiuyZht0jp5TjZK9tovthKwhoG/Kf/1EoWT+QrX4Hf+70DwbPmz0xGr2zHfslHRBeIUCRAlav0O9zTfIbp9hZm1DYQauZx6g7p6m6C/dvQto2q1dDBIEZmDJVvBcbQren1NeXH1q02JxpFU3nYHbqEakcv1d/4TWqPfx0zm8HdO8zG9xq898IYfav3sGOXxTveBbydvoMRbxd1/PhUg+uNd9gbnM7er/0Hyr//h63pm5+0ZeMi9M1v/jr/63/9j/G/P/zwvfzwh98/oef4kz/5I/7Tf/q/jvqcp8vf//23+ZVfOTBF9fDveya/1/lOKtCEOMt8y55uNVpNpXF7etDZ3CeOh9aJJNVf+7e46Q6M7/4rUzI7aPjD7A5fQb/TxaKRZYwZiu/orzEwYLBypcHkya0pkYVCqyoNYLCRoO4J46/nqfn8RHSRq5y3iZLfN90yiImNjUkXe/lb8yGGP/MAsyMuL79s4fXCzTcn2ZG9gStf/9/4myVmuiahep4YOfbSAS8quq7sw/onUHceWFx8/HGrwuxw+xcX+zk/U9hfM7CGmzQsL16aBN06DXyMkaCkQziORVHFMAxNwC4Q6sugGvXWN6vXsaMx7KuuAZ8Xe9MA2s4ykG1naMik2QSPBzo6XILB1hbNJKPjY70rgSTLllmsXWuSzSo2bTJIpTQdHS6Dg62Fm98P4TDcfPOh1WsqM3bIeHAhhBBCCPHJ4nHNd6//EzzvwbyxN/BXBikS4LXQXfzP8J/iOPDRRwaByhj3711Nsemjp7NII2Dw4Z4OMGBK32reiN1FIJAiEoGn8o+AA1/V/0CICl4aZElQIErC7sNXqbGjaxHZSpzsqMbUNlG3QUSPoWoOhrZbPdNw8boVzGaN93PXM+3RPXT+4xb899WB1rpyIgc/3nzGJPdgjGFvNyGVp02P0MlemspLVBcJU6SOlw6G8LoN6g0fhgHKBdU/gNu0cSZ34x0bwvPh+2hDoVwXD3UKbpSm6SHolkCDrS1GdYrOO+fju3ERbjwOwO4n1lLcMYAZDtM/4ybad77DQD7O1YGPmebtpx4I45oJQrUM8/a+gvt2O3rBZWf2F38W/cmf/BEr9t2DHewHP3ia7u6es3BE8Kd/+t+xrOML2Xzzm7/OrFlz+K3f+uRA2Je+9FUefvjRUz28I9x003X8xV/8TxYuvOWMf6+zRQJoQpxFKjOGZ+2bOKk0OpUG00Cn0q3x0Ctfw5k9G6d32sTBl0CAxuI78bz0GpszXWTD3XiiAQI2lPtM5hdW8fPoXVSsJPU6bN5sUCy2gmdLlrQCPStWtPN+dBE37H2WRsXhM5XVdLIXjaJMgCYmNhYDdJMzErzqWcwsI0hXSo8PKHjyH21+68OVtDX68CgbVxsYNHBRWMolrEvoIRO9xIGfgLq/dfjHu7goPhghrMtYOJhOk916Oh3GEB9wOW+7VxNUFaKeGgvcVUTsLGHK4DHQHm+rdL3ZxNq6BZ1I4nZ0YMVC7NqZZNOgQSLRqsir11uBsRldFXpe/T6hj95ElcvoUIh3uYmXR75ArNMkkWid99BQ6+d4yy02tZpCKU0mY1CrKUC3qgqXPY1n7YHnac5f0Cp3l4ydEEIIIcQxJRJw+Wdi/I/K/2Ymmyl90Mducyo7rVlMn+4y1XTZutWguWUnc/KvEtM5PAUb5fHQGenhndBNWPlRgrUMX/tanJER+Mu/9POycyu/xt+SJ46fGkEqeLCpGgE8rstgKUKh0ppa2W6MotC4jqau/fhprUeVUcc1LPw+E59dZ1doHum524HW5ofl+2IwXi9cdRW89x40GvD8863/bxhgXeLgNVxidgaPW8XExUsdv65RMUKYrsa77/tpwEcDXLDrihGC/P/Zu+/4OKprgeO/KduLVqsuy5K73GnGxjbFGAwYg6km1EBCgJBQE2pICHkJJQnhQQiQwCOE0CE0YzCB0MENYzDuvUiy+mq1vczMfX+sJFtYNq7YhPv9fPyxNDs7c0e71h6fufccX6qZdKwenxLHbhNYvSohFkdNp/CJMEnhI6oF0ITBHNvhLPcfwaErPsO5ZA7C46Ft2FgeLv0lrpIkzvJcM4DjwmsojTQQiNSSyfNiOjzYMzHiup+Ir5w+y2ZjhI77r74pPGbMOH7xi1u7bQvsw2YJfn/eHj2eEALTNHG73YB7jx57W77Jc30T5BJOSdqH1HBbLsHi92/eaBhodbXY5s3Ffffvt7ukUw234dAMHAMrCKfdJBIKhgENqTy8IsZhg1oYNszC6cx9YDc2Khx8sMGIESZHHGEwebLB3Moz+DD/FMrCy+ktNoKiksBNEg9+YtgxKKGRqOWhxQqSTkN9vUJtrULd6jTXL/0hJ6ano2OQEE7iwoWFRlzxkq+241aSZLFjChWG5Mb91eBi9Ojc37A5uABQBwtsioGChYJFmaijXN1ES/4AWr2VqB4XMXsB7ZaXgNlKAS04jDhGVpA2NcyswDA1MkkDdflytLpaEgcfRrue++AXIrcEU3QUqpjY+i9878xAaBpmRQWprEbg/RlMjv2LoiKB3y/w+XKFbOvqcr8+8/MF2WyuEUEgkDtO56zCzuMITcMxcwaO6S/v4XeQJEmSJEnSf6epU3Ox6gbbIN4Sx1HvGciAARZDhlgMG2Zx1FEGV1h/IZhuwMpatJs+EkmF/KbVTEi/RfUhLi69ycOUKQZHH21SUiI4V3mGKmUDMdVPXPEiUPAQI51Vacu4yTRH8KWaaVOCKEKQ1DxomNjIAgIFgWZmidmDaJpgQHYFRRWrcA9KAPDhh7mGWQcfDIsXw9y5sGhR7vvGRvjoo9y1KYPBOSRJgdWEjkEMLyEKsBQdC7XjfCBQESgdrQEEZAzSmgtLs+PJtJHNWKRMGwhBurCcNlsxQrVhKDZiip/3/FNZU3k049PvkDb0rrjU+ebr9F//PsmKASRcBSRcBWysHEdfWx1uI0Iqq2FLx9BSCer13nj6FeE0Y6jhtn3zZviG2O02CgoKu/3RNK3HJY733fcnrrji0l0+l2ma3H//PZxwwgROPPEYHnzwvq7/k3T66hLOl156gbPPPo2JE8dx8snH8ctf3gDkZs998cUCXnjhGQ4/fBSHHz6K+vpNLFgwn8MPH8Xs2Z/wwx+ez9FHj+XLL7/Y5rLKv//9YU466ViOO+4o/vjHO8hms12P9bQk9aKLzuXRR//W9TjADTf8jMMPH9X1/VfPZVkWjz32CKeddiJHHz2Wiy46lzlzZnU9Xl+/icMPH8UHH7zLlVdexjHHjOfCC89h8eIvd+nnvKfJGWiStA911g9TIhFEkRMAfcUytBXLEU4nRlUflGx2m0s6O58/yB3GtDmpq1Npb1fwGu3Y8j2UDQtQ6Lfo188iFFJYulRl3fwwz8/NLSscdmSAq25UMRqPofz3L8KyAjKWjtkUzU35RsdHBBsZFjOchmwh77+f+8WuplM8aP6YY8w3sJElK8CuZDDRUABFWGRVB3YrjYaFMUzHVp0BugcXzz4LAwfmOnKecw4sWJALLo46CrTBFs6hKTJLXWQtjTbyCCkFRNUqAmYrOHRa8TAh/SZ5tKFi5roPGQqaqoACWdUOGYtsOI06djy1Y0+naLbA4xE0NqpEo7lZb6P6NjN6/SckfCV4i4oAiHtdtOoaw8KfUJc8AVwFVFRYLF+eW7oZiSjEYtDcrHDkkQbhsILa1kLFlrMKIdcenFw798yk4/+r79xJkiRJkiTtrlAoV1Zj0iSDUaMM7rzTgcsFvXtvTjDkt6ymuv1T2pUATiWDrlpkFRdkDBybNmE7uy//nl/C3LlabvWA0crB9sXE0j58VjteYmSwo2EQJISJTlA08poxhnVUMFBZhcNKo2Ogk8FCI4WOgkIirZPM2PCoLvpOXdU1ppdegp/9LFfPt/Pm8KBBMHt2rkTJSy/lYlwA7bQs1mKdGD4alDI8WhK3kSRftJJBR8VE6yiqAgoCEAi0WJQQPgJWFruSIWXaUYWKWT2E+e5elIeX81nlqSwqPw69OJ/JH/6KhLcYrbwAHLm4VEnBiLpZrGyaDL1zBXy/rD6DVEucoxc/gD8bIiH8NPmrYeBgSl2tpDQv1jc8G6vzfRAIiP+6OsPPPvskb7wxg5tvvpWqqr48++yTfPjh+xxyyKge91++fCn33Xc3v/zlbxgx4gAikXYWLvwCgKuvvo6amo307dufH/3oMiA3c66+fhMAf/3rX7jiiqspL6/A5/Px+eefbXX8+fM/xW638+c//42GhnruuOM3+P15XHbZT3foeh555J+cfPIkbr31NxxyyBhUVetxvxdeeIZnn32S66//BYMGVTNjxnRuuulnPPHE8/TuXdm138MPP8hPf3oNFRW9efjhB7nttlt49tmXd3hJ694iE2iStA+JYAHZMWNzNdAAxelAXZX7EDb7D4RAPgK2mXwRwQLaho3F+ebrDCyF/r38xDdFqU+1sLDvVLL+3L4uFzSsS3HUhhc5ofUTirUWDHTmLzmSWZnzOeWQEI2tOnoyn6yhoah2vFYEXZjoGNRSwVOcu7kDUqqVH2X+wgT+g4EK2FAVsJMljYKFip0UUStIGg0PcbQzNtcH25ngInuaTnaxRkpxsUg5iJC9BFciwwJjDCOSCxhjzaUXtbQpQdwigYsEOgampZFU3DRqvXArKTb6DqHoxO+Tl+/E7xe4XFBSkusIFQgIPJta8W+MoheUd43T6RRk3HmoiRrcqTYSroKOOnLQ2KgSCin4/YJgULBwocbcuTr9zDiX1iUoGd2r2y9Y4fej1tWihtswZQJNkiRJkiRpK8kkXbVn4/HcDP8xY0yOOsrknXd0mptz5TciEYWidRvRsyk2ab3wiwjeTASbmsZSbaTR+M+mYcz8Mtd1varKQqwMYVeytPvLqYjPJ23acIg0TlKoQAsBgoS5gCcx0WgRBaDraKrAnW1HFxlalGI0TRBWg+QbLQRECMfUeNf4L74YDjhg8/VYMQXVK7Db4Z57YOHCzY+pZ1oovxX4aUcIBUt10qCWUWFtwEJBQ6B2dNE0UclgJ44X3UiSUPIJK/m06MVs9Axn/Agbgbb1DGv4ACORxZdYQF6tTiRYidYewjeyX1cVESWZxGm3qPS0kWloI+4swO8XNEfcfFl8OX1PNhlV9xr4y2hKlpKoaaMm3sLCPlPJvl3C1KnGXq9Isq33wd4+96xZHzNp0hFd348ZM47f/e73e+Vczz//DBdccBFHHTURgOuuu5l58+Zsc//GxgacTifjxx+B2+2htLSMQYMGA+D1etF1HafTSUFB4VbP/dGPLuPQQw/b7nhsNhs33/xrnE4n/frlEnEPPPBnLrnkclT16xcu5ufnd4zF1+MYOj3zzJOcd96FHHvs8QD85CdX8fnn83n++Wf4+c9v7NrvnHPOZ9y4wwG4+OLLuOCCs6irq6Wqqs/XjmVvkgk0SdrHOttA2+bNQV2/HiWbxagejFk9uGufnpIvnR8sCxaezQFRGyPqZtErbxPlg9ysmHgSM5vPJK85l+Cpr1c4eOE/mWq8jDudxmNF8alx+iqfsvihhbx4xk0MaC2iytFOQNTTonmJKHkUaa1AitmuEyDpwmslOTPxPMdl32A8H+Ej2vGBbsMhclN8dXIzzlQsHFYKS6hYKNjP3NwN9KvBhRHT0L1mj8GF/cwMjb8tYQkjWGUbilOkGJZeiGLEcZKgkFZSuFhHP4JKG2WiBjsZLFSEUPAaEVq0Et7UTmSKUkiZK3fn8pNPdHRd4HbnlmGW2Qvwl3twZSMIcrMBXS7omx+mNuplYyyI5qajCQOcdFKGo482ee89jY8+ygVnhYUWyaYgaxr9pL+M0W+ss+s6lEgE4fnm79xJkiRJkiTtK0qoFTUeQfH4d2gG/vTpOjNn5uKqiopc86uZM3WOOSa3nHPePI26OhWPRxAtqCSJCw8x2hyltFsFqIZBvhYGVeW9uiEUVeQ6rQPk9w+QaPHSkjVI6T4wk7nauUACJ1kclNOAToYsdmrUPtiNNAndC/Y8itO1+EQ7LUYhAZoxFRVlkIU6fHOMu2V82/57HzX/05uK2+oIXN++1ePqSNAHZHGsTpFHmC/FKBrUIoqsBpykUBCksQMCGxY2DOJ4CGnFtCkF+EQEp5lCTcZwr9yAsX4DWlZjo7eaimwNo1s+orU1SLErTn68FSt1MPqa1ai1tSjRCFWaznmD3uKJTC/q6tx4PLk6yf2POw/rLTetz8wl3dFkYNWwk5lfcgabZubSB9/7XvfGWXvatt4He/vcBx10CNddd3PX907n3snWxWIxWltbGDp0c1MGXdeprh4CiB6fc+ihYygtLeOss05hzJixjBkzjiOPPBqn09nj/lsaPHjo1+4zYMDAbscaNmwkyWSCpqZGSkvLvv6idkA8HqOlpZkRIw7otn3EiANYvXpVt239+w/s+rozIdfWFpIJNEn6znO5SH/vXDKTjsdZtxHtrw8i3J5u1fR7Sr5s/mDRWDP+PFY2TSbT0MaYA/wcd04ex0zXmTcPmjemOWj+45yReoACJYTDTBNV8whZQXx6gqG177DyjQOoqRpH7/oG4rYyPGYILRnGnWkn6Smgb3IpvzVuxmXPMjS9EJ8SwSdi6GSxI8hgI4OOnQwu0ihADC/1opgYPoKD2hg4YnXX2LcMHup/X0Lkf/LQ/8ei/89Xb/W4PtKiaUAxq9cMAJvOiNTnFBkNLFUG004ew8QSHGQoEfXU0hsLi1IaO4rCZmkQxbxoP49X1WmcKATTp+s0NytUVZmEwyrxOESjCn2PClJy0Bi0d3KzAYXfjxKJ0N/bRMNRpxBVCojXKV3BxdSpBskkLFmiUVS0OTijd5CaqnEUbZxOpsLCXpw7jtbcRHrySXL5piRJkiRJ//22aKikJRPoLvfXNlQKhWDu3O5xVeffX3yhccstaSZNypXMEEJwzz0D+cxzOBMTb6AZENf8uIjiyMT4rGwyNc6B9PNvTm6Vj8inpmEcZWufR5gmDpFFxUJ0LJIspw6NLApgJ02ltY719CU/z8JumaTTdpK4yCOCodhAKOinZba6DqtBIfZ9F0vfHowNk5abisi8a6fgsRa00u7JEfUMcPw+jY0sfY1VBNVmQnoJLpKoZhaviGAni4KJikqQVkxTJ21zs0mUc7D5KUcm38a7JE7aVMh4+xLIVwiG6zFtLvIwiNgLKVq9CpobUUwDYbOBqmKVljI2/g6DjnBRc/R5WyyTdFE/6Vwe+HAqjkAIkR/EDAQJuCCrC+bN05g0ydhrSyq39z7Y2+d2uVw9dtxUFGWr+mSGsXeTiF/ldnt49NEn+fzzz/j00zn83//9lb///WEeeeSf+Hy+7T53TyQCFUX9Rn8GWy7VVJTcv1HLsra1+zdGJtAkaT8hggWYpcVYRx7dtaSzM4nz1eRLTx8s9A4Sdxbw4RI4Ipnme98zmDTJIP3Is5ifvIpLS6OYuZlhBUYTeUqItOFA1RWGNb3PW6PuZpFP0LduFk5HPvamOlIJJ5ucQ4iTT7HVwvjUBxhCpVGrIGHGcANOkjhIY3Yk0FQgiYNlDMWumuQTIe/08NbX2wB13y9l9duD8BPGeV2WN/9zAhMfewd7abbbvgee8QWlv2+gNlFJJRtZrfQn5KvEqyWJRfNxGGnyCdNOHs0UAYISmqnTKqlV+zAgvYxTMy8QbTqJuXPzKC3N/dySSYtUKlfHTFEgdMxp2O0dswHrahEeL9mTTuLAqVOpTKa3qsFQX68QCikUFAiSyc3x4IqRp5NKQe/UR7g6jpOefFLXbENJkiRJkqT/Zp0NlcyiYqzevRFt4W3W9O0UDivE4woVFd3/k+z3C+rqVMJhhX79cqUz1q5VMAyF5w68HfE5jEl/TIHZQFJx8Zb9RFae8j/kt3cs9eyIlW02WH3QGRyS/AR/y6coGFhZhSw2vERRoaNgfy6l5iZOBTV8VnQ2VlOIMAK3lmaC+R6WgCx21NO6JxRSM+2YF6rUN5eTxUE+zeRbIfQ3M7Qf4CbvHwm0yZufo5wOyu9BxaJQNKKZBjF3IUkc9Eqswk4a2DwnyU6GEhooyTZR3TE7TVdAqArC0ihKbKAgtYmwvzdZmxtHqp3lnuGU+S1865Zi5eeD14fRrwKzejBKuI38pXOwn9a9TExDg8K8NUWkUsVYa3M/u169LPr2tWhszL0WwWDPM6V21468D/bWubclEMhn3bo13batXr0CTdu1dIrX66WgoJClSxdz4IEHA7lk1IoVy6jeYhXSV+m6zqGHjuHQQ8fwgx9cygknTGDBgk856qiJ2Gw2LMvcpfEArF69inQ6hcORm4W2ZMkiXC43xcUlAAQCAVpbW7r2j8dj1NfXbTW+7Y3B4/FSWFjEokULOeigQ7q2L1q0kCFDhu3y2L9JMoEmSfuZbks6t5F82dEPlgJasVbPZpGrnPzEJpyJpo5ipBZ2kUIng2oIBkU/482WMAuGn8/y/pMpalnOce/+ErcwGCtmI8wUCVPFIyKkcRIznWjko2NgI4OdLEZHlyADjSRu8pUwrXopumlQcFprt3GaMxWsCwW+5nbK2YSPCC5SuN+MEz/AQfYfOp7Jm7uOqqdbBH4fxi6yJISbhepBuN1gdzrZEKnEQzsaBnbSlBAnQJgwAT5TR9OmFZAn2jnReBXfR1ni8R90/dxcLnC5css46+pU2lJugpOOxxw0CCHA6tO3K5gIuuj2YZ1MwnvvaaxcqWIY4PPlAovqaotwwk3z0POZ/JOJ2JRcwwY580ySJEmSpO8CJdSKbcuGSpqKKCr+2oZKgUCuydOWSS/Ilc/Ystv5lvsKdz7/dD/Ek2vWUJJaT43ah0SvATx4SZKPPjK7lv111k1LNSYpKxMkRxyLVduEumoVejREZ4UnC7Wr7piKIECYXsvfYz19CNMbj7mJTZRiopHGwbDAUgBEBtI32jHvUzGF2pXoKqcBFQMdk2yTndQUG45rsmh3CRQ7EACr69wKug3K0+sRSm4caezomB0z0Cx0TAQaKcWJV8QQgE1YoDvJCie6lcGVjeBMtyOU3BgttxerejCieSPZgw7Gqqjsuuu7rRq9s2drNDUpuN2QlydIp2H1apVoVKFfP6vba7Gn7cz74JtyyCGH8swzTzBz5gyGDx/JW2/NZO3aNQwcWL3Lx5w27WyefPJxKioqqarqw7PPPkUsFtvm/p988hGbNtVx4IEH4fP5mT37E4QQ9O5dBUBpaTlLly6mvn4TLpcbv9+/U+PJZrPceedvufDCi2lo2MTf//4wZ5xxVlf9s0MOOZSZM19j/Pgj8Xp9/N///XWrRgGlpeV8+uk8hgwZgc1m73EM5557AY8++jd69apg4MBBvP76a6xatZJbb/3dTo13X5EJNEna32yxpFMN95x82dEPFjXcht2IEff1IZIppVdyNQgBnR19FBXFrhIwWum74t98HByO31/A0FVzyI9sxOFSsBkJTJuGM5MGBHbSFNFEHb3QMPESw0KlmQLsZDru2ln4RISWTAEGFmrHeEQGzBsVxH2gCgUvSRTqyWLHQiFAG+6mNI1TijCv0fDdFesILhSaKGG1Ooh+1hrKHK1ENQ+aBmscQ/El2ykR9axhACYaZdSznj5E9XxUp4OwWYyz0qT3htmU6CcTiRRs9XMLOBL0fu9ZPEtmo7SFQNfJHjae1LkX9LjUYPp0nY8+0ikpsdi0SSWdhpUrc4FFXl5umWegfwEmMnEmSZIkSdJ3hxpuQ4nHsSoqum3/uoZKwSAcNbyJuTMjJFL5aMVBIhGF5maFyZO7L9sLBmHMmFyCrKREYKvoS2trP4gqnHNKlvJymDo1t7xsy7ppRwxrJvtWnPdaK8kog7A8B3Bg7E2qxQog1+tSoCLIzQpTgCpW4yBJHmGWMIIMNgazAgONtef3odeZm0g/bWftwj64SJNPG4W0onXcXu6MOOO6H6/RBv8ryLyrYz/XwHqBjvPlmgZkNQe2bAtCyZ3dREXHQKCSi987YmoBGrklqKqlIISFR8RICQfCAk+ylaylUeMeSlGVE8XIkLF7SCsubFvEtT2ViQmvaaVldpwR5QWsChWRySg4nYJUCjZuVDj5ZHOvdsTc8rWFzcnPnt4H35QxY8Zy0UU/4qGH7ieTSTNlylROOGEKa9as/vonb8PZZ59Pa2srt9/+axRFZcqUqRx55ATi8Z6TaF6vjw8+eJe///1hMpk0FRWV/PrXt9OvX38gV3T/9ttv4/zzp5FOp3nhhek7NZ5Row6ld+9KrrjiEjKZLMceezw//OGlXY9fcMFF1NfXccMN1+DxeLnkksu7unx2uuKKa3jggf/llVdeoqiomH/967WtznPmmWcTi8X4y1/upa0tRJ8+/bjrrnu6deDcnyniqwtZ/8s0N0f39RB2i92ukcns+lTMbyt53V/vuec2F9f86gdLZ3FNJdSK53e3sXqdjQ0bVI5ueBankfs3YaFiOtzYnSqWqlNTeggPDrqHJqOAS5Zex+D2uTgTbWSEjZRlR8umyCc3kyxCHqsZgJsY/VlHmDxeZwqHsIB8QviJoABr6UMBLfQ5tAbzTBXjaRssBA0DBQsd0bHoU8XAhqIoKCJXMFXFRDvAwH5uFusFldAXJfzbcRJDk59jerzU2vvTYuThSEYoNBuZ5T6G+e7DmdD6EmcbT5HGThoXm/RKsgMHM3FCGkdjLS8Mu5VnPxu81c/tmuDjHLbpZaxwDEc8hJ6Og2GQmTCR+O1/6JZEC4Xgd79zoGm5BgQrVqjU1alEIqDr8KMfZTjnnK/vEiTf598t8rqhqGj7NTok6bvu2x63fp3v6u9B+G5de2f8KTQNUVSMpqmYpoXS3IRiWcRv+fXWM9A6aqaps2azaWWCunYfi/LGs3DQGRw0zt5j98XOhlrz5m3u1Dh6tNlVp7az9AbkliTOmaPx/osRzlv8S1SbSia/mHBYoSiylu+JZ1Cwck2oUHKJqY5kVQxPx4w0i5UM5N8cxyBWUcVGvMQopYElDOFdjuVAPmcIy/HTTgEhLEBBIYudOq0SvwhTYDUTx4WLdMdi0VyiDBTCzhICqUZMNByk6EyvGR0JNoAMDgCcHcs7AYTNhmKYuRSbEFhobAweQNvwsdizSTI1zYS0AoJmK86qIipHerElNpeJSX/v3K7XIPWfOSyfn0T1e5ijj+d5cRZRw4Wm5TrU/+lPKYYO/foUwu6857f32u7tDqB7wnfp3/tXfZuvfUfiVDkDTZK+pXq6q9ZZ3L6TCBaQHTOW/s0z0MoDtLeXoEXTuYKpdju6z4VlGAjDpCK0mF+lf0Vz1UFkNibZ0FLOwFQjhqpjidwdOAMdAx0bWQppxUAljpuVDGITvejFJvJpI40dBxnyacNBhvSnDtRPTUzUjlppueKsAlCAFgqJOQsoydahm2k8xBFAdqGN9EIHdjLYlBR9U8tZ4xhCrGQgQ1Of08tsot7w8iljmFd2Ege0vMdB5mcdi0odCEVliLIcS7WwJYoQHi9Hn+Fjk8dg9myNlhaVYFAw5bBGSl6fxcZ1cfISjbTaPDgDXgqdbTg+eBfzmSdIbXEHZssltLoOw4ZZ9OuX6xAUCilMmGB+Kz7cJUmSJEmS9rTO+LOzpi/5AZS28HYbKm1ZM63XuF4UNUU4oOEVzhyZxbaNmmkuF101fzuTZS5XLvEyd+7mxMuYMSaZDPznPzrr2wpZGhzHMdFXqW8FxQxgqjpJ04mLBDrdy6NYQBInWkcXzP6sZThLmM+hNGoVjNQW4zGSLLeGMYa5lNCInQwe4h2pMTDQMFEoNjcRw4OJhk4uXlexMNABQRYbRsoghR0bJmkc6BgdizZzy0pzrQQsbGS74mgAJZvtSK8pZBQ3hmbD4dXxRhtZ3+Kjpmoqa4ZOpv/SmfTeOAt7ahOVQ93dysR0vQa+YsJ5vfEa7RyffZXiSsGHfS8gFgO7HUpL9/78m55e230x80ySvkom0CTpW2pHP1g6PxT7zpuDiHvQ1+oItwdRUkJiUxt6NE5S9xPVCkmGPNjXf0K6IclGUUGJvh63FUcRBiomLRRSQyXFNLOWftRTQhoH5WziCD6khEb8RHCQpokiFnAwnzGa7ynPMVQs6aqSZqGSxoGNLGlcNFCGkhIksePExEaGTEfwYKGQxEXCEaB/di0NVgW2DesImWl6qQ30s+topDmgdjEl6RrW6gPRdZ1B2aVkVTu6U0fbuAqzMEN66mm88lEJixdrGIaCrsPQoSbO9jai69uoyISwXG4yqodYGAjkU6xnsc2eRfrUM7oCvp6W0LpcEIvl6qTti9oMkiRJkiRJX0cJtW6zRMie1K2mb20twuXeZkOlrWqmAfbeThQnuJbOIR7quWZap2Bwc53aLVdoVFTkbm6++qpOIqFQUiLQdfi47EycThjS+gnlqY1k0VlONQeyEL6SQFMALwnayaOdAMU0MpjlOMjSX68lz2zFIZKczr9yyzVtDlIOH0osV/jfQiGDHQWBkyQu4lho2DFROs6lYZDFRhQf7WoQ3TKw095R+2xzAwEBGNjQc2nJbgm0zrFaKGiqRchdwrJMf54zfopzXB/cvXP/SVg19jwWVkzGnQpx6U88BPoXbPUa2IuKKY2orFpVAir0b5zF3KITqY8WfuNLKLd8bSVpfyATaJL0Lfe1Hyxb1lRrqEd54M/Y53xCoiGCEouTsvvJ2r20+vqwpLkP3ribgdpy8rNhGp1V9Eksw0EaBYskFk7SPOa+nH8lTqKVIAnc3M9POYXXsJPBRCWKlyQu5qrjmVl9NZXtafo3rEOxDAx0dA0UVUFk02gYVLIeCxUnKexkMFDZQBUFWgS/EiWt+hAuF+mURbFVR72tkoAaocBoQJgqIUeAjN3HgORKnHaTtOpGFxkKrRa0VG5JaKh6Gm8wrSuo6pwx9s47Onp7Mbc5NdypODFbCbaOSrKp9gxGkRuMbLd6HftjbQZJkiRJkqRt6lieZ5s7GyUeR3g8ZMeMzSW09sa0+S3iT0c8Qtrj32YSbFdrpn1VT13qi4oEkUiuAH5FhYllqYSSbt4svoC53qMZs+ZZhqUWUEITAGZHF06gK3nlIEWSUsIE0DDwE2EEX6IYGqqVRRcpXJgY6GRVG3mpJnSyZLChIbCTQcfYItllkcaJraOkCVgkcDPbOQEdg4J0Ky2iCD8RXCTRMDHRyOAghZ0g4a7x9SRp96PYdLxKnLWhfAb4ghQmW3Gn2kg480kXF7C2rpCQkibA5rrJW74G1dW55F7jxjz09hrcqRCTJwe6Vrp8U4lYSdrfyASaJH1HiGABZrCA2N33YTzyBA2Pvk2lfTEpd5BQXh/aCofga02QSFgk/HnMYxxHJ15HV0yEohIRXhK4cZAinYJVDERRIF+00p91xHHRTBFp7IBCHmFOEy/ja7FRYa4lrAYpsBqwkUFVNAQaGhkEGgoKCpDG0VFjwsSjpmjVS9iUP4JmdyX2RDu9+ByHkuEAawHlRg0mGlHhoyRTi1ZQiLs9yfDkZ2QVGzF7kDZfJa5EG6owsHx5zPrc2y2oqvK24FHamNVQxKqKIxgQ+hRXqo2kMx+XlUbLxEh7SrEHC7oVV4UdW0IrSZIkSZK0P9hyiaRVUYESieCYOQMgV/9qLxHBAqzSYsR2aiJZgXyEx5MraF/k7NreU4H77dlWl/q8PEE0qjB3ro1MJrdiIBBQOMp8jzHWXLIuO0pMxRJqR6/L3BJLO9muWV0mKh4SNCplVOj1RPUAzkwMnQwp1YduhbGRRU+3YykaIEjgwkWqY8aYgoUg94hKSnFhKlkcpElbDmwY1FJObzbRruQRE25q6U2lWkOB1ZQrv4KKiwwgsAC12/y0HBULdyaM3YhTX9qfpOVm+IKnGJP9BEc2Rtrm5Uv/eOKVZxAI5O4Yh0IQaw/SX/dg73gNOkuUDPSHyKRclN3kIdDfyCVin9s6EZs54ijUVFIm1KT/ejKBJknfNS4XG0+6jMfnTOZH62/FcLiJeMspaVnKgLYazHgUK6mTdo1nbbqCOkcFCbuPsvgaCs1mXCLBj6yHSeLgcX5Atb6GSmMjAoUAYTRMLDRUh40RYjl69EVWqoP50H0cx2VfJ5iqxzI1DM2OioLAJIGbVrUIXTXxGu1k0Qk5e7FB6UPWVswQbwN6Sw02MhSJJgxhx0TDQMNHBL8VJhOKkdS8uIwYWWEjL9MCrS2gqgi3h+C7r6AEzsCqKMeIJhm94UX61s1Cicc4vM1Pa8EhrK6cwICN76OZGRKqh3ZPGXl5btqGHkZNuJAAm5fJytoMkiRJkiR9G/S0RFIUOTHJLbHMTNr+Esm97as104TfjxKJbLdmWk+21aV+1SoNIcAwBPn5oKpAa4jq7Czi+cX0GeFB/9yLCGm58aCSwoXe0UVTxcJPlKQjj2p9Ix4lRbESw3A7UbOgCQs1CgIFU9FJ627c2Xb8dDbuyh21M91loeIRMRRVAaGSwI2LFIen3sNQ7KwW/ejHWlRMfFY7Wkcrggg+vMQ7mhwI0DUwtqh9TMcMOtPEYWbxJZuYEniHQ2qmk8gvJurtjRaLMGz1dPr2tXC5vsdzz3XWiyvn+IYjOTb+CsUjQA3mXgN3tAlt8kmoHUs9t0rEhkK4HnkI5zNPYpWUdp/ZaPfu5jtDkvY/MoEmSd9BgYAgWjqA+ckTGdv4KvntG8mL1RJL27AUlUa1jInWf3CoET7ITqA6s4I+tjqcJW5iqSD2UIhp+itYuodV2mB8sRh2UsTxksGBQzNwZ9twKFn6H+qhIl1Hfts63JtasBAYqLQ4yvAbTdhJkUcbMcuLobtYZR9KwnKx2HYog8wVHG58iGNjmJaEk3zaiAkPdVYlAWxYgAZ4SNKcKQfhxE8biiKwiRQClbCjlLwyP9aatQzTn+Wv667nXONlKjOvYZQUEXZX4nBHOLDuTRb3P4H1Bx1M1YaPMVIG/j75zOk1jmcWTiM8z9FViHbLDkCyNoMkSZIkSfuzPbVEcm/qVjOtrhbh8W6zZtq29FRio6lJYcMGlepqk0AA6upUfD4oV1spiUYZekwZnqCDtcsG4lY24RYxbBhYKFioWEAKF24tjT0bYiWDKRJNFFsNaNk4NiULpplLZpkWAgUlm+2YIQYC0dFfE0xys8Y0DFQgY9kxhIaGRRoHNWoVg61lrGAAWaoZqi7HZaWwUEnhpFUvxaPVkc1kcIgklqWgKBqq2LImWm62XFL14I42cXrJDNJ9i6htL8GKgsvlYkCRYKT5Cc89cwIzPyzpqhc3330G4c9h4tJPqC6vxRbs/hr0lIhVW1tR2togmcQaOhyyma6ZjeKCC3b3bSFJ+x2ZQJOk76CuAKP5TBzZKMevfBAzC7Gsi1BeBQ35Q3GGGhiRrWGAfT39rI0U9Hah57lJrEqStPkJu8uZYH7CWm0wBhpOBAq54qxCAJaFATStjDHIX4+iAKaJYtOxmQa2TJI0TgxUbJgs1Yaz0epDvh4lL89gVr+LKEk/R+O6GtZmhpDWbRQZ9eQRISsaiOKliGZUDEDBMgUONUu7lk+ANrA0VMBpJEk2KUQNDyMci+mTXcXI6CesFyUkWkuw2QR9+xj4Ynb6b/qYx0bcjWfMWYytbiFqy+f1OR2BRVGuZtrMmTrxOEyYYMpZZ5IkSZIk7ff21BLJvWrLmr27UVurs5TGRx9p1NeraJqgpMTiwAMtPB7o188ilVLwpv3oH3txpqKkUi42eodi5WcYGvoEDzFAJY2T1Qxgue8QDs+8T9pUWegey9DEfMqNjahGltwCzw6ahq5YqEYaQ3VhoOGwkh0zw3JJtKyiYRdpABSRS7g5lQyrGMhqZRAl1FNKI3WO/qx12ekVrsVAoUkpI6V7SdnzcFgJTEPHEDoqJjZMBAoWGmnFSSOlhKwAlUYdiY2tLPH0R9jBZoOiIovKQV7ExlqWz2qnj0+j2AjR1BRkbXMhH2Qv5NnoSRxR0szIYXkcNzWv66bxVonYZBKtrhaRl4diGCjCwioq7prZmJ1yIngDu/qOkKT9kkygSdJ3VC7AsLHgjWPpZ3+fqLeA5myAsjJBhRIh6/VCfR5DlY14Yu1gC2JFEziMBPX+ahqNEirVGvoVtNEYr0DJ1uBQsmBmURRIq24UYeEJ1dJiy6OwwARFASV3t8wl0sTdXjzpBMISGDYXPiNKodnI2sqTqawUlH68iFVWf9q9JTisBI2ZcgqsJrzEaKGAdgK4lCQaCXRhsFwbxiCxnCKzAaFomIoGloUj2kq7rx8lQYOx/nV4V8RYmaki22QwQltCcFMNbiVJoZphWvRRKn91CY5AH373OwdFRQKvV6CEQlSaIZpaCvm//yvko48s8vO3npEmSZIkSZK0P9lTSyS/CZ01e3f7OCL3x2YDux0iEQWPR+BygcslaG4uZG35OA6LvoxhF9j1IOm8IuqMwTiTbay0DaVeraDZKmRwQQvpTXYUxeIAcwEV5ho0smhY3SuQWRaW201UcZPy5KOoClayFT2bxlBt6NkkulPDSua62wMYmp1GRyWfOSdSlAyzxhjCfEZxmOMLHIpBUvOgmhnS2HFYSVAEAoWkI59GrRRftpWg0Yqh2Um4CqjNlBBJOyikmZjlYX28mGwiQruzBIdDEI1q+BIxygpc9Fn1Dr1bv0RLxggbPrLWeDYUnEnUVUCtK581H0LKY/C97+WSkl9NxCqpJEo2g9B1hN2OcOaC4c6ZjUpbm0ygSf91ZAJNkr7jorYgEb0AXbXok1lF5YaNONUMqjCJuQr5omgS49c8jTPaiuX10xCopiVvCP3a6jFjJqm0wiatNwnTQVCL4CRJVnfRrhfgMiMUpuqJR71kC21omg6ZNMLuQsmaKA47WDaEojEsv542Rwmz3SfT/9qTaXlyJSVWPa1af3Qd4ik3G9U+uESCrEizlv4YLj+FjghxxQOxBK1WAFQFU7XnGgcoOqZmJ6n6cJJCqBruob2Jr/TiNdopMhvoa64kjhtDOMmq4Jw7i9nXljH4zrOIRBQSrUkOWv0ihyQ/wZGNM8Dy8oV3HO3+0zE1V9cygc7gQpIkSZIkaX+zJ5ZIfhtMn653dVwvLc2tHli/HhYtUtF1C79fYDaFsDW0YZ98BCKQxTnrYw6Nf0i2uR3D6cHpgjJbO/XOIfhNgep2IgT4RAR7ah1uK0YWGwITFROh6aiKAE3DChaSatPQsylaiqqxNDvOVBhbOobQbWSKSqiL+HEoKdqTTmr0ftSolThjEfKyTbzCqTypfp/XszVcpDxNoW09vc3V9BMrsWUs1KxAKCqtjkLmV53B4sIjmLbq9/RvW0DMcJJJC4pEMwElzBvKFD4W4zmVV9ENMBx+nPF2QsubyVQF6bvmPTaZJcS0KrxWOycrr6KE4I3C71NebhGLKcybpzFpUq7D/FaJWJstt7okHsccPgLRcTe5c2ajyN8PZjZK0h4mE2iS9B21OcAoIDR0LIcveABbNEREC6C4dbxGHJUEG63eFJ3wYwqbXkOUlqE2FdFn4RwqUmsxVZ0RbQtBZFFVgxqlkuW+Q7ApFl6rnXe9JzFBTKfMaoSYgcjPR4lEQJArzJrNkLF5iHpKUZ02FARBb4beHz6H74s5VMaXkZ9ey6pkNUsYyhptIP2M5RTRxkEsIJb08alyFE8M+BUD17/LkdE38aoJ4s4CVCNDWA9iy/eSiWXxp1uoLxzG+7XVbEwdzmniBfqwlpRwIFBwkWSNNpiwvQTvl7P54KXjWLq0jAkbX+IIazotWgk1RiV5tDMpPp3mRotFB58P0C24kCRJkiRJ2u/soSWS+7NQCObO1bp1XC8qEowYYbFunYoRTTJg8YuMaP+E3p5WyuaoWIePxxgyjEBNLetKBlHTnkefpk/pHV+Gzw8LHYfiSTSjILBZGdKqG0SuIYCl6mQUFw6fHdPrQmmPQjKJCFQQjpg06eVYHoXiRDuG6iZZ2gd9YG/Ci+O85zqBRpwMD88iIGqI4eMlTuVfTAMEx5lvMjnyAi4iKICDTG4JqOok5ihEMbKcWP93jki/w3qzgHYtiDMeplTESCoe3nacyC+Mu8hoLjRL4XDxMb2sGuJ2D2+kj+Xwmvm0qSW0UIKigGla+NV2jhLvMMucAvjx+wV1dSrhsNJV77dbIjbUilVUjBKLYQYLIZ3aembjdrqvfhctWDCfq676MTNnvofP59ujxz788FHcccfdHHnkhD163G/SmWeezFlnncNZZ+29zsC7SybQJOm/hBJq3eGA6KsBxnrnBMYufxxbNoHDMDCFndXekSTdhRzv/ZjAz28g9akb27w5DA3PIi0aiepuFCMLuorHqZDFTVlbLbaoQa1rIJ85D2O6/UwyvfI5KfEv8ga7ESX52FaugDVriXgKaE168OopGgqG06oEIdzOxFUPI1bAiuxovsgMYZCxlGrxBSZZPEQppYEELhJ4AIUhyc+pXv1vXgpeTEO/w6ho/RVpHHhFjEo2EnDHiWYsapX+vJF3DhuWqyzUp1FsNTDIWt5VM2KlOphV6lACDoPCRA3z3oxiNjkYa35Ci15Ck1JC2oKU6sShQr+GWaxJTsbvL9gquJAkSZIkSdof7aklkt+0HYlzw2EFJRSif0ErRjKfhCu3XzAoSCYFN/R9joqmV3FoUeyxNpQlcZTP5yFcLjKjxtA/0ka/2CKsvDQ2XcEfW4YrGybTliSl2TGzCrqRQhG5umcZxY7welHUDG2Gj6yw0UhfXh54C+Wp9QxomkV71ovirkRRIGwWE1vp4m3zWP7ZfBbuAifPx0/CkwkRIkhYLcDjEeRnQpydeZJKNqAJEzdJVHLV1kx0Qu5elJj1eFMtuDMqkcoKljcchp7exGfmgfzD/iM22gcSyyiowBPq93nffSIjK1qoTxeQrGvjePVjGpzF+ESGvolllImNOM0kbi3DBdlHWRe7nBguPB5BILBFfPvVRKzThf2jD3qc2Wjf6++KPe/2229j5swZnHLK6Vx//S+6PfanP/2el19+gcmTT+KWW27bNwOU9jmZQJOkb7tkEsf0l7HNnY0Sj3dvH72NwlzhsEI8rlBRkWusbTdSRL1l1AZHEI/BkINtVOS5cSlJPKFaEiRJf+9cjIGD8PzhTmxlJRSur8Gw3Kg+N7ZMHDMrWCiGE2jbSNrIUJVYyJXO1dT1GkXoyJMYbPsUqzXMSjGA+b3PYJltBEet/Dsp1UPWLMFuF9j8Flooia4L0g4vX6QOIIWNgaxgKEtxksDARqPSi4Rw4SRFoRLie8bTlJx1IudfXYH+9CScb76OGvTh0AdjJeK4khmays+kPlpBLKYQt9z8Q7uY6uxSNLJsUnuTxIVdAa/ZSsrmZW24kHJnK4VmlHVWFYiuEm4k7H701EbcqTY2GIVbBxeSJEmSJEnS7tvRODeZpPd7L/ODlfNwGjEUn5d1vcbxZfUZNEfclOgtVG6chVtEUUMNCI8Hq7gYtakJdVMdts/no1gWwuNBLc6DhA1l4wby3VHmlIxnTVOAccrbuKw47XovStRWXGoaXUmRSVrokQbsqorHEePIDU/xhXc8y6bdyNFjUvxnQT6zZ+sMsa9B0wXvLhyEYbiIxQQhowCLXKJPBQqVVo60vc+Q9BI8SgJVEejCBAQaApdIUOVtwRaJgWWhtbYwSHxMld3PYnshxZlWonoQmw1UFQwj93fWH2StFqQxCl6HgpbnIRBqp8hooK+2gojwkDac2BQYnfoIz7Ji3ii8gMmTe15hsWUi9r9tZmNxcQnvvPMWV131MxyOXNONdDrN22+/SUlJ6T4e3a4zDANdl+mf3aXu6wFIkrR7HNNfxjFzBkLTMCsqEJqGY+YMHNNf3uZzAgGBxyOIRHJ9gxLOfNI2L1YqS8YbJC8PgrTiijblujM5XTieexr3Q/ejr1iGfeUKbG1N2D16ruumw0GsLUuR1UxvbRN2p0KDozdZS+PI2JsccKhG/JZf89IBt3KrfgfTKy5H71VCvjdL2uWntFRw6KEGdiOJpgtUVcElktg9NpboB/AWk6ihFyY6tfQirvoQmk5S82J4/PTRajh52GpcLrCdfgLO3kG8iz7FPvsT9BXLobyUA381iZtuSlFWZuWKqNoL+NA+EScpPFYEu5WiVGkkP9PEksBYYo4ghj9I1uGlwhsmEMg1EwDwGu2kbR42xoI0NyuMHm3K5ZuSJEmSJEl72I7GuY7pL1Pw0QwKS1RqlEoSaY0hK6fTd/5LNDcrjK1uwZkIobS1ITwehNsDmo7ID4Kq5mZPORwItwcjC9lICmFaJCMGdWEfUT3ABucgFE3FppiEyodg8ztR0imsjIEQKsv1YXxkjicSMjhiw9O435pJurI/C1f5mJh8g+Nr/s6kxfdzY/gXXOZ6HIeVAkDXwaMmOd/6J7fEfsFFiQcJEMYm0rk7t+rm/7IrlomtuR4lHM4V8c+kUaNRHK319E8tYxiLGe5eQ39rFSV6a9fTVRUSidxsvIJB+awqGU8ftYbeiZWkcKJpuTGs1gbR4qxgaGgWpx3Z2NXV9OuIYAFmvwHf+uQZQHX1YIqLS/jgg/e6tn3wwXuUlJQyaFB117ZMJsO99/6Rk06axMSJ47j88otZtmxJt2PNnv0xZ599OhMnjufKKy+joaF+q/MtXPgFP/nJj5g4cTynnz6Fe+/9I8lkcrtjrKnZyE9/egkTJ47j/POn8emnc7o9Xl+/icMPH8U777zFFVdcysSJ43jrrZlYlsVjjz3CaaedyNFHj+Wii85lzpxZWz3vP//5Nz/+8Q+ZOHEcF1xwFp9//lnXPgsWzOfww0cxa9bHXHjh2UycOI5LL72ItWtX79R1tbWFuOGGa5k4cTzTpk3lrbdmbvea9xcygSZJ32JKqBXb3NmYRcWIomJwOBFFxZhFxdjmzUEJtfb4vGAQxowxaW5WaG5WaFML+NI/Hle4nlGJj/DPfgf7O2/j+OQjhBC5r2fOQDhdWHkBhBAosTg0NJJKKWQiaWIJjfzkJuKqjyZ7BcLuRPh8ZFQHtg/ep6EBHvrPEBbXF7FsmcaHi4uIp21UspFYc5J0OjczTFNBCEGyY9q4qubqPrRQSBY7oGCaYFm5YEBRFDQNfL5ccsvx1ptooVYyhxxK+uiJZA45FC3UiuOtN8nPB6cTvF5BXp5gpncar6mnomFRSQ0Om8ncspOZV3UmvXsL9JJ85ujjCaSbKDQbcakpytRGeumNLPSOJ2ovYPJkY4eDC0mSJEmSJGnH7Gicu+V+lYcW0mewnXZnCQ1WCQMbc4mgo8/wga6jJOIIh2PzSUwT4fGiGAYimSa+qgFj8Sr0mg2IeAJHWyOj2t9lhLmQhrxBrHMNRbUM6sNeQocczcpjL2Ge6yj+4zqJz93jqDLXMCi5iOL4eo5e8gDqXx/m4OXPMKr2VVJZjVZvb4SmMSE8neMj/wJyMe3p1gucwisYQqPZykdBoCKwWWno3usz952Vqy0mVA3h9mBpdtzEGWit4KfJe/hl9tf8yX4T15f8g6riOL16WRx4oMnll2c566wsM71nsrbiCNx6Bt1IY5qC+rxq8sYN5tBJHg6pbuf0Cc37vMu8EmpFW7t6m/+n2VumTJnK66+/1vX9669PZ8qUk7vt8+CDf+b999/llltu49FHn6Siojc/+9mVRCLtADQ2NnDLLTcwfvwR/OMfT3Hyyafy17/+pdsx6upque66K5kwYSKPP/4Mv/nNHXz55Rf87//+YZtjsyyLW265Hl238be//YPrrruZhx66v8d9//rXvzBt2tk8+eQLjB49lhdeeIZnn32Sn/70ah5//BlGjz6Mm276GTU1G7e6trPPPo+///0phg8fyY03/oz29vBX9rmPK664hkce+SeBQD433vgzDMPY4eu6/fbbaGpq5M9//iu//W1ueWxbW2ib172/+NbN4Xv//fd54IEHWLFiBQ6Hg0MPPZQHH3xwXw9LkvYJNdyGEo9jVVR0297ZPloNt22zzkVn0mfePI26OpV45RmMsz6mtOZLFEPLTW0P5KNtqkNfsxpjUDWiqJhUSzvqylWYeNAa24iEHYCgNROkzAyxzj0SzWVjaGwhxdEa3EoSdU6a9b96ioZ11+ApdODVkoxveh1PqIb+Yg1puw+HuwqvUU7McqOo4BVRmiMagXQ7xTTyrjKJoAhTxXosNDI48JLClQmTrajEO7IvfDXQ6mA2N2GbN4fYoBMoKirH4xE0NqqknE4+KTifRdkTcCbCFAzMI69fkJNGm2QyWd56S+ezfmdgq4Hh7bMoNWvwV3ro9/3JeI48meNL03LmmSRJkrRNMm6VpF23o3HulvvpOgwbZtGvn0U64sUXquGACc2Y5QPIHjYe/bP5KOEwIi+Akk6jJOKYlVVoGyHd2I49mURVLCxFAyFQMXFbMfplV6ClodXdmza1gAesn1IZ91MY3cC45EZq1BKq08voba4krrhJUkCh2UrFnJc4bJ3CUobQZi9G1wHdTTSicJjxCf92nkgmA4fzMY2UEBN+DmQBAlAABfHV/BmKZYGikGtlAFgWiirQhIkuMpQUZmjwDaTcDNM/8iq9SwWDbj2LqipBMAjJJNjtNj766CJKW5dgE1no3YvqkQ58PlCa21GCXjKBfdhFcxdK1OxJxx13In/72wNdM8YWLVrIb35zR9dMrGQyySuv/Itf/OI2xo4dD8CNN/6STz89mRkzXuWii37AK6+8SHl5BVdeeS0AlZV9WLNmNU899XjXeZ544jEmTTqhq2h+796VXH319Vx55aX8/Oc34dgy2dth/vx5bNiwnnvu+QuFhUUAXHrpT7nuuqu22nfatHM46qiJXd8/88yTnHfehRx77PEA/OQnV/H55/N5/vln+PnPb+za7/TTpzFhwjEA/PznNzF37mxmzHiV8867sGufH/zgEg499DAAfvnL2zjttBP54IP3mDz5hK+9rsbGBubMmcUjjzzOkCHDALjppls577wzd/AV2ne+VQm0f//73/zqV7/i2muv5bDDDsM0TVauXLmvhyVJ+4wVyEd4PLl20UXOru2d7aOt7XzwuVzwve8ZTJpk5ArgizYqHrAwKg8HrxfhdCFcLrQ1q9DWrMEYPJzlS1TqG4bjj2iUxVdSriRwkqLFXsaqVH+Sqouwq4x+qaVUZVfQLjxEs060KLjnz+JEvRcvKd/niPYXOTw7nZUMJIqXwWINzmULKUk18aJ1AiGtkCEsozBbQxQvb9hO4QWmkTHsXCb+ips4bpHAoQrUwiD2H03DChbk7lBtJ9DKFyH8/jLy86G62iCVUnA6BbFYPolEPj/6UYaqqvQWwQXMm+fky4LzqbEmc/jQZkZd4MNRXkDfvfaqSpIkSf8NZNwqSbtnR+PcnvZzucAd654ISp17AdqSRTg+eBeRzSLcbsySUkyXl+VGfwq+/AhDceG14miawK6kEKbAKZK0iTwqkyvRRZaX1VOpii/njLUfU6i04LeWEzCbyCNOWrNjoWGz0kR0P/UtBZTEvySsHEA8q2C3C4QAIfKoUGso0kLggoJUO24Rp0LUUMUGVEVB6UicCV1H6ZjZYwWDZEcehP3TubluoEYWUilUXUdoKllTp10LojgdtKZLsaEwwfYxpVUTu5ZXdv4fIDTJi/70kRTPeg2tVzvC7kdp/koXzX2kc+muWVSMVVGBEongmDkDyNVc29vy8/MZO3Y8b7zxGkIIxo0bTyAQ6Hq8rq4WwzAYOfKArm26rjNkyDDWr18HwPr16xg6dFi34w4fPqLb96tXr2LNmlW8/fabXduEEFiWRX39Jj788D2eeOKxrseeeOIF1q9fR3FxaVfyLHfckT1ex+DBQ7q+jsdjtLQ0M2LEAd32GTHiAFavXvWVcW4+nq7rVFcPYcOG9d32GTZs8z5+fx6VlVVs2LBuh66rpmYDmqZRXb15fFVVffB692xn0r3hW5NAMwyD22+/neuvv55p06Z1bR8wYMA+HJUk7VsiWEB2zNjcBwy5RNFW7aO/RjCYq4egrc3dvTMrKsCxOUixCnK/nOsXhlgVKcJlMzFNsKsGUeFnvedANlWN5fbV5zE28S7nJV+gd3YtMdOJaSr41CQNedVsipVyYHwWb0XHcbDyCZuUEprVEuqtYtxKFnfiC/pZKzlXDbHBquIVcSr/USbSqPUi4cxN83pFnItm2Tg8/S7lzhBDxvtwnTyhq6X21wVa/j75jBljMnNm7lef35+rA9fcrDB5ssFBB22+xffVBGMg4CEY9Oz2ayZJkiT995NxqyTtvh2Nc3c4Hna5iN/+B9ofeQLb7Fk41Cx6aQGfiPE83zqUH2tryFfasVltpC0nMVsRpmXiFxGcZgxdsfhCP4REO5ymvIo3r5BGZQCx2naGshgnaVKmAxsmGhbrzH4sC5dRpn1JpaOJZWY+qZSCrkOR3o7D70Y48vF4BJW1jZSmNxATXjS7DTQ3IpFAQQGHAwQIRcEcPAxz6DDEqhUobSEslwuzrBzFyKDHY6Q8ARL2fGIRBbsdSqt99HVvJN3DqpRgELj4VESRQOmhi+a+stXSXUAUOTEB27w5ZCYd/40k96ZMOaVryeHPfnbDXjlHMpnglFNO58wzz97qsZKSUk499QwmTpzUta2wsHCnju/aR2twv+66amo27INR7RnfmgTa0qVLaWxsRFVVTj31VFpaWhg8eDA33HADgwYN2tfDk6R9pvMDrqf20Tujp+RTMglmaxZnsBfx+hhlvkaC0VoKUktRVYXV9uE0GGUMaJrDFGcpT6lnMUBpoE9qOboJimLR7KwgXlBFW9RFoVVDP3U9PmJspBLThBHKUgbEF2MjjanomDYnfa0NXKQ8SVTJ5yXvCPJcuZplRWqM5aHD+DJ7GN+/IMuIS3ykt/gA3ZEA6qtLVz0esd06Zp0Jxh21I23WJUmSpP9uMm6VpD1jR+PcHdkvmYTp033MXX8liuM8ivVWKqrymLu6mOKyVTSvqaTVyoICTjOO3UjhUFKoIovbiJFU3fSOrWCE+SHhwgEk/SU01yksVsZSLmrpyzoUIImLqOLCLeIMMpfR4qwgqEcY4G6g1cjDY7QTNJpY3utkykrziW1ow7IAFDS7ht2pIrCh2O0IRcUqKERJxME0MUtLQVUweldii7SD3YFqZBGmhXC5cFRXMeZgO6mUidMpcMfCKJYXIUBbu3rr+NTl2u+6aO5OiZo9acyYsWSzWRRFYfTosd0e69WrApvNxpdfLqS0tAzI3ThZvnwp06adA0CfPn35+OMPuj1vyZLF3b4fNGgw69ato6Kid49jsNny8Pvzum3r06cvTU0NtLS0dCXUlixZ9LXX4/F4KSwsYtGihRx00CFd2xctWti1jHLzOBdx4IEHd13XihXLOOOMs7bap7Q015U0EolQU7ORqqq+O3RdVVV9ME2TFSuWdZ1748b1xGLRr72Ofe1bk0CrqakB4C9/+Qs33XQTvXr14rHHHuOCCy7g3//+d7cplZL0nbKHPvi2TD5lDFjZGKBtfRRvvIX5xefRZHdwIu/QK7KCiOpivWMQG7xDSWRslLgEY1Of8IZjCl8Mv5Dh8xZS0L4Wl5WkINNA0YYQrnQeq+jLRrUPac1LIe20Wxb9zLXoIoOh2LA0G1FbEJuVxmMlOEa8y/tiCoWFTiY0/4sR7Z/gMmPk9fIwonw0wrV1kvDrAqitZ5aJPVPHbC/WapBJOUmSpG+XXY1bbTYNRfkGB/oN03VtXw9hn5HXvovsXsQFF5CdcmKug2Z+Lhay78J+Mx4P8+Xr7QTK8tEHFRBqL2DxhynGbHyKE7yfEFA24ks2oos0PhEhI+zYSKErFjYlQZOtHKFq9BdrWBH1UtfYi0RCwWVLkUq7aSNARnHRpgRJKF4CtNHHWsfT/itJu4OMjM6iSqkhJLzMLpnK5wVnUlYGFYEWjEgRdSkP/d31aIYN4ulch1BNxerbD0VTsSoqUFHQ6jch+vUjO2ggSiKBEoki8gMYDgdqUxO+RAvePD9KewS1qQGruBjfX++HWAy8XozDxpI99fTu8WlpMZQWsyffpbv6uivFhah+H0oshnC7N2+PxcDnQy8uRLPvnX9PqqqgKAp2uwZoPP/8SwC4XLl3kqIoqKpCXp6XM86YxkMP3UdBQYCSklKeeOJx0ukUp59+OrquMW3aNJ599kn++tc/c8opp7Fs2TJmdixDtds17HaNiy76AT/84YXcd98fOeWU03A6naxbt5Z58+Zy/fU39TjGcePGUllZxZ133saVV15DPB7nkUdytTV1XcVu17DZcj8fm03ruJacCy64kIcf/itVVZUMGlTNa6+9yqpVK/ntb+/o9ryXX36BPn360LdvX5555ilisSinnXZat30ef/z/KCjIp6CggIce+guBQIBjjz0GXf/66xowoD9jx47j7rvv5MYbf4Gmadxzz904HE40Te025v3NPk+g3X333TzyyCPb3eeNN97AyqXl+fGPf8zxx+eK3t15550ceeSRvPnmm5x99tbTA+HbH4h8Vz9s5XXvgj3wwSfOPBNTV6l5cg7RdZvQvR7WjJjK/MCZvDvbS4N7MNcUtbEy24+aWBCREigKtFl5BNRaJo9p4ZOmQbSkPBxEDREtQFL34U5FGWA2sFLtzxp1IHO08UwxXqXA3o4nFUfHRAiFiOrHVG0IRcFuJCjUQ4wrXcOx1rtUZz+mtbCC/D4VjKhsx/be62TdKtlzzut+ETsYaJWW5v4ooVaUms377Srbi69i+/frWEUliKpK1PYIrn+/jq73MMYOX/t6J5PYXnkJfc7s7Qc93zLy3/d3y3f1uqX/Tns7bs1mzT074P1QJvPff43bIq99N3gDuT8A2ztWT/slk2Sffpk+j85jsBFDafWyrm0cX1afwSGplxjY/BoxrYh433Hkr5hHdfILLMBGGjtZVE0lq6jkizDOUh/ZBi8VmbUsaxxKEjdBPYknHadFLaPFXUFBtpE8ox10G2FbKe+rx1DnHs6Hvlxs2pAJMu2HPo7Ph3nzBBuiQTKuPAr75OMYPohUvA3bZ5+h1daAaaBuWE9m/BHEf3EbihDdbqp2u8nqcudu5s6bg7JxI5bHi1lQhNbQhFFaiijvlVuZ8dp0DMP6RmqJ7dLr7g3AqDE4Zs7AsqytVpSkvYHtvwd2g2UJhBBd47bbc/F25/e5Ol65xy+55KcYhsmvf/1LEokE1dVD+NOf7sfp9GAYJsFgMb/73R+4//57eP75ZxkyZBiXXvoT7rzzf8hkTDIZk6qq/vzlL3/j4Ycf5JJLfggIyssrOOaYSdv92d1++x+5667f8oMfXEBpaRnXXHM9P//5lRiGRSZjdn2WZLNmt+OcdtpZtLdHuPfee2hrC9GnTz/uuuseSksruj3vssuu4B//+DurV6+kV6/e3HXXPbjd/q32+dOf/kBtbQ0DBgzirrv+FyFUDGPHruumm27l97//HT/+8Y/Izw9yySWX09jYgGla+/XvSkUIseNrk/aCUChEW1vbdvfp3bs3CxYs4MILL+Spp55i1KhRXY9NmzaNcePGce211/b43Obm/X8a4PbY7dp+/QbaW+R17zuhENz7qxh+I4SzPJ+EK5dUmj1bJbKujf913oxmV/mysYxYTEHToMLWQFmJSd4Dv+SBBxyc+/nNBKPrsSUiOJQMGey0ZPKo0frwW/8fcBU4mZr+FweH3uWQxEcE1TBhAtSpvVF1FZcVw5GNEbUX4B1cRp+WBRiqHXPAQNQRQ3JtyJubUCyL+C2/3rXE1x6cMaaEWvH87jaEpnXr/vl1Y/y619vx3NNdBVS3Ch6+gaBnb9kf3uf7grxuKCra/4vDStL2yLh193xXfw+CvPZ9ee2O557G+NfrzNtQihb04zHa8SWaWFU5kZKGL6lv0ol5ilEMg34t8zgoPQ+NLEJRURFk3X4MAxxmkpqyUWQ1ByX1i5ivjqY+U0CBPcohxhxqbX2Z7zoCh5kg4EgysDxCY73gybyfssrsT5NZgDfdynGjmrnmfzw4nRBZ30abEqRswZsUfJSL+bS6WrQVy8GysKr6YPauRI1Gdjj+60yqCQHuB+5DZLNdDcJwuXYpht6V1RC79bp3xunz5qDEYwiPl+zow76xLpy7a1+/53dVff0mpk2bymOPPcXAgdU97rNgwXyuuurHzJz5Hj7f1nHdt/XaYcfi1H0+Ay0YDBLcgfVTw4cPx263s27duq5AJJvNUldXR3l5+d4epiR9Z4TDCo1GIbaKINYWnZNHjrSYlwqyyBrPyHXTyc+q+Av89PK2k5ds4iP3VLJv26hs+YICW5jW/qMItQqMSIq45SKuaPTXN3JgZQtLs4N4yvg+r/qncPOA/2Nq7BlcTVGCmTZE2sRNHFVT8QWTeCsU1Jgdm8OBfcNKTDsYw0bsdh0Ex/SXcbz6MsLnzTVKyGZ2ubvP3qjVsL8UUJUkSZI2k3GrJH27dMVTpUUkQiWoJghPrtbvwA3vIOIJ2vMHU1BmkZ2/lF7GRgxFxy7SGDYnqpGEVApLc2NqdnzJZuqDQzHdbg4yvmRkNktWuIjkV+K0ORlga6BN5FFdkqC4bjFW3M+ZkftI4kJ3avSvyjDIjGG7vAkUBW9hEWV+P9mDDiF9zHHY5sxCW7UK4XRi9h+IWT0YbDZEc9MOx38iWIAZLEBbuhht6RKUVArFMhE2O2avCsy+fVEbG3YsPt2LJUq2az+szSZJsB8k0HaU1+vl7LPP5v7776esrIzy8nIeffRRAE444YR9PDpJ+u8RCAg8nlx3yqKizRNUEwmFoUMthp15El/eBsPbZxG015CxeVjf93jyYxkqX7wVbzpESWwZ3kwYyseQKcpHiyr0SjSSn+fGVxWgj2Gh64JDDvHRbP8x/3rJx8T6v1Ce3YimWAiPGyXfjzVmGKKwELF+NSgKwuNBratF6TcAYtFuLcx3hrKpDuczT6I0NaHoGsJmx6qowAwW7lJyakfbrO+M/aWAqiRJkrTzZNwqSfuHznjKXlFBRcRi1SoV1TIoi9RS0rIMYVhUWLVQWEmb1gh5fkQqgZqMYWp2EBYOIwkI4s4CbNkk5a2Lydo8rAuOIIOdfE8aoylMSCnGqVscnLcRg+xjVgAAPPNJREFUV0s90ahCpHQANlc+fevmUx5dSTI6CC3tQK3N1UkUbjdWfj6Od94iPfkkUj+6FDXchlHVB7aIH3cl/rPNnoXa1Jg7R14AJZ1GX70SJRrB6td/h+JTx/SXu1ZDWBUVKJHILt9w3hWdyUBJ2l98axJoADfccAO6rnPDDTeQSqU44IADePzxx8nLy/v6J0uStEOCQRgzxmTmzNyvB78/l0xrblaYPNnAX+Lmwz4X0FBwAvmijYQzn8FrZnJw43QaRAmZPv1pWt5GZetKhIDleYdCUwQ3TcwrnErUXsAhB5icfnqWjz7SmTnTx2kOD+HCfjRYQwgbXvoURenXPB+jtRmjd2/MXhXoq1cinE6UdBp1Uy1KOt29NflOcL74PNq6tVgFBVhuN0o6jbZqJfTJ5gKUnUxO7XD79J2wN5JykiRJ0jdHxq2StO9tGU9VV+fiKXXRMopbl5G1OTF69SKYWI1YuxiygohWgmFzkrL8KMLCUGyoqonl9mJmFYQKCd1PjW8ItaIXbjdEBDjtjWiKxYqpV9ProBY23Poo2VI3VlEJrkyCoNZOypmH1RIC1co1CFBAbWrErB7StcIgO2o0VkkpSjbLlnWWdjb+U0Kt6EsWYVX1QW1sgEwa4XRAKom+cT2Jk0/52vhUrob47ikrK+fjj+dvd5+DDx71tfv8N/tWJdBsNhs33ngjN954474eiiT9V5s61QBg3jyNujoVj0cwebLB1KkGySR4PIKGbCFmUQHuZCt962YR0ktIOks4cIRBjXcM9V+Av70BX2INGYKs6380iYEHUGS1Mn9+IS6XYMkSjTJ7C9XNswh7K0j6S0gkFFZmE1S6lqNuWI9SPSQ3fR3Q1qyCbBah28hMnLRVC/MdoYRa0ZYvRXi9oGqg6Qh37lehumE9xoEH71JyakfbrO+ovZGUkyRJkr45Mm6VpH1vy3gqnYLePjt52nJUv4nRtwzlgCFYG12oK5bjCTeSSHvZWDgcu2ZQ2LIS07RwBh04Dh2KuTLEHPs4ittWE3UU4FYhkQCvF2yFfrxttXz2mUY4XMSomEE0mIczC04jiWZmSDl92ONtiKRA5HlzCbRIBCWVxOqYYaYo7JH4r3PmXXbkgejr1+Zmr0UiCJsNy1dK9rDxO3yMfbkawuF4Erf7XhKJa0inz9+r55KkHfGtSqBJkvTNSCbh0ENNRo0yUBSFQEDQWfLF5eo+Q62/1YaIxmhRKunT38Lng6EjVVKVB2EsW8tsvk+v7EaGJBdy4OezSNu8fOkfz78/PJPlG2yUxdoJh+I0OXvjTOfOFUu7iZf2IW/dYtRNtbkaZS4nVlkvskdNIHXe93c5gaSG21AME6OqD/r6dQAIhwNMEzUWwxwyZNeOvRdqNezppJwkSZIkSdJ3Tftxp7F8roY2Zw7+yDpc4RCq24GztR5lVhtmrwqyRx6NNncuZAOElRKiwoPqjlNhrMNRGkQEfASumMQBw4/Cf+/vEWqIT1aX4/WC2y3wxNsRHg818SAfvKJR3O7DbIsSc7vple+mSrVjS0UxbC4Ul4WSToMCwm5HOF3dZpjtifiva+ZdIo4xbARKvwEoqWSuq7vdjlVauuPH2GerIdJ4vTeiqlG83htJp6cBjq99liTtTTKBJklSl2QSpk/XmTtXIx5X8HgEY8aYXTPSOm05Q211qIDDdS9DSsJUVhd27ePKRogWBilYt5EDYu+Q8BbT5uuNM93OoZteZdkyhTcSFxLTCmgzvNhFO61mCek05OeDUlaKaUZRa2vRFywAwCov72rTvas6gwHhdmPoNrS6WtRoBGGamH37kTr9rF0+NuzhWg2ygKokSZIkSdJuefUtHzNDF9LnkCmcuPYv9IktIZb1YjcCFCkp9NUrMaMRrAMPIG/kgRyyYDFGex16Xl/Ug08mMXYcVmkZIlhAHuA49jCMf72OO6rlunrGc109/+06hc/WFxNPwueecUyKT4cEbMrk0WjLo0+6kWTlIKh0oKxYDpBbZRGLdpthpoRaMQ4dTXbUaBSFXYr/elrJgGGgdXTz3JHj7evVEHb7u6hqrjOxqkax298jk5E1JKV9SybQJEnqMn26zsyZOkVFgooKi0hE6Zpp9r3vbU6iuVy57ydNMgiHvfR+b3Su9Xab1e3DVRwygUFLFhLSSzA8udoJMd1Jfb3C8PbZVAZOotkqYJF1AJNTr6KbKUJGCQeWteFOhjAGDkJrqCdbUYFVUIgajeKc/jJKJkPq4kt7vIava7PdLRgoKcGsqEBtbUGNRkmfcjqivNde+MnuHllAVZIkSZIkaeeFQjB3rkZRkaDQKyhY2kBbsD+eSAOp9gzZgAOburkuWOqHl6KclosljUA+2R7ir/TU08jGwP7op7jaalG8Hj6vnMrTm84ikwGnE+ZXnoljE4xon0VediNrtL5oI/sxoG8WMnGs3pUACLcHxbJyM8yOOwHHc0/33PFyF+zMTLZQCMLh7qtOdvYYe5rDMb3b93b7dJlAk/Y5mUCTJAnoHmB0dt/s/HvePI1Jk4xuH6iQazgQDAooO420d+sPV/uIkfR6Yy5fhKqwJxQcDkE8rrApHqBcq+HA4lp6t8zgIPEZXhGlV3ouKSVAUcFAMkdOQF/4BWavCkR+EH3FMtTaWpRoBNejfwMFUudcsLmF9k602e4WDIRaEV4f6a+pqbatwEKSJEmSJEnaP4XDCvG4QkWFhTvRhiMbo7bkIIK2NXhDtdAeBVf3umBfe+PS5cJ28bmsVaYwd2YEe2k+UXsBTSs0TDMXGytuF7MGXMCC+BSslhB5fQP84k9e0oEWsh03epW2NrSajZi9K7H6D8Dx3NN7tuPlDqxk2N7qE5drx46xd2Sx21/vtsXhmEEsdh9g+wbOL0k9kwk0SZKA7gHGlvx+QV2dSjis5JJlPdnGh6sSaqV8kJv0xjCro6VEIgpCQImzHdP0MrL9Y8ab79DqK2GROBJvvIlBnlrUUQeSnTAR29w5WIWF6CuWoa1aifB4sIJB1LY2HDNn5BJ1HQHFTrXZ3olg4GsDC0mSJEmSJGm/FAgIPJ5cR/l8bz5pmxdHNsY6/wHYXAMJHhRDMb6+LlhPKxyOOyePlKeAefM0QiEFuz3XUMDr3Rwvt6kFxD0FFJebBALpXHLO5d7qpq8xbAT6ws+7dbxMeJ2YEQXHh3NQdqPj5fYSgju6+uSbXg1hs32Iqoa7bVPVMDbbR2SzE7+xcUjSV6n7egCSJO0ftgwwthSJ5JJGgcA2kmdbEMECzH4Duj7gRbAAa9xYBuU1MGHIJg4/NM7RQzfRz91ITXAk/aMLaaSENnsJCcvJJlslol9f8tYvRggQHg9qUxNqbW1H3TIPimEivD6M0nJs8+aghFoJr2kl9Z85JHwdQYfDiSgqxiwq7tpnR8bbk87AQtOgosJC02DmTJ3p0+X9B0mSJEmSpH1NCbWirV3dY7wXDOaaXzU3K2yIFbKqZBz2tmZsoUZKy8GhG6jRCNnRh/UcDyaTOJ57Gs/vbsP9+zvw/O42HM89DclkV0mTW25J86tfpbnssgwFBRYtLQptbRCNKoRCCi6X4Mgjza4VDJ03fYWmYVZUIDQNx5uvo61cgfD7MQxYskTlww91Plmcz9J5SWY+FSWZ3LM/t6+uPnE46Po6lxTcs+fbGVsu33zmmZ637wsLFszn8MNHEY3marO98cZrnHDChH06pk719Zs4/PBRrFq1Yl8P5b+aTKBJkgR0DzCamxXSabq+Hj3a3OVli+mpp5GefBJOu0VBvAa/1yI84SSWFh5OQI/RLvKIxxUMA6qqBP0P8aLEY11tvLWGepRoBKFpKPE4SiKeW9ZZXIzVHmPmU1H+dmec5fOTfLQoyJIlKkbHDTPh96PEY6jhtp0edygECxYofPjh/hlYSJIkSZIkfadtJ7m1palTDSZPNrAseMMzjc96T6Wqt8Fg98bN9ce2Ucajx2TXzBk4pr/ctU8wCGVlApsNfD7IZhVqa1Xq6xXKywUXXpjtasClhFqxzZ29eaZZ503f0jLUSDtKUxMrVqisWqWiqlDsCJO2e3l9dtEeu3kbCsHatQobNuRWn/j93W+S+/25kivhsLKNI+xtJg7HawAkEnDVVbm/gY7t5l49++LFX3LkkaO5/vqr9+p5vqseffRvXHTRLixJ3k/IKRSSJHXZsrtmXZ2KxyOYPNnYqgvnTvnKcsmEM59F75RQvyJCW9aLS0QQPidDhpgccICFra17G28lFsP16N9Q29pyM88GDMKsHowSbmNti4/XZxdRUCDQ8jx4jXZWrSoBYNgwa5fabG+5ZLOxUWHlSo2BA00CgVxgBDu4rFWSJEmSJEnaa3a0fEf35lcqgcA0CphI+mvKeGyV7AJEkROTXB3dzBbLKqdP13nnHZ3Bgy0OG9hEojZMTTzIMWfkc8EFm+NoNdyGEo9jVVR0O5dVXIzIC2DVbCLS5CHPmUdQhPGlmlg8YCrOkiDz5tFjTeId9dWyJLouaGhQcLsVyso2x7M7s/pk5wmczsfR9c+2uYeiRFDVFgBmzoSWFnjzTTj9dFDVZny+HyKEf5vPN4xDSKUu2uURzpjxKmec8T1mzHiVlpZmCguLdvlY0t6TzWax2b75engygSZJUpfuAcaeLZjfWTvhledyAUbJ0HyS7rEMXfkazarAo3mxtYW3ao2duvhSUMAxcwZGaTmiuBgl3IZZ18Qn4jScvYK4iwQb28cxfPV0UKFxYx4D/SHc0Z1vs71lLYiqKot161RWrFDRdRg+PFcfbu8GFpIkSZIkSdL27Exyq1NX8ytA8PU1vbaV7BJ+P2pdLWq4DTNY0LUUsjw/wcTGf9G3bhaObIx208vKV8bTNukk8stzhXOtQD7C48nd5C1ybr6eSARjUDXNvQ7CemEppUoNhtPD4gFT+bL6DPzW7t+87aneWTwOixap6LqF358r5dLcrDB58q4n6rZH1z/F57tqh/d/8cXNf59+eu5rp/PlbT8BgMcxjKEYxuidHl8ikeCdd97m0Uf/SSjUwhtvvMb3v//DnT5Op/r6TUybNpXf/OZOXnzxOZYvX0a/fv259dbfEo/HuPvuu9i4cT0jRx7EL3/5G/Lzczf9Lcvi8ccfZfr0lwmH26iq6suPf3wFhx02ruvYS5cu5o9/vIMNG9bTt2//Hse5du1qHnjgz3z55ec4nS5Gjx7DlVf+nEAgsM0xf/nlFzz88IMsW7YEm83O0KHDuO22O/D7/ViWxVNPPc706S/T2tpK796VXHTRxRx99LFAbonrVVf9mHvvfZCHHrqf9evXMnBgNb/4xa1UVvbhjTde47HHHgHg8MNHAfCLX/yaE088mWg0ygMP3MvHH39AJpNl8OAhXHnlzxg4cBCQm7n20UcfcMYZZ/HPf/6dhoZ6PvroU9577z889tgj1NbW4nQ6GTiwmrvu+hOuvVSsWibQJOk7antdJbcMMPb0ObestbAqcAYuF5StnU16dR2ZfBdqD9PoU+dcgPB4u3X5bBp3Mu99cSbFHdPOv6w+A4DKmlno7TVkUi60nWyz3VMn0oEDLRYvVlmzRqWiwiKb3buBhSRJkiRJkrR920puJW1+xMY6Iuvb8O1m0fvtJbu2XOHQ2YjruOi/GL5+OlF3MW2+3tiT7YxcNx3tJQOuOAfI3VDOjhmbmzlHR7mRSKTrBnJm0rm8vCGG3wjhLM8n4cpdQ6R5927e9hTjFhUJRozI3SxOJiEa3UOrT7bDsiqwrMBWDQJ6snIlvPpq7utXXsl9P2jQjpwjgGVVfP2OPXj33bepqupDZWUfjjvuRP785z9xwQU/QFF2bznr3//+N6666ueUlJRy553/w29+80vcbjdXX/1znE4nt956M48++leuu+5mAF544RmeffZJrr/+FwwaVM2MGdO56aaf8cQTz9O7dyWJRIIbbriWQw8dw69+9Vvq6zdx3313dztnNBrlqqsu5+STT+Wqq35GOp3ioYfu59Zbb+LPf/5rj+NctWoF11zzE048cSpXX30dmqbx+efzsazcJIInnniMt96ayXXX3UxFRW8WLvyc3/72VgKBfA466JCu4zz88INcccU1BAL53H33ndx55//w0EN/55hjJrF27Rrmzp3Fvfc+CIDX6wXgV7+6EYfDwd13/xmPx8urr77ENddczjPPvITfnwdAXV0N77//Lrff/gdUVaOlpYXbbruFn/zkKo488mgSiQQLF36OEHtvkoNMoEnSt0RP3X92xb7sKvnVTp+GzcWC4edjr5pC+7oQP/iRh8qDeshK9dA106AA21oHkUguAOg81sd5J+JOhSi7yYPaf+d+Tj11Iq2utshmYfVqlY0bVUpK9m5gIUmSJEmSJG3fV5NbhgErVqhEVsWxsn5efrSUYav03Ypvt0x2WakU2O2QyaBGI91WOAQCghK9hd4bZhF1FxPz5MqJRDQXfq9CwbLZGKHjuvbvvLm75Y3hzjpsQRcMOzLAzJmFFMUEfnXPzArrKcaF3A3zZFLwwx9mCATYo6tPemJZ5YTD7+LzXYzN9nnX9uXL4Sc/gaam3PdC5BJmnXWNEwkYNiyXQOvMZRUXw4MPwuDBm4+fzR5EJPJ3LKt8l8b3+uuvctxxkwEYM2Ys8XiMzz//jIMPHrVLx+t0zjnnM2bMWACmTTub2267hfvue4iRIw8EYMqUU5g587Wu/Z955knOO+9Cjj32eAB+8pOr+Pzz+Tz//DP8/Oc38vbbbyKExU03/QqHw0G/fv1pbm7k7rvv6jrGiy8+x6BB1Vx22U+7tt18862cfvoUNm7cQGVl1VbjfOqpf1JdPYTrrrupa1u/fv0ByGQyPPHEY9x774MMHz4SgF69Kvjyyy949dWXuiXQLr30J13fn3/+hVx//TWk02kcDiculwtN0ykoKOzaf+HCL1i2bAmvvfY2drsdgCuuuIaPPnqf9957h1NOyU0/zGaz3WbqrVixHNM0OeqoiZSWlgHQv/+AHX1ZdolMoEnS/i6Z3KrVdXbM2NyH7y5EBDvarnpv2LLTZ+fdL4D6TAFGSRBvVXq7z9+yhXaQXNODzrF3TTuPFjJ5coBA/52/lp7Gp+vQq5cgP9/kRz/KUFW1dwMLSZIkSZIkafu+OpNrdV2A5hVRSmhiWfXJJN0FzJyZy7TsTnybPu4E9Lmzsc+ZhZJKIZxOMoeNI33cCV37BIMwbnALfBwn5OiNZkI6nVseWdXPj9PcQKJjuSfQ443hLW+O742axNuKwTvLkvTp883Ft6Y5gHD4bTye/8Ht/jOQS4I99RScfz68+27PzzMMWLo09/Uxx8ATT0BZ2ebHE4mriMdvBey7NK6NG9ezdOkS7rgjN5NL13UmTpzE66+/utsJtP79B3Z9Hex4rfv1G7DFtiBtbbmmZ/F4jJaWZkaMOKDbMUaMOIDVq1cBsGHDOvr3H4jD4eh6fNiwkd32X716FQsWzGfSpCO2Gk9dXW2PCbTVq1d2Lcf8qtraGlKpFNde+9Nu27PZLAMHVm/zejsTZW1tbZSWlvZ47NWrV5JMJpky5Zhu29PpNHV1tV3fl5aWdSXPAAYMGMghh4zm+98/m9GjD2P06MOYMOEY/P5t18jbXTKBJkn7uR0tkLojtjV9G3If0rtTmHRHdHb6/GrSq7VV4fjjd/7cezrA2Nb4Ou/6HXSQrHkmSZIkSZK0P+icyWV9OIf0qjrcTi+r+p/M0uozKLLtmfjW8dabaKFWMoccCnYbZLJooVYcb73ZLQ4/+gwf7e+4MZvbaTFKsNtzZUCqi9oRoueGVlveGN7S3qhJ/HUx7jd/c9hOPP47MpkJ+P2XoarNlJXB22/D738Pt9ySm4X2VYoCd9wBN9wAqprbZllFRCJ/I5vtOfGzo2bMeBXTNDn11Mld24QQ2Gw2rr32xq6lhrtC17dOu2y5TVEUhLC22md3JJNJxo8/gssv37rm3Jazv7Zktzt63N55PIA//OFeijrqDnb6ajH/r14bsN3rSyYTFBQUcv/9f9vqMa/X1/W109l98oimadx77wMsWrSQTz+dy4svPsfDDz/Iww//g/LyXts83+6QCTRJ2o/tSoHU7dnW9O1vsqtkT0mvKVNMTjxx55NeeyPA2CudSCVJkiRJkqQ9q2Mm18ZBJ/BUOI6vKoAZ2BwX72582xWH+/zg9SKcLnC5MJubtorDHeUF9D5nDJWvziDhs9ALfLiyEWytzSSPn7JL5Vf2dE3i/THGzWaPJRSahd9/GXb7u6gq3HwzLFuWm2H2VeefDzdtXl1IJnMMkcjfEKJ46513gmEYvPnmG1xxxTWMHn1Yt8duvvk6/vOfNzn11DN36xw7yuPxUlhYxKJFC7sti1y0aCFDhgwDoKqqL//+9xsdyyJzSa8lSxZ1O86gQdV88MG7lJaW9ZjA68mAAQOZP38eF1982VaP9e3bF7vdTmNjQ7dx7SybzYZlmd22VVcPJhRqRdM0ysp2bvmtoiiMHHkgI0ceyEUX/YgzzzyZDz98j7PPPn+Xx7g9MoEmSfuxHe3+s6O+bvr2N9FVsqekV2mpRiaz68fckwHG3uxEKkmSJEmSJO1Z3qogkZIy4lkoYs/Ft2pDPdrSJSipFIplImx2zF4VmH37ojY2bBWHp6eehgPwz5uDEsrVNstOOZn0iafs7iXuEftrjCtECe3tL+H3n4fD8ToAX5nQ1GXL7en0FCKRpwB1t8cwa9bHRKMRTjrp1K1mmh111ERmzJj+jSXQAM499wIeffRv9OpVwcCBg3j99ddYtWolt976OwAmTTqBRx55kD/84Xecf/4PaGjYxLPPPtntGGeccRavvfYKt912C+ed9338/jxqa2t45523uPHGX6Jp2lbnPf/8i7jwwrO5++67OPXUM7DZbCxYMJ+jjz6WQCDA2Wefz/3334MQgpEjDyQWi7Fo0Rd4PF4mTz5ph66ttLSc+vpNrFq1gqKiEtxuN6NGjWHYsBHcfPN1/OQnV9G7dyUtLc3MmvUxRx11NIMHD+3xWEuWLOazz+YxevRhBAJBli5d3NW1dG+RCTRJ2o/taPefHfVNT9/eXuODbSW99lSzhN21tzqRSpIkSZIkSXvO3opvbbNnoTY1ItxurLwASjqNvnolSjSC1a//1nF4D7XNbKXFkDF7PsE+sn/GuAqatgIAy4IZuWo12O1w4IHwxReQycDrr+ceV1XQtJXA7nXH7DRjxquMGjW6x2WaEyZM5Omn/9lVf+ybcOaZZxOLxfjLX+6lrS1Enz79uOuue+jduxIAt9vNXXf9L3fffSc//OF59OnTl8svv5Jbbrmh6xiFhUU89NCjPPTQ/Vx77RVksxlKS8sYM2Ysqtpz0rGysop77vkLDz/8AJdeeiF2u4OhQ4d3NTO45JLLCQTyeeKJx9i0qQ6v18egQYP5/vd/sMPXNmHCRD788F2uvPLHxGJRfvGLX3PiiSdz99338fDDD3LHHb8hHG4jGCzgwAMPJj9/2/+APR4PX3zxOc8//wyJRJySklKuuOIaxo4dv8Pj2VmK2Js9PvcDzc3RfT2E3WK3a2T2s1+63wR53Zs5nnu6qwbaV1td72wNNNjchXPevM1dOEeP3sNdOHey8YHdrpFpj+3RZgnfBvJ9/t0irxuKinxfs7ckfbd92+PWr/Nd/T0I8tr39rXv6fhWCbXi+d1taOvWojY2IDwehMOBEg6jJhMkfno1qR9e+rXHka/7jl27pi0lGMwtnXz/fTj6aDj4YHj2WRg4MNeR85xzYMGC3ONHHZV7Xig0F9McsncuYDfI1/3bee07EqfKGWiStJ/bXqvrXfFNTN/elcYHe7JZgiRJkiRJkvTdsafj284yKtmRB6KvX5srnRKJIGw2LF8p2cP23gyX7yKH49Wur196CX72M7jzztwMNIBBg2D27Fz9s5de2pxAczheJZHY/xJo0n8vmUCTpP3d17S63lV7a/r2rjQ+2NPNEiRJkiRJkqTvnj0V33aVUUnEMYaNQOk3ACWVhFgM7Has0tI9MFqp05YJtIsvhgMO2PyYEB4UJY7dDvfcAwsXdn9eIrFFVwFJ2st2v+KeJEnfCBEswOw3YL9PJHXesRN+f7ftwu9HicdQw21bPUdp2/nnSJIkSZIkSdLeIIIFZMeMRWtuQmluQqgKwjBQoxGyow/b7+PxbxNNW4WuL+36fsvkWSJxDS0tq0kkru7xcV1fgqat/iaGKUmATKBJkrSHbdn4YEvba3wg8nf+OZIkSZIkSZK0t6SnnkZ68kkoloVWV4tiWbtVRkXqmc32Kvfem0uMPfBAbptlFRMOv0w8/j+Ah3j8t4TDL2NZuZUqDzyQ2//ee3PPl6RvilzCKUnSNu1KR8zOO3aOmTMwYavGBz0dZ1eeI0mSJEmSJEl7zV4qoyJtJoTgrrse5o47ct9fcQW0tvbnJz95CyGKuu2bzR5DKDSLBx44jttuWwvAtddCc/PfuPrqn6Eoe6YjpyRtj0ygSZK0tZ3sovlVu9L4YE83S5AkSZIkSZKk3SWCBZgycbbHCSH4/e9v5557Grpt//Wv15BK/YNrr71+q+f86U//4K671nbbdscdDaRSt3PjjbfIJJq018kEmiRJW9ntjpi7csdO3uWTJEmSJEmSpP96m5Nnf+jaNhWY3vH1nXf+FqBbEu2ee/7AXXf9ruv7U4BXt3gMkEk0aa+TNdAkSepmq46YDieiqBizqBjbvDkoodYdPtauND74tjRLkCRJkiRJkiRp5/SUPPszuWTYH7bY7847f8v//u8fga2TZ38EXul4Xqd77vkDv//97Qix+11YJWlb5Aw0SZK66eyiaVVUdNsu/H7UulrUcJucxi5JkiRJkiRJ0k7ZVvLsyo6vO+eb3dDx9513/pZPPvmYDz98r2v/PwLXdXzd+byrOv6WM9GkvU3OQJMkqZud6aIZCsHatQqh0PaPqYRa0dau3qnZa3vT/jYeSZIkSZIkaf+wo/GttPMefvjBbSbPOl1P95lo20qedbqSrWeiPfzwg3tiuNt1xRWXct99f9rt49x++23cfPPP98CI9h976mezP5Iz0CRJ6mZHOmImkzB9us7cuRrxuILHIxgzxmTqVKN7j4HdbEawx+1v45EkSZIkSZL2Czsc30q77Jlnnur6+hS2Tp51+upMNOg5edbpSuA/bK6h9swzT3HZZT/dqbHdfvttzJw5g1NOOZ3rr/9Ft8f+9Kff8/LLLzB58knccsttANxxxx/R9d1Pp1x99XVy2em3iJyBJknSVtJTTyM9+SQUy0Krq0WxrG4dMadP15k5U0fToKLCQtNg5kyd6dO7f4h0NiMQmoZZUYHQNBwzZ+CY/vK+uKz9bjySJEmSJEnS/mFH41tp111wwUVdX78K3L2dfa8H/g8YBzzKtpNnkEuuTd/i+y3PszOKi0t45523SKdTXdvS6TRvv/0mJSWl3fb1+/Nwuz27dJ4teb1efD7fbh9H+mbI3waSJG1tOx0xQyGYO1ejqEhQVJS7W9L597x5GpMmGQSDPTQjAESRExOwzZtDZtLx32ijgP1tPJIkSZIkSdL+YUfjW2n3XHzxpbS3h7saAnTONNtWcuzijj/b80e6z1S7+eZfcfHFl+7S+KqrB1NXV8sHH7zHccdNBuCDD96jpKSU8vLybvteccWlDBxYzdVX55ZfvvTSCzz//NM0NTXi9XoZOfJAfve73GLU9977D4899gi1tbU4nU4GDqzmrrv+hMvl4vbbbyMWi3LnnX/qOu6AAQOx2+289tqr2Gw2TjnldC6++LKuc2/YsJ677votK1Yso7y8F1dffR3XXvtT7rjjbo48ckKP13bFFZfSv/8AVFVj5swZ2Gw2LrnkciZNOoH//d8/8N577xAMBrnmmusZO3Z81/M+//wzHnzwPlavXoXf7+eEE07ikksu75p9l0wmufvuO/nww/dwu92cf/73tzp3JpPh4Ycf5D//+TexWJS+fftz+eVXcvDBo3bpddqX5Aw0SZK2qaeOmOGwQjyu4Pd3n2rs9wvicYVwOFews7MZgfD7ux/T70eJx1DDbXv/Arawv41HkiRJkiRJ2j/saHwr7b6f/ewGbrrpl13fX8/2Z6JtT0/Js2uvvX5bu++QKVOm8vrrr3V9//rr05ky5eTtPmf58qXcd9/dXHzxZTz99Ivcd98DHHDAwQC0tLRw2223MGXKVJ566gXuv/9vHHXU0dtdtjlz5gycThcPP/wPLr/8Sv7xj//j00/nAGCaJjff/HOcTid/+9s/uP76W3a45tvMma+Tl5fHI488zhlnnMWf/nQXv/rVjQwfPpK//397dx5d073/f/x5ElISGQQpidAKUiUJJSIxVb6uNlS1VMlFqSGtVAdDDS31FdwQJTUWadz64Rq6vre/e5Wy2tVLvz+tUpcaiy7zFBFjG4TYvz9yc5oQR2jO2ZH9eqyVtXJ29vns9yexkpf3Hj6LlhIZ2YJJkz7g2rW8K/AyM8/y7rtv88QTDfn00+UMHz6GNWv+weLF6fYx586dyY4d/yY5eTozZsxl27ZtHDiwv9BxU1NT2LNnJxMm/IXFi1fQrl17Rox4i+PHjxWr7tJEDTQRuS9+fgZeXgaXLxcOEpcv5z0rws8v74/B/SxG4AqlrR4RERERKR2Km2+lZJREE80ZzTOADh06smvXDs6cOc2ZM6fZtesnOnTo6PA9GRlnqFChAi1btqZ69RqEhj5B9+49AcjKOkdubi5t28ZSo0YgISF16dq1O56enncdLySkHv37JxAcXIu4uOd44okG/PjjVgC2bv2BkydPMHbsBOrVq09ERGMSEhKLNbe6devRr99AgoNr0afPq3h4eODr68fzz79IcHAtXn11IJcuXeKXXw4CeVfVBQQ8yrBhI6ld+zHatHma/v1fY8WKZdy6dYvs7GzWrPkHb7zxDs2aNSckpC7jxyeRm3vTfswzZ86wdu1qJk6cSkREE4KCavLnP/chLKwxa9euvluppZZu4RSR++LvD1FRuXz5Zd6vDx+fvLCRmWkjLu73y9uLsxiBK5W2ekRERESkdChuvpWSM2xYXvur4O2c/kD/Yrw3Hec0zwAqV65MdHRL1q5djWEYxMS0xM/Pz+F7IiOjqF69Bi+/3IWoqGhatmxFTExbKlSoQN269WjatDmvvNKT5s1b0Lx5C55++r/wue2umIJCQuoVel2lSlUuXMhbFvbYsaMEBFSnSpWq9q8/+WTDYs2t4Lju7u74+PgSElLXvs3/P/8fungx71hHjx6hUaNwbLbfG8thYRFcvZrN2bNnuXLlMjdu3ODJJxvZv+7r60utWrXtrw8d+oXc3Fzi47sWqiUnJwdfX99i1V2aqIEmIvft+efzzips2eLOyZNueHkZxMXdtG/Pl7/oQPktm3E7eQLDq1KhxQhcrbTVIyIiIiKlQ3HzrZScYcNG8t13m/j2238BeY2x4jbQ8rVt267Emmf5OnXqQmpqir3Ge/H09CI9fSnbt29j69bNLFjwMQsXzict7f/g7e3NRx/NZdeun9i69Qf+539WsnDhPBYu/JTAwKAix7t9dU+bzVYiK3UWNW7BbfmNslu3Su6Ky6tXs3F3dyc9fQlubu6FvlbxIVzeVg00EblvFStCjx43+dOfbnLxog0/P6PoM3MOFiMwRWmrR0RERERKhWLnWykxM2ak2JtncO8FAwru9/1/Pt+48V+kpk4r0SZaVFQ0N27cwGaz0bx5dLHeU65cOSIjo4iMjOK11wYTG9uaf/97K23bxmKz2QgPb0x4eGP69RvISy915ttv/0XPnr3vu7ZatWpz9uwZzp/Psl8xtm/f3vsepzhq136MjRu/wTAMe3Nt166f8PT0IiAgAB8fH8qVK8fevbupXj1vldLLly9z/PgxGjduCkC9eqHk5uZy4cIFIiKaOKVOV1IDTUQemL8/+Pvf+wyF4V+F3FLUqCpt9YiIiIhI6VDcfCt/zIwZKfbbNyHvmWbFufoM8hpo5/n9Ns7k5IkAJdZEc3d3Z9myz+yf38umTf/LqVMnady4Cd7ePmzZ8h2GYRAcXJs9e3azbdsWmjdvgZ+fP3v37ubixQvUrv34A9UWGRlFUFBNJk36bxIT3yI7+zfS0j4GwFbCa1107dqdzz5bTmpqCt269eDYsSMsWrSAHj3+jJubG56enjz3XBfmzZuJr68vlStX5pNPPsZm+/1R+7Vq1aZDhzgmTRrPkCHvUK9eKBcvXmDbtq2EhNQjJqZVyRbtZGqgiYiIiIiIiIhLFNU8G3GfY+S3ypzVRPPyqlTsfStV8mbjxm9YtGghOTnXCQ6uxfjxk6lTJ4QjRw6zY8d2Vq1aTnb2bzz6aHWGDHmH6OiWD1SXu7s7ycnTmTJlIoMGvUJgYBCJiW8zatRQPDweeaAx76ZatQCmTZvJvHkz6dcvHh8fHzp16kLfvr9fK5iY+DZXr2YzatRQPD296NWrD5cvXyk0znvvjWfx4nTmzPmIzMyz+Pr60bBhGDExrUu0XlewGSVxM20plpl55d47lWIeHu7k5OSaXYbLad7Wonlbi+ZtLQXnXa2at8nViJRuD3tuvRer/h4EzV1zt567zb0kmmcFOWs1zj/C1T/3nTt3kJg4kJUr/y9BQTVddtyiPMz/5ouTU3UFmoiIiIiIiIg4VXr6wvtqnqX/52MAd38+WlFXovn4+DJgQMIfrLb02rjxX3h6VqRmzVqcOHGcmTOnExYWYXrzzArUQBMRERERERERp1qy5FP7511w3DwreGXZ98AFB/u/C/w/4J8FjlOWG2jZ2b/x8cezOXv2DL6+fjRr1pwhQ94xuyxLUANNRERERERERJwqPr4X48aNAeAfwGzgzSL2u/22TPj9SrOimmiz+b15ln+csiwu7jni4p4zuwxLcrv3LiIiIiIiIiIiDy4hIZFhw35vjb1FXvOroNubZ23btrN//i7w4W37z/7POPmGDRtJQkJiSZQrcgc10ERERERERETEqWw2G6NGvX/XJlpRCwJ89tk/GD16rH1bwSZaUc2zUaPex2azOaN8Ed3CKSIiIiIiIiLOl99Eg7wVOSGvCfY1hW/DLLiaZn7DLX8BgneB/71tfzXPxBV0BZqIiIiIiIiIuERRV6LdrXmWb9iwkYWuRFPzTMygBpqIiIiIiIiIuExRTTQounmW7/YmWv42Nc/EVXQLp4iIiIiIiIi4VH4TrXLlyixfvow+ffoxYECCw/cMGzYSX18/liz5lPj4XiQkJKp5Ji5jMwzDMLsIZ8rMvGJ2CX+Ih4c7OTm5Zpfhcpq3tWje1qJ5W0vBeVer5m1yNSKl28OeW+/Fqr8HQXPX3K1Hc9fcHzbFyam6hVNERERERERERMQBNdBEREREREREREQcUANNRERERERERETEATXQREREREREREREHFADTURERERERERExIEyvwqniIiIiIiIiIjIH6Er0ERERERERERERBxQA01ERERERERERMQBNdBEREREREREREQcUANNRERERERERETEATXQHjIbNmyge/fuhIeHExkZSWJiotkluUxOTg5dunQhNDSUffv2mV2OU504cYL33nuP2NhYwsPDad++PbNmzSInJ8fs0krcsmXLiI2NJSwsjO7du7Nz506zS3KqBQsW0K1bN5o0aUJ0dDSJiYkcOnTI7LJcbuHChYSGhjJ58mSzS3G6jIwMRowYQVRUFOHh4XTu3Jldu3aZXZZT5ebm8tFHHxX6HTZ37ly0bpGI5LNypgVr5VqwVrYF6+VbUMYtyEo5F6yVdcuZXYAU3/r16xk3bhxDhw6lRYsW5ObmcuDAAbPLcpmUlBQCAgL4+eefzS7F6Q4dOoRhGCQlJVG7dm0OHDjAuHHjuHr1KqNGjTK7vBKzdu1akpOTmTBhAhERESxevJgBAwawbt06qlSpYnZ5TrFlyxZ69epFWFgYubm5zJgxgwEDBrBmzRo8PT3NLs8ldu7cyYoVKwgNDTW7FKe7dOkS8fHxREVFkZaWRuXKlTl69Ci+vr5ml+ZUaWlpLF++nKlTp1K3bl12797NmDFj8Pb25pVXXjG7PBExmdUzLVgr14J1si1YM9+CMm4+K+VcsGDWNeShcOPGDaN169bGqlWrzC7FFBs2bDCeffZZ4+DBg0b9+vWNvXv3ml2Sy6WlpRmxsbFml1GiXnrpJWPChAn217m5uUarVq2MBQsWmFiVa2VlZRn169c3tmzZYnYpLvHrr78aHTp0MDZt2mT07t3bmDRpktklOdW0adOM+Ph4s8twuYSEBGPMmDGFtg0ZMsQYPny4SRWJSGlh9UxrGMq1+cpitjUM5dt8Vsu4hmG9nGsY1su6uoXzIbF3714yMjJwc3PjhRdeoFWrVgwcONASZ+vOnTvHuHHjSElJoUKFCmaXY5orV66UqU5+Tk4Oe/bsISYmxr7Nzc2NmJgYtm/fbmJlrnXlyhWAMvWzdSQpKYm2bdsW+rmXZd988w2NGjXirbfeIjo6mhdeeIFVq1aZXZbTNWnShM2bN3P48GEAfv75Z7Zt20abNm1MrkxEzGblTAvKtQWVtWwLyrcFWS3jgvVyLlgv66qB9pA4fvw4AHPmzGHw4MHMnz8fX19f+vTpw8WLF80tzokMw2D06NH07NmTsLAws8sxzdGjR1m6dCk9e/Y0u5QSc+HCBXJzc++4lL1KlSqcO3fOpKpc69atW/zlL3/hqaeeon79+maX43Rr1qxh7969DB8+3OxSXOb48eMsX76cxx57jPT0dOLj45k0aRKff/652aU5VUJCAh07diQuLo6GDRvywgsv0LdvX55//nmzSxMRk1k104JybUFlMduC8m0+q2VcsGbOBetlXT0DzWQffvghaWlpDvdZu3Ytt27dAuD111/nmWeeASA5OZk2bdqwbt26h+6PT3HnvWnTJn777Tdee+01F1XmXMWdd0hIiP11RkYGAwcO5Nlnn+Xll192doniQhMmTODgwYP87W9/M7sUpzt9+jSTJ09m0aJFPPLII2aX4zKGYdCoUSOGDRsGwJNPPsnBgwdZsWIFL774osnVOc+XX37J6tWrmT59OnXr1mXfvn0kJycTEBBQpuctYmVWzbRg3VwLyrZSNCtlXLBuzgXrZV010EzWv3//e/7DCg4OJjMzE6DQHx8PDw+Cg4M5ffq0U2t0huLOe/PmzezYseOOs3TdunWjc+fOTJ061ZlllrjizjtfRkYGr7zyCk2aNGHixInOLs+lKleujLu7O1lZWYW2Z2VlUbVqVZOqcp2kpCQ2bNjA0qVLqV69utnlON2ePXvIysqia9eu9m25ubls3bqVZcuWsWvXLtzd3U2s0DmqVatW6Pc2QJ06dVi/fr1JFblGSkoKCQkJdOrUCYDQ0FBOnTrFggULymSYEhHrZlqwbq4FZdvbWT3fgvUyLlg354L1sq4aaCbz9/fH39//nvs1atQIDw8PDh8+TLNmzQC4ceMGJ0+eJDAw0Nlllrjiznvs2LG888479tdnz55lwIABpKamEhER4cQKnaO484bfA0bDhg1JTk7Gza1s3XHt4eFBw4YN+f7772nfvj2Qd7n3999/T+/evU2uznkMw2DixIl89dVXLFmypFCoLMtatGjB6tWrC20bM2YMderUYdCgQWU2VDz11FP254DlO3LkCEFBQSZV5BrXrl3DZrMV2ubu7o5hGCZVJCLOZtVMC9bNtaBsezur5luwbsYF6+ZcsF7WVQPtIVGpUiV69uzJ7NmzqVGjBoGBgaSnpwPw7LPPmlyd89wepPKXQK5Vq1aZPqORkZFBnz59CAwMZNSoUZw/f97+tWrVqplYWcl69dVXGTVqFI0aNSI8PJzFixdz9erVQmdvypoJEybwxRdfMG/ePLy8vOxn4r29vcv0w4QrVap0xzMwPD098fPzK9PPxujbty/x8fHMnz+fuLg4du7cyapVq0hKSjK7NKdq164d8+fPJzAw0H4L51//+le6detmdmkiYjKrZlqwbq4F62RbsGa+BetmXLBuzgXrZV010B4iI0eOpFy5cowcOZJr164RERHB4sWLLbWyiVVs2rSJo0ePcvTo0TtWrdu/f79JVZW8jh07cv78eWbNmkVmZiYNGjTgk08+KdOXuC9fvhyAPn36FNqenJxc5oOVFYWHhzNnzhxmzJjB3LlzqVmzJu+9916Zf5j+2LFjmTlzJhMmTCArK4uAgAB69OjBG2+8YXZpIlIKKNNaj1WyLVgz34IyrlVZLevaDN1PISIiIiIiIiIicldl78ZzERERERERERGREqQGmoiIiIiIiIiIiANqoImIiIiIiIiIiDigBpqIiIiIiIiIiIgDaqCJiIiIiIiIiIg4oAaaiIiIiIiIiIiIA2qgiYiIiIiIiIiIOKAGmoiIiIiIiIiIiAPlzC5ARFxj9uzZzJkzBwCbzYaXlxeBgYFERkbSq1cvQkJCCu0fGxvL008/zQcffGBGuQ9s9OjR7N69my+++MIlxzt//jzz5s3jp59+Yt++fZQvX57t27e75NgiIiIiZY0yq3Mos4r8cWqgiVhIhQoVWLx4MQC//fYbBw4cYOXKlaxatYrJkyfTpUsX+75z5szBx8fHrFIfWGJiItnZ2S47XkZGBmvXriU8PJxGjRqxf/9+lx1bREREpCxSZi15yqwif5zNMAzD7CJExPlmz57NokWL7jjTdP36dRISEti2bRtffvklwcHBJlX4cLp16xZubnl3w9/teywiIiIixaPM6hzKrCJ/nJ6BJmJxjzzyCOPGjePGjRt89tln9u2xsbEkJSXZX48ePZrnnnuO7777js6dOxMeHk7v3r05ceIEFy9e5O233+app56iffv2rF279o7jbNiwge7duxMeHk6LFi0YP358obNuP/zwA6GhoWzatInhw4fTpEkT2rVrR1paWqFxDh48yKBBg4iKiiIiIoJnnnmm0D75dRa0f/9+BgwYQOPGjWnatClvvfUWp06dKrRPaGgoaWlpzJ49m5iYGKKiohgzZsw9zwzmBxERERERcR5l1jzKrCLm0S2cIkLdunV59NFH73kWKjMzkylTpjB48GDKlSvHpEmTGDFiBBUrVqRZs2a8/PLLrFq1infffZeIiAiCgoIAWLduHUOHDqVr1668+eabZGZmMn36dC5fvkxqamqhY4wfP54uXbowd+5cvv76az788ENCQ0Np06YNAK+//jpVq1Zl8uTJVKpUiWPHjnHmzJm71nz69Gl69+5NcHAw06ZN4/r166SmptK7d2/++c9/UqlSJfu+y5Yto2nTpkyZMoUjR46QkpJClSpVGDFixIN+a0VERESkhCiz5lFmFTGHGmgiAkCNGjU4d+6cw30uXbrE0qVLqVevHgBnz55l4sSJDBo0iDfeeAOAsLAwvvrqK77++mv69u2LYRikpKTQsWNHJk+ebB+rWrVqJCQkkJiYaB8PoEOHDrz55psAREdHs2HDBtavX0+bNm04f/48J06c4P333yc2NhaAFi1aOKz5008/5ebNmyxatAg/Pz8AGjRoQKdOnfj888/p06dPoZqmT58OQJs2bdi7dy/r169XGBEREREpJZRZlVlFzKLrOEUEAMMwsNlsDvcJCAgoFBwee+wxAGJiYuzbfHx88Pf3t59hO3z4MCdPniQuLo6bN2/aP5o3b46bmxu7d+8udIxWrVrZP7fZbISEhNjHqly5MkFBQcyYMYPPP//c4Vm8fD/++CNRUVH2IAIQEhLCE088wbZt2wrtW3Ae+fsV5xgiIiIi4hrKrMqsImbRFWgiAsCZM2fs4eJubl/hqHz58gB4e3sX2u7h4cH169cBuHDhAoD9bN/tTp8+Xej17WOVL1+eK1euAHnhJD09ndTUVJKSksjOzqZhw4aMGTOGyMjIIse/fPkyDRo0uGN7lSpVuHTp0j3nl5OTU+S4IiIiIuJ6yqzKrCJmUQNNRDh48CAZGRm8+OKLJT52/lm0Dz74gPDw8Du+HhAQcF/jPf7448yaNYsbN26wfft2ZsyYweuvv863336Ll5fXHfv7+vqSlZV1x/asrKx7hi8RERERKT2UWUXETLqFU8Tirl+/zsSJE/Hw8KB79+4lPn6dOnWoXr06x48fJyws7I6PRx999IHGLV++PM2bNychIYFff/2Vs2fPFrlf06ZN2bx5c6Ezd4cOHWL//v00bdr0gY4tIiIiIq6lzCoiZtMVaCIWcuvWLXbs2AFAdnY2Bw4cYOXKlRw/fpwpU6ZQs2bNEj+mzWZj9OjRjBgxguzsbJ5++mkqVqzIqVOn2LhxI0OHDuXxxx8v1lg///wzU6dOpWPHjgQHB/Prr7+yYMECgoKCqFWrVpHv6devH3//+9/p378/gwcP5vr163z00UfUqFGjxM5erlu3DoBffvmF3Nxc++uwsDD7qk4iIiIiUjzKrMqsIqWRGmgiFnLt2jV69OgBgKenJzVr1iQ6Opo5c+YQEhLitOPGxcXh4+PD/PnzWb16NQBBQUG0bt2aqlWrFnucatWqUbVqVRYsWEBGRgbe3t40a9aMadOm4e7uXuR7atSowZIlS0hJSWHEiBG4ubnRsmVLRo8eXWg58D/i7bffLvJ1cnIyXbt2LZFjiIiIiFiFMqsyq0hpZDMMwzC7CBERERERERERkdJKz0ATERERERERERFxQA00ERERERERERERB9RAExERERERERERcUANNBEREREREREREQfUQBMREREREREREXFADTQREREREREREREH1EATERERERERERFxQA00ERERERERERERB9RAExERERERERERcUANNBEREREREREREQfUQBMREREREREREXFADTQREREREREREREH/j9THjHXKpq6kAAAAABJRU5ErkJggg==", | |
| "text/plain": [ | |
| "<Figure size 1400x600 with 2 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "transient": {} | |
| } | |
| ], | |
| "source": [ | |
| "# Visualize the distributions\n", | |
| "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", | |
| "\n", | |
| "# Plot 1: Full distribution vs Full distribution (should match)\n", | |
| "ax = axes[0]\n", | |
| "samples_full_2 = sample_from_gmm(n_samples, means_2d, covariances_2d, weights_2d)\n", | |
| "ax.scatter(samples_full[:, 0], samples_full[:, 1], alpha=0.5, s=20, label='Set 1', color='blue')\n", | |
| "ax.scatter(samples_full_2[:, 0], samples_full_2[:, 1], alpha=0.5, s=20, label='Set 2', color='red')\n", | |
| "ax.scatter(means_2d[:, 0], means_2d[:, 1], marker='*', s=300, c='black', \n", | |
| " edgecolors='yellow', linewidths=2, label='Mode centers', zorder=10)\n", | |
| "ax.set_xlabel('Dimension 1', fontsize=11)\n", | |
| "ax.set_ylabel('Dimension 2', fontsize=11)\n", | |
| "ax.set_title('Full Distribution vs Full Distribution\\n(Both have all modes)', \n", | |
| " fontsize=12, fontweight='bold')\n", | |
| "ax.legend(fontsize=10)\n", | |
| "ax.grid(True, alpha=0.3)\n", | |
| "ax.set_aspect('equal')\n", | |
| "\n", | |
| "# Plot 2: Full distribution vs Mode-dropped distribution\n", | |
| "ax = axes[1]\n", | |
| "ax.scatter(samples_full[:, 0], samples_full[:, 1], alpha=0.5, s=20, label='Full distribution', color='blue')\n", | |
| "ax.scatter(samples_dropped[:, 0], samples_dropped[:, 1], alpha=0.5, s=20, label='Mode-dropped', color='red')\n", | |
| "ax.scatter(means_2d[:, 0], means_2d[:, 1], marker='*', s=300, c='black', \n", | |
| " edgecolors='yellow', linewidths=2, label='All mode centers', zorder=10)\n", | |
| "ax.scatter(means_2d[0, 0], means_2d[0, 1], marker='X', s=300, c='red', \n", | |
| " edgecolors='black', linewidths=2, label='Missing mode', zorder=11)\n", | |
| "ax.set_xlabel('Dimension 1', fontsize=11)\n", | |
| "ax.set_ylabel('Dimension 2', fontsize=11)\n", | |
| "ax.set_title('Full Distribution vs Mode-Dropped Distribution\\n(One mode missing)', \n", | |
| " fontsize=12, fontweight='bold')\n", | |
| "ax.legend(fontsize=10)\n", | |
| "ax.grid(True, alpha=0.3)\n", | |
| "ax.set_aspect('equal')\n", | |
| "\n", | |
| "plt.tight_layout()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Testing with PQMass...\n", | |
| "\n", | |
| "1. Full distribution vs Full distribution:\n", | |
| " Mean p-value: 0.6052 ± 0.2902\n", | |
| " → Distributions MATCH (p > 0.05 means match)\n", | |
| "\n", | |
| "2. Full distribution vs Mode-dropped distribution:\n", | |
| " Mean p-value: 0.000000 ± 0.000000\n", | |
| " → Distributions DIFFER (p < 0.05 means different)\n", | |
| "\n", | |
| "✓ PQMass successfully detected mode dropping!\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Test with PQMass\n", | |
| "print(\"\\nTesting with PQMass...\")\n", | |
| "\n", | |
| "# Generate a second sample set from full distribution for comparison\n", | |
| "samples_full_2 = sample_from_gmm(1000, means_2d, covariances_2d, weights_2d)\n", | |
| "\n", | |
| "# Test 1: Full vs Full (should have high p-value)\n", | |
| "print(\"\\n1. Full distribution vs Full distribution:\")\n", | |
| "p_values_full, mean_p_full, std_p_full = pqmass_test_multiple(\n", | |
| " samples_full, samples_full_2, n_ref=50, n_repeats=10\n", | |
| ")\n", | |
| "print(f\" Mean p-value: {mean_p_full:.4f} ± {std_p_full:.4f}\")\n", | |
| "print(f\" → Distributions {'MATCH' if mean_p_full > 0.05 else 'DIFFER'} (p > 0.05 means match)\")\n", | |
| "\n", | |
| "# Test 2: Full vs Mode-dropped (should have low p-value)\n", | |
| "print(\"\\n2. Full distribution vs Mode-dropped distribution:\")\n", | |
| "p_values_dropped, mean_p_dropped, std_p_dropped = pqmass_test_multiple(\n", | |
| " samples_full, samples_dropped, n_ref=50, n_repeats=10\n", | |
| ")\n", | |
| "print(f\" Mean p-value: {mean_p_dropped:.6f} ± {std_p_dropped:.6f}\")\n", | |
| "print(f\" → Distributions {'MATCH' if mean_p_dropped > 0.05 else 'DIFFER'} (p < 0.05 means different)\")\n", | |
| "\n", | |
| "print(f\"\\n✓ PQMass successfully detected mode dropping!\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "**Interpretation:** \n", | |
| "- When comparing two sets from the full distribution, p-value should be high (> 0.05), indicating they match\n", | |
| "- When comparing full vs mode-dropped, p-value should be very low (≪ 0.05), indicating they differ\n", | |
| "- This demonstrates PQMass's ability to detect mode dropping in generative models" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 5. Workflow 3: Dimensionality Scaling\n", | |
| "\n", | |
| "We test how PQMass performs as the dimensionality of the data increases.\n", | |
| "\n", | |
| "One key advantage of PQMass over other metrics (like Wasserstein distance) is that it scales well to higher dimensions without requiring feature extraction.\n", | |
| "\n", | |
| "This corresponds to Section 3.2 and Figure 3 of the paper." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Testing PQMass across different dimensions...\n", | |
| "\n", | |
| " Testing dimension 2...\n", | |
| " Same dist: p=0.1639, Different dist: p=0.000021\n", | |
| "\n", | |
| " Testing dimension 5...\n", | |
| " Same dist: p=0.1040, Different dist: p=0.000000\n", | |
| "\n", | |
| " Testing dimension 10...\n", | |
| " Same dist: p=0.2785, Different dist: p=0.000000\n", | |
| "\n", | |
| " Testing dimension 20...\n", | |
| " Same dist: p=0.6117, Different dist: p=0.000000\n", | |
| "\n", | |
| " Testing dimension 30...\n", | |
| " Same dist: p=0.1242, Different dist: p=0.000000\n", | |
| "\n", | |
| " Testing dimension 50...\n", | |
| " Same dist: p=0.6534, Different dist: p=0.000000\n", | |
| "\n", | |
| "✓ Dimensionality scaling test completed\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Test PQMass across different dimensions\n", | |
| "print(\"Testing PQMass across different dimensions...\")\n", | |
| "\n", | |
| "dimensions = [2, 5, 10, 20, 30, 50]\n", | |
| "n_samples_dim_test = 500\n", | |
| "n_components_dim = 5\n", | |
| "\n", | |
| "results_same = []\n", | |
| "results_different = []\n", | |
| "\n", | |
| "for d in dimensions:\n", | |
| " print(f\"\\n Testing dimension {d}...\")\n", | |
| " \n", | |
| " # Create GMM for this dimension\n", | |
| " means_d = np.random.randn(n_components_dim, d) * 3\n", | |
| " covs_d = [np.eye(d) * (0.5 + 0.5 * np.random.rand()) for _ in range(n_components_dim)]\n", | |
| " weights_d = np.ones(n_components_dim) / n_components_dim\n", | |
| " \n", | |
| " # Test 1: Same distribution\n", | |
| " s1 = sample_from_gmm(n_samples_dim_test, means_d, covs_d, weights_d)\n", | |
| " s2 = sample_from_gmm(n_samples_dim_test, means_d, covs_d, weights_d)\n", | |
| " p_same = pqmass_test(s1, s2, n_ref=50)\n", | |
| " results_same.append(p_same)\n", | |
| " \n", | |
| " # Test 2: Different distribution (drop one mode)\n", | |
| " means_d_dropped = means_d[1:]\n", | |
| " covs_d_dropped = covs_d[1:]\n", | |
| " weights_d_dropped = weights_d[1:] / weights_d[1:].sum()\n", | |
| " s3 = sample_from_gmm(n_samples_dim_test, means_d_dropped, covs_d_dropped, weights_d_dropped)\n", | |
| " p_diff = pqmass_test(s1, s3, n_ref=50)\n", | |
| " results_different.append(p_diff)\n", | |
| " \n", | |
| " print(f\" Same dist: p={p_same:.4f}, Different dist: p={p_diff:.6f}\")\n", | |
| "\n", | |
| "print(\"\\n✓ Dimensionality scaling test completed\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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", | |
| "text/plain": [ | |
| "<Figure size 1000x600 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "transient": {} | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Key observations:\n", | |
| " - Green line (same distribution) stays above p=0.05 → correctly identifies match\n", | |
| " - Red line (different distribution) stays below p=0.05 → correctly identifies mismatch\n", | |
| " - PQMass maintains discriminative power across dimensions\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Visualize results\n", | |
| "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n", | |
| "\n", | |
| "ax.plot(dimensions, results_same, 'o-', linewidth=2, markersize=8, \n", | |
| " label='Same distribution', color='green')\n", | |
| "ax.plot(dimensions, results_different, 's-', linewidth=2, markersize=8, \n", | |
| " label='Different distribution (mode dropped)', color='red')\n", | |
| "\n", | |
| "ax.axhline(y=0.05, color='black', linestyle='--', linewidth=1.5, \n", | |
| " label='Significance threshold (p=0.05)', alpha=0.7)\n", | |
| "\n", | |
| "ax.set_xlabel('Number of Dimensions', fontsize=13)\n", | |
| "ax.set_ylabel('P-value', fontsize=13)\n", | |
| "ax.set_title('PQMass Performance Across Dimensions', fontsize=14, fontweight='bold')\n", | |
| "ax.set_yscale('log')\n", | |
| "ax.legend(fontsize=11)\n", | |
| "ax.grid(True, alpha=0.3)\n", | |
| "\n", | |
| "plt.tight_layout()\n", | |
| "plt.show()\n", | |
| "\n", | |
| "print(\"\\nKey observations:\")\n", | |
| "print(\" - Green line (same distribution) stays above p=0.05 → correctly identifies match\")\n", | |
| "print(\" - Red line (different distribution) stays below p=0.05 → correctly identifies mismatch\")\n", | |
| "print(\" - PQMass maintains discriminative power across dimensions\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 6. Workflow 4: Simple Generative Model Evaluation\n", | |
| "\n", | |
| "We demonstrate how to use PQMass to evaluate a simple generative model.\n", | |
| "\n", | |
| "We'll train a Gaussian Mixture Model to fit data and use PQMass to assess the quality of the fit.\n", | |
| "\n", | |
| "**Note:** In the paper, more complex generative models (VAEs, diffusion models) are evaluated. Here we use a simple GMM for educational purposes that runs quickly." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Generating ground truth data...\n", | |
| "Generated 2000 training samples and 500 test samples\n", | |
| "Data dimensionality: 10\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Generate \"true\" data from a known distribution\n", | |
| "print(\"Generating ground truth data...\")\n", | |
| "\n", | |
| "n_train = 2000\n", | |
| "n_test = 500\n", | |
| "n_dims_gen = 10\n", | |
| "n_modes_true = 4\n", | |
| "\n", | |
| "# True distribution\n", | |
| "means_true = np.random.randn(n_modes_true, n_dims_gen) * 4\n", | |
| "covs_true = [np.eye(n_dims_gen) * (0.3 + 0.4 * np.random.rand()) for _ in range(n_modes_true)]\n", | |
| "weights_true = np.random.dirichlet(np.ones(n_modes_true))\n", | |
| "\n", | |
| "# Generate training and test data\n", | |
| "X_train = sample_from_gmm(n_train, means_true, covs_true, weights_true)\n", | |
| "X_test = sample_from_gmm(n_test, means_true, covs_true, weights_true)\n", | |
| "\n", | |
| "print(f\"Generated {n_train} training samples and {n_test} test samples\")\n", | |
| "print(f\"Data dimensionality: {n_dims_gen}\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Training generative models...\n", | |
| " Training GMM with 2 components...\n", | |
| " PQMass p-value: 0.0000 ± 0.0000\n", | |
| " Training GMM with 4 components...\n", | |
| " PQMass p-value: 0.6828 ± 0.1760\n", | |
| " Training GMM with 6 components...\n", | |
| " PQMass p-value: 0.2755 ± 0.1506\n", | |
| " Training GMM with 8 components...\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " PQMass p-value: 0.2333 ± 0.0481\n", | |
| "\n", | |
| "✓ Model training and evaluation completed\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Train generative models with different numbers of components\n", | |
| "print(\"\\nTraining generative models...\")\n", | |
| "\n", | |
| "n_components_list = [2, 4, 6, 8]\n", | |
| "models = {}\n", | |
| "generated_samples = {}\n", | |
| "pqmass_scores = {}\n", | |
| "\n", | |
| "for n_comp in n_components_list:\n", | |
| " print(f\" Training GMM with {n_comp} components...\")\n", | |
| " \n", | |
| " # Train GMM\n", | |
| " gmm = GaussianMixture(n_components=n_comp, covariance_type='full', random_state=42)\n", | |
| " gmm.fit(X_train)\n", | |
| " models[n_comp] = gmm\n", | |
| " \n", | |
| " # Generate samples\n", | |
| " X_gen, _ = gmm.sample(n_test)\n", | |
| " generated_samples[n_comp] = X_gen\n", | |
| " \n", | |
| " # Evaluate with PQMass\n", | |
| " p_values, mean_p, std_p = pqmass_test_multiple(\n", | |
| " X_test, X_gen, n_ref=50, n_repeats=5\n", | |
| " )\n", | |
| " pqmass_scores[n_comp] = (mean_p, std_p)\n", | |
| " \n", | |
| " print(f\" PQMass p-value: {mean_p:.4f} ± {std_p:.4f}\")\n", | |
| "\n", | |
| "print(\"\\n✓ Model training and evaluation completed\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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", | |
| "text/plain": [ | |
| "<Figure size 1000x600 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "transient": {} | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Interpretation:\n", | |
| " - True distribution has 4 modes (green vertical line)\n", | |
| " - Models with ≥4 components have higher p-values (better fit)\n", | |
| " - Models with <4 components fail to capture all modes (lower p-values)\n", | |
| " - P-value > 0.05 suggests the model adequately represents the distribution\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Visualize results\n", | |
| "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n", | |
| "\n", | |
| "components = list(pqmass_scores.keys())\n", | |
| "mean_pvals = [pqmass_scores[c][0] for c in components]\n", | |
| "std_pvals = [pqmass_scores[c][1] for c in components]\n", | |
| "\n", | |
| "ax.errorbar(components, mean_pvals, yerr=std_pvals, fmt='o-', \n", | |
| " linewidth=2, markersize=10, capsize=5, capthick=2,\n", | |
| " color='steelblue', label='PQMass p-value')\n", | |
| "\n", | |
| "ax.axhline(y=0.05, color='red', linestyle='--', linewidth=2, \n", | |
| " label='Significance threshold', alpha=0.7)\n", | |
| "ax.axvline(x=n_modes_true, color='green', linestyle=':', linewidth=2,\n", | |
| " label=f'True # components ({n_modes_true})', alpha=0.7)\n", | |
| "\n", | |
| "ax.set_xlabel('Number of GMM Components', fontsize=13)\n", | |
| "ax.set_ylabel('PQMass P-value', fontsize=13)\n", | |
| "ax.set_title('Generative Model Quality vs Model Complexity', fontsize=14, fontweight='bold')\n", | |
| "ax.legend(fontsize=11)\n", | |
| "ax.grid(True, alpha=0.3)\n", | |
| "ax.set_ylim(bottom=0)\n", | |
| "\n", | |
| "plt.tight_layout()\n", | |
| "plt.show()\n", | |
| "\n", | |
| "print(\"\\nInterpretation:\")\n", | |
| "print(f\" - True distribution has {n_modes_true} modes (green vertical line)\")\n", | |
| "print(f\" - Models with ≥{n_modes_true} components have higher p-values (better fit)\")\n", | |
| "print(f\" - Models with <{n_modes_true} components fail to capture all modes (lower p-values)\")\n", | |
| "print(f\" - P-value > 0.05 suggests the model adequately represents the distribution\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 7. Comparing PQMass to Other Metrics\n", | |
| "\n", | |
| "We briefly compare PQMass to other sample-based metrics commonly used in the literature.\n", | |
| "\n", | |
| "**Note:** This is a simplified comparison. The paper (Section 3.2, Table 1) includes more comprehensive comparisons with Wasserstein distance, MMD, and Jensen-Shannon divergence." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Comparing metrics on 2D mode-dropping task...\n", | |
| "\n", | |
| "PQMass p-value: 0.000000\n", | |
| " → DETECTS mode dropping\n", | |
| "\n", | |
| "Mean difference: 1.6647\n", | |
| " → Simple metric that doesn't capture distributional differences\n", | |
| "\n", | |
| "Variance difference: 1.4545\n", | |
| " → Also doesn't capture mode structure\n", | |
| "\n", | |
| "Key advantage of PQMass:\n", | |
| " - Provides rigorous statistical test with p-values\n", | |
| " - Captures distributional differences beyond simple moments\n", | |
| " - Scales to higher dimensions without feature extraction\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Simple comparison with basic metrics\n", | |
| "print(\"Comparing metrics on 2D mode-dropping task...\\n\")\n", | |
| "\n", | |
| "# Use the 2D samples from earlier\n", | |
| "# Recall: samples_full has all modes, samples_dropped is missing one mode\n", | |
| "\n", | |
| "# PQMass\n", | |
| "p_pqmass = pqmass_test(samples_full, samples_dropped, n_ref=50)\n", | |
| "print(f\"PQMass p-value: {p_pqmass:.6f}\")\n", | |
| "print(f\" → {'DETECTS' if p_pqmass < 0.05 else 'FAILS TO DETECT'} mode dropping\\n\")\n", | |
| "\n", | |
| "# Mean difference (simple baseline)\n", | |
| "mean_diff = np.linalg.norm(np.mean(samples_full, axis=0) - np.mean(samples_dropped, axis=0))\n", | |
| "print(f\"Mean difference: {mean_diff:.4f}\")\n", | |
| "print(f\" → Simple metric that doesn't capture distributional differences\\n\")\n", | |
| "\n", | |
| "# Variance comparison\n", | |
| "var_full = np.mean(np.var(samples_full, axis=0))\n", | |
| "var_dropped = np.mean(np.var(samples_dropped, axis=0))\n", | |
| "var_diff = abs(var_full - var_dropped)\n", | |
| "print(f\"Variance difference: {var_diff:.4f}\")\n", | |
| "print(f\" → Also doesn't capture mode structure\\n\")\n", | |
| "\n", | |
| "print(\"Key advantage of PQMass:\")\n", | |
| "print(\" - Provides rigorous statistical test with p-values\")\n", | |
| "print(\" - Captures distributional differences beyond simple moments\")\n", | |
| "print(\" - Scales to higher dimensions without feature extraction\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 8. Practical Guidelines for Using PQMass\n", | |
| "\n", | |
| "Based on the paper and our experiments, here are practical recommendations for using PQMass:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "PRACTICAL GUIDELINES FOR USING PQMASS\n", | |
| "============================================================\n", | |
| "\n", | |
| "1. CHOOSING THE NUMBER OF REFERENCE POINTS (n_ref):\n", | |
| " - More reference points = finer tessellation = more sensitive test\n", | |
| " - But too many can lead to empty cells and numerical issues\n", | |
| " - Recommended: n_ref = 50-200 for most applications\n", | |
| " - For high dimensions (>50), use more reference points\n", | |
| "\n", | |
| "2. SAMPLE SIZE REQUIREMENTS:\n", | |
| " - Need enough samples to populate Voronoi cells\n", | |
| " - Minimum: ~10 × n_ref samples per distribution\n", | |
| " - Recommended: 500-5000 samples for reliable results\n", | |
| " - More samples = more statistical power\n", | |
| "\n", | |
| "3. CHOOSING THE DISTANCE METRIC:\n", | |
| " - Euclidean (L2): Default choice, works well in most cases\n", | |
| " - Manhattan (L1): Can be more robust in high dimensions\n", | |
| " - Custom metrics: Can use feature space if needed\n", | |
| "\n", | |
| "4. INTERPRETING P-VALUES:\n", | |
| " - p > 0.05: Cannot reject null hypothesis (distributions may match)\n", | |
| " - p < 0.05: Reject null hypothesis (distributions likely different)\n", | |
| " - p < 0.01: Strong evidence of difference\n", | |
| " - p < 0.001: Very strong evidence of difference\n", | |
| "\n", | |
| "5. MULTIPLE RUNS:\n", | |
| " - Run test multiple times with different reference points\n", | |
| " - Report mean and standard deviation of p-values\n", | |
| " - Reduces variance from random tessellation\n", | |
| "\n", | |
| "6. COMPARISON WITH OTHER METRICS:\n", | |
| " - Use PQMass alongside other metrics (FID, IS, etc.)\n", | |
| " - PQMass provides statistical rigor\n", | |
| " - Other metrics may be more interpretable in specific domains\n", | |
| "\n", | |
| "7. COMPUTATIONAL CONSIDERATIONS:\n", | |
| " - Main cost: Computing pairwise distances for Voronoi assignment\n", | |
| " - Complexity: O(n × n_ref × d) where n=samples, d=dimensions\n", | |
| " - For very large datasets, use subsampling or batching\n", | |
| "\n", | |
| "============================================================\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(\"PRACTICAL GUIDELINES FOR USING PQMASS\")\n", | |
| "print(\"=\" * 60)\n", | |
| "\n", | |
| "print(\"\\n1. CHOOSING THE NUMBER OF REFERENCE POINTS (n_ref):\")\n", | |
| "print(\" - More reference points = finer tessellation = more sensitive test\")\n", | |
| "print(\" - But too many can lead to empty cells and numerical issues\")\n", | |
| "print(\" - Recommended: n_ref = 50-200 for most applications\")\n", | |
| "print(\" - For high dimensions (>50), use more reference points\")\n", | |
| "\n", | |
| "print(\"\\n2. SAMPLE SIZE REQUIREMENTS:\")\n", | |
| "print(\" - Need enough samples to populate Voronoi cells\")\n", | |
| "print(\" - Minimum: ~10 × n_ref samples per distribution\")\n", | |
| "print(\" - Recommended: 500-5000 samples for reliable results\")\n", | |
| "print(\" - More samples = more statistical power\")\n", | |
| "\n", | |
| "print(\"\\n3. CHOOSING THE DISTANCE METRIC:\")\n", | |
| "print(\" - Euclidean (L2): Default choice, works well in most cases\")\n", | |
| "print(\" - Manhattan (L1): Can be more robust in high dimensions\")\n", | |
| "print(\" - Custom metrics: Can use feature space if needed\")\n", | |
| "\n", | |
| "print(\"\\n4. INTERPRETING P-VALUES:\")\n", | |
| "print(\" - p > 0.05: Cannot reject null hypothesis (distributions may match)\")\n", | |
| "print(\" - p < 0.05: Reject null hypothesis (distributions likely different)\")\n", | |
| "print(\" - p < 0.01: Strong evidence of difference\")\n", | |
| "print(\" - p < 0.001: Very strong evidence of difference\")\n", | |
| "\n", | |
| "print(\"\\n5. MULTIPLE RUNS:\")\n", | |
| "print(\" - Run test multiple times with different reference points\")\n", | |
| "print(\" - Report mean and standard deviation of p-values\")\n", | |
| "print(\" - Reduces variance from random tessellation\")\n", | |
| "\n", | |
| "print(\"\\n6. COMPARISON WITH OTHER METRICS:\")\n", | |
| "print(\" - Use PQMass alongside other metrics (FID, IS, etc.)\")\n", | |
| "print(\" - PQMass provides statistical rigor\")\n", | |
| "print(\" - Other metrics may be more interpretable in specific domains\")\n", | |
| "\n", | |
| "print(\"\\n7. COMPUTATIONAL CONSIDERATIONS:\")\n", | |
| "print(\" - Main cost: Computing pairwise distances for Voronoi assignment\")\n", | |
| "print(\" - Complexity: O(n × n_ref × d) where n=samples, d=dimensions\")\n", | |
| "print(\" - For very large datasets, use subsampling or batching\")\n", | |
| "\n", | |
| "print(\"\\n\" + \"=\" * 60)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 9. Scaling to Production Use\n", | |
| "\n", | |
| "This notebook demonstrated PQMass with small-scale examples for educational purposes. To use PQMass for full-scale evaluation as in the paper:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "SCALING PQMASS TO PRODUCTION USE\n", | |
| "============================================================\n", | |
| "\n", | |
| "WHAT YOU WOULD NEED FOR FULL-SCALE EXPERIMENTS:\n", | |
| "\n", | |
| "1. COMPUTATIONAL RESOURCES:\n", | |
| " - GPU: For training generative models (VAEs, diffusion models, GANs)\n", | |
| " - RAM: 16-32GB for large datasets and high-dimensional data\n", | |
| " - Time: Hours to days for full training and evaluation\n", | |
| "\n", | |
| "2. LARGER DATASETS:\n", | |
| " - MNIST: 60k training, 10k test (as used in paper)\n", | |
| " - CIFAR-10: 50k training, 10k test\n", | |
| " - FFHQ: Thousands of high-resolution face images\n", | |
| " - Use 1000-10000 samples for PQMass evaluation\n", | |
| "\n", | |
| "3. ADVANCED GENERATIVE MODELS:\n", | |
| " - VAEs: Train with frameworks like PyTorch or TensorFlow\n", | |
| " - Diffusion models: DDPM, DDIM, etc.\n", | |
| " - GANs: StyleGAN, Progressive GAN, etc.\n", | |
| " - Normalizing flows: For exact likelihood computation\n", | |
| "\n", | |
| "4. FEATURE EXTRACTION (OPTIONAL):\n", | |
| " - For very high-dimensional data (e.g., images), can use:\n", | |
| " • Inception features (for FID baseline)\n", | |
| " • VAE encoder features\n", | |
| " • CLIP embeddings\n", | |
| " - Paper shows PQMass works well in raw pixel space too\n", | |
| "\n", | |
| "5. COMPREHENSIVE EVALUATION:\n", | |
| " - Compare against multiple baselines:\n", | |
| " • FID (Fréchet Inception Distance)\n", | |
| " • IS (Inception Score)\n", | |
| " • FLD (Feature Likelihood Divergence)\n", | |
| " • Precision and Recall\n", | |
| " - Test on multiple datasets and model architectures\n", | |
| " - Report confidence intervals\n", | |
| "\n", | |
| "6. IMPLEMENTATION OPTIMIZATIONS:\n", | |
| " - Use efficient nearest neighbor libraries (FAISS, Annoy)\n", | |
| " - Batch processing for large datasets\n", | |
| " - Parallel computation across multiple GPUs\n", | |
| " - Cache distance computations when possible\n", | |
| "\n", | |
| "7. CODE REPOSITORIES:\n", | |
| " - Official implementation: https://github.com/Ciela-Institute/PQM\n", | |
| " - Includes optimized implementations and utilities\n", | |
| " - Pre-trained models and evaluation scripts\n", | |
| "\n", | |
| "============================================================\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(\"SCALING PQMASS TO PRODUCTION USE\")\n", | |
| "print(\"=\" * 60)\n", | |
| "\n", | |
| "print(\"\\nWHAT YOU WOULD NEED FOR FULL-SCALE EXPERIMENTS:\")\n", | |
| "\n", | |
| "print(\"\\n1. COMPUTATIONAL RESOURCES:\")\n", | |
| "print(\" - GPU: For training generative models (VAEs, diffusion models, GANs)\")\n", | |
| "print(\" - RAM: 16-32GB for large datasets and high-dimensional data\")\n", | |
| "print(\" - Time: Hours to days for full training and evaluation\")\n", | |
| "\n", | |
| "print(\"\\n2. LARGER DATASETS:\")\n", | |
| "print(\" - MNIST: 60k training, 10k test (as used in paper)\")\n", | |
| "print(\" - CIFAR-10: 50k training, 10k test\")\n", | |
| "print(\" - FFHQ: Thousands of high-resolution face images\")\n", | |
| "print(\" - Use 1000-10000 samples for PQMass evaluation\")\n", | |
| "\n", | |
| "print(\"\\n3. ADVANCED GENERATIVE MODELS:\")\n", | |
| "print(\" - VAEs: Train with frameworks like PyTorch or TensorFlow\")\n", | |
| "print(\" - Diffusion models: DDPM, DDIM, etc.\")\n", | |
| "print(\" - GANs: StyleGAN, Progressive GAN, etc.\")\n", | |
| "print(\" - Normalizing flows: For exact likelihood computation\")\n", | |
| "\n", | |
| "print(\"\\n4. FEATURE EXTRACTION (OPTIONAL):\")\n", | |
| "print(\" - For very high-dimensional data (e.g., images), can use:\")\n", | |
| "print(\" • Inception features (for FID baseline)\")\n", | |
| "print(\" • VAE encoder features\")\n", | |
| "print(\" • CLIP embeddings\")\n", | |
| "print(\" - Paper shows PQMass works well in raw pixel space too\")\n", | |
| "\n", | |
| "print(\"\\n5. COMPREHENSIVE EVALUATION:\")\n", | |
| "print(\" - Compare against multiple baselines:\")\n", | |
| "print(\" • FID (Fréchet Inception Distance)\")\n", | |
| "print(\" • IS (Inception Score)\")\n", | |
| "print(\" • FLD (Feature Likelihood Divergence)\")\n", | |
| "print(\" • Precision and Recall\")\n", | |
| "print(\" - Test on multiple datasets and model architectures\")\n", | |
| "print(\" - Report confidence intervals\")\n", | |
| "\n", | |
| "print(\"\\n6. IMPLEMENTATION OPTIMIZATIONS:\")\n", | |
| "print(\" - Use efficient nearest neighbor libraries (FAISS, Annoy)\")\n", | |
| "print(\" - Batch processing for large datasets\")\n", | |
| "print(\" - Parallel computation across multiple GPUs\")\n", | |
| "print(\" - Cache distance computations when possible\")\n", | |
| "\n", | |
| "print(\"\\n7. CODE REPOSITORIES:\")\n", | |
| "print(\" - Official implementation: https://github.com/Ciela-Institute/PQM\")\n", | |
| "print(\" - Includes optimized implementations and utilities\")\n", | |
| "print(\" - Pre-trained models and evaluation scripts\")\n", | |
| "\n", | |
| "print(\"\\n\" + \"=\" * 60)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 10. Summary and Conclusions\n", | |
| "\n", | |
| "This notebook demonstrated the core concepts and implementation of PQMass." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "SUMMARY OF PQMASS\n", | |
| "============================================================\n", | |
| "\n", | |
| "KEY CONTRIBUTIONS OF THE PAPER:\n", | |
| "\n", | |
| "1. Novel Method:\n", | |
| " - Likelihood-free two-sample test for comparing distributions\n", | |
| " - Based on probability mass estimation via Voronoi tessellation\n", | |
| " - Provides rigorous p-values for hypothesis testing\n", | |
| "\n", | |
| "2. Statistical Rigor:\n", | |
| " - Grounded in chi-squared tests on multinomial distributions\n", | |
| " - Theoretical consistency guarantees (Propositions 2.1 and 2.2)\n", | |
| " - No assumptions about density smoothness or parametric form\n", | |
| "\n", | |
| "3. Practical Advantages:\n", | |
| " - Scales well to moderately high dimensions (10-100+)\n", | |
| " - Detects fidelity, diversity, AND novelty simultaneously\n", | |
| " - Computationally efficient compared to alternatives\n", | |
| " - Works directly in data space (no feature extraction required)\n", | |
| "\n", | |
| "4. Empirical Validation:\n", | |
| " - Tested on multiple datasets (MNIST, CIFAR-10, FFHQ, astrophysics)\n", | |
| " - Compared favorably to existing metrics (FID, FLD, MMD, etc.)\n", | |
| " - Successfully detects mode dropping, memorization, and noise\n", | |
| "\n", | |
| "WHAT WE DEMONSTRATED IN THIS NOTEBOOK:\n", | |
| "\n", | |
| "✓ Core PQMass algorithm implementation\n", | |
| "✓ Null test validation (chi-squared distribution)\n", | |
| "✓ Mode-dropping detection capability\n", | |
| "✓ Performance across different dimensions\n", | |
| "✓ Evaluation of generative models (GMMs)\n", | |
| "✓ Practical guidelines for usage\n", | |
| "\n", | |
| "APPLICATIONS:\n", | |
| "\n", | |
| "- Generative model evaluation (VAEs, GANs, diffusion models)\n", | |
| "- Quality control during model training\n", | |
| "- Model selection and hyperparameter tuning\n", | |
| "- Detecting mode collapse or overfitting\n", | |
| "- Comparing different generative models\n", | |
| "- Scientific applications requiring rigorous uncertainty quantification\n", | |
| "\n", | |
| "LIMITATIONS TO BE AWARE OF:\n", | |
| "\n", | |
| "- Requires sufficient samples to populate Voronoi cells\n", | |
| "- Performance depends on choice of n_ref and metric\n", | |
| "- Very high dimensions (>1000) may require feature extraction\n", | |
| "- P-values should be interpreted in context with other metrics\n", | |
| "\n", | |
| "FURTHER READING:\n", | |
| "\n", | |
| "- Paper: Lemos et al. (2025), ICLR 2025\n", | |
| "- Code: https://github.com/Ciela-Institute/PQM\n", | |
| "- Related work: FID, FLD, MMD, Precision/Recall metrics\n", | |
| "\n", | |
| "============================================================\n", | |
| "\n", | |
| "✓ Notebook completed successfully!\n", | |
| "\n", | |
| "You now have a working understanding of PQMass and can:\n", | |
| " 1. Apply it to your own generative models\n", | |
| " 2. Compare different models statistically\n", | |
| " 3. Detect common failure modes (mode collapse, etc.)\n", | |
| " 4. Scale up to larger datasets and more complex models\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(\"SUMMARY OF PQMASS\")\n", | |
| "print(\"=\" * 60)\n", | |
| "\n", | |
| "print(\"\\nKEY CONTRIBUTIONS OF THE PAPER:\")\n", | |
| "print(\"\\n1. Novel Method:\")\n", | |
| "print(\" - Likelihood-free two-sample test for comparing distributions\")\n", | |
| "print(\" - Based on probability mass estimation via Voronoi tessellation\")\n", | |
| "print(\" - Provides rigorous p-values for hypothesis testing\")\n", | |
| "\n", | |
| "print(\"\\n2. Statistical Rigor:\")\n", | |
| "print(\" - Grounded in chi-squared tests on multinomial distributions\")\n", | |
| "print(\" - Theoretical consistency guarantees (Propositions 2.1 and 2.2)\")\n", | |
| "print(\" - No assumptions about density smoothness or parametric form\")\n", | |
| "\n", | |
| "print(\"\\n3. Practical Advantages:\")\n", | |
| "print(\" - Scales well to moderately high dimensions (10-100+)\")\n", | |
| "print(\" - Detects fidelity, diversity, AND novelty simultaneously\")\n", | |
| "print(\" - Computationally efficient compared to alternatives\")\n", | |
| "print(\" - Works directly in data space (no feature extraction required)\")\n", | |
| "\n", | |
| "print(\"\\n4. Empirical Validation:\")\n", | |
| "print(\" - Tested on multiple datasets (MNIST, CIFAR-10, FFHQ, astrophysics)\")\n", | |
| "print(\" - Compared favorably to existing metrics (FID, FLD, MMD, etc.)\")\n", | |
| "print(\" - Successfully detects mode dropping, memorization, and noise\")\n", | |
| "\n", | |
| "print(\"\\nWHAT WE DEMONSTRATED IN THIS NOTEBOOK:\")\n", | |
| "print(\"\\n✓ Core PQMass algorithm implementation\")\n", | |
| "print(\"✓ Null test validation (chi-squared distribution)\")\n", | |
| "print(\"✓ Mode-dropping detection capability\")\n", | |
| "print(\"✓ Performance across different dimensions\")\n", | |
| "print(\"✓ Evaluation of generative models (GMMs)\")\n", | |
| "print(\"✓ Practical guidelines for usage\")\n", | |
| "\n", | |
| "print(\"\\nAPPLICATIONS:\")\n", | |
| "print(\"\\n- Generative model evaluation (VAEs, GANs, diffusion models)\")\n", | |
| "print(\"- Quality control during model training\")\n", | |
| "print(\"- Model selection and hyperparameter tuning\")\n", | |
| "print(\"- Detecting mode collapse or overfitting\")\n", | |
| "print(\"- Comparing different generative models\")\n", | |
| "print(\"- Scientific applications requiring rigorous uncertainty quantification\")\n", | |
| "\n", | |
| "print(\"\\nLIMITATIONS TO BE AWARE OF:\")\n", | |
| "print(\"\\n- Requires sufficient samples to populate Voronoi cells\")\n", | |
| "print(\"- Performance depends on choice of n_ref and metric\")\n", | |
| "print(\"- Very high dimensions (>1000) may require feature extraction\")\n", | |
| "print(\"- P-values should be interpreted in context with other metrics\")\n", | |
| "\n", | |
| "print(\"\\nFURTHER READING:\")\n", | |
| "print(\"\\n- Paper: Lemos et al. (2025), ICLR 2025\")\n", | |
| "print(\"- Code: https://github.com/Ciela-Institute/PQM\")\n", | |
| "print(\"- Related work: FID, FLD, MMD, Precision/Recall metrics\")\n", | |
| "\n", | |
| "print(\"\\n\" + \"=\" * 60)\n", | |
| "print(\"\\n✓ Notebook completed successfully!\")\n", | |
| "print(\"\\nYou now have a working understanding of PQMass and can:\")\n", | |
| "print(\" 1. Apply it to your own generative models\")\n", | |
| "print(\" 2. Compare different models statistically\")\n", | |
| "print(\" 3. Detect common failure modes (mode collapse, etc.)\")\n", | |
| "print(\" 4. Scale up to larger datasets and more complex models\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## References\n", | |
| "\n", | |
| "**Main Paper:**\n", | |
| "- Lemos, P., Sharief, S., Malkin, N., Salhi, S., Stone, C., Perreault-Levasseur, L., & Hezaveh, Y. (2025). PQMass: Probabilistic Assessment of the Quality of Generative Models Using Probability Mass Estimation. *ICLR 2025*.\n", | |
| "\n", | |
| "**Related Methods:**\n", | |
| "- Heusel et al. (2017). GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. *NeurIPS*. [FID]\n", | |
| "- Jiralerspong et al. (2023). Feature Likelihood Divergence. *ICLR*. [FLD]\n", | |
| "- Gretton et al. (2012). A Kernel Two-Sample Test. *JMLR*. [MMD]\n", | |
| "- Salimans et al. (2016). Improved Techniques for Training GANs. *NeurIPS*. [Inception Score]\n", | |
| "\n", | |
| "**Code:**\n", | |
| "- Official repository: https://github.com/Ciela-Institute/PQM" | |
| ] | |
| } | |
| ], | |
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| "name": "python3" | |
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