wehub-resource-sync
8571 lines
621 KiB
Plaintext
8571 lines
621 KiB
Plaintext
{
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"source": [
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"# Tail-GAN: Learning to Generate Tail-Risk Preserving Scenarios\n",
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"\n",
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"**Chapter 5: Synthetic Data Generation**\n",
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"**Section Reference**: Section 5.4 (GANs for Time Series)\n",
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"\n",
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"**Docker image**: `ml4t-gpu`\n",
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"\n",
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"> **GPU recommended**: This notebook trains models with PyTorch/CUDA. It will run on CPU\n",
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"> but training may be very slow. For GPU acceleration:\n",
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"> ```bash\n",
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"> docker compose run --rm ml4t-gpu python 05_synthetic_data/02_tailgan_tail_risk.py\n",
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"> ```\n",
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"\n",
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"\n",
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"## Purpose\n",
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"\n",
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"This notebook implements **Tail-GAN** (Cont, Xu, and Zhang 2022), a GAN\n",
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"architecture that uses differentiable sorting to preserve tail risk\n",
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"characteristics (VaR, ES) in synthetic financial scenarios.\n",
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"\n",
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"## Learning Objectives\n",
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"\n",
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"- Understand how differentiable sorting enables gradient flow through quantile\n",
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" computation (VaR/ES)\n",
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"- Implement constraint projection to enforce the relationship $W \\cdot VaR \\leq ES$\n",
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"- Generate portfolio PnL scenarios that preserve tail risk structure\n",
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"- Evaluate generation quality using relative VaR/ES error\n",
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"\n",
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"## Book Reference\n",
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"\n",
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"Section 5.4 discusses how Tail-GAN targets specific risk metrics rather\n",
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"than general distributional matching, filling a gap left by TimeGAN.\n",
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"\n",
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"## Prerequisites\n",
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"\n",
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"Requires ETF data. The generator clamps outputs to [-1, 1], so returns\n",
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"are scaled using 99th-percentile normalization with headroom for tails."
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]
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},
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{
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"cell_type": "code",
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"id": "8bf97af8",
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},
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"outputs": [],
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"source": [
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"\"\"\"Tail-GAN: Tail-risk preserving scenario generation (Cont et al., 2022).\"\"\"\n",
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"\n",
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"import warnings\n",
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"from pathlib import Path\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import plotly.graph_objects as go\n",
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"import polars as pl\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"from IPython.display import Image, display\n",
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"from plotly.subplots import make_subplots\n",
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"\n",
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"from data import load_etfs\n",
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"from utils.paths import get_chapter_dir, get_output_dir\n",
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"from utils.reproducibility import set_global_seeds\n",
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"from utils.style import COLORS, plot_fidelity_comparison"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "97771745",
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"metadata": {
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"execution": {
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"status": "completed"
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},
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"N_EPOCHS = 3000\n",
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"BATCH_SIZE = 1000\n",
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"LATENT_DIM = 1000\n",
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"N_COLS = 100 # Time steps per scenario\n",
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"N_STRATEGIES = 32 # Number of portfolio strategies\n",
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"N_SCENARIO_MULTIPLIER = 10 # Multiplier for number of scenarios (batch_size * this)\n",
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"RETRAIN = False # Set True to force retraining even if checkpoint exists\n",
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"SEED = 1 # Seed pinned to preserve §5.4 prose numbers (VaR 13.1% / ES 11.3%)"
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]
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"set_global_seeds(SEED)\n",
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"\n",
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"# Paths\n",
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"CHECKPOINT_PATH = get_output_dir(5, \"tailgan\") / \"checkpoints\" / \"tailgan_model.pt\"\n",
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"\n",
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"# Device setup\n",
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"cuda = torch.cuda.is_available()\n",
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"device = torch.device(\"cuda\" if cuda else \"cpu\")\n",
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"Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c40e3071",
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"metadata": {
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"exception": false,
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"status": "completed"
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}
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},
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||
"source": [
|
||
"## Tail-GAN Architecture\n",
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"\n",
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||
"Tail-GAN uses **differentiable sorting** to compute VaR and ES in a way that\n",
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"allows gradients to flow through quantile estimates. The generator learns to\n",
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"produce scenarios where the relationship $W \\cdot VaR \\leq ES$ holds, ensuring\n",
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||
"realistic tail risk structure."
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||
]
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||
},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "8856aae0",
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"metadata": {
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"execution": {
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}
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},
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"outputs": [
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||
{
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"name": "stdout",
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"output_type": "stream",
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||
"text": [
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||
"Device: cuda\n"
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||
]
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||
}
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||
],
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||
"source": [
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"ASSETS_DIR = get_chapter_dir(5) / \"assets\"\n",
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"if (ASSETS_DIR / \"tailgan_architecture.jpeg\").exists():\n",
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" display(Image(ASSETS_DIR / \"tailgan_architecture.jpeg\", width=800))\n",
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"\n",
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||
"print(f\"Device: {device}\")"
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||
]
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},
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{
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"cell_type": "markdown",
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"id": "e3bcad50",
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"status": "completed"
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}
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},
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"source": [
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||
"## 1. Configuration\n",
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||
"\n",
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||
"Hyperparameters following Zhang et al. (2022):"
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||
]
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||
},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "89515882",
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"status": "completed"
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}
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},
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"outputs": [
|
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{
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"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
|
||
"Return matrix shape: (5, 100)\n",
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||
"Epochs: 3000, Batch size: 1000\n"
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||
]
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||
}
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||
],
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||
"source": [
|
||
"CONFIG = {\n",
|
||
" # Training\n",
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||
" \"n_epochs\": N_EPOCHS,\n",
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||
" \"batch_size\": BATCH_SIZE,\n",
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||
" \"lr_D\": 1e-7,\n",
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||
" \"lr_G\": 1e-6,\n",
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||
" \"b1\": 0.5, # Adam beta1\n",
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||
" \"b2\": 0.999, # Adam beta2\n",
|
||
" # Architecture\n",
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||
" \"latent_dim\": LATENT_DIM,\n",
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||
" \"n_rows\": 5, # Assets per scenario\n",
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||
" \"n_cols\": N_COLS, # Time steps per scenario\n",
|
||
" # Tail-GAN specific\n",
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||
" \"temp\": 0.01, # Neural sort temperature\n",
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||
" \"alphas\": [0.05], # VaR/ES quantiles\n",
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||
" \"W\": 10.0, # Scale parameter\n",
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||
" \"project\": True, # Project onto constraint set\n",
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||
" # Noise distribution\n",
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" \"noise_name\": \"normal\", # \"normal\" or \"t5\" for Student-t(5)\n",
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" # Strategies\n",
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" \"n_strategies\": N_STRATEGIES, # Number of portfolio strategies\n",
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" \"Cap\": 10, # Investment capital\n",
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"}\n",
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"\n",
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"# Derived shapes\n",
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"R_shape = (CONFIG[\"n_rows\"], CONFIG[\"n_cols\"])\n",
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"print(f\"Return matrix shape: {R_shape}\")\n",
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"print(f\"Epochs: {CONFIG['n_epochs']}, Batch size: {CONFIG['batch_size']}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e162b3c7",
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"metadata": {
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},
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"source": [
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||
"## 2. Data Loading\n",
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||
"\n",
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||
"We use ETF returns and create portfolio strategies to generate PnL scenarios.\n",
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"\n",
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"**Important**: Returns are scaled to fit the generator's [-1, 1] output range.\n",
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||
"We use a conservative scaling factor to keep most returns well within bounds\n",
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||
"while preserving tail structure."
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||
]
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},
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{
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"status": "completed"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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||
"Loaded returns: (1894, 5) (days x assets)\n",
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||
"Raw return range: [-0.1122, 0.0879]\n",
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||
"Scale factor: 0.0548\n",
|
||
"Scaled return range: [-2.0469, 1.6051]\n"
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||
]
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||
}
|
||
],
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||
"source": [
|
||
"def load_etf_returns() -> tuple[np.ndarray, float]:\n",
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||
" \"\"\"Load ETF returns for scenario generation.\n",
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"\n",
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||
" Returns:\n",
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||
" Tuple of (scaled_returns, scale_factor) where:\n",
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||
" - scaled_returns: Returns scaled to fit [-1, 1] range\n",
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||
" - scale_factor: Multiplier used (for inverse transform)\n",
|
||
" \"\"\"\n",
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||
" # Use the data loader (handles Docker/local paths)\n",
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||
" df = load_etfs()\n",
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||
"\n",
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||
" # Compute returns\n",
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||
" returns = (\n",
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||
" df.sort([\"symbol\", \"timestamp\"])\n",
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||
" .with_columns(pl.col(\"close\").pct_change().over(\"symbol\").alias(\"return\"))\n",
|
||
" .filter(pl.col(\"return\").is_not_null())\n",
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||
" )\n",
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||
"\n",
|
||
" # Pivot to wide format\n",
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||
" pivot = (\n",
|
||
" returns.pivot(values=\"return\", index=\"timestamp\", on=\"symbol\")\n",
|
||
" .drop(\"timestamp\")\n",
|
||
" .drop_nulls()\n",
|
||
" )\n",
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||
"\n",
|
||
" # Select subset of assets\n",
|
||
" n_assets = CONFIG[\"n_rows\"]\n",
|
||
" data = pivot.select(pivot.columns[:n_assets]).to_numpy()\n",
|
||
"\n",
|
||
" # Scale returns to fit generator's [-1, 1] output range\n",
|
||
" # Use 99th percentile of absolute returns as scale factor\n",
|
||
" # This keeps ~99% of returns in [-0.5, 0.5], with headroom for tails\n",
|
||
" abs_returns = np.abs(data)\n",
|
||
" scale_factor = np.percentile(abs_returns, 99) * 2 # *2 to target [-0.5, 0.5]\n",
|
||
" scaled_data = data / scale_factor\n",
|
||
"\n",
|
||
" print(f\"Loaded returns: {data.shape} (days x assets)\")\n",
|
||
" print(f\"Raw return range: [{data.min():.4f}, {data.max():.4f}]\")\n",
|
||
" print(f\"Scale factor: {scale_factor:.4f}\")\n",
|
||
" print(f\"Scaled return range: [{scaled_data.min():.4f}, {scaled_data.max():.4f}]\")\n",
|
||
"\n",
|
||
" return scaled_data, scale_factor\n",
|
||
"\n",
|
||
"\n",
|
||
"returns_data, SCALE_FACTOR = load_etf_returns()"
|
||
]
|
||
},
|
||
{
|
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"cell_type": "markdown",
|
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"id": "b70033f2",
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}
|
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},
|
||
"source": [
|
||
"## 3. Scenario and Strategy Generation\n",
|
||
"\n",
|
||
"Create return scenarios and portfolio strategies for PnL computation."
|
||
]
|
||
},
|
||
{
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"cell_type": "code",
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"execution_count": 7,
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|
||
"source": [
|
||
"def create_scenarios(returns: np.ndarray, n_scenarios: int, seq_len: int) -> np.ndarray:\n",
|
||
" \"\"\"Create rolling window scenarios from returns.\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" returns: (T, n_assets) daily returns\n",
|
||
" n_scenarios: Number of scenarios to create\n",
|
||
" seq_len: Length of each scenario\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" scenarios: (n_scenarios, n_assets, seq_len) return scenarios\n",
|
||
" \"\"\"\n",
|
||
" T, n_assets = returns.shape\n",
|
||
" max_start = T - seq_len\n",
|
||
"\n",
|
||
" # Random starting points\n",
|
||
" starts = np.random.randint(0, max_start, size=n_scenarios)\n",
|
||
"\n",
|
||
" scenarios = np.zeros((n_scenarios, n_assets, seq_len))\n",
|
||
" for i, start in enumerate(starts):\n",
|
||
" scenarios[i] = returns[start : start + seq_len].T\n",
|
||
"\n",
|
||
" return scenarios"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6a1a9d90",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.001992,
|
||
"end_time": "2026-06-13T02:38:02.934152+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:02.932160+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Portfolio Strategy Weights\n",
|
||
"\n",
|
||
"Random long-short portfolios whose PnL distributions we want to preserve."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "3eca697c",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:02.938574Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:02.938506Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:02.949733Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:02.949432Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.014031,
|
||
"end_time": "2026-06-13T02:38:02.950172+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:02.936141+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Created 10000 scenarios of shape (5, 100)\n",
|
||
"Created 32 portfolio strategies\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def create_strategies(n_strategies: int, n_assets: int) -> np.ndarray:\n",
|
||
" \"\"\"Create random long-short portfolio strategies.\n",
|
||
"\n",
|
||
" Random weights normalized to sum to 1.\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" n_strategies: Number of portfolio strategies\n",
|
||
" n_assets: Number of assets\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" weights: (n_strategies, n_assets) portfolio weights\n",
|
||
" \"\"\"\n",
|
||
" # Random weights (can be negative for short positions)\n",
|
||
" weights = np.random.randn(n_strategies, n_assets)\n",
|
||
" # Normalize each strategy\n",
|
||
" weights = weights / np.abs(weights).sum(axis=1, keepdims=True)\n",
|
||
" return weights\n",
|
||
"\n",
|
||
"\n",
|
||
"# Create scenarios and strategies\n",
|
||
"n_scenarios = CONFIG[\"batch_size\"] * N_SCENARIO_MULTIPLIER\n",
|
||
"scenarios = create_scenarios(returns_data, n_scenarios, CONFIG[\"n_cols\"])\n",
|
||
"strategies = create_strategies(CONFIG[\"n_strategies\"], CONFIG[\"n_rows\"])\n",
|
||
"\n",
|
||
"print(f\"Created {len(scenarios)} scenarios of shape {scenarios[0].shape}\")\n",
|
||
"print(f\"Created {len(strategies)} portfolio strategies\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0f8b1e5b",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.002062,
|
||
"end_time": "2026-06-13T02:38:02.954492+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:02.952430+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 4. Neural Sort (Differentiable Sorting)\n",
|
||
"\n",
|
||
"Differentiable sorting is critical for gradient flow through VaR/ES computation."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "ed38c481",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:02.959041Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:02.958958Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:02.961246Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:02.960967Z"
|
||
},
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.005045,
|
||
"end_time": "2026-06-13T02:38:02.961530+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:02.956485+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def deterministic_neural_sort(s: torch.Tensor, tau: float) -> torch.Tensor:\n",
|
||
" \"\"\"Differentiable sorting via neural sort (Cont et al., 2022).\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" s: Input elements to sort. Shape: (batch_size, n, 1)\n",
|
||
" tau: Temperature for relaxation (lower = sharper sort)\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" P_hat: Soft permutation matrix. Shape: (batch_size, n, n)\n",
|
||
" \"\"\"\n",
|
||
" n = s.size()[1]\n",
|
||
" one = torch.ones((n, 1), device=s.device, dtype=s.dtype)\n",
|
||
"\n",
|
||
" # Pairwise absolute differences\n",
|
||
" A_s = torch.abs(s - s.permute(0, 2, 1))\n",
|
||
"\n",
|
||
" # Sum of differences\n",
|
||
" B = torch.matmul(A_s, torch.matmul(one, one.T))\n",
|
||
"\n",
|
||
" # Position scaling\n",
|
||
" scaling = n + 1 - 2 * (torch.arange(n, device=s.device, dtype=s.dtype) + 1)\n",
|
||
" C = torch.matmul(s, scaling.unsqueeze(0))\n",
|
||
"\n",
|
||
" # Compute permutation scores\n",
|
||
" P_max = (C - B).permute(0, 2, 1)\n",
|
||
"\n",
|
||
" # Softmax to get soft permutation\n",
|
||
" P_hat = torch.nn.functional.softmax(P_max / tau, dim=-1)\n",
|
||
"\n",
|
||
" return P_hat"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2550b7ea",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.001975,
|
||
"end_time": "2026-06-13T02:38:02.965535+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:02.963560+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 5. Score Functions\n",
|
||
"\n",
|
||
"Scoring functions for VaR/ES constraint enforcement.\n",
|
||
"The S_quant function has better optimization properties than the general S_stats."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "89eb78c6",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:02.970272Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:02.970200Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:02.972038Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:02.971799Z"
|
||
},
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.004794,
|
||
"end_time": "2026-06-13T02:38:02.972322+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:02.967528+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def G1_quant(v: torch.Tensor, W: float) -> torch.Tensor:\n",
|
||
" \"\"\"G1 function for quantile scoring.\"\"\"\n",
|
||
" return -W * v**2 / 2\n",
|
||
"\n",
|
||
"\n",
|
||
"def G2_quant(e: torch.Tensor, alpha: float) -> torch.Tensor:\n",
|
||
" \"\"\"G2 function for quantile scoring.\"\"\"\n",
|
||
" return alpha * e\n",
|
||
"\n",
|
||
"\n",
|
||
"def G2in_quant(e: torch.Tensor, alpha: float) -> torch.Tensor:\n",
|
||
" \"\"\"G2 integral function for quantile scoring.\"\"\"\n",
|
||
" return alpha * e**2 / 2"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bfe22716",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.001959,
|
||
"end_time": "2026-06-13T02:38:02.976310+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:02.974351+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Quantile Score Function\n",
|
||
"\n",
|
||
"The core scoring function combines VaR and ES constraints with indicator\n",
|
||
"functions. This formulation has better optimization properties than\n",
|
||
"general scoring rules because it directly targets quantile risk."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "e0367104",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:02.980723Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:02.980657Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:02.983182Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:02.982952Z"
|
||
},
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.005221,
|
||
"end_time": "2026-06-13T02:38:02.983512+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:02.978291+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def S_quant(\n",
|
||
" v: torch.Tensor, e: torch.Tensor, X: torch.Tensor, alpha: float, W: float\n",
|
||
") -> torch.Tensor:\n",
|
||
" \"\"\"Quantile-specific score function (Cont et al., 2022).\n",
|
||
"\n",
|
||
" This scoring function has constraints on VaR and ES with\n",
|
||
" better optimization properties than the general score.\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" v: VaR estimates (n_strategies,)\n",
|
||
" e: ES estimates (n_strategies,)\n",
|
||
" X: PnL samples (n_strategies, batch_size) - transposed from discriminator\n",
|
||
" alpha: Quantile level (e.g., 0.05 for 5% VaR)\n",
|
||
" W: Scale parameter\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" score: Scalar loss value\n",
|
||
" \"\"\"\n",
|
||
" if alpha < 0.5:\n",
|
||
" # Left tail (losses)\n",
|
||
" indicator = (v.unsqueeze(1) >= X).float()\n",
|
||
" rt = (\n",
|
||
" (indicator - alpha) * (G1_quant(v.unsqueeze(1), W) - G1_quant(X, W))\n",
|
||
" + (1.0 / alpha) * G2_quant(e.unsqueeze(1), alpha) * indicator * (v.unsqueeze(1) - X)\n",
|
||
" + G2_quant(e.unsqueeze(1), alpha) * (e.unsqueeze(1) - v.unsqueeze(1))\n",
|
||
" - G2in_quant(e.unsqueeze(1), alpha)\n",
|
||
" )\n",
|
||
" else:\n",
|
||
" # Right tail (gains)\n",
|
||
" alpha_inv = 1 - alpha\n",
|
||
" indicator = (v.unsqueeze(1) <= X).float()\n",
|
||
" rt = (\n",
|
||
" (indicator - alpha_inv) * (G1_quant(v.unsqueeze(1), W) - G1_quant(X, W))\n",
|
||
" + (1.0 / alpha_inv)\n",
|
||
" * G2_quant(-e.unsqueeze(1), alpha_inv)\n",
|
||
" * indicator\n",
|
||
" * (X - v.unsqueeze(1))\n",
|
||
" + G2_quant(-e.unsqueeze(1), alpha_inv) * (v.unsqueeze(1) - e.unsqueeze(1))\n",
|
||
" - G2in_quant(-e.unsqueeze(1), alpha_inv)\n",
|
||
" )\n",
|
||
"\n",
|
||
" return torch.mean(rt)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0773272a",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.001972,
|
||
"end_time": "2026-06-13T02:38:02.987520+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:02.985548+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 6. Score Criterion Module\n",
|
||
"\n",
|
||
"Wraps the scoring function for use in training."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "392b57c7",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:02.991966Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:02.991902Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:02.994032Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:02.993786Z"
|
||
},
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.004717,
|
||
"end_time": "2026-06-13T02:38:02.994226+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:02.989509+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"class ScoreCriterion(nn.Module):\n",
|
||
" \"\"\"Score criterion for tail risk constraints.\"\"\"\n",
|
||
"\n",
|
||
" def __init__(self, alphas: list[float], W: float):\n",
|
||
" super().__init__()\n",
|
||
" self.alphas = alphas\n",
|
||
" self.W = W\n",
|
||
"\n",
|
||
" def forward(self, PNL_validity: torch.Tensor, PNL: torch.Tensor) -> torch.Tensor:\n",
|
||
" \"\"\"Compute score across all quantiles.\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" PNL_validity: (n_strategies, 2*len(alphas)) VaR/ES estimates\n",
|
||
" PNL: (batch_size, n_strategies) PnL values\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" Total score (lower is better for real data)\n",
|
||
" \"\"\"\n",
|
||
" loss = 0.0\n",
|
||
" for i, alpha in enumerate(self.alphas):\n",
|
||
" v = PNL_validity[:, 2 * i]\n",
|
||
" e = PNL_validity[:, 2 * i + 1]\n",
|
||
" loss = loss + S_quant(v, e, PNL.T, alpha, self.W)\n",
|
||
" return loss"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2f67e8d6",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.002027,
|
||
"end_time": "2026-06-13T02:38:02.998347+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:02.996320+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 7. PnL Computation\n",
|
||
"\n",
|
||
"Simplified version of Transform.py - computes portfolio PnL from returns."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "15e69386",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:03.003010Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:03.002937Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:03.005163Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:03.004804Z"
|
||
},
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.005,
|
||
"end_time": "2026-06-13T02:38:03.005458+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.000458+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def compute_pnl(R: torch.Tensor, strategies: torch.Tensor, Cap: float = 10.0) -> torch.Tensor:\n",
|
||
" \"\"\"Compute portfolio PnL from return scenarios.\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" R: (batch_size, n_assets, n_cols) return scenarios\n",
|
||
" strategies: (n_strategies, n_assets) portfolio weights\n",
|
||
" Cap: Investment capital\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" PNL: (batch_size, n_strategies) portfolio PnL\n",
|
||
" \"\"\"\n",
|
||
" batch_size, n_assets, n_cols = R.shape\n",
|
||
"\n",
|
||
" # Convert returns to prices (start at 1)\n",
|
||
" ones = torch.ones(batch_size, n_assets, 1, device=R.device, dtype=R.dtype)\n",
|
||
" prices = torch.cat([ones, R], dim=2)\n",
|
||
" prices = torch.cumsum(prices, dim=2)\n",
|
||
"\n",
|
||
" # Compute PnL for each strategy: sum of (weight * price_change * Cap)\n",
|
||
" # Final price - initial price for each asset\n",
|
||
" price_change = prices[:, :, -1] - prices[:, :, 0] # (batch, n_assets)\n",
|
||
"\n",
|
||
" # Portfolio PnL = sum over assets of weight * price_change * Cap\n",
|
||
" # strategies: (n_strategies, n_assets)\n",
|
||
" # price_change: (batch, n_assets)\n",
|
||
" PNL = Cap * torch.matmul(price_change, strategies.T) # (batch, n_strategies)\n",
|
||
"\n",
|
||
" return PNL"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "71b6e533",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.002166,
|
||
"end_time": "2026-06-13T02:38:03.009685+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.007519+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 8. Generator Architecture\n",
|
||
"\n",
|
||
"4-layer MLP with BatchNorm, clamp output to [-1, 1]."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "6c4eefbb",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:03.014195Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:03.014129Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:03.016740Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:03.016505Z"
|
||
},
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.005389,
|
||
"end_time": "2026-06-13T02:38:03.017068+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.011679+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"class Generator(nn.Module):\n",
|
||
" \"\"\"Tail-GAN Generator (Cont et al., 2022).\n",
|
||
"\n",
|
||
" Architecture: latent_dim -> 128 -> 256 -> 512 -> 1024 -> output\n",
|
||
" Uses BatchNorm and LeakyReLU(0.2).\n",
|
||
" Output is clamped to [-1, 1].\n",
|
||
" \"\"\"\n",
|
||
"\n",
|
||
" def __init__(self, latent_dim: int, output_shape: tuple[int, int]):\n",
|
||
" super().__init__()\n",
|
||
" self.output_shape = output_shape\n",
|
||
" output_dim = output_shape[0] * output_shape[1]\n",
|
||
"\n",
|
||
" def block(in_feat: int, out_feat: int, normalize: bool = True):\n",
|
||
" layers = [nn.Linear(in_feat, out_feat)]\n",
|
||
" if normalize:\n",
|
||
" layers.append(nn.BatchNorm1d(out_feat, 0.8))\n",
|
||
" layers.append(nn.LeakyReLU(0.2, inplace=True))\n",
|
||
" return layers\n",
|
||
"\n",
|
||
" self.model = nn.Sequential(\n",
|
||
" *block(latent_dim, 128, normalize=False),\n",
|
||
" *block(128, 256),\n",
|
||
" *block(256, 512),\n",
|
||
" *block(512, 1024),\n",
|
||
" nn.Linear(1024, output_dim),\n",
|
||
" )\n",
|
||
"\n",
|
||
" def forward(self, z: torch.Tensor) -> torch.Tensor:\n",
|
||
" \"\"\"Generate return scenarios from noise.\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" z: (batch_size, latent_dim) noise\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" R: (batch_size, n_assets, n_cols) return scenarios, clamped to [-1, 1]\n",
|
||
" \"\"\"\n",
|
||
" x = self.model(z)\n",
|
||
" x = torch.clamp(x, min=-1, max=1)\n",
|
||
" return x.view(x.shape[0], *self.output_shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "52ee7b4b",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.001974,
|
||
"end_time": "2026-06-13T02:38:03.021102+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.019128+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 9. Discriminator Architecture\n",
|
||
"\n",
|
||
"Takes sorted PnL, outputs VaR/ES estimates with constraint projection."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "20b654b2",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:03.025551Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:03.025490Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:03.029025Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:03.028786Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.006163,
|
||
"end_time": "2026-06-13T02:38:03.029293+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.023130+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"class Discriminator(nn.Module):\n",
|
||
" \"\"\"Tail-GAN Discriminator (Cont et al., 2022).\n",
|
||
"\n",
|
||
" Architecture: batch_size -> 256 -> 128 -> 2*len(alphas)\n",
|
||
" Uses neural sort for differentiable sorting.\n",
|
||
" Projects outputs onto constraint set W*v <= e.\n",
|
||
" \"\"\"\n",
|
||
"\n",
|
||
" def __init__(\n",
|
||
" self,\n",
|
||
" batch_size: int,\n",
|
||
" alphas: list[float],\n",
|
||
" W: float,\n",
|
||
" temp: float,\n",
|
||
" project: bool,\n",
|
||
" strategies: torch.Tensor,\n",
|
||
" Cap: float,\n",
|
||
" ):\n",
|
||
" super().__init__()\n",
|
||
" self.W = W\n",
|
||
" self.alphas = alphas\n",
|
||
" self.temp = temp\n",
|
||
" self.do_project = project\n",
|
||
" self.strategies = strategies\n",
|
||
" self.Cap = Cap\n",
|
||
"\n",
|
||
" self.model = nn.Sequential(\n",
|
||
" nn.Linear(batch_size, 256),\n",
|
||
" nn.LeakyReLU(0.2, inplace=True),\n",
|
||
" nn.Linear(256, 128),\n",
|
||
" nn.LeakyReLU(0.2, inplace=True),\n",
|
||
" nn.Linear(128, 2 * len(alphas)),\n",
|
||
" )\n",
|
||
"\n",
|
||
" def project_op(self, validity: torch.Tensor) -> torch.Tensor:\n",
|
||
" \"\"\"Project onto constraint set W*v <= e.\"\"\"\n",
|
||
" for i, alpha in enumerate(self.alphas):\n",
|
||
" v = validity[:, 2 * i].clone()\n",
|
||
" e = validity[:, 2 * i + 1].clone()\n",
|
||
" indicator = torch.sign(torch.tensor(0.5 - alpha, device=validity.device))\n",
|
||
"\n",
|
||
" # Check if constraint violated: W*v >= e\n",
|
||
" violated = self.W * v >= e\n",
|
||
"\n",
|
||
" # Project onto boundary when violated\n",
|
||
" v_proj = torch.where(violated, (v + self.W * e) / (1 + self.W**2), v)\n",
|
||
" e_proj = torch.where(violated, self.W * (v + self.W * e) / (1 + self.W**2), e)\n",
|
||
"\n",
|
||
" validity[:, 2 * i] = indicator * v_proj\n",
|
||
" validity[:, 2 * i + 1] = indicator * e_proj\n",
|
||
"\n",
|
||
" return validity\n",
|
||
"\n",
|
||
" def forward(self, R: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:\n",
|
||
" \"\"\"Process return scenarios to VaR/ES estimates.\n",
|
||
"\n",
|
||
" Args:\n",
|
||
" R: (batch_size, n_assets, n_cols) return scenarios\n",
|
||
"\n",
|
||
" Returns:\n",
|
||
" PNL: (batch_size, n_strategies) portfolio PnL\n",
|
||
" validity: (n_strategies, 2*len(alphas)) VaR/ES estimates\n",
|
||
" \"\"\"\n",
|
||
" # Compute portfolio PnL\n",
|
||
" PNL = compute_pnl(R, self.strategies, self.Cap)\n",
|
||
"\n",
|
||
" # Transpose for per-strategy processing\n",
|
||
" PNL_T = PNL.T # (n_strategies, batch_size)\n",
|
||
"\n",
|
||
" # Neural sort for differentiable sorting\n",
|
||
" PNL_s = PNL_T.unsqueeze(-1) # (n_strategies, batch_size, 1)\n",
|
||
" perm_matrix = deterministic_neural_sort(PNL_s, self.temp)\n",
|
||
" PNL_sorted = torch.bmm(perm_matrix, PNL_s) # (n_strategies, batch_size, 1)\n",
|
||
" PNL_sorted = PNL_sorted.squeeze(-1) # (n_strategies, batch_size)\n",
|
||
"\n",
|
||
" # MLP to predict VaR/ES\n",
|
||
" validity = self.model(PNL_sorted) # (n_strategies, 2*len(alphas))\n",
|
||
"\n",
|
||
" # Project onto constraint set\n",
|
||
" if self.do_project:\n",
|
||
" validity = self.project_op(validity)\n",
|
||
"\n",
|
||
" return PNL, validity"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4fb63612",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.00199,
|
||
"end_time": "2026-06-13T02:38:03.033313+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.031323+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 10. Initialize Models"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "c8ff4f38",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:03.037809Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:03.037739Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:03.702371Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:03.701947Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.667612,
|
||
"end_time": "2026-06-13T02:38:03.702924+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.035312+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Generator params: 1,334,132\n",
|
||
"Discriminator params: 289,410\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Convert strategies to tensor\n",
|
||
"strategies_tensor = torch.tensor(strategies, dtype=torch.float32, device=device)\n",
|
||
"\n",
|
||
"# Initialize models\n",
|
||
"generator = Generator(\n",
|
||
" latent_dim=CONFIG[\"latent_dim\"],\n",
|
||
" output_shape=R_shape,\n",
|
||
").to(device)\n",
|
||
"\n",
|
||
"discriminator = Discriminator(\n",
|
||
" batch_size=CONFIG[\"batch_size\"],\n",
|
||
" alphas=CONFIG[\"alphas\"],\n",
|
||
" W=CONFIG[\"W\"],\n",
|
||
" temp=CONFIG[\"temp\"],\n",
|
||
" project=CONFIG[\"project\"],\n",
|
||
" strategies=strategies_tensor,\n",
|
||
" Cap=CONFIG[\"Cap\"],\n",
|
||
").to(device)\n",
|
||
"\n",
|
||
"criterion = ScoreCriterion(CONFIG[\"alphas\"], CONFIG[\"W\"])\n",
|
||
"\n",
|
||
"# Optimizers\n",
|
||
"optimizer_G = torch.optim.Adam(\n",
|
||
" generator.parameters(), lr=CONFIG[\"lr_G\"], betas=(CONFIG[\"b1\"], CONFIG[\"b2\"])\n",
|
||
")\n",
|
||
"optimizer_D = torch.optim.Adam(\n",
|
||
" discriminator.parameters(), lr=CONFIG[\"lr_D\"], betas=(CONFIG[\"b1\"], CONFIG[\"b2\"])\n",
|
||
")\n",
|
||
"\n",
|
||
"print(f\"Generator params: {sum(p.numel() for p in generator.parameters()):,}\")\n",
|
||
"print(f\"Discriminator params: {sum(p.numel() for p in discriminator.parameters()):,}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "95b31a42",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:03.708040Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:03.707890Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:03.725843Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:03.725414Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.02112,
|
||
"end_time": "2026-06-13T02:38:03.726317+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.705197+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Loading checkpoint from 05_synthetic_data/output/tailgan/checkpoints/tailgan_model.pt\n",
|
||
" Loaded model trained for 3000 epochs\n",
|
||
" Final D loss: -0.165748, G loss: 100.332498\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Check for existing checkpoint\n",
|
||
"SKIP_TRAINING = False\n",
|
||
"if CHECKPOINT_PATH.exists() and not RETRAIN:\n",
|
||
" print(f\"Loading checkpoint from {CHECKPOINT_PATH}\")\n",
|
||
" checkpoint = torch.load(CHECKPOINT_PATH, map_location=device, weights_only=False)\n",
|
||
" generator.load_state_dict(checkpoint[\"generator_state_dict\"])\n",
|
||
" discriminator.load_state_dict(checkpoint[\"discriminator_state_dict\"])\n",
|
||
" SCALE_FACTOR = checkpoint[\"scale_factor\"]\n",
|
||
" history = checkpoint[\"history\"]\n",
|
||
" print(f\" Loaded model trained for {len(history['d_loss'])} epochs\")\n",
|
||
" print(f\" Final D loss: {history['d_loss'][-1]:.6f}, G loss: {history['g_loss'][-1]:.6f}\")\n",
|
||
" SKIP_TRAINING = True\n",
|
||
"else:\n",
|
||
" if RETRAIN:\n",
|
||
" print(\"RETRAIN=True, training from scratch\")\n",
|
||
" else:\n",
|
||
" print(\"No checkpoint found, training from scratch\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5aa62659",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002274,
|
||
"end_time": "2026-06-13T02:38:03.730798+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.728524+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 11. Create DataLoader"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "e7416614",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:03.735516Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:03.735438Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:03.741048Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:03.740596Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.008871,
|
||
"end_time": "2026-06-13T02:38:03.741723+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.732852+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"DataLoader: 10 batches of size 1000\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Convert scenarios to tensor dataset\n",
|
||
"scenarios_tensor = torch.tensor(scenarios, dtype=torch.float32)\n",
|
||
"dataloader = torch.utils.data.DataLoader(\n",
|
||
" scenarios_tensor,\n",
|
||
" batch_size=CONFIG[\"batch_size\"],\n",
|
||
" shuffle=True,\n",
|
||
" drop_last=True, # Discriminator expects fixed batch size\n",
|
||
")\n",
|
||
"\n",
|
||
"print(f\"DataLoader: {len(dataloader)} batches of size {CONFIG['batch_size']}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cbb3c976",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.003713,
|
||
"end_time": "2026-06-13T02:38:03.749444+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.745731+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 12. Training Loop"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "c05093ff",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:03.754481Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:03.754395Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:03.756569Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:03.756249Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.005215,
|
||
"end_time": "2026-06-13T02:38:03.757066+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.751851+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def sample_noise(batch_size: int, latent_dim: int, noise_name: str) -> torch.Tensor:\n",
|
||
" \"\"\"Sample noise for generator. Supports Gaussian and Student-t.\"\"\"\n",
|
||
" if noise_name.startswith(\"t\"):\n",
|
||
" df = int(noise_name[1:])\n",
|
||
" z = np.random.standard_t(df, (batch_size, latent_dim))\n",
|
||
" else:\n",
|
||
" z = np.random.normal(0, 1, (batch_size, latent_dim))\n",
|
||
" return torch.tensor(z, dtype=torch.float32, device=device)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "e5052cc3",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:03.761784Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:03.761714Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:03.763603Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:03.763307Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.004758,
|
||
"end_time": "2026-06-13T02:38:03.763912+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.759154+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"if not SKIP_TRAINING:\n",
|
||
" # Training history\n",
|
||
" history = {\"d_loss\": [], \"g_loss\": []}\n",
|
||
"\n",
|
||
" print(\"=\" * 60)\n",
|
||
" print(\"TRAINING TAIL-GAN\")\n",
|
||
" print(\"=\" * 60)\n",
|
||
" print(f\"Epochs: {CONFIG['n_epochs']}, Batch size: {CONFIG['batch_size']}\")\n",
|
||
" print(f\"lr_D: {CONFIG['lr_D']}, lr_G: {CONFIG['lr_G']}, W: {CONFIG['W']}\")\n",
|
||
" print()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "d97107be",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:03.768377Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:03.768311Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:03.771193Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:03.770930Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.00573,
|
||
"end_time": "2026-06-13T02:38:03.771701+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.765971+00:00",
|
||
"status": "completed"
|
||
},
|
||
"title": "# compliance: skip cell_size"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"if not SKIP_TRAINING:\n",
|
||
" for epoch in range(CONFIG[\"n_epochs\"]):\n",
|
||
" epoch_loss_D = []\n",
|
||
" epoch_loss_G = []\n",
|
||
"\n",
|
||
" for i, R in enumerate(dataloader):\n",
|
||
" R = R.to(device)\n",
|
||
"\n",
|
||
" # Sample noise\n",
|
||
" z = sample_noise(R.shape[0], CONFIG[\"latent_dim\"], CONFIG[\"noise_name\"])\n",
|
||
"\n",
|
||
" # Generate fake returns\n",
|
||
" gen_R = generator(z)\n",
|
||
"\n",
|
||
" # ---------------------\n",
|
||
" # Train Discriminator\n",
|
||
" # ---------------------\n",
|
||
" optimizer_D.zero_grad()\n",
|
||
"\n",
|
||
" # Real data score\n",
|
||
" PNL_real, validity_real = discriminator(R)\n",
|
||
" real_score = criterion(validity_real, PNL_real)\n",
|
||
"\n",
|
||
" # Fake data score (use real PNL for scoring fake validity)\n",
|
||
" PNL_fake, validity_fake = discriminator(gen_R)\n",
|
||
" fake_score = criterion(validity_fake, PNL_real)\n",
|
||
"\n",
|
||
" # Discriminator loss: maximize real_score - fake_score\n",
|
||
" # (minimize fake_score - real_score is equivalent)\n",
|
||
" loss_D = real_score - fake_score\n",
|
||
"\n",
|
||
" loss_D.backward(retain_graph=True)\n",
|
||
" optimizer_D.step()\n",
|
||
"\n",
|
||
" epoch_loss_D.append(loss_D.item())\n",
|
||
"\n",
|
||
" # ---------------------\n",
|
||
" # Train Generator\n",
|
||
" # ---------------------\n",
|
||
" optimizer_G.zero_grad()\n",
|
||
"\n",
|
||
" # Generator wants fake validity to score well on real PNL\n",
|
||
" PNL_fake, validity_fake = discriminator(gen_R)\n",
|
||
" loss_G = criterion(validity_fake, PNL_real)\n",
|
||
"\n",
|
||
" loss_G.backward()\n",
|
||
" optimizer_G.step()\n",
|
||
"\n",
|
||
" epoch_loss_G.append(loss_G.item())\n",
|
||
"\n",
|
||
" # Record epoch losses\n",
|
||
" d_loss = np.mean(epoch_loss_D)\n",
|
||
" g_loss = np.mean(epoch_loss_G)\n",
|
||
" history[\"d_loss\"].append(d_loss)\n",
|
||
" history[\"g_loss\"].append(g_loss)\n",
|
||
"\n",
|
||
" # Log progress\n",
|
||
" if epoch % 100 == 0 or epoch == CONFIG[\"n_epochs\"] - 1:\n",
|
||
" print(\n",
|
||
" f\"[Epoch {epoch:4d}/{CONFIG['n_epochs']}] D loss: {d_loss:.6f}, G loss: {g_loss:.6f}\",\n",
|
||
" flush=True,\n",
|
||
" )\n",
|
||
"\n",
|
||
" print(\"\\nTraining complete!\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "20e0bb7c",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:03.776231Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:03.776155Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:03.778109Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:03.777856Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.004645,
|
||
"end_time": "2026-06-13T02:38:03.778421+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.773776+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Skipping training (checkpoint loaded)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if not SKIP_TRAINING:\n",
|
||
" # Save checkpoint\n",
|
||
" CHECKPOINT_PATH.parent.mkdir(parents=True, exist_ok=True)\n",
|
||
" checkpoint = {\n",
|
||
" \"generator_state_dict\": generator.state_dict(),\n",
|
||
" \"discriminator_state_dict\": discriminator.state_dict(),\n",
|
||
" \"scale_factor\": SCALE_FACTOR,\n",
|
||
" \"history\": history,\n",
|
||
" \"config\": CONFIG,\n",
|
||
" }\n",
|
||
" torch.save(checkpoint, CHECKPOINT_PATH)\n",
|
||
" print(f\"Saved checkpoint to {CHECKPOINT_PATH}\")\n",
|
||
"else:\n",
|
||
" print(\"Skipping training (checkpoint loaded)\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "32ff3c75",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.002215,
|
||
"end_time": "2026-06-13T02:38:03.782757+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.780542+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 13. Generate Synthetic Scenarios"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "6f89919f",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:03.787254Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:03.787188Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:03.996252Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:03.995895Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.211726,
|
||
"end_time": "2026-06-13T02:38:03.996512+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.784786+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Generated 10000 synthetic scenarios\n",
|
||
"Shape: (10000, 5, 100)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def generate_synthetic(\n",
|
||
" generator: nn.Module,\n",
|
||
" n_samples: int,\n",
|
||
" latent_dim: int,\n",
|
||
" noise_name: str,\n",
|
||
") -> np.ndarray:\n",
|
||
" \"\"\"Generate synthetic return scenarios.\"\"\"\n",
|
||
" generator.eval()\n",
|
||
" with torch.no_grad():\n",
|
||
" z = sample_noise(n_samples, latent_dim, noise_name)\n",
|
||
" synthetic = generator(z)\n",
|
||
" return synthetic.cpu().numpy()\n",
|
||
"\n",
|
||
"\n",
|
||
"n_synthetic = len(scenarios)\n",
|
||
"synthetic_scenarios = generate_synthetic(\n",
|
||
" generator, n_synthetic, CONFIG[\"latent_dim\"], CONFIG[\"noise_name\"]\n",
|
||
")\n",
|
||
"\n",
|
||
"print(f\"Generated {n_synthetic} synthetic scenarios\")\n",
|
||
"print(f\"Shape: {synthetic_scenarios.shape}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3aae4503",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002313,
|
||
"end_time": "2026-06-13T02:38:04.001149+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:03.998836+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 14. Evaluation\n",
|
||
"\n",
|
||
"### 14.1 Fidelity: Visual Comparison with PCA and t-SNE\n",
|
||
"\n",
|
||
"We project both real and synthetic scenarios into 2D to assess whether the\n",
|
||
"generator covers the same regions of the data manifold."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "b7ee1deb",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:04.005929Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:04.005842Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:07.415127Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:07.414757Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 3.412468,
|
||
"end_time": "2026-06-13T02:38:07.415744+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:04.003276+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"findfont: Failed to find font weight semibold, now using 700.\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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gObYqVbF3C8zx5QevotPrWLF2G7v3HyUxKYV3Pv2J9yY+S68ubQ3nZdx5rXIcOno6zz5zlh+W9nter3Y1QoIDuHTlOolJKezce5jYuATD+9S0YR0q+mbPSrOzs+Xbjyfywx+zOX7qHBqNlqsRN7gacYNd+44wfc4ynnq8P8+OGlygsa2srOjcrjmd2zVHo9Vy6kw4u/YdYeHyDYbP2eWr19FqdYYi9oH+vrRqVp+de4+gVmexct023N1cDDPyGtevZYj3XvfWqLHONSsqn49OgRS076J8Jorr3jGLWxvsfo6dPGf0uFb1+yeMLP3ZupdnMXeAc7hnWZ6NjRIbpRJ1VhaA4fNqyviDpc71+8+SMjOMZ5U5OBjH62B/N7F47+chR0n+fAghhCi/JPEkhBCPuN0HjholIIb270bj+jWxtbXl1Nlw/vx3ocXGqlypotGMoBwVvNyB7C9ando1Z+W6bQAcOHKSsxcuU/3O0qyCyL3cB+Dshct5nrtlx35ef2EUdraqPM8BGPN4f1av30FGZma+/VlyV7ucLeTbtmjEuNc+5uSZ7BkE30+ZSfNGdfH0cAPA18fL6LrJ779s9AUwt5xZIaX5nucY0KsT3/76L5C93O52XILh2L0zbqqFVea3b94jNTWNKxE3uBF9i30Hj7PqTtHvGXOXM2p4X2xV+b9v91JaW1OvdjXq1a6Gra0tf89cBGR/4b23Ts6wAT0MtWyWrNpEQEUfw7G+PdoXatzSVpTPhKXHPBd+1WJJ2HslJqUYNgqA7IR1jaohBbq2pD5blnDjnp0XY2LjDEkngApeHoZ/517mmXFPQuja9agSie/e3SSv37hp9LpHRt3M81whhBCPNvP7UwshhHhkJCTeXd6gVFrz7KjBtGnRiPq1qxntMGcJjo4ONG1Y2+S/3MmZZ54ciLtr9l/FdTo9b3zwncmMDY3W/F/0IyKjDQkagIkTRvHj5LeM/vv+szdRKrO/tKWmpbNjz6H7xu3p4caQfl3ue17OrnY5/1liVzsrKyteHveE4XFqWjr/zl1ueNywbg3D6wVw+NgZGtSpbvT6enu6c+L0BWzv1Mwqzfc8R49OrQwzOvYcOGpI4Pl4e9KyaQPDeYlJyfz570IiIqNxdHSgVvUqdGnfgrFPDDSco9XpDEXJ8/Pv3OWs3bTT6PlCdiH1c7kSiDWqhpjU+mpUvyZhoUFAdtH7fXfqe7m5OtOuZePCPPU85U5uqLOySE5JtUi/RflMFDeehvVqGs1m+fnPOUTHxBoexyckseJOQrk4wi9d46W3JxvqnllbW/Pa+JH51mrLUdjPVkm9P3m5cu2GoYaSXq9n+py7yyOVSmujRJ5frkTfyTPhhhpXG7ft5eLliDzHKM5zalSvplGicvp/y9Bosn8Xh1+OYOuug4ZjHds0M7leCCHEo0tmPAkhxCOufu1q2NgoycrSoNFoGfLU61Ty9+Xa9ehi1Sspqgrennz76UQmffQ9t2Ljib0dz4RJn+NbwRNfH29SUtK4eMX8F6vVG3cY/u3s5EC/nh3NFoCuV6sah45lJ7PWbNxJl/Yt7hvXE0P7sGTVZlJS04r4zIqudo0qtGxaj937s2cpLVu9hRGDe+FTwRM7O1veemUs73zyI1qdjgXL1rN+y24qBfiRkZFJdMxtw5fLZo3qAGXznjs6OtClQwuWrd6CTnd3TU2/nh0MS9wgO3k3bc5Sps1ZirurCz4VPFGpbAjPVXg4uFJFfLzvP6Ni9/6jHD91HoVCgbubC16ebtjb2nL1epQhGWVtZcXYJwaYvX7YgO588s0fRm09OrfBxsYyt09VqwQZ1fB5YtzbBAb4okDBJ+9MKPJspKJ8Joobj52tindefYa3Pv4BrVZL+OVrPDbmDYKDKqLV6rh2PYp6taqZrf10PzmF9m/eum2086WDvR3vvPaMoXD6/RT2s1VS709eQoID+Oy7P5k+ZympaenciL5lODZ8UE+8PN0Nj9u1amyYpXXp6nWGjnkdNxdnIqNjCAsN4sJF8wnkYr3Hdra8+eJTvPf5z+h0erbvPsSAJ1/B28uDi5cjDLOzwkKDGDGkV7FfDyGEEA8PmfEkhBCPuEB/Xz6cNB5Pj+z6JPEJSZw8e5EuHZrzzMhBZRJTjaohzPztc0YN64v3nWV40TG3OXriLOGXr6HX63FzdaZzu+a8+vyTQPYMgXWb7m7l3qxRXfO7jgEtmtwtirv/0Ani4hPNnpebi7MjIwaX3ZepZ0fe3YFPnZXFtDlLDcfatmzEH9+/T6P6NbGyUpCYlMKJ0xe4cOkaySmphIVU4uknBxIUmL0FfVm95wN7dzZ6rFRamyQi3FxdeH7MUKpWCSI+MYmzFy5z/NR50tIzsLayomXTenz7ycQCzXBp36oxtWtUwUapJC4+u6D0sVPnDUmnalWC+eaTiUaFxXPr0r4FXneWNObo2719wZ/wffhW8GLc6CGGRFZMbByHjp7m4NFTqNX336UtP4X9TFgintbNG/DXDx/QrFEdrK2sUGdlcT78KhcvR1DRtwKD+nS+bx/mHDl+lsPHzxiSTh7urgzu24UZv31Op7YFn1lT2M9WSb4/5gQF+PH1h6+RlJxqSDqpbGwYNawvzz811OjcNi0a8fSTAw2x3YqNJzUtne8+eYOQoLyX9xb3OXVo05RfvnqXOjXDAIiNS+DM+Uuos7JwcLBjaP9u/PbNeyb1qoQQQjzaFGp1ppTyE0KIh5xGq+Xwsbtbylf09TaqWQOQlaXhWmQUqanpBAX64eriTGJSCufCrxjOaVi3Okpl9heWi1ciDMtdPNxdqVI50HBefseKIjomltjbCWi1WlxdnHF3c8bF2cko+aBWZ3E0V8HhwIo+ec7eufd5hYVUwt3NhcioGMOXW1tbFfVqVTW6LiMjk+O5du9S2SiNdpnKVKs5dvJuQfRa1UJwdHQo0HPMPTZAw3o1TBJnx0+dNyypUSqtzRZGT01L59r1KNIzMvFwc8HbyyPP3cQs+Z4X1OHjZ9BotAA4OthRq3qVPM9NTU0jKuY2SUnJODrYE+DvW6Sd0TQaDbduJ5CUlExqWjq2tir8/XwKNGPlj38XGJY81a9djd++/Z/JOfm9LhGR0UTdzF5y5uhgb7YIdmpqGpev3SA9I8NQYLl+7WqoVDbF7hsK95koTjy5pWdkcC0ie0xfHy98vD0LlCwE058FKysFSmtrHB3s8fR0M9nZ8V4FibEwn63ivh6F/V2p1eoIv3yNzEw1ocEB+f4OSU1LJyIyGoVCQVhIJaysrLh05Tqxd2qoebi5UCWkksWfE2QvXbx+4yaZ6ixcXZwICqxoNtlvic+wEEKIB5sknoQQQgghzNDr9bz01hccPHoKgE/enkDn9s3LOCohhBBCiAeL1HgSQgghhMjl8LHTTJ2zlOibsYaZN2GhQXRs27SMIxNCCCGEePBI4kkIIYQQIpfb8YlGOylWqxLMFx+8gpWVlMYUQgghhCgsWWonhBBCCJFLXHwi4ZcjsLaywtvLnUoBfve/SAghhBBCmCWJJyGEEEIIIYQQQghRImTOuBBCCCGEEEIIIYQoEZJ4EkIIIYQQQgghhBAlQhJPQgghhBBCCCGEEKJESOJJCCGEEEIIIYQQQpQISTwJIYQQQgghhBBCiBIhiSchhBBCCCGEEEIIUSIk8SSEEEIIIYQQQgghSoQknoQQQgghhBBCCCFEiZDEkxBCCCGEEEIIIYQoEZJ4EkIIIYQQQgghhBAlQhJPQgghhBBCCCGEEKJESOJJCCGEEEIIIYQQQpQISTwJIYQQQgghhBBCiBIhiSchhBBCCCGEEEIIUSIk8SSEEEIIIYQQQgghSoQknoQQQgghhBBCCCFEiZDEkxBCCCGEEEIIIYQoEZJ4EkIIIYQQQgghhBAlQhJPQgghhBBCCCGEEKJESOJJCCGEEEIIIYQQQpQISTwJIYQQQgghhBBCiBIhiSchhBBCCCGEEEIIUSIk8SSEEEIIIYQQQgghSoQknoQQQgghhBBCCCFEiZDEkxBCCCGEEEIIIYQoEZJ4EkIIIYQQQgghhBAlQhJPQgghhBBCCCGEEKJESOJJCCGEEEIIIYQQQpQISTwJIYQQQgghhBBCiBIhiSchhBBCCCGEEEIIUSIk8SSEEEIIIYQQQgghSoQknoQQQgghhBBCCCFEiZDEkxBCCCGEEEIIIYQoEZJ4EkIIIYQQQgghhBAlQhJPQgghhBBCCCGEEKJESOLpIZGalk5WluahGUcIIYQQojzIyFSTkZH50IwjhBBClLZHKvGk1epITkklOSWVlNQ0dDrdfc/XaPJPsmRkqklJTSuR8Qtj1Ph3WbFum8X6A0hNTTNJMpXEOEIIIYQon/R6PckpqWi1Bb9n0ev1aLRas8dSUtNINXPflJGpJj0jw+i8nHum3P+p1Vl5jpv7mvvdvxXGd1P+5bPv/rJYfwAZGZkmSaaSGEcIIYQoD5RlHUBpOnnmAs+9/gkODnZYKaxQZ2VRNTSIF595nLq1qgKg0+lYtGIjS1dt5mrEDZQ2Srw83OjWsRXDBnbH2cnRqM+xL77PpavXmTHlM8JCg4o9flE5OtqjsrHs2/nE8+/w1PB+9O3RoUTHEUIIIUT5lJKaRtdB45j688fUqBqS77mHj5/ht6nzOR9+BWtrKwIDfBnarxvdO7XG2jr7b53dBz+HHj3Tf/nU6L7px99ncf3GTX7+8m3DeUobJTZK43uOsU8MZNjA7mbHz32NWp2Ft5c7o4f3o3e3dsV5CbCztUWvL1YXJr7+ZTparZYPJ40v0XGEEEKI8uCRzCBM/+VTAv19SU1N47Pv/+aND75j/tRvcHVx4sMvp3D4+BneePEpWjapj7W1FTdjbrNu8272HTpB53bNDf2cPBNOxI1oatcIY/narbz+wqhijW9rqwK9Hjs7W7RaHRmZmTg62Buu02i16LQ6VCobkz6nfP0eKhvTdr1eT1aWxuw1uanVWUbnpKVnoNfpyVRnkZySikKhwMnRIc9x8ostNTUNW1sVSqUSjVaL0to631iEEEIIUT6kpqUDkJ6eQXJKKtbW1jjY25mcdyM6htff+4ZhA7vz0xeTUNmouHwtknlL1tKwbg38fL0N59rZ2vLrP3P54fNJ+Y792vNPGv3xqyByrtFotcxdvIbPvvuLir7e1K1VlcxMNY6ODuj1etLSM7C3s8XKKjshptdn3/PY2apM+hw/dhh5ZYQy1WpsVabX5HdOplpNVpYGnS57JjyAg719nuPkF1tGRiYoFNjZqtDr9Wh1OrnPEkIIUe48komnHI6ODjzz5CC27NjP6XMXyVSr2bB1Lz9OfoumDWsbzvPz9Wb04/1Mrl++dgvtWjamc/vmfPbtX0x4Zvh9bz7yG3/77kNERsXg4e7Ctl2H8PPxYs5fXxJ7O54vfpzKvkPH0euhamglJk4YTc1qoYa+Ro1/l8cH92Jg705AduLoh99nsnHrXrRaHRW8PRg3eohR4iw1LZ2f/5zDhm170GRpqRJSiUkvj6FqaBBvvP8tMbFx/PrPXP78dyHW1lasXfC7yTgFiW3o2Dfo3qkVx0+d5+LlCBQKBcMG9uCZkYMK/mYJIYQQotSNGv8eAG988B1WVlZUDQ3i16/fNTnv8LEzqLPUjH1yoCHxUaVyIO++9ozJuUP7d2PmvBUcPHqKxvVrlUjcSmtrnhjSmzkLVrP34HFuxyfyzS//MnxgD2YvXAXAv1M+pYKXB7/8PZcV67aSmZmFl4cb40YPoUfn1oa+vpvyL+npmXzyzgRD2+wFq/hv8WqSklNxcnBgQO9OjBkxwDCzS6/XM2fhauYuWUt8QhLeXu68MGYYnds3Z/p/y9iycz8A+w6dAOD7z95k+dotRuNoNJr7xvbZd3+hzsrCRqlk/+GTZGRm0qppA/73xjizCUIhhBCiLDzSiScAdVZ2rQCl0prVG/ZRKcDPKOmUl/SMDDZt28eXH75K/TrVsbVVsXXnQbp1bFnk8QEOHDnJC2OH8e5rz2JzZ0nbB19MQY+eZbN/wt7Olp///I+J73/LgmnfGs2Iyu1/n/+MUqlk4fTvcHdzYd+hE7z98Y/4VvCido0qALzzyY/EJyTx1/cfElypImfOX2bfweOGm8oBI18xWWp3r4LGtmbDTr766DVq16jCwaOnePntL2jSoBb161Qv1OslhBBCiNKzcPq3dB00jl++eiffpXaeHm7odHq27NhPl/Yt8u0zKNCPXl3b8uvfc5n688coFAqz5+XMus7N3t6uwDN6dDodGq0G6zv3WEnJKZy/eIXFM743lE74e+YiNm3fx5Sv3iU0pBLrNu/i02//oFKAL7WqVzHb76wFK1mxdhs/fDaJKiGVuHY9ijc++BZ7O1ueGNobgH/nLmfOwtV89NZ4mjSsTezteP5btIbO7ZszbtQQYm7FmSy1W752i9E40/9bVqDYduw5zLuvP8OHb40n9nY8z7/+CXMXr2HMiAEFep2EEEKIkvZIFRfPkXZnuvilK9f5+c85VPDyoGa1UK7fuEmgv2+B+tiwdS/ubi40qlcTpbU1vbu1ZcXarcUaHyAosCJPDO1tSDqdC7/C4eNnmPjCaDzcXLG3s+OlcSPQanWs27zbbP/hl66x58BxXh43Ajs7W9LSM6hTowotmtRl/Zbsa85duMz+wyd5+9WnCQkOwMrKilrVQ3nysT4Feg6FjW1wvy6GhFfj+rWoElKJk2fDCzyWEEIIIcqv5o3rMqBXJz74Ygp9hk/grY9/YMGy9SQkJps9/+mRg7hy7QYbt+3Ns89f/5nLwJGvGv136kz+9w45yaromFh+/GM2aemZtG3RyHD89RdGGdXrnLdkHaOG9aVaWGWU1tb06tKWZo3qMnfxWrP963Q6Zs1fyVOP96OiXwVS09LxdHdlQK/OrN28C8jeTGbOwtWMGtaXFk3qobS2xreCF68+/2S+sd+roLE1rFeDXl3aGsZp37oJp+QeSwghRDnySM54Gv/Gp1hbWeHi7ET1sMpMnDAaRwd7rKys0OaxC8u9VqzdRpcOLQw72nVs3ZQZc5cTGRWDv1+FIo0PUOmexNe161GobGwICQ4wtNnZqggJDuDa9Siz/Z+/eBW4Oz0+txZN6gEQfiUClY0N1cMqF+j5mlOY2Px8vI0eOzrYk5qaXuSxhRBCCFE2MjIyycq1a5yzkyMKhYI3X3qK0cP7sv/wSU6eDWfanKX8M2sxv379LqHBgUZ9eHu6M7R/V/6YvoD2rZqYHacoNZ5ySgTY29tSKcCPrz96jRpVQ7h+4yaODvZ4ergZzr0dl0BKaprJvVCNqiHs2HvYbP8xsXEkJqXw9S/T+fbXGUbHcu7lYmLjSE5JpU7NsELFnlthYjN3j5Ui91hCCCHKkUcy8ZRT3PteQYF+HD1x7r7XX74ayelz4Vy5FsnCZRsM7UprJSvWbuW5p4YWaXy4u+Quh7W1NTq9Dp1OZyiACaDVag11BMyxtrZi9bwphplT97JSWKHV6dDp9Fhbm5/ifj+FiS2PWfRCCCGEeMB89fM0duy5m/xYu+B3w//3K3h70rtbO3p3a8eEp4cz4tm3mLNwNf+bOM6knycf68OyNVtYsmqTxWLLL1ll7h4LMPmjo1arRZnPPRbAVx+8RqP6Nc0es7pz06Mp4B8zzSlMbHKPJYQQorx7JJfa5aVH59ZE3bzFxq3mp33r7+w0snztFpo1qsuGxX8a/ffexGdZtWEHWq3OYjGFBAWg0Wg5c/6yoS01NY2LVyKMZhrlVrNaKBqNlr0Hj+XZb42qIWi1Wo6dPJvnOTZKJVpd3s+lKLEJIYQQ4sFho8z+A5Yu1/3A+288Z3T/Y21tZbhHys3J0YGKfhVIz8g027eTowOjhvdj2pylpKVnlMwTyIebqzOeHq6cOH3BqP3YqfOEBJm/j/Hx9sTLw42d+8zPiAKo4O2Bl4cbh4+dzvMcG6XS6DW1RGxCCCFEeSWJp1yaNKjN8IE9mPzD38xbspbIqBhiYuM4fPwMX/zwD6vWbycrS8PaTbto27KRyfUtm9QjMSmZPQfyTvgUVnClinRo3YQvfviH0+cucu16FJ98+yfuri50bW++kHlwpYr07NKGL3+ayuYd+4mJjePU2Yv88PssVq3fbjinS/sWfPbdX+w5cIybMbfZtH0fv/z9n6Gfir7eHD1xjriERMOSwuLGJoQQQogHh52dLR7uruw/fJLEpOQ8E0SrN+7g/cm/sOfAMaJjYrkRHcOs+Ss5fuocHds0zbP/Qb07Y2dna9jlLbecek25/8vIVFvsuQGMGtaPGfNWsG33QaJuxjJ19hJOnrlgKBJ+L4VCwbjRQ1i4fAOzF6ziRnQMl65cZ+7itXw3ZYbhnLFPDmT2gtUsWbWJ6JhYjp44yzuf/mTop6KfN+cvXiMq+hbJKalm/2hZ2NiEEEKI8uqRWmpnbW2Nk6NDvkvUXho3gnp1qrFs9RbmLFyNQqEgwN+Hbh1b0b1za/YeOI5Wq6VN84Ym1zo6OtCyaX02bd9H6+YNCj2+nZ2KTLWtSft7E8fx29R5vPvZz2iyNNSpVZWfvngrz2V0AO+8+gxzF69h+n/LiL0dj79fBbp2aGm06977b4zj37nL+enP2aSlZVCvdlUmPD3ccPz5p4by/W8zeWLc2+h0OtYu+B1HR3tUucYtSGxOjvYolcax2tvZYatS5Rm/EEIIIcqHSS+N4e9Zi5m3ZC2hwYH8+vW7Jud079gaW5WKhcvXc+nKdRQKKwIq+vDZuy/RvvXdGk5OTg5G9wQqlQ3PjR7KN79Mx8Hezui8P/9dyJ//LjQap3e3trw87gmzcTo5OWBjY2P2mI1SiZOjg0n7kH5d0el0/Dl9IfEJSQQF+vHDZ5MIruSf5+vRu1s7XJydmLtkDXMWrcbdzYWmDesY7SLXv2dHHB3smbt4LX/PXEzlSv48O2qw4Xi/Hh05c+4yz73+CWnpGXz/2ZvY2dqSe+JYQWKzs7PFztb43lGlsjF6LYUQQoiyplCrM03nRosHTr8RL/HcU0Pp0bl1WYcihBBCCPHQ+Oir31Aqlbz72jNlHYoQQgjxQJKldg+4rCwNx0+d53ZcAqGVA+9/gRBCCCGEuC+dTseN6BhOngk32ZVPCCGEEAX3SC21exit3rCDGfOW89SIAVQNDSrrcIQQQgghHgrXrkfx4qTJNKhbg7492pd1OEIIIcQDS5baCSGEEEIIIYQQQogSIUvthBBCCCGEEEIIIUSJkMSTEEIIIYQQQgghhCgRkngSQgghhBBCCCGEECXikUg86fV6NBoNer2UsxJCCCGEKAi5fxJCCCGEJTwSiSetVkubXqPRarVlHYoQQgghxANB7p+EEEIIYQmPROJJCCGEEEIIIYQQQpQ+STwJIYQQQgghhBBCiBIhiSchhBBCCCGEEEIIUSIk8SSEEEIIIYQQQgghSoQknoQQQgghhBBCCCFEiZDEkxBCCCGEEEIIIYQoEZJ4EkIIIYQQQgghhBAlQhJPQgghhBBCCCGEEKJESOJJCCGEEEIIIYQQQpQISTwJIYQQokz9MX0BZy9cLuswhBBCCCEeCBu37WXlum1lHUaBKcs6ACGEEEI8GKJuxvLlj/8AYGurIiykEo8N6I6zk2Ox+j1z/hJ1a1W1RIhCCCGEEOXKe5/9TEpqGtbW1lT09WZo/24E+vsWq88b0TGkpKRZKMKSJzOehBBCiIdQckoqanWWRftMS0/n5JlwHhvQnR6dW3Pk+Fk+/eZPi44hhBBCCFEW0tLTSUtPt3i/B4+eol2rxgzs3QmdTsf4iZ+SkZFp8XHKM5nxJIQQQjxEzpwLZ9mqjUTfjMFGqaRxw7oM6NMNlcrGIv0rlda0aFIPAL8KXox/4zPDMZ1Ox9LVWzhy/Ax2drb079mRWtVDAZgxbzmHj51BZWND5SB/Hh/cC1cXJ4vEJIQQQghRVLG345i/ZDUXwrOX/YdVqczQAT3x8vSw2Bh1a1UlNDiQlk3rs27zbq5E3KB6WGUA9h48zsate1BnZdGqWUO6dWwJwO79R5m/dB3W1lb4eHsxpF9XKgf5Wyym0iQznopp5dpN9Bj4FE3b92fIyBc4dPREWYckhBDiERUVHcM/M+YTfTMGgCyNhj37DzN30YoSGS/ixk1cXZwNj7+bMpPz4Vfo3L45tatX4b3PfyYiMhqAFo3r8diA7vTp0Z6MTDWffvtHicQkhBB5uRpxnaeef4Om7fvTqfcTTJu5oKxDEkKUMY1Gw5S/ZnH+wiX0ej16vZ7zFy4x5a9ZaDQai48XG5dAplqNq3P2H982bd/HzHkraNa4Lm1bNGb+0nWs3bQTgNDgQB4b0J1BfboQFOjHxPe/ISNTbfGYSoPMeCqGP6bOZvK3v6PX6wG4GRPLsNEv8e8f39CyWaMyjk4IIcSjZtfeQ2Zvko4eP03/3l1xcS7+DKPUtHReeedLMtVZXLsexXuvPwtAVpaG5Wu30LBuDZas3ASATqvj8LEzBPr74uBgz74NO4iMuklWloaTZ8KLHYsQQhTU1Yjr9Bw0xlAT5WZMLB9O/pHz4VeY/NEbZRydEKKsnDx9nrj4BJP2uPgETp4+T/26NS0yzlc/TcNWpeLCpauMfWIgfr7eACxctp4sjYZV67cDoFZnsefAcbp3ao2TkwPnLlzhXPgV0jMySE5J5WrEDapVCbZITKVJEk/F8NvfcwxJpxxqdRafffUrqxZNLaOohBBCPKrM3ThB9hK4hMQkiySebFUqHhvQndS0dP79bzk3b90GICk5BQUKHhvQ3ej84ErZU8JfemsyXdq3oHe3dlhZWXHwyKlixyKEEAX1+ddTzBbiXbhsNe++8TxOTrL0V4hHUV73TgDxCYkWG6d7p1Z4ebqzev0OLl+NvDt+QhIDe3eiUoCfoc3D3RWA76fMIC09g87tm+Ngb8ePf8x+YGtDSeKpiG7FxuX5QbwScb2UoxFCCCEguFIAp89eMGm3s7PDx9vLImPkrvFUs1oIY1/6gFbNGuDt6Y67mwupqel0bt/c6Jq09Azi4pN4akR/bFUqNm7ba5FYhBCioE6Z+d0I2X803rb7AL26dijliIQQ5UFwpYA8jwUFWq6eUk6NpxaN6zLqhffYsecQbVo0oma1EK5dj2Jo/24oFAqjay5dvc6Lz46gQZ3q3IiOIebOH/seRJJ4KiJXFydsbJRkZZkuaXB0cCiDiIQQQjzqWjZvxN4DR0z+ete1Y2tsbVUWH6+ibwX6dGvHb1Pn8f4bz/H+m8/x/uRfmL1wlaFw+OsvjCLQ35emDWsz/OlJeHq4obKR2w8hROlyc3EhgiizxyoHBZZyNEKI8iKkciVqVg8z+cNdzephhFSuZPHxlEolLz7zON/++i/NG9dj/NhhvPH+twx56nUCKvoA0K9HBzq0aUqXDi2Z9OH3hFYOJC4+AU8PN4vHU1oUanWm/v6nPdg0Gg1teo1mx6rpKJWWu9kdMfYVdu45aNI+/uknmPTacxYbRwghhCioxKRktmzfw4WLV3B0dKBls0bUr1PDIn2npWdw+txFGtevZdR24vQFmjasjUKhICNTzaUrESQmpQBQt2YYjo4O6HQ6zpy/jJ2disqV/Dlw5BTNGtUB4OyFy/hW8MLN1dnsuKJslNT9kxBlYcnK9bw66WPuqZJBaEglNq+cU2pxZGRksGLtZuLiEmjVvDG1a1YttbFLU3TMLSJvRFO/Tk2sra3LOhwh8qXRaNi55yBHjmWXAWhQrxatWzS22P/7Dh49Rc1qoTjY2xm1VQ0NxsXZEZ1Ox5VrN7h1Ox6dTkdwYEVDDairETdISU0ntHIgFy9HUCnAF2cnR25Ex6DRaI2W6JVnkngqBrVazeNjXuHQ0ZPodDpsbJT06taRH79632JjCCGEEEKUBUk8iYfNl9/9zrRZC0nPyEChgLDQysz8+zt8K3iXyHhnzoVz6swFbJRKGjWsw+3bcbz+zuckJacYzmnXuhlff/p2iYxfFhITk3nymdc4cfocOp0OBwd7nhw2gHcmji/r0IQQZUgSTxYQHXOL8ItXqF+nhhQmFEIIIcRDQRJP4mGkVqs5eOQE/hV9CArMu7ZLcf23cAX7DhwxPFYoFBw6etJsYeCJLz/L0AE9SyyW0tRnyFiOnzpn0v7FR28yfEjfMohICFEeWJV1AA8D3wretG7RRJJOQgghhBBClGMqlYqWzRqVaNIp/NIVo6QTQFxcAtciItHpdCbnb9i8vcRiKU3XI6M5ecZ8EfepMxeUcjRCiPJEEk9CCCGEEEIIYSGnzoSbtOn0OvT67F307qXRmCajHkTXrptPrAEkJCaXcjRCiPJEEk9CCCGEEEIIYSG2tjYmbZ4e7tjZ2aKwUpgca9+meYnGo9PpSEpKuf+JxdS4QR3s7GzNHqsSGlTi4wshyi9JPAkhhBBCCCGEhTRuUAcrK9OvWa2bN8LF2bg0R7061RkxtGRqH+l0OiZ/O4WOvUfQue8T9B4yliUr1pXIWJC9jHHooF4m7XZ2tvxv0oslNu69jhw7xTsffc3Lb37MvEUr0Wg0pTa2EMI8qRQphBBCCPEQ2HPgGNP/W8aN6FvodDpm/T4ZdzcXtFodv02bx9adB3BxduS50UNp2qhOWYcrxEPLy9ODJx7rz7zFq8jMzC4m7ubqwqsTxuLo6MCipWuIjYunZZOGdGjXosTi+OSrX1i1drPhccyt23z5/e+4u7mW2CyrT959jZBKgcyYu5ik5BTCQirz7psvULNalRIZ717TZy/kt79no9dn75+1Z/9hVq/fyj+/fiGbJAhRhmRXOyGEEEKUG1HRtzh8/Ay9urYt61AeKGcvXOaltybz2viRNKhTA6XSGnc3F6ysrFi4fAPL12zhfxPHcflaJN/88i+L/v0OZyfHfPuU+ychiiczU034pavY2CipEhJkdhZUScnIyKBLv5FkZqpNjtWtXY2/f/my1GIpLSkpafQYNNrsc35twliGDe5TBlEJUTr+nrmIp58cVNZh5EmW2gkhhBCiwOISElm2egtzF68l/NK1YvcXHRPLinXbDI+jbt5i1YbC7/B0bz8xt26zfM2WYsf3oJg5bwVD+nWje6fW+FTwxNPDzfAld+2mnYx+vD9hoUF07dCSOjWrsGXngTKOWIiHn62tilo1wqhapXKpJp0Abt2ON5uAAbgVG1+qsZSW3fsO5fmcd+87XMrRCHGXWp3Fxq17mbVgJfsPnbBIn//MWmJUzP+fWUss0s+0OUvRaLXFju9ekngSQgghRIFcux7FsLFvsP/wCWJu3eaz7/7iv0Wri9XnzZjbrMyVMCrrfh5U4ZcjsLFRMnL8u4x49i2mzVlqWGpyI/oWwYEVDecGBfoTdfNWWYUqhCgFfj7eJvWkcgRXCijlaEqHk3PeszgdHe1LMRIh7lKrs3j6lQ+Zv2wdiYkp/Ld4DZM++r7Y/U6bvQSdvviL1yzVz/3IvGkhhBDiIZIeewaVSyDWqrtfOLTqFNRJEdh71ShW31t2HqB547p8/PYEAPR6PRfuzHpavHITrZvVp4K3JwAxsXHs2HOIQX26GJbP1apehb0Hj+Pn60W7lo0BWLtpFzdjbvP3zEV4ebpTyd8XgCvXbpicC5CRkcm23QeJvZ1AjWohNKxbw2w/LZvUM4o9I1PNtl0HiYy6iU6nY/jAHjg6OhTr9ShP1FlZ7Np3hPcnjiNTreaDL6bg5+NF906t0Wp1RrMtlNZWaDXm/5q5cPkGFq3YAGBIXAlRWtRqNW+89wXbd+9Hq9FSt04Nvvp4EhX9fMo6tAeOUqlk+JC+/DF1jlG7SmXDM6OHlVFUJatl04b4VPDiZkysUbtCoWBgv+5lFJV4EJTkvdO58CskJCaxZMaPWFtn/7/43IXLQHZtRgd7O+rVrgZk/393xrwVPNa/G3Z2tvw9cxGD+3Zl664DKFDQrVMr7GxVrNu8G51ez9RZS7CyUjBqWD8AEhKTTc7NsXv/US5dvY6fjzftWjVGaW2dZz+57TlwjPBL18hUq2nfqglVQioV6XWQGU9CCCHEQyI99gxJ17YSf2EZWnX21tladQrxF5aRdG0r6bFnitV/oL8vV67dICIyGsi+ma96Z4vs23EJLFqx0XDuklWbuB2XCGQvn/tt2jy++XU6sXHx/PLXf8xdvDbPca5djzJ7bmpqGs++9jEHj54mMTmF76fMZMa85Wb7iImNZ/narQCkpKYx8vl3WLNxB1kP6e5GFbw86NW1LVVCKlGrehW6dGjByTMXAfD2cjea4RR1MxZvLw+z/Qzu24X//vqK//76ilm/Ty6V2IXI0WfoMyxduZ64uAQSk5LZsWs/vYaMJT09vaxDeyCNHTmUt19/ntDKlfBwd6VRg9r8+u1H1K5ZtaxDKzFffjzJ8AcQyE60PT36MZo0qFuGUYnyrKTvnXwqeJKZmcXx0+cNS9qqhVUGQGVjwy9/zzWce/DoKfYeOIadnS2QvQzuvc9+5tr1aNZv2c0Hk3/Nd6y8zn1/8q8sXbWZ5ORUVm/YzsT/fZNnH9PnLEN7Z6ndu5/+xC9//0dScmrRnnwuMuNJCCGEeEioXAKxtnVBm5lE/IVluAR1IunqJrSZSVjbuqByCSxW/x3bNCUjI5Ovf55OdEwsVUIq8cyTg6gc5E//Xh155uUPeWbUYCB7BtJf339guNbaypofPnsTpVJJo3o1mbdkLcMGdqd7p1ZcunrdUBDz8LHTeZ67dPUW7O1s8fHOTpo0rFedZau3MPKxvib9nDwTbhh7ycpNhIVU4rP3XirW8y/P2rduwp79x+jZuQ1ZGg0HDp+ke6fWALRt0YgFy9bTuH4trt+4yb5Dx3nh6YdzxoN4cG3etoez5y+atMfFJfDTb/8y6bXnyiCqB9+APt0Y0KdbWYdRampWD2Plgn/YtfcgCYlJtGnRFBcX80sOhYCSv3eq4OXBVx+9xvwla/nyx6l4ursybFAP2jRvSKP6NUlLS+filQhCgwNZsmozQ/ob/7xOnDCK4Er+ZGRk0mPoePR6Pd06tuSTr39nzBMDUFpb53vu2QuXOXryLH27twegRtUQ5i5eS3RMbJ79AJw6G86psxeZN/VrbFUqiksST0IIIcRDwlrlhHtYv+y/2mUmEX8+u9Ckta0L7mH9jKaQF1XPLm3o2aUNer2e1Rt38NJbk1k66ye8Pd2pVT2UnXuyC7jWqRGGl6e74Tr/ihUMO6O5uTqTkpqW5xh5nRtxIxr7O38FBHB2cqRfzw73jTkyKobaNUpnK++yMqBXJ46dPEfXweNQoKB968b0v/PaPD64J29//COdBz6DlcKK554agm8FrzKOWAhjO3bnXfD+yInizTgQj55WzRvf/yQhKJ17p3q1qlKvVvZMw/MXrzLhzc/57dv3CA0OZGCfTixbvYXRw/txPvwKn9wpZ5AjuJI/AHZ2tigU2Uvr80oEmTs3IjIaezs7o/OGDeyOzX12q71+I4aw0EoWSTqBJJ6EEEKIh4q1ygmXoE6GGycAl6BOFrlxCr8cQUBFH+xsVSgUChrXq0VsXAKpaem4ODsyqG8XZszNXvpW0C19lUpro91U8hPg50NcXKKhb61Wy9ETp7kRdTPffgIq+nDqrOlMioeJna2KL95/hbT0DGyUSmxs7t7iOTk68POXb5OamoZKpTI6JkR5UbVKcJ7HAipWKL1AhLAwnU7H7bh4XF2cUVnoS7ywrJK8d4qJjQOyZz4BhIVUwtXFies3bhIaHEiPzm0Y8ewkHB3t6dujvaEO1H1jVlqj0+rgnplK9/L38yEtPYMRQ3qZJKDy6yegog/hl66hVmehUtkUKKb8yJ2HEEII8RDRqlNIurrJqC3p6iaL/NXuemQ07376I/VrV8fR0YHtuw/Ssml9XO7sJNSwbg2+nzITlcqmwDOM/P18uHLtBr/8/R8BFX0MxcXNGdCrI6s2bGfCpM9JTUnh6PHTpKenk5GeRtWwEHQKlaGfKpXvFr/s36sjT034H6+99zXVwoKxtrJ66IqL53CwN72pzPEwPl/x8Bg+pC/f/PQXsbfjjdptVSpem/B0GUUlRPHMXbiC6XMWEReXgJ2dLZ3at+LdieMNs3pF+VCS906ZmWre+OA7wkIq4efjxYnTF9BoNNSvXR3I/v926+YNmb9kHYtnFHy3u8qV/Pnix3+o6Otttih4jlrVQ2lUrwZPTXif1s0bYGerQqlUMnp4v3z7qVU9lJrVQhn70ge0aFIXlcrmwS8uPunD7+ky8Fm6DHyWA0dO5nvurdvxvP6/b+j52HheeOMzQ4FTIYQQ4lGXUwwzpy6Be9UBRnULcopmFlX71k345at3qVU9FE93V14bP5KvP3rN6JzqVSszpF8XozY/H296dWlreOzl6W6oNeDm6swPn0/C9c623/md6+jowL+/fkaArzcHDh0jKyu7ULher+fc+YvostIN/VTwunudk6MDM377jK4dWtx3arkQouwsmDmFqlUqo1AogOzE9O8/fia72okH0vpNO/j+16nExSUA2buyrlq7mU+/zr9AtChdJX3vFOjvy/RfP6Vdq8Y4OznSv1cnZv/5Ja65ao/VrBZKp3bNcHVxNrp27BMDjB6PHNYX6zszk7784FWqhgYX6NwPJ43n9RdG4uZq3L+5fkY/3s9w3afvvshzTw2xyB+uFGp1ZpnvlZuekYFGo+WFNz/n+aeG0uKeLZBzm/TR97i6OPHU8P6sWLeN/YdP8PePH+Xbv0ajoU2v0exYNV2yy0IIIR5aOTuz5K5LkPuGyqVS+2JvC5yX8xevsmLtVvYePM6cP78s0eVcvQeP5cTpcybtCgWsWzqDamEhJTb2o0Tun0RZSUxMRp2Vlefui8Wl1WqZu3AFcfGJDBnYE98K3hbp98Tpc6zduJ0APx+GD+lrkT7Fg2v0c29w+uwFk3Y7O1vWL/0XOzPLnkTpK8t7p+SUVGbOX8mGLbv59pM3CAkOKJFxyoNycReRs9bQ2ir/CVipaens3n+UFXN+wc3VmTFPDGDh8g1EREYTmM/UfCGEEOJRkHNjpHIJNEwNzymaqU6KKLEbpxxenu589+kbJV5DKD4xyWy7Xg+Xr0RI4kmIB5yrmb/KW8qmrTt56c2PSUnJ3rTg+1+n8tjA3kz+6I1i9Tti7Cvs2nsQ/Z0/6X/x/e/M+uM76tSpXtyQy9z+Q8f4b8EKbsfFU7tGVcaMGoqXh/v9Lyygs+fDWbpyI1qdlu6d2tGoQW2L9V2WbsfFm23PyMgkPiEJP19JPJUHZX3vZGer4n8Txz3USScoJ4mngoq5FYeTo4NhipjS2hp/vwpE3YyVxJMQQggBZm+QrFVOJX7jVDU0iKqhQSU6Ro4aVUO4Hhll0m5jo6RF04alEoMQ4sGj1Wp56c1PDEmnnLY5C5bRrnVTundpV6R+v/35b3buOWjUlpCQxLOvvMOeTYuLFXOO7bv2s//QMfx8vBnQpxsODvYW6fd+5i9ZzXc//4VOl51RO3v+Ilt27GXGn9/g7eVZ7P7/mDqHqTMXoL+TsVu2cgP9enfh3YkvFLvvshYSXImbMbEm7e5uLvjIzqLlSlndOzk7OTJmxID7n/gQeKASTzq9Dqt7ZkVZWVmZ3cVm4fINLFqxAcDwi0wIIYQQD77/vfUiu/cdJjUt3ah9QJ9uJTpTQgjxYFu4dA0pKalmj02dtYDuXdpx5ep1zpwPx97Ojgb1apnUXDFn+eqNZttvRMVw4vQ56tSsVuSY1Wo1415+j1Nnzhvaps1ayA9fvk/tmlWL3G9B6HQ6/p4+15B0ynE7Lp4/pv3He29MyOPKgomMusm0WQtNvqstW7mBPt07Ubf2gz1bbNyY4Rw5foqMjExDm0Kh4InH+pt8pxXiYfdAJZ68PT1ITEohNS0dRwd79Ho90TGxhq0JcxvctwuD+2YXN82pUSCEEEKIB19QYADL5/3Fh1/8xNlz4Tg5OjJ8SG/GjRlR1qEJIcqxhMTEPI9lpGcyb9FK9uw/bGhbtXYzo0YMvm+CJ0udleex5OTiFSb+c9pco6QTQFJyCp98+TPz/v25WH3fz/nwSyTksbT5xEnTOnuFtW7TdrMTCABWr9/6wCeealYP4/cfPuX3f+Zw8fJVPD3ceWxQL3p161jWoQlR6h6oxJOLsyP1alVl1oKVjH1iIKvWbcfF2fGhXw8phBBCCGNVQoOZ9dd3ZR2GEOIBMrBvD7764U80Gq3JsZo1w4ySTgBZGg1zF63gw6ov51tgv0mjekSuXG/S7uzsSMtmjYoV847dB8y2X74aQWTUTfxLcMc/Tw93rKwUJjOeAFxcHIvdv82dnbPMUZVwrcAcarWaqJu38PH2LJFi3zWrh/HT1x9YvF8hHjTlYo7fL3//R5eBz3Lh4lXe/fQnugx8lvSMDABeffcr1m3ebTj39RdGsW3nQdr3forpc5fx9itPl1XYQgghhBBClHuXr0QwddYClqxYh1qtLtGxDhw6xktvfMTLb35sdvfJsuTt5cHI4YNM2kNDKlGvlvnZNSkpqVy6EpFvvx+/+6pJzR5ra2smvTqu6MHeoVDkfcwqv4MW4O3lSf06tcweG9SvR7H779OzMzY2NibtVlYK+vXuUuz+7+en36bTtf8ohjz5At36j+Kr7/8o8TGFeFQp1OrMMi+AlJGpJivLeIqqs1N2Fj0tPQMbpdJkh5yMTDV2tqoC9S/bAQshhBBCFI7cPz0cPvnyZ1at22yYteLi7MSXH79VIjuHvfzmxyzNNfNHoYBRIwbz0TuvWHys4ti0dSd/z1hAWmo6nTu0YvwzT7BkxXp27jE/u+il50YTUrlSvn2q1Wp+mDKNvQeO4u3pzoTnRhWrtlOOKX/NZPrsRSbtIcGBzJ1eskvtABISk5j47mecOHUevV6PvZ0twwb35fmnLbO0efnqjXz1wx+o7yxXVFpb8+yY4YweMdgi/edl6qwF/P73bJP2J4cP4MVxo0p0bCEeReUi8VTS5MZJCCGEEKJw5P7pwbd+0w7e++Rbk3ZvLw9WLZxq0bF27zvE8KdeNmm3srJiw7IZVAkNtuh4lnb5SgQ//jbNpN3D3Y3/TXoRRQnPLsqLRqPh+Vff49iJs4Y2VxdnfvzqfWpWDyu1OK5GRBIVHUPdWtUtvqNeQmISq9dtQavV0r1LO4vslnc//YY9S1R0jEm7u5sL65bOKPHxhXjUyF2EEEIIIYQQD6GVazebbb8VG8eBI8dp0qCuxcaaOXeJ2XadTsf0OYv49H+vW2ysklA5OJCeXTuwduM2Q8FrJydHRj4+sMySTgBKpZK/fv6CXXsPcvDwCXx9vOnXq3OJ1CPKT1CgP0GB/iXSt5urC48P7VcifeclIcF8ofnEpORSjUOIR4UknoQQQgghhHgIabSaPI9lpmfmeaworBR5l45VWJVd4qYwunZqQ9NG9Th74SJ2dnbUql7FbA2istCqeWNaNW9crD5i4+JxcXJEpSpYuZKHWXBQIGfOhZu0VwoomeSaEI+6clFcXAghhBBCCGFZ7du0MNvu4uxE86YNLDrWmJFDzLZbWVnx9MihFh2rJLm5udC8SQPq16lRbpJOxbV05QZ6DxlLz4FP0anPE/zv0+9KvMh8eTdu7OMo79lVz9ramnFjh5dRREUXGxfP3IUrWLJiHRl3NugSoryRxJMQQgghhBAPocH9utOwnnERcaW1Na+/+LTF63Y1ql+HYYP7GO3CZmVlxXNjHicoMMCiY4mC273/MF98N4WYW7cByMxUs27jdj74/IeyDayMtWzakF+/+5gmjepR0c+HhvVq8/0X79GpXauyDq1Qps5aQN8hT/PdL/8w+dvf6D7wKbZs21PWYQlhQoqLCyGEEEIIE3L/9PDYtG0XO3YfxNXFiaEDe+Pv51NiY504fY5Zc5dibW3F06OGERIcWGJjlQSNRsPGrbtITkmlQ9sWeHm4l3VIxTL+tf9x8PAJk3altTWrF0/DzdWlDKIqGp1Ox5oNWzl+8iwBFX0Z1K+HxQudP0jOng9n1Lg30OuNv87b29uxZtG0R/q1EeWP3EUIIYQQQgjxEOvUrlWpzeSoU7MaX348qVTGsrS9B47w3sffkpScAsD3v0xl9IjBPPvUsDKOrOhu3Yoz267RarkZE/vAJJ5SUtIY+8IkLl+NMLTN+G8Jv3z7EdXCQsowsrKzaPk6k6QTQHp6Bus2bWdAn25lEJUQ5slSOyGEEEIIIUS5p9Vq+WjyT7TuOoSWnQbxxnuTSU9Pt0jfGo2G9z/93pB0ymn7Z8Y8jp04Xay+k5JS+O3v2Tz/6nu899E3nDx9vrjhFliV0CCz7Q4O9gQFVrT4eDqdDrU6y+L9/vj7NKOkE2TvQPfZ179afKwHhToz7zpd6elS60mULzLjSQghhBBCCFEupaSmEXH9Bm6uLjwz4W1OnD5nODZ/8Sr2HjjCjnXziz3O5m17SEhMMmnX6/UsXLaWenVqFqnfhMQkRj77OtE3bxnaNm3fwzsTx9OnR6cix1tQT496jN37DpskIoYO6ImdnZ3FxlGrs1i+eiP7Dx1DrVZTOSiQfr27EFzJMvW99h04arb93IVLJCWl4OLiZJFxHiRdOrRmzYZtJu1Ka2u6dGxdBhEJkTdJPAkhhBBCCCHKnTUbtrJp6240Gg2RN25y8MgJVCobFLkqmF+LuMG0mQt46knzu+oVVFo+M0Qy85lZcj9/Tv3PKOkE2TO3fv1zBr26dcDKqmQXoIRWDuLPnz7nt79ncT78Mm6uLgzq34NBfbsXq1+NRsOvf85k8/Y9ZKrV2KlUVPD2wsEhO5l1+WoEv/09m0mvjsPD3a3YzyOv10mhyPtYaboVe5tz5y8RGhKEn2+FUhmzdcsmdGzbgs3b7xYTVygUPDVyCN5enqUSgxAFJYknIYQQQgghRLly9MQZ1m3cbnh8/UZ09jKurCxsVSqjc7fv3l/sxFP7Ns34+sc/ycoyXSbWoU3zIvd7+NhJs+1x8YmcD79E9apVitx3QVULC+GHL9+3aJ+vvf0Zew8cAbITabG347Gzs6VLh1aGzQgyMzPZve8wvbt3LPZ4rVs0Zv7iVSbttapXxcnJodj9F5VOp+P9z35g89ZdaLRarKysaNG0IV9+/Caqez6nJeGLjyexc/cB1m3ega2tioF9ulGzeliJjytEYZV9elgIIYQQQghRYJFRN4mKjinrMErUvjtJjRyOjtk7dOm0OpOCyr4Vij/DxM3VhWdGP2Y0mwqgSaN6dOvctsj9OjqYT4ooFApcXQpe2PvIsVN89f0f/PTbdCIio4ocjyWcu3CJfQePGh5rtToAMjIyuXj5mtG5t2/HW2TMCc8+Sa0aVY3avL08eP/tlyzSf1H9+tdM1m/ajkarBbITUbv2HmTyd7+XWgytWzbhk/de4703JkjSSZRbMuNJCCHEQ+vi5atERcVQt3aNR7L+gxDi4XL85Fk++/pXQ5HlkOBAPnzn5VKZNVPa7q1JVKNqFU6fuYBGozVqt7FR8vILoy0y5ugRg2ncoC6Llq8lIyODdq2b0b1zu2L12a93F6O6VDlqVg8r8JKsDz773qiWz38LV/Dai2MZ0r9nsWIrqmMnzxol/5RKaxQK0OshPsG4TlaAv69FxrSzs2Pab1+xa+9Bjp04Q4C/Hz27tjfMrioruWfl5bZl+x4+eKtsk2JClCeSeBJCCPHQiY2LZ+I7n3PmXDh6vR6VyoahA3rx0vOjyzo0IYQokrS0dF596xOSU1INbZeuRPDSGx+zfN6fFi0UXVA6nY6om7dwd3XBwcHeon1XrxbKlWvXDY+tra1o17oZ+w4eQ3tndom7myufvv8avhW8LTZu7ZpVqV2z6v1PLKC+PTtzPvwyS1asNyzjqxISxBcfvVmg63fuPmBSQFqr1fLjlGl069i2TP6oUjnYuGC4lZUVDvb2pKal45jrc+Dh7kbzpg0sOnar5o1p1byxRfssjtRU87sqyq5yQhiTxJMQQoiHzrsffsPpsxcMj9XqLGbNW0rl4MBS2UVICCEsbdGyNUZJpxwJiUksX7OZoQNKd/bLgqWr+WfGfOLiElAqlbRu0ZiP333FYgmwdq2acfzkWW5E3TS0BVT05aMlr5KZnolam0Wj+nUsMlZJm/jSM4x5cgiHjp7Ez6dCoRJbazftMNuuVmexadsuBvTpZqkwC6xJg7pUCQki/NJVQ5uTkyOOjg60a9McKysF1cNC6dyhVZ5LDR8W1cJCzNbxqhISVAbRCFF+SeJJCCHEQ+VW7G2OnTxt9tiyVesl8SSEeCBFx8TmfayU6z3t2nuQb3/6C50ue7mVRqNh6469vPvRt3w7+V2LjGFvb8cr48dw8MgJLl+NwM3VheZN6uPp4W6R/kubh7sbXToUfov7e0pO3XOs7Mr1/vTNh3w8+ScOHj6OVqejSkgQb77yLPXq1CyzmMrCKy+M5rlX/kda2t2ZTyqVDS+Pf6oMoxKi/JHEkxBCiIdKXHyi4cvQvZKT00o5GiGEsIzG9euwYMlq88ca1S3VWGbPX2b29+zu/YeJi0/Aw93NIuOoVDa0bNaQls0aWqS/B1HfXp3N1hGyt7Ola8fCJ7IsxcvDnZ++/gCNRoNGoymTpZ7lQfWqVZj513f8O2cRFy9dI8Dfl9EjBhFaWWY8CZGbJJ6EEEI8VMJCg3F3czEpcApQt071MohICPEoSklJI+rmTYIC/S2yrXqHdi2oWT3MaBkxQL061WnZtHQTM7dvJ5ht12q1xNyKtVjiSWQvaxs2qDfzl6wyJPtUKhvenji+yHW19h86xt79R6jg7Un/3l2KlTRSKpVlXuA7P7Fx8fzw8z/sP3wcK2srWjRtyOsTnsbJyXJLAAP9/XjvjQkW60+Ih1H5/S0hhBBCFIGVlRXPjR3Bl9//gU6nM7R7e3kwbszjZRiZEOJRoNFo+HDyT2zZvoesrCwcHOwZOqAn4595sth9//nTZ/w5bS47dh9AoYC2rZry7FPDLRB14YRVqWzYWS83RwcHQoIrlXo8lrB9135++3sWV65F4uLsSK9uHZkwbiRWVmW3nC3Hay8+zaC+PVi7aRsO9nb06dkZN1eXQvej0+mYMPEDDh4+YWj7+995fDf5PerWfvj+MKPRaHhmwltE3rhbJ2zV2s2cv3CJ2f/8UHaBCfEIksSTEEKIh86APt0IrhTAnAXLiU9IoE7N6owaMahIN+pCCFEYX3z/O+s33V0alZaWzvTZi/Dy8ix2AXCVSsWEcSOZMG5kccMslmdGP8buvYdISTUudj5scG+LzO4qbYeOnOSt979Ec2e3vPiEJGbNW0pqWhpvvz6+jKPLFhTkX+w/nkyfs8go6QSQlJzCR5N/ZNHs34rVt6XFxsUz9d/5nDxzHk8Pd4YP6UPTRvUK1cey1RuNkk45Lly8wvZd+2nbqqmlwrWoVes2s3nrHmxslPTp2alc7eInRFFJ4kkIIcRDqUG9WjSoV6uswxBCPEJ0Oh0b8tiFbNHSNaW+81x+MjIyOHz8NN6e7oSFVi7UtUGB/kyb8hW//j2Tcxcu4erizJABPR/YzRumzV5oSDrltmbDNl5+/qlCLWmLio5hyt+zOHXmPE6Ojgzs253+vbtYMtwi27Jtj9n2iMgorl6NJCjIv5QjMu9mTCyjnptIXFyCoW3P/kO8/tIzDOlf8J+hs+cv5nns1JkL5TLx9Opbn7Jr70HD483b9zB0YC8mvvRMqcaRnp7Oa+98xq69h9BqtdSpVYOvP32LQH+/Uo1DPDwk8SSEEEIIIYQFpKVlkJ6RafZYQmJiKUeTt3/nLGbqzPmkp2cA2bXxvvnsHfx8KxS4j6Agf7765K2SCrFURUWZzooByMjIJOpmTIELRd+Kvc3o5yYa1Rj8/JtfuRYRyUvPj7ZEqMWi15vfeANAo9OUYiT5+3Paf0ZJJwCdTs/f0+cyqG/3Ai9/DA7MO5FWOTigOCGWiJ27DxglnXIsXLqaIQN6EpTP87G03kOeIfzSFcPjPfsO0XvIWPZuWoS9fdFqi4lHW9kvWhZCCCGEEOIh4OTkgK+Pt9ljoSHlY5er3fsPM+WvmYakE2QvPZr47udlGtPIZ1+nTbeh9B4ylj+mzilSP8dPnuXDyT/y5v++YM36rUZ1/vITkMcsDns7W/z9fAo8/rSZC81ubLFg6WqSklIK3E9Jaduqmdl2/4o+5WoXthOnzpltj09I4sLFKwXuZ1C/7ni4u5q0+1f0oWvHNkUNr8Rs2bHXbLtOp2fTtt2lFsemrTuNkk45EhKS+P7XaaUWh3i4SOJJCCGEEEIIC3lm9DCsrBRGbXZ2tox/uvjFxS1hweLVZme+XLh4hXMXLpV6PMdPnmXiO59z9vxFMjPVxNy6zT8z5vP1j38Wqp+psxbwzItvs3rdFrbu2MsHn//AhIkfFCj5NObJwWZ3ZuvTs3Ohdnw7fS7cbHtmppqTZ88XuJ+SMubJwdS7Z3dXRwcH3nvjxTKKyDwXF0ez7VZWCjw93Qvcj52dHX/88DkN6tbCysoKpbU1TRvX5/cfPisXRePv5ZjPkk5nJ/OvSUnYuedwnseO55EUFOJ+ZKmdEEIIIYQQFtKnRyc8Pd2Z+d9ibt2Ko3JwIE+PeoxqYSFlHRoASUnJeR6LvR1X6nH+M2M+Go3pMq+Vazbx4riRBUr8JCQm8c+/800SagcPn2DNhq306tYx3+vr1anJd5PfZcpfs7hy7Touzk707dmFZ58aVqjn4unuZrZdoVAUauZUDo1Gwzc//c36TTtITUsjJDiQCeNGFrnYtFKp5K+fv2D7rv1s2b6HtPQMWrdoTKXA8lW3Z0Cfbhw/aZrgaFCvFl4eBU88QfaS0D9++syQgLREwmn56o0sXr6OpORkalavyvNPjyjS+3uvQf17sGDpGrT31BtzcLCnT/f8P8OWVL1q3r8DggIrlloc4uEiiSchhBBCCCEsqGXThrRs2rCswzCrQb1anDht+qXe3s6WBnVLf0OG65FRZtvTMzKJjLpZoCVgW3fsIysry+yxbTv33zfxBNC8SQOaN2lw3/Py8/jQfuzad8hkllX9OjWLVJ/nf59+x6atd5dYXbx8jTf/9yV//fw5NauHFTlOvV5PQmISer2eTVt3sXnbbvr36Uq7PJbimRN7O46VazZz+lw4tioVTRrVpUeXdtjY2BQ5rhy9unXk6rVI5i5aSUZGJgqFgrq1qzH5wzeL3KelZjj98scMZvy32PD4emQ0+w8e5d8/vilUjTRzggL9mfjyM/w0ZZqhVpyLsxMfvv1yoWbeFddjg3rz9Y9/cis2zqjdzs6WVyeMKbU4xMNFEk9CCCGEEEI8IkYOH8imbbuMtplXKBSMemJwoXZvsxT/ir5EmEk+2dnZ4udTsC/yDvZ5fym3tVUVObbCatSgNu9MHM9vf8/mdlw81tbWNGlYl0//93qh+4qNi2fbjn0m7VlZWUyfvajIhd3j4hNYunK90ewwvV7PspUbqFuzOu5maiLdKy09nZ9//5fEO7Pn1Go1m7ft5nZcPI0b1uXqteu0adEUV1fnIsUIMP6ZJxk9YjDHT53Fz7dCqRbWzktaWjoLlqwyaU9ITGLqrAW8O/GFYo8xqG93enRux7ad+1DZ2tCuVTOzy0BL2qLZU3ju5f9x9vxFdDodlQIr8uXHk/CtYL6GnRD3I4knIYQQQgghHhEuLk78+/u3zPhvMYeOnsTF2YmhA3sWeflWcY16fBAHDx9Hc8/yoh5d2hU4EdaxXQvcfnIhIdG4sLdCoWBwv+4Wi7Ug+vbsTN+enbl6NRJXN2fcXF2K1M/Vq5Emr0mOyBvRRY5v09bdRN6IxsbGBk8PN8NMIJ1Ox4nT52jbqul9+9h/8Jgh6ZQjPT2Dz7+eQqZajUKhwMZGSd+enflu8ntFjtXBwb7Ys9As6cy5i3nuWnn23EWLjePgYE+Pru0t1l9RBAUGsGbxNNRqNWq1GicnpzKNRzz4JPEkhBBCCCFEOadWq5m3aCX7Dh3HxdmRxwb2ol6dmkXqy8XFiQnjRlo4QsjIyGDpyg1cuHiFkOBABvXrft8lQo0a1OaLjyfx+z+zuXL1Oi6uzvTq1oEXx40q8LhKpZLPPpzIux9+Y0g+2djYMPqJQUV+jYorKKh4M3RCQyphY2Njdglh5UoBhe5Pr9cze/4yVqzZxJVrkQBcuaakZvUqODo4AGBtbV2gvqJv3jJp27JjL4lJyahUNlhbW5OVpWHRsrVUCQ1mQO+ubNyaPcvOy9OdDm2al5tdHgvD19cbKysFOp1pcX6PPOp7PehUKhUqVenNGhQPL0k8CVGO6HS6crnLhhBCCCHKTkZGBmPGTyL80lVD2+Ztu3ll/BiGDe5ThpHdFRUdwzMvvk3MrduGtpnzlvLHj5/dd5lU21ZNCzTTJj9NGtRl9aKpbN62h9S0NNq1bmaxZEBCYhIHDh0nOSWFkMpB1K1VrcTv19xcXejRtR3LV200arezs+WpkUMK3d/Bwyc4ePg4nh7uXL12HZ1OT1aWhvCLV6lXpwZKpZK6tavfvyMwqWWUkppuSPgp7nldZv23hCtXr6NWqwGIvhnD6bMXGPPkUGrXrFro51GW/P18aFCvFoeOnDRqt7JSMOKxfmUUlRAPBkk8CVHG9Ho9GzbvZMfuAySnpBAYUJHe3TuWm91vhBBCCFG2Zs9fbpR0AtDp9Pw+dQ79e3cp1cLDefnmx7+Mkk4AcXEJfPXDn/z67UelEoNSqaRrpzYW6SsqOoYTp85x81YsBw4dx8pKAcD2XfsJq1KZcU8NL/HaO++9MQFPd3fWbNhKUnIK1aqE8MKzT5otuJ6ckkpiUjI+3p5mC3wfPXEaAJWNkiohwYRfuoJOpyc1LR1NloZRjw/C2cmxQHE1aVSXLdv3GJJN6enpoM+eMWWlUBide+t2nCHplEOn07F6/ZYHLvEE8OVHb/G/T79j/6FjaLVaPDzcGPfUcJo2qlfWoQlRrkniSYgytmrdFjZu2Wl4HHH9Bn9O+4+Xnh9dLgopCiGEEKJs7Ttw1Gx7Wlo6Bw+foHXLJqUbkBmHj50y2370+OlSjqRw9Ho9h46c4Mjx0+h0OurWrk58QiLrN+1ADxw5dorMzExCKwdRwdsTgAvhl9mz/wht7vO6azQajh4/zeWr13FzdaFp43q4uhSu4PbzT4/g+adH5Hlcrc5i3uKVHDl2Cp1Oh729PT26tMt3BpmXpzuurs7EJyShAN58bRyVAgp+z+lgb89Lz49m1botnD4bTpWQIPbsP2y2JpVLHs/3RtRNNBpNmRTOLg4XFyd+/Op9kpJSiI9PJDDQT1YrCFEAD9ZPuhAPmaysLHbsPmDSrtVq2b5rP08OG1AGUQkhhBCiPHFwyHtGU15f7Etbzoyge1lbl8yXcp1Ox9nzF0lITKZyUECRt7Kfu2gl+w4cMTw+eOQEEdejqF41hIz0DDLuFJO+dOUa7u6u2NxJlJw6cz7fxFNGRiZT/p7FtYhIQ9vGrbt4fuwIgoMKX6MpL4uWr+XQkROGx+np6SxevhYPdzejGUX169Tk1Jnzhsc2SiUVvDyo6OdTqKRTDg93N6P7VA8PN77/5R9ybZaHk6MDvbt1MClEDuDi7FyqSaeExCS+/+Uf9h06hpVCQasWjRncrwcHDh/n9u14AgP8aN+meYGXZ7q4OOHiUr4Kbut0Ok6ePo+jo73ZWXFClCVJPAlRhpJTUsnMNL87xq3YuFKORgghhBDl0cC+3dm977BJe6C/X4Hr8hRUbFw86zdux9ramh5d2hf4y3WLZo1Yv2m7SXuzxvUtGh9AfHwiv/0zm5hbsYa2xg3rMmJoPxQK8wkwc25E3TRKOgHExScQF59AUlIytra2hnadTk9CQhLeXh4AqFSmy9ly275rPxcuXiY6+hYpqanY2tri5+PNgqWreePlZwscY34yM9VGSafcdu09aJR4atywDmcvXDQ639nJiRFD+1sklpeff4oqocH88fcc4hISqFG1Cu9NeoGExGSmzVxgcn671gWv6XUzJpZFy9aQlp5Blw6tCl0wXqfT8fQLb3Ht+g1D24LFq5i/eBUd2jQH4PLVCA4eOcEr48cYZrY9SDZt28XXP/5FXFwCAMGVAvjsg9cJC61ctoEJcYcknoQoQ64uzjg5OZKSkmpyzN/PpwwiEkIIIUR507ZVU0aPGMTs+csNu5xV9PPhq0/fsug4/85ZzB//zDYsmfrlzxm8+co4+vTodN9r33z5WS5dvmpUiyq4UgBvv/68RWMEmLd4JTG3YklKSiH65i3UWVlcjYjEp4IXXTq0LnA/Fy9fM2lTkJ24SkpOIcDFGRdnJ5KSU7KP5cppNW5YN9++Dx4+zvGTZ8nK0txpSeFWbBzqLA0JiUm4uboUOM68pGdkoNFozB5LSU0zeqxQKHhy2ADatmrKxUtXcXF2om7tGvdNoBVGr64d6NW1g1FbUCAMG9yHdRu3E5+QiIODPe1bN6dju5YF6nPFmk1M/vY3w/Ocv3gVXTu05tMPJhY4rpVrNxslnSD7j78ajZao6Fv4+XoD2UtXN2zZyYihD1ah8IjIKN7/9AejHRCvXLvOK5M+YcX8v2UpoCgXJPEkRBmytramS8fWLFm+zqjd1taWDm1blFFUQgghHlR6vZ7la7YSHORPvVp3ZzvExMax/9AJXJydaNmsPsoCbpsuyo/xzzzJ40P7sffAETzc3SxezPjylQh++3um0VbxmZlqvvz+d9q0bHLfRImLixNzpv7Izt0HOH3uImEhQXRoV/B7GZ1Oh1qdhZ2dbb7npaalce7CJW7FxnHh4hVDe1JyCj//Np1mjevj4lywWVrOZs7z9HTn2vUbhgLdYaHBnDl3kUx1Jm5urlhZWdGxXUvq1sp/ptnla9dzJZ2y6fV6rkVE5pvs0Wq13IqNw8nJESdHh3zHcHVxxsvLg1gzs+SrhASbvSYo0L/Ua4g2b9KAZo3rk5qWjr2dLdYF/P2TkZHBNz/9ZZJcW79lJx3at6BTu1YF6uf02XCjx3q9Ho0mO7l6Oy7BkHiC7J+DkpKSksbNW7cI9PdDpVJZrN95i1YaJZ1y3IqNY8uOPQV+nYQoSZJ4EqKMtWvVDCcHB3bsPkBCYhKVgwLp2qnNAznNVwghRNmav3Qd/85dTvdOrQ2Jp/DLETz/+ic0aVibyKibLF65kR8+n1TGkYqicHN1oXvndiXS95IV64ySTjnU6ixWrd3MiMf6F6if1i2bFKrYuU6nY82Gbezcc5D09HQq+vnQu3tHalYPM3u+VqtDp9NzNVftpBypaels27mvQDO0AGrXCMPN1cWwOxuAvZ0ttWqEGWpn2dqqaNq4Hh3btsDXx5vgoIACzVayszWfQLPNox3gwKHjrFiziaTkZKysrKhfpyaPDeqNra35JIVCoaB/r65MnTkfnU4HQHJyKukZGSQnJxN+6UqeCaj7ibxxk1NnzqNS2VC/bs1iz9BSKBT3TaTda8v2vaSnZ5g9tm7jjgInVCoF+N0TCyisFOh1epydjXfyK2zx9/zodDqysjRYW1vx0Rc/sXnbHrKysnB0cGDIgB6Mf+ZJi4wTF5+Q57GbN2PzPCZEaZLEkxDlQKMGdWjUoE5ZhyGEEOIBFhEZzc69R+jZpY1RAmH2gpUM7tuFcaOHoFZnMfyZNzl64iz161i2NtCD5PTZC8TFJdC4YR3s7PIu3P0oUWeZX7IFkJGpLrFxl6/eyNYde9Hr9SQmJXMjOoYzZ8P54J2XCa5kWoTbxdmJCt6eqNWmMzw8PdzMzljR6/WcPhvO5asRuLo406hBbRzs7VEqlTw3dgRzFiw3FAGv6OfD6y89g6uLM6fPXkBpbU3tmtXuOxPrXtWqhhAReYOo6Fvo71TcdnZypGb1Ktiame1y8dJV/vp3LpmZapwcHbCxUXL42ElQwMjhA/Mcp3bNqrz24tPs2nOQnXsPkpCYhK+PFwePnODgkRN0aNuSfr06FzjuQ0dOMG32Qs6eC8fdzRVfH29Wrt3MiKH9aFCvVqFeg+JS5FGwHsCqELW8Bvbtxr9zFhGfkJNgVOBgZ4dOryfwnqRU6xaNixKqEa1Wy6p1W9i97zAZGRmEX7rK9cgowyyn1LQ0ps9ehJeXJ0MH9Cz2eE0a1mXjll0m7VZWClo1K/7zEcISJPEkhBBCCPGA0+l0fPXTNCZOGMWq9dvRcTfxdOL0BQb16QJkF0Ru2rAOJ8+EP5KJp6sRkbzx7mSuXLsOgL29HU+PHMqT+XyxL464uEQ+/fpnjhw/hbOjE2NHDaVfry4lMlZxdevUhsXL15q0W1lZ0b1LycyyysxUs3vfYTRaLWfPhZOUfLfm5bsffcMfP31udpbMsMG9WbthG5nquwkxVxdnfH28TWasZGVl8ef0uVwIv2xoW7NhK88//QSB/n74+njz2oSx3I6LR6fT4e11d8Z58yYNCv18FAoFKpUNbet7E3HFFz9fH1JSUlGpVHi5qujYPNBkqVlKahoff/UzZ+4sCbOyUtzZba4iR4+fZmDf7vnOFgqo6EvHdi3Ye+CISSJly/bdNGtcD18f7zyuvmvZyg0sX7ORE6fOAZCUnEpcfCK1aoQxd9FKalSrUugEXHF0bNuCLx3+IDUtzeRYz24dzFxhnp2dHb//8BmfffMLJ0+fR6Gwok3LpjRtUo+z5y6SlZWFk5MjXTq0tkhybfHydezaexAAnV7P6bMX0Gl1eHi4Ge3kt2jpGosknvr06MTCpWuMlp4CdOnYhqCg0l1WKUReJPEkhBBCCPGA+2/RGlo0qUdQYEWTYwmJybjm2pnM1cWJuIQkk/MAFi7fwKIVGwAMszQeJhPf+dxoiVZ6ega//DmT6tWr0KRB/sWiC+tWbBxd+4807DIF8NIbH3H0+Bk+ePsli45lzs2YWK5HRlMtLAQnp/svcWpQrxZ9e3Vm+aqNhjaFQsHI4QMKtOHJlWvXuXotEjc3F2rXqFqgOj4pqamo1WquR0YbJZ0Aom/eYvnqjTw+pK/JdaGVg3h69GOG5JOzkyNuri5YWVmZzFjZtfeQUdIJsotIL1iymtcmjDW0eXq43zfevMTcus3CZWu4EH4ZhUJByzoeNAvV8lhbZ5Yf0GCrssHBFrrX1xPmn0p67BnsvWoYrp+/eCU3bkQbHut0eq5HRuNgb4+XpzspKan3XaZ25txFw8+sr1MK8el2ZGqzv+qdPnsBb3d71EkRRuPmlpCQxLZd+4w+r5BdhDsuPgEvKyvOhV+iXm3z15cElUrFO2+M5+MvfiLzzqw7hUJBn56daNuq4LviAVQODuTvX7401IvKSQBlZqpJSk7G3c3VKClUVGnp6ew7eNTwWKPRotXq7hzLMKo/lpCYWOzxIPu5/PPrF/w7ZzG79h7C1taGnl07MKBPN4v0L4QlSOJJCCGEEOIBFpeQyOwFqxj9eD8WLFvPufAr6HR6du8/Ssum9XF2diQ1Ld1wfmpaOp7ubmb7Gty3C4P7Zs/I0Wg0tOk1uhSeQek4duK02bpAer2eBYtXWzzxNPnbKSZf4gFmzVvC6y+OwcmpYAWwCysjI4O3PviKvQeOotPpsLVV0a9XFya+9Mx9r33vjQl069yWteu3YWVtxYDeXfOstZRDq9Xy75xFHD951tDm6eHO80+PwMvTI99r3VxdcHZy4vbteJNjTo4OHD1+2mziCWD44L7Y2Nhw+OhJNBoNri7O9O7eiQB/PxISk3B1cUahUBhm79zrWkSkye5yGRmZbNy6ixOnzqFUWtOofm3atW6WbxItM1PNr3/OIDEpGcj+PB04GY2dOo3GdUN5vp8TWpdGKBIPYKVNw9rWBZVLoOH61LQ0Tp4+j4uzE4lJKUZ9x8TeJiS4UoHqfubUgfJ1SqGaVzwZGiVHo7zJ1Cqxs9ERf2EZ2szshLO55NOViMjsOlFmlrAlJafi5elRoN3RTpw+x6EjJ1Crs6gcFEirFo1wsLe/73V56dKhNU0a1mXZqo2kpqbSpWNrwkIrF7m/e5NLtrYqvG0tV1c1MTHZqBi6ykaJvb0d6ekZaO/sFpkjNCTIYuPa2dkxbszjjBvzuMX6FMKSJPEkhBBCCPEg00PHts24dj17xkRiUgo6vZ5bsdlf5qtUDuTIibNUD8v+snb0xDnGjR5SZuGWlbi4vGcXJN3zhd8SDh09abZdrc5iw9bdDOjd1eJjAnz85c/s3nfY8DgzU838xauo6FuBxwuwTXyTBnULlYTbtfeQUdIJ4HZcPPMWr+KF+xRPtra2pmunNmzZsceoXam0xs/PB/2dgtnmqFQ2PD6kL/17dyE1NQ0XZyeWrdrI/MUrydJoqB5gRav2vbFW3k0a2VprcLfPIDrFCYVCYZRQ0ul0TPl7lqHWE0DkjWiuRdxg9BOD84zj8LGThqRTjkytkh3nHahaRYerQwrWcduyn6+tC+5h/bBW3U06Zqk16HQ6GoY5kpqkIi757vJBW2st/TtUKVDCp26t6ixevo74dA0ZGiV2Sg31/W4RnuBDsEM42sx0k6RXbm53lih6eboTmWv2FWS/1g4O9lQPC8lz/MtXIvjpj+ns3nsYvV6Pq6szVSoHse/QUV4ZP6bQhcWNYnN1YdTjJbMc1tK8PN2xt7cnPf1usr9GtSocPnbSKOllZ2fL+KctU1xciAeBJJ6EEEIIIR5gHu6uTJwwyvB4yj9z0Wh19OuZXQNl2IAevPnRdyQlp3D12g10Oh0tm9Yrq3DLTJNG9bC1VRmW7Bgfs+xsJwBnR8c8jwUUYOlaUajVarbv2m/22LJVGwuUeCqso8dPm20Pv3iF1LQ0HB3yTzi0admEQf17sGrtZtTqLFycnfCv6IudrYp6dWved3wHe3sc7O2Zs2A5++8scfJ1SsFHGc+pbX8QGNyL8xeyk071/W5hp8yejeLiVxdnp7vv0fFT54ySTobnd+I0kTdu4l/R/Ht228ysNshOPiVYV8eV84Y2l6BORkknADc3F2pVssHLKomgzu6sOaInJi4NF0clEwZUwt/+usnSPHPs7e0YO3IoM/9bwtEoqO93C2d7GFLbHp06GVTOeN2T9MotOCiAwICKRFy/QXBQAFevRaLX67G2tiIwoCKjHh+EjY2N2WuTklP4fepsDh0+aVjul5iYzNnzF7G1VbF91356dm2fb/wPCxsbGzq3b8mKNZsMbUGBFXFzdUaBgsSkZCoHB/L0qKFUr1qlDCMtvmMnTvO/T77j2vUb+Pp48/7bL9OyacOyDkuUU5J4EkIIIYR4iNSpGYYuV32mRvVr8t2nb7JjzyGqV63MpJfHFGgGxcPGycmBJ4cN5J8Z84zqVwUF+jN8cB+Ljzd6xCBef/dzk3Z/Px+aNCqZxF9aeobZ3d4gOzlQEvKqBabX6ylombBxTw1Hr9MbJX58KnjTt2fBdmNLTUvj0JEThsfx6XaGWT/O6Qdo26QqmVE7sSadm/Eart9259Un2hj1EXH9Rp79X78RZUg8Xbt+g9u34/Gv6EsFb08qmamrBtmJLm/FRaO2pKubTGY8AbTv0o/DG3/FUaWmdyMlZ24F0TQkk6qVK2Bt60Ki2oF9O/Zia6uiXp0aeS5dq1qlMh+8/RIXL19Dn3ELm4SdXL4SQXJKKkejfah4eDmPDeyNm5uL2eufHvUY8xatRKFQ4OnhjtLamq4dW9O1U1vDUj5zDhw6RmJSChmZmUbtqWnpJCUlZ9fYekQSTwCd2rfCzdWFnXsOkpySSmhIEF06tLrv0tMHycYtOxn57ETDssIbUTEMfPw5vv3sHUY81r9sgxPlkiSehBBCCCEeIm1aNDJpq1erKvVqVS2DaMqXZ58aRq0aVVi4dC0pqSk0aVifJx7rh4ND0WvQ5GXwgJ4cPXmGuQtXkJWV/eWsol8Fpv3+tcXHyuHm6oKfbwWiomNMjlWvGloiY9arU4PLVyNM2kNDgvJcXhUXn0Ds7Xh8Knjh6uKMg709r74whtNnw4mKjsHb24M6NasZJUgjb9wk/PIVnB0dqVOrmtHsm6SkFKP6OZna7PpG9f1uoctKplX1JM5pnTh/JY2jN7yxdXDk1z9nMmJoPxo1qAPkX1zc08ONtPR0/pkxn4uXrhraGzWow/DBfQiuFGDYKRGyk07d6ulxUGmxtnXBJagTSVc3oc1MIv7CMkPySaPRkJaeQZWwGri7vUP4vn/RZiZRM8yOCl4VsbF3Y99VNzbM+NfQ99KVGxjz5BCq5bHsTalUUiWoAjFndnDwXDiaO5+96l63ORp+lt+nJjHp1XEozNRycnVx5tmnhpOSmkaWOgt3d9c8X5PckpJTUFpbo1AoTBKR6iwNTk55z/4rbzQaDRs27yQpOYW2rZri51uhSP00alDH8Nl6GL35vy+NalkB6LQ6Pv36F0k8CbMk8SSEEEIIIR4ZrZo3plXzxvc/0QI+/d/rvPHSs2zYvIOAAD+aN2lQ4mO+8MyTfPD5D0aJGEcHByaMK3o9mWvXbxAXl0CAv6/JrI3WLRpz7sIlzpwLN7S5ubrw2MDeJv1kZWXx34IVHDl+Cr1ej5WVFS2bNWJQv+4oFApq1QijVg3jYuZ6vZ75i1exZ//dulUuzs6MG/O4YRaSt5cHjo4OpKamGc7J1Co5c8uD0CArkpJTuB2XwLXUAOwcbYHsmk7zFq+iVo2q2NnZ0rBeLdZt3GZSrykwoCJVQoL5b+EKo6QTwKEjJ/D38+G5sSPYsmMPJ06dw9ramha13Ahxu2VU08k9rJ+hwHdG4lU27o9i975DZGZm4unhTq9uHWjYYQzx55cY+k+wqs6GbauMxszMzGTW3KV88PZLZndh06pTiL+wjJioq6Skw5lbFajhHWeo+XQ0Cs6HXzZJXN2Oi+f4ybMoFArq1q6ORx4bEJhTObgSSuU+PD3ciM1VKF6hAGcnR1o2ezCWXx05dopJ739JQmJ2EfYfpkxj2KDevDz+qTKOrPyJvmma3Ia8l56WJo1Gw849B9HqtLRq1gg7O7uyDkkgiSchhBBCCCFKjKurM4MH9Cy18bp2aoNPBU9mzFnCzVuxVAkN5qknBhMU6F/ovu6d5aNQKGjSqB7DBvU2zEZSKpWMG/M44ZeucPVaJG6uLtStXd1sPaDV67dx+Njdous6nY6dew7g7eVBu9bNzMZw9Phpo6QTQFJyMrPnL+XNV8YZYujRpT0Ll642nGNrraF+QAp+PlW4EZVdLLuGd5xhpzfIrol1/uJl6taqjp2dLS88O5LFy9dy7sIlrKysqFe7BgP6dkOn03E411K+3A4cPk7Hdi3p0aU9Pbq0N7Sn3jqFwt7PsKwuJ/mkTopg/b5ItmzfbTj3dlw88xYswKlHBVwd7s7yij67CltrjSHeHMkpKVy8fM3srCd1UgTazCTS1NaG55oz+8tOmV1cPT7BuND+9l37WbJinWG20vLVGxncv2eBE0Z1a1UjtHIQWq0OvV5vSD4EBQYwfEhfalS7fy2j4yfPMuO/xURFx1C5UgBjRz1G5WDzhdBz02g0zF+8ip17D2Jva8eAPl1p3bJJgeLOTafT8c5H3xiSTpC9Y+Ps+ctoVL92kfp8mFkrrdFotKbtVnnvAFkadu09yEeTfzK8j44ODrz24lj69OhUpnEJSTwJIYQQQgjxUKlXpybfTr5/Ye77Wbpyg9EsH71ez/6DR/H38zFJFFUJCaZKSDBR0TGs37wTrVZL3drVCa4UYDhn74EjZsfZs/9InomnI8dPmW2/EXWTmFu3qeDtCWTPvHJ3c2XX3oOkp8TTOPA2gb6h2Dt5kKAKIEOzPtesn7vJJ2Wune0qeHvSv3dXEhOTCAyoaFiCqdFo0GhNv2QDhmWUOTQaDavWbTXMZgoMqEjfnp0JCw3GWuWE0rUKu/ctN7rG1lpDfd9bRF1PxaNOA8PSPGsiqO8XbxTv/eQUIXe2ySRzV/Y4OcmnnB39cr8ncfEJRkknyE7CLFy6mprVq+Dmar4eVG5WVlY8N/Zxdu09xInT59BqtVStUpkuHVrnWZA8t+279vPW+18aXuMLF6+wY88BfvvhU2pWD8vzOo1Gw9MT3ub02QuGth17DvDk8AG8OG5UnteZs2f/YW7HxZs9tmz1Rkk83aNpo3rs2HXApL16tZJZ0lsQaWnpvPvxt6Sl3d1RMDUtjcnfTKFh/dr4l9CmDqJgHr3KkkIIIYQQQoh8abVaDh89afbYgcPHzbZv27WPr374gw2bd7B5225++HUqy1dvBLKTVhkZGWavy731/L3yK1Cu0+kM/46LT8DZ2ZExTw7h2eEdqFLJCwdnT9zD+lG3YSuORlcwFBx3t8+Ow8nJkapVKgOQkJjEz7//yxff/cZv/8zmk69+Mcy0UiqVedZUql3TuHbaomVr2bJ9N5l3Cm1HXL/BH1PnGOpupaVnGI7lcLfPwE6pITkD3MP6oXLyxT2sH14VAozizeHs5ERo5Up5vi72XjWoVbseoZWDDG2ZWiXRKU40bVwfXx9vQ/vxk2fNFojX6XScOnPBpD0vNjY2tG/TnBfHjeKV8WPo2bVDgZJOAL/8McMksZeekckvf87I97olK9cbJZ1y/Dd/ObF5JJHykpZu/rMJmN0J81E3b/rPVAkNhlylwgL8/Vgye0qZxbRizSajpFMOjVbLoqVryiAikZvMeBJCCCGEEKKc02g0pKSmFWgGiiXodDqjOlG5mfsinpiUzLKVG0ySGJu37aZR/Tr4V/QhrEplzl+4ZHJt9VxLsaKiY4iKjsHLy4NKARWpV7s6J06dNbnG16cCvj7epKSmMWvuEs6ez95BzsnJkX49u1A7qD0ql0CsVU74+TrRq2cv1q5bi4tNCtEpTjg6OvDUiMGGOkkz5y7h4uW7s7vS09OZv3gVfj4VCA4KYECfbnz3yz9cvhpBZqYaZydHalYPo3OH1oZrUtPSzCblNBoNO3YfYOjAXrg4O+Hh7kZcfILheHRK9nK8sFrNjZbmhTQZydV4Z6KvXDacq1KpeGJYf6ytrdmx+wB79h8mNTWNqmEhdO/c1lAkXaFQ8NzYx9mx+wDHT57FWmlNo/p1aNHUuM6Ywsq0yLjhmJkC5Jam0WiMCrPndu686Wclt737zM+g02i17Nx9kP69uxQ4jjYtGmNvb0e6mQRUm1blZ7bTgSPHmTlnCTdjYgmpHMjTox4zSjCWFqVSye6NCzl09CQbt+6iTdNGtGxZOrXz8pKYlPfOncmpqaUYiTBHEk9CCLOuXb9BamoalYMCsbOzLetwhBBCiEdSRkYGH3/5M9t37UetzqKinw/jn36Crp3aFLlPtTqLpORkXF2c85yVYmNjQ5XQYC6EXzY5VrtmNZO2M+fCjWYg5XbyzDn8K/rQr2cXfvlzhtEMJzdXF7p1aoNGo2Hm3CUcO3HGcCysSmXGPDGEhvVrG82+cnCwZ/iQPgBGSSeAlJRU5ixYxisvjCHIKzuJk5aeTlhoMLVeeYnLV65ho7KhZrUqhud+K/a2SeFwyJ6ldfzAWgL9hpGpVqPV6VAA9kotAa7p2NgojZbqJSQkm+z0lSOn7pFCoaBn1/bMnr/MKEmXpPOmXbv2RtdYq5zo3PtJ6jSN5cy5cOzsbKlbuzoO9vYsW7XRqE7UgUPHOHvuIm+88iwuztnP28bGho7tWtKxXUuzMQHUq12DZSs3oNPpSEvPIDklFVuVCk8PN+rUMn2fzYm5dZu1G7dx8dJVnJ2daNW8ES2aFqw+lFKpxMHB3uxMFRcXp3yvdc7nuEcBd+TLYWdnx4RxI/n2p7+NPscN6tZiUN/uheorx7ETp5k6cyERkTcIqOjHqMcH0ahB7UL3k5WVxYlT59i0bTfzFq001Fe7fDWC3XsP3XdJYklqVL82jeoX/jmVhE7tW/DPjHlmZ/B1aNuiDCISuUniSQhhJC4+gX9mzCfyRnYhTpVKRe/uHWnbqmkZRyaEEEI8et764Ct277tbXPtG1E0++PwHfCp4Uq9O4eo46fV6Vq/fyvZd+8nMzMTBwZ5O7VrSqX0rs+cP7NONX/+aSUrK3dkCvj4V6NzBNJGhymdZlc2dWUX+FX2Y9Oo49uw/zK3YOPz9fGjetAGODg6s27TdKOkEcCH8MivXbmbk8IG0bdWU8ItXcXZ2pF7tGtjZ2RIXn2CUdMr9PHfvO0ygvx9LVqxj7/4jZGk02NnZ0aldS7p0bG10fqqZpAeAr1MKrtpzxF9YxrKtyWiysgj29zAU6j4XG872XfsNSUAvT3dsbW1NltIBBPj7Gv7duGFdnJ2d2L5rP/EJiQQF+tOxXQuibt5i197DuLo40ahBHZydHAHwqeCFTwWvXPGmsWP3fpMxklNS2L3vEN07tzN5PfKaveTm6sKQAT35/Jsp3IyJBcBKoaBZk/p5znjLLT4+kR+mTDUkjhKTkvlvwXLOnL9I+1bNqBwceN+ZU507tGL5qo0m7b27518QeuiAnqzdsBWdzjjR4OXpTusWhZ99M6R/T+rXqcH8JatJTU2jTYsmdOvc1pDoKYzd+w8z8Z3PDYnI65HRHDx8nK8+eatQ9aJuRN3k93/mkJSczLpNO0hLS8fOzhZXFydAQXpGJr/+NYtfv/0ozz4yM9UcOnqSmzG38KngTaP6tbG1VRX6OZV3oZWD6NurM8tWbjBqb9W8MS0LmAgVJUcST0IIIzPmLDYknSB7x5fFy9cSUNGXkHzqCQghhBDCsm7GxLL3wFGTdq1Wy4w5SwpdQHzL9j1s2LwDrVZHXHwCGo2W23EJODo60LxJA5Pz/Xwr8Pbrz3Pg0HFux8UT4O9Hw3q1zM6SqlWjKnZ2diZ1nKytrWlY7+6MCDdXF6Pd33LkVTfq4JETDBnQk+BKAUZFsQGSU/JePpOamsbq9VvZsftuAeSMjAxWrduMm6sLTRrVNbQHVPTF0dGB1NQ0oz7i0+1wcXMhIzUOL91pbtl6UMM7DjulhgyNkvh0O06cPmdIPNnaqujcvhWr1m026sfZyYk2d5IN6bFnULkEUi0sxFA3KjMtgYXzp3PgzN0d1dZs2MbzY0cQHGT8nAFiYm7nObPqRtRNw7+vXLvOijWbuHT5Gg4O9rRo2pAeXdphbW2885hOpycwwA8nRwcge7aQQqFgwZLVPDN6WB6vcLbtuw8YzVaKT0gk/NJVDhw+wZGjp/D28mDk4wNN3rvc3nr1OZKSUti5+wAarRaVyoZe3TowduRQwzlXIyI5f+Eyjo4ONKhbE3t7O2pWD+OV8WP47Z/ZhiVyvj7efPHRm0VKFgGEhVbm3YkvFOna3H77a5bJe6TRapny96xCJZ7mLFhOUnL2TLqc1zkjIxOVygZ7OzsAwi+azkrMkVO7LHfh9I1bdvLSc6NxcyudZbul6d2JL9CySUNWrt2MVqelU/tWsqNdOSGJJyGEwc2Y2DzX2e87dEwST0IIIcodnU7Hlh17SE5Oo32bZiVaA2nrjr1cjbhB/TrVCz3bqCiuR0bnuXzt5q3YQve3Y/cBklNSOXMu/O5W6Fez/8hkLvEE2duRt2/TnCtXr3P1eiTnLlymZvUqJl/sbW1VPPXEYP6ds8jwBVmlUjFsUG+TL7harZYNW3ay/+Ax0jMyqVE1lOQ86rNoNBqjGTv7Dh5ly/a93L4dh5+fD5lqNbYq09kbVUKCWL95h9k+d+45YJR4UiqVDOjdldnzl5GcnELUzVtkZqqpHBxIxbpD0cVswd5GSwO/7ALhGRqlYae5e2d6BVXyx9/PhwsXr+Lq5kzDerXp2rE1bq4upMeeIenaVqxtXXAP64e1ygmtOoVTO/7CIf08vk7uhnpPmZmZzFu8kkmvPmfUf3rsGdyc3bGysjJ8NmytNYYd67w8PYDs5YNT/pqFWp1djys1NY2NW3aSlpbO0IG9jPo8dOQEKhsbo1lVAKfPXiAtPR0He3uzryNAZFSuP1ZmZXHuwiXDDKSMzEzi4hP4+995fPDWS3ku61QqlXz1yVvExsUTEXGD0MpBhmV2er2eOQuWc+DQMcP5K9Zs4tnRw6gcHMiwwX3o37sLe/YdwcnFkSYN6podo7Rdvhphtv3KVfP32ebcjovnemQUAFbW1lhbW6HVZr/nmRmZhsSTi7Nznn2sWrfFZLe+uPgEVqzdxJPDBhQ4lgdJh3Yt6NBOltaVN5J4EkIYpOex2wyQ5040QgghRFk5cuwUb33wJfEJ2TNFvv7xT8Y8OcRopoQl3IyJ5flX3+N65N0v2Q3r1eaXbz80FKcuCWGhwdjaqswW864SGlzo/hISk7kQfuVu0umO8xcuc/b8RapXNd0KXaPRMHXmAqPdwyp4ezH+6Sdwc3NBr9dz5Ngpjp3MXiY3sE83VLYqdDo91cNCzNaJ/G/hCg7emeHk65TCqZPJXLkRj5enOzZKpVEipVaNMEPSafe+w8xfvNLQz7WISJKSU7BVqajqlz1DKVOrxL+iL3Ur27JJk4CvU5YhmZPTr7mZUo0b1iUpJZUfp0xDr9Pj7eWBvZ0tP/+9gJfG9Mbd/SRxd+o0nbnlQaY2+33PncBauHQNO/dkz7BycLAjS52Fn4+3oeC3yiUQa1sXtJlJxF9YhktQJ5KubiLhdpRhBlVuUdExxN6OMySTcieumjWsxp6DZ7C11hiW/qlUKsMSs117DxmSTrntO3iUHl3bG5bxASY7yuXQ6/UkJ6eSlJSCt5eHyUwpgApenoaC8bG34w1JJysrhSEhmJKSyqmz4dSvU8PsODm8PNzxuvNa5Th24oxR0gmyC7/PXrCMdye+gEKhwM7OrtwlGlxdnLkVG2fS7uKad5LoXrlrFVkpFFT08yXi+g2T8wb06ZpnHydPny9UuxAlpWhzEIUQD6WAir445boRya1Grh1nhBBCiLKm0+l4+8OvDEknyC7C++e0/zhy7JRFx/pw8g9GSSeAw8dO8uufMy06zr1cXJzo18t0Zy5HBweeemJwoftzc3Umw0z9IWdnR44cP232mq079plsWR9zK5ZFy9cCMHv+Mmb8t5hjJ85w7MQZZs1bypk7SQZzSae4+AQOHTkBZCedqnnFU9/vFv7eLqjVWYZESgO/GGpUzKJvz85A9pfwbdu24OtkPDPK29OD5jXcaFPDis51tAzs1Y5nhzZHHb2TXjWjqOF9G1+nFAJdEmnkf5NqXvHUDcmOS6tOIT32bl2p/QePUTkogKphlfGp4IWVlRVZ6Ymc3TeXkOBKhnukGt5x2FpraFC3lqGw8pWr1w1Jp9xWrNlEUnJ2zNYqp+yZTjnJp/NL0GYmoVXYG2ZQ3St3YjN34qpD9Qy6talF40rx2NlosHf2ZODQUXi4uwGYTXpA9myz3DvqAdS5Uyze1ykFW2uN4byEhCS+/+kXpv/5NR9+/iN7D5juItemZRNDDS9trqVlPt5eKJV3E1VF/QPmvXW/csTGxhGZa1lheZPzub1X724dC9yHl6cH/hXv1gZrWK8WFf18UCgU2NrZYm9ny/AhfXh8aL88+1DZmE+M51eTTYiSIDOehBAGyv+zd95hbtR3/n/NjLq0att7b67rbmNsAzYdbJqB0BIIhIRLv1yS++WS3F3KXe5y6SEFSOgdjGkGYxtT3Ou6rb27Xm/xVu1KWq26NJrfH/LKK+8aTAklmdfz8DzWdzTf+UojiZ33vD/vj0bDNSsu5sHHnk2z9ldVljF7xtSPcWUqKioqKirpvPHWNtye4XHjiqLwzPOvMmP65A/lOOFwmL37Jr743fDmFr52160fynFOx7e+egcFeTmsfmkdvhE/dTWVfPnOmyktLnzPc527eAFrTgljliSR4sL8CTtBAezZN7GId7Cpmda2jpRzaSxbtu9m2pQ6jnf3MeL3U1lRytRJtYiiSF+/K3UsT8hAOK7BoIkzp8SNmDWfuuwhYiMjGCWYVlyKIyPpmgkHvJQZWzFkJMWNURcTgDtsoLa+FDniQ9IfR2coZtjfg9OqIxoLYDeEmZo7BAIMBO0sWHQxctSPp2U1ciQpXCrmMvr6B9Jex6gIFvRpMNZWseTKlXTue46e421I0hC7Djby/R8fTeY3neb9k2WZI81tKWeUpLNgLV2Kp3lV6jlZVRcS2btx3L6VFaVppaOjwtXoumcW+KFgEqIuA2fNFUi6k+9JQX4uB5vGu1q0Wi05WZlpY0vOnkffse1Yol2pMsKW4y4KcjKYntt/IkgdnnjmRZwOOzVV5al9c3OyuOuOm3nxlQ2MjPjpGxgkLyebosJ8gsEwgWAQo9FAbVXFhO/PuyEIArKczCKLRKNkWMzYrBkIgoDAO4eWfxAURaG59RiDQx6Ki/IpKSp4T/vfedsN+Eb8vLhmPaFwBINBzyUXnMuX77zlPc1zw8oV/OG+h/H7A4iiwLzZ06mrrmTRwjmUlxZjMp2+DBKSTr71GzdNOK6i8lGiCk8qKippNEybRF5uNtt2NhIIBqmtKqdh2qQJ7dUqKioqKiofFyOB0wdLf5jl4YmEctqcpdMFPL8bfn+QVze8SSKR4MLzFr9r2/gbrl3xjq6GM+XsBbNZOH82R1raCIXCmExGCvJyMBoNTJ8ycRnU6QQpRVFoPdo+4TbvsI8f/8/vyM5Kloi9uWk71VXl3HnrZ8jNyUIQBBRFISInRY7RUrFS6zHynLkIOcmLYiUeTpWkBTrWY7dIeP2MK0nLzC7AUX1ZSpDxHXsVnTkfgCJrBVrzINq4Dq1WS0ntVBwZutRzJb0VnbUYRdSO60jnMIYxaOKgsWHIrEPSWfCb5tLd34hBEyfHHCCWCPPahrcoyM897fuu0590l8hRP76O9Wnbi01dnHPWdN7Ysi/1fufmZHPjBOd8IuHKVrYsTXQCWDh/Flu2707rSAhJkcloPPn+DbiG2LJ9D5K5AId1GKNWprrKzGpJZlJOepC6oihs3rorTXgCKC8r5itf/CyJRIKnVr3Mpq27aG5tY+hEaWJxYT6PPLWa22+5bkIX3DtRXJzPn/76KLHYye+a3WZlydnzKCw4/XsOpMpAR91806fUMWvG1Hftsucb8fPH+x5JC2qfMqmWz9149Xsqrf2Xr32Br9x5C929/eTn5ryrSDQRhQW5/OA7X6VxfxO+kREqK0rfMaj9VC5atpi+fleaCGm3WvnTfY/wL//2X2i1GhadNZvf/M8P0U2Qlaai8mGhCk8qKirjyMvNZsWlE1uEVVRUVFRUPgksXjgXnU5LNBqbcNuHhclkpKqilJYJRJY5s6a/5/mef3kdP//NPYTDSYHj13f/lW98+fNcvfyiD7rUd0UURb50+43cc/8TaQLLwvmzmVxfPeE+dTWVbN2xB+/wCDX5CnEpC4sti/raKrKMw2ToIph1J3OUdGIMMdQBmnRHVkvrMbbu2MvZC2YzY/pkdu89AEBE1tDkcjK3xENOdtKJY6+4GElnSYlDoyJLQVE5G18fTitJE0WR82Yly5HSBBlBIKNwIdGRLqy2OAP9EYJBGX+iHTn+OEaDPj3kW5apra5g+65GDCdazY++pktnzCfQs53w0GG2bAvS3ptNjjlAgTWYFKaAgUFNWuD3KAaDgcFBNw888gxOm57puX3opRiS3prKeJIjPs6ugoXzb6Wj243VmkF1ZRnBUIgXX9nAkZY2jEYD82c3MH1S2TjhytexPvU6ICm4hEJhbrpuBTv3HKCl9RgWi5mz5s1i4fxZqf0OHW7hLw89lRJQ9ZLC4toI9ZUOZhaMD1IHUmWDEyGKItddfRk+v5+29k5yc7LIznRgtWbQerSd519eNy7Y/N3Yt/8wWU4Hvf2u1FggGKKmuvwd9kp29PvF7+7jSHMbNlsGudmZ7D94mMMtbdx03RXvuO+q519NiU6KouD1+li7/k00ksRnb7z6XYWrsRgMBirLS8/4+ROh02nTssTeC1qtljs+dz3dPf30DyTfwxtv/0aqAUAkEmHN2je4suuLvPTMXz7QOlVU3glVeFJRUVFRUVFRUfnUYbdZue2Wa/nTfY+muXJmTJvMZRedeY7KmfCv/3wXX/2X/8A/xmVVWJDLV7/42fc0j2twiJ/98k/EYifFsmg0xv/9+h7OmjuT/LycD7ROWZbZf/AIvf0D5GZnMW1K3TiHRlVFGT/816+yd18ToVCI2ppKisbkyIwlEomy/+ARfCN+CjICNBQJjIS7aB4WWb6kEtm1mQtqunAHko4eT8jA5OxewqYEQ4qW/lNMaQebmjl7wWxuWLmcTKeD7Tv3IscCLKkXqCyuSWUFjQoppzp76hfcyMrMAV5/ayuDQx6KCvO4cH4xllgTQ4dPtpRXEnHC3qME+ncTSDg5ejxZTpdQYDASwu3xMn1qPXk1S5F0FvbuO8Szz7+Kd3iYnt5+ItEolWUlOJ12zjrvIibNnJQSwfLEI/RqzCnRadQNFJfj3HT9lTyz+hVCoeRFvclkJBqN88KapFCUZ/GTyPFSP3k6lVNWjCuds0ojqRKocDjCb/7wQEosAOhoaya6wEB5oWOccOVpWY2jegWdPR4efep5XINDABQXFXDXHTeP61inKArPrH4lzbUXkTVsO6ojP2skJeqODVIHzqjDcf/AIFUV48WWnXv2pwlPI/4Abe2dmE1GKstLxwk6I/4Axzq6KC8rJjc3m+HhEbRaDU6HjZ6e0+c77di1jz/f/1gqH8rjHcblGmLKpBp27t7H4oVzT1s6F4/H2XfwMJDMjDt0uJXACZHm/kefYcjt4a47bk5zjX0aKCzIpbAgl29890cp0WksB5qa2bV3P7Ma/vbRGq+9/jZPr1qDb2SEKZNr+cLnrif7lPJPlb8/VOFJRUVFRUVFRUXlU8ltN61k1vTJPL36FcLhCIsXzuXSC89FFD/c/jlTJtXw7KN/4IlnXqSru48p9dVctfzC91ya8txLr6WJTqPEZZnnXnyNL91+4/teoz8Q5Pd/fpDevpM5RZlOBzd/5koK83PTWtmbjEbOmjfzXefcuWc/rsEhJtdVEwl6EbSDFNkEigsjaA0moqF+cjPNSGKYvT1Q5zyO1ahBURx4B8eXFel0yTVoNBouvfBcLjp3Dp6W1YQDbtqOu9nSrKHaOUBhbgYlkRDSmHDqQDDEgZd+x9Y2K8aMTFZecTGzZ05DjvoZOtxByJV0UBmcNYCAHBlGScj4vAMcHCigJstLodUPCgwGoadvAFPGeiLWBalsS1EUqSgrJh6XKcjL4dvfuDMl3I0KRFk2HTPi491ABfm5zJ4xlWmT62g52o5GI3GkpY0Nb2xOvYZRB9XRcIJvnp38d0SW2NWdSV97L5HGvcyfKzBtch17tryId6iXsZdruZYAvoEu5OIlZFWPF66GXS388S9r09xsXcd7+NNfHuXfvv3ltO/FgGuIIbcn7fzopTj12W68w0bKSopoPnqM+mx36jVaMzIY8Qf40c9+iyiKzJoxhWXnLBwnbsZjE5egxuNxFEVBEATWb9zEmtfeSAlf2VmZfOHWdAFCEIRUWabJaMA0RuwRpYm/4/F4nNUvv8awbyRtPBAM0e8aoiAvh9aj7acVnhTlZGlte2d3SnQKRyIM+0Z44tmX2LFnP5+/+VqWnbvwPbmfPgm0tLafdtvmrbv/5sLTPQ88zj1/ffzkeo52sOr5Vzlr/ix0Wi2T66tZcen5qZB8lb8fVOFJRUVFRUVFRUXlU8v0qZOYPnXS3/w4dpuVO2+74QPNEQmPb28/SjR6+m1nwpq1G9NEp+M9fWzf1cjWHXuYNqWecxfN54Kli97TnMc6uoCkAGAwO2jzZ9CQ4UIvxRk88hI2SzJHySLqmZJ3nERCIRSTWLtfwGpLoNePvrYYPX0DGAx6/vLQk5y9YA41VeVEfV3EwsPsPtDOplYzEVnA7bMxK9qPFOkmv6gGva2EWMZMdr74e7RChArLEHt7/Dz8RDeBUIglC+dhypmWEp6UeAQ55kfS24gnJIYCXoyaOCgk/xOgc9iKzhxHjvho3/UIWiFGZMxlkUYjMTA4hGvQnXKhjWYrFXkH8Hh9yLJMk8uJPwyKEuPSE93KdDptqmzxxVc2jHtP+/wW8HsY9o2g1+n49d33j3E1tXDocAtXL6sF724a8n0p0UcvxcnPCKIoCv5ENgUnyupGxaeor4sdR0bSRKdR3B4vhw63MmVSTWrMYNCnRB04GaRu0MRRJBPVC25Db36O3u5jOBxRlKz57NrfxrYxne1eee0Nuo73csfnrk873pRJtRN2+JsyqQZBEGg52p5ygY3iGhzigUef5VtfvSM1ZjGbqK4qp7mlbdxcM6dPGTcGSUHR7w+k3HNjGR4eoSAvhwzL6TPVtFot9bVVHDrcksqoikZjDA+PYDYnxdTunj5eenUDiqK85+/Ux01xcT77Dx2ZcNuM6X/b39FwOMzDjz+XNuYd9hGLxdi3/zAN0+pp3N9E1/FevvONL6LXq5lTf098uLeDVFRUVFRUVFRUVFQm5MJliyZ0SAiCwIXLPtgF7L4Dh1P/7ut30dnVQzye7AYWCoV4ee3rvLlp+xnN1dHVzZrXNtJ1vIdI5KQgNprHBKDT6RAkLXLW+Rxt70YUBDRScnuGLYueEyJYNBZj/6EjiKJAOBxh34HD/OHeh9m2cy/GrHr6Y6UnRCdN6hjHfRaGfAohHDiqV/DG9lZ2HXciiQnMuigOYzI8/rUNb+Pr3oGgCDhqrkRvLUWQtEhaC5aCBeTN/hot7hxiCQmtlKDbZ2FbVx6BqJaAYRqS3koiNoLDGEYvxcmzpOcX+QPB1L9HQ8FNRgPTptSSmemg2HScvt5OIpEIz724lsPNR9P2NxknDpOWJAm9Tse2XXvTSulGeXVTC4LGjEGTFIOs+khKFHKHjFjz0l0pks6CMaueEf/pA/f9p4Tx26wZ1NVUph6PBqmH4xryp6xEZ8mjfPZN1E+axoxJpRQ6YHDQPW7eg03NdB7vSRu7cNlicnOy08bsNivLL0nmh+6YoBMiwPHu3jTxFOC6qy6lrkhEL510Uc2cPoW5DdWEBsd3m7SYTQBkOu1pLj8ArVaDxWJm2pS6CY8/ylXLL8Rht5FUKiEYDKHVajCbknOfGOaNTdtO23hgIjq6uvndnx7kN3+4n6PHOs54vw+Tf/nanSnn4VjKS4s5e8Gcv+mx9zc1EwqdbPwQi8VSDlC315sad3u8p+2mqfLpRXU8qaioqKioqKioqHwEVFeWs/LKS3jy2ZfSxpdfuoy6mqoPNLconhS0xgYxjx1/c/P2dw1ef/b5V1ICVSQSZe/+JirLS8jKdKRKsWy2DExGA4ocw9P8JJKQQFaS97Prs91E4tnoSwq59qrL2LVnP4oC+jEXu4qi8PKrrzNn5jSOe41pGUIAncM2orKEfeo0qnQWevoGcBjDKAoIwsmudk6ph8GDjyCKArqMQhQ5ipjQE/X3EPF1YHBWUTV5Pof3beLIoAOdJANQl+NlWrEfa+kVGFwatM3bU8IOJF1JoijR3HKM9Rs34bTpmVU4iEWvIOmtFNRcSdPRX5Jl03HNWRr29hpwDQ5x3wNP8O1v3JkqF1swdwZHWo4Sj8uIopg6Fw3TJmEw6GlvPz7hOfCHEhiKLiLa+wAGTZQZ+SdL+wLmueQVlKAoCoebj+Id9lFWUkR+Xg5VFaW8tuGtcfMJgjBh5tIN167g/oef5uixDvr8FjSSxMz5y5g0ORmaP9ZNtXNX32k/Mz29/WmlaxkWM9/66u3s2XeInt5+srMymdUwJdXRLho5vbsvcorzz5To44qFmbhHYrilBsoqasjPSgbPR4Y7scWDWPJOBqbbzBrm1dvY1jTMpLoqWo62pzKNptTXcudtN7yrkyYr08n/+9ZdiKLE3gkEkMxMOwCBQJBQOHxSkHoH7n/kaf543yMkEknV6pEnV3Pz9Vfy5Ttvedd9P0wqyoq593f/zfd//As6u7rRaDTMbJjCH3/5k7/5sXOyMtNcdnH5pGinP6VsecA19Ddfj8pHiyo8qaioqKioqKioqHxEfOurd7Bk0TxeeuV1lESCSy86j7nvozveqcxsmMrrbybzhMaW7WVlOlP/Hvb63nGOo20daa4ovV5HfU0lnd095GWamVEwRGFOBlW1k7GWLMLVeA9iIozNIPNWewGVTl/KpbO3N5vcE13q9BM4LIZ9Iwy5PeRmJ0Ov8yx+PCFDSoTq81vIzswkNNhEVqaDgz0GRs1iDfkumlxOypwhBCTioWES8TCiqEWQdCiKjCBI+Ht3sqBMS4Gg51B7AJt2BL3RQmV5PWZdAm/byxRmhFlcOYTbD+6QEU/IQCwWJxYP89rrb6XWphv0Ul07mbqFK+hp38fmZg2TcjQYNHEcxjCekAGHwc+W7XtSzh6LxUQoHKFx/yFAICc7k4vPP4drVlwMgM2WcdpzUVZRhc1yK9277mHYH0cRJMSs+dxwxbUMDfTw9BP303T8pAto9sxp3LByOVMm1XLglFKqhqmTONjUgkajYfrU+pQrKMNi5itf/Cy9fQP4RvwUF+WPc2mNuqmyMiMTnieATJue0GATxqz61JhWqz3t53pSfTV79x8aN27NyBiXvaSzFqPRW8nER46+C6u1JiU6xUODBAf2YXTWJjsTRv14WlazaJII2NjWBA1T6xFFkfPPW8TF5y857ft9Klqtlq/d9TnuvudhwpEIrhNuL4vFTHFhco2ZTsdpXW1j6e0b4E9/eSwlOkFSfH3o8VVcuGwR1ZXv3KHvw2bJ2fN485UnPtJjApQWFzJ1cg37DiQ/n9oxGW6VFemh9QUfsNGCyiePT4zw1HjgCIea2ygvKWT+7NO3i3x1w2b6XYOpx9Mm1dAw9Z3tkioqKioqKioqKiqfFObMmMacGe+vPfrpuGjZYrq6e2g92k6GxYJ32EeGxZx2IV9WVpy2TyQSJRyJYLMmBZDR7Jex4oLVamGKtYbrLqklT2rF6ChLuWB0lgJ0/gDewQAaUWFvb3bKOZRnT+CwW7FmTJyno9FokCQNgWAQs9JPacYIJTYTjX05RGQNtRX5WCM78XX6WNwwhf5jMRQF7MakqDYjf4DigmzAT6r2SRRR5AiC1og+o4SIuxmNMYuKyjqmnXse3rbXEOQAgsaAIkcJuQ6gyFGys5xI9mxGXHoacgoJRmD/weR7MVriddhlp8kXp3xKJ8Get5mU4+bQgBOzLoYnZEi97oT/KLCMAdcQf7rvUYwGPbNnTCUQDKHX6TCbjKmOaAvmzuTtLTvTOssB1NdW4cjQEWvbRFmOjkSOCb2tFL1Fg0b2seu135KjGcRjcaQCy3fu3kdFWQm33bySHbv3se/AYTQaiWg0xt79h1KlS8+98Cq33Hg1UyfVpo6Xn5eTyrJq7zzO2vVv0Xm8B6fDzrmL5jNj+mRmTJvMrrefJ1/vSQtVryzJwRHbg68zKWqOFZ/gZFi3JJ0UGWY1TGFP40GajrSmfR5WXnXJuMYApwaoj3Y51Fry0FryUOJhPC2r0zr8GcxOrr1hBRcG4vgDQQrycsYFoJ8J1gwL3/76F5g7exr33v8EWq0Wu93KqIfwomVLzihc/MVXNyDL8rhxRVF44eX1fPMrt7/ntX1a+flPvsd3vv8zGg8cQqPRYLMlxca8MaWZuTnZNEz72+f2qXy0fCKEp2dfWMd9j6xi8YJZPL16LRctPZs7brl6wueuXrOBLKeDvJzkHZRIdHxnEBUVFRUVFRUVFZV/JPR6HV/+wi0ca+9iz75DvLLujbQuYFqNhksvOBdICk6rXniVXXv2E4vHyc3JZsWlyxBFkTyLn9qsdHFBL8XJEAYQRRFDZl3KBQNgrb6KPc8+SZ8/2UVsT08W8WAP3miU//zZb4Fk2UxOdnq79OrKcn71+7/gGxkBxYrb58agdTOzSMKYv5i5FRHkiA9Jb6W4cgYrrnLSsu1+RiJDZFuiGKxFOExhdJZSlEQBgqRDFLVEhtvRmnIBBY0xC72tBMeJDnDZ9VelBAw5FkSRowiSDr2tjOrcGRS69iPp4zz1djLbaWzg9pFBB/1DAUbiFjLsOZh0rUzKcdPkcqaeE45rKCtOhl5v2rqTTIP3RFmgJiXuHTzYyEBHCTmls8nIMDN7xlTWv7EZQRCwmE1Mn1LP1ZcvwdOyGgEBUWdBZ8omHuwnikz39l8Q9g8RjmtTJYej7Nq7n7PmzWTe7AbmzW7geE8fP//1n9OeE4vHeeSJ1fzH//v6uJKzru5efv+nB4mdEML8/gAPPPoM4UiEBXNnsvIzt7Hv9T8ieAeYWTiIJucsFlTHU+dJZz0pbEYiUV5Ys57tuxqJRqNUV5Wz4tLzKSrIQ5IkvnDrZzjQ1ExzSxsmk5G5s6aT6XQAEBpsQmctRhoToG4pmM9Q05NoDHYA7BUXI+ks4wQpSW9NnW+njnHd0XwjftZv3MTh5jZMRgPz585g3uyGib5SAIiiyHmLz6K2qpJ1r79N5/EeHA4b55w9Py2s/Z0QhdPHKgvip6sr3gfFbrPyp9/8hN6+AYbcXqoqSnj9ra3s2nOAWCzG1Ml1XLhs8fsSClU+2XzsZ1SWE9z/+Gp+/L2vMGNqHT19A9zype9xw9UXYzZPXC97xSXnMvNvnLqvoqKioqKioqLy90sikeDp1a+wYeNmBBEuOG8RV15+4ce9rA9MeVkx5WXFLJg7g41vb6O3b4C83GyWLJxHYUEuAE888yK7Gw+k9ukfcHHfg09yw8oVbAoZUiHeoyVt0wtHsBoyEQQTRmct3mEfR1ra0Ot0VOa4WXn9bdQ2dXC4pY3WtnYGwxFqCqDvRFa3KIrEYnG0Wg0ajYZZM6bidnuSohOAaKQzXE2Dw0VZoYOyYjfI6SJCTd1kyoq+Rd/OX5GI+gE/yBAL9GNwVIGSdJRoMwqJjXQj6e1oDHaspUtTAgaA3lpKoG83MX9P6vmCqCHkaUHQGJAjPmrs3fTqFeqz3SlByRMyIIoiFlsWhsxryO/uo7vzaFr+Ur8yhatnJTO0YsMtEwp4DXkufB0b8XiGue+ZHUSjUcwmI5FIlIXzZ3H1iosJDTYhR3xoTJk466/D17EuJaolYhFASc05loScHnS9d9/4cjZIdhc70tpGda6SJvBs2LgZUQmTZwmnnFQAa9e/xfw5M8jOLeKcq76Lu/k55OgIAiOQSD9Pozz8xHPsP3gy8L6l9Ri///NDfPebX8RmzUAQBKZOqk05r4Z9I+zcvQ9joo9MoRWNwZaaM+rvZ6Dx3hPnHTQGO76O9TiqV2AtXZoSnYC08+0d9rF2/Vscbj6KXq9j+tR6tu9sxO3xpp5/rKML16Cbyy46b8L3apTCglw+e+PExoh345ILzuHeB54Y53oSRSFVlnkmHDjUTCwWY/qJ8sFPM2Nddheffw4Xn3/Ox7sglb85H7vwNDjkYWQkSMOU5I9OQV4O2VkOjnX2MKV+4pDFzTsaaevoZlJtBZNqKyd8joqKioqKioqKisrp+MZ3f8yW7btTj3ftOcCW7Xv4nx99933PGY/H2d14EIvZxKS66g9jmcRiMbbv2kdzaxsmo5H5c2dQWlz4rvvl5+XwmWsuHzfuG/FPmK8jyzJHj3WwYtl0Blt7sOmTmT5zioeorSolHuxDZ85n61treGbdERKJBHkWP5PzfNRNms6MOTcza8ZUfvTT/2FhxUhaUHdWpoOK8hJuvHYFZpMJSRL59vf/O+34ox3zHBnDFBXk0dfvYv+AiLT/NRbOm0VpoRNfxzo0pmxichStpYDoSBcJOUzYfQRj9hSsxYsZaLwXOeonFnShMTrxtq0hs24lIfcR/L07CQ81ocgxBEEkkYiTiAXRGBwo8XCyBE8QKXKSJiiNijyzZkxOBUk3LPkcGdvvZ2BgkLgs48hdxpVnL0t1DHPm1eLyHExlXjW5nNRnuzHpExgzMvnLS7vSsrj0eh1vbd5Bw9RJVFYk3WSjotCouKK3laGJ+ulTLERkz7hzOHVy7bix0yEE2/F1HksTjQb6j48LWQfweIcJhcOYjEYknQVb2bLTij0ArsGhNNFplFAoxNYde7hw6eK08fUbN/Hy2o3IsoxeijOvbJiGSaXQshpzwQJcjfeQiPoRdRYy66/F37MVOeJj6PBT444xKkhFZQ2//eMD9PQNEI/HMRkN7NpzgFA4TGV5ep7Qxre2cs6i+ansqw+b/Lwc7rr9Ru6+95GU+CSKArfetJLK8vGh76fSuP8QP/jJr1Id/5xOO9/9xhc5Z9H8v8l6/16Ix+P84b5H2PjmVuKyzNzZ0/nKFz6L1Tpx+a/K35aPXXjy+kbIyDCl1cdmWMx4h0cmfP4F555FT+8AR4918ZeHV3HFpefxhc9eM+55Tz//Gs+88BpAKjlfRUVFRUVFRUVFZeuOPWmi0ygb39rKvgOH37Xd+kS8sGY9v777r/hGkq6MosI8/uvfv01tdcX7XmcsFuP3f36I9s6T3c+27tjD9ddc/o7lQaM0tx7jSEsbFrOJWTOmYs2wMOwbOW0LeI93mKsvW06fw4e7v42cuBtrVjnxQC8aSwFBWc/z6/eTSCQvITwhA/7wCM2H92OzrsJSfA7V1vY0l9AoXq+PSCRKIpHAYbchSVJattFoxzxFkdh/8AihUBh9fIC9rdns3nuAGy+bTpHBTTzoQm8tRZC0iHoHI65mAmGR/QdbqCs5TkleNpIiI8b1KHKMkOsAfYF+ov4eEtERBEmPqDGgMWYR9rQSdjcjiBq05lwSUT9yLIBeGaaqxEl7r4+mXieZ5igFZXVcc8UlAMhRP962NVgNCs6a5E1wSe9FK8aAZOe2+QsW8ptdjZSbjmLQxFlY0k1ElsgtqCJgnMuQ7xn0UjKYfKy7aO/+Q1RWlKZKGeWoH1/HegAEUYPGYOf8Bom+IT1uXwRZTuAacpPpsJOd5URRlNR1VcO0Sax7/e1x59lgMFBVP49Ax1CyTO1ERtKM/AFGvOPPXYbFgkGvT63HfXQtXu8wkiSRkWFJiT2j4tPg0HhRbJRTtx1r7+KFNetTjyOyhm3tNkSpi/kzjLibHk+JTnmzv47OkovWnMvQ4acIuQ6gMWait5WmZTx5Wlazpzebt7fsxOMdBpJh4aIoEI1GKSrISyszjMfj9PUPUFVRdtp1f1Bu/sxVLDv3bJ5/eR2yLHPpReedkYAcjUb51vf+i2HfyWtjt9vLv/3o/1j16B9THRRVxvPVb/8HO3fvTz1e/eJr7Nl7kMfv/41ayvcx8LG/49YMC35/MG0sEAhhO40SecUlJ22Q1115Ebfc9f+47cYrxn14rll+PtcsPx9I/pgsuvRzH+7CVVRUVFRUVFRUPpW8vWXnabe9sWnbexaejh7r4Kc/vzutlOZ4dx/f/Ncf88KT976nsph4PM6zL6xl05adeH0+4rE42VknO9MpisLql15j5vTJaLXju8WNPufBR59NBUoDvLx2I7d/9jrKS4sxGo2EQqFx+5WVFiWzkKovQR7+FdGQgm+gGUkSEaRBmkbKiMj9qedHZE0qUNwz1IsSexGnVYvbR1op2LDPT0/vAP/zqz8BUFJcSHlZMS2tx4D0LCVZsLC9TUd9djytQ97LbzRx+/k6dOZ8NKZMrKVLeX31rxHCGqKyyLA/wkCvC4/XxrKr/5l4oIfgwD5CrgNEfV0k5CiKkkCjt2Iru5CwtxVNcBA5Okws0I8lbzaBgb3Egy4ESYfDZCYzu4BJ1WG0Oi0mq4IY9xLo7SbgaiTkSpYqWosX0dm6i8GBvUT3HcJWeRmzZ8/DYjZx1xe/wFuvryFjZD1WfQKjyUT5wmvpHBLTXjOQJj6NMtqlbTRDaVRcMeHjC5fls73TyWOrXkOv06LRSNz34JPUVFfwhc9dj0ajoaggj0svPI+X176euhGv1Wi46borMFoc6MaEdg8ceoosu57+AZF9/ellfOcuno8oishRPwfe/DPtbUcIRkSaXE5mlvQwra4YWlanxKf8vBxEUZxQ4CwqyEt7vHPP/nHPicgadneYmFIdIMOSzH1y1l+PzpIsFZV0FszZ04n5e9HbSlPHHRtCvmnj9pToBEkR1+8PoNfrCIZCacKTIAg47LZx6/iwyc/L4c7bbnhP+7z06utpotMo0WiMJ555iS/fecuHtby/Kxr3H0oTnUbpPN7DqhfXsvKEiKzy0fGxC0/ZWQ4Mej0HD7cyua4K15CH3n4XpcUF77qvTpdcvmpoUlFRUVFRUVFROVPeqY293Xr6bafj8WdenLBrlWvQzRtvbePcJQvOaJ54PM4dX/l/HGxqBpIZNZFIlPraKupqTjqngsEQx7v7KD+lS90oexoPpolOkLzwfuyp5/mXO87nonPnsOrlN1Pb9FKc0hwNC+fPRo76GW5/jeOuMEpoGE9Yj8MQIeqRsBj2o5cSaaLEaIlcwwmRoaS4gC1vBlLPicbitLQeo77uZIRGZ1c3VmsGJcWFdHZ14zCGMWji2DPzaR0pwxc5ntYhz2EMQyJMOJqH1ZGJo3oFXb1eNhzU0pBvwqCJE5Uljgw68IQMFHd4mT5lFjpLIYnICHLMj6S1IMeDSBoTEU8zAmDKnY4ix4gMtxN07SPm7wVAa8lHURJEh9swWUuIhVxEA9C381cIoo54aBBRY8CYPYV1uwbZun2Ihnw/Bk2cXa+uYv/hLj5/y7VkGEUWVMeJh6YSGW5HkaMMNN5L4ZRbmVvqRUwk3UVDAT0DriG8wz6qK8vp6u6luDCfkKed3u52vH6ZmL2G2SXp4spQbz+5p4S2N7e0sWnrLpacPQ+AOTOnEY5E6Ozqoaa6nLPmzUyVC0o6Cxkl57F3/e/o63eRSCjsbDfjCQWxWa1kZTpYcvY8Fi9M5lb1tO+jrbWJcOxk+eGWNgnoYv5MI1FfF8aseuw2K2fNm8XbW3akrS3T6WDurOnpn8tTOvqNfh7rs90klKQYpDE4CPRsQWfOSbmqzPkzEbXGcSHkjuoVDHYf4kD7eNHBaDQQCoVT7q1Rpk6uTQWbp9YVi7FtZyPNrW0YjUYWzJlBWWnRuDn/1gy6T+8eGyusqaSzq/HgabcdOHhEFZ4+Bj524UkjSXzm6ov5t5/8lvPPWcCmbXtZfvG5WDPMALy6YROV5SVUlRcTCod5avVaAPz+IK9t3MJVly1Dq/3YX4aKioqKioqKisqnhKuXX8RDj60iHI6kjZtMRq68/KL3PN/waSIiAFzvcOF4KqteXJsSnYCUU+pISxsVZSWpm67AaZvwAOw/eGTCcYPcQ+/hV5iclYfzpuVs2n6QSHCYOSVuivNsSKEOoiE43nGEWHAQb8hAQhEZDBqwG33EgiIOozHNnaOX4kzN92GzJm8aO+02bjzfyuY2a7ILnD/ApPoqTEZj2lp8vhHOP/ds8nOzCUeiTJ+RTWn1LHqeWw8cT7mpRkvRBEHAUrwYR36yq15375Fxzxmlp7efKTXF+DrWI0haNFJSVNBoHCjyyVwle8XFhD2thFz7CUeSF/GmnGlYy85P5grFgoSH29FlFBEd7kwGmIsSos6CMbOORNZS3nz0ARQlfR19Bw/TfPggTnlvMiTc6MReeUkqIHuw8U9Mqc6j8XAPu7sd7Dl0DJ/PT2FBLi1Hj/GL397LVcsvYuuOPci+YTwhAxF5E6+9uZsv3X4jhdUrGOo9TOPRlyc8z/sOHGbJ2fN4a/MOVr3wasp5dKyji0yHnZkNyc57ctTPwU0P09M7kNp3bkWEvb1WlpyzmEsuOCdt3t0tQY64HCfWk/wsJsWnDKbMrSPvRHkgwNUrLiI728n2nY2EwxHqaio5/7yzMRrTO/FNnVzL9p170z5PDfkuMoxgd+ZjLz8/rYRubEmfcczxRpF0FsKaQnJyMunp6ycePykIS5LElEm11NVU0tHVjVajYfbMaVxx2QVpc0xU4rp9515WXnkpZ82bOeF7PkoikeDosQ4cDjtZp4hZ74dFC2Zz7/1PTBgdc9b8d17LRDy56mWeXb0Gr2+Emspy7rrjRupqJs5V/jRTXJh/2m0F+bkf4UpURvlEKDY3X3c5ZaWFHG4+xuduWMHSxfNS2wLBcKr+W1FIleVZLCb+7Z/vZFaD2t1ORUVFRUVFRUXlzHE67Pz4+//MT3/+e9yepOCQ6XTww3/9KhbLew8YnjNrGhvf2jpuXJIkFi2YfcbzbN6anjtlNOgJhcIkEgn6BgYoKUqKO5XlpeRknz7bRdJIE457QgYkbQZyxIcxvJVFsxtwcgRRFpD0VnTWpINqyO0hENIRjOlSodgAkhiievK59G1LlpnppTgzCweZVF2MzmgH5zwObn6YEU8/VkVPbv1FhKICb7y9bdxajrV38cf7HkmVER5pMXDHZ8tYOG8WO3fvQ1EUIrImJSjV11ZxpFvhyMaXMJmM5OVkAaQ9ByAUjnDo4D7k7hfJMEB+UTllDVfjbVuTKo/T28oQRA3etjUk5CiCpIMTpXiJRIyRrjcQtSYUJY4gCChyFJ21mOhIFwBKLIjRUc3+rsGUIHDqOrqPNWLL9aWFd2dPv4OB3b9HkaNYLHDRtd9i5OXtdPS4qSwrSYkyiqJw970Pk53pQBTHvLZQiGeeW8M3vvx5jJn1wMTCkyiJDA65efb5V9IEi3g8zmNPPU9tTQUGTQJPy2oG+joJxzWp8zxa3rhrx/ZxwlM0Gp2wJDAia4ho091AgiCwZOE8lixMXtcFQyG8Xh9GgyGtzG1KfQ2zZkxl14mSO4cxjEknU1E9hczaKxG1ZkwlFxHoWIMc8aVcVe9EQV4OdpuVyfXVdHR24x0eQZJEsrMy+cKt17Ps3LMJhyNoNNKEWT87du9PE50geU5eWLOe2TOmpgLkT+XJVS9z7/2P4x32IYois2dO46c/+NYHCrOuq6nivCULWL9xc9r49Kl1LF2y8D3N9bs/PciDjz2berzVvYfG/Ye49/f/TXVl+Xte20OPPctLr75OKByhYWo9X77zlk9M5tTSJWfxu7ycVCD7KGaTieuuvuxjWtU/Np8I4Qlg0fyZLJpAtb3qsqWpf5uMBu76/PUf5bJUVFRUVFRUVFT+Dlm8cC5nL5jNzj37EUSBWdOnvO8W5VdedgHPv7SOIy1t48ZHW4afCeZT3CBarRZrhoURfwDdiTynyvJSbv7Mle84z+wZU9m5e9+4cUdWAeayi9n68q+QIz5gF6IoUFxazfTzbkbSWQgNNhGXBYIxXaqcKlX2po1zwVnVzJ4zj0OHW8gQBiix9GHKyMRUejG/uPtRRrwJGvLBoAmwd/sGZOP4rl0er4/efhezxjgPwuEwDz2+ih9896vcsHIFz7+8jhG/H0EQmFRXhXd4hGdWr0k9X1EUFEAc06AoFI7QerSdsswoMf0wPSMaXt4/wPWm4xSOueox584g5Gk5KUQ5axCAsLuZyNARBEmHwVmLRu8g7D5CPDyEEnQhCICioCgJAoMHyTCdvFl+alB43FCCId+KJbMCSWdBjvoJ9GxBby1FjvnRGBxE+zehl4QJHRh9/S6MBj3WjJOiRZ7FT19PB/5AkAyLmeqqcjqPtYxzfM1qmELj/qYJXTKxeJwDh5qZVqZHjvjwR4Rx59lpDGHxu9L2k6N+JhVr2DReX0Wn043rFOf2eOns6sFus7Jr7362bt9DLB5Hr9dz7uL5XLRsCZAUqG6+/koWzJ3BocOtmE1GppbpySyo4+3tB9nwxmaGfSPkZVk4/6xqZr2L6ATJ783lFy/lyWdfYlJddep9KCzI4+wFcwAwGPSn3X80e+xUQqEQXcd7qKwY/5nevH03v/jtPSQSyWMlEgm279zLt7//3/zx1z9+1zW/E//179/mmedfYe26t4jH4yw+ex43Xrv8Pc0RDod5esz3Z5RQOMJ9DzzJf//nd97TfD/8yS9Z89obqce9fQPs2L2Px//6209E1zhRFLn7lz/ihz/5BQcONZNIKFSWl/Cv/3wXdpv1417ePySfGOFJRUVFRUVFRUVF5aNEFMVxmTPvB41Gw32//28efnI1m7fuRKfTsfySZamL6zPlyuUX8drGTWmCgdFooKgwn5/84FtkZFje0ek0Sl1NJVMm1fLqujfRaDVkOR1kZTr47A1X8dBTzzPsMjAj3wdAIqHw3GYv9lo3FeUWjFn1aLLmsre5Ja2cam9vNg01mWTkTSUDUh25QoNN6KzFbNvTzOCQG0gvO1P8Q5QUFdB1vCe1viG3h8L83DTnCyQzrTq7epgzaxrTptSydcdeLMIAIcXG0y9sTD1vVOQZCNqYMW0S+w8dIZFIoNXI1FSV0x/QIQjCiZIwiW1vv8IVZ2dizJ6COXs6R90mWvftpETrxmDQY6udgclegvvwU6ksJr2jgqivC0SJeMgNSgJB0qKzVyKHBom4m8nO1JCbacbrHU7lUQVDEbYd8hKLx3nlNT3Tp9Rz5aWLiBxfSyTood8dxSXXU2rpxo6PEuMA+yU5LTcLQBJFNNJJ51qexU9tloeIrEFMhAAT1604j00v7CERTZZ69vktzJ3dwLzZDWx4Y3Nqv7GlcQBCIgTosZacQ+SInYjckjrPvSMmnMYQk0s0yFF/SjTztKwmU/SxuCGbN/eeFKUEQeCq5RemhBxFUXj6uTVs3rYLRVHoOt6Ld9hHXU0FWq2WSCTCK6+9gd1mZf6cGal5qirK0rrKvblpO6teeDX1uG/Qz0PP70GyVtEw9d3FpwVzZ5KbncXm7bsJBIJUV5Zx1rxZ7yg4jfJOZayn2/b4Uy+kRKex7N1/kN6+gfckQE/E1csv4url770MeJSOrh6CwfENBQCOHut8T3P19g2wdsP4bomDQx4efOzZT0zgeWF+Lvf+7mf4fH7ichynw/5xL+kfGlV4UlFRUVFRUVFRUfmA6HQ6brtpJbfdtPJ9zzFrxhTu/PwN/PWhp4hEkllE2VlOfvaf353QZTERsizz14ef5sChI2RlOhgeGSEWj3Przcl1DfR20pDvTtunPtvNrl3bqTjhWlm4dCWNxx6m63gPiUQC30gAa4aZcy9O78h16HALO3YdIhzZw5DbQyKRQBTFtLIzAZJB14FjHDg6jKAxUViQR3dP3ziX0ChNR1p5+InnyBAGqM3y0OcOEfJnY7Q40rvBDcKCeTNSDrD//sUfIdCGJySmzXmsLwrGAjIrl7L2jZ28uu5FAEpsThQF9N27uXbREeSoH21GIYlYAF/7enTmfCRjFnLUD4qMqDGh1dvRmXIIu48QGTrETZcsZd+u14kG4wSiInuODFJUUIhWo0GWZXY3HkAItbOgRmHPgXZ2djmIyPvRS3GWTo5RXuTEaRqkd+RkB8BwJEJ5WTEm08lcLE/IQDiuoTAng2DnK2hKl4JrI/NmVOP1x8nXzaS0rJpEQuGhx1bRcvQYUU8z5YUaSu3GlKvJpIci3RF8nSGsJedwwfkX0NTag98fAKDfb6bYEWFWXTaeltWpTnpyxIdGb+Wiyy5iKLKOLdt3Y9DrufyS89IEpK079rBp68mukX0DLmKxOG3tXdRWnwzI37R1V9p+pzK2dHWseLbxzS00TK2nu6uNvTtep9NtoLAgl0g0xsFDR4jGYkyqq+ayC8+jorwk9Zl+L8yfO4NNW3eOc4xVlJWQl5s94T5Dp8lySyQU+vpdH1h4+qDk5+ag0WhSETZjyc52TrDH6Wk80DRhMwWApuaj72t9f0s+CQ4sFVV4UlFRUVFR+Zvi9nh5df1bHGk+itFoYP7cGSw+ay7CmPIQFRUVlVFuu2kl1yy/mE1bd2K1Wlgwd+Z7KgHcumMvBw4lw8X1eh05+qRD6qlVL3PVJYtTos2puT42uRE5ehGSzoLRaODrd93KU6teZtULaxGEZL7qPfc/zq03r6SoII9X17/JmrUbU8ft63cx6PYwua6GU3/ecow+pNBBSmbkkVl3Ic3H+rj/gQeZVdiPTR9hT2/SreOw28jOdvIf//UbotEocSkptph1MtW2DtyCjql5vtT6hwJ62juO09HZTUlxIWWZUfRGD+H4ya5reinO7GI3SrAbb39zWlZO57AtKWRZG3Edt2HRxTA4a0BRiAcGCMudgIIgiIgGOyTiRIaP4ay5CkHSEvUdR/A1MntqBZGElh2dTiqrDow7J7ua/Xi8Mt1DjjQX2YGOALb8IpZcuIynn1vD7saDJGIBqosMSFIl8biMJEkIQvL5bk0DS2qTJXKe5lUAaPRWqqckM6S6unv5zR/uJxaLARBIWOns7aU4T6Eh30WzO5urznYgJUKpTK9snYV/+doX2LxtFz29A2RnOTlrdj24NqYdR9JbySi/lLv/8gwdXd0nG0Gte5NwOMKVl18IkNbCXlEgFksKHW6Pl7gsp1xcPt/pA/kVRcHt8QInnV6j53TI7eXI4QPsXX83OjHG8KCD1S8P4B8JMHVyLXq9jp2799F2rJNvf/3OM3I4nUpRQR43XXcFq15cmxLkKitKueUzV512n0n11bQcbR83bjQaqK+tfM9r+LCxWi2cs2ge617flDYuigI3Xf/OpbunMpo1NxFn4shU+cdEFZ5UVFT+IQmFwmzZvpu29i6sGRbOmj+LooK8j3tZKn9nBIJBfvOH+/EOJ0tavMM+Vj3/KkNDHq76AJZ5FRWVv2+sVgsXnxLsfKY0HmiacLzreA96ZYgMI4yENONyfaochrTgZu+wjx2795Gfd9LhMeT2cO/9j/P1f7qN104ptcnKcnK8p48ht4eszJPdvOprq8i0GRnsHiQeGmQIqKy4mBVzRBS/HxTQijKlmTGWX3kh+w4cJho92Xmux2ciyxjDYghTkNGDQZMUo7Z12Dna2UvslQ2p52Y7jJSbJYwnArJHhbXSgky0Bht9vd1ISpj4mEugXEsApzGEO5hFdmEtSjyMxuhEiYcIeVoRJR2ixojOlE0sNISkzSDia8ecN4eQ60CqS17etFuRO3dN+N6HIxF2HgnjdNhSY3kWP+UOH56eRs6/9nIONx8l4BtkfrkPq1HgyGCMPr+exQvnUVlRgsNuo6SogKi/LyUGAVhLl6a6vL224a2U6ATgyMyjO2YjHzdTqvI522lHp9WmBZ4D2KwZXHz+OWlrjpqWjjvOgebjdHR1j3t9b23ewbmLFmC3W4mMOXeCANYMC74RP4pCmoOoqrJswvcquZ+QcsWNOr1GQ89j1mKat9yPTowRjmvo9gi43V4Aunv6Ug4nt8fLrr0HWDh/1mmP807MmjGV6VPr6e7tx2Q0vGto9u23XMebb2/D4/Wljd+wcjkGg+E0e53E5/Pz7AuvMDjkYf7sBs4+a877Wvc78Z/f+wZ6vZ71GzcRDkfIy83mjs9dz1lz31t3vEl11dRWV4zLtNNqtdx03YoPvM4de/ZxqKmV6qqy97y2vzVuj5d773+C3Y0HsFhMrLj0Ai6/eOm776iiCk8qKir/eARDIX599/30D5zMKNi6Yw+33HD1GeUGqKicKVu370mJTmPZtHUXy849Oy00VkVFReXDQHwHN6UpazIl05bz2AvbicjJ50VkDQHzXMob5qREp9BgE7v3dqWV06TK4oaTZVCnluxoJIkp9TVotVq0Gg1anZY5M6dxyQXnohGiGLOnEHIdIOQ6QDzsodA6QsyUxXAilyVl1RQYB9BFd9EdLkodb9SddXw4gyxrNOUUHQwaGPEHKCrMS1ufFHPhN87BqOzHQIDZRUPk5mZRXlmH0VmPwf06DfmuNDdUfkayY7ZsrCSz7ho8LauRIz4UJYEo6dAYM8mefgeBni2IGhNRfw8R33H8PTtAkREkHYoSx9u2hrKiOjZvS3+/AAYULYJgSlurVpQJxzXYpRju5ufoaG1mcU0Ig0YgHNfgCSXFiubWY1y1POkmkqN+fB3r0953X8f6lIjU0TleFEIy0jyUxbLcbEY/GWPFqok43XE6jpknfH4ikaC9q5sGe7KT3PHu3tS20uJCDh5uxmwyoT3RQc5kMnLh0sX09btwe7wU5OeOC3y++PxzuO/BJ9LEUZNOprbETdPh4ZQDyjviT+0zPObfAH396R3N3isajSaVZfZu5OZkcf8ff86f/voYBw81Y7NZuHr5xWckIO/Ys49/+d5/pTKYnnz2JWZMm8zvf/EfE3bde79oNBp++N2v8r1v3UUwGP5AJWi//p8f8P0f/5Ldew8gyzKFBbl87Uu3Ull+ZiXBExEMhrjrmz/g0OGW1FhVRSl3//JHn4hAcJ/Pz+e++C/09Z+8fth34Aitbe18458+/zGu7NOBKjypqKj8w/Hmpu1pohMk/2h6/qXXmD6lTi2BUvnQ6O7pn3BclmX6BwZV4UlFReVDZ8b0yRyeIGelvLQYu92Kfe7F5JXPZfuuRgKBILk5mTgcdoJCDhaSopOvcyPmwBB6KZ4SaFK5SjDhxXCexY9HMnDOueek3DNy1E/UdxQpq57MupUMylFCrgNEvcmuYRk5UyiffCNASvApM7ZgM0SpyxrCoIkTk0VyM4JoJT0NUychR7xMjXrItUTY1S0SkdNFKndskEtu+g59e+5Fa7AiaXTYypYh6SxkuBopdCqAK63M0BsxsXT2+UR9XZgLFuA79ipaUzaCqMFetRzkCI7qFXhaVqMkYoQ9R5PldzoL1rKlDB9dQyjspSQzQWmBk77+gVR3OADzlAr2u/I5crQ3ba1tbitTJhURj/iYltsLCmllggB27WAyY2rMeyRoDOjtVfR17CPY14Jr8K9UzfssToed4VNK2PRSnIYiP2P/shlqeoLM+uvRWU5200ueqy4Gg3qO7XgQOTqCKSOL2nnXEx/YjBzxUaQ/nvpMnIrdlgHAuYsWcOhwaypMPiPDzLzZDUyqq0ZOJCjMz2PerGk8+/wrqc+pKIosOmsOV1x2QepvsCmTarjrjpvZ8MZmXENucJYxpcSDyWRElESaep1EZA3GMW4irTZ9XTnZWePWOUowFOLgoRYSSoJJddVkWCYW1d4L+Xk5/Pu/fu097/fjn/1uXPD3nn0HefDxVR8oM+50aDSaD5x75HTY+f3//Qc+n59QOExuzunf6zPlf39zT5roBNDa1sFPf343//Oj737g+T8oDz72bJroNMrTz73CrTet/ESIY59kVOFJRUXlH46JavAhaZ91DbrV+nSVD42srIkDOwVBIMvpmHCbioqKygdh7qzptLZ1sGNXY2rM6bDzmZUn26/nZGdy0bLFPPLEala/tC5VAjV9aj03XHU+kt5Klm2EhvzuNIEmHNfgi1lYcvY8mo600t3TB8DkHBe5liChuJ4506sBiPr7GWp6HFFKdq7TWYuJhz0oKAhjZBA5GkAOu1PCjj7i47LZIsePJ0UnBNCKCfILyymc/hm8bWsIDIxQaB0C4EB/Vtr6QuhxH34SJTxALOZFtJXh61iPzlZKQo5SlG1AEmMYtAOIJDAa9Eyafxdm0cdw+zqi/h505nwESYuSiDN44EH01mJsZcuwli5lYO+fURIxRF0G2dPvQGfOIerrIuQ6QGToEDdfdimth3z4PFoUwUxebja5WVZKi0XCAQN5mmPJtcoayuoXUjN3Aa6mpzEZjfgDAZpczpSwk2fxM6NYh6dlNUZnfUp0kmWZHW8+w75OifyMIAZNN28f+D2zZ13AsY6u1L6BqJZJOW7Ki/KQ9FbMBQsYPPgwctBF385fkTf76+gsuamuda7+Ljbt7SXTGEwKYPtDmA+8wFe/cB30riMvM06ubYTO9Gz6ZL5WSdKpZjDo+dqXPsfefYfo6OrGYbcxZ9b0NGHn0aeeTxNHE4kEb7y9jdycbM6ad7K0qrqyjOrKstT65EjSNZabnUl9xM3e3mwwGXDYbXi8w+TlnCwLtduszJk5bcLvyL6Dh3n48edSJZ0ajYZrrrh4XNi5oihs27mXXXsPkEgkmD6lnrPmzfxQXUhHWtro7ZvYmbXxza1/E+Hpw8RqtXxo4d2btuyccHzrjj0fyvwflP0Hj0w4HovF2LP3IOcuWfARr+jThSo8qaio/MNhOU0rXFEU0zrIqKh8UM6aO5M3N20nHA6njc+YNhnHmKwPFRWViYlGo+w/dISSokKyTyPk/j0SjUZ5+rk17D90hLycbK6/5vIzchREIlFCoTA3rFzOOWfPp629E6vVwpT6GqQToc6jrN3wNnv2HUwba9zfRHZWJheftwJYTXHeEAZN8qJ41IlzySUXkmExc9N1V/DH+x5BCLWTYw6SaZHJzC8E10bC2vkMNN5LIupH76hCMjjp3/MnQkNNCIKI0VlPLNhPaKiJsPcoeksRtvLzsZYmc4VKCvNx2Kx0eEzoou2EohI7u5w0P7+ZRfMXYgYMHi+2SIQZ+cn1SWKC1iEbF82NIAgmRJ0FSW8n7GlFCtkZbl+HpLdhclZR6UhQFIsR9bZgcpTizJKQDE6i/h7kiI9wLIS1bBn+3u0oiRPlhpIeb9sa5MjwiYc2Aj1b0FWvILNuJUNAzN+LPHyE8kIHUkUpjupk3o2nZTVCxMdnlmTg9ZYSkbXkTl5JptPO1pd/hdt1nGgsjnfYR74+QjhWTjShxWgvo6TcjBzxEXI3YcqeSsjTQmdHOy5PhH5/Nv1+84nOgDGMWV1cf83l7N70Avl6Dwa9SE52NkUllVhLl+HrWIeksyCHhkhE/Qw1PU5m/XX4OtYTj/g4cqyflkEbg0Z9qotcxOPlza37uXTZCsy+Lq7Ky+CJZ1/kcPNRRFFk3uwGrr/m8tRnqLunn5279xGORKirrWTqpNq0cPx4PM6eveMD2AG279ybJjwBY0QnH5LeirV0KYL2NeTEQQRxkD3dWUyfWo/VmkE4HCYajTGlvobLLj5vwmDxYCjEQ4+tIhaLneyWF0+Wt9VUlWMza1JZZ488uZqdu/el9j3a1kHTkVbuvO2GcfO+X0Tx9C7799JUwO8P0t3bR2lxwRllSn0SGQ2iP5X4acY/apzO0//dlp8/cbdDlZOowpOKiso/HGfNm0Xj/vHhq9Mm151WlFL5ZJNIJDh0uJVAMEh1ZRlOh/3jXhIAdruVL995C8+/vI6W1mPo9Xrmz2ng0gvP/biXpqLyieeHP/0VTzz9IqFwGFEUaZg2iYf+/HMslg+3RLV/YJDX39yC2WziwqWL0Ol0H+r87xXvsI/b7vo2x7v7UmPPPP8K//Oj7562/XwsFmPVC2vZsauRWDxOptPB5ZcsY9E7BBRv37n3NOONXHbReVhLl1IV8ZGZ6cDjGcatncFdVyyktLiQtzbv4MVXNhCJRFBkIwlJoKi8CCE6RDTQT+fWXxHwjxCJi7hCBcyxNxMd6UIQRCS9DclgQ9SaCPTvQlESaE05SAYnvo51qXVkWMxUG7W88KaGziENEXkAOgbYu/8Qn7/xcgqmFtK3+TnicpgMg4yckFhcG6YwS4/G6MRWeSmuxntQEnFi/n4UJUEiHiYy0onWlEtipA0h7ifsaQFJjxx2ozVmEQu6EAWBYP8eJL0dOeJFiYdxH36SeDBZZmPOm4Ug6YgMdzJ0+CnM2dOxFi9m+NhalEQcOerHVn5BSsAYFdQEwGG34ai5EklnYefa3zLY33Wiw2A2tVk6BDnA2flBSmbcwLTpM1DiwZTwEnQlO8b1uPxp5Xh9/uR3onHfIW667gpmT6tioOkZxEQIRY6SUbgAX8c65IgPnTkXR/UVeFtfQJR0qQDxuKJny9EMIrImNd8orW0dSLplGLPqGe5rYtg3gsGgRxRFBgaH8I34sWZY2LZzL48//ULKQbdl+26mTKrltptXpkSUeFwmFp9YSAiFI+PGor6ulOg0mmWVWXsloihSWuzmrKUzyauYi06nPe1nfSwHDjWnRKex3fIisobGvbuZmtOLHPExOOROE51GaTrSSnPrMWqqyt/1WN5hH/5AkLycrNO6pKoryyksyJ2wNP/cxe/uoEkkEvznz37LutffJhqNYTToWXHp+XzzK7e/676fNBqmTWLT1vGup2lTPhn5q5+5Zjmvv7mVRCKRNl5dWUZdTdXHtKpPD6rwpKKi8g9HbXUFV6+4mJfXbiQUCiEIApPra7j26ks/7qWpvA/6+l38+a+PpVovC4LAOYsWsOLSZX/zY7sGh3hhzXqaDreiN+iZO2s6F5+/BK325B/ARQV53HX7TSQSifd091JF5R+Z+x58gvsffjr1OJFIsHvvAW6581949pE/fGjH+d2fHuSRJ1enQrR/9fu/8KMf/PMH6qQUDIZ4/NkXOXzkKEUFeXxm5eXv2hFrLH+49+E00QkgHI7w81/fw9MP3z3hPs8+/ypbtu9OPR5ye3jgkaexf+lWykqLJtwnEo1NOB6ORFLh0tFolEQiQU52JjWZMRy5No62dfDM6jWp5wuSia3HdBgMLqbX5DDcf5hh3whyQuT1tiIGg00caz3A8tlOzJn1iJIOJR4GJYGkt6HIMSwFC+jct4re422MhBS0GaXUFmno6mzFqRumk+yT4eZ+C2te28i80mFisTjxeJyRIFQW6igorURRElgK5uPv2YLGlI0cGcZgL0eOB0lEAyixENGRLiSDg4QcQZT0uBrvIWf67SBqMDpqiAX70RjsaPQ2EEWiIz3ERroRJB3G7Clk1q0kPNTMoOsA0ZHjRLzHUBIxNKZsYiPdJOQIfTt/i8FeQSIWIuROv9nl61iPIbMuJTqNCh/7+nJoyHehE6PUFpuS/8/QWVLC1SjHfPlE5PEizSgafQa5k04GpQ8fWwuQJt5I9delzWkrW4YsPAyMF4VGy+TcHi8PPbaKeDyOXqdLOoY8A9x7/+N85xtf5NnnX0UnxlLnCeDAoSPsP3SE6SfEA4NBT3lpcaokcCyTasdfvI8G3uusxUg6C23HOul3DZKXOYe8XDm1/UwZFQ1O7ZbX5HJiCW5HjthIyFG6BoJp+439/LW1d76j8BQMhXj0yec52NSMoihYLGZWXHI+c2ZNXPr37//6Nb75rz9hxB9Ijc2ZNZ2br7/iXV/Pz399Dy+/+nrqcSgc4fFnXsTptPO5G6951/0/SXzra3dwuPkoQ25Pasxus/Kdb9z5Ma7qJNOm1PHdb36JP9z7EB6vD0EQmFJfw3//57c/7qV9KlCFJxUVlX9IFp01h/lzGujpG8CWkYHdrgYCflp56PFVKdEJkpkMr7+5mcryEqZMqqG59RjrNm6iv99Fbm42y85ZeEZ3Kt+NQDDIb/7wACP+ZOhrzB9nwxubGXJ7uHWCTAZVdFJROXMefGzVhON7Gg/idg+/Y8nDmbJjzz4eenxVWov3EX+AH/74l6x59q9nnOMy5Pbw0iuvc/BwC7Ic5+0tu5BlORWS/OwLr/Kb//kh06bUndF828dkM42l83gPvX0D5OflpI0HQyF2TODMUBSFNzdvP63wVF9bye4JSp6m1ZXgbn6O1pZDtHd7aBpwUp/dRaati8mRKDuPjnecRWQNu9qiVOd04vcHQQFvSE+l08dIRE/bgIYmTzEXLFiZLJ1qXoUgaTHYKjDlzeS4K0Dzvt2EYhK9IybKHY3sdOlAUTBo4uSYAxRYgxg0yW5wpuARXH1GIrKWJncx9dluulxRjMajOHIqU86j6HAnIBALDiDp7ShyFEUQEBBIRIbJKJhPyN18ouzsSZREjHh4GI3BjiAmz39CkUGR0Zhz0Ojt2CsuRtJZ0GYUorXkI0dGiPq7EUQtsaALvb2cuPcYoqQnMnIcuX0tktaMxujEWro0WdYWcjPS9TbNA2b6Rowp59JoB7eCCFyUVU97x3EOHmwkK7GPXKcR44nSsflVMZTwMAMBc1rQ94ypFYQGmzBm1SOdIljFw15s5RckS+1OCIuKHEOO+dEYHET63mLujBo27zg07vwunD8LgJ2796e6GaY7hmD9G5shHkgLoR8Vnw4dbk0JTwBXLr+Qu+95OK0M3Ww20zfg4js/+BlGg565sxu44Lyz0Wg0GLPqCYcj3P3HBzh6rCO1T011BZ+/uRK9PulSjMVi7Np7gOaWY5jNRubPmUlhwckAdYDJ9TVoNBoicVLd8gyaODPyB8h2ZJOQowgI2BNNpw3Xf7cQ6UeffJ4Dh07mAfn9AR59ajXZWc4Jv4/Tp07ihSfv5fmX1zHgGmLB/JnMmTGxSHUqr65/c8Lx1S+99qkTngrzc3nm4bt56rk1tBxtp7y0iOuuugyL5ZNTjXDFZeez/JKlNLe2YbNax/0eq5weVXhSUVH5h0Wr1Z5xm1yVTyZ9/a5UuO2p7N57AI1G4s9/fSx1h3PYN0Lr0Xbu+Nz11E9wZ/W9sH1nY0p0Gkvj/iYGXENqSL2KygfANzL+uwVJt0J3X9+HIjytfn5tmug0yrBvhNff2sr55579rnMEQyF+84f7U53Etu1spLdvAINej+1El69gMMT//vrPPHTPL85oXTrtxKV+oiikLrDH4vcHU2LAuNcyPDLhOMBlF51H27FOvMO+1FhVboKlcwvpO/Y27cc9yfBmoMdnAoK0HDmIKI8X7jN0ERaVdROPmYjGFDxhIxn6GLIi0JDvYm9vNoe6ZC4g6fYZRZC0RH0dvPqWh4jPjieUzKZJBmZH8YfjeCP2lOgUjmtQFAWJMGHZSuMJp9CogOAdDpJh86LR24kMt6Oz5BMZOY6oMSLHRkjIkaTLSWtCkWMEBw+gs5aRiI6c6FbXCoIAyFhLzyU4eJBA92YSchSNwYlGb8fXsT6VlyRKemQxiKgxkIgFEDUm5JAbU2Y9sdAgipIg4m1DY8zEWXctOksu1tJl9Gz5KUoiTmVRLh0ntL+xjhp7/mSeff4Vtm3dTEO+i6gmTlu7hqpZKym39lKUOcTiqiGOufzs6sklImuYXiawuGoYX+fG5OfIWoy3bQ3xsBeAmL+HgcZ7yZ5+B4GeLcSDQ0QDvWhMSbGFiI/F1WZisWp27TtKIpHAaDRy8flLmFSXDIwfWwp3qmNImxhOiTPhuCZ1LoGUYDZKSVEB3/3mF9m2cy9Dbi85WZlsfGsrTUdaAYhEIqxd/yaDg25uueEqAF56dUOa6ATQ3NLGK+veZMWly4jFYtx9z8NpTqpNW3dx03VXMLNhysnPqsXM1Ssu4qlVLxORNTS5nMzIH6CkuBCjQY+t/AL8PVtxSj4WVIywu9NEbdYQ8bCPY8MR3j7op2JyD9Om1GEyjs8F9Q77ONjUPG5cURQ2b999WiHYZDKmZWWdCYlEIs0llbYO7+m/+xPRPzDIcy+uJRqLcckF51BZXvqe9v+wMJmMfPbEOf+kIoqiWlr3PlBvv6qoqKiofGqR5cRpt8XlOK+ue3NcLX4ikWDt+rc+8LH7BwZPu23ANfSB51dR+UemsqxkwnGzyThhOc77IRKbuNQMkqVtZ8KOXfvS2tePloiEIxHiJ8r3AJpbj41rMnA6lp23cMLxyXU1E+bXZTrtZJwm96q05PQ3V5wOO9/+xp1ceP4ScrIzWTDZweULHGijxznUo0+JTg35LiqcPnp8JrYdlXAWpedM6aU4c4r6MGoTGEx23uwsZySixxvSYTNEMeuiOIxhsmwGPC2rGezvYtf+Nv74Qi/b9rZyvPMoeeLBk2HWJ4SkcFyD2SBRZPOnxIy9vdkc81jwhM00DTjTnEKeoBaDOIIc9hKPeFHkKJGR4ygoSedRxEdCTpb4CQggiCTiIUKuRuJhN4l4UpRKxEMIggaN3kbEexQQEAQRzYnw8Wign76dvyIeHEJjdJI1+SZ0lgIMmfUIgojWUoCoNZE9/Q4y66/FmDUZnTkfX8c6ov4+hpoeR0nEEUQt08+6Er1en3LU1GZ5KMuKMamumjc3bcdhDJ987T3ZPPniVgzFF2KwJsP2p9dms3KRmduvrOPcGg/ycDOCxoBkcDJ0+ClCrgPEQ4OYC+Yg6iwkon76d/6GiO840UAvOksBOnMumfXXI+mtCHKAq85v4N//39f51te+wL989XYsZhOHm5NCVH1tZeq8jz1PJp3MpMwuHBmatNJBSJa/z6wyI0fTxeQMo8ji6dncsHI5kiTiD4wXUPbsO5j6Tu06xZ2XZ/Gjl+LsaUyOb9vZSM/xY+RZTh4nkUiw6oW144TZBXNn8m/f/jIrLjqby+dlMGP6ZIpOOKP8PVuxli5Do7cyY3IpS2qDhANu+twhNhzUkV9UyuZtu/jDvY+M+/sCwB8ITihoA4ycRlB/vyRzrib+jldVTvwbOhFPPfcyV37mTu578EkeemwVN9z2dX7x23vf83p27NnHXd/8Psuvu4Mvfu3fPjHd6FQ+GajCk4qKiorKp5aC/BwynY4Jt02dVEd37/iwTuC04++F09mrBUEgP0/tbqKi8kH4t29/Gf0EId+fveHqcd3Z3i/nLJo34bher2PJwom3ncqpbdClMSW1clxOGz/T0r3P33wtixbMSZXqARQV5vGf3//mhM+XJIlLLxrfsMBmzWDxwrnveKx9Bw6z/vVNDLiG2H3YxdY9zQz0dZIhDqDXyGkOloGAmd4RM9On1lFeWpyaw2EMk1AkzFk1FMz9Jjkl03ir1UIgpsUb0tHvN9EfyGDB9Hzcrm72HOrgjSNGPEENm1rNHGrtQUyEcBhPCnMRWcNgwIDDaqaivBitVkuTy4lWp2PlYjvTq2xMynGjl5KCQoYuwpR8NzoNJOIhtKYctBmFIAgosQCCqCURCyLpMhC1ZjTGLESNnoQcRUnIKHIUjcGKIGoBATkWwN38HEoshNaSR/b0z2PKnoTOnJ/McIr6UVCwli4j0LMFJSET8/cASWeRIscY6XoDgJzpn0djykSO+E4EjAtIeisGRyXm8D7++c4rWDFXR36WmbzCMm753F309ic/V31+C0cGHSkhJxaP03Ksn8y6lWRNvpHMgnqKc61kSX1JMe0EYU8LIVdSkDFmTyGjYD55s7+OqLOAIiNHhtFZCtAYnTiqV6Cz5OKoXoG15JxkGHqGhabDrfzk53fz4GPP8sf7HuEn//t7rBkWZs2YmnaemlxOiosK0Gm11NVUMCBXpkQnvV7PjZdNQx/cl8ybOiE+jXaq83VuJDTYRG+/a8LPp6Io9PUnb/LEx3yfRsv8GvJdiEpSJD7aejgl3o0Vn0b8/gn/n28za5ia00tJvg2LLTsZ+K63Ikd8+DrWYS5YgEGvp6SoALs1gyGqqaqpx2xKln11He/h4OGWcfPm5WRhOZGJdSrVlWUTjn8Qvnj7DeN+E3U6LXfdfvMZ7e8d9vGr3/81TShXFIXHn3mRxv3jyy5Px9ubd/C1b/0HO3fvp6/fxe7GA3zjuz/m9Te2nPEcKn/fqKV2KioqKiqfWgRB4DMrL+fPf32caDSaGp82pY5ZM6aw8e2tE5bifRht2efMmsbGt7bi8Q6njc9smHJaMUxFReXMaJg2iWceupsf/fx3tB3rxGa1ctvN13DjdVd8aMe49MLzeHXdW2l35UVR4EufvxGr9cw6550qQBfk59LaliwHkjQnLwbnzJp+xsKTKIr83399j5ajx9ixax9FhfnvKiDNnzMDu83K21t2MuwbobK8hHMWzUer1dDcegyH3Tou4Nzt8fLksy+lXBsRWcPu45nAcSqKnMzIT/52jnWwFBXmo5E0FBXm0d55nGAwhLVqKrOWVGKwlfC7+1+kvaOL431eHmwbZmZ9Fjp7JTdeex4106fx6P0HaOzJTnMqNfbmoIkPERBOvufF1mFmFgzgtNlxZDnIzc5i2jQRrUaDIAfxmHMIt3alQqFnF/Zh0CjozU4MmfXJ4HKNCVHUgtZMQo4iagygKIiihoQcQmcpIOpLlmXpbKXEAv3JMHFBIiGHANBa8nHWX4/RUUHI04a76XH01lKigb4TeU3rCHvaiAy3IWrMIIjoMgqTTitvlJDrAJmTb0jLWhIkLdnT78Db+gLxkBshsoHKkkwkfTl6axmC3INWq0kGd4cMeEKGVAleiW0YbeQokm4ylrxZ6CyFuJueRI750VtLETR6lHiYkGs/GmMmWks+mXUrk2HiOgvZ0+/A3fQ4GkPy/1HmoiXsOdBGV3cvuWY/U2ctBqDlaDsvvboBvRQn25I89pDbwwOPPsO3v34nDdMmceBQMwZJZmpuHxmGpMPHaDBww7IMRgxziMgSFWUlaIRoKuTc07I6lXM12qlOZy0mP9c74edaEATycrMAmDq5ll17kl39xpb5nVMfJurvo8zUits/vswPwGQ0jJt7om55juoVeFpWEw+5U13/AsEQoigyvWCEvb2GtEyt3t4Bpk6qTZtXo9Gw/JJlPPbU82nOp7zcHM6aN2vC1/lBWLpkIdafZ/DQo6vo7RugtKSQ225emSqPfDdeee0NYqdxf77wygamT510RvP88S+PpolXALIs86e/Psq5S969O5/K3z+q8KSioqKi8qmmqqKM73/nK+zee4ARf4CaqvJUePjSJWfx4GPPjttn6ZKzPvBxTUYjX/niZ3l57UaajrRi0OuZM2vaGeXCqKh82Bxt7+IPf3mSpuY2CvNzuOu262iYmgyzjsfj/PIPD/H62zuwZlj4p89fx6IFH/4F0IfN1Kl1PPnA7/6mx/jN//6Q9W9s4vU3t2E06rn2ykuorjzz5gNzZk3j9Te3pHKSJtdXM+wbYcQfQHPChVBZXsL3v/uV97y26sry97SWuppK6mpOlkKteW0jGzZuTrWur6+t4pbPXIXxxEX43n2HxpUKRWQNB/vs1FVoybCYGfEHaHIlS9oMBgMXnHc2v/jdffhGkuWFRqOBkf4D9Hlr2PDSi/T2DSCKIvWV+dj0Nnp8Jr5/+43k5iRdoC29pF24jx4zTA7nnD2PLdv3EBwZosg6gt2egcloQJCSzjfF20yUpIOncv516Myr6D1+jBxnEIs5j2ybhMFsR5S0KPEIgqRFa8oh4utCEDUocgyDtZRYsB+tOQ+NwZEMIB/pJnZCgBIkLYocR1BAjnjRW0sJ9CQdG67Ge0hE/ci6DBLREVyN96K3lSBqjUjaDOTYCJIuIxlgnoihJGIIokTY3UpwID383dP8LPGwFyUeQm8rQxA1aE25uJuT/7+anDmLcLaHWFwEAbRiglxLgLocHxbfRgK9RRgya/C2rSHi60CRo2DJR6PRnyjj06AxODA6TooPctRPoGcLktZCPOwBrZX1q37FW82mpNssy0NP80bmX/INduw+MC5Qu89vobdvgJ4TYsukqsITgpKCpLemCUpWYecJMUcH6FKCzqjrC5Id9qK2s+ge8DF7ZvJGztiyVYAZ0yanbuRcfvFSOrq6GRx0p8r8FlYFqChy4mleRWG2hZ7e9DI/gKxMJ2tee4Oe3gGys5ycu2g+FeUl47rlASiigX29dhK9W0goIo7MAkxF5xNufTCVZTV2/tPdxJo7azrZmU627NjDyIif6soyFsydieGUrKsPizkzpp1xGPnfivbO4xOOd3b1fMQrUfmkogpPKioqKiqfejIsZpacPb40ZmbDFGQ5wdoNb+EaHCI7K5MLzluUFjT6QXA67Nz0ITowVFTeLw889jxXXnoe/+8bt7Phre186wf/x0tP/B69TsdTz79GU/Mx/vzLH3Kss5sf/e8feeqv/4fNmvFxL/sTwdIlC1m6ZOJcpXdjVIAe7WpnNOj5j+99g9LiAg42tVBaUsj8OTPefaIPmT2NB3l1XXq3q6YjrTy9eg03X38lAInE+BwavRSnPtuNIBQyZVINQx4vOfkyUdtCZs+Zx/qNm1KiEyRLnmoyPbRuv5+hgSxAmyZYiKLIzj0HOG9WHjprMXm52SmX6Ngw7ezsTM6aN4umI61YhAF0mgQtfTpqqgowKAkU+aQjQzI4kHRmiqdegUWfFDEUOYYhaxLRka5UiZnWUkDE15Eq7xK1ZmLBfnQZhcSCLpREnJi/j0R0BEFvQ2ctIR50kSCGgoLWnEM00EsiHma446eIkh5JbyVr0mcYPPQoiaifyHAn2dNvZ/jYK0RHuhEEEUHSJUv9LHmIGhOB3u1AUjCzV1ycFIuGmogHXUg6C5HhdrQZhXjb16IoMoIgoRNGqCgvJTDQhAL0jZhwWhQynXYEQcDftxN/3w7C7mYi0ShBIRdTVIsu1IwA6G1lyFE/7uZVhDwt2CsuwdexLi1QvKenm1homIb8AIcGnCccRGH2v/EnJM2kcUHhow6sUXdM1NdFPDiEgkLWGMeQq+lZBvu7GFZ2UjF5CYIgjOuwFwyFeWlzmNauh4Bkl7iLzl9Cc8sxDh1pxaDXMXd2AxcuXZQ673able98/U4a9zfR7xoiPzeb+nI7vqMvICcSmM0myhquptG1G+Rk2abDbsM1OMTgkBuAvv4BDjY1c/tnr2NSXXVKfBrl3geewNvTSG1WhHBcw/rDQUrLW4kL08iO78OgGfOZzcp8x06V5WXFlJcVn3b7J4WLzl/Cb//04ISup8svOu+M57HbrBPmW9repQPg34q29i7+/ae/oulwK2azic+svIw7b7vxY1mLShJVeFJRUVFR+btmzqxpzJk1jUQigSimRxs2HWllx+59xGJxJtdXM2fmtA8tP0ZF5aPkP//1n1L/vvLSpfzmz48w7POTk+Xktdc3c8v1yykqyKWoIJeGKXVsfHsnKy4Znwuk8t7JdDpSnbfGMuWUEpyPki2nCfXdu+8QK6+4BINBz7Qpdbz06oZUOdCoYGTSyWTmFOKsvhhNx3qyIj4kfS8mnUJ7R7qrYbTkSUgEmZrbR4s7h/psd3pns0Abvs7DSHoryxbN4khLG1ohmuamOffc5dzzwOMMDroBO0cGk/s29g7zuQs0GPQ69NZSFEEk5uti6PBTqTUk4iHCnqOEh9vQmvNOLk4QTrilFES9DUlrQmvKJRZ0IRkchD2tKPEwgqTF4KjE43bh9wwQjiWIyBbypBDZmQWEhg6jxMMoYrJEzuioIM/0dfp2/go54mPo0CNIOit6RyWCoiCIGtAYsRYvJjiwf9w5SMjRpNglahEkI1pLAbGRbhQ5iqA1ordXIigJcjNtBMUcIpEIzvwC7A4nSjyIqMsgHhoi4u/G7Q3QMaih2zdEXkaQ8swImZkOzLkzCHlaiIcGTwSMDwEC8dBgKttpSwdkCV4MmjhmXSzVGRC/m/qKTo56Twa6O4xharM8KJKJwtykkKCzFqOgIAgiUV8Xxqx6tu1p5uU1nVgkH33+N8nJPsTtn72OTJs+1c1QAQ43HyVTkemSku4h77CPZ1e/wr995yt89sarU+9VJBKlcf9+ItEYk2qrsNutzJ6ZdPbIUT8dux/h2NFmfD4/oihgzxrg21++E/dIBLPJxMtrN44rh08kEry8duO4UrSjbR0cbj4KWFKf7YicLFW99aZrOHIkm7bWPQyFzcyaUc/yS5Z9oL8X4vE4+w8eweMdpqS4gKqKstT6evtdZDpsGAzjSwQ/bOw2K1+761Z++bv7kE+UygmCwMorLznjMjuAKy+/kD/95dFx48svWfahrfVM6eg6zuXXfh6/P5gcGBzipz//Ay1H2/n5T773ka9HJYkqPKmoqKio/ENwquj0yro3eOW1N1KP9x88zN59h7jzthvSQn1VVD5tvLphE7VVZeScKAPp6x+itKggtb24KI++d+jKqPLpJxSauIOeLMtEYzEMBj052ZlcdtF5vLAmKQg4jGGMWpnSilryJq9My7yRIz6ivi5stpMuuUQiQVvvEIcOD7Nsapwsm0xDfj8CQlouVGF5A5LuMHLEh41dfOmWizi662liIQVRZ+fCS6/FYrOfEJ2S9Pkt6KU4NU4XA4NQUpiPHPMTCw6AoAElnnQG2cuJjvQgx/wIsg7oQ++oSglAGmMmJBIIkpY+eRJ9B/YhJfzk23rJMMhIGgPm/NkMKJM52PIITqOIP6KnY9jMW8dirDzbR3ZmHWHvUXTmAgI9W9CZc9BZcsmcdCMDe+4mHnITC7rQ6O3obckW9EoizkDjvejM+WQULSIe8aDEw3iaVyGHPUh6G5LORmbdNYwcfxvRWkrE14HWmIlGb0cOuxFEDQZbMQZFQdKaiIc9CBoDAiBpzXiCOvq8yU5wtVnJzm/HhvT0a2q4rPhsTLkNDAEh14FkKR5CWqB4/PUn2NuTnXLwADS5nMwsdJGV6cTj9bH+oC7ZMTBkIJrQMq02n+GjL6TK6kRJl8pp6uru5alVL6EoCv4T4s2Aa5CHHnmUz57vTOUpuRJVxKI7MWlJK10TlTD7t6/h7GXXAtDa1s59Dz5FKJTM2xJFkUsvPJel5yxEjvrpO/gUB/fvwR8WaHIlBc/EQDc71v6GZSu/h6Sz0NnVPeH34Hh377ibUV3dvWmfv7EMDnm49pprgGtO95V7R6LRGN5hH3abFZ1Oy5Dbw933PJzq2AfJUlmDXs+jTz2Pd9iHTqfl3MUL+OF3v3rG+XDvl2uvvIRFC2bz/MvriESiXHzBkvdU5gvw+Vuuxe8PsOrFtQSDIUwmI8svXsqXbv/oXUY//d+7T4pOY3juxdf4/re/mvY7pvLRoQpPKioqKir/cAz7Rnhtw9vjxg83H+VAU/O4sFAVlU8LW3fu46EnX+BXP/lOaiyhJBirpYqCiDxBG3CAp59/jWdeeA3gtC3BVT751FZX0HV8fLZKQX4u1ozkRbVrMFkWs3jhXFDAbrcyuURDVuEkJJ0F77CPxgNNJKL51JVUYsyqZ9FZRvYdOIyiKBxubktlW+3tcrLMGmV4eAS7zZrKhaqprmDa9Bko8dqUgGVlBzMmlZKQ88msvx6dJZe9+5uAk+V3npAh5YiKxDU4aq7E27aGeGgIORYgEQ+h0VuJeFohIaM156K3VxLxtJGI+pMh4YDWmIUg6ugchCfe7sSqNzIjfwT3iIIix8gsmU1G3hyeenY7x084fgyaOFpRZnZhHz29ZnKynGgMmcTCg4gaAwON92EtXcrgwQdREjKCRk8iMkws0EdCjpA15WY8zatIRP1E6cVWdRlxfw9BV9L9pDE4MNgriY4cZ/DQo+gsBQiSFl1GIeHhY0T9vWhNOUi6DGIjSeFEtJYiaS1E/MeJylEEQcI9HMRhiIIQpdtnoctrYSBgJtHr5bJrQNJZyKxbScBRTejEseNhD7bKS5F0FmbPnMYzXd1pZXT12W4yHXYkUaS2uoLcfJnm4VKMZgezplaSGNgwLqdpNJh7x663J/zNiI104XVHsTvzcFSvoGvn6wDYjcmGIGPPtzawj9DgVLT2au5/5JmU6ARJofOFNeuprion2zBMf08H/rCQEq5Sji3fID3HGimuXYjDbhuXGwVJl8+pN6McDttpv09Oh33C8UQiQTgSwWQ0TrhdURReWfcGG9/aRiQSQa/Xc86iebR3HE8TnQBe2/A2R1raMJmSc0WjMV5d9yaiIPAf3/vGadf2YZGfl8Odt93wgeb42l238k9fuJnefhe52ZnoJuhM+lEwUbdBgFgszltbtnPZRUs/4hWpgCo8qaioqPxd4fX6iMZiZGc5VdfOO9B2rDNlKT+V1qPtqvCk8qnkzc27+MNfn+CXP/k2uTknO5jlZDnp7h2grKQQgO6+AWZNr59wjmuWn881y88HkqUgiy793N983SofPucuns++A4cZcJ10tmk1Gq68/EIANr61ldUvvZYSCkRRZOWVl5BbPhOA3XsP8MiTq1O/k88LApdeKLPs3IXcdN0VPPT4KrzDPkRRoLTAyfkNWoxaCU1Mg9VqYYlTIKP8fCaVZqDEg2kZP0oiTtTfi8aYia9jHUZnPeVFRZj0MDU7Kfx4QzqcxhDukBF71XJ0ljwy61amHDyi1owSDyEICoKkI7fhTqKBXkID+4grcbTm3FSeUszdgjI8QKUjiyxzBAB/VEdCEYj3d2DUi+SJzRzHwd7ebIqsPqblD6GX4gwGBUzZU3E3r0JRZEKeVpREjED/HlASCKKEOWcOI11vk5CjKCE3QwcfRW8tJgZoTNkM7r8fOepDa8pFY7ADEA26CPvaERSI+nvInHQDgwcfQo74EAQRvb0KQdKSOFEKKGj0KIAiR4mHvYgaIygKiCRr1xQYCJiTDjOLDznqTwVmR30dKHIsmVcV9eNqvIe82V/n7AWz6e3pQO5bj0kTRRDAZM6gsmYSpswqQp4WHJYwZ2X6cFQny3KD4dKUgAZgLV2aOk44crKz7Fj6/BbkjAYc1fOQdBZKa2ZzZPfLAAgCBGPalOBnd+ajsxZzpPUYfn9gwvn2NB5ixaXL6AkXsbc3kRKuRsUnhzFMVdRM1mAT5yyaz/2PPD1ujnMWzR83NqW+hqwsZ5rzDpKltFMnp/9NkEgkWPPaG7y9ZSehUIjcnGwuufAcpk9J/119c9P2tKy1SCTCy69uxDU4RF5udtpzW9s6CIUjKeFplA1vbuFf//lL76vsLhwOs2nbLiRR4uwFs//mzilIdvUrLsz/mx/nnXDa7XQd751wW2lJ0Ue8GpVRVOFJRUVF5e8Ar9fHw08+R+vRdgCyspxce+Wlqe5uKulYLObTbjObTB/hSlRUPhzWvbGVex96ll//9DtpohPAOWfP4bFn19AwtY6Orh527D7AN75088e0UpXT8cQzL/LY0y8Qi8ZYdt7ZfPWLn33fGTJmk4lvfvnzbN/VSHvncew2K2fNm0lWppPBIXea6ATJC+mnn1vD5PoaNBqJx55+IU2cVxSFF19Zz+T6ambNmMqg20MkEsWsh1lFQ6lMp6bBHK6otVOYbSEhb8ffKRAezMRaugxfx3qURJzIcDsJOUIiFiQRCxLo243BWcPV8yU6O+LEEiJmXTL7SeeooX7SVFrb2olEYpRXrECfUYy3bQ0ISceKNqMQf89WdNYyRM34i/O4LIOSoMLpIypLyXW6nNRnu9EG40T9PWRZRRryXTS5nJQ6RjBrowRjWjpj0zDlNhDytBDsb0SRwyiJOIl4CAEBU9ZMBEHCYK8gFhpEDntJxINE/T0466/D2/oiEV8HgiBiyKzHUb0Cb9sawr07kuKQwYbOnI//+NugCICApLehzygiNHQIRAklkUCJR9DaSggPNQEJlHgIh9VKjytOTBbRSglyzAGshih1xUY8LatPvOcnA8UlgyP5nkf99O38FTnTb+e8+gj+glqCw91oDTYcOeUYnfX4e7ehCCKJWFL8CbmPEBzYR8h1AI0xKyWg+TrWYyi+kIPNx4lGo0QiUfT6dJeL0WikfNJiJJ0WAGdWPo6aFbiPrMagiTMjfwAAqyOXyrm3IOksEwbfn/ysJj+XzqKZOA41U5vlSSvt9ISNOKK78XVGqC05h2uvuoy169/EO+zDYjEze8ZUhtwefvrzuzEZDcyfO4P5c2YgSRL/dPvNPL16DYdOOGbqa6u4esVF4wSbF1/ZwIY3Nqce9w+4eOCRZ/jyF26horwkNf7Wlh0Tvobeftc44SkSiaQ9VhSFQDDI4JCb7/3n/zFvdgMXnb8k5Vh8N156dQM///W9BILJkjO7zcoPvvMVzj5rzhnt/1Ew6Pbws1/8kR2796EkEsyeMY1vf+NOcnOyPtC8d95+A//0je9zqgGvsqJEvbH4MaIKTyoqKip/B9zzwON09/SlHg8Ournnr4/xvX/5Mnb7x9NR5JNMVUUpOdlZaW4ASDoC5s6a/jGtSkXl/XP3fU8w6PbwmTu+nRr7w/99n9qqMq6/6mKOtLRz4TVfxGTU8+XbP0N2puNjXO1Hw7H2Ll569XW0Wg3LL1lGfl7Ox72k0/JP3/wBL76yIfX4QFMza17byNrnHjyj/ROJBHsaD7Lv4GFEQWRWrYX6aQtZvHBuspSOZBhzaLCJfQc9aaLTaJlVRIYDh5rR67SIiRB5lvC4rJvGA03k5+WQ6bCj1+vIsvhTotPohb8mbyle32ZGPP3otFoynQn6dv4KjSmbeNCF1lJAPDSIxpRNLNCXdBK5DpCl15ExqZb+QS/IURxZBWRUXMZP/vf3uD1e8ix+EPUsn+7DpEsgSDo0phyiwx2IYlLUcNRcRdjTnMpTArDkTGN/+w5EJZa2TlfAQHmBgM6cT0H+CMMjHcwq6MOqjyArAq6AkUWLFyPpLNgrLiE4cAA5GkDSmpLHE0Si/h4ScgSNOReNOZeI5yhKIkpCjjC4/wE0xixAQJAMiNJJQUbUJF0txuwGiAWIh70o8SAGRxWWggXoMwrQmLPwtDxPIhogHvHiLLiW8NBh5MgIihLHaRHw+k24R6J0DxspsAZxZgiUFOYkQ8+bHkdJxNMCxZ211+BqvIdE1M9Q05NoDHb0WgFDVjIo3Fq6DElnJuBqJOw6gKLIWAoWpEQnAK0lD3vFxfg61uPq72L/Gz9k1/FMwnEN7Z3HyciwMLPKlOyAp+i46vIL0em0yFF/KoT8wgsu4nC+g8HDz5JIKDidNqrn34pGn8zeqakqx2g0ppXajTLqKprVMIVNmwoJx0cwaOIp4XBZgw6tEEllT501z8KCuTMIBEOAwi9/95e0Mrf2zuMMuIZYfskyHA4bd3zueqLRGIqijBPRAGKxGJu27prwO/jGpm1pwtPw8PgyP0kSsZjH3+CyWjMIBE7mEg37RlJCXiweZ/O2XbQcbefbX/8CWq123P5j6e7t5yf/8/uk6HoC77CPf/vxL3j56b+Mc1V9HCQSCe78yv9Ly9Z6a8sOWtraefaRP3wgd9alF5zL4S9+jvseeIJAMIQgCNRWV/DAn3/+YSxd5X2iCk8qKioqn3LaO46niU6jxOJxtu3ay4VLF38Mq/pkIwgCX7j1eh58bFUqfNTpsLPyyktUoU7lU8mjf/5vEqfc3jWcuGgyGQ387N+/QVyWkUTxH6IM97d/eoBHnngu5Zy4/5Fn+Mqdt3DDtSs+5pWN50hLGy+9+vr48eY2/vrQU9x688p3neOhx1axZ99BICkktfg8DB/fzvxLvoGksyBH/amMJYN80mmRZ/FTm+UmFNPQ2JeDKAoIiXBax7mx4pN4wmU0fWo9L76ygb4TDcNGS52cDjtvbjtIV/sADmMcT0jDksomqvMlFDmK3lqKxpRJ1uSb8HWsAyARDSBHvChyFItFwFZeiKS3Yqu8nJ/+8q94vMPkWfxMyhkiyxTCP6ygzykib+pnGTr0CIlYkJCnGSM1mHNnYnBcjPvwU8hRPxqDHVNWLUUFHfT2dHNoIJk9VWIbZnaRiyyzHZ21hPL6+Qj8Bd9gC4qcIKw4mVxXQpZ4kKg/D2/bGhQlhiBpELVmdPYyYv7+Ex31mtGE3WgMTlBktJZCQoMHIRFHURLobWXIkWFi/j5c+/6KpLNgzJ6CoDUR7NmOkpCR9DYESUc8NMhw+6tozXkIgoTeUkQ00IvG4MDd9CRRXxeizoyoMaA151Gjc+GPStQH+hHNxWTnl2MvvyD53kZ8xGPBtEDxqK+LrEk34Gl9MeVaspWex3DHBkRJh69jHdbSk/k3giARD/Qjx/wAGLOnkFmXDJ43lV7M/jd+gFaIpgLKa6oqsNDP/CoZnUlH2awbKSgqT/v8QbIjXpbYiqOyLHWskc7X0ZzIi9LptFx/zWU89Ngq4vF46jnnLlqQEnb0eh1f/MLtvP76BpSB18myyFxVpScvNzsteyr5OgQsZhMb3tg8LlsJkiVx5y05KyUIyb5WdNZi4KTwNCqcRTSF49xJowy5vWmPK8tLTnTKS2fZOQsJBEMMDp0s67vsovNY9/rbhCNR4vE4kROlizVV5YgnfrNdg0Ps2XfoXW+QPfv8K2mi0yjBYIjnX17H9ddc/o77n8qBQ8088uRzuAaHqKup4nM3XUOW84PdvFi74a000WmUvn4XL6xZnyoLfr/881du5+t33cruvQfIy8v52Mv/VFThSUVFReVTz/DI+Dtqo4yMTJyRoAJZmU6++eXPM+AaIhqLUZif+w9xQa7y94nBoH/X52g+QOvvTxOHm1t5+PHn0lw9sizzuz89yPnnnU12VuY77P3R88zqV04b5P7qhrffVXg62taREp0gKQKF4xoG+jrpbnycvPrL8HWsT3UVq5u6EPG1w8RicfYcGUIT8mDRKxTpPDiMUWzyEbr0CYKRZDczOOGKChtpmJZsr67Vavni565i/avPsONw8sK+rqaCgvzcEyVImpRgtbUjiwzDENU1pQiSFmvpUnSWXKylyxhqehytKQtRZybqPUbM34NoLcVaupS2rkE83uHUawIQBYV4QqA9Og2bqxFRa0JR4ggI6DKK0VmLGTr8FJHh9hOd3CDsaaG0pByNJJHQeNnXrWF2eYwsuw2tVk8sOICnZTU6eQBnhhZJ50TvrEGUdKkg7VgwWUaX7FxXQnSkG0EUAS1KNH6i256ILqOYkKsRFBnEZKlaPOjCkFWPHPIQDw4Ql3RkTb2VQN8OlISMHB1GIYHWlEPEexRFSaDEoxjsFWhMmdirLmX42Foivi7kyDBacx45DV8k0LMFAH1gP4oUw2ACc9ZkJJ05laelMdhREnH01lKivi6G29cR9fegMycvwhU5xuChR9GYsknIURgTHG5w1qDEIwiSFo3kIKN4MebchpSY09zWx67jThzGZAdFvRQnImuIS5kYMwyU5mWg9D5P1H5d2udPMjhTIpSkt5JRch4tO55gyLWX2L5D2CqXM2v2HKZPqafsO0Vs27GHPY2HUAQYCQQ41t5FeVkxABkWM8svv5yof05q3ZCePTWW493jb9JBMs+ut2+A6soyQoNN+Do3polXY4UzS/Fi7DZrKlh/LCVjuocCXHzBObS1dxGNnsy/0ul0XH3FxRTm57L/UDMej5fS4kIqyku49qpL+NNfHmfvvoMkFIWqijKKCnLT5uzp7Z/wNYzFN+J/h23v7e/CV9a9wX/8129Spbf7DhzhtQ1v8Zc//C+F+bnvsvfpaW45dtptLa3t73vesUiSxJyPycX+3//3B55c9TKRaISZDVP57f98H7vd/rGs5ZOCKjypqKiofMopLy1GkqQJw7Irx1i+Pw1s3bGH3r4B5s5u+EB/0LwXcrLHX4R6h32sXf8Wh5uPYjDomTtrOkvOnqcKUyoqn1sUlVkAAQAASURBVAJeWLNhQiEnLsu89OrrfO7G99cSfSzBYIhnVq+h43gP9bVVrLhk2fsuDXknl6Ut4/R5dKO0tLWnPR7b4csz1It+gg5k1151KT/62W9xDbrpd8FlswQKc/Qc2fJXGqZOorpmMo9s8BKRk6JTXbaXkvJsMm1JgVOO+tF43mLZdAMXX3AOBmcdJqOR3/zh/rS16KU49dluBn0yZQkBnZTMBRoVnQQEBI0BSRCTjp+ID3moCbFtDWFlemoOhzHMpo5CFpYmHao5GYeRI0XoMorQmHKQQ27iEQ+DBx8h7G4mEQ9jyKpH0ppR4mEEjYGC/Hxy7Drm1EPMryBozOhspYTdR0jEwyTiQURdBlpLPoKSQJGjKImk2yYW6EMQtSBpEcSTLhhB1CLqrKAkUBIxIt42EvEogqTF4KwhNtKNIGmJeI8h6SwIkg5FjuJqvDd5HEmHpLcjCCKi1oSkyyAe9qIxOhEkLeaCBfhPCEw6Sz5yZBhFiTPS9QbmvNkE+u5H1JhAA5ais/D3biPgaiQeGkIUdSAIRIbbiQy3k1FyHmFPKygJovSSOekGhg49SiLqJw5kTroR//G3AFAScQRJhyD9f/bOO7CN+u7/r5vasuU94tgZTuIkZCckbAh7NECBUkZpSylt6eTp0z4t7a/t072e7lJWW1ootEDDHiEJhOy94yTOcux4ybJkbZ1u/P44W7GxAwmkjFavv6w73VdfyXfS3fs+n/f7aEvXgca1LH58E109SWpHVlM7ooqMYe/zr/dZ6pUnocXX2Gbm2/6E7CzM7X9atCUnOgXqF/DEs8vYuKGHaZV22+aGl55gV1Mrt9z4QRRFZv2m7bkExo6OLjZu3s6N1y1g1owpuX0x2rxk0H4XbV5CoH4BguymozOI2+WisNBPUVHhsMeQIAgU9aXaqf4aJIffFh2bnsJfO3+QcOYsqOXiC87m0cefGTSGy+Xi3LMGm5bX1lRz52dv5bWV6+js6qa8rISzzzg152E07ZTBZuQTxo3lFz/6Bodb2/i/39w/7FzLSt/c/+jcs+bx1LMvD/s+558z7023H8jv7v3rkPPLcCTK3fc/zPe+eecJjTWQcfXH9iCtH1v3lsd9L/DBmz7N8pVH/b1eenkZs8/ZxOYVz+D1Hp9H178jeeEpT548ed7HhMO9LH1tFdFYnMMtbVSUlxLou4gZM6qWKZMnvMszPD6OtHfyhf/+Dof74r9FUeSSC8/hW//z+Xd8Lul0ht/84cFB5fhPPruIrmCI666+7B2fT548ed5bHDzUwqe+eBfhiF3x8PRzi3no0YXc99sfvaX2k0985Dp+/fs/k0qnBy0XRZFPfvzDb7r9cIEIGcM20J468eip/sAqkPoxdYyqrcHv8yIIAj2CkzGOHgzdoDPYzdRzP8PX5/jZvnMPhhan1tmEImSGvRAvKK1HUm3PGEE8Ks47JD3Xspc2ZLyjLiPd/AxWMkTXlnvIJjoRVQ+qMBJTiyK5S22Dbi1GqruRsmKRgFtnXJE9xp7uABuOVDC9sotAgf07Vzj6EiTVS7jpKTK9zWTj7ViWiSApSIqHwtGX2gbbqR6MbIJsvA3ZXYYgqSieckwthpFNYhlpu4XOW0Vg3NXEWpaRaN+A4q0AC7saSBAQZRepUCPOkgbSPXvANHEV1WNm02Sih7DMLGAhqV4k2YnlLsHIRNGTQSw9TWDcVUSbl2BqcbToYWRXCYp7JHY0HSCIyE77ZohlZAluvQ/VW4WoenEF6hFVL6ngDpJdW4m3rUMQJUTZiaOgDi3agmkaJFtXYZkaiqsUR0GdPZZl0HvwJTCzCJKK6q0i3roC1VuFFm9DcZcSbV6CKKk5A3iwW+sKR1/C9hV/4fDBJqplmaBRStO+gzTtO4imZXMVdgN9luoLO5GFErKGltvnBu5/3spTcRaPo6snyaq1G3FI0B5zoxkSHXEvHdt2ctbpc2jafygnOvVjWRZPP7+E6VMngZEaVD01cN/cs/qPLFydIRi2faLG1Y/msovO5bWV6wZVIAFMmTyB4r5jV1K9OEZcSLL5xVzFGwwWbufOno7X4+G1VeuIRKLU1Y7g/HNOH7aasqK89ITPHUaOqKJ+7Cia9g2uCios8DNz2uQ33f60OTM4fe4sVq7ZMGj5FZfOZ8yo2uOex5H2Tjq7uoddt33n7uMeZzguPO9M7vvTo0Pa7SrKS7nikvnH2Oq9z979B1mxasOQ5b29Mb727Z/xm599+52f1HsE8d2eQJ48efLkOTEivVHu+ePf+Or/+zGf+uJdLFuxDo/bRXlZMZ1d3TgdDq7+wMV86tYb3nIi0jvN1771k5zoBLbp5HMvLuWxJ59/x+eyduOWYT0g1qzfTCQytLQ+T5487y2uuOS8YasTZUnisovOfdvj/+Dnv8uJTv0caevkl7/701saT1VVfv6ju/B63QOWKXz+0x9l5rRT3nT7GdMm4XAMbrV0SDrTRsQJFBbklkWbl2BodgtOdyiMLEuUl5VQU1HAKRVH3086nbHTymSTubOnc/rpZ1J5ynVHq0D2LhxUsTKwpWngfAOudM54POaajVPMIAiinbCmepE95YiSi2TnRrIJuwVKdhYhOwMggJXu5so5Ak7FHiOhKTSU9lBSUkRBgR89HbHT7bBFDdkZQHaXIrtLcBU3YOlpos2L8VTNwzQy6MkggqSCYCfhZROdZOMdYJmIsgtTz5BNdBA9tJhsogvLzGJkYsiuAKKkongqyCY6MI0MWm8Llp4GQcJdNh093Y0gyiDItqF3uod4x2Yy0RayyW5ExYOnchbu0lPw1863K5/MLFq8zW4LNLNkeg9haAmyyQ6yyS60VDemFicTbcEysqSC23GXTcFRMMpu/cv0YmYTFDVch+wqwtTipHv2YOhpsExMPUUq1IjiG2FXL2GheCspnnRTbj8QRJniiTeCICFKKpLDj6d8+qB9SZDdPLfRyolL/a11AFWV5RiCky3tpfZ6RWfBHBmXlEZPdeMoqLM/l779L9G+iejhV0n1NAKw70BzTqAcFRh8TDXtP8S+g83D7vPRWIyuYIhYy3L0ZCi3L6reCvy155NIRNm7ewdlymEckl21trfpAE89+zKf/ujVTB9jH2uKonDaqTO54doFffM5xE9/dS/f/MHd3LOwiX0HmzFMM7ePDdzXJ4wbzcxpkxlVZwu4kjT8ZbVlWWzftYennlvMq8vXEB9gIP5G3HrzdZwxbzYOhwNZlpl6SgOf+9QtwxqeD8cvfvQNvvW1z3P63Fmcdfocfvydr/KN//7scW3bj8/jOeZ55HAG6SeCKIrc85sfcPYZp+JyOXE5HZw+dxb3/eaHb8tY/N3m9W3eA1mzfvM7PJv3Fu/f/2qePHny/AeydfsuvvDV75JMpojFEySTKbweN2efcSpFgUKKAoWoqsoZ82Yhiu+PewtH2jvZ03Rg2HXPvfgK11556Ts7n7bh/RNM06StozNvPp4nz3ucCePGctP1Vw66AJAkic/e/pG37e9kmibbd+4Zdt36jVve8riXXXguF88/i4VPv0Q8leKaD1x03C0ZHreb2z/2Yf72+NN0d/fgkHTOqE8yqb4WxVkwqAok3PQUgfoFVJSXEk8kEc00s8encMomad2uklpQVzDouZLqRVK9Od+gfvovxDUty/OLXmH9pm1omkY6owEWHfQZO7srue2mq1E9MrKrCADLMimddCNdW++3W9cECVl2o5bZ7VOmFsfQ4lSUV+APVLMrWMyF1TspLayluGwEzoI6evYuRE91021kESQFy8jarWiGhmaB4qvCyETp3fcs2Xg7RjaJ4ipClN1k4x2YetI2uxZkJElBkB1YRpZk11YQBARRwTI0MpGDOEsmovU2I4hKX1uck2wigyQ5bPHLtIUeS8+ABZaeBVEHK4uoeJEcPnw1ZxM58Dyp4A4kRwGi4kWLHSabCmIaGoqnjGw6jCAqmNkEzuIGNNPA0mLoqRCOgpGo3hHo2TiCKCMIAqLsJrz3SYon3kho18MIgLNgJLKjgExvM0amF633IGpBHYIgUTj2ilyqHYDsLCTavAQBYVArXNG4q0n1pQP2djWRSCRoF45WJPWjqgr/738+z5Ztu7Ay3dQ69+JURDLRZhRfNbKrCIe/llS4CSMTJRHciiA70VM9dG19AL9j5tGqOP2opxiA1+vB5x2+1VQQBBStxRbtcml83r62u8V0hyJkslDiTuNVg7kWwCMtBxC6NS6e5eHKKy7EWzYpJ6wEu0Pc88DfyOo6DkmnvihIV5eOoRuMrx+da9+TVC+ZjMbd9z/EocOtuTktW76Gj3/kOhrGj80ty2az3PvnRwdVLr24+DU+9fEbqKsd8YbHtdPp4JorL+GaKy95w+e9EZdddB6XXXTeW97e7/cyZ+ZUVq/bNGTdBy49/y2P209JUYCffu9rb3uc9xJvZGLu9/3nttlBXnjKkydPnvcVP/z53SSTdtl4NpsFIJ5IsnN3E9P7TF+jsRjhSG+ubPzNiMbivLDoVXbs2ouiyMyYNpmL5p/5pnG9J4tkMnnMu0Pp17WevBOUFhcdc13JG6zLkyfPe4fP3X4Ll154Ds8vehVJFFlw+YUnzTdOFERMhnrqieLbqzCVJIlrrnprQvvoUSO568t3cKi5lWjndoqF/Tg9RbkL5UD9glxLUnvzdh58chPB7h58QhexXpGkw8e+3ko8/hLGn34D6ZaXyPQeJtWzB2/FzJyPjmVkMbJxZGcgdyH+4N+eZWfj3txcnA4VWZa57KLzKPdEqZ80F8VpC/aB+gWEdj9GNt5B78FFyO7SXNqdICkUjrYvsvuNsQGKqydydp2LRFtXThwxtASKtxItdoR422pU30i7vcxXjdZ7GD3VjZlNoPqqMPU0RiaKaWromoLkLEJUXGixw1imAZYFpobsKcdZMJp0ZB+GFkeUFCwziyBKCKaBKDsxjRSuwDgAO6kuGQLCdpWWZdhtfooLUXFhpiOIihcsO7UuvPefZON2W5Hqr0GU3eiZMKYWs43KLQtX8QS0aAuCIJGNHcHUomBZKH2VPN07/4qe6EBSPBSMvoT4kZUYmShdm+/GWTgay2ULq6Ko4vDXkgztRM/0YvUewldzzlHRqU+ESgW3QiaKaWh4quYRa1lOpvcQpqFROPYKMDLI3mpm10ZQSdAZdw8SnirKSvH7vJw+e2Lf/uVAT4exDA09GcRbNYdk13YE2YkgO7H0NK7SKUT2P4upxSn2JPC7BaLJo/5QYPslzZg6iXJ3lB3bDTL60WPLIenMnlxBoGI84dhuyERzaXz9Amsiq7Cjs5SJZT2DWgAbSnvQUl683mq8xaMHVfOsWrMpJzoNFMOW7HIwaqyKOkCMXb1uxyDRCewk4SeefpG7vnxHruJy1bpNQ9rl0uk0/1j4HF/54u1v6Vh/p/nuN+7kv+76Htt27MGyLBRFYcGl5/OhD17+bk/tPcltH72eH/zs9yReX9kmwBfv+Ni7M6n3CHnhKU+ePHneJ3T3hDlwqCX3WJIkslm7hLwreNQDQZHlYT0/hiObzfKbPzyY81CwLItnXlhCc8sR7rjt5pM4+2NTP2YUJcUBukND29veLDL4X8Gps6fxyvLVOYGvn8kTxw9rRJ4nT573JmNG1fK52285qWOKosiM6aewbsOWIetOnzfzpL7WibL4lZUsfnUlmUyG6oIUteMquXq8XUHSLz5leg/zq78sI9gdom5kNR1dDjYcCtLanWLBgvF8+Jor8Pr9CEUNZHqbSYd2o3pH2D5JyRBaon1QAtrhTX9j395W+i8pKrxxwiknGR3cVjvFQgfhvUdwl03BW2F/PpahkY23YVk6RjqC4q3KmVj3t871o6fDhPcuxFU6GU/VHFxF4wGINi/GzKYQZQ9mNoGZjeMoqENUvbhLp9J76CWyiU5MPYUjMAZECUwBQRARJAemlsBCAkFEVBTs3j4DsJBdxdBnri17yhEECT0TIdN7CNlZiKh6KRx9CeGmp9HTKzCzWUw9jaj4EGUTZ2AMWrwdyesGTCwjSzZ2BMVTiWUZqL4RuMumkGhbh8M/EgQJLWJX/eqJThz+Eaj+OvRkF5nejG1ybmjEDr9KMrgdQRDxVM2loPYcHAV1dKz/OZaRRUt0IruKbENz2YVaUIvsDKCnetATXYT3PIGouJAULw6/nb7nrz2faPNirFQPkX3PIAgi2XQYIxkkuPU+yqZ+gt79zzBmRCHR7jCWBSFvnI64l+qCFOecPnVQ4psgO3EUjkGQVERRJd3ThCA7MTMxTFPDWzUXLXoI2WX7P8kOHxMn1PP8ZoGMYbfalZeVcsN1H8CKH8CbbeSGcwt4am2WnmgGh2xw4RSTCaNNtGjLIEF1oBeTq2YKsQ1Lcib7TllnemUXoiTiD1QMaREF6IlEgMEtov1iWMZ3Gq7sJoxMFC3awq49+4Y9Bru7ewh29+TOFXbs2jvs89raOwn1hI/7BuG7id/v5b7f/Ijm5iO0HGlj8qTxFBbkK7/fiAfv+Rkf//RXcumCsiJz8/VXccUlb79K7P1MXnjKkydPnpNIbzTG4dY2Cgv8b1hu+1ZQZQVRFDBNuzrI5XKSTmcAEAf4mcyZNe24otUBNm/blROdOoMhDre0kc1m2bJtFwCf/Oj170jl05c+eyvf+v4v0XU9t6ymupJP3HL9v/y1X4/f5+WO2z7C088vZu++AyiKwqmzpnL5xe9fs8s8ed5rbN+1h8WvrKR+TO377tj65lc+y6e+eNegttzx9aO5845b37U5bdi0jedeWpp7fKTXxZH1u3A4C1hw+QWALT51pfx0dgUBu12psrwUKGVCKZSVFOUuKJ3F43D0NGJkooQaH7V9i1LdqN4qZFdRTrBI9jQTcKXpiHup8MYHpZu1hQVGBZykgjtIBXdgZlOkw02ke/aCKKGnQmAa6Klu3GXzSUf2kw7uAGxTa1/N2YT3/hOAVN9y1VtNtHkJmd7DGJkIjoJRiLIjVx1lGRrp0C4kZxGmlkCUnWBZSIoHUXYiSCpGOoyZTSBKMrIzgOofiSCpaJGDJLq2IIgKkuK2faBiR+yqKEECQcDQYrboBlhYgACCAFi5VDwt1ma33ZkarqIGtHgbWAZ6KoTsLkGUHAiW3dpmmTqZWAtIDvR0D5g62WQIydFndO0oJJvoIBXajcM3AknxIkgKAgKxtjVEDy0BQUZUHUiq1/bKklRMI026exeukgbSZhNaoh1RlBBEhZJJN5Dua33r98CK7Hsm5/FUMOayXGVU56bfo/iqKfZkEcXR9LbHiRt+Zk/wMGe0C3d8Db0HPejJEKLDh54KofVuQvZV2/uYnsZdOoVwX4VTzNCQFO8g/ydViHHtWZWYJVeD5KKivBRDi5Pq6UBy+Kkogk99oJSMZzpiZD0yaSSH306fO0YL6ExHMas37Ka55QiNwSKmV3YBtml30ZgLkVQvqe7G3Bj963bv2g7Anu6ALaAaMoosU1VTh0OqQYu24CppwOnYPuxxKAgCqnr0nEl+A5/Nk+nB+fTzi3lx8TIM3eTcs+Zy3dWXnXS7hdraamprq0/qmP+unHX6HPZte5XnF71CVzDE9R+8HKfT+eYb/puTF57y5MmT5yTx1HOLWbZiDWafEeWo2ho+/pHrjulRcKL4/V4mTxzHth22v4iqKBQU+IjHk1RWlKOqKqfOmsqCyy447jHb2+2TsUhvjP0Hjpp4WpbFqjUbKSkKvGk5ta7rJJIpfF7PWz7RueDcMxg7qpaHH3uKUCjCtCkT+dDVl75rP9TVVeV8+hM3YhgGoigOa1ScJ0+et8Z1t3yWdRu20N9h+50f/JpH/vQrxo6pe1fndbyUl5XwxEN3s/iVlRxsbmHShHrOOG32uzqnlWs2Drt81bpNXHHp/Nx3s6ZljzlGZsC6ge15ZKLo2WROdBrYvpdWt9Pxsh3b/vp0s8pSL3C0kjXRttYWYQDVV2PPJ9qCIDnoPfCC3XZn2S2Mouwm0bYaUXKgeCvJxtvJxjtyAoPirQBBIB3Zb3suqR5bJLJMJNWH4ipBKZuCpWcQJAXRX4sgOzC0uG0cjoUoqijeKiTZhWUaWKaFmU0hCClcReMQFTeaaaCne2yhwzsCU0+Q6dlLcMdfyEQOgmkgqX4k2YWeiWBmekH12V5QsoNUTyOi6gMEjEwYy8wiyi4UXzWKr5qODb/ESPdi6ilExYWoeLEMjVRoV58BuYlpaFhZu3LCWTgaBJFE5yZiR1YiIIIArtJpSIobQZQxTZNMZB9YJtlkEEQB2VWEKDnBMogdWU1xw/VEmxfb4tPBl3KiU///tmzqJ+jc9HvbLytie0RVl1Ywef4CPtDnpWQnCdptjbK7FEF22qKa4kLvM4v3Vc0jEz2E7C4la2iIkmOQ/5PqryW8dyF6KoQLKJ5wLfGOjSS7tmHpaTxVc0iHdtvCVuxpZGdg0Dz7W0AH0t8C+pnbbmbt2tWkW17EpQQoKyuh0O8j2rwEV1ED8fa1g8Y6dcZ4Yvsex8rG2dMdyLX9zT/3dNwuO7XRVdIAwJyZ09i2Y2iq2/j60YOqgWbPmELjMNVRY8fUDakaMk2Tpv2HSCSSjB418ririr7x3f9j0ZLXco83b9vJspVrufsX3zuu7U8GmYzGitXr2dnYhKzIzJkxhVkzprxjr/9e5dIL336Yxb8TeeEpT548eU4CGzdv55XXVg1adrC5hX/88zlu/ch1J+11vnPXnXzmS9+kvcMWjJwOOwXk+//vv3C7nCSTmRNKAykrs8vBOzqDQ9a5nA7Wb9rGgssuGLaCyrIsXlryGstWrCOVSuH3+bhw/pmcMW/WW3pvo+pqTjhx5V/N+yUVME+e9wvf+eGvWbt+y6BlXd0hPvmFr7P02b+9O5N6C4iiyIXzz3xbY7y2ch0Hm1uZ3DCOmdPfPCL9jYglEsMuz2QyZLN6LgmrbuQIXC4XqVRqyHMnTqgf9Fi3VCLiBOT0clx9FUUDk70k1cuI+nkUF21g9dpNmJZFMOTn/MkQ8EqMdOzB0kWcReOw9AxGNo5laLhKJ1My6UZiLcsRBAEt0WELTpaJ4qnAMnUS7euQXSU4Ckbirz2fTLiJZPBolYnDV0OycyuG1ovkKATLwrJMjEzE9koqmYSpJ9FTISTsKiEL0NM9ZBPtWEYWRJlsohNBVEkFt2LqGSwzi4VAMrQbV+FoLMsELMxsgkD9VWQi+0gGt5MO7cHQonZ1lOxE8Y9ASDoxMr0YmSiqrwYzG8eyTCxDQ3GV9LXygRZrIXLgedxlU5BdJXaroJHB1BJIjsCANL1SLFPDsnQs00RP9yAqHrT4kb5EPRHJVYyraByCYAuLsquYdM9eZFcJlqn1Jd+lcJWdQuHoS3OVTZEDz+Pw1djm6n30/28NLU68bY3tlxWx/Ymy8TYC464a9L/v9+vSU91kY0fQk0GcReNwFI4h02O3mGUTnSAI6MkgDn8tWqIdU0+jJ4OUTLwRSfWQ6t5FsmsrqeAOIrKbePs6wK56cxWNR3IE6Nr0O6y+SrP+eQxs8TMNjcDYy4m3rckZ43uq5lHv2oLYMBLJ4R/sAdVnct7/XH/tfNItS5h5ymgOt0c4qBVSE3Axbswo5s2ZMeRYmTxxHJdeeC6Lli7PVWrXjRzBh6/9wKDnzZg2mYPNLaxYvSHnZVleVsoNr3teVzDEvX96hO5QD3D0++Xi888e8toD2dN0gJeXLh+yfOPmHby6fA3nnDn3DbcHO2nt6ecXo+sG5509701f8/UYhsEfHniYg81HrSD2Nh2g5Ug7V11x0QmN9Xp0XefXd/+Zpa+tJpPRmHpKA1/67K0nza8vzztLXnjKkydPnpPA2o1bh12+s3EvyVQqd7fs7VJdWc7Cv/2BJctW0XKknZlTJ9Ewfiyf+OzXWLV2I9msTqCwgE9/4gZu//iNbzrejKmTeXnpCrTs4LvgBX4fXq8HXddJplLDCk9Ll63ixZeX5R5HYzEef/J5PG4X06dOevtvNk+ePP92vLj41WGXHzh4mLb2Tqr+Ay4ounvCfPoL36C55Uhu2ZTJ4/ntz77zlqs860fX0d3dM2R5zYiqQfHrqqrwwQ9cxN8eezpXnQtwyqQJTJk0Pvd4xeoNLHp5EQ2BFpyyjt/vZdzYwcleAE8+u4hgdwin00Gwu4fd+2OUBcbwmStHIIkiVp9XkiApyJLdPiZI9nx8NWeip3sQBBnT1JBUH+mevViGhiCpKN4KXEV2hUkmOqAi19SJti7HNDKIsgewcu1uouzBNDKkunciiDJGphfZVYwFpEONZNN26p0oyiAImFqcePtaREECLBRvFYYWQxRlMtHDqAW16Emnnd4XO4xp6mBZCKIEooogqzgKRyEIEr6RZxHa9SiWZSJKKqq7jkwyiJmNk012ofpqkFwBMuF9drshYJlZW0BSPJh6EsvIIDkCyK4SZE85mch+sExMUsjuUrRYK6aeRBAknAWjUDzliIpdVW3paSxTR3IU2m1/jgL7+UYaLXoYSfFQNvVWQrsfI9m1lUT7BlTfiFybYrR5Sa6FUk/1oCeDqAV1th+XodG54VeUz/oCrsBowBaf/DVnoUVbMTIRLEPD0OJIijsnNiIIZHoPoXirsLBsf7BsCsVV0mcIfr7dOiipCJLDFqoGkE10Etx6f26fkBRvbh+MtSxHT4awsBAQiLetybUNWqkeunc+hJEM4giMpWQYk/3+aiojEyW47U9IqheXt4i5l36M3lc3sHLlcnaG9/LKa6uZMW0y13/w8kE39i6cfybj6kfxxJMvEAz1oKgKh1vbOGXi+EHv4YMLLuGcM+dy8FALfr+P+jF1Q6qoH/zbEznRCezqpxdfXsao2hrG148eclz3s2zFumOGs6xYs+FNhadf/f5PPPyPp3KPX12+hsWvrORn3//6G243kG07dg8SnfpZvmo955wxl0Cg4LjHej13fu37rFm/Off4tZXr2NG4l0f/9Ou8z9T7kPdH1naePHnyvMfRMtqwy03TfMPWhhMl2B3if3/8G3537195eclytmzfzU233cmyFWtzRuPhSC8/+Nnd/PXRhSSSyTccz+FQ+eztH2Hq5AZEUUCWJSrLS5kwzj7RKQoUEigc/qRh2Yp1x1i+9m28wzx58vw7kznG96FlQSw+fNXOvxvf/dFvBolOANt27OGXv//zWx7zgvPOwOcdbJasyPKwkeezZkzhK1+8nfnnnM7pc2dx6y0f4uM3X5trx9t/oJlnnnk6JzqldZlle9zs2teWqxAxtDjdoR6WrViLKIqMrKli5vTJnDlnElOrY6TTtgiS6T1EumcvguwkMO4qHAW1WHrabuHDTrmT3cVIigdDi+cEBkdBHa5APbHWFXRs+CV6ym53Kxh1IVq8jWy8A9PIoGfjtrF25KCduKcnQZTIJjowtDjOovFgmRjpMHq6B0u3RQ/FV4WrdDKmngLTwELAGRiHr2oOlbPvPPphmQbl029H9dcQb1tHsnMLouzEUzETb8V03MUNaL3NCIJIsmOz3fYnSii+KiR3GXqiHT0ZxNDthFZBVFA8FZh6ikT7BtsIXJBwl03BUzETTB0zG0d2FOIumYTDOwJJ9eEsHo+pxcAyARFB8eIfeQ6yq8gWnAyNbKKTbKobIxNBdpfiDIymcOwHkFQ/VjZFx4ZfoiW67NY9I9vneaXgH3URksOf8/PSYkdIh/eheipRvRWUzbgDBBE9FaJzw6/R4rY4pMU76dp6P3qyKydEZuO2v5WeDvd5Ytn/Uz3Vjb92vi2YBcaQTXWjxTts/zA9jat4Aqq3EkFScPhrcRaNy3lMmVocUfVSNuMOZHexneK39QHS4X1YWBSOvQLZXYyeDNneVEaGbDqM6i7FERhLccP1Qyq1/CPPwVsxE3/tfPR0hGy8jUzvIbxVc9mwbR/LXl3K5JI2xpeEKXNH2bBpG8+8MLilrycc4d4/PULLkXbS6QxN+w7ywIN/H7bttbgowPhxY3A4VFKvS+tt7+jiSFvHsMf1+k3bhl3eTyBwbPGl0P/GwsyR9k4eefyZIctfW7mOFavWv+G2Azlw6PCwy03THPI9dyI07T/I2gEhDpZlkUqlOXSohW9+9//oCUfe8ti7djdx++fv4uyLP8QlV3+MX/zugUFCfJ5/DXnhKU+ePHlOAq9vUeinuqripN2ViUbjfOzTX+H5l16hrb2T/QcP89Nf3cMry1aTzWZzP5qGYZBOZ/juj3/DN7/7f/zlb//MmZAPR1GgkG9//QtcfP7ZzJk5lVF1NUiShCAIXHHJ/GH9jUzTJBqLDTtepDc67PI8wxOJRHnk8Wf41vd/wY/+7w+8unzNMe9g5snzfmfaKQ3DLi8qKnzDO/v/Cjq7utm2c3cuYOGdwDRN1h+jQva1lW9dtC8KFPLlL9zGBeedyYRxYzhj3mz+6/O3UX8M36yK8lKuuGQ+1151KadMHD/oe37Nhi1Dkr2iGQdLdypolppL9tp3oHnQd9XAGPqemI6nfPqg1+y/6O8XObRoS84c2jKyZPv8nxRfNYIokwo3YZoaphZHi7fhqZpHvG0NqqcSSfWBZSKKMqaeRPaU21U+shNJduMoHIPDX2On5QkiejKIKDmQnQEchaMomfQRZNWHu3SSXelSMsEWlKrm2Z5BfZ+HpHiIt9nejXoqhKnFEBX7fXir55JNdWMaGlq8HdlRYKfeKW7S3Y0k2tZi6mkEBBRXsV3t1LMXPRlEdpUCdvWXw1+Lv+4CRMVj+zoZGkY2TqJjI+6KmRRPvBE90WlXR1kmouy0K7JiLXbFkOwk0bnFNm4P78PQoujJIJ6qeZhaBGfhKBAkTC1OT+OjGKmQPYbisf2i2lbjrz0fyeEHyyATbcEydSwsnMUTUD1llM/6AnJfu2D3zr8SObiYjg2/xNTiCIoLyV2KIKmYRoZE52aSwZ1koi0UN1yH7C5FcgYI7XoYTB3LMpHdpfbnmU1iCaJd8ST2VRMJAno6jJ6OYGgxECXKpn4CV2A0gfoFfS1yvbnKskTbajstL9GOqcXRE50ojkIkh5+yqbdipHswtPig/VD11xDv2Ei0eQmS6s197l1b72fnxqW5/Tity4RTdhXimvVbBokTr7w2NP0W4MWXl2EYRu6xYRj8/Yln+fYPfskvfvsA3/reL3j2xaW2kNLdiJYefM7kkHQqvPZ8s9k3vnF5xcXnDesjqqoKH/rgZW+47eJXVh5TbFny2uo33HYgb3SOW+D3Hfc4r2fHrr257xfTtOgJR4jG4qTSGdZt3MIPf343TfsPnfC4LUfa+cyd/4/N23aSSmcI9YR55LFnuOt/f/aW55rn+Mi32uXJkyfPSeCs0+ewfdceWlrbsIBUMo3DqfLBD1x80l7jkcefpit49AIpGosTjcXJ9vkL6LqBKImYhn0ikU5nME2TTVt3oBs6H7/52F5TBX4fd37uE32eIy0UFvg5Y+4sRtXVDPt8URSpGVFFS2vbkHW1I/OpJ8dLMpXiV3f/iXCkF7BTEZ98dhFdwRDXXf3GJ4158rwf+cG3/5tLrvrYoLvVsixx13995h2bQzab5S+PLGT7TtscWBAEpk+ZxA3XfeCEPPLeCqZpYlrDX+wNvFh9KxT4fVx20ds3s+2/mD7Q46cr4cmZLAMYzlr85dW4Shrwdg2Oih8oVmmFp+OrOQNRdhPv2sKR1sM8+dqvOdLrYtL4Gs6ZOQtXSQOGFidy4AUy0eYB7VR9AoyeRnbZPoSiqBI9+BIAsrsYT8VMIocWocWOIMpu9EQnouzGNDKogQr8NeeQ6kvlM7JJLEOzTcAdhVimTmjXw7bhdaLLFopCuxFkN50bfo2jYCSS6kNyBkhHDqDqaVI9e+yWMEHE0HoJbv+z3RZmGXYym6fC/hAEEVNPY+oZMHUEyYHiKrHFn76LaMsyEWUHqq/aTnjLROja/Acc/hpcpZNxBuqJt60h1b2TTPQwIIChISpuuz1QT2MaGsmurXbCntknTogKkuRAkFUkZ4Dg1vtQPZUonnL8dRcS2WebcwM4CseQjbWSTQYRRYVo82JcpVOJt92PAMiecjwVs0i0rSMd2k2gfgFV875G986/ko2309PXKig5C3EUjkGwTFylk8kmOkllogiCZQs52x9ElJ2ke/baRvGeCrzV8+je/iCWqZNNdiGnQjgKapFdRXir5tK5+R6yiQ5E6WiLf7TlNRTP0TZcQVTwjzgz9z/uPbgoZ2Du8NfalVy189GiLUQPvzrEkDy0+zFSwR3IrmIcBbUUjrmUrq33Y2pxJvmO0BFVSGZVtrSX5vb/TCaDrhuoql230dLaPuzxE4vHifRGKS6yP+sXXl7G6nWbcuuzus7iV1ZQ6opS52vHrfooDbgIhlODxFuAyQ3jh32NfpxOJz/57tf4f9//P4J9rbaFBX6+euftlJYUv+G2bvex23o97uO3h5g9cyqLlq4gkzl6gzMc7sXpcuA+gXFez5hRtbm/k6kUun70+9HjdpPNZnniqRf5nzs/dULjPvi3J4YVDF9dvpb2ji4qK8re8pzzvDF54SlPnjx5TgJOp4PPf+oWnn3xFR578nm0TIbS0mIWPruIm6+/imQyxYuLl3G4tY3iogBnnz7nhBM/djY25f7OZnVSqTSiaN+VtSwLQRDIZnVEUUBAwDvgLtj2nXuI9Ebf8M6U3+fl8ovPO+75XHrhOdz350cH3TFTVZULzzvrRN7WfzRr12/JiU4DWbN+MxfOPzPvYZDn346KslKWv/QoP//NH9m0ZQcV5SV84Y6PM3H82HdsDs8vWpYTncD+/ty0dQdlZcUnbKx7osiyTMP4enY27h2ybsa0NzcYf3X5Gv700OO0d3RRUV7KLTdezfyzTz+pc5xcq9CRCpPWZboS9u+IQ9KZU9uLM70XsG8uTJwwlqJAYU5E7Ijb7UwJs4APzLRT/jyVM3hx9SGadu7uWx9j1fpd7Nrbyn/dUUemdRHZeDumZRGzKkjGdXxWiOKSckTZiaWn8VbNJTXAWLzfBDvesQFJdpNNdqH4qsnGjuDwjwRBwlk8DsVXRU/jPwZVUhmGTqxzJyYSarwTV9EYsj17sSzTTqQTBLLJIMUTbyS062FMUyPZvR0sC9lZhKtoPJneg2jRFoys3RoqOwsR5IE+iBZYFqLssoU0hx/TzCL1pdnZHlMqlqjgrphBaNcjdmUPUDnnv3KG26ahYcTaEEQJxVOBWljXZ/Yt2sbp6QiZyAGsbLKvgskNloHsLLYrpAyNrByhYOzlJNpW50Qny8iixVqQXcUIooIWb8M0syQ6/gSWYbe1TbsdSfWgRQ8NMuC2TB3TyNjCnKMAb/VpxI+sRlRclE++GYDeg4vRYkdIdG2xPyNLR5CcCKKMr3oenVvuxdLTiKrPPm9JBUEUKRhzGdGWZbYAKUoIigvVW4WeDJIK7qC7T5i09DSOgpG5/3F470JbxIodyfldwVHPKkF2kuk9nHsP0eYlZON2a5vircwJUqVTb6Nr0+9wOiScssHGtqJBomvdyBGoqpJ7XBQo5NDh1iHHj6qqeD1H57Fq7fCJk6u3HmHM/EKMTJQPnubiH6/EqC8KoogZIgmBQOX44wodmDl9Ms89/kc2bt5BVs8yZ+bU40oYvuzCc/ndvX8dIsKIosi1Cy590+378fu8fPrWG3nsyefZf+AQq9dtJplKU+D3ceWHb+fiC87mW//z+eMer58pkyfQMH4sjXv2oWlH7SxkWWLs6DoAOjq7iESiFBYe/7nSoWH8qMAW/pv2H3rfCE9H2jv52z+eItjdw9TJE/jggovftSTo4yXfapcnT548J4mMlmXN+s2UlRQxoroSh6rS0trGT395D7+550F2791PMpmipbWNh/7+5DHjr49FfwKd/Vr2j7AkSiiKnGsLsJN9QBQFpk+ZmHu+ZVlEY3FOJg3jx/L5T32UaadMpLqqgjmzpnHnZ2+luurf3xz4ZHGkvXPY5aZpDps0mCfPvwNer5dvfe3zPPX3e7nn1z94R0UngPWbhm91W7dh+OUnm/+581P4fYP9mMrLSrjzs7e+4XYvv7KC//nWj2ncs49Ib5Tde/dz13d+xouLl73hdieCrutkpRJCUY1MMsyEQCt+R4bpVUEmjClHdhag+u1KWFEU+eTHPsyUUQ4cUl/lrXMkH735JpyySaq7kUhvlJXrd+VEqX4ivVG2rn/FTiRTy3lph4eHl2VYuNZgW2ML27ZtRSqYgKdqDtoAY3GwBYV0aC8CAnomgqOgDkl24SioQ89EEBBIh/YOqaSKJnS279xDNJEhm44R7k0Q7jqMu3giouxCdhUhWCA7Cok2L0F2lSAgICleJEcBlbO/SNGEa3AU1PW1uymIkgNBkLAMzU5dEyRABEFA8VVhWSap0G6SXdsw9BSWniEb7yATa7X9oBQXnspZSKoPy9DIhJts/yvLwFXcgOQsRHIUoPqqEAUJV+lkPOXTkZ0BuxrM1JFcRYCF6q3CVdyA7CzE4a9FVL1IsoPg1vvQkyE72W3URWiJdrTeZvRUCNHhQ3aV9Hlk2Z9T2dRPoHrLc62RpqGhp3ro2nIfqVAjZjaB7ClHEBWSXVvQ4m1o0VbSob1IqpeCUecjqm7b+FxSEWU3lpFGchXRufluzGwSQXJQPuMOvFWnIilezGya0M6HyMbbscws7opZlE3+KBUzP4urdDKWaZDo2Eg23p6rXurfF/q9xAwthhZrAUHMJdZFDjzfN/9uW3zauxAjE8VRMJKSiTdSPOHaXBVUom01Dn8tBSWj0AU3DaU9JKLdbN+5h41bdpBMpTnUfFRoOuv0OcMKPKedOiNn6N/vSzQc4ZiWaz0NeCVuPj+ASzUIRjTWHPTRdKCDF14+/mN75vTJzJ09/bhEJwC328X3vnHnoFY9p9PBnZ/7BLW1J1a5Xlc7gv/+wicxDBNFUQgUFiCKIoZh8NyLS3n470+e0Hj9/Oan3+bsM07NVaIWFvg4bc6MXLWWKIoo6onV0Yyorhp2uSgKjBldO+y69xorVq3nQ7d8lscWPs+ry9fwq7v/zI23folo9OSe559s8hVPefLkyXOS2Lh5+6BS4352NO6lrLSEwOvuyLy8dDmnnTpjWA+lWDzBvv2HcLmcjBs7ClEUufHaBbywaBmappHJZEinM1iWhcfjpqqijNYj7aRSGVSHyvj60YMubNxuF5XlpSf9PdfVjuCjtdec9HH/UygtKRp2uSAIx1yXJ0+et0fmGJ53GW34kIiTzfj60fzz4T/w6BPPcri1jYnjx3D1By5607vVDzz4d0xzsP+baVo88OA/jlmpFYsnsCxriNA1HKZpcs+fHqFp30EUoYbxgRZkM8mptWEaxtfjD5QPSrQDKJC6uWyOn/kzi5ErzqNqxCjMbCKXHBYRxx3TR+Zgt8LMaefw9+c2sLv9aCXJlvZSApE0me0h5o3qxchEbdGkr1rFyETZt/1lgu0HSKYNBDVL7dQrKbU2orhLMU2NRHCrLWJYBoqvGslZyrYN60llZCzTjSiYuBSd9g4dV7FM5awvEm1e0tdKF0dEIJvqxlU8wTbItnR6Dy2x2+1E2Ra50hFMI43iKkYQZbLJ7pxpt6OgjqIJ19G56fcI6TBmNokWb8cVGIsWa7VvFkkqgiXgLptiVzelekj2VXYJspOCqjmY2RTJjk22VxVQOPoSJNVLonMrkf3PYmpxjJTdYpVNdiKq3pxXkuqpJB05gGVm0YDCsZcRb1uN6q1Ci7chiioOXw2p4I7cZ6/4qom3rUHx2MKT1uf3lIm19Bmbgyi7yfZ5TllGFstIg+wgEdyK4qsi2rwEU4tjanEcBbX2cy3TTvKzTCzTwFV6Cs7COpyFdRjZJIn2dViG3Vopu0vsZLzicUiqF1/N2SS7jorC/tr5ALl9zLJMFG8VJO0qr0zvQRxF4xAEsa+drsRO2TO0QWOoXrs90tDiubFkdzFVDdeh+F9k966tTKloJ57w4g/U0RXs5rf3/oXPfeoWamuqqasdwcdvvo5nXlhCZ1cQp9NJ/eha6mpHEIsn8Hk9CILA6FEj2X9gsHgKUD92VM7jLLx3IW3tnVimRXumFpfXgW4YvLx0OWUlxcyeeWIV8sfLGafN5qUnH2TpstVkNI1zz5yH1+t+S2NFo3G279o7rPD13EuvcOOHrjzhMf1+Lz/93tf48DVX8OeHHx8y9uSJ4/C4T2y+H7vhGpYuWzXE+3TenJlUv09SVX/66/uGBBe1HGnnDw88zFe+dPu7NKs3Jy885cmTJ89J4liJTMlUeliDyEhvlGQqNehH07IsHvr7Ql5avByXy4nH7aIoUMhtH72eUXU1/PDb/81X/t+PyGZ1LMtClEREQSQciVJXW4OmZVEVmXg8weZtO6kfO4riQCGXXHAOiqIMmcObEemN8uNf/IHV6zaj6zpTT5nIV774SWpr8j5OJ4N5c2awbMXaIaXuUyc35Pwh8uTJ8/bYvn03/++Hv+TAocN43G5Gj6qhZJjj61ghEW/E1u27eOjvdrvDhHFj+NhN11BeVvKm2/n9Xj75setP6LVaj5F8NVwiVneoh0efeJamfQfpCfeiqgpnn3EqF553JhXHuAmxbcdumvYdBCBrqRxOjGB6ZRe6rqOqSq7FbSCGWkpHKImlJygSlpINXJ4ThySHn6KS8QjCqmEDE8pKi3EUjWfb7idyyyq8ccIpJx1xL4UHtmBUFSPITtvk2ltBoH4B25fdQ+fhRgASmostzW52HXmGBbMFXKqJpHjJGlkUbyX0VSP16GXE0wJO2eBAj5/aQIx0yv5NDIXCFAa3Ehh7Ob0HF2GZWdK9B1D9I5FdRXjKp9Pd+A/SPXuRXSW4y6ZQUHMWnVvusZPkEl24SxoQRAlTTyEpLmRnEamubTgLau22uEQQPd6OprjtFkLLwNRiRA6+CAjIriKkvjYxy8iCIBJvXZUz0O6nv31Mix7KeRop3ipbINLTZHr24iqdTOHoS4g2L8EJJEONSM4AvQcXASC7iigYcxmJ9vX0Ni/B0GKIihvVV2ObsIsKod2P4S6bguodgZ7qxswmcy12ZjaFSJ9XlaiA7ED1jcDS0/Tsfgw9HUFPhbDMLHo6TGDsBwju+DMCAqahg6SSCTcR2v0Y/pqz0HqbESUHkupDEBVMPYXZJwZ5q+bSteUezGzSbu9TvLZAqHjQUz3IriJK6hegRVtAchDe+0+y8XaMVAhJtau5lD6BiQHeatHmJQTqF9C4r42mrUspFg9QVFrFhNM/gOzwUTHpGtZv3MqIMh9TxQAdcbuVUtd1lry6MueZOXniOCZPHEdbWyd/efSfbN+1h+279iDLMuefezoXn382V1wyn9/f99CgdjGv18NF88/E0OK2UGeadIdsAbGhtGeQt9SaDZv/ZcIT2O2/F84/822PE0skjikyJ49R9XW8zJg2mY6ubpYuW4Xe52s6ZnQt1119+QmPVVtbzS9++E1+dfefaNp/CJfTyXnnzON/vnRiXlHvFs0tR2jv6Bp23bpjBFe8V8gLT3ny5MlzkqgfU8fiV1YMWe52OfEPSPYIR6LsaTqApmn84Ce/4xN9olI8keRH/3c3i5Yszz23KFDIuLGj+PPDT/C1//o0U09p4LQ5M+gOhdm2YzeRaBTDMIjFbDPL2poqiosCCIJANBYnFovztTs/fcIXVKZpIooin/nSN9k34E7d+o1buf0Ld/HPh+5+W6aReWz8Pi933PYRnnlhMXuaDuBwOJg7e9pJMQjOkycP7Nqzj2tuuSN3dzsSidLS2kZd7QhmDvBUKgoUctmFJ3bcvbDoVf73x7/JmYLv2t3E0mWreOD3P6amuvLkvYk+iosCw15wFBUVDnpsGAZ33/8w3aEedu89kPOR27P3ABs37+AjH76KmdNPGTLOwO96h6TTUGpfCJum3art7LtY7xefGvfs408PPY5gJG1D5OYWajoOU1NVOcjMedopE9m8beeg13K7XVRVlvPMC0toPdJOIFDIqJIs40vCuSS9UKaMg+EC4p1bUKRdFI/pZvKsi3hpq4iVsn1YwiknGUNGFhLEIyGcxYWIbje+qrkku3ehJ+3Py1LtSqpyb4JKXxKHZNCbldnRVUJ5pRM9GaJz8z2AgJEJ22l0okrJxBv7ZmxhGhn0VDeqbyTRltfsChrLxDIymIaG4q3EMnX0TJRUaBfpnj14K+fgRiRpbsM00hiZXrs6xzJtE3VXMZaRJd2zBwDFW0U23oZlGXbinXfEkGqvUOOjWJaBngzmjLRlVxGZ3oNIjgIkZyDXJte19QEEIBM5iOWtQnGX4K2aS7RlGYn2DZjZFAgWjkA9kuJGFJW+VLssWrQVyeFH9dqtSYKoYGSiCLIDQ4siKl4QRQrHXIG7dBI9ux/LtbzJrjLAQHYUEtz5VwTJgamnEWUVS5ARRIlk11a7ksk0kF3F+GvPp/fA81iWQSbWgmlm6dj4G/R0GEEQcZVNA0sn09cmqHgr8VTNQ4u2oPprCDc9hSiqeCrnoKd7SCZTiIKC3Pd/ev3nuOb5X/D0uiwZQ6bCC+GtPUzqfJmPfPhqdEtl9QEfAZcypE30SJvdIp/qbkT11yCpXp54+kU6OoM4JJ2AK01H3MuLLy9j5IgqJk6o5ytf/CQrVm8g2N1DVWU5Z8ybhdcpHK3akj1sPFJKQ2kPTtk2GO8Xn4Yzwj4W7R1dPLbwecK9UebNnn5SBKXjpbqynMqKsmG/o6YNsH54q1x64TmcedpsWo+0U+D3UfU2qpNmTp/MX+79+due07uB2+VEEIRhxfz+Fs/3KnnhKU+ePHlOEuPGjmLyxPHs2LVn0PKrrriQ3XsP5Hx71mzYgmVaeL0eXn51JSvWbOA3P/sOW3c0snV746Bte8IR2jo6EUWBw61taJqGaZqEeiKoqkJpcREZTaO9owvDMDl4qJX2jiBut4sz5s3C43ahKgpbtjfy+MLnaWlto7i4iLmzp3HxBWcPab841NzKMy8sYf/BZsLhXjZv24XX4x7UDtjTE+GxJ1/glhuuJpPR2LxtJ92hMNWV5UyZPAFJkv51H/K/IdVV5Xzq1htzBvF58uQ5eXz/J78hlbYj7fsRRZG29k7u+vJniMYSVFaUMXvGFJxOxxuMNJTf3ffXIUl0kd4od9//ED/41n+flPkP5JoFF/Obe/4ydPmVlwx6vKNxL6GeMKGeyKDwAi2bpTvUwxNPv8iUyROGVMH2t9gMTNZK6zKNwSJOmVKYM5kO1C/AFBz89dGFfVUc9nOmV3bR0tJOYUEBI8ddlROobrjuAxQW+FmzYQvpdJoJ48YgSxIPPPh3ABLJFIcOH0EbX0NtoYxT1pla2cXODo2Du3bm5vH81pWcFVJoD8aAwb9dXQkPLb0apSUS2Xg7sfZ1ZGNHAHCVTqZ8zBmIz+9GM9IUuVJ4HVl6Mw4yukTFxMvJtP7NTlJTPDgKR9sVRIZGuOlJBEnFVTQOANkZINK+nXQ8iAp4K+diGqlctZLiKUdP99h+SbKLbLILLAvJWYBouMA0yEZbsCwTqSxAYPSlRA48D9BXBRW1/zbsKmnR4cNZPAEtfgR/7flEmxdjpXrQUyEEUSGbiVAy6SYSbavt7UydTOQAXVsfoGzqrRSOvYKO8H7MTIR0ZB+WpdO19X5E2YUoO1H9NcjOIrDs/ViQHbb5ueJGcvgR+qqxCsZcRvTgS5jOQpLB7YiKB0EQwTToPfgS6Z5GMr2H+tIDfciuQgpGXUxw6/19nk4q7pJJZJPdSM4C9FQEIxNBlFQk1UfxxBvB0HCVTiYV3IGVTZONt6GnwwCohWMwMxHbt8pVjJ4KkU0E6dryBxRPBYIgIUoqguwkeGQXB1u6yWQ03EqWIh9UjJ5HVV9llL/2fDoan6Gr4zABV4Bwym5xzRgym7bs4Mx5s6mrHYHLW0RHb3TI8VZWWkyquzGXlmeVnM3+g81DEuk64l7Wb9zGxAn1lBQXceXlFw4aJ9XdmKsMLKlfQMHyR9nSLuXG6BewJow7Pg+8JctW8v++98tchf1zLy7lsSef4+5ffO9fntbZzxc//THu+u7Pc1VJYFsK3HHbTSdlfJ/XQ8M77An4XqO0pJiG8WPZtbtpyLoL57+3w33ywlOePHnynCQEQeDjN1/L+k3b2LZjN7IsMWPaZKZObmBP0wFeWPQqS15dhSiIuH1O3C67YiiVzvC7e/+Kz+cZVKZsmiamadHdHWZEVQUHD7WwfeceNm/bRbC7B1VREAQIBkO5mNmsriPpEslkio2bt3PmabN56vnFLHl1Jd2hcN/I+2nv6GTv/oP89+c/mbtDEuwO8fv7j5aDdwZDJJMpdF1H1w0ymoYkivh8Hg4cbCbUE+a39/xl0IVNVWU5d3zy5hPuuc9DXnTKk+ck0hUM8dRzL7NyzUbS6QySJKHIcu44s2PRHVx39Tlvafz2ji66gqFh1+3YNTSx7mRw84evRsvq/OOfzxKO2Cml11x5KR+9cbDPXqTvYnm4xMyMppHsE3rqx9QNWjdn5lSWvLKSgDOeE3u2tJdSXFbNqNk35qoztGgLB4JHKzEGVkdBX+vagOooRVFYcPkFLLj8AizLovnwEX75+z/mnl9XO4JUOkNjUytFBZOYVROiwC0wc0S3bRTeN4+MIbN02WoCgULCfUl6ufdlyITkGTiLNFLBHX3pb7bo1G8gfdOHruTPDz/Ojk6DmsI4imRy1Twnpa44ITOLpPrwVM6isE8MSgV3kAruQPFW4SgYSfHUO9j0yv2Ew+0AtMV8VNVXc8Wl84nse9quXDGyKO4yZFdxX0WURSbajKR6kfy1djtc5ABGppd0aDe9zlex9DRq4ShSwZ1kzRCKuxxnURl6qgdRdtOz+wksI9PXQncp8SOrSUf22+bgkpPIvmcobrDbNrPxDjK9h8Ey6Nx8D5LqtRPiRAlR8WFkesG0zxcC469ClF2o3hFEmxfb8zd1ZFcxksNP2dRbibUsx1E0nkTbaixTR4sdQZCcYFmUzfg03Tv+QjbRQTbZaaf+lU7OJc9Fm5cgql5IBZFUP5Lqx1M1l3DT02STXYiyE9lVguwuIbTrYVRPJb4RZ2AaGqmubaBnECUnouJCcZci9o0ryk7cZdOIHl6KZZlYRhZnwSgQJGLB3USDHehZF5vbK/r2yxTdu/YQqNlDom0dksPPkUwde4J7CaecQ8SiPfsOMKquhnPPnsfCp18atJ8JgsC5Z81D9ZciOfwYmSipQ8/jd2Ry1UppXc6JWenX+X4Gu0PsbGxClmWmntKAf+Q5uaqpKy+7gD/88W+2x1mf6FRSUsR5Z88b5ttgMKZp8pNf3DPE1mHr9t088tjT3Pzhq990jIG8/MoK1m7YQlFhAdd98PJhW5OH49yz5/HgiJ/y8D+epivYzcTx9Xzkw1fj97+5x1ye4+f73/oyn/vyt2g9YrdZi6K9X95yw4n9n99p8sJTnjx58pxERFHk1FnTOHXWtEHLx9ePpn5MHY88/gwu11AD2T37DjB9ykSKAgW0dwTpDvWQzmRyQtSR9g62bGtkwrhRFPi8NLccoaMzOKSfXhAE9KyOJIr0hCMYhsWepgMDRCebg82tlBQX8dKS18hms0RjcZr2HWLX7ibS6QwulxOXy4lpmvSEI4iCCAJopkUimeKlJa/RG41jmAbSALPHtvZOXl66YsidvTx58uR5p0imUvz2nr8QjcVwqA6SyTSGbmBZFg7VFtpFUWTM6Jq3/Bo+rxdZktBfV/Fkr/MMs8XJ4daPXMetH7mOeDx5TBPgUbX2+xqu+tTntS8Ah2vJKAoU8vGPXMdjC59nT7fdxlZdM5qbrr8y17qlRVtwlTRgddni2nDVUeWVdqLYoY0PsfqAn9bOKOVlxZx71jzqRo5g++uqghVZZsqk8USjMaZOn8Hpp9ZzZMsjuWTPxuDRWHvDMGgYP4bVazflWk0SiSQWcNZpsyG1ctjPJNIbZcv2XYBFY5uM6WrgmtNcuFWTZHA7irsMxVuRE6kKR19iG3drcWRnId6quax75SHC4aNiXpErxZr1myguq2LuVNsgWpAUpL5qLEGU0dNhDC1ONhVCzsRxFtTiDIwh0bXNrsxqfgVv9emk29dhajEESUFUPQiSim/kmaR7mjD1JJaRtSuB+gyyLSOL7AzYn4GpE21ejLdqLuF9z+Lw15CJtmDqSdI9exEVF+6KWQh9glO69yCipGJmYqS6tpN27MZbNZfeg4tsf6Z0CFFxE2tZTjq8j97Dr+SS/WR3GXq4CVF2Em1egq/mbHr2/AMsC9lZlBMbw01PkY0cACwUdymYBploM4aRRs/YrXOCKBMYdyXhvQsxtTga7YjOQgSw10sqzoI6u3Kuz59JkJ1YehrT1JFUH3o6AliYlo6V7iGV6CWtS6xvrSCmOdjSXmrvn9kYwe4efH1ikV/vJplVBu27/WJR/42zOeP9yB84l6UrttATjlBdVcHF586mpjCdOx7CTU/hJMq8Ub2k0/ogkRRgUsO43P7y8tIVPL/oldx++9Szi7jpw1cxtcTeX8aMruV/7vwUq9ZuIhzuZd7IaubMnDrsOePr2bh1B+HI0OosgNdWrTtu4UnXdT71xbvYtuPoMfrI48/ww2/9N2ecNvu4xqgfM4pvf+0Lx/XcPG+N6spy/vnwH1ixaj1tnUFmTz+FUXVv/ffsnSIvPOXJkyfPO4QoirhdLhLJZN8SC03LktGyyLJMzYgqkskU3SHbm8AwzdwJSigUJp3O0N7ZxWmnziSRSA1r4miaJpppYpgGfp+XcWPrho0OTySSLF+1jldeW01VZTnFxYVs2rITXdcJFBbSG4sRi8ZJplIYhoklWliWLWwJgkBnV4iVazZQWOBnUsNg/6jtO/fkhac8efK8a2zYtJ1oLAZAw4SxrFq7ESwwDTPnXze5oZ7amhFv+TW8Xjenzp7OyjUbhqxb8A58/71R8tTIEVVMnzKJWDyRE28ACgv9FPi9VJSXMXLE8JHiDePH8s2vfo629i6cTnVQyIGkenGVNAAwvn4ULpeLAik4qDoqY8hUnnIdsY4l7NyxidauQjriXjo6u9ixay+fuvVGFGX4yw+/38f0yaNxp7YNes7rzZbnzJjKxPH1PPXsIpatXAfAmJGl7HrtD0yotnA5nTmvpHTPXrp2PsqDi6N0dMf7PjsPRzqjrNjj5cJT7N9R2VlI4ehL0KItSM4i28BalJGdhRh6io4Nv6a3O5kT1wZ68WzfvJqJhXbbi2VkyURtryxX6WRKp3yc4PYHSYUa0VNBMqJEyeSbycSOkE10YOppEu3rAQtBduIK1KMngxiZXlLdjTh8I3CXTcU0NNLdjSQ6NiJKDkTZieKtRBBEsskggqjQe3ARAgJaqhvVW4WeiQAWlmXa40sKlqkDlt3ypvqRHH70ZMhuv5NU0r2HAGx/qaKrSHbvIpvoRBeCFE+6kVRwJ66SiejJIJg6mcg+nIV265PiLsmZdqsFtcRaVyHKTpzFDQhAKtRIsmsrlplFlF24ixtIdmxE9VSi0Y7qrSLVtY10z1779fv8q4Q+c/VsvANP1WwyPU19ghNIDp+dGBg5CKJM2nSweJ8tOoFdCddfQTRermPk6HmEm56iyNfLqbVhNG3wvutwOJgxbVKulW58oZ+5X/oYouLJpTVGD9sCj6ukIZdIN6quhj179w8SSceMruXUWVMB2xfquZeWDtrfs7rO3/7xNOPHjs61+RYXBbjikvnDHh9vhEM5trePJB7/5f5fHl04SHQCyGQ0fvDz3/P8aX864Xn9O7Jj114e+Ms/aGlto7y8lI/e9EFmT//Xmb+/EccrBr5XGJp3mCdPnjx5/mWcf97pfX9Z9EbjhCNRkskUHo+LRUteY8WajbbYM0B0EgQB07JIpTMkEkleeW0VpmEgisduzTIMk3RGo652xBDfkmQyRTQWz90di/RG2bRlB5ZlkUpl6AlH6OzspifcS793oWlaA+YD8USCRCJJbzRGNBYfNH7e4ylPnjzvJv3pUAB1I6v7/Izsiy9BEJg5/RQe+dOv3vbrfPcbdzJj6uTcd7GqKlx39WVcd9Wlxz3Gxs07+Omv7uWeP/6NYPfwrXtvhZs/fBUfu+lazjhtFkWBAkbVjmBC/RjKSkv4+M3XvuG2giBQXVVOYYGfV5ev4Re/fYD/++0DLF22KtfKoygKN33oSkLpQvZ0B3IX7hecdyajxoxj6W4HjX2iUz+GYfDiy8uYOW3yoNbiCm8ch6RT5HdQznaMTJTCQCnbDxu0dScwszGmVHTikHSqqyqoqx3BpIZ6TMti4oSxTJwwlvpKKHWF6emJIPjqKZl0I65S2zw+fHg1HuPwoPfoUzMUpFeTGGDcHGr8O5EDL9Cx4ZfoqR4kh5+CURei9R5GT4UodiXY1VVENGNX0qR1mRJ3knHeXTmvHnfFDNurSbfHlVQvhXUXIDsKEUQJSfURb12Bs6AO1VOJIMpYRhpRclA56wuo/hGIsotM7yFbmDI1iidcS0HdBWCZuXQ5xVdN0birUDzlyO5Su5oom7Tb+pwB9EwY2V2CqLgxMr0kOzZimQZG1k7fFWUnqfBePFXzyMRb0VM96JkoslqAIIhYhkb00Mu2p1JftVO6uxEzm0R2BiidehuIMqKkovpH4CwahyA7cz5gQF9CXRpTi+Eunw4ICKKMICr4Rp6LadhtaIKkUDr1NnzVp2PqCWRXMa7SyRQ1XGd7KOlp9FQIU08RPbSEbKKTdGQfejqCoSUAAdPU0dM9eBWDQufRBLUKr31+EkoXUjuyGkn14q2ai5nppWH8WNxuV04sKi4K8MmPXo/H7bbb3/qqo8JNT5FNdOZaTSWHH9Vfk0ukAwgU+Jk+dRKXzfFy1qmn8NEbr+GO227O+ajZ1XZDyWQy7G468IbH4/EwZfKEY6ZVXnzB2cc9zmsr1g27vD/Q5p3ANE22bt81rIfRu836zdu4/Qt3sXLNBg63trF+41Y+/+XvsGTZ8JWWeQaTr3jKkydPnneQr37xdmLROIuWLiedziCKIjXVlRT6fTS3tNkmpJKEZVoYr0us0HWdhGEMm2QxHOl0hsf/+RzFJUUU+H20d3SRytjilSRKCMJRX6FYPEkqlcKy7DYV6BeQLERRBCxM0+qrsrIf90SiSLJEIpkaZFI+c/rkIXPJk+c/meGM43VdJ53R8Hryfmgnm+qqikGPJzeMY3LDOHp749z1lc8wdnTdSXkdr9fNH371PVqOtHOkrYOJ4+tPyMvkK9/8Ea8uX5N7/JdHFvLNr36Wi88//gvFYyGKImfMm8UZ82aRSqXZf/AwHreLutoRx+0n9+eHn2D7zqMXm4dbjrCn6SCf/oSd9DapoZ5vff2LbN3eiKZpTJxQT3lZCQAHDneTTg/9LJpbjlBaUsyHPng5Tzz5AsXOCONLwhiii6lz5mFlDxFL6Wzd3sj4CpNXt2uMLjUo9KZpqBvLldfZXkZN+w8R6jnaQq4ZEvGMQm/aQSI6mpF9bXOdqRBmb5DaQIzWqJ+MIeNTM5w/9jAOSSeVjDFi2g1Em5dgJUOke5uxLAMt3kbBmMuIt63GWTiaNKDFDTxqlpjmIGPItMfcVPpiFPpNBNlJoH4BgN2ylomQjbejRVvwVM7AMJJkwgcxs7YQomciKL5qZFcRAJLiJd6xAdU3EjkZQnYEMPUkkuojm+ika/M9dgWTKNmm4rEjRFtew1dzNsmt92EZGlrvISzLJN2zF9VXjewM4C6bQmjnw1iW3VJoWQayqwjZWdTXarfNbl3TUyjeCgRBxukcZ1cnBbejeqtwl03BMjT0TAQt2orsKsJMR3Jm3noqhNa7CbWgFtlVbIszkhvVP4J0ZD/p0B5S3TsRZTeyswhR9hBvXYEoOTC0GKqnksi+Z/BUzsTIRO20utKpSKoXUfZgGhqCnsLIRMEyMKyYnbCnxQELsBBVH1gWiqgxorKYlqhOhTeeS0ksn3QNHrcbLd5J19b7MbU4Dm8Vk+srGTfOj1x9CSNqRiEIAoYWR4u25FrpjEyU8N6F9v+pL60RGCRE+WvnEz7wMhXFFnWeOCUTRvadO701Bibmge0NdejAPgocCeqnnDvsMfzdb3yJL9/1Q3qjdrWnIAhccN6ZXHn5Bcf9urJ87Dmr6pvLBu0dXbz8ygqaDx+hsNDPWafPYerkhuN+/ddWruOHP787d2xXVZbzv3d9iSmTJxz3GP9K/nDfw0N8tAzD4J4HHmH+2acfY6s8/eSFpzx58vxH8/2f/Ja/PrqQjKYxqWE89/36h1RWDn/X6GQgyzI/+t+vMm5sHUteW02Bz4eqKmzetiu33jAMLI6KSwOFpuMVnfpZtnId88853fZuymSwLHsMSRSQZZloLE40Hs+ZxA6MaDUMwxbBLFt0OoqFJEqoikwsFsflPOo/MHnieOaffdqJfizHxeHWNnrCvYwcUUlRoPBf8hp58pxMtu9q4ns/v5fOYIh5s6by35/7KEWBAgC27dzL/Q/9k9//9Bvv8iz//ZgxdRJLXl1FV7B70PJzz5p70kSngdRUV1JTXXlC27yw6NVBohNANpvlJ7+4l/PPOf2EUqjS6QxbdzSSStmJca+vfHC5nEyeOG7Idrqu07T/EAD1Y+oGveahw62DRKd+9jTtZ+++g4wbOwoAr8fN6XNnDnleUaCAtvb0kOWBQnv/nzt7OlMmT2BP404csVUEPBKK2oPqP4XNryxEJIspuvCVjaQlnSGtZpk6YjqFhX6AIRHzHXEvm9ttX6q5tfbvlaR6KZl0M909v8cw25hWGaQxWMSs6g4ckk5al+hKTaRt3T7Gj5mLmzU4gWwmguIoJHrQNpa2sCif9XmEzjZe2rsaSOGQdCwLMvgoLCoDwNDihBr/jp4KgSjirzkbV0kDhhYn3dNEKrgD2VWCZemkw/sQBBFP1VyK6hcQOfACya5txFpew1EwCmfhaDLRZlsA6t6JkY4giBLuilmIoky6Zy/Jrq2kQo3ITtvIXPFVkwnvByy06GEUbxW+qrlgQaTpafRMBMs0wGmSTXSCpxwt2YllGkjOwpzxuGmksSwTARFRPvr7rqdCmEaaZNc2DC2Gp2wamViL3fqnehEQ0FMhfFVzSfU0IikenIHxJLu2gKljkqSo/gp6dj+OpacxzCyKr5p0ZD+WqZNNdOCvPR8tdphY6wrC+59FTwZRC2pxBMYiiAqZWAsYGhiAIIGl24KTsxhTdiOpPs4odTFi7CT27G+n0LOfqlIPgeLDRA4uIdq8GFOLI6pePFWz6d3/AiLdOCLLMCvs/2O/oOQfeU6ula4ff+18JNU7KJEuUL+AV1dtZfnyVsYXHsbjsPC0ipx3yQ05gWjaKRN5eenyIceDw+GgYdyYQcsGJuYVjv0ATzy7jE2b1jOtogunrPPayrVcc+MdFBb4B2039ZSJPPf4Azz30iv0hHs56/TZ1I8ZNeQ134gL5589pNUO7PTdN0vX6wqG+OXv/0Smz1A91BNm/4FmPvTBy5k3Z8abvnawO8TXv/NTNO2osNPW3sl/ff37PPf4A6jqsdsJ3yn2Hzo87PLmliPv8Ezen+SFpzx58vzHcvm1n7C9P/pYtXYjM8+6gm2rn6WkpORtj9/bG+PbP/wlazdsxeFQuPLyC5k9Yyqr1m5k05Yd9EZjeD22Ca1hGOi6TncojGXxOqHnrWFZFr2xOM+8uATDMActFwQJLauDIGDoxuvWCYPa/FRFIZU+egEhCCJOlwNBEFAUha9/+TPc++dH2LJ1F5u37mTths187vaPnrQ7VIlkkj/+5TH2H2zOzWnenBlce9Wl+SS4PO9ZdMPgWz/6PafOPIWZUyfy/OLlfPJL3+H3P/sGZSVF7/b0/q1RFIXPfeoWFi15jZ2NTSiKwuwZUzj3rLnv9tRyLFq6Ytjl8USCZSvXHvfd830HDnH/g/8gPeA7+pwz576pz17jnn089PcnSSRsz0Gv18PN11/F+PrRwBtfSDUfPsK4saPIZDS27mgkmUwxvn40lX0X7v1z+Ns/nhqy7TlnHv0fuF0ups+YhaFNyF3sxzq2kEpEc747miEjKjKhtD3nC+ZUo/prGDO6FlmW0XUdh2RHzzd1CHQF2ygo2kd5WSmzZ5yC6i1n7JlfYH/r/+IkwfTKLgxTpDWisHCdQGllI9DIc8DZc6dw8ZkzkJyBnNigpyMIkkqibTUj6hfwP3dOZf26NSiRlQScEoX+CjDTmJmYvY1lYBoZRBzEOzbgCIwhcuAFEu0bECQFxVuBw1dDJnIAyzRIBXdi1F1ANtGFng6DZSIqbooariO053GSHRvtih9BwlMxE1/VPBRfNZEDzxNrXQXE7GolVxmS7MIRGEMqtBtR8WCkegjtfsw2JBdtEUlU3H0t9BZatAWzZw+S6kOQnSjucjK9hzAyvVhGGiQnyeB2RMUDCDgK6hAkFSMTJRncjhZtxVk4GtldTMGYywhuvQ9TixM1NGRnAEF2QjaJpHgw9SSi4iHS9AyCqIAMrqJxCJIDqaCWZPcurHSEyL5nUP016KluZHcpop5CFFWsbCLXrpfu2YMoOey2PUFG8ZShuEvxj7qIyL5nyIT3MTogMvfGWwEI7X6MVHAH8SOrAZBdxVTM+iKS6kGLtuTSCxOBerRoc05Qsr2+Fuf2Vz0dIXLgBYonXJvzOpOcRWxau5hnXtgGwJZUXyLd/v0IntWc13cjrrqqnCsumc+zLy7NnV8pssxN1185xOh/YJvfztfuZefWHqZVHE3Ma2pP8fcnnuX2j98w5PhSVZWrrrho2OM2nc6wccsOurt7qK4qZ9qUiUME7uuuupQNm7axbMXa3DwL/D6+8/UvDTvmQJYsW5UTnQby0uLXOHXWtDetAHts4fODRKd+eqMxnnvplWO+r3eSAr9viOgN/9pAiX8n8sJTnjx5/iM5cOgwq9dtGrI8nclw62e/zlOP3vu2xk+lUlyw4GY6u47ecf/xL/5AWUkx55w5F4/bxb4DzUR6o0yZ3IDX46H58BH7JNqhkkqlT7i66Vhks3ZMsCAI9Js2ZXV7WTo99CRh4OuapokxILVJlmVkSUQSRZwOB9VVFfz10YWDfAG2bt/NZ7/8LR78w89OSsrGE0++mBOd+ue3au1Gqqsqhr3TnifPe4H+mOOvfP5jCILA/LNP5Tf3/o3PffWH3P2zfJXTvxqf18MHF1zCBxdc8m5PZVjENxDNxeO0YDVNk78+snCQ6ATw6vI1NIwfmxORXk8imeSPf31sUMtIPJ7gj399jG9/7Qu4XM5cZdJwBAr9HGpu5d4/PzLoIuyMebO55kr7854zcyqZjMbiV1bQG43h83qZf85pnD535pA2Ikn14qmaR0/jo4hqAaIoDDJp7qfSG89VggTqF3DJBWezaNFLTKsMYulxdvTEyGYL6ezq5h//fJb1G7dyxydvxu0t4szL7qBx5f2Ew72IosDinSpFr6sMW7ZmGxPGj6bUWpJbJqletHgboqgQbnoKf+18plV2YRSV28IKoKfDZKLNOUNzV1G9LVghEN67kEy0BSMbR1HLKRx9Kaq3HEuA0K5HMNI9dG74dc4TylUykfLpn8LQEmjRw1iWiSipyK4yTD1NrHUFsrsYZ1E9mchBtEQHIGBkIviq59HbvMR+viOA5CrKJeEJkoqnYiaCpKKnetBiLViWgWVksSxwekdgmhogYGbjSKoPUfWhRVvIJrsRRAVHgUXl7P+iZ/djxNvXYeq2p5Sv9lwSbattr6lMFFF2YhlZEEQkxY2jcDRqQR2JI6vs9jhBpGza7ciqF9VfQ2j3Y6i+avRkN1gG2XgbjoI6ZFcRvspTSYX32mJXxybbuF2UUBzFfabq9j5iGVkSbaspHHsFkX3PIAgiXVsfoLjhQ1iGhmVoSKoPyVFA8aSbUL3lABRPuJYQkI23kwput//nDj/+2vOJNi/OiVAOfy09exeip7oJ9W2n+msINz1Fz4G1VHiddMS9ZAw552v22sp1OeEJYP45pzNtykSefXEpLa3tjBtTR1Fh4ZDja2BiXijYxPRKu3VuoAn67r37SSSTuQS+NyPYHeK39/wl14YHtlD02ds/MmSMn3z3f9i1u4kVqzdSWhLgsovOPa5qo9Yj7cMuj/RGicUTFPh9b7h9T6T3mOuC3eFjrnsnWXD5Bfzh/oeHLD8RH63/ZPLm4nny5Dkh9h9o5nf3/ZWvfPNH/PDnd7Nq7VDx5v3A/Q8+ekxhZ8euoWXGJ8KTz77MBQtu4WBzK1rfib2Fha4btHV0EemN4vG4GVVXg2EYdHQGkWUJ07QTl7LZ7EkTnQZiWQMb+I4f3TBylUWmaaIqCmWlJfj9Ps4+81SWvDLUVDGdzvDAXx97mzMGTcuydUfjsOs2bNr2tsfPk+dfha4beNyu3LEjCAKfv/1GTp15Cp/7nx8SjSXe5RnmeTe5+MLhL1T8Pi9nnn58SUUHD7UMupAcyJZtw5sZg31z4PU+JWAbHfd/306aUE/JMJV5hQV+pkyewF8fXTjkzv+K1evZsWtv7vGZp83m1ls+xKi6kSRTKV55bTWvvvQIvYdftSucNNvvSIt3Etx6H9l4O6bWS3FRgIbSHhySPmj88VNOG2T4fOasMXzisgpqKvz0Ji2cBSMZXz8qd8wdbG5hw+bt9uuE1zFuTB2nzprK+PrRnDHexK0MToZ1SDpdjf8k09tsezaNuwrZVYTqrSITayUd3k9478KcGFE84VoKR1+Kngwe9VgyNPR0hOKJNyJItrG06q3EVdyAwzuCaPNitHgHeqITZ2AckqMAR8FIJNWLs6AWxVOOluiiY8MvsbIpFE85JRNvxFM+BVFyoCXa0ZMh0j1NdvWSkcHUYjgKRpGJtaB6KpFUH4q7BMVRmBOdHAV1uEom4as5Gz3VDaZhC3+SimVqALjLpiGIIoLkQPWPpLjhekTFjSAqCKKIu2wakupBVD04A2ORVB+WodG9/c9oiU60XrsNSYsdQc/0ku7Zi56JIkpOEkdWgSBj6inMbILIvmdR/faNKcvQMFI9CLILCwvFV40gyjj8tSQ6NpJNdGLqKTLR5j5DcgE9ZQcIWGYWPdVtG6QnQyTaVuMqnUQm1kImvI+urfeR7tkLoozkKERxlxLZ9wxavBPoM38ffQmS6ssl5flr52Okewa10rnLp+XM6lPBHSQ6txyt0ktZhFNHWxL7eX3wimVZPPP8EjZv3Ul3qIdV6zbxs1/fO+xNUEn14q+dPyi9eKAYa1n2OeXx8uSzLw/5rmjv6OLlY1ReTpxQzyc/dj1XXXHRcbe4lRQHhl3ucrnwuF1vuv2cGVOHXS4IAmedPuu45jCQXbub+Mo3f8SNt36Rr337JzTtP3jCY7yej990Ldd/8HJcfaE9DofKFZfM587P3vq2x/5PIC885cmT57g53NrG3Q88TNO+g2iaRmdXkH/889khPhXvB0bVHjtGe6Bn0YnyX1/7Pj/42e84cLAZwzDIZDRS6TSWafV7YNLc0kbrkXba2juxLOjoDLJ1u92ukNX1XIXSewHTNLEsE0kUEUURyzRJaxqmaTJ96iQcqkpvLD7o5Kifwyeh51039EEVVwPJaNrbHj9Pnn8VdbXV9MZiQ072v/Tpm2kYN5of/vKBd2lmed4LXHDuGVxywdmD2oWdTgff+MpnT8jf6a0wXDtMbl3f96okSXzmEzcxYdwYBEFAEATG1Y/mjk/eTEdX9yBj74EMTO/qCUf4/X0PcfDQYQzDoDca44VljTQdCubEo3R4Px0bfpnz3SlquJ4x4yZSXuxiWmUQh6SjaVlKigMkMwJC6blHxae9C/G7BMqrajmUGI23oGRI+/X+pt2DTKAD465CkL04ZT03fj8BVxoXYdujCcjG2vDXno+oejGNNFqslWyyGz0dwV9rR95Hmxcju0oQJBW1cBRCX7VRaNfDdtUPIIgyiqcc0eHLzVtPhjAyEVzFExBlFw5/LaahYWpxehofzX0elbP/i4JR5xOoX5ATwSwsLD2NJLtziXP0LZPddhuZ4q/B0JMo3iocBXUYWozQ7n/Qsf4XKO5SJGcRpp7G0lOYWpxUzx7Skf0oHturzMhE6d7+Z0RRQVK9qL4akl1b7NY9PY3qr8E/8lwsy8TUU6SC21E8pUgOP7K7zBa3BIFk9y7iHeuxLBNHYHRfu5xFJnqIjo2/JbjzYdI9e+1UumQnhhazK71MnWT3LtK9zaS6d5HsbsToE61MPQWiiKtsCq6yKQiigqT60BLtpEJ7CO9ZiJVNgyD0+VJlQBARFTfZZJBMeB8dG36JFu/E0OJEDrxgpwjG29DTEaLNS/re3zkE6hcgqV4k1Wu32JVORnYVkwpuz+1TcdecIdV5AKNqB1d872xsGpJuZ1kWC59ZNKT6vD8xb6Cf5UAxdmRN9ZtWEPVjmuYxE+K273x7N1oHcvbppw7bTnfGvFnH9Z124fwzmTJ5/JDl55459039pV7PqnWbuO1zX+fV5Wto2n+IJa+u4uOf+Sqbt+48oXGG487PfYKXnnyQhx/4BS8tfJBvfvVzb8tI/j+JfKtdnjx5jptXX1uDrg8VRZa8uoqzTp/zvvrive2jN/Ct7/+KdGaoeHHbx69/S2Ou27iV5avXA/2JcDa6biDLR2uNkslkzlcjndE40taOab61aqR3AssCwzSRJQlRkfH7vDSMH0s40sszLzYRCoURJZHS4qJBXgXVVXYp+58ffpynnnuZ3t44Y0bX8JlP3Mz0qZOO67XdLhejams42NwyZN3ECfUn5w3myfMvQJYkbrn+Azy3aDk3XHNpbrkgCHz9S5/gf/U/EIsn38UZ5nm3+c5dX+LD117B0mWr8Xk9LLjswhNKxRtVV0OB3zds1dO0KROPuV3D+LE89dzLQ5YLgsDE8Ucv8IoChXzq1hvt1m8s3C67auFwa9sxxx5Yrbt81YYhbYAZQ+aFLTBmtAsyUUKN/8iJLBWzvojqLUf1lKHIMrFIF44WJ4vXtdEdCvPUcy/zzAsiH7piLnXOaG5Md/XZZIwnhp1PwJkiFOwma6qMmHY+qrec2pk3sn3XHpwkcBAhlHDj8bjpiHspHHUOLrmTbLyD7uAOnEXjME0dTANR8aCnQ2AahBr/jq/mDPRkCD3VjeQoRBQVFF81qe5diJID01WMt2JGzjcIQSSbDCIpHrR4Wy7VzlM1j8i+Z5BdJWR6D+EoqAMveCvnIKm2d0x/+1WqZw9WNkUyuB1BUnAUjgZByLWc+Wvno8VbibfYRtau0skUjr6EnqanMEKNmIYGloUjMAYtdrivmkkGBFJdWwEBBJFM9DCSYr+24q1Ai7agJzoRJBV32VRMQyNy4HksI2sLO5ZJNhGkdOonCG69H8vQbG8qy7BvvCHiLByLFjvcl3zXSyrchCiqiLIDSfEgSgp6JoqZTZHpbcY0tdznJikeJEcBhtaLIEgo3kqK6q9EUj2kQ3tJ9TSip3r6PLkUjEwvguQEQcDSU0iCbLfujbsq50XVvfOvSKrProjq+6wMLYae6iHc9FROdOpPuXOVNJD1zyHUvhCHFqPA78NfO5/zA9C4/0ECSohwyknGkFFkmcsuOnfQto179uX2ywpvPPdcTdPYd6CZhrFVaNGWXAufkYlSXTOGTW1leNLbc2LpnsjIXEvr8SAIArIk5SwWBnIyRe7Ro0by8Zuv47mXltLe0YXX6+HM02Zz4XlnHvcYf/jl93no70+ybMU6ZFnkwvlnce2Vl775hq/jd/f8ZVBVZyqdpqcnwqe+eBd3fPIjXHz+2ZSVFp/wuP04nc4TNm7Pkxee8uTJcwK0d3YNuzwWjxNPJPH7jv+E+b3An373Ez52x1dI9939FUWR886axxc+9bG3NN5rK4/6HAUKC0gMaEMwDR1JloaIc+FIL1gnnlb3TmNZFgjgdNjlxVu278pVhjmdKslkmq7uEDXVFYCAqip89MZr+L/f3M+jTzybG2fr9t187r+/zf2//eFx38G6esHF/P6+h0iljn6eVZXlnHf2vJP3BvPk+RfwwSuGj7EWRZFvf/Uz7/Bs8pxMmvYf5B8Ln6e3N8a8OdO54pL5b+kibsK4sSd8N78fURS5+cNXcd+f/z6oiumcM+ce098JoKK8lPnnnM6SVwe3SV84/0xKS4ZejLlcg6uAa6orKS4KDFv1NFDwen2qYD/JDGieabhiq5GdhQAUN1yX893pF1n01l0sf+YlVEXJbasIGgc3/5OqWQ2oiv15OxJbGFtbxr7mwecoqXSGV7d0sU3pIZxyoi++nwvnn8lF888iUL+Ah/5yD9sOBO336HTwsZuvZdyMS+10ut2Poae6SXZts42xVR+CqKB4ytFT3VhAvGMLpqkhym6MTATFUwaGhig57Aob1UcquB1P1RySXdtItG/AMrM5oUdPBgnUX0mibTWWZaBFD4Nl9rUgCnYlTqyF4gnX5vywkl3bcul4kuIhEzmAICk4C8cgiDLR5iXInnJMPT0okc72iSpGT4UQFSfp0G5E1YcoO1E9FRjZBHoqhKGn7HMSI4MBuAJjSfceQpBUTD2NLDrQ0z3oySCCpCKqXpzOEtK9B1F9lQS33o9hpMHI4CqaYAtdgoCe7CbS9CRKX9uhFm9D6z2MqUdRPONQXEUIkoNUaA96qgvTyPR9ThaOwDgU1YcgKVhGMYLsQE+FyMaOoFbOwFM5A8VXRc/u/vZ+wTZMFxXbZD0TRRDtm4Gqp4yKWV8k1PgoZjZBKrgDQVJxlU7GXTaFeMtKtLgtrA4UgfRMlJeXLiHRuROnbAs4breLSZaDylOu4wsfOZtD254iEk8Td8/hjNPPpLzYkxOQgNyNuQpvnPEl4UGeTU7ZyD3XUVA3qM3v45PdbN++hcSh53GpBhdNuZii6qohx5VlWezeu59wJMqo2hE5s39BEJgx/RTWrt88ZJtZ008Z9hgdjmQyxYFDLYyorhiSqNfP5InjmDxxHJqWRVHkEw6AkWWZj954DR+98ZoT2u71HDh09GZlIpkk3neTJ9jdw6YtO9i9dz9f/vxt73pC8qvL1/D3fz5Lb2+MiQ313P6xDw/7HfzvQl54ypMnz3FTWV5Ge8dQ8cnn9eL1HJ/B4XsFy7IwBYtbbryGrdt2oWWzfOHTt3DFpcNfKB4Pft/Rsmen00FJUYCeSATTtJPiykqLGVFVyZ6m/ciyTEVZKU6HSjz+/vB6MXS7dTCrZzEME6fDTrYrLSmip6eXeDJFKp1h2ikTueOTN1NbUzXsXXVNy3Lfn/7Oz39413G9bk11JV//8mdYv3ErPeFeRtZUMX3KRJQBFyN58uTJ807x5LMv8+Nf/CHXBvzq8jUsfGYR9//2h+945PfY0XV8+2tfYOuORtLpDBPGjaHidabZw3HFJfOZ3DAu1/ozfcok6ga0oEciUdZs2Jy7iJ05bXLuO1cQBG760JXc++dHB90QOO3UmZwy8WirTGVFGTsbj3o+9eNxgJrYknssOwuJt61B8ZQPMhxvbDUG3ZRxSH3tcaJOqFdj4mnX0rT+73S0baBCMtiZVDBFJy6nA0/fOYmW0ejI9N8UM3hh0auUl5bw/JK1uIrGUy/0ohsGhQU+9h84TDabRenz/TG1OKnQbiwji2CBwzcCQ/axan8JhNcScKbwez1Uj5qMyz/CbnVzFWGZWbKJDoxUCEdBLap3BMmubSieckw9hewuRes9jOwoJLj1PmRXCdGeVtKmF4/HQ9WUjxE58AJ6qptUcAchoHD0JUQOvEAquMP+fFxFGOkwRjaOoItYgoAoO9FTPSSDO5BdxUiOAiw9nUvoc5dNxeGrId6+3hZjBJGi8R8k2bEJQVRss24LJEcBWT2FpHrJproREDCzKSTVg6kn7NQ7y0ByFFAy+RYSbasRZWefB1MaU4sjOwrIJrv6DNfbscyjbYcAirMYPRlET6dJRw7gqZhFuqcR2V2Mng7ZZub+GhRvlS3IYZ9fCZKCocUwjQzR1uU4i8cB0L3zYbRYC2Y2Sb/wpHjKsEwDxVWCoScxB1QfFTd8iJ7Gf2CoXlRPZZ/Pk5d0aDeAbTHgDOTEoI5gL+mugzhlk7Qu0xgsoqG0h317d+F0PkVJ7fmomcl9glEGv88a1OKp+muYPaOYV15bTTjlJK3LuQqmDn0UBdpGDC2G5PDjqzkTR0HdIAP+8ooaXmuaQLx9P9t6DnD2maXUjRx8vN79wMN0dgVzy2ZOP4Ubr1uAKIosuOx8gsEQBw4dzq2fdsrE476B99Nf3cszLywhnc4gSxLnnDWX/73rS8cU21X13T0/87hdRGNxLMsikTj6HdX/HZZMpnht5bo3Tf/8V/Lg3/7J7+/7a+47bt+BZlas3sBf7/35v634JGha5r19m/0koOs6Z172UZY/9+d/ed98njz/zrQcaedXv//TkHa7Ky+/cFBE8vuBJa+u5JkXlgxZfvP1VzHzBO4ADaS7J8yV139yUBysZVkYhsFHb7yGA4cOk0qn2bh5O5qmo+sGpmXQ2RXCsmxxauBJttNpCzupVHq4l3vHEQQBWZaxLAtJEvG43RQXFQ66o3XnHbdy/bVXALCn6QA333bnsGONHFHF4w/9/h2Zd548ed4a+fOnoei6zkVX3kJsmBsGd3zyI9xyw9Xv2Fxa2zpob++itLRo0EXo2+XgoRbufuBhtAE+elWV5Xz29o/k2u3ADpHYuqORcLiXtvYuDrceQRRFpk2ZyEXzzyKVSvOTX90zyITcIel8YI7C2JElfelh84k2LxlU4dF/sb1sxVoWPvNSbtuBlSKjZt9ExpB5/rnnmFYZxCnr7OkO0JnwccsNH6QoUMj//fb+Yd+f3+8jegxT9ltu+CBTGmpzooGhp+zqINlOZ3tircG+lhgOSSfg6vttdlbwpTs+TuzgcxiZKJaRxcjGERUPxQ3XY6R7cml8/W11giCS6T2Enk1zpFsjHDdJZlW2tJcyeuwEbvnQJUT3P9lX3VSM7Aygp23/qVx1TusqMrEW20BckCgYfQnR5iWYWhxHYCyBsZcTavwHkupFEGX8oy4i0bYaLdFJJrwfANPI4Cwah56w/Y4sU8NdPhNBgHRoD6al2xVYFkiy0xZRvFVk0z0Y6QjOonEExl5O78FF6FqMVHA7ouxGlF2oBSPRoofR02GwTATZDaaG5CjAXTYVd8kk2tf/EsvU+3yaPBhaHMlRgKS4UTzlJLt3gmkgu4opn/UFEm2rycY7SIYaEWUnjoI6LFMnFWpEEETcFTMxtQRa/AimFkOU3UiOAkw9ieobQfn0TwEQ2v1YLvFP8VbiKKjFWTwBtc8Avr9KyTKyWFi8sj2DK9M0qErJIelMqwpy1qmTKBo1f1CLXD+v36fXbtjCP59+CfQE0yqDFHolJowbg9vlHPLcfo60dfLrP/x5UFWjKIrc9tHraehrjb3nj38b1MrXz7VXXTYo/fdQcyvdoR6qqypyFVFvxh8femzYJLcFl1/AXV++4w233b13P8tWrKUn3EvNiErOP+f04xLG3y4/+cU9PP7UC+i6TqgnklteP2YUkyfaNg1jx9Tx2U9+5F8+l+HQNI2Lr/oY8cTQ35Hj+Vzfr+TPIvLkyXPc1FRX8plP3MQLi5fRfPgIgcICzj7jVE47dca7PbUTZmBb3ECWrVz3loSndDpNOBzhrv++gx//4g+0tXeRyWg4HSofuuYKDh5upaW1jZa2Do60dZHRNCRRxN2XemULT/T9DYoi4/O6EQWRTEYb1rz7ncayLPtusCKjaVk0rZdMJkN5WSmyLOH3e6mpscu/dV1n3/5mIr1RDMNAkWUcTgeqoiAIAhXHecKTJ0+e/zxWrFrPwmcWkcqkOWPuLK67+rL3jPC1btO2YUUngBWr170jwpOu6/z54ScGJbCOGV3LJz7yoVxbnGEYLFq6nDXrNpNMphg/bgyXX3zecV30Pf7UC4NEJ4C29k5eW7mOi88/msbndDqYM3Mq//fbB2gZ4Pu0dNkqmluO8Lnbb+ELn/4YLyx6lb37D+L1eDhnRjmjC7vQBRer9nrY+tSjuBwW54xPM7LSylWkAEyd3MBTz72c+/3rj6mPZr1cPGEyP/3VvWQMWwgIuNJ96y0ONrcQCBQc8/290c2cYNcRlu1+lFikk6wpUVlWSJXfA5ZOtGsPZei0SOVkDDk3H+Jxdu5tZfLY+YT3LrTT7LIClqkTbV5MoH4B/pHnIDmLiDYvxjJ1TEPDUVBH24G9LN5bhlvJ5jx/Gvfs47U1O5h/5rUkAvWkgtsBkJ0BHIVj8I88B9VbbpuLO4sIbv8TeipMJtyE6qnE8lgUjr2C7p0P2VVFkorqq7arq9yl6Mkgqq+KZHcjApAONeIsmoAgqQiiSjbejuqtsh8bFgK2QGVg4QiMRYu3YWSTiIoHTJ2urfcju0vJRA5imQaWZVA86UYSbUeDZySHH8lRiBZrxcj0YmoJkt07cZdMJBHcjmWZ6OkIoqRgmVkK6xcQ2vk3MA0sy0TxVZNoW423ai5dW+9HlBwIgoyZTZJNdiEIIpKjwBawFA+mFsOyTLtqyBkg07OXbLyd7p1/A6xBvk6CpJLpbSbRsQlX6WR8NWcTPfgSlpElE21G8VWj6QKHuwO5/xHYfmVb2kq5sPL03D7rr7X3AT0dRlLsZLp+IcnQ4kypczDtri+x/2AzDmJ4kmvpv3U38LkDWbT0Nds/Sj/62qZp8vLLL1NXnMXy1LF77/5h9+dNW3YMEp7qakdQVzuCSG+UZ19cSuuRdkqKizhj3qxjfjc8O8xNWoCXl654Q4Fk05Yd/PXRhbkbqp1dQbbv3MOX7vj4v1x8+vIXbiMajbF42cq+c2uREdUVTGw42tZcWjw0tfOdYvfeA8OKTgA7dw2tEv134b3xK54nT573DaNHjeSO225+t6fxtrAs65gR1JFIdNjlb8RPfnEPz760lHQ6gyJLaFmdQKAASRAZPWokbe2dbN3eiGVZdId6MAwdURTQDYNkMkVhoZ90KoOW1bAsC1WRcbvdGIZJdyTyhv5PU0c7cTsEdhzKEEvZJ+c+l8jkOgfJjMXWAye3WkoUBUzDtE9ELYtkKk2wO0RVZTnTp0xi5jQ7bvgvj/yTbTt2U1Ic4MChFnsbUcDpdBAo8HPLjfbFWTyRpKOzi6JA4bvea58nT553n9/e8xf+8sg/c483bNrOoqUruP+3P3xPiE8e17FTT13ON48MP1HWb95Ga2s7M6ZMpra2GoCXX1kxSHQC2H+gmWdeWMJ1V18GwKNPPMv6jVsB29dky/ZGHn/yeW772Ie59IJzhvg29RONxTnS1jHsul279w0SnsBO6moZxmx8/4Fm9h04xNjRdXz0psF+LbGO7dzzyCu0dthtPxHgnyGd2ZP8XDOzIfe8wkI/N1x2Ck+8tIWUZl+ed6cLueHqC0h1N+YqqQaJQEDrkQ4WXHbBMY3XzzxtNkuXrRqy3LIsdm5+jTpfJ1lTBMukq6ubWMLP9FMmk2jZTIEzw7TKYK7qpZ+e7naiytbcY0n1osXbEEWFcNNTfZVdi3Nm5Kq3CkuQ6OzJ0FDaM2S85t0rME4dhxZtzi3LJoMkgzsw9aSdsFbSQKJ9E1qsDUPrRVK9KO4S/CPOILj1vj5vIwXFW9Vn1p0lmwziLm5AzyYQFTemFkOSfGAZFE+8ka4t95JNdJBNduEun4qVTZDsjoFl2pVcWgxTT2NmEwiyg0y8HYwMWjaFKCoYCIiKl549jyEpPjDtljxZ9VEw5hLiR9agxVpJ9+yxRbBUCNlRgKHFsJCxLBOnv4ZI01MIkoyIF2dgNILkINPbjJ4M2Z+dp4LCsVfQu+9ZTD2Fs3C0bf6eCpGNtyM5CoatcMr0HsLUYjlfp+IJ1+bW66kQWrQ11/6YjbdhGRp6MsjI0WewZf/GIftMdc1o3E5nny8XRJuXoKfDaDE71Tdy4IXca/RXQ/lHwvjRNYSb1jEwszfavGTYiqdkcNcQTyiHpFNqbaP3MCilpx7zPHG4VODuUA+//P2fBtg87Gft+s186tYbGTO6dsjzo9H4sGMnkyl0XR/2e9myLJ5f9OqQeWUyGRa/upKbPnTlsGOeLERR5Hvf+jJ3hj/Bnx96nH0Hmge1/ymyzFmnz/mXzuGNKC0pss+nzaH/t8LC4f2z/h34/+yddXhc55m+7wNzBjWaEYNlybYsW2bHdmzH4TgMbqhJmxSTlNst7263u21/C+22u93CNoWkTTENNtiwg6aYWbZkyWKWhung748jjT2RTOF0576uXPGcOfCNBs45z/e+z3PMM3gsnuDp5zdgWRbnnbmMkuJg9rkNm3cCcMbpi97q8eXJkyfPm44gCEytqaazq2fCc3VjF/Ynyy9/czcPPPIkYM9AdfUMkUqlcToVJEmiu7efQKGf0XAYn9eDpumAgDA2DrC9k0pLigCLQKCQwaFhhoZG0Se5YDiahdNd3Hx+IafPchNLmuxuT3Hvi1HOXeDj3IUeAl6Jtv4Muw5n+MnDo6f0uo6FfZK02wJFUUDAbr+rqiznK5+/FZfLSXdvP7v32j4JXo8Hr8dNIpHENC2cDgcz66dRWlTEY0+u5aV1r6LrOoIgsGDebD54/ZqcZLw8ed6rjIyGaW7tYOWyhQAkU2lGRsNjBvzQNzDMzj0HuHT1me/kMN9VDI+GuPu+RyYs33+ghUeeeI5rr7rkHRhVLgvnz6GqspzevoEJz11xyflveP+6rjM4NEI6k+Eb3/5B1iRXFAXOPXMF//Htr7Ftx95Jt922cy/vv+ZywuEoW7fvBqC9sydnrHff9yitbR186bMfn9QnzyHLiKI4aZXtZL/Nk/k+jtPTN0D99LoJr+/uJ/by5NpXASguDlJRVkoGmXW7R1h53kA2ETU13ESNp5fPXTOVznQDSG7mzJyC1vssaijMlMIU3ZGJYl9BgZeBwWHed8VF3H3fIzlpXvPmzOKKS84nnkiyeevOnO1qp06ho6uHTCaIQzSYXhQlrctsOuClfsXZuGrrGO14GJesH1VhZbcPVjsOYGTknPZBIGtUHWp+CMvQUBN2NZHsLsI75RxSG4+klY2LChW+OLW+BP1bf4TirUT2FOOtWsngjl9gqDESfVsB2/cp3r8VU4uBZaGnRxAVL4O77sQydSSnn9KFt9keTKKD1OhBBMkJokzJ3JsZ2PqTsYoiC0FyMrL/T0hOP0Z6FAQRNdqJJLvtaNsxc25BkMYS8exWNksQEEQnggCmngHZhZboR5QUHGVlBBrWEG59ikysm5F9fybYcDWiqKBnwhiZCLK3nEy4DckZwNTiYwbjB5BcQSTZTemiTyKIMlpqhNGme9ElBae/lmD9FUS7XiIT7bAT9JQCRIcXyeEDXyWSw4e3wq7GlxQfxbOvJxmcSWJgB2ocFJ/t6zRuIG4/30Co9XEMNWabfAdn2NVh3kpmmc3MnVHCvtYjhvlFfidXLXcT7XwRYczI3dLTSO5ixNQoWmrINpQ3VARJsZ9z+pFcRTn+T0e3mx6dpjeO5SwnrfdnPyfj/lIFbgHZ6SdQ3sD0uqk5/k3jzJ87a8KyZ55fN8FbVNN1HntyLV/87McnrF8/vY7tuyb+5tTWVB9zMiCZSjE8Mvl1Z1d336TLj2Z4NMTw8AjT66a+Id+8omCAL3724zy99mXWbdxKIpGkbuoUrrj0/JNuNXwrqKwoY9H8uRP+roIgcOO1V75Do3rrmfTTous6n/ryvxKOxnDIMnf8/gH+/ZtfYNlieyZ7/0G7nC8vPOXJk+e9yuUXn8ev7vpzzmyQoihcfME5x9lqIo8+8Vz238lUGl3XMU2TVDqN0+lE13T6M0MYhomm6VimiW6YgD3LIYi2+BRLJJAliaHWdjRVO6HoBDCv1smqOR4kScCliFywqICz5/swDQunYqfnzatzM2eqi8UzXHzsv48dgX08ZFnCNM2cmRl7FkvA6VKonVpNbU1Vdjaps8s+TjqjEonGKC4KUlwUxMKipqqS6spy/njvwzkXJZZlsWtPE16PJztjnyfPe5mOrl7+cN9jWeHpQHMbd/7xL9z+g28C9g37Y0+/mBeejmLdhq3H/O3bsHHbu0J4Avjed77Gl//x3xkesVPdJEnimqsu5qILTj42fDI2bdnB4089Tzye4JUNW4nFExT6fQiCPTP+/MsbueuPD+TEhB/N+PlscNj2DVRVjb7+XIEslUrR2zfAtp17WbFs8YR9uN0uFsydnTUdP5rlSxZOWFZ6nEjyskkMcu/64wM8+eyL2XbFWDxBNBpjdsMMwG7pGxeeFH8NktMPmSgz/e1jN+jPYmSiKJ4A9XOn0L1hV87+e/sGUTWdnbv345BlFi2YQ0lJEalUmob6aTTOqqf1cAdYFqUlxZimybS6GpYsmkdbexedXT1ZQUkzpWxr1WAoRUlxA5s7g1Q4u+lJgjSm282p8xPwWpiGSsmYaBCcuYZQyyOYpu33JEtB+/+eUmR3Ee6iRhR3gEzBGRDdgEvWKfclUA37mEVBGVONodJHoP5y4r0bcfprALAMFS3ez+iB+8lE2pGcgWzinhbrsf2URJnShbfhDk5HcngY2v0bXIHpmEaGYP0VxHs3Ijn96OkRJMWLGusC00BwuHEGZpKJHsZIh0ExUAqmoiZ6EAUZU4vjcBfjLpplV/ZEO8EywLQQHR7QVQxDBSxEyWV7SVk6giAiiBKxrpdxuEsQRBlTT6OH23AFpqMlBlH8NaSG9mCZOkYmhjtYT3JoN/7a1US6XsayTDBU0pHD9G+/3R6vKOEoqEZ0+LLtc05/LXomwvD+P+MNtWQrjmK9drUVpoGhFjDSdC+YOogyZQtvQXYXITuDtnAme7D0DGULbyV06HG0SBtXLK5n5cqr6OgNESxwMN3bBuoAJhZmOoyeGsZV1IAoKTgLa0EUsQyV1NBeHL4qnIVTCc5cgxrtmuBpNv55MTLRnHZTgFWrzuKee3uzXmaLK22xt7yqPrv9de+7lNvv/GOOoDRjeu2kVT2tbR0TlgG0d3aP2SnkCtKfue1mPvfVb5FOH/GYkiWJz9x686T7AXC7XHg87hx/t3GKjtMGm0ym+MZ3fsCrW3dhGAZej4ebb3wft3z4/cfc5kSIosilF57LpReei2maE9Kl3ym+9/++zj/8y/fZuWcfpmnh83r5yE3XvqOVWG81kwpP6zbtQBAFHvztD3G5nDzyxAt8419/wve/82UWz5/9do8xT548ed50Zs2czhc+/VFeeuVVhkZGqaos57yzVpxy3/nRZfzjNwMWYJkWujY2y2rZwkomk+uZIQjCWO0TCNi+E5qm58zOHot//Ugpqxf5EMXcqFqHJICUu0wUBWbXOPnHG4pp7tF4cN2ptRPKkoQpiKhm7s2OfWOjUlVRRigc5YVXNmIaJoZp2ul3Wu7rELBb7cCuYCib5IZly/bdXLvmEiRJOqUx5smT571PYcFEf5NxCvzHfu7tZnZDPY/f/2teeuVVhkMhzlyx9A3Pnh9qa+feBx/P/q6OjIawLItoDAr9RxJTn37uZd5/zeWs27hlwj7mNtqmuWWlxYiiSDyR5LUdOB6PXSHU3tE9qfAEcP01lxFLJLI3qJIkcfaq5Sw9bcGEdefPaaCstITBoeGc5dVVFVkxaZzWtg72NTXjHjsPjDMaihCNxfEX+CgtPeK78tob8vF0tvEb9qsaPUiKjw2vbieVSmGaJm63C8Vh395ous6W7bu59KJzs+lVz72wnsefyvWsqawoY3bDjAnn6KPb97bv3MuhtnbKCzSqvAZO4TDt6ZmsvmA1569aSOTAHxEQsqKBpPjw165mpOkeJJfdNSK7gra3k7+OeN+rpEabuOLS87nzd0MoWg+VBUlcsk5caWTO+R8l0vQHBEEkcvgZe3t3EUWz308m1EJyaA96OoxlqLhL5+Gvu5Do4acxnYXo6VEU/1QwMhhqnOF9f7QFKqUAp7+W0KHHwdQxjfSY8XbaFo0EAUtLYwkystOPaWg4PGVoyUHcxY0Y6RCIElpqBIenBD1pJ6gJksNubfNV2ULLaDMgkB49OPa8grd8KVpyACMdwjI1ShfexvC+P6LF+0iH25BdRajRLiSHF12NIisFmIZGOtRGrOffQBAQJAcOb8WYcBMBQcBdMg9RcqDGOrEsA0GQcJXMIdL+DIYWJ9G3FdNQMdKhHANyyeFFH1smSk4Gd/2aQP2Vdnuk7EGUXUiKj3jvJgL1VxI+9BiipFAh7qNh1XiFUgLJU4y/djXRDnsS0jJUsExkdxGFMy4n0vpXDDWO7ApkPZzcJY1kIu14yk/LSXH0164mObA9R3QCWLxwLvHElax9+lEaAu0oikJtTRULzvpwdvuqynK++bXPsX3XPsKRKHVTq2mcVZ8T/jKOz+thZDQ0Ybnb7Z60gmnBvNnc9fPvc+fv7uVwexcV5aV85IPXsnjh3AnrjiOKImedsYynn3s5Z7kgCMcNI/rnf/0hG17dnn2cSCb55W/uprqqfEKb7+vh3SI6AQQK/fzix//GwOAwA4PDzJld/65oJ38rmfTVdfcNsGLpguwNwprLzqOw0Mfff/t/+OG/fe1tHWCePHnyvFXU1lTz4TdoBDuttoaDLW2AfUIbr2A6+mRvmHaFk8B4nZONZVmYlonX66G8tJim5taTMhH/wvuKWDnbM0F0Oh6iIHDZ6X5OG9WoLpFPqfUuo6qIojQhdQ/AMExeWr+ZaVOr+dHP7iKTUXEqCoIgUFRUiMMhZwUoRXFQXGRfgBf4Jr+J1DQNXTfywlOePP8HOees5ZQUB7OVROOIosD7r77sHRrV5IiiyHknGUV+MmzYtD37+2qYZvbf6XSGAp83e8OUzmS4ePXZtLS250SnBwOFXHXZasC+oTl9yULWvrQ+5xgOh4Py0hJ7neP4iHg9Hj7/yY/Q0ztAOBKlZkol/qNEwUg0xtYxk/UZ02v5zG0389iTa9m9pwkEgcUL53LVZatzzoMDg8M8/tTzDA2PEgwEUJS+nATYeDzB4oVzc9L5TNNk/6EeOtsUKsUBSkuKUByOHBPmKy+9gMsvPg9N0/nJL36LOIk/1boNW7n4grOJxRM89dxLVPjiOSbRe/cfZP++3dRXSJO2URYFAxxqawcgnHZTF/RTW6VTZUU4Z1k9yc6nECXFblPTk1m/n5Gme+xKH9mFKzgTNdqBlRol1rsR2V1sV70MvcjffeL9tG57gEwyhNdfSv3yjyA7C5Abb8iKbTBuPu0l3r8NQRCRXYGxZwQi7c+iRjsx1TgIElgmkquIkQP3o8a6sUwNUfGhJvqQ3SWo0S4cBVXoDGOJst1yZ8+SoRTWosV6sFIjZKJ2+5aRDuGrPpNI2xMIkgPL0tFSQ4iyGwHbkFyU3Ti85TgD0+xWPC0JloErMA1v+ULifZvREoM4PaUkejdSOvcm+rf+FF1Pk4l2IkoKsiuAw+HBMjS0xCB6JoSpxhFlJ74pZ+IKzgBBJDW0FyzTTthLDmKkw0iuAIXTLkJPDOAsqMHUUiAIqJF22+9KEBGVAiw9TcH0yxjadQei7EIQJNskffvPwDKQnH7KFt5KvHcTRiZKoncj3solpEdbcgRQQXaNJeGVZ03Fx/FVrSDeuxFBlLPv00jTvdmUw0ykHT09ir92NUZ6FMVfk03TSw035YhPpmnS032YhpJhFFFBFAVC4QjDh56iYu712e+Cy+XMCfsZGh6hu6efoqIAtTVHbCTOWLGEjkmsJlaevnhSoQpgxrRavvvtr0/63LG4ZPU5CILAy+s3k0ymKCkp4rILz2XWzOmTrh+Nxtm4efukz9374F/fFOHp3Uh5WQnlZSXv9DDeFiYVnirLSznQfDhn2bmrlpHJqHzln/+LRfNnMXP61LdlgHny5MnzbubTt93MV7/xH+i6jtvlIpVKI8sSsiyh62OCkwAgIkjAmNg0rt8IgkBVZTm1U6o4eOjwCYWnhdNd1Fc68LhOXZhRZIGqYgclfpmF010nbTxuWZMbVI6TSqXp7hugpCiIKIpkVJXCwgJUVaOkKEj/4DCBwgLqaqcgSSI1U6qorixn05YdE/ZVN3VK3uMpT57/o4iiyH/9+zf4h299n/4BW1Rxu118+pabmDN75ptyjM3bdvHiy5vw+Txcf/VllE7SDvZOEI0fMfB1u1x4vR4SiSRge+uNT9QvWTyfAp+Xr/3dbezcvZ+evkHKSos4beG8nN/O919zOcVFQQYHRxgaGSXg91NTU4XDIaMoCiuWTl7tdDTVVeXZtrdxWlrb+dVdf85W+L74yiZmN8zg1o/cwIduvHrS/Tz02NO8tO5VhkdCtLS243DITKubysjIKKOhCIIgsOy0hdz64Ruy2+i6zq9+ew8dbc0sqhxClXW6evponDUD6TUmzKIo4nQqxGKTp0TFEwksy6L1cCclrvCkRs3hQ49SopZw6/UreHHbALv3HkCSJE5bNI9kMsX6Tbav0niKnt32pNK1808UBwNITj+u4tkkejeTHjmAKHvJhA4hyG5cwXpSQ3twly6wzcHVOM5gPZaWwMhEGd33e0oKfMjlDdnXpcYHGGm6F1E68p6G255ET42iRtqxZA+lC24h2bue5OAujEwEUSlAKahGTw6hxfsY2nMXWmIAQZDwVq0YE6UEW1QyNbRYr50Ep/gRZTeZcBsWFumRA0iKf8yPKEgmZHswxbpeQpAUwMLU08juUgRRRJTdGJkwWrwfyVGAlhxBSwzaYpcokwm1Ijl8ZGI9IEAm0okgyEQOP4PDW4aWGkaUFARJwVnUAAgk+7faBuhOPwICkrMAIx0i1PwophZDVHwICJh6yv636EBSCsiMtgDYJuWBaZiGiiR7MJQCBEFCSw6hRjoYbbpnLAHQNikfbbon68VUuvA2XMHpOLzlhFoeIRPpJBPpQHYXYxmaLbwZGggiid7NCJZAtPsVBOyqLGss6c/hLgFRprjxBkaa7iUTOkT/1h9RuvA2u80xNUr/1h8hj7UejouXylhL5TgbNqzDGngBt6yT1hWaBmyPp0PN+3EqEz2hTNPMhguMC9jT66Zyy0fej9fjYfnSRUQiMda+tIFMJoMsyyw7bQGXX3zeiX4STglBELhk9TlcdP5ZpNJpPG73MYUtgFAocsxW60j01IN/3iqGhkd45vl1tB3uxF/g48yVS19XGvZbza49+0mk0px+2oJ3TSXVpKM44/SF/Pw39xIKRwkeNSNy8fmrSKdVvvfjX7+pwlN37wDf/s/bOdB8mNqpVXzr65+mYcZEV/1TXTdPnjx5Xg/Nhw7zwssbGRoZpbqynAvOXcXUKVWTrnvG6afx8//5f/zmDw/Q3dPH7FkzCIcj7D9wCFVNI0sixlirnSSKIAgY+hFxyTItDjS34nYqVFWUZ2dVj0Vbn0o0YSEKtsfSqSKJUFooMbfW+aYl3pmmSSadIZ3OoCgKgmDPXK1YtphF8+dw1qqlbNuxl0QySf30OlYsW0wqnaaltT2n3FtRFNZcfuGkx+jrH2T9pm2EI1Fqa6o5Y8VpeD2eN2X8efK8VRzu6OEb//pjAEKRGO2duY+Pcw3+f5Y5s2fy6L13sGXHbuLRBCuXL8blOnaS3KnwtW9+l5fWvZp9/Kf7HuWf//5z74qZ9PrptRxqbc8+XjBvFq9u3QWW7bMH9sz4Zz9hp8rKsszS0xaw9Bj7E0WRC88/kzNWnMb9f3mC3fsOYJomU6orufaqS15XcpJlWdz74OMTPKYONLeyedvunGqLcfYfaMn+zYuCARTFgapqdHb1sHjBHBAEioMBvvS5j+fcHG3asjMrOrlknbQu09RXBFI/ywsKGNz1a4obb0TxHRHGGqaV093Wl9MiB/ZNtyAIeNwuQikXaV2eYNTslEuRnH4yBJnd4GP1eWdmq7wef+r5nP1lDJk9fX4WVw1lK9HGq7DSIwcwMlFMQ8VRUI0W7yc9ehBXUQPpUPOY2GERqFuNocYZ2v0btHgfuqRQWH9FVnTq3fgfWKaOq6iB4sYbiHasJR1qJRHqZDSmMxhNkjnwY4or6qlVIoCJ0z+VkrkfItz2BKmhvWixHkSlAFfRTLylC4l0voAW68FRUI2eHAZEMmOpeQ6v/Xc01Bi6PoLsTlE863oiHc+BZZCJHEYUHSiFtYBAJnIYLAtX8WxExYfiqyY1sp94/xYE0QGWjig5QJSxEIh1v2J7lMsKDn8NmWgHDk8ZqZEmwBpLoatGGJt8Ex0FGGoEh7sYwSMhu4pIh1sx1AhYFpLsxhmYnm3ncxc3cnRNuW0+/jLq0F50wOGrslPqTA3T1O1KJ8lBYNpFtt+VUoChxBFlN7Gul1C8ZdlWz8TALuK9m7IeUuP7AnAWNRDpfB410oGo+ChdeBvhQ49hZKJoySHcJXMw0iGKG2+kf+uPMNU44UOPEai/kqFdd2CqcTRDxVlYl+P5dDRtTZspGPsOjIulO/tKEYQhZibDEzyh1m3cOsE4v629k788+nRWHL7ogrM458zlDI2MEgz4s9dT6XSGl9a/yr6mFhwOW5BavnTRcQWjEyGK4kldr9XUVBIo9BOOTBSZXtu2+04RCkX40e13ZScFRkZDHO7oIhKNcf45Z7zDo7M50HyIf/z2D+jptSs3fV4vn//Uh7n6yovf4ZEdQ3hyu1x86+ufJhZP5AhPYLfdBQP+4xqDnSr/9b+/ZcHcWfzke//Io0++yL/+4Jf84Rf/8YbXzZMnT55TZc/+g/zm9/dlZ4mGh0fZ39TC5z71kZxS5aNxuVwsWtBI/YxaXE4nL76yEa/Xja7rGKaJLEsYholpWZhm7myOaVk4nQ66e/uJj53IjkcsZfKzx0c5fbZCoffUK4MEQaC+SqGoQCKjWafs93QsNE1neCSUnXFXFAdpNYOFSf30ugnJRori4CtfuJVNm3fQ1d1HUVGAVSuWUBQMTNj3/gMt/OYP96OPeV/t3X+QV7fu5O8+8zEKfN43Zfx58rzZFBcFOGvlkRtxn89DTXV5zuOpUyrfiaG9J1i2eKKf0Bvhr08/nyM6gd3a+58//CXnn73yDSUnvRmcdcYytu7Yw/Cw3QZdUVbK6nNW4fW6EQSReXMa+MB1V2Y9mk4Wr8fDR2++jnQ6g6ppOS1zp0r/wBByphOndKRNDexUt67m9TCJ8LR9177sv0VRYG5jA62HO4lGYyQSSRYvmsf7r75swoz8vqZmgu50VnQav+He0CoxvyGKmOpmcOcvKVv0SRRfOYYa58yZCfalIhRHUuwbtP0aHbKcTRucOaMOXXDy2Fadc2enKfI7WFw5iCgKFJVU89AmlX3NvweO+FqtuXw1SxfPZ+2L67EsC8Mw6e1uZ8W0CKGwSGtbB3VTp2SrsI72pJIcXvSxNDhLz9jeP55igjPXABDtWIvk8KFLCpahMrTrDsoW3srQvj+hp0YQZBeB+itRfBX4a1cT7tlBLB7n0GAAWRJwySrRgf2MlvmYMqWW8sWfsj2EgjPR0yEsPYPsChCYfimGlkSNdoFlZMWndKjVTrQTwDJ1nMF6jMFdINhVTOlwK6KkYBoZJEcA2VNKydybMbUEfZt/CIChximabbd7Dez4JUYmgmWqODylCLILxVtBJtqJYYEggMNbSTp8GEF0oI3sB8tCEGUKp19MerSZTLQDxVeFIIooBTWYatxOrTMyWKaG7LITgBVfFcLY9ZRlGWjJQbvCaIxo18sYasL2r5Jdtn+VZdh/FwQy0U4U/1S7MslTipYcsseU6MfIhBmBrCl5OtSMGuvCUBNIipejZwz0dAhLS42Jb1UkejcSrL+C/q0/RRQEBEGyTfIVHxVLv8hI0z2IkkL08NPI7pKs6CSIck4L6dEMpgrpjQRz2kMzhsyO3lIurlo1wRNqy1ii5WvZtaeJG6+9Imse7nQqTKmqyD5vGAa33/nHnMTn1rYOunv6ue59l066zzcTURT5+Ieu539+9pscW4cCn5dPfvyDb/nxT4aX128mkUgSjcUxDDOrkzz7wnrOOmPZpEmhbyemaeYEX4Bd8fn9H/2KeXMamDlj2js4umMITwDz5xy7pPnsM5a8aQOIJ5Js3bmff/3G5/C4Xbz/fRfx+3sepb2zl7qpVa973Tx58uR5PTz5zIsTfIw0XefZ59dx60dumLD+uo1beeDhJwC7HWLbzj2EQhG8Hg8et5tMRiWTyZBKZ1BVLadlzfZMMgmHo0Sj8Qlm3Meib1Tnl0+E+Pr15SdeeRIK3BIZFTbsP7HQdTKIoohlWeiGgWJZCIKAKIocPNjKjddcQXtnN+WlJbjduVULHrf7pGaIHnrsmazoNM7wyCgvvLwx62mSJ8+7jdqaKr7xpdve6WHkGePZ59dNujyRTPLKxi1ccM6qt3lEuXg9Hr702Y+zfuM2Wg934vf7WLV8CXW1U0688Ungcjmz3q1g36B0dPYgCAK1U6tPqqJBSLZP2qa2qHKISiU9wZsGmHA+dbuczGuciabrfP6THzlmJYOiOLKVS6+94fbNuAa17c/oySH6t/4o68fjEtM01vkpjUl4igpwFDZw9qrTs6Eh9z74OPF4kt6hOI+GU1yxRKCkOMjsWQ1sbXezr7k9e3zDMHjh5Q1UVpRy+pKF3HDtFTz4yFN0dhxg5bQohV4RxR3g1Y4g0VQXy5wK8Aiu4tl4q1YSPfw0gijjKKjGMo4k2/lrLwCgc8fddHccYjiiEhGmsbzqMG7iDGy/HaWgGsNdglI4lUTvRiSHh2jHWoZiIkNRL3sHy3DKRjbdrHc4Q8PZ1yIpPhJ92xkd8xpyFtYBMNryCJlwK5alIwiyXbET6wFTQxAEZHcxLr/dPSLKbkywW/GSAxh6CskVwNLTGOkwowceIBU6OLaubcAd7ViLv3Y1BVXLsQwVy9KRnQEKpp5LvHsdrsAMZFcxnvLTiPesQxQdGHoS0eHF0lO4i2ajxnrQEv3omShaagRJdqOpcdxFM1Hjg+jJEQRRwumvwQK0WA9Of63t8ZQezTEtDx96DD3ejxrrRHR4QZCxTB1TTyM5/ZhaElH2kR5tsVsPk0N4ihuR3EU4CqrJjDaTGtpLYsyXS4v3YRlatu1RFB04C+uwBAE1fBhBUgjOuIJM1PaSihx+BldwBpZlUrbwlqyYpPjKKR7z7bIMDS3ei6OgGkG0P9vjHlBHV/EZapwlDV4eeWGiIDVl6gyC1YsmLD/aO+1o7NRla9LnwBamOifxflq/aSvnnb0i69E5Gbv3HuDHt9/FgZY2vB43q89dxZc/f8spt3jdeN2VVFWWc/d9jzIaCjOrYTqf+NgHqKk++Uma4dEQe/YcYMqUijddaNm+ay/PvbA+m8rpdrlYuKCRyvJSRkbDpxxQ9Gbz0iuvTvBIBPv37N6//JVvfu1z78CojnDMT8Nnv/bvfPKj17NgbkPO8t37mvnV7x7gf7//jTdlAINDo/gLvNlZa1EUqSgvYWBoZIKYdCrr5smTJ8+pYprmBFPRcbq6eycs0zSNvz79QvZxNGaLR4lkimg8gSSK2fL+GdOn0dLaRjgSxTItRFHEHDeSNcwJF+fHo75K4XNXvX4jQkGAAo9AXbmDsoB80i13oihMetFS4POQGovZNU0Tp9OJz+clFk/wh3sewl/gw+FwcN7ZK7nsonNPaazhSJSh4ZFJn2s5qi0lT548eY6HJB7bF+94z72deD0eLrrgrLf8OM2HDvOnex/OprIWFwX50AeuzjH2nozSKXNweoOQCOW0qblkndKKmgneNAAL5zeybceeCcvLS0uOaTIMtpfVrj1Nk7bNlVXNRPV/Mtu6NLD99rH2sSE8hTVMryxmyWtalpoOHmLTlh24XU6WL5rJnJJe3A4dp6JQ4PNiNm3CKZXkVHIBvLp1J6cvWciKZYuZVlvDT/7rX6ipAFN0s6uvzG576i2lsiKGW1ZJh9ow9SSKt9I2to522v5Lst1qFO1YS0qqYs+u7SQywpiAF6G7x8nVi6N4XWDqGcqXfoFE78YcM+toWmJ9h1153Vh6JCDEMi1Crc/ica8hMbQru1wYE0oSfVuxjAwOXwUlcz9MvPsVBF8VyZEDKAVT8E89j0yoGT0dAizb5NtdgpEO4/CUo6WHba9HLUGsZyOWpSMpPsoXf4rk0G705Ah9m//b9nSS3Sg+WyQY2X+3/XcABFEiEz5ke0Al+pGUAgQElOLZaKkRrEQfgqhgGRksLFvwcnhR44OAhanFER0+PKULiHSsxTJU0pHD9jZmBsHhRvFVET70GJaeQY33gCDZop8ZRUv02i167iDBhuuI96xDEERMQ0WUnOhaguK5NyMpXkYO3E96tIVE32ZMPY2zsBZv5XLCbU9iqjEykXa7jTLRj+QsxBWcgad8Ic7gjCPG46KMtzTX98dQ44TbnkRLDtmVYYaKnhyiaOGthA49nvWAqlj6xWwVX6jlEWaVhFk8w8OO1iOThT6fl2vXXDLpd2du48yc4IFx6mfUHddDczLDcbDF486u3mMKTx1dPXz+q9/KXgeGIxoPPPIkQyOj/ODf/vGYxzsWZ686nbNXnX7K2wF8979v57Enn0fXdQRBoHFWPT/6z38mUHjqrcWvRdd1Hn/6+azoBJBKp9m8bReXXXRuTvroO8VwaKLoNE4kEjvmc28XkwpPu/c1Y5rmBNEJYMHcBnRdZ8/+luNWRZ0SE+5jrGM7l5zkug88+iwPPvasvcYp3NDlyZPn/y6iKB6zv3yyE27fwBCpVCr7WBBAVVU0TcO0LCRFwbIsW4iKRSkKBnE4HIyOhjEtCyvHSFywy91P8HtV4Bb54vuKccpvLBLWpYj88wdLeXmvfSFzMuLTZKKTKIo4FIVgoBBN0ykvK6HQX0A8kSQSjaKqdjy1pmk8s/ZlioKFx4zwnnScTieyLE+oeAI7EjhPnncrXT39PPDos8ddZ0pVOdevuehtGU937wAbt+zCX+DlvDNPR1He2ZaAt5vLLjmXVzZumbC80F/AmSuP5ZR0cuzas58/3/8Yo6EI8+fN4iMfuBa///W3tL2VJJJJ7vzdvQwMDjM8MoppWhQFR7njt/fw7X/8u+O2iogOL6WN19C84S4UKcPiykEEQaC8qo6GlR+btE1o/pxZrFi2OCdMwuVycdP71xy3ymrB3NlccO4qHn3iOZLJFG63ixnTarnpBrtNTfGVU7bwVga2345lqKjhwyiFddlWtteOZc8+u0pnvELLJQukdTdbuoqYVm/gFDUWVQ5lK7nGSY/dTAPoukFCqKA9Gp9QhdUSqWHJ8gZCrY9jqnEyVjcOb6UtMFgmouxGGkuya2troWXIy2DCm60am1EUpXPYYv7sSmRXgETvRnxVK4gcfiZ7fKFoObA91/dqqIgVU4dQxFQ2Rc9dOg/TUNFTo1imjmVkAIuCKeeQGtqFZWikw22YWhItOUg6fAgBWywZH69lmQiSgh5qxlPcaCfixXoxtCQYOg5vJcmh3fiqVjCw45doiX5EyQmWZVcCRTuxtBQZqxtnwVT01AjieHqdw4MoOREdvrHqJg+aGsdV3IjDV0VqaLdtGG6BqcWxTA1EGcU/hUysC8VbScbqxtLTmFrc9ocqrLXbAtU4anIAQVIwtSSu4HRSoWZEhxdTT+PwVtpVVw4fRiaKp6QRLTk0Vrn1HMGZa3AHZ5Lo24qW6EN0ePFWLkeNdYKlY5mqPe5Yj90CKEj4a+3K62jH2iOflXSY0eaHSIVasi17IwfuJzW01zZm95RgGSqKt5J476Ycz6eRpnuyvl5GJoriDvDBD3+IhS09dHb1EAwUsnTxfGRZ4slnX2Tbzr3oms68ObO4ePXZrD5vFQeaW3MmUn0+L9ecwOPneOLM8Z777Z8ezIpOR/PKhi309Q9SWVF23OO+Wdz74OM89NiR74tlWew/0MI//b//5mf//Z03vP+n176MZU5MqDYNE1XVJlT1vxOcfcbp/PCnv540EGjF6Sd/7f1WManw1NzawczjGHbPnFFLc2v7myI8lZUWEY0niMYS+Au8du90/xDlZRNTRk5l3euuupDrrrJNanVd56zLP/qGx5onT56/fc47eyUPPfb0hOXnnr1iwjK/zzfWLmefgvwFPjKqhizLGEeJSqZpMjA4QqCwgHA4gmlZE4QUAcY8CI4vPE2vVNAMM5tw9EYQBHDKAoNh/ZRS7sCufgIBt8tJIpFCFOzZ+oaZ03EqCpu37UIQBAp8uRf/G17dzopli9m2cw8bNm1n8cI5nLly2TGP43I5WbxwLlu27Zrw3MpJ/ETy5Hm3oGoa/QPDkz6nGzqbt+9l/pyZb4vwdLDlMJ/5+r9z3qpldPb08/ATL/Dz//rmW37cdxMXnLOKyy/ZxhNPv5D9nXW7nPzz33/+DSX+PPz4s3zvhz/PJpLu3LOfZ9a+wu9++V+Teta902zfuY+WQ4fpOeqmdGQ0xGgowp59Bzlt0bxJt4tEY/zqrj/T09tPgVLMvNJePAVeGmfVUzHvhklFJ7Bbym+87krOWLGE5pbD+KUh5i46E6//SNWuocYnGCRnMirdPf3IkoQg2K0igcICfF5vdpt47yYcBdWoYTuJW4v3Emy4etKxjBu0T+Yb5ay+CKWzB5Jhgu50TpVV46z67L/Ly4rx+bz0xyfsntq6emR3EYq3EpU+RNmDGm6zU+MkBadvCr7KpaRHDpBJNqGZrpxWRZesE07IuKe/H2vkVfTkCIO77kSUPciuAIIoM7ciRFuZicehIgiws6+UoDtNZWU5RrIfyVeFK1iPp/w0Qi0Po8V7kT1lyO4iJIePeM86ZHcJemoYZ+FUTCONqadJ9G7CU7oALTFoG4ObGqLsxDJNRMmJmhzAqfhQfBUIomSLWaaGnholcvgZJKUAU0vgKVuIIDnQ4v1jCXFOWxSM96AUTiMdOgimjigq+KeeR7jtSQTJgYHt/eQK1BM5/LS9fz0DkgNBlHEGpiO5AhipEVJDe3EVNeAMzMBIjeDwVaKnRkDP4C6ZS3JoD87COpLD+xEECTXeg+TwYBoqnuJGtOQgppZEjffa49bT9nuW6EMUHYwcuB9DS2DqSSxDwyRBvHcjemrYTgcsXwJY6GkZPT2K7ClFi/UQHW2yhT7LpLjxhrEEwmFSQ3sZAdzBmaSG9gLgrVyKp2wBim8K0Y7nMDJRMDI5HlDjlVNHG44vmu9n0fwj35E7f3cve/cfzD5et3ELh9o6+OoXbuXLn7uFHbv209ndQ0lxEUtPm097Rw/3P/QE/YPDVFWUsfq8VTmf76WnLeCZ59flTKgCTK2pZlrdxGrGcTo7J6+UMk2Tltb2t014euzJtZMu375zL+FI9A1XPQ0MjSDLMoGAn3g8iabrCKKAx+V6x1vsxikvK+HaNZdw31/+mrN8dsMM1rwLrCkmPdOmUumcm6bXYpomqdREZfP14PN6OP20edzx+we45eZreOypFykrLaa2xm6dMwwTURQQBOGE6+bJkyfPG+WcM5djmiYvvLyJaCxGcVGQS1afw4K5s3PWC0eivLptJ8lUmlA4QllJMbIs4S/wEYsn8Ho9SKKArhuYponX48Y0TSRJQtV0u9XOtH/fJFFCm6SiZzJ2taVZt09h+WwP0htIxDJNGI4a/OmFCO9b6afQJ2b3PxnjM9PjN2wO2YFDcaBpGoZhkk5LmKbF+k1bWTx/DoZhUFVZPqGsOxqNccV1t7DnqIulutopPPCH2yktKZr02NetuRRVVdm99wCWZeF0Orn4grMmvCd58rybmFFXw39++0sTlr+4fgu3//peqipK+dD7r3xbxvLH+//KB6+9nFtuvhrdMPjgbX/Ptp37WbJoztty/HcL3/qHL3DjtVfw/EsbKfT7uOqyC/H5Xn/lZDKZ4j//5xdEY3EUhyNbRTYwOMydv72Xr3/pk2/W0N80BoaGc0SncUZGQ7S0tR9TeLr/oSfo6e3HKenMKQuhyA6SyTShcBTPmLH2scQngKlTqih1RYh27ibV9TSusfXH24mMjF1pPC4+PfnsixxsacXpVHA67XPDwZY2nnruZa64cAWhlkfQU6PoySGUwjo7sWzMoHu8VeloTls0j5fXb57gGxUMFDKjfjaydBsPPfB7+uNHzlkV5WUsnDeb7Tv3EgwUMq2uhisuOZ97H3w8Z5LI7XFTUVGK5a2lcNqFCJJC5PAzCKKMpPgIzLgMy1BxlzTiLppFdFeE/rgtSh8thB0IT+X6kjpMXwH9W3+EnhpFECM4vGUIkoKsp7l8VQ29XSkyySiLZ/qYvfh9THE0oSdHMI0MolJAuO0J0qPN9jauALIrYBuJmxrWmJm17C7CU7GE4T2/wzQN1Hhfth1PdpeQCR1CKahBjfdiakmM1AjOwlqCDVcTbnuSTLidTKQ9a4wdbLiagmp7gi7U8gh6OowgOhAwbN8nI41SUIMuuSlb/CliXS8hSA4sw/YjUkrqiHY8h6WnkD2lKN4ytMQglqEiuwIEZ76PaMdzgG3ULkgOnIW1uIpnE+/egGlkSIVasu+JKDnQ1BgOXyVGOoTin4qWHER2FZGM99kVVYJMUeONJHo3YpkaqdFmBFEC07AFQ0HGsnT0xACWZeIsrkdUvGjxfvTkIIgSWCbpWCdqYsD+LHrt4wWmX4aeGkGL99keUXoa2V2Cw1dB8ZgZO0Bw5poc0dVXsYTEwI6s79O4J9hr/dO6e/tzRKdx+gcG2bl7P0tPW8CyJfZ/AHv3N/Pr39+b/dy2Hu6grb2T2z56I3Nm24UkBT4vn771Jh54+Ek6u3oQRZG5jQ1cf/Vlx/xeA0ydWp1zTZd9D0SRmTPqjrtt7tiHUDWNKVUV2bTIUyGRnNy31DAMYvHEGxaezjh9Mb/89d0oikJRkZI15geB05csfEP7fjP56hduY/HCOTzy+HOk0xlWrVzKB6674g1NsLxZTDqCqTWVPPPCxrGbotw33jRNdu1rZvmSNy9t5Kuf+wj/+l+/4uoPfZG6qVX8y1ePnKg//w//wRUXncNlF551wnXz5MmT583gvLNXcu5ZK8hkVJxOZUI7QHdvPz/71R9IpVI4ZJlYLEH/wBCV5WVEY3EKfB77B96yL1wTiSSJZAqXS6G0pIj+gWEMw0BxyGOpd/JJC09ANonua9cVv+6IWwvYczjNhYt9FPpEInGTtj712OtbFpIkYVkmWBAoLMCyIKrrCILdiqjpGul0hn0HDnHOqtMxrYkTGK9u3cn+A4dylrV3dPPJv/sn/vKnn096bKdT4WM3X084HCUSi1FRVnpcn4I8ed6N7D/Yyk9/dTeHO3v4+E1Xc80VF7xtF4JNzW3ceI2dSiRLEssWz2XfwUP/54QngFkzpx/XW+hk6e7t599/8L/ZdpYEtiF2oNCPIAhs37X3DR/jrWC88mcyjmU8nEyl2NfUnFOdM97m5fLFqSiLEmp55ITik+KvQXL6bd+ilkfw116QbSeSnP4cj6htOyb/+23dvpuLllejp0ZR470o3kq7va7havq3/4JEbIS2V+9gxqov4HAdudGsmzqFqy5bzV+ffiErPhX4fHz05utsz9hCg49+/LNs3tXMaCjC1ClVdHe1cfdvf0xfzK6ymlpTzW0fvZHPfuJDbNi0neFQiM7OHqLRGL/5/X04HA4uPncpi8ZMv2VXAIB476Zskp2k+FhyxpVs2PM7TNPMjqV7FILFCnff/yhzamTKZTfOwHQc3jIs3Z4QsiwDfXQvlUV+rEI/s0tLKZ7dADQwcuB+MkN7Gdn/Z8BClN24S+cRmH4p0Y61KKZGJtSKMmZm7a+9YEyQqUVPjeAsmIKhxfHXXcjowfsBAVNPYZma3c5WOj/7GkxDRY11IogKarwPU40R7ViLp3Q+iq8c2VOBlnwGLAtnYZ1txG2opEOtKL5KwoceQ5QUPGULMQ0VIzWCFuuyE+wQcAWmIwoiouwhE2knNXIAh2cjTn/tmBh3tCgjYBqZrMDjKmrAEgQMNYYgSnY1FBbpkQNIzsJs+6GRDuEqrwMjg792Nf1bf4ShRgEBh7sYZ2EdBVPPJdr+LMnhJgRRtn2mRAmHrwJJKSA10kQmfNhuK8TC5bdbPSVXEdGO5xBFBaVwGsLYtdB4wiAcEZLUaFf2cz9uDG9ZBrKrGIenhOF9f0RUCrL7cJc0YqhxBg9PbBsep6dvkNc2Dz/z/CsTKuoty+LZ59dlhSewBeIvf+4WorE4sizhcZ84RfOjN13L8y+un9Bud9YZy06q2mlkNMSv7rqHzq4enE6FQn8B1199OfPmTLT8OR4L58+hp3eiqF5eVnJK5uTHYnZDPeecuZwXX9kEHJmQnTVzOpdffN4b3v+byQXnrHrHAzMmY9IrnhVLF/BfP/0t/3vHn/nkx67HORYxm1FVfv6b+4hE4yxfOn+yTV8XVRVlxyz5vv0H3zzpdfPkyZPn9bBpyw7uffCvhCNRFs1v5CM3XUug0J+TAHQ0Dz/+TLYUWZYlZs2cxshomB279uFyOgmFIwiCit/vY2BwGLfLiXcsAluSJJxOe6bEMA0UUcHlVEilTr7NDeCZbXE+dH4hlcWvz6fFsiwWTHNxsDvDcMTkj8+HiaWOXekK5PSMDw6PAEdaA/sHhqgoL8lepMxqmM6htg407Ui6SqG/gI6uiSbtYHuknIhAwE8g8MYNIvPkeTvpHxzmF3fdxwvrtnDdVRfy/e98ORuS8nYRjsTwFxw5ZoHPSzgySb8QeY/Mk+W+v/wVQ8/10VBVjVQqjcfjfkOVVLn7VHnkiefo6Oxh3pwGLjr/rNdVDTBOVUU5pSVFDA2P5iyvriqn6hg3ieMBGJO1qR2K1rDMWYCRiU5olwPYsWsfL617lXAkSu3Uai48eyUuck2zj24nGudYkzG6YeAuaSQTaQfLyno6rX1lB+teVlhaZWCYHTy/50dcfeOnCMgj2Tj78885gyWL59N8YD8uIUTj4tXIskxquIlo54tITj8Xnm2PY+PG9VgDL9BQrGNZFv1xH51dPdz/0F/5+IfeT/30On73pwfp7OzBMSYgi2aKoaa/MChMpaxialZYy0Q6GTlwf7bSZVpdDbfcdCUbX3mSve0qrQMS4UgEyeFl+8699B6Ks6o+xszGpQSmX0a04zn01CjpsF21JLuLkd3FWHo6K+DZVUXjk0cCrqIGPGULkBRfVlgRBBFDjWPqaaIda/FWrQTLQBRlMtEOHJ4yBnf+ElF2Icpugg1XE2r+C3pqhNTwXlLD+4l2vYwW70NyFuLwVuCtWEq0Yy2mGqd/648omfNBIoefBssCQcDIRLE8ZWRiXWAZaMlBnIV12ffcUOPZz4GeDmPpSdRwG+7SeVjBFZjqMxA7SPiwbYEgOTzZKqtw25MAdkVWJoqo+GyPp3gvkrMQy9ARHR605ACCIGBkwiiF0zBCLQiiw/a5Mg0SHc8hiApYJqLDh6PANnAf3vcHjEwMS09hiTJaoh+Ht5zA9MsItz2BIDkw9TRGOoYou9BSwwQb3pdtnzNNFTXWjSBIOP21CJIjO2ZLT5OJ2FVjktOPv3Y1iaFdWJaBkYmAaWIZaTKxbgRBxFU0C8lVlK0Q9Bt9TC2MoBrSBAP+ySxo+o4RnNPbPzjpcn+BD8uy2Lp9N1t37EE3DObPmcWqFUsmTJjU1lTz0//6Dj/5xV0caG7D4z6SanciRkfD3Pb5b9Dd0weA1+Nm+rSp/PZPD/CPX/n0cZP0Xsunb7mJV7fsZGT0iMG2LEl8/pMfOel9nIjv/+s/cM8Dj/H02lfQdZ0zVy7lIx+85g39Jv9fYlLhyako/Ns3v8DX/uW/eeK5V5hWOwUsi8OdPZimxQ/+31eyYlSePHnyvJe5+75H+PHPf5u9wdrX1MxzL67nD3f8cNKyXF3XOfSaNDXTtNjX1EwylaLE46YoGCCZSpFIJCkvK6XQb3tBDQ2PIkn2bHMymQIBZNlBoLBwzFj85Mc9vVKhpVfF65aQRAunwz7BniyyJFBcIDGz2sm9Lw+dUHR6LfZYjww4lU7T1dPP1JoqHLJMMpni61/8BOs2bmVkNMyU6grOWL6Eex/866T703UDwzCyf588ed7rpNJpfnv3I9z78NOcuXwxf77jP495Y/9W4y/wEU8caUOIJ1KUlkx+QZ/3yDwx4XCUzq4ePGO/96OhcPa5tKri8bhZc/kb9+7q6OrhU3/3zeyN1H1/+St3/eEB7vjpd0/ZvLyjq4cHH3mKjs5uorE4LpcTj8eNLEmUFAcpKy05Zptdgc9L3dQptHd2A+QYa89unEdw5qIJotPQ8Ag/+9UfeP6lDTgcDirKSwlHojQdbOWLt1yFlHkpu66/9oIJlVLz5jSwdfvuCWNZOTeAocYJzLiUVGEdir+G1sOdbN/wGNG0j/Ud1WM+TfDYA3ey5oxgjrDlcwlM8x7CyETRwi3IJY2TVmKFDz2aFdlCqSOmwXv3N5NOZzAtk117m3LGNi7MdQ/EmHWWfTx3USOJ/u2230/ZAnwVSzDUOOXCXq5cXsh1V63kP+94Bq/3SGVJKOWiezhG4UAfsuM53KULGN7zW7sFTCmgqOFq9EyY5OBujEyU0QP320KcKOFwldom4WqMePcGkoP231CU3UjOQmRXkPRoM5ZpkBjYhSi7ERUfsqeUTKgVS09jmDqe4kYyoWZKF97GwNYfo6dG6N/2UwTJiSg5cZctQJQUMqEWAjOuINz6OIYaY2DHLxAEEYe3AqWwlvTwflIjTQiCBKKEs7AOLAtBkDHURI4htyi70RL9GI5C9u/Yw8bWQ8wrG6auOEnAKyArtpg7ntY37pnk8FXi8JZjaHEMLWm353nL0ZKD6OkQDk+lbQ4uiKiRDjsJzzLAsgg1P4TsLMQ0UsjuYrBsIUtLh9FSQ4CA7CkFLCxDxdAShNuewNLTuIsbMdU4hpbAyESQPaVZM3hBdiEKov26AUF25ozZXToPT/lp6OlRjEyUkaZ7sCwDQVLG2vwk+//xXixDIxPpINTyMIKkYOlp/IFi5tb0k4zbvw3j4lMwUMjiBXOBI5OFkiRRVlZCT2//hO9TedmxE5IffOQp1h0VyHCotZ19B1r49C03Tai2XzBvNnf+738ec1+TYVkW//mjX2ZFJ4BEMkXTwUMsXjiXzdt2cemF5570/srLSvjzXT/md396kP0HDlFaHOTmD7yP2Q31J974FLjxuiu58bq3p03+b41j1ngvnNvAfb/5L5587hVa2+0TzdlnLOHS1WcRKHzn4wLz5MmT542i6zp3/u6+CbP6A4PD3Pm7e/nqF26bsI0oiiiKkk1rAwhFIrbJ4NiJeNzrCaCyvJRINEZHdy+xaBwE0DQd07LAAl3XGBweniSx8/iMezH9ZX2Uve0ZPnZxgBvPLkSUBEzDQpSEY6eDjlHglYinTT5wTiE//MvIKYtPr0XXdUZHw8yZXU8g4Ke0pJirX5OiMmvmdHZOUt1UM6UyLzrl+Zui6WAbv7/3MaZNrcbpVPj1Hx+asE5tTSUfvuGqt3wsDfW1bN2xj8aG6ZimyfZd+/n8Jz74lh/3bxVROjK7ffrSBWzcvCMbVS1LEje9fw1XXnrBGz7Ov3//Zzmz9wCHO7r4n9t/w7f+4QsnvZ9YPMEvfn03qVTKjhhvqKe59TCaqjF77nSKi4LceN2Vx63Eu+7qy7j9jj/kGGvXTZ3C2atOR1KUHNEpHI7yPz/7DS+v24yqaaQzKrF4AlXTmD6lmJZt9zF72hEPpugkHlFXXHI+7R3dDI8cqcyaPUVkUU0q29Y33nbUt/c+ZpUcuQEfvwlv7U2TUEW8TGztMw0VyWV7R0mKj+DMNXa7WqTDFiNI51R2jWOaJrphoGt61lB+nPHj+qypXDT2WlzFDbhL5xEZOsyODY/Tr+2nsXSEkoAT2elnNOWeUPGcMezjlpdlKCsaYXjPb8fMuhWchXWEDj2OIIh2cpueQo31YOrprIgkCBICAmq81674ifchCDKCw4Xk8OIunYeWGEBL9CMpBQQbriYTasFdPJvE4G4Ufw2C7MLIREn0bqRwxuUM7/mdfU1hGRTUno+pRu1UOUPFFayndOFtDO26E1ONgSgRmHYRguwiPdKEocaxTAN3cDpYFqnRg8T7txLtXoc7OBPZU4y3aiVDu+5AkJyMhkJoaSeLK+3v1GBMJpqGxgVLcDhkLD2d9YYC8JQvItq+FkwDIxNGchehp0IIiEiKF1NP4grOIBNpR1R8WEYayVWEnhhAT48CFg5vBZLDRzp0yG6ds0wEQURyFlI06zrSoWZSQ3vJjBzE4avC4asAsBP6HB4sU0eNduIKzEAQZTsZr387Dl9ltjrNMo9U8XlLF6L4ygnOXEOo5RHIRNHTIZy+KQiybcouiDKeknnoehozPUpqaC+CpOAqslvQ5s2uo7VrhPiAhCQJzJvTwJrLLySRTPLHex9mX1MzgiCwcF4jK5efxgMPPTHhe33BuWdMWAa2cLx+09YJy5tb2tjX1HLKbXCT0dLaTm//xEosXTcYGh4lHp/cs+l4BAr9/N1nPvaGx5bnreG45gKBwgI+cO3xDcXy5MmT573Knn0HiScSkz63c/fkrV+iKLLstAU5J2RDN+wqUNMimUqjqRqiKOB2uzjQ3IrD4cAhyyiKg1g8DghjCXiq7WMgiqQ59cCGo43Af/KwfXGuahbN3Rm+dE0JLoeA0wG6KdAzrDEcVVk60wdYiKLAnsNpMqpFRre4+fwAf3ohQjQ5MYL1VFBVlbqp1ZyxfMmkz//bt77CDR/+HInkkdQURXHwnW/83Rs6bp487zaKiwJceck5x13n7SrP/8C1l/Hlf/o+I6EIHV29KIqDFUvfPK/O/2v4C3zMmF5La1sHbpeL889eSSgcIZVM8YmPf5DV5535ho+h6zqvbt1JIpnENEwcigOv14MsSWzavOOU9rV5686cpCqPx8Wi+Y0kk2luvO5Kli9ddEK/wClVFXzz659j6/Y9hMJR6mqrmT9n1qSf4ZfWbyYUjqIe1WoNEBrpY+FSg0zCieScmSMEHe0RlRpuosBfw99/6ZNs37WP/oFhKku9NFRJ6JGWCR5RopmcUJkEtoAjlJ6HlN6S09pnGiqCIBLteC5H8LIMFS1uV18EA4W80mLkiE5gi20+r115U15WysDgUM7z/XEfF684EnwhKT4ORKbTs38zLllHppuWUegPlnPu1Tejhydvs88YMnFHLZno7qzoVDL/o6SGdqMnR8jEusba6zSwTEwjjSA5kF3BbHsegIUFXtDTI1haCjXeS+nC24h1vYSRieIsrEONdQF2UqAoKRipUUrm3kyidyNavJ9Iu70vBAHR4SXRs8H2bbJMBATCbbag4fTXoKWG0VMjjBx8AKWgBkGQwTKxk+CiIDoQJMfY4zCZWDeB+suJ925E8VWRTKUZims4JYOkaX+2NndX4XFoKPUNrFg8i1DzQ7a5uL8WV8kc9MQAiq8KNd6L7C5Bi/eCqSLIbmR3EWqsGzXWjaT4EWUXjsI6kqMHQRDtNr0xEc9yWLagZNnm4pLDi7dqOf6aVbiCM+zqJjWOpPjstkbLRHL6kT3lxPs2Y1km6XArrsAMUqEWTFNFlJy4gjNJDe2xE/oK6/CWL8ZbeVr28+GvvcAWO13BrKA27mMlOjwoig/D4UaL9dhVV2rcbjn0BFl1xUc5+xrfmNG1gK7rfPe/f54jWG/ftZf+wSE+/IFreO7F9WPWCKVcdMHZLJyX2xo7zuGO7mO2Wre1d04QnuKJJF3dvfgLCqiuKp90u9cSicTwF/gQRWGCv5yqajTMnHZS+8nz3uGYwtOL67Zwx+8fpLd/iMryEj7xkes498xjR17nyZMnz3uNoqIAgiBMenI9XhvDVZetJhqLs2ffAcC+QK2sKGNgcJiBwSPx6bFEElEQcLmcSJKEz+fNzmzKDhmvx008kXzTfFTGxSeAkdggHueRG4m97Rnm1TkBiUTa5PmdcUZitqH4zecHKPSJTKtwsKfdmjCLeyqoms7Vay49pnnv/DmzeP6Ju/n+//yS5pbD1NRU8pXP3Ur9KSSf5MnzXqC2popvfGli1eQ7wcK5Ddz+X99k3aYdnHH6Ii5bfebrDibIY/PB66/i57/+E8NjXknBQCEXrz6HC859cwxdn31+HZFoLHt+MNIZMqpKcTBwyvsKhaOTLvd4XLhcrhN+FjRNIxZPUOgv4OxVp5/weN29fThkGVmW0I/ywarwmziEDLKzNCv4jFd8jHtEAVm/peDMNSxfuijra5MZjOKtOp30yIEcISlYXMXjO0YniESF/gJqp9WjJ/3ZdQEC9VeS6N2Y21rX9mQ2CU5y+KiqKGT5jHZePihl9+vxuLn2fUfMoa+5/Czu/MOjWT8qp6Qzo8rFOWcuzx4rkUzy2FPrcUtFLK484qfz8n6LigV9zJvTkNPGCPZ5VLTSzA70YOljlU7+WlJDu/HXrrZbvSK26CTKLiTFh56JZNu6JMWbTUuTXMGjPJRGkN0lRMf8kpyFdRhqFMnhRXL6KZx2EYO77sRU4wztuoPC6ZcSaX9u7DgKRY03Ej38DJahkom0UzL/Y0QOP0V6cBeCIOEqasDhKSMT7cbIhDD1NK5gPRYmRiaKnh5Bz4ziLp6Dq7iR1OBuJKUg254mu4uI+Wegmw+SMY8Imo2lo+zsKyWRSBLtWIueDmGZOrIzQGp4H4IgIio+fNWrSA3txumvJRPtQHK4cZfMR412IcoeRNmFp2IJ8Z5NSLIbBAl3cQPp0CEy4TZEhyf7txYkB3o6TDrUgis4k2jH2mxKoRrvw8hEcJctyL4fkrMQPR3G1FIYWsJufQScRQ2kx9L29HQIyeEjE+3Aoy4a29cAI033YmoJRMmJGrd9MN2l8yioOYdQ81+yYqjDW4Ya7UaL9yL6a7MtqqnhpqyP2a49TYyMhnBK+ljLqX0t29s3gNvt4utfzA3l6h8YYs++g8iyxKL5cwgGC7PfnWNR+JrOp6eee4nnXliPPvY9mDGtlo/efN0JvQzraqtRHA5qqqvo6OrJeW7BvNnMnzPruNu/10mn0/zyN3/mpfWvAnDG8iV87hMfwuVynWDL9y6TCk9dPf1863u3c8Ul5/CBay+lqbmNf/nez/jTL79HTXXF2z3GPHny5HlLqK2ppnFWPfsPtOQsFwSB69ccu9rT6VS45cPvZ+/+gzz6xHMMj4SxLItEMkVxcQA1o2FaJqlUmkQyDQIICFimiTEm6iQSSQrKSyl2KoSjUURRYNys+80Qoo6uhhpnY1OKZMairU/Naav74/Nhplcqk25zqjgVhVAoctx1KspK+eF38yERef720Q0DTdNwj11IZlSV+x56msHhUc4/ezmL588+wR7ePGbV1zGrvu5tO97fOsVFQb7xlc/QdLCVSDTGtNopJ5XgdDKoqsZL6zdTXBRgeORI5YI1VlW7auXkFaXHom5qdY5XyziCIFA7peqY21mWxeNPPc+6jVvJZDIU+HxcdMFZnHXG8SeiS4qCtIgCleVldB3l33JoQOBQqISll30oW2U0Lj6p0S4GkgXEIsMUCS44RvKdu2gWiq86R0iauewGZveuY9eeI55LDlnm/ddcgaUnc3yEABK9G/HXrs6aQGc9kiAnCe60edOorVFpjU/H5y/GITt4ZcMWSlwRGoqHKS4s4yufvZlXdzQTjwzRWNxNRZEHIdEObruSpPVwJ6KVprE018y9sXSUloP7mTengY/edB2//dMDtLV30nq4CzUV5oazfRw4FGVKVQmNqz6eFcqiHc/hCjagxfuR3UXZlizZXZL1/xn321L8NYRaHkFPh9GTgyAIqLFuXIXTbONvLYIW68EZrKdkTAisWPrFsYS3GMN7/4BlaAiSA3fRbGKdL+IoqM5W3ow2/wXGzLAlZyGWbldu29U6Apg6ouwmUHcRkfZn0DP2tYGvYgm+6hUkSuYS63oFy9QRRBlv1UrKk8/Th4DPmeHVrkpmFEVxyTpLqgeY7txLcsj2RBJECUOL4/RNwTQyWGmDWNfLCKJjzLw8gBrvRTv8lF2pJTkRFR+RtifBAskVxB2sx100m3So1TYJ1xK4AjOQPcU4/bWMNj+ElhwgNdKEs6AGUfGBoWJkIph6iky4lUzoEJaexlXcSHq0BVONZU3eLcvAVGMIDq9dZSe7UBP29yHU8ki2vVBPjSCIDhR/je3zJEiYhkq0/TnUWLf9HogOu6pJUmzhL9pBuO1JPGULSPRuzgq1w2Oi03j6JBxpAT36twTgmbWv8MQzL2QfP/bkWj5w3VUsW7KAhvpplJWWMDg0nLON2+1m6eIjAWO79jbx1LMv5azTeriDex98nFs/cgPHo7SkmFUrlrJu4xZ8Pg+DQ6OYpsG8ObP45tc++5ZXBCeTKR5+/Bki0Tjnnb38TfeCOhGf/tK/sK+pOfv4vr/8lZ279/PHO//nbR3H28mkwtPWnfs4a+VpfO1zHwXgiovPIRSJsW3X/rzwlCdPnr8pfvBv/8BX/+m7HGhuxbIs3C4nN91wNeeds/K424VCEf5036PZ9gVN0+12OodCZXkZGVVlcGiURCKFZVoIooBmGFiWiWXZ6w8NjVDg82LoJlOnVNHZ3feWp0hNJi7FUuabIjqJooi/wEPzobbjrrd9517Wb9pGNBanfnotF55/JkWvYxY/T553O7f/+l5KiwNZ24L/+OGdbNmxl+rKMh5+4nnu/PF38mLQexhRFJnbOPPEK54iI6Mh0uk0ixfO5ZUNW0gfFVNe6C/gi5/++Cntb9GCOby47tUcE1+AVSuWZiscJuPptS+z9sX12cexeJwHH3mSAp+XRQvmHHO7M1cuY8v23UyZUokoivT1D6JqGo2zZvK+Gz7BtBm5N3ixlMmv/7yerm672sPjhDWny9RVMSH5DpggJMU6n+dD11/FGcuXcKC5Da/HzdLT5lPgFrPVVHZy2BERK9rxHL6qFUQOP2N7EBkq7tJ52eS58UqsUiFK7Zxp3PXQNtraOwG7smm0epgFs9OU8QqXnjO+Xx+S04/ir8mOze0wsyJAWpdpGiqisXQUl6xTKe/HUFcTCPj54mc/zp2/u5dYPMHMiiKK/WESGYF7N4jcMCVJQ8XsrJm4w11EYNrFxPu3YqhxZFdgzFA8jq96edb7KtTyCHpq1K7iKZ5Fsn8blmWSsgzbI0qQEF1BAvVXZoVAxVdOUcPVDOy8A0l2YUkKRY03EDr4YNajqGT+x+z3wNTJRDtx+Kox1TimqaHFe3EFZ2D5awABUXQQ7XwBLAtR9qB4y9GSAxhqgkjH82P+UyLu4tmEDz2GZGSYVqqRTKosqBhmS3cFc8pGqSsTcKjtmJIt0OjJoTEBphNTT2GoURBEZFeAosYbibQ/SybShqmbSIqfosYbGD3wAPZMoIUzMB13yRwiY69DkBwIlgPL1PDXrkZSvCSG95Ho24plhpBdRbbwZJm4yxaQCbdiaSni/VvxVSwh1rsJh7cMHQvZGUB2BpBcQdRYF5KvkuCY0CmKDtR4L6apkd73R0w1juQM4ArWg2UgiDKC7CIz2owgOe1KsuKZmEYKyeHF4S3HMlTSo81o8T6SkPXjCrU8wpSSGTmft6PbT2uqK7P/7usfzBGdwPYvu+8vjzOnsR6vx8Onbvkgd9//aDZQp6qynBuuvQKv50hi5+atuyb9DdjX1Ewsnjhh1dO1ay6htqbaTs7TdebPtZPzHI7Xl9h8smzYvJ1vfPsHdtAP8Ns/PcBFF5zFv37zy2/pccdZt2FLjug0TvOhwzz7wjoufBPatd+NTCo8jYYi1E3NnQGZNrWakdHw2zGmPHny5HnbKC0p5ne//C9aD3cwMDTConmNeDzuE2738oYtOZ4ZsiyhKLZvwbTaKew/cAiXU0FRFDvG1zTRNH08YRgBe1Z7NBxh6pQqevsGsaw3Zu79TiEIApJkv36fz0v9tNpjrvv8Sxt49Innso+HhkfYu7+Zr3zh1klTBPPkea+Szqg8+dwrPPLHnwAQjSV4acNW7r3zB5SXFfODn97FE8++nBee8kygsLAAhyzj83q4+IKz6OjqIR5PEgwWcv37LsPn85x4J0chyzKf+8SHeXHdJvY1taA4HCxbsoDlSxcdcxvLsnhlw8QqKYCX128+rvBUXVXOJz72AR5/6nkEoH5GHStPX8wVl5w/aRXD3fc/mhWdAJIZ+OvmBB84V6QoYAtj/lrbrH34wEMM9neSMWSKZ1xEgbYfIxMlfOhR6meuyWnzTg03ZUWn8dY+V7Et4OjJEUKHHkeUFCxTQ5A92bS0cVzFs5FkD1ub41nRCWz/pR09JSiOfoqCgSPVV7KP1vg0Bte+SnVlOQvnN1IdhEM+iXAcdvaVkjZkXjnkY3ltiMXF7mx1kq7rNB86TKG/gMEkiMNCNj1w37ZnqF7sRpBd+CqXo/hrGNjxC9RYN4Ig4a85i3j/NjLhtjGD65kY6VH01ChqvBeHuwRLzyA5CzEyEdtHSBCx0BAsk1jXSyjesmzr1/C+u7H0FJak4C6eTbTzRUxTwzJUXO4SRFGiuPFG+rf+CMvU0JPDiJKD9OhBBNGBnhyiYukXMbQEA9t+iqklEUQZpaAaU0ugJgbo3fhdTCONkYkiKgWkQ4dwFFSTGt6H1+NGVtyoDi9n+yEw9UqmKM2YWgqHr4LA9MsItz1BamgvppHG1FMgiDi85QTr16B4y/BVLrWNzTMRZFeQaPtzOAum2J9tU8NUY4TbnrSrkwRprIqsD9lTSrTjOfy1qxElBUnxguXBMlTMTAzZU0xw5hoMNcHwvj9gakmiXa+gxm2Dd09xI4LkwDI0UiP7wTJR/HU5RuLm2N/M6a9BchZS3HgjkuK1TcbBbiN0l+AsnIq7yK6ci/e9iuT04yqejeKbghrvzracekoXEO/fCpkoxeygNOBkKEyOMf7shhnU1U7Jfi/27DnyeQZyWvOaDhxi6WkLKAoG+NwnPkwkGsMwjEknCI8WxY/GsqyxEJ7jC0+CILBsyQKWLTk5z8F0OsOO3fuIx5PMnFGXfU2ngmmafOc/fpQVncbH+/RzL7NqxRIuWX18b8Y3g+279h3zuV17mv5vCU+WZREKR2lt78ouC0fs/vDxZcGAP3syyJMnT573OjOm1TLjOILJa+kfGMx5XFZawtDwKKZpkU5nkGUZy7KorCgjFo8TCkcwTRNJEqkoK0WWZXRdxzANunr60DUdW456ayue3iwkUcTCPl+IokiB10PRWCT3NVddPOk2qqrx7AvrJyyPxeO8smHLm5IClSfPu4X2zh4qy0vHBGnYd+AQDTNqKS8rBmDlskU8/MTz7+QQ87xL8bjdrFx+Gi+v34woikyrtStoHLJ8Uh5Lk+FyOblk9TknfVOl6zqJxOSpUuP3BMejoX4aX/7cLWiahizLx/SRCkeiNLfkVsk6JZ3G0lEGh9TsvUa0Yy0JKtizeSOxlGXfVL/4GMsWzuSiBQVZj6ij0/XG/634a7BEF6nhJhK9m0GQME0VyeHF0BJoqWFMNYYa9Y619x1pw/NPPZeDLROreDOGzM5uH4vmpPB63GRUlQde6KVz8HB2naoXy/nsJz7E/DNv4p7HtzAa6+Ngy0F0XUfNVBO1IlxQJDOnBFRNy0nLHW+PAhiIykhOP0YmSmq0iVSoheTQHgRBxFu1Ak/5ImRfFb0b/wMrE2Fg5y8pX/QJLMtE8VaiZcJIig93caNdmWRkEGUXppZET4fQ4n2EWh7BV7WCwV13gmUgu0soW3gLyaHd6OkQgiDjLKoDUUZyBQm3PYEgKQiiguKvRI12gihj6kmc/qmE255ASwyip0exTB1X4TRMLYHDXUIm0ompJzG0BJIrgGWoODylpEeawDQQJRd1y79A6Vh7IXQAThyeI95ggemX2sJapB3J4UV2F+Gbchapod2o0XaCM9dQMu9DqLE+Yp0voCX6UQqmULb4U+jxXqJdr9gtenqG0kW3kQm1IMluuxXO1BjY+UsUTynu4ka0xAB6cggjE6Gw/oqj2kQLSA3tRXYXj3k0udBSwxRP+yAj++9GT41imSrJ4b14yxciKT58VSsY2H47lqGip8OULvgYis824x43GRdEGdkVwF97AUo2Pc+NoSdJ9G4m7TxgpzoWzSI90kxqtAnLMtDTIWRXkDmzZ7JnqAp/uhdRFFm8cA4XnHOGLcSO7SOgRnBKdgVbmTdBlT+Zbc17rTh8PL+n2Q0zaD3cMWF5WWkJxUXBY273eujo6uGXv7k7RzA6beE8PvSBq0/Js3Dj5u3H9L178pmX3hbhqbLy2G3ZVW9Sy/a7kWOai//l8bX85fG1ky4HuOXmq7n1Q9e+dSPLkydPnnchuq7z/EsbaW3vIpVOZ71bCv0+ptXW0NXTh9PlpLg4SHdvH263G3+BD9M0CYej+P0F2Yqq8ZQ7+2LzvWP0K4oiskOmtKQIWZKRZAmvx83iBXP5+pc+iaIok243PDKaUyV2NF2vaQHJk+e9jsvpZDQUyaYN2cJTXfb5dCaDyzn5dyXPO4eu64ii+LYlDh6L911xES6Xi/WbtpJIJKmbOoUrLj3/TfOROhEOh4Pqqgp6evsnPFc39UiVwdDwCPsPHEJRHCyYNzunDWd8P8dDVXOT7472p0nrMsGGq7PtcXt27+DggJvBhDdbybFlVwsz6y9k/vRAjugE9sTIut0jvLz+SWLxOHXVxVyyyKRADqOnhpGKGuykM9GB4AxgqHHUeD8jTffY1S5jbXMu18EJ4x4XxyTJfj8Od3RTqcQZkI5UmfT2DfDM2le4+sqL+eLnl/G1b36XGdOmUuDzIooirYPQ+Yf7+ebff55Cf8Gkf+9INI7P5+VgeCrTfW2QiWIZGpLDiyApdgqdGifRuxF3yRzUSCcOp12FJUoKpqHicAVtkQWyBtqZSDuWZVBQtRLL0jEyUUaa7sNU44hjXk+KrxzTVEn0bx9rzRMRJYXRpvvIRDsw9TSyuwQjE8YZnIEW68EURJKjB5GiXYCFIDqQHAWYporDW4GWHELxVZIKHUJ2FiKIDhyFdWhjBuDIFuVLv4A7OB3J4cnx8ho30zbUOOG2J+30ujEMNU463Ip4VNuZr2oFowcewFBjmHoaPT1KpP3ZsUomH6aewdJTjOy7m/KlXyDRuxHL0kmHWhEEEcPhQXYXIzsDY5ViKkO77qBs4a1Eu17OmtEjSoiyB9PI4PKUEu9eh+KrwrIMO4UuNXKUp9Od2ZRCSfER7Vh7zBbS8eckxZdtn8wa64/5n6VGm9BTo+jJIRSv3UonSSJLapOsXv2RrEiWGm4i2vkiguxCkF2UBTMsqT6MJJhU+JLEVQejKTcJs5A5sye2D5umSUtrO6l0hpkzarPf87POWMauvU05bbwOWea6MRP+yUgkkwyPhCgtKcLjPnGHwTh33/coyWQKwzCJxuKIosi2nXuZM7uepaedfEqrYRy7u8B4A+E6p8Kay1bz69/fx+hruskK/QXHnLz9W2BS4ekD11zKVZece9wNvSfRipInT548f0vs2rOfr//z9wiFoxiGQSgcYXpdDfPGkjcqK0q5ds0lnLZoLhs2bUfTNLp7+jFNcyzZTuS1FU2yJCMKIubYzelb7fH0RrBvxgQEBAzDIBKJ8dtf/OCkU5wKCwuylV6v5fUkNeXJ825mSnU5gijw0zv+zII5M3n4ief5py9/Ivv8/gOtzHsL/IHyvD4OtrTxvR/+gqaDLYiCyLIlC/nnf/g8JW/yrP3JIooil110LpdddC6GYSBJ0ts+hisuOZ+f/eoPZNQMbpcbURRwuVxcdMFZgO0B9dSzL2XPWw899gwfvenaSW9cj0VpSRElJUVH0gHd6azoVFxzCYqvguDMNfTuvhctFUEzgxPS63bsPczpp38QsIXDdRu3smtPE82thxkZCVFeVoIgCLT3jPDbQZNbLy3H7SZrzO0unQeAqcazUfVHt+edvmQhW7fvzh5vXBwrK3Lh9ZdQMPV8+jb8P1yyvfzoFqc9+w5y9ZUX0zTmI/na6hFN19m+cy/nnb2SNZdfyK/u+jO6brflHzzURjyewOVUuP/RFygrFHj/mW4KfF7cRbMRZCdavJ/RA/fbrWzecoIz30fk0OPZ6pdA/ZXEul4iM9QO9OEqasi2FAqChKEn8decfSRZzlNGydwPZqtwFN8UHL5KW+xSCjDUOFq81/bEKmnE1NOIkhPZXUTRzDV2Kp6RwciEkd0leMsW2AJYJoaa6ENUfGSinUiyC0FSCMy8inDLo2PjEVEK60j0bkRyeLNCzHgaXLRjbTZFLjm4C8vQUPy26bcaPkxmtBlnUQOCIJEOt5Ho345lqEiuAJ6CqaTDh0j0bkJSChBkF+7gTFKhZixTY2jXHQQbrs6KbIgSguzC0tPInmIK669gaNcdmGqc/m0/BQQEQcThq6So4WpChx7HMtKosR67pVDPUL7ok+iZMOmRA+jJEQa3/wzLUBEVH/66C1CjXbbgd+D+7OfPskyKG28g2rGWTKSTkQP3Z33HJMWHv3Y1I033oEc6GR3bTo33ongrEZ0FuIIzUaMdGJkog7t+TXHjjSi+cjv1zulHT45gmipudwFza0TisRi6KRDJONk3Us2N778el8uZ8xnt6R3g17+/l9FQGLCFpSsuu4BzVi3H5XLyd5/+KNt27qWtvYtCfwErli2aUO3UdPAQr27ZxZbtuwiFo5QUB3AqCqtWLmXN5ReesGKpr3+QgcEhhoZHaWvvwjDstEynU6FmSuUpCU9nLD8Nf4GPaCw+4bnVb1Iq6YlQFIWf/fd3+M53f5KtqJw5o45vfv2zf9OpdpNO53i9HspKi4/7n9d7av3lefLkyfNexjRN/vHbP8iW54qiiMftpq29i4HBYQKFfi6/+Hyue9+l1E+vo29gkClVFSw7bQHz58xi1fIl1NRUo+sGpmlfpCuKgyWL51Pg9yGJIqIgjIlTp4YsS5xslbGiOHDIxyx2BY7UXgmCgCxLyLKMy+XE63EjCiIIdjWHKAr86b5H2HlUktDx8Ho8LJvk4kCW5ROmJOXJ815DliS+/fVP8/KGrXzre7dz9sqlrFy2EIBYPMEL67dw5SVvfUn/3wobXt3Gj39+16TpbG+UaDTOZ79sJwyZpoVuGGzcvJ3PfPGf37RjJJMpzNc5m/5OiE7JVIqNm7eTzmTo6R2gpbWd8rJSvvL5W6goL6Wzu5cnn3kxZ7JEVVX+eO/DaJp2nD3nIggC77/68ux5qT/u4+BwkFF5EStWnAHYyXeumos5OBzMaUE7eh9gVzjd8dt7efjxZ2g93MmOXftoa++iZcwcGSCtiWxpd2YNuQEC0y8lMP3SbIsTHKmuAbtt8H1XXJSt3gq605QEnMyaPZ/gzDUovgr2DFaS1mVcsu2VM874e5dKHTvAIzHWOtRQP42vfP5Wzly5DK/HjdftYsG82TidCk5JZ1pBL4fa7LYmQXKgp0Oo8R67esnU8VWtsEWmaAdavA89HSLRuxFXsCF7LEvP2AbZpfNwl87DzMQIHXocPR1Gi/diZMJEu17GGBPhbENsBUFSSI82o0basxU7pp5GFBVkdxHBmWtwBWdQuvA2ZMWP6PBkxaji2dcje4pxeErRYr129ZSk4PCWM7T7LtsDSlJQAtPQU8Oo0W56N/4Hemo0Jw1OT44w0nQPmfBhuwLJMm1hyeHFWWS/Rn2svU+NdmOoMQRJoXDaRRTNvh6lYAqWZaKnw4iSG0F2UrXyG9k2xuE9v8u+NtlVTHq4CUsQ7ba24HTKFt4KooSRjtpJfq4goqgQ791EoP5KtNQIRiZKOtSKFu8ldOhx3EWz8NdegKHFs/v2115AZtROUxZkF1q8DzXWjRrvtSf20iHcRY3oqWFSQ3uJHH4u5/3QUyNkIm2kRg6gp22BT3TagmZqaA+u4tmYhkomdIiRpntsMXVMtFITfaiRDtRYD16XTGlZGf7SmTTMO4d//PKnmDfnyGfFUOMkh/Zz15/uz4pOYIulDz36NJ1jvmwOh4MVyxbzweuv4vKLz5sgOj37/Dp++Zu7efzptWzZvptDbe3sP3gIVdN48ZVNvPjKpmN+N8YRRZFUOsOhto6s6ASQyahsfHX7Kf22yrLM17/4yQnVmGcsP433XXHhSe/njTJjWi2//9V/8/TDv+PJv9zFH+/8n7c9We/t5vh3H3ny5MmTB4BNW3dmo2gzqko0GssKSD29A3znn75EQ/207PrOsXYzSRIpKLDNFU9bMIcDza1UlJfQ3tHLaChMV08vK5Yu5sV1mzB0A8uyMDi1mxNJlDBN84TVUk6nQjBQiCRJjIyMks6ok64XDAYwTQNBFCn0+xkeHsn6OYE92zV+Ma1pGg88/ATzGmcin0DQAjvBxOV0smHzdjKZDFOqK7ny0guoqiw/pdecJ897gYXzZvHAb3+Ybbcbp8Dn5aHf/+idG9h7iHg8ztUf/DTNh45458yYPpWH/vRLCguP7T1yKtzz4OOTzn63d3azbsMWznwDwvhDjz3Nb/5wPwODw3g9Hi67+Fy+8vlb3/FWvnEMw2D33gP09A1QVlrM4gVzcDgc3PPAY+zeewCf15M9tw0ODROPJyktKWbn7v2T7i+ZTHGw5XDODezRaJrG7r0HiERjTK+bSl3tFBrqp/GNr36WV7ftJBq1004Xzm/MEdxKy6fgCDTAUe1V4yxeMBewE6EOtrQCduWTrts3qMMjIaqryvF6PDglnQJtPzAju3247Ul0XaejvYvhkVFMy6KwtZ8F532KohK7fencs1awfOkiOrp6ME2T+OA++oxKig0ZtyIwb8Fidm4/YtA8zpLFdjVVw4xpiKKYc4Nc4YsTSrlobDgylrIiD5efWUcoHCE51pp+dPthKAZG0TnI0VfRRg5iagkA0uFWBrb/HMvSEQQJd+k8BEkZ875qp3D6paSH9yNI9s12YPqlgJDTVii7i0iPNpMa2svwmEhi6WlEZwGWlsgKJ46CaiSHl1g8zsBAJ/tDMuUdmzjz9LmYAxtx+msxtLht6j3WLjbubTQuOhU13pAVnSxDpWLBx8iEbDEmObjbrrByF1O+6JM5aXCyuwRBlHEGpuPwlGOoUcxMDNFZQGD6pSQGd6JGuxFEGYe3AgSItD6JGu2iqOFqTC2FZeroqWFEhweMDMVzbmJwx88BsCyT0vkfJdrxPGqsi0y4FUO1/8bRrpftN0mwwBLQUyEcnjK7mmnnLzC1JKaRxumrGhPGjGw1k+wKYpn251GNdWWrqQTZRUHN2ST6tyErhcie4mwqort0HunRFmLdr6AlB2yxLxPD0JKYhgaa/flQ/DW2QGeZSE4/7qJZKL4p2fd2vC0v2rEW2VOKqqfGvLPAVTgVp55GT20j0Z7BNVZdNZ6KGB7pQ04PAhMF32079jB1StWE5UcTiyd4eq39dxsYHM4uj0bjjIyGKSkOsv7VbZx39vGTpMvLSjCMya9zPR4XLa3tOcECJ+KiC85i8cI5PPDwU0SiMc49azkrli0+6e3fTP4vBevkhac8efLkOQnGTVZN0yQSieWc/FLpNL/+/X185xtfzJYoL1uykPbO7px9uFxOPnLTdTz4yFOk0mk8bg+RSIze3gEKfF7CkRhYFk7FQUY9uRljURSPuqk9tvDk83kIFPopKS7i07fcxJPPvcTDjz0zyf4EggE/oigSiycYGBwa64e3wALTMhFFR/b1FBcFiccTtHd2Uz+97oTjlWWZNVdcyJWXXYCqahNKuvPk+VtkXHRKpdOEwtG/afPQN5vPf+07OaITQGtbJ5/58j/zp1//6E05Rmf3RDEje6z2rtctPL3w0ka+98NfZM8XiWSS+x96AsMw+Ycvf+p17fPNJJFM8rNf/YHevoHssqeefYkPf/Aa9uyb6GtkWRbrX93GtLoajjfPYR3jXNQ/MMSPbr+LwcFhXG4XikNm8YK5fOgDVxMMFp7Q1PcD113JL359N9FYLLtsyeL5LD1tPgCHO46EIjkcDhTFkfWQisUSFBUoLKocIugrRHL68ddeQLjtSVJDexkcHqFryMnewRIaS0cxh3t5+ZHvc+kHvoXTEwDA7XYxMDjMI399dkxA2o3y2At89KZrWXP5agYHh3PGMG/OrGzrjt/vY/myRbyyfguyLFHhizOrJESgpJJyzzCGaocOhFoewchEKXFkqPDF6Y/7ctoPd/aVcr7TFgEEUUJSCnH4KlCjXeiZYQTJgbdqBSVzb8ruT0+OoCWHMbUkkuBDEGWiHWtxFc9GEERMQ6Vs3ocAGDlwP6mhvaSG9uLwVeIsrEUprCV08CEEScFZWIcgyoyEE+zb34wsmowMtxMbasXofoQlC2biKSimqPb9hNueJBPpYOTA/eipESxDtQ3PC6qJ92zEGZiGqacQZBfhtqcomXszyV13jFWaCZTMvfk4aXB+3EWNxHrW261mQCqzf6zSKWInAE5ZiRrtIpUOkxzYRXJwL5LTh54axshESIdaME0j29Jn6klE2UO043m7ginRb7fWbf8ZDm85argNQZDwVCwhE27HVKOkQ4cQnYVoiQGwLGR3sS3iOf2oiX7UaCcOTxkOXyUObxnp0Wa0uIzDV5EVn1JDe7Li33h7J0Dx7Otz3g9ECUGQwdKRnQEQJbBMtFgPenIId+m87PaS4qNs4S3Zz1Oo+SEsU0eL99uXipZh+0w5vFgOL3pqhNTQXkawRclxXzVDcBNKTd76lTnG5OXRtHd2Z+0VNC3XZiESjVFSHCQWS5xwPwArT1/EgbFKqXHKy0ooCgZIHSNd73iUlhTz6VtvOuXt3ms8+8I6DrV2MHvmdM475/gC31tNXnjKkydPnpNg1fIluF1ORkLhCTMuFeWlZDIZdu1tysZTn7H8NHr7Btjw6rbs+jOm1+J2OxkcGsY5Zio8MhomEo1j6DqSLCMAkiwjm2Z2tvZ4mKaJjpW9CTiWT5Sa0VAcMitPX8zVV17MD3/2GxyyjHaU35IgCIiiREGBl9FQBMMwcToV0ukMmYx9opckCVXVcCiwcP4RI9djGYofC1EU86JTnv9zNB1s484//oXbf/DNd3oo7xk2bt4x6fIt23ZPuvz10NgwnWfGZuVfy7zGySt3Tobf3/PQpL/HTz7zAl/9wq0nVSV6LJ59YR2PP/k8qqZyzqrlXPe+S095f08/93KO6AQwGgrz0GPPHLOCNh63bxIXLWjkhZc3THje7XYze5LKA9M0+ca3f8C+Ay3ZyZKy0mIsCxpmTmPl6aedcLxVleX8899/jj37DhKNxZkxvZaa6srs80dXDggC1FRX0nrYjo1XFIWgO43HaVI9dUb2Bt1bupBwz3ZUVaM7Wkw042RnX6ldYUScpl3rWLTyCsAWzh5+3P7bjFcrZVT4wz0P851vfJHP3Xo97Qe3MpwJUFVZnh3bpi07+OtTLxCLx8lkMjgcPqbMWERD1RCFSoKRfXdnvZfGq2BmViQgaldZj1dQhVIuyiqn4nckiOpp3KXz8JYuxFFQxWjTfaRGD4Ig4CmZmxUvxj2BTC2JnhpGdhdlK6GSg7vthDtRySYCBqZfOlZVY1cseatWkujdiOwuweGrIDD9UiLtz9HRvg8LaBu1/+Zzy0bwKRr9vR0suugGJMWu9NaSg2SiXciuILKnFFFy2UbYvipEyUn5aZ9lZN+fMJJDjDbdY6fwyW4czgCJ3o0o3rIJaXCZaBdlC2/B4S0nNdqEaWqkI20gyOiZEKLsQpScqNEuAtMvwzI0Yj0bsAwNPS2jFNRgGhkESaF/649wB2ciiA5cxY1osV4wdRK9GyldeBvD+/6IGu0kE27F4S7FU76QwPTLGG15mETvJoxMOHs80eHBFZhBYMZlxHs32YmJ6gCy2xYVx1scASw9jad0PsmhPdnP7NHtnUA2vc9U42Qi7RhqDAQJyeHBXTYPf83ZhA/9lUy0A8tQcQVnTth+PCnPMnUykXZEhw/TMnAVNR6paJNdOIsayIw2o8X7s4buktNP7azLENfeCZMEw8w9RlVjarjJ9pRSfBT47M+BU9JZXO9mW/MRkWm81W3WzGmT7ue1nLlyGbv2NDEaiqBpGoVjYT0OWaahvu6k9vFmsmXHbn72yz/Q1t5Jgc/LFZdc8K4SsoZHQ3zqC/+UM7Eyva6GX/3ku/j9EyvY3g7ywlOePHnynAQej5vP3PYh/u37P81ZXlwUYFqdXRadzhyZcREEgeuvvowLzj2Drp4+iouCTKmq4Fv//j/ZdVRVIxZPYhrGWCubiShKaKp2UqLTOOMtf0DWkymdSmfnnAXB7skfGBzhH7/8abq6e+nt7Uc3DARBeI2po8WMuqkkEgdxOZ0YpsnQ0AiSKKLrOoIo4ZAl/AU+/AX2iau8rPSE5dZ58uTJ83qYLIwAQDdO/jfyRFy75hLuefDxnFYQgIXzZ2dbpV4PQ8Mjky5PpTOMjIYpLyt5Xfv93g9/wV8efSr7eNuOvax9aT2//PF/nFIL3649Tezc3URPXz+arlPoL2D+nFkIgoDP6yWemFiJMGNaLQC1NdVcvPpsnln7Sk4b9s03vG/SJLtH/voce5uas48ty2JgcBinorBj9/6TEp7Avlk9bdHk78niBXOzAg/Y1RCyLBONxpleV0PF1CksXlpJ9fSF2Rt0b+VpRA628GrXZjojhQBkDLuyKOhOo0S9LBrb/47d+7Ki06ySULYCKZVK0dS0h2q5CZ8WparuXNxjLXoHW9q454HHsmP0ej121Y+rkNlnXGVXtIxVmwiSgmvMq6g06CVUXsOmbvtz3h/30VAJV1+zGndJHWC3WBlqIttS5S6ahaHFbXNpdZFtXq0ns9U0Dl/FmIF6DEkpIDW0105t81WO7StOtGMtguRAlmyfnvChxxAEEWfhVNxFjUiKD0fVhYTj63HJJoIAimQwmnLjlHWUzBChloezoobDW2l7UBkZCmdcTibajph02W1znlLiPRtQCqeixXqQHD4EyUFB9Rmosa5sepu3aiWDO39pezaJDgw1yuCuO6lY+kX8tavp2/zf6KlRsEwE0YHo8OEqasDS00Q7nkOQ3WMthhayM4jiKSM4cw1Du3+DpafJRDtxF8/GskychVPRkkMIogOMDEUN19C/5b/BNDD0FN6KpfY+LRNP6QJSoUMYmSii5ETxT7WrybpexlVkG+wLopz11RqvaEqPNGMYSTKRjuznIh6P0bn2xww7z2bBgtOorio/8n6IMo6CaqzwYfuz46uioOYc4r0bESQHTn8taqKfdKgFb/mi7GdbjQ8w0nRvNvnQMlQsSSXYcA3ecttvcLwiqqByOd6SuROEMMUb5P1XX8Yf7nkop030tIXzJhXlx9Pzxl9r3dQp1FUXUy7sRa5USCRTHOg2EUVbeHa73Vx64bnH/sIfRUP9NE5fuijH6F8QBN535cWnlI73ZrD/QAtf+vt/zVZUptMZ7vrj/YQjEf7xK595W8dyLL77g9snVPO2tXfx/R//in/75y+/I2PKC0958uTJc5LccO0VTKmu4B+/9QM0XaOivIyaKZWIY+LNnFkTTQGLggGKjkpsc7vdhMNRVE3DMAw0Tcsx8wYwxy7iJUnKnuhP5N8kCAKSKKI4HJhWbqODLS6JGKbJD356J6+s24w+5ic1vm8BEEQRj8fNtLoatu/ej4Bgj08QEEQRC3ApDgQB4okku/ce4IpLzudjN1/3ev6cefLkyXNCZkyr5UBz64Tl08cE/zcDl8vFb27/T374v79m+869Y4EHp/PFz3z0De13ak0Vg0MTxadAoZ/SkqLXtc+unj4efvzpCct37TnAM8+/csJ2taN5af3mnNawcDjKhle3ceH5Z/HB69dw9/2P5Jx7ystKWbVySfbxpReey5JF89l/oAWHw8GiBY3ZmPXXsmXbrkmXv1bsm4xwxPajOZEXitOp8Jnbbua+v/yVwx1dCILAyuWnceO1V+Sch19LSd0ZdD6Z21qYMWT64z4uPqotdnySJ5RyZY3EF1UO0TRUhDTyMkbAieDwZT16AF7ZMLkZ/pZtu1hz+YXZCqPxqhVDSyCNiRRnXPEhZp1lGyr7pSFKhFbk8HqM0hLcJY2o8QH6t/4IU43jDNZn09DGE80EBGRPMd6q021RSk1k15c9ZcjuYpyFtQRnrrFfV8sjJGMjONyFFM+4mGjHWvRIh238XVhLvO9VUqNNeKrPYTDlx9B1KguSuGSdwyE/kmgyw6PmiGgObxlYBg5fJZ7yhYiym4SxCwUwtDiiUoAa7wHBviXV0yHSbU/iKpqFJYikQ63Ee7egZ0IIgkhg5lXEu9djqnH6tvw3zoKpmHoaBAHJVWQn4/mqEMeS+zKRTtR4D5aeQXYGcAXsarxQ819Q/FPQk0O4AjMQRJnAtIuI925CEB1YlokgKYzsvRNBkLEAU0sytPvXuAIzQFJIhQ+hJ4cQJQeyuxTJ4QWEbGtc4fRLs15KYAs5arSLWPc61EQfiq8K2V3Enh4FsW8DTklH6x/kJy9v5OLV57CgvB8jE0WQXUhj3lgYKplYF4M7f4GzoAbZXYTTX0um+aFsq1zx7Otz3mtnsJ7SBR9jcOev7ATAaDve8oVZw/HkwHZcxQ2EWh7J+YyO+3MtXjiXmimVbNm+m3Q6Q+OsembNnD5pEt14et64aOivvYDrVrk5eMDF4CgEK8ppcGVoqJ/GogVzOGvlMoLBwmN+N49GEARuev8aliyax979zSiKwtLF86muevs9Qu/64wNZ0elonnjmRT7/yY/i873zIWxbdkxeFbzx1W1v80iOkBee8uTJk+cUWLViKV/5wm0TWgxWn3cmpSXFx912NBRmYHAIVdXIqCoWFtaYSJRbeTQmPIkiTqeCrutomn5c8UmSJIKBQtLpNJn0kfSccdFJFAVEQeTxJ9fikGUURSajHjE6tQBZFJk5o47dew8wOhrO7sMWyHSwwOtx4/G4UTWNkuIgF5y7iory0pP+++XJ83+ZmTNq+eKnPvROD+M9xX98+6vc9PEvkTrqd83lcvJv//KVN/U4pSXFfPfbX39T9/nJj32AXXsOTEh5u/H6K1+3ufgr6zfnVLkezbqN205aeBoYHM4GZhyNYZiEQhGWLVlASXGQDZu3E4vFqZ9exxkrTptQWTCedn0iDNPE5/NmW/XG0XSdxQvmTLpN/8AQ9zzwGO2d3cTiCVLpDNUVZUydWs35SyqZveCMnNYiQ40TkEf4u898jGgsjiiK+E4ihXtaXQ0z66fR8hovsYryMhbMm42u6+zYtZ/2zm46unooKyk+0o4n6yyZMoyecbJxR4iNrQUUBH/LxeefzbIlC4hEY5MeU9N1EtERzMHnESQHDl8VaqQdLdaDWFiXbbsqK/VRVlo8ZvY8nHNDP9J0L6YaR1R8FDfemOOHZKVGsSzTTlkbOYDiq7bNpd0laIaaTfUbP07L7udp2/Eqo1GNXQPlNDau54qzpqP3bwdAsAQkpx89OcLwzp+xot5gYCiMYYqkdZmBuBeHZDK/oRJBjNkimhpHFB2IDi/Fs69HjXYR73sVBNEWipQC0uFWTC2FaaSR3UEkd7HtNzSyHxBt83A9hSBIeCuWUDj1XHwVy+jf+iP01CiJ1C7b88hVhKtwGggCarwXUXTgrTgNPR2yDdgtEyQHBbXnMbL/boxMFC05hLtkTjbdMN67CX/t6rHkuNFsa5/kDuIsmklqeB96apSUqSGICpau2j5brqDdvqjG0RIDWJaJqadI9G9Fdh4RVcJtT+IKNmBhYapx1HgvVvEq+pvvwCMrBMa+WkF3mi0bnqF+dRUuz5jYOtamZxmq3SKpJVEFiaLZ70dSvKRCLVnBKxGcSaJ/W85nw0iPIjsDqIk+26g92oXir7Ffa3KE1GhztjJu3Ih8/LMWnLmGoeFR+geGSKbSFPh81E2dMqldgqT4sp/BcW8phwALFy3FKjmHsw0HVZVlk4pWJ4MgCDTOqqdxkonet5POrsl9ATMZlc7uHubMnvk2j2gix0r6O9b54+0gLzzlyZMnzzFIpzO8smELB5pbcbtdrFi2mHlzGvj/7L13fBv3ff//vIHDJAhwT1GLkqi9LVmWPCTvvR3HzrSzk6ZJmrZp2rT9Nm3a/JrVpBnOcIYTr8SW95C8JFl7D0qkJC5xggRJbBxu/P444kSIlC15yUnxfDz8sPAB7u5zhwOBe937/Xpdf/UaZjfUs2f/IQRBYMHcWXa73Rux4fUdbN2xB03XyGiZEdNu64vU7XLa7XWSKGGaltG3LEuk0+k3FJ0Uh8yUyXX4C3z09Yfp7Q2hadk2OquiSddNFIdAKpVG8ogjYpQ4ypRcoKqqnEgkxqS6WtuU1cREzWQwTRNZkvF6PSPzdVE3oYYDh46wYtkiQv0DfPv797Fz934EQWDZ0oV89a8+cc76yPPkeb+w/1Azf/jTs/z7179Agc9re16MHs9zehbNn8NLTz/At7//M44db2fSxFq+8lf35nj7vF+ZN2cmP/ivb/CzX/2BlrYOioKF3H7zNdx47eVveZ1vlOTnHzm3zoTmoy24XU7S6XTOnXuHQ8blsqpFJk2sPaPvtjNhev0kBgeHrBj1UdubPXOa7Y04mkwmw49//juGI1GisTgHG5swDJP2jk6KnIM0bd+IGTlEwwX35KRwZStM/CWWB+Fov5ksuhqzPY2y3POh23l+/Wvs2nMAXTeYO3sGV1x6IYZh8OOfP8DxVssrShRE9u5vZFr9ZJxyEQurQxQHg7S0drC7u4y0LpPuD/PAw48jSSKTJ9bSMY55fUWJD713PYYatStaDEFGTycxhloYOv4sxSMJYzD+Bb0oKSOVTpawoCte+3VqpAPJVcRA44OQNZfWM2jJftsgHKyqFkov4mcPb6XY6WIwGSCti+zed5B0apgblswmE+shHWnFV7WMvr0/x1BjlPsdGGYx7T0x9nSXUFdicOHUFB5RRPaWk4l0oEY7wASlcIItdAiyyzLKBkTFh5YaBAS7YkiUFJTAJOLdOzD1NIIoI8puJMWHqHjR1Th6KkzpvHsZOPgAaqQdZDfu4hkE668n0rYew8iQSQ6QHGzG1NKIsgeDBJgG0baXEUQFY8TbSBAkgtNuHDFCbyfStg5v1XLCjQ/ZKX4lsz5EvGc7GVcRWiqMlrSqrxwFVRQ23IEabbd9mExdRXT6MbQUWiKEw1tOYPKVtol9MnSA4LSbABAQ6G58Epekkcgo7OiswKtkbE+v3qjCjOppJEL77NY1XY0je8tRh9sQENBTYRRfuWVEDmRi3bZZefbcUHzl6IoX2WMJxKZpILmCDDavJT1S0SZKTnsbAO6iBpLhRvR0hK0bn+fh5w/a527z0RZ27T3AX336o7Zf6WhGe0tl8detRvFVjHnte0F/eJDfPPBHDh1pprSkmLvvuPFtC0NVleU51aJZFMVBTdX747tp/txZbNuxZ8x4NozhXJAXnvLkyZNnHNJplR/85P4c49UDh45wzRWrWXPxCqZMrmPK5LqzWufTz71EZ1cvoijidrkBE93QSaczmIAoiUiiRDDgp6yshI4TXaTSqi1QjYcgCCiKgzkzp+PxuJk6RSMeT/Dsi6/mJIgIQFpNo2ku2ju6MExjZNT68aMoDmoqywkNDCKJIiuWLWb33oMMDUdwOBwIWBUBlgG5yNTJdTgVBw6HjKZp3PO5v6e7p8/e3gvrX+PosRYevD/XEytPnv9rZDIZu1VoNIlkikRirGFrnrFUVZbz3W/947mexlti0YLZ/HTBN9+x9V2+ehXf/9/7x5xTkiRx+03XnPF6pk+bjCzLBAOFZDQNTdOQRAlFcTB50tl9t50JV19+Ccda2lnocjIQHkLNZCgvLeHrX/3cuNVf+w4ctquFOrt67Lv0qprhWFeaiUGZE+3HqBgVFa+nI0hOv93qNtpvxlU8A3fRdOCkr42RSSI63LhLGnA6Fa67ag3XXbUmZx4bXt9ui04AtTWVFBcHEfQk1y8vpDRYTuORowA0lIbZ011KWrcur9a/+jr3fuQO9uw7lFP5JAgCV62agaG2IcguDMPg2PEWunqTiOhU+iN4kgcRJSUn5Wy8C/rihtvRU+EcXx1Jsdr9BpvXYhoaWiaB5PCSjrThKKhGdhflHLOWrb9GNFK24JHlUNMJrr3iQzilTejpCMMtLyB7SsnoKk5/HZOLHMy68EouVAXoW0d6IIlp6mjxPkCwfJcEASGuILnGtpZKih/J4R1py6tHHPGFMnUVBBHT0BEkJ66iacjOQox0lJ4d38PhKQVBwtCSVjWUqQGC3TrWvf2/rcojI4OndBb+2pXE+/aQGjhCMnwYQ1cRHW6chZMom/dxez5ash/T1Mgk+gET2VOGqHgZOPQAircSV2AKmbgXLTWIaWgo/gkUVJ+Hrs5isOkxnIUT0dUYvsolDB1/9rSfBUl2U9xwO4NNj9mVP42hIqKqk6hqVRFV+GI4DdES/CrPw1Vs+SlF2tZZnky1FyDJHls8zRqRn3puKL5y+/lThUstNYiWHMBdOhtP2dwxnw9f5XlkTIm1j5/0k8vS1d3Ltp17WTlO4mfWm2o02bY9SfHRdLSFrTv2kE6rNEyfynmL5502GCGRTLJj1376B8JUV1WwcN6scT3kTkd3Tx8f/fTfEB4ctsde3bCVf/r7L5xVW/KpfPSuW6wbyad4EK65+IL3zQ3Xv/3rT/LJL3wtp7K1vKyEr37xE+dsTnnhKU+ePHnGYdvOvWPSfgCeX//auO0GZ0Lz8VYSyaQtJImCgOyQEQWBGfWTUTMZfF4PixbMYe6sGWzetpvtu/dxpOnYSMWTgGkaOTHWkiRimNDV20dNZQWTJ07gRFc3V6xZxWubthOLxa1lBcvHRBAENF23U4VkSQLA5/OiKAput8v+7+JVy1BVDVEUcDhkBsJWol+g0I8kWRcKC+fP4bGnXsgRnbIcb+3g5Vc3n/P41jx5zgXdvf08t34jXT0henoH+NXvH7efMwyDLTv2Mbvh3LYL5PnzQ5Zlvv1vf8c//Ot/2/5RPq+XL372o9TVVZ/xekpLirlwxVJeem0zDlnGMXLh53Y5+eid74xvn2EYtqhkXfB8kte37qSnN0RFeSnLly48rW/TaKEmHs8VaCMJ3Wp1I0QmNZyTwjVaqMn6zaSH24j37BqTHBcP7cXUrBbO0dVPoxnPXyzok5lfmSJYIOMpKGZr2wD1wYzt+ZQVn0L9YQKFfv76cx/ntU3baG07QaDQz8rzlzBpYi3J/kZ0LcHBLX+ivWuYPd2Wn1SZN06VP47D1Ya3/GRl1uku6P11a8b46kTa1qMlBtCS/Si+KnTVan/TEiFKZn4Qh7cMV+3lJNufQ0u1EnSbY4QngOG4zuRRyWiZaCcOX9WIWTeovZsoKGogZqhWMtrQMcxMEkQJ2RWwjMwdPiJt63AVz8DUUiPG32kEyYG7aAaC7ATTQJQ9aGoMQ42DoSE6XIgOD+pwG1JJA+noCYxMHCOTxBWcgquoHsnpJxPrIdG3l5CuIipeJIcPwxFHKaxDlL04g/UkB5tPvveiA0NLI45UykXa1mNqKZxF0zDUKEYmjpYcwFU0DV2NYqgxUnobIICexuGtQE8PoQ61kOjdSzrSClhG4rIrQKL/IIHJV5GOtmNqKfvYOXyVFE64JMdPqaQ4SFv7iTGiZVQroKS8Bj0dIRluxFFQlSOuuoumj6nieyOxB8YKl5LDh+wuxtRSpAYOY2SSpAabMbXUiFg7je7WDoKOAXrSY8+N4y3tY4Sn0ZW0vDHgAAEAAElEQVSH47Xt7e+rZO1zG+3XHzh0hF17D/CZe+5CGvk9mqUvNMAPf/obItGTfwvWv/I6n//Uh+3K4TfjR/f9Lkd0AiuY4kc/++3bEp7mzp7Bf/3r3/K/P/8dLW0nKPB5uWLNKr742Y+95XW+09RWV/KnB37M40+9yLHWdqZNncSN11z2ttJU3y554SlPnjx5xiFbQqvrOpqmoygOBMEy2z7R2cO0qWcW/zqarq7enOolwzRJp1WcToVJE2vxF/iIxhLs3d9IX2iAKZMmMGXiBI4ea7UEI01ndMu2IFgR0aZpcux4OwVeHxNqqxgcjiCKItdfvWbEBPwIfX0hZMWBYRi4nAqptDriL2Xi9Xgo9BdQXBykqCiQM2dFkRFFkU9//C4eeuwp+vvDI9sWuPCC85g/p4Gnn3/ptPvc2HQ0Lzzl+T9JIpnkcFMLQ5Go/e8skiyxaN5M7rzl6nM4wzx/rsybM5OnHvkF23fvI5lIcf55C9/SxcS///Pf8L0f/ZIXXtpALJ5gxrTJfOFTZydgnYppmrzw0gY2vG7d+KirreaaK1dTP2Uihf6CM06wmjxxgv1vl8tJWlXtxwUFPtK6TGdqAuIor5jx4uiD9dczcPgRyzfolOS47AX2aDPwUxmvlSjoTuGSNRyuQoL111NY8gh7Tpi251PQbVUPZdtBA4V+u5LKav2zEuPcJQ3ous7W5kfJqB5bdOiIFNIX96J5XMy6tIFkfyOSq8iudpGcfnxVyxg8+hSkI0Ta1uGvW8NA44OYiQG7rU6Nd+PwlGKaBqVzP8ZA40MICBzZ+nue2Q3doSjBAoWJ5aV0R+OcarsjyzKVpQVE2l4kk+xHSw6AoaMl+wlOu5dox6skQgcwdBVf5XkAlndRtAPZXQwm+CddhpEeRk9H0OJ9OIPTSPTtHjHittDVKEpBLfHubZimZTkgym5EhxcTAz0ZJt6zC1GxhBKHp3SkamsNmWgnw+0vkQjtJ9a9DVdwKpKzEF/1ChJ9exju20ek/WWUwjrL4LxsPsMtL5CJ95Ds20dYVxFEeUTMaSDWvRXBXYzDV4mppZDdxRhaitTgUetmnaeU4lkfJNrxKmrkBOHmtUhKAa7gZNt3Kz14lPRQC8Wz7iTRsxPT0EgPt+LwlJGKthM5sQFBEJHdRQSnrWZqwqCl+SAr6jrZ1FaN4PBx57XzKZk6z37Ps++piUnJKDEJIN69yxZRs2JPtnUw69EkjRLZsgiSwzIsB9LDbcQ6tyBICu7S2XbLnTK8ieklVsXMqcJktrJndDurGumwDdFdxTNQfBV2pVUqHmbn5gNA7o3bY8fb2Lu/cUxa5dqnX8wRnQD6Qv28sP41br7+yjGfy/HYf/DwuOO9ff10dvdSXfnWjckvOH8JF4xT8fV+wuVyccct157radjkhac8efLkGQef18vx1g76Qv0YhomiOJhQW0VZSTGF/tN7bJyOltYOdF1HEkWMkaqlrG+TKIr4C3yEB4c50nwc0zSJxeL4vB4m1dVSUV7G4OAQ6bSas05BsDybXE4FwzBIpdO8unEr0+on09Z+YmQ/POi6hgl43C4S8SQOhwOHwxKhptVPIuAvwOFw8B///FUef+oFNm3ZycBAGBMoLgpyyw1XUj91Il/78mc40nycaCzOlEkTKC6yfjxPGXWBcCpTJ08862OVJ89fAlMm1vKf//zXnOjqZdvO/dx07Zo3XyhPnrNgyYK5b2t5URT50ufv4Uufv+cdmU+of4D/+t5P2bp9L4risIMnfvrL3/PXn/34WaVPTayrYd6cBvbub6SqstyugCopDlLg8+KUNC6Zm3sBe2qFB5xsQTpdctyprz/VE+q8xfM5uH+PLSaBdQFeXVVB1dzbkRQfl61eyX33d7Knu9R+nSiKXLZ6Zc78Wg69Ria0hcJgJSUzbkRSfKQTQ5S6hnD5NDKGZG8jrcu0h512u6Chq7ZYkTXAFhAwdBXSERK9uzBNHTXaidNfh56JIbmCZBIhq+IpNUjZvI/TvOVXHG0+hJkMAj4Goyr9QylSqTReb+7xXL1yAZmuF0kPt5OJ9SAIEoLDjeKrItrxKpl4L2qsCz0dwVs6D1fxNIbbXwZAjXQAAoaWoHTevaTDR0gMNJLsP4Q40j4XmHotob33oacjlteQKFm+SoKMpBRgmhoYBoLsxtQS6OlhglOvw1lQnSPEecvmo0ZOYGgJMvFeEEQibeuQPaVWdZOhkuw/hCtoGYQ7C2owTR1TT6OrMcDEP+ly3MHJiA43ir8WXY0z0PggIsBIhTiAMzAFxVtG8Yxb6d39E1KDzRiZOIGpV6H4KihuuIOuzf9BJhGid9ePcJfMQov3YmgpkoPNZBIhdDWC7C6maMZtKL5y6hffgdf4Lsn4IJNnTaJ2yiJS3ZuItIXwVi0n0vI8pp6xWyVH+5Ml+xsZankOLTmAw1dJcNpNSIol6mnJfkxDI9qxgYLalaetREIQLb8rScHUVdsYPtK2HqeUweMvYbAjt71NkiSWLVmQ084arL8ed0kDRiZJPLSXeNc2ux0wWH89Rw68zonhsYEGAEeOtuQIT6Zp2i2sp3KwsfmMhafThQtIkkSB98w98fK8M7y1SI08efLk+QtnaDhii05g+UocPdZGIOCnvKzkrNfX0dlNIODHoTiQRAlZsv4TBAF/gQ8TaG0/YYtR2bvXkWiUu++4waq4GrU+UbQeGYZh3wnu7Oph975DJBJJLrzgPLsPvrg4SEGBF5fTiTLq7q3L5SRQWEhtTRXLly6ktKSIkqIgqqqS0TQMw8A0TdSR9YuiSMP0qSxdNM8WnQBuuObSnMdZaqorWHPxirM+Vnny/CVRU1WeF53y/MUTHhzi//vBz9nw+g5S6TSRaIymoy109fShaRqvbtp61uv88J03c+uNV7N08TwuvWQlSxbOpX7KRMqKPNx+YQHVpT7rgnfajTmtZpaYYF2Uq7FeIm3r7eQ40zRIDx7DNLQxFVLZi+jR65haV8atK700lA1R4bPGJtRWc+3N99jLTps6iU989ANMnDydlFTFjGlT+PQ9dzG9fjJgpfP9x3//mJ/8bh0797WwbesmGjf+HDXWQ6L9OYoLHaQ0mcGkK2f/66dMtNsFRUnBNA1LiBgRXGRPMcUNd+CfcBGe8oVoiRCmrpKOtKGZMsmhduSR6qCsmLah2ceRUDCnekWSRBomSFx20WIm1FYzrX4yH/rATaycX0Z6uB2HrwJv5WIcvkqcgSkIkkIydABTz+DwluPwVhAP7aX/4ANkYl0IgoSndC6SqxBDjRHaex9KQQ3qsNV6Zuhp/HWriXdtRvFVWUbnpoFSUIO3cgkOXwWKrxJMMDJRFF8FsrsYUXJaFTuS0z4GguwiHe3AFZiMpBSAIJAeOmZVWCVClM67x0rWk5xoiT4rWMVZgLtoOoqvGjBJD7fRt+cnqLFeW9AZOv4MWjKMlhrC4SnBWTgZT8UiBNOwzw/ZXTwy58W2P5LiK6d03j0IkgyGTmqgEckVxNDTiLILRBFnYDKKt5JI2zrUWI/VhlhQRaBiJg1Lb8VXPNlKEEyGLWEuk7BFUy0RyvHLUvy1OAvrECQFNXqCwebHGTj8iN06KDrcpIdbiXZssEWnYP31diWSILtIhZswtDROv7WeVLiJ8OFH7Neff82XqJ8+yxbfioIBPnLXLVRWlNnnZ/azp8Z6SIYbx1QTSooPb+ms037Wx2udO10V59l4PF175epxx5csnPu+8WLKoqoqL7+6mY2vbz9tIt2fO/mKpzx58uQ5BVXN0HjkKNPrp9DS1kEqlQasL9uy0rMXncBK7lEUhaqKMqKxOKl0GlmUECSRyooyMhnN3g6QI26ZpskXPvURfvrLPzAcjaKOVD5pejYFT8RfYH1pG4bBnn2H+Ie/+SxXXnoR0VgMVc1w971fIhqL295MYH3Rl5YU4fV6uOWGq+jt6+fVTVvHxGNveH07y5cupOo0Jckul4uf/c+/863v/ITdew8iigJLFs7ja1/59FuODM+TJ0+ePH8+vLpxK0PDkTEXTJ2dPVSUldht2meDKIqsWLaIFcsW2WO6rqMONo0x0x5tnGxV28Bw6zrUWBeKtxLRWYBo6BhaAlPPkBw4PCY57tSL6GxVSG25n+qqFdTL8ykIlNktdOHBIf70xPMcOtyMKIrMm9PAXXfcm3MRbZomP//NQyP7L1veVJUhjjYdwud6ELfLycTJ03nlhQHS+sl99xcUsObiFTn7RjpCpOV5INfPSle8DDavRfFWktRP0BFKIWb2IgkG+kCMormfpmRkH3vDcfpPaZmq8MWYVDDIwqp+Ll99e05KIJh2NVP2+Jp6BtldjLOwzq6+Sg+3kYl1261axTNuRVfj9Oz4HoYaY/DoU7gCk0lhpdzFTmwAQHYXUTXjayR6d1FQu9La7khbGQIo/gm4i6blJOr17foRTn8dhqEiCiKCaSB7iimou5jQvl8gCDKGlsDpt7aj+CeQiXXhLJwIpmmZl5sGzsIJODzlpAabycR76d7+35TP/ySRjtdI9O1FTw/jLLTEQ4enBEG2hMH0cDvhw48giDKe0ll2W1qyvxF3SQOiKOEumUVqoNHyrYr3Wn5RqUEUXxW+mvNJDRy2W+gAZE9xTvWdv26NfewyumpVOg234/CUEmlbl/NaV/EMDF21U/Oy7aSipCA4vEhOPwW1VvWdp3xhjthq6CqC5MRZOMHyf2peSzJ0gPRwK87CiSNpdKXc8+HbiURjJJMpykqLbRFqPNPyU8/PLHW11VRXVdDZ1ZNz/smyzHmL5+WMCYLA4gVzeH3rTk5l8YIzT2W745ZraW3v5Mln15PJZBAEgTmzpvFv//jlM17He8GzL7zCt79/H7F4HIBgwM83vvZFzl+68BzP7J0lLzzlyZMnzykkEkkymQzBgJ9AYBbpVBpJknA4ZJLJ1FtaZ1EwwLVXXsJjT75Aob+AQqx2vUK/jxuuvZym5uMjIo1JVUV5jvDjL/CxasVSfv/IE/h8HuLxONFYglg8gSiKVFSUIQgnBR5RFIgnEng9HlwuKyHlb7/0Kb72z98mnVbxeNwoioMVy5dw6w1XsnjhHDxuN69uPP0d6cNNx3KEJ1VV+fYP7uOV17aQVlVmTq/ny1/4OPVTzt77Kk+ePHn+HEgkkjz4p6doPHyUqsoy7rz1+rdUAXsqr2zYwmNPvkA8EWfJwnncfceNeDxnH2BxLuk40Y1TcSDLEpp2UkHJaBppVT2rNrvWthO8unErA4ND1FRVcPGqZZSWWN+JkiTZVSmjW+KyF8DR/mPIhVMRzTSmaVgx9+YJFHEC6nALkrMQQZCt9LJYT44HTnYdbbseINTeiHHkIMGAn+KyGkqm3UDlqIvoTCbDD3/6G8KDQ4B102fn7v309vXz5c/fY1+YH2tpyxHd0rpMY6iIBZV9hPoHmFBTxcT5N/P5qSIbXt/OQHiI+kqTRcvWEAwG7H3zVi0n3PggssuqLh5drZX11ZE9xby8WyeoH0ISXRR7UgwmRDY/+TilVfWUFBcxobpqjAg4mHQhKgUIejxHcNPTEZyFdbiKp+UYUwuSA1kKjogS5WMMqwOTr7SPZ9m8exhofBjZZe1L6byPEztx0lza6a9DUrwjiXBxy+h7xJ/K1DPoqWH8dWtGKonupW/XjzB1FTVuiVyZWDfu0tn469YwdPwZAHQtgSA5SUfarCS6WBeytxxdtdo11fARPGXzTgpGQ8eIdW4mE+0idOA36Olh9PQwkrMQxV9jpcUdf5FQ3wnMTAQXQzgUjy3MJMNHSPTts43qFX8tgiDi8FZjqJaxtRbvpXTORxBEGXdJA4qvOieB7tTqOz0VRnaXWAmCI2l5iq+STCKEIDpQIx12cqGejuAunfOG7aRqpIP0cCtaKmyf78Mt60j1NyKIEq7iGSPbt0JsGNVul329v8D671TGS1s8dX+y3POh2/ntg4/ZSZGF/gJuufEq+/M9muuuWkNoIEzz0ZP+iAvnzeaSs/QN/bsvfYpPfPQODhxqora6kkkTT+/pdi7o7unj//3XD3MS8gaHInztn7/NM4/+8s/uu+CNyAtPefLkyXMKhYUFFAUDhAeHEMAWbwAmTnjrpqt//+XPUFdbzZPPrieeSDJ75jQ++4kPUV1ZztBQhN8/spZDh5vHlBdfsHwJM2fUc93Va1j71It4vV68Xi+h/jCSJOFyOjFNk/hINHtff5jXNm7n0ktWIMsyuq6zafNOLjh/CdFIDN0wKPT7EEWByZMm2Al9b5TU53bntgD89d9/k+0799qPd+09wKf+6us8eP8Pxv0BkSdPnjx/zoT6B/jYZ/6W3r5+e+zxJ1/gO//xjyxaMPsNlnxj/ve+33L/A3+0H+87cIT1r7zOb372/+Fyud5gyfcXRUUBxFaR6qoK2to77XHLw9DPhRec96bryGQy/HHtc/zh0SdRFAdlpcW0d3Sya+9BvviZj9qeUTA2ha79RBePP/kCx1vbkeWnmT93Jjdc+UE48gC6GiE9cARBUvCUzSMw+Soy0U6S4Ua7Qiq7vk3bD7H+hQ7mV1iptj29IbRQNbfOzr2I3rOv0RadRnOis5vmY612AEnilJtVTkmjodQSfrICXaRtPWX113PbTVfb7X56z3r0AuuCX431Etp7H8ZI+5/sCuYIAtm590d03OkfgQy6ITKQsM6fUMzBlu17uOaKS1hz8QoONDbZLfQAquGgbuEHkZTDp61aeaNEvex4VhTLzg0g1rXFFp1MPcPAod/b7XVaaohw02NEu7YgCCKZRAiHu4RMsh/JFURLWn5AA40PUtxwB/GuzSf9qxw+1Hg3kiuAoasMNj9OKtyEqWeQnFaLn2maJPoPIEgutHAT7qJ6dC2NaWRIDR5FV+MovnKK6m8g1X+YTKKXTKzb2nelAE/ZPIpn3Ep71yC/fbKLGYFWavxR4qKBz5sBWgntv59M3PK/cpfORnIVMXD4EdTICXR1GMU/AS3Rj6mn6T/0eyoWfxFdjdF/8HcYmQQOT6l9zJz+iQgOF76KRZZ4Jco4CqrR1djIedCFw12CaRpIrqAtOgmyi9Rgs91Oqg63kol2Io4IY5LiG1PN5/TXET2xAdPI4C6ZieKrYeDwI6TC1ufEV7UMTM1+vW/iVew60MLR460U+LwsW7KAygorhfFMEvWyBIOFfOHTHyHUP0AqrVJdWX7ayniXy8ln772b9hNdDAwMUllRlvM3YDgSpbOrh2Cg0J7L6SgKBli1YukbvuZc8ejaZ3NEpyyJRJInn13P7Tdfcw5m9e6QF57y5MmT5xQEQeCaKy7htw8+ZnsuART4fFyy6vy3te47b7ueO2+7fsx4Kp1m3pwG0mqGE53d6LqOz+flijUXMnumlcDzD1/5LBevWs6zL7wCwKS6Wnbu3k9oIMyx4204ZBlFceD1uHl+/auc6Orm3o/cwdHjbUSiUQQY09O+c/cBaqoqAJgzazput5tkMje62ul0Mn/OTPvxsZY2duzaN2YforE4v/3DY++YUW2ePHnyvF/4wU9+nSM6ASRTaf6/H/yMP/zqB29pnZFIjN8/8sSY8db2E/z+0Sf52F23vqX1ngtWrVjK7r0Hqa4sR3E46O4Noaoqi+fP4cuf/zglxUVvuHwsnuB/fvJrXlj/mn0TpbOrh1kN1vffiy9v5O47bhx32Ug0xo9//oD93aVpGjt27WM4EuXeD9xuVekIMhoKW497aH7tKYIBP6vOW0RVuWkLN/FEgueef445pQM569f6Xqfp8EKmzTjpUdMfPn3rYKg/bAtPUyZNwCHLZDQNp6TZyXcpTaZg8rVIjuP2xX2w/voxAsHoFjNR8VE0IsDo6QgHX7uPTcd9JNICs6bVUKMcttfdGCqioTSMS7a2GY9a+xR0hPniJ29n/cY9dJzoprgowEXnz2VCiYTkGr9qJdt2d6oxdWrwOPG+/8bpq0H2FOekqQ0cfgQ4mRzorVpui2dqrIuyefcQ6XgNLdmPGu3ENHQwMmQSIdxF9WipQdzF08gkQmDqltG3pCB7iimqu80WOtR4N1q8z/JvkhRcxQ2o0XYEBPRMHEFSMLQEksOHlhqieOYHGTj0AIYaGxG0bmfo+LOY6FbKm+iwqpV8VQQmX4Uge7j/gftIxeNQCNG0wnDaiTRsMHdyBiPSYa3fWQhAerCZRO9edHUYyRlAUgqQZA/JwWbrvDn4W0xDIxHajyA68FWfj5boJTV4nKGWFxFlF8bsJOpwmyXGxXuQ3SVoqUFkT6llFu+ttNsRDUNFll2YWgpBdiEJom0Snh5uzWknHd0SF+/dDTBiKu4gNdhMMnQAAE/ZXIJTrcTVwea1qMkh/vDAz9l77ORvww2vb+fDH7yZ2dNqx5wbbXsfo/PIbgZf305Yns+FF140JmjmbG5QTqipYkJNlf3YNE2eeHodr72+DX3EcmJa/WQ+8sGb3/AG6tul+VgLz6/bgMfr5rqr1lAyjrfpWyESjZ32ueHI6Z/7cyQvPOXJkyfPOCycPxu/38drm7YxNBxlUl0NF12wjGCw8B3djmma/OHRJ9m2Y489VlQU4I6br8XlVHjhpY08/fzL+At8XLB8MSvPX5LT811aUsRjTz6PJElIkkQw4GdiXQ0ABxubaG0/kdP2cCq6fvIui8vl5N4PW2XQg0NWeXig0M9dd9yQU/F06PDRHEFuNK2j7nTnyZMnz18Ku/YcGHf8WEs7kUjsLRnVbtm+G1XNjPvcjl373rLw1NLawW8efIye3j5m1E/h7g/cSNFI29aZcuhwM9/54c851HgUxamw8vwl/P2XPn3ato8JNVXc8+Hbeeq5l6zHtdWsWLaIqy676Iy8/ta/vImu7l5bdAKrIqi17QSzGuppbTtx2mW3bt8z5oYJQHtLM50HkngU0AQP+w4eJpY8Sm93Ke0dMvsOHObDH7yF+SPdks1HGplT2j2ueNN/+DGmTK6zKziqR27YjEdN9cnnvB4P11y1mseeeJ6gO2WvWw2sYNa8ZRiZOfTt/QVmYsCuvArWX8/A4UdID7eTHm63RaeKxV9E8ZWjeMvY8sx36etuZ7jfMgrPDDVh1iRI65aPVPb/WaGrtvJkQp7i9HPnTdfniErDrWFM00CUrAASLTVoixbZVj5BduEuakDxVeAuaiB6YhN6Jo7DU2ZXOGnJATKJPnQ1iqQUjHgHrSHRuwvFV0Us3EZXb5o//fx+NLmU1Q0lFDn6kT1FZOI9iLKL9HA7ruAUHN5yimd+kETvLlLhZgxdxTvSEpYVUQwjg5FJICoFKN4KvOXziZsGarwbZ3AKWqIf2VOCNiLYiKJExeIv2kJWuPFhUsMtGGoU2RlE8deSiXWhJUIMHX+GIWUpw5EoFb4UDskgnHSzp7sUp6xTW6lT7rc+vw5vBaaWIhHaj+jwgCDgKZtLYPJVVgKhKJOOdGCoUdKRDjB0EB2kho4RmLiGWM8uDC0FRobh4y8guQL2nE1MJMVHJhFCcgWtii98pCNt1riu4vBVjhz/MK6i6YBJKtxEJtbDwOFH8JbOw1u58GS7pCjjLJxoiVRaimRoP7K7GIevMsf3LFh/PVs3Ps/eYwdzznHDMHjsieeZeu+lOablx9pD/PLZXuaVx3HJGqGeQ/zvfSf45MfutM32x+N4SztbduwhmUwxY9oUli6ae1oT8a079vDyhs05Y03Nx/nTE89z1+03nHYbb4dvfecnPPbk8/Zv31/8+mG+9pVPc/Xll7ztdV94/lLWPvXimHFBEFh90dm1Fb7fyQtPefLkyXMapk6eOOYuzTvN5m27c0QngHB4iCeeWUeoP0w6bRmOJ5NJ/rj2WeKJBFesudB+7bVXrkbXdQzDwKkoY1riOk50s3TRPFwuF6nUWH+qubNn5DyePGkC//i3n6etvRPTNJlYVzPmomHGtMkIgjCu+DShtmrMWJ73N8ORKBte3077iS6KgwEuWL7krPxY8uT5v4BzVCLoaCRJQlHe2s/p8dJAsxT6C97SOjdt2cHf/tN/2oLWzt0HeOaFl/nlj79N9WkCIk6lt6+fz37pG8QTCQC0RJLn171GX98AP/3BN0+7XMP0qTRMn0o6reJwyGcVLnHwcDOiKNjVQVmGI1F0wyBQ6D/tsuO1vGWri1IJDwUFtexqEUglD9sVQFlx5unnXmLe7BkIgoBiDtjC0KnijSKpOS15sxumMaG2mvaO3JstM2fUU1drteQn+xtR/LVcuOI86mqr2bFrP5J6jFm1Ncw+7zoEQUCNdCAIIqZp2AlgAKaukol14fBV4fBVUdxwG4rPev+iSYMnd+gEnCfT6XpiPjgB/rKppHXLvDk7/xkTC7h84aUcOLCP1IkQXmcvafURSuuvJNK2Hi0Ztk3YrSSyOgabHkNLDjAAFM+4FW/VUhJ9+4h1b0V0uHEVT8NbuYRUuAnZXYyuxhhofMiq1JHdBKZcjcNdhOQqshPoNKWKB7cNIpsxemIOYIgHQxpXnT+NGUEDUfGSGjiCIIio0c6R9LVyJGUlyXATejrC8PEXSA0cJlh/Pf66NfQf/B16esjyNVIKSEfaKKi5AMlVyHDLC5hKAaLooGzePZi6irukAV2N4atYRCK0n3SsEy05gOQM4CmfR2DylYSb15Ls20sydAC9sOTk8QU7ebChNIxuBu3WP9kVxNRVS9Dx1yK5ivBPuAjFV37SHF4QyCT6cRZOQksPg6mRDjcRVmNgGsiuIJKzAIe7BD0TQ/FWjlSSWQbuAOnhNhT/BNvLydBVghMvQ3C4GD7+AlqyH4evwm4njYes/bCS65KkI60556uWGkSSPZZvlytIYPKVtn9U1qdrf2sap6QRdKdy0hCHhiMMDAxRVLUUd9F0JMXH8+v/iGkYdEU8ZAxp5PUGz6977bTC04bXt/PHtc/aj/cfPMzO3fv5zL13jZtst33n2Ip7gD37DnHbjVejKGeeencmvL5tF3964rmcsUwmw39996dcvHL52/ZguuD8JSxfupDN23bljF9zxSVMmVT3ttb9fiMfN5QnT54855Cde/aPO77h9e12usVoXn5tC+m0mjNWU1VBoNA/RnQCKC4K4HQq3HHLtUiSlPPcyvOXjCusiaLIpIm1TJ40YdwLh/opk1g4f2wsrtfjOW0rRJ73J4ODw/z3D37Oupc30tR8nM3bdvHdH/2Cw03HzvXU8uR5X3G6NuuF82e/ZS+mRQtm2wlpoxFFgTveoq/H9370qzFVVINDEf7nx/ef8Tp+++Bjtug0mj37D3Gk+fibLu90KmedaOp0KgiCQEV5rlm7KAqIgsDKN/Bnqa0Ze8Mj6E7hdugUFFrVOEfaIuzpLiWlybhk6yIaLO+uWNza1+nzVtOZrLZFJ7DEm7295VTPujrHV0oURT798Q9yyYXnU1pSTHlZKVdddjEfu9uqUstWFw02r0VXY0ycUMONV63k/FlByp3dpMNHAMuIWnYXIUqKHUdvee00IUgKkuJDdgWIdW1BH/F46mzegmkYOSKAU7LEuuKSKu758B3MnT2D+qmTuPzyK7jxtk/yo/t+ywN/fJ6ndmgcOd7Lrh1b6dj9gJVSZxq2yBGsvx5v+XzcpZZvWTJ0gETvHlIDh+22uawoUTzjVjylszG11Eg7o4io+HAW1KBGWpFcQTvxTpBd7Gx10B+1RBynpFHhs/ant30fWiZNJtqJIDkw9BSGlqJv789JDh5nsHktoqSg+Gtw+CrQ0xEGDj/CYPPjZGJdGFoSZ/F0+7l4aC/DretJRzrIxLpID7cS6XgNxV9rCWSHHyHc9BjpSAd6agjTtNrsApOvQlJ8VhqcpGAaGlUV5bhH2rd6Yj4USWfNlDY8DpXismqKGm5DkBSSoYMkBw5jGhpaaohY52b6D/7W9mfyVS1DjXaiJa123fKFn8Fd3GC1xA0dx8jE8ZbPp2z+p2wRSJAcowzc15BJhDC0FGq0A0dBNaLis5IMB5sQTIHCyZfhHnk/Im3rcBRYnwur3TBJ+OhatGQYyemncNJlpKMdJAcaSYaPWCmCwEDjQwy3rrPPWwCfW2B+ZYjpJYP2ewZWGiJDO0kNHLbH+npOML8yxOSiSM7nseNE17if3VQqbVdJjuZ4azu79h4cZwlIjnMTFawW24w2fgXp2+GZ5185zTzSrH/19XdkG9/91tf52lc+y/KlC1mxbDHf/MZX+Me//fw7su73E/mKpzx58uQ5hxi6Me54PJEYt6IonU4zODScY7A4b04Dz7zwypi7vhXlZTRMnwrA/DkNTKz9PDv37CetZpjVcPKu7Fvhu//xdf7zez/jtY1bSaVVZs6Ywpc/f887kvCU571j3SubiESjOWOapvHEM+uYMW3KOZpVnjzvPz5z7100H2tly/bd9t/mSXW1/Ms/fPFtrfc7//51vvpP36KlrQOwBPxPfuwDzBvlq3emRCIx2jo6MQzDig4XRZSRdpW9Bw6/ydInae8Y/yLRNE2ajra8YcvMW2Xxwjl0nOiiproSwzTp6Q2h6wY11VXcdtM1zJ/TcPplF8zh1Y1b6e0L2WM9MR9zZzdQOcdqGwoUFtDTa1UAja7ccDqduEcCRCRJ4rrbPsH9v3uU9Mj3qcvl4rYbryLgl2wRIYsiaVy6tJrrrlozZk6nejWNTorLijcwNo4+3Pgw6UgbAO7S2QQmX2kvN9i8FndRA+7UgZyqrdHeUUFflNkzp9nekADPvPCKHWE/OlWvpbWDObOmU9xwO3pqMCclsHjGrQwAmVgPiZB1g2y02Xh27qPTzARRpmzePZZINmJSrqUG0ZIDOHyV9Iy8P9n5ehwqI+F/JIdP4CmsQkv24/TXkgw3QzpC364f4fTX2aIYWL5D6eE2MrHuEdFIQXJ4CUy+iqHjz1gJc7qKICk4CqrREiGSoQP06yomkA43WfMonIizsI5E3z4wdQabH7dbz5SCGoxMksxwM3fcuJrfPvwckpli1aQOyr0JDIefifNvIRPtRI20k0mGEEQZ2VOGIDlJDTaR6AvTu/snBKfdRGjvz9HVKIaeRkuFGW59EdPIYJqGlbZnGqiJENGOV60qJIcPQXLYJt2ZaCeGnsHQUzCSYpz1yUqGDqBGTqAU1BCYcpVdYZb1gZLdlp+SOtyGGuuicMrVRDpetY6RICJIDjwVC1EjbVbLZ6zLPs7+utUsqQ1zcMiqBMxWfAEUVc3AU+DMOcfPqxskkxz72uLTeLy1H9kIWpzRkkS2uupI83GWLpo3ZpkZ06bQ1d07ZryuthqvxzPudt4Wxvi/0wH00/yGP1tEUeSGay7lhmsufUfW934lLzzlyZMnzzlkzqzp9gXHaCZUV9kXDKNRFIVgINdnyuFw8NlP3M2fnnieQ4ebEQSBObOmc9N1V9iRzgCBgJ/VF614R+btcrn4xt994R1ZV55zx9HjbeOOd3X3kkgm31Wjzjx53i32HTjM0HCEpYvmvmPJcKIo8v3/+ieONB9n74HDTKipZNmSBW97vXV11Tz06//hcNNRwkMRFs+fjaKM39b3ZrhcCql0mshwlOxtC1mWCBT68Z5FO8iUSRPYsn33mHFBEJg5Y+oZrycWT7B1+266evooLy1h2dIF48axA6xcvoTu7j627thDXW01tdWV1NZW8emPfxCf1/uG21EUB1/49IdZ/8rrHDp8FJdTYcmieZx/3kL7O3DViqUcbjpG0J3KuSA+/7xFCEaKZL/VRjehpoqvf/VzHG9tR1Uz+At8dB7dwvH+w5SW11A8/cYxhttwMmVPVTPs3d/I0HCESTULCbDrtElxWUYLOHomZrWElc62/ZWyrVZ6OoIpmPj8QWpKAEI5PlQZ08ncRReNOT6HDjfb/x6dqheNxclo2rgJZJLiIzD5ynHNxrOMl2YW69qCt2o5kZbnMYGEKmJKlShqgplFA3Q60/Z8sz9PREHAVViN4iujZNZdRNrWYSKgDlvtZHomRlHdbfa2R4tdouREVHx2lY9SMIF49w5E2TUi3FliVLx3P9HefThcVstm9vgCDEgKydABkqEDOHyVOAvrCE67yT7m1c5G/u6zt9G694+QqcElD1NQMoVI2zpkTzm6GkUQHUjOAN7KJUQ7XkVyBtDTQ+jpKH27foSpq1Y7X+EEUoNHiXVuBgzAqhIzMgnSg0fJRE+gGRJRoxTZ6aE0aDBw+BEMXcXQ4giijKFGSYb2MyQpMLJdLdlPJt6DrkYITL3WOv56hnSkDUdBNf4JFxLt2IiAQKTlebTUIIIg4a1ahsNbjrd8Pt7y+VZLIJBJhNBSg+jpCIUekbrJ03h6W4KgO05PzMeUSXXccdvNeJ3Yn4PBpseYWBVk/5F4TtUgwOoLx1aMJvsbkaN7TyuiejyRMcsArL7ofA42NucIzU6nkxuvvXzc179drrj0Ql54eeOYcZfLyWWXXPCubPMvlbzwlCdPnjznkJXnL+Hg4WaOjRIAvF4PH73rVn730ONkMpkxrx/Pa6S4KMi9H7nDfv3pTBnzvDl79jfywvrX6OkNUVZawqUXr2DRgjnnelrvCgUF3pwfb1kURRlX+MyT5/3MsZY2vvr1b9HRaUWiu11O7vnw7dz9gZvesW1Mr5/8rlT8zJh25oLO6Wht78TtcjE8fLKKUdN0hiNRLr901Rmv54N33MCTz64fk7a0ZNG8M/YcGQgP8oMf309/eJBUSsXlUnjt9W187hMfyqnYzSKKInfcci2XXnIBJ7p6KC4K2omrZ4LX4+G6q9aMW30ElvfSXdfOp/fIc0QSUQ4OVLN48VKuvGTJGAFJFEWmTp7IY08+z2ubtqGIGeZXhvC3dTAznqR69nXjVi919/Tx458/kFNFumhmFZfONm0B7FTxBnIFHNll+X4JkkIyfIR417aRtLA16Kkwir+WRN8+qktcOCQNtyOEaZoongDzL7iXopKxrZvOESHz1FS9IwMlLHcW5qTqZec2nqg0WqA6XdKdlgwT2nsfGTFI07F2Uqk0GV1EdshMqi1lUU0/hm6Q0mS6ox5ME6bNu5jiKVPs46j4J5JJ9OMumm77J2W3nZ3HqccKQE9H0NMRlIJq2yTbEJxsbFJwhCMoooYmmFRPu4CaGdfZ+xqYfCVGOmpvK9veNroSjZ4XqS33IzlrcoRAPR3BWTgZXY3iLKgh0vI8YCXDWSLYVvRUGEFSKFvwKRRvGT07f0gm1oOJiewqwls2Dy01RHLgEInYEB2DLlKaSsYQaVMkZtSIOEQTSfaglMwmPXQcLREi1r0dyVmIKLlw+ieQSfZjmjqhvfchu0vIxLowddUyKffVUNxwuy3Yya4gBbWrEB1u4l3b7Pc/WH89wy3rSA0dRUsNAeDwlNKw9GaqKjYSGezBU72SmvqTptejhcDSkiKmFCzj6GuHSIcHKQoGWHPxBSxZNHfMean4a/EHyykJtDP/FBE1rcuct3D8v1lej4cvfe7j7Ni9n7aOToqChSxbvIBA4PQ+cGAJsH949EmGh6MsnD+LO26+5oxuTFxw/hKuvuISnh7VEihLEl/63D1v29/p/xqCqqbHjyb6C0LTNFZe/RE2PH3/uCZlefLkyXMuMU2TQ4eP0tLWQTBQyMJ5s3C7XbS2n+CZF17heEu7nWp38arlOVVMed5Z9h86wi9+/dCY8bvvuPEvUnzas+8Q9z/w6JjxCy847127e5jnz4c/t99PN975STq7clswRFHgR//9/1i0YPY5mtV7x+8eepzXt+xk8/bdhMNDgFWlVF1Vzh9/9+NxBZ/Tcaylje/88JccOHQEl1Ph4lXn86XPfeyMq7F+84c/8acnnqOnN4RhmIiiQHlZCddeuZp7PnzHWe2XYRgcPd6GpmlMmVR3WqP3N0NXY4SbHicZG0BxBymaclmOgDRaeDn1uyAr2hT5HXbb3+hlNE3js1/+BgcPNaHpOsFAIdMmlbNs4jANUyupHDn2p27ndAJONkUOsL2VRj+HIGJqaQwkDNOgbOatKL7xhbot23fz4KNPUuGLMb1k0DZPnzlrHnfdejl9e3+BIIgUTlxjm28PHH6ETKxnJJVuNf1Hn0NNDKE4oKThA+ipMJH2V3JEMclVRM+O76GrMY6cyPB6a7EtJBgmKLLJlClT6OkN0dgpMLEoSVllHQtWfwbZWWBvNxk6gOwuxllYh79uNQONDyEgIDoL7OMhyC7cwXrSkbaTx2PE3BsgOO1GFF8FDz/6KHr3elzyScP6lC6z5LIvMHVaw5jKtVPfIzXWk1P1FZx2I3pqECSnLTIBeCoWEWldBwhWtdiUq+yWw0zCurnjCk4hWH89id499O79OaLswlMyi6KRFLlD677JYKiD7SfKqfIncMkakmhQUmBSXR60q7Qy8V56dv4PWjIMpoEzMAXZXWQdq0MPYKgxBElB9paTiXbhCkzGMFREpQDBPNkaJsguXEX1qMNtOedfuHkt8a4tmKZhJ/1pyX4UXxWyu4hg/fW2ATmcrHgy9Qx6JmZVjNVfz4EjHWzYvJ1weIjamkouvWTlGDFZV2N073+YI4f3E4lYQrchupm85EMsWLj4DT7NZ8fjT73If373J+j6yZTnutpqfvXjb+PznVl73oFDTbyw/jXcbjc3XXd53lriLfD+/xWRJ0+ePH/hCILArIZ6ZjXU54xPnFDDZ+656xzN6v8m61/eNO74ulc2/UUKT/PnzuT6oUt54aWNJJNJJEli8cK5XHvl6nM9tTx5zoqduw+MEZ0ADMPk0bXP/J8QntJpFUVxcOEKKwI+Go1TFCzE43GjZs7OdHfKpDp+9N//8pbn8uJLG+nq7rMfG4ZJd0+IF1/edFbCU1tHJ7/67SMMDVvCQNZzaeH8s3s/U6k0qZRBsP56hKNPvGn72+49ucbGtjeS3EcimcLjduVUL913/0PsHmWGnIwPUueKoEgBuvvjzFz5iRyvpuz2oh0b0BIDtoeR1Xa3hoHGBxEBX+V5JMONOfPNClKYBhIgIY5plwsPDrH/4BFEUWTu7BmsPH8JGzfvAKxkturaydx8w5WokTYEhJxUvWT4CMnQAQA8tZfw8NNbOHigjeU17VT6U4TDQzRc/BX8Ey7KSa1zFk5E8VYypA/z8tGMnaq3qKqXan+MmOogmUwxa0Y9s2efFNWGjj5hi2qZmOVF5fBVIpZdQm/XcWRBQI11Y0ZPICkFKP4aABKh/XhK5xLt2kwm1o3sLkF2BQCrKkqpuACz9yW7wmt0RU377t8zoepz9twNXSU49ZqT/lTNa5E9FST69iA5TgoT/QdHDNkNFcVbaZmha0n69vwMAQEEAdHhoXfPT3H6ahCdBTi85aTCTaSH2xg4/AhacgBRUhBH3sdI23r8dWvoHdTRMzIZQ7J9uHRDpHcYJi68jOLJllVCrGsLin8Cpp7BNDJoyX5ExUukbT2yM0BGVxEVH5lYF4qvikyyn3SkDVH24ApOoWTWXQwdf9ZuLwxOv9EWn8KHHyE93IrkLESQFETZjRrtAENHjXVRNOM2UgNNDLU8h8NXab+HguwCQUQb8Sc7tOE+fr9+0G63Cw8OcejwUb74mY/lpOZKio+y6VfhENIkU2k0Xadm3h24/ON7kDYeOcq6lzfR3Wu1766+aEWOn9l4GIbBj372mxzRCay/Lfc/8Cif++SH3nD5LKd6p+U5e/LCU548efK8TzlwqIkt23cTi8WpnzqJi1ae9+4YJ74Jmqb9WVQ7vBP0hgbGHQ/1h9/jmbx3XLxqOSuWLSbUH6awsACf970/x/LkebuEh4ZO+9xwJHra5/6SmDljKvsPWibihf4CCv0F9r/Ppm3tnSDUP/7f0v6BwTNeh6Zp/OLXD+e0rqVSKX730OPUTaimuCj4pusYjkR55LFnaDxyFF3XKSku4vpLF1DGyUj28drfjFMMhUd7I2XN5bNiTziS5sChIzmvrykCj2IwEMnQr0xD8VXktG6pEcvbMT3ciomJv26N3cIWaVuHIIg4CyfirVyIo6DKFp1MQwNdtUSnU6qgsoLWhq0HWPv0i/Y81z79Infeeh0Xr1pOR2c3wUAhE2qqiMUTDCScOJxBBC1mG0SnBg4ju4tx+Cp5dlMLO3Y3AQLbT1SwpKYHfbgTp++31M270RZuJKefgtqVOAsn0tUWI60/e/JgCBBTHYSTbhxGBbWyyxYrBNllteft+xWyK4CzcAJyxUoef7mJQ0334ZQ0lk2KMKPWhUuII7uLMHQVwTQQZBeJ/oOo0ROIsseuzsoej3DjQ7ikNImMYnsI7ekutdomMzESvbts0UlAINa1xW6jSw40kT7+HAgintK5lMz6IEPHn0UdaCSTCCE6LN+x4LQb6NtzH6aWAtmJp3g2qcGjaKlBMAycRfUIpoG7dDaGrhLv3o7DU4bsLkZ2l6DGujC0BF2b/wNZhFDSTVx1MLPMOtc8jgxpXUKPn0BX40Ta1pGJ95IaaMIZmISW6MPQUqTCTTgDk1EjHTjcxaSHW8E0EUSFggmryCRCmHoKNXqC1ODRnHNVkjwnPcbUkx5jzuAUhprWgiiBKFnte9FO4qG9aMmBkdemcRWNiDEj+2maJieO7CLo9uQkL2YyGda/sokP3Xmy9Xl0S2fW5D/W8QqOU4RgsESnn/3qD/Z53dLWwS9+8xAfvftW5s6awenYf/Dwab8Dtu3ce9rl8rzznF3WaZ48efLkeU94ZcMWfv7rBzlw6Ait7Sd48aUNfO9HvyKZHD9G9p3GNE3Wv7KJf/x/3+Er//Dv/Nf3fsqBQ03vybbPhnRapf1E1zt2YVlVUTbu+Nm0qPw5oigOqqvK86JTnj9bzls0H0UZ35fsvMVv3wT8z4ElC+cy7RT/KVmWufXGqxHF9/Ynf1EwMP74m/iwjOZw0/ExqZtgiUI7dx94w2VTqTS/ffAxPvjxL/KzX/2BHbv2MTg0THSoj4ObfkcsnrBfG2lbb0fHZ5k7++SF7GhvJEHxUT3vAzmJdb3dHTgcco5xelM3bDxssm6fxJw51vmXTbDzT7gId0mDnXwnSgqRtnVWW9eIMCW7iyioXTnGa0lXY6TCTQiyi2D99baglZ1Pb3uu6ASWgPeHR57A6VSYN7uBqooy/vDok3zjm9/l//vR7/jftSc40Re1q6qs6qU63HXXsGPvSZEiqjrZ1FZNKiPT09VCuOkx+ns7ONY+wL7eCqJJA3dJA1OnNdg3q4LuFA7RIJx00x31UOO33k9hRHxyFtSSHm4lE+tCSw3hr1vNH55t4lDTCcCqNNvS4ufg8SF0ZDLRTiuVbiTZTUsOIAgSrqL6McfD5SnAFN0c6iuyK2/SusyhviIc/gkEplxJVJ7B622lNLb009PZynDri3irlgMmCCKCYJl/ZzENHUF0IMpuFF8VkdaXECUnwkjbnBKYhLtsLpLDh6Gn0JMD1nNlc3F4yzENHUNPUzrvXhRfBQ5PKcmBw2jJfkoLHRzqK2JmWdhus8sYImV+E4eZYKDxQdRoJ/G+vRiZGJlYNyVzPork9IMgkejbh6FGyST7kZRCBFHCNDJkop04CydaVUyCRKJnF6aWwl06m5KZH8RVPG2Ub1YAh6/KSv8bPG75Zxn6yL5niIf22su6iuqR3SWYumq3gxbPuJWCydez90Su6JSlvfNkYuapbabBaTfmfK5O/Uy++NLGMWnPpmny4ktjjb9H4y8oOO1z+QCV95b/G7ew8+TJk+fPiHRa5dkXXx0zHuofYPO2XVwyTjrIO80LL23g2RdesR93dffyy98+zGfvvZspk8/MXPbd5uXXNvPcutdIp9OIosj8OTO5/eZr3rL/B8Cai1dwvLU958eNIAhctnrlOzHlPHnyvEv4/T4+9IGb+cVvHsr5/E6eWMvtN111Dmf23iHLMp/62J3sO3iEpubj+Hxezls874wqg95pLrnwfCLRGKH+AbJvR2lJEasvOvMUKFVVT/tcKp1+w2UfeHgtu/YcsKtVk6k0HW3HWT29GKckcKIvyvLLPzhu+xvAgnmzONDYxK49Bwi6U3Zq3OILP4WzoBJ5VPVScSCNIAhMmTyBQ4ePkk5b827qhksvmcWyJfPteWmmwrYjEVqeexS/38eyheejDL8+pvWP0os43HSMguR2nFImt7pJjWLqJ1sns+15id5dbG/TxlycA2Q0jUONR1myaC5rn36RraNSC4djGda+HuP2VTolRQHAqgIbSopjKr+yLYelwTiHm44xODjM7u4yIulNPPvSNu758O1Mr5/M1ZdfzNqnX7TFh8Gki3lzZ1JUqtv+Va5gPcNt6y1vJklBUnycOLCW7s5ORl+ipnWZfT3F1FUZlBWoI0l3cSTTwFk4gcCkXD/CrMA31LqO+to2kmqYPV0nU9MW1MaYVF7BpvWP8MgLjQA4JZhfeYL+gUFmqFEUX6UlCo74ImXfG2/lYmRnEE/ZHIZbXrBb+wJTryUT7SA93IogiJavkp5GcvgwdZV41za8VUtxl8xEQCDetRl36VziPfcjyh6QYfKST7I49hLGQB8pzRLIFtTGKKupRo11IXtKyST6EWUPppFBKZxAMrSX4pkfpHfnDxFECQQBd7AeQXRgihLq0PGRtL4q3MUNmFoaQbIE+sDkK5EU3xiPsWwbHoCraDpgkgo3kYl2oiVCtteUrsbGTT10O7zEKAfiY87DklF/i9RIxxh/tVOrArNpkQDdvWODUMAy9X8jJk2sZerkunFTfK+/evwwgr8kHnjocV7btI2y8hK+8MmPUFpSdM7mkhee8uTJk+d9RndPH+nT/KhuaTvxrm9f13Ve3bh1zLhhGLy6aev7Qnjae6CRtU+/aD82DINdew/gUBx84JZr3/J6G6ZP5ZMfu5MXX95Id8+ZewjkyZPn3POJj97B7FnT+ONjzxKLJzhv6XzuvOXaM0ou+kvBEuEbbAPsc8XVl1/MsZZ2hoaGSaZSuFxOAoWFZ+UfVz91ErIso2namOdmzagfZwmLwcFhDhw6gprJ5IgwVUETU4+T0gIcGZrAheO0v2UvdAVB4EMfuImVy5fQdKyFgBxm1oIL8PktQ+HsRXJ2mQVzO9m19wAL5s5icGgYVVWprqrgH/7ms3a1WSKZ5Ac//jU9vScvlDdt2clHb7uEUvYAYAKvHjLZuPOXlHujTC8ZxF9Uzopr7kDxBnEXNRDv2UUqfIRk+Ai+ikV2e56ejuDU3viiUtM0tu7IbS/KthH29iVt4SnStp7glGvxFxTkVJ2dbDl0Mjg4DEBDadhqZcvAw396mq9/9XNcvGo5kybWsnP3fjIZjWsnOpk+xwpHGWxei5YYYLD3KQwtgewupmzePcS6tqD2nWB+ZchujRu9TVUrxOGrQh1uJRPtRCyciKd6FXpqiETXRpLhxlzj9tQgPiXDohkllJUaHB8qYlbFELUVU5CcBTz9dKO9X1lBzSX3ER4cojgYoGSW5bE5WlwZLdZkkV0BkqG9+OvWoMZ7bNHG4asiPeJ55C6djbtoOu6i6fb+9++/H1NXEWUXzsKJxLu3cs0Nd9JxoJAT0SANRbU0TKkk2vI0mZhEJhHCFZiEoasEpl5LvGszejpC7MQGXMXTSA004S6qRxzxpDIMiBsFCGRQZGssKzpl32NX8Ywx4o+nbK69D76KxTgKqgg3Pkw60oapq7iC9fbyoxntM3bRBct46rnc5wVB4OJVJ9Pwsp81xV9rv2enfq5GU1ZSRFtHJ6dSVlo8ZuxU/uNfvsqX/u7f7MRTWZK45YYrufKyi9502T9XVFXl8hs/zPGWDnvswUee5H++/c9ccemF52ROeeEpT548ed5n+P0+BEEY965l1rPj3SSRTJFIJMd97v3idbR5665xx3fu3s9N117+tqqeZkybwoxpU97y8nny5Dl3nL90IecvXXiup3HG3PfrB3nuhVdJJFPMmTWdz37ibupqxzfWPRekUin+uPY5jra0Uz+5jpuuu/yMhLzKijL+9oufZMPm7fT0higvK+GC5YtP24I3HgU+L9ddtYbHnnw+5/vwvCUL3vAGyHAkimmauF1OZFlC06xWoaZu8DeLFJSUctHFVkviG13oglUtMWli7bjbkRSfvcydt11HaWkR23bsxev1MHPGVK667KKcVp6Nr+/IEZ0AZNIc3fEwJQunIQCdXb2oPftQxNKT1UIdIlHvdm645jJcxdNwl84mE+smNXAYxVedk8w3fc5yhJeaxvx+cMgys2bWk1bVnEqy0W2EsZRAcNqN9vqGjz3JtZct5/d/slr3sq8N+CQ0XOzu9tpm3VmxaCA8SGd3LzVVFUycUMPECTUk+xuJtL/C0NGQ1WpYt5q+PT9DSw+CaeKvW40rOAWHt5yM9kc8yjGC7hQ9MV/O/AoKi5AUH4KkoGVSDLTsZsO2g+zrKWFVg8m0iWUw4lMVaVtvt4S5gcKiFJYdfTGS00+3MYtEuj3nOGQ9vIaHoxQHAwwdf5ZTyY6ZWmqMIfnQ8WfQ1TiGlrKMw8dJIJYUH96q5fTt+pFd6VUy5yMkQ/vQ0xH69v4ClyAyo0QjWH8+kuLDGHm9rkYRZTdl8z+B4qtAcngINz6Mlh5GTw/hKqrH4avAX7ea5q2/Y7h7D6ZpMph0UuxtozgYIFCzCFewntRgM3o6QmrgMN6qpSi+GqIdGyioXYmvYhEAgingKKi2jO4lBae/Dj0TIzXYTGqwedy0xWzlYHFxkIymceBQEw5ZZt6cBu7+wE1MP6UVeLzP3OjP1WhWX7yCX/7m4THjay5aMWbsVOpqq/njAz9m++599PX1c96SBTnVV+8mr2/dyTf/60e0tp/A6/Vw83VX8Ldf+tS7vt1vfPP7OaITgKpm+Pt/+XZeeMqTJ0+ePBZFwQCzGqaNMSuVJIkVyxa969v3eT0ECv12itBoqt9jg9rTMdqfYzSappFKp9+W8JQnT5487wX/+G/f4fl1r9mPX9mwhT37DvH7X33/PbsoeiM6Orv5xOe/xkD4pCH4Aw+v5ec//BaVp/HDG00g4H/bCZmrVixl8qQJ7Ny1n4ymMWfW9DEXr6dSWVGG0+kknU5TW1NFS+vJi69QKkCFv4QLli+xx053oXs2yLLMlZdexJWXXnTa1xw5ejzncVZUMTMaqu6gouEatuz+rxwhJys+bdu5lxuuuQxJ8VE841a7Smuw6TFMQB5VsXLjtZfz+FMv2G1ysixz523X2yJYRXmZLYBl2whTmoyrYuUYE/TZkwv53Cc+xMbNOxBTbUwtNamqncIrjU4i6WbbrNsla7ZYpDhOVtW0tp9g964jFOm9lAWHMbRHMA0VPT2MKDkRHR7UaAe6GrMSzhpupqjPBX07cUqaPT+3109psR8tGcZVMotdu/fgF9P4FYOZpQO8dqiIWKyFZQvkMWmF47WEDfedrCofLW6lNJmoZymCPGhX/bhLZxOYfCWRtvWkh9vRkv1W6p6o5BiSp4aOk4n1IAgSkjOAJLsRCyciSMqIUNiEq3gaQ0efxNRVECUkZyHJ0D57HWZiANM0bBHHV7WM0N6fo6tRDD2NlgwzdPxZApOvYuj4s6QjbSMm3xkQJUpm3UUsZXDoyDGqCwwcosFg0knQlWYgPIQ7GMJQY4iKD0F2WW2PpsBA44OkB4+SDDdRNu/j+CoWocZ66dnxPQw1hjM4leKG23OOgbt09rgtcvt3vcqvH9uJQ5aZP6cB0zQxTROPe6xgPRyJ8urGrbS2nSBQ6Gfl+UtOK/TOnTWDj9x1Ky++tIHunj7KSku47JILzirhcsmCuWf82neCnXv286FPfJlMxqrajMUT/O/Pf0dXTx/f/69/ele3/eqmsZ0LAOHwEDv37GfR/Pc+qTkvPOXJkyfP+5AP3nY9jzz2DHv2H8IwDEpKirjh6svO6Mf+20UQBK649EIefPTJnHFFUVj9HvhLnQn1kydyYqRkejTlZaXvSVVYnjx58rwdevv6WffypjHjQ8MRfvPAH/nS5+85B7PK5dvf+1mO6ARW1et/fe9nfPdbX3/b6x8airBlx26Gh6NMnjiBBfNmjpugWlNVcVapfE6nwhVrVrH26RepLC/F5XTS0xtCURzceuPVXLFmFQU+7xmvL5VKk1bVt/3d4vXkGhnboo8uUzj1OhRfGft6KpjqT+YIOdk5ZJEUH86KlRx49SeE+sMYhkHUvZTLy9KUlfpYtWIpc2fNYN/Bw0iSxNzZM3L29/qr1/DzXz+Eruv2+nW5hM9efAktrR109fRRVrSAmnKrYmdiTTFTPngzAMn+RiRXEQvMvWzf22wnxWXnWltTZbc+vbJhC48/9QIATslgUXUL9eXNBPweREnBW7EIQVIwtVSOx9biBXMplVvp6otxPN5A+UQ/MyaVED3+BADD8mxeaQpQ5nVQ5U/gkjW8SobdHV7qJw5RWmy1G/rrLNFzdEuYlhpk6Piz1E2/hbLSEvpC/ZR54xS5k4STbvb1VfDlO1fhyrTYwpO3dF6OIAfgq1qOGmlFT0eItK3DW7WcTKIfvKaVeqdYx1sQZVs0Gm5/mXhoL6KkoBTWIchu1KHjpIfbiLStw1+3Bj0VRvHXWu14yTC9u/4XU1eRnAHchXWoQ8dJ9O4lEToApmGlHwYmWemIusrQ8Wc42ufEKalohkhcddAZKUAzRPxOFX80RVHQaaXwCRKGoRMP7bVN1AUEBg4/gjtYT6xnpy1SFTfcgeIrt49BJtGLUjAhp0XOW7WcaPurPLvpZCWZIAgIgoBhGLz48kamTZ1kPzc0HOG7P/xFTjjM7n0H+dAHbmLBvFnjfobeD23EZ8O3vvMTW3QazTMvvMw3/+lL+HxjDdjfKURxbMVdFkmU3rXtvhF54SlPnjx53oe43S4+dOdN3JK8klQqTTBQiDBO2fa7xbIlC3C7Xby2cRuDQ8NMqKni0ktWvifC15lw8YXL2XugkfDgkD0myzI3XHPpuZtUnjx5/iJ5+dXNPLL2GaLROPPmNHDPh28nUHjm6WzjcaDxCLquj/vckeaWt7Xud4rd+w6OO75r7xsnyp0Jx4638dNf/cFu+9q8bRcbNm/ns/fe/ZYrVg3DIJVO43G7uXjVckpKiti8dRfxeIL6G6/iopXLziq5M5FM8uhjz9o3gKoqy7n5uivess/hsiUL2HfgsP04K/pUTpxLYdD6bp0ybQZ7dsdyRCew/Aez6GqM15/7MYlIvz0mR3bys/vCfOmvP4/H7SYQ8LNqxdJx59EwfSpf+tw9bHh9G+GR7/dli+fzwCNraT568txbMMXDmgUunJ6gLQplRZFiIlx74RSe3tBCWpfpifkoKSni7jtuBCAai/PUcy/Z60rrMr1RDxMKe1A9bgqLJtrrPNVjS/HXUlJWQ7Awwlyngr9uBZG29cjuYhy+StqHC0nrMh2RQvriXoLuFINJF/MrQ6TTJy9tR7fFSU4/ir+OwabH0JIDhIGPf/Aa/vDIk5QL1k2soUwxd9x+GyXFRST7ewlOvxEt3o+r+KTHo6t4Bq7iGUiyB2/5PHvukZbnERDQkmEwDbREiKIR3ypDS6JGOxHoQpAUPGVzkd3FZGI9uIqmI0gO9HTEFp3USAf+utWE9v3KbscrX/BJHN5yBg4/Qrx7B3qyH8lZiKdy8Uj10zMkQwdIhg5gZCxhZjDpJJFRUCSdgEslo4skndMpm3f1yfWkh5GchXgrF1Ox+Iv2ehI9u3D4qkYqnSzRCSyBSZS9GJkEkbZ1eEpno/jKUWO9hPbeh67GKAF6GZsE3NNz0hw82d/Ia5uP5ohO2Qq3J59dz/y5M9/T37zvFm3tYz2pwGp527O/Mafy8p1m9UUruP93j44ZLy0pYv7cme/adt+IvPCUJ0+ePO9jPG73OYt7nTe7gXmzT95ZymQy9Pb1U+gvwOVynpM5ZfEX+PjyF+5h0+adtHV0Eij0s2LZIqoqy8/pvPLkyfOXxX2/fpCf338yKe9I83Fe3biV3973nbclPk2cUHNaL7/KyveHwC+e5sLvnbhb/sjjzzIciaLrOl6PB0GA9o5ONm7ezuoz8GzJkkymUDMZXt+6kw2vbyeRSFJaUszVV1yC2+UkkUjS0dlNLJ7AX+A7rRgzHvc/8Eeamk+2x3V19/LTX/2Bv/vSp87KqyrLzBn1XH/1pTz74qu24BaomsctN99ov+aqSy+i+WgLPUPD9pjP57VbFnU1RtuuB0hE+klpliF21mdpSkELO3ZsY9XKk/4t3T19bN+1j2QyxYzpU5gzczqiKFJdVc4do4I4nnhmXY7oBHCoNcKkkjjTJ0oMjvJOyvpJXbTmehadb3CspQ2fz8u0qZNsseDosdYcU3inpFHiTTGcciKqBQRE2TajDtZfT7RjA4rfarHKtm4NHH6E9HCb3SbnLKwjWH89td1DwCuAJWhlRSeXrOH1l9o+VenhNtRoJ56yeQTrrwcgNdhMom8v8e4dlATruWt1gMhgPYbo4eIFH0BxF9qeVIauIiCM7LvVCqclw5imgSgp+CdchL9utT0/PRNDKahESw2heCuJdW3BW7Wc3h3fRxAdiJIT2VOMqatk4n1k4t04fBUEJl+VU+mUGjyG6PDYiXmS4iPc9Bi+quUEJl+JocZIDR2zkuqC9Si+cjxlc9HVOJlYF1UFEUKGSEYX6Y566I15qSxI4HboVBTER1rzVBAE22zc1FXAxNTVnJTBohm32qKTfT5WLyfWvRVDjdGz43sUz7yTgUO/xxhpl4xLxcBYO4SyMqsSLnt8pfBxnJLbThvMvodH+q1qSJfelWM8nj3/T+fHNppQ/wCyJBMMFr7h695tiouC9Pb1jxkXRZGpUya+q9v+p7/9PFu376HxyFF7zO1y8d///g/v6nbfiLzwlCdPnjzvQw4cauJfv/UDjhw9jqHr+HxeJtRU89G7b+GKNe+9KeBLr77Oiy9vIplM4pBlzl+2iOuvvtRO6zkXeD0eLlu98pxtP0+ePH/ZJBJJfveHx8aIQ719/fz6gT/yV5/56Fte95RJdcydPZ29+w/njDscDu6+44a3vN4s23fvY8++Q9TVVLPm4hVv6W/1eUsW8MqGLWPGly2d/7bmdrylnRdf2kA0ZsWtK4qDSRNrKQ4GOHT46BkJT/FEgkf+9Az7Dh6mte0Eg0PDTJpYi7/AR6h/gB/+5NeYpklBgdXyNBAe5E9PPIemaVwyqmU8k8nQPzCI3+/D6zlZDdXTG8oRnbKoqsrmbbu5+vKLxzyX7G/MuVCOJxJ0drRS4IhSOfk8AC5etZzlSxfS2dWD3++jtCQ3kSsQ8PM3X/wE23fus1reSopYuni+3SqnRjpIxgZIabKd/DbaZyk+cBywfiNs37mPPzz6BGWeCINJF5u37WL2zOl87O5bMbVEzgX87r1jq9vSusyL+yVmTvfbflJATgJaQIFFC8Z6xYy+OTVaVAgn3cie5UjOKHo6wom9DxJKFODVW8gkByiadoN9/ExdJRM72VLvn3Q5kuJjYp2PmTPqOdbcSNCdsrYnawT8biYtvMVui+vb+wsMXUWNWmnAkuIjMPkqkgONVsVOxwZkVwB/sNzen0Qyyas7WtF7juGRVcoLIViWoWfH95A9pWiJEIq3EsnpR3IVEWlbZ89PdgUxdJXSeffaiXPhxgcxDQ1RcuKftAZ1yBL3tNQAgiCjRjoYaHyQ4obbbdEpPdyC5AzgKZtL6dyP0n/wdyT69pEIHcBd3ACmgalnSA00kol1k0n0o0Y7UIfbMLQkTqWACeUe2ntjTApGUHWJPd2l3LbShZDqsRPqRMmJMzCZTLSTVLiJgYMPoMasqixpRPQanVQ3WvSpWPxF2/8ptOdnAIiKj4rFX2RFQS+tDz+ecz4IgmCbgCv+WiSnH58L5leGcsTTlCYTzfgQU21EujflnGuxnp0k+vZZbYJYxuSnClEtrR08+Men6O2zqqsmT5zAnbddR0nxG6c9jsYwDB5d+xwvv/Y6oihxxZpVb9mr7rOfuIvPffmfx3yHLF44h4qysVVh7ySSJPHcY/fz9Asvs/7lTVRWlvPpj33gXW3vezPywlOePHnyvM/48X2/41++9QMymma3YkiSSDqt8s///j1EQXxPBZcdu/bxxDMnf1xlNI1XN25FUZRxf3znyZMnz18Cew40khzlrZPz3P7GccfPhv/+5tf51//8AZu37SaTyVBXW81fffojTJn01lq5wEqh+9xXvsG+AyfDKX5032/53+/+P6rPsiL0a1/5DG3tnbS0nTTnnjJpAn/3159+y/MD+P2jT9iiE1htJ03NLcyfOxNFcbzBkif51e8e5eixVgzDoKcvhKbpHDp8lAXzZuFUHJzo7gHTZEZBbkLpS69t5qKVyxBFkVc3beX5da+RSCSRZZlFC+Zw6w1XIssyg6Mqjip8MQaTLtK6ddl0pPk4nSdaSIRbMN0TufjC85leiVXFMXKh/MKrO9i44TVml3bjkjVU3zauv/UTuFxOXC5nTrteKpVGURy2OOhxu7nwgvPG3W93SQPBicPseekZez6jfZZWLZxnH9M/Pfk8ZZ4I00sGbaHqwKEj7Nu3hxpHI3o6Yq9T141xt5fKiDlVPWB5J42uQskyNBzhcNMxHLLMzBn1BAOFDA4N5xiY7+ur4CsfXEmhV2bz09+hv/cEx8N+qvwJAm0nmJVMUdFwDUPHnyUVbrI8oAwNLREitPc+KhZ/EcVXzodvv5J9r7QTDg3RHqukbEINE0sNom3rkbOi2NRrSQ+1oKcG6dv7C9scW5RcmA6PvQ9ZL6hoz35+/ODrdHX34pQ8zK+ME4urTMgcJuBzkdFVnP46ZE8xin8iQ8efyUl2Gzr+LEZqkHjXZrxVy4m0PI/k8CHKLgTZzeCRPyIpBSCIYOgIkhMtNWzt275fIruCiA4PguwCTgoVkuIHQcDIJEgPt+IpnYWQdGGqUfT0EKnhVtRoB1qyH0EQMTJxCpUhZlVDWK9FqVvOvNkNOAZeJNG3D0NXEWUnntI5tmdToncP8b49SIofxV+LIDpQY10AORVfo8+Z4pl32qITQPHMO1F85SxZVI4oCqx7ZROh/jBVleVcvnqV3S6arWirHBwiPLiHBZWW0X32HF24aCG+4slkwvttk/VsmyRYZu9GJoka682ZU0Yu4bGHfkpv+GSr7vHWdn76yz/wta985oxb9z7/N//C9p177cfbd+5l6469/Ns/fumMlh/NNVesprs7xI/u+y2DQ8M4HDLLli7klz/61lmv661y9WUXc/Vl74/f6nnhKU+ePHneZ3zruz/BMAw7kQZA160f1zVVldz/wKPvqfC0cfOOccc3bdnBVZdd9BfRh58nT548p1JeWnza5wr9b/+usd/v4//75tdQVZVUSsX/Dqzzhz/7bY7oBFa71f/71v/wk+//21mtK1Do56Ff/w+vbNhC09FWZkybfFatauPR1tFJe3snmqYTiUSRZAmP243DIdMX6mfponlvuo6u7l6OHmsFQNd1NM26QWMYBn19/dTWVJJKpmGcr6ZYLE4imeJ4azuPPfG8Pa5pGlu370ZxyNx8/ZXUVFciyzIlrqEc4SY0lOLA/r1cOjtDqU/jSH+a+3/XyV23XM5Ej1UZ1Ljx52zeEGL2qCqOPY0hMmuf5a7bb7C3eaT5OE88s47Orh6cTifnL13I1VdcPK7B+miqpi5j1uyuHK+ttC6juSawaCThq6Wtg2QyiSG5rMS6kaS8xlARw0efoHJy+YjvkdXeNnf2DDZu3j5mWwtmT8ox54bcKpgsr2zYwhPPrLN/t7hcLq6+/GJe3bSVnpFOoxRBbr/tBoqLguzcvZ+ndugE3UF6Yj764l7mV4ZoOnIARUiSHrbeX3fpbApqLyS09z67tatsxDtpQoWfSXU1rBlpo7OT/kbaAuNdm3EFJqPGuxEEkcGmx8gkQqjRDiTnyRasrBdUb08nRjQC+EjrVhvjgso+Ogd0CjwG7sIqBMmB4q8j2vFaTrKbfR4lBzANnUyiH1FSrNf7qkgMHMI0dLTUELIzgCA5MU0N0zQQRAnJYR1LX9UyjEwCPRPDGEnjMw0N0eHD1DXr95Zh4CqcSFqQMHWV9GAzpqkjCCKSsxDR6UeIiYiiQWWByvz5M4h0vEoy3IShq5haEsPURqQtAROrTdDQ0ghSGskVRJQUxBHxyTAyDDQ+iCgp9jmjxnoZOPT7nPNi4NDvcXgsYXDRgjnMrJXfsFVu4vybSScGae/ossTncCkLFi7hpusuR3I4cpLyEj27Rh3jMMPtL6Ml+1F8VcjuIiRXEY0b72eSvxdVDeb4o4X6BzjSfJwZ03JF6PHY+Pr2HNEpy4svbeCu269jxrSp4yz1xtz70Tu496N3EOoPU+j3oSj/d1OX88JTnjx58ryPWPfyRpJJq4z41NLcdNryhOjq6XtP5xSJxsYdTySS6Lr+pj+S8/zlE4nGeH3rTrq6eyktKWbFskVvyQMlT573E1Mm1TFj2hQONx3LGRcEgdtuvuYd246iKO/Yxchrm7aNO75n/yFSqRQu19hI8zfjopXLuGjlsrc7NQCOHm9j38EjVuKSYFX7pNJpigIBpk2dfNo0q9EMDUfsf8uyjNOp2N+P6RHvJI/HNW57YaG/AK/HfdobKlt37OX6qy+lwOflopXL2PDaKznCzcthgwtnCbhknZRm+QsBPPfKdv7+rz7CYPNa+np2sqDSMk3OaYnbd4hbb7gKp1Ohs6uX++5/0PZBSqfTvLxhM2lV5babrs6ZUyQaY8Pr2zne2k6hv4CVy5fwwduvp7KijG0795LJZJjVMI3L16yyjdmz/z+1FW9BZR9OuSynhQngiksv5FhLG92jfl9UlhWwalqCTDJJ2lAonnwZ6d6N6OnISAWRZTrd2dXL40+9YJlD+yxT9FQqxXPrXuUbf/cFOrt6yWgak+pqcDisirZdew/apuTZeTaGinDJfaTig6CruEtnUzzjVqSRFq5sa9dA48PIrsCYfRgtVGSrY2RPMYGpVzPc8gKmnkFLhpGUAgRBQpCsY5RNrxuOeez30ylpNJSGkUSDgCtNRnMgx7oQ/XWkBptx+E6mLOpqjEjbekwthatoOroasUUaf91qBhofQhAVTENHcvrBNJE9pWTiPRhqFGfhJNtrKTXYjOKvRUuGSQ+34iioJhPtxBWYjGlkEEWH/dqC2lWkw0fIJPsx1BhK0XTAxNTSeErnkBo8hqHG6T/4e/T0EADesvmo8S6MTJzUQCPd4WZMU0N2FyM6vCj+CQimJR6KzgIULFFKcgWRnH5cxTNQ4322ECgqPoLTbmTg4O9tYbBi8RfRU+GcCsBsq172/TEySZLhRspKiiktLiKVVlnmDVLWcDHSyDkiKT672k6QHDj9dQiy0xLbRlL81FgXhVOutvy30sM5n8lTP0Nnwquvj//30zRNXt247S0JT1lKS8683e8vlfzVQp48efK8j/A4T35hns54tqKs5L2cEpMm1uakx2WZUFudF53y0D8Q5vv/ez/R2Mkfdpu27OSzn7ib2urKczizPHnePv/971/jb77+LRqPHMU0TXxeLx//0K2cv3ThuZ7auJwuKc80TQxj7PfJe82hw82AiSiKFAUDZDIZDMMgECjg9lMEl9NRU12JJEnouo4gCNTWVHL0WBuA7YU0edIEzHH299JLViIIAtHTXIiqqkoqreKTZa654hIqy0vZuWs7frmRmqAbnzuEltFyBCWA/v4wpujCX7cabefJi9fGUJH9Gk3TUDMZnE6FDa9vyzHfzrJt516uufISO1QkEo3x3R/+Iqf1b/feg9x9x41ceskFXHrJBePuR11tNeVlpfT2hXKqdxCsC+BT2+V8Xg9f+cK97D94hO7ePspKipleJdC041GOtQ+woyOIITzAiqWzOK8qRHrwGAOND1I27+Ps3ncwx8cJrMS+eDxB09FWZs+cNmZ+p56nWaEHAIcfh8NhC0MAiq+csnn32KITjG35Gy1UZPFWLSfWtdnaZiYGpo7gcOMMTLG9kk4ehGmk9RZ7X7yKSoknyXBKoczhw1FQjRrvxjFSqeTwVWBqqRzvK1H22u13wVEtf8n+RiSHF4criJYMk4n3YJo6SsEEXMEpOcbtpiCSSfRawsqIJ1Qm0YszMMUWhTKJfqKdryM5g7iDU9HkIdRoJ6ahYmopZHcJCGAahvV/XcVZPB3R4UGhAkNPkx5qQUtb6XjOwCRKZt2NpHhtccgVrCcW24bsClrvgb+OeNc21ETINpjP+lk5CyeQHm7HUGMkendRULsSyenPqUDL7p8gu4iH9ua0KWafG2xemyNUja62ywpuAM7CiaSHW5HdJURarMpFf7CcPVtF+/NmLycITJk0Ycw5OB7BNwiMeLtJpnng3LnC5smTJ89fOIZh8PTzL/HVf/wW3/jmd3NilE/H+ecvprCwAGDM3Vqvx40gCNz9gRvHW/Rd4/LVq/B4cpP15JEf5XnyPPviqzmiE1g+M08+u/40S+TJ8+dDaUkx9//k2zz8mx/y4+/+G8899is+OKpd6v3G0sXzxx2fXj95zN/xc0FXdx8Taqvtxw6HA6fTicPhYN6cN06qyuIv8OV4IJWVFNMwfQp1E6qZOWMaixfO5R+/+nn+5oufoGH6VLxeD7U1Vdx1+w1csHwxwGl9tCrKy/B5T5qMT6yrobpmEinvfFwuxRaERgtKAEXBAIKRItK23v4OB2goDeOULDGmuqrCFsYGxrmZA5Y4NTx8MmL+lQ1bckQnsETEJ59dP+6NqSyCIPCxu2+lrLTEFnVESWTyxAn4PB7rQl/N/bstSRLz587kyksvYtGCOTSeMHhhV4IdHUHSukxG03jl9b3sH5iEMzgVUVIYbF6LqA3aotOpFSeGOb531JxZM+x/jxatZJefqrl34CycYIk6zWvR1Ri6GiPWtcUWnYAx+3CqUGHqGUJ777OqnJx+Sud+DGdwKk5fDQJgGppdSROcdiNzl9+I2+22PancjgySYFBUIFGz9LMo3nJkT6mVljfchiifPE8AnP46MvFuTEz8dWuQFB/Rjk307fkJgiQju0tw+KpAlNGSA+ipQQwtib9uDYqvAn/dGvRMnHS4CUl2I0gKSuFEDFOz/J3CTQiyi8JJl6HGejC0FFoyhK6n0dMRMok+tEQIQVIQHB6MTAJDS6AlB5DcRaiRdox0FNldRHDaTTi85Th81Ti8FSCIll+SGsdVPANP6VyG29aTiXWjpQYBqxpLkF0onlJkbzklM++0TdQd3nLKF3+BwJSrCEy50vZxssWnpsdsscpTNjdHnMuawWdfq0Y6cqqjJKef4LQbEWQXqXCT3YbpKKgmE+uyxcNJC25h0pTpY861lecvobgoeNrPymhuvfGqcX3mvB7PWzYYz3OS/K3qPHny5HkXMAyDz33lG+zYtd8ee27da3ziox/g4x+67Q2X/dev/zVf/tq/Y6oZkCQMw0BxyCxeOJcPfeBmrr78vRV8ykqL+coX7uW1Tdvo6OympLiIVecvpbrq7Ixq8/xl0tTcMu74qdHced5dDh05xo9+/iCNTceprizn8/d+gKWLrLSptKryre/9klc2bsdf4OVz936ASy9afo5n/OdFXW01daMEk/crf/Xpj7B3/yFOdPbYY/4CH1/7ytszBH+nKPQXUFVRhtfjoS/Uj64bBAN+ZkybgiRJZ7ye665aQ3VlOVt27CGdVrl8zSouvOA8WxjK8smP3Tnu8msuWsG+A4eJRE+KPJIkcd1VJy8uG48c5Ze/eRjRTDG/MsTRkIYsS5iYNJSGcyqeLr1wkX2hXFs3lc3HCymXj9stegf6q7jx2svsdddWV477N9LtdlNSfPIiuaW1Y8xrAAaHhult2U5pzczTeuiUl5Xw1c/fTeuO36GlPQSKqyiactm41SXjsXHz9hyvnCyvbG3ikr/7FENHn0BPR5jm72a/PLYKzOl0Mn3q5DHLJ/sbWTJvKgcb6zl0uJmgO4VXUfE5DSat+JAtRAw2r0VLDDDUug4zE0dPRxCVAgaF6ai9m/B7BmBkH4AcoSLb4pb1XCqacdtI1dTHCR18gETfPpSCGmRXAEFyoEbaCJbP59P3fJA/rX2OI/2HSepOLpuZYMKEatswPHP0SVyBKRiGiqElcvdrRJgRgUjbOpz+OvoP/QE9E0N2l+IoqMLUVUxDB9PExEB0uIm0rbPNu41MEkGUMUaMzPVMHFPX0NQwDk8pnrK56KlhBFECRExMy4RdlMDIIIgOlIJaZFcQPdmPlhxAlH0YasyqoIp3U1B3Cf0Hf4c+IlJJDg+ZRAgB6NnxPQRBQlOHEUwrqa6o4Q6iHa+SifXg8FXY4lP0xEYgN+UQPY2uxpAUn12BFm582DoGriD+utUovgok2ZPj/5QVqrLnbrK/8aToNMpDK4urpIFI63qr7S7ShtNfR7T9JT76gavZse8Y+w8etgID5s8+o/bdLKUlxfzLP3yR//zOT+2W3uKiIP/6j3/9vhDu/9zJC0958uTJ8y7w7Iuv5IhOYN2l/OVvH+Hm668Yt2Q3k8lw/wN/5GBjEzddexn7DxxGkCT+5q8+cc7T44qCAW645rI3f2Ge/3N4vZ4xFU/Z8TzvHY89/RIfv/smGqZN4qUN2/i7f/0ezzz0v7hcTh55/AW6evr446+/Q0tbJ3/3r99j0fyZFAUK33zFed63pNMq+w8eRs1ozJw+lUDAT6DQz8O//iFrn1nHgUNN1FRVcPtN1+Dzvf3PY/OxFg42HmXa1InMnFH/ltaxasVSHv7TUxT6fTkG7W/FQ2rRgjksWjDnLc0jEPDz5S/cw4bXt9PW0UlRMMAFyxfb7cGmafLIY88Q7u9hUe0AuioQN7w0dRdz4SwRQ42yQAjRnp7OqlUXMm+Si0j7PiSnn5L66/nUDAfbtm1B71mPx2lw0dWXUTl5Ys5x2L5z35i/nZdevML2QQJOazhfXZhEH9jGYLJ5XA8dsJLHMtETFLhBCtTarwvWX0/X/ofobztKU+/zzFx4KYHA2N8ko5MHR5NIJEFy221tBT4vVVXlPL3TtEUnWZb5wC3X2l5TWZL9jbb3z8fuvI5j7SGOHztCtbYOn5zENfQKujplRLRYw0Djg6T6GwETQynnoRfDnOh5FqeksaAqxKTaYuaWW22vo4UKSfFRUHsBqXATpq6SiXai+MrR1TjpoWMjht0egtNuzBHiquuv54uf/Rjhzt24CutwKop9TIePPY2RSeDwVSDCuK1iguxCkF3o6Qjx3t2IslX95fCUYGTiaIkQrsI6VNmF6HBj6iqpcBNqrBtTV5G9ZRiqG0GwRKXg1Gvo2fkjQERXowiik2RoLw5PqSVQZUbOH9NAdgVR/BMonLSGeNc2lIIaRNmHqSdRCqpRo52ISgGhvfdZ1V6iw6oaSoSQnQES/YcQENC1BAgSiq+CisVfRFK8aMkBMole65ysXUkitB9Tz6BnYgSn3WhVKWkJ4l3b7PdAV+P0H/wdmZF9g5PG9O6SsdWNkuKzx7P/z4pTyf5GTC2Fu3Q2rqJ61OE2FG8lKt3InlJMTPR0hGjL0yxbeD0rli0a99w9E1ZfuIKLVy5ny449OBwyi+bNHtcvLs/Zkxee8uTJk+dd4NWN4xsUZjIZXn5tMzdee/mY5154aSMHG5sA60fbgpF0mqzZeJ4870eWL13AY08+P2Z82ZIF52A2/3f5hy/da//78ktW8J/f/yXReAKXy8m6V7fw8btupChYSFGwkEXzZ/LKxh3cdE2+deDPleZjrfzyt4+QTCYBqzX76ssvZvVFK5BlmZuvu4Kbr7viHdmWqqp88e/+Hzt3H7Dbu+bOns4P/uufz7oK4PzzFpJKpVj3yiYSiSRut5uLVp6X0zr3XlHoLzhty3jHiS5efm0z1YVJRF2gux+e35egbmKQo9HJ3LzcEhf8Ey7KuYjOXii3HG9lMJrBHVzBhCkBKifn7l+g0M8XP/tR1r28iaPH2/AX+Lhg+eIx1Rkrli1m34HDY9rq6qYtwumxWqw69z1IRJ6FXzuIU8zkpNWdegEP8PqORp5+poOAM0FP7CDii43cdtPVY/5mn87fsa62GsFIMXDseXp6+ohG4zidDm6/uJwufQayUsCCebPGvcGm+Gvtlqqho08wqW41xWoYLVF3Mn1ulCeQKCk4PCW4ixq4//HtnOix5pPWZXZ3ldI6mMJTLzJn5vQx++kumo63cjGZWA/JcCOGliDatRUzk0SUXQSmXouk+HAVzyA1cBgtMUC0YwPOwolkerdgDB3CWX+9VbVz+BHb0NoVmIyWCueIXKONzd2lc0iG9qOrUZSCakSHF0wDLTWIw10Cokzl0i8TaVtHavA4arQDQexHkBwIkhNBEMkkQjjcJQy3vYQzOBmj/yAYOn27/xdnYAqGlkDx15CJdqGlhxFlJwICsiuIu8g6FnpyiOG2dValU7QT2VtOevC4laI3yp9JFB2kh1sRRAcI4FBKMTIJFP8EwKRv7y9Qh9swAXfZHNKRNkw9QzrShqGn6d72HZwFNYjOAlt06z/4e5LhI2iJEJJSgKdsLoKkkB5uY+DwI7ZpPORW6ZmmSeORoxw41GSJPgsKmVDjyzmP1UgHyb79yJ5iihpuR0+FUfy19vHPruvtIIri+9bH78+ZvPCUJ0+ePO8CbpfztM95PePfed65e//443sOcMsNV74j88qT551m1YqlDA4Ns3HzDjRNQxRFliyax5WXXniup/Z/liefe4XZDVMpHWnZ6Q0NUFN1MoWppqqc3r7+czW9PG8TTdP49e//aItOYLV3P/nseuqnTmJCTdU7ur3v/PCXYyp49x04wr//94/5t3/80lmv75ILz2fViqVEojH8Bb73ZUjFtl17iSeSNCUATE6EIZE2aWnrYMWyhQTrrxpzgZv99x8efZKt23fb48+9JHLX7V4WjtxMylJcFOT2UemImqZxsLEZwzSYNmUSTqfCtKmTuOOWa3nq2ZeIxmJIksSiBXO44borMbU4rz/1HYbC3WBuBQECRZWcf80HctrnRs8xPDjEn554DtMU6cn4qPDFGEy6eOSxZ2iYPpVCf4EtBFy+ehWNR45aFU4jyLLMNZctp/fQo+zdvYOhmE5jqIiG0jCe3hAzZkpMPe/D47bvJfsbUfy1OSJNtg3LWVhHUcPtls/QqFS6rLgzFMvQ3PpUzvqyqXg7du1jzszpY8QGSfFRPONWBpvXkh5uJ9a5xWotcxUhuQqJdrwKWJVL7rI5ZBL9lmG1t8wWxwYOP5KToiZICrqWwFu1dMy2gvXXE+/bQ7x7J0YmTibWjSApOAsngWkgOXykI204CqrRU2HLiD4ZRk8PYWgpBEEGQSA93I7iqyST7EfxVeEsqMY/4WL69/8KQZBQh1st0SneY7XiGSqC4AZBJBVuInTwAev4hJuQnAHrfXOXkIl1IUoOECRK592LOzgZyeFhsOkxHAXVGIPHcAYmI0oK6eFW1KEWwo0PIyCAIIGhMtj0OO7gNLT0IA5vGclwM6ZpkBo6jiswGdFZAIJIom8venoYQXLgKZtHyaw7SYaPMBA6gJYcIFU6D2/lQnQ1Rt/eXyAIVkXRn15qYvvOvVZCojvFqxu3cuO1l3PhBeedthoKn2X7MLpVL8/7k/ffX/o8efLk+Qvgpusu57l1r425Sxko9HPJheN7q2QyYxNuALRMZtzxPHneDwiCwA3XXMaaiy8gFBqgqChAob/gzRfMc8b89P5HuP8Pa8d97htf/RRXrD6ZbPXKpu388cl1fP/f/zbndYJw8t9vZEr86BMv8scnX3zT1+U5dzQdbSF2mjao3XsPvePC08uvvT7u+MbN29/yOmVZpigYeMPXaJrGvgOH6ezupay0mIXzZuW0ob2btLV3ESj0MzQcoan75HgsFmf2zBk5bUGjaTxyNEd0AksUfOhPTzOrYdqY1rMsTUdb+PXv/0g8bvkGOZ1OPnDrdcyf08B5i+ezeMEcBsJDeL1u++bVM6/sYcsBnQXZ8FATXj6gkyjcfdr2/P0Hj9if6wpfjOklg7Y304FDTSxbON0WhfwTLrL9HU909VBSFGTViqUEHWH2Hz7GUEy3PZ32dJcyvzJE6/EjTGgYe/E/usUumK0ianx4pHpGpaB2FYqvfEwqXTa5LpMJ2SLZaFN3p6RRQqPtK5RldBVNtmJJkxRMXcVRWIogOUiGDgDgKppGKtyMKClITj/uoum4i6YzcPgRkqEDtuDkLp2NICmW6fnRp5EUH6mBw3bVkxrvI9z4KJg6SmEd7tLZJAcaiffuRHJYggymjpYIgeQk0rYeQZTthDbT1FCH28HUyST6cBZORHYX2f5PzsAU1BFj7dRQK4IgIspOJGcBDk+5lYSnpYh3bUF2lwKg+GsoqL2QSMvziP46W/jKejFF2tZjGhqZaCeCIKIl+ymddy+R1hdJhg7Y/kml8+4htPfnmOkIyXAjruIGtNQgnuIGMsl+u93N1FLomQSCIFppecEpBOuvsz4vRdNxl862K9AcBVUMND5EevAoouKjI5SwRafRCYlPPruehfNn28b8wJu26mVp6+ik8chR3G4XC+flriPPe09eeMqTJ8+bYpom0Vgcj9v1vrwz+X5k3pyZfOKjH+CXv32EzIhwFCj0881//sppj+HsmdPYvG3XOONjUzry5Hm/4fN6ctKg8rxzfPIjt/LJj9z6pq974eXX+c1DT/L9f/9biosC9nh5aTEdnT1MnGCZY3d297F04fjeOLdcdym3XHcpYF34r7z6I297/nneWRKpNEePtzI0HKXA52XKpAn294ph6G+y9NmTVse/+ZFOq2e0fDKZYv+hI6TTKrMa6t9UcAKIxRP88Ke/oae3zx57Yf0GPveJDxEMvvveZIrDwdQpdRxpOm57HYmCQG1tFXNnnf47+WBj87jj6XSaYy1t43pjpdMqv/ztI6RSqZzX/+7Bx5g4oZpAoR9JkigrLc5Zbveu7TSUhnPGGkrD7Nm1/Yx8IQeTLlKabBugS/pgjkG34q/FrfjG8Xcs52CPn8Pdgi0CZcWn4FCK8xzVnNqAObrFzq4iGhGdBEkhNdiMK1ifk0oHJz2B/GKIeTUJhmJRW+xyShor6jqZ4hfp2/sLyuZ9fIzXlZFJkgw35gg86eEWFN9JcdbUVasiabRJNiDJHgw9jSg5reS7+usBgd49P0WL96Inw4iig8HmtfiqltG743/Qkv3I7hJKZt0NQGroGKZpoKnDOAsnWsKXu+T/Z++8w+Mq73z/OWX6aKRRL5blJtly7zYGY2Mb03sNJBACCemFbLLJ7s3ezd3cLTdbSDYdSKGFEjCmGGxsDDbgXuQm23KTZXVpRhpNPXPK/eNIxxo0so2BQLLn8zw8D/Oeed/zzsyRrPOd7+/7o7PuIZz+8n5haSnhhpX9QpiAYeg4cyoQRBl/+XwijWtR492oiS6ceaNJ9zWbwlE6ir9sPvkTbibSuBZRdJDsOdYfMF5BTuVFOP0jzPnJHiSnH1egCgMDyZ1P98FnSUfbEB1eHP5ylEgTsreIvqa3MABBcqKrSZRYK4nOOoqm3UfHrl+apXuRk7hyR+Pwl1Iw6dMo0VMY6QSxtp2koy0AuIJjkWSP9RkOdqANuNoEQUR0+nH6yjh1+FUCrhS1RaGMDomqptJw5PgQx+DZePq5lzP+pn551To+d9et1I4fd8Z5J0+1EO7pZWRF+fv+XaOqKqvXbaCtvYsL5s447yy89xKNxvmPnz1svZ55s6bxra/em7Wc9ZOMfQdpY2NzRrZs381rr79FuKcXj8fDxRfO4fJlixAGf31uk5V777qVm667nPUbNuHzelmy6IIzCndXLF/MkWONdHZ1W2P5wTyuudLOYbGxsTkzr6zZwJPPreK///X75L/nj+UlF8/l8WdfYdKEcRxvbGb7rv1892v3fEw7tfkgtHd08Q//9J8cONSAoZvOlaPHGlm4YA45OX6mTf7wy0wmTxzP1u27h4xPqj3zDRzAoYZj/PaxZ0mlUgA8/+JrXLn8Ei5dctEZ5732+lsZohNAdyjMi6vWcvedN5375s+TObOmcvR4I1MmjScWi6Ok0/j9PubMmIr7DKX0jjP8Gz+cW2vfgUMZotMAqqqye8+BrMHrmhKlJrcRGfMGfaDczS2rVOeeHOIAGmDa5FpWvvI6uq5nOJW8To0Rjnq0lDxEgMlGSionpR3NHNNkOhO5uF2Z709HZzfJVIqysdcQPvxcVheRnuqjbfuDGWLM4NDvQNUyxtZMpH5/HdPLOq3Xm5Pjx5fjGZINNRD0HeusywgBHxB4lGgLDm8xSA4E0fzMBtxVALHWnURbt4JhmCKQ5KDn2KsAyA4fgq8E2ZWHEm1B19O07/wFGBqyp5CS2V/H6S8h0VWPw1tihoZ7CnB4CnHlVxNv3212mTN0K4RbU/pAlFATIUTZTbqvGTF3FOEjL6MmutG1JK6cSmRPPvnV19G67UHQFHQtieT0WQ4y2ZOPIBZTUHsbktNvlRmagli+5djq2v8YSt8pBEGicPSdxDrqQFdJx9pQIidxeItx5o4mHWvFUJMkw0eJd+5D8gRRek6ArqD0nSJYcwOS00e8Yw/xjjoMLY0giP3d8nxmaeJ7OigOdrUJokzxtPuItmzGJXUzo8z8mc/WIfH9cOBgw5AvctOqypPPvMg//t03snbQjMbi/O6xZzl6vBEwc54unD+LG6+9/JzueRqOHufr3/k/dIfCAPzmd3/kovmz+fH//f4HDif//Ne+x9HjJ63Hr77+FgcOHuHpP/z3X1TwuS082djYDMveA4f447MvWo8TiQSr125AEiWWL134Me7sL4e83EDWIPFsBHL8fPebX2DXngO0tnVSXJTPzGmTh7Xm29jY2Azw8OPP09bexVW3f8Ua++1//x9qa8Zw6/WXc+zEKW68+1sEcnx852v3DBGnbD48GhubEWXR6pD2YfLvP32Iru4QgRw/kUgfhmE6knbvrecfvvcNxowe+aGf89tfvZcvfP3v6I30WWM+r5cHvnrfGeepqsqjf3zeEp3AdFC/svoNxteMOWNJ4N79B7OPHzjEk8+sZNWaN0kmU8yYPokv3XvnGV1UhmFw8PBROrq6qSgrYdyg7nLDMW/2dJpb2nhn8w58Pi8+YHRVJTffcOUZ582aOYU3396ctcx+7DCfjXKGcnplGLeZEmmitMDLqfah5W4jSrzDZt3k5QW49careHbFKjRNI6XJHA4VcdMFLks0GyzADMf8uTM5ePjokPHpUydawlwo3MOjTz7PiZOnADPM/ZYlFfj7RSdX7ihLjOioewQ1EUL2FGQEdncffJZU70m0ZIjysfPwOiU6OloZU5kmEKgiv6AYT0ENSm/jkGwod8GEjC5rYLp4Bpw8qUgjouxBzB2FIMqWMwcg1lkHgCi7zXBwsErzPEWTKZj0aSKNa9H1NErPcWvtvDFX4PQVA6bLSxBlnIGRGGoKNdlD8uiruPJrCFQtJXf0MjQlRtv2B81OhKKIK290v1BViBJtQXR4UaKtiA4PYv97kggdMmunDQ09HScROkS8Y4/lIBOdfnqOvYq3eCqp3pPIngIkTz5qohuHvwzD0Ej1HDffo/waDMFAT0dRUz2IkgdViSE5A6T6TmKkEzhyKlCTIXQlBoKE7M5HTfWgJkN07PwFztxRKL3HTdFJcuApqLUELsAKHFciTTgDlUNcbdGWzfjKL6Aw0kVTcyuGblDfmW+JTrmBHCbUjAEgnkjQ1xejID/vjF/k7tmX/fdHXzTK8camrL8DnnvhVUt0ArNEduO726goLz2nZik/+Kf/tEQnMH/vbNy0jSeefoHPfOrGs84fjnVvvZMhOg3Q2NTMq6+/yVWXZW+Q8EnEFp5sbGyG5a23t2Qd3/DuVi5dcpHtevoIcDgczJ017ePeho2NzV8YKx59cNhjbpeTf/zbL//5NvM/lG279vDPP/45zS1m2/Exoyr5px88QPXY0R/aOXbu7s+lcblwFjhIJlMYGIiiyHVXLfvQzjOY0aMqefoP/80Tz6zk6LFGqkZW8OnbrqeosOCM8w4fOW5lFr2Xs2VRDXdTuW3nHja8c7pr7MlTLby7eQd333kTra0d5OUGuOiC2YwdUwWYLoZfPvw4zS1t1pyxY6r4wmc/dcYvdQRB4KbrrmDJxQs42dxCMC/3nLKzRpSXcsM1l/HiqrWoqplRkxvI4Z7P3DKsM6G2ZhyiKKLr+pBjk2prss7xFNZSPftmNjz9JinNvMFPaTLHoqO55KrFZwxYnj9nBrXjx7HvwGFEI8lIdwOycTpAfHBp1HBMn1LLlcsv4fX1b5NOpxEEgUm1Ndxy/Wlh7uE/PE1La7v1OBkLs29nA/MmleD252eIPYERC0nH2pA92a4po79E7gQOWaS8OB9BcmBoadDTJDr24i2aQrzzdAB+oGopTn8pkuy1OvyFG1ZiqElc+TVoybCZtcRpMWrAmePJrzUDx4vM0i5DTZrn6sdbPBWnvwRf+QXEdv7cGpf6BZ9UXxOeYA2J8GGzQ12sDdlTiKGnTcErdBhRcqIpUbrrn0ZXogiiA1fuSFLksL89H1/XHgpy3OS7PciuAIJkXquaEiXesQdRdCB4ChAkJ7HW7SRDZkdkR04F6Vgbif4Qb0NXMTAQEPofa6jJbgw9jcNXSsEEs4Q72W0KNUq0BdlTSLLnKOgqotNvhqMLImlVsQLEtWQPsfadpGNtqCmzjM9XNhtv8VSrq95ASZ2/bB6iw5PReW7AgRZpXIeaCNFZ9xAuXxnVY0dx7PhJs2S0tQhfoJDPfvpmDMPgmedfYeuOOlRVxefzcvmyRSxcMCfr9XkmF5AoDD2WSinU7avP+vxtO/acVXhqbGrm2ImmrMdeX//OBxKe9h/IXr4LcODgEVt4srGx+esgHO7NOh6NxkinVZzOP0/Ip42NjY2NzSeZnt4If/N3/0wicbpk6tiJJr72nR/y8jMPf2j5iINLRERRxOs103Q+6n+P84N5fO3+u9/XHF0/Qzj9WYLrZ06fzOtvbMwYi8UTdHZ24x8UEKzpGgcOHuG3jz5ruSJ27z3AZ26/gZnTJ/PiqrUZohOYpYmr123g2ivPLtQFg7nvO+fl4gvnMnP6ZBqOHMflcjK+ekzW0p4B8vICXH35El5ctTZj/JKLF1BRXjLsvNIxc/nmNyazbcceWts7KS0uZO7saRmdc9PpNFt37LEClufPns7YMVXkBnIGBYknMoSA95ZGDRCLx9m5ez/RWIzqsaNYvnQhFy2YbQp+eQEK8oPWcxubmjNEp4HAaJeo0haRmTX7liGldN6iyYMem3sx1CSu3Cq8JTNR+gUVAIe//HSWUH4NiXDmzfmAoOUprCXRVY+mxi3BQ5R9JNMxHP1lfYaaxJ1fTax1B6QiiA4PgZGLcQYqzbyowysQJAeuQBXe0pn4S2ehRNvprHvIKhl0+MvNcG9DI9a6nWjLFkTJiSC7cflHILpy0DUFIdqGriZJR1sJH16BKDlxBcdRUHs7Bw/s5MkXt5BMt+KSDIKeGLnF5XzqqotRexsyHF0OfxmSM4Ca6CbRuQ9BcuLOH4+naCLJUINZThg5hcNfSqpflJKcOaQTnQiGgeTMITjO7KioRJqsToPvdXAFqpaRCh82P4dAJbK3gEDVMsINLyA5/Oi6iiS7ceeNI2/MlTj9p6/XQNUy4u07LdFJiTQNEp2W9Xf2W0bb9gfN0kOfQfX8z1FY/Drh7hZmz8xh9OzPILtyWPHSat7dsuP0tRiL89zKV8nLCzAlSw7qrOmTM54/QH4wj9GjKoeMq5qaVfgFSA5ybA7HgMj8fo+dCyMrK4Y9VjVyxAda+8+NLTzZ2NgMS1VlRYZtdIDSkmJbdLKxsbGxsennmedXZYhOA4RCPaxa8+Y5iRznwoL5s1i1ev2Q8Xmzpn8o63+Y1IwbjcfjIZFIDDk2bcqZs6iWL7mIU82t1B86Yo2ZQltmA4N4PImu63SHM0tcXn7tDWZMm8Suuv1Z19+958CH9pmEwj00NbeSH8yzyiv9Pi8zpk0a8tzuUJiXVq1j/8EGXC4nc2ZO48rli1iyaAE11WPYuXsfhm4wdfKErDfIR4818ubbW+gOhRlRUcbSRQuyZkCBecP7q0eezCgf2rajjhuuvYxFF87LEALksmWoUsASIAZKowacU8eOn+TXv/ujVTa5eu0Gpk2p5e47brLcZYOJRjOdbkFP0gqMPqWM5wJ/aca5tGQo4/HgkrmB8rcM+h33ZtnYUSv/6L3imSe/lmjrFiRXAF/5XARDoO/U2yi9jbhyq/AVTcMQDJLdBxEQECS3GYTeH1IeaVyHoaXR0lFkdxAl0ogSraa7/inUVB+iw0fxjPuJtWxCFB2koqcwDB1BB11NIgoigmyWHgqGjrdkGs6cSlKDhLKC2tuQPIX8ac1BkmnR6t7XFvXTFm1l/6mZzBo/n+76Z5DdeajJMILsBgxTGPSX95/DINGxF1/5XADS0TYkhw+VTrR0HCXWgSCKyO4grtxRRJo2mO+hmiQwcjGyr4Ro6+lOlQ5/OcmeI+jpGA5vEbqm4C+fT8+xVSRDhxEdXtz+ctKJblK9x+k5toqCCbcMeu/WkuptJNa+G1fuSNPZNnIxkjufSONatFQEV+4oRNmD7CmwwuELJ9yAcPBZ0tFW0n2nQKpm09bMTpEDvP3u9qzC09gxVVy27GLWrNtolb36/T7uuuPGrNUaPq+XqsoKGpuahxwbznWYcb7RVZSVFtPa1jHk2IJ5s846/0xcffklPPyHp+jo7M4YL8gPcv1H5HL9qLCFJxsbm2G5dMlF7D/YkJHPIAgCV162+OPblI2NjY2NzSeMjq6uYY+1tQ9/7P3y3W98gaPHGjnUcMwaGzVyBH//3a+cYdbHg9Pp4I5br+XRJ54jPehb/6WLL2RU1Zm/qXc4HNz/uTs4eaqF1rYOigsL6AqHhwhJ6f4MJI/LnTEeCvcQ7ukd1ln13gym99IdCpNIpigvLR62bMcwDJ5b+RrvbN5urTd2dBWfu+uWDNfRAPFEgp/+8vdWVlY6nWb9hnfp6g5x7123MqK8lBHlpcPuae+BQ/z20Wesc7W0tlO3t55vfvkeykqLhzx/Z93+DNFpgO0bX2TO1HF4C2tp6+jkpTf209zyc/K9KQoqZ3LzNcvw6SFLdDIMgyf/9GLG34IAdXvr2bFrH3NmTR1yjlFVFThk2frc26KmcyqccHP7xea6AzlOgwWuwcHTA48lp59EV32/M6nGdBmJMmLuKLR0jHRfsxXUPbDmgIBlCIbVTS/ZfRB/+XyUWCsYOul4J6I7j54jLyEgYGBmHYUbVuIrv8AaV2KtyP3CC6kIrXWP0djUQk8kwbamXIobNnLz1Ytw8A4Asq8ENd5BqvcEhqaYQe8Ob3+nwFEkw4cz3queY68SE0cQ6euj1B9lfGE4I1z7+OE6KpMN6ErUfN8cflJ9p1B0M8jbkVNBuq+ZNGb+lCd/PE5/hfU+yr4S9MhJBFE0w9IDVQiSMyOzKtXXQveBJzAMHdkdxBkYSTrWhtFnlo85c0YgOXz0HF1FqveENS9vzBX0HHuVROc+Ep376AbyxlxhiX8OvynEZjrZ1p4WPH3FCKJjmBJLUyBKKQqKkr2D5uDcufdyxaWLmTd7OgcPH8PrcTOptnrYgH+AG6+7nF898mSGUF5RXsrihfOGnTOYv33gi3zvH/6NZPL0z0nNuNHcd/et5zR/OGRZ5tc//Wf+6V//m7p99RiGwbTJtfzdd76M0/mXlQFrC082NjbDUlZazLe+8jne2LCJU82tFOQHWXzRvKzfbtnY2NgMxjAM4okEHrf7L6rrio3N+TBr2mRefGVt1mPzZg+9MT9fvF4Pjz30n7y7dSf1B48wdvTIYR0vnwSmTBzPP3z/G+zecwBFUZg4oTqrSDIcI0eUW9lKo0dVMrqqkuONp7NURElE1EXGjc38u8Qhy3g9HqZOqWXHrr28l+EcV72RPh57agVHjp4AzHymG6+7PGu3wE1bd/H2pm0ZY0ePN/LcC69x1x1DM1227dyT9UZ57/6DtLV3UlpSlHVPA7zy2vohglkqleL19W9zV5YMmYb+1zCYUn+UMblhmnY/RenkW/j105sx0lGml3XillUOndzBb5+M8sBX77XmtLV30tUVyrrW/n11GcKTpkRRIk34Cmu58rJLWPnK66fXifqpHjc6472XnH48hbWcaDxFS/MJCvU6Al6RAU/K4MBvb9FUEuHDYJwuiRJE2Qrqlpx+2to72bv/EBKlTBpdjb90Fp788ZYQ1Xt8DU5/OUq0BaevjFD9U6SjrYhOP0XTPk+sZRNqIkT79p9iaCkEyYU7OLbfTbWM3hOvc3BvPSe7DI50V5DSZPqONfKL3z7P97/1WfTocdM9Jco4cipQeo6TjrYgBqqQvaWEDz8PDBVtdHU3I3Nl2qM+kqqMWzZLE4+Gcqn1bUFXnIhOPwW1txJp2oDRo6ApvUiuvCHiqqbErABvQ1fN4HWlD8mdZ2ZO9Z1C8J/OK3PnVxNt3mI6tQQRV3AcouhA0RS0VC+SKxdf8XSS4QY0JYqhKWbAer+7qWDCLXRjhq8PlBBCplvtTE62ZPfBYUosR/a7z7yUFBfR3tE55Bocd5b7kfxgHgvmzTzjcwaoqqzg+9/+Ett21BEK9zKyspyZ0yadUawazIK5M3n+yV/xzHMv09EVYs7MKVxx6eIP5e+firISfvWTH1nlgH+pf1PZwpONjc0ZKS0p4o5brv24t2FjY/MXxLYde3j19TcJhXvw+30sunAeyy650G5IYPNXy2XLLubJZ1/McCKBGeQ8bcrED/18C+bOZMHcc7uh+rjJ8fuGDQF+v/z0x/+bv/8/P2bfgcPousHIEeXkB/MI5GQGYc+ZNQ2328W1Vy6j6VQrHZ2nXWcjKyu4bOnFWdf//eN/yhC2eiN9/OGJ5/juN+8fIgxt21GXdY26ffUoSnpIJEH7GZxv7Z1dZxSeUimFtvahZTwAJ5taso77s7iuwgk3SVXGIaRo2Pw7nEaE2rKQVQYXTrhJRZs50XjKcqVlu8kdcOYE3IeItu0YEiitpxPMn5hH5Yi72LK9jlQqRe34ccyZOTVjvXQ6zSOPPsPxo4eYXtZJu6zi9OZx0VVfJt3+LloqQkfdI2BopOOdOPtzmfzl8+moe9gK5gZ4/Y23eWX1G9baL4sit97oYP6cGRlOKkGU8VdciNJzDMnhR5WcyN4i+prewpVTSbxzHxgaCCLO3JHInnzLTdUlTmXXyT2Wg2uAvmiUvfsPMtZ/HC0VQZDdSIJodnfTFFKRRjQ1bok770WWZUpKijnZm7K6E7pllYuqTpHj8iI6/ZTO/iZOfwnRtu3oWgrRkYPszkdyeBH85eh6Gl2J0l3/lJUvhaYgyqYb0J0/HsEwSIYOmcKbvxxf+Twc7gIkhxdv0VREpx/B0DHUFIIgIbly8ZdfQDp+Oq9L9hZbYecDeIun4g5Wk3hPwPtARthwTjbgjCWWA8+55oql/PaxZzJymHL8fpYsuoBEV71VHjnAgAB6ppD9bARy/CxdfOH7mjOYwvwgX/78Z857/tn4SxWcBrCFJxsbGxsbG5sPjb0HDvHEMy9Yj6PRGK+sfgNRFD7QH3Q2Np9kRFHkof/+Zx76w9O8s2k7kiSxZNEC7vn0zR/31s5K3b561qzbSFt7J8VFBVx6yUXMnD75495WVkqKC3n4Z/9GVyhMLBanqrKCrTvqeOW1N+iN9CHLMnNmTuWGa5YDpmPpb791P3sPHKazs5uy0mIm1VZnFcFbWtszRKcBdF1n87ZdXH/18ozx1DDlP5qmoWoqTjKFpzM5vcpKzuwCczod+P0+otHYkGN5eYGsc+bNmc5b72zJuFnv6k3xbjLA2DEpktEQM8p6ADJKu8AMyx+gpLiQivLSjJD2AQFrfJ5O9/4ncefXmCKLmkSQ3cQ66zDUJBUjF3PnrVkymvp5ff07HDx8lFL/6Qyo48060fXbufk6U5Aw4t2kkz2oCTPjJnfsVURbNuH0laHQiiCItDbuzRCdwPzc/vTCq9SOqyDdss4aT8e7iLZuRXLm4s4bgytQRSp6CiVyknjbTiR3vhnWnTvKclQNiBrhPtUSnQaymAbes3joBKoUQtdSyLIbDB1P0WQMTTG7zhk6zpwKJGcAQ01aIounaDK+omlcNWsc7Y8/ixI+xNFQLnMrOygKunE4ZII1N6IlQ0Sjp4i1bMXMd8rB4S3E0NIo0RYMQ0POr0VAR3IF8OTX0nN8Na78GkTJia5E0bUUiDJa//8rkUZ8JdOHDVN3F05EjbebYmJ/kLqmRNCVqBUKH2lci5oIYRg6Yr8gpSbD9Bx71eqaF2lch6GraEoU2Z2X0TlRcvrPKEwBTJ5Ywze/8jk2vruNULiHqsoKLr5wLq50M5GTb2YIVVr/3rSUeQ2/X/HJ5qPjEyM8vbFxK/WHjjGqqpzLl1yEJGVX9J5duYZTLadV13mzprBg7vQ/0y5tbGxsbGxszsSbGzdnHX/r7a0sWbTAdj3Z/NXidrv52v13v+/ubx8new8c4veP/8kq4Wpt6+DRP5rlQJ9U8QlMZ0Fhfxe1ubOmMXvGFELhHnxeLx5PZt6TJElMP0uYOUA0Fh/2WF8WwWfihOqMzm0DjK6qxOvxDBmfM3Mq6zdsIhTuyRifMXUSxUUFdIfCvPHWJk6eaiE/mMuiC+cxZvRIwMzXXLhgDq+ueXPIuosuyp5BU1pSxF133MTzK18j0tfH8cYmYrE41WNH8/KWKLX57RgGeNwu6jvzLQFFEARGVpZnrPXp267nN7/7o5mbBSi6A2/VFRSWdVv5PmZnNTOI2VCT/ZlGQwPSB7OzzswZGhBznJLG6GAEpWM9hnE5werrSHYfJtZZh5roRpQ9RI6vBkD2FpBfextaMsTbe7qzri8ZSU7seJzSfA+SK4C/fD7tu36NoWtoSi+GKCGKMkY6iaqYZWWaEsEZqEQQzfdjsEhS1d9hLFfuZlSghxEBN3vbS0hpMiWFOehaC3o6QVprxZVbRaBqGUr0FL6y2VZJmbdwIvFBzqC8MVfg9JvZXl+6fQEtB7pR4634cmvR4mYXvI7dv8YTrAFRxDB0DC2Fno6hpnotgUYQJATZiTu/GknyIjo8gIEoOcmpXETPkZcQJReC6MDhK8XQVdREiI66RyiovR0w86bUZA+Sw4eWjtLX/C6S04/TV0KwX2QSBIFk+ChOPU13/VMIgmiVLpqfeRXdB/5IOtaBrimW6JXqa0KUPFZW1kDnRE3T6Gx4FQmGlFgOFp9GjigfImJqimhleA0u1UtEuzl+KsTG1S9hyOuYO3MaSxcvyNpdclfdfl5f/3a/8F7I8iWfXOH9L51PhPD0+z+u5OXVb3H50gt5esVqDjWc4IEv35X1uevf3sqYqkpG9LcXzRnUUtXGxsbGxsbm4yUc7s06HunrQ1XVc85LsLGx+ehZt/4dDMMgpaRxyDKiaN76vb7+7b+omy9RFCksyP9Aa4wcUY7T6cwaZFwzbvSQsSWLLuDAwYYM8cnj8XDDtZdlXd/tdvH1L36WV9as58DBBlxOJ+NrxlBbM5aDh4/w6B9XEI+bwcZNp1rYu/8Qn/30zUydNAGA5UsWous6G97ZRiKRIC83wOWXLsra1WuA6VNqmTKxhs3bdvPoH5/D43bjklRqizpxyU56eiK4XE5qi0KW4+miC2aTH8zLWKestJj/9d2vcuDQEfr6YlSPraKosAAl2oae6iMVaTSDtNMxJEMfUio1HLp22o3VFvXjklTKcuJ4nCrhhpXkjlpGImQGi3uLp2Kop4ObA1VLcfpLwF+CJL2bdf2gJ4moJ5BcJVaQuSvHLCE09DR6KgKyCy3dh2EY6OkYouxBjXeSP+0+oi2bLXGmeNq9+HweukM97D7ZiH+mQI4nwhh/nGBZLd7IetIIloPJkVNhBWkHRi62RLREqD5jjxnuH3c+Dj2MJKroqRD5tbfRsfs3GGqSRKgeT9EUnDkVKIaBoadRIk2Istsso5Mc6EqUWOsORMmJr3wurtwqtFSEniMvYRga6VgbrpwRGBhgaCTDRzAMja79jyM6/aRChzEMDXylKH3NGHoaTfYQrL4ep7+EQNUy2rY/iKGnUeNduAKV6JqC01eG7C2wXqMgOdFSPcTbduH0l6CpCQxNQdc1/KOXk4qcQEtFeHf9CtqOb0U2kuiil7Laq6gp6LSEpIH3RVHShHt6yQ3k4Ha7rPfuvWHy4cMr0HSdHXuPs+lYDiktCSRZtWY9bR2dQ7LQdu+t5w9PPnf6Gmw3hXdBELJ2pbT5YHzswpOqaTy94jX+6/9+lwnVo7nx6mXc/NkHuPfTN5IbyP7LasnCOcyc9uHXy9vY2NjY2Nh8MCpHlA35Rh/MGxdbdLKx+WSxadsu6vbWk0gkkSSRstISZk2fPKR19/tF13XWvfUux0+cYurk8cyfM+ND2vFHh9vt4qrLL2HFi6szxkeNHMHMLDehXo+Hb33lc+ys28/JJtOlNGfWtCF5U4PJywtw562my+OPf3qJzVt3sWnLTo4eP4mmaYwbU2XluOi6zsuvvmEJT4IgcMWli7n0kouIJ5L4fd5zynyRJInuUNgSnQbyg5Kqg6PRMSwaKSDoCS4JKuTXXMGcOdnD6iVJyhC5NCVKpHEdguTA4S9H6T1Buq8ZMXfUkFKpwQzO5Jk6eQJvbtyMS1IJepK0Rf3sbi3ismkGutJH696nCIV7ESQXRYX5yNLpf0MGCzbTp07k5dfesMoKB8rgQko+lZMuJ6donDUvd/SlSO58UuEGYm07SUUaER0+JNGBribR1STuvDFEWzbjK7+AzrqH0JUofU0beXJ9N/nBPBSlkg2Hulk2OU2BT2fyCA2B025eXY3Tc2xVhvNLU2JETm1ElJxIroDlzhnIsSqovR0tGbJKCB3eIiIn1uIJ1pAI1SPKXgw1RdHUe0iFj9DXupV0XzOa0oeuK+SNWtbvaurPd+J0fhKpCGoybAlEAyVyGAapSBOaEiHRtQ/JkYPgcCM5fHiLp5nClJqg58hLSLW3mZ+36ESQHDj7O9YV1N5Guq/F7CDo9OMuqMFbPJV4xx4MTcEAsxOhIOEpmoy3ZBqCw83efYd4Z+tuxhcmSaoyBzr8NHSsxXn5TYzyHkFLRVAiTWys6+KNDZtIJpM4nU4uumA211yx1HJPv7dUr7MrxM6TXsvBN8Cuuv1ccekiigpPd9Bb9+Y7Wa/RtW++YwtPHwEfu/DU2RlCUdJMqDa/ScgP5lJWUkRjUwtTJ9VknfPaG++wbfd+Jo4fy8L5fxnBijY2NjY2Nv8TuHTJQuoPHc1wDQiCwBXLF398m7KxsRnCu1t3snP3PhQlDYCm6ZxqbsUwdG689vLzXre9o4svfvPvaR4UjTGptoZf/tf/we12n2Hmx8+iC+dRXlrC5m27SCSSTKgZy/w5M4YVzR0OB/NmT2fe7Onv6zyvr3+b7Tv3WI8jfVESiSQul9Mq5wLo6OwiFo/jGxQULstyhrgVTyTo6YlQWJA/JND89D7NW76g53SW0oDDqXzqtZSK+9FSEeQiF6vWvMmxEyfJzcnhwvmzsnYyHpyjMyRIu/eEle/zXvEp0VWfkcmzfOlCGo8foZi9uGUVgLSzkqkLL6dx5+OcPNWCJOikNZHGE8eonjCZcbNutQSbAVdMfjCPW2+8ij+98CqF7h7GF4ZJG05qF95IXsX0jP0GRi5GcvrMwO+02aVNlFy48mvQlT4EBNLxTgwgfeQlnL4yDJ+BHpjO4YaHEAQoKy2C0iK6STHS20FXqI/8MVUIsot0tI14204EyYmnaLLVva27/ilS4SPI3iKCNTfi9JsurI66R0iGG+jY/WvK5j5A7uhLMXSVzr2/R1eTAHgKJmDoOrI7j55jrwIgyR50dx7pRBfxth2gphAdXgTZjZroJnrqXSTZa4kyksOPlo6SX3Wrde5ww0oE0UEq0miGtIsirpxKK1BdU2JWWHn48ArS8S6UvpNIrlzrMx3Yj6EmkWQvnsJaCibcgqEpJDr3oSXMjojuInNciTQRa9lK2/FDhBMBDnUFiSkOJhabIfd7d77FjHu+jBJpYtfRBKvWrLfOpSgKb7z1Lh63m0uXXGRdiwNd/ADi8QS1ReGMzDIwO+02t3ZkCE/tHdkD/z+o8G6TnY9ceHrmhdU0t2bvwvD5u24iEo3h82XWQft8HiJ9Q2upAW6+djkdnSGisRg/+dXjbNpax3e/fs+Q5/3pxdd57iWzhed7W4/a2NjY2NjYfDSMKC/lG1+6hzc2vMup5jYKC4IsXjif6rGjPu6t2djYDOLxp1bg9Xgs4WmA1rYO5s89f4fSD//1JxmiE8D++sM8+Ivf870Hvnje634YtLZ10NTcyoTqsQSGqayoHjvqI/99tXV7Zkc8p9NBIpGko7M7Q3jyeDy4Xa73TgfMEPMVL61h87ZdqKqK2+1m6aIF1g35YGZOm2wGyPdnKQ2EYvv9PibUTkbQx9HTfpifP/F2hmN115793HHLdcyZNTVjPSXSRKq3EclTYPp8+oO0dSVGoruedLQto1TKep2ByiGZPHdcEqCzfSR9CYPK2UuZPLGGlr3PWN36XLJGgSdJc8TPH9/s43/Nz88orxroXjZ/zgwm1dawf99uvNGtFOQ6cIn7UaKlllAluQJI7ny6D62gvfUkneEkKXEsY0sF3IaO7DFFCUF0oKWjSO6gJZJ1hhMZ74FZsmiKKpquIfS7sQTZZYlwhqZYwogoOZG8RUgOH5HGtdZ7kzfuGtq3/xQ13klH3SMEx11N575H+91XcUTZi5rsoXjafUSaNpDoNHOxPEWTKZx4B+07f0k61ka8ax/u/AmQ6jWdZ04/SC4z2FtLW+WQ3fVPUzzt3gynkOjwoatJnIGRVqA6gJYMUVB7G+HDKzC0NGoyhOTMMfOk+oPEB+/nbJleg68BtBjTy1LUd+ZbolNSlWmKGCiRJpyBSt7Z/GTG+z3gint70zYuXXJRhqA44CJzdTyGW+5kelnnEPGppKggYy/lpcWcOHlqyB7P1Aig/tARNryzlZ7eCKOrKlm6eAEF/XlzNmfmIxeeCvLz0PXswo8oiuQFcoj0xTAMw7LM9UaiBPNyss5ZsnCu9f/XXLaYm+95gG9+8dNDFP6br72Um6+9FABVVVl41Wc/hFdjY2NjY2NjczYqykv4zO03fNzbsLGxOQOdnSFcLid5uQFi8QSqqiLLEj6vl8L8vPNaU1VVdtUdyHrs7U3bgPcnPPX0Rti8bReBQA4L5p5/lUMymeRv/+H/sWX7bnRdx+l0cPXlSz82ISyZSmU8Lisppre3D03TMsYvnD8rayAywKo1b/a/p/1rJpO8svoN8nIDQ4SikuJCbrnhKla8+JolPvl8Xu6582ZkWQb8bD7QO6RM2jAMXn5tHbNmTM5S1iegK30gOq3yrZ5jq3D4ShEd3gxRaAAl0mSVeQ1k8hi6SjDgZfzCewEIN6yku+MUSVWmvjPfEncQQEmlONhwlGmTa63MpoH133pnC29u2Ey4p5eq8nwum65TIEWsEqwBASnZ28j+fbtp64r3CxMRXJLKLQt9VJaAp2gKkdZdxBUZl5CirMYsGywp9pEfzCMU7nlPyaKMo+xSBLnVEmEcORWk+5pJhg4Tqn8GQXIguQKU1Nx4+rX3C2+xlk3InnzTfWVotO/8BYamgCDi8FeAoeH0lRFt2Yw7WGOdw1c0DYevBFd+NWqy2xSXeo6arieHB4e3iPbtP8HhL0dL9eDIqUCNd4KuWqV9kcZ1pOOdpHpPgCCgRE7izhtruZj0VB9Gf0C5lo6CriI4PLjyxoKhY2inRWtv8VSrq1z3wWdJhg5jGDrOwEjUeAfJ0GG6Dz5L3pgrcRdMwOWtg1iYGWWmQWXAhTez1mu54pJR83oc/H4DtPcJ1vU0IDoNCHk1C+7heOM/4ZbjllAFUDt+3BBBafnShTz0+6cyTCqCILB8yUKysXVHHU8+s9J63NrWQd2+eh746r22+HQOfOTC09KLs3daGMDtcpKb42dH3QFmT59EU3Mb3aEeRo2sOOM8gGg8jiiKiMN0wLOxsbGxsbGxsbGxGcq4sVU0NjXjcjlxuZzWuNfrYdTIEee1pq7rGIae9Zj6HlHlbPzmd0/x6B+fsxxZpSVF/Mc//x3VY4cGfZ+NH/3bz9i0daf1WFHSPP/ia5SVFnP3HTeeYeaHy9Hjjax4aQ3HTzQhSRKFBebNan4wl7FjqkglTUHK6XRy4fzZXDlMibJhGLy7ZWfWY+9s3j5EeAJYMG8m06ZM4NDhY8gOmdqasRklhMdOnMy6Xm+kj67uMMWD3CLOQCWu3JFoqQi6puAvn0+kcS2GmsQdHEOgahlaMpQhOg0us/OXz6f3+BoMXSXVe8LMiIo0AaClImiCx3Kr7G4tskSHoCdprSc5/db6b27czAsvr7GONbaEeDqkcOclAXIDpplhIHfqSLvIliMS4cRpN0xKk1mxKcUD907nwO71tLecsIwT/hP/zYVXP4A3p4Cbr7+C3z72LEF3FLeskkhLHOwZwRh3gHT6JLqaRJAc5JTNJe7YT6JzH8neY8jufII1N2SUuA0Ib2oyjJbqxZk3Gi0RNkUnUcIdHIee6iOn8mJibdtREyF8JTMpmHQHgiHgyKmgo+4RM0i8ZBbpRCe6YpYOOoMjUWOdqIluNCWCt2gKDl8Jwerr6ax7CC0VoWXrf+DylaClo8juPDSlD0NPk+g+iGHoiJIDQXLj6nco5Y5eTuf+J3C48hAAQ0v3C2p5OHNH4ck3878SoUPEO+pQU32IkgPZnYfkziMVOmyOJ7qRHD7G1s6jfudqjP73ub4zH130MH/hFUixLWipCAvGRXhrv0Ft0WlHVDjhtro9Dnz+A5lhAP5AIRde823eWreSjpO9uFwO5s6aytWXLx1ybU+cUM0X7vkUa9e/Q2t7B6XFRSy75EImTqge8lxd11m1ev2Q8VgszvoNm7n5+iuy/vwMR29vHz/815+wZXsdbpeLG6+9jK984TPva42/ND72jCdRFLnnzuv5wT//jAVzp7Nj9wE+ddOV+Lxm+d0zL6xm0oRxTJowllg8wW/+8CfAbHn69uZdfO7OG5CH+SbAxsbGxsbGxsbGxmYo9919G+9u3kEimem+ufWGK887i8npdFI7vpr99YeHHJszc6gYMhzvbt3JI48+neFEaGvv5G/+/l9Y+dRv3teeVFXlzXe2ZD328qvr/mzC0++f+BO/euQJdN1A0zRC4V4qK8qYOd0MMR43uoqv3n8XLqcTn89zxmYMmqaRSCSyHov0RYed5/N6h+1WmJuTvdpEkqQhsSiDu4mRitB73BR9MrrZ+Usy5gyUWKnxbjrqHkb2FpHua8bQFNREF5I73+xSB1QUuki9/TiAJT4FPUnC6QImVI/NWNcwDNZv2JQx5pJUJhR209yasISngTDyhiMnLBfMYFRVZf/21XR3NGe6rXo62PraT1l43XeYOKGav/3W/WzZXke4eTdbDrUhOtKsf/0FZpZ3UlHoJFg6BlewmkS4AUQJNRFCT8etEjcl0mQJbwCSw48oe0h21SPKbmRPPmqqF6XnOJ6iyUiyBwGh3z1UabmKOuoeIRU+gjO3CtlTgFOUMDymkKf0HMddMJF0vANBlFD6mgmMupS+prcQHV4MXUNPx0hFTprz3UE0JUa8fQdaOokA6JKMK28Msiffcqk5XHmkE91oSi8ObzGkBZS+JpI9R0lHWyid9RUAdDUFhgo4URPdOPxlOPNGk+w6YApWHjdBOcLk2hpa2zqIJ5IsmSJTPf82KirHoCmlhBtWMmZEkljkEJqqWY4oXXBz9WVLrM9tsLip6zpr3tjI25u2E4vFGTNqJFdfvoTRo4YvAawdP47a8eOGPT5AJBKlpzeS9VhjU/NZ5w8mkUhw6XWfyciY+n8P/ppddft4+Of/9r7WOh+27drDqtVvAnD1ZUuYNePP08H0YxeeAK6/cgnVY6o41HCcqy5dmNGxriA/D6/H/MdPEkXKSooAyPF7+fQtVzO66uzOKBsbGxsbGxsbGxub04wdXcWvf/p/+eXDT3Dg4BHycnO49aarueX6Kz/Qun/3N1/iy9/6B3ojfdZYRXkJ3/rK5855jRUvrs6a0dra1sG2XXuYM+PcRaxkUhmSYzVAX3R4kSYbR4838vyLq0kpCsuXLGTurGnnNK+zq5vf/PaPlotGkiQK8vPoDvdQXFzI/NnTmTdnekaI+JmQZZmRlRWczHLDO+4886kunD+LXXv2D3nfZ0yblHVf7+0mBpyxm50SacroEpfWFBzeEpRoC05/uZV75CmspRK46rIlrFqzHsMwSGky3ck87rrz+gx3HpjutcHX2uCyrN6YTrDmhowwcr+ngGwEPUmivbGM4PUBt1VvqJVo9zFyy6ZSVFjAlcsX83/+dQ+yy3ytbVE/e9s0epJRZgVV2rY/iOwpRJCcuPJGm13dEOioewR0lXSiC6e/HEGUMQyVeOd+DN28Rh3+cgQliq4mMbQ0jpwKZG8BWipC98FncQerUSKNiJIT0R1EkD1W97xA1VLCDS+S6NpHtPltREcOgiBiaAqdex4BQBAkguNvINa6wzxm6ORULiJy4nVEhw9BdiOIDmRXENmdj6/8Aqs8UNcV1FQYdA2Hv4xA5cW07fwFal8z8fZdtG17ENlbhCAIyO4gnoKJCJIDQ00iGAbOnEoc/lLADCPPKyhj5MxPn87gimxCU4qRnH5cgSrURIhpkyfQ0tbB/q4RzJlVwrwpZVQOEpIGd0lc+crrvPX2FlySSokvydHjjfzi4cf5m69/npLiwqyf+7ni9XpwOp0ZjVMGyA/mZpkxPP/1899lDTZf99Ymjhw9cd4/w+fCv/zHL1jx0ml34CuvvcHtN13NA1+77yM75wCfCOEJYNKEsUyaMHbI+OBSPbfbxe03nn+XDRsbGxsbGxsbGxsbEyWtUliQz6TaakRRJJFIkkymcLuzB1qfC9VjR7PiyV/zzAuv0HSqlYkTxnHD1cv7s4TOjVg8PuyxyCCR4Vzw+72MqCjlVHPbkGMTaobeewzHH558nl8+/JglHr34ylqWL72YH/3ggbPOXbNu45BSQ1EU8Xk96LrOkkULznkfA1x75VJ+/ciTpFXVGvP7fVx6yYXvey2AsWOquPPW63jp1XX0RvqQJIkZ0yYNK0TGIl207nsBp5jG1Z+1O+AqGq6bna4pyO480pqCM6cCpa8Zh78Mw9CHZEJdNLWAaRM/S/2RFhwOB1MnT8Dj0El01We4XFwuJ4WF+XR1mVlQgzv3pXNm4/SXZpS3Tasex9oNMuqg9w3A8I6mJdVLQ2s8owRvwG21JOf0tXLkWOMQ90tTJJeOmI9R5WGKfTHSmoIrd1SGY0iJtaNEToKho0RbKJh4B511D2NoKZAcOHOrkBxedFceutZJMnQIJXqKYPV1dB98lkTnPuJtO3H4yxAdPkTRgdJzLKN7noGBmopg6GlEWcOZNxql9zhqMgyAr2wOvuLp+IqnW+9J79FXSPWeMLv7BarMvKdoCwICkeOrAdPN5imaSjraCqL5eYsOH+78GtREN7qaJNXbSDrWjih78BRNpmDCLWYA+OEVCKKM7M7DE6wm3rnXXC+/NsM9p6UiJEKHUHqb6Du10fx8c0cxpqqSsWPdQAQj2kGiK4insDajfNNdeRnvbt4xJA+qLepn47vbzlgKl0ym2LF7H72RPkaNrKB2/Dgre3oAp9PBgnkzeXPj5oxxQRC4eMFc3g9btu3OOq7rOq+sWc83vjS0cdqHwYGDDbzw8utDxp9+/hWuu/pSxo4e2sHyw+QTIzzZ2NjY2Nj8JaDrOqvXbeDdzTuJxeOMGzuKa65YSmVF2ce9NRsbG5tz5lRLG79//E/oupnJpOs6O3fvQ9M07vn0LR9obb/fy+c+wBrzZs9g+869Q8Y9bhcXzHn/IeNf+cJn+MH/+c8M8cfn9fK1L959TvO7QmF+/dsnhzRMWrNuA1detviswefDBYQDyNL53Y6NGzOKB752Hxvf3UZXKMyI8lIuXjCXvLzAWecmkynaO7vIyz2dgQQwe+ZUZk6fTFd3GJ/PM6wDa/Wa1+g58hJOQSGpyYgF81k8SbRcRcN1szPi3aT6WnD0i06GpqClesif8HnQUpagNFhQuGiuGSIuGX2EG0znzcCaA0LVZUsu5olnXgCwyuj60n7uv+ES4HSo+UDu1F135LLyhRdx6F20Rf2MqCjj7jtuZNPWXew78W7Ga01pMlJgHDl+nzX2XtFq8HPbmESpsw7ZW2h1iHP6S8zw9aOrEIMO0+XlKyN66m3ceWNJGDoIAujm9TmQuSQ5/SS7D+L0V1hd8gTJieTw4ymcSKqviXTUvLY0JUrPsVdRehuR3Xk4fKVI7iCp/pDvAQzVLK0dcKyFDj5LqvcEhqZYAlakcR26bnbCcwWqECRH/+soRXS4iXfswVCTpqAE+Etno0Tb0JXe02tXXgyYYiSAoatoSpRUpBF/mWksibZuIRGqJ1h9HcHq6/rzofYQb6/rF87cZ+yeN7hLYmf983ik2JA8KICOru6Mz2mwS6q5pZ1fPvI46UQvQU+SNev8VI8bzRc+e/uQctdrr1yGIIi8u2UHqVSKwsJ8rr5sCWPHvD/BJj9/eIfUiPLS97XW+2H1ug1ZnaSGYbB67Qa+/PmPNmPKFp5sbGxsbGzeB3964VXe3bLDeny44Rg/O9nMd7/5BburiY2NzUdKc2s7W7fvpqy0mPlzZnygtd7dvMMSnQazZ99Benoj5OWeXcD4qPjUzVfz+hsbOXzkuDUmigL33X0bXq/nDDOzs3TRhRT9pIDHnlpBe0cXY8dU8bnP3HLOXxisW//OsGLD6tffOqvwdOXyS/j5Q4+RSmWW6QiCwPVXLzu3F5GFstJibr3xqnN+vq7r/O7xZ9n47jY8bjcej5sZ0yZx+01XWzfZoihmBIm/l2079rBryxuML1ROl6U1NeJ0T2VhjZi1m50pciyjbfuDYOiosXYc/nLU/pKzWMsmy7EDmYJCR90jGIaGGu/E6StD9hYgufMthwzAnFlTcbmdvLVxC13dIQoqavjU0oWMHFGeIWINnGNS9QhKry0jEhZxlS6gssZ0nC1ZtIC9+w/ROUiocDgc3HDN8oz3YNyYUbjdbpLJZMa4S1IZn3sEXekjraUQc0cRaVxnOZ4MLYk7OI7csVdZTiIAd8EE9FQEV+5IAlVLLbFGibagxrstcUiQnLgCVWjpGD3HXsWVX2M6xtSk9RyAQOVi/BUXEG54AUNTTCdTwUgMdAxNsbrLRRrXofWX9QFoSh8gWJ+VoSmk+k4hOf2Wm81fOgvJFSRU/xSyO2h2thNEDM3MHNM1hWTvCdp2/hJ3fjWCoSPIbujP8hogULUMKRSwxEpnoIpkuIF0tBVBlPAUTkRNhkjHOpBdp4Uad7DGur4Gu6Vceg+zR3SjaVpGuSRARenpvLH3Xg/PrniFdKI3wyXVcOQ472zeweKF8zM+X1EUue6qZVx12WISyRR+n3eIM+pc+Or9d/Hmxi1Dfv/mB/O46br3F1L+fnA5ncMfc52/y/VcsYUnGxsbGxubc6QvGmPL9t1DxlOpFG9v2sF1V53/DYSNjc1fNocajvHz3zzG4SPHyPH7uf7qS7nztus/tPV/+K8/5dU1b1o3K5UVZfz03/+RirKSs8zMTu8wJWuGYRDpi36swpPT6eT3v/oxzzz/Cpu37cbv93LL9VcyY9qk815z6uQJ/PhH3z/P/Qwf9O04w7EBAgE/33vgi/zLf/zSypuSJInP3nkTE2rOHmz8YdDR2c0//suDlpNMEKC8rARd1/F63Od8w7tp607LVRROuK2b+007D3LlZV9Ej7dmiE4DaMkQhpxHqKuD5lAC3YiiF8xnXp4wRKwaLCgY8W7LHaXQSt64q6zMIckVsNwvUydNYOqkCUPOO1jECjestIQdIx0lr6CM4KjTeWF+n5dvf+0+tmzfTePJZvLyAlSPHcXJphba2ruYPnUifp8Xp9PBbTdexeNPv4DW76JzSSpXzRLJDQRQolFkTyFKtAVdT9O2/UFLNPOWzCTSuBY12WMGhaejqIluPEWTLVfWwGvX9TSpvmYMPQW6ZjmSwg0vovSdIhU6jLM/Q2ogqN1TNJnc0ctIhA6RDB22xCpBcqClYyiRU6Tj7VZ3OYe/FASBVG8jyVADrdv+A1feWJy+MtJiF1qqh1Rvp/l5N6zMyOlSkz3oWhJd6UNy5eIpmoyWjhNv34kaayOa6MJfOhsBwNDxFJkh1loqkvE6U72NRJs3I0hOc/9jLsNIJ+mqfxpN6cWdNw5XoAp34USUyAkSnXtIKQo96QLycgNW1lh5WTFNp1qp78y3rkuv18PCC+dkvR7a9j9LqP0o08uGuqTq9tYPEZ4GkGWZHP/5yyizpk/hew/cz09+8XticVOwKy0p4uGf/st5r3kuXHf1ch576vQ1O4Asy1x39aUf6bnBFp5sbGxsbGzOmVC4Z8g/2AN0dA4NirSxsfmfQWNjM/d/4++J999EhMK9/OSXv6e9o+tDCW19ZsUqXnntjYyxpuZWvv+//x+P/uY/zmvN0VWVWbvPeTweSouLzmvNDxNZlrnj1uu449brzv7kj5jLll7Mgz//7ZAOgIIgcP1Vy4eZlclVly3hwvmzWbV6PUlF4YpliygrLT6nubqus+GdrWzbuQdV1ZhUW82ySy7E6zl399djT63gcMNpB5lhQHNLOzl+P1u213H91cvPWBI4QDRm5m+9tzOcoigYoier6AQgeEo5eOQEySTohgDoJFu3srJjIp+9ddmQeYPDy12BKlKRRmRvUfYOemdgsIgV7+vm0OoH6emJoAkeckZfxaLazHJCt9vFoovMUrCXX3uDX//2SevYylde57N33syk2mpmTJtE1cgKtu/cSzKVYlKljD9dj+QKkD/hViKNaxFFB6lIo1km5zMs51Oq9yRqogvZk4/Db7rudE2h59gqDDVJYORiAlXLaN/9a7RUGF1LIbuD/WVnAoLkQJCcaOk4ya4DCIL5uQmS0xxXYvQ1vY3kysMZGEHemCtMd5OawDB0RMlNOtqKlF8DgOzKRSqZTqrnKFqyl0RHHd7iaThyKtDTMSRXLlo6ihI5RbTlp2BoIEjmuWKt/XtyWp3zJHcQLRlGEATUZAiHKFmfV7L7MIZgWJ93oGop3fufwDA06C8ndPorCDe8iJ7uA8NATXYjOn1EGtfh9JVxqjPBCy++RDSh45I1lk/RqB1bRmVFGQ6HA4enl13NuVSNHsdlyy4mP5iX9XpIx0LMKO8AgyEuKVESz3hdfVDu/9yd3Hf37by5cRN5ebnMmj7lIz0fQEVZCQ987V5+8ovfWQK4y+Xk21/7PIV/Bse+LTzZ2NjY2NicI0WF+ThkOSPMdYDy83Qd2NjY/OXz0KNPW6LTYF545XW+eO+d51UeNphVq9dnHT/UcIzm1vbzcj0tmD+Tzdt3WaHMA1xx6aIzOnz+J+L1evj773yVH/34ZyT7xSdJkrjrUzcyeWLNOa+Tlxs4LyHtiWdWsmPX6cyr9o5ODjUc41tf+dw5hbZ3dHbTdKqFdHrov11d3SHyg7koShqP5+zCU8240Vm/aKkcUT5sKL2mRGnY/HviCZWk6qS+M9/K4slTD3AqtJTqwqFzBkrOBMmBw19Ouq8ZMXeUlZ10NtFpAMnpx1lyIVu3/Ngqd9zVGiRy6G06QnFuu+nqIXOOn2hi7fq3KfVHLWdXOp3miWde4B/+5j6MRBv5hbUsnFZo5QUlukosB5YrUIWWiuAKVKHE2giOuxotGUJLRXD4S5E8+aiJbhz+MmRPAcnQYWRPAa7cKiR3PpHGtTjcQQw1gSA60LUkajJsuafcBRNQ412osTYEyYnDX47k9GOoSdp3/xo11o7kCpA35korY8osdTS/PJNceWbek6H3d8RbRip8hGjbdtKxdrR0DKk/JBxATYb7g9E1RKefommfJ9ayCVF2kY53ILsL6DnyEqLkxFcyA9lTRKJzD7LbFDQCVUtRIk1EW7f0B4uPBzBzqaIt6FoaQTRIhg7TdeBJ87N25oABzpxKy9EVjh9j5SYfUcVlBomXdhIJqxxulFhw+ZeQXOsoLY4wf06AYPXyrNfIgKipHV5BbiCH3t6+DJcU8GcRgiRJYuniiz7y8wzmluuv5NJLLmLNG28jCHDZkosJBM7t5+iDYgtPNjY2NjY254jX4+GiBXNZvyEzfNTn83Lh/Fkf065sbGw+bo4db8w6nkymaDh6nGlTJn6g9d+bJTOAYRhEo7HzWtPr8fDNL3+ODe9speHIcXJyfFwwdya14/88pV9/aSxfupAF82bxyuo3SKYUli+56JwdSx+EtvZOtu/cS3NrG+0dXaTTKnl5AeKJJHv2HWTm9MlnXWNAcMrNzSHxnmtJ13UqR5Tj8bjPaT/LLrmQfQcOZXR1c8gy1181fKmOEmkiGQtnuEp2txZZuTrhtsNQc/pnRFOiVoaT5ApY5V2GppDqPYGrPzspWG0Gjw8IP4PnDy7d05QoR7Y/m5GxVVsUYndrEVu27+aypRcPCWWv21dPqT/K+MLMfWupPk7seJygXybVe4JU7wnLzeMprEVTolYHOtljqmm60kdH3cOUzv4mgZGLkdz59BxbRSphZklJDr8lOg28Ji0VQfbkW+4pNd5tuafScg85hRfSO6iUznydfcieAhz9Yo/DlUekca1VXuj0lZGWPUjSgFiUB4C/fL5VvujwlZIKH7EyqoJjrkBPx2jf+QvQNURnDsXT7sUdHIPTV0y4YSWyKw9NiSJKTkvEijSutUQnwMq6Gihz6z74LIamkAwdRleTiLIHQZDQtRSp8FEMDETZg8NbjKEpluutqzuNz5mmT3FldDBcs0di/tXBjA55rY17cebVDMksGyxqjhtTxf76But6SGkyc2ZN44K52TP0enoibHh3Ky2t7ZQUF3LRBbMpKhw+E+2TSF5ugFtvyN6t8qPEFp5sbGxsbGzeB9deuZS8vBw2bdlFNBajZuxoLr900ceah2JjY/PxUlxUwJFjQ8UnSZKo+BA6Xs6dNY1jJ5qGjBcWBBlfPea81/X7vFy5fDEsX3z+m/sfhN/vzeqO+Sg51dzK8cYm2to7rbFQqIdIpI+Dh4+ek/BUXlZMQX6QlJImFO6xymwAigsL3lc+YV5ugL/5xhd4d/MOmppbyQ/mcuH82WcMJPcU1uIqvYDdmzdZrpIB8SnoSTJj1OyM5w8IL4NFDKe/HCXaguwpxDB0K3hcEERkT75VdjdYtAIz0yfcsJJEtJukKme4raaXdbK7tYiWtvas3QDDCTdJVbaeOzAXzYfkGoG3ZCZqv4tpID+q59irVgc2h7+UQOXFdNQ9jK5Eadv+IEXTPm8GjatJ3Pk1GGoKQXIgS0HLxTUgmA0IaoNLDpVYK5LsItK4FsmVizNQSU7lIit3CSBQdQme/PHW+xA+vAIA2VuAu3ASvcdXWyKeI6eC9l2/Nl1hgUq0VC+C5LSOhw7+iVTfSaujnih7iDRtwOEryeiMB6YrKmfERRkZXL7yC+g58hKkInTXP0XeuGuItWwi1XuSdLQFRAlnYCSSO49kVz16OoYge/pDy2XURDeuQCWa0mdeA63HrRymcMJNV9zNke4gKQ2SqRTu3AB9njm8vv5PHGh6HXidERVl3HnrdZSVFg8RNUtrluLxr6W74xS14x0UT7yR8hGjs17H7R1d/OSXv7PcrQcPH2XT1l185Qufoaqy4uw/PP/D+WiLF21sbGxsbP7KEASBRRfO43sPfJEf/eDb3HXHjWf8g9vGxuavn8/cfmPWfJyLLpj9oWRn3Hf37UM6sMmyzLe+eu8HXtvmk43T5aS9Y2hpm6pqtLR1nNMagiBw201Xk+P3MW1yLZUjygnm5TJn1lT+7Z++x7gxo97Xnvw+L8uXLuTeu27lhmsuI5Djp/7QEU40nsrarh1g8uzLKCwZkTGW0mTyyqcxamTmuKewlsDIxQSrr7PK02RPPqWzv0lw7JUUT7sXyRVAQLBEqHDDSpRoW4ao4AxUWiKW05PH7tYivI40BzryLUEp35uiINdFoqs+Yw/Tp060xLG+JDjEFDPKOvB7IL+wnGD1dTj9JabgNRBefngF6Wir+RqKJlMw4RbcwbGUzv4motOPrkQJ1T+FloogyO7+TKbTZa0DXeYG3oMBIW1wyaHTV4bS14qWiqCrCQKVF9Nz5CUc3iJEpx9RdCLJZm6Vs98JNYDsLaX32CoMPY0ge3D4y1EiJ0nH2kj2HCMd6wDDDDH3FE3GMDSirVtJx9oRHB7yxlyBluoh0bmP7oPPokTb6Tn2ar/z6zip3kY66h4hHW2zRMNYyyYEQUSJd5IKH6HnyEv4yi9Aduche4sBAV3pQ0v2IIgSkjMXh6cQSfagpnow9DS6lkKQ3aQTXeT4c5he1kmOM8X0sk4KvUmCniQlxUXk5QaIJxL85tGVHGg6XVZ6qrmVX//2SVRVzRA1zc+wlPya6yksqaSs0EvQnd1dCvDa628NKalWFIWX35O/Z5Md2/FkY2NjY2NjY2Nj8wGYNWMyP/y7b/LLRx6nuaUdt9vFJQvn8/ff+cqHsn4g4OeJR/6LZ1esYmfdAQoLg9xx87WMHlX5oaz/QYlEouiG/qE7P3VdRxQ/3O/Jm5pbeXfzDibWVjNl4vgPde1zJdIXZfPWXXSFwlSUlTB31rRhS918Hg9+n5e+95RUOhwyOTm+cz5nzbjR/K/vfpXtO/fQF41TM24UE2rGnlc7+MFs3raLFS+tIZUys69Kiou4965bh3whI4oiX/78p1mzbiN1e+uRJImZ0yezfEn2jJsB18973T/4zTyzwWV2ZnB4F+2bf4vL6TS71Q0KHveVz6V61AjKDvyMKn+YtC7SFvEQV3MoHz0VKbSRSL9DauB8o0aOYNaMKfz2sWfYLye4eqaA0+lg1owp5I2+1Fp7sCMJQHYHyam8GF/JdOs5Tn8JRdM+T6j+KWR3EENXQVMG5SuZpXADAlo299bg57mBZM8xZG8R3fVPk462WrlLaCmcgcpB5X4FVslbvGMXhqEjCCLO3CoMNQkGIAgIgoihJpACI0zBT4mi9LWgxjsBidxRy/FXzCfV10Sicx/xjjrSsfb+4+Atmmo6o9IJUpEmAqMvs5xPsief3LFX0XPkJQRBpLPuIWRPIVqqBz0dR010IbmDuPPHI+hmBlU63omBgaEp+Cvmo/Q2IooOirRT9AYM5jjb0HSRpCoTSfv5TH9Xtl11B0gkhubt9fRG2H+wgWmTM68nwzDQcFnX03Dh+AANx05kHT9yNPu4TSa28GRjY2NjY2NjY2PzAVm+dCHLly4kGo3j9bo/dMHE7XbzmU/dyGc+deOHuu4HobGxmX/8159w4GADYOal/P13vsLECdUfaN2Dh4/w4wcfYv/Bw8iyzAVzZ/KD737tA4fg3vWFb7Px3W3ouo4gwPjqsTzzh5+Rm5vzgdZ9PzS3tPPzhx7NcE5seGcrX/vi3VmFu6KiAibV1nD0eCPd3WF0wyAvN4dRVSOorCh/X+cO5PhZsmjBB34NA5xqaePp517OcDm1d3TyyKPP8P1vf2nI831eLzdccxk3XHPZsGsmEkkOHTmOJIlMqB6Dw+HIKgYMlKQZhsHmIw7SrftMEQVo1WXurDRwRuqJnHyz390ynls/9TkObPgVHu0UI3JjJPwXMHmMjpaKWg6pAZLJFPWHjjB1QhWTi1rwuQxcLhfNLW2UHX+dgvE3DHEkDaBEGvGVTLcea0qUWMsmSwDSlChqogtP0WRLZApWX0dH3SMY8W5LAOlr2kg62gaijK9gQr/ItYzu+qdwBirRFVOMFCQnsreIWMsms0tcv+gE4PCXWV3tAJw5FQiyh2TXAXOuICL2O6QE2Y0zMAqArv2Poym9CLIX2R1EjbejKTG8xVPN9yd0mHRfM4LktNxdmhKjbfuD6EqUniMvIbvzMroPSrW3W8fTmoIjpwJibYiaB11NkQofw5lTTjragijKCK48gtXXExhxEVrxdMINK/EA4105dPWqhPtUBM8cvn7rAkpLikh01RPry3QHuiSVoCdJW9RPNGp2ZBy4nt7cuJn1GzbRG+mjqLCAKy5dxMz3hNwPJsfvy5qp5/eduwD8PxlbeLKxsTkjp1ra2PDOVrq6QpSXlbB44TwKC/I/7m3Z2NjY2Nh8IvH7vWd/0l8Bqqry5W//gM5BXfEajp7g69/5IS/88Tfn/T6Ewj185YH/bTl8FCXNW29vobWtg8cf/q/z3u//+qf/4K23t1iPDcPMaLnva9/j2Ud/ft7rZkNVVf70wqvsrNtPUWE+t998jVUquXLV60PKdbpDYV5/421uyRL4G8jxs3DBHERRYOzokRgGSJKI1+thwbyZw+7h5KkWTja1kB/MpXb8uA/sbMrGth11WUvr2js6aWxqft+5N9t37uGZFatQFDME3OfzcvcdN1EzLnvmDsD2bVvpO7ke96C72qB+iOeee56777zpdBlcw0oKq5YydfIE4p0qAuDK1UGNZogjA+ys24+W6mP+qF7csoukKlPXZmY8dXWcQhRXWvlT73UkDeRPFdTejuT0Wa4lQXbjDlajRIbmwSmRJtN1ZOg4A5UkuupJho+QijQhuXPpPbaaeMceAAxDM0vt0lEMTUH2FqPGOxFFB+HDK0hFmjB0DW/JNAom3JIhWImSCyXa0p/l5MBdUGsGlsc6SIYOkY620HNsFUqkCTDwl81DdPpQ491mRz1/Od6SGRiq2WVPcvjJG3OFKSw5/RRPu4/u+mes4PLB3Qe1ZAjZU0haU3D1dyfMn34/vSfWEW3ZhG5opKMtOHIqUOOdOH1lJLr24S2a3N+Zz3SWOV0BSoJRJi/6Ik5/KQCJLlNkHO01cEkqKU02u971B9iDKYwP8ObGzbzw8hrrcWdXN489tQK32zWscL5g3iyeW/nqkPELL/jgzWVSKYV9Bw6RTCnUjh9LfjDvA6/5ScMWnmxsbIal4egJqyYa4NiJk2zftZdvfvkeSkuKPubd2djY2NjY2HxcvPr6Wxmi0wCRvih/WrmKz95583mt++QzLw4pKwM4fOQ423btYc6Mqee17qtr3sw6vnP3PhRFwel0nte67yUajfO5L3+XEydPWWMrX3mdH/3g2yxcMIeGI8ezzqs/dGTYNW+67nJyAzls3raLWDzB+OrRXLn8kqwOKU3T+MOTz7Fn30FrrKS4iC/de2fWAO1sHGo4xtbtuykvL+GShRcM695LJFPDrpE8w7FshMI9PPnsi+i6bo3FYnF+9/if+OHffROn0zFkjqZECTW8YHU2GxwcTmwzsfiVGV3OBsrhPPk1xGO9ROMp/D4pQxwZoC8azeiaNrgb34QaB1oqQrx9Z0ZekOT0kzv2Gho2PogRO0mkL0rFhMst0Qkg0bkXX/lcc/+pCEqkic5kLhs37CZXaSTPJ5HUn6S0ZhlKtAUMDV1NIHuLSHTuwzA0BEHGMFQEQcJTNBlBcqImQv0d9vLQlT4cORXkjblykCtrLYIgIohOM8BbciA6fCi9jSAIaEoMDA0tnUDTFDMHSnSgaynyKq+wAtJ1LUUy3JDxXg10GASING3IemwgvFwQZUt0Aoi2bMZbOAmlrxk9HT0tSE27j/CRl0mFj9Bd/xQFtbcTaVyHoaWt7n7d9U+beV/9a0uuAAEiLJ+m89b+FLVFIfI9CXpTLmomXUBJcaF13ezb9hrvjbs2DIM3NmwaVni66ILZRPqivPX2FhRF6XdjzmD5koXDX9iYPws7du+js6ubirJSpk+txeE4fT0fPdbIw48+Y5UICoLAZcsu5vJli8647l8atvBkY2MzLC+/us4SnQZIJpOsWbeRu+745Fj9bWxsbGxsbP68NJ1qGfZYc2v7ea978gzrHj3aeN7CUzKVXQhRVY1oNEF+/ocjPD3y6NMZohOYrq1//+lDLLpoHg6Hw3L0DMbtdg27piiKVinn2dj47rYM0QlMB9KfVr7KfXffdsa5uq7znb//F97evN1yMpWWFPHf//6PWd1LtTVj2bp9d5bX4h4SGH42du7enyE6DZBIJDhwsIHpUycOOaZEmpCMxBBhaMDlkuxpJJA3OyODKZGIcfjoKWIxU9x0OByM7kszYcG9GeLT2NEjeXWN+TiccGd04yudfDMBVwJPYS2JrnorL6g30scvHnqCeE+KOSMMtNYTHGt+hrkXXYHWdxxDTSK5Anjyx+PJH48SaaI54uFXj/weVVVxSV6ml3XSHdqBpvTh95Wh0IrTX25a9ABDUzBII8ruISVuhqaAYJbeqfFOeo6tssrstFQEsf/1ObwluAtqEYBEdz1qMgyGgej04codjdLbiODKQ3L4ECUnvcfX4PSXo2spZE8BhppE1xQE2Y0SM8PUuw8+i6GlSYYOARCoXEi0bQdGIkS4YSWuwCgS4cOWqyu/5ob+YPKT6JoCGDhyKjIEqUDVUsKpXgRBpG37g8ieQtREl+WIEhAyMrEGRMZJ1VAc7KYvLOEWRUbkVzNhoZkBpSlRug+toMLTTNQfpC2aKTh2d4eHvUYFQeCqyy5h6aIFdIXCBPMC+LxndnZ2dYf42a8fpac3Yo2tffMdvnr/XeT4fei6zqN/fD4jl8owDF57/S3Gjxvzicnx+zCwu9rZ2NhkRdd1Gpuasx47duLkn3k3NjY2NjY2Np8kZk6fPOyxqZMmnPe61eNGDXvsg2RHjRudfd2iwnzy83PPe933smnrzqzjHZ3dHDl2gnmzp2U9Pm/29A/l/Dt278s6fuBgA6nUUMFrML9/8jk2btqWUT7X1t7JD/7pP7M+f9qUWibV1mSMCYLADdcsx+UyhTxVVak/dIR9Bw6f8fxpNT38sbSaddxTWIur5AJLdAIs8SksjKN41OyMDCZDS9Nxci8+oZO0LrKrtZi+hEHDof001z1ldZQDGDdmFFMnT6At6rfWBrhg7kzKKkZlhJ8PCFYrX36d9o5O+hQX7zRWkFRlktEQR/eus0QnK++oP6Nq1eo3rS95U5rp2tJ1nVOnWhEkBwUT70AQZQRRxpFTgStvLM6cClyBKtzBajQlRqRxLU5/Oa7gOAon3oHDb2Z/JTr3Eap/xnJl+YqmYahJXLkjKZp0J8Hq63DnjbWyp9B10v0lf5LDizNv1OnPVZTJKZ9vvY6C2ttx+kpw+stRYq2kek4Q76gz35OiyYiyFwEBJdpCsucY4aMvE++oI9XXhIBAus8UmNPxDvpOvUs61oYa7yR39HIkVwA1EaL7wBNIDh9qMmzmQkVbcPrLcfpKKJ39TWRvAanekyT6xa6BoHd0lXy/xJix46kYN4/CoC+j66GRNssrw4mhgf6VI8qGjL0Xt9vFiPLSs4pOACtfWZshOoEpBK9ZZzrDjhxrpDfSl3XuzrrsP8t/qdjCk42NTVZEUcTvzx6WFwj8+UI4bWxsbGxsbD55zJ8zg6mTh3aFGzOqkqsuu+S81/3UTddSkB8cMj5j6iSmTj5/QetH/+sBPO7MG01JkvjON75w3mtmY7iSPUEQ8Hq9XH35UqYMEuZEUeTC+bO5+MK5H8r5s7mGwHRRZMtjGszaN97OOn6o4RidXd1DxkVR5N67buWzn76FeXNmsHjhfP7m61+wRLQjx07ww3/5Kb/+7ZM8/Ien+N///CC799ZnPcfkYToMyrLMhPFjh93z3ItvoKxiVMaY4PCz5Io7h3SFi8lVpFXVDCE3IKVK7G4tIqFItLU29ucaneaypRdz6ZKF1I4fx6TaGj5z+w3ceuNVWfdhGAZ1+06/tgERCU67aLKV9A12x7kkldois3y1LxrF0NJ01j2CpiYwdJV0X7MZvC25UGKthA+voGv/Y6iJELInn4La24m2bEYUHcjeIjPEPNULgDNQhbugBl/5XETZh6bE6Dn2KprShzMwEkF2mcKbKOHMHWU6n3qOk+g+iKaabpxo2w68RVMJVC1DS4YIVl+H6PQjyl5E2Y0zp6I/zPxK3AU1yN4CnL4yBNGB5MrF0NJoqQgIIrHOOgw1ac7xlSDKHpy+MqItm/GVX4ASbUFXoiDK+EcsQPYVW2V45vvoQ5R9gEGy+yCaEkVTov0uqhOkoy1oShRBciLIbqvUcuBaGDnzThQ9s3zTIcssu8Tssqiq6pCqj/eLYRjsrz+c9dje/Yes5ww//wOd/hOHXWpnY2MzLAsXzMmaibDwgjl//s3Y2NjY2NjYfKL4xX/+E798+AneemcLmqZzwdyZfPULd32gjn5+v5eHf/6v/OdPH2b33gM4nU4WXTSPb375sx9or1OmTODlZx/m3/7r1xw5foKSwkK+/uXPsmDeBw8GHsyVyxdz8PDRIePjxlRRUVYCwL133UpnVzedXSHKS0vOOXvpXJg2uZbmlrYh49XjRp+xnA+GdxYZhkE8kcx6TBRFpk+pZfqUzM5zqZTCI48+m1FClEwmefypFYwaWTEkn6qyoowF82fz+FMr6A6FwYCCgiBfu/9ucob5IhTA6XTwtS/ezZ59Bzlx8hS5gRzmzJpGjt9Hoqs+I4Op7UgLW5pKGRGI4pB0q9vZ7tYicspHWi6mk6daeOLplbR3dAJQWlLMp2+/nhHlpWd8/wYzWEQaYHDe0QDBvFy6Q+GMIOykKhPRx5DsOYaa6EJL9+EpNB2GhqGhRFvRdQV0jXS0FYe/DF/5BVbQuejK6ReLDLRUj/neH15BousAmhIh1XOc3sY3+jOfJNyFtehKFMPQ0dNJBCDVdxItHUdPxzA0Bc3QUXobUaLNuPLGIhg6/rJ5GJqCGu/A4e93Cgkikca1BKuvI1h9Hd0HnyUVaUTvL88zdIVkz1HcuaORvQUEa26ku/4pDE8+6f69Ro6vxukrw/AZeAonEz70PKLsRnblAdBzzAz4VvpOIcoetFSkv9RPIdFdj6Gl8RZPRZCcGGoSBBFDSyNIptAUqFrKdH8pLk+A9Rs3090dpqK8hGWXXESOz8fDf3ja6tQ5eWINN157edY8tbMhCAKyLGctrR3IeBo7eiT+YbrlfRCh/ZOILTzZ2NgMy/IlC0kmFd7ZvB1FUfB6PSxbfCFzZp1fvoKNjY2NjY3Nn5+e3ght7R2MGjkCt3toecn54nQ6+caX7+EbX77nQ1sToKKshP/4l7//UNcEGDd2FA/97F8+9HUHc/vN17CvvoG16zei66Zloay0mH/+h+9kPK+osICiwoJzWlNR0jgc8jl1plu8cD6Hjhzj6LHTndPycgPcfN0VZ507b870rDELFeUl77tD3YGDDRmi0wCqqrKrbj+XXHxBxrhhGLS0tFFUmI8kigiiQGFBPrv27Gfp4gUZYqau66xet4F3N+8kGosxZvRIrrl8KTOmTcpYc0BIGshgGjO6ktZYAe1RnyU6gelOGlG9wPz/lMKvHnkis/Ng7Bi//d0f+N53HrBCzjUlihJpss4hCALTJteys27fEBHJW7IUyRW3uusNFp8uvnAuK15aPSTI/NarJuB0SGjpKCCQCjfgKZhIqu8kRjqBK7cK0ZlDKnQYQ0sTOb7a3Ed/iLnk8CJ7CswucrF2dD1NtGUTksOsWtBSPQiihKdoGvH2PYgO0z3kCo5BTcfQ03EEQcRTOBHZlYshiAiSy1xLieItnka0bTvJ0GEEyYnkMF+PEms1u+s1rCRQtdTMpNIURNmNw19OOt4Oukoq0khO1SVEGtciSk70/uekIo24AlUIkoOcEQvp2v9o/3tgZleBWUKoq0mrK58gOUlHW82AcjWO5MrFVzYbT/54ug8+S6Jzn/leSKaTsrv+aQpqb6d2/Dhqx4+zPs9E+AQPPvw8XYOaJuzZd5C29i6+98AXz0tQnz1jCu9u2TF0fOYUwHT03XHLtfzusWdNN14/F18494zdHP8SsYUnGxubYREEgeuuWsblyy4m0tdHXm4gowuDjY2NjY2NzScXVVX5wY/+k7c2bkHVNDweNzddezlf/9JnP+6t/VXzox88wOfvuo3N23dRVlp83mV0W7bvZs26jXSHwuQH81i6+EIunH9mh5bT6eCrX7iL+kNHOHmqhfxgHjOmTjynv9+++Lk72bJtd4b45Ha7+NtvffF97z2VxeVxpmOHjxznxMlT5Ph9GQ6nltZ29h9sYMqgUrwXXl7Dhne2Wo+PHmvk5w89xne+8QWKizLFvAFhCMDr8XDV5Ut44eU1GaHSNdVjLHfJ7r0HMkSnUn+U8YVhkmofe/fsYtbsuRklfIPPcd3Vl9Lc2o4QP26JSD3Omdy49CrS4b3EO/ZYnew8hbVoSpS5EwIoyhLWb9zEoS5Ii4VceeUiZl04j0RXPcHq6+k58hJ6Og4YuPwjMDAonnYvAPH23cQ791r7dQerSXTuRfbkkz/hVlPYER0ke49h6BqGoeEKjiUVPmoJNloyhCC7KZ39LURRQnLn07LpnzEMFU/+BHQ1hpaK4PSXoatx02WUjpHuM68TT9Fk3MFqkuEGnGB24wO6DzxJMnzULMPzl6NEWxAEEQQBQ1Po2vt7XLmjkD35BMovoLPuIQxNIRVpxOEvNx8bGg5fCa68sWDoGJqZBSZIDpw5IxAMHUNTECQXupZCkNx4Cmrx5JvXi64pCJITV24VgaqldNc/TSp8hLbtD1I6+5s4/SXW59ne2oic7AEyyyE7OruGXIPnyjVXLKW9sytDCJ42pZalixZYjydOqOYfvv8NdtXtJ5VSqJ0w7n256/5SsIUnGxubs+JyOSlyndu3cjY2NjY2NjafDH7045+z7s13rceJRJLHn36B4qICbr/5mo9xZ3/9VFVVUFX1/lxCg9m5ex9/fPZF63Eo3MOzK15BlqWzBpELgsDECdXvO4zd7/fyx9/9hOdfWsOePQcoLi7kU7dcc87OrMFMqBmLKIpZM6ey5Tm1tXcOu1ZbW6d10x9PJHh3y9AA93Q6zcZ3t3LTWZxdixfOp7KijK0795BMJpk4oZpZ0ydbbpb3ljyFE26SqoxbVhG63kKJjrS6xEmuAM7A6a5juYEc/vZb97P/YAO9LXUUj5hE9fiJJLsPEmvZiiC78ZfNs0SnAfHqoqmLueTibxGNxQnk+K29DAhaUu1tVlc+gPyaG5CcfjQlSipiChqJZIpQuAc6olRUzydYOcMK2w4fXoE7dwxpZwDJnYcke3AFx5qd7CQnejqKKzCSRGcdgaqlRBrXInsKEB1eckcvM/d6eAWC6ED2FCK58zDUFIam4CmajLd4qvX6RFcOsp4mnehCiZzE0NLI/jKro54gSDhyRqBEGk2RqfcEOSMvIdayCWd/Fz/ZW2SKWoaOIEgUT/8iksNr7kFy4ApU4S2dia9kuuVo0pQ+s6xO0hiIRkp2HyYVMjOWBIcPyemnoPZ22rY/iK5E6dr/GP7y+SiRRrRUhIQiZQ0ch+zd7kLhHlateZODh4/idruYN2v6EHeex+Pma/ffTWNTM52d3VSUl1JWWjxkrRy/70PLefukYgtPNjY2NjY2NjY2Nn9lqKrKG2+9m/XYipfW2MLTJ5w3NmzKPv7WpvPqgDcgAJ2tXEiWZW694UpuveHK932OweTlBrj68iW8uGptxviii+ZldXOUlhQNu1Zp6elj4XBk2NDnjs5Q1vH3MnZMFWPHVGU9Nm7sqIzHA13yppd1kueXLAFoIDcqpUnUbd1JKqUwoWYspSVFpkg2SFxzBiqRXAFT3AjV48gpHyJeSbKcNUdocFe+ASKN6whULbMynZraIry0pY8Jhd24ZZXDR48zbq6TGdMmZcxVE11oSgRnYKTlVjIDwUtRUz2IcQ/hwytQk2G0VC/OQGV/17x1GFqaZO8x1EQIKZGDp2AC+MsRJCdO/wiSroP9weEShp5GjXdZuUqiKGOoKTxFk0nH2lF6G5E9BWipHgxNIXTwWQRRQnLlUjDxTqKnNiLmjiLVewKHt5hExx5Skcb+TCoZQXKg9Lui0rF2s5TP4UWQ3WY+VegwXfufQJCcyJ5CBMlBOtJklTmWzv4mXfsfIx1tJVT/NA5/Ga7cKnLGLCT11vNZr4vKisxud/FEgp/+8vdWx7poNMYrq9+gszvEHbdcO2R+VWXF+y5X/WvDFp5sbGxsbGxsbGxs/sqIx5Mkk6msx3r7srfvtvnk0NUdJp1Oc6qlnXBPL5IkUVyYjyRJ72udSF+UF15aw579B9F1nSmTxnPD1Zd9qIHmw7Fk0QKqx41m5+796LrG1EkThhV8asaNpqqyYkjGVHlZCZMGObcK8vNwOp1ZA5vL+8PbPwhVlRXMnjmV7Tv3WGMpTcZVdhE+b8QaC1Qt5ejJTh559BlSqdM/Z0sWLeDaK5cBkOiqt/KlgtXXEW5YiZoI0bnnd8juPEu8Ghw2PnjOgCtKTYQwDJ2C2tsswaq7/ikEBJKGh2ffjpHSnJZA5pbTvPPGc5Q76pH0BILsRk31oqVjkI4DA1lhBkpfC4ahIrlyrXwlyeFH9hSgK1Hatj9o5UQZho4gSgiiA0Fy4vCXYqhJIo1rCVQto+fYKhKd+6xMJ2egEskdRDB0dE3BGagk3rEHQ0/jDIwgUHkHocMvkI62oKYiOHwK3QeewOkvRxBlHDkVpHpOkOw9gaGlkL2FlEy/n2jLZtREiLat/0k6GcLhDuLJqwHDIBU9haEpxNp24MypxB0cgzO3imSoATURoqPuEQpqb0OQPWhKH5IzB8lhOsMKfCWMHbMjoywOzFLM9163W7fXWaLTYLbtqOOKZYsIBnPP9xL8q8UWnmxsbGxsbGxsbGz+yggE/JSVFtPa1jHkWM3Yv67Q2g+TSCRKw9ETjKwsO68Ssw+L0pJCNm3ZQWKQeHg8Fsfn9Z7zGoZh8KtHnqCltd0aq9tbT3NLO9974IvI8kd/K1hZUTbELZINQRC4/947WLX6TXbW7cMwYPqUWq66fEmGS8vtdnHJxfNZvXYDYGYwhRNuJFcOFy8wuy4PDv5ubGpmy7bdJJMpxteMYVKljCc4KkPseW9Q+J23XsfE8ePYvfcAsXiC2rHlTCttRx+kdfUcf52nX2zNEJ0A3njrXSZOGEdFIEHk5JsZ4pK/fD7tO3+BoZkLBftL5gZIdNVnzFEiTaiJEEq0BaevDC0ZtgQsUhFcuaPYe1wipbUCp91ZQY/ZgTDU0UxJeRVdyVziXUfxCAFcUho1GcIdrEZTIhjpBHJOBVoyZHavS0eR3UEEyYmm9KErUdKaAoKMIIj4yy9AkBwYahJPsIZo23ZIRdCSITzBauJtO/szlUaRP+EWJKefjrpHEAQRTYmariXZja4ppKOtyJ580tEWJIcHLdWLgJkRVTDxDroPPImhJc18K1HC0BQiTRvIqVxE+85foCa6EAQBR6CS/Am3EGlch66nSYaPgGF29JO9JSQ69mIIIqm+Jox0gvZdvzTLAHXNzImSHFbHwfvvuYN1b71D3d6DAMyYNpEl7wnCB2jJ8nsVzJ+51vYOW3jKgi082djY2NjY2NjY2PwV8oV7buef/u1nGTk7HreLL3/+zo9xV59c/t9//ZoXX12LoqSRJIn5c2bwL//4Nx9qJ8Bzpby0hGQq09UjCAIGBuFw7znd2B48fDRDdBqgqzvEnn0HmTl98nntbc++gzzy6DOcPNVMSVERd915IwvmzjynufvrG9i2o46UojBxQjXz50y3gs+9Hg83X38FN19/5pymKy5dTG4gQP3O1ylxtOELuKm96FaCwdyM7KS9Bw7xx1V7MQwz9afl6GZiY1UmT5lBwfgbMlxFg4PCBUFgVNUI3tm8g1Mnj1KobGbbSZEx42oZM/NmIo3rCHU2My5wnGisiJSWeUu9e089o69caJXXhRtW4iu/gM66h/uDsJ1ITr8ldgyIT609In2hBDnuBAx0hTN0nL4yZG9BhnvKEsoa3844d0qTreD0hGcSf3qnk0PHjlHqV4gpAeZU9TFpVADZaf6n64rpbpK9GBgZrirJmYPsKwYD1HgHjpyK/v36SHYfJhGqRxBEXLmjcAYqibXvxOEvR3L6EUSZSOM6PPm1CAgYhk7eqGW4ApVEW7eTCh0m2bkPh78cT9FkZFcQpa/JEtl6jryEw1MIgOAvQ5S9pGOtxFq3oyZC6OkYiBKyJ5+CCbfi9JcSqFpG2/YHATAAQ1PoPb4GZ24VSvgIhpbGwCDVa+ZLOXwlFEz6NLGWTaaL7OCzeIuncsWli7ni0sVnvAZLirKL0oIgUFJceMa5HxVvbtzMy6+9QTqtsnjhPK676tLz6sT3UWELTzY2NjY2NjY2NjZ/hVx12RKKCgt49I8r6OjoYuyYkdx3922MHZ293Ol/Mo/98Xn+tPJV67GmabyzeTs/+vEv+NEPHviz70fXdSbX1nCqpY1EIonb46KirJTcgJ/m1vZzEp46+9vCx2JxevuiOB0y+cE8RFGkK0tY8rmwY9c+vv7dH5JOm93Fmlva2b33AP/4d9/g8mWLzjj3tbVv8drrb1mP6w8dYffeA3zl85855xvkk6da2LPvIJIkcuW1t+HoeccUScJvowRclmhiSD5eWr/fEp3ADApv7+6kuPUkoriyP0g7e1D4w394mpbWdkr9SdyySm9M5rG1Ib49xU2w+jpC4T/gllWCnmRGh7wBdMHFwXAliabVeOQUxY2bcMoCotNP8bT7iLZstkQpd+VlPPL4SxxvbMIlqUwv66S8uJWaZC+i5BxSkic5/ZY7a/rUWlatWZ/xOgGcTietET+HjpklgwN73H4SAv4Utfmm2JdTOpNI0wYc/jIK+h1Kwerr6Nz/BImOOpw5lUhOH1qqBzXeSc+xVeSNuYJEqJ5U70kc/lK8JTMt8c7hL0WUvehqHC0VIdZZh+jKQU/10bL1Pymc9GkEsAQ4QXbhCVaTU3kRia56kFyE6p8iHW1FkJwUTrnH6rTXuuXHqKkukqFDiJILOXcUsqeAWMsmJIePSONaHN4i9HQCZ94os4xQTZAKN2DoOnq6D0GQTceZoYMgITl8BKuvs0LK09E2JNmLp7CWjs5uVq/bwNFjjeTk+Llw/izmz5kBwNzZ01m/cfOQMPrpUyZSkB88p2v5w+T//devM35/bdq6k7VvvsvP/+OHf/a9DIctPNnY2NjY2NjY2Nj8lTJ31jTmzpr2Zz9vNBrn1bVvklbSLF92MYUfw83Y+2HlK2tJpRRUVUWWZZxOB4Ig8Nbbm62x90MkEuXxp1ewa89+8nJzuf2ma5g149wdRgX5QXJyfNSOH5vlWN45rTGivJQjxxrp6Oy2xhwOmYkTxjGi4vzatf/qt49botMAuq7zm9/98YzCU6Qvytr17wwZP3qskT37DjJ96sSznvuV1et5/Y2N1uM16zZy41WLqM1vMkWcQcHfIWk6ffETGfMHStHKShWKCiNDgsIHhJ0Tjacsp9iAYBNOuElpsGX7bq64dDGjZn2a17Z00BYdmrk1eWINv3jocY43NhFwwYUjuwilUvhzchk75/O4g2Nw+EosseaNNS9wvLHF2mN9Zz5uuYPm1jYqy8sIVC3lVHsvr6x+gWPHT5Lj93Hh/NksXbyAosICbrz2cla8tNpyNjpkmTtvu45X17yVsS+XpFJbFCLco6LrOqIoEm3bgaFrGc/TlBipnqMYuobo8JJfeys9x14l0bmPROc+dCWKpkRRE104/KUo0VNoqYiZJZXoRundiTO3CtlTYJbkFU0ldPBZ1EQXrVv+H6LkNjOicipI9zUT6luBKHtxF9QQbliJ5PCj9pf6de1/lJLp99NzbJXZuQ7BEtmcvjKQHKR6G+mufwpDV1ETXbiDY5E9+eSOuYquvb9DV5MYuooge5BkD7K3BF2NgZaibfuDFE37PACypxBX7kicgUp6eiL85Je/IxaLA9DTG+GpP7XQG+njsqUXk+P38bX77+alV9dZXe3mzprGFZeeWXz9KGhsaub5l14bMr5tRx3r39rEJYuGlgp+HNjCk42NjY2NjY2NjY3Nh8Zra9/in//9F1a4+c9+/Sj333snd99x4wde+9kXVvHiK2uJxmJMnjieL9/36aztyd8PsXicQw1HifSddi/IskwwL0AqpZBMKvj9537bFIlEuev+b2eUuW18dyvf/PLnzrmb4Lw501m/cRPxeCJjfELN2HN+veGeXlLvCeFOp1W6unuoHT/unNZ4L8eON2UdP9XcZokZ2Thx8tSw3egajp44q/DU2taRITqBmafzwqsbmfj126DldWs8ULWU5NDcZ8AUdqKuCUB3xvMHZy1FotGMOYMdTdGoKUQ4Pblcft1ns4aL90b6LPdSbVGIeNp0F3UmNApPvo3TV2w5i1K9J9n43AvW/IE5AF1dYSrLy2itf5mHXmkjmjCFpXBPLy+/tg49eoyll93AwgVzmDJpPPvrG5CFFNUjPORX1Fo5WAPrmsHjKilVJnfctcSbN2AkQuhqHD3VR0fdI+SNu4aeIy+Z2U+efPLGXYOWDFMw4Ra6NIVE5z40JYrk9CN78jHUJMnug3iKppAMNyCKTkSnH1FyAeApmoISOYEjpxw11YOmxNDFBN7i6UgOL2lAV5NEWjYR66zDUJNmWWHeGEIHn0aPpWjd/hN0NYWW6gEEELwYhk6y5wgIMhgqDm8xIOD0lyN78nHmVhFr24YrUEWq7xTpeDtoAg5PEU5/qZn51HMUXYkSqn8K2R3ElTvSEiDfen2tJToN5o23NrH4ovm4XE5Kigu57+7bsl9o50A0Gqe9s5OqyooPlLf25sYt6LqR9dhb7279xAhPn5yiPxsbGxsbGxsbGxubv2gikSj/98c/z+iop2oav3z4MY4ebzzDzLPz7z99iB8/+BsONRyjuaWd1Ws3cM+Xv0tX6PzKxgZ45bX1eDyejDFVVYnG4oyoKMXvP/dAb4DfPf7skGwlXTd46PdPDSu+vJdAjp8vf/4zVI8zg+Adssz8OTP47J03n/M+du05QG3NWCoryvC43bjdLirKSxlRXmqV4b1fcnNzso7n+H1nLJcL+IeWo1lzc3xnPe/e/YeyjsukOLV3ZcZYpHEdZYV+RmQJNXc7dCYV9wx5vqacFptGV1UOKwSMG9TdrGbcaP7397/O7Tdfw/VXL+f73/4y1165jIajJzKEnqQq887JCqJJmd5wm+l06hdvPIW1aP1OpffOOdA9AskVoKXpGJMKmnFJp6+dUn+URMvbdNY/j6ZEycsNMH/meMb6j5Nu30yiq54ZUydZzw96kta6qdwL8eZVEqy+zhRp/OWkUz2kwkfo2PlzBARcwXEUTfs8PUdeovfEWpLdh/s72ZVb2VhmqZwbLRUh0bmXdLQN0ZVD6exvWqJUvH0XyZ5jaMkwrsBIBFEEw0CJ9Ocs+csQRAfJrgMokSYkV4BA1TLURCeSKwC6hproRlNMJVGUnMjOHFzBatREt5k95Sk0O/G58wDTgRc+tIJE5z6QXbiDYxFl8+dbS4XR0jEEQ8eVMxLZW4TsNt2YgwXIbNloAKlU6rzLVAdQVZX/9cN/57Ib7uZT93yDy66/m4f+8NR5rxc4w8+P3/f+fnd9lNjCk42NjY2NjY2NjY3Nh8KqNetJpYa2utd1g+dfXH3e6/b0Rljx0poh46FQD79//E/nvS7A7r0HmFRbPURsUBSFL9336fe93o7d+7KO90VjVresc2FEeSlf+fxn+PGPvs+//dP3uP3ma3C7Xe9rL6IoUjmijBnTJjJz2iSqKsuRpPO/Bbzh6uVZx69YvviM80ZVjaC8rGTIuEOWz6kU1OEYKgQNCDWSkTTL5WpuyAj0/uztVzKyssJ6fp7fwZ2X5OJ1almfPyA+5fh9XLrkoiHnGzumimlTajPGvB4P8+fMYPHC+VaodI7fnyH07G4tIpJysbu1CKc7Dy0VQYmYzjFBEJhUWwMwZM7omukEq6+jN65beVIDhBNu4opIItpNuGElSrTNKt0byKu65OL5XDApiEtSaYv6OdQVpFmt5ZorlpLoqrdcV+7gOPJrbkSQnBiaQirSiL/iArr2P04qfIRUpIlo23YMNYnDX4ogOVETXaSjbRiagqGlUZM9pKMtGJqC5PThClShqQmSPUdJho8giA5ceaMJjFiI7MkHXSPVe4Lc0ZfjLZmGM6cCyZWLv3w+kca16Kk+RIcPyZOPKLkQEBAkN7K3GFduFXq6DwbK7kSH2WlPV0n1niDa/C5a2nQr6UqfKfK5AsjuPATRQU75BQiym1TvcQTx9HU1WIAsKszPeh3KskxeXnbx9Vz54b/+lDXr37ZKVvuiMR763VO88PLrZ5mZnSsuXUSOf6j4JEkSt9xw5Qfa64eJXWpnY2NjY2NjY2NjY/OhkFLSwx5T0sMfOxv79h8aki00QP3BI+e9Lpg3/7mBHBYvnM+hhmNEIn14vW6mTp7ApZcMFSDOxnAOKUEQzivraqDr2/tl2uRa9h0Y6hQqLyuheJiuXGfjM5+6kd5IlOdWvkYsHsftdrF86UIe+Oq9Z537+btv5/GnX7CcbwX5QW654Uryg3lnnTtj6iRefu0NNO10HlHQk8TnMigsqbRKpILV11kCTECM8MBX76W1rYN4PEGxN0K85e2MTKfBz7c6xQGXLb2Yyooytu3YY3Xgmzd72jmFoM+bPY0N72yBroFsKPOWu6xiFCNn3ppxHoDrr76UU82ttPWYj8MJNwXFFSxfuhDJ6SUVWMCh429nlPylNJldLUWMHZ0k3NNAQaQLtysziDzRVc/iyTKzxpXTqk0kv7CMmtEl9B59iUjqdC1iMnyESNMGnP5ylGgLupqkY9cvEWQPouhAcvpJhg7hzjcFMjXRjSu/BgGBZOgQsqcAUXab5W+hw7Tt+BlKX4sZ5i3KoGtoSh+aEkPpPY7sLkBNdqMpfUQa11FQezuRxrVoqQi9x9dgaGmUWCuunEoEyYmmRM3MKS2Nw2eKl7IrD8mdh55OoEROIhdPRZCcAEgOL4LsxukrRxBF1GRP/1ouHN5ivCXTSIQPI3sKcOVWZYTMhxtWEqy+josumM2WbbtIv8ehOH/ODHze83cRKYrCmxs3Zz327IpXuP7qS9/3mm63m3/54Xf5hx/9J6FwLwBer4dvfvlzVA0SXj9ubOHJxsbGxsbGxuavBFXTePSpF6mtGcMFc067CI6eaOLtzbsI5Pi4fOmFeD6G9vA2/zNYvuQifvXIExkCwQCXLz3/4N3KinIEQRjSvQugoOCDBZfPmDqJtzdtI8fvZfagAPCLLphzXuvddN3lbN+5d8h4zbjRVFX9+W4EZ8+cQv3hI+zsd2CpqsapljbA7OJ3yw1X4j6P3wVfvf8uvnDP7bS2dlJUlI/X6zn7JCAYzOVrX7ybULiHVEqhtKQIQRDOaW5eXoA7b72Op557GaU/t6pXK6J69qUU1V6Q0fEtWH1dhrgzOBNLlmWcgcozPn+AiROqmTih+pz2N5jSkiLuuuNGnl/5GimtD4BxY0fx6duuz+hIN0BBfpDvPfAl9m57lbZeifKKUUybUoskSWhKlHmTS9m0oxA47Xhqbe9ETau87MhnRlkHjU3NjK6qZOLCG6zX5gxUIrkC5BAhz9VEoLKG3qMvZbiiNCVmik1KlDSQN+5auvb9HkMzRd6CGfeQCh8m1duIoabM8rZEN7KnEAMD2VOAw1/W36XOgZaOk+4+aHaNA0TZi+AQUJMh1LYwouzBlTsG2RMk0VWPno7Rc2wVrpxKtH4xTEtHkb1FiAOfkcOLw1dKItRAvH0n7vzxOH0lyO58ErF6dNFJOtaBw1uIp2gy6Vg7qZ7jSM4cJIcP2Z1HOt6F7A6SjrXS17Sx371VhrtgAk5/6RABsrSkli/d92lefu0Njp04ic/nZcG8WR84PDwSjWV1hILp6jxf5s6axqrnfse7m3eQSitcOG/Wef1sf5QIipLKnkT1V4Sqqiy86rNsfOX3Hyi4y8bGxsbGxsbmk8wjjz/Py2s2cMlFc/n6F+4AYP/BI3z9+//KlcsW0tjUSiye4OGf/ONZb/jsv59szpdf//ZJfvvYsxki0RWXLuKHf/+tD7Tu57/2vSGlapIk8euf/F+mTp5w3uvGEwl++fATNJ1qscYqR5TzpfvuxOs5N1Hlvfzy4Sd48tmV1k3muDFV/Mc///0HDkI/Hxqbmnln0w5+9ps/oKRVxP6f/aLCfB7+2b9+LHs6XxKJJAcPH0WURGprxuF0np8bLBvJZApZlv5/e/cd31T1PnD8k53uvSkdtKXsvUFQQEEURHHvhevr3tufe+89wYGIAwFBQRDZsvcqq6W0pXs3O/n9ERqJTaGUtin0eb9evDTn3nvuk5u0vXlyznNO+PedoWinWzILwGauwlieRaklDB+9jrDjjHTL3LkMS8E/BIVGE9ZxoquOkisZ4tedP9fksHd/Fhq1moy9B2gfE+yqCQXOEXV9+w0kptslrhFPKn2oazQRgMNuxeGwE9njRtc+5Qf+xFydh9o3AktlDnabCbul2rnSXGgawSnnU3HAOU3WYbOgUOuwGkqcK8iFpjmn6FmNKNR6zBU5GEt2odQGoFTr0PjFYKnOw1JTiMNuQaULchbyDohHqfXHbjNjLN6Jw2ZBG9DOVafJXJWH2jcSpVKFSheIT0QP8ta8gcNqQKn1J7LnzRRv/x5L9WEcgF90b9TaAIKSzqYscyE1+RtRKFQo1XqUWn9M5ZkoFEr8YgcS0eVKjMUZrmLmuqBEAuKHOc97JAFpM1e5/v+/RfO379zDspVrKC2rICmhnWt1wYYaN+kGjzXWBvXvzTuvPtXgfk41chchhBBCCHEayNiXxY7d+xk9fCBWm93V/t2Pc7n2svFcc+l47HY7V97yCGs2bGNAn25ejFaczm654QqGDe7HzN8WYLXaOGfkMAb263XS/b72/GM88dybrN+4FZvNRkR4KLfffNVJJZ3AWafnvv/dyK6MfeQXFBEVGU56WocGj8bx5LabruTKSyawaesOIiNCSU9r3CpyTSEhPo6XXv8Iq9XmSjoBFBaV8Po7n/HGS497LbYT5eOjp1ePLsff8QQcyMxm5m8LOJidg0ajoV/v7kwYNxqdTnvcYw1FO6k4+LfbFDerqZKiXTPBVk1Y+xH4hHaq9/i8wwVM+e5nyorz6BlTSIDPfpKLS0npe4lr+pdKF0hsUg9u6DgEgKUr1mCsLnUrRL6zMJROESUUFmSj3zMLfVg61blrUOkC8Y8d6JzCdqQGksY/1pVUqR19ZbfbKNr6FQ6bGaVKR3jXq6nK+QeFQknh5s/Q+sWgUGmO/NOi8Y8GwGEzg8PuKgpetn8eVkMhusD2OBQKLJU56ALicTjsOGxWlGotDosBY+k+InvcSEXOShw2C0q1HrVPGL7hnak6vN45aqn8AH4xAwhPnYChZDdqXQBmqwGHzUpF5l9oA2IxVx9GqVRhLs9CEdKBgs2fAypQqFConElJpdoXUOCwWTCV7cNSnY+hZCcOq9GZ+CrZg9VYQkjqBFfSqTbhB7iNUPtn7Uam/zTH9Ti/oJAt23dx/503HTe5WOuGay7htbc/cVuJzkev47abrmjQ8acqSTwJIYQQQpziLBYrb334Nc88fDs/z3EvULprTyZXX+pcwl2pVNK7R2d27TkgiSfRrBo7TelYgoMCef/1Z6ioqKK8spJ4D6uWNZZCoaBTxxQ6dWy6BFFgoD9nDOnfZP2djC3bdnps37B5ewtH0rwcDgcHD+VitVhJTGiHSqU65v7FJaV89MV3rul7FouFlavXU11Tw/VXXVxnf5PJzJ+Ll7Np6w4AendNpnesn6s+UFZVHNlbZ+GwVGFX+hBSlsZZZ3lOPNntdj6f+gPFJaWAs6B4z5hC9uzehr/egU7rXrOplkKhqFOI3GRz/rdjmjMWhUOBSheItaaYgs2fu0YzOWxmrIYirIYS18p62sB4CjZ/gd1mAocDfVAiptK9BKecT+Hmz7Cbq3D4OQhNm+hKhqFQotL6o1CqsRrL8G83jIqshViq8px1nRRKTGX7UQCmymyw27Fbq9H4RWCtKQK7lcKtU1DpglGq9ehDO6JQaagp3Ip/dF9MZfuxmSsxluzGXF2AsXjXkel94LAYcNgtmCsPodIF4rCZ0AW2x1KZg81UgdVcgUKpRRvWEbU2EBTOa2EzlmMzllO8cwZqfbDz2iaMonjndKyGEkr3zHLVe7IaSnA47GgD491er98X/F3ndaypMbB46T9MumDssd+cR1w0fgwRYaF898MsCouL6ZCUwOTrLyO1Q1KDjj9VSeJJCCGEEKIVW7NhGxu3eP7QeNaw/qR2SODL72YydvQwoiLrDvevqKxyW1I5wM+X8vJKj/39NPtPV+LKUy0dIVqDwEB/AgP9j7/jacBut7PvQBYhIcGNKkxeS6VWYfVQd8vTanHNyWAwsmbDZvLzizCZzRgMRixWKx1TkggOCuJAVja+vj7079P9hKYvARzKPczU736msKgYcK4ud/nF5x8zAbrin/WupNPRtmzbRUlpmVvhc4fDwadffe8qjg6w4O8SspJjmDQkgML8bA7u/hvgSEIoGNP+FSjUvpx5xqA659izL/NI0snJZHOOXOoVU0BBUTHxsTFUajqzYsEqUCjo2b0TCfFx9OjWiVlzg+sUL7eiI7nf1ejsJfiEd0ITEMfhdW876zfZzGj8Y7EailCqfSnJ+AVDaQbByedSkbUQhUKJQqFGGxCDw2HHZqqgbO8cND7h4BftmpoXkjqB4l0/YijchtrHuYqfpSqXws2fowuMR+Mfg1IXRM3hdTjsVlTaQLA7sJmP1G8ylaMJiMNUugcUSqCMgPZnYjUU4rA6Vyf0jeqB2j+G/HXvgsPG4TVvOFfJs1ShUChR6gJx2G3O0VY40Po5Rz5p/KKxGIpRqrQoFBrMVYdRhvhiqchB4XCg1Pih9gl1TecLTBh5JFlVjt1qAKA0YyYOuxVzVS5avxi32l/l5ZWUV3j+23nwqGm6DXHGkP6tJindUiTxJIQQQgjRiqnVqnrrmChVSgqLS/l5zkIuOn8Un0z5kU3bduNwOJj/10rOOWswQYH+VFZVu46prKr2mKACmDR+NJPGO1fVqa3xJITwjhkz5/H5lOmUlVegUqno06sbLz71QKOSbgP79WLJ8tV12gcP7NMUoTZIcUkp7308lbLyCnLy8sk6mINOq6VTegqz5/6JxWKla+c0lEoli5eu4porLqR7l+NPo3Q4HGzetouXXv8Qk9lMRHgofr4+VFZV8dU3P/L4Q/8jOCjQ47GlR1YB89TnfxNPGXsPuCWdau3en0fJkDHk5q13te0sDHUlhP5e9g8jhg2sM3XTYDC6PdaprHSKcNb+sVptHDyUx96sT10jmhYvXcmY0cMZM2o4l110HtN//g2rzVnfSa1Wc9lF5xEcGgU4V36zGUucI51sZnSBCShUGkLSbqYyewmGwjIsVXkU75yOw2HDWlOIPjgZtW8YgQmjqMhaCKYKdKFpBMQPcxtxBaD2CUcX1B7/2IEUbP4cu7kKU0U2kUlnU3ZgPg67DYVSjSYgDktlDkqNH0q1L1q/SMxVeSg1ftjMVdhtZioPLkYXmIDaN8w1ustH609U37s4vOYNbKZyaoq2odIFolTpjqyohzOJptJiqsgEpcZZQ0rjh8PhwG414DCVYSrdiy64A6byLOyWKiw2A2qfUFRqH8r2/47dZsZmKkep8XNOQzwSr8Y/FrVvmNuIJz8/X7RarcdEZUiw5/eX+JcknoQQQgghWrHe3TvRu3v9NULKKyq5/KJ/h/irlErsDgdqtXOKSXpqEqvXb6Vzxw7YbHbWbdrO/Xdc19xhi1OM2Wxm5+69hIeHERcT5e1w2ryVazbw5nufuerA2Gw21qzbxCPPvMKHbz53wv09/uAdZB/KZX9mtqstPa0DD909+YT6yTtcwPSf51BcUk7/Pt05b8xZboWXj2XuH4spK6/AarWRfSgPANOR911tEfbCohKiIsOxWq38+Ms8uqSnHnO6nMPhYMp3P7F46T8cyHI+t9y8fDokJxAVEYbFamXDpm2cNXywx+Pj28WwcUvd6YYatbpO0fWcIysC/pdOZaUye7HbamWdIkpcCaPyikqsVisajfsXCB2SE1Cr1VitVnQqq1vNJkfYCPZu/gW92tle29f8hUvp26sbfXt3J71jCtt27Aaga+eObiNbwbmqnUKhciWdAKpzVxGcfC6+kd0xFu9yTk0zlqL1i3FL/Px3pb/aAurmiuwjBbnbE5gwCpuxhOi+97hGVpXunYNaH4JPeOcjRcdNOOwWlCo9upBkHFYD+uBkTJWHcDjs2E0V2BQKTBVZBKWc50pw2cxVVOeuQheYQE3xdhw2C9aaIvyieqM8UsxcExiPtTofhUKNw2FDoVDiwI7DYgCHFZRq9MEdUCg16IKTMRRu/neklMYPQ6FztUdtUBLmyoM4bGbMZQcAZ1IrvMtVbgk3rVbD4AG9+XvZP27XWaFQMHzIAI/vDfEvSTwJIYQQQpzCggIDuP6KC1yPDQYjVpudkWc4b4SvmDSOux55icP5RRzMOUxgQAD9ejVtcV5xaps67RemfPsT1TU1KJUKunfpxKvPP1LvKBHR/Kb/OMet+HCtDZu2kXe44IRXogsOCmT6lPdYvnItGfsz6dwx5YQLvi9esoonX3gTs9kCwIJFS/lx5jw+e+/FBi3dvm1nBgDVNTXY7f8ugFBUXEqAvx/gnBocFemcwlVZVUX2oTwSE9ods8/NW3e6Rv7Uysw6RHhoCCqVkpr/jCw62sD+vVjxz3q3KW8Aw4cNxM/XPZETHh5a5/jahJGfzhedXwj/ZDroFFHiljAKCY+tk3QCCPD349yzRzB73kK3mk2WkKEYHf5syo1wJaNCfIwcrvLH4XCwfdcehg8ZgL+fb72vYW2BbKVKe6Tw90hXfaaKrIWEpE5A6x9HacZM1HrnFE6zXy/+WLwWi8VCl05ppHb4N+l0dAH1wPYj3FbLC2w/gogeN1O4+TNUGn8cNouzT4cdh1KDWh+CAwc2Qwk2Uxlq3wg0/rHYS/c4p+spwG41UrT9W2L73wdA6Z5ZWGuKsZpK8Q3vjKF4F3ariZriHfgpu4FSDXYL2G0o9YEolRocNgs2cwV2mxkcCvShCSg1vliNpVgNxfhE9MBqLEGtC8JhNQHgcNhw2IzOaXpHRjpZqnJR+0a4rtPRyafx545CqVSycvUGjEYjEeFhnDf2LJKT2tf7HhNOkngSQgghhDiNDOjTHbvj3w91XdI78OV7z7JyzSa6d0lj5PC6Uz5E27V0xRo+/OwbV00vu93Bpq07eOiJl/n0vRe9HF3blXnwEKVl5djsdrRqNb5+vqhVKux2BwWFRSeceKo1dHA/hg7u16hjX33nU1fSqdbuPfv5+vtfmXz9Zcc9XndkmpJG7Z6EUR01Yuq/CZrjrSy3c9dewJmAVyigtjSdzWajorKKkOBA0tOS6z3e18eHu267jkV/r2BXxn78fH0Y2L8XA/r2rLNv105pREVGkF9Q6GoL8TESEqAmPLIdfh0G8ceWH9iUp3IljEJ9TYw5e3i95z9r+GCSEuJZu2ELCushOqX2pXuPXqxas9FVMLw26eS6Jtrjr7Znrsh2rYh39Cim2tXaDCW7MRbvcu2flnVJcgAAU0NJREFUX1jM9pXvsSnXObLq72X/MLBfLy6bdD7awHhnce4jBdSdSayFrv5V+lCKd07Hbq7CaN0PDlAoVUcKhiudtZ50wditNaBQY644iN1qQKUNwjeqB37R/Sjc/Bm2mkIKNn9BQPxQrDXFGMv2ow1qj9YvioD2Z1Kw8SMcNgs1xTuJ7nsP1YfXYqk6jFKlRRvYHlPZAeDIm0ChxGEzE5R0NlW5/+Cw27BU5aLxCcNqLEOtD3ZO7/OLwliSgc1c6RoZpgxMwFC6F5upHL+jRn3ZzFWYK7IZf+4oxp1zJkaTCa1Gw9btu1myfDUdkhNoFxt93NemrZLEkxBCCCHEaaRPz8512hLiY0mIj/VCNKK1m/7zHI+F5Lds30lOXr5MuzuGrKwc5v35N1qthvHnjjzhYtj1WbdhC2Vl5a4kj8Fqw2g2ExoSTGCAPx1T60+kNJdtOzLqjAqqtXzV2gYlnvr16c5fS1bi66snMNCfiooqANrFRVNSWo5SqXCNdgKIbxd73ASbTqdz/lerJT4uhoNHpvABqFRK+vTqRkpy4jH7CAoM4MLxY44bv1Kp5Pabr2LWb3+yZbszaRPbYSB9hiYQGt0Rldafu++4gUWLV3Dw8EESAlVcMOq8475eSYnxJCXGu7X17N6JX39bgMmCW9JJq9XSvevx617VJku0gfHYFTo2rN9C7uECosOT6Birdk2zU+kC0UYNYec/r6FXuU/r+2ftRvr2dl6/o5NWpRkzAY6MpHLWg1KgQKn1R6HSYq44iFITgLF0L7rAeHwiugJgNZZiN1djt1ThsFlAqSIw/gyqcv9BH5KCuSoXBQpUal+U2gAcdgvWmkJCUi+gOncVPqHpVBdsBquRkoxfCEo8C0PhNhx2C8aSDJRKNcojNZ5wWHE4bFRkLyUgfjg1hduwW6oxWQ0oNb6oNH4oVBrMldmYq3JRKjXY7WbCO11C0fZvsZlKsZlKsRhK8OHfEWQ2U4Xr+paVVfLJl9OoqPy34Hj/vj25fNL58uWOB5J4EkIIIYQQoo2qb4VDu91BUVHxKZ14ysnLJyNjPykpicTHxTRp3+9+NIVpP85yTYf7YuoPPHjPZCaef85J9etwOJi34G/S0zqQl1/oSj457A5qqmu49YYrGzStran5+Ojq3abVNuwj5djRwykuKWXz1p10TElmz/5M1CoVKckJVNcYcDgc+Oid54mKjOCayycet89+vbvx97JVOBwO2sXFEBDgT1FxKb6+Ptx92/UNStKciKDAAK654kJXsva/CYZ2sdFce+VFx+3HarVSVV1DYIC/xxpZfr6+XHflJL6b8Ss1Nc4V13x9fbj6son4+vg0KFaf8E5UVFbx/iefUVBY5GrvGKtg3IAgfAOcNZ22ZWSz4VBYnWl94Ew4piQnotL6owtMoDp/Iwql8/WuXRXOVJ6FLiiB0E6XHlkhT43VWIzDasDhsBOWfjE2czXFO6ej8Q1HrQ/GULwLHHbK9s7FZqlC4x9DdN97sBmdK/Kp9KGYKrNRa/0p3PwZap9wZ1FyXSAOqwG1xg9zeRZ+cUMo2zsb7FYUuiCiet9O+YH5mKvzsRmKqCnYjN1UicYnHJu5CofdgqX6MGp9CBpdFPaaIuxWA3YMUJmDqXQvKm0gCqUah91GVe4q9CEdqMhahKk8C41/jKvg+LQfZ2GqKSXa/9/rtWbdJtI6JNK3d/cGvUZtiSSehBBCCCGEaKO6dUlnz77MOu1+vr506pjS8gE1AavVyqPPvMaylWux2+0olQoG9uvF6y88hlp98h9/tmzbxXczZrmNFLPabLzx3uececagk6qNVVlVTUlpGb6+Ppw5bCA7du+ltLQcrVZL395dGzSyqDl0SEogIT6OrOycOtvOGVX/VLKjaTQarr/qYvILijhcUEhMVCQ6nRaj0URkRBg1BgP7M7Px8/EhKTG+QaNGYmOiuPSi85g5ZwEmk4mgwAA6JCdw49WXNHo6YkM0dkSL2Wzm+5/msHHLDnA4CAwIYPRZQxnmYfpjl06p/N9j97B7zwEUCgVpKYkea0Udy+8L/nZLOgHsznUQfUDL+IucU/C0Gk290/pqz1edt4GSIyOddEGJKJRqyvb/fmQvBfqwdLT+UagiBpG1cyulpTXo1FaqKiMYlWbAdGihW72p0j2zMBRuw1CyC1Cg9glDpfVD6x+FzVxFRdZCNPoQLMZS7OYqLDYz2qD2WGsK0fimYakpxG63YirPdCadVBp0IR0wFG4hvMvVlO2fR03+RuyWGkwVWThQAA6Uar1zeqDGD7vNjN1mRKHWoVAoUSiUVOdvxGosQxecglKtQ6XxozRjJpaaQizV+Wj8nQnswqJiivIPuZJ18O/ItE1bdzZL4sloNDLnj78oKiplyMA+TZ5UbW6SeBJCCCGEEKKNuvGai1myfLXbNCqFQsF1V01C24BaMq3R2x9+xZLlq12P7XYHK1dv4JW3P+HxB+446f7n/LHI4/REs9nCH38u4bJJ5ze6b18fPXq9HqPRiK+vD317dXNt69q5Y6P7bQrPP3U/9zz8nOu9olAoGD1iCBdfcO4J9RMVGe42pS4oMABwJju7NeI5DuzXi57dOrM/8yB6na7BSauWtnrdJt79aCp79jlXTgsKCiCtQyI/z/odfz9fevWou+iDRqOha+c01+Pa1eWOLnhdW3uodnrd0bbtyPAYy8ptJUy83NlHWkoSQYEBlFdUuiWdFAoFfXt1c64wV7j533aV8/dC7apwPhFd8QntiMVYwfK5H2A1OqejWawqjFXLmDk9k7P7haA+Um+qtg/FkdXpAGoKtlCEgpDU8a4i6EqtPwFhnajOW4tK649CqSa0x01U5f6D1ViOqTwTpVIN2gAiul+PoWg7pvKDVGQtxC+6H6ayAyiU1djMldgtNaBQ4BczgLD0iynbP4/qvHXYTOUo1D74RHVD4XBgNZZhLNuLShtEaOp4jKV7nCvqGUpQKFVYqvKchc8DerutQlhq+HcUoqffDSdry7Zd3Pfo81RUOqenfvXtjwwb1I/XXni0watKepsknoQQ4iRYrVZ+mbOAVas34Ofny8UXjKFHt7r1VYQQQojWKCI8jK8/fZ0vvv6RLdt2EhwcyCUTxzFi2EBvh9ZoCxcv99j+198rmyTxdKwPlkev1tYYarWaoYP61nkOCoWC4UO9u2R7x9Rk5v70BQv+WkZ+QTFDB/WhQ1KCV2Oqpdfr6JyeesLH/fjrPP5ctBybzcYZQwdw5SXjT2hUXObBQxzKOUx4WAgdU5PrTXhlZh1i+k9zyDx4yNVWXl5Jxr5MuqSnsnTFGo+Jp6MZinZScmARRqua8PQLCQqJ9Fh76GhqtcpjX+qjRk6pVCpuuOYSvvx6BuUVzqm3Wq2Wc0adwaIlKzm8fw2poUWEhkWRlNjOWT/J9m+Red9I5+iePf9MwWqswGhVs7Mw1LW6H+ZsSu3daB8ah81cTUXWQhxWIz4RXdEGtKciewnm8kyqcldhs1ShUvugUOux28yU7/8dtU+Ya3pfVe4/qH2jsBxcgt1mArUvuuAkDEXbsdvMWA1F2G0mqnLXgMOOJiDOWUC8eCcOmwVzeeaRGlNmUChQqDQolGrMZZloAmKxGovB4cBmrqB0/x9o/WOwVOVit9ag1Aai8glzJsVMfxMepMJgMLtqYtXq2QyfA5564S1X0qnWslVrmfHL3JNKdLckSTwJIUQjWa1Wbvrfo+zYtcfVtnDxMu64+WquvvxCL0YmhBBCNFxEeBiP3Hert8NoMjUGo8d2Qz3tJ2rc2Wcye+7COu1qtZoxoxs27exYzj17BAqFguWr1mEwGIgID2PcOWeS2iHxpPs+WUqlkjENnFrX2j36zKss+nul6/HWHbtZsWpdg1ZztFqtfPnNj273gLExUdx201UE+PvV2X/V2o3YbHYsFvdVAcvLKzEaTZSVVxzzfA6Hg7/W7KfqwA60Sgum9ZvRRg9laEc7DkuVs0h4YHyd4/r06uYxEdu3V1e3xwnxcTz1yF1k7D2A2WIhOSGe9z/95sgKflrM5kBKDzjoUGBkQn81Nks12oA4/GL64x/dB0PRTgyVBaiUdlciZlNehGtUUGXJIaqUh7AfMqNAgdo3zFWYXKMPxmYIwGYqx1S6D11IBxQOG+Yy58gwjX8MwcljqchahNVQQnXBZpQaXwAUSg2W6nwsVXkoFEp0QUlYagqwWw0otQGo9CEoHHZ8I7pRXbAFS/VhirZ9g81ciVKlQxfYnprCrdgt1TjsVuc1VKiwGoqwVueBw4pKF4TVWOJMuNktrqUTEyOV7M9Vuk1P7N2jK317d6Mp7crYS25evsdtC/9eLoknIYQ43f306+9uNxzgHM7/2dQfuGjCWHx9G1b8UQghhBBNJz21A5u27qjTntZEq8H16tGFSRPG8vPsP1yjn1QqFXfdei2hIcEn3b9SqWTcOWcydvRwjCYTPnp9k04d27FrD+9+NJXde/YTEODHuHPO5JYbrmiy/k8FuzL28teSVXXaN23dwaIlKxg5fMgxj/9ryao694C5efnMnD2fa66o++VjdXUNSqUCPz9fqqtr3LZZrFYS27c75vlWrt7An0vWo1OF/1tXqPBvMrXRJHdIJyR1gtv0u1rnjBxGbl6+W6wpHRIZd85ZdfZVqVSuum4bN28/knRyOlzlj05lJcCyg/KScLT2MhQqLSr9HvyieqINjMf3SAH62kTM0XWjupx5BirbJjBVYLeZ8Y8dSEXWQufKevoQ9OGdMRbtQKFQYi47gMNhR6HS4BvZg7D0i1Fp/QlMGEXuqpdw2C3ogpNQaQMwlmRgt9Rgs1Sh9gkHhx2lWg8KFaAAqwmVbxiBSedgqsjGUn0YY9k+VLogfCO6odIFOxNPNhMO7CiVGgLbn0nxzu9x2C0oVDo0PuGofSMwlmRQnbsafXAyNnMVGqWDjolhhHYeQ4VRQ0qHRBLi4475OjaG1Vr/KMpjbWttJPEkhBCNtGzVOo/tRqOJFavXM/rMoS0ckRBCCCHuueN6br/vKddqYAA+eh333nF9k53joXtvYfy4kcyb/zdarZYJ541u8pXzlEplg1cwa6isrBxuu+cJDEYTANU1NXzx9QwOFxTx9CN3Nem5WrOlK9bVO2Vy1ZqNx008bdi83WP75m07sdlsqFTuU9xSUxLZtmM37dvFsitjn+vcGo2a0JBgRp917HvGlavXA2CyOaex9YopACA/v5CeZ93hMenk7F/D5OsvJzsnj7zDBURGhB03yQWQ/5+C5DqV1ZXwqrb5ERTZDmNJBobCbRTZzChUWkJCw1HlVLjVOzLZ1OhD00nt2Bm7JYHSPbPAVEH5gQUAKNTOfZUKJb6R3bGZq7BU5YLNjMYvmuDkc13PrSZ/gzMZpFQT3uVqwEHRjmkYCrficNhBoUKh0qDCH11QApbKHMzVeQSnjKMie4nzfEoNDrsFh92GQqXFL6YflTnLcRjKsBqKseiCMZTuBYcDpUqLxi/KucKdw47NXInDbsNSU4guuANWQxFa/1iSfHII6e058dcUunZOIyI8lMKikjrbzhjav1nO2Rwk8SSEEI3kd4wRTZ6GWQshhBCi+XVOT+Wbz95k6rSfyTp4iHZxsVx35UVNPhohPS2F9LRTa+W/z7/5wZV0OtqCRcu4+7brTmpFvuZUUVHFodw82reLw9/f96T7CwsLrndbaHDQcY+vr5aXw+HwmNAa2LcXa9dvAZyJhLy8AowmEyPPHMpN11ziVmzdk6qqasCZAOoU8W8CwmqzUZ65kNC0C46Z+IiPizmhxGh0lPuKgCE+Rlch7YCkCYQnxlO860fnynSF29D4x6ALSmD4xIcxLl7Lpi07USoV9OzembGjnVNH9x0sZMduJZGOg4SFhRAcGIA+JBVD4VZXAkql8UUZmICpIgtrTSHmqkNo/aMACIgfhqEkA4VCSdn+eThsZiyVOSjUPijsNrA7a08pVBo0uigUSg1KlZayvXMxVWShUCjxCe+MpaYIhVKJ1VhK4ebPUGkCsZmrUChUztXyLFWAHaUmAJXGeT9fc3gDdpsFpUqLPrgDSo0vQUlXugqh11fcvak8/tD/eOSpVzAe9bPbo1s611w2sdnO2dQk8SSEEI104fhzWLJ8dZ0bjKjIcAb26+WlqIQQQggRHxfDEw/+z9thtDqZWYc8tlssFvbsz6Rfr5NfBt5qtbJu41Z27NqDVqulf58epKUkNaovu93Os6+8x59/LcdisaDVahh79oiTLhI/7uwRfPTZt3UKNmu1Gi6eePxV+rp3TfdYO6lzeqrH4uQ6nZY7b7mW1es2sWdfJv5+vgzs36vBydDUlCS2bt7gtpLazsJQBqVasZsrKd0zq97pdo3RrXMaMdGR5B12jqyqrWEUFJlKSpozwRKcPBa7qdI5zU0fQmDCSLT+4Vw0YSwXTRjr1t/seQtZsXwpPWMKyVdbyS8oIiYmkhRdID4R3TGWZuCwGlHpAglMGEnZ/t+xVOVhLN6FT2hHVFp/58p2cROpOjALdfk2sJlBqUap9kGp1KJQqlCoda7V8lRafxw2M7YjxcR9IroSknoBFVkLsdYUYyzbj81SicNmRqULRKHUoNIFYSqrRqHUAEqshhLnCCmbCewWtIGJKDW+OGwWind8h9o3Al1QYrMmnQAG9+/Nr99/wk+//kFxSQkD+/bizOGDmvWcTU0ST0II0UgD+/Xihqsv5uvvZ7qKRUaEh/LKsw97OTIhhBCidSspLePN979k7fpNKBQKBvbvzQN33twko1lE/eJioti9Z3+ddpVKRWITjAiz2+18OmU6GUedY92GLZw/diQjRxx7+pon73z0FfPmL3Y9NpstzPrtT0KCArn95qsbHader+eNFx/jqRfediVXQkODeez+24kIDzvu8aNGDGHP3gNkZee42kJDgpl4/tn1HqPTaTljSH/OGHLi06PGjDqDkkMbXUmnTXkROFS+pA+5EJVhbZOPulGpVNwx+RrmzV/Mlu27UCqUpPcc6CosbzNXUZG1CIVKg1oVAkBF1iKPya/8giJX0unopJnRWkJURAH6miIUCiVqn1DX8WHpF7tW6zNXZGNQxfD1tF/IPHiIDqGlDGxfQEBgMP5BoSg1vqh9QgFwWI34RnanpmALhsJtqH3CUeud8SlUWlRaP0JSJ1C6ZxZ6wFSZg91mwG6uRKULQqULOlK4XIFveBfA4SworlCi0gYANvzbDaV4xzTs5iqsgG/nK5vkmh9PaEgwk6+/rEXO1RwUZrOp/vVATxNWq5Vh465j2dwpJ7Q8phBCNERZeQXLVq4lJCiQwQP7oFQqvR2SEEKcNLl/Es3Fbrcz6erbOZRz2K09JTmBaV++46Wo2oZdGXu58Y5H66yuNmLYQF597pGT7n/Tlh18/MV35OTlU1lZhVarJSY6gsjwMJ55/B78fE8ssXj2hGs8rvgWER7K3J++POl4AbbtyMBisdCjW6cTuoez2+3s2LWX7JxcwkND6dm9ExqNptFxlJSWkZl1iKDAAJKT2tcpKF9eUcn6FXPIyrcSGh7D0MF9CQsNwWauavapXkezmatcSaHaEUq1U85UusA6yacly1ezavHPdAwvdSXNTDY1OpWVi4f5ER8ViC4okYD4YW7HHf28XnvnU3JyD7tqTYX6GDDZ1XTv2hWdRkFYp8tQaf0wV2SjDXROA7RUHUYX1N5jfDZzFaUZM7HUFGIxFKHAWf9J4x+LsXQvDocdtT4IlSYAAHN1HnZLDb6R3VEqNVhNZdgtNWj9YghKGt1i1/5UJncRQghxkoKDAjl/7EhvhyGEEEKcEmbPW1Qn6QSwd38Wi5esOuWmkJxK0tNSeOX/HuKDz75hf2Y2Pj56Rp05hEfuvbVJ+t+weTtbt+/GYrUCUGMwUlZegdlsYX9mNt06dzyh/iqP1DZqaHtjdO2c1qjjlEolXTunNfr4o82cM5+lK9a4yjfExkQx+frL3WpuBQUGcNbYuqsPqrT+rsRHZVU1RUUlhIeHNlu9UXNFdp0kU+0oIk8jr3x89K6peqUGPSabMwVhsqmxBA8lsH2ox8RN7fPKys5xSzrp1VZKDD7sLAwlIrqa5HahVGQtJCR1Aj7hnTAU7cRhNaILau8xPkPJbozFu7AaSzFXHsJuNaL2CUPrH4vNUoPNXAV2C1abCbumGn14JxQ1BShVOowle1CqdWC3EZw2AZ1/HD7hnbBarazdsIWdu/ee9PTS05UknoQQQgghhBAtZlfGvnq3bd+1p00knux2O/kFRYQEB6LX649/QBMaOrgfQwf3w263N/ko7f0HDrqSTkfLzsnDrxEr9CUltGPv/qw67cmJ7RsVX2s0d/5ffPXtT1itVgID/ImMCCM3L5/pP83h1hsbNo3Lbrczc858Vq3ZiNVqRaVSMah/by4cf06DXuOy8gpUKtVxk1Vms4W9BUoU9mQ6JPR3jVCqTe54GnnVvUs6M30WcNi9nBY6nY4ePXvjc5z3RXV1DeBe4Lx21FS2uSOpOpNbwqv2/ErfGDZs3c+uPfvw0evp13MAYWHlGIt3YTNVoPGPQe0TRnX+JqzGUrDbQAEKHNhxgNoXn7BOmMoOgEJxZDU8KzhsqLQBmCuyCYgdWO/00vHnjiI1JYkly1dTVFRCXGw0I4YNaNBUztORJJ6EEEIIIYQQLSaxff21hJISj7/U+6nuux9+5evvf6G0rAKtVsPI4YN58uE7W3xKa3OUBtDptSiVCux292ouarWa4OATXzHvjslX8+ATL2M9Kpml1Wq445bG13dqTdZv3Mq7H02htMw5nbCouJSCohK6pKeye89+KquqGzRyafHSVSxbuRaAmhoDVdU1zJ3/F4EB/pw9cli9xx08lMuPM+eRfSgXhUJBx9RkLrvofI+v1Y5de/hm+q8YDAYAfHy2cOUlE1wjvo4eeXU0vV7HTddcwjfTZ7qmTQYFBnDlpRfg24BkZGJCOzRqtcdRUykp6YSkJtZJeGmCU/nkq+/Zs/eAq23FP+u48rzuxPsePeWuGmPZPuzWGqzmcnRBiSjUemymSpRqLQ6bCYfDht1ciVLtjNU3soergHnpnllkmzq6JZ1qTftxNn6+vtTOmMw8eIj1m7Zxz+3XEx0VcdznfbqRxJMQQgghhBCixVxw3mimTvuF4pJSt/aY6EjGjh7hnaBayB8Ll/Dux1NdU6rMZgu/zV+MxWLhxWce8nJ0Jy8+Noa0lGT2Zx7EbHbWkfL396Nr57QTru8EMGRgXz5++3m++vYncvPyiW8Xww1XX0zn9NSmDr3FWa1WZv62AJvN7tZeVVVNQVExMVERWC11R495snL1BhwOB3v3Z1FYVOJqf+ejrxjYvxeBAXVXu6uuqeGjz79zJZIcDge7Mvbx8ZfTePjeW9xqTFXX1DDlu58xm82uNoPBwNTvfuLpx+7B38/9tTWbLSxdsYat23ehVqvp3bMrTz58J5lZh3DgIDmxvVvis6ysgkVLVrA/M5vAAH+GDupHl07O19jXx4dxY87i198WuJJP4Fzpr0e3TqhUqjoJr42bd7glnWqf388LM3j01jH4hSah0vpjrshG4xuFw2ZG7ROGxiecoKSzKc9ciKFwG3abCZU2CLvNjFLtgy4wwTV9r3bqXm7mZo+vyf7MbNrFRhMaEuRqMxqNLFi0jGuuuNDjMaczSTwJIYQQQgghWoxer+fjd57nxdc+ZMv2XSiAXj278sSDd5z2C3RM/+k3V9LJYrVSWVmFxWJl+s+/ERMdyVWXTiTkqA+qp5ozhvRn87adhAQHUV1T40wK6HUMHzoAnU7bqD67d03nrZefOKm4SkrLeOmNj1i9bhM2m52e3TvzyH23Eh8Xc1L9noycvHyqqqoJCQmiotJ9Hlp5eQV9e3Vr8HuhqrqGvPxCt6QTQHFJGT/P+p3rr7q4zjHrNm51JZ2Odji/gD37Mt1qFG3Ztsst6VTLYrWyeetOhgzs42qz2+188uU09h34d4rkvgNZZGXncMXF4+v0UVZewZvvf0FFZSUAOcDO3Xu5eOI4V78jhg2kXVw0q9dtxmQy0aljCv16d0elUgHOpNLRibJdezxP5zUYDORXB5Aa7UxgaQPjUSjV6IM7oFA6UyMV2UtxABr/GFQaf6yGYhQo0AbEoVBpXKv31U4tpPDwkaj/5XA4qKqqRqNW1Ylhf+ZBj7Gd7iTxJIQQQgghhGhRCfFxfPLuC9jtztEe3ko4GY1G1m3YSmBgAN27pjf7+WpHedntdsrKyl1T0mw2O1u3Z/DRF9/x6P231VnR7FSRnNSe666axG+//0VhkbOOz+ABfThvzJlejeuWux4nK/vf5MDa9ZuZfOdjzJz2UaNqbM35fRGz5/1JdbWB3j27ctO1l7oVAm8IP1/n1K3oqAhKS8vdkk8+ej0XX3Bug/vqmJrE2vV1R94EBwWwdftuzGYLWq37intlZZX19lde7r7N/J9VEI/23xUSt+/a45Z0qrVm3SbOOmNQnWlmS1escSWdjvb7n38zoG8P1xTUlOREUpITXdsNBiO/zJ7Puo1bsdlsdO2cxvhzRxEWGoKvT/2vae30vtrV+ZQqrWt1vrL9v2Mo3AaALjSN2p9CpVqPUuOHQqXFZqqgdM8sVzHz/n0iWbx0ldt0UIVCQUhwEAEeRpqd6PvkdCGJJyGEEEIIIYRXeHOE07QZs/h0ynRqapyjPtq3i+W15x8lKTG+2c7ZIak9+QVFGE0mtzpIOq0GHx89BYVF7MrYR6eOKc0WQ3Pr0bUT3bukU11jQK/Ttnjtqv9avGSVW9KpVnFJKb/Mns8Vl0w4of5ef/czZvwy1/V47/4slq9ax7Qv3sbXt+EF1MPDQknpkMjefZl06ZRGSWkZlVVVaLVannz4ThITGl7v7Nyzz2T6T3Pc2tRqFe3bxWG3210J3qMlJ8WzeGndvhQKBYkJ7nXYunZK49c5C1yj9Y7et3ZKXK3MrEP1xpmVnVMn8ZR1sO5rA84phyWl5URGeC7G/fnUH9wSXJu37uRgdi6P3Hcb/fv2ZPmqdXXijW8XS1xsFOB5dT6/iB6uxJPGLwprVR4+EV0BcFiNzu0lO92KmUeEh3HtlRfx08x5lFc4E2iJ7dsxeGAfVv6zvk7cwwb3q/f6nM4k8SSEEEIIIYRoUzZv3cG7H09xS/4cPJTLfY89z8xpnzTbeW+98Qo2bN5OVVW1W3taSrKrCHFtoenGqi3g7M2RFQqFok7dH2/J2Heg3m37s7JPqK+iI8mq/8rNy+fbH2Yx+frLTqi/qy+byNfTfmHfgSzCQoNpFxfD+WNH0q1zxxPqJzoqgsnXX8G0H2dRU2PEx0dPdFQEOq2G1JQk9HpdnWO6pKfSITmBff9ZNXBQ/951Vl4LCw1h7OgRzFuw2K39nFFn1Nk3JLj+6YGettVXdF6tVuPv7/k9lJl1yOOoqtKycjZs3sag/r25bNL5/Prbn67phPHtYrn+qkmufWvrQmkD412r8/nF9AbAoXDgH90HQ9FOtIHORHRtokkfllanmHm3zh3pkp7KodzD6HU6IiPCcDgc6HU6lq9ah9lsxs/Pl9FnDqVPr271Xp/TmSSehBBCCCGEEG3KD7/MrbPyGkBObj5rN26hX6/uzXLe9LQUPn33hSP1hjaj1+lISU6gXVy0a5+kExjpcrR9B7L4v5feZfeRFbY6pibz5MP/I7VD0nGOPL0da/RYSlLCCfW1YeM2tylVR9u0dfsJ9QXO1d3uvPVa8guKqK6uoV1cTJ0pcQ113pizyD6U6za6K8Dfn4vGj6mzr8PhYP3GbWjUajQa5/kSE9rRr3d3+vX2/N4/e+Qw0jt2YNOWHQD06NaJhPi6K1T26dmVPxYuqZNcjYmOJLVDYp39hw3ux8bN2+uMyurXu3u9q979t5bV0QoKndsG9O1J7x5dyMrOwUfvQ1xsFBl7D/Dt9F85eCiX0JBgRgwbyOAB7tPhapNPgFtyqfb/61u9T6lU0r5drOuxQqFg/LmjOGfkGVRVVxMUGOD10X/e1HafuRBCCCGEEKJNKq+oqndbaUl5s547PS2FKR+/zoeff1tn5a1+fXoQEx15wn0ajUZuv/dJt9FSuzL2ces9TzJj6ruEhYaedNwAeYcLePfjKWzcsgOtRsPI4YO5Y/LVrfoD9RlD+tMhqT37DrgXdY6MCOPC8eecUF//nSZ2tNCQkEbFBxAVGd7oY2vp9Truvv16tu7YTdbBHMJCQ+jTs6vH0U7fzZjFug1b3NoCA/zp36fHMc/Rvl2sW3LFEx8fPbfdeBU//TqPA1nZKBQKOnVM4ZILx3msXZbYvh3XXTWJ2fMWUlRUgkajYWC/nkwYN7rec8TE1P8zEnfUNo1G46oLdSAzm4+/+M6V4CooLGLGL79htVo5Y0j/Yz6nk6HTaRtdWP900np/QwghhBBCCCFEM+jfp4fHYsxarYaB/Xo1+/kVCgWTr7uM5avWsWXbLpQqJX16dmNQ/8ade/bvf7klnaxWGxWVleQXFHHdrQ9x7tlnculF4wgNCW50zFVVNdx4x8MUFZe62r6bMYt9Bw7y7mtPN7rflvDJOy/y6lsfs2rtRmx2O316dOWBu29Gqz2xhED3rukkJ8azP9N9ip5KpeKKi89vypAbRalU0qNrJ3p0rTsip9bBQ7l1kk4A6zduZfjQAcdNLDVEXGwUd99+PZVV1ahUynpHLtXq3iWdbp07UllVjY9e5xqFVZ92sdF07dyRbTt2u7VHR0XSs3tnj8f8tXSVx1pXi/5ewbDB/U7Zgv6nCkk8CSGEEEIIIdqUSy88l98XLHZLICgUCq685AICA+uuRNUcNBoNZ54xiDPPGHTSfR06lOv6f4fDQWlZuetDdnV1Dbv37OOjz50r5jW2oPv3P892SzrVWr1uE7v37KdjanLjgm8BgYH+PP/0A03S11svP8kjT7/Krox9OBwOQoIDuWPyNXROTz3+wa3A/v+M/Dra3n2ZTZJ4qhXg79fgfRUKBYEeVoGrz3VXXsTCv1ewbuNWrBYr3bp05JxRw+sdfXc4v9Bje3lFJQaj8bjJMXFyJPEkhBBCCCGEaFP0ej1TPn6N72bM5p+1G/D38+ei8ecw9BRdcapX9y5M//k3AIxGk9vIjtDQYAAKi4rZlbGv0QmS3Rn7PbY7HA62bN/VqhNPTSkmOpKpn7xOTl4+5eUVpKd18OrqjCfqWEXfTyTx05QcDgdLV65hzbrNmExmOnXswOizhh0zHrVazZhRwxkzaniDzhEbE0lhUXGd9uCgQHz0+kbHLhpGEk9CCCGEEEKINkev13PjNZdw4zWXeDuUk3bm8EF0TE1m95792I5KOgUFBdAu9t/C5SezYl7tMvSeJDayILonBoOBz6fOoKKykkkXnNtqE1pxMVHExdR/TeqzYNEy5s5fjNliZviQAVxy4bgWTVz16NaJWXMXUlnlXucswN+f7seYotcQNpsNq9V2wjWNfpw5j5Wr17seL1tZws6MfTx41+Qmq4901vDBbN+5p05x+NFnDTuhaXaFRcVUVdWQlBjfJHG1Fa0i8VRWXonZbAacGcfjVfJ3OByUlVcSGOCPSnXqZJeFEEIIIYQQojl89t6LvP/pN8xftIycnDyiIsPp0sl9dFNi+8YniK669AJmzV1ITY3BrT21Q2KTrQI4a+6fPPTkyxiNJgA+nzqD88eObPU1pBrqhdc/YNZvf7oer9+4jcVL/+GTd19osRg0Gg233ngl036cRU7uYQDiYqO54uIJjVpRb9OWHSxbuYZ1G7dSWVVDVGQYaR2SmDBuNMlJ7Y97fFlZBf+s3VinvaiohLUbtjB0UN8TjsmThPg4brvxSub/tYyD2bmEhQYzYuhA+vVp2Hs3v6CIx599jW07MrDbHUSEh3L3bddz9shhTRLf6U5hNpvqriPawp566X02bdtNaVkFrz5zH4P61V9NP2NfFo8++zZl5ZXo9Tqee+x/9O5+7Mys1Wpl2LjrWDZ3SqtecUEIIYQQorWQ+ychTk0Oh8Pjinl9enXj6ssmnlTfm7fu4LV3PmPPvkxUSiX9+/bgqUfuOqmi5bVsNhtdB4ypk9gCePvVp5h43tknfQ5vysrO4dJr/4fdXvfj9/NP3u+VBEbt1LOI8LBGHf/XkpXMnreQjL0HXPW/tFoNPbqm4+vry8P33kJ42LFXVNy2I4PPp073uG1Av15cPsn7RdsBLrvuzjpF5dUqFVM/fZ3UDkleiurU0SqGCz376P+Y/d17pDQgI/rmB1MZP/ZMFv36OXdNvpJX3vkCh8PruTMhhBBCCCGE8LraFfMmjBtNUkI8HZISuHjiOK68ZMJJ992jW2e+/fwtlvz+PUvn/8DbrzzVJEkngOk/zfGYdAL4bvqvTXIOb1q6fLXHpBPAslVrWzgap4jwsEYnnUwmM/MXLcNktrgVnTebLRzOL8JisbBi1fpj9OAUHhZS/7bQ+re1pI2bt9dJOgFYbTam/TjHCxGdek6pr6/KK6rYtmsfb734EADnnDWY9z+bxr7MQ6QkyRxLIYQQQgghhGjKFfM80TdDMWajyVTvNrPZ0uTna2nBwUH1bgsKDGiRGKb/NIfZ8xZSVV1D185p3DH5mkbVqQIoKCrGZDJhNpnrbKuqrgaguLTuKoj/FR0VQaeOKezcvdet3dfXhwH9ejYqtqaWezi/3m3FHlZ6FHU1+4in0rIKCgqLPf6z2ezH7+AohcWlBAX6u1Wdj4oMo7CopKnDFkIIIYQQQgjRQiZNOBeNxvO4iDFnN2zlstZs7OjhHhNMapWKiyee2+znf/nNj3nz/S/Yuz+Lw/mFLFy8ghtue9DjSm8NERQYgFKpxMdHj1LpXpxbp3UWBI+Pi21QX9decRGDB/RBo9GgUChITUnijpuvadAqe4VFxbzw+gdcfv1d3Hr3EyxasuLEn8xx9O3VHZVK5XFb184dm/x8p6NmH/H0yrtfsmP3Po/bvnrvOcKOLO/ZEGqVEpvN5tZmtdpQq+u+CX6a/Sc/z3EWbpOpeEIIIYQQQgjRegUFBXDLjVfywSdfu31+65yeyi3XX97k5yspKcff3wettmlWTTsetVrNq889whPPveEaOBHg78c9d9xAQnxck5/PZrOxdUcGJSWlBAYGMOf3RXX2KS2rYMp3P/Pg3ZNPuP/AAH969+zKug1biI2J4lCOs1C5UqEgKiqCkOAgBg3o3aC+9Hodl1w4jkkXjMVutze4rmBRSSnXTH6A4pJ/Rx1t3LKdydfnNOlqlVGR4Ywbcyaz5y50a4+JjuSKi8c32XlOZ82eeHr5qXuarK/IiDCqawyUlJYTGhKE1WolJ6+A2OjIOvtOGj+aSeNHA/8WxxRCCCGEEEII0To9eNfNnHXGQD798nsqqqo59+zhXH3ZhU16jh9+/o3X3vmUwqIS1GoVvXt25fP3XiYoqPmnu/Xq0YW5P33J2o1bMBlMDOzfq1kWbygrq+D9z76m6EiCK+9wAQWFRQQHBaJQuI9O2r3H8yCRhrj0wnHodTpWr9uEXqejxmAkKSGeIQP7MGrEEPz9fE+oP6VSiVLZ8ElZX06d4ZZ0Auegk2+mz+TKS8Y36ZTQJx78H2kpyfz2+yIMBiN9enXj1huvwN//xJ5jW9UqajxVVlVjMBixWm2UV1RSUFhMRHgoCoWC0rIKfPQ69Hodvj56hg3qwzuffMc1l53P3PlL6ZDYjriYuoknIYQQQgghhBCnlj49u/HJu92ape+Vq9fzyNOvYrc7S75YrTbWrNvMpdfdyR8zpzTLOT3p16t7o48tKSlnx+4MOndMIzTUc92oX+b84Uo6Afj7+WI2W6iuMdRJBjW2uDg4a4lNumAsE8aNwmyx4OfbskmYrTt2e2yvqTGwdWfGSV1nTy6ZeC6XtMC0yNNRq1jVbtrP87jpnmcor6zkwy9/4KZ7nsF0pEjZK+9+yYo1m1z7PvC/a7HZbDz8zFtkHcrjqYdu9VLUQgghhBBCCCFOFW9/+JUr6XS0XRl72fGf4tatjc1m4/rbHqTfiPFceeO99Bsxnutve9BDKRor23ZkuLUFBPgTHhaC6T8F3NUqFVdfNvGkY9NoNC2edAIIqadgu0KhIDoyooWjEcfSKkY83XLtxdxy7cUet/13ql5ocBDPP35nC0QlhBBCCCGEEOJ0kV9Q5LHd4YAdOzLo3DGlhSNquAcef5G/lqxyPbZabfy1ZBUPPP4ib7385HGPH9C3Bzt278NsMmO12YiOiuB/k6+hc3pqc4bdrC69aByr122qU9O5e9eOxMfFeCkq4UmrSDwJIYQQQgghxKli7frNvPPxVAoLi+mcnsLD990qIyxOAanJCWRmHarTrlQqGdC/lxciargFfy2rp32522O1Wk3Xzmls2bbLrV2r1fLIfbdyxuD+VFZVExUZ3myxtpQhA/ty923X8cXXM6isqkapVNC9SydefvYhb4cm/kMST0IIIYQQQgjRQN//OJvHn33DNcVpV8Y+Fvy1nFnff0JKh0TvBieO6ZH7bmPZqnUYje5TzoYN7tfqR8gYDKZ62o112i48fwx5hwspLCp2taWmJDFy+GA0Gg2+vj7NFmdLu+KSCVxy4Th27NpLRHgoMR4WHhPeJ4knIYQQQgghhGigV9/+tE5dnaqqap584S2+//IdL0UlGiKlQyI/THmPp194m70HMvHR6xkzejjPP3m/t0M7rpjoSA7l5NVpj/Ww0FZwcCCP3n8bW3fspqSkjHZxMaSexklRtVpN967p3g5DHIMknoQQQgghhBCiAfZnZlNSWuZx245de1o2GNEoPbt3ZtYPnzZ5v/sOZJGTm0/Pbp0JDPRv8v4fumcy9z7yvFvSU6VS8eDdkz3ur1Qq6dG1U5PHIURjSOJJCCGEEEIIIRogODAQpVLpcWU0vU7XIjHsytjLOx9NYXfGAfz9fRk7egS33XRli5xb1FVUUsoDj73Izt17cTgcaLUaLpk4jrtuu65JzzNh3GiCgwJ5870vyMsvICYqkvvuvJHhQwc06XlaQk2NAa1Wg1ot6Yi2Ql5pIYQQQgghhGiA0NAgOqYms3P33jrbzj37zGY/f1Z2Drfe8yQ1NQYAqqqr+erbH8kvLOKZR+9u9vOLuh5/5nW30W5ms4Vvf/iVpMR4zh87sknPNXzogFMy0VRr4+btvPbOp+w7cBCVUkm/Pj14+tG7CA0J9nZoopkpvR2AEEIIIYQQQpwqpnzyGu3jY12PlUolw4b05+lH72r2c3/17U+upNPRFixaVu8UQNF8CouK2bxth8dtv85Z0MLRtG55hwu45+Fn2bs/C4fDgdVmY9WaDdz14P95OzTRAmTEkxBCCCGEEEI0UHRkBMvmz2Dl6vXsztjP8GEDSU6Mb5Fz7z9w0GO71WolY+8BBvbr1SJxCKeS0nLsdofHbVXVNS0cTev2/Y+zMRjrrsyXsfcA6zduo0+vri0Sx29/LGLj5u307tWNcS0wSlE4SeJJCCGEEEIIIU7Q4AF9GDygT4ueMy42il0Z++q0q1QqkhJaJvkl/pXaIZGQ4EBKyyrqbOve7fRYZc1sNmO329Hr9SfVT05ufr3b9mcdbPbEU2FRCRdcfsu/KwNOnUH7+Fh+m/EFQUEBzXpuIVPthBBCCCFOK0aTmRqDEYfD/Vt463+WfxdCnHquveJCNBpNnfbB/XsTFRne4vHMmruA7oPGEpc2kPTeI/nos+9aPAZvUiqV3HbT1SiV7h+rI8JDueWGK7wUVdPIO1zAbfc+wfCxlzN87OVcd+uD7N6zv9H9dUzr4LFdoVDQs1vzr753x/1P/Zt0OuJgdi633vtEs59bSOJJCCGEEOK08POcPxl32R2cM+kWzrvsDkrLnd/AG4xGHnn2bUacfwNnX3QLc+Yv8XKkQojGSk9L4dXnHqZDUnsUCgW+vj6MG3MWrzz3cIvH8u0PM7nh9kc4lHMYg9FEQVEJjz/3Og899XKLx+JNF5w3mk/eeZ4RwwbSvWtHrrxkAt998TbhoSHeDq3R7HY7t9/3JOs3bsNms+FwONixaw933PcUVVWNm0J42UXnER5W95oM6t+L1A5JJxvycW3cvN1j+4ZN25r93EKm2gkhhBBCnPLWrN/Kl9/9yitP30uX9A4oFArXtum//EFVdQ3zf/qYrOxc7n70FQb27U6Ehw8AQojWb8jAvgwZ2Be73V5npE1LeubFd+qMrAT4etovvPrsI16IyHt6dOtMj26dvR1Gk1m4eIXHqXEVlVXM+HUuN1x18Qn3GRjozxcfvMJ7n0xlw+bt6LRaRg4fzO03X9UUIR+X3W732G6T0cAtQhJPQgghhBCnuB9+nc8VF51Ll/S6UxkWL1vDLdddjJ+vD507dqBf764sXbmOi84f7YVIhRBNxZtJJ4DyikqP7WazhaKiIsLDW37qn2gamQdz6t2WfSiv3m3HExMdyYtPP9jo409GSnKix/poHVOSvRBN2yNT7YQQQgghWjGLxUqNwejxX23dpqzsXAqLSzln0q2cNeEmnnv9E6xWKwAFRSXExUS6+ouLjiS/sMQrz0UIcfpQq1Ue2xUKBUFBQS0cjWhK3bt2rHdb5/SUFoyk6bz+3KP4+fq4tfn7+fKyF6aptkUy4kkIIYQQohX7ZsYcvp3xm8dtj917M6NGDATg4KE8Zn7zNhaLlXsee4Vf5y1m0vjRKBVKjp4NY3fY0Sg83wL+NPtPfp7zJ4DHKTRCCFFryIB+/LV0ZZ32pMR2Hgugi1PHwH696NIpje07M9za42KjmHje2V6K6uR065bOkj9+4K0PvmDv/izSUpK4746bCA2VJGlLkMSTEEIIIUQrdsOVE7nhyonH3Cc6KpwzBvdxfZs7qH8PMg/mHtkWRtahXJIS4gDIPnSYwQN6euxn0vjRTBrvnIJntVoZNu66pnkSQojTzk/ffsAZYy5h+869rkR1XGwUS3+f7uXIRFP46K1nefejqSxduQabzU7/Pt25+/brUatP3RRCRHio16b6tXWn7rtGCCGEEEIAMHbkUGb/8Tf9enXFZDLz19I1XH/FBABGjxjE19PnkJLUngMHc9i4dReP3XezlyMWQpwOlv4xg7y8QuYuWMwZQ/qRltL8q5OJlqHX63no3lt46N5bvB2KOA1I4kkIIYQQ4hQ3dtRQDhzM4db7nkWr1XDu6GGMGTkUgEnjz+bgocNMvuf/CAjw44kHJhMcFODliIUQp4uYmAhuuvYSb4chhGjFFGaz6bSfwF87VHzZ3Cmn9NBAIYQQQoiWIvdPQohT2V9LVvHeJ1MpKCwmKTGeh++9hW6d6y+aLYRoPnIXIYRoFt/98CsZ+zIZMqAPZ48c5u1whBBCCCFEG/HN9F946vm3sdvtABzKyWPN2k1898Vb9OvTw8vRnZi5CxazZt1munVKY9LEc70djhCNIoknIUST2p+ZzaSrb6e4uBSAKd/+RGqHRObM+AwfH5/jHC2EEEIIIcTJeeuDr1xJp1oms5n/e+ldfvvpCy9FdWLKyys5/9KbyDqY42p79Z1P+fX7T4iNifJiZEKcOKW3AxBCnF5uvedxV9Kp1p59mTzw+EteikgIIYQQQrQVJSXlde5Fax3Iym7haBrvnkeedUs6AeQXFHHr3U94KSIhGk8ST0KIJlNVVcWevZkety3/Z13LBiOEEEIIIdocf3+feuvS+Z5Co+/XrNvssX3bzowWjkSIkyeJJyFEk7HZHHWGNf+7zdbC0QghhBBCiLZGq9XWW8fpvLEjWziaxrPZPN9T2+12ua8WpxxJPAkhmkxQUADt4mI8buvWJb2FoxFCCCGEEG3R5++9QI+unVAqnR931WoVZ581jKcfvcvLkTVcp/QUj+3JSfGoVKoWjkaIkyPFxYUQTeqVZx/ixjsewWg0udpCQ4J59blHvBiVEEIIIYRoK/z9/Zk94zOysg+xbece+vfuQUR4qLfDOiFvvPg4F1w2mfKKSlebr68Prz3/qBejEqJxJPEkhGhSQwf1Y8kf03nngylkZh+iZ9dO3HXbtbKinRBCCCGEaFEJ8e1IiG/n7TAaJTkxnpULf+Sdj6awbeceOiS15747biI0NMjboQlxwiTxJIRoctGREbz0fw96OwwhhBBCCCFOWf7+/jz+4P+8HYYQJ01qPAkhhBBCCCGEEEKIZiGJJyGEEEIIIYQQQgjRLCTxJIQQQgghhBBCCCGahSSehBBCCCGEEEIIIUSzkMSTEEIIIYQQQgghhGgWkngSQgghhBBCCCGEEM1CEk9CCCGEEEIIIYQQollI4kkIIYQQQgghhBBCNAtJPAkhhBBCCCGEEEKIZiGJJyGEEEIIIYQQQgjRLCTxJIQQQgghhBBCCCGahSSehBBCCCGEEEIIIUSzUHs7gJbgcDgAsFqtXo5ECCGEEKcilUqFQqHwdhgtSu6fhBBCCNFYR987tYnEk81mA+DMCTd5ORIhhBBCnIqWzZ2CWt0mbptc5P5JCCGEEI119L2Twmw2ObwcT7Oz2+2YzeY2+W1lQ1x166N8+/FL3g7jlCfXsWnIdWwach2bhlzHpnE6XMe2eA9xOtw/nQ7vvZYg16nh5Fo1jFynhpHr1DBynRqmtV2nNjfiSalUotfrvR1Gq6VQKNrct7jNQa5j05Dr2DTkOjYNuY5NQ67jqel0uH+S917DyHVqOLlWDSPXqWHkOjWMXKeGac3XSYqLCyGEEEIIIYQQQohmIYknwUXnj/Z2CKcFuY5NQ65j05Dr2DTkOjYNuY7CW+S91zBynRpOrlXDyHVqGLlODSPXqWFa83VqEzWehBBCCCGEEEIIIUTLkxFPQgghhBBCCCGEEKJZSOJJCCGEEEIIIYQQQjSL1lnyXDS7h//vLUwmMwAvPXU3PsdYtebBp9/AYrG6Ht98zSS6pHdo9hhbO5PZzMPPvAWARqPmtf+7/5j75x4u4JsffqOkrJxhA3tz3jnDWyLMU8KBrBym/TyPqqoaRo0YyMgzBnjcr6i4lOff+NSt7akHbyU0JKglwmxVLBYr3/00l52799O+XQzXXT4ePz/fk963rTEYjXzzw2/s3X+Q1A4JXH3p+eh1Wo/7vvb+FHJy812PJ4w9kzOH9W+pUFu1/3v1I0rLKoDj/0yWV1QxdfpscnLz6dY5lcsuGotapWqpUEUb4nA4mLtgKSvXbqamxkD/3t24YtK5AJSUljN1+iwO5xfTp2dnLp5wtmvJ57Zq+T8b+Wn2Ah6883riYiIBqK6uYeoPc8g6mEuntGSuvHgcGk3b/PiQd7iQH2ct4HBhMd07p3HheSPRajWA857wux/nsntPJontY7n28gn4+pzaK0I2hs1m58dZC9i4ZSfRUeFcd/kEQoIDvR1Wq7B+0w5+X7gMm93BWWf0Z9jA3q5teflFfPPDHIpLyxgyoBfjx4zwXqCtxO8LlzP/rxU8++j/CAzwA6CkrJyp38/mcH4Rvbp34pILzkapbLvjaJasXMeiJauprKqmQ1I8/7vpcgCqawx8PX02mQdz6ZiayFUXn+f6XeVNbfeVauMuOn80l1xwDqvXb8VqtR1z3zXrt3HR+NFcOnEMl04cQ2x0RAtF2bqpVWounTiGMSOHsGb9tmPua7XZuPPhl1CpVJw5tB9ffDeT+X+tbKFIWzeD0cgdD75ASHAgQwf24s0Pv+afdVvq2dfEvgPZrvfipRPH4Ovr08IRtw4ffjmdFas3MmrEQA4eyuO5/yTkGrtvW/Pqu1PYtnMvo88cxJbtGbz90Tf17rt1ewbDBvV2vffSU5NaMNLW7fxzhnPpxDFs3LrL9aVGfZ566X3yDhcycvgAFi9fy5ffzmyhKEVb89GXP/DNjN8Y0r8nl04cQ/8+3VzbHnjqDSoqqznrjAHM/uNvpv/yuxcj9b6y8kq+/mE2u/dmUl1jcLU/98an7DuQzagRA/ln3WY+/HK6F6P0norKah546g0iIkI5a1h//li0nLeO+nvx7iffsXbDNkaNGMjeA9m8/PYXXozWe76ZMYffFizhrDMGUFFZxWPPvePtkFqFJSvXMeX7WfTq3olunVN57rVP+HvFWsCZrLvrkZdQKODMof34evps5v25zMsRe1dOXgFzFyxl3cbtWCwWV/vDz7xNWXklI4cPYN6fS5n20zwvRuldM+cu4vX3ptCzWzqXXHAOZw7994vQF9/8jIx9WYwaMZB1m7bz/uffezHSf7XNrywE/Xt3xWo7dsLpv/vrtJ5HAbRVKpWSQf16UFBYfNx9/1m7Bb1exwP/uxYAnVbLtJ9/55yzBjd3mK3eX0vXkJQQx+03XAqA2WLh5zl/MrBvd4/763RaBvXr0ZIhtjpWq5XZf/zNl+8+S0J8LMMG9mbcZXdQUFhMZERYo/dtayoqq/lr2WpmfvM2ocFB9OnZmYuuuY87b7683hFh3TqnkdYhoYUjbf169+gMgOo43zxm5xxm2669zPvhQ3RaLSlJ7bntgee5+ZqL2vxoE9G0ikvK+HHWn3z7yUuu0Tu1dmbsJ7+wmM/efgaVSkl0VDjPvvoRl190rpei9b43PpjK7TdcylMvf+BqKywu5Z91W/jt+/fx9/Ola3oKV932KLddf2mr+Pa8JfnodXzx7v+h1+sACA0O5OV3vgTAaDQx78/lTPv0ZWKiIxjUrwfjr7iTkrJyQoPb1ojsX35byHOP3UmPLmmMGj6Q8Vfeyd79B0lJbu/t0LyqT4/ODB/c1/X4cH4hGzbvZMSQfqzZsBWVSsWDd14PgK+PD1O+n8W5o4d5K1yvcjgcvPbeV9x3+zVcc9tjrvaMfVkcyj3MR288gVqlIi4mkseff4+rLjnPi9F6h8Vi5bOpP/PCk3fRq1u627aS0nKWr97InGnvExjgR/fOaVwx+WFuv/Gyekf0txQZ8SQa5NlXP+aZVz7k94XLcThkIcQTlZWdS6ejRkekpyaRlZ3rxYhaj6zsXNLT/ntt8urdv7zC+Q3a8298yur1W1sixFansKgUHJAQHwuAXq+jfbsYDuYcPql925qcvHzCQoJdHwxCg4MIDg4gL7+o3mM+mTKDJ154jxm/zsdsttS7n/AsKzuP5IR2ri8ykhPbYTSaKC2v8HJk4nSTsS+L+LgoNm3bxSPPvs0Hn093TQfNys4lrUMCKpXzNjg9NZHcw4Vt9md64d//EBoSRM//fIDJPpRHXEwk/kcS8THREWjUagqLS70RpldpNGpX0glg07bddElPAeBwQTF6vZaYIzMC/P18iYmK4FBOvse+TlcGo5HColLSUxMB5xe0qcntyZT7XdfPEDgTK1u2Z7jeP1nZeXRyuw9OJOtQ271mM36dT+8enUhObOfWnpWdS2pygmtqfseURAqKSjAaTd4I06ty8vKxWK3U1Bh49Nl3eOODqeQeLgCcX/DFREW4pidGRYah1+saNFCiucmIp9PQ/U++hs1mr9MeEhzI0w/ddsL9vfZ/92Gz2ykoLOGLb38hv7CY6y6f0BShtlrV1TU8/sJ7Hre1bxfDfbdfc0L9GYxGdEfdsOj0WmoMxpOK8VRQUFjMi2997nFbp7RkbrnuYmoMJoIC/V3tep2OGoPB4zERYSE8++gdOBwOsrJzefLF93nygckMG9SnWeJvrWqMRnT/+dZCr9NSU1P3PXUi+7Y1BoOxzrc/ep2W6np+Nh/433VU1xioqKzih5nz2ZlxgKcfurUlQj1tGDy8H3VH3o9tbWSAODnHu9epqKwi93Ahm7buZsxZQ/hr2Wrue+I1vnr/OWoMJvS6f/8mazUalEoFBqPxtBvJ8+xrH1NSWu5x29svPkxJWTk//PoH7738aJ3t/71OcORvdI3nv9Gnsrc//pbMgzketx1dYwZg2T8bWPj3P3zw2uOA8/faf/+W6HRt4z7vaDU1RlRKpdsMCb1e1+auw/G8/fG3REWGu2Y9/Pfvok6nw2g0Ybfb21z9ouycwyxduZ53Pfw++u89m1qtRq1WUWMwuiWF24LyymqsVhtz5i9hzFlD2LBlJ7c/8ALTP3/VeT083fe3gp9DSTydhi65YAx2e92bMV0jb6aOrokQGRHKR1/NOO0TTzqdlksnjvG4zb8RRZlDg4PYeyDb9bikpJywNlAQOyDAr97rWFtsMjQkkILCEld7cWkZoSHBHo/R63WuaXaD+/fEZrfz17I1bS7xFBocREVlNRaL1VXktbi0nLDQuu+pE9m3rQkNCaK4tByHw4FCocDhcFBSWlHvz2b3Lmmu/+/WOZUrJj/CUw/eIlPETkBocBDFJWWuxyazmeqaGnk/ihN2vHud2gL3j917EwqFgqEDe3HmhBspKi4lNCSQoqPeh6XlFahUKgID/Ov0d6o775zhx6y7NvO3v6iorOKRZ98GoLy8itffn8IdN15GaEig28+r1WajrKKSsNDg5g3aC0YNH0hlVbXHbT5HfahdvGwNn3/7C++8/Mi/9zHBQZSWV2Kz2V2j6EpK28Z93tGCgwJx4HCbYljcRu53G8LhcPDGB19TUVnJ0w/d7moPDQ5ix+59rsclpeWEBAe2uaQTwPRffqesopL7n3wNALvDwdMvf8h9t19DSEiQ2+/t8ooq7DY7wUEBXorWe8JCgjCaTDx6z40EBQYwYmg/lv2zgT37DzrvbY+6Tna7nbLyylaxEJMknk5DA45KFDW1XXsOENIGfsDVanWT1hHq07MzH301g+KSMsJCg/njrxX069WlyfpvrXz0+uNex749u/D4C+9RWVVNgL8ffyxq2LWx2+1k7M1qkx9YQ4IDSWofx59/r+Lc0cPYvmsflVXVHmsonMi+bU18XAw+eh2r1m5mcP+erFq7mQB/3wYtoLAz4wDBgQGSdDpBnTomU1hUyu49B+iYmsSCxavo3LHDMVdWFcKT493rdElPQa1WcTi/iJjoCLJz8lEoFPj7+9GjS0eeffVjsnMOEx8XzfxFK+jTo/Np+fPcu3unY24/+8xBdO6Y7Hq8e28m55w1mPi4aPz9fDGbLWzcuote3dL5e/la4mIiW8UHmKbWtVPKcfeZ/9cKvp4+h3deepjwsBBXe2REKFERoSxevoZRwweycesubDYbSQlxzRlyq6NSKenVrRPzF63g8ovO5eChPPZnZdOtc9rxDz7N2e12Xnr7C0wmM08/dLsrQQnOzwjvfTaNwuJSIsJC+GPRcvr16urFaL3novNHM/So1f7WbtzG+LEjiAgPISw0mMyDOWQezCWxfSzz/1pB7x6d22SCLjY6gnaxUWQezKVH146UlJVTUVlFcFAAMVHh2B0O1m3aTt+eXVi6cj2REaFEhod6O2xJPLVVb374NQcPOevoPPbcu7SLjeLhu28AnMPXH77rBiIjwti6Yw9ffPsLAAWFJdQYjLz6f/d5Le7W5v9e/YiCwhKsNiv3PPYKaSmJ3H7Dpa6pei89dTc+ej0J8bGMGTmEq259lOjIcIqKS/nw9Se8HX6r0L1LGr26deSKmx8mJDiQ6hoDn7z5FOCcqvfKu1/yxnMPArBg8Urm/bkMh8PBwZzDBPj5cs+tV3kzfK+5/cZLeeqlD/jlt4VkZedx5+QrXMPb3//8e/r27OIq0H6sfdsylUrpujaJ7WPJys7j8ftudt3EvPLOl5w7ehjdOqe6TRutqKwiJ6+QJx+4xZvhtyoffvkDGXszMZstPPv6J0RHhrmmdj/xwnvcdPVFJLaPxddHzy3XXcwdD71IYvtYDh7K4+Wn7vFu8OK05OujZ/I1k7j+zidJSmjHvgPZ3HnzFeh1WvQ6LVdfej433vUU8XEx5OTl89YLD3k7ZK+Ij4smPi7a9VijUdOtc5oruXTrDZfw4FNvuH5HPvvoHd4K1asKCot59rWPSU1O4PkjK8P6+uh58cm7USgU3HHj5Tz72sdM/+V3srLzuP+Oa1Cr297HrFuuu5j7n3ydhUtWk52Tx/VXXOA2TbGtmvfnMuYuWErfnl1co3l69+jENZeOp11sFOedM5yrb32U2OgICopK+ODVx70csXckJ7Zzq+2kQEGfHp0J8He+h667YgKT732G+LgYDuXm8+bzD3grVK9SKpXcNfkKHnrmLZIT25F5MIcLx41y/S6//cZLeeT/3nb93m5MqZ3moDCbTVIpug3aumMPVdU1rse+vj70ODKNZPX6rXTvkoqPXk9JWTm792SiUCgICQogOTHeNV1HwIbNOzAdVYw0OCiATmnJWK1W1m7cTr/eXV1F8AAyD+ZQUlZBp7Qk+Yb/P/buP0hVdQ2dOia7kiIGo5Et2/e4vtnOySvg4KE8VEol4WEhJCXEnZbfUDdUaVkF+zMP0S42iqjIf1eo275rHxFhwW6r1tW3r4Ci4lIys3NJjI91+xZ78/YM4uOiCA0OwmA0smnrbhQKBQH+viQltMPXR36Ga+3M2E9ZeaXrsU6rca10t27TdtJTk9ymKeflF5GbV0BKcjxBgaf/KFrhPQWFxRzKzad9uxi3n2+AQ7n55BcUk5aS4Ppg09Zt2LyDjqlJ+Pn6uNoKCovJzsknKTGuzdZiMxpNbNy6y61NrVa5jUwpKS3nQFYO8e2iW8XoAm+prKomY28W0VHhdVaUbKsOFxRxIMu9hlh4aDCpR62Sm3kwl5LSMtLTkuX+4ojV67fSu3snt8+eOXkFHM4vkt/bOO/tD2QdIiYqwrW4Qa2CohKyDx0mKSGu1YxSlcSTEEIIIYQQQgghhGgWbW9SpBBCCCGEEEIIIYRoEZJ4EkIIIYQQQgghhBDNQhJPQgghhBBCCCGEEKJZSOJJCCGEEEIIIYQQQjQLSTwJIYQQQgghhBBCiGYhiSchhBBCCCGEEEII0Swk8SSEEEIIIYQQQgghmoXa2wEIIURzePLF91m45B8A9Dodie1jmXztJAb16+HaZ+/+g3w57Vc2bdlFtcFAXHQkI4cP4PKLzsXXR88nU35kyvezXPsv+vVzfH30Lf5chBBCCCEaY+jYa/jy/edI65BwzP02bt3FZ1//xJ59B9FqNfTt2YUbrryAhPhYAMZddgdV1TVM//w1YqLC3fp/84WH6N+7q2u/ktLyOv3/PuMjgoMC6rTfct+zbNmeAUB6ahJfvf9co5+rEKL1khFPQojT1rmjh7Fq/rfM/eEDBvTpxiPPvk3e4UIANm/P4KZ7niEkKIAPX3+c+T9+zItP3oVCoWDR0tUA3HLdxaya/y0fv/GkN5+GEEIIIUSzKS4p4/4nXqNPjy78NOUNpn36MsMG9mbaT/Pc9vPR6/j4qxnH7e+J+yezav63bv88JZ0APnnzKVbN/5bJ105qkucihGidJPEkhDjt+fromXztJJQKJRu27gTgjfencMagPjx45/Ukto9Dr9eR2D6OG66cyPnnDPdyxEIIIYQQJ+fSGx/EZrdz7e2PM+icq3jlnS897rdn/0FMZjPXXT6BoMAAggIDGDViII/ee5Pbfldfcj5/r1jL9l37WiJ8IcRpRBJPQog2p7C4lD37DzJ21FBvhyKEEEII0Sx++OI1VEolUz98gVXzv+Xhu2/wuF9KUjx6nY73P5/GvsxsrDabx/1ioiO4eMLZvPfpd80ZthDiNCSJJyHEaa/GYOSzb37GbrfTq1s6ZWUVAESEh3o5MiGEEEII7woPC+GTN5+itKyCex57lVETb+bex19l+669dfa97vIJZB3K4+/la+vt7/k3PmXQOVe5/o2/8s7mDF8IcQqQ4uJCiNPWvD+XMe/PZeh0WhLjY3nxybuIjY6ksLgUgMKiElKS4r0cpRBCCCFEy3j302l8/7OzdlN0VDgzv34bgJTk9jzz8O0AlJSW8/UPc7jz4ZeY/sVrRB71RZ2/ny83XDmRD76YztCBvTye44n7JzPu7DOa94kIIU4pkngSQpy2zh09jCcfuKVOe0RYCKnJ7Zn/1wq3Ve6EEEIIIU4nCqXC7fFdk6/grslXHPOY0JAgbr7mIn6Y+Qd79mW5JZ4AJp43kp9m/8lPsxc2ebxCiNOTTLUTQrRJ999xLX+vWMcbH0wlKzsXo8lM5sFcvpr2K3PmL/F2eEIIIYQQJy08NJg9+7JwOBz17rN6/VZeeutztu/ah8FopKS0nC++nYlWqyE1uX2d/dUqFf+76TK+mvYrDurvVwghasmIJyFEm9Sja0c+e+tpvpr2K7fe/xw1BiNx0ZGMHD6As4b1B+CvZWt4/Pl3XceMvMC5usuML18nPi7aK3ELIYQQQjTULdddzEdfzuDFtz5j/JgzPRYY79OjEwVFJbz98TfsO5CNVqshJak9bz73IJERYR77HTaoDylJf7Bhy846255/41Oef+NTt7apH75AWoeEOvu+/v5Ufp7zp+vxoHOuIiI8hNnfvXeiT1UI0YopzGaTpKmFEEIIIYQQQgghRJOTqXZCCCGEEEIIIYQQollI4kkIIYQQQgghhBBCNAtJPAkhhBBCCCGEEEKIZiGJJyGEEEIIIYQQQgjRLCTxJIQQQgghhBBCCCGahSSehBBCCCGEEEIIIUSzkMSTEEIIIYQQQgghhGgW/w8po8+0MSJQCwAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plot_fidelity_comparison(\n",
|
||
" scenarios,\n",
|
||
" synthetic_scenarios,\n",
|
||
" title=\"Tail-GAN: Real vs Synthetic Distribution\",\n",
|
||
" n_samples=1000,\n",
|
||
" flatten_method=\"mean\", # Average across assets for visualization\n",
|
||
")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "436f5591",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002702,
|
||
"end_time": "2026-06-13T02:38:07.421605+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:07.418903+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Interpretation**: Overlapping point clouds confirm that synthetic scenarios occupy\n",
|
||
"the same region of feature space as real data. Gaps would indicate missing regimes.\n",
|
||
"However, Tail-GAN's primary objective is tail risk preservation (below), not\n",
|
||
"distributional matching -- slight visual differences are acceptable if VaR/ES align."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "47f81115",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.002602,
|
||
"end_time": "2026-06-13T02:38:07.426848+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:07.424246+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### 14.2 Tail Risk Metrics\n",
|
||
"\n",
|
||
"Both real and synthetic scenarios are in scaled space, so relative error\n",
|
||
"comparison is valid. We also report unscaled VaR/ES for interpretability."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "49db3f50",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:07.432824Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:07.432643Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:07.434907Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:07.434627Z"
|
||
},
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.00564,
|
||
"end_time": "2026-06-13T02:38:07.435141+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:07.429501+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def compute_portfolio_pnl_np(\n",
|
||
" scenarios: np.ndarray, strategies: np.ndarray, Cap: float = 10.0\n",
|
||
") -> np.ndarray:\n",
|
||
" \"\"\"Compute portfolio PnL (numpy version for evaluation).\"\"\"\n",
|
||
" batch_size, n_assets, n_cols = scenarios.shape\n",
|
||
"\n",
|
||
" # Convert to prices\n",
|
||
" ones = np.ones((batch_size, n_assets, 1))\n",
|
||
" prices = np.concatenate([ones, scenarios], axis=2)\n",
|
||
" prices = np.cumsum(prices, axis=2)\n",
|
||
"\n",
|
||
" # Price change\n",
|
||
" price_change = prices[:, :, -1] - prices[:, :, 0]\n",
|
||
"\n",
|
||
" # Portfolio PnL\n",
|
||
" PNL = Cap * np.dot(price_change, strategies.T)\n",
|
||
" return PNL"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "83143c2b",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.002617,
|
||
"end_time": "2026-06-13T02:38:07.440598+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:07.437981+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"Compute empirical Value at Risk and Expected Shortfall from portfolio PnL."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "c2fc4795",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:07.446354Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:07.446283Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:07.448062Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:07.447788Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.005015,
|
||
"end_time": "2026-06-13T02:38:07.448261+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:07.443246+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def empirical_var_es(pnl: np.ndarray, alpha: float) -> tuple[float, float]:\n",
|
||
" \"\"\"Compute empirical VaR and ES.\"\"\"\n",
|
||
" var = np.percentile(pnl, alpha * 100)\n",
|
||
" es = pnl[pnl <= var].mean() if np.any(pnl <= var) else var\n",
|
||
" return var, es"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "76ada0a7",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:07.454019Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:07.453953Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:07.514734Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:07.514355Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.064272,
|
||
"end_time": "2026-06-13T02:38:07.515171+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:07.450899+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"============================================================\n",
|
||
"TAIL RISK COMPARISON (α = 0.05)\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
"Value at Risk (5% quantile):\n",
|
||
" Real mean: -9.132109\n",
|
||
" Synthetic mean: -10.326623\n",
|
||
" Relative error: 13.1%\n",
|
||
"\n",
|
||
"Expected Shortfall (mean below VaR):\n",
|
||
" Real mean: -11.507471\n",
|
||
" Synthetic mean: -12.802239\n",
|
||
" Relative error: 11.3%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Compute PnL for real and synthetic\n",
|
||
"real_pnl = compute_portfolio_pnl_np(scenarios, strategies)\n",
|
||
"synth_pnl = compute_portfolio_pnl_np(synthetic_scenarios, strategies)\n",
|
||
"\n",
|
||
"alpha = CONFIG[\"alphas\"][0]\n",
|
||
"\n",
|
||
"# Per-strategy metrics\n",
|
||
"real_vars, real_ess = [], []\n",
|
||
"synth_vars, synth_ess = [], []\n",
|
||
"\n",
|
||
"for i in range(CONFIG[\"n_strategies\"]):\n",
|
||
" r_var, r_es = empirical_var_es(real_pnl[:, i], alpha)\n",
|
||
" s_var, s_es = empirical_var_es(synth_pnl[:, i], alpha)\n",
|
||
" real_vars.append(r_var)\n",
|
||
" real_ess.append(r_es)\n",
|
||
" synth_vars.append(s_var)\n",
|
||
" synth_ess.append(s_es)\n",
|
||
"\n",
|
||
"# Aggregate metrics\n",
|
||
"real_var_mean = np.mean(real_vars)\n",
|
||
"synth_var_mean = np.mean(synth_vars)\n",
|
||
"real_es_mean = np.mean(real_ess)\n",
|
||
"synth_es_mean = np.mean(synth_ess)\n",
|
||
"\n",
|
||
"var_re = abs(synth_var_mean - real_var_mean) / abs(real_var_mean) * 100\n",
|
||
"es_re = abs(synth_es_mean - real_es_mean) / abs(real_es_mean) * 100\n",
|
||
"\n",
|
||
"print(\"=\" * 60)\n",
|
||
"print(f\"TAIL RISK COMPARISON (α = {alpha})\")\n",
|
||
"print(\"=\" * 60)\n",
|
||
"print(f\"\\nValue at Risk ({int(alpha * 100)}% quantile):\")\n",
|
||
"print(f\" Real mean: {real_var_mean:.6f}\")\n",
|
||
"print(f\" Synthetic mean: {synth_var_mean:.6f}\")\n",
|
||
"print(f\" Relative error: {var_re:.1f}%\")\n",
|
||
"\n",
|
||
"print(\"\\nExpected Shortfall (mean below VaR):\")\n",
|
||
"print(f\" Real mean: {real_es_mean:.6f}\")\n",
|
||
"print(f\" Synthetic mean: {synth_es_mean:.6f}\")\n",
|
||
"print(f\" Relative error: {es_re:.1f}%\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "96e51aac",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.00354,
|
||
"end_time": "2026-06-13T02:38:07.523799+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:07.520259+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Interpretation**: VaR relative error of ~13% and ES error of ~11% show the\n",
|
||
"generator captures the tail structure reasonably, though not perfectly.\n",
|
||
"ES error is typically comparable to VaR error because Expected Shortfall\n",
|
||
"depends on the conditional mean below VaR. Points near the 45-degree line\n",
|
||
"in the scatter plots below confirm per-strategy accuracy, not just average accuracy."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6714f44b",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002621,
|
||
"end_time": "2026-06-13T02:38:07.529053+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:07.526432+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 15. Visualization"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "2724588a",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:07.535118Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:07.535008Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:08.480183Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:08.479790Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.949199,
|
||
"end_time": "2026-06-13T02:38:08.480889+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:07.531690+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.plotly.v1+json": {
|
||
"config": {
|
||
"plotlyServerURL": "https://plot.ly"
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||
},
|
||
"data": [
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||
{
|
||
"line": {
|
||
"color": "#0a1628"
|
||
},
|
||
"name": "D Loss",
|
||
"type": "scatter",
|
||
"xaxis": "x",
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||
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = make_subplots(\n",
|
||
" rows=2,\n",
|
||
" cols=2,\n",
|
||
" subplot_titles=[\"Discriminator Loss\", \"Generator Loss\", \"VaR Comparison\", \"ES Comparison\"],\n",
|
||
")\n",
|
||
"\n",
|
||
"# Loss curves\n",
|
||
"fig.add_trace(\n",
|
||
" go.Scatter(y=history[\"d_loss\"], name=\"D Loss\", line=dict(color=COLORS[\"blue\"])),\n",
|
||
" row=1,\n",
|
||
" col=1,\n",
|
||
")\n",
|
||
"fig.add_trace(\n",
|
||
" go.Scatter(y=history[\"g_loss\"], name=\"G Loss\", line=dict(color=COLORS[\"amber\"])),\n",
|
||
" row=1,\n",
|
||
" col=2,\n",
|
||
")\n",
|
||
"\n",
|
||
"# VaR comparison\n",
|
||
"fig.add_trace(\n",
|
||
" go.Scatter(\n",
|
||
" x=real_vars, y=synth_vars, mode=\"markers\", name=\"VaR\", marker=dict(color=COLORS[\"blue\"])\n",
|
||
" ),\n",
|
||
" row=2,\n",
|
||
" col=1,\n",
|
||
")\n",
|
||
"var_range = [min(real_vars + synth_vars), max(real_vars + synth_vars)]\n",
|
||
"fig.add_trace(\n",
|
||
" go.Scatter(\n",
|
||
" x=var_range,\n",
|
||
" y=var_range,\n",
|
||
" mode=\"lines\",\n",
|
||
" name=\"y=x\",\n",
|
||
" line=dict(dash=\"dash\", color=COLORS[\"neutral\"]),\n",
|
||
" ),\n",
|
||
" row=2,\n",
|
||
" col=1,\n",
|
||
")\n",
|
||
"\n",
|
||
"# ES comparison\n",
|
||
"fig.add_trace(\n",
|
||
" go.Scatter(\n",
|
||
" x=real_ess, y=synth_ess, mode=\"markers\", name=\"ES\", marker=dict(color=COLORS[\"copper\"])\n",
|
||
" ),\n",
|
||
" row=2,\n",
|
||
" col=2,\n",
|
||
")\n",
|
||
"es_range = [min(real_ess + synth_ess), max(real_ess + synth_ess)]\n",
|
||
"fig.add_trace(\n",
|
||
" go.Scatter(\n",
|
||
" x=es_range,\n",
|
||
" y=es_range,\n",
|
||
" mode=\"lines\",\n",
|
||
" name=\"y=x\",\n",
|
||
" line=dict(dash=\"dash\", color=COLORS[\"neutral\"]),\n",
|
||
" ),\n",
|
||
" row=2,\n",
|
||
" col=2,\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" title=\"Tail-GAN Training and Evaluation\",\n",
|
||
" template=\"ml4t\",\n",
|
||
" height=600,\n",
|
||
" showlegend=False,\n",
|
||
")\n",
|
||
"fig.update_xaxes(title_text=\"Epoch\", row=1, col=1)\n",
|
||
"fig.update_xaxes(title_text=\"Epoch\", row=1, col=2)\n",
|
||
"fig.update_xaxes(title_text=\"Real VaR\", row=2, col=1)\n",
|
||
"fig.update_xaxes(title_text=\"Real ES\", row=2, col=2)\n",
|
||
"fig.update_yaxes(title_text=\"Loss\", row=1, col=1)\n",
|
||
"fig.update_yaxes(title_text=\"Loss\", row=1, col=2)\n",
|
||
"fig.update_yaxes(title_text=\"Synthetic VaR\", row=2, col=1)\n",
|
||
"fig.update_yaxes(title_text=\"Synthetic ES\", row=2, col=2)\n",
|
||
"\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "24d4d6eb",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.005183,
|
||
"end_time": "2026-06-13T02:38:08.492952+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:08.487769+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 16. Results Summary"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "374d2595",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:38:08.503983Z",
|
||
"iopub.status.busy": "2026-06-13T02:38:08.503881Z",
|
||
"iopub.status.idle": "2026-06-13T02:38:08.506565Z",
|
||
"shell.execute_reply": "2026-06-13T02:38:08.506173Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.008993,
|
||
"end_time": "2026-06-13T02:38:08.507093+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:08.498100+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"============================================================\n",
|
||
"RESULTS SUMMARY\n",
|
||
"============================================================\n",
|
||
"Checkpoint: 05_synthetic_data/output/tailgan/checkpoints/tailgan_model.pt\n",
|
||
"Training epochs: 3000\n",
|
||
"Final D loss: -0.165748\n",
|
||
"Final G loss: 100.332498\n",
|
||
"VaR relative error: 13.1%\n",
|
||
"ES relative error: 11.3%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"=\" * 60)\n",
|
||
"print(\"RESULTS SUMMARY\")\n",
|
||
"print(\"=\" * 60)\n",
|
||
"print(f\"Checkpoint: {CHECKPOINT_PATH}\")\n",
|
||
"print(f\"Training epochs: {len(history['d_loss'])}\")\n",
|
||
"print(f\"Final D loss: {history['d_loss'][-1]:.6f}\")\n",
|
||
"print(f\"Final G loss: {history['g_loss'][-1]:.6f}\")\n",
|
||
"print(f\"VaR relative error: {var_re:.1f}%\")\n",
|
||
"print(f\"ES relative error: {es_re:.1f}%\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f8a157eb",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.004989,
|
||
"end_time": "2026-06-13T02:38:08.517277+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:38:08.512288+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Key Takeaways\n",
|
||
"\n",
|
||
"1. **Differentiable sorting**: Neural sort enables gradient flow through VaR/ES\n",
|
||
" computation, making tail risk metrics trainable loss components\n",
|
||
"2. **Constraint projection**: Hard projection onto $W \\cdot v \\leq e$ ensures\n",
|
||
" the discriminator's VaR/ES estimates remain economically consistent\n",
|
||
"3. **Tail risk preservation**: The generator learns to match real tail\n",
|
||
" distributions rather than just marginal statistics\n",
|
||
"4. **Scaling matters**: The generator's clamped [-1, 1] output requires\n",
|
||
" careful input scaling to preserve tail structure without saturation\n",
|
||
"\n",
|
||
"**Next**: See `03_sigcwgan_signatures` for a signature-based approach that\n",
|
||
"eliminates the adversarial discriminator entirely.\n",
|
||
"\n",
|
||
"**Book**: Section 5.4 compares Tail-GAN's targeted risk approach with\n",
|
||
"general-purpose distributional matching."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.14.5"
|
||
},
|
||
"papermill": {
|
||
"default_parameters": {},
|
||
"duration": 12.086923,
|
||
"end_time": "2026-06-13T02:38:11.137511+00:00",
|
||
"environment_variables": {},
|
||
"exception": null,
|
||
"input_path": "05_synthetic_data/02_tailgan_tail_risk.ipynb",
|
||
"output_path": "05_synthetic_data/02_tailgan_tail_risk.ipynb",
|
||
"parameters": {},
|
||
"start_time": "2026-06-13T02:37:59.050588+00:00",
|
||
"version": "2.7.0"
|
||
},
|
||
"jupytext": {
|
||
"cell_metadata_filter": "tags,-all"
|
||
},
|
||
"ml4t_provenance": {
|
||
"source_py_blob": "7e6e5d857230981aad92ebe9996054e6c66c421a",
|
||
"executed_at": "2026-06-13T02:38:11.553148+00:00",
|
||
"executor": "gpu-docker",
|
||
"production": true,
|
||
"parameters": {},
|
||
"notes": "executed-state finalization batch (2026-06-13 run)"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|