Factor-Risk-Decomposition/notebooks/02_factor_analysis_and_diagnostics.ipynb

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   "source": [
    "# Factor Analysis and Diagnostics"
   ]
  },
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   "source": [
    "## Purpose\n",
    "\n",
    "A **factor** or **signal** is a vector of scores for the stocks available at a date:\n",
    "\n",
    "$$f_t \\in \\mathbb{R}^{N_t}.$$\n",
    "\n",
    "The question in this notebook is simple: when we rank stocks by $f_t$, do the higher-ranked stocks tend to have better subsequent returns?\n",
    "\n",
    "In practice we measure this with the **information coefficient (IC)**, a Spearman rank correlation. After ranks are centered, a correlation is also a cosine similarity:\n",
    "\n",
    "$$\\rho(u,v)=\\frac{\\langle u,v\\rangle}{\\|u\\|\\|v\\|}=\\cos\\theta.$$\n",
    "\n",
    "So the IC is a geometric question: does the signal vector point roughly in the same direction as the return vector?\n",
    "\n",
    "We test four common factor ideas using data we can build from prices alone:\n",
    "1. Momentum\n",
    "2. A crude value proxy\n",
    "3. A crude quality proxy\n",
    "4. Low volatility\n",
    "\n",
    "Momentum is the only one that looks useful in this dataset. That is not a universal statement about factor investing; it mostly tells us that the other three proxies are too crude for this public-price-only setup.\n",
    "\n",
    "### Timing convention\n",
    "\n",
    "The raw factor formulas use `.shift(1)`, so the score at a given return date is based only on information available before that return was realized. That is why the IC code can pair `factor.loc[date]` with `returns.loc[date]` without looking ahead.\n",
    "\n",
    "## Terms used\n",
    "\n",
    "| Term | Meaning |\n",
    "|------|---------|\n",
    "| **Factor / signal** | A vector $f_t \\in \\mathbb{R}^{N_t}$ assigning one score per stock |\n",
    "| **Momentum** | Trailing 12-1 return: recent winners may keep winning |\n",
    "| **Value** | Cheap stocks may outperform; here proxied crudely by inverse long-term return |\n",
    "| **Quality** | Profitable/stable firms may outperform; here proxied by a rolling Sharpe-like ratio |\n",
    "| **Low volatility** | Lower-risk stocks may outperform on a risk-adjusted basis |\n",
    "| **Information coefficient (IC)** | Spearman rank correlation between signal ranks and subsequent return ranks |\n",
    "| **Information ratio (IR)** | Mean IC / std(IC), annualized here by $\\sqrt{12}$ |\n",
    "| **Rank** | Cross-sectional ordering of stocks; useful because it is robust to outliers |\n",
    "| **Turnover proxy** | How much the signal changes; here approximated with rank autocorrelation |\n",
    "| **Walk-forward** | Split into subperiods to see whether a result is stable across regimes |\n",
    "| **Return panel** $R$ ↻ | The monthly return matrix from notebook 01 |\n",
    "\n",
    "## Outputs\n",
    "\n",
    "This notebook writes factor exposure CSVs, monthly IC series, IC decay, subperiod diagnostics, and a cross-factor correlation matrix.\n",
    "\n",
    "## Notebook Structure\n",
    "1. [Setup and Imports](#setup-and-imports)\n",
    "2. [Load Data](#load-data)\n",
    "3. [Factor Definitions](#factor-definitions)\n",
    "4. [Information Coefficient Analysis](#information-coefficient-analysis)\n",
    "5. [Subperiod IC Stability (Walk-Forward)](#subperiod-ic-stability-walk-forward)\n",
    "6. [Factor Decay](#factor-decay)\n",
    "7. [Turnover via Rank Autocorrelation](#turnover-via-rank-autocorrelation)\n",
    "8. [Cross-Factor Correlations](#cross-factor-correlations)\n",
    "9. [Conclusion](#conclusion)"
   ]
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   "metadata": {},
   "source": [
    "## Setup and Imports"
   ]
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    "\"\"\"\n",
    "==================================\n",
    "Setup and imports\n",
    "==================================\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "\n",
    "palette = ['steelblue', 'coral', 'seagreen']\n",
    "\n",
    "os.makedirs('../data/processed', exist_ok=True)\n",
    "os.makedirs('../images/02_factor_diagnostics', exist_ok=True)\n",
    "\n",
    "RANDOM_STATE = 3"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returns: (251, 501)\n",
      "Prices: (252, 503)\n",
      "Sectors: 11\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Load return panel and sector mapping\n",
    "==================================\n",
    "\"\"\"\n",
    "df_returns = pd.read_csv('../data/processed/returns_monthly.csv', index_col=0, parse_dates=True)\n",
    "df_prices = pd.read_csv('../data/processed/prices_monthly.csv', index_col=0, parse_dates=True)\n",
    "df_sector = pd.read_csv('../data/processed/sector_mapping.csv')\n",
    "\n",
    "print(f\"Returns: {df_returns.shape}\")\n",
    "print(f\"Prices: {df_prices.shape}\")\n",
    "print(f\"Sectors: {df_sector['sector'].nunique()}\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "ac67fd55",
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   "source": [
    "## Factor Definitions\n",
    "\n",
    "We compute four cross-sectional factor matrices. Each matrix has the same shape as `df_returns`: one row per month and one column per stock. Each entry is a percentile rank centered around zero, so the scores live on roughly the same scale.\n",
    "\n",
    "The factors are:\n",
    "\n",
    "- **Momentum:** trailing 12-1 return. In code this is an 11-month rolling sum shifted by one month, so the most recent month is skipped.\n",
    "- **Value proxy:** negative 60-month trailing return. This is not true book-to-market value; it is a rough price-only mean-reversion proxy.\n",
    "- **Quality proxy:** 12-month mean return divided by 12-month volatility. This is closer to a recent-return quality proxy than a true profitability or balance-sheet quality measure.\n",
    "- **Low-volatility:** negative 60-month trailing volatility, ranked so lower-vol names score higher.\n",
    "\n",
    "The caveat matters: momentum is a fairly standard price signal, but value and quality are usually built from fundamentals. Here they are learning proxies, not production-grade definitions."
   ]
  },
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     "text": [
      "momentum: (251, 501), non-null months: 240\n",
      "value: (251, 501), non-null months: 191\n",
      "quality: (251, 501), non-null months: 239\n",
      "lowvol: (251, 501), non-null months: 191\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Compute factor exposures\n",
    "==================================\n",
    "\"\"\"\n",
    "def cross_sectional_rank(series):\n",
    "    \"\"\"Rank cross-sectionally, scaled to [-0.5,0.5]\"\"\"\n",
    "    return series.rank(pct=True) - 0.5\n",
    "\n",
    "# Momentum (12-1): sum of returns from t-12 to t-2\n",
    "mom_12_1 = df_returns.rolling(11).sum().shift(1)\n",
    "df_momentum = mom_12_1.apply(cross_sectional_rank, axis=1)\n",
    "\n",
    "# Value (proxy): inverse 60-month momentum\n",
    "mom_60 = df_returns.rolling(60).sum().shift(1)\n",
    "df_value = (-mom_60).apply(cross_sectional_rank, axis=1)\n",
    "\n",
    "# Quality (proxy): 12m sharpe-like\n",
    "mean_12 = df_returns.rolling(12).mean().shift(1)\n",
    "std_12 = df_returns.rolling(12).std().shift(1)\n",
    "quality_raw = mean_12 / std_12.replace(0, np.nan)\n",
    "df_quality = quality_raw.apply(cross_sectional_rank, axis=1)\n",
    "\n",
    "# Low volatility: inverse 60m vol\n",
    "vol_60 = df_returns.rolling(60).std().shift(1)\n",
    "df_lowvol = (-vol_60).apply(cross_sectional_rank, axis=1)\n",
    "\n",
    "\n",
    "factor_dict = {\n",
    "    'momentum': df_momentum,\n",
    "    'value': df_value,\n",
    "    'quality': df_quality,\n",
    "    'lowvol': df_lowvol,\n",
    "}\n",
    "\n",
    "for name, df_f in factor_dict.items():\n",
    "    print(f\"{name}: {df_f.shape}, non-null months: {df_f.notna().any(axis=1).sum()}\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "e9ea22e1",
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   "source": [
    "## Information Coefficient Analysis\n",
    "\n",
    "The **information coefficient (IC)** is the Spearman rank correlation between a factor vector and the return vector it is meant to predict. Since Spearman correlation is Pearson correlation applied to ranks, the IC can be read as a cosine similarity between centered rank vectors:\n",
    "\n",
    "$$\\rho(u,v)=\\frac{\\langle u,v\\rangle}{\\|u\\|\\|v\\|}=\\cos\\theta.$$\n",
    "\n",
    "- IC near $+1$: the factor ranking and return ranking are almost perfectly aligned.\n",
    "- IC near $0$: the factor is not directionally useful in that month.\n",
    "- IC near $-1$: the factor points the wrong way.\n",
    "\n",
    "The **IC information ratio** annualizes the signal-to-noise ratio of the monthly IC series:\n",
    "\n",
    "$$\\text{IC IR}=\\frac{\\text{mean monthly IC}}{\\text{std monthly IC}}\\sqrt{12}.$$\n",
    "\n",
    "A small positive IC can still matter if it is stable, but a tiny IC with a noisy sign should be treated as weak evidence, not a discovery."
   ]
  },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing IC for momentum... \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing IC for value... \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing IC for quality... \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing IC for lowvol... \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "IC Summary\n",
      "\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>ic_ir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>momentum</th>\n",
       "      <td>0.0060</td>\n",
       "      <td>0.1905</td>\n",
       "      <td>-0.6010</td>\n",
       "      <td>0.4032</td>\n",
       "      <td>0.1091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>-0.0216</td>\n",
       "      <td>0.1576</td>\n",
       "      <td>-0.3878</td>\n",
       "      <td>0.6297</td>\n",
       "      <td>-0.4750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>-0.0025</td>\n",
       "      <td>0.1945</td>\n",
       "      <td>-0.5120</td>\n",
       "      <td>0.4798</td>\n",
       "      <td>-0.0448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lowvol</th>\n",
       "      <td>-0.0259</td>\n",
       "      <td>0.2360</td>\n",
       "      <td>-0.5288</td>\n",
       "      <td>0.5478</td>\n",
       "      <td>-0.3808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "            mean     std     min     max   ic_ir\n",
       "momentum  0.0060  0.1905 -0.6010  0.4032  0.1091\n",
       "value    -0.0216  0.1576 -0.3878  0.6297 -0.4750\n",
       "quality  -0.0025  0.1945 -0.5120  0.4798 -0.0448\n",
       "lowvol   -0.0259  0.2360 -0.5288  0.5478 -0.3808"
      ]
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   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Compute monthly IC (Spearman rank correlation)\n",
    "==================================\n",
    "\"\"\"\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "def compute_monthly_ic(factor_df, return_df):\n",
    "    \"\"\"Compute Spearman IC using shifted signals and aligned return rows.\"\"\"\n",
    "    common_dates = factor_df.index.intersection(return_df.index)\n",
    "    common_tickers = factor_df.columns.intersection(return_df.columns)\n",
    "\n",
    "    ic_series = []\n",
    "    for date in common_dates:\n",
    "        f = factor_df.loc[date, common_tickers]\n",
    "        r = return_df.loc[date, common_tickers]\n",
    "\n",
    "        mask = f.notna() & r.notna()\n",
    "        if mask.sum() < 20: # sample size filter\n",
    "            ic_series.append(np.nan)\n",
    "            continue\n",
    "        \n",
    "        ic, _ = spearmanr(f[mask], r[mask])\n",
    "        ic_series.append(ic)\n",
    "    return pd.Series(ic_series, index=common_dates)\n",
    "\n",
    "ic_results = {}\n",
    "for name, df_f in factor_dict.items():\n",
    "    print(f\"Computing IC for {name}... \")\n",
    "    ic_results[name] = compute_monthly_ic(df_f,df_returns)\n",
    "\n",
    "df_ic = pd.DataFrame(ic_results)\n",
    "df_ic = df_ic.dropna(how='all')\n",
    "\n",
    "print(\"\\nIC Summary\\n\")\n",
    "ic_summary = df_ic.describe().T[['mean', 'std', 'min', 'max']]\n",
    "ic_summary['ic_ir'] = ic_summary['mean']/ic_summary['std'] * np.sqrt(12)\n",
    "display(ic_summary.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5659b941",
   "metadata": {},
   "source": [
    "The `mask.sum() < 20` rule is a sample-size guardrail. The `mask` keeps only stocks with both a valid factor score and a valid return for that date. If only a handful of stocks are available, a rank correlation can become mechanically extreme; with two stocks, Spearman correlation is always $+1$ or $-1$.\n",
    "\n",
    "The threshold drops those degenerate months before they leak into mean IC, IC IR, and decay statistics. We use the same kind of guardrail throughout the project."
   ]
  },
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AIrVPgVuRBkwXLSIiIiIipek6WUREPIECtyIiIiJSKQpsiIiIVC+dW0WkOAVuRURExGPp5kVE3KHuuyIiIlIfKXArIiL1jm7gRURERERExNspcCsiIuLl1CpVdAyIiIiIiNQ/CtyKiIhHUOBJRKThqErPCPWqEBERkYZCgVupFAVYREREpCHRtY+IiEj10oM4kQtT4FZEROQCdFEpIiIiIlLz9KBUpCQFbkVEREQqQQF9EfEkCnaIiIjUPwrcioiIiIiIiIiIV4tN3s8lx5aSEBoLOZ3AP7CuiyRSZQrcioiIeIgWqYexHDbHQtrWdVFExF22E2wbrLouiIiISAN2dA9X7fsQP2cuLdKPwCuH4LqZ0KJDpVdZZz0ZUpPgbCLEtIaAoNrbrngkBW5FROSCqvuixVO6c3pSV/fuJzcz5tBXOLGY23EqcFG1b6Pt2T30j1/OkfAO4OwBDke1b0PEG0WnH8e2LE4FNXN/ofw8+iSsYsDx5WT7BrKw7bU1Um895feyqurL5xAREc8Tnp0EH72JnzO3aOLp4/D6IzByKgy5+oLXvXV5nvLLz6Zl6iFYsAEObIXEOPNGcDgMnQwDxtdqecSzKHArIpVS1onNNz+HDmd/JiGkZR2WrHy6aZTa4JefjdNykO/wc3+htLMMO/IdAA5sxh6cDckjIKJJ9RXsx++5au+HOHDSMi0OvguGcXeCpWaC0oA5nQw9spD+CSsBSApoDCGj4aLh0LhF+csd2AbzX2P4qaMABOVncv3ut2BFtrnBOvfmMCsDjvwMzdpCWFSNfiQREU/kSQ/LpX4JyMvg6j3vQVYyAMdDW+PjzKNpxnFw5sHid2HfZpj8UPVeW1eHs4mMPfAFnc9sx8d2wt5z3s9IgUXvwJo5XBw1mB3R/Sp2jyH1ggK3IlI9bJtJe98nNvUQ+ZYPRByB4VPAP6CuSyZSa1qkHmLSng/I9fHni653uL/g4vcIzM90vQzKz4TP/gG3/wV8q+HibN038O3rOM6dFhQGI26s+vpFvFFeLnz9X1fQFiAy+zQs+8T8a9EROvWF8CYQFkl0+hnyHT4MOvY9bPip1OocOGHJ+xzesIbv2l9Hpl8o4dlJzAjeB5sXQ04mhDWG+5+DoNBa/rAi4s3U+KBuaL97Ph9nHhP3fUxU1ikAzgQ2YU6nW8h1+PGroB2w8kvAhkM74D/3wcWXm9a3TcpoaGTbhOSmkuVb86kJ/PMyYdG7sHYe3fJzS75pOUx6h9BI2L3BlD8tiRFp8+kXv5IlbScBOh4bEgVuRaR6/LyO2NRDAPjY+bByNmxfAePvgq4D67p0UgG6SK2kzDTG7f+cAGc2Ac5sxhz8EpzDwOFz/uXidsGPSwHI9gkkx8efsJwUOLoHlrwPV8yofJlsm4HHf4AN3xdtLrw9rVMOmBc/fGwCSIMmVH4bIt4oOxM++Zvpjgg4sUgIaUXz9KNY2Gae4/vMvwJTy1hNfEgrlsWOp1PyT/SNX4OFTZuU/dzy00skhLSi3dnd5oarUOppc34cM73GP6KIiEi9ZtuMPvgVrQruQQmJ4OsO08guDLyOngYd+sCXz0HKacjPg82LzMPUrgPhkomQlwdHd8PRPfzi4C6C8jNx4oC4ltCsHX2TAjkTFG3ubzefgMxUyEiF7AwICDapDELCaXM2iUy/UE4HxZy3yD7OXHqe3MSg4z9AXoZreqZvMHuienLx5SOhbU8ICjFvJMaZ6/WdawAIy01hwv5P4OwwaHT+bRXfT+3P/kxIbhrxoa3A2f3C9yfiURS4FZGqy8+Hxe+7XjpxmJZHySfh41nQeQBceZf7JxcRb2PbMO9/hOWmuCa1SIuDdfNh8FXlLmbZ+TDvDdfr1S1Hcja4CZN2f2AuENfMgTbdoeugSpTJyfAj39HnxBrXpHXNL2Nty5H0ObGG4UcWmInfvg6BIXDxCPM5kk/RJnkvITmpHAlvX/Htiniw57/ZTnBuKlfveZ+YjHgA8ixfvu1wAwciuxGSk8JdMSdh+3JIOFj+ioLDYfQ0Pj0eDVikhERxKKwT1x2fA2lJhOSm0eHsz0Xz+/qD02m6bK6dZ3LVNYquhU8sUn3U1V1EPEnnM9vpemYbALkOP/xu/gMpW7NLztSuJ9z3HKz4HDYuNL1fsOHndeZfMYXtbB044eQROHmEYcVn2Ee5rin4P9snEJxDoddwaNvDBEid+XDoJ9i2jLu3ryIgv1gZffzYGD2IDc2HkeMbxMXdzvldjWkNU34P8Qc58uELxKYeNHl8v30dbv7DhXdSfh4jD8+h18lNRdP2vg2tOkPrbtChN8R2Of86sjPNPUJg8IW3JzVCgVsRqboti+H0MQCOhbZhcduruS1jmaslE3s2mFaFN/w/c3IQt5R1g6SbJs/U5fQ2OLgKgGxHAAHOgguyJe9D5/7QuHmZy/U+sQ4SDwNwIrg526MHEEgWy2Ov4PK4+WamL/8D9/wLoiowaBIw7JygLWNvZ22SCcRuaTaE4e3CYPln5r2vXjDBpFPHIDfLdfGZ6hcO2f0rNJqtWmyXwbZh11qwnSYI76PLrzqRnkLf+JX0PrG26CFLUCiz29xEfFhrM4t/OAwdCkOvMfXh1DFIS4LUM2zfuZ/g3HROB8UwcPrdprV6/Hbz/QJHw9vDpGc5/OpfaJOyH4A0vzBCh10F/a6AVV/C6q8gPxe+/wgmP1h3+0KqTbdTWxhwfDkHG3WG/G6q3yIitaTr6W2uvxe1m8yVrTrD1u2lZwwKhbG3w/AbYNNCc82beqbUbBm+ISQGNyckN43o7FPmYWsFBeRnwZYl5l9opGk9e2iHuZYASiQR7DUcRt3CqlUnLrzi5u2Y1/Fmpu14gdDcVJNC4ed152/ckZkOn/2DXie3lpyek2nu0w9sNa15+401vWTPTc9m27D1e5j/urmGve0pE/CVWqcrC2kwGlIwwS8/m66ntpLv68POqL41ui3f/Bz44RPX65WxYzkb1ASuexx+WgUL3jQnqqw0eP9pGHsbXHKVBkSSeiMsO4nL4+a5Xi9udzUtUw/TO3Ed5OXA1/+F258uNVhRSE6KyZUJgMX3ba7Cthxgw7bogVweehZ2rjZdsT77J9zxf+DnZs7o7SvoWxC0dWLhmHQ/9B0NxQP/l99sunttWGAuxuL3l/5suSnm4vayGyq1bwTIyTKB8Z2rzetGTeGyKXDRZeDjud3U6s0507ZpkRZHr8QNsHknw/KLbsJS/CMIv+Np4tefLXvZJi1L5MBbmlG0TwaWl6M2tBFfdZ5Gx6SdgMWBRl341fA+5r1h15numVlpsPUHGDzJDFYm3mvnGsYc/AoLm8gTa+DDZ2DK7yr0sEtERCohK4PYgoekqX7h7I3sfuFlAkPMAKKDJpqUfns3QWgjaNUFWnXmtdWJrnvUh67oCqeOsvC7FTTKOk2uw5+hfTqZIHBwmEmTkJVuBg9LT2Hd1r00yjpNu7O78XfmmO2lJcGOFSWKkO0IYH9kN7Y2HcTN140rmOpG4BbI8Q1keevxXLn/UzNh/uvQ7qKyzzlJJ+CDv0DBIKp5lg8/Nh1MeHYSnXOPuwLJgAlmJxwy56/CwduyM+GbV2DbsqL55r4Mv/hnmdevAXkZjDg8Hwsnm5tdqhy81UyBW5H65uAObvnpJSKyzY9xvu0LXFRjm+tzYrXrh39fZHcSQmPNG5YFPS+Fjn1g9vOm1a3thO/eghOHYeK91TPokkgdsmwnVxyY7erytLNxb/ZF9eRwRCfaJe8x9TBuJ2z4tlQe2WFHvitqmdtvDCccrVwt97AsmPRL01X7TLwJqn78N7jpEfDzP3+hEo/AnJdcL39oM5GRfUeXUXgLxt8NOdnmaToWRDaF6Fg2J/vS+8Q601Vs1ZfQ/woICa/y/mpowrLPwpt/hIQDRRPPnoCvXzBd9kbcaH4nlWesRoTkpDBx30c0Sz9W6r1DER1Z3PYa7oqOBcoJ3FaW5WBfVM/S04NCYfj1sPBt001z8btw65+rd9tSa5qnxsHsd4tyIgPs3wJv/wmm/gnCIuuyeCLSAPSLX0GPU5vZ1GxouYGyettbb+8mfO18APZHdjMDernL1w/6jDT/irNOlpynWTt2NUlzTRrav/x9t/aU2c8+zlweaJ9ZFBjOzwOHL3TqA70u47WDQeQ7Kn8PvDeyB4fDO5iePSmnzGCqY28vOdPhnSaPf4bpXZThG8y8jjcTH9YGgM5X9oSzifDzetM7MC8Hju2BV38LN/wW/IPg83+Ze5DiThyCjQtKj41h24w+NKfgoTV0ObMDvtjJm1Z/UgMauWarN8deHVDgVqSe8M3PYejRRbBhHRHFpl96dCFkX1cjrT+CctPpF2+6h2M5WNWyjOBQYIgJNn3/kQlUgBmI6dRRuPFhCIuq9nKJ1ArbSf/45bRMM6kOaBTDsjZXApDrE8Ditldz3e63zXuL3zOj0/sFwu4NXL1nMW2T95r3gsNh1C3w/eGS6w8MNk++3/yDabW5fwt8+ndTb8p56OGXnw2f/g1ys6AgkLw9uj8jy5wb0wr4ml/ByKmm9UBBi94V32zHz5lLr5MbTXeqFZ/DuDuqvs8akOaph5m472PISzcT/IPMCMGHdpjXZ+Jh9nOw7DMYMM7kGC6vFadUXG4OV+39kKYZx4umBYWyKfwidsQM4Gxg47op14DxsO4bkwN+3xY4sA3a19zDVakZjbJOc9W+D83NLnAoohPN0o4QmJ8F8QfgjUdMUL6sUctFirNtorJOkhxQQ4F+2zaDGyUcNNchwXoIWxNCclJod3Y3+9xp9Xku28bPmUOu4wIP5s+1bwuXHl0EwOhDc2BVpEnz01DsKkoHVqn9XkPyHX7Qoy/0GGpSFZyMgyatzHU2kH+4jFQOFWFZfN9mIrfvfMmkXlozFy4aAVHNTe+uTQvhSLEc+01a8mnzG0gOjCqxDiKbmnE42nQ3Qd7kk5CeDO88boLghWki/IPMfMsKWvku/dB8ttCigCxbf3AFbV22L2e6tZofm17iyt8rlafArUg90CL1EGMOfkmj7KIuDzkOf/ydOSYHzoovYPSt1b7dgcd/KNFi8KzVpOwZHQ4TmGraFr76j7nROboHXv09TP0jNG9X7WUTL5OfT1RmIkG56RwvyDXpSXycubRMPUx0RjyNMxPhlXe4/0ScGRygMB3B5IfI2ZHvWuZoeHsTkNuwAHKz4bXfmws4bEp0jh49rfwbqWbt4JbHTJqR3Czz5P7Tf5iAbhl5qEYf/BqSTOvCk0FN+b7NxAunJbGsom5RxaxrMYKup7eaz7jhWzPyrocMMBiUm46FTYafZwY6u5/czMjDc80AcwCRzcwAEjGxphXE9x8VBXBPH4MFb5jgfo8hJs9YbFelk6kK24Z5L7uCtqn+EaxqOZpxU6ewcuHuui2bn795UPLl8+b1onfg7n+YFrgHt8OP35v/uw2CcXeVm06jXdLP9E5cy44m/dQdsZYF5qZz9Z73CCoYDfxIWDvmdryZRtlnmHbkE3PzezbRBG9vfMQMTiNSFqeT8fs/o3PSDo6GtQW7zwV/+x3OPHMeObjd/DtxyJybW3cz547W3Uxr7yO7iwZfSirohh3ZDO76m3rQVLczCUz96WWC89LpeXIjOAe435PGmc+4A5/T5cx2zgQ2geDLofsQcx4737GQmQ5fv1hy2qJ3TOvO4ddX7fN4g9xs2LsZClqTeuK9AwBBIaZOVrPkwMbme/7+I9Oj9eNZJm1DVnrJGdv1gim/J3npeQZbbdHBpD/44lmT89Z2mn8ALTrC9TNNUDjljBnXJjvDHGuTHzLznE00A6UV2B7dn45nfiIoPxNfO4/+CSvpfmoLX3eepuuVKlDgVsSb2Tb9jy9nyLElRV31fP1Z1nwUhyI6cutPLxWMTP819BlV7gBJlRGRdca0xgPTSu+yKbC8dHfUEnoONWX4aJbp2pF62rQmvH4mdBlQbWUTzxeQl0HnMzuISY+HV9+FxDimFbRc2teoG9gX123gyraJzDpFm+S98N6X3HtgO752yQEKiodNNzW/lAFtusOOc56ij55uLizPJkJmWom3Uv3C+bHpJQzrXW57WKNNd7jlT/DB0+ZCdc8G033pht+WGASn94m1dE4qCAYGBDO/403k+VSw9UYx6f7h/BhzCQMSVpgbge8/dnswJcvOx6YCXdbOo1HWadqe3UNU1kl482M4dZRfZKTgxGJp20kedxHY7dQWxhz6qmhCu17muyoMzrfpbnIeH9xuBoQ4XNBCIS/H5D3d+gN0HlB2cF7c0vvEGjjyAxSMMj2n0y2cCm7GuAulGaktvYbDmjmmBVz8AXOzdGS3OS8W2rAAsjLKrnPbVzBx30c4sGmRGgdpV5Zs+SI1xseZy1V7P6RRdsGgNjGtmdfyZpwOX84ExcCdfzW/1ScOmd/8tx8z+cVH3apgmZS2+D3XebtV6iHzcLZz/zJnbZZ2hEHHv6dl6mHYlFvyzYSD5t/6gkFNff1drcFLSEowvXKmPaHzS3VJT4b3nyK4oHdN04x42LTIPLh3x/cf0+WMuXaMyjplBo1d/hnTAxqzL6o7W2PKGXhqwevmPqrg4WRYTrKZvvQDc8024sYKX0d7VUqF/T+aa2LgQKNu2FYDTDk1dLLJP3v6uLnPKC46FvqPg/5j3RswMyQcbn3MtKZdOdtMG3y1aXhV+Fsx+lYz0G5hnv6+Y8zDoq9eMMHcgp5+S9tOYmWrMdwX/DN5q+fia+cRnJfONbvfgRNdoWmbat8VDYECtyJeyseZC18+z9BjRQnDj4e2psWMh/lx7WmwbbY1HUCfhLXmBL7wLdPiq5jo9OP0PrGWY2Ftwe7p/gk+K51xBz4rak02eFJByoMLBG4BmreHX/zDPBk8use0Ivz4r3DF7SZR/AXKUG8GzGnIDu/k1h0vmtbgZeh4dpcZEGvwVbVeNJxOupzexiXHlhbdmJd5srQ4GxDJ6aAYDkV0Zkd0X8p89BAQBFf/ytzI5+WYC6mug/jodGMSg1uAZTHM4UaAs20PkzOxcD0/r4N/zDAPTRwObsvKJzy7WJ7Oax7k7IHgyu4Fl43NL2XA2S1FF2lDrr7gBVff+JUMO7qQ3VE9wdmjavlbt/7ArTtewKfwyX8xDmxGHpoLB/uZ4KgnOHHYlKnAjzGD6H3rb8u+aG7Xy/xLjDPd2rb+UNRSYs8G+OLfcP3/8+gBzDxRbMp+hh1Z6Hq9sN1kTgU3q9MyleJwwJjp8N6T5vVPq8qeb/tyAKyAkWbgQoAdq2D2czgKHtb62nkmyHv5TbVT9vrqbCLsXGO6sl58ednXIvn5XLn/U1qkHwEgzS+M0FseI2dlsRyA4VEw4/9MWpsDW01L6s2LTJfeUbeaIK5yWgsFgwGt/qrktJWzyw7c5mQzYd/HZV83hUZC2llzrBUqHrS1HGZU+5NHzJgUh3fCvP/B1Q+oZ0d5bBtSz8CJOPokrCU4N40j4R1K3yvlZMGH/1c6D2hhV/KCrvHl2rXWlUbOxqy3sCFOZPZpBsSvoMfJzdCnUcmW+7vWmWsGINsngE+73kXnM9sZdrTg3LfsE3PvN+oWj/uOqys4bO1a6/p7X2T1t2j1Cr5+MOEecy1hO80Dm56XQr8xZrC1in73Dh/TC7DwHHhuqp+QCHNMffOKef3Nq2ag3YIeZCn+ESxrbVLG5fgGwZjpvJvajisOfE7LtDiC8jPh3SfMYMuNW1TPPmhAFLiVBs1bg4DBuWlM3PshpB91TVvTciQbmg/nwcYtAPME9sdml9Dp9E/mQm/3BpNPr2PB6NY/fs+UXa/ja+fR/fSPsCzQPJ0tQ4n9NLI9vPeEa7CXNL8wQodUMJ9SaCO47Sn46r/w00pzslnwpnliOO5O954MivexbVj9NSx+j9ASgTgLoppxwBlO+7MF3ZgXvWtaJrboUGvFa5F6CF5/l3HH95V6L9UvnLiIjhwNa8up4Kbccs1lvLNwj3srbtcTHvqfuYhuFA1A4jeVyG/VrqdJLfLh/5mbsqw08w8o0dZu6GTTzfpAFXNoFV54DbvWfB/YsOQDmPqH8hc4tMOVb63LmR1m/jHTK75h24ZVX8HidykV4giN5FS+P00yT5jB0z79O9z9d9ONqw6Z/ML/cLXM3h7dn2VtJtD7Qr9nMa1h/F0wehqLPviYEXHfmPQUu9bAvJfNIHWVvfFyOiH9rLmx97Cbt5oQnp3E+H2fmuMC2NB8WNmDhHmCDr3Nv/0/mteWw5yf+4wyrz9/1uSX276cMY2TWdRuMh2SdsHGz4q6MBba8K2p9/4Btf85vJiPM9fs03e/MLmGCwNf+340ub+Lt0i0bZj7kuscle0IYE6nW5ka0QQ4J2gTGGxS3Kyfb7qx5mSa1rfz/lfUEq9lJ4huVb0fKD+fgcd/oOOZnWxoMbzCvREczjwaZZ8x3bWlZu3faoIeBbJ9AgnIz4K4XRD3M7TuWnL+9d+4grYZviEEdx9Q8PCvp0mTkJVuGkPE7TL/kk6YLs5dB0KnfiaAeGwvvPUnc/3w41JWnPJlc/NLoYL3QF7VKrOCYtKPM/ToQqIzEmCjaUE4vOC9/gmr4K2NJnDVpjvk55veT8fMeAVpfmGcCm5mxi/ITDU9aq68u9xtRWaehC9fc71eETuW3VEXcXfLZNi5Guehn3Bgm5a87z5urhP6X2EGm5r3smu5Za2vJC0ggs3NL2VYz1bmfgpg5RdmsKmLLsM/L6xUflFv+B798rPJL6slbX4e7DG9PrN9AjkS3r72C+cp2l9kHhYmn4SOfU1qhqo659xU/Fix7OY82Ly96S2UeNgMsmreYWG7a8nxDSyxbGpAI+Z0vpXJu98xsYP0syaH7h3/5zHp17yFoiMiXqZJRgKT9n7g6hKT6/Dju3bXsT+qdFL2XJ8AVrYaw7iDBV0evn0D7v2XCaasnVvyB+CHj03AdNh15W47IC/DXDzE74eCi8evOk/n1sBKtOzzC4DrfmNSJyz/zEzbsAA2LTbBrchmXJ7iS3JAJAcbdan4+uuAN1wEVaeyHnwE5abTOnkfuT7+kBRjTsqWZXJxffUf2L3etcyRsHasaTmKKZNHQkAQc7/ZztAj35mLY2eeuSC+51+VG1jPtmmSeYKw7LPk+ARAQigEhJjB8hwO00oiN4vGGScIzMug94m1pqVvMcdCW7M/shvDr7qSN9efLRn48qtggKS6Rhdvf5EJCHz/ock15cwDp5OMrGwcts3BRp3pNvKW6tlWoYFXmsGUUk6blqCHd5qblnNlpsOX/yk5wvqqL02e3l7D3N+e0wnfvQXr5rkm7WjSj5+i+3DjpBEQFMKH87Yyae8HBTdIaSb9yp1/NQGTumDbjDo0B86YB1qJwc1Y1np8xdbhF8DO6L6k+Ycxae+HpkfDliXmmB17e8UCr+nJZtmN35mWhF0GwnUz63VgLzQnmYl7PzQtOgA69WNNxKi6Ltb5XfcbWPmleZjZa1jJwTqn/M7ks3bm0e30VkJzUkwX6YKg9I4m/fBz5pguthkpsPV797vmNnCWnc8lx77nosT1ZjCxc+1YYX7vbnq4KMXJ4vfMwKpAnuXDvE43czLkPA+LfHxMr5GeQ82Dr20FvaPi98OcgryUfgFcH9CUEyEtSfOPgB9PmQBbUBiENy4z93i5Us7AF88y+NhPAIw++BWkXuH+ALBOJ5P2fkCblP3saNIXuNj9bdcG26Zl6iEsbJM/3pudPGIeODpNr7XNTQdzKrgZYw9+ad5fNRtaF3tAmpluficK8ul/0XUG064957ctMMQ8+ClsnFGWlp3gmgfh838CcOnRRZwNbMyBhtpa8Vz5+Yw78BmRWafLnyduJ7z1R/PQLSjUFTwkIJivO04j0zeY6dv/g78zx9zT9BtbZi8l/7wsJu77yFyLArujerGl6RBznh84BAaO540vVzP24GzapOw3x8o3r5hgWWaqOccDdBnArrDeRSu+5Crw8StqEVmQB/luy4fDEZ34ufHFFW6d6va9TV6uue6wbfO7V8VB8Dqd2cHYA7NJ8w+Hy/5ufhML+B/bjVXQNf9Aoy44HQ08pNW6K9DVjRmrzrYcppXv6w+XfGPwVRzLK3vMmhyfQL7qPI3rf36LJpknTFqowuCtBil3m9cd5S+99BL/+Mc/iI+Pp0ePHjz33HMMG1b+DeGyZcuYOXMmP/30Ey1atOD3v/899957b62WWaS6xKbsZ+Lej8wFAUB4Yz5rNeW8Nw+7oy5iXO5PcHS3GQTnhQdK5NE7GtbW5NUCWPK+aWEyeFKp9QTmZTB59zuQYVqWZPiG8EXXGSanW2U5HGaQlqhmMOflgiBUnulydCaewnG2hx1dCLlrYei1ld+W1HwL859WM23Hi64BW3j+IwgIhmZtIflUifxL65sPZ23Lgu6/xQKza1qOolXqIfNU9ky8aaF07a+Lglb5+SZNQNxOExRu0wPLdrq6EYdlJ8Hyz7l1x0IaZ50sKls54xGVNWTfqaCmrIgdS1xEJwCGx7QGK7kadlA1adcT2j1TYtJrxb7bbtXdtd4vAEbcVBRsWPAm3PakuVEsbv6r5ol/QQvlsNwUM/3r/5rRdN0ZhDDPpIAp3nV8dctRbGg+3BwDBS0JbMvBt+1v4MZdr5qccCePwOx/w02P1Ek35J4nN7py1GU7Apjf4UYzqnAlxEV0YkH765hw4HPTsnLNHHODOPyGCy7bPDWOixLXw+adpkVKod3r4b0nTLqN6miN4UH88zJh0bvctm2uq7VzUmBjIq/9DfbSA3VdvPMLDoext5X9XteBJjfyZ/8AZz6xqcUGFukziiU+w4jOSHAdd6yZY7pHqhv+BfWPX8nA+OUlJ0Y2NQ84Ni00eRPjdsLrj5gHZXs2mIdQBd2Zv2t/vfvBw7Aocw7rO8b8RibGFb2Xm03L3DhaphVMO7Kg5LIXjYCrf3nBXkitk/fB//5pAvgF/J055iH9Nb9yr5zbl5sAEdDz1GYzInls7QQCLigz3TV4E8C8Djd5XG5zd7361Vqm7HqVRgVBJzoPYGX4FVi2k8FHl5jz5u4N5jiJKRhsafWXrt41Pze+uGrX3T2HmnuB7z/Cwmbcgc/5tNtdXrs/q9XW711B2yyfIALbdoGY1ixMML+p/eNXmOsNKOopAeDwhRsf5tQuc526sfkwhhxbYs7f375uehgWf/BqOxl7cHbRumLasLjV1aUezmb4h/F151sZemQR/U6sNhM3LyqaISgMJt4Hy4+WWI4B48z12ZL3XdfdvnY+Hc7+TIezP3MstDUM/G117TXjdLzJ1V7YY+27N6H7EFrmdDDp+Cra4+foHsYemI2vbXoB8MnfYcZfXL0gAg8W7f/9evBQaZVucNSqs0n7s3mxeR0dCyNvgfMM/prtG8yXXW7j7iPvmx62SQlmkPKOfcz6WnU2LX11DVOu6hk5pJZ88skn/PrXv+aPf/wjW7ZsYdiwYYwfP564uLgy5z948CBXXnklw4YNY8uWLfzhD3/gwQcf5Isvvqj1sotU2Z5NTNrzgStomxDSEu7++/lbfFAwYvyVd5vu6FAUtHX4sqTNVXzR9Q5WthpTNP93bxUNbgCQdpbWyfu49ue3iSkI2hLSqOpB2+J6j4QZT0P3wRDTBvwCS8+zewO8+SjX73qDtmd3mye64hEC8jJMt97P/lEUtC2UnWFaaBYGbYNCYeqfWNNqdFHOxmKcDl++bX+DCfhSkONx6/emG+Dqr+E/95lAxrpvzLH66m+5Z8tfmbTnfa7f9Tp3bPs3LP2gZNDWTWl+YTDpl3zY4z5X0FYKXHx5Ua6r+P3wxqNwJqHo/e0rXPk4s30C+azbXfzUpKDlT16OyWmdnlLWmkua81JR0NZywKRfsqHFZWVe9Of4BjK30y0QGGom7NloAhW1LDo9nsvivnW9XtzuGjPabxXsi+oJE4s9ZF76Ycnf5TIMj5vPlJ9fp+uZbcWCtpbJeQYmEPP2n0zevnrAx5lLn4RV3L7tOVj1pStom+EbwtyOU+tHgLrbILjht+QX/63sPRKuuh8sBydDWnAkrOCByJl42L2xzorqNU4eYeBxkxvSicXOxr3NYIG/egnG3QG3/wVCCpLPnImHV39rzjUFvm8zgX1RPcpbe/na9oB7njXrH3ObGTX+Qt1Et/0As58zDyzLYNlOLjm2hGv2vOcK2qb6hZPtU3AN9eP3cHz/hcuWk22CPMUtfMczrrMO74T//aboAQUw5NhiV2tVr+J0Mu7AZzTKTjKvm7WD636DbTlwOnzZ3GxI0byrCnLfpiaZnP9AvuXD2paXV70cw28wAyQCfs5cJu35oN6cFyotLxeWfep6ObfTVJj2OFwxg11N+rCrSR/e7/lL8yDk3Ho7+UHTG6rA5mZDzIMgTPoodq4xf9s2HPmZCfs+ocPZn820wFC46eFyB5K1LR9Wth4Hkx8yLWmLm3hP+b25eg0zKbru+htccpW5vi3QMi0O/jeTwUcXm/FSqqjLqa3wysyioC0FqQy2L+f63W8xbccL9E5Yg48z73yrcQnJSYGP/1pyQOBje2B+QVoJZz4BhYFbvwAOh3es8meQShg9zTzca9zCjMfgxuCvGX6hMP3JojqUehq2LIa5L8HLv4a/ToN3/lxUZ6QEr2px++yzz3LnnXdy1113AfDcc8/x3Xff8fLLLzNr1qxS8//vf/+jdevWPPfccwB069aNjRs38s9//pPrriu/O7hITarM060OZ3bCx5+7TmL7G3Xl2w438IC7A4K16AB9RxU9GQtpBDf+nh3bzfo2NR/GpZ2amFxsYE6OP602T8TSkphcfF2hkXDbU5xZn+T2Z3ZLbNei1h22DenJfPLNalqkHqb3ibWu1nst0w7Tcu/hgq58F51/nVJzbBtsJ22T9zHq0NdQbMCMfY26cToohkFh6WaU45SCbmctO5sWZI2iYW/5+VdTAqPgqvtMqgSAea+YYzKnjC6tQEB+Fu2SS+ebPRbahiPh7fB15tG/ZbAJ/mammbL7B4BfIDsSMsj18eNsQGN2NenN/X37Y8dXPTdsvePjY24c3n/K7MOTR0w3qRsfhoho0zK6wNI2E0kNaMT3bSbSIyDdXHAnnzQB92lPlD/Y1uGdJlBBwYjYU35nBmk5Tz7gs4GNzTH1/lOmdcuqL00ah3JG5a4qv/xsmmQkmBYgS7Yxft/PtEo9WHSDMXAC++xKBHXK0m+MOWYXvWNef/sGNGoKnfuVnnfjQvqcKBqog6BQ6DPajCacnWn2T3qyGen+zT+YG9I6zglcFVGZiVy194OiAEhB9/VtMYPY0GI4Wb51lDKjJnS7hHkdb2bg8WUcCW/PwEn3m94qBTY1G1rUGnf1VybYK2VzOmHOS/gWDKy6udlQVsWOpXvbYnmQW3aEu/8GH/zF/M5lF3sYedmNbM+oQv328TEB3GKDDL361VqaZCQQlJfO+C6RBd2gz5rrtfw88yDLchQEbor9diYc4oZdr9O82FgHdOzLh6Fj6HZ6K8OPLDA5exe8aVqqna/F25qvi87ThY78bFrqd3XveGqdvI/g3DT2RFVTy838PPjhEzNY1zk5naOyTplB+i4aXu7iHmnTQlqnmF4AaX5hhN78hxI9jn6K7sdlJ1ea1rXbl8PlN5tzWm42FORNTw2ohrRLlgWTfkn8gQM0Tz9qrq8/mmWOk4qmgaovNi109Rg6FNGJ42Gl0xvYlo95cNZzmEkJsGejGZjpnFRQ+Q4/uGKGGXgZYOHbJs/1+m8hfj+FYUYbC+u63xSci0+V2l4JF48wD88//psJdl08wgx+dj6W5WrJ+GZeP9ok7+WyuG/N9Yszj4Hxy+l8ZjsrW13BidCW5vexHL75OThsJ3nFehL55Wdz+eF5dDu9tWjGxi1MC8pty81vWUF9vezIt6Y3ULdfnjf85OPM5aq9H0KGObefCG5O48yT5hpr8yLz+xzZHJ+CFuh07Ftu0FtqWHA43Fk6/nZBEU1MK/Q5L8Khn0r+vudkmvQePS6t1qLWF14TuM3JyWHTpk088sgjJaaPHTuW1atXl7nMmjVrGDt2bIlpV1xxBW+88Qa5ubn4+ZXuxpidnU12drbrdUqKCRY5nU6c5/lBa3CKPYWv8H6pyrJVUdZ23S1LdZb5nBYM5a6vYL4up7cV5L0y8+2O7MnCdtfitHzMsmWtz7aL/mGbaaOmYWWkgmVhX3GHyRW0bUfRcsOux8rNwVpZ0CL98E+lipTmF0bwbU+ZE7N9puQ2qeb9FBxOQkgrEkJa8WPMIH7Z6gzW6q+wTplAdc9Tm3HuWmu6Nrqrpo89d7/bqqyvgscP1XG8204uObqY3onr8cvPxt5gRr196NzFAkP4rvk4dkf1AstiwJUFN8MZqaald0xr0wXmnM9RZvm6D8Hq8yPWliUlR0cG7I59sfuOhpTTWId/InPvdjN4A3A6MJrIwWN4+1RMiRucvleWPUDRkvk7Srw+b52q7LSylPf5z6237nL3+67Kb17zDnDnX7E+nmXqYUYK9juPQ1QzV64xu+cw9gT2Atsm3/LFecNvsV7/PVbaWTi0A3vh29hXzChje06sBW8W9gvAecUMM8iCO8dKu15wxQwcC94wb33/MXaHPu53zSvjO3vhnOPiV1f2pF3SLq44+KUZQAbgIHQuNk9CcEtiRk8r1VWsvPVdqCxOpxMGT8LKSMVaZYIX9uf/wr7jmaIutJguhdb811z7bkWrsQydPqOopS3A7X/Bev8prOSTkHQC+40/YI++1XynTVqW3TWtus+P7sznTv05/BM37HrdlZvUxoKLhvOu3aeoztt23dbbaj4XHIrozKEIc7T1xypRLw6Hd8SOboV18igc+Rnn4V0Q66F54UudW6rnu3B72fXzcRwxLd2SAhqztsWIomOluPAmMOP/sD77J9bBbWZz/a/AHn4DfFvy2ujc7Vb0u870DXYNrHPFgGK/Cx37Yn36d6z8PNixAtuysK9+APLzsJZ9Cmvm0LzghteJA0ZNhSFXk/XtTrZGD6BX4gYis09D3E6cP602PZrKknoGa+VsrIL1rG45kkuPmYf89uL3sDv2Lfn7UMY+bpu0m6v3md4Onc9sx5nVGfxL95wKykll8LGl+Dlz2R7dH2d+99K/07YT9m3BWvYJVrHWwkdD27CzSV/GHipIWbHsE+zug6vUrdbpdGIX//4vcExV6Tx6JgFr0Tuu3+lFbSdzdVhUibqc6/DHHjgea/ln4MzHXvAG7NmEBdh+gSZlUMG8Vb6+9PFlXsebuXHXq4TnJMPxfdhf/gf7ut+YBwXnU9v19nzLVkXB+nzzc7CXf+76bla3GFnyd+Hc79vhY3LX9htbOLH0fJ36Y7W/GOvAVhMQ/vq/JTad6RvMilZjGd2ht3vXOU6nOV//8nk4cdjkK67A8WhjcSiiM0d6tGNA/AoGnliF5cyjUXYSE/d/bObZ8TzTfSNIDogk3+FDcG469r5c7ktOKkrRB9hbfLnX8sWynSWn9x6JPe4O8A+CUbfCz+s4tugrYgvS8UVmn4b3n2JCo24sjx1HakCjojIXnEPHHPySphnHAUjxb8TXnabROmWfa6wWe/5r0Kro/ObsNgji3Ky37qqNeEFdxCRqo+65u42IaNOQIzfb9Ao5thfr2B5zPZt6BmfLjud9kFCfVOQ7tGzbE/rBXNjx48dp2bIlq1atYsiQoq4kzzzzDO+88w67d5fOqdG5c2duv/12/vCHogTvq1evZujQoRw/fpzmzUu3NnniiSd48sknS00fOXIkvr5eE+euVglni1obNGtUfiuWsuar7mWrMs0dxZc737JlzefutIps15F2Br9TRa0p8kMjyWvcyq2AhG3b5OXl4evri2VZ7pXFtslKOEpEQSsm2+GD7R+I7R+E0z8QZ3BEhS+Sq/U7s218Uk/je8ac1G0fP3JadiYhJbvUsmkn4mmUdYZ8hw8+wWE4A0NwBoSUHCW6jO2Wt5/cnc+dfVCVY+VC66/KsuUdE76nj+KTdv4W1s6gMHIbtyq1f93aRrkrdWId24N/fg42Fs6wSPLDmxCfXnSSa9YoGGwbK88cA7ZvQLn1oyr7qSoqsl136m2l96cb5XPnOHY4nbTMTcSRmVay7L5+5LToXOo3wspKxy9hv+umKDe6Nc6QRiW228I3y/Vbl+PjT3xoLM0iK9DV3bbxi9+HI8cMTJXbtB3OoLAq1dvifFJOuX53yuIMDCW3SauSwdILcPv8kJRBdEYCwblmf9u+/hwNbonT4YOPM4+W6UdNcAfID2tCXuMWZW8wLxe/Ewdw5GaXnG5ZZDv8yfUJIM0/nMgm5Q8UUZVzuk9yIj7JJ8mPiCY/Iqbsz+904nv6KFZ+LvmhUeY4Sc50zROck0Z05omi4IV/EHlNWmH7V2IAwwuoSr290Pqq+/ql+LVChl8IJ0Oa19i1VE1fh5WlOs5xvvm5NE+Lw1Fw7OQ0a49dmGalvOu6pHRCC3qTpPmFVeg3qTquiYNy04nJSHAd7xl+IfjnZ+NbrMux7RdAbuNW2OfkHHdkpOCXaAImeQ5fjoe1wbasUmVpnHGC0BzzGfPDGpMX1QK/hP04Ch7GnQ6KJi0govwyO534H9+DVewBq9M/iGOBzXAWOxe0CMjH99QR128VgDMgmFO+4WT6hmJhE5KbSmRuconfKBvIb9SM/IhoAPMblmUe1J4Kbkq6f1EXcHeur879/A7bidNyVOkYtXKzyTyVSGBeBpl+IQQ1bXlOTlMbv8SDrnNmfmgUeU1alb2y/DwCju4qFezIi4ghP7JZqdmrWh+tnExzfi4IHpwNjHSl+qmte6qqLFsd92M+yYn4JpnUT87gCHJjSre2Pd92yyublZuF37E9FD8jOP2DyA9vjDO4UYmeE+6qyjn43LL5nj7mqktVYTsc5EW14nheyeufwu1aOZlmW8V7L1gW+aGR2D5+4PDB9vHFkZ2BT0E6P9vhILdZB9e53ff0cXxSS7ZKtrHIad3d7fvS6j5PVdd3UdVtuHvfWpltVma7ld1G4TQfZx75lk/F7gG8WF5eHkuXLiU5OZnw8PMP6FehSOTSpUt54IEHWLt2bakVJycnM2TIEP73v/+dd7Cwqjr3gti27XIvksubv6zphR599FFmzpzpep2SkkJsbCxffPHFBXdmfVW8pVC5rYTKma+6l63KNHe42yqqrPncneb2dnesxDH734C5uLP7X4E9/q4LPwkv4HQ6OXnyJNHR0TgcjgqVpVHWafIcvsy4ekjFE8qXsb5zt1ml78y2sT78C1bBwAD2wCv5j3NQyWV3rsbx+b+ADqUXb9zCtJrpOQwsq9R2y9tP7s7nzj6oyrFyofVXdNnzysvB+vxZrD0boHAk0WbtzDFoWeafnz92z2Gm+1g5x0pl6yPA/+ZuonnaERJDWnD3pIFVWl+N7adq3K479bYq+/NC5XP7OB7XDeu7N7E2mMF0bCzs6U9A27KX/+HttxgZZ/L02f6B2Hf9nRcK0q345Wdz396XsdLMb90XnW/jaHj7in+2nWtwFIyWbbfuhn37X6pUb8HkUrMWvo21fj5gWrnaHftid7gYIptD4+Ym9ce5uefcUJHfZN/8HK7f/SZNC/KMHw1tw9edbuWave/RMq1ZwWfujj3t8fMPZJSZSsJLj9Ei/UiZb9tYMHA89sipptXMecpcoe/n2D6sNx7Boi0Aztv/wgs7Suao/NWVPbHmvYxVmNIHsCObsiR8ELsaX0yvkxu57MgC1zrsDn2wb/h/ZZazOlSl3l5ofdV+/ZKXi/Wf+7DSkrCxeKfnr5h+7WVV3m5dXIeVpUL1NvmkyQvavB34+JllbZur975P2xSTg9HuNxZ7wj0X3EZ1nZerdE3cIcO0/D0np6vt44t96XUwdHKZD0yxbdPKvqDF8KqWo9nYfFiJskRnxHPzzlewsLEDgrF/9aLp/npkN463TKOXdL9Q3un5ILk+AWWW2fr+I6wVn5eafjYgiq86TSPNP5whxxbT98QaoGmZnzcpoDF+zhxXkLzQ6cBoFrWbzJQbriiaeHA7jveecC33Xs9fmi7sbl5flfgubJsgO4NMK5hflZe2LDcb4g+aYSICgk1qg8Ic/LvWYP34PdaRn4GiHj52216m5WqICXizeTGOeS+b98IbY9/779KDexbfpwveKDjnFKwvKBT7Vy+VuUy11Mc9G7E+/isW5h55Qbvr2N34olq7p6rKslW+H8tKx/rP/VhZbbEth/luossJqlfC0nfepn/CKk6EtKTT1Teb1AVVuK+qyn11KbYNP6/DitsJSYlw9gQkncAq/uAkKNQcx8ER5ncmNwdys0y9yM2BZu2wr7gdIpud/zuzbdi2DGvxe1jpZ8/zCTua68kbH4YuA4om5+dhvfekKWuBgxGdaPPQX8+zrgvvk6qcp6r1u6jCsu7et1Zmm5XZbmW3UVf3aHUtJSWFyEj3UuBUKHD73HPPcffdd5cZwIyIiOCee+7h2WefrZHAbZMmTfDx8SEhIaHE9MTERJo2LftCoFmzZmXO7+vrS+PGZQ8cEhAQQEBA6fw+Doej3Ivxeq/YCea8+6Cs+ap72apMc8c5J9Nyly1rPnenubO++ANFI7gDXHIV1hUzzvuQouzVWkXHbgXKcjaoiZmnOkanr4nvbOK98NJDkJuNtf5bmnZryYnQWLPs0d3w5X/KL87p41hfPg8bvoVxd5bebnn7yd35ytyoG8tWZH3nWX+Fly1PVoYZUOpQwYnUxxfr+t+WmT/xgkdlZesjkOsbSFyjTiWXren6Xd0quN0L1tsq7M8Llc/t49jXDybcYwL5G77D6jsaq335Oae3xwygefoRup3eipWThfX5P/FtNZ08H3/6J6zEKmjRvb9RV45GdKjcZ+s+2HT7P3UMK26XuZmuSr3NzoQv/m1GlC807Hqsy2/Gqub9ft6yWBZ5vgHM6zSVO/e/CWlJtEo7zLSf/mu6uAKENcaa8jusCw0OERLB593uJDblADEZxxkakQkJB7FPx2OZ2yVYPx9rz0aY9MsSA66cW2a3vx9nPnz7WkHbuYJl572MT5s7TC7Awmm71hTlYS/cXNIJRifNYcixxQQXH/iw90isq+7DOl+QuqqqUm8vsL5qv37xD4BBE2DJ+1jY9Elci8NxedW3WxfXYWVx97s4cRjeeMTkQ/cPgg4X0yOzKX75Oa6gLeGNscbcVroO1+B5uUrXxN0uMQPUffoPfApzAbbpYY7/wgEjyzPuDvjfTLCdDIhfzs4mfUpsd9iRBa5gnXXZFKzQgkHZ2nSDbpfArrWE5KbR58Qa1re8vHSZTx0zeZXBtHq77jew4C1IPU2j7DNM+fl10v3CiM4sdi/WsY8ZmG3tXEg0g0tHZpfMr3s8tDWbmg3lQKMuYJ1TpzpcDG16wOGfiMw+TZczO/i5Se+iferuOdM1zTyEdk3PzYYju821z6EdcHQvuDmoUiHr0HasV39n8rSHNzY5Tgvfu+o+rOCw8y7P4Ktxrl+AoyBFmnXpdeUvUx31setAGHubq5yjD33NmaAYHI6LLrzs+dTCde1DEy8y+Zlt2+TOLG/Z8ta3dq7JKQxYF12G1bQ11emnmAH8FGMCkF3adKv6CqtyX12WHkPMv0IFY4zgdEJIuFvnWNeWLvSd9Rlp7iG+/9gMtmqX3UXcGnUL1rn3Gg4z7kHq8w+5xjzZF9mDdrX1m+zu+qoyX2WXdfe+tRLbrNR2K7uNurpHq2MV+ZwVuuLdunUrf/vb38p9f+zYsfzzn/+syCrd5u/vT79+/Vi0aBGTJxcNlbRo0SKuvvrqMpcZPHgwc+fOLTFt4cKF9O/fv8z8tiKeIDgnFT5+viivZ59RJsl9FVu+1iuRTc2gDQvfBmxGH5rDR93vJTznLHz0JuSbUVJ3NunDsthx3Nfd1wy0cXC7+R+TF5LXH2Zs44tZ3Wo0af4RdfuZPEl6CnzwdNEIsf6BcNOjpYM4IsXzvJ2PZbG0zVVEZ8TTJDMREuMYmT+XNS1H0jehIE+9w5eVsVdcaE3lczjg0uvgq4IHN8s/h6jJF1qqbFnp8M7jEF+QY9HhYwbM6zOq8uWrojT/CLjpEXj7McjLKQra+vjCjb+HwqDLBdiWg7iIjsRFdGRoQSuzl+dspOfJTQw+tgQ/Zy6cTYR3Hzff7Zjp520ddkGbFpUcbRoT8Bngt5y1rcz+DMs+C3OKBrjj0uvMMgfMoCclgrbDroeRU3VOPFf/K8j5/lP8nTn0OLkJEo9ATGxdl6r22DZ8+3rRIJY5mbBrLaPPnW/CPRDoZYPXdbuEOZ1upcfJzRxq1Imx06e5d/w3bWPq8MYF+DtzuOWnF+HsfGjcgsuOprhyTxLZDAZeWXLZUbfCz+vBdtIvYRU7YgaUfN+2zaChhakPhlxtBktq1cUMhnjyCMF56a788/j4mt+SgRPMb3WfUbBvM0fmfEBs6kFsLPY36krH66bz2bYLjHR/+U3mdxAYeHwZuxv3crW6rZLEI/DWH12DKrktpjXLA7pxNrAJow59TUhumhlA6q0/mV4ZBSl86D0SOpUxuOS5ImPYHtOfixPXcyawCVEDx1fu81TE4Ens2Lydnqc24WvnccmxJYAb5/a6tmsdfPZPE1xv0hI69SM2JZLjoW3Id1wgzJGebAK3mOsPRtxYK0X2aJbl9rVEpQSGwPg7YfgNkJQAGSlmHIyMFFPvGrc0A6+VJbQR8zpNZdz+z8jwD2VPZA/G1FxJRTxOhQK3J06cOG/A09fXl5MnT1ZHuco0c+ZMpk2bRv/+/Rk8eDCvvvoqcXFx3HvvvVCQ5uDYsWO8++67ANx7773897//ZebMmdx9992sWbOGN954g48++qjGyihSFT7OPCbu+wjSC1ofxHY1Nxm6QS1t0EQzamnCAZpknmDwscV0TNoF2eaCOy68PUvaTDI51jr1gk59zXL7foTv3jSjRQPdTm+lY9JO5naaClTTaMjeLD8fPvlrUaAlKAxufcwMhODlHiqvK6TUijwff+Z3uJHpu1+D3Cy6nd5K87QjZrRggEsmcDa37N4wbus1DH742AQe928hJmAgiSEXaJV2rvw8+PQfRUHbgGC48WHPeHDRqjNc/QB88WzRtAn3mOlVkOsTwJZmQzjQqCu3pywqGpxy00L4eZ0J4vQeWfEVpyfDkg+KXl/5C1jwBjjz6Z+wgn1RPTgdFM24A59DYf67HpfCqFvMee/Ibg5+/ibtkvfgxOKHNhMYOeqWKn3WeisolK1NBzEgfgW+dr4ZDOfOZ+q6VLVn19qiHiLB4SaweE4AbndUL7p0GVD28h6u8GELwNiKXBNefhPZW34gID/LPAA5/BMc/onexecZe1vpdAtNWpYI+o46+BWk94CQgl6XO1e7HqwQEW0CMRSMGH7HMxx78U+0TDsMBSkPGt/+qOmhUciyoFM/Znf1JzLzJHkOP1IDGvFQbFfYtv38n6ltT/Pv0A7T6vb0dler2ypZ+UXpoG3jFtCmu2nBnZ1pfqeyM0138aZtze9iiw5sKejm+2H3+7g76RuI22mCiQXXmoRGmkYYblrW+kr2RvbgdHBT7vEr3RO02lkW37eZQJuUfYTlJNM+eY/nP/xJSjQPagtbRJ86BqeOcS2Q4/BnX1QPlrU+T9D7h4+LHvT0G20ahUjtCAkv+i2pgMSQFrzb60GC7AzyLPfHFBCpDyoUuG3ZsiXbt2+nY8eOZb6/bdu2Mgf8qi433ngjp0+f5qmnniI+Pp6ePXsyf/582rQxScTj4+OJi4tzzd+uXTvmz5/Pb37zG1588UVatGjBf/7zH6677roaK6M0DDUSALJtRh6eS/P0gsHIwpuYYEFZucsEfHxg0v3w2u/BdtI/YVXRezGt+ablTSUGxnDp2Bva/Rs2fQfffwSZafg5cxl7YDZk6tktK2dD3C7zd1iUGfXTky/cxaskBUWbelsQeGyUfca8ERxubvyXHKjaBnx8Tc7Hb14BYMDx5XzT6Wb3ly9stVcYkAgKgxl/gZjq7T5ZJb2GsWz9TgbEr2B7zAAG9S3VprDSkgOjYPJTJmC76B1zU5uebFL3bPyOZhGXkxBagd+DRe+6uqFy0QgYOB7SkmD5Z/jYTkYd+pq48A60SCu4dmsUY1LhFAamYrswp/OthGedwbYsUgMiqUT4uMFY12IEHZN2Epl1Go7tgbXzgPZ1Xayal5sN371V9HrS/dC5Pxzbx7pvv6V18n4y/UL4oc0EupxvPfVRSATzO0xhQPxyIjNPEZJXclDJI2HtiO1aOgUSACNuJGfzUvydOSaQ999fwujppmv1gjeL5ht/p+mZUygolC+7TKf3ibXYWGyLGcgviwdtz5EUFF3xzzXiJnj7TwAMPbqIpunHYNnP9ExMMwP0BTd3jVrvlvRk+KngOjIwFK68ywSHwyv2MDHDPwxuexIWvw9rvi5646r7ICj0fIuWYFsOjoWXv89qgtPhy5amlzD8yHdmwpqvzYNCT5SfB1/8q+iBX1hjc24p6H7v78yh+6ktRGSdhuwycmX+uBQKcvTj6w/DbqjN0ouIVFiFArdXXnklf/7znxk/fjyBgYEl3svMzOTxxx9n4sSJ1V3GEu6//37uv//+Mt97++23S0277LLL2Lx5c42WSaQ69Dmxhu6ntpgXvv6mS2xNdlepD1p0gEsmwpo5RdNCI2Hqn8hZlVD+cj4+pltgr+EceenPxKYeNINiLHobfIbXStE90tE9pgUCmAHIbvidgrZS/XoNMylLig2+wsipVeuOX1zvkbDsU0hLouPZXURlJnImKMa9ZdfOg40FN60+vuZ32JOCtgV+bDaEH5uZvHTlhFwqz+GAAeNMl96Fb5uWdQDH93Hj8X3sbNybDc3d+J2M22VujilotTz2NvP38Btg5xo4dZRm6cdoln7MTLccJkdmUOnjICUwqvo+Xz2W7/BjUdtruOHnN03u0qUf0qjbfZwNLBl8qne9D1Z/bQYlA2h/MXQZaIL/sV1Y2zKHtS3rLsWJJyjeWvehke3hzHEWLFmHf342u6N6cV95LXhDG7Gw/bWMOfglAfnZkJkGc1+CJe+brs1gfie6DCy1aL7Dj03Na26watr2MEHnguu33onrIHEdhd+0Ewebmw2GHDd7I2xZUpT2oe8ouOiyypfNxxeuuB1iu5hjs8uAkgMtebAd0f0ZdHwZAflZsG2ZOTd7oqUfmmtWCtKn3fMvk5f1wFZ2fr+EDkm7CHBm0zItDj56Bp/Gk1051ZunxsHcYjGDsbdDuM4xIuLZKhS4/dOf/sTs2bPp3LkzDzzwAF26dMGyLHbt2sWLL75Ifn4+f/zjH2uutFIn6t0FfnXJyTZdoE4dhZNHmLh3J5FZp8jxCeC79hVo1Z2dyWWHv+HixPVF06550AQlG5BKH2cjbiJl03LCc86S4/DH/5Y/mVHeOU/gtlBQKIvaTebWHf/F35kDmxcT26UlR8Ib1r6HgoGYZj9XNFjA8Bugdde6LpXUV2Nv5/hP22mRfoSEkJY0q8ZWo/j5m3yLBQOtDDi+nO86XH/h5XZvOKfV3i9NF9mGqlG0GVzn4HbTCrlgIKHup3+k++kf4cSXZkC4boNNLs3iwZ/8fPjm1aLXI28pehDp6weTfon95h9cAyNBQd7KWP3mVFV8WBt+bDqIPifWQl4Oow9+xeddZ5jAuDc4stukM0pPhql/hOgLPDxMPgUrvjB/W45Sg47KOYJCoGUndjfOcmv2/ZHdiQ+NZVjcd3Q9s81MLAza+vrD+LvqbH8vbz2O635+m8D8zFLvOSjoifXyPmKjx3EkovzrOst2FrW+xIL+Vci1Xlz3weafF8n1CWB7dH/6J6w0gex184GL67pYJe37EVZ9af52+MD1/6/owW/PS1l0OIIf049z7e63CczPgkM7mHgmi3mdbiY4N92kpCsM0vcfZ3qCiIh4uAoFbps2bcqqVau4//77efTRR7HtgpFILYsrrriCl156iaZNlR9G6rGUM2aE8d0b4MA21yBYAMUvCSfvfhdS3LjQ2b0BvnmF3ilFI+qubz6cgT2HVnfJ3eZ1gfqAIL7schs9Tm5iT1RPpjavWLfQ1IBGrIwdy8jD8wAYdWgO7/f4JXk+DSx30oI34Uy8+btV56J8dSI1wdeP2V1n0DL1IAmhsdxXkNak2n5/+l9hgjmZqXQ+s53kwEjYlUFYdg6p/hGlAw3xB+HzZ6EwkDj8hvIHyGho2vWCe56FTd+R9d37RUGSk0dg2RHTurlRDIQ0Al9f8PEzXddPFAx81Kw9DDgnENK6K1tjBppWcsCRsLbEXnptbX+yemt1y9H0yTkISSdomXaYixI3sK1ptbfNrl7ZmbD0g4JAUUE9/OETuOG3519u0btFg7kOHK9eIjUgwy+M7zpcT9eJ15kHMqcLWskPux6imtVZuU4FN+e13r8jJDeV4Nw0buodzeI1PxGVeZKLEteb/OlJJ7g26R12Nu7NitbjylxP2+S9RS22O/aBqJpL++cNfmx6CX1OrMHHzoeNC/Dr3pVcn1rIs+uO1CT48rmi16OnlTkGw8mQFnzVeRqTd79LgDObtin7uHLfJ4TlJBcNmNfuIpPmQ0RqldfFGjxEhQK3AG3btmX+/PkkJSWxb98+bNumU6dOREZG1kwJRepaTpbpOrtjZemRscsRnnMW3n+SgJa3kO0bVHqG1CTTgqmwCyqQ6/BjTcuRbGk6hNKdzuR8zgY2ZlVs5Ue/3R7dn85nttMq9TAR2UkMObaY5a2vdGNJ93j8CWrnGtiy2PztHwjX/tqkkxCpQfkOX+IiamjQO/9AuOQq+P5DHNgMOr4MPlnGHUCmTxDp/mH4OvPgZyA3x+TJK2xt3uNSuLwCeXEbgoL0Mu/EN6bbqS10TNpJi7QjRe+fTTT/yjLhF6ZV1DlWtxpNYF4m/vlZLGl7NXeXlZNcKiXPx9+0GH/nz1CQA/RQo6oNYFej9m6Cea8UBc8K7VoLaWfLX+7wTtixwvwdFGbynkrNaX8R3Pdv2L7C/F5WZsDCauZ0+JIaEElqQCR06cVP+0wqv+3R/Rl1+GtapZoB0rqf/pHWKfvhsvYFvbKKXJS4oejFgLKDuw1Jun84uxtfZNK3ZaXT4+QmV2oeAFLPwPzXTGv3xi0guhU0aWX+j2puUkXUBGe+6RmWnmxed+pnzvPlOBEay5zOt3LD/g8gN8vkaS4U1dw8FKqpsoqIVLMK/Vpde617rSFmz55d2fKI1C7bLreLl29+jslNterLoouE4sKiTC61pm2gSSve3JGO0/Lhhp/fICI7CRLjmJTxAV92nl7UejMzHdbNMzlZCxPqA4fDO7C07SRSAvQApE5YDpa0vYbbdr0MeTn0PrGOvZE9iQ/zvNyW1S7ltMlZV2j83Q2+tYnUE4MmcHLtUqIzS6ZNCcrPJCizoNVo9jnLtOoM1zygrtblyPINZkuzoWxpNpSHhrWEn9eaBz9H9xS1eixu6GST57EMuT4B7qWwkMpp18u0PN/4Hf7OHEYf/BLyh3rWQ7n8PJj7clEuZAq63zdrB0d3m0DNliVAGUFnZz58+0bR61G3VGjwJ6kkXz/oU7MB2+p42H02qAlfdJnBQ81PkD3/LQLys8xYBh//Fe54xjVfWHYSbVIKGmVEREOnvlXedn2wqdlQ17gbfU6sYVvMIDPg79E98MnfTPAWSjdoCQyFkTebFATVbfF7cLAgXUdYFFzzK5OT/TyOh7WBqX8g792nTAtsINsnkICb/wDBYdVfRhGRGlKhwG1ERETNlUSkLnz8V9M9PKY1RMfS8YzNmaBoYlMOMCB+OeSWHH2XZu0KBhkYCM3bl7i5T927HYAvO09nyq7XCc5Lp0VaHOP3f8qidpNNDtvn/loiYEtQGIy7g6+ORClQUMfOBjY2rewWvYOFzehDX/Fhj/vqulg1y+mEr14wA45QkI+t9+V1XSqR6hEYzIc97iMiO4nojHgmtMjj0LZtRGfE45+fTZ7lS1BIsAkU+QWYBxYTfmH+bmAqFSgJjzKDPA4s6J1g2yYQl5dr0ghZDt0Y17Uxt5GybS3hOcnEph6ChW+ZnKSeYu28kkHbdhfBVfeaPKP/ud+kTNi0EDp2LJ2jd8sSSDhg/m7WDqozT3YN8fjeN/WN5YB+Y3kvLoTrf36TRtlnzDEz50UINMdL95M/FuXaHjCuzN4BDdGZoBjTonXvJsJzkumY9JPppfLWvKL8sGXJSjOtcbctp3HEaE4Hu5FC0ek0PRszUmDQhLIfwGxcaBrTUPC9XvsbCHEzLtGuF3M7TWXCvo+xsJnfYQqTo1u5t6yIiIeoUOD2rbfecmMuES+ScNB0zTtpunxOKHMmC3oOheFT3MqdlhzYmK86T2PqvncgJ5P2yXu4a+s/8CnshktBMv3el8OoW82Fx9Ht1feZpPIuuYqE1Ytpln6MqKxTDD62FKjHrS/WzoUDW83fYY1h4n16gCD1i2WRHBhFcmAUjOrF11klf2sVSKlGlmVa4/n61XVJpFBAEN+1v45rd79j8lWu+8Z0afaE7uCpSSY/MpjrrEn3Q59RReegjr1h3xY4m0iblP0cLp5WJTMdlnxQ9Hr8XQq4SbnS/cOZ22kqN+581QxEu2MF/VoF8mPMILqcKmjB6eNrjj8pMuQak8YEGHl4LgH5xbqotO5u0mplZ8DJo2ag5uP7zTggAEd3c/OxfWxsdikbWgwn31HOecG2TaB3Y8HgcNuWwc2PlpglNnk/bHyvaMKEX0C7nhX6KHERHXnz4plYtk2WX0iFlhXxZLqObTiU2EUarvw8CAgGhy84y3l63G0wjLjRpEOogJMhLeDmR8l790l87fyioK3DBy6+HIZdV6cDOkg5fHxY3PYabtr5P3ztfPomrIZDO6BtxS4QvULCQVjyftHryQ+qdVwDpYs+kfrreFhblra5ijGHvjIT5r9m8lK2v6huC7bkfcgpSFnSb0zpFrP9rjCBW6BX4oaSgdtln5jWeRTkpG7TvdaKLd7pTFAM37W/jqv2fQTA0KOLicpMJDA/y8zQY6j7LTgbirY9oHkHiN9fMmg78Eq4YkZRftji90gHd8C8l+H0cXzsfAbFL6NT0k981/56oIxrjaUfFAVtwfSCfO1h2rW5loONuhCVmciE/Z8U5aAfPMmkgKmEbN/gSi0nUleqcn2ua/v6R4FbqZfc+rHy8YX7nzMB3DMJcPIIa1ZtJCrzJDk+/myLGcgtN1R+wCva9eK79tdzxYEvsHCyq3Fvek69WwFbD3c6uClrWo5i2NGFpvvcl/8xg3HUJ7nZ8MW/i7q7Dbm67m/iRcTj6UbAO+2M7ktU1kn6JawyAZBP/wF3/RWatDQzOJ00zjhB48wTHA1vX/MFOrqnKEVCYAiMnFp6ns79TR7L1DO0O7ub0Jxk0vwjiMw8CTvnm3l8/WHM9Jovr9QLByK7mQHsfjBd5ruf3lr0pie0Qvc0lgVDr4HP/wVAnuWD71X3nj8tSbuecO+/YcXn5K/4Ah/bSVTWKabseg3WZJvBxApb1a/6ClZ8UbRsZFNIOgE5mVy190PWNx9O1zPbCCgMrncZqPouXkfXTVJdFLgV8fE1I6FGt2L9weod2GJfVA+OhbXBaTnI9g2mp4K2XmFzsyG0Td5jcgImnzQtlALqURe6Re+50oPQrB2MvKWuSyQiIjVoVasx9AvPhj0bTR7Kj56BfmPh8E6I28mtBbnO0/zCYERHaBRdpe2Ve7PqdJpzaqERN5Xd0tHHB/qOgWWf4MCmx8lNrGtxOcPjvjUDkwFcem2VyykNzPAbTI+jn9e5JiUGNyemVdmDKDZ43QezNWYgUZknWdVqNDe5k0vazx9GTuWj0zGMOfglTTNM61u+ewsObDODiv28Hha9U7TM+LvNoHdfvQA7V2NhMyh+WdH7zdrDdb9RShQRabDOPxSjSAPz0IReJf5Vh0y/UHXP8TaWg0XtriXbJ9C83raMTmd21HWpqsfezbD+G/O3r78Z4EE5KUVE6jXbcsB1M81grACnj8PCt2H3+qIBKoHQ3FT44OkS06rV1h+KRqKPjj1/S8e+o12DkvU8uYkOZ3fRNqVg2Yhok4NTpCIcDpj8EKeCYlyTtkUPUH7/8jh8+KHNRGZ3ncGJ0AuP81Hc6eCmfNrtLjY1G1o0ce8mePFBmPe/ommXT4VBV4J/INzwW/O6mFS/cJj6B/O+iEgDpcCtiEgZUgMa8X2bouHqRh6aS0hOSp2WqcrSk01rhkJjbnNrwD0REakHAoLg5j9CcHjJ6UGh7GvUjbMBUeb1ySPw8V/xKS//f2VlZcDiYoMMjb+rKE9mWSKamJQJBQHlKw4U61Y99jbwD6je8knDEBDE3E63cCi8I7sb92JX44vrukT1ltPhy8rYK/iq87SilvUZKSVz1g6/vmgBy4LLbmBux5tJ9wslxb8RczvdAuGN6+YDiIh4CKVKEBEpx+6oixgXdAJ+WklgfiZjD87my85eml/r5FH49O+Qfta87tQPBo6v61KJiEhtioyBGf8HmxdBZDMzsFd0LN98+xMRWWeYsus1gvPS4fBPjEl1sKD9da5Wr1W2/NOic1C3S9zLrd7/CtMqGPBz5pppbXpA9yHVUyZpkFICIvm68zSC7Ayclm6Ha9rhiE5w1b/hy+fhQEFu4T6jYOztZbZ2PhDZjQONuuCwbZxKjyAiosCteD8l/ZYaY1kw8R6I2wWpp2mdcoAep7YAXtY6Y8cqmPNfyCkY4CEkAq5+QF0DRUQaouhWZlT4cyQHRjGn0y3ctPdtyMuhy5ntpPpHsCq2CgO1FjqyG9bOM3/7+sPY0tsvU4feJPs3IiLHBHydWDjG36Xzl4i3CYuEW/8Mu9ZCXg70Gnb+emw5cKqaSw1SDEG8iVIliIicT1CoGUihwID45ZCf7/7y+fmw9EP47J+wbj6cOga2XTNlPVdeLnz7Onz+z6KgbXSsaW0V2qh2yiAiIl7jRGgrk2eyoJVt/4SV9E5YU7WVpiaZHh+Fg4oNvca0/HWHw8GO6P6ulzui+0OztlUrj4jUDYcDegyBi0dooDERkQpQi1uRGqQnefVEh4s5FN6Rtin7iMhOgp9WAZEXXs7phDkvwtbvzeufVgEwwz+CuPCO7I3qTlx4x4qVxZkPW5aYgPDFI0zOwrIkJcIX/4Kje4qmXTTCtCDWAA8iIlJMqeuVK++Gb14B4LIj38LqpjDk6oqvOC/XBG1Tz5jXbbrD8BsqtIotzQYTmXUKh+1kVasxuJFgQURERKTeUOBWRMQNG5sPKxrNeuUX0PqO8+f9s2347q2ioG0x4TnJ9Dy1iZ6nNrGu+WVguxngz82B2f823cwAln8Gl9+EZcdgW6blgo8zF374BFbONl3RwAz+Mv4u6DdW3UvrAT0QkqrQ8SNuGTCOdVt2M+j4D+b1wrfNOYiuFVvPgjfgyM/m7/DGcMPvzj8gWRnyHX4san9txbZbQaoX4kl0PIqISHEK3Eq10UWG1GfHwtpyPCSWFulHIDGOdhF7ONjoPDewyz6FdQX5/CyHCZzmZMH+H03O3HwzyMqg+GWwpAnYF50/qJqVAR/PgkM7iqalJcHcl7klMNqVg3B43HzITiqaJyIapvweWlawZa+IiDRoa1uOJN9yMOTYUjPh+w8Z3Hw4a1qOKnG+Kvf6b9Mi2Pid+dvHD258pNbT9OjaVOozHd8iIg2DArciHkAXXl7AstjYYhiT9n4IQP/jKzgY0aXsYOvaufDDx0WvJ/0S+ow0f186GXKyYcO3sOgdM23lFwxtlsCqVuW0iE07C+8/DQkHzGu/QGjbA/ZuAqBx1kkm7f3gnPI6YNAEGHEjBIZUzz4Q8XL6rRWpmA0tRpDn8GP4EROAHRi/nIFtIsoc3KyEI7th/qtFryfeqweIInVE5z4REe+mwK2IiJsORnTmVFAMTTITaZF+hJaphzgW3q7kTD9+DwveLHp9xR1FQdtC/gFmcBa/ANeNbf+EVThsJytix5UM3iadgPeehDPx5nVQGNzyJ2jV2bTcXfgOHN1dcv1tepj8hE3bVPMeEBGRhmZLs6EM79Ua5r9mJqydax4oXnkXBIeXXiBuF3z6D8jPM68HXln6PCgiIiIiblHgVkTEXZaDjc2HM+7A5wD0j19RFLjNyYKlH8LaeUXzD78BBl9V/voGjjej6s57GYC+J9YQlpNClm8QfDgHUs7AmeNm3QDhTWDanyE61rxu3Q3unMW89z9l0PHv8XXmsbblSMZPu1m5bEVEpPoMvBJ8/WHOS4ANO1bAga0w7g7oNdycc5z5sPxzkyrIdprl2nS/cOtcERERabDUK+DCFLgVEamAPVE9GJe0ApJO0DZlHzHpx/HLz4aXX4KkhKIZB06Ay2++8Ar7jwWHA3vOS1jYdEr6yUw/ec58TVrBtMchoknJ6ZbF/qju7I/q7po0XkFbERGpbn1HQ0CQCd5mZ0BGCsx+DrYtg2HXw5IPIG5n0fytu5kc6xUcjExERERqnwKonktXUiJSL9XUice2fGDINfDNKwBM2PcR4TnJRTP4+sPIqXDJVe63eu07GsvhgK9fLGqlREGe2rBIk/pg/J1ld0kVERGpLT2GmoDst6/DzjVm2r4t5l8hywGXTTHBXB+fOiuqiIiISH2gwK14BHeDbHoKJB6h90jSF35ASG5ayaBtbFe4+gFo0rJS66R5BzibCGFRJmAbEmFSKYiIiHiKsCjTkvbn9fDNq5B6uui9iGi47jcmuCsiIlJNHprQC6fTSWJiIjExMXVdHJFapcCtiEhF+fmzpelgLj26CIBchx9+Y6eZHIBVCbQ2baMBxaRe0kM3kXqo60Bo2xOWfgDbl0On/jD+LggKqeuSyQXoN1lERMR7KHArIlIJPzYdTER2En75OaxtOZLbL7msroskIvWQAizi0QKD4cq7TcBW+dVFREREqp0CtyIilZDv8GVp20l1XQwREZG6p6CtiFQTPbAUESnJUdcFEBEREREREREREZGS1OJWRERqVHW3nFBLDBEREffonCl1QcediEj1UeBWRGqULtxERERERERERCpOgVsRERERkWL00FGqQsePeBsdsyIinkuBWxGRStAFroiIiIiIiGfTfZt4OwVuRUREREREPJSCDiIinkO/yVLbFLgVERERqWW66BcRqV76XRURkfrIUdcFEBEREREREREREZGSvCZwm5SUxLRp04iIiCAiIoJp06Zx9uzZcufPzc3l4YcfplevXoSEhNCiRQumT5/O8ePHa7XcIiIiIiIiIiIiIhXlNakSpk6dytGjR1mwYAEAv/jFL5g2bRpz584tc/6MjAw2b97MY489xsUXX0xSUhK//vWvmTRpEhs3bqzl0ouIiIiIiEh9oxQNnkPfhYjUR14RuN21axcLFixg7dq1DBo0CIDXXnuNwYMHs3v3brp06VJqmYiICBYtWlRi2gsvvMDAgQOJi4ujdevWtVZ+aRh0oVB3qrLv9b2JiIiINBwPTeiF0+kkMTGRmJiYui6OiNfT/ZRIzfKKwO2aNWuIiIhwBW0BLrnkEiIiIli9enWZgduyJCcnY1kWjRo1Knee7OxssrOzXa9TUlIAcDqdOJ3OKn2OBsm2XX9WeP9VZVnB6XRi23bRviu2P6nrferJ3215+8kby1wfePJ+d1cFvp8GVW9r47utD8dPWerD53L32PakOlCOUvW2LGV9Z+5Oc1c1r+9X43tUvizVqa6OAQ/Ybp0f75UtS23U70pso/g8btVbb+SN5+qaVl/qsrvrqw/fWTnqbb31Nl5wbeYNKrLfvCJwm5CQUObT0JiYGBISEtxaR1ZWFo888ghTp04lPDy83PlmzZrFk08+WWr6yZMnycrKqmDJJcjOcP2dmJhYa8uK+SFITk7Gtm0cDkeJ/Ukd71NP/m7L20/eWOb6wJP3u7sq8v00pHpbG99tfTh+ylIfPpe7x7Yn1YHynFtvy1LWd+buNHdV9/o8RV0dA56w3br+zipbltqo35XZRvF53Km33sgbz9U1rb7UZXfXVx++s/LU13rrbbzh2swbpKamuj1vnQZun3jiiTKDpMVt2LABAMuySr1n23aZ08+Vm5vLTTfdhNPp5KWXXjrvvI8++igzZ850vU5JSSE2Npbo6OjzBnylbJlWUSWuaFekqiwr5sRmWRbR0dE4HI4S+5M63qee/N2Wt5+8scz1gSfvd3dV5PtpSPW2Nr7b+nD8lKU+fC53j21PqgPlObfelqWs78zdae6q7vV5iro6Bjxhu3X9nVW2LLVRvyuzjeLzuFNvvZE3nqtrWn2py+6urz58Z+Wpr/XW23jDtZk3CAwMdHveOg3cPvDAA9x0003nnadt27Zs27aNEydOlHrv5MmTNG3a9LzL5+bmMmXKFA4ePMjSpUsvGHwNCAggICCg1HSHw6Efh8ooFliv8P6ryrICBQ88XMfuOQ856nSfevJ3W95+8sYy1weevN/dVcHvp8HU29r4buvD8VOW+vC53D22PakOnEeJelv2DK4/z3teqe7rpoZ0rNTD7db5d1bZstRG/a7ENs6d54L11ht547m6ptWXuuzu+urDd3Ye9bLeehsvuTbzdBXZb3UauG3SpAlNmjS54HyDBw8mOTmZ9evXM3DgQADWrVtHcnIyQ4YMKXe5wqDt3r17+f7772ncuHG1ll9ERERERERERESkJnhFjttu3boxbtw47r77bl555RUAfvGLXzBx4sQSA5N17dqVWbNmMXnyZPLy8rj++uvZvHkz8+bNIz8/35UPNyoqCn9//zr7PCIiIg2dRiAWERERcY+um0QaLq9p0/zBBx/Qq1cvxo4dy9ixY7nooot47733Ssyze/dukpOTATh69Chz5szh6NGj9O7dm+bNm7v+rV69uo4+hYiIiIiIiIiIiMiFeUWLWwpayb7//vvnnce2bdffbdu2LfFaRERERERERERExFt4TeBWREREpCLUrVBERERERLyZArdSo3TTLCIiIiIiIiIiUnEK3IqIiIiIiEdSIwARERFpyBS4FRERERGpQQo+ioiIiEhlOOq6ACIiIiIiIiIiIiJSklrcioiIiIiIiEi9ox4PIuLtFLgVEREREallCiZIWXRciIiISHFKlSAiIiIiIiIiIiLiYRS4FREREREREREREfEwSpUgIuLl1K1SREREREREapruPWufWtyKiIiIiIiIiIiIeBi1uBWPpSc5IiIiIiIiIiLSUClwKyIiIiIiItVCjS9ERESqj1IliIiIiIiIiIiIiHgYBW5FREREREREREREPIxSJYiIiIiIiEgJSnkgIiJS9xS4FRERERERkXpHwWcREfF2SpUgIiIiIiIiIiIi4mHU4lZExA1qsVE3tN89h74Lz6HvQkRERESkYVCLWxEREREREREREREPo8CtiIiIiIiIiIiIiIdRqgQRERERERERKUGpeURE6p4CtyJS63QRKA2JjncRERERERGpDKVKEBEREREREREREfEwanErIiIiIiJSjHpLiIiIiCdQ4FZERERExAMoWCgiIiIixSlwKyIiIiJSDykQLCIiIuLdlONWRERERERERERExMOoxa2IiIiI1Cq1BK1+2qciIiIi9Y8CtyIiIiIiIiJeTg9wRETqH6VKEBEREREREREREfEwCtyKiIiIiIiIiIiIeBilShARKUZdzERERERERETEEyhwKyIiIiIiIrVKD8tFREQuzGtSJSQlJTFt2jQiIiKIiIhg2rRpnD171u3l77nnHizL4rnnnqvRcoqIiIiIiIiIiIhUlde0uJ06dSpHjx5lwYIFAPziF79g2rRpzJ0794LLfvXVV6xbt44WLVrUQklFREREREREpKFSi3IRqS5eEbjdtWsXCxYsYO3atQwaNAiA1157jcGDB7N79266dOlS7rLHjh3jgQce4LvvvmPChAm1WGoRERERERERERGRyvGKVAlr1qwhIiLCFbQFuOSSS4iIiGD16tXlLud0Opk2bRq/+93v6NGjRy2VVkRERERERERERKRqvKLFbUJCAjExMaWmx8TEkJCQUO5yf/vb3/D19eXBBx90e1vZ2dlkZ2e7XqekpEBBENjpdFa47CJ1xel0Ytt20XFr26XeFxHPonorcg4vqAOl6q2I1Lwq/jao3kq9Vaxu1LfjW/VW6pOKHMd1Grh94oknePLJJ887z4YNGwCwLKvUe7ZtlzkdYNOmTTz//PNs3ry53HnKMmvWrDLLdPLkSbKystxej0hdczqdJCcnY9s2DoeDIDujxPuJiYl1VjYRKZvqrUhJ3lAHzq23IlLzqvrboHor9VXxuuGJ58yqUL2V+iQ1NdXtees0cPvAAw9w0003nXeetm3bsm3bNk6cOFHqvZMnT9K0adMyl1uxYgWJiYm0bt3aNS0/P5//9//+H8899xyHDh0qc7lHH32UmTNnul6npKQQGxtLdHQ04eHhFfh0InXL6XRiWRbR0dE4HA4yrZIn7rJasYtI3VK9FSnJG+rAufVWRGpeVX8bVG+lvipeNzzxnFkVqrdSnwQGBro9b50Gbps0aUKTJk0uON/gwYNJTk5m/fr1DBw4EIB169aRnJzMkCFDylxm2rRpjB49usS0K664gmnTpjFjxoxytxUQEEBAQECp6Q6HQz8O4nUsyyo6ds9pea7jWcQzqd6KFOMldaBEvRWRmlcNvw2qt1IvFasb9fHYVr2V+qIix7BX5Ljt1q0b48aN4+677+aVV14B4Be/+AUTJ06kS5curvm6du3KrFmzmDx5Mo0bN6Zx48Yl1uPn50ezZs1KLCMiIiIiIiIiIiLiabwicAvwwQcf8OCDDzJ27FgAJk2axH//+98S8+zevZvk5OQ6KqGIiIiIiIjUtIcm9KrrIoiIiNQKrwncRkVF8f777593Hvuc0UXPVV5eWxERERERERERERFP4jWBWxGpGrVMEBERERERERHxHsroLCIiIiIiIiIiIuJh1OJWRERERDySeouIiIiISEOmFrciIiIiIiIiIiIiHkaBWxEREREREREREREPo8CtiIiIiIiIiIiIiIdR4FZERERERERERETEwyhwKyIiIiIiIiIiIuJhFLgVERERERERERER8TAK3IqIiIiIiIiIiIh4GAVuRURERERERERERDyMArciIiIiIiIiIiIiHkaBWxEREREREREREREPo8CtiIiIiIiIiIiIiIdR4FZERERERERERETEwyhwKyIiIiIiIiIiIuJhFLgVERERERERERER8TC+dV0AERERKdtDE3rVdRFERERERESkjqjFrYiIiIiIiIiIiIiHUYtbEREREREREREvp95aIvWPWtyKiIiIiIiIiIiIeBgFbkVEREREREREREQ8jAK3IiIiIiIiIiIiIh5GgVsRERERERERERERD6PArYiIiIiIiIiIiIiHUeBWRERERERERERExMMocCsiIiIiIiIiIiLiYRS4FREREREREREREfEwCtyKiIiIiIiIiIiIeBjfui6Ap7NtG4CUlJS6LopIhTidTlJTUwkMDMTh0DMaEW+geivifVRvRbyP6q2I91G9lfqkMMZYGHM8HwVuLyA1NRWA2NjYui6KiIiIiIiIiIiI1AOpqalEREScdx7Ldie824A5nU6OHz9OWFgYlmXVdXFE3JaSkkJsbCxHjhwhPDy8rosjIm5QvRXxPqq3It5H9VbE+6jeSn1i2zapqam0aNHigi3I1eL2AhwOB61atarrYohUWnh4uE5sIl5G9VbE+6jeingf1VsR76N6K/XFhVraFlJiEBEREREREREREREPo8CtiIiIiIiIiIiIiIdR4FakngoICODxxx8nICCgrosiIm5SvRXxPqq3It5H9VbE+6jeSkOlwclEREREREREREREPIxa3IqIiIiIiIiIiIh4GAVuRURERERERERERDyMArciIiIiIiIiIiIiHkaBWxEREREREREREREPo8CtiIiIiIiIiIiIiIdR4FZERERERERERETEwyhwKyIiIiIiIiIiIuJhFLgVERERERERERER8TAK3IqIiIiIiIiIiIh4GAVuRURERERERERERDyMArciIiIiIiIiIiIiHkaBWxEREREREREREREPo8CtiIiIiIiIiIiIiIdR4FZEREREquTtt9/Gsiw2btxY10WpNrfffjtt27Z1az7Lssr8N2/evGot086dO3niiSc4dOhQta73fM733a5YsYIpU6bQsmVL/P39iYiIYMiQIbz88sukp6fXWhlFRERE6ivfui6AiIiIiIg3CwoKYunSpaWmd+3atVq3s3PnTp588klGjBjhVlC5Jj3++OM89dRTDBkyhKeffpoOHTqQkZHB6tWreeKJJ9izZw///ve/67SMIiIiIt5OgVsRERERkSpwOBxccskldV2MSsvIyCA4ONjt+T/77DOeeuop7rzzTl577TUsy3K9N378eH7/+9+zZs2aGiqtiIiISMOhVAkiIiIiUitWrlzJqFGjCAsLIzg4mCFDhvDNN9+43k9JScHX15d//OMfrmmnTp3C4XAQERFBXl6ea/qDDz5IdHQ0tm3z61//mpCQEFJSUkpt88Ybb6Rp06bk5uYC4HQ6+fvf/07Xrl0JCAggJiaG6dOnc/To0Rr5zIsWLeLqq6+mVatWBAYG0rFjR+655x5OnTpVat6ff/6Zm2++maZNmxIQEEDr1q2ZPn062dnZvP3229xwww0AXH755a50DG+//bZr+TfffJOLL76YwMBAoqKimDx5Mrt27Sqxjdtvv53Q0FC2b9/O2LFjCQsLY9SoURX6TE899RSRkZH85z//KRG0LRQWFsbYsWMrtE4RERERKU2BWxERERGpccuWLWPkyJEkJyfzxhtv8NFHHxEWFsZVV13FJ598AkB4eDgDBgxg8eLFruWWLFlCQEAAqamprF+/3jV98eLFjBw5EsuyuOOOO8jIyODTTz8tsc2zZ8/y9ddfc+utt+Ln5wfAfffdx8MPP8yYMWOYM2cOTz/9NAsWLGDIkCFlBlPdlZeXV+Jffn4+APv372fw4MG8/PLLLFy4kD//+c+sW7eOSy+91BVMBti6dSsDBgxg7dq1PPXUU3z77bfMmjWL7OxscnJymDBhAs888wwAL774ImvWrGHNmjVMmDABgFmzZnHnnXfSo0cPZs+ezfPPP8+2bdsYPHgwe/fuLVHWnJwcJk2axMiRI/n666958skn3f6c8fHx7Nixg7Fjx1aola6IiIiIVJxSJYiIiIhIjXvkkUeIjIzkhx9+IDQ0FICJEyfSu3dvfvvb3zJlyhQsy2L06NH861//Ijs7m4CAABYvXsyIESM4fvw4ixcvZsiQIRw/fpxdu3bxm9/8BoCLLrqIvn378tZbb3HXXXe5tvnRRx+RnZ3NjBkzoKBF66uvvsr999/PCy+84JqvT58+DBo0iH//+9/83//9X4U/W3p6uiswXGjo0KGsXLmSe++91zXNtm2GDBnCiBEjaNOmDd9++y2TJk0CYObMmfj6+rJ+/Xqio6Ndy9xyyy1Q0Iq1U6dOAHTv3r1EaoazZ8/y9NNPc+WVV/Lhhx+6po8YMYJOnTrxxBNP8MEHH7im5+bm8uc//9m1XyoiLi4OgHbt2lV4WRERERGpGLW4FREREZEalZ6ezrp167j++utdQVsAHx8fpk2bxtGjR9m9ezcAo0aNIjMzk9WrV0NBy9oxY8YwevRoFi1a5JoGMHr0aNe6ZsyYwerVq13rAXjrrbcYMGAAPXv2BOD777+HgnQBxQ0cOJBu3bqxZMmSSn2+oKAgNmzYUOLfG2+8AUBiYiL33nsvsbGx+Pr64ufnR5s2bQBcaQwyMjJYtmwZU6ZMKRG0ddeaNWvIzMws9bliY2MZOXJkmZ/ruuuuq9RnFREREZHao8CtiIiIiNSopKQkbNumefPmpd5r0aIFAKdPnwZgyJAhBAcHs3jxYvbt28ehQ4dcgdt169aRlpbG4sWLad++fYlWn7fccgsBAQGunK87d+5kw4YNJVqVFm6jvHIUvl9RDoeD/v37l/jXpUsXnE4nY8eOZfbs2fz+979nyZIlrF+/nrVr1wKQmZnp2j/5+fm0atWqUtuv6OcKDg4mPDy8Uttq3bo1AAcPHqzU8iIiIiLiPqVKEBEREZEaFRkZicPhID4+vtR7x48fB6BJkyYA+Pv7c+mll7J48WJatWpFs2bN6NWrF+3btwfghx9+YMmSJUycOLHUNq6++mreffdd/vKXv/DWW28RGBjIzTff7JqncePGUJCn9dwg6fHjx11lqC47duxg69atvP3229x2222u6fv27SsxX1RUFD4+PpUeIK345zpXWZ+rrAHF3NW8eXN69erFwoULycjIUJ5bERERkRqkFrciIiIiUqNCQkIYNGgQs2fPdrUyBXA6nbz//vu0atWKzp07u6aPHj2aTZs28cUXX7jSIYSEhHDJJZfwwgsvcPz48RJpEgrNmDGD48ePM3/+fN5//30mT55Mo0aNXO+PHDkSgPfff7/Echs2bGDXrl2MGjWqWj93YYA0ICCgxPRXXnmlxOugoCAuu+wyPvvss/MOkFa4nuL7EGDw4MEEBQWV+lxHjx5l6dKl1f65HnvsMZKSknjwwQexbbvU+2lpaSxcuLBatykiIiLSEKnFrYiIiIhUi6VLl3Lo0KFS06+88kpmzZrFmDFjuPzyy/ntb3+Lv78/L730Ejt27OCjjz4q0Qp01KhR5Ofns2TJEt555x3X9NGjR/P4449jWZYrCFvc2LFjadWqFffffz8JCQmlBt/q0qULv/jFL3jhhRdwOByMHz+eQ4cO8dhjjxEbG+sa7Ky6dO3alQ4dOvDII49g2zZRUVHMnTvXlau3uGeffZZLL72UQYMG8cgjj9CxY0dOnDjBnDlzeOWVVwgLC3Pl6n311VcJCwsjMDCQdu3a0bhxYx577DH+8Ic/MH36dG6++WZOnz7Nk08+SWBgII8//ni1fq4bbriBxx57jKeffpqff/6ZO++8kw4dOpCRkcG6det45ZVXuPHGGxk7dmy1bldERESkoVHgVkRERESqxcMPP1zm9IMHD3LZZZexdOlSHn/8cW6//XacTicXX3wxc+bMKZX2oE+fPjRp0oRTp06VaFlbGLjt06ePKz1AcQ6Hg+nTp/PMM88QGxtbZkvTl19+mQ4dOvDGG2/w4osvEhERwbhx45g1a1aZ66wKPz8/5s6dy0MPPcQ999yDr68vo0ePZvHixa5csYUuvvhi1q9fz+OPP86jjz5KamoqzZo1Y+TIkfj7+wPQrl07nnvuOZ5//nlGjBhBfn4+b731FrfffjuPPvooMTEx/Oc//+GTTz4hKCiIESNG8Mwzz9CpU6dq/VwATz31FKNHj+aFF17gj3/8I6dOnSIoKIgePXowc+ZM7rnnnmrfpoiIiEhDY9ll9W8SERERERERERERkTqjHLciIiIiIiIiIiIiHkaBWxEREREREREREREPo8CtiIiIiIiIiIiIiIfxusDtSy+9RLt27QgMDKRfv36sWLHivPNnZ2fzxz/+kTZt2hAQEECHDh148803a628IiIiIiIiIiIiIhXlW9cFqIhPPvmEX//617z00ksMHTqUV155hfHjx7Nz585SI/MWmjJlCidOnOCNN96gY8eOJCYmkpeXV+tlFxEREREREREREXGXZdu2XdeFcNegQYPo27cvL7/8smtat27duOaaa5g1a1ap+RcsWMBNN93EgQMHiIqKquXSioiIiIiIiIiIiFSO17S4zcnJYdOmTTzyyCMlpo8dO5bVq1eXucycOXPo378/f//733nvvfcICQlh0qRJPP300wQFBbm1XafTyfHjxwkLC8OyrGr5LCIiIiIiIiIiItLw2LZNamoqLVq0wOE4fxZbrwncnjp1ivz8fJo2bVpietOmTUlISChzmQMHDrBy5UoCAwP58ssvOXXqFPfffz9nzpwpN89tdnY22dnZrtfHjh2je/fu1fxpREREREREREREpKE6cuQIrVq1Ou88XhO4LXRuq1fbtsttCet0OrEsiw8++ICIiAgAnn32Wa6//npefPHFMlvdzpo1iyeffLLU9M2bNxMaGlptn0OkpjmdTlJSUggPD7/gExwR8QyqtyLeR/VWxPuo3op4H9VbqU/S0tLo27cvYWFhF5zXawK3TZo0wcfHp1Tr2sTExFKtcAs1b96cli1buoK2FOTEtW2bo0eP0qlTp1LLPProo8ycOdP1OiUlhdjYWNq1a0d4eHi1fiaRmuR0Ojl58iTR0dE6sYl4CdVbEe+jeivifVRvRbyP6q3UJykpKVBG49SyeE3g1t/fn379+rFo0SImT57smr5o0SKuvvrqMpcZOnQon332GWlpaa7Wsnv27MHhcJTbFDkgIICAgIBS0x0Oh34cxOtYlqVjV8TLqN6KeB/VWxHvo3or4n1Ub6W+qMgx7FVH+8yZM3n99dd588032bVrF7/5zW+Ii4vj3nvvhYLWstOnT3fNP3XqVBo3bsyMGTPYuXMny5cv53e/+x133HGH24OTiYiIiIiIiIiIiNQ2r2lxC3DjjTdy+vRpnnrqKeLj4+nZsyfz58+nTZs2AMTHxxMXF+eaPzQ0lEWLFvGrX/2K/v3707hxY6ZMmcJf/vKXOvwUIiIiIiIiIiIiIudn2bZt13UhPFlKSgoREREkJycrx614FafTSWJiIjExMepKIuIlVG9FvI/qrYj3Ub0V8T4Nvd46nU5ycnLquhjiJj8/P3x8fMp9vyKxRq9qcSsiIiIiIiIiItJQ5OTkcPDgQZxOZ10XRSqgUaNGNGvWzK0ByM5HgVsREREREREREREPY9s28fHx+Pj4EBsb2yBbG3sb27bJyMggMTERgObNm1dpfQrcioiIiIiIiIiIeJi8vDwyMjJo0aIFwcHBdV0ccVNQUBCAK73H+dImXIhC9SIiIiIiIiIiIh4mPz8fAH9//7ouilRQYaA9Nze3SutRi1sRERERERERkUp4/pvtrr8fmtCrTssi9VdV86RK7auu70wtbkVEREREREREREQ8jAK3IiIiIiIiIiIiIh5GgVsRERERERERERGpFsuXL+eqq66iRYsWWJbFV199VeL93NxcHn74YXr16kVISAgtWrRg+vTpHD9+vFbKd/vtt2NZFvfee2+p9+6//34sy+L2228v9d7q1avx8fFh3LhxtVJOFLgVERERERERERGR6pKens7FF1/Mf//73zLfz8jIYPPmzTz22GNs3ryZ2bNns2fPHiZNmlRrZYyNjeXjjz8mMzPTNS0rK4uPPvqI1q1bl7nMm2++ya9+9StWrlxJXFxcrZRTgVsRERERERERERGpFuPHj+cvf/kL1157bZnvR0REsGjRIqZMmUKXLl245JJLeOGFF9i0aVOJgKhlWbzyyitMnDiR4OBgunXrxpo1a9i3bx8jRowgJCSEwYMHs3///gqXsW/fvrRu3ZrZs2e7ps2ePZvY2Fj69OlTav709HQ+/fRT7rvvPiZOnMjbb79d4W1WhgK3IiIiIiIiIiIiUmeSk5OxLItGjRqVmP70008zffp0fvzxR7p27crUqVO55557ePTRR9m4cSMADzzwQKW2OWPGDN566y3X6zfffJM77rijzHk/+eQTunTpQpcuXbj11lt56623sG27UtutCN8a34KIiIiIiIiIiIhU3Su/hbSztb/d0EZwzz9rZNVZWVk88sgjTJ06lfDw8BLvzZgxgylTpgDw8MMPM3jwYB577DGuuOIKAB566CFmzJhRqe1OmzaNRx99lEOHDmFZFqtWreLjjz/mhx9+KDXvG2+8wa233grAuHHjSEtLY8mSJYwePbpS23aXArciIiIiIiIiIiLeIO0spJ6u61JUm9zcXG666SacTicvvfRSqfcvuugi199NmzYFoFevXiWmZWVlkZKSUiroeyFNmjRhwoQJvPPOO9i2zYQJE2jSpEmp+Xbv3s369etdaRV8fX258cYbefPNNxW4FRER8UTPf7O9xOuHJvQqd14REREREZFqEdrIjZm8Y7u5ublMmTKFgwcPsnTp0jIDr35+fq6/Lcsqd5rT6axUGe644w5XqoUXX3yxzHneeOMN8vLyaNmypWuabdv4+fmRlJREZGRkpbbtDgVuRUREREREREREvEENpSuobYVB27179/L999/TuHHjOinHuHHjyMnJAXClXyguLy+Pd999l3/961+MHTu2xHvXXXcdH3zwQaVz7LpDgVsRERERERERERGpFmlpaezbt8/1+uDBg/z4449ERUXRunVr8vLyuP7669m8eTPz5s0jPz+fhIQEAKKiovD396+1svr4+LBr1y7X3+eaN28eSUlJ3HnnnURERJR47/rrr+eNN96o0cCto8bWLCIiIiIiIiIiIg3Kxo0b6dOnD3369AFg5syZ9OnThz//+c8AHD16lDlz5nD06FF69+5N8+bNXf9Wr15d6+UNDw8vNz/uG2+8wejRo0sFbSlocfvjjz/y/9u77/A4qnv/4+9dddnqzU1y7703jI1xt+mYbgMhBEIgEEi4kOR3geReuMklBMINNXQImGKqjY3BNrhj3Hu35SJZsq1mq2vn98eRtZLVVtJKuyt9Xs/jx9rZKWd258zOfOec79m4cWOjlU0tbkVERERERERERMQtJkyYgGVZ1b7fqVOnGt8/78J5qlqutm1V5c0336zx/c8++6zs7y+//LLa+YYMGVLnbdeVWtyKiIiIiIiIiIiIeBkFbkVERERERERERES8jFIliIiItBDPLdhW9vf9M/t7tCwiIiIiIiJSM7W4FREREREREREREfEyanErIiIiIiIiIiLi405m5pb9nRAZ6tGyiHuoxa2IiIiIiIiIiIiXsizL00WQOnI4HG5Zj1rcioiIiIiIiIiIeJmAgABsNhvp6enExcVhs9lqnL+osKDs7/x8tdX0BMuyKCwsJD09HbvdTmBgYIPWp8CtiIjUSoNaiYiISH2Vv45A1xIiIi7z8/OjQ4cOHDt2jMOHD9c6f3ZeYdnfOSENCxhKw4SGhpKUlITd3rAAugK3IiIiIiIiIiIiXqh169Z0796doqKiWud9e/nesr/nTujcyCWT6vj5+eHv719rC2lXKHArIiIiIiIiIiLipfz8/PDz86t1vrwSZ6AwODi4kUslTUEJL0RERERERERERES8jAK3IiIiIiIiIiIiIl5GgVsRERERERERERERL+NzgdsXXniBzp07ExwczNChQ1mxYoVLy61atQp/f38GDRrU6GUUEREREWkszy3YVvZPRERERJovnxqcbN68eTzwwAO88MILjB07lpdffpnp06ezc+dOkpKSql0uKyuLuXPncumll3Ly5MkmLbOIiIiItBwXBlPvn9nfY2UREREREd/mUy1un3nmGe644w5+/vOf07t3b5599lkSExN58cUXa1zurrvu4qabbmL06NFNVlYRERERERFPUutsERER3+YzLW4LCwvZsGEDjzzySIXpU6ZMYfXq1dUu98Ybb3DgwAHeffdd/uu//qvW7RQUFFBQUFD2Ojs7GwCHw4HD4WjQPog0JYfDgWVZOm7FPSyr7E8dU6XKfSaUfi7PL9xeYdp9M/rVaZWNXm/1PYq4XaV6W8W5we1Ul8VV3nKsNEW9qANdJ4tbeUs9a+ZUb12k49En1OW78ZnA7alTpygpKSEhIaHC9ISEBFJTU6tcZt++fTzyyCOsWLECf3/XdvWpp57iiSeeqDQ9PT2d/Pz8epZepOk5HA6ysrKwLAu73aca14sXCrFyy/5OS0vzaFm8RfnPhNLPpappddHY9Vbfo0hlX6w/XOH15cM71Wn5C+ttQ88DrlBdFld5y7HSFPWiLnSdLO7kLfWsuVO9dY2OR9+Qk5Pj8rw+E7g9z2azVXhtWValaQAlJSXcdNNNPPHEE/To0cPl9T/66KM8+OCDZa+zs7NJTEwkLi6O8PDwBpZepOk4HA5sNhtxcXH6YZMGy7M5f/Tj4+M9WhZvUf4zofRzqWpaXTR2vdX3KFKZu+ttQ9fnCtVlcZW3HCtNUS/qQtfJ4k7eUs+aO9Vb1+h49A3BwcEuz+szgdvY2Fj8/Pwqta5NS0ur1AqX0uj1Tz/9xKZNm7j33nuhXNN6f39/vvnmGyZOnFhpuaCgIIKCgipNt9vtOjmIz7HZbDp2xT3KPSDT8VTqgoeGdru96ml1Xm0j1lt9jyKVubveumF9LmywcdcvzYe3HCtNUS/qSNfJ4jbeUs9aANVbF+h49Al1+W585lsMDAxk6NChLFmypML0JUuWMGbMmErzh4eHs23bNjZv3lz27+6776Znz55s3ryZkSNHNmHpRURERERERERERFznMy1uAR588EHmzJnDsGHDGD16NK+88grJycncfffdUJrm4Pjx47z99tvY7Xb69as4KEx8fDzBwcGVpouIiIiIiIj3eW7Btgqv75/Z32NlERHxFjo3thw+Fbi9/vrrOX36NH/6059ISUmhX79+LFy4kI4dOwKQkpJCcnKyp4spIiIiIiIiIiIi0iA+FbgFuOeee7jnnnuqfO/NN9+scdnHH3+cxx9/vJFKJiIiIiIiIiIiIuIePpPjVkRERERERERERKSl8LkWtyIiIiIiIiLiO5SPU0SkftTiVkRERERERERERMTLKHArIiIiIiIiIiIi4mUUuBURERERERERERHxMgrcioiIiIiIiIiIiHgZBW5FREREREREREREvIy/pwsgIiLNR/kRgzVasIiIiIiIiEj9qcWtiIiIiIiIiIiIiJdR4FZERERERERERETEyyhVgoiIiIgPUUoSEREREZGWQS1uRURERERERERERLyMWtyKiIiIiIiIiEiTKt+LCPUkEqmSWtyKiIiIiIiIiIiIeBkFbkVERERERERERES8jFIliIiIiIiIiEgFGgxTRMTzFLgVEREREamH5xZsA8sixMolz5bG/bMGeLpIIlIPyrMpIiLeSqkSRERERERERERERLyMWtyKiIiIiIiIiJSjVBEi4g0UuBURERGvpZsmERERERFpqZQqQURERERERERERMTLqMWtiIiIiIiIiIgPUa8kkZZBgVsRERERERERkRaqfBAYBYJFvIpSJYiIiIiIiIiIiIh4GQVuRURERERERERERLyMUiWIiEgFypclIiIiIiIi4nlqcSsiIiIiIiIiIiLiZdTiVkRERESaHQ20IiIiIiK+ToFbERERERERERGRelCqOWlMCtyKiIiISK10UyIiIiIi0rR8LsftCy+8QOfOnQkODmbo0KGsWLGi2nnnz5/P5MmTiYuLIzw8nNGjR7N48eImLa+IiIiIiIiINK7nFmyr8E9EpDnwqcDtvHnzeOCBB/jDH/7Apk2bGDduHNOnTyc5ObnK+X/44QcmT57MwoUL2bBhA5dccgmXXXYZmzZtavKyi4iIiIiIiIj3UcBXRLyVT6VKeOaZZ7jjjjv4+c9/DsCzzz7L4sWLefHFF3nqqacqzf/ss89WeP3kk0/y+eef8+WXXzJ48OAmK7eIiHgnDV4kIiIiIiIi3spnAreFhYVs2LCBRx55pML0KVOmsHr1apfW4XA4yMnJITo6upFKKSIivk7BXBERaWmUw9q76fsREWm5fCZwe+rUKUpKSkhISKgwPSEhgdTUVJfW8be//Y1z585x3XXXVTtPQUEBBQUFZa+zs7OhNOjrcDjqXX6RpuZwOLAsS8et1J1llf1ZdvxUNc3VZb1ZufJyvsxVTXP3stVwZ719fuH2sr/vm9HP/OFr3w8+WubG5qnPxNe+i4bU5bps4/w/LLecB1zebmOuX5qPRv79Lv9bQ/nfmxrWX9dtNEadqvR72xT1tiGaw3m/ic+Ndd6GL17/+tr308DtevX9rSeOgeo+O10j+IS6fDc+E7g9z2azVXhtWValaVV5//33efzxx/n888+Jj4+vdr6nnnqKJ554otL09PR08vPz61lqkabncDjIysrCsizsdp9KZy0eFmLllv2dlpZW7TRXl/Vm5ctLaZmrmubuZavjznrbkO/Rm/himRubpz4TX/suGlKX67qNQAoBm1vOA3XZbmOtX5qPxv79bsp6Vp9lq3Ph721T1NuGaA7n/aY+N9Z1G754/etr309Dt+vN97eeOAaq++x0jeAbcnJyXJ7XZwK3sbGx+Pn5VWpdm5aWVqkV7oXmzZvHHXfcwUcffcSkSZNqnPfRRx/lwQcfLHudnZ1NYmIicXFxhIeHN3AvRJqOw+HAZrMRFxfndT9s4t3ybM4f+PMPuqqa5uqy3qx8eSktc1XT3L1sVZ5fuB0sixArlzxbIfc1sCtkQ75Hb+KLZW5snvpMGrLdKluAN7KG1OU6baO0tW0eIQ0+D9Rpu424fmk+Gvv3u8nqWT2Xrc6F18mubsMT5zJ89Lxf07rcsT53b8MXr3997ftp6Ha9+f7WE8dAdZ+drhF8Q3BwsMvz+kzgNjAwkKFDh7JkyRKuuuqqsulLlizhiiuuqHa5999/n5/97Ge8//77zJw5s9btBAUFERQUVGm63W73upODSG1sNpuOXam7cr0Yyo6dqqa5uqw3u6DHht1ur3qau5etcX02KK27DdKQ79Gb+GKZG5unPpOGbNcTZW5IXa7zNsrV24asr87bVb2QWjT273eT1rN6LFvjastdJ9djP3zm/Ost223ic2Odt+GL17++9v24Ybtee3/rTdc5ukbwCXX5bnwmcAvw4IMPMmfOHIYNG8bo0aN55ZVXSE5O5u6774bS1rLHjx/n7bffhtKg7dy5c3nuuecYNWpUWWvdkJAQIiIiPLovIiIiIiIiIiLiOzRYoDQ1nwrcXn/99Zw+fZo//elPpKSk0K9fPxYuXEjHjh0BSElJITk5uWz+l19+meLiYn71q1/xq1/9qmz6rbfeyptvvumRfRARERHxdropERERERHxPJ8K3ALcc8893HPPPVW+d2Ewdvny5U1UKhERERERERERERH38bnArYiIiEh9lG9FilqSioiIiIiIl1PgVkRERERERERERBqNGlHUjwK3IiIiUoHym4pIVXRuEHEv1SkREamNArciIiIiIiJeSsE9ERGRlkuBWxERkSamm3AREfEU/QaJiIj4DgVuRURERERERKRGyk8pItL07J4ugIiIiIiIiIiIiIhUpMCtiIiIiIiIiIiIiJdRqgQREREREXEb5VAVEXGNzpeeoc9dfIla3IqIiIiIiIiIiIh4GQVuRURERERERERERLyMUiWIiIiIiPiQ8IIM+qetZ09Mf0BdPEVExHXl0wSgVAEiXk+BWxERERERX1FcxFV73iKy4Ax9Tm2CwvEQGOTpUrmFggkiIiIiFSlwKyIi4gVCis4y+dBnFPv5823HKzxdHJFmqVkEBn9aTGTBGQBCi8/B5u9gxAxPl0qkVjG5J7FbJaS3aufpooiIiPgMBW5FRES8wEVHv6Fz1l4AgovyoHgw+Ad4uljiYRr1WCrIz4UfPqo4bc0XMHQq+Pl5qlQitWpz9ijX7n4du+VgYdfrlOJDmpXE7AP0Td/IpoQxOrabULN4GCviAgVuRUSk2fG5C7n0o/Q6vaXsZWLOIfj0ObjmQbBrHFERKbX6M8jNBsDChg0LMk7CrrXQb6ynSydSraEpK/GzSgAYn7wQ8i+H4FBPF0uk4XKzmbXvfQIdhbQ9mwyOyWDXgzQRcR/dDYqIiHja8nnYsSpO27EKFr8OllXdUiLSkuScMa1rgRKbH0s7XuZ8b/VnOleI98o4SZfM3WUvWxflwPIPPFokEbdZ+xWBjkIAwguz4OC2WhcREakLBW5FREQ8KfWwCdICuf6t+Lbz5TjO/zyvWwAr53u2fCLiHZbPg6ICALbGDWd73FDSQtuY907sh8M7PFs+cYvnFmwr+9dsrP+68sPJdQvM758LgotzuWzfe1y1503a5bi2jEiTyD8HPy6sOG3Td54qjYg0UwrcioiIeNKy98v+XN92HAeje/Ndp8ud73/3Lmxa6pmy+YjygY5mFeyQ5uXU8bLAa52lH4ON35q/A0NY32482GxsbHORc57Vn7mnnCLuVJhfduwW2/zZlDDKTLccsPCV2luKWxYTD39Bl8w9JGUfZPbu1+HLFyHvXBMUXqQW6xeZ4G15u9dCbo6nSiSCzSrBVpqaRpoH5bgVERHxkISzx2HPjwDkBISzLW4YgRSxM3Ywk7u2NkFbgC/+CUEh0Gd0g7YXWJJP9zM7SAttq8EzRJqKZcGCl+GnxRAaDrPurntd/u5dE+gCuOgq8nJaAbAvqi/TIr6HrHTYtwFOHoGEjo2wEyL1tPX7ssDW3pj+rOowhU6Z+4gqOA3Ju2DLMhg0sfrlt6+ke8bOitM2fAN71sP0O6DPGLDZGnknxBt5fDyDwvyy9DUObByK7EnXzN1QUgzbVsDIGU1bHhFMK/Abd7xMTN5JDkT1gaOBkNjT06WSBlLgVkRExENGHXd2p1vfbjwl9gCwisyEi66GnAz4cYEJ2Hz8N7jud9BrZP02lpnGdTtfJSY/3byevxsmzYHw6EqzBhedIzbvJOnnu2GLSP2t+NgEbTGD2PDhX6H/xTDjTghp7ZyvuAj2bYT9G83fYAJSJcWwe5153ToKRl0GS/YB4LD7wejLYNHr5v3Vn8NVv27a/ROpjmWZlAilNsePpMTuz/KOM7lq79tm4jdvQc8RFevCedlnYMErZS+3xw6lx5ltJp/o2Qz46GnoPRqu+Q34BzTJLomU2fBN2WCRe6P781PbcSZwS2m6BAVuK/F4sL0lWL+IuLxUALpn7IDXHoEOPWHMFdBrhAbO81EK3IqIiHhAu5wjdMreb15ExrMjdnDFGWw2mPYzKMyDzUvBUQIfPg3XPww9h9dtY6mH4N0/E5Of4Zy2dbkZif7ia2H05VBcaIJD21dy5/4t2HFwMrQtOIaDXZmVROqjx+mtsP7jym9s+wEObYPL7zGtcLcsg+0rIe9szSu85AYIDK44bfAkWP4h5J81rbwuvRnCY9y7I82cggmN5NA2SD8KwPHWSaS3agdAckQ30+p85xoT+Fr6b5j5i4rLWhZ8+YI5roE90f35rvMVrGs3gTsKfjAtbgF2rYENfWHkzCbeOWnRigphlTM9zfp2F3MmJJ7UVu1pc+44pB6ElIPQtkutq4o7d4IeZ7azN7p/s+oNpfOqBxQXVXhYVubYHvPQOKYd3PAIxCV6onTSAArcioiIuElYQSaX73uXQnsQC7tdX/2MlsXocq1tGX8djhP+lXP92e0msONwmECro9hceF3/CPQY6lKZErMPwOsfmgAwkBUYSVBJAcEleVCUb7pgr1sAeTmmZV+5BPgJuSlwdDd07OPyZ1D+Ql0X6dKStcs5zORDnzonXHqLCagufBUKck2LwX//t+sr7NATBl1aeXpQCAyfCis+MeeItV/BlFvdsxMiDVG+te353LbnTf0Z7NtkfofWLzIBh4k3QlhpL5BN35n0H8C5gNYs72gCs2eDIuCqR82Djk+eMfOu+ASGTIaAwKbaM2npNi8153CA3qM4ExIPwI7YISZwC2Z8gtoCtycOMHv3awQ4ihh8cg2sLYCRs5T+Q+pn2w9lx+WByF4cjOzF5NyNkJZs3j99At5+An72JETFe7asUicK3IqIiNTC1VYDw1N+IDYvDYAr9r4D+YMhOLTSfF0zdtHh/MjY0W1hwAQ4sbPSfIDp0nTlvSZdwrYfTHB13l/g+v+oNXjb8/QWJh/6DEoHKEhp1YEvut8MNht3+W023fwsh/Pmo1SBXzBBJfnmxY7VdQrciih4D5H5p5i17338zw8OMmSSSX9is0Hn/iZv9f5NFRfyDzSpUAaMh+g25R7kWGCzm2nVdXEcMdOkSSgpNiOc978Y2nZu3J0UqUnGSWer2PAYDkb2rvh+RCxMuB6WvGWO8U3fmmDsRVeZvLXn038A33W6nHz/cr+lNhv0Hwc7VpmeImczYOMStbqVplFSDCvnO1+PuxY25UJpyoRLjy82vZi2fg+T51b/QCH7NLz/JAEOkxrHzyoxx/3hHXDFvVWnDxGpjmWZ64BSP7UdR2rrRCbPmAsHtsA3b0LaEcg5DW8/Bj/7b+eDMvF66vsoIiLiDkUF9DizvexlXN5J0zq2tBVrmT3rmXbwI+frCTeAXy35pux+cOWvoW/pCPIlRaal3oJXoCCv8vy5OYw/soBpBz8xNwIAPYczv+dt5Ae0MjfAs+6Cu/4GnfqZ91tHmZvenz3F6wN+Q7GttEw7V5sWvyLikrCCTK7Y+y4hJaV1s+sgmHmXswVVeAzc/P/gsl+aBzed+pmb9N++Adc+aB7IxLaHuA6l/xLN65ry0oVFwfDp5u/iQvNwR6Oaiyf9+LUJyAIMn27yMV9ozBUw9XYIKg3KFuXDsvfhn78u6yXCoIkciuxV9TbGl+vZsuIT031dpDFlnTKpPbJKxwvoPhTadS17u9A/2Dn4ZP5Z58OLCxXmw/tPQs4ZALIDI5zv7V4HLz8Ex/Y24o5Ic9Mxa19ZapoTrZNIbV2aDsFmg26D4NYnzLUEQEYqvP14WY5m8X5qcSsiIuIOu390tlI97+AW+PJFE5Sx2WDXOvjo6bJWeHuj+tGj30Wurd/PD65+wLSe3bnG3BCv/xr2/GhGqe8xzHQ1/WkRLP+QQfnlcmUOnQIzfkHxogta9bbpBLf+ybRWahVRFhgq3LaN5IhudMncY96rY7qElkL525oJyyImL40iv0Cyg6Iatq79m7lx50uEFJvWV6dCEoid/Tvwu+CS22Yz9XLolIZtr7xLb4HkXXBiP2SehPl/h5v+oIFIxGU2y4Flq1u7nvD8M4w8sZzYvJMU2wPgTDQEBMHBrWYGvwCTxmD5kSo2aDM51gdMgO/nmZQJlsMZ8A2PMbnevztY9cbbdjat1NXqVhqL5aDNuWN0ztwLL74OJw9XfP/iaysvM/hS09qW0pQf/cZWfN/hgPnPmhy4QFZQFPN6/4KEc8e44tjnJtd5Zhq8/gfnOARBIY22i9I8DE1dVfb3hjZjK8/QKgLmPA6v/948eEg/Cu/+2dwH6PjyegrciohIi9DoQbbNS8v+XN3+Ukac+B5/q9hMj4iD+CSTj89hgrZ7ovuzuMvV9KjLwF9+fnDtQyZv4NJ/Q1GB6Wr37/82N69pyXAmpWz2InsAa9pP5OJZd1WbL+25hedbCR+v8JnsjepnArcoXYI0DY8EwosKmHT4M/qe2kSxzY8vu99cv8FhLIsRKcvhp+WElAadsoKi+Lz7LdxRRbqURhEQCNc9DK/81rSi2b8Jls+DiTc1zfbF4+qdpsSymHDkK/qmb2RvdD/I61x7N+3CAlj5CXO2f2p+6847m1xxvv7joFV4zetqFQ4z7jStxpe8BXt/Mg87rrgPglvVvOz4603gFuW6lTpwlMDJZIiMh5AqjrGSYtj2A7ds/4CY/PSq19HvIkisojV4x74QmWAeoB3YbFrpRsQ631/6nvOYDQrli+43kxfQisORPWHmM/Dx38xgUo5iWP6BeaAx/joYOrnyQ0CR0gHuEnMOmRfRbTkY2bPqGSNiYe4T8MYfzMOuE/vNPcQt/888cBOvpZovIiLSQK0Ls0z+qNJgzfq248gIjmHmgY9My6Hv55kclZZJObArZiBLOl9V55ZNUJo2YfTlJlD75YumVS84bwJK7YwZxJoOl3I2MIKL6zHIxaHInhTb/Ezr4J2rTaunugSZRcrpeXoLvU5vZX9Ub3D09YpjKSL/DLz2KH1PmZsdf6uEaQc/hoyxdRq0I6g4l2kHPyYp29kq8FBEDxZ3uYYC/yZuxRIZZx7uvPOEOd/88BG06wa9RjRtOcS3bPiGgWk/AtDn9GZ44QHTU6TboMrzWpbp9fHNm5CVXnYzaWHDxgUDbAaFmtzOrorrYFqJpx8zDyqj29a+jFrdSl2dPgHzn4Pje821Wfvu0HUgbXPCOB2SQO/Tm+G5f0D2KWIuXLZ9d5Meocew6gces9th8EST9gPLBGJbR5rUVvnnTLAMzLavfYgzewOcy0bGwe3/ZR7Or/7cnMfPZcLCV2DNF6Y+OUrMPpw+wa3JhwgsyWd1h0n1e+gozUL51raMuQJO1nCNFdMW5jwGb/7RtO4+ssM89NJDXq+mwK2IiEgD9T61uaxr586YQWCzsz+6H3QNNTe3UBa0ZdBElvhfXL+gbXlRCebCa8tyWPy6ufgCSOoDU29nyeYqct/WQaF/MEciutM1c7fXpUvQ4Fc+pLgIFr3OtIOLAOiUtQ/eOQSX3+vREY07Z+5m6sH5cEF6k5DiXPjwL2bEZRdan8SdO8HM/R8QUZhZOsXG6vYTWd92nLkp94QuA2DSnNJBn4BPn4M7/+rMbSdSXvrRCgOBAWbwmnefgOHTYPKtZuC81EOm9eCe9aY1YKkSmx+bEkbzY7vxlNj8uW9yd9MatygfwmIgsB6tuOI61G3+Rmp1q9+aZsayzKCsi98wPZYovTY7tgeO7eG6ahY73jqJnbFDmHztFSYA64qBl8CyD8y14dHdVc8z7Q7oPgT2Vuxtgp+/GdRs8KWmde7ONWZ6Rip8+UKFWc+XZsKRhZA507QglhYlrCCT7md2mBeh4TBwAnxTS37khI5wy3/Ca783LbvXfAkjZrh+fEuTU+BWRESkISyrNHBr7Iot10Jp9OUmj9S6Beb1kEkw65dYX+9wz7ZtNhh0CXQbDJuXmZvdHsPM9M3bXFhBzfZF9TWBW5QuQeoutCgH3vrPyjeth7bBi/ebgNCwqdWm8WgMdkcxo04sY3jKirJpGcExfNvpSiYf+pTIgjMm7+CCV5y5qauz8Vuu2/Wvsm7ief6hhNz4O9bv9oKcsmOugOP7TGv5glz46Gm462nlu20ivhLw83MUw8fPmAHtSnuDhBadpWP2ATPD+kWw5ycT5MqrYrC7roN5N2QcmSHluoEHt6o9vYG7qdWt1CYnA774J+zb4JwWmWAC/KUDOlXSfSgf+Q3iRFhHACbXJagVGWeuz8ql0SoTFGqOz5Ezal5HbHuT/ubYXvj2HTi8vdIsJTY7fpbD/A59+47pcSEtyuCTa7BT2jhkxAzXUx607w7DpsCPC82DthUfw/SfN2pZpf7qFLhdunQp9957L2vXriU8vGKuoqysLMaMGcNLL73EuHHj3F1OERERr9T27FGiCk4DcDSsMznlBzey2WDqzyCxt+k612tk43QRbx0JF13l9tUeiuoJyQFQUqR0CVInbc4eZeb+D6DIBHuKbf5saDuWPqc2E1aYZUbUXvCyaTHebTC07UKrwkLOBYQ1XiD3yE5u2vFixXyFfUbzQfAlFPoFs6DbDVy361UCHEXmZrtDDxNYvlBRISx8FTZ9W3YhnRbalq+63sDPug6C3Q1/aNJgNpsJPKcfNf9OHobdPzpHO/cCGtzP88YcW1I22NLp4Di+63Q5JTZ/7o8/bnqLFBdC9qnKC8a0h8lzoOcIMhdWDiZ5hHLdSnVOHIB3/2Ryf583bCpMuQ0Cg00O2oNb2LPie2LyTpIe2pbes2+DNp04saAB5/PLfwVjrzR/B4VCYIjZXl2vozr0MANIHdxqurWHRUNMO4htzyvLDnDrtucILc6F7SthxExIqiLvrjRLQcV59E03DyOKbf74D59etxWMu9YMoFdUAD8tNg1O1GrbK9XprPHss89y5513VgraAkRERHDXXXfxzDPPuLN8lbzwwgt07tyZ4OBghg4dyooVK2qc//vvv2fo0KEEBwfTpUsXXnrppUYtn4iItCx9Tm0q+3tn7ODKM9jtZkThPqN9LuhZ6BfszHF4Pl2CSC16ndrMNbtfp3Vp0JbwGD7udQdr21/Ku/1+ZQIq5x3bYwZfef9Jfr7lae7c/Fdm7J9HcNE5t5UnqDiPiYc/hzf+UBa0dWDnh8RpMPt35jgHToW24dtOVzgXXPgvOLqn4soyTsLrj8Kmb8smbYkbzhc9byInyMu6GAaFwNTbna9Xf2a6Crdwzy3YVuFfS9Uxax9DTpZ2wfYLYFHX2ZTYA0zQf8R0uPsZEzCiNOjUayTM+AXc90+493nzuglby9fqfKtbSn+vzvd0kZbt2F7T8+N80LZVpMmjPOtuE0SldMCmwZeyqOts3ut3L990uQbadGr4tu12iEs0/8JjIDi0/teBNht0HWjykA6fZlLihMdQ6B/C2vaXOudb9Bo4HA0vu/iEfuk/EegwPSZ2xg6ufSDIC4VFwchZ5u+SYjOgqXilOrW43bJlC3/5y1+qfX/KlCk8/fTT7ihXlebNm8cDDzzACy+8wNixY3n55ZeZPn06O3fuJCkpqdL8hw4dYsaMGdx55528++67rFq1invuuYe4uDiuueaaRiuniIi0DP4lhXQ/U9raKDCE/VHNMJVAnzEmpyHAjlWeTZdQmA9bv+fGHZ8TUpxrRs1N97JgWUu3ZTlTDn3qHKSoYx+Y/TtOfm+6ohb6BcPMe8xxteAlEwgtJ7T4HN0zdlDkFwiManBxup/exvjkr2lVfLZsWmqr9nzX6XJOhbatNHDf3pgBTI8vgLVfmrxvb/zRtNqzHCboWVzkzFftH8iixMvYEzOAECu3wWVtFF0HQXxHSDtiAhjJu5TyRAgtOsuUg/OdEybP5dTpNhVnim0Pd/wPZKaboJOfD6TZuORG07Icy3T7HXSJcja2YO1yjsDb/4bC0pz/SX3g+oehVYSni+ZW2+OGMCBtHbF5aWbgs20/mDyn4hZem/qmuIhBJ9dC6eCQm9qMYUB91jP2SvhpkRk4b8tyGHMlxCe6u7TSQHUK3J48eZKAgIBq3/f39yc9Pb3a9xvqmWee4Y477uDnPze5N5599lkWL17Miy++yFNPPVVp/pdeeomkpCSeffZZAHr37s1PP/3E008/rcCtiIg0WNeMXQQ5Sge46DuWYr9m2C2z5wjwO58uYU1puoSmvYEPzz9jRjx/5i+Qf47znbgGpv0I//yRq8M6syV+JAejemLZfCC40FztXAOfPV8WtN0aN5wBcx82A61wQQ7BboPgvhdM4DblIKQc4Mi2rbTPOYK/VWzyRp88YgbQqA/LYuzRxQwrP9JyYDDL20xka/yImgcHnDwXUg7AkZ0meFtQXHme6LZw/X+w56ds727FarOZfLef/cO8Xv25ArctlN1RbLpa79vIdbt+ILS4tFV7tyEm32ZVKQ9sNo8OIlhnCR1hyKWw8VuT23n5PJh1l8uLtyrMJj43hdaFWfDtFqYc3E/rwtKWmhmR4OfPjLRcSmx+HIjqBXhREEcqaJ99iMv3vQelrRHp3B9u/L2zlW0zYtn8WJE4jav2vm0mfPsO9B5VcV8dDvPQ0U9DHDUb21aU9Ww6ENWbzOCY+q0npDWMvQq+e9ccI8v+Ddf/h3vLKg1Wp5rbvn17tm3bRrdu3ap8f+vWrbRt29ZdZaugsLCQDRs28Mgjj1SYPmXKFFavXl3lMmvWrGHKlCkVpk2dOpXXXnuNoqKiKoPQBQUFFBQUlL3OzjY/1g6HA4e6HYgPcTgcWJal41bqrlwQouz4qWqaq8t6wgWBFIfD0SjTyqdJcAycANtLKs9XS/kqfMbn/+GGuuvq91jbtMBgbF0HYdu7Hs5m4DiyEzr2rXazfiVFtM85TGqr9g3fB0cJEw99Tr9TG50tOEuV2Pzws8znnZhziMScQ2QFRvFpj7kufe644xh19/pq2Uad62NTbnffBmwfP4OttDXqlrjhLE+aST+bvfSGsZploxLMvz6j+axgO0NSVzLu2BJsWFjfvoN14+/rXnZHCbYvX6wQtN0f2Zsut/2GLStTKuxLlfXbZodrHsL2xT/NKN42W+k/u/mX1AvrkpvMAEzW9sr11lPHRXX6jsH23bvYcs5g7VmPlXbUtKYs5/lyQbv7ZvRr+HZdmc/Vc62Lnr8g8Fjtfrh5u7Vto87rcvNvcHTuSUadWEZS1gHYYO5xzrc3zPVvRfAV95Y7hutRZlc/u4Z8xnVZdsIN2LavxFaYj7XhG6xhUyG+cs/MC6+T2+QkM3v3685Bfo5A7/ILlGZ+6V76steZrTh2dzUDgjaEt19fXbjdpvoe67s+yyIx+wCX7X/f5CsHrK6DsK57GPwDa04j4KnrXzcsmxzeFavHMGx7f4KcM1gr52NNuAFOn8D240LYsgyKi6HrQKw+owksak2hf0jF7TbR9+PStGpUur910/FY5++2Ca7Navxdtixsqz/jfJ+hDQljoJrPxeFw1P77OGIGtnVfYTubCbvW4ji6F9pXHfNrsKa4RvIRddl3m2W53kzgvvvuY/ny5axfv57g4IpPq/Ly8hgxYgSXXHIJ//jHP+pWYhecOHGC9u3bs2rVKsaMGVM2/cknn+Stt95iz549lZbp0aMHt912G7//vfOif/Xq1YwdO5YTJ05UGWR+/PHHeeKJJypNnzhxIv7+ekIlvsOyLIqLi/H398fmTTnIpEmkZjq77baJDK3TtIZsw91lcXeZ3caysOdmEZCebF76B1LYvmeV+f7Kl5daynxhva1qWU98JvazGQScMi0mcwIjCGzXsdrt+qcn43cuE8svgKI2XbACgur93drPniHg1DFnQWw2SlpFUhIWixUQiN/ZM/jlnMZWVFg2S0l4LMXR7ardxoVq+4xrmubu9bm7PjbVeSC4OI/4syfKguslraMojulQY/7Laj9Ph4PAE3uwFZsb7tTW7Skod3NZ22dnsxzE5Z4kpFyO3KKY9jjC6tYSpS7fjyu/t03x/dS0bHh+BlH5ZhDFs4HhnA6Nr9f6mmK+mpbDDfXR3dt1929hfcpX7bLFhQSm7MNWUlLpLUdwK4qj22EFhlS5aEPK5+7zZV2XjcjPILL0eM/zDyWtdbsK6zo/n91y4LDZaRMZSkDaIey5OS7vI4Bl96OobTdSzjk/38aqy67OV99pTVFXGrKsq+uzFeYRmLLf+XAuNIxjAfFY5c7NjfF76+p8jbmsraiAwBN7wDJd5/P9QwgpriaNj82GI7g1jpAwLD9/sPth2f3M/34BZbl43f39uKqm/S9fb+u6bG1la8hxYSsqwH4uEyuoFY6Q1nUqS13LF1yUS8K5EwA4gkIpalv3IOuF31k7ex4BZ44DkOcfQlrr9vW+hq1pmitl8dj9XRMrLi5m6dKlZGVlVTmOWHl1ikT+8Y9/ZP78+fTo0YN7772Xnj17YrPZ2LVrF//85z8pKSnhD3/4Q0PLX6MLL4gty6oxKFXV/FVNP+/RRx/lwQcfLHudnZ1NYmIin3zySa0fpog3cTgcpKenExcXh93HBkSShqvqKa2r0xqyDXeXxd1ldov0Y9gWvYbt0FbA3Aw6LrkJxlWdgsflVmBV1NuqlvXIZ1KQi+Ovt+NvFXMuoDUhv3sV/AMrb/fobuxvOK8DrIhYrFv/zPOr01wqc4Vp03pje/EBbKc7mHVdPBtr+PTKueksBxzYgu2Dp7A5SrAi47HuewFsNpc+l9o+45qmuXt97q6PjV6nLIsvPviMGQc+JNBh8qFZfcZgXf1Arek0avw8Ny/F/sU/AUhp1YEPe/28LAhc02cXVJzHZfv/TfuzpQ9U/PyxrnrADAxYR3X5flz5vW2K76emZQNL8rl753PYCnIptvnzxoDfkBvQuuL6iovAP6DG9bm63YbMV9NyuKE+unu77v4trE/5qly2pBjbW/+J7ZjJ8WqFtIZuQ7C6D4EugyA0rOHbcGG5mpZtrHOtn6OIudufJ7wwC4DPut/CkYjuFT93yyLEyiXPFsp9YxOwPf8rbJYDKywaa/z1Jq9vRKz53+5nBu4p/Wdb9Bq23esAsOKTeKH9nLJUSY1Vl12dr77TmqKuNGRZV9dne+dxbIfMgzqr10isa35j0j3VsF1PX/+6c1nb4jewrfuq0vQiewD5fiGEFWVXu83zLLsftOkMib1YmB7KidZJ5AaGlW23Id+Pq6rd//L1tpp8s01R9yrIy8H2w8ew/mtsjigArC4DsSbN4fmNzofIdT43FObBgc0mN3Ppte/5+a7a8xZJOQcBcFz3sHNgxjqo9J1N7UnO//6SiMIMAL7qegMzbp7ttuta6ngeaAmys7OJiopyad46BW4TEhLKBvh69NFHKwRBp06dygsvvEBCQkL9Sl2L2NhY/Pz8SE1NrTA9LS2t2m22adOmyvn9/f2Jiam65UVQUBBBQUGVptvtdgW/xOfYbDYduy1VuYdTZd+/q9Masg13l8XdZU5LhtMnICza3JC1inR9hN+CPPh+Hqz9ChzlWi/1GIZ9zBXVr+eCB4W1lblCva1qWXd/Jq4Iac3BiO50y9xFq6KzZrTucddU3K7NBt+9V3Ffsk5he/txWifdwtmgiNrLXH7a7h/NdwXQsS+2iTdR9SNXO/QYCp36wcEt2DLTsKUnm5sOVz6XWj7jGqe5e33uro+NVaeyTpkBLDYv5cozKc7pPYZhu/oBbP7Vj4dQVXkrbXfQJbDmC0g/Sttzx+iWuYv90X2d8124bEkx3c9sZ+SJ5cTkl461EBiC7YZHsHWp11Addf5+av29bYrvp4ZlC/1DsA2dAqs/w98qZmDaOtZ0mGTmSzkEy96HvT9BYk+YfCsk9WrYdhsyXw3L4Y76WBXLgsPbITDE2T3U3fW2qX+Dv/sAjpX2SIyIw3bX3yA0rJrzaD234cJyNS7bSOfaEr9AVnWYwvSDHwEw7uhikiO6VvG5mzQo9k3flg06aBs2Dduwiqn2Krny1/Cvh+HUcWxpyVxa/CWLu5jfxEary67OV99pdbxWqa4cdV6fO3+DD2yBQ6UDSUUlYLv2oep/j7zp+tedy064nrwNS50tbSPiWBE2mB1xQynwC6Lt2WNcF3kSdqyGnNNVb9pRYgY5O7GfmaXTToa25fMecxp+jeSqGvffVrGu1WlZN/5OlRTD+kXm3iDvbIW3bAe3YHtlK1NiBrCm/aXkBEXWrT4W5sNrj0L6UZOWacad0P9isNmIO3eiLGhLdFvsvUa4fi9Tzb4C2AOCWNNhItMOfgLA5MOfYc8c77brWup6HmgB6rKfde7736lTJxYuXEhGRgb79+/Hsiy6d+/ucqS4vgIDAxk6dChLlizhqquuKpu+ZMkSrrjiiiqXGT16NF9++WWFad988w3Dhg2rcZA1ERFpZkqK4Zu34MJWCHY/E8Rt3x2m3m6CuVU5thfm/bXiRW5kvBmoq+eIGruENxfr2l9Cl8zd2LHgh49gwPiKM+zbCMk7zd/Rbc1F5KnjkHmSa/Le4ONeP+NcoIs9V6zSEcHPu/ja2pfpNRIObjF/7/7RBG7FvQ5vhxXzzedsVczLlRzehaTZv6vQYrPe7H4waQ68/yQAY45/y8HIXjjKt+K1LOJyU+h7aiP87a/MyHfeNOX6tyL0tiegXdeGl6U5GTWr9MFTMQPS1nMwshfM+xp2rXHOc3Q3vP4o9B5NpP+I+g924kssywzKsnK+eT37t9B3rKdL1TB71sOqT83fdn+zTy60sPUG7hq1fW90P6YXboFje4nJT6df+gZgUKX5/BzFZjAzSs89QybVvvLgUDN4z6sPQ2E+vc5sJbV1B7YkjHJL2aUeHA4zKNd5E292z++Rrwlpzfyet9Hn1CZOtE5i5k2z2bhoZ9nbKWFJMG0mTLkNju+F9GOQf84EHvPPQu5ZOHkYyqepAhJyUxiQ9iNQ9x4szdLx/TD/784GBmByKA++FPZtgMw0wKL36S10P7ODH5Km1W0ww69fM0FbMN/P/Gdh1zpCgi5mSGq58Z1GX+7WAYP3RA+ga8ZuumfsIKgkHz78X/za30yJvQXWJS9Tp8Dt1Vdf7dJ88+fPr295avTggw8yZ84chg0bxujRo3nllVdITk7m7rvvhtI0B8ePH+ftt82IinfffTf/93//x4MPPsidd97JmjVreO2113j//fcbpXwiIuKFcjLgo6edQcXyHCWQlW7+Je8yIw5fmIx/11r45O9QXJpH1S8ALroaLroKAir30GiuToW2YVv8cAam/QhFBeYGKbj0BtdymMDHeZPmQIee8OYf4UwKkQVnuGbPG3zc82cubatz1l5z4wDQrht0GVj7Qj2Hw8JXzN+718GE6+u+k1K1nAxY8hZs/b7SW0fDOrMzdjB7Yvrz64BA922zxzDo2AeO7CQq/zR9Tm3kSER32Po9Ew+vpH3OYaLzT1Va7FRIPF91u5HbFLStLDwG+o+DLcsILsnjhl2vVHzf7g+OYvP3rjXcYlvHtrjhrG83vsrVNRvL5zmDtgCf/sM8fPJVmenwWbnxRibPhQ49PFkiz7DZYOrP4DUzsPWo40shdzaEVnyA2DVjF5wzKRXoPRrCXGyMFJdoWt5++FcAxh1dRHpo27oFZ8R9dq2BlAPm74ROXvvwxV0PJmpyKrQNPyRNNy/8qgnq2e2Q2Mv8q0puNhzby09Lv2dY6koAumTubqwi+5a8c+bB8tkM57QBE+DSm00DkOIiWP81fP8R5J/F3ypmwpEFcHgUdKp+cN8y21bAptKHSTa780H5rjXc4r+V4OI88zo0HAZe4t59s9n4tvMVxOamElVwGlIPMr5kIUs7Vd1QUppOnQK3ERERLszVeK6//npOnz7Nn/70J1JSUujXrx8LFy6kY0czSEpKSgrJycll83fu3JmFCxfym9/8hn/+85+0a9eOf/zjH1xzTdV5CEVEpJlJ3m1uqs5fXNn9YcR0E4TNPm26fGekmi5JZzPgjT/A1aU5MS3LdNf+5i0oHXSJjn3givsguo1Hd8tT1rSfSI/T2wgpyYNtP9CuV3dOhHWk55ltFQOtvUeZm+Zb/2SCtxknico/zYwD88AaXXMLZcti+IlyAcKLr3WtRXNErNn2if2Qeggy0mpfRmrmKIGfFpsUGAXlBo6IjIdBE2HgJcxffbJxtm2zwaS5ZUGXS458ZVp7b60iLOIfyK6I3uyIHcLxsI7mRkeqNuYKM7p4ea0iTeqTwZfCth9g2QdwLhM/y8GgtHWmpaJ9gmnZE5foqZI3jhWfmG6u5RUXwvtPEdLlZ+QFtPZUyeqnpBg+/puz226vkaaldUuV2JM90f3peWYbocW58MULpqVsOQPSf3S+GD6tbuvvMxrGXgWrPsXPcjDtwEdQNLlFPdT1CiXFFVM1Tbqlfl3HxSk0HHoMY9W+IBKzD5KQe4L43NTGubYqzIdv3jQtVifcYFq0e7MlbzrvK9p0gct+WbHRh3+A+b0cNJFtrz9L//QN5vpl/t/h7mdqXHV4/hn48mXnhCt+BQHB8NVLkJdDaLEzZy7Dp0Og+881hX7BLOh2A9fveoUARxH90zeQ0jqJXbGD3b4tcV2dArdvvPFG45XERffccw/33HNPle+9+eablaaNHz+ejRs3NkHJRETEq6xfBF//y5mPNiwGrvudyeFY3rlsmPc/psVtcaEJ9F56C2SfMus4b8AEuPyeltn1rlSBfyhrOkxi4hGThmh88gI+7H0no48vdc40aY4z0BoRC7f+iewXHia8MMsMGnVgC3Sr3F31vA45h2h7rrSLXnwS9BjuegF7jTSBW4A964BO9dnNZs3l1j4nj8BnzztbMAEEt4bJc2DwpHI3xY0UuMUEXfZH9aFbxk5z01OOAzuprTuwM3YQk264jm++O9h45WhOEjqyM2YQfU5vJtc/lNBLZle8+Rs21eTRW/0ZRSs+JcBRhL9V2pV847emJfSYK6BjX99PEbP684o9BSbPhZ1rTffh7FPM3P8B83vehsNe58xynlFSDAtecea1jYyHK+71/e+pgX5InEZS9gGT83P3OtiwBDAtqqNz08oGMyQu0TycrauJN3N06yYScw6bgZ9++gZGX+bmvZAabV4K5/Otd+wD3YZ4ukTNyoGo3iTklqYE2PMj0NG9G1j+gXlIDCYV042/d+/63engVmdqlcBguPHR6tOshbRmWcfLiMw/TWLOYdNg5IsXoPWMKs/LdkexyctdWNqidsB406LWZoOk3vDlCyYXPVBs88d/xPRG283ToQks7XgZUw+Z3iiXHPmKtNB2nA5tnPGspHY+ciUiIiJSB3s3wIJyT6w79jU5/lpHVp63VTjMfcJcTG1dbqaVv5kH0wJg/HUt/gYYYHvcUCbmmxa28bmpXLXnbSIKSlsedBkIFw4GFRnPysSpzDjwoXn9/TzoWn3qgxHlW9uOu7ZurWZ6jYSlpa1udv8IcQrc1ktuDrzzRMVugIMmmsBWq6btfbUicSpx504QWnyO1FYdSBwynPmpIaS0SiwbxX1ScKsmLVN9NEX3WFd92/lKNrUZQ2ZQNL8aO7TyDEEhcMmNvJWZxKCTa+if/hNBJQXmvb0/mX+jLjM5vn2A3VFCcHGuydd3dI9pPX50T8WWtpPmmpaT/cfDq7+DnDO0P5vMhOQFLO14ufef+zPTTUvb80Hb83ltQ3ysxXAjyA0MY0mnK7l8/7/NhEWvEdXrLjKCY+mTvsk54/Dp9fue/fz4IWkGN+94wbxe+QkMneym0jd/rp4bq52vqMCkOzmv/MNjcYuDkb0Yc/w782L3OohzY+A2+zT8+LXzdVoyvPI72nWczYkwL7uGK8w39wrnTb61+qBtKctmZ3GXa7l5xwtlD4/6d4xjW/yISvOOOf4dbc4dNy+i28LMu5zHclgU3Ph7vnn7HXqf2sz2uKFMb+Trsd2xg5galQMblxDgKGLm/g/4oM8v3LNyy1I9rSMFbkVEpHkpyKsYtB05ywzCUF2eL0q7NV31a4htB0v/7Zxu9zfdlAZOaNwy+xDLZjej277xBwDanz3ifHPSnCqX2R/Vh9PBccTkp5vBjw5tM6MCX6DN2WQScw6ZF9Ftoe+YuhUurgPEtDODRRzZSXBULvn+3tnlzpsCeZUsft0ZtI1LhFl3168lmhtkB0Xx5sAHTY43m537L+nP0QXbPFKW5sKy2TkVWnu6l3OB4axKnMr6duP5ZewJM7BZdmle4bVfmta5se0boYCW+Vefrs5FhaaVeMohSD3IjXt2Ep2Xhr9V2vNiexXLXHKjyVkOEB4NNzwCr/8BSkwX0XMB4aSHtoEtpxlwcj+BjgIyg2I4ENW7YftZFw6HGagmPBZCKj6o6Jy5B17+qzM9gt3f9A5p373pyuflDkX1YmvccAakr4fiQqYd/JhPu8+h+5kdZobA4MoDbtbBqdA27I3qR4+M7SZf7o8LgZb9+TfZb9yPCyHnjPm754jqc7ZKvZ0OiSczKJrIgjNwZCdBUbkUuOva6oePKo4hUVIEeTlcvedNlnW8jB1xVTxc9JSl/4bM0l5GHfvA0CkuLXYuMJwlna/k8n3m/uLi5EWcaN3R2Xq1uIg+6RsZmrrKvLb7w7UPmYeo5dls7IodXJayoPHa25Yz/eek7d5OfG4KUQWnuWLfe1A4wIUFa5ByED78XwgKhcvu1m+VixS4FRGR5mXZ+2awMUpbgE77mWtPdW02uHg2RLeDr140ubaueRA692v0Ivucjn2g3zjYvsI5re9YqGZAKMtm58d245l+8GMz4fsPIf6CwcNKihl39Bvn64uurvtIuTabaXW76lOwHHTO3KOcXHW1bwNsKW15HhQKcx4zg1p5mvLWekyhX7BJjzBypskjufoz88aaL0xuP3favxkWvWYGxrnlP6s9p1TpxAF4/ynIOV02Kb62ZS66xpz3y2vf3QQ+P30OgFEnSnMC74fyw8Akh3eBc/9Z9XqLCiH1oEnRExnn+j5UJTcb5v0FjpQOsBmVAG26MDw7lJCicwxOW+ucNzLetLTVjXAlKxKnMsBKgVPHiM9NYfae1wlwFJk3B0xocF7Nte0voVvGDpPWZdWnBPb+tak7tTl5BPZtNANsxnVoUBlanLxzsOL8wII2MziUuJ/NxoHIXgw9ubr02movu2OrT3nlsjOpFdMO3PWMGWT2wGb8LAeTDn9OVP4psPp5vnXm0T3m4SUmrz6X3VOnh4uHInvBiJnw4wL8rWKmH/iQte0nwiffwp71TD6fHoHStD3eMsBqQCALul7P9bteIbQ4l3Znk+GD/8Ev6gpK6pNGKO8czPurMwD+2u9h2u1gdfD8d+zldBUsIiLNx/F9sG6B+ds/EGbdVfcLgX5j4bdvwG9eVdC2JpPnUmQ3+X4d2GHiTTXOvi+6HxnBpQHAIzton32o4gyLXjMXhEBWYGT9Wz/1cnY/65LRskZA9i8pNC0V6ymwJB++fMk5Yert3hG0Fe/g528GCwwsbQW0ZTmczXTPus9lwfxn4d0n4NQxE6xc/Lrrx/PBrWYgxHJBWwALG2eCYzkY2ZOdMYPMjfPFs00vjNv/2wR6qvqNGDiBn9rUPCp9UvZBeOkh2uY4B0YmN8eMJP7sL+C1R83/r/wOVn5KRP4Z1/alvNMp8K9HnEFbgIyTsGsNY45/VzFo22ukCXwoaFulYr9AuPZBcxwD0fmnnG/WdVCyKmSExLEnprQlWt5ZBqWuqXmBvLOw8F/w0oPw7dvw+qPOVtPimtWfQn7pZzZwgsmLL43iYLkeBl0z3XRttXyecxyK0VdATFu46Y+mp1ypoamrTN7b6pzNNKmdXv89rJwPp467p2zlFRfBF/90DlQ84Yb69TaZPBcSTPqHmPx0Zh6YZwYELRe03RfV1+sGlMwOjuazHnMp8CvNhX9wC9MOfITtfE8WV1kWfPlPZ9AWwFEMC19l2sGPCTifkkmqpBa3IiLSPJQUm9xTlsO8nnC96W5fHy14ADKXRcSyuPM1jEj5nm1xw7k0pl2Ns1s2Oz+2HV820MGIlO/5NLyzeXPDN2UDwRXb/Pi662xuqO930L4HtIqEc5l0zN6Pf0lhWS7UZsuyGHt0McNSV7Evqg84+ta9tTIw9ugSZ1f4LgNh8KXuL6v4tuBWJn/nmi9M99b1i4C+9V+fZcGWZbD4TcjLqfjekZ1wuKrcBhV1PbMT3vvY/AYAdOhhBnRp05kXNp2tUP/7zHC9+/aqDpPJCoohvCCDQr8gxg7szOJdp7FbDsYc+5ZWxWch5zTX7Hmdte0mElp0Fv6+GYryK67oxH44sZ/bgLTQtqzqMAlwoRzJu0wL4vOfS6tIiIqH1MPOrsWUdqudcqtpEa0WSzVr09nkM178etmk462TaJ/gnpyd69pNoPeZbWA5GHJyNVsSRlbqUm6zHPRN3wj/+N+Kx3zeWVj1GUy6xbWNFRfRJWMXbc4d43RwPBR0q9y1ujnLOQNrzECp+PmbYJo0mpTWieT6tyK0+BxJWfvxcxRRYm/AtXJaMmwtHdMgpDWMvtz87ecH0+/ghxNFXHy0dMCyNV9C12pa+H73HhzYbP5O3gXfvsPNIfEciOrN9thh9S9feT98ZFLVALTt6ixrXQUEwrUPUfTig87W/pjf1Z2turM/ug+HInpwvxeex9NbtePz7nO4au9bBDiK6Ja5i8mHPuObzleV9YiqLT1K//T1cKT0gVZwK9NTb4PpadfzzDbiclNY0O0GzoTU2lemRVLgVkREmoc1X8LJw+bvhE71v7ASlx2I7sOBaJP71JUQ356Y/kzNXA0ZqSRlH6RtTml+3A1vlc2ztNPlnGydWP9C2e2m1e2GbwhwFJGUfaBCS5FmafkHDCvNjdY9Y6fpOjp+dq2Lldc++5DJ/wgQEGy6wHvhzYN4gZGzTJdRywHrv8a/V/e6PxyxLDiwBVZ8DEd2OKcHtzItRzcvNa+XfWDSqlRzLPZN38DEw184W0L1GA6zH4IA0zKoeGsD8iHb7GyPd974jx3Rn93pZn2HI7oz/eBHdMg5jJ/lYOzxbystS4+hkHXapEwoFZ+bYvIcHu4BnWro0bF9JXz6D5PvkdJc0zf/0aRCcJSYPN4ph+BMisnr2bZz/fezpRk5k8NrfqBT9n4AtsaNwF2ZmrOCY2DwRNj4LUElBQxJXc2aDpPMm44SOmfuZtTxZcTnpjgXCgiCkhLT8mzdVyYAX52SEji8DbatgF1ruawg1/ne/34JvUbQuaAjR8K74ajHw7smZ1km6Lb2S/Pg5fJ7zcMJV3xfLjfqsGmuLyf1YtnsHIrsSd9TGwl0FJKUfZBDkT3rv8Jl7zvP2xddXSlVyeaEUQw6uY7wwkzYv9EZOC0v65QztVM5sXlpxOalMfDkjzCmvXlgU18Ht8KKT8zfdj8z7kVNY2bUJq4DC7pez4iUHzgTHEe/qTOgc3+WLPb+HmIpYUl82e0mrj7wHpQU0/v0FvwdReQERsLHS8zDlLOZJlf8yJnm97hUbG4KFycvcq7syvvMb33XQfD5/0FBLtH5p7hh58t81/Fy1x5utjAK3IqIiO87kwLLPyh9YTP5Cf30E+dtLJuf6Wr9+f8BcNHRb4goyDA3rMCmhFHuyUlbGriltEvfwajeZjTgLcth4xJz8X31A2YgM0/KzzXBr4aM/L72S5MzuLzlH0BH14PV/iWFTDr8uXPCpFtMHk2RqkTGQb+LTBfP3Gx6n97CtvjhLiwIfo5iepzZBi++BmlHKr7Zb5zJSR4aZvIJnj4OyTvpEHKIY+FdKq1raMoKLjq2xDlh4CVweQNvql2UGxjG/J638uvgbaZ77nn+gTBkEoy6DKJLB4A7kwI715K2+hvic1Pxs0rgg7/Az/+n8ootywSzyw+S2WUgXPc7E9SmNHgQl2j+Sd3Z7Szuei1jj35DQUAwe6P7uneQn4tnw+bl4Chm0Mm17I4ZaI6RnxZzeWZaxXn7jTPdp1d/boK2RQWmdR+jK693+0r4+jU4V016kuJC2L6Sy1lJnn8oO2MHQ2abhudYbgwOB+xdDz98bFqkn/fJ3+D2J2tf/nSK+S2nNDfqxdc2XlmlzIGoXvQ9tRGALhm76h+4Pb4PdpWmeWkdBcNnVJrFsvmxOWGks9XtugVguyB9zZovyq4fGTrFXNPtWmsGwQWCS/Lg7cfhtv9yuWgVWo3mZMAnf3f25Bt/fcOCwKWORPbgSGQPAPp1960A5dGIrjD7tzg++Ct2HKaxAEC57AecPm4GIY5PokfrERyO6M6M/R/ib5V+VyNnmaAtQJ/RkNCR9H/9ibi8kwQ4iogoqEdaoRZAd7UiIuLbSkrgq5edLS9GzlSOP282YLxpKZN5knbnyrWg6NyfFTFT3bONzgPMzVxhPp0zdjPW/xt45i+Qf845z1v/aXJcViOwOJ9ARwFnAyPcU6byUg7Bms/Njbh/oBmEKakeI2FvXgaLnF1+j4V1pEPOEXOT8cnfCen6c/ICqgkKOxzmhnnvT9ywc7kZLRrMiNzDm2SsYp/TZKOk+4LRl5vALTA4dTXb4oZWPYBcQZ5pKZWWzMXJG+l+Zjutiy5IiRDVBqb/3LRQPW/8dTD/7wCMOr6Mj8M6V2h1O/zEcsYcX1qxPJNvrdNgMQ1l2fxg0hxI6gM/LYJ23UzdaRVeccbotnDRVcSPvhze/2/Yv8nk5Xzvz4R0vI28gNKAbGGBeai1Y6Vz2cGXwqy79SDSzfL9Q/mu0xWEWLnuH/gwMt6kE1n/NYGOQuZufx4uyPiRHtKGuBvuMwN9Aoy7xgzSVJQPG5YQ3q8X2UFRzgUObKkYQMIELHeF9eRIRHfa5RxhwNldZTlyQ4pzTW7Q59ZAnzEkOHo3rCeLu1iWCawtn1f5wQ3Asb2lgx/2qHk9y9535kYdcyW0aoTfaakkObyraSFeVECXzD0stRxYVdSfwOJ82LHaDHZ6YLO5Pk/oZIKebTqZa5fzLp4NgUFVbm9H7FBGHV9GoKMQNi8juP9g8v1MOpDg4lzYUhq89w+ES26E1pEw5gr+9ekqZuyfZ64xc7Ph7ceI7DyXzOA65Ox3lMAnzzgflHQdbOqpQK+RLO5yNdMOfoKNC/LQ+wc678fSkpmelkyhPdB8h5Smmpg8t+IyMe2Y1+cXTDiygNaF2fzYbnxVj65aPF0FiIiI7zpxwOS1Pd8VNTy21kGyxMP8/M3F75cvOKdFxsO1v8VaXsWNXH34B0D3obBjFSEleQxLXVl5nuzT8NZ/0jppDmeDyt30WRb909Yz7ugiAhxF7IgdDOfckP/QsuiYvZ8hqatgvbPrNIV58NWLZlChutj9Y1nLZYB17cazrt0lXLXnLRJzDkHOGaYe/ITPesxxBiYsy+QM3fqDuaE6mwHA+VuZYps//lfcW6fgl4KZLVS7rqar/+HtRBWcpkvmHmdKkrOZpsXogc2QlV62SKW29B16mAFpeo+snJO531j44UM4dZz2Z4/QIadcq9tVn1YI2q5qP4mxU27zXGqPHkMrBp2r4+cH1/4W3viDSeuTcZJZhf9mfq/bCCnKhTd+Dynlzg0TbzbnSqUs8T3jrqX4pyXOFmalDkd0Z2u8aYH26/NBWzABp9GXmda2jmJGHl/Gki5Xm/dOHYeP/tcZtO062LTq7jGU3gFBlPWtKC6CA5vZs/hLumbsMtu2HLBjJTewkhOtElnW6TLPdUHOzTaDX+66YNC2Np1Ny+Pv3jXlXfYBsb1/wanQNlWvJ+UQbF9h/g4NV1qsJlRiDzBd23evI7T4HG3OHiUlzFwfBRbn0/v0Zrpm7KLd2SOwyVFx4cPbK+csj4w3x3I1Cv2D2RE7xAzCWFxIv7Sf+KntOAAGnlznzCc+ZJKpQ6XOBYbzeY9buHrPmyTkpsDZDK7e/QYf976j4gORmiyf5yxvWAxcfX+TPhj0dntjBnAmJI6o/NOcC2jN7MnDICzKBPb3bTDnsmN7AcqCtgV+QQTNfqjKcURK7AF81/lK/BzF7n+Y1kwocCsiIj7Hv6SQUceXwk9rnTczNrvJy9mSBufwsHoH7QZOIHvxvwkvzKTIHkDADY9WbqXWUL1Gwo5Vztd+/tD/YjPy9IJXzMj1mWlck/cGH/W6g9zAMEILc+C9/2LikY1li/U9tQn+7176JFzKzthBdb+gtCy6Zuxk1PGlxOalVT1PWrJpsYeLI2If3AofPe089odPZ601Cmw2FnW9ljv3vVo6ONsBhqWsZGfcENNVd+O3ptt2Vdp1w//SW+o3UnITa2nBYq/d3zFXlt3YDkldxcHInrBhCSx5q2Lr9nIc2DgQ1ZvuV88xrburY/cz3VI/MQ80Rh1fysdhnRl0ci2s/7psth8Sp7KpzVjG+kpwMzgUbvoD/Os/IOcM7c4d5bJ9/yY29yQUm9aSBAabVC7nu5L6IK89ZptKeDRr2k9k3LFvyPcLIXjEFBg+lc/XnKp+mTFXmGM77yy9T29hQ9uLOBcQBu8/6axPPYbBDY9UPfikfwD0HM6i/cGEFJ1lQNp6RmVthHNZALQ7d5Qr974NhRdX28Kx0ezfBJ89X/awEEoHEh0/2zxktdlMK/SV88FRzJSDnzCvz12U2KsIVXz3rvPvi69tUdd8XlGveo2E3eugNBVVTmAkLH6Tn21ZRJCjoPL8AUEmzUtOFd3fJ1xf62DAWxJGMThtHWAxMG0dGxNGE+AoYFCaKQN2P1N3LlDoH8JnPW/lruPvQ9oRwoqyuXrPm3zc62e17+P+TSaNB6X3Ftc+qFbdFzDHYjXHY49hpl4f2sbRT98kMecQDux82+lKZtYyaHSVdV5AgVsREfE1SVn7mHj4SyIKy+V5i0+Cy+6BxAYMlCBNxz+AL7rfTL/0n9gdM4Ab2nRy/zZ6jyI5vAsRBRnsihnIqJtudbbIuPUJeOOPcCaFyIIzXL3nTTa1GcPYo99ASV7ZKorsAWbk37wcJh/+jD6nNrG6w6XmZjonwwRYAoKqboVhWSRlH4BX32ZW+Rx+lHadHnUZxHUwKRswXT9Det3r7DZdnU3fmVZL5/O69Rtnupl/bQZ4yg0Ig2t+Y/K6YTH6+HeMOrG0YhdbSruzdR1oBo/oMRTCouv+GUvL1m2wybOafpT2Z5O5cefL8NMFgy4ldIL4RIhL4tNDxaSHtiEvoDX31xS0Pa/vGJO/+dQx2p9NZtLhz8vyKwKsbn8pm9qMrXEVXiki1gRvX/8DFOXTMfuA872oBLjhUUhwQyt/8aiNbS9id8xACvyDuXfqkNKpNQRug1uZQZqWvI0Ni7HHluDvKILsE+b9+CS45sGqg7YXyAtozbr2lzDq9rth2w9kLP6AqILTtCo6ax4SVhHoahRFBbDkHfhxgXNaSGuY9UuT27L8A5cJN8C+jXDyMHF5Jxl5fBmrEydXXN/h7WagKoCIODMomQd4RQDVU3oMxYEdOw76p61n0Mk1YDko/yggMyiayEGjofsw6NTXBGfPZUHqIfMvLRnikkxe8lpkBUebcQt2r6N1UQ7dM3YSUXTG5K8F6D/OtNytQr5/KMx9HN78I5w6TkRBBjfueBEij8HwaRVa6UJpr6Tj+2D+s86B0y69xZnSpB5a7LFis0GXAczvdTtReabnTUaIF+bb9iEK3IqIiO9IPcRVe99xvvYLME/sx1yhHIA+5nRoAt93rGH07IbyD+DTnreVvRxV/gI9LBpu/ZPpspyZRkx+eoXBuc4FtGZJpytJD23LxUcX0fOMGUm+/dkjzN5dmlO2/GD1IWEmGBvTlpGn7OQERtDr9GYScw5XKFJKqw781HYcl910jfPme9BE2LwU8s8x5ti3fNe5mhtqR4m5AV5TbhCxHsPgql9XDhx3GWBaMn3/IXYsczNyXucBJv9iz+EmsCZSX3a76ab8xT8BiM8tF7QdMB6m3l6hlVLymW1VraWG9fuZXLelrW7LB23XtZvA+nbjG7wLHtO2C1z7II73nzJ1FJPnm9m/Nd2/pVnIDQyr2wLDZ8CaL+FsBl0y9zinh4bDjX+oe+vSADNY3oIDcPOOF0w+ypXzYZib8snXJP0YfPhXk+P6vK6D4Yp7zajzF/IPML9nrzwMjmKGpq7kUGRPUlt3gMw0OJMK35a7/rvkhlpba0ojCA3neFgSiTmHnXlLS1Mt7YodxJb4kZwOief+6QMqLtcqwqRZ6Dqo7tscdVlZK98hJ1fTqixPus087KhJ60iY+ycy//k7IgvOEFqcC9/PM/Vg4HiTlzwrHfZuMF38y7cM7jGs6R5yNGMK2LqH7nJFRMR3tOnMnuj+9DyzjaNhnUm87bdmFNkWrsU+0W+IiFi49U/kvPAwYUXZzul9xvBu0MWmpQawqOtses68mswPn3cO4HWhvBw4ngPH9zKqirfTQ9qwusOlHI7oYVohlG8xNWmOGaylIJe+pzayLX4Yaa0uSFeQn2uCV/s2OKeNmAFTf2byZlZl/HUc2ryZzll7ORfQmlYjp8KQS02AWcRdBozn3MK3aHW+m39UG5h1V/1uzqvSdwynF7xDTL4zVy5jr2JtgZvW70k9h7O4yzUMS1nBociejLjlPj2AbOkCg8zDigUvO6f5+cP1/wFRVbcqdMXp0AT2RfWlR8Z2k2t2/ddAN/eUuSq718H850wOd0p7eEy+FUZMrzlnc5vO5mH80vewY3Hl3rexWyXwU0nF+eISzcMh8Yi90f3LHkzn+YcSMnYWr2d1qn4w1Ibq2AfadIHUgxUfEPYaYY6F2oRH83GvnzHu6GK6n9mBHQeUFJn0URu/rXqZqDZwZRUPxkU8RFcHIiLiU75Pms6RiG7sihnE/QraSkNEJTC/1+3M3P8BQcV5rEqczLTZN5O/8IIBNLoO4t1+v6Jv+kZi804SUFJIr7hgKMw3/3LOQHblLrCZQdFEzrqNfx8Jrz43butIc6O6+A1sWEw4soAPe//ctJI9kwJH98CqT52tlmx2mHGn6eZXE7sfX3S/iciCDLIDI7lvUjMIdIn38Q/g285XMuLEcpIjujLy9l+6tyW33Y+17Scy88A8ADYljGbwpDlwYR31UXtjBrA3xrRMG6GgrWAGWsr89iPng8LLftmgrtrnrWs/ge4ZO0yr21WfEdDrPor83NzrwnIw6sRyWL/cOS2+I8x+yLUAG+bBTMq672l77liFFp0VTLnVpZQR0jh2xA3BhkWJzZ+90f341SVDyVtQxx4VdWGzmcH7Pn2u4vRx17i8inOB4SzqOptVHSbzs1YHYOMSKMitOJN/oOn50GOYScEQXEvqqnpSYwupD10hiIiIT8kLaM2u2Erjk4vUS2ZwDO/1vce8sNmYVk1roBJ7AFsTnIMF9brwwruoADJO8uU3a4koOENOYAQHI3tzX/9BkFzLDc2IGWZQp1PHaHvuGLN3vwY7s0zLqPKCW8N1vzOpEFxhs5MZHOPavD6uudwI+eJ+HI7sweHIHgCMbIT0G/uj+/KF/WZsloODkb0YbLP55Ock4hI/fxZ2vY4xx7/jQGRvLh000S2rPRMSz97ofib1T242A9J+ZEPbcXVfUVGBybN++jjEtIf4JDpllpAVFM1Fx76pmOKh70Vwxa9MPnhX+fmxuMs1XLn3HVoV5ZAVFEVsUifTWyS6DXToYVKNiMdYNj+2xY9o2o32HcvZr16ndWmahOSwLiS1717n1eQERcLU20zL9k3fwYHNEBlngrWd+jf9wH11pN++lkuBWxEREWnZ3DEifUAQxCdxMCqr7sv6+ZsBxt55HIB2Z49Wnie2Pdz4e6UGEZ/VkBvOQ5EaeFJajvRW7fi8xxwALnXjete1m0DPjB1gORiauoqt8SPq3up23QLYWtqi9vg+ACplAbXZzaBOY6+s1+9rVnAMbw14wPQ80YOaBmsWn59/AJsSRjPu2DcA/Nj2YpIasr7gUNOKd/Rl7iqhSKNS4FZERETE07oOZFfMQHqf3mJeh7SGDj0hsaf5v2Mf5b8UEZF6ywiJg34XwbYfCCnOZeDJdfzU7mLXV1BYAKs/r3GWfL8Qgm98GLq5IT2POx6qSrOxsc0Y8v1DKPYP4Hh4Z08XR6RJ6Q5ARERExAss6XwlO2KHkBvQmrlXT9CgGCIi4l7jr4PtK8FyMCR1FVsT6tDlfeM3zhQ+vUfDxddCWjLr12wkJj+NEpsfKztM4XZ3BG1FLmSzszN2CCFWrgszizQvCtyKiIiIeAHL5udsRaKgrYiIz/La7umx7aH/xbB1OSEleQw8uQ4YXvtyxUWw6jPn6wnXQ0JHaNuF1cdaRi51ERFPUeBWRES8gtfe5IiIiIg0F+Nn49j6A3ZMrlvO3lL7MpuXQs4Z83evkSZoKyIiTULNOURERERERERagph27Iw16QyCSvJhyds1z19SDCvnO1+Pu7aRCygiIuWpxa2IiIg0KrWmFhERd9LvSsOs7jCJbhk7CS7Jhy3LaNerCyfCqmlFu+0HyEwzf3cbDO27NWlZRURaOrW4FREREREREWkh8gJas6bDpLLXE458hc0qqTyjowRWfOJ8ffHsJiqhiIicpxa3IiIi0mI1l1ZbzWU/vIU+T++h70KkcWyLG0bf9A3E56YQl3eSAWnr2ZIwquJMO9fA6RPm7079IKm3R8oqItKSKXArIiIiIiIi0oJYNjvLOs7i+l2vAjD6+Hfsi+5LbkCYyWt7aBsse9+5gFrbtjh6cCbiHRS4FRGRJqcLQRFp6XQeFBFPS22dCIMnwaZvCSopYNKhzzkbGA5/expys50zdugBnXXOEhHxBAVuRURERERERFqiSbeQv3UVwSV5dM7aW/n9VhEw8y6w2TxROhGRFk+BWxGRZkgtuUTqT/VHpHlQXRZxQasIVneYxMQjXzqn+QdCz+HQdyx0HwIBQZ4soYhIi6bArYiIiIiIiEgLtS1uKMHFuUTln+JwRHemX381BIW4tKwekIiINC4FbkVERMQrtPSbv5a+/yIiDdFczqEe2Q+bnfXtxpe9nO5i0FZERBqfArciIi1Yc7nJEfEWqlMiIiIiIuIudk8XwFUZGRnMmTOHiIgIIiIimDNnDpmZmdXOX1RUxH/8x3/Qv39/WrVqRbt27Zg7dy4nTpxo0nKLiIiIiIiIiEjTu39m/7J/Ir7IZwK3N910E5s3b2bRokUsWrSIzZs3M2fOnGrnz83NZePGjfy///f/2LhxI/Pnz2fv3r1cfvnlTVpuERERERERERERkbryiVQJu3btYtGiRaxdu5aRI0cC8OqrrzJ69Gj27NlDz549Ky0TERHBkiVLKkx7/vnnGTFiBMnJySQlJTVZ+UVEmiM9tRYREREREW+h+xNpjnwicLtmzRoiIiLKgrYAo0aNIiIigtWrV1cZuK1KVlYWNpuNyMjIaucpKCigoKCg7HV2djYADocDh8PRoP0QaUoOhwPLsnTcipNllf3ZpMdFQ7Zb1bKe2o/6KldeailzpXpb1bK++D02xfoaext1+B7dumxDeHNdcfUz8dRnVwde83vr6vnS1ePC3fP5mPum963w+sLzL24+r3jlZ9fY5WuK+l1dHTj/DzfUXW+qK/U9D3jTudZTx4U3bcNTy9Zx/TTludHd9dabeOK3wMXvscrfwoZsoyHzNTN12U+fCNympqYSHx9faXp8fDypqakurSM/P59HHnmEm266ifDw8Grne+qpp3jiiScqTU9PTyc/P7+OJRfxHIfDQVZWFpZlYbf7TFYUaUQhVm7Z32lpaT6x3aqW9dR+1Ff58lJLmS+st1Ut64vfY1Osr7G3UZfv0Z3LNoQ31xVXP5Prh8W7NJ8necvvravnS1ePC3fP1xy4uy57+2fX2OVrinNjVftw/bD4snobERHRZN+jp373XJnmqd+pqnjquPCmbXhq2bqun0Y6fmo6ZgMpBGxeec6sL0/8FjT191jT+rzp/NOUcnJyXJ7Xo4Hbxx9/vMogaXnr168HwGazVXrPsqwqp1+oqKiIG264AYfDwQsvvFDjvI8++igPPvhg2evs7GwSExOJi4urMeAr4m0cDgc2m424uDgFbgWAPJvzR7Cqh2HeuN2qlv35zKYruzuU3wdq+QwurLdVLeuL32NTrK+xt1GX79GdyzaEp44VV3jqM2kM3vJ7W9X37eo0V9fXkPmaA3cft97+2TV2+ZriPFDdPriz3npTXanvecCbzslNcZ3n7dcgnlq2ruunkY6fao/Z0ta2eYR45TmzvjzxW9DU32NN6/Om809TCg4OdnlejwZu7733Xm644YYa5+nUqRNbt27l5MmTld5LT08nISGhxuWLioq47rrrOHToEEuXLq01+BoUFERQUFCl6Xa7XcEv8Tk2m03HrjiVe9DVpMdEQ7brqTK70wUPGGvbjwr1tqplffF7bIr1NfY26vg9um3ZBrh/1oAm2U69eOgzaSxe8Xtb1fHu6jRX19eQ+ZoDdx+33v7ZNXb5muI8UMM+uK3eelNdqe95oJmdk2vl7dcg3nztXMu1qVu2W+Mxa4PSuttseOK3oIm/xxrX19LOP6Xqsp8eDdzGxsYSGxtb63yjR48mKyuLH3/8kREjRgCwbt06srKyGDNmTLXLnQ/a7tu3j2XLlhETE+PW8ouIiIiISMugQW9ERMTd9NsitfGJHLe9e/dm2rRp3Hnnnbz88ssA/OIXv2DWrFkVBibr1asXTz31FFdddRXFxcVce+21bNy4ka+++oqSkpKyfLjR0dEEBgZ6bH9ERERERDzB1RtE3UiKSHOl85uI91B9rJ3PtEF+77336N+/P1OmTGHKlCkMGDCAd955p8I8e/bsISsrC4Bjx47xxRdfcOzYMQYNGkTbtm3L/q1evdpDeyEiIiIiIiIiIiJSO59ocUtpK9l33323xnksyyr7u1OnThVei4iIiIiIe6mlTP3psxMREZHa+EzgVkRERERERERERLybHk66j8+kShARERERERERERFpKdTiVkRERKQRqcWBiIiIiIjUhwK3IiIiIrVQ8FVERESagq45RKQ8pUoQERERERERERER8TIK3IqIiIiIiIiIiIh4GaVKEBERERFphtTdVsR7qD5Kc6VjW6RxqcWtiIiIiIiIiIiIiJdRi1sREREfolYNIiIiIiIiLYNa3IqIiIiIiIiIiIh4GQVuRURERERERERERLyMUiWIiIiIiIiItBBKuyQi4jvU4lZERERERERERETEyyhwKyIiIiIiIiIiIuJlFLgVERERERERERER8TLKcSsiIiIiIiIiIh6nHMwiFanFrYiIiIiIiIiIiIiXUeBWRERERERERERExMsocCsiIiIiIiIiIiLiZZTjVkRERERERKQZUr5QERHfpha3IiIiIiIiIiIiIl5GgVsRERERERERERERL6PArYiIiIiIiIiIiIiXUY5bERFpVMqtJiIiIiIiIlJ3CtyKiIiISJPSAx0RaQo614iIiK9TqgQRERERERERERERL6MWtyIiIiIiIiIiUiO1Yhdpegrcioi0ELrQkobQ8SMiIiIiItK0FLgVERERERERERFpAdQgw7cocCsiIl5LFxUiIiIiIiLSUvnM4GQZGRnMmTOHiIgIIiIimDNnDpmZmS4vf9ddd2Gz2Xj22WcbtZwiIiIiIiJSs/tn9q/wT0RERCrzmcDtTTfdxObNm1m0aBGLFi1i8+bNzJkzx6VlP/vsM9atW0e7du0avZwiIiIiIiIiIiIiDeUTqRJ27drFokWLWLt2LSNHjgTg1VdfZfTo0ezZs4eePXtWu+zx48e59957Wbx4MTNnzmzCUouIiIiIiIiIiIjUj08EbtesWUNERERZ0BZg1KhRREREsHr16moDtw6Hgzlz5vC73/2Ovn37NmGJRURERERERESkoe6f2R+Hw0FaWhrx8fGeLo5Ik/KJwG1qamqVlTM+Pp7U1NRql/vLX/6Cv78/v/71r13eVkFBAQUFBWWvs7OzoTQI7HA46lx2EU9xOBxYlqXjVsTTLKvCy5rqZKV6W4dlfc19050PVBttv8p9fs3psxPv4i2/t01Sp6RlacbnULfWW2/6nOpblmZ8veEpHjsne9Px6Gbe8nvbbOk80KTq8vl6NHD7+OOP88QTT9Q4z/r16wGw2WyV3rMsq8rpABs2bOC5555j48aN1c5TlaeeeqrKMqWnp5Ofn+/yekQ8zeFwkJWVhWVZ2O0+k85apNkJsXIrvE5LS6t23gvrbV2WlcrKf3767KSx6PdWmqvmfA51Z731ps+pvmW5fljFRlKe3g+pP286Ht1Nv7eNS/cdTSsnJ8fleT0auL333nu54YYbapynU6dObN26lZMnT1Z6Lz09nYSEhCqXW7FiBWlpaSQlJZVNKykp4aGHHuLZZ5/l8OHDVS736KOP8uCDD5a9zs7OJjExkbi4OMLDw+uwdyKe5XA4sNlsxMXF6YdNxIPybBUvemrq3nVhva3LslJZ+c9Pn500Fv3eSnPVnM+h7qy33vQ5eVNZxDOa8zGg39vGpfuOphUcHOzyvB4N3MbGxhIbG1vrfKNHjyYrK4sff/yRESNGALBu3TqysrIYM2ZMlcvMmTOHSZMmVZg2depU5syZw+23317ttoKCgggKCqo03W636+QgPsdms+nYFfG0C3p91FYfK9TbOi4rFyj3+emzk8ak31tpju6fNcDTRWhUbqu33vRb401lEc9o5seAfm8bke47mlRdPl+fyHHbu3dvpk2bxp133snLL78MwC9+8QtmzZpVYWCyXr168dRTT3HVVVcRExNDTExMhfUEBATQpk2bagczExEREREREREREfEGPhNCf++99+jfvz9TpkxhypQpDBgwgHfeeafCPHv27CErK8tjZRQRERERERERERFxB59ocQsQHR3Nu+++W+M81gWj4F2oury2IiIiIiIiIiIiIt7EZ1rcioiIiIiIiIiIiLQUPtPiVkRERERERMSb3D+zv6eLICIizZha3IqIiIiIiIiIiIh4GQVuRURERERERERERLyMUiWIiIiIiIiI+DilbRARaX7U4lZERERERERERETEy6jFrYiIiDRLankkIiIiIiK+TC1uRURERERERERERLyMArciIiIiIiIiIiIiXkaBWxEREREREREREREvo8CtiIiIiIiIiIiIiJfR4GQiIiKNSANkiYiIiIiISH2oxa2IiIiIiIiIiIiIl1HgVkRERERERERERMTLKHArIiIiIiIiIiIi4mUUuBURERERERERERHxMgrcioiIiIiIiIiIiHgZf08XQEREREREREREGub+mf09XQQRcTO1uBURERERERERERHxMmpxKyIiIiIiIiIi0kKptbb3UotbERERERERERERES+jFrciIiJeSk++RUREREREWi61uBURERERERERERHxMgrcioiIiIiIiIiIiHgZBW5FREREREREREREvIwCtyIiIiIiIiIiIiJeRoFbERERERERERERES+jwK2IiIiIiIiIiIiIl1HgVkRERERERERERMTLKHArIiIiIiIiIiIi4mUUuBURERERERERERHxMv6eLoC3sywLgOzsbE8XRaROHA4HOTk5BAcHY7frGY2IL1C9FfE9qrcivkf1VsT3qN5Kc3I+xng+5lgTBW5rkZOTA0BiYqKniyIiIiIiIiIiIiLNQE5ODhERETXOY7NcCe+2YA6HgxMnThAWFobNZvN0cURclp2dTWJiIkePHiU8PNzTxRERF6jeivge1VsR36N6K+J7VG+lObEsi5ycHNq1a1drC3K1uK2F3W6nQ4cOni6GSL2Fh4frh03Ex6jeivge1VsR36N6K+J7VG+luaitpe15SgwiIiIiIiIiIiIi4mUUuBURERERERERERHxMgrcijRTQUFBPPbYYwQFBXm6KCLiItVbEd+jeivie1RvRXyP6q20VBqcTERERERERERERMTLqMWtiIiIiIiIiIiIiJdR4FZERERERERERETEyyhwKyIiIiIiIiIiIuJlFLgV8VJPPfUUw4cPJywsjPj4eK688kr27NlTYR7Lsnj88cdp164dISEhTJgwgR07dlSYp6CggPvuu4/Y2FhatWrF5ZdfzrFjxyrMs3fvXq644gpiY2MJDw9n7NixLFu2rEn2U6Q5cVe9feWVV5gwYQLh4eHYbDYyMzMrvH/48GHuuOMOOnfuTEhICF27duWxxx6jsLCwSfZTpDlpqnp73oIFCxg5ciQhISHExsZy9dVXN+r+iTRH7qi3Z86c4b777qNnz56EhoaSlJTEr3/9a7KysiqsJyMjgzlz5hAREUFERARz5syptn6LSPWast6eV1BQwKBBg7DZbGzevLnR91GkMShwK+Klvv/+e371q1+xdu1alixZQnFxMVOmTOHcuXNl8/z1r3/lmWee4f/+7/9Yv349bdq0YfLkyeTk5JTN88ADD/Dpp5/ywQcfsHLlSs6ePcusWbMoKSkpm2fmzJkUFxezdOlSNmzYwKBBg5g1axapqalNvt8ivsxd9TY3N5dp06bx+9//vsrt7N69G4fDwcsvv8yOHTv4+9//zksvvVTt/CJSvaaqtwCffPIJc+bM4fbbb2fLli2sWrWKm266qdH3UaS5cUe9PXHiBCdOnODpp59m27ZtvPnmmyxatIg77rijwrZuuukmNm/ezKJFi1i0aBGbN29mzpw5Tb7PIr6uKevteQ8//DDt2rVrsn0UaRSWiPiEtLQ0C7C+//57y7Isy+FwWG3atLH+53/+p2ye/Px8KyIiwnrppZcsy7KszMxMKyAgwPrggw/K5jl+/Lhlt9utRYsWWZZlWenp6RZg/fDDD2XzZGdnW4D17bffNuEeijQ/9am35S1btswCrIyMjFq39de//tXq3Lmzm/dApOVprHpbVFRktW/f3vrXv/7VBHsh0rI0tN6e9+GHH1qBgYFWUVGRZVmWtXPnTguw1q5dWzbPmjVrLMDavXt3o+6TSHPXWPX2vIULF1q9evWyduzYYQHWpk2bGnFvRBqPWtyK+Ijz3T+io6MBOHToEKmpqUyZMqVsnqCgIMaPH8/q1asB2LBhA0VFRRXmadeuHf369SubJyYmht69e/P2229z7tw5iouLefnll0lISGDo0KFNvJcizUt96m1DtnV+OyJSf41Vbzdu3Mjx48ex2+0MHjyYtm3bMn369EopF0Sk7txVb7OysggPD8ff3x+ANWvWEBERwciRI8vmGTVqFBEREQ3+3RZp6Rqr3gKcPHmSO++8k3feeYfQ0NBG3Q+RxqbArYgPsCyLBx98kIsuuoh+/foBlKUxSEhIqDBvQkJC2XupqakEBgYSFRVV7Tw2m40lS5awadMmwsLCCA4O5u9//zuLFi0iMjKyifZQpPmpb72tjwMHDvD8889z9913N7DUIi1bY9bbgwcPAvD444/zxz/+ka+++oqoqCjGjx/PmTNn3LofIi2Ju+rt6dMoU8ErAAAJTUlEQVSn+fOf/8xdd91VNi01NZX4+PhK88bHxyulmEgDNGa9tSyL2267jbvvvpthw4Y16n6INAV/F+YREQ+799572bp1KytXrqz0ns1mq/DasqxK0y5Ufh7LsrjnnnuIj49nxYoVhISE8K9//YtZs2axfv162rZt6+a9EWkZ3F1vq3PixAmmTZvG7Nmz+fnPf17v8opI49Zbh8MBwB/+8AeuueYaAN544w06dOjARx99VOGmU0Rc5456m52dzcyZM+nTpw+PPfZYjeuoaT0i4prGrLfPP/882dnZPProo41UepGmpRa3Il7uvvvu44svvmDZsmV06NChbHqbNm2g3JPJ89LS0sqeUrZp04bCwkIyMjKqnWfp0qV89dVXfPDBB4wdO5YhQ4bwwgsvEBISwltvvdUEeyjS/DSk3tbFiRMnuOSSSxg9ejSvvPKKG0ou0nI1dr09/yC0T58+ZdOCgoLo0qULycnJbtgDkZbHHfU2JyeHadOm0bp1az799FMCAgIqrOfkyZOVtpuenl6v320Rafx6u3TpUtauXUtQUBD+/v5069YNgGHDhnHrrbc28t6JuJ8CtyJeyrIs7r33XubPn8/SpUvp3Llzhfc7d+5MmzZtWLJkSdm0wsJCvv/+e8aMGQPA0KFDCQgIqDBPSkoK27dvL5snNzcXALu94unAbreXtQ4SEde4o9666vjx40yYMIEhQ4bwxhtvVKrDIuKapqq3Q4cOJSgoiD179pRNKyoq4vDhw3Ts2NFNeyPSMrir3mZnZzNlyhQCAwP54osvCA4OrrCe0aNHk5WVxY8//lg2bd26dWRlZdX5d1ukpWuqevuPf/yDLVu2sHnzZjZv3szChQsBmDdvHv/93//d6Psp4m5KlSDipX71q1/x73//m88//5ywsLCyJ48RERGEhIRgs9l44IEHePLJJ+nevTvdu3fnySefJDQ0lJtuuqls3jvuuIOHHnqImJgYoqOj+e1vf0v//v2ZNGkSlF6QRkVFceutt/Kf//mfhISE8Oqrr3Lo0CFmzpzp0c9AxNe4o95S2tIgNTWV/fv3A7Bt2zbCwsJISkoiOjqaEydOMGHCBJKSknj66adJT08vW/Z8awURcU1T1dvw8HDuvvtuHnvsMRITE+nYsSP/+7//C8Ds2bM9tPcivskd9TYnJ4cpU6aQm5vLu+++S3Z2NtnZ2QDExcXh5+dH7969mTZtGnfeeScvv/wyAL/4xS+YNWsWPXv29OAnIOJ7mqreJiUlVdhu69atAejatWuFFr4iPsMSEa8EVPnvjTfeKJvH4XBYjz32mNWmTRsrKCjIuvjii61t27ZVWE9eXp517733WtHR0VZISIg1a9YsKzk5ucI869evt6ZMmWJFR0dbYWFh1qhRo6yFCxc22b6KNBfuqrePPfZYjet54403qt2WiNRNU9Vby7KswsJC66GHHrLi4+OtsLAwa9KkSdb27dubdH9FmgN31Ntly5ZVu55Dhw6VzXf69Gnr5ptvtsLCwqywsDDr5ptvtjIyMpp8n0V8XVPW2/IOHTpkAdamTZuaZD9F3M1mmQokIiIiIiIiIiIiIl5CCfFEREREREREREREvIwCtyIiIiIiIiIiIiJeRoFbERERERERERERES+jwK2IiIiIiIiIiIiIl1HgVkRERERERERERMTLKHArIiIiIiIiIiIi4mUUuBURERERERERERHxMgrcioiIiIiIiIiIiHgZBW5FREREREREREREvIwCtyIiIiIipW677TZsNhs2m42AgAASEhKYPHkyr7/+Og6Hw+X1vPnmm0RGRjZqWUVERESkeVPgVkRERESknGnTppGSksLhw4f5+uuvueSSS7j//vuZNWsWxcXFni6eiIiIiLQQCtyKiIiIiJQTFBREmzZtaN++PUOGDOH3v/89n3/+OV9//TVvvvkmAM888wz9+/enVatWJCYmcs8993D27FkAli9fzu23305WVlZZ693HH38cgMLCQh5++GHat29Pq1atGDlyJMuXL/fo/oqIiIiId1LgVkRERESkFhMnTmTgwIHMnz8fALvdzj/+8Q+2b9/OW2+9xdKlS3n44YcBGDNmDM8++yzh4eGkpKSQkpLCb3/7WwBuv/12Vq1axQcffMDWrVuZPXs206ZNY9++fR7dPxERERHxPjbLsixPF0JERERExBvcdtttZGZm8tlnn1V674YbbmDr1q3s3Lmz0nsfffQRv/zlLzl16hSU5rh94IEHyMzMLJvnwIEDdO/enWPHjtGuXbuy6ZMmTWLEiBE8+eSTjbZfIiIiIuJ7/D1dABERERERX2BZFjabDYBly5bx5JNPsnPnTrKzsykuLiY/P59z587RqlWrKpffuHEjlmXRo0ePCtMLCgqIiYlpkn0QEREREd+hwK2IiIiIiAt27dpF586dOXLkCDNmzODuu+/mz3/+M9HR0axcuZI77riDoqKiapd3OBz4+fmxYcMG/Pz8KrzXunXrJtgDEREREfElCtyKiIiIiNRi6dKlbNu2jd/85jf89NNPFBcX87e//Q273QwZ8eGHH1aYPzAwkJKSkgrTBg8eTElJCWlpaYwbN65Jyy8iIiIivkeBWxERERGRcgoKCkhNTaWkpISTJ0+yaNEinnrqKWbNmsXcuXPZtm0bxcXFPP/881x22WWsWrWKl156qcI6OnXqxNmzZ/nuu+8YOHAgoaGh9OjRg5tvvpm5c+fyt7/9jcGDB3Pq1CmWLl1K//79mTFjhsf2WURERES8j93TBRARERER8SaLFi2ibdu2dOrUiWnTprFs2TL+8Y9/8Pnnn+Pn58egQYN45pln+Mtf/kK/fv147733eOqppyqsY8yYMdx9991cf/31xMXF8de//hWAN954g7lz5/LQQw/Rs2dPLr/8ctatW0diYqKH9lZEREREvJXNsizL04UQERERERERERERESe1uBURERERERERERHxMgrcioiIiIiIiIiIiHgZBW5FREREREREREREvIwCtyIiIiIiIiIiIiJeRoFbERERERERERERES+jwK2IiIiIiIiIiIiIl1HgVkRERERERERERMTLKHArIiIiIiIiIiIi4mUUuBURERERERERERHxMgrcioiIiIiIiIiIiHgZBW5FREREREREREREvIwCtyIiIiIiIiIiIiJe5v8DcdWhZXTlfaYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1400x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Plot IC time series\n",
    "==================================\n",
    "\"\"\"\n",
    "fig, axes = plt.subplots(4,1, figsize=(14,12), sharex = True)\n",
    "\n",
    "for ax, (name, ic_series) in zip(axes, ic_results.items()):\n",
    "    ax.bar(ic_series.index, ic_series.values, color='steelblue', alpha=0.6, width=20)\n",
    "    ax.plot(ic_series.index, ic_series.rolling(12).mean(), color='coral', linewidth=2, label='12m  MA')\n",
    "    ax.axhline(y=0, color='black', linestyle='-', linewidth=0.5)\n",
    "    ax.set_ylabel('IC')\n",
    "    ax.set_title(f'{name.capitalize()} Factor IC')\n",
    "    ax.legend(loc='upper right')\n",
    "    ax.grid(alpha=0.3)\n",
    "\n",
    "axes[-1].set_xlabel('Date')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/02_factor_diagnostics/ic_time_series.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cce2714",
   "metadata": {},
   "source": [
    "## Subperiod IC Stability (Walk-Forward)\n",
    "\n",
    "A positive full-sample IC can hide a fragile result. Maybe the signal worked in one window and did nothing elsewhere. Walk-forward analysis splits the sample into non-overlapping windows and recomputes the diagnostics in each one.\n",
    "\n",
    "This is not parameter tuning. It is just a stability check: does the sign and size of the signal look similar across regimes, or is the full-sample average doing too much storytelling?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fba556cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-31T11:08:39.040999Z",
     "iopub.status.busy": "2026-07-31T11:08:39.040823Z",
     "iopub.status.idle": "2026-07-31T11:08:39.075634Z",
     "shell.execute_reply": "2026-07-31T11:08:39.075169Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean IC by Subperiod\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>period</th>\n",
       "      <th>2006-2011</th>\n",
       "      <th>2011-2016</th>\n",
       "      <th>2016-2021</th>\n",
       "      <th>2021-2026</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>factor</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lowvol</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0029</td>\n",
       "      <td>-0.0361</td>\n",
       "      <td>-0.0295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>momentum</th>\n",
       "      <td>-0.0131</td>\n",
       "      <td>0.0276</td>\n",
       "      <td>-0.0079</td>\n",
       "      <td>0.0174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>-0.0287</td>\n",
       "      <td>0.0258</td>\n",
       "      <td>-0.0258</td>\n",
       "      <td>0.0183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0179</td>\n",
       "      <td>-0.0331</td>\n",
       "      <td>-0.0095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "period    2006-2011  2011-2016  2016-2021  2021-2026\n",
       "factor                                              \n",
       "lowvol          NaN    -0.0029    -0.0361    -0.0295\n",
       "momentum    -0.0131     0.0276    -0.0079     0.0174\n",
       "quality     -0.0287     0.0258    -0.0258     0.0183\n",
       "value           NaN    -0.0179    -0.0331    -0.0095"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Information Ratio by Subperiod\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>period</th>\n",
       "      <th>2006-2011</th>\n",
       "      <th>2011-2016</th>\n",
       "      <th>2016-2021</th>\n",
       "      <th>2021-2026</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>factor</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lowvol</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>-0.560</td>\n",
       "      <td>-0.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>momentum</th>\n",
       "      <td>-0.237</td>\n",
       "      <td>0.547</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>0.325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>-0.593</td>\n",
       "      <td>0.461</td>\n",
       "      <td>-0.390</td>\n",
       "      <td>0.353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.576</td>\n",
       "      <td>-0.676</td>\n",
       "      <td>-0.170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "period    2006-2011  2011-2016  2016-2021  2021-2026\n",
       "factor                                              \n",
       "lowvol          NaN     -0.043     -0.560     -0.420\n",
       "momentum     -0.237      0.547     -0.130      0.325\n",
       "quality      -0.593      0.461     -0.390      0.353\n",
       "value           NaN     -0.576     -0.676     -0.170"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " % Months with Positive IC\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>period</th>\n",
       "      <th>2006-2011</th>\n",
       "      <th>2011-2016</th>\n",
       "      <th>2016-2021</th>\n",
       "      <th>2021-2026</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>factor</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lowvol</th>\n",
       "      <td>NaN</td>\n",
       "      <td>51.7</td>\n",
       "      <td>43.3</td>\n",
       "      <td>41.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>momentum</th>\n",
       "      <td>48.3</td>\n",
       "      <td>61.7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>47.5</td>\n",
       "      <td>63.3</td>\n",
       "      <td>43.3</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>46.7</td>\n",
       "      <td>48.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "period    2006-2011  2011-2016  2016-2021  2021-2026\n",
       "factor                                              \n",
       "lowvol          NaN       51.7       43.3       41.7\n",
       "momentum       48.3       61.7       45.0       58.3\n",
       "quality        47.5       63.3       43.3       60.0\n",
       "value           NaN       40.0       46.7       48.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Subperiod IC stability (walk-forward)\n",
    "==================================\n",
    "\"\"\"\n",
    "windows = [(2006,2011), (2011,2016),(2016,2021),(2021,2026)]\n",
    "subperiod_results = []\n",
    "\n",
    "for name in factor_dict:\n",
    "    ic = ic_results[name]\n",
    "    for start, end in windows:\n",
    "        mask = (ic.index.year >= start) & (ic.index.year < end)\n",
    "        sub = ic[mask].dropna()\n",
    "        if len(sub) < 12:\n",
    "            continue\n",
    "        subperiod_results.append({\n",
    "            'factor': name,\n",
    "            'period': f'{start}-{end}',\n",
    "            'mean_ic': sub.mean(),\n",
    "            'std_ic': sub.std(),\n",
    "            'ir': sub.mean() / sub.std() * np.sqrt(12) if sub.std() > 0 else np.nan,\n",
    "            'n': len(sub),\n",
    "            'pct_positive': (sub > 0).mean()\n",
    "        })\n",
    "\n",
    "df_subperiod = pd.DataFrame(subperiod_results)\n",
    "\n",
    "# Pivot for display\n",
    "pivot_ic = df_subperiod.pivot(index='factor', columns='period', values='mean_ic')\n",
    "pivot_ir = df_subperiod.pivot(index='factor', columns='period', values='ir')\n",
    "pivot_pct = df_subperiod.pivot(index='factor', columns='period', values='pct_positive')\n",
    "\n",
    "print(\"Mean IC by Subperiod\\n\")\n",
    "display(pivot_ic.round(4))\n",
    "\n",
    "print(\"\\n Information Ratio by Subperiod\\n\")\n",
    "display(pivot_ir.round(3))\n",
    "\n",
    "print(\"\\n % Months with Positive IC\\n\")\n",
    "display((pivot_pct * 100).round(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b0bc01a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-31T11:08:39.077211Z",
     "iopub.status.busy": "2026-07-31T11:08:39.077036Z",
     "iopub.status.idle": "2026-07-31T11:08:39.399850Z",
     "shell.execute_reply": "2026-07-31T11:08:39.399204Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot subperiod IC\n",
    "fig,ax = plt.subplots(figsize=(12,6))\n",
    "x = np.arange(len(windows))\n",
    "width = 0.2\n",
    "colors = ['steelblue', 'coral', 'seagreen', 'goldenrod']\n",
    "for i, name in enumerate(factor_dict):\n",
    "    vals = [pivot_ic.loc[name, f'{s}-{e}'] if f'{s}-{e}' in pivot_ic.columns else 0 for s, e in windows]\n",
    "    ax.bar(x + i * width, vals, width, label=name, color=colors[i], alpha=0.8)\n",
    "\n",
    "ax.set_xticks(x + width * 1.5)\n",
    "ax.set_xticklabels([f'{s} - {e}' for s,e in windows])\n",
    "ax.set_ylabel('Mean monthly IC')\n",
    "ax.set_title('Factor IC by Subperiod')\n",
    "ax.axhline(y=0, color='black', linewidth=0.5)\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3, axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/02_factor_diagnostics/ic_subperiod.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c6cc2c9",
   "metadata": {},
   "source": [
    "## Factor Decay\n",
    "\n",
    "A one-month IC tells us whether a signal is useful right after it is measured. IC decay asks how long that usefulness lasts.\n",
    "\n",
    "For a horizon $h$, we compare today's factor ranks to cumulative returns over the next $h$ months. If the IC stays positive as $h$ grows, the signal is slow-moving and may tolerate less frequent rebalancing. If it drops quickly, the signal needs faster trading and is more exposed to transaction costs.\n",
    "\n",
    "In vector language, a slow signal keeps a small angle between $\\mathrm{rank}(f_t)$ and $\\mathrm{rank}(r_{t:t+h})$ for several horizons. A fast signal loses that alignment quickly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29d1c1d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-31T11:08:39.401596Z",
     "iopub.status.busy": "2026-07-31T11:08:39.401424Z",
     "iopub.status.idle": "2026-07-31T11:08:45.405229Z",
     "shell.execute_reply": "2026-07-31T11:08:45.404664Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1m</th>\n",
       "      <th>3m</th>\n",
       "      <th>6m</th>\n",
       "      <th>12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>momentum</th>\n",
       "      <td>0.0015</td>\n",
       "      <td>-0.0023</td>\n",
       "      <td>-0.0032</td>\n",
       "      <td>-0.0048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>-0.0229</td>\n",
       "      <td>-0.0400</td>\n",
       "      <td>-0.0463</td>\n",
       "      <td>-0.0458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>-0.0041</td>\n",
       "      <td>-0.0128</td>\n",
       "      <td>-0.0167</td>\n",
       "      <td>-0.0251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lowvol</th>\n",
       "      <td>-0.0251</td>\n",
       "      <td>-0.0621</td>\n",
       "      <td>-0.0873</td>\n",
       "      <td>-0.1108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              1m      3m      6m     12m\n",
       "momentum  0.0015 -0.0023 -0.0032 -0.0048\n",
       "value    -0.0229 -0.0400 -0.0463 -0.0458\n",
       "quality  -0.0041 -0.0128 -0.0167 -0.0251\n",
       "lowvol   -0.0251 -0.0621 -0.0873 -0.1108"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "IC decay at horizons 1, 3, 6, 12 months\n",
    "==================================\n",
    "\"\"\"\n",
    "horizons = [1,3,6,12]\n",
    "decay_results = {}\n",
    "\n",
    "for name, df_f in factor_dict.items():\n",
    "    decay_results[name] = []\n",
    "    for h in horizons:\n",
    "        # Forward returns at horizon h\n",
    "        fwd_returns = df_returns.rolling(h).sum().shift(-h)\n",
    "        ic_series = compute_monthly_ic(df_f, fwd_returns)\n",
    "        decay_results[name].append(ic_series.mean())\n",
    "\n",
    "df_decay = pd.DataFrame(decay_results, index=[f'{h}m' for h in horizons]).T\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(10,6))\n",
    "df_decay.T.plot(ax=ax, marker='o')\n",
    "ax.set_xlabel('Horizon')\n",
    "ax.set_ylabel('Mean IC')\n",
    "ax.set_title('Factor IC Decay by Horizon')\n",
    "ax.axhline(y=0, color='black', linestyle='--', linewidth=0.5)\n",
    "ax.grid(alpha=0.3)\n",
    "ax.legend(title='Factor')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/02_factor_diagnostics/ic_decay.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "display(df_decay.round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0205e12",
   "metadata": {},
   "source": [
    "## Turnover via Rank Autocorrelation\n",
    "\n",
    "A factor that re-ranks the universe every month will produce high turnover, and this contributes to lowering net returns after transaction costs.\n",
    "\n",
    "**Rank autocorrelation** is the correlation between this month's factor rank vector and last month's. If the rank vector barely changes (autocorrelation $\\approx$ 1), the portfolio barely trades. If it changes a lot (autocorrelation $\\approx$ 0), turnover is high. In linear algebraic terms, the rank vector at $t$ is nearly parallel to the rank vector at $t-1$ for stable factors, and nearly orthogonal for unstable ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65a97db6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-31T11:08:45.407354Z",
     "iopub.status.busy": "2026-07-31T11:08:45.407075Z",
     "iopub.status.idle": "2026-07-31T11:08:46.547766Z",
     "shell.execute_reply": "2026-07-31T11:08:46.547146Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "momentum: rank autocorr = 0.888, turnover proxy = 0.112\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value: rank autocorr = 0.977, turnover proxy = 0.023\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quality: rank autocorr = 0.898, turnover proxy = 0.102\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lowvol: rank autocorr = 0.997, turnover proxy = 0.003\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Rank autocorrelation as turnover proxy\n",
    "==================================\n",
    "\"\"\"\n",
    "def rank_autocorrelation(factor_df, lag=1):\n",
    "    \"\"\"Averages cross-sectional rank correlation between t and t-lag.\"\"\"\n",
    "    common_dates = factor_df.index[:-lag]\n",
    "    autocorr = []\n",
    "    for date in common_dates:\n",
    "        f_now = factor_df.loc[date]\n",
    "        f_lag = factor_df.loc[factor_df.index[factor_df.index.get_loc(date) + lag]]\n",
    "        mask = f_now.notna() & f_lag.notna()\n",
    "        if mask.sum() < 20:\n",
    "            continue\n",
    "        rho,_ = spearmanr(f_now[mask], f_lag[mask])\n",
    "        autocorr.append(rho)\n",
    "    return np.mean(autocorr)\n",
    "\n",
    "turnover_proxy = {}\n",
    "for name, df_f in factor_dict.items():\n",
    "    rho = rank_autocorrelation(df_f, lag=1)\n",
    "    turnover_proxy[name] = rho\n",
    "    # Rough turnover estimate: 1 - rho\n",
    "    print(f\"{name}: rank autocorr = {rho:.3f}, turnover proxy = {1 - rho:.3f}\")\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(8,5))\n",
    "ax.bar(turnover_proxy.keys(), [1-v for v in turnover_proxy.values()], color='coral')\n",
    "ax.set_ylabel('Turnover proxy (1 - Rank Autocorr)')\n",
    "ax.set_title('Factor Turnover Proxy')\n",
    "ax.grid(alpha=0.3, axis='y')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/02_factor_diagnostics/turnover_proxy.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ac614b4",
   "metadata": {},
   "source": [
    "## Cross-Factor Correlations\n",
    "\n",
    "The cross-factor correlation matrix asks whether the factors are actually different from each other.\n",
    "\n",
    "If we stack factor vectors into a matrix\n",
    "\n",
    "$$F_t = [f_{1,t}, f_{2,t}, f_{3,t}, f_{4,t}],$$\n",
    "\n",
    "then a Gram matrix has entries\n",
    "\n",
    "$$G_{ij}=\\langle f_{i,t}, f_{j,t}\\rangle.$$\n",
    "\n",
    "Because our factors are centered ranks, these inner products are proportional to correlations. A high off-diagonal entry means two factors are nearly collinear and probably redundant. A value near zero means the factors are closer to orthogonal and may diversify each other.\n",
    "\n",
    "This matters because combining four factor names is not the same as combining four independent sources of information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d488e92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-31T11:08:46.549585Z",
     "iopub.status.busy": "2026-07-31T11:08:46.549416Z",
     "iopub.status.idle": "2026-07-31T11:08:49.782467Z",
     "shell.execute_reply": "2026-07-31T11:08:49.781755Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>momentum</th>\n",
       "      <th>value</th>\n",
       "      <th>quality</th>\n",
       "      <th>lowvol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>momentum</th>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.444</td>\n",
       "      <td>0.860</td>\n",
       "      <td>-0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <td>-0.444</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.412</td>\n",
       "      <td>0.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>0.860</td>\n",
       "      <td>-0.412</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lowvol</th>\n",
       "      <td>-0.104</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.109</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          momentum  value  quality  lowvol\n",
       "momentum     1.000 -0.444    0.860  -0.104\n",
       "value       -0.444  1.000   -0.412   0.210\n",
       "quality      0.860 -0.412    1.000   0.109\n",
       "lowvol      -0.104  0.210    0.109   1.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Cross-factor rank correlation (time-averaged)\n",
    "==================================\n",
    "\"\"\"\n",
    "factor_names = list(factor_dict.keys())\n",
    "corr_matrix = np.zeros((len(factor_names), len(factor_names)))\n",
    "\n",
    "for i, name_i in enumerate(factor_names):\n",
    "    for j, name_j in enumerate(factor_names):\n",
    "        if i == j:\n",
    "            corr_matrix[i,j] = 1.0\n",
    "            continue\n",
    "        df_i = factor_dict[name_i]\n",
    "        df_j = factor_dict[name_j]\n",
    "        common_dates = df_i.index.intersection(df_j.index)\n",
    "\n",
    "        corrs = []\n",
    "        for date in common_dates:\n",
    "            a = df_i.loc[date]\n",
    "            b = df_j.loc[date]\n",
    "            mask = a.notna() & b.notna()\n",
    "            if mask.sum() < 20:\n",
    "                continue\n",
    "            rho, _ = spearmanr(a[mask], b[mask])\n",
    "            corrs.append(rho)\n",
    "        corr_matrix[i,j] = np.mean(corrs)\n",
    "\n",
    "df_corr = pd.DataFrame(corr_matrix, index=factor_names, columns=factor_names)\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(8,6))\n",
    "sns.heatmap(df_corr, annot=True, fmt='.2f', cmap='RdBu_r', center=0, ax=ax, square=True)\n",
    "ax.set_title('Cross-Factor Rank Correlation (Time-Averaged)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/02_factor_diagnostics/factor_correlation.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "display(df_corr.round(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f5fea3fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-31T11:08:49.784322Z",
     "iopub.status.busy": "2026-07-31T11:08:49.784108Z",
     "iopub.status.idle": "2026-07-31T11:08:50.390890Z",
     "shell.execute_reply": "2026-07-31T11:08:50.390284Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved factor exposures, IC series, decay, and correlation matrix.\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Save factor exposures and IC results\n",
    "==================================\n",
    "\"\"\"\n",
    "for name, df_f in factor_dict.items():\n",
    "    df_f.to_csv(f'../data/processed/factor_{name}.csv')\n",
    "\n",
    "df_ic.to_csv('../data/processed/ic_monthly.csv')\n",
    "df_decay.to_csv('../data/processed/ic_decay.csv')\n",
    "df_corr.to_csv('../data/processed/factor_correlation.csv')\n",
    "\n",
    "print(\"Saved factor exposures, IC series, decay, and correlation matrix.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a76a4424",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "The diagnostics point to one usable signal in this price-only setup: **momentum**. Its full-sample IC is small but positive, and it is more stable than the other proxies. That is enough to justify carrying it forward, but not enough to pretend we have found a large or universal edge.\n",
    "\n",
    "The other three proxies are mostly useful as negative examples:\n",
    "\n",
    "- **Value:** the inverse 60-month return proxy behaves more like a bet against long-term winners than a true value measure. Without fundamentals, it cannot distinguish cheapness from price drift.\n",
    "- **Quality:** the rolling Sharpe proxy is highly correlated with momentum, so it is not adding much independent information.\n",
    "- **Low-volatility:** the signal is stable, but in this sample it points the wrong way.\n",
    "\n",
    "The factor correlation matrix explains why a naive composite is unlikely to help. Momentum and quality are close to the same direction, value is partly the opposite direction, and low-vol is more independent but has negative IC. The next notebook builds the signal we will actually trade: sector-neutralized momentum."
   ]
  }
 ],
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