stefan-jansen--machine-learning-for-trading/08_financial_features/06_robustness_sensitivity.ipynb

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    "# Robustness and Sensitivity Analysis\n",
    "\n",
    "**Chapter 8: Feature Engineering**\n",
    "**Section Reference**: 8.6 — Combining Features and Controlling Search\n",
    "\n",
    "**Docker image**: `ml4t`\n",
    "\n",
    "## Purpose\n",
    "\n",
    "A robust signal maintains performance across reasonable variations in\n",
    "parameters, regimes, and implementation choices. This notebook teaches how\n",
    "to assess robustness through parameter sweeps, regime conditioning, and\n",
    "signal × state interactions.\n",
    "\n",
    "## Learning Objectives\n",
    "\n",
    "1. Conduct parameter sweeps as response-surface analysis\n",
    "2. Define robustness as the **breadth of the near-optimal region**\n",
    "3. Analyze regime-conditional IC with clean conditioning variables\n",
    "4. Build signal × state interaction features (gating, scaling, conditional)\n",
    "5. Apply RAS correction for parameter snooping\n",
    "\n",
    "## Data Policy\n",
    "\n",
    "All examples use **real ETF data**."
   ]
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    "\"\"\"Robustness and Sensitivity Analysis — parameter sweeps, regime conditioning, and signal interaction features.\"\"\"\n",
    "\n",
    "from __future__ import annotations\n",
    "\n",
    "import warnings\n",
    "from datetime import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "import polars as pl\n",
    "from ml4t.diagnostic.metrics import pooled_ic\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "from utils.reproducibility import set_global_seeds\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
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     "parameters"
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   "source": [
    "START_DATE = \"2015-01-01\"\n",
    "END_DATE = \"2024-01-01\"\n",
    "SEED = 42"
   ]
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   "source": [
    "set_global_seeds(SEED)"
   ]
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   "source": [
    "## 1. Data Loading"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ETF data: 225,336 rows\n",
      "Symbols: 100\n",
      "Date range: 2015-01-02 to 2023-12-29\n"
     ]
    }
   ],
   "source": [
    "from data import load_etfs\n",
    "\n",
    "etfs = load_etfs()\n",
    "\n",
    "etfs_filtered = etfs.filter(\n",
    "    (pl.col(\"timestamp\") >= datetime.strptime(START_DATE, \"%Y-%m-%d\"))\n",
    "    & (pl.col(\"timestamp\") < datetime.strptime(END_DATE, \"%Y-%m-%d\"))\n",
    ").sort([\"symbol\", \"timestamp\"])\n",
    "\n",
    "print(f\"ETF data: {len(etfs_filtered):,} rows\")\n",
    "print(f\"Symbols: {etfs_filtered['symbol'].n_unique()}\")\n",
    "print(f\"Date range: {etfs_filtered['timestamp'].min()} to {etfs_filtered['timestamp'].max()}\")"
   ]
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     "text": [
      "Computing features for 100 symbols\n"
     ]
    }
   ],
   "source": [
    "prices_wide = (\n",
    "    etfs_filtered.select([\"timestamp\", \"symbol\", \"close\"])\n",
    "    .pivot(on=\"symbol\", index=\"timestamp\", values=\"close\")\n",
    "    .sort(\"timestamp\")\n",
    ")\n",
    "\n",
    "symbols = [c for c in prices_wide.columns if c != \"timestamp\"]\n",
    "print(f\"Computing features for {len(symbols)} symbols\")"
   ]
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   "source": [
    "## 2. IC Computation Helpers\n",
    "\n",
    "All IC statistics use HAC standard errors via the `ml4t-diagnostic` library\n",
    "to account for serial dependence."
   ]
  },
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   "source": [
    "from ml4t.diagnostic.metrics import compute_ic_hac_stats\n",
    "\n",
    "\n",
    "def _ic_stats_with_icir(ic_series: np.ndarray) -> dict:\n",
    "    \"\"\"Compute HAC-adjusted IC stats and add ICIR (mean IC / std IC).\"\"\"\n",
    "    stats = compute_ic_hac_stats(ic_series)\n",
    "    std_ic = float(np.std(ic_series[~np.isnan(ic_series)], ddof=1))\n",
    "    stats[\"icir\"] = stats[\"mean_ic\"] / std_ic if std_ic > 0 else np.nan\n",
    "    return stats"
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   "source": [
    "### Compute IC Series for a Momentum Signal\n",
    "For each date, compute cross-sectional Spearman IC between the momentum\n",
    "signal and forward returns."
   ]
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    "def compute_momentum_ic_series(\n",
    "    prices_df: pl.DataFrame,\n",
    "    symbols: list[str],\n",
    "    lookback: int,\n",
    "    forward_horizon: int = 20,\n",
    ") -> np.ndarray:\n",
    "    \"\"\"Compute daily cross-sectional IC series for a momentum signal.\"\"\"\n",
    "    momentum = prices_df.select(\n",
    "        pl.col(\"timestamp\"),\n",
    "        *[(pl.col(s) / pl.col(s).shift(lookback) - 1).alias(s) for s in symbols],\n",
    "    )\n",
    "    forward_ret = prices_df.select(\n",
    "        pl.col(\"timestamp\"),\n",
    "        *[(pl.col(s).shift(-forward_horizon) / pl.col(s) - 1).alias(s) for s in symbols],\n",
    "    )\n",
    "\n",
    "    mom_long = momentum.unpivot(index=\"timestamp\", variable_name=\"symbol\", value_name=\"signal\")\n",
    "    fwd_long = forward_ret.unpivot(index=\"timestamp\", variable_name=\"symbol\", value_name=\"fwd_ret\")\n",
    "\n",
    "    merged = mom_long.join(fwd_long, on=[\"timestamp\", \"symbol\"], how=\"inner\").drop_nulls()\n",
    "\n",
    "    ics = []\n",
    "    for date in merged[\"timestamp\"].unique().sort().to_list():\n",
    "        day_data = merged.filter(pl.col(\"timestamp\") == date)\n",
    "        if len(day_data) >= 10:\n",
    "            sig_vals = day_data[\"signal\"].to_numpy()\n",
    "            ret_vals = day_data[\"fwd_ret\"].to_numpy()\n",
    "            valid = np.isfinite(sig_vals) & np.isfinite(ret_vals)\n",
    "            if np.sum(valid) >= 10:\n",
    "                ic = pooled_ic(sig_vals[valid], ret_vals[valid])\n",
    "                if not np.isnan(ic):\n",
    "                    ics.append(ic)\n",
    "\n",
    "    return np.array(ics)"
   ]
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   "source": [
    "## 3. Parameter Sweep: Response Surface\n",
    "\n",
    "We vary the momentum lookback period and observe how IC changes. The goal\n",
    "is not to find the \"best\" parameter but to understand the **response\n",
    "surface** and identify a **robust region**.\n",
    "\n",
    "**One knob at a time**: The sweep varies lookback while holding everything\n",
    "else constant (return metric, normalization, label horizon). Changing\n",
    "multiple parameters simultaneously makes it impossible to attribute\n",
    "performance changes to any single choice."
   ]
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     "text": [
      "Parameter Sweep: Momentum Lookback\n",
      "------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Lookback   5d: IC=-0.0097, ICIR=-0.03, HAC t=-0.80\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Lookback  10d: IC=-0.0139, ICIR=-0.04, HAC t=-0.97\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Lookback  21d: IC=-0.0245, ICIR=-0.08, HAC t=-1.60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Lookback  42d: IC=-0.0272, ICIR=-0.09, HAC t=-1.68*\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Lookback  63d: IC=-0.0195, ICIR=-0.06, HAC t=-1.13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Lookback 126d: IC=0.0136, ICIR=0.04, HAC t=0.79\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Lookback 189d: IC=0.0241, ICIR=0.08, HAC t=1.35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Lookback 252d: IC=0.0011, ICIR=0.00, HAC t=0.06\n"
     ]
    }
   ],
   "source": [
    "LOOKBACK_RANGE = [5, 10, 21, 42, 63, 126, 189, 252]\n",
    "FORWARD_HORIZON = 20\n",
    "\n",
    "sweep_results = {}\n",
    "\n",
    "print(\"Parameter Sweep: Momentum Lookback\")\n",
    "print(\"-\" * 60)\n",
    "\n",
    "for lb in LOOKBACK_RANGE:\n",
    "    ic_series = compute_momentum_ic_series(prices_wide, symbols, lb, FORWARD_HORIZON)\n",
    "\n",
    "    if len(ic_series) >= 20:\n",
    "        stats_result = _ic_stats_with_icir(ic_series)\n",
    "        stats_result[\"ics\"] = ic_series  # Keep for later use\n",
    "        sweep_results[lb] = stats_result\n",
    "\n",
    "        sig = (\n",
    "            \"***\"\n",
    "            if stats_result[\"p_value\"] < 0.01\n",
    "            else (\n",
    "                \"**\"\n",
    "                if stats_result[\"p_value\"] < 0.05\n",
    "                else (\"*\" if stats_result[\"p_value\"] < 0.10 else \"\")\n",
    "            )\n",
    "        )\n",
    "\n",
    "        print(\n",
    "            f\"  Lookback {lb:3d}d: IC={stats_result['mean_ic']:.4f}, \"\n",
    "            f\"ICIR={stats_result['icir']:.2f}, \"\n",
    "            f\"HAC t={stats_result['t_stat']:.2f}{sig}\"\n",
    "        )"
   ]
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize response surface\n",
    "if sweep_results:\n",
    "    fig = make_subplots(rows=1, cols=2, subplot_titles=[\"Mean IC by Lookback\", \"ICIR by Lookback\"])\n",
    "\n",
    "    lbs = list(sweep_results.keys())\n",
    "    mean_ics = [sweep_results[lb][\"mean_ic\"] for lb in lbs]\n",
    "    icirs = [sweep_results[lb][\"icir\"] for lb in lbs]\n",
    "    hac_ses = [sweep_results[lb][\"hac_se\"] for lb in lbs]\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=lbs,\n",
    "            y=mean_ics,\n",
    "            mode=\"lines+markers\",\n",
    "            name=\"Mean IC\",\n",
    "            error_y=dict(type=\"data\", array=[1.96 * se for se in hac_ses]),\n",
    "        ),\n",
    "        row=1,\n",
    "        col=1,\n",
    "    )\n",
    "    fig.add_hline(y=0, line_dash=\"dash\", line_color=\"gray\", row=1, col=1)\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=lbs,\n",
    "            y=icirs,\n",
    "            mode=\"lines+markers\",\n",
    "            name=\"ICIR\",\n",
    "            line=dict(color=\"darkorange\"),\n",
    "        ),\n",
    "        row=1,\n",
    "        col=2,\n",
    "    )\n",
    "\n",
    "    fig.update_layout(title=\"ETF Momentum: Response Surface\", height=400, showlegend=False)\n",
    "    fig.update_xaxes(title_text=\"Lookback (days)\", row=1, col=1)\n",
    "    fig.update_xaxes(title_text=\"Lookback (days)\", row=1, col=2)\n",
    "    fig.update_yaxes(title_text=\"Mean IC\", row=1, col=1)\n",
    "    fig.update_yaxes(title_text=\"ICIR\", row=1, col=2)\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f81842d",
   "metadata": {
    "lines_to_next_cell": 2,
    "papermill": {
     "duration": 0.004779,
     "end_time": "2026-06-13T03:32:55.638489+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:55.633710+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 4. Robustness: Breadth of Near-Optimal Region\n",
    "\n",
    "Robustness is **not** a scalar score (mean/std ratio). It is the breadth\n",
    "of the near-optimal region: how many parameter values achieve performance\n",
    "within 90% of the best. A robust signal has a broad plateau; a fragile\n",
    "signal has a narrow peak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "efe570ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T02:54:55.637904Z",
     "iopub.status.busy": "2026-06-16T02:54:55.637814Z",
     "iopub.status.idle": "2026-06-16T02:54:55.640243Z",
     "shell.execute_reply": "2026-06-16T02:54:55.639965Z"
    },
    "papermill": {
     "duration": 0.006044,
     "end_time": "2026-06-13T03:32:55.647868+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:55.641824+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "def compute_robust_region(\n",
    "    sweep_results: dict[int, dict],\n",
    "    metric: str = \"icir\",\n",
    "    threshold_pct: float = 0.90,\n",
    ") -> dict:\n",
    "    \"\"\"Compute the robust region as parameters within threshold_pct of best.\"\"\"\n",
    "    if not sweep_results:\n",
    "        return {}\n",
    "\n",
    "    params = list(sweep_results.keys())\n",
    "    values = [sweep_results[p][metric] for p in params]\n",
    "\n",
    "    best_idx = np.argmax(values)\n",
    "    best_param = params[best_idx]\n",
    "    best_value = values[best_idx]\n",
    "    threshold = best_value * threshold_pct\n",
    "\n",
    "    robust_params = [p for p, v in zip(params, values, strict=False) if v >= threshold]\n",
    "\n",
    "    return {\n",
    "        \"best_param\": best_param,\n",
    "        \"best_value\": best_value,\n",
    "        \"threshold\": threshold,\n",
    "        \"robust_params\": robust_params,\n",
    "        \"robust_fraction\": len(robust_params) / len(params),\n",
    "        \"robust_range\": [min(robust_params), max(robust_params)] if robust_params else None,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ebd5620",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T02:54:55.641218Z",
     "iopub.status.busy": "2026-06-16T02:54:55.641157Z",
     "iopub.status.idle": "2026-06-16T02:54:55.643328Z",
     "shell.execute_reply": "2026-06-16T02:54:55.643102Z"
    },
    "papermill": {
     "duration": 0.005953,
     "end_time": "2026-06-13T03:32:55.656376+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:55.650423+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best lookback: 189 days (ICIR = 0.08)\n",
      "90% threshold: ICIR >= 0.07\n",
      "Robust parameters: [189]\n",
      "Robust range: 189 to 189 days\n",
      "Robust fraction: 12% of tested parameters\n",
      "\n",
      "Robust fraction 12% (<25% of parameters near-optimal)\n"
     ]
    }
   ],
   "source": [
    "if sweep_results:\n",
    "    robustness = compute_robust_region(sweep_results, metric=\"icir\", threshold_pct=0.90)\n",
    "\n",
    "    print(f\"Best lookback: {robustness['best_param']} days (ICIR = {robustness['best_value']:.2f})\")\n",
    "    print(f\"90% threshold: ICIR >= {robustness['threshold']:.2f}\")\n",
    "    print(f\"Robust parameters: {robustness['robust_params']}\")\n",
    "    rr = robustness[\"robust_range\"]\n",
    "    print(f\"Robust range: {rr[0]} to {rr[1]} days\" if rr else \"Robust range: None\")\n",
    "    print(f\"Robust fraction: {robustness['robust_fraction']:.0%} of tested parameters\")\n",
    "\n",
    "    frac = robustness[\"robust_fraction\"]\n",
    "    band = \">50%\" if frac >= 0.5 else \"25-50%\" if frac >= 0.25 else \"<25%\"\n",
    "    print(f\"\\nRobust fraction {frac:.0%} ({band} of parameters near-optimal)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d0863ad6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T02:54:55.644355Z",
     "iopub.status.busy": "2026-06-16T02:54:55.644297Z",
     "iopub.status.idle": "2026-06-16T02:54:55.650565Z",
     "shell.execute_reply": "2026-06-16T02:54:55.650306Z"
    },
    "papermill": {
     "duration": 0.010028,
     "end_time": "2026-06-13T03:32:55.669006+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:55.658978+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "# Visualize robust region\n",
    "if sweep_results and robustness:\n",
    "    fig = go.Figure()\n",
    "\n",
    "    lbs = list(sweep_results.keys())\n",
    "    icirs = [sweep_results[lb][\"icir\"] for lb in lbs]\n",
    "\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=lbs,\n",
    "            y=icirs,\n",
    "            mode=\"lines+markers\",\n",
    "            name=\"ICIR\",\n",
    "            line=dict(color=\"steelblue\", width=2),\n",
    "        )\n",
    "    )\n",
    "\n",
    "    fig.add_hline(\n",
    "        y=robustness[\"threshold\"],\n",
    "        line_dash=\"dash\",\n",
    "        line_color=\"darkorange\",\n",
    "        annotation_text=f\"90% of best ({robustness['threshold']:.2f})\",\n",
    "    )\n",
    "\n",
    "    if robustness[\"robust_range\"]:\n",
    "        fig.add_vrect(\n",
    "            x0=robustness[\"robust_range\"][0],\n",
    "            x1=robustness[\"robust_range\"][1],\n",
    "            fillcolor=\"lightgreen\",\n",
    "            opacity=0.3,\n",
    "            line_width=0,\n",
    "            annotation_text=\"Robust Region\",\n",
    "            annotation_position=\"top left\",\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1f98bcda",
   "metadata": {
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add best-point marker and display\n",
    "if sweep_results and robustness:\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            x=[robustness[\"best_param\"]],\n",
    "            y=[robustness[\"best_value\"]],\n",
    "            mode=\"markers\",\n",
    "            name=f\"Best ({robustness['best_param']}d)\",\n",
    "            marker=dict(color=\"red\", size=12, symbol=\"star\"),\n",
    "        )\n",
    "    )\n",
    "\n",
    "    fig.update_layout(\n",
    "        title=\"ETF Momentum: Robust Region Analysis\",\n",
    "        xaxis_title=\"Lookback (days)\",\n",
    "        yaxis_title=\"ICIR\",\n",
    "        height=450,\n",
    "    )\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54076f0c",
   "metadata": {
    "papermill": {
     "duration": 0.003315,
     "end_time": "2026-06-13T03:32:55.718581+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:55.715266+00:00",
     "status": "completed"
    }
   },
   "source": [
    "### RAS Correction for Parameter Snooping\n",
    "\n",
    "After sweeping N parameter combinations, the best IC is upward-biased.\n",
    "RAS corrects for correlation-aware multiple testing — nearby parameters\n",
    "produce correlated IC estimates and count as fewer independent tests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "29f3e441",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T02:54:55.689870Z",
     "iopub.status.busy": "2026-06-16T02:54:55.689755Z",
     "iopub.status.idle": "2026-06-16T02:55:06.222797Z",
     "shell.execute_reply": "2026-06-16T02:55:06.222370Z"
    },
    "papermill": {
     "duration": 9.554035,
     "end_time": "2026-06-13T03:33:05.275888+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:32:55.721853+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== RAS Correction for Parameter Snooping ===\n",
      "\n",
      "Best uncorrected IC:           0.0260\n",
      "Rademacher complexity (R-hat): 0.0098\n",
      "RAS-adjusted IC (lower bound): 0.0021\n",
      "Parameters tested:             8\n",
      "Time periods:                  1992\n"
     ]
    }
   ],
   "source": [
    "from ml4t.diagnostic.evaluation.stats import (\n",
    "    rademacher_complexity,\n",
    "    ras_ic_adjustment,\n",
    ")\n",
    "\n",
    "if sweep_results:\n",
    "    ic_matrix = []\n",
    "    for lb in LOOKBACK_RANGE:\n",
    "        ic_s = compute_momentum_ic_series(prices_wide, symbols, lb, FORWARD_HORIZON)\n",
    "        if len(ic_s) >= 20:\n",
    "            ic_matrix.append(ic_s)\n",
    "\n",
    "    if len(ic_matrix) >= 2:\n",
    "        min_len = min(len(s) for s in ic_matrix)\n",
    "        ic_matrix_aligned = np.array([s[:min_len] for s in ic_matrix])  # (N strategies, T)\n",
    "        best_ic_values = np.array([np.mean(s) for s in ic_matrix_aligned])\n",
    "\n",
    "        # complexity is the empirical Rademacher complexity R-hat, NOT the count\n",
    "        # of parameters tested. It expects a (T periods x N strategies) matrix and\n",
    "        # measures how correlated the swept ICs are (correlated -> fewer effective\n",
    "        # independent tests -> smaller snooping penalty).\n",
    "        r_hat = rademacher_complexity(ic_matrix_aligned.T, random_state=42)\n",
    "        # The snooping bias is about the cherry-picked HIGHEST IC, so deflate that.\n",
    "        best_idx = int(np.argmax(best_ic_values))\n",
    "        ras_adjusted = ras_ic_adjustment(\n",
    "            observed_ic=best_ic_values,\n",
    "            complexity=r_hat,\n",
    "            n_samples=min_len,\n",
    "            kappa=0.05,\n",
    "        )\n",
    "\n",
    "        print(\"=== RAS Correction for Parameter Snooping ===\\n\")\n",
    "        print(f\"Best uncorrected IC:           {best_ic_values[best_idx]:.4f}\")\n",
    "        print(f\"Rademacher complexity (R-hat): {r_hat:.4f}\")\n",
    "        print(f\"RAS-adjusted IC (lower bound): {ras_adjusted[best_idx]:.4f}\")\n",
    "        print(f\"Parameters tested:             {len(ic_matrix)}\")\n",
    "        print(f\"Time periods:                  {min_len}\")"
   ]
  },
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   "source": [
    "## 5. Regime-Conditional Performance\n",
    "\n",
    "A robust signal maintains predictive power across market regimes. We use\n",
    "42-day realized volatility on SPY as a clean, pre-defined conditioning\n",
    "variable. Tercile thresholds use expanding-window percentiles to avoid\n",
    "lookahead bias.\n",
    "\n",
    "**Warning**: Avoid conditioning on variables derived from the signal itself."
   ]
  },
  {
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     "status": "completed"
    }
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volatility Regime Distribution:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (3, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>vol_regime</th><th>len</th></tr><tr><td>str</td><td>u32</td></tr></thead><tbody><tr><td>&quot;high_vol&quot;</td><td>854</td></tr><tr><td>&quot;low_vol&quot;</td><td>600</td></tr><tr><td>&quot;normal_vol&quot;</td><td>810</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (3, 2)\n",
       "┌────────────┬─────┐\n",
       "│ vol_regime ┆ len │\n",
       "│ ---        ┆ --- │\n",
       "│ str        ┆ u32 │\n",
       "╞════════════╪═════╡\n",
       "│ high_vol   ┆ 854 │\n",
       "│ low_vol    ┆ 600 │\n",
       "│ normal_vol ┆ 810 │\n",
       "└────────────┴─────┘"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spy_prices = prices_wide.select([\"timestamp\", \"SPY\"])\n",
    "\n",
    "spy_vol = spy_prices.with_columns(\n",
    "    (pl.col(\"SPY\").pct_change().rolling_std(42) * np.sqrt(252)).alias(\"realized_vol\")\n",
    ")\n",
    "\n",
    "# Expanding-window tercile thresholds\n",
    "spy_vol = spy_vol.with_columns(\n",
    "    pl.col(\"realized_vol\")\n",
    "    .rolling_quantile(0.33, window_size=100_000, min_samples=63)\n",
    "    .alias(\"_q33\"),\n",
    "    pl.col(\"realized_vol\")\n",
    "    .rolling_quantile(0.67, window_size=100_000, min_samples=63)\n",
    "    .alias(\"_q67\"),\n",
    ")\n",
    "\n",
    "spy_vol = spy_vol.with_columns(\n",
    "    pl.when(pl.col(\"realized_vol\") < pl.col(\"_q33\"))\n",
    "    .then(pl.lit(\"low_vol\"))\n",
    "    .when(pl.col(\"realized_vol\") > pl.col(\"_q67\"))\n",
    "    .then(pl.lit(\"high_vol\"))\n",
    "    .otherwise(pl.lit(\"normal_vol\"))\n",
    "    .alias(\"vol_regime\")\n",
    ").drop([\"_q33\", \"_q67\"])\n",
    "\n",
    "print(\"Volatility Regime Distribution:\")\n",
    "spy_vol.group_by(\"vol_regime\").len().sort(\"vol_regime\")"
   ]
  },
  {
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   },
   "source": [
    "### Compute Regime-Conditional IC\n",
    "Split the IC series by volatility regime to check signal stability."
   ]
  },
  {
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   "outputs": [],
   "source": [
    "def compute_regime_ic(\n",
    "    prices_df: pl.DataFrame,\n",
    "    regime_df: pl.DataFrame,\n",
    "    symbols: list[str],\n",
    "    lookback: int = 126,\n",
    "    forward_horizon: int = 20,\n",
    ") -> dict[str, dict]:\n",
    "    \"\"\"Compute HAC-adjusted IC statistics by regime.\"\"\"\n",
    "    momentum = prices_df.select(\n",
    "        pl.col(\"timestamp\"),\n",
    "        *[(pl.col(s) / pl.col(s).shift(lookback) - 1).alias(s) for s in symbols],\n",
    "    )\n",
    "    forward_ret = prices_df.select(\n",
    "        pl.col(\"timestamp\"),\n",
    "        *[(pl.col(s).shift(-forward_horizon) / pl.col(s) - 1).alias(s) for s in symbols],\n",
    "    )\n",
    "\n",
    "    mom_long = momentum.unpivot(index=\"timestamp\", variable_name=\"symbol\", value_name=\"signal\")\n",
    "    fwd_long = forward_ret.unpivot(index=\"timestamp\", variable_name=\"symbol\", value_name=\"fwd_ret\")\n",
    "\n",
    "    merged = (\n",
    "        mom_long.join(fwd_long, on=[\"timestamp\", \"symbol\"], how=\"inner\")\n",
    "        .join(regime_df.select([\"timestamp\", \"vol_regime\"]), on=\"timestamp\", how=\"inner\")\n",
    "        .drop_nulls()\n",
    "    )\n",
    "\n",
    "    regime_results = {}\n",
    "    for regime in merged[\"vol_regime\"].unique().to_list():\n",
    "        regime_data = merged.filter(pl.col(\"vol_regime\") == regime)\n",
    "        ics = []\n",
    "        for date in regime_data[\"timestamp\"].unique().sort().to_list():\n",
    "            day_data = regime_data.filter(pl.col(\"timestamp\") == date)\n",
    "            if len(day_data) >= 10:\n",
    "                sig = day_data[\"signal\"].to_numpy()\n",
    "                ret = day_data[\"fwd_ret\"].to_numpy()\n",
    "                valid = np.isfinite(sig) & np.isfinite(ret)\n",
    "                if np.sum(valid) >= 10:\n",
    "                    ic = pooled_ic(sig[valid], ret[valid])\n",
    "                    if not np.isnan(ic):\n",
    "                        ics.append(ic)\n",
    "\n",
    "        if ics:\n",
    "            stats = _ic_stats_with_icir(np.array(ics))\n",
    "            stats[\"ics\"] = np.array(ics)\n",
    "            regime_results[regime] = stats\n",
    "\n",
    "    return regime_results"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regime             Mean IC     ICIR      HAC t    p-value\n",
      "------------------------------------------------------------\n",
      "low_vol             0.0708     0.25       2.77*     0.0059\n",
      "normal_vol          0.0253     0.11       1.26     0.2096\n",
      "high_vol           -0.0344    -0.09      -1.12     0.2648\n",
      "\n",
      "IC range across regimes: 0.1052\n",
      "IC range > 0.04 across regimes — interaction features are warranted\n"
     ]
    }
   ],
   "source": [
    "if spy_vol is not None:\n",
    "    regime_ic = compute_regime_ic(prices_wide, spy_vol, symbols)\n",
    "\n",
    "    print(f\"{'Regime':<15} {'Mean IC':>10} {'ICIR':>8} {'HAC t':>10} {'p-value':>10}\")\n",
    "    print(\"-\" * 60)\n",
    "\n",
    "    for regime in [\"low_vol\", \"normal_vol\", \"high_vol\"]:\n",
    "        if regime in regime_ic:\n",
    "            r = regime_ic[regime]\n",
    "            sig = \"*\" if r[\"p_value\"] < 0.05 else \"\"\n",
    "            print(\n",
    "                f\"{regime:<15} {r['mean_ic']:>10.4f} {r['icir']:>8.2f} \"\n",
    "                f\"{r['t_stat']:>10.2f}{sig} {r['p_value']:>10.4f}\"\n",
    "            )\n",
    "\n",
    "    if len(regime_ic) > 1:\n",
    "        ic_values = [r[\"mean_ic\"] for r in regime_ic.values()]\n",
    "        ic_range = max(ic_values) - min(ic_values)\n",
    "        print(f\"\\nIC range across regimes: {ic_range:.4f}\")\n",
    "        if ic_range > 0.04:\n",
    "            print(\"IC range > 0.04 across regimes — interaction features are warranted\")\n",
    "        else:\n",
    "            print(\"IC range <= 0.04 across regimes — signal does not depend on regime\")"
   ]
  },
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   },
   "source": [
    "### Book Figure: Conditional IC Box Plot\n",
    "\n",
    "Static matplotlib version for print — box plots with fold-level IC\n",
    "observations overlaid, showing distribution stability across regimes."
   ]
  },
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    {
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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if spy_vol is not None and regime_ic:\n",
    "    fig_mpl, ax = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "    regime_order = [\"low_vol\", \"normal_vol\", \"high_vol\"]\n",
    "    regime_labels = [\n",
    "        \"Low Vol\\n(bottom tercile)\",\n",
    "        \"Mid Vol\\n(middle tercile)\",\n",
    "        \"High Vol\\n(top tercile)\",\n",
    "    ]\n",
    "    grays = [\"0.85\", \"0.60\", \"0.35\"]\n",
    "\n",
    "    ic_data = [regime_ic[r][\"ics\"] for r in regime_order if r in regime_ic]\n",
    "    positions = list(range(1, len(ic_data) + 1))\n",
    "\n",
    "    bp = ax.boxplot(\n",
    "        ic_data,\n",
    "        positions=positions,\n",
    "        widths=0.5,\n",
    "        patch_artist=True,\n",
    "        medianprops=dict(color=\"black\", linewidth=1.5),\n",
    "        whiskerprops=dict(color=\"0.4\"),\n",
    "        capprops=dict(color=\"0.4\"),\n",
    "        flierprops=dict(marker=\".\", markersize=3, markerfacecolor=\"0.5\"),\n",
    "    )\n",
    "    for patch, gray in zip(bp[\"boxes\"], grays, strict=False):\n",
    "        patch.set_facecolor(gray)\n",
    "        patch.set_edgecolor(\"black\")\n",
    "        patch.set_linewidth(0.8)\n",
    "\n",
    "    for i, (ics, pos) in enumerate(zip(ic_data, positions, strict=False)):\n",
    "        jitter = np.random.default_rng(SEED).uniform(-0.12, 0.12, size=len(ics))\n",
    "        ax.scatter(pos + jitter, ics, s=8, alpha=0.3, color=\"black\", zorder=3)\n",
    "\n",
    "    # Annotate and finalize the regime IC figure (must be in the same cell as\n",
    "    # the plot to avoid the matplotlib-inline backend auto-displaying the\n",
    "    # un-annotated figure on cell exit).\n",
    "    for i, regime in enumerate(regime_order):\n",
    "        if regime in regime_ic:\n",
    "            r = regime_ic[regime]\n",
    "            ax.text(\n",
    "                i + 1,\n",
    "                ax.get_ylim()[1] * 0.92,\n",
    "                f\"IC = {r['mean_ic']:+.3f}\\n(t = {r['t_stat']:.1f})\",\n",
    "                ha=\"center\",\n",
    "                va=\"top\",\n",
    "                fontsize=8,\n",
    "            )\n",
    "\n",
    "    ax.axhline(y=0, ls=\"--\", color=\"black\", lw=0.7)\n",
    "    ax.set_xticks(positions)\n",
    "    ax.set_xticklabels(regime_labels)\n",
    "    ax.set_ylabel(\"Information Coefficient (rank IC)\")\n",
    "    ax.set_title(\"Momentum IC by Volatility Regime\")\n",
    "    ax.text(\n",
    "        0.5,\n",
    "        -0.12,\n",
    "        \"126d momentum, 20d fwd returns, 42d vol window\",\n",
    "        transform=ax.transAxes,\n",
    "        ha=\"center\",\n",
    "        fontsize=8,\n",
    "        style=\"italic\",\n",
    "        color=\"0.4\",\n",
    "    )\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1138f6cb",
   "metadata": {
    "papermill": {
     "duration": 0.003661,
     "end_time": "2026-06-13T03:33:06.212907+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:33:06.209246+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 6. Signal × State Interaction Features\n",
    "\n",
    "The text (§8.6) describes three interaction templates. We demonstrate all\n",
    "three using momentum (signal) and realized volatility (state).\n",
    "\n",
    "| Template | Construction | What changes |\n",
    "|---|---|---|\n",
    "| **Gating** | Zero signal in high-vol regime | Active sample (turnover, capacity) |\n",
    "| **Scaling** | Divide signal by volatility | Position size |\n",
    "| **Conditional** | IC computed within each regime | Separate testable hypotheses |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "20abc9bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T02:55:07.122427Z",
     "iopub.status.busy": "2026-06-16T02:55:07.122353Z",
     "iopub.status.idle": "2026-06-16T02:55:07.129557Z",
     "shell.execute_reply": "2026-06-16T02:55:07.129104Z"
    },
    "papermill": {
     "duration": 0.01232,
     "end_time": "2026-06-13T03:33:06.228862+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:33:06.216542+00:00",
     "status": "completed"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Signal × State Interaction Templates:\n",
      "\n",
      "  momentum                 : IC = -0.0804\n",
      "  momentum_gated           : IC = -0.0516\n",
      "  momentum_scaled          : IC = -0.0877\n",
      "\n",
      "Conditional IC by Regime:\n",
      "  low_vol        : IC = -0.0379  (n=597)\n",
      "  normal_vol     : IC = -0.0574  (n=745)\n",
      "  high_vol       : IC = -0.1496  (n=854)\n"
     ]
    }
   ],
   "source": [
    "if spy_vol is not None and len(spy_vol) > 252:\n",
    "    interact_df = (\n",
    "        spy_vol.join(\n",
    "            prices_wide.select([\"timestamp\", \"SPY\"]),\n",
    "            on=\"timestamp\",\n",
    "            how=\"inner\",\n",
    "            suffix=\"_price\",\n",
    "        )\n",
    "        .sort(\"timestamp\")\n",
    "        .with_columns(\n",
    "            (pl.col(\"SPY\") / pl.col(\"SPY\").shift(63) - 1).alias(\"momentum\"),\n",
    "            (pl.col(\"SPY\").shift(-5) / pl.col(\"SPY\") - 1).alias(\"fwd_return_5d\"),\n",
    "        )\n",
    "        .drop_nulls([\"momentum\", \"fwd_return_5d\", \"realized_vol\"])\n",
    "    )\n",
    "\n",
    "    # Gating: zero momentum in high-vol regime\n",
    "    interact_df = interact_df.with_columns(\n",
    "        (pl.col(\"momentum\") * (pl.col(\"vol_regime\") != pl.lit(\"high_vol\")).cast(pl.Float64)).alias(\n",
    "            \"momentum_gated\"\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # Scaling: risk-adjusted momentum\n",
    "    interact_df = interact_df.with_columns(\n",
    "        (pl.col(\"momentum\") / pl.col(\"realized_vol\").clip(0.01, None)).alias(\"momentum_scaled\")\n",
    "    )\n",
    "\n",
    "    # Compare IC: raw vs gated vs scaled\n",
    "    print(\"Signal × State Interaction Templates:\\n\")\n",
    "    for col in [\"momentum\", \"momentum_gated\", \"momentum_scaled\"]:\n",
    "        valid = interact_df.drop_nulls([col, \"fwd_return_5d\"])\n",
    "        if len(valid) > 100:\n",
    "            corr = valid.select(pl.corr(col, \"fwd_return_5d\", method=\"spearman\")).item()\n",
    "            print(f\"  {col:25s}: IC = {corr:+.4f}\")\n",
    "\n",
    "    # Conditional IC by regime\n",
    "    print(\"\\nConditional IC by Regime:\")\n",
    "    for regime in [\"low_vol\", \"normal_vol\", \"high_vol\"]:\n",
    "        subset = interact_df.filter(pl.col(\"vol_regime\") == regime)\n",
    "        if len(subset) > 50:\n",
    "            ic = subset.select(pl.corr(\"momentum\", \"fwd_return_5d\", method=\"spearman\")).item()\n",
    "            print(f\"  {regime:15s}: IC = {ic:+.4f}  (n={len(subset):,})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c49d7e",
   "metadata": {
    "papermill": {
     "duration": 0.003624,
     "end_time": "2026-06-13T03:33:06.236244+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:33:06.232620+00:00",
     "status": "completed"
    }
   },
   "source": [
    "**Interpretation**: Gating suppresses the signal during high-volatility\n",
    "episodes where momentum historically underperforms. Scaling normalizes by\n",
    "recent volatility, producing a risk-adjusted signal. The conditional IC\n",
    "reveals whether the signal works differently across regimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ccfc0811",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T02:55:07.130460Z",
     "iopub.status.busy": "2026-06-16T02:55:07.130349Z",
     "iopub.status.idle": "2026-06-16T02:55:07.715475Z",
     "shell.execute_reply": "2026-06-16T02:55:07.715014Z"
    },
    "papermill": {
     "duration": 0.575989,
     "end_time": "2026-06-13T03:33:06.815831+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:33:06.239842+00:00",
     "status": "completed"
    }
   },
   "outputs": [
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J3jQpkmL6qG6i7VToJblyk36oXqEYJg4Jfb5GFMNrroeXnqtThncWHmsq9D0v90dPOMaKhUlDO2lCRGhFg17kKWSERC8XJsAEmIAlBGxW8NIPXLGCOcXyLT1kpy/cJLwZ5OmjzVFUaPmMHrLkjcmWKU2Y/pN3lDIGrJs3TPw9MoKXvK371kwKY3/czDXYtPsYrh9eJn5wpHd3zZwhwjOhXawheOnHrXGnUYgfL674EZfLyJ99vqJk7W7i/ykFW4HcmRHXJQ5oE9uarYcEy92rJggPEoWErNp8MEzbnRxjYUC3puJvpghe2vhHG+roh79q+aLiPootXr3lEC5dv4937z+KHzYq5GG+cXSZ+De9RFRq+JdYZl87d6j4m3yhofpb//8lx9gk1yd4adl66fp9YmlYzZ3lUvCaMnepH+R9XLPtECge+8UrD3z56ic211EhBuTppUIvbw8evxIimARTmXo9xNL1hNlr0aZxdbRvWgN7j5zHwHGLhGddejENsaJMJJSRRNtrTvG2FMdMoTDk6aOlfVoZoFCOkX+1MTksgeo0xFsKOO12aYfsUN93HTwDCm2gEJjPPr6gMBMqFKJEWTeoGBK88u/kWaVnhXZp0X28CBNZOq2/8IyS4CUhSVlNtIslgrdtk+pYteUgpi3YKF6MaRzoO0WeWl3BSy95JIhptUc3dpk2VdIYXNy3UPCWvGjVg1ZLZKGwK/LcntszL0zYllzt+vfgYjGGpvIguzKkgUITtAvtCUiYwBX08kJFScGr77nauNNosVGZQo+0C4VqUdz9xgUjjD0C+HMmwASYgF4CNit4dVtLm0NG928nhJkstNRNS95XDiwO94NMGQnoR5N+LKgoLXgpto5+kGS8moyN1P0RorrVFrwUA9u65wR8+vwFa+YMDeOxlSJV32Y/ijcePGGJxiOsL0aPfniJLxVTBK8UUytmDBIxkeQpbNNrori//u/lkCNLWhHbvOvAGZF549axFeF+yMnzmDZVckxfuBlrth7Esa0zw4x7RN9lfYJ38do9mLV0m4ixNMfDa+4zQzctmbxf39ylGFVaJaCXAXoxIG87vaA9ePxSxIprvzhJ4XJ65xwRG96uz2Sc3T0PE+esE15eEseT5q4HhRJo55k21H5ajajQoDca1aqAYb1bipcN8qBVKl1QeCVlIY87eblJPFH8MK1eUMiEsZcGKdSW/T0AxQvm1NgjryRlxKBC2T10s5D0GTlXhLpQSkCKNU/pnhj29vZiZYdSEnZuVVvca0jwjpmxWqTMMlQo3IDCDqTHnTJykPdeu1gqeOkljpgGBASiROHcWDCpjzCrK3jlHKHPKeZWu8h5Kr32hgQvZb6gDBhnds0RKRtlkVkxzu+dLzYDmsqD7jckeMlrTHH3UmiqLXhpbnt6+YRb0SGvNr0gGgs1Mfc7y9czASbw6xCwWcFL3hz6gaKNM+NmrRHektWzh4TZkBGR4KX4PPLkqCV4SUDRD5QUvKP/XiV+wKV3RXsKqSl433t6i3Rjn7x8sHR6/3BihDZMFazyJ+r/XjaMmKH2EdvSdXqIpWJTDt8wRfCSaDl65qrgTj/GXQfPEBtSyLuaNtXPTVIk1mizobbgffz8DWq3HiI8ed3b1hMbzShO0JxlTH2Cl0QUtYuWn8kbp1aRYsaUuStDP2gzJMVxyiLDcbQFr4y7nTuhF67deii8bBRXSdkBeg6bLbyy9N/0adxFCIIphcaFNgad2DYLZy/dEjG39LIk4zLJBnlcz1y6KTbWUWwthRdQGT+oQ4S5m2V4irZI1W2T9N5LD6/0KNMGqaG9Wmg2J1F6rxI1u5okeOXzgF5snLQ2Tcm6abMphaYcOH5JxNzqZlag6ywVvHSvzOMtX/YiErwUtlCmWN4wWGQmEVpNorE0JHjlxj1dwUuxtRQHLQWvqTwiErzkGacXInMEr+4zMKJNa7orZ+3/moIPn8JvvDR1XEyZ+3wNE2ACvyYBmxW82qmd5CYd2rG9cHJfTXwsLVPT8qnuUjXFPVLsWfIkCVULadAVvPRDQz84e1ZPDBcTq5bgpXymHfpOET9ItNHIUO7g6s0His09tIStXWTcp6kb6owJXhIxf7QbhlJF84DGjwrFNZPnkl5WtIs+wUufU8wu5SEe1qeliOml8S5TzPS8vvoEL4V1kPeNYjV3rRivVwxR3bQ5SjtdmbmPBH0xvIbm7oipy7Ft3yncPLpcE4NJ9ekTvCQ8S9TqKnbL0wa8pvUqiX/TJqdStbtjUI9mmDRnvfDW6jsAQV8/5EsAieh9Ry6IEAbtmF5991CcaYtu4+Aqsi+EXXLWvp42PlVo0Ee8nO5ZNTFc1gC6Vlfw7j18XsQm68Z+6xO8unHOsm75PNDOaKGvH3IlQ3sjoLzOVGGlnaVBvkTR32iTqvYY6Hp46cWB5rj2XgRZN6227Dt63mhIg6mC11Qe5gheynxBMeDaMdWy/VJw6wpxFrzmPkn4eibABNQgEC0EL3VcesTokATavEObrqRHkOIMKXWULNKD81enRiK+kYrSIQ26gvfStfto22eSiDWVMa9Ur/y7qaJS9sFYlgaKBaQ+UYjHoqn9RBopQ0WGX8jd1vI6+fdFU/qadFhERIKXYoJ7jZiLj58+i/g7eSIepTGi2OBDG6ZqhCaJnV7DZosNNtoeXmqXzMpAhxRQZowjm/6Gvf3PhP7GvgSG8vDKDTWUumrcwA5h0ibRCxLNmVMXbmo251A9Sm1a0zd3KbUWCRdiJTcJkbgbNnmpEL26seA01hS+QCEQ2mnzKPvEo6evxC59fanGDPEicU9hDHlzZhQeePJKd2oZGiNLhcaT4oXz5MgYxgQtcVM4Bi27R1RkdgAKaZg2skuYpXe6jw4qqdt2mCZTx4Wrd0HePcqxTeE3VEjo0yoKsdL2FlOarb9GzQ/3MkT5l2u1HiLS4i2c9FeYlxd6OaA+UXw9/btM3R4ibIZi/GUmFxJzXQb+LWJ9jS2d6xO8+njoCl56Oa3cuK+Ind+6ZIwmFIs21lGubAoDopc8KpH18JrKg+oyFNKg6+Gl8CliR89Ver5qF5krXTt+n1426eWONtTqS0vGHl5jTzT+nAkwAaUIRBvBSx2mTTrrth8RqaUoxVTo30LzipYsnFvk7KQ8ttv3nUL6tO7YuGCk5gdFbcFLoonEFsUpkkeSEs7T0jOl2qEfOVMFL+2Wp1RAFG5AG4lq/lZCc2pa1fJFNONOG7zef/RCy/pVxEY13VKicC7N8ci08a9Rp9HiNDHyPqVJlUwsUdOPEL1A0E5pU4ruSWs/fvyAt89X3Hv4DFdvPRTpxGaN6SHGQRb5I0hxn5S1gU5Bo5AHB3s7kaNXV/DKvLv0GQmwnu3DHg1sqJ3GTlqjzU+UGYI8pLQBiOJqEydMgI9en3H+37tClFPKI+3wCaXSkumbuyRGyFNG8cxUb1BQsOBC3lHKY6sreOUGTcqOsHXJaA2GnQfOiBPkaKOaTDtnyljSNbTxkvK70sujzL0r75VeOVp2JxHm7OQk8rDSi4Epmwjp+0DfV9rMSaEEFK9KL0HfvwcIcX7+3ztCeJJXmrzVFPtat90wceR1jYrFBBdKi/X67QfBQ1vwUvaAKk37i3Gk/KyUvox28FNGCOllpM2Pv5UthCQJE+DJizeCbcVSBTWhO5RfmGKlaVWE4ropzzB5Zx0cHEQ2E7UEL/El7zrNRcqOUr1SMZGWjDZ7Uj9pFUaeDhhZwUt1mcrDVMFLNilrxtOX78TLPYWOyHGgdIdVmobm365RsbjYfEZx5RQDTN9rFrymfjP5OibABNQgEK0EL3l8eo+cIzxg8keXfljXbjss8kO+fE0HT8QVm296dWigydFJ4NQWvFQHeTMmzFmL42evCe9Y/tyZ0fvPhiK9kz6PiL4B1T1pTfuaOydWav6XDmOgzUeGim68Kole8kqTd4wEQuqUSdGodgUhmE31oErBK+uk7BTkqSJxQXk6SdRr7yaX19FGtrVbD8Hrsy8yp08lDpygXMa6MbzyehIDB09cNstjuefQOQyaELq5ThbyWtLufllorlAGABLhFBdLP8LxXeMKL2b1ikVF+iXtkAYlBa++uUuCjuI+n73yEAed1K5aUngdB4xdGE7wyqXwbm3qomubupo+kcigrA0UB0wpycwplC6LdsXTC4r2YS5kgzx5tDmMBMvz1x6gbB0kDmnsTD2KmuzQCx/Ftl+780ikuaI8zCR8SxbJLQ460D6Rj4TwmBmrcP32Q5FJhERsqwZVRUYF3XhgegbQgQUv3rwXqxyUllBukKNNafRiTB5depGgo8iLFMiBxrUraLK50FxYsHq32ORGHuvsmdKIZ8bCNbtFZhg1BS9xocM8Fq4Oe/AEPSukt5+uUULwkh1TeJgjeGlvxNiZq3Hnv6dwcnREu6Y10KHZ72Lq0Yvv5LnrhQefQpkoxSCFElG4Bgtec76dfC0TYAJKE7A5wat0B6PaHolSEqf60hBFddtssX4SIjVaDBIhGhTPyYUJMAEmwASYABNgApElwII3sgS17qdlWd3d4XIpWu0csAp2I0pN0SlSlHWC4rQpnIMLE2ACTIAJMAEmwAQiS4AFb2QJat0/bPIyPHn+BtkypxXLebSZiJbmKaeo9nHIClYZ40xRzlXawERpzZz1pJaKcR3mDjEBJsAEmAATYAKqE2DBqyBiigdct+Mo3np4IiAgCJRpgLICdGpRy2AqLAWrj/amKEaYcu82rFkew/u0ivb94Q4wASbABJgAE2ACtkGABa9tjAO3ggkwASbABJgAE2ACTEAlAix4VQLLZpkAE2ACTIAJMAEmwARsgwALXtsYB24FE2ACTIAJMAEmwASYgEoEWPCqBJbNMgEmwASYABNgAkyACdgGARa8tjEO3AomwASYABNgAkyACTABlQiw4FUJLJtlAkyACTABJsAEmAATsA0CLHhtYxy4FUyACTABJsAEmAATYAIqEWDBqxJYNssEmAATYAJMgAkwASZgGwRY8NrGOHArmAATYAJMgAkwASbABFQiwIJXJbBslgkwASbABJgAE2ACTMA2CLDgtY1x4FYwASbABJgAE2ACTIAJqESABa9KYNksE2ACTIAJMAEmwASYgG0QYMFrG+PArWACTIAJMAEmwASYABNQiQALXpXAslkmwASYABNgAkyACTAB2yDAgtc2xoFbwQSYABNgAkyACTABJqASARa8KoFls0yACTABJsAEmAATYAK2QYAFr22MA7eCCTABJsAEmAATYAJMQCUCLHhVAstmmQATYAJMgAkwASbABGyDAAte2xgHbgUTYAJMgAkwASbABJiASgRY8KoEls0yASbABJgAE2ACTIAJ2AYBFry2MQ7cCibABJgAE2ACTIAJMAGVCLDgVQksm2UCTIAJMAEmwASYABOwDQIseG1jHLgVTIAJMAEmwASYABNgAioRYMGrElg2ywSYABNgAkyACTABJmAbBFjw2sY4cCuYABNgAkyACTABJsAEVCLAglclsGyWCTABJsAEmAATYAJMwDYIsOC1jXHgVjABJsAEmAATYAJMgAmoRIAFr0pg2SwTYAJMgAkwASbABJiAbRBgwWsb48CtYAJMgAkwASbABJgAE1CJAAtelcCyWSbABJgAE2ACTIAJMAHbIMCC1zbGgVvBBJgAE2ACTIAJMAEmoBIBFrwqgWWzTIAJMAEmwASYABNgArZBgAWvbYwDt4IJMAEmwASYABNgAkxAJQIseFUCy2aZABNgAkyACTABJsAEbIMAC17bGAduBRNgAkyACTABJsAEmIBKBFjwqgSWzTIBJsAEmAATYAJMgAnYBgEWvLYxDtwKJsAEmAATYAJMgAkwAZUIsOBVCSybZQJMgAkwASbABJgAE7ANAix4bWMcuBVMgAkwASbABJgAE2ACKhFgwasSWDbLBJgAE2ACTIAJMAEmYBsEWPDaxjhwK5gAE2ACTIAJMAEmwARUIsCCV0N7tnoAACAASURBVCWwbJYJMAEmwASYABNgAkzANgiw4LWNceBWMAEmwASYABNgAkyACahEgAWvSmDZLBNgAkyACTABJsAEmIBtEGDBaxvjwK1gAkyACTABJsAEmAATUIkAC16VwLJZJsAEmAATYAJMgAkwAdsgwIJXgXF48/GbAlbYhNoEEsd3hu+3QPgH/lC7KravAIEUieLg3advCFHAFptQl0AsBzskiueM997f1a2IrStCwMXZAU6ODvD2DbDIXsrEcSy6j29iAlFJgAWvAvRZ8CoA0QomWPBaAbKCVbDgVRCmyqZY8KoMWGHzLHgVBsrmogUBFrwKDBMLXgUgWsEEC14rQFawCha8CsJU2RQLXpUBK2yeBa/CQNlctCDAgleBYWLBqwBEK5hgwWsFyApWwYJXQZgqm2LBqzJghc2z4FUYKJuLFgRY8CowTCx4FYBoBRMseK0AWcEqWPAqCFNlUyx4VQassHkWvAoDZXPRggALXgWGiQWvAhCtYIIFrxUgK1gFC14FYapsigWvyoAVNs+CV2GgbC5aEGDBq8AwseBVAKIVTLDgtQJkBatgwasgTJVNseBVGbDC5lnwKgyUzUULAix4FRgmFrwKQLSCCRa8VoCsYBUseBWEqbIpFrwqA1bYPAtehYGyuWhBgAWvAsPEglcBiFYwwYLXCpAVrIIFr4IwVTbFgldlwAqbZ8GrMFA2Fy0IsOBVYJhY8CoA0QomWPBaAbKCVbDgVRCmyqZY8KoMWGHzLHgVBsrmogUBFrwKDBMLXgUgWsEEC14rQFawCha8CsJU2RQLXpUBK2yeBW8o0NpthuLxs9fi3y5xnFG0QA6M/KsNkiVxU5j4T3PjZq7Bhp1HsWbOUBTMk0XzQf+xC/DP0YvYtnQMsmdOq1r95hqu0KA35ozvhdzZMph7q81dz4JXgSFhwasARCuYUELw+q0pGfpwbHnOCi3+tatgwRt9xp8Fb/QZK/H84qOFNYK3U8ta+K1MIXz89BljZ66Bv38Als8YqNqAkuA9eeEGsmZMjXkTeot63r7/hFY9J+D9By9sWjSSBa9K9FnwKgCWBa8CEK1gggWvFSArWAULXgVhqmyKBa/KgBU2z4I3FCh5eLu1qYuq5YuI/9+x/zSWrt+HfWsmif8fNGExTl24Ab9v/kiTIil6dqiPymUL49XbD/i9xSBc/GcBYjs7Yf2Oo9h54DQ2Lxol7us3ZgHy5siIVg2rhhs5EryuceNg18EzWDK1PzJnSIWp8zciSaIEmLVsGzYuGCEEr+/Xb5g4Zx2On7uGOM7OaFS7Av5sXhP29nZYt/0IDp+6gnSpk+PwySuIHy8u/h7VFYdOXsGWvSdgBzv07dwI9aqXEfUHBARi5pKtOHjiMgKDglCtQlH079IEjo6xNLYypE0hbMV2dsSIv1qjbPF8GD5lObb/cwrOTo6i3i6t66JwvmzoPWIOjm+dqenb7y0HYVivlihROBd6j5iLhG7x8OrNB1y7/QB5cmTEqL5tMWvpNpw8fx3uyRJhyrDOyJUtvcKz2rg5FrzGGRm9ggWvUUQ2cYESgtdxZUZ8dnBnD68VRpQFrxUgK1QFC16FQFrJTFQJ3pvPP1mphz+riWVvh5xpEuqtVwpeErGv333A4AlLULRAdvRsX19cf+PuY6RIlhgJ4sfFvYfP8We/qTi7ay6cnBzxW6O/MGV4ZxTMkxWdBkzH1VsPsWf1BLgnTYRqzQYIAZoza3hRR4I3YQJXODs74cnzNxjSswXqtBmKXSvHo3TdHhrBO3TSUvh9+45R/drC58tX9Bg6Gy3qV0aDmuWESJ29bBv+6tgQJQrnxpqtB7F59wm0blRViFxqy6S563Buz3w4xnIQwvnt+4+YOLgjQkJC0HXwDFQpV0TYk7Z6dWiAMsXyYO+R89i65ySObvlbMNANaSAmxgTvw6evMKBrU2RI646B4xfjxWsPkP0ShXJi2fp/8MbDE4un9rP6XGDBqwByFrwKQLSCCSUEb8qVLqKlb9r4WaHFv3YVLHijz/iz4I0+Y0UtjQrB+9kvAI2mH7Y6qAQuTtjct7JBwStjeOkC8qD26lAfdnZ24vrrdx5h067juHH3Eb76fYfnp8/Ys3oiMqZNIby/FJbQpE4lNOw4EqWK5EH6NO74/bfiqNKkH87vmS+8orpFCl7y/lZt1h+1KpeEk6Oj8Mjmr9xBCN6sGdOgYJUO2LlivLBJZc+hc9i857iI/SWReuHfOyK2lsq12w/RZ+Q8nNgW6nUNDv6BvJXa4cim6cKjWrhaJ+xZNQEp3ZOIzw8cvyQ80gsn9w1n68NHb5Sv3xtXDixGnNhOFgleik2W3u3Fa/fg0dPX4uWAyoWrdzFiynIc2jjN6nOBBa8CyFnwKgDRCiZY8FoBsoJVsOBVEKbKpljwqgxYYfNRIXipC7bq4aWQhr9GzYPX5y9YNn2gEKov37xHvXbD0KdjQ9T8raTw8pas3Q2rZg1GlgypxVL/ifPXUbtKKVy6dh+VyhTEknV70bZxdazZeggLJ/+ld9Sk4O3Wtp4IZVi3/TAObZwuNspJwUvhDeX+6KURnWTo8vX7IK8vCUVdwXv/0Qt07D8Np3bM1tRJtnavHI+4LnFQtl5PpEieWPNZcHAw0qRMhtWzh4SzRV7lItU74/ze+Yjv6hJpwbt6y0HhKZ8+squoX5+HWOHpbdAcC14FSLPgVQCiFUyw4LUCZAWrYMGrIEyVTbHgVRmwwuajSvAq3I1Im9OO4fXx9cMf7YejbtXS6N6uHvYePo+12w5h48KRmnq0Be+L1+/Rssd4lCmWFzUrl0CRfNlRsWEf8e+ECeKhQ7PfjQre957eOHflNupWKy2u1fXw7lg+DhRbSyUiD29Egjd1imQoVK0jDm2YiqSJw2ef0BXPuoKXQjdmjO4uYnGp3H3wDB36TsW5PfM0/dON4dX28LLgjfQ0tS0DLHhtazwMtYYFb/QYJ9lKFrzRZ7xY8EafsaKWsuANHS/dTWsUwtCm10QsmPQX4sRxFrG5y/4egEQJ4mHttsNYteUgdq4YJzy8VEjg0gaxgxunIpaDg4gBPnzqMpZOH4D8uTIbFby6F0jBS5vWyJtLG9fGDGinN4ZXO6QhIsGbNlVyjJq2Eh8+eWNQ92ZInDABKMb2+at3wjttTPC26D4epYvmQcsGVeAfEIhYsRyEx3jcwPbIkSWdEOIrNu4XHm25aY0Fb/R6HpjVWha8ZuGKsosjK3g97uxHgcuhmxk4hlf9YWTBqz5jpWpgwasUSevYYcGrX/DSXynmlEISti8bK/67YecxuMaNjYY1y2P11kNYM2eIRvAOHLcIsAMmD+0kDB49fRUDxi3EhX0LxGYxfUU7pCEiwUtid8LstSJsgjJBNKpVAR1b1NJkaTBH8H77HoC5K7aLLA2fvHyQJlUytGlUTWxwMyZ4KeZ22ORlIn6ZBHOTOhWxec8JzF66DY6ODmjweznsPnQOo/q2YcFrna9v1NbCgjdq+ZtaOwteU0nZxnUseG1jHExpBQteUyjZzjUseG1nLLgl1iPAMbwKsGbBqwBEK5hQUvA+afASsV1/bgKwQvN/uSpY8EafIbdVwev87jQSH6iKj9UOwt89NCcpFw5p4DnwaxJgwavAuLPgVQCiFUwoKXivFdmG5LmqW6HVv24VLHijz9iz4I0+Y0UtZQ9v9Bovbq0yBFjwKsCRBa8CEK1gIrKC9/WZeSjyqL9oKQte9QeMBa/6jJWqwVYFb7xr4xDvxgT4FJ0C+y/PRXd9ik1VqtvR1g4L3mg7dNzwSBBgwRsJePJWFrwKQLSCiUgL3r19UcRzAQteK4wVVcGC10qgFajG1gXvl3xD4PTuNJw9TvOGU/bwKjDj2UR0JMCCV4FRY8GrAEQrmGDBawXIClbBgldBmCqbsgXB6/jppuhlYKK8mt5KDy8L3rATgD28Kn8h2LxNEmDBq8CwsOBVAKIVTLDgtQJkBatgwasgTJVN2YLgTby/ajgPbvyL/eF6bx5I8Ma9Nw/2AZ/Zw8seXpW/DWzeVgmw4FVgZFjwKgDRCiYiK3jf7uiAQp/Xi5ZyDK/6A8aCV33GStVgC4I35UoX0R2PhvcQHDed+LcUwb45ugnhS4VzaPOmNaXmPduJXgRY8CowXix4FYBoBRORFbx+a0qKVmYOvo7LSbogVc3pVmj1r1sFC97oM/ZRLXgdvj5H8i05BLDAhHkQmDgfvEsv1ghe/+RlhPeXBW/onOKQhujz3eKWKkeABa8CLFnwKgDRCiZY8FoBsoJVsOBVEKbKpqJa8Mp8u9rdJE+u9PAGu6aFg+8LFrz/B8SCV+UvBJu3SQK/pODddfAs5izfLo7Ky5M9I8YPag86b1pfOXjiEpas2yfOnabj/X4rUwiDejSHs5Oj5nIWvDY5t8M1igVv9Bgn2UoWvNFnvKJa8MrNadrC9kPtC0h0rDEcfEPTkclCf9fe2BZ9KCvXUha8P1leunYfS9fvxc17TxAQEIhU7klQvmQBNPvjN6RIlihC6Fv3nsTRM1exYFIfiwZnxcb9uP/oBSYPCz2aWLfUbjMUHzy9cHTL33CJE1t8HBQcjGpN++PL12+4uC80a5AtlBevPfBH++G4cmCxLTRHbxt+OcH76OlrNO48GjPHdEfeHJkwf9VOXL/9CJsWjdQLaMPOo0jkFg/5c2WBt48v+o2ejyrli6BHuz8017Pgtdn5HaZhLHijxzjJVrLgjT7jFdWCN9HRRnD0uonAhHkR++VeAe5TxU1C8OoWPnWNQxrknPjn6EWMmLoM3dv9IZxZri5x8OzVO+w9fB6Z0qdE07qVolzwfvvuj+Z//IY2jaqJtuw7egGzlmzF5y9fWfCa+Yj85QTvvBU7xBvVnPG9BKovvn4oWbsb/lk7GWlSJjOKjzzD/z16ibkTQu+nwoLXKDabuCCygjfByqS4n6CO2LjGMbzqDykLXvUZK1VDVAte2rDml7k5vEsvgXYqMjp0ggVv+FFmDy+EN7diw7/Qo109NK5TMRykkJAQ2NnZYd32I8ID7OX9BQnd4qFhzfLo2qYuXr55j1qthyA4OFis+Do4OAgBSnZnLtmKgycuIzAoCNUqFEX/Lk3g6BgL374HYPLc9Th08rL4/ySJEiBz+lQRengb1y6P5Rv248CGqXCM5YBGnUahTtXSmL1sm0bwPnv5DmP+XoVb958iRfLE6P1nA1QsVUD0idp/+NQVpEudHIdPXkH8eHHx96iuOHTyCrbsPQE72KFv50aoVz306O33nt4YO3M1rt16iDhxnNGxRU3RZyq9R8wVDN56eOLfmw+RIa07po3oirSpkqF68wF48fo94sR2EtcunT4Al6/fBzkZpQebVtXL/dELN44uQywHB429V28+4NrtB8iTIyNG9W2LWUu34eT563BPlghThnVGrmzpFXlU/XKCt9+YBQJiv84/3/zL1++NsQPaoUyxn/kbDdHtNGA6cmZNj14d6msuee/tr8hgsBF1Cbi5OsLvexACgkIsqijZsti4krQrCn+YL/6btvbfFtnhm0wjkDSBMzw/+8Oy0TKtDr5KGQKxHIAEcZ3w0SdAGYNmWqHv5tcCQ/G14HBxZ6IdRfHDKYE4bEK3eNc4hIAUZc2sIWZdHsfJXognH79AizqWzM3Zovvw6qRl90XmLntHIGXohmPtcvXWQ7TqOQFXDy6Gk1aIou51j5+9hqOjI5IlccNHLx907D8No/u1ReF82aAvpGHinHV4+/4jJg7uCBLNXQfPQJVyRdCifmVMmL0OD5++FKLO2dkRc5ZtR1BQcISCd1ivlli2YZ8QzqlTJBWr0r3/bCjaQQI7MCgYtVsPQcNa5dC07m+4ff8Jeg6bjXXzhyNj2hRC8JI4/qtjQ5QonBtrth7E5t0n0LpRVSFyicOkuetwbs98ONjbC0FdtXwRtG1SHSREidHiqf2QPXNaIVAfPn2Fvp0aIWumNKC+xnN1waQhHaEvpGHJur1GBS/ZG9C1qRDPA8cvFnZ6dWiAEoVyYtn6f/DGw1PUr0T55QRv9yGzxMB1b1dPw4/eTOiNqGr5ohEy3bH/tHjz2L5srAhzkCUo+IcSY8E2VCZAX+YfISHiIWRJiTXTAdfduyGe51lxe7ru/1pihu8xkUAsB3vwd8tEWFF+mR0c7O0Q/MP6z0K7VyfhsLUighscQ0jqcoKE/aG2sHt1CnY+zxCSJC/sPEMPpaCifV2UY4uiBpDn0s4O+PHDwmehg735Lf/mCcxPav59kb0jTlKg6/twVo6duYqR01bg9M454jPyzJap11NzXc/29UUowbsPn7By0wFcunYPXp+/wNvnKwZ1ayq8wrqCl35bClfrhD2rJiClexJh68DxS9h54DQWTu6LojU6Y82cociWKY34zJQYXhK8P0J+YPzMNUidMhma1askvLRS8F699QB/jZqP41tnCI80leFTliNp4gSgPpDgvfDvHc2q9rXbD9Fn5Dyc2DYz9PsQ/AN5K7XDkU3T8eGjN8gpeGjjNA2HMTNWi7jm9k1rCMFbME8WtGpYVXxO4RWrNx8UIaGWCl5te4vX7hECecrwzsL+hat3MWLK8jDticx0+OUELw0mDV6fjg013MjDO6Z/W5Qtns8gS1qeGDtjNZZO7y8Es3bhkIbITEHr3RvZkAZaNqVQhoRe50WjXVqes17jf8GaOKQh+gx6VIY0yAwN2rG5MqyBCNKhE9qhDRzDG4UxvDbm4W3ZYzz+PbhYbEgnsfrG42OoYJy8TKz4tm5UDbVaD0bBPFnRtXUdsTpMYrFI/uxCDOsKXvIAl63XU4QVyEIhDxQuOXtsT5Sq011s6pLL/qYK3qIFsgvPa0BAEHauGIcbdx9rBO/+YxeFINfehzR/5U7Rl3ED24cTvBTSSWL51I7Zmjbmr9wBu1eOx90HzzF44hIkThhf8xm9CPxRo6xwCuoK3hPnrgvvMTkBlRC8q7ccFH2bPrKrqJ/+3XvEHBzfGirOI1t+OcE7d/kO/PfkJeaMC32ToxjeErW6Yd+aSSLGRV/ZvPs4Fq7ZLd7QsmZMHe4SFryRnYbWuT8ygve770dk3JoGl1IPQaK3/7DgtcKQseC1AmSFqohKwet6dy7iXxqAd83eijAGKtppyqTgpfy8jl63wII3CgWvQvNNCTP+AYEo/0cvEY/bskGVMCa7DJqBovmzo0al4qjYsI9GFNNFJPqk4KVVX8rkRNqACnnMC1XriEMbpiJpYrcwNsmTWqBKB6E15H4hcwQvhR78+PFDhFJoC17y8EqPrSke3ogEr+cnHwybvFTsadJXIhK8r95+EKEVVw8t0dy6cvMBEQs8a2wP8Td9MbzaHl4WvErMbC0bNNjNuo4V7v3c2TNg4erduHj1rnhDoUIT+Ozl25g2oov4f3pT2nvkvBDI2m9tcWI7a5YPWPAqPEgqmYuM4PW4sx8FLtcXJ6zFuxo6V9jDq9JA/d8sC151+SppPSoFr/Tmap+gRkcIu69PIbpI2RocP95AiLObEMYseFnwyrlPv/e0ZN+lVW0R0uiWwBVPnr/ByGkrUa9aaRF3S5vah/ZqidJF8+DY2WuYOn+j8HaSh/f8lTvCI7px4Qhh0j1pIoyathIfPnljUPdmSJwwgYh5pbSmtauUAgnpeHHjCJFNHtG/F20R4Q0RpSWjkAby8GoXbcErY3jr/14Wzf+oLGJ4ewybjfVaMbzaIQ0RCd4UyZOgfocR+K1MQfESQBvabt1/IjbYFS+YM0IP73f/ABSr0QVLpvVHtsxpRIz4vzcfYOD4RVg0pR8QEoJlG/4RG+i0N62x4FXySazH1rZ9p4SQ/ej1GbmzZxQb1jKkDX04UowuxdtIFzrllfvv8ctwVk5unyV2WFJhwavygClkngWvQiCtZIYFr5VAK1BNVApetzN/IvbzPXjX/F2YniTfmh1BcdPhY/WD4u/6Qh8U6Hq0NMFZGn4O2+mLN4UQu/vgmfCgpkvtLsIZmtStKAQshTNOmL0WJOgqlCqAtx4fxSY0Erzk0e0/dgGOn70mxPKxLTNEJoa5K7aL+z55+SBNqmQipRhtEHv7/hMGjV8kQgdyZ0+PFMkSixjayAhe6snTF28xZsYq3KYsDckSo3fHhmGyNJgqeOk8gjfvPDF1wUZcufEfvvsHihDOfl0aI1/OTBEKXmoHhVbQajhxXDVrsLh31PSVoPRvlNO4Ue0KYqMbC95o+dgIbTQL3ugxeCx4o8c4yVay4I0+4xWVgpdOU6Miha2k5vgpdKOaPGSCBe/P+cSCN/p8t7ilyhH45WJ4lUP30xILXjWoKm9TKcH748E2ZP+8C5/bfFC+kWxRQ4AFb/SZDFEpeJPuKi5id3UFry49FrwseKPPN4pbqgYBFrwKUGXBqwBEK5iIjOB9vbcvinguAMUJav/bCs3+ZatgwRt9hj4qBS9lT6GNaV8KDIsQmMPX50i+JQd8ik6Bb87u0QeuCi1lD68KUNmkzRNgwavAELHgVQCiFUyw4LUCZAWrYMGrIEyVTUUHwUsITBXHKuOKcvMseKN8CLgBUUCABa8C0FnwKgDRCiZY8FoBsoJVsOBVEKbKpqJK8FKcbtLdxUUmhu9paxntJQveUEQseI1OFb4gBhJgwavAoLLgVQCiFUyw4LUCZAWrYMGrIEyVTUWV4DU3LpcFLwtelb8KbN6GCbDgVWBwWPAqANEKJiIjeD9saYRUXy8joM1TjuG1wlhRFSx4rQRagWqiSvC6PF4Lt9Md4dHwHoLjpjPaExa8LHiNThK+IMYSYMGrwNCy4FUAohVMREbw+q0pGboU2PIcXp+ZhyKP+uNapX+RPE0OK7T816yCBW/0GXdrCd7EB6ohIHlpzQY1fYdORESNBS8L3ujzreKWKk2ABa8CRFnwKgDRCiaUErzap64lz1XdCi3/NatgwRt9xt1agpcEa7BrOng0uCfgxL/YHy6P1oQ7dMIQORa8LHijz7eKW6o0ARa8ChBlwasARCuYYMFrBcgKVsGCV0GYKpuypuClrshjhA0dOsGCN+IB501rKn8h2LxNEmDBq8CwsOBVAKIVTERG8Aavygt/e1cR0sAeXisMFsfwWgeyQrVYQ/DKjAzUZBmz+6sL3ok7r+Kl5xfM71DOrJE0JHgpXMvt2Xp8LzkdiTMWN2gzZeI4ZtXHFzMBWyDAgleBUWDBqwBEK5iIjOClpdDLSbogVc3pLHitMFZUhbU8vM4epxHkmtakTU9W6nq0q8YagldmZCA4H6sdhL97GbPz6sa0kAYSvA/eeGNF14pmzRmDgvf/B+xcK7INEYVrseA1CzdfbCMEWPAqMBAseBWAaAUTLHitAFnBKqwheKWIMuWkLku7Rp5JKoGJ8lpqwubvs7bg9S6zGH6ZWrDgZcFr898NbqDtEGDBq8BYsOBVAKIVTCgleH08HiD7/vy4nHkqUpXuZoWW/5pVWEPwyrRWagpec5fdo+NoW1vw0nh9T1fbrEMniGvyrdmFZ9i79JLoiDlcm9nDGyOGkTthJQIseBUAzYJXAYhWMKGU4KWmaoc4WKHpv2QV1hC8Mq0VC97ITTFrC97vaWria64eSHygqia8wZQexLSXD7UE76XUQ5D6t2EGkXJIgymzja+xNQIseBUYERa8CkC0ggkWvKGQY7/Yg3jXxoOWhW15mZ0FrxW+FApVYQ3BK19O/JOXEa3+lrWlWYdO0D0xVfBObVkSSeLFNnk0DcXwUr7xzMHXNfsVDBlkwWsyar7Qhgiw4FVgMFjwKgDRCiZY8IZCtoZXU4nhtIbgdTvzJ1werYPaHl77gM/4UOeCElhs0oaagpfYOXrdhNObk4h3YwJ8c3QDhaJ8zdFN/L9MUWYKmJgmeEdsuoSXH30xsG5BZE/pZgoCcQ0LXpNR8YUxiAALXgUGkwWvAhCtYIIFbyhk5x0VkPjzRbzK0g/2pcZYgbxlVVhD8JIAoiwNagteqsMcYWYZsai7S03Bq/2CRgKXxkoKX9d788ziGtMEb9v5x8Sgs+CNurnPNUcfAix4FRgrFrwKQLSCCRa8oZCdNhREEv/7uJmoBZLUXmwF8pZVYU3B65m5EwJKz7CsoUbukqKaBa9leCU/CmWgF4dPFTch0bHGwhj97WP1gyYbZsEbioo9vCZPGb4wBhFgwavAYLLgVQCiFUxYKnj1HTThtiIp7rnVQYp6S63QcmWroA13VN64FAYanVLWuILWrCF4XXf/hvifzqnKQsO7jZ+CdGzLlJoeXil4AxPmgYPvc3hV2iI2q7HgBdjDa1vfA26NbRNgwavA+LDgVQCiFUwoKXhpc4fwlLQ8Z4WWK1eFw9fnSL4lBwve/yN12VUJbl7nWfBGcopZQ/BqC1z5EkHxvD7Fpprc+pjq4e1RPQ8KZkhqMgdDHl7JVR6yY8ggb1ozGTVfaEMEWPAqMBgseBWAaAUT1hK8tMkm4bHGcH53yqz4QisggDxowSMkGdwcvuFjKw9rVGtRHdbw8LLgtWhowt0UVYLX3NjrmCR4Pb98R/81oS/cdQqnR92iGU0eTBa8JqPiC2MQARa8CgwmC14FIFrBhLUEr/YRqPJEKCW7R4L6h1MCi0zKtt0Mzo28DrdtTpBrd8oagjfZWnfECvJhD69Fs+nnTWoKXul11PbwyjAHc79fMUnw3n/jjck7r6oieB855I9w9Yo9vJH8wvDtUUKABa8C2FnwKgDRCiYsFbxvrqxD4dt/4lbZY0icsbhoaUQhDXJXufYPtFLdS741B344JrA4xRXl4KUNP+eDiqNErAu/vOCVYuqLYyp8af5QqWEKY0fW8a7ZW4tfVFRpmIJGrS14Ex1thNgv95p16AR1lwVv6KAb8/Cy4FXwy8GmbIYAC14FhoIFrwIQrWDCUsH7em9fFPFcEEYcmip4AxPmtVic6kMS2Q1QUoyP9h+Gkc7jzBYMVhgmTRXW8PBqew/V72BH0wAAIABJREFUyqIg6/hY7aA41jYmFmsJXjph7VOlzZpc0uYyJaHs4PtC0e9kVI2n0h7e774fkXFrGtEdFrxRNapcr5oEWPAqQJcFrwIQrWBCScH7fV0pOAZ9gUPrm+FaHvqj+hzf09bSJMaXnlVKqUR/t6SE2XBm4Y5/KXgH+E/EFOfBLHj/n7GCxiO6Cl6aF7F8X4gUXVFVrCV4ZcwuHTwR58EaeJddjOC46Uzutpz/ao21yQ1R4MIz/73DsqN3hSUlYnhlNhoWvAoMDpuwSQKKC957D59j2oJNGNi9GbJmTB2m0w+evMLkeesxuHtzZM6QyiaBWNIoFryWULP+PUoKXn1eX9kjuWwa4F5GI3jlD61f5ubwLr3Eos5rxwZ/qH3BomOBdQVvZAS4RZ0w4yb28JoGSy7vR6WIU0vwOn66iaS7iyPYNa3wzJq7Sc00gtHzKtqwRhvXsv7/hLXBdQua3BF9IQ1S8HoiOUJgh8A2Twza4xhek1HzhTZEQHHBO2zyMrz78AlLp/XX282O/achTcpkGN6nlQ1hiFxTWPBGjp+17raW4KU4W8oZGpgor0bwSlFibqJ8bTbk1XI73VH8ydylXGlHCt6qfntx0KWmTQsIFrymfTPkBi5LX4JMqyXiq9QSvPIlTx46wYL35zhQDt4C6ZPga0CQ+KNSgpfCGTIHX49wxYMFrxLfGrZhbQKKC97qzQegW9t6qPlbCb192XPoHOav2on966ZYu6+q1ceC1zS0H59cQNA3LyTPVd20GxS+ylLB672+GpIF/IeANk81LYrIw0sxm/TDHBw/vRCoHg3vIdGRxnD0umn2yVDaCLQ3w1nqmSVx9PrtK3T5PueXF7xSTO0IrI16jruhlmBUO4ZXCl5L54QSXzMWvEpQNN3GC09fjNx8Ce0r5cTpe29Y8JqOjq/8hQkoLngLVPkTa+cMRa5s6fVivXn3MVr1nIDrR5bFGOwseE0byshuuDKtFsNXWSp49W1QM0XwBqQoJ06EIm+spSdDafcm/sX+cHm0BvaBPhZ7Zinv7AvPL1iSeCEmeVWFXZa6FodYRHY8jN2vtodXCt61AU3RwmmDxV5zY/2wluCNSu+n2oKXDphwvTdPtTEyNoa29rncsDawbkHsuBQaeiA9vBTbu+vSE9BnSeLF1tt0GdLw+OYpxD7XF99LTsf3N/+iyKP+uOtUFjkDIs4hzh5eW5sR3B5TCCgueIvX7IqpwzujTLG8eus/duYqBk1YjEv/LDSlfdHiGha8pg3TryB4pYgikUuFhK5P0SmIf2mAiEO0C/gMSk9lSZGxwc4ep+GXuQW+5uxudhyvFLzrUyxFs7cdkMXdDV6/H7KkOarfE9MEL80D35zdFeeWdFdxsXoQmfjwyDZKLcHreneu+O7Q98n12jhxqhqFCv3qJSLBu/PSE+y68kx4f0tnc49Q8P53cRcKXK4POlmNCmWjoX/rZqXRNcKC91efgdGz/4oL3vZ9pyCVexKM6d9OL5GB4xaJGN9VswZHT2J6Ws2C1/hQau8AvlZkW5SENVjDw6steOlwCNpwQ6mUKGeojEO0dHMRCRuyaR/gDUevW+Lf5opnKXjP5NmI0reaIG2SePCrc9T4AEbBFTFB8NIhIe7rUwh6anlg5YtkZOLDIzu8agnemJRVIbKMte+XonZe+7KYtT80U4z08E7ceRUP3njjtzyp0bxMVrMFLz2fSQRH9JxmwavkaLItaxFQXPAeP3cN3YfMwrDeLdGoVgU4ONiLvgQFB2PttsOYOn8jpo3oguoVi1mrj6rXE9MFL8WLTdr5L6rkTWPW8ZXa4GOa4JX90f1R0Ba8lHNVO88rCZ54NyaIzSAkhMgr5/xsN2J9fQGKvzRUKKUZCVz68Scb5CWm5V0q5ornRBuz4bRvVjzMOwspbgxBrTgn8L7FO9W/I5ZUEBMEr3ZmDbUFryUvQJaMi757WPAqRdI0O1LwruhaEbP/uSmyNYxpXFTcLAUvZW8wtJFNhjTc2dhD49Wle8mzy4LXtDHgq6IfAcUFLyFYsHoX5i7fgcQJ4yNbprQIQQjuP3wBr89f0K5JDfTt3Cj6kYqgxTFd8MrlM3NzPcYUwUuilZb5UtWcrumSIcEr8+3KDVBS8FI4w+eiU8UpZ7Q8S7lTZcYFMkob2wzlE5Wbkug6KZos9XxReyhm1anceAScHCpiV80VzRIChVYEuaY1Kw+qOV/8yApeEgFn/3uHUtnc9cYyyrGSOYnVCDmwpuAltlF1mpuSgpdSkdELIb0wWjrPzZln0fHadacf4MitVyDBqy1+qS8yXRn9mz7XV3QFL2Vm8EpYggVvdJwM3GaTCagieKn2h09fYd+RC3jyInQHabpU7qhesShyZtW/mc3kFtvghSx4jQ/K6zPzxIYIKldyL0HKws2N36TwFZaGNJgjeHV/oN3XuYtNZrTc7FtgmGYTW9w7c0SYgyzeZRbDL1MLvT3WFrwyHZlMURaRUNZnTArelDWn483evhYLXink1PJaUtsjK3jli1rT0lnE6oRusUaKNmsJXhkuY2m6ush+1ZQUvHK+04uY25k/QQw9GtyPbBNj1P3kxaVCHlxdwUvpymQxR/B+jZ0aqb5exssi8zmkIUbNFu6MJKCa4P2VEP8qgpc8ZR0q5bRoaGVWA7pZ11tqkUELbooKwSt/vEkYyqwNFL5Anl7tEpFw1Cd4dUMnTMUhBW/Fjsswc9Esi09b086P+rF66AY9pUtkBa88icrQykR0FLzk/aRNXAHJSyMgZTnhYU++JYfYsObyaF2UZTFQS/DKjZpqzTGl56y17BkTvGkSx8XLj19Fpobs/z+YQrtt+jy88vNvpf5GnlMVcTnzVKQq3U1vlziG11ojzfUoSUBxwbti4360aFAFjrEc9LYzICAQ67YfQdsmUZOLVUl40lZMF7xy+X6/c1vkaxoaO2pu0RW8icsPQfDO2vByr47Uvw0z15xF15sreEkcyhQ9poY06Hp4tQXvlwLDREyvPDXqh2N84f2lQhvbPlXarLdf2oJXenQtEbxyA9XCgD9Ru+OsSAleuXs+2DUdPBrcs2g8jN0UWcF7+cQm1HnWFsdSjUf2yn3CVRcdBa+2x5g6JF+eaIWAQmQiWikwxjsyn6sheCksiFLxUWHBG3Z0KGwhTWJX9C0OHL/9CstuxxbhCzI/L61qbDjz0OCRw7qC9yvi4q1DFlGJS8tz4jkVkWOCBW9kvi18b1QRUFzw5irfBrtXjkfs2M56++T79Rv+aD8cd06sjKo+K14vC17jSKXgfWmXCf728eCdoxsK3/4TFDtGD1hrFEsEL7U3TchjXEo9JIwwNxTDK3PlvmseuhFMnrAmDwXQjukNipsOFAdLJaId9tqCV8bbWiJ45T0Us9q7Uy+MW7gU82P3FKLpe9paZg0BLTWTR5GKpTHAEVVI4QhvvHxRIrM74jjHMqtt8uIj/6xBq/edcDZhJ2SoMyPKBa8SWRR0Ba/cCClzPasZYhLRIKgheGUqspgoeGkcnTxOi5h8SwqFLdDKRfuPXcQzhE5OHN2oKPwCgjB551Xh2aX/GlrdkIL33ppWKPR5vWgCPYtZ8FoyGnxPdCGgiuA1pfPRRfD++BGCaQs2Ysf+0wgMCkKlMoUwul9bxHZ20nSTBa/xEafDG5x/fBFil8pn15ziQfvSLjMcWoem1VG7mCN4Xx0Zh6KvJmiapJuNQXpLzyYfiAzVR2qu012ClV5EGVspBS8l0qflafqxomOIKQuDIeGoT/DSvZTyzByxKsXSVOcZaN60E+hH09LjhbXbpMYJZTIucVDdgsimZ0nWlLlyY0M3VPdfAUMrE9oe3qnOg5AtZULFPYnaAvVXELzX1nYzmsPV2NjJuUXfGbczHeHvXtpmD0cx1hd9n2vPCUs3GeoTvCRy/fwDMWf/LSF+6SQ2Y4L3zYLC4hhhFryWjCTfE90IqCJ4l0zrF0YQakP56uePzgOnRxsP78Zdx7Bq8wHMm9gHLnGc0X/MAuTLlRn9Ov+MwfxVBO9Fp+pI02ybRXNcnlZGGyNSfr0ibCRFqBdUDQ+hvkaaI3g/bGmEfF9/birTl5NS37KfFLxn823CzktP0cl1E3K9mo371a9j/vmvGGffH/E/ndMk0g89RCI0/tLQj58+wUv9k0cYU6iEKUX+0M52nYMGDdpjxKaLWPqtgkX5YfXFFZvSBlOvkYKXYsYpdtzUQku6VNImccWVtV1QO2iVQcFLXuovDw+j1bfliC6CV/t4aeqnrXl4L67qglJeiyL1nZZzS4ZpRJXX2tQ5Z+512oLXkk2G2scK13nSWrw0t/q+HG1qV4Pno3No9KSBmBcNzpc0S/BSWMP9BHWQot5SDmkwd1D5+mhBQHHBO3HOOiEGHR31L0N+9w/AjMVbMLiH9XfpWzIirXtNRKXSBdGqYVVx+8nzNzDm71U4uuVvjbmYLngfXN6F8neaRir8QApemfqG4MlwgbvO5eDWdL8lw2PWPeYIXmqv9HxQJeYK3pEhU3HtmSdkLky5gWpVstFw970sBK/T25MiL69cjjb04ycFgK6HMLKClza+jPZpgNgZK5ntQaM2OX66IWKQzfEymzpglFuU+NHRqCR46xbNaNKtXZeeRJok8cTu9edLK6JErAsG5y314b83XujvPynaCl7tw0zMnQ8mATXxIhnScHhBe+FVj8xLrFwFkWKeBW/YQdA+Za3UjcZC8FKYUrJcNZDM54II45FlVepNqPxb+HAlGdKg7eGle2TcbvCqPPjimApuzQ7onQEcw2viF4MvsykCigtem+qdAo0p90cvjB3QDmWL5xPWnr/yQI0WA3HlwGLEie2EZ8+ewccvKMKaUqVOa7Qlr1+9MHpNVNm5c/UYSryZiNcOmRGv+gJNO81pj/++NvCLlQT+ifIgq8dafEZCvHcthGS+/yKfwzXcKr4dKfP8bpCBLp8v+7vANfgT7Gr+PLDBWHvix3XEw0dPEBQcYnS8vFYUQ+ag0KU+Krrto/bE31sZ/yWsixSlfu5kjne0BYIS58Wiz7Vxy9MOSePFxqx2ZbD1wmNsv/gEM9ymiHPqvZs+EHbfPL6JEEdXxD3VGd/z90dA6irh2pb79G+w932OQPcy8Kl1RPN54iXO8Cs4DN8KDYcp8yezxyq4XB2HXgnPYESDIhiz9TJ6fOmOdEniaeyaYoc4J9hWFD9c08LpxR5NG7QbbqodQwNBbbv/2hsJ8EV4a1uVy25wzLTHvdmsw3BxioWlXSrg9cJCyOtwGzeDcwM1d4S73/lMH7z+9BUbYnVH06C5KJMmKAxffRWa2y/Ht6fEPKGiPX7m2pFtifPvWDGGsjxOUBsOn27Cp+ZhUY9/1pbwz9oqTNOJD7WD7vUrMQ1BiUOfZUqOF50vFM/FCdtmd0XRgP14UWAm3FLl0jtmxr6nNK+fBacXfXF+sEZvn8iwMTt0jaWcleYj7VF7Yn28AZfz/cSf9I2HsX7dfe2FOVuPo2W5bMj5YJQY/0WBf+Lxj4zI7vgc7ewXCtvpHZ5hbrw5aNqkY/i572gPx1j2uDK7KlIFP9J8Lp9n9HylQs97fe1JHP9nSJ/BLyZ/wARsjAALXiMDUrRGZ8wZ3wvFCuQQV3p88ELFhn1weuccJHKLh9GjRxsd0pEjf8Z4Grr4l7bjOgo3Mw5H3npjDLK0Np/HswsiU+A1TXs8G99DktQ/RZcp7TlnX1jcf3D471hz8gHWnnqIuimfo0uqS0C10E2bptgZ6ToqtB0FewEVZv5kNN0OKDESKDnKNDuVQ4DzozEq9WWMalwY/VafRxfvbsiUPD7Q+ITp7aH5LOs+P1rTBu3BM6lfEXwvqG23nn9CyR+h4S8RFfn9evzOB12XhG4CHNmoEDLtzo/k9h5C8O741sCYGYzMsBNo9XPM9d1gdr9engA2Vwg1lbqc+Zx1G3FulBhDOCUAAj5jtO//50YEvRN85H3lZwCFeoe72ux+GahPETvT7Uzvl5FRVaQ9pn5PrfCcP3TjFQ7feAXXp8ZTAdJzY4HbPHRp31X156rRLxdfwARsgAALXiODQB7e8YM6oHTRPOJKXQ/vqlWr8CMkYo9hs+ZhPS76qly/brXR6RBVdrw/vkeib/fgGxIXLmlCRRwVc9pj/+okfJ1SwSVZ5nD9bOzZHY/jV0TWtjsNMpB8Avx8EPT9C1z9Qr0SP0hEmNgex1h2WL1qFSIerdB+Bc1OAC/75Ej947Gw7t8zOEzbqD2aPiVKDTs/D8DZDXYfrovcqHd9EuC2XTZxz+5B1TF4/QXceeGFXGkTYmKz4hpbsl/2by8ixNkNIYlC79EubT+2RXCOVgguPhIh8X4e3OK4LVRIBdY/DlPmT8tMTxHr0hisKHAfzcpkwcy9N5H/6VhUDdmu6Z8pdoiP82wHBBUdAYcnO0WbAmuF9aCaasfQgHeYfwLvfb4hd8h/cHGOhYwkyg0UOQ9vvfyEoesuiqualMqMttdCX1A8fiTHP0knhbs7xOM6vgUE4ZNLDjj7vcAAlynhxln3JnP7Zf/6JBy3VRQCNSR+egQ0Cz0wwFw7sh1yzINq7oDTosRY9a2N5ntg9+GG+HdI0rAeXOLjcGG0GHuaR0GVV4RjYWl7pCE7O8DRwR4LJvRG3JDP8EuQG7HjJdY7YsaeGzS3qF/0fbDz90ZI0vwIcU4QzpYxO5HhHNlxNzRXibOdz3PY+Tz7/1iF71tE/ZLPEfpe5E6bCPZvzgI/guARkgweIckRF1+Ryf6JsN06zkrsitsL1dr/DL+T7SKPvJ2dHVZN+BPfHdwQN/ij+Eg+o/1f/yv+3zlVIb3PeWdHe4PfR/6ACdgqARa8RkamVc8JqFy2MFo2CF1qPn7uGkZPX4UT23562mJ6DO/T/aNRymOy8JQlaX/JorkcUV5HGd8bUXoyH48H8L26FOk8tmo2u1FDbpU9hsQZfwrIiBpnTgwvtfdG3JqajWv6YhKdV2WAh2M2JK84VJygJmMOn2b8C51v/zzSk/JjGjvfPqIE+4ZiM81Nyi83PM3PeVvExNLGsPjXx1t02ppsk9O7UI+q0nlSaRd6sgRx8P7zNxHHO7VlSaPz7tDNlyL3KJXEzt+x3uGnV/dCzfciNEK7uOyqhBeeX3Amz0aLORhrlMxXTFk5XO/Ni1Rsq+jX/tC9BMRbxrpSPmdKgxfRfKB0eVS/Epki9PVZxvDe/zsfctvf0hvzboyV/Fz2S/6/JRu7TK0rKq6jOeH8fI+IvTU3b7J8jlC76bkiWb3K0g+TP9RFHI/T4jAZWT7AHYFtQgVwmLnv7AAnRwe4zI0l4naLeIaGqsm9Ct7rqyFe4BuDGXQ4hjcqZg7XGVkCLHiNEFy3/TDWbjuCBZMoS0Ns9B09Hzmzpguz6S6mC16ZQ9dSwfvxyQVxco+hI4Wl/Yg2usi8t3K4PJEcSeBh1g+ruYJX+4dAX9ukUE9YebIQvHR4BB0XfCv/cvQ7lwzytCP6YZLn28tNbLrTjvL1Ovi+wIc6F8LNSKUEL9Xx5vkdHMi9J5zgNTc9kjUEb/MyWeD7LRC7rjwTP+7GiszsENvJAdmCr4sffu+EJeDmdR7HqrwJd+JUsrXu2POtPN7mm6Ca4JUvGfJlKDKbuQwJXiliIxK8hjY+GmNq6udS8N6dnlfETevb5GmqrZgueLWFvbkb8nSfI9ob/NYENsOHaxsx0vlnjDfVpe8lR25aMyR4jT2TWfCaOpv5OlsiwILXyGgEB//A5HkbsPvQWQQFBaFCqQIiDy+JX1l+FcFLIQ0+bT+YPX8NHdKg4XdlnTiEIiJvrbbgJeHsECeR0fPedRuqtuCVO+Z3pV+B+XeTon2lnFh29K4mJya1x5C3UveENu22KyV4ZVYC8mhKD++za3vMPl5Y+9CLuHfmiBzC39LUhH3gZ3iXXmz2/NC9wfPLd/GC0Ld2Pjx689lkwUuZHRp86Atfp5T4x6egUcErj1n2yT/0lxG8ap2Mp6bgVSPPc6QnqQIGTM2q4ecfhJcffcXJat2WnRI1yxdnXcErV2zomqeuFRDi8wypUqQOtwKjK3gzeG4P40BgwavAALMJmyOgquB98fo9bt1/Al9fv3Adb1zHuMfG5mgZaJASgpeWt344JkBgorw2123tY4H1ecqMNdiY4KVwhez780d4dru0QZ7dgDZPYcymvjaZKnilbRLWdi+PC1OUm1K36Hp4dQWvPO1I/lfer89baYngFae6PV4r8veaUmgX/xOPz7hfejdKZ3MXIQ2RFbwytVpgwrxw9LoZ6SV7ErsbzjzA1aeemNKqOM7efWey4KXl3jnepUU6pXVfa6Cz0xI8L7EW6c63wIlcG5C1SJ0wmKTgTVlzOq7tnYq+TjMgj242hacp1yjt4XVfnwJ+mVrAp9hUuK9zF2nhTPHwJt1VXIwPlch6mfX1W1fwRnQsbUTcdE+SU6u9poyd2teYKnhlSkPt54iu4KVc3hcyTsWbvX1FiBIVGoNXb1+jTtyT4Z4RuoI3odd5kYZReuZZ8Ko9+mw/KgioJng37zmBsTNWwcHBAQnixYW9vV2Y/h3fqrXbPCp6rmCdSgheeviR94W8GT9oB7YNlYfbe6GczxLRIksE7xsTPLjU/38TNNMrLKleKULvOpUVuSG/+35Exq1pIjzvXRehuYLX2LKsFLxJSnaF2+mOYtzoBLZNKZdi/YtU6FUjn+Z4T1qWpx+pB2+89S7PR3RUsKEfxohEsr7pI2NWPasfEsv7lM/zwJ5VkfLw2gV4I9ExrUNY2oR/uTVlKpN4p7LarosQuFRWda+Ifx9/wOz/nxylG4Ora3fUss1Y7BC6iWttQFPxwy9zHJPXvUj5n+2ka7QFryUcTOmX0oJXey7ohilE9AKkHSagluB1crTHi3mhqeAiK3jlyyMLXogXU/pONC2dRROjXiB9EvSskVcTw0u8/iu9C/4nh2pOurv69AO8Dw/SG6NvSPDKVTYWvKZ8u/ma6EZANcFLqbvKlciPIT2aGzyEIrrBMtTeyAreyJ68ozZHeUSrpYLX2MOT7BrbuCZFM51YFj95VtHliDbC6WOiluBNnruGOEBClh5uZ8Q/6xXNaBOCl04Uc/B9iRDvp7jmmwrfamzXCN55u05jW5zGZp22pi3OSeTTEceyWLrBSMafLku8QPy4J44XG+t6VcKJ228xaedVkHeLRHpEZeaiWZoNO4eDKqGc00V8qbJNxFfHZMFLG+LI42tslUCyU0vwvvL6ilhbyisqeM2NcVX7WaikfVM9vFLw0jHB8mVQfh/ki4z08mvPAXqhlR5f3TEnwfvu9n5kP1tXeHXjXR0rPLzyOmNOCo7hVXImsC1rEVBN8Baq2hEzx3RHmWK2t0SvNFwlBa+5u3aV7os+e9qC91TF+8ic1vhBGtp2TBG8b3d0QKHP6w0ut+qz4bQyA947Z4dflhZIWdj4yX2mCF7t8A1jHl46fpiOSY6dr51ewdu8dFZxnj15Y+jUMBnTq0+8kWeYlqu/p60lTi6TxfHTTSEo9c0LUz282scAk+eTlvClh3fyzqs46FLTLMEr65Ub3ZLuKgYH3+died1SwUtL7lSWpFivCWFIkSiOyYKXjlu9vn2oCGOQhUSAd9nFSL4lB84m7IR87o4ISFFGMJa8JQ/p4X1d6R/YpSmv2NdKsqIxJU94ZEImHL4+F32Rc0GOqxSFhuaDvE96TdWIiaWQBjUEr6XzSbEBVNGQuYJXrhCRp7dK3jRwO9MRLo/WihYaEryGVi50BW/Q0yMazzDZMxYyxoJXxYnBplUjoJrg7T1iLrJmTI2ubeqq1nhbMRxZwUtxmLQkTsUWPRragldfLKSxcTBF8L4+Mw9FHvU3uHFNnw3t439N8VqZK3iN2ZRtkjvwJYe6QftRKlsKNC+TFZReSxbtmF593kqNt8a9LPzT1oRvzu6IKNRBzhtjIsqY4N2VqDOQND8+VdpsbCjF5/qElRSQPkWniHabW2TfyTv+zT8QYxoXgzmCV9ubpS14ZfquRw75hQeLMmlQPyVXOpL1j/rtsX3bMuEd1n3JoX7FfrEXgYnyWBRfL1kZOz7aFF66c8FUwSvvk/NUDREpBW+CLUWRyeEJTjg3RNamq0zpVphrZFulOFejrWY3SqUbkm/NDn/3MkaP9ZbHbGs/R+j5oR2mok/w0kugnNe6HHUFb5wkmfDN8zGS56ouqmHBq9Kgs9koJaCa4F2/4yhmLN6M8YP+hJNjrHCdLF8yf5R2XMnKIyt45Y+irQpez7VVkDcodJle39KwMZahOR1fw6H1LYOXGnvARifBW9VvL2j5kTIhmCN4aR5QXlvHTzfwPV0t8UMYkeCN6DNt0LqCt2LHZeJj2v1Nu75XJRuNhHFjm5xL15An0VSPlb5JoC146fPBdQsKwfvu0ze0mX9Mw9PQBKKNPWlON0O+RF/g4nNHXCZfHsn2N8RFHHwVcfIeDe5puI6zn452rbpg0MKNWB27He7lnokEhX8exSoZ06YgGg9zS2QEL4lt2mhGGwIpdETm9JWedZlbV5+HN/aLPYh7dx588w8VTdbOE62GiJSCt+D2JKI+esGIKK+2IY6SN3nEQ5zcxEuGre1pMHcOGLre1Dza2rl3yZZuOAPNTXopo3kRmt7wOT7UCT2AZdzCpZgfu6dYNaKVDVl0Ba8UuvJzYxuJ2cOr1CxgO9YkoJrgzVOxbYT9uHUs/Gk/1uy4knXFdMGr7Um1RPAai8+lsaBcvZlO1sJD1wpI2vCnp5E8v6J4Pwmz5EZ/0g4/MOUACrU8vPSD4/JonWjmt6SlUff5oHCCl5Yj/6yUU6TbkkuSpvwQKiF4tT1BtIQvBS/VT4J8VsolyBJ0XQhBU4otCl6Kcyx9qwmypUwoEvpT0Ra82v0iwUeFROBs1zlo0KC94EChHXRoiHPZn3lMtT2OlhyuERnBK+/1dy8Le3+/srq1AAAgAElEQVRv4Q3UPrxCe0PclwLDwnjek2/NIYQPebS/p68tVpAoFIL+q5bgPXX/LRqczSjYRlbwqtFGU+a2Na+JjODNmcQuNAQqTU3xUkB7CGhFStemnNe6K4fGBC9xiGiPBAtea84UrkspAqoJXqUaGB3sRFbw0qYi+mGltGTkgTJ1adlabKwheKkvusKY4nqzf96Ftw5Z4JWwRISC11i8rRA48Z3FQQb+gT8MopOxxH4hceFtJOewFNzaO8rpoIPGr4dqvDDSwyvTCNH/k/e3Sr604shcfUX7R0tpwUtL+L079dJUS+0Zl3ZfOLYRzS3yIpHn0aPB/TCXUSzvDyc3kz3F8mYZDkH/rx0O8j/2zgM6iqp74Dc9JCQkhJAQmtTQm/SmAiIgTUFQEFBBRUDEAqLyF0VQLPhZAAtVEJEmoCiIgHRp0nsJJRBaAoGE9PI/9613eTuZ2ZnZndnshvfO+c4n2Tev3Pdm5jf33aJHw4vAO+xoHUAHrsyK3ZmWPL1iN8gNrgiwuC3EpO2xjlUOeN9dtBNmpj9UAHhJq+pohjJngJfXzNPg+XFQ22TTi1pdtBNGG11yJMTnSVqV/gyIUAOIkOSo2Ym9PYEa3tX7zsOgvZZ0zo4CL8lbzZzIVc8+M/txBnjrex9iH2y4lyk0oF7gPb95KtQ/9jrwjsD8fHN/qMtC/GFUHGkRwGvmzhBtmyUBAbwGSNZZ4EWHHf7YzhFNkgHTUGwCQbQYpELZ3NOwLepNqNR5vK7ucn+oByl+MbIPTr4hKfDyoI3hyPxzb9sck+Kx2+1Di6HpxQ/h3xL9wbv641YbNFmQ1AC81KeWF7YW4H1j/nZISslgjmsYRoiAt0JkCKw9cBGGtK/JklHwhdeg2gNecmiTHlcWeDnNDbL+SQ543620haWOVrMFpkbkXtQLtpyEPhefg+iwYN3Ay0cp4c1BCHiHztwErf+ziVbaeNj/6DMN5G3gJcBLx+W8hhfblWq00NkrZN8k5hiE96fWeMdya+mIDa8lVGEFloGPsviRDTL2wcM0an95cwAEX0w5jI6EfJY3Z8xO9ADv4by6UPI5y7G6nqLVEVNPm+5aVyvw8mZROBc0adAKvPj8mZPXE3Kr9LAxyUEN79kVr0HDq8rpru2dzAngddddJcZlTwKmAW9eXj4sXLEO9h46BWnpmQXGgKl6i0TJTIaE1ACHp8Lb4aH9JhZ3A96UeS3Az8cbKmfvg9UBz0L9p/4zM9A4a63hw6R2uvxRPAIoFjm7QKp3ILirjTmEdHhaNLx6gPfaxo+gwbkPICkwFiIyTrDuroc0g6ev/h98OqAlA1myv+NtejvULQfBAb4sGgFGbsAkEHKQhBobNTtdNYDhtafYx7jcj2HY4Jet3eELsVPEGRh47UVmKyqX2riAHFc/UmCfDpu5CSZ4jWYmBXr3b0bceqi82WJfOCzjK6jTqB2zfybg/XDFXvYb2vUqlaVLZ8HI1Jflj+v/A96d/p2hWdZqQBOUrDIPsOP990oshRce68Kale5T3rYef3dE6+iohpccEuloH9cxdNcbcLvpZ9aPYyXgJcCl/0doxoIaebX9ovGWLlBNquF1NA25AN6CKyAFXkxcwz8X7Gl48fnzSsoIKF8qxOa+1AK89pyNBfA6eqeI6wpTAqYB7+ffLYaFK9ZDu1aN4M9Nu6HTQ02heFAxWL56Czz8QGOY/PZdx5DCFIDTfS96EBI6rHa4GbK1Q61T8JGpBUDC4YYNvDBxVlMGb3gs7M7Aq6aVNRp4+XTHJO6EoMbwbOJ71uQSUuDFf2NJ/y9dKIEwv1z80TSCDh1dohZPWtQARpq5imxWqR0cT7WcfQwWtUIdr5lCT/C0rBwWb3io3/fQrdhGuPb0FV27j5cjaqBrtXichV0yCnhzVw+A8leXWZNRoFlAVnQbdsyPUSEIpDNm1wav0PsgoPfvbPxS4CVnMT2T42Vlb60i1nRiZhikRcbrfO+cL2A2wveNGn78YMYYvGjHSWuNZh1o60s2u3gNmUKo7Rc9c+PrIvAu+ec0DD9a1/pnZz4QHLnW0bEX1nWOanilwIvjp2dE8X0W+3P66MT7e1jyMAa8t7v+ZZ2qFuC9uG4iOz2L6x0PgcUjbMQkgLewdo3o1xkJmAa8D/UeBa8P7QtdO7QA/O+Zn42GKveVhU+/+RlQ+/vm8KecGbf7XDujEiQ8ftTh8eALCDVOqLkJX9/H5mHlcKMGX+juwIvxeEvBVVVgcwXwHg/rAa8kPG8XeNHEAf+HBbW9GL6ML7z2hn+ZGQG8Y0qsgVGPtbV2RwD+RU4/dnSuBerwIy0zujWsivwAZq0/yj6GMCXwAN8Fslmd7G1HjBRx9Z/p0OrcWFYNgbdTt0EsTrAe4N3w/WDFvjM3j4NKcZ/DlKxXoW1MBrNXJu3n9FqHmTYZC+1z6LOZ/VsKvI44UmkBXoqTi32SWQmfQpiNLSUDZq4/Bv1aVwOljHPSkF4IjXT6QVEmtEKW3kcIAu/HK/fBlMSW1ksdgdZ7ScNr8d3YatdZlCKpUAxeFC4Cr5LZE34AoUaf/EDw/u6eOB4eCNgNiQPufohqAV57kXME8Oq9Q0R9d5CAacBbv/1gWDLjfRaLt/ugt+Hd1wZB4/qxsHnHARj/2RwoMqmFp3g5dNSJiy9NKmDWy8jZjUYgEJ52DLbmNIeovksK2J0q9YHRF+pubgd76sxQTQ4hZ9KAzmNBXncgEaIg2aeMrEmD1ni8WoE3IC8FsnttLKDVkM7x0oltUGt7Twj2umP9CTXgfxQbbNUakob35c51oVGlSKuJA14Q6O8DFUqFFDiqJwBC5yKvzGSrwxHakaJG9aetp6zgo6axk2p4eY0mjoGA973md+xqkvm5U5+TLndnCTUYlIcEQpf0WbqBVxo/d17p76BDlwGsTQLeL/84yIBvQt+milt554wn4TG/X2XvRQJehOmO4aehQ9osWeC9Nbc5BPj5QGD/bZY5rX6ERXwgO1qzgJePw402uhhxQZpsBOWEWnR7ET7kgFcaq1fpGSMNgab3mSEHvDu6XlOEc6X27yXgVZorvxZHE/NtsjWqAS+Fn8M9hEUpvbAW4KXQZGgqlhX9IJRtM8y6bAJ49d4hor47SMA04G3ZfThMeXcYtGhcGzAJRbVKZWH4s4/Bui3/wthJ38GeNd+7w/ydH4MTwCsXSB4HpNcG0vlJ2G/BZ3YluFmyOYTlJMC1W+mQ2HmtappXalEtvi7fsxzwUsIArIeOa3Iew5jxrP6dVawpe9EatAIvtqMlhihCGEYAqOdzGNBmEf9fCryUFpRiZ6Jz1bpDFwE1NlTkbFMJKrEOhRzC/ybwIVMItY8kgqnl2d2hincczIj41gawMag9zuPjBzIZ8Ko5wOEYcGwXq70Bgw/czUiGTnnF4lfB+ICJLEoAHrFrKacvXICMtSOgo+86Vp2PIEDAu3xXnDX7mlybtA5lwoIhu9ffBapk34iDaUt+gwO5dWFU+DLonDlHFnilTjooWzQzSG04DkJ3jXEouoEWDa81/FhUG5YBLr3a06w/3omQ9lGr2GgY0r6WrGh5e+3s8LosFitLK51yAdKrD4C0Kk8XCFtFDfHJKQiWtKwf1UHg/XHZYngteRBkBJaDwIyLsKFjgubnBLUjgBespil4CrE7aoRm4EXTFtTwSkOQ7fnxJeie84PNxyAC7+W5D0PxLPvx0Xk/inivquAz6CBbKgG8eu4OUdddJGAa8PYb9gHUr12VmS78uXE3vDN5BnR/pDVs2XkQalarAF99MNJdZODcOKZ4afZul3YkTdGqBi/ODdTxq8mZp1rufriQmGIa8PL523G0qBlG7QLB7O5SL0HZrlNkJ6IFrI0GXgbGK9tD2M1/AL3S63gfgkneUyC37ANWKJECL/4bkyRM6NMUEOQQfvGIUlqMAl7aYxj9AIvUhILGh2NQ0xbTGGk/jLvwKGsPtbx41L5/y1IYl/e6rjivUjto/mWtFXjxI6Dd2pgCIcV4mZLjjz3gPblwEHNquznoGrtUC6yq3VVa2uAdV/HjBu1tpfa7tE4U3k6pX2u2vqg27MMZIZiPAGNJTHChgHMihQNzNMEGAu+ypbPhpVvDAUPz4T3hCPBKEyeoydeTf1eCe/r4QG3/9prfqwIvnRSSLKTA+/uSL+H5O2/Z3JcIvEmzmkF+vv2PewxNVj7/jFXMZM8rgNeTd969O3bTgPfW7TuQl58H4SVCmHTnLloDm3bsh0oVYuCVwb2gRGhw0ZD6FC9dL3h+0tIHntLLqLAFxQNvUuJVON95q2bNjRYQpfnxdfFvDXf3AoRctLnE4o7AS0fGC4LfgSSvaNh0vTS0v7+21S6UQIWiNqBJAtlg8rCpFXjxiPLr1YesiS3UPpKkwCt1ktMLvKRFXFX8Vfgp/VH4bMBdm027kRIUNvHB7cug08kBcMM7GkrmXbFqp5hm+txPcK35N7D0SK5dDe/5fb9BiwN94VCD2RDR4EnZngh4XwxeBI/nz5fV8FIKbbI95WUbvSAa0qoOYA5ieooW4KU6qElGLTsWKXjqBV50XJMbqxJkoSYYk6cgHGeXrM+02Vq19DheBN6pM76GCd5jIL7CECh/YSbsbvEXlI1tpUdcihpoXY14SGWltSCnVYyhfDz8MfjhdBSMi1kFYy/1hUu+1eCbIQ8wzT2CMcXC5jWxUuBduulfGHm2jc0JhVbgxSyZtbI2W5/DdIImgNdDNpkYpo0ETAPee0bOTgCvFFaMPM5z1iaPXz8C3ppwEIonboO19Vax0FFaiquAVy0VJo5Vi4ZXawg1mjtp5zD6wSnfhnAyIdkmDS4et+P/0AlLWlDTi05fBMP875jEAV94fBYl/J3AB00IapQLh0a7ezE74LQe62WXQw/wqsEzdkDaJ5ovb47x4187Ycylh+B65CNw3rsahLX7UDG5Bg32wPqZ0Dl+JNwu2RJCb2y3Ai+N+0anP2HdrWqKcsJ2yBTGnsMdxUMmx7rblfpBetw6WH3/FhYRAsvRxS8z+14WzP/qFgg+8jUzA0Bw1CIbuQXQAs0sY1aFrixOKoELJZOgNvUCrxR6qB3pMwbXE50hpUku9CanQOD9bPoX8EnAW3CjzlgoeXgyrKk+H+q17KXlMWGt46ic+U7woxKLknOfrgGZWFnpeS91liRzKbRBT49qw0ySpHKyB7yUlIXfE1qBF5+r6YkWDS8qIATwmrghRNOmS8BU4M3Pz4frSbcgNS29wEQqVyhj+uRc0sEUL012j3JjoYQTZLNrJPBK43g6Kgv0Eq66MBT2R4+ACrXasExOetIL24vlKB2TMxpebEsNVs0AXnQw8025AO/tCGbmHhlZuUAOamoyt+eIRC80Cp9FWkcefCpEFIeqx9+FLgF/Q9JAS5QKacFTg4TzR+CljK/ZT/Y0vFpgwx7w0ouVxqDlSJtP3oHH+AhfCH50L9xstwj2+bZhx7pkBy2dY+bSRyH/9jkIfO6IosjJefAx35Uw1H8GpIbUgrhkbxvznLOr32cJOPhU0QQJWmQj1zl/nVWT22gcy6rIhxIjwCTwlNpBz1x/FLaduMJsv+3FI1YCZhob/4wh50iKWMGPXwmYlQTMAy+1p+c5Qe06Kmd+XLjWGPbPnpOj2r3pit/VgJcyOF7Ni4Io76ssrN7O0sNVgVf6sYTPmWZ/VoPbZbtAfscf2NS0Ai/JISM1CSovLQ+7yr0N5TqMEza8rtggog/DJWAa8O749yi8PXkGXL1+U3bQRzbONXwyhdLgFC/57E4aBiO1mTQSeKkt6cNPw7BsqvD2kaHVH2FHrnpeZM4CL2oUULOAxZ5JA/5eGMBLwiKgwn8rgZlU9gS8GNYLtbx8oZBFlBZWDnixfrNr0+xGRkCAOJFwE0ZnTmbNS4GXxoBjbnJ1qo2DnNxeIeBFbdN9DbtZTTew7tqD8fDM3ljrZVrSPfP7g4cdBPXA+FXs3iLHHTm5YhSFzLUvw7X8SCg98G6cUenY0RTk2MWbcP3oaqaFxIKaM94Bc/fGRdDj3LPWqAxYh8CPNPl6Q23JAS8bc1QbuNl+MdPUeWcnQ2LnP1kaZJQvJhKQOo7R/pLbK/xcCXiVIkrQhzA6xOGHGt7PeJLgk3rempmNn7fW54W7AS+etMjZxmudjyvq8WvBUmD/V/j3AK+5tQe8uI/wgxHts6Vrj/d4qdUdoWzJYMh8zOLUicCbMqM2pPiqZ8CkcdHzNfzmP1B11D5XiEj0ISRgqARMA94uT78JFctFw7BBPSA8LAS8vLxsBl42upShEym0xtwcePVqauSgjByCzAZe3izBKzAMGh9+Hg613cCc17CohTZzF+B9v09TzcepZJOL85s2uK3VBIBeenxaWKxDGl7872L+PtA7b55d4A1d9TDEXb1lBV4pNBoJvNhW7T8bQJSXxenrn/qLoGJDSwY1paIEvKTp5B13+LHjyQP213WTJYuYloQoeNT9y7JZqsDLj5XuH7mPUQSWYqd+hKROaxTnJwVeghKETNTy+t08qOmEiP+gsgdyaArjd/OQol+BXIYuGjwlrMB/631uZObkwjezpjLZFraGl+y13R14lbIo8nut2PzKEJ5riZ+7zrcXrCz+KtPw4ukgH28Xf6fr5D52pDGmEXjDvgtQVSLwGztsTiQcC+sB99/6CeD1/EJ75YqOhQQclYBpwNuw4/Pw5YQR0LZ5fUfH5hHX5X1VwiFnFrmHnZEaXl5D5kiYIRI+r+Et1nw0oL3h2rDXoU7PDzStjx4NLzZI0Ir/jc5qfPB8NY2hvdzv2J4ZJg0kBDpyxn/rfdGSLS8mQHi4bjkGveQ1TxBCmkUefLAvtWQPGEUCTS1Iw2sPeOt7H1KNxcvbBEtjwiJQBq1sx8KzYdFyEnB25avQ6uZ3bJ2lcGiJgVsRrjf8CAb9GWoTgxb35bSVW2BZsb4WGAgaDLX6WMw27JXZ875hkSSw/JPTHNK7/GK1ryYNL389hWmj9eBDhZHW1158Xrk5Uft5fqGQUbEbM+FQK/y68x9G0uvoQ0FpTPxzJ+D8rywjGxWcK5osYcEPDUpegP+m65ROjE5dSYbfV/7AgJcyvP0a8R407jZGbWo2vxth0uDpwMvLgI8xjnbuR1uuYB/TShFVyCZbKvTNs56HJ30WWEOTOQK8OJaw3MuWJD8CeHXta1HZPSRgGvA+M2oytGvVEAY+YfE6LqrlzFeNoFzJ4rpj5zoKvPiSRc3SzXaLmb2jUqEXn15NjbS9c3GHoOXmZnAq9n0IbjGaPWi1aNOoHYyRW/bObsh65qymLeCpwMtrXvUCLwqGnKoGt68FrWOjrYCBv2Es4uEpExlIS4G3o+96eN3/fzah8XjNI8oTj0Ln5/Rn8jcSeOVMDFCTpAd4WSiwzD/g5jPXAT/SUDt5tfcx9mGFjpdY8Pi/+9k3bcwxUDO+ee1PVm2tmrkLbT4ealEuMV2nWIGX9jq/UQkc5e5XusfsxS62B7xSqLR3g2g1mdEDvJiGFj8qsCB832y/xBolAqM1kMMeD7xKzxME3o0rp7M4zCgzNJXQ85yguWsNjWdPVgS89j4MND2MTK6kpOHl9wzeHw9mLrHeB+TvoVdO0ggkjgJv1dz9FqkI4DV5d4jmzZCAacB76PhZGPH2FzBl/DDw9vYGANsjkEZ1bdOpmjE5V7R56ouGzEteb7IIR4GXP+rlNTDSueKRFx6XOgu80igLeoFXTesqHbenA2/5iGCY0LeZ7q2HADdj/VG4v1Iki+HLZ0izB7wdwk/B6MxXITu8njW2Kmn3STtuD3gpdSmBttqLlNfwygHvkZnd4GFfS8QILRpefn8UsF30LwGQdUsWePEDI33/LAb7WBwF3uaDvrOakdBphlbg1XKPYZ3siLpMi0v3LsLlnVojIKvMA3Y/WvlxIMRRetlWNaJhSDv55BPUh1LECgorh05ywUdRu5vP7D7Jpjjy12bs3wyC/UuwVNNagTf+19et5jV6nxNGAS+Z6GB7KKeeTSprzgqp+6Z18gL+HRC+oQ+7h/E9wgPv30smQbOM36FSsRuQE1wR7tQewT4K8V7R82zf9stH8MTtD6wfxo4AL4UoE8Dr5MKLywtNAqYB74ate2HMxG8hPSNLdnJFxWnt4Cd1ITYmXDfwypkvKH3x8wKkFxo76u2+wyaoPF+PnB30PBTlFsodgNd/biV2jKZm0oDa5Jg7eyD7mTjZPWemSYPWsFH27nTKekbe5bSGBLxoG7xg60lrNAhs69HSZ2Fk6susWT5+LGrueOBd4j2QRZCQg1SEKXJm0wO8cuHUSJNkBPDmN3wFcs5tgDz/sAIa3ncX7YIHb33HAAuLmn03yZ3X8L6fOQ6ef/Ft65LYA16sJJUNrY89TS1/DcW6RbjU+4FMa7Ryzzk2XiXtJT0f7DnX0ZgoyQXuFf45QW3we0otMQVqeN0NeHH89lIxF9pb97+O+ec9asRpX/DAS+s+wO8n5lBKkRuwCT0OybTv6cTCEeAl8zQBvIW9c0T/jkrANODt0Oc1aFCnGrz83OMQVqJ4Aae10OJBjo7Zra778aN+dp2GlAbrKPCSVgnblbPTC9v6Ausy6PSP7P+NBl6vH+tDQk4klHnGkgpWrejV8JJjBDr0kA0v2bGpAa+avbAa8GqJ5as0Xy2pX9VkhVEOFm49ZYUZKfBiNjPMztYqtgzL0IZlWK3rLLIADyc89JBJA4YzQs91NeC1eHtfgMTOa1jUAGlB7XHqtVPQ/+YUWVtlHnhXl/8K6rcfYnfachpetJONWlIToNMcSLl8ir3oXw7bytpBhx2Mazx6/nb4MmYG1Eheyf6utjdoEOu3bYUBpzqyf2KkiVEvvmIdnxzw8ppSHl75NL48wGKoL7+kQyyuLhb+GrrnjQBepUggWoGXwAnNMfL9LTGiyURKDnjVxo770fuf8TYa3l+8BgBq0PUUtQ8utbZ4DS/WlUYlUbvelb8rAS+a85A5iRR4+fHZsx2XzuPk7pXw4JGn4HyLH8Ev9nHIunEG7vu1Luyu+imUbT1c07Tp+RrvVQXKv3Za0zWikpCAO0nANOBFp7Wpk16BVk3quNN8DR+Lq4EXXwj0spI+8CiuJk4yO7wuO/oyGnhhcVsGHKUG79IkyxJzI+F4iR5Q5rGZmuoTAGX5hEJItiXPu6uAV0+SDOlkCHidecGi09f4xbuscXzJ4540vNQn9oGaPjSf6FgmCfrE9WY/STW8ZEuJYFerxeMMljFLk7TwGl5yxFJKMYswlJSSDs8mvicLvMf/+h+0u/QO60KLIxkPvKRFtDpP9fkbUk78VQB4CWoWlZ0E2Tl5MMFnCrzSuZ5qkgscEx8rWAq8uK8PLhwOj0TEQbFyjZlDF68pjVpag0FhetWBUOz0fPZRSfci1ZN6yvMAp+b4Ze8GoTVC05f4pDtAJijSa7SET8Mx4fE5mjxJ4/1ie3ziA3LSUwPeX/echeJ7J1qBt9S80rApqxnUHvKbpvueKqkBL+772Jgw2UQuC7achP3nEtnzCROzYMprZ+5HXQN3oDI9r9G8JHSXxbmPTmXouW0PePWEyJOm375xfA3U2fG45g9FHBsBLz6PRFgyBxZcXFLoEjANeEe8/SXUq1UZXnjafliiQpeAkwNwFHilqSFxGFpMGvCFQIHxpdmQKCUlPiwzKnaHyF+bOw280qMwvcCrFipMKn4CIPp70IDtVuClPO5KS+ashtcZ4JWm/HV0W700cxO0ji0D/dtUt9p8ygFvo8qlITElHS5cT4FOh7tBZe+zVjgjzTAPvLwmUzo2vk/8Dfdm4IVVVvtNvj4C76UbqfCh7+eytsqoQfU6MoeBjxanpZR5LcDPxxsC+2+z7n9rIgQV4P09tB9kVNOX7hfXqesmi+Z6YMZsmDzUNhUxAUav2j4sTi3vGEpHzZQMBNtAcwaMF0xgSGYL9DEqBTjUDKNtrJ5CGm06nuc/UKTtUCIULQ6tBFhybSDgYqphmode4NX7nMAx+N04yJ5Z9o7qce6tYqOZnbu0oNYfZYUFHTzRERQzHMrV1SN/M+vy5iU88F6s9gacrPQaS7iC696t9DmrQyHW03tKgHKpt6wkJHpFQ1zZ5yCoXGPdwHtx3URoevFD5kQrgNfMXSHaNksCpgHvgl/+gi9mLINJY4eAv59vgfE/2LKBWXNyabtjPpzMPMX1HC/hAOXC72gFXgQC9MTP8wtjaU/p5SY1kzAixI87Aa+aRqMwgZe0jlqzrCltUt6Ol7R1UuDlj7NRY3l938/MOx4/gLyybzMNHRbSHEk1mdK+MQIAFsrgZS88Hu6p+MQU+DJkqmzGL9RSo53x18mtVYGXAu+TwxntfwrFlv/0PkhOOAnhG/oCn8qYZP1nUFfdH3S82cIjaasKaKntwaQc8NJHBd3/0sgNahpLLQ8rPlYyAty7i3ZCqZBiMLJLPS2XF6hz1w+gAlztfVy2DT4Lm//VreCbikkNziuCllTD6wjwann+kfOeNNscOV/SZCiiCb+vHRKWyRcpAS+ag3x3xxIiDu93ChlI93VmdFuWqU9PyZhdm30Y34FgONt8vm7gJYXAgeCuUH+oPs29nnGKukICZknANOCt285iV6hUDm2YY9acXNquK4GX97D2vXGAaWB4kwWp1tgM4L294ikod3Md3H72uqqcKR2lVg96bFBOw3t5+RCoeGu9amizhD0LrMkqIio3L/iiDw2A1PRsyMzOkx27MxpeAr3+ratrTjohNwgyjcAXNoHn4by6sLrafKb1RfhB6KHCh+fiHVrwdwLHj0PmwIBelpenXJECrz3wQPvC3dASfgx6RzXFrZqGl8BLCrx03J7/Wj7cPLoWSv6X3W/60UgGqCijc/t+Yx+a9kKCyc2VB160C5aCkz3gpfXgUw+jjS/KRJoamO5LZ4GXt0mlDx1ar1rlSioe79u7OUnualpCKZgMTbcAACAASURBVIxhm3zkBr6PhdtOQrkTn0Jfb0usV79lD8Hl5DsAfTZrjpKgBrwEtXLplXk5RYQEwmcDWrIQfljspWJWfYiZXEEqYzLnmZL1KqzNac96lwKvXuUKTQFPU2LzDrB/Hm7+i27gxec5mkIUL98EatQq2vH1TV520XwhScA04C2k+bi8W1cCL/9CQA1M2JYXbDQuUsA1AngPrJ8JneNHWm391LSo/AI4ApBywKt1UdX6U3NaU7te6zicqccDL2lAMUHCvnozbdL4Uh/4ol/zmyXgP2ZeopBS+DsBsBzY8WOUggEdLcvBJDnB3W7wjux4qF0tYanINlsKvNSGPeAlrbbel78a8ErNO3g58cf6lB4Yoy3wUCsNVeYs8PKZ+Ah4+ZjPCH9ovxyflArlI4prsmOmsHX4QYQnREqFN5/i68idtExeuRe6ZsyGjumzGfBSwhM+dbPafaEGvAS1mGFwusQWnRw+A/19WJhIhFw8LUG5SNN2q43Dlb9LgZfMefBU5kBuXSvw1irlxT6ssKiddCmNP/HHjlAvx+L86Qjw8u3GRBRzpZhEX0IChkjANOAdOPJDGPzUo/BAi6L9Jdhr4mKW7Umvc5jci5DX4KbWGlFggaUvBB5oCVL4l5gRwCsF3GtrxkCDK1Ph9FO3VV+ujgDkXU/gqpDipz3POwpLrT9PAF7++JqOMTGOrj3A3LL2J+iboBwNQS/woiyVQE0P8K7xehzqDbJEC5ErZGvMnwDQ37A+Au+1K1cg6qcysC3qTZhwto1Vwxu6f5JD0VHUgNeeVpA+QDCObnbJ+tbQYvx9RuM3QsOLGs2ftp2EbcctqWUJeAnu8G8IvG1qxsCs9Ufh4XrlASN5qBU+bbVcFkY8rSgVEggV17RiZgze2bdtmlQC3t7pX0HbjMVW4E1KvArnO2+VdTCTG6NW4MVr+eQumKlw67HLzKZ9ZGfLMT9mI+M/HtVkUli/Sz8q5ICXwv/R3nIUeCmrIc7139LD4P5r0x2GZwG8hbVjRL/OSMA04G3ZbThMGPMcdGhzvzPjc/trH/ngd3DEltAeUCjBs/SFwNtakmaJD6OEL2KE6Os9djgsRynwZm4eB5XiPocNHRPsvsiS4nZAxtn10CR+kkOewDhgPaYQRRF46wZeYE48asC7dfMf1kgNcgutBrxyYIARCdC0QJrchIA3p/l70LFeeeV9pSGahz3gRWewgCd+gys30qHM3CDYHz0C3ozrxEK2/bT1JHSJHwn1w1Pgeo+duvY2wlzpX5sxW0Y5udgDXj4ZCB/Fgs8QZyTw8tpdnCSBD398j2CKUQu2nbBAsVxsZKmApJEk+N+pbXSUeurSEGsmNr6OEvC+kvIylIuwZJ3U+pzg26UPCj59s9zYpMBLzmpSUwdPAV7eFEkOeAnunQVeVAh4H50D9e+sgmMBbaFm5mYBvLqeHqKyp0vANOAd88G3EFkqDEa/ZOsF7ekCk47flcBLLwQKJUQvLrIjlIaSsud8pHUdHAVev7mVIRIsL+FDbTeAnE2t3Bj44OauBl41G2CtMnOmHtkpUjglDNU1YnMpaN6oqaIJAR9qS9r39fzSMLfqWmb/q1TkwEDudIAcmb7Neh6qd33fvuZOB/BuKDsJajz8qnV42E++XxhERUdbgfds5ddg6OF2TMuJ4dVeSRkB5R3IcIidUPrjT6vsLyAXe8BLpyjYBv9RKr0P8Xeyj9Vi0oAe9Ct2x0HrGjE2MqV1QZtUTDfds2llJiOK2kACQ+jFPJZJKRmycZb1AC8fXm9w0kuagBedK/fEXYPyuaetwJu37V0od+ozWPXAeWhUKVLTLaH2vOJBn9fwUiphaQgyaVxrTYNwcSX8sPTKTLZq0ck+fFjGV3Amz7LeNFcMU4hF70cePyU6BTvt2wCq5uwXwOvi9RbdFa4ETAPepas2wUdfL4D3Xn8GQmSSTBSVKA0IvAvCX4fipasV0IYpLS2Bg1z4HXsvSOkLQRq4XKoZVnuBaNl6jgIvfzSt5wjOGeAlJ7ld5d6Gch3GFZiemkmDHvtkLbJztI7UccqeIxX2IQe8cXmVmBbzYG4d2Fr3Z7v2tnLAKxfPlfYb2hd26jbIMODdWHshVG/So4C4ypQsxoC39NIacC20GTxzepBhwIudycWSVnN0kmpwsR0KB8jHU9UDvHgkjyYJShpKOSctDLmFIIzJRLBQbGYtmcUo3rGc/fPM9UeZthjj2L4XMJGFXKOCphxo3iBNW0xOcJeCGrPoEajhpWfP9FqH7e49ftHVnlc88GLWQTRbwELAK42QIo1u4ej9aOZ1fJIPvh+MIIL2yJgdkYd7fHfIJYTROkYBvFolJeoVRQmYBrz3SpSGN+b9A0NvDNOlbbJnq2bP7lYJeBGc0YFNCtBqLxB7Gxq1SPjiizryETyeP9+qCbi153uoeXgUbG+7E+6rbHGqkCuFAbw4DuxXSTPsKcArDTulBrxyGcLQDAJj4ToKvLzWEj3zMduZT8pZFhkEgffpfi/a9b7PXPoo5N06D8UGH5bdH3ySFKUsaQS8JVc/AulZOdD30jtW4P3kVifIrTHIrtOV0t58+ZvfIMrrKowbWtDumeB/bM9Gsk5gtK/5GNjScGrYrx7gpT6lzlhq6ap5kwc0ZcCjfS2JFlD2QSfnQ1r1AQXgCYEfIRod4P5XfS1bdypKCW8I2q4UbwLhwYEMeOk0anHlpdC6bRdN70615xXvrEf2zJSsBQEdNeAEwdihJwPvkGJ/Q7EAP0jPzJaNd61JoDKVBPA6KjlxXVGQgGnAWxSEo2UO7gC8ZPcl1diovUDszY9eFgN8F9g4B9HLfeV9c6DJg/KhrkjTSu3r0fCSWQFeq9ekoagAL69lpONrpcxa9GJvtzaGifuvnPbwV24HqOwVB0P9Z2gCXgInXmsm/SjjP2DU4vriONScG6l91EQntfoeysa2KrAdeeDNzs2DXhfeYhnGUBvqiN08dUAaQV5zRr/xUIXw6OXtzX5CoEKYoux30nsN5cPbYuJ/YwQEtMFWC53G98mPCbWteB8ifKPZgrTQPdqhbjlmmmEvwoTaswxNaTCiweQVe62axXn3b4aoY59YL6UkG9K5Ry2tyZzbsrxDID+yAQNee88JpZMUteeVHPDag1qjYmOryc6Z33kNLx9lBe3LMcQhFh7inekLryXgvehdBUrmXoFkDeEl5foUTmvOroS4vjAkIIDXSakj8D5x6wNoAttlM1PJNe+ohlcaZ5eiOii9iOjoUskJxCzgpYcqto9JEzBbmtbCX7unzgyIadxf66WsXlHQ8GKK1G0nLrPQS1q0VFjnvjWtmAkDanbn5/SH+j6HWKgy1PAeb/0rswFVKnJ98GY3GeW7WUMiYRtagFfNaUmLeQQBb/j6PixNdpdr06xH964AXjQl4E0GUINIgFIQ+mpYQ8JhpBTUcN5st5hlx1ILnaYEvGrmFfgxNGP9URalAddXrb6W+x3rIECvO3QRPq68hkVkobI7chg0uT69wHz4jyHSbNP6LoqZCW069rPpmoBXqtmXyz7JX8jLiUw31O4PtdMRXQ8XEypTZj5s2ppSGwAmlN9jSoY4/vl6xrcBFHta+7OZn74AXhM2g2jSdAmYBrx5efmwcMU62HvoFKSlZxaYyDeT7zqpmD5LEzt4b9EeaHRlKnTP+UGzA4A9TQZ6fGMsVbnICnLmDviyIc2AFGzVwvxoeQEO9fseHvP71To3e5obevk8V/WKNUyWM8CrdNRtb9xhcyLhWFgPKPPYzALVPMWkgbepVXuh4yRRO5f2Y0uo53OYwSjG74zyvgYvVjwBC86Xhd7de9u1t1Xqg+zJs8o8YJPWdGSx32BsX0tQfKWiBrwZceuh8uZuMK/0d9CgzROyGkwC3uL7JrKjdbRrRK3rjr27YF7gc3ZT0NobGx3bq2l4eeBFDS9mNqMYtlKI5TV1/ImLmcArnaMzwCuN94spbUfFxrEY3AixByu/C39s3MQy+slpt2ksfDILpVjMmEjm/ls/FYjeohZGUap9xw8QckxTik7h7sBL7wKUH8oV9/qJhJuqdvfOvNLoA0UArzNSFNd6ogRMA97Pv1sMC1esh3atGsGfm3ZDp4eaQvGgYrB89RZ4+IHGMPntFwpNXiv/3AZfz/4FEm/cgro1KsOksYOhQtko2fH8uXEXzFjwO5y/eAUCA/xZmLWxL/eHAH8/Vn/+ppPgtf09XTFB7QGvWlpX9mDs/Kd1rPZsZZ0B3sN7N0D0/rfY0fg1/+oQ2H8b65PanF/iE2j/mG2sYHxpblq7EBoXj4e+2dNY/X9L9JOFT6XF5zUQjgAvJa6Q0yp7CvCSExOaGKRl5QDCB59OWE52FHmAD1hPjkyq16ZkMPvP2JhwGNuzobV5PL7PLV4R7tR+2QZ41cKcYQN3/vkUqp0Yr2jrrRYzGduQA95WsdGQevovh9J508TsAS/JXipjchxTuj+NAl5+rfQCLJ+WWu/DlYdJDP32+vxt0K/CJfbhihC7rf4ia4ITZ4GXEo5I729HgFct9Ji7Ay+dwhHw5ibHwfzNJyGi+VD7Yf/0LjBXXwCvE8ITl3q0BEwD3od6j4LXh/aFrh1aAP73zM9GQ5X7ysKn3/wMqP19c/hThSK402cvQd+h78MXE0ZAvZpVYPoPK2D/4dOw6LvxsuNBaC8ZFgINaleD5Nup8Mb706Hjg03g5ecetwLvte3fwuv+/wOtpgOeALy7Ny6CHucs6aETghqzFKFUlDQ30mgBemx3qW0BvLbONigXs4EX+0BY2ncu0cYjnAAks2I3CN01xrr+WoBXDWjVNMA88AYfncr6R83y+RRfiM3d7xTw4odZWlaurJkHHwmAfyAQ8KKph2/KBciMbmPzvCDgzQ6vCxkVujGNNIKhXg0vD7zDZm6CVrFl7IaU4wehBn/2Hrh0LZ++uFrOPhiZ+rIVeHEfypmS8B/dfKSY8LmRcNC/LZTvt8yma0eBF/fohaRUCPL3YdEgUOOuNme9Hw2ufinxsZ1xvxzIq6vpfndmnAJ4nZGeuNaTJWAa8NZvPxiWzHgfqlcuB90HvQ3vvjYIGtePhc07DsD4z+bA30u/KBS5TZuzHI6fvgBfT3qF9Z+SmgYtuw+HP378GMrHlFYdE2qGT5yOh6kfWq5HDe+hbb/oegHbs1WzFxQeU0tmVOgKya1n2MAn/gPNGq72Pm4zfr0aXvR4xoIe8bdP/mkF3tRSreB2179UgZeHZAbKz6SpylNa4fbVk1BjdQP253tVw8s7qqGzkhbgTZnXAmLzDgA6E4ZUf4Rdg9pQjLTBO6MpLQgdDbeqEQ1D2tVi1fiMXLy3vpZQU0YCr/+VLQwcvyr+Nfx+rRK09NnBjtYpHrXuTWbnAinwlo8I/s9bPgcm9G2qeCWZOqA2NL36AGvUFIyeoseGlwdevdpJNfizJycpGOK/I/KvwHu3ejPgXVh2JizceqoA8FJsYgqDxwNvxoJWgM6GIQP/cQp4KZNaPos2fLdQ+mDMsDahryVGrbTgPG6kZLB7ICjA18itYkhbPPDi81KLCZOzHQvgdVaC4npPlYBpwIsQOeXdYdCicW0Y9e5UqFapLAx/9jFYt+VfGDvpO9iz5vtCkdkbE76B6NIl4Y2hdyMMPNhrFHww5jlo08ySltJeeXHMFKhV/T54ZUgvVm3tgYuw7vcfdQGvvaM7egDKZVuTi9FLDy/edo7Gr5aqmJ8nPWjxBR+fdAf633cJBl57kVWRto19/uo7CBo//Y2NqJYuncU0QlQcAV68luakJ2EF9emMSYO9a9X2hdG/E+zUKFdSE/AeWDgcOmfOsZoQUCQCHJecrap0vHJB/em4lY8+gNdpAd78+I1Qdn0XWFN9PtRrablX+KJHwysFXmnkEKNlz8sCzUKwrNxzzq4c6eMAZZXacBwDdKXoKdLxkokF/t1dgJccJ1f4dgZ0Vpuf3Z/Zlko1vPS8QudItCG3OeVSSD5C97f0gxajPWRGt7b5oEeZUGxg1LLzBYFXTYMr1VwbvVeMaI/PoObMR4vWsVB/+8P7Q+ked5UnWq9nz+iIYnqqi7pCAm4hAdOAt9+wD6B+7arMdOHPjbvhnckzoPsjrWHLzoNQs1oF+OqDkYYKIDMrG9LSM2Tb9PHxgdD/kl+MePtLqFG1Aox47jFr3c79x8Co53vDIw8qa3CwMtoffzlzGfwy6wNm5oDlwPkkmPLjr8yJJqv9LMiuMVB1XoHLLQ4/GY+tl61b7Of7IS+kImQ++ovN78HT/CCryf9BdtN3rX/HtnwSNkNuTFvZ9uSukev0cPwNePfn3dafXgxexOLvYsm5r5vNWPJnVIRDaRWg3itbbJr6a84b0DPtS/Y3dIiIfvFue6pC4SrgmLHcGZ6t5zJW99RPz0CDmwtkr8VA7tk5eZCbZ6spok6ufNeE/aej49Y9WDsXvPjdJqhTviQ0qx4Fk5fvgymDWkKl0pY9J1fG/bwLjsbfhAlPNmHX4fXXb1vuh19GP6JpaLtOX2N9URs+lzZB4IoO7FrcX7jPsPzc/DR0u7+i3Ta9U85DsXlVYUvFD6FR19EF6t786y0od/Iz2Nn9KhuvXEGNXHpmDnj/N45poVPh1yv3AQGvI/tDkyAA4PFPLXbyfVpWYf+/ePsZu3IkWaGc8P5EueG96r/7A8jouQ5yyz5gd+2u30pn64XJEx6qYwkxh2PA/p9sVVXTsH/edlp1nEoNYV+PNqoAg9vXtKzxf2399sBOGLc1iDlCYkHg5Z9BNG+0HR836jXw+S+MG9Y9t/g5qHjtF/AaYUmOgSUtJREi55Vh/703ajjE9r570qf0rKK9jdfUKh8OlSJD4Pe9F9h64G9YJj4p/+y+k5kNA77aAM+2q6G6ZzUJ2YRK/PPOmTXUOjTqb3/0cKjWy7GT1uBA99OWa52/qHfvSsA04L11+w7k5edBeAnLS3ruojWwacd+qFQhBl4Z3AtKhAYbKvXf1m6HaXNXyLZZ5b4YmPbhKPYbanjLRpeCV194wloXNbwTRj8LbZvXVxwTQvsH/5sHM6eMZsBMBYF3zLwd7EWQcf84yGxyF0aVGgteaYGIOz3WyVZR+r3Et/4F+sC6vpc3Q1bsAEh/aFaB9uSuket095lr8OnK/dafCCrwD9J5pc1vBddvp0PF4Xttmlr/3WArJGPqysghlpeR3oJjxnJraJbeS+H0L6Pg/mvTZa/Fh3Rmdi7k5MoD7/WZlpemo+PWPVg7F4xfbPlYqF0uHJbuiIPFr3W02zzWP3bxJozv04RdQ//Gi9SupYaPXLwJ7y/eDaN7NIAmVUqDd+p5CPmxGvs5u2I38Dv/G1zNLw1nuh1ifagVXMd/Sw+Dqo8XfKkmL+gEgbcPQ+5zZyE4wPKBIy0lgv3g1h3LRw+2tSNiKIyP7woUOcSR/aE2ZvodnfjOX0+F3s0rQ3CgP/yw8ThMG9IWIkMLxsPFa7yykiF0dml2r+SWfRCCf+3A/jvw34kwK+IbKFW7MzxYywKy0kJrjeuH/fVpWRVoLWg9tYx78fbTmvYK39acv49BpagSMH3NYWvf+DsCJc55zvCH4Nlpf1svkT7nfBM2s7ki8E5+ayykpN39SJW7Fy8e+B1q/2NRNkj3htKzit/LNcuF29wTz0zdAA/ULgPPPmQBdbnS5/O1NnPTIktX1im+pDH4JB1kz6xPVuyD6ynpgFEnzCr4nMO0wgi8lXr+z6Fu8N4URUjA0yRgGvC6qyCmzl4OJ+Li4euJFg0z2vC26DYcfp8/GSqWk4/UsPjXv+Hb+b/Ctx+/zmyS+cIDr5wZgpwc7KUPxvqY1hXjeGJoMkojiRmu0PZXGsSenGWU+lbri8ZH9rd0NLk8uzsLRyZNI8rqKxxV7pzxJDwasAH881LhqH9bCOu3xqFtwB/x6W3AXnpgtSgN7mTSQEe1NWPCVI/TUUZ0LE7hmfBIGmOpYtFi0kByRlMItBuOLRvGbHlpLdAOtcRfveFoVkVI7LzWflrh/xqzFxNZaQ/x601RGvDzhNoad+FR+DRgLIsowUcr0btP1OqTPNHEAIsWO2pqk+xaj0YOhFrX51lDxcmtA5pKbD2WACX/SxNMmdIcseV05Bpcb8zwlp6Vy7TLjSpFsmnwbeHcqcwIegVKl69pTaPOx1P+8M034Vry3VM2uXuRd0qVJpZRelbxJh9o1sDfE1rsnLXUUdsPZv5Oz3A0AVMz0TBiHFanwajhENX5U4eaFCYNDolNXFTIEjAVeA+fOAtrN+6GK9duwCf/NxRyc/Ng9s9/QLOGNaFeLctRoasLOqyhuQU6rdWpUQm+nfcr7Nx7lJkpYEGzhW27D8Nn777E/j197gpYte4fBshloiKswy0WGABeXl6QmpENvT5dC78VfwqyYgdqSnWqBqF8qBoMYo8Zm3jw4D3EjQZemiA5ocjZ4QYsfwiSb1yH8523WsGHYsEiLF2t/RYUK1UFQqMsmYL0FoylG+R1xyGnt6ICvGi3eCIhmTmeqdmPonylobbU0tIqrckb87dDUooFWjCrWdfr/wd5fmGwrNhIqHFqPCTcSIMqT861m1aY2jYLeL8IehsqR5VwW+BFJ6s+/1RmST/42MhS4CXnRJQXxfwtDOCl9eLthwl4KbMd1ZF+bJDtMsZI/v3tLjbAK+e46Czwonx4u3aEcbV0ypiqG9P0os2vOxZKPoHPWozMgR8dQ9pbHEfNKAJ4zZCqaNMTJGAa8GL82jcnfQ9tm9eD9Vv2wpGNc5k8Jn05Hy5fvWGNclAYQlr2+2YGskk3b0GdGpWZw1qlCha7MrTRXbFmizWKxOOD/w9OnIkvMMxNv3wJpUqWYH9/5IPfYU6p9yAipJiml7Aa8GKbqDnBIOR+Nw8yLSsBr1TjSt7hShpetdiWNLED62eyIPN8OR/QCPye2lpg7nIOR/iCLLW6I1QoFQJpPeRtk7WuNT2QHXF6u7R1GjQ5PRqOd95fALg9ScNLwEqxdNW0tFJnFwIWCqelVfYIzhcSUyAyJJA5L1K/ep3gsD//uZUgKbAGhDy5umD3CqcEfEVew4sxgZNzguDpq/8H00LGQflSIZruNa3zltYjRyny7kcTB3vpnfnrUbs++kwDOAb1oCYchI21F8JHu0MKxFLmneOkwCuX7lltLno1vPiROnzW3XCDGH+XIhnQb5hwA8PVUeGBN/DCb1Bi1xiWVrhnzmpY+sYjqsB7cd1EaHrxQ9Ycr+GlzH63m34CqbVs43vzGl4eeLXGmXaF1lRtbez9js96/8ubIKXhOHCFNloArzOrJa71ZAmYBrw9nn0HBj/VBbp3bAW1H3zGCrw79h6FNyd+BwiMRaXoAV4+ZWtalaftigBfKCU39IW0qk9D0OkfAWN8Xu+x0+Ya0rAohWjSCrykGeUbl8bfpd944K0QURzik1IBnUO6bqoIZyu/BgFtJzq1tM6YFtgLh+VJwEthwijNqxrworYQ/1fjP092DDE3fvEupjl0RLNFwIVavyB/X9YWFbWxUD176+i/sBGcuhMGFYdsUNwrPPDiPsZweX0vvcPs5enkw6mNZudi6QeEHhB5d9EumJn+IItuEnB1CwuvN3ZTgCrw0nBwvRC4EWA/02HLqRd4pSHYpOuKc+azzeH4eODFMIkYCQbLyPCt8PmglrYmDSe2QZN/HoZjdb6AEo0tiYauL+kDZe/shlJw1QZ47YVQ5D+2iiLwWu+X/z5A1DTWzu55At7jrVZAaDX7vgFKfQmTBmdXQVxfGBIwDXgbdnyexbYtU7qkDfCimcPTwyfC/nUFHawKQwBG9InA+23pKVDOL0k2JTDfh57YuHyMRmxDLp4nAjTWU4JnM4DXZ+d7EHXsE1jcIo4Fgf969SFATdDka83hRp2xkNFY3XHPntxRS4ulbOvhupenqAAvr6E9mZCsyw6XhEbA4gjwkoYPX76lSgTBrPVHDQVePLH4Obc/tB2sHBZJCryYsKbn+bGyyQ90bxSVC6QfEFqBl+SGUE5lR9dr7IOBt5HF3/h0vvhhg0kVsGgJtyU3fNoz0n6UpsoDb0RIYAG4fmnmJqumn9rggZdPOCEHvNh+u7UxNh/BuT/Ug0zv4lA1d79NFkY9wIsphXE9MOpKRlYuc/BCUyql4opQX87uP0r8gu2oZUZ0tq/knzpBrazNIIDXWUmK6z1NAqYBb4c+r8FHb78ATRrUsAFejNawZNVG5iRWVMrLs7ZBl/RZLA6qmuZJD/CSNpjkpBbAXk6eWoGX4rjybZwt/hAE9P69QLPoUIcB9RdXXgqJgTWYjWmFgESY4fMMJLf5XhG+XbHezgBvibmRcLxED12pkM2a0924yBYNulatKj8efIkiyPRv45gttdRxDttGyPhmiHKILb5/expepWx9/PU88JIjJ5n3aHUQNWp90A6UsnvZa5PWbVHZSRB205JwAU1z5ICZT+eLHxbHEizhuxwFXrxWK5hjXalJhfTDiDcloDm/5z8RGkTcgZuP7bSaWd3wjob3SiwtoOHFa6R23PTv8Jv/QEBeCiQ2/gRKVGwKJVKPKmalk2p4CXhpTGr3hicALy9rtfk4u6fpNE8Ar7OSFNd7mgRMA150BkM73snvvAhoB7t+yeewecdB+HT6QnhpUA947skuniYrxfEi8NbL3gLPeWEiBi+42vuYYl09wEsvDGeAF218fVIvqGqe5YBX6kVN46A5YFavdcnVADWQ9X0O6Uq+YdbiOwO8dp2szBqwnXbpRe+oWYKzQybgte6/lAwG0Fo1xicXDoJmWavh5qBrNkOhhCirA56F+k9ZtPlyhQde3nQn8tfmBaKVODtXteu12oESXK2oOBmKXbfYvzsCvGgzXD6iOEufq6foAV4ym8H20TlS6iglBV782Hkibx487b+QJZiIWmIJBXbapwF8FTpVFngzZtcGr9D72Icz3Zt76syAsGPTmJYXksWGXwAAIABJREFUC/67UrkKmoCX7KhR+4zaXUyUo5RljeRGa8LbKOuRqSvqorMaRsrQ80Hp6LgIeBMHXYEsr1CHmhEmDQ6JTVxUyBIwDXjx+PGzb36GBb+sg5zcXDZNb28veLJHOxg7oj/4+HgX8tSN6x6BF8uHFf8ASsGqZFNLL26tDllYHwtqe9GpgcKUaR291v7if+rF4IQvSva4BLzfh0+H3bdjICztOER7XYXX/f9nSrpXrXOlekrgqmbDK4DXVtJoR4rH7hgnNzYmTLfnOH1E8XudP7VwBHjxlAOzmDly2qF3H/H1tQIv1kvPzIZpoe8y+10sOH85DbFUw4tmFBiZA4/o9YArP0491/GRPNrUjIHWsdE2IuJD2+EP+OHV7No0Brx4klX8mOVjBaNRbK+/CIZ0qGljw4u/Jc5qyswN0npsgKoLLXCFDqW+a5+xAi9+WFep9yDzV5B7bvIaXjruJxjX8jGo17bZmX3i6LWu/LhFk7HwcwsgZugeSE7VH+sc5yiA19GVFtcVpgRMA16a1O3UNDgVdxHy8vKgaqWy1kQUhTlpo/seO38ncxia+Fgsi6yALwI8csWXe+CFVZDa4B2r57FWADVqjFr7I0cGLcBLcUZnBH8ECTfuwPiAidYQTFpB3qj5ybVTVIBXz0vdDHnyGkBHHGnkQsSRIyaOF/fPo0+8ojh0XsNLZjRoMoPmNK4GXoI/e8fNk1fsY+l3UVv6ZtarNsArB8y8BvWp1tUgLSPbGoJOD7hKgbdVjWjo2aSyaug4taN+HsilwMunm9YCvBiqsOHuXpCWHwzJz14H/nmDMbvL12zNlAXS5weFbsOTBQyXJwVetH1WM9lxd+DlI2J0bFBBU4xrZ+/3oAAf8PfzEcDrrCDF9R4lAdOBFxM7JFxNgvz8fKhcoQz4+xe9DC3TVx+xiZUavSAa0qoOYM5kGFYMXw4UJF8rgBq1i7T2pwd42Rf+3CD4MespNkym8ckPhuIOxs41aq7UjgBeYyTK23g64kgjB7y0H3GEXxX/Gnr3HqwJeOlUIa1qf8AkLHikrve0wxmpqMHhXZvrYBjZpT5UPvp/Vg0oQhzaUyO8TejblGlxi/n7woKtJ1kIOAzl1791ddgbd409R/DoHcOFIQR3rFde17DpqB+TSUxXsbVWiwTBf/DgIPCjJ3T/JHa/5/mXgOzwegzqMUnN1YYfyWp4ScuPodkePPIUHCk3EsI7TLYBXjSJiKrTRRZ4pc6b5KBGTl5aPsQoYgnOwWz7WF2L9V/lwgByAbyOrJS4xtMlYBrwXktMholfzoMNW/cx2MUSVCwQXhzQjYUrw6QNRaVIgRftZv1uHmLxKbHwwItBxgPP/wZX+l9xyfS12gzfmN0M6ngfshnT+RY/gl/s47LjJOCt4n0WWvjuYHWu+VeHnH530xO7ZIIynRQV4CWtopZjW7NkTUetap7wcv1TbGdeG4vOZ3QUvihmJrTp2E8X8JJm0dUnCfbsQBFgca3SsnJgQp+mLJYtD/Y4Vh6YKXoGTZxsoqkOflzoyezGC1CP85OamYY0ljOC4+lfXmHAiwVPsVArix++qY3G2QXebeEvQqub31kzN/JhELUAL2rNr6dkWO3HSVZaI1LQPnZHO14amyMflY7e9wJ4HZWcuM6TJWAa8A5541M4e/4yjBn+JNSsVhGyc3Lh3wMn4PPvl8ALT3ctUk5rUuCl41fcGBg7F+GXXtBaoyYYtam0Ai/a2tXwOwc5lXtA9p0bUOLyarvHxpHzo2BjZlOo530YoryvsuEqxe01ai5a28GEB5eCm0DkE4ttLvE0G15Hs6VplZOWeph5DUPPqTkGybVF6ap54KXMgFgfnR6bPNhXE/CSGU1u8QrMCdPVwGtPC0fAwsOXFuCNT0yBVrFlrEfypFF1V+DFhfriuy+ZcyoWXNdbaVnw3trr0KPdg9Cj6X0FbHivbfwIGpz7AP7KaQ8P+663rps07ndy9Zcg7OQ3BdZVSe78xwHFnra3n/lU0Vrqa7k3jKhD85MLC2dE+0ptCOA1U7qibXeVgGnA2+DhITBxzGDo+nALm7kv/vVvmDpnOWxe/pW7ykT3uBZvP8vilJIWjHfMySjfFQLjV1mPYN0VeH1mV4KbJZtDaM+FVu2UPTvJoJXt2XEspk6lcj2kGWT3+lu3/Iy+QCkclqcBL6aoxX1VmBpeZ9ZGDngjVzZnZj5Yptc6DBhiSqnwNrxYh+K+5vmFuuyEhMamBF5kYyqNciAFXj5zGiUEwegm/JE89UHJRjDLW4VSxXUtAa/hlWoMUUOLWmgCPjUNr1zyEh540azkyK0STBv99uONAG2HryVb0lJT4VMJX/atBvlPH2A/EfDGe1WB8vlnIDWkFgRlXCywrvbkjrLHxDeUHc6eoArDbEDLwtE97sgJipb2BfA6IyVxbVGTgGnA26Hv6zDq+d7QtYMt8B44egaGvP4J7F79XZGR5YZDlwscQWIqVNTsSr3K8YWP9m9k02u2EJJPrIVa//QEe+YJBBMUhkwtexvWL/HXExB8yRKjl7TYCPc32ttqVc2en1z7jgDv7asnocbqBrC76qcOJbwwY570ktZip2hG/862eX7fb9DiQF842mIFhMVaMjohtO4N7AJv3RhmA3tyfSkBL28i5OwYtV5P8Cc9QlcCKf6UB7XRfD0ERGkqYRyHEclGeOBFO94KpUJhbM+GLNrGil1nbWI6a3GMkyYv4YGXn5cS8ObHb4Sy6y0hKPkTIAJeNGeg8GRy62oUqCqtn9b1N6ueFmdIM/oWGl4zpCradHcJmAa8Pyz5E3b8exS+mfyqjQxWrNkKv63dDrM+H+PustE8PjngxSNYLBhzlA+jhC98VwbNx6O/YUfrqKb85e1eUUPtm3IBMqPbKMqA12BRiCJXzsve4jgCvPbi92reCAZXJO2hpwKvNNMWxd/9Nut5WJ7TQzfwojOod/ZtG5t4g0Vutzk5QORTMPNH5XyWRARDAi6MI4taewzVhetLcWWNAl4+YxdNBh21+IgLBO1agZePhDD2259hXuBzFoDlQF4JeGnNsf7RyIEQ9ui37FoC3l3l3oamFz9kf5MDXjVnQT3rr2W+etozoq6alt2IPuTaEMBrlmRFu+4sAdOAF5NNnDgTDzWqVgBv77sxd69cSwIfHx+IjAizymXJ9++5s4xUxyYHvHQRPfBvN/2EhSZzNfDiA/Xr5NawP3oElO70iexc8GXcfFVpOFlxFBR/yPLyUSu8Bou02AJ41aSm/3eExiB/X91H2/p7Mv4KKfASBI7J/AgO5NbVDbxk/1sYGl6Ujr1saVLvfynw0vWk2SVp82YHfKQHdNDSmtGOXzlsY+uxBNh24q5T7NiejeCXXXHMBAmTNRDAagFAfH7wMXoxCsT8wOcgNOeyJuDFsZEpCh/XO2HPAmh8+HnY12QZC1d2rwIvJpzg7biNvwvlWxTA6ypJi37cSQKmAS9mWtNahg7srrWqW9Y7nZBiN4wQQe6d2i9D9E9lgODXFZPBjE0LcjuAUtY0HINcznu1sdELHZ2IrnffCajRzgmp4NJQUUpjlAuHhXXt2fC6o4ZXbQ3c/XfpvqKPpGEZX8GZvMqg5mEvNWkg4C2sDyt04EMtLmYkw49ELBRKzAjgpXis2K4zdtt8GC5+j2CbWNIzc9gc0JZYbQ2kewwBuFzuKRjSKoadAJEGdv7L7aFkSEABG14eeA81mA0RDZ4ssG195twHUV7XTNfwIqy35pwEC/v+oRMcR8LPOTt2AbzOSlBc74kSMA14PVEYjo45ISmdaX9iY8KhY/1y0KhSpE1TFJc3s2J3l2aJohfoL8X6wpmIx6BU9+9lp3jpxDZo8s/DoPRCkruIHPMKS9tmb63UgPf6xeOQuncmQGR9iGncnzUlgNfR3a98HQFvfIUh4NPuK6sz5CNpq9hFamGY3A14yT4WbWOp1IgJh8SU9AJRLCiqBH9/YLY11NyilpWK1DHNqIxbaBsaWaIYLNx6ytpXw/tKsZMCjPXraCQI6RG8FuClGN+ozY2q3bnAhqHf5XwAjDRpKCzzAaU7RMkcxvg7sWCLAnhdIWXRh7tJwHDgzczKhpu3UiAiLBT8/HzZfO+kZcBvf22HjMws6PZwS4gIdyx/t7sJj8ZDwIv/Lh9RnAWX5wtFZkhtOM6lwEtHpJ8GjIXw4ADwedKS6lRaHIU91FxjIoDk1jPcamnUgPfC/t/ZMSpmeArrt0YAr0mrhxosWNwWyoQFs+gdGIM6I249PJU60yHgJQ2xK09IeNHwDmH0d4Tf8qVCrPFh+fp4f/DAK3e9VDNsFPDSOHibXrQFx+Jq4M1e2BoqZu6F890Pg1/JglE5KK25nOa+KAIvRmbArHH/nrlm40Ro0m0o26wAXldKW/TlLhIwHHi/m/8b/LR8Haxe8DFLNJGXlw/9hk2Ao6fOg5+vL5QIDYYFU8dBmagId5GB0+NA4MUYmmsPxLMHmTTEDAIv2vKmNHxHMV+804OQaYDien4R9Db4+XhD0IDthgKvGWM2ok014I1b/io0SfwG0EOcZOIo9Bsx3qLcBsZ3Rgct6LMZ8D7A2LPDUyY6BLwkJ7yXMNKJqwtmJuNtY6l/1JyO7FKvwHCkwKvkUMZfSMBrlKOiJTpDHMQn3WGZ2yh9MZoyfL36EOgNfeaIhvfWnu8h9coJKNt1iuyS0f0qB7xq2eD07AF30fDSGuPYy0cEOxTjWs+85eoK4HVWguJ6T5SA4cDbf/hEaNaoJowcbHFE2LnvGLw4ZgqsnDMJypQuCZiQolKFMvD+G896orxkx4zAi4VMCKQ2WRTRgDITyQXNR61LbEyYoXnUrdmhQsax8SkB78ndK1naz7i2v0Fg5fYevy6Xtk6DJqdHw6G2GyCicnPrfMiG9+oPHaFW1ma4A8Fw65nr7HcBvOYsOw+8UUtrwobkqvBZliVyi16TBnNGqL1VPDE5fvEG/Hkw3sYsQQlOpcDLR0qgXqUaXkoNbBTwYj98kgYcPz5rsH38f72pdvkUydg2AekXz7RStOFVk/Dxv/4H7S69Ixu9xkhIxbHiB4BSymU8kVixOw461qtgqpMoD7xKH0tqMnP2dwG8zkpQXO+JEjAceB94/BX48K3noVWTOkweU2cvh31HTsGsKZYwZH9t3gMTv5gPm3750hPlZRd48Uc5z2cCXgrfJQe8Wjym9QqMXnSfhk6GcnlnIGPgGdkm5BIE6O3LneorwSsBb9rMOlA+/zQbMnmJo3kDQrCSnaE7zc+TxnJ+ZjuoFpwMWU/tZd76mIZ2fo7Fblot2L7Uhtdd5i01TVByOsL7Pj8gjEVn4cGT5iHnmEZtGwm86MS25VgCPNKgAguFxscB1gu8UhMDAtL/63W/w8BLzx85Da+RwKtmHkEmYHod+fTsS1KKyMVh1tOOs3UF8DorQXG9J0rAcODFDGvzv34H6taoxOTx8rivoGK5KHhjqCWF6KFjcfD0iElwYP0sT5SXKvDyntxUmSIaYIIGn9TzslmiEHil2ZqcERBGZ8CXGz5YB/ouYEf4SulYlUwAnOm/MK9VA96IGYHW4e2pM4OFR6Li6pS1hSknV/S94fvB8LT/QpZpMGpJTZiS9SocK9GdHbGrwZanAK+apprkTE5KWoBXa5t619DZ5BZmAO/x4/ug1LYXIfj+F6BYgyE2U3Il8NL6GPmxIV0fHqovXE+B1jVjLCY/Li4CeF0scNGdW0jAcODt+OQbMOr5J6BL+2Zsgu2eeJVlXOvesRX796Z/DsD4z+bAxmVfuIUAjBgEmTRgW3IPaD4mp1xUA7kUno6OCyGXtDjYBtro+ewYz4D3YK8bzAaRvLWpj3sReCnDU7xXVau2F+UhgNfRnSd/HQEvxWrGGLydug3SZLrjrsBL2bHQ/hLBXU1TTZIh2HEH4EVnOwStCX0tz2mthQdefG59vfoglAwJBGc0vPayqbkSeGlurgBesz5otK6jAF6tkhL1ipIEDAfeyVN/gu27D8PENwfDvwdPwv9mLIG/l35hjczwybSFcOh4HNMCF5WiBrw4Twq+bi99pjOxN0mWvH0YPVQJaFc9cJ45qkgf6EUNeJPidkDdze0AtbcUdgzlgyYNl84egcq/1YN/S/SD+2/9VGALCuA19q48sHA4dM6cw2JPh+4aAwMzZsOw3l012Ui6K/ASGGGWNIRGPsOaPem5A/BS7FccpyPPGx546VmDJ1NDO9Z22KTB3YCXzyxn7N0AzLkZQ8VNG9wWggIsUYwKowjgLQypiz4LWwKGA++N5BQYOPJDOHvhMnh5ecHrL/aBZ5+0xF68nZoGnfqNhuf7dbX+rbAFYET/PPAq5UYn4JWLNckfM77Vs5HDQ6IjOXwB4Yu4Z1NLCCBy4voq5jf4/bRXAeA99csr8MDtGUVKu8mnSiaBIvCe3LUC6u7oxWx1i5WqAqVWPwLX/GOZ/S4WAbwObz/ZC3csGguPp3/FwtcFnV4AGINXzZSBGnJX4MWwUmgT+0rnerqgRWpOIKdJVHp+GLkqzoQ+kwNenEfvFlUMA17U6mI0D3QuM9K3Qc2Gl3535ENA6/qojUFrO87WE8DrrATF9Z4oAcOBF4WQnZ0DR06eg6hS4Tbhxy5dSYTDx89Cs4Y1IaxEcU+Ul+yYeeBVeqBFrmwGfjcPyXoi04sQIRWPRx0tSn2TTevPuf3hSZ8FsC3qTajUeTzrBjU+11Y+D61y1sDN/yIWONq/O12nBXgxCP7tqychPfGMNb2pAF5jV3Hp0lkwMvVlyPMLhUSvaBiQ/LnHA6+jEuKBF9vgU/ZSmwTTznz4qo2P18xixjU9heZAUR7w4xo/rKPDijkMvKR1Ro1569hoZhZ2MiGZ7ZOiBryu+KDRsp4CeLVISdQpahIwBXiLmpDU5sMDr9KRFaVFTW7zPaRVedqmSf6oU6v2S25MGDLoQlIqfCaBZgLeg7l1oJ7PYZs0w3hNv8tDIMjfVzFsmdr83fF3JeA99PPL0PjadIjrHQ+BxS2xoBF6a6xuwP5bAK+xq0nAi62i3fSXIVNlkzTI9equGl5HJWTUSY6j/dN1zsT6lZplEKT6+ng5DLw4Lh5szQJeOgFTij3Mh41z5jlsb32MtEl2Zh8I4HVGeuJaT5WAAF4DVo4HXiV7NEw1iiW7ZMEA9ajVmbX+KPvd0QcthrsZv3gXcyCRaoeST6yFWv/0hNT8YCjudccGePEBPCppIOT7h95TwCsFWzI5EcBrwA3BNSGA964w+JBUZmpw1VbQmdBnPPDyJhlmAC+lPzbKicyerTDKjA83Z5aNrQBetd0pfhcSME8CAngNkK0W4LXXDa9Z0OrxLW2PwpDJaS987pxnIaGo7C71kjXrUeKPHaFezlabrGMGiKTQm8j9oS6k+JW1pg7GAaEN75F5A6FR8oICmty0+S0hJucUJD9rSUQhijESWPX3RnjhfBfW2F857WF/7BTo36a6psaLmoYXJ42aTDNtRLUI1ijgJe0u9lnUgNesKAr4nMYEQ3pNSbSsq546QsOrR1qiblGRgABeA1aSB16lbGtagdfRBy2+SO1l7SENJo5jdcCzUP+paWxICHpVc/dDnF9DCOy/zQBpuEcTOC8sfHY5BN746fcD5BfMOoeOfYEx99tkZnOPmXj2KPBjbthRSxIajMFbrMFgqzOl2syKKvAaGW9bTYZyvxPwKiXMsNcmr+Hln1XOAi9mmGsdW4Z9DNH4EKjx5IsHa0fmS9do0fBeSExhWfTMSj5hpE2yM7IQwOuM9MS1nioBAbwGrBwPvKTF0XMMx2t4HXnQEmTb65OANx2C4aB/Wyjfbxmb+Y3ZzaCO9yE4HtYDQnsuNEAa7tEEAm+Z3FNsMJQ+2B7wuseoi94oeODFCA164KUoAi/CnJyzmitXHlPsYjxuZz6ucbz8sb+zwMsf9Q+buQnSs3IBw4OtO3TR4XFKZSp1jpP+TmNAhzk9z289ayeAV4+0RF0hAWMlIIDXAHnKAa9cLEfUMJxISIaH65azCWfEA6/WBy1CLsVxVNNc4BQReLO8Q+CCVxU2Y9J8ps+qA1V84uBqzTGQ2+w9A6ThHk2Q5hpHQ3a5CLy3vyoLCcGNIfKJxe4x0CI+Cinw6oGsogi87rDc9LzRsxb8uMnpjfc3MBJ4+bBpCJ+OjlNO1vaAk4AXtbz3V4o0xexAAK873AFiDPeqBATwGrDyUuBVckygF41Ui4uharaeuMyO0rQAL2Y4Qi1Nz6aVID0zFyJCA+HjFXvtvhhCd45mM71y0mK2QMCLIIza3fCHJ0NucEUDpOEeTST/1MkaW/d45/0QGlWd2fAGfOVj47TnHqMtuqPAPd/rSDsIKFkF+l56Rxe8COA1Z18I4L1P1qwGYRQVFRjpBosZjoUCeM3Z06JVIQEtEhDAq0VKKnX0Aq8UagmQqRt7D1o8lnt30U525EcFbXf3nUvUlL1HatsqF77LAJEUehOUPQ4HgkkmMOauAF7XLwuG6Wu0uxeULlEMBl0brzkNL45UAK856+Us8OLz53pKBnwz5AHrAIuChpdgFJ+xeBKHmuV9Z6/Dw/XKG7YQAngNE6VoSEhAtwQE8OoWWcELpMCL2ld8YEqTSNCLhpxWVu45xzx2l++Ks2nUHvDy5g90UfmIYIhPuqMppBkPvPhgr7esJBwpNxLCO0w2QBLu04Qc8GYl/Av3rW1TIOWw+4y66I1Eml1MT9g9Abzm7Ae877ceS4DWNWNYRkZHCp4yVSh1N3mQs8Arl8GtfERxiE9K1XUqoDYX3jlOWpdgFP+Oz2aMpoEmFY5GzpEbiwBetRUSvwsJmCcBAbwGyFYKvDzYonOINIMQPkj7t67O4uaidvZOVg4bRWRIIGBwdEypqVSo7YiQQEhKybBWC/T3sdG4KF2PYchK5l0F74EH4NKJbdDkn4fhUIPZENHgSQMk4T5NyAFvypm1ELulp1Xj6z6jLbojEcBbdNeWn5kZwEvt6/lIUpO2vTi4BKOlSgRZ46Jje444EsuNQ81pTm3sRv4uojQYKU3RlqdIQACvASulBLykIaBwYRRuBzUX/dpUZ3a3WIdKzZgwplmw94CnB/Yj9cvD1mOXmSkDFq2xPQkE0ZGLMrDRkb8BonCbJgTwusdSEPDiBxl+0E3o20zzwISGV7OoCr1iUQLeGuVKsmczFS1+FVoWQItzsZZ2jKgjgNcIKYo2PE0CAngNWDEp8ErTb+KxIR6L8Zl8MAbmwq2WsFkEvfdXjmR/G9uzESAUH0+4yUwV+KgOUg0F79GsxcniXgHehD0LoPHh55l8KdHG1TWjoeGVaSJ9sAF7XmsT/L2g9aOM2hbAq1XKhV/PKOCl7Go0I60nV1oloKThRRMNPHFDbW6NmHAYPmuztUl78c219ov1BPDqkZaoKyRgvAQE8BogU3vAiw9sjL6AWlseeFFrgNpcXovAaxYQiI9fvMk0uHxYHvIkpmxVerMm8cCb+0M9KJ9/Gk613wTB5ZsYIAn3aSIpbgcU2/YaS6ohgLfw1kUpFa2WEQng1SIl96jjLPCiKdfXqw8BKQLIZMso7SpJSQl4pTBKigSEXbQjlvpjOCJ1AbyOSE1cIyRgnAQE8BogSynwkrYAmyawRWhF5zR0gpArWK9j/QpWzQL+Gz3cMRqDFHj5l4CjwBvfaRuUX9OKDYXi1BogCrdrAqNQ/FuiH+SFVoKQa1ugRubmIj1ft1uA/9Lp0r3Qs2llzUMUwKtZVIVe0VngJRik5yWZg+lJVKJFCBgCEpNZSM3GpDCKkSjwdI3GwyfZ0NKPXB2Cern074626eh1wqTBUcmJ6zxZAvck8K78cxt8PfsXSLxxC+rWqAyTxg6GCmWjVNdxyreLYfbPf8CRjXNt6kqBF38kDQEd0aHmAh3O0LwBH6RYeMczglgCWMoyhPXIaYI/dmtUKZK18dUfB5kWWOuLgTS8x+p8ATUPj7ongPe0TwOm6aVSlAFfdRMXQgX+XqjB2ayrDUUAr5qE3Od3s4DXyKQTKC0+GgQvPSnwIhinZ+WwSBZqMc61roJS31qvN7KeAF4jpSna8hQJ3HPAe/rsJeg79H34YsIIqFezCkz/YQXsP3waFn033u6azV28Btb8vQsOHYvTBbyoSSAzBNQs8CDLmzUQ8FqSShyxQjEOin6TOxLDv63df4FlBaLMa/YmsnvjIuhx7llIDqoJYWnH7gngjfeqAuXzz7C5nvZtAEFPb/eU+7NIjJOAV69mSwCv5yx/UQNekjylbUeFRUcn4/EK4PWc/SxGWjQlcM8B77Q5y+H46Qvw9aRX2IqmpKZBy+7D4Y8fP4byMaVlV/nXtdtg2e+bYfzrz0C3gW9pAl48EsOCXunSHO1ku4sgi8dcdHRGx728rS8PvFtPXGHhcpyJC3lw+zLodHKAdZ5n8ytDwLOHi+buBgD/uZWgFFy1zk8Ar+uXWi4VrZZRCODVIiX3qOMs8FLILkqiQxnPMHwjH+/X2dkqQSeaj6HDsJzpglLsXHweY2kdG61pWAJ4NYlJVBISME0C9xzwvjHhG4guXRLeGNrXKtQHe42CD8Y8B22a1Ssg6M07DsDXs5fD7M/HwO3UNOj45BuagBc1tWi+gFpXPB7bduIys8dFyMUHJcbQ5e16+ViPmLgC4/dSQgnS8BrxwKRQZDjRn3P7Q3z10UAOcKbtskJsGBNt8OYMAnhdvxgCeF0vc1f36Czw4nhpn+B/G23KQPJQUhrYe7bKAS8Bup7II5RCns9Q5+p1ov6ESUNhSV70W5gSKDLAm5mVDWnpdxMx8EL18fGB0OJB7E8j3v4SalStACOee8xapXP/MTDq+d7wyINNbdYi7sJlGPXuVJg1ZTRERoTBpSuJssB7MyXL7hr+se8CzNt0gtXp1awyHL10A45dTIZ3n2gMRy7cgGU749h/1yobzupQ/YEPxMKmI5egVGgxGN18GrkBAAAgAElEQVS9ASz55wyr+/Oohx3eM5cO/g51djzOrp9Z8hvo1fs5h9vyhAuTZjeDqjl37XePBbSF6EF/ecLQi8wY3/zxHwgK9IXxvfVFAgkP8Qe1e6vICMnDJ+Lt4wUhgX5w6479Z6G9afLPSWeecfb6OHrpJkxYssfmeYv17T1bn/ziL/bcfqJFFWvTjoz1/aW72fV67wMztkaAnzf4+nrDnXRL0iO9Be9NUYQEPE0CRQZ4f1u7HabNXSEr/yr3xcC0Dy0OWqjhLRtdCl594QlrXdTwThj9LLRtXt/metTuvvzOV+Dl7WX5e34+ZOfkgp+fL3w9caRVI4yaW3vlcPwNGPfTLlZl5KP1YMeJK7Dr9DX43zOt4FD8DZi9/hhM7NcU6pQvWaCZd36ymEZM6teMHbkt2nYGVrzZyeF9dmHfKojd2pNdv7raD/Bgp/4Ot+UJFyZ80xiq/Ae8iV5RcKni81C923ueMPQiM0Z+D+uZVKCfD2Rm50K+notE3UKRgDcA+Pl5Q2Z2nsP9889JZ55xWp7F0uftzHVHYf3hBFg4qkOBy0fN2QqlQ4Pg7V6NrL9h/VX/XmD/XvBKewgO9FOdt6P3gWrDDlTw8fYCby8vyM51bL2K+fs40Ku4REigcCVQZIBXqxinzl4OJ+LiGbBiQRveFt2Gw+/zJ0PFcvYjNShpeOWiNPDjIccH/Bse1eFx2JZjCYCJIvC3C0mpUCGiuKzTGR830l5aTK3zP33hArTdUINV31h7IVRv0kPrpR5Zj0waMFJD+eH/Qmp6tlMvZY8UQiEPWq+tIw1X2PAW8sLp6N4IkwbszlHzF61DlTr+4t7EjJVpmdlQLMCXPZOlRe65y/tZaDW/MOL5rXWeavWESYOahMTvRVEC9xzwosNav2EfMKe1OjUqwbfzfoWde4/CL7M+YOu7fPUW2Lb7MHz27ksF1ttR4MWGXpq5iSWg0PpwpM4x7BgC8oS+Ta3Ob1oyqiltVnzgt1sbw37e0DEB9ISJ8sQbQACvJ66aZcwCeD1n7TwFeKWhHclfAiWtlOQCQTU9M4c9g6kMm7kJggL8rL4YWp6jAng9Zz+LkRZNCdxzwIvLiBEXps9dAUk3b0GdGpWZw1qlCmXYCn85cxmsWLMF/l76haHAi5qE89du63YQ450pjHhg3mvAe3n5ELj/1k8gNLye9wATwOs5a+YpwIsS5Z3QtGhqpQ5tdGJHYSW1ZoNTivZQGKssNLyFIXXRZ2FL4J4EXqOFrmbS4Ex//MN29PztEBsTxmLuOlpQW1xvmcVW+F7Q8FKiDQG8ju6YwrtOAG/hyV5vz0YB7xvzt8OdzGwwM5IBgmdsTDi0rlmGxTDH1MFYlLKp0TOY4kjzZhGYlAJDqGmJdCOAV++uEvWFBIyVgABeA+TpKuA16oGJ6XYP5tZhCRi0JKswQESF1gQB7+5SL0G9flOFDW+hrYT+jgXw6pdZYV1hFPAacYqlJgOyE6b0xVgfQ0BizHS5QqHM8DdMQHHhegr8e/Y6TBnQCr5cfZBdosXMzKjnt9r8tPwuNLxapCTqFDUJCOA1YEXNBF7Kv86nKHY240+JOZFwJq8SlBpsiRxRlIsAXs9dXQG8nrN2RgGvow6OeiRFwIuRBijCTqvYaMWTM9LoYh8YWx1PyciMgfexUBuDAF41CYnfhQTMlYAAXgPkaybw0sOW7MX0Or3JTW/h9++wPz/1wiQDZu/eTQjgde/1sTc6Abyes3ZGAa8rZizNZIla20aVIhnMyhUeeOl3Mn/QmgxILi28K+aq1IfQ8Bam9EXfhSUBAbwGSN4VwIt2YusOXdQd5UFuemaH/jFApIY1YQXeqp9CvS6vCZMGwyRrfkMCeM2XsVE9eDLwqikR+LCSKC9MfzyyiyUrp9Z07wJ4jdppoh0hAcclIIDXcdlZrzQTeCmMDqUZnjOsndMjRuC1Z7PmdAdu1EBS3A4otu01SGn0f1CrRU8BvG60NmpDEcCrJiH3+d2TgffTAS0VtbskYVIS4HNzSPvaUKFUcfYTD7IYSx0d4MrLxFQXwOs+e1WM5N6VgABeA9beTODF4fE55o0CXj054A0QkVs0EREaIIDXLVZC2yAE8GqTkzvU8iTgXbDlJDsto6LlmUpx1KWRHEj7i2YRWDAb5sud6zITCb4I4HWHXSrGcK9LQACvATvAVcBrlFYWQ/9EhARq8iw2QDxu04QAXrdZCk0DEcCrSUxuUcmTgJcEpuc5iJCMRS78GDmjoTPbthNXZBNYrD0Yz2BYKfSZqxdR2PC6WuKiP3eQgABeA1bBVcBrlFYWzSSw0LGcASLwiCYE8HrEMlkHKYDXc9bLE4HXqOcgAe+xhGQ4mZAsC7xandtcteICeF0ladGPO0lAAK8Bq2E28L67aCfEJ93RHODcgCkVySYE8HrWsgrg9Zz18kTgNUq6qClG+9195xKtTUqVEwJ4jZK2aEdIwHEJCOB1XHbWK80GXgqjozWFpQFTKpJNCOD1rGUVwOs563UvA+/M9UcB46VTTF9cNQG8nrN3xUjvHQkI4DVgrV0FvOgY4WzSCQOm67FNCOD1rKUTwOs563UvAy+fiS3Q3wcysnJZpIYJfZtaFxChGB3XPhvQ0i0WVZg0uMUyiEG4WAICeA0QuNnAiw9LdIZQixdpwFSKdBMCeD1reQXwes563cvAS6EjcbUoQRD+Nx/9wRUpk/XsFgG8eqQl6hYVCQjgNWAlzQZesv8SwOvcYgngdU5+rr5aAK+rJe54f/cy8KLUMGxZZEggC0e2cs85JkgBvI7vJ3GlkIAZEhDAa4BUXQW87hLSxgCRFUoTAngLRewOdyqA12HRufzCex14+YgPcg5qQsPr8i0pOhQSKCABAbwGbAqzgdeAIYomAEAAr2dtAwG8nrNe9zrw8itFMXf5DG6j52+H2JgwGNK+llssqjBpcItlEINwsQQE8BogcAG8BgjRBU0I4HWBkA3sQgCvgcI0uSkBvHcFLJdVjWL19mxa2eSV0Na8AF5tchK1ipYEBPAasJ4CeA0QoguaEMDrAiEb2IUAXgOFaXJTAngF8Jq8xUTzQgJOS0AAr9MiBBDAa4AQXdCEAF4XCNnALgTwGihMk5sSwCuA1+QtJpoXEnBaAgJ4nRahAF4DROiSJgTwukTMhnUigNcwUZrekADeuyJOTMkAtNkd3L4WtI6NhrTMHBg+azO4Uxx1YdJg+i0hOnBDCQjgNWBRhIbXACG6oAkBvC4QsoFdCOA1UJgmNyWA11bAaLPboW45eKRBBVixOw62HXevOOoCeE2+IUTzbikBAbwGLIsAXgOE6IImBPC6QMgGdiGA10BhmtyUAN6CwIt/wRTDJxOS2Y/uFEddAK/JN4Ro3i0lIIDXgGURwGuAEF3QhABeFwjZwC4E8BooTJObEsArD7z8X9/v0xQqlCpu8kpoa14ArzY5iVpFSwICeA1YTwG8BgjRBU0I4HWBkA3sQgCvgcI0uSkBvLYCfnfRTohPumP9Y6C/D3wz5AGTV0F78wJ4tctK1Cw6EhDAa8BaCuA1QIguaEIArwuEbGAXAngNFKbJTQngtRUwZlYjU4aIkECY0KcpBAX4mrwK2psXwKtdVqJm0ZGAAF4D1lIArwFCdEETAnhdIGQDuxDAa6AwTW5KAK+tgBdsOQn7ziVCUkoGs+N9q2cjk1dAX/MCePXJS9QuGhIQwGvAOgrgNUCILmhCAK8LhGxgFwJ4DRSmyU0J4C0o4BW74mDlnnMCeE3ee6J5IQGtEhDAq1VSduoJ4DVAiC5oQgCvC4RsYBcCeA0UpslNCeAVwGvyFhPNCwk4LQEBvE6LUCSeMECELmlCAK9LxGxYJwJ4DROl6Q0J4C0o4uMJyfDxir3Qo/F90LNpZdPXQE8HwqRBj7RE3aIiAQG8Bqyk0PAaIEQXNCGA1wVCNrALAbwGCtPkpgTwKgMvZVwzeQl0NS+AV5e4ROUiIgEBvAYspABeA4TogiYE8LpAyAZ2IYDXQGGa3JQA3v9v787jbCz/P46/MWSNhGwRUSiEvviKwkSIRLYoRcjaNNllHevYd4Psyr7Lvu+yl6goEWXfdzP8Hvf1ezhfIzPjnLnP4T5e568y59qen/sc77nOdW5eBra5ewKvzaB05wgBAq8NZSLw2oDogy4IvD5AtnEIAq+NmF7uisDrZWCbuyfw2gxKd44QIPDaUCYCrw2IPuiCwOsDZBuHIPDaiOnlrgi8Xga2uXsCr82gdOcIAQKvDWUi8NqA6IMuCLw+QLZxCAKvjZhe7orA62Vgm7sn8NoMSneOECDw2lAmAq8NiD7ogsDrA2QbhyDw2ojp5a4IvF4Gtrl7Aq/NoHTnCAECrw1lIvDagOiDLgi8PkC2cQgCr42YXu6KwOtlYJu7J/DaDEp3jhAg8NpQJgKvDYg+6ILA6wNkG4cg8NqI6eWuCLxeBra5ewKvzaB05wgBAq8NZSLw2oDogy4IvD5AtnEIAq+NmF7uisDrZWCbuyfw2gxKd44QIPDaUCYCrw2IPuiCwOsDZBuHIPDaiOnlrgi8Xga2uXsCr82gdOcIAQKvDWUi8NqA6IMuCLw+QLZxCAKvjZhe7orA62Vgm7sn8NoMSneOECDw2lAmAq8NiD7ogsDrA2QbhyDw2ojp5a4IvF4Gtrl7Aq/NoHTnCAECryPKxCQRQAABBBBAAAEEPBUg8HoqRzsEEEAAAQQQQAABRwgQeB1RJiaJAAIIIIAAAggg4KkAgddTOdohgAACCCCAAAIIOEKAwOtBmW7fvqO+I6ZqzuL1uhUersBiBdSlRR0lfCqBB73RJLYC67f+pIat+0XqJn78AO1e/o35s3MXLqlD6Fht2r5XSZMk0sdVSqt+rfKu589bulFDxs7W6bMXlDtHVnVv85kyZXguttOi/X0ClvOYKYs0f3z3SD+JTX14LXrnMrsVHqHuAycpICCe2n/5caRBajQK0U/7/4j0Z1/Wr2JeUzHVI6Zae2c1/tvrtes39XWv0dr980Gdv3BZWTOnV4uG1VW4QK6Hen+LqV57fz2kzn3H6+Cho0qfNpXaNqulYoXy+C8oK/NrAQKvB+WdOm+VJkxfomE9g5U40VNqGTJCeV/JZt5oePhewAq8XQdM0MxvQlyDx5GULGli8/9fdR6uiIgIdQiurX9OnlWj1v3Vo209vVk4rw4eOqbqDbtoYEhT5cn5ooZPmKvdew9q2shOvl+In4548vR5fRLUQ+cuXFaaVM/8K/DGpj68Fu2/aFZt3KUegyfr3PlLqlS22AMDb+Vyb6pMiYKuwRMmiK8ECeIrpnpEV2v7V+L/PVohN2zSfL1fpqhSP5vCbMKMnDRfq2cONL/cx/T+Fl29bt0K1zs1W+rD9wNVvWJJrd20WyEDJmrplD5KmSKZ/+OyQr8TIPB6UNJPgnoqsGh+1a76jmm9dvMehfSfoJUz+nvQG01iK2AF3u6DJmnJd73/1dXNm7dU8N1Gmjqio3Jky2R+3mfEVJ05d1G92jXQsHFz9MvBIxrSPcj87NLlqyryXhMtmhyq59Onie3UaH+PwJpNu9V/1IxIgTe29eG16L1LrM/wqbpx89YDA2+tSm+rQuki/xo8unrEVGvvreTJ6Tk8IkJ5Az/TzNFdlDN75hjf36Kr1w+7flFQh8HaOH+Y4sa1thCkap93Vo2KJWX9wsMDAacJEHg9qNhblYPUtVVds0NoPQ4fPaFyH7XW9iWjlCghxxo8II1VEyvwNmk3QM8kT6ankyZWwXw5FVS/ivnvQ0f+UfnabSPVZsbCNZq1cK2mhnVSi5ARSpsmZaTd+eIffGnqy0d3sSrLvxo/KPDGtj68Fu2t0b29RRd4D/91XEkSJzQfc9etUU7Fi7xmmkZXj+Mnz0T7WvTeSp6cnn/c97vqBIdq7exBZoc3pve36Oq1YNlGzfp+XaRPu1p1DdNzqVOqecNqTw4qK/UbAQKvB6UsWK6h2REslC+naX3i1DmVrBqs9XOH8FGPB56xbWKdC7RqkPzppLL+Uu0XNl2pn02uAV2aav+Bw6pSv5P2rh6nOHH+f5di4fLNGjV5geZP6KGm7QaZnd+mdSu5plG2VitZZxLfKf6/j2xjO0faSw8KvLGtD69F711ZUQVeq2ZWmLpzR1q/dY/6hk3Xd8Pamx3F6Opx4tTZaF+L3lvJk9HzlavXVatJN5ULLKQGH1Uwi47p/S26ei1Yvkkr1+/QxMHtXIAd+4xV/IAAczyMBwJOEyDwelAx67fi7m3qqWjB3KY1O7weIHqxya69B2R9VLdnxRj9+ddxs6u0a9loc8bQelg7vDMXrDU7F9YOSIa0qRTcoKprRtYOb0jLOq4dfC9O9YnqOrodXk/rw2vRe5dQVIH3/hHrteijAnleUqPaFc0Ob1TvjXd3eKOqtfdW4v89W2G3Yev+ypzxOfPp1N1f7mN6f4uuXtYO75wlGzRleAcXoLXDmyb1M3xfxf8vKb9cIYHXg7LW/qKHSr35uvm2v/VYvWmXuvSboDWzBnrQG03sFti4ba9adxupDfOGyJwbLNfQHF+4e4a397Ap5o4MvTs01NCxc/TrH39pSLcvzDSsM7z/rdBE30/qZf7y4GGfQJRneGNRH16L9tXn/p4eNvDWbNzVnOe1vtwUXT1iei16byX+3bP1XmaF3fy5s5u7KNwNu9aqY3p/i65eW3ftV3DHodo4f6irT+vTsuoVS6hq+eL+jcrq/FKAwOtBWb+dvVyTZ63QiF7WXRoSqnmX4cr1UmbzZsPD9wLWra4ypktl7pRhfWu5fegYs+N0tx5BHYaYL120/7K2Tp4+pwYt+6pz8zoKLJbffGHN+gvbOqLyao4sCps4X1t37tPsMV19vxA/H/FBgddacmzqw2vRexfNgwLvseOn9d2cFXo3sLC548aS1T9o4OgZWjipl9KmTqmY6hFdrb23Ev/t+e/jp/Xpl73MXRo+rV7GtdCAePHMJ1oxvb9FVy/rF5RSNVqYL2dXq1Bc67b+qI69x5ovB1t3hOCBgNMECLweVCwi4rZCh03R/GUbFR4erhJv5DP34bXCLw/fC1i34hk/fYmO/n3K3Iqs9Fv/MUcU7n6B0NoB6dB7rLbs3KckiRKq1gdvm49f7z6sL2YMHz9XZ85d0Ks5spqPBLNkSuf7hfjpiNat4D6o10Hh4RG6dv2GqdF7pd9Qm6Y1zYpjUx9ei/ZfNItWblW3QRN1/fpN3ZHM66jTV5/qneL/Mb9Qdug9Rnv2/S7rY/TsWTKoRaMaej3vy2YiMdUjplrbvxr/7nHl+p36osPgfy3SCqidmn9q/jy697eY6mXV2boDkXV7s/Rpn1XLxh+q5Bv5/BuV1fmtAIHXb0vLwhBAAAEEEEAAAQQsAQIv1wECCCCAAAIIIICAXwsQeP26vCwOAQQQQAABBBBAgMDLNYAAAggggAACCCDg1wIEXr8uL4tDAAEEEEAAAQQQIPByDSCAAAIIIIAAAgj4tQCB16/Ly+IQQAABBBBAAAEECLxcAwgggAACCCCAAAJ+LUDg9evysjgEEEAAAQQQQAABAi/XAAIIIIAAAggggIBfCxB4/bq8LA4BBBBAAAEEEECAwMs1gAACCCCAAAIIIODXAgRevy4vi0MAAQQQQAABBBAg8HINIIAAAggggAACCPi1AIHXr8vL4hBAAAEEEEAAAQQIvFwDCCCAAAIIIIAAAn4tQOD16/KyOAQeL4FzFy6paMVmmjayk159OcvjNTlmgwACCCDgtwIEXr8tLQtD4OEEho2bo+ET5kX75GVT+ypD2lQP12E0z7o/8K7ZtFtN2g3U4m9DlSnDc7HuP7oO2vUcrXlLN5qnxA+Ip+RPJ1XO7JlVqWxRvVO8oFfHpnMEEEAAgUcrQOB9tP6MjsAjFzh+6qxOnDrnmkdwp6F6q3BevV+2mOvPcmbLpAQJ4sd6rvcHXmvcH3btV2Cx/EqcKGGs+48p8B45dlKdmn+i8PAI/X38jFas36GFKzapSvni6vTVJ14dn84RQAABBB6dAIH30dkzMgKPpUCZmq30fpmialj7Pdf8cpeso87N65hwunLDDuV66QUN6RZkdmcPHz2uS1euKVXK5Ho3sLCC6lVR3LhxTNuLl6+q5+BvtXzdNhOYC+R5Was27HQdadiz73fVbNxVmxcO19NJE2vnT7/p42Y9FBbaXGET52nfgcN6IeNz6tyijvLmetE1n3Vb9qj38Kk6+s8ppUuTUvHixdPz6dNoRK/gKE2tHd7TZy9oVJ8WkZ6zdvMeNW47QH06NFK5wELmZ217jDZrPXP+opIlSaRC+XOpTdOaZo2zF61Tj8GTtXb2YCVJ/L+Q3qz9YAXEi6sBXZrq1Jnz6j18irbs2KfrN27phefTqnaV0qpQushjWXMmhQACCPi7AIHX3yvM+hBwUyCqwJsgfnzVqFhS+XJnV/yAAOXJlVVhE+eraME8SpMqhQ4cOqrOfccpuEFV1apcyoxaJ7iXzp6/pKDPPlCK5MlMiBwydnaMgdc6PlGvVnmlfja5xk9bon9OnNHSKX0UJ04cbd/zq+m3wUcV9GbhvDp5+pyZR5pUz3gUeK15NmzdX9eu39CEQW3NvPuFTderOV5Q5oxpdebcRYUO/U5ZMqXToK7NdO36Tb1V+Qu1aFRD1SoUN8+3gnTJqsEa1buFChfIpbrBoSbgt2hYXRG3b2vHj7/p6N8n1arJh25Wg6cjgAACCNghQOC1Q5E+EPAjgagCb4829WPcoWzdbaTCIyLUr1Njbdm5T/Wa99Giydb53DRG6P4jDVHt8G6cN1Qpkic1bayA+0lQT62ZNVCpn01hdoAzpEulXu0auNS7DZykY8dPexx4rXPMU+au0oZ5Qx5YyZkL12rwmFlaN2ew+XnIgIna+8sfmj6ys/n/UZMXaM7iDVo0uZcJ5UXea6K6NcqpXs13/ejKYCkIIICAcwUIvM6tHTNHwCsCUQVe6whD8SKvRRpz2drtWrRyi/448o8uX7mqcxcuq3D+XCZ4jv52oabNX60V0/q52jxs4N22OMx1pvfPv47r3Y/baMHEnsqaKZ1eK1XPnLetdM8ZYzsCrzXXu4F276+HNG3eau377U9duHRFFy5e0Z07t7V9ySizlv0HDqtK/U6a9U2IXn7xeVlmH74fqE+rlzE/7xs2TeOmLlb+3Nn12ivZVTBfThUrlNsr9aJTBBBAAIGYBQi8MRvxDASeKIGHDbwTZizVkDGzVKd6WRPoUqZIptHffW/CoRV4x0xZpJkL12jxt71jFXiPHDuhsrVauwJvvtL11bVVXZV/+7+ufmMbeJu2G6TrN2/qm74t9dP+P/RR0+4qX+q/KlOikDmuYe0yDxg13RV4rYGrfd5ZuXNkNV+4a9x2oNbMHOjalbZ+vvvng9qw9SdZu9hbd+1T1fLF1SG49hN1LbFYBBBA4HERIPA+LpVgHgg8JgIPG3itc7RZMqVXx3tC3L3B09r9bRkyQmtmD9QzyZOZ1VnnYd+s9EWMZ3jv3eG9P/BWqN1WJd7Ip68+r2ZL4N24ba8+b9VPg7s2U8mi+c0t2hav3GIC9t3H0jXb9HWv0ZEC7/QFa9QvbJry535JTydLrNCvP3c93zrWERAvnuv/J85YqpGTF8g6qsEDAQQQQMD3AgRe35szIgKPtcDDBl7rHOv6rT+qTZOaSpAgwJzZnTp3ldnttXZ4b9y8pdI1WujFzOn10QelzJ0LJs9abo4/3P2HJ6I6wxtd4LWOClihtHWTD5UxfWpt3blfVqC8O25UuNZdGg4fPWF2Wa0vqJ06c8HccWLJqh9Uq/Lbri+ULVn9g9p0H6l2QR+bs8e/HDiiiTOX6uKlK5EC79Vr1/VW5SBdvXZDk4Z8bY4v3H2UrdXKfKnOCsPWWANGzTBne8NCv3qsa8/kEEAAAX8VIPD6a2VZFwIeCjxs4D1/4bLah47R5h0/m9tzlQssbL6UdvHSVdeXx6xAa925wQqa2bNkNMG3TY9RsQq8t8IjzF0T5i/bqNu3byuwaAHdCg/X7dt3NDCkaZSrvvcfnogXL66SJ0ui117JpmrvlVCxQnlc7e7cuWPO4M5ZtF7WWEVef0Wv5siikZPmRwq8VoPmXYbr4J/HNG9c90jjWnd5WLt5t7ltmmVj3cmiZeMa5tgHDwQQQAAB3wsQeH1vzogIIGCzgHVbsWxZMpjbgPnqYR1bKFW9uerXqqCalQJ9NSzjIIAAAgh4IEDg9QCNJggg8OgErH8tbcX67Xr5xUxKnOgpbdv9i4aPn6tpIzubOyb46mEdfWgf+o3WzBqkpEkS+WpYxkEAAQQQ8ECAwOsBGk0QQODRCVi3KQvqOER/HTup+PEDlO2FDOZfdyuYL4dPJ2XdDzhLprQKaVnXp+MyGAIIIICA+wIEXvfNaIEAAggggAACCCDgIAECr4OKxVQRQAABBBBAAAEE3Bcg8LpvRgsEEEAAAQQQQAABBwkQeB1ULKaKAAIIIIAAAggg4L4Agdd9M1oggAACCCCAAAIIOEiAwOugYjFVBBBAAAEEEEAAAfcFCLzum9ECAQQQQAABBBBAwEECBF4HFYupIoAAAggggAACCLgvQOB134wWCCCAAAIIIIAAAg4SIPA6qFhMFQEEEEAAAQQQQMB9AQKv+2a0QAABBBBAAAEEEHCQAIHXQcViqggggAACCCCAAALuCxB43TejBQIIIIAAAggggICDBAi8DioWU0UAAQQQQAABBBBwX4DA674ZLRBAAAEEEEAAAQQcJEDgdVCxmCoCCCCAAAIIIICA+wIEXvfNaIEAAggggAACCCDgIAECr4OKxVQRQAABBBBAAAEE3Bcg8LpvRgsEEEAAAQQQQAABBwkQeB1ULKaKAAIIIIAAAggg4L4Agdd9M1oggAACCCCAAAIIOEiAwOugYjFVBBBAAAEEEEAAAfcFCLzum9ECAQQQQAABBG/24OsAAAEKSURBVBBAwEECBF4HFYupIoAAAggggAACCLgvQOB134wWCCCAAAIIIIAAAg4SIPA6qFhMFQEEEEAAAQQQQMB9AQKv+2a0QAABBBBAAAEEEHCQAIHXQcViqggggAACCCCAAALuCxB43TejBQIIIIAAAggggICDBAi8DioWU0UAAQQQQAABBBBwX4DA674ZLRBAAAEEEEAAAQQcJEDgdVCxmCoCCCCAAAIIIICA+wIEXvfNaIEAAggggAACCCDgIAECr4OKxVQRQAABBBBAAAEE3Bcg8LpvRgsEEEAAAQQQQAABBwkQeB1ULKaKAAIIIIAAAggg4L4Agdd9M1oggAACCCCAAAIIOEjg/wDAXlPBGYK1LQAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rolling IC comparison: raw vs gated\n",
    "if spy_vol is not None and len(interact_df) > 252:\n",
    "    ROLL_IC_WINDOW = 126\n",
    "\n",
    "    ic_raw, ic_gated = [], []\n",
    "    for i in range(ROLL_IC_WINDOW, len(interact_df)):\n",
    "        window = interact_df.slice(i - ROLL_IC_WINDOW, ROLL_IC_WINDOW)\n",
    "        valid = window.drop_nulls([\"momentum\", \"momentum_gated\", \"fwd_return_5d\"])\n",
    "        if len(valid) > 30:\n",
    "            ic_r = valid.select(pl.corr(\"momentum\", \"fwd_return_5d\", method=\"spearman\")).item()\n",
    "            ic_g = valid.select(\n",
    "                pl.corr(\"momentum_gated\", \"fwd_return_5d\", method=\"spearman\")\n",
    "            ).item()\n",
    "            ic_raw.append(ic_r)\n",
    "            ic_gated.append(ic_g)\n",
    "        else:\n",
    "            ic_raw.append(np.nan)\n",
    "            ic_gated.append(np.nan)\n",
    "\n",
    "    fig = go.Figure()\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            y=ic_raw, mode=\"lines\", name=\"Raw Momentum\", line=dict(color=\"steelblue\", width=1.5)\n",
    "        )\n",
    "    )\n",
    "    fig.add_trace(\n",
    "        go.Scatter(\n",
    "            y=ic_gated,\n",
    "            mode=\"lines\",\n",
    "            name=\"Gated Momentum\",\n",
    "            line=dict(color=\"darkorange\", width=1.5),\n",
    "        )\n",
    "    )\n",
    "    fig.add_hline(y=0, line_dash=\"dash\", line_color=\"gray\")\n",
    "    fig.update_layout(\n",
    "        title=\"Rolling 126-Day IC: Raw vs Gated Momentum\",\n",
    "        xaxis_title=\"Trading Days\",\n",
    "        yaxis_title=\"Spearman IC\",\n",
    "        height=400,\n",
    "    )\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5db24822",
   "metadata": {
    "papermill": {
     "duration": 0.005587,
     "end_time": "2026-06-13T03:33:06.827679+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:33:06.822092+00:00",
     "status": "completed"
    }
   },
   "source": [
    "The gated signal avoids the worst IC drawdowns during high-volatility\n",
    "episodes, at the cost of fewer active days (reduced breadth). Whether\n",
    "gating improves net performance depends on the IC gain vs breadth loss —\n",
    "a question for the modeling chapters (Ch11–12)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58d8fecb",
   "metadata": {
    "lines_to_next_cell": 2,
    "papermill": {
     "duration": 0.005773,
     "end_time": "2026-06-13T03:33:06.838997+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:33:06.833224+00:00",
     "status": "completed"
    }
   },
   "source": [
    "## 7. Implementation Variants\n",
    "\n",
    "Different implementation choices are hyperparameters. A robust signal\n",
    "should not depend critically on one specific choice. We compare five\n",
    "momentum variants with the same lookback (63 days)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a03ce692",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T02:55:07.716843Z",
     "iopub.status.busy": "2026-06-16T02:55:07.716724Z",
     "iopub.status.idle": "2026-06-16T02:55:07.719785Z",
     "shell.execute_reply": "2026-06-16T02:55:07.719436Z"
    },
    "papermill": {
     "duration": 0.009028,
     "end_time": "2026-06-13T03:33:06.853578+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:33:06.844550+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "def _compute_variant_ic(\n",
    "    signal_df: pl.DataFrame,\n",
    "    fwd_long: pl.DataFrame,\n",
    "    symbols: list[str],\n",
    ") -> dict:\n",
    "    \"\"\"Compute HAC-adjusted IC for a signal variant.\"\"\"\n",
    "    sig_long = signal_df.unpivot(\n",
    "        index=\"timestamp\", variable_name=\"symbol\", value_name=\"signal\"\n",
    "    ).filter(pl.col(\"signal\").is_finite())\n",
    "\n",
    "    merged = sig_long.join(fwd_long, on=[\"timestamp\", \"symbol\"], how=\"inner\").drop_nulls()\n",
    "\n",
    "    ics = []\n",
    "    for date in merged[\"timestamp\"].unique().sort().to_list():\n",
    "        day_data = merged.filter(pl.col(\"timestamp\") == date)\n",
    "        if len(day_data) >= 10:\n",
    "            sig = day_data[\"signal\"].to_numpy()\n",
    "            ret = day_data[\"fwd_ret\"].to_numpy()\n",
    "            valid = np.isfinite(sig) & np.isfinite(ret)\n",
    "            if np.sum(valid) >= 10:\n",
    "                ic = pooled_ic(sig[valid], ret[valid])\n",
    "                if not np.isnan(ic):\n",
    "                    ics.append(ic)\n",
    "\n",
    "    return _ic_stats_with_icir(np.array(ics))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "37fdd870",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T02:55:07.720673Z",
     "iopub.status.busy": "2026-06-16T02:55:07.720613Z",
     "iopub.status.idle": "2026-06-16T02:55:07.727203Z",
     "shell.execute_reply": "2026-06-16T02:55:07.726677Z"
    },
    "papermill": {
     "duration": 0.012728,
     "end_time": "2026-06-13T03:33:06.871968+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:33:06.859240+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "LOOKBACK = 63\n",
    "returns = prices_wide.select(\n",
    "    pl.col(\"timestamp\"), *[(pl.col(s) / pl.col(s).shift(1) - 1).alias(s) for s in symbols]\n",
    ")\n",
    "forward_ret = prices_wide.select(\n",
    "    pl.col(\"timestamp\"),\n",
    "    *[(pl.col(s).shift(-FORWARD_HORIZON) / pl.col(s) - 1).alias(s) for s in symbols],\n",
    ")\n",
    "fwd_long = forward_ret.unpivot(index=\"timestamp\", variable_name=\"symbol\", value_name=\"fwd_ret\")\n",
    "\n",
    "impl_results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "367fdf0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T02:55:07.728137Z",
     "iopub.status.busy": "2026-06-16T02:55:07.728059Z",
     "iopub.status.idle": "2026-06-16T02:55:14.499572Z",
     "shell.execute_reply": "2026-06-16T02:55:14.498933Z"
    },
    "papermill": {
     "duration": 6.21939,
     "end_time": "2026-06-13T03:33:13.097188+00:00",
     "exception": false,
     "start_time": "2026-06-13T03:33:06.877798+00:00",
     "status": "completed"
    }
   },
   "outputs": [],
   "source": [
    "# Simple price momentum\n",
    "mom1 = prices_wide.select(\n",
    "    pl.col(\"timestamp\"),\n",
    "    *[(pl.col(s) / pl.col(s).shift(LOOKBACK) - 1).alias(s) for s in symbols],\n",
    ")\n",
    "impl_results[\"Simple\"] = _compute_variant_ic(mom1, fwd_long, symbols)\n",
    "\n",
    "# Risk-adjusted (divided by rolling vol)\n",
    "vol = returns.select(\n",
    "    pl.col(\"timestamp\"), *[pl.col(s).rolling_std(LOOKBACK).alias(s) for s in symbols]\n",
    ")\n",
    "mom2 = mom1.join(vol, on=\"timestamp\", suffix=\"_vol\")\n",
    "for s in symbols:\n",
    "    mom2 = mom2.with_columns((pl.col(s) / pl.col(f\"{s}_vol\").clip(lower_bound=1e-6)).alias(s))\n",
    "mom2 = mom2.select([\"timestamp\"] + symbols)\n",
    "impl_results[\"Risk-Adjusted\"] = _compute_variant_ic(mom2, fwd_long, symbols)\n",
    "\n",
    "# Skip-month (skip most recent 21 days)\n",
    "mom3 = prices_wide.select(\n",
    "    pl.col(\"timestamp\"),\n",
    "    *[(pl.col(s).shift(21) / pl.col(s).shift(LOOKBACK) - 1).alias(s) for s in symbols],\n",
    ")\n",
    "impl_results[\"Skip-1M\"] = _compute_variant_ic(mom3, fwd_long, symbols)\n",
    "\n",
    "# Log returns\n",
    "mom4 = prices_wide.select(\n",
    "    pl.col(\"timestamp\"),\n",
    "    *[(pl.col(s) / pl.col(s).shift(LOOKBACK)).log().alias(s) for s in symbols],\n",
    ")\n",
    "impl_results[\"Log\"] = _compute_variant_ic(mom4, fwd_long, symbols)\n",
    "\n",
    "# EMA-smoothed\n",
    "mom5_raw = prices_wide.select(\n",
    "    pl.col(\"timestamp\"),\n",
    "    *[(pl.col(s) / pl.col(s).shift(LOOKBACK) - 1).alias(s) for s in symbols],\n",
    ")\n",
    "mom5 = mom5_raw.select(pl.col(\"timestamp\"), *[pl.col(s).ewm_mean(span=5).alias(s) for s in symbols])\n",
    "impl_results[\"EMA-5\"] = _compute_variant_ic(mom5, fwd_long, symbols)"
   ]
  },
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   "id": "ea528f25",
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    "execution": {
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variant               Mean IC     ICIR      HAC t\n",
      "--------------------------------------------------\n",
      "Skip-1M               -0.0072    -0.02      -0.41\n",
      "Risk-Adjusted         -0.0152    -0.05      -0.95\n",
      "EMA-5                 -0.0189    -0.06      -1.07\n",
      "Simple                -0.0195    -0.06      -1.13\n",
      "Log                   -0.0195    -0.06      -1.13\n",
      "\n",
      "ICIR range: 0.04\n",
      "ICIR range across implementations: 0.04 (threshold for cross-implementation stability: 0.3)\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'Variant':<18} {'Mean IC':>10} {'ICIR':>8} {'HAC t':>10}\")\n",
    "print(\"-\" * 50)\n",
    "\n",
    "for name, r in sorted(impl_results.items(), key=lambda x: -x[1][\"icir\"]):\n",
    "    sig = \"*\" if r[\"p_value\"] < 0.05 else \"\"\n",
    "    print(f\"{name:<18} {r['mean_ic']:>10.4f} {r['icir']:>8.2f} {r['t_stat']:>10.2f}{sig}\")\n",
    "\n",
    "icir_range = max(r[\"icir\"] for r in impl_results.values()) - min(\n",
    "    r[\"icir\"] for r in impl_results.values()\n",
    ")\n",
    "print(f\"\\nICIR range: {icir_range:.2f}\")\n",
    "print(\n",
    "    f\"ICIR range across implementations: {icir_range:.2f} (threshold for cross-implementation stability: 0.3)\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
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     "status": "completed"
    }
   },
   "source": [
    "## 8. Robustness Summary"
   ]
  },
  {
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   "id": "7b292d0b",
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "ROBUSTNESS REPORT\n",
      "==================================================\n",
      "\n",
      "1. PARAMETER ROBUSTNESS\n",
      "   Best: 189d (ICIR = 0.08)\n",
      "   Robust range: 189-189d\n",
      "   Fraction near-optimal: 12%\n",
      "\n",
      "2. REGIME ROBUSTNESS\n",
      "   IC range across regimes: 0.1052\n",
      "\n",
      "3. IMPLEMENTATION ROBUSTNESS\n",
      "   ICIR range: 0.04\n",
      "   Best variant: Skip-1M\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\" + \"=\" * 50)\n",
    "print(\"ROBUSTNESS REPORT\")\n",
    "print(\"=\" * 50)\n",
    "\n",
    "print(\"\\n1. PARAMETER ROBUSTNESS\")\n",
    "if sweep_results and robustness:\n",
    "    rr = robustness[\"robust_range\"]\n",
    "    print(f\"   Best: {robustness['best_param']}d (ICIR = {robustness['best_value']:.2f})\")\n",
    "    print(f\"   Robust range: {rr[0]}-{rr[1]}d\" if rr else \"   Robust range: None\")\n",
    "    print(f\"   Fraction near-optimal: {robustness['robust_fraction']:.0%}\")\n",
    "\n",
    "print(\"\\n2. REGIME ROBUSTNESS\")\n",
    "if spy_vol is not None and regime_ic:\n",
    "    ic_vals = [r[\"mean_ic\"] for r in regime_ic.values()]\n",
    "    print(f\"   IC range across regimes: {max(ic_vals) - min(ic_vals):.4f}\")\n",
    "\n",
    "print(\"\\n3. IMPLEMENTATION ROBUSTNESS\")\n",
    "if impl_results:\n",
    "    print(f\"   ICIR range: {icir_range:.2f}\")\n",
    "    best = max(impl_results.items(), key=lambda x: x[1][\"icir\"])\n",
    "    print(f\"   Best variant: {best[0]}\")\n",
    "\n",
    "print(\"=\" * 50)"
   ]
  },
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   "id": "93bace1d",
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     "status": "completed"
    }
   },
   "source": [
    "## Key Takeaways\n",
    "\n",
    "1. **Robustness is breadth, not a ratio**: The fraction of parameters\n",
    "   within 90% of best performance, not mean/std\n",
    "2. **One knob at a time**: Vary lookback while holding everything else\n",
    "   constant; combine best single-knob settings afterward\n",
    "3. **Regime conditioning requires care**: Use pre-defined conditioning\n",
    "   variables (not derived from the signal); only condition when the IC\n",
    "   range across regimes exceeds noise (>0.04)\n",
    "4. **Correct for snooping**: After sweeping N parameters, apply RAS to\n",
    "   deflate the best IC for data-mining bias\n",
    "5. **Interactions multiply search**: Signal × state combinations must\n",
    "   enter the searched-set accounting from §7.4\n",
    "\n",
    "**Next**: `07_event_studies` — event-based signal validation"
   ]
  }
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