diff --git a/book/_toc.yml b/book/_toc.yml
index e50fadd..06ab718 100644
--- a/book/_toc.yml
+++ b/book/_toc.yml
@@ -24,3 +24,7 @@ parts:
- file: scm/backdoor_criterion.ipynb
- file: scm/frontdoor_criterion.ipynb
- file: scm/causal_discovery.ipynb
+ - file: prescriptive_analytics/overview.md
+ sections:
+ - file: prescriptive_analytics/fractional_uplift.ipynb
+ - file: prescriptive_analytics/budget_constrained_optimization.ipynb
\ No newline at end of file
diff --git a/book/prescriptive_analytics/budget_constrained_optimization.ipynb b/book/prescriptive_analytics/budget_constrained_optimization.ipynb
new file mode 100644
index 0000000..14bb917
--- /dev/null
+++ b/book/prescriptive_analytics/budget_constrained_optimization.ipynb
@@ -0,0 +1,920 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d6b70f64",
+ "metadata": {},
+ "source": [
+ "# Budget Constrained Optimization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "826633ac",
+ "metadata": {},
+ "source": [
+ "> [Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing](https://arxiv.org/abs/2004.09702) 논문에 제안된 방법을 Python 코드로 재현했습니다."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b841ee2c",
+ "metadata": {},
+ "source": [
+ "- 기존 uplift 모델은 이질적 처치 효과를 추정할 수 있지만, 비용 대비 이익을 충분히 반영하지 못합니다.\n",
+ "\n",
+ "- 마케팅처럼 예산이 제한된 환경에서는, 비용을 고려하면서 효과를 최대화하는 처치 효과 최적화(treatment effect optimization) 접근이 필요합니다.\n",
+ "\n",
+ "- 이를 위해 다음 알고리즘들을 활용합니다.\n",
+ " - Duality R-learner\n",
+ " - Direct Ranking Model (DRM)\n",
+ " - Constrained Ranking Models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21447276",
+ "metadata": {},
+ "source": [
+ "## Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0f91658c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
+ "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
+ ]
+ }
+ ],
+ "source": [
+ "%pip -q install fractional-uplift"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "9114f7da",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "from sklearn.ensemble import GradientBoostingRegressor\n",
+ "from sklearn.dummy import DummyClassifier\n",
+ "\n",
+ "import fractional_uplift as fr\n",
+ "from econml.dml import CausalForestDML\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "RANDOM_STATE = 42\n",
+ "pd.set_option(\"display.max_columns\", 50)\n",
+ "\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "57cbb979",
+ "metadata": {},
+ "source": [
+ "### CriteoWithSyntheticCostAndSpend Dataset\n",
+ "\n",
+ "CriteoWithSyntheticCostAndSpend 데이터셋은 \n",
+ "수익(spend)과 비용(cost)을 동시에 포함하고 있어, 비용을 고려한 처치 최적화 실험에 적합합니다. \n",
+ "\n",
+ "- 주요 컬럼:\n",
+ " - `treatment`: 광고/프로모션 노출 여부 (0/1)\n",
+ " - `spend`: 사용자가 발생시킨 매출\n",
+ " - `cost`: 해당 고객에게 treatment를 줄 때 들어간 비용\n",
+ " - `treatment_propensity`: treatment 할당 확률 (상수 0.85)\n",
+ " - `sample_weight`: 비균등 서브샘플링 보정을 위한 모집단 가중치\n",
+ " - `criteo.features`: feature 컬럼 이름 리스트"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "b2b3d7a2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(72053, 19)\n",
+ "(20333, 19)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " f0 | \n",
+ " f1 | \n",
+ " f2 | \n",
+ " f3 | \n",
+ " f4 | \n",
+ " f5 | \n",
+ " f6 | \n",
+ " f7 | \n",
+ " f8 | \n",
+ " f9 | \n",
+ " f10 | \n",
+ " f11 | \n",
+ " treatment | \n",
+ " conversion | \n",
+ " treatment_propensity | \n",
+ " cost_percentage | \n",
+ " spend | \n",
+ " cost | \n",
+ " sample_weight | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 44 | \n",
+ " 12.616365 | \n",
+ " 10.059654 | \n",
+ " 8.964588 | \n",
+ " 4.679882 | \n",
+ " 10.280525 | \n",
+ " 4.115453 | \n",
+ " 0.294443 | \n",
+ " 4.833815 | \n",
+ " 3.955396 | \n",
+ " 13.190056 | \n",
+ " 5.300375 | \n",
+ " -0.168679 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 0.85 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 100.0 | \n",
+ "
\n",
+ " \n",
+ " | 187 | \n",
+ " 12.616365 | \n",
+ " 10.059654 | \n",
+ " 8.904597 | \n",
+ " 4.679882 | \n",
+ " 10.280525 | \n",
+ " 4.115453 | \n",
+ " 0.294443 | \n",
+ " 4.833815 | \n",
+ " 3.955396 | \n",
+ " 13.190056 | \n",
+ " 5.300375 | \n",
+ " -0.168679 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 0.85 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 100.0 | \n",
+ "
\n",
+ " \n",
+ " | 484 | \n",
+ " 22.377238 | \n",
+ " 10.059654 | \n",
+ " 8.214383 | \n",
+ " 4.679882 | \n",
+ " 10.280525 | \n",
+ " 4.115453 | \n",
+ " -2.411115 | \n",
+ " 4.833815 | \n",
+ " 3.971858 | \n",
+ " 13.190056 | \n",
+ " 5.300375 | \n",
+ " -0.168679 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 0.85 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 100.0 | \n",
+ "
\n",
+ " \n",
+ " | 528 | \n",
+ " 12.616365 | \n",
+ " 10.059654 | \n",
+ " 8.350682 | \n",
+ " 4.679882 | \n",
+ " 10.280525 | \n",
+ " 4.115453 | \n",
+ " 0.294443 | \n",
+ " 4.833815 | \n",
+ " 3.955396 | \n",
+ " 16.226044 | \n",
+ " 5.300375 | \n",
+ " -0.168679 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 0.85 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 100.0 | \n",
+ "
\n",
+ " \n",
+ " | 1108 | \n",
+ " 14.617627 | \n",
+ " 10.059654 | \n",
+ " 8.489929 | \n",
+ " 3.907662 | \n",
+ " 13.253813 | \n",
+ " 4.115453 | \n",
+ " -2.411115 | \n",
+ " 4.833815 | \n",
+ " 3.809530 | \n",
+ " 42.176324 | \n",
+ " 5.737292 | \n",
+ " -0.560340 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 0.85 | \n",
+ " 0.090777 | \n",
+ " 36.459294 | \n",
+ " 3.309655 | \n",
+ " 1.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " f0 f1 f2 f3 f4 f5 f6 \\\n",
+ "44 12.616365 10.059654 8.964588 4.679882 10.280525 4.115453 0.294443 \n",
+ "187 12.616365 10.059654 8.904597 4.679882 10.280525 4.115453 0.294443 \n",
+ "484 22.377238 10.059654 8.214383 4.679882 10.280525 4.115453 -2.411115 \n",
+ "528 12.616365 10.059654 8.350682 4.679882 10.280525 4.115453 0.294443 \n",
+ "1108 14.617627 10.059654 8.489929 3.907662 13.253813 4.115453 -2.411115 \n",
+ "\n",
+ " f7 f8 f9 f10 f11 treatment \\\n",
+ "44 4.833815 3.955396 13.190056 5.300375 -0.168679 1 \n",
+ "187 4.833815 3.955396 13.190056 5.300375 -0.168679 1 \n",
+ "484 4.833815 3.971858 13.190056 5.300375 -0.168679 1 \n",
+ "528 4.833815 3.955396 16.226044 5.300375 -0.168679 1 \n",
+ "1108 4.833815 3.809530 42.176324 5.737292 -0.560340 1 \n",
+ "\n",
+ " conversion treatment_propensity cost_percentage spend cost \\\n",
+ "44 0 0.85 0.000000 0.000000 0.000000 \n",
+ "187 0 0.85 0.000000 0.000000 0.000000 \n",
+ "484 0 0.85 0.000000 0.000000 0.000000 \n",
+ "528 0 0.85 0.000000 0.000000 0.000000 \n",
+ "1108 1 0.85 0.090777 36.459294 3.309655 \n",
+ "\n",
+ " sample_weight \n",
+ "44 100.0 \n",
+ "187 100.0 \n",
+ "484 100.0 \n",
+ "528 100.0 \n",
+ "1108 1.0 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "criteo = fr.example_data.CriteoWithSyntheticCostAndSpend.load()\n",
+ "\n",
+ "features = criteo.features\n",
+ "train_df = criteo.train_data.copy() \n",
+ "test_df = criteo.test_data.copy()\n",
+ "\n",
+ "print(train_df.shape)\n",
+ "print(test_df.shape)\n",
+ "display(train_df.head())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "958d999f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Train\n",
+ "X_train = train_df[features].values.astype(np.float32)\n",
+ "T_train = train_df[\"treatment\"].values.astype(int)\n",
+ "Yg_train = train_df[\"spend\"].values.astype(float)\n",
+ "Yc_train = train_df[\"cost\"].values.astype(float)\n",
+ "W_train = train_df[\"sample_weight\"].values.astype(float)\n",
+ "e_train = train_df[\"treatment_propensity\"].values.astype(float)\n",
+ "\n",
+ "# Test\n",
+ "X_test = test_df[features].values.astype(np.float32) \n",
+ "T_test = test_df[\"treatment\"].values.astype(int) \n",
+ "Yg_test = test_df[\"spend\"].values.astype(float) \n",
+ "Yc_test = test_df[\"cost\"].values.astype(float) \n",
+ "W_test = test_df[\"sample_weight\"].values.astype(float) \n",
+ "e_test = test_df[\"treatment_propensity\"].values.astype(float) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "576b8d00",
+ "metadata": {},
+ "source": [
+ "## Duality R-learner\n",
+ "\n",
+ "Duality R-learner는 다음 두 단계를 결합한 방식입니다.\n",
+ "\n",
+ "1. 사용자별 처치 효과(CATE) 추정\n",
+ " - $\\tau_r(x)$: gain uplift (매출 증가 효과)\n",
+ " - $\\tau_c(x)$: cost uplift (비용 증가 효과)\n",
+ " > 본 노트북에서는 이론적으로는 Duality R-learner 프레임워크를 따르지만,\n",
+ " > CATE 추정 단계에서는 R-learner를 직접 구현하지 않고,\n",
+ " > `econml` 라이브러리의 `CausalForestDML`을 사용합니다.\n",
+ "\n",
+ "2. 예산 제약을 고려한 듀얼 최적화\n",
+ " - 라그랑지 승수 $\\lambda$를 학습하여 최적 정책을 도출합니다.\n",
+ "\n",
+ "우리가 풀고 싶은 문제는 다음과 같습니다.\n",
+ "\n",
+ "- 예산 $B$ 하에서\n",
+ "\n",
+ " $$\n",
+ " \\max_{z_i \\in \\{0,1\\}} \\sum_i \\tau_r(x^{(i)}) z_i\n",
+ " \\quad \\text{s.t.} \\quad\n",
+ " \\sum_i \\tau_c(x^{(i)}) z_i \\le B\n",
+ " $$\n",
+ "\n",
+ "- $z_i = 1$ 이면 고객 $i$ 에게 프로모션/광고를 집행, $z_i = 0$ 이면 미집행\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c4f11422",
+ "metadata": {},
+ "source": [
+ "### 2. CATE Modeling: CausalForestDML"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "74a38e00",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "prop_model = DummyClassifier(strategy=\"prior\")\n",
+ "\n",
+ "y_model = GradientBoostingRegressor(\n",
+ " random_state=RANDOM_STATE,\n",
+ " n_estimators=300,\n",
+ " learning_rate=0.05,\n",
+ " max_depth=3,\n",
+ " subsample=0.8,\n",
+ ")\n",
+ "\n",
+ "def fit_cate_forest(Y, T, X, sample_weight, name=\"\"):\n",
+ " model = CausalForestDML(\n",
+ " model_y=y_model,\n",
+ " model_t=prop_model,\n",
+ " discrete_treatment=True,\n",
+ " n_estimators=500,\n",
+ " min_samples_leaf=50,\n",
+ " random_state=RANDOM_STATE,\n",
+ " n_jobs=-1,\n",
+ " )\n",
+ " model.fit(Y, T, X=X, sample_weight=sample_weight)\n",
+ " return model\n",
+ "\n",
+ "tau_r_model = fit_cate_forest(Yg_train, T_train, X_train, W_train, name=\"tau_r (spend)\")\n",
+ "tau_c_model = fit_cate_forest(Yc_train, T_train, X_train, W_train, name=\"tau_c (cost)\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "1ca49bce",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "== τ_r(x) 요약 (Test) ==\n",
+ "count 20333.000000\n",
+ "mean 1.389563\n",
+ "std 3.072315\n",
+ "min -4.889611\n",
+ "25% 0.042572\n",
+ "50% 0.379860\n",
+ "75% 1.537881\n",
+ "max 22.812970\n",
+ "dtype: float64\n",
+ "\n",
+ "== τ_c(x) 요약 (Test) ==\n",
+ "count 20333.000000\n",
+ "mean 1.545375\n",
+ "std 1.643977\n",
+ "min -1.031990\n",
+ "25% 0.191356\n",
+ "50% 0.966007\n",
+ "75% 2.487680\n",
+ "max 8.429497\n",
+ "dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "tau_r_train = tau_r_model.effect(X_train)\n",
+ "tau_c_train = tau_c_model.effect(X_train)\n",
+ "\n",
+ "tau_r_test = tau_r_model.effect(X_test)\n",
+ "tau_c_test = tau_c_model.effect(X_test)\n",
+ "\n",
+ "print(\"\\n== τ_r(x) 요약 (Test) ==\")\n",
+ "print(pd.Series(tau_r_test).describe())\n",
+ "\n",
+ "print(\"\\n== τ_c(x) 요약 (Test) ==\")\n",
+ "print(pd.Series(tau_c_test).describe())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e6e387a2",
+ "metadata": {},
+ "source": [
+ "### 2. Duality: 예산 제약 하에서 $\\lambda$ 최적화\n",
+ "\n",
+ "목표는 다음과 같습니다.\n",
+ "\n",
+ "$$\n",
+ "\\begin{aligned}\n",
+ "\\max_{z_i \\in \\{0,1\\}}\\quad & \\sum_i \\tau_r(x^{(i)}) z_i \\\\\n",
+ "\\text{s.t.}\\quad & \\sum_i \\tau_c(x^{(i)}) z_i \\le B\n",
+ "\\end{aligned}\n",
+ "$$\n",
+ "\n",
+ "- $z_i = 1$: 고객 $i$ 타깃\n",
+ "- $B$: 사용할 수 있는 총 비용 예산\n",
+ "\n",
+ "이를 위해 라그랑지 승수 $\\lambda \\ge 0$ 를 도입합니다.\n",
+ "\n",
+ "$$\n",
+ "L(z, \\lambda)\n",
+ "= -\\sum_i \\tau_r(x^{(i)}) z_i\n",
+ " + \\lambda\\left(\\sum_i \\tau_c(x^{(i)}) z_i - B\\right)\n",
+ "$$\n",
+ "\n",
+ "고정된 $\\lambda$ 에 대해:\n",
+ "\n",
+ "$$\n",
+ "s_i(\\lambda) = \\tau_r(x^{(i)}) - \\lambda\\, \\tau_c(x^{(i)})\n",
+ "$$\n",
+ "\n",
+ "- $s_i(\\lambda) \\ge 0$ 이면 $z_i = 1$ (타깃)\n",
+ "- $s_i(\\lambda) < 0$ 이면 $z_i = 0$ (비타깃)\n",
+ "\n",
+ "듀얼 목적함수 기울기는\n",
+ "\n",
+ "$$\n",
+ "\\frac{\\partial g}{\\partial \\lambda}\n",
+ "\\approx \\underbrace{\\sum_i z_i\\,\\tau_c^+(x^{(i)})}_{\\text{cost\\_used}} - B\n",
+ "$$\n",
+ "\n",
+ "이며, gradient ascent 업데이트는\n",
+ "\n",
+ "$$\n",
+ "\\lambda \\leftarrow [\\lambda + \\eta(\\text{cost\\_used} - B)]_+\n",
+ "$$\n",
+ "\n",
+ "- 예산 초과($\\text{cost\\_used} > B$) → $\\lambda$ 증가 → cost가 큰 고객 penalize\n",
+ "- 예산 미만($\\text{cost\\_used} < B$) → $\\lambda$ 감소 → 더 많은 고객 선택 허용"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "id": "5fe3e686",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def duality_learn_lambda(\n",
+ " tau_r, tau_c,\n",
+ " budget,\n",
+ " sample_weight,\n",
+ " lr=1e-5,\n",
+ " n_iter=200,\n",
+ " verbose_every=20,\n",
+ " scale=1e4,\n",
+ "):\n",
+ " tau_r = np.asarray(tau_r, float)\n",
+ " tau_c = np.asarray(tau_c, float)\n",
+ " w = np.asarray(sample_weight, float)\n",
+ "\n",
+ " tau_c_pos = np.clip(tau_c, 0.0, None)\n",
+ " total_pos_cost = np.sum(w * tau_c_pos)\n",
+ "\n",
+ " B = float(budget)\n",
+ " lam = 0.0\n",
+ "\n",
+ " for it in range(n_iter + 1):\n",
+ " score = tau_r - lam * tau_c_pos\n",
+ " z = (score >= 0).astype(float)\n",
+ "\n",
+ " cost_used = np.sum(w * tau_c_pos * z)\n",
+ " gain_used = np.sum(w * tau_r * z)\n",
+ "\n",
+ " grad = cost_used - B\n",
+ " lr_eff = lr * scale / (total_pos_cost + 1e-12)\n",
+ " lam = max(0.0, lam + lr_eff * grad)\n",
+ "\n",
+ " if it % verbose_every == 0:\n",
+ " print(\n",
+ " f\"[iter {it:03d}] \"\n",
+ " f\"λ={lam:.6f}, \"\n",
+ " f\"cost_used={cost_used:.2f}, \"\n",
+ " f\"gain_used={gain_used:.2f}, \"\n",
+ " f\"grad={grad:.2f}, \"\n",
+ " f\"selected_ratio={z.mean():.3f}\"\n",
+ " )\n",
+ "\n",
+ " return lam\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "id": "233a6a45",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[iter 000] λ=0.016964, cost_used=25337.54, gain_used=30819.14, grad=5337.54, selected_ratio=0.846\n",
+ "[iter 020] λ=0.252245, cost_used=22393.95, gain_used=30436.29, grad=2393.95, selected_ratio=0.763\n",
+ "[iter 040] λ=0.348683, cost_used=20936.68, gain_used=30006.85, grad=936.68, selected_ratio=0.721\n",
+ "[iter 060] λ=0.385365, cost_used=20327.41, gain_used=29783.81, grad=327.41, selected_ratio=0.703\n",
+ "[iter 080] λ=0.397241, cost_used=20092.89, gain_used=29692.07, grad=92.89, selected_ratio=0.696\n",
+ "[iter 100] λ=0.401432, cost_used=20045.66, gain_used=29673.22, grad=45.66, selected_ratio=0.694\n",
+ "[iter 120] λ=0.403259, cost_used=20017.88, gain_used=29662.05, grad=17.88, selected_ratio=0.693\n",
+ "[iter 140] λ=0.404121, cost_used=20011.53, gain_used=29659.48, grad=11.53, selected_ratio=0.693\n",
+ "[iter 160] λ=0.404512, cost_used=20005.44, gain_used=29657.02, grad=5.44, selected_ratio=0.693\n",
+ "[iter 180] λ=0.404553, cost_used=20000.21, gain_used=29654.91, grad=0.21, selected_ratio=0.693\n",
+ "[iter 200] λ=0.404553, cost_used=20000.21, gain_used=29654.91, grad=0.21, selected_ratio=0.693\n"
+ ]
+ }
+ ],
+ "source": [
+ "BUDGET = 20000\n",
+ "\n",
+ "lambda_star = duality_learn_lambda(\n",
+ " tau_r=tau_r_test,\n",
+ " tau_c=tau_c_test,\n",
+ " budget=BUDGET,\n",
+ " sample_weight=W_test,\n",
+ " lr=1e-5,\n",
+ " n_iter=200,\n",
+ " verbose_every=20,\n",
+ " scale=1e4,\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bc2f0da6",
+ "metadata": {},
+ "source": [
+ "### 3. Cost Curve & AUCC (Test set 평가)\n",
+ "\n",
+ "Test set에서 정책의 성능을 Incremental Cost 대비 Incremental Gain 곡선으로 평가합니다.\n",
+ "\n",
+ "Duality 정책 점수는 $s_i = \\tau_r(x^{(i)}) - \\lambda^* \\tau_c(x^{(i)})$ 로 정의하며, 이 점수 기준으로 내림차순 정렬한 뒤 관측 데이터에 대해 IPW 방식으로 성능을 추정합니다.\n",
+ "\n",
+ "1. IPW incremental value / cost\n",
+ "\n",
+ " 각 샘플의 propensity score $e_i$와 가중치 $w_i$를 사용하여\n",
+ " IPW로 추정한 incremental value와 cost를 계산합니다.\n",
+ "\n",
+ " - Incremental value (spend)\n",
+ "\n",
+ " $$\n",
+ " \\Delta v_i\n",
+ " =\n",
+ " w_i\\left(\n",
+ " \\frac{T_i Y^{(v)}_i}{e_i}\n",
+ " -\n",
+ " \\frac{(1-T_i)Y^{(v)}_i}{1-e_i}\n",
+ " \\right)\n",
+ " $$\n",
+ "\n",
+ " - Incremental cost\n",
+ "\n",
+ " $$\n",
+ " \\Delta c_i\n",
+ " =\n",
+ " w_i\\left(\n",
+ " \\frac{T_i Y^{(c)}_i}{e_i}\n",
+ " -\n",
+ " \\frac{(1-T_i)Y^{(c)}_i}{1-e_i}\n",
+ " \\right)\n",
+ " $$\n",
+ "\n",
+ "\n",
+ "2. Cost Curve\n",
+ "\n",
+ " 점수 내림차순 인덱스를 $\\pi(1),\\dots,\\pi(n)$이라 하면,\n",
+ " 상위 $k$명 타깃팅 시 누적 incremental value와 cost는\n",
+ "\n",
+ " $$\n",
+ " V(k)=\\sum_{j\\le k}\\Delta v_{\\pi(j)},\\quad\n",
+ " C(k)=\\sum_{j\\le k}\\Delta c_{\\pi(j)}\n",
+ " $$\n",
+ " 이고, $(C(k), V(k))$ 점들로 Cost Curve로 구성합니다.\n",
+ "\n",
+ "3. Random baseline 및 AUCC\n",
+ "\n",
+ " AUCC는 budget $B$까지의 Cost Curve 아래 면적으로 정책 성능을 요약합니다.\n",
+ "\n",
+ " budget을 지정한 경우, 커브를 $x=B$에서 절단하고\n",
+ " 교점 $(B, V(B))$를 선형 보간으로 추가합니다.\n",
+ "\n",
+ " 모델 커브의 면적은 사다리꼴 적분으로 계산합니다.\n",
+ "\n",
+ " $$\n",
+ " \\text{AUC}_{\\text{model}}(B)=\\int_0^B V(C)\\,dC\n",
+ " $$\n",
+ "\n",
+ " 랜덤 기준선은 전체 집단의 단위 비용당 기대 value를\n",
+ "\n",
+ " $$\n",
+ " \\text{slope}\n",
+ " =\n",
+ " \\frac{\\sum_i \\Delta v_i}{\\sum_i \\Delta c_i}\n",
+ " $$\n",
+ "\n",
+ " 로 두고, 예산 $B$에서의 기대 value를\n",
+ " $V_{\\text{rand}}(B)=\\text{slope}\\cdot B$로 정의합니다.\n",
+ " 따라서 랜덤 기준선 아래 면적은\n",
+ "\n",
+ " $$\n",
+ " \\text{AUC}_{\\text{rand}}(B)\n",
+ " =\n",
+ " \\frac12\\,B\\,V_{\\text{rand}}(B)\n",
+ " =\n",
+ " \\frac12\\,B\\,(\\text{slope}\\cdot B)\n",
+ " $$\n",
+ "\n",
+ " 입니다.\n",
+ "\n",
+ " 최종 성능 지표 AUCC는\n",
+ "\n",
+ " $$\n",
+ " \\text{AUCC}(B)\n",
+ " =\n",
+ " \\frac{\\text{AUC}_{\\text{model}}(B)}\n",
+ " {\\text{AUC}_{\\text{rand}}(B)}\n",
+ " $$\n",
+ "\n",
+ " 로 정의합니다.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "2b65bc8a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def make_cost_curve(\n",
+ " score,\n",
+ " T, Y_value, Y_cost, \n",
+ " e, w, \n",
+ " n_points=101, \n",
+ "):\n",
+ " score = np.asarray(score, float)\n",
+ " T = np.asarray(T, float)\n",
+ " Y_value = np.asarray(Y_value, float)\n",
+ " Y_cost = np.asarray(Y_cost, float)\n",
+ " e = np.asarray(e, float)\n",
+ " w = np.asarray(w, float)\n",
+ "\n",
+ " # IPW incremental\n",
+ " inc_value = w * (T * Y_value / e - (1 - T) * Y_value / (1 - e))\n",
+ " inc_cost = w * (T * Y_cost / e - (1 - T) * Y_cost / (1 - e))\n",
+ "\n",
+ " # score 내림차순 정렬\n",
+ " order = np.argsort(-score)\n",
+ " inc_value = inc_value[order]\n",
+ " inc_cost = inc_cost[order]\n",
+ "\n",
+ " # 누적합\n",
+ " cum_value = np.cumsum(inc_value)\n",
+ " cum_cost = np.cumsum(inc_cost)\n",
+ "\n",
+ " n = len(score)\n",
+ " idx = np.linspace(0, n - 1, n_points).astype(int)\n",
+ "\n",
+ " x = np.concatenate([[0.0], cum_cost[idx]])\n",
+ " y = np.concatenate([[0.0], cum_value[idx]])\n",
+ "\n",
+ " max_cost = float(x[-1])\n",
+ " max_value = float(y[-1])\n",
+ " return x, y, max_cost, max_value\n",
+ "\n",
+ "\n",
+ "def _clip_curve_to_budget(x, y, budget):\n",
+ " if budget is None:\n",
+ " return x, y\n",
+ "\n",
+ " x = np.asarray(x, float)\n",
+ " y = np.asarray(y, float)\n",
+ "\n",
+ " order = np.argsort(x)\n",
+ " x = x[order]\n",
+ " y = y[order]\n",
+ "\n",
+ " if budget <= x[0]:\n",
+ " return np.array([0.0, budget]), np.array([0.0, 0.0])\n",
+ "\n",
+ " if budget >= x[-1]:\n",
+ " return x, y\n",
+ "\n",
+ " j = np.searchsorted(x, budget, side=\"right\") - 1\n",
+ " j = np.clip(j, 0, len(x) - 2)\n",
+ "\n",
+ " x0, x1 = x[j], x[j + 1]\n",
+ " y0, y1 = y[j], y[j + 1]\n",
+ "\n",
+ " # 선형 보간으로 y(budget) 추가\n",
+ " t = 0.0 if x1 == x0 else (budget - x0) / (x1 - x0)\n",
+ " yb = y0 + t * (y1 - y0)\n",
+ "\n",
+ " x_clip = np.concatenate([x[: j + 1], [budget]])\n",
+ " y_clip = np.concatenate([y[: j + 1], [yb]])\n",
+ " return x_clip, y_clip\n",
+ "\n",
+ "\n",
+ "def aucc_from_cost_curve(x_cost, y_value, inc_cost_all, inc_value_all, budget=None):\n",
+ " x = np.asarray(x_cost, float)\n",
+ " y = np.asarray(y_value, float)\n",
+ "\n",
+ " # x 정렬\n",
+ " order = np.argsort(x)\n",
+ " x = x[order]\n",
+ " y = y[order]\n",
+ "\n",
+ " if budget is not None:\n",
+ " x, y = _clip_curve_to_budget(x, y, budget)\n",
+ " X_max = float(budget)\n",
+ " else:\n",
+ " X_max = float(x[-1])\n",
+ "\n",
+ " # 모델 면적\n",
+ " auc_model = np.trapz(y, x)\n",
+ "\n",
+ " # 랜덤 기준선\n",
+ " eps = 1e-12\n",
+ " slope = float(inc_value_all) / (float(inc_cost_all) + eps)\n",
+ " Y_at_Xmax_random = slope * X_max\n",
+ "\n",
+ " auc_random = 0.5 * X_max * Y_at_Xmax_random if X_max > 0 else np.nan\n",
+ " aucc = auc_model / auc_random if (auc_random and auc_random > 0) else np.nan\n",
+ "\n",
+ " return aucc, auc_model, auc_random, slope\n",
+ "\n",
+ "\n",
+ "\n",
+ "def plot_cost_curve(x, y, max_cost, max_value, title=\"Cost Curve\", budget=None):\n",
+ " plt.figure()\n",
+ " plt.plot(x, y, label=\"Model (sorted by score)\")\n",
+ "\n",
+ " # 랜덤 기준선\n",
+ " plt.plot([0, max_cost], [0, max_value], label=\"Random baseline\")\n",
+ "\n",
+ " # budget 표시\n",
+ " if budget is not None:\n",
+ " plt.axvline(budget, linestyle=\"--\", linewidth=2, label=f\"Budget = {budget}\")\n",
+ "\n",
+ " x_arr = np.asarray(x, float)\n",
+ " y_arr = np.asarray(y, float)\n",
+ "\n",
+ " xb, yb = _clip_curve_to_budget(x_arr, y_arr, budget)\n",
+ " plt.fill_between(xb, yb, 0, alpha=0.2)\n",
+ "\n",
+ " plt.xlabel(\"Incremental Cost\")\n",
+ " plt.ylabel(\"Incremental Value\")\n",
+ " plt.title(title)\n",
+ " plt.legend()\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "b735c814",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[전체] AUCC=1.4121 (auc_model=4.82e+10, auc_random=3.41e+10)\n",
+ "[budget=20000] AUCC=4.6471 (auc_model=8.51e+08, auc_random=1.83e+08)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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87bffTN0cogyPmSoiIiIiI2CmioiIiMgIGFQRERERGQGLf75GsoXIvXv31Aar3MyTiIgoY5DqU1IoWUqPyPZJz8Og6jWSgOpl+1gRERGRebp9+zZy58793NsZVL1GkqHSvykv288qo2nwzQ4EhETBx8MJ2z6uZ+rmEBERGY1sUyVJEf3n+PMwqHqN9EN+ElBZWlA1tEU5hEfHwtXR3uJeGxERkXjZ1B0GVWQUXar6sSeJiMiqcfUfERERkREwqCIiIiIyAg7/kVEEBEciTtNgZ2MDH09n9ioREVkdBlVkFG/+uBf+wZHw9XTGgdEN2atERGR1OPxHREREZAQMqoiIiIiMgEEVERERkREwqCIiIiIyAgZVREREREbAoIqIiIjICBhUEREREWX0oGry5MmoXLmy2vXZx8cHbdq0wcWLFxOdU69ePbWBYcLLgAEDEp1z69YttGjRAq6urupxhg8fjtjY2ETn7NixAxUqVICTkxMKFSqEhQsXPtOemTNnIl++fHB2dkbVqlVx6NChRLdHRkZi0KBByJIlC9zd3dG+fXs8ePDAqH1CRESU0UXGxCE6Nh7WxqTFP3fu3KmCFAmsJAgaPXo0GjdujHPnzsHNzc1wXt++fTFp0iTDsQRPenFxcSqg8vX1xb59+3D//n1069YNDg4O+PLLL9U5169fV+dIMLZo0SJs3boVffr0QY4cOdCkSRN1zt9//42hQ4dizpw5KqCaMWOGuk2CPAnUxEcffYS1a9di6dKl8PLywuDBg9GuXTvs3bv3NfYaERGR+Tp3Lxg9Fx5CZEw8+tTKjx4188HD2SHRORJwnboTiDtPI/AgOBIPgqMQEBIJBztb+Hg4qZ055GuuTC4o7OOe7P0vPQhBbLyGsrm9VMLFHNhomqbBTDx8+FAFMBJs1alTx5CpKleunApykrN+/Xq0bNkS9+7dQ/bs2dV1EhiNHDlSPZ6jo6P6XoKhM2fOGO7XqVMnBAYGYsOGDepYAikJ7n788Ud1HB8fjzx58uD999/HJ598gqCgIGTLlg2LFy9Ghw4d1DkXLlxA8eLFsX//flSrVu2lry84OFgFY/JYnp6esCRXH4YiLl6Dna0NCmZzN3VziIjIBI7ceIKeCw8jJPL/o0VeLg7oWzs/mpXOgUPXn2DHxQDsvfIYoVGJR5ReJKeXM4r4eiCTqyMu+IfgSkAIYuJ04UuR7O7oU6sAWpfPCSd7u3R5XSn9/DarbWqksSJz5syJrpfs0h9//KGyUa1atcLYsWMN2SoJaEqXLm0IqIRkmN577z2cPXsW5cuXV+c0atQo0WPKOUOGDFHfR0dH4+jRoxg1apThdltbW3Ufua+Q22NiYhI9TrFixeDn55fioMqSMZAiIrJu2y8G4L0/jqoMVZV8mdGpSh7M2nEVVwJC8c2mS+qSUBY3RxT19VAZqeyezsjm4aQyTwHBUXgQEomHwVG4+SRMZbHuBUWqS0ISrMXEScYqFCOWn8KiDTsxOesG5OgyC5m8TJO4MJugSjJDEuTUrFkTpUqVMlzfpUsX5M2bFzlz5sSpU6dU1kmG5FasWKFu9/f3TxRQCf2x3PaicyTyjIiIwNOnT9UwYnLnSDZK/xiS9fL29n7mHP3zJBUVFaUuevJ8REREluafk/cw9O8TKiiqXzQbZnWtCBdHO7QulwtrTt3D91sv4/qjMJTL4416RX1Qr2g2lMrpBVvblw/bBYXH4FJAiBruexoWjSLZPVAipydyebsgODIWfx26hdu7F2FEzCx4PojAwT89UHXAbFh1UCVzq2R4bs+ePYmu79evn+F7yUjJPKiGDRvi6tWrKFiwIMyZTMSfOHGiqZtBRERkdAHBkVhz6r4KqE7cDlTXtS6XE990LKvmRgmZEiKB1Ztlc6rhOkf71K+P83J1QOV8mdXlmdvsY9E/+AcgdgFgA5yzL4F8zYfCVMwiqJIJ32vWrMGuXbuQO3fuF54rc5/ElStXVFAlQ4JJV+npV+TJbfqvSVfpybGMi7q4uMDOzk5dkjsn4WPIMKHMw0qYrUp4TlIynCiT3xNmqmSeliVafeIuIqLjDH+ZEBGRZXkUGoXTd4Jw6k4QDlx7jAPXH0M/K1sSTj1r5senzYsnm32SieSO9kaeTP7wIrC0JxBwVp4BqD0UJeqNBuzsrTOokjnyMhF85cqVquRB/vz5X3qfEydOqK+SsRLVq1fHF198gYCAAMMqvc2bN6uAqUSJEoZz1q1bl+hx5By5XsiwXsWKFdWqQCnroB+OlGMJ+ITcLisK5ToppSBkGFLKOegfJykp3yAXazB53QX4B0fC19OZQRURUQYgq+3m776uJpXnz+qKvFnckD+rG7K6O+HWk3A1F0oWIcnXs3eDnpnTJCr4eassVPMyOeDj4fx6Gi6R3IlFwLrhQEw44JYNaPcTULABTM3e1EN+sppu9erVqlaVfm6SzLCXDJIM8cntzZs3V7WhZE6VlDWQlYFlypRR50oJBgme3n33XUyZMkU9xpgxY9Rj6wMaKaUgq/pGjBiBXr16Ydu2bViyZIlaEagnGaXu3bujUqVKqFKlilptGBYWhp49exra1Lt3b3WeTKSXoE0CQgmorH2SOhERZRyyUnvRwZuYuvFiolV6LyNVC2RRUplcXiiT2wsNi2dHnsz/L3H0WkSFAGuGAqeX6I4L1APa/gR4JJ4TbZVB1ezZsw1lExJasGABevTooTJIW7ZsMQQ4MnQmWSIJmvRk2E6GDmW1nwQ4Ut9KgqOEda0kAyYBlARk3333nRpinD9/vqFGlXj77bdVCYZx48apwEzKOEi5hYST17/99lu1KlDaIBPQ5f6zZs1K514iIiJK/UjQ7suP1ORuyUAVzOamAiCpITVm1RmcvqtbbV86l5eaNH7jcThuPg5Tk8kl0JKVeIWyuaOgj5v6WiyHJ0rl8oK7kwnDhvungKU9gCdXARs7oP5ooNZQWa4Pc2FWdaosnSXXqar25VbD8N+B0Q1N3RwiIqskH+mbzz3AD9uuGAInPXtbG8Rpmho983C2x4gmRdGlal41mTzh/aNi4+HskD71ntJEGnxoHrDpUyAuGvDMBbT/Gcib/NSb9JAh61QRERGRzpm7Qdh75ZFaACQZIrlIZXEJiOQ6CXyc7W3V9452tth07oEqXSDFMYWLgx1qFsqKe4ERKgMVEROnrm9bPhdGNS+W7BwomVBuVgFVxFNg9WDgwhrdcdHmQOuZgOuzKwHNAYMqIiIiM3L7STi+2XQRq0/cS9P9JfjqXiMvetcqgMxujuq6+HhNjSbEaxpyZ3rN86DS6vYhYFlvIOgWYOsANP4MqDpAN7nLTDGoIiIiMgOB4dGYuf0Kft13E9Fxus2IGxTzgbODrZrnJNu6qK+RsSrrFJFk02LJYPWqmR89a+aDt6sumNKTMgc5vV2QIcTHA/u+B7ZOArQ4IFN+oMMvQK4KMHcMqoiIiExEAqX7gRFqi5eZ268iKCJGXV+zUBaMalZcTQ5/EclARcbGqa1hJEOVluKaZiX0IbCyP3B1q+64VHug5QzAOWPMQ2ZQRURE9BqCpz2XH6oVebefRqhAyj8oEiFJNhUu5uuBT5oVQ90i2dT8ppeRDJSroz2SJKYypms7gRV9gdAHgL0L0OxroEI3sx7uS4pBFRmFLL9N+JWIyNrdeBSGbRcC1OXg9cdqm5bkeDrbwy+LK7pVz4f2FXInWo1nFeJigZ1fA7umylI/IFsxoMMCILuugHdGwqCKjOLf92uxJ4nIqslQ3Mk7gdhw1l+VNbj2MCzR7fmyuKJBsewonsMDObxc4OvljBxeznAzZe0nUwu6q8tO3dyrOy7/LtBsCuCYQSbTJ2HF7yQREdGriY2Lx+EbT7HxrD82nPFXK+wS1oWqkj+zmmwulwLZ3NndCV3aCKwcAEQ8ARzdgVbfAaU7ICNjUEVERJSGob05O6+q2lBPwqIN17s52qFB8exoUjI76hTJBk9nB/ZtUrHRwNaJwP4fdcc5yuqG+7IUREbHoIqIiCgVman5e67j282XVOVx4e3qgDeKZ0fTUr6q2KZZFc80N0+uA8t6AfeO6Y6l7tQbkwB7y5iPy6CKjGLUitMIioiGl4sjJrcrzV4lIotz9l4QRi4/hTN3gw1lDwbWK4Sq+TPD3i6DlzJ4Hc6sAP79EIgKBpy9gTazgGItYEkYVJFRbL8QYNj7j4jIkkTGxKntX+buuoa4eE2t1hvTsgQ6VsydorIHVi8mAtgwCji6QNcVearq9u7zzmNxXcOgioiIKBmyufD+q48xZtUZXHukW8nXvLQvJrxZMtl98ygZDy8BS3sAAWdlZ0Gg1kdA/dGAnWXONWNQRURE9B+paL7n8iPsuBiAnZceIiAkSl3v4+GEz9qUQpOSvuyrlNA04MRiYN3HQEw44JYNaDsXKNTQovuPQRUREVm88OhYXH4QiuDIGMTGaYiJi0ds/H9f43SbDe+8+BBHbz1VQ3x6Lg52aFchF0Y0LQYvF8vMrhhdVAiwdhhw6m/dcf66QLt5gEd2WDoGVUREZFFDdneeRuD8/WCcvx+CC/7BuOAfghuPw1TyJCUKZnNDvaI+qFc0Gyrny8zVfKlx/xSwrCfw+ApgY6sb6qs1FLC1jhWRDKqIiChDCYmMwYFrT3A5IAQBwVF4EBz53yUKD0OiEB2nK3WQVFZ3J2R1d4SDnS3s7WzgYPvfVztbuDvbo1qBLKhXJBvyZM6Y1bxNSiLWw/OBjZ8CcVGAZy7dZPS81WFNGFQREZFZk+G4M3eDsPvyQ+y69AjHbj1VQ3fP42hni0I+7iiew1NtCVPM1xPFcniooIrSQcRTYPVg4MIa3XGRZrpyCa6Zra67GVQREZFJRUTHYeelALXNy93ACHWdjawU+49kpJ6Gxzyzj155v0xq/zwp5ZLd0wk+6quzmlQu2Sd6DW4f1hXzDLoF2DroCnlWew+w0lITDKqIiOi1C4uKxfaLAVh/2h/bLgQgIibuhed7ONmjRqEsqF04G+oUzga/LByiM6n4eGDf98C2z4D4WCBTPt1WM7kqwJoxqCKjeLNcTgSFx8DLlatjiCh5geHR2HJel5GSoTz9Ni8idyYXNCvlq7JPkuPQ/pumo0FTmaiyebyZfTIXoQ+Blf2Bq1t1xyXbAa1mAM5esHYMqsgoRjcvzp4komQdufEEP2y7gj1XHiUqV5A3iyual86B5qVyoFQuT1Ynzwiu7wKW9wVC/QF7Z6DZFKBCN6sd7kuKQRUREaULmVw+bdNFbL/40HBdMV8PtfGwFNGU77nNSwYRFwvsmgLsnKLLI2Yrphvuy17C1C0zKwyqiIjIaAKCI7H5/ANsPPsAuy7pgik7Wxu8VSk3+tUpiPxZ3djbGU3wPWB5H+DmXt1x+Xd0GSpHvpdJMagiIqJXIvMplx69jX9P3cfJ24GJbnuzbE589EYRBlMZ1aWNwMoBQMQTwNEdaDkDKNPR1K0yWwyqyCgaTNuhivD5eDph27B67FUiKyBVy3/bfwMrj99FZMz/J52Xy+ONN0pkVxPPC2RzN2kbKY1io4GtE4H9P+qOfcsAHRcCWQqyS1+AQRUZRXhUHEKjYuEexR8pIku378ojzNh6GYeuPzFcJ/Ojulb1Q+OSvqpWFGVgT64Dy3sDd4/qjqv0Bxp/BtizeOrL8BOQiIhSJDo2Ht9suoifdl0zzJWSSefdq+dD5XyZOOncEpxdCfzzARAVDDh7A61nAsVbmrpVGQaDKiIieqlrD0PxwV/HceZusDruUtUPHzQorCqakwWIiQA2jgaO/KI7zlNVt3efdx5TtyxDYVBFRETPpWkalh69gwn/nEV4dBy8XR0wpX0ZNcxHFuLhJWBZT+DBGd1xraFA/dGAHYs5pxaDKiIiStbZe0H4ZuP/60zVKJgF098qx+yUJTmxGFg7DIgJB9yyAW3nAoUamrpVGRaDKiIiSuRKQCi+3XIJa0/d131Q2NpgaOMi6F+noJpHRRYgKlQXTJ36S3ecvy7Qbh7gkd3ULcvQGFQREZFy63E4vt92GSuO3YF+N5lWUmeqUWGWRrAk90/phvseXwFsbHVDfTLkZ2tn6pZleAyqiIismOzFt/1CAP44eBM7Lz1UmxiLRsWzY1jjIiiew9PUTSRjkTf38Hxg46dAXBTgmQtoPx/IW4N9bCQMqoiIrFBASCT+PnQbfx66hXtBkYbr6xbJhiGNCqO8XyaTto+MLCIQ+GcwcP5f3XGRpkCb2YBrZna1ETGoIqP4om0pVVHZ2cGWPUpkxiJj4jB7x1XM3nlV1Z0SsqLvrUp50KWKH/Jxbz7Lc/swsKwXEHQLsHUA3pgEVHsPsOH8OGNjUEVG0bA4JzcSmTvZ4Hjc6jO48TjcsJ1Mt+p50bx0Djg7cD6NxYmPB/b/AGydBMTHApnyAR0WALkqmLplFotBFRGRhQsIjsRna8/j35P31LGPhxPGtyqJ5qV9WQXdUoU9Alb2B65s0R2XbAe0mgE4e5m6ZRaNQRURkQUKi4rFyTuBOHDtCRbsuY6QqFhINYTuNfJh6BtF4OHMwo4W6/puYHkfINQfsHcGmn0NVOjO4b7XgEEVGcXpO0GIjouHo50tSufmX0JEpgiitl0IwJEbT3D01lOcvx+iVvbplc3jjS/alEKpXPz9tFjxccDOr4GdU2SpH5C1KNBxAZC9pKlbZjUYVJFR9P3tCPyDI+Hr6YwDo1mNl+h1kKBp39VHWHHsLjac8UdETFyi23N6OaNC3kyoX9QHbcrnYuFOSxZ8D1jeF7i5R3dc/h2g2RTA0c3ULbMqDKqIiDLgHKmf917HquN38SA4ynB9/qxuKoCqkNcbFfwyIae3i0nbSa/JpU3AqgFA+GPA0R1oOQMo05HdbwIMqoiIMljV887zDuBuYIQ69nJxwJtlc6JdhVxqNZ8Nl8lbj9hoYOtEYP+PumPfMkDHhUCWgqZumdViUEVElEFcfxSGzj8dUEPtkpUa2bQY6hfLBid7lkOwOk9v6GpP3T2qO67SH2j8GWDvZOqWWTUGVUREGcCVgBB0mXcQASFRKOzjjkV9q8LHw9nUzSJTOLsK+OcDICpIVyKh9SygeEu+F2aAQRURkZm76B+CrvMP4FFoNIr5euCPPlWR1Z0ZCasTEwlsHA0c+Vl3nLsK0OFnwNvP1C2j/zCoIiIyY2fvBeGd+QfxNDwGJXN64o/eVZHJzdHUzaLX7eElYFlP4MEZ3XGtj4D6nwJ2rDdmThhUERGZIU3TsPb0fXy68gyCImJQNrcXfutVFV6u/BC1OicWA2uHATHhgGtWoN1coFAjU7eKksGgiojIzASERGLsqjPYePaBOq7g542FvarAk1XQrUtUKLDuY+Dkn7rj/HWAdvMAD19Tt4yeg0EVEZEZZaekkOekNedUdsre1gYD6xfCoPoFucLP2vifBpb2BB5fBmxsgXqjgdpDAVuu9DRnDKrIKLYMq6s+EFgjhyht7gVGYPTK09hx8aE6LpXLE1Pal0WJnJ7sUmuiacDh+cDGT4G4KMAjp24yet4apm4ZpQCDKjIKdyf+KBGlRXh0LBbsvYHZO64iNCoWjva2GNKoMPrVLgB7O1t2qjWJCAT+eR84/4/uuEhTXbkEtyymbhmlED8JiYhMICYuHn8dvo3vt17Gw5Aow9ypKR3KoJCPB98Ta3PniG51X+AtwNYBeGMiUG0gwAr5GQqDKiKi1yg+XsOa0/cxbdNF3Hwcrq7Lk9kFw94oqrabsbW14fthTeLjgf0/AFsnAfGxQKZ8QIdfgFwVTd0ySgMGVWQU83dfQ0hkLDyc7dGndgH2KlEywqJi8dHfJ7DpnG5VX1Z3R7zfoDA6V/FTw35kZcIeASsHAFc2645LtgVafaerkk4ZEoMqMor5u6+r/ch8PZ0ZVBEl487TcPT59Qgu+IfA0c4WgxsUQu9a+eHG+YjW6fpuYEVfIOQ+YO8MNP0KqNiDw30ZnEn/NJo8eTIqV64MDw8P+Pj4oE2bNrh48WKicyIjIzFo0CBkyZIF7u7uaN++PR480P2Vp3fr1i20aNECrq6u6nGGDx+O2NjYROfs2LEDFSpUgJOTEwoVKoSFCxc+056ZM2ciX758cHZ2RtWqVXHo0KFUt4WIKKmjN5+gzcy9KqCS7NSf/arhg4aFGVBZo/g4YPtk4Lc3dQFV1iJA321ApZ4MqCyASYOqnTt3qiDlwIED2Lx5M2JiYtC4cWOEhYUZzvnoo4/w77//YunSper8e/fuoV27dobb4+LiVEAVHR2Nffv24ddff1UB07hx4wznXL9+XZ1Tv359nDhxAkOGDEGfPn2wceNGwzl///03hg4divHjx+PYsWMoW7YsmjRpgoCAgBS3hYgoqeVH76DzTwfVvn3Fc3hi9eBaqJg3EzvKGgXfA359E9j5FaDFA+XeAfrtALKXNHXLyFg0MxIQEKBJk3bu3KmOAwMDNQcHB23p0qWGc86fP6/O2b9/vzpet26dZmtrq/n7+xvOmT17tubp6alFRUWp4xEjRmglS5ZM9Fxvv/221qRJE8NxlSpVtEGDBhmO4+LitJw5c2qTJ09OcVteJigoSJ0vXy1N1S+2aHlHrlFfiUjTwqNitXGrTqvfC7n0++2wFhoZw66xVpc2adrX+TVtvKemfZFT007+beoWUSqk9PPbrGZGBgUFqa+ZM2dWX48ePaqyV40a/X+Po2LFisHPzw/79+9Xx/K1dOnSyJ49u+EcyTAFBwfj7NmzhnMSPob+HP1jSJZLnivhOba2tupYf05K2pJUVFSUakfCCxFZvqM3n6L597vx6/6b6nhw/UKY3bUih/usUWw0sGkMsKgDEP4Y8C0N9NsJlHnL1C0jS56oHh8fr4blatasiVKlSqnr/P394ejoCG9v70TnSgAlt+nPSRhQ6W/X3/aicyTIiYiIwNOnT9UwYnLnXLhwIcVtSW7O2MSJE9PUH0SU8UTFxmHGlsuYu/Mq4jWohRtSd6pOkWymbhqZwtMbwLJewN2juuMq/YA3PgMcnPl+WCizCapkbtWZM2ewZ88eWIpRo0apeVp6EsTlyZPHpG0iIuPVm3oUFoV7gZG4HxiBu4ERWHrkDi4+CFG3tyufC+PfLAkvFwd2uTU6txpY/T4QFaQrkdB6JlC8lalbRdYQVA0ePBhr1qzBrl27kDt3bsP1vr6+amguMDAwUYZIVtzJbfpzkq7S06/IS3hO0lV6cuzp6QkXFxfY2dmpS3LnJHyMl7UlKVlpKBcishyxcfH4ec91/LjtCkKiEq8yFlncHPFF29JoWir5/xfIwsVEAhtHA0d+1h3nrqwr5untZ+qW0Wtg0jlVsgGvBFQrV67Etm3bkD9//kS3V6xYEQ4ODti6davhOim5ICUUqlevro7l6+nTpxOt0pOVhBIwlShRwnBOwsfQn6N/DBnWk+dKeI4MR8qx/pyUtIWILNvZe0FoM2svJq+/oAIqKX6ew8tZreZrWSYHPmhQCBs/qsOAylo9vATMb/j/gKrmEKDnegZUVsTe1EN+ixcvxurVq1WtKv3cJC8vL5VBkq+9e/dWQ2gyeV0Cpffff18FMdWqVVPnSgkGCZ7effddTJkyRT3GmDFj1GPrs0QDBgzAjz/+iBEjRqBXr14qgFuyZAnWrl1raIs8R/fu3VGpUiVUqVIFM2bMUKUdevbsaWjTy9pizUrl8kQOb2f1VzqRpYmMicN3Wy/jp13XEBevqSG9T1sUV0N83PSYlBN/AmuHATFhgGtWoN1coFDiBVJkBTQTkqdP7rJgwQLDOREREdrAgQO1TJkyaa6urlrbtm21+/fvJ3qcGzduaM2aNdNcXFy0rFmzasOGDdNiYhIvXd6+fbtWrlw5zdHRUStQoECi59D74YcfND8/P3WOlFg4cOBAottT0hZrLalAZKlCImO0xtN3GkojDPzjqPYgOMLUzSJzERmiaSv660olyGVhS00LTvnnAmUMKf38tpF/TB3YWQuZqC4ZLykdIZkuIjJ/nyw/hb8O31aV0L9sWxqNS3KuFP3H/zSwtCfw+DJgYwvUGwXUHgbY2rGLrPTz2ywmqhMRmaNtFx6ogMrGBvixSwVUK5DF1E0icyC5CJk3tWE0EBcFeOQE2s8H8tU0dcvIxBhUEREl42lYNEYuP62+710zPwMq0okIBP55Hzj/j+64cBOgzWzAjQE3MagiI+nz62E8DotWE9Xnd6/MfqUMTWZFjFl1Bg9DolDYxx0fNylq6iaRObhzBFjWEwi8Bdg6AI0mANUHcSNkMmCmiozizN1g+AdHqgrSRBndPyfvYe3p+7C3tcH0t8rB2YFzZKxafDyw/0dg60QgPhbwzgt0XADkqmjqlpGZYVBFRJSAf1Akxq3W7Rs6uEEhlM7txf6xZmGPgJUDgCubdccl2wKtvtNVSSdKgkEVEdF/Q35n7wXj87XnEBQRgzK5vTCofiH2jTW7vhtY0RcIuQ/YOwNNJwMVe3K4j56LQRURWbUTtwOx9tQ9bDjrj9tPItR1jva2mP5WWTjYmXTTCTKV+Dhg11Rg59eAFg9kLQJ0XAhkL8n3hF6IQRURWSWpjC5ZqQV7bxiuc3awRd0i2dC7VgEU8vEwafvIRILv67JTN3brjsu9AzSfAji68S2hl2JQRURWJzw6Fh/+dQKbz+k2UZd9+1qUzoG6RbPB1ZH/LVqty5uBlf2B8MeAgxvQ8lug7NumbhVlIPzfg4isipRJkBIgJ+8EqWG+aR3LolXZnKZuFplSXAywdRKw73vdsW9poMNCICvn1FHqMKgiIqtxJSAEPRYcxp2nEcjk6oB53SqhUr7Mpm4WmdLTm8CyXsDdI7rjKv2ANz4DHFgehlKPQRURWYVD15+oDFVwZCzyZXHFgp5VkD8r58lYtXOrgdXvA1FBuhIJb/4IlHjT1K2iDIxBFRlFn9r5ERIZCw9n/kiR+dl9+SH6/nYEkTHxqJQ3E37qVgmZ3RxN3SwylZhIYNOnwOH5uuPclYH2PwOZ8vI9oVfCT0Ayij61C7AnySxtOfcAAxcdQ3RcPOoXzYbZ71RkhXRr9ugysLQn8EC3ryNqDgEajAHsHEzdMrIADKqIyGL9e/IePvr7BGLjNTQr5YvvOpVXk9PJSp38C1gzFIgJA1yzAm3nAoUbmbpVZEEYVBGRRVpy5DY+WX4K8RrQtnwuTO1QBvYs5mmdokKBdcOBk4t1x/lqA+3mAZ45TN0ysjAMqsgoQqNi1TYfNjY2cHfijxWZTnRsPL7ZdBE/7bqmjjtX8cMXbUrB1taGb4s18j8DLO0BPL4M2NgC9UYBtYcBttwkm4yPn35kFI2m7YR/cCR8PZ1xYHRD9iqZxJWAUHz413G1h5/oV6cARjUrpoJ9sjKaBhz5BdgwCoiLAjxy6Caj56tp6paRBWNQRUQZnmRJ/zx0G5PWnFUr/KQG1dfty6BxSV9TN41MISIQ+PcDXckEUbgJ0GY24JaF7welKwZVRJShxcdrGLn8FJYevaOOaxbKgulvlUN2TxZvtEp3jgLLegCBtwBbB6DRBKDaQMCWCxQo/TGoIqIM7euNF1RAZWdrg5FNi6JPrQKcP2WN4uOBAzOBLROA+FjAOy/QYQGQu6KpW0ZWhEEVEWVYv+y5jrk7dRPSZbivQ8Xcpm4SmULYY2DVAODyJt1xiTbAm9/rqqQTvUYMqogow9ag+mztOfX98CZFGVBZqxt7gOV9gJD7gJ0T0OwroGJPgIsTyAQYVBFRhrPv6iMMW3JSLfDqVj0vBtYraOom0esWHwfsmgrs/BrQ4oGsRXTDfb6l+F6QyTCoIqIM5ey9IPT/7ajadkaqpI9vVZIlE6xN8H1gRV/gxm7dcbmuQPOpgCM3yCbTYlBFRBnGtYeh6PbzIYRExaJK/sz49u1yaoI6WZHLW4CV/YHwR4CDG9ByOlC2k6lbRaQwqCKiDOFuYATemX8Qj8OiUTKnJ+Z3r8SNka1JXAywdRKw73vdcfbSQMcFQNbCpm4ZkQGDKjKKed0qqeEYR+6tRungUWgU3p1/EPeCIlEgmxt+7VUFns4O7Gtr8fQmsLw3cOew7rhyX6Dx54ADa5GReWFQRUZROjeXLlP6CIqIUUN+1x6FIZe3C/7oXRVZ3Z3Y3dbi3D/AP4OByCDAyQto/SNQ4k1Tt4ooWQyqiMhsXXoQgo+XnsS5+8HI6u6I33tXQU5vF1M3i16HmEhg06fA4fm649yVdXv3ZcrL/iezxaCKiMxOWFQsvtt6WRX3jI3X4Olsj996VUWBbO6mbhq9Do+uAEt7AA9O645rfgg0GAvYcciXzFuaNkP6/fffUbNmTeTMmRM3b95U182YMQOrV/+3eSVZna3nH2DtqfvqK9GrbIy87vR9NJq+Ez/tuqYCqjdKZMe6D2ujRE5Pdqw1OPkXMLeOLqByzQp0XQ68MYkBFVlmUDV79mwMHToUzZs3R2BgIOLi4tT13t7eKrAi6/TpyjMYtPiY+kqUFhHRcRi65CQGLjqG+0GRyJPZBb/0qKQWQeTO5MpOtXRRocDK93TlEmLCgHy1gQF7gMKNTN0yovQLqn744QfMmzcPn376Kezs7AzXV6pUCadP/5eqJSJKhdtPwtF+9j6sPH5X1Z16v0EhbP6oLhoUy85+tAb+Z4B59YGTiwEbW6DeaKDbasAzh6lbRpS+c6quX7+O8uXLP3O9k5MTwsLCUvtwRGTldl16iPf/PK5W+WVxc8SPXSqgesEspm4WvQ6yz9CRX4ANo4C4KMAjB9B+PpCvFvufrCOoyp8/P06cOIG8eROvwNiwYQOKFy9uzLYRkQWLjInD3J3XMGPrJfXZWjaPN+a8UwE5vLi6zypEBAL/fgicW6U7LtwYaDMbcMtq6pYRvb6gSuZTDRo0CJGRkWpS6aFDh/Dnn39i8uTJmD//v6WvRETPERUbh78P38bM7VfwIDhKXde5Sh5MeLMknOz/P6WALNido8CyHkDgLcDWHmg0Aag2CLBN09opoowbVPXp0wcuLi4YM2YMwsPD0aVLF7UK8LvvvkOnTtx/iYiSFxMXjxXH7uD7rVfUljNCinkOb1IUbcrnYrdZg/h44MBMYMsEID4W8M4LdFgA5K5o6pYRma5OVdeuXdVFgqrQ0FD4+PgYpzVEZJGuPgxF31+PqKrowsfDSU1Gf6tyHmanrEXYY2DVAODyJt1xidZAq+8BF29Tt4zIPIp/urq6qgsR0YsCqk4/HcDDkCg1Ef29egXxTrW83AzZmtzYAyzvA4TcB+ycgKaTgUq9ABsbU7eMyPQT1W1e8Itw7dq1V20TEVmIaw9D0fm/gKqYrwcW9amKLNy3z3rExwG7vgF2fgVo8UDWIrrhPt9Spm4ZkXkEVUOGDEl0HBMTg+PHj6vVf8OHDzdm2ygDcXWyg7uTvfpKJK4/CkPneQcQEBKFotkZUFmdEH9ddurGbt1x2S5A86mAE7caIsuV6qDqww8/TPb6mTNn4siRI8ZoE2VA24bVM3UTyIzckIDqpwNqdV+R7O5Y1JcZKqtyeYuuMnr4I8DBDWg5HSjLhUxk+Yy2frVZs2ZYvny5sR6OiDJwyYS+vx2Bf3CkCqgW962GrBzysw5xMcDmccCi9rqAKntpoP9OBlRkNV5ponpCy5YtQ+bMmY31cESUQc3ecRWXA0KR1d0Ri/owoLIaT28Cy3sDdw7rjiv3ARp/ATg4m7plROYbVMkWNQknqksBUH9/fzx8+BCzZs0ydvuIKAO5/CBEFfUU41uVRDYPJ1M3iV6Hc/8A/wwGIoMAJy+g9Q+6kglEVibVQVWbNm0SHdva2iJbtmyoV68eihUrZsy2UQby5brzCAqPgZerA0Y353ZF1ig+XsMnK04jJk5Dw2I+aFmGm+FavJhIYNMY4PA83XGuSkCHX4BMibcxI7IWqQ6qxo8fnz4toQztnxP31BwaX09nBlVWatHBmzh68yncHO3wWZtSLyy9Qhbg0RXdVjP+p3XHNT8EGowF7BxM3TIi8w6qgoODU/yAnp6er9IeIsqA7gdF4OsNF9X3I5oWQ05vbops0U7+Daz5CIgJA1yzAG3nAoXfMHWriDJGUOXt7f3SvzplbpWcExcXZ6y2EVEGIL/7Y1edRWhULCr4eatq6WShosOAdcOBE4t0x/lqA+3mAZ4c6iVKcVC1fft29hYRJeuXvTew5fwDONjZ4Kv2ZWBny2E/i+R/BljWE3h0CbCxBep+AtT5GLBlwV+iVAVVdevWTclpRGRltl8MwBdrz6nvRzYthiLZPUzdJDI2TQOOLgA2jAJiIwGPHED7+UC+WuxrImPVqQoPD8etW7cQHR2d6PoyZcqk9SGJKIOVT/hg8XHEa8BblXKjd638pm4SGZuUSPjnA+DcKt1xoTeAtnMAt6zsayJjBFVSj6pnz55Yv359srdzThWR5XsSFo3evx5BSFQsquTLjM/blOZqP0tz9yiwtCcQeBOwtQcajgeqD5Y6OqZuGZHZsk3LhsqBgYE4ePAgXFxc1EbKv/76KwoXLox//vknfVpJRGYjOjYe7/1xFLeehCNPZhfMfqcCHO35QWtRw337fgR+bqILqLz9gF4bgZofMKAiMnamatu2bVi9ejUqVaqkCn/mzZsXb7zxhiqlMHnyZLRo0SK1D0lEGajA56gVp3Hw+hO4O9nj5+6VkYX7+lmOsMfAqveAyxt1x1IVvdX3gIu3qVtGlCGk+s/LsLAw+Pj4qO8zZcqkhgNF6dKlcezYsVQ91q5du9CqVSvkzJlTDR2sWvXfuP1/evTooa5PeGnatGmic548eYKuXbuqoE5KP/Tu3RuhoaGJzjl16hRq164NZ2dn5MmTB1OmTHmmLUuXLlUV4eUceS3r1q17Ztn4uHHjkCNHDpWha9SoES5fvpyq12vJ6hfzQfPSvuorWabYuHh8vPQklh+7A1ng933ncpyYbklu7AXm1NIFVHZOQItpQMdfGVARpWdQVbRoUVy8qCvyV7ZsWcydOxd3797FnDlzVMCR2gBNHmPmzJnPPUeCqPv37xsuf/75Z6LbJaA6e/YsNm/ejDVr1qhArV+/fokKlzZu3Fhl1I4ePYqpU6diwoQJ+Omnnwzn7Nu3D507d1YB2fHjx9VWPHI5c+aM4RwJxL7//nv1OmXo083NDU2aNEFkZGSqXrOlmtyuNGZ1rai+kmUO+X3w13GsOH5XlUz49u1yaFAsu6mbRcYQHwfsnAL82hIIuQdkKQz03arbEJlV8YlSR0ul33//XVuwYIH6/siRI1rWrFk1W1tbzdnZWfvrr7+0tJKmrFy5MtF13bt311q3bv3c+5w7d07d7/Dhw4br1q9fr9nY2Gh3795Vx7NmzdIyZcqkRUVFGc4ZOXKkVrRoUcPxW2+9pbVo0SLRY1etWlXr37+/+j4+Pl7z9fXVpk6darg9MDBQc3Jy0v78888Uv8agoCDVXvlKlFFERMdqvRce0vKOXKMVGr1WW3/6vqmbRMYSfF/TFrbUtPGeusuKAZoWGcL+JUrj53eKM1UdOnRQk9IlMyTDcqJixYq4efMmDh8+jNu3b+Ptt9+Gse3YsUMNN0qG7L333sPjx48Nt+3fv18N+cn8Lj0ZlpO5XpJN0p9Tp04dODo6Gs6RDJNk254+fWo4R+6XkJwj14vr16/D398/0TleXl6oWrWq4ZzkREVFqUxZwgtRRhIRHYe+vx3BlvMBcLK3xbxuldC0lK+pm0XGcGULMLsmcH0X4OAGtJkDtJ0NOLmzf4nSKMVBlQQgMgndz89PzS26du2aut7V1RUVKlRA1qzGr1siQ3+//fYbtm7diq+//ho7d+5Es2bNDGUbJNDRz+/Ss7e3R+bMmdVt+nOyZ088TKE/ftk5CW9PeL/kzkmOTNyX4Et/kflcRBlFVGwc+vx2GLsvP4Krox0W9KyMekU5Zy7Di4sBNo8H/mgPhD8CspcC+u8EynU2dcuIrCeoksBGAimZd/THH3+oEgoNGjTA4sWLVUYmPXTq1Alvvvmmmjguc5xkzpRkxSR7lRGMGjUKQUFBhotk8yxVqx/2oNqXW9VXsoxVfkOXnMTeK4/h5miH33tXQY2CLPiY4QXeAhY0B/bO0B3LvKk+W4GshU3dMiLrm6guk71lkrcEVzIxXFbt9e3bV01QHzRokJoInp4KFCigMmJXrlxRx76+vggICEh0TmxsrFoRKLfpz3nw4EGic/THLzsn4e0J75fcOclxcnJSqxITXizVw5Ao+AdHqq+UsckUx0lrzmHtqftqP7+571ZCxbyZTd0selXn/9Wt7rtzCHDyAt76TbfCz8GZfUtkJGmu2CdZKslYyfCXDHP99ddfao5Rerpz546aU6VfZVi9enVViDRhMCd1tOLj4w1tkXNkRWBMTIzhHAkIZY6WlITQnyOZuITkHLle5M+fXwVPCc+R+VEyb0t/DpGlmLXjKhbuu6G+n/ZWOdQqzAxVhhYTCawbDvz9jm7bmVyVgAG7dDWoiMioXqkMskzg/uabb/Dll1+q4a2kk71fRupJnThxQl30jyffy56Cctvw4cNx4MAB3LhxQwU0rVu3RqFChdQkclG8eHE170qyZYcOHcLevXsxePBgNWwoWTTRpUsXNUldhi2l9MLff/+N7777DkOHDjW048MPP1ST8KdNm4YLFy6obNyRI0fUYwmpjyWV5D///HNVNf706dPo1q2beg4ZliSyFEsO38bUjbqSKeNalsCbZXW/R5RBPboC/NwIOPRfCZkaHwC9NgCZ8pm6ZUSWSUuliIgIVVahfv36mp2dnZYvXz5t4sSJ2q1bt1L7UNr27dvVEsWkFymlEB4erjVu3FjLli2b5uDgoOXNm1fr27ev5u/vn+gxHj9+rHXu3Flzd3fXPD09tZ49e2ohIYmXBJ88eVKrVauWKoGQK1cu7auvvnqmLUuWLNGKFCmiOTo6aiVLltTWrl2b6HYpqzB27Fgte/bs6nEaNmyoXbx4MVWv15JLKlT9Yotaci9fKWOSUgkFRq1V7+NX68+bujn0qk78pWlf5NSVSvg6v6Zd2sQ+JUqjlH5+28g/KQm+JBP0yy+/qEyPFLxs27YtevXqhYYNG3Ij1RSSIUNZBShZPUubXyWT1GVOla+nMw6Mbmjq5lAq/XvyHob8fQJx8Ro6VMyNqR3K8Pc6o4oOA9aNAE78oTvOVxto9xPgyawjUXp/fqd4779q1aqp6uefffaZqlWln49ERBnb0iO3MXL5KcRrQLvyufBVu9IMqDKqB2eBpT2BRxcBG1ug7kigznDA1s7ULSOyCikOqmSOkdSjIiLL8fv+Gxi7+qz6vktVP3zeuhRsZWM/ylhkwOHoAmDDKCA2EnD3BdrPB/LXNnXLiKxKioMqBlRElmXermv4Yt159X2vmvkxtmVxZqgyIlnR9++HwNmVuuNCbwBt5wBuXLVJZLZBFRFZVoZKH1ANql8QHzcuyoAqI7p7FFjWC3h6A7C1BxqOB6oPBmxfaWE3EaURgyoyilHNi6l94lwcOXfD3J26E6iKe4ohjQpjSKMipm4SpWW4b/9MYMsEID4G8PYDOiwAcv9/H1Qiev0YVJFRtC6Xiz2ZAQRFxGDQ4mOIidPQtKQvPmzI7UkynLDHwKr3gMsbdcfF3wTe/AFw8TZ1y4isHoMqIish1VNGLjuF208ikDuTC75m2YSM5+Y+YFlvIOQeYOcENP0SqNRbKhSbumVElNJMVfny5VM83+LYsWPsWCIz9Ou+G9hw1l/t5zezSwV4uTiYukmUUvFxwO7pwI4vAS0eyFII6LgQ8C3NPiTKaEEVt2Khl7n6MFQVjrSztUHBbO7sMDOcR/Xlugvq+9HNi6NsHg4VZRgh/sCKvsD1Xbrjsp2B5t8ATvw9I8qQQdX48ePTvyWUoXWdd5AV1c18HlV0XDyalMyOHjW471uGcWULsKI/EP4IcHADWkwDynU2dauI6Dk4p4rIwudRfbL8//OoprQvy9IJGUFcDLD9C2DPt7rj7KV0q/uycaUmkUUFVXFxcfj222+xZMkS3Lp1C9HR0Yluf/LkiTHbR0SvYMmR21h/RjeP6keZR+XKeVRmL/CWbjL6nUO6Y5mI3uQLwMHF1C0jopdIdYW4iRMnYvr06Xj77bfVxoJDhw5Fu3btYGtriwkTJqT24Ygondx6HI5J/+rqUQ1rXBTlOI/K/J1fA8yppQuonLyAjr8CLaczoCKy1KBq0aJFmDdvHoYNGwZ7e3t07twZ8+fPx7hx43DgwIH0aSURpYosGhi65ATCouNQJV9m9K1dgD1ozmIigXXDgb+76radyVURGLALKNnG1C0jovQMqvz9/VG6tG4Zr7u7u8pWiZYtW2Lt2rWpfTgiSgc/7bqGIzefwt3JHtPeKqtWZZKZenQF+PkN4NBPuuMa7wM9NwCZuKCAyOKDqty5c+P+/fvq+4IFC2LTpk3q+8OHD8PJycn4LSSiVDl3LxjTN19U349rVQJ5MruyB83VqSXAT3UB/1OAaxagy1Kg8eeAvaOpW0ZEryOoatu2LbZu3aq+f//99zF27FgULlwY3bp1Q69evdLSBiIyksiYOHz09wm1DU3jEtnRsWJu9q05ig4DVg3S1Z+KDgXy1gIG7AGKNDZ1y4joda7+++qrrwzfy2T1vHnzYt++fSqwatWq1au0hYhe0fTNl3DxQQiyujticrvSLJ9gjh6cA5b2AB5JNtEGqDsSqDsCsOVm5ERWF1Tt2rULNWrUUJPURbVq1dQlNjZW3VanTp30aCcRvcSey48wb/c19f1X7cogizuH482KpgFHFwIbPgFiIwF3X6D9PCA//88kstqgqn79+mpOlY+PT6LrZcK63CZ1rMj6/DO4JuI0DXbc2NUkrj8KU1XT5XO7c5U8aFQiu2kaQsmTFX3/fgicXak7LtQIaDMHcM/GHiOy5qBKKjQnt7ny48eP4ebmZqx2UQbj4+ls6iZYreDIGPT59bDajqa8nzfGtypp6iZRQnePAct6Ak9vALb2QMNxQPX3AdtUT2klIksJqqTAp5CAqkePHolW+kl26tSpU2pYkIhebz2q9xcfx9WHYcjh5Yy571aEswPn5pgFSRsemAVsHg/ExwBefkCHX4A8lU3dMiIydVDl5eVlyFR5eHjAxeX/WyY4OjqqeVV9+/ZNn1YSUbImrzuPnZcewtnBFvO6VYKPBzOGZiH8CbDqPeDSBt1x8VbAmz8ALplM3TIiMoegasGCBeprvnz58PHHH3OojxJZfPAWwqNj4epojy5V/dg7r8GSw7cxf8919f20juVQKpfuDx8ysZv7gOV9gOC7gJ2Tbt++yn0kzW/qlhGRuc2pGj9+fPq0hDK077dehn9wJHw9nRlUpbP4eA0/7b6GqRt1BT4/bFgYLcrkSO+npZeJjwN2Twd2fAlo8UCWQkCHBUCOMuw7IiuR6qDqwYMHKlMlBUADAgLUcGBCXP1HlH6ehkWrPf22X3yojqW4pwRVZGIh/rpCntd36Y7LdAJaTAOc3E3dMiIy56BKJqnfunVLVVLPkSMHiwsSvSZHbz7B4MXHcT8oEk72tpjwZkl0qpyHv4OmdmUrsLI/EPYQcHDVBVPlupi6VUSUEYKqPXv2YPfu3ShXrlz6tIiInhnuk6KeUzZeVKv9CmR1w8yuFVA8hyd7ypTiYoDtXwB7vtUdZy+lG+7LVoTvC5GVSnVQlSdPnmeG/IgofYRFxaq9/Dade6CO3yybE1+2Kw13p1T/6pIxBd7STUa/fVB3XKkX0ORLwOH/q6KJyPqkuvrcjBkz8Mknn+DGjRvp0yIiUm4/CUf72ftUQOVob6v28vuuUzkGVKZ2fg0wp5YuoHLyBDouBFp+y4CKiFKfqZJNlMPDw1GwYEG4urrCwcEh0e1PnjxhtxK9ogPXHmPgomN4EhaNbB5O+OndiijvxxpHJhUbBWwaCxyaqzvOVVFXzDNTPtO2i4gyblAlmSoiSt+aX+NWn0FsvIbSubzwU7eKyOHFYSWTenwVWNoD8D+lO64+GGg4HrB3NG27iChjB1Xdu3dPn5YQWbmYuHh8vuYcft1/Ux23KpsTUzuU4bYzpnZqKbBmCBAdCrhkBtrOAYo0MXWriMgMpWm269WrV1WFdfn63XffwcfHB+vXr4efnx9KluRmrtYof1Y3eDjbI6v7//eEpJQLCI7EoMXHcPjGU3X8ceMiGFS/EMslmFJ0GLB+BHD8D91x3ppA+/mAZ06TNouIzJeNlsqlfDt37kSzZs1Qs2ZN7Nq1C+fPn0eBAgXw1Vdf4ciRI1i2bFn6tTaDCw4OVnsoBgUFwdOTy+FJ58iNJ3hv0TE8DImCh5M9pr9dDm+UyM7uMaUH53TDfY+kar0NUHcEUGcEYMdVl0TWKDiFn9+pXv0nK/8+//xzbN68WW2krNegQQMcOHAg7S0msjLy98yCvdfR6acDKqAqkt0dqwfXZEBl2jcFOLoQmFdfF1C5+wLd/wHqj2ZARUQvleo/u06fPo3Fixc/c70MAT569Ci1D0dkdWLj4rHtQgB+P3ATuy/rfmdalsmBr9uXgRvrT5lOZDDw74fA2RW640KNgDZzAPdsJmwUEVl0UOXt7Y379+8jf/78ia4/fvw4cuXKZcy2EVlc3am/D9/GkiO3ERASpa6zs7XBqGbF0LtWfs6fMqW7x4BlPYGnNwBbe6DhOKD6+4BtqpP5RGTFUh1UderUCSNHjsTSpUvVh0B8fDz27t2rNlnu1q1b+rSSzN6Hfx1XNZUyuzniu07lTd0csyJDe6NXnsaW8w/U6JLI4uaIDhVzo1MVPzXJn0xE3pADs4DN44H4GMDLT1d7Kk9lviVElP5B1ZdffolBgwap7Wri4uJQokQJ9bVLly4YM2ZM6ltAFuHgtSfwD46Er6ezqZtiVg7feIJBi44ZMlO1CmVF5yp+at6UVEknEwp/AqwaCFxarzsu3gp48wfAhUVWieg1BVUyOX3evHkYO3Yszpw5g9DQUJQvXx6FCxdOYxOILHMS+s97rmPy+gtqE+TCPu74sUsFFPX1MHXTSNzcDyzvDQTfBewcdfv2Ve4D2Niwf4gozdK8PlhqUsmFiBILiYzBJ8tPY+3p+4ZNkGXfPk5CNwPxccCe6cD2yYAWB2QpBHRYAOQoY+qWEZE1BlXyF7jUotq+fTsCAgLUnKqEVqz4b+UMkRU6dusphvx1AreehMPBzgZjWpRAt+p5OQndHIQ8AFb0Ba7v1B2X6QS0+AZwYvaQiEwUVA0ZMgRz585F/fr1kT17dn5YEP1XJmHm9qv4fttlNdyXy9sF33cuj4p5OT/HLFzdBqzoB4Q9BBxcgRbTgHJdTN0qIrL2oOr3339X2ajmzZunT4uIMmCphI/+PoEjN58ahvs+a1MKXi4Opm4axcUA278E9nwreXbApyTQcQGQrSj7hohMH1RJmXbZloaIgFXH72LsqjMIiYqFu5M9PmtTEm3L52bXmIPA27rJ6LcP6o4r9dJNSHdwMXXLiMhCpTqomjBhAiZOnIhffvkFLi78z4msU1BEjAqm/jl5Tx1X8PPGjLfLwy+Lq6mbRuLCWl25hMhAwMkTePN7oGRb9g0RmVdQ9dZbb+HPP/9U29Lky5cPDg6JhziOHTtmzPYRmZ0D1x5j6N8ncC8oUlVE/6BBYQyqXxD2dqw7ZXKxUcCmscChubrjnBV0xTwzJ94BgojILIKq7t274+jRo3jnnXc4UZ0MOlXJg5DIWHg4p7lKh9mLjo3H9M2XMHfXVVWIO28WV3z7djlU8ONkdLPw+Kpuq5n7J3XH1QcDDccD9v/f+J2IKD3ZaFIjIRXc3NywceNG1KpVK/1aZaGCg4PVnLSgoCB4enqaujmUyuG+/r8fwYFrT9Rxp8p5MLZlCdaeMhenlgJrhgDRoYBLZqDtHKBIE1O3iois7PM71WkF2Z6GAQFZk3uBEei54DAuPghRk9G/6VgWTUv5mrpZJKLDgfUjgOO/6/ojb02g3TzAi5u7E9Hrl+pJINOmTcOIESNw48aN9GkRkRm54B+MdrP2qYDKx8MJf/evxoDKXDw4B8yr/19AZQPUHQl0+4cBFRGZTKozVTKXKjw8HAULFoSrq+szE9WfPNENjxBldPuuPEL/34+qcgmyd9/CXlVUUU8yMZmxcOxXYP1IIDYScM+uy04VqGvqlhGRlUt1UDVjxoz0aQllaNW+3Ar/4Ej4ejrjwOiGyOg2nLmP9/88jpg4DVXyZ8a8dyvBy5XFPE0uMlg3d+rMct1xwYZA27mAezZTt4yIKG2r/4gs2crjd/Dx0lNqu5nmpX0x/a1ycHawM3Wz6O4xYFkv4Ol1wMYOaDgOqPEBYMtSFkRkHtL0v9HVq1cxZswYdO7cWW2qLNavX4+zZ8+m6nF27dqFVq1aIWfOnGoPwVWrViW6XRYmjhs3Djly5FCFRhs1aoTLly8/M9zYtWtXNXne29sbvXv3RmhoaKJzTp06hdq1a8PZ2VlNtJ8yZcozbVm6dCmKFSumzildujTWrVuX6rZQxrf44C0MXXJSBVQdKubGD50rMKAyh+G+/bOAnxvrAiovP6DXBqDWEAZURJSxg6qdO3eqoOPgwYNqD0B9AHPy5EmMHz8+VY8VFhaGsmXLYubMmcneLsHP999/jzlz5qjnk3IOTZo0QWRkpOEcCagkmNu8eTPWrFmjArV+/folWgbZuHFj5M2bV9XXmjp1qqoK/9NPPxnO2bdvnwoQJSA7fvw42rRpoy5nzpxJVVsoY5u/+xpGrzytPsO7V8+LKe3LqOKeZELhT4C/ugAbRwHxMUCxlsCAXUCeKnxbiMj8aKlUrVo1bdq0aep7d3d37erVq+r7gwcParly5dLSSpqycuVKw3F8fLzm6+urTZ061XBdYGCg5uTkpP3555/q+Ny5c+p+hw8fNpyzfv16zcbGRrt79646njVrlpYpUyYtKirKcM7IkSO1okWLGo7feustrUWLFonaU7VqVa1///4pbktKBAUFqfbKV0tT9YstWt6Ra9TXjCY6Nk77cu051X65fLX+vHrPycRu7NO0acU1bbynpk3KqmkHf5JfRlO3ioisUFAKP79Tnak6ffo02rZ9dg8t2bbm0aNHxor1cP36dfj7+6thNj0pvFW1alXs379fHctXGfKrVKmS4Rw539bWVmWT9OfUqVMHjo7/r6osGaaLFy/i6dOnhnMSPo/+HP3zpKQtlDFdfRiK9rP3Ye6ua+r448ZFMLJpMTUcTSYSHwfsmgosbAEE3wUyFwT6bAGq9AX4vhCRJU1UlyDm/v37yJ8/8V5aMmyWK5fxCu5JECOyZ8+e6Ho51t8mXyWYS8je3h6ZM2dOdE7StuofU27LlCmT+vqy53lZW5ITFRWlLgmHIsk8SHJ00cFb+HztOUTGxMPLxQGT25VG89I5TN006xbyAFjZD7i2Q3dc5m2gxTTAycPULSMieqlUZ6o6deqEkSNHqmBC/pqPj4/H3r178fHHH6Nbt26pfTiLNnnyZJXR0l9kkjyZXkBwJPr+dgRjVp1RAVXNQlmwcUgdBlSmdnUbMKemLqBycAVaz9KVS2BARUSWGlR9+eWXapWcBAgySb1EiRJqeK1GjRpqRaCx+PrqtgF58OBBouvlWH+bfNWvPtSLjY1VKwITnpPcYyR8juedk/D2l7UlOaNGjVL7BOkvt2/fTlUfkHHFx2v448BNNJy+E1vOB8DRzhZjWhTH772qwtfLmd1tKnGxwJaJwO/tgLCHgE8JoN8OoHxXDvcRkeUGVTJkIhkqWQV37do1tdrujz/+wIULF/D777/Dzs54tXxkyE4Clq1btyYaPpO5UtWrV1fH8jUwMFCt6tPbtm2byp7JfCf9ObIiMCYmxnCOrBQsWrSoGvrTn5PwefTn6J8nJW1JjpOTkyr1kPBCpttupsOcfSo7FRIZi7K5vbB6cE30qV0AtlzhZzqBt4GFzYE90+V/GKBiT6DvNiBbURM2iogojVIz+z0uLk5zcHDQLl26pBlDSEiIdvz4cXWRpkyfPl19f/PmTXX7V199pXl7e2urV6/WTp06pbVu3VrLnz+/FhERYXiMpk2bauXLl1erD/fs2aMVLlxY69y5c6JVetmzZ9feffdd7cyZM9pff/2lubq6anPnzjWcs3fvXs3e3l775ptvtPPnz2vjx49Xr/P06dOGc1LSFmte/bfvyiNtx8UA9dWcRETHqtV8BUetVSv7So7boC3Yc02LjeMqMpM7v1bTJvvpVvd9mVvTzqwwdYuIiF7p8zvVJRVKlCih7d+/XzOG7du3q0YmvXTv3l3dLsvax44dq4IiKV/QsGFD7eLFi4ke4/HjxyqIkvIOnp6eWs+ePVWwltDJkye1WrVqqceQsg8SICW1ZMkSrUiRIpqjo6NWsmRJbe3atYluT0lbrDmoMkdRMXHaO/MPGEol9PvtsHYvMNzUzaKYSE1bN0IXTMllbl1Ne3yN/UJEZiuln9828k9qMlv//vuvKoQ5e/ZslCpVKq0JMqskQ4YyYV3mV3EoMP3nTw35+wT+OXkPro52+PbtcmhS8vnz3+g1eXwVWNYTuH9Sd1x9MNBwPGD//5InREQZ9fM71SUVZIVfeHi4qoQutZ9ky5aEZJI4kSnJ3wmfrT2nAip7WxvMeaci6hThhrsmd3oZ8O8QIDoEcMkMtJ0DFGli6lYRERlNqoOqGTNmGO/ZyWLsv/oY0XHxakVd9YJZTNqW2TuvYsHeG+r7aW+VZUBlatHhwPoRwPHfdcd+NYD28wEv49W1IyLKkEFV9+7d06cllKF99PcJ+AdHwtfTGQdGNzRZO1Ycu4MpGy6q78e2LIHW5fjBbVIB54GlPYCHFwDYAHWGA3VHAnap/q+HiMjspfp/tnXr1qnSCbKNS0KbNm1CXFwcmjVrZsz2EaXYuXvBGLXitPq+f90C6F0rcSV9eo1kquax34D1I4HYCMA9O9BuHlCgLt8GIrJYqS7++cknn6jgKSmpDSW3EZlCcGQMBi46iqjYeNQrmg0jmxTjG2EqkcHA8t7Avx/oAqqCDYABexlQEZHFS3Wm6vLly6qKelJSZf3KlSvGahdRqiamj1x2CjcehyOnlzO+fascC3qayr3jwNKewNPrgI0d0HAsUONDwDbVf78REVl+UCVLCqWaer58+RJdLwGVm5ubMdtGlCIyKX39GX842NlgZtcKyOTG5fkmGe47OAfYNBaIjwG88gAdfgHyVHn9bSEiMpFU//nYunVrDBkyBFevXk0UUA0bNgxvvvmmsdtH9EJHbz7Fl+vOq+8/bV4c5f10Ww/RaxT+BPirC7DhE11AVawlMGA3AyoisjqpDqqk8KdkpGS4T/bEk0vx4sWRJUsWfPPNN+nTSqJkPAmLxuDFxxAbr6FFmRzoXiNx9pReg5v7gTm1gYvrADtHoNlU4O0/ABcGt0RkfdI0/Ldv3z614fDJkydV8c8yZcqgTp066dNCoufMoxqx7CTuB0WiQFY3fN2+DGxsbNhXr0t8vG4T5O1fAlockLkg0HEBkKMs3wMislppKhYjH16NGzdWFyJT+OPATWw5H6CKjf7YpQLcnVj36LUJeQCs7Adc26E7Lv0W0HI64OTx+tpARGSG0vRJtHXrVnUJCAhQpRQS+uWXX4zVNqJkXfQPwedrdfOoPmlWDCVyPn8fJjKyq9uAFf2BsADAwRVoPhUo11X+0mJXE5HVS3VQNXHiREyaNAmVKlVCjhw5OORCyuuqoh4ZE4cP/jxuqEfVsybnUb0WcbHAji+B3dNl8BXwKQF0XAhkK/p6np+IyBKDqjlz5mDhwoV4991306dFRC/w1foLuPggBFndHTG1Q1kG9a9D0B1gWW/g9gHdccWeQNPJgEPizdSJiKxdqoOq6Oho1KhRI31aQ/QC2y48wMJ9uo2Sv+lYFtk8nNhf6e3COmD1QCDiKeDkCbT6DijVjv1ORGSMkgp9+vTB4sWLU3s3olfyKDQKHy89pb7vVTM/6hX1YY+mp9goYP0nwF+ddQFVzvJA/10MqIiIjJmpioyMxE8//YQtW7aoUgoODg6Jbp8+XeZckLWZseUSQiJj4eFsjyGNiqTL40tdqmK+HhjRlPN40tXjq8CynsD9k7rjaoOARhMAe1aqJyIyalB16tQplCtXTn1/5syZRLexTpD1+uvQbfgHR8LX09noQdWVgBD8eei2+n7CmyXh7GBn1MenBE4vA/4dAkSH6Ap4tpkDFG3KLiIiSo+gavv27am9C9ErT06Pi9fwRonsqFYgC3szPUSHAxtGAsd+0x371QDazwe8crG/iYhSiBUTyaztv/pYFfm0s7VRNakoHQScB5b2BB5K7S8boM7HQN1PADv+90BElBop/l+zXbuUrfhZsWJFqhpA9Dzx8Zphs+SuVf1QMJs7O8uYNA04/juwbgQQGwG4Zwfa/QQUqMd+JiJKz6BK9vwjep3+OXkPp+8GqS1oPmxYmJ1vTJHBwJqPgDPLdMcFGwBt5wLuXFVJRJTuQdWCBQvS/CREaamcPnXjRfX9wPoFkcWdNamM5t5x3XDf0+uAjR3QcCxQ40PANtUVVoiIKAFOmiCz9O2WS7gbGIGcXs6qLhUZabjv4Fxg0xggPgbwygO0/xnwq8ruJSIyAgZVZHa2nn+AuTuvqe/HtWIJBaMIfwKsHgxcXKs7LtYSePMHwDWzcR6fiIgYVJF5ufM0HEOX6IpO9qiRD01L+Zq6SRnfrQO6vfuC7wB2jkDjz4Eq/aSwnKlbRkRkUZipIqOoWiCzqnie2S3tVbejY+MxePFxBEXEoGxuL4xqzhIKryQ+Htj7LbDtC0CLAzIXADosAHLqivcSEZFxMagio/iuU/lXfoyvN1zAiduB8HS2x49dKsDJnpXT0yw0AFjRD7j2X7He0m8BLacDTh6v/D4REVHyGFSRWdhwxh8/77muvv+mY1nkyexq6iZlXFe36wKqsADA3gVo8Q1QriuH+4iI0hmDKjK5U3cCMXypbh5V39r50bgk51GlSVwssGMysHuaLPUDfErohvt8OIxKRPQ6MKgik5Lhvnd/PoiQqFhUyZcZI5oyAEiToDvA8j7Arf2644o9gKZfAQ4uRn2/iIjo+RhUkVF0/ukAHoVGIau7E/7sVy1F9zl26ym6/3xIBVSV82XCLz0rw8GOBShT7cI6YPVAIOIp4OgBvPkdUKp96h+HiIheCYMqMorrj8LgHxyJkMjYFAdU3X4+hFDJUOXPjAU9KsPNiT+OqRIbBWweDxycrTvOWR7o8ItulR8REb12/BSj1+5BcCT6/XZEBVTVCmTGLz0qw9WRP4qp8uSabquZ+yd0x9UGAY0mAPZpL2lBRESvhp9k9FrFxEktqmN4FBqNYr4eDKjS4vQy4N8hQHQI4JIJaDMbKNrM6O8VERGlDoMqeq2mbLiAwzeewsPJHrPfqcgMVWpEhwMbRgLHftMd+1UH2s8HvHKnz5tFRESpwqCKXpsNZ+5j3m5dLaqpHcsgf1Y39n5KBVwAlvYAHp4HYAPU+Rio+wlgx19hIiJzwf+RKd1pmoa1p+9j1PLThlpUTUvlYM+nrPOA438A64YDsRGAmw/Qfh5QoB77j4jIzDCoonR163E4xq4+g52XHqrjqvlZiyrFIoOBtUOB00t1xwXqA+1+Atx90undIiKiV8GgitItO/XTrmuYvvkSomLj4Whni4H1C2JA3YKsRZUS904Ay3rqVvnZ2AENxgA1hwC2rONFRGSuGFRRuvhm00XM3H5VfV+9QBZ83rYUCmZzZ2+nZLjv4Fxg81ggLhrwygO0/xnwq8q+IyIycwyqyCg+aFgY4dGxajXf/N3XDAHV2JYl0KtmPtjY2LCnXyb8CbB6MHBxre64aAug9Y+Aa2b2HRFRBsCgioyiS1U/9XXJkdv4fK2sUAOGNymK3rXys4dT4tZBYFkvIPgOYOcINP4cqNIPYDBKRJRhMKgio9l87gE+WX7KsMJvYL2C7N2XiY8H9n4LbPsC0OJ0W8x0WADkLMe+IyLKYBhUkVHceBSGIX8dR7wGdKyYG6ObF+eQ38uEBgAr+gHXtuuOS3cEWn4LOHnwp5KIKANiUEWvLCo2Dv3/OIKw6DiUy+2Nye1KM6B6mavbdQFVWABg7wI0nwqUf4fDfUREGRiDKnplX62/gIv+oer7u0ERsLfjsv/niosFdkwGdk+TpX6ATwndcJ9PMf4kEhFlcAyq6JWsPnEXC/beMBzbcWL18wXdBZb3Bm7t1x1X6A40/QpwdOVPIRGRBWBQRWm24Yw/hi45qb53drBFZEw8e/N5Lq4HVr0HRDwFHD2AVjOA0h3YX0REFoRBFaXJ9gsBeP/PY4iL11C/aDacuhOEyJho9mZSsdHAlvHAgVm64xzlgA6/AFm4MpKIyNJw8gul2snbgej/x1HExGmoVSgrPmxYhL2YHNli5pfG/w+oqg0Eem9iQEVEZKGYqaJU7+k3/p+ziI6NR6W8mTDsjSKws2W19GecWQ788yEQHQK4ZALazAaKNuNPGxGRBWNQRany76n7OHE7UM2her9BYa70Syo6HNjwCXDsV92xX3Wg/XzAKzd/0oiILByDKkqxyJg4fL3+gvq+fYXcyOzmyN5LKOACsLQH8FC26bEB6nwM1P0EsOOvGRGRNeD/9pRiC/fdwN3ACGRxc0SbcrnYc3qaBhz/A1g3HIiNANx8gHY/AQXrs4+IiKwIgypKkcehUZi57Yr6/t1qeeHsYMeeE1EhwJqPgNNLdf1RoL4uoHL3Yf8QEVkZs179N2HCBLXdScJLsWL/rzwdGRmJQYMGIUuWLHB3d0f79u3x4MGDRI9x69YttGjRAq6urvDx8cHw4cMRGxub6JwdO3agQoUKcHJyQqFChbBw4cJn2jJz5kzky5cPzs7OqFq1Kg4dOgRr8uP2KwiJikXBbG6oX+zZgOHzNqXwZ9+qWNS3KqzGvRPA3Dq6gMrGDmg4HnhnBQMqIiIrZdZBlShZsiTu379vuOzZs8dw20cffYR///0XS5cuxc6dO3Hv3j20a9fOcHtcXJwKqKKjo7Fv3z78+uuvKmAaN26c4Zzr16+rc+rXr48TJ05gyJAh6NOnDzZu3Gg45++//8bQoUMxfvx4HDt2DGXLlkWTJk0QEBAAaxAQHInFB2+p77tXzwfbZKqm587kioLZ3NXFKob7Ds4Ffn5DVzbBMzfQcx1Qeyhga/a/UkRElE5sNFkjb8aZqlWrVqlgJ6mgoCBky5YNixcvRocOusrUFy5cQPHixbF//35Uq1YN69evR8uWLVWwlT17dnXOnDlzMHLkSDx8+BCOjo7q+7Vr1+LMmTOGx+7UqRMCAwOxYcMGdSyZqcqVK+PHH39Ux/Hx8ciTJw/ef/99fPLJJyl+PcHBwfDy8lJt9/T0REYx8d+zaiua4r4e+Lp9medulpzd0wk+ns6waOFPgH/eBy6s0R0XbQ60ngm4ZjZ1y4iIKJ2k9PPb7P+svnz5MnLmzIkCBQqga9euajhPHD16FDExMWjUqJHhXBka9PPzU0GVkK+lS5c2BFRCMkzSOWfPnjWck/Ax9OfoH0OyXPJcCc+xtbVVx/pznicqKko9V8JLRs5Sda7i99yAyircOqgb7pOAys4RaPo10GkxAyoiIjL/oEoyRDJcJxmj2bNnq6G62rVrIyQkBP7+/irT5O3tneg+EkDJbUK+Jgyo9Lfrb3vRORIARURE4NGjR2oYMblz9I/xPJMnT1aRrf4i2a2MZs7Oa4iKjUcxXw+Uy5O4rxPacTEAq07cVRssW5z4eGD3dGBBMyDoNpC5ANB7M1BtAGDNQSYREWWc1X/Nmv2/AnWZMmVUkJU3b14sWbIELi4uMHejRo1Sc7H0JFDLSIGVZKkWHbyZoiyVlFt4HBYNX09ntLakcguhAcDK/sDVbbrjUh2Alt8Czhln+JaIiF4Psw6qkpKsVJEiRXDlyhW88cYbamhO5j4lzFbJ6j9fX1/1vXxNukpPvzow4TlJVwzKsYyZSuBmZ2enLsmdo3+M55HVhHLJqObu0mWpimb3QPkXZKks1rUdwIp+QOgDwN4FaD4FKP8us1NERJTxhv+SCg0NxdWrV5EjRw5UrFgRDg4O2Lp1q+H2ixcvqjlX1atXV8fy9fTp04lW6W3evFkFTCVKlDCck/Ax9OfoH0OGGOW5Ep4jE9XlWH+OJTp7Lwi/79dlqbpY21yquFhg2+fAb210AVW24kC/7UCFbgyoiIgoY2aqPv74Y7Rq1UoN+ckKPilpIFmjzp07qzlKvXv3VsNrmTNnVoGSrMaTQEdW/onGjRur4Ondd9/FlClT1ByoMWPGqNpW+gzSgAED1Kq+ESNGoFevXti2bZsaXpQVgXryHN27d0elSpVQpUoVzJgxA2FhYejZsycsUXh0LN7/8zii4+JRJV9mlPezoixV0F1geR/g1j7dcYXuQNOvAEdXU7eMiIjMnFkHVXfu3FEB1OPHj1X5hFq1auHAgQPqe/Htt9+qlXhS9FNW2smqvVmzZhnuLwHYmjVr8N5776lgy83NTQVHkyZNMpyTP39+FUBJzavvvvsOuXPnxvz589Vj6b399tuqBIPUt5LArFy5cmryfNLJ65Zi4j/ncO1hmNrb74OGha0nS3VxPbDqPSDiKeDoAbSaAZTWlesgIiLK0HWqLE1GqFO15tQ9DF58XLYDVlXSy+ROWZaqx4JDhonqB0Y3RIYSGw1smQAcmKk7zlEO6PALkKWgqVtGREQZ6PPbrDNV9Hodu/UUo1acVt93rJQnxQFVhiYV0Zf1Au4d1x1XGwg0mgDYZ9wFBkREZBoMqgiSrPzj4C1M+vcsYuI0VTm9c+WMU/ohzc4sB/4dAkQFA87eQJvZQLHmpm4VERFlUAyqrFxUbBxGrziD5cfuqOMaBbPgw4aFYW+XoRaGpk5MBLDhE+Dofxtn56kGdPgZ8Mpt6pYRpYqsRJbSMkT0aqSagMzDflUMqqzcZ2vOqYDK1ka3WXLb8rnSNDE9k6sj7GxtkM3DzIfNAi4Ay3oCAedkSiFQexhQbxRgx18FylgkmJJdJiSwIqJXJzUvpf7kqyzO4ieJFdty7gH+OKDb1+/T5sVRJX+WND/Wt2+XM+8NlWU9xvE/gPUjgJhwwM0HaPcTULC+qVtGlKYh+/v376u/rGWXBlkFTURp/30KDw831LSUWphpxaDKSgWERGLE8lPq+zblcr1SQGX2okKANUOB00t0xwXqA23nAh6WWRKDLF9sbKz6EJDN5l1dWUON6FXpt76TwMrHxyfNQ4EMqqw0Kh++9BSehEUjf1Y3dKueFxbr/klgaU/gyVXAxg5o8ClQ8yOAf9lTBiabvOt3fCAi49D/gRITE8OgilJu+8UA7Lz0EA52Nhj2RhE4WOKkdBnuO/QTsGkMEBcNeObWTUb301XbJ7IEVlOYlyiD/D4xU2WFWappmy6p71uVyYm8WdyM8rg/br+CmLh4ZPd0xuR2pWFSUhF99WDgwhrdcdHmQOuZgGtm07aLiIgsmgWmKOhFNp71x9l7wXBxsEO7CsYrIXDkxhNsuxCA7Rf+v3m1Sdw+BMyprQuo7ByBpl8DnRYzoCKyEjt27FAZh8DAwBTfJ1++fGpP15ettixUqBD27ftvX9AMoF69ehgyZMhzb58wYYLads0adOrUCdOmTUv352FQZUXi4jVM36zLUr1ZNie8XBxgMWRZ+e7pwC9NgaDbQKb8QO9NQLUBktM1deuISLaz6tFDBTyykX1SstG93CbnmKM5c+aovWJr1Khh0kCI0mbMmDH44osv1DYz6YlBlRVZeuQ2Lj0IhZuTHdqUzwWLERoALGoPbJ0IaHFAqfZA/11AzvKmbhkRJSElIP766y9EREQYrouMjMTixYvh5+dnttMmfvzxR/Tu3TvdnoNFXNO330qVKoWCBQvijz/+QHpiUGUlQiJj8M2mi+r7TpX94O5kIdPpru0E5tQCrm4D7F2AVt8D7X8GnM1zw2qidKuzEx1rkos8d2pUqFBBBVYrVqwwXCffS0BVvnziP4SioqLwwQcfqCXuzs7OqFWrFg4fPpzonHXr1qFIkSJqSXz9+vVx48aNZ55zz549qF27tjpHnlseMywsLMVtPnr0KK5evYoWLVok+jAfPHiwqmkkbcubNy8mT55suP3WrVto3bo13N3d1Qa8b731Fh48ePDM0Nv8+fNVBkweQ7J0O3fuxHfffaeydnLRv54zZ86gWbNm6vGyZ8+Od999F48ePTI8nryebt26qdulTakZ6po7d67qF1n9Ju3UZ3N27dqlKo37+/snOl8yadKfydE0Tb02eT+dnJxU2Q/p74Tv6ciRI9Xzye0ypPrzzz8bbpfXX6VKFXWbvI5PPvlElRBJmMmTfpc2ZM2aFU2aNElR/4hWrVqpgD49WcgnK73Mj9uu4FFoNHJ5u6BF6bQXNjMbcbHAzq+BXVPl1xjIVgzouBDwKW7qlhG9dhExcSgxbqNJev7cpCZwdUzdR0mvXr2wYMECdO3aVR3/8ssv6Nmzp5oPldCIESOwfPly/PrrrypomTJlivoQvXLlCjJnzozbt2+jXbt2auiwX79+OHLkCIYNG5boMSQYatq0KT7//HP1PA8fPlQfynKRNqTE7t27VeDm4eFhuO7777/HP//8gyVLlqgAQtoiFyFV7vUBlQQJEhRIG99+++1Er1Feh7w+CSqlLpK8xkuXLqmsyqRJk9Q52bJlU/PDGjRogD59+uDbb79VWT4JTCQA2rZtmzpv+PDh6rlWr16tgtDRo0fj2LFjL50zJW2Q1/Dvv/8iODhYZeMGDhyIRYsWoU6dOihQoAB+//139fj6cgNym7wXyVm+fLlqowQvJUuWVAHZyZMnDbdL4Ld//37Vf2XLllW7AuiDn7t376J58+YquPztt99w4cIF9O3bVwWcEqjpyc/De++9h71796rjlPSPkGBNhgAlsJOgLT0wqLICNx6F4Ze919X3vWvlz/glFILuAsv7ALf+mzBaoZtuQrojiyASZQTvvPMORo0ahZs3b6pj+XCUD+GEAYdkXmbPno2FCxeqDISYN28eNm/erDIb8iEvt8uQjj4rU7RoUZw+fRpff/214XEkeyTBm36eUuHChdUHet26ddX95QP7ZaSdknFJSDJR8liSPZOMkgREelu3blXtkIBBMjJCggQJMiTTVrlyZUO2S66XwElPao9Jxki2S9GToUfJ4n355ZeG6yRAlMeWIEzaJn0iQ1sNGzY0BB65c798MZIMvUobcuXSTQn54YcfVEZO+lTaIEGWBJ/6oEqCL7mPBCzJuXXrlrpfo0aNVJZLAk4JZoS0VQI4eQ/ldiFBm96sWbPUa5LXK31arFgx3Lt3TwVI48aNM+wcIP2eMKiTgPlF/SMBsZB+kj6XQC/h+2VMDKqswBfrziMmTkMFP29UypsJGdrFDcCq94CIJ4CjB9BqBlC6g6lbRWRSsppXMkameu7UkiBCPrglYJLhIvlehnKSZpgkK1KzZk3DdfIhLR/Q58+fV8fytWrVqonuV7169UTHkiU5deqUyq7oyXNKNkmCnuLFX57dlsxH0uBLsilvvPGGCuQkE9ayZUs0btzY0C75QNcHVKJEiRJqbzm5TR9UyQd7woDqeeQ1bN++XWW+kpJ+kvZJsJCwLySTJ217GQl69AGVvv+kby5evKiCI3mdMsn7wIEDqFatmnrPJKByc0u+HE/Hjh3VSkoJlqRfJPMkw2729vY4ceKEyshJQJsc6Rt5/oT1ouT9Dw0NxZ07dwxz7ipWrJiq/tEHVfqq6bIbQXphUGUFJRQ2n3ugNkzuXatAxi0WGButm4i+/0fdcY6yQIcFQJaCpm4ZkcnJ73Vqh+BMTYYAZQhOzJw5M92eRz6Q+/fvn2hej15KJ8ZLwCeZp6RzwyQoW79+PbZs2aICDcm+LFu2LMVte15gktxrkMAkYQZOT+YdyRBeepGhRHluyVbJ3C95vUmHaRPKkyePCsikTyQjJUOJU6dOVUOT+qDmVSXtt5f1j96TJ0/U15QEsmmVsX4LKVWuBIRi2BLdWPabZXPBL3MGHR57ch1Y1gu4d0x3XPU94I2JgH36jIkTUfqTLIZkVyQg1E82TkiG9WQoTIYG9UM1krmS4TP9UJ5kmWReU0KSUUka/Jw7d05NiE4rGVqSoULJcCX8w1QmoMs8Kbl06NBBvSb54JZ26edY6bNV0gaZ+yMZqxeR16zfhijha5C5SlJPSzI+yfWVZPEOHjxoCBSfPn2qhr6elxVKOFwnQ2z64U3pPxlmS5jlkrlKnTt3VsOJ8lwJs4fJcXFxUUGOXGQumQzjSVBaunRplQWTAEs//JeQ9Ju8zoT9LO+/zGV70VDmy/pHTyazy+MkzYoaUwafXEPPExwZg36/HUFoVCxK5vRE93Te369OkWyq9tWb5RLPO3hlZ1YAc+voAipnb6DTn0CzrxhQEWVwMgwkwz0SbCS3ea1kI2Qysszl2bBhgzpPJi3L0I2+tIHUu7p8+bI6R7IjUpZBhqcSkvk4UrBTsmIy/CTny2RufZYsJWRVoWRDzp49a7hu+vTp+PPPP9Vkagleli5dqobLZIhPAgYJIGQul0wWP3TokJqgLQFOpUqVXvhcEhhIcCSr/mQCtwQhEphIsCaBjQSVMqS1ceNGNblfAjAZ9pI+kX6QidkSPMiwnX4O0ovIsGb37t3VEJpMyJeMnmTdEs7pkqBXAkiZuyTP+SILFy5U87ukDdeuXVPzvCTIksBYXps8l2QpV61apTJ9kvWSeVZCsloSiL7//vuqX+V9Gj9+PIYOHfrC1/Ky/tGT16cfok0vDKos1OgVp3HtURiyujvhk6bFYJ/Ok9N71cyPMS2KY3RzI62+i4kA/h0CLOsJRAUDeaoBA/YAxZob5/GJyOTkg1ouz/PVV1+hffv2anm8ZCNkmEs+LDNl0s0NlayMZCjkA1pWkkmBzoSTlUWZMmVUZkQCHykDIFknmfScdOL5i2TJkgVt27ZNNC9LsicyWVqCJJkjJUGQlHeQD3/JskhAIO2UFXQSZMkco7///vulz/Xxxx+rIFMyWjJMJZkkaatkbCRAkKBAAjbJ1kkApw82ZIhNXp9kh+T5ZAJ90rlHyZEMnqyglLlP8tjSXzJhPCF5DgnS5PklOHwRb29vtaBAslnyWDIMKJPbpQ+FZPwkqycBlGSwJFDWl7eQuV3ShxKEyvspQbMEizKn60VS0j8yuV5+TuT50pONltoiI5RmslzVy8tL1QB50X8kr8o/KBLVv9qq9hSe1rEsimT//zLg9JTd0wk+ni9fSfNSDy8CS3sCAfJXoQ1QeyhQbzRgx9FqIv0HhPyVr69vROlPJrvLxHTJgiQ3IdrSSXAj5SiSDrdmFLNnz8bKlSuxadOmNP1epfTzm59SFmjVibsqoJJhv9cVUBmFNPrEImDdcCAmHHDzAdrNBQo2MHXLiMjKSdZFJkLLh65kQqyFBBEyH0qGVjNqQCVkzpmUi0hvDKosjCQelx+9o76vX9QHGUZUCLBmKHBaN7aOAvWAtj8BHtlN3TIiIsVc9yVMT1LEVIbjZChOMnUZVZ8+fV7L8zCosjBn7wXjckAoHOxsUKtQ+q1wSGrAH0cRGB6N7F7O2DasXurufP8UsLQH8OQqYGMH1B8N1BoqA/np1VwiIkqBF5VPoGcxqLIwy4/pslTVCmSB22vc3y8yJg5h0XEIj0q8FPilw32H5gGbPgXiogHPXLp9+/ImLt5HRESUETCosiAxcfH458Q99X0Dcx/6i3gKrB4MXFijOy7aHGg9E3DNbOqWERERpQmDKgty9OZTPA6LhpeLA8r7mfF2NLcPAct6A0G3AFsHoPFnQNUBUhba1C0jIiJKMwZVFmT/1cfqa9ncXrCTfWnMTXw8sO87YOtngBYHZMoPdPgFyFXB1C0jIiJ6ZQyqLMj+a7qgqnQub5id0IfAyv7A1a2641LtgZYzAOf0q9dFRET0OnF5lYWQieInbgWq78vk9oJZubYTmFNTF1DZuwCtvtdNSGdARUSvkVQ6l6rar1u9evUM+xWashxEmzZtzKpNlohBlQXNp4qOi0cWN0fk8DKTCstxscC2L4DfWgOhD4BsxYC+24CK3Tl/isgKyQe7BDZykWKMUrl6xIgRqpI1vV4rVqzAZ599xm43Mg7/WYgDhqE/r0S7qJtM0F1geR/g1j7dcfl3gWZTAEdXU7eMiEyoadOmWLBgAWJiYnD06FG1wa78nyXVyun1yZyZK63TAzNVFjZJvbQ5DP1d2gjMqaULqBzddUN9rX9kQEVEcHJygq+vL/LkyaOGo2Tz382bNxt65vHjx+jcubPaXNfV1VVtCfPnn38m6jkZuvrggw9UlkuCA3m8CRMmJDrn8uXLajNj2cNNNidO+Bx6sv1KgwYN4OLiojb87devH0JDQ58ZMpNNmrNnz6426J00aRJiY2MxfPhw9dy5c+dWQeLLyH0GDx6s9o/LmjUrxo4dq3bA0Pv999/V5syyUbO8ni5duiAgIMBw+9OnT9G1a1e1ybK0t3Dhwome9/bt23jrrbdUG6VdUgldNnl+nqTDf/ny5VOvs1evXqoNsln1Tz/9lOg+qX0Oa8SgygLIL+a5+8Hq++K+ppn4PbBeIXzZuhi+yHscWPwWEPEEyFEW6L8LKN3BJG0ishry4RwdZppLgsAgtc6cOYN9+/bB0dHRcJ0MBVasWBFr165Vt0ug8+6776qtUhL69ddf4ebmhoMHD2LKlCkq2NEHTvHx8WjXrp16XLl9zpw5GDlyZKL7h4WFoUmTJsiUKRMOHz6MpUuXYsuWLSrwSWjbtm24d+8edu3ahenTp2P8+PFo2bKlup88tmzf0r9/f9y5oyu8/DzSXnt7e/U6vvvuO/VY8+fPN9wumTsZjjt58qSa9yXBSsJtcSQIO3fuHNavX4/z58+rDYIlONPfV16LBEO7d+/G3r171abPkhWMjo5O8fsxbdo0FdgdP34cAwcOxHvvvYeLFy8a9TksHYf/LIDUpgqPjoMM+vmaaD5VzSwhKLjzfTg8OKG7QupOvTEJsHcySXuIrIpsQP5lTtM89+h7gKNbik9fs2aN+jCWzE1UVBRsbW3x448/Gm6XDNXHH39sOH7//fexceNGLFmyBFWqVEm0wbEEOEKyNvIYW7duVfvTSXB04cIFdb+cOXX9IlmYZs2aGe4vGwRLAPfbb7+p4EzIY7Rq1UoNRUpmSkhG5vvvv1ftLFq0qArgwsPDMXr0aHX7qFGj8NVXX2HPnj3o1KnTc1+3ZOa+/fZbNdQpjyNZMjnu27evul0yRHoFChRQz1m5cmWVOZP+unXrFsqXL6+CHn1mSe/vv/9WgaQEafrpH5LFkoySbDPTuHHjFL03zZs3V8GUkCBU2rd9+3bVXmM9h6VjUGUBbj4OV1+zuDvBwe71Jx+9rv2LXLs/gV1MCODsDbSZBRRr8drbQUTmr379+irLIpki+dCW7E379u0Nt8fFxakASIKou3fvqiyIBF8yFJiQBFUJ5ciRwzBcJpkcCWL0AZWoXj3x9ldyTtmyZQ0BlahZs6YKHCQ7ow+qSpYsqQIqPbm+VKlShmM7Ozs1dJhwqC451apVSzTfVdojmSF5vfIYMr9MhjAlUyVDfdIOIcGUDF9K1kj66dixYyqAkWHJGjVqqHPkPleuXFFZpIQkaLx69SpSKmGfSltlGFL/uoz1HJaOQZUFuP1EF1T5er7erJBNbCRyHJiILBcWqePonJXh+NYCwDvPa20HkdVzcNVljEz13KkgQUyhQoXU97/88osKbH7++Wf07t1bXTd16lQ1PDZjxgw1n0rOl7k/SYeYZPVgQhIE6AMRY0rueYz93PqhSLksWrRIzZuSYEqO9a9bsmw3b97EunXr1DBnw4YNMWjQIHzzzTcqmyVDpnLfpOSxXuW16l+XsZ7D0jGosqBMVQ4vl9f2nE5PLyPPtkFweXoBGmywq9BwxJTrgeyh7ihthrVHiSyaZEBSMQRnLiQDJMNoQ4cOVROzZQK2zNWRCdDvvPOOOkc+1C9duqSyNSlVvHhxNan6/v37KoMlDhw48Mw5CxcuVAGNPlslz60f5jM2mX+VkLRHhi0lSyVDlTJBX4YRJcMmjhw5kmzwIqsl5VK7dm01WV6CqgoVKqjhOR8fH3h6ps+82tfxHJaAE9UtQECIrsZLFvf/T/ZMN5oG70tLUWh1SxVQxThnxY2mv2Po9Sro88cJ9P3t2f8IiIiep2PHjiqwmDlzpjqWQEMyMTKBXYboZBL4gwcPUtWBsqKwSJEiKviQYSuZWP3pp58mOkdW0snKQDlHJsTL3CGZvyWT4vVDf8YkmScJHmVoUVYz/vDDD/jwww/VbbLSTibVy3XXrl3DP//880wNqXHjxmH16tVqCO7s2bNqbpoEhvrXIpPWJRiV13r9+nU1z0lWSL5sAn1KvY7nsAQMqiyAfu1Neu/3Zxsditw7hyDPrmGwjY1AaM6auNJuA0Jz10nX5yUiyyVzqmTFnUwAl6zRmDFjVFZEhr5k2b/M60lYCTwlJNu0cuVKREREqMntffr0wRdffJHoHJmjJRPZnzx5oiaEd+jQQQ2pJZw0b0zdunUztEeG7SSgkpWN+gyUZM1kBaJk5CRjJRmohCTokknxMu9JSkVIIPrXX38ZXousTpTgTFY9SrAlw6ky38lYWaXX8RyWwEZLWCiD0lVwcLCqURIUFGTUH8LRK09j8cFb6FrVD50q+yE9OD8+C7+tA+EUfB2ajS0eVBiGh2UHArZ26vYeCw6pVYi+ns44MLphurSBiHTkg0wyBVKRXLItRJS+v1cp/fzmnCp6MU1D5vO/IceBz2AbH41otxy4Xf8HhPv+f2kzERERMaiiF7CNCkTuXSPgdXODOg72a4Q7daYhzjkT+42IiCgJZqooWS4Bx+C3bTAcQ+8g3tYB/lVG4XHJ3twImYiI6DkYVFFiWjyynpoL3yNTYaPFIsrDD7cbzEREtrLsKSIiohdgUEUGdhGPkGfnR/C4s1MdBxZohbu1JiPekSs7iIiIXoZBFSlu9/Yhz44P4BAegHg7J9yrPglPi3bicB8REVEKMaiydvGx8Dn+HXyOfw8baIj0LoxbDWYhKrPxKwoTERFZMgZVVsw+zB95tr8Pd3/d9glPinTCveoToKVyLy8xq2sFZPNwgo8na+YQEZF1YlBlpTxubUXunUNhH/UUcQ5uuFtzMoIKpa5qcUKujvZwd9JdiIiIrBG3qbEyNnHR8D34OfJt6qkCqogspXClzbpXCqiIiExNtnnx9uZu7mRaDKqsiEPwTRRY0x7ZTv+kjh+V7Imrb65EtFd+UzeNiKxAjx49YGNjY7hkyZIFTZs2xalTp2CObty4odp54sSJdH0e2fS5c+fOyJMnD1xcXNS+et99990z58kGxrIvopOTEwoVKqQCyaRkY+p8+fKpbVaqVq2KQ4cOPbMVi+w9KH3v7u6O9u3bP7NhtWz+3KJFC7Xfn4+PD4YPH47Y2Nh0eOWWh0GVlfC8tgaFVzaH68OTiHXyws1G83C/+kRodk5GefxVx+/ip13XMH/3NaM8HhFZJgmi7t+/ry5bt25VGyq3bNkS1uzo0aMqePnjjz9w9uxZfPrpp2rz5ISbO8uedBLo1K9fXwV5Q4YMURtFy6bQen///TeGDh2K8ePH49ixYyhbtqzamDogIMBwzkcffYR///1Xbd68c+dO3Lt3T22QrBcXF6eeJzo6Gvv27cOvv/6qgrdx48a9xh7JuBhUWTib2Ejk3DMaebcNhF1MCMJ8KuJK2w0IztfEqM+z6sRdzN9zHfN3Xzfq4xKRZZEsi6+vr7qUK1cOn3zyCW7fvo2HDx8asjGSHQoMDDTcR4IIuU4yR3ryQe/n56eyKW3btsXjx4+fea7PP/9cBSseHh4qAJHnkudMaP78+SozJJmdYsWKYdasWYbbZGNdUb58efX89erVS5c+6dWrl8pM1a1bFwUKFMA777yDnj17YsWKFYZz5syZo9ozbdo01d7BgwejQ4cO+Pbbbw3nTJ8+HX379lX3LVGihLqP9M8vv/yibpfNgH/++Wd1XoMGDVCxYkUsWLBABU8HDhxQ52zatAnnzp1TAZ70VbNmzfDZZ5+pDJgEWvRiDKosmFPgFRT8pzWyXPhDHQeUHYRrLZcgxj2XqZtGRITQ0FD14S1DWTIclVIHDx5E7969VWAhAZdkbySASmjRokX44osv8PXXX6tMkARgs2fPfuYcycDIeefPn8eXX36JsWPHquyM0A+dbdmyRWXWEgY5SclQ2osuAwYMSNU7LgFQ5syZDcf79+9Ho0aNEp0jWSi5XkjAI68z4Tm2trbqWH+O3B4TE5PoHAkkpW/058jX0qVLI3v27ImeJzg4WGXR6MW4VMtCeV9ailz7xsA2NgIxzllxp94MhOauY+pmEVE6kuH3lGSLS+XyxPzulRNd1+fXwzhzN/il9+1TOz/61C6Q5jauWbNGBRkiLCwMOXLkUNdJAJBSktWRYcQRI0ao4yJFiqhsy4YNus3fxQ8//KACL8naCAmeJAsjgZyeDJNJ5kc//CWZIMnSzJ07F927d0e2bNnU9RLwSWbtRV4278rTM+U7U8hrkaG8tWvXGq7z9/dPFOgIOZZgJyIiAk+fPlVDd8mdc+HCBcNjODo6PjOhX86R2170PPrb6MUYVKWSpECnTp2qfrhkvFp+catUqQJzYRsThpx7P0WmK7q/qEJz1sTtet8h1tXH1E0jonQWEhkL/+DIl56Xw/vZenKPw6JTdF95jlchWSV9xkgCARlukyEmyQrlzZs3RY8hWSUZ8kuoevXqiYKqixcvYuDAgYnOkf+rt23bZgjorl69qgIvGTLTkwnZXl5eqX5dkm0zhjNnzqB169Yq4GvcuLFRHpNeHwZVqaCfBCjj1LKqYsaMGSotKr+8Mm5vajkiLqPQqm5wCroGzcYWDyoMw8OyAwFbO1M3jYheAw9ne/imoABvFjfHZK9LyX3lOV6Fm5tbogBE5jRJEDNv3jw1hKfPWGmaZjhHhqyMTZ+xkueV/88TsrNL/f+Z+uzb88g8KfnseBHJkjVs2BD9+vXDmDFjEt0mmbKkq/TkWDJgsmJQ2iyX5M7RZ9nkqwwTyny1hNmqpOckXTGof8yXZeuIQVWqJJwEKOQXRNKzMglQJkCajKbhXbtN6H95MRy0aES75cDt+t8j3DfxfxREZNlkWC6tQ3NJhwNfF5kALoGUDGEJ/ZCbzGHKlClTskNrMlFb5lUlpJ9orVe0aFEcPnwY3bp1M1wnxwmHtHLmzIlr166ha9euybZNhsqEDKu9zKsO/8l8JZk8LsOOMscrKcnErVu3LtF1mzdvVtfr2yoTz2VFZZs2urqD8fHx6ljmngm53cHBQV0npRSEJAWkhIL+ceSrPL+sGNQnC+R5pP0y+Z1ejJmqFNJPApRlrs+bBJhUVFSUuujJ2LfRaRreujUJ5Rw2AxoQ4tcI9+pNR5xzJjA/RUTmRv5P1M/NkeE/KRsgWaNWrVqp6ySLJfWaJkyYoD7cL126pOY9JfTBBx+gZs2a+Oabb9RQmZQVSDj0J95//331R3ClSpVQo0YNNdIg9bBkdZ3exIkT1WNJpkzmaEnbjhw5otoloxISVEgWSB47d+7caoXg84YGX2X4T4b8JKCSkQ95Xn3/SOZJH2TKRHfpK5lHJqsFZRhzyZIlieZdyX0lKJPXLEOdMpoiw5z6RIC0XYY75TyZBC+BkvSTBFLVqlVT58iQowRP7777LqZMmaLaIlkzqW0lKzfpJTRKkbt370ouWtu3b1+i64cPH65VqVIl2fuMHz9e3SfpJSgoyKi9vmbeeC1qXCZt128TNS0+XjOFql9s0fKOXKO+ElH6ioiI0M6dO6e+ZiTdu3dP9H+hh4eHVrlyZW3ZsmWJztuzZ49WunRpzdnZWatdu7a2dOlSdf7169cN5/z8889a7ty5NRcXF61Vq1baN998o3l5eSV6nEmTJmlZs2bV3N3dtV69emkffPCBVq1atUTnLFq0SCtXrpzm6OioZcqUSatTp462YsUKw+3z5s3T8uTJo9na2mp169ZNl3553mdF3rx5E523fft2Q1sLFCigLViw4JnH+uGHHzQ/Pz91jnw2HThwINHt8jMzcOBA9VpdXV21tm3bavfv3090zo0bN7RmzZqpvpX+GzZsmBYTE6NZ8+9VUFBQij6/beSflwVeBFUgLVeuXGpVhj5NKuSvBimgljQV/bxMlfwFJktlU7MS5KXkLXx8FchqnImSaVHty61qkqvMyTgwuqHJ2kFkDaQqthSDlNVqkj2hlHnjjTfUvKDff/+dXUap+r2Sz2/J9L3s85vDfymUNWvWl04CTEpSpa8lXWpjY9KASr9EW1YUJTcBlojodQsPD1fzXmVITf7v/vPPP1W9KZkfRJReGFSlUEomAVozU01yJSJ63gR4mdgt87IkAyET15cvX/5MAU0iY2JQlQovmwRIRETmQSaYS2aK6HViUJUKb7/9ttqfSirzyooI2RdJVoUkrT5LRERE1odBVSrJUB+H+4iIiCgpBlVkFLJvmGxzIRPVOb+K6PXg4m0i8/p9YlBFRiEbsepLKhBR+tJvoyJFiWXuEBEZZ8WokKrzacWgiogog7G3t4erq6ua4ykfAPr98ogobRkqCahkax7ZEzEtez/qMagiIsqA5QJy5MihChXevHnT1M0hsgje3t6vvGk0gyoiogxaO69w4cJqCJCIXo1kfF8lQ6XHoIqIKIOSYT9uU0NkPjgQT0RERGQEDKqIiIiIjIBBFREREZERcE6VCQqLBQcHw9LERoYhPioKsZFxFvn6iIjIegX/97n2sgKhNhpL8r42d+7cQZ48eV7fExIREZHR3L59G7lz537u7QyqXqP4+Hjcu3cPHh4eqs6MMSNoCdbkzfb09DTa41oj9iX70tzwZ5J9aW6s8WdS0zSEhIQgZ86cLyy2y+G/10jeiBdFuK9Kfrit5Qc8vbEv2Zfmhj+T7EtzY20/k15eXi89hxPViYiIiIyAQRURERGRETCosgBOTk4YP368+krsS3PBn0v2o7nhzyT7Mb1xojoRERGRETBTRURERGQEDKqIiIiIjIBBFREREZERMKgiIiIiMgIGVRZg5syZyJcvH5ydnVG1alUcOnQI1mLChAmqOn3CS7FixQy3R0ZGYtCgQciSJQvc3d3Rvn17PHjwINFj3Lp1Cy1atICrqyt8fHwwfPhwxMbGJjpnx44dqFChglo9VKhQISxcuDDDvw+7du1Cq1atVIVg6bdVq1Y9U0F43LhxyJEjB1xcXNCoUSNcvnw50TlPnjxB165dVQFAb29v9O7dG6GhoYnOOXXqFGrXrq36RaowT5ky5Zm2LF26VL1vck7p0qWxbt26VLfFXPuxR48ez/yMNm3aNNE57EedyZMno3LlymrXCfldbNOmDS5evJior8zpdzolbTHXfqxXr94zP5cDBgxIdI6192OayN5/lHH99ddfmqOjo/bLL79oZ8+e1fr27at5e3trDx480KzB+PHjtZIlS2r37983XB4+fGi4fcCAAVqePHm0rVu3akeOHNGqVaum1ahRw3B7bGysVqpUKa1Ro0ba8ePHtXXr1mlZs2bVRo0aZTjn2rVrmqurqzZ06FDt3Llz2g8//KDZ2dlpGzZsyNDvg7zWTz/9VFuxYoXsEKqtXLky0e1fffWV5uXlpa1atUo7efKk9uabb2r58+fXIiIiDOc0bdpUK1u2rHbgwAFt9+7dWqFChbTOnTsbbg8KCtKyZ8+ude3aVTtz5oz2559/ai4uLtrcuXMN5+zdu1f155QpU1T/jhkzRnNwcNBOnz6dqraYaz92795d9VPCn9EnT54kOof9qNOkSRNtwYIF6mflxIkTWvPmzTU/Pz8tNDTULH+nX9YWc+7HunXrqteU8OdSfl/12I9pw6Aqg6tSpYo2aNAgw3FcXJyWM2dObfLkyZq1BFXyoZ6cwMBA9eG8dOlSw3Xnz59XH3z79+9Xx/Ifrq2trebv7284Z/bs2Zqnp6cWFRWljkeMGKECt4Tefvtt9R+XpbwPSYOB+Ph4zdfXV5s6dWqi/nRyclKBkZAPI7nf4cOHDeesX79es7Gx0e7evauOZ82apWXKlMnQl2LkyJFa0aJFDcdvvfWW1qJFi0TtqVq1qta/f/8Ut8VcPC+oat269XPvw358voCAANWnO3fuNLvf6ZS0xVz7UR9Uffjhh8+9D/sxbTj8l4FFR0fj6NGjaigk4f6Ccrx//35YCxkGkqGXAgUKqKEoSVkL6ZuYmJhE/SNDTH5+fob+ka8y3JQ9e3bDOU2aNFEbhp49e9ZwTsLH0J+jfwxLfB+uX78Of3//RK9J9r2S1H3CvpMhv0qVKhnOkfPltR88eNBwTp06deDo6Jio72Qo4unTpynq35S0xdzJEIkMnxQtWhTvvfceHj9+bLiN/fh8QUFB6mvmzJnN7nc6JW0x137UW7RoEbJmzYpSpUph1KhRCA8PN9zGfkwbbqicgT169AhxcXGJ/vMQcnzhwgVYA/lglTF8+bC6f/8+Jk6cqObvnDlzRn0Qy4e5fPAn7R+5TcjX5PpPf9uLzpH/pCMiIlRwYGnvg/61J/eaEvaLBAoJ2dvbq/+4E56TP3/+Zx5Df1umTJme278JH+NlbTFnMn+qXbt2qh+uXr2K0aNHo1mzZupDy87Ojv34HPHx8RgyZAhq1qypPvSFOf1Op6Qt5tqPokuXLsibN6/6g1TmPY4cOVL9sbNixQp1O/sxbRhUUYYmH056ZcqUUUGW/EexZMkSNaGZyNQ6depk+F4yKPJzWrBgQZW9atiwoUnbZs5k4rL8cbRnzx5TN8Ui+7Ffv36Jfi5lEYj8PErgLz+flDYc/svAJG0rf+kmXSUhx76+vrBG8ldjkSJFcOXKFdUHksYPDAx8bv/I1+T6T3/bi86RFW8SuFni+6Bv94tek3wNCAhIdLusDJKVbMbo34S3v6wtGYkMU8vPjPyMCvbjswYPHow1a9Zg+/btyJ07t+F6c/qdTklbzLUfkyN/kIqEP5fsx9RjUJWBSeq5YsWK2Lp1a6JUrxxXr14d1kiW88tfWvJXl/SNg4NDov6R9LbMudL3j3w9ffp0ouBg8+bN6j/XEiVKGM5J+Bj6c/SPYYnvgwxVyX+qCV+TDI3IXKmEfScfKDK3RG/btm3qtev/g5ZzpOSAzD1J2HcyXCtDfynp35S0JSO5c+eOmlMlP6OC/fh/MtdfAoGVK1eqn6WkQ8fm9DudkraYaz8m58SJE+prwp9La+/HNEnjBHcyE7LsV1ZBLVy4UK0i6tevn1r2m3DliyUbNmyYtmPHDu369etqab4so5bl07LaRb/kWZYSb9u2TS15rl69urokXTbcuHFjtfRYllRny5Yt2eXXw4cPV6t7Zs6cmezy64z2PoSEhKgl53KR/wqmT5+uvr9586ahjIG8htWrV2unTp1SK9iSK6lQvnx57eDBg9qePXu0woULJyqpICukpKTCu+++q5Z3Sz9JXyYtqWBvb6998803qn9lRWdyJRVe1hZz7Ee57eOPP1arweRndMuWLVqFChVUP0VGRhoeg/2o895776nSGfI7nXCpf3h4uKGvzOl3+mVtMdd+vHLlijZp0iTVZvm5lN+rAgUKaHXq1DE8BvsxbRhUWQCpsSK/2FJTRZYBS80gayHLoHPkyKFee65cudSx/IehJx+6AwcOVMv65T/Rtm3bqv9cErpx44bWrFkzVT9JAjIJ1GJiYhKds337dq1cuXLqeeQ/H6kBk9HfB3lNEgQkvUgJAH0pg7Fjx6qgSD5cGjZsqF28eDHRYzx+/FgFUe7u7mrJes+ePVUgkZDUlapVq5Z6DHmPJEBKasmSJVqRIkVU38lS97Vr1ya6PSVtMcd+lA8x+XCXD3UJFPPmzatqAyUNttmPOsn1o1wS/r6Z0+90Stpijv1469YtFUBlzpxZ/T5JfTkJMBPWqRLW3o9pYSP/pC3HRURERER6nFNFREREZAQMqoiIiIiMgEEVERERkREwqCIiIiIyAgZVREREREbAoIqIiIjICBhUERERERkBgyoiIjM2YcIElCtXztTNIKIUYFBFROmuR48eaNOmjVX3dHr3wfLly1GvXj14eXnB3d0dZcqUwaRJk9QG18aQL18+zJgxwyiPRWSpGFQRUYYXFxenNmq1Vp9++inefvttVK5cGevXr8eZM2cwbdo0nDx5Er///rupm0dkNRhUEdFrJxmVDz74ACNGjEDmzJnh6+urhrkSCgwMRP/+/ZE9e3Y4OzujVKlSWLNmjbpt4cKF8Pb2xj///IMSJUrAyclJ7WofFRWFjz/+GLly5YKbmxuqVq2KHTt2GB5Tfz95nKJFi8LV1RUdOnRAeHg4fv31V5WNyZQpk2qbBGp6KX3cjRs3onjx4ipT1LRpU9y/f1/dLq9NHn/16tWwsbFRF/39R44ciSJFiqi2FChQAGPHjkVMTEyK+/LQoUP48ssvVRA1depU1KhRQ72ON954Q2Wvunfvbjh39uzZKFiwIBwdHdXrTxhwyY5l0k4/Pz/Vnzlz5lT9oH+/bt68iY8++sjQfiJ6ln0y1xERpTsJMoYOHYqDBw9i//79anisZs2aKhiQrFOzZs0QEhKCP/74QwUC586dg52dneH+Egh9/fXXmD9/PrJkyQIfHx8MHjxYnffXX3+poGDlypUquDl9+jQKFy5suN/333+vzpHHb9euHdq2bauConXr1uHatWto3769aotkf0RKH/ebb75RgYqtrS3eeecdFYgtWrRIfT1//jyCg4OxYMECdb4Ek8LDw0MFZfK48nh9+/ZV10nAmRLy+BLEDRw4MNnb5XUJafOHH36ohvAaNWqkAsuePXsid+7cqF+/vgrAvv32W/UaS5YsCX9/f5XpEitWrEDZsmXRr18/1T4ieo40bcNMRJQK3bt311q3bm04rlu3rlarVq1E51SuXFkbOXKk+n7jxo2ara2tdvHixWQfb8GCBbIRvHbixAnDdTdv3tTs7Oy0u3fvJjq3YcOG2qhRoxLd78qVK4bb+/fvr7m6umohISGG65o0aaKuf5XHnTlzppY9e/bn9sHzTJ06VatYsaLhePz48VrZsmWfe36zZs20MmXKvPRxa9SoofXt2zfRdR07dtSaN2+uvp82bZpWpEgRLTo6Otn7582bV/v2229f+jxE1oyZKiIyCZlInVCOHDkQEBCgvj9x4oTKoMiw2PPIEFbCx5AsjwzZJb2PDN1JJktPhtkk86Unw4syXCbZnoTX6duS1sdN+Hpe5O+//1aZs6tXryI0NBSxsbHw9PRESsmwXUpIpkwyTQlJNu67775T33fs2FFlsWQIUrJwzZs3R6tWrWBvz48JopTibwsRmYSDg0OiY5mno59s7uLi8tL7yzkJ5/ZIQCLDg0ePHk00TCgSBkzJPe+L2vIqj/uygEeGPbt27YqJEyeiSZMmauWeDL/J/KiUkmBvz549ah5W0jakRp48eXDx4kVs2bIFmzdvVsOJMkdr586dr/S4RNaEE9WJyOxIBurOnTu4dOlSiu9Tvnx5lVGS7FChQoUSXWQifFoZ63Els5Zw8rvYt28f8ubNq1bvVapUSc3PkgnhqdGlSxcV+M2aNSvZ22XCv5AJ9Hv37k10mxzLRP+EgapkpyRzJhPpJeiTTN3z2k9EiTFTRURmp27duqhTp46aMD59+nQVwFy4cEFlf2Ro6nkZG8n6dOvWTWV6JBh6+PAhtm7dqoK0Fi1apKktxnpcGWKU1YGSDZJhQ8lKSRAlqxYlOyXlENauXasmlKeGrESUSe3Dhg3D3bt31aR7mfR+5coVzJkzB7Vq1VIT1IcPH4633npLtV8mqv/7779qArpkpoRMlpegSR5PhjJlgYAEWRL06du/a9cudOrUSa0OzJo1axp6k8iyMVNFRGZJVqNJoNG5c2eVTZHA4WWZEllZJ8GPBBhSMkCKbR4+fFiVCXgVxnhcWTUn95WMVLZs2VSW6M0331RlCmR1oVRNl8yVlFRILVkFuXjxYrWSUoYRZfWerKyUoE9fUkHaLPOnZIWi3D537lz1uqRcgn6V4Lx589Q8K7mfBFsSeOnnjUkh0Rs3bqh5Y9J+InqWjcxWT+Z6IiIiIkoFZqqIiIiIjIBBFREREZERMKgiIiIiMgIGVURERERGwKCKiIiIyAgYVBEREREZAYMqIiIiIiNgUEVERERkBAyqiIiIiIyAQRURERGRETCoIiIiIjICBlVEREREeHX/A6qrTN29hm9FAAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "score_test = tau_r_test - lambda_star * tau_c_test\n",
+ "\n",
+ "x, y, max_cost, max_value = make_cost_curve(\n",
+ " score=score_test,\n",
+ " T=T_test,\n",
+ " Y_value=Yg_test,\n",
+ " Y_cost=Yc_test,\n",
+ " e=e_test,\n",
+ " w=W_test,\n",
+ " n_points=101\n",
+ ")\n",
+ "\n",
+ "inc_value_all = np.sum(W_test * (T_test * Yg_test / e_test - (1 - T_test) * Yg_test / (1 - e_test)))\n",
+ "inc_cost_all = np.sum(W_test * (T_test * Yc_test / e_test - (1 - T_test) * Yc_test / (1 - e_test)))\n",
+ "\n",
+ "# 전체 AUCC\n",
+ "aucc_all, auc_model_all, auc_random_all, slope = aucc_from_cost_curve(\n",
+ " x, y, inc_cost_all=inc_cost_all, inc_value_all=inc_value_all, budget=None\n",
+ ")\n",
+ "\n",
+ "# budget AUCC\n",
+ "aucc_B, auc_model_B, auc_random_B, slope = aucc_from_cost_curve(\n",
+ " x, y, inc_cost_all=inc_cost_all, inc_value_all=inc_value_all, budget=BUDGET\n",
+ ")\n",
+ "\n",
+ "print(f\"[전체] AUCC={aucc_all:.4f} (auc_model={auc_model_all:.2e}, auc_random={auc_random_all:.2e})\")\n",
+ "print(f\"[budget={BUDGET}] AUCC={aucc_B:.4f} (auc_model={auc_model_B:.2e}, auc_random={auc_random_B:.2e})\")\n",
+ "\n",
+ "plot_cost_curve(x, y, max_cost, max_value, title=\"Cost Curve (Duality score)\", budget=BUDGET)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/book/prescriptive_analytics/fractional_uplift.ipynb b/book/prescriptive_analytics/fractional_uplift.ipynb
new file mode 100644
index 0000000..412a666
--- /dev/null
+++ b/book/prescriptive_analytics/fractional_uplift.ipynb
@@ -0,0 +1,1994 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "8530047b",
+ "metadata": {},
+ "source": [
+ "# Fractional uplift modelling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "91104063",
+ "metadata": {},
+ "source": [
+ "> Google LLC의 [Fractional Uplift – End to End Example](https://github.com/google-marketing-solutions/fractional_uplift)를 한국어로 번역 및 정리했습니다.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "601fb081",
+ "metadata": {},
+ "source": [
+ "fractional uplift 모델은 프로모션 비용이 사전에 확정되지 않는 상황에 적합합니다. \n",
+ "\n",
+ "예를 들어, 특정 조건이 있는 쿠폰을 제공하는 경우 쿠폰을 제공하는 시점에는 실제 비용을 알 수 없으며, 비용은 사용자가 어떤 상품을 구매하느냐에 따라 달라집니다.\n",
+ "\n",
+ "이러한 상황에서 일반적인 uplift 모델링은 한계가 있습니다. 일반 uplift 모델은 처치로 인한 증분 효과(incrementality)만을 다루고, 비용은 고려하지 않기 때문입니다. \n",
+ "\n",
+ "반면 fractional uplift 모델은 여러 지표를 함께 고려하여 최적화하도록 설계되어 있습니다.\n",
+ "\n",
+ "일반적인 uplift 모델은 하나의 지표(예: 전환율 또는 지출 금액)에 대해 조건부 평균 처치 효과(CATE)를 추정합니다.\n",
+ "\n",
+ "$$\n",
+ "f_\\text{uplift}(X) = \\text{CATE}_y(X) = E[y \\mid T=1, X] - E[y \\mid T=0, X]\n",
+ "$$\n",
+ "\n",
+ "fractional uplift 모델은 여러 지표에 대한 CATE 추정치를 결합하여, 다음과 같은 단일 점수를 계산합니다.\n",
+ "\n",
+ "$$\n",
+ "f_\\text{fractional uplift}(X) =\n",
+ "\\begin{cases}\n",
+ " \\dfrac{\\text{CATE}_\\alpha (X)}\n",
+ " {\\text{CATE}_\\beta(X) - \\dfrac{\\text{CATE}_\\gamma (X)}{\\delta}},\n",
+ " & \\text{CATE}_\\beta(X) > \\dfrac{\\text{CATE}_\\gamma (X)}{\\delta} \\\\\n",
+ " \\infty, & \\text{그 외의 경우}\n",
+ "\\end{cases}\n",
+ "$$\n",
+ "\n",
+ "각 항의 의미는 다음과 같습니다.\n",
+ "\n",
+ "- $\\alpha$ (Maximize KPI) \n",
+ " 모델이 최대화하고자 하는 지표입니다.\n",
+ "\n",
+ "- $\\beta$ (Constraint KPI) \n",
+ " 제약으로 작용하는 지표로, 가능한 한 낮게 유지하고자 합니다.\n",
+ "\n",
+ "- $\\gamma$ (Constraint Offset KPI) \n",
+ " 제약을 상쇄하는 데 사용할 수 있는 지표이며, 선택적으로 사용됩니다.\n",
+ "\n",
+ "- $\\delta$ (Constraint Offset Scale) \n",
+ " constraint offset KPI의 스케일을 조정하는 상수입니다. \n",
+ " constraint offset KPI를 사용하지 않는 경우에는 필요하지 않습니다.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "7f3eb8c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
+ "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install -q fractional-uplift pandas numpy statsmodels tensorflow tensorflow_decision_forests matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 134,
+ "id": "e4d6d469",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "os.environ[\"OMP_NUM_THREADS\"] = \"8\"\n",
+ "os.environ[\"TF_NUM_INTRAOP_THREADS\"] = \"8\"\n",
+ "os.environ[\"TF_NUM_INTEROP_THREADS\"] = \"2\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 135,
+ "id": "97a2ff86",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import statsmodels.api as sm\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.ticker as mtick\n",
+ "from matplotlib.lines import Line2D\n",
+ "\n",
+ "import tensorflow as tf # tensorflow decision forests가 eager mode로 실행되도록 하기 위해 필요\n",
+ "\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "id": "77fb1695",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow_decision_forests as tfdf\n",
+ "import fractional_uplift as fr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 137,
+ "id": "27a1ae4b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# tfdf 사용 시 출력되는 Keras 학습 로그 숨김\n",
+ "tfdf.keras.set_training_logs_redirection(False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "acd5db70",
+ "metadata": {},
+ "source": [
+ "## Load the Criteo data\n",
+ "\n",
+ "[Criteo dataset](https://ailab.criteo.com/criteo-uplift-prediction-dataset/)은 uplift 모델링을 벤치마킹하기위한 공개된 데이터셋입니다. 여러 incrementality 테스트 결과를 모아 구성되었으며, 각 행은 사용자 한 명을 나타냅니다.\n",
+ "\n",
+ "데이터셋에는 다음 정보가 포함되어 있습니다.\n",
+ "\n",
+ "- 사용자 특성(feature) 11개\n",
+ "- 처치 여부(treatment)\n",
+ "- 결과 라벨 2개: 방문(visits), 전환(conversions)\n",
+ "\n",
+ "이 데이터셋은 전환이나 방문과 같은 단일 KPI를 대상으로 하는 표준 uplift 모델링 문제를 위해 설계되었습니다. \n",
+ "\n",
+ "그러나 이 노트북에서는 사용자에게 프로모션을 제공하는 상황을 가정하여, 보다 현실적인 uplift 모델링 문제를 다룹니다.\n",
+ "\n",
+ "이를 위해 다음과 같은 추가 지표를 사용합니다.\n",
+ "\n",
+ "- **Spend**: 사용자가 전환했을 때 지출한 금액을 의미하며, 특성(feature)을 기반으로 생성됩니다.\n",
+ "\n",
+ "- **Cost:** 또는 쿠폰 비용을 의미합니다. 사용자가 전환한 경우에만 발생하며, 처치된 사용자(T=1)에게서 전환이 발생했을 때에만 비용이 발생하도록 설정합니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "id": "7ee1e798",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "criteo = fr.example_data.CriteoWithSyntheticCostAndSpend.load()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "id": "92c3c0ab",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ " f0 f1 f2 f3 f4 f5 f6 \\\n",
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+ "187 12.616365 10.059654 8.904597 4.679882 10.280525 4.115453 0.294443 \n",
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+ "1108 14.617627 10.059654 8.489929 3.907662 13.253813 4.115453 -2.411115 \n",
+ "\n",
+ " f7 f8 f9 f10 f11 treatment \\\n",
+ "44 4.833815 3.955396 13.190056 5.300375 -0.168679 1 \n",
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+ "1108 4.833815 3.809530 42.176324 5.737292 -0.560340 1 \n",
+ "\n",
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+ "44 0 0.85 0.000000 0.000000 0.000000 \n",
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+ "1108 1 0.85 0.090777 36.459294 3.309655 \n",
+ "\n",
+ " sample_weight \n",
+ "44 100.0 \n",
+ "187 100.0 \n",
+ "484 100.0 \n",
+ "528 100.0 \n",
+ "1108 1.0 "
+ ]
+ },
+ "execution_count": 139,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "criteo.train_data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bdef041d",
+ "metadata": {},
+ "source": [
+ "## Experiment Analysis\n",
+ "\n",
+ "먼저 쿠폰 제공의 전체적인 효과를 일반적인 A/B 테스트로 분석합니다. \n",
+ "\n",
+ "사용자에게 구매 금액에 대한 할인을 제공하는 쿠폰을 제공하므로, 캠페인의 증분 RoI(incremental RoI, iRoI)를 평가합니다.\n",
+ "\n",
+ "iRoI는 다음과 같이 정의합니다.\n",
+ "\n",
+ "$$\n",
+ "\\text{iRoI} = \\frac{\\text{Spend}_{T=1} - \\text{Spend}_{T=0}}{\\text{Cost}_{T=1}}\n",
+ "$$\n",
+ "\n",
+ "최소제곱법(ordinary least squares)과 델타 방법(delta method)을 사용하여 iRoI와 신뢰구간을 추정합니다.\n",
+ "\n",
+ "**주의**: 델타 방법을 이용한 iRoI 신뢰구간 추정은 처치군에서의 spend와 cost 간 상관관계를 고려하지 않습니다. 이로 인해 신뢰구간이 실제보다 넓게 추정될 수 있습니다. 다만 이는 핵심 주제가 아니므로, 단순화를 위해 해당 방식을 그대로 사용합니다.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "id": "7c472fde",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def perform_ols(input_df: pd.DataFrame, target: str) -> tuple[float, float]:\n",
+ " \"\"\"statsmodels를 사용하여 최소제곱법(OLS) 회귀를 수행합니다.\n",
+ "\n",
+ " target 변수를 처치 여부(treatment)에 회귀시키며,\n",
+ " 이는 두 집단 간 평균 차이를 검정하는 t-test와 동일한 역할을 합니다.\n",
+ "\n",
+ " Args:\n",
+ " input_df: 회귀 분석에 사용할 데이터프레임.\n",
+ " target: 효과를 추정할 대상 변수명.\n",
+ "\n",
+ " Returns:\n",
+ " (effect_size, standard_error) 튜플을 반환합니다.\n",
+ " - effect_size: 처치 효과(평균 차이)\n",
+ " - standard_error: 처치 효과의 표준오차\n",
+ " \"\"\"\n",
+ " Y = input_df[target].values\n",
+ " X = input_df[[\"treatment\"]]\n",
+ " X = sm.add_constant(X)\n",
+ " model = sm.OLS(Y, X)\n",
+ " results = model.fit()\n",
+ "\n",
+ " effect_size = results.params[\"treatment\"]\n",
+ " se = results.HC0_se[\"treatment\"]\n",
+ " return effect_size, se\n",
+ "\n",
+ "\n",
+ "def estimate_incremental_roi(data: pd.DataFrame) -> tuple[float, float, float]:\n",
+ " \"\"\"델타 방법(delta method)을 사용하여 증분 RoI와 그 불확실성을 추정합니다.\n",
+ "\n",
+ " 이 방법은 처치군 사용자에서 spend와 cost 간의 상관관계를\n",
+ " 고려하지 않기 때문에 정확한 추정은 아닙니다.\n",
+ " 실제로는 신뢰구간의 폭이 더 좁아질 수 있으며,\n",
+ " 여기서 계산된 값은 보수적인 추정치입니다.\n",
+ "\n",
+ " Args:\n",
+ " data: 분석에 사용할 데이터프레임.\n",
+ "\n",
+ " Returns:\n",
+ " (incremental_roi, lower_bound, upper_bound)를 반환합니다.\n",
+ " - incremental_roi: 증분 RoI 추정값\n",
+ " - lower_bound: 신뢰구간 하한\n",
+ " - upper_bound: 신뢰구간 상한\n",
+ " \"\"\"\n",
+ " effect_size_spend, spend_se = perform_ols(data, \"spend\")\n",
+ " avg_cost = data.loc[data[\"treatment\"] == 1, \"cost\"].mean()\n",
+ " cost_se = (\n",
+ " data.loc[data[\"treatment\"] == 1, \"cost\"].std()\n",
+ " / np.sqrt(np.sum(data[\"treatment\"]))\n",
+ " )\n",
+ "\n",
+ " inc_roi = effect_size_spend / avg_cost\n",
+ " inc_roi_se = np.abs(inc_roi) * np.sqrt(\n",
+ " spend_se**2 / effect_size_spend**2\n",
+ " + cost_se**2 / avg_cost**2\n",
+ " )\n",
+ "\n",
+ " inc_roi_lb = inc_roi - 2.0 * inc_roi_se\n",
+ " inc_roi_ub = inc_roi + 2.0 * inc_roi_se\n",
+ "\n",
+ " return inc_roi, inc_roi_lb, inc_roi_ub"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "id": "b78d9b3b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Incremental RoI = 0.97 [Lower Bound=0.90, Upper Bound=1.03]\n"
+ ]
+ }
+ ],
+ "source": [
+ "inc_roi, inc_roi_lb, inc_roi_ub = estimate_incremental_roi(criteo.data)\n",
+ "print(\n",
+ " f\"Incremental RoI = {inc_roi:.2f} \"\n",
+ " f\"[Lower Bound={inc_roi_lb:.2f}, Upper Bound={inc_roi_ub:.2f}]\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "664e5bd8",
+ "metadata": {},
+ "source": [
+ "처치가 분명하고 측정 가능한 효과를 보이는 것으로 보입니다(iRoI의 하한이 0보다 충분히 큼). \n",
+ "\n",
+ "그러나 동시에 비용도 높아, RoI가 확실하게 1.0을 넘는다고 말하기는 어렵습니다. 즉, 이 프로모션으로 손실이 발생하고 있을 가능성도 있습니다.\n",
+ "\n",
+ "이러한 상황이 바로 uplift 모델링에 적합한 경우입니다. 프로모션을 적절한 사용자에게만 타겟팅한다면, 더 높은 iRoI를 달성할 수 있을 것입니다."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3fd3f766",
+ "metadata": {},
+ "source": [
+ "## Uplift Modelling\n",
+ "\n",
+ "이제 이 프로모션 캠페인을 최적화하기 위해 다양한 uplift 모델을 학습합니다.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ea86385",
+ "metadata": {},
+ "source": [
+ "### Distillation\n",
+ "\n",
+ "이 노트북에서 사용하는 uplift 모델들은 모두 meta learner입니다. 즉, 여러 개의 머신러닝 모델을 조합해 하나의 uplift 모델을 구성합니다.\n",
+ "\n",
+ "예를 들어, 아래에서 설명할 T-Learner는 처치군의 반응을 예측하는 모델과 대조군의 반응을 예측하는 모델, 총 두 개의 모델로 이루어져 있습니다.\n",
+ "\n",
+ "이처럼 여러 모델을 함께 사용하면, 실제 서비스 환경에서 추론 지연(latency)이 발생할 수 있습니다. \n",
+ "\n",
+ "이를 해결하기 위해 fractional uplift 패키지의 모든 메타 러너는 distill 메서드를 제공합니다. 이 메서드는 전체 uplift 모델을 근사하는 단일 모델을 생성합니다.\n",
+ "\n",
+ "아래 예제에서는 distillation을 적용한 모델과 적용하지 않은 모델의 성능을 함께 비교합니다."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "868108a3",
+ "metadata": {},
+ "source": [
+ "### The T-Learner (baseline)\n",
+ "\n",
+ "fractional uplift 모델과 비교하기 위한 기준선(baseline)으로 기존 uplift 모델을 먼저 사용합니다.\n",
+ "\n",
+ "여기서는 비교적 단순하면서도 성능이 안정적인 T-Learner를 사용합니다. T-Learner는 하나의 KPI에 대한 uplift를 추정하기 위해, 대조군과 처치군 데이터를 각각 사용해 두 개의 모델을 학습하고, 두 예측값의 차이를 uplift로 계산합니다.\n",
+ "\n",
+ "전환(conversion)과 매출(spend)에 대한 uplift를 각각 추정하는 두 개의 T-Learner를 학습합니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "id": "d2f0d38c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_dataset = fr.datasets.PandasDataset(\n",
+ " features_data=criteo.test_data[criteo.features]\n",
+ ")\n",
+ "distill_dataset = fr.datasets.PandasDataset(\n",
+ " features_data=criteo.distill_data[criteo.features]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "id": "6ba9653f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_base_regressor():\n",
+ " # 예시로 사용하는 기본 회귀 모델입니다.\n",
+ " # 실제 프로젝트에서는 tuning 인자를 사용해 하이퍼파라미터 튜닝을 수행하는 것이 좋습니다.\n",
+ " # 자세한 내용은 tensorflow decision forests 문서를 참고하십시오.\n",
+ " return fr.base_models.TensorflowDecisionForestRegressor(\n",
+ " tfdf.keras.GradientBoostedTreesModel,\n",
+ " init_args=dict(verbose=0, max_depth=6, num_trees=300, shrinkage=0.1),\n",
+ " fit_args=dict(verbose=0)\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "414804f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for target in [\"conversion\", \"spend\"]:\n",
+ " print(f\"\\nTraining the {target} T-learner\\n\")\n",
+ "\n",
+ " train_data = fr.datasets.PandasTrainData(\n",
+ " features_data=criteo.train_data[criteo.features],\n",
+ " maximize_kpi=criteo.train_data[target].values,\n",
+ " is_treated=criteo.train_data[\"treatment\"].values,\n",
+ " treatment_propensity=criteo.train_data[\"treatment_propensity\"].values,\n",
+ " sample_weight=criteo.train_data[\"sample_weight\"].values,\n",
+ " shuffle_seed=1234\n",
+ " )\n",
+ "\n",
+ " t_learner = fr.meta_learners.TLearner(get_base_regressor())\n",
+ " t_learner.fit(train_data)\n",
+ "\n",
+ " distill_t_learner = get_base_regressor()\n",
+ " t_learner.distill(distill_dataset, distill_t_learner)\n",
+ "\n",
+ " criteo.test_data[f\"{target}_t_learner_score\"] = t_learner.predict(test_dataset)\n",
+ " criteo.test_data[f\"{target}_t_learner_score_distill\"] = distill_t_learner.predict(test_dataset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "id": "32668b60",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
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+ " f0 | \n",
+ " f1 | \n",
+ " f2 | \n",
+ " f3 | \n",
+ " f4 | \n",
+ " f5 | \n",
+ " f6 | \n",
+ " f7 | \n",
+ " f8 | \n",
+ " f9 | \n",
+ " ... | \n",
+ " conversion | \n",
+ " treatment_propensity | \n",
+ " cost_percentage | \n",
+ " spend | \n",
+ " cost | \n",
+ " sample_weight | \n",
+ " conversion_t_learner_score | \n",
+ " conversion_t_learner_score_distill | \n",
+ " spend_t_learner_score | \n",
+ " spend_t_learner_score_distill | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1007 | \n",
+ " 13.221509 | \n",
+ " 10.059654 | \n",
+ " 8.471764 | \n",
+ " -1.666396 | \n",
+ " 13.059169 | \n",
+ " 2.230907 | \n",
+ " -14.476362 | \n",
+ " 9.170324 | \n",
+ " 3.793946 | \n",
+ " 39.917532 | \n",
+ " ... | \n",
+ " 1 | \n",
+ " 0.85 | \n",
+ " 0.051091 | \n",
+ " 37.386848 | \n",
+ " 1.910123 | \n",
+ " 1.0 | \n",
+ " 0.035170 | \n",
+ " 0.053963 | \n",
+ " 0.217223 | \n",
+ " 0.484556 | \n",
+ "
\n",
+ " \n",
+ " | 1080 | \n",
+ " 15.643772 | \n",
+ " 10.059654 | \n",
+ " 8.232822 | \n",
+ " 3.907662 | \n",
+ " 11.029584 | \n",
+ " 4.115453 | \n",
+ " -1.288207 | \n",
+ " 4.833815 | \n",
+ " 3.858041 | \n",
+ " 34.180688 | \n",
+ " ... | \n",
+ " 1 | \n",
+ " 0.85 | \n",
+ " 0.469323 | \n",
+ " 37.690649 | \n",
+ " 17.689078 | \n",
+ " 1.0 | \n",
+ " 0.015709 | \n",
+ " 0.015331 | \n",
+ " 0.661332 | \n",
+ " 0.196871 | \n",
+ "
\n",
+ " \n",
+ " | 1295 | \n",
+ " 13.236812 | \n",
+ " 11.119309 | \n",
+ " 8.329031 | \n",
+ " -2.570015 | \n",
+ " 11.561050 | \n",
+ " 1.128518 | \n",
+ " -18.659676 | \n",
+ " 5.814641 | \n",
+ " 3.855652 | \n",
+ " 34.621266 | \n",
+ " ... | \n",
+ " 1 | \n",
+ " 0.85 | \n",
+ " 0.007478 | \n",
+ " 45.282760 | \n",
+ " 0.338639 | \n",
+ " 1.0 | \n",
+ " -0.233079 | \n",
+ " -0.195807 | \n",
+ " -12.409683 | \n",
+ " -8.438211 | \n",
+ "
\n",
+ " \n",
+ " | 1670 | \n",
+ " 19.622574 | \n",
+ " 10.059654 | \n",
+ " 8.305211 | \n",
+ " 3.907662 | \n",
+ " 13.253813 | \n",
+ " 4.115453 | \n",
+ " -1.288207 | \n",
+ " 4.833815 | \n",
+ " 3.803324 | \n",
+ " 41.176485 | \n",
+ " ... | \n",
+ " 1 | \n",
+ " 0.85 | \n",
+ " 0.010958 | \n",
+ " 44.872755 | \n",
+ " 0.491714 | \n",
+ " 1.0 | \n",
+ " -0.137972 | \n",
+ " -0.126640 | \n",
+ " -10.253737 | \n",
+ " -9.813921 | \n",
+ "
\n",
+ " \n",
+ " | 1940 | \n",
+ " 12.616365 | \n",
+ " 10.059654 | \n",
+ " 8.681976 | \n",
+ " 4.679882 | \n",
+ " 11.029584 | \n",
+ " 4.115453 | \n",
+ " 0.294443 | \n",
+ " 4.833815 | \n",
+ " 3.864711 | \n",
+ " 13.190056 | \n",
+ " ... | \n",
+ " 1 | \n",
+ " 0.85 | \n",
+ " 0.993622 | \n",
+ " 19.274877 | \n",
+ " 19.151940 | \n",
+ " 1.0 | \n",
+ " 0.002356 | \n",
+ " 0.001106 | \n",
+ " 0.003596 | \n",
+ " -0.081532 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 23 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " f0 f1 f2 f3 f4 f5 \\\n",
+ "1007 13.221509 10.059654 8.471764 -1.666396 13.059169 2.230907 \n",
+ "1080 15.643772 10.059654 8.232822 3.907662 11.029584 4.115453 \n",
+ "1295 13.236812 11.119309 8.329031 -2.570015 11.561050 1.128518 \n",
+ "1670 19.622574 10.059654 8.305211 3.907662 13.253813 4.115453 \n",
+ "1940 12.616365 10.059654 8.681976 4.679882 11.029584 4.115453 \n",
+ "\n",
+ " f6 f7 f8 f9 ... conversion \\\n",
+ "1007 -14.476362 9.170324 3.793946 39.917532 ... 1 \n",
+ "1080 -1.288207 4.833815 3.858041 34.180688 ... 1 \n",
+ "1295 -18.659676 5.814641 3.855652 34.621266 ... 1 \n",
+ "1670 -1.288207 4.833815 3.803324 41.176485 ... 1 \n",
+ "1940 0.294443 4.833815 3.864711 13.190056 ... 1 \n",
+ "\n",
+ " treatment_propensity cost_percentage spend cost \\\n",
+ "1007 0.85 0.051091 37.386848 1.910123 \n",
+ "1080 0.85 0.469323 37.690649 17.689078 \n",
+ "1295 0.85 0.007478 45.282760 0.338639 \n",
+ "1670 0.85 0.010958 44.872755 0.491714 \n",
+ "1940 0.85 0.993622 19.274877 19.151940 \n",
+ "\n",
+ " sample_weight conversion_t_learner_score \\\n",
+ "1007 1.0 0.035170 \n",
+ "1080 1.0 0.015709 \n",
+ "1295 1.0 -0.233079 \n",
+ "1670 1.0 -0.137972 \n",
+ "1940 1.0 0.002356 \n",
+ "\n",
+ " conversion_t_learner_score_distill spend_t_learner_score \\\n",
+ "1007 0.053963 0.217223 \n",
+ "1080 0.015331 0.661332 \n",
+ "1295 -0.195807 -12.409683 \n",
+ "1670 -0.126640 -10.253737 \n",
+ "1940 0.001106 0.003596 \n",
+ "\n",
+ " spend_t_learner_score_distill \n",
+ "1007 0.484556 \n",
+ "1080 0.196871 \n",
+ "1295 -8.438211 \n",
+ "1670 -9.813921 \n",
+ "1940 -0.081532 \n",
+ "\n",
+ "[5 rows x 23 columns]"
+ ]
+ },
+ "execution_count": 145,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "criteo.test_data.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "id": "6c84863c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_cumulative_incrementality(\n",
+ " ax: plt.Axes,\n",
+ " results_data: pd.DataFrame,\n",
+ " model_names: dict[str, str],\n",
+ " x_col: str,\n",
+ " y_col: str,\n",
+ " title: str = \"\",\n",
+ " x_label: str | None = None,\n",
+ " y_label: str | None = None,\n",
+ " x_format: str = \"{0}\",\n",
+ " y_format: str = \"{0}\",\n",
+ " order_col: str = \"share_targeted\",\n",
+ " random_baseline_name: str = \"random\",\n",
+ " x_lim: list[float] | None = None,\n",
+ " y_lim: list[float] | None = None,\n",
+ " show_legend: bool = True,\n",
+ " ) -> None:\n",
+ " \"\"\"Plots the cumulative incrementality of any x and y metrics.\"\"\"\n",
+ "\n",
+ " baselines_data = results_data.loc[results_data.name == random_baseline_name].copy().sort_values(order_col)\n",
+ " models_data = results_data.loc[results_data.name.isin(model_names)].copy().sort_values(order_col)\n",
+ "\n",
+ " raw_model_names = list(model_names.keys())\n",
+ " clean_model_names = list(model_names.values())\n",
+ "\n",
+ " ax.plot(baselines_data[x_col], baselines_data[y_col], color=\"k\", lw=1, label=\"\")\n",
+ "\n",
+ " for raw_model_name, model_results in models_data.groupby(\"name\"):\n",
+ "\n",
+ " if raw_model_name.endswith(\"_distill\"):\n",
+ " raw_model_name = raw_model_name.removesuffix(\"_distill\")\n",
+ " label = \"\"\n",
+ " line_style = \"--\"\n",
+ " else:\n",
+ " label = model_names[raw_model_name]\n",
+ " line_style = \"-\"\n",
+ "\n",
+ " color = f\"C{raw_model_names.index(raw_model_name)}\"\n",
+ " ax.plot(model_results[x_col], model_results[y_col], color=color, lw=1.5, label=label, ls=line_style)\n",
+ "\n",
+ " if x_lim is not None:\n",
+ " ax.set_xlim(x_lim)\n",
+ " if y_lim is not None:\n",
+ " ax.set_ylim(y_lim)\n",
+ "\n",
+ " ax.set_xlabel(x_label or x_col)\n",
+ " ax.set_ylabel(y_label or y_col)\n",
+ " ax.set_title(title)\n",
+ "\n",
+ " ax.xaxis.set_major_formatter(mtick.FuncFormatter(lambda x, pos: x_format.format(x)))\n",
+ " ax.yaxis.set_major_formatter(mtick.FuncFormatter(lambda y, pos: y_format.format(y)))\n",
+ "\n",
+ " # Add legend\n",
+ " if show_legend:\n",
+ " handles, labels = ax.get_legend_handles_labels()\n",
+ " handles.extend([\n",
+ " Line2D([0], [0], alpha=0.0),\n",
+ " Line2D([0], [0], color=\"0.7\", lw=1.5, ls=\"-\"),\n",
+ " Line2D([0], [0], color=\"0.7\", lw=1.5, ls=\"--\")\n",
+ " ])\n",
+ " labels.extend([\n",
+ " \"\",\n",
+ " \"Full model\",\n",
+ " \"Distilled model\"\n",
+ " ])\n",
+ " model_legend = ax.legend(\n",
+ " handles=handles,\n",
+ " labels=labels,\n",
+ " loc='upper left',\n",
+ " bbox_to_anchor=(1, 1)\n",
+ " )\n",
+ "\n",
+ " del(baselines_data)\n",
+ " del(models_data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3418e936",
+ "metadata": {},
+ "source": [
+ "이제 두 개의 T-Learner를 평가합니다. 평가는 모델이 타깃으로 선택한 사용자 비율에 따라 incremental spend과 incremental conversions이 어떻게 증가하는지를 시각화하는 방식으로 수행합니다.\n",
+ "\n",
+ "만약 모델이 사용자를 무작위로 선택한다면, 전체 사용자 중 50%를 타깃팅했을 때 incremental conversions과 incremental spend 역시 전체의 약 50% 수준에 그칠 것입니다.\n",
+ "\n",
+ "반면 uplift 모델이 제대로 학습되었다면, 사용자 50%를 타깃팅했을 때 50%를 초과하는 incremental conversions과 incremental spend을 기대할 수 있습니다.\n",
+ "\n",
+ "아래에 제시된 uplift 곡선은 타깃 사용자 비율 전 구간에 걸쳐 이러한 성능 차이를 직관적으로 보여줍니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "id": "dcba6c3d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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IQvxEyhhNGN1/yxheKIxv/hnAMBU3MuVUTDk3hmWEDJjoncRHWIQ23KY3hTG6aMAa+9DTGRzxQsP0fgZSvBBR2AqL0CVWTHMOLdZx2+n7ExmYpsyU6o0bN34klkTkyRQWuicDy+4i63fA7WQZgDG8oOuvR7RP9WDAeL2Z3v7LL7+ogO6PP/5Q+5JBAssO9PXjPiYsbQgPbrtxsBH6mGAAzBT/Pn36KO8DpobTi4wip3GJQExv8+euR2T3Fbeb89Dwl14YDDKZSk+BNbwSRmN4k8EHA3sGwgyAKYAx4Lp06VII89jIfL9ROV4ZPBqXAejfPecLyzMsOozzWVrBcj/eFIX20QhN6GNK//4pdoXGuCuU8TGsB7qhCcsfJTSR3Rf6MRn6nMRzbuiAXBDiKxJnmEacYfx9sEyesBySZe88ZxkLEFE5T8ZU7BDRdS0y5+kviUNDQ0GP28Zyefp0cZCM7+F041I7lgJyuzkPSzg5+BqZ7smMCfg98MHrxu7du1WJIkvj+RqvjVH5PX3pNTU69llEsNyP5aksY+U+1S0uwjuuQ6+ffm0NfX1n7BLagoLHHgcJw+v2/KntiMq+4HpxnULvY3qTCYIQfxGxy4QxHimJDoyDJaKP1PEGP7wgx7hrS0TrFN50/SLJz+IFhnX0Yc3LjIuoLC8iOKrHrI7Xr18r/w2OGNEjiBdtekiFHqH8FHw/oY9ATBCZbaWgSUGGwRqDOvqiMdA29sfQt2vs2LHhtkgPvZ9DHxN6ZgtFG3olMHOJPmsMfOhrwGym2Nrmz12PyOwrQh8NHg8cMWdnRWahcfmcP7SYFB4M6nms8UHBluapFL84EhvZbY3q8UohMLSoyHko7oTXmj68QDIy0K+FQiC99aZPn65GRrmtDIQp8IUmrGMqsujbSh+NsERN41HiiJYRU/tCEOIbEmeYRpxh/H1UqVIl6DmzU7hs+hBSVIvqeTKmYodPXdfC87YKq8FSVOPQ0FDUohckRTzGDdxWilDGQhavCRSWGEswDuWD1zAOoIVupBMR9PBiJUPDhg2V+TqvM8ZiV2R+T196TY2OfRYRzFjjunBfcpCK+5LfJz28wjquv/SaT488DjyGReiB9LDeH5P7QhCErx8Ru75yaNDIizcDrshkd4W+6WNpHoMP4+AqptaTQRBHgJh2Hh2EF0xRlKKxJQMYBjI6UU1f1+H6cuSHoghNRUMHfWEFQxwZ5kXYWJSgyb3++ufAgI6jk9evX1ejtEy/phiko5t1UoD50u+Ty2JWFR8ceWMATHGI3TRjc5ujuh6R3Vc6DLL4oPEsDVlZLjJz5swQwWtkYTMIHnNPnz6N0vui43jlfmJZHtf/SwLPsGCJAEereZ4xNpRnMBxZ+P2H7kBJQk/Tj2HemHzqGA7v9x/ZfaEfkzyueNNiPFIcHR0sBSG+IHFGzMcZ4UEhhAbfLJ+nUMUs56icJ2M7dgid6cWsG2NCZ3lHRxzKuJLlp9w2GpTTNoNm6KEboLBMldvNB2MV7pNZs2apgbTQWUiR2T7uN2Zyx/Y1NaZjd5aAUjhirKXj7e390Xf5qWsrr+/GnTJpa0KjemPxifvw/Pnzqlz2U+b/Yb0elX3B9eL3xXsR42XxdyEIQvxFPLu+ctixjiduBkJRHZ3kCBTfzwtvWBds3vRFF+x2xM/jeoZeLz5nfX9U4SgqCX0B1kfWjD+Hf3+qxXREcL25jnq3y9AwM4hp6YRd3dgZyNhDgu9hlyYKZcZZP1GB3xW3bfny5WoEk2V9+j4g9CRi4MD0evpkfc73Sf8qBjXGcJkMJiJqzxzd2/y56xHZfcXyw9DfI0UvCnURLZ/rdfTo0TBf40jx56TER8fxynIMBnt//fXXR69xOyMbpIa3fgwMjUfkGbDqXZIiA7MTuN84sq5DgT509hXn4w0Xu4PSKySiYzi8339k9wUDY46m8xg13vcTJ06M9HYJwreAxBmxE2dElGlDgYqdfaN6noyt2CE0XD92DqTflzHMZIqJOJSCHsXA+fPnK78m4xJGEjrG5LVeF10iuuZTiOHyQkPRjp0JP6cE7kuvqTEdu3P5oeN0XidDZ+VFNPBH+5I5c+aEiLN4vQ89kMTrNbMhOW9Y5ZR6d0fCYzas32Fk9wXjVHbopJhnHNOFV/4oCEL8QDK7vnI4atKmTRtMnjxZZSgwbZsjVkyT5msc5YoIBk80gKSpKc0kaYLJm1AaOjI7gn9HBwykmC3DVHO9BTGFi7t372LdunXKIJJGrFFdJg0zmYnDZfFCyO1gyj9f4/J4EWXQxQvhl2RrMHDiSO7w4cOV8Sz9mjhKxACK5tv0cNDTz7ktHC1kKQONYelRwIsrW1zzRjqyRveh4Sguv1Ome9M/LHQwx+CN7b/pSZE7d25ljE5vCu4DfsfcD7oXSHhwpJojbAxAeCywHILfD1s76y28wyK6t/lz1yOy+4qlkPxt0M+EmXsMyFgSogdO4cHAiAbEHF3nb40p9gy+GKTyN8fjOqqlntFxvFJMZJkLyzApKNGPhEIOzwm8ueENmLEpa1RgG3TuR24vDYzpfzFt2jQ1Eh5Zb5t+/fqpbDyW/fDGjb9VHqv0feE5Rh9l5bazZTnPaTT853fNkVuW3WzZskVla9EnRb9BIyw/5c0fvzvOH9l9weVyn3M+3vwxEOZvm6Ilb9IEQdCQOCN24ozwoHDA6zmFIvob0QszsufJ2IodwoKDg4wx+T8FEApfvLbHRBzKWIHfBR+scgid5cN14HLoc0abAopVFHCYLa57i4YFM/XYAIplh7zuc/COZvMU1SiSDRkyJE6uqTEZu/N6yHiI5YtcLgequEweh5GBGXTcL7zWc3/zu2HcT29T/maMs6p4DLPslKb+3B4euxTVWBXA6cx+47GjX/O5Htx3LM9lRh+3P7L7gq/xd8FMTMapzJrkdlJIFgQhHhPX7SCF8FuCs81uaMJqYc2Wv2PHjjXkyJHDYG1tbUiaNKmhZs2ahtOnTwfNw/f06NEjzN3NFsp8LW3atAYrKyvVerty5cqG2bNnB83DlrxcxqpVqz657sbrybbGxqxZs0a1Kea28cF15mezfbQO2xLnzp37o/XkPmHrZ2M2bNhgyJUrl8HS0jJEm+srV64YqlSpolpPJ0mSRLXiPn/+/EetsCPbElxn9+7dhvr16xuSJUumPpP7um7dumo9Qu/TDh06qM/md5I3b96PWnDrrbn53YUmdCtonTlz5qjXHB0dDV5eXmGu49mzZw2NGjVSrZ9tbGzUPmvWrJla9099P2zdzO+D3wu/H7ZtLl68eIhWzfp3ZNw+Orq3ObLrERER7as7d+6o1umZM2c22NraGpydnQ0VK1Y07Nq1K8Jlso02l9ugQQO1X7l/7e3tVWtxbhNbbn/qNxNWS/bIHq/hnRd0+Jtli3M7Ozu13fwO+vXrZ3jy5Emk91vt2rU/+p2x1Tpb03N7+Z1wncL67UR0nuFxWbZsWbWMNGnSGEaOHGmYPHmyes+zZ89CzMt9V716dfW98/vh98SW76dOnQpx3uvVq5f6DZqZmX20LpHZFwEBAYahQ4eqtuWcr0KFCoZLly6p7Q+rxbkgfK1InGH6cUZE5/fbt28bLCwsQpyXInOejK3YQT++eH3T8fT0NHTq1EmtHz+Xy3rx4kWY8U1k4tBPUbp0abXszp07f/Ta6tWrDdWqVVOxG+OTdOnSGbp27Wp4+vRphMtkrDBo0CBDiRIlQsR9vE7u2bMnxLxRiduj45oaHfuM32Po78PNzS0oluOxzWPs2rVrH10Xw4v/dXh91+OkYsWKGQ4fPqyuyTVq1Agxn6+vr2H06NHq98h5EydOrObjtdnd3T1oPq5DuXLl1LWan2u8LpHdF/fv3zfUq1dPxW3cvt69exu2bdumlsffkyAI8Q8z/hPXgpsgCILw7UEDXGYEsnQmuo2yBUEQBEEwDVh1wgxE2pqEVbYoCIIQE4hnlyAIghDj0H/DGJYAs4SgTJkyInQJgiAIQjyBnquhcykWL16sSgorVKgQZ+slCMK3h2R2CYIgCDEOvVEY5NIfhd5r8+bNU2ax9LtjC3ZBEARBEL5+9u3bp7qI0heVXl/0z+I1n9d/+mXR10sQBCE2EIN6QRAEIcahATybFrDzEQ1qaazM4FeELkEQBEGIP7BJERv4sHkWs7nYNIDG8DSTF6FLEITYRDK7BEEQBEEQBEEQBEEQhHiDeHYJgiAIgiAIgiAIgiAI8QYRuwRBEARBEARBEARBEIR4g3h2fUELXZorOzo6Kv8ZQRAEQQgLdqV69+4dUqVKBXNzGWP6lpBYQRAEQfgUEicIQswgYtdnQqGL5ouCIAiCEBkePnyINGnSyM76hpBYQRAEQYgsEicIQvQiYtdnwowu/aSUMGHC6PxOBEEQhHjE27dv1eCIft0Qvh0kVhAEQRA+hcQJghAPxa6AgAAMGTIE//zzD549e6ZKPNq3b48BAwZEWBq4b98+/PLLL7h8+bK6geD8fJ8x06ZNw9ixY9Vy8+fPjylTpqBYsWJBr3t7e6NPnz5YsWIFfHx8UL16dUyfPh3JkyeP1Lrr60ehS8QuQRAEIbLXDeHbQWIFQRAEIarXDEEQooc4NQ8ZPXo0ZsyYgalTp+Lq1avq+ZgxY5QwFR53795F7dq1UbFiRZw7dw4//fQTOnfujO3btwfN8++//yoxbPDgwThz5owSuyhmvXjxImien3/+GZs2bcKqVauwf/9+VWrQqFGjGN9mQRAEQRAEQRAEQRAEIeYwM9ARL46oU6eOyqSaN29e0LTGjRvDzs5OZXuFxW+//YYtW7bg0qVLQdOaN2+ON2/eYNu2bep58eLFUbRoUSWi6QaxzADr1asXfv/9d7i7uyNp0qRYtmwZmjRpoua5du0acubMiaNHj6JEiRKRSjd1cnJSy5LMLkEQBEGuF4LECoIgCEJUkftKQYiHmV2lSpXC7t27cePGDfX8/PnzOHToEGrWrBnueyhGValSJcQ0Zm1xOvH19cXp06dDzMPuV3yuz8PX/fz8QsyTI0cOpEuXLmie0LDUkSci44cgCIIgCIIgCIIgCIJgWsSpZxezrCgaUWiysLBQHl7Dhw9Hq1atwn0PPbhC+2rxOZfj5eUFNzc3tZyw5mH2lr4Ma2trJEqU6KN5+FpYjBw5EkOHDv2CrRUEQRAEQRAEQRCEqMFiLH9/f3WfKwjfMhYWFrC0tIyUx12cil0rV67E0qVLVTlh7ty5gzy4aFTfrl07mBL9+/dXPmChu2YIgiAIgiAIgiAIQkzAyqWnT5/C09NTdrAgALC3t0fKlClVApPJil2//vqryu6i5xbJmzcv7t+/r7KowhO7UqRIgefPn4eYxuf0zaLXF5U+PsKah+/Vl8GTBn2+jLO7jOcJjY2NjXoIgiAIgiAIgiAIQkxD72k2aOP9LRNCeHMvXRuFbznD0dfXFy9fvlS/i6xZsyrLKpMUu6hOh145/pD5ow6PkiVL4r///gsxbefOnWo64QmgcOHCygusQYMGahqXx+c9e/ZUz/m6lZWVmkZDfHL9+nU8ePAgaDmCIAiCIAiCIAiCEFfwxl5vtsZsFkH41rGzs1NaDpOk+PuwtbU1TbGrbt26yqOLxvAsYzx79iz+/vtvdOzYMdz3dOvWTXVZ7Nevn5pvz549qhySHRp1WG7IzLAiRYqgWLFimDhxIjw8PNChQwf1OrsodurUSc3n7OysssLYqZFCV2Q6MQqCIAiCIAiCIAhCbBBR9oogfGuYR/L3EKdi15QpUzBw4EB0794dL168UKmZXbt2xaBBg4LmGTJkCBYuXIh79+6p5xkzZlTC1s8//4xJkyYhTZo0mDt3rurIqPPdd9+p1DYuh4bzBQoUwLZt20KY1k+YMEHtJGZ2sdMi3z99+vRY3gOCIAiCIAiCIAiCIAhCdGJmYOGjCcMMLdYlU/AyJWhQzwwxd3d3lRkmCIIgCN/i9aJ9+/Ymd402FeL7dy8IgiDE7LXC29tbeRMx4SOici1B+JbwjuTvwqTzIanD7du3D3/99Vdcr4ogCIIgfBY+rne/qT3n5+eH3377TTWdSZAggcrabtu2LZ48eRJivgwZMqjBLOPHqFGjQsxz4cIFlC1bVgUy9CsZM2bMJz+f/pu1a9dW3ibJkiVTzXDYrt0YxhaFChVSjWeyZMkSplg3bdo0tY787OLFi+PEiROfvU8EQRAEQYhfsPKMscu5c+fielWEr1Hs4sFD4zEGuIIgCIJgamy58BSlR+1ByznHsPLUQ7zz9gNe30Hg+VXYuHgCqg+YhzJTziO+4erqqjKv6bm5fPlyJRg1bdpUGYWy+cyZM2eUTQH/X7t2rWoCU69evY+WM2zYMNVOXX/QP9N4pLtatWpInz49Tp8+jbFjxyprg9mzZ4e7XgEBAUro4nocOXIEixYtUkKWsT0CRwI5T8WKFVWA+tNPP6Fz587Yvn170Dz//vuv8vUcPHiw2ob8+fMruwNaLgiCIAiCzv1XHhiw/qKKBabsvgn/gJCN1gIDDVhy9B6azTqKFScefPR6fIeWQry2Z8qUSQ0w8b6evt1sFPe1w21h7JInT54Y+4ywBgaNH8yuDw3jnkSJEsXYOn1NxKlnlyAIgiCYMr7+gTh25xVcHKyRK2XCoHbf3n4B+N+WK/jn2AP1/PEbLxy5/QoDVp9FbrM7uGJIDx9kU68FwhPxDfpmMtNpyZIlqgnMjz/+qLwx2TGKpRjskmwMG8uwYQyzriiQ6Tg6OiJFihRhfsbSpUuVaDV//nzVaZmNbChOsZHN999/H+Z7duzYgStXrmDXrl3Kp5OencwOZ6YZhTIuZ+bMmSrtffz48eo9OXPmxKFDh5SXp+7/yc/o0qVLUGMbvod+oVyX33//Pdr2oyAIgvB1cv7hG8w+cAdbLz1F4AdToPE7b2DX1ecY36wAsiRzwJ2X7/H7mos4ce+1ev3E3dfqPX2qZUfNHM4w934Ft5dPcfqhB+Jr5lPp0qWV8MIBK2Z8M/ubg0s9evTAtWvXYMpwXdn1LzwsLCzCjWGii5MnT6qBPMJBPPqNcwBRL3dlZ0JTwNfXV8VYcfE9fLWZXYIgCIIQF7x854NJu26i9Og9aDv/BGpPPoTKf+/HxF03cOjmS9SdcihI6OpaPhO6lM0IKwsz+MICZw1Z4QPtgp8Cr9A1SfzL7GL3ZJYmli9fXolbzJIaPXp0uL4J9CGhUBh6pJFliy4uLihYsKAKhI3LDY8ePYpy5cqFCJ4oRjHIc3NzC/Nz+B4G08YNafgeZoldvnw5aJ4qVaqEeB/n4XQ9YGMmmfE8bGjD5/o84cGGN/ws44cgCIIQf6DNzt87rqP+tMPYclETuipkT4o/auVAQltLnH/kjtqTD+LXVedRbcIBJXQxPiiW0Rl2Vua44+qBHsvOoOigNSg8Yh8KznmGjhueR3UlAF+PuHlEwe6bTeh47efgGEWabNmyqYErZk4fO3YsaD4OhNWvXx8ODg5KxGnWrBmePw/eJxys4uAVB9iY6cS4o3nz5nj37p16nRnftEzggJsxXGbHjh2Dnm/YsEFZGDBWYabZ0KFDQ8QdXNcZM2aoTHTaMAwfPlzFG61atULSpEmVsJQ1a1YsWLAg3DLG/fv3q8E9ZrGlTJlSDZAZf0aFChXUAGG/fv3g7OysxDJuX3jwczkPH5yf0KJBn8Z9EVXevHmjMtq5bO7vSpUq4fz54Fj19u3bat8xluJ3UrRoUTWIaAy/Bw4mMhbkMjgIqWeUUczkQCLfW6NGDZX9ZgybC/J1fg85cuQI0SRQ36fMsGeMyXk4+Pm5SGaXIAiCIHzgvY8//tp0BevOPobvh1KDJA7WeOvtjzsvPTBx182gfWVtaY7ZlYAK+a3hbeWE+Yc0by47eKOo+XX0sViBHLV/hE+ufhgwYHi82sccqWWwx/K+yJiIMrOqRYsWIYx3Gewx6GTwxtHK/v37q4CIWVV66QMzsIzRRSy+ljhx4o8+i9ONha7Q74loHgpTXl5eKrDlKGpY83xqFHrkyJEqeBYEQRDip9A1cus1lZ1FGhRIhW4VMiNHCu3aVjd/KvRbfQEHb7pi1elHQe/zCzCorC5jXiFYpEgJVzyMyor4eQIjUiFO+OMJYJ3gk7O9fv1aZXxTMKJwFBp98IsClS50USiiMMSsr++++075axoLMOvXr8fmzZvVdZqCGAfMuHzaKLBUcu/evahcuXKIz//vv//U84MHDyphZvLkycoLlMvTs8RpWaBD4YnLZda6paWlsmRgxvjWrVuRJEkS3Lp1S8UKYfH48WPUqlVLlRYuXrxYxQzMEqdgYyxo0WKBgt/x48fVIBrnZ1xVtWpVxAZNmzZVwh23iWLZrFmz1H67ceOGisnev3+vtoP7lqIdt4WlpxxsNM7OHzdunLKJ0Pcf9zGtLDidwiQHClu3bo2+ffsGCVb8n+9hxj8HOjl4yn3EY4T2GDoUCZmBz3m+pDGDiF2CIAiCQB+q9z7ouPAkLjxyV/ujYLpEaF8qA2rmSQlvP38MWH8ZG88Hm6yXtbuHCgd+B47aw9bPE0sscyGT+VMkhxu8zO1h134NzNKXgk88zO6hIDVixAhVzsiAkaOa3bp1U4/QqecMSHmDwNFSYxjo6eTLl09lcHXt2lUJRgyuvkYo2BlvFwU08R0VBEH4+uF1bOimK1h45J56PqRuLrQvHTwg4+MfgI3nnmBOm8JYfeYx1px+hAQ2lkhoYw68vAqD6y1Ywx82Zr4INJjhHezR3WIDAt2fwqfwDyiJ+AVFIe4zZu5EBL27Ll68qPw09eslxRVmgLGEj1lFuijGzCHaH5A2bdqo91KQ4eBXzZo1sWzZsiCxa/Xq1UqcYuY54UAUBRRdUGFmFzOTmGFlLHa1bNkyyMJAzzqj4FKkSJGgjKbwYIYSt4FCDrOTuO1szsMBPwo8FH/0mEf/TGaKcX5uS2yIXYcOHVKZdvQg1WMtilMUErnPKAByINN4MJP7ad26ddi4cSN69uwZNJ0ZYX369Al6TrGLcR+tHzJnzqymcX76s+pwuyliNWrUSD3noCbFRApuxmIX/VT1eb4EEbsEQRCEb56Hrz1VueJdVw84J7DGtJaFUDKzS5BvF0dydaGracoX+MutP2z9vIJHWAGk8riKUy/NkKdaW2So9zvglDre7lc9vZ+PBg0aqCCTwhcDOX2kVBe62Ghmz549H7VTDw07HnJElyns2bNnV+n5xmUMRH8enkcGp4fumhj6PeEtl+vHkU56cPAR1jyf8uZg4Pi1CnWCIAhC2AQEGjBg/SUsP/EAtO4c3iAvWhbXMlyeunvhyK1XmH/4Li4/eYvndy9hkP1atHbxB4p0AP7rB7y9jQ/uBgpPcwdcePAW693SovPEk3B0SgS0j0JWsJW9lmEVF/CzIwGFrshw9epVJRAZDwzlypVLZX7xNV3sosikC12EJYLGTWNYasgMIQpOvA4zg4iljrrAxDK9w4cPq7hFh1nczD5nNhI7OBNd1NL54YcfVAkmm9WwaQ5jnlKlSoW7LSVLlgzydyXM2GKm1KNHj4Kyoih2GRN6W6ICM+J0mEVFoSkizp8/r9aHFhLGMFuNg5eErzMTjV6lzLhnbMbXKfwZE3pfEe5HXegKvW0eHh7qMzp16qS+Kx0uP3Q5ZljL/hxE7BIEQRC+aa48eYt2C04on640ie2wuGMxZEqqBQ/uXn74fvEpHL/7GuYIxJ+W/6Dj620q2CUGS1ucsCmHun9tQNa8RbFq1SrlG/EtwYCUGVk0h+eoHsUuXei6efOmKisIHVSFBbPDGJTSi4IwYPzzzz9DGJPS+J5CWFgljPp7GMgysNKXw/dQyGLwrM+jlzXocB5OJ8wwK1y4sBplZVCrjyjzufGIpiAIghD/OX3fDUM2XsbFx+4wNwPGNMkPT19/9F97UTWw4SCZTmK8Q4nbswGL09qEa5u1/x2SA1WGwCtFEfT47S8sWPyPKtX7e/nf6poTZX9HBiGRKCWMS5ixRNEnukzoQxuUc9nGHl0ss6PARoGGAhnjETae0aGAw+yusLKFjMvkQpdccjCPg3aMGxgrMHOM3x2zoWJqW6KCsV/YpwYV9f1AAcq4RDR0aSnLDrmt3EZ22+ZAYJMmTZSnqTFhlaeGtW268MnPJnPmzFEDnMZwkPFTy/4cROwSBEEQvkneevth7oE7mHvoLjx9A5AjhSMWdSyG5AltqW4AhkA42FjitYcvHCwNmGw2DpUsgoMK/+x10W+XLybMX6VEEKZlx1QnGlODWVwUgmgYy5FRClr02hgwYIASpxgUcRSU3hp8XffLohcE9xE9KuhVwfICjtTyOZfJUUldyGIpAQNTjgCyBODSpUuYNGlSiOA1NBx1pajF8oYxY8aoz+U6MTDVM65YasmSAZYu0LiWWWcrV65UAbIOSxGZTs+RRRrN0ruDI5LGpQ2CIAhC/OXFO2+M2noNa888Vs8dbSwxsnFe1MmXCuXH7sX9V1pWNwfC8prdQWnzy2hnuR3JbQMBH46I8QbfHCjRDajwO24/dkXjGo2VLxL9jHi9i8/wes/mL9OmTVMenaHFC5qkU1yhUfnDhw/VQ8/uYlkbX9cHqSIDBSsKWczoYgklB8boC6rDv+k5RfEmqtDInTEBH/T7+vXXX8MUu7gta9asUeKOnt3FbDLGOWnSpEFMENXtKVSokIqN6EcWXkkm15k+Yg0bNgwSqZh1/6XQ+5QDwnfu3FGZeLGBiF2CIAjCN4WXbwAWHb2HGftuq8wtUiqzC0Y2youTd1/jyJmzGOQ5BraFW8CieFeMKmuNRMfmIfOrD0JX0hx4WrAPavUYqQKnbyFoDQ1T8SkIMXOLIhBHCCkc0SCWASt9HQjFMGMoirETEYWnFStWqDR5djCkZwPFLmO/K6a0M1uMQhUzrei9Qc8LvUyS8HMpmNHrg0EbRwYpsLHsgJlauuGpsV8EP4vCFj+P4hkDUHYGYlCuQ2Pcly9fqs9jUMjtoNFtaNN6QRAEIX7gHxCIo3deIWsyR9x++Q7fLz4ND98A9ZqluRlWNXJCjpTvAV9PNCmUBp5+ASjsfwHFTv6IhM7JAWsH4Lk74POhfC9LFaDKUCBFHnXNYZzALGd2IQxdxhZfodDFMj4OGvE6zO1myRqzhujjybI/djpmF2WKHxxY4uvs4shOfFEtZeMy6tSpo7ovh47LeD3na4xfOCDHTHKW9HEg7X//+1+4y+T7GIPQQ4zxCmMMilphwfXmNjAW4iAoY0R6VDG20cspYwsONJ4zyvoijL24vxkfccCSg4LskElfMR6jFLe4z5mVt3btWpUtR9GOJv2fm3kWGg5iUvxkjMdOjdynp06dUk0HjGPA6ELELkEQBOGbgd4aTWcexSM3zW8rk52nGpG9+9AZFca6wgBtJC6TZUp03jUE2DMchX0/lBcwkK3wO7a/yYgWddqqDCRmJEWmI2F8g0IRH4SjfzSN1aHo9CmvDo4sGrcdDw8GxixFCA+KXBzVTJ062B8tffr0H5UphoaCGzsARQQDVSlbFARBiJ8eXK88fNT/KZ3s1LR7rzzRZl5Iz0edjGZPYVjTDzDXeib2ckqrlSa+vAGYeQFuRlkv2WoC5X8FUhdWgsOwwYOV0EPhgMbreqnYtwBN4JnlTXsBGpnT/4lZUhSP9KY1FFM2bNigBKJy5copUYgiyJQpU6L8eTRMZ0YZRSZmhxvDAS0KVfwuRo8ercrtaCDfuXPnCJfJbHQ2n2FmE8v5mNnFwbqwYCzC+IOZX4wNuS7MTmeGeWzDbKyCBQuGmEYvLWa9cR1pE8FsdQ7s0Y+U+14f0GMTIg5g0puMA43Mro9yqW04cH/T12vs2LFqP3FQkmInDeljAjNDZN3jhBDwC6ci6e7uHqn6WEEQBCFu8fYLQNOp+3DxuTdSmb1GU/M92BxYErcNwUJJdrMHKGt+EQ0sDiOP+Yfg1cIayFUfgZWHYPiUBWqUjh4O//zzT7jeUd/S9SK02BWbsH02vcH4vykS3797QRAEU4beWpN338I9Vw/ce+UBN09fvPP2V9YFhCbzIxrmVX+feeCGn1acw4PXWnkiSQgP/Gy5Gq0tdsHKTHvPR1jYACnyAqkLAQVbAym1AbBXr16pTCNmKLObHQWT8LJ7IrpW0ECdAzvMSjb2lhKEbxnvSP4uJLNLEARBiPcYfDzQb8ZaXHzurAxkZ1j+jaZ+g+ELKyS1eI9fzJajksVZJE9gCaQqCDhX1wJWPpJmx5t3Hmjbtq0aFaTYxZTu2E5JN1XiSugibAggCIIgCGFhbWGOuQfvwD/QEG4nZp3MSR1gC82AOy2eoZ/lSpSxuITEFj5A4AehK39zoGQvwNcDePcEcM4MJMsJWIQ05T59+rTq4MfsGpbA009SEITYR8QuQRAEId7CLkmzN+zBwdtv8CjQGRbwx/T0+5C/7hx0u+yAu09fYNjdrkhs7gmU6wuU7QtYhjSZv3jxojI9dXV1VWJXrVq14mx7BEEQBEHQYIEShSwri+DBp2vP3iJ7ckdVHmdpYY6elbLAyc4KGZIkQFIHGzjaWqrmMw62lrCx1DrAebm/RNcpm3DjfXIkgxtW2IxCajZl9ngP0KooaU6gzgQgvda1NyLmz5+vvJtYmsXGLSytFwQhbhCxSxAEQYh3PHjlicm7rmLd2ScIYDckOKvprROeR8m09kCaIvgplQHm5jmAI78DmSooE9nQLF++XPkL0KyTo7P0OxAEQRAEIe5ZdfoR5h+6i3FN8yNPaifsvfYCXRafQusS6TGoTi6Ym5vhpyrZIlzGzYfP0WP2VtzwSw4HeGFB7vNIXX4JML86YGUPlP8NKNnjo+ytsMqqaLw9Z84cFTfQc0rKDgUhbhGxSxAEQYg3vPH0xZCNl7H5whP4q8Yx5jBHAAJhgfLmZ9HbZzZw2gMo3RvmiT+Mtpbq+dFy/Pz80LdvX0yePFl19Jk1a5Yy1BQEQRAEIW5YefIhvPwC0K5UBtU9cfreW8pYvv60w2hWJA3WnnmsMr1eefjCEImssFUn7mLQ+ovwNiRFErzB9EaZkbtYE22GGqOAHHWARGk/uV73799XHf6YCT5v3jxl7i0IQtwjYpcgCILw1cEg9fEbL1x+8hY+/oGolz+Vms7ShN2XGeyaqe5Jdw3JldBV1uoG5hR4DOvUfwIp8gEOycJdNrsF0fSc3QI5MtujRw9VDiEIgiAIQvTBbog0jX/13hfpXexha6WVFd568Q7nH7rDw9cfrz184ebhizuuHjh40xWW5mYonskZOVIkxOofSmHwhsvYcvEplp/QOiUWyZAYqZxs0Wj6YSR1tEXd/ClRJWdyJLCxDGpWc+qeG1acuI/NF5+p2+Gy5hfwt90CJM2yLXjlSvwQqW3YuXMnWrRoAQcHBxw+fFh1GhQEwTQQsUsQBEH4qgLj/225gnVnH+ONp5+altbZThO7DAZYHpmAPw0HsM2sGPYZCqjXW9kcwpCfesEqsSaIRQQDVb2z3759+1C6dOkY3iJBEARBiP94+QbgylN3nHvojvMP3+DCozeq86HuHb++R2kUSJtI/b3v+kv8b8vVj5Zhbgb8XDUbsiVzVM+TONhgWqtCKH/yAYZtvgLfAIMSsvjQcMeuq89ha2WOitmTwd3LD6fuu8FXS/2GBQLwi+Uq/GCxCeaFOgPOmSK9PYGBgRg1ahQGDBigDOiXLl0KFxeXL99RgiBEGyJ2CYIgCF8Nw7dcxYLD99TfHN3NltwReVInRIB/ACx2DcSlI5uxLKAzLhgywwKBGGK1GG3a9wI+IXQxU2zq1Kn45ZdfULJkSaxcuRIpUqSIpa0SBEEQhPjFIzdPJLC2ROIEWtOXhUfuYfS2a2HOm9jeCp6+/kHP0znbo1y2pLC3soCzg7V6PbG9NUpkclHeXCFKEU8/woit1/DeR+uY6GhjifLZk6JSjmSqSc3G809w/5Untl5iFpdGchs/lPY7hjaWO1DQ/DbgkAKoPDDS2/bmzRu0a9cOGzduxKBBg9TDwkLLShMEwXQQsUsQBEH4KqAJ7fzDd9XfYxrnQ/2CqWDj9xbYNwruE3fib7cyWBIwHIEwV52XprcqhNIJs2htwSPA09MT33//vRqV/emnnzBmzBhYWUVsRCsIgiAIQtgwa6vV3OMY2Sgv6uTTBps4MMVMLGZvFUjrhHxpEiF7Cke4JLBWXRONqZY7hXqEB0WuG8/fY9CGSzh+97WaljNlQvSrkR2lMyeBtWXw8n6pmg0XH7tj99UXcE5gjdL+x5F59/cwowZnZgFl7lVzNGAbLKJFhHGH5k2bNqFOnTpyGAiCiSJilyAIgmBSZYorTz3E7RfvUTFHMjWKa2FupkoPpu69peZpVzI9Ag0GLN+8A94XN+C9tz9WBPSFK7RAtW6Sp/izS2ukcLJlkUOEn3fr1i0VtN6+fRvLli1TvhuCIAiCIHwelx67o/Xc43jn7Y+rT98GiV1lsiTBqQFVPmuZgYEG5dF54t5rnLnvhtP33fDsrbd6zc7KAj9XzYqOpTN+JJoRem7mcw5EPv95gF0+YNOPAG04ndIB7g+ArNWBXPUjtR4cFOvSpQuyZcsmHZoF4StAxC5BEATBJLjz8j1+XX1BBbFk7qG7SOpog8UdiyFzUgd0LZcR8w/fw6Kj943eVTfor8zWbvgr8TaUKl8NUEJXxGzevFl1WkyaNKkyo8+bN2+MbJcgCIIgfCtCFzO63nr7o0j6xPihQpag16LS6EVvQnPk9itlSn/o5ku4ffDp1KGVAQfFBtXJhbTOEXRLfv8SWNIAeH4JsHIAAv2BXA0Bl8zAselArbFcuQjXx9fXV3VoZtOatm3bYsaMGdKhOR6QIUMGldHPhxA/EbFLEARBiPNsrgWH72Ls9uuqsyI7KlbOmQz7b7xUhrYHbrxE2/kn8PKdj5qf5QklE7nD4fUl2MIXtmb+yFG8Or6r1RLWlq0jZSo7dOhQDBs2DPXq1cPixYvh5BS58gVBEARBED7mypO3aD3vuMrELpQuERZ0KKqu5+Hh4x+gPLU8fQPg6ROgPLvYcfHsAzecffAGLz5c83W4rGIZnVE4fWL1yJ8mEeysP+GT9fYpsLg+4HodMDMH/N4D6UoCDWcCZ5cADaYDidNHuIjHjx+rxjWnTp3C9OnT0a1bN+nQHElevnyp/My2bNmC58+fI3HixMifP7+aZuoNgCiE3b9vPLgaEnq2LVy4MMQ0PqdwRk83wTQQsUsQBEGINTha+8jNS3VgKp1FKzF89d4Hk3bdVEIXyxxGN8mH1Ins8OKtN3osO4ORWzVD2+QJbdCmRHq0KJoWLoeGAMdnaMFrkwVA7uqR+vzXr1+jTZs22Lp1K/73v/+hf//+MDf/uOxBEARBEITIm9FzUIpdkvOnTYSFHYvB0dYqwvmbzz6m4oHwYOYWzejLZU2CstmSKq8vqzDKFMPF/RGwqC7w+g5gbqlldLlkAZovA6xsgWJdPrkIdmX+7rvvYG1tjQMHDqBEiRKR/3wBjRs3VllxixYtQqZMmZTgtXv3brx69crk987JkycREKA1PThy5IjaluvXryNhwoRqmp2dHUwB7l8enzGBn5/fV+9hKxG+IAiCEGN4+wXg0E1X/L3jOtrNP4HC/9uFsmP2ouPCk/AP0Fp/J0toi2ENcmN4wzxY0qmYErrOPXyDhtOP4OQ9N5XJ9VcpSxxKPQ09y6SCy+lJmtBF6k0BcjeI1LqcO3cORYoUUSWLFLv+/PNPEboEQRAE4QtZfPQ+XN/7IEcKR2U9kDACoYtZ2m3mnVBCVwJrC6R1tkP25I5KzKqROwX618yBVd1K4uKQ6ljfozR+qZYdRTM4R03oevMAWFDzg9BlpQldCdMAbdYB9s6RGpgbP348qlSpgjx58uD06dMmJXRx/ZgJFxcPfnZkYHbTwYMHMXr0aFSsWBHp06dHsWLF1CAjs+qNy1tZFlqzZk0lIFEUW716dYhlPXz4EM2aNUOiRIng7OyM+vXr4949rTM3ad++PRo0aIBx48YhZcqUcHFxQY8ePZRYo/PixQvUrVtXfUbGjBmV/1pE0OKCXbn54GeSZMmSBU37nIoA7pPOnTurZVM0q1SpEs6fPx/0Ov1juW3JkyeHg4MDihYtil27dn2UcfbXX3+pclougw2WmFHGfbN9+3bkzJlTvbdGjRp4+vRpiPfOnTtXvW5ra4scOXKoTEUd7k9+F//++y/Kly+v5vnUPvoakMwuQRAEIUZgi3F2UGTGVogLj7kZsiRzwGsPXyV0kYYF06j/7718hzUHzmDm6XfwCzRDhgS+mJbrKnKfG68Fq0ubAfcPaQuqMQoo+OmyRbJkyRIVEPAiv2fPHhUsCIIgCILw5fxWIwfsrS3QtEha1Q05PN56+6H9ghOqfJEDW2t+KPWhmUw04uupZXRR8LKwAQJ8AIfkQLuNQKJ0n3z7u3fv0LFjRyW4/P7770pYsLQ0rVtmL78A5Bq0PU4++8qw6rC3/vT+oODCx/r165VQaGNjE+68AwcOxKhRozBp0iQVrzVv3lx1vWTMRsGqevXqKFmypBLP+F0wM59izoULF4Kymvbu3auELv7P5kPMyCtQoIBqKKALYk+ePFGvM1vpxx9/VAJYbMJyWIptHHClWDZr1ixUrlwZN27cUILa+/fvUatWLQwfPlztL9psUKBjRlm6dMHHLkU9loIOHjxYPed+YWdxTuf+Y8UCPWn79u0bJFjxf75n6tSpKFiwIM6ePav2TYIECVRJpg6PeQq9nIeC19eOaf1yBUEQhK8SjtRuufAETYqkDfLocLS1VEIXyw/ZCrxgukTImyaRGvm1tQr22XjxzhtbLjzF+nNPcP6h7nNghprmxzHafzYSXvxQ5pA0Z7DQVXkQUOKHSKV3//LLL5g2bZoKdDiKZSqp54IgCIJgitxz9cCcg3dUN8UyWZOifoFUqlEMYVb2ibuv8d+lp7j5/B0S2lkjkZ2VErn2X3+JxoVTw8bSIsxM786LTqmuikkcrPFP5+LRL3QRa3ugaBdgz1+Avzdg5wy03aAZ0n+Cq1evqg7N9Olau3YtGjZsGP3r941AUYoZRxRUZs6ciUKFCqmMIQpZ+fLl+0gEYsYTobi4c+dO1QyAMRszjei1yqwkvcnBggULVCYTy0yrVaumptEPjEKOhYWFylqqXbu2Kpnk51NMosB04sQJlS1F5s2bp8S02OLQoUPq8ymw6cIfxSmKgRRWOSBLPzM+dLgv1q1bh40bN6Jnz55B05kR1qdPn6DnFLsoCnI/Z86sHeecf9iwYUHzUBijiMXjmzC77cqVK0pwMxa76DmmzxMfELFLEARB+CyYys4yw3mH7mDnlecINFDgskLjwlqWVpNCaVAlZ3JkTebwkZkrR3e3X3qGjYfP4vBTcwSqPuCAuRlQ2v4BmjpdR92kL2FmUx+wTqCN0N7aqb256jCgdO9Prp+xqSxT5Lt27SqmsoIgCIIQQTfFGftvY+vFp+qaTs48eIPJu28ib2onZEvuiL3XX6jM7PCYe+gOhjfIi5KZXYJiheN3X2P8jusqZnC0scTCDsWQMUmCmPkePF8Dl9dpQpeNk1a6mOzTosaqVatURhfL7Rg3ZMuWDaaKnZWFyrCKq8+OLPS5ouhEMUa3kBgzZowSrjgAqcOsLWP4nNYThGV+zNRydHQMMY+3t7cq+9PJnTu3Erp0mOXF7DBdxKT4Vrhw4aDXKYhRMPtcmLWmwywqCk0Rwe1g5hZLLI3x8vIK2g6+PmTIEGXozxJEf39/9fqDBw9CvIeWHKGxt7cPErr07X/xIXPNw8NDfUanTp2CMt0Ilx+6HDOsZX/NiNglCIIgRAmO6m65+BTzDt3FhUfuQdPpt8FsLh2WKOplisTXP1B1WFx39hF2XXkO3wBG0lpgUiC5JRoUy4baeVMi6ePdwMMngK854OsBvLoF3N2vLaTacKBU8OhWeNBIlv4ODG7EVFYQBEEQwofX50EbLmHFyYdB0yrlSIaKOZJhz9XnOHDTFRcfu6tHaBoVSq2sCdw8fLHu7BPceemBFnOOoXGhNCifPamKFfSsbVsrc8xtV0QZz38x/j7A88vA0/Pa4819LUZY3RF4eRWwSwy0XgOkKhDxYvz9g0q3mHU0Z86cEEKGKcIBxMiUEpoCLIWrWrWqerBckRlczDIyFrsiggIQRaqw/KPofaUT2kid+4gZYTGFLsYR3bT+U9tBAYrZaKHRRTeWHTKrjRlfWbJkUZUITZo0UVUKxrD0MDRhbb/hg78aP5vw2C5evHiI+YwFwvCW/TVjaYotPbt3765KTsJT3flDoYla1qxZlekda1t1+KXyB8QvkyZwbGvKEX3Oa9yNq1evXti0aZOqaaXqzBphUz+xCYIgxDXMyOqy6JQapSU2luYq0O1YOiOyJg856qa3Fj9z/w22XXqKjecew83LP+i1zGaP0cDyKOqVyIP01XsCt/cCy9oCzy6E/eH06PpE6SKvATyfM2AoW7asSn+noaggCIIgCB/j7uWH7ktP4/CtVyq7ul7+VOhaPjNyptRu4EtmcobN9hs4fvcV3DyDDb9zpUyIjmUyonGh1EFZ0z0rZcWYbdew7MQDrDnzSD30WKFpkTT4vmxmpHOx/7KvwfstcHwmcHQq4B1KfJtbCfDzAhxTRiqj69mzZ8rbid32Jk6cqHycQmeiC9FLrly5VOmeMcz6ouG68XN6RhGWP+qxXGREpbBgFhdFTTYa0MsY6YNFreBzoRgVFbgdPN44CBueb+zhw4eVCKiXz1KkMjbi/1ySJ0+OVKlS4c6dO2jVqhW+JSxNpaUnuXTpklJ9WXYSFjwRtWjRAiNHjkSdOnWwbNky1XnhzJkzqlMGYWrk5MmTVYtT1qJSGKOpHWtSdZM1fslMDaRyyvrWDh06qDpZLk8QBEEIH3srC+W3RV+u78tlQqvi6eDiYPNR4Lz2zCMcvOmKY3dewdM3+DyfBG9Q3+IIGlocRO7kDjAr97MWmC6oBTw+pc1k7QDkawYkSKaVMNo4AMnzAmm1ACU8mKbNEcMVK1YosYvXClMzlRUEQRAEU+GRm6fqjnzj+XtlMD+tZSGVzWUMk0O2XX6m/rayMEOtvCnRtmR6FEqX+CNhiL5dwxvmVXYGA9ZdwuM3XmhdIh3al8qIpI7hG5RHmmcXgYV1AO8PIgWzt5LmAAyBWpaX73sgcQbNo4v/RwCFBd5zcpCMpuVlypT58vUTgnj16pXavywNpUcXyxBZHsp7dXYcDJ3MwvI5fgfM4KK3FT219Pv2sWPHqvfQgypNmjQqWYaeav369VPPP0X27NmVoT3tLJgEw9iQ3lQx4eFKbcM464vQo4udPVmeSe2C+4BlsjTMZ8kixS1uP5NzuF00pedvizpGdGWnDR06VIm5LFvkvvDx8VHfh5ubm/K2ja/E6V2AceohYRcG1prSvC4sOFrPL+fXX38NYWBHMzrWyfJkRVV+wIABQT8idjGgmkkFmamprNndtm2bEtr0mlQa4DE7jCmDVD0FQRCEkPD8yguvpYU5prQsiBdvvZEl2ceZXB4+/vhu1lFce/YuaBqNaMtlTYp6N/9EGfuHsLS2Zc0E4HodWKMZkiqs7IFiXYBSvYEEIT0NPsXNmzeVoebdu3excuXKcAdNBEEQBEEALj9xR/sFJ1WDGTaSmdeuqCovDAw04MS91yiRSbsO05i+b7VsyJcmEQqkS4SEtuF3W9ShELblxzJKKDNnulh0QWHLLhHgkAzIXlsTv27vZpSivZ48j1a66JgiwniG9468waf4wKwhlpcJ0QsrplgyN2HCBOUXxQSTtGnTKs+oP/744yMhhgOVrO7id7F8+XKVAaZ7UdGO4rffflNxHrtlpk6dWnUxjEqmF03tOSBKnYHaADs6UkyKbpiNpWel6VDfoO/Yf//9hz///FMl2rx8+RIpUqRAuXLl1PqQv//+W4mDpUqVQpIkSdQ2v337NlrWq3PnzmpfUjiklsJyxbx58yrRLz5jZtCLOeMY1qJSaOKJJ/QPQIctN/m68ZfCkkUKWTR9Y2oeDya20mSrUR0e1HxOsWz+/PmqewFVTB2mNTLri6pyeF03qH7yocMDjz9Yd3f3z06pFARB+Br49+QDnHvojhEN80SY3s8A+Ye5u7H9jo/K4Opi+R/KZkqIHFmywtzSCji/Anh+KeSbbJ2AlPmBtMWBYt9rAWwUYZeaNm3aqKCBXWv0AMlU4PWCI2lyvfj2kO9eEART5I2nL2pPPqQyr9gheUGHokjpZKeEoGGbr2DB4Xv4s1ZOdCmXKa5XFXhyFkiRn6qZ9tz1NnByDnB8RvA8GcsDhdsDOeoAltYRZoDr1Tw///yzssMJ7XVkatcKGrFzII8VS3qVUnyCcSVjN2Y8CUJkiezvwmTqOyhYsW42IrM61rnqyqcOn3O6/ro+LaJ5Qvu3MJXR2dk5aJ6wYDkMVWdBEIRvCZYh/rnuEvwDDco8vkzWJGHP6PEKk2bOwPaXBWENP8y2/huFzG8BbCBj3ETG3BLIWg3I0xhIUxRIlI6RzmeninPAY/jw4Wqggi2uZfBBEARBEMKHgla/1ReU0JXexR4ru5UMytaavu+2ErpItJQdfimnFwKbfwFK9QKqDgW83ICtfYHbe7TXOUhWvBvgEtyFLqIMcPo0MzmCWUT06hIEIX5jMmIX63Jr1qxpsmWE/fv3D1HPqmd2CYIgxFcevvZE96VnlNBVN38qlM4SRmmhnzewoTu2XniMSb4/qknDbRahUJVWQOpCWoekJ+c0f43stYDcjaJcohieFwR9HFjKzhJ4+jaIqawgCIIgRMyiI/ew48pzWFuYK48uOysLHLrpik3nn+DfU1o3xkF1cqFBwdRxsyt9PYGHJ4GjU4BbO7VpbFxzZ58mfL2+rdkeNJgB5I5cNtCGDRuUATozwI8fP47cuXPH7DYIgmASmITYRZO5Xbt2KUO2iOAJ6vnz5yGm8Tmn66/r04xrr/lcL2vkPC9evAixDJYxskOj/v6woLEcH4IgCPGd9z7+2H31GUZtvY7XHr5IkdAGuVMlxAv6esANeHFZKxk4txTYPwan39jjF9/+6r0dk91E0+6LANsPafiZKkT7+rEpCUdn6duwfft2ZfopCIIgCELEXHzkjhH/XVN//1ErB/wCAlHkf7tUYxmdnhWzqC6Lsc7JucCFlVqzmsDgxjYKZnLp2VxOaYHmy4CU+SKVAT5o0CCMGDFCMsBNFBNxVBLiKSYhdtEwjqWFtWvXjnA+mgju3r07hGcXR/U5nbBmk4IV59HFLWZgUcH/4QetXT3nZbkkW48WLlxYTduzZ4/qdEATPUEQhG8Vb78A/HPsPqbtvRWivfiztz4YtfUaJuy4hrbW+9DNbDVcHOxw640BE/ybYEtgCTVf2XS2+KPrj4DFB1+NGLpe8HxOU839+/crL0dBEARBECJm15Vn6Ln8LHwDAlEtV3K0K5VBDW55+QbAJYE1quVOjtp5w8nijgncH2sG8wG+2uP+EeDh8ZC2B/T0tLABPF9pjzRFgHpTAYeQTc7CwtXVFS1atFD3efTmoim3ZIALwrdFnItdFJl489KuXbtPtojv3bu3MpsfP368EsZYb82WmbNnz1av8wRGIYzdFdi6k+IXuyywNFI3vcuZM6fq6MhOEOzgyM4QPXv2VJ0aTbWEUhAEIaZgoPv0jZfqvDR1zy08dfcOeo1OWsUzOiNVIjvce/gQZ1zNMcerApaiBEp4X8G+wAIIhLmy3KqXPxWG1c+jujXGBGwQwmvArFmzVEcZdtGNj0atgiAIghCd+PoHYsR/V7HwiObFxfLFsU3yq/smR1srbOxVGlmTOcIiOrsmRgQzec4uAbb8CgQExxxBUNwq3hUo8zNg7/xZH3HixAk0adJEmVgzMaJSpUpfvt6CIHx1xLnYxfLFBw8eqDaboaFZ/b1797Bv3z71nG042T1jwIABqmMjBS0a2+fJkyfoPfRt0TttMIOrTJky2LZtW4iboqVLlyqBiy1Lzc3NVTnM5MmTY2mLBUEQ4pa9119gws4buOvqgXfe/iFeS+Vki+q5k2PxsQcY1SgvmhZMCewcCMOV6dhvlQ/j/ZvhoiET9gQWUvNXyZkcfaplQ86UMdeV9tGjRypoZafdOXPmKLFLEARBEISIuf3yvfLevP7snXpuaW6G0U3ywck+uANhjhSx2FXe5z2w+Wfg4krtubkVkKogYGkDWFgDSXNoZvQJg+1ooloSxzihV69eKFiwIFatWiUey4LwDWNmMOFCWWZxVaxYEUOGDIGpIe3EBUH42qA3x/gdNzBz/+0Q0x1sLJDEwQZtSmZAq+LpYGtloczp01q8Bv5tAzw5EzSvIWstbM/8Ow4+NUfjwmlQKF3iGF3nvXv3qo5JHLBYs2YNihYtiq8NuV58u8h3LwhCXLHj8jP8uPwsvP0D1fOEtpZY/n0J5E7lFDcr9OwSsLKtZjCv45QGaLUGSJbjixfv5eWFHj16BNkdTJgw4avxW47oWsHstLt376qKJcloF4So/S7iPLMrPPhjv337NrZs2RLXqyIIgvDV8+SNF3otP4vT993U87Yl06NUZhdsv/wcWy4+xSsPX9TMk0IJXSStsz2wfUSw0GWTEGiyAGZZq6AGoB4xCcdhWLL+22+/qUGP5cuXI2nST3t0CIIgCMK3jnE3ZZLO2Q5LO5fQru1xwcGJwN7hQKBv8LQCrYAao4Ib2nwBvOllpc7Vq1exaNEi1XlREATBZMUuqtssXREEQRC+jGN3XqHbP6fxxtMPjjaW+LV6dvx36SkWH70fNE/25I5wfe+j/LkUT84B55drfydMDbTbBLhkjpWvgl0WO3XqpMoPKHbRh/FTno6CIAiCIGikdLKFcwJr1UW5QFonzG9fTD2PdQL8gO1/AidmBU+zcwbqTQZy1o2Wj9i6dStatWqFxIkT4+jRo0FNygRBEOTuQRAEIZ6XMajuS/6ByJvaCaMa51VlDbdfeigz2hp5UqBj6QyqHFF1KXp1W3us7gD4vgdS5NVKDByTx8r6Xrt2DY0aNcLDhw9V2SL/FgRBEAQh8iw78UAJXU52lljQvhgSx6bQ5e8DHJ8F5G4IrO0CPDiqTU+QFMhaFagyFHBIFi1NzjgYRrubWrVqYcmSJUrwEgRB0BGxSxAEIZ6y8tRD/L7mAljFUDVXckz6rgB6LDujhK4UCW2xqlvJkCUNbveAmWUAP0/teYayQPNl0VJiEBnWrl2rGpOkTp0aJ0+eRI4cX+7hIQiCIAjfAleevMW0vbfQt3p25c9J+lbPEbtCl5838G9r4NZOYM9fQICvZoPQcBaQo1a0fYybmxtat26tsrqGDRumGpex6ZggCIIxInYJgiDEQ2btv42RW6+pv5sVSYMRDfPC0sIc1XKnwKl7bpjTtkhIocvLHZhdMVjoytsUqD9N65AUw/j7+2PgwIEYNWqU6ro4f/58ODo6xvjnCoIgCMLXzq0X7zFx1w3lv8m2YxcevYG7l5/qktyyWLrYWxE/L2BJA+DBMe05ha7keYGmC4EkWaLtY9iZmf5c9Hem2FW9evVoW7YgCPELEbsEQRDiGUuO3Q8SurqWz4Tfa+TQShQBtCiWDrXypNTajj+/Ajw+DVgnALb+Bni91hZQ5meg8mDgw3tikpcvX6JFixaq6+LYsWPRp0+foHUVBEEQBCFs7r/ywKRdN7H+3GOVwU1KZ3HB4Vuv1N9D6uZSdgWxgscrYE5F4M0HL1ALW6DyAKB4N8DCKto+hubz3bp1Q65cubBnzx5kyJAh2pYtCEL8Q8QuQRCEeMS5h28wbNNl9fcvVbOhV6Us2HrpGUpmcgkqZVBCF4d/T84DTs0NuYBKg4ByfWJlXVmqyNFZtg/euXMnKlWqFCufKwiCIAhfM8M2XcHio/eCui3SquCnKlkxZKN2/a+XPxWKZ3KJnZV5eh6YXxPw89CepysJNJoDJEobbR/h4+ODn376CTNnzlQNbKZOnQpbW9toW74gCPETEbsEQRDiCW4evuix9Az8AgyomScFqudOjhZzjuHYnddI62yHJTVtkAFPte6K2/sDT84Gv9ncEij/W6wJXXPnzkWPHj1U1yQa0adJkyZWPlcQBEEQTAWDwYDHb7xw6bE7Lj52x/Vn7+BgY4l0zvbKaoD/507tBDsrCwQEGmBtqflSWVqYKaGrcLrEKJXFGe5e/vhj7UWcf+Su5u1fK5Y8L989A+ZW0UoWSYU/gQr9ovUj2LCGFgfnz5/HnDlz0Llz52hdviAI8RcRuwRBEOIBgYEG/PTvORU0MzhO5miD2pMPqWDY1soc32XwRsr1bYBAT8AQqL3JwgbI11QrM2DXxViAWVw9e/bEvHnz0LVrV0yaNAk2NjHvCyYIgiAIpnCtvv78HY7feYXjd1/j5L3XcH3/QSiKAAszM1TIkRTNiqTFnZceShyjqHX6gZt6GPNbjexI6WSHGOf1HeCfJprQZWaheXPlqhetH7F79240b94c9vb2OHToEIoUKRKtyxcEIX4jYpcgCEI8YOreW9h/4yVoz/HE3QuLjmq+GdWyJcLARNuQ9uLUYJGLFGwDVBkCJEgSa+t4//59NTp78eJFZULfoUOHWPtsQRAEQYgLfPwDcPiWK7ZdeoZdV1/gtUdIccvS3AzZUzgib2on5EjhiJsv3qv577/2VI4DJMBgwO6rL9TDGEcbS+RN44Q8qZ2QO1VC5E+TCBmSJIidjK6FdYG3jwCntEDLlUDyXNGa8TZ69Gj8+eefqFKlCpYuXYokSWIvXhEEIX4gYpcgCMJXjH9AIGbuv40Ju7Q247TvCAwwoEDqBOhtvxMVH80EHvgHv8EhJVB/CpC1aqyu565du9TorIODAw4fPozChQvH6ucLgiAIQmxBsYYWAv+efKAErvc+wddhe2sLFE6fGCUyuaB4RmclVNlaWeCNpy+azTqKG8/fB83Ljopls7rAxtICj9y8cOXJW6R3sVd+XHwvX481E3odD1dgalHA5y3gkhVovwVwTB5ti2eXxfbt22P9+vUYMGAAhgwZAgsLi2hbviAI3w4idgmCIHyl0Nuj94qzuPbsnXreung6FTDXTv4GBbfWAV4FhHxDgdZA9eGAXaJYW8fQo7PLli2Di0ssmeYKgiAIQgzg6x+Ii4/f4NQ9N2UXkCqRLVIktENSR2vsu/4Sy048UOWGOskT2qB67hSokTsFimZ0hpWF5r3l5Rugrtskkb017K0tVXli3fwp0bJ4euRP42RaHYoDAzSPLgpdZuZA82XRKnRdunQJjRo1wosXL7Bx40bUrVs32pYtCMK3h4hdgiAIXxkUkCbtvonJu2+qTC5HW0sMK22NBhWywYwB6KxGWkBKD420xYFCbYEsVQCHpLG6nm/fvkW7du3U6CzFrqFDh8rorCAIgvBV4uHjj2XHH2Dv9Rc488AN3n5G1gBhwAyu+gVSo0nhNCiYNhHMjTKwmJU95+BdzD5wG/v6VtS6JAMY3yw/kjjYwMlOe25yrO4IuN3V/q76F5A0W7QtmoNhXbp0QebMmXHq1ClkyZIl2pYtCMK3iYhdgiAIXxkUuibuuhn0/Nfsr9Dw8A9A8jnA2X+Ad08BlyxAl72AbcI4WccrV66gYcOGePbsmRK76tevHyfrIQiCIAhfArOvlhy7h5n774Tw23JOYI2iGRLDwcYKz9564ekbbzx/642MSROgedF0qF8gFRxtPxatbr14j76rzuPcwzfq+fYrz5TxPMmc1MH0viwahz09B5xaCFxZr03LWAEo1TNaFu/r64u+fftiypQpaN26NWbNmqUM6QVBEL4UEbsEQRC+IujXMemD0JXAxgJzq9mg5O622ounFgAPjgBWCYDvlsaZ0LVq1SplPp8hQwacPHkS2bJF38ivIAiCIMRkeeKD15546q6JV/dfe+Dfk4/g+t5HvZ7BxR4dSmdEqcwuyJLMIdwSQ3ZdNM7kWn/2MR6+9oSHbwDmH76rPofm8oPq5lKZXyZHgB/w8ARw/T/g8lrg7ZPg12wTAc2XRsvHPHnyBM2aNcOJEycwdepUdO/e3bTKNgVB+KoRsUsQBOErwc8/AK3mHofhQ/emzW0zIuPqGkCgP5ChDHDvkDZj3UlAshyxvn7+/v74/fffMX78eHz33XeYO3euMqQXBEEQBFPmxVtvLD56H/8cv483nn4fvZ4msR1+rJwVjQqmhuUHv62wLAY2nn+CTeef4taLd9jbt0KQcLPu7GPVMVmnfLakGNU4L1I62SHWoc3B80tazPDyGmDrBNgn0bozU+S6vRu4s1/z5QqNlT3Q8l/A5suv7QcOHFBCF83n9+/fj5IlS37xMgVBEIwRsUsQBOEroes/Z+DmqZVQjGuQHRm3twG8XgPJcgHPLmkzFe0M5Gsa6+tGM1kKXAcPHsSECRPQu3dvGZ0VBEEQTJqbz99h1oE72HjuCXwDNA+uBNYWSJXI7sPDFgXTJkaDgqlhbRm2yKULXUM3XcHCI/eCpl1+8lZ1WiSVciRDSidbePkFoFzWpGhUKHXsXiN93gNXN2mP+4cBb62EMkLsEgNZqwHZawOBfoBLZs0iwcbxi1aF+4pxQr9+/VC2bFmsWLECyZNHn8m9IAiCjohdgiAIXwGXHrtj77UX6u96+VKiwd3BwIvLQIJkWuDJwDVVIaD6iFhft+PHj6Nx48Yqs2vPnj0oV65crK+DIAiCIEQWdy8/TNh5A0uO3UcAO70AKJw+MbqUzYiquVLAwqgE8VPw/X+uu4gVJx+q593KZ0a9/KmQM2WwKNSuVIbo/XI8XwNnFgPXtmhZVk5pgURptf8ZEzADiw+fd8Cl1cCVjYBfcHdIWDsC6UsCqQoCvh6Ahyvg8RLwctOyvfw8gZx1gXpTonW13717h06dOim7g19//RUjRoyApaXcjgqCEDPI2UUQBOErMMf9dfUFVb6YI4UjJhZzB/7ZBFhYA3mbAsemaUFt47mApU2srRdHZ2kk++OPP6JIkSIqeE2dOnWsfb4gCIIgRAV6aa09+xijtl6F63stU7pqruT4oUJmFEqXOMo7k10V+6w6jw3nnoD62Jgm+WPOg4slhq43gJNzgfMrNEEqKjhnBvJ9B2StAqTID1gY3Qb6eQP3DgLru2vLTZkfqDosWlf/2rVraNSoER4+fIjVq1erQTJBEISYRMQuQRAEE+W9jz9m7ruNPdee4+rTd0hsb4UlnYrD3NEGqDIEMLcC9vylzcyglCUGsYSXlxd++OEHLFq0CD169MDff/8Na2vrWPt8QRAEQYgK3n4B+H7JaRz44J2VKWkCDK2XG2WzJv3sHTluxw0ldNFHc2LzAqiTL1X0fSk0iN83EnC7D3i6At7uIV9Pnhco2lEb+HrzEHB/CLx9rGVq+XpqmVyGQCBzZaBASyBNUSB06eTdA8CRKcDdg4C/V/By26zXyhijiTVr1qB9+/ZImzatalyTI0fs+4oKgvDtIWKXIAiCCXLklit6/3sOL99pHaBc7C0xu1k2JKXQRUr2AuZXA/y9gUwVNa+uWOLu3btqRPbq1atYvHgx2rRpE2ufLQiCIAifk4H14/KzSuiys7JA7ypZ0bF0xgh9uMIagNp99TmsLcxRM29KNa1z2YzKeL5P1WyokiuafKcC/IEDY7WHISDka+aWQNbqQMnuQPrSH4tXkSEwEDD/sN0sc7y5Q/vbMSWQrTpQaRBg7xxtjWv++OMPjB07Fk2bNsW8efPg6Phlnl+CIAiRRcQuQRAEE+KZuzdGbr2qRop10jpZYVmCCUh7wAvIuEnz4zg8AXh8GrBxAupP+7yA9zPYvn07WrZsCScnJxw9ehQFChSIlc8VTBeO1i9cuDCuV0MQBCHckvsB6y9hx5XnStya374oSmZ2idTe8vT1x95rL7Hp/BPsvf4CPv6ByJM6YZDYlcTBBpt7lYmSx1eEvL4DrP0eeHRSe563GVC4vdYpkR0TmW2lC1VRgV5c1/4Dzi0FMlcCyvXVpmcsp2WGZ6miNbuJxlji+fPnaN68uWpcwy7NP//8szSuEQQhVvmMs6UgCIIQ3fj4B2DGvtuoNH5fCKGrYnorbLP4GWlfHwHePgXePQMurQH2jdZmqDUWcIp5n6zAwEAMHz4cNWvWRIkSJXD69GkRuoQw8fPzw2+//Ya8efMiQYIESJUqFdq2bYsnT4KPa/L69Wu0atUKCRMmRKJEiZRp8fv370PMc+HCBdWty9bWVpW/jBkz5pN7/cGDB6hduzbs7e2RLFkyZYLM7AJj9u3bh0KFCsHGxgZZsmQJU6ybNm0aMmTIoD67ePHiOHHihHzjgvAVMm7HdWUeTz1qcvOCkRK6xu+4juoTDiDvkB3osewMtl1+poSuTEkSoEK2ZMr7SydahC6DATg1H5hZVhO6OJDVeB7QeA6QoTSQNDuQwCVqQpfHK+DccmDZd8DYrMCG7lonxtMLtewuwsGz0r2B5LmjVejiYFjhwoVVBjgb1/zyyy8idAmCEOuI2CUIgmAC/G/zVYzedg2evsElCz1y+WDey9ZI4PlI89BovwU4PhNY3VFrA56nMZCvWYyvm7u7Oxo2bIgBAwZg0KBB2LRpExInjj4vD+Hrw9XVFe3atUO6dOmwfPlyJRixRMXX1xeenp44c+YMBg4cqP5fu3Ytrl+/jnr16oVYBoWuy5cvY+fOndi8eTMOHDiA77//Puj1t2/folq1akifPr0SV1kGM2TIEMyePTvc9QoICFBCF9fjyJEjylOOQhaPW+MyXM5TsWJFnDt3Dj/99BM6d+6sshZ1/v33X3VzNnjwYLUN+fPnR/Xq1fHihdYRVRCEr4N5h+5i2t7b6u/hDfOiRp4UkXrfIzcvXH/+TnVaTOtspzosbvmxDHb3KY++1bPDPLoyuYjbPWBxPWDzz4DveyBdKeCHQ0DeJp+/zBWtgLGZgfXdgBvbtJghaU6gwh9Ax+2flx0WySw6DhSUL19enbt5/pQOzcLnUqFCBXWN1uEA1MSJE00mq71BgwaRnp+DbGZmZnjz5k2MrpcQEiljFARBiKNyRcbKyRLaquclMrlg2YkHKrC2NgfGZjyD+nfGaTPnqANU/BNY0wl4ek6bVrYvUKF/jJcvXrp0SQldFDcoSFAkEASWozDTacmSJSrwZEfObdu2qQxAlrhSwDJm6tSpKFasmMq6okDG0X7OT6NidvIkU6ZMQa1atTBu3DiVDbZ06VIlWs2fP181P8idO7cSp9gMwVgUM2bHjh24cuUKdu3aheTJk6vsw7/++ktlmlEo43JmzpyJjBkzqrIakjNnThw6dAgTJkxQghbhZ3Tp0gUdOnRQz/meLVu2qHX5/fffwz0AfHx81MNYsBMEIW5Ycuw+/tp8Rf39a/XsaFEsXZjzuXn4qjLHXpWzIEeKhGpam5LpUTtvSuRJ7YTkCW1iJiuJ2VzsrLhzsGYmb2kHVB4EFO8KmFt8PL/7Y+DiSsAxFZCxLJDwgxm+52vg6iagUNvgmECZyxuA5HmAnHWBXA2AZDFrCs+Bjq5du+Kff/5R1wQOUEjjmm8bCkIcdArNzZs31SCZIMQ0InYJgiDEMqtOPcSgDZfRtHAa1MqXEuu2bsPmZ4mV0JXM0QZzCtxB/pMfhK5SvQEbB2B+dcDnLWDnDDSaDWStGuPruWLFClVaxoDk1KlTyJw59ro9CqbN2bNnVWkiR+8XLFigsqT4iCg7kDeLLFfUS1z4ty50kSpVqsDc3BzHjx9XAivnYUaA8c0SxajRo0fDzc0tzOxCvoflkxS6jN/DzqHMIitYsKCah59lDOfRR48psDGTrH///kGvc734Hr43IkaOHImhQ4d+Yu8JghDTLDv+AAPXX1J/dy2XCd0rZA5T5Dp+9xWGbrqCp+7euOvqobK3eK4qlC4WspcpdP33wTuLZvP1pkTcVfnZRWDXkODnzpm18sbbe7RmNS5ZtJJHUu5XoNIAwDFymWxfyq1bt1TjGv6/bNkytGjRIlY+VzB9atSooeIEY5Im/fwOqIIQFaKcw8p01IsXLwY937Bhg0rhY6cNBoiCIAhC+Ea3fVaex6+rL8DLLwD/nnqI5rOP4d+HieDhZ0D+VA7Y2LMM8ufIChTuABRoBZxdAuwdrgldaYoB3Q7GuNBFzyVm7jBY1UUHEboEY0qXLq2CV2b7fQpvb2+VWcXjif5c5NmzZ8pPyxhLS0s4Ozur1/R5jEUroj/X5wlNZN4T3jzMwvLy8lJZjCyHDGue8D5XhwIZhT398fDhwwjnFwQh+vn35AP8sU67V+lcJiN+r5kjKDNrzelHyoOr7Jg9KPjXTnT754wSuujFNa5p/tjzlXp5A9gxUPu7/O9Au81hC13M2tLJXgMo0hFIWQAwMwde3wau/6cJXSnyAoFG3oSJ08ea0EVrAw5c8PzJwQoRumIelovSizIuHvzsqEBvzBQpUoR4WFhYhFkGyEEnli5+LvoyR4wYoa7ZHFQbNmyYWm/6dzLGSJMmzUfiG7WNSpUqwc7ODi4uLip73NhDlDEBrQ24PL7er1+/j/YDM9s54MXMcS6H9gerV6/+7G0R4iizi+mpTOHnyOmdO3dUlw3eDK1atUqlr5pKHa0gCIIpcfS2K3ouO4tXHsGDAjS7dYQnalocR8PCGVG8QXfNB8TdDri+FXj/LHj0liWLeRqFXdoQjfBmvlmzZkrgmjx5Mnr27CmmssJHsMyPwSRF0du3b6vywm7duqlHaOGUxxODwhkzZsT7Pcmgng9BEOIGilm/r9WErg6lM6jSRGMB6+DNl9hy4WnQ84w0nM+eFH2qZYeDTSwVvAT4Aeu+B/y9tM6I5X8L6aHFm+i7B4Ajk4FHp4CeJwGHD4MDdSZo/3u9AR4cBZ5fBjKUAdIWj7WuzEGbERCgysP/97//oX79+qpcjWXsQuzse1oBxFWmFgenTBU2RKCgRR/Qw4cPqwoFengyU5xiLD05qWdUrVpVzefh4aGyu0uWLKmsFejNSR9Pxr968xraHvBvWhnQ+oDP161bpwQyHQpdLOGl7UHWrFnV57du3VplsTELXogbonyk3rhxI6gDFwUuHjhMV+XBROFLxC5BEIRgWJo4ZONl5R2iY2EGVMySEPWfTUFV3z2wzVUTaDhWC1TPLgU29dbMZBOlByr8rrUet4j5wILBQJMmTYKMNJm9IwhhwS6L7M7JB0dR2aWTwhfL/XQ/LV3oun//vgo+9awuwpHd0GbvHHllh0a+ps/D1vXG6M/1eULD6aG7JoZ+T3jL5fpxNJYjznyENU94nysIQtxz5LYr+q0+r7Si9C722HP1BRYcvodtP5UN8uJqVSI9siRzQMF0iZUfl5OdVeyv6IGxwJOzgG0ioP60YKHL3we4slETuZ5d+DCzGXBjO1CoTchl2CUCstfUHnHAq1ev0LJlS+WPyJt8Zrrw/C8IoWEGuIODQ9BzxgvUEGIKZm9xsJbHY/bs2VUXZybksApNz8AeNWqU8uqkdkEdgxnoixcvVrGN7jNat25dZZvADDHqG3xfo0aN1OsUtIyb2tCrkwOA/D1QNCOZMmVSnzFr1iwRu+KQKN89cXSWaXqEX2idOnXU32wJztT/qPL48WNV3rB161Z1INIbhqmFxj4eoeFNGFMJ6b/Bz2WHMKYtGsNOIDRGZJYC0whpfEtzXB0e1H369FGeNDxAqehOnz79o7IFQRCEL2HHlWdBQpeDjQV+rJwNTfMkROLVTQG/M1q77wYzAEMgsGMQcHSq9sac9YCGMwFr7cIbk+jdkyhWlChRAitXrkTKlClj/HOF+AHT+jlKSnP4gwcPKrFLF7poQrt3716V9m8Mg0F2JKI3FtvTEwpijC+KFy8eNM+ff/6plmVlpd2Q0viewWt43UD5HgpwFNL0Mkm+h0JWrly5gub577//QryP8+gBKj3CuE67d+8OKrHgevE5R3oFQTA97rx8j44LTyLgQ2XR/VeeQa/tu/4ySOwqmsFZPeKMhyeBA+OCs7RoMv/+JXBoAnB+OeD1oWyRZvUFWwMluwPOmWBK0MOTA2Ms8+INf2gPRCHm4YAMM6zi6rOjAv08jTO7dUEppmAzG2Phlff2efLkCbH+jEn0ATc2zKFWYLxeHOzldZ+dpG1tbfH06dOg2IQws41ahV7KSK866hjMFjOGFk/0ChW+IrGLXyzTVXli279/f9DBy1beURWKaDDLg4k/AopdTPNjYBxRS3u9ZThLJdipicEnUw15Y6Z3UdJbhlN15YFJNZav8YDVg1/e1LGzEpVlptwygKVayww1QRCE6ODQTVf8/K/WPTFrMvpxlYadnzuwpL5mNMtuSc2XAu+eAVt/1UxmCUsa6OERC6OkobsnsROeLiwIQnjwGkohiJneLKegoMWYgINPFKd4I0SPT47o8nXd64ojrhSTWAbAQJ0dD3mt5nt4HeYoKzsxEmYN0OydJQgcFGNn0EmTJqmuieFRrVo1JWq1adNGjebyc7lOPXr0CCovZPzAUVtmInTs2FGJbBR4GRPoMIZo166dink4UMY4gqUOendGQRBMhwevPVF78kF4+2mD8cUzOqNSjmQqcyt3qoRIZB/c5CJO8XmvlS8aArSMbVoTENoT0Kw+wEfrtFikA1C0M2Afh6JcOMydO1edq2lnwzItdtcVYh+W5ppyKaExFJHC6rxIQSq07xVjgS8ldAzLfRXWND15JzrQ/b0YR6ROnTrEa2JtELdE+VfCgK9Vq1ZYv369GnHVD14asJUqVSpKy2JqIDOzjE3iaOoWEdHRMpymsfPmzVNpi3qtLdeByzp27JjKbBAEQfhcVp9+hLdefhi97Zry5aqYPSlmtC4MWysLwNwBsHcBEiTTyhcOjgfOLdOCX47kNpgeHADHMPRdpMjP8nQOHlBcEITIwBscCkIcoKIIxIxrCke9evVSpuwbN25U8+m2BzoUxXTzWR5zvGmqXLmyCnrZyYulBzociGK2GIUqZlolSZIEgwYNCiqTJPxcDphxICxDhgxqxJYCG7svMlOLQTZFKxrU6jCGYExAwY7iGT07eAOnxxDku+++w8uXL9XnUTDjdtAfRbK/BcG08A8IVGb0Xh+Ert9rZEfX8plNz2uSN/UbugOv7wCOKYGM5YJfo6hVZYjWTTFL5Rj35vwcWBHD8zXvn3gO5rmTGS+C8LkwyYWDWMbQ/zO2B1x5/08/LsYyenYXk1/0MkjGIkyqod8X7Zt02wVmphcqVEg95yAbRa0HDx5IyeLXLnbly5cvRDdGHZYMRjWtkcEwg8umTZuqEWEqod27d1dCVXhER8twvk7l2Hg5OXLkUME75wlL7GKpIx867NokCIIQmr3XXyjPkMAPg1VVcibDtFaFYGP54fxoaQM0XQzsGgT82woI+GBYn60mUGUwkCxnrOxUlnFx4IKp3BT5eW4XhMhCoYgPQhsB3cSVUHSKTLcmZnlx0CkieFyyNDI8KHJx0M14JDV9+vQflSmGhoLb2bNnI5yHN3ZStigIpsutF+8wdvt1lUVtbWmGyc0LokYeEy3BP/Q3cGUDYPYhFtjYE3BKA2SuqD1nuaKJcu/ePZWtS/sYJgeEto4RhM+BCSfUD+iVxcEpVhhQ/Irtsj/GwoMHD1YDY2y4wIEuDtwxQ1wf4Ordu7fy+aLxPDUDJtbQikHH0dERffv2VXERM8bKlCmjkmsomtFGgcsW4obPrpGhqPTo0SOlYPLBulfWs0Y1q4BlkDxwWPPNkViW0bCbR3hER8tw/s8yCvqMhDdPaGi+SGVXfzAjTRAEwZgrT96i+z9ngoSuyjmSYnqrwrB5fk4zpNUFgMMTgdMLNaErQ1mg006g5YpYEbp4EWZpGP0Wy5Ytq7w3ROgSvlYoatEUVkpvBeHb4fxDN1Qevw9V/j6A7Zefq94uE78zYaHr5k5g94fsUmZxv3sKJEwT690TPwfenzGzls1D2MRGhC4humCyysCBA5WlQNGiRfHu3Tu0bds21newvb29Os55jHM9KOwy45x2Bzr0+ab4RdGKwhzFrYYNG4ZYzl9//aW2h5qBbtXALPJPVa0JMYuZITLDr0aw3EVv4WkMF8OUYQpNkYWCE/0wjJdFsYttP/UsrNBky5ZNlScaZ24x2KWPF71n6APGEV4uUzebJfwhMXuMKYgcSeYyjDO1CH05WA7B8srIZHZR8KJqa9xhShCEb5On7l6oO+UQXN9rmVrFMibG4o7FYet2A1hQE/ByA2qP17ovremkvaneVM2ANpYCXp4febHmOZOCF0vRpXtSzMPrBQdJ5Hrx7SHfvSBEH4GBBvy5/iKWn3gYNK1ERmf8UTsn8qUJOYBtMry6DcyuAPgYVYQUbg9U/QuwNd37Bw6MsdkHM154086sG2bjCrF/rWAJKbOYKZpI6aggRO13EeUyRopENMSjJwbrV7+kJp7v17sj6VAJXbNmTbjviY6W4fyfmWlMPzTO7oqorTjrcMVgThCE0Hj6+mPNmceYsfdWkNCVK2VCzGtXFLbvHgCLG2hCV+rCQNKcwD+NtTeW7v1xK/EY5MKFC2oUioIXxa646uIjCIIgCFGFg+ps+LLh/JOgab0rZ8XPVbOZ7s588wBY1ixY6DK3BGqNBYp0hCljPDBGsYvZKjIwJgjC10iUxS4ax9HzivWqXwo7MbJDYujMMfpthEd0tAzn6yx54DQa4hKuB8sxjbPBBEEQPsWN5+8xcH2wwWZaZzss6VQMjr6uwOL6wPtnQLJcQP0ZwD8NAX8vIGs1oPLgWNu5NAKnFyKNNnft2iUp1YIgCMJXw5M3Xui8+JSyCiBWFuaY2rIgqucOe4A6zmHRDK0KdgwEfN9p02ydgGZLgEzlYcqcP39eNa6h4MUSrJo1a8b1KgmCIMSeZxczseiLFR3QxI3GyPTbuHXrliovnD17tuq8FB5sGU6vL5YlXrt2DdOnT1ctw3WjXMIOUXPmzFHeX1evXlVeYMYtw5kmylJMzsfOUBTv+BqFLunEKAjCp8oobj7/ELyy21zaRMjoYq/+TupojX+/LwkXCy9gSUPgzX0gcUag+VLNjPbtY8AlK9B4bqx0W2IGK0vDW7dujWbNmqnybvEOEPT0b0EQBFPM4Lr02F1dawn/rzf1UJDQlTe1Ew70q2C6Qhe7Lc6pBGz+SRO60hYHyvwCdN5j8kIXjcJ5H8T7JN4bidAlCMI3J3bRz4pCE9t9v3r1StUYGz+iAk3g1q1bh+XLlyNPnjzK2G3ixImqK4IOuyKws1PoluHM5sqfPz/Gjx8fZsvwcePGqZbhbBfObLTQLcMnTJigDJqZ2cU2oixfXLt2bVR3hyAI3xD3X3mg+ZxjaDT9iBppJodvueLuK0/19/imBZAqoTWwsg3w8qrWXrz5cmBDT+DRCcDGCWixQhvhjWGePHmiOt3MnDlTDQqwgxJLvYVvF2Y58zpLX0sHBwc1cERYosJ28oIgCHEtdP244hzqTDmExx+usRvPP8GrDxYBjQqlxqZeZZDSyUSvZde2AlMKA0/OABY2QPURQIetWqflJFlgqtCTuHv37sp8u3nz5qqDnAyMCYLwTZYxVqlSRf3PLgVfalBPKDjxER40HmOL8OhuGU4js2nTpqmHIAjCp1h58iEGb7wML78A2Ftb4OrTt0hgY4m+q86r11uXSIdy2ZJqM+dpDDy9ADRdpI3uPjwG2CQEWq+JlYD34MGDKpOL/oUHDhyQjFVB8b///U9lPI8ZM0aVtepwsIkDTcx4FgRBiCum77uNTR88ue698sC0vbew4qRmRt+scBqMbpLPdL+ci2uAtZ0BQyBgZgHUnQgUaAlT59GjR6r7HO+rODj2/ffff5EfsyAIwlctdrHsL7aggMYMskOHDsXaZwqCIISGwXe/NRfU3yUyOWNsk/xI62yPX1aew1N3b6R3sccftXKG7LSUuTKwumNwRlebdUCawjF+zpw8eTL69u2rPBH//fffEBmtwrcNS1RoFcDBKloC6DBLmrYAgiAIccXe6y8wbofm4/tjpSwYsvEybr/0UM2KfyifGb9UzWaaIkxgALBrCHBksvbczBzosgdIVQCmzp49e1QmFxMAOEjGrvSCIAjftNhVvnzs1Zvzonb//v1Y+zxBEITQnLr3Gn0+ZG+1L5UBg+rkgrm5GbZffoa1Zx6rQHx80/ywv78XSFUISOACBPgBazppQpdtIqDteiBVwRjdufQl5IgsvQ/pR8iSc3bOFQSdx48fI0uWLGGWN/r5+cmOEgQhziwCei8/q3zdS2Rywcz9d+AbEIjkCW0woVkBlMqSxDS/mTcPNT/OO/uCp1UdZvJCFwfGxo4di/79+6NixYrKTiZp0g+Z6YIgCPGIz7oTevPmjfL3oPk7yZ07Nzp27KgMDQVBEOJTAN5l8Sn4+geiaq7kGPhB6KJ5br/VWqbX9+UyoYjVPWBJS8ApDdBxO3BpLfDwuObN1W4jkDJ/jK4nG3ywexI9mFasWKF8CwUhrAYzHL0P3fF49erVKFgwZsVYQRCEsPDw8cf3i0/jrbc/MiVNgON3XoHW9FVyJseYJvngnMDadHfchRWa0MWyRUMAkLowUKI7TBn6K7MpF32KKXbRx5GWB4IgCPGRKItdp06dUmbwNDrW013//vtvDB8+HDt27EChQoViYj0FQRBinSQONiiULjFc3/tgUvMCsDA3w/mHb9Bm3nEVmBdKlwi/lE4KzKsIBPgCST+UMu4bof1fZUiMC12bNm1CmzZtkCxZMhw/flwNPghCWLBpCw2ImeHFbC7e7Fy/fl2VN27evFl2miAIsc7S4/dx/fk7JLKzwsPXnkro+q5IWoxqnNd0yxb1bsqlemti171DgLkVUG9qrHRa/lwuX76sGnM9ffoU69evR/369eN6lQRBEEyrG+PPP/+MevXq4d69eypQ5oMm8jSZ/+mnn2JmLQVBEOIAGtDPblsEizsWh721Jc4+cEPrD0JX4fSJsah9Edhs6g64PwASZwQaTAf2Dge83YEUeYFC7WJs3dgMhF30eD5mGcLJkydF6BIihDc2FEd37dqFBAkSKPGLGdqcVrVqVdl7giDEOh1KZ0S5rElU2aJfgEFldA1vmMf0hC5e17f/CcytrFkVEHZdfvHB77BsHyB5Lpgq9PAsXrw4rKysVOKCCF2CqcHfPEXYz2XIkCEoUCC4hLh9+/Zo0KBBiAZ3X6pVLFy4EIkSJYIpQC2G++zcuXORfk+FaNgH8V7s4gnyt99+C+EFw7/79eunXhMEQfiaufjIHX9tvqI8LQizuZzsrXDy3mu0mXcC77z9UTRDYizqWAyOp6YAN7cDlrZAs8XAm/vA6UXagmqOibER3tevX6sBhhEjRqjHmjVrpIxciBRly5bFzp078eLFC3h6eqoGMNWqVZO9JwhCrPHynQ8CArVr7PO33iqzy9M3QF1bp7YsCEuLKN+exGwm15nFwJTCwNGpwJOzwNVNwIVVwLzqgKcrkDwvUPYXmCL0Y2SiAo3oKXAdO3YMWbNmjevVEr4RKDhRkOGDQiubJnFwbf78+SrD3BhmHNasWfOzhTE2Z9q9e3e0rr/wDZYxJkyYEA8ePECOHDlCTH/48CEcHR2jc90EQRBilS0XnqLPqnPw9gtEhiQJ0KZEetxz9cDEXTew4fwTZZ5bPKMz5rcvigSPD2tZXKTWOC2Ta34NWr8CeZoA6UvFyDqyPTj9ud69e4etW7eKUCEIgiB8Ndx68U4NHFXOmQzfl82ElnOP4/lbH2RL7oC5bYvC1srCtAzoV7UHHn8YzHfJClQbDtw7oAlfJEtVoPFcwNIGpgbFA3p4Hj16VHVq7tmzp+llzAnxnho1amDBggWqIuH58+fYtm0bevfurfxCN27cGJRAkyJFii/6HAcHB/UQBGOiPHTCk2anTp1UOiwFLj5oiNy5c2e0aNEiqosTBEEwCWbsu40ey84ooatC9qQonckFf6y7iCp/78f6c5rQVS9/KizoUFSVN+LkHMAQCBRoDRRqA1xcDTw8BljZa92YYoBFixahVKlScHFxwenTp0XoEqKEubm5MiIO7yEIghCTnL7vhiYzj+Kpuzf2X3+p/n7k5oWMSRIouwBmUZsMdw8Cs8trQpdNQqD6CKDtBuDYtGChi6WLLf8F7EyjrMkYZu3SR5kNbPbt24devXqJ0BUP8ff3D/dBcSm65/0cbGxslJCVOnVqdUz+8ccf2LBhgxqwZVlgWNlavr6+SpxNmTIlbG1tVWOdkSNHqtcyZMig/m/YsKF6j/48dBnjp/Dx8VHZYFwvWjuwzJe/FWO4funSpYO9vb36vFevXkWqtHDlypUqk54e50WLFsWNGzeU3UiRIkWUIMcMtpcvXwa9j1luw4YNQ5o0adT+4nZQFDTmxIkTqpkQ9weXw8Hv0Fy6dEktm5/BLLo2bdrA1dUV3zJRzuwaN26c+hLbtm0bdNAzLfGHH37AqFGjYmIdBUEQYpSN559g9DbNd6NL2YxoWzIDmsw8okabCcWvvtWyI09qo46ztf/WOjDVHgd4vwV2DtSms5TBKXW0rh8vyCxDmDFjhup8O23aNHWxE4SosG7duo/KWxgsUUQdOnSo7ExBEGKMPdeeo/tSbUApewpHVcr42sMXWZM5YGnn4kiW0ISuaRzd2vMX4PkKSJEPaL4UeH0HmFMReP8csEqgeXTmDvYDMhVowcAsLt7Ec3CMyQlfmjEjmC6hBRFj2LhIbyZHaGEQWtTScXZ2VseLzp49e5TgFBpaaEQHlSpVQv78+ZX3NxNmQsNjmFlfFI0oNukJNoSiEbeN2WLMGvvcwTqKaVeuXFFJO6lSpVIxEpd38eJFVerLpk9M8KHIRu8v7uvBgwdHatmcb+LEiWrdGbe3bNlSVcBNmjRJCWfNmjVTvqmM6wmnjx8/HrNmzVKCFss86cnLphJcl/fv36t9zxLQf/75R/mlMzvOmDdv3qj9yv05YcIEeHl5Kespfha/z2+VKItd1tbW6gvhF3/79m01LXPmzOqLEwRB+Nq49Ngd/VafV39/Xy4TelbKgqYzjiqhi23QRzXKh2IZnbVA99ACoMwHY0eHZECzD/5c2/oD755qJvUle0Xr+j169AhNmjRRogQvgt9//320Ll/4dgjLkJjHFjt48oaIQZ0gCEJ0s/r0I/y25oLy6SqSPjFuv3wPN08/5EyZEP90KgYXBxMrAWSpX5MFwOGJQKVBwJFJwIFxmk0Buy43XQgkC2nnYgrwhpgxwvLly/HLL7+oJAQmJAiCKUJLpAsXLoT5Gi2TKPKUKVNGJdkws0snadKk6n8axX+ukMvlUyzj/xS6CAViClqcTj9c6h0Uv+hLTrJly4YjR45EKDDqcFnVq1dXf1OUYvUb/cRKly6tpjHeMs5qYzIRhSl665HRo0dj7969SjDjAPeyZctU9te8efPUYDfjNt4fMNlIZ+rUqUoo47rrzJ8/H2nTplWZZVz/b5Eoi106FLfy5s0bvWsjCIIQi3j7BaDrktNqpLl8tqT4uUo2dF58UpnlJnW0weKOxZAmsT3g/lgzovV4ASRKC+RpHLLU4fQC7e96UwCr6BudZjo1S8c5yHDw4MEQI3SCEF2UKFFCRFRBEKKF43deqQEi3RtqxYkH+H3tRfU3rQAuPHqjhK78aZxUo5dE9tamsecfHAPuHQLK9dWeO6YEMlcCltQHHp/WprHDco1RgLXpDfDzZpZ+niyj4uAFszmE+A/FmPAI7c8WUdfl0PMyQyg2shDD85CjsT3XN3v27GobmdUUnc10mL3FLLfQAhArKWgVQtitmqWLxpQsWTJSYle+fPmC/mY5ITHWTTiNjYLI27dv8eTJkyAhTIfPz58/H7QuXKZxVQfXxRjOS4EsLN+y27dvi9gVETx5Un2kOT3/jgimIwqCIHwN0Ah3cN1cmLr3FiY3L4AB6y/h8K1XsLe2wIL2RTWhy88L+LeVJnQlywWkLR68AF9PYNOP2t+FOwAZy0ZbAPD333+rUZ7y5curFGt9JEsQohOmubNcgJ4VgiAIn4t/QCDGbL+O2QfuoG+1bOhZSev4Z2VhrhKlmDn9zssP9155IkVCW9MRuvx9gf2jgEMTNB9ONpt5+xg4Oh14dVObx9oRqDsRyNsEpgjLr9q1a6cyVOjrkytXrrheJSGW0M3d43Lez4UCTsaMGcN8jd5eLNWjr9euXbuUeFulShVlah9dWZAsf6T/begyyOgwuTfOqNQFvdDTQnejjI5tqlu3rsoKC03KlCnxrRKpI9nJySnoi+LfgiAIXzPGo0nVcqdA5RzJMHbHDaw58wgW5maY1rKQ5s9F345NP2mtxu2cgRbLAac0wQvaN1Irb3RMBVQdGm0XK6Y306fg119/VenIsRF0CPGfxIkThxhF5e+AXT2ZqU0PCEEQhM/BzcMXvZafxaFbmhGyp2+wL1CNPClQKUcynH3oho4Lta6G45vlNw2h6/Vdrdvi03Pa8xy1gS2/AO6PtOc2TkCR9kDxH4CEpnezSO/kAQMGqJvbxo0bq5IlJiYIgqlDDylmV9GPNjx4LLO6gQ9aLjDD6/Xr18pfjMJReP5jkYHlfnw/s6toJB8WOXPmVL5dxhw7dgzRDbeTQvXhw4fVALcOn+sVHVyXJUuWwNvbOyi7K/S6UCBcs2aNMuyX+4ZgInUHxdrVsP4WBEH4Gtue9111AeOa5kOWZI7w8g1A39XnseXCU/X6sPq5UTFHMm3mY9OBCys0I3p6dCTWOr4oWNagd2TiiK/tlw8EXL9+XaVM04STo1cMXgUhuqD3Q+jujMwYZAciCmGCIAhR5c7L92g7/4TqqmhnZYGxTfOhTj7NA4ewe7G3nw/6rdZKGTuVyYjSWZLE/Y6+uglY3wPwcQfsEgMlewBHpgLeb7QBrFK9tE7LNo4wRXiTTh8g2h2MHTsWffr0kW6LgknC0sBnz54pcen58+eqDJDe3yxNZMO7sGB1A7ORKEoxVlm1apXy56JPF6Ggo3tgsXthVGMYli+2atVKfT6N4fk57I7IZbJcsHbt2vjxxx/V8umnRc/T7du3R6qE8XPg4DZN7emDzk6M1FvOnTuHpUuXqtdpcP/nn3+iS5cu6N+/vypX5noZ06NHD8yZM0edF+gzRlHw1q1bqjpk7ty532zXbcvPKXngaLBuSH///n2VPsuU2eispRUEQYhuNpx7jP5rL6pR58EbL2N80wLosvgULj52h6W5Gf5qkActiqXTZr69B9gxQPubLcczBY+2qNc29dZKHvI2BbJpJpRfAtst86LLcjJ2mqFxpyBEJyxzEQRBiC7YTbH9gpNK6ErnbI9ZbQor03ljeM9Azy7X9z7IntwRv1bPHvdfwJ7/AQfGan+nKQYUbAVs/Q3w9wZSFwFargQSaL49pgizTZjpwm55LPGqWLFiXK+SIIQLBSIKV8w2oijFLoy0T2BMQiErLNi5cMyYMbh586YSaYoWLYr//vsvaH4KVGzCQHGHcTPFn6hCQel///ufEoofP36MJEmSKA9TveMk/+byKUKxcyLLKJlJ+ddff0X7t01hzd3dXa0LhWzqKuxGSZN+vbRy06ZN6NatmxLm+Lqe0amjZ4fRAoWaDEXG9OnTq4y48Pbzt4CZgVehKMCdR98u7my2uKRxHM2TXV1dlQpr3BUgPkMzOZZ08sCUlGFBMH3+3nEdk/fcUn+XyuyCjqUz4I91l/DinQ+cE1hjRqtCKJ7JKLi9vE4rbyjQCqg/TevO5HYf2P4HcG2zNk/CNEDX/UCCzx+l5kjXwIED1SgXL1q8+PIiL8QfTOl6wes2PV0YTIX2iwhvhFWIH9+9IEQn7KzYfPZRnLznhrTOdljXvTSShOqq6OsfiPE7r2PW/juwtjDH+h6lkSuVCfwOLq0BVncESvQEHFMAOwdqnRaz1wIazzNJA3rCWzZ2ZeaNcZEiRVS2i/gtxv9rBUvX6F9Ffytjg3JB+JbxjuTvIsqZXWfOnMGECRPU3yyzYUrh2bNnVY0oVc9vRewSBOHrYe/1F0FCV+cyGfHW2w+dF2vdlTjSPLddEaR1DhXc5m4IBAYAOepoQtexmcCuwdrIL8sai3cFyv8G2Gkp1Z8DBwmYbkzvAo5gsVVxeJ1pBOFL4agg0/bpC8dg2vhY498idgmCEFnob9m8aDrcdfXA/HZFPxK6rj97h19WnsPlJ2/V8341spuG0EXYUdnKHjg6DTg2NbjJTK1xgIVpemR6enqqe6zFixejZ8+eKrOFyQaCIAhC+Fh+zslWzzrYsWOHyvJiahxT/VjSKAiCYEq8eOeNviu11r0lMjlj1elHcPfyU8+bFE6DIfVyw8HGEvD10MoWy/0KJPzgN6J3XrqzH9j2m/Z3hrJAzTFA8i/rdnTq1CmVycVz6s6dO2OlzbPwbcP0+I4dO6qmB7oVgSAIwufSuHAaZUBPXy7jjK+5B+9g/I4b8A0IRGJ7K/yvQV7UzhfHBu+PTgHOmQCbhMCxacDeEdrgFUWvyoO1ASwTHWy6ffu2ihdu3LihTKpbt24d16skCIIQP8WuLFmyKG8ZmijTqE3vosCSCEnRFwTB1Ji46yZeefjC1socx+68VtNypHDE/xrkQZEMztpMrje1ksXnl4Dnl4GO24ODXu+3wIYe2t+F2wN1Jn5xQDxv3jxlJEnfAmbIpk2b9ss2UhAiAT0pWP4iQpcgCJ9bRrf46H2Uy5YUGZMkUNNCC119Vp7D+nNP1HN2Oh7ZOC+SOcZx6dXbJ8CyZoC5FZAgKfBcM8tHxvJA3UmAc0aYKlu2bFHilouLi+q+RvNsQRAEIXJE2a2MpYostWEXBHZwKlmyZFCWFw3TBEEQTAUG3qmd7GBuBnj7BaoMrsF1c2FzrzLBQteFVcDsCprQxSC4ytCQYhY9utwfAonSA9WGf5HQRbPI77//Hp07d1bGnAcOHBChS4g1qlevrjIKBUEQooqPfwB+XX1BNXdhYxd2MjYmMNCAfqsvKKGLDV9GNsqrLALiXOgK8AdWdwI8XwGerprQZeOkeXG23WCyQhf9PGmMTbPssmXLqnO3CF2CIAgxnNnF7h9lypTB06dPVVaCTuXKlVW2lyAIgilw5Yk7Bm64jNP33dTzslmTYHTjfEiVyE6bwc9L6750ZlFweWLjuZpZrc6NHcDZJXQ0AhrMAGwcPnt9Hj58qMoQLly4oDK7WE4mCLEJW2mzvfWVK1eQN29eWFlZhXi9Xr168oUIghCmHUC3Jadx5sEbNXjErsXMljYWutjpeM2ZR8rLa0qLgqiZN47LFgl7cO0eBjw4oj0P9AeS5wW+W2KyIhd59eqV8ldkIgG7xfXv3/+b7qYmaESxp5wgxGsi+3uIktjl5+cHOzs7nDt37qMsrmLFikVtDQVBEKIZ/4BA7LzyHBN338TN5+8QaIDK5vqzdk40L5o22JD73TNgaVPg2QVNyKLRfPl+gLlF8MI8XwMbe2l/l+gOZCj92etFA/rvvvsOCRIkUG2BCxcu/KWbKghRpkuXLur/YcOGffQafxvMJBAEQTDm8hN3dF50Ck/dvZHQ1hJTWxZSZYzGNxwDN1zCv6ceKiFs4ncFTEPoYrfZ7f2B4zODp+VtCtSdbLLdFvVGYBwYY3e+bdu2oVq1anG9SkIcow9M0eOV9+GCIED9Hox/H9EidnFh6dKlk4BYEASTY/vlZxi66TKevPEOmpYzJTstFkVqPZtLx9pBG/G1TwI0mQdkqhDydT9vzafr/TPAJStQmW3Jow5vAsaOHatGZZn9umzZMiRJkuSzliUIX0ogb/4EQRAiyaGbruj2z2m89/FH5qQJ1PVU9+rSr3F/bb6KpccfqAr/8c3yo27+Dw1e4hJew9d1Ba6sD55WfYQ2cGWiJvRk/vz56N69u8q83bdvH9KnTx/XqySYABYWFkiUKJHyxyb03ZTO3cK3isFgUEIXfw/8XfD3Ea1ljH/++Sf++OMP1Q3E2fmD540gCEIc8s+x+2pk2TijlR2iJjUvABvLME6CLEdsuQIIDAAShwom3z0H/m0FPDoJmFsCDWcCVlEfSeOobIcOHbB27Vp1zmQ2zadOyIIQW3h7e8PWNo69dARBMOkbiom7biihi52MZ7UpAie7kCPok3bfxPzDd9XftAloWDANTAJDAPDmgZa5DQNQoDVQ8kOjGRM9H7N5yJw5c5Sn55QpU+T8LIQgRQrNYkMXvAThWydRokRBv4toFbumTp2KW7duIVWqVGrEgWU5odNvBUEQYisYn7rnFsbvvBFi+h+1cqBL2UzBI19UwQ6O08SrMloHWTiFEZQ/PQ8sbwG8fQzYOgFNFwFpikR5va5evYpGjRqp7nfr1q1DgwYNPm8DBSEaYZniiBEjMHPmTDx//ly1sc+UKRMGDhyoms506tRJ9rcgCApeP2e2KYyZ+27j1xrZPxo4mnforup2TIbUzYVmRUyoq7B1Ak3cWtMJsLABKvaHqfLgwQNVtnjx4kXMnTtXzsNCuL/HlClTIlmyZMpWSBC+ZaysrCKdQBBlsUtu2gRBMBV8/AOx6+rzoOcWZlCjz1VyJQ+eidlbNKI/OUcb5c1aDUie++OFXVwNbOgJ+HtppYst/wVcMkd5nVavXq0yuljyffLkSWTPnv2zt08QopPhw4dj0aJFGDNmTJB/F8mTJw8mTpwoN1mC8A3j6x+IDece4/jd1xjXVGtAlcTBBgPq5Ppo3pWnHuKvzVfU332qZkP70iZg9u7+GLi1CyjcTrvuH/xbm168a9iDWybAzp070aJFCzg4OIifpxApeIMvVQKCEHmiLHaxDa4gCEJcZ3RxlMvWygK/VM2GjgtPIsAA/Fk7V0ihy98HWPv9B98OM6DmmI+FLg9XYMsvwJUN2vPMlYEm8wG7RFFaJ39/f1WuSI+uZs2aqY6LDGAFwVRYvHgxZs+erfzjunXrFjSdnZWvXbsWp+smCELcEBBowKIj9zDn4B1lQk/Y0KVIho+tSvwCAjFr/238/SGbukvZjOhZKQviHLd7wKJ6wJv7gJk5YGEFvLisZWjr2dwm5p84atQoDBgwQBnQL126FC4uLnG9WoIgCPGOKItd5M2bNyp74fbt26qNOb27WL6YPHlypE6dOvrXUhAEgT5Y3n6YuPMm3L38lBGum4cv/lh3SQldNfOkQIfSGYL3k/dbzXvr7gHA3ApoNBvI0yjkfry8HtjSB/B0BcwsgHJ9gXL9AIuonRrpodC8eXMcOHAA48ePx88//yzmoYLJwbLaLFmyhHnjJWURgvBtDhwN2XgZS47dV8+TOdqgY5mMyJbC8aN5Lzx6g36rL+Das3fqeYti6fBHrZxxf61zvakJXe+eAIkzAulKAksaaq9R6LI3LX9h3kO1bdsWmzZtwqBBg9RDMnUEQRBMROy6cOECqlSpAicnJ9y7d0+VQlDsogkz6845ciwIghATXaF++vcsXN/7Bo0oj952DY/feCGDiz1GN8kXHHS73de8tziyy86LzZeG7LjITk2bfwbOL9OeJ8sNNJgOpCoQ5fU6fvw4mjRpAl9fX+zatQsVKoTq7CgIJkKuXLlw8ODBjzp8cfCqYMGCcbZegiDEDTP231ZCFy+dA2vnQqsS6T7y5mI219jt1zH34B0EGoDE9lYYXDc36hdIFfdC1/2jwIqWgNdrIEl2oO0G4PI6wP0B4JgSKNYVpgTvoejP5erqqsSuOnXqxPUqCYIgxGvMo/qGX375Be3bt8fNmzdDdAqpVauWymqICkOGDFEXSuNHjhw5InzPqlWr1Dz8bLbm/e+//z4apeIoCU387OzslDDHdTXm9evXaNWqFRImTKic/GnK+/79+yituyAIsceJu6/RefFJJXRlSpIAizoWw9ZLz7D3+ktYW5pjWqtCSGhr1CXq/mFN6HJIDrTfHFLoev8CWFRXE7qYzVW2L/D93igLXTzXzJo1C+XKlUOaNGlw+vRpEboEk4bXxp49e2L06NEqm4uDVBywopcXXxME4dth3dlHGLPtuvp7UJ1cKqMrrO7Fo7Zew+wDmtBFgWvXL+XRoGDquBe66LO5uJ4mdKUqCHT4D3C9Duweqr1eoT9gbQ9T4Z9//kGJEiVgb2+PU6dOidAlCIJgimIXDZe7dv14pITli8+ePYvyCuTOnRtPnz4Nehw6dCjceY8cOaKMHClOnT17Vpnl83Hp0qWgeWi8O3nyZNVtihkX7BZZvXp11dZXh0LX5cuXlTHk5s2blUj3/fffR3ndBUGIeS49dkenhSfh7ReIitmTYutPZXHn5XvV8pz8VT83cqdyCvmmAi2BGqOALhSxjDJWnl8G5lQCHp3QvDxarwEqDwQsbaK0Tl5eXuo8RN8jtgnfv3+/ErwEwZSpX7++yiZgBiKvjRS42DmU06pWrRrXqycIQiySwSUBnBNYqyzpDuEYzB++5aq6LpJJzQtgUvOCcHGI2vUyRnhxDVjTGQjwBXLUAdr/B7y8DixrDvh7A9lrAQVbwxRg1nevXr3Qpk0blQV+9OhRZM4c9eY3giAIQtQxMzA9IQqw5en27dtVyYOjoyPOnz+vWpdTOOrYsSMePnwYpcyu9evX49y5c5Ga/7vvvoOHh4cSqHQ4SlKgQAElbnFTUqVKhT59+qBv377qdXd3d+UltnDhQuWpw8CepRwU7YoUKaLm2bZtm8pMe/TokXp/ZHj79q0q5eTymSEmCEL0c+vFOzSbdQyvPXxRLKMzFncshs0XnqLvqvPq9Z+qZMVPVbJpM5+cB+RqACQIx+T1+jatDbnve8A5E9ByJZAka5TXieXbLEO4cuWKOu+0a9fui7ZRiP/I9eLbRb57wVQGjU7de43rz9+jfakMyP7Bk+uZu7fy6TI3/zhLy93TDzUmHVCm9a2Kp8PwhnkRp+i3K3pG2f4xgLc7UHUY8OiU5tPl5wFkqapZF0RxECumfBLZsIb3HJMmTVIDZHGeESeYJHKtEAQTyeyqV68ehg0bFmRmy5M2vbp+++03dQMYVVhiSIGJghkzrris8OBoCMsSjWHWFqeTu3fvquwy43koSBUvXjxoHv7P0kVd6CKc39zcXGWChYePj486ERk/BEGIWe66euKdtx/ypnbCvHZFsO/6C/RbrQldncpkRO/KH8Qqthun0fzKtnTb/jhAPjwJWN5cE7oylAU67/4soWvHjh0oXLgw3Nzc1LlEhC7ha4JZiPv27Yvr1RAEIZYIDDRg7PZrqDPlEIZsuoLlJx7g7AO3oNdTONmGKXSRQRsvKaGLnph/1s4Zd98ZPTZPLwKml9QazuiU+xWoPhx4eg5Y2kQTumhZ8N0SkxC6eK4tVKgQ7t+/rypIfvjhBxG6BEEQTF3sYqcx+lsxw4ulPOXLl1fdnZjlRd+PqEARihlXzKyaMWOGEqvKli2Ld++0Ti+hoZDFLC1j+Fwvn9T//9Q8XHdjLC0tlcl+RGWYI0eOVMKZ/kibNm2UtlUQhKhTNVdyLO5YXHl0nX/ojh+Xn1O+Ic2KpMGA2h+6QLk/AtZ0oaqlCVjm5iGD5PU/ADvpR2QACrcHWq+Ncncm+huNGDECNWrUQLFixZTfBjNKBeFr4uXLl+oY5vWLnZQjm1UtCMLXx3sff3y/5DSm7b2tnpfPlhQ9Kmb+uOw/DDaef4IN557AwtwME74rAHvrz2re/mVwoOraFmBaMWDTj8DLq8DJucGvq+v/Y2BpM8DnLZC+DNB8OWBlh7iEVSbjxo1TA+m0amG3elahCIIgCLFPlK9eFHpYskhvLXYVofDFkYvQGVeRoWbNmkF/58uXT4lf7BK1cuVK5YdjSvTv31+Z8+sws0sEL0GIGdPcnCkTIkcKrTy4ZGYXXH36Ft3+OQ3fgEDUypsCIxt96LwY4Aes6qAZ1KbMr/l0Bf1In2qZXvTnohE9XyvWJbgEIpKwVJkZXBs2bMDAgQMxePBgaRMufJXwGGZWIhu9LFu2DH///bdq+MKs6pYtWyJDhgxxvYqCIEQDD197ovOiU7j+/J1q4jKmcT5lKv8pnrp7Ycfl5xi/QzOu71kxCwqmSxz734nrTWDrb8Dt3dpzdlYs2QMo2CZ4Hn8f7Rrv6Qokzwu0/DfODek5WE9LF3a4ZcXL//73PzWgLgiCIMQNUT4D05OLIk+ZMmXUIzpheWG2bNlw69atMF9PkSIFnj9/HmIan3O6/ro+jd0YjefRszA4z4sXL0Isw9/fX3Vo1N8fFjY2NuohCELMlVuM23Ed0/fdRupEdtjyYxkksrdWwXeHBSfVKHWJTM5qlJmjzYpdQzQxy8YJaLoIsLIFPFy1ssUTcwB/L82IvulCIHOlKK8Tm180atRInTM2btyIunXrRv+GC0IskjhxYtWQhQ/6VC5fvhzz589XZvW8FgqC8PXDckUKXfTjmt22CAqkTRTuvH4BgVh89D42nX+Ccw/fBE3PnzYRelbKgliH124KXYYAwMIaKNULKNsHsE4Qcr5tvwOPT2nXeJYu2jggLqEnMOMF+nStWbNG/S0IgiB8ZWWMHPll6eKcOXPUCHF0wiyx27dvhxCqjClZsiR27/4wyvMBZplxOsmYMaMSrIznYQYWvbj0efj/mzdvcPr06aB59uzZo8qUmFkmCELs4+HjrzK3KHSRBgVTIaGtlfLrotD17K03siZzwKzWRYJbo19eDxydqv3dYLpWmrj7L2BSfuDIZE3oSl0E6Lzns4QuZpiy9MDW1laVLYrQJcQn6LvJ45rXRzZdCF3+LwjC18svVbMpX8uNPctEKHT5BwTip3/P4a/NV5TQxcTnIukTK5uAJZ2KwcoiyrcJUYODUzSWZyaXDleCQle2GkD3Y0DlQR8LXWf/AU7N58xA43mAc9jdJGMLZsvS4oD+vzSjF6FLEATBNIjyVYzBMU/oNKmnKNWgQQOVrksD96jCjon79+9XgfaRI0fQsGFDVR7UokWLMOfv3bu38veib9i1a9dUN0euT8+ePdXrLGv66aefVNowszAuXryItm3bKgN8rifJmTOn8izp0qULTpw4gcOHD6v3s1NjZDsxCoIQfTxy80TjGUew48pzWFuY4+9m+fFr9RwIMBjQfekZXHv2DkkdbbCgQ1E42Vtpb2L54p7/aX+X7AkkywnMqQQcHKeZ0LOkkd0WO+8CkkRtZJoiAEuW2f21fv36yoievoSCEB/Yu3evuv5R3Grfvr3qJswOx8zyEgTh68W4ubqlhTkG1smlDOjDIyDQoDobb7nwFFYWZmr+4/0rY/UPpdC5bCY14BSjeLkBSxoAt/cAazoHd1vMURf44ahWluiS+eP3PTkLbP5gK1LxDyBrVcQVzIblvQw7LtauXVsNHmTPnj3O1kcQBEH4wjLGggULqseYMWNUpxH6frAcgplRHMlgOURkYXBNYevVq1dImjSpKos8duyY+pswEKcQpnePKlWqlPq8AQMG4I8//kDWrFmxfv165MmTJ2iZ/fr1g4eHh1onZnBxmRTImJ2hs3TpUiVwVa5cWY3CsIvk5MmTo7orBEH4Qk7ee41uS07jlYcvkjjYYFabwiicPjHOP3yDgRsu4cIjd9hbW2B+u6JIk9jIi8PCCmi7ATg2HchcWRO6vN8ACdMANUcBOepE2ZtLL3mmyEURnG3Ce/XqJd2ThHhD6tSpVck+B3xmz56tshWlPF8Q4gf/HH+AK0/cMbhubthafciAjsA24Pc1F7D+3BNYmpthWstCqJY7fCuPaMfnHfBPE+DZRSBBUqDx3OBrtmNy7REWD45pnZUDfIBsNYGyfRFXsKkVB8oZL0yYMEENyCsvUUEQBMFkMDMYDwV9Juw0QkN5GtYHBAREz5qxc0z58qhYsaLK4DI1WB5Js36aV3NkXBCEqNNx4UnsufYCuVImxJx2RWBvZYEx269jxckHapDX0cYSU1oWRIXsyYK7K9KXS+fUAuC/vkCgP5CmKPDd0vCD5E/ADK4mTZoo4Z4lCdHtSSh8u5jK9YL2A02bNlX+mMK39d0L8ZszD9zw3ayj8AswYGyTfGhaJPyO4Qz7B6y/hKXHH4D2l1NaFELtfGHbh8QIvp7A0qbA/UOAXWKg/RYgee5Pv+/KBq3zMoWu1IW1zsp2cXMuo8DFcyn3JS0P2EleEL4EuVYIQszw2cX4zMpidheN31nW6ODggGnTpkXbijEwpH8X04MFQfj6ocH8kI2XsfdacIOICc0KoGPpjFj9Q0ncfvEeFcfvU8a6FLoaFUqNPX0rBAtd9PSYUhi4tFZ7fmQKsPknTejK2xRot/mzhC4Gq9OnT1fiOn3/KN6L0CXER1i+SKGLTWC2b98OLy8vNT0axrwEQYgjDt10VRnSFLpq5kmBJoXTRDj/oiP3lNDFJCQ2fIlVoYvX8fnVNKHLJqEmWIUldAX4B5c1kqPTgZXtNKErey3teh8HQhfPlVOmTEGFChWQOXNmFS+I0CUIghCPyhhnzZqlSgk5qqG3LGc78/Tp00frinEkVDxEBCH+MGDdRVUy8czdGxVzaAIWPbgG1c2lBLCuS07DNyAQOVI4Ylj9PCiW0Tn4zXf2aYEuSxUPTwSSZAN2DdVeK/87UOH3zypb5M1+t27dsHjxYlWyOG7cOFhbW0fbNguCKUHLAHrL0LeL5TY3b95EpkyZVGY2uzTSD1MQhK+DV+998L8tV7Hu7GP1nE1cxjTJF2Ep3Y3n7zBi6zX198DauVC/QGrEKgfGaaWLds5Ai+VA6kLBr3m/BS6v1cznH53UplnYAJY2gM9b7XnRzkDNMYB5xGWaMYFukcJ7IPoDc8DfyiqGfc0EQRCE2BW7aP5Ony16XOXPn//LPl0QhG+CvddfKKGLJRM18oT0Bdl15Tl+WKqNSlfPnVyVVFhbmofdhpzdFZsvBf5tDQT6ad2aPlPounv3rvIZvH79OpYsWYLWrVtHx6YKgsny888/q5uzBw8eqGYtOvSpY1OGzxW76K+5cOHCaFxTQRAi4ujtV+q6+cbTT13+2pXMgL7Vs8PBJvyw3sc/AD8uPwtf/0BUyJ4UHUpniD1/LhtH7W96apIqg4GEH5pCvbgKHJ4MXFkP+HmGfC8zufgwMwcqDwZK9/6s6/2XwoEBxgt37tzB8uXLlVeXIAiCEA/LGBkkczRDhC5BECJbvvjn2ovq7w6lM6JBweCR5O2XnwUJXbXypsDUlkZCFzsusuMSPbkodOVrrnl7XF6njfpaOwK1//6swHfr1q0oXLiw8khgUwwRuoRvgR07dmD06NFIkyZkmRObvdy/fz9aP2vt2rWoVq0aXFxcVKbJuXPnPpqHpUB8zfjBTMvQMQe7nNnb2yNZsmT49ddfVQe0iKAJP7PO6ZHFsk1mrr1//z7EPPQYZfkRm9ekTZtWxTWhoXcfM9g5T968efHff/999v4QhOgkc7IEymQ+Z8qEWNe9NIbUyx2h0EXGbruuuhu7JLD+ZAbYF8OOibv/AqaXAmZXDJ5Oj65GszShy+sNsPV3YEZp4PwyTehyyQpUHQb8dBHoexP46RLQ6wzQ5wZQ5qc4EbpYvVKkSBH4+vqqLu4idAmCIMTjzC5eHNnlkCf8Fy9eKDNnY9q2bRud6ycIwlfO2G3X8MTdG2md7dCnWjY1jUH6wiP3MOK/q/APNKBOvpSY+F0B1S49SOhaVA94cIRnHaDKEG1E9819YPcwbZ5qwwCnqJVg8Hw1fPhwDB48GLVq1VIZXSzfEoRvAZbhUDQKSxyKaldGV1dX9OnTR5VEsovpoUOHVKdmdjtmKTA/i953LJukV1h48LVhwz78poEQ68eGNxS6UqRIgSNHjuDp06cqxmB22ogRI8JdJoUuzrtz5074+fmhQ4cOQeVHhCI3hbgqVapg5syZuHjxIjp27KiEMc5H+HnMYh85ciTq1Kmj3tugQQPl0WPcAVoQ4oJkjrZY8X1JZE3uACv9uvkJX6+5h+6qvyl08f0xxqEJwC6jxlJmFoD7I8Dpg8jO+4ZzS7V5PF21aeygXOpHIG2xOBG0woLnn0GDBqlzTcOGDVX2qjSZEARB+MowRJGNGzcaHB0dDWZmZgYnJydDokSJgh6JEyc2fCu4u7vTOVP9LwhC2Jy698qQ4ffNhvS/bTYcvPFSTbv78r2h6YwjahofvZefMfj5B3z85h2DDIYRaQyGa/9pzwMDDYZF9QyGwQkNhvm1DIaAMN4TAW5uboY6deqoc9fQoUMNAVF8vyB87deLmjVrGgYMGKD+dnBwMNy5c0f9Dpo2bWpo3LhxlJbVunVrQ7Zs2Qz79u0zNGjQwLBnzx5Dv379DF5eXiHmu3v3rtr2s2fPfrSM8uXLG3r37h3uZ/z3338Gc3Nzw7Nnz4KmzZgxw5AwYUKDj49PmO+5cuWK+ryTJ08GTdu6dav63T9+/Fg9nz59uopXjJfx22+/GbJnzx70vFmzZobatWuHWHbx4sUNXbt2NXyN373w9bPj8jPDgRsvovw+13fehmLDd6rr7R9rLxhilEenDIYhibXr9LIWBsO55QaDx6vg1/n3kkba63xMKWIw3NptMDVevnxpqFKlijr/jB492hDI+EMQYhC5VghCzBDlMkaO5HIElCUBzPByc3MLenB0WBAEQWfhkfuqoRK7Q5XK7IKFh++ixqQDOHHvNeytLfC/BnlUNyiV0eXvA3i8Ct55lQYCPxwGstfUnh+dphnVW9oC9SYD5pE/fbFkiWUIzD7ZvHmzGq01j8L7BSE+wFK92bNno2bNmqokp1+/fipL6cCBA6q8MSqcPXtWZVmxiykbylSsWFEtgyV/UYGZYEmSJFHr0b9/f3h6Bnv2HD16VJUPJk8e3GW1evXqKjPr8uXLYS6P72GGFn/vOszg4u/9+PHjQfOUK1cuRDMKLpf+fYxl9Hn4PmM4D6dHhI+Pj1o/44cgfCmHb7mix9Iz6LTwFM49fBPp9/kHBOLHFWfx/K0PMiVNgAG1c8Xcl+HrAaz9XrMdyN1I89fM3xyw/9Bshsb0sysAt3YBlnZAteHAD0eAzJVgSrBypVChQjh//rzKDuV5MkZLPgVBEATTKWN8/PgxfvzxxzBLIQRBEIyZ0Cw/8qdxQuNCqTF002UsOqr5ApXM5KJKKdImsgHuHQQurgKubABcsgAdtgGW1oCFJZAondZ+fN9IYP+Hm/FKAwCXzFG6mWapVLZs2ZRnEbvPCcK3CAWlGzduYOrUqXB0dFSDVjRd7tGjB1KmTBmlZZUuXRoLFiz4Iv/Oli1bqk7OqVKlUoL0b7/9pgQn+n2RZ8+ehRC6iP6cr4UFp9PbyxhLS0s4OzsHvYf/Z8yYMdzlsrQ5vM8O73N1WPY4dOiHTrGC8IUYDAYsOnIPI7deU92K2cQlT6qEkX7/2B3XcfjWKzW4NLN1YdhZx2AXw+OzgFe3AMdUQJ1QfpoXVgEbewH+XkCi9JoQliIvTG1fz5kzR3VmLlCgAFavXq38/ARBEIRvSOziyOapU6fkhlEQhE+fYCzM0blsJvy984YSuhj7st15+5LpYX5pFbBgCPDuSfAb3j4Bnl8EUhfWngcGAFv6AKcXaM/L/w6U7BmpPU+vnr59+6rOsW3atFHePCLSC986zML6888/Q0zz9vbGuHHj1O8lsvz999/Ky4YdHm/fvq0M6GkuH9pgPiL+z95dgEdxdWEA/uIuOMHd3d2Le3B3dyhSKNAfp3iRAi1W3N3d3d2CO4EQIbrzP+cOCQmahITY9z7PktnZ2clkl2Tunjn3nMD6WEIyuCTgVr58ebW/9OlDH9COTiQ7TTpbBpLMLn5gpvB48c4b/VZfxMGbL9X9ClmTYFrjvB9rW37HtktPMfvAXbUsF5cyJfnQETGyFJNgljeQqqheiF74vQd2DgFO/aPfT18ecP7nY7ZXNPH+/XsV9JcAfufOnTF58uQw1zEkIqJYEOySYrHSDenq1atqcCqFYoOrWbNmRB4fEcUwx+++xqYLTzCsRnbVWXHeYRdM23NLPfZHzexokcMKWNkMuLFFf4KlA5CtFpCzAZC6GGD84cqznzewth1wbZNepL7aBKBgu1AdgxSnlsLY0mlRsli6dOnCaQgUp718+VJN45OpexJQMjExUQHhmTNnqmwk6XAYlmCXjY2NavYgNyncLlMjJfAl0wWDB7HConDhwurr7du3VbBLCtPLlKLgpBi+kMe+RNZL85zg5GeTMguBz5Gvgfv52n6/ts3Xvm8g+YDMD8n0o3ZdfY7+qy/gjZcfLEyNMbhaVjQvkjrU57HbL9zRb9UFtdy+ZFpUz5Us8t8UEzOg7G8f7z+/CqxpC7y4qt8v0UfPzA48x0cTLi4ucHZ2xrVr17Bw4UI22iIiisvBrsCuSsG7JwWSk7B0LyGiuOmhqxe6LDkLV09fJLW3RDJHK/xvsz7Q7ftLJrQomga4sV0PdBmbAWUG6B2YTD+5gipTF9d10ANdJuZA3blA9tqhOoYjR46gXr166kP3gQMHUKxYscj4UYliDKlVJx0FJctIztNSz0oyGCRIJVP8hg8fjpYtW4Z7/1Ijq2PHjmqa8KFDh8Id7JLsMBE4pbJo0aIqmCbBq8CpiVJDRzqiZcv25dpD8hypJ3rmzBnkz69niO7du1d1Yg0Mpsk2kt0mwb7AC3ay38yZMwd1Z5Vt9uzZg169egXtW7aR9USR7fR9VxXoyuZkj6mN8iBjGLKy3nj6osN/Z+DpG4Ai6eJjQOUskXeg3m7AoYlAmd8AM8uP52/J5NoxGAjwAWwSA3VmARlC1sCLDrZt26a6t8rvvdTjk+mLREQUe4S5QrMMGL92Y6CLKO5y9/ZD+0WnVaArR3J7pHS0RP/V+ofXtkluoVu5DPqGmSsDZYcAHQ8ApX79PNAlDk3Qa3hJQKzpqlAFuqTehkxZLFOmjKrPJR92GegiAoYMGYKqVauqulgyxe7UqVOoU6eOmoYoWdoy9dDKyipML5VkcUkw2c3NTZ379+3bp+4HBpgkk0qCV7J/IbW45H5gzSuZqjhixAj1e3rv3j1s3LhRZVRI4fhcuXKpbSpWrKiCWjINWYpF79ixQ/0sMt3oa9lTWbNmReXKldWFOckKk+B3t27d0KhRI1UbLLBWmGS4tW3bVhW6X7FiBaZOnRpi+mHPnj2xfft2TJw4EdevX1cBQSnhIPsiimwDK2fB9CZ5sb5r8TAFut56+aLpPydw96UnnBwsMb1JvlBPewwzH3dgsTNwZCqwsdvHQNf2QcDWfnqgK8MvehH6aBboks8sUltPZqvIOEF+txnoIiKKhSKpy2OsxxaxRB+9eOetVZt2ULU2zz9il7bwyF0t3YCN6n7f337VAobF0zQfj9C9ZNe3adowB70t+al5oXqKh4eH1rRpU03+pPXu3Vvz9fXl20PRRlSfL+LHj69duXJFLXt5eWnGxsba+vXrf2ifkyZN0vLly6fZ2dmp/aVIkUL79ddfNX9/f/X4/Pnz1c/86W3YsGHq8QcPHmilSpVSx2ZhYaFlyJBBPf/T1+jevXtalSpVNCsrKy1hwoRa3759NT8/v6DHXVxc1H737dsXtO7169da48aNNVtbW83e3l5r3bq15u7uHmK/Fy5c0EqUKKG+d/LkybWxY8d+9jOuXLlSy5Qpk2Zubq5lz55d27JlS4x77ynm2HvtufbeV//9CY83nj5a1amB5+Gd2s1n77RII+fzfyvp5+kxqTTtyQV9/aHJ+jq5HflL0wwGLbqRvw9Vq1bVjIyMtBEjRmgBAQFRfUhEPFcQRRIj+SesATK5eivFbGV+u5Arr1LHq2TJkogrZDqIFPqVq9oypYIornrw2gvN553A/ddeSGBjjtbF02DSzhswwAjOJgcxvoQxTFIVAjJVBsy+kz3y8iYwtxzg6w4UaKt3dPoOqe8jHeUkU+Tff/9VGRxE0UlUny9kSm/wLoXSiVGyrCKqCHyrVq2wYMECRAXJKJPf/7t37wZNQYxOovq9p+hPhuH/Hb+PoRuuoHSmRJjbooCqdxkWbl5+aPbvCVx67KbOw8s7FAlTRliY+HoCSxvqnZQtHICWG4BkeYELy4F1HfVtKo0GinZFdHPu3DlVn0umOi9dulRlgRJFBzxXEEWTml2LFy9G69at1eCyR48eap1ME5CCtzLYlekBRBQ3BBg0tFpwUgW6Usa3QrPCqTFu+zUV6Gpgsg9ja2aGcWG9zt93vX8LLG+sB7pSFQMqj/3uUzZv3oxmzZqpD/EyZSl79uw//kMRxUIynTBwCqF8uJZphZ6eniG2CZw+GJNs3boVv/32W7QMdBF9z0t3Hwxedwk7r+rNENInsoWZSeiK0Ae69vSdKkZ/5ck7Feha2j4SA11SdH5VK+DVDcDcDmi+Vg903d4NbPgQ3JKOydEw0CWfUaTTolygl3p8adOmjepDIiKiSBbmzC6phyHFZ6Vex6dtyOfOnRuU7RXbMQJPcY3BoGH9+cc4//AtiqRLgKo5nYK6L47ddh1tiqdBz+Xn1VylxiZ7MaqUFYwrjQzdzn299NofD44C9imADvsB20Rf3VxqBEmTDLnVqlVLdVCS7Ami6Cg6ZHZJYfovne4D17PBTOx87yn62nrpKYasv6zqXEqAq1eFTOhSJn2oOy5Kfa7Ju26qrDCDBsRXga7CyJI0kv6fGQKAGYWA17cB26RAw/+AlIWAx2eBBdUBP08gZ32gzhz5o4PowsfHR9Xgmz17Ntq0aYMZM2bA0vJDMX2iaILnCqJoktklUwVq1Kjx2fqaNWuqq6tEFDtN2nUT0/fdVsv+Bi0o2CWBr//aFkLlKYdUoKuexUmMyvYKxr/MD92O/X2BlS30QJeFPdB42TcDXVL4WronSbHqkSNHYtCgQerDPBF9mYuLC18aomhCuiUOXHsRO67o2VxZktphYoPcyJ4sdBdsfPwDsPL0I1UuQDo2imo5nfBbtaxI7hi2RhNhYmwC1JwOHJkC1JoB2CQEXt8BltTXA13pygC1ZkarQNfDhw9Vd2aZti0X5Nu1axfVh0RERNE52JUyZUqV/pshw4fOah/s3r1bPUZEsc+mC0+CAl3Ni6RGuax67R9F0zBhxw08fvseKeJZ4Y+27WDskCh0A165Uiw1Pm7vAkytgCYrAaevT6WSAatMoZYsCemUJt3aiOjbUqdOzZeIKJqwtTTF1afvYGwEdCmTAT3KZwxVja73vgFYdvIB5hy8i2fvvNW6TElsMbxGdhTLkDByDvbhScDtIZDDWb+fuqh+Ex4vgMV1Aa9XgFNuoOFiwNQc0YV8VpEantbW1qrcSoECBaL6kIiIKLoHu/r27atqdcmHTmnXK+QkInPhpXU3EcUulx+74dfVF9Ryh1Lp8FvVrB8f9HiBU4uHYNG9mjIhCmPr5oJNwlAOugP8gS19gCtrAWMzfaAcOIj+gkWLFqFjx46q3sbevXuRJk2aH/7ZiIiIIpNME56482ZQUMvMxFidKxPYmod6yuHCo/cwdc8tNeVRJLG3UIGypoVTwdQkEjKp5ELUoYnA/rGAqQWQJCeQKNPHx33cgSX1gDf3gHhpgKarAYtIqhMWjtd73LhxGDx4MCpUqIAlS5YgYWjHJUREFLeDXVLcMWnSpJg4cSJWrlwZVMdrxYoVqnYOEcWu4rkdFp2Gt59BdYkaUDmL/oDU/rmyDt7bhmCAa29oUpA+nxNKZPzOgPLNfeDOHuDOXuDuQcDHDTAyBpznAhkrfPEpvr6+qkbgzJkzVdc3+WplFYlTNYiIiCKIFJ+XzOgkDpZoVjiVqslVPAyZWJsvPsGwjVfUcqr41uhUOj2c8yeHhalJ5LxHXq7A8qZ6aQGRpRpglyRkN8YVzYCnFwDrhECztYBtsGzvKCRZ3zJOWL9+PYYMGYLhw4fDxCSSXiciIop9wS5Rp04ddSOi2G3Jift44uaNdAltMK1xXpjIvIuXN4Gt/QCXA/jLrwHuasmQyMYEg6vn/PJODAY9wHVsOnB3f8jHrOIBlccB2b/89+Tx48eoX78+Tp8+jb///ls1xwht8V4iIqKoJFMP/7fpqlp++vZ9mM9fT93eY/C6y2q5XYm0GFglS+RkcgXP6FrTTg90mdsC1SYBuRt+7Jh8ci5wfCbw3hUwswGargISpEd0cPnyZVXm4MWLF9i4ceMX6wsTEVHcEupg15s3b7B48WK0bNnys45CciVFphh96TEiirl6lMuoplxUzpEUDhbGwJ7/AUemQQvww1KtEv426NmcI+vmgYO12ecdFi+u0AfGr27q6ySLK2URIH05/ZYsj1709gsOHDiABg0awMzMDIcOHULhwoUj/eclIiKKKNP33VL1LKVwfLdyIWvdhqYDcr9VF+D23g+5UzhgQGQHusTBP/WLU8amQPqywJNzwMvrgK8HcH4Z4OuubydTF2v+BSTPh+hg2bJlqvh8+vTp1cWxT+sKExFR3BTqYNf06dNx8eJFdO/e/bPHpK22fBiVtqkyR56IYi4ZYBs0TQ2qjY2N0LXsh0HjjW2qhsdTLT76mw3CIY/kanWdvMlRKXvSjzt4+xA49Q9wdiHw/o2+ztwOyN8SKNQBiJf6u/U2Jk+ejP79+6NkyZJqinTixNFjigQREVFo3HnpoYrJi6E1ssHaPGyTKeYdccGR269hZWaCyQ3zqAtPkSbA70ONrjH6fYM/cG3T59slzgaU6KNnY5uEa3JIhPLz88Ovv/6qagY3a9YMs2fPVgXpiYiIRKjPVGvWrFF1ur5GCkf369ePwS6iGEwCTX9suqI6Pcm0xRA1QTJXwbr8izD0pDHcPYxhYWqM/pWzoHWxD4XiPV4C234Frm4EtAB9nWMqoHAnIG9zwPL7WZ8eHh7q6qwEuOTvyZgxY2BqGvUDaqKYKm/evKGeOnX27NlIPx6iuHIuHb7xCvwCNJTJnAgVswWreRUK156+w/jtN9TykOpZkS6RbSQdKYDnV4CljQC3Bx8zsHM20Kcn+r3XbwY/IH15IFPl0HVa/gmePn2qyhycPHlSXZDv0qULyxwQEVEIof4UeefOHWTMmPGrj8tjsg0RxeyOUQuP3Yd8Nj7p4oqSGRMFPT7n4B2MPqL/ycid0hET6+dGhsQfBuCuLnoLclf9KjbSltKDXGpgHLrisDdv3lT1Nu7fv49Vq1ahXr16kfBTEsUttWvXjupDIIoTzVwkQFUqk37OlIyss/ffqO6Lf9TMHqYgzDtvP/RYdg6+AQaUz5IYTQqlirwDl3P3f3UAj+eATSKgYDsgf+uQBemjoYMHD6oyB1J8XkoeFC369U7OREQUd4U62CUnlCdPniBVqi+fdOUx42hytYeIwsYvwIChG65g2Un9yu6IWjn0QJd0XTw8Gcu0XzB6q/5YlzLp0eeXTB9rhzy9CCx2Bjxf6JlcDRcDTrnD9P03bNiAFi1awMnJCSdOnEC2bNn4FhJFgGHDhvF1JIpE+2+8QM/l55HA1hx7+5ZR64qmT4Dk8axQJYcTUiewCfW+fPwD0HHRGdx64YGEthYYVy9X5GUruT8HFtbQA12JsgJttulNY6L5RbkpU6aoqYtS5mD58uVIkiR6B+aIiCjqGIdlKoS08v2adevWqW2IKGZ56+WLlvNOqkCXjKmH1ciGZkU+1NU6uxCbdu7Ab1vvqbudy6RXUxeDAl0uB4H5VfVAV5IcQNtdYQp0BQQEqKnPkn1SoUIFNR2BgS4iIooJ9S2n7bmF1gtOqSLy9pYfm7RI5+K6+VKoc2ZY9td35QUcu/saNuYmWNC6oAp4RQrprLigKuD2UL+fJFu0D3RJmYNGjRqhT58+6N27N3bt2sVAFxERRUxmV7du3dRJJkWKFOjcubPK9Ar8sDpz5kxVUHrp0qWh3R0RRQN3X3qg7cLTcHnlqQbXUqerfNYPV0kfn8G+TUvQ268bNBijaeFU6F8p88cnX1wJbOgKBPgCqUsAjZcClg6h/t6vX79GkyZNsHv3bowdO1YVpI+0K9hEpM7Xcq5euXIlHjx4AF9f3xCviqurK18lolCQ4FafFeex5/oLdb9J4VTqQlFwnUqnD1PG0sgt17D54lOYGhvh7+b5kSN56M+nYSKdkudXBl7f1u87pATKDEJ0duPGDdSpUwcPHz7E6tWr4ezsHNWHREREsSmzS04s8mG0R48eiB8/vsrikpss9+rVS11pYY0dophFaoK8eOet2qKv7lzsY6DrxTXsnjcMnby7wB+mqJXbSU1tVMEogwHYMwJY214PdGWtATRbE6ZA15kzZ5A/f371defOnRgwYAADXUSR7I8//sCkSZPQsGFDuLm5qfO21MmTEgTDhw/n608UCt5+AWjx7wkV6JKaXOPr5cLoOjlDNnQJgwCVIXZbdV8UE+rnDlEvM0L5+QBzSqlzvJI0F9DhAJDw6zV5o9ratWtRsGBBtXzq1CkGuoiIKNTCVGRr1KhROH78OFq1aoVkyZKp+jqtW7fGsWPHVGbGj5DnywdpCZx9ixSuzpIlCywtLZEzZ05s3br1s6tjQ4cOVcdmZWWlpkbdunXrs6vXTZs2hb29PRwdHdG2bVuVHk0U12RJao9/WxXE+q7FkdXpQ7dEVxesnDMKHT07wAfmqJA5ASY0yANjYyP9ivDqVsChCfq2xXsB9RcBZpah/p7z5s1D8eLFkThxYtX9rXz58pH00xFRcEuWLMHcuXPRt29f1eW0cePG+Oeff9Q5U87tRPR90iXxwiM3xLM2w9rOxdCgQMpwvWwyXt13/QWqTD2IybtvqnWDq2ZF7bzJI+dtcHsMTMkBvPowJk5fDmi3G7BJgOjI399fXWSXi+2VK1dW9Txl/E9ERBTh0xgDFSpUSN0iklypmT17NnLlyvXN7Y4ePaoG52PGjEH16tXVtEmp9SMfmHPkyKG2GT9+PKZNm4aFCxcibdq0+P3331GpUiVcvXpVBciEBLqkZbHM9/fz81MBuw4dOnAaJsUJK08/RPpEtsifWq/PUSTdx4Gu5vYEM2dOwp8ejdV959yJMLZBAZhJjS7PV3oh+qfnAWMzoMZUIG/TUH9fHx8flRk6Z84ctG/fXv2eBv5OElHke/bsmbpIJGxtbVV2l5DzqZwriej72pRIgzMP3qBfxUzhnmp445k7/rf5iuraKBytzdC3YmY0D6yXGRkOT9bra4rCnYHKY6AKdUZDz58/V6VTDh06hIkTJ6oaXSxzQEREYRXl7RMlo0qCT3K1OV68bxfHnDp1qrq6I11YsmbNihEjRiBfvnyYPn16iC4tQ4YMQa1atVTwbNGiRapTZGBx/WvXrmH79u3qanbhwoVRokQJ/PXXX6qji2xHFFt5+vhj0q6b6L/6ItouPIWnbu9DPO7l64/hux7jT4/K6n6nokkwoVFBPdDl8QJYUF0PdFnFB1puDFOgS+pslCpVSgWh5XddAl4MdBH9XFJzUy70iPTp06spxIEXnCwsIqkQNlEskyKeNdZ1LhbuqYYv3X3QYPYxFegyNzFGh1LpcKBf2cgNdB2bAZyaqy+XGwZUGRttA10yW0TKHMhF6j179qjp1gx0ERFRjAx2de3aFdWqVVPTDUNzAvx0O8nakvXCxcVFXbkOvo2Dg4MKagVuI19l6mKBAgWCtpHtpWaJpEh/Kyvl3bt3IW5EMcFDVy+M2nIVRcbsUZ2jRKOCqZDU3jKo1fmCIy4oNX4/Fp7Wr/r+Xt4JA2sV0AeY7s+ABdWAl9cAOye942LqYqH+/nv37lUDV/ndPHz4MNq1axdJPykRfYsUeJYPj6J79+4qmytjxoxo0aIF2rRpwxeP6BsBqr3XnwfdV9P6w0nOx1LgPktSO+zpWxq/Vc0KB+uPnRwj1MVVwMEJwI7f9PvlhwGl+iA6kgvWM2bMQOnSpZE6dWo1a0OWiYiIfto0xogk2VRyMpOryqEhH5aTJPlQQPsDuS/rAx8PXPetbaRWUHBSu0QK7Qdu8yUydVKK+xLFFH4BBgxccwlrzz2Cpunr0iSwRrdyGVEvfwp1f8P5xxi/8Swee+lx71TxrTGkWlZUzJ5Uf8K7J8DCGnrXJvvkQMtNQIL0oR64TpgwAQMHDkS5cuWwbNkyJEyYMJJ+WiL6nuC1NaVIvXyglPIAEvCqUaMGX0CiLzAYNPRacU5lYv2vVna0KJom3K/TkduvsP78E5VUNc45F1LGt4681/z6FmBdR0AL0O/nbw2U6I3oyMvLCx07dsTixYtVIF7GDubm5lF9WEREFMNFWbBLpjX17NlT1c2KCdOZBg0apFKpA0lmV8qU4StKSvQzHL/7OijQVTJjQrQpnhalMyUKuiK94dxD9FxxUSV4JoErepRKjgaVSuvTFkXg1EXXO3prcgl0xU8bqu/t7u6uMkWkRbgEu0aOHAkTk/B1qiKiiHHw4EEUK1ZMXeARRYoUUTcpBC2PyVRjIgpp9sG7KtBlZWaCYukT/FAXxyHrL6tlmbKYO6Vj5L3Ud/cDq1p9DHQlyBRta3TduXNHdYW9ffu2aqLRpEmTqD4kIiKKJaIs2HXmzBm8ePFC1dwKFBAQoAbcUoNLpg1++uE4adKkqmhlcHJf1gc+HrhOujEG3yZPnjxB28j3DU4G+tKhMfD5XyL1TFjThGISqSfyd7P8MDU2QvmsIbMdb12/hEErbwMwRwuTnfituD0sKzeRuRn6Br6ewNIGHwJdqYBWm4F4oasncv36dTVd6vHjx6pluCwTUdQrW7asqtn1aXazFKqXx+QcTEQfXXj4FhN33lDLf9TMjgyJ7cL98vx94A5cXnkikZ0F+lXKHHkv88OTwLImQICvft/IFKj3D2Bmhehm06ZNaN68ORIlSqQ6wgY20CAiIvppwa68efOGujikTEsMjfLly+PSpUsh1klXRGkrPGDAgC9mgRQtWlTVG+nVq1fQOskMk/VCui9KwEq2CQxuSQaW1OLq3Llz0D7evn2rgm1SRyiwppDBYFC1vYhiw5SLwOytSoHTEYMeDIDnoZnovN0CXlpyFDO5jmHORWCSr3GIbbC6LfDknF6MvsX6UAe61qxZg1atWiFVqlRqenLmzJE4oCeiMJGpxV86l79+/Ro2NjZ8NYmC8fDxR8/l5+Bv0FAtpxPqF9Cn/4fH3ZcemLnvjloeWj0b7C0jqUbXk/PA4nqAnydgZKJndpX/HXDKjehEAuvDhw9XWd/SUEqa10iNXSIiop8e7KpduzYimp2dHXLkyBFinQy2EyRI8Nn6QDLtUYpVShtiKWovNb9Onz6tOrsJGcRLIExOnlKDRIJfUoA3WbJkQT+DdHGUjo7t27fH33//DT8/P3Tr1k21OJbtiGJykOvw7VcYv+O6yuiSjlEhBPhBW1gLA2/nxW2tGJKYemJal7owSRZsaqLMedzWH7i5DTCxABovD1WNLsmOHDx4MMaPH4/69etj3rx5sLW1jYSfkojCSqYIBZ4jJRgdPEtZPnRevHhRTW8koo+GbbiCe6+9kMzBEqPr5Ax3R8BLj9zQf81F+AYYUCpTIlTP9XHmQYR6cw/4rw7g4waY2wK+HkDqEkCx7ohOJLguUxV3796t6uH2799fNYkiIiKKkmDXsGHDEBVkUH7v3j3s379f3ZfB+NKlSzFkyBD89ttvKqC1fv36EMExOWl6enqiQ4cOKoOrRIkS2L59e4i6YFITQAJckl0mJ1hnZ2dMmzYtSn5Goh9x0sUV2y8/w+Unbrj25B3cffzV+hn77mBM3U+mA5iYYZFta2wyWMPESMOMtuWRMFmCkIGuI1OAU//Ix2LAeS6Q6vvZji9fvlTB4gMHDqiismwTThS9BGZMSGaXXGiysvo4nUmKQEvdLrkARES6M/ffYM3ZR5Ak6SmN8oarW+Lzd974c8cNrD7zSN23szTFiFrZwx00+y77FECG8sDNHYDPO8DCHqgzCzCOPvUy5QJ1vXr11Dh9586dahxOREQUWYw0Gf1GU5LFJXVEJNU5upHpkfIBQmqd2NvbR/XhUBwjv7Yz99/BhJ03gjotCnNTY1TImhgT6+eBlfnHAe6VJ26YvOsmdl/T69VJx8V2JdPpD/r7ApfXAMdnAM8+TC2uNBoo2vW7xyFTFSVY7O3tjRUrVqjfVyKKnucL6Sjcr18/TlmMg+89hZ10K37y1hudy4SuA3Fw/x27hzHbrsPLV6+DVydvcvSvnBlODpFYN8vvPbC8KXBnD2BqqWdmp48+5+R//vkHXbt2VWVGpHkNmzwRfcRzBVE0KVAvUx4mT56MlStX4sGDB/D1/VAA8wMp9B4RZGAoHVq2bNkSIfsjik28/QzYeP6JCnTVyJ1MdVnMkdwe6RPZfuymCOD2C3dMWn8UW+/qGV9ylbpl0TRom9NMb0v+6DRwfing8Ux/gqkVULIvUKTLd49h7ty5KkNSavrJwDVFivDXMyGiyBdVWdpEMVGtPMnD9bzZB+6oQJfIm8pR1ejKmyoeIsWL68DpeUC534GVzYG7+wAza6DJCiBt9OiuKhfDZKzw77//omPHjpg6dSobPhERUfQMdsmVYbk607dvXzWdUOr0yFRDmU44dOjQCDswuRL66JGe+k1EIUnW1j8tC6gaXY0Lpfriy7P76nN0XXIGPgEajGBAjXQm6GG5FRmubwXOvg65sZ0TUKgDkL8VYB0/1APXTp06YcqUKRy4EsUA0plYMrukiYt0Jf40sZvdGCmuW3riASpmT4KEth/r2oXF3IN3gwJdvSpkRM/yGSNv2qIUo5caXe9dgZvbgbf3ATMboOkqIE1xRAfy+UCmLV6+fFnV8pRGVERERNF2GmP69OlVfSspEC+1P86fPx+0TtoGS02tuIDppvSzPXPzxvmHb1E5xycdFr9g1emHGLjmIgI0oKTxRQyx3YLMvsG6nxqbAomyAElz6dMcstUGTM2/u9/79++raYsycJUGD1JXj4hixvmiSpUqKiNbgtVOTk6ffQiXrmgUO997+j6pf9lp8RkksbfArj6lw9wx8Z9DdzFyy7WgQFevCpki72V/cAJYUk+vzWVuA/h66kXpm60BUhVBdLBjxw5ViF7+/0unZskCJ6Iv47mCKJpkdj179gw5c+qFr6XbmgzgRPXq1VXnQyKKeG88fdH83xO4/dIDkxvkQe28yb95ZXnUVn3AXc/kAMaazoWpr0EvOp+zHlCoI5A0J2D2sWlDaOzatQuNGzdWv/dHjx5Fvnz5fvjnIqKf5/Dhwzh06JCqmUNEH9175Yn+qy+o5dp5koc50CU1ugIDXT3KR2Kg6/lVvZHMpdWAFvAx0GXpADRbC6QogKhmMBgwevRoNdtDup8vXrwY8eN/O2OciIgoWgS7pC7P06dPkSpVKpXRJd1U5EOvFKoO3s6ciCKGh48/Wi04hVsvPJDU3hIF0ny99seEHTcwfd9ttdzBZDMGmS6FSt7IVgsoMwhInDXM31+SP8eOHaumLf/yyy+qm2mCBMG6OBJRjCAFoaNxTxqin+61hw9m7b+D/47fh4+/QdXY6lcpc5j2ceHhW/yx6apa7lY2A3pXyBg5B3t2EbCx+8f75naArztgnRBosV6/iBXF3rx5g+bNm2Pr1q2qRqBcBJeu50RERDEi2FWnTh1V76Nw4cLo3r07mjVrpmr3yNSI3r17R85REsVRPv4B6PjfaTWYjmdthsXtCiFFPOsvbrvl4tOgQNdA06XoZLoZMLEAGi/T25GHM626ZcuWqiafBLukM6qJSfRpY05EoSf19QYOHIjZs2cjTZo0fOkoTpux7zZm7rsNzw8dEwukjodpjfOGaPLyPZ4+/ui14jz8DRqq5XRC34qZIq9GV4YKepfF9OWApxeBd4/0epstNgCJwhagiwwXLlxA3bp1VcBLmkvJtGkiIqIYFeySDI9ADRs2ROrUqdWUpowZM6JGjRoRfXxEcdaLd97ou+oCjtx+DRtzEyxoXQgZEtt9cdtHb7wwcO1FtdzVfAs6GX8IdElHpnC2Hr969aoKbsvU5Q0bNqBmzZo/9PMQUdSSc7aXl5fKyra2toaZmVmkdFMmignevfdTgS7pZNy3YmaUyZQozIGqEZuvwuWVJ5wcLDGqTo7IC3QJ+2RAj3PA6jZ6oMshJdByIxA/HaLaf//9hw4dOiBLliyq5EG6dFF/TERERGEOdh08eBDFihWDqan+1CJFiqibv7+/eqxUqejR6pgoJnP39kOVqYfw2tMX5qbGmNuiAHKndPzitv4PT6PnJh+4e/sjr/VL9ApY/sOBrpUrV6JNmzZImzYtTp8+rYLZRBTzM7uISNexdHo1bbFS9qThClJtv/wUy089VKUCJjbIDUfr7zd5CbPdf3zoltxev7/nf8CDY4CF1OhaE+WBLl9fXzWrY+bMmSoLfNasWbCysorSYyIiIgp3sKts2bKqZlfixIlDrJdC9fIYW5cT/Tg7SzPUK5ACR2+/xoT6uZE56RcyugwBwK6hmHbwKc4EOMPOxA/T/EfCzNxMD3SlKxPm7ytB6wEDBmDSpEmqGP3cuXNhY2PDt5QoFpAPo0RxuSzAgiP30KZEWjVVMb6NOSrncAp3d+SBa/UOxx1LpUex9Akj+GgB3NkHHJ6kLyfLA9w7BFxYBhiZAPXnR/nUxUePHqF+/fo4c+aMCnJ17NgxcjPbiIiIIjvYJcVtv3Qye/36NT8UE/2AB6+9YGtpqgbgos8vmdCvotGX64f4eQNr2+HElVuYHjBErRpp/DdSmrwG6i0JV6Dr+fPnapqTdGyTDJAePXpw4EoUy9y5cwfz589XX6dOnaouXG3btk01ncmePXtUHx5RpPALMKDrknPYfe05rj9zx+SG4e9IajBo6LvqPN56+akpkHKujnBersD6LvpyAZm2+ETP6hJVx4e7DmdE2bdvnxovSGMq6fAqdXyJiIhibLBLik4KCXS1atUqROdFyea6ePGimt5IRGEnNT+azD2upkEsa19YfbUw/UoheG83YHlTvHC5iJ5+o2CAMZyND6CWyTGgygQgS9Uwf//jx4+jXr16KrNr7969nI5MFAsdOHBAFY0uXry4KjswatQoFeySwtLSaGb16tVRfYhEkUI6FUugy8LUGPXzp/ihfc0+eFfV0rQyM8HURnlVqYEIZTAAm3sB7k+A+OmBQh2AueX0xwp3Agq2Q1SRC95//vknBg0apGZzLFu2DIkSJYqy4yEiIvqWUJ+hHRwc1E1OdHZ2dkH35ZY0aVJVmHLx4sWh3R0RfXDnpQcazTmGp27e6uqzb4Dh66+N+zNgfjX4uBxDJ/9+eKbFRzqjJ/jDbCFQtNvHuh6hJL/PMv1Aau1Js4mzZ88y0EUUS0knxpEjR6oC0ubmH+sLlStXTgW8iWKjuy898O9hF7U8tVEeFMsQ/imH5x++xcSdN9Ty8JrZkD6RLSJUgD+woQtwdYM+XbHuXGDPCMDPC0hVDKg4ClFFujPLRTEpdSC3HTt2MNBFRESxI7NLpj0IaVfer18/TlkkigC3nrujyT8n8NLdB5mS2GJp+yJIaPsxazIEv/fA/KrQXt/B7+iBswHpYQcv/GM2AbbZKwG/jAjT937//j06d+6MhQsXolu3bpg4cWKID8BEFLtcunQJS5cu/Wy9ZHe9evUqSo6JKLKN2nIN/gYNZTMnCneNrsDGMT2Xn1P7qpbTCQ0KpIzQ44SmAes6AJfXfAh0zQG8XgE3tgDGpkD1SYBJmKuPRIgrV66oGR7SnXn9+vWoVatWlBwHERFRWIQ593rYsGEMdBH9IMmoWnz8PmrNOKICXVmS2mHZtwJdwswKKNYdCyyaYKVPERjDgOlm05AuZXKgzmzAOPS/zi4uLmoqk3RdXLRoEf766y8GuohiOUdHR9Vg5lPnzp1D8uTJo+SYiCLTwZsvsef6C5gaG2FI9Ww/tK+hG67g/msvJHe0wui6OSO+pqXsL2NFwNQSaPgfkKU6sK2//liRzkDirIgKK1asUDW55GKYdGdmoIuIiGJtsEuKWDdv3hzJkiWDqakpTExMQtyI6Pv+2nsbQ9ZfhpdvAAqnja8yuhJ8K9D1wRHHGhjpUUMtDzJditK2D4H6C/VAWCjJ1IP8+fOrDqrHjh1Tv89EFPs1atRITT+S7Az5oG4wGHDkyBGVrd2iRYuoPjyiCL+oNHbbdbXcomiaH5pyuP7cY6w79xjGRvpUSAcrM0SK3I2AHueBLNWAI1OBN/cAOyeg9AD8bH5+fujdu7f6uyEBLpnqnDFjxp9+HEREROEV5nxoKU7/4MED/P7773BycmK3NqJwaFQoJZadfIB2JdOhdbE0MJYR9Jf4+wJ7/gBK9MHRZxo6LDqNAIOGusaH0M5kG1B3NeAYuqkU8sF29OjRGDp0qCpSLTX24sWLx/ePKI6Q3/+uXbsiZcqUqrFMtmzZ1NcmTZpgyBC9qytRbCEB3RlN82HanlvoWT78QRof/4CgoFmP8hlRIE38CDxKAGcXAZmrAjYfaonZOwGuLsDhSfr9SqMACzv8TJIB2qBBAxXgmjZtmip1EOGZbERERJHMSJNLX2EgxemlzXCePOFv2xwbSKFOKc4v2TH29vZRfTgUA1x65IacKRyC7nv7BcDS7BvZkL5eev2Oa5uw3b4+eryuC98ADUVNrmG+6VhYlukDlP0tVN/77du3KnNj8+bNaiqyBKuNwzDtkYhiz/lCLlhdvnwZHh4eyJs3L7M14tB7T2G34tQDDFhzCUnsLXCwf9mvd0oOjyvrgVUtAfsUQJejgKWDXrtraUPg1g4gbWmghRSr/3mBpsOHD6N+/foquLVq1SpV8oCIIhfPFUTRJLNLrgiHMT5GFKfJ78uEnTcwY98djK+XK6io7TcDXe+eAMsaA0/PY7mhAn57WQcGTUNli8uYgj9hmb5EqKc1yIfaOnXqqALUEuyqWrVqRP1oRBQDpUqVSt2IYqt7rzyRJqHND+9HMqn/PnBXLbcrkS5iA10vbwIbuurLOerqgS5xcq4e6DI2A6r++dMCXTJWkSwumdZcrFgxVatLuq0TERHFmWDXlClTVPvy2bNnq86MRPR1/gEGDF53GStOP1T3X3n4fP/lenQGWN4E7u5umGHUGn/7/qJWNzI7jFGYBZP4aQDnfwDj7w+6ly9fjrZt2yJDhgyqsGz69On5dhHFUfJhdvXq1di3bx9evHihpjYHt3bt2ig7NqKIcvzuazSeexxNCqXCiFo5vl4mIBR2XHkGl1eeqkZX48IRGCD28QBWNAN8PYA0JYHywz6e/3d8yNj+5Q8gUWb8DJLl2b59ezVm6NOnD8aOHQszs0iqS0ZERBRdg10NGzaEl5eX+tBsbW392cnQ1dU1Io+PKMaSwFbvFedx6NYrVdR2dJ2caFToG4NlyZg8uwheWwZjgW8ZzDHUwluDtXqoi+lG/Gq8HEapiwENFwM2Cb5bWPbXX3/F1KlT0axZMxWclt9XIoq7evXqpf4WlC1bFkmSJGENHop1pDzAoLWX1OnUoOGHAl0SHJ65/7ZablksDWwtwjxk/rJnl4Gdg4FXN/Ti8/XmASamgJcrsKoVYPADstYAinTBz3Dz5k3UrVsX9+7dU9lcUquLiIgozmZ2EdH3ryz3WHYOL9x9YGlmjKmN8qJS9m9PB/D29cfivVcwy2s8XkOfzpDeyhN9/eeiqslJIE9ToPpkwPTbXRul05oMVqXT4l9//aUKUrOwLBH9999/KnuLU5kptpJi9JKJldjOAoOqZvmhfcmFqsuP38HKzAStikXQTIb7R4H5VfRlmaYo3ZRtE0sHGWBdJ8DtARAvLVBrxk+Zvrhu3Tq0bNkSyZMnx6lTp5A1a9ZI/55ERETRNtglJ0Ui+rpHb7zQ7J8T8DdoyJDYFjOb5kOmJF/ppOTlCm+DCZZfcMWM/Xfw0r20Wp0mvhV62u1FzWfTYSKzFX/5H1Csx3cHv0eOHFGFZcX+/ftZWJaIgkih9HTp0vEVoVjpyhM3zD6o19caUTsH7C1/bBrerP13gronx7cxj5BjRMrCQMLMQJLsQMk+QNKc+vojU/Q6XSYWQINFH+t3RRJ/f3/VgXXcuHFwdnbG/PnzVQMqIiKi2CRcOdl37txRJ0b5KtOkEidOjG3btqmCt9mzZ4/4oySKQVLEs0bH0unw/J0P/lcrO6zNv/Jr5uWKXdO7Y6inM5766NlayR2t0LNUctS90Q+m9w8CpuZA3TlA9jrfnW4xffp0VWujSJEiWLlyJZycnCLjxyOiGGr48OH4448/MG/ePFhZWUX14RBFaH3MAWsuqoLyVXMm/W4m9fece/AGx+6+hpmJEdqX/IEAscynPPUPkLcZYGal19rseBAws/y4zfOrwL5R+nLV8YBTLkQmqdfXuHFjdUHszz//RN++fZn9TUREsZJxWJ9w4MAB5MyZEydOnFDTIaSopbhw4QKGDftQYJMoDjnp4ooGs4/hwsO3Qev6/pIZE+rn/nqgy98Hlxf2QlfXeirQldTeAiNr58C+TlnR4GJbPdBlbgc0W/PdQJfU0GvRogV69Oihpizu3buXgS4i+oxMb37z5o26QCXn8Xz58oW4EcVUf+29raYcSiH54TV/7KKru7cfft9wWS3XzpMcyRx/IDC8dwSwtR+wpL4+VVEED3TJus29AYM/kLkakC9yZ0/I2D1//vyqS/Pu3btV50WWOSAiotgqzJld0olx5MiRKoMkeMpzuXLlVGYJUVwyZfdNTNl9K6hWyL+tCqrlbxbF1TS8W9MLXR+Uhi/MUS6tFWa2KQ1Ln9d6LY/XtwGbxECz1YBT7m9+f8mulMKyt2/fxtKlS9XVWiKir5UhOHPmjGpaEZEF6lu1aoUFCxbwRacokzulA+wsTDG8ZjYktgsWTApHgft2C0+rwJlMXexRPmP4D0oyug5N/HCAjWRg8Pk25xcDD48DZjZAlXGRVqdLsr+lOYVcFCtQoABWrVql6nQRERHFZmHO7Lp06RLq1Pk800SuFL969Sqijoso2jty+xWm7tEDXY0LpcLIOjlC9Txt72j8eiEx7mtJkdzWCJOal4Cl/zvgv7p6oMshFdB2x3cDXVu2bFGDVsnsOn78OANdRPTdvxlSkHrWrFlqSqNkYwe/RSTJ/K5YsSISJEiggmrnz5//bBtvb2+VjSrb2NraqtpBz58/D7HNgwcPUK1aNdVNVsYZ0mVW6g19i3SFbtq0Kezt7eHo6Ii2bdsGZaEHunjxIkqWLAlLS0ukTJkS48eP/2w/EhDIkiWL2kYy4bZu3Rru14MiV7ksSbD/1zKokzdFuPfhF2BAlyVnccLFVQXOFrUphJTxw9nF+PoWYOuv+nKZ3/RpjJ/yfAXsGqovlx0EOKZEZJAxggSkO3fujI4dO6rpiwx0ERFRXBDmYJcMHJ8+ffrZ+nPnzvHkSXHGaw8f9F5xXpXjaFI4FcbUzQknh1BMdTg9D/P2XcQOQyGYGWuY2bIYHE39gCUNgOeX9IyuFuuB+F+vEWIwGFTdnerVq6sPa9JBST6IERF9iwR1JAAUEeTilmSKSa3OZcuWIUOGDKo5hq+vr3rc09MTJUqUUAWwv6Z3797YtGmTCipJiYQnT56oTNVAAQEBKtAl+zx69CgWLlyoMsiGDv0QIPgKCXRduXIFu3btwubNm3Hw4EF06NAh6PF3796pQFzq1KlVppvULZLg35w5c4K2ke8nmbISKJPxTe3atdVNpn9R9OD23g8PXb2C7iew/Xan4m+RWl99Vl7A3usvYGFqrLK0cyQPZ5H4e0eA1W0AzaBPSyzd/8vbSaDr/RsgSU6gcGdEBsn+LlasmPodk26s0qHZ3DyCiu0TERFFd1oY9e3bVytRooT29OlTzc7OTrt165Z2+PBhLV26dNrw4cO1uMLNzU2Tl0++UtxiMBi0NvNPaqkHbNYqTNyvefn4h+6Jr+9qp4cW0tIPWK+eu+CIi6b5eWvawlqaNsxe08ak0rRnl7+5C1dXV61atWqakZGRNnLkSC0gICBifigiivXni82bN2uVKlXSXFxcfnhfzZo10zJlyqTt379fq127trZ3716tf//+2vv370NsJ99LfvZz586FWP/27VvNzMxMW7VqVdC6a9euqW2PHTum7m/dulUzNjbWnj17FrTNrFmzNHt7e83Hx+eLx3X16lW1j1OnTgWt27Ztm/qb+fjxY3V/5syZWrx48ULsY8CAAVrmzJmD7jdo0ED9rQ2ucOHCWseOHWPkex/b+AcYtBb/ntBy/7FDO3bn1Q/vb+LOG+q8nH7QFm3v9efh39GTC5o2OoV+Tl/SUNP8/b68ncshfZthDpr24KQWWb/vjo6OWvr06bULFy5EyvcgoojBcwVR5AhzZtfo0aNVWr9cIZZpAdmyZUOpUqXUlSNpY0wU2605+xh7rr+AuakxpjXOCytzk1A976SbA1obhsAfpqiW0wktiqQC1ncB7u7T63U0Xa23I/8KaQIh0xaPHTumup8OHjwYxl+qAUJE9AVSq2vfvn1Inz69qrkZP378ELewkGwnaYxRunRpODg4oGzZsiqLS6b8hYZkVPn5+aFChQpB62RsIZli8jdOyFfJWpX6YoEqVaqkMrMkc+tL5DmSgS5/KwPJ95C/lVKcO3AbGbcEz3CR/d64cUMV8A/cJvixBW4TeGxf4+Pjo44v+I0i3uRdN3Hg5ktVY8vOMlyNxYO4efnh30N31fJY51womzlx+HcW4Kd3XExdHKg/HzD5wrE9OQ+s66Qv528FpNRrfUYUyYiUacmB2d+nT59GrlyR2+GRiIgoOgrzCEEGh3PnzsXvv/+u0vkl4JU3b15kzBj2Ip5SN0Ru9+7dU/ezZ8+upidUqVLlq8+RVGz53vIc+Z4yuK5atWqIIpxykpdjfPv2LYoXL66+R/Djk3oe3bt3V9MnZAAsdUKmTp2qaoYQBXfw5kssOHoPBk1Do4IpUTmHkwpUnX/4BhkS2SKr03emBLm6qC5LO5/bofuyc/DxN0aB1PEwrl4uGF1YClxeDRibAo2XfnPAu3jxYjUNRz4MSgeltGnT8o0iojCZMmVKhL1icm6dP38+cuf+dm3Br3n27JkaT0hgKjgJbMljgdsED3QFPh742Nf2K7W9gjM1NVXBvOD7/fRvaPD9xosX76vf+2vfN9CYMWPUNHOKPHuuPcf0fbfV8jjnXMieLJzTDT9YfOI+PH0DkDmJHZzz/WDR9hT5gTY7AbskgNknpQ2k7sHpf4Htg4AAXyBeGqBCxNbKe/36tZrGu3PnTtVMatCgQbwoRkREcVa4L4fJ1Ve5/YgUKVJg7NixKhAlQSqpx1GrVi11xVgCX58KrKEhg0m5YiXd56SGxtmzZ5Ejh14cXIrMTps2Te1LBrMSGJOrsVevXg264iwDAak7JvU85Mpy69atVSBB9kcUWL9DOi3KgFrGp6JUxkTqq2RyjawdihpZ7s+BRTWx/H1h/PauDgwaUCFrEkxvkheWbi7A1g91PMoOBtKV+eIupFaNdD6dMWOGKjA7c+ZMWFn9QBt0IoqzpMZWRJk0aZLK9Ja6W1IXSArQd+rUSd3iMgkuyN/sQJLZJZnwFDEevPZS9TJFq2JpUCvPjwWnJDNs3mEXtdy5TPrwdSh9/xZwewgk/TAuSJTp82183IFNPYHLa/T7maoAtWcCVvEQUWQsLBdv5f/c9u3bVV06IiKiuCzMwS4JSq1evVpNhXjx4oUqlv1pB6bQqlGjRoj7o0aNUllY0lnuS8Euyb6qXLmy6sYkRowYoQJW06dPx99//62OTa5cy3RKCZqJRYsWqaux69evR6NGjXDt2jU1CJCi3oHTHKRgp2SHTZgwAcmSJQvrS0KxzAt3b/Rcdh7H7r5W9xsXSol8qeIhT8qQGQjf5OuJF4taY9rL8lgc8Ita1aBACoyukxOmmj+wpi3g5wmkKQkU7/nFXUixZin4LP9X5fdCuiiFayBORPSBBKYkI0u+yjlVsqBkWrRcvPrSefdrbGxs1DlbbnLRSTKyJfAl2dLBi8F/TdKkSVUwXzKwg2d3STdGeSxwm5MnT4Z4XmC3xsBtvrRfGZsEJ90bJaM7+H4/7fr46X6/ts3Xvm8gCwsLdaOIJ4GpTovP4J23P/KlcsRvVbP+8D5XnX6I156+SBHPCtVzOYV9B37ewPImwNOLQONlQNqSXw6GLaiuN6ExMgF++QMo2g2IwPO5/E5Lt0WZ9ivdFqX5AhERUVwX5oI/vXr1QvPmzeHi4qKm/UmtjuC3H6kxsHz5ctXBqWjRol/c5ns1NOSYZIpB8G3kmAoXLhyiBsj36nl8CetwxA2XHrmh2rTDKtBlbW6CqY3yYEzdXKhfICUyJrEL1T5euXli1LQZKPmwfVCgq0uZ9Gq6hamJMbBvFPD0vH5Ft85svb7HJ6R7WL58+dR0XelSJtkSDHQR0Y+QvyXyYVjOdXJhSsoQBNYDlOn/4SXnVAnGS8Dr0KFDoXpO/vz5YWZmhj179gStk5pZDx48CBoDyNdLly6FCF7JBS7pKCn1Qr9EniMBNKkJFmjv3r3qwpyMBQK3kb+xktkdfL+ZM2dWUxgDtwl+bIHbfG18QpFv9oG7uPr0HRLYmGNG03yqbuaP8A8wYPZBvVZXh1Lp9PNzWBgC9AtX94/ogSsrx68Ew5p+7LbcehtQrHuEBbq8vb1VcLlNmzaqhp78/jHQRUREFM7MLmldLIPk4HWyfoQMZGXwKCdsCZ6tW7fuq4PY79XQCPz6vW2+V8/jS1iHI2649NgNrzx8kCmJLWY2zY8MicNWx23d2UcYvPoMvAx6hkS+pKboWz0/imdIqG9wdz9wZKq+XPMvwCHkFAzJTpRsi379+qFEiRJYsWLFZ/+fiYjCY+DAgaqOj0yzkwL1gcqVK6cypMNCsrgkoytPnjzqYpVke0swLbBRjWRSSeBKMlQDA1lCMqPkJhei2rZtq45Fzr8SwJJamjIeKFKkiNpWpmHJeEAusEmJAjlHy/67du361eyprFmzqgzw9u3bq4xvCWh169ZNZXYHZm43adJE1dWS7z9gwABVf1T+7k6ePDloPz179lTF9ydOnIhq1aqpi3FS6HvOnDlhft0pYnQsnQ5P3d6jRu5kcHL48en8Wy49xaM371XwrH7+ME41lfoGW/oA1zcDJhZ6VlfgNMbgwbC17YH7hwELe6D52s+3+QH3799HvXr11Dj633//VQEvIiIi+ijMl8VkgJouXTpEFLmSKrU+5EqzpGBLTRGprxUd63C4ubkF3R4+fBjVh0SRoEnhVJjfqiDWdC4W5kDXxfsvMWDVOXgZTJHL6A4WVDBgTc+KHwNdD08BK5rLKFnvwJQ15DReyWqUenLyIVI+aEkhega6iCiiyIfiOnXqfLZeLgC9evUqTPuSaY8SqJJ6VFLvUrJK5MO2BKzExo0bVfMaCRQJCTbJfQlABZLgktTflDpD0h1RgmDBSyGYmJhg8+bN6qsEwaSbpHyf//3vf0HbSParZL3K1K1AS5YsUc08ypcvry7MyYWD4EEqGcdIAW/JBpcMs759+6rmOMGnX0qHafm55HlShF/KN0g5hMD6oBT53vsGYPreW3jj6avuW5qZqG6JQefUHyAXlmbtvxNU+yu0XZWDHBgPnFkAwAhw/gdIU+LTbwBsGwBc2wiYmAONlkRooEuyDOX/7suXL3HkyBEGuoiIiCIis2v48OHqiui8efMipFC2dGPKkCGDWpYTt9Qnkiuss2fP/mzb79XQCPwq65ycPtZekPty9Tm09Ty+hHU4Yi8f/wD4+Btgb2mm7pcJR9txaV3eZfkl+GrGqGh8CrNrJIFR0WDBrAcngMXOgK+73pK80pgQz7916xbq1q2rPnxJBkHDhg1//AcjIvpkuqE0Z/m0E6E0hUmePGyFviUoLzchzTMWLJAP/h/JOrl9izSNkeYbcvsamZK1devWrz4ufzPl5wreFVIyxb7XcCZXrlzfnXIpNRPlRj/frefuaDX/FB6/fY8X7j74X62IDTLuuPIM15+5w8bcBC2Kpgnjwe0G9o/Wl6tNBLLV/HybgxOAU3P1YJiUK0hbKkKOW6bjSmMnyXCUzEcJ7CZIkCBC9k1ERIS4ntnVoEEDvHnzRl0JltofUlco+C0iTuRSH+tLvldDQwbwErAKvo10pZGsseA1QL5Xz4PiDoNBQ79VF1F/1jE8efs+7DtwOQTN3w99V11Q0yFS2ZvgzxblYFS088dt7h8DFtfVA11SkL7pKsDcOujhTZs2qRpyUqxZ/q8y0EVEkUGyq2TankwHlGwoOe9JVohMm5aMqZhIAmG//fZbUK0tih1NYgIDXckdrVA0XYII7bQ8Y99tdFt6Liib28Fav9AVamfm618LtAUKtv388cOTgX0j9eUq44AcdRERZOwqmZmDBw9Wnca3bNnCQBcREVFEZnbJNEMJFMl0Apli9SNFs2VqoBS0lekQ7u7u6kqsTEXYsWPHF7f/Xg0NORYpoC81STJmzKiCXzIgkDodUlsktPU8KO6YvPsmNl14AlNjI9x77YlkjmHIVjy3GNjQDXMTDsTuRzlVsdyZLYvCIbnDJ4EuZ73zYroyQKNlQYEuqXMjmZLy/1X+f0pmxI80eSAi+pbRo0erelcy9VD+/kg9LPkqNawCa22Fx6dZXT/Tn3/+GWXfmyKel68/2i08rQJdaRPaYG3nYohnYx4h+5YLWr1XnMcJF1d1v1ouJ/T+JVPYd1R/IXByDlDgCzWyDk8Bdg/Xl8sOAQp3RERNQZbsb5luLBfIZPovERERRXCwS64kSTBKamD8KJlOKFeTZVqFfMiXaQWy719+0TvYyRQIqccRWIsjsIaGDMrlSq4EtD6todG/f39V+0hqb8hVMDnO7du3q+kSgSTtWwJcUs9DujBKvZBp06b98M9DMcuWi0/x197banlM3Zwolj4MdUCkVsemntgdkA/jHunF6IfVyIYcwQNd7s+Blc31QFf6ckCjpYCZHkyTabPyAVMyE+UDqGRbyP9FIqLIIDWKJKNLznVSn0o+PEs3RqmjJedSoqgmWVc9lp3HxUduiGdtpupnRlSg6/Q9V7RdeBpu7/1Up+U/amZHvfwpwnfB1sQUKNrl8/VHpgG7P3Q1LTsYKP3rjx/4hzGrXKDNlCmTGs+mT58+QvZLREQU2xlpMgIOAyn6unLlShWYimySxVW2bFmV/RLdyPRICdBJsXrpIkUxy5Unbqg36xje+wWgfcm0GFztyx1Av+jUv3i4aTRG+jfDDkMhtapWnmSY0jDPx4GzwQAscQbu7AUSZwfa7wkKdJ09e1YFWCWbcdmyZUHBXSKKnaLD+UKmLMpFnytXrjC4Fcfe+5hizLZrmH3grsqSXtquMAqkiR8h+/ULMKDSlIO4+9ITuVM4YEqjvCprLEyubQbuHwEq/AGYfiEAd2I2sK2/vlzmN6DMgB8+biltIM0TpFOqdCSV2QjW1h9LIBBR7MFzBVHkCHMqiUwhlOwpybiKTDIwvHPnjqolQhSRXnn4oMOiMyrQVSpTIgyskjXUz/U+NheT1x9BBd8JKtBlYmyE1sXTYJxzrpBXiI/P0ANdplZAvXlBga6FCxeiePHiqs6GTAdmoIuIfgbJHJUMrtevX/MFp2ipRq5kSGpviYn1c0dYoEssPn5fBboS2JhjcbvCYQ90uT8DNnYDjs8ETv/7+eOv7wA7P0wDLj0wQgJdjx8/RpkyZVSzppkzZ6qxAwNdREREkTyNUWp1eXl5qTRqOfGamYUs7CnTsyKCXAl99OhRhOyLKLhhG66oeiDpEtrgr8Z5VcAqNN4emoM2Wz1xVnNW96Vo7vCa2ZE5qV3IDZ+cA3b/oS9XHg0kzqKaLkg9Obky26ZNG9V9LPjUWiKiyCZd3H799VfMmjUrxPR/ouhAygDs7Vca1uZhHpp+1VsvX0zZfUst96mYCXYfui6Hmkx+2NgDeP8GcMqtF6X/1PZBQICvXq6gzMAfPmYp3SGNamR8ffDgQRQpUuSH90lERBQXhXlEMWXKlMg5EqKf5Pfq2VTdDglUOViZhbqwbYsjKXBb02Bv6o/R9Quq4raf1fvw8QBWtwUMfkDWGkD+1ipoW69ePZw7d041U5DaG0REP5vUyJSLVblz54a5uTmsrKwi5WIVUWi98/bDI9f3yJZMn+IZkYEuMXXPLXW+z5LUDg0LpAz7Ds4uAm7tAEwsgDqzP5/CeGO7/rixGVBlvHRKCvexSlURmT0xcOBAlCpVSjVhks7nRERE9BO7MRLFZEkdLNVUhtC69dwdLeadxFM3DUltTbGwbUlkdvpC7RXPV8DqNoDrHcA+OVBjGvYfOIAGDRqoLK5Dhw6hUCG9xhcR0c/Gi1UUnUhwp/+qi9h74wUm1M+NmrkjtiP2nZce+O/YfbU8pFo2mJqEsXLHm3vAjt/05XJDgMSflDzw8wa2f8jkkoL1CcPf6EFqeErW9+rVq1XDGunSbGoasYE/IiKiuCZcZ1KppTV//nz1derUqerK07Zt25AqVSpkz653piOKTk66uOKlu4/KxgqLS9vnodlxJ7h5G5AukQ3+a1sYyR1DZkMoD44Dq1oD7k9UnS7N+R9MnDlPXaGVRgtyhTZRokQR9wMREYURL1ZRdDLvyD1sv/IMZiZGSBU/4guvj95yDf4GDeWzJEaJjGHothzYZGZ9F8DXA0hVDCja9fNtjv0FvHEB7JyAUuHvvHjt2jXUrVtX1elas2aNWiYiIqIoKFB/4MAB5MyZEydOnMDatWtV63Jx4cIFDBv2oeUyUTQiUxC7LDmDrkvPYvPFJ6F+3oudU9Buv5kKdOVJbovVnYp9HuiSeh5H/wLmV9UDXQkzwbPpJjT8dYqqjSOdlHbs2MFAFxFFua1bt6q/R5/auXOnumBF9LNcePgWY7ZeCyotkCelY4Tuf/vlp9hz/QVMjY3wW7XQN6EJ8vI68PQCYGYD1J4JGJuEfPztQ+DgRH254kjA4pPanaG0atUqlfEtDSROnTrFQBcREVFUBrskU0XSq3ft2qVqfgQqV64cjh8/HpHHRvTDpFZHh/9O45WHL7I62aNcltDVv/DZNwEd9wHPER8ZbH3wX/tiiG/zSa0Of19gTTu9C5MWAOSoh5tl5qBgtZbqg6NMRxg3bhynIhBRtCDn74CAgM/WGwwG9RjRz/DeNwC9V55XWVfVcjqheZHUEbr/hUfvocuSs2q5RdE0SJ/INuw7SZIN6HwEqPcvED/t51lfW/oC/u+B1MWBHHrTmrDw9/dX3calzEG1atXUBeTMmTOH/TiJiIgo4qYxXrp0CUuXLv1svUxlfPXqVVh3RxQpDAYNq888wrjt1/Ha01cFquY0zx+q4rfagT8xdPdznNPKwt40AHM7Vfq8g5OPO7CiGXB3P2BsqgrTrnsYDy2LlUby5MnVFdosWbLw3SWiaOPWrVvIli3bZ+vlb9Xt27ej5Jgo7pHz8t2Xnkhib4FRdXJ83uglnPwDDPjf5qtY9KFOV/38KTCwyg+ch+Ol0W+fOjD2Y9H6qn+GuSj9s2fP0KhRIxw+fBiTJ09Gz549I+w1ICIioh/I7HJ0dMTTp08/Wy+d5uRDPlFUu/rkHerMOor+ay6qQFf6RDaY36ogUoamJsjR6Vi06yRWBJSFMTT81aIo0ia0CbmNx0tgQXU90GVmg4BGyzFo7W3UdXZGxYoVcfLkSQa6iCjacXBwwN27dz9bL4EuG5tP/s4RRYIz912x4Og9tTy+Xm44Wn+SMR1OHj7+aLPwtAp0SdxIglzj6+WCuWkYh7kBfsCT819//OoG4MA4fbnGVCBJ2OrUHjlyBPny5cONGzewd+9e9OrVi4EuIiKi6BLskqtR0ilGrkzJlSiZ/iAnb0nHlrbmRFHNN8Cg6oHYWphicNWs2NazFHKHph7I9a04tm0x/uffXN0dUCUrSmdK9Hl3pnkVgafnAesEeFt7MSp3HY/x4/Wb1N+wswtf7Q4ioshUq1Yt9eFamssED3RJbcGaNWvyxadIlzuFI36tlBmti6f5/Pz6A4ZtuIKDN1/CyswEs5rmR6fS6cMXRLq0CphTGljb4fPHnl8B1nXWl4t0BfI0DlPnyb/++gtlypRB+vTpcfbsWZQqVSrsx0dERESRN41x9OjR6Nq1K1KmTKlqf8iUCPnapEkTDBkyJKy7I4pwUuh2vHMulMmcCIntLUP9vIe2udAloB8CYIJauZOhQ6l0ITd4ehFYUg/weA44pMLFPP9Djaqt4eXlpWrYSd06IqLoSgLylStXVpmnKVKkUOsePXqEkiVLYsKECVF9eBQHmJoYo2vZDBG6z6O3X2HN2Ucqo2tB64IonC5B+HYktbgOT9GXE39S1N7LFVjWGPDzBNKVAX75X6h36+npiQ4dOqgSIBJslt9DM7NPSiMQERFR1Aa75MqUZHRNmzYNQ4cOVfW7pBtj3rx5kTFjxog/OqJQ1ukYu+06mhdNjdQJ9Kk4DQqmDNNr5+njj/ar7+KNwQo5k9tjXL1cIa8KuxwCljcBfN4BibNjmWk9tKrcCHny5FGF6CX4S0QU3acxHj16VAXnpYOylZUVcuXKxQwTinR3X3ogRTzrsE8r/A5vvwAMXn9ZLTcrnDr8gS5xYwvw6gZg4QAUaBvysY3dgbf39Rpe9eYDJqahrpNXt25duLi4YPny5WjYsGH4j4+IiIgiN9iVIUMGXLlyRQW3+AGfopqPfwC6LjmL3ddeYO/1F9jeq1TYBtOer2C4vQ99LqbF9WfuSGhrgTktCsDSzCRkjQ7puhjgC0PKouh9MimmzemLjh07YurUqbCwsIiUn42IKKJJEF9qC8qN6Gfw8vVH839PwtrcRJ1fP6uD+QNm7LsNl1eeSGxngV8r/0A3Q00DDk3Slwu2BSztPz724DhwfTNgZAw0XAxYxw/VLjds2KDKeyRNmlR1W8yePWz1vYiIiOgnBruMjY1VkOv169fM5KJoYdKumyrQZWFqjEFVs4Yt0OXrBSxtiKn3UmNHgDPMTYwxu3l+ODlYfdzm0mo90AUNXqnLo+LsBzh9fj/mzZuH1q1bR8rPREQUWfbs2aNuL168UDU3g5O/a0QRbeqeW3j89j2SO1qpDowR5dZzd/x9QK8/90fN7LD/tGtyWLgcAJ6cBUwtgSJdQgbBdg/Xl/M2A5Lm/O6upLSHzH6Qsh916tTBggULYG8fLHhGREREP0WY88nHjh2LX3/9FZcv62njRFHZ1WnOQb2z2LTGefFLtiShf7IhAFjbHvse+GNqgLNaNbJODuRPHe/jNi+u6VMXoOFx0gpIO+AgHj17pRoyMNBFRDHNH3/8oTK6JNj16tUrvHnzJsSNKKJdf/YO/x5yUcv/q5Ud1uZhLhX7RQaDhkFrL8EvQEP5LIlROUfS8O/M7z2w/Td9OW9zwDZY4fybO4AHx/QgWJlB392V/F5JXTwZK48bNw5r1qxhoIuIiCiKhHnUISnZUpA7d+7cMDc3VzU/gnN1dY3I4yP66rSIvisvqIuuzvlSoFL2MA50dwyG97UdGOr/p7rbsmhqNCgQrO6WjwewsiXg54V7xmmQsdt6lC1XHsuWLUOCBD9QE4SIKIr8/fffKsukeXO94yxRZJKA1OB1l+Fv0FApexKUzxqGC1Lf4OtvwB+bruD0/TdqauT/aucIX+fFQNJ0JsAXsEkElPo12A8QAOz5Q18u3BGwT/bN3Zw8eRL16tWDt7c3m9YQERHFxGDXlCkfOtUQRaHx22/g3msvODlYYmiNbGF78rGZwIlZmBtQGw+1xEhqb4n+lbN8fFwiaJt7qUK1r/0sUGjKJfQfMAj/+9//YGISrJYXEVEM4uvri2LFikX1YVAcsfL0Q5y5/wY25iYYXjNi6lW9cPdGl8VnVaBLDK2eTU2P/CFSdL7DfsD1LmAXLCB3cSXw4ipg6QCU6P3NerZz585F9+7dVcOmVatWsaYtERFRTAx2tWzZMnKOhCgM3ZdkAC3GOeeCg1UY6nTc2Qfs+A2PtQSYYainVv1WLStsLIL9KpyeB1xaBX8D0GStD+YuXYdatWrx/SGiGK1du3ZYunQpfv/996g+FIrl3nj6Ysy262q59y+ZQtbCDKezD96g8+IzeP7OB3YWppjSKM+PZYtJzTrjD9U8LGwBp1wfH/P3AfaN1pcl0GUVrMRBMO/fv0fXrl0xf/58dO7cGZMnT2bTGiIiopga7Nq6davKbqlUqVKI9Tt37lRFOatUqRKRx0f0GemUuLZLMRy48RKlMgWrrREa94+qGlyjbX+D92tjFEobHzVyOX18/PEZBGztD8nfmnLJAX9t2I1MmTLxXSCiGE+mV82ZMwe7d+9Grly5YGYW8kLBpEkfutER/SB3b3/kSG6P1x6+aFUszQ+/niddXNHsnxPwDTAgY2Jb1UwmXSLb8O8wwA9YVAvIXAUo0vVj0CvQ8ZmA2wPAzgko1PGLu3BxcYGzszOuX7+OhQsXqjIfREREFIODXQMHDlSFNz8lXZ3kMQa76GcwMzFGhbAUpA9UbjCO2pTDlnWuMDYChtfIHlTrw//pZfjMqQIbI3+cck+MTovOwtbOLuIPnogoCly8eBF58uRRy582mfmhmkdEn0iVwBpL2hWB23s/mJqEuRdSCH4BBgxed0kFusplSawa0tgGz8YOj0OTgPtHgGeXgRz1AHunkNMXd3+o1SVF6c2tP3v6tm3b0LRpU8SLFw/Hjh1TdWyJiIgoegnzaOHWrVvIlu3zGklZsmTB7du3I+q4iD7zz6G7eOnhg18rZg734Nk/wIA/jvqp5WZFUiNbMr0d+KvbZ4B/KyGhhR+eGSdDgVHHYWTJQBcRxR779u2L6kOgOCZMZQa+YtGx+7j1wgPxbcwxuUGeHw90ebwAjkzVl6tNDBnourIeWCeZXOOScQQAAFHfSURBVBpQoC2Qr8VnF3ZHjBihOptWrVoV//33nwp4ERERUfQT5oiBg4MD7t69+9l6CXTZ2NhE1HERhXDqnquq/zH7wF3svPo87K/O5TUIeHUXQ9Zfxo3n7ohnbYY+v+jTE88e2ArPmeVVoMvTJhWS9jsCIylIS0RERKF2+4UHRmy+CldP3wh51V55+GDKrptq+ddKmeFg/ePBMxycAPh5AsnyATn12p3KjW3AmraAZgDyNAOqTpCUxxDdxmvUqKECXdKwZuPGjQx0ERERRWNhvjwmhbp79eqFdevWIX369EGBrr59+6JmzZqRcYwUx71090HXJWcRYNBQK08yVMmRNGw7ePsQ3ut6oJd3B2wPKKCmL/6vVg51xXnBrMkoeHkoUicyhr9dSti03wlYx4+sH4WI6KerW7duqLZbu3ZtpB8LxW4Td97AtsvP8PydN6Y3yffD+xu//TrcffyRM7kDGhRI+eMH+Oa+3oRGlB/6MZh19wCwsgVg8NenNdacFqKO17lz51R9Ljc3NzWF8dO6tURERBQLgl3jx49H5cqV1bTFFClSqHWPHj1CyZIlMWHChMg4RorDZNphz+Xn8MLdBxkS22J0nZxhri3jvnM0OrzviWOG7DA3Mca0xnlQOr0j2rZti4rvliN7DjNotklh2mZzyOkMRESxgGRkE0W2Cw/fqkCXnKJ7lM/4w/s7//AtVp5+pJaH18wOE7lS9aP2jwUMfkDa0kD6svo6j5d6RleAL5C1BlDnb8BY2tTopPh8p06dVAmPvXv3Ik2aHy+4T0RERNEw2CWD5qNHj2LXrl24cOECrKysVFenUqVKRc4RUpxlMGjov/oijt55DWtzE/zdLB9swlirw+3GITQ9lx2XtbSwMTPC3JYFkczUAyVKlEA632to5GwGGJnAqPEyIB4HsEQU+8yfPz+qD4HigD933FBf6+ZNgUxJfqzmpWRyD9t4Rd9fvuTInzoC6mJJra7Lq/Xl8sP0r5oGbOwOeL4EEmUF6v4DmOhTJX18fNRMhr///ltdHJs+fTosLS1//DiIiIjopwhXlU/JrKlYsaK6EUWWEVuuYu25x+pq7rRGeZEhcRgHz69uY+ySrbislUB8Ux8s7FgeT6+eQP7GjZEioR2WtHYCvF8BxboDyX98ugUREVFctO7cIxy+/UplT/eqEP6srrdevlh5+qEqSv/ozXtVjH5glSwRc5C2iYHOR4GbO4AU+fV1ZxYAN7cBJuaA81zATA9mPXz4EPXq1cP58+cxd+5ctGvXLmKOgYiIiKJ3sGvPnj3q9uLFC9WZJrh58z7UQiD6QSUzJsTykw8x1jknKmRLErYne7zExfk9sNy3s7o7s0UhbFw4Hb///ruqtbGuTSqYX1kOJMgAlBnI94qIiCgcXF55Ysi6y2q5W7kMSBnfOsz7eO8bgNFbr2HVmYfw9tPHlVJXc1SdHEhsF4HZVAkz6jfx+g6w47eP9buS5lSLMr5t1KgRrK2tcfjwYRQsWDDivj8RERFF32BXYBeaAgUKwMnJKcz1k4hCq1yWJDjYvywS2VmE+UUznPwHw1wrQoMxqmW2x9j+3bBhwwYMHToUQ5uXgcni2vqGNf8CzKz4phAREYXDuG3X4ekbgMJp46Nr2Qzheg2HbriMVWf0+lxZnezRqlhq1MydHFbmH2tnhdvTi8DT80C+Fh/XBfgBa9sDfl5AmpJAka7QNE3Vpf3tt99Qvnx5LF26FAkTJvzx709EREQxI9gltQsWLFiA5s2bR84RUZy24fxj5EnpiNQJbNT98AS6xDr7ZjinXYSlCbBzch+8fHALmzZtQvWKZYFZxfSNCrYDUn9YJiIiojAbVy8XHK3N0LNCxnAVkV9/7rEKdMlT/26WH79kSxJxF1IfngKWOAPeboB1AiBLNX397uHA4zOApYMqSP/OwwOtWrVSncYHDx6sLuyamERAoI2IiIiizMe+yqHk6+uLYsUiJkAwZswYlR5uZ2eHxIkTo3bt2rhxQy9w+i2rVq1S3SClUGjOnDmxdevWEI/L1TnJ4JHMMymgX6FCBdy6dSvENq6urmjatCns7e3h6Kh35vPw8IiQn4vCZ8vFp+i14jzq/X1MtS0PL3dvP4zZrv8/enVwCazgg9OnT6N6hVLA0obAm3uAfYqPBWqJiIgoXGS64VjnXHByCHuW9L1Xnhi87pJalg6OFbMnjbhA190DwKJaeqArVVEgTQl9/eEpwLHp+nL1Kbj88K2arSDTFyUDfOTIkQx0ERERxcVglxTplNTuiHDgwAF07doVx48fV90d/fz8VNF7T0/Prz5HOkE2btxYBafOnTunAmRyu3xZrxchJA192rRpKgvtxIkTsLGxUXWavL0/BlAk0HXlyhX1fTdv3oyDBw+iQ4cOEfJzUdgdvPkSvVacU42RKmRNgsThyeh6dgnY+iumbL+MVx4+8HN9jF9SmeDYsWPIkNQBWFgduHcIMLcFnP8BLO35VhEREYXR5cduWHrigbq4GF4+/gHotuxs0BTI7uXCX9j+i4GuJfUBP08gXVmg2Ro9i+vMQmD3hwtdv4zAsks+KFy4sLp4KhfFatasGXHHQERERFHKSAvjSKVnz55YtGgRcuXKpW5mZnqL5kCTJk0K98G8fPlSZXhJEKxUqVJf3KZhw4YqGCYBqkBFihRBnjx5VHBLfpxkyZKhb9++6Nevn3rczc0NSZIkUdMvpejotWvXkC1bNpw6dUpdzRPbt29H1apV8ejRI/X873n37h0cHBzUviU7jMLvzP03aPbPCbz3C0C1XE6q82KYp0L4+0CbXRbrnzmir28nGIxMUCfBU0zq1xZGbg+BRbUB1zuAdUKg2WogWV6+ZUT0U/B8EXfFxvd+99Xn6L7snDpndy+XAX0rZg7zPvwDDBi55RoWHL2HeNZm2NqzZLgyw77o7QNgdmngvSuQuSpQb77eZfHqBmBVK0AzIKBoD/TZ+k5dGG3WrJkaP8qFUSKiqBAbzxVEMbJm18WLF1VgSQTPphI/mnouv+Aifvz4X91GsnT69OkTYp1kba1fv14tu7i44NmzZ2rqYiD54yFX7uS5EuySrzJ1MTDQJWR7Y2NjlQlWp06dz76vj4+PugX/o0Q/RgKTa88+xvCNV9SgWbovTm6QJ1w1Py5vnIrhj+rhtJYZMAIKJDXF5F7t9G5LC6oD7k8Ah1RA83VAwvAV0CUiIorL5h9xwf82X1VZ2HLObl8qXaie5+njjwM3X6qLWxcevsXlJ25BXRcn1M8dcYEuP29gZQs90OWUB6g3D3h9G7i6HjgyVQW6PLPUR6URe3HixElMnz4dXbp0YbMlIiKiWCjMwa59+/ZFyoEYDAb06tULxYsXR44cOb66nQSyJEsrOLkv6wMfD1z3rW0kgyw4U1NTFWQL3OZL9cWkYClFnJWnH2LAGr1WR6G08TG7eX6Ym4ZtZq2vvwEjlu/FkstZYYAxTAy+aF8iDXpXzQV4vAQW19UDXYmy6IEu++9n7REREdGXM7FE40Kp8L9a2WFm8vVz9nvfAOy9/gJbLj1RXwODW4FsLUzRrVwGlM8acrz2Q0zMgAwVgDf3gRQFgb9L6MGuD14mKobcvTfByNhUla8oWrQo32YiIqJYKszBrsgitbskU+zw4cOIjgYNGhQio0wyu1KmTBmlxxTT1cqTHPOP3EPNPMnQoWQ6mH5j0Pw1E7ecw3+XJePOGMVxEWP6d0OqhHaArxewrJFejN4xNdByE2AbMsBJRERE3/bWyxfdlp7D4duv1P1BVbKgQ6l0X8yG8gsw4PCtV6qz8s6rz+HlGxD0WOoE1iidKRFyp3BE7pSOSJfQBsbhyOT+JmMTIHtd4Pwy4NRcfZ2JBbQM5bH9gSVq9/oPRYuXwIoVKz67KEpERERxNNhVt27dUG23du3aMB9Et27dgorEp0iR4pvbJk2aFM+fPw+xTu7L+sDHA9dJN8bg2wROv5RtXrx4EWIf/v7+qkNj4PM/ZWFhoW70Y47eeYWi6RKoQbKlmQk2dy8RriCX2Ld/H/45+g4wMsU4iwVoOHAuYGUHGAKAte2Bx6cBq3h6YVoGuoiIiMLs2lN3de62NjfBpAa5UTnHx7FVoACDhj933MCKUw/wxssvaH2KeFaqFmeNXMmQPZl95E0XlJIF9smBu/uBNW0BXw8gXhqg3O/wSF4CbTv3xMqVC1QtV8nUl2x+IiIiit1CfbaXuleRUbOpe/fuWLduHfbv34+0adN+9zmSci7toWXKYyDpqBiYii77kICVbBMY3JIsLKnF1blz56B9vH37FmfOnEH+/PnVur1796qplFLbiyLH3wfuYOy262hbIi2GVMuqBr3hDXRt2rIFPdffgSFBelQ0u4CGnYcCVo76gzsGA9c3AybmQKOlQMII7PBEREQUhxRNnwDjnHMhZwoHZEn65cLJk3bdUOd4kdDWHNVzJVNZ23lTOkZ+PSyPF8DCGjKqBN490delLgE0WIQbj16hTolyePjwIVatWoV69epF7rEQERFRzAt2zZ8/P1KmLi5duhQbNmyAnZ1dUL0sCaxZWVl9tRtk6dKlMXHiRFSrVg3Lly9X7aLnzJmjHpdBlQTCRo4ciYwZM6rg1++//646LNauXVttkzVrVlSuXBnt27dXHXj8/PxUdpkUrw9NJ0YKuxn7bqurvsLO0jTcg18JSMp7O3HLecQr2xZ25kYY2ak5kDSlRE+BA+OAE7P0jev8DaQuxreLiIgoDK48cVNF4+PbmKv79Qt8vWzDrqvPMWOfHugaVScHGhZIGe4LWWHm7wusaA68e/xxXf7WQJXxWLtxM1q1aqVmDJw8eVKN/YiIiCju+EmjkS+bNWuW6sBYpkwZNeUw8Ca1FALJQEUeD1SsWDEVIJPgVu7cubF69WrViTF4Ufv+/furjLEOHTqgYMGC8PDwwPbt22FpaRm0zZIlS5AlSxaUL18eVatWRYkSJYICZhSxpu+9FRTo6vNLJvSqkClc+3nz5g1mtS2AQ+vmImHZ1mrdkBo5kDjZh0DXvlHA/jH6xhVHATmcI+6HICIiigNeuHuj9fxTqPHXYdx+4fHNbe+/9kSflefVcqtiadC0cOqfF+gS2/oDD49/vF9xJPwr/4kBg3+Hs7OzurApmf0MdBEREcU9UVq0QKYxfo+LiwvKli0bYl39+vXV7Wska+h///ufun2NdF6UoBlFrmUnH2DCzptq+ddKmdG1bIZw7efixYtY1LcqxhXzwBGnQbhlMEax9AnQQK42y/+jPf8DDk/SN644EijWLSJ/DCIiolhPCsxLMfoX7j7ImNgWTg4fLxJ+ytsvAJ0Wn4W7tz/ypXLEb1V/cubU6XnAmWCzDnI3wYsMDdGoUiVVA1ZmAPTu3Tvyp1ESERFRtBStK3RK1tedO3ewZcuWqD4UCocDN19iyPrLarlH+YzhDnRJFt6m8R0wsmYKNPTtg9NaZliYGmN0nZww8vcB9o4Ajk3XN640Bijahe8XERFRGI3ffh0nXVxha2GKv5vnh43Fl4eJBoOG39ZdwrWn75DAxhwzm+aHuelPyugyGIBzi4Ct/T+uS5gZJxM1RN18+VTDIanDWqpUqZ9zPERERBQtRetgl9TuevToUVQfBoXTczdv9bVuvuToXSHsReJ9fX1V56Qrm2ehUWNnVPdrAG9YwMbcBONKWSDN0YHAlQ2Aj5v+hCp/AoU78P0iIiIKo62XnmLuIRe1PKF+LqRPZPvF7d55+6HPivPYfe0FjI2AvxrnRdJvZIBFPA049S9g0Ls+aiaWWGGojBblKqrSFVKInvVXiYiIKFoHuyhma1AwJdIlskGuFGHvxvT06VM1VdX09VUkbjgGowL06RHFkxlhnP84pDh89uPG0m683BAgT5OI/hGIiIhiPVdPXwxYc1EtdyyVDpVzOH1xu1vP3dHxvzO4+8pTZXKNc86JYhkSRv4B+noCplaAsTFgbAIUaAtslq7cGv59lgXth45QtVonTJgAc3O9qD4RERHFbQx2UYTy8vWHX4AGByszdb9Amvhh3sfhw4dVoCtzQmM41RmEY4assDH2xaDUN9H0yWiouJm5LZCtFpC7kd5iXAbAREREFGZ/H7ijam9lc7JX9TW/VGN108WnGLTmIjx9A5DMwVJNc5SLWZHu5U1gZXMgRz2g9K+A5yu98zI0bHlgg54rTqsarI0bN478YyEiIqIYg8EuijAyGP511UVVw2NuywJfnQLxref/9ddf6Nu3L4oWK4asrcdg2/U3sDHyxUqHGcj+9BQgga6C7YEKwwALO757REREP6h9yXR47xuAStmThuim6OtvwIbzjzH30F3cfK53ZiySLj6mN8mHhLYWkf+6X14LbOwO+HroxegLdwTWdgDcn+CmKzD4uDVOnNgZoiM3ERERkWCwiyLM9L23seXSU5iZGKkpEekThf65np6e6NChg7o626dPHyT9pR1m7L8LEyMNM00nIfv7i4BjKqDWDCAti84SERFFlER2FhhRO2TAaMmJ+/hrz208e6fX35R6mW1KpEXP8hlDBMQiRYAfsPN34MQs/X6akoDzvzCcmA3jO3vg5adhxotCOHBsjarvSkRERPQpBrsoQuy88gwTd91UyyNq5UDBMExfvH37NurWrYvnD+/i6rQGuGgeHwP231WPjbZYhNK4COSsD1SfAliELVuMiIiiXqtWrbBgwYKoPgz6hGRzWZmbfPa6TNtzC5M+nNMT21mgdfG0aFI4VVCJgkjl4wGsagnc3q3fL94LKPc73K7shO3ekSrDe69VNUxeugTGLGFAREREX8FCR/TDbjxzR+8V59Vyy6Kp0ahQqlA/d9OmTShQID9KJ3qLOwNSYMMzRwxyyaUe6267Fw2xQ7+iW3sWA11ERLEsACbNS4LfKleuHGIbV1dXNG3aFPb29nB0dETbtm3h4aFPp/sab29vdO3aFQkSJICtrS2cnZ3x/PnzENs8ePAA1apVg7W1NRInToxff/0V/v7+IbbZv38/8uXLBwsLC2TIkCFWBus6/HcaLeedxL1XnkHrZu2/ExTo6l0hEw4NKIvOZdL/nECXIQBYVEsPdElB+oZLgF/+wJX9q+C9uAlMjIAnicug+pBlDHQRERHRNzGzi36Iu7efGixLwdqi6RJgSPVsoXpeQEAAhg8fjlWzRmNfm5Qws7WHs1tn3ND0QFkzxyvo8/4fIF4qoP5CwOQnDLKJiCjCvHr1StVg3Ldvnwo2SfORvHnzYsmSJUEd8yS4NX/+/KDnSGApOAl0SXfeXbt2wc/PD61btw6a8v41vXv3xpYtW7Bq1So1xa1bt24qe/jIkSNB5x8JdCVNmhRHjx5V+2/RogXMzMwwevRotY2Li4vaplOnTup49+zZg3bt2sHJyQmVKlWKFf9LDt16iUO3XqnSAybGesfkfw7dxbjt19WyFKrvWjbDzz0o6bSYvyXw5h7QZCVgaoF7Y0sgu/clwBrwdUyPZO2+/t4TERERBWKwi37I+O03cP+1F5I7WmFm03wwC0UdD7lS36pZY5QOOIjLXe0xO6AkJvvWgz9MkcDGDKPSnEflO6MAcxug8XLAJgHfJSKiGEaCTidPnsR///2HKVOmoEePHti+fTsMBkOI4JYEnb7k2rVravtTp06hQIECap00MalatSomTJiAZMmSffYcNzc3/PvvvyoYVq5cObVOgmlZs2bF8ePHUaRIEezcuRNXr17F7t27kSRJEuTJkwcjRozAgAED1EUYCcT9/fffSJs2LSZOnKj2Ic+XYN3kyZNjRbDrtYcP/th0VS03K5IaKeNb479j9zByyzW1Tupy/dRAl7+PCmwp+VoA6cogYMcQmFzbgDSS8KUBWva6MK80Uh8bEBEREX0HpzHSD+lePgPKZk6EaY3zIJ6NfqX+W86dO6c+tBw7eQrtK2TGsoBy+NO/kQp0Vc6eFDvLPtIDXaLubCBJdr5DREQxkPy9l4yp0qVLqwyrsmXLYty4cbC0tAwxVVCmEWbOnBmdO3fG69evgx47duyYmroYGOgSFSpUUNPXTpw48cXveebMGZUBJtsFypIlC1KlSqX2F7jfnDlzqkBXIAlgvXv3DleuXAnaJvg+ArcJ3MfX+Pj4qP0Ev0U3j9++R/3Zx3D7hQcS2pqje7mM2HHlGYZu1H/2LmXSo1eFjD/nYDQNODkXmFkU8Pz43rtvHqwCXQZNg4t1Xhh3PQGTBvMBh+Q/57iIiIgoxmOwi35IYjtLzG9dCPlTf78g/aY5I1G+VDHEixcPp06fhUv5vzHC0CZousSsQq+QYE9ffeOyQ4CsNfjuEBHFUMWLF1dZVZs3b/7i4zKFcdGiRWqKoATBDhw4gCpVqqhphuLZs2cqEBacqakp4sePrx77ElkvmVkSJAtOAluBz5GvwQNdgY8HPvatbSR49f79+6/+zGPGjFGBvcBbypQpEZ3ceemB+rOO4u5LTyRzsMTKjkXx+M179Fx+TsWdmhVJpc7HUj8t0vn7Apt6Alv7Aa53gHOL1OrTK/6E3e2NKpvrTvHJSNt/P5A4S+QfDxEREcUqDHZRmPn6G7DvxovQb//2KQ4NyI9qj8djQbvcqm6KQ6Jk6LLTC74GoFL2JOiS2QNGq1sDmgHI0wwo1Y/vDBFRDDZp0iQ0bNhQTWeUoJZMF5TpgYEaNWqEmjVrqiyr2rVrq6CYTFmUbK+YatCgQWoqZeDt4cOHiC40TcPANRfxxM0b6RPZYHXnYrA0M0Hbhafg7WdA6UyJMLxG9p8T6PJxBxbWAM4uhGqv+Mv/YCjaA+NGDEWCo/9Tm/jmaYmMFdtG/rEQERFRrMRgF4WZdGlqPf8URm3R6318y+tD/8JrfHaUtLoNYyMj1ChTAOZm5ui98ryaSpEmgTX+rJgYRssaAX6eQNrSQI0pwM8YbBMRUaSxsbHBqFGjcOvWLRXUkmmKffr0wZw5c764fbp06ZAwYULcvn1b3ZdaXi9ehLywIh0Tpe7j1+p8yXpfX1+8ffs2xHopkB/4HPn6aXfGwPvf20a6QlpZWX31Z5YaZLJN8Ft0IUGsKY3y4pdsSVRGl72VGdosOIUX7j7InMQO05vkhWko6m5GiB2DgYfHAQsHoOlqvM3eErVq14bFkT+RNp4xNIcUsKyqNwsgIiIiCg8GuyhMLj92w5yDd9TyN6cuernixczqSLCnDxzNA+BllxZovQ3+1adh0u5b2H/jJSxMjTGziBvsV9YF3J8CibIADRax8yIRUSwj0wo7duyopikeOnToi9s8evRI1eySjoeiaNGiKmgldbgC7d27VxW4L1y48Bf3kT9/ftVVUaZGBrpx4wYePHig9he430uXLoUIpEm3RwlMZcuWLWib4PsI3CZwHzHJk7cfp11KM5m5LQrgvqsXWs47ievP3JHQ1gL/tioAO8uf1PX49u4PGV2S3rcEF7wSqbps3rcPo2cRvUi9UfWpgIXtzzkeIiIiipUY7KJQCzBoGLzukqqjUSN3MlTO8eUr69qt3fD4MycSvziEAA1wz98VV2rtwJBzdig0ajem79Ov2o+wWo5se1oCr28BtkmApqsAq5B1VoiIKGaS6YtSh0um80kdrn379qn7EpDy8PDAr7/+qjok3rt3TwWWatWqhQwZMgR1O5QOiFLXq3379qqro0yB79atm5r++KVOjELqZLVt21ZlkMn3k0BZ69atVZBKOjGKihUrqqBW8+bNceHCBezYsQNDhgxB165dVWaW6NSpE+7evYv+/fvj+vXrmDlzJlauXKl+pphk+ckHKP3nPmy//FTdP3P/DVrMO4m6M4+qZSszE/zTsgBSxLP+OQckhcH2fmhCU7gT/jt8T703qRNaYUvHdDCCBuRqBGQM2RyAiIiIKKxMw/wMirOWnXyAC4/cYGdhit+rZf3iNvIBZuLoceiS8B08reLDrNlytNwRgEtHTgdtk9DoHdqbbEYDv82AdUKgQGugUAfANmQhYiIiirmkA6IEnWQao6enp6rF1aZNG3Tv3l1NNbx48SIWLlyosrckeCVBqBEjRgQFnMSSJUtUgKt8+fKqC6OzszOmTZv22fQ8KYTfqlUrdX/y5MlB20p3RAmeSbAqkImJiaoPJtMqJdAi0y1btmyJ//1PrxUl0qZNiy1btqjg1tSpU5EiRQr8888/QYG46E7qc83YdxsTdt5U9w/feoVtl59hw/kn6r6JsRGc8yVHt7IZkSrBTwp0CSlR0GwNAvaPR7/Nb7BzVSts75wdJR2fweitjz4mqDzm5x0PERERxVpGmoyIKMykI5NcQZYr1tGpJkdkeenug/IT9+Odtz+G18iGVsXTfraNTBWpW7cu7t+/j+VzJqKKc3O0XXpZTVm0MTdBleyJUevpFBR9swmmCdICJfsCOZwBs49t6ImIYpu4dr74EglELViwIML36+LigkyZMuHq1avImDEjopuoeO+liYxkYa8680jd71Q6He6/9sS2y8+jLsj1yXTVDk3roGuKa6iW0eTjA8kLAJXHAikLRslxERFFFY4TiCIHM7soVMZsvaYCXTmS26N50TSfPb5pzXIM69MBAVbJVDctmX4ybvt1FeiyNDPGio5FkePccODtBsA6PtBiA+AYvVqyExFRzLJ161Z06NAhWga6ooKblx86LT6DY3dfw9gIGFo9G848eKsCXeYmxpjdIj/KZo6CLGqPl4DLAex7GR/92jfE6pr+SOsggS4jIGt1oGh3IGUhNqchIiKiCMNgF4VKxexJccLFFaNq51RXhgNJHZahvw9B3ttTcbiFJbR642CTNSs2XniCWfv1QvbjnHMhx6vtwOl5+sC27lwGuoiI4pDIyOoSUmeLdO7efqgz6wjuvvRU2dTTGufFlotPsenCE5gaG2Fm03xRE+jy9YK2rBGMHp/Ggwt+2NHAGgktDIBjKqDJKiBxFr6FREREFOEY7KJQkWL05bMmhlmwtuSvXr1CkyZNUNzvEOqVNodmYgKj+E6qY2P/1RfUNh1Lp0MtJzfgn176k0r3Z+FZIiKiCCbdFEtlTARv3wCMq5cLS44/wPYrz9QFqulN8qJCtiQ//zU3BMBvZWuYPT4NN28NDXJZw8rID0icDWi2FrDXO28SERERRTQGu+ib3RcfunohTUIbdT94oEs6XEl9rvpp3dGkVFr8658TV5I0xJXV3rj98oh6bunkQH+/2cDCTYCfF5CuLFB6AF9xIiKiSNDnl0wqi6v9otPw9jOoqYxTG+VB5RxRE1R6vbwLEtzeDt8AwMbKDKaaH5CiENBkhV7SgIiIiCiSMNhFXzV22zUsPfEAUxrlxS/BrgjPmzcPXbp0QYeahfA+Q2n84qu3c8d9+cddLeYxvo1pr8bC5LWX/li8tIDzP4BxsGK0RERE9EOeuXkjkZ0Fdl97jqEbLuP5Ox+1vmCaePi9ejbkSuEYJa/w2Vkdke/5cv2OQwqYejwC0pTUA13m+kU0IiIiosjCYBd90ZozjzD3kEtQZychLdy79eiFRZv2ony30dhsmhEGg57tVSpjQuRPDGS/Pg05PI4gCd7AKH4aIMMvQMaKQNqSgJkVX20iIqII4uMfgBbzTsDLNwCP3rxX61LGt8KgKllRJUdSGBl9rLH5s/j5+WHhb43Q2moXJLXMP215mLvsASzsgbpzGOgiIiKin4LBLvrMuQdvMGjdJbXcvVwGODlaoud/x7Dl2GX42VeCU4vquPph24rxnqJ3s3rI6nMBWNkC8H4L2CcDGqwEUhRkZyUiIqJIMnPfbdx87hF0v1mRVBhSLRsszaImi/rZs2do0KABsnufRJuqltCy14bp9W36gxVH6uMDIiIiop+AwS4KYcP5x/ht7SWVzSVTFwuljY8Gs47CX4MapMo14njWZiiSNh66JLqAnL+0As4vBjb3AbQAIHl+oNFSwC4pX1kiIqJIcvWJG/7aezvofq8KGdGzfMYoyeYShw8fVoEuMWbVHhinNAH2jAACvIG0pYF8LaLkuIiIiChuYrCLFINBw+D1l7Ds5EN1v0i6+GhZJDVa/XsM/jCBtds9/F6/MErkzooU8a0/DKYLAtc2A5t66jvJ2QCo+RdgZslXlYiIKBLce+WJfw+7YOnJBzDIhSgA/6uZDS2KpY2S11vTNEybNg0jBvdDkcKF8M+SNUiaNClwej5w/zBgZg3UmMpMbyIiIvqpGOwixdjYCOYmxpAYVveyGVA8jR2azD2EABMLJMUb7B1eGdaLqwNelYGqEwAjE+D5VWBdR30HBdt9WB81V5SJiIhiuxvP3FF56kFoH4JcYnSdHGhSOHWUHI+Hhwfat2+PjauX48GgZIhvcRdGa2oB3u8Aj2f6RuV+B+JHTSCOiIiI4i4Gu+I4KW5rYarX9vitWlZUz50M7148QsOZZwFLe6SxNWBrl/KwXlwVcH8C3D8K+LgDmgFY1gjw9dC7K1Uey0AXERFRJGROBU5NzJDIBo5WZnjj5Se13zGjSV5UyRk1dbBu3ryJunXr4vGDe3g5LDWs/d8AfgBeXv+4UaYqQOEPF8WIiIiIfiK9lV4UOXjwIGrUqIFkyZKpgdz69eu/+5z9+/cjX758sLCwQIYMGbBgwYLPtpkxYwbSpEkDS0tLFC5cGCdPngzxuLe3N7p27YoECRLA1tYWzs7OeP78OeJakOv39ZfRct5J+Afo3RYl6HXowH60XnxJBbrSxzfHhg55YL28LuB6F3BIBTRfp3dSWtUSeHsfcEwNNFgEmJhF9Y9EREQUq8oLrDz9EJWnHILbez8V9Pp945WgQNfURlEX6Fq3bh0KFCgAQ4Avng1Nowe6RL5WQJOVQPt9QO8rQONlgHHUFMsnIiKiuC1Kg12enp7InTu3Ck6FhouLC6pVq4ayZcvi/Pnz6NWrF9q1a4cdO3YEbbNixQr06dMHw4YNw9mzZ9X+K1WqhBcvXgRt07t3b2zatAmrVq3CgQMH8OTJE3V1Mq546OqF+n8fw3/H7+P4XVccu/sa/v7+aDhgIqZeMoaxtSMyJbbBioYp4LCkKvDiKmDpCORtCpyYDfxXB3A5CJjZ6ANZ6/hR/SMRERHFGhLY6rf6Avqvvogbz92x4IgLhm28gqUnHqhqARMb5EaN3D8/0CVjhYEDB6oxU6WKv+Bi90Sw8NBrfaJIV6DmVCBTJSB5PsAhBTO+iYiIKMoYaTKiigYks0uuFNauXfur2wwYMABbtmzB5cuXg9Y1atQIb9++xfbt29V9yeQqWLAgpk+fru4bDAakTJkS3bt3VwM0Nzc3JEqUCEuXLkW9evXUNtevX0fWrFlx7NgxFClSJFTH++7dOzg4OKj92dvbI6Z4/PY96s86iidu3nC0NsPkhnmQ1RGo3H8m3joVVNtUzJYEk0tosFnVAHjvChibAgb/z3fWcDGQtcbP/yGIiGKQmHq+oKh77+cfccEfm67CxNgIv1bMDJfXnlhx6qEKdI1zzoUGBVL+9LdHLho2btxYZdj/OXY0esffC6OHx/UHczcF6sz86cdERBQbcJxAFAszu8JKglEVKlQIsU6ytmS98PX1xZkzZ0JsY2xsrO4HbiOP+/n5hdgmS5YsSJUqVdA2X+Lj46P+EAW/xTQv3X3Q7J8TKtCVPpENtvQoiYd3bqHwoKVBga5uZTPg72b5YePvCni7AcZmeqDLNsmH2hudgEqjgQ77GegiIiKKYCddXDFqyzW1PKhKFri8+hjoGh9Fga4TJ04gf/786mLj7t270aeE/cdAV+aqDHQRERFRtBOjCtQ/e/YMSZIkCbFO7kvg6f3793jz5g0CAgK+uI1kbwXuw9zcHI6Ojp9tI499zZgxY/DHH38gpnLz8kPzf0+oQXOKeFb4vVpWtJi6FXfeW8IocUaYmxjhT+ecqJXvwyA6fnpVtwvv3wAJMwMtNwF2IV9XIiIiijjP33mjy5Kz8DdoqJHLCVefvMPac49VjS6Zulgnb4qf+nJL8v/s2bPRo0cPtP8lKwb/tQLJ7q0GdvypbyAXwRot/anHRERERBTrMrui0qBBg9Q0hMDbw4cfalTEEA9cvfD4zXsksrNAvpQOaLXgtB7o0gxomD85DjSwQK3DtYDnV4GXN4EFVfVAV6KsQKstDHQRERFFMmkc88rDB5mT2KmAlwS6ZCqjFKP/2YEuLy8vtGrVCp07d8awzvUxvfAjJNvZATj4IdBVbohet/NDp0giIiKi6CRGZXYlTZr0s66Jcl/qYFhZWcHExETdvrSNPDdwHzLdUep8Bc/uCr7Nl0j3R7nFVDlTOGBhm0IYufECNl58Bk0zIJeDL/7qUBlpLv8FrBujb7i1n16QXgJdibMDLTcCNgmj+vCJiIhivf/VyoH3fgF47xuAbZefwcxED3RVzen0U4/jzp07qlP1zZs38d+iRWjmvxy47w88uyBVVoHqk4ACbX7qMRERERHF2syuokWLYs+ePSHW7dq1S60XMj1RakoE30YK1Mv9wG3kcTMzsxDb3LhxAw8ePAjaJjYJ7D8gbcsHLjuGs489gQA/DCieAJt+c0Ya1yPA/g+BrtTFgQfH9UBX8vz61EUGuoiIiH4Kc1NjvHvvh9P338Da3AT/tiz40wNd0gioQIEC8PDwwPHjx9EsrzVw/7D+oNTxrL+AgS4iIiKK9qI0s0sGUrdv3w667+LigvPnzyN+/PiqYPynOnXqpLos9u/fH23atMHevXuxcuVKNTAL1KdPH7Rs2VIN1AoVKoQpU6bA09MTrVu3Vo9LV6S2bduq7eT7SFaYdGqUQFdoOzHGFG88fdF83gm0KpoGo9eegKvBCsYBPvi7aV5UzJMW8HgBbOiibyxZXPeP6Ms5GwA1pwFmVlF6/ERERHGBl68/3nyorXn3pSfiWZthfutCyJMyZH3RyCQ1T//3v/+pW/Xq1fHff//B0coUmOb8caOq44HsX++aTURERBRdRGmw6/Tp0yhbtmzQfQlACQlWLViwAMOHD1df7927p9anTZtWBbZ69+6NqVOnIkWKFPjnn39UR8ZADRs2xMuXLzF06FBVcD5PnjzYvn17iKL1kydPVl0aJUVfuizK82fOjH0ts0dsuYrLj9/h15XnoBlbwQq+WNWzLHKkiCcpb8D6zoDnS8DCDnhxRX9S+aFAiT6swUFERPQT+AUYUHHyQbh7+6ss7OSOVqrsQIbEtj/t9Xd1dUXTpk2xY8cOjBw5UtUplXESdv4OeL7QN8pYCcivXzgkIiIiiu6MtMB5btGQBL2MjIxUwCu6kQ6QkiUmxeolOyy62X/jBVrNPyXzGFXgKp65hg09yyFVAmt9g7OLgI3dAWNTwOAPmFgA9ecDWapF9aETEcUq0f18QVH73m84/xg9l59Xy7bmJtjeuxRSxPtwrv4Jzp49qy7+ybEuW7YMFStW1B94dRuYURDQDICFA9DjLEsbEBFFAo4TiOJYzS6Jwe3fvx8jRoyI6kOJcTx8/NFz8Qn9jpERHC1NsKZ7mY+BLpHDGUhbSg90iZp/MdBFRET0k8c6M/ffCbo/sGrWnxromj9/PooVK4aECROqoFdQoEvc2KoHuoTzvwx0ERERUYwSbbsxSkbX/fv3o/owYhxX9/eoMmIl3Iz1Dop2FiZY2qEY0iX6ZDqE613g0Wl9uWg3IHfDKDhaIiKiuOv43de48cxdLedJ6YAmhT6vVxoZvL290bNnT8yZMwft27fHtGnTYGlpqT/49gFwfBZwZqF+P29zINMvP+W4iIiIiGJ9sIvC7t7DJyg/+RACzPVAl7mJMRa0KYxsyT5MnXh9BzgzHyjaA1jeBPDzAtKVBSr8wZebiIjoJxu19Zr6amwETKifG8ayEMnkQmK9evVw6dIlVfdUmvYozy4DB/8Erm6UXtb6ulTFgKp/RvoxEREREUU0BrtiiXmbD2H42rMwTpxB3bc2M8GcFgWQP3U8vW7XzZ3A2naAzzvg2CxA8wfipQXqzQNM+N+AiIjoZzp3/41qIiNaFEmDDIntIv177tq1C40bN4atrS2OHDmC/PnzA8+vAgfGAlc3fNxQOjRX/B+Qvjwb1hAREVGMxChHDPfc7T3aTNuIK562KtBlbgI0KZIaPctlQjzzAL0Q/eHJ+rTFQCrQlQZovAywjh+Vh09ERBQn/bb+kvpqY26C36pljdTvZTAY8NeYITi0ZAJ6VsyNXj17wc7qEbB6BnB5TciNreIDdecASXNE6jERERERRSYGu2Kwtafvod+qizAY2arsrSbZzNEztxGSmN0Gjq4ETv0L+Hp8fIK5LVCsO5C1BpA4G6/WEhERRQEvX388fO2llvtWzARz08jrF/T27VtM6lodfVNeRM8GVgBuAtu6fHnjRFmBJsv1C2JEREREMRiDXTHQaw8f9F5yAgdd3AEjE0nVwjCzhWjtshNw+cITjIyBrDWBapMAmwRRcMREREQUaNOFJ/DwDUDqBNZoVSxtpL0wFy9exIr+VfBHIQ+YSj2w+OkA2yRAQADw7AIQ4AMYmwCZqwL5WwHpygHG0bZRNxEREVGoMdgVw2y79BS/rjwLDz/9vhEMmGg6C3UtTgMO6QFzGz2Dy9xa77ZYoC1QqD1g7xTVh05EREQAlp54oF4H6b4YWUXplyz+D08Xd8aoInJRDEDOBkCt6YCphX7//lHg1k6gcCfALinfFyIiIopVGOyKITx9/DFg9UVsvvQ0aJ0xAvCX2XRUi/8EKDQYeHMfqD7p45MMAfoVWyIiIooWjt95jQuP3GBiZIQ6eZP/+A6lCc3zK8DdfeqrweMFHt44j9JeL5EiMNAlAS2/98C5xUDBD90XUxfTb0RERESxEINd0Z0ErJ6cg+vF/ThwIQUg9bmMjGAFb0w2m4XKid0AKydg11B9+4wVgcyV9WUGuoiIiKKVibtuqq/WFiZIbG8Z/h09uwQcmQbc3Q94vghaLZMQU8vozt4YmrEZjFIUAE79Axj8ATNrIFstwCZhRPwoRERERNEWg13RUYA/cHU9cHM7cHsPdnukQT+/jnA31tuS1zQ+gkFmy+Bk7gO89tSfY2wKlOgDpC8XtcdOREREX+Tu7Ycz913Vcs3cycL/Kr19CCysAbx/o983s8Zru6yYve0CXvuYokOPX5HZ+xyMrm8BHhzTt0lXFijdn4EuIiIiihMY7IpuDAZgbTvgyjq80uwx3q8hVhtKwwBjZLRyx4icL1HEZS3g7gr4fWgRXqC1XpvLIQKmQxAREVGkmH/kHgyavtytbIbw7cTfF1jdWg90OeWGVnEkpq45hn59BqN06dJYPn8GEu3tA9w/om8vxedL9gNS5I+4H4SIiIgommOwK7rZPUwFut4bWaOi9zi4wkGtrpMnKf6sXwWmAd7AP0tVaXqU/Q3IWQ8wk1biREREFF1pmob/jt1Xy2kT2sDJMZzn7j1/AI9OAZYO8Kg2C617D8fq1asxcOBAjBgxAqbPLwKPzwAW9kC9+UDGChH7gxARERHFAAx2RScn5wJHp8HPYIzCHmPwzlwPdDXIHR/j6ueBkYkxYGINNFoCWDgANgmi+oiJiIgoFM4+eIOXHj5quW3xtOF7za5tBo5NV4sPCwxGxV/q4fHjx9iwahlq1qoNmJoCyfPpQa74aYHEWfneEBERUZwkdUwpGnh2diuWbNyGDr69kdl7Ad6ZJ1Lrm8a7ivE3q8Lo6JSPG8dPx0AXERFRDDJj3x31Vbow1s4XjrIDt3YBq9uoxce2udCuWz9USOGDR1MqoeaN3nqtz0BZqjLQRURERHEaM7uigwfHsXfDAgz2bxssBKmhmfEujHy/QF93ew9QpAunLBIREcVAJTMmxIVHb5ErhQNsLcI4/Hr/Ftg2AAjQM8OSe1zEjkYyWHgNPNipb3PvEJC7USQcOREREVHMw2BXFLj9wh3DN15FjbRGaJjiNbRVrZDYkBVm8IMfzGALT8w2nYziibyAXAOBXA2ABOmj4lCJiIgoArQunlbdpHZXqL25D9gmBpY1BlzvwMPPGIfv+yFX5jRwsjeV6p16AfqczkCyfHyfiIiIiD5gsOsnO3jzJbouPQt3b3/4372C2mbjsSjgF4z3awA/IzNkTGSFf+tlQyr75YBjKsBIDWWJiIgoFjAK7Xn94ARg/xggZRHgwVG4+QB1Nprjj783IlnJkpF9mEREREQxGoNdP9HCo/fwv81XEWDQUNDoOpyND6Kq72jc0ZKr5orlsiTG1EZ5YGdp9jMPi4iIiKKTK+uAvSP05fuH4e2vYfDl9FiyeyucnJyi+uiIiIiIoj0Gu34C/wAD/th0Ff8d11uOVzY+AX/NBL/6d1b341ubYWDVrKiXLwWMjZnJRUREFGc9vQis7xJ0Vy6QLfOtgMlrV8DMjBfDiIiIiEKDwa6fYNqeWyrQZQQNJY0vYp8hD3xgAWMY0LJ4OvT+JRPsmc1FREQUt3m8BJY3Afy8gladdWqG1p1nRulhEREREcU0DHZFMoNBw9Wn79RyArjhoCG3Ws5u64XJ7SsjUxK7yD4EIiIiiu78fYGVLQC3h0GrnuXqhoJ1R0XpYRERERHFRAx2RTKpQ5s54A52ww6v4IgE2ht0LZUGratWDX2RWiIiIorVDEf/gvGDo0H3vYv3R9JfBkfpMRERERHFVMZRfQCxvVZX//k7MOOmnr1V2XAY2wdUR5tqJRjoIiIiIuXVq1eYOmdR0KuhleoPSwa6iIiIiMKNmV2RZP+NFxiw4jSee2mqNldPq53o8ftUGBkzvkhERES6kydPYkj7OthYw0NfUbwnjMr+xpeHiIiI6Acw2BUJ7rx0R7sFp+CvSepcACbbLUet/v8CDHQRERGRZG9pGubOnYv9M3pi9i82sJQRWcaKQIU/9BoIRERERBRuDHZF8MB12cn7GLzuMjQYwQgG/Gv2J8o2GAqYWUXktyIiIqIY6v379+jatSteH/0PGxpZA/ADbJIAtWcx0EVEREQUARjsiiAv3X3Qd8VZHLztKmXpYYIAzDSdirKlygMZf4mob0NEREQxmIuLC5ydnfHq/jXc7hEf0Lz1B+r9A9gkjOrDIyIiIooVWEAqAuy59hxlx+/+EOgCrOCNDWZDUCmTLVBhWER8CyIiIooErVq1+mmv67Zt21CicD40TvEULr3jwTww0FWiD5C21E87DiIiIqLYLk4Hu2bMmIE0adLA0tIShQsXVkViw8P4zh54+OnL8eCOjQ6TkKPhH0Dz9ZyOQEREFAPLEgwdOhROTk6wsrJChQoVcOvWrR/a5+l1s6Atdsa9Thp+zecFE39P/YGkuYGygyPmwImIiIgobge7VqxYgT59+mDYsGE4e/YscufOjUqVKuHFixdh3lfZvJnxh+k8ZDJ+jFXl3iHjwMNAjjoMdBEREUVDr169QsuWLZEqVSosW7YMGTJkQP369eHr66seHz9+PKZNm4a///4bJ06cgI2NjRojeHt/yMQKh3zaBVTNaAYzY01fYekI5GkCNFsNmLCqBBEREVFEMtLk8mUcJJlcBQsWxPTp09V9g8GAlClTonv37hg4cOB3n//u3Ts4ODjAzc0N9vb2wIPjMCQrCGNTk59w9EREFFN8dr6gKNe8eXOVzT1nzhxMmTIFPXr0wPbt2/HHH3/AwsICyZIlQ9++fdGvXz+1vbx3SZIkwYIFC9CoUaPwvfeP9gNn/wOsHICS/YBEmXlRjIiIOE4giiRx8lKiXLk9c+YMBg0aFLTO2NhYTVM4duzYF5/j4+OjboFk4Bo4kFUcswFeH6YkEBERfRB4noij15aipXPnzqFFixYoXbo05s+fj7Jly6qbuHv3Lp49e6bGBIEkYCUXyWSM8K1g1zfHCinK6LdA7u6R88MREVGMwnECUeQwjavTFwICAtRV2uDk/vXr17/4nDFjxqgrvp+SbDAiIqLvef36tQqaUNQrXry4CnJJCYNPSaBLfGmMEPjY13CsQERE4cVxAlHEipPBrvCQLDCp8RXo7du3SJ06NR48eMAPL+G4eiFBwocPH3JKD1+7n4b/7/jaRRXJ7pHaUPHjx4+yY6CQJk2ahNGjR6N37964c+cOzp8/j06dOqnbj+BYIeLwbzZfu6jA/3d87aICxwlEkSNOBrsSJkwIExMTPH/+PMR6uZ80adIvPkdqeMjtU3KVnjVYwkdeN752fO1+Nv6/42sXVWS6PEUPUnB+1KhR6la7dm1UqVJFBb4CSxoEjgmkG2MguZ8nT55v7pdjhYjHv9l87aIC/9/xtYsKHCcQRaw4OfI2NzdH/vz5sWfPnqB1UqBe7hctWjRKj42IiIh+HkdHR3Ts2FEFvA4dOoS0adOqC1/BxwiS7SFdGTlGICIiIooZ4mSwS8iUxLlz52LhwoW4du0aOnfuDE9PT7Ru3TqqD42IiIgikWRxHThwQE0dkRqe+/btU/flQpiRkRF69eqFkSNHYuPGjbh06ZIqZi8dGiULjIiIiIiivzg5jVE0bNgQL1++xNChQ1XBWZmaIG3HPy1I+62pCsOGDfvi1EbiaxdZ+P+Or11U4P87vn6xjdRQk4tet27dUhe69u/fjzZt2qB79+7q8f79+6v1HTp0UDU6S5QoocYIlpaWYfo+/N0JP752fO2iAv/f8bXj/zui2MNIYy90IiIiiqNatWqFBQsWRPVhEBEREVEEirPTGImIiIiIiIiIKPZhZhcREREREREREcUazOwiIiIiIiIiIqJYg8EuIiIiIiIiIiKKNRjs+oYZM2YgTZo0qvtS4cKFcfLkyaDHpItT/PjxkTJlSixZsiTE81atWoUaNWogLhgzZgwKFiwIOzs7JE6cWLVlv3HjRohtvL290bVrVyRIkAC2trZwdnbG8+fPgx53dXVVr5c8ljdvXpw7dy7E8+W5EydORGw2duzYoHb3gfi6fdvjx4/RrFkz9f/KysoKOXPmxOnTp4Mel94b0m3VyclJPV6hQgXVeS2Qj48PmjdvDnt7e2TKlAm7d+8Osf8///wzqDNbbBIQEIDff/8dadOmVa9L+vTpMWLECPV6BeJrpzt48KD625QsWTL1+7l+/foQr+X3XqfAv29NmzZV/88cHR3Rtm1beHh4BD1+7949lCpVCjY2Nuqr3A+uevXqWLNmTaT8X6Afx3HC93GcEHE4Vgg7jhXCh2OF0ONYgSgak26M9Lnly5dr5ubm2rx587QrV65o7du31xwdHbXnz59rGzdu1JIkSaKdOnVKW7p0qWZpaam9fPlSPe/t27daxowZtfv378eJl7VSpUra/PnztcuXL2vnz5/XqlatqqVKlUrz8PAI2qZTp05aypQptT179minT5/WihQpohUrVizo8T59+milS5fWbty4ofXq1UvLnz9/0GPHjh1T9/39/bXY6uTJk1qaNGm0XLlyaT179gxaz9ft61xdXbXUqVNrrVq10k6cOKHdvXtX27Fjh3b79u2gbcaOHas5ODho69ev1y5cuKDVrFlTS5s2rfb+/Xv1+LRp07SsWbOq/7t//vmnlihRIs1gMKjHZH/ye+zm5qbFNqNGjdISJEigbd68WXNxcdFWrVql2draalOnTg3ahq+dbuvWrdrgwYO1tWvXSiRQW7duXYjX8nuvk6hcubKWO3du7fjx49qhQ4e0DBkyaI0bNw56vG7dulqjRo20mzdvag0aNNCcnZ1DnIdq1KgRqf8fKPw4TggdjhMiBscKYcexQvhxrBB6HCsQRV8Mdn1FoUKFtK5duwbdDwgI0JIlS6aNGTNGGzdunNawYcOgxxInTqwGIaJDhw7apEmTtLjqxYsX6kPhgQMHgoJ/ZmZm6gN1oGvXrqltJJAlqlSpos2aNUstX716VbO2tlbLvr6+6kOiBBVjK3d3dxVU2bVrlwr4BQa7+Lp924ABA7QSJUp89XEJWiVNmlQFsQLJa2phYaEtW7ZM3e/cubPaj/Dy8lL/J+X/b+CHMwlwxEbVqlXT2rRpE2KdBFyaNm2qlvnafdmnwa7QvE7y90yeF/xv2LZt2zQjIyPt8ePH6r4EXGVd4IA5W7ZsavnNmzcqMPbgwYMIe+8pYnGcED4cJ4Qdxwrhw7FC+HGsED4cKxBFL5zG+AW+vr44c+aMmpISyNjYWN0/duwYcufOraZLvXnzRm33/v17ZMiQAYcPH8bZs2fRo0cPxFVubm7qq0zxFPL6+Pn5hXgts2TJglSpUqnXUsjruXfvXvj7+2PHjh3IlSuXWj9+/HiUKVMGBQoUQGwlUzSrVasW4vUR/2/vTqBsrt84jn9liBoG0RCZUGRpM9SxpBNlOyfTEHVyapJEKEciI/uRIyonIlsNRUVJ5GTLVigtMiRLiyzZlWyHxO9/Ps//f+//3jEz7lw0d67365zL3P33+97fvfOd5z7P8/2OccvWnDlz7Lho3bq1lc+q/HXixIn+67du3er27NkTNK5xcXFWjhx43Ok9q/evjjuVopUsWdLKklW6nJyc7KJR3bp13eLFi92WLVvsfHp6uo1Ds2bN7DxjF5pQxkn/q3Qx8DNMt9fvk9WrV/uPQ5XQnjlzxi1cuND/+dezZ0/7fFCpPCIP84TwMU/IOeYK4WGuED7mChcGcwUgl+V2tC0S6Rt3Dc2qVauCLu/Zs6d9kysDBgzwKlWq5NWoUcMyQE6ePGk/q0xv9OjRXuXKla1UTyVSlwplv+mboHr16vkvmzZtmpWDZlS7dm2vV69e/mwIlfWo/LFBgwZWNqqSHmU8HThwwOvYsaOVBrVu3dpuGy2U/aFjxlfyFJjZxbhlT9kzOqWmpnpr1qzxxo8fb+XEkydPtutXrlxp7+Fdu3YF3U/HkErFfJmDnTt3thLSWrVqWYnZwYMHvYoVK1o2jcrX9B5v3Lixt3PnTi+a3qf6tlvZRTExMfb/0KFD/dczdqF9WxvKOKkMRL8LMlLJ7NixY+1nHVv63FSpt/7XeWXG6pjU8ajH0+efPgf1ewaRgXlCeJgn5BxzhfAxVwgfc4XwMFcAIktMbgfb8qqBAwfayWfQoEH2jX2BAgXckCFD3Pr1693cuXPdo48+alk6lwJ98/jDDz9YlkhOKBvi3XffDbqsYcOG1iBcWTa//vqrNb3v0KGDGzx4cFQ0q9+xY4fr1q2bW7RokWURheNSHDcfZcEoW2bo0KF2XpldOvbGjRvnUlJSQnoMvVfVXDpQu3btLDNTiySoGbmynpRhqMuipUn4jBkz7PjQsVO9enW3du1aWxhBTdgZu39f2bJl7XdF4MIJTZo0cVOmTLHfJVr8Q+/jpk2buvHjx0flognRinnC2Zgn5AxzhfPDXCF8zBUiC3MFIDyUMWZCpUz58+cPWjFQdL506dJn3X7Tpk1u6tSptqLZsmXLbEWtUqVKuTZt2lhZ45EjR1y069q1q/3BtnTpUleuXDn/5RovlXscOnQopLGUtLQ0K/1JSkqy8dQKjwpMqGRN56OBAqD79u1zNWvWdDExMXZavny5GzVqlP0cHx/PuGVDJYfVqlULuqxq1apu+/bt9rPv2Ar1PSw6djds2GDHso6z5s2b2wp5eh9Hy3HnK4/r3bu3e+ihh2wFS61I2b17d1sxTRi70IQyTvpf7/NAKtfWCo1ZHYcK4DZu3NglJibacafVa/X517Jly6g6DvM65gk5xzwh55grnB/mCuFjrnBhMFcAchfBrkwULFjQ/tBQX5vAb4d0vk6dOkG3VcZqx44d3auvvupiY2NtqV71qBLf/7osWmn/NYGdNWuW9d2qUKFC0PUaR/2hFjiWylJQUCLjWMr+/fstC2n06NF2PuN4RstYNmrUyLL/lFXjOylTqW3btv6fGbes1atXz46jQOpBlZCQYD/rONQEI/C4O3z4sPVJyuy4O3HihGUcKHNGge5oPe7k+PHj1jMqkPZZn3HC2IUmlHHS/wr0B2b36nNSY63eXhlt3LjRMu70xYlE83GY1zFPCB3zhPAxVzg/zBXCx1zhwmCuAOSy3K6jjOQlxVXrrx5AWlFLqywWK1bM27NnT9DtJkyYELRU/OrVq72iRYvaSoP9+/f3r6wVrbSiXVxcnLds2TJv9+7d/pNWt/Pp1KmT9eNasmSJ9TSrU6eOnTLz8MMPW88zH618mZiYaK+BVm1Uj6VoFdizSxi3rGn1U/WbUk+kn376yXqcaRXPqVOn+m8zbNgwe8/Onj3bW7dunZeUlGS9j3w90gL16dPH69Gjh//89OnT7ZhNT0/32rdv7zVv3tyLFikpKV7ZsmW9uXPnelu3brWegyVLlvT30BPG7v8roH3//fd20q9LrbSrn7dt2xbyODVt2tS77bbb7HfDihUrrBehehRmpNUdtcLoJ598EvT5qj5e+vzTYwwfPvyiHhvIGeYJoWGecGExVwgdc4XwMVcIHXMFIHIR7MqGgi76g1cN1tWY/quvvgq6XoGvhIQE/xLyPoMGDfJKlCjh3XjjjfYHTjTTH4CZndLS0vy30R9+ClIVL17cAhLJyckWEMto/vz5Ns5qiulz7Ngxa9BcpEgRr1GjRt7evXu9S2UCy7hlT0EBNfhXUFrvNQWeMwYP+vXr58XHx9ttdPxs3rz5rMdZv369d/3113tHjx71X6ZjUH+gKXCtxRQUUIsWhw8ftuNMn21q6q+G/GrGH9j8nLH7r6VLl2b6+aY/AkIdJzWZV3ArNjbWjqd27drZxDijcePGBX1xIvq802Pq80+fg/o8RGRhnnBuzBMuLOYKOcNcITzMFULHXAGIXPn0T25nlwEAAAAAAAAXAj27AAAAAAAAEDUIdgEAAAAAACBqEOwCAAAAAABA1CDYBQAAAAAAgKhBsAsAAAAAAABRg2AXAAAAAAAAogbBLgAAAAAAAEQNgl0AAAAAAACIGgS7gBzIly+f+/jjj/PMmHme55588klXokQJ2/a1a9fm9ibhHB577DF3//33M04AkAcxT8DFxjwBAEJDsAv4n/3797unnnrKlS9f3l1++eWudOnSrkmTJm7lypV5dozmz5/vJk+e7ObOnet2797tatSocdZtdH2xYsUiftKuyZ22J6vTddddl6vbRoAKAKIb84SzMU8IDfMEAPj3xeTCcwIRqVWrVu7vv/92U6ZMcRUrVnR79+51ixcvdgcPHryoz6vnLFiw4EV57F9++cWVKVPG1a1b10WanO73a6+95oYNG+Y/r/1KS0tzTZs2tfP58+e/qM8PALi0MU/4dzFPAACcDzK7AOfcoUOH3BdffOFeeukld/fdd7uEhAR3++23u9TUVNeiRYugMTpw4IBLTk52V1xxhbvhhhvcnDlz/NedPn3atW/f3lWoUMEVLlzYValSxYI0mX279+KLL7prrrnGbiM7duxwbdq0sSwrlR0mJSW53377LdvXZ/ny5badykRT8Kd3797un3/+8T/P008/7bZv335BMp/+/PNP17ZtW1eqVCnbN+27gk0+59r+rPZ77Nix9liFChVy8fHx7oEHHsj0+ePi4izbzncSPZfv/Msvv+wqV65sr4uClf369XOnTp3y33/gwIHu1ltvdZMmTbLXR88nmzZtcvXr17fz1apVc5999tlZ31Rnt296XAVIZ8+e7c8yW7ZsWUhjouPl2Wefteuvuuoq16tXLys9BQBEFuYJ58Y8gXkCAEQSgl2Acy42NtZOCnCcPHky2zEZNGiQBTDWrVvnmjdvbgGgP/74w647c+aMK1eunPvggw/cjz/+6Pr37+/69OnjZsyYEfQYyhjbvHmzW7RokZUYKiijkskiRYpY0E2lk9oeZS3pm83M/P777/b8tWvXdunp6e6NN95wb775phsyZIhdryDb4MGDbXtUwvjNN9+c12ut4JH2ad68eW7jxo32fCVLlrTrQt3+jPv97bffumeeeca2U5er7LJBgwZhbZ+eWyWZ2kbt+8SJE93IkSODbvPzzz+7mTNnuo8++sj6lynYpACcAmSrV692EyZMcC+88ELQfc61b88995wdDzqvcdZJmXShjMkrr7xi2/zWW2+5FStW2HE0a9assPYfAHDxME84N+YJzBMAIKJ4AMyHH37oFS9e3CtUqJBXt25dLzU11UtPTw8aHb1l+vbt6z9/9OhRu2zevHlZjmKXLl28Vq1a+c+npKR48fHx3smTJ/2XvfPOO16VKlW8M2fO+C/T9YULF/YWLFiQ6eP26dPnrPuMGTPGi42N9U6fPm3nR44c6SUkJGT7CqelpXlxcXGZXqd9mzVrlv183333ee3atcv0dqFsf2b7PXPmTK9o0aLe4cOHs93Gc21bZkaMGOElJib6zw8YMMArUKCAt2/fPv9let1iYmK83bt3+y9btGhR0GOHum9JSUk5HpMyZcp4w4cP919/6tQpr1y5cmc9FgAg9zFPOBvzBOYJABCpyOwCAnpx7Nq1y8oSlX2jUrSaNWta5k2gm2++2f/zlVde6YoWLer27dvnv2zMmDEuMTHRyv30TbCyhVRKGOimm24K6helzCxlHSkLyPftscreTpw4YX23MqPsqjp16ljZnE+9evXc0aNH3c6dOy/466rm/e+//76VAqrcbtWqVTne/oz7fe+991rJqMoOH3nkETdt2jR3/PjxsLZv+vTptv8qadTz9+3b96xx13PpdfFRNtm1117rL4sUlYUGCue1CeV+f/31l2WB3XHHHf77xMTEuFq1aoW1/wCAi4t5QvaYJzBPAIBIQoN6IID6NikAo5PS8Z944gk3YMAA6zflU6BAgaAxU7BJ5YuiYJDK2lSepkCUAh0jRoywErlACpIFUoBKATIFezIKDM5cDArWHTt2zPbhsssuC+pP4uuVJc2aNXPbtm1zn376qZUhNmrUyHXp0sV6ZYW6/Rn3W+OzZs0aCywuXLjQyj7VA0sll1mtEJmZL7/80spJVWKq0kFts14LvQ6BMj5/KMJ9bXLzNQUAXBzME5gnZMQ8AQAiE8EuIBtqWB7YqPxc1JdJ/Zo6d+7svyy77B8fZZApM+nqq6+24FMoqlatav2nVEXgy+7S8yuApD5doVKjeDW1Vw8rbYePglCipu+BQZqUlBQ73Xnnna5nz54W7Apn+wOzme655x47KbCoINeSJUtcy5YtQ34MZZkpayuw35YCc6Hsu5rIa+VNNceXjL3NQtk3Zaup/1dO76dFBRQI9fUp0+vw3XffBb0OAIDIxTyBeQLzBACITJQxAs65gwcPuoYNG7qpU6da4/mtW7dak/nhw4fbCnqh0qqCarq+YMECt2XLFssOC6UxvLKS1Oxdz6Vm5np+ZTupeXtWJYkKqClQoxUXtaKgVgNUsEir+wVmaJ1L9erVXePGjd3jjz9uDeT13GoUr8d/8MEHXdmyZe12yrrSc6g0b8OGDdZgXgG3cLdf9BijRo2yQJuCU2+//bZlmPlWaszJuKtkUdlcCi7qMUNp9K4MvkqVKlnwTq+7goUqfxRfADGUfdNKl7q/yiK1Wqea04dyv27durlhw4ZZQFWvocbcl1EHAIgczBOYJzBPAIC8hWAX8L9VltQ7Sav3KcumRo0aFqjq0KGDe/3110Meo44dO1pGkoJEejxNjgOzvLKi1QA///xzV758ebu/gkjt27e3/k5ZZQUpCKWSwq+//trdcsstrlOnTnYfX7AmJ5SBdNddd9n2K/ilgIyCNJMmTQrKXkpNTbWeZRqj/PnzW3Ap3O0XZXFpZUQFGnWfcePGuffee8+2ISdatGjhunfv7rp27Wo9xZTppdfvXLQPCjSpBEGrWqps1ZcdplKVUPdNx4kCdOq3pew3Bc1CuV+PHj2sV5mCbb6y1+Tk5BztOwDg4mOewDyBeQIA5C351KU+tzcCACKFAlX169e3DDZlfQEAADBPAIC8hWAXgEuayh31jb1KIRXgUmlh8eLF3YoVK3J70wAAQC5jngAAeRMN6gFc0o4cOeKef/556/mlHltqlJ9xFUcAAHBpYp4AAHkTmV0AAAAAAACIGjSoBwAAAAAAQNQg2AUAAAAAAICoQbALAAAAAAAAUYNgFwAAAAAAAKIGwS4AAAAAAABEDYJdAAAAAAAAiBoEuwAAAAAAABA1CHYBAAAAAADARYv/AB3/ILk4ky4NAAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "evaluator = fr.evaluate.UpliftEvaluator(\n",
+ " metric_cols=[\"spend\", \"conversion\"],\n",
+ " is_treated_col=\"treatment\",\n",
+ " treatment_propensity_col=\"treatment_propensity\",\n",
+ " effect_type=fr.EffectType.ATE\n",
+ ")\n",
+ "\n",
+ "models = {\n",
+ " \"spend_t_learner_score\": \"Spend T-Learner\",\n",
+ " \"conversion_t_learner_score\": \"Conversion T-Learner\",\n",
+ " \"spend_t_learner_score_distill\": \"Spend T-Learner\",\n",
+ " \"conversion_t_learner_score_distill\": \"Conversion T-Learner\",\n",
+ "}\n",
+ "\n",
+ "results = evaluator.evaluate(criteo.test_data, score_cols=list(models.keys()))\n",
+ "\n",
+ "fig, axs = plt.subplots(ncols=2, figsize=(12, 4), constrained_layout=True)\n",
+ "\n",
+ "plot_cumulative_incrementality(\n",
+ " axs[0],\n",
+ " results,\n",
+ " title=\"Incremental Conversions vs Share Targeted\",\n",
+ " model_names=models,\n",
+ " x_col=\"share_targeted\",\n",
+ " y_col=\"conversion__inc_cum\",\n",
+ " x_label=\"Share of Users Targeted\",\n",
+ " y_label=\"Incremental Conversions\",\n",
+ " x_format=\"{:.0%}\",\n",
+ " y_format=\"{:,.0f}\",\n",
+ " show_legend=False,\n",
+ " x_lim=[0, 1],\n",
+ " y_lim=[0, None]\n",
+ ")\n",
+ "\n",
+ "plot_cumulative_incrementality(\n",
+ " axs[1],\n",
+ " results,\n",
+ " title=\"Incremental Revenue vs Share Targeted\",\n",
+ " model_names=models,\n",
+ " x_col=\"share_targeted\",\n",
+ " y_col=\"spend__inc_cum\",\n",
+ " x_label=\"Share of Users Targeted\",\n",
+ " y_label=\"Incremental Revenue\",\n",
+ " x_format=\"{:.0%}\",\n",
+ " y_format=\"${:,.0f}\",\n",
+ " x_lim=[0, 1],\n",
+ " y_lim=[0, None]\n",
+ ")\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ed581503",
+ "metadata": {},
+ "source": [
+ "uplift model들이 제대로 작동하고 있는 것으로 보입니다. uplift curve가 대부분 random baseline(검은 선) 위에 위치해 있는데, 이는 사용자를 random으로 선택하는 것보다 더 나은 성과를 내고 있음을 의미합니다.\n",
+ "\n",
+ "두 모델 모두 incremental conversions를 최적화하는 데에는 무난한 성능을 보이지만, incremental revenue를 찾아내는 데에서는 spend T-Learner가 확실히 더 우수한 성능을 보입니다.\n",
+ "\n",
+ "이는 conversion T-Learner가 spend에 대한 정보를 전혀 알지 못하기 때문에 충분히 예상 가능한 결과입니다.\n",
+ "\n",
+ "하지만 과연 이 방법이 uplift model을 평가하는 올바른 방법일까요? 일반적으로는 그렇지 않습니다. 사용자 비율을 기준으로 타겟팅하는 것이 동일한 비용 비율을 의미하지는 않기 때문입니다. 예컨대, 모델이 선택한 사용자들이 오히려 가장 cost가 많이 드는 사용자들일 수도 있습니다.\n",
+ "\n",
+ "다음 섹션에서는 보다 현실적인 마케팅 목표를 기준으로 uplift model을 평가해 보고, 이러한 환경에서 T-Learner들이 적절한 fractional uplift model과 비교했을 때 얼마나 잘 동작하는지도 함께 살펴보겠습니다."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "353e5314",
+ "metadata": {},
+ "source": [
+ "## Fractional uplift modelling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3000ba3e",
+ "metadata": {},
+ "source": [
+ "### Objective 1: Minimum Cost per Incremental Acquisition (CPiA)\n",
+ "\n",
+ "첫 번째로 살펴볼 마케팅 목표는 incremental acquisition당 비용(cost per incremental acquisition, CPiA)을 최소화하는 것입니다. 여기서 acquisition은 conversion을 의미합니다.\n",
+ "\n",
+ "즉, 우리가 얻는 incremental conversion 하나당 가능한 한 가장 적은 비용을 지출하는 것이 목표입니다. \n",
+ "\n",
+ "CPiA는 다음과 같이 정의됩니다.\n",
+ "\n",
+ "$$ \\text{CPiA} = \\frac{\\text{Cost}_{T=1}}{N_{\\text{convert}, \\, T=1} - N_{\\text{convert}, \\, T=0}} $$\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6377e331",
+ "metadata": {},
+ "source": [
+ "#### Fractional uplift model\n",
+ "\n",
+ "CPiA를 최적화하기 위해 fractional uplift model에서 다음과 같은 metric 설정을 사용합니다.\n",
+ "\n",
+ "- Maximize KPI ($\\alpha$) = Conversion \n",
+ "- Constraint KPI ($\\beta$) = Cost \n",
+ "- Constraint Offset KPI ($\\gamma$) = *사용하지 않음*\n",
+ "\n",
+ "아래에서는 이러한 설정을 바탕으로 fractional uplift model을 학습합니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f516ac56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_data = fr.datasets.PandasTrainData(\n",
+ " features_data=criteo.train_data[criteo.features],\n",
+ " maximize_kpi=criteo.train_data[\"conversion\"].values,\n",
+ " constraint_kpi=criteo.train_data[\"cost\"].values,\n",
+ " is_treated=criteo.train_data[\"treatment\"].values,\n",
+ " treatment_propensity=criteo.train_data[\"treatment_propensity\"].values,\n",
+ " sample_weight=criteo.train_data[\"sample_weight\"].values,\n",
+ " shuffle_seed=1234\n",
+ ")\n",
+ "fractional_t_learner = fr.meta_learners.FractionalLearner(get_base_regressor())\n",
+ "fractional_t_learner.fit(train_data)\n",
+ "\n",
+ "distill_fractional_t_learner = get_base_regressor()\n",
+ "fractional_t_learner.distill(distill_dataset, distill_fractional_t_learner)\n",
+ "\n",
+ "criteo.test_data[f\"cpia_score\"] = fractional_t_learner.predict(test_dataset)\n",
+ "criteo.test_data[f\"cpia_score_distill\"] = distill_fractional_t_learner.predict(test_dataset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "id": "f707b4c2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " f0 | \n",
+ " f1 | \n",
+ " f2 | \n",
+ " f3 | \n",
+ " f4 | \n",
+ " f5 | \n",
+ " f6 | \n",
+ " f7 | \n",
+ " f8 | \n",
+ " f9 | \n",
+ " ... | \n",
+ " cost_percentage | \n",
+ " spend | \n",
+ " cost | \n",
+ " sample_weight | \n",
+ " conversion_t_learner_score | \n",
+ " conversion_t_learner_score_distill | \n",
+ " spend_t_learner_score | \n",
+ " spend_t_learner_score_distill | \n",
+ " cpia_score | \n",
+ " cpia_score_distill | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1007 | \n",
+ " 13.221509 | \n",
+ " 10.059654 | \n",
+ " 8.471764 | \n",
+ " -1.666396 | \n",
+ " 13.059169 | \n",
+ " 2.230907 | \n",
+ " -14.476362 | \n",
+ " 9.170324 | \n",
+ " 3.793946 | \n",
+ " 39.917532 | \n",
+ " ... | \n",
+ " 0.051091 | \n",
+ " 37.386848 | \n",
+ " 1.910123 | \n",
+ " 1.0 | \n",
+ " 0.035170 | \n",
+ " 0.053963 | \n",
+ " 0.217223 | \n",
+ " 0.484556 | \n",
+ " 0.025971 | \n",
+ " 0.011292 | \n",
+ "
\n",
+ " \n",
+ " | 1080 | \n",
+ " 15.643772 | \n",
+ " 10.059654 | \n",
+ " 8.232822 | \n",
+ " 3.907662 | \n",
+ " 11.029584 | \n",
+ " 4.115453 | \n",
+ " -1.288207 | \n",
+ " 4.833815 | \n",
+ " 3.858041 | \n",
+ " 34.180688 | \n",
+ " ... | \n",
+ " 0.469323 | \n",
+ " 37.690649 | \n",
+ " 17.689078 | \n",
+ " 1.0 | \n",
+ " 0.015709 | \n",
+ " 0.015331 | \n",
+ " 0.661332 | \n",
+ " 0.196871 | \n",
+ " 0.048494 | \n",
+ " 0.030863 | \n",
+ "
\n",
+ " \n",
+ " | 1295 | \n",
+ " 13.236812 | \n",
+ " 11.119309 | \n",
+ " 8.329031 | \n",
+ " -2.570015 | \n",
+ " 11.561050 | \n",
+ " 1.128518 | \n",
+ " -18.659676 | \n",
+ " 5.814641 | \n",
+ " 3.855652 | \n",
+ " 34.621266 | \n",
+ " ... | \n",
+ " 0.007478 | \n",
+ " 45.282760 | \n",
+ " 0.338639 | \n",
+ " 1.0 | \n",
+ " -0.233079 | \n",
+ " -0.195807 | \n",
+ " -12.409683 | \n",
+ " -8.438211 | \n",
+ " -0.363601 | \n",
+ " -0.103153 | \n",
+ "
\n",
+ " \n",
+ " | 1670 | \n",
+ " 19.622574 | \n",
+ " 10.059654 | \n",
+ " 8.305211 | \n",
+ " 3.907662 | \n",
+ " 13.253813 | \n",
+ " 4.115453 | \n",
+ " -1.288207 | \n",
+ " 4.833815 | \n",
+ " 3.803324 | \n",
+ " 41.176485 | \n",
+ " ... | \n",
+ " 0.010958 | \n",
+ " 44.872755 | \n",
+ " 0.491714 | \n",
+ " 1.0 | \n",
+ " -0.137972 | \n",
+ " -0.126640 | \n",
+ " -10.253737 | \n",
+ " -9.813921 | \n",
+ " -0.158326 | \n",
+ " -0.219133 | \n",
+ "
\n",
+ " \n",
+ " | 1940 | \n",
+ " 12.616365 | \n",
+ " 10.059654 | \n",
+ " 8.681976 | \n",
+ " 4.679882 | \n",
+ " 11.029584 | \n",
+ " 4.115453 | \n",
+ " 0.294443 | \n",
+ " 4.833815 | \n",
+ " 3.864711 | \n",
+ " 13.190056 | \n",
+ " ... | \n",
+ " 0.993622 | \n",
+ " 19.274877 | \n",
+ " 19.151940 | \n",
+ " 1.0 | \n",
+ " 0.002356 | \n",
+ " 0.001106 | \n",
+ " 0.003596 | \n",
+ " -0.081532 | \n",
+ " 0.113706 | \n",
+ " 0.011170 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 25 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " f0 f1 f2 f3 f4 f5 \\\n",
+ "1007 13.221509 10.059654 8.471764 -1.666396 13.059169 2.230907 \n",
+ "1080 15.643772 10.059654 8.232822 3.907662 11.029584 4.115453 \n",
+ "1295 13.236812 11.119309 8.329031 -2.570015 11.561050 1.128518 \n",
+ "1670 19.622574 10.059654 8.305211 3.907662 13.253813 4.115453 \n",
+ "1940 12.616365 10.059654 8.681976 4.679882 11.029584 4.115453 \n",
+ "\n",
+ " f6 f7 f8 f9 ... cost_percentage \\\n",
+ "1007 -14.476362 9.170324 3.793946 39.917532 ... 0.051091 \n",
+ "1080 -1.288207 4.833815 3.858041 34.180688 ... 0.469323 \n",
+ "1295 -18.659676 5.814641 3.855652 34.621266 ... 0.007478 \n",
+ "1670 -1.288207 4.833815 3.803324 41.176485 ... 0.010958 \n",
+ "1940 0.294443 4.833815 3.864711 13.190056 ... 0.993622 \n",
+ "\n",
+ " spend cost sample_weight conversion_t_learner_score \\\n",
+ "1007 37.386848 1.910123 1.0 0.035170 \n",
+ "1080 37.690649 17.689078 1.0 0.015709 \n",
+ "1295 45.282760 0.338639 1.0 -0.233079 \n",
+ "1670 44.872755 0.491714 1.0 -0.137972 \n",
+ "1940 19.274877 19.151940 1.0 0.002356 \n",
+ "\n",
+ " conversion_t_learner_score_distill spend_t_learner_score \\\n",
+ "1007 0.053963 0.217223 \n",
+ "1080 0.015331 0.661332 \n",
+ "1295 -0.195807 -12.409683 \n",
+ "1670 -0.126640 -10.253737 \n",
+ "1940 0.001106 0.003596 \n",
+ "\n",
+ " spend_t_learner_score_distill cpia_score cpia_score_distill \n",
+ "1007 0.484556 0.025971 0.011292 \n",
+ "1080 0.196871 0.048494 0.030863 \n",
+ "1295 -8.438211 -0.363601 -0.103153 \n",
+ "1670 -9.813921 -0.158326 -0.219133 \n",
+ "1940 -0.081532 0.113706 0.011170 \n",
+ "\n",
+ "[5 rows x 25 columns]"
+ ]
+ },
+ "execution_count": 149,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "criteo.test_data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b51f3134",
+ "metadata": {},
+ "source": [
+ "#### Evaluation\n",
+ "\n",
+ "가장 낮은 CPiA는 가장 낮은 비용으로 가장 많은 incremental conversions를 달성하는 경우에 해당합니다. 이를 다음 두 가지 방식으로 평가합니다.\n",
+ "\n",
+ "1. 총 비용(total cost)에 따른 incremental conversions를 시각화합니다. 동일한 비용에서 incremental conversions가 가장 높은 모델이 가장 우수한 모델입니다.\n",
+ "2. incremental conversions에 따른 CPiA를 시각화합니다. 동일한 incremental conversions에서 CPiA가 가장 낮은 모델이 가장 우수한 모델입니다.\n",
+ "\n",
+ "이러한 지표들은 아래의 UpliftEvaluator를 사용하여 계산합니다.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "id": "46fddcae",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "class CPIAUpliftEvaluator(fr.evaluate.UpliftEvaluator):\n",
+ " def __init__(self, **kwargs):\n",
+ " kwargs[\"metric_cols\"] = [\"spend\", \"conversion\", \"cost\"]\n",
+ " super().__init__(**kwargs)\n",
+ "\n",
+ " def _calculate_composite_metrics(self, data: pd.DataFrame) -> pd.DataFrame:\n",
+ " data[\"cpia__inc_cum\"] = data[\"cost__inc_cum\"] / data[\"conversion__inc_cum\"]\n",
+ " data[\"cpia__inc\"] = data[\"cost__inc\"] / data[\"conversion__inc\"]\n",
+ " return data\n",
+ "\n",
+ "evaluator = CPIAUpliftEvaluator(\n",
+ " is_treated_col=\"treatment\",\n",
+ " treatment_propensity_col=\"treatment_propensity\",\n",
+ " effect_type=fr.EffectType.ATE\n",
+ ")\n",
+ "\n",
+ "models = {\n",
+ " \"spend_t_learner_score\": \"Spend T-Learner\",\n",
+ " \"conversion_t_learner_score\": \"Conversion T-Learner\",\n",
+ " \"cpia_score\": \"CPiA Fractional T-Learner\",\n",
+ " \"cpia_score_distill\": \"CPiA Fractional T-Learner\",\n",
+ "}\n",
+ "\n",
+ "results = evaluator.evaluate(criteo.test_data, score_cols=list(models.keys()))\n",
+ "\n",
+ "fig, axs = plt.subplots(ncols=2, figsize=(12, 4), constrained_layout=True)\n",
+ "\n",
+ "plot_cumulative_incrementality(\n",
+ " axs[0],\n",
+ " results,\n",
+ " title=\"Incremental Conversions vs Cost\",\n",
+ " model_names=models,\n",
+ " x_col=\"cost__inc_cum\",\n",
+ " y_col=\"conversion__inc_cum\",\n",
+ " x_label=\"Cost\",\n",
+ " y_label=\"Incremental Conversions\",\n",
+ " x_format=\"${:,.0f}\",\n",
+ " y_format=\"{:,.0f}\",\n",
+ " x_lim=[0, None],\n",
+ " y_lim=[0, None],\n",
+ " show_legend=False\n",
+ ")\n",
+ "\n",
+ "plot_cumulative_incrementality(\n",
+ " axs[1],\n",
+ " results,\n",
+ " title=\"Cost per Incremental Acquisition (CPiA)\",\n",
+ " model_names=models,\n",
+ " x_col=\"conversion__inc_cum\",\n",
+ " y_col=\"cpia__inc_cum\",\n",
+ " x_label=\"Incremental Conversions\",\n",
+ " y_label=\"CPiA\",\n",
+ " x_format=\"{:,.0f}\",\n",
+ " y_format=\"${:,.0f}\",\n",
+ " x_lim=[0, None]\n",
+ ")\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0ac57c64",
+ "metadata": {},
+ "source": [
+ "fractional uplift 모델은 기존 uplift 모델보다 명확하게 더 우수한 성과를 보입니다. 동일한 비용 대비 훨씬 낮은 CPiA를 달성하고, 더 많은 추가 전환(incremental conversions)을 만들어냅니다. \n",
+ "\n",
+ "반면 T-Learner는 일부 경우 오히려 CPiA를 증가시키는 모습을 보입니다. 이는 T-Learner가 비용을 고려하지 않기 때문에, 증분 효과는 크지만 비용이 높은 사용자들을 타겟팅하게 되고, 그 결과 전체적으로는 더 나쁜 CPiA를 초래하기 때문입니다.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "75dda433",
+ "metadata": {},
+ "source": [
+ "### Objective 2: Maximum iRoI\n",
+ "\n",
+ "이번에는 CPiA가 아니라 수익(revenue) 관점에서 살펴보겠습니다. 모든 전환이 동일한 가치를 갖는 것은 아니며, 어떤 전환은 다른 전환보다 훨씬 더 큰 매출을 만들어냅니다.\n",
+ "\n",
+ "따라서 가능한 한 낮은 비용으로 최대의 추가 매출(incremental revenue, spend) 을 창출하고자 한다면, 우리의 목표는 iRoI를 최대화하는 것이 됩니다.\n",
+ "\n",
+ "$$\n",
+ "\\text{iRoI} = \\frac{\\text{Spend}_\\text{T=1} - \\text{Spend}_\\text{T=0}}{\\text{Cost}_\\text{T=1}}\n",
+ "$$\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10405922",
+ "metadata": {},
+ "source": [
+ "#### Fractional uplift model\n",
+ "\n",
+ "iRoI를 최적화하기 위해 fractional uplift 모델에서 다음과 같이 지표를 설정합니다.\n",
+ "\n",
+ "- Maximize KPI ($\\alpha$) = Spend \n",
+ "- Constraint KPI ($\\beta$) = Cost \n",
+ "- Constraint Offset KPI ($\\gamma$) = *사용하지 않음*\n",
+ "\n",
+ "아래에서는 이러한 설정을 바탕으로 fractional uplift 모델을 학습합니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c23b3cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_data = fr.datasets.PandasTrainData(\n",
+ " features_data=criteo.train_data[criteo.features],\n",
+ " maximize_kpi=criteo.train_data[\"spend\"].values,\n",
+ " constraint_kpi=criteo.train_data[\"cost\"].values,\n",
+ " is_treated=criteo.train_data[\"treatment\"].values,\n",
+ " treatment_propensity=criteo.train_data[\"treatment_propensity\"].values,\n",
+ " sample_weight=criteo.train_data[\"sample_weight\"].values,\n",
+ " shuffle_seed=1234\n",
+ ")\n",
+ "fractional_t_learner = fr.meta_learners.FractionalLearner(get_base_regressor())\n",
+ "fractional_t_learner.fit(train_data)\n",
+ "\n",
+ "distill_fractional_t_learner = get_base_regressor()\n",
+ "fractional_t_learner.distill(distill_dataset, distill_fractional_t_learner)\n",
+ "\n",
+ "criteo.test_data[f\"roi_score\"] = fractional_t_learner.predict(test_dataset)\n",
+ "criteo.test_data[f\"roi_score_distill\"] = distill_fractional_t_learner.predict(test_dataset)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8d235815",
+ "metadata": {},
+ "source": [
+ "#### Evaluation\n",
+ "\n",
+ "가장 높은 iRoI는 가장 낮은 비용으로 가장 많은 추가 매출(incremental revenue) 을 달성한 경우에 해당합니다. 이를 다음 두 가지 방식으로 평가합니다.\n",
+ "\n",
+ "1. 총 비용 대비 추가 매출을 시각화합니다. 동일한 비용에서 추가 매출이 가장 높은 모델이 가장 우수한 모델입니다.\n",
+ "2. 추가 매출 대비 iRoI를 시각화합니다. 동일한 추가 매출 수준에서 iRoI가 가장 높은 모델이 가장 우수한 모델입니다.\n",
+ "\n",
+ "이러한 지표들은 아래의 UpliftEvaluator를 사용해 계산합니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 152,
+ "id": "0ef46b07",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "class RoIUpliftEvaluator(fr.evaluate.UpliftEvaluator):\n",
+ " def __init__(self, **kwargs):\n",
+ " kwargs[\"metric_cols\"] = [\"spend\", \"conversion\", \"cost\"]\n",
+ " super().__init__(**kwargs)\n",
+ "\n",
+ " def _calculate_composite_metrics(self, data: pd.DataFrame) -> pd.DataFrame:\n",
+ " data[\"roi__inc_cum\"] = data[\"spend__inc_cum\"] / data[\"cost__inc_cum\"]\n",
+ " data[\"roi__inc\"] = data[\"spend__inc\"] / data[\"cost__inc\"]\n",
+ " return data\n",
+ "\n",
+ "evaluator = RoIUpliftEvaluator(\n",
+ " is_treated_col=\"treatment\",\n",
+ " treatment_propensity_col=\"treatment_propensity\",\n",
+ " effect_type=fr.EffectType.ATE\n",
+ ")\n",
+ "\n",
+ "models = {\n",
+ " \"spend_t_learner_score\": \"Spend T-Learner\",\n",
+ " \"conversion_t_learner_score\": \"Conversion T-Learner\",\n",
+ " \"roi_score\": \"RoI Fractional T-Learner\",\n",
+ " \"roi_score_distill\": \"RoI Fractional T-Learner\",\n",
+ "}\n",
+ "\n",
+ "results = evaluator.evaluate(criteo.test_data, score_cols=list(models.keys()))\n",
+ "\n",
+ "fig, axs = plt.subplots(ncols=2, figsize=(12, 4), constrained_layout=True)\n",
+ "\n",
+ "plot_cumulative_incrementality(\n",
+ " axs[0],\n",
+ " results,\n",
+ " title=\"Incremental Revenue vs Cost\",\n",
+ " model_names=models,\n",
+ " x_col=\"cost__inc_cum\",\n",
+ " y_col=\"spend__inc_cum\",\n",
+ " x_label=\"Cost\",\n",
+ " y_label=\"Incremental Revenue\",\n",
+ " x_format=\"${:,.0f}\",\n",
+ " y_format=\"${:,.0f}\",\n",
+ " show_legend=False,\n",
+ " x_lim=[0, None],\n",
+ " y_lim=[0, None],\n",
+ ")\n",
+ "\n",
+ "plot_cumulative_incrementality(\n",
+ " axs[1],\n",
+ " results,\n",
+ " title=\"iRoI vs Incremental Revenue\",\n",
+ " model_names=models,\n",
+ " x_col=\"spend__inc_cum\",\n",
+ " y_col=\"roi__inc_cum\",\n",
+ " x_label=\"Incremental Revenue\",\n",
+ " y_label=\"iRoI\",\n",
+ " x_format=\"${:,.0f}\",\n",
+ " y_format=\"{:.1f}\",\n",
+ " x_lim=[0, None],\n",
+ " y_lim=[0, 5]\n",
+ ")\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eca57559",
+ "metadata": {},
+ "source": [
+ "이번 경우에서도 fractional uplift 모델이 기존 uplift 모델보다 더 우수한 성과를 보입니다. 일반적인 spend T-Learner 꽤 괜찮은 성능을 내지만, RoI를 최적화한 fractional learner에는 미치지 못합니다. \n",
+ "\n",
+ "또한 이 경우에는 distilled 버전이 전체(full) 모델의 성능을 충분히 잘 따라가지 못하는 것으로 보이며, 추가적인 튜닝이 필요함을 시사합니다."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a7289d07",
+ "metadata": {},
+ "source": [
+ "### Objective 3: Maximum Incremental Conversions with an RoI Constraint\n",
+ "\n",
+ "마지막으로 살펴볼 경우는 iRoI가 특정 목표 값 이하로 떨어지지 않도록 유지하면서, 가능한 한 많은 추가 전환(incremental conversions) 을 만들어내는 상황입니다.\n",
+ "\n",
+ "이 예제에서는 iRoI 목표값을 2.0으로 설정합니다."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8c6e744f",
+ "metadata": {},
+ "source": [
+ "#### Fractional uplift model\n",
+ "\n",
+ "이 문제는 constraint가 포함되어 있어 조금 더 복잡하지만, fractional uplift 설정을 통해 해결할 수 있습니다. 이를 위해 다음과 같이 설정합니다.\n",
+ "\n",
+ "* Maximize KPI ($\\alpha$) = Conversion \n",
+ "* Constraint KPI ($\\beta$) = Cost \n",
+ "* Constraint Offset KPI ($\\gamma$) = Spend \n",
+ "* Constraint Offset Scale ($\\delta$) = iRoI target \n",
+ "\n",
+ "이 설정은 uplift 모델이 다음과 같은 값을 추정하도록 만듭니다.\n",
+ "\n",
+ "$$\n",
+ "f_\\delta(X)=\n",
+ "\\begin{cases}\n",
+ " \\frac{N_{\\text{convert}, \\, T=1} - N_{\\text{convert}, \\, T=0}}{\\text{Cost}_{T=1} - \\frac{\\text{Spend}_\\text{T=1} - \\text{Spend}_\\text{T=0}}{\\text{iRoI}_\\text{target}}},& \\text{Cost}_{T=1} > \\frac{\\text{Spend}_\\text{T=1} - \\text{Spend}_\\text{T=0}}{\\text{iRoI}_\\text{target}}\\\\\n",
+ " \\infty, & \\text{otherwise}\n",
+ "\\end{cases}\n",
+ "$$\n",
+ "\n",
+ "이를 직관적으로 보면 다음과 같이 해석할 수 있습니다.\n",
+ "\n",
+ "1. 사용자의 iRoI가 iRoI target보다 높다면, 항상 해당 사용자를 타겟팅합니다.\n",
+ "2. iRoI가 target보다 낮은 경우에는, \n",
+ " incremental conversions을 얼마나 만들어내는지를 \n",
+ " net cost으로 나눈 값으로 사용자를 정렬합니다. \n",
+ " 여기서 순비용은 iRoI target을 초과해서 발생하는 비용을 의미합니다. \n",
+ " 즉, iRoI target을 가장 적게 훼손하면서 가장 많은 incremental conversions를 만드는 사용자부터 타겟팅합니다.\n",
+ "\n",
+ "아래에서는 이를 실제로 구현합니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "263b9561",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "target_roi = 2.0\n",
+ "\n",
+ "train_data = fr.datasets.PandasTrainData(\n",
+ " features_data=criteo.train_data[criteo.features],\n",
+ " maximize_kpi=criteo.train_data[\"conversion\"].values,\n",
+ " constraint_kpi=criteo.train_data[\"cost\"].values,\n",
+ " constraint_offset_kpi=criteo.train_data[\"spend\"].values,\n",
+ " is_treated=criteo.train_data[\"treatment\"].values,\n",
+ " treatment_propensity=criteo.train_data[\"treatment_propensity\"].values,\n",
+ " sample_weight=criteo.train_data[\"sample_weight\"].values,\n",
+ " shuffle_seed=1234\n",
+ ")\n",
+ "fractional_t_learner = fr.meta_learners.FractionalLearner(get_base_regressor())\n",
+ "fractional_t_learner.fit(train_data)\n",
+ "\n",
+ "distill_fractional_t_learner = get_base_regressor()\n",
+ "fractional_t_learner.distill(distill_dataset, distill_fractional_t_learner, constraint_offset_scale=target_roi)\n",
+ "\n",
+ "criteo.test_data[f\"roi_constrained_conversion_score\"] = fractional_t_learner.predict(\n",
+ " test_dataset, constraint_offset_scale=target_roi\n",
+ ")\n",
+ "criteo.test_data[f\"roi_constrained_conversion_score_distill\"] = distill_fractional_t_learner.predict(test_dataset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "id": "2a9a778f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " f0 | \n",
+ " f1 | \n",
+ " f2 | \n",
+ " f3 | \n",
+ " f4 | \n",
+ " f5 | \n",
+ " f6 | \n",
+ " f7 | \n",
+ " f8 | \n",
+ " f9 | \n",
+ " ... | \n",
+ " conversion_t_learner_score | \n",
+ " conversion_t_learner_score_distill | \n",
+ " spend_t_learner_score | \n",
+ " spend_t_learner_score_distill | \n",
+ " cpia_score | \n",
+ " cpia_score_distill | \n",
+ " roi_score | \n",
+ " roi_score_distill | \n",
+ " roi_constrained_conversion_score | \n",
+ " roi_constrained_conversion_score_distill | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1007 | \n",
+ " 13.221509 | \n",
+ " 10.059654 | \n",
+ " 8.471764 | \n",
+ " -1.666396 | \n",
+ " 13.059169 | \n",
+ " 2.230907 | \n",
+ " -14.476362 | \n",
+ " 9.170324 | \n",
+ " 3.793946 | \n",
+ " 39.917532 | \n",
+ " ... | \n",
+ " 0.035170 | \n",
+ " 0.053963 | \n",
+ " 0.217223 | \n",
+ " 0.484556 | \n",
+ " 0.025971 | \n",
+ " 0.011292 | \n",
+ " 0.160407 | \n",
+ " -1.164981 | \n",
+ " 0.028235 | \n",
+ " 30.174107 | \n",
+ "
\n",
+ " \n",
+ " | 1080 | \n",
+ " 15.643772 | \n",
+ " 10.059654 | \n",
+ " 8.232822 | \n",
+ " 3.907662 | \n",
+ " 11.029584 | \n",
+ " 4.115453 | \n",
+ " -1.288207 | \n",
+ " 4.833815 | \n",
+ " 3.858041 | \n",
+ " 34.180688 | \n",
+ " ... | \n",
+ " 0.015709 | \n",
+ " 0.015331 | \n",
+ " 0.661332 | \n",
+ " 0.196871 | \n",
+ " 0.048494 | \n",
+ " 0.030863 | \n",
+ " 2.041546 | \n",
+ " 1.063659 | \n",
+ " inf | \n",
+ " 30.259451 | \n",
+ "
\n",
+ " \n",
+ " | 1295 | \n",
+ " 13.236812 | \n",
+ " 11.119309 | \n",
+ " 8.329031 | \n",
+ " -2.570015 | \n",
+ " 11.561050 | \n",
+ " 1.128518 | \n",
+ " -18.659676 | \n",
+ " 5.814641 | \n",
+ " 3.855652 | \n",
+ " 34.621266 | \n",
+ " ... | \n",
+ " -0.233079 | \n",
+ " -0.195807 | \n",
+ " -12.409683 | \n",
+ " -8.438211 | \n",
+ " -0.363601 | \n",
+ " -0.103153 | \n",
+ " -19.358949 | \n",
+ " -1.164981 | \n",
+ " -0.034047 | \n",
+ " 10.612205 | \n",
+ "
\n",
+ " \n",
+ " | 1670 | \n",
+ " 19.622574 | \n",
+ " 10.059654 | \n",
+ " 8.305211 | \n",
+ " 3.907662 | \n",
+ " 13.253813 | \n",
+ " 4.115453 | \n",
+ " -1.288207 | \n",
+ " 4.833815 | \n",
+ " 3.803324 | \n",
+ " 41.176485 | \n",
+ " ... | \n",
+ " -0.137972 | \n",
+ " -0.126640 | \n",
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+ " -0.219133 | \n",
+ " -11.766439 | \n",
+ " -1.164981 | \n",
+ " -0.023002 | \n",
+ " 0.024123 | \n",
+ "
\n",
+ " \n",
+ " | 1940 | \n",
+ " 12.616365 | \n",
+ " 10.059654 | \n",
+ " 8.681976 | \n",
+ " 4.679882 | \n",
+ " 11.029584 | \n",
+ " 4.115453 | \n",
+ " 0.294443 | \n",
+ " 4.833815 | \n",
+ " 3.864711 | \n",
+ " 13.190056 | \n",
+ " ... | \n",
+ " 0.002356 | \n",
+ " 0.001106 | \n",
+ " 0.003596 | \n",
+ " -0.081532 | \n",
+ " 0.113706 | \n",
+ " 0.011170 | \n",
+ " 0.173577 | \n",
+ " 0.932336 | \n",
+ " 0.124512 | \n",
+ " 3.947203 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 29 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " f0 f1 f2 f3 f4 f5 \\\n",
+ "1007 13.221509 10.059654 8.471764 -1.666396 13.059169 2.230907 \n",
+ "1080 15.643772 10.059654 8.232822 3.907662 11.029584 4.115453 \n",
+ "1295 13.236812 11.119309 8.329031 -2.570015 11.561050 1.128518 \n",
+ "1670 19.622574 10.059654 8.305211 3.907662 13.253813 4.115453 \n",
+ "1940 12.616365 10.059654 8.681976 4.679882 11.029584 4.115453 \n",
+ "\n",
+ " f6 f7 f8 f9 ... \\\n",
+ "1007 -14.476362 9.170324 3.793946 39.917532 ... \n",
+ "1080 -1.288207 4.833815 3.858041 34.180688 ... \n",
+ "1295 -18.659676 5.814641 3.855652 34.621266 ... \n",
+ "1670 -1.288207 4.833815 3.803324 41.176485 ... \n",
+ "1940 0.294443 4.833815 3.864711 13.190056 ... \n",
+ "\n",
+ " conversion_t_learner_score conversion_t_learner_score_distill \\\n",
+ "1007 0.035170 0.053963 \n",
+ "1080 0.015709 0.015331 \n",
+ "1295 -0.233079 -0.195807 \n",
+ "1670 -0.137972 -0.126640 \n",
+ "1940 0.002356 0.001106 \n",
+ "\n",
+ " spend_t_learner_score spend_t_learner_score_distill cpia_score \\\n",
+ "1007 0.217223 0.484556 0.025971 \n",
+ "1080 0.661332 0.196871 0.048494 \n",
+ "1295 -12.409683 -8.438211 -0.363601 \n",
+ "1670 -10.253737 -9.813921 -0.158326 \n",
+ "1940 0.003596 -0.081532 0.113706 \n",
+ "\n",
+ " cpia_score_distill roi_score roi_score_distill \\\n",
+ "1007 0.011292 0.160407 -1.164981 \n",
+ "1080 0.030863 2.041546 1.063659 \n",
+ "1295 -0.103153 -19.358949 -1.164981 \n",
+ "1670 -0.219133 -11.766439 -1.164981 \n",
+ "1940 0.011170 0.173577 0.932336 \n",
+ "\n",
+ " roi_constrained_conversion_score \\\n",
+ "1007 0.028235 \n",
+ "1080 inf \n",
+ "1295 -0.034047 \n",
+ "1670 -0.023002 \n",
+ "1940 0.124512 \n",
+ "\n",
+ " roi_constrained_conversion_score_distill \n",
+ "1007 30.174107 \n",
+ "1080 30.259451 \n",
+ "1295 10.612205 \n",
+ "1670 0.024123 \n",
+ "1940 3.947203 \n",
+ "\n",
+ "[5 rows x 29 columns]"
+ ]
+ },
+ "execution_count": 154,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "criteo.test_data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c72e932f",
+ "metadata": {},
+ "source": [
+ "#### Evaluation\n",
+ "\n",
+ "이를 평가하기 위해, iRoI가 목표 iRoI 값인 2.0과 같아지는 지점에서 달성할 수 있는 최대 전환 수를 확인합니다. \n",
+ "\n",
+ "아래에서는 다시 한 번 uplift evaluator를 사용해, incremental conversions에 따른 iRoI의 변화를 그래프로 시각화합니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "id": "a40a2611",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "evaluator = RoIUpliftEvaluator(\n",
+ " is_treated_col=\"treatment\",\n",
+ " treatment_propensity_col=\"treatment_propensity\",\n",
+ " effect_type=fr.EffectType.ATE\n",
+ ")\n",
+ "\n",
+ "models = {\n",
+ " \"spend_t_learner_score\": \"Spend T-Learner\",\n",
+ " \"conversion_t_learner_score\": \"Conversion T-Learner\",\n",
+ " \"roi_constrained_conversion_score\": \"RoI Constrained Fractional T-Learner\",\n",
+ " \"roi_constrained_conversion_score_distill\": \"RoI Constrained Fractional T-Learner\",\n",
+ "}\n",
+ "\n",
+ "results = evaluator.evaluate(criteo.test_data, score_cols=list(models.keys()))\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(10, 4), constrained_layout=True)\n",
+ "\n",
+ "roi_plot = plot_cumulative_incrementality(\n",
+ " ax,\n",
+ " results,\n",
+ " title=\"RoI vs Incremental Conversion\",\n",
+ " model_names=models,\n",
+ " x_col=\"conversion__inc_cum\",\n",
+ " y_col=\"roi__inc_cum\",\n",
+ " x_label=\"Incremental Conversions\",\n",
+ " y_label=\"iRoI\",\n",
+ " x_format=\"{:,.0f}\",\n",
+ " y_format=\"{:.1f}\",\n",
+ " x_lim=[0, None],\n",
+ " y_lim=[0, 5]\n",
+ ")\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d9e251b3",
+ "metadata": {},
+ "source": [
+ "fractional learner가 단순한 T-learner들보다 우수한 성과를 보인다는 점이 분명하게 드러납니다.\n",
+ "fractional learner는 iRoI가 2.0인 조건에서 약 3,000건의 incremental conversions을 달성합니다.\n",
+ "\n",
+ "반면, conversion T-learner는 iRoI 2.0에 도달하지 못하며,\n",
+ "Spend T-learner 역시 iRoI가 2.0일 때 약 1,000건의 incremental conversions만을 만들어낼 수 있습니다."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/book/prescriptive_analytics/overview.md b/book/prescriptive_analytics/overview.md
new file mode 100644
index 0000000..ad8685a
--- /dev/null
+++ b/book/prescriptive_analytics/overview.md
@@ -0,0 +1,5 @@
+# Prescriptive Analytics
+
+- Prescriptive Analytics는 데이터를 활용해 최적의 의사결정을 도출하는 분석 방식입니다.
+- 접근 방식은 크게 Prediction + Optimization, Causal Inference + Optimization 으로 나눌 수 있습니다.
+- 이 섹션에서는 Causal Inference + Optimization 에 집중하여, 개입의 인과효과(CATE)를 기반으로 가장 효율적인 정책·전략을 선택하는 방법을 다룹니다.
\ No newline at end of file