diff --git a/.dockerignore b/.dockerignore
index 2e6e678..4791eba 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -1,5 +1,4 @@
 venv
-output
-bin
-__pycache__
-.pytest_cache
+**/output
+**/__pycache__
+**/.pytest_cache
diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml
index c15fac9..bb488f9 100644
--- a/.github/workflows/pylint.yml
+++ b/.github/workflows/pylint.yml
@@ -19,7 +19,6 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install pylint
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-    - name: Analysing the code with pylint
+    - name: Analyzing code with pylint
       run: |
         pylint $(git ls-files '*.py')
\ No newline at end of file
diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
index 602dcf0..8312b7a 100644
--- a/.github/workflows/python-app.yml
+++ b/.github/workflows/python-app.yml
@@ -17,8 +17,10 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
+        sudo apt-get update
+        sudo apt-get install -y build-essential swig libglpk-dev python3-dev libcdd-dev libgmp-dev
         python -m pip install --upgrade pip
-        pip install flake8 pytest
+        pip install flake8 pytest pytest-cov
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
     - name: Lint with flake8
       run: |
@@ -28,4 +30,4 @@ jobs:
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
-        pytest
+        pytest --cov=src --cov-report=term-missing --capture=no
diff --git a/.gitignore b/.gitignore
index 2e6e678..eba188a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,11 @@
 venv
-output
+output*
+bns
+data
+plots
+*meta.txt
+compose.yaml
 bin
 __pycache__
 .pytest_cache
+*.svg
diff --git a/Dockerfile b/Dockerfile
index cd0c712..160dec4 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,8 +1,20 @@
-FROM python:bookworm
+FROM python:3.12-slim
 
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    swig \ 
+    libglpk-dev \
+    python3-dev \ 
+    libcdd-dev \ 
+    libgmp-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
 WORKDIR /workspace
 COPY . .
 
+# Install python packages
 RUN pip install --upgrade pip
 RUN if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
 
diff --git a/README.md b/README.md
index f72325e..f09b867 100644
--- a/README.md
+++ b/README.md
@@ -2,104 +2,144 @@
 
 Code for paper "Imprecise Probabilities for Privacy–Accuracy Trade-offs in Bayesian Networks" presented at [UAI 2026](https://www.auai.org/uai2026/).
 
-## Set up Python environment
+## Preliminaries
 
-Create and activate a Python virtual environment with: 
+### Experiments
 
-```bash
-python3 -m venv venv
-source venv/bin/activate[.fish]  # use `.fish` suffix if using fish shell
-```
+`<name>` is the name of the experiment to run. Each `<name>` has its own directory, which is named the same way. Each of these contains the experiment logic, configuration file (`config.yaml`), eventually generated models and data, output directory, and a `Plot_results.ipynb` notebook to plot results.
 
-Install all dependencies with: 
+`<name>` can be one of the following:
 
-```bash
-pip install -r requirements.txt
-```
+1. `cn_privacy`: run membership inference attack against a Bayesian network (BN), its related credal network (CN), and compute the theoretical privacy estimate of BN.
 
-Upgrade all Python packages with: 
+2. `cn_vs_noisybn`: compare two privacy techniques, namely the CN and a noisy version of BN. All models are naive Bayes with target variable T. First, the CN and noisy BN hyperparameters are fine-tuned so that they achieve the same privacy level; then, their accuracy is computed in terms of most probable explanation (MPE) on variable T.
 
-```bash
-pip install --upgrade $(pip freeze | cut -d '=' -f 1)
-pip freeze > requirements.txt
-```
+For additional details, we refer to the paper.
 
-This updates the requirements file with the upgraded packages.
+### Attacks and defenses
 
-## Experiments
+Each experiment requires the user to specify one defense and one attack mechanisms, plus additional related hyperparameters. Below, the mechanisms and hyperparameters names are reported.
 
-`<name>` is the name of the experiment to run. It can be one of the following.
+Implemented defenses:
+- `def_idm`. Requires: `ess`.
+- `def_ran`. Requires: `delta`.
 
-1. `cn_privacy`: run membership inference attack against a Bayesian network (BN), its related credal network (CN), and compute the theoretical privacy estimate of BN. The pipeline and results are described in the paper.
+Implemented attacks:
+- `atk_mle`.
+- `atk_cen`.
+- `atk_ran`.
+- `atk_ent`.
 
-2. `cn_vs_noisybn`: additional experiment, not reported in the paper. It compares two privacy techniques, namely the CN and a noisy version of BN. All models are naive Bayes with target variable T. First, the CN and noisy BN hyperparameters are fine-tuned so that they achieve the same privacy level; then, their accuracy is computed in terms of most probable explanation (MPE) on variable T.
+## Running code
 
-## Run code
+### Using Docker (recommended)
 
-### With Docker (recommended)
+The `compose.yaml` file contains a set of pre-set experiments. Additional ones can also be specified. The `generate_compose.py` file helps in generating them automatically.
 
-1. Build the Docker image:
+Generate models and data for all experiments (controlled by `config.yaml`):
 
-```bash
-docker build . -t bnp:2025
+```sh
+python -m experiments.cn_privacy.generate
+python -m experiments.cn_vs_noisybn.generate
 ```
 
-2. Run the experiment:
+Run one or more experiments with:
+
+```sh
+docker compose up [service name]
+```
+
+Results will be available under `experiments/<name>/output_*`.
+
+To check the status, run one or more of the following:
+
+```sh
+docker compose ps
+docker compose logs [service name]
+docker stats
+```
+
+### Local computation
+
+Create and activate a Python virtual environment: 
 
-```bash
-docker run [-d] [--rm] -v bnp:/workspace bnp:2025 python -m experiments.<name>.main
+```sh
+python3 -m venv venv
+source venv/bin/activate[.fish]  # use `.fish` suffix if using fish shell
 ```
 
-3. Results available at: 
+Install dependencies: 
 
-`/var/lib/docker/volumes/bnp/_data/experiments/<name>/output/`.
+```sh
+pip install -r requirements.txt
+```
 
-### Without Docker
+*Notice*: if some package is missing locally, see the `Dockerfile` for additional packages to be installed (names refer to Ubuntu/Debian).
 
-1. Run the experiment: 
+Upgrade dependencies: 
 
-```bash
-python -m experiments.<name>.main
+```sh
+pip install --upgrade $(pip freeze | cut -d '=' -f 1)
+pip freeze > requirements.txt
 ```
 
-2. Results available at: 
+*Notice:* each of the following command will overwrite any related output. 
 
-`experiments/<name>/output/`.
+Generate models and data (controlled by `config.yaml`):
 
-## Test code
+```sh
+python -m experiments.<name>.generate
+```
 
-Run tests with:
+Run an experiment: 
 
-```bash
-pytest
+```sh
+python -m experiments.<name>.exp def_mec=<def_name> [param=value] atk_mec=<def_name> [param=value]
 ```
 
-Test results are available at: 
+Results will be available under `experiments/<name>/output`.
+
 
-`test/<name>/output/`.
+## Testing code
+
+Run integration tests:
+
+```sh
+pytest [--cov=src] [--cov-report=term-missing] [--capture=no]
+```
 
 ## Formatting and linting
 
 Format code by running:
 
-```bash
+```sh
 black .
 isort .
 ```
 
 Lint code by running:
 
-```bash
+```sh
 flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude=venv
-flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude=venv
+flake8 . --count --exit-zero --max-complexity=10 --ignore=E203 --max-line-length=140 --statistics --exclude=venv
 ```
 
 Analyze code by running:
 
-```bash
+```sh
 pylint $(git ls-files '*.py')
 ```
 
-## Plot results
+## Running actions locally
+
+Install `act`:
+
+```sh
+curl --proto '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/nektos/act/master/install.sh | sudo bash
+```
+
+Run `act` with: 
 
-Use the `Plot_results.ipynb` notebook available for each experiment. Plots will be saved at: `experiments/<name>/output/plots`.
+```sh
+sudo ./bin/act [-W <path_to_file>]
+```
diff --git a/compose.yaml b/compose.yaml
new file mode 100644
index 0000000..7a007a7
--- /dev/null
+++ b/compose.yaml
@@ -0,0 +1,77 @@
+version: '3.9'
+services:
+  cn_privacy_def_ran_atk_mle_delta0.1:
+    build: .
+    command:
+    - python
+    - -m
+    - experiments.cn_privacy.exp
+    - def_mec=def_ran
+    - atk_mec=atk_mle
+    - delta=0.1
+    - n_bns=1000
+    image: bnp:2025
+    volumes:
+    - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns
+    - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data
+    - ./experiments/cn_privacy/output_def_ran_atk_mle_delta0.1:/workspace/experiments/cn_privacy/output
+  cn_privacy_def_ran_atk_mle_delta0.3:
+    build: .
+    command:
+    - python
+    - -m
+    - experiments.cn_privacy.exp
+    - def_mec=def_ran
+    - atk_mec=atk_mle
+    - delta=0.3
+    - n_bns=1000
+    image: bnp:2025
+    volumes:
+    - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns
+    - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data
+    - ./experiments/cn_privacy/output_def_ran_atk_mle_delta0.3:/workspace/experiments/cn_privacy/output
+  cn_privacy_def_ran_atk_mle_delta0.5:
+    build: .
+    command:
+    - python
+    - -m
+    - experiments.cn_privacy.exp
+    - def_mec=def_ran
+    - atk_mec=atk_mle
+    - delta=0.5
+    - n_bns=1000
+    image: bnp:2025
+    volumes:
+    - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns
+    - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data
+    - ./experiments/cn_privacy/output_def_ran_atk_mle_delta0.5:/workspace/experiments/cn_privacy/output
+  cn_privacy_def_ran_atk_mle_delta0.7:
+    build: .
+    command:
+    - python
+    - -m
+    - experiments.cn_privacy.exp
+    - def_mec=def_ran
+    - atk_mec=atk_mle
+    - delta=0.7
+    - n_bns=1000
+    image: bnp:2025
+    volumes:
+    - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns
+    - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data
+    - ./experiments/cn_privacy/output_def_ran_atk_mle_delta0.7:/workspace/experiments/cn_privacy/output
+  cn_privacy_def_ran_atk_mle_delta1.0:
+    build: .
+    command:
+    - python
+    - -m
+    - experiments.cn_privacy.exp
+    - def_mec=def_ran
+    - atk_mec=atk_mle
+    - delta=1.0
+    - n_bns=1000
+    image: bnp:2025
+    volumes:
+    - ./experiments/cn_privacy/bns:/workspace/experiments/cn_privacy/bns
+    - ./experiments/cn_privacy/data:/workspace/experiments/cn_privacy/data
+    - ./experiments/cn_privacy/output_def_ran_atk_mle_delta1.0:/workspace/experiments/cn_privacy/output
diff --git a/experiments/__init__.py b/experiments/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/experiments/cn_privacy/Plot_results.ipynb b/experiments/cn_privacy/Plot_results.ipynb
index 6492fc0..27260ec 100644
--- a/experiments/cn_privacy/Plot_results.ipynb
+++ b/experiments/cn_privacy/Plot_results.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "b53ad4f3",
    "metadata": {},
    "outputs": [],
@@ -15,32 +15,77 @@
     "import numpy as np\n",
     "import sys\n",
     "from pathlib import Path\n",
+    "import re\n",
+    "import ast\n",
+    "from itertools import cycle, product\n",
+    "from sklearn import metrics\n",
+    "from natsort import natsorted\n",
+    "import xarray as xr\n",
+    "from pprint import pprint\n",
+    "from tqdm import trange\n",
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "from scipy.interpolate import interp1d\n",
     "\n",
     "sys.path.insert(0, str(Path().resolve().parents[1]))\n",
-    "from src.config import *"
+    "from src.config import *  # noqa"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "ce91ef75",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Choose config file\n",
-    "config = get_config(\"config.yaml\")\n",
+    "config = load_config(\"cn_privacy\")\n",
+    "\n",
+    "# Choose results path\n",
+    "cur_dir = get_cur_dir(config)\n",
+    "folder = \"cn_privacy_v4\"\n",
     "\n",
-    "# Get results path\n",
-    "res_path = get_base_path(config) / config[\"results_path\"]\n",
+    "# Get metadata\n",
+    "complexities = dict()\n",
+    "with open(f\"{cur_dir}/{folder}/exp_meta.txt\", \"r\") as f:\n",
+    "    for row in f:\n",
+    "        exp = re.findall(\"^- (exp\\d+).\", row)[0]\n",
+    "        nodes = re.findall(\" Nodes: (\\d+)\", row)[0]\n",
+    "        edges = re.findall(\" Edges: (\\d+)\", row)[0]\n",
+    "        compl = re.findall(\" Complexity: (\\d+)\", row)[0]\n",
+    "        complexities[exp] = [nodes, edges, compl]\n",
+    "\n",
+    "out_paths = [x for x in os.listdir(cur_dir / folder) if \"output\" in x]\n",
+    "def_pars = natsorted(\n",
+    "    set([re.findall(\"atk\\w+_(\\w+\\d\\.?\\d?)$\", x)[0] for x in out_paths])\n",
+    ")\n",
+    "deffs = [\n",
+    "    f\"{d}_{p}\" for d, p in product([\"def_idm\"], [x for x in def_pars if \"ess\" in x])\n",
+    "] + [\n",
+    "    f\"{d}_{p}\" for d, p in product([\"def_loc\"], [x for x in def_pars if \"ess\" in x])\n",
+    "] + [\n",
+    "        f\"{d}_{p}\"\n",
+    "        for d, p in product([\"def_ran\"], [x for x in def_pars if \"delta\" in x])\n",
+    "    ]\n",
+    "deffs = natsorted(deffs)\n",
+    "atk_mecs = natsorted(set([re.findall(\"_(atk\\w+)_\\w+\", x)[0] for x in out_paths]))\n",
+    "exp_names = natsorted([x for x in complexities], key=lambda x: complexities[x][2])\n",
     "\n",
     "# Choose where to save plots\n",
-    "plots_path = get_base_path(config) / \"plots\"\n",
+    "plots_path = cur_dir / \"plots\"\n",
     "create_clean_dir(plots_path)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "25752955",
+   "metadata": {},
+   "source": [
+    "## Power vs Error plots"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "52eff632",
    "metadata": {},
    "outputs": [],
@@ -52,7 +97,7 @@
     "        \"font.size\": 9,\n",
     "        \"axes.labelsize\": 9,\n",
     "        \"axes.titlesize\": 9,\n",
-    "        \"legend.fontsize\": 7,\n",
+    "        \"legend.fontsize\": 6,\n",
     "        \"xtick.labelsize\": 8,\n",
     "        \"ytick.labelsize\": 8,\n",
     "        \"lines.linewidth\": 0.8,\n",
@@ -62,57 +107,42 @@
     "        \"axes.linewidth\": 0.8,\n",
     "        \"text.usetex\": True,\n",
     "    }\n",
-    ")\n",
-    "\n",
-    "# Colors\n",
-    "bound_color = \"#ff441a\"  # red\n",
-    "BN_color = \"#3934fe\"  # blue\n",
-    "CN_color = \"#28bd6b\"  # green\n",
-    "alpha = 0.2"
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "3bc7788b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Plot BN and bound (semilogx)\n",
-    "def plot_bn_bound(exp: str, ax):\n",
+    "# Plot BN\n",
+    "def plot_bn(path: Path, exp: str, ax, fill: bool = True):\n",
     "\n",
     "    # Import results\n",
-    "    results = os.listdir(res_path)\n",
-    "    r_path = [r for r in results if f\"{exp}-ess1.csv\" in r][0]\n",
-    "    result = pd.read_csv(f\"{res_path}/{r_path}\")\n",
-    "    error = result[\"error\"]\n",
-    "    bound = result[\"power_bound\"]\n",
-    "    bn_cols = [c for c in result.columns if \"BN\" in c]\n",
-    "    bn_mean = result.loc[:, bn_cols].mean(axis=1)\n",
-    "    bn_max = result.loc[:, bn_cols].max(axis=1)\n",
+    "    files = os.listdir(path / \"bns\")\n",
+    "    r_path = [r for r in files if f\"{exp}.\" in r][0]\n",
+    "    res = pd.read_csv(f\"{path}/bns/{r_path}\")\n",
+    "    error = res[\"error\"]\n",
     "\n",
-    "    # Plot bound\n",
-    "    ax.semilogx(\n",
-    "        error,\n",
-    "        bound,\n",
-    "        \"^\",\n",
-    "        color=bound_color,\n",
-    "        label=\"Theoretical estimate\",\n",
-    "        markersize=4,\n",
-    "        zorder=4,\n",
-    "    )\n",
+    "    # Select what to plot\n",
+    "    bn_cols = [c for c in res.columns if \"BN\" in c]\n",
+    "    bn_mean = res.loc[:, bn_cols].mean(axis=1)\n",
+    "    bn_max = res.loc[:, bn_cols].max(axis=1)\n",
     "\n",
     "    # Plot BN (avg-max)\n",
-    "    ax.fill_between(error, bn_mean, bn_max, color=BN_color, alpha=alpha, zorder=2)\n",
-    "    ax.semilogx(error, bn_mean, \"-\", color=BN_color, label=\"BN\", zorder=3)\n",
+    "    (line,) = ax.plot(error, bn_mean, linestyle=\"dashed\", color=bn_color, label=\"BN\", zorder=3)\n",
+    "    if fill:\n",
+    "        ax.fill_between(error, bn_mean, bn_max, color=bn_color, alpha=alpha, zorder=2)\n",
     "\n",
     "    # Title and axes\n",
     "    ax.set_xlabel(\"Error\")\n",
     "    ax.set_ylabel(\"Power\")\n",
-    "    ax.set_xlim(10e-5 * 0.5, 1)\n",
-    "    ax.set_xticks([10e-4, 10e-3, 10e-2, 10e-1, 1])\n",
-    "    ax.set_xticklabels([\"$10^{-4}$\", \"$10^{-3}$\", \"$10^{-2}$\", \"$10^{-1}$\", \"$10^{0}$\"])\n",
-    "    ax.xaxis.set_minor_locator(LogLocator(base=10.0, subs=\"auto\"))\n",
+    "    ax.set_xlim(-.05, 0.8)\n",
+    "    # ax.set_xticks([10e-4, 10e-3, 10e-2, 10e-1, 1])\n",
+    "    # ax.set_xticklabels([\"$10^{-4}$\", \"$10^{-3}$\", \"$10^{-2}$\", \"$10^{-1}$\", \"$10^{0}$\"])\n",
+    "    # ax.xaxis.set_minor_locator(LogLocator(base=10.0, subs=\"auto\"))\n",
     "    ax.tick_params(axis=\"x\", which=\"minor\", length=2, width=0.5)\n",
     "    ax.tick_params(axis=\"x\", which=\"major\", length=3, width=0.9)\n",
     "    ax.set_ylim(-0.08, 1.08)\n",
@@ -121,38 +151,65 @@
     "        True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1\n",
     "    )\n",
     "\n",
+    "    return line\n",
+    "\n",
+    "\n",
+    "# Plot bound\n",
+    "def plot_bound(path: Path, exp: str, ax):\n",
+    "\n",
+    "    # Import results\n",
+    "    files = os.listdir(path / \"bns\")\n",
+    "    r_path = [r for r in files if f\"{exp}.\" in r][0]\n",
+    "    res = pd.read_csv(f\"{path}/bns/{r_path}\")\n",
+    "    bound = res[\"power_bound\"]\n",
+    "    error = res[\"error\"]\n",
+    "\n",
+    "    # Interpolation\n",
+    "    f = interp1d(error, bound)\n",
+    "    error_lin = np.linspace(error.min(), error.max(), 15)\n",
+    "    bound_lin = f(error_lin)\n",
+    "\n",
+    "    # Plot bound\n",
+    "    (line,) = ax.plot(\n",
+    "        error_lin,\n",
+    "        bound_lin,\n",
+    "        linestyle=\"\",\n",
+    "        marker=\"^\",\n",
+    "        color=bound_color,\n",
+    "        markersize=3,\n",
+    "        zorder=4,\n",
+    "        label=\"Theoretical estimate\",\n",
+    "        mec=None,\n",
+    "    )\n",
+    "\n",
+    "    return line\n",
     "\n",
-    "# Plot CN (for a given ess)\n",
-    "def plot_cn(exp, ax, ess: int, color: str, type: str):\n",
+    "\n",
+    "# Plot CN\n",
+    "def plot_cn(path: Path, color, exp, ax, type: str, fill: bool = True):\n",
     "\n",
     "    # Import results\n",
-    "    results = os.listdir(res_path)\n",
-    "    r_path = [r for r in results if f\"{exp}-ess{ess}.csv\" in r][0]\n",
-    "    result = pd.read_csv(f\"{res_path}/{r_path}\")\n",
-    "    error = result[\"error\"]\n",
-    "    cn_cols = [c for c in result.columns if \"CN\" in c]\n",
-    "    cn_mean = result.loc[:, cn_cols].mean(axis=1)\n",
-    "    cn_max = result.loc[:, cn_cols].max(axis=1)\n",
+    "    files = os.listdir(path / \"cns\")\n",
+    "    r_path = [r for r in files if f\"{exp}.\" in r][0]\n",
+    "    res = pd.read_csv(f\"{path}/cns/{r_path}\")\n",
+    "    error = res[\"error\"]\n",
+    "\n",
+    "    # Select what to plot\n",
+    "    cn_cols = [c for c in res.columns if \"CN\" in c]\n",
+    "    cn_mean = res.loc[:, cn_cols].mean(axis=1)\n",
+    "    cn_max = res.loc[:, cn_cols].max(axis=1)\n",
     "\n",
     "    # Plot CN (avg-max)\n",
-    "    ax.fill_between(error, cn_mean, cn_max, color=color, alpha=alpha, zorder=2)\n",
-    "    ax.semilogx(error, cn_mean, type, color=color, label=f\"CN, $S={ess}$\", zorder=3)\n",
-    "\n",
-    "    # Legend\n",
-    "    if exp == \"exp0\":\n",
-    "        ax.legend(\n",
-    "            loc=\"best\",\n",
-    "            frameon=True,\n",
-    "            fancybox=False,\n",
-    "            framealpha=1,\n",
-    "            facecolor=\"#e6e6e6\",\n",
-    "            edgecolor=\"#8c8c8c\",\n",
-    "        )\n",
+    "    (line,) = ax.plot(error, cn_mean, type, color=color, zorder=3)\n",
+    "    if fill:\n",
+    "        ax.fill_between(error, cn_mean, cn_max, color=color, alpha=alpha, zorder=2)\n",
+    "\n",
+    "    return line\n",
     "\n",
     "\n",
     "# Plot title function\n",
     "def get_title(exp: str):\n",
-    "    with open(f\"{res_path}/exp_meta.txt\", \"r\") as meta:\n",
+    "    with open(f\"{cur_dir}/{folder}/exp_meta.txt\", \"r\") as meta:\n",
     "        for row in meta:\n",
     "            if exp in row:\n",
     "                pieces = row.split()\n",
@@ -164,45 +221,669 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "ade37b54",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w6/WGqDWM78zMzL6Gfz1I++lxEsX+7ca6EvyfRTWM7czMTKjvXSP/K19wiNZGegz4PZSaERxOuFMMDw+HPv7u7i21+//tD2PczP7YLcp1NRoA+d+/TtpPAHCUHYV4I6zgZ67UHunGNmQlhyXe2H3jqJ5g+6dSO6Qb9y8xQrj3dnPbs95xqZp2xtxRfzcBANGi/b+D9v9endz+r6WZtk+U1227sa4QS4R7bze3Uesd46K8RxBGcB/W6kwS5f2GVn9GdC5bAI6sK1euaHh4uOy5ZDKpycnJ0t9PPfVUzXUsLi5qbGws9DaXlpZkWZYsy9Lg4GBjBW6TTCajdDotyfv/JJPJuu9JpVKlxysrKwdVtIYtLi6WHo+OjtZc9umnn666b6enpyv+H476/u3GurK0tKSFhYXS3zMzM5Gs9/r16/rEJz5Rcxl/u2H/V74LFy5IkoaHh/ccw2oZHh6uW+9rvTeZTCqTydTdT636HjzzzDOl/0U9wXomqVRPfcH9Pjk5WbY/0um0FhYWNDc3p6WlpbrbinJdwf21u8y7Bb9/ze5nAEC0iDc8ly5dKj1OJpM6f/582evd2Ias5rDEG8F2R6P7I7g/d//dLfuXGCH8ezstRgir3nEpSlF+B6L8bgIAokf730P7v7JObv9HLarrtt1YV4glwr/3MMcSUd4jqCeTyWhiYkKSV/+np6erLhvl/YZWfkZ0NhKugCNsaWmpYgM32BBcWVmpebJfWlrqqhvbly5d0tDQUKlhMjIyUtY4qOTq1aulx2EvQgYbZZ10QS94Yq8VuM7NzSmTyXTVvm1WlPu3G+tK8PueTCYj2+cXL16sG0z5AXkj21xaWir9n5544onmC9gEP1jtlAZyI/srk8mU/b07kK90bJibm9PQ0JCGhoY0MTGhqakpjY2NaXBwsO55Iap1Xbx4sRSo+EFTNU8++WTpcVSBPABgf45ivLHbwsJC2ecNntt83diGrOYwxhunT58OtVxwvwbrdDfuX2KE8DotRggjzHEpSgf1HdjvdxMAED3a/7T/q+mm9n8Uorpu2411hVgivMMcS0R5j6Ce4H2DenUkyvsNrfyM6GwkXAFHVDqdrtrATaVSZc9X63Xinxwayfhul0wmo6GhIV24cKHUePIzxy9cuKCRkZGqjc9ggzfsRcjghb8bN240Xe4o7T6ZV9r36XRaFy5c0NTUVFfs1yhEuX+7ra6k0+myehHsbbZfYXqR+AHjY489Fnq98/PzpcetDtD9oPbKlSst3W4UgsH57n2zu6d3KpXSyMiIpqamKh4XM5mMRkZGKgYIUa7LL+v8/LySyaRWVlY0NDSkhYWF0rr8niIjIyOlHkzz8/N7EsoAAK131OKNoEwmo6WlJU1MTJRdwBsfH6/Y07Lb2pDVHKZ4I9iWCPv/De5HYoTKiBHap9HjUtTb9u33OxDldxMAEC3a/7T/u7n93071rtsGXwuDWIJYImrNHOOivEdQy8LCQinxaXh4WOPj4zWXj+p+Qys/IzpfvN0FANAeS0tLNRu4Fy5cKJ2kFhYWlMlk9jRmmultcv78eS0vLzdc3ij4J7nx8XE99thjun79eqlnhX9iXV1drTv1VhjVGsXtFGy4S6o79Ggzjct27t9mRbl/u62uzM7Olv09NTXV0u37/69GkmOefvrp0uNmg3Q/0H/ssccaurkwNDRUen8tnfg9CO7rixcvlr22+/NMTEwonU5rfHxcU1NTOn/+vG7cuFFKUPWXf/zxx7W2tnZg6/KNjo7q2rVrevLJJ7WwsFC158no6KhmZ2dJtgKADnEU442pqSnNzc1VfG1mZqZqu6Pb2pDVHKZ4Y/c0ZPXs3ofECM0jRohWs8elKEX5HYjyuwkAiBbt/3K0//fq5PZ/OzVy3TYMYgliiahEdYyL4h5BNcHRtsLOehHF/YZWfkZ0Pka4Ao6oxcXFmg3c0dHRspNIpV4njc6nLnmNPX8e5Fb3aEilUlpeXtb8/Lymp6c1MzOja9eulf0f6g0h2Yzr169Hvs5mBOdTHx8f1+rqatnP8vJyWeOh0X0rtXf/tkuU+7fVdSU45GkymWxpokomkykFfGF75wTf06ylpaVSr4VGp83wl68XAHXa92BhYaHUc6LSHOa7P086ndbs7Kzm5+c1Ojpaqhvj4+NaXl4u1ZNMJlMW0ES9rqBkMqmZmZmq563h4WHNzMyQbAUAHeQoxhvVjI6O1u1luR/EG9Hb3Xu62kVm3+4Lu8QIzSFGaJ2DPi5FKfgd6KTvJgCgHO3/HbT/u6/93y71rtvuF7FE+Pc0i1hir4O6R7BbcGSq0dHRhpI693u/oVWfEd2BhCvgiKo2n3pQ8KBf6SLW1atXu2rO7fn5+T2NkWQyWZZ1v7KyUveCXaM6pcfJ7vmEU6lU2c/w8LAmJydLFyS7ad+2U5T7t9V1JdgoDDsHfFSCwxkHhzmuJYo554PHtUbruF/OTvlOh7GyslJKJE0mk2UXQny7h5YeHR2tOtyzH4j4Ll26dGDrCpqYmNDQ0JCWlpaUTCZLx6rJyUmlUimtrKxoZGREY2NjXbV/AOAwO4rxxsTEhGZmZko9Pf2LbP4F2IPo3CF1TtvksMUbwd7lwR6puy0tLe2pv2fOnImsHMQItREj1Nau41KUdv9vO+W7CQAoR/uf9n+3t/9bLcx12/0ilqiNWKK2Zo9xB3WPYLfg/77R0dT2e7+hVZ8R3YGEK+AIqjWfelDw5JDJZMqCIH8dnZBNXcvy8rKWl5e1urpatax+lrEv7LCTYfnDgraTv7989fZ9p+/XThLl/m11XQnWiXbu87A9P3Y3YhsNiIJzyPuBfyM6cejuWlZWVvT4449L8j7vtWvXKn7m3QFovWPg+Ph42f8ieHElynX5gvOlz8zMaG1tTbOzs5qentbs7KxWV1dLibNLS0saGRnpiv0DAIfZUYo3gkZHRzU9PV0aTXdxcbFsRN2FhQUNDQ1Ffp4i3jgY09PTpXJmMplSm8T/nCsrK5qamtLY2NieUXei7MlNjFAbMUJt7TouRWn3d6BTvpsAgB20/2n/H4b2fyuFvW67X8QStRFL1NbsMe4g7hHstrKyUtpfyWSyoVEFo7jf0IrPiO5BwhVwBNWbTz0oGAQFTxjNzKfeDv4Qm/UaOo899ljp8e5goZlGT7X3t0sjQ7leuXKl5r6dmprqyMZfs6Lcv91UV3aXL6rgy5+TupHth+1xsnu5RgOg/fQ2qbT9ThYMBEZHR7W8vFy1fu1+PkxgGFwmuB+iXJfkHW/8oHVycrLqsNrB3nLpdLoreuoDwGF2lOKNevyeyv75Lp1O68knn9yzjK/T25DVHNZ4Y35+vvT/zWQympiY0ODgoCzL0sjIiObm5kpT1wfLHPz83bR/iREOd4zgC3Ncinp7vqi+A1F8NwEA0aH9v4P2/17d1P5vhWav23Z6XSGWIJYILhO033sElQRnLvrEJz5Rd3lfVPcbWvEZ0T1IuAKOoHrzqQcFGwzpdLrUkF5cXDxUvRJ2nxyDJ7tmToLNDF96kILD0dbb94899ljV4TdXVlb09NNPd0RQF5Uo92831ZWD2odPPfVUw9M07O5JUs3uY04jo9GtrKyUei1IaiohJ1jOTv4OzM3NlYZwn56e1uLiYs3y7q53YT5b8D21gtn9rGt3T8d6gXUwOFpaWioFTgCA1iPe2CvYbllYWCg7T3VTG7Kawxpv+AkblS6e+tMCrK6u7nktWHe7af8SIxzeGKGSWselKB3EdyCK7yYAIDq0//ei/b+jm9r/B63R67bdVFeIJYglfFHeI6jm6aefLj2uN51tcL1R3W9oxWdE9yDhCjiClpaWykZ0qiWVSpU1mP2TaJg52btZtSHnwzZqgyfeTmgo7Z5PvZbx8fGq2dhPPfVU1XmIu1WU+7fb6kowoIiigZfJZLSwsBCqjjQ7XG7wfxw2qSadTpeGaPY10+OkGxrBFy5cKF3AmJ+fDxUkNnMxq1rQGuW6GukpV2n7V69ebbgsAIBoEG/stfscGTxPdVsbspLDHG+kUimtrq5qcXFRs7Ozmp+f1/LystbW1koXYIOff3c7s9v2LzFCY7ohRqim1nEpSgf1HdjvdxMAEB3a/3vR/t/Rbe3/g9LMddtuqyvEEo05rLFElPcIKllZWWloSlNflPcbDvozoruQcAUcMWHnUw8KZvkuLS1paWmp4XV0ut09H4Mn2lQqVdZYq9fgymQyZY3fdv+fGp1PvdZ6FhYWqvZG6VZR7t9uqyvB7VXq/duoiYkJTU9Phwrkgt+xRhqau3sdPP744zX/z+l0es8c243M5x3U6Q3iiYkJXbp0SclkUsvLyw19zmCAEGb+8FrTckS5rv3o5oAVALoZ8UY4u89/3dSG3O2oxBujo6OanJzU+Pj4nourwekMdpe/2/YvMUJjOj1GaMRBtZ8P+jvQ7HcTABAN2v/h0P6vvJ5Obv9Hqdnrtt1WV4glGnOYY4mDvEcQXN/u78hBauVnRHch4Qo4YlZWVho+AY2OjpYd/CcmJrpieN/dWc61BE+GlT5bcA7gy5cv11xXcCjLdgc/UnNZ25VMTU3tqQuHRZT7t5vqSjCYDZalGXNzc0qn0w0Nu+tr5ML+5ORkWR3MZDIaGRnRhQsXSsFkJpPRysqKpqamNDQ0pE984hNlw79evHix4TIGy9mJx7+xsTEtLCwolUrp2rVrDZfxiSeeKD2en5+vuWy9wD2qde3ez2HqSTAY7sT9BABHwVGKN8JcUPPtvmC7+/N1Uxtyt6MebwR7QadSqYoX27tp/xIjNFfOTjlmRXlcilI7vgNhvpsAgP2j/V8Z7f/6urX936j9XrftprpCLNFcOTvl+BflMS7K+w27Bac0beR/F/X9hoP8jOgyBsCRMjk5aSYnJxt+3+zsrJFU+pmenj6A0kUrmUya0dHRusvt/mzz8/N7llldXS29nkwmzdraWs3t+ssuLi7u5yNEYnx8vFSe8fHxptYxPz/fMZ+nnuDnDVtPo9y/3VZXpqenS2WYnZ1tah3z8/MmlUrV/KyVDA8PG0lmZmamofetra2V3lvrZ3h42CwvLxtjjEmlUkaSSaVSDW0raHJycl/fo4MQ/F+EOd7VWk/wf7e6ulp1Wf//IKni+STKdfn7Lcz3OXgsTyaTNZcFABycoxJvLC8vV40dKgm2XSqdp7qtDRl01OKNoLW1tVD7o9v2LzFCeJ0UI0R9XKqm3TF3GGG/mwCA/aP9Xxnt/9q6qf3fTNvHmOiu23ZbXSGWCO8wxxJR3iPYLXjPoNF9HeX9hoP8jOguJFwBR0wymQx9wqz03iguei0vL5vl5eWaJ5/98hvsfoPHbwTVWq5ewzfYsB4dHa3Y2BsdHQ21rlb9H4wp32/NNHD9hlaYhmMrP1c1zQZAUe7fbqsrwbJU+65UMz093VTwY8z+A4qZmZmyBrIf9ExPT5f9r/w6vJ8gz5idxni9Rnyrvgerq6ulMkXRSJ+ZmSk7blbap8GAudZ+j2pdwQsKtfbf7mN5N1ysAYDD6qjEG8H2xejoaM02VPDCWq3P1m1tSN9hjjdWV1erfqZgWyxMe6zb9i8xQjidFCMcxHGpkk6IuaP8bgIA9of2/160/2vr5PZ/Jc0mm0d53bbb6gqxRDiHPZaI8n5DUHDbjZ5/or7fcFCfEd2FhCvgCPEP/M0GL8GTQrMWFxdL6zjoEUiC5fVPZNPT02ZmZsZMT0/vyVgfHh6uu87dWduTk5Ol9QUDjXo9DVr1f9jdeGi0cRvM4A7TcGzl/jXG+3yLi4tlP8F9ND4+vuf1Wvslqv0b5bpa9T8NBkFhegUsLi6aVCpVNcALw2+07qcXiK9WGfwAYL//v7Dfo1bss909toeHh83o6KgZHR01w8PDdX+qBWbBeuBfEKh0zEwmk3WDu6jWtTu4SaVSVdfV6E0fAEC0jlq8EdyWv73x8fFSzDE+Pl52vg7Tzuq2NuRhjzf8/bG7/bG7nRP2gn637V9ihPo6KUbYvZ0ojkudGnNH/d0EADSH9j/t/8PW/o+i7XNQ1227ra4QS9R32GMJY6K932DM3lGlmjn/RH2/IerPiO5DwhVwyK2urpr5+fmyDPhUKmXm5+cbPrD7J7IwiUnVtLqB7DfSgie7Sj9hs9DX1tb2nDx3/9RqFAfLdVD/h9XV1VJgsLusfgZ8tZ/l5WUzPz9fsZEeRqv37+6hp8P81GqARbV/o1xXK/+ns7Ozpf2eTCbN9PS0mZ+fN8vLy2ZxcdHMzs6ayclJk0wmzfDw8L5HEQo2jg8qqz+4jWZ72xmz08MjzD5oVcJVo3U/7MWQ4Pmi0k8qlQp9/ohqXcvLy3WP5fvpUQkAaN5RjzfW1tbqnu/8/0mYtlM3tCGPSrwRZl80EkuGXWe79+9uxAjVdVqM4IvyuNSJMfdBfDcBAOHR/qf9f1jb/8ZE0/Y5qOu23VBXdiOWqO4oxBK+KO837Dfp0xf1/YYoPyO6DwlXwCEWHAIymUyW/YS5EFbJ+Ph4w3PiBrW6gezzg0A/m9hvwE1PTzfV8PLXF2wsjo6Ohj75HtT/IbjPo/wJu8+7MQCqZL/7N8p1teM745c5lUqVyu33Ltk9fO5++UHiQU0B5zd099uz2a9rYYaA7vaEK/8zVKq3zdy0iHJds7OzZnR0tGxdw8PD+zovAQCaR7yxY3V1tdR7MYpzXqe2IY9avOFvs1IsOTMzs6+e1524f2shRtir02KE3aI4LnVyzH0Q300AQG20/3fQ/j+c7f9OTrjydWpdqYVYYq+jEEsERXWPYPcxab91J8r7DVHeB0F3sYwxRgCAlrt06ZIuXLig0dFRLS4utrs46GCHua4sLCxoYmJCk5OTmp2dDfWeTCajubk5pVIpjY+P11338PCwlpeX91XOkZERraysaHl5WcPDw/taFwAAQCsc5jYkDvf+JUYAAABo3GFuHyJah7muEEsAaLV4uwsAAEfVlStXJElTU1NtLgk63WGuK+Pj40omk3r66adDBUB+UOPzg6CxsTGlUimlUqmy5aIIGtPptFZWVjQ8PEzwAwAAusZhbkPicO9fYgQAAIDGHeb2IaJ1mOsKsQSAVmOEKwBok6GhIaXTaXEYRj2Hva7Mzc1pampKs7OzmpycrLns1NSU5ubmQq13ZmZG09PT+y6fv83FxUWNjo7ue30AAACtcNjbkEfdYd+/xAgAAACNOeztQ0TnsNcVYgkArUTCFQC0QSaT0eDg4KEcshXROip1ZWRkROl0Wmtra3WXTafTWlhY0OXLl5VOp5XJZCRJyWRSqVRKTzzxhCYnJ5VMJvddLv//Pz4+rvn5+X2vDwAAoBWOShvyqDoq+5cYAQAAIJyj0j7E/h2VukIsAaBVSLgCgDZYWVnRk08+qZmZGTLYUdNRqSsrKysaGRmJrJdIVCYmJrSwsKC1tbVIAioAAIBWOCptyKPqqOxfYgQAAIBwjkr7EPt3VOoKsQSAViHhCgAAdIRLly7pwoULWl1dLc2N3k7+vOzz8/MaHx9vd3EAAACAI4cYAQAAAEAziCUAtAIJVwAAoGNMTExoZWVFy8vLbe3hkU6nNTIyoosXL3ZUDxgAAADgqCFGAAAAANAMYgkAB42EKwAA0FEmJiaUTqe1vLzclu1nMhm9853v1OTkpGZmZtpSBgAAAAA7iBEAAAAANINYAsBBIuEKAAB0nAsXLujMmTNt6e0xMTGhsbExTU5OtnzbAAAAACojRgAAAADQDGIJAAeFhCsAAAAAAAAAAAAAAAAACMludwEAAAAAAAAAAAAAAAAAoFuQcAUAAAAAAAAAAAAAAAAAIZFwBQAAAAAAAAAAAAAAAAAhkXAFAAAAAAAAAAAAAAAAACGRcAUAAAAAAAAAAAAAAAAAIZFwBQAAAAAAAAAAAAAAAAAhkXAFAAAAAAAAAAAAAAAAACGRcAUAAAAAAAAAAAAAAAAAIZFwBQAAAAAAAAAAAAAAAAAhkXAFAAAAAAAAAAAAAAAAACGRcAUAAAAAAAAAAAAAAAAAIZFwBQAAAAAAAAAAAAAAAAAhkXAFAAAAAAAAAAAAAAAAACHF210AYD8ymYy+8IUvlP4+d+6cent721giAACAzpfNZvXqq6+W/v6Jn/gJJZPJ9hUIaANiCQAAgMYRS+CoI44AAABo3GGNI0i4Qlf7whe+oF/4hV9odzEAAAC62u/8zu/o53/+59tdDKCliCUAAAD2j1gCRw1xBAAAwP4dljiCKQUBAAAAAAAAAAAAAAAAICQSrgAAAAAAAAAAAAAAAAAgJKYURFc7d+5c2d+/8zu/o4ceeqgtZXEcR8vLy5KkkZERxWKxtpQD3YH6grCoK2gE9QVhvfjii/rzf/7Pl/7e3aYCjgJiCXQj6goaQX1BWNQVNIJYAkcdcQS6FfUFYVFX0AjqC8I6rHEECVfoar29vWV/P/TQQ3r00UfbUhbHcXTjxg1J0qOPPsoJBTVRXxAWdQWNoL4gLMdxyv7e3aYCjgJiCXQj6goaQX1BWNQVNIJYAkcdcQS6FfUFYVFX0AjqC8I6rHEEUwoCAAAAAAAAAAAAAAAAQEgkXAEAAAAAAAAAAAAAAABASCRcAQAAAAAAAAAAAAAAAEBIJFwBAAAAAAAAAAAAAAAAQEgkXAEAAAAAAAAAAAAAAABASCRcAQAAAAAAAAAAAAAAAEBIJFwBAAAAAAAAAAAAAAAAQEgkXAEAAAAAAAAAAAAAAABASCRcAQAAAAAAAAAAAAAAAEBIJFwBAAAAAAAAAAAAAAAAQEgkXB2ApaUlDQ4OamFh4cC2kU6nNTU1pZGREQ0ODmpwcFAjIyOam5try3oAAAAA7A9xBAAAAIBmEEsAAAAArUfCVQQymYzS6bTm5uY0MjKisbExZTIZ3bhx40C2d+nSJQ0NDSmdTutzn/uc1tbWtLa2posXL+rChQul11q1HgAAAACNI44AAAAA0AxiCQAAAKD9SLjaJ78XxsjIiBYXF/XEE08c6Pbm5uZ04cIFjY+Pa3FxUcPDw6XXxsfHtby8rBs3bmhkZESZTObA1wMAAACgccQRAAAAAJpBLAEAAAB0BhKu9umZZ56RMUZra2uan5/X6OjogW3LH2pXkubn5ysuk0qlNDk5qUwmoyeffPJA1wMAAACgOcQRAAAAAJpBLAEAAAB0BhKu9imZTLZsWzMzM5JUN4DyA5eFhYWKPUGiWg8AAACA5hBHAAAAAGgGsQQ63fVnr+j6s1faXYwynVamTiuPRJnC6LTySJ1Xpk4rj0SZwui08kiUKYxOK89RRcJVF5mbm5Pk9fSoJfi6/56DWA8AAACAzkccAQAAAKAZxBJoxsuf+jW9/Klfa3cxynRamTqtPBJlCqPTyiN1Xpk6rTwSZQqj08ojUaYwOq08RxUJV11iZWWl9HhoaKju8n4vl8uXLx/IegAAAAB0PuIIAAAAAM0glkAzrj97RTeeu6obz13tmFE3Oq1MnVYeiTJ1Y3mkzitTp5VHokzdWB6JMnVjeY4yEq66xNWrV0uP6/UCCS4TDGaiXA8AAACAzkccAQAAAKAZxBJoRnCkjU4ZdaPTytRp5ZEoUxidVh6p88rUaeWRKFMYnVYeiTKF0WnlOcri7S4AwlldXW36vZlMptQrJKr1HLR3vOMdoZbL5XJlfzuOI8dxDqBE9QW3264yoHtQXxAWdQWNoL60T2HtbcVOJGXFu6N57bpuu4uAFjlqcYRELIHDjbqCRlBfOoNxXZnsttx8NvBkcIGdP+yB47J7esre72a35W7e3rNscBXB5yUpcebOPeUo3FqTyWb3PC9j5LiO4usZSdL2a99XLGbL6utX/OTgns+Sf+u1qtut9Hx88IzsvoGyl53NDTlrbxc/R2Ad5R8q8NhS7/3v2LOZ/Fuvy93aqPleEyhL7NgJJe64u7yorqvsKy/VL0NgPYm77lfs+Imy9TjrN5V74wd7lq30fl//u9+3d7kuQCxxdBy1WII4Yv9u/LE30kbp7+eu6q0/ek6nP3r+UJXJGOMd140r47reY9eVMa7kGtn9A7Ls8vEunI11uZsbWlv+6p7y/PB3f1fJD70vcNoxshI96rnrvj3bzv3wezL5vPyFze7zS+lv73c8eVbxwTNeGYp1xCrktfGdb8q2LUlS5qvf2lOm71/+bSU/8HDpud4HH5Ld21e2qfxbr6tQPJ+HYfX0qu8d79rz/Pa178jky79Xma9/u2qZ4mfu3NPWcbe3lP1eY8eaRj5TtfIMnv9g6M9USzOfqVKZ3vj8F3X24z8a6jNVY/X0KnFuJznWrzf1PlOl8uz+rh30fmq0TK3YT2HLdGLo/gP7PtWy/t3X9taj//wHOnb3qdDrcB1HViEvE0+UnYuaPUZUOm7vPibVchDHiGbLFNV+2v2ZOvF8G8ZhjSO6444QlMlkGlr+9OnTpcc3btwoBSVRreegvfLKK029b3l5WTdu3Ii4NI370pe+1O4ioItQXxAWdQWNoL6EZIws15GVy8rO+z852bni7+JzVvFvt6dXa8M/uWc1dy9d1tsf/r+psOumVKe6du1au4uAFjlqcYRELIGjg7qCRlBf9sEYWaV24c5vK/B3/tRZbd37zrK32dkt3bP4W7LyOVkhN/XWR/6UtnetZ+B739GZ5d8PX1zb1vd/fnLP82ef/Y/qf/27Vd93T/H360u/LUnaOPcu3Tj/ePlCjqNz/+5zocsitfcz7dapn8n5tpfwFXt4782YTkQscXQctViCOGL/sk/98z3PrfyDS+q9+Df3t2JjZBXy3jk4l5VVyMtyC7IcR5bryMQT2r7rgT1vO/nCstb+9X/Y8/zz//207v6ZD8lyHcl1ZDkFWa6j9Xd9ULeH3l+2bOLm27rzC78jq5RkZWSpSsJx0Wtjf1GF4+XJCqe++ZxOfucrevUP99az7/yD/4/O/fiDZc/lTp7RG49P7Fn27v/rt5TYuFlz+0GZRx7T+ntGyp6Lb9zUW7/266W/K5Xp5Uv/rKxMr43+BRVOJMuWOfmtL+vUi+FHosudPK03Hv/EnufvXvwtJW6Xf6ZaZbr58GO69d7yz5S4eV13//586LJIjX2mauW562c/FPoz1dLMZ6pUpq/9w3+snv/575Q9t9/95B9b6n2mSuXZ/f0/6P3UaJlasZ/ClumOn//IgX2farm2svf89bVP/mMNPTpQYenqYsX9FDwXNVv3Kp1Ldh+Taqm0n+I3r+vu//xvZWxbxvJ+ZFkydkym+FuWtfOabevNj/2snGMnJUmFT/6TPdt54Z99ToP/xU/sWqe987dtq9B/XLfe3JKKEaGRJVnS8dVvyM7nqpZHgeeNZSuf3FDu5M3SehKf/JRiu8oTyfn2gB3WOIKEqy6xnwZ7MKCJaj0AAAAdw7iyc/5Nr8CNsFz537fe9cFSkOS9z+jEd76q5LeeC70pp29At7ZuS64ry7jeb9fV5hu3pK9+Vfrxn5BsZu1G5yCOAAAcea4ru5ArT5oqJtNbbkG3U3tHGRpc+bz63vy+t0yhfsLUxv0PKXumOHKScWW5RirkZDfQa1mS4rczStx4vey5WAM3Vb3ta886JMnKbze0Gju7tXc9buOjp+z9TFaTn+kN7d4RjX+mbSXW3ix/spnPtHFzz3piG7caW4mREmtvSZJyC/9Oxo4p9nf/dsNlAQ4SsQQa4Xz7JbkvvrzneffFl+V86zuKvSclyxhZuaxiWxuK5bZl57PaPnuvFCveNi6OznTslRc08IP0zrm7kPMSnqrInRhUPni9p8h+7o+V+8He0VWyr2fkfGdVA2ePlT0fX88onvGOzf4pJ347I9sphPkX7Kwn85as3FZ5WbY3tPn2hraub+5Zfuv6pjbf3igrj+XklVh7Y8+ylttYWWKbG3vOWfHbmdLjsGWK37ohq5AvX/fW3vfVYjmF0rmv/Pnyc3G9MtlbG3vWE28gwaT0npCfqVZ5tn94I9RnqqfRz1StTM7L35V15YriD72j9NxB7acw5XFffLmsPAe5n5op00Hvp0bKVHjp7grvqM7fTztHRkvGsuRacRV6+mXsYvKPtZP8U0ooKr62/XpG+Ve/vWfdzsuv6M33fVyJc3eXJyPZgaSiQGKRLEu3e07KNT2lckiWcvc+qlsn7y1LYtpTHj/hybLlJnqVX31TPRXOJVvXN/Wtd/+M3IcfLlvP3nXacmNxmZiXAuOXRYPv0vf/6kcb+h/7Ys9/Xcdf+M6e5/M/fFvpux+T8/4PNLzO2+/7yabK4pen71vf2vO8++LLcr79Utd04jhMSLjqQlH16GhlL/NGPfhguCzVXC6n117bGcp8ZGREjz766EEVqybHcUqZuz/6oz+qWGx3bimwg/qCsKgraES31BcT6BUoY2SMKzeblbu1IXfzttytTZmtDTlbG3K3NpU4e5f6hh4uLmskGRVuZfTWv/hludubMrkKU6NUcO4DI+q5a7C0TRlp660TWm+g7PFCXh9KPSjLKl56KwZ2y//oM7Jf3dSPX/yf9wwb34k6uR2Ig3MU4giJWAKHG3UFjeiW+rK7bSiZsueM48jd3pC7uSl3a9NrM25vque+dyh+MllqHxrXaPvlb2v9i//RayNub8nNbtduK9q23vOjPyHLkjcVUHFaoJuWo+zW7dCf4XTcUupccYyo4kV9Y0lv/udYQwk9qaEhr90bsPntmG6+sBx6HZZt6UPDe6eSuPHqC8pe35uIVc3gmTNKnX9sZ72yZBxHr33x34dehySl3vVu9T/0SNlzm99KKNPgZxr+8Ef2PH/j+y9ou4HPdPrsWZ2Oe731B4e9EUxMoaDX/uB3Q69DKn6md5W3GTa/mVDmhatV3rGXZVsa/shHtLbyvL62+j1J0qOJ/o6fBkTq/LYgDsZRiCWIIxpnnIJMLicnt62Vf/Y5VUszTvyby3rwpz8g51ZGJlueiHT6iSeLU9ga7xxqpNuvfUebN8NPQ9VvW/rAvXd5p2A/Vcq29ZVvVT9HXH/hbQ38eHnC1T13nNHxR4rn4eJ1n/zbb+j6lWdCl0WSHn7vexQ/fZe3muK0fevbGX37hcXQ5envH9DwY3vPfW987Yty/Ol0Q7j3/vt1ongOdV1XX/nKV2TsnVvD11+o/n8OlunRD3xA8V1TdN3Krun2914IXZb+gQENf6TCZ/r6F+QE2l31yvTInzunE7vWk3/zNb11dSl0WaTwn6lWeW598zV9+H+t/5nqufdcY5+pVpkSX/qqPvSXn9gpYxP76YPnz+srX/mqJOlDH/qg7Fis5mcKW56D3E/NlKnR/XT3/ed0rPR9klxJ2bfe0Nq3viw3lvB+4gmZWLzi3+tf+b3q5f32W7r+/3hSJu6/1/tt4t5jY8XKEpyMHZOJxbS7R8IP3hk+qejY371QNVHk7Zcz2virF0Ovq6LB+xt+y7HP/Eb1F39vUVs/PrqPAjWn77d/s+ZrG00kXO1HrfIc+/yzemzyF1tYmsZ0cjtwP0i46hLBYXQb7dURrLxRreegffe73w213De/+U297307PRFjsVhHXEDslHKgO1BfEBZ1BY3YXV/MrptYpeSl0nOSjFt82UtGknGLT7s7rztu8T2OjGu811xXxsnL3d6Su7UhZ2tLZntTJpdT3zvfVdqWdwPL1fqz/1m5174nN5uVyW7LzW17UWoV/e/9gGJ9xWGMLUnGyM1l5dxaa+h/YrZue8O+x2KKxfukeFyJwTsqL2zHZPf3y+4/Jrv/mGID/u/jOvauR8uSqq4/e0UbP7gu6bpuXv2qznzsscrr7CB2FySFIRpHLY6QiCVwdFBX0Ai/vlRvE6rU9ttpE9ZqO3qJUP77/HaecY23HtctJrgbufm83OymzNa23O1NWbGY4oNni+srtjtllHnm/5TJbcvN+W3ErEwuK1NllKhTj/+8+h96WN6sDJYkI/fGG8q9mg7/j3Fdaeu2rESPZNmybEuWFVNs4Fjdt1qJHtl9/bL7B9R77wM69p73S7a9k5gvyTzxpKyeHsX6B2T19KrshkhgOf9h4s57FTt2omw7vWfv0vGHP1ilEMEbLFbpV98DQ3sWTfzl/1bu1taexSXJcV19ZeUrkqTh4WHZsZjs/gElTpe3lY3r6v4L/6hGEfaWJ548Lbuvv/wz3XGXTnzoo3uW9VZhVXpaibN7e/3f9V/9rfL6Uen/EXja6unV1V/825Kke3/uz3ifyRg9+Pc/U75iq8ofxRXZ/QOyEz3ln+nOe5T80V3TFdYpV+zYcX397/z90t/pT8/pjh/be9O20xBLHB1HLZYgjqjNOI7cXFZudlvuZrHDXC4rWdLNr39bN7/yjarv3fj+27r17Zf3jCglyRvFyra9XKtizlWsv7GprKyeHh1/+Ee8c0jx58ZzK95o5FVsXd+Ude4DSj48JCvRIyseV98736O+d767bDnnvgeL5fM63MmyvGtCllVsOxQfB5bpe8e7ZPf2la3n9suvVhzZJlie4z/9X+v0+Q96nykeV3zw7J7l7v/b/0CmlMztnVNKp5mdB6Vfdm9/6TzsOI4Kq6+ocPyUzv39zyiz8nVt/e7fqlmmk7/w13X6w8Oy+wf2dDDs/S/+K539ub9U9f172HbFfXtu+lKpHXrjyyva+t3qU2JtXd+Ufdd71X9v+RSSfXfdp2NP/YvwZZFk91X6TP+1zv7Znc9Urzybr2e0+cobe64FBj9TGFa8R3ZPedui2meqV6abX/lGWZl2f6a6ZbFtmUSvCqve1Hd99z6gWCxW9TOFKc/GK6/r9EcfU+KOe3Xuk95Uzl4IUD5ynQk8KD3u6/e+W4HXj/3c/10Df+oJGUmuceUYyXEl1xjljbR+9Sva+t3/Z80yrVx9WfZ/+ffkuEauUfHHyClu2zFGxkhO4DVXltzr3uNS+WL3Sn/hf6m6rdJiz39dx79XI5nohRd1+2ZBzvsfqb5MhGLPf13xbzxf9fX4N55X7PmvNzV601Euk6Xi6UGSXUwCti1LpdOTLO95Y0rL7LzHW87284bLnrNlx2w5X/2aNmqUZ+25ZWW+vNKx9ycOaxxBwlWXaDSwCA7TGwxooloPAAAIp2KPfXfnxlWlm1e7nzPFm1Zy/ZGZ3FKik4q98I1x5RQKSlx/XZYx2nr5217j3HVLvf4tFVfv3Y+S5F+LseQaV6ZQkNnelsltyz52onhBxkiyZBmj3Js/0Oa3vlocKWBb7nbW+53drnwjLBbTvX/zl2RZxRtPlmRZtpzbt5R/87W9y1f7H2azxUQr78eyY1p/+VVtXt/SwJn+qu+zenrLEqb6hx7WwHvLA7LEqdPqufs+2QPliVVWT2/5zZ4aXv7Ur5U97tSABkcTcQQAHF7GGMl1vDac43ijTBQKMrltmXxe+a1N9bz1A1mSNl98vnThdk+b0LK8pHSVcp/KnnOdgpcon8sqdmpQtgJXhSVtvfxt5V57tZQk5Y8q5bcZTb58CpDed75bZ//cXym1DX3ZV16qmlxV8fMXp5Oziv8LS5J2JcHU4iVMDajvgSHFTw2WRi61LG8kp76HHlGsf6DYniz/HesfkBVP1N3GyY/+ZOjyVBM7flKx43unSWpUpYQln+M4yqdflST13Pdg1Rv2lm2r995z+y6L3Tegnr7GbqZXEj+ZbGj5689e0Y3nrpYen/nYY7IsS/Hk/tsqdqKnZv0zxRtzrvEeu0Z68w+fK5VHkm48d7VULqATEEscbcZx5Ga35GzcLo5Mta3Cxrryr31Pudd/oL4HH9LAIx+UJL3yW/VHP6w0opQkxU+dVn/q3ZId85Jf7Jh3Pad4TSd27Lh3Dh44vvO4r99LkkokZMXiFUcaX/2V+sk3b11ZVep/+J9qLhMbOK6TH/nJuuuq59rnqo9I4vvuv5rXXT/z0zWXieKcJTum2IlTujb7r+sumv61/113/NSfqLyanl6pp3ffxQkmYaU/+7/XXX71V/6Fzn78Y2XPWbGYYgPH910Wu6dHCiQ+hSlPpWuBjSYNVlLtM4UtU2nUzERCiidKCU7Fy6vFpCYTeFxMJDJSYbuggu29J7Odl205MsWUAq9fhim954Vf+Zd1y/P1f/Sruuszny4lL5liYpNrzM6P6yc8BROdjFyz5SU8uUaudpbzk6AqTTZ67LP/sm4CxI2539DGJ2fqlr1c9alN66k1KlFwmVaNltRoeXYnBVmWZPuPJWWzWVky6u/v30kwCry+O+nIe7/X2cJLKLJ0e+G3VG9s4NP/9rd15+iPFctj7SQtFR9bVrXnrUByU/my9q73BZd9/pPzqjdx+Ll/P6+P/OWfDn0vYT+eu/CbqjfGIfcnWo+Eqy4xNLTTM63ROc+DAU1U6wEA4CgwbjEJqZD3fnJZufl8Wa9940V8xd75xeQn1w306lepl16pM3PwOUn+PaviS6WbXF6SlLXzBu301NuZ0s7ambYkn5dVKMjE47Licdlxb1hhI+n2lS/KZL1pVdztnd8mu1X6OzjK1Omf+4saeM/7y/8fr31PWy9W70Gxh+PIWV+XlYiX8qUseb30QrG9nvXxwTPqf+gRWXasdBPs+f/ln6mQsfTgX/9rpaSqnYQp70aYFaJnZzx5el8Xq4I3bSRukqDzEEcAQHfy2pO7kqnyeS+JqZDbaZf6SfXym5CWd6MyFvMSh+y4jHFlJRKK9faVXQQu3Mpo89tf8xKlSu3E7Z02Y3ZbJrstU9hJmLr3b/y9PaMV5d/8oTa/EX56OHdrU87mbW+KumL5jfGS5RtJuIodO6n+1Hu89mHxBm38znvlrF0vS4zanTAVGzjmjWhQo0167NEP6dijHwpdFlR2/dkrktQxbeMoO0r4SVNGpnTj0Ls56N00dNzijUR358aha1xlHaNcwVXOcfXGP/7VimXslP8XQCxx9LjZbTmbG3LWM3I31uUWHGW/97JyP3xFue+/okLmemlZ4xTUN/RerS1/TZmvfrPuureubyr+3h/T2R/7qOKn71Ds+ImqHd76H3rYG8WySbuv1VTTqms4nVYeSbrxx1c7rkyd9n866PL4ydiliQdkdh5XSYZaa2C/vfT7X9KJx0b8tcjvTuEa12unuFLeNSq4rhzHKG9cOa5UcI0Kjqtb/XfIWNLKD28V2zE7SU5usc2jr31NfVdX6pYnu/JVfWvpSy0ZmajeiES+Vo6WlPhG+DKde+VF9Y8My7YsxWxLseLvPUlF8p+rkgQVTDpSebLTrS9f1TdDlmd08wc689HHaiYTOY6jL37xi5Kkj//Ix5sabfH6s1f05a9+re5yua98Vfdee7Flx6RbX64f5649t6wbf3y1649JaB4JV13i/Pnzpcdhht1Np73h01Op1IGsBwCAw6I0EkAhLzdfTKrKbnk3l/L54uhQxeQn2/aSeILJTrJKozfJjpX/LWvfPRtyr39f+bff9G6CbXvlKiVMZbdktrd3XsvndOrMPcq8/0fLevhZktaf/f2ym2X1ODfXVFi/5SWG+f+BGtP+VZO48x4lBs/sDLNu23I2N9Rz9/2yB47v3PQqS5oaqDnKVDC4yFundOZD7Qsegjdtgs8R0KBTEEcAQOfxk/rlFkoJVW5x6jw3m5UKuWIylUqJ+X4ylRWLewlG8bhigbaSs3FbhetvqPD2G8oXfwqZG7pra1OWcbUVK+jk+R8rK4ezflO3vvifGip7/sbbivmjChlJlvFGhmqE63pT3gXah5Yd0/EPfkTu9tbOyKOVkqVKfw94/4tdes7eqTsmfrGx8uDA+G3lTmgb1+so4Y/oYIyRq52EKj9hynWNCsERGKSdlEHjquBKWcdRrmCUc91iUpVRtuAq6zjKFv/2xZ7/uo5XuKnEDRJ0EmKJo8EYI3dzQ/m3X5O7sSFZklsoaPPbX9XG174s93blsT2y30tLPb165enfC72t1z//VT3431Wfeiwqla7V1Fr2oI+5nVYeSVr99GzoZVtVpk76Pxlj9NKnPht6+Rf/6Wf1geEPlZKmSsnY8toYMpIbeK20nV3JUH5nip1ny5dJN7Dfrn16VrF//MvKu24xicoo77pl26+pxxuR7vpm9evJx/7N/zd0eVo1elOYkZt8xy7/G8U+PFJKaopZ8n7b5clOwd928G9bFZfZnSj15f/tlxQ23Tj+m7+p4T/7J5v78CG9+Jnw02+ufmpWZy9/+ABL4+mk739wO40sexTPJfCQcNUlhoeHS49XV1frLu8HLqOjoweyHgAAuoUxRnL8UaoK3jQn236P/a1AEpHX08O/gWXF47J7+5rerpvLKn/9TZntrWJC1Hbpd3BUKbf4uslu6+6/9j/s2ebG176sjefr91zw2fms7Ny2nNu3ZGxb/thZVk9vQwlXVl+/+h5I7YwYYNmKnTqt7Ze+JXtgZ+o9e+BY8cbXseLNsUACVd9AxVGmToz8mE6M/FiFrYbTKVP4VetVwk0SdBLiCABond1T/Ml1vIT+fFYml/OSqnJZbyRUqTTEqSVLsgPJVD09ivdVnzZZkjaev6r8m6+VkqvcrcoTC/ip687GLTkb66WpQ2QsuYV6EzbsFT81qJ77Hiy1Dy3bVuH6W3JuZbxEqFLbsEay1MCxitOj3PGJv9ZwedC5Kk3f10puaVQp7/d3/unem6bf/ief0bv/5WflFEPCna42O9Oqu5LygQSqXMFV1vFGqNouuMoWXG0XnPA3L4tq3QzkBgk6BbHE4WaMkbuxrtybr8nd2pTd2ytn67ZuL39Jmy98XXIKVd+buPNe9T30sHrvPqeP/dv/o4WlDucjl3+93UUo02nlkaTH/s3nmhqF5iBF+X8KJjsFp/N1XD952jvHlyVDqTh5QXEdD3zuV/WAwiZDSevZgt//VsV5CorTdHtsyyot77hGBdeURpjykqGKj12jvOO9XnDd8se/9A93ZkwIYzP86LHNaHxKPo+XjKRAUlKlJCcpZtuKF5OWKiY17XlOij39G15S1O5EqeKoT+3QaceATiuPRJnC6LTyYAcJV11kfHxcCwsLunq19k3XlZWd4RunpqYObD0AAHQKb5SA/E5SVXH6EzeblZvbVjAStfybWTFvujp/FKiq6y5egMq//YbyN96Su7VZTJjaGVnq+Ic+qoH3/kjZ+/Jv/FBvXf5cQ5+jcCuj+PGTxakIjXfTzm7s4oflOiocP6We+x5QLJ4o3Qzrue9BubfXZQ8MyB447t3wGtg7DZ//d+zUacUGij2ZAlOB3PvX/6eGyhO1TprCr1avEm6SoJMQRwDA/pWS+B0/oao4xV/eS6Iy+ZzcXE4ypjh1tCVZxen+iiOPWrGYrBDtT0lyczkVrr8pZ2vDmzZvl/Urf6DCjbdDl9+ybCXuvHcnqcu2FT9zVvHBM7L7jnltxP4aI0sV24uJO++T3dNTtu5TPz6mUz8+FrosODidNIVf1NP3eTdNd6buc4vPFYojNTiud/PUkTcClSlOz25kdPvKsjIVpgNZv7Kit750RYkPfVDZglHWcQJJVK6yBadsVKqo1Jvqhg4c6CTEEoePcRwV1m+q8PYbcnPbsnv7FD95Shvf/IrWfm++4nvip+/QwCMfVP9Dj6pv6L2K+6NdAi0UTKIKTnHnJ1EVdrUFylsDXiK1iok3kj+BQXHatWKWlBXzX6ucmGOMn/zkJUjlHTfwt5c4Vfq7+FowqargGkXfsohG3LaUsC0lYl6S0+1bGVnG6M47zipu22UjQNm2pfju0Z7KkqAqJ0jZbU58AoCDQMJVh8hkMrpw4YKSyaRmZipnBF+8eFELCwtaWVlROp2uOqTu5cuXJXk9R4K9R6JeDwAArVQ+9V+umFDlJVYFR24yUvGGVlxWLKbYseMNBXHGGG187Tnl33pD+eveiAFme6vmewoPPiQ3791gk+t6CWCm8en3JMk+mZQdj0uxhKxYTNt33iPF4t6Nr+DUe8WRAYIjSqm3X9+49l05x04qfnKwrKfafX/j7zVVHqmzpgLplCn86s2Zzk0StApxBADsn3FdGadQnlCVz8otjkqlfKUp/rzRUUsJTE20OyXJFArK33hLhes7UwHm335Tzs01SUZ2/4D6/vrflWVZZe3hWPJMxYQrq6dXPXfdp8Q955S48169/MPXZBK9uvcnR5U4fUfZsrFjJ/Tg//tXm/yvoRN1Sru9XicJf5SJ3dPuOMUp+xxXxd/ec8bsuWUaGGnCv3Hn3TL1pnoxyjnSdsFLoPrhr1TvCJP+9GzTozNUY1tSXzym/kRMAwm7+Nv7uz8R00ufnFflCbp20IEDrUAscTg4337Je/Dxj9dczs1uq3BrTYXrb8q4RrG+fsVPnCq93p96jzLxuDftsf/cez6gUz/5Mxp474+EShgHGrU7qdpPovKn9C0Uk6kcUz+JypKXyBOzLMVjXoeDStvbGUHKLSZDGRXKkqPKE6kKTmC5Roe0bCE/WSoRs9QTs5WI2eoJPE7ELPXY/vPe3/4yiZgtOxDHOI6jL37xW5Kkjw4/1HGjoQFAJyHh6gCFmY/c9/jjj5f13qgU4AwPD2t6elqXLl3S1NSUFhcX9yyTTqd16dIlJZNJPfPMMxW3FdV6AACIkjGmNEKVKeS9aVeyWztJVbun/ovFvFECEj2y60y5sps/YoC7vam+d7677DXLsrR+5YvFm1zhONubkhWTnYh7IxjE47ISveXr7esPTKNyPDCy1M4IU/0PPbKnl+Dpn/2EzvyZvxCuHI6j3K1s6HKH0e6pQKqVJagVyU3+DR+/x9wL/+Qzdd/DTRI0izgCAKJjAklUO1P85WRyWW90qmxWMu7OgKjewFRSzJZlx7w2Z6L+FH9hFdaua+NbX1Hh+pvKv/2GCmvXvblLqnC3NpV783XFjx0rtnu9JPz+hx6R2dpUz70PqOeeB9Rzz/3queec4oNnSzdEHcfR5he/KEnqueveSMqPztXOdrs/0oQpTsFXa/o+15VcqSx1ypTSGL2kKas48oElFaeR8W4AejdJVRqFKlsIjEjl7DwXHJUq9vzXdfwrX61a9vg3nlfs+a/Lef8HQn3WnphVSpwKJlF5j73kqt6YXTX58vqzV3Srwmhbu9GBA80iljh6Cr/zH70Hk79Y8XVn47byb78mZ+O2LNsf7b08ecK4rtxCXv3v+YC2XvqmTjz2J3TqJ35aPXfdd9DFxyFUKYnKFEeicgIJ1X4SVWkq311JVJYl2aqfROVvs+AabeedPe2CbHE6YP93Q1PytYgleQlQfkJUPJgcZQWSpPYmVCVithK2xahRANAmJFxFwA9i0um0nnrqqdLzly9f1vDwsM6fP196LplM1lyHv55q/KDn0qVLGhsb0+zsbKk3yMLCgp588kmlUinNz89X3VaU6wEAoBHVp/7b8kYPCLAsqzhKVTz01Ct7tucUVLjxdmC0AO+nNGLA8ZO697/9H0vL+sle8eSZiglXVm+/4qeSO9OtHPMSp/rf/T71v/Nd5ds+c6ce+KVPewlWfeHLv3sakHb3IIxyKpAoy1Lptf2Wze9p71/82Rn625Xj7vSkv33lqm5dWam7Pm6SoB7iCABonjfFn1NMqAqMhJrLyuSzXvK+P8WftJPQYUmyi4n7dvgp/sKXy5VzM6P8228onjytxNm7yl4vrGe0/uzvh1+hbSt+4qT6H3pEVqBn+Zk//YTO/Oknoio2mtBJ0/dJ0bbbg1P1GAXayf4oE275aFTSzngT61euVp2+7/aXV3TqIyMVbwi6xuxKoir+Lt0wdbVdcNTowBJ9v/2boZbZ+sAHykalCiZTBZOrYvb+bmbWimkqLdsp9Qudh1gCknTjj6/KffHl0uM7fuwjZa/n164r98Pvye7rK41mZYzRxvPLGnjkg7JiMbnZbbnZrHruuldnP/HXZPf0KjZwrOWfBZ1tbxJVcXTKOklU8pOpmkii8rdbcE2pLVD6qZBQ1c7BpyxLxQSpneSo3YlSZX/bVtlIU3ESpgCga5FwtU9TU1Oam5srCwD8x+l0WhMTE5K84CWVSml1dbXiemZnZ0tzklcbvtc3MzOjJ554QrOzsxobG9ONGzckSalUShcvXtT09HSoske1HgAAgkojVBXycvP5slGq5OwMS14+9V9csWMn9h1Y5tfe1tYLXy8lVhXW3pbcGiMG3L6l3FtvKNbfXz5iwLselXEK6rnnAfXee04993g/sVOnQ5fRisWUOHNnw5+hU6YBkepPBdLOsuzWSNnKRqtyvIQqbyhxszM1iZEs2xuSPGZZSsR3Lv6kf+3XQ5ebmySohjgCABpjjJHJZeVsbchZvyl343ZpBFTL8mZ2tmxrZ1QqO6bYsWM1b+Dstzzu7Vvlif3X31Th7TdK012f+OhP6dSPjxU7HXhtZLuvyg1My1L89B2ldmfp5857ZMUTB/IZsD/d0m7ffYPUGMktTuVXcL2bk/l8QblYr4xsvb6+XUxCtIq3Scun8gveKLUtS7GYymKk1Rpt5Vd+ZU5nH/5ndUelikrs+a8r/o3n6y4X/8bz+pMbP9DZj3048jLs9pHL4WMJoBpiCfhWPz1b9jiYcJW/8ZZyP3xVseMnypK2b/7Bf9LtK3+gwvqajn3gw7J7+9Q/9B7ZfQMtLTs6h59A5Qam9CsUOx+WpvRzy9sEKnVLbDyJyuclUjkVkqh2HjeTbN2MmKXi6FGBxCjbVk88MKKUXWV0qZitmCUSpgAcKL9zeK7gKue42swVtJX3jtUP3XFcvXGm/m0XEq72aXZ2VrOzs/UXrGN0dLRq4FPJ8PBwJNuNaj0AgKPDuG7ZKAI7U/9tyS1OyeKzJFkxb5o9u7dXlr2/izfBG1ux5GklBs+Wve5kbujWHy2FX6FtK37ylPpS7y0b2eDMz/1Fnfm5v7ivsjajk6bvkyr3vm5XAlGYnuDBshlj5BjJLfawyzuu8sVRq9zAnEGWJNuSbMtST6x2bzLXGG3lHZ3+lU+rJ1fQ7WxBt3OONvNOaZkzAwmNvavxRDscPcQRAFCfm8vJ3d6Uc/uWnPWbMo7jTTWW6JE9MHBgyVTVbL7wNWVfvVZKsDLZ7ZrL517/gQrrt7xOBv0Dip845SVR3fuAYicH1VNM7O+955wSd90nu6e35vrQOdrZbi+NKiF/FKoqU/j98mf0rn/52dKywaSpUicDeR0MvOaxJUuuemK2YrHYnvVV4hqzkzxVcHXry1e1XmMk2O2Vr+iFZ74Uevq+emxL6ovvTOc30BP3pvUrjlT18ifndTPkulY/NduShCsgCsQSkLzzz9pzOyMKrj23XDon5d56Xfk3f6jYiRNl0weuX/1D3b7yB97jZz+vgYc/qL73fKDto6vj4ASTqfIFV44Vk7EsZbbzMip4nQ+NSm0Df1Qq20+kKkuwDldPHNcom3eUdZzAKJV7E6qcA8yksiypPx5TX9wujVTZ50//G4+pJ74zulTCtvc9giUARKFQ7BieLx4vt/KOtvOOtvKOvP4p3nHalhSP2YrblrKF4P0OtAMJVwAAYA/jOKVp/0whXxxefMub/i+Xk+QNAy2VT/0Xi/DGl7O1qcLbr++ZDtC/sXXyx0aV+NiflDH+iAEFWf3VhzwvjRhw7wMdO2JAJ03fV21EqXaMclVvdCvfjeeu6ruff1YD54dLF21McQIhvwd+3LZk16mjpnjj6HauoNtZp/i7oNt5R/Vil1vbBRlj6NUGAEATTKHgJVhtrMu5lZGbz0mWJTuekN3XV3bD8CC421veKFU33tLA+/ZOe7b1nW9q6zvfaGCFjgbe/agUi5et69yFS1EVGW0SZbs9OC2Pq51RqMqm7Qt0JHCNSqlTktHtKyuVp/C7uqKNqys69eGRujGa67qlNCzLskrT92wHpvLbfcN0u+Aov2tUqmOzv173YnPfb/+mNkIkXPXEbC+JqsK0fv60fz0xu2a7+86nf6PudgCgW1XrJHf6IyMqvP2GYidOlh3/N7/9Vd38/H8o/W0lEoqfHCTZqovVG5nKa0uUlpbrusrHemXJS76KxWLF62ThrmE5rilPmio4FUelKhxkIpWkvrhdTJ7aSbruj8dKz/UnbPXWaSMAQLs4rlHOcVVw/JGqHG0VHG3l/MQpIxlLsqS47SVVDfTEaxyrnSrPo1VIuAIA4AgyxkilpKq8N3pAbntn6r9CwYtgJS9pPhaTFY/LiscV6+k9kIB16zvfUPYHr5QSq9yN9ZrLl40Y0Nuv+KkTStx1nxJ33qN48kx5YtXd98vu7au4nuvPXpHU/qlAOmn6Pqn2iFKtTgZ76VN7e+xX8+qvzOnhf/VritUZrcqX9ROrgslVOafpXnZ9iZjyjlFPnIs6AADUYxzHGyV1c0OFWxm525uSbFkxW3Zvr+J9/QeyXTeXU+HGm2VJ/YXrb8pZ3xkLp++d75Z97ERZmzl2MllxfXbfgBL33F8arao0HfXxkwdSfrRXI+1215hSolTB8X6XJVIVBwcOTs1jZMkyXgcXy9JO5wFJCXtvGzf9a/+iall/+JnPKfmR83vKVEqcKiZSbeUdrfefkWvF9EevrCnruA1P39PI9H3Hv/0N9Y0Ml41KNRDfSabqT8QYaQIAaqjZSe4Pn1X/2fJplbdfeVk3fu/f7ixox3T3f/Pfq+8d72pFcdGEuslUgXZEtZGpYralRKDd4LqWYsa7MR+P2bJ3nWtd12gz72gj52gjX9BmrjyhKn/Ac/v5o1H1xcsTrvsTMfUXk6x643boBDEAaBfH3ZlxI1dwtJ33Yq6tvKOC43oxXzHWi9mW4ratgZ4Yx7cuRcIVAACHlHHdnWn/CnmZXFbutjftn5vb1s5QPaY0SlVp6r/+/U39V7E8TkGFG28rf+MtDbzn/Xte3/jWV7T98rfDry+XVf+7HpEVT5TddHjg7/7ThsrlJxa1O+Gqk6bvqzei1EElgzmuf+PJKF8cPrfgunrwc5/RA/IvHZmd4cwthU7+K7huWUKVNx1gQTmnuYtF/Qlbp3oTSvYndKovoWRfXCf6EopzYwgAgKqM68rktuVsbspZz8jdvC1jLFm2ZPf0KX7i1IFs19m8rdvLXyolVzk31+RPnFJN9tVr6j2XktXTI/vYCdl9Axp45IPKvfED9dzzgHrvfUA9d3tJVrFTg/SgP0Iqtdtf+tRndfLDI6XEqpzjFm+Keq/7t0H9i+rl0/M0X3dufXm55hR+61dW9PX/9EUV3veBqqNSlSS80YILBbfy63X0/fZvhl72jv/f0/rIX/hTTW0HAFCnk9yn5/T+//XvlP52Nm/rxv95WXJ3RsC48y//dxp4bzTTu6I5xuwkTbnG1E2msvxppPxkKstSoonOfkaSsWytbeW1VXC95KpigtVWvrk2QD29xVEryxOoYmXPkUgFoNu4pphUVZwCcDPvaDvnaDPvqOC6UunI7SVVJWxbvfGYBnqiTc/JO67WNnPqP3UwHdZQHwlXAAB0MeMUStPpufmcl1RVnPrP5PPeSFbFNBXLtr1RqmIxxY4di2zqvz1lcl0Vbt5QYddUgIW1tyXXC9x7z6UUGzhWNnVh7MRgxfVZiR4l7rpPPfeeU+89O6NWRXFjK5hY1M7RpDpp+j6p9oW74DLNlCvYy99LrNoJTPy+/d6FJCnmX0Rq4EaU6xpt5L2EqvXAqFXbTd446olZOtVXTKrq9xKrTvYl1BNjyH0AAOoxxsjksnK2t+Su35Rz+5aMcWXJKiUyRZGoZFxHhbXryl9/U3Zfv/oeGCpfwLK1/tznw68wFpd9/KT63/P+sml2Eqd/XCdGfnzf5UVzOmFk2ref/XLFdvvac8u69vkv6cRj3mhSMUuybUvxiEY9dYtT+ARHpdouuLr1T+uPBLv+6/9SG5+c2df2bUvFG6PFqXt6AlP4+DdO/92/ZlQqAGiBep3k1q5+VTeff7F0vsw88+/lbm2UXj/zC/+lTpynPdNKTmm0S2+0k3xxtCj/qq2KI1vuN5kqyDVGW/5oVTlHG7mCNnKObp24T8aKafmHt/b9uXpi1q7kqfhO2yAeU1/xMYlUALqVl1RlStP/bRccbeZcbecLyjleUpV/DI9ZluIxy0uqsqNPwdkuOHprPas31rN687b3+431bd3YzOsD957U//LTD0e+TYRDwhUAAJ3OcWS5jpzbt+Q6Bbnb/tR/WzKun0TiDUEqP6kqHq86hV7kxdva1OY3lsumYzGFfM33ZF+9pp57H5CdSMjuPya7r18D732/cj98ZU9iVfzMnWU3uqIUTCxq12hSu8tR6bVWlqvehTtfrWQwf9hzp5hcFRytynGD40kYxYq9+3tiVkNJgMZ4w5zvjFbl/d7MO3XGq6gsZls61RsvJVad6osr2ZdQb9xmxAoAABrg5nMy2W0V1m96U/Q5BRkj2T09svsH9t2uM4W8sq9eU+7NH6rwdnFawBtvSo43akPfQw+r74EhGdfZGe3VdWUPHJO7uVG+MttW4uzd6rnnfvWU2p/3K3H2blmx2L7Kiei1cmTa0rR/rjdald9J4Du//Jmq73nz135dZz/24Ya2Y4zXAcFLoHJKU/ZsF/zpexxtlzonlIs9/3Ud/9rX6m4j/o3nFXv+63LeX3kkk96Yrb6Era1bNxUzBaXO3aeBnnhgKh9bPTHaxADQKcJ0kvvub/6OznzsMW2++Ly2XtyZ7nXgfSM69ZM/e5DFO9L8Udv9kdtzjpdc5Za6F+6MculdB9v/uTXvuDsJVYEEq61q18escG3cvritE71xHesJjki1k2zdF2f6XwCHgykmVfn3MLJ5Rxt5R9t5Lz4zxhurygSSquIxW32Jg0mz2cw5xYSqbb0ZSLDKbFW/5/aDm9sHUhaEQ8IVAAAdxhQKcrNbcm6vK5e5od63vi/JUvbVa4rFY8Wp/2KR3LAKy9naVOHt12XFe9Rzz/27Xizo5hd+r6H12X19GnjP+7zPUpQ4e5dOfvSnoihuKLsTi9o1mlS7pu+rJsyFO58/dYo39PlObw+vl54pjq0m2f6Q50308jfGuwG1XjYVoHchyW0is8qypJN+YlWfl1h1qi+hYz0xbiIBANAEU+wQ4Gyuy7mVkZvNSjKy4wnZPb2yYvufqtpZv6mta9/R9uoLyr7ycs3k/vybr6lw+5asWFx2X79iJwe95P6HPyh3e0s99xYTq+6+Xz133Ssrnth3+XDwDmJk2mAnAaeYUJVzXBUcIzfYli3eGN28uqKNq1+pur71Kyu69eVlnfzwSNk2so6rW9tex4CyhCrH+7uZNq3U2BR+pxZ+S3f+yR8tu1Hq3zyN2ZYcx9EXv+hN7/7Ine9RjIRDAOhIYTvJ3fzat3T9j68qu/yfSs/Z/cd0xxNPcu0jAjvTAe5MJZx3XRVKJ3VLtrzrYIkGOxhW4hqj7by7K6nKe1x1quAQbEs63hPXyb64TvYmdLIvrhO9cZ3sjSvByO4ADhFTvH/hx3vbBUdbOUdbBVfbeUc7k8Ab2ZatuG0pbls60Rs/sPPm7WzBS6Za39Ybt7Ol5Kr1bKHhdV3fyGkr76g/QRzXDiRcAQDQZsZ15W5vyd3aVOHWmtytTUmSFbNlxXrk9no3qWLHTyp2wMGum8sqf/3NPdMBuhvrkqT+d79PZ/7sX/KmKvSnMzSS1dsnk92bRR87mVTP3ed2bmzdc049d9/XstG3aqmUWNSOUa4Ocvq+Znzk8q+X/b37RlSh2NMj77pyXKO3N3LFcMTs6qXXeF3NOW7ZaFW3i0lWhSbvQh3riSkZGLHqVF9CJ3rjDGUOAMA+GNeVm92Wu7mhwq2MzNaGJON1COjpVfzEyUi3d/OPlrT+7O+Hf4MdU3/qvXvam3f9lb8ZabnQWvsZmda7KepNy1dw3bIprb1WppFlJMv22rLVboz+8DOfq7ut7/3KrO741X+uW9mCbmXzWs8WlNvHjdBqemO2Er/8yztT/CViex5HNXIGAKBzNNJJbvWz/0r3vn+nXXZ2/K8qfjJ5AKU63PxRLx1/1MvAdICSP2qVN3J6X3x/123zjqvNfPkUgBt5xxvNfR/Nid6YrRN9cZ3oientH3xPcSenjw7/iE7093CNDMCh4sV5Xqy3XfBGqdrKe0lVRsW0KmNk27ZitqXEASdVGWN0a7tQNgXgm+tZvXE7q82c09Q6bUu660SfziX7dW6wX+eS/bo/2a8eEmXbhoQrAABazBgjk92Ws70p59ZNuRvrMsbIsi1Zu25SOY5bY037l/3+NW1f+04pscq5uVZz+dybr8m5fUuyLFmJPtnHTio+MKC+offK3dpS773nAtOxnFPs2PEDLX+zqvUIbPVoUlFM3xcVf9hz13i983amAax0I0qKWZYSTV5IKrhGG7nCTnJVrqD1rFOc97xxfXF7z1SAJ/viirdoBDgAAA4zY4xMLitna0POerHt6hpZti0r0SP7+IlILk66uawkye7pLXs+ccfdFZe3jx0vtjkfKLZBzylx1/2KDRzbd1nQWcKOTLsz2oRKnQT80VclyW/VxpqYyufWl5e1fmWl7nIbV7+iNxb/sOr0ffXELKkvkDxVnkzFFD4AcNTt7iTnjVD4RUnSxz/+ceWuveiNJm/bev1z/8ib3llS37se1fGRH2t5ebuJP/qJY/xRL73fxpjiDXpvxKoopgN0jdGtbEE3t/PayDqlUauavS4meYlfx3tj3khVvXGd8Eet6o2rp3j9znEcfTGdkSQdp0MigC5V6hDueKMHb+UdbeYcZQuOHKOyWTfiMW+kqmMHfMxzjVFmKx+YAnC7OHpVVtuF5o7tcdvS3Se9xKoHBr2kqvuT/brnZB+jEHYYEq4AAGgBN5eVu70lZ/2mnPWbMq4ry5Ksnl7Zx47te2jpaozrqnDzhgpvv6HeB4b29PTf/u7LWn/uC6HX525tqvcd75Ld2182neG9U/9jqPdff/aKJLV8FKndavUIbOVoUo30TIyiXMHe/f4FJL+Hv2t2+ub5vfP2exHJNUYbxYSq29ni71xBW/nmgoxEzCpOBeiNVuX/9O6zByEAACjn5nJytzfl3L7ltV0dx2u7JnpkD0TXdi1krmtr9QVtp19U9tVrSv7Un9bxD31UxnVlsttyCwXFz9wl2THJddR7LqWB943o2PuG1XPfOxi95wB1SrtdqtxmfulTn9Xxx4YrdxQwkmXtHn11fzdFX/nns6GX7/vt39RGhYSrnphdSpoa6CkmUcXLR6dKMCoVAKBJxinIzeUUP9EnY4xO/9m/pNvLf6Stl7+l5E/+LOeXCgquq1xgut+gmO3d7LYjaPduFxzd3Coos53Xze28bmULTU8p3BOzitP+JYpTAXrJVcd7SKACcHg4pen/dpKqtoqjVTnGyJKRjCXLkmK2rUTM0kALjoOuMbq+kdObgSkA31jf1pu3s01P8doTs3Tvqf7SiFX3J73Hd53oo6NNlyDhCgCAA2AKeS/BamNdzq2M3HxesiQ7npDdP1CWrBTJ9oyRs35T+bff2JkO8Pobyl9/Uyp4cz6f/cR/o74HhrwbWIW8TCEv+3iVKV9sW4k771XPPefUW5oO8H7FT9+5r7L7N0vaeeOm3qhSrRzlanfPxKgYFYfPNfJuQjmuCq4pTc1nZGRJsoo3ofZ7AckYo628W0qoup11tJ4raDPnqJkwI2ZJJ/uKo1X1J4pJVgn1xW0uEAIAcABMoeAlWJXarjnJsry2a1+fLDsWzXYcR7kfvqKt1Re1nX5BhRtvlb2+9dI31PfQI7JiMcVOJNVz8pTsvgHFJv9f6rnvHUyD00Kd0G53jdFbX6rcdl97blmvfOGPdeKxkdLoq1FMoee6RrdzheKUgAXd2vbat+4vfbKh9QwkYhrsT+j0QI8G+xMa7E+oPxHN9wgAgEpMoeAN6yHvek/vPecU+xM/q7MTv6jE6TvbW7gO4ZqdEVG2C44c17tGFlU7wt/GeragzFZeN7e9JKtmRjc53hPTiV6vw6GXYBXXyb64euO0JwAcDo670yE8V3C0nXe1kS8om/fuZZjiaFWyvATYuG1roCfWkuTSgut6iVV+UlUxweqt29nSPZZG9cVt3Zfs3zMV4B3He0mY7XIkXAEAEAHjOHKzW3I2bnvTBGY3JVmy4nHZPT2K9/VHuz3X1cZXn/OSqooJVia7XfM92R+8ovjgWVl2TPbAMcVODurYIz3a+MqzXkJVMbGq955zStxxj6x4tM2EYKJTK6ft2y3MqFKtHOWqWcEpUxzXC0Ky+YK24/2yZOntzZxixZujUYxW5W8z67iB0aqc4rSAzfXMs6TixaOdxKpTfQkd74mRWAUAwAHy267u5oYKtzJytzcl2bJituze3kjbrs7Wpravvajt1Re0/d2XarZZsz/4nnrue1DxE6fKkvwHHv5gZOVBfa1ut/tTWzvuzmhVedeV4xq9/E8/W/V9b/7av9DZfZSt4BrdLiZWrRd/384WGu4wcLxnb3IVN0MBAK1mHEfBKymmkJfd26PEmbuO7DUWf5rAXMHVVsGbss+SNyJK3LYUj+///5ItOMps+wlWjY1eZVsqXgvbGbHqRK83WhWjmgA4DBzXeKMJFqd738w72sp5o1UVHFcKnJ/8pKreeEwDLToG5h1Xb93O6s3b2dIUgG+sZ/X2RrbpkQiP9cR0/6liUlVgxKrTAz1H9nx82JFwBQBAE4zryuS25WxuqLB+U2bztpdxb9uye3oVP3Eqkm3k3/iBem68odzpu8pftCzdevYZuVuboddnWbYG3v3+8kSqO+7Wg7/06X2XNYxgolO7EprqjW7la+UoV/U4rindhMq7bmnaFK8Xnj8nuTdlimUk27iyJPXFY7L3MRpZ3nF3TQXoJVflm4w0jiViOtVfPh3giV4uIAEA0Ao7bddNOesZuaW2qyW7py+Stmsl68t/pJuf/w+SqdF+sG31vfM9Ova+EQ08+iHFTya5CNlmB9Vud4rTWrvFdm3OqTy1dcySYralrZWvaOPqStX1rV9Z0a0vL+vkh0fqbjvvuFoPJFbdyha0kXMa/gwne+N7kqsSMaa3BgB0AKc8adjZ3lbPXfcduXZVwfVu6m8VR0spTjjs3cSP7W/k9P2OXjWQiOnssR6dHejR2WM9SvYnGNUEQNdzjVHBlQpGymzlte3ktJ13tZ0vKOe4kjcBoCwjxWLebBu98ZgGelqXppItOHrzdk5vrm97SVXFBKsbG7mmZuiQpFN98Z0Rq5L9un+wX+eSAzrVFz9y596jjoQrAABCMMbI5LJytrfkrt+Uc/uWjOvKsixZPT2yj52IpBHlbG4o+92XtH3tO9r+7nfkbm3qZP9xvf2RP+WVo1DwpgN0CooPnlVu63t71mH3D5SNWNVzzzn13H1OsWPH912+Zu1OdGpXQlOY0a2Cy7aqfG7xxpNjvNGq8v5wuo6RSpeGdm5A2ZalWEx76pzrumq0Fjqu0UauoPXAaFW3c46yTQx3Lkm9cVun+uIaLCZVnepP6GRvnBtRAAC0mDFG7taGCmvX5azfPJC2a2lb+byM68ju7St7PnH2rorJVnb/MQ088kEde9+I+t/7I4oNHIusLNif/bbbjTFyjYojsXpt2pzjdRrwW7VGki1Ltl17auvv/+pc3e19/1fn9MiHZ8ueyzmu1rd3EqvWswVt5htLrrIs6VRvXIP9PTo9kNBgf4+S/XHFI54aHgCAqLi7RriSMYodP9mu4rSMN02gUc7xR0wxkmUpZqk40nvz5+79jl412N+jO44ldPZYr84M9DC9MICuZYrHWn8KwK18QVt5V1u5grZzjn6w7R1rr13fVE88pljMUiIWU1+itakoW3lHb677I1Ztl0atWtvKN73O0wMJ3XeqXw8MDpRGq7o/2acTfYkIS45uRsIVAABVuPmcTHZbhfWbctZvej3FjGT39MjuHyib5qRZxrjKv/4DbaVf1Pa17yj/+g+kXTn1sa3bsrdvy7l9U1b/gOxjJ2T39avvoYelWEw99zyg3kByVawDRwWolOjUjlGuPnL511u6vaDgzSc3MF1KITBald/VI2ZZgSkAo7mp4xqjzdzuqQCdhm8++RK2pZN93ohVO9MBxpk+BQCANjOOo8LtWyq8/Ybc7JbsRHRt1yBn/abXhk2/oOwrqzrxsZ/SyY/8pIxxZbJZufm8YqdOy+rplclllbj7/uIoVsPqe8e7Ii8PohG23V5q2xaniAh2GCh1F/BHUbMsJRps19768rLWr1Qf3cq3fmVF3/uDZ5V/9AOl0asaGWlC8m6IJvsSGhxI6HS/N3LVqb4EI7ECALpLISc3t60bT8+r753vVu873yu7p6fdpToQXpvD1VbeUa7Y9rDkJXL3JZprY5ZGr9rO6+ZWc6NXnRno0R3HenTmWI+StCUAdJlSUlVxlo3tvJfIupn3Omcb488AaGRbtjcFYMzWiT6pP+bFgKf647Ltg78/sJEt6I3bO1MAvrm+rTduZ3Vru9D0Ou843lNMphoo/u7Xfck+HWvhSFzoTtQQAACKjFOQu70tZ3Ndzq2M3GxWkpEdT8ju6ZUVG4hkO252W9urL5SNYlWTZcvtO67+93xA8cRO1vwdf/6v1nzb9WevSFLbp8WrNo1fJ03bF6Wd0aqMCq4XpBSKPT+8C0CBXv2WvJ798QhHmJDkWjG9tZHTRt6fFtCbMqWZ4XFty5s6ZSepKqFkX0L9if0Nww4AAKLl5nNybq4p//Yb3mhTff2RThVojKvc6z/w2rHpF5V/84dlr2+99C0NPDIsy7YUO35KibsHFesf0N2/+HeUuOs+JU7fEVlZcDBqtdtf/8Mv6+SHh5Ura9tKfmeRqDsMhBndyvfqr3xOG5+cCbVs3Lb2JFed7IsznQ8AoOu529vKfu+ast9bVfZ7q9IXfk/97/xVxZNn2l20SBQcV9sFV5v5ggquN0FVzN5f22Mr7+jtjZze2sjpxlaO0asAHHqmdM/C6yyzXXC0nXe0mXeVzTvaSWH1kqpitqWEbelEb/Vp8tywB88myrqeLXgJVaXkKm/Uqmamh5e8hLG7TvTqXCCp6lyyX/ee6lNflx3HXddoK+ftv+SxHpJ824iEKwDAkWVcV252W+7mhgq3MjJbG5KMFIvJ7ulV/MTBDLtdWHtbN/7D0zWXSdx9nwYe+ZD63vMBXf3hW5Ida3gUAL93ersTmmpN49eOUa6iUG20qrzrynGNVBzE3QtMoh+tyi9DzjGlhKrbOcfr1X/ifsmylXl9vaH1WZKO98ZKCVXedIBxHe/hBhQAAJ3M2dpU4cbbKty6IcuyZPcNyIpFc6HQzW5r+5WXi50FXpS7uVF12fwbP1DijruUOHNXWbt14OEPRlKWw6wTOkq4xuilT3226uvf+dRn9dBvfGZXYtXBtBGNMXrwc79aGrHq1rY3LWC+wQv5iZilwb6ETg94iVWDAwmd6Kl+owBoJdcYbeW9kYczm3kd743rvmR/u4sFoIu5uayy379W+rvnnnNdn2xVcF1lC642co4KrpFklLBt9TXZcdEYo5vbBb21kdVbGzndDnnDntGrAHSbvOOq4BrlCt4U75s5b7SqbMHZSS41RpZlKx7zRgg8XiOp6iAZY5TZyhenAQyMWnV7W1v5xkYv9sVsS/ec6NX9g15ilZ9cdc/JPvXEu3O07YLjajNb0O2tgq7fzurmZq406vSHHzqrY32k/bQL/3kAwJFhjJHJZeVsbchZvyl3Y13GNbJsW1aiR/bxE5E1KJ3NDWW/+5JkWxp474+UlSE2eFZ2/8D/n70/jZLzOvM7wd+7xL7mhi2RiY2gSAKkSABcJVFWiazFVapFIl1ub93TtiSPZ3r6zBkfqfVlvsyHHnHOnFPjOTUuUlPTtmem+2jEsrvtY7tssbSUdpEEJQEUQJBIEASQe8Ye8a733vnwRiQygdwzMjMy8/7OSQKIeCPiTWbkG/e5z//5/xc5WxnxBKkHz5I5c470wx/H7huMnkcImPzBul9/4XT6TrpILTcl32G3uFyJtqjKD6OvhRP9hiJqbprRZH9sCxbsgYg2l+oLxFUNPyQQSzSd1iDqSscsCkk7Ela1navyCVtvFmk0Go1Gs1tQElGv4pdmkG4Lw45hZbq3lgVoXn6b8n/+n0Gu3IhKjJwk/eh5MmfOERs8pMUsG2A7ByWkUgipkJ24CCEJpKTys7cp/+ztZR/XfOsi/sVfkH/qfNfPp+WLSFjlhfMiK7FOcVXCMulLLRZXZWKWfj9qegovjOKvKi2fqhsglMLEAAzi9saaSRqNRtNBBT7+5O35f6cefHQHz2bjCKnwwijCyg8lhgG2aZLc4H5bICRzrcjFarbpryrg7rhXDaYj96rBjHav0mg0vUnYGQIXcn6dGcUASoRSGG23KhOwrSgCML2Dw9VSKcot/66oakEkoC82thaOWQZH8klG+u46Vh0tpjiUT2Cv08Cg1/ACQcsLqToBpbpHww1QCkwjis/Np2OYhkGp4e/0qe57tOBKo9FoNHsa6ftIz0HUa4h6BSUEhkEksEpnuuY4pJQkmLyDe+Mazth7BJN3AIU9eJDUg2dRnocMo/xoM5UhefoMwdQ46TNPkHnkCZInPoZhd+9jeaGr1E66SK3kbrXwmF4SXCnVjgMUCjeUeELQWe+bRiSq2qqJfiEVTf+uoKrhRX+64cYKjoRlUkjakWvVfCSgTcza3cVGr6FU9F5xA0FeTzlqNBqNZgtRYYDVqmE1ani3C9ip9KZjA5UQqDDATCQX3R4bOLik2MqIJ0g/9Bjps+dJP/w4dr64qdff72zVoEQUFSGRhoXEoNzykYZBKDsBEZHDaWd9O/1n/89Vn/P2n77GI0+9uuFzkkrRuEdYVffCNcf3dEjFTPpScfpTd6MBk7aOu9b0HkIqWoGg7gaUWz5eKDGAmG0tana5wcYiUTQajWYeJZHNOqJSmr8peeLBHTyh9bFIZNXehLNNc0PxTkpF196ZdlRgxQlYbakxkI4xnE9xMJfQ7lUajaanEPPxfwucqkKB40ukal/d2sWdbXZEVdaOJlYIqZhr+kzV3UWuVdMNr+1WuH4StslwYbGwaqSYYiib2BPXbKWieMCWF1Ju+Mw1PPxQAAaWCcmYRV8mrmveHkULrjQajUazp1BhGAmsGnVEvYL0PcDAjMUwk0kMs3sTSaLVxLv5Pu7YNdwPry1yrOoQzk4RzEwTHx4lnitEES+2TfK/+m8xrK35GL7XVWqnXKRWc7fqsNMuVwtzy30hcUM53/SxjMh+NrZBq/Ll6MRH1NuCqo64qrXBjXbbNMgnbQoJm9k7N7GFzyeffIJMIt7V89bcJRDR1FDVCag4AaGM3CLOHM6T6uJ1RqPRaDQaAOk6hJU5vNkZ7FoFGUtgZQuYGxRRi1YT98NrUVTgh++T/fjTFJ7/rXlHWOn7GJksZjqDbDWx+4dInz1P5ux5UqcewrBjXf4O9y/dHJQQMhJZuYHECQVhKPCtOAYQKrWsO0Tt529Tf/Piqs9ff/MitZ+/vSaXKyEVdT+k7obz7lUNL1y14XkvmZg1L6rqS8foS8VI2nqtpelNOoMYDS+k4vg024JCq/27l0zprXiNRrNFSEkwPb7opuTxB3boZNZGR0TQbEdcQRRptZHPeakUZSeYd7FabX8tZhocyiUYLqQ4nEuQ0GsLjUazg3ScqsIFTlVOKPCCKBZQKejobGzTwDbNHRdVQRRxN9P0maw5XK7Z1EKD731/jNmGj1AbE1alY1Ykpuq7K6o6WkwxkInv+PfbTYRUUTygG1Cq+5SbHlJGyrmYbZCMW2R1ROCuQf+kNBqNRrOrUVIiXQfZahDWK0jHAcCwLMx4AjuXXOUZ1odo1Gj+6k3cG9fwJ27DKi2D2MFh7IEDJA6PLLp9q8RWsLSr1E64SK3F3Wrhsdt1fgsFVm4YFTGdeEDLiDZ3urV4n99wX+BW1fAFTX/9k/wQORDkElEU4ELHqnQ7LkUIwQ/GqgC6EdVlpFK4gaDhCcqtu5t3VnszMG0aVJ1gh89So9FoNHsJJSWy1SCYm0I0G9H6NpNBJlLrfy6lCGencMau4l6/ij9xCxZsgDof/JrsE8+AYWBm88SHDmOlMhz8B/8Ndv8QsQNH9CTlFrDZQQmlFIFU+G2nTV9KwMA02hvxMQtL3XWJWG6Ne/tPX1vzOS/lchVKGTlWuXddq5q+WLe4Kpewo1jAVIy+dJy+ZIz4FsR1azTdJGg7DVScgKobEAqFYURT+NlETF87NRrNtmBISTA9Mf9vK9+H3Te4g2e0NEopfKFo+SFOKDAwsIzIoX2910svlMy2fGaaHnOtYNU44lzC4kguyZFCiqE91rjXaDS9TyDkfE/i3vi/yKlKgTLaTlWRqCrR3nPeafxQMt2461I1VXeZqnuUWv6CHken3+Wt6TlzCZujxRSjfXdjAEeKKYqpvbl+9kNJywuptXxKDY9qKwSiePFE3CSX0u6KuxktuNJoNBrNrkJJifI9RKuJaFSRzQZKKQzTwIgnsHP5LX196fvUfvxXy95vxBOkHjxL5swTpB56nFj/9m5uLOcqtRMuUk9/88+37bVWQt4jsPJD2U4vj/KuuxUP6HeEVb6g7oXzf19tw2c5snFrURRgMWmTTexcxvp+xG8Xv2XHp+oECNUugmyLXMLek8WfRqPRaHYeJULCWpVwdhIZ+O0hgig2UIi1xwwrJfFuXsf54Ne4199D1CvLHhuWZjDzRRKHRzGsu6Lt9MOPb/Tb0KyBjQxKhDJaz94dHIgiApdyhJBrnCp+5F+uPSYwEJK5lh8Jq9oCq/W6tBpAPtkRV0XOVcVkTMdea3YFQnYGMUJKToDjR+//mBX9DlpxXSNoNJodQCnC8uz8PxOjJ3fwZO5HyMjpvRmECBkNPG5UZDVec5luelTdcMVjDWAoG2c4n+JIPkkuoduhGo1m61g45B0Ihd9OR2j5kYufUMwHuxtEDqi9EP+3EDcQiyIApxoe03WXcmv1aNbl6EvF5h2rRoophospjhZSFFJ71zVbtQe3W56g3PAoNTxcP9rLMdvxgP3ZvSks26/oFYZGo9Foeh7pe0jXQdQqiEYNJQWGaWLE4piZbFcXJkpJgsk7uDeuYcTi5J781N37pMBMprDyRUStMn977OAw6UeeIP3I46ROPoRh79zH60quUjvhcrUTSKUI20WNGwoCoeYFVpbZEVhtvJkTSrnIrarRFlf5YmNlR8o2FwmrCkmbfDKGrScatp1O86TuhZRaAV4YOTPETJN0XIvdNBqNRrO1SM8lrJQISzMopbBSKezk+t2sANwP36fynX9HWJpd8bjYoaNkzp4nc/YciSPHMEwteNku1jooIdXdKWg3FIRoGsGuAAEAAElEQVQSOs6sm13XroYXSmpesMi9yg3XLvqDyKG1kIyiAPvTcfpSkbhKT+9qdgtKKbxQ0vJDyk5IzY0aTiYGyZi5p5tFGo1m92AoiajMzf87fujoDp5NhFKKQCiaQYgTCAyi/ZXYBtwrq27ARxWHyYbHSnryuGVyJJ9gOJ/iUC6hxdwajaardERVvpCEQuGGd12q3OCuw29nrWhbRltU1Vv7yi0/ZKreFla1naum6u6qQtaVyFqS/rjisRNHGO1PR65VhRSZfSB27cQDNt2ActOn3PAJ2sNyMdskFbNI5/b+/4f9jP7pajQajabnUGGAdB3CRg1ZryIDHzAwYzHMVLrrjSDRauLdfB/3xjXcG9eQTgsAK1ck/dhTEPgoJTEsGytfJPv4MwRzM6TPPEH64bsuVnM/eRNn7p0dEzUt17TpsBMuV9uBkJ1CJ8o1j6JUolmRbgis/FAy5/jMNX3KToCzziZTh7hltAVVHXGVTSEZI643f3YULxQ0vah5UvcCpLrbPMkndfNEo9FoNFuLUgrZahKUphG1ahQbmM5ser1rxOJLi61sm9TpM2TOnif9yLltd2PV3GWlQYlrf/LPeezcEzihwBcSAwOjHS2RtLu/Ud8RlNS8cP6r7oZ463BUA7AMg2LKpi8Vpz8doy8Vp5DsreaCRrMWgrYjQc0NKLcCwrZrcdwyt8x1uOWvzylOo9FoFiHFouHQ+MHhHTsVISMRQtMPCTfhZiWlYrLhcavqrCgCKCZtjuSTDOdT9Ke1Y4hGo9kcHbFoICOnKi8QOIHACSVeIOiMdhtKYRjmvKgq02PJFEopGp5gquFGUYBtx6qpukfD25iwyjDgQDbBSNux6mghxdG+FIezcd786Y8B+NSTI1iWtcoz7W4CEcUD1p2AubpHrRWglALDIBEzySQtLFP3FfYTWnCl0Wg0mh1HCYH0HESzgahVkJ4DGBi2jRmPb3iyf9nXU5Jgchz3xnu4N67hT9yGJUxRRb1CWJkjdfJjWOksRiKJYRgkvvBfLfm8nabJTgmaVmraLDxmtwuuIoFVZ8pfEkrZNuKNmlAb2cRZiJSKshsw1/KZa0UT/evBMg3yCZviPcKqpL2589J0ByEVrUBQcwLKjo8fKgxDEbcsMhucNgql5MZci1/eqTLb9PjNhw5uwZlrNBqNZi+hhCBs1AhnJpGehxmPYeXyG1orSN/HjMfvPreSWMV+YgcOE0xPYOUKpM+eJ3P2PKnTZzATyW5+K5oNsNqgROVnb3Pnhz+l+PSTm17b3otSCieIxFV1L5gXWAXrdGu1TYO+1GLnqlyPNRk0mrUiVScmUFBxfJp+5FBgmwYJ2yS9Re5/pabPpYkavxyvMllz+R/+zjnScb1dr9Fo1o/dqrPQ+il28Mi2vv5Sbla2aZLcgJuVGwpuV11uV51l3eQPZBOMFlMcySVJx/d2Y1+j0XQf2b5mhUISSIUbhLTaLlVeKKEd/QcK0zCxzEhUlU3YPbe/r5Si6oZM1915x6pOJOB6o987WAYczCfbwqo0I8UURwtJDhdSJJa4rguxtwcHXD8a2K62fEp1j1a7X2QYkIxbFDNa7Lvf0RWcRqPRaHYEFQaE9RphrYxqNVAKDNPETCSwc4Utec2wUqL24zdwb7yPdJorHmvEE6ROnyFxeIRY/9Cqz72wabITLlKrNW067EaXq3CBwMoLRXu62MA0oin6pL25jRWlFE1fzAusSo6PXEO/yTAgH7cptKMAi21hVSZu6QV2D6GUwg0lDS+MmideiFRgmSbJmEkqtrHmSd0NuDrd4MpUnWvTDfy2A0TDD7XgSqPRaDTLIn2fsFoinJuej6u28/n1P5FSeDffp3nxx8hmnQP/4L8BKZFuCyUVdt8AA5/7u0jfJfvEMxiW3v5ZyNxP3gR2blDi/T/556seM/1nf87gs09t6nWUUoSmjTDjXJttUvej6ORwLYvdBcQtY5FrVV8qRlaveTW7HC+MXKwqLZ+qGyCUwsQgYVvktrCZNl33uDRR5dJEjfGqu+i+t29V+NQp7Tyo0WjWj92oLfp37MD2Ca4CIam5AZ6QG3azUkpRcUNuVRymGt4SY7GRCPZEX5rTgxntSK7RaFalM7QdiCii3Q2j6D/HD/GFom1IBIBpRIIq2zS2dB24GaRSVFpB26Wq7VrVFld5G0zksE2DI4UkR4spRvs6MYBJDuWT+zaSVUqF4wuaXkC54VNq+PhhW0hsGSTjFv25xE6fpqbH0DtuGo1Go9k2lJTIVoOgPIeoVwADM5HAzOS2ZRFr2DatX/9i2ftjB47MxwSmTj2EYa+9eF/oLrUTLlJrcbdaeGyvCq6UUvMRgW4YFUJSRfMkd2NUNr/YXxgTONcK1hSXkkvYHMomGMrEKaRiZOM2ltl7xZembevrRxEgFScgEArDgIRtkk1sbOJEKsWdisOVqTpXpxvcrjhLHvfeVIOmH5LRk+kajUajaaOUQjotwvIsYbWEYZiY6TSGuX7RuApDUhMfkr79PqXm3cZa6+qvSI6eIjZ4CKvQhxmLkzg80s1vY0+x3c60SnUisCXTP/o55Z+9vepj6m9epPbzt8k/dX5dr+UEgpmmz0zTo+IEiGzUcG3cI+xYjqRtLnKt6kvFSMe0uEqz++k43dbdgErLj2pABTHbIr1Bp9u1oJRiouZyaaLG5YkaU3Vv2WN/erOkBVcajWZDiGSK5EOPIRs1MEysVHrLX1MqRdOPYogtc2MDkUIqJusuH1XdZR3mcwmLBwezHO9L71sBgEajWZqOqMpvu1W1AoEbSNwgbA/HGqAUyjCwDQPLMrAtk4Rt9Gx9EwjJbNNnpuHNi6pmGtGf63Ul7hC3TIaLbceqYpqRvhTDhRQHc4l9398I2/GADSdkruFRbbWH8ZUiETNJxS1yqd7c51dKMVv3+OWHZTxf8OLj2+tuqblLb75DNBqNRrNnUEohXQdRKxOW51BSYsbjWNmNRaashHBaeB9ew71xDTORovjZz83fJ30fMLAHDhDOTQN3XazSZ86Rfvjja3KyWop73aV2wkXq6W/++ba9VjfpNJ8C0XawErLtLqWw2pMl3dj47sQEllo+s2uMCYxbBgezSQ7nEhzMJbSApofpRIDUvZBKK6AZiKh5YkUT6un4xt5DbiB4f6YxL7JaLd8+l7A5P1LEDYR+v2g0Go0GJSWiUSOYnUI6LcxYbMNrYNFq0vzlz2i881MKrcZ997sfXKHvs7+PYelIldXYLmdaIRWBlNGG//wQgeL2n35jzc9x+09f45GnXl3xmE6ExEzTY6bp0/DXHueQjln3iatSMf0e0uwNOk63TT+k3IqcbpUCsx1xlY9t3XpdKcWtisPliRqXJmrMNf1ljzUNeORQnudO9PPUaN+WnZNGo9nbBIVB8mfOYlkmyVOPbOlrRddXQc0NkUqRsNfvaOUEgltVhztVl2AZ580juSQPDmU4mE30rDBCo9FsPaLdOwikwg8jQZUTCJxAEMpIVNUJALQMA9syiFkWyS1c63WDphcy3VgoqPKZqXuUWv6SLn9rIRUzGS6kGOlLMVpsO1YVUwxm4zr6vY0XRPGAtVZAqeHRcIOoRjAMkjGTfDrWk/+vlFLM1T2uT9a5Ptngg8k616fq1FoBAE+fHtSCqx2kt682Go1Go9m1SN9HNKqEpRmk52HYFmYqtaFJ/uVQShJMjuPeeA/3xjX8ydugouWokUyRe+YzKBllbpupDLGDRyh88jcJa+UNuVgtx1LuUr3sIrWTyLaDVWTjK+/a3SqFaXZPYLWRmEADGMzEOZxLciiXoC+ls7d7GT+MppYqjk/VWRwBUtiEtfxMw+PqVJ0rU3VuzLUQauU3zvH+NBdGipwf6eOBoUxPFmQajUaj2V5UGBBWKwSzkygRYiZS2PmNRWYHpRkab/+I1rvvoMLgvvvNbJ7C879F4RMvarHVGtkqZ1rZHiTw2k2AoL35b7VdWjtrhDP/amUB1VoIhWS2FTDb9Jhp+WuadM7GrQWxgFE0YKILzrEaTS/Rcbqttp1uw7bTbXwTTrdrRSrFzVJr3smq4tx/ze5gmwaPHsnz3PEBnhwtktPRWBqNposY9ta1/UIhqbbjA2OmuW7HqVYgeH+2yVRjabe/mGVwsi/N6cEs2YRuX2o0+4VO9F8oJH57PeeEAi+QhELezf4DrHYPIWFbpM3evk5IpSi3fKYb/n1uVa11DMrcSzZhcbQtrBpZIKzqT+t+xkKUiuIBW15IueEzV/fwQoFhGFgmJGMWfZl4z/0/mxdXTTXaAqvoq9pavr749e0qSqme+172C719JdJoNBrNrkKJkLBZR5RnEc0mhmlgJpIbbjAtReRi9T7ujcjJSjrNpc/FdQjrNTIPP4aZTM9vNhR/4/e6di5wv7tVh51wuepFOo2nIJS4QuKHEtWe0bANg7jVPfteX8h5gdVcy19TdnkuYXMol+BwLslQJq6tyXsYIRVOIGh4IaVWgBuGoCC+yQiQUEpuzLXmRVazK0yfQxRL+NjhPBdG+zg3UqQ/Hd/Q62o0Go1m7yHdFkF5jrA8h2GAmUpjWJkNPZc3/hH1n30P9/rVJe8Pcn0c+t2XyV/4JGZMfxatlW4603aisP0FgwQKhYHRjsHurgCuFQhmmh6zTZ9SK1h16nkgHeNILsGdD65ghz6f/tQnsLQoT7PHEDJyum14IWUnoOULDMC2ot9Ba4NOt+t5/bG5JpfbIquVnJTjlsHjw0WeO9HPuZGidsTVaDRdRoGUEI9jmN3f25qPD/RCLGP965xQSMbKLW5WHJaaayskbR4czHKsL4W9Beev0Wh2no6oKmj3CDouVU4gkUqhVKSrMuiIqsx2ckHvr5m8UDDT8O8KqurRnzNNH7HaFPgK9KdjDBdS84KqkfafhZQW6y+FkKodDxhQaviUmx5SRt2ouG2SjFtkezAesFT3IseqztdUnUpzeXHVUmQSNnN1j8F8covOUrMSvfeu0mg0Gs2uQkmJdFuElTKiOodSKhJZ5fJdfZ1gbpryX/7FIher5YgdOEL6kcdJHX8AK9vd87iXpdytFt633wRXUilCofCEwAujIipqPIFlmm2BVXc2TqRUVNxgXmRVW0NMYMwyOJSNBFY6JrD38cLI4rfshNTcqLFoGQYJ26SQ3Hhzue6GXJ2uc3WqzrWZxqrivAPZOOdH+rgwWuTMobwW5mk0Go1mEaLZwJ+ZQDYbGLaNlc1uer3j37m5pNgq+eBZPho8hnfgKKefeh5TC2jWxWadaYVUhDISWLmhQMgowMKaHyTo3hpBKUVlQVRgc5UJaNs0OJxLcCSf4kg+QcK2EEIwc9Xt2jlpNL3Awhqh7gVIBSZRBMh2NJ9CKXl/psnliSrvTtZXdCdI2CbnR4o8d7yfJ44WSerITo1Gs0UYQqCkwIgluv7cgZCUHZ9QKhLW+uIDlVLcqbl8MNfEv8eR0wCGC0keHMwy1IMOIxqNZn2o9uB1KBV+GDlVOYHAbYuqhFJR+J+KVFW2aWKbBum4tSsSA5RS1LyQmXucqmYa/orOpqthmwaH8kmOFpIcLaYYLqYYLqQ4UkjquPdV8ILIvaruRPGA1VYItFMw4ia5VAzL7K33VqnhLXCtihysyqsMgN/LgUKSMyNFHjvex9mRImdHi1potcPoLqNGo9FoNoT0XMJahbA8iwpCjJiNmdl8cwkiEde901hWNo8/dWdJsZURi5M6fYb0mXOkH/44sYEDmz6HtbCcu1WH/eBy1Wk6+UIuiE4BAwPL7G7jSSlFMxDMNdcfE3gol+RwLkEx1ZsZ3JqIUEocX1BzQ0rteBzDgLhlkk1s3MVKKsV41eXKVCSyulVxVjzeMuBjB3I82XaxGi4k9cafRqPRaO5DiZBgZpJgdhoztfHYwKVIPfQYtR//FSrwwbLJnf8Ehc/8HvbBI7z/gx907XX2ExtxplVKEbQbBm4o8EUUE2i2YwJjdnfXB0HbsXWm6TPb9AlWWexm4hbD+SRH8kmGMome20zWaLrB0jWCIm5ZZDbhdLseAiF5b7rBpfEqV6bquCsMbGTiFhdG+nj2RD8fP1IgrqM7NRrNVqMUplNHhf2Yse4KT1t+SNUNsDbg3llyfN6baS7p/neyP83Zg3nScS0m0Gh2Ex1RVSCiQWs3vCuo8kIxv1evAJNoADtm7R5RFURrz7mmHzlW1V2mG/68uGotiRrLkYlHMYAdUdXRYpLhQoqhrK7j1oJSkbNtyxOUGx6lhofrRz8P04RU3KI/21uRiuVG5Fw1Ntng+lSdDybrlBvrE1cN5ROcHe3j0WN9nB0tcnakyFBBi6t6DS240mg0Gs2aUWFI2KgRlmaQbgvDtDCTSYxUenPPqyTB1Ph8TKCZzjD4h38/uk8KlOchRUjs4DDBxC0AYgcOk37kCdKPPEHq1EMY9vbbqK7kbrXwmL0kuBJSEUg533QKZTSVYhpsaPNlNToxgaVWwOyaYwItDmWTHM7rmMBeRymFG8p2BIhP0w1RRBNOiZhJOr7xn50bCj6YaXJlqsbVqcaK8R4QxUs+cbTAk6N9fHy4oN3PNBqNRrMiotXAv3MTJUKsfGFDm3phpUTj7R+BaVH8zN9EKYVyHWQYYKazFD71W2DbFD71W9j5YvS6YmWXI83yrNWZthN14QYCd4tjAgFavph3sSo7q0cFDqbjDBeSDOeT5BJ2T20oazTdQLabKU1fUG5F7m6KKJJ+szXCenBDwdWpBpcnqlydarQFl0uTS9g8fSwSWZ09nNdxWBqNZlsxPYdDP/h3zP4AjGSKI//4ayRPPLip55RKUXdDGn5IwjbXJZRoBYL3ZxtMLdFUHsrEOT9cpKjjsDSankWpSFDlSRAKpusenlA4ocQLxN16RSkMw8S2jLZT1fYI4btFyxcLXKqiP6frHqXW6kPey2EYMJRN3HWrKrS/iknySX3dWw+deMCmG8UDVpo+QXs9HrNNUjGLdK539u8rTX9xLOBkndI6xVWD+QRnRop8/FgfZ0aLnB3t44AWV+0KeuedqNFoNJqeREmJdJqE5TnCWgUMMONJ7NzmJvil7+GOXcUdu4b74TVkqzl/n2HbhJUyWCaGZWPlisTzBfp/8/OIVp30w49Tu3YTgPTHHt3UeWyU1dytOuxmlyulFKITERhKXBHFpgDRVL9hkOzytK5UioqzsZjAQ7kkh3RMYM8TCEnLF9TcgLITELZdrBK2SS65uSmU2YbH1ek6VybrjM21EKvEjx7rT3NhpMiFkT5ODWb0NJFGo9FoVkUJQTA7RTA7iZlMYyVT63u8UvjjH9F464c47/8aUGDZpB89H8VyF/qJ9w9hpdKkjp/emm9iH7IWZ9pb3/8JifNPEOn7tyYmENrrXTdgtj013QxWFtHFTINDuSTDhSSHc0kS2i1HswfxwihypuL4VJ0AqSKhY8K2tlVY2PIFV6ZqXJqocW26EQ0YLUNfKsYzx/t59ng/Dx3M6VpCo9HsGJZ318VbuQ7mOten9xJKRdXx8YQiaa89QjCUkhulFjcrzn1ihUzc4okjBYbz2kFco+kVlFL4IhJX+WHkUtX0Q5xAEAjJhGMCism6S8y2sU2D7C4b+Oj0GmbaYqq74iqfxhr6DssRt0yOtEVVkbAqcqs6nE9qd9MNEgg5Hw84V/eotgKUUhjtoYtM0sIye0O0Vmn6d4VVU3WuT9SZW6e4aiCX4Gw7FvBM27nqYHFzn9+anUN3JDUajUZzH0oplOcQViuElTkQIUYsjpXNbXpBHcxN03jnp7TevRhFpCz1+mGIcJtkH72AkUjNv2b23LPzx3zwJ18D2DEh01rcrRYeuxsEVwstgX0hcUM5v0FitR2suh2bcm9MYNnxEWuICRzIxDmsYwJ3BVIpnEBELlatgFYgMBTYVuQSYcU3/rMLpeTDuRZXpupcmaozu0reedwyeexIngujfZw7WmQgE9/wa2s0Go1m/yGcFv6dm0jfw8rl1yXEUVLgXHuXxts/wm87tt594hB37CoDn/s7mPFEl89aA2tbu3/4f3+Nh//Fn5Hs8noXos3j2QVRgSuJOACycYsj+XbERCau17qaPYeQUY1Q9wLKrQA3FKAgblvb7o7Q8ELenaxxabzGB7ONFR0NBjNxnj3ez3Mn+nlgKKt/NzUaTU9gLhBcAVibGJL1Q0nJifZW1jpkqZRivO7x/mzzPjdA2zQ4cyDHg0NZLUzVaHYIqTqiKokXSlqBoOVHUYBSgYECDCzLIGaapOM2KEnajhZF2YSNafZ2/Gcg5LyQaqG4arbpEazWbFiBYirG8AK3qo64ql/XaJvG9QVNL6Ta8inVPVptAZxpRgKrvkxvxAN2xFVjU3WuTzb4YLLOXN1b13P0Z+OcHe3jsXYs4JnRIgcLWoC8l9CCK41Go9HMIwMfUW9HBvpuFBmYSmFsckGtpMC9fpXGOz/B+2hs2eOMWJzU6TOkzzxB+mOPYSaXjipcOKG+U+5RT3/zz7f9NbvNQoGV2y64ovIjmui3TWNLCgdfSEptB6v1xgQeyic4kEnomMAeZ35CveVTdYN28Q4J26KwSfvkuhvy3nQksLo201j1/TOUjXNhpI/zI0XOHMrrKSONRqPRrBslJcHcNMH0OGYyhZ3Lr/mx0nNpXnqLxsUfI2qV+w8wTbJPPEv+md/QYqstYq3OtI23LtJ46yL5p8535XWbfshMMxJZVVaJCjSIhBzD+SRHCknyid6Y3NVoukUnSrzlR0MYDS+qESzTJGGbFJLbOwhRdQPenYicrMZmmyv+fh7OJ3j2eD/PHh/gxEBaN0Y0Gk3PYQaLG79mKrOh5/GFZLbpEbPMNYuj3EBwaapO2Qnuu+9kf5rHDuVJxnpbqKHR7BWEVPhCEohoX9bxBa1ALNo7NYjWXzFrZbeqjUbqbSVKKZq+YKru3RcFWGmtHs2+HJYBB3P3uFUVUxwpJHWSRpeQUtHyQ5puSKXpU2r4+GHk9ByzDJJxi/7czu+HVFsd56rGvHPV7DrFVX2ZOGdHizx6rI9HR4ucGe3jUFGLq/Y6+kqh0Wg0+xwlBKLVICzNIpo1MAysRGrTkYELkc0Gc//2fwJ1vzAiNnSI9CPnSD/yOKkHHsawV28uLJxQ3y3uUb2AvEdg5YcShcIAzPnIlO4v/O7GBEZRgTomcO+xaEK96eMKiaEg1oUJdakU41WXK1N1rk7VuV1xViygTQM+diDHk6ORyGpYT4toNBqNZhNI18Eb/wjpOljZPIa5NuFuWKvQuPhjmr96E+Xfv0FnJtPknvssxU//Dnaxv9unve+RnVhsIfj1//X/sebH3f7T13jkqVc3/JoVJ5gXWbVWiwq0DA7nkgzno6hALQrX7DUWRolXnICgHSUet02yie2fWC+1fC5PRE5WN8utFY8d7Uvx7PF+njnez0gxpesJjUbT05gL1ppGPIFhrV/gpJSi7gZYprFmsdVUw+Pdqfp9zp2DmTjnhwv0pbSruEazFYQy2tcPhMINBA1f0PJDQiGJQpnBMAxsyyBmGtsaz9wNhFSUWn4kqLonBtBZpcZaiXTMuj8GsJjiYC6BvcY6X7M2wnY8YMMJmW14VJs+iuizJhkzScUtcqmd7ffUnICxyTofdKIBJ+vM1NYvrjqzQFx1drTIIV077Et091Kj0Wj2IUoppNMirJYQ1RJKKsx4PGoibcFiwMzmSZ78GO71K9ENtk32iWcpPP/bJEdPreu57p1QL/3srR1zuep1Oo2mKB5QEAg1L7CyzI7AqvvFhFKKViCYbfnMNdcfE3gol6BPxwT2NEqpyILaDyk7IXU3miIyDSOaUI9tbonphYL3Z5rzIqv6KiK9XMLmiaMFLoz08fhwgUxCL3E1Go1GszmUlITlOfyp25jxxLpcrQCq3/33OO+/e9/tdv8Qhb/xN8k/8xnMRLJbp9vTzP3kTWBro8AXOrc6iwYLDB76F3+2ZRE2gZDMtgVWs63VowJzCYsjuSgqcFDHUGj2GPNR4m5I2VkcJZ6wLdKbiBLfKDMNj0sTNS6NV7lTdVc89uRAmudODPD0sX6OFPbH9Vmj0ewNzMC/+/dNuFt5QpK0VxdrCal4b7bB7Xuuq+mYxRNHChzVg28azabp1Dd+Owqw1XascgJBIKP9fZTCNM0Fa63dtR/qBmLeoSoSV0Uiq7mmj1Abt9gazMTbMYBphotJjhZSDBdTFJK7S3i2m/ACQd2TtHzJxbE5nCAyXjAwSMZMCpmd7fXUnWBeVHW9LbJar7iqmIlzZqTQjgWMogEP92lxlSZid119NRqNRrMppOciGnXC0jQy8DEsGzOVWfOk/kqoIKD13q9o/fodBv7wH2DG4yghkG4LJSW5p54nLM2Q/+SL5J/5DFZ2fU2rDgvdrRbepgVX0YZHVIgJvEDiy7sL260UWMHimMC5lo+7hpjAbNyKBFY6JnBXEEqJ4wtqbkhpQUMxbplkEptzsQKYa3pcmWpwZarG2FwLsUrD8lhfigsjfVwYLXJqMLtljVSNRqPR7D+k5+JP3EK0GliZ7IbitTNPPLNIcJU4fprib3yOzKMXurL23k101u/dXq8LqQikxAskTijaEcZqWwYLpps+s+uICjxaSHEknySnReGaPYYXCppeSMUNqbkBUkaOs8nY5qPEN4JSiqm6x6/Gq1yeqDG5QgSIAZweyvLciX6ePt7PgezOx5hoNBrNRlgYKbgRwZVSiqobEFvDGrXuhfxqskbTX+wyc6yY4sLRot7b02jWiVIKX0SDI34oaLZFVU4g5mP9FGC3HasStkV6F+2Bdq4v03X/vhjAmrt6CsZyxCyDw/lITHW0GAmqhgtRPHtiDcJRzcZRSkVxlV5IueEzV/dw/IAPSwLLVJxQkfPTTgmR6k7A2FTHuarB9ck606sMXtxLIR3jzEgxElcdK3J2pI8j/VpcpVkevdOj0Wg0exwlQsJGnbA8i2w1MAwTM5nCTqa68vxhtUzzlz+jeektpBPZ8jcvv0Xq9BkMyyY2eAgr34cRi5F76tObajDd627VYb+6XEUCK4kXRhGB4QKBlW0aJCxzyxaBG40JPJhNzLtY6ZjA3kYphRtKGl5IueXT9EKUYWAbBomYSXqTzWIhFTdKTa5O1bkyVWem4a94fNwyefRIngsjfZwfKTCQ0Q0RjUaj0XQXpRRhpYQ/cQszFls1YlspSevyRcxkmtTpRwCQvo/0XOyBQ8RHThDrP0DxN36P5PHT2/Et9BwL1+/dWK9HDYkopswJBWBgGWCbxpZNzEqlKLejAmfXGBUYuVglOZRLEteNR80e4t4hjCgmUBGzLDKbjBLfKEop7lRdLk1UuTReY7a5fF1hGPDIwVzbyaqPvrSOu9JoNLsfI1zgcLWB/V4nEIQSkvby13ClFLerLu/NNlg4H2eZBheGC5zo35izlkazXxBtN95ARPv4ThCJq7xQIGU0OIIR7enbpkl6h9ZVGyVsO//OC6rmowB9fLH6YPZy5JM2RwopRtrxf8OFFMPFJIOZhB6+3SaEVO14wIBSw6fc9JBSoYBEzCQRt0jFE+STBmCQiFnbJkxquAFjk41FsYBTGxBXPdIRV7VjAYf701pcpVkXutOp0Wg0exAlZRQZWJlDVMtR1FcisWrTaM3PryTezes03vkp7vWrcM9cd/PSWxQ+83vY2fxigdUmFylLuVstvG+vC66EVARK4IUSLxRthyEDk2iDYy223xulM80/1wqYbfmUWzomcC8StBuYFSeg6gaEQmEYkLBNcsnYpguNhhdydarO1ek616YbqzqhDWbiXBgtcmGkjzOH8sRt3bDUaDQazdYgfR9/8jaiXo1crayV11VhZY7SX/5r/Ns3sAcPEj96HBUGmIkU8aPHsLMF0v/7/xOGtb+3XRau3zezXg9l1JRo+QKhFJaxtcMF/oKowLk1RQXaDOeTDOeTDOioQM0eQiqF224ILjmEEd+Z9blUio/KDpfGq1yerFFuBcsea5kGjx7O8+yJfp4a7SO/A85bGo1Gs5WYwd1roJlKr+uxUilqXkjcWn7t4gvJr6fqTN8jaO1LxXjuWL928NRoFiAWxAC6YRQD2AoiYVW0Wx5hmyYxyyAb310xd00/ZKZ+NwZwuuEzU/cotfwVnX9XwjDgYDbBcLHtVlVItoVVKX192QG8IHKvqjkB5YZHtRUCChODRNwkl4rdJ3YTmxDVrZWmG3B9qrEoFnCqsj5xVT4V45F7YgGPDmhxlWbz6CuVRqPR7CGk6xDWK4SlWZQQmLEYZjbbtTgN6bk0371I852fEpZnlzwmdmiYwvO/c7/YapMs527VYS+6XCml8ENJYMaQhs1008MyLYz2FH9yi8UnG40JPJRLcjiX4EBWxwT2OlIpnEC0XayCyLFBRc4MSdvCim+u2FBKMV5zuTIZiaxulZ0Vi2/TgAcP5HhytMj5o0WOFrVVr0aj0Wi2FqUUYa1CMHErmijOr+5q1Xjnp9T++j+hwqi5Fc5O4U+Pkzv3XBTXPf/Ztb/XQfeu39e7XpdtN6umF+KFEsOAmGUS26KowKYvmGn6zDQ9KqvEWxjAUDbO0XwUFZjVjQDNHsIXEt+XVJwoNlNIhWlEETbdGMLYKB2H3MvjNS5P1laMoYlZBh8/UuC5EwNcGCmS0b+jGo1mD7MZh6umH6KUwlxmfVXzQt4Zr+Ldsyf40FCWRw/ltcOMZt/ScasKRCROb/iClh8SConCAANMohjAmGmQT+4eV02pFHNNn3HXpBYYjP1yIqqTGt59caLrIWmbHCkkOTovrIrEVYfySd1D2CFUe7ii5QnKDY+5hofrCwwMTBNScYv+7Pav/5teyFhbVNX5c3Kd4qpcyubMSJFHj/Xx6GgfZ0aLjGhxlWaL0NWmRqPR7HJUGBDWa4SlGZTnghlFBq42lb8egvIsjbd+SOvXv0AFS9jzGyaZxy5QeP53SJ56aEsWLSu5Wy08Zi8IriJr4WiKOAwl0rAxkCRtC7OLIrZ7kUpRcQPmmjomcC/jtW2ryy2fmhsgZCR0SsYsCl2Y9vZCwQczTa60naxWaoQAZBMWTwwXuTDax+PDBd2w1Gg0Gs22ocIAb/IOolrGSmcw7JU/g4LyLOW//Av8OzcX3W4VB0gcHMZKZ7fydHcdS63f17JeX7gWlhJsM3Lb7HaNsTAqcKbh4awyXBC3TI7kEwznUxzK6cECzd5BSEXDC6n4Bk0B707UMS2TWA/E2YRScn22yaWJGu9O1FZs8iVsk3NHCzx7fIBzI0VSsa1zgNZoNJpewgzvOlxZ63C4CqWi7oUkllnTlFo+v5ioLXL6TNgmz472cSiX3PgJazS7BKUUoYwGon0haQWRY1Wz7bprRAdhmia2FYnT07tob7zlh8w0olpoph3/N9P0mG36CKmAtkisVlnX8/anYwwXUhwt3nWqGi6k6E/vnHBfE9GJB2y6UTxgpekTCgkY2LZBKmaRyW3ve7jlhYxN1flgoj7vYDVRdtb1HNnkQnFVkTOjRUYHM/r9ptk2ds+VX6PRaDTzKCmRrQZBeQ5RrwAGZjKJlctvyeuFczM0f/nz+263cgVyz36WwidewC72b8lrw+ruVh12s8uVkAovFDQDQSCigi1mGdgxE5OtsWS9PyYwQKiVzX8NYCAd53A+waFcUscE7gKEjFysam5Ape1UZgCx9iZAN35+c02fq1N1rkzVuT7XbBflyzPal+LCSJHzI32cHsru+olIpRQNN6Ta9DnYl9INWI1Go9kFhPUq/vhHoNTqrlZS0rj4Y6o//M8QLhYS5z/xAgO//3fX7Saw11lu/b7cel0qhRdEa2F/wVrYXCHeZiP4oWSm5TPb9JhtBauuWfKdqMBCkv60jgrU7A2UUrihpOWHVJyAuhsSCkEthJgJhZSNae6cWCkQkmszDS6P1/j1VA0nWL4eTsUsnhwt8uzxfj4+XCShI8g1Gs0+xFgguDLiiTU/ruGFGBhLNqSnGx6/mqyxcKl0KJfgmdE+krYWtGr2FlIpAqEIhMQLoxhzJ4i+pFKAgQJswyBmmaTj1q6pC0IpmWv6SwqrWptwq7JNg0O5hTGA0Z9HCkkteu8h/FDi+CF1J2Cu7lFtBSilMNrx4JmkhWVuX9x2JK66Gwt4farOeGl94qpMR1w1Ggmszo4UGRnMYO7y/oJmd6MFVxqNRrNLUEohXQdRKxOW51BSYsbjWNl8V5XaSsn7IgjjIycws3lkowZA4vhpCp/+HbKPPbWqE0A3WIu71cJjd4vgSimFLyLRkxOEgHFfVKBcRQC1XgIh5yMCdUzg3kQpCBWUmj41X1B3Q6RSWKZJ0jYppDb/Oyuk4sNSkytTDa5O1ZlueCseH7cMHj1c4MJokXNHiwxm174B2KsopWh6IaW6x3jJwQ0FUir69O+IRqPR9DQqDPGnxwnLs21Xq5U3F4PSTORqNf7RotvtvkEO/N3/NanTZ7bydHctK63fF67Xg86keCCQqvux2UopGguiAqurOG+aBgxlEgwXklFU4C6aUNdoViIQkctt1QmoOAGBjISNcdskm7BRyiS5g70xLxS8N93g0niNK1N1fLF8nZpL2Dw52sdzJ/o5ezi/J9beLS9EqaiBpNFoNOul/PFPEi9Pc+LgAXIff2pNj/FDSTMISS5xDb1TdXh3urHottODGc4dKWi3EM2uRkg1HwXYEZ+3gkhkJSOTHwyimsTuAafPtaJU5FY6vVBU1XbxLbd8VpkxWZGEqTg2kGWkmG47VSUZLqY4kE3s+gHavYZSCi+QNL2QStOjVPdw2qI60zRIxiz6MtvnMuZ4IWPTDT6YqDM2FQms7qxXXJWweWSkwGPH+jg72sfZtnOVFldpeg1dxWk0Gk2Po6QkrJYJ56aQnodhW5ipNEYXo+WUUvjjH9F45ydIp8XQy/81SimU6yCDADOZovD8bxHOzVB4/rdJHD3etddeC09/88+39fW2mlBInE5MilJYhkHC6n5MCmwwJtA0OJiLYgIP5hK60bQLCKXE8QXlpscd1yBUBn1lh1TCJpuwu/Leangh701HLlbXphurivUGM3HOjxS5MNLHmcP5PTNt3vJCSg2P8VILxxdYhkEmZZNN2ZQaS0SuajQajaZnEM0G3p0PQQqs3MoNIyUljbd+SPXHb9zvavXJ32Tg9/8OZkJHqSzFau60pZ+9xfgPfkrsiScIpMIkcrPq1lpYSkXJCZhpRo2G1dYsCcvksI4K1OwxpIpcbhteSLkV0AoEKLAtg6Rtkb6nSdHlOZ814QSCK5N1Lk1UeW+6sSiy6l6KqRhPHevjueP9PHIovycafH4oKbfrilLDZ3ggxUPDxZ0+LY1GswsJ+g6gDJPkqY+tac822iv0iZn370XeKLV4f6656Lazh3KcOZDTYivNrmA+BlBIAqFwgpCWH4mrQiFRGHTeyjHLxDYNsvHu7J1uNYGQzLbdqRa5VTW8NQ1VL4dlwIFc5Op7pBCJqg7l4nx05RekTHj++SexLO1a1WtIqWj5IU03pNz0KDcC/DASWMUsg2Tcoj+3PUPPji/4qBwyUZN8/85VxqYajJdarKfEyCRsHj5a4NFjd52rjg1ltbhKsyvQHVSNRqPpYUSrgT95G+k6WKn0qnEn60UGPs7VX9F45ycE0xPzt7sfXcfuH8LO9xHvG8RMpWlN17Hyo9suttorSKVwA0Hr3pgUo7sNnYUxgXMtn5KOCdyTdGJAogaKT8MLwTAwkdgGxE216SgQpRQTNZcr7ajAW2VnxSLJNODBoSxPjvZxbqTISDG1KzYr1oLri6gZUnFoOAEmBpmUxcA2Fa0ajUaj2RxKCIKZSYK5KcxUGjOWXvUxstWg9tPvLhJb2f1DHPi7/4TUAw9v5enuetbiTvvB/+1VHv4Xf9Y1NysvlMy2XazmWj5ilZ3dQrIdFZhP0Z/evilfjWYr8UJB0wupuiE1N0DIaI2esC0Kye2LClmJph/y68k6l8arvD/TXLFWHUjHeeZEH88dH+DBA9k9UaMKqai2fCbLDrM1FzBIJyxyKTty1tBoNJqN0o6IWgtNPySUkLTvHq+U4tpsk5uVxe4jF4YLPDCY7eqpajTdQEhFKCW+UPj3xQBCJwjQNExsyyBhW6R3wVCxVIqqE8zH/s00vEhk1fCoOMG6BCz3kk/aHMkn205VUfzfcCHJgVwC+57hfiEEs9c2971ouksgJI4XUndC5hoe1aY/n46SjJnza8qtxvFDbkw3uD5R53o7HvDO3EJx1fSqz5GOWzwyUuRsJxZwtMhxLa7S7GJ6/9NFo9Fo9iHS9/CnJxCVEmYqhZ3rrtAqrMzR+MXPaF5+G+Xeb+PpXr/K0FOfxozF52/rNE52S1xfL6Da+e/NQOAGIsp673JMCmwmJjBysdIxgbuDQEhavqDiBFQcHyGjzbSkbZFPRo1CKQWbqUv8UPL+bIMrU3Xem6qvGruTiVs8cbTIk6N9PD5cIJvYO0tLLxBUmj7jpRY1J8AwDDIJLbLSaDSa3YZoNfHHb6KCYFVXq4VY2TyFT/0Wlb/6t4BB/vnfYuBz/wVmXH8OrMRq7lYdmm9dpPnWRfJPnd/Q69yNCoxcrNYSFXhgQVRgZhc0WzSa1ei43NbckIrj44UKw1DELKunInDqbsDliRqXJmqMzTVXjLU5mEvw3PF+njnRz6mBzJ4QQyqlaLghUxWHyYqLkJJkzKQvG5///lprcKLWaDSalTDm/7MygZDUvZDEgn1ApRRXZxrcqrrzt5kGPDPax2hx9UEFjWYr6UQABiIaZm4GAscP2/HD0ZveACwzcqvqpTXQSrihmHenWuhWNdv0CFabHlkB2zQ4lI+EVMNtt6qOa1VmD+3b7lUCIfEDiR8KvCAapmh6gpYXzrtXmRgkYiaFzNYPzbu+4MZ0gw8m64xNRrGAt+fW51yVils8fHRxLOCJA1pcpdlb6KurRqPR9BBKCILSDMHsJIZpYeXX3hRa9bmVxL3xPs13fop74xossSwyk2lyz3yGwqdeXCS2Wtg4mfvJm1p0tQqdyMCWLxDtyMB4N2NSlKLqBsy1Amab64sJPJRLciib2FPCmL3KfAyIG1J2Alp+xxLYJBWzuxajUWr6XJmuc2Wyzthcc8UoD4DRvtR8VODpoeyeiPPo4IeSatNjvOxQbQYoFJmErUVWGo1GswtRUhLMTRFMT2Imk1jZ3NofqySi0SD18OOIRo3cM58hdfJjW3i2e4e1uFt1uP2nr/HIU6+u6/m9UHKn5nCn6uKsFhVomxxpR2Mc1AMGmj3AIpdbx6fphu2hHpNEzCQZ6533eLnlz4usbq4SJ3K0mOLZ4308e3yA0b6945Lr+CFzdY/bcy28QGBbBrmUjWX2htuYRqPZYygFq1w/lVLU3ADLWLxHeavqLhJbWabB88cHOKj3QjTbhFQqEpoIRRAKnEDSDEIcXyJUlBSBUphtUZVtmSRjvb+3LZWi3PKZXiSsiv5e36TYui8d40g+ydFial5QdaSQZDCT2FN7tXsNKRV+GAmq/DAanmh5IU0vpOWFbcfT6HpuKLBsI4oHjJlkk1v7nvcCwY2pBten6nzQFlfdmWutOCxxL3ELzoz23RVXHSty4kBOvyc1e57e/0TSaDSafYBSirBeIZi4g5ICK53FMLu3Wdr81ZvUfv59RKW05P3xI6MUnv9tsuc/seTU/sLGyQd/8mdacLUE90cGKmKWSawLkYEKkKbNrapLyQnWHBPYn45xOJ/kcDZJX1rHBO4GvFDS9AKqbkjVDZASTCMqqgqp7mzMC6n4sNTi6lSdq9N1pureisfHLYOzh/NcGO3j3NEiQ9m9teEWCEmtFTBZdpiru2BAKmbRl9XxQhqNRrNbkW4Lb/wjpOdi5XIYK6zHlBDU3/xrVBBQ+NRvIn0P6brEhg4SGzhI+vQj23jmuxshFWf+1as0gxAho6ED29z80IFSioobcKviMtXwVhRuFDsxGYUU/Sn9Wa7Z/fihxAnud7lN2Ca5ZG+9x2cbHpcmalyeqHGrcr+T9kJO9Kd59kQ/zx7v50ghtU1nuPX4oaTS9BgvOVRbPqZhkE3aW94g02g0+xclJdmxy5hOEydhkDhwGLvQf99xUilqbogXqkUC3dmmz9WZxvy/bdPgN04N0p+O3/ccGs1mCWXkVOWHEjcUkdgkEHjtQYooBjB6H9qmSTpu7Yr97KYfLnKrmm3HAc42o7XbRknYJofbblUdYdWRQpLD+SSpmNXF70DTTVZ2qeoMDSmUigSuMTsSEua3sX/jBZFz1dhkR1zV4PYqTrT3koxFzlWPHivyyNEC3sx1DuVMPv38p7As/f7U7C90tafRaDQ7jHRa+DMTSKeJlUpj2N23ag4rpfvFVqZJ5uNPU3j+t0meeHDZjdp7Y0FKP3tLu1y16UQGttr58BAtkrsRGRg1lkLGaw6V7BGkaVOZba74mE5M4KGcnuLfLQgZvX/qbkC55c9vMMRti0wXLbCbXsjV6TpXpxpcm6njBCs7Qgyk41wYLXJ+pI+zh/MkuhyDudMIqai2fKarDtNVD6UUybi1KNZDo9FoNLsPpRTh3Az+1B3MRAI7m1/xeH9mgvJf/gXB1DgYBrFDIyRHT5I88SBWOrNNZ91d5n7yJrB9MeBKKXwhafoCNxQYQMw0iXVh7RAKyXjd43bVodF2+rwX04CD2QTDhRRHcknScb2xq9ndCNl2ufVCSq0AL4yi6WNmd11uu8VU3eXSeORkNVFzVzz29FCG544P8PTxPg7mktt0hluPkIpay2ey4jBbc5EK0jqKXKPRbBPK9+j75Q8BqF57h9TxB+4TXEmlqLR8XKFI2Hc/R+peyC8na/P/NoBPHu/XYivNppDt/fJOFGDLFzSDEC+QbVd9hcLAxMC2DGKmQS5h9/x+XCglc03/nhjASFjVWqZWWQsGMJiNc6SQ4mhbUNWJAexP95a4XhPRyy5VS+EFgg+nG1yfanB9os71qTq3ZtcnrkrETB4eLvDosSgS8NHRPk4euutcJYTgBz/4cGu+AY1mF6AFVxqNRrNTiBC7UcW9cQ07lcLOFTb9lEqEqDDETNzdvFRSkHzwLPU3/xqUwsoXyX/iRfLPfRY7X1z1OZeKBdnvLlehjKaMO5GBZhcjA51AMFF3Ga95tNoiLsylP651TODuQykVuVj5IeVWQMMLkAos0yRpmyRT3fkZKgXjNZf3pptcnarzUdlZ0Q3CMODBoSxPjvZxfqTISHHvRHl0kFJRcwJmai5TZQepFPGYSXEb8u41Go1Gs/UoEeJN3EZUy1jZ3IpusUqE1H/2fWo//R7I9npLKWo//jaFT/1fMGO7N+6ps3bf6rV6Zz3c9AWyHaGdsMyurB/qXsitqsNEzVvW1fVgNsEDAxkO5xPYXXQG1mi2m0590PKjGPF6OybQbLtY5ZO9dT1SSjFei0RWlydqTDeWd8s1DHj4QI5nT/Tz9LF+BjJ7p4GvlKLhhszUXCZKDkJK4jGTQiauawuNRrOtKLE4lsyw799XqroBnpAk7bvCdC+UvDNeXeS+c264wKE9JIjVbC1CRoMXgZDza5lWIHEDESVctgUnlmkQM02Ssd53q1JK0fDCxRGAzejv5Za/LoHKvaTjVtuJN8nwghjAQ7kk8T026LoX2A0uVUvhBYKbM02ut52rxibrfLRecZVt8tDRAo+OFjk72sejx/o4eTCLrYf7NZpl0Z1ZjUaj2WaUlASlGRIzE2CCmc1j2pubxBaNGo1f/pzmr94kc+YJCs//NtL3kZ6DYVokjz9A8bO/T+LoCTKPXcCw1nb5v9fdqsN+dLmSSkWL6y2IDBRSMd3wuFOLIgOXYz4mMBcVY/06JnBXEIioIVlzA8qtgEAqDCBum2QT3ZtU8kPJtZk6b5ZtJjwLZ/zGisdn4hZPHC1yYaTI40eL5PagYE9KRcMNmK66TFVcQimJ2Sa5dKzr7gCOH/Kjq9OkYhZPnh7s6nNrNBqNZmWk7+HdvoHyPez8ykMM/vQ45f/4FwQzE4tujx04woG/9092tdhq4dp9K9bqsuNm5YV4C9bDZhfWw1IqppoetyoOFTdc8piYZXCyL80Dg9k9uW7R7B869UHVCag4UX0AkLBMMonuudx2C6kUt8pOOy6wSqm1fM1qGXDmcJ7nTgzw1Ghf12LRewXXF8zVPe6Umji+wDINcikby9xb36dGo9k9rCa4kipyTkwsaJRLqfjlRBU3vOt+fnoww+nB7NaerGbXoRa4VflCRikPfpT0EAiJYQDKwDDAtiLByW5wq/LDtltV01vsVtXwFv1erBfLgAO5haKq5HzceT7Z+/9f9hO7zaVqKfxQ8OF0k7GpOh+0nas+mlm/uOpjw1Es4NnRPh4dLXLqUE6LqzSaddIbVwWNRqPZByilEI0aweRtQs9DxuJgbnwKXCmFf/tDGu/8BOeDX9NeBdL41Zukzz6JlcsRP3ocO5PHsCwGPvdfrPs1lnK3WnjfXhdcdYpKJ4jy5BVRhny3IgOrbsh4zWWy4bUtle8nZhrYToVE0ORvPPk4ybjeyO11pFK4bceHcsun6S+Mm7RId1HoU2r5XJ2qc2WqzvXZZvt9tPzybqSY4sJIFBX44IFsz0WSdIPOtPlc3WOi7OCHgphlkN2CRoiQinc/qvDdy5P85NoMXiCZrrpacKXRaDTbiGg28G6NYVgWVia37HFKhNR+8l3qP//+/LoZAMOk+NnP0f87L2HYu3udtXDt3s21eiAkbth2s5Jgm3RlPQyRu+vtqsPtmksgll4P96dinB7MMFJMY+/BtYtmf+AGgpobUm75UW2pwLa6Xx90C6kUN+ZaXJ6ocnmiRnUZISRENfLHhws8d7yfC6N9e855ORCSSsNjvORQafkYhkE2qSMDNRpNb6DCewRX9wzZRk4si135Pyg1FwncD+USPHFk88kLmt2PbDtvOn5IzQ2peSFCRu8hBdhGFAOYsC3S8d7/vBdSMdPwmKy7TNY8ptp/llr+ikkAq5FP2pGQqpiKos3bMYAHctp9t5fYrS5VSxGEkg9nGlyfrLe/Gnw021zkUrgacdvkY8N5Hh1txwIe6+PUoRwxLa7SaDZN738iajQazR5Aug7+1B1Eo46VSmNlE7DBxbf0PVpXfkHznZ8RzE7ed79yHaTTjJysNrEwXM7dqsNedrkKpcQNJM0gRMjuRga6oWCiFrlZzUcG3oNBtNlxsj/DwUyMH//oGoBe/PYwfhhNqVccn4oTzL9vErbV1ckuIRU3y615kdVUffkID4icIM4eznNhJIoKHMru3aZA0w2Zq7uMlxy8MJo2zyZtcl2KaVzI7bkm3708xfffnWLunp/BX/1qgqYbkumRaSeNRqPZqyilCMuz+BO3sVJpjBWcqfzJO5T/8i/uWzvHDg1z8O/9b0iMnNzq091y7l27b3atfr+7a7SuMK3Nr2mUUsy2fG5VXWab/pLHWAaMFtOcHszQn947MWSa/YUXSmpuwFzTp+ULDAOSttVzMYEdpIJpz+Tf/GqCdyfrNPyl61WAuGXyxNECz50Y4PxIkVRsc67dvUYnjnyy0mK66gGKVEyLrDQaTQ8iFl+r73W4cgLBwuVbxQn4sOzM/zufsPnEsf6eExdotgcho6HRVtt5s+GHKKUAg5hlkrQtLLP397ekUpRbPpO1triq7jFVc5lp+MtGlK+GbRoc6kQAtuP/OjGAmT0mLt+t7AWXqoX4oWCq4jJZcZgsO0y0/5ysuExX3XWLqx48kuexY5G46uxoHw8c1uIqjWar6L0ryi5nbGyMr3/967z11luMjY0BcPLkSb785S/zpS99qWuvYxgGX//613nppZc4eXLjm8Ovv/46X/3qV3nppZd48cUXOXny5PzzjY2NUalU+OY3v8kbb7zB1772NV566aVufQsazb5AhQHB7BRBaQYzFr8bcSLWb00blGZp/uKnNN+9iPLcJY4wSD/yOIVP/zapBx/dtMBjJXerhcfsFcHVoqZSGFkix0yTWBem94VUzDQjkdXcCvELuYTNyb40x/vT8xvWQiy/ya3ZOTobEg0vpOQEOL6A9nsmHe9uDEjTD3lvqsGV6TrXpus4wcrXj4wlOZGW/N5TD/HYcJHEJiNLe5mWF1Ju+NwpNXF9gWkYZJI22S0QWdWcgB9emeZ7lyd5f6K+7HEjgxnGyy1OH853/Rw0extdR2g0a0dJSTB1h6A0g5XNYZhLf9Yppaj96NvUf/bXoBZ8fpomxRf+gP7f+vyud7XqsNTafb1rdaUUoVS0Ou6uqnvurgC+kNyputyuOjjLRHVk4xanBzOc6M8Q15vBml1IICR1N2C26dPwQ8AgZVs9G60XCsm1mQaXxqtcmkjgKwPmKksem4qZnB8p8tzxAR4/WthzdYZSiqYXMlvzuDPXIpSSuG1SzGyty4GQkl/cKDE21eD//PfPb9nraPY2upbYv9wbKcgCcYxUCjeUxNuKKyEV707d3c8wDfjE8X7dgN9HBELiCPCEwXvTdbywLUYhEmhkuryn2W2UUtS9kMlaJKqarLlM1SOR1XJuuavRn47NC6k6blVHCkkGM4k9mQyw29hLLlUAjh8yWXHbQiqHiXL0NVVxmK15G3Jei1kmDx7JtcVVkcDq9JG8vrZrNNuIFlx1kVdeeYWvfvWrvPDCC3zjG9/g3LlzQFRAfPGLX+TrX/863/72tzdVjADzRdNXv/pVvvrVr67rseoeNffY2BhjY2O88sorvPLKK8s+7ktf+pIubDSadaCkJKyUCKbHAaJGkLGxBY6oVyn9p3+N9+H7S95vpjLknv0NCp98kdjAgQ2f80JWc7fqsNtdrpRSBFJF0w9hp6kEyS5M5yqlqHkhd9oF4EqRgaN9KU72Z+hPxXSWew8TCEnTF5RaPjU3QCowiaZiutlAUUoxWfO4Ml3nymSdj8qtFYstw4DTg1meHO3jieEcH156G8OAc0eLWNbeaoIAuL6g3PQYLzvUWwGWaZBJWvRvwbR5ICQXr8/x3XenePuDuWV/j/uzcX7/yRH+6OlRHj5a0L/HmnWj6wiNZu3IwMe/cxPRamLlVr7mGoaBaNQXia3ih0c48Pf+CYmjJ7bjdLeF5dbua12rC6nwQkHDF4RSYtI9d9dOjPatqsNUw2Opj1IDOJJP8uBglgPZuP4c1ew6QilpeoLZpkfVDTGUIhGzKCR7053NDyXvTde5NFHjylQdb75Zdf/vXiZu8dSxPp49PsBje7Rx4/qCcsPj1lwTx7/rlGtbWyuSuzPX4q8uTfC9d6coNyK3v3/4wmk9uKFZN7qW2Ofc63C1YB8oEBKlmF9bXS81aS5w2z9zMEehR10XNZtHKYUvohSHmhtQcwOcIGTaM9sBgZBL9u5edMsX7QjAtriq7jJV85ZNjFiNoWyc0b40x/rSjPalGC6mOJxP7jmXzt3GvS5VbiBourvXpQqg4QZMlJ0lRFUu5WUcnteKbRk8eLjtXHUscq46fThPvEtDUhqNZmP05tVoF/Laa6/NT2V861vfWnTfSy+9xLlz5zh//jznz5/nxo0bFIvFDb/WxYsXN/S4r3zlK+t+TLFY5Otf/3pXJ2E0mr2OaNbxJ24jfRcrnV1U6G4EM5NDNu53U4kPH6fw6d8m+8RzmPHubuSuxd1q4bG7TXC1ZGSg2Z2mkhdKJuoud2ouzRUiGA5lE5zsTzNcSOlpmR7GDyUNL2Cu5VP3BChF3La6PvHlh5IPZhtcnWpwdbpOxVneCQ2ixsfjwwWeHO3j8eECufYGmRCCm3vw7eQFgkrTZ7LsUGlFhWk2aTOY777ISinF9ck63708xQ+uTFF3wiWPi1kGv/HoYb7w7DE++fCBPdl80mwPuo7QaNaOcFp4H0XNPju3toZ0/rnP4t64hnSa9L34R/T95h/dF7Wy21lp7b7cWr0zeNDyBa0gcuGJmQbJLjnWhFIxWXe5VXGoL7MmTtompwYynBrIkNaNDs0uQ0hF0w8ptXwqLR+pDBK2Sb6LceLdxA0EV6YikdV70/UVXSDySZunj/Xz3Il+zhzK78l6NRCSWivg9lyTStPHMAwyia2PDGx5IT+8Ms13Lk3y3njtvvv/4ic3+e8+/+iWnoNmb6FrCc29DlcL96GdQNLZqrg3SrAvFePhA7ltOUfN9iCVwgsljh9Sc0NqXkgoFKCwLZOEbVKwYqSsaA0Qs8yeWLP4oWS64bWFVS6TNY+pukvVXXo/bjUKSTsSVvVHwqrRvjRHiyktrNpB9ppLlVKKaiuYF1LdGwHY2OB7dyG5lM2xoSzHBjMcO5BldDDDQ8MFTh/R4iqNphfZW7uMO8TY2Bhf/vKXAe4rbDqcPHmSL33pS7zyyit88YtfXPa4tfDmm2/OP2exWKS/v3/F49944w1eeOEFvv71ry95/7lz57hw4QJjY2OUSiX6+/s5d+4cTz75pJ4g0WjWgfRc/KlxRL2CmUxj5wpdemJB9qnnKf+H/x9YNtnHn6bw/G+TOPbAlhVFT3/zz7fkeXcSqaJpiaYf4nU5MlAqxUzD507dZa7pL+tGlI1bnOxPc7xfN5V6GS+U1L2AuYZP0xcYRiSy6nbzpNzyuTJV5+pUnQ9mm8u6J3U4WkxxYaTIhZE+HjyQ3ZONj4X4oaTW8pkoO5QaHgYGqYS5ZU2Q2ZrLX/96mu9enuT2XGvZ4x4/3sfnnznG3zw/TCHdm64Fmt2DriM0mrUTVEr44zcxE6llhw2k72HGo88JJSWiWcdMZTjw9/4JdqGfxPCx7TzlbWE1Z9p7Xa6EVLihoOmHhO3Bg0QXmy0NL3KzmljB4fVAJs7pwSzDhWRPbqBrNMshlaLpCyotn7mWj1RRTZlJ9GYzqOWH/HoyElldm2kgVqg3+tMxRmyHUxnB33rhPLHY3tsyllJRcwKmKw6TVRelFKm4Rf8WO+tJpbj8UYXvXJrkJ+/NLGgsLuZAIclQPrll56HZe+haQgOg7nW4ag8WKBWt+WKmgVKKX08vjhJ8eqSvJz+7NGtHSIXbjgKvOgENP0TKyAkobpkkbQsrvvhnLOXG3KG6gZCK2abHZM1rC6si56rSCnvpK5GKWYwUUxzrT807V430pchr17ZtZy+6VEmlKNW9pUVVFRd3hUH7tTKQSzA6mOHYUCSqOjaU4dhQJK4qZvSes0azm+jNK9kuo1M0vPDCCyse9+Uvf5lXXnmF119/nUqlsuGJkrGxMb7yla8sW6ws5PXXX2dsbGzFYurkyZO8+uqrGzoXjUYDKgwJStMEs1OYdgw7X9zY8whB85c/I370BPEDh1FSIJpNDMsm9+xnsPsGST/88e4JufYBWx0ZWPdCxmseE3WXYJnNa9s0GC2mONmfZiCtI1J6FTcQ1L2QuaZP0w8xDIOUbXU1KlBIxUfl1rzIarLurXh8zDI4eyjPhdE+zo0UOZDd2mnrXiAUkmorYLLsMNdwgej3dauaIK4v+On7M3zv0hS/ulledoPncF+Kzz89yh8+PcrxA9mun4dm/6LrCI1mdZSUBLOTBDOTWJncsu6x9Ys/pv7zv+bgf/m/wzBNpO8RP3AEu38Iw9y7E6BrcaZ9/0/+Odknz9PyQ5xQYAC2aZLs0mSsVIrphsetqkt5GZdO2zQ40Z/m9EBGN0E0uwqlFK1AUGkFzDY9hIxcIrrteNst6m7Iu5M1Lk1UuT7bXDLGs8OBbIJnj/fz7Il+TvQl+eEPfwiAuccGOxpuwGzdY3yuRSAkcdukmNl6kdxUxeE7lyb57uVJZmpL135x2+Szjx7m5eeO8dxDB/b8UI2mu+haQgP3O1xhRmvlQCqkUhiGyUTNpbFAHHDmQI5iF/e7NNtDINpCFl9QcQIcP0QZBiYQs3tnbSKVotIKIlFVve1cVXOZafgItX5pVcwyGC6kONaXYrQ/zWgxzbH+FP16n31b2WsuVQBCSqar3iJ3qk4E4FTFWdERdq0cKiYZHborpur8OTKYJqvrYo1mz6AFV13gtddeA1g1B33h/a+99tqG7HQhsu/92te+tupxY2NjfPGLX+Sv/uqvNmUXrNFolkZJSVgtE0yPg5JY2RyGsf6mhVIK5/pVqt//j4SlGRKjJ+n7m38bA0n8wGHsvkEMyyL21PNb8F3sTcL5CZ+7k/vdjAycbEcGNlaYZDiQjXOyP8PRQhJ7Dzf5divRpJ+k5gbMNX3cIHI9S9oWxVT3Jkiafsh70w2uTtV5b7qBE6w8/dKfjnF+pI8LI0UePZIn0aVYn15GSEWt5TNVdZipekilSMZM+jJbs3EileLdjyp87/IUP742s+xEUjph89tPHOHzzxzjyVMDe67xpOkNdB2h0ayMCkO88Y8Q9SpWLr/kWltJQeW7/57mOz8FoPyf/jX9v/vHpE4+hJlMbfcpbyuln761ortVh/LP3ubm939M4akLXXWzcgPB7ZrL7aqLL5Z2bCkmYzw4mGG0L6XXxJpdQ6dWqDoBMw2PQEoswyQVs3tSEFNxAi5PRCKrD+daK7pEDBeSkcjqeD/H+tPz1wMhds7xYivwAkGp4XFnrkXTC7FNk0zSwra2trHl+oKfXJvhO5cmufxRZdnjHhkp8LeeO87vXTiqXXM1G0bXEhoA7nW4sqKWnxcKTAykUnxQas7fn45ZPKSjBHsepRS+kLhB5MJfcwLcUIEBVtvBKpeM7ajgSClFwwvviqraf07VvWVrg5UwDTiUT7bdqiLXqtG+FAdzyZ5cf+01AiEJQokfRn82XZ/blRA3VJhXpwGT3eZSBeCHgqmKOy+kmneqKjtMV90VhxPWgmUaHOlLMTqU5fiBtkPVUORaNTKQIaFTTjSafUHvXgV3CQuzy0+dOrXq8cVikUqlwje/+c0NFzcnT57k3LlzKx5TqVR48cUX+cY3vrHqsRqNZv2IVgN/8jbSdbDSmflidr3YjSqlf/0v8D+6Pn+b99EYwfQd8s9+dtm4FM39LBcZ2I3JfakUs02fOzWX2RVsjjNxixN9aU70p8nE9Udsr6GUwgkENTdktunhhwrTiByUuuVkpZRisu5xdarOlak6N0srNzwMA04PZnhytI9zI30c60vti+ksKRV1N2C66jJVdpBKEY+ZFLZw0vxOqcX3Lk/y/Xenlp0wNw145sEhvvDsMV78+GFS+vdYs4XoOkKjWRnpuXi3xlBhiJ1f2uFV+h6lf/c/4d64Nn+be/0qVja/58VWANf/2dpdIab/7M8ZfPapTb+mUoq5VsDtqsN001/yGNOA0WKK04NZBrSQQLOLcANB1Q2Ya3i4ocIyDJJxk7TZe2vCUtPnUltk9VHZWfHYY32ptpPVAEeLe/fa2HHLHS+12pHkkEnaWxZJ3kEpxdU7Nb5zaYIfXZ3BWWagoz8b5w+eHOGl547z4JH8lp6TZu+jawnNQmQsjiEEhlIYtk0gJE1fYFsG4zUXJ7grfnn0UE6LV3oQqRReKHH8kJoXUnfDyF3HiJxiE5ZJIbVzwwtOIJjqiKoWxAG2NhirNpiJM9qX4lh/OhJWFVMcKaSId8mBV7MY1e6bdERVgVA03RDHD2n5AtcLkSoSU6FAoTCUoukrLBNyqRixHh4KdvyQyYo7L6RaKKyaq3sbiqxcSNw2OTqQXuRQdWwow+hQhiP9aWKWft9qNPud3tsx2GW89dbdadLVpkk6x1y8eHFRUbRevv3tb696zMsvv8wLL7yg8841mi4jfQ9/egJRKWGmUhuO9xOtBvn3LpKauMG9bYrYwWFaM03E279k4NknN3/Se5itjAwEqHshd2puFBm4jIWsbRqMFFKc6E8ztEWOPJqNI1X0/qi2nawCEU2nJ2MmqS5tVARC8sFsc15kVVkmSqdDOmbx+NECF0b6eOJoYd/E6iilqDtRnMdEySGUkphtkkvHtmyzr+4E/OjqNN+9PMW18dqyx506mOMLz47yuSdHOLSHm1Ca3kLXERrN8oT1Kv7tGxixOFZm6SjXsFZh7t/8K4KZyfnbjESSQ//w/0D8wOHtOtUd5cn/8RtYCyIWZVtcXvdCpIK4aXTNoTEQkjttN6vWMo6dmbjF6YEMJ/ozJHSzRLNL8MLIOWK24dPqRIvHrB1tai7HdN3j0kSVSxM1xqvuiseeGszw3Il+njnWz6F8cpvOcPtRSlFzokGOyfYgRyq+dZHkC5mre3zv8iTfuTzJeGlp0ZtlGnz6kYO8/NwxPn32kG7IabqGriU0HVIfe5Q7v/dfY9fLPPWpT2MmkszUXUwDlILrc635Y3MJm2N96R08Ww103KsUfihoBYKGF9LwQoQEDIhbJgnbIh3f/j1mpRQVJ+B2xeF21WG8Gomrqm64+oOXIJ+073OsOlpMkdYDjl1FyruCKj+MYv9a7cg/xw/xfAEGdJRHBgaWBbZlYlsG+SUGYIWQJGPRbb0QCVh3gkXuVAsjACvNlffi10IqbjE6mOHYgY6o6q6w6mAhpZMPNBrNiuhPtU1y/fr11Q9ahs1kpq/EK6+8QqlUWlMRtJDXXnuNb33rW7z11ltUKhVOnjzJSy+9tKZc9m5z/PjxNR3n+4ulKkKIHbNBX/i6e82KXQNKhITlOYKZSQzLwsjkUIaBWKc9rgpDmu/8hMbPv0faX+ywYqazFH/nZXLPfIa3/v4/BqD4lJ4GWwohFW4oaPoSoSSmYRAzDYz2wlfK9dsWd/CFZLLhMVHzqK8wpTOYjnGiL83RfAK7vWm6mdddDn1tWT9SKVq+oOoElFoBoZLRdHrMIjk/jaOQcuP/PytOwNWpBlenG3ww2yRcxX94uJDk/NEC544W+diB7CKBUTd/rr32flEqmpiaa3hMlB38UGKZBtlkDMu0OgchlhE0boRQSH7xYZnvXZ7irbE5wmWeu5iJ8Xvnj/JHT4/wyNHCno1TWY6tuF5p1oeuI7YOXUvsXpRShHPTBFPjmOks2PaS6+1g6g6l/+X/g2zW52+zigMc/NJXiB86uqf/Hy71XrkrtBIopYjbJrYZ7ajLTWYkVN2Q2zWXqYa3bNzC4VyC0wNpDswPH6g9/TPYTehry9KEQlL3QmabPg0/xCCKQ8klulMrdAulFBN1j8sTdS5P1JhuLO0qB1EP7cEDWZ493sdTo30MZu66y63lZ7/b3itNL2Su5jJedvCDaJAjk7gb+Rhd+7pXY3TwQ8lbH8zxncuT/Opmednr4qlDWV565hi//+TRBQ5be+faqGuJnUfXElvDbq4jwlwfpLMEYUgoJAnb5HbVwVuwln70YBYlJXvjSrQ76CQy+ELS8gUNX9Dyo+EIpcA0IW4ZJG3zHlGLZCsutQtrqzAU1P3IufZO1Y2+KssPV6xE0jYZKaYY7Yu+RorR13KJAnvl83C7CEXHnSoS6rmBxPFCHD8SVPlh++e6QFQVianagqqUvaIQXUmFuGfdtPCzfjs+95VSVFoBk2WHqYrDRDsGsBMH2Nig6G8h+VSMkcE0x9uxf6OD6cipajDDQC6x7P8jpeS96a2ae9httYRm59irdYQWXG2SSqWyruP7+/vn/14qlbpe3Fy8eJGvfvWrvP3222t+zNjYGOfPn+fChQu8+uqr81Mxr7/+Ol/84hd5/fXX+da3vrWtNsA3b97c0OPefvttSqVSl89m/fz4xz/e6VPQdAulMN0Wdr2MISUynoysVTfwPImZO+TGLmG7rcV3GSb1U2epPXSemzKB+PN/if+z6Hf4e6/9v7AePt2N72TXowBpWAjDRpomYGAqQTdmCxQQ2Em8WBbfTi37MzZFQMqvkfJr2OWQW3fgVhdef63oa8vySAWehFZo0BQglYFlKGJmFGuzWZSCcmBw27EYd02q4cqTyRaK4ZTkRFpwPC3Jxxxwysy9Dz9+f/PnsxZ28v3ihoq6K5lrRTbVpgGpmLFlTlZRlKPk0kTA5cmQVrB058My4bHDMZ47HufsoRi2WaH0YYUffrglp9XT3LhxY6dPYd+j64itQ9cSuxQpidVLWE4TEV9+PZaYHafw659jLhBDeMUhZp/9HT58/wa8v3+ubz/68Y8Rho2wYijAVLJLa2MDL5bGjWcR1tJRXKYMSXlV0l4NVQ659hFcW/JITa+wb68tbUS7XqgHBq40UCqqFWI9ZjikFJTadcdtx6Qhlj9BA8VwUnI6KziZFqRtB2ZnuDK7uXPo1fdKIBQNL6oxnGDra4wOnVrjl+MBlycDluv3pWIGT4/G+OSJBKNFC8MY59e/GN/Sc9spdC2x8+haYmvYC3WExMC3U5hKUM0cAisS4Nqhy/VfvsnYDp/jXkYqCNtfnjDwBHgKwIjEVYbCMsA2NtZe2AxKQUsYlAKDsm9TDgz+zX9+D1+u70QsFMW4YjAmGUgoBuKSgbgiaykMowkCmIXyLJS35lvZcyilEApCGQ2ZhxLcIHJB80KFL4jEd4YiWv0pTCIXTcuM3k9b7b70q1/9sivPo5Si5ilKLUnZkZRbinL77yVHsgGt333kEgYHsmb7y2Ioa83/OxPvrKtb0ZcPzTtw5c7mX1dzl16tJTS9wV6tI7TgapNsZiG93sJoLbz88su89NJL6ypELl68yNtvv33fY1566SVOnjzJ+fPnOX/+PNevX1+TRbFGs1cwAg+7VsYKPISdQG0ipi5z6xq5scv33d46fILK2WcQ2bvRhOH//JeL/r6fBVfRTKqJMC2kaaOUgYHsWjMpNGN4sQxeLIMyl/n5KknSb5Dya8RDpyuvq+kOHZFVIzRoiag5aKGIm9EmRjdohAY3WyY3HYv6KiKrjKU4nhacSAuOpmTPNW+2Gi+MGiAlR+IFCtoNkNQW/o+oe5LLEyG/mgiYaS4/HXG8z+ITJxI8ORJbUFxrNDuLriM0mrsYYYBdmcUUISKxTMyJUqRvv0/u+qVF67HWkROULnwWZe2f7Q0F9wmtuvHpJkwbN5bFi2dRxtLPGAtapL0qyaCh18WanmepesE2FElTbXujcyWUglm/LbJyLVpi+ZMzUYykIpHVibQgufFtil2BkIpWEDXmaq6MagzbIJ/c+jV9049qjV9OBEw3lq41DODhgzafPJHg8SMxYlYPvbE0expdS2iWxTBAQWjFEdZdt8O0V9Vrty7SEVcFMhJXuRKCeXFVJKyyDEiaYHRpj3KtLBZXmZQDg1JgrltclbEkBxKKobicF1YVYqorg637CakUQkaCqlAqQtERU0UDq74g+qERrVUNFKbZFlQZkIp1Iv06/+N7+wcgpKLqLhBSdcRVjqLsSNYZGHMfBlBMGQxlLQ5mzUWCqqGMNR+DqNFoNNvJ/tmR3Aa2wop3Pbz22muMjY2ty273S1/60nwRsxTnzp3jhRde4I033uDll19e15TKZjh27NiajvN9n4mJifl/nz9/njNnzmzVaa2IEGJeufvcc89hWXt812sPIwOfYHYSUS5hjA5jJpKbfk7x4APM/A/XUEFkOR07MsqdU4/jDR5Z9H4p/fQt3nrvg7vn8t4HnIml6H/mwqbPYTexbGRgF3bFAyGZbPhM1F1q3vJjEwPpGCf6UhzNJ4lZOyfQ0NeWxYRS0vQEpZZP1Q1RCo5YUQRIt/LkG17IpYka79yp8VHZWfY4Azg1mOHCSBQVeKwv1ZX36GbY7veLFwjKDZ/xcgvlhgwaBiNxi8QmBKprec03P5jje+9OrRjjcbCQ5A+fOsofPDXKyYPZLTuf3cpOr1s1i9npn8deqiNA1xK7DdFq4t0awzh6CDOZWvIYpRS17/w7WtcvLbo9/xuf49jf/FsY5v4Q0wqpaHg+b75zGWXAx88+Qsze3LaOVIrZps/tmkfFCZY8xjYNjheTnOrPkE/qbaTdxH68tnTixctOQKnlI2X364VuIKTiRqnF5Yka707Wqa9Qm8Ytg48fKfDs8T7OHS2Sjnf/59hL7xWlFHUnZKbqMFF1yUrFQMwiHbe2vN4KheTijRLfvTzFxbESYpliY3QwzReeGeUPnxrhYHHpz669zE6vXTWL2emfx16qJfZCHeEJRc0TvD/XpNaOoo2ZBp998rF25LRmvYRS4ocSN5A0fUHDC3HF3WFg2zSIWQZ2l/au14NSirITLIoEvFNdfyzgQDrGyYE0pwYznBzIcHIgTT65dBygZjFCKoIwivzzQ4kXiCjqrx355wYC0wBbRXl/hgG2GUX9df7c6f3kpZBSzjtbPfbYxzEX1Nx+KJmqOEy24/4mO38vO8zU3GX3ateKZRoc7ksxOpjh+IEMxwYzjAxmODaU4ehAekv3nTUbo5dqCU1vs9Pr1q1C75RtkoV2vOudDun2m6pT1Lz00kvrOofVzuPFF1/kjTfe4OLFi7zxxhu88MILmznNNfHhhx+u6bh3332Xs2fPzv/bsqyeuJD3ynlo1ocSgrBSIpi+A4ZFrFDc0GJXKbXocUqEKCB7/pO03r1I/+f+Nulzn2DsRz8CFr9fxv7Za/c939g/e42hTzy9sW9qFyGVwheSphfitYtW2zJJLOc8tQ6UUsy1Au7UXGaa3rKL/lTM5ERfmhP9GXKJ3vuI3K/XlkBIGl5IqeVTcwLAIGab5FPxrjVN/FByebLGO7crvD/TWPY9krBNzh0tcGG0jyeGixRSvbv5sFXvFy8QVFs+EyWHSsvHANIJm6HC1jUapFJcuV3le5cn+dHVGRx/6Y2jVNzitx4/wheePcZTDwxuuaX2bsbcJ+KEXkbXEVuHriV2D0FplmDiI+xUGjMWX/HYRUMQpsnQH3+R/DOf2eIz7A0ih5eQuheiJBgITAUx297w9dwNBXeqLrdrLl649JhxIWlzejDLsWJqRwcQNN1hL19blFK0AkGlFTDX8gmlwjYNcsnu1QvdIJSS92eaXJ6o8u5kndYya1qApG1yfqTIsycGeGK4QHIbG0s79V5peSGzNZfxkoMbCOK2QTGT2PLIQICbMw2+c2mS7787RbW1tPg0Hbf4nXPDvPzccc6d7O/J5uh2oWuJnUfXElvDXqgjpJBIBNNNf/724/1pErHe2+fsRQIh8UKJEwiaXrT+DtqbhAaRUCZuW6QS278fOC+uqjjcrjjcrjqRuGqF9cRSZC3JUELx9MdGeGAox8mBTE/vb+40gZDzgqoglDi+oOWFOH6I40ciq2hJoEAZGIbCtiIhVTJhk0nFduWawfFDJuuCckty4807TFUjcdVE2aFU99isb1vcNjk6kObYUDYSVQ1lGR3MMDqU4Uh/Wtefu5he+UzU9CZ7tY7Qq6xNst4CZaHd78LCaLO88cYbjI2NbYm97kJb329/+9vb1ijRaLYT0Wzgj99EhgFWOoOxAZGPUpLWlV/SePvHDP2tf4gRjyNbTTAM4kdGST7wMIOf/weYiSRC3F8Izf3kTUo/e+u+20s/e4u5n7zJwLNPbuh762WUUgRS4QQCJxBIFeWOJyyzK4VI0w8Zr7mM1zy8ZfxqTQOOFlKc7E9zIJvoqQ35/YwfShpe1DCpewKUImFb5JLdK1KFVFybafDO7QrvTtYIxNKlomXAx4cLfPqBIZ4cLZKw91/BEAhJrRUwXmpRangYGKQSJgO5xJa+7kS5xfcuT/H9d6eYqrpLHmMY8PTpQb7wzDF+8/EjpHtQLKnRLIWuIzT7GSUlwcwkwewkVja3prV3/tnPEMxM4k/e4vA/+qekTu+Mi8B2EkpFyw9p+CEG0RpZmWrDIRJKKUpOwO2qw3TDX3KT3DRgpJDi9GCGgXR8VzYHNPsHJxBUnYCZhkcgJZZhkopZ2yLQWStSKcbmmrz9UVRzuMsIHAEycYsnR/t49ng/jx0pELf35mb0QvxQUm54jJdaVFsBlmmQTdpkU1u/pm+4AT/49TTfuTTJB5P1ZY978oEBXn7uOL+law1ND6FrCc1SSKloBiGlVrBokPD0QGbnTqqHUe3hXzeQ1NyAihMQSgXtYWrbMknYFukdWFd0xFW3K04ksKo63KlswLkqE+fkQJoHBrOcGsxwvC/JL9/8KQCfeuzIvhdFKKUIhMIPRVtUFdVfjhfS9ASuHyLvmco1O45mlkk6YZLbhjXLVqCUotoK7jpUle+6VE1WnHsE6Dc29BrphMXIQIZjB7IcH2qLqoYip6qDhZQelNVoNHuG3flJ0EOcOnVq/u/rzU7v5jTJq6++CrDu4ubixYurZqsvLMIuXry4/pPTaHoYJSXB7CTBzCRmKo29TIzJanh3blL57r8nmLwNQPVHb5B78lPEBg4SGziAsYaojw/+5M9WvG8vCa6UUnhCUndDAikxiQqVbkUGTjU87tRcqm647HH9qRgnBzKMFlPE9cRET+CFgroXMtfwafoCw1DEbYt8wu5as08pxUdlh3duV/jleJXmClNgDw5l+fQDgzx3on9f2miHQlJtBUxVHGbrLkpFDlL92a1tvjbdgB9dneG7lye5eqe27HHHD2T5wjOj/MFTIxzuS2/Z+Wg0W4WuIzT7FSUl/sQtwmoJK1dY02eK9FxUGHLwf/Xfonyf+IHD23CmO0coo7iSlh8CxqJhBKXWP0scCMl43eV2xaW5TJMmHbN4YCDNyYEMyX0oLtfsHrxQUHVCZhsuTqiwjGiNmjZ7a4uz3PJ561aFt2+VKS3jmASQT9o8NdrHcycGOHM4h71HJ34XIqSi2vKZLDvM1lzAIJ2wGMxv7TBH57V/9WGZ71ya4Gfvzy47dHO4L8Xnnxnl888cY3RQCxU0vYeuJTRLIVQkzJ9sePO39adi+3JPaymUUrihxPFDal5IzQ2j6FgVORLFbZP0DnwOK6Uot4K2qGpz4qpTAxkeGOrEAt7vXLXUEPhe5t64Pz/suFNFkX9uIFEoUEQTnQosC2Jth6p8Orarh7OFVMzVvXkRVUdYNdGOAHTX6Y62FIV0jJHBDMeHshxb4FR1bCjDQC6hB3g0Gs2+oLd2I3YhFy5cmP/7Wux7x8bGgPUXIavx+uuvA6xaqCykr6+PSqXCCy+8wLe//e1lj1tYtK23gNNoehnpuXh3biI9ByuXxzDWX1CFlRLVv/5LnGuXF93e/OXP6P/tLxA/eGRNz7Ocu1WHveJyJZXCDQQNP2zHPJhdaegopSi1Au7UXaYby0cGJu1OZKDOoe8V3KAtsmr6UVPRMEjZVtetrKfqLu/crvKLO5UVGx7DhSTPnxrk+VODHNhi96ZeREhFreUzU3WZqrpIBcmYQTGztXEsQkp+caPMdy9P8vMVGh+FdIzfu3CUzz9zjEdHNxb7qtH0CrqO0OxHlAjx7txENOpY2fyy13Hn/V8TO3gEO19ENJsYtkXyxIOLYwV7hLmfvAnQlXV6KCVNT9AMQgwM4pt0fXUCwVipxUTdXXZ9fDiX4PRglsN6M1zTw/ihpOYFzDaimsEwDFIxi2Kqt8RJgZBcmqjx1kdlrs82l41a6UvFeOZ4P88e7+ehg7mecuTaKpRSNNyQqXaDT0hJMmbSt8XDHB3GSy2+c3mS712eYq7uLXlM3Db5zceP8PKzx3jmwSHtuqDpaXQtoVkKqcANI2ekDsf28YCakAovjFIVKk5IwwsQMtLVxKxoT3onPoO9UPBR2eHDUoubpRa3K866xVWDmTgnBzM8MLi8uGo/cG/c37yY6t64PwUY0crMtkxipoFtmxTj1q6vgfxQMFVx24Iql6l27N9kxWG64kaubZtkIJdgdDAzH/3XcaoaHcxQzMS78F1oNBrN7kYLrjbJwmLi+vXrqx7fKYC6aYG7cMJjYGBgzY/pnMsbb7yx4rELi7atsAfWaLYbpRRheQ5/6jZmLI6dza/7OaTnUv/Z96i//WMQi12UrOIAg3/wd4kdWJvYClZ2t1p4zG4VXHWEVnUvRCiImQbJLsQjtHzBnZrLRN1dNprBNGA4n+Rkf4aDOR0ZuNN0pslqbsBcw8MNFYYBSduikOpugVZ1A35xu8o7dyqMLxNHB1HD41OnBnj+1CDH+9O7vtBeL1Iq6m7AdNVlquIipSRmmxQyWz/FdWO6wXcvTfKDK1NUmksL4WzL4PlHDvLSs8f49JlD+yJaRbM/0HWEZr8hAx/v1g2U72Hnll5/K6Wo//yvqf3gP2EPHqT/c3+HWHGAxNFjGHZvNhA66/jNrNNDETlaNdtCks3Ga/uhZKzc4lbVYSlDrLhlcqo/zanBDNm43hbS9CaBkNTdgLlmFDG+VTXDZlFKcavi8OZHZX55p7psXZpL2Dx/aoBPnhzggaHsvqlLHT9kru5xe66FGwhilkEuZWOZW39Nd7yQH703w3cuTXLldnXZ4x471sfLzx3jd88fJbcPm9Wa3YmuJTRLIZVitrlYVDpS3Fiaw25EyGj/uRUIKo5PwxMo1PwgQzpu78jnb9UJ+LDUan81magtPwyxFPeKq04NZvbFILGUat6ZKhASPxA4fuRQ1mo7VCkFtAVVCoW1R+L+7qXphXddqubdqqL4v7m6t6zIf62YBhzqS7WdqSIx1chAitmP3mMoa/HiZ57f9/GTGo1GsxJ749Nmh3nppZd4/fXXeeut5d1pYHER8uUvf7lrr79acbIUnaKsWCzyta99bcVjOxMwAH/8x3+87tfSaHoJFQZ4E7cIqxXsbA5jnQtFJSXNS29R+9G3ka3movuMeILii39I8TO/ixlb+yZw6advrehuNX/cLnS5ElLhtB2tpIJ4u+jZDKGUTNWjyMDKCpGBfakYJ/vTHCumtUBjh1Eqeh/U3JDZpocfKkwDkjGLQpen0p1AcHmixju3KytOladiFs8e7+fTDwzyyKHcvml4dOhMmM/UouI8ELLd/Iht+XRfueHx17+e5nvvTvLhdHPZ486MFPnCM6P87oWj9Gf3n9uYZn+g6wjNfkF6Lt6tMZSQWJnskscoISi/8b/QuhT9PoSzU9S+/x84/L/9P657zb5dLHSp3cg6PRCSRnsC2zQMEvbmhFahlNwsO3xYdhBLKK0G0jEeHMxytJDaF446mt2HkIqGFzLb9Km5AUopEjGLfLJ7EePdou4GXLxd5a1bZaaWcUwyDXjiaJHPPjjEuaNFYvskyt4PJZWmx3jJodryMQ2DbNImm9z6bWilFO/eqvKdSxP85L0Z3GBpAdxALsEfPT3KF54d5YFD6x/C02h6AV1LaO5FSLXI1X0wHScd6811dDcIhMRtuxpV3YCWHyIxMIlcC7OJ7RdYSaWYqnuRuGquyYflFuUVnPbvZaG46lRbYLVXxVWhuBv1F4QSNxA4XhSr7ngCLxQYMC+oMjCwbCNyp7K2Z1B0u1BKUWn6UdRfOXKrmloQ/1d3lu+BrJW4bXJ0IN12qIrcqTouVcMDmfv6J0IIflBbXdCr0Wg0Gi246gpf+9rXeP3117l48SJjY2PLTlx885vfBKLCYjmb3Uqlwle/+lWKxSJf//rX1/T6C6dY1pPB/sILL/Dyyy/zpS99acXjOuf9wgsv8NJLL635+TWaXiOsV/HHPwIFsUJx3Y93b35A9bv/gWB28p57DHJPf5r+3/vb2Pn1P+/1f/bqmo/dLS5XQipaQUjDF6AUMcvcVAGkVGSHfafmMrVCZGDCNjleTEU2ynu0GN0tSKVwfEG1PZUeCIllmCRjJqkui6xCIbk63eCd2xWuTNWXtUq2TYNzI0X+xqlBzo3sn4ZHB6UUTS9ktu4xWXLwQoFtGWQSNra1tb8vfij4+ftzfO/yJO/cKC37O3ygkOQPnxrhD58e5fRh3fjQ7H10HaHZD0i3hXvzOoZlYaWXjjSRrsPcv/3/4n10t7GGaZF79rOYPSq2gsUutetZp0dCqxAnkJgGmxZaSam4VXUYK7eWjOU93pfioaEcRe3coulBhFS0/JC5lk+l5SOVQdw2ySV6T2QlpOLKVJ03Pyrz3nR92TXtcCHJb5we4tMPDNKX7i1Hrq2iE00+WXGYbTt3pBMWA9sU0z5Tc/nupUm+c3mSqcrS7sa2ZfCZs4d4+bnjfOrhA9j7rB7U7D10LaG5Fy8UVNy74p7hQu/FcW+GQMj5BIWqG+IGAqUUpmGSsE2yidi2rx38UHKr4nCz1ORGqcVH5RbOMmLfe8nELR46kONjB7N7TlyllJp3pooi/6L1Xsu9G/knFyykFGCZBrZlYJsmybhJdo+4U3UQUjJT8+5zqOqIq5YTia+HXMpmZCBzV1Q1lGF0KMvoYIaDhaSOS9ZoNJotYm99Yu0Q586d4ytf+QqvvPIKX/7yl5fMHh8bG+OVV16hWCzyV3/1V8s+12c/+9lFUydrKXA2mmH+6quv8uKLL3Ly5Mll7YRfeeUVLl68SLFY5Fvf+taGXkej2WmUEAQzkwSzU1jpDEZs/YWLUpLq9//yPrFV8oFHGPz8f0li+NiGz+/J//Ebe8aSNZR341BMaAutNr6J2QoE4zWX8drykYEGcCSf5ORAmsO55J6ZbNmNSKVo+ZFtd6kVEIrIyjkZi2y7u/1aY3NNfnG7yqWJ6rKbGQbwyKEcn35gkGeO9ZNJ7L+lT9MNKTc97pRauL7ANk3SSWvLNy6UUly9U+O7lyf50dVpWp5Y8rhkzOI3Hz/M5585xjMPDmnHDc2+QtcRmr2OaNZxb17HTCQw40s33cNKidl//S8JSzPzt5mpDIf+0T8l9cDD23Wq62ahuxWszY02EJK6F+KGbaGVZWyqKaSUYrzm8sFcc8m18nA+yWOH83oQQdNzdOqGcsun1PIRUhGzLDKJ3nQqmKi5vPVRmYu3KzT9pde0qZjJJ04M8NkHD3B6KNNzYrGtYKFr7kTJIRSSRNykkIlvy8/RCwQ/e3+W7/xqkl/dLC/rbvzQcJ6XnzvO5y4cpU8752r2ELqW0NzLjBMsuhYezu1uwdVCgVXFCXHD6DPYajvD7oQ4qeGFfFhqcWOuyc1yi9sVZ83xgAdzCR46kOORQzkeOpjjSGH37mMLqfBDMS+m8vzImarVFlN5vph3poK2O5UFtmViWwb5dG+u+TaLFwimKpFD1cQCMdVkxWW66iLWkyW5DIP5BKODGY63o/86LlXHhrIUM/tD6K/RaDS9xv7rOm4RnSLklVde4cUXX+TVV1+dnyp5/fXX+eIXv8jJkyf51re+teLEx8Js8oW2uSux8DH9/f1rPufO+bz88su88MILfPnLX+bkyZMUi0UuXrzIf//f//e8/vrrvPTSS3zjG99Y16SKRtMrSLeFd/smMvCx8oUNb3oahknhU7/J7F/8CwDsgYMM/tHfJ332/L7YSF2NQERCq1YQYmCQsDY+pR9KxVTDY7zmUnaWt1wuJm1O9mc41pciYe8NwdpupDORXnGCqFmiwDYMkjELK97d3w2lFBM1l4u3q/zyToXqCpGSx/rT/I1Tg3zi5AAD+7DY9ELFRKnFZNWj6YaYpkEmaZHZhgnzqYrD9y5P8b13J5lcZroc4KkHBvnCs6P85uNHyOpGsGYfo+sIzV4lqJXxb32IlU5j2Etf573xj5j7N/9vpHM3YtYeOMjhf/zfET9weLtOdUMsdLdaeNtSgitfSOpugCcUVjeEVkBgp/jp7eqS4o+hTJzHDxf25RpI07sopWgFgooTOeCGUmEbBun49sf9rIWWL/jFnQpv3apwu+Ise9yZQ3le+NgQTx/r2zd1qesL5uoed0pNHF9gmQa5lI1lbv2aXinFtfEa37k8yQ+vLD/UUUjH+IOnRnjp2eM8fLSw5eel0ewUupbQLKTU9Of/nrRNCtsQ5dpNVhNYbfcQgVKKmYbPh6VmFBFYajG74P/xSlgGHB/I8MjBSFz10MHd5TbbcahyA4EfyPmYv5Yf4vhhJBxStEVVBoah5sVUcdskHbf2bM+k4QaRmKrcdqmqtB2ryg6lxtreHythmQZH+lKMDLVFVYOZtltVlpHBNKkuDzVrNBqNZvPoK3MX+frXv84f//Efz09pdKY8Tp48yde+9jW+8pWvrPocr7766nyW+lrte7/61a/y1ltvrTgVshznzp3j+vXrvPLKK/PPU6lUKBaLvPDCC3z7299e93NqNL2AkpKwPIc/dQczHsfO5tb+WCGQThMrm28/l0A0m8QOHSVz7jmSo6cofOq3MGx9CfWFpOmFOPNT+hsTWimlqLgBd2oeU3UPoZae9ohbJsf7Upzoz9C3i4rUvYaQiqYfUmr5VJwAKaOiequaJaWWzy9uV3jnTpWpurfscUPZOM+fGuT5U4McLaa6fh69jhcI5moO12cDWoEiMdkgl44xkN96kVXLC/nx1Rm+e3mSX9+uLnvcsaEMn396lD94apThgaVjpTSa/YiuIzR7jaA0gz9xCyuTxbCWXjO3rv6K0n98HcRdAXXyxMc49MV/ipVZ+9p9J7jX3arDvS5XfiipewGukJEg3d58fFXFCailDxDaSbhHbFVI2jx+uMChXGLPNjg0uw8nENScgOmGF8WMmyapmNWTrqZSKT6YafDmrQrvTtSWjSofzMT57IND/I3TQxzYJ45JgZBUGh7jJYdKy8cwDLLJ7YsMLDU8vv/uFN+5NMntudaSx1imwScfPsDLzx3jM2cPE+/CNVej2Q3oWkIDkfaltkCEejDb++vBjsCq5oVUe0BgFQrJ7arLh3NNPixHAqvWMs6W95KKWTx4IDsvsDo9lNk1Qmw/lHiBwPUFNcen7gQ03AWRf4aBZbbdqUyDbDLWk+u4biGVotzw50VUd/+MxFWNFQaA10oiZkbRfwciUdXI4F2nqiP9aWI69lij0Wh2FVot0GXOnTvHq6++uuHHv/DCC4vyz9f6mHK5vOHXBPjKV76ypuJLo9kNSN/Hn7yFaNSiJo+5tuJGKYU7dpXq9/4jZibL4N/6RyinBUoSP3AYu2+Q9INnt/jsex+lFIFQi5pHG53SdzqRgXV3xUi4w/kkJ/ujyMC9XND1MqGUND1BqeVTdXwkBjHTJLNFIqumF/Kr8Srv3KnyYWnpDXWAXMLmuRP9fPqBQR4cyvb8ZlK38UNJpekx0W58KCmRQD5p0peNY21hgS6k4pcflvje5Sl+9v4s/jKxn/lUjN89f5TPPzPKx4/37bufkUazVnQdodkLKKUIZiYIZiaxsrll1+H1n/811b/+y0W3Zc9/kgN/58vLumH1Eku5W3V4/0/+Odknz9PwArxQYpkGqS40W+peyPtzzWiq3l4cD5OJWzx2KM9oMaU/ZzU9gRcKam7ITMPHDQUmUSOy2zHj3WK24fHWrQpv3yov66IbtwyePt7PZx8c4syhfE+6cnUbKRU1J2Cy0mK66gGKVGz7RFaBkLz1wRx/dWmCd8ZKy0Y2nTiQ5eXnjvEHT41yoLC747M0mo2iawmNwqCxQBw02GNOp0opfKFwA0HDC6m6AW4oMQBzhwRWTiDazlVNbsxF8YDLia3vZTAT56GDOR5uC6xGiqme37MOhaTphtRciRsoLn9UoeVLgvZ+nkIRs80ornGPRv51CIVkpuYyWXaZWCisqjhMVdxl9zjXQyEdm4/6Gx1qu1QNRk5VQ/neF0RqNBqNZu305k6HRqPRbJCwVsEf/whMEzu3dtt4f2aC6nf/A95H7c2F8iytX71F9sIniA0exIz1VpG6E0SFsaTmhdFksrHx5lHFCbhRbjGzgg1zPmFzaiDDsWKKZGx3TATtNQIhaXiRk1XNCVD8/9n78+A4zzzB7/y+75snMhP3QYIkwEM8dJQkHhIpVUmqEknV0V3VJYlS9c6O7XFPS5qeWdthe13VNXbE7oY3ZkqKiY1xxM5sq2rbHo/XdqvEnrHH7R73SCVRUlWpWKJIFS9JFC+QuIG8j/d+n/0jAfDKJAEKifP3iWAQmXgTeAAkkM/veX7P71ctC52INibodryAM6MFTgzlOTderLugHjF0Hulv5aktXTy0rpmQvrpO/bh+QKHiMpypkC3ZKKApYtCejBAEiojR2IB9YKLEu6fHeP/sGNk6pbINXePJ+3p4bl8f33hgDVH5HRZCiBVPBQHu2BBuZgIj1Yym1X59ti5/cUuyVdu3D9H2zeeXxaJzvepW07JHP2bgyK9p3btnXuawFdfnQrrMSI0qn1FD54E1KTa3J5b8Bo9Y+aYruk2WHMqOh6ZpxMPGgm+ezpbt+ZwaLvDR1SyX6lRMAtjalWD/tm4e39ROYokmjM0npRQlyyNbNhlKV/CCgEhIpzWxcBuvl8aK/OLUKO+fHaNo1k6AS8ZC/N7u9Rx6rF8OdQghBOCGoly/jNbetLhr2YFS2F6A6XgUbI+i5eH6CjQI6RoRY+ETrHKmw6V0tXLVpXSZsaLNbNKrNA3625q4dybBKklHYulWuAyCamKb7fqYtk/erK7r2q5PEARcyXroOvQ5Hk3REEZ8Zc5vyrbHaNZkLFetTjV2XULVRMGqu+48Fz0tMTZ0JtjYnbwuoar6f/Mi/w4KIYRYOCvzlVQIseoo38MZG8bLTmI0JWfd7k8FPoVfvU3xt+/DTW3snOHLRNf+YSOGu6woVQ3Sio6HFyhCuk7sLhKtlFJkTJeLmQpZ0615TdjQ6G9tYnN7E23xsCyaLgLHCyjZLumKQ9H2QSmiIYNUrDE/Dz9QnJ8scWIwx+mRIo5f+wSRrsGDvS08dU8nj/S1EV9lCTyeH1RPl2dNJos2SiliEYO2ZOSmn8s8rBbUkCs7fPDpOEdOj3JxrFT3uvvWt/Dcvn5+f8/6BTv5LoQQYvEp38ceGqhWmE213HbOEO3fQmLX45SP/xoMg+6/9Sek9nxtAUf75dyuutW08df/nM7HH/1Sn8f2Ai5lKlzNm7e8umsqIGFlOLj7PqLhpZnMIlaH6QMak2WbouWjaYpYKERLfGluMCmluJypcOxqlt8NFerGHi2xEF+/p4unt3WtmlblluOTrvikSwFcTBMOGSRjIULGwvyNKVQc3j87zjunRrk0Xjve0DTYu7WTFx7fyDMP9RKLrK6YUAghbsc1rlX40zVoXeBkJn8qyafs+BQsl5Iz1ZJOqyZXRUMGTZGFW+cNlGKsaHM5XeZSpppklauzHn2zaEhna9e19oDbupNLch1SKYXjVdsymrZH0fTImy4V2wOlUFSfC5GwMXWANoTvB6Ri1YMx8UgIYxkfYvUDRaZkTyVVWVPJVCajeYvR7Py0/gsZGuvam+jrurH1X39Xgg0dCTlgKoQQApCEKyHECuBXyjjDAyjPveMGz/W8Yp7MX/0FztDADffriRTt33mR5seebsRwl43gukQr368mQ8VCcw/ClFJMlB0uZioU7FsDHQ1Yk4qyuT1Bb7O0DFwMtudTtD3SJYeyU90oiYQMmqOhhiRZKaW4mjM5MZjjd0P5G0qe32xrV4Int3Ty1U0dtMRX14aiHyiKpst4zmQsXz15FQtrC3a63PF8jp1P8+7pMY5fTNc9+dWZivIHj27guX39bOttbvi4hBBCLC3Kc7EHLxFYJqHUbF4HNJK7v0aotZ34lntJPLC74WOcL3eqbjWt+NFxCr/9mOZH5/61eX7A5ZzJQLaCf9Nrr67BPe1N5C6cQlfBqqvyKZYGP1BTSVYOBctFKUU0bNAca0zsMB/ypsvHgzmOXclW23LWYOgau9e3cmB7Fw+va10Vcel05dzBdJlM0WIk7xMLa7Qnow1tTT7NDwJOXMryzskRPjqfrtvGaV17E4ce6+O5ff30tjc1fFxCCLEcude1nG6JhRfkdcwLAsq2T9Z0yJkugQIdqsk9kdCCtqTz/ICrObNavSpTYSBTxnRn1xauJRbi3jXN3NuT4r6eFP3tTUtuHuB4AbbrYzk+BdOhaLqULA8/UGiAhkY4XO1M0JZYOYeYLcdnLF+tUDWaNatvZy3G8ibjeQvv5oDpLjRFjJlEqo3dyWqVqqnba9uW3nNBCCHE0iMJV0KIZUsFAW56HHdiBD0aw0ikZv1Y8+LnZP/tmwTmda0DDIOWp75D2zPPYsRX7yJeoBQVx6fkeChVLfMcDs89sJg+SXQpW6mZUBPSNbZ2JtjWuTRPCa10ljuVZFV2qDgeTLWIbGRS00TJ5sRgjhODedKV+u0ke5tjPLmlgye2dLKmOVb3upUoCBRFy2UibzGas/CnWng0Ny3MYplSis+HCxw5PcYvPx2nXCNJEiAa1jnwYC/P7+vj8R3dsvgghBCrVODY2Fcuonxv1nNxv1Qk3N5B4v7/87LbCJhNdatpg//sp9z36Ouzvj4IFFfzJhezlWq7lZtsamvigTUpYobGB+dnt3kkxHzxA0XF8UhXXHIVh0BVN1NTDTqgMR88P+DsWJGPrmQ5N16qWwe2ry3O/m3dPLmlg+Yl2v5wPk3HG2PZagUIpRTxiEFbIjJT8aLRBtNl3jk1ypEz9VuUxyMG39rZy6HHNrJnSwe6xBtCCHFbnnGtynhHA1uZuX5A0XLJmi4F00WhETYWPsGq4vgMZCszFawGc2bdxN2b9TbHuHdNaqZFYE8qumTmM54fTLUDrH6fCxWXkunOJBYpFOGQTnRqrXAhv+eNoJQiW3ZmWv1Vk6quJVflyrOrSnYnnc1RNnQkZtr+beicersrSfst3QOEEEKIuZGEKyHEshQ4Ns7wFfxKCSORQpvl6W7l+xR+9Va1heCUymQZo6WdLT/6r4n29jVqyEueHygqrkfZ8QmUImLodxW0BYFiuGhxKVupeZIoYmhs70qytTNJZAFOzYoqpRSWF1CwXNIlG8tTaBrEQkZDW34ULJffDeU5MZRnMGfWva41HuZrmzt4aksnmzqaVlWgq5SiZHlMFmxGshUcPyBiaKTiIQx9YTZ9xvMW750Z5d3TY4xk6/+c9mzp4Ll9fXxr5zpSq6zimBBCiBsFjo11+Qs0TcNoStS9zivk0KMx9GgMv1zESDYTWbNhWb7W733jz/H8gLzlYnkB0dDdzZevp5RiuGhzIV3G8m6dO69rjvHg2mZaphJBfL9+ZVAh5tP0QZyc6ZAuO3hBNUZMRBd2Q3WuhvImH13J8slgnopb+/clETF4YnMH+7d1r5rYo2S5TBZthtMVXL96qOP6yrl+nfaK86Vse/zq03F+cWqUc8OFutft2tzOC49v5Fs7e0muggQ4IYSYDwrwr1s/ap/H9Zrp9cSKUz20WXJ8NFWtjJ+KLVwlJdP1uZQuc36yzIXJMqMFq24y9fUMDTZ1JKrJVWtS7OhOLVoF/SBQeIHCDwI8X+H5wUwrxkLFpWi52K5f/YGiYRhT1cJixrJu/ed4PuNTB0vHcua1pKqpJCunRgw0V2FDZ117nL6uZLXd33SVqs4E6zubiEdkK1wIIUTjyKuMEGJZUUrh5bO4I1fQQmFCqZbZPzYImHjzv8UZvHTD/bkhh7Crr9pkK2/qtHLJ8dCoBii6NvcgzgsUQ3mTyzkTu0agFAvp3NudZEt7gpAkWi0IpRSm61OwPCbLNo6n0DWIhQ1a4o37GViuz+mRAicGc5yfLNddAImHdfb2t/PUPZ3cv6Z5VVVJUkpRtj3SRZuRjInl+YR0jWQsRLOxMAs/pu3x4bkJ3j09xukrubrXbehs4rm9ffzBo31s6Ky/oS6EEGL1UEGAPTSABuixeN3r/HKJyZ//OVo4Qtt3XiDU1kl0Xd+sD0ssJYFSlB2Pou1haNqXrtA63Xb7i3SZco1qsF2JCA/3tjS0QoEQN5uOH3Kmy2TZwfMVIV0jHg4t6bl62fY4MZTjoys5RgpWzWs04MHeFvZv6+KRvjYioeX3d2iubNcnU7IZSlco2x4hvbppG1qgeCNQilMDWd45Ncpvzk3W3VDtaYnx7L4+Du3rp787uSBjE0KIlcTXw3Bd4lNz7Mtt+7l+QMXxyVsuuanqSpoG0ZA+cwig0Rwv4HKmwoXJEucnywzmzFklWMVCOtu6k9y3ppkdPUm2dSWJhhrTWUGpagKV51cTqPypt10/wPYCbMfH9gIcL8BxfVw/QEOjmlGlgVb9GIauEQ7p1eSq6PLbslVKUTBdxqYTqnJTLQCnEqoyRXtWP7s7aU1E2NDRRH9Xkg1dCTZ0NNHXlaSvs4melrhUwxRCCLFolt+rtxBi1VKehz06hJ/PYCQSaMbc/oRpuk5s49aZhCstFCZ03xOU/td/BpdHSX/4ER2PPdKIoS9Jnh9QdnzKroeGRtTQ7+pUkusHXM2bDOTMmu1PEhGD+7pTbJSe5wsiUApzalEkXXZw/QBd04mHdeINTLLygoDPx0qcGMpxdrRYt4x3SNfYub6Fp+7pYvf61lWx0XG9iu2RKdkMZyqYjo+haSTiIZLxhZmS+UF10+PImTF+c24Cu0YVOoBkLMTv7V7Pc/v62LmpfVWc+hdCCDF77uQogVkhlGque03g2Ez+q/8eL5cGIP2v/yXr/uP/+5zn8EuB4wfVzaZA3fWc+XpZ0+GLyTI569bWva2xEA/1trAmuXRam4iVz3R9CqbLRNnG8QIMXSceNjAiS/c56AeKcxMljl3Jcna0iK9qxx89qShPb+3iG1s76UhEa16zknh+QL7iMpypkCnZaEAiFqIjtXBf+2jOrLYMPD3KRMGueU0kpHPgwbUceryfx7dLi3IhhPgyPOPGBP1U9O6SooqWy1DeojJ1GCBsaMRCCzMf8IKAq1mT85Nlzk+WuJIx6762X68lFuLeNc3cN1XBqv9Lrj+7foDvK7zgWhKV6/lYXoDrTrX78wJcL8D1fJhKoNI0DUU1+UjTquufhq6h69pUwvPSTl6/E88PmCjYNyRUTb89lrMwaxwgmStD11jbFp+pTNV3XaWqDZ0JqbQvhBBiyVp+K51CiFXJL5ewBy+DCjBSzXe9+dD0wG6sgfMoy6Tnj/5TPvlP/x8z7zv/T/9sVSRcuX5AyfEwHR9du/tEK9sLuJKrcDVv1UyuaY6GuL8nxYbW+JJuObESXN/uI1Opnjwz9GrlhaYGlkwOlOJypsKJwRwnhwuYdVp2aMC9PSmeuqeTfRvbSS7D01pfhuX4ZEs2Q5nqyXIdjUTcWNBNj6uTZd49Pcr7Z8ZIl5ya1xi6xld3dHPosX6e/soaol+ycocQQoiVySvmccdHMZrrJ1sp3yP9v/6PuGNDM/eFe9YRautYiCHOm0ApipZH2fUJ6dUT819G0fb4YrLMZOXW1+JExOChNc1saI1LopVYELZXrYQ7UXKwPB8diIcN4gt0EOBujZdsjl3J8vHVHEX71qRFqFbgeGxjOwe2dbOjJ7nif6emK0uM5y1GsyaBUsQjBu3JyIJ97Zbj8+vPJ3jn1AhnrubrXnf/hlZeeLyf39+znhap4CeEEPPC16+9dod0jegc56yuHzCct5gs28TDxoK03AuUYihnzrQIvJQp1zzIe7P2pjBfWdvCA73VJKue1N0dUnD9AMvxsV2foulRtFyKpoM/PQYNUNUqVFBdZw1NJVAZukZTVMdY4nOmuarY3lQSVbU61Wj2WkLVRMGiztnaOUnEQjOVqfq7EmzouJZY1dsWl64YQgghlqWVNSMQQqw4KghwJ0dxJ0bR403o4dkvyCnfRzOMqY/j45dKGC2trH3pv0CPxckeP0Xm6LGZ6zNHj63oKleOF1C0XWxfYUyVgb6bgNRyfS7nTAbzZs1Aqy0e5oGeFL3NsRW/sL2Y/KlWkDnTJVNx8ANFSNeJLcBJ9JG8xYmhHJ8M5cmZbt3r+triPHVPJ1/b1EFncuWfJr+e7frkyg7DmQoF00XTNBLRhU2yKlRcfn1ugiOnxzg/Wqx73fbeZp5/rJ/v7llPZ3NswcYnhBBi+QkcG2dooFpttk4LaqUCMv/2MPbA+Zn7Ius2svaP/69ooeVxKlkphTWVjBKoL1/VquL6XEiXGSneWuklGtJ5oCfFlo6EHFIQDed4ASXbZaLkUHI89Kn2mAvVHuhuWa7P74bzHLuSZSBr1r1uR3eS/du7eWxj+5du+7kclC2PdNFieKpFeSSk09wUXrAKGkopPh3K887JUX71+QRWneoW7ckI33+0j+cf62dbb/1kXSGEEHfH16+9jicis3/9U0qRM12uZCugNFpi4Yau5abLDucmSpwbL3JhsoxVp9Xs9VLREA+sbebB3ha+0tvMmjkmWHl+gD1VmapsuRRMl6LpVatTaaBUtZJXJGSQjC3ca+hiUEqRLdlcyXlkK4rPSpcZL1hTiVUWhdus8c6WplVbBW/oTNDflaCvM1mtUjXVArA1sXDJ4EIIIcRCkYQrIcSSFdgW9tAAgW1OVbWa/QkH88Jn5N7+N3Qe+jsYqVYCyySydj2hts6ZSf35f/pntzxupVW5Ukrh+AFF25tqDaHd9cn8suNxOWsyXLBq9l3vSkS4vydFj7Q/aRg/UJTdaoJVznQJAkXI0GmKhBq+QZetOHwylOfEYI7RGpuF0zoTEZ7Y0sGTWzrpa2tq6JiWGscLyJdthrMmuXK1ckUiurDtO1w/4LNxl5MjHhfe+Q1+neNnHakof/DIBp7d18eOdS0LNj4hhBDLlwoCnOEBNF2vmzillCL/7l9jfnZy5r5QRzdr/+TH6LH4Qg31S/ECRcFyMT2fiK4T/hKnrG0v4GKmzGD+1vlzSNe4tzvJ9s6knOQWDeUrsHw4P1Gm7AVoKGKhEK3xpV1dKFCKi+kyx67kODWSr1v1oi0e5htbu3h6WxdrV8HhAdudqp6bNSlVXAx9YVuUA0wWLI6cGeOdU6OM1EmAM3SNp+7v4cXHN/Lk/T1f6m+pEEKI27u+wlVilpXuy47HSN4iZ7qkoqGGzEdtz+fCZJlz4yU+nyiRLteuuH69eNjg/jWpmQSr2VZ/DQKF5fpYro9p+xRMh4LpYrs+SlUTgXStWv1rJVanmlZt/Wcxmp2qUjX9L2sxljex3euT3K7c1eeIhQ3WdzTR15mgv/vGKlXr2pukYr4QQohVZ2XOKoQQy5pSCi+XwRm9ih6OEErO/gSk8n3yv3yL0kfvA5D+X/9HOg/9EbFN2zCaEjPXpT/86IbqVtNWSpWr6VP5RdvHCwJCukbsLoOdou1xKVNhtFQ7yWZtKsr9Pc10Jpb2gv1y5QeKig9lT+PUSAFN1wnrOokFSLKqOB4nhwucGMxxKVOpe10yavD4xmqS1fae5KqqzuD6AYWKy2jWJF2sbqY2LXD7DqUUX4wUOXJmlA/OjlOyardWiYR09j+4luf39fHVHd2yuSuEEGJO3MlR/EqFUKr+3Lz42/cpHf/1zG0j2UzvP/ivCKWWfnKvUoqK65O33GrVn9DdbxR4fsDlnMlAtsLNOSK6Bls7E9zX3Tzndi9CzJYfKMqOx3jB5KqpoylFn+/THG1s5Yr5kK04HLua4+OrWTKV2pUWQrrGI31t7N/WxYO9LSu6GgVU/6bkp2KOyaIFQDIWoqN54Q52OJ7Pb79I886pEX53OVu3rdDWtSleeHwj33tkw4IePBFCiNUsuL7C1R3Wf8uOx0jBIm+6REMGbfPY3jVQipGCVU2wGi8xkKngq9v3oYsYOvf2JHmwt9omcFN74rav664f4LgBrh9gOh5F06VgelRsr1quimpyVSRsEAnpJKIrbwvUdLxq27/stYSqsakEq/lq/deRirKhcyqpqitJX2c1oaqvM0Fnsxy2FkIIIa638mYbQohlTfke9sgQfj6DkUjOtAScDa+QI/NXf4EzfO10hpeZwMtlbki2gtrVra5/33JNuApU9TRPyfHw/Gr1o9hdbhblTJdL2QoTdU4fbWiJc39Pitb40m4/sVyZrk+6bDNetBi3dUKaIhk1CBmNfel2vICzY0U+Gcrx+Vip7sJIxNDY09fGU1s6eWhdy6o6sewHinzFYSxnMlGwUUoRixi0LWCSFVRPlr93Zox3T48xdJuEuJ2b2jn0WD/f2tlL8zwupAkhhFg9vGIed3wUo7l+slX51McUPvibmdtaNMbaP/mHhDu6F2KIX4rrB+QsF9dTREL6XSeP+4FiMG9yMVPBrbHTsam9ia/0NNM0h1YvQsxWoBQVxydbcUhXHAIFIU0R1xWaVq1GsFQ3x1w/4PRIgWNXspyfLNesqAzV36H927r42pZOUitwA/V6SimKpstY3mIsa+IriEX0BT/YcWG0yC9OjfLLT+sf7GiOh/n9Pet54fF+7t/QumSfZ0IIsVL5+rW5Zb15ZtnxGC1Y5EyPiKHPW6XLku1xbrzEuYki58ZLlOq0l52mabClI8Gu9a082NvCPV2JG9YUXT/AsnxcP8D1AsqOR8XyqDg+lu1Vk4k0YKpqVSSkEQ7ptCWWflL5bCmlyJWdalJVzmQsZ96QYJWvk5A+F7oGa9ua6O+aav03lVRVbf2XIBFb2fMsIYQQYj7Jq6YQYsmothC8jHJsQs1zOwVvXviM7L99k8C6Vs5eC4XpOPQf0rzvGzdcW6+61bTlWOUqUArT9SnZHr6CsK4RC889AUYpRcZ0uZSpkKnRt10DNrY1cV9PasUvcC8GLwgomC7jJYey4xPSNRIRgyajuuXQqMpRfqC4MFnixGCeUyMFHD+oeZ2mwVfWNvPUPZ3s7W8nvopKRCulKFne1KkxE18pomGd1kR4QSt6mY7Hb85NcuT0KKcGcnU3ozqaNP7wya08u6+f/q7kgo1PCCHEyhM4Ns7QAEYiUbfFt3nhU7L/7l9fu8MwWPvSf0F0/caFGeRdClS1ClDR8qqtt+9i/gzVecJw0eZCuozl3TqPWtcc46G1zTTH5KCCmF/VysZBNcmq7OD6aiqGqFbDDQKfpbr3qJTias7koytZfjeUr/m7A5CKhnhySwdPb+tmY/vKb1lesT0mCxbDGRPL9YmENFJN4QWt4pUvO3zw2QTvnBrlymS55jW6Bo9v7+aFx/vZ/+BaaR8khBCLRClFoF1bo73+4O1owSRdcVFKYXsBEcP40gdn/UAxkK1MVbEqMpS37viY1niYB9akeGBNMzt6kjNtDz1fMZSp1EyoUqq6Dm0YEDZ0QoZG8wKvwTWS4/lMFGwm8lOt/7LmDQlWllt7XjQX8chU67+uBBu7kmzoTLC+Pc7Y5bO0N+l846knMeZw2F0IIYQQtcluuRBiSfDLReyrl9AMAyORmvXjqi0E/x2ljz644f5w11p6/ug/I9q74ZbH3K661fXXLIeEKz9QVFyPsuMTqGrVofBdBJ5KKSbKDpeyFfI1Tq0aGmxuT7Cj+1pQLOZPxfFIl6ubJAEQD11bAAmC258Mu1tKKQZzJieG8nwylKdk1z6tDNWTZ0/d08lXN3esuopmjheQLdlcnSxTtjxCi7DhESjF6Ss53j09ym8+n6i76JKIhvjWzrVsjmXZ2hniySd3yMKJEEKIL0UFAc7wAJquo4VqzwHczCTp/+1/BjX9+qTR/e//R8S33r9wA70LtheQtxy8QBEN6Xd9In6iZHMuXaZc4zR/VyLCw70tdEiFSTHPbM8nb3pMliwsT2FoGvGIQVNk6W9CFi2P44M5jl3NMlas3bZe1+DhdS0c2NbNrg2tK76a7nTMMZypkK+4GLpGMhYiGV+42NvzAz4fd/ndiMeFd47i1+lH1N+V4NBj/Xx/bx9rWuMLNj4hhBC1Of5UqacpsZBOya4eKBjKW9UDs7pGPHz3rynZisPnUwlW5yfL2HWSpKcZWvXAwcbWOBtb47TFQtW5tlJcHi3OXKeUtmITqmzXZ6JgMZ6v/pso2Nfezltk63SUmKv2ZIQNnYmpSlXXqlT1dSboSN3a+s/3fT6Y/HxePrcQQgghqmTXXAixqJRSeJlJ7JGrhBKJuhs5tdRqIQiQ3P01un7wx+jR2C2PuVN1q2lLvcrVdKJVyfYBRdi4u9YnSilGSzaXMpWaJZ9DusbWjgTbu5LE5MTqvJquZjVadLBcD0PXSURDDV9YmCzZnBjKc2Iwx+RtgvueVJSntnTyxJZOeltu/V1ayWbad+RMRnIWKEjEDDqaows6jqF0hXdPj/LemTEmb7MZ9dUd3Ty3r58DD64lbMAHH3xQ81ohhBBirtzJUfxKhVCqfivBUFsHqV1fpfjb9wDoPPQfknp430INcc78QFG0PcqOR9jQiYXuLpHD9gI+mygxVrr1Nbo1FuLh3hZ6krducghxt1w/oGi5TJQcSo6HrmnEwwYt8aWfjOQHik/Hihy7muWzsSJ1cnnobYmxf2sXT93TSdsKT1ScblM+mjWZLFiARlPUoHOBY46BiRLvnKrGHPVaFDVFQ3xn1zpeeLyfnZva5e+aEEIsITdXqU+XbYbzJroGLfE7JzD5gcL1fGw3wHZ9LMe/4WDupWyFyVm0sOtKRLinI8HWjib6WuMrPlnacm5OqLr29njempe2fwCGrrGmNU5f53Trv+mEqiQbOptISgVdIYQQYtFJwpUQYtEo38ceGcTPZwilmtH02Qdi9VoIdr7wR6T2fr3uAuBsqltdf+1SS7jy/ICy41NxPUAjYmh1W7vcTqAUwwWLy1mTintrolXE0NjemWRrV5LICg+QF5JSiorrky45ZCo2SkEsHKIl3tjNhKLl8bvhapLV1ZxZ97qWWIivbe7gyXs62dKRWHUL6bbrk5mqZlVxfCKGtuAtA4umyy8/Hefd06N8MVKse93WtSme39fPdx/ZQPd1CXG+35iKaEIIIVYfr5jHHR/FaK6fbAWgaRrJ3V9Fb0qgR+O0PPHMAo1wbtRUC+6C7VXnYHdZ1Wq6feDnEyW8m7JGEhGDh9Y2s6ElvurmUaIx/EBRsj0myw4FywWliIYNWhscP8yX0YLFR1eyHB/M1awCBxAP63x1Uwf7t3WxtSu5on93ptuUj+WqbYP8ICAW1mlLRhb06y6aLh98Os47p0a4MFqqe92j93TywuP9PPNwL01RWUIWQoil6Ob56PmRIsmogYZGhlsPBihVTdLyfYXrB7heACgCpRivuIyUbAYLFuYdqlhFQzpb2pvY2pFgS0cTLSss8ce0PcYL1ypSjU9VqJqYSqgqmPOTUAUQCxts6Lyx9V/f1L/ejqYVn7wmhBBCLHcSLQshFkXg2NiDl1COTai5ZW6PtS0y//ZN1HXJVrdrIXi9vW/8+V2Nd7G5fkDJ8TEdD03TiBh3t0HkB4rBvMnlnFmz/HMspLOjK8k9HQlCEszNG9cPyFsu4wULywsI6TqJaGMTeSzP58xIgRODeb6YKFHnEDmxkM7e/naeuqeTB9Y2L2irvKVAKVWtNJY1GcuZKCAZC9GZWriT5a4fcPxihiOnRzl2Pn3LYtm09mSE7z2ygWf39nHv+pYVvRklhBBicQWOjTM0gJFI3DG5369U0CJROr73t9CMpbnE4E3NxWw/IKLr6MbdvYaars/Z8SLpm06shw2NB9c0s6UjsWLaoIjFEyhF2fHJlB2yFafaOj6kk4qGlsX8r+L4/G4ox0dXcwze5rDH/WtS7N/Wzb6NbURDK7uacsX2yJRsBtMVLNcnbGik4iEMfeE2p/1A8bvLGd45NcrRLybx/DoxR1zj//TUVp5/bCMbOhMLNj4hhBB35+YKV76vKFS82z7G0DV0TSNk6EyaLpeyFQZyJrZ/+ySr3lSUrZ0JtnYk6G2OLes1RNPxbqhIdfO/knX77+FcxMIGve1x1nck2NDZxLr2qX8dTazvSNC+wInXQgghhJhfS3M1VAixovnlIvbVS2iGgZFIzfnxejRG2zPfJ/Nv/mcAknu+RteLtVsILnd+oCg5HmXbQ9c1ond5Et/1A67mTQZyJm6NhdVE2OC+nhQb25qWdbC8lCilqDg+E1MbJaCIh0O0xBv30usFAefGS5wYzHN2rFDzZw3VhZWH17Xw1JZO9vS1Eb3LVjrLme36pAs2V9JlbNcnEtJpTUYWbJNUKcXFsRLvnh7lg7PjdU/GhQ2Np7+ylucf6+dr93bLqTYhhBANp4IAZ3gATddrtvtWqroRo2k6gWWiGTrRDZuWZLJVMDUfK9ouuqYRu8ukDqUUV/MWX0yWuHl6taElxu51rdJ+W3wp0xXYshWXdMXB8wNCxsK0HJ8PgVKcnyjx0dUcZ0YKdQ8QdCYifGNrJ9/Y2kVPauXF79ebrp47nDUpmdW/QclYiGRsYf9WDmUqvHtqlCOnR0mXareUj4Z1Dj64lm3JPNu7Qjz15A4MQ/6mCSHEcnDz2l9TxLjt2rFSitGSzeeTZQZyFZw6a4cAYV1jS0cT93Yl2dqZJBFZPq8Ntuvf0O5vLHdjQtV8Vqhqihr0tjWxvqOJDZ0J1rU30dtevb2uo4m2hCRUCSGEECvZ0lsRFUKsWEopvMwkzuggRrwJLXx3pzkDxyaypo/mrx0kumELzfu+Pr8DXQJmNoccD02pu060cryAgZzJ1bxZc9G7ORri/p4UG1rjy2Ihfzlw/YCc6TJerFazChs6yQZulARKMZCpcGIwz8nhfM0WkdN29CR5aksnj23qILUKW0IEQbWa1XCmwkTBAiAVD5GMLVw1q3TR5v2zY7x7apSr6Urd6x7e2MZz+/r5zu51tDQtj5YxQgghVgZ3chS/UiGUqt1KsHTsV1iXztF68A/Qo3FiG7eih5fea5XjB+RNFzdQRAz9rudiJcfj7FiR3E2n3GMhnT3rW1nfEp+P4YpVynJ98pbLRMnG8QIMXSceNjAiy2Ouni7bHLua4+OrOXK3OUCwt7+NA9u6uX9t84qOO10/IF92GM5WyE4lNyVjIToWsHouVNsg/eqzCX5xaoTPhgp1r3toYxsvPN7Pd3atpymi88EHHyzgKIUQQsyHiKERcctUfB09HEUL1Z5DVByfzydLnEuXKdj1qzclIgbbO5Pc251gU9vSbWfnegHjhWst/sZuqFBlkivPX0JVIhZiXXsT69ubWN+ZYF17vFqdqj3Buo4mWprCklAlhBBCrGJLegXnxz/+MQBbtmzhj//4jxd5NEKIL0P5Ps7YIG4mTSjVjKbPLlhTvoc1cIH45u0A+OUSmmEQ27ydxP07GznkRaGUwvJ8iraHF1SDZv0ObVxqsVyfyzmTwbxJrcPFbfEwD/Sk6G2OSUA4D9RU24/Jsk12qsVMPGzQ2sBqVqMFixNDOT4ZzJO9zamsDa1xntzSwRNbOulKLuxC/1JhOT6TRYvByQqW5xML6wtarttyfI5+Mcm7p0c5eTlbt73j2rY4z+3t4/t7+9jYnVyQsQmxUkkcIcTd8Yp53PFRjObayVbO+DD5D/4dBD7j/79/TtcPXl5yVWYDpSjaHmXHJ6RXE6Pu9uNczla4kKmgbnrx3tzexMO9LUSW6AaUWNpcP6BguUyWHMqOh65pxMMG8QbGDvPJ8QJODuc5djXLxdscINjSmeDAtm6+urmdxDJJILsbfqAoVBzG8ibjeRulFPGIseDtgQKlOHs1xy9OjfLh5xPYbu22UJ2pKM/u7eP5x/rZsuZaxXHfr39wR4jVRmIJsZx0J6O0l4aZTPvEujdCy42vuRNlh9NjBS5mKzXXiAFSkWrngwd6UqxviS16crRSinzFZbJgMVGwmSzaM2+nCxYTRXsmsXk+JGKhajLVVIWqmepUU/83y0FIIYQQQtzGkl7xePPNN7l06RKtra0S3AixjAWOjT14GWWbhJpbZr3o6OWzZP7qL3BGrtLx7L9HuKsXo6WV6JoNdU/rLGfTC++2HxDWdWKhuQe3FcfnUrbCcMGqmdTRlYhwf0+KnmRUEq3mgeNVq1mNFS1cXxE2NFLRUMO+tznT5eRwhhNDeUamKjTV0t4U5oktnTy1pZP+9qaGjGWpCwJFvuIwlK6QLtnXWngs0EbW9IbHu6fH+PXnE1hO7Q2MpojBt3au47nH+nlkSwe6tPQUYl5IHCHE3CnPxRm+gpFIoNVI+FeuS+av3oCg+pqmHJtQW8eCjjH94UcAdDz2SM33u35A1nTwA0XUuLsKsQAFy+X0WJHSTa/fibDBoxva6FngajVi+fOCgLJdPaCRtzw0FLFQiJb48tjAU1NVdT+6muV3QwUcv3YyT3MsxNfv6eTprV1saFu5cYhSiqLpMp63GM1ZBEFAJKzTmggv+Cb1eN7i3dOjvHtqlLF87RgxbGh84ytreWGqTXlIkkWFuC2JJcRKkDNdPhrKcTln1nx/PKTz4JpmvrI2xbrmhUuyUkpRsT0yJYeJgsVkwa7+K04lVE0lV93cKvHLiEcM1rVXk6nWdzRN/atWp1rf0SSV5YUQQgjxpSzpjIVdu3Zx8eJFcrkchUKB5jqnbIUQS5dfLmEPXkTTDYzk7H+HzfOfkvk/DqOsalCY+evDrP37Pya6buOKSxTyA0XRcam4PoYGsZAx549RtD0uZSqMluya71+binJ/T4rOhGwOfVnBVDWriaJNznLRgKawQVOkMYvWFcfnQtlgoGIwOXS+bnWkRMTgsY3tPHlPJ/f2pBb9NNpiMR2PibzFYLqC6wXEIgtbzWo4U+HI6TGOnBllolD791HXYN+2Lp5/rJ+DD60lvoJP/AuxWCSOEGJulFLYo0OgQAvVbvude//f4mUmZm63Hvz+TBXahXL+n/4ZUDvhyvED0hUHA4jexXwaqvPyC5kyl7O3bkxt60zw4NpmQrOs1CuEHygqjke64pCrOARKIxrSaW7gAY35ljddPh7McexKlsly7UoSuga7N7Syf1s3O9e3rOjfkbLlkS5aDGdMLNcnHNJIxUMYeu2/m41iuz6/OTfJO6dGODWQqxsj7ljXzAuPb+S7e9bTtkqrHQtxNySWEMuZHyhOjOT53WihZkWrLe1N7FnfwrbOJKF5PPTneD65skO25FT/L9/4f6507e35TKYCiIb1qWpUCTZ0Ns28vb6jiXUdTbQlFrbqpBBCCCFWlyW9w/fKK69w+PBhAH7yk5/wj/7RP1rkEQkhZksphZedxBkZxIg3oYVntwCpfI/8+/+O0se/vOH+UEsbRlNyRQVHCvC1EBNlB13XiRranL++nOVyKVNhos7i94aWGPf1NNMWX9gF4JXI9gJypsN40cb1A8KG0bDNEqUUV7ImH15O87vhAn5Q++cXNjR2r2/lqXs62bm+lfAqPansB4pc2WEoUyZbcqrVrOIhmpsW5nlfslx+9dkE754a5fPhQt3rNvckeX5fP997dANrWuMLMjYhViuJI4SYG6+Yw89nCDW31ny/efFzyid+M3M7umEz7d96foFGV5X+8CMyR4/NvH190pXpemRNl7CuY9zlxlGm4nB2vETFvbGqVXM0xN6+Njrk5LuYBaUUFdcnW3FIlx28QBExdBLRha98dLc8P+DsWJGPrmQ5N16qm8yzoTXOge1dPLG5k5YVHG9ajk+2ZDOUqVCxqy0gE/GFq5w7TSnF58MF3jk1yq8+HadSp4JuayLC9x5Zz6HHNnLv+pYFHaMQK4XEEmK5Ktge718dJWu5N9wf1jV2rWth34ZW2r/EnNbxfEazFiPZCsNZk5Hpf5kK6Xls8XezlqYwa9vi9LZVE6jWTL09Xa2qIyWdHIQQQgixeJZ0wtX+/fvZuXMnJ06c4NVXX+WVV16hv79/sYclhLgD5fs4Y4N42TRGMoWmz+6EeWCZTP4v/wPO4OUb7k8+8iRdL/wRejTWgNEuPKUUpuvhGHGUVk2amUtJf6UUGbOaaJUx3VverwEb25q4tydJc3TlLnwvhEApyrbHeMkmb3poWrUFXFODKhI5XsAnQzl+fTnDcJ12EJoGD6xp5ql7Otnb39awsSwHFdtjvGAxNFnBCwLiEWPBqll5fsCJSxmOnB7jo/OTdU/ntSYifHfPep7b18f9G1plAUiIBSJxhBCzF7gO7shVjKZkzff75RLZ/+MvZ25r4Qg9/8F/jGYs7BxkurrV9NvTCVcVxyNneUR0/a5a87p+wBeTZQZvatesAff1pLivO3XXSVxi9TBdn4LpMl6ycYMAQ9OJh0PL6rkzlDc5diXLicH8LYmH05rCBk9s6WD/tm42dzSt2Lmt4wXkyjYjGZNcpbqBnIyFaF+EdqKZos2RM2O8c2qUoUyl5jWGrvHEvd288PhGvv7AGiKh1XkQR4j5IrGEWI4ynsGJwTzedWWtNOCR9a08tbmdZJ31w7LtMZ63mMhbTBYsipZHxfao2D6mc+3/aqs/u24i9t2KhnXWtsbpbW+it72aSLWmrXp7bVuctW1xqQwvhBBCiCVtyc9U3nzzTe655x6UUuzatYvDhw/zjW98Y7GHJYSoI3Ac7MFLKNvCSLXMegHWLxeZPPwvcCdGZu7TwhE6X/gjmvd+vUGjXXiOF5C3XWzXQyNAV8z6pLNSiomyw6Vshbzl3fJ+Q4PN7Ql2dCdJSCD6pdieT67iMlaqVrOKhgyaY41r/TFRsvnN5QzHrmYx3aDmNV2RgO881M8TWzppW8UVFqarWQ1OlsmWHUJ6tZpVyGh8cqFSisvjJd49Pcb7Z8fIV25NeIRqEuXXH1jD8/v6efL+nlVbeUyIxSZxhBB3ppTCGRsGpaGFbp0/KqXI/rt/RVApzdzX8fzfIdy1ZiGHeUN1K4DM0WNMfvhbort2UrA8oiH9rqoHTZRszk6UsL0b51/t8TB7+9poicnhBVGf4wXkLZfJkoPp+ugaxCMGTfryicXKtseJoTwfXckyUqhz4AP4Sm8zB7Z180hf24pN5vEDRb7iMJo1SRctAlU9bNOxCElWrhfw0flJ3jk1yolLmZotoaBaQfeFx/r5g0f76GpZGQfUhFgqJJYQy8mko/FxOUpwXTpUTzLCs/etoT0e5uJYiS9GClwaK5GvOJQtj7LtkS07VOzaSdZfViIWojMVpas5Vv3XEqOrOUpn87X/e9vitEq7PyGEEEIsc0t+FWjz5s18/PHH7N+/n2w2y4EDB3jllVc4dOgQmzdvpr29/baPlx7rQiwcv1zCHryIpukYydSsH+flMkwc/m/xc5mZ+8Jda1nzx/85kTXrGzHUBef5AUXHo+L4hA2NWMhgtqGkUorRks2lTIVSjbYBIV1ja0eC7V1JYuHZVRMTt/IDRdmpVrMqWC46GvGI0bDktUApPh0t8uHlDOcmSjWviYcNvr6lg/bSFdojiifu68EwVufPuGx5jOVNhjMmfhDQFDHobF6YzY900eb9s2McOT3Glcly3eu+0tfK84/183u719OaWL1JcUIsFRJHCHFn1VaCWULNtdtOlU/+FuvCZzO3mx7YTfO+hd9svL661bTP/1//Hzb+f/85sZA+500axwv4bKLEaMm+4X5DgwfXNrO1M7ls2r+JheUFAUXLY7JsU7R8NA1iIWNZtdTzA8W5iRLHrmQ5O1rEV7WzebqTUfZv6+Lr93TSmVz4pKOFEASKouUynrcYy5r4CmJhbdE2fy+OFXnn1Cjvnx2jaN56yAqqlbZ+f896Dj3Wz4P9bbJJLUSDSCwhlouy7fKvLsB4vozrBgS+orMpjG36/PP//TM+H8rXrcr+ZbQmIvR3JdjUnWRTT4r+rgS9bXE6m2N0NkelKpUQQgghVo0lPet58cUXOXHixA33KaV4/fXXef311+/4eE3T8LzaCxRCiPmjlMLLTuKMDmLEmtDCs19sdidGmTj83xGUizP3Rfu2sPbv/RgjUbutyXLiB4qy61GyPXRNm9kQCoLaVYxuljVdPh0v1ky0ihga2zuTbO1KEpEKOnfNcn1ypstY0cIPFJGQQXM03LCF65Lt8dsrWX5zOUOuRktIgL62ON+5bw1PbO4grMMHHww0ZCxLnecHZEs2g+kKhYpLyNBJxQ0MvfEbWqbt8eG5Sd47M8qpgVzdkuk9rTGefbSPZ/f1sbln9ommQojGkjhCiDu71kowUfP9bnqc/Lt/PXPbSLXQ/bf+3oJv7t9c3Wpa4aPjOMdPEN+7Z9YfSynFaNHms4kS7k0lY7qTER5d30YyuqSXScQimD6YkS475CoOStOIhvSGVsBthImSzceDBT6+mqNo136Ni4Z0HtvYzv5tXdzbk1pWX99sqam28ZMFm+FMBccPiBgaqabworSALFQc3js7zjunRrg8Xvtwh6bBvq1dvPDVfg4+2EsssjoP4QixUCSWEMvJ/+3waT46nbnhvjTw+V18rGQsREcqSjIWJhUPkYqFScbDJGMh2pJR+rsSbOxOsrErQfMqrr4vhBBCCHG9Jb2SePHiRS5cuDCzwHP9Qo+qcwJPCLGwlO/jjA3hZScxkik0ffYLfyoISP9v/9MNyVbxbV9hzR//5+jR5V0OXymF6foUbA+lFFFjbifvHS/g3GSJ4aJ9y/tiIZ0dXUnu6UgQkkSru+IHipLtMV60KE4lwzVFQg1bYFdKMZA1+fBSmpMjBfwaPSEMXWPfxja+c+8atncnZ54vvt+Y0t5LWdF0GcuZjGZNfKVoihp0LEA1Kz8IOHk5x5Ezoxz9YhK7TnvHeMTgmw/38ty+fvZu7URfhI0ZIcTtSRwhxO1VWwkO1W8l6Ptk/vrnKO9acnj33/4HGImFTy6uVd1q2tA//xkts0y4slyfs+MlJivODfeHdY2He1vY3N60IpNLxN1RSlFxfDIVh0zFwQ8UYcMgGQsvq+pnlutzoWxwqWKQHrpY97rt3UkObOvmsU3txFdo1WTT8cgUHYYyZUzHJ6TrJGIGzQvQmvxmfhBw/GKGd06Ncux8Gq9Oz8D1HU0ceqyfZ/f20dvetMCjFGL1klhCLCfR+Oy2+GJhg/s2tLCxK0lzU5jmeJjWRITejibWtVf/pZZRxU4hhBBCiKViSSdctbW10draetePl8VSIRpL+R721Yv4pomRapnz75ym67T/3h8y8cbPUI5N4uG99Px7/1HNTZ/lQimF4wfkLRcvqFah0rXZJ0UppRgsWHwxWb5l0TURNrivJ8XGtqZFOXm7EpiuT7biMFGy8RVEDZ2WeONOZDlewImhHB9eyjBcsGpe09EU4Zv3dnNgW/eyakUy39ypalZX0xVKpktogU6ZK6W4PF7iyJkxPjg7Trbs1LxO12Dfti6e29fHgQd7ScSW798pIVYDiSOEuL1qK8Fc3VaC6DqJB/aQm/xr8D2an/wWTTseXNhBUr+61bTiR8cp/PZjmh/dXfcapRSDeYtz6fItSe/rmmPsWd+6YhNMxNyZrk/edJko2bi+IqRXD2YspySr6cMeRwcynBzO4/q1Y4y2eJivb+3k6a3d9LYs7wNP9diuT67sMJypUDBddE0jETPoSC1Oi8Srk2XeOTXKe2fG6sYd8YjBt3b2cuixjTxyT4fMSYRYBBJLiOXkP9m/lb88cglNg7XtTXQ3x4iGdGJhg0QsxIP9bezb3sX23mY5uCuEEEII0QBLerfwrbfeWuwhCCHqUL6PPXiZwLIIJe/+pLvelKTj+/8ezuggnc/++2j68g38XD+gYLlYfkBY14mF5rbAUrBczo6XKNzU3sHQ4IGeZrZ3J5fVQv9S4QeKouUyVrIp2x6GphNvYDUrqLbr+PByhmNXslhe7SpJX1nbzO/dv4Zd61tXbQKdUoqS5TGaMxnJmiilSERDC7IBki7avH92jCOnx7gyWbt1B8DWtSme29fPd/esp6c13vBxCSHmh8QRQtR3p1aCUN0oTDz0KKH2LswvztDxvb+1gCO85nbVraYN/rOfct+jtdv7lB2Ps+Mlsje1cY4aOnvWt7JBXtsFYHvVOG6i5GC6PiFNIxbRaYosr9i04vgcH8xxdCDDWI1KyQAhXWNPXysHtnXzYG/LioxDPD8gX3EZyVRIl2w0NOJRfdGSrAoVh19+NsF7p0c5N1Kse93uLe0cemwj3965Tg53CLHIJJYQy0l3S4z/2zPN9CR1vvHUE4SW8UFmIYQQQojlSGZfQog5U0GAPXIFv1K+62QrpRR+qYDR3E7TjgeXdaLVdHu6kuMR0jXiobmdkHf9gIuTZa7mb62AtK45xq51LSQi8ud6rkzXJ1N2mCjbBAFEw42tZuUHik/Hinx4Kc0XdRJ4msIG39jaybfu7aG3ZfVu8DleQLpoMZSuULY9woZGywJUszJtjw/PTfLemVFODeSo1wigMxXle49u4Nm9fexYV6fyhxBCCLEMzbQSpHYrwev55SJN9z5My9cOLszgbnKn6lbTalW5CqYq/FzIlLm5U9fGtjg7e1uJhpZv/CG+PNcPKFouk2WHkuNVk3LCBq3LrOKsUorLmQpHB7KcHM7XbU3XGQn43sMbeXJrF6noyostg0BRMF3GcyZjeYtAKWIRg/ZkZFEqzbhewLELad49Pcrxi5maLeUBelpjPLe3j+f39dPfnVzgUQohhFgp1rdU16KlupoQQgghxMJbeassQoiGUkrhjA7iF/KEUs1zelzxw3cwUi00PbALv1gk3NFFuLt32SZbBUpRcXyKUxWpYiF9ToGtApxwEx9ezeH4t7YP3L2+ld7mldnaoVH8QFGwXMaLNmXHx9A1msKNrWZVtDx+eyXD0YEsuZuqJ0zrb2/iO/f18MTmDqJzTMhbKZRSFE2X0azJWN4kCCAZb3w1Kz8IOHk5x5Ezoxz9YhLbrV1xLB4xOPjQWp7d289j27tW5Gl/IYQQ4o6tBKf4ZgUjkSLU1rFAI7vVbKpbTbu+ylXB9jgzVpyZo09rChs8sqGVtSmZX69W0wdl0mWHvOWCUkTDBi2xxh3KaJSy43H8ao6jA1nGS7WrWTWFDZ7Y3E5b6SpdUcUT93ZjGCsnFpmuljtRsBjJmLhBQDSk05IIL0plaKUUnw7lee/0GL/6bILyTX+DpkVCOgcfWsuhxzZK3CGEEEIIIYQQQixzknAlhJg1pRTu2DBeLo2RnEuyVUDunb+ifOI3oGmoICD16JOEu9Yuy5M3Siksz6dge/gBRAxtzgu6Jcej0NSNF4rBdclWugY7upLc19NMSBZeZ63ieGQqDpMlh0BBLKzT0sDT6dMnyT+8nOHUcAFf3XpiOaRrPLaxne/c18PWruSyfK7PB9v1yRRtrqTLmLZPJKzTkog0dBNEKcXl8RJHzozxwdlxsmWn5nW6Bvu2dfHcvj4OPNgrrTuEEEKsaHdqJWie/xQj2Uy4swcCn8javkU7GKGU4v5/+Tp5yyUa0mc1bwiU4kK6wuVs5ZYqlls7Ezy4ppmwsTwPeoi7N31IJltxSFeqsULE0ElFQ8tufq6U4lKmwtHLGU6NFOpWs9rWleSbO7p5bFM7IQ0++ODKAo+0sSq2R6ZkM5iuYLvVQzbJWIiQsTjVyUayFY6cHuO9s2OM5W6tWj3t4Y1tPLuvn9/fvY7mpuWX5CeEEEIIIYQQQohbLaudxU8++YQ33niD48ePc/HiRbLZLJOTkzdc8yd/8idkMhleeeUVnn766UUaqRArkzs5hpsew0i1zHpxWvk+mf/jMOanv5u6Q5H/4G9offq7y26BG8DxAwqWi+MHhHWdcGhuX4MXKC5mygxkTVToxtP1Pckoe9a3rsgWD43gBUG1bUTJoez4hHSNRDTU0EQe2/M5MZjnw8sZRgq1F9M7ExG+uaOb/du6G5r0tZQpVW3pMZypMDHVKjMZD5Fobmw1q3TR5v2zYxw5PcaVOm0dAbauTfHcvn6+u2c9Pa2rt7WjEKuJxBFCgJeZBEXNVoJePkvmr3+O8lySe56g7fdeRI8sTkKAUoqC7VF2vFlXkLW9gN+NFMhZN1YbTUVD7N3QSmeisXMQsbQopTBdn5xZbRnoBaoaK0QaGys0Stn2+Hgwx9GBDBOl2gcJEhGDp7Z0cnBHN31tTTP3+76/UMNsKNv1yZRshrMmJdNF16pJVslFOjBRMF1+/dk4754e49xwoe516zuaeHZvH3/w6Ab6u6RloBDLlcQSQgghhBBCiHqWxa7+J598wksvvcTx48eB6uIZ1O5JnU6nOXz4MCdOnODcuXMLOk4hVjI3M4E7PoyRap51olTgOmT+zf+Edena76IWjRF79PfI/e4sHY890qjhzjsvUBRtD9P1MTSIzbEtnFKK8bLD5xMlLO/GlmaxkM7uda2sb4ktyyS0haSUouL6ZMoO6bJNoCAeDtHa4MSm8aLNh5fTfHw1d8vPb9pDvS18+74edq1vXbVtIWzXZ7Jgc3WyjOX5REM6bclIQ5/XpuPxm3OTHDk9yqmB3C0VLaZ1pqJ879ENPLu3jx3rbt9GSQixckgcIURV4Dq46XGMZO0N//yRv0Y51bZkpd++R+qRJ4i0Lnw7wUAp8paH6VTnEbOZQ+Qtl09GCtjXzdE04N7uJPf3NK/aedlqZHvVJKt0ycbyAgxdJx42luVzQCnFxXSZowNZTo0U8OtUs9rRk+SZ7T3s29hONLSyKri5fkC+7DCcrZCdSjRLxhrfkrzueLyAjy+mOXJ6jI8vpOtWGGuOh/nO7nU8u7ePnZvaJcYXYhmTWEIIIYQQQghxJ0s+4eoXv/gFzzzzDFANag4cOMCuXbt47bXXal7/p3/6pxw+fJgLFy7wzjvvyIkSIeaBm8vgDF/FSKXQtNkt4gaWyeS//pc4QwMz9+nJZnr/5B/yyX/+XwMsi4SrQCnKjkfR9tDRiBranBdMK67PZ+MlJis3nUZWiiY7xzM7txOLrM5KSLPl+gFFy2W06GC5HiFdJxENN/SEuh8ozo4W+PByhvN1qiUlIgbf2NrFt+7tYW1zrOY1K10QKPIVh5GsyUTBQtM0UrEQyXjjphh+EHDyco4jZ0Y5+sUktls7CS4WNnjm4bU8u7efx7Z3LcvNNiHE3ZM4QohrvFwaTdNqzuWtKxcxvzgzczu55wmatj2wkMMDqnOvnOlg+wGx8OwONwwVLD4dL3J93kMqavDV/o6GJ+SLpWE6TpgoOZQdD03TiIcNWho4F22kku3x8dVqNavJOm2xp2OQg9u7Wb/CqrX6gaJQcRjLm4znbZRSxCMG7Q0+xFGPUorPhwocOTPGrz4bp2R5Na8LGRpP3tfD8/v6eer+HqKz/BsmhFi6JJYQQgghhBBCzMaSXoHK5/O88MILKKVoa2vj7bffZufOnVy6dKlucLNr1y42b97MpUuXePPNNyW4EeJL8op5nKEBjGQKTZ/doqFfKjB5+F/gTo7O3Bdq66T3H/xXFM5fJXP0GADpDz9asklX020oCraHUoqoMbsT9tfzA8XlbIVL2Qo3H37tiIdh9DzhwCFs3DuPI185lFJUHJ/0VDUrgFg4REu8se1tCpbLbweyHB3IkK+zoL6pvYnv3LeGr25uJzrHamcrheX4TBYtBicr2J5PLKw3dCNEKcXl8RJHzozxwdlxsnU2oHQN9m7t4rl9fRx8qJfEIrUZEUIsLokjhLhGeS5eehy9KXHr+4KA/Lt/NXNbi8bo+P7fXsjhAdVqstlKtfXbbCrJBkpxbrLMlZx5w/29qRiP9bcRNlZWpR9xK9P1Gclb5CwXDUUs1Pg4oVECpbg4Wa1mdXqkgK9qV066ryfFMzu62dvfTmQFVbNSSlE0XcbzFqM5iyAIiIR1WhONPWBzOyNZk/fPjHHkzCijudqt5AEe2tjGs3v7+M6udbQlpXWpECuFxBJCCCGEEEKI2VrSu5D/+B//Y3K5HJqm8Ytf/IKHH354Vo87cOAAP/3pT7l48WJjByjECueXS9hXL2I0JdCM2SWVeLk0E2/+d/j5zMx94Z519P6D/5JQSzvn/y//5cz95//pny3JhCvbCyhYLq6viBga+l1s2EyWHT6bKFFx/RvujxgaD69toa8lyi+HayeMrHauH5C3XMYLFpYXEDJ0krHGLrYrpbiUqfDhpQynRvK3JMgBhHSNxze185371nBPZ2JVtoYIAkWu4jCcrpAu2eiaRjLe2GpW6aLN+2fHOHJ6jCt1Ko0BbF2b4rl9/Xx3z3p6VthJfyHE3EkcIcQ1Xi4DSqHpt85py6c+wp24dkii7VvPE0otbOtd1w/ITFWCnU1LNMcL+N1ogazp3nD//T0pHuhJrco52mrieAFjRYuJkk3YMGiOhpbtz7xkexy7kuXolSzpOocJktEQ39jaycHt3axrWVlz3LLlkS5aDGdMLNcnHNJIxUMY+uJUpyuaLr/6bJz3zozx2VCh7nXr2uN8f28f33+0j43dtdu0CiGWN4klhBBCCCGEELO1pBOuDh8+jKZpHDhwYNaBDcCWLVsAOHbsWINGJsTK55sVrIHzGLEmtNDs/lQ4EyNMHv4XBOXizH3R/ntY+/f+FKMpSfrDj2aqWwFkjh5bUlWuPD+gYHtYrk/I0ImF555oZXk+n0+UGSvZt7xvS0cTD61pIRLS8X2/xqNXL6UUZcdnsuyQrTiAIh4ONbwViOX5nLia48PLGUaLt/7MADoTEb51bw/7t3XRHFudrWkqtsdEwWIoXcHzA6INbuthOh6/OTfJe6fHODmQpfYZf+hMRfneoxt4dm8fO9Yt7OawEGJpkzhCiCrle7iTozWrWwWWSeGXb83cDnX00PrktxdyeDheQLriYOgQqpEQdrOC5fLJSAHLu9ZO2NA1HutrY/0KS0YRN/IDxWTJZrhgoaPRHAsvy0SrQCnOT5Q5OpDh7GixbjWr+9ek+OaOHh5dYRXbLMcnW7IZylSo2B66ppFo8AGO23H9gOMX0hw5M8axC2k8v/bPIxUL8e3d63lubx+7Nrcvy+eeEGL2JJYQQgghhBBCzNaSTri6ePEimqZx8ODBu3p8Lpeb3wEJsUoEtoV95Tx6NIYWnl2CiVIBmb9644Zkq/j2B1nzd/8z9GgMqFa0utlSqHKllKLkeBQtD13XiIXn3iIuUIorOZML6coti+atsTCPbGilo2l5trhoJNcPyJku40UL21OEDY1kNNTw1hFjRYsPL2X4eDCHfd2G3TQNeGhdC9+5r4eH17Vi6KtvQd0PFLmyw+BkmVzFwZiqZhUyGpN05gcBJy/nOHJmlKNfTGK7t/5cAGJhg4MPreW5ff08tr1rVf5shBB3JnGEEFVePosKVM3W4IUP3yEwKzO3O5/792d90GI+WK5HpuISNvRZvZ6PFCzOjBdvqESajBg8samDllWaFL8aKKXImS6DORMvCEhEwsty/le0XD66muO3AxkyFbfmNaloiKe3dnFgeze9LbEFHmHjOF5ArmwzkjHJTVWzS8ZCtKcWpwWfUopzwwWOnBnjl5+OU6rTRt7QNZ68r4fn9vXxjQfWEL2LdQIhxPIksYQQQgghhBBitpZ0wlVrayv5fJ50Oj2nx3300UczjxdCzE3g2FiXz6MZIfTI7BOENE2n4/f/kPG/+CnKtkjsfIyev/0PZjZtbq5uNW2xq1x5U+3rbF8RDel3dVI1a7p8Ol6k5NxYtSqkazy4ppl7OhMNTyBabmwvYLJkMz5VCSweNmiJN/bkth8ozowW+PWlDBfTtVvTJSIG+7d18c0dPaxpXjmbHHNRtqrVrAbTFfxAEY/odDRoM0QpxeXxEkfOjPHB2XGydVqp6Brs3drFc/v6OPhQL4nYkp6+CCGWAIkjhADl+7jjIxg1qlu5mQlKJz6cuR3f9hWa7t+1YGOzXJ9MxSVi6Oh3SJ4JlOKLyTIDOfOG+9ekojze305kBVX/ETcq2R5DOZOy49EUCdEUWV5zwEApvpgocXQgy9nRQs225QAPrG3mmzu6eaRv5VSz8gNFvuIwmjVJFy0CBU0Ro2FxxWyM5UzeOzPGkTNjjGTNutd9pa+V5/b1853d62hPLt54hRCLR2IJIYQQQgghxGwt6dWqzZs3c/z4cd5++23+8T/+x7N+3Ntvv42maezZs6eBoxNi5QlcB/vKRTRdm6lKNRdarImO5/4D3JGrdPzB30a7ri1IrepW179voROulFKYrk/OcjE0jVho7gvbjhdwbrLEcI1WdP2tcXb2ttxVtayVzHR9Joo2k2UHXWNBqlkVLJejA1mODmQo1Dm9vLmjie/ct4bHN3UQvYvnwnLn+cFUaw+TfMUhpOuk4gbGLFr73I100eb9s2McOT3GlcnayW8AW9emeG5fP9/ds56eVmkTJISYPYkjhJiqbqUCNOPW+Wj+3b+GYKqapKbT+fzfWbAWWbYXkDEdwrNItnL8gJMjBTLmjRWB7u1O8uCaZmnrtUJZrs9IwSJTcYiFDVriy6tScN5yOXYly2+vZMnWqWbVHKtWszq4vXvFHPQIAkXRchnPW4xlTXwFsbBGa6JxrcjvpGS5/PqzCY6cGePTwXzd63rb4nx/7wa+/2gfm3pSCzhCIcRSJLGEEEIIIYQQYraWdMLViy++yPHjxzl+/Dh//ud/zt/9u3/3jo/58Y9/TC6XQ9M0XnjhhQUYpRArg/I87KuXUL6P0dQ0t8cqhV8sYLS007TjwRsSraB+datpC13lyg8UecvF8vzqqfo5Lv4qpRgsWHwxWca76ZhyKhrikfWtdMtJ2BuUHY+xgk3OnErmiTU20UopxcV0hQ8vpzk9Uvs0edjQeHxTB9+5t4d7upING8tSVpraEBmeaoXZFG3cqXPT8fjNuUneOz3GyYEsdQ7405mK8r1HN/Ds3j52rGtpyFiEECufxBFitVNBgDsxihG7dV5vXvwc69LnM7ebv3aQyJp1CzIuxwvIVGzC+p3bCBZtjxPDeazr2j8bmsa+vlY2tM4tXhHLg+sHjBdtxoo2IUOjJRZeNkl1gVKcGy9xdCDDp2PFutWsvrK2mW/e28OeDa0ropqVUoqy7TFZsBnJmtieT8TQSDUtXutH1w84cTHDkTNjfHR+Es+v/cNIxkJ8e9c6nt3bx+7NHXdMABVCrB4SSwghhBBCCCFma0knXL3yyiv85Cc/IZ/P8/LLLwPcNsD52c9+xquvvoqmaWzevJk//uM/XqihCrGsKd/HHryMcmyMxOwST0rHPwQNEg/vxS8WCXd0Ee7uvSXZCm5f3er6axYi4cr2fLKmi1IQC829+lTBcjk7XqJg31gpydDggZ5mtncnpX3gFKUUJcdnNG9StD3ChkFzgzdNLNfn+GCODy9nGKtReQygOxnhmzt62L+ti1Qs3LCxLFXuVDWrwXSFouli6BqpplBDqln5QcDJyzmOnBnl6BeT2G5Q87pY2ODgQ2t5bl8/j23vWrTNGSHEyiFxhFjtvEIO5btooVsTk8LtXcS33o/5xRn0pgTt31mYTUHHD5icZbLVSNHizE1JK4mIwRMbO2iNr77520rnB4pMxWE4bxIoGn44Yz7lTZePpqpZ5cza1axaYiGe3latZtWTWhnVrEzHI1N0GMqUMR2fkK6TiBmk4ouzzKiU4ouRIu+dGeODT8comrUrGxu6xhP3dvPcvn6e/soaolKRWghRg8QSQgghhBBCiNla0glXLS0t/PznP+eZZ55B0zRefvllXn/9dfbv3z9zzcDAAG+99Ravv/46x48fn7n/zTffXIwhC7HsqCDAHhogMEsYyeZZPaZy7jS5d/6q+nilaH7sacJda2sm0typutW0Rle5CpSiaHmUHI+IoWMYc1vAd/2A8+kyV/PWLe9b1xxj17oWEpEl/Sd1wUx/r4fzFqbrEw3pDW8DMlqw+PByho+v5nD8W5N6NGDn+ha+fe8aHl7fsmw2cOaLUoqS5TGaMxnNmigUTZFQQ6pZKaW4PF7iyJkxPjg7Trbs1LxO12Dv1i6e29fHwYd6ScTk90cIMX8kjhCrmQoCvIkRjHjtKlCh1nbavvU8iYceRU+2YDQ1vtKn6weky84dk62UUnyRLnM5a95wf08yylf724mswtbPK5lSioLlMZirYHuKZDS0LBLvA6X4fLqa1WixZuVWDXiwt4Vv7uhmd18roQa16l5ItuuTKzsMZyoUTBdd00jEGlchdzbG8ybvnRnjyJkxhjNm3evu39DKc/v6+L3d6xd1vEKI5UFiCSGEEEIIIcRsLfndzQMHDvDzn/+cl19+mVwux8cff8zHH388k9ixefPmmWuVqi5z/fznP+fhhx9ejOEKsew4Y0P45QKhWSZb2YOXyfzvP4epZeXC+39D2/7v1a1aNJvqVtdf24iEK8cPyJkOfqCIhfQ5VVhSSjFStDk3WcK5qRVBImywe30rvc0r45Tyl+UHirzpMFKwsL2AeDhESwMrEPiB4vRIgQ8vp7mYrtS8Jhk12L+tm2/uWDmnyefC8arVrK5OlilbHqGQRnOD2nukizbvnx3jyOkxrkyW6163dW2K5/b189096+lpjc/7OIQQYprEEWK18kp5AtchFKv9OquUIrBtUo88gV6j5eB8c/2AyYqDoWu3nYO4fsDJ0QLpyo1VgnZ0JXlwbfOqS5hf6SqOx2DOpGh5NEVCtMSXfkJSznT46EqOj25Tzao1Hmb/ti4ObOumewUk9nh+QL7iMpKpkCnZgEY8qi9q0lLZ9vj1Z+McOTPG2av5utetaY3z7N4N/MGjfWxZk1rAEQohVgKJJYQQQgghhBCzseQTrgAOHTrEwYMH+eEPf8jPfvazutft2rWLn/3sZ+zcuXMBRyfE8uWXCniZCYxUy6yud9PjTP4v/wP418rzd77wd29bGWvvG3/+pcd5t6ot7TwKlkfY0IjOsYVgyfb4dKJE9qbFdF2rbvzc19NMaBmcwG40LwjIlF3Gihaur2iKGLQ0sJVE3nQ5OpDh6ECWol27VcSWzgTfua+Hxzd2rLpKCEopiqbLWM5kJGeBonryvHn+N0VMx+M35yZ57/QYJweyNU/3A3Smonzv0Q08u7ePHetm9/dGCCHmg8QRYrVRSuGNj6LXSbYCCCplQu2dC5Zsla44GJp223lz0fb4ZCSPeV37YUODRze00d/W+HGKhWN7AaMFi3TZIRLSaW1qbCXcL8sPFJ+PFzk6kOWzsfrVrB5a18K37u1h1/rWZVGl63aCQFEwXcZzJmN5i0ApYhGDtmSkoe3hb8fzA05cyvDemTF++8Ukrl878khEQ3xrZy/P7uvnkS0d6Mv8ZyGEWFwSSwghhBBCCCHuZFkkXEG1lO/rr7/On/7pn/L222/z8ccfk8lkAHjkkUc4cOCABDVCzIHyPeyhKxjxxKwWTf1Sgcm//Bco61qZ/rZvv0Dzvq83cJR3z/MD8paL7c+9qpUXKC5mygxkzVsW1HuSUfasbyUVXTZ/PhtmujXMaNFCqWqLuqZIY5KblFJcSJf58FKGM6MFghrr62FD42ubO/j2vWvY0ployDiWMtv1yUxVs6o4fnUDKxGe92oQfhBw8nKOI2dGOfrFJLZ7awtHgFjY4OBDa3l2Xx+Pb+9e9htPQojlS+IIsZoE5SKBbRJqbr3hfr9UqCZhaTqgCHf2NHws3lSyla5x22Sr0aLNmbEC1+dPNIUNntzUQWsDq6WKheUFAZOlajVcQ9NojoUWLXlnNrIVh4+uZPnoSpa8VfuQR1s8zIHtXezf1k1XcnlXs5puQT5RsBjJmLhBUG0N34B4Yi5jOj9a5L0zY3zw6TiFSu2qYoau8dUd3Ty3r4/9X1lLLDK3g1ZCCHE7EksIIYQQQgghbmdJZwz8k3/yTzh06BAbN26cuW/Tpk289NJLvPTSS4s3MCFWAHdiFJSPFr7zifHAtpj8V/89fiE3c19q3zdo++ZzDRzh3VFKYbo+OcvF0DRic6hupJRivOzw+UQJy7sxiSQW0tm9rpX1LbElvTGwEGzPZ6JkM1FyAEhEQg1LprFcn48Hc3x4KcN4ya55TU8qyrd2dPONbd2rLhFOqerp89GsyViumiCYjIXonOcWH0opLo+XOHJmjA/OjpMtOzWv0zXYu7WL5/b1cfChXhKx1fXzEEIsHRJHiNVIKYUzPlKzulXmr9/Ey2dIPfp1Uo99Az3c2KpCM8lWQEivPR9XSnE+XeZS1rzh/u5khK/2t8+5Oq1YmgKlyFUcBnMmvoJkNLRk20P6geKzsSJHBzJ8Pl6qXc1Kg53rWvnmjm52roBqVhXbI1OyGUxXsF0fQ9dIxkKEjMVLdpwoWLx3ptqqfChTu3U8wH3rW3huXz+/v2f9orY4FEKsPBJLCCGEEEIIIWZrSe+E/vCHP+RHP/oRmzdv5tChQ/zgBz+QPuhCzAO/XMJNj8+qlaDyPdL/5n/CHR+Zua/pvp10vfjHSy7xyA8UecvF8nwihj6nhfyK4/PZRInJyo2JJBqwrTPBA2uaCRurqzXdzUzXZ6xokylX28I0crNkpGDx4aUMxwdzOP6tFZQ0YNf6Vr59Xw8PrWtZsps2jWK7PumCzZV0GdudqmaVjMz79yFdtHn/bHWz48pkue51W9emeG5vH999ZAM9rfVbGAkhxEKROEKsRoFZJjDLt1S3socGsK9cACD31r8m1NJG5IlnGjYOL6gmWwGE6syfXT/g1Gjxlrn39s4ED/WuvrndSlW0PQazJqbnkwgbdZ8Piy1zXTWrQp1qVu1NYQ5u72b/ti46Ess7uWe6Mu5w1qRkuuhaNckquYiHJSq2x68/n+C9M2OcvpKre11Pa4zvP9LH9/dt4J41zQs3QCHEqiKxhBBCCCGEEGK2lnTCFVRPvV68eJHXXnuN1157jdbWVl588UVeeOEFnn766cUenhDLjvJ9nJEr6LGmOyZMKaXI/s2/xh44P3NfdMNmev7Of4JmLK0T57bnkzVdlILYHE7DK6UYyJmcT5dvaVPX0RTmkfVtq76VSdn2GCtaZE2XsK43rP2HFwScHinw4aUMl+qcZE5GQxzY1sU3d/TQvcpOMQdKkSs7jOVtJgoWAKl4iGRsfr8PpuPxm3OTvHd6jJMD2Zon+wE6U1G+98gGvr+3j3vX3zl5UwghFprEEWK1cSfG0CO3zguKxz6YeVuLxUk98rWGjcELFJmpJKp6hxVsz+fYUJ6y48/cp2vw6PpWNravvrbQK5Hp+ozkTXKmSyxs0BJbevGUHyg+napmda5ONStdqx7yeGZHDw+va1nW1ay8QJEu2ozmLbJTlYqTsdCiVobyg4BPLmU5cnqU355P43i1W5U3RUN86+Fent3Xx6P3dKIv45+DEGL5kFhCCCGEEEIIMRtLOuHqz/7szzh8+DBvv/32zH3ZbJaf/vSn/PSnPwXghRde4MUXX+TAgQM0N8vpNiHuxJ0cQ7kuRjJ1x2sLv3yLytkTM7dDHd2s/Xt/ih6NNXKIcxIoRdHyKDkeEUPHMGa/+Op4AafHbj1ZHzE0Hl7bwqb2OyelrVRKKUq2x3DBomR7RIzqRkkjvh850+XoQIajA1lKdu0T5Vu7Enz73jU8vql91VUasxyfybLPZDmAy1maYiHak5F5/Vn4QcDJyzmOnBnl6BeT2G7tzY5Y2ODgQ2t5dl8fj2/vXtabTkKIlU3iCLHaBFYFv5S/pbqVm5nE+uLTmdvNjx9Aj925pfjd8ANFpmKjVP1kK8utJltV3GvJVk1hgyc2tdMWb2ybQ9F4rh8wVrQYL9mEdZ2WJfgzzZQdfjtVzapYJ/boaIpwcHsXT2/rpiOx9L6G2QoCRbbscDXnkTcVxtUciVh43mOJuVBKcXGsxJHTo3zw6Tj5ilvzOkPXeHx7F8/t62f/g2uIR5b08qUQYoWRWEIIIYQQQggxW0t6xeLll1/m5ZdfBuAv//IveeONN3j77bfJ5XIz17z55pu8+eabAOzatYsf/OAHt/RYF0JU+ZUy7uQoRurOCwF+qUDpk9/M3NYTKXr//j/ESC6dRQTHD8iZDn6giIX0OS0aZyoOp0aL2De1q9vS0cRDa1qIhFZXUs+0QCkKpstwwcZyfaIhndYGbJQopbiYrvCrS2nOjhZuqS4G1cS3r23u5Nv39bC5Y3VVO3D9gFzJZjhjkilZjBV9YmGNjlQUYx4Tzi6NT212nB0nW3ZqXqNrsHdrF8/t6+PgQ70kFrHViBBCzJbEEWK1cdMTaOFb52ylj38J07V7dIPWr3+7IZ/fDxTpikOgIFJnrmK6PscGc5jXVbHpbIrwxKZ2onOoUCuWHj9QpMsOw3kTNEhFw0uqLaQfKM6OFjg6kOXcRKnmNboGuze08s0dPTzYu7yrWVVsj4mCxVC6gu14lB1FMgrtyfmNJeZismDx3lSr8sF07WrGAPeub+HZvX18d896OpuXzkEvIcTqIrGEEEIIIYQQYraWza7p888/z/PPPw/AiRMneOONNzh8+DAXL16cueb48eMcP358psf6Cy+8wD/6R/9osYYsxJKifB9n+Ap6LI6m3XmR1Ug20/WHLzF5+F+gHJu1r/wp4c41CzDSO1NKUXI8CpZH2NDmtEETKMXFTIWLN7Wsi4d0Hutvpzu5utrUTfMDRd50GMpbuH5APByipQGtFAOlODta5Mj5Ca5kzZrX9KSifPveHr6xtYtkdNm8TH1pQaAoWi5jWZPRvIVSiqaIQXsySjI6fxsj6aLN+2fHeO/MGAMT5brXbV2b4rm9fXz3kQ30tMbn7fMLIcRCkzhCrHSBbeHlMrccqvArJcpnjs/cTu5+nFBL+7x/fj9QZEyHQKm6yVYVx+fYUA7rumSr7mSEJzd1ENJX50GHlUApRd50uZozcf2AZDS8pBKVJks2v72S5djVXN1Kup2JCAe3d7N/WxdtTcu3mtX0gY2hjEmu7BDSNZLxEImoQTy8OD8T0/b48NwE754e48yVXN1W5T0tMb73yAae3dvH1t6lc8BLCCFAYgkhhBBCCCHE7S3LneydO3eyc+dOfvKTn5DP5/n5z3/OW2+9dcNJkwsXLvDqq68ueHBz8eJFXn31VY4dOzYTeG3evJlXXnll5mTMfDh8+DA/+tGPOHToEAcPHmTz5s1s3rx5Zgy5XG7m9M2Pf/xjDh06tCTGLRaPmxkncGxCs6huNU2PNdHzR/8pBAGx/i0NHN3seX5AznJx/LlXtTJdn1OjBXLWjYvtvakYe/taV+XJei8IyJRdRgomfgBNEYOmBrRr8PyAjwdzvHd+kskalZQ0YM+GVr593xq+0tu8pE7EN1rZ8pgsWgxnTBzPJxLSaU1cqwrg+7Xb+82F6Xj85twk750e4+RAtu5mR2cqyvce2cD39/Zx7/qWL/15hRBiqZE4QuKIlcjLTqIZoVvmxaUTvwHv2ry39envzfvnnk628n1Vt0Js2fE4Npi/obLsmlSUJzZ2LKnkHDE3ZcdjMGdSsj0SkVBDYoi74QUBZ0eL/OZyhvOTtQ8X6Brs6Wvjmzu6ebC3ZdnGHkopiqbLWN5iNGsSKEVT1KCz+dohovmIJebCDwI+uZTlvTNjHP1iEser/fmbIgbPPNzLs3v72LutS/4WCCGWBYklJJYQQgghhBDiZktjRexLaGlp4aWXXuKll14C4Gc/+xmvvvrqDadMFsprr73Gj370Iw4cOMDPfvYzdu3aBVQDkZdeeolXX32Vt956ayYI+TIuXrzIxYsXee2113jttdfqXvfyyy/fMbBZyHGLxeGbFdyJUYxkataP8YoFQq3tRNb2zSmpqVGUUpiuT85yMTSN2Bxb/o2XbE6PFfGu612na/DQ2ma2dSaXxNe4kBwvIFNxGC1YKBRNkVBDqguYrs/Ryxk+uJimWONUeSJi8MyObr65o4euVVRdzHZ9cmWHwUyFUsXF0DUS8RCp+Py9LPuB4uTlLEfOjHL0i0lst/ZmRyxscPChtTy7r4/Ht3fLZocQYtWQOELiiJUgcB3czCRGMnnL/eXr2oPHdzxItHfD/H5upciaDp6viNaZmxdtj4+Hcjj+tTl4b3OMr/a3y5xjmbI9n5GCTbrsNKz9+N2YKNl8dCXLR1eylB2/5jXdyWo1q29sXd7VrGzXJ12wuZopYzk+YUOjuWnxqosppaZalY/xwadj5Mpuzet0DR7f3s2z+/o48OBamlZRNWMhxMojsYTEEkIIIYQQQsAKSLi6fPkyhw8f5o033uD48eN3fkCD/PSnP5053THdv33aoUOH2LVrF7t372b37t1cunSJ1tbWho6ntbWVV1999Y4nQZbauMX8U0FQbSUYjd22laBfLuKMDhLfci9+uYSRTBFZs2FJJCL5gSJvuVieT8TQ53QC2Q8U5yZLXM1bN9yfjBh8tb99WS+03w3b85ko2UyUqlWmEpFQQxbmC5bLLy+m+c3lzA2tY6Z1NEX43gNrOLC9m1h4dVQW8wNFvuIwmjWZLFgoIBkL0dE8v4lm1c2OUT44O062RjUxAE2DfVu7eG5fHwcf6iURW/bTASGEmDOJI2qTOGJ58cslgFvm+ZUzxwnMay20W/fPb3Wr6ytb1Uu2KlguHw/lca878LC+Jcbj/e3LtqLQauYFAeNFm9GiTUjTaIndWlVtwcfkB5weLXB0IMuFOtWsDF3jkQ2tPLOjZ1lX0p2OJYbTFdIlG03TSMYMEqnFO7QyWbB4/+w4R86McnWyUve67b3NPLevj9/fs4HultgCjlAIIRpHYonaJJYQQgghhBCrzbLcYX3nnXd48803+fnPfz5Trlepa4u4u3bt4gc/+MEdT1HMl4sXL/LKK68A3BIgTNu8eTMvv/wyr732Gi+99FLd6+Zi165d7Nmzh4sXL5LJZGhvb2fXrl088sgjs/raF2vcYmG56XEC27ptK8HAsZn8V/8Sd2yY5ieeIbnrMaK9/WgNqHg0V5brkbM8lILYHFv+lR2PkyMFijedcO5vjbNnfSthY/G/voViuj5jRZtMxSak6SSjoYZsNkyUbN67MMnHV3P4wa2N6za0xnn2wV6+urm9IRW1lhqlFCXLY6JQbRkYBAHRsE5bMjKvG1Tpos37Z8d478wYAxO1N5sAtq5N8ezePr77yAbWtMbn7fMLIcRyIXFElcQRK4dfzKNHbjxAoIKA0rFfzdyOrOsnvvX++fucU8lWt6tslZ9Ktrq+umx/a5y9fW3LNuFltQqUIltxGMqZ+ApSDYoj5mK8ZPPbgQzHruao1Klm1ZOKcnB7N09v7aIlHl7gEc6f8lQsMZSu4AUBsYhB+zzHEnNh2h4fnpvkvTOjnBrI1W1V3tVcbVX+7N4+tq+TVuVCiJVBYokqiSWEEEIIIYS4ZlkkXBUKBd5++23eeOMNDh8+PHP/9QHNgQMHeOGFF3jxxRdpaVnYxZxXX311Zgy388orr/Daa69x+PBhcrnclz6ZsXnzZl5//fW7fvxijVssnMAyq60EE8m61yjfJ/O//c+4Y0MAFD74GyJrNxDfvGOhhllToBRFy6PseIQNHcOY/YKyUorhos2n40Wuz/kxNI0961vY1J5owIiXprLtMVq0yJseIUOjORpuyOL8lWyF985PcnqkUHPRfUdPkuceXMeu9S2LfhJ+IViOT6ZoM5gpYzo+IUMjFQ9h6PO32WM6Hr85N8l7p8c4OZCtu9nRmapudnx/bx/3rpfNDiHE6iJxRG0SR6wMKggIygX0phvnttaFT/Fy6Znbrfu/N2/zLz9QZCoOflA/2Spruhwfzt+QfL+prYlHNrQueqKOmJui5XI1Z2K5AYmosagHJlw/4PRIgaMDGS6ma1dTMnSNR/va+OaObu5fu3yrWTleQK5sczVdbT8eMnSScWNeY4m5mG2r8njE4JmHe3l2bx/7tnVJ21AhxLInsURtEksIIYQQQghxzZJOuPon/+Sf3FCW9/pgprW1lRdffJEXXniB/fv3L9YQgWoJXOCO/cSvf/9Pf/pTfvjDHzZ0XHeyXMctZkcFAfbwFfRIpG6lKqUU2bf+F6xL52bui6zbSGrP1xZqmDW5fkDWvLaRM5cNIi8I+HS8xEjRvuH+1liIxze20xxdvqebZ0spRcn2GC5YlGyPaMhoyKlupRTnJkq8+8UkF9O3VlXSgD0bWnnuoV62dafm/fMvNZ4fkK+4DKbLZEsOul5t89Exj20+/EBxciBzx82OWNjg4ENreXZfH49v75bNDiHEqiNxRGMt13GvNIFlogJ1SzvBaN8WWp76NsVjH6CFIyQf3jcvn88PFOmKQ6AUkTrJVpmKw4nhPP51meD3dDSxe13rqki6XylM12cob5I3XZrCoUWtEDVWtDg6kOX41RwVt3Y1qzWpKM/s6Obr9yzfalZKKQqmy2jWZCxvgtJoihrz3n58Li6Nl3jv9Cjv36ZVua7Bvm3XWpU3RZf0MqMQQsyKxBKNtVzHLYQQQgghRC1LeiXkhz/8IZqmoZRi8+bN7Nq1i4MHD3LgwAE2bdq02MMDuKFH+5YtW+54fWtrK7lcjjfeeGNRg4TlOm4xe142TWBVCKXqn64q/PoXVE5/PHM71NbJ2r/3p+jR2EIMsSbbC0hXbEK6RnSOLQQLlsvJ0eItC/FbOxM8vLZlxSedBEqRN11GCjaW6xENG7TGI3d+4Bz5geLkcJ4j5ycZKVi3vN/QNZ7c0sH3v9LL+hXetm56Y2Q8ZzKaswiUIh4x6EjNb5uPsaLPyRGX//eHR8nV2ezQNNi7tZPn9/Vz8KFeErEl/RIvhBANJXFE4yzXca9EQaWEVmN+q0djJB7aS9NXdmOkWtGMuc2pa/ECRaZiEyiI1GnLPVl2+GQkf0OF2W2dCXb2ro4KpyuB4wWMFS0mSjZhozGxxGy4fsDJ4TxHB7JcztSuZhXSNR7tb+NbO3q4b01q2T7HLMdnsmgxOFnB8nyiIZ3WRGTRqnNlijbvzbJV+XP7+vnunvX0rPCYTwix+kgs0TjLddxCCCGEEELUsyx2Y9va2tiyZQuPPvoojzzyyJIJbACOHTs28/adTmVMX3P8+PEbgovFsFzHLWYnsC2cscHbthKsfH6K4ofvzNzWmxKs/fv/kFBz6wKMsDbT8ciabrWF4BySo5RSXMmZnJss39BWLWxo7NvQxrqWlb0A7AeKrOkwkrdwA0U8ZNDSgM0Rxwv46GqW9y9Mkq24t7w/FtJ5Zkc3331gLe1Ni7M5s1AqtsdkwWI4Y2J7PuGQTnNTeF6T+goVh/fOjPH2yVGuTNbeaILqZseze/v47iMbWCObHUIIcQOJI+bfch33SuTls+iR2gclAssk2rf5tocvZv15goBMxUHdJtlqomTzyWiB6wpAcG93kgfXNC/bRJjVxA8Uk2WbkbyFhkYqFl6UhJ/RgsXRgQzHB3OYdSq59jbHOLijm6/f00lzbHlWs/IDRa7sMJSZqoyraSRjIZLxxVmiMx2Po+cmOXJmjFMD2RuSJq/XmYryvUc38OzePnask1blQoiVT2KJ+bdcxy2EEEIIIUQ9Szrh6qWXXuLNN98km83y1ltv8fbbb8+8b7o/+oEDB9i4ceOijfHChQt3/dj56j3+05/+lDfffJNjx46Ry+XYvHkzhw4dmumHXstSGPftzPZn6jg3VnrxfR/fr91moNGu/7yLNQaYaiU4dBmlhwiUBv6tC9VeLk32b/7VtTtCYbr/+L/A6OhZtLGXHY+85VVbCKII6q3y3sTxA86Ol5i8KQGooynMvg2tNIWNRf151DMfzxfPD8hUXMaKNl4Q0BQxiEV0QBEE8/c1lx2PDy9n+fWlbM02Hs2xEL93bw/P7OgiEam+rCzF7/mX5XgBubLNUMakUHExdEjEwsQjUxs9SuH7s3ve1hMoxcmBLL84OcpHF9J4dT5eZyrK7+9Zx/cf3XDDZsdK/L6LuVkqr0Vi6QuC2hvZK4XEEbOzEuMIWB2xhPJcPLOCkWy+Zb6vPA8VCkGs6Ut/PdXKVtXvU9jQa/7tGCvZnB4r3XDw4b6uBPd3J1f835rFMJ+v9UopcqbLUN7C9RXJqFE9QKCCukk3883xA04OF/jtlRxXsmbNa0K6xr6NbTyzrYvt3cmZJL7lNtcpmS7jBZvhbAU/UMTDBi3x0HVfz/z/vlz/O3j9236gOH0lx3tnx/jtF5NYdVuV6xx4cC3ff3QDj23vmjlgsty+9+LOJI4Qc7HSX98llpidlRhLrIY4Qqxu8nwRsyXPFTEX8nwRs7VS44glnXD1+uuv8/rrr3PixAn+4i/+gr/8y7/k4sWLADcEO9OT+YMHD/L0008v6Bhzudycrm9vb595O5PJfKkg4eLFi+zevZs9e/bw+uuvz5wKOXz4MC+99BKHDx/mzTffZNeuXUtq3LMxMDBwV4/7+OOPyWQy8zyaufv1r3+9aJ9bN0tEcmn8WFPtCwKfjuNHCDv2zF3ph5/k6tVRuDq6QKO8RgG+HsLTI+jKZy7nqF0jSjHegdKv+1OmFAkrQyib4eOh+R5tY8z1+eIFUPQ0Cl71dlSHRnRLLHvweSnExYqBr279BM2hgN2tHjuSJqF8keNHz8//IBZZoBQVR5GpBBRsBUoRDWtEjPn9hufMgN8Nu/xuxKVg1d7dChuwc12Yx/uj3NsTQteyTFzMMnFxXociVpDFfC0SS9+lS5cWewgNJXHE7a3kOAJWRyyh2yaR7AR+9LrqlkqBpqE7Jl6qHX8k/aXGEaDhGjFAoVN7fmKHmijFO6q9jackK5Nkzn3BB+e+1KcXs/BlXustH7KOhq00orpinqe3d5R1NS6WDQYqBm6NWAOgLRzwlWaP7UmfGBUmzg0xscyeV66vKNoB6UqA6SpCOsRDGvoitLs/efJ3jJeqrcrPjHoU7dq/1xqwvTvEY/0Rdq2PEAtVUJOf8+vJzxd2wGLRSBwh7kRiCYklVmossRriCCGmyfNFzJY8V8RcyPNF3M5KjSOWdMLVtJ07d7Jz505effVVLl26xNtvv82bb745E9xcuHCB1157jddee43W1lYOHDjAH/7hH/Lss882fGxfZiI91wDjZsePH+fjjz++JXg5dOgQmzdvZvfu3ezevZsLFy7cUqJ3McctGijwCRWz+HVaiwCkLpwiXMrN3C5t3EFlw9YFGNytFODpEXwtNKdkKwWY0RbMSPMNmzt64NFaHiXi1T4Zvdy5U4lWRU9DQzUs0SrnanxWDHHF1FE1fipdkYA9rS6bE0FDPv9iU0phe5C3fNIVhRdAxIBkBDStdhudu+EFinMTHp8MuVzM1M/6v6fD4InN0anNjhX4DRdCiAaSOKI2iSOWP92qEOjGtTuCgI6Pf4HT0onduRa7a92X+vjTyVbq08/QUHDv9luuscIJyrH2G+bjqcoECTv3pT63aCw3qM73S55GRFc0GQtUymrqc181DS6UDTJu7Xm1oSm2JnweaPZZEw1Yjh0pA6UwXUW6HJC3FaCIhzRaYvMXS8xF0Q44M+pxasRlrFT/NOnaZp3H+iPs7YvS3rQ4YxVCiKVEYonaJJYQQgghhBDimmWRcHW9TZs28dJLL/HSSy8B8Itf/IK33nqLt956ixMnTpDNZjl8+DCHDx9G0zQ8z1uwsS3Eae1pL7/88kwQU8uuXbs4cOAAb7/9Ni+88AIff/xx3Y+1kOOerf7+/lld5zgOIyMjM7d3797N/fff36hh3Zbv+zOZu48//jiGYdzhEfPPnRjBTbdjJFI1329+cYbc0LXSzeG167nv7/0IPRJZqCHOCKbaV9heUG0jOMuVdMvzOT1WwrRu/N1ek4zw6PpuoqH1jRjuvJvL88XxAkaLFumyS6cOTREDfZ53HpRSXMpUeO9Cms/HyzWv+craZp79yhruX5Oa9c9rObFdn0zRZihrgu2xVtPYGg/PtM2YL1cmyvzi9Cjvnx2jaNZ+jepIRXn20Q0ceqyPDR3xRf/bIpaPpfBaJJaHpTj/azSJI6pWehwBKz+WUEphfXEGLRpDm0q6si5+TrZcIFwukBi+yH1PPk1s8467Gos71bZa1+H8f/NnAOz4W394wzXDBYuzEzfOGXeuTXFPx5q7+pxi9u72td7zA8ZLNmMlmy5doylsLNicfihv8tuBHJ+MFbC92gk/G1pjHNzezROb22falC83FdsjXbQZTFfAC9gS1klEQ4sSO5Utj48upPng7BgnB4p1atRV447f3z3dqrx5RcZ5YnYkjhBzsVTngI0ksUTVSo8lVnocIYQ8X8RsyXNFzIU8X8RsLcX533xYnqtY19m/fz/79+/nJz/5CZcuXeLVV1/l5z//+YKdeLi+rO1cP+eXeVK1trbe8fEHDx7k7bff5vjx47z99tscOHBg5n2LNe7Zunz58qyuO3PmDA888MDMbcMwlsQf8sUYR2Bb+JkJwqlU3So84eYWjFQLfjGPFomy5o/+M8LxeM1rG8kPFDnLwVcaTdHwrB83XrI5M1bEDa4tF+saPLS2mW2dyWW7OFzv+eIFARNFh9GihaFptDZF5v1rDJTi7GiRI+cnuJK9tTKYBjy2qZ3nHuxlU0diXj/3UuD5AfmKy3CmQqZUbbOZjIXoapnf3wvT9vjg03F+cXKEcyPFmtcYusaT9/Xwg69u5Kn7ewgZ1d/j63teL5W/cWJ5kOeLuB1dl8oVEkfUt5zjCFj5sURgmWhKEQpfm0dbZ49fe3xLG01b7kW7i99z1w/I2T6hkE7l2AmKx6oft3TsBM2P7gZgomzz6U3JVo+sb2XLCpwrLnWzec76gSJTcRjOmwQKWuLReT+8UYvl+XwymOfoQIahvFXzmoih87XNHTyzo5t7OhPLMp7z/IBsyWY4Y5KrOOiaRjIeptVY+NfZsuVy9Is0v/58nN9dyuIFtdOsomGdAw/28tzePh7f0TUTdwgxbam8HoqlS2IJiSVuZznHEis9jhDievJ8EbMlzxUxF/J8EbezUuOIZZ9w9c4778yU8p3upb6Q5jrRv75s7vUBRiNcX9b3rbfeuiG4WcrjFnfHmRhBC4Vv2/Is2ttH56G/Q/79f0fz4/uJdPcu4AirXD8gYzooBZHQ7P6wBoHi3GSZK/kbE4KSEYPH+9tpb1r4Cl2N5AeKrOkwnKtuiiSjoXnfFPH8gOODOd67MMlEybnl/RFD4xtbu/iDr6ylJ1W/ReVypJSiaLqM5S3Gsia+UsQjBu3J+U1oU0rx2VCBt0+O8KvPxrHdOqf5O5t48fGNPLevn+6WlfW9FkKIpUziiPokjlja/Er5hjmLXyljXvhs5nbq0afuKtnKCxTpqYSRkK4x+M9+OvO+wX/2U+579HUKlsvJkcIN1XL2bmhlU7skWy01tueTLjuMl2yCQJGIhgg1eGFLKcVgzuToQJZPhvI4fu35b39bnG/e28MTmztoWobVrJRSlCyP0ZzJaNYkUIqmiEFHKrrgYylZLr/9YpJffzbB7y7XT7LSgEfu6eD5x/p55uFekrHZH3wSQghxK4kl6pNYQgghhBBCrCbLbmXr8uXLHD58mLfeemumXzpUF7yud+jQIX7wgx80fDxbtmyZeXuuPci/zKmM48eP39In/WbXByHHjx+/4X2LNW7RGH65hJ/PEWpuue11gWNjJFtY+/f/IXpo4RdYHT8gXXEwgPAsT9GWHY+To0WK9o2luPta4zyyvnXWH2c5UEpRsDwGcxUsT5GMGvO+KWK5Pr8ZyPDLi2kK1q3lzRMRg2/d28Pv3beGlvjKWoQ3nakWH5MVLNcnEtJINc1/y8Bc2eHI6VHePjnKUKZS85poWOebD/fyg69u4pF7OpblaX4hhFhuJI6okjhi+fMKWbTrWoJXPv0EgmsVMZv3fn3OHzNQirxZTcIP6RqF335M8aNrP/viR8eZ+PVvObNmC/51vzK7elsk2WoJUUpRcXzGijY5y0FHoykSmvf57s1M1+eTwRxHB7IMF2pXs4qGdJ7Y3MHBHd1s6Vie1aymW5BfTZepOD4RQ6O5AfHEnRRNl6NTSVYnB7L4dZKsALauTfGVDpe9/VH+4JtflZPGQghxlySWqJJYQgghhBBCiBsti4SrTz75hDfeeIPDhw/fcGLk+oCmtbWVF198kRdeeIH9+/cv2Nj27Nkz8/ZsyuBOj79en/PZaGtrI5fLceDAAd566626110ftNwcwCzGuEVjqCDAGRtEj92+Mo4KfALbIr55+6IkW5muR9Z0Cev6rBekhwsWn44Xb9jUMTSN3etb2LzCNnbKtsdQ3qRoeTRFQrTG5zfRqmC5/PJimt9czmB5t540b28K8wcPrGX/9m7i4ZWzCO9e1+IjP93iIxYiGZ/flz8/UHxyKcNbJ0c4dj5dd9PjvvUtvPjVjXx3z3qaV1hlNiGEWIokjriRxBHLn/JcgkoJI9lcva0UldMfz7w/tnk74a41c/64RdvD9hWxqQq011e3mnbhv3kd5//5k5nb27uSbOtKzvlzifnnB4qC5TJasDFdj7Bh0BwNNzSpSSnFlazJ0YEMvxvO4/q157+bOpr45o4evra5Y1nGGX6gyFccRjIm6aKNQpGKh+hc4GpWBdPlt+cm+fXn45wcyN02yWpbbzO/t2sd3961jr7OJj744IMFHKkQQqwcEkvcSGIJIYQQQgghbrWkE65+8IMf8Pbbb98w+b4+oNm8efPMqZGdO3cuwghvLJF74cKFO14//bVcX0p3Lo4fPz7zMa4/TXO7zwW3BiULPW7ROF4+S2CZhFK3VrfychnswUs03b8Lv1QksrYPPda04GOsOB45yyOi6+izSLbygoBPx0uMFO0b7m+JhfhqfzvNK6j9gRvAQKZC1vKJhnRa5zkJZ6Jk896FST6+WntRfn1rjGcf7OVrmzsa3mJkoQSBomC6jOYqjOctlIKmaGNafIzmTH5xcoR3T42SrtGaEaA5HuZ7j27gxcc3cu/621ehE0IIMT8kjriVxBErg29Wq2dOJ9K448O4E6Mz70899vScP6bpeJRtj+hUstXN1a2maadOYpw6if+VB9nQEuPhtc138yWIeeT4AYWyy2jRwg8gHjZoiTc2qd90fY4P5jh6OcPoTfHatFhI58ktnRzc0c3mjuV5UKZseUwULIbSFfwgIBrWaUs2NontZoWKc0Mlq9vkWLFjXTPf2bWeb+9ax8bua4mQvu/Xf5AQQoiaJJa4lcQSQgghhBBC1LakE67efPNNNE27IaDZtWsXP/jBDzh06BCbNm1axNFdc+jQIQ4fPsyxY8due931JXRfeeWVu/pc00FJa2srP/7xj2977fUnb2qVMl7IcYvGUL6HOz6CEb91EVv5Hum/+gvc0UGsi5/T+u0XCbV1LOz4lKLkeBSs6gaOPovF6YLtcXKkQMW9cWH4no4EO3tbFrxdQ6N4fkDW0ci7Gl22S0ssMq+L91ezFY6cn+T0SIFa6/Lbu5M8/1Avu9a3Lst2HrWULJfJgs1wpoLrB0RCOq2JyKyed3PheD6/OTfJ2ydHODWQq3vd3q2d/OBrG3nmoV6iy/A0vxBCLGcSR9xK4oiVwc9n0cPXEmrKp65Vt9IiUZIP7Z3Tx3P9gKzlEgnpM3PCWtWtpsX+4n8kvncP+/raV8wccjlyAii4GmdGChiGQaLBbQOVUgxkKhwdyPK74TxencyfLZ0Jvrmjm8c3Lc9qVtPVcQfTFYoVl5Chk4gZhIyFO/CTrzgcPVdNsjp15fZJVveub+H3dq/jWw+vo79bqs0JIcR8kVjiVhJLCCGEEEIIUduSTriC6sLegQMHeOGFF3jxxRdpaVl61UF+/OMfc/jwYY4fP87Fixfrlrh94403gGqAUq/XeS6X40c/+hGtra28+uqrNa+Z/n68/PLLtx3X9Oc7cOAAhw4daui4xeJwMxMQeGihW6tW5d//G9zRQQDMc6eJ9m2hafO2BRubUoqC5VFyPGLXbeDcznjJ5uRo4YZF5bChsXdDG+tb4g0c7cLxA0W67DCULVP0IG4oEpHQvGxYKaU4N1HiyPlJLkyWa16zZ0Mrzz3Uy/bu1Jf+fEuB7fpkSjZD6Qpl2yOkN25T5NJYkbdPjvL+2TFKllfzmu6WGM/v6+PQ4xvp61yep/mFEGKlkDjiVhJHLG/K9/GLefREdY6hPJfKZ7+beX/y4X3o0du3Gb9eoBRZ08XQtJkE9XrVraaFTp/igbFLGFu77vKrEHcrUIqi5TGSLzNk6oQ0RSoWImQ0blmn4njValYDWcbqVLOKhw2e2tLBwR09bGxf+GrKX5ZSiqLpMpYzGclZKKVIREN0NC9cy8BcebqS1Tinr+Rum2R13/oWvrN7Pd/a2Uu/tPQUQoiGkVjiVhJLCCGEEEIIcaslnXD15ptv8vzzzy/2MO5o165d/PCHP+S1117jlVdeqdnD/OL/n70/f5LqTvN8z8/xNfYdggiWgEAISUggBaAFSZlKEaRyX0HK6u57225XSerqmflhpq2l1j/QaaKnZ/reGbNboL5tMz9NZUFbr9VdlYCUmcrUxiIJtLFEsBOr77uf5Ts/BMRCLHgQHhu8X2aZ5uf41/18I3TCOY+f5/s8vb3av3+/GhoadOzYsWnfa/fu3RNWb0wV4Bw4cEB79uxRZ2fntOV09+/fr1OnTqmhoUGHDh2a93lj4XnFguzhAfmrJn/JmrvwldIn/zS6HWxdrcaXfrFwczNG8ZytvOOVnGx1JZ7TN0PpCftaqkJ6pqNR1aEl/VFVEmOMEjlbV+M52a6nqpBfFWVa9O16RqdvJPS7C8PqS+YnPe/3WfrWxmb97LF2rWlY/olrrmcUzxTVH8tpODXy89ZUBOalZWAmb+v9rwd19HSfevrTU47x+yx959FVeuXZ9Xr+kdZ7pgobACxnxBHEEfciL5+TjCfLGmn9l7vwtUw+N/r8bNoJGmOUyDsjrdICYxelM1W3uuXy/+uAVj335CxmjrmwXU+xrK2BVH6kiqtfqg6MZOSUu5KrNHJuXIxm9fGlqM70JaetZrVpRbVeeqhVuzY0TTiHlot80VUkVdDV4YwKjqtgwKeG6uC8/E6nEs8U9dG5IX1wdkhf3iHJasvahpFKVk+s1loWdQDAvCOWIJYAAAAASrWksximCmz+3b/7dzp06JBOnDiheDyuhoYGdXZ26le/+pVeffVV1dXVLcJMx4KQ/fv3a8+ePTpw4MDo6ozDhw/r1VdfVWdnpw4dOqSGhoZp32d8j/Px5XfHu/U++/btU3d3t15//XV1dnaqoaFBp06d0q9//WsdPnxYe/fu1TvvvDPj8co1byy84mCfLJ9fls83Yb+TiCn6Pw6PblvBkFb9k/+bfKGFWaHrekaxXFG266mihC/ejTE6N5zR5Xhuwv7NK2q0ra1uwb7wnk/pgqPr8ZzSRUfVoYCqQgF5nnvnF95B0fF0/GpMf+gZVixrT3q+IuDTns0r9eNH29RcHZriHZYPY4zSeUcD8Zz6Yzm5RqoI+dRUU95WjLeO9eXVhI6d7tMHZ4dUdLwpx61fWaNXdnXoZ0+tU0td6dUkAADzjziCOOJe5KYT0rhqRpkvxtoJBlpaVbGh9Gq2GdtVznYU9o/FEneqbnVL9OMTinx4XM3P7Cz5eJi9nO1qOF3QcKYoSaoK+ssWR0wlU3B08lpcH1+OaihdnHJMVdCvFx5o0Z6HVmpd4/KrZnVr4cb1aEbRVFF+n6WaioBqKhfmq7FYuqCPzg3rg7ND+urqzElWj65r0A9vVrJa00ySFQAsJGIJYgkAAACgVEs64Wq8d999V6+//vroBf+tHuqxWEynTp3SqVOn9MYbb+jgwYP68z//80WZ49tvv61XXnlldLVHNBqVNBKMvPXWW3rjjTfu+B4HDhwY7Uk+XfleaWQlSE9Pj/bv368333xzQrDX3d2tI0eOTLvSZD7mjYXlZtNyE1H5ayeWszauo+h/+2uZwliVo5Z9f67QqtULMi/H8xTNFuV5KmmVs+sZfTGQ1MC4L/QtSdtX1+uBluXfHiFnu+pL5BTL2aoI+tVQWZ6kp0zR0QcXo/rgYkSZ4uQbLnUVAf1oyyp976FWVYeXzcf8lHJFR9FUUdeiGeWKroJ+S7VVwXmpIhVNF/TeF/06drpffbHclGMqgn79oGu1XnluvZ7Y0FT2ZC8AQPkRR0xEHLE8GWPkxKPyVYwkeRvPnXDNX/f0d0q+Lim6npI5W6HbKtGWUt3qlgv/9q9IuJoHnjHKFBwNpAtK5pyRhKBwYN4WoRhj1BPJ6OPLMX3Rl5Q7TQbQQytr9N2HWvX0+iaFA74pxyxl6bytwUReN6I5uZ5RZcin5tryL9yYSixd0IfnRtoFfnU1oRlyrPTYaJLVaq1uXn4JbQBwLyKWmIhYAgAAAJhoWdyJf+edd/RP/+k/lTQW1Iw3ft9rr72mnp4e36Fn/QABAABJREFU/at/9a8WbH7jdXV16cCBA3f9+u7ubvX09JQ8/o033ihL8DHXeWPhGM9Tsf+6fBWVk74gTrx/RMW+q6PbNTu/pbqnvr0g87JdT5FsUZakUAlfwhcdT5/2JZTIO6P7/D5Lz3Y0qX2ZVwsqOp4GUnkNpgsK+n1lS7SKZYv6Q09En1yJynYnfxa21ob1s8fa9MIDK0r6b7BU2a6neLqgG9Gc4tmifJal6gq/quehZaDreTrZE9XR03062ROZdpX5Y+sa9KvnNugH21erpiJY9nkAAOYHccT0iCOWFy+fk3FdWb6RRQ2Wz6+V//AvVey/Ljs6pNqdz5f0Pq5nFMsWFfD7JiXxPPL/PaCc7erjq3EV3bEKn9tX12vTPbAYYilzPE/xnK2BZF4Fx1Mo4Fd95fxdc6YLjk5cjemTy7HRClq3qw6NVLP67kOty7ItedHxFEsXdC2SUTo/krxWWxmQ3zf/cVI0XdBHZ4f0p7ND+voOSVbb1jfqh9vX6KXH29XeRJIVACwlxBLTI5YAAAAARiz5hKtjx47p9ddfl2VZMsaou7tb+/bt044dO9TQ0KDe3l6dOnVKv/nNb3Tq1CkZY/T2229r48aNi7aqBJhPTiohL5dVoG5idati/3WlT/xxdDu4sl0r9v2TBZlTwfEUyRYU8PkUKKHyUKbo6NMbSWXtsepMFQGfvt3ZrMYyJSctBtczGs4U1JcYqTZQVxEsy2r0gVRe750f0mfXE1MmBG1ortIvt7bryY6mean8tBA8zyiVtzUQy6k/kZcxRlUhv5rnIclKkm5Eszp6uk+/+2JAsWluMtVXBfWzp9bplV3rtal9cUrDAwDuHnEE7iVuMi7LPzlRxFddq4atO+UL33nBgjFG8VxRxkhB/+RrRtv1dOpGYkKy1UMraki2mkcFx1UkU9RgqiBjjCpDAdXPU3s7zxj1DGf08eWovuxLyZ3ixrEkPdJaq+8+tFJPdTQtu0UcnmeUzNnqj2c1lMjLyFJ1eP5iivEiqYI+PDukD84O6ZtrMydZPb6+UT+42S6wbRm2ZgSA+wGxBAAAAIBSLPmEq3379o0+PnTo0KQe6hs2bNDu3bv1L/7Fv9D+/fv1L//lv5QxRm+88QbBDe45xvNkD1yXv6p64n7jKX7sv0i3vtb1B7Tqn/xf5QtXKPLhcUmat5YfuaKjaM5WyO8rKdknnrP16Y2E7HGZQ3XhgL7d2azq0JL/SJqSZ4zi2aKuJ/JyPE/VofK0vItmijpydlCnrsWn/ML+sbY6/XJbux5tq1u2re2yBUdDyZH2HkXHVSjgU0N1eRLVblewXX1wdkhHT/fpq6uJKcdYlrRr8wq98uwGvfjYKoWDd26NCQBYmogjcK8wnicnHpGvYmKVIeM68gWDskKlJZNkiq4KrlHFFEk0nmf0WV9yQrvqtfWV2tZG0nm5GWOUKboaTBUUyxUVsHyqnse2gam8rRNX4/r4clTRrD3lmJpwQC9uatGezSvVXr/8qlnlio6GbrYMLDiuwkGf6qtD8/Y7vWU4mR9tF/jN9eSMY5/YMJJk9dLjq9XWuPx+xwBwvyGWAAAAAFCKJZ3d8M477ygej8uyLP3VX/3VpMDmdm+88YYikYj+9b/+14rH4/o3/+bf6J//83++QLMF5p+TTsg4jqzKiatgs2dOTmgl2LD7Jwq1rZUkXfi3fyVpfhKuskVHsbytcGByS5Kp9KcK+mIgOaFK08qakJ5b36zQFCv2l4NU3tbVeF5521FVKKCqMiSNJfK23j03pE8uxyatPLckPbO+ST/f1q7O5uqp32CJc1xPiayta8MZxTJFBXyWqisDqp2H1fzGGPX0p3T0dL/e/2pA2XE3Ecdb1VCpfbs69MunO7S6mVXmALDcEUfgXuLlMhPaCY7uz+cVaF5RUuK943pKFexpr7nPDqcVy40l47RUhfT0usZlm9S/FLmeUSJXVH+qqJztKOT3q74iOC+/Y3OzmtWHl6L6sj85bdvsLavq9NJDK/VkR6OCyyweK9iuUjlb1yPZ0TbkNZUB1cxThbBbhpP5sUpWMyRZWZb0xIYm/XD7Gn338XatWoZtGQHgfkUsAQAAAKBUSzrh6tChQ5Kkzs5OvfrqqyW95u2339Y777yjRCKhv/7rvya4wT3DGCNnsF++iopJ+zNnToxuBxpb1LjnZ5KkyIfHFf34xOjjciZd5YqO4nlHYf+dk62MMbocz+nccGbC/vWNlXpybeO8rzyeDznb1fVETomcrcqgX/VlaIWYKTr63flh/eliRM5td0UCPksvPrhCP320Tavq7twyZinK5B0NJHK6Ec3J9TxVhfxqqZuf9h6pnK0/fDWgo6f7dGkwM+WYoN/S7q1tevnZ9dq1eeWybccIAJiMOAL3Eiceky8wFrrbsWEF6htlPE/+6jtXoDLGKJG35bOsKa+7+5J5Xb3ZEluSasN+Pb+hmWujMik4nmLZogZSebmeVBn0q2Ge2qhni45OXI3ro0tRDU/TNruuIqDvbFqhPZtXqm0ZxRWeZ5QpOEpkixpI5JXJOTKa3zbktwwl8/rgm5Ekq3M3Zk6y6ups0g+7RpKsWkmyAoBliVgCAAAAQKmWdMLViRMnZFmWuru7Z/W6HTt26OjRo+rt7Z2nmQELz8um5RXyCtTVT9hvWZZWvPIXSvzpmNKffqCWvf+LfKGRL/BvVbe69bhcCVc521Es7yjkn/qmzXjGGH0zlJ5wE0eStrTW6tHW2mW3ar7geBpI5jWcKSjoL8/NkoLj6o8XI3q/Z1h5x5vwnCXphU0teuWJNVpRM783EuaD43qKpQu6FskqmbUV8PtUW+mX3xcs+7E8Y/TF5biOnu7TR+eGZLtTL+V/YFWtXn52vX765Fo1LcPfKQDgzogjcK8wris3GZOveqSyqfE8Df3/Dsh4nirWPSD/T/5M/qrOGd8jZ7vKO54qp2iVnC44+nIwNbod8Fn61oYWhadoO4jZyRQdDaWLimaK8llSVSgwL0lsxhhdjef04aWoPr+emLRw45bH2ur00sOt2rG2YdlUsyrYrtJ5R0PJnIaTBbmekd+yVBn2q6l2fpLWbhlM3Kxk9c2gzvWlph1nWdL2zubRSlYr65dPEhsAYGrEEgAAAABKtaQTrm6V7t24ceOsXtfZ2Tn6euBeYQ/3yxeeJjnE8ql2x7Nq/P5ehZpXSppY3UqSoh+fKEuVq7ztKJYdaUdyp2QrxzM605/U0LjV1ZakJ9c2aEPT8mqH53iehtNF9SXz8luW6srQ/sMxUk/ar//2bo8yU7S6e2Z9k/5s+xqtrl9+K6PTeVuDibxuRLJyjVFV2K/meapmNZzM670v+nXsdL8Gbkvsu6Uq5NcPd6zRy7vWa9t62uMAwL2OOAL3CmMXZIyRZY0kyBT7rsrLjlTvzJ07Iy/7kxlf73pGiYIzZQKV43n6vG9iu7mn1jaoNrykvyZY0lzPKF1w1J/MK1N0FPD7VFcRmJdrT9uTPr4c08eX47qRnPoauDYc0IsPrtB3N69cFlVyXc8ok7cVzxQ1mMgrW3RkjFQR9Km2MjjvVdcGE7nRSlbnZ0iy8lnSjo0t+sH21frutnatIMkKAO4pxBIAAAAASrWkv0ltaGhQIpFQT0/PrF53axVJQ0PDPMwKWHhePis3nZ5U3eoWN5NWqG2tgk0to/vGV7cav28uCVcFx1X0VrLVHb7sLjiePr2RULLgjO4L+Cw9v75ZrfPc8qGcPGMUzRZ1I56Ta6SacGDOLRBdz+iTyzH9XX9YOc+SNDHZ6onV9fqHO9ZqQ/MyS0pzPUVvVrNK5Wz5fZZqq+bnxojtejpxIaKjp/v02cWoplnIr8c3NOpXz27Q97tWq4qbhwBw3yCOwL3CKxZHVizclLvw9ehjX0WVKh94eMbXJwuOZDTp+tUYo68G0srYY9ehD7ZUa21DVXkmfp+xXU/RbFEDqYIc16gi6CtLy/Gp9CfzOhkP6FLWL6evf8oxD7XW6HsPterp9U1LvppVvugqlbM1lMwrkirIM0Z+n6XKkH9BqtEOxHP64OxIktWFOyRZ7XygRT/Yvkbf3damlmWQwAYAuDvEEgAAAABKtaTvPt8qw3v06NFZve7o0aOyLEs7duyYp5kBC8uODMkKTt2CzSsW5KuoVKChaXTf7dWtbplLlauC4ymSLSpYQrJVuujo1PXEhPZ4lUGfXuhsUX1F+VvJzQdjjFIFR9diWeUco5qwXwHf3G5WeMbos+sJHflmUJFsURPunkl6uLVG/2jHOj3UWjun4yy0VM7WQDynvlhOnjGqDgfUPE9JdVeHMzp2uk/vfTmgZNaeckxTTUg/f2qd9u1ar42rltfvEgBQHsQRuFe46ZR8/rGwPX/hq9HHVY88Lss/fUiftx3lilNXt7qayKs/XRjdbq4K6vH2qRd3YHo529VwuqDhmxV9q0J+VYfKn+Bku57O3Ejqo8tRXYpmNdVXOZVBn779wAq99NBKrWtcuolzrmeUztuKZYoajOeUL7qSLIVDPtVXB+e8uKUU/fFblawG1dOfnnbcrSSrW+0C5yvGAQAsLcQSAAAAAEq1pBOuXnvttdG+5//m3/wb/fN//s/v+Jq//Mu/HH28b9+++ZwesCC8YkFOIiZ/zVjiiJtNK/7e36ru2W5ZPr8qNjwoa1wy0FTVrcY/N9uEq6LjKZotKOjz3bFaUTRb1Gd9STnjSg41VAT07c4WVQb9szruYskUHV2P55TKO6oKBdRQObebJsYYfdWf0t9/M6D+VGHS8xuaqvQ/7Vynre11y6bVne16iqULuhrJKp2zFfBbqpunala5oqM/fTOkY6f79M315JRjfJb03MOteuXZ9frOY6uW/Ep+AMD8Io7AvcLLpWUFRyol2ZFBObHh0eeqt05/Te8Zo0TeUdDvm3R9Gc/bOjs0lmQS9vv03PrmBUl0uRcYY5QpuhpI5ZXI2fL7fGWpgjuV4XRBH12O6cTVmLJTtCCXRmKJ7z3Squc2NKtiicZbuaIzUsUqUVAkPdIm0++zVBX2q2mBkpj6Yjl9cHZQH3wzpN6B6ZOs/D5LT25q0Q+3r1b3VpKsAOB+RCwBAAAAoFRLOuFq79692rBhgy5duqQ33nhDkmYMcP7yL/9SBw8elGVZamho0F/8xV8s1FSBeePEIrJ8E2+UJP7w98p9/bly575U3TMvqurhx0efm6661S2zrXJVdD0Nl5hsdSOZ15cDKY3v7raqNqxnO5Z+KwtppGViX7KgSKaocMCnhqq5twE5P5TW3309oKvx3KTnGoOenmmy9Y9f2q5AYEl/HEsaubmUzjvqv1nNyhijmor5qWZljNG5G0kdPd2vP34zeHPl+2Srm6r08q4O/eKZDq1qqCz7PAAAyxNxBO4FxnVkCgX5auskSfmL58ae9PtV9fC2aV+byjvyjJl0DV50PZ3uS064Xt/V0bRsFkYsJtczSuZt9ScLytqOwgG/6iqCZV8w4XpGX/WPVLM6P5SZcozfMnqw2tX//O3H9GBrXVmPXw6O6ymddxRLFzWYzKlgezLGqDLkV8MCVbGSpL5Y9mYlqzsnWT21aaSSVfe2tgVpZQgAWLqIJQAAAACUasnf4T906JB27Nghy7L0xhtv6K//+q/V3d2tnTt3qrOzUydOnFBPT48OHjyoeDwuY4wsy9KhQ4cWe+rAnBnHkRMbkq+yenRf4cYVZb84ObLhOnIS0ZKrW40fU0rCle16imSKCtwh2coYo4uxrC5EshP2b2yu0vbVDUt+tbztehpMFTSQKsjvs1RfEZjzjZPL0az+/psBXRiefJNkRU1Irzy+Wub6V/JZWvJVrYrOzWpWwxll8o6CAUv181TNKpkt6ndfDujo6T5dHc5OOSYU8GnPtna98ux6PbWp5Y4tLgEA9yfiCCx3XrEoM+4yp3C5Z/RxxYbN8lVM3Tau6HrK2K7CtyVbGWN0pj85oe33o6tq1UoFnxnZrqdY1tZAKi/bNaoM+tVQOfeFGbeL52x9cjmqT67ElMw7U45pr6/QdzevUGjgnMJ+aWNL9ZTjFpoxRrmiq1TO1mAir1imKGOMAn5LVaGAqsML99XT9Wh2tF3gpcGpE9akkSSrpx+8mWS1tU2NJFkBAMYhlgAAAABQiiWfcNXV1aW/+Zu/0csvvyxJOnXqlE6dOjVpnDFja3T/6q/+Si+++OKCzRGYL04iNhKw30yoMp6n+NH/PPq8FQiq5Rf/eHT7TtWtbimlypXtehrOFuX3WQrMkNDiGaOvB9O6nsxP2L9tVZ0eWlmzpJOJHM9TJF1UXzIvWVJtxdxbgfQl8vr7swP6qj816bmGyqD2Pd6u3Q+ulE9G79+Y06Hm1Wg1q1hWffG8ZKTqCr+a68p/I8L1jE5fiuno6T59cn54QjvK8Ta31+mVZ9frJ0+uVX0Zqo8BAO5txBFY7oxd1K1SVMZ1VLh2cfS5qs2PTf0aY5TI2Qr4Jif190azimTt0e1VtWFtWVl7+1vgprztajhT0HC6KCOpKuRXVai8VXs9Y3R+KK2PLkX1Vf/ESsG3+H2WnlrXqO893KpHVtXK8zy9P3xuipELy3Y9pXO2YpmiBuN5FR139PfUWF3+yl8zuR7JjrYLvDRNVTBp5Hf5zIMr9IPtq7VnW7saqokpAABTI5YAAAAAUIoln3AljZTxPXHihF599VV9+umn045raGjQoUOHtHv37gWcHTA/jOfJHu6Xf1x1q8xnH8se7BvdbnjpFwo2rRjdLqW61fix0yVc2a6nSLYovzVzspXjevq8Pznhxo3Pkp5a26iOxqlX3C8FrmcUzRbVl8jJNVJ1KDDnak1D6YKOnB3U59cTk26UVIf8+sXWdn3/kVaFAyPtWlx36hZ5i63oeIqk8ro6nFG26CoU8M1b24/BRE7vnunXsdP9Gk4VphxTUxHQj3es1SvPrteWdQ1lnwMA4N5GHIHlzDi2rJvXqMW+ayMJWDdVPvjolK/JO66KrjepRWAkU1RPdKx6aFXQr2fWNS3pxRGLwRijbNHVQKqgWK6ogOVTdXjuizJuly44OnElpo8uRxUdF0uN11Id0ksPrdSLD65UQ2WwrMe/G8YYZQuuktmiBpN5JbIjCYEBv6XKcEA1lQv39ZIxRtciWX14dqRd4OUZkqwCfkvPbF6hH3at0e6tbSRZAQBKRiwBAAAA4E6WRcKVNLKq5OTJkzp27JgOHTqkEydOKB6Pq6GhQZ2dnXrllVf0y1/+crGnCZSNk0rIuI4s/80EnUxKiT8dGX0+0NKqxhd/NOE1T/3m/5j7cW8mW/mkGZOtCo6rk9cTShfHEodCfkvPb2jWiuql2Y7Bu7ni/1o8J9v1VB0OKOCb2yr1eK6oo+eGdOJKTLcXZgoHfPrJo6v040fbVB1auh+3xhilcrb6YzkNJHLyzEiiU8s8tJcp2K4+PDekd8/068zl+LTjdmxs1q+eW6/vPt6uyiX8uwMALH3EEViujF2UfCOxQP7yhdH9VkWlwms7J433jFGy4Ch0WyvBouvpzEBydNtnSc+tb1I4UN5qTcuZZ4xSeUf9ybwyRUdBv1/1FeWt0mSM0aVoVh9eiupMX1LuFFVdLUldaxv0vYda9fia+kVvzW67nlI5W9FUQUPJgmzHlSxLlSGfGqtDC5qw53qevrme1PHzw/rkQkR9sdy0YwN+S7s2r9QPt6/W7q1tVMcFANw1YgkAAAAAM1l2d7F3797NahHc84wxcob65K8YqxKV/OCYTGGsbd+KfX8uK1Delc6ON5JsZUkK+Ke/AVNwXB2/llDWHku2qg759UJni2rDS+9jxRijVMHRtVhWedeoKuhX1RyTeNIFR++dH9KHl6KTWuAFfJa+93CrfrmtXXUVi78afToF21U0VdDVSEbZgqtQ0Kf66lDZb+wYY3T2elLHzvTrT98MKlecurpXS21Yv3y6Q/t2dahjZU1Z5wAAAHEElhtj26OtxQtXekb3V258ZHRRxnh525XrScHAxGu5s0NpFd2x69Un2uvVRAKKpJH4J5a11Z/My3Y9VQT9qq8s7+8mZ7v69FpcH12Kqn+aqq71FQF1b16pPZtXakXN4i1eMcYoU3CUzNgaulXFyrIU8FuqCvlVu4BVrCQpV3D06cWojl+I6ERPROm8M+3YoN/SrodW6ofb12j3Y6tUxzkOACgjYgkAAAAAU1l6mREA5KYS8ooFBWrrJUl2bFiZ0ydGn6/eulNVD20t6zFvJVtJUnCWyVZNlUF9q7NZFYHJN34WW7rg6Hoip3TeUVUooPqKua3kz9mu/tAzrPd7Iiq63oTnfJb04qYVevmJ1WpeolW+jDFK5mz1xbIajI8k8NVUBtRSV/75RlIF/e7Lfr13pl/Xo1OvQPf7LH3rkVa98tx6ffuR1hkT/QAAAO4nnm1LPp+M8STLGvmfMap66LHJY0erW01MthrKFNQ3LsmnrTasB5qrb3/5fafguIpkihpMFWRkVBUMzHlBxu2uxXP66FJUn16Py3YnV7OSpC2r6vT9R1q1c13DnCvv3q2iM76K1UjimU+WKsN+NdYsbBUrSRpO5nX8QkTHLwzrzJW4nGl+d9JIktVzD7fqh9tX68XH2lS7BFovAgAAAAAA4P6x5BKuPvvsM0WjUTU1Nenxxx9f7OkAC854nuyB6xOrW/3pqGRuJvf4fGr+yT8s6zEdzyiaLUpm9slWK6pD+nZn86LdIJhOtujoRjKvRM5WRcCvhjmucC46nv50MaLfXRhWzp5YocmS9Gxns/6sa41W1VXM6Tjz5VY1q8vDGeVtV+GATw015a9mVXRcHT8f0bEz/fr8UnRSm8VbNrbWau+udfrZk+vUskR/ZwCA5YU4Avcc15YVCMqyfFr5q9dkR4fkplOq3vLEpKGZoiNjjHzW2DW543n6ejA9uh3wWdq5tnHBE2iWkkzR0VC6oEimKL9lqTocKOv1cNHx9PmNhD66FNXV+NQLDqpDfn1n0wq99NBKtddXlu3YpfK8m1WssrYGEzklc7YsScGAT9UVfvl9C5u0ZIxR70Baxy8M6/iFiHoH0jOOr6sK6oUtrere2q7nHl5JkhUAoCyIJQAAAADcjSWTcPXWW2/p4MGDisfjE/bv27dPBw8eVF1d3eJMDFhgTiImzy4qUDHy5Xtx4IZy35wefb726e8ouGJV2Y7nekbRbEHmHkm2ytuu+lMFRTNFBf0+NcyxJYjjefrkckzHzg0pVZjcwmLn2gb9gx1rta6xaopXLy5jjBLZm9WsEiPVrGorA6qpKG81K2OMevpTeveLfr3/1eC0rT5qKwL68c612vtMhx5d13Bf3+wDAJQPcQTuRcYYebYtf2jsus0KhlXzxBb5qya2XnY9o3TBnXQtf344o7wzVpH18bY6VQWXXkXa+eYZo1TeUX8qr0zBUdDvU31FsKzXogOpvD6+HNPJqzHlbG/KMZtWVOt7D7XqmQ3NCgcWNn4q2K7SeUfDybyGkwU5niefZakq7Fdz7cJX5rUdT2euxEYqWZ0fViRdnHH8upZqdW9t0+6tberqbKIqLgCgbIglAAAAAMzFoidcJRIJ7dixQ729vTJmpBSKZVmjj//mb/5Ghw8f1pEjR/Sd73xnMacKzDvjOrIH++SvGmvzkfzjkdHHViCopu/9smzHcz2jSLYoz0ihGb60zjuuTtyWbLWyOqRvLaFkq6LjaSCV11CmoIDlU11FYE43UTxjdOpqXEfODSqWtSc9/2hbnf7RjrXatKJmilcvroLtajhZ0NXhjAqOq3DQp6Z5aAcSzxT1+y8H9O6Zfl0Zzkw5xrKkXZtXaN+u9ere2qbwfXiTDwAwP4gjcE/zPMmY0es3Y4wsS/KFJ1dEShdHkt3HV2qK5WxdvZlwL40slNh4n7USdDxP8ZytvkReRddTRdCv+jkuxhjPM0bfDKT0x96ILkxzLRwO+PStjS363kMrtX4Bf/+uZ5TJ24pnihpKFZTO2bIsKRTwqaYyIL9v4Rc+JLNFneyN6vj5YX16KaZ80Z12rGVJj69v0p5tI0lWna21CzhTAMD9gFgCAAAAQDksesLV9u3b1dvbOyER4FZgI40EOp7nqbu7W729vero6FiMaQILwolFZDxXln/sT7P+Wy9JlqV87zeq//b3FahvKsux3JttBD1jlnWyle16GkoXNJAqyCdLteHgnNqCGGP0RV9Sf//NoAbThUnPP9BSrX+0Y60ea6+fy7TLzvOMkjlbN6JZDScLkjVSUaqmsrwf847r6WRPRO9+0a+TPVG50/QMXNdSrb3PdOhnT61TW+PCt0oBANz7iCNwLzPuxGQUYxflq6qR5Z+YvO64njJFR+Fx1/OuZ/TlQGp022dJT95HrQQLjqdodiQ+MJ5RZSigqlD5romLjqcTV2P6Y29Ew5mpKzOta6zU9x5u1bc2tqhygRYc5IqOUjlbw8mCIqmCPGPk91mqDC1OFStJuh7J6pMLwzpxIaJvriembTcuSRVBv557eKW6t7bphUdXLdqcAQD3B2IJAAAAAOWwqAlX//pf/2v19vZKGgloDhw4oO7ubm3YsEGJREJHjhzRv/yX/3J0pcm+ffv0ySefLOaUgXljHFv2cL/8VRNb0wVXrFJD909l9GNVbthclmO5nlE0V5TrGYVmaGexlJOtXM8okinqRiInI6kmHJhzotW5obT+7usBXR9XDeCWtQ2V+kc71mr72qXVCi9fdDWcyuvacFZ5x1Vl0KfGmvK2SJGky0NpHTvdr99/NaDkFBW/JKkq5Nf3u1Zr36716upsWlK/JwDAvYU4Avc8b6RqlXFsye+XsYsKNLRMHGKMYnlbfsuacN3VG81OuH5/bFWdasOLvtZq3mWKjobSRUWzRfkkVYXKW8kpkbP1wcWIProcU86eXJ0p4LP0zIYmff/hVj24omber4VdzyieKSqZczWYzI1WjKoI+VVfPbdFKHOZ09nrCX1yIaLjF4Z1I5qbcfyKurB2b23T7sfa9MzmFVTDBQAsCGIJAAAAAOWyaN+6JhIJvfnmm7IsS52dnfrtb3+rDRs2jD5fX1+vvXv3as+ePdq7d6+OHTumkydP6j/+x/+on//854s1bWDe2JEhyUiWb+KXzF6xKF9FpSo2PCirDElOt5KtHNcoPNtkq5qQvrVhcZOtXM8onivqejwn1zOqDgfnfCPlSiyrv/2yXxej2UnPtdaG9Q+2r9WuDU2LctNiKp5nlMgWdT2SVSRdkM+yVFNZ/mpWqZyt978a0Ltf9KunPz3tuJ0PNOvlXev13cfbVXUf3MwDACwu4gjcD4zrSbKUOv6+Uif+qNCKNlVt3amml34xOiaVd+S4nsKBsfghWXB0KTZ2TdtYGdTmJdgCu1xczyhdcNSfzCtTdBTw+1QXnltr8dtdi+f0fs+wPr8xdYWmxsqgfvBIq/ZsXqnaimDZjns7Y4yyBVfxdF4Xo44yBSPf5ZhCQb+qQgFV1y7OdXiu4OizSzF9cmFYJ3siSuWcGcdvbq9T97Y2dW9t05YltpgFAHDvI5YAAAAAUE6Ldmf84MGDo4/ffvvtCYHNePX19Tpw4IAeeOABSdK/+lf/iuAG9xyvWJAdGZC/pnbyc/mcKjo23vfJVsYYJXK2ridyyjtGNSG/AjO0QixFMm/rf3w9oJNX45Oea6oK6uUnVus7m1YsejWvW3JFR0OJvK5FsrIdTxVhv5pqQmW9SeF6Rp9djOrdM/365MKwHHfqvh9tjZX65dPr9IunO7S2pbpsxwcA4E6II3A/MK4jyVOx76pMIa/CtYvyjYsVskVHadtRxbjrYc+MtBK8dfVmSXpqbeOSWTRQTrbrKZa1NZDKy/aMKgI+1VeGyvb+njH6qj+l93uHdTEyeVGGJG1ortJPH23TMxua5i1eKDqe0nlbkVRBw8mCio4r4xnZrlFNWGqqCcs/x5jobgwn8zrRE9En5yM6cyU2bcwgSQG/pScfaNGebe36zqOrtLq5atqxAADMN2IJAAAAAOW0aAlXR44ckSR1dXXpF7/4xYxjOzs79eqrr+qdd97RqVOnlEwmVVdXtxDTBBaEPdQvy++XZY18WW7HhhWoa5RxbPmqa+SrnpyINVueMYrNKdmqRYEytuQolTEjq9avxfPK2o6qQwE1VM7tpoLjenq/N6Jj54ZUdL0Jz9WGA/rltna99FDrjO0WF8qtViHXoxnF0sXRalZ1VeVdPX89ktWxM336/ZcDiqaLU44JB3166fF27du1Xk8+0CLfIpwPAAAQR+B+YGxbsnwq9l0d3Vdxs7247XqK522F/b4JiffXEnmlCmPVhR5eWaOGyvmruLQYcrar4XRBw5mR69WqkF9VZUx2KjiuTlyJ64+9EUWyk6+JLUk71jXop4+266HW8rcN9DyjTMFRIlvUUCKvZG6klXcw4FNVyK/ayoBc11M4sLDX4cYYXRxM6/j5iD65MKzegemr30pSXWVQ397Sqj3b2vXcwytVe4+dhwCA5YtYAgAAAEA5LVrC1YkTJ2RZlrq7u0sa//LLL+udd96RJPX29urxxx+fx9kBC8dJJeTEI/LX1kuSjONo+NC/l+X3q3r7s2r4zo/m/EW+Z4xi2eWXbJUpOLqRyClZcFQZ9KthjqvWjTH6eiCl//plvyKZiTdQgn5LP3+sXT95rE2VQf8077Bw0nlbg4m8bkRHWidWhnxlr2aVKTj609eDevdMv87eSE477vH1jdq3a72+37WamyUAgEVHHIH7gVfIyUsn5OXGqitVrB+psBDP2wpY1oTKVbbrqSeaGd2uCfm1pfXeuCHomZEkpIF0QcmcI7/PUk04UNbKXfFcUX/qjerjy1HlHW/S8+GATy9uWqEfbVmlVXUVZTuuJBVsV6mcreFUXsPJglzPyGdZqgr71VwbLuuxZsN2PH1xJa5PLgzr+IWIIqnCjOPXNFdpz9Y27d7Wru2dTXOuRgwAwHwglgAAAABQTouWcBWPx2VZlnbu3FnS+M7OztHH0Wh0vqYFLCjPLqp444r8VdWjiTTp05/ITcYlSYmj/0WVGzYr8Oj2uz/GzWQr2/UUCkyfSLSUkq1ytqu+RE6xnK1wYO6JVpI0kMrrv37Rr3NDk1dj79rQpH+8c51aahbvhoY00jIkli7oWjSrdM6W32eptjIgfxlX7XvG6IvLcR0706ePzg2rOMUNJUlaURfWz59ap18+06HO1rlXWAMAoFyII3A/8AoFFQdvjO2wLIU7NqnoerJdTxW3XddfimVlj2vr1rW6Qf5lXo3U8TzFc7YGknkVHE/hgF/1ZU7+vxLL6v3eiM7cSMiboiteU1VQP3xklfZsXqnqcHm+PnE9o3TeVixT1FA8p2zRlSVL4aCl2srgov53S+ZsneqJ6JMLEX16Map80Z12rGVJ2zoa1b2tXbu3rtLG1tqyV/wCAKDciCUAAAAAlNOiJVzdMj5omcn4furxeHyeZgMsHGOMiv3XJElWYOTGgXEdpT75w+iYUNtaVT3yxF0f41ayVdEduUExnaWSbFVwXPUnC4pkCgr4faqvCM75S/uc7erI2UF9cDEy6SZKR1OVXn16vR5etXgJRcaYkZYhyaIGEzkZY6m6ovyr2fvjOb13pl/vfdGvoeTUq9ODfku7t7Zp3zPr9ezDK5f9TToAwL2NOAL3MmMXZA+MJVwFV7bLX1mldM6eVNkpb7u6HM+Nbq+oDqltESsjzVXBcRXJFDWYKsgYo8pQQPWV5fvqwjNGX/Yl9YeeiC7HslOO2dhSrZ8+2qan1zfN+ZrYGKNccaSK1VAyr1i6KKOlUcVKkm5Eszp+YaRV4DfXpk48u6Ui6NezD61Q97Z2fefRVYs+dwAA7haxBAAAAIByWPSEK+B+5cSjcpNxBeoaRvdlz56Rlx5r7db0o1/JusvqRssp2aroeBpM5zWYKsrvs1RXhkQrzxh9cjmmv/9mQJnbVmbXhgP6hzvW6MVNi5dUlC+6Gs64Gs540sWYKsIB1VeHytoaJV909cHZIb17pk9fXk1MO+6RtfXa98x6/WjHGjVUz72aGAAAAO6e8TzJdVQcl3BVsX6TXM8oYzsK39aq7XwkMyFJpqu9ftlVGjLGKFN0NZgqKJ4vyidLVaFAWa/V87ar41di+uPFiGJZe9LzlqQnOxr108fatHnl3BZk2K6ndM5WNF3QUKKgojMSj1SE/KqvDpb1mn+2XM/o3I2kjl8Y1ifnI7oenTrp7JaWurBefKxN3Vvb9MyDK1QRWvz26wAAAAAAAMBSQMIVsAg8uyh74Jr81WNf5BtjlD75p9HtwIpVd13dyjNG8WWQbGW7niKZovqSeVmSaisCZbn50BvJ6L+c6dONZH7Cfp8l/eCRVXr58dVlawkyG65nFM8UdT2aUSSZ10DKVUXQUlNtWH5/edoGGmP09bWE3j3Trz+dHZq2DUhTTUg/3blWv3ymQ5tX15fl2AAAAJg74zjyXE/O8MDovvDaDcrZrixpQjJVMm+rLzVWvbSjoVKNVcsngd71zMjPkCwoZzsK+f2qC8998cV40WxRf+qN6JMrMRWmaKddEfBp94Mr9KMtbVp5lxWbjDHKFBylsrYGE3klckUZYynolyrDAdWUsULX3cgVHX1+Kabj5yM60RNRMjc54Wy8TW212rOtTbu3tuvRtQ3yUfkWAAAAAAAAmISEK2ARePmcjGdk+ceSoYrXLk1oG9Lwwg/vqrrVrWSrwh2SrQqONynZqrUmpOcXINnK9YyGMwX1J/PyjFQTLk+iVSxb1N9+1a/TN5KTntvWXq9/8nSH1jRUzvk4s5XOj9x4uRHNyfWMKkM+NVaHVBMuT5KVJA0n83rviwG990W/+mK5Kcf4fZa+vaVV+57p0LcfXaVgmZK8AAAAUEaeIzcVl3HGkmKCbeuUKjoTrt+MMTo7nBnd9lnS1ra6BZ3q3XI9o2imqBvJnFxPqgz61VBZ3kSxy9Gs/tAzrC/6kpqqS15LdUg/2rJKux9coarQ3X01kis6iqQKujacVcFx5ZOlivDItf5iVxmLpgo63hPR8fPDOn05Jtudvleg32dp5wPN2rOtXS8+tkprmqsXcKYAAAAAAADA8kTCFbAI3HRSvsDEP7/UyT+OPvZVVqv2yW/N+n09YxTP2cq7nipmSLZyPE+nbtyebBXW8xua5zXZyvWMYrmibsRHEo+qw8GytAkpOp5+3zOs310YmnQjobU2rD9/ukNdaxoW9KZH0fEUSxd0LZpVOmfL77NUWxmQ/2YSnetOXl0/WwXb1Sfnh/XumX59fik25Y0kaWSF+r5d6/WTnWvVfJer9gEAALAwjOPKiQxO2Ge1rpFrjILWWMLVcLao2LhKRQ+21Kj6LhOHFooxRom8rWuxnIqeUU2Z2wa6ntGZvqT+2DusK9MsQti0olo/fbRNT3Y03dWxx1etjaVH2h/WVC1+FStjjC4PZUZbBV7oT804vrYyoG8/skrd29r0rUdaVVsZXKCZAgAAAAAAAPe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+      "text/plain": [
+       "<Figure size 2400x2400 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Layout 4x3\n",
-    "fig, axes = plt.subplots(4, 3, figsize=(10, 9.5))\n",
-    "# fig.suptitle(\"Power vs Error\", fontsize=18)\n",
-    "\n",
-    "exps = [\n",
-    "    \"exp0\",\n",
-    "    \"exp1\",\n",
-    "    \"exp2\",\n",
-    "    \"exp3\",\n",
-    "    \"exp4\",\n",
-    "    \"exp5\",\n",
-    "    \"exp6\",\n",
-    "    \"exp7\",\n",
-    "    \"exp8\",\n",
-    "    \"exp9\",\n",
-    "    \"exp10\",\n",
-    "    \"exp11\",\n",
+    "# Choose what to plot\n",
+    "params = dict()\n",
+    "params[\"def_mec\"] = [\n",
+    "    \"def_idm\", \n",
+    "    # \"def_ran\"\n",
     "]\n",
-    "ess = [1, 1000]\n",
+    "params[\"atk_mec\"] = [\n",
+    "    \"atk_mle\",\n",
+    "    # \"atk_cen\",\n",
+    "    # \"atk_ent\",\n",
+    "    # \"atk_ran\"\n",
+    "]\n",
+    "params[\"ess\"] = [\n",
+    "    1,\n",
+    "    10, \n",
+    "    100,\n",
+    "    # 1000\n",
+    "]\n",
+    "params[\"delta\"] = [\n",
+    "    0.1,\n",
+    "    # 0.5,\n",
+    "    # 0.9\n",
+    "]\n",
+    "\n",
+    "# Filter results\n",
+    "res_path = {}\n",
+    "for def_mec, atk_mec in product(params[\"def_mec\"], params[\"atk_mec\"]):\n",
+    "    arg_str = \"ess\" if def_mec == \"def_idm\" else \"delta\"\n",
+    "    arg_vals = [x for x in params[arg_str]]\n",
+    "    for arg_val in arg_vals:\n",
+    "        res_path[(def_mec, atk_mec, arg_str, arg_val)] = (\n",
+    "            cur_dir\n",
+    "            / folder\n",
+    "            / f\"output_{def_mec}_{atk_mec}_{f'{arg_str}{arg_val}'}/results\"\n",
+    "        )\n",
+    "\n",
+    "# Layout 4x3\n",
+    "fig, axes = plt.subplots(len(exp_names) // 3 + len(exp_names) % 3, 3, figsize=(8, 8))\n",
+    "# fig.suptitle(f\"Power vs Error\", fontsize=15)\n",
+    "\n",
+    "# Colors\n",
+    "cns_num = len(res_path)\n",
+    "base_palette = sns.color_palette(palette=\"RdBu\",n_colors=cns_num+5)\n",
+    "palette_cn = dict(\n",
+    "    zip(res_path.keys(), base_palette[-cns_num:])\n",
+    ")\n",
+    "bound_color = base_palette[0]\n",
+    "bn_color = base_palette[1]\n",
+    "alpha = 0.2\n",
+    "\n",
+    "# Loop over results\n",
+    "for i, exp in enumerate(exp_names):\n",
+    "\n",
+    "    # Plot BN & bound\n",
+    "    ax = axes.flat[i]\n",
+    "    path_bn = list(res_path.values())[0]\n",
+    "    bn = plot_bn(path_bn, exp, ax)\n",
+    "    plot_bound(path_bn, exp, ax)\n",
+    "\n",
+    "    # Plot CNs\n",
+    "    for res in res_path:\n",
+    "        (def_mec, atk_mec, arg_str, arg_val) = res\n",
+    "        path = res_path[res]\n",
+    "\n",
+    "        cn = plot_cn(path, palette_cn[res], exp, ax, type=\"-\", fill=True)\n",
+    "\n",
+    "        # Legend\n",
+    "        # cn.set_label(f\"CN, {def_mec}: {arg_str}={arg_val}, {atk_mec}\")\n",
+    "        cn.set_label(f\"CN, $S={arg_val}$\")\n",
+    "        if i == len(exp_names)-1:\n",
+    "            ax.legend(\n",
+    "                loc=\"lower right\",\n",
+    "                frameon=True,\n",
+    "                fancybox=False,\n",
+    "                framealpha=1,\n",
+    "                facecolor=\"#f0f0f0\",\n",
+    "                edgecolor=\"#a8a8a8\",\n",
+    "            )\n",
+    "\n",
+    "    # Title\n",
+    "    (n, e, c) = get_title(exp_names[i])\n",
+    "    ax.set_title(f\"$N$: {n}, $E$: {e}, $C(\\mathcal G)$: {c}\")\n",
+    "\n",
+    "    # # Log Y scale\n",
+    "    # ax.set_yscale('log')\n",
+    "    # ax.set_ylim(0.9*cn.get_ydata().min(), 1.1*bn.get_ydata().max())\n",
+    "\n",
+    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
+    "plt.show()\n",
+    "fig.savefig(\n",
+    "    f\"{plots_path}/results_linear.pdf\", dpi=1200, bbox_inches=\"tight\", transparent=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "538c8d24",
+   "metadata": {},
+   "source": [
+    "## Summary statistics plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1558a5b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_cn_privacy(deff, atk_mec, exp_name) -> float:\n",
+    "\n",
+    "    # Retrieve def info\n",
+    "    def_groups = re.findall(\"^(def\\w+)_(\\w+\\d\\.?\\d?)$\", deff)[0]\n",
+    "    def_mec, def_par = def_groups[0], def_groups[1]\n",
+    "\n",
+    "    # Get CN info\n",
+    "    cn_path = (\n",
+    "        cur_dir\n",
+    "        / folder\n",
+    "        / f\"output_{def_mec}_{atk_mec}_{def_par}/results/cns/power_cn_{exp_name}.csv\"\n",
+    "    )\n",
+    "    cn_data = pd.read_csv(cn_path)\n",
+    "    cn_cols = [c for c in cn_data.columns if \"CN\" in c]\n",
+    "    cn_mean = cn_data.loc[:, cn_cols].mean(axis=1)\n",
+    "    cn_auc = metrics.auc(cn_data[\"error\"], cn_mean)\n",
+    "\n",
+    "    return cn_auc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "429482d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def map_label(label, verbose=False, show_cen=True) -> str:\n",
+    "\n",
+    "    if \"exp\" in label:\n",
+    "        val = complexities[label]\n",
+    "        if verbose:\n",
+    "            mapped = f\"$C(\\cal G$$)$: {val[2]} ($N$: {val[0]}, $E$: {val[1]})\"\n",
+    "        else:\n",
+    "            mapped = val[2]\n",
+    "\n",
+    "    elif \"def\" in label:\n",
+    "        def_par = re.findall(\"^(def_\\w+)_[a-z]+(\\d+\\.?\\d?)$\", label)[0][1]\n",
+    "        if \"idm\" in label:\n",
+    "            par_name = \"$S=$ \"\n",
+    "        elif \"loc\" in label:\n",
+    "            par_name = \"$S*=$ \"\n",
+    "        else:\n",
+    "            par_name = \"$\\delta=$ \"\n",
+    "        mapped = par_name + def_par\n",
+    "\n",
+    "    elif \"atk\" in label:\n",
+    "        mec = re.findall(\"atk_(\\w+)\", label)[0]\n",
+    "        if mec == \"cen\" and not show_cen: mapped = \"\"\n",
+    "        else: mapped = mec.upper()\n",
+    "\n",
+    "    elif \"Avg.\" in label:\n",
+    "        mapped = \"Avg.\"\n",
+    "\n",
+    "    elif type(label) is float and label < 0.005:\n",
+    "        return \"$^{\\dagger}$\"\n",
+    "    elif type(label) is float and label < 0.01:\n",
+    "        return \"$^{\\ddagger}$\"\n",
+    "    elif type(label) is float and label >= 0.01:\n",
+    "        return \"$^{***}$\"\n",
+    "\n",
+    "    else:\n",
+    "        mapped = \"\"\n",
+    "\n",
+    "    return mapped"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dedf89b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Build tensor of results\n",
+    "data = np.zeros((len(deffs), len(atk_mecs), len(exp_names)))\n",
+    "res_tensor = xr.DataArray(\n",
+    "    data,\n",
+    "    dims=(\"deff\", \"atk_mec\", \"exp_name\"),\n",
+    "    coords={\"deff\": deffs, \"atk_mec\": atk_mecs, \"exp_name\": exp_names},\n",
+    ")\n",
+    "for (i, j), k in product(product(deffs, atk_mecs), exp_names):\n",
+    "    res_tensor.loc[i, j, k] = get_cn_privacy(i, j, k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "221001bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Useful functions\n",
+    "\n",
+    "# Best attack for a given defense, marginalized over experiments\n",
+    "def best_atk_for_def(deff):\n",
+    "    def_res = res_tensor.sel(deff=deff)\n",
+    "    df = def_res.mean(dim=\"exp_name\").to_pandas()\n",
+    "    atk_best = df.idxmax()\n",
+    "    value = df.max()\n",
+    "\n",
+    "    return atk_best, value\n",
+    "\n",
+    "\n",
+    "# Best attack for a given experiment, marginalized over defenses\n",
+    "def best_atk_for_exp(exp_name):\n",
+    "    exp_res = res_tensor.sel(exp_name=exp_name)\n",
+    "    df = exp_res.mean(dim=\"deff\").to_pandas()\n",
+    "    atk_best = df.idxmax()\n",
+    "    value = df.max()\n",
+    "\n",
+    "    return atk_best, value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83e16f45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.reset_defaults()\n",
+    "plt.style.use(\"seaborn-v0_8-paper\")\n",
+    "plt.rcParams.update(\n",
+    "    {\n",
+    "        \"font.size\": 5,\n",
+    "        \"axes.labelsize\": 9,\n",
+    "        \"axes.titlesize\": 9,\n",
+    "        \"legend.fontsize\": 7,\n",
+    "        \"xtick.labelsize\": 6,\n",
+    "        \"ytick.labelsize\": 6,\n",
+    "        \"xtick.major.width\": 0.5,\n",
+    "        \"ytick.major.width\": 0.5,\n",
+    "        \"lines.linewidth\": 0.8,\n",
+    "        \"figure.dpi\": 300,\n",
+    "        \"savefig.dpi\": 300,\n",
+    "        \"axes.edgecolor\": \"black\",\n",
+    "        \"axes.linewidth\": 0.8,\n",
+    "        \"text.usetex\": True,\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "vmin = 0.25\n",
+    "vmax = 0.75\n",
+    "colors = ['#ffffcc','#c2e699','#78c679','#31a354','#006837']\n",
+    "cmap = LinearSegmentedColormap.from_list('Custom', colors)\n",
+    "kwargs = {\"vmin\": vmin, \"vmax\": vmax, \"cbar_kws\":{\"extend\": \"both\"}}    #RdYlBu #coolwarm_r #Spectral"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0663086",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot single experiments\n",
+    "fig, axes = plt.subplots(len(exp_names) // 3 + len(exp_names) % 3, 3, figsize=(9, 11))\n",
+    "\n",
+    "for i, exp_name in enumerate(exp_names):\n",
+    "\n",
+    "    # Retrieve results\n",
+    "    exp_res = res_tensor.sel(exp_name=exp_name).to_pandas().reset_index()\n",
+    "    yticklabels = list(exp_res[\"deff\"].map(map_label)) + [\"Avg.\"]\n",
+    "    exp_res = exp_res.drop(columns=\"deff\")\n",
+    "    xticklabels = list(exp_res.columns.map(map_label)) + [\"Avg.\"]\n",
+    "\n",
+    "    # Add averages\n",
+    "    exp_res[\"Avg.\"] = exp_res.mean(axis=1)\n",
+    "    exp_res.loc[\"Avg.\"] = exp_res.mean(axis=0)\n",
     "\n",
-    "# Loop over subplots\n",
-    "for i, ax in enumerate(axes.flat):\n",
-    "    plot_bn_bound(f\"{exps[i]}\", ax)\n",
-    "    plot_cn(f\"{exps[i]}\", ax, ess=ess[0], color=CN_color, type=\"-\")\n",
-    "    plot_cn(f\"{exps[i]}\", ax, ess=ess[1], color=CN_color, type=\"--\")\n",
-    "    (n, e, c) = get_title(exps[i])\n",
-    "    ax.set_title(f\"Nodes: {n}, Edges: {e}, Complexity: {c}\")\n",
+    "    # Plot\n",
+    "    ax = axes.flat[i]\n",
+    "    sns.heatmap(\n",
+    "        exp_res,\n",
+    "        annot=False,\n",
+    "        yticklabels=yticklabels,\n",
+    "        xticklabels=xticklabels,\n",
+    "        ax=ax,\n",
+    "        cmap = cmap,\n",
+    "        **kwargs, \n",
+    "\n",
+    "    )\n",
+    "\n",
+    "    ax.set_xlabel(\"Attack mechanism $g$\")\n",
+    "    ax.set_ylabel(\"Defense mechanism $f$\")\n",
+    "    ax.set_title(map_label(exp_name, verbose=True))\n",
+    "\n",
+    "    # Colorbar\n",
+    "    cbar = ax.collections[0].colorbar\n",
+    "    ticks = [0.3, 0.4, 0.5, 0.6, 0.7]\n",
+    "    cbar.set_ticks(ticks)\n",
+    "    cbar.set_ticklabels([str(x) for x in  ticks])\n",
+    "\n",
+    "    # Separate plots\n",
+    "    n_rows, n_cols = exp_res.shape\n",
+    "    lw = 3\n",
+    "    ax.axhline(4, color=\"white\", linewidth=lw)\n",
+    "    ax.axhline(8, color=\"white\", linewidth=lw)\n",
+    "    ax.axhline(n_rows - 1, color=\"white\", linewidth=lw)\n",
+    "    ax.axvline(n_cols - 1, color=\"white\", linewidth=lw)\n",
     "\n",
     "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
     "plt.show()\n",
+    "\n",
+    "# Save figure\n",
     "fig.savefig(\n",
-    "    f\"{plots_path}/results_logx.pdf\", dpi=1200, bbox_inches=\"tight\", transparent=False\n",
+    "    f\"{plots_path}/privacy_by_exp.svg\", bbox_inches=\"tight\", transparent=False\n",
     ")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d558218",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get best attacks\n",
+    "best_attacks = pd.DataFrame(columns=[\"def_mec\"] + exp_names)\n",
+    "best_attacks[\"def_mec\"] = deffs\n",
+    "best_attacks.set_index(\"def_mec\", inplace=True)\n",
+    "best_attacks_val = best_attacks.copy()\n",
+    "\n",
+    "for d in deffs:\n",
+    "\n",
+    "    # Compute best attack for each exp\n",
+    "    def_res = res_tensor.sel(deff=d)\n",
+    "    atk_best = def_res.idxmax(dim=\"atk_mec\").to_pandas()\n",
+    "    val_best = def_res.max(dim=\"atk_mec\").to_pandas()\n",
+    "    # val_best = round(val_best, 2)\n",
+    "\n",
+    "    # Populate the dataframes\n",
+    "    best_attacks.loc[d] = atk_best\n",
+    "    best_attacks_val.loc[d] = val_best\n",
+    "\n",
+    "best_attacks.reset_index(inplace=True)\n",
+    "best_attacks_val.reset_index(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d51ac8a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot\n",
+    "fig = plt.figure(figsize=(7, 3))\n",
+    "\n",
+    "data = best_attacks.copy()\n",
+    "data_val = best_attacks_val.copy()\n",
+    "\n",
+    "# Get data\n",
+    "data[\"Avg.\"] = data.apply(\n",
+    "    lambda row: best_atk_for_def(row[\"def_mec\"])[0], axis=1\n",
+    ")\n",
+    "data_val[\"Avg.\"] = data_val.apply(\n",
+    "    lambda row: best_atk_for_def(row[\"def_mec\"])[1], axis=1\n",
+    ")\n",
+    "\n",
+    "d = {k: [best_atk_for_exp(k)[0]] for k in exp_names}\n",
+    "d[\"def_mec\"] = [\"Avg.\"]\n",
+    "data = pd.concat((data, pd.DataFrame(d)))\n",
+    "\n",
+    "d = {k: [best_atk_for_exp(k)[1]] for k in exp_names}\n",
+    "d[\"def_mec\"] = [\"Avg.\"]\n",
+    "data_val = pd.concat((data_val, pd.DataFrame(d)))\n",
+    "\n",
+    "val = data_val.drop(columns=\"def_mec\").astype(float)\n",
+    "annot = data.drop(columns=\"def_mec\").map(lambda x: map_label(str(x), show_cen=False))\n",
+    "\n",
+    "# Plot\n",
+    "xticklabels = val.columns.map(map_label)\n",
+    "yticklabels = data[\"def_mec\"].map(map_label)\n",
+    "ax = sns.heatmap(\n",
+    "    val,\n",
+    "    annot=annot,\n",
+    "    fmt=\"\",\n",
+    "    yticklabels=yticklabels,\n",
+    "    xticklabels=xticklabels,\n",
+    "    cmap=cmap,\n",
+    "    **kwargs,\n",
+    ")\n",
+    "\n",
+    "# Move x-axis on top\n",
+    "ax.xaxis.tick_top()\n",
+    "ax.xaxis.set_label_position(\"top\")\n",
+    "\n",
+    "# Labels\n",
+    "plt.xlabel(\"Model complexity $C(\\cal G$$)$\")\n",
+    "plt.ylabel(\"Defense mechanism $f$\")\n",
+    "\n",
+    "# Colorbar\n",
+    "cbar = ax.collections[0].colorbar\n",
+    "ticks = [0.3, 0.4, 0.5, 0.6, 0.7]\n",
+    "cbar.set_ticks(ticks)\n",
+    "cbar.set_ticklabels([str(x) for x in  ticks])\n",
+    "\n",
+    "# Separate plots\n",
+    "n_rows, n_cols = val.shape\n",
+    "ax.axhline(4, color=\"white\", linewidth=lw)\n",
+    "ax.axhline(8, color=\"white\", linewidth=lw)\n",
+    "ax.axhline(n_rows - 1, color=\"white\", linewidth=lw)\n",
+    "ax.axvline(n_cols - 1, color=\"white\", linewidth=lw)\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "fig.savefig(\n",
+    "    f\"{plots_path}/privacy_best_atk.svg\",\n",
+    "    bbox_inches=\"tight\",\n",
+    "    transparent=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "956d52d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get ATK-CEN AUC\n",
+    "atk_cen_auc = pd.DataFrame(columns=[\"def_mec\"] + exp_names)\n",
+    "atk_cen_auc[\"def_mec\"] = deffs\n",
+    "atk_cen_auc.set_index(\"def_mec\", inplace=True)\n",
+    "\n",
+    "for d in deffs:\n",
+    "\n",
+    "    # Compute best attack for each exp\n",
+    "    val = res_tensor.sel(deff=d, atk_mec=\"atk_cen\")\n",
+    "\n",
+    "    # Populate the dataframes\n",
+    "    atk_cen_auc.loc[d] = val\n",
+    "\n",
+    "atk_cen_auc.reset_index(inplace=True)\n",
+    "\n",
+    "# Compute difference with best attacks\n",
+    "diff = atk_cen_auc.drop([\"def_mec\"], axis=1).astype(float).to_numpy() - best_attacks_val.drop([\"def_mec\"], axis=1).astype(float).to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9da2c46e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot difference with best attacks\n",
+    "fig = plt.figure(figsize=(7, 3))\n",
+    "\n",
+    "data = best_attacks.copy()\n",
+    "data_val = best_attacks_val.copy()\n",
+    "\n",
+    "# Get data\n",
+    "data[\"Avg.\"] = data.apply(\n",
+    "    lambda row: best_atk_for_def(row[\"def_mec\"])[0], axis=1\n",
+    ")\n",
+    "data_val[\"Avg.\"] = data_val.apply(\n",
+    "    lambda row: best_atk_for_def(row[\"def_mec\"])[1], axis=1\n",
+    ")\n",
+    "\n",
+    "d = {k: [best_atk_for_exp(k)[0]] for k in exp_names}\n",
+    "d[\"def_mec\"] = [\"Avg.\"]\n",
+    "data = pd.concat((data, pd.DataFrame(d)))\n",
+    "\n",
+    "d = {k: [best_atk_for_exp(k)[1]] for k in exp_names}\n",
+    "d[\"def_mec\"] = [\"Avg.\"]\n",
+    "data_val = pd.concat((data_val, pd.DataFrame(d)))\n",
+    "\n",
+    "val = data_val.drop(columns=\"def_mec\").astype(float)\n",
+    "labels = data.drop(columns=\"def_mec\").map(lambda x: map_label(str(x), show_cen=False))\n",
+    "annot = np.round(diff, 4)\n",
+    "annot = np.concat([annot, np.atleast_2d(np.zeros(annot.shape[1]))], axis=0)\n",
+    "annot = np.concat([annot, np.atleast_2d(np.zeros(annot.shape[0])).T], axis=1)\n",
+    "print(annot.min())\n",
+    "\n",
+    "# Plot\n",
+    "xticklabels = val.columns.map(map_label)\n",
+    "yticklabels = data[\"def_mec\"].map(map_label)\n",
+    "ax = sns.heatmap(\n",
+    "    val,\n",
+    "    annot=annot,\n",
+    "    fmt=\"\",\n",
+    "    yticklabels=yticklabels,\n",
+    "    xticklabels=xticklabels,\n",
+    "    cmap=cmap,\n",
+    "    **kwargs,\n",
+    ")\n",
+    "\n",
+    "# Move x-axis on top\n",
+    "ax.xaxis.tick_top()\n",
+    "ax.xaxis.set_label_position(\"top\")\n",
+    "\n",
+    "# Labels\n",
+    "plt.xlabel(\"Model complexity $C(\\cal G$$)$\")\n",
+    "plt.ylabel(\"Defense mechanism $f$\")\n",
+    "\n",
+    "# Colorbar\n",
+    "cbar = ax.collections[0].colorbar\n",
+    "ticks = [0.3, 0.4, 0.5, 0.6, 0.7]\n",
+    "cbar.set_ticks(ticks)\n",
+    "cbar.set_ticklabels([str(x) for x in  ticks])\n",
+    "\n",
+    "# Separate plots\n",
+    "n_rows, n_cols = val.shape\n",
+    "ax.axhline(4, color=\"white\", linewidth=lw)\n",
+    "ax.axhline(8, color=\"white\", linewidth=lw)\n",
+    "ax.axhline(n_rows - 1, color=\"white\", linewidth=lw)\n",
+    "ax.axvline(n_cols - 1, color=\"white\", linewidth=lw)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73430f4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Best attack for a given *class* of defenses, marginalized over experiments\n",
+    "print(\"\\n * Best attack for a given *class* of defenses:\\n\")\n",
+    "\n",
+    "def_class = \"ess\"\n",
+    "def_labels_subset = [x for x in res_tensor[\"deff\"].values if def_class in x and \"idm\" in x]\n",
+    "res_subset = res_tensor.sel(deff=def_labels_subset)\n",
+    "avg_atk = res_subset.mean(dim=[\"deff\", \"exp_name\"]).to_pandas()\n",
+    "best_atk = avg_atk.idxmax()\n",
+    "print(f\"Class IDM + `{def_class}` ->\", best_atk)\n",
+    "\n",
+    "def_class = \"ess\"\n",
+    "def_labels_subset = [x for x in res_tensor[\"deff\"].values if def_class in x and \"loc\" in x]\n",
+    "res_subset = res_tensor.sel(deff=def_labels_subset)\n",
+    "avg_atk = res_subset.mean(dim=[\"deff\", \"exp_name\"]).to_pandas()\n",
+    "best_atk = avg_atk.idxmax()\n",
+    "print(f\"Class LOC + `{def_class}` ->\", best_atk)\n",
+    "\n",
+    "def_class = \"delta\"\n",
+    "def_labels_subset = [x for x in res_tensor[\"deff\"].values if def_class in x]\n",
+    "res_subset = res_tensor.sel(deff=def_labels_subset)\n",
+    "avg_atk = res_subset.mean(dim=[\"deff\", \"exp_name\"]).to_pandas()\n",
+    "best_atk = avg_atk.idxmax()\n",
+    "print(f\"Class `{def_class}` ->\", best_atk)\n",
+    "\n",
+    "# Best attack, marginalized over experiments and defenses\n",
+    "avg_atk = res_tensor.mean(dim=[\"deff\", \"exp_name\"]).to_pandas()\n",
+    "best_atk = avg_atk.idxmax()\n",
+    "\n",
+    "# Best defense, marginalized over experiments and attacks\n",
+    "avg_def = res_tensor.mean(dim=[\"atk_mec\", \"exp_name\"]).to_pandas()\n",
+    "\n",
+    "print(\"\\n * Best attack overall ->\", best_atk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46c5d3f0",
+   "metadata": {},
+   "source": [
+    "## On the KL divergence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87afd57e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_kl_exp(deff, atk_mec, exp_name) -> tuple:\n",
+    "\n",
+    "    # Retrieve info\n",
+    "    def_groups = re.findall(\"^(def\\w+)_(\\w+\\d\\.?\\d?)$\", deff)[0]\n",
+    "    def_mec, def_par = def_groups[0], def_groups[1]\n",
+    "\n",
+    "    # Retrieve BNs\n",
+    "    bn_dir = cur_dir / folder / \"bns/rpop/\"\n",
+    "    bn_atk_dir = cur_dir / folder / f\"output_{def_mec}_{atk_mec}_{def_par}/bns_atk/\"\n",
+    "\n",
+    "    bns = [os.path.join(bn_dir, f) for f in os.listdir(bn_dir) if exp_name in f]\n",
+    "    bns_atk = [\n",
+    "        os.path.join(bn_atk_dir, f) for f in os.listdir(bn_atk_dir) if exp_name in f\n",
+    "    ]\n",
+    "\n",
+    "    kl_pq_vec, kl_qp_vec = [], []\n",
+    "    for sample in trange(len(bns)):\n",
+    "        bn_str = [bn for bn in bns if f\"sample{sample}\" in bn][0]\n",
+    "        bn = gum.loadBN(bn_str)\n",
+    "        bn_atk_str = [bn for bn in bns_atk if f\"sample{sample}\" in bn][0]\n",
+    "        bn_atk = gum.loadBN(bn_atk_str)\n",
+    "\n",
+    "        d = gum.ExactBNdistance(bn, bn_atk)\n",
+    "        kl = d.compute()\n",
+    "        kl_pq_vec.append(kl[\"klPQ\"])\n",
+    "        kl_qp_vec.append(kl[\"klQP\"])\n",
+    "\n",
+    "    return kl_pq_vec, kl_qp_vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "600cb119",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_kl(deff, atk_mec) -> pd.DataFrame:\n",
+    "\n",
+    "    frames = []\n",
+    "    for exp in exp_names:\n",
+    "        print(\"### EXP: \", exp)\n",
+    "        kl_pq_vec, kl_qp_vec = get_kl_exp(deff, atk_mec, exp)\n",
+    "        df_pq_exp = pd.DataFrame({\"exp\": exp, \"KL\": \"PQ\", \"value\": kl_pq_vec})\n",
+    "        df_qp_exp = pd.DataFrame({\"exp\": exp, \"KL\": \"QP\", \"value\": kl_qp_vec})\n",
+    "        df_exp = pd.concat((df_pq_exp, df_qp_exp), axis=0)\n",
+    "        frames.append(df_exp)\n",
+    "    df = pd.concat(frames)\n",
+    "\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ae4e7c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Plot (example)\n",
+    "# deff = 'def_idm_ess10'\n",
+    "# atk_mec = \"atk_cen\"\n",
+    "\n",
+    "# df = get_kl(deff, atk_mec)\n",
+    "\n",
+    "# sns.boxplot(x=\"exp\", y=\"value\", hue=\"KL\", data=df)\n",
+    "# plt.show()"
+   ]
   }
  ],
  "metadata": {
diff --git a/experiments/cn_privacy/__init__.py b/experiments/cn_privacy/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/experiments/cn_privacy/cn_privacy_v5 b/experiments/cn_privacy/cn_privacy_v5
new file mode 120000
index 0000000..2391a63
--- /dev/null
+++ b/experiments/cn_privacy/cn_privacy_v5
@@ -0,0 +1 @@
+/home/niccolo/JML_Backup_results/cn_privacy_v5
\ No newline at end of file
diff --git a/experiments/cn_privacy/config.yaml b/experiments/cn_privacy/config.yaml
index 321871e..7ccc44b 100644
--- a/experiments/cn_privacy/config.yaml
+++ b/experiments/cn_privacy/config.yaml
@@ -1,27 +1,27 @@
 ## Configuration file
 
 # Paths
-base_path: experiments/cn_privacy/output            # Base path for output
+cur_dir: experiments/cn_privacy                     # Current directory (contains all the following)
 bns_path: bns                                       # Where to save ground-truth BNs
+cns_path: output/cns                                # Where to save CNs as obtained by def-mec from BNs learnt from pool
+atk_path: output/bns_atk                            # Where to save BNs as obtained by atk-mec from CNs
 data_path: data                                     # Where to save data as generated from ground-truth BNs
-results_path: results                               # Where to save the experiment results
-meta_file: exp_meta.txt                             # File of metadata
+results_path: output/results                        # Where to save the experiment results
+exp_meta: exp_meta.txt                              # File of metadata for experiments
 
 # Models
-n_nodes_vec: '[10, 20, 50, 100]'                    # List of models' number of nodes
-edge_ratio_vec: '[1, 2, 4]'                         # List of models' edge ratio
+n_nodes_vec: '[10, 20, 30, 50]'                     # List of models' number of nodes
+edge_ratio_vec: '[2, 3, 4]'                         # List of models' edge ratio
+n_modmax: 3                                         # Maximum number of variables categories
 
 # Data
-gpop_ss: 10000                                      # Sample size of general population
+gpop_ss: 5000                                       # Sample size of general population
 rpop_prop: 0.5                                      # Sample size of reference population = gpop_ss * rpop_prop
 pool_prop: 0.25                                     # Sample size of pool population = gpop_ss * pool_prop
+samples: 200                                        # Number of data samples
 
 # MIA
-n_samples: 20                                       # Number of data samples
-n_bns: 500                                          # Number of BNs to sample within the CN
-error: 'np.logspace(-4, 0, 20, endpoint=False)'     # Type-I errors vector
-ess_vec: '[1, 10, 50, 100, 1000]'                   # List of ESS
+error: 'np.logspace(-4, 0, 30, endpoint=False)'     # Type-I errors vector
 
 # Other
-seed: 42                                            # Global seed
 num_cores: 'multiprocessing.cpu_count() - 1'        # Number of threads to use for parallelization
diff --git a/experiments/cn_privacy/exp.py b/experiments/cn_privacy/exp.py
new file mode 100644
index 0000000..0a0349a
--- /dev/null
+++ b/experiments/cn_privacy/exp.py
@@ -0,0 +1,63 @@
+import gc
+import multiprocessing  # noqa: F401 # pylint: disable=unused-import
+import sys
+
+import numpy as np  # noqa: F401 # pylint: disable=unused-import
+from joblib import Parallel, delayed
+
+from src.attack import attack_mechanism
+from src.config import create_clean_dir, get_cur_dir, load_config, map_sys_args
+from src.defense import defense_mechanism
+from src.mia import mia_vs_bn, mia_vs_cn, theoretical_power
+
+
+def main():
+
+    # Init configs
+    config = load_config("cn_privacy")
+    cur_dir = get_cur_dir(config)
+    create_clean_dir(cur_dir / "output")
+    num_cores = eval(config["num_cores"])
+
+    # Get command-line hyperparameters
+    def_mec, def_args, atk_mec, atk_args = map_sys_args(sys.argv, config)
+
+    # Init the vectors of experiments
+    exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()]
+
+    # Defense mechanism
+    print("## Defense mechanism: [", def_mec, def_args, "] ##", flush=True)
+    create_clean_dir(cur_dir / config["cns_path"])
+    _ = Parallel(n_jobs=num_cores)(
+        delayed(defense_mechanism)(exp, config, def_mec, def_args) for exp in exp_vec
+    )
+
+    # Attack mechanism
+    print("## Attack mechanism: [", atk_mec, atk_args, "] ##", flush=True)
+    create_clean_dir(cur_dir / config["atk_path"])
+    _ = Parallel(n_jobs=num_cores)(
+        delayed(attack_mechanism)(exp, config, atk_mec, atk_args) for exp in exp_vec
+    )
+
+    # MIA vs CN
+    print("## MIA vs CN ##", flush=True)
+    create_clean_dir(cur_dir / config["results_path"] / "cns")
+    _ = Parallel(n_jobs=num_cores)(delayed(mia_vs_cn)(exp, config) for exp in exp_vec)
+
+    # MIA vs BN
+    print("## MIA vs BN ##", flush=True)
+    create_clean_dir(cur_dir / config["results_path"] / "bns")
+    _ = Parallel(n_jobs=num_cores)(delayed(mia_vs_bn)(exp, config) for exp in exp_vec)
+
+    # Compute theoretical power
+    print("## Compute theoretical power ##", flush=True)
+    _ = Parallel(n_jobs=num_cores)(
+        delayed(theoretical_power)(exp, config) for exp in exp_vec
+    )
+
+    # Clean
+    gc.collect()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/cn_privacy/generate.py b/experiments/cn_privacy/generate.py
new file mode 100644
index 0000000..bee8241
--- /dev/null
+++ b/experiments/cn_privacy/generate.py
@@ -0,0 +1,43 @@
+import gc
+import multiprocessing  # noqa: F401 # pylint: disable=unused-import
+
+import numpy as np  # noqa: F401 # pylint: disable=unused-import
+from joblib import Parallel, delayed
+
+from src.config import create_clean_dir, get_cur_dir, load_config
+from src.data import generate_randombn
+from src.learning import estimate_bns
+
+
+def main():
+
+    # Init configs
+    config = load_config("cn_privacy")
+    cur_dir = get_cur_dir(config)
+    num_cores = eval(config["num_cores"])
+
+    # Generate BNs and data
+    print("## Generate BNs and data ##")
+    create_clean_dir(cur_dir / config["bns_path"])
+    create_clean_dir(cur_dir / config["bns_path"] / "gt")
+    create_clean_dir(cur_dir / config["data_path"])
+    open(f'{cur_dir}/{config["exp_meta"]}', "w").close()
+    generate_randombn(config)
+
+    # Init the vectors of experiments
+    exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()]
+
+    # Estimate BNs from rpop and pool
+    print("## Estimate BNs from rpop and pool ##")
+    create_clean_dir(cur_dir / config["bns_path"] / "rpop")
+    create_clean_dir(cur_dir / config["bns_path"] / "pool")
+    _ = Parallel(n_jobs=num_cores)(
+        delayed(estimate_bns)(exp, config) for exp in exp_vec
+    )
+
+    # Clean
+    gc.collect()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/experiments/cn_privacy/main.py b/experiments/cn_privacy/main.py
deleted file mode 100644
index edc5fb9..0000000
--- a/experiments/cn_privacy/main.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from src.config import get_config
-from src.data import generate_randombn
-from src.run_exp import run_cn_privacy
-
-if __name__ == "__main__":
-
-    # Load config
-    config = get_config("experiments/cn_privacy/config.yaml")
-
-    # Generate BNs and data
-    generate_randombn(config)
-
-    # Run experiment
-    run_cn_privacy(config)
diff --git a/experiments/cn_vs_noisybn/Plot_results.ipynb b/experiments/cn_vs_noisybn/Plot_results.ipynb
index f602c56..210b0ba 100644
--- a/experiments/cn_vs_noisybn/Plot_results.ipynb
+++ b/experiments/cn_vs_noisybn/Plot_results.ipynb
@@ -1,8 +1,16 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "80ba8653",
+   "metadata": {},
+   "source": [
+    "Ensure the output directories are named as `output_<...>_<def_arg><def_value>`, where `def_arg` can be `ess` or `delta`, and `def_value` its value."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "b53ad4f3",
    "metadata": {},
    "outputs": [],
@@ -13,37 +21,76 @@
     "import numpy as np\n",
     "import re\n",
     "import sys\n",
+    "import ast\n",
+    "import seaborn as sns\n",
     "from pathlib import Path\n",
     "from natsort import natsorted\n",
     "from sklearn.metrics import roc_curve\n",
     "from matplotlib.ticker import LogLocator\n",
+    "from matplotlib.lines import Line2D\n",
+    "from matplotlib.legend_handler import HandlerLine2D\n",
+    "from statsmodels.stats.proportion import proportion_confint\n",
     "\n",
     "sys.path.insert(0, str(Path().resolve().parents[1]))\n",
-    "from src.config import *"
+    "from src.config import load_config, get_cur_dir, create_clean_dir"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "7b61e02b",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Choose config file\n",
-    "config = get_config(\"config.yaml\")\n",
+    "config = load_config(\"cn_vs_noisybn\")\n",
     "\n",
     "# Get results path\n",
-    "res_path = get_base_path(config) / config[\"results_path\"]\n",
+    "cur_dir = get_cur_dir(config)\n",
+    "# res_path = cur_dir / config[\"results_path\"] / \"inferences\"\n",
     "\n",
     "# Choose where to save plots\n",
-    "plots_path = get_base_path(config) / \"plots\"\n",
+    "plots_path = cur_dir / \"plots\"\n",
     "create_clean_dir(plots_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "49a48f2f",
+   "execution_count": 11,
+   "id": "e07e1ceb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Names of experiments\n",
+    "folder = \"cn_vs_noisybn_v3\"\n",
+    "pattern = re.compile(\"output_.*_(ess|delta)(\\d+\\.?\\d*)\")\n",
+    "exp_names = [\n",
+    "    re.findall(\"(\\w+\\d+)\\.csv\", r)[0] for r in os.listdir(cur_dir / folder / \"data\")\n",
+    "]\n",
+    "\n",
+    "# Choose what to plot\n",
+    "def_mec = \"def_idm\"\n",
+    "atk_mec = \"atk_cen\"\n",
+    "out_dirs = [\n",
+    "    item\n",
+    "    for item in os.listdir(f\"{cur_dir}/{folder}\")\n",
+    "    if pattern.match(item) and atk_mec in item and def_mec in item\n",
+    "]\n",
+    "\n",
+    "# Get ESS/delta list\n",
+    "def_arg = pattern.findall(out_dirs[0])[0][0]\n",
+    "x_values = [pattern.findall(i)[0][1] for i in out_dirs]\n",
+    "x_values = (\n",
+    "    sorted([int(x) for x in x_values])\n",
+    "    if def_mec != \"def_ran\"\n",
+    "    else sorted([float(x) for x in x_values])\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ad31867b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,55 +98,42 @@
     "def split_data(data: pd.DataFrame, col: str, threshold: float = 0.5) -> tuple:\n",
     "\n",
     "    # Split results based on probabilities\n",
-    "    cert_idx = data[(data[col] > threshold)].index\n",
-    "    data_cert = data.iloc[cert_idx]\n",
-    "    data_uncert = data[~data.index.isin(cert_idx)]\n",
+    "    cond = data[col] > threshold\n",
+    "    data_cert = data[cond]\n",
+    "    data_uncert = data[~cond]\n",
     "\n",
     "    return data_cert, data_uncert\n",
     "\n",
     "\n",
-    "# Accuracy function for BN\n",
-    "def get_acc_bn(data: pd.DataFrame, col: str, vs_col: str) -> float:\n",
+    "# Accuracy function for a BN\n",
+    "def get_acc_bn(data: pd.DataFrame, col: str, vs_col: str, alpha=0.05) -> list:\n",
     "\n",
-    "    return sum(data[col] == data[vs_col]) / len(data)"
+    "    succ = sum(data[col] == data[vs_col])\n",
+    "    acc = succ / len(data)\n",
+    "    lower, upper = proportion_confint(\n",
+    "        count=succ, nobs=len(data), alpha=alpha, method=\"wilson\"\n",
+    "    )\n",
+    "\n",
+    "    return [acc, lower, upper]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "38f1e202",
+   "execution_count": 13,
+   "id": "84251635",
    "metadata": {},
    "outputs": [],
    "source": [
-    "res_path = get_base_path(config) / config[\"results_path\"]\n",
-    "dirs = natsorted(\n",
-    "    [f\"{res_path}/{dir}\" for dir in os.listdir(f\"{res_path}/\") if \"results_\" in dir]\n",
-    ")\n",
-    "\n",
-    "res = {\n",
-    "    \"ess\": [],\n",
-    "    \"eps\": [],\n",
-    "    \"acc_cn_cert\": [],\n",
-    "    \"acc_cn_uncert\": [],\n",
-    "    \"acc_cn_tot\": [],\n",
-    "    \"acc_noisy_bn\": [],\n",
-    "    \"cert_cn\": [],\n",
-    "}\n",
-    "\n",
-    "roc = {\n",
-    "    \"roc_cn_cert\": dict(),\n",
-    "    \"roc_cn_uncert\": dict(),\n",
-    "    \"roc_cn_tot\": dict(),\n",
-    "    \"roc_noisy_bn\": dict(),\n",
-    "}\n",
-    "\n",
-    "# For each ess...\n",
-    "for dir in dirs:\n",
-    "\n",
-    "    # Get results\n",
-    "    files = [f for f in os.listdir(dir) if \".csv\" in f]\n",
-    "    data = pd.concat([pd.read_csv(dir + \"/\" + f) for f in files])\n",
-    "    data.reset_index(inplace=True)\n",
+    "# Build an inferences data set for each output folder\n",
+    "res = dict()\n",
+    "for out_dir in out_dirs:\n",
+    "    inferences_path = os.path.join(cur_dir, folder, out_dir, \"results/inferences\")\n",
+    "    files = [os.path.join(inferences_path, f) for f in os.listdir(inferences_path)]\n",
+    "    data = pd.concat((pd.read_csv(f) for f in files), axis=0)\n",
+    "    data[\"cn_probs_1\"] = data.apply(\n",
+    "        lambda row: row[\"cn_probs\"] if row[\"cn_mpes\"] == 1 else row[\"cn_probs_alt\"],\n",
+    "        axis=1,\n",
+    "    )\n",
     "    data[\"bn_noisy_probs_1\"] = data.apply(\n",
     "        lambda row: (\n",
     "            row[\"bn_noisy_probs\"]\n",
@@ -108,245 +142,273 @@
     "        ),\n",
     "        axis=1,\n",
     "    )\n",
-    "    data[\"cn_probs_1\"] = data.apply(\n",
-    "        lambda row: row[\"cn_probs\"] if row[\"cn_mpes\"] == 1 else row[\"cn_probs_alt\"],\n",
-    "        axis=1,\n",
-    "    )\n",
+    "    x = pattern.findall(out_dir)[0][1]\n",
+    "    res[f\"{def_arg}{x}\"] = data\n",
+    "\n",
+    "# Retrieve AUCs for each result folder\n",
+    "aucs = dict()\n",
+    "for out_dir in out_dirs:\n",
+    "    auc_path = os.path.join(cur_dir, folder, out_dir, \"results/auc_meta.csv\")\n",
+    "    data = pd.read_csv(auc_path)\n",
+    "    x = pattern.findall(out_dir)[0][1]\n",
+    "    aucs[f\"{def_arg}{x}\"] = data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "581cee1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.reset_defaults()\n",
+    "plt.style.use(\"seaborn-v0_8-paper\")\n",
     "\n",
-    "    # Store ess\n",
-    "    reg = re.search(\"nodes(\\d+)_ess(\\d+)\", dir)\n",
-    "    n_nodes = reg.group(1)\n",
-    "    ess = reg.group(2)\n",
+    "plt.rcParams.update({\n",
     "\n",
-    "    # Store avg of eps and std\n",
-    "    with open(dir + \"/exp_meta.txt\", \"r\") as f:\n",
-    "        eps_vec = [\n",
-    "            float(re.search(\"Eps: (.+)\\n\", line).group(1))\n",
-    "            for line in f\n",
-    "            if \"Eps: \" in line\n",
-    "        ]\n",
-    "    eps = (float(np.mean(eps_vec)), float(np.std(eps_vec)))\n",
+    "    # ---- base ----\n",
+    "    \"figure.figsize\": [8, 4], \n",
+    "    \"font.size\": 9,\n",
     "\n",
-    "    # Split CN results based on probabilities\n",
-    "    data_cert, data_uncert = split_data(data, \"cn_probs\", 0.5)\n",
+    "    # ---- axes ----\n",
+    "    \"axes.labelsize\": 10,\n",
+    "    \"axes.titlesize\": 11,\n",
+    "    \"axes.linewidth\": 0.8,\n",
+    "    \"axes.edgecolor\": \"black\",\n",
     "\n",
-    "    # Compute accuracies\n",
-    "    vs = \"gt\"\n",
+    "    # ---- legend ----\n",
+    "    \"legend.fontsize\": 10,\n",
+    "    # \"legend.labelspacing\": 0.3,\n",
     "\n",
-    "    acc_cn_cert = (\n",
-    "        get_acc_bn(data_cert, f\"{vs}_mpes\", \"cn_mpes\") if len(data_cert) > 0 else None\n",
-    "    )\n",
-    "    acc_cn_uncert = (\n",
-    "        get_acc_bn(data_uncert, f\"{vs}_mpes\", \"cn_mpes\")\n",
-    "        if len(data_uncert) > 0\n",
-    "        else None\n",
-    "    )\n",
-    "    acc_cn_tot = get_acc_bn(data, f\"{vs}_mpes\", \"cn_mpes\")\n",
-    "    acc_noisy_bn = get_acc_bn(data, f\"{vs}_mpes\", \"bn_noisy_mpes\")\n",
+    "    # ---- ticks ----\n",
+    "    \"xtick.labelsize\": 8,\n",
+    "    \"ytick.labelsize\": 8,\n",
+    "    \"xtick.major.width\": 0.6,\n",
+    "    \"ytick.major.width\": 0.6,\n",
     "\n",
-    "    # Compute CN certainty\n",
-    "    cert_cn = sum(data[\"cn_probs\"] > 0.5) / len(data)\n",
+    "    # ---- lines ----\n",
+    "    \"lines.linewidth\": 1.0,\n",
     "\n",
-    "    # Compute ROC\n",
-    "    roc_cn_cert = roc_curve(data_cert[f\"{vs}_mpes\"], data_cert[\"cn_probs_1\"])\n",
-    "    roc_cn_uncert = roc_curve(data_uncert[f\"{vs}_mpes\"], data_uncert[\"cn_probs_1\"])\n",
-    "    roc_cn_tot = roc_curve(data[f\"{vs}_mpes\"], data[\"cn_probs_1\"])\n",
-    "    roc_noisy_bn = roc_curve(data[f\"{vs}_mpes\"], data[\"bn_noisy_probs_1\"])\n",
+    "    # ---- dpi ----\n",
+    "    \"figure.dpi\": 300,\n",
+    "    \"savefig.dpi\": 300,\n",
     "\n",
-    "    # Store results\n",
-    "    for key in res.keys():\n",
-    "        res[key].append(eval(key))\n",
-    "    for key in roc.keys():\n",
-    "        roc[key][ess] = eval(key)\n",
-    "\n",
-    "    # Debug\n",
-    "    assert (data[\"cn_probs\"] >= data[\"cn_probs_alt\"]).all()\n",
-    "    assert (data[\"bn_noisy_probs\"] >= 0.5).all()\n",
-    "    assert (data[\"bn_probs\"] >= 0.5).all()\n",
-    "    assert len(data) == len(pd.read_csv(dir + \"/\" + files[0])) * len(files)\n",
-    "\n",
-    "# Debug\n",
-    "length = len(res[\"ess\"])\n",
-    "for key in res.keys():\n",
-    "    assert len(res[key]) == length\n",
-    "for key in roc.keys():\n",
-    "    assert len(roc[key]) == length\n",
-    "assert res[\"ess\"] == sorted(res[\"ess\"])"
+    "    # ---- tex ----\n",
+    "    \"text.usetex\": True,\n",
+    "})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "b6274696",
+   "execution_count": 15,
+   "id": "2ce6f5be",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Style\n",
-    "plt.style.use(\"seaborn-v0_8-paper\")\n",
-    "plt.rcParams.update(\n",
-    "    {\n",
-    "        \"font.size\": 9,\n",
-    "        \"axes.labelsize\": 9,\n",
-    "        \"axes.titlesize\": 9,\n",
-    "        \"legend.fontsize\": 7,\n",
-    "        \"xtick.labelsize\": 8,\n",
-    "        \"ytick.labelsize\": 8,\n",
-    "        \"lines.linewidth\": 0.8,\n",
-    "        \"figure.dpi\": 300,\n",
-    "        \"savefig.dpi\": 300,\n",
-    "        \"axes.edgecolor\": \"black\",\n",
-    "        \"axes.linewidth\": 0.8,\n",
-    "        \"text.usetex\": True,\n",
-    "    }\n",
-    ")"
+    "# Retrieve related epsilon\n",
+    "eps_median = []\n",
+    "eps_uq, eps_lq = [], []\n",
+    "eps_up, eps_lp = [], []\n",
+    "for x in x_values:\n",
+    "    data = aucs[f\"{def_arg}{x}\"]\n",
+    "    data_eps = [i for i in data[\"epsilon\"].values if i is not None and not np.isnan(i)]\n",
+    "    eps_median.append(np.median(data_eps))\n",
+    "    eps_uq.append(np.percentile(data_eps, 75))\n",
+    "    eps_lq.append(np.percentile(data_eps, 25))\n",
+    "    eps_up.append(np.percentile(data_eps, 95))\n",
+    "    eps_lp.append(np.percentile(data_eps, 5))\n",
+    "\n",
+    "# Get CN certainty\n",
+    "cn_certainty = []\n",
+    "for x in x_values:\n",
+    "    data = res[f\"{def_arg}{x}\"]\n",
+    "    cn_certainty.append(sum(data[\"cn_probs\"] > 0.5) / len(data))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "0e9e8e36",
+   "execution_count": 16,
+   "id": "ba1c060b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2400x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Ess vs eps\n",
-    "fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n",
-    "\n",
-    "eps_mean = np.array([x[0] for x in res[\"eps\"]])\n",
-    "ax.semilogy(res[\"ess\"], eps_mean, \"-o\", label=\"Mean\", markersize=4)\n",
-    "ax.set_xlabel(\"S\")\n",
-    "ax.set_ylabel(\"$\\epsilon$\")\n",
-    "ax.set_title(\"S vs $\\epsilon$\")\n",
-    "ax.set_ylim([1e-5, 10])\n",
-    "ax.set_yticks([4, 1, 1e-1, 1e-2, 1e-3, 1e-4])\n",
-    "ax.set_yticklabels([\"4\", \"1\", \"1e-1\", \"1e-2\", \"1e-3\", \"1e-4\"])\n",
-    "ax.yaxis.set_minor_locator(LogLocator(base=10.0, subs=\"auto\"))\n",
-    "ax.tick_params(axis=\"y\", which=\"minor\", length=2, width=0.5)\n",
-    "ax.tick_params(axis=\"y\", which=\"major\", length=3, width=0.9)\n",
+    "# Plot: ess vs eps\n",
+    "fig, ax = plt.subplots(1, 1)\n",
+    "\n",
+    "ax.semilogy(x_values, eps_median, \"-\", color=\"black\", label=\"Median\")\n",
+    "ax.fill_between(\n",
+    "    x_values,\n",
+    "    eps_lq,\n",
+    "    eps_uq,\n",
+    "    color=\"#7981e4\",\n",
+    "    alpha=0.3,\n",
+    "    linewidth=0,\n",
+    "    # label=\"Quartiles (1st \\& 3rd)\",\n",
+    "    zorder=2,\n",
+    ")\n",
+    "# ax.fill_between(\n",
+    "#     x_values,\n",
+    "#     eps_lp,\n",
+    "#     eps_up,\n",
+    "#     color=\"#aab0ee\",\n",
+    "#     alpha=0.25,\n",
+    "#     linewidth=0,\n",
+    "#     label=\"Percentiles (5th \\& 95th)\",\n",
+    "#     zorder=2,\n",
+    "# )\n",
+    "label = \"$S$\" if def_mec != \"def_ran\" else \"$\\delta$\"\n",
+    "ax.set_xlabel(label)\n",
+    "ax.set_ylabel(\"$\\epsilon$ ensuring $\\epsilon$-DP\")\n",
+    "# ax.set_title(\"Balancing privacy\")\n",
+    "\n",
+    "ax.set_ylim([1e-9, 100])\n",
     "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n",
-    "# ax.grid(True, which='minor', linestyle='-', linewidth=0.5, color='#bfbfbf', zorder=1)\n",
-    "ax.legend(loc=\"best\")\n",
     "\n",
-    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
-    "plt.show()\n",
-    "fig.savefig(\n",
-    "    f\"{plots_path}/s_vs_eps.pdf\", dpi=1200, bbox_inches=\"tight\", transparent=False\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "90bf5cc9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Ess vs CN certainty\n",
-    "fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n",
-    "\n",
-    "ax.plot(res[\"ess\"], res[\"cert_cn\"], \"-o\", markersize=4)\n",
-    "ax.set_xlabel(\"S\")\n",
-    "ax.set_ylabel(\"Ratio of (maxmin CN prob. $> 0.5$)\")\n",
-    "ax.set_title(\"CN certainty\")\n",
-    "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n",
+    "# Second axis\n",
+    "ax2 = ax.twinx()\n",
+    "col1 = \"#66b983\"\n",
+    "col2 = \"#3b8f5b\"\n",
+    "ax2.stem([0] + x_values, [1] + cn_certainty, linefmt=col2, markerfmt='o', basefmt=\" \",)\n",
+    "cn_proxy = Line2D([], [], color=col2, marker='o', linestyle='-', label=\"CN certainty\")  # Just for the legend\n",
+    "ax2.set_yscale('log')\n",
+    "ax2.set_ylim([1e-9, 100])\n",
+    "ax2.set_ylabel(r\"Ratio of $\\underline{P}(t^* \\mid \\mathbf{x}) > 0.5$\", color=col2)\n",
+    "# ax2.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n",
+    "ax2.tick_params(axis='y', labelcolor=col2)\n",
+    "ax2.spines['right'].set_color(col2)\n",
+    "\n",
+    "handles1, labels1 = ax.get_legend_handles_labels()\n",
+    "handles = handles1 + [cn_proxy]\n",
+    "labels = labels1 + [\"CN certainty\"]\n",
+    "ax.legend(handles, labels, loc=\"upper right\", bbox_to_anchor=(1, 1), handlelength=2.5, handletextpad=0.6)\n",
     "\n",
     "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
     "plt.show()\n",
+    "\n",
+    "# Save figure\n",
     "fig.savefig(\n",
-    "    f\"{plots_path}/cn_certainty.pdf\", dpi=1200, bbox_inches=\"tight\", transparent=False\n",
-    ")"
+    "    f\"{plots_path}/joint_{def_mec}_{atk_mec}.svg\", dpi=1200, bbox_inches=\"tight\", transparent=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "b5c19b7e",
+   "execution_count": 17,
+   "id": "1f7ebeae",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Accuracy\n",
-    "fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n",
+    "# Store accuracy results\n",
+    "acc = {\"acc_noisy_bn\": {}, \"acc_cn_tot\": {}, \"acc_cn_cert\": {}, \"acc_cn_uncert\": {}}\n",
     "\n",
-    "labels = {\n",
-    "    \"acc_noisy_bn\": \"Noisy BN\",\n",
-    "    \"acc_cn_tot\": \"CN (total)\",\n",
-    "    \"acc_cn_cert\": \"CN (certain)\",\n",
-    "    \"acc_cn_uncert\": \"CN (uncertain)\",\n",
+    "roc = {\n",
+    "    \"roc_cn_cert\": dict(),\n",
+    "    \"roc_cn_uncert\": dict(),\n",
+    "    \"roc_cn_tot\": dict(),\n",
+    "    \"roc_noisy_bn\": dict(),\n",
     "}\n",
-    "for key in res.keys():\n",
-    "    if \"acc\" in key:\n",
-    "        ax.plot(res[\"ess\"], res[key], \"-o\", label=labels[key], markersize=4)\n",
-    "\n",
-    "ax.set_xlabel(\"S\")\n",
-    "ax.set_ylabel(\"Accuracy\")\n",
-    "ax.set_title(\"Accuracy\")\n",
-    "ax.legend(loc=\"best\")\n",
-    "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n",
     "\n",
-    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
-    "plt.show()\n",
-    "fig.savefig(\n",
-    "    f\"{plots_path}/accuracy.pdf\", dpi=1200, bbox_inches=\"tight\", transparent=False\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5c336efd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "roc.keys()"
+    "for x in x_values:\n",
+    "    data = res[f\"{def_arg}{x}\"]\n",
+    "\n",
+    "    # Split CN results based on probabilities\n",
+    "    cn_cert, cn_uncert = split_data(data, \"cn_probs\", 0.5)\n",
+    "\n",
+    "    # Comput accuracies\n",
+    "    vs = \"bn\"\n",
+    "    acc_cn_cert = (\n",
+    "        get_acc_bn(cn_cert, f\"{vs}_mpes\", \"cn_mpes\") if len(cn_cert) > 0 else None\n",
+    "    )\n",
+    "    acc_cn_uncert = (\n",
+    "        get_acc_bn(cn_uncert, f\"{vs}_mpes\", \"cn_mpes\") if len(cn_uncert) > 0 else None\n",
+    "    )\n",
+    "    acc_cn_tot = get_acc_bn(data, f\"{vs}_mpes\", \"cn_mpes\")\n",
+    "    acc_noisy_bn = get_acc_bn(data, f\"{vs}_mpes\", \"bn_noisy_mpes\")\n",
+    "\n",
+    "    # Compute ROC\n",
+    "    roc_cn_cert = (\n",
+    "        roc_curve(cn_cert[f\"{vs}_mpes\"], cn_cert[\"cn_probs_1\"])\n",
+    "        if len(cn_cert) > 0\n",
+    "        else None\n",
+    "    )\n",
+    "    roc_cn_uncert = (\n",
+    "        roc_curve(cn_uncert[f\"{vs}_mpes\"], cn_uncert[\"cn_probs_1\"])\n",
+    "        if len(cn_uncert) > 0\n",
+    "        else None\n",
+    "    )\n",
+    "    roc_cn_tot = roc_curve(data[f\"{vs}_mpes\"], data[\"cn_probs_1\"])\n",
+    "    roc_noisy_bn = roc_curve(data[f\"{vs}_mpes\"], data[\"bn_noisy_probs_1\"])\n",
+    "\n",
+    "    # Store results\n",
+    "    for key in acc.keys():\n",
+    "        acc[key][x] = eval(key)\n",
+    "    for key in roc.keys():\n",
+    "        roc[key][x] = eval(key)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "9186101e",
+   "execution_count": 18,
+   "id": "c158f122",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2400x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# ROC curves\n",
-    "fig, axes = plt.subplots(2, 3, figsize=(16, 8))\n",
+    "# Plot accuracies\n",
+    "fig, ax = plt.subplots(1, 1)\n",
     "\n",
     "labels = {\n",
-    "    \"roc_cn_cert\": \"CN (certain)\",\n",
-    "    \"roc_cn_uncert\": \"CN (uncertain)\",\n",
-    "    \"roc_cn_tot\": \"CN (total)\",\n",
-    "    \"roc_noisy_bn\": \"Noisy BN\",\n",
+    "    \"acc_cn_cert\": \"CN (certain)\",\n",
+    "    \"acc_cn_uncert\": \"CN (uncertain)\",\n",
+    "    \"acc_cn_tot\": \"CN (total)\",\n",
+    "    \"acc_noisy_bn\": \"Noisy BN\",\n",
     "}\n",
     "\n",
-    "i = 0\n",
-    "for ess in res[\"ess\"]:\n",
-    "    ax = axes.flatten()[i]\n",
+    "colors = dict(\n",
+    "    zip(labels.keys(), sns.color_palette(palette=\"seismic\", n_colors=len(labels)))\n",
+    ")\n",
     "\n",
-    "    for key in roc.keys():\n",
-    "        fpr, tpr, _ = roc[key][ess]\n",
-    "        ax.plot(fpr, tpr, label=labels[key], linewidth=1.3)\n",
-    "\n",
-    "    ax.plot(\n",
-    "        [0, 1],\n",
-    "        [0, 1],\n",
-    "        color=\"gray\",\n",
-    "        linestyle=\"dashed\",\n",
-    "        linewidth=1.3,\n",
-    "        label=\"baseline\",\n",
-    "    )\n",
-    "    ax.set_xlabel(\"FPR\")\n",
-    "    ax.set_ylabel(\"TPR\")\n",
-    "    ax.set_title(f\"ROC (S = {ess})\")\n",
-    "    ax.grid(\n",
-    "        True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1\n",
+    "for key in acc.keys():\n",
+    "    accuracy = [1] + [x[0] if x else np.nan for x in acc[key].values()]\n",
+    "    lower = [x[1] if x else np.nan for x in acc[key].values()]\n",
+    "    upper = [x[2] if x else np.nan for x in acc[key].values()]\n",
+    "    ax.plot([0] + x_values, accuracy, \"-\", label=labels[key], color=colors[key])\n",
+    "    ax.fill_between(\n",
+    "        x_values, lower, upper, color=colors[key], alpha=0.25, zorder=2, linewidth=0\n",
     "    )\n",
     "\n",
-    "    i += 1\n",
-    "\n",
-    "plt.legend(loc=\"best\")\n",
-    "plt.subplots_adjust(hspace=0.5)\n",
+    "label = \"$S$\" if def_mec == \"def_idm\" else \"$\\delta$\"\n",
+    "ax.set_xlabel(label)\n",
+    "ax.set_ylabel(\"MAP accuracy\")\n",
+    "ax.legend(loc=\"upper right\", bbox_to_anchor=(1, 1), handlelength=2.5, handletextpad=0.6)\n",
+    "ax.grid(True, which=\"major\", linestyle=\"-\", linewidth=0.5, color=\"#bfbfbf\", zorder=1)\n",
     "\n",
     "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
     "plt.show()\n",
-    "fig.savefig(f\"{plots_path}/roc.pdf\", dpi=1200, bbox_inches=\"tight\", transparent=False)"
+    "\n",
+    "# Save figure\n",
+    "fig.savefig(\n",
+    "    f\"{plots_path}/map_accuracy_{def_mec}_{atk_mec}.svg\", dpi=1200, bbox_inches=\"tight\", transparent=False)"
    ]
   }
  ],
diff --git a/experiments/cn_vs_noisybn/__init__.py b/experiments/cn_vs_noisybn/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/experiments/cn_vs_noisybn/config.yaml b/experiments/cn_vs_noisybn/config.yaml
index d8b5777..b7b6d2a 100644
--- a/experiments/cn_vs_noisybn/config.yaml
+++ b/experiments/cn_vs_noisybn/config.yaml
@@ -1,38 +1,36 @@
 ## Configuration file
 
 # Paths
-base_path: experiments/cn_vs_noisybn/output         # Base path for output
+cur_dir: experiments/cn_vs_noisybn                  # Current directory (contains all the following)
 bns_path: bns                                       # Where to save ground-truth BNs
+cns_path: output/cns                                # Where to save CNs as obtained by def-mec from BNs learnt from pool
+atk_path: output/bns_atk                            # Where to save BNs as obtained by atk-mec from CNs
 data_path: data                                     # Where to save data as generated from ground-truth BNs
-results_path: results                               # Where to save the experiment results
-meta_file: exp_meta.txt                             # File of metadata
+results_path: output/results                        # Where to save the experiment results
+exp_meta: exp_meta.txt                              # File of metadata for experiments
+auc_meta: output/results/auc_meta.csv               # File of metadata for AUCs
 
 # Models (Naive Bayes)
 target_var: 'T'                                     # Target variable
-n_nodes: 10                                         # Number of nodes for each BN model
-n_models: 10                                        # Number of models to evaluate
+n_nodes: 20                                         # Number of nodes for each BN model
+n_modmax: 4                                         # Maximum number of categories for covariates
+n_models: 20                                        # Number of models to evaluate
 
 # Data
 gpop_ss: 1000                                       # Sample size of general population
 rpop_prop: 0.5                                      # Sample size of reference population = gpop_ss * rpop_prop
 pool_prop: 0.25                                     # Sample size of pool population = gpop_ss * pool_prop
+samples: 50                                         # Number of data samples
 
 # MIA
-n_samples: 30                                       # Number of data samples
-n_bns: 50                                           # Number of BNs to sample within the CN
+error: 'np.logspace(-4, 0, 30, endpoint=False)'     # Type-I errors vector
+
+# Noisy BN
 tol: 0.01                                           # To find eps s.t. |AUC(eps) - AUC(CN)| < tol
-error: 'np.logspace(-4, 0, 25, endpoint=False)'     # Type-I errors vector
-ess_dict:                                           # Eps list to evaluate for each ess
-  1:  'np.arange(0.1, 10, 0.1)'
-  10: 'np.arange(0.1, 10, 0.1)'
-  20: 'np.arange(0.05, 5, 0.05)'
-  30: 'np.arange(1e-3, 1, 1e-3)'
-  40: 'np.arange(5e-6, 1e-2, 5e-6)' 
-  50: 'np.arange(5e-7, 5e-4, 5e-7)'
+eps_vec: 'np.logspace(-8, 2, num=300)'              # Epsilon to consider for noisy BN
 
 # Inferences
-n_infer: 1000                                       # Number of inferences to perform
+n_infer: 100                                        # Number of inferences to perform
 
 # Other
-seed: 42                                            # Global seed
 num_cores: 'multiprocessing.cpu_count() - 1'        # Number of threads to use for parallelization
diff --git a/experiments/cn_vs_noisybn/exp.py b/experiments/cn_vs_noisybn/exp.py
new file mode 100644
index 0000000..5110a16
--- /dev/null
+++ b/experiments/cn_vs_noisybn/exp.py
@@ -0,0 +1,73 @@
+import gc
+import multiprocessing  # noqa: F401 # pylint: disable=unused-import
+import sys
+
+import numpy as np  # noqa: F401 # pylint: disable=unused-import
+import pandas as pd
+from joblib import Parallel, delayed
+
+from src.attack import attack_mechanism
+from src.config import create_clean_dir, get_cur_dir, load_config, map_sys_args
+from src.defense import defense_mechanism
+from src.inference import inferences
+from src.mia import find_epsilon, mia_vs_cn
+
+
+def main():
+
+    # Init configs
+    config = load_config("cn_vs_noisybn")
+    cur_dir = get_cur_dir(config)
+    create_clean_dir(cur_dir / "output")
+    num_cores = eval(config["num_cores"])
+
+    # Get command-line hyperparameters
+    def_mec, def_args, atk_mec, atk_args = map_sys_args(sys.argv, config)
+
+    # Init the vectors of experiments
+    exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()]
+
+    # Defense mechanism
+    print("## Defense mechanism: [", def_mec, def_args, "] ##", flush=True)
+    create_clean_dir(cur_dir / config["cns_path"])
+    _ = Parallel(n_jobs=num_cores)(
+        delayed(defense_mechanism)(exp, config, def_mec, def_args) for exp in exp_vec
+    )
+
+    # Attack mechanism
+    print("## Attack mechanism: [", atk_mec, atk_args, "] ##", flush=True)
+    create_clean_dir(cur_dir / config["atk_path"])
+    _ = Parallel(n_jobs=num_cores)(
+        delayed(attack_mechanism)(exp, config, atk_mec, atk_args) for exp in exp_vec
+    )
+
+    # MIA vs CN
+    print("## MIA vs CN ##", flush=True)
+    create_clean_dir(cur_dir / config["results_path"] / "cns")
+    res = Parallel(n_jobs=num_cores)(delayed(mia_vs_cn)(exp, config) for exp in exp_vec)
+    auc_res = pd.concat((i for i in res), axis=0)
+    auc_res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False)
+
+    # Find eps s.t. |AUC(eps) - AUC(CN)| < tol
+    print("## Find epsilon ##", flush=True)
+    create_clean_dir(cur_dir / config["results_path"] / "bn_noisy")
+    res = Parallel(n_jobs=num_cores)(
+        delayed(find_epsilon)(exp, config) for exp in exp_vec
+    )
+    auc_res = pd.concat((i for i in res), axis=0)
+    auc_res.to_csv(f'{cur_dir}/{config["auc_meta"]}', index=False)
+
+    # Run inferences
+    print("## Inferences ##", flush=True)
+    create_clean_dir(cur_dir / config["results_path"] / "inferences")
+    _ = Parallel(n_jobs=num_cores)(
+        delayed(inferences)(exp, config, def_mec, def_args) for exp in exp_vec
+    )
+
+    # Clean
+    gc.collect()
+
+
+if __name__ == "__main__":
+
+    main()
diff --git a/experiments/cn_vs_noisybn/generate.py b/experiments/cn_vs_noisybn/generate.py
new file mode 100644
index 0000000..557f062
--- /dev/null
+++ b/experiments/cn_vs_noisybn/generate.py
@@ -0,0 +1,44 @@
+import gc
+import multiprocessing  # noqa: F401 # pylint: disable=unused-import
+
+import numpy as np  # noqa: F401 # pylint: disable=unused-import
+from joblib import Parallel, delayed
+
+from src.config import create_clean_dir, get_cur_dir, load_config
+from src.data import generate_naivebayes
+from src.learning import estimate_bns
+
+
+def main():
+
+    # Init configs
+    config = load_config("cn_vs_noisybn")
+    cur_dir = get_cur_dir(config)
+    num_cores = eval(config["num_cores"])
+
+    # Generate BNs and data
+    print("## Generate BNs and data ##")
+    create_clean_dir(cur_dir / config["bns_path"])
+    create_clean_dir(cur_dir / config["bns_path"] / "gt")
+    create_clean_dir(cur_dir / config["data_path"])
+    open(f'{cur_dir}/{config["exp_meta"]}', "w").close()
+    generate_naivebayes(config)
+
+    # Init the vectors of experiments
+    exp_vec = [f.stem for f in (cur_dir / config["data_path"]).iterdir() if f.is_file()]
+
+    # Estimate BNs from rpop and pool
+    print("## Estimate BNs from rpop and pool ##")
+    create_clean_dir(cur_dir / config["bns_path"] / "rpop")
+    create_clean_dir(cur_dir / config["bns_path"] / "pool")
+    _ = Parallel(n_jobs=num_cores)(
+        delayed(estimate_bns)(exp, config) for exp in exp_vec
+    )
+
+    # Clean
+    gc.collect()
+
+
+if __name__ == "__main__":
+
+    main()
diff --git a/experiments/cn_vs_noisybn/main.py b/experiments/cn_vs_noisybn/main.py
deleted file mode 100644
index f0f9b7b..0000000
--- a/experiments/cn_vs_noisybn/main.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from src.config import get_config
-from src.data import generate_naivebayes
-from src.run_exp import run_cn_vs_noisybn
-
-if __name__ == "__main__":
-
-    # Load config
-    config = get_config("experiments/cn_vs_noisybn/config.yaml")
-
-    # Generate BNs and data
-    generate_naivebayes(config)
-
-    # Run experiment
-    run_cn_vs_noisybn(config)
diff --git a/generate_compose.py b/generate_compose.py
new file mode 100644
index 0000000..9aece97
--- /dev/null
+++ b/generate_compose.py
@@ -0,0 +1,77 @@
+from itertools import product
+
+import yaml
+
+# Set hyperparameters
+names = ["cn_privacy"]
+def_mecs = {
+    # "def_idm": {"ess": [1, 10, 100, 1000]},
+    # "def_ran": {"delta": [0.1, 0.3, 0.5, 0.7, 0.9, 1.0]},
+    "def_loc": {"ess": [1, 10, 100, 1000]},
+}
+atk_mecs = {
+    "atk_mle": {None: [None]},
+    "atk_mne": {None: [None]},
+    "atk_cen": {None: [None]},
+    "atk_ran": {None: [None]},
+    "atk_ent": {None: [None]},
+}
+
+# Initialize the `compose.yaml` file
+init = {"version": "3.9"}
+with open("compose.yaml", "w") as f:
+    yaml.dump(init, f, default_flow_style=False)
+
+# For any configuration ...
+# (assumption: each defense and attack mechanism has at most 1 hyperparameter to be set)
+data = {"services": dict()}
+for name, def_mec, atk_mec in product(names, def_mecs.keys(), atk_mecs.keys()):
+
+    def_params = list(def_mecs[def_mec].values())[0]
+    atk_params = list(atk_mecs[atk_mec].values())[0]
+
+    for def_par, atk_par in product(def_params, atk_params):
+
+        # ... set the related volume, ...
+        app = (
+            f"_{list(def_mecs[def_mec].keys())[0]}{def_par}"
+            if def_par is not None
+            else ""
+        )
+        volumes = [
+            f"./experiments/{name}/bns:/workspace/experiments/{name}/bns",
+            f"./experiments/{name}/data:/workspace/experiments/{name}/data",
+            f"./experiments/{name}/output_{def_mec}_{atk_mec}{app}:/workspace/experiments/{name}/output",
+        ]
+
+        # ... set the command, ...
+        command = [
+            "python",
+            "-m",
+            f"experiments.{name}.exp",
+            f"def_mec={def_mec}",
+            f"atk_mec={atk_mec}",
+        ]
+
+        if def_par is not None:
+            command.append(f"{list(def_mecs[def_mec].keys())[0]}={def_par}")
+        if atk_par is not None:
+            command.append(f"{list(atk_mecs[atk_mec].keys())[0]}={atk_par}")
+
+        # ... and create the experiment
+        data["services"][f"{name}_{def_mec}_{atk_mec}{app}"] = {
+            "image": "bnp:2025",
+            "build": ".",
+            "volumes": volumes,
+            "command": command,
+            "environment": {
+                "PYTHONWARNINGS": "ignore"
+            },  # Ignore Python warnings to control the size of container's log
+            "restart": "on-failure",  # Restart the container if exited with non-zero status, useful when running out of RAM
+        }
+# Print number of services
+print("Number of services: ", len(data["services"]))
+
+# Write file
+with open("compose.yaml", "a") as f:
+    yaml.dump(data, f, default_flow_style=False)
diff --git a/requirements.txt b/requirements.txt
index 6e6f634..29bd7d8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,15 @@
+arviz==0.22.0
 astroid==3.3.11
 asttokens==3.0.0
 black==25.1.0
+cffi==2.0.0
+clarabel==0.11.1
 click==8.2.1
 cloudpickle==3.1.1
 comm==0.2.3
 contourpy==1.3.3
+coverage==7.11.0
+cvxpy==1.7.5
 cycler==0.12.1
 dask==2025.7.0
 debugpy==1.8.15
@@ -14,6 +19,9 @@ executing==2.2.0
 flake8==7.3.0
 fonttools==4.59.0
 fsspec==2025.7.0
+h5netcdf==1.7.3
+h5py==3.15.1
+hopsy==1.6.1
 importlib_metadata==8.7.0
 iniconfig==2.1.0
 ipykernel==6.30.0
@@ -21,19 +29,24 @@ ipython==9.4.0
 ipython_pygments_lexers==1.1.1
 isort==6.0.1
 jedi==0.19.2
+Jinja2==3.1.6
 joblib==1.5.1
 jupyter_client==8.6.3
 jupyter_core==5.8.1
 kiwisolver==1.4.8
 locket==1.0.0
+MarkupSafe==3.0.3
 matplotlib==3.10.3
 matplotlib-inline==0.1.7
 mccabe==0.7.0
 more-itertools==10.7.0
+mpmath==1.3.0
 mypy_extensions==1.1.0
 natsort==8.4.0
 nest-asyncio==1.6.0
 numpy==2.3.2
+optlang==1.8.3
+osqp==1.0.5
 packaging==25.0
 pandarallel==1.6.5
 pandas==2.3.1
@@ -45,30 +58,37 @@ pexpect==4.9.0
 pillow==11.3.0
 platformdirs==4.3.8
 pluggy==1.6.0
+PolyRound==0.4.0
 prompt_toolkit==3.0.51
 psutil==7.0.0
 ptyprocess==0.7.0
 pure_eval==0.2.3
 pyAgrum==2.2.0
 pyarrow==21.0.0
+pycddlib==3.0.2
 pycodestyle==2.14.0
+pycparser==2.23
 pydot==4.0.1
 pyflakes==3.4.0
 Pygments==2.19.2
 pylint==3.3.7
 pyparsing==3.2.3
 pytest==8.4.1
+pytest-cov==7.0.0
 python-dateutil==2.9.0.post0
 pytz==2025.2
 PyYAML==6.0.2
 pyzmq==27.0.0
 scikit-learn==1.7.1
 scipy==1.16.1
+scs==3.2.9
 seaborn==0.13.2
 six==1.17.0
 stack-data==0.6.3
 statsmodels==0.14.5
 swifter==1.4.0
+swiglpk==5.0.12
+sympy==1.14.0
 threadpoolctl==3.6.0
 tokenize_rt==6.2.0
 tomlkit==0.13.3
@@ -79,4 +99,6 @@ traitlets==5.14.3
 typing_extensions==4.14.1
 tzdata==2025.2
 wcwidth==0.2.13
+xarray==2025.10.1
+xarray-einstats==0.9.1
 zipp==3.23.0
diff --git a/src/attack.py b/src/attack.py
new file mode 100644
index 0000000..b82a628
--- /dev/null
+++ b/src/attack.py
@@ -0,0 +1,121 @@
+import inspect
+
+import numpy as np
+import pandas as pd
+import pyagrum as gum
+
+from src.config import get_cur_dir, set_seed
+from src.mia import get_ll
+from src.utils import centroid_cn, maxent_cn, mle_cn, mne_cn, ran_cn
+
+
+# Apply attack mechanism to a BN, namely, derive a BN from a CN
+def attack_mechanism(exp, config, atk_mec, atk_args) -> None:
+
+    # Get current directory
+    cur_dir = get_cur_dir(config)
+
+    # Read data
+    gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv')
+    base_path = cur_dir / config["cns_path"]
+
+    # Set seed
+    set_seed()
+
+    # For each data sample ...
+    for sample in range(config["samples"]):
+
+        # ... read the related CN
+        bn_min = gum.loadBN(f"{base_path}/bn_min_{exp}_sample{sample}.bif")
+        bn_max = gum.loadBN(f"{base_path}/bn_max_{exp}_sample{sample}.bif")
+
+        # ... retrieve rpop, ...
+        rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(bn_min.nodes())]
+
+        # ... and derive the BN
+        atk_mec_fn = globals()[atk_mec]  # Get the related function
+        sig = inspect.signature(atk_mec_fn)  # Get its signature
+        args = {
+            k: v
+            for k, v in {
+                "bn_min": bn_min,
+                "bn_max": bn_max,
+                "data": rpop,
+                "n_bns": atk_args.get("n_bns", None),
+            }.items()
+            if k in sig.parameters
+        }
+        bn = atk_mec_fn(**args)
+
+        # Save results
+        gum.saveBN(
+            bn, f'{cur_dir / config["atk_path"]}/{f"bn_{exp}_sample{sample}"}.bif'
+        )
+
+    return
+
+
+# Get the BN inside a CN with max entropy distribution
+def atk_ent(bn_min, bn_max):
+
+    bn = maxent_cn(bn_min, bn_max)
+
+    return bn
+
+
+# Get a random BN inside a CN
+def atk_ran(bn_min, bn_max):
+
+    bn = ran_cn(bn_min, bn_max)
+
+    return bn
+
+
+# Get the centroid of a CN
+def atk_cen(bn_min, bn_max):
+
+    bn = centroid_cn(bn_min, bn_max)
+
+    return bn
+
+
+# Get the maximum likelihood BN inside a CN
+def atk_mle(bn_min, bn_max, data):
+
+    bn = mle_cn(bn_min, bn_max, data)
+
+    return bn
+
+
+# Get the minimum likelihood BN inside a CN, i.e., the maximum negative likelihood (MNE) BN.
+def atk_mne(bn_min, bn_max, data):
+
+    bn = mne_cn(bn_min, bn_max, data)
+
+    return bn
+
+
+# Get the maximum negative likelihood BN within a CN
+def mne_bn(bns_sample, data):
+    """
+    Given a list `bns_sample` of BNs,
+    find argmax_{BN in bns_sample} -ll(BN | data),
+    where ll is the log-likelihood function.
+    """
+
+    mne_bn = None
+    mne = 0
+
+    for bn in bns_sample:
+
+        # Estimate the likelihood of data
+        bn_ie = gum.LazyPropagation(bn)
+        llr_im = data.apply(lambda x: get_ll(x.to_dict(), bn_ie), axis=1).dropna()
+        llr = np.sum(llr_im)
+        neg_llr = -llr
+
+        if neg_llr > mne:
+            mne_bn = bn
+            mne = neg_llr
+
+    return mne_bn
diff --git a/src/config.py b/src/config.py
index 016bb82..04d1d0d 100644
--- a/src/config.py
+++ b/src/config.py
@@ -1,47 +1,101 @@
+import os
 import random
 import shutil
+import sys
 from pathlib import Path
 
 import pyagrum as gum
 import yaml
 
+IN_PYTEST = "pytest" in sys.modules
+
+
+# Get arguments as passed from command-line for experiment
+def map_sys_args(sys_args, config) -> tuple:
+
+    # Store parameters
+    params = dict([arg.split("=") for arg in sys_args if "=" in arg])
+    with open(f'{config["cur_dir"]}/{config["exp_meta"]}', "a") as m:
+        m.write(f"\n Defense & attack parameters: \n {params}")
+
+    # Get defense and attack mechanisms
+    def_mec = params.pop("def_mec")
+    atk_mec = params.pop("atk_mec")
+
+    # Get defense parameters
+    def_args = dict()
+    if def_mec in ["def_idm", "def_loc"]:
+        def_args["ess"] = int(params.pop("ess"))
+        assert def_args["ess"] >= 0
+    elif def_mec == "def_ran":
+        def_args["delta"] = float(params.pop("delta"))
+        assert def_args["delta"] >= 0
+        assert def_args["delta"] <= 1
+    else:
+        raise Exception("Defense not implemented")
+
+    # Save attack parameters
+    atk_args = dict()
+    if atk_mec in ["atk_mle", "atk_mne", "atk_cen", "atk_ran", "atk_ent"]:
+        pass
+    else:
+        raise Exception("Attack not implemented")
+
+    # Exceptions
+    if len(params) != 0:
+        raise Exception(f"Unused parameters: {params}")
+
+    return (def_mec, def_args, atk_mec, atk_args)
+
 
 # Read configuration for experiment
-def get_config(path):
+def load_config(name: str):
 
-    with open(path, "r") as f:
+    subdir = "test" if os.getenv("USE_TEST_CONFIG") == "1" else "experiments"
+
+    config_path = get_root_path() / subdir / name / "config.yaml"
+
+    with open(config_path, "r") as f:
         config = yaml.safe_load(f)
 
     return config
 
 
 # Set global seed
-def set_global_seed(seed):
+def set_seed():
 
-    random.seed(seed)
-    gum.initRandom(seed)
+    random.seed(42)
+    gum.initRandom(42)
 
 
-# Get root directory
-def get_root_path():
-    return Path(__file__).resolve().parents[1]
+# Create an empty directory
+def create_clean_dir(path: Path):
+
+    # If directory exists, clean it
+    if path.exists() and path.is_dir():
+        for item in path.iterdir():
+            shutil.rmtree(item) if item.is_dir() else item.unlink()
+
+    # Else, create a new one
+    else:
+        path.mkdir(parents=True, exist_ok=True)
 
 
-# Get base path
-def get_base_path(config):
+# Get output path
+def get_cur_dir(config):
 
     root_path = get_root_path()
-    base_path = config["base_path"]
+    cur_dir = config["cur_dir"]
 
-    return root_path / base_path
+    return root_path / cur_dir
 
 
-# Create an empty directory
-def create_clean_dir(path: Path):
+# Get root (project) directory
+def get_root_path():
+    return Path(__file__).resolve().parents[1]
 
-    # Remove the folder if already exists
-    if path.exists() and path.is_dir():
-        shutil.rmtree(path)
 
-    # Create a new folder
-    path.mkdir(parents=True, exist_ok=True)
+# Only perform an `assert` if code is running in `pytest`
+def safe_assert(condition):
+    if IN_PYTEST:
+        assert condition
diff --git a/src/data.py b/src/data.py
index 31fdf2e..6f22f63 100644
--- a/src/data.py
+++ b/src/data.py
@@ -1,30 +1,35 @@
+import ast
 from itertools import product
-from pprint import pformat
 
+import numpy as np
+import pandas as pd
 import pyagrum as gum
+from numpy.random import randint
 
-from src.config import create_clean_dir, get_base_path, set_global_seed
-from src.utils import compact_dict
+from src.config import get_cur_dir, safe_assert, set_seed
 
 
 def generate_naivebayes(config):
 
-    # Set seed
-    set_global_seed(config["seed"])
-
     # Set paths
-    base_path = get_base_path(config)
-    bns_path = base_path / config["bns_path"]
-    data_path = base_path / config["data_path"]
-    results_path = base_path / config["results_path"]
-
-    # Create empty directories
-    create_clean_dir(bns_path)
-    create_clean_dir(data_path)
-    create_clean_dir(results_path)
-
-    # Set BN (Naive Bayes) structure
-    bn_str_gen = (f'{config["target_var"]}->X{i}' for i in range(config["n_nodes"] - 1))
+    cur_dir = get_cur_dir(config)
+    bns_path = cur_dir / config["bns_path"]
+    data_path = cur_dir / config["data_path"]
+
+    # Set seed
+    set_seed()
+
+    # Retrieve hyperparameters
+    n_modmax = config["n_modmax"]
+    gpop_ss = config["gpop_ss"]
+    pool_ss = int(gpop_ss * config["pool_prop"])
+    rpop_ss = int(gpop_ss * config["rpop_prop"])
+
+    # Set BN (naive Bayes) structure
+    bn_str_gen = (
+        f'{config["target_var"]}->X{i}[{randint(2, n_modmax+1)}]'
+        for i in range(config["n_nodes"] - 1)
+    )
     bn_str = "; ".join(bn_str_gen)
 
     # For each model ...
@@ -32,66 +37,125 @@ def generate_naivebayes(config):
 
         # ... generate BN, ...
         bn = gum.fastBN(bn_str)
-        gum.saveBN(bn, f"{bns_path}/exp{i}.bif")
+        gum.saveBN(bn, f'{bns_path / "gt"}/{f"exp{i}"}.bif')
+
+        with open(f'{cur_dir}/{config["exp_meta"]}', "a") as m:
+            m.write(
+                f'- exp{i}. Naive Bayes: {config["n_nodes"]} nodes. Complexity: {bn.dim()} Max categories: {n_modmax}\n'
+            )
 
         # ... and generate gpop from BN
-        data_gen = gum.BNDatabaseGenerator(bn)
-        data_gen.drawSamples(config["gpop_ss"])
-        data_gen.setDiscretizedLabelModeRandom()
-        gpop = data_gen.to_pandas()
-        gpop.to_csv(f"{data_path}/exp{i}.csv", index=False)
+        gpop = generate_unique(bn, config["gpop_ss"])
 
-    # For each ESS ...
-    for ess in config["ess_dict"].keys():
+        # For any data sample ...
+        for sample in range(config["samples"]):
 
-        # ... create results subdirectories and metadata files
-        meta_file_path = (
-            base_path
-            / config["results_path"]
-            / f'results_nodes{config["n_nodes"]}_ess{ess}'
-            / config["meta_file"]
-        )
-        meta_file_path.parent.mkdir(parents=True, exist_ok=True)
+            # ... sample pool and rpop
+            shuffled_idx = np.random.permutation(gpop.index)
 
-        with open(meta_file_path, "w") as f:
-            f.write(pformat(compact_dict(config)) + "\n\n" + "#" * 50 + "\n\n")
+            pool_idx = shuffled_idx[:pool_ss]
+            rpop_idx = shuffled_idx[pool_ss : pool_ss + rpop_ss]
 
+            gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx)
+            gpop[f"in-rpop-{sample}"] = gpop.index.isin(rpop_idx)
 
-def generate_randombn(config):
+            # Debug
+            safe_assert(pool_ss == len(pool_idx))
+            safe_assert(rpop_ss == len(rpop_idx))
+            safe_assert(sum(gpop[f"in-pool-{sample}"]) == pool_ss)
+            safe_assert(sum(gpop[f"in-rpop-{sample}"]) == rpop_ss)
+            safe_assert(sum(gpop[f"in-pool-{sample}"] & gpop[f"in-rpop-{sample}"]) == 0)
+            safe_assert(
+                sum(~gpop[f"in-pool-{sample}"] & ~gpop[f"in-rpop-{sample}"]) == gpop_ss - pool_ss - rpop_ss
+            )
 
-    # Set seed
-    set_global_seed(config["seed"])
+        # Save gpop
+        gpop.to_csv(f"{data_path}/exp{i}.csv", index=False)
+
+
+def generate_randombn(config):
 
     # Set paths
-    base_path = get_base_path(config)
-    bns_path = base_path / config["bns_path"]
-    data_path = base_path / config["data_path"]
-    results_path = base_path / config["results_path"]
+    cur_dir = get_cur_dir(config)
+    bns_path = cur_dir / config["bns_path"]
+    data_path = cur_dir / config["data_path"]
 
-    # Create empty directories
-    create_clean_dir(bns_path)
-    create_clean_dir(data_path)
-    create_clean_dir(results_path)
+    # Retrieve hyperparameters
+    n_nodes_vec = ast.literal_eval(config["n_nodes_vec"])
+    edge_ratio_vec = ast.literal_eval(config["edge_ratio_vec"])
+    gpop_ss = config["gpop_ss"]
+    pool_ss = int(gpop_ss * config["pool_prop"])
+    rpop_ss = int(gpop_ss * config["rpop_prop"])
 
-    n_nodes_vec = eval(config["n_nodes_vec"])
-    edge_ratio_vec = eval(config["edge_ratio_vec"])
+    # Set seed
+    set_seed()
 
     # For each configuration ...
     for i, (n, r) in enumerate(product(n_nodes_vec, edge_ratio_vec)):
 
         # ... generate BN, ...
         bn_gen = gum.BNGenerator()
-        bn = bn_gen.generate(n_nodes=n, n_arcs=int(n * r), n_modmax=2)
-        gum.saveBN(bn, f"{bns_path}/exp{i}.bif")
+        bn = bn_gen.generate(n_nodes=n, n_arcs=int(n * r), n_modmax=config["n_modmax"])
+        gum.saveBN(bn, f'{bns_path / "gt"}/{f"exp{i}"}.bif')
 
-        with open(f'{results_path}/{config["meta_file"]}', "a") as m:
+        with open(f'{cur_dir}/{config["exp_meta"]}', "a") as m:
             m.write(
                 f"- exp{i}. Nodes: {n} Edges: {int(n * r)} Complexity: {bn.dim()}\n"
             )
 
         # ... and generate gpop from BN
-        data_gen = gum.BNDatabaseGenerator(bn)
-        data_gen.drawSamples(config["gpop_ss"])
-        data_gen.setDiscretizedLabelModeRandom()
-        gpop = data_gen.to_pandas()
+        gpop = generate_unique(bn, config["gpop_ss"])
+
+        # For any data sample ...
+        for sample in range(config["samples"]):
+
+            # ... sample pool and rpop
+            shuffled_idx = np.random.permutation(gpop.index)
+
+            pool_idx = shuffled_idx[:pool_ss]
+            rpop_idx = shuffled_idx[pool_ss : pool_ss + rpop_ss]
+
+            gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx)
+            gpop[f"in-rpop-{sample}"] = gpop.index.isin(rpop_idx)
+
+            # Debug
+            safe_assert(pool_ss == len(pool_idx))
+            safe_assert(rpop_ss == len(rpop_idx))
+            safe_assert(sum(gpop[f"in-pool-{sample}"]) == pool_ss)
+            safe_assert(sum(gpop[f"in-rpop-{sample}"]) == rpop_ss)
+            safe_assert(sum(gpop[f"in-pool-{sample}"] & gpop[f"in-rpop-{sample}"]) == 0)
+            safe_assert(
+                sum(~gpop[f"in-pool-{sample}"] & ~gpop[f"in-rpop-{sample}"]) == gpop_ss - pool_ss - rpop_ss
+            )
+
+        # Save gpop
         gpop.to_csv(f"{data_path}/exp{i}.csv", index=False)
+
+
+# Generate unique data points from a given BN
+def generate_unique(bn: gum.BayesNet, n_samples: int) -> pd.DataFrame:
+
+    # Generate data
+    data_gen = gum.BNDatabaseGenerator(bn)
+    data_gen.drawSamples(n_samples * 2)
+    data = data_gen.to_pandas()
+
+    # Ensure data items are unique
+    data_unique = data.drop_duplicates()
+    check = 0
+    while len(data_unique) < n_samples:
+        data_gen.drawSamples((n_samples - len(data_unique)) * 5)
+        data = data_gen.to_pandas()
+        data_unique = pd.concat([data_unique, data], axis=0).drop_duplicates()
+        check += 1
+        if check >= 1e6:
+            raise ValueError(
+                "Too many iterations, please check the data hyperparameters."
+            )
+    data_unique = data_unique.sample(n=n_samples, ignore_index=True)
+
+    # Debug
+    safe_assert(all(data_unique == data_unique.drop_duplicates()))
+    safe_assert(len(data_unique) == n_samples)
+
+    return data_unique
diff --git a/src/defense.py b/src/defense.py
new file mode 100644
index 0000000..ca7c0ae
--- /dev/null
+++ b/src/defense.py
@@ -0,0 +1,188 @@
+import inspect
+
+import numpy as np
+import pandas as pd
+import pyagrum as gum
+
+from src.config import get_cur_dir, safe_assert, set_seed
+from src.utils import get_bn_counts, check_consistency, get_min_max_bns
+
+
+# Apply defense mechanism to a BN, namely, derive a CN from a BN
+def defense_mechanism(exp, config, def_mec, def_args) -> None:
+
+    # Get current directory
+    cur_dir = get_cur_dir(config)
+
+    # Read data
+    gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv')
+
+    # Set seed
+    set_seed()
+
+    # For each data sample ...
+    for sample in range(config["samples"]):
+
+        # ... read the related BN
+        bn = gum.loadBN(
+            f"{cur_dir}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif"
+        )
+
+        # ... retrieve pool, ...
+        pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, : len(bn.nodes())]
+
+        # ... and derive the CN
+        def_mec_fn = globals()[def_mec]  # Get the related function
+        sig = inspect.signature(def_mec_fn)  # Get its signature
+        args = {
+            k: v
+            for k, v in {
+                "bn": bn,
+                "ess": def_args.get("ess", None),
+                "delta": def_args.get("delta", None),
+                "data": pool,
+            }.items()
+            if k in sig.parameters
+        }
+        cn = def_mec_fn(**args)  # Keep only `def_mec` args
+
+        # Save results
+        base_path = cur_dir / config["cns_path"]
+        cn.saveBNsMinMax(
+            f"{base_path}/bn_min_{exp}_sample{sample}.bif",
+            f"{base_path}/bn_max_{exp}_sample{sample}.bif",
+        )
+
+    return
+
+
+# Estimate a CN from data by local IDM
+def def_idm(bn, ess, data):
+    bn_counts = get_bn_counts(bn, data)
+    cn = gum.CredalNet(bn_counts)
+    cn.idmLearning(ess)
+
+    return cn
+
+
+# Build a CN by bloating each BN parameter with a fixed-size random interval
+def def_ran(bn, delta):
+
+    # Initialize the extreme BNs
+    bn_min = gum.BayesNet(bn)
+    bn_max = gum.BayesNet(bn)
+
+    # For each node ...
+    for n in bn.nodes():
+
+        # ... get the CPT, ...
+        cpt = bn.cpt(n).toarray()
+
+        # ... get a matrix of eta's, ...
+        eta = np.random.uniform(0, delta, cpt.size).reshape(cpt.shape)
+
+        # ... perturb the CPT, ...
+        cpt_min = np.minimum(1 - delta, np.maximum(0, cpt - eta))
+        cpt_max = np.minimum(1, np.maximum(delta, cpt - eta + delta))
+
+        # ... and store it into the extreme BNs
+        bn_min.cpt(n).fillWith(cpt_min.flatten())
+        bn_max.cpt(n).fillWith(cpt_max.flatten())
+
+        # Debug
+        safe_assert(np.all(cpt_min <= cpt))
+        safe_assert(np.all(cpt_max >= cpt))
+        safe_assert(np.all(np.abs(cpt_max - cpt_min - delta) < 1e-6))
+
+    # Build the CN from the extreme BNs
+    cn = gum.CredalNet(bn_min, bn_max)
+    cn.intervalToCredal()
+
+    # Debug
+    safe_assert(check_consistency(bn, bn_min, bn_max)==0)
+
+    return cn
+
+
+# Build a CN by DEF-RAN, where each delta is the size of the intrval under local-IDM
+def def_loc(bn, ess, data):
+
+    # Initialize the extreme BNs
+    bn_min = gum.BayesNet(bn)
+    bn_max = gum.BayesNet(bn)
+
+    # Learn a CN by local-IDM
+    cn_idm = def_idm(bn, ess, data)
+
+    # Extract min and max BNs
+    bn_min_idm, bn_max_idm = get_min_max_bns(
+        cn_idm, exp=f"{np.random.uniform(0, 1, 1)}"
+    )
+
+    # For each node ...
+    for n in bn.nodes():
+
+        # ... get the CPT, ...
+        cpt = bn.cpt(n).toarray()
+
+        # ... get the intervals lengths, ...
+        len_idm = bn_max_idm.cpt(n).toarray() - bn_min_idm.cpt(n).toarray()
+
+        # ... get a matrix of eta's, ...
+        eta = np.random.rand(*len_idm.shape) * len_idm
+
+        # ... perturb the CPT, ...
+        cpt_min = np.minimum(1 - len_idm, np.maximum(0, cpt - eta))
+        cpt_max = np.minimum(1, np.maximum(len_idm, cpt - eta + len_idm))
+
+        # ... and store it into the extreme BNs
+        bn_min.cpt(n).fillWith(cpt_min.flatten())
+        bn_max.cpt(n).fillWith(cpt_max.flatten())
+
+        # Debug
+        safe_assert(np.all(cpt_min <= cpt))
+        safe_assert(np.all(cpt_max >= cpt))
+        safe_assert(np.all(np.abs(cpt_max - cpt_min - len_idm) < 1e-5))
+
+    # Build the CN from the extreme BNs
+    cn = gum.CredalNet(bn_min, bn_max)
+    cn.intervalToCredal()
+
+    # Debug
+    safe_assert(check_consistency(bn, bn_min, bn_max)==0)
+
+    return cn
+
+
+# Create noisy BN by adding Laplacian noise (Zhang et al., 2017)
+def noisy_bn(bn, scale: float):
+
+    bn_ie = gum.LazyPropagation(bn)
+    bn_ie.makeInference()
+
+    bn_noisy = gum.BayesNet(bn)
+
+    # For each node X ...
+    for node in bn.names():
+
+        # Get the joint P(X, Pa(X))
+        joint = bn_ie.jointPosterior(bn.family(node))
+
+        # Add noise to P(X, Pa(X)) and normalize
+        noise = np.random.laplace(scale=scale, size=np.prod(joint.shape))
+        noisy_joint = np.clip(
+            joint.toarray().flatten() + noise, a_min=10e-9, a_max=None
+        )
+        noisy_joint = noisy_joint / np.sum(noisy_joint)
+        joint.fillWith(noisy_joint)
+
+        # Compute the conditional P(X | Pa(X))
+        cond = joint / joint.sumOut(node)
+
+        # Fill noisy BN
+        bn_noisy.cpt(node).fillWith(cond)
+
+    # Check noisy bn
+    bn_noisy.check()  # OK if = ().
+
+    return bn_noisy
diff --git a/src/inference.py b/src/inference.py
index 7fad96d..273a6b7 100644
--- a/src/inference.py
+++ b/src/inference.py
@@ -1,21 +1,33 @@
+import inspect
 import math
-from tempfile import TemporaryDirectory
 
 import numpy as np
 import pandas as pd
 import pyagrum as gum
+from more_itertools import random_product
 
-from src.config import get_base_path, set_global_seed
-from src.utils import add_counts_to_bn, get_noisy_bn, random_product
+import src.defense
+from src.config import get_cur_dir, safe_assert, set_seed
+from src.defense import noisy_bn
+from src.learning import learn_bn_params
+from src.utils import get_min_max_bns
 
 
-def run_inferences(exp, ess, eps, config):
+def inferences(exp, config, def_mec, def_args):
 
-    base_path = get_base_path(config)
+    # Read config
+    cur_dir = get_cur_dir(config)
     target = config["target_var"]
 
+    # Read data
+    auc_res = pd.read_csv(f'{cur_dir}/{config["auc_meta"]}')
+    eps_vec = [
+        i for i in auc_res.loc[auc_res["exp"] == exp, "epsilon"].values if i is not None
+    ]
+    eps = np.mean(eps_vec)
+
     # Set seed
-    set_global_seed(config["seed"])
+    set_seed()
 
     # Set list of evidence
     evid_vec = [
@@ -24,29 +36,39 @@ def run_inferences(exp, ess, eps, config):
     ]
 
     # Store ground-truth BN
-    gt = gum.loadBN(f'{base_path / config["bns_path"]}/{exp}.bif')
-    gpop = pd.read_csv(f'{base_path / config["data_path"]}/{exp}.csv')
-
-    # Learn BN from gpop
-    bn_learner = gum.BNLearner(gpop)
-    bn_learner.useSmoothingPrior(1e-5)
-    bn = bn_learner.learnParameters(gt.dag())
-
-    # Learn CN from gpop
-    bn_copy = gum.BayesNet(bn)
-    add_counts_to_bn(bn_copy, gpop)
-    cn = gum.CredalNet(bn_copy)
-    cn.idmLearning(ess)
-
-    # Learn noisy BN from gpop
+    gt = gum.loadBN(f'{cur_dir / config["bns_path"]}/gt/{exp}.bif')
+    gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv')
+
+    # Learn BN from gpop                            #TODO: save results
+    bn = learn_bn_params(gt, gpop)
+
+    # Learn CN from gpop (defense mechanism)        #TODO: save results
+    def_mec_fn = getattr(src.defense, def_mec)  # Get the related function
+    sig = inspect.signature(def_mec_fn)  # Get its signature
+    args = {
+        k: v
+        for k, v in {
+            "bn": bn,
+            "ess": def_args.get("ess", None),
+            "delta": def_args.get("delta", None),
+            "data": gpop,
+        }.items()
+        if k in sig.parameters
+    }
+    cn = def_mec_fn(**args)  # Keep only `def_mec`` args
+
+    # Learn noisy BN from gpop                      #TODO: save results
     scale = (2 * bn.size()) / (len(gpop) * eps)
-    bn_noisy = get_noisy_bn(bn, scale)
+    bn_noisy = noisy_bn(bn, scale)
 
     # Run inferences
-    gt_mpes, _ = run_inference_bn(gt, target, evid_vec)
-    bn_mpes, bn_probs = run_inference_bn(bn, target, evid_vec)
-    bn_noisy_mpes, bn_noisy_probs = run_inference_bn(bn_noisy, target, evid_vec)
-    cn_mpes, cn_probs, cn_probs_alt = run_inference_cn(cn, target, evid_vec, exp)
+    try:
+        gt_mpes, _ = run_inference_bn(gt, target, evid_vec)
+        bn_mpes, bn_probs = run_inference_bn(bn, target, evid_vec)
+        bn_noisy_mpes, bn_noisy_probs = run_inference_bn(bn_noisy, target, evid_vec)
+        cn_mpes, cn_probs, cn_probs_alt = run_inference_cn(cn, target, evid_vec, exp)
+    except:
+        return
 
     # Save results
     results = pd.DataFrame(
@@ -62,12 +84,12 @@ def run_inferences(exp, ess, eps, config):
         }
     )
 
-    res_path = (
-        base_path
-        / config["results_path"]
-        / f'results_nodes{config["n_nodes"]}_ess{ess}'
+    results.to_csv(
+        f'{cur_dir / config["results_path"]}/inferences/{exp}.csv',
+        index=False,
     )
-    results.to_csv(f"{res_path}/{exp}.csv", index=False)
+
+    return
 
 
 # MPE function for BN
@@ -89,15 +111,14 @@ def mpe_cn(
     bn_min: gum.BayesNet, bn_max: gum.BayesNet, target: str, children: dict
 ) -> tuple:
     """
-    Get the MPE of a CN as: argmax_t log P_lower(target=t | children),
-    together with its lower probability.
-    bn_min and bn_max derive from a binary CN.
-    The DAG is assumed to be a Naive Bayes model with `target` the target variable.
-    Return the MPE, its probability, and the lower probability of the alternative class.
+    Get the MPE of a CN as: argmax_t log P_lower(target=t | children).
+    bn_min and bn_max derive from a CN.
+    The DAG is a naive Bayes with `target` a binary target variable.
+    Returns the MPE, its probability, and the lower probability of the alternative class.
     """
 
-    lp1 = get_lower_posterior(bn_min, bn_max, target, 1, children)
-    lp0 = get_lower_posterior(bn_min, bn_max, target, 0, children)
+    lp1 = nb_log_lower_posterior(bn_min, bn_max, target, 1, children)
+    lp0 = nb_log_lower_posterior(bn_min, bn_max, target, 0, children)
 
     if lp1 > lp0:
         return (1, math.exp(lp1), math.exp(lp0))
@@ -106,72 +127,73 @@ def mpe_cn(
 
 
 # Get a value from a BN's CPT
-def get_cond(
+def cpt_value(
     bn: gum.BayesNet, x_var: str, x_value: float, parents: dict = None
 ) -> float:
     """
     Get P(X=x | parents) from the BN's CPT of X.
-    x_var is X, and x_value is x.
+    `x_var` is the X name, while `x_value` is x.
     """
 
     cpt = bn.cpt(x_var)
     inst = gum.Instantiation(cpt)
     inst[x_var] = x_value
-    if not parents:
-        assert len(bn.parents(x_var)) == 0
-    else:
-        assert bn.parents(x_var) == set(bn.ids(parents.keys()))
+
+    if parents:
         for var in parents.keys():
             inst[var] = parents[var]
+        safe_assert(bn.parents(x_var) == set(bn.ids(parents.keys())))
+    else:
+        safe_assert(len(bn.parents(x_var)) == 0)
 
     return max(cpt.get(inst), 1e-10)  # Smoothing
 
 
-# Get a Naive Bayes log-joint
-def get_naivebayes_log_joint(
-    bn: gum.BayesNet, target: str, t: float, children: dict
-) -> float:
+# Get a naive Bayes log-joint
+def nb_log_joint(bn: gum.BayesNet, target: str, t: float, children: dict) -> float:
     """
-    Get log[P(target=t, children)] from the BN's CPT of `target`.
-    The BN is assumed to be a Naive Bayes model with `target` the target variable.
+    Get log[P(target=t, children)] by exploiting the BN factorization.
+    The DAG is a naive Bayes with `target` a binary target variable.
     """
 
-    sum_log = 0
+    sum_log = math.log(cpt_value(bn, target, t))
     for var, val in children.items():
-        sum_log += math.log(get_cond(bn, var, val, {target: t}))
-    sum_log += math.log(get_cond(bn, target, t))
+        sum_log += math.log(cpt_value(bn, var, val, {target: t}))
 
     return sum_log
 
 
 # Get the lower posterior from a CN
-def get_lower_posterior(
+def nb_log_lower_posterior(
     bn_min: gum.BayesNet, bn_max: gum.BayesNet, target: str, t: float, children: dict
 ) -> float:
     """
     Get log P_lower(target=t | children).
-    bn_min and bn_max derive from a binary CN.
-    The DAG is assumed to be a Naive Bayes model with `target` the target variable.
+    bn_min and bn_max derive from a CN.
+    The DAG is a naive Bayes with `target` a binary target variable.
     """
 
-    lp_lower = get_naivebayes_log_joint(bn_min, target, t, children)
-    lp_upper = get_naivebayes_log_joint(bn_max, target, 1 - t, children)
+    l_lower = nb_log_joint(bn_min, target, t, children)
+    l_upper = nb_log_joint(bn_max, target, 1 - t, children)
 
-    return lp_lower - lp_upper - math.log1p(math.exp(lp_lower - lp_upper))
+    return l_lower - l_upper - math.log1p(math.exp(l_lower - l_upper))
 
 
 # Run inferences on a BN
 def run_inference_bn(bn, target: str, evid_vec):
     """
-    The BN is assumed to be a Naive Bayes model with `target` the target variable.
+    The BN is assumed to be a naive Bayes model with `target` the target variable.
     """
 
     # Store information
     cov = sorted(list(bn.names()))
     cov.remove(target)
 
+    # Set seed
+    set_seed()
+
     # Debug
-    assert len(cov) == bn.size() - 1
+    safe_assert(len(cov) == bn.size() - 1)
 
     # Create object for inference
     bn_ie = gum.LazyPropagation(bn)
@@ -186,8 +208,8 @@ def run_inference_bn(bn, target: str, evid_vec):
         probs.append(prob)
 
     # Debug
-    assert len(mpes) == len(evid_vec)
-    assert len(probs) == len(evid_vec)
+    safe_assert(len(mpes) == len(evid_vec))
+    safe_assert(len(probs) == len(evid_vec))
 
     return mpes, probs
 
@@ -195,19 +217,19 @@ def run_inference_bn(bn, target: str, evid_vec):
 # Run inferences on a CN
 def run_inference_cn(cn, target: str, evid_vec, exp: str):
     """
-    The CN is assumed to be a Naive Bayes model with `target` the target variable.
+    The CN is assumed to be a naive Bayes model with `target` the target variable.
     """
 
     # Store information
-    with TemporaryDirectory() as tmp_path:
-        cn.saveBNsMinMax(f"{tmp_path}/bn_min_{exp}.bif", f"{tmp_path}/bn_max_{exp}.bif")
-        bn_min = gum.loadBN(f"{tmp_path}/bn_min_{exp}.bif")
-        bn_max = gum.loadBN(f"{tmp_path}/bn_max_{exp}.bif")
+    bn_min, bn_max = get_min_max_bns(cn, exp)
     cov = sorted(list(bn_min.names()))
     cov.remove(target)
 
+    # Set seed
+    set_seed()
+
     # Debug
-    assert len(cov) == bn_min.size() - 1
+    safe_assert(len(cov) == bn_min.size() - 1)
 
     # Compute all combinations of evidence
     mpes = []
@@ -221,8 +243,8 @@ def run_inference_cn(cn, target: str, evid_vec, exp: str):
         probs_alt.append(prob_alt)
 
     # Debug
-    assert len(mpes) == len(evid_vec)
-    assert len(probs) == len(evid_vec)
-    assert len(probs_alt) == len(evid_vec)
+    safe_assert(len(mpes) == len(evid_vec))
+    safe_assert(len(probs) == len(evid_vec))
+    safe_assert(len(probs_alt) == len(evid_vec))
 
     return mpes, probs, probs_alt
diff --git a/src/learning.py b/src/learning.py
new file mode 100644
index 0000000..9bdc4a7
--- /dev/null
+++ b/src/learning.py
@@ -0,0 +1,66 @@
+import pandas as pd
+import pyagrum as gum
+
+from src.config import get_cur_dir, safe_assert, set_seed
+
+
+# Learn BN parameters from a given BN and data
+def learn_bn_params(bn, data):
+
+    bn_copy = gum.BayesNet(bn)
+
+    learner = gum.BNLearner(data, bn_copy)
+    learner.useSmoothingPrior(1e-10)
+    bn_learnt = learner.learnParameters(bn_copy)
+
+    return bn_learnt
+
+
+# Estimate BNs from rpop and pool
+def estimate_bns(exp, config) -> None:
+
+    # Get current directory
+    cur_dir = get_cur_dir(config)
+
+    # Set seed
+    set_seed()
+
+    # Read data
+    gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv')
+    bn = gum.loadBN(f'{cur_dir / config["bns_path"]}/gt/{exp}.bif')
+    n_nodes = len(bn.nodes())
+    gpop_ss = config["gpop_ss"]
+    rpop_ss = int(gpop_ss * config["rpop_prop"])
+    pool_ss = int(gpop_ss * config["pool_prop"])
+
+    # Debug
+    safe_assert(gpop_ss == gpop.shape[0])
+    safe_assert(n_nodes == gpop.loc[:, ~gpop.columns.str.contains("in-")].shape[1])
+
+    # For each data sample ...
+    for sample in range(config["samples"]):
+
+        # ... retrieve pool and rpop, ...
+        pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes]
+        rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, :n_nodes]
+
+        # ... estimate BN from rpop, ...
+        bn_learnt = learn_bn_params(bn, rpop)
+        gum.saveBN(
+            bn_learnt,
+            f'{cur_dir / config["bns_path"] / "rpop"}/{f"bn_{exp}_sample{sample}"}.bif',
+        )
+
+        # ... estimate BN from pool, ...
+        bn_learnt = learn_bn_params(bn, pool)
+        gum.saveBN(
+            bn_learnt,
+            f'{cur_dir / config["bns_path"] / "pool"}/{f"bn_{exp}_sample{sample}"}.bif',
+        )
+
+        # Debug
+        safe_assert(len(pool) == sum(gpop[f"in-pool-{sample}"]))
+        safe_assert(len(pool) == pool_ss)
+        safe_assert(len(rpop) == rpop_ss)
+
+    return
diff --git a/src/membership_attack.py b/src/membership_attack.py
deleted file mode 100644
index c9c704a..0000000
--- a/src/membership_attack.py
+++ /dev/null
@@ -1,367 +0,0 @@
-import math
-import traceback
-
-import numpy as np
-import pandas as pd
-import pyagrum as gum
-from scipy.stats import norm
-from sklearn import metrics
-
-from src.config import get_base_path, set_global_seed
-from src.utils import (add_counts_to_bn, get_ll, get_llr, get_noisy_bn,
-                       sample_from_cn)
-
-
-# Get the attack power related to a fixed error
-def get_power(llr_ref, llr_gen, ground_truth, error) -> float:
-
-    # Compute the threshold
-    t = np.quantile(llr_ref, 1 - error).item()
-
-    # Test: L(x) > t => reject H_0 => assign `x` to target_pop
-    y_pred = llr_gen > t
-
-    # Compute power (i.e., true positive rate)
-    power = sum(ground_truth & y_pred) / sum(ground_truth)
-
-    return power
-
-
-# MIA: membership inference attack
-def run_mia(model, baseline, rpop, gpop, ground_truth, error_vec):
-
-    # Compute llr(x) on reference and general populations
-    llr_ref = (
-        rpop.apply(lambda x: get_llr(x.to_dict(), baseline, model), axis=1)
-        .dropna()
-        .sort_values()
-    )
-    llr_gen = gpop[[*rpop.columns]].apply(
-        lambda x: get_llr(x.to_dict(), baseline, model), axis=1
-    )
-
-    power_vec = []
-
-    # Get the power for each error
-    for error in error_vec:
-        power = get_power(llr_ref, llr_gen, ground_truth, error)
-        power_vec.append(power)
-
-    # Compute and store AUC
-    auc = metrics.auc(error_vec, power_vec)
-
-    return power_vec, auc
-
-
-# Get the maximum likelihood BN
-def get_maxll_bn(bns_sample, rpop):
-    """
-    Given a list `bns_sample` of BNs,
-    find argmax_{BN in bns_sample} ll(BN | rpop),
-    where ll is the log-likelihood function.
-    """
-
-    maxll_bn = None
-    maxll = -np.inf
-
-    for bn in bns_sample:
-
-        # Estimate the likelihood of rpop
-        bn_ie = gum.LazyPropagation(bn)
-        llr_im = rpop.apply(lambda x: get_ll(x.to_dict(), bn_ie), axis=1).dropna()
-        llr = np.sum(llr_im)
-
-        if llr > maxll:
-            maxll_bn = bn
-            maxll = llr
-
-    return maxll_bn
-
-
-# Find eps s.t. |AUC(eps) - AUC(CN)| < tol
-def get_eps(exp, ess, config):
-
-    # Get base path
-    base_path = get_base_path(config)
-
-    # Set seed
-    set_global_seed(config["seed"])
-
-    # Init hyperp.
-    eps_vec = eval(config["ess_dict"][ess])
-    results_path = base_path / config["results_path"]
-    n_samples = config["n_samples"]
-    n_bns = config["n_bns"]
-    error = eval(config["error"])
-    tol = config["tol"]
-
-    # Read data
-    gpop = pd.read_csv(f'{base_path / config["data_path"]}/{exp}.csv')
-    bn = gum.loadBN(f'{base_path / config["bns_path"]}/{exp}.bif')
-    n_nodes = config["n_nodes"]
-    gpop_ss = config["gpop_ss"]
-    rpop_ss = int(gpop_ss * config["rpop_prop"])
-    pool_ss = int(gpop_ss * config["pool_prop"])
-
-    # Debug
-    assert gpop_ss == gpop.shape[0]
-    assert n_nodes == gpop.shape[1]
-
-    bn_theta_vec = []
-    bn_theta_hat_vec = []
-    cn_vec = []
-
-    # For any data sample ...
-    for sample in range(n_samples):
-
-        # ... sample pool and rpop, ...
-        pool_idx = np.random.choice(range(gpop_ss), size=pool_ss, replace=False)
-        gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx)
-        pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes]
-        rpop = gpop[~gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes].sample(rpop_ss)
-
-        # ... estimate BN from rpop, ...
-        learner = gum.BNLearner(rpop)
-        learner.useSmoothingPrior(1e-5)
-        bn_theta_vec.append(learner.learnParameters(bn.dag()))
-
-        # ... estimate BN from pool, ...
-        learner = gum.BNLearner(pool)
-        learner.useSmoothingPrior(1e-5)
-        bn_theta_hat_vec.append(learner.learnParameters(bn.dag()))
-
-        # ... and estimate CN from pool (by local IDM)
-        bn_counts = gum.BayesNet(bn)
-        add_counts_to_bn(bn_counts, pool)
-        cn = gum.CredalNet(bn_counts)
-        cn.idmLearning(ess)
-        cn_vec.append(cn)
-
-        # Debug
-        assert len(pool) == sum(gpop[f"in-pool-{sample}"])
-        assert len(pool) == pool_ss
-        assert len(rpop) == rpop_ss
-
-    # Debug
-    assert len(bn_theta_vec) == n_samples
-    assert len(bn_theta_hat_vec) == n_samples
-    assert len(cn_vec) == n_samples
-
-    # Run MIA against CN
-    auc_cn_vec = []
-    for sample in range(n_samples):
-
-        # Retrieve sample-related info
-        y_true = gpop[f"in-pool-{sample}"]
-        cn = cn_vec[sample]
-        bn_theta_ie = gum.LazyPropagation(bn_theta_vec[sample])
-
-        # Extract random subset within simplex
-        bns_sample = sample_from_cn(cn, n_bns, "inside")
-
-        # Get the maximum likelihood BN
-        best_bn = get_maxll_bn(bns_sample, rpop)
-        bn_ie = gum.LazyPropagation(best_bn)
-
-        # MIA
-        try:
-            _, auc = run_mia(bn_ie, bn_theta_ie, rpop, gpop, y_true, error)
-            auc_cn_vec.append(auc)
-
-        except Exception:
-
-            # Debug
-            with open(f"{results_path}/log.txt", "a") as log:
-                log.write(f"{exp}: error with sample {sample} (CN).\n")
-                log.write(traceback.format_exc())
-
-    # Compute Avg(AUC(CN)) across data samples
-    auc_cn = sum(auc_cn_vec) / len(auc_cn_vec)
-
-    # Find eps
-    eps_best = eps_vec[-1]
-
-    # For each eps ...
-    for eps in eps_vec:
-
-        auc_bn_noisy_vec = []
-
-        # ... run MIA against noisy BN ...
-        for sample in range(n_samples):
-
-            # Retrieve sample-related info
-            y_true = gpop[f"in-pool-{sample}"]
-            bn_theta_hat = bn_theta_hat_vec[sample]
-            bn_theta_ie = gum.LazyPropagation(bn_theta_vec[sample])
-
-            # Get noisy BN
-            scale = (2 * bn_theta_hat.size()) / (len(pool) * eps)
-            bn_noisy = get_noisy_bn(bn_theta_hat, scale)
-            bn_noisy_ie = gum.LazyPropagation(bn_noisy)
-
-            try:
-
-                # MIA
-                _, auc = run_mia(bn_noisy_ie, bn_theta_ie, rpop, gpop, y_true, error)
-                auc_bn_noisy_vec.append(auc)
-
-            except Exception:
-
-                # Debug
-                with open(f"{results_path}/log.txt", "a") as log:
-                    log.write(
-                        f"{exp}: error with sample {sample} (BN noisy, eps: {eps}).\n"
-                    )
-                    log.write(traceback.format_exc())
-
-        # ... and compute Avg(AUC(eps)) across data samples
-        auc_bn = sum(auc_bn_noisy_vec) / n_samples
-
-        # Condition on |AUC(eps) - AUC(CN)|
-        if abs(auc_cn - auc_bn) <= tol:
-            eps_best = eps
-            break
-
-    # Store found eps
-    meta_file_path = (
-        results_path
-        / f'results_nodes{config["n_nodes"]}_ess{ess}'
-        / config["meta_file"]
-    )
-    with open(meta_file_path, "a") as m:
-        m.write(f"- {exp}. Nodes: {n_nodes} Eps: {eps_best}\n")
-
-    return exp, ess, eps_best
-
-
-# Membership attack against CN and BN
-def attack_cn_bn(exp, ess, config):
-
-    # Get base path
-    base_path = get_base_path(config)
-
-    # Set seed
-    set_global_seed(config["seed"])
-
-    # Init hyperp.
-    results_path = base_path / config["results_path"]
-    n_samples = config["n_samples"]
-    n_bns = config["n_bns"]
-    error = eval(config["error"])
-
-    # Read data
-    gpop = pd.read_csv(f'{base_path / config["data_path"]}/{exp}.csv')
-    bn = gum.loadBN(f'{base_path / config["bns_path"]}/{exp}.bif')
-    n_nodes = len(bn.nodes())
-    gpop_ss = config["gpop_ss"]
-    rpop_ss = int(gpop_ss * config["rpop_prop"])
-    pool_ss = int(gpop_ss * config["pool_prop"])
-
-    # Debug
-    assert gpop_ss == gpop.shape[0]
-    assert n_nodes == gpop.shape[1]
-
-    bn_theta_vec = []
-    bn_theta_hat_vec = []
-    cn_vec = []
-
-    # For any data sample ...
-    for sample in range(n_samples):
-
-        # ... sample pool and rpop, ...
-        pool_idx = np.random.choice(range(gpop_ss), size=pool_ss, replace=False)
-        gpop[f"in-pool-{sample}"] = gpop.index.isin(pool_idx)
-        pool = gpop[gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes]
-        rpop = gpop[~gpop[f"in-pool-{sample}"]].iloc[:, :n_nodes].sample(rpop_ss)
-
-        # ... estimate BN from rpop, ...
-        learner = gum.BNLearner(rpop)
-        learner.useSmoothingPrior(1e-5)
-        bn_theta_vec.append(learner.learnParameters(bn.dag()))
-
-        # ... estimate BN from pool, ...
-        learner = gum.BNLearner(pool)
-        learner.useSmoothingPrior(1e-5)
-        bn_theta_hat_vec.append(learner.learnParameters(bn.dag()))
-
-        # ... and estimate CN from pool (by local IDM)
-        bn_counts = gum.BayesNet(bn)
-        add_counts_to_bn(bn_counts, pool)
-        cn = gum.CredalNet(bn_counts)
-        cn.idmLearning(ess)
-        cn_vec.append(cn)
-
-        # Debug
-        assert len(pool) == sum(gpop[f"in-pool-{sample}"])
-        assert len(pool) == pool_ss
-        assert len(rpop) == rpop_ss
-
-    # Debug
-    assert len(bn_theta_vec) == n_samples
-    assert len(bn_theta_hat_vec) == n_samples
-    assert len(cn_vec) == n_samples
-
-    # Compute theoretical bound
-    compl = bn.dim()
-    bound = math.sqrt(compl / pool_ss)
-
-    # Find power (beta) for any error (alpha) given theoretical bound
-    z_alpha = [norm.ppf(1 - i).item() for i in error]
-    z_one_minus_beta = [bound - i for i in z_alpha]
-    beta = [norm.cdf(i).item() for i in z_one_minus_beta]
-
-    # Init results
-    results = pd.DataFrame({"error": error, "power_bound": beta})
-
-    # Run MIA against CN
-    for sample in range(n_samples):
-
-        # Retrieve sample-related info
-        y_true = gpop[f"in-pool-{sample}"]
-        cn = cn_vec[sample]
-        bn_theta_ie = gum.LazyPropagation(bn_theta_vec[sample])
-
-        # Extract random subset within simplex
-        bns_sample = sample_from_cn(cn, n_bns, "inside")
-
-        # Get the maximum likelihood BN
-        best_bn = get_maxll_bn(bns_sample, rpop)
-        bn_ie = gum.LazyPropagation(best_bn)
-
-        # MIA
-        try:
-            power_vec, _ = run_mia(bn_ie, bn_theta_ie, rpop, gpop, y_true, error)
-            results[f"power_CN_sample{sample}"] = power_vec
-
-        except Exception:
-
-            # Debug
-            with open(f"{results_path}/log.txt", "a") as log:
-                log.write(f"{exp}: error with sample {sample} (CN).\n")
-                log.write(traceback.format_exc())
-
-    # Run MIA against BN
-    for sample in range(n_samples):
-
-        # Retrieve sample-related info
-        y_true = gpop[f"in-pool-{sample}"]
-        bn_theta_hat_ie = gum.LazyPropagation(bn_theta_hat_vec[sample])
-        bn_theta_ie = gum.LazyPropagation(bn_theta_vec[sample])
-
-        try:
-
-            # MIA
-            power_vec, _ = run_mia(
-                bn_theta_hat_ie, bn_theta_ie, rpop, gpop, y_true, error
-            )
-            results[f"power_BN_sample{sample}"] = power_vec
-
-        except Exception:
-
-            # Debug
-            with open(f"{results_path}/log.txt", "a") as log:
-                log.write(f"{exp}: error with sample {sample} (BN).\n")
-                log.write(traceback.format_exc())
-
-    # Save results
-    results.to_csv(f"{results_path}/{exp}-ess{ess}.csv", index=False)
diff --git a/src/mia.py b/src/mia.py
new file mode 100644
index 0000000..789466e
--- /dev/null
+++ b/src/mia.py
@@ -0,0 +1,318 @@
+import math
+
+import numpy as np
+import pandas as pd
+import pyagrum as gum
+from scipy.stats import norm
+from sklearn import metrics
+
+from src.config import get_cur_dir, safe_assert, set_seed
+from src.defense import noisy_bn
+
+
+# MIA attack vs a BN
+def mia_vs_bn(exp, config) -> dict:
+
+    # Get current directory
+    cur_dir = get_cur_dir(config)
+
+    # Init results
+    power_res = pd.DataFrame({"error": eval(config["error"])})
+    auc_res = pd.DataFrame({"sample": range(config["samples"])})
+    auc_res["exp"] = exp
+
+    # Read data
+    gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv')
+
+    # Set seed
+    set_seed()
+
+    # For each data sample ...
+    auc_bns_dict = dict()
+    for sample in range(config["samples"]):
+
+        # ... read the BNs as estimated from rpop and pool, ...
+        bn_theta = gum.loadBN(
+            f"{cur_dir}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif"
+        )
+        bn_theta_hat = gum.loadBN(
+            f"{cur_dir}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif"
+        )
+
+        # try:
+
+        # ... and perform membership inference on gpop
+        power_vec, auc = run_mia(
+            bn_theta_hat,
+            bn_theta,
+            gpop,
+            sample,
+            eval(config["error"]),
+        )
+        power_res[f"power_BN_sample{sample}"] = power_vec
+        auc_bns_dict[sample] = auc
+
+        # except Exception:
+
+        #     # Debug
+        #     with open(f"{results_path}/log.txt", "a") as log:
+        #         log.write(f"{exp}: error with sample {sample} (BN).\n")
+        #         log.write(traceback.format_exc())
+
+    # Save results
+    power_res.to_csv(
+        f'{cur_dir}/{config["results_path"]}/bns/power_bn_{exp}.csv', index=False
+    )
+
+    # Return
+    auc_res["auc_bn"] = auc_res.apply(lambda row: auc_bns_dict[row["sample"]], axis=1)
+
+    return auc_res
+
+
+# MIA attack vs a CN
+def mia_vs_cn(exp, config) -> pd.DataFrame:
+
+    # Get current directory
+    cur_dir = get_cur_dir(config)
+
+    # Init results
+    power_res = pd.DataFrame({"error": eval(config["error"])})
+    auc_res = pd.DataFrame({"sample": range(config["samples"])})
+    auc_res["exp"] = exp
+
+    # Read data
+    gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv')
+
+    # Set seed
+    set_seed()
+
+    # For each data sample ...
+    auc_cns_dict = dict()
+    for sample in range(config["samples"]):
+
+        # ... read the BN as inferred from the CN
+        bn_theta_hat = gum.loadBN(
+            f'{cur_dir}/{config["atk_path"]}/bn_{exp}_sample{sample}.bif'
+        )
+
+        # ... read the BN as estimated from rpop, ...
+        bn_theta = gum.loadBN(
+            f"{cur_dir}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif"
+        )
+
+        # try:
+
+        # ... and perform membership inference on gpop
+        power_vec, auc = run_mia(
+            bn_theta_hat,
+            bn_theta,
+            gpop,
+            sample,
+            eval(config["error"]),
+        )
+        power_res[f"power_CN_sample{sample}"] = power_vec
+        auc_cns_dict[sample] = auc
+
+        # except Exception:
+
+        #     # Debug
+        #     with open(f"{results_path}/log.txt", "a") as log:
+        #         log.write(f"{exp}: error with sample {sample} (BN).\n")
+        #         log.write(traceback.format_exc())
+
+    # Save results
+    power_res.to_csv(
+        f'{cur_dir}/{config["results_path"]}/cns/power_cn_{exp}.csv', index=False
+    )
+
+    # Return
+    auc_res["auc_cn"] = auc_res.apply(lambda row: auc_cns_dict[row["sample"]], axis=1)
+
+    return auc_res
+
+
+# Get theoretical power
+def theoretical_power(exp, config) -> None:
+
+    # Get current directory
+    cur_dir = get_cur_dir(config)
+
+    # Read data
+    bn = gum.loadBN(f'{get_cur_dir(config) / config["bns_path"]}/gt/{exp}.bif')
+    results = pd.read_csv(f'{cur_dir}/{config["results_path"]}/bns/power_bn_{exp}.csv')
+
+    # Set seed
+    set_seed()
+
+    # Compute bound
+    bound = math.sqrt(bn.dim() / int(config["gpop_ss"] * config["pool_prop"]))
+
+    # Find power (beta) for any error (alpha) given theoretical bound
+    z_alpha = [norm.ppf(1 - i).item() for i in eval(config["error"])]
+    z_one_minus_beta = [bound - i for i in z_alpha]
+    beta = [norm.cdf(i).item() for i in z_one_minus_beta]
+
+    # Save results
+    results["power_bound"] = beta
+    results.to_csv(
+        f'{cur_dir}/{config["results_path"]}/bns/power_bn_{exp}.csv', index=False
+    )
+
+    return
+
+
+# Find eps s.t. |AUC(eps) - AUC(CN)| < tol
+def find_epsilon(exp, config) -> dict:
+
+    # Get current directory
+    cur_dir = get_cur_dir(config)
+
+    # Init results
+    power_res = pd.DataFrame({"error": eval(config["error"])})
+
+    # Read data
+    gpop = pd.read_csv(f'{cur_dir / config["data_path"]}/{exp}.csv')
+    gpop_ss = config["gpop_ss"]
+    pool_ss = int(gpop_ss * config["pool_prop"])
+    auc_res = pd.read_csv(f'{cur_dir}/{config["auc_meta"]}')
+    auc_res = auc_res[auc_res["exp"] == exp]
+    eps_vec = eval(config["eps_vec"])
+
+    # Set seed
+    set_seed()
+
+    # For each data sample ...
+    eps_dict = dict()
+    auc_noisy_dict = dict()
+    for sample in range(config["samples"]):
+
+        # ... read the BNs as estimated from rpop and pool, ...
+        bn_theta = gum.loadBN(
+            f"{cur_dir}/{config['bns_path']}/rpop/bn_{exp}_sample{sample}.bif"
+        )
+        bn_theta_hat = gum.loadBN(
+            f"{cur_dir}/{config['bns_path']}/pool/bn_{exp}_sample{sample}.bif"
+        )
+
+        # ... get CN AUC, ...
+        auc_cn = auc_res.loc[auc_res["sample"] == sample, "auc_cn"].values[0]
+
+        # ... init results, ...
+        eps_dict[sample] = None
+        auc_noisy_dict[sample] = None
+
+        # ... and find epsilon
+        for eps in eps_vec:
+
+            # Get noisy BN
+            scale = (2 * bn_theta_hat.size()) / (pool_ss * eps)
+            bn_noisy = noisy_bn(bn_theta_hat, scale)
+
+            # Perform membership inference on gpop
+            power_vec, auc = run_mia(
+                bn_noisy,
+                bn_theta,
+                gpop,
+                sample,
+                eval(config["error"]),
+            )
+
+            # Condition on |AUC(eps) - AUC(CN)|
+            if abs(auc_cn - auc) < config["tol"]:
+                eps_dict[sample] = eps
+                auc_noisy_dict[sample] = auc
+                power_res[f"power_BN_noisy_sample{sample}"] = power_vec
+                break
+
+    # Save results
+    power_res.to_csv(
+        f'{cur_dir}/{config["results_path"]}/bn_noisy/power_bn_{exp}.csv', index=False
+    )
+
+    # Return
+    auc_res["epsilon"] = auc_res.apply(lambda row: eps_dict[row["sample"]], axis=1)
+    auc_res["auc_noisy_bn"] = auc_res.apply(
+        lambda row: auc_noisy_dict[row["sample"]], axis=1
+    )
+
+    return auc_res
+
+
+# MIA: membership inference attack
+def run_mia(model, baseline, gpop, sample, error_vec):
+
+    # Create objects for inference
+    model_ie = gum.LazyPropagation(model)
+    baseline_ie = gum.LazyPropagation(baseline)
+
+    # Retrieve rpop and evaluation set (i.e. the rpop complement in gpop)
+    rpop = gpop[gpop[f"in-rpop-{sample}"]].iloc[:, : len(model.nodes())]
+    eval_pop = gpop[~gpop[f"in-rpop-{sample}"]].iloc[:, : len(model.nodes())]
+    ground_truth = gpop[~gpop[f"in-rpop-{sample}"]].loc[:, f"in-pool-{sample}"]
+
+    # Compute llr(x)'s
+    llr_rpop = (
+        rpop.apply(lambda x: get_llr(x.to_dict(), baseline_ie, model_ie), axis=1)
+        .dropna()
+        .sort_values()
+    )
+    llr_eval = eval_pop.apply(
+        lambda x: get_llr(x.to_dict(), baseline_ie, model_ie), axis=1
+    )
+
+    power_vec = []
+
+    # Get the power for each error
+    for error in error_vec:
+        power = get_power(llr_rpop, llr_eval, ground_truth, error)
+        power_vec.append(power)
+
+    # Compute and store AUC
+    auc = metrics.auc(error_vec, power_vec)
+
+    # Debug
+    safe_assert(len(rpop) + len(eval_pop) == len(gpop))
+    safe_assert(len(eval_pop) == len(ground_truth))
+    safe_assert(len(power_vec) == len(error_vec))
+
+    return power_vec, auc
+
+
+# Get the attack power related to a fixed error
+def get_power(llr_ref, llr_eval, ground_truth, error) -> float:
+
+    # Compute the threshold
+    t = np.quantile(llr_ref, 1 - error).item()
+
+    # Test: L(x) > t => reject H_0 => assign `x` to target_pop
+    y_pred = llr_eval > t
+
+    # Compute power (i.e., true positive rate)
+    power = sum(ground_truth & y_pred) / sum(ground_truth)
+
+    return power
+
+
+# Log-likelihood function
+def get_ll(x: dict, theta):
+
+    # Erase all evidences and apply addEvidence(key,value) for every pairs in x
+    theta.setEvidence(x)
+
+    # Compute P(x | theta)
+    ll = theta.evidenceProbability()
+    if ll == 0:
+        ll += 10e-9
+
+    return np.log(ll)
+
+
+# Log-likelihood ratio (llr) function
+def get_llr(x: dict, theta, theta_hat):
+
+    # Compute log-likelihoods
+    ll_theta = get_ll(x, theta)
+    ll_theta_hat = get_ll(x, theta_hat)
+
+    return ll_theta_hat - ll_theta
diff --git a/src/run_exp.py b/src/run_exp.py
deleted file mode 100644
index ec51f00..0000000
--- a/src/run_exp.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import gc
-import multiprocessing  # noqa: F401 # pylint: disable=unused-import
-from itertools import product
-
-from joblib import Parallel, delayed
-
-from src.config import get_base_path
-from src.inference import run_inferences
-from src.membership_attack import attack_cn_bn, get_eps
-
-
-def run_cn_vs_noisybn(config):
-
-    # Get base path
-    base_path = get_base_path(config)
-
-    # Set number of threads for parallelization
-    num_cores = eval(config["num_cores"])
-
-    # For each ESS and each model ...
-    exp_vec = [
-        f.stem for f in (base_path / config["data_path"]).iterdir() if f.is_file()
-    ]
-    ess_vec = config["ess_dict"].keys()
-
-    # ... find eps s.t. |AUC(eps) - AUC(CN)| < tol, ...
-    res = Parallel(n_jobs=num_cores)(
-        delayed(get_eps)(exp, ess, config) for exp, ess in product(exp_vec, ess_vec)
-    )
-
-    # ... and run inferences
-    _ = Parallel(n_jobs=num_cores)(
-        delayed(run_inferences)(exp, ess, eps, config) for exp, ess, eps in res
-    )
-
-    # Clean
-    gc.collect()
-
-
-def run_cn_privacy(config):
-
-    # Get base path
-    base_path = get_base_path(config)
-
-    # Set number of threads for parallelization
-    num_cores = eval(config["num_cores"])
-
-    # For each ESS and each model ...
-    exp_vec = [
-        f.stem for f in (base_path / config["data_path"]).iterdir() if f.is_file()
-    ]
-    ess_vec = eval(config["ess_vec"])
-
-    # ... run MIA attack on BN and CN
-    Parallel(n_jobs=num_cores)(
-        delayed(attack_cn_bn)(exp, ess, config)
-        for exp, ess in product(exp_vec, ess_vec)
-    )
-
-    # Clean
-    gc.collect()
diff --git a/src/utils.py b/src/utils.py
index e980b0f..11ae36e 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -1,168 +1,621 @@
-import io
-import re
-from collections import defaultdict
-from contextlib import redirect_stdout
+from fractions import Fraction
+from tempfile import TemporaryDirectory
 
+import cdd
+import cdd.gmp
+import cvxpy as cp
+import hopsy
 import numpy as np
 import pyagrum as gum
-from more_itertools import random_product
-from numpy import random
-from numpy.random import random_sample
 
+from src.config import safe_assert
 
-# Log-likelihood function
-def get_ll(x: dict, theta):
 
-    # Erase all evidences and apply addEvidence(key,value) for every pairs in x
-    theta.setEvidence(x)
+# Create the BN storing the counts of events
+def get_bn_counts(bn, data):
 
-    # Compute P(x | theta)
-    ll = theta.evidenceProbability()
+    # Init the BN
+    bn_counts = gum.BayesNet(bn)
 
-    return np.log(ll)
+    # For each node ...
+    for node in bn.names():
 
+        # ... create the CPT storing counts of events
+        counts = []
 
-# Log-likelihood ratio (llr) function
-def get_llr(x: dict, theta, theta_hat):
+        n_parents = len(bn.parents(node))
+        if n_parents != 0:
+            cpt = bn.cpt(node).topandas().reset_index()
+            parents = [str(x[0]) for x in cpt.columns[:n_parents]]
+            cpt.columns = parents + list(cpt[node].columns)
+            domain = [int(x) for x in cpt.columns[n_parents:]]
 
-    # Compute log-likelihoods
-    ll_theta = get_ll(x, theta)
-    ll_theta_hat = get_ll(x, theta_hat)
+            for idx in range(len(cpt)):
+                counts_cond = []
+                query = dict(cpt.iloc[idx, :n_parents])
+                query_str = " & ".join([f"{k}=={v}" for k,v in query.items()])
+                data_cond = data.query(query_str)
+                for node_val in domain:
+                    counts_cond.append(len(data_cond[data_cond[node] == node_val]))
+                counts.append(counts_cond)
 
-    return ll_theta_hat - ll_theta
+                # Debug
+                safe_assert(sum(counts_cond) == len(data_cond))
 
+        else:
+            domain = [x[1] for x in bn.cpt(node).topandas().index]
+            for node_val in domain:
+                    counts.append(len(data[data[node] == node_val]))
 
-# Parse the credal network
-def parse_cn(cn) -> tuple:
+        counts = np.array(counts).flatten()
+        bn_counts.cpt(node).fillWith(counts.tolist())
 
-    # Get the DAG
-    dag = gum.BayesNet(cn.current_bn())
+        # Debug
+        safe_assert(sum(counts) == len(data))
 
-    # Cast CN to string
-    buffer = io.StringIO()
-    with redirect_stdout(buffer):
-        print(cn)
-    cn_str = buffer.getvalue()
+    return bn_counts
 
-    credal_dict = defaultdict(lambda: defaultdict(list))
-    current_var = None
 
-    lines = cn_str.strip().split("\n")
+# Get the BN inside a CN with max entropy distribution
+def maxent_cn(bn_min, bn_max) -> gum.BayesNet:
 
-    for line in lines:
-        line = line.strip()
+    # Init an empty BN
+    bn = gum.BayesNet(bn_min)
 
-        # Variable identification
-        var_match = re.match(r"^([A-Za-z0-9_]+):", line)
-        if var_match:
-            current_var = var_match.group(1)
-            continue
+    # For each variable ...
+    for var in bn.names():
 
-        if current_var is None or not line:
-            continue
+        # ... get the maxent CPT, ...
+        cpt = maxent_cpt(bn_min.cpt(var), bn_max.cpt(var))
 
-        # CPT identification
-        cpt_match = re.match(r"^<([^>]*)>\s*:\s*(.*)", line)
-        if cpt_match:
-            condition = f"<{cpt_match.group(1).strip()}>"
-            raw_cpt = cpt_match.group(2)
+        # ... and fill the BN
+        bn.cpt(var).fillWith(cpt.flatten())
 
-            # Extraction of inner lists: [[x,x,x], [x,x,x], ...]
-            vectors = re.findall(r"\[\s*([^\[\]]+?)\s*\]", raw_cpt)
-            for vec in vectors:
-                prob_vec = [float(x.strip()) for x in vec.split(",")]
-                credal_dict[current_var][condition].append(prob_vec)
+    # Debug
+    safe_assert(check_consistency(bn, bn_min, bn_max)==0)
 
-    params = []
-    for var in credal_dict:
-        for cond, vectors in credal_dict[var].items():
-            params.append((var, cond, vectors))
+    return bn
 
-    return dag, params
 
+# Get the BN CPT inside a CN CPT with max entropy distribution
+def maxent_cpt(cpt_min, cpt_max) -> np.array:
 
-# Compute a random subset of BNs from the CN
-def sample_from_cn(cn, n: int, where: str) -> list:
-    """
-    Sample random BNs from the CN.
-
-    Parameters:
-    - `cn`: the given CN.
-    - `n`: number of BNs to extract from the CN.
-    - `where`: can be `inside` or `outside`.
-        "inside": the BNs are taken from within the credal set;
-        "outside": the BNs are vertices of the credal set.
-    """
+    # Transform CPTs into pandas dataframes
+    cpt_min = np.atleast_2d(cpt_min.topandas())
+    cpt_max = np.atleast_2d(cpt_max.topandas())
+
+    # For each row in the CPT ...
+    cpt = []
+    for row in range(cpt_min.shape[0]):
+
+        # ... get the maxent credal set, ...
+        c = maxent_cset(cpt_min[row, :], cpt_max[row, :])
+        cpt.append(c)
+
+    # Reshape the CPT
+    cpt = np.array(cpt)
+
+    # Debug
+    safe_assert(cpt_min.shape == cpt_max.shape)
+    safe_assert(cpt.shape == cpt_min.shape)
+
+    return cpt
+
+
+# Get the max-entropy distribution inside a credal set
+def maxent_cset(vec_min, vec_max) -> np.array:
+
+    rank = {v: k for k, v in enumerate(sorted(set(vec_min)))}
+    vec_order = np.array([rank[val] for val in vec_min])
+    s = 1 - np.sum(vec_min)
+    out = vec_min
+
+    while s > 0:
+        idx0 = np.where(vec_order == 0)[0]
+        idx1 = np.where(vec_order == 1)[0]
+        idx0_len = len(idx0)
+        idx1_len = len(idx1)
+
+        if idx1_len != 0:
+            diff = out[idx1[0]] - out[idx0[0]]
+            s_cond = s / idx0_len < diff
+            mat = np.stack(
+                [
+                    (
+                        (s / idx0_len) * np.ones(len(idx0))
+                        if s_cond
+                        else diff * np.ones(len(idx0))
+                    ),
+                    vec_max[idx0] - out[idx0],
+                ]
+            )
+        else:
+            s_cond = True
+            mat = np.stack(
+                [(s / idx0_len) * np.ones(len(idx0)), vec_max[idx0] - out[idx0]]
+            )
+
+        mat_min = np.min(mat)
+        q = np.argwhere(mat == mat_min)
+
+        if np.any(q[:, 0] == 1):
+            if len(idx0) > len(q):
+                vec_order[~np.isin(np.arange(len(out)), idx0[q[:, 1]])] += 1
+        elif not s_cond:
+            vec_order[idx0[q[:, 1]]] += 1
+
+        out[idx0] += mat_min
+        s -= mat_min * len(idx0)
+        vec_order -= 1
+
+    return out
+
+
+# Get the max likelihood BN inside a CN
+def mle_cn(bn_min, bn_max, data) -> gum.BayesNet:
+
+    # Init an empty BN
+    bn = gum.BayesNet(bn_min)
+
+    # Store counts
+    bn_counts = get_bn_counts(bn, data)
+
+    # For each variable ...
+    for var in bn.names():
+
+        # ... get the MLE CPT, ...
+        cpt = mle_cpt(bn_min.cpt(var), bn_max.cpt(var), bn_counts.cpt(var))
+
+        # ... and fill the BN
+        bn.cpt(var).fillWith(cpt.flatten())
+
+    # Debug
+    safe_assert(check_consistency(bn, bn_min, bn_max)==0)
+
+    return bn
+
+
+# Get the max likelihood BN CPT inside a CN CPT
+def mle_cpt(cpt_min, cpt_max, cpt_counts) -> np.array:
+
+    # Transform CPTs into pandas dataframes
+    cpt_min = np.atleast_2d(cpt_min.topandas())
+    cpt_max = np.atleast_2d(cpt_max.topandas())
+    cpt_counts = np.atleast_2d(cpt_counts.topandas())
+
+    # For each row in the CPT ...
+    cpt = []
+    for row in range(cpt_min.shape[0]):
+
+        # ... get the MLE credal set, ...
+        c = mle_cset(cpt_min[row, :], cpt_max[row, :], cpt_counts[row, :])
+        cpt.append(c)
+
+    # Reshape the CPT
+    cpt = np.array(cpt)
+
+    # Debug
+    safe_assert(cpt_min.shape == cpt_max.shape)
+    safe_assert(cpt.shape == cpt_min.shape)
+
+    return cpt
+
+
+# Get the max likelihood distribution inside a credal set
+def mle_cset(vec_min, vec_max, counts) -> np.array:
+
+    # Number of variables to optimize
+    n_par = len(vec_min)
+    p = cp.Variable(n_par)
+
+    # Log-likelihood to maximize
+    objective = cp.Maximize(counts @ cp.log(p))
+
+    # Constraints
+    constraints = [cp.sum(p) == 1, p >= np.maximum(vec_min, 10e-9), p <= vec_max]
+
+    # Solve the optimization problem
+    problem = cp.Problem(objective, constraints)
+    problem.solve(verbose=False)
+
+    mle_vec = np.array(p.value)
+
+    # Debug
+    safe_assert(len(vec_min) == len(vec_max))
+
+    return mle_vec
+
+
+# Get the max (-likelihood) BN (MNE) within a CN, i.e., the min likelihood BN.
+def mne_cn(bn_min, bn_max, data) -> gum.BayesNet:
+
+    # Init an empty BN
+    bn = gum.BayesNet(bn_min)
+
+    # Store counts
+    bn_counts = get_bn_counts(bn, data)
+
+    # For each variable ...
+    for var in bn.names():
+
+        # ... get the MNE CPT, ...
+        cpt = mne_cpt(bn_min.cpt(var), bn_max.cpt(var), bn_counts.cpt(var))
+
+        # ... and fill the BN
+        bn.cpt(var).fillWith(cpt.flatten())
+
+    # Debug
+    safe_assert(check_consistency(bn, bn_min, bn_max)==0)
+
+    return bn
+
+
+# Get the MNE BN CPT inside a CN CPT
+def mne_cpt(cpt_min, cpt_max, cpt_counts) -> np.array:
+
+    # Transform CPTs into pandas dataframes
+    cpt_min = np.atleast_2d(cpt_min.topandas())
+    cpt_max = np.atleast_2d(cpt_max.topandas())
+    cpt_counts = np.atleast_2d(cpt_counts.topandas())
+
+    # For each row in the CPT ...
+    cpt = []
+    for row in range(cpt_min.shape[0]):
+
+        # ... get the MNE credal set, ...
+        c = mne_cset(cpt_min[row, :], cpt_max[row, :], cpt_counts[row, :])
+        cpt.append(c)
+
+    # Reshape the CPT
+    cpt = np.array(cpt)
+
+    # Debug
+    safe_assert(cpt_min.shape == cpt_max.shape)
+    safe_assert(cpt.shape == cpt_min.shape)
+
+    return cpt
+
+
+# Get the MNE distribution inside a credal set
+def mne_cset(vec_min, vec_max, counts) -> np.array:
+
+    # Get the credal set vertices
+    vertices = vertices_cset(vec_min, vec_max)
+
+    # Get the vertex that has the maximum (-likelihood)
+    vec_best = vertices[0, :]
+    mne_best = counts @ -np.log(np.where(vec_best > 0, vec_best, 1))
+
+    for row in range(vertices.shape[0]):
+
+        vec = vertices[row, :]
+        mne = counts @ -np.log(np.where(vec > 0, vec, 1))
+
+        if mne > mne_best:
+            mne_best = mne
+            vec_best = vec
+
+    # Debug
+    safe_assert(len(vec_min) == len(vec_max))
+
+    return vec_best
+
+
+# Get a random BN inside a CN
+def ran_cn(bn_min, bn_max) -> gum.BayesNet:
+
+    # Init an empty BN
+    bn = gum.BayesNet(bn_min)
+
+    # For each variable ...
+    for var in bn.names():
+
+        # ... get a random CPT, ...
+        cpt = ran_cpt(bn_min.cpt(var), bn_max.cpt(var))
+
+        # ... and fill the BN
+        bn.cpt(var).fillWith(cpt.flatten())
+
+    # Debug
+    safe_assert(check_consistency(bn, bn_min, bn_max)==0)
+
+    return bn
+
+
+# Get a random BN CPT inside a CN CPT
+def ran_cpt(cpt_min, cpt_max) -> np.array:
+
+    # Transform CPTs into pandas dataframes
+    cpt_min = np.atleast_2d(cpt_min.topandas())
+    cpt_max = np.atleast_2d(cpt_max.topandas())
+
+    # For each row in the CPT ...
+    cpt = []
+    for row in range(cpt_min.shape[0]):
+
+        # ... sample randomly from the credal set, ...
+        c = ran_cset(cpt_min[row, :], cpt_max[row, :])
+        cpt.append(c)
+
+    # Reshape the CPT
+    cpt = np.array(cpt)
+
+    # Debug
+    safe_assert(cpt_min.shape == cpt_max.shape)
+    safe_assert(cpt.shape == cpt_min.shape)
+
+    return cpt
+
+
+# Get a random distribution inside a credal set
+def ran_cset(vec_min, vec_max) -> np.array:
+
+    # Get the credal set vertices
+    vertices = vertices_cset(vec_min, vec_max)
+
+    # Sample weights for vertices
+    n = len(vertices)
+    w = np.random.dirichlet(np.ones(n))
+
+    # Draw the linear combination of vertices
+    ran_vec = w @ vertices
+
+    # Debug
+    safe_assert(len(vec_min) == len(vec_max))
+
+    return ran_vec
+
+
+# Get the centroid of a CN
+def centroid_cn(bn_min, bn_max) -> gum.BayesNet:
+
+    # Init an empty BN
+    bn = gum.BayesNet(bn_min)
+
+    # For each variable ...
+    for var in bn.names():
+
+        # ... get the centroid CPT, ...
+        cpt = centroid_cpt(bn_min.cpt(var), bn_max.cpt(var))
+
+        # ... and fill the BN
+        bn.cpt(var).fillWith(cpt.flatten())
+
+    # Debug
+    safe_assert(check_consistency(bn, bn_min, bn_max)==0)
+
+    return bn
+
+
+# Get the centroid of a CN CPT
+def centroid_cpt(cpt_min, cpt_max) -> np.array:
+
+    # Transform CPTs into pandas dataframes
+    cpt_min = np.atleast_2d(cpt_min.topandas())
+    cpt_max = np.atleast_2d(cpt_max.topandas())
+
+    # For each row in the CPT ...
+    cpt = []
+    for row in range(cpt_min.shape[0]):
+
+        # ... get the centroid credal set, ...
+        c = centroid_cset(cpt_min[row, :], cpt_max[row, :])
+        cpt.append(c)
+
+    # Reshape the CPT
+    cpt = np.array(cpt)
+
+    # Debug
+    safe_assert(cpt_min.shape == cpt_max.shape)
+    safe_assert(cpt.shape == cpt_min.shape)
+
+    return cpt
+
+
+# Get the centroid of a credal set as the average of its extreme points
+def centroid_cset(vec_min, vec_max) -> np.array:
+
+    # Get the credal set vertices
+    vertices = vertices_cset(vec_min, vec_max)
+
+    # Compute the centroid as the average across extreme points
+    centroid = np.sum(vertices, axis=0) / len(vertices)
+
+    # Debug
+    safe_assert(len(vec_min) == len(vec_max))
+
+    return centroid
+
+
+# Get the credal set vertices
+def vertices_cset(vec_min, vec_max) -> np.array:
+
+    # Define the (in)equalities (i.e., get the H-representation of the credal set)
+    n_par = len(vec_min)
+    A = np.concatenate(
+        (-np.eye(n_par), np.eye(n_par), np.atleast_2d(np.ones(n_par))), axis=0
+    )
+    b = np.concatenate((vec_max, -vec_min, np.atleast_1d(-1))).reshape(len(A), 1)
+    bA = np.concatenate((b, A), axis=1)
+    bA_frac = np.array(
+        [[Fraction(x).limit_denominator() for x in row] for row in bA], dtype=object
+    )  # Needed for numerical stability
+    mat_frac = cdd.gmp.matrix_from_array(
+        array=bA_frac, rep_type=cdd.RepType.INEQUALITY, lin_set=set([len(A) - 1])
+    )
+
+    # Get the polytope and extreme points. Each point is a row of the matrix `vertices`
+    poly_frac = cdd.gmp.polyhedron_from_matrix(mat_frac)
+    ext_frac = cdd.gmp.copy_generators(poly_frac)
+    vertices_frac = np.array(ext_frac.array)[:, 1:]
+    vertices = np.array([[float(x) for x in row] for row in vertices_frac], dtype=float)
+
+    # Debug
+    safe_assert(len(b) == 2 * len(vec_min) + 1)
+    safe_assert(A.shape == (len(b), len(vec_min)))
+    safe_assert(bA.shape == (2 * len(vec_min) + 1, len(vec_min) + 1))
+    safe_assert(vertices.shape[1] == n_par)
+
+    return vertices
+
+
+# BNs sampler from a CN
+def sample_from_cn(bn_min, bn_max, n_bns: int) -> list:
 
-    random.seed(42)
-
-    # Parse CN
-    dag, params = parse_cn(cn)
-
-    # Store variables indexes
-    var_idx = {
-        var: [idx for idx, elem in enumerate(params) if elem[0] == var]
-        for var in dag.names()
-    }
-
-    # Cases
-    if where == "inside":
-        sample = sample_inside
-    elif where == "outside":
-        sample = sample_outside
-    else:
-        msg = "'where' can be either 'inside' or 'outside'"
-        print(msg)
-        raise ValueError(msg)
-
-    # Draw n random BNs
-    k = 0
+    # Get the DAG and extreme BNs
+    dag = gum.BayesNet(bn_min)
+
+    # For each variable ...
+    cpts_dict = {}
+    for var in dag.names():
+
+        # ... sample `n_bns` CPTs from the CN
+        cpts_dict[var] = sample_from_cpts(bn_min.cpt(var), bn_max.cpt(var), n_bns)
+
+    # For each sample ...
     bns = []
-    while k < n:
+    for i in range(n_bns):
 
-        # Init an empty BN
+        # ... init an empty BN ...
         bn = gum.BayesNet(dag)
 
-        # Sample from CN
-        next_sample = next(sample(params))
-
-        # Fill the BN's CPTs
+        # ... and fill its CPTs
         for var in dag.names():
-            array = np.array([(next_sample[idx]) for idx in var_idx.get(var)]).flatten()
-            bn.cpt(var).fillWith(array)
+            bn.cpt(var).fillWith(cpts_dict[var][i])
 
         bns.append(bn)
-        k += 1
+
+        # Debug
+        safe_assert(check_consistency(bn, bn_min, bn_max)==0)
 
     # Debug
-    # assert(n == len(bns))
+    safe_assert(len(cpts_dict) == len(dag.names()))
+    safe_assert(len(bns) == n_bns)
 
     return bns
 
 
-# Given a parsed CN called `params`, sample a BN inside the credal set
-def sample_inside(params):
+# Sample from two extreme CPTs
+def sample_from_cpts(cpt_min, cpt_max, n_bns) -> list:
+
+    # Transform CPTs into pandas dataframes
+    cpt_min = np.atleast_2d(cpt_min.topandas())
+    cpt_max = np.atleast_2d(cpt_max.topandas())
+
+    # For each row in the CPT ...
+    credal_dict = {}
+    for row in range(cpt_min.shape[0]):
+
+        # ... sample `n_bns` points from the credal set
+        credal_dict[row] = sample_from_cset(cpt_min[row, :], cpt_max[row, :], n_bns)
+
+    # For each sample ...
+    cpt_samples = []
+    for i in range(n_bns):
+
+        # ... build the CPT
+        cpt = []
+        for row in range(cpt_min.shape[0]):
+            cpt.append(credal_dict[row][i])
+
+        cpt = np.array(cpt).flatten()
+        cpt_samples.append(cpt)
+
+    # Debug
+    safe_assert(cpt_min.shape == cpt_max.shape)
+    safe_assert(len(credal_dict) == cpt_min.shape[0])
+    safe_assert(len(cpt_samples) == n_bns)
+
+    return cpt_samples
+
 
-    p_1 = [
-        (vecs[0][0] - vecs[1][0]) * random_sample() + vecs[1][0]
-        for _, _, vecs in params
-    ]
-    p = [[x, 1 - x] for x in p_1]
+# Sample from a credal set K(x | pi_x), i.e., a constrained polytope.
+def sample_from_cset(vec_min, vec_max, n_bns) -> list:
+    """
+    We assume a credal set is a polytope in a space of #X parameters, defined by a:
+     - Multi-dimensional rectangle, i.e., inequality constraint Ax <= b, and
+     - Hyperplane (provided all the variables sum up to 1), i.e., equality constraint A_eq x = b_eq.
+    This is true if the CN has been learnt by local IDM, for instance.
+    """
+
+    # Define the rectangle
+    n_par = len(vec_min)
+    A = np.concatenate((np.eye(n_par), -np.eye(n_par)), axis=0)
+    b = np.concatenate((vec_max, -vec_min))
+    rectangle = hopsy.Problem(A=A, b=b)
+
+    # Define the hyperplane
+    A_eq = np.array([np.ones(n_par)])
+    b_eq = np.array([1.0])
+
+    # Define the polytope as a constrained rectangle (i.e., get the H-representation of the credal set)
+    constrained_rectangle = hopsy.add_equality_constraints(
+        rectangle, A_eq=A_eq, b_eq=b_eq
+    )
+
+    # Sample from the polytope
+    mc = hopsy.MarkovChain(constrained_rectangle)
+    rng = hopsy.RandomNumberGenerator(42)
+    _, constrained_samples = hopsy.sample(mc, rng, n_bns, thinning=10)
+    constrained_samples = constrained_samples[0]
 
     # Debug
-    # assert(np.sum(np.array(p), axis=1).all() == 1.)
+    safe_assert(np.all(vec_min <= vec_max))
+    safe_assert(n_par == len(vec_max))
+    safe_assert(n_par == A.shape[1])
+    safe_assert(n_par == A_eq.shape[1])
+    safe_assert(len(constrained_samples) == n_bns)
+    for i in constrained_samples:
+        safe_assert(len(i) == n_par)
 
-    yield p
+    return constrained_samples
 
 
-# Given a parsed CN called `params`, sample a vertex of the credal set
-def sample_outside(params):
+# Check the consistency of a BN as sampled from a CN. Returns the number of inconsistent CPTs.
+def check_consistency(bn, bn_min, bn_max, verbose=False) -> int:
 
-    yield random_product(*[vecs for _, _, vecs in params])
+    n_issues = 0
+
+    for var in bn.names():
+        bn_cpt = np.atleast_2d(bn.cpt(var).topandas())
+        bn_min_cpt = np.atleast_2d(bn_min.cpt(var).topandas())
+        bn_max_cpt = np.atleast_2d(bn_max.cpt(var).topandas())
+
+        # Check if probabilities sum to 1
+        sum_vec = np.sum(bn_cpt, axis=1)
+        probability_consistency = np.all(np.abs(sum_vec - 1) < 1e-5)
+
+        # Check if the BN CPT is >= min CPT
+        min_consistency = np.all(bn_cpt - bn_min_cpt >= -1e-5)
+
+        # Check if the BN CPT is <= max CPT
+        max_consistency = np.all(bn_cpt - bn_max_cpt <= 1e-5)
+
+        consistency = probability_consistency and min_consistency and max_consistency
+
+        if consistency:
+            continue
+        else:
+            n_issues += 1
+            if verbose:
+                print("Variable: ", var)
+                print("probability_consistency: ", probability_consistency)
+                print("min_consistency: ", min_consistency)
+                print("max_consistency: ", max_consistency)
+                print("BN CPT: ")
+                print(bn_cpt)
+                print("BN min CPT: ")
+                print(bn_min_cpt)
+                print("BN max CPT: ")
+                print(bn_max_cpt)
+    
+    return n_issues
+
+    
 
 
 # Check BNs sampled from a CN
-def are_all_bns_different(bn_vec) -> None:
+def are_all_bns_different(bn_vec) -> bool:
 
     signatures = set()
     for bn in bn_vec:
@@ -176,70 +629,37 @@ def are_all_bns_different(bn_vec) -> None:
 
     print(f"({len(signatures)}/{len(bn_vec)} different BNs.)")
 
-
-# Add counts of events to a BN
-def add_counts_to_bn(bn, data):
-
-    for node in bn.names():
-        var = bn.variable(node)
-        parents = bn.parents(node)
-        parent_names = [bn.variable(p).name() for p in parents]
-
-        shape = [bn.variable(p).domainSize() for p in parents] + [var.domainSize()]
-        counts_array = np.zeros(shape, dtype=float)  # float, not int
-
-        for _, row in data.iterrows():
-            try:
-                key = tuple([int(row[p]) for p in parent_names] + [int(row[node])])
-                counts_array[key] += 1.0
-            except KeyError:
-                continue
-
-        bn.cpt(node).fillWith(counts_array.flatten().tolist())
-
-
-# Compact a dictionary to be printable
-def compact_dict(d):
-    new_dict = {}
-    for k, v in d.items():
-        if isinstance(v, np.ndarray):
-            new_dict[k] = (
-                f"np.ndarray: [{v[0]:.2g}, {v[1]:.2g}, ..., {v[-1]:.2g}], length={len(v)}"
-            )
-        else:
-            new_dict[k] = v
-    return new_dict
+    return len(signatures) == len(bn_vec)
 
 
-# Create noisy BN by adding Laplacian noise (Zhang et al., 2017)
-def get_noisy_bn(bn, scale: float):
+# Extract BN min and BN max from a CN
+def get_min_max_bns(cn, exp: str = ""):
 
-    bn_ie = gum.LazyPropagation(bn)
-    bn_ie.makeInference()
+    with TemporaryDirectory() as tmp_path:
+        cn.saveBNsMinMax(f"{tmp_path}/bn_min_{exp}.bif", f"{tmp_path}/bn_max_{exp}.bif")
+        bn_min = gum.loadBN(f"{tmp_path}/bn_min_{exp}.bif")
+        bn_max = gum.loadBN(f"{tmp_path}/bn_max_{exp}.bif")
 
-    bn_noisy = gum.BayesNet(bn)
-
-    # For each node X ...
-    for node in bn.names():
+    return bn_min, bn_max
 
-        # Get the joint P(X, Pa(X))
-        joint = bn_ie.jointPosterior(bn.family(node))
 
-        # Add noise to P(X, Pa(X)) and normalize
-        noise = np.random.laplace(scale=scale, size=np.prod(joint.shape))
-        noisy_joint = np.clip(
-            joint.toarray().flatten() + noise, a_min=10e-10, a_max=None
-        )
-        noisy_joint = noisy_joint / np.sum(noisy_joint)
-        joint.fillWith(noisy_joint)
+# Generate a random CN with local IDM
+def generate_random_cn(n_nodes, edge_density, n_modmax, ess, s_size) -> tuple:
 
-        # Compute the conditional P(X | Pa(X))
-        cond = joint / joint.sumOut(node)
+    # Generate a BN
+    bn_gen = gum.BNGenerator()
+    bn = bn_gen.generate(
+        n_nodes=n_nodes, n_arcs=int(n_nodes * edge_density), n_modmax=n_modmax
+    )
 
-        # Fill noisy BN
-        bn_noisy.cpt(node).fillWith(cond)
+    # Generate data
+    data_gen = gum.BNDatabaseGenerator(bn)
+    data_gen.drawSamples(s_size)
+    data = data_gen.to_pandas()
 
-    # Check noisy bn
-    bn_noisy.check()  # OK if = ().
+    # Learn the CN by local IDM
+    bn_counts = get_bn_counts(bn, data)
+    cn = gum.CredalNet(bn_counts)
+    cn.idmLearning(ess)
 
-    return bn_noisy
+    return cn, data
diff --git a/test/cn_privacy/config.yaml b/test/cn_privacy/config.yaml
index 0124b59..5e950ff 100644
--- a/test/cn_privacy/config.yaml
+++ b/test/cn_privacy/config.yaml
@@ -1,27 +1,27 @@
 ## Configuration file
 
 # Paths
-base_path: test/cn_privacy/output                   # Base path for output
+cur_dir: test/cn_privacy                            # Current directory (contains all the following)
 bns_path: bns                                       # Where to save ground-truth BNs
+cns_path: output/cns                                # Where to save CNs as obtained by def-mec from BNs learnt from pool
+atk_path: output/bns_atk                            # Where to save BNs as obtained by atk-mec from CNs
 data_path: data                                     # Where to save data as generated from ground-truth BNs
-results_path: results                               # Where to save the experiment results
-meta_file: exp_meta.txt                             # File of metadata
+results_path: output/results                        # Where to save the experiment results
+exp_meta: exp_meta.txt                              # File of metadata for experiments
 
 # Models
-n_nodes_vec: '[10, 15]'                             # List of models' number of nodes
+n_nodes_vec: '[4, 5]'                               # List of models' number of nodes
 edge_ratio_vec: '[1, 1.5]'                          # List of models' edge ratio
+n_modmax: 3                                         # Maximum number of variables categories
 
 # Data
-gpop_ss: 1000                                       # Sample size of general population
+gpop_ss: 20                                         # Sample size of general population
 rpop_prop: 0.5                                      # Sample size of reference population = gpop_ss * rpop_prop
 pool_prop: 0.25                                     # Sample size of pool population = gpop_ss * pool_prop
+samples: 2                                          # Number of data samples
 
 # MIA
-n_samples: 5                                        # Number of data samples
-n_bns: 10                                           # Number of BNs to sample within the CN
-error: 'np.logspace(-4, 0, 10, endpoint=False)'     # Type-I errors vector
-ess_vec: '[1, 1000]'                                # List of ESS
+error: 'np.logspace(-4, 0, 5, endpoint=False)'      # Type-I errors vector
 
 # Other
-seed: 42                                            # Global seed
 num_cores: 'multiprocessing.cpu_count() - 1'        # Number of threads to use for parallelization
diff --git a/test/cn_privacy/test_integration.py b/test/cn_privacy/test_integration.py
index 4dca807..7a0d2ca 100644
--- a/test/cn_privacy/test_integration.py
+++ b/test/cn_privacy/test_integration.py
@@ -1,15 +1,139 @@
-from src.config import get_config
-from src.data import generate_randombn
-from src.run_exp import run_cn_privacy
+import sys
 
+from experiments.cn_privacy import exp, generate
 
-def test_integration():
 
-    # Load config
-    config = get_config("test/cn_privacy/config.yaml")
+def test_generation():
 
-    # Generate BNs and data
-    generate_randombn(config)
+    # Generate models and data
+    generate.main()
+
+
+def test_def_ran_atk_mle(monkeypatch):
+
+    monkeypatch.setattr(
+        sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mle"]
+    )
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_idm_atk_mle(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mle"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_loc_atk_mle(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_loc", "ess=1", "atk_mec=atk_mle"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_ran_atk_mne(monkeypatch):
+
+    monkeypatch.setattr(
+        sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mne"]
+    )
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_idm_atk_mne(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mne"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_loc_atk_mne(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_loc", "ess=1", "atk_mec=atk_mne"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_ran_atk_cen(monkeypatch):
+
+    monkeypatch.setattr(
+        sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_cen"]
+    )
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_idm_atk_cen(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_cen"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_loc_atk_cen(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_loc", "ess=1", "atk_mec=atk_cen"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_ran_atk_ran(monkeypatch):
+
+    monkeypatch.setattr(
+        sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_ran"]
+    )
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_idm_atk_ran(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_ran"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_loc_atk_ran(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_loc", "ess=1", "atk_mec=atk_ran"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_ran_atk_ent(monkeypatch):
+
+    monkeypatch.setattr(
+        sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_ent"]
+    )
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_idm_atk_ent(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_ent"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_loc_atk_ent(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_loc", "ess=1", "atk_mec=atk_ent"])
 
     # Run experiment
-    run_cn_privacy(config)
+    exp.main()
diff --git a/test/cn_vs_noisybn/config.yaml b/test/cn_vs_noisybn/config.yaml
index beb5d12..fc9eeed 100644
--- a/test/cn_vs_noisybn/config.yaml
+++ b/test/cn_vs_noisybn/config.yaml
@@ -1,38 +1,45 @@
 ## Configuration file
 
 # Paths
-base_path: test/cn_vs_noisybn/output                # Base path for output
+cur_dir: test/cn_vs_noisybn                         # Current directory (contains all the following)
 bns_path: bns                                       # Where to save ground-truth BNs
+cns_path: output/cns                                # Where to save CNs as obtained by def-mec from BNs learnt from pool
+atk_path: output/bns_atk                            # Where to save BNs as obtained by atk-mec from CNs
 data_path: data                                     # Where to save data as generated from ground-truth BNs
-results_path: results                               # Where to save the experiment results
-meta_file: exp_meta.txt                             # File of metadata
+results_path: output/results                        # Where to save the experiment results
+exp_meta: exp_meta.txt                              # File of metadata for experiments
+auc_meta: output/results/auc_meta.csv               # File of metadata for AUCs
 
 # Models (Naive Bayes)
 target_var: 'T'                                     # Target variable
-n_nodes: 10                                         # Number of nodes for each BN model
-n_models: 5                                         # Number of models to evaluate
+n_nodes: 5                                          # Number of nodes for each BN model
+n_modmax: 3                                         # Maximum number of categories for covariates
+n_models: 2                                         # Number of models to evaluate
 
 # Data
-gpop_ss: 1000                                       # Sample size of general population
+gpop_ss: 20                                         # Sample size of general population
 rpop_prop: 0.5                                      # Sample size of reference population = gpop_ss * rpop_prop
 pool_prop: 0.25                                     # Sample size of pool population = gpop_ss * pool_prop
+samples: 2                                          # Number of data samples
 
 # MIA
-n_samples: 5                                        # Number of data samples
-n_bns: 10                                           # Number of BNs to sample within the CN
-tol: 0.02                                           # To find eps s.t. |AUC(eps) - AUC(CN)| < tol
-error: 'np.logspace(-4, 0, 10, endpoint=False)'     # Type-I errors vector
-ess_dict:                                           # Eps list to evaluate for each ess
-  1:  'np.arange(0.1, 10, 0.5)'
-  10: 'np.arange(0.1, 10, 0.5)'
-  20: 'np.arange(0.05, 5, 0.1)'
-  30: 'np.arange(1e-3, 1, 5e-3)'
-  40: 'np.arange(5e-6, 1e-2, 1e-5)' 
-  50: 'np.arange(5e-7, 5e-4, 1e-6)'
+error: 'np.logspace(-4, 0, 5, endpoint=False)'      # Type-I errors vector
+
+# Noisy BN
+tol: 0.05                                           # To find eps s.t. |AUC(eps) - AUC(CN)| < tol
+eps_vec: 'np.logspace(-8, 2, num=10)'               # Epsilon to consider for noisy BN
 
 # Inferences
-n_infer: 10                                         # Number of inferences to perform
+n_infer: 2                                          # Number of inferences to perform
 
 # Other
-seed: 42                                            # Global seed
 num_cores: 'multiprocessing.cpu_count() - 1'        # Number of threads to use for parallelization
+
+## Notes
+# 1) Suggested pairs (ess: eps_vec) for n_nodes=10: 
+#   - 1 : 'np.arange(0.1, 10, 0.1)'
+#   - 10: 'np.arange(0.1, 10, 0.1)'
+#   - 20: 'np.arange(0.05, 5, 0.05)'
+#   - 30: 'np.arange(1e-3, 1, 1e-3)'
+#   - 40: 'np.arange(5e-6, 1e-2, 5e-6)' 
+#   - 50: 'np.arange(5e-7, 5e-4, 5e-7)'
diff --git a/test/cn_vs_noisybn/test_integration.py b/test/cn_vs_noisybn/test_integration.py
index 1ef1eea..4e88562 100644
--- a/test/cn_vs_noisybn/test_integration.py
+++ b/test/cn_vs_noisybn/test_integration.py
@@ -1,15 +1,139 @@
-from src.config import get_config
-from src.data import generate_naivebayes
-from src.run_exp import run_cn_vs_noisybn
+import sys
 
+from experiments.cn_vs_noisybn import exp, generate
 
-def test_integration():
 
-    # Load config
-    config = get_config("test/cn_vs_noisybn/config.yaml")
+def test_generation():
 
-    # Generate BNs and data
-    generate_naivebayes(config)
+    # Generate models and data
+    generate.main()
+
+
+def test_def_ran_atk_mle(monkeypatch):
+
+    monkeypatch.setattr(
+        sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mle"]
+    )
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_idm_atk_mle(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mle"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_loc_atk_mle(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_loc", "ess=1", "atk_mec=atk_mle"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_ran_atk_mne(monkeypatch):
+
+    monkeypatch.setattr(
+        sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_mne"]
+    )
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_idm_atk_mne(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_mne"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_loc_atk_mne(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_loc", "ess=1", "atk_mec=atk_mne"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_ran_atk_cen(monkeypatch):
+
+    monkeypatch.setattr(
+        sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_cen"]
+    )
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_idm_atk_cen(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_cen"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_loc_atk_cen(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_loc", "ess=1", "atk_mec=atk_cen"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_ran_atk_ran(monkeypatch):
+
+    monkeypatch.setattr(
+        sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_ran"]
+    )
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_idm_atk_ran(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_ran"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_loc_atk_ran(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_loc", "ess=1", "atk_mec=atk_ran"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_ran_atk_ent(monkeypatch):
+
+    monkeypatch.setattr(
+        sys, "argv", ["def_mec=def_ran", "delta=0.3", "atk_mec=atk_ent"]
+    )
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_idm_atk_ent(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_idm", "ess=1", "atk_mec=atk_ent"])
+
+    # Run experiment
+    exp.main()
+
+
+def test_def_loc_atk_ent(monkeypatch):
+
+    monkeypatch.setattr(sys, "argv", ["def_mec=def_loc", "ess=1", "atk_mec=atk_ent"])
 
     # Run experiment
-    run_cn_vs_noisybn(config)
+    exp.main()
diff --git a/test/conftest.py b/test/conftest.py
new file mode 100644
index 0000000..8948023
--- /dev/null
+++ b/test/conftest.py
@@ -0,0 +1,8 @@
+import os
+
+import pytest
+
+
+@pytest.fixture(scope="session", autouse=True)
+def enable_test_config():
+    os.environ["USE_TEST_CONFIG"] = "1"
diff --git a/test/unit/__init__.py b/test/unit/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/test/unit/utils.py b/test/unit/utils.py
new file mode 100644
index 0000000..0949ca5
--- /dev/null
+++ b/test/unit/utils.py
@@ -0,0 +1,12 @@
+import numpy as np
+
+from src.utils import maxent_cset
+
+
+def test_maxent_cset():
+    vec_min = np.array([0.3, 0.4, 0, 0.1])
+    vec_max = np.array([0.6, 0.8, 0.12, 0.17])
+
+    out = maxent_cset(vec_min, vec_max)
+
+    assert np.allclose(out, np.array([0.31, 0.4, 0.12, 0.17]))