From c7c53322fc3b1733c20e64c63641b8ca631aff8b Mon Sep 17 00:00:00 2001 From: Nicholas Landry Date: Wed, 15 Nov 2023 14:46:00 -0500 Subject: [PATCH] Fig2 (#11) * added generative models, plots, tests, fitting infections per node, and more --- .gitignore | 4 +- Data/erdos-renyi.json | 1 + Data/watts-strogatz.json | 1 + .../erdos-renyi_experiment.py | 0 collect_erdos_renyi.py | 67 +++++ collect_frac_vs_beta.py | 4 +- collect_watts-strogatz.py | 77 ++++++ convergence/determine_mcmc_parameters.ipynb | 3 +- erdos-renyi.py | 119 +++++++++ fitting_ipn.ipynb | 6 +- frac_vs_beta.py | 7 +- infer_contagion_functions.py | 7 +- lcs/generative.py | 32 ++- lcs/inference.py | 235 +++++++++++++++++- lcs/utilities.py | 71 ++++-- plot_er_experiment.ipynb | 188 -------------- plot_fig2.ipynb | 174 +++++++++++++ run_dynamical_inference.ipynb | 72 ++---- sbm.py | 120 +++++++++ tests/test_contagion.py | 1 + tests/test_generative.py | 14 ++ tests/test_inference.py | 3 + tests/test_utilities.py | 12 +- tests/unit_test.py | 3 +- watts-strogatz.py | 120 +++++++++ 25 files changed, 1030 insertions(+), 311 deletions(-) create mode 100644 Data/erdos-renyi.json create mode 100644 Data/watts-strogatz.json rename erdos-renyi_experiment.py => _OBS/erdos-renyi_experiment.py (100%) create mode 100644 collect_erdos_renyi.py create mode 100644 collect_watts-strogatz.py create mode 100644 erdos-renyi.py delete mode 100644 plot_er_experiment.ipynb create mode 100644 plot_fig2.ipynb create mode 100644 sbm.py create mode 100644 tests/test_generative.py create mode 100644 watts-strogatz.py diff --git a/.gitignore b/.gitignore index 12d456a..4a63c63 100644 --- a/.gitignore +++ b/.gitignore @@ -148,7 +148,9 @@ tutorials/test.* # airspeed velocity files Data/frac_vs_beta -Data/erdos-renyi_experiment +Data/erdos-renyi +Data/watts-strogatz +Data/sbm #slurm config files config.json diff --git a/Data/erdos-renyi.json b/Data/erdos-renyi.json new file mode 100644 index 0000000..7187008 --- /dev/null +++ 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0.5991507572971346, 0.788926115323576, 0.45167152900664775, 0.4968258676950459, 0.9412426015937573], [0.7277347674487323, 0.714993348240822, 0.4102017667480149, 0.6557214510673044, 0.7724914514797783, 0.7607430544139404, 0.40207620099392605, 0.9530156835438841, 0.6255079924139799, 0.8490116191338464], [0.8471368967443083, 0.8002330021682146, 0.8197595916351061, 0.771962947666529, 0.6601206552742509, 0.7489787208097067, 0.7819144164349152, 0.4449623814196926, 0.33260427458654696, 0.8468913948019056], [0.22252473951515783, 0.719501163091238, 0.35188639844076286, 0.7167233458624187, 0.4507722007722007, 0.5765878779875875, 0.8872106386471844, 0.7532586624746185, 0.31497529456480433, 0.7043905708859346], [0.48835582620144835, 0.8592548787699585, 0.265796649785742, 0.5887485415715519, 0.36676217765042984, 0.6438369880845098, 0.6367814359978905, 0.7142573076158283, 0.6344739581584338, 0.7459384928961094], [0.48771099795517414, 0.8091179853144997, 0.3170623013161413, 0.28708877401619504, 0.6664842232013355, 0.3117892707998714, 0.36698197562238233, 0.6077384375256716, 0.5173278764274869, 0.7862454135143033], [0.25352895572166234, 0.521795085678877, 0.5915521767292811, 0.3750820063219419, 0.8006061204993905, 0.5316576120972386, 0.5541971505583365, 0.5527463891824512, 0.8909031198686371, 0.3800806888661409], [0.2955928901874849, 0.46943365364956047, 0.3297206742110841, 0.5441655708650163, 0.7073439128923896, 0.38370118845500845, 0.30641867051550886, 0.6579777907656341, 0.2406481641096364, 0.28471396036575003], [0.256061953848569, 0.6221856445829169, 0.6256271593811026, 0.566998600068676, 0.5106220181834549, 0.41349636580207605, 0.2617111379644447, 0.503223654957759, 0.42598409017524275, 0.6584573588471534], [0.2552614760781894, 0.3418198805432281, 0.7187235978423344, 0.6376285187029497, 0.7197709011037896, 0.44314335995668064, 0.25078301333359854, 0.4474022025538814, 0.616698398476873, 0.39202086667852276], [0.24525014170847947, 0.3582514036335107, 0.5974659449170187, 0.4207267394492423, 0.2583765617008841, 0.42188506220859845, 0.21946612223091433, 0.4710877476748887, 0.3387602462886715, 0.6708860759493671], [0.23646456500947066, 0.6102564102564103, 0.3656340920759884, 0.5294810434259654, 0.29996320974532975, 0.5868358897014883, 0.2380228868160401, 0.7070006645286181, 0.36465029393989334, 0.5272816007808687], [0.2892213271960107, 0.33659892586295315, 0.657722470672111, 0.8530477759472818, 0.7477368560728924, 0.7272567717779208, 0.319397463002114, 0.7330747017472217, 0.693567037916134, 0.5813079428714608], [0.288436463161156, 0.6383042245111211, 0.6367929201201994, 0.4702164886532786, 0.3147371190406957, 0.5390298747189206, 0.25479460251922326, 0.6065518405943938, 0.5902300907996225, 0.7086785009861933], [0.3319863310913431, 0.6549592916146669, 0.6466757830305399, 0.2462118907077222, 0.5826581339778627, 0.5429365606474307, 0.30269449898218515, 0.47581281016650434, 0.700408211560078, 0.6551396213365609], [0.5843774993335111, 0.608362143474503, 0.6243392862727927, 0.7022330909324924, 0.6360344411362355, 0.6836184950565389, 0.37690854119425543, 0.7469102289783796, 0.4899893103747147, 0.7689138821683124]]]} \ No newline at end of file diff --git a/erdos-renyi_experiment.py b/_OBS/erdos-renyi_experiment.py similarity index 100% rename from erdos-renyi_experiment.py rename to _OBS/erdos-renyi_experiment.py diff --git a/collect_erdos_renyi.py b/collect_erdos_renyi.py new file mode 100644 index 0000000..8261ce5 --- /dev/null +++ b/collect_erdos_renyi.py @@ -0,0 +1,67 @@ +import json +import os + +import numpy as np + +from lcs import * + +plist = set() +clist = set() +rlist = set() +beta = [] +frac = [] + + +data_dir = "Data/erdos-renyi/" + +for f in os.listdir(data_dir): + d = f.split(".json")[0].split("_") + p = float(d[0]) + c = int(d[1]) + r = int(d[2]) + + plist.add(p) + clist.add(c) + rlist.add(r) + +clist = sorted(clist) +plist = sorted(plist) +rlist = sorted(rlist) + +c_dict = {c: i for i, c in enumerate(clist)} +p_dict = {p: i for i, p in enumerate(plist)} +r_dict = {r: i for i, r in enumerate(rlist)} + + +ps = np.zeros((len(clist), len(plist), len(rlist))) +sps = np.zeros((len(clist), len(plist), len(rlist))) + +for f in os.listdir(data_dir): + d = f.split(".json")[0].split("_") + p = float(d[0]) + c = int(d[1]) + r = int(d[2]) + + i = c_dict[c] + j = p_dict[p] + k = r_dict[r] + + fname = os.path.join(data_dir, f) + + with open(fname, "r") as file: + data = json.loads(file.read()) + + A = np.array(data["A"]) + samples = np.array(data["samples"]) + + ps[i, j, k] = posterior_similarity(samples, A) + sps[i, j, k] = samplewise_posterior_similarity(samples, A) + +data = {} +data["p"] = plist +data["sps"] = sps.tolist() +data["ps"] = ps.tolist() +datastring = json.dumps(data) + +with open("Data/erdos-renyi.json", "w") as output_file: + output_file.write(datastring) diff --git a/collect_frac_vs_beta.py b/collect_frac_vs_beta.py index d8e89c5..53458e3 100644 --- a/collect_frac_vs_beta.py +++ b/collect_frac_vs_beta.py @@ -67,8 +67,8 @@ ipn[i, j, k] = infections_per_node(x) - psmat[i, j, k] = posterior_similarity(A, samples) - spsmat[i, j, k] = samplewise_posterior_similarity(A, samples) + psmat[i, j, k] = posterior_similarity(samples, A) + spsmat[i, j, k] = samplewise_posterior_similarity(samples, A) it += 1 print(it, flush=True) diff --git a/collect_watts-strogatz.py b/collect_watts-strogatz.py new file mode 100644 index 0000000..5b7a19b --- /dev/null +++ b/collect_watts-strogatz.py @@ -0,0 +1,77 @@ +import json +import os + +import numpy as np + +from lcs import * + +plist = set() +clist = set() +rlist = set() +beta = [] +frac = [] + + +data_dir = "Data/watts-strogatz/" + +for f in os.listdir(data_dir): + d = f.split(".json")[0].split("_") + try: + p = float(d[0]) + c = int(d[1]) + r = int(d[2]) + except: + p = float(d[0] + "-" + d[1]) + c = int(d[2]) + r = int(d[3]) + + plist.add(p) + clist.add(c) + rlist.add(r) + +clist = sorted(clist) +plist = sorted(plist) +rlist = sorted(rlist) + +c_dict = {c: i for i, c in enumerate(clist)} +p_dict = {p: i for i, p in enumerate(plist)} +r_dict = {r: i for i, r in enumerate(rlist)} + + +ps = np.zeros((len(clist), len(plist), len(rlist))) +sps = np.zeros((len(clist), len(plist), len(rlist))) + +for f in os.listdir(data_dir): + d = f.split(".json")[0].split("_") + try: + p = float(d[0]) + c = int(d[1]) + r = int(d[2]) + except: + p = float(d[0] + "-" + d[1]) + c = int(d[2]) + r = int(d[3]) + + i = c_dict[c] + j = p_dict[p] + k = r_dict[r] + + fname = os.path.join(data_dir, f) + + with open(fname, "r") as file: + data = json.loads(file.read()) + + A = np.array(data["A"]) + samples = np.array(data["samples"]) + + ps[i, j, k] = posterior_similarity(samples, A) + sps[i, j, k] = samplewise_posterior_similarity(samples, A) + +data = {} +data["p"] = plist +data["sps"] = sps.tolist() +data["ps"] = ps.tolist() +datastring = json.dumps(data) + +with open("Data/watts-strogatz.json", "w") as output_file: + output_file.write(datastring) diff --git a/convergence/determine_mcmc_parameters.ipynb b/convergence/determine_mcmc_parameters.ipynb index d15c24e..1cf3f51 100644 --- a/convergence/determine_mcmc_parameters.ipynb +++ b/convergence/determine_mcmc_parameters.ipynb @@ -21,8 +21,7 @@ "metadata": {}, "outputs": [], "source": [ - "G = nx.karate_club_graph()\n", - "A = nx.adjacency_matrix(G, weight=None).todense()\n", + "A = zkc()\n", "n = np.size(A, axis=0)" ] }, diff --git a/erdos-renyi.py b/erdos-renyi.py new file mode 100644 index 0000000..1824f0e --- /dev/null +++ b/erdos-renyi.py @@ -0,0 +1,119 @@ +import json +import multiprocessing as mp +import os + +import numpy as np + +from lcs import * + + +def target_ipn(n, p, gamma, c, mode, rho0, tmax, realizations): + x0 = np.zeros(n) + x0[random.sample(range(n), int(round(rho0 * n)))] = 1 + ipn = 0 + for _ in range(realizations): + A = erdos_renyi(n, p) + x = contagion_process(A, gamma, c, x0, tmin=0, tmax=tmax) + ipn += infections_per_node(x, mode) / realizations + return ipn + + +def single_inference( + fname, gamma, c, b, rho0, A, tmax, p_c, p_rho, nsamples, burn_in, skip +): + n = np.size(A, axis=0) + x0 = np.zeros(n) + x0[random.sample(range(n), int(round(rho0 * n)))] = 1 + + x = contagion_process(A, gamma, c, x0, tmin=0, tmax=tmax) + p = beta(p_rho[0], p_rho[1]).rvs() + A0 = erdos_renyi(n, p) + samples = infer_adjacency_matrix( + x, A0, p_rho, p_c, nsamples=nsamples, burn_in=burn_in, skip=skip + ) + + # json dict + data = {} + data["gamma"] = gamma + data["c"] = c.tolist() + data["b"] = b + data["p-rho"] = p_rho.tolist() + data["p-c"] = p_c.tolist() + data["x"] = x.tolist() + data["A"] = A.tolist() + data["samples"] = samples.tolist() + + datastring = json.dumps(data) + + with open(fname, "w") as output_file: + output_file.write(datastring) + + +data_dir = "Data/erdos-renyi" +os.makedirs(data_dir, exist_ok=True) + +for f in os.listdir(data_dir): + os.remove(os.path.join(data_dir, f)) + +n = 50 + +n_processes = len(os.sched_getaffinity(0)) +realizations = 10 +probabilities = np.linspace(0.0, 1.0, 33) + +# MCMC parameters +burn_in = 100000 +nsamples = 100 +skip = 1500 +p_c = np.ones((2, n)) +p_rho = np.array([1, 1]) + +# contagion functions and parameters +cf1 = lambda nu, beta: 1 - (1 - beta) ** nu # simple contagion +cf2 = lambda nu, beta: beta * (nu >= 2) # complex contagion, tau=2 +cf3 = lambda nu, beta: beta * (nu >= 3) # complex contagion, tau=3 + +cfs = [cf1, cf2, cf3] + +rho0 = 1.0 +gamma = 0.1 +b = 0.04 +mode = "max" + +tmax = 1000 + + +arglist = [] +for p in probabilities: + c = cfs[0](np.arange(n), b) + ipn = target_ipn(n, p, gamma, c, mode, rho0, tmax, 1000) + for i, cf in enumerate(cfs): + if i != 0: + A = erdos_renyi(n, p) + bscaled = fit_ipn(0.5, ipn, cf, gamma, A, rho0, tmax, mode) + else: + bscaled = b + c = cf(np.arange(n), bscaled) + print((p, i), flush=True) + + for r in range(realizations): + A = erdos_renyi(n, p) + arglist.append( + ( + f"{data_dir}/{p}_{i}_{r}", + gamma, + c, + bscaled, + rho0, + A, + tmax, + p_c, + p_rho, + nsamples, + burn_in, + skip, + ) + ) + +with mp.Pool(processes=n_processes) as pool: + pool.starmap(single_inference, arglist) diff --git a/fitting_ipn.ipynb b/fitting_ipn.ipynb index 9850315..094c1dc 100644 --- a/fitting_ipn.ipynb +++ b/fitting_ipn.ipynb @@ -18,8 +18,7 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from lcs import *\n", - "import networkx as nx" + "from lcs import *" ] }, { @@ -28,8 +27,7 @@ "metadata": {}, "outputs": [], "source": [ - "G = nx.karate_club_graph()\n", - "A = nx.adjacency_matrix(G, weight=None).todense()\n", + "A = zkc()\n", "n = A.shape[0]" ] }, diff --git a/frac_vs_beta.py b/frac_vs_beta.py index 67cd51f..0ca7acd 100644 --- a/frac_vs_beta.py +++ b/frac_vs_beta.py @@ -44,8 +44,7 @@ def single_inference( for f in os.listdir(data_dir): os.remove(os.path.join(data_dir, f)) -G = nx.karate_club_graph() -A = nx.adjacency_matrix(G, weight=None).todense() +A = zkc() n = A.shape[0] n_processes = len(os.sched_getaffinity(0)) @@ -54,7 +53,7 @@ def single_inference( nb = 33 # MCMC parameters -burn_in = 10000 +burn_in = 100000 nsamples = 1000 skip = 2000 p_c = np.ones((2, n)) @@ -79,7 +78,7 @@ def single_inference( for k in range(realizations): arglist.append( ( - f"Data/frac_vs_beta/{b}-{f}-{k}", + f"Data/frac_vs_beta/{b}_{f}_{k}", gamma, c, rho0, diff --git a/infer_contagion_functions.py b/infer_contagion_functions.py index d32af3d..eb8878f 100644 --- a/infer_contagion_functions.py +++ b/infer_contagion_functions.py @@ -6,8 +6,7 @@ from lcs import * -G = nx.karate_club_graph() -A = nx.adjacency_matrix(G, weight=None).todense() +A = zkc() n = A.shape[0] p_gamma = [1, 1] @@ -39,7 +38,7 @@ p_gamma, p_c, nsamples=1000, - burn_in=30000, + burn_in=100000, skip=1000, nspa=10, return_likelihood=True, @@ -69,7 +68,7 @@ p_gamma, p_c, nsamples=1000, - burn_in=30000, + burn_in=100000, skip=1000, nspa=10, return_likelihood=True, diff --git a/lcs/generative.py b/lcs/generative.py index c961cce..8d69d3e 100644 --- a/lcs/generative.py +++ b/lcs/generative.py @@ -5,12 +5,31 @@ import xgi +def zkc(): + G = nx.karate_club_graph() + return nx.adjacency_matrix(G).todense() + + def erdos_renyi(n, p, seed=None): - return nx.adjacency_matrix(nx.fast_gnp_random_graph(n, p, seed)).todense() + if seed is not None: + random.seed(seed) + + A = np.zeros((n, n), dtype=int) + if p == 0: + return A + if p == 1: + return np.ones((n, n), dtype=int) - np.eye(n, dtype=int) + + for i in range(n): + for j in range(i): + A[i, j] = A[j, i] = random.random() <= p + return A def watts_strogatz(n, k, p, seed=None): - return nx.adjacency_matrix(nx.watts_strogatz_graph(n, k, p, seed)).todense() + G = nx.watts_strogatz_graph(n, k, p, seed) + G.add_nodes_from(range(n)) + return nx.adjacency_matrix(G).todense() def sbm(n, k, epsilon, seed=None): @@ -18,11 +37,10 @@ def sbm(n, k, epsilon, seed=None): # ratio of inter- to intra-community edges p_in = (1 + epsilon) * p p_out = (1 - epsilon) * p - return nx.adjacency_matrix( - nx.planted_partition_graph(2, int(n / 2), p_in, p_out, seed=seed) - ).todense() + G = nx.planted_partition_graph(2, int(n / 2), p_in, p_out, seed=seed) + G.add_nodes_from(range(n)) + return nx.adjacency_matrix(G).todense() def projected_bipartite(k, s, seed=None): - H = xgi.chung_lu_hypergraph(k, s, seed) - return xgi.adjacency_matrix(H, sparse=False) + return 0 diff --git a/lcs/inference.py b/lcs/inference.py index db9e733..c6465d0 100644 --- a/lcs/inference.py +++ b/lcs/inference.py @@ -20,6 +20,50 @@ def infer_adjacency_matrix_and_dynamics( skip=100, return_likelihood=False, ): + """A function to infer both the adjacency matrix and the contagion dynamics. + + Parameters + ---------- + x : numpy ndarray + A T x N matrix of zeros (susceptible) and ones (infected) + A0 : numpy ndarray + An N x N adjacency matrix to initialize the MCMC algorithm. + p_rho : list or ndarray, optional + A 2-array specifying the parameters of the beta distribution + for the prior on rho, by default None. If None, it assumes a + uniform prior. + p_gamma : list or ndarray, optional + A 2-array specifying the parameters of the beta distribution + for the gamma prior, by default None. If None, it assumes a + uniform prior. + p_c : list of lists or ndarray, optional + A 2 x N array of the priors on each entry in the c vector, by default None. + If None, it assumes a uniform prior. + nsamples : int, optional + The number of adjacency matrix samples desired, by default 1 + nspa : int, optional + The number of dynamics samples per adjacency matrix, by default 1 + burn_in : int, optional + The number of iterations before storing the first sample, by default 100 + skip : int, optional + The number of iterations between each sample, by default 100 + return_likelihood : bool, optional + Whether to return the log posterior, by default False + + Returns + ------- + samples, gamma, cf + (1) `samples` is an S x N x N array where S is the number of samples + (2) `gamma` is an 1D array of size S*D, where D is the number of dynamics + samples per adjacency matrix. + (3) `cf` is a 2D array of size S*D x N, where is row is a sampled contagion + vector. + if return_likelihood is True, also returns a list of log posterior values. + + Notes + ----- + We assume beta priors for conjugacy. + """ n = x.shape[1] samples, l = infer_adjacency_matrix( x, @@ -56,6 +100,40 @@ def infer_adjacency_matrix( skip=100, return_likelihood=False, ): + """A function to infer the adjacency matrix. + + Parameters + ---------- + x : numpy ndarray + A T x N matrix of zeros (susceptible) and ones (infected) + A0 : numpy ndarray + An N x N adjacency matrix to initialize the MCMC algorithm. + p_rho : list or ndarray, optional + A 2-array specifying the parameters of the beta distribution + for the prior on rho, by default None. If None, it assumes a + uniform prior. + p_c : list of lists or ndarray, optional + A 2 x N array of the priors on each entry in the c vector, by default None. + If None, it assumes a uniform prior. + nsamples : int, optional + The number of adjacency matrix samples desired, by default 1 + burn_in : int, optional + The number of iterations before storing the first sample, by default 100 + skip : int, optional + The number of iterations between each sample, by default 100 + return_likelihood : bool, optional + Whether to return the log posterior, by default False + + Returns + ------- + samples + An S x N x N array where S is the number of samples + if return_likelihood is True, also returns a list of log posterior values. + + Notes + ----- + We assume beta priors for conjugacy. + """ # form initial adjacency matrix A = A0.copy() if not isinstance(A, ndarray): @@ -121,9 +199,7 @@ def infer_adjacency_matrix( num_entries + delta_entries, max_entries, p_rho ) - delta = compute_delta(new_l_dynamics, l_dynamics) + compute_delta( - new_l_adjacency, l_adjacency - ) + delta = (new_l_dynamics - l_dynamics) + (new_l_adjacency - l_adjacency) if np.log(random.random()) <= min(delta, 0): nl = new_nl @@ -157,8 +233,39 @@ def infer_adjacency_matrix( def infer_dynamics(x, A, p_gamma=None, p_c=None, nsamples=1): - # Our priors are drawn from a beta distribution such that - # the posteriors are also from a beta distribution + """A function to infer the contagion dynamics. + + Parameters + ---------- + x : numpy ndarray + A T x N matrix of zeros (susceptible) and ones (infected) + A : numpy ndarray + An N x N adjacency matrix + p_gamma : list or ndarray, optional + A 2-array specifying the parameters of the beta distribution + for the gamma prior, by default None. If None, it assumes a + uniform prior. + p_c : list of lists or ndarray, optional + A 2 x N array of the priors on each entry in the c vector, by default None. + If None, it assumes a uniform prior. + nsamples : int, optional + The number of samples of the dynamics desired, by default 1 + return_likelihood : bool, optional + Whether to return the log posterior, by default False + + Returns + ------- + gamma, cf + `gamma` is an 1D array of size S*D, where D is the number of dynamics + samples per adjacency matrix. + `cf` is a 2D array of size S*D x N, where is row is a sampled contagion + vector. + if return_likelihood is True, also returns a list of log posterior values. + + Notes + ----- + We assume beta priors for conjugacy. + """ if not isinstance(A, ndarray): A = A.todense() @@ -196,6 +303,25 @@ def infer_dynamics(x, A, p_gamma=None, p_c=None, nsamples=1): def count_all_infection_events(x, A): + """counts all the infection and non-infection events + + Parameters + ---------- + x : numpy ndarray + A T x N matrix of zeros (susceptible) and ones (infected) + A : numpy ndarray + An N x N adjacency matrix + + Returns + ------- + nl, ml + (1) `ml` is an N x N matrix where the rows indicate node labels + and the columns indicate the nu value. This matrix stores non-infection + events. + (2) `nl` is an N x N matrix where the rows indicate node labels + and the columns indicate the nu value. This matrix stores infection + events. + """ n = x.shape[1] nus = A @ x[:-1].T @@ -213,7 +339,26 @@ def count_all_infection_events(x, A): def count_local_infection_events(i, x, A): - n = x.shape[1] + """counts all the infection and non-infection events + + Parameters + ---------- + i : int + The node index in question + x : numpy ndarray + A T x N matrix of zeros (susceptible) and ones (infected) + A : numpy ndarray + An N x N adjacency matrix + + Returns + ------- + nl, ml + (1) `ml` is an 1 x N matrix where the entries indicate the nu values + for node i. This matrix stores non-infection events. + (2) `nl` is an 1 x N matrix where the entries indicate the nu values + for node i. This matrix stores infection events. + """ + n = A.shape[0] nus_i = A[i] @ x[:-1].T x_i = x[:, i] # select node i from all time steps @@ -230,25 +375,66 @@ def count_local_infection_events(i, x, A): def dynamics_log_posterior(nl, ml, p_c): + """Computes the portion of the log posterior from the dynamics + + Parameters + ---------- + nl : numpy ndarray + An N x N matrix of nu counts for each node + ml : numpy ndarray + An N x N matrix of nu counts for each node + p_c : numpy ndarray + 2 x N array of beta parameters for the prior on the contagion + vector. + + Returns + ------- + float + The log of this portion of the posterior distribution. + """ a = nl.sum(axis=0) b = ml.sum(axis=0) return sum(betaln(a + p_c[0], b + p_c[1])) def adjacency_log_posterior(num_entries, max_entries, p_rho): - return betaln(num_entries + p_rho[0], max_entries - num_entries + p_rho[1]) + """Computes the portion of the log posterior from the adjacency matrix + Parameters + ---------- + num_entries : int + The number of non-zero entries in the lower triangle of the adjacency matrix + max_entries : int + The maximum number of entries in the lower triangle of the adjacency matrix. + p_rho : numpy ndarray + A 2-array specifying the parameters for the prior on rho. -@jit(nopython=True) -def compute_delta(a, b): - if (a == -np.inf and b == -np.inf) or (a == np.inf and b == np.inf): - return 0 - else: - return a - b + Returns + ------- + float + The log posterior value from the adjacency matrix portion. + """ + return betaln(num_entries + p_rho[0], max_entries - num_entries + p_rho[1]) @jit(nopython=True) def update_adjacency_matrix(i, j, A): + """Flips an edge in the adjacency matrix + + Parameters + ---------- + i : int + row index + j : int + column index + A : numpy ndarray + The adjacency matrix + + Returns + ------- + int + -1 if an edge is removed, +1 if an edge is added. + """ if A[i, j] == 0: A[i, j] = A[j, i] = 1 return 1 @@ -286,6 +472,29 @@ def _count_mask(array, boolean_mask, axis, max_val): def _check_beta_parameters(p, size): + """Checks that the parameters for a beta distribution are in the right format. + + Parameters + ---------- + p : numpy ndarray + The parameters of a beta distribution. If p is None, + this function constructs an array of ones which corresponds + to a uniform prior. + size : numpy ndarray + a 2-array specifying the size that p should be. + + Returns + ------- + p + An array of the specified size. + + Raises + ------ + Exception + If the parameters are not positive. + Exception + If p is of the wrong shape. + """ if isinstance(p, (list, tuple)): p = np.array(p) diff --git a/lcs/utilities.py b/lcs/utilities.py index 84a53a9..401d869 100644 --- a/lcs/utilities.py +++ b/lcs/utilities.py @@ -11,8 +11,8 @@ def to_imshow_orientation(A): return np.flipud(A.T) -def posterior_similarity(A, samples): - meanA = np.mean(samples, axis=0) +def posterior_similarity(samples, A): + meanA = samples.mean(axis=0) num = np.sum(np.abs(A - meanA)) den = np.sum(np.abs(A + meanA)) if den > 0: @@ -21,9 +21,9 @@ def posterior_similarity(A, samples): return 1 -def samplewise_posterior_similarity(A, samples): +def samplewise_posterior_similarity(samples, A): ps = 0 - n = np.size(samples, axis=0) + n = samples.shape[0] for i in range(n): num = np.sum(np.abs(A - samples[i])) den = np.sum(np.abs(A + samples[i])) @@ -38,14 +38,31 @@ def hamming_distance(A1, A2): return np.sum(np.abs(A1 - A2)) / 2 +def fraction_of_correct_entries(samples, A): + n = A.shape[0] + nsamples = samples.shape[0] + num = (np.sum(samples == A) - nsamples * n) / 2 + den = nsamples * n * (n - 1) / 2 + return num / den + + +def f_score(samples, A, threshold): + Q = samples.mean(axis=0) >= threshold + tp = np.sum(Q * A) + fn = np.sum((1 - Q) * A) + fp = np.sum(Q * (1 - A)) + + return 2 * tp / (2 * tp + fn + fp) + + def infections_per_node(x, mode="mean"): match mode: case "mean": - return np.mean(np.sum(x[1:] - x[:-1] > 0, axis=0)) + return np.mean((x[1:] - x[:-1] > 0).sum(axis=0)) case "median": - return np.median(np.sum(x[1:] - x[:-1] > 0, axis=0)) + return np.median((x[1:] - x[:-1] > 0).sum(axis=0)) case "max": - return np.max(np.sum(x[1:] - x[:-1] > 0, axis=0)) + return np.max((x[1:] - x[:-1] > 0).sum(axis=0)) case _: raise Exception("Invalid loss!") @@ -108,21 +125,21 @@ def ipn_func(b, ipn_target, cf, gamma, A, rho0, realizations, tmax, mode): def robbins_monro_solve( f, x0, - a, - alpha, + a=0.02, + alpha=1, max_iter=100, - tol=1e-3, + tol=1e-2, loss="function", verbose=False, - return_values=True, + return_values=False, ): x = x0 val = f(x0) - it = 1 xvec = [x] fvec = [val] diff = np.inf + it = 1 while diff > tol and it <= max_iter: a_n = a * it**-alpha x -= a_n * val @@ -130,19 +147,31 @@ def robbins_monro_solve( val = f(x) xvec.append(x) # save results fvec.append(val) - if it % 3 == 0: - match loss: - case "function": - diff = abs(x - xvec[it - 2]) - case "arg": - diff = abs(val) - case _: - raise Exception("Invalid loss type!") + match loss: + case "arg": + diff = abs(x - xvec[it - 1]) + case "function": + diff = abs(val) + case _: + raise Exception("Invalid loss type!") if verbose: - print(it, diff) + print((it, x, diff), flush=True) it += 1 if return_values: return x, xvec, fvec else: return x + + +def fit_ipn(b0, ipn_target, cf, gamma, A, rho0, tmax, mode): + f = lambda b: ipn_func(b, ipn_target, cf, gamma, A, rho0, 1, tmax, mode) + bscaled = robbins_monro_solve(f, b0, verbose=True) + + f = lambda b: ipn_func(b, ipn_target, cf, gamma, A, rho0, 10, tmax, mode) + bscaled = robbins_monro_solve(f, bscaled, verbose=True) + + f = lambda b: ipn_func(b, ipn_target, cf, gamma, A, rho0, 100, tmax, mode) + bscaled = robbins_monro_solve(f, bscaled, verbose=True) + + return bscaled diff --git a/plot_er_experiment.ipynb b/plot_er_experiment.ipynb deleted file mode 100644 index bd474ce..0000000 --- a/plot_er_experiment.ipynb +++ /dev/null @@ -1,188 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from lcs import *\n", - "import os\n", - "import json" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "mean_infections = []\n", - "ps = []\n", - "plist = set()\n", - "clist = set()\n", - "rlist = set()\n", - "beta = []\n", - "frac = []\n", - "\n", - "\n", - "data_dir = \"Data/erdos-renyi_experiment/\"\n", - "for f in os.listdir(data_dir):\n", - " d = f.split(\".json\")[0].split(\"-\")\n", - " p = float(d[0])\n", - " c = int(d[1])\n", - " r = int(d[2])\n", - "\n", - " plist.add(p)\n", - " clist.add(c)\n", - " rlist.add(r)\n", - "\n", - "clist = sorted(clist)\n", - "plist = sorted(plist)\n", - "rlist = sorted(rlist)\n", - "\n", - "c_dict = dict(zip(clist, range(len(clist))))\n", - "p_dict = dict(zip(plist, range(len(plist))))\n", - "r_dict = dict(zip(rlist, range(len(rlist))))\n", - "\n", - "\n", - "psmat = np.zeros((len(clist), len(plist), len(rlist)))\n", - "spsmat = np.zeros((len(clist), len(plist), len(rlist)))\n", - "ipn = np.zeros((len(clist), len(plist), len(rlist)))\n", - "\n", - "for f in os.listdir(data_dir):\n", - " d = f.split(\".json\")[0].split(\"-\")\n", - " p = float(d[0])\n", - " c = int(d[1])\n", - " r = int(d[2])\n", - "\n", - " i = c_dict[c]\n", - " j = p_dict[p]\n", - " k = r_dict[r]\n", - "\n", - " fname = os.path.join(data_dir, f)\n", - "\n", - " with open(fname, \"r\") as file:\n", - " data = json.loads(file.read())\n", - "\n", - " x = np.array(data[\"x\"])\n", - " A = np.array(data[\"A\"])\n", - " samples = np.array(data[\"samples\"])\n", - "\n", - " ipn[i, j, k] = infections_per_node(x)\n", - "\n", - " psmat[i, j, k] = posterior_similarity(A, samples)\n", - " spsmat[i, j, k] = samplewise_posterior_similarity(A, samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'PS')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "l = [\"Simple contagion\", \"Threshold contagion, tau=2\", \"Threshold contagion, tau=3\"]\n", - "\n", - "for i in c_dict:\n", - " ps_mean = np.mean(psmat, axis=2)[c_dict[i]]\n", - " ps_std = np.std(psmat, axis=2)[c_dict[i]]\n", - " ipn_mean = np.mean(ipn, axis=2)[c_dict[i]]\n", - " plt.plot(plist, ps_mean, label=l[i])\n", - " plt.fill_between(plist, ps_mean - ps_std, ps_mean + ps_std, alpha=0.4)\n", - "plt.legend()\n", - "plt.xlabel(\"p (for Erdos-Renyi)\")\n", - "plt.ylabel(\"PS\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "l = [\"Simple contagion\", \"Threshold contagion, tau=2\", \"Threshold contagion, tau=3\"]\n", - "\n", - "for i in c_dict:\n", - " ps_mean = np.mean(psmat, axis=2)[c_dict[i]]\n", - " ps_std = np.std(psmat, axis=2)[c_dict[i]]\n", - " ipn_mean = np.mean(ipn, axis=2)[c_dict[i]]\n", - " plt.plot(ipn_mean, ps_mean, label=l[i])\n", - " plt.fill_between(ipn_mean, ps_mean - ps_std, ps_mean + ps_std, alpha=0.4)\n", - "# plt.legend()\n", - "plt.ylim([0, 1])\n", - "plt.xlabel(\"mean infections per node\")\n", - "plt.ylabel(\"PS\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "l = [\"Simple contagion\", \"Threshold contagion, tau=2\", \"Threshold contagion, tau=3\"]\n", - "\n", - "for i in c_dict:\n", - " ps_mean = np.mean(spsmat, axis=2)[c_dict[i]]\n", - " ps_std = np.std(spsmat, axis=2)[c_dict[i]]\n", - " plt.plot(plist, ps_mean, label=l[i])\n", - " plt.fill_between(plist, ps_mean - ps_std, ps_mean + ps_std, alpha=0.4)\n", - "plt.legend()\n", - "plt.xlabel(\"p (for Erdos-Renyi)\")\n", - "plt.ylabel(\"PS\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "hyper", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.0" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/plot_fig2.ipynb b/plot_fig2.ipynb new file mode 100644 index 0000000..0b7d301 --- /dev/null +++ b/plot_fig2.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from lcs import *\n", + "import os\n", + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "fname = \"Data/erdos-renyi.json\"\n", + "\n", + "with open(fname) as file:\n", + " data = json.load(file)\n", + "p = np.array(data[\"p\"], dtype=float)\n", + "ps = np.array(data[\"ps\"], dtype=float)\n", + "sps = np.array(data[\"sps\"], dtype=float)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'PS')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "l = [\"Simple contagion\", \"Threshold contagion, tau=2\", \"Threshold contagion, tau=3\"]\n", + "\n", + "for i in range(sps.shape[0]):\n", + " ps_mean = np.mean(sps, axis=2)[i]\n", + " ps_std = np.std(sps, axis=2)[i]\n", + " plt.plot(p, ps_mean, \"^-\", label=l[i])\n", + " plt.fill_between(p, ps_mean - ps_std, ps_mean + ps_std, alpha=0.4)\n", + "plt.legend()\n", + "plt.xlabel(\"p (for Erdos-Renyi)\")\n", + "plt.ylabel(\"PS\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "fname = \"Data/watts-strogatz.json\"\n", + "\n", + "with open(fname) as file:\n", + " data = json.load(file)\n", + "p = np.array(data[\"p\"], dtype=float)\n", + "ps = np.array(data[\"ps\"], dtype=float)\n", + "sps = np.array(data[\"sps\"], dtype=float)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'PS')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ceDwejh07Fv17U1MTu3btoqKigoaGBr71rW/R3t7O//zP/wBw66238vjjj/PNb36Tm2++mX/+85/8+c9/TmhwMzDIFaqmsq9vX06O5Qq5eKvzLS6ceWHc5naa0Gj3tHPSdZIubxcCPXN83HGcs6adRUNJYs/DWHFk8EhK43S67O/bT31xPVaTNfXGGSCEwCf7wMK4lp1PBYY39DsTueGGG7jhhhsmehkG48SEipt33nmHSy+9NPr3O++8E9B7Fjz11FN0dnZGmzCBPlfkxRdf5Ktf/SqPPfYYdXV1/Pd//7dRBm4w5hxzHMOn+HJ2vG5fN7t6dsWURw9tABhUgyP28Sk+tnZt5ajjKCuqVoybdyeoBmly5i7aElADHBk4wtJpS3NyPCEEra5W/IqfkBbCqlnJwxA3BgZnMpPGUDxeZGJIMjAAkFWZl5peyrhCKh2WVS7DbDLT7Gwe0QAwFfXF9SyftpwCa0HqjUfB/r79HBg4kNNjWiQLV82+atRrHwwMsrN7J26fmzq5jrpZddjtdsrsZYavx8BgCnJGGooNDCaCgwMHx0TYAOzrzz7V1epupcPTQWNZI4sqF+U8zQMgazLHHMdSb5ghilDY17eP82ckb4GfCFmV2du3lxPOEwgEVk69doFAEQpWKffvh4GBwdTAEDcGBknwyb4xubjnClWoHBo8RLOrmUUVi5hZNDOnkZwTjhNjJuxOuk/SWN5IeV55Rvs1O5vZ27eXgBpIuI2iKmMi9rJGCYHFNtGrMDA4YzDEjYFBEvb370cVE1ellC4BNcCu3l3s6t1FkbWImgJ9IndNQQ1Wc3YXeU1oHHUczfFKY9ndu5tL6i9Jay29vl4O9B+gL9CXcntZk8lnggeWCgFKEBQ/aCpQYggcA4NxYkqVghsYjCfOoHNKdhX2yB6OO4/zVudbPH/8eTa2bGRf3z56fD1oIv2Kp2ZXM34lfufvXNHr76Xd0x73OUVTaPe0s61zG387/jdeb389LWEDetork9eaU4QGIR/4ByHkCQsbdJGTIZs2bUKSJBwOx6kHQ97crDMJTz31FGVlZaM6RnNzM5IksWvXroTbxH19pxHpvAcGY4MhbgwMErCnd0+0DHuqIhAMBAY4OHCQzW2bef7Y82xu3cz+vv10ebsS9tsRQnBk4Mi4rHFv796oEJFVmZOuk7zZ/iZ/O/433ux4k5Puk1mlxhRNyfVSk6OpEPTookb26SJnKKqs/4SRJCnpT9zRCJoCsh/UcX5tpzFjKUDq6+vp7Oxk2bJlOT92MiaTaPzBD37AqlWrKC4uprq6mmuvvZbDh0ffEDQVRlrKwCAOPb4eunxdE72MnKMIhR5/T8zwyxJbCdPyp1GZV0llfiXFtmLaPG24Zfe4rMktu3mn6x0CaoBeX2/O+unImozNPIZpIKHpqSehgRLQU1ApF+WHcJqws7Mz+vCf/vQn7rvvvpgv/aKiIt55552R+wOoQTDH//oOhULR6dkGE4vZbGb69NyPG5lKbN68mS996UusWrUKRVH49re/zfve9z4OHDhAYWHhmJ3XiNwYGMRhb2/2YxamGq6QixPOE2zv3s6G5g387fjf2NWza1zXcNJ9km5fd86EDeQgcqOpEHRDwAUBJ/gd4BsAXz94+/Q/+wf159IRNgBqKJqmmj59evSntLQUSZJiHisqOjWiY8eOHZx33rkUlNfw3svXcfjAqSq7Bx54gLPPPpv//u//jimfdTgc3HLLLVRVVVFSUsJll13G7t27o/vt3r2bSy+9lOLiYkpKSjj33HNHiKm///3vLF68mKKiIt7//vfHCDJN03jwwQepq9PL788+++zoBPBEvPTSSyxYsID8/HwuvfRSmpubU75lDoeDL3zhC9TU1JCXl8eyZct44YUXos+vX7+epUuXYrfbmT17No888kjM/rNnz+b73/8+N998M8XFxTQ0NPDkk09Gn58zZw4A55xzDpIkcckllwCwfft2rrzySqZNm0ZpaSlr165l586dMcc+dOgQF154IXl5eSxZsoRXX30VSZKiM7ziRYU2b97M+eefj91uZ8aMGdx9990oyqnf1UsuuYQ77riDb37zm1RUVDB9+vSkA06H09zcHO0fV15ejiRJ0eGkGzZs4MILL6SsrIzKyko++MEPcvz48ei+8SI+u3btQpKktP6t4rFhwwZuuukmli5dyooVK3jqqadoaWlhx44dWR0vXQxxY2AwjFZXKwPBgYlexoQRUANJK5GmBEKgBt2oAZfuUcn0J+gGdxf4BnVR43fqIifogaBX99Qk+knVOkzO3Hvzne98h0d+8B+88/orWMxmbv7il2NSXMeOHWP9+vU888wz0QvpRz/6UXp6enj55ZfZsWMHK1eu5PLLL2dgQP/d/tSnPkVdXR3bt29nx44d3H333Vitp8znPp+PH//4x/z+97/n9ddfp6Wlha9//evR5x977DEeeeQRfvzjH7Nnzx6uuuoqPvShD3H0aHwTemtrK//2b//GNddcw65du7jlllu4++67k75uTdO4+uqr2bJlC3/4wx84cOAA//mf/4nZbAZ00fexj32M66+/nr179/LAAw9w7733jujI/Mgjj3Deeefx7rvvctttt/HFL34xGiXbtm0bAK+++iqdnZ0888wzgD6u4cYbb+SNN97g7bffprGxkXXr1uF26xFNVVW59tprKSgoYOvWrTz55JPRSeOJaG9vZ926daxatYrdu3fz85//nF//+tf8x3/8R8x2v/vd7ygsLGTr1q388Ic/5MEHH+SVV15JeuwI9fX1rF+/HoDDhw/T2dnJY489BoDX6+XOO+/knXfeYePGjZhMJj7ykY+gaenfVLS0tFBUVJT05/vf/37C/Z1OvZ9XRUVF2ufMBiMtZWAwBE1oo+o9YzBJkP1U/CQ3HZAz5it7wZakHF8JgrUATOnfWz70vQdZ+169m/XdX7uDD1z3SQJeF3kllYCeivqf//kfqqqqAHjjjTfYtm0bPT090cHBP/7xj3nuuef4y1/+wuc//3laWlr4xje+waJFiwBobGyMOacsy/ziF7+IDpa8/fbbefDBB6PP//jHP+auu+7i+uuvB+Dhhx/mtdde49FHH+VnP/vZiNfw85//nHnz5kUjKwsXLmTv3r08/PDDCV/3q6++yrZt2zh48CALFiwAYO7cudHnf/KTn3D55Zdz7733ArBgwQIOHDjAj370o2i0AmDdunXcdtttANx111389Kc/5bXXXmPhwoXR96yysjImhXTZZZfFrOXJJ5+krKyMzZs388EPfpBXXnmF48ePs2nTpuh+Dz30EFdeeWXC1/PEE09QX1/P448/jiRJLFq0iI6ODu666y7uu+8+TOHfieXLl3P//fcD+r/L448/zsaNG5MeO4LZbI4Kh+rq6hhj+HXXXRez7W9+8xuqqqo4cOBA2r6g2tralP6kRMJF0zT+/d//nTVr1oy5D8kQNwYGQ2h2NeORPck3kgO6oTNv6k32NpgMCL1yypa+32D54lPCY8b0GgB6OttpCIubWbNmRS/SoKecPB4PlZWVMcfx+/3RNMSdd97JLbfcwu9//3uuuOIKPvrRj8ZMyC4oKIj5+4wZM+jp0b1aLpeLjo4O1qxZE3P8NWvWxKS+hnLw4EEuuOCCmMdSDT3etWsXdXV1UWET75gf/vCHR6zh0UcfRVXVaIRn+fLl0ecj6b/Ia0lEd3c399xzD5s2baKnpwdVVfH5fNGRQIcPH6a+vj5GEJ1/fvKmlAcPHmT16tUx3bPXrFmDx+Ohra2NhoaGEeuF2Pd+NBw9epT77ruPrVu30tfXF43YtLS0pC02LBYL8+fPz+r8X/rSl9i3bx9vvPFGVvtngiFuDAyG0OnpTL2REgBPD+TF/8I1mARY8xm4cz8mTJTllaa/n+zXU0ujPHdKlIAevUlzRIRVOpU2iFwYNVWJpqaGGzM9Hg8zZsxg06ZNI44VuZN/4IEH+OT1H+PFl//Oyy+/zP3338/TTz/NRz7yEf2c1tj+SJIkMd7TevLzc9OrKN5rSZWKufHGG+nv7+exxx5j1qxZ2O12Vq9eTSg0Nk0th5LNetPhmmuuYdasWfzqV7+itrYWTdNYtmxZ9DVFIkdD/51lObaisqWlhSVLliQ9z7e//W2+/e1vxzx2++2388ILL/D6669TV1c36teSCkPcGBiEEULQ6+9NvaEc0E2lqhytfDEYB1QVJMBkTr2tJIGtAA1QLHYspjS+6iKm4GQppVwhRFjgjPLincDIvHLlSrq6urBYLMyePTvBGjQWNNSw4Etf4Ktf/Sqf+MQn+O1vfxsVN8koKSmhtraWLVu2sHbt2ujjW7ZsSRi9WLx4MX/9619jHnv77beTnmf58uW0tbVx5MiRuNGbxYsXs2XLlpjHtmzZwoIFC6JRm1REKstUNbZZ55YtW3jiiSdYt24doHuG+vpO9VlauHAhra2tdHd3U1OjR9O2b9+e9FyLFy9m/fr1CCGiInXLli0UFxfn9IIf7zX19/dz+PBhfvWrX3HRRRcBjIigRKJ/nZ2dlJfrncOHp6AyTUsJIfjyl7/Ms88+y6ZNm6IG7rHGMBQbGIRxBB3IWvy+LzEoAf3i5D79SsUnNd4e6NytG3szIK2qKVXWzcLjiexPbT7WUnTHVkNxj3HFFVewevVqrr32Wv7xj3/Q3NzMm2++yXe+8x3eeecd/H4/t992G5s2v8HJ44fZ8torbN++ncWLF6e9/G984xs8/PDD/OlPf+Lw4cPcfffd7Nq1i6985Stxt7/11ls5evQo3/jGNzh8+DB//OMfRxh/h7N27VouvvhirrvuOl555RWampp4+eWXo1VZX/va19i4cSPf+973OHLkCL/73e94/PHHY4zPqaiuriY/P58NGzbQ3d0dNbw2Njby+9//noMHD7J161Y+9alPxUSSrrzySubNm8eNN97Inj172LJlC/fccw8AiYa23nbbbbS2tvLlL3+ZQ4cO8fzzz3P//fdz5513RqMmuWDWrFlIksQLL7xAb28vHo+H8vJyKisrefLJJzl27Bj//Oc/ufPOO2P2mz9/PvX19TzwwAMcPXqUF198cUT1WSQtlexnqLj50pe+xB/+8Af++Mc/UlxcTFdXF11dXfj9Y9sg1BA3BgZhenxp5rTV8N2yuxu0qd3kb0rh7dOjZt37YfBk2u99SsEaKfnOomGjhiAolOxaPQpNFyfJUFI8L7SRzQLRL64vvfQSF198MZ/5zGdYsGAB119/PSdPnqSmpgazyUR/Xw83fP5LLDj7PXzsUzdw9ZWX890MSo7vuOMO7rzzTr72ta9x1llnsWHDBv7617+OMCZHaGhoYP369Tz33HOsWLGCX/ziF0mraiKsX7+eVatW8YlPfIIlS5bwzW9+MxqRWLlyJX/+8595+umnWbZsGffddx8PPvhgjJk4FRaLhf/6r//il7/8JbW1tVEPz69//WsGBwdZuXIln/70p7njjjuorq6O7mc2m3nuuefweDysWrWKW265JVotNXyadYSZM2fy0ksvsW3bNlasWMGtt97KZz/72agoSpebbropWrKe6Dzf/e53ufvuu6mpqeH222/HZDLx9NNPs2PHDpYtW8ZXv/pVfvSjH8XsZ7Va+b//+z8OHTrE8uXLefjhh0dUcmXKz3/+c5xOJ5dccgkzZsyI/vzpT38a1XFTIYnxTqJOMJmMTDc4s3ij/Q06vWl4bjp2n2qBP60RiqqSb28wemQ/tL8b+5itEKbNB1shVtVKnVxH3aw6bPbYBnYSEmX2svh300LT+9SkipAMQ0MQ0BRd2AgwSxIFJhtWKcP7RZMF8ssSnETVy9BTSSdLHtgzNLfL/vhjHMxWsBdDpq/DANBTTBdeeCHHjh2LMWPnmrVr13LppZdm1P9mqhAIBGhqaorp2RQhk+u34bkxMEDPC/f505tbFONz8HQZ4mY88MTxQoW80LUXShugaFbCXQUCRVNGDhAVQo/YZCBshouaCKoQuNUgdpOZfJMVE+kZhdEUPXoTr5OyEiCtaJIaBFGYtjkZIRL32lFlPe1nL07P23SG8+yzz1JUVERjYyPHjh3jK1/5CmvWrBlTYeN0Ojl+/DgvvvjimJ3jdMAQNwYGwEBgID2/jSrrF6QIAbfe1M0+dm3EDQBfAuGpaTDYDIEQlM5MuLusySPFTcgT0wgvGYlEzXCCmoosVPJNVuxSml+vcmCkuNE0/fF0EEJ/HelOHFeCcVNZp86t6NGsvFJD4KTA7XZz11130dLSwrRp07jiiitGeFRyTWlpKW1tbWN6jtMBQ9wYGEB6VVIAcpzqFHcn2LPr+4Ac0NMKad50n5EE3Kkv9MGwUBla8SLC/ycEivADplMeFaGmJWzSFTUx+wjwqjIhSaXAZMWcKsWjhtBUGRUwSSbMJnN4gngGjgE1mJ64ESK96eSRdJ29JOEMKwO44YYbuOGGGyZ6GQZxMH5rDQzIxEwc5yLr6wN1VuZl4ZoKPQfAkg9VCzPqWDuhaJp+dx9J50gSIIXXL4X9GhKYcqTYvGkKTyH0FE9IoIuaU08p6N1R004XAYrQcGvBtEXNcGSh4dKC5EkW8kzW6JlVoaEiwv/VUIRA88tgsWOWzBRbizApGY6/iFRNpUpNDZltlRKhQdCpp6jGcgCpgcEYYIgbgzMeTWj0+/vT2zjeRUfTwNMNpRn2qRho0iMSkQqg6kWTo29OyK+/Hk3WOzELNewNUUAo+utNB0nSfyobobAy9fbx0ITeUygTEqgRRWjYpPTSLALwaaGshc3QpfiFQkioSEioaPGPqSkgbKioeAODFIkMg3nppqYynWsV8SXlV6Tv6TEwmAQY4sbgjGcgMIAi0pwgHS8tBXpZePHM9KMV3n69y3GEoFs3x1YvAWv8MtIxJ+QHZ6suJnJRRCn0lBDO1uzFTWAwbV9MKmShpi1u/JqMksNCUlWEo0mJEISbQtqQFT9+yUKBKUOhmyo1pYZi/WLpIkT42BP0e2lgkAVTJA5uYDB29PrSTHtA/MgN6CbNtKM/IRg4PvJxOQBd++KX6I4lIR/0HoHOXXovmVx3hwj5dDGXDemmpNJATmaiHbZdIBsRMFo0OZxegoCmR3syIkFDvyhZTCOPkqATsoHBZMUQNwZnPGmbiSGxuIH0Oxb3H9NTPPFQQ7rA8TvTX1O2REXN7rERNUNxtma+j6qCfzBnS9CE7nNJhkDg08Z+dlCCk8dEqbxaKOV6Y/cXiZsCqvLoImCqnHEvIAODicQQNwZnNBn5bbQkFw/Q+4Okirq4OsKN2ZKdJ2w0jtfbJReEfNB7eHxEzdBzetPsIxTB35++vydNUkVvfJocTiFNPEKARwuhZVQ1leD3czRRmwhG9MZgCmGIG4Mzmoz8NmogtRBwJelwHPKCoyW9cwkBfUfB2ZHe9ukQ8g4RNTny1WSCM8PeHGMg7hQSRx9koRKcZNGJza+/ibmoGocjzUieEic1FWkUmAFP/eH/KJs5rBGdmpm4aW5uRpKkpEMWN23ahCRJOByOjI49VUjnPTAYGwxxY3BGk3YJOKR35+rrix/+1zRdrGQaiRhshoHmzPYZTsAD3Qf1sRETIWoiZBK9UUIQzGxAZjrIIn4cREPgTaeJYw6pLGtI+vPwD34S3daf9triRBdzEbUBPaKYI3P3ZGIsBUh9fT2dnZ0sW7Ys58dOxmQSjT//+c9Zvnw5JSUllJSUsHr1al5++eUxP69RLWVwRpORmTidjrGapldBDe+W62jRL+7Z4OrQK5jyyvQ5RHllYE6j6ifg0qMlqdJg44mzDQqnpd7O2zsmIkwIvSR8+AwonyajjbPoO3D4neifn3vmb/zgBz9h6/bXoo8VFhay6909gG4wloWKNY1qr5DPja3Erv9FU3ObTlKCk6NdwRTBbDYzffr0iV7GhFJXV8d//ud/0tjYiBCC3/3ud3z4wx/m3XffZenSpWN2XiNyY3DGomoq/YEMqnjSbazm7oqt+vU7dIEyGpSg3num9zC0bYOu/eBsj+/x8Tt0U3LXvsklbCD96E0Oq6SGowyrQgoJldAEpKNqaqqjP8UlJUhIMY8VFZ0a6bFr917Ov+hKCqoaeO/l6zh85Fj0uQce+iFnr76E/37q98xZei55ZdUgNBwOB7d89maqZi2iZMYcLlv3EXbv3Rfdb/fefVx69bUUT59NyYw5nHvh5byzc1fMGv/+6j9ZvPK9FNXM4v3XfozOtpNR0alpGg8++CB1dXXY7XbOPvtsNmzYkPQ1v/TSSyxYsID8/HwuvfRSmpubU75PDoeDL3zhC9TU1JCXl8eyZct44YUXos+vX7+epUuXYrfbmT179ojxB7Nnz+b73/8+N998M8XFxTQ0NPDkk09Gn58zZw4A55xzDpIkRadtb9++nSuvvJJp06ZRWlrK2rVr2blzZ8yxDx06xIUXXkheXh5Llizh1VdfRZIknnvuOSB+VGjz5s2cf/752O12ZsyYwd13342inEqNX3LJJdxxxx1885vfpKKigunTp2c0ILO5uZlLL70UgPLyciRJik5J37BhAxdeeCFlZWVUVlbywQ9+kOPHT1Vuxov47Nq1C0mS0vq3isc111zDunXraGxsZMGCBTz00EMUFRXx9ttvZ3W8dDHEjcEZS3+gHzWTctt0xY0SPNV4TpWh71jy7TNFCL01/uBJPdXUtgP6j+u9djr3QvcBPWozWUnlvQl5s49yhRFC4FcCcX9csg+f4sen+PEoPvpDroTbZvMjxiAC9ND3fsSD/3Evmza9gMVs5ubb7oh5/tiJJtY//wLP/PG37HrrNVBDfPT/9/+jp6ebl599mh3/epWVZy/n8g9cx8CAXoH2qZu/SN3MWrZvfoUd/3qVu++8A6v1VDDf5/Pz48ee4Pf//QSv//2vtLS28/Vv3x9Nez322GM88sgj/PjHP2bPnj1cddVVfOhDH+Lo0aNxX0Nrayv/9m//xjXXXMOuXbu45ZZbuPvuu5O+bk3TuPrqq9myZQt/+MMfOHDgAP/5n/+JORy53LFjBx/72Me4/vrr2bt3Lw888AD33nsvTz31VMxxHnnkEc477zzeffddbrvtNr74xS9y+PBhALZt2wbAq6++SmdnJ8888wygz4268cYbeeONN3j77bdpbGxk3bp1uN1uAFRV5dprr6WgoICtW7fy5JNP8p3vfCfp62lvb2fdunWsWrWK3bt38/Of/5xf//rX/Md//EfMdr/73e8oLCxk69at/PCHP+TBBx/klVdeSXrsCPX19axfvx6Aw4cP09nZyWOPPQaA1+vlzjvv5J133mHjxo2YTCY+8pGPoGWQLm9paaGoqCjpz/e///24+6qqytNPP43X62X16tVpnzMbjLSUwRlLRikpyCy87+7SG9f1H8/YzJkxSlAXNnSP7XlyRSR6kyg9lWlVVRwCapD3v/Bvoz5ONmz44DPk57jh3Xfu/QZrLnwPAP/+1du47qM3EAgEyMvTzxMKyfzPkz+jqkp/T9/YvJlt27fT03QAu11PUf34+9/luRde4i/P/Y3P33wDLW1tfPUrX2TRwkYAGufHGohlWeYXj/2IeXP1yMbtX/gsD/7nj/XfN4udH//4x9x1111cf/31ADz88MO89tprPProo/zsZz8b8Rp+/vOfM2/evGhkZeHChezdu5eHH3444et+9dVX2bZtGwcPHmTBggUAzJ07N/r8T37yEy6//HLuvfdeABYsWMCBAwf40Y9+FI1WAKxbt47bbrsNgLvuuouf/vSnvPbaayxcuJCqqioAKisrY1JIl112WcxannzyScrKyti8eTMf/OAHeeWVVzh+/DibNm2K7vfQQw9x5ZVXJnw9TzzxBPX19Tz++ONIksSiRYvo6Ojgrrvu4r777sMUHsGyfPly7r//fgAaGxt5/PHH2bhxY9JjRzCbzVRUVABQXV1NWVlZ9LnrrrsuZtvf/OY3VFVVceDAgbR9QbW1tSn9SZHzR9i7dy+rV68mEAhQVFTEs88+y5IlS9I6X7YY4sbgjCUjMzFkJm4CTt0I7BvI7BxnCsm8NzkQN6cbS5cujv65olp/33p6+2io10d+zGqoiwobgN27d+HxeKhsWBBzHL8/wPGmZgC+/KXPc+vtX+P/nl7PFZdezEc/8qGokAEoKCiI+fuM6TX09PaBGsLlcNDR0cGaNWtijr9mzRp2794d9zUcPHiQCy64IOaxVHfvu3btoq6uLips4h3zwx/+8Ig1PProo6iqGo3wLF++PPq8JElMnz6dnp7kn//u7m7uueceNm3aRE9PD6qq4vP5aGnRKx4PHz5MfX19jCA6//zzkx7z4MGDrF69GmnIKIs1a9bg8Xhoa2ujoaFhxHoBZsyYkXK96XD06FHuu+8+tm7dSl9fXzRi09LSkra4sVgszJ+f2aDghQsXsmvXLpxOJ3/5y1+48cYb2bx585gKHEPcGJyRKJrCQCAD4aHKmbeuH63P5nQmUfTG78yJATbPbGfDB59J+LzNZNIrp8bAQ5xntuf8mFbLqa/qyIVRGeITKiwoiNne4/UyY3oNm15+bsSxykpLUYTG17/171z70Q/xr1deZ8M//sn9D/2Qp596ko986AP6Oa2xlwdJkk6l3DIsC8+W/Pz8nBzHao01QUuSlDIVc+ONN9Lf389jjz3GrFmzsNvtrF69mlBo7Js8ZrPedLjmmmuYNWsWv/rVr6itrUXTNJYtWxZ9TZHI0dDUqizHVsi1tLSkFCXf/va3+fa3vx39u81miwqic889l+3bt/PYY4/xy1/+ctSvKRGGuDGY8gghYu6E0qHf349GBl8WiWZKGWSPsw0KpsVOiMyRkViSpJSpofSmTE1efEnKsleevZyu7h4sFguzZzXEPCcQuFR92vm8eXM5e8Ei7rz9i3zips/z2z/8X1TcJKOkwE5tbS1btmxh7dq10ce3bNmSMHqxePFi/vrXv8Y8lspUunz5ctra2jhy5Ejc6M3ixYvZsmVLzGNbtmxhwYIF0ahNKmw2fR6Xqsb677Zs2cITTzzBunXrAN0z1Nd3Kqq4cOFCWltb6e7upqamBtBNyMlYvHgx69evj/nO2rJlC8XFxdTVZTh4N8PX1N/fz+HDh/nVr37FRRddBMAbb7wRs18kRdfZ2Ul5eTnAiBRUNmmp4WiaRjA4tt+phrgxmPIcGTzCtPxpVOanP5wxo5ELAEqOeoUYnCLk0/sCRaI3mmak8TJASTID64pL17L6/PO49vob+OF/3M+C+fPo6OzixQ2vcPUHr2LOwnncf99DfOhD61gwZw79nT1s3/ku1334mvROril84+t3cv8DDzJv3jzOPvtsfvvb37Jr1y7+93//N+4ut956K4888gjf+MY3uOWWW9ixY8cI4+9w1q5dy8UXX8x1113HT37yE+bPn8+hQ4eQJIn3v//9fO1rX2PVqlV873vf4+Mf/zhvvfUWjz/+OE888UR6rwPdl5Kfn8+GDRuoq6sjLy+P0tJSGhsb+f3vf895552Hy+XiG9/4Rkwk6corr2TevHnceOON/PCHP8TtdnPPPfcAJLzZuu2223j00Uf58pe/zO23387hw4e5//77ufPOO6NRk1wwa9YsJEnihRdeYN26deTn51NeXk5lZSVPPvkkM2bMoKWlZYShe/78+dTX1/PAAw/w0EMPceTIkRHVZ5mmpb71rW9x9dVX09DQgNvt5o9//CObNm3i73//e05eayKMaimDKU+Ts4nt3dtRMyjnzdhvM05h+DMOZ9upsnn/QHZTq89g/EKOO55BkiReeuZpLl6zms/cegcLzn4P19/0eZpaWymdVo7ZbGJwYJDbvvhVVqy8kI/dcAtXX3k53/3ON9M+9x1f/Dx33nknX/va1zjrrLPYsGEDf/3rX2lsbIy7fUNDA+vXr+e5555jxYoV/OIXv0hYVTOU9evXs2rVKj7xiU+wZMkSvvnNb0YjEitXruTPf/4zTz/9NMuWLeO+++7jwQcfjDETp8JisfBf//Vf/PKXv6S2tjbq4fn1r3/N4OAgK1eu5NOf/jR33HEH1dXV0f3MZjPPPfccHo+HVatWccstt0SrpSJG7+HMnDmTl156iW3btrFixQpuvfVWPvvZz0ZFUbrcdNNN0ZL1ROf57ne/y913301NTQ233347JpOJp59+mh07drBs2TK++tWv8qMf/ShmP6vVyv/93/9x6NAhli9fzsMPPzyikitTenp6uOGGG1i4cCGXX34527dv5+9//3ta5ujRIImxqFucxLhcLkpLS3E6nZSUlEz0cgxGSb+/n3+2/hOAxrJGzq4+O+U+iqbw/LHnM0tL9R3Tm/MZ5J6qBXr0pucg+LIblGk1lVBX8T7q6mux2c6sgLRFMlFstpMqMRtJR8WbnVVmycOU8gjDkEyQXw6ZpITDlVanK1u2bOHCCy/k2LFjzJs3L/UOWbJ27VouvfTSjPrfTBUCgQBNTU3MmTNnhEjM5Pp9Zn0LTCEO9B9gbulc8nJcUnq6cdJ1Mvrno46jzCyaSVVBVdJ9+vx9mQkbMIYGjiXONsgrnXwNB6cIenpKJt+UvHOwX1MSDgVVhIYtje7HMQhNN9pbbOltH/TovaJMZjCdHpeeZ599lqKiIhobGzl27Bhf+cpXWLNmzZgKG6fTyfHjx3nxxRfH7BynA0ZaahJy3HGc/f372dS2Cb/h9UiIqqm0ultjHtvetR05xRyejPvbQPoN/AwyJ+TT526dWUHknBIQCkqSieeyUBP6c4Ck+yYlnXStEBB0n/oMpTPGZIrgdrv50pe+xKJFi7jppptYtWoVzz///Jies7S0lLa2NoqKisb0PFMdQ9xMMvr9/ezq2QWAO+RmU+smfPLourWernR4OwhpsWWZXsXL7p74fTYi9PgzTC9pcYYRGuQWI2ozKoQArxZCxPHfCAS+FIJ/+EiKtFFCegQn2cJCntjIpxLIfIDsJOWGG27gyJEjBAIB2traeOqpp6isTL+wwWDsMMTNJCKgBHir862YlIlH9rCpdRNeOc4MoTOcoSmpoTS5mujydsV9TtZkHAFHZidSA0ZUwWDSo4r4IsanyQnTUREUIeIak1MjdIET96lIxCZOdMeISBuMMYa4mSRoQuPtzrfjpqG8ipfXWl/DHXJPwMomJwElQLc38biB7V3bCcWJtvT5svHbnD5hdIPTm6CmIg+JwshCJZhmFWFOU1MRYZMo4qkYNwwGY4shbiYJe/v2Ju294lf8bGrdhCs0iQcijiMt7pakIiWgBni3590Rj2eckgKjgd+UQMT850zGq4XQ0CMx3hTpqKFknZpSZRgqoFIJm8g2xk2DQRxyVcB9eljWpzitrlaODB5JuV1ADbCpdRNr69ZSai8d+4VNYhKlpIbS4m6htqiW+uL66GN9vizmFhlfwpMeRfOjqAFcLi8lJYVkWtV8uqGa/EhAKIPeT6qkYjJl+capLrDln/LYqGn0Kwo5IY/MSskNTmuEEPT29iJJ0ogRFJliiJsJxhl08k73O2lvH1SDbGrdxMV1F1OeVz52C5vEOAIOHEFHWtu+2/MuVflV5FnykFWZwWAWfVQMcTPpESj0uN8G3oPLZbRPyJYCkxUpG2UomcCaHzYLJxZUAhF7fGvvaVMWbpAbJEmirq4u7fEZiTB+q3KIM+hkb99eFpQvoLqgOuX2sirzVsdbKCKzrqwhLcTmts1cNPOijEYOTAZ8so/B4CDOoJM5pXPIt2Q+GK/Z1Zz2tkE1yI7uHayZuYZef2/capKUGD1upgRBpY+2wQ1YTPmc8aGbLFlROIMZ1iybm0pFetQmAe0hJy0hB6uLZp160FIGcy7L7nwGpyVWq3XUwgYMcZNzOr2ddHo7KbOX0VjWSENJAyYpvrVpW9c23HJ2JmFZk3m97XXmlM5hWv40puVPm1QN/zSh4Q65cQQdDAZ0MeMIOmJKt/v9/VxUd1HGxx3e2yYVHd4Omp3NaUd7RmBEbqYMAgVZM4z32TIQsjDHmmUH4VDim4D9/i4OhP1uJm0atki0JtANoUEomZHdOQ0MEmCImzHCEXSwvXs7e/v2Mq9sHvPK5mE3n/rSONh/kA5vx6jOoQiFo46jHHUcBaDYVsy0vGlRsVNkm5gmT/v69nFk8AhqCoNil6+LE84TzC2dm/axu73dBNTMxcau3l0x73/aDDdLGhicxvQquW05oQqN7d42WkOO6GM9ipc62xDPYPc+Q9wY5BxD3IwxATXA/v79HBo4RENxA43ljfgVP/v79+f8XO6QG3fITZOrCYA8c15U6JTnlVNqK8VqHp1JKxV+xZ+WsImwu2c3NQU1FFoL09o+k5TUUGRNTtm5OP6ORkrK4MzBrQbxpzHKIR0CmsybnpP0K7FNSHsVT6y4cbbp0+ALKkZ9TgODCIa4GSdUodLkaqLJ1YRZMmfn/ciQgBqgzdNGm6ct+liRtYhSeyll9rLoT4G1IGfnPDxwOG1hA3r06Z2ud1hbvzbltiE1RKe3czTLyxyj2ZjBGUav7KHBPrpiBacaYIunGW+ccvCeeA1Ju/fDnMxS1AYGyTDEzQSQycU/13hkDx7ZQ7unPfqY3WynIq+C1TNWYzZlb+QKKAGanE0Z79fj7+HI4BEWlC9Iul2ru3X837t0ZucYGJxG9CjeUYmbLtnN256WmGaCQ3GpAQKaTN7Q6NDACZi5EmzpRXANDFJhNPEzIKgG6fR2cnjw8KiOc3jwcMaVXxH29+1P2aAw25TUqDiNhvwZGKRDr5K44ikVxwP9bPE0JxQ2p84xLHojNOg+kPV5DQyGY4gbgyiHBw5nPaQzqAY54TiR9bkVobC9a3vC7pTukJuBwEDWx88ao1LK4AzDo4bwZTEodo+vk52+drQ0Osz2yHEEVN+RxHOqDAwyxBA3BlEUobC7N/lE7UQcHsg+ahNhIDDAoYFDcZ9LpyPxmGD0uDE4A+nJsGqqPeTkcCDx+JjhxK3KUkPQN7rosYFBBEPcGMTQ5mmjx5fZ/KWgGuS443hOzn+g/0Dcqd0TIm40kXw+joHBaUomqamQprDT1556wyFEqrJG0HMQtCwHeBoYDMEQNwYjeLfnXbQMJgQfGTgy6qhNBA2NbV3bYs7f4+vBp2SXLhsVqjG52ODMJG7aKAG7/Z0EtMw//73xzhHy6uZiA4NRYogbgxG4Qi6OOY6ltW1IDaW9bbo4Q072953qAzRxKakz12/j9MvGgO0zGJ8m40mjUrBLdtOczbw2oDtRdKh7b1bHMzAYiiFuDOJyoP8AgTQu7kcHj+YsajOUw4OH6ff3I2sybe621DuMBWdoA7+AojHgC9HtDqAZEueMJZXvRhYqO7zZfzYTdkP2O8DdlfVxDQzAEDcGCZA1mb19ye+gZFWOjn7INQLB9u7ttLhaxkQ8pcUZGrlxB3QvhD+k0uUIoE6x1NxUW+9kJW7aaAh7fV34sun6HcabrCqr70jWxzUwAEPcGCSh2dVMv78/4fNHHEeyG2mQJu6Qm109u8bs+CmZYuLGL4++waEqBN7gKTEZVDU6nH5C6uQ3eSqaoMsZoNt1Zkbcck1PElNxr+zheDDxd0P650gQvRk8aZSFG4wKQ9wYJGVXz664vWdkVebYYG69NvHQmMCL6hQRN5oQdLkD9LiDo45auAPKiESUogo6nYGciKexwhNUaHf48SsqQUXF6R870X2mENAUXHGG1CpC4x1fblLFCauyNMUwFhuMCkPcGCRlIDgQHcQ5lKOOo4S00/zOagr0uJG1sPAIqWhCjOqiLjiVkhqOJgTdrgCe4ASlCBOgCkGPO0ivJxjTPG7QF5oS0abJTm+cOVD7/V14ctQiIWlVlpGaMhgFhrgxSMm+vn2EhnyZyZrM0cGx8dpMGlQZtMkbqQAIKCqdjtiUkTugZB298YUUFC3xvgLo9QQZnCRREb+s0uHw4w2NFFwC6POc5uJ7HBiemhpQfBwN9OXs+Emrsnz9+rRwA4MsMMSNQUqCapD9/adKs487jp/+UZtJXinlCSp0OUeafTUhcPmzi66ku5/DF6LXE5ywOioBDPiCdLkCScWYkZ4aPX1DPDGaELzjbcv5v3vSqqy+0/wmymDMMMSNQVocdxzHGXSiaApHBs+AcLHin+gVJGQghbhwBeSMozchVSOgpB+p8gQVetzjLwBDqkaHw48zTSFmpKdGR0BTcIZ9NwcD3dE/55KkVVkDxyd9BNVgcmKIG4O0EAje7XmX447jBNNo7jXlmYRmYg1BtzuYMhqhCYErkFn0xpXAa5MMX0jBN44m44iwyUSsGOmp0dMje3Aqfg5lMDsqo+MnG/WgBGGweUzOa3B6Y4gbg7Tp9feyr2/fRC8jezK5A5xkZmJFE3Q5Avji+Evi4fLLaU1nBl0MeYPZiZRB7/gJh2y7JhvpqdHRo3jY7mtL+/cpUxJVZUXpH/uqTIPTD0PcTFYC6c92GU8mtDR7NKgy9GaQTptEkRu9IspPMIOIRSbRG3dQyfrCFVI13ONQQaUKEdc4nC5Geip7OkIuBsc4TRuvKiuKqwOC7jE9v8HphyFuJiPefn2+ymDLRK/k9ME/qP+40mzrPkkiNxqCnhTG2US4/HJa4xPcGaawhuPwhcZ8TIMnoIxqhqmRnprcJE1NgWEsNsgYQ9xMNvwOvb+DEOBsS/9ibJCcSEmpoxnkFHehmgY56uMxWnrd2UccVCFwpzDe+mUVeZQRDUXLvkIrXXIRHTLSU5OXhHOmIvQfY1Tq1uCMY8LFzc9+9jNmz55NXl4eF1xwAdu2bUu6/aOPPsrChQvJz8+nvr6er371qwQCkyeFMCoCbug9FPshHmwCb+76SpyRaAICzvCfNeg7pj+WCCU4Kb5IB31y2h6bRDhTRG+yMRInOs9YzXTKhQCLMOg30lOTkaCm4EyW+gp5wdU+fgsymPJMqLj505/+xJ133sn999/Pzp07WbFiBVdddRU9PT1xt//jH//I3Xffzf3338/Bgwf59a9/zZ/+9Ce+/e1vj/PKx4CQF3oP6hffoQih37X4nROzrtOBoDPWTBx0gytJ+/gxKHfNFE9QweEfffQoWfRG1jT8odxUO2lC4PCNTbQrVwIM9I+TkZ6anKSaQm50LDbIhAkVNz/5yU/43Oc+x2c+8xmWLFnCL37xCwoKCvjNb34Td/s333yTNWvW8MlPfpLZs2fzvve9j0984hMpoz2THjkA3QdBTXCXrmnQe1gXQAaZ4x8c+ZizDYIJ8vwpGviJ8M9YEVA0+ry58/wkit7EmyM1GtwBJedREUUTORNgEYz01OQk4ZypCI7W1CllA4MwEyZuQqEQO3bs4Iorrji1GJOJK664grfeeivuPu9973vZsWNHVMycOHGCl156iXXr1iU8TzAYxOVyxfxMKpQQ9BxI7fHQFF0AyRMfVcgJAdf4NeeKJ26E0E2KwyNlkLJSKhBu+z8WgyQVIeh1B3KaFVOFGGEa1hB4RmkkHo5AT6XlklwLsAjZlpUbjB09sjfukN4oQoP+4+O3IIMpzYSJm76+PlRVpaamJubxmpoaurrim2g/+clP8uCDD3LhhRditVqZN28el1xySdK01A9+8ANKS0ujP/X19Tl9HaNClXVhk65gUSNCaAzuOhNFjcYKd9f4CLWQP/F5ZD84To58PIW48Qb1CEWXK0C3O4icRSVTPEZTGZUK17CLuTeojolHxhdSMup0nIxkgzxHiyoE3kk2BPRMRxYqjlQpYSM1ZZAmE24ozoRNmzbx/e9/nyeeeIKdO3fyzDPP8OKLL/K9730v4T7f+ta3cDqd0Z/W1tZxXHESNBV6DkHIl9l+cgC6D+Qu6hH06v1f2t8Zv6iQKutD8cZjxIE/xeA9V6deoTaUFOLGNyRN4gspdDj8DOYgEtDnDhFUxsbsqmgCd/CUUHCPYVpmwJubY3uD2Q8BTYdMuzgbjD0pS8IDTvDE92QaGAzFMlEnnjZtGmazme7u7pjHu7u7mT59etx97r33Xj796U9zyy23AHDWWWfh9Xr5/Oc/z3e+8x1MppFazW63Y7fbc/8CRoOm6cIm28ZUIa/uwalaDCYp8/0Femm0u0NPD0VwdUDl3OzWlAmeHj0tNB6N8uKlpIbTfxxmrABz+OOQpMdNQBkZ8YiYaT1BmYoCG4U2/TiK0LBI6d0/DPrlUTWpSwenT6bYbiWoqBk1BIxHUMjsUE8iCxVJkjAjISFhwoRJkSjSLNjNZsySiRpLEYvzqpGkzH5Xx1p8BBWVkKphM0+pe7zTml7Zw8K8quQb9R2BourxWZDBlGXCxI3NZuPcc89l48aNXHvttQBomsbGjRu5/fbb4+7j8/lGCBiz2QyQPFc7WdC0cKVOx6nS5GzxO6D/CFTMBbM1/fN7enRREy9K4+2Bsvr0j5cNAj0lBWMfKVKV9ASkEoSBE1C1QI8qJYmKJSvNVlRBjzuIx+Rgu9pEh+xiVUEdlxTPw25K/FHzhpQxqzSKWV84ehOURydshBC8rZygRYSjYvE+esP+ac8rqOP9JQsxpSlwQqpGMIv0lio0AsgUSund0Lj8MtOKJtnNzxlMr+JFEyL578lgM9RfMLbfUwZTngkTNwB33nknN954I+eddx7nn38+jz76KF6vl8985jMA3HDDDcycOZMf/OAHAFxzzTX85Cc/4ZxzzuGCCy7g2LFj3HvvvVxzzTVRkTOpkIMQcusX2IALZF9u+6d4+/UfkwnMdrDYw/+1gSUPzDb9MSTwdOs/yfw6mqanacobcrfG4fgHT0VGxjpy4x9M//329kF+OVjyk2+WZAaTX4R4V23huHZqwOBWXyt7/V2syZvHQms1JpOEBEiS/iOAwRylcdLB6ZNRR+npOa710iIGMCFxtrkBExIaAg2BQEMT+p9tVgnNpLHb38k7vjZ8msy1ZUvTimZlU/49oHnZrBzGS5DLLUuYYSpNuY83pFKR6mJqMG4oQmNQ9VNpKUi8kSrDQJN+M5IMOQCeLrCXQEFFbhc6xVA1FbNpEl4jx5AJFTcf//jH6e3t5b777qOrq4uzzz6bDRs2RE3GLS0tMZGae+65B0mSuOeee2hvb6eqqoprrrmGhx56aKJeQiyqfGoOStAzfi38NQ00f27KJD1dUFqnC6axwD3ELD7WZZ3ppKSGMtgEJTMTPh1QtLhmX1VoHNI62au2I6OLnzmmadRJ5exSW3GLAK/4D7En0MH5ljmUSUm+uMeY0ZqV3SLAdrUJgBXmepaaaxNua0airjSf+fZpPOvYx4FAN/4BmY+VL08aycp0kKcQgmNaD9vUpmjJ+x61NS1xowmBJ6hQkmdEASYLvbInubgBPTU1XNxoqv794u7Uv4d9/aeeyy+D8tlQPkf/8xlGj6+HGUUzJnoZ44okpkQ+J3e4XC5KS0txOp2UlJTk9NhORzP/2PZYTo85IVTMgZIx+CDIAeh4NzaaUn8BjEXUTQBt2zOvLJOkhNGeAV8opj+KEII2McgOpRk3upCtlApZZZ5DlakY0IXPfq2DfWobKgIJiSWmGZxlrsMqpfe6NSEQCMxp+nfGCk0I/qHso1d4qJFKuMKyJGXEoyzfRnmBlePBfv48uAdZqNRaS/hE+dkUmm1x93EFZPrTnDauCJWtahMnwtGyWqmULuFCQ3CVZSnVptSfcZvZxMyy5BG7ycKg4uct70lW5M9gpi21eJuK1FiLuLg4De/fkg/r5eGuDv3H25NeoUV+uf4dVz4H8nJ7DZisvNP1DudNP2+ilzFqMrl+T2jkxmCS4uqAounZmZWTETESD0UJgLkwt+cBCLqyK5lPovWHVkkNaj7eUZvpErp3Kh8r55gbmGuqijHOmiUTy811zDFN4x2lmTYxyH6tgyatj/Mss2mQKqLbCyEIIDMofDiEj0HhY1B4cQo9wlUlFTPDVEatVEqFVJixQXe07NPa6RUerJhZY5mfVirHGQiRbzMzz17JDRUr+ePALjpkF0/1v8OnKs6hLE4aMN1Bni7hZ7NyBIfwIQFnmxtYaqrlbfUEx7Qe9qntXJaGuAmpGgFFJc8yucP2LSEHfx7cjU+TGVT9fKrinIle0pjQr/jwqSEKEojfKAf/poubTPEPQvsgtO+Egko9olO1SE/nn6b0B/rxK37yU6TdTycMcWMwEiUIvj4oSlG1kAma0D0/I87lB/sYiJvh5d2jJKRqyKruJ9mpnuSQ1okATEgsMdWyzDwzaSSmWMrjUusiWrUBtivNeAnyunKEWqmUUqkgKmSCJL6wdwsX3aqLXYAdC9NNpdRKZcwwlaZtoM2WXs3NHlVvo3CBeU7a5xMCupx+Sgts1OaX8plp5/G/A+/Sr/r4bVjgVFuLotsHwhVMqTip9fOWchwZlTysXGRpZHo4DbXUXMtxrYd24WBQ81JuSv375fIr5BXH/vuJcMTMNMERM4A9vk7+5jyAGk679cgpSqanMIrQ+IfrKOcWzqTeVpZ4w2yEzXB8/fqP2QbVi0Z/vEmKT/bhDDoNcWMwtghg0tsXXe25FTf+/viRlLHy3aTqb5Mh3pCKJgRb1KM0a3ouv0GqYKVlFsVSXtrHqTdVMMNayj61nf1aBx3CSYc4VTknAcXkUSYVUG4q1P8rFSAQdGpOOjQH3cJFEIWTWj8n6QcVSsmnxlSCGRMqWvRHERoaGgoaqm73pVIqYqW5AbuUns9EFipblGMIYLapkjnmzH4vBODwhfCHFKYV5fOZSl3g9Cpenup/h09UnB29iKWaLq4KLSwude9WtVTMRZYFFEin7rpLpHwaTJWc1PrZp7VzkSmF8RS9Ck4VNszhaJQiVH7b9w4+IfOZyvMoMaf/b5xLhBC85jnOG55mABrt0zga7MOtBfFrMvmm+P+G+ogQgWnyf9PERRYqb3ta6LZ7OLugNu2WClnjbD1txU1ACaAIBUfQwfTC+G1WTkcMcTOOaAj6PSH8IZWSfCsleZbJW6UR8unh2/zy3BxvqJF4KGNhupYDmTdHTIEnIPOGepSTWj8mJC60NDLLVJnVsSySmbMtDcwVVRxUOzFhojwsYkqlfCwJIkAl5nwWmqejCY0+4aFDc9ApnPQLD078OLX0hOKg8NGuDfJey3xqTWUpt39HbcZNgAJsnG/Ovg9SUNHocPopz7dxY+W5/N/AbtplJ7/v38lHy5cz1145otRej57oF2ofIbYoR+kVetRiqalWr9aK8xlaZpqpiz+tn7NFIKUA1bshK5Tl62LhdU8TnYreRuAZxz5uqFg57hEcWag859jPwYDetG5N4WwuK57Hf/VuwakG6FW8NCSIbPhCCmaTiTzLJP1+SZOm4AB9ipfVhQ2UjmXUwd2pt44wn36XRK+szyR0Bs+s4cun37/kJCWgaPR6AiiqHlYe9IVw+eXJLXKcHbkRNyFfbLPAoaToUuwPqeTbMvRC5Dgl5VcU/hk6HBU2F1sWUG8afWlpiZTPBZbMxYJJMlEtlVBtKuFsICgUuoSTPs2DhO7zMRP7Y5FM0ZLtXUoLLgJsVA6y0FTDSvOshIKqVRvgmBa+uFrmY5dG95UhhG7MzguZub78bJ5z7uN4sJ+nB3dhlyyo4VSQXlYu4rbQiXh+kv0bVJgKqZXK6BAO9qsdvCeN99kdlCnLt9Ite3jTo4/lMCPREnLwmvsEl5fMz/ZlZ4xbDfKnwd10yC5MSFxTupgVBbVUuHtY5nGwJT+PHtmTUNy4AwoFdjN5lolPqY0Wtxpko/sYZ+XPoDFv2ticRFPB1ab7b04zIuJmMJhh9egUxxA3Y4wAHH4Zpy804otaFWJyi5yAUy9ptxel3jYZ8bw2ERI08vOHVJr7vLiDCnkWM9OKbVQV2bGm82WdaQl4ElSh8YxjX1TYrLUspM6Uo2hWjrBLFmZJlWlHkmZay9iptnBY6+Kw1k2n5mSNZT7TwhVeEfwixFuKPqhwiak26mnJBQFFJeTUWFewhM2mo+zxdxEQqY3E06QiLrQ0ppUKXGaeSYfi4LjWw3JRF5O6ioeiCjxBmRfcB9AQLLRXcVb+dP7i2MsWbzMNtrKxu7gOoVN28aeB3bi0IPmSlY9VLGeWTf+dW9L6LgukEFvy8/B6OqGwbuTrEIKArGIxSXCa9CdUhWCXr4Nu2c2qwvqkrQSyxtF6WosbT8hzRvW7McTNGCJrGr3uUMpOq5Na5LjaoWohAL2uIJ6gQkNlAeZ0K6k0DTy9iZ9XZVDVaDm4pgnaHQG6nP6oGAwoKm2DftoH/ZTmW6kqtlOWbyNulkDTIEfhV1VorB/cy3G5b9IKm2ywSGbOt8yhTivnTeUYLgJsUPZxlqmOs8wzMUkmhBC8qRwniEK5VMDZ5twPnNWEwOFVeI91PueWNNDnD2KKjnGQov8zQfSxRBGmeFRLxVRJxfQKN4fUTlZaZqXc521PK+2yC7tk5urShZSY81gVGmS7r43nHPv5fNUFlI6h/+ZwoIdnHPuQhcY0cwHXV5xNRbjnS7HPwQxHO/OLdIO0x9dDQcCNLy9WlHrDk9RD6uTr8lHkd+K1FyOy7KPVKbv5h+sIFxQ2xBjRc4KzTQ8tTpbv3hwRETcCgTPkpCLvzGhoOPVjlpMUd1ChwxHIqIV8ROS0DeqDGAOKGm1KNmH4BpADPo52e2jq99LrCXKgw4U/lObr8vaCluKOPNypeNAbYm+7k84hwmYokSjY0R4P77YO0tLvG7kOv0MXOKNEFRp/GdzLoWAvJiQuOU2EzVBqTWVcY13BbFMlAtijtbFB2YdT+DmiddMhHLq/yNw4pj12/LKKEjBTJhVQIuVTLOVRKNkpkGzkS1bskhWbZMlI2ABIksQys96U8YjWTShFZMgrgmwLNQNweXFj1ER8ZckCZliK8QuZ9YN7UXNRpROHnb52/jS4B1lozLVVcPO0VVFhA9DYuR+A0vDF6bjVwnsOv4ZpWG8XT3jaeUjN0XDdHFE7cJKr313P0tadozpOQFN43X2Ck7lOsyiB03Iop0855T90BB0Tt5Bxxojc5BhVCPo8oaQziNI5hsMXwoFePWM1m7BZTNitJuxmMzbL+NVA+EMKbU1HGcw7deful1X2dzhpqCiguiTFXWwiI/EQQgEvzQ6R0XwlRRN0uQJ0uQIU2sxYzCZAYHe1Y/MHIGpEDZtSBZTmWynNT10hpAqN/ze4hyPBPszhiM3M00zYRLBLVi6yLKBO7WObeoJ+4eVFeXf0+ZXmWZSZJq6j8miZKZVRJuXjEH4Oa12cZR6ZxgH9d2SrcgIFjRnmEs4tONWp2iKZuK78LH7Vt5U22clr7uNcUdKY03UGNYVXXEcAOLdgJleXLIwxMNvkALN7jwEQqF6K5D+Aw2xG+AdY3rydXXPfoz+naNFSeiGYVINBa/tbAJg5cJJ9s0bXUE4AhwO9zLLn+HPpbIXimtwec4KJRG7gzDIVT47f+tMEv6zR4fCPStgMRw8va3iCCv2eEB1OPycHvHQ4A/R7g3hDCtoYNJnWEPR7g3S5Alh8fZi02DJuTUBzv4/jPR7UROHvgEefYJ4AATj9Msc6+kY1ONIbUnH6ZZx+BdXTH516HelNo2giGhULKMnvupUhwsaCiSvtS05bYTOUOeZpXGNdwQypFBWBiqBWKmWRaWqXjkqSxFKTLlQOqp0oIn4046TWT3s4UnWBae6I1ESFpYAPlS4B4E3vSY4E+nK6zt3+DoJCpdJcwLqSRSMqs+Z1HcKsqQwWVuIqnUG5WRecx2xWGrsOMLNPH4nhCcZ+TkMpft/Hk2lu3XtX4ndiy8HQXKcaoF/JbVUkjpbcHm+CEULgk4dEbgKOiVvMOGOImxwiK+qoZ/ekgxAQVFRcAYUed5CWAR9drgBOv5xWA7RUhFSNTkcAV7RTrEZ+ML5vpt8bYl+HK/4sIE/8qE1kOnWHw8+AL4SUowGaFsWHSSTuSiyAXk8goRgcLmw+Wr6cKnFmtGcHKJDsXG5ZzHvMc5lrquK9lvnj3gV5LJhtmkYRdoIo0cqvoQSFzHa1GdBNyMVSPt7gyBuUxfk1nF+gRzCfd+zHqQZAaJS7e5FGkQoVQrDVG26QWNgw4j03aSrzug4CcKR2KUgSVRbdd7OlSvcRrTr+BvkB54jPYS6+D3KBPeSneEjFZKU7N+mfpmBu+1kRcCau7JyC+BU/Gqd+B5whI3JjMIUQ6KmiAV+IdoefVoePfm8Qn6xm7Nhx+nXRMfxLMT+Q+As8qKgc7HTS7RoiUlRF73Icb32DPvo8oeg5zFpuet3Y5NQfXEXV04bx2Opt4WhY2FxfcTY10uk5uycZkiTRaK5hjWU++Smqi6YKJkliSXjA5wG1A22YZ2aHepIAMqXksywc5XElGAFxRUkjtdaSqP9m1eF/csXev3Hl7ueY5uzMan1Hgn0Mqn7yJAvL80fOdKvvO0G+7MdvK6C1cg5A1Ey7p7iC3uIarKrM6oOvIamx604Wualw97C49V3yctwTKh6Vw7wsuRI3rSEHSq49UKdR9MYzrJO1rMl4Qqdvd+uhGOLmNERRBa6AQrcrEI3qdLuD9HqC9HuDDPpkHH4ZV0DGHVTwhhR8skqXM8BAnJJ1AEko5IUSh+I1ASf7fRzr1tNUIWcnLn+QbncgZWTJouYmcmNP867EG1Jwx7kzP+DXw+bvK1nAXHsFvgwmUxtMbuabqsnDipcQTdqpadFdmpPj4aGb77HMjRqngwnGQFgkE9eVnYVdstAmO1kv9Lv8Ur+DS/e/zAVHNmUsFrZ69YvpyoKZ2IaX6QoRNRIfm74YEX6+Ohy56VG8vL3gEoKWPCr9A1zW+27M7vHETbFvkPceepXL977AstZ3uWLPXyl3J6lozAGVLl3MhMLzonIlbhSh0Rpy5ORYUZytuT3eBDLUbxPhTIneGOLmNEcTAr+s4gspeIIKroCCwx9i0Bei3xuizxOkxx2k2xXAn6KyqyDQnXSwJOgN2na1OmhpaabfE8IXHluQDEkoSOlM802CSZOxqIn9PSPW6Q3FXLwcip9OxY0ELMmvRg33CjE4PTBLJhab9ajIfrUdIQSKUHlbOQHAAlPNiAnirkD8FGe5JZ+bwiMonior4Tezl3OsZhECaOg7wft3/oUF7XvTSlV1yW6aQ4NISKwqHFluX+Xqotw7gGIyc7zm1HiAKoseuelRPPhtBbw5/2IEcI7jOItcpyIPqhDRVHl+0MN5x/7FVbueY+ZACwIJv62A/JCPS/e9RH3v8ZTrzZaI3+b4dP01VHhGl8obSs5TU56esemcPgEM9dtEOFN8N0a1lEHamLQQlc79qCYrQrKgSWaEyYomWdBMFv2/kgWzFsScYTTGrAZQ0hhwmAhbhncjmhD0uoPUluUjAYfCLe5n2copMNlwB5WJLsI3yDELTDXsU9tx4qdVDNKnuXETiE50H443qFJeIKLzpiIU+xxce2In3tJC/lBaws9NHtRZF9BUs4CVJ96i0tPLipPbmdNzlJ1z30NvaW3CNW0LR22W5FXH7Z/T2KFHbU5WNSJbT3Xkq7QUYEIiJFRcWhBnfg32iiW8d+AA7+/aTndeOYO2cP8bv5flPfuZ33UIc9hQ3VYxi30NK/HbCrng6GZqB1t5z9HNlPoG2dewkvhNpLLDpKmUe/Sob3N1I3O7D2NXgpR5+xksHv38un7Fh1MN5K7/kND0njeV83JzvAlkeFoKzpyKKSNyY5ARJi2IVfFgkx3khfrJD3RR6G+j2NtMqecY5e5DlHibMj7uaH039jT8NsMJqRoDXv28BwN6WH5RXjVATiveDCYHNsnCwnD1107lJAe0DgAusMzFFmeshCZG+rMsSog1h17Fqsp8WrEz01JMQCj81XmAwcJK/nnWB9k+70KCljxK/A4u2b+BCw6/Rn5wZFTRowbZ69dN9xcUjhRXhX4XtYO6+DlSuzTmObNkojLcA6dH9uAJKmyZtpSW/CpsQuHDHVvIV4Ks7t/PdXufZWHnfsxCpadkOhvP+iBvLbocd0E5isXGlkWXc3DmcgAWt+9hzaGNWJTsqxeHU+7pwyw0ApY8PHkl9Bfrn7FcpaYAmnMdvTlNfDfx0lJnyhgGQ9wYTApGJW6EwBYecpgproBCT8BLq+wAYFFelZ7KS7dJocGUYpF5BmYk3AT0NJJUkXRGlS+k4IqUVwvBBUc3Uxxw4bMV8s6Cy/hI+VlYMNEcGmSnvx0kieaaBby88jrdI4NEQ38T7393PfM6D8Yce4evHRXBTGsJdbaR5vXGzv1IQGdZHZ78kc9Xh1NTHSEXsqohJBMv1K7Ga7ZTHXRy2/G/clHfPmyazGBhBa8vfh+bl17NQFhcRJFM7Jt1Hlsb16JKZmoHW7ls7wsU5qhqKCJi+kuqQZKi4mZaDsXNyZAjty0xXB05aQY60cQTNz7Fh6wmrio9XTDEjcGkwKxmL25sigcS9C9Jh11hP0CttYQScx6+UOZVZgZTg3zJynyTfnG1YmaVZU7KfSL+rKWtO6kdbEU1mdmy6HKCtnwqLAVcFh6o+YrrqF4eDsgWO+/OXc0rKz5EX3E1Fk1hZdNbLG3ZCUKgCI13fG1A/KiNVQkyp+coAEdql8VdV6QcvCt4KvXgseTzwoz3IAAzGoPWIl6cuZpXl3+Y7vK6pKMFWqrm8dqydfhtBZT6HVy+529UOTtSvj+piPht+sLN8frGIHIT1BTas4jeJkQN6ZPCpzCqphJIYA84EzoVG+LGYFJg0bKvmLJkGbWJcFLVq2dOpaTO7KhNoeJnmbOJRa4W5ng6menrZVrQQbHsxa6GkMZo/MB4sdxczxzTNC6yNKYcpgm6h760/ShL2vTOze/MXYOj6NQAzfML6qmzlhISKi84DyKGRBCchZW8tuwD7G1YCcCStl2saN7Gfn8XXi1EicnO4rxhkRRgbvdhLJqCo6CcntKR5eFwqhy8d5iR/mThdP5SdzEvzLiAX8+5mv1FDaT7Gz1YXMWryz9Ef9E07EqQi/f/XY84ZRsVESJaKRWJ2AwWVaEhURDykh/MXVlyzo3FU7xqyqskLrA4E8SNYSg2mBSMKnITJ/SaLkGh0BUu560V5Wjo1WVnMld3bmOuL/nYjJBkIWC2srViMe+W53YUwViTJ1m50JL+mqcFHbyvfSsAR2YspaV6fszzJkniQ2VL+GXvVo4H+9nj72RFwRATsSTxTtUyHKqJi9rfobFzP3usHjDDqsL6EXO7JE1jfjiFdXTG0oTRlkhayil8aELEDNttKowVRLKqYbakN5srYCtg09J1nHd8C7P6jrOy6S3yZB/7G85Na/+hFAVc5CkBVMnMYFgQqmYLjsJKKrx9THP30GrPzQDMHtmDTw1RYM5RfyZHKzS8JzfHmgDiVUpFGGtx45W9FFqzLxDJBUbkxmBSkHU5uBBYMygBH067NohAUCblYwpZGPSGxmScxVTBroaY5dPTCK35VXTZyxm0FuE121GGzjoSCiWKn0t7d1EyCnE52clTg3yk/Q1sQqG5oIa3as+Ju900SyGXFM8F4O+uI7iHiPVBv0yvJ8hbRfN4afr5bMvLo8kssAmYpVbg8Mv4Qkq0ZLtuoJmCkJeANY+WqrkJ11ZmzseMCRWBh+SRz6CcWbRNM1vY1ngxe8KCZmH7PixZ+DQiqafBokq0IT18+krCqSlX7lJTAmgK5dAsG/KAL8fRoHEknt8mwlhXTDU7m8f0+OlgRG4MJg3ZlINbFN+o/DYt4YZuDZI+GTtRZ9ozhTneLswI+mwl/F/DZSOeN2sqNk3Bpslc3bWdBn8Pa3t387fa907AascWSWhc0/EW5bIXh7WQv9auJuRVqLVasZpGRlNWFzZwMNBDh+ziRedBPla+gn5vKDqlG2Bf6Ry22xxAgGvdbq7pfZPna9+LGr7w280mLm3fB8Dx6YvRTIm/ojUBpVI+A8KLQ/gokfITbpvVGAZJ4vDM5czpOUpxwMWMwVZapyUWW/EY7reJ0F9cDZ0Hos/niubgAEvyqjMeGyKEiL+PowUKEhvOJzPxysAjuEIuNKGNmGGWC5xBJ/2B/tQbjjFG5MZg0pBNxZRVzT5nLwuVDuEASFoxcyYx39MOwLGimXGfV01m/BY7TlsR/6w+GwEsdrdS68/tIMnJwMW9e5nj6yYkmXm2dg0Bsz3aHylebM8kmfhQ6RJMSBwJ9vHmYFuMsAFwiwCHzHqU5WNuH/O9HVzX/i+s4cG0lZ4epnn7UCRTTNO+eHiCCmWSXg7uEP6k22Y9QFOSaKucDUBdX3PGu0f9NiWxvqL+sNgp9Q5gzmHljk+T6VYy+044EuhNvM8U9t0ki9yoQsUdGp1XMRHNruYxOW6mGOLGYNKQjbixZfhFNpQO4UBFUISd8vBFYjwxCY3z+w8yPTA5Qt8moTHXq1eIHCtK3HguQk9eOXtL9Wqjy3rezd50OgmZ4+nkgsFDALw8/Xx6805Nhg8qKg5f/AtytbWICwv192RL8Dh+Edsv5pCqv7+1Uhk7a95LSLIw29fNx1o3Y1dDnDdwGIADJbM4GZCSDuLVxY0erXGI5CMfZFXLugKwLTzParqjNakQUYf9+1uVIKV+BwD9RbHixm8vxGcrxISgwhMrjDUE2a82M2PxsUAfu32d0Sq3EXj7YBxmb40FyTw3MDapKU1onHSdzPlxs8EQNwaThmxMxZZR+D1aNP1LsMFUMSHTr892HOOSvj1c0/HWpBAGdf5e8jQZr9lOZ156kax/TTuLkGShNjDAEvfk+FIbLQVKgHVduoF4R1kjh0tGlmo7/KG4xvOQqjFHqaZcKiCIEp02DhASp6aSLzbPoLWgmj/VX4LfZGNmoJ9PtvyTBeHI2TvlC/DLKu0O/4joD0BAUZFVbUjkJvmFTJB99MZRWIHHXoxFU5kx2BZ3m4Ci0ueJ/fxG/DbuvBKCtpEps6jvZlhqyu1XGEgw3DYdOmQXQS11evlEcIB3fXqpuzvZd88Ujd4kS0vB2DTza/e0ExxFcUguMcSNwaQh03JwsxrEJLILaatCo13TP9wNpsqsjjEqhGDloN7HpFz2MGMS5KjnefQv+hOFMxBp5uK9lnzerlwMwMW9e7CkcVGZ1AjB1V3bKFSD9NpK2VS1IuGmfZ5gTLTCL6t0OgNoQuK95vlISJzU+jkZ9nUd03pQ0CglnxnhifOd+ZU8XX8pXrOdqpATE4Kmghr67GVAeEyIJ0i3O/ZcnrA3LCJuXCKAmqJEPyvfDeipqWmzAajrb467yYBXxhdSCQyZTxcRN8P9NhEiqalpQ0zFqhA4/KcG+maDJgTNKYzFzcFBdnhPCbWk4sYx9cSNrMrIWvLvxrGI3EwGI3EEQ9wYTBoyjdxYR5GS6hJOZFTysTJNyk0paibM8XZRMeTOaslEh3KFYH5Y3CTy2yTinfIFOC0FlCh+VoXTKlOVlY6jzPN2okgm/lb7nqjRNx6KpgsPAHdQptsViFbaVZgKWWbSU3vblCb8QuaQqpfXLzLPiIkU9uaV8ceGy3GFxylsqxjptfGFFNoH/fhCChoCb7gXUwE2rJgRCFwi+c1BaNhg3COBPn7Z+zZdcmrvRSQ1NWOwFbMaKzq8IYVg+NgD3lMRl2kuPSIz3G8TIaaZX/h9c/hOVSv2eUJJ03LJSJaaag05eMcbK1hcyW6s3B2gTi3RnipqA7kXNz7ZR7cvtwbx0WCIG4NJQ6bl4KMRN5GUVP0EpaRWOvSoTXf4Dn2xuxXTBDbHqwi5KZc9KJKJ5sL4d9qJUEwWNocjHBcMHKRISW5unaxMCzq4pFdv1LepakU0epIMf0ilyxmgzxMa4RI5y1xHqZRPAJl/yPvwEsSGhbmmaSOOM2gr5qnZ7+N/Gq7gZOH0uOdShaDbHaTLGYwKAEmS0vbdBJXYFW72HKdb8UQ7JSdjsLASr70Ii6ZQ44jdfqj/KKhoeIIKkqZR4dHntfUNH/cQxllQgWKyYFNDFPsdyJqGe0i1oiYEfe7sUhxuNUhfnCZ2bSEn27ytI/6tQpqaOJWlqeBqz2odE0UyM3GEgBrAn8PParOrGTGJersb4sZgUpHJNPFsxY0mBK1Rv03mKSmTUDFn05MnTHnIzTxvJwL42wx9FlCBGmS2N3njvLEkUiXVUlCNbLJmvP+h4nra8yqxCZWLevfkenljjkVTuKbjbSxC43jhDHaWpd/kz6/E/10wS6Zwegpc4T40C0w1WKT40aCA2U5Xfurfx+Cw85WGU1PONEzFEQYVH53hiE1aQyeHVk0NSU15gsqIdNegP0SpbwCLphKy2HDnl8U9pDCZGCjSp4JPc/cw4JVHXBr9iorDn13qeXj0piPkYqu3JWEfK1ey754p5rtJR9xAbqM3kyklBYa4MZhkpFsxJWlKRkJoKL3CRRAFGxZqpOKM9jVrKp8/8SI3nPxH1v6Sc8JemxOFMxiwl3CwWDesTmRqar43nJIqzCwlFUWS+Ge13uBumauZmgmoADMJjaXOJi7p2UVhhnekF/fuoSrkxGu28/L085POYMqEaaYiloTTUxISC82ZRcXSIV1TsSZEVIgcCJzyuQyo/uQX9jARcVM72IpJUxDoQmY4iioo6td/n/qLqqPv5T5/Fz/reTM6CR1OmYrLnF34EnhsHL4QgSzM0G0hJ3K4B1aX7OZt78mkDTrdyb57nG2TwvSfLj4lvQqvXHUq7vZ2Jx33MBEY4sZgUmFO01QcKQEXQtChOfCK9MPXp1JS5Rk3saoIuSlR/FSFXFn5S2yazFmuJgB2hMcWHCiZBUCjpx1bChPgWJCvBJgZ7lNzPI0S8ER05ldyoLgBCbisZ9e4XQwsmsI5g0f53IkX+UDXNs4fPMxnm15mmfNEWmuY4+nkvHCa8KXpF+Cz5OV0fcvNdSww1XC+eTYFkj2nxwbS7nUDp6I3B/yx3ojmNCpnBoqq8NkKsaoyNY4OXAEZRY3//laE/TZ9JTVoQuMfriM849hHv+pjl+/UMM7IvKlknYoFunk70/JwRWi0hpz0yB7e9DSPKFUfjiuZ50/2g7c3o/NPJOlGbnIlbprC32mTCUPcGEwqLGmaiq1hw9xerZ2NykFekPcwoKX+QAshYvw2mVIy5I4oG3/JUmczdk1hwFpMc4HurejKq2DAWoxVqMx3j39uf663Ewnd/+O2jq7fz+aq5ciSmXp/L42esX0tNjXEBf0H+cKJF7iyZyelig+POY8eeyl5msy6ru18tO31pOMhhpZ9v1PWSFNR/CGVo8EimbnAMpcF5vhemtES8dy4CaCk6NYdVDQGFB+dihsJiXPydTHbFEo3NaUL8Zn9TTgT9PpBiGhTx5NFFfzvwC7e9rZEn+4Zkk6O9L8pD7nJVxJ/9mVVoz+L8vDDgR62pCFsIEVaCvRuxVOEdAzFkBtxE1JDdHhGPz0+1xjixmBSkW5ayqp6aNH62a3qufAQCq8oB1IKnH7hxUcICyZqpbKM11c85EKZsb9EiKiReGf5/FOpD0mKRm+WTkBqKtsqqXi4rYVsr1gIwCW9u0blTUpEgRLgot49fPHEC6zt20OhGsRhLeQf1efyy7kf5Hez3semactRJBNzfF3c3LRBTwUOv8ANK/venKTsezKThxV7eJKOM41OxZGU1GxbOYvz9TRZqtLpCNHU1EBLwgqiEsVHseJnv83GfwabaQoNYJXMfLh0CQBeLYRX1YVKyGqn314CwMxA8i7XnizKwz1qCCVNo37ScnCA3kN6BGcK4E9znZ6QB2WU7RtOuk6ijmIEzlhhiJtJhl+E2Kqc4E+hbRxRJ09ZXS7RhGCv2s4BtQMx7IKTTjm4pGm4Q71sUY4BMN9UzTSpKC2BE5klNdNUPmIaczpEIjdtebrx86wM/CWzfd1UhtwETRb2lcyJeS4ibmb5ujP2i4wGs6YyJ2xkTqcrcTpsrViEx5xHueyNirlcUCz7uKx7J1848QKrBw5i12T6bCW8MP0C/nvOOnaVz0c1mRGSiW2Vi3lq9lW05U/DJhSu7NnJJ1pfo3xIy/lMyr4nM3rFVPpjGA6GU1JL8qtpsJZhQsKpBhhM4/euv7gGvzUfuypHB6wOZ6a/j5cKC7ixtganFqTcnM9nK1exoqCWcrMeZYpEb1wBmbY8vXosnREeoykPT4VPk5MLIVWG9p1jcu5c4lf8KCI9wSIQozYVNzknX0oKDHEzaZCFym6llefkdzmidRNC5ah2+okbWai8phxil9rCDvUkW9RjaEO+UNIpB9fkfl5TDqGgMV0q5QLzXC63LE4pcIampBqk7GZJlYZbmh8prmd/sS5I0vWXRJr27SuZQ8gcW5HksBXRnleJCcEi1/iFv+v9PdiEgtuST7e9PPUOaSCbrLxedRYA7+0/QL6SnfF7KGUhNzc3b+A8x1GsQqUzr4Jnatfwm9nv50DpbLQ4QnXAVsIf6y/jleqVhCQL9f5ebmr+O6sGDlEdGMy47Hsyk66p2KH5oympRfZqbCYzM6165KQ5zdTU8dJ6ABa4R5aQa0LwOl3cVT2NoCRRK5Xx6bLzqLbqvaSqLfp/exQPmhA4fTLt4Qqxmf7UjSwjs73GipTRm/6jk35SeLp+mwjOUPbiZiAwMKr9xxJD3EwwmtA4rHbxnPwue7Q2FDQqJH0y9oDwEkpTgU8FfCLEP5T9dAgHZkxIQJPWx2blSIxXIFkVlCZUdrg24iVIMXYutjRikiRskoXLLYupTCJwnMKPmwAmJGaasruQR9JSLmsBrw/xlyzwJO8VUhbyMC9ckbSzPH6Z8USkpiIpqeOFtTmrEAJdwHXby7BrMhf27xvdwYTgfd07sGsy3fYy/lS3lt83XMGx4rrUa5Yk3i1v5Ddz3k9zQQ1WoXJp724+ffKVrMq+JyvpiptIt+TZtnIKzTb9z3Zd6KdjKlY0wb58PX3Z6GmP6c0UFDIblYNsyNMfu1gp5lLLIvz+U8I/InJ6ZA8Ov4wqBB35euRmemAAUxrpjcAoysNTkdJ3IwS0bR+Tc+eKVDOlhuMIOLI+12SN2oAhbnKGoikMBh0j0iyJEEJwUuvnr/JutqlNBJApJo+LLQtYZzmLYvSKjV4xNpNbxxuH5uNleS8DwosdC++zLGGtZSFmJNrEIBuVg1Ehl8x3c9D1L/rUPqyYucS6CLt0KgJikyxckUTgtAj9jmuGVIY1Qa+RVETSUi5LAW5rQbSb7CW9u5P6S85xHEUCThROZ9AWv/z8UHE9GhLTg4NUBF1ZrS8jYroS5yYlFUWS2BguDV/hOMGMUUwNX+I+yWxfN7Jk5vnaNXqTuwyFmMtayJ/r1vJyzSoCJr2nb67LvieSdBv5RcTN0vxTJemzbbrQbw4Npvz+cvhCtOZPw2u2k6+FaPDp/p1BzcuL8l66hJN8TeOR7l6W2uZhkiQCihr1ykQiN92KB1dAFygD1mJ8ZhtWoVKT5oXW4QvhCSr4Zf3Y7qCCKyDj8MsM+mT6vUF6PfpPOmbiCEnLwSO4Oib1SIZ0zcQRsk1LKZpCq3vyvg+GuMkRzc5mPrX5Zv4ob+W50Lu8Ih/gTeUYu5VWjqk9dGoOXMKPIlS6NRcblH28rhzBTQA7Fs43z+FD1hXMMlUiSRLVJv0C2K2Nw0VujOnUnGxQ9uEjRAl5XG09i2mmYupNFVxuWYIVMz3CzSvKAfxCTlgO3uLbS6tfjwJcaGmM3q0OZbjAeXWIwIn4bRqyqJICvY9KUTjFEqkq2laxCLclnzLZy7mDR+LuZ9Vkzgrf4SSLEvgteTSFu9OOxxDK6qCDEsWHLJlpKYjfRXY0tBVUc6C4AROCD3ZuzarMPU8NcmnPLgDeqlyCwzaKURmSxN6yufxm9vvZUrmUP9etzXnZ90QRaeTnI5Qw2usSfgaEFwlYlFcVfbzOVooZE24tyICaWByFVL37sJBMHC2qA2ChuxUhBP9SjuIlSLmw8L8d3VwQkvBYTg3LHPTpDfqqLXpUulf2okW8M5JER9h3MzNNESyAXk+QLleAHneQPk+Qfm+IQV8Ihz+EK6DgCeo/Pa5g2kXkScvBh9K2HbSJ6yiejEzTUtlWTLW521LOr5pIDHGTI3r9vUhIaAjcBOgSTo5rvezR2nhLPc6rykGel3fxf/I2/qHsp094sGBiuamOj1hXstA8PabnSo2k58G7xdQWNyfUXv6pHERGpVoq5v3WZRRLpy4oNaYS3mdZih0LA8LLP+R9BOWR4fH+YBsHXf8C4BxzA3VJ0kqnBE4hwbDAadUGGBQ+JEi6bzKKFD8mBIpkwmvWX4NssvD6NN1fsnrgAAVx/CVLXSfJ02QGrUWcKExeary/ZDYQbug3xn1iIoMymwuno5gsY3KOV2rOxWkpoFz2cFn3uxnvv7ZXr4bqs5WwLVyFNVo81gK2TFtGb15uPEaTAbtkoQA9zZTIVHwy7DebYSqjwGSLPm6VzNTZ9EGeTUlSUw7fqQ7Ch4p1302jp50BzY0TP2ZMfNtjp1GWac+PHTEhqxruoEyFpQAzEjIqXk4Jicj26ZiKMyXexPJEpNPMUD+oE/om5xy1TNNSilDwhDLv9t7sas54n/HEEDc5YnXtap674mmutZ7D+yxLWGOez9nmehpN1dRKZZRK+VjCb7eE3ob9Wus5rLDUx02RVJt0cdMvvNEum1MJIQR71TbdMIxglqmSKyxLYtJIESpMhbzfuoxCbLgI8Jr/TTzKKdOeT3Gyy7EBgUadtYGlptQpFF3gLIkKnE2K/kVUI5WQF2cN6RDpl+K2FMSkMvaXzKYzrxy7pnBh397YnYZM/95Z3pgyBXKsqJaQZKFM9lI7xpPC53v1PjTHCnOckhpC0GzjxRnvQUNiuauJhRmEsWf6elnhPAHAP2rOQ8sylXimEElNJRrDEI1cShUjOvUOTU3FI6hoMWXYrQVV+Mw2CtQgvSH937TeVM7CsNm2LX/k/CyHV0ZCojSaQjslwiKm4jp/35iIek9QwZmGT8erhZJ2MY6hYxcomffeGWsyjdxA5tEbd8hNr39yNzU0xE0OsZqsFEt51JhKmWuu4ixzHe+xzONy62I+ZD2b663n8zHrKj5mXcUFlrnkS7aExyrCTgE2BII+kf2AyGQEhUKLNsA2pYkX5T3sUXOTP9WExtvqCXaFj7fENIOLzI1JS69LpHyusi6jhHx8IsjW/mdwyt0oWoidjheRRYASSzXn25akPehyqMCJkM0sqegaw3dEruGN7iSJ16p0f8lyZxNVQ3wDDb4epoVchCQLe8NRmWQoJgtHivWQ/1iOYyhS/MwIDCIYXVfidGgrqOLtisUAXNW1neI07ixNQuN93TsA2FM6h7aCqhR7GCQzFQ9NSdWbKkbMg5odrpQ7mcB3M+iLvYhHUlMKcFjSo8tzpcqoIO+II25UIeh2BSkJr3NwyDq78ipQkShSAzGNMnPJgC+Usk+OJgSeNHttoQSgc3cOVpY7NKGlPXphKJn6bibbHKl4GOJmHJEkCbtkwSalTgFIkkRNOHrTkyPfjSJUOjQHO5WTvCTv4f/J29msHOaw1sWA8LJbbaNdS6+ZVyL0Uu/DHNN6kIDzzXM41zI7LUFSKNm5yrqUSqkQWQTYNvAcOwb/hkcZwG4qYGX5OvIz7AETETjVUjF5WEcnboaYiYfTVlDFoeJ6TAgu7d0Vvfs8N9znZV/pbELmxGJ2KJGqqUXulrSqR7IhkpLqzKsYF9/Jm9OW0plXQZ4m84HOrUgpGqutGjhMVciJz2xn0xRtrjfelCYRN5GU1HSplDzJSmjYrKY6aykWTHi1EL3DZgR5Q7pxdziHi+vYmp+HywR2LJwlS9g1hZBkoddeGneNAUWlPM46FZOF7nCaMF3fTTb0eUIjhN1wUpaDD6XnAAQmj3XAr/izmsydSeRGE9qkT0mBIW4mNdU58N0EhMwetY1/yPv5k7ydjcpB9msd9AsvAighjwWmGmaFL/pvKccJZll+LoTgTeVYtNR7rWUhCzNsOZ8nWbnSspRKywxUITMod2LCzDll6yjEhklkHga2SRbeZ1nKddZzyc8yJQWn0lIua2Hc5zeHu+LO9nUzz9tBacgTnbadSbnxyYJqPOY8CtRQtMFerpmXw67E6aBJJl6Y8R5CkoUGf0/SuVylIQ/v7d8PwD+rziZgzv08ptORZI38IimpyOc8OEzcmCUTDbYyILbfTUBR6U3gV2kpqOH5Ir3wYZlWQINf368jvxKRJEqbKMIUifaMpbjRhKDbFUhaQZWRuBEatL+Tg5XlhmxSUpCZuOnydhHIcmjxeGKIm0lMJHLTJ9yoabYQH852tYndaivdwoWGoAAbc01VrDHP5zrrSj5sO4cLLHN5r3keJeThR2abml3vgkNaFy1iAAmJyy2Ls5rdBLrB8b1FFzM9rxETZpaVXkaZbTpWJfv0nCRJmEZZ8huN3CSYv+S0FfFOuW56vbRnF+c6jui9fApqGAi3mE8HIZk4WDJ2k8ItmhLtLpvzEvAkDNqK2Vijp+8u6tsbv7OzEFzZswOrUDlZUB2NYhmkJuJlCSATEKf8JcNTUgBynCnbkdRUpN9NSNX0SqMEOiCI4J+F+jmv9viiomS4mXg4EXHjEv6YBp7t4yBuQO/V0+1KPIjTlebw3iiDJ8E9NjchmZJpGXgEn+IjpKa+cXQGnRzoP5DVOcYbQ9xMYkrIw44FFUF/Fr4bRai0hdNM55gb+LD1bP7NupI1lvnMNVfFTCi2SGbea5mPBDRrfdF+GOnSq7nZqeoX4nPNs6LCLFtsQubssqu4vOZz1ObrgmE04iYXRD03cdJSEd6uWIzXbKdC9nDeUCNxhkQu6vM9HdjU3JZbzvZ1YxUqTksBfbb46YOxYm/JHA4X1WFGcE3H21iHzbVZ6G5lrrcLRTLxj5rzToseNOOFVTJThP6ZHhoVGZ6SAl24DL+0z7aFm/mFBlE0jW538ghHmzZIUII6WWado4O6sME0npl4KIXYsGJGQ+ASp4RERNxUBZ1ZtQ3IhKCi0ueOfzHPKHIToXXbKFeUGzKtlBpKsuiNO+Rma+dWXjn5CoNpNHucDBjiZhIjSdKoSsI7hRMFjQJsLDXVUiLlJ/W+VJmKWWrS0xRblRP400wBBYXMv5QjaAgapAoWmUY//TgyY8o8xJ80oeJGiCGG4vhpKYCQ2cq/wqXhAIPWwpTl3/HotpfTb9MnhTem6H6cKZFU2bGimeMvHiSJv08/D7clnwrZzaU9p8rD7WqIy8N/f7ticcJmhwaJiZfyOanpkZBZQ/xmAr08eyi11mJskpmAUDjgHEBRk3s3mjRdzFzlDVCsBihVfGhIdOYl97Xps7BGNh30WPJxWgowIZjuH/sRB96QwmCcCqq0e90MxdcPfcdysKrRkW1aCuKbir2yl+1d2/l7899pcbdk5eeZKAxxM8mpHoWpeGjTunQrjJab6yiXCgii8LZyImXHUiEEW5RjeAlRTB6rLfPSPlcyhg/QlDQVszpxE3nztBC2sBfJPaQ5WTz2luqjBwDeLWtM6j9IyFhNCheCuZ5OYHxTUkMJmO28OP0CBHC28wTzwzOKLurbS5EaYMBazNZwdZVBZgz33biEP9rfaXjzyuG+G9MQ301rnF5TQwkImQ7hAGCJOCVCe+2lI+amxSOR+TkSvakb49RUhEin46EoQsOXTeSoY2fCSenjRbZpKYiN3PhkHzu7d7KhaQPNruYpJWoiGOJmkhOJ3PQId/r9FwBVaNGUVCYVQmbJxBrzfEzhsQgntOS9DPZrHbQLByYkLrYsSKsSLK11DMt7T5aUlNdsT9nwTkgm1s+8iJemn8+OLFJSEQ4Un5oUXpSjSeEzAgMUqQGCJgutE1he3VJYw7ZyfXTF+7u30+hu4xyHfuf7j+nnTtkJ3RPN8IhIJL08XSod0WNKVkdWQFWhf990pbiZOqn1I4AKqRBPQUP08VR+mwjlccrBh+4/Fs38EtHnDRJQYt+LtJv5DSXkhe69qbcbQ0aTlnIGnQSUALt6drGheQPHncfRmJxdmNPBEDeTnDKpABtmFDQGRfohx27hIoRKHlaqpMzC++WmQlaY9Q6k29VmvCJ+mLZbc7JL1SdYn2+eQ4UpcbomUyShIg3xY9gmWtykMBMPx2MtYF/pnOyiNmGctiLa8ir1dvk5mhQeqZJqKpwx4U3x3pi2jG57GQVqiI90bEEC9pXMpqWgJuW+BvEZmpaKzK+D2JRUhKAce7Pk8MuUq+HBlsKV9GYqkpKaY5pGc2ENofBNTUd+ejdSkXUObzgYNRUH+lO2C8gVQkC3KxhTIp6V7waga9+YdxZPhKIpo6picgadvNz0MkcdR1GnYOPY4RjiZpJjkiSqsvDdRL7U6k0VWVUJLTHVUiUVIaPypnJ8RHrKL0L8SzmKAOaaqphvyv1soqGpKcsEi5viqJk4dwIuHQ6EG/8tdTWP+steEhqNnrHvSpwuqsnM32asRg6LLL/JxmtGT5tRUSLlIwEyKt3ClTAlBcRczD1BhUFfiHKpEBtmZFQGEhQxuEWAXuFBAmabpqGYLLxWfTaHi+qiM6dSERE3boIxHdh77aUETRbsmkxVlgMds0ETgi5nIPqeZC1uNAXkiUmfj8ZvA6ChoWTZBmQyYoibKUBNhkM0NSFoDVdIZD0kUpJ4r2U+Zkx0CSeHtVOljpoQvKEcxY9MqZTP+eY5OfHZDMcS7hQqCQ3rGHUtTZfSaI+b9CI3ueJQST0qEjVBB188/jcu7XmX6sBgRneHVQEHl/Ts4tYTL1AVcqIhcaIoc5PzWDBgL+HvNefhN9n4x/Tz8J8mgywnCrNkogQ9NbU73CF8RpyUFOifY1nTYmYvmSQp2l+rK8HNVFPYoDxdKqUg3GV9d9k8np+5BjnNGWV5kpU89DU5h/TlEZKJtnw9XVofnjg+XqjhHjiyJtKbDp6I0OhERraMJiV1OjI20/IMckr1EN+NECKlkOgVLoIo2DAzXcq+JLtEymeluYHtajM71RZqTWWUSPnsUVvpEi4smFhrWRB3NlYuiPhuLIoPJjj3m6w78VgSMNt5peZcLg6bbVcNHmHV4BF6baXsL5nFgZJZeOIIrkLFzxLXSZa4TlIzxCjoN9n417SzJlVjvAOlszlQOnuilzFlyMeKn8SG11IpH6fw0yPcQHLPnSeo4vLLMXbR6aZS2tRBujUny8yxTR6FEDSpp1JSo6FMKqBLOHEIH9M4Ne29Nb+Ked5OGvy97CA3w1LTRe+BEyC/bBSXRtkLJPazCSEYCAxQmWYKL11GG7k53TDEzRSgUirEjIkQCg7ho1xKnhqJ9LWoM1XETBrPhoWm6bRqg3QJJ28qx1hmrmOvpqc23mOeF616GAsiaamJNhPDkLRUkjLwsWJP2Tz2lc5hjreTpc5m5ns7qAo5uaRvD2v79nCyoIb9JbNoKpzBbG8XS10nmeXrxhS+ZKmYOF40g/0lszlROMMw605xZprKOaYljmqUmQpoUfXvAAkpafTW4RvZ7mF6NA2uNw8dOhNuQHhxEcCMlHWTzug6h4ibobQW6CnuOl+vHqEc53YFsqpx0uHBWxyi0JreyJQYUkRu9vXtI6AGci9uFEPcDMUQN1MAk2SiSiqmSzjpEW7KSXyBFTlISQ1FkiTea5nH3+Td9AoPm5RDgD7VfI55dHduqZhM4iYSuXGPc1oqgiaZOF40k+NFM7GrIRa6W1nqOkm9v5fZvm5mhzsOD6Utr5IDpbM5VFw/qSI1BtmTj5WKFDc3ZUNuOGZIJXFTUqn2t2MhiEK/8EQjxwAnwimpOlPFqCsjI5VdwyumuvPKCUkW8rUQVUEHveGZU+NJSNV4t3uA98yswZKpuAolTg91ebs4NHiIEtvompzGwztB6bDJiiFupgg1phK6VCfdwsVCEjfJ6xMefISwYKJWKsvJuQslO6vMs3lTPR4t/zzPPDsnx05GJC1lneA7EpNQo6XYznFOS8UjaLaxp2wee8rmURrysMR9kqXOk1TIbgatReF01WwctqLUBzOYUlRJJdhSfG0PFTezskgdRZqHtogBuoSL6nB5uCZEtCHgaFNScKocfHjFlCaZaMufxlxfF/X+3gkRNwB9QR9Hu9wsmF6MOROBE4p/M+aTfWzr0jsZu0NuZE3Gasp+1t1wjMhNLIa4mSJE+91orqS+m5ZoSqo8Jpw8WuaaqugRbno0NxdbFuT02ImQhIpNdiNNsIO/WPbrFSiSGf8ki4A4bUW8VbmUtyqWkK8G9fXlIIyvCTAZkw8mHVWmYsySCQsmlAQ+tGLyKMCGgka9KTthMN1USos6QJfmZLlZr4DqEk78yNiw5OTGKZLS9odnYeUNiTC1FlQz19dFg6+HneULRn2ubPCJEK6AwtEeDwuqi9P/PMQx9goh2Nq5lWA4Gi0QDAYGqS7IXZWp4bmJxRA3U4RpUhEmJPzIuAlEKyKGIoQY0pU4t/lcSZJYbZmX02Omgz2Y2YyrsSCakrIUTN5ZR5KUs0qjtzqWsLd/Dh+e+yY1hVNjjsyZQAE2isLz4GxYUIg/HsUkSayznoWGyDglFWG6qRRU6B3iu4lUSc02Vebk5iYyC8tDEIfwMV06NeespSBSMTUxvhsAf/j9dfpkjvd6mF9dRFqriJMe2te3j75AbGPCgcBAzsRNSA0hj/E8rqmGUQo+RTBLJqZJepohUUn4oPDhIYgZKWcpqYkmL+SY6CVQMkFl4BOBqpnY3z8bRbPwdpcxAmEyMbQZZyq/S75ko1DKPspYQh75WNEQ9Ao3ilCjN065SElFiDcLC6A7ryLqu5kWGr9+N0PxDZmtN+AN0dyfZmRE9sW0aoj4bIYzkMP5WUbUZiSGuJlCnCoJjy9uIl8+tVLZmJVnjz8T3ykz0+7EE0FAsfJuzzz8ShbVHUNo80wjpOl3++2eKjo9ozelG+SGKtMQcTPGQXdJkqgJz7Xr1ly0aYMoaBRiz7jjeTISiRtNMkW7FTeMc7+bCAEUtCGNM3tcQbrdaXQAFiKamhrqsxlOfyB3UenRzJQ6XTHEzRTi1JeNO+7zLSJSJZXblNSZTskEdSfOhH+2nsNbnct4q2PpqI5zwqk39zNJuqh8p3t8+4yMljc7lvC7/e9jIHB6makLscdEYmyM/c1LJE3UJZzRKqk5pmk5bdg5fNDnUGJSUxOAQOAb1k+opd+HK5hG+ifkHeGzGU5ADeSs8Z7RwG8khriZQlRJxUiAlyCeYfOenMKPU/gxIVGXpYnQID5RcTNJIzctriqaXbooOe6cgaJl97HWBNHjXDxzDxIarZ5qurxT4/epy1vOrt5GvEo+27sWTfRycsrQqA2kTkvlgukmXdz0CU90AvjcUbZ/EMPWXT5sFtZQIv1u6v29EzavyTfse1YTcLzHEzO6Ii4hb1yfzXAGArlJTRlpqZEY4mYKYZXM0R4XPcN8Ny1Dpv+OxxffmURJuMRyvLsTp4MqJN7oOCv6d1mzctKV3eDJLm8lfsWO3RxiYUUrC8v19v3vdE9MtUomaAL+1b48+vfjzlocwckbacuU4amgsU5LARRhpwAbGgKBoEIqHHXTzpA1tr9LiZSHCQkZFd8wg3RXXjkhyUyBGpw4300c03ZIERzt8aAl0Vtdrpa4PpvhnI7iRlZljjuOE1CyH+KZCwxxM8WoSTBEsyWHjfsMhiDEhHYnTsW+vjk4gsXkWwIsqWwC4KgjveGFw4mkpOaUdGGWBOfWHEVC0OKeTo+vLFdLHhMODsyi11+GzSRTW9gHSOzsaZzoZeWEIuzRGU4R7OMgbiRJikZvIDdG4uHixiSZKEnQzE+TzFHfzUSlpoaaiofiCSgJDcY+TWZbz7tpHT9n4mYS9bhp87Txkx0/4SPPf2RC12GImylGtelUv5sIHhFgQHiRYNQt0Q1iyVdD2MJTi92WkeX3E4lPtkfTLxdMP8iyymYATrpqCKqZXfyEOCVu5pZ2AFBq99JY3gbAjkkcvQkoVt7uXALA+dMP8Z4ZBwA4MlCPOzS5/s2yYXhKCsA2TgUDQ2fTzc6BuFHMBWjDytMjnYqHm4phSGpqgkzFicQNQK87SM8wg7EQgq2eFoJpGnwHA4Mj0nHZMJk8N+0efTzP4sqJrbY0xM0UI1Ix5SKAP/zBi0RtqqWSmEZYBqMnkpLymPMm3UymrV2LCWlWqvIdLK5ooTLPRbndhSrMnHDWZnSsXn8ZHrkAi0mhrvjUXfK51UcAQZNrBn3+3LeMzwVbuxYTVG1U5LlYNq2J6YWDzCzqRcPErt75E728UROvOmk80lKgNwMtlwpYaJo+InqUDarJhmqO7ceUqGIK9CGaAA0T5LvxE0oqPk72+3AHTzUZbQoN0qd4QUksioaiCAVXKH71a9prVPyoYuKrSiO0u3Vxs6JqxYSuwxA3Uwy7ZIma8CJTf42U1NhRPEnNxD2+Mg4ONABw4cw9SJLe56yxXP9iOTo4M9nuI4hEbWYVd2MxnTJLlud5mF+mH3MyVk71+krZ3z8bgItm7sEk6RciXZTBgf5Z+OTJ1VU6E4rIIz+OqLBIZkzptZQbFXbJygetKzjfMmfUxxKSBWEyZyRuOvMrkMO+m8pRioBs0BAEkkxg1wQc63ETUjUUobHf36U/ocSvkIrHaEvCJ5PfRggRjdycNe2sFFuPLYa4mYJEojfdmgufCNEbFjmGuMk90R43k8hMLAT8q/0sQGJBeSszhnQRbizT00jtnqq0L+p6SkqP9ERSUkM5r0YXCiectfT7c9fjZLQMfR8ay9qYWXTqIjGzqI+aggFUYWZ339yJW+QoqY6TkoowXtGbXKGadJGmDBthUhadMeWP6SsDw303ky81BacMxof8vQS0cBRHk0nqOB7CaJv5TSZx4ww58cgeTJLJSEsZZE6k302PcEUngE+TiigYRUdSg/iURrsTTx4z8RFHHd2+CiwmhdVhf0mEUruPmoIBBBLHHOlFbwaDxTiCRZgklVklIy8gFXlu5pXqd2M7eiaP9+bIYB1dvkr9fajdH/OcJMHK6qOAbroOKNmla6VxiI4kI1nDvKkmbjSzLm5UU2zkpgg7FkxoCNyMrLBpLRiSmpoA4lVMDaffH+DN/rZTDwgBanqpqdGaiieTuImkpGYWzSR/gj2KEy5ufvaznzF79mzy8vK44IIL2LYtfjfHCA6Hgy996UvMmDEDu93OggULeOmll8ZptZOD6vAX3qDwcUzTL0ZG476xYbKlpUKqJdqo77zqIxRaR14MGsNppKNpiptISqq+uBebOf6Q0nPD0ZtjjpmTokFeSLXwZmf4fag5TFGc92F2SRcVeS5kzcq+/szTKjOk0mjrhYmghLykHrqp1vJBDd98DU9LSZKUvJlf/hBT8QT4blJFbgBOan04AqEY/w0JmvcNxxVyoWjZDweeTOKmzaMLvDn/f/b+O8yx9LrPRd9vR+SMQuXQOYfp6cmJwxkmkZREkaIsKtE68rGOaOk8fHyvTdmSrnRt6frYlq/vObZl06aCrUAdWqQCM4ecRHJynukcqrq6ckQVMrD3/WMDqIRcQBW6By+fejgNbAAbYe+9vrV+67c82y9jbpemBDdPPfUUX/va11hcrG/I3he/+EU++9nP8lu/9Vu88sornDx5kve///3MzJROP6bTaR5//HGuX7/Ol770JS5cuMDnP/95+vrq0xfc6tiFhgfrBLFgWj/sTkmqNbRbWerlmf3EszY82ionw1dKbrPPdxOByXQ8wHKq+n6vdUlNlt0mZI8y4pkERFt0Tr04fZBE1oZPX+Vk6GrJbYRY0968MbuHTK52QbiCzIgUKs5z2w3CUmUB9064FDeTXL4clZN02JQRKwQ3i8bWC/WUzdLdOHMpAunS7uytpFpwEzNTTOWtOeZjKXKFAKzGzE1hQnijtFNwU9DbDHuHd3dHqDO4+Vf/6l/xG7/xG8V/m6bJBz7wAd7znvfw4Q9/mMOHD/P2229XeIaN/P7v/z6/9Eu/xKc//WmOHDnCH/zBH+BwOPjCF75QcvsvfOELLCws8JWvfIX777+f4eFhHn74YU6e3F1V9m7Qte7E5xcO3KI5E6E7bKTgTryyLnPz+uwezi8M7Pi+LKecvD5rTWZ/oPctZKm0S6pDTdHnslL41TxvoikHcwkfAoNhz1TFbe+MXADg8lL/rhrkLSTdvDlr6Wge6H2z7OcAsNc3gUdbJZnTeWdhqObXGJaCqEIhKJy7VpoKVwms9FstcyPly+ZiLdApUClzk5NkJuxWZno35kzFqZyBuWrMYmIFNKZpeeAANXdMQeOlKcM0WE7tjsHhZnJGjqlV6xxyywU3X/ziFzl27Fjx31/60pd4+umneeaZZ5ibm+POO+/kt3/7t2t6rnQ6zcsvv8xjjz22tjOSxGOPPcYPf/jDko/5m7/5G+69915+5Vd+hUgkwrFjx/jd3/1dcrnybXCpVIpoNLrh73Ygss5/olOSag2ykcOVs8ody3nNzULSzfcnjvPdG6dZ3uEL/PcnjmKYMgPuaYY80xW3Xeua6q+Yyb+aH7fQ65rHrlQ+GYcdywx5pjB3MXtjmvDszeMYSIx4JhksoRFajyTMovbmtZl95GoYTeFCp1f4AFCFglfsvHbAix29iq3Draa5yclrXV+bdTeVOqZgrTQ1mNj54CaLQcosXTZaNGLF7HmB1UJpqsayFDQe3EzGJkkbtQdRrWQ6Pk3WzGKTbXTlv6/dpK7g5tq1a5w4sWZx/rWvfY2Pf/zj3H///QQCAf75P//nZQOTzczNzZHL5YhENlrFRyIRpqZKryCvXr3Kl770JXK5HF/72tf4jd/4Df7tv/23/It/8S/Kvs7v/d7v4fV6i38DAzu/4m4FkXWZm6FOSaoluPMlqbSQSeY7PW6shPP3Ct6c27m68li0i+vRHiQMHuh9i2qzC/d4J5BFjsWUm/lk+fLG1aXqJan1FLI3Fxf7ayp5NZuryz2Mr4aRRY77e9+q6TEH/eM41QSxrJ3zi9WP/31y14bhkLtRmipl3LeZSsFNzhDkzN0VQ2/GkNYFN2XawVdIki3h2VIQFffHd8nvpkRpyjRNrhpbRc7pnEEya9RcloLGg5vry9cbelwrKJSk+tx9TR2u2ih1BTfZbBZdX0sn/vCHP+S+++4r/ru3t5e5ucqDwraDYRh0dXXxX/7Lf+HMmTN88pOf5J/9s3/GH/zBH5R9zOc+9zmWl5eLfzdu3GjZ/u0kTqFzRh7iDnlw2/NeOpRmQ0kqf7DeWFlbkZxfGKxLx9EoOUPw7ISVMT0evorfVt39VJezxezOpcXSpalYRmcqbgXGIzUGNxHHEoPuaUwkXtnhzqlMTub7+c/hdNclPHptrqyyZHAqfBmAV2f2Y1S46EeEZ8vxFNzh4EYgCFXokipQzqXYMOFLlx7mz88/WlOmaicwhIop1vYlK20sS9mFig0rU7VoJLfs96QtSEbIuHJJAukVomaCN3M3yeyQeV2pjqlpM8pqmZLVajJTl9dNPBsnkd1akqtEMptkKla5lAxWEDYWHSNntPazGl+xxMR9rvbQwNb1y9+7dy9PP/00AGNjY1y8eJGHHnqoeP/4+DjBYG0lklAohCzLTE9vTK9PT0/T3d1d8jE9PT0cOHAAWV47qA8fPszU1BTpdOkoWdd1PB7Phr/d5Nmbx/iLC+9pii38EbmXo3J7/JBuR9bExFb5KWdITMSs37cup0kbKhdqyARslzfn9hTnRxUyJ7VQ8Ly5tNRXcrF7bbkHEEQcCyW7jcpR2IcLCwNEd3C8wSsz+1nNOHCr8WKpqVaOBEaxySmiaWfZFnkFiREpvOV2m1Bxs3OaNq+w16SnKZe5mU94mU96iaZdzCfbw5dos8Zmc+YG1sYwfHNykC9efM+G6fbrdTd6coJvZN7itdwY54zagvLtsllUnDMNRo3y5nuxdA4jU3twA/Vnb8ZWxjCoMp0ceHXmVf7o7T/iqfGn6nr+eilkbvpdjc22azZ1BTe/8iu/wmc+8xl+8Rd/kQ984APcc889HDlypHj/d7/7XU6fPl3Tc2maxpkzZ3jiiSeKtxmGwRNPPMG9995b8jH3338/ly9fxjDWvtCLFy/S09ODpm3fGrzVXF3u5o25vSwkPTw1fnI3sqsd6sBT9LixVvJTcT9ZQ9kQZLwxt6el3+NC0s0L09b8qHu6z6GXadUuxZBnGlXKsJpxMBXbWrqspUuqFN3ORfpdMxhIxdlWrWYp5eTV/CiF+/ve2uCiXAuqnONE2OqqemVmf8nvbFAKlg0qQtLOZW8qedusRxNKSbHz+OraDKiFCiXJnWR9SQogWzK4sY6zlLzEUspV/H0WuGEPM6YofF5bJIV1HIwbzRk8WY3NouKb5mIV52KTWCJRs5EfUHfH1Gh0tK7tzi9Un1LeKPFMvBic3ZKZm1/6pV/i//w//08WFhZ45JFH+PKXv7zh/omJCf7+3//7NT/fZz/7WT7/+c/zx3/8x5w7d45f/uVfJhaL8elPfxqAn/u5n+Nzn/tccftf/uVfZmFhgV/7tV/j4sWLfPWrX+V3f/d3+ZVf+ZV63saukMyqPDW+1tU1thKp2WStw+5QzNzkg5tCSarfNcvhwBiqlGUp5WZ8detqvxlkcjLfvH6WrKHQ75rhUGCsrscrklEMXC5u6ppKZlUm8hfBWktS6znbfR4wubA4yJWlnqrbbwfThKfGTxbF1FZLev0cD11FkzIsJD1ci27MDjvR6RP+so/dKd2NVZKq/bVKtYPfXPd7rKS32kk2Z25MScHcFEgWghtJt7L55zZ1t73p8POLPV0syhStMObNWE0+NNtl/WtkzGzRPLUSq8lsy0TFS8klllJLNW07l5gr/n801ZqGmkLWJmgLYlfbY1htXcGNYRjMzMwwPT3Niy++yL/7d/+ORGKtTvgf/+N/5Md/vPYx55/85Cf5N//m3/Cbv/mbnDp1itdee41vfOMbRZHx2NgYk5NrJ7KBgQG++c1v8uKLL3LixAl+9Vd/lV/7tV/jn/7Tf1rP29gVnp04nvflWOFM14X8bccadk7t0HoKmpuCx814XkxcMLs75LeCjVYIi00Tnhw/yWLKjVNJ8Njgy1VFxKUoTPW+stS7QWB6PdqNgUTAtoxPr98no8e5yOm8juXJ8VOspFtXtrmwOMDN1TCKyPJQfo5WI+hylmOhawC8Mn1gQ/Zmn9SFVOGJHULHQeuzwz5hr8ucb3NpKmeKYukUrMxfO5ArMR+rXDu4pFs6kpur4aJoPWam+BNlmilFYTid4ScZKmqhbhqNe8TUSoos2fxoiOvGPNkaykHJbI5kqnYdTT3BzfXo9Zq2M02zGNwAXFu+VvNr1ENRTNwmWRuoM7j5l//yX/Lrv/7ruN1u+vr6+Pf//t9vO2vymc98htHRUVKpFM8//zx333138b4nn3ySP/qjP9qw/b333stzzz1HMpnkypUr/Pqv//oGDU47cj0a4eLiAAKTRwde5c7IBfx6lETWVnRZ7dB+FIMb1UkyqzKT8AEUp2YXLpTXo91N7xx6e36YS0sDCAzeN/QSDrWx1Wm/aw67kiSZ04vBGRT0NvWXpNZzV885uuyLpHIaT4ydqScDXzOJrMYPCo7M3Rfw1iAitlcIQk6ErqCILDMJfzHjFhZufFL1728nhMW1lqQKbA6EZuI+ssbabQttMsnd2BTIAGTLtINL6gqKYmUYzi8MkjDTfCfzDquk6M4a/NepGQ7FF+mXrEzb+A4EN2BNCI+bKSbN2n1lFqPVxf8FMkampgnhhmkwtlJbFjeajpIx1spnV5dLG15ul+LYBfctGtz8yZ/8Cf/xP/5HvvnNb/KVr3yFv/3bv+VP//RPN2hgOmwkmVV56oZVjjoZvky3cxFZMnlk4DUAzi8McXNdjbxDm2CaxVbwqOLIf0cCvx4tim/9tlUG3NOAaMjevxwzcW+xO+qennfocTWuK5CEyb78MMyCoV8mJzOWL7FtJ7iRhcnjQy+hSlkmYqGWdE/9YOIoyZxO0LZc1pG5gECwX4pwlzLCUakXJ1svqA41zeGgpUF4bvIwwpTYW0JEXIqd0N346xz3sDlzUyhJDbgsP5hY1t4W2eGctPW72CwqlkwVI20FLIe6Xwbg/HKIb2feIUoSJxr/KOEmkssxEJ+hP19GnDSXS7aPN5uYmeKaMVc07KuF5ZVV6ti8piGak7FJUjWWu2bz87gK2qxry9cwmywSXD8J/JbN3IyNjfGhD32o+O/HHnsMIQQTE1snCXew+MHEMWJZOz59lbu61wRdPc5Fjgatlf+TN05u6AzosPs4cilUM4cJrKj2ot5mwL3R1+JE3vr/3PxQU9rCk1mVb42exTBlRjyTnKpyQa+FQmnq6nIPGcMKbHKmjEdbJWjbXg3eq8d5qP91AF6cOshkrLxupV7GV0JcWBwETB7pfx1ZlD8pCwSHpR56JR8AIcnNncowh6WeLeWkO7ouoUkZZhN+xuaPVTXLK+ARdvQWGufpKBVnSZViczv4zXU6KrdqBee7LyoWxYng69lclppPujFSlhYq5L2Eri5j9v4PlklgR+Vx9Sgxu3X/YHwGP3YcaOQwiuMPWsmUscycWXsmBsDIpFlKlBceb6aW0tTocm1CYljT2+zz70MRCquZ1WLA0yzmk/Mkc0kUoRBxRKo/YIeo2+fGZtsYbauqSiZT+5f3bmI02sX5/Mn5PQOvbOnwuKfnHRxKkuW0qy3m9TSDidUg3xm7g+m4b7d3ZVsUxMSrih1DyMUSRv+m4GbQPYNXW21KW7hpwvdunCaaduLRYjw6+ErD+pL1RByLuLUYWUPh+nKEq8u9gJW1acbzH/SPc8B/AxOJ74zeSSq3/QAga0hFAf7x4DUizvKlBxmJY1JfSeO7LsnDnfIwB6Xuoo+KU01xX36K+Lcmh5hJ1R5QtLI05WnACXl95iZrSMWuuD7XHAF7ft7RLgc3hqRtHiUFbHUpnk34yKWsi+M8UVxD/xXZNonI2XlcPYpb2Ji0BcgKyfK7ycbWlaZa3zW1TH0+NACykWZupXmi4lQuxWSs9mzrXNwKbrod3Qx6BoHm624KJakeVw+y1D4SkbqCG9M0+YVf+AU+9rGPFf+SyST/8B/+ww23dYBUTuHJ8VMAnAxdoafEyVmXszzY9wZgmYvNJ9pD/NcIqZzCkzdO8pUrD3BxcYBvXLurLdLhjeJeJyZeTjmIpp1IGPQ6N3pbCLGmvXlzbmRbbeGvze7lWrQHSeR439CLdbV9V0KItUnhY4v7Gc13Cm2nJLWZh/rewKPFWMk4ePLGqW23x788fYDltAunmuDunnNlt1OQOSH3E5DKl3OEEHRLXs7KI+yXIugoHA6Mst+1QsqQ+PxYpOb9rVcTUw+NjHlYn0majgfImTIOJYlPXyWQz8rttqi4VNYGCpmbtahnJu4rZm6uG3Ok1VnMrIPY2C+h5bwAZCWFSZslmB6Iz2zQ3TS73NIMJCPNYjxNJlebdGM5tVzRbG8sWpu3TYFC5iZkDzHitUrnzdbdtGNJCuoMbn7+53+erq6uDeMMfuZnfobe3t4Nt3WwtAKxjB2vtspdPeX9BfZ4JxnxTGIg8eT49i8KO41pwpWlHv78/Ht5Z2EYsAzuYll70U22URaSbl6aPtCUTEC9eNd53BRKUhHnAlqJgONQYAxFyrKY8jSsn5pcDfDcpOUZ9UDvW3Q5mjMMz4udfVIXH8830FxY8ZE2FJxKkoijeUJMTc7y+NBLSBhcWe7j/MJgw8+1kHTz6sx+AB7se7PkZw5W1uKUPFBzxkMSgl7Jx13yCMfkXv63oRlUYfBG1MUzC7VlNzzCjtKiadwNZW7WCYoLv70+1yxCUCw5zu+yqHhz+amAKaQN/jez64IbABUZfeYnySV7ubjOZXvMkZ8zFZ+hW3hRkEiQ2TLjqR2QjAwmML9aW0OAgcFiqvxxWau3TYFicOMIscdrDZsdXR5tqltx0bzP3R7mfQXqumr84R/+Yav247ZibCXMuYVhrHLUq6hS+R+SEPBg3xuMr4aZjgd4e36YY6HrO7Wr22I1Y+OZ8RNcyw9f9OkrPNL/OpIw+PLlB7mwOMhe7wTD3spDHks+d9rGX1+5n0RWZybu44PDLzSlhFIr692JCyWpzXqbArqc5ZD/Bm/Nj/Dm3B763fWNIIlnNL41eicmEvt9NzgavL6tffdiJyy5CQnXmp7EYTBoTzKWsEoBd/lXUIREro5VYDUijiXu6jnHc5NHeWbiOMdcKfbYwYZKDoMcBgam9d+mkb/NxMAgQ45lM0HWNHjyxsniYMxy2SU7Kifkgbo1KgCSkKzxBrYMH++d489vdvHHNyKc9MTwqpVP+pIQBIWT6SZrPGSkkgLoaqz3uRlfsYKbXvciSS2I32EJ3xeSHkyTHT1+1lNKTFwgK9vQjBRZQ2Ih6cHAhQ2NHDneqxxm2pXi6WXL8+ZE6CpCWGZ+AAOJWWQEPcLHDXOBcWOR4A6aLdaCZGYQpsHcaopub212CQvJBUL2rYuk5dRyxcBnM7FMjHj+PBayhVAkBbtiJ5FNcHP1ZrFMtR0yuUxxBES7ZW5urbGytwDpnMKTN04BcDx0jd4aOl1cWpJ7et7hmZsn+OHkEYa9U3XZ4e80pmm1Kv9w8ggZQ0XC4HTXJc5ELhZ1RSfDl3ltdj9Pjp/ip5zfxabUrsvKGRLfHD1LImudFK9He3hnYYijwfpWLduhUJZaLnZKWeZ95TgWuspb8yNci3YTTTno0w1rACMCAxPTNK3/x/p/I39Rz5nwB2OHiGXtdNni/OzgDTQpmN9y7TFgiWZFvu9BIJAQCLH2bwWJgHCWFcjeH4gydtM6wT7gSxCRujlnTNbV/VEKGQkXNjzCxpFIgpXVVd5ecfG3o6f4F4dGUaXant8wTf5u1slUPIgqZXkgX7LdjAud43J/XX4w5fhwZIEfLHgYTdj44/EufnWkeqkuJFxND27cwlbRZ6ccKgogiAsP0wmrROPsC7NiD4CeRsIgbaisZmy4td05p5QrS1n3Wcf4fMKLgYRdSfNh5QRCWGMvPP5xvj9xjIWkh5m4n4hzkQl7kKyQcGcT+DKr9Mt+buQWGDcXOUn7DUaWjAzxtMRqMovLVv03W053U6u3TYFC1san+1Bl65ww4h3hnfl3uLZ8rSnBzWTMOn+4VBcebbeF6xvptOg0mR9MHmE148Cjxbin+52aH3c0eI2IY4GMofLMzeMt3MPtsZB08+XLD/D0zZNkDJWIY4FPHHiSu3vObxBM39V9Hp++Qjxr49mJ+t7PMxPHmI4H0OU0J0OWUdz3bx5jMblzqzJP1kpxjxtdpHIampShy7FUdvuAbZV+1wwguDZ/qFgucQsbXmHHJzkISE6Ckouw5CYieeiRfPxgeh+XVn3oksE/2TPFQTXAiBxijxxmr9zFfjnCAbmbA3I3++UI++Qu9spd7JHDDMshhqQgg1KQASlAj+Sr2PnzQCCKJgxCWobD7jhhyc2I1FgZTSDoF37ukIe4T97HKWWAPXKYsOziM8NTuJUs1xM2/vxm7e7Ny1mFL09Yq79P9c3zmL2X/VKEkHAh509VXuyclAeaEtgAKAL+16EpBCbfX/Dy6nL1Vmy/cBb3p1l4qK8klZPtrDiHmQnfy1LgLFeMo5imhMOWxWnPZ59UDZfDWlTsZsfU5tEL6ym0gxc8pML2JeySWszI6XKWvT6rG/dcvtS5XnfzntnX2GNa39mCGSNu1jfPaSeQ8j4zc6u17VupdnDDNBiL1udQvl5vU6Cgu2mWqHh81erE7Hf3t8Uk8PV0gpsm8tJYnHfyfifvGXgVVa69rikJeLj/dSQMri33bpmrstsYJrw0fYC/vPjI2sq69w1+fN8zBO0rW7ZXJINHB15FYHJxcYBry6WHoW7m3Pxg/jM0eWzwZe7rfZt+1wxZU+E7Y2fIGTtzABUM/M6lrZVgn3sWqUIrMsDJvLD4tYVe0kZlXcZKVuILYxH+atI6Sf/S4BT99tbayIe0LP/qyDV+5+Aocv5jHJAC9ApfXc+jo3BC6mev3FUy4xDQsvzykJUB+epMoKaAAeCPb0SI52T2OhK8P7yITaj0Sj6Oyn3cJ+/jpDTAcbkfpcw07EbZ60zyIxHrgvL50W4SucqnRVlIdfvRVMMjand4XvQc5nrfh5kJniXmGECRHMwsWo/v8m+8gHpd1jloPulr2r7WSznNzfr7ZvPdlaUWEIcDVsb20lJf0W7hRf8Bcgj2r07wv41+l17Tep5xY+vjdxvZsI7r+dU0Rg1Ol7FsbIuPzXRsmmSuvsxboVNqfXBT0N2Mr46Tzm3/fFM072uzkhR0gpumEUtl+T+esGqPx4LX6HOVnxhbjpA9yqkuK1PxzPhx0rsgpC3FasbG31y5jxemDmOYEkPuKX7q4Hc5Eb6GVCHW6HYucipv0f/U+Mmq3VMzcR9P3zwBWJmfIc8MQsB7B19Fl9PMJnzFIZKtRDGyOPMnlzdiewEYqFCSAktX8qN+nYiWJpaTebaMQDVnwjdmfPzaW3v55qwfE8GPdC3wYLD1Ph0AvbYMQW2jQHef1EWwxot1SLg4Iw9VdfQ944vxgbAVMPzbK3388/ND/NexCE/MerkSs5HeFKS+uuzkh4seJEz+wdDUlt+VJAQ+yYEsWnPK+kTPHF1amvmMyhcnqmezmj1rqmYxsRAsu/ZsuEmRdGaWrIt7l2/jBdDjtLIGU9leaHJQWBsyhlT+uC+4FBcyN132pS3b9Drn8WgxMobKlbyNwWV3P386+F4WVReebJyfWLR0fTs1SLMepHxwkzNNFuK1lec3Z2+uRevPtBT8bNYHN36bH5/uwzCNusXJpWjXTinoBDdN42tvTjIZzeJW49zb83bDz3Nn5AIebZVY1s6fnn8v3x07zeWl3l3pGAJrdMRfXniEiVgYRcry6MArfGjkedxabZ4PZ7vP48+XpyqV2xJZjW9cP0vOlBn2THKm62LxPqea5JH+1wCrZf7marDMszQHd9Z6bymhcClhHbTlxMQAQeHkDnkIr2TjfV2W4O/rM4EtnW9vRB38P98Z4Q9vdBPLyQzak/zGgTF+bmCmNW+kRoQQHJZ6cVE+eyAjsV+KcFTuQ62xJPSp/lkOueJkTIlLMTvfnvXzX8Z6+PXzw/z8qwf4x28P839d6+Hvpv38tzErs/cjkQWGHTtfWrDJJr80ZC1OvjHj5+Jq5UxKUDhLTuRuBCc6ao2BR8zWS07ZGFgaWZ3FFSuA2JK5yQc3Swk7y669tOqU/+rMPr5y+b4tC5icXHlBY8gamZzGYr5dPVyiS1AIikNjz63rwpuyB/nj4ffxpmeYR+LWMTtrLOKoYYTBTlLI3ADMrdSWfVmvu0nn0kyu1m/bUKosBWvZm+22hEdTUaLpKAJBr6t3W8/VCjrBTZP4xJ0D/O6He3l08JW6ylGbUSSjmKlIZG2cXxzkW6Nn+cO3PshXLt/PqzP7WEi6W94ynjME3795lK9du4dkTidkW+InDzzJocCNurouFMmwzOgwubRUujxlmIJvj97JasaBV1vlvSXM6/b6JjkUGAUET4zd0dJgz5NvA1+QPRimjFuL4dG2tpkKBMNSiGNyf/Hi9J7gMrpkMJ7UeXvVughNpVT+9eU+/uWlQcaTOm45y/8yOMX/5/B1jrmrz0raCWQhcUzuKxrdrceJzml5sOj+WyuaZPJbB8b4/aNX+dWRm3wkMs8Jdwy3ksVAcCNp45kFL/99PMJsWiWsZfh4T32dZs3khCfOw8FlTAT/ebSHbIVGMkXIxVlI26WeklR0U9YGYGZJBQRuRwa7vnGnvS4ruInGVZKyO5/1aW5pN5OTeXHqIBOxMJeXNq7gK3VKFZhOBzEROJUEzjKNFIcCYwhMJmMhllJrWca0pPL1nrs5FzpDTzZLWggOzDzD4SZkJZqFtG62UzSZJZWp3qE4n1zL/NfrbQNWQFSYUxW2b9S9NUt3U8jadDm60OTWD5Wtl/aoe9wmPLjXxfiN+stRm+lxLvALR77BZCzI6EqE0WiEpZSbiViIiViIH04exaXGGfJMc8B/o6RB4HZYTjn51uidzOZTxcdDV7iv5x1kqbG24YhjiVNdl3h15gBPjp+kxzm/oXvq+cnDjK9amaEPDL9Q1rzuwd43mVwNspx28dT4SR5vcFJ2NQp6m5tm3iws7xuyHgWZw1LPFvM4p2LwUGCZb8/5+dupAG9EnXx12k/WlJAweX/XIh/vmcOltN88Nl0oHJX7eD03Vpx63Cf87JFCSA2WgyQBfbY0fbY09wcsbZZpwkJG4XrcxvWEzrW4jfm0ws/1z2CTd9fo6Wf7p3l12cl4Uuevp4P8RE/54zkkXCw2wVul1pJUVnEQt221t59YtH6cXb6tGS+nLYcsG+RyEqsJBcnpJeoawbN6jbqGHlVgbKWLrGldSq5FuzdYWdQU3CSs4yxcQbDvUpMMuKcZW+nm3PwQ9/ZubNa44BkilE4xyTw/sGv8vyafYzg2xRORO0hXKIvtBJKxUdsyt5qiz1/5O19Mrp3TGykfFbI2TtWJXd34WoXgZiY+w2p6FZfWWIm1nUtS0MnctCWKkeVM9BLH9Ovc3/s2P33ou3zq0Ld5sO8NBt3TyCLHasbB2/MjfPnyg7w1N9y017642MdfXnyE2YQPXU7zweHnebDvrYYDmwJ3rZuE/kxeVwOWAeCrs5Zh23v6Xy0pTi6gyjkeG3wFgcHlpX4uLrXGNKrgcXM9Z2WZ+t2zCAQONILCRb/wc0YeKuuK+4F8aeq1qIu/ngqSNSVOeFb510eu8QsDM20Z2BRwCZ3DUi86CsekPvbJXQ0HNuUQAoJaljO+VX6iZ55/vPcmv3d4lMPu+u3tm41bMfiFAUu/8VeTQcYT5VekzRrFUGtwE3WOlDSrGZ+3fk+bS1Jgbe51WIuF5Zh1kU9pflYc228DLlAY5wGWkeB6rWClTqkCswnL+LVSNyLA4Xxp6sLiAIa59XPoUixzv2873WQRHI9e5+evf4ueROUFp2maLXU3lksEN9XiyrSRZiW9wnJqmYVU/TqiciUpAIfqoNtpndu2k70ZX1nrlGpHOpmbNuS9M69ycvkqCUnjf/Y/yIQ9hFePc1y/xvHQNTKGzM3VEBcWBriy3MfTN08Sz+qcjVxoOJORyck8c/M45xeHAOhxzvH44Mu4muSNIUsGjw6+yl9depBLS/3s9U3g01f47o07AMsXZ7+/+gDWiHORs90XeGHqMM+Mn6DHOY+nRv1PLWgohLPWyehqpgeByY96nQTl/TW3Ovbb05zyrPJa1EW3nubn+qe5wxvbNRO1eglITu4SexryXbkduM+/wrMLq7yy7OI/j3bzWwfHUEp8FLpQ8GAjSuVjZDVt42vX7yaZ1XCoKRxKEoeSwqEmcStpHLYAXiWLT80RUDMopWJJIRF1bZ08v5rKMrdilcHDJTI3AB5XhoUVjWhMhfx8pKQthMDAFb9Rcd+rkTUkrufHeShSlqyhcGMlzF6fpRHJ1VCumItZQWK4hJh4PcOeKexKinjWxmg0woh3asP9EeFBQSIqGfx/B+7lH0y+hj+zyqfGnuCFwEG+HzxGbtPsI8M0eTp7kXlzlR9RTzRkClkNYWYRpoGZXySksgbLyQxee+XXWkgusJxqzKm8ICbeXJIqMOIdYSo2xbXlaxwP1289YphGccZVu2ZuOsFNm9GdmOdEXuhlN9J88saT/E3vfVxZJ9hSpRzDnmmG3NO8NL3Ci9OHeGn6EPGMjYf6X6/YwVSKyZif7904zVLKjcDkTOQCd0YuVm19rpeIY4nTXZd5ZeYAT42fQJczZAyFXucs9/bU7gl0R9clxqJdTMWDPDF2hh/d+2zd77kch6RuIlmrZDZhhhhxJAmp9WcufnVkgosxO8fc8ZpN7NqJd2tgA1a24xcHp3jn7REuxhz819FuywunxEcSktxEjfLBTTqn8NVr9zCftLITq5mtOp3vrftvv5rh1/ffYHCTLUDM1kNO3prhuTprTan2OtPoWumMYEFUvLy68WKasFmZDld8nEZLVOMrYTKGglNNsM97k9fn9nE92r0W3FQpS2WygmjC2qZacCNLJgf8N3h9dh/nFwa3BDeykOgVPsbMBV7RTP5w+P08Pv0yR1bGuGfhPPtXb/L17ruYWJfNeCU3yg3TyoxMGsuMyI35PlVDMjIbWuLnVlJVg5v5xHyx9FMvpdrA17PHu4cfTvyQq8tXMU2zbo+amfgMGSODLutlX2O36ZSl2gnT5PGZVxDAefcAV5w9qGaOH7/5LMdKKNuFgLPdF3io73UEJu8sDPPN0bNkjdq+1lRO4anxE3z58kMspdw4lQQf3ft97uq+0PTApsDZyAUCNqs8tZRy41QTvG/opbpeTxImjw2+giplmIwFeWWmORPVnej4JSd6xlot3TRDnPA0pqlwKganvbFbMrDpYHkC/erIJAKT7837+NJk6Q69SqWpnCn45uidzCe92JUkH9nzAz44/DwP97/G2ch5jgavccw7z35ngrCWRhUGixmVz492s9kOZXP7d4Grs9bvs1RJqkAxuIltXcsmbF0sug9i1KCNKcWVdRPmh/PBxmg0Utz/asHN0oolhnapcRxqdd+VQmnqejRCPLP1uYuDNM1FUrLG3/Xey1/1PsCqbCOYXuFTY0/wnplXUYwsV3OznDPWupCaoZ8qx+bS1FI8Q66K581odLRub5sClcpSAIPuQWQhE01Hq04iL0Uh6Op19badeV+BTnDTRpxYvkpPcoGUpPBE12m+3PcAb3qGkTD50NSL3D1/jlJtUsdC13nf0ItIIse15V7+9uq9VbuJCsMu386bDh4KjPLJg99ryJ+nHmTJ4NEBSzcjiRwfGHqxppPaZjx6nIfy9vwvTR3kwmI/M3EfK2l7zcHdZvokH5gmWsbqMpgwgxz3tEc3U4ed54xvlV8ctPQ3X5oM8725rUOBHUIrORPKNOHp8RPcWImgSFl+ZOR5BtyzjHinOBoc5Wz3BR7uf4Nf3TvKvzg0yv91/Cr//thVdMngYszBU/Nrr5VVnCTspU0wr+QzN93+8hPkCx1TqwmFbG7rhSirOlnwHCat+cp/GCXIGaJYktrrnaDHuYAup0nmdKZjAUwhY24qA9mljRPKF1asslXYUVv7dsC2QsSxgInEhcWtoxb68sHNohknlncrvuzu4wsjH+AtzzACOLt4kftufofns1eAtWnsrRy8uVlUnDNNFmOVz3tZs/x3WomckSsGLOWCG1VWGXBbn18jLeEF875+V3vqbaBTlmobbLkUD81ZF+vvB48RU6wD7uvddxFT7NyzcI6H597AmU3w3a7TW4SFe32T2JQf8vVrdzMZC/Hlyw/wkT3PbWmtXE1bgt7CsEuvtsojA6/T59q5FtwuxzI/sf8ZJGEQsjfuSXHAP87oSoTLS/08MXZmw32qlMWupLApaexKCrucImiPcix0DblElkhFpkt4ULMxJDNHzhQsSV4OOtvPFKzDzvF4eIn5tMKXp0L8l9FufGqW096NF8Gg7CeW21gieXVmP+cWhhGYvG/wpZJiWYHAvc5bKKhl+UTPHP/jZhd/ejPMWd8KLsUom7VZTmSYj6URQG9AlG0W1lUDTc2RzsisxBX87q1GcqYks+zaiz05my9TVRe931wNkcpp2JUk3c55JAGD7mnL8iHaTdi7du6RURhwHEMRKpdjLxZvX8wHNyFn7YHF4cAo0/EA5xaGOBW+vOFUaBMqYeFm1lxh3FjkoGwFX0lZ52s9d3PePcBdsy/zm0EnWWFyIiMxaBvm73LnWDBjDZVoamF9O3iBudUUIXdjGbNKzCfnMTHRZA235i673Yh3hOvR61xbvsbZ7rN1vUZh7EKfuz31NtDJ3LQND869iSOXZlbz8Ip//9odQvB0+ARPhE8BcOfSJT4y+RxyiZH1fa55fmzfsziUJAtJL3916cHiPCbDhDfnRvjzC49yLdqDhMGZrgv5bM3Oe4t0OZa2FdiAFd893P86hwKjhOxLONUEkrBOyhlDIZp2MhP3Mxrt5vziEN+fOM7fXb23pFNyt/AiCwktX5Kaxs9+V7pTVurAJ3vneCiwjIHg313t40psnS+NEJhdH4J1xoaXFvt4buoIAA/0vcmwd7rk87rRt3SifTCyQL8txUpWseZyCYkV53DJxxf0Nn1+Ow6t/EVSiPK6m80kbGEWPYeKM58qUeiSGvFOFjVvBR3M9Wg3Rl5M7JA9HHTfj1/rQdvkbF3I3ATctZdf9vkmUKQsSykXP5g4ykzcuyGhXSxNGVstMi47u/lf+oaZVBSGMhn+08Qovzb6LMKEFFnitGYEyuayFMBKMku6Bs+bellfkqoUqBXM/K4tX8Mwa9+PZDZZfI12FRNDJ3PTVNSFy0hmDqNOm/NIcoFTS1aK9DuRMxglWm9fDhwkrtj40OQLHF4Zw55L8ZW++7d4OITsUT627xn+9uq9LKddfPnyAzzQ9yZvzu1hOh6wXs+xwCP9r1Vsu75V0OUsjw68Vvy3aULaUEhkdRJZjWRWJ5HVWc3YeG12HzdXw3zp0kN8aOR5Ajbr4iAQVkkKNuhtjjeot7kdMGTdSqW32i3yFkAI+F+HJlnMKLy54uRfXe7n/31olIieIW7rxrT3ormOkV55jcnVAE/cOA3AydBljofKt9qWagFX8mLm3744xBNzPu7p13CUCTSu5PU2e0IuVFH5t+p1ZphdshXbwSuRVewsug/jio9hS5cuUxsmxezvXu+abmXAPYOEwVLKzULKSyQwSJ/9EFL+nKhJdgQSJgbpjCCWsC5BPncGaptMgCZnORwY5c25vbw+t4/X5/bhUuOMeKfY452k15HgVcaYMpfJmLkN7s8v50aZZBUFiQ8yBNIy3ZkYezIermgqC2YMp2h+NmVzWQosCfdcLEWvr76hqdUoBB7lOqUK9Lh60GWdVC7F5OpkzVmYgt7Gr/txqs2dsdZMOpmbZjFzjq6//l/4xPjT6PUMJDNNHpu2RMTn3IPccHSV3fScZ4j/2f8gaaEwHJ/mp8a+hyO7dcXj0eN8bP8zdNkXSeZ0vjN2J9PxAKqU4aG+1/lYmWGXtwNCWAGPT4/R41xkxDvFkeAod3Vf4Cf2PY1bixFNu/irSw8xGrU+67BwFadpK2kruJkwQ5x4F+ttJsIPcrXvxxiPPMps4AzL7v0k9XBNviW3I4oEn917k2F7kuWswu9d6iealfMjDcDuu4f5TIivXb8bw5QZ8U5wb2/lMSzl/G2OuBM8FLCckv/oqhejRIBpmmYxc7M37EStItz1uvJeN1UyN8XnlyRWXMP5rNHWxdpkLEgiq6PLaXrXZX51OVv8dyx2mAHH0WJgA9aoj8K+FkpSTlsWRa+vBfv+3rd4bPBl9nitLM5qxsGbc3v46yv38+V3fhw568HAZDy3dp67kpvlvGFllu5X9hNzDvGF4Q9wzRHhcNo6Zy+arTnm5RJlKbCGaTabap1SBSQhMewZBurT3bS7eV+BTnDTLFanwTQYis/w02NP4M7Utuo/Fr1OX3KetFD4Xvhk1e2vO7v5i8H3EJN1ulOLfGrsO/jTWwMVu5LmR/d+n0G3lRIf8Uzy9w59l2Oh67eM30qzCdpX+Pj+p+lxzpE2VL527R5em9lLr/AXt4nHre9tXvgZsO38jKO2QAjSihtTUkjpQaKuPcz5T3Ez8gjX+n+U670/wmT4QZbd+6s/122EQzb4J/vGCWkZJlM6/8eVQZZVK0AWho+vXb2LVE6jy7HIY4OvVLUnqGTe96n+GRyywXg0ywvXtuq+FmJplhIZZCEYCjpRq2Qb1jqm6gsiknqQRc/WbsQrS/mSlGdyi4btoMfK9lxeKL2q1/OlqUJw4/ekayqDrUcSlubuA8Mv8vePfp0PDT/HIf8oNjlFKqeTiB4D4MkFOy9P72feWOW5nJUdPy71MShZWey0rPJM6DiHUlaQEc21Zi5VqcwNQCKTI5ZqfFxPKap1Sq1nj2+tNFUrxUngbay3gU5w0zz2PMLMRz/PqmwjnI7ys6PfoSu5tea7Hj2X5uHZ1wH4QfAIq2pts2qmbAH+bPC9LKlO/JkYnxr7Dn3xrYMdVTnHj4w8x88d+SYfHHkBV5m5LbKR48jydQKp9ho41wrsSpqP7vkBhwPXMRH8YPIYfzo2QiY/pTqXslbDku5+1waBWdmBKZWvWOcUB3F7N/PeY5i7Mml69whoWT637wZOOcelVZ0vvjROKpvjfzw3ykpSxaUn+dDw86hS5QuWHRW9wgBSn5rjI/us+7/1zhQryY0r/6tzVhA+ELCjKRJKlcxNYTp4Mi2TytR32s8qjg3Bh2mu6W32+DYOdOwVPj7qz49iWMoST2/t+Cnobhbywz4D7nS+ZbyxA06RDIa90zw6+Bq/cPSb/OjeZxnMl5kl10VemB3gO+nLGJj0CT8n5Y1dVlP2IKG8sDtqtOYcKMwswiita5lfbY5RKlgZvblkbWUpWBvFcGPlBplc9bqgaZqdzM27kUzoIP996DFmNQ+uXJKfHvsuI7Hy01wfmHsLZy7FvObmpUB9Xi2Lmpv/MfgYk7YAjlyaT44/ycGVrW6jQlA2qAEIpKL8zNh3+PDU83xi/ClEHcKyWxVZMnmk/3Ue6H0TgclT8z5+5+IASxkZe2YJAJezOUMRb0XSqqem7UxJIam3p4FXK+m3p/nH+yaQJcE7k1H+3bcvMrYQx65KPHRiEbtWfSVedeSCkDh1YA99PjvJjME33trYjVVoAd8TthoGqmVuVMXEYbMCjehq/VLLtOor/vdU3E88a0OTMgy41hZVB6QI++UI3bYcg/YkhgkXp7dmlXXJeu/FzI07A4INJneNIgmTPtc8j4bnUZGRlFUcw39AWkrgws4Dyr6SIttovrw4L5nkGvSWqbpvZunszXwsTbNOu0upJbJGFlnI+Gy+qtsHbUE8moecmWNsZazq9oupReLZOLKQiyMc2pVOcNNkVlQnfzb4XkYdXWhmlp8Yf4YTebHwesLJRU4vXQbgO1131C1CBogrNv5i4D1ccvWimAY/OvEDzi6cr00EapqcWrrMz49+i0hqCQBvNs5QfKbu/WgEBxoDIoC2S5p2IeBseIx/uu8GDjnHxZiDXz83TDDvVhrxNFfkdyuRVsq3j24mVsZ/5XZnIBLiJ+8cQGBNepYlwc/cM8xgoJeYvbfq46sFN6v2XkzFzkdP9iKAV28scS2frbH0Nnkxcdgq/VTT3EDjpSmAlLrmu1PI2gx5pooz52QkImJtm9M+K0A4N7k1uNEkB6m0RDxpHft+t3XRz0n1laYqIQupOMVe0uYxczpMfhK1zPlm3DVAT9YKSl2x603bjw37VKY0lcmZLCdrVFNXoVCSCtqDNc2EE0IUsze16G4KJaluZzdKhexuO9AJblpAStb4v/sf4q28Ad8Hpl/igbk314IO0+SxmVeQMDnv6md0GxFwRlL4Su/9vOzLD5+cfZ3HZl6pmIFxZJN87OazvG/6ZVQzxzVHN+fc1iC949sYpFYPB+Ru9shh7pH3cEzqIyRciAbT0o3SK/k45Y3zLw+N0qOniGdyBIS1IrY72rcLoNXUmrkBiNvencFN1LWH431ePnqqF79D5RNn+hkJOQnpgyT0MFm58u+nWnBTyCQMBBycHbb0IX/92k1yhsnMSorVVBZFEgz6rQxjo8HNZlO9cmQUJ6ZQrJJUXm+zd11Jyi1sG0Z2nAxZ57qL0ytkN5VjdMnBYr4k5bJnUBVr23p1N9XoX6elS09+nMmlQ7w+t7fktqaQCOdLU3JqoiVdgqW8bgrMrTRH31eP3qZAoSX8+vL1itstJBZ4PS+jaGfzvgLtHXrdwhhC5mvddxFVHdw3/w73zb+DJxPjG91nORS9wUBijrSQ+V7XqW2/likknug6zbLq5D2zr3HH0mU8mTh/23svmU3R9Uhskg9OvoArlyQrJJ4KneBl/wEiqUUOr4yxf3UcPZcmVcPAu0bpFb6iK6gQgqBwEcRF2swybUaZNqLEaK2YV0aiJ7/S7LWl+ReHRvnSVQEZSGBr+on2VqKe4CajesgqTpTsu6dtPq35iuW4u0eC3D2yNprBIXtwKj5WHAP4Vy5QamaTgoST8sdXRnUX5z4BvO9ohLcmlplZSfGDK3MoeaXycNCJIlvrU1moSEgYFcz3Ck7F64ObiG0v1+OvVX/TQpBWPSwvGaxkHChSlgH3WpbXJzaWcQd8Oi5dYTWV5fpcnH1da2MqNMm+5m/jWbvgZxscAVGOISnIlLlMWLhJe3M8tQLPTR6hzzlH2LF1IKWqhsGc4IZscCo+va1FZynKiYoBlhJpcjkTWd7eAm82r72sJ7gpZG4mY5PEM3Ec67SfhmlwceEiL02/tCGzM+wdrvicO71QLUUnc9NKhODZ0HG+3n0WA8Gx6CifGH+aR/LR73PBI6w0yydACF4KHOSve+8jI2T2xSb4qRvfxZm1pgDLRo5Hp1/hE+NP48olmdU8/PfBx3k5cBCEYFr3M6N7UUyDw9HR5uxTCXQURqTSQjdNKAxIAe5UhjktD9IjvCgt+ol2CTfqOkGnSzH4Rz0XADD02i/utyP1BDfw7sveFNq/yxHSh8iqzrJ6JLewVzRXizo3Tv92aAofPGZ9xk+cm+H1cevCXChJFagmKi5kbqIxFdO0dDo+NYJc4xo3rXq5smx52wy5pzeIpr2bMlE5xcmhbisrdG5yo0hXkTSWVqx9LZSkoPmZG1lI3KfsY78c4UhglBHPJIYp8e2xM2RyW2UAHtn63Z/XVO5cvNjUfYHy7eBg+QYtVBnHUAu1etysx6W56LJbwfT16HUAVtIrPHXjKf79K/+ev7z4l8XAZp9vH3/v0N/jgL+yRrQd/G86mZsd4E3vHlYUOz928wdFTcuC6uJF/8Gmv9ZF9wCrip2P3XyGnuQinxp7gu+FT/HA3FuE8x4uL/v281T4BNn1WR0heNMzwntnX+PE8jVe87emzXe/FEGpoRbsEXY8sp19ZoQVEiyZCRaNOFESmA1MMH5g7k0ORW+QEwJDSNiEHUlSMISMiYQpZNR89iG9Tl/wbiMn2zGl+jQZMXs3ntWturLbEUNSWXEMVtzGr3ZzU5xj1d6Hnl5EbJoRtDkQ2ICQiJZwJD496OfF64uMLcQZW7C8WApi4gKq0EmTKPvUbkcWIUwyWYlESsbv9iOEhFPxE81u7bbcTFLxcHXJCtj2eCeKt0sIPGwMTLKKg8M9Hl4aXeT8VJQPn+jZENCtiYnXLujZFmZLhYD3DLzKzAUfSyk33584xiMDr2/YJpDPPl1TVfri4wTSURa05i10KmVuwBrHEPY0nr0yTbOhshTAiG+EmcQMr0y/wttzb3N+4XzxPOtQHJzuOs0dkTvw2/xVnsnCpZUfKLtTdDI3O8R1Zw9/NvgoK4odA8F3IneQk1rTRjthD/Gng4+xqLrwZWL8+MT3CaeXick6X+p7kCcid2wMbPK84xkih0R3apFQXmTcTMLCTVCq70cvCYFXOBiSgpxSBrhf3sdxqZ8BEcCNrab0Z398lvvm3yGQWSGcjhJJLeFNTuKO38Abu44vdhX/6iVcSeuEnXgXdgAVqDdrA5DQu941LeErzuGKbfIAQkgEtX5MSSZm39ouuzkQWM+qvQ+jRNeQJAQ/eqq3+GvXFYm+Tc62ahVBriRZAQ5YpSmHbAXxbqX0xPPNLCXsLKddyCLHkGetJOXGtkW8mlFc7A27UCTBYjzDdHStzBxNZoinJMDE71rLZpiSglmhPX672JQM7x18BTB5Z2GYq/ksVAE7GjoKOSG4pKrcsXipqa9fTlBcYCWVJbWNcQyxTIxkLolAELTX9p0WWC8qPrdwDhOTQfcgH9v/Mf73M/877x16b82BDYBbrb0poVV0MjctRiBwoOERNlyOCC/tO4Izm8CtuhghScxMsWqmSJBpKCNRDqtV/L187Oaz9CXnueLs4evddxFXyp8AE4qNy65eDq6Oc3z5Gt/rOt20/VGQ2Set6QjcsesYQiHmqE+YJguJgHASwEp7Zs0ci2accWORaIlVqzAN3jvzCgBvu4d40zfCHgJ4hYYwcwjTQDJz+f/OYQqZBc/hbbzTW5tGgptCS7g9WXqG0u1EuSGWmwnpg8ykrpGwhdDT86jZtVEf7lKZGyGR0K25TuXo8dq5b2+Q71+ZZ0/YhbzJJbBaOzhYpaloTGV5VcWp+ABwKYGa3tPNWWu/B9wzaPJaNmqz3gYsAbKmSOzrcnF+aoVzU1G6vda5Z2LROk49jiyKsvGcl5N1lGxj07Brod89x+nwZV6d3c/3bpyiy76IS7M6u4QQBISTSXOZc7rGjy1f45nQ8abpD6tlbgDmV1P0+hvr1JxNWNk3n+6ru5Np2DOMT/cRz8Y5GTrJme4zdFVwy69GpYGdO0UnuGkqAjsabmHL/+m4sCFvWtWYsocAFC/QADnTIIYV6MTMFEtmfNtD3BKKjT8bfJRAeoV5zbNlkngp3vSOcHB1nKPRUZ4Kn2ioRb0Ue6UwmlDANOmZ+z6DM98lJ1ReOfjZkivVWlGETFi4CUtu5owVrhlzGz63E8tXiaSWSEqqNU1dcTOijLDUhPfUDNpNjNtIcAMQt0Vu++AmYesiU+Pno0l23EqIaHaWVccg/ug5wMSJVizLZhUncVs3MXu3lf2q4YL0/qPddHls7O/amgGtprkBK7i5gaW7KWRu7LIHWajkzMrtyOP54GbvupIUlCizCUFWtm473O3h/NQK5yejvOegdbEcX7KCG79n6/ktK9lQaO3xcFf3OcZXw8wmfDxx4w4+sucHRTfpQnDzqt3JT66scnLpCi8Em7PYEWYOYeQwK2Ts51bTDQc3xZKUo/7MsyZr/MqpXwFA3mZFQUJqC81NpyzVRDyam7uUEQ7LPfRLfrzCsSWwKYcsJDzCTq/kY78cYU8Z0W29mEJiXvfWFNgAXHN2syrbcORS7F0tb0BYD37hoFvygmkyOPUtBme+C4BsZvDEmydeDklu7pSHOSh1o6Og59I8OPcmAM+GjpFQ9OKAzHZh0XMIavyN7ASNBjcxe0/1jW5xas3aFAjrQ4A1iDKhhwGBrvcx7zvBWM/7Ge39ELOBO4jbe2sKbAAUWeLscACfY2s2oZbMjacoKtaLM5+EEFWzN9GYQjSmIoTJoG9tHIRAbGlrz8qO4m/6YI+1gr+xmCi6LN/MZ27W620K7ESXoiyZPD70EoqU5eZqmNdm1vSFfmFdlF+3W8HjHUuXkJpobCpVCSCT2RyrycYyV43qbQrIkrztwAbAqTkrCuZ3ivY5q3bYgF3Ub7TVKDZUTkj9eLBhCom3821+zfC8kZHYL0UQRo69N79Mz8LzACQ0qybsXa19YFstCCHolrzcJY/wofkrOHJpZjUPr/r2oWwyGtt1hMSqvX+DQdpuk1YaC26slvDb19U5J9tK6mcq4VHDxTlKMXsfc76TJMKPsOQ5WHMGqB5q8roptoPL5Iy1kpC7SnBTKElF/CmEfW1V7i6Rmc4oa/d7bCr9+UzEhakVy74/n7kJuLde6JvhUlwLPj3Gg33WwueFqUNMx33WPknWvk9IWZZlHU82wYGV8aa9rlzDUOX51cZsMBrplGoFnhb8thuhE9y0KTbUHfEK6BIezshD+CUnR+ReNBTe9Fjisj2xyWIreaMMSUGcBhwY+3NCy29hIHG578e5EXkvAN4Wddk4U3PsW7ROXm9GHkYSCt3CW1On1k6R0EMYskZKq03z0Gpysg1jG/qCuO32zd5EXXsayrCFNKuzypQkTEnGKfuavGdr1FKWctpyyLKBYW68iLqqiIoLJam+cHxDMF6q8ysrbwxy17eER5NZVlNZJLEWaK2nmS7F1TjkH2Ov9yYGEt8evZPZuBcPNhQkcph8K2CdB880sS28Jt1Ng+MYap0G3mraoVMKOsFN2yIJCb2FkigFiUNSD4flHpR8eloXKkfkXhZ1LzdtQSRMjlZxrayECxvDOY3Do3+CL3aVnFC5OPhTzPuOE3UOYyKwp+fR0kvNeVMFTJOhqW8iMFlwH8LpOc1ZeYR+qT2CiAKFUk5Sb4/9arQkVeC2HcUgJJY3ec/USkDrR8qfZlWho8uty27VUpYSArz5jqnpda64dtmNUubxqwmZpVUNIUz6QkkyqhuwzhnlxMTrOdxj/a4uz65yPT9Costtw65sfT0rc7MzJQ0h4OH+13GpcaJpJ//3pYd54sYZPFjB2LPuMFkh0ZecpyefFdku1cpSAFnDZClRn94ymU2ykrFGXXSCG4tOcNPG2FpUmvJi54w8TETaejHzCjv7pS7ezLcGHo9ea8iKXCA4mbNx9Pof40pMkJHtnBv+WZbd+wArS7CaT/N7Y80tTflXLuCNXcMQMmPdjwOgC6XiFObdIGazbOyTbZK52W5wk9DDbaUfahYxWw+5BktuiqTiU60g1qnU3krbCKqkI2o4pftdlvne1PLGAZGuMvtXKEmFvSl0zcAUEmnVVVJvA5ZQej3dHhs+u0omZ/L0Jaujp89vLw7QXI8pJAypde7om7EpGX583zPs990ABBcXB5hetDrWxskWx9I0y9SvWjt4gfnV+oKbQknKrbqxVeiI3Qk8TfQG2g6335noNsJOc4MbgWBECnFSHqgYOPVIPpY9x8kImWB6hZ7kfN2vdTRlcPb6n2FPz5NSvbwz8uktbd8Fp9dm6m6EkWVw6lsATAbvI6W19oLSKGnVQ1a1VjgZ1YNRp3FeK2hUb1PAlNS8cPb2Ytld2ZG4GiHdukA65db/FhVRPTAIe6xtpqMbg5tyfjfFklTXWok6rfpwoZcs827O3AghOJQXFk/mA6p+vx1NKh0wttLMrxRuLcnjQ6/w8f1P0eucJZewzlPnYxp/xqMAHFwZx52JV3we0zR5NTvGE5lzfD97mVeyo7yTm+BqbpYJY4kFI0Y6u4JRQ81pMZ4mm6u9NrWdTqlm41LbI3PTXkvZDhuwCbXUaJqGsKNxWO7BLWo7cQyq/Vx1D3MweoUTy9eYrGcQW3yJ9918CsVIEtfDnB/6VEkB5bJrD/2zT1mZG9Noyqq/Z/6H2DJLpBQPE+H7t/18rWKzODWlBXa9lTrTBOOt260l3JrzFNnWczgVHw7ZW/SVaSWqpJPJJStu0+NxAsktwU0p3c38ssZCVAdM+kJrwU1K9eAV0S3bw9bgBqyW8OeurnVZ9fnsyLIdSlRpck2eMVUrXY4lfnTvD3h7VeNVQOhT/PnoP+DH9e9zl7jA6cVLPN11suzj58xV3jKsqdllz9tZIPEkNsnNGf+HcaulA0oTaxxDl6e283XB42a3S1KqpO565qhAJ3PTxtgrDNerh7Bwc0YeqjmwAcsRNeW/B4BDK2MoRm3tiWdX5/nx8SdQjCQrjgHeGfmFsp0hq/Y+spKOkkviTGy/7VzLROmdfRaAG92P7Wh6u142t063Q2lqu2UpuP10N/W2f5ejSx/BIbc+XV+L7mbA7wOsi2c6u5YdsMnODY+fWdR56nXrYtkbSmLX17Y1ZA2HvvW7NoVMTt5abhoJWaZ+ALIQdHtsxU6yzezm0Foh4LDLcvkVcgKHbZr/nPkwAEcWR4nGyu/bFcNybe4WHk7LgxySehiWgnQLLz5hzzeJWCSNFcYT71Tcl+loimQmV3GbAtttA28W7WDeV6AT3LQxzdLcdAtvzX4760k49xBXfehGtmo7pNPU+NGlKR65+R1kM8ui+wDnh36m5ImuiJCKQwK9se13TQ1MfwfZzBB1DDDvObrt52sVOdlGSt+4Ytvt8pkh6025qGRU723TEm5IGisNCok349d6EDugR6rWDi6Q6HIEcGoyJjCzUjp7MzFn45k3QuRyEl3+JHcfXtj0PALFvrVcV+67V2SJA3njwW6vDUWW0EpobmDn2sHLIQsJX15L9PDeb7IatnPdjOAhgW00y0q6hM7IzHHdsMr3x+V+jsl9nFWGeVA5wOPqET6inuIT2p38tHoPJzxWp+hs6nrF/Uhkcrw5vszYfLxqiapdOqXaYexCgU5w08Y0S3PjqKEOXxIhmPedAvLC4hIoSOwTIX5i9h0OTj+FAKb9d3Jx4Cdr0pE0S3fjio0RWn4LExjt/kDNpoW7QSnDu93umEorzTsp3S5Twud9x9tCC1UP1drB7bIbSchE8uWOUrqbsWk7P3griGEIekMJHjg+t2VMgk1yky4xOqVUSarA2eEAAjjaa2WwymVusjvYDl6OQN7ML8oKZ7ov8XbIMmT8h9Lf8tTV46RyGxUdY8YCGXI40YmI8hk6SQi61V4EEvHcMrHsUsX9MIGpaJI3xpeZWk6WbBHPGlmW8rMAd9vjpl06paAT3LQ1ipBR2Z5jpIy0rQzQnO8kJjAUn8GbXt1wX5fwcDe9PDr+DboXX7YCi8j7uN7zwZr1M4W0vys+jpxrzLwK02B46hsAzPjvIN7mbrmlzOBysn1XMx7NKEkV2O3gZjvjPAqk9IDlbXOLUa0sVfDZiXgLwc3GY+7CuMLz7wQwTcFgJMa9R+eRS5yCXIqfpBbc8lln5fLBzf6Im9/88BEeOmBdgFXJhlTi/GbIGuzyINaCU/GCabWunwsOMaN6CYkon859i69fu4ucsXaOu2JYmpe9UriqO6/NFPg16xw1lyrv0C4Za4KkrGEythDnjZvLLMY2dlLNJ+YxMbHJtl0fe9ApS3Wome1mb7ar20lrXqJO6yR/Z9QSyznROSkNcNJwcOr6/8C3eoWcULg08JNMhe6pK2uS0vwktQASBu7Y9Yb2Mbz4Gs7kFFlJZ7zrPQ09x05hCoWErfRAut3U3TQ3uOnatZbwpB5mvOvh7U2XFoJZ/x3N26kdpNpkcEde1Nydz9xMrcvcPHNplr97YxYQ7Old5a7Di0hlvkaXEgAhiG0ybqyUuQHQVRlp3fmhXGkqU6mcvQMENgU3hpD4bsQaJPxz8rfwxuN898YpTBNWzRRT5jIAe+TqmRPZSBdHc8yWCW6UbBx/9MKW21PZHJdmVjk3ES2OaVivt9nO2INMztigwWqETlmqQ83YGi0p5Wm4JLWOWf8pAI5Gr7JPhLlDHqI3tczRq1/AmZomrTg5N/LzFScaV2I5Hzw14ncj55IM5GdVjXc9ssVjo92I2yKYZValu+lU3MyylNUSvgu1fyEx6z9NRvUy5zvR8NMsu/btugaqUWrO3KwrS5mmybffmeLrb00BcGoE7jiwVHGNUphFtVlAnqkz+1hed7O7wY0/b04YJ00yb7w35oxw3j2ALEx+R/1DLi3188LUIa7mszYR4ampaUOYaUKaFdwspG9uGViqZBP4Vi4hGamy4xpWUlnemYxyZWaV6ZglZN6u3mZuJc25yWjNIuZSdMpSHWpmu5mbZgQ3C+6DZCUbtkyUw4klAquXOHL9j9CyK8T1MG+P/GLdc3fWUyhNNTKKIbLwImouTkILMRO4s+F92CkqDZi8XTI3sDulqSXXPtKaNRog6t7b0G8yJ9tY8LavGL0alTQ3yjqH5Ijb2m4lmeWvXrnJ9y5YF+j3H4nw2JFAxcDGJrlQ8p2ICVv3hixdVqnv4lZWd9NCJ+da0ISCGytQWTTXppR/L3yKtJA5K13kx6Tv8/LMfs5lLLH1Pql0RnYzci6NSwlgk9wY5JhP3Vx3XwLfykWEaWVllFxlb535WJqbK1ZwE3ZsT28zu5IilTU4Nxklnqo/wLErdpQaB8DuBJ3gps3ZbseUownt5KakMu89BsDw5Nc5MPZFZCPDsnMP74x8mrTm29bzr41iWKhrFIMwskQWXgDgZviBshmRtkEIYvbesnenNP+uCKENSW3YgbccO90SnpPtLHiPbLhtJnCm7g6wOd/JW05EvB61wmJmvc+Orsr4Hdb7fHlsEYCPnuzl4YNdVedMrZ8gbmzK0mUqaG5KUcrITxYC9N3vuCsM0VxYF9ysqA5+GLSC39/S/wSX4zxpKYZsKgzUON5FNjIIIYqlqbm0VZqSc0l80UvFwAaqBzfQnDbwaCJDMmsFNJmcybnJaHGKe620izNxgU5w0+Zsdzq4vQmZG1grTdnT8whMZnynuTD095rSPpyTbazmOy/q6ZoKLb+Jlo2RVty3xGo7qQUqil1NSdm2S3AjNMO8b+tz7mxL+Kz/JOamoMSQdaaDd9X8HAlbhFXnYLN3bUcRQirrUuyUN06fL5SmJAGfONPPPXusoEaTbOhS+SDFtWmCeCFLZ0hq3YNXS5WlNEXC4fSwUzOmyrFZVFzgJf8BFlQXfnOV08H/CUBq+SRLidqCG8m0Sk1rupvrSNkkvujFLbOnlCqDiw3TYDVrBafbCW5mVzYKy3OmycWpVZbjtQc47eJMXKAT3LQ5tm1kXgQCR5PayWO2HlbzWYexrvdyrffDTc2UrOluaixNmSY98z8EYDJ4d/tnbSjdJbWZ3WgJb1VAtVOlqYQtQswxUPa+JfeB6k8ipGIAf6tTTnezebbV2eEA3R4bP33XEKcHN97nVsr/DjfPoCqUWhvRu5UaJKopEh6Hvut+N4G87mbB2Jg9yUky34ncQVwILjisYZXppbN89do9JT1wNiPnu6ACWj8CiURuBSn6esmhmkq2cuYmkYtiYiALBZ/uq+VtbSGTM1iIbdX25EyTi9MrLNQ456qdOqWgE9y0PbpQkBv8mnQUpGZ1rQjB+aGf4bV9v8Jk+P6ml0/W/G6uUdLMYRO+1UvYU3NkJZ1Z/5mm7kurqKS3KbAbouJm620K7ERwYwqZWf/pitvM+45XFQgvug+WddK+1SiluxFIODZlbg73ePjV9+7nSO/W912uNKVLzi0dWRnVQ1Zx1V2SgtKZG12R8NpUsm3SMRUlQcbcqEG57uzhTwJ9JCSJ3qyBx/QSz9r4u6v3bPHA2YKZQxg5FEklmB+qOpmbKbmpZKYRFdzhY/msjUP2kcw01uk0t5IuOy3CBK7MrjK7yQ+pFJ3gpkPd2BrMvjRDTLyeUs66zWLV3ktWsqEYSZyJiarb98xZWZsZ/x27vsKrhYzqrunimdRa8/lWonXBTetbwpc8B6uX1YTEdPCusu3hWcXZcKdfO1Iqc2OXXUh1ZDc3l56q3R6z9zRUhpSFsqWMpisymioh67vb+WgXWrGhY8ncmkH5K4/1u/t4NMrnwl/EqSZYTHm2eOCUQjbSSLk0Q6YVKN40lspuq1bQ3axmLTGzS/FvMWSslc0lqc2YwLX5OBNLlUtknbJUh7ppVHfTDDHxjiEkoq78KIYquhtnYgJPfBQDqS5NRbPIKg6W3fvresxmP5BypFUP5g53HLQquGl1S3hWcdUclGRUD3P+0kMPZ/2nd/wzbyWlRjDUO5FclXTs0tagcXNJqkDM3lPV46Ycmzum9PwMKrtz9zNp5XQ3UTPBJDGECR9ZjfHBxRf52NDTqFKGiViY741bHjjlULIxfKuX6BPWZzxjRrdkh9a2LR9UFPQ2TiXA/GqaXK6+ScvrhcTVGF9McGOhdKAlIe26geBmOsHNLUCjmZtmiYl3iqUadTfd+azNgvcoadVbcdtWkJXtzPlO1uWEHHPU2JYsBCl15zxWTKG01BuolaWpWf+purRWUdeeLbqnmL2v7R2t66VUcONoYCJ5qSxNucxNQg+TUut/DdjaMVUYsOl07fyxvZnNZn4Fruas1vkeyYsu2XBnE3w4/jzvH34RgcHFxQFenC4feLvjY8i5JB5suNAxMItGgJup1DEVy1nBjUvxkzNN5lbrc3mvlrXZzORykquzMYxNFTCX5tqWgWAr6AQ3twCNBinNLku1moLdvbvCKAYtvUQwak3TnQzdu2P7tp6sbAchmAreXVNwZch6XeWmnRQVp1vsKNqq4KbRoGR9e7gpFOZuExHxepQSZamCeV89bNbdaJK9rOkeQiJpa8xnZfNz6qp1WXK7HLs+hqFUO7hhmuvGLXTx3S7LzfrOhQuc0q7wcP/rALw0fZBz8+W676wMixCCPslazJQrTZULblazCyxnpgHwKNZnv3mcRiXKCYmrMbea4tzkMul1Gp92ciYu0AlubgEaNfK7pcpSWD4vCS2AwMRTZhRD9/zzCEyWnXt2bYZRwT3VlFQmww9UbYeP2XrqEmDvpJlfq0pSxefXvGXHTTTKdoKS9e3hC97Dt80E8/VsztysN++rB5cSQKxrx3bJrfld6uuCG1kIVNm6LCmyhGzbXR1HIXOzZMYx8o0OU+YycdJoyAxIAa64erni7EHG5LGZVzgSGOVMlzU64anxk9xYqRz09Uk+AG4ai5glallyLoXYnCoBLq++CEBE34NDsRZZyWyO5URt7dvzq+WFxNWIpXO8NbFMNP9a7SYmhk5wc0vQSOZGQUbbznydXWKta2praUrOJehaegXYvawNsKGLI6s4mAzdX3GWUS1dUuvZyY6pVgc3ABPhB5s6hHK7QUnCFmHWf0dtLeK3IOqmEQDOBkpSAIqkYpfXLlrlSlLbZX1ZqlCSKqA7dvei6UJHRcbAZNm0tC+FrM2wFELOC+af6LqDrJAYjk9zYHWcu7rPs993AwOJb1w/y1yi/HEWER5kBHHSLJml9DUmcm7j7SuZOaaSlwDY59qoO5xerk1YPFNHlqcUWcPkwtQKk0vJthq7UKAT3NwC6CgbVlC10Cx/m50mWmHOVNfCy8hGhrjeVfTF2Q02t6im9ADTwdKjH0wh151hyiqOppgj1sJOBDcIidnAGWsY5Ta7pzKquylBSdS9d9eGe7aazZmbzeZ99bC+NNW64GbteNoc3DhduysqFkIU50wtmDHSZpYbxjxglaQKLGkuXghYGpv3zLyGwOTRgdfodc6RMVS+eu0eVjOlj2lFyESE9R1NmIslt9ncMVXI2nTb9uFWN4r2lxMZUlXawusRElfCBG4sxnlnPEM2t72hm83m9jy6bzOEENioLwtzq4mJCxRGMdjSC+jptQNdGFm686MWJkP37sqYggKl/DdijgHmfce33J6whRvqxNmplvBMEwdmViPq3stE+MGKLs3VaEaAdLsjCRl53flis3lfPRQCGlXYGipt1YIm2RH5S5G+KbhxuLy7eagDG0XF1415cph4hZ2g2CjEfy5wmLRQ8GbjhNLLyJLBB4ZfwKevEMvY+erVe0iX8cBZK00tlbxfWZe5iWZmmU5Zme29zrNbtjWB6ZXK2Zt6hcTVmI9KfPPt6bpHNrSSzlniFqHeYOVWExMXyMk6KyVGMQSX30LLrpJW3Mx7ju3W7gGlgxuAJc8hVpzDG25rdKDoToiKTSGTKTPoUG7RmSFh6+JG5L11d7nF7T1MhB9sun7ndqVgtFfKvK8eLN2N1LKsDViLNy2/v5szN5LmwKbusqg4H8QsmnGuGJbZ3j6pa0t3UFZSmLRbn1Nfft6TTcnw4ZHnsCtJ5pNevjV6Jzlza7RWEBXPmCukza2mfeudii+vWou8Htt+3GrpRdDcSgrDKK2oaVRIXA5ZqCiSxnIiwzfemuJmFT+cnaIT3Nwi1NsOfquJidez7MzrbgqlqXWjFqaCd2NKu3uyyynlnVNnAmdI6msCwnr1NgWqOeo2g4ziKpsBO9jdunJAVnEyHnlP1cDPkFSW3fsZ7fkgk+EHSOzwMM5bmYJLcb3mfZuRhYJD9pT1t2kWBd2NTdm0r5KEbttd/5RCcDNrrjBnriKAEam0f9O43Tr2+/PBDYBHj/OhkedRpCxjKxGeGT+xxQPHLWx4sGFiMlmiJVzJJcA0Wc7MMJO6Bgj2usp7fGUNk/kyYxO2IyQuhW3dHLJMzuSpC7Ocn4o28RUaoxPc3CK8WzI3AMt58aknP4rBu3oZR2qWnKQx479jV/fNkLTK3ipCYjJ0HxnVTUoLFDur6iWlBeouvclSfduX09t47Sr7u1orEDQllanwfSxumuQNlq5mzn+a670/wpz/FNk2cz69FSi4FNdr3lcKlxJoaeYG1jqmNmduAGy7bObnFXYkBEY+JOgT/rLn4/H88Mq+dcENQMSxxOODLwMm7ywM88rMVhPQ3nz2ZqJkacpAySW5vPq8ta3tQNWAc6ZMaWq7QuLNlBqyWs3NeCfoBDe3CPW0gwvEtgZu7jaxdaMYXImJYtbGGrWwM0LbctQy78aQNSZD9xPdVKKqB0NSy5aMyjEYqE8TUS64iXh0nLpCwNl6UfqC9yhToXswJaVYehrr+QDL7n1bpnx3qJ2CqLjQIrwd/FovNrm1AWZBVFzwuFmP3elGLhHoKzt0+ZKEhE+sHVt75fKt3ZP2IAYCXyaGK7NRBDzineKB3jcBeH7qCOMrG7M/1VrCo6kbzKZGEQj2urZqbTYTS+dYTW4scTVLSLwevYGZYjtBJ7i5RbDVMYLBjoq02yq87SAklvOjGHrmvo83dh0TwVTg7l3eMchWKEmtJ6O6rY6cbVCPqNhlUwi59LqyN+WDGyuA7PfvjAdMzDHAtd6PNK305LHfehYIzaaZmZv17eCtQpMcGzxuNqA60TfpbgSCQWnn5rAVSlM6Cn2i/GeallRmdSug3Jy9ATgRvsYh/xgA5xY2GvxZLeESCTIslphldSH+GgC99kM1t/dvnjfVbCExbB2f0S50gptbhHoyN7dqp9R6CrqbwIplhjXvPUpa23079p2cVFyPmV/YpSMJ8Nhr/52UCm6EgC6PdWHs9+/ce23WbCePXeH04M6Nr2hXFElHEVrLOpyajS45SpakANCc2LWN9wWEg7DYuU6//nzJ6JDcU/S2KUcp3c16jgavA3A92kN23YBNWUj05FvCb25qCZ8xokznZhBI7HWWtp0oxUIsTSZrtWhncgaL8eYJiQu0OqvXKJ3g5hZBEhJ6je3gt7KYuMDyJtO3yeDumfatp1ENTSOkauyYkiVBwGV9595asxZCKln28jtU9Lyo0+fQcNlurSzISMhJj8eGrUR5492EKvSGRi7sFlql4EbRsOsby9Fh4cEm1Ibn7tXLgBTg4+qdHJeqdz/eLKO7KdDlWMStxskYCqPRyIb7CqWpzbqb13M3rPvth+oqNZqsZWvmV9OUaaDaFp3MTYdtU6uO5lYWExdIaz4S+bLMsnOkbYYb7mTmJqV6axoMGXBqKPkypLfGzE1GcZb0i+nybLyI9Pl27v1uFyGs4EaSBMOh9tQB7BQ2Rce7Ax13zUKRVBxqef8j1eZCka3fuIxESFiBuVfs3O/TLtSahkMWRMVdqSU0Y6vvixCw13cTgMtLG4Olgqh41lwhlW8JnzKWmTKjSAj220tPtq/EzEoS02hNSUoT9m1147WSTnBzC2GvUXdzO5SlAKYDd5KR7Yx3PbLbu1JkJ4MbhERK81XdLOxeuyjYFLmkKHMz5fQ23ZuCm4EdLE1tl26PDYdmZZr2vMuDm6DDRberdLtyu+K3VSgzac5im3hIuIqloZ0MbmplVXWwrDiQMOlJzJfcZp9vAoDRlQiZ3Fpw4BI6XmHHBCaNJUzTLGZt9kldeKg/kEjnTEYXYiQyzRUSA21d9uwENzuMEODUGot0aw1uboeyFMB08G5eOfT/YNUxsNu7UmRHgxuqz5myazJufWPpqJbsTSkTPUlAl3vj6jns1re4xrYrI+sCGp9D25Fur3Yl7HLS52lsSvdu4bdX0G5oTux5UXGXWAvMvaI9L67VdDdh+xIeLUbWUBhd2VSaEj4AbppLTJlRZswVJATH5L6yE8KrMdOCrA2ATWpPvQ20SXDzH/7Df2B4eBibzcbdd9/NCy+8UNPj/uIv/gIhBD/2Yz/W2h1sIl67Sk+Dqf5a6ssqMmqbpglvB3Y6uKnWMRV2bU3l1xTcKFszN0GXjrKpW0UIQd8tkL1RZbFFAD0Sat8Tb6sJODX8jvb/3tYTsFfws1Ed2DUZDaU46wmsErxW52ianeCmo7LuplJpqm+d383rOauzar8UwSl0lOzu+8esp5O5qcAXv/hFPvvZz/Jbv/VbvPLKK5w8eZL3v//9zMzMVHzc9evX+cf/+B/z4IMP7tCeNodujw2/QyvWj+uhlnLT7ZK1aUdMIW9rLlIjVBrDIAkIubZ+3x6bWtX/r1RZKuIp/d52smuqUQYDji2B2VDQQZ2+hrcNfqdWV+dcOxB0VApu7MiSxIDm3aJ7acfSVEF305uYR5ilB0ruzwc3o9HIhplTYeFGRSZJhllzFTmftYGNYxjaAb2TuSnP7//+7/NLv/RLfPrTn+bIkSP8wR/8AQ6Hgy984QtlH5PL5fjUpz7Fb//2b7Nnz+5Nh64XuybjtatIAoLO+oOQWtrBbwcxcbuyGwaCWcVZNqDyOrSSviCKJHDqFVazQpAp4fob8ZR+fz1eO0qbRwkj4a0aG5sq03sLCaKbhSTA79BqFpe3A7JUJbiRJFDt7HduDfZ9bViamtO8JCUVzczSlVoquU3QFsWrrZIz5Q1dU+tbwgEOSN3F87pkphHG1tlTu0W7dkrBLgc36XSal19+mccee6x4myRJPPbYY/zwhz8s+7jf+Z3foauri1/8xV+s+hqpVIpoNLrhb7foXrcyDpUoJ1RDETJKFUFZO4iJNaW9L4SNUihJ+RzqjupQyvndhN3lf0OVLmwZxbWlC0uRRNnfpCwJeny76wxdCbdNoctdev9G3oXCYo9dRZYELl1p+6C0gFNXcKqVvyu3PcRAiQCoHTM3CFFsCS+nuxEC9pXtmvIBVmfYUbl3w31qA7obQfN/BxJS0Vm6HdnV4GZubo5cLkckslFQFYlEmJqaKvmYZ599lv/23/4bn//852t6jd/7vd/D6/UW/wYGdkecqsiC4LqLh0tXcDQgLK6WvdntspQkYF/Xzplr7SSF4Cbs1otGdztBqeBGUwS+CgFMJZfeUnqbkFur6G68U27FjVApgOnz2W8ZQXSz8DvWzgG3iluzU7OCm0oX4UH/Xtz5zPeGxwodtYEuolZTze8G1oKb0ZWuDaWpESnEHinMvfLeLQvWRnQ3IW2w6QGOJjlqao3fLW6po35lZYWf/dmf5fOf/zyhUG1tjp/73OdYXl4u/t24caPFe1masEvfMh+lkexNtY6p3S5Lhd06bl1puCOsnSkGNy69bKagFZQKbkIuW8VTlUtTywYrGXVr8Fnt/fT6bG2pXyl425TD8rxp38CsFQTWlbw9tlujNOXUFSQhYa8w3mQocBhFCBwlSq7tmL0p6G7647NsGQOeJ2Bbwa+vYJgy15bXRo8oQuZ+ZR8j8tbrXCMdUwGtD4/aVffjKtGuzsQFdjWsD4VCyLLM9PT0htunp6fp7t46Y+bKlStcv36dj3zkI8XbDMMSaymKwoULF9i7d+M8H13X0fWdFYFuRojSeoaQS2N8MV6Xa2QlIz8JsWOOnSVfX1j6DACfUyOWbi9l/3YpzJUKu3XS2dIiwVZQqh087K4cxBZGMSzGttqtlxITd3srBze6IhN260w3eaLwdikM+azESMjFhanVHdqj3ce/rgX+VhEVO3VrMeRUncRLiGZDthBObz9glVw3D4T0CQdzZnt9x1O2ADkkXLkk3kyMZW1rMFDomnpp+hCXl/s4GBiv+rz1BjcSEnbZTUTfw3JmuvoDaqSd9Tawy5kbTdM4c+YMTzzxRPE2wzB44oknuPferXb7hw4d4s033+S1114r/n30ox/lPe95D6+99tqulZyq4XdoJVPjqizVffKplLmxo+1qmjC0zhNlfWr8diEr23FoMk5dwedQy9vFNxlD1ljyHMLIT8n22JWioVklyo1i2BzcqLKoSeDejqWpWtq9A04Nn+PWuMhvFyE2Hnu3iqjYmTdfLKe7GfQMgmoH1Y6nxEiQdszcZCWFKZvV1l25NGUZ+t1Y6SKZrf59ybkUwqh9cWWXPQgh4VR8uJTmDRvV2zxzs+tlqc9+9rN8/vOf54//+I85d+4cv/zLv0wsFuPTn/40AD/3cz/H5z73OQBsNhvHjh3b8Ofz+XC73Rw7dgxNa88LarkWW6gsCi1FpeBmN0tSkoBe79oJxqnJO3bx3ylysr34fQkh6v7utsO87zjXe3+EOf8pgoHaSrLlLmxpZWNZKuzWawqK260lXJFFzQ7K7xZhsdumbOig28my1HbKloXsW6ngRkJiwJ1fuNoDuPStJVcnOsruX862sCYqni27TcC2QsAWxTAlrke3Viy2YiLnas+KO+S1zquI3rzuYlsnc1OZT37yk/ybf/Nv+M3f/E1OnTrFa6+9xje+8Y2iyHhsbIzJycld3svGcWpyxROMz66h1uF5U6kstZti4qBrq5Ot/zZbLWdl+wad1GY331ZjSipx3wECd/0U7H0U3JVPhOtHMaRVD1HXXqZC92yZwF2tJFXAqStt5fpbytumHCMhZ1tqhprN5oyp26bsyPuWhPV9NIorH9y4SlgUdDu70eT8+7L7kQRbBroKIdoye1NwKq6UuYHyXVPlqKdjyqH4iv/tUUM45Aot93WgS+29YGgLKf1nPvMZPvOZz5S878knn6z42D/6oz9q/g41ka4qFw5JQNClMbVcm5ZBFwoyEjm2piV3qw1cEpT0E/E5tLbTaGyH7LrMDex8cAMwHHKiKDL4h6y/2DzMvA0L16BgFiYE2ANW8KPbuLZir2g+GKlDHN3vd7AQW97u22gKe0p425TDpsp0e21MLCVbuEe7z+bgRpIELptCNNFabxS/U6PLo3N9vn6xqyxZHmAADnVrgDToGVz7h8PSn3ltKsvxjUMpvcLBvBmr+/VbyU27VQYKp6PYcimSZY7Dfb6bvDB1mPGVMMmsik3ZOnBzPfXobjZPh+/S93A9/lrNjy+FLFQUqT0rJQV2PXNzO6PKgpCz+gUw5Kqv86acaHi3ylJBl46tRAnKYyvfsXOrYcg6iiJvyEYFnI05TW+HveFNK1tnEEYeguOfgIG7YP/jcPKn4chHYeAugn37KgY2uiLhr8NQsl1KU64K3jbl2PLZ3YYESnyXO1GaCrt1AjWc60pRGHYKW8tSqqTS61zn82K3NCyltIrtaOaXUGzMa1YZuK/MEE0Anx4jZFvCQOLqck/V5621HVwW6pYRCT61e9tiYFubZ22gE9y0lLC7tvZZpybX5XlTTndj34WylBBWm3ApJHHrCBqrYZWkNgq2d1p343eoJS9eAGgOiBwFbz8oa9tEPLaKoxjKuRKXw+fQtpQEdoNGpn73+ey3nQ5sM/4SZcOdOAbDLh2fXW3INNC1rtvNrthRxNq/+1x9yNK6c6PNB0LCqclbyvkudOQ2vKTV4ncDsDcvLK6lNKXkEmXby9ezXm9TQAhBlz5S9bGVaPeSFHSCm5YhicpC4s2E6rhIlsrc6CgoYue/zqBTr9i5c7t0TWVlO+ESGbadLE3taSDzoClS+YCI+n6jBdohe9OIQFiSBMPB9lvdNwunLqOXOBZ3oh087NaRJNFQV9rmhd360tSge3DjxpIEdh/AloyjEAJPW+puCsFNeVExrOlubq6GSGSrnTcNlFz1EuvmklSBgNaHIho/d9nkTnDzrsXv1NBqFDsChJxazcK/Utqa3ShJCQG9/sorf69DqTrE8VYgK9sJlfCW2SkzP1miYTO6ngq6r646Mzew+8FNLd425bidu6bKBbGtzty4bQo21QpQgiUGuVZj83dZKE3ZZBtdjhLGc3ZLdzMcdDIYcCCtO8362jC4uZkXFfckF5CNXNntvHqcsH0Js9bSVA0dUw5la+YGQBIyXfpQ1ceXo5O5eRdTb7pflSW8NWY5SmVudqMkFXRq2Kv4raiShLvBC1E7kVPsJR2lg05tR+b39PsdJVfltVCuG8qRH+RaL2GXjk3dvVPHdgKUoEu/bUqlmymXJS3lC9NM1pdmgw3oblybzg+FjqlBz2Bpi4K87kZgBe5He73FAKkdO6YWVRcxWUcxDSKpxYrb1tM1pWRjJFMS0Vj577dc5gYgpA8i53uKcjl46byPN694aql2oXcyN+9OXDaloQt6qMZVTynNzU5nboSAnhonLvu2UZqqVFLZSRxOd8kJ3JIkGlqt1st2xLAhp17SbqDR+VhCCPp2adq2IotttRzD7Zu9KXesKLJUdABuBeuDm0ATMzdD7jKZBcdGx26HKnO010O/345X2NtPd7N+iGa8cmlqbz64mVgNEc+UPz6TWZWXrvfx1ed6+OYLEcamtx6PmmSv2NEkC5WgPoBpwovnA1ybdHF+zMPkfOWFuUC0vTsxdIKbllBPa+16avW80VG3DEHbaY+boFPDodZ2wiwlcqzpNVwaQ0FHW/iTeL2+svfVm6Wrl36/vWYvmlJIkii5j93b2O/+bQYYjTLgr93bphwjIedtUSrdTKWFQCt1N+uDG4+tfufuzZkbp+rEo3nw2XylH2DfOo5EYAnGj/X5CJVoJ99tCn435SaEF/BoCSKOBUwEV5Z7t9yfycm8NH2A/3Hucd6cGcQwBCB44VyA6YWNwVApMfFmuvRh3rnm5cbM2mf22iUfufLVM1TJhiQqn/vnV3ffAqQT3DQZTRENZxsKnjfVtxPYNlkU7aTHTT1ZG7DM5Ox1DtIUee8cTZa2lflpFn5v+RNFK0XFdk3irpGtJ/N6KRUcbSco6/bYdrwNHmBvHd425bBr8raCxXbErklF3UspWtUOblOlLc9dyyiPAus9bgo4VSdDngp6ENUGZQIYpyZzZ1cXPrtW8wxsGyoeWvt7KGRuepNzVbucCl1TV5bWgpucKXhrbpg/Pf8YL0wdJm2oBG3LPHRskoGuOKYp+MFbQRaia99FpZJUgdfH4rwzarWqn96/iE3LEUsqXLyxdbhugWp6m9mVFP/sK2/zT770BslMhSipxXSCmybTVWP7dzlq9byxrQtmZCRsVaaFNxO/o/aszfrH1EPQqRdfo5GOnmYT9PvL3+fS2WYyoSz37AlWvGjVyuaLucumNCzKBZAlsWHcxk4Q8egNCaBLcbjbsyNaqZ2i2vHVKp1RKSuEehZ36z1uCjhV59YuqS0PLB/wh1UnfodKt9deU1PHASlCQGqtB9K0zUdGyDhyaQLplYrb7vXmS1OxIKtpG5cWe/mL84/y9M2TxLM2PFqMxwZf4icPPMmgd56zhxfo8ifJ5iSefSPEStz6TMuJiQtcmlnhK69Zr3VkKMq+/hgn9y0BcG7UTTxZ+rxTyeMmmzP44otjpLMGNxbjdTXVNJtOcNNEhCS2vYp3ajLOGrIc9nWi4p0WEzeit6inRVQS0LeuI8djU+vO/DQTTVNxOssf0LIkGjYwq8TBbldxyvp28djUDbqLSBOyTUM72FZ9uMfNo4dKdM40SLfXxgePd++IXgqsTKSuSOiKhKZIqLJAlQVK4U+y/hotl1ULKDxlhqhul+0GN5tLUgCKpJR0Kt5AidJUgaDiQBYCmyLR47NVDGJ7hQ+/5MRLawN1Q8hM2iy34kpzpgDcWpJuxzwg+MuLj/DtsbMsp13YlSQP9r3B3zv4BAf8NxHCciqWJbjv2Dw+V5pURubp10MkU0rFstTUcpI/e34Mw4RTAz7uO2B93gNdCULeFDlD4vXLpR9fSUz8rXemmVhO4tIVfv8nTyHt4gLi1m9jaSM8ukIzlvAht06sio25TaiQz27upJg44NTqMhws4NIVVFmQyVWX4ofdWx2Pw26dsQas3ZuBw1l9FkvEozO70rw6s9eucmqgfLaoEXq8di7PrAK1z5OqxEDAwZkhPy+PVu4A2Q6qLLh3b7AlE8ndNpX3HYnw9kSUt24uY9TQJdIopwd9HOqu/jt6ZWyR85OVV/al2LXMTYkOwlJdheVo5FxiPbD8sSELCb/sYC4bQ8KSCcyUODbtqOyRLC2MW9gQCExa9yMYt4cYTMzQl5jjDd/eitvu800wFQ+SzOmoUpZT4UucCl9BlTeWeQpOxapi8uDJOb77ShexhMKzb4Q5/QiUqkZGExn++IfXSWUNhoNOPna6j7TpYXl1CiHg9IElvv1iF+OzDmYWY3T5UyhCw6OE8apdeNRwyX2+NL3Cs5ctTdGn7x/a9dJvJ3PThgRr8LxZn7nZqeBGU8SGjEo9CGrrmpIlUXJOVcil7dooB5e7ujCvmU7FkoD79gab/n7X+900SwR9sNvNPXsCLRHoBpwqHzze05LApoAQgmN9Xt53tLtlAUDEo9cU2ADsDTVWHqmWLdEVuent+4okSgZV9joc1xsujVbI3IBVmiq+hqZsKX8JBAflHuS88aksJNyt1t04ChPCK4uKAQ4FxtjrvcnJ8GV+5vC3Odt9cUtgA6Bk1xZ8Ns3goZOz6FqOxVWZ//7cKJncxhmEqWyOP3nuOsuJDCGXzs/cM4giSzgUL27Fyiz5XBn29lkzut68HGaP4y6OeR5lyHkCn9ZdUky8msryf788DsDdIwFODzZ3YdYIneCmDanF82a95mYnOqU8NoWjvd66tTbrqaU0FXbrJeu0qlTZabeVuN3VL0xhl960rq6TA7665j3VSpdHR+RHYjRDx1NgT9jFA/tCTe1q2x9x8fiR7pJli1YQcGp84Fg3h3rcTQ3UCpmnWvE61LpLZboi1RQkNFtUHHRpZcsOtR6rDX+/Ni9I5X/DIWVj6STo0pDWfbH9wr/FE6fVHjk3bUFMwJ9ZxVllNpQmZ3n/8Evc3/s2diVddjvJTCOMtaGoLnuOh07MoSlwbS7GX750AyMvYDZMky++eIOJpSROTeYX7hveEPRF9L04ZR+9tgN87PgRHJrMwiq8NWaW9hvKY5om//PlcVZTWbrcOh86Xt2AcCfoBDdtykDATpdHL7t636C5aXHmptdn51CPZ9viMK+98iBNS6RafvW0k3OcCsiSqCm4UeT6BlCWI+LROdxT2yq/XnRFJuDU6PY2/3McCDh4+GB42yJdRbKCgbPDgR3P1MmS4I5BiggOXAAANTlJREFUP+891NU0X5izw4GSotlK1NsRVmsg4W1gNEIlKh2PtQZojkY/ZyGsOVNlCCnODcGMItZGQzjRGZZCWx7TjOBG5P9XirSsMatbWeBqc6bqwZ7a+Fw+d4ZP3hVBlgRvT0T5m9cmME2Tv3tjgvNTKyiS4GfvHd7yu4lk05xShonY9uK3e3nfkW4AvnNumpVk+SnlP7w6z4Vp63l/6uxgST+w3aA99qLDFuyKzEjQyR2DfvZ1ufA61A0rSllIaCgIBI4yU8K3iyILDnS7GfDba26rrIQsREW/jW6vreKB4daVlpqRlcKlywittovNdsXkmiLVtcpvhB6vrWUjI3q8dh45FK7Jq6kUHrvC+49277rJXpfHxoeO97Bnm23nQ0EHww28l8GAs64gsdagutmZm4rBTY0C+21l5lyRsncpQsInb8rM2FRsiswhuXtD4FO8X9jLBia14sFGvyhfkqnV76YeHMkpxLqxDjIKh7rC/OSdAwjghesL/Ndnr/Hc1QUE8JN3Dmw1wjRNgktvEFl4ETlf6rpz2E+vz0Yqa/Ctt6dLvvbkcoKvvzUFwAePde+6zmY9neCmzZGEpcE5FHFzasDHYMBRrGfbUdFRkFowMNOpW2Uof5N1CL4yz6fIoiZTuZ2a5VTAZVOtids1sN025bsaWOXXS6/P3lLTwS63jfcejqDXYeSmyoJ9XS7ef7S76dmFRlFliXv2BLlrpDE9kUOTOTvcmD+Rpkj0B2rPIgRqtFloZseUEJWFw7VkkyRRuhW8ZrqPg1z+9xJWtgaW9wcHcEulf/+KkLdd4g9IToalIE5Kfza1TAjXchl6E3McW76GN71a9TWFmcOZmCz+2y57EEJwvM/LR05aXjnX5iwNzQeOdXOsb6uG0BO7hpaJIuVSdM8/D6aJJAQfPWE9/uWxRW4sbGzoSGcN/uLFG+QMk0Pdbu7Z09qFWb10uqVuITRZosdro8drI5bOMTPvZCZWfXhavUQ8OoMBZ0ucgf0OlWslbu/xVm7ZLBB0aYwtxMm1srVlHW5dAbW21XfYZWlaapnNspnhkIPBHWitrqeTpVECTo3HjkT43vkZ4unSJl5OXabfb6fP56ArP1G6HdnX5cKuyXz/0hzZOn5z9+4N1u3Uu569YRfX52rrDqzVAbyZgmm/Q62YZdUUCY9dIZrIlt3GsV09leaA7hNw8+WSd4cVJxdYa7sOKA7ucHczlk0wtVx6orZX2ImZjXc9BoQTSUgckCO8lruxpfuqMCE8klxEy6VxZZOEU0uEU8uE09b/+zKx4vbzmpsvDH8As8oC1p6aJWHrIidrG/xt7tkTJJ7O8cS5ae7bG+SBfVvLccLIElh+p/hvW2qO4PJbzPuOMxh0csegj1fGlvjbNyb4hw/vLWa9vvbWJLMrKdw2hY/d0b9BlyNV0RTtBJ3g5hbFqcns9XmwSQo0qUNalgTDQWfNM64aQZUlXDaF1WR23W2lxwOUQhaCsFtjarn19t5CgNOm1Jy50RQJv0NlIVa+Pl0Kpy5z59D2XYjbCa9d5fEjEZ44P1P8roMujT6fnX6/vS1cp2ulz2fn0cNdPHVhllTWqLr9wW73trNjEY9ty3FSClUWuGssNzk0BUUWZGuwY6hGLfq3gFOrGNy4mlFijhyD+UuQjG65K6Q4EViOGYqQOOu0LsB9fjuL8TSpzNbv0ivsTJhLDe2KhoJLWN+7R9jpF35umAsbtllRnUQVB55snH90+SvIZVrPVxQ7ei5NML3C/tWbXHQPVHl1A0dighXX8BZ/m0cPdXH/3iB6mSYC7+pl5E0Txn3R88T1MAl7N+8/2s3bE1HGFxO8MrrIncMB3p5Y5oVr1nv7xJmBDeVFe2IKX3wMGK6yz62lU5a6hXFJGiNeT+NeEevQVYkjvZ6WBjYFNpemen125Dpy/+EaXZy3i0OTUSQJlNpLBOE6y2ZCbH+V3644dYXHD0e4e0+Aj93Rx/uPWinxWymwKRBy6Tx+NIKryoRty5/I15TX3FODXqde5+9mZW9qOQar6W6czSjBShL031XyLlWS8eWP3aP2CB7Z2mdFCIbKzEbzisazp0Gx8fsaloIly1xXnZZQV8YkLRQmbAFe9+7hO12n+fOB9/D/2/dj/Ke9H+WlwEEA7l44V1M62JZeQMkmcCq+LfeVC2ykXBp/9HzJ+7oXXkDOJXDbVN6bN9D85ttTTEWT/NUrlrPxg/tD7Otasy9wJCbomfsBwqwclO8EnczNLYxT1rGbBt0hG+9MRhsqh4B1ftjf5d5Wm3c9+B0a44vWSkFXpbp1NA5NxmNTiFZZ1W4Xl65YgY1Ue+DR5da5MFW7CduJfu+O64h2Ersmb2uieTvhyZv+PXlhpmR2rtn+RHvCTt68uVzxuK63Q89jU5lfLd9aXCu1ZG6qdUxtZ/zHBnwD4B2A5Rtb7gopTlQhc8C20XjO79DwOzUWYxs/C10o2FFJUF/2FcC/KbixylPdvL6pPPW9rtOcdw8SVZ0sqU7Kibpe9u3n7MIFepKLDMZnGHOWF1BbmPiTs2hS7Ysxf/QcklH6vUq5FN1zz3Oz62Hu3RvixdFFZldS/KcnL5PJmfT57Dx+ZG2fnPEbdM+/AGb17OZOcPstF99FuCQNt6zj0pVtTXgeCjhrGvnQLByaXPRZ6fU2NourWTOGKuGuQ0xcoJ529ZGQk6O91Q0CO7QPNlXmvYcj9Pi2/v5O9DfXn8ihKVW7T+r1fmqGqNhlU2oah+J3VDYjbaqH0cBZKKFL6VU93OnoL/mQoaCjZCDqaaAlXCDwl8j6eIWdPuHbcFtGUhhzRljSXGUDG4CEYuMN7wiQz97UQDCTwJaqrRNLycbxrl6puI0tNUsg+g6yJPhIXlycyZlossQnzw5YmW3AHRvNC5HbI7CBTnBzS6NLCjbJSjP3+x0NzV8KufSWTrUuh89hGcnVOih0M36H1nDLca24bErZ6cPlsKlyzWaFdzdh2neHnUeVJR7eH97Qst7l1jncU36ScqNUcyyutVOqQDPKUqVGLpRClkTFY6Fhj5tS2LwQObrl5i7VhVMu/RnpskR/Ccd1XwOlKQ92lBLOvQDDUqjh+X8vBg5hIBiJTxNJLlTd3i1sBJfeqOm5A8tvIczqU7v90XPYk9Ps63Jxst9ajH30VG+xOcGzepWuhRcb66RoIZ3g5jZBElYmoJ62VbsmMxzaueGH6/E7VPp89oY7siRRv76lHnRVQpeluoMbqO5347IpPLg/1LYdQh2qI+XNBo/2eoouxJVcXBul328v21avSKLuTEwln6laqSc7GawQCDXdfbrnZN3Ha8Rj21IeayRzE6wwKVsWEgflSEMeOlHVybn8hPS7F0prY9bjxoYtNY8zfrPidlp6CXd8rLadME0i85b+5uNnBvgnHzjEHfnxCt6Vy4QXXm67wAY6wc1thVtXau86kixvkXqEvM3EbavfZn4zYbfekplGkG8BB6jRwG89lTQ0qix4+EC4qeMPOuweJwd8fPB4T/P0I5uQJFF2AeJzqHUHVG5d2fZs33qCm3JlM0mAvdnHgKxC/511PURgLQrXrzMcQkOrU466WW+zGa9w0LupPFUrzwcPAXBgZRx/urKez53v1gosv1Ux4Aguv1lXQCLnklaAI9ayf77oBUKLr9b8HDtNJ7i5zej322u6cA4HnTsmIC6FyP9tB5sitWzYYbErppHMjaf0yV8S8MD+UMv2ucPu0Or5V+UE2Y3MWhOi9tbxUuh1HnPBMvvo0JWWZLoI7gVXV10PcWoypwZ9nBrwcbzfy7E+LydCQbo9NiJuq2wfdun4HFrJc5aOgktUD/hGpNCGsTm1Mqf7uOLsQcLkbIXsjR0NNV8a0zJR3LHrpbdLzuBITNW9H/bkDP6opf3xL79Tc/lrt+gEN7cZshCMVCk1RTz6jrR87wStEha79PxJqE5BMVi6m1LlgjNDfnq8rR3O1+H2w+fQCJQw6mtUvLydMQz1znfz2lWUEtq4pnjclGPgnopC3VKokoSuSDhUGacmM2D3YFdlHJqCU1Nw6Qp+u1rSBDNQJWtTQM53TzXC84HDAByLXi87dNMjNp4Lg8tvl9TUBJfebGgfAALRd4jMPUdg+e2Gn2On6AQ3tyEem0qkTPbAqcsMBnZ3dk8z8dlVdLW5P2NZEmveQQ1kbmBraepgt4v9keYLTju8OyiVvalXTFxgO5nDeoMbIUTJ/WyKx005nEEI7t/WU5Qa3QBWlm7zwrDW4AYssXIj5alxe4hxWxDFNDizeLHkNu5NwY2cS+BdubzhNlfsBnq6ujC5LKaJK7615b4d6QQ3tyn9AceWi35BZ3M76VgFtXdv1IpLV9bSzw1obmCjqLjHZysK8Dp0aISh4MZhmpJoPEjZTjt4vcENQKBElrhVGqUifWegTJdULXhlW7HEsxm3rhbLbQKBr47gBmCPFMZWb3lKCJ4PWtmb00tX0HJbvYo2BzcA/uj5NR8b0yC4/FZ9r3sL0wlublMUIRgJbjzo9oSd2JTbT8ja47VzpNfDcMhB2K3j1OVtBXDugt5GVisO5qtEQXfjc6jcvzfUGn1Bh3cNmrKxbdnnUBvutms0KFKk0lmYaoRKOBW3PLhRbdB7uuGHCyEIKuWzth6bSsCh4RV2lDoHF8tCYkiqf8jkFWcvc5oH3chwammjP41A4CoxrFMy1hyIvatXUbLVB3HeLnSCm9sYr10trrS6vbaG09jtjiTynWJuG3tCTo71ejkzFOBYn5eRkJOIR8dlU2o2Gi4KRBssSYFlwBZyaTx8IHxbjlbosPPsWVeaqnfswnrcNrWhLsOAU2sooCqdudmBRVb4ENh9jT+8TGmqgNeuctjb2CTsLuGpuyMLIXg+YHVO3bl4EdlY09O40JHKBFnelcuomSiB6Dsl779d6Zx1b3MGAw4CTo0B/+742ewWkrC6ILrcOsNBJ0d7PNwx6Odgt5tur162o6w4LBMaLkkVeORgV+tXqB3eNXR7bcWgoJFOqQKyJBr6XTZSkgJrsbDZq6elmpsCkgSD95Z0Lq6FUJXgBuBEIESvr/4mAUkIeiVf3Y875xkiqjhw5ZIci14r3l6qJFVAmFn6Z55EyrV+2HA70QlubnMUSbD/NtPZNIosBD67ylDAycl+LycGvAwFHXjtajGr49BklMKydhuZG6CTsenQdArC4u2OefBUGQBaikaDG9g4Z0oSNGXYb024u2HvexoKcPxK5YG+DknFK9sY8Nurjsko4LIpDATshFw6vcKLVKchhiEkXswP1Lxr4QIiP+6gUnADvOsCG+gENx3exdgVmW6PjUPdbu4Y9HOg271xFdZAG3iHDq2kYDi3nbIU1K+7EYKSbdC1sn5CeMs8bsrhG4S9j4JUX0AlC4lABd1Nj+op/vdQwFGyQ1WSwOtQGQk5OD3o42iPh16vnS6PjioUIsKz5THVeMO7h4Sk4c+scmBlHNjaBt6hE9x06ABYWR2/Xd2oS1Jvn5b5DrcHTl3hQLd725PH6w1ufHZ1W5nI9bqbnRzSW8Q30FCAU6k01a1utHYYDjoJu3VkSRByaezrcnF60M+hiJsutw1tnTW0S1fQFEG/VH8XZUZSeMVvtbrfs3AOxRQ4ajARrIaaWUXJxrf9PO1CJ7jp0KEcasdwr0P7cawJk+TrnTG1nZIUbHQq3jUdmrcf9r4XpNpfv1xwIwtBl7rVe2gk5OSOQT97wy6CTm2txL0JgWXO6BB6XT45BV7x7SctZCKpJQ4ltuFbk0dPL3Dy8v/Ficv/CS29tO3nawc6wU2HDuXYpqC4Q4dW0AwtV70uxdsNbmyqXBRDt3pcRUW8fbDvsZoDnKDiKKmKCSnOki3gAmrWNxZKi32i/uxNQtF5w7cHgDPz23QLNk1GJr6GbKRRczEOjv0FUgkfnXpoZEhos+kENx06lGObguIOHdoVTZGwa7Wf/rcb3MCa7mbXOwg9PbD/8Zo8rFQh41O2ZnA3l6Qa2g2biiwJApITZwmPmmq86D9IDkEkfhPfSmnX4loILr+FN3YVQ8hkZCeO1Ax7b355W5O+vY7dn5/XCW46dCiFkDplqQ63NbXqbrx2FUcTWrcLHVM74nFTDXc37KstwClVmmpGcCMJy4wRaEh7s6I6ecNvdU7tufk3qJnKE8NLoWTjDE19E4Cb4Ye4OPhJDCETWLlA/8z36n6+Au3gqdYJbjp0KIXqqHv4XocOtxK1BDd9fjuPHalvynY5CrqbHfG4qQV3BPa/v+qYhs3BjUvW8MjN6U4qlKa6hLtuUz8ZicXIh4jZIqi5OHtv/nXd2ZaB6e+g5uLE9TCTwftYdfRzrfcjAPTNPdvQuAZN2d7k+WbRCW46dChFJ2vT4Tanku5GEnB60MfDB8LoTRrZEnBqyNIOetzUgisMByoHOKFN7eDNyNoU8DpUJAGSkOo29dsvRXDKLi73f4ycUPDGrtI9/8OaH++OXadr6TUArvV+GDPfSTbnO8FE8D7Aygg5Ezfr2i+PbfezNtAJbjp0KE3H46bDbU65jimnLvPYkQiHe+r3YKmEIkt0e+3tN2fNGYKRh8pmam2Silte08R0K80LbhQhcOe/h3pM/fqEn4hkfT9JPcxo9wcAGJj5Lo7ERNXHCyPLyMRXAZj2n2HVMbDh/huRR1l07UcysxwY+2JdJS9fG+htoBPctC+OYN2eDB2aSMfjpsNtTqmyVL/fzgeP9WzLsK8Sg4E2XTT4BqDnVNm7C6UpWQjCJVrAt0MgHwzUaurnxcEeKbzhtln/aRY8h5FMg33jf1W126l37lns6XnSiosbkfdu3UBIXOn/GHE9jJZd5cCNLyIK08UrIMT2ps43k05w064MPwCHPwKOwG7vybuTTuamw22OTZWLbeWSgDNDfh5q8aDXvgbmMO0YvaesIKcEheCmXAv4dvA7tGLSqK+KsFhH4Yjcg7Q5yyQEV3s/TEr1YE8vMDz19bLPYU/O0jv3LACj3R8gV0Y/lJN1Lg7+FBnZjisxwZ6Jv62q6XHqCmqtE4pbTHvsRYeNeHqtoMbuh0Mfhsix3d6jdx+dNvAO7wI8NgWnLvP4kQgHu5tXbilH289bG34IbFuzJ4UJ4etHLjQLVZaK7fHOCqZ+EoIjci+aKJ0Zycl2rvT9OCaC8NLrBEqJgU2T4cm/QzINFl37WfAcrrhvKc3P5YGPYyARWn6L3rnvV9y+XufrVtLmv7RbDNXe8ATaDXQdWftvSYaBs5borWMqt3N0gpsO7wIOdXv44LEegi0qQ91yKJo1pmFTi7hT1rBLalPFxOtZ3zpdztRvn9SFR1TOfK04h7gZfhCAPZNfpV+PMxJ2crjHzUjIyd7km3jiN8hJKtd7PlRTR2jUOcJoz5qmxx+9UHbbTnBzu6LaIXRge89h85ROjXp64ciPgn9oe8/foTY6gWSHdwGDQUf7Z1N2Grsfhu7fcvOI7t8gLG4mfudaUFDK1K9H+Ogp003lsSt0e22MhJ0c6fXQfeaj4B9BzqXou/YluhwKHptKl5oidMPytJEO/QiH9g6yr8tFr8+O166iyuUDnZnAnUz77wRg7/hfcWDsLxiY+jbhxVdxxW+gZOMosthd9+lNtM+e3C50H4f5S2DkGnt819Hy9ym6taqYuwQ3nodcdYFXhwbpZG46dHj3EhiB+DxMvVm86YAervCA7WFTZByaTDxtXTf6JT8XjCkAPNjYJ5X2GvI6VA5FSmSTTv8MPP2vYfE6XPomHPwQvPNlyCTAO4AYfhC7JGNXZILr1nGvjy+TzJS+do32vB9begFv7Cr+lYtszi9lFQdivBucXRDaZ1Ud9j1W70fRNDrBTbPRXVb2ZuZc/Y9VdAjuq75daD+4InDtaYjN1v86HSqj6CB3Do0OHd7V9J2xApyo1Vqttrh71e/UiKcTgGXqd405AI7IfVsFxIAsCYaDZRZhjiCc+El45U/g0rctIfDEq4Cwbi/zXrrcOmMLpSeDm0Lm/NBP44mNYk/NYUvPYU/NY0vPoWei1kTxhavW343nYPKNTnBz29F9HOYu1p+9CR2o/aJq81jR+MWvw+pM/fvYoTydrE2HDh2EgJGH4dzfQnq15S/nd2jcXLSCG0lI9Ek+vMKOXkZA3OO1YatksNh7B8yehxsvwOVvW7fteRi8pTvCAEJujfGlOIZRZgMhEXWNEHWNbLhZMtKc8qdRE7PW9Si1AoP3lN+3HaAT3LQCzQmhgzDzTu2PERKED9X3OpIEg/fCub/Z1pCzDpvoBDcdOnQAUG2WFODCVxuXGtSIU5PRVYlUxoosBqVg2W3tmkyPt4a2+qM/AQvXrAy/3Q8HPlhxc1WSCDg05lbrmwpus9lRA93AoHWDp9dqgtlFOkqyVtF9HKQ6YkffoFXSqhdHYGN3VYft0/G46dChQwFnEAbv25GX8tfo7jscdCDVYmas6HDn34fIcbjj561/V6HLU//crHJu17tJJ7hpFZoDwgdr3z5SQUhcjd7Tne6eZtLJ3HTo0GE9oX31Z9YbwF/DNO2QS6s4F2wL7h44+4vgH65tc12pe/6XrxPcvMuoNXvjDINrG5N3ZRUG7mr88R020gkUO3TosJn+s6C31ujQbavcki1LYkdGWHR5am95l6X2mAK+mU5w00pUe23RfqQJZSX/MHj7t/88HTqZmw4dOmxFVloukhWA114+ezMQsKPKrb9sB106ck11L3DblNpKZDtMJ7hpNdWyN5oTfMPNea2Bu+vT+aynxpTlu4JO5qZDhw6l8PZbHjgtZL2h33qcukKXu349TCMoQhB0VS+RQftMAd9MJ7hpNaqtsuA3fMjqemoGNg/0nKj/cZFjsPc9lpq+Qydz06FDh/IM3F2TMLdRvHZ1S9ZECBgJOdnJBEmtgVQ7jVxYTye42QkiR7fMKgGsLEs9ouOaXus42Ly1b99zwppdBZ3sDVgt+erOrI46dOhwC6LaLf1Ni5CF2NJ91OXWcdYp8q0b1Q7yWrbGqcm4bJUrATZVruy1s4t0gpudQLVBV4npq8F9zV8BFLxvaqH3lOXCWaAzt6pTkurQoUN1QvutLqQWsb4lXFME/f4WZ5MLhoX7HtsgbehyV74+ee3ta5XXCW52isixDVExUDrgaQaeHgjurbxN3xmrhXw9dn99WZ/bkU5JqkOHDrUwdG/ZMQbbxedQiyLdwYATpdWK3Z5T1nXDHYE9j1gZbCDg1FAqdG+1a0kKOsHNzqHoG4MZbz/Yfa17vf6zW4OpAgN3ldfm+N7l2ZuOgV+HDh1qweaF7gY0jjWgShIum4LXrhB01ibsbRhPH/ScXPu3bwCGHwCsElnIVTp7I4n2NO8r0AludpL12ZtWuwqr9o0lpwKD91Q2DHy3625svt3egw4dOtwqdJ9o2SI16NQZClUpk8ua5VLfKKoDRh60ylIbXnyvJZwGwmVKUy6bglxioGe70AludhJFswILuw+8fa1/vfBByyCwwND91UthzmDLjaraFrvPCkA7dOjQoRYkyTqvtoAut469klhX1v7/7d15UBRn+gfw78zADMcwA0rkHA6jsmC4wqEm60EyUXFjJOZaNxvAuKZ2Vy0rlBvdaMRUEkksTTSK7q66rm6pa2nimkQ3KxI84kXAazVq1GggKqA/PABFlOnfHx1GJ3L0DDPT4/D9VHUV0/12zzOPCA9vv2+/QO+hQMyvxN4XaykU4kKanm2sURUUB4QkwsdTBV0rY2vaex6PK2Bx42w94sT7m86gUIiDixVKIGog8FAfaed1xVtTSpU4oE7qquxERID4dHknLM1gwcML6DMc0D4k/szqZQS69bTuGqHJgF9w+23CHgV6xLY6LdwVl1y4F4sbZ/NQO/whUBZ8uwNxo8S1UaTqirOmwlI6171LRF1XWIq0yQgqT7EICUm0/YGrnt7iitu+96warlSKvTBS1yjUh1uOs2lPRH8EhMdYLAuh9lBYvf6Us/HP1K7A2nvC2h7if9TbNxwSjsvRh3du4VIi6to81EBEP+BMyf3HlB53n2ysN9ydYdX9YeDc10B9jfT38fQRC5u2fqYb0sXi58eytq+h9hV78q2gjB4EffUVXD7/PQDXvyUFsOeG2uIfIXcEzuHpbZ4ZQERks4Couz83lSrx656DgcQx4hPgA6Isp4576YGYEeLMVilTytVaICaz4z9Wg+PF4kXRyq93hVK8/W7tg0qVSgQlZ+KWVyAA154C3oI9N9S6gCjg0gnbztUbgBuXgds37RqSQ0Q+3vaAOiIia0T0/2kRY4PYm9MRhQIIfkTs2flhd9u9OBo/scdG6mSPwJ8eEPv9dsB05+7+sEfFZ9nYQOvtBdPDT+L2qS+h83bdWVIt2HNDrfMLFgetWcvDC4geBPR91v5LS9hbj1jxmQ5ERPag9hVvN0kpbO7l7f9TL07q/b04Xjqxx8baWaz+BnHQcctT8PUGsVenE3qFdENj5BB4ql3/D0KXKG4KCwsRFRUFLy8v9OvXD6WlpW22Xbp0KQYOHIiAgAAEBATAaDS2255spFDYdmsqNEn8j+2hASIfA37xK9dckNM7wKHrwxARWUWhEIuP2GfuPsLD2x/ok2n7sjDah8SiSRskPs+mk8L8vREdFmz1mB05yF7crFu3Dnl5ecjPz8eBAweQmJiIYcOGoaam9e657du3Y8yYMSgpKcHevXthMBgwdOhQnD9/3smRdwHWzpry0gOBP+ut0fYQ/7OGp9o+O8DelCqxd8lBj04nIrKZt7/4R2HEgJ8Km04+Nd3bH/jFCLusY6hQKPDwQ1qxVyg0qdPXcySFIAiCnAH069cPaWlpWLRoEQDAZDLBYDBg0qRJmDZtWofnNzc3IyAgAIsWLUJ2dnaH7a9fvw69Xo9r165Bp9N1On63ZjIBh9cCzU3S2j/8RPsF0a16oGIfcK3SPvHZytBPfEAVERHZ7tS21n+e60LFMUJ2Zs3vb1l7bpqamlBeXg6j0Wjep1QqYTQasXfvXknXuHHjBm7fvo1u3Vp/RsmtW7dw/fp1i40kUiqlj0nxC+64p0ejBXobxZkDcq2+rQ9nYUNEZA/Rg1z2ifayFjeXL19Gc3MzgoIsR28HBQWhqqpK0jWmTp2K0NBQiwLpXgUFBdDr9ebNYOAAUqtIfVpxeLr0awZEiQOOI/qL94KdhdO+iYjsx0Mt9ti7ypCDe8g+5qYz3n//ffzrX//Cxo0b4eXV+syeP//5z7h27Zp5q6yU+ZbIg0YX1vE3breelk/LlELlKc5W+sUIIP4FcUyOPZ8QrFKLvUPe/uLgPF2YOAiO076JiOzHp5s4ecTFyFpuBQYGQqVSobq62mJ/dXU1goPbX/Ni7ty5eP/997Ft2zYkJLS97LxGo4FG0/mBVF2W6qena1451/pxpar11cetodGKswSC44GbV4Ha78XtVl0r8ajFqZEav5+2n7720IjHlJ7WT8MkIiLbdX8YaLgE1ByXOxIzWYsbtVqNlJQUFBcXIysrC4A4oLi4uBgTJ05s87w5c+bgvffew3//+1+kpqY6KdouLCCy7eKmR1+xOLEXb3/xQVNhjwINl4G6KrG3RaMTixo7jPgnIiI7C08HbtQC9dUdt3UC2W+U5eXlIScnB6mpqUhPT8f8+fPR0NCAsWPHAgCys7MRFhaGgoICAMAHH3yAmTNnYs2aNYiKijKPzdFqtdBq7fhLlu5qWQ/F1Gy538Or0w+FapdvoLgREZFrUyqBnkOA45/LHQkAFyhuXnrpJVy6dAkzZ85EVVUVkpKS8OWXX5oHGVdUVECpvDs0aMmSJWhqasLzzz9vcZ38/HzMmjXLmaF3HSpPcWrf1Z+NV2p5YB8REZHaRyxwqo7IHYn8z7lxNj7nxkaXTwPndt197aUH4rLEap2IiKhFw/9ZP8lEggfmOTf0APE3WK4yG5bCwoaIiO7ngMLGWvztRNJ4aAC/EPFrKQ/sIyIikgmLG5KupaCx5oF9RERETib7gGJ6gPhHAPU1LtHlSERE1Bb23JB0nt7iSrVEREQujMUNWUfFzj4iInJtLG6IiIjIrbC4ISIiIrfC4oaIiIjcCosbIiIicissboiIiMitsLghIiIit8LihoiIiNwKixsiIiJyKyxuiIiIyK2wuCEiIiK3wuKGiIiI3AqLGyIiInIrLG6IiIjIrbC4ISIiIrfiIXcAziYIAgDg+vXrMkdCREREUrX83m75Pd6eLlfc1NXVAQAMBoPMkRAREZG16urqoNfr222jEKSUQG7EZDLhwoUL8PPzg0KhsDiWlpaGb775xqp99359/fp1GAwGVFZWQqfT2TXu1uKw13nttWnrmC25uve1I3PVXtydPaejNrbmpbV9XTFXbe1vKzc/f/2g5qqjdsyV9HaOyBXgej/fu2KuBEFAXV0dQkNDoVS2P6qmy/XcKJVKhIeHt3pMpVLd9w/R0b7Wjut0Ort/87f2PvY6r702bR2zJVetvXZErtqKxR7ndNTG1ry0tq8r5qqt/R3l5kHPVUftmCvp7RyZK8B1fr531Vx11GPTggOK7zFhwgSr97V23BFsfR8p57XXpq1jtuRKajz2YMv7dDZXbR1nrqw7bktuHvRcddSOuZLejrmS3u5BzJVUXe62lCNdv34der0e165dc8hfQu6EuZKOuZKOuZKOubIO8yWdK+SKPTd2pNFokJ+fD41GI3coLo+5ko65ko65ko65sg7zJZ0r5Io9N0RERORW2HNDREREboXFDREREbkVFjdERETkVljcEBERkVthcUNERERuhcWNTM6ePYuMjAzExcUhPj4eDQ0NcofksqKiopCQkICkpCRkZGTIHY7Lu3HjBiIjIzFlyhS5Q3FpV69eRWpqKpKSkvDII49g6dKlcofksiorKzFkyBDExcUhISEB69evlzskl/bss88iICAAzz//vNyhuJwvvvgCMTEx6N27N5YtW+aw9+FUcJkMHjwY7777LgYOHIja2lrodDp4eHS51TAkiYqKwtGjR6HVauUO5YEwffp0nD59GgaDAXPnzpU7HJfV3NyMW7duwcfHBw0NDXjkkUdQVlaG7t27yx2ay7l48SKqq6uRlJSEqqoqpKSk4LvvvoOvr6/cobmk7du3o66uDitXrsSGDRvkDsdl3LlzB3FxcSgpKYFer0dKSgr27NnjkP9z7LmRwbFjx+Dp6YmBAwcCALp168bChuzi1KlTOHHiBDIzM+UOxeWpVCr4+PgAAG7dugVBEMC/9VoXEhKCpKQkAEBwcDACAwNRW1srb1AubMiQIfDz85M7DJdTWlqKvn37IiwsDFqtFpmZmdi6datD3ovFTSt27tyJkSNHIjQ0FAqFAv/+97/va1NYWIioqCh4eXmhX79+KC0tlXz9U6dOQavVYuTIkXj00Ucxe/ZsO0bvXI7OFQAoFAoMHjwYaWlpWL16tZ0idz5n5GrKlCkoKCiwU8Tycka+rl69isTERISHh+NPf/oTAgMD7RS9czkjVy3Ky8vR3NwMg8HQyajl4cxcuZvO5u7ChQsICwszvw4LC8P58+cdEiuLm1Y0NDQgMTERhYWFrR5ft24d8vLykJ+fjwMHDiAxMRHDhg1DTU2NuU3LffyfbxcuXMCdO3ewa9cuLF68GHv37kVRURGKioqc9fHsytG5AoCvv/4a5eXl+OyzzzB79mwcOXLEKZ/N3hydq02bNqFPnz7o06ePsz6SQznje8vf3x+HDx/G2bNnsWbNGlRXVzvls9mbM3IFALW1tcjOzsbf/vY3h38mR3FWrtyRPXLnNAK1C4CwceNGi33p6enChAkTzK+bm5uF0NBQoaCgQNI19+zZIwwdOtT8es6cOcKcOXPsEq+cHJGrn5syZYqwYsWKTkTpGhyRq2nTpgnh4eFCZGSk0L17d0Gn0wlvv/22PcOWjTO+t/7whz8I69ev70yYLsFRuWpsbBQGDhworFq1yl6hys6R31clJSXCc889Z48wXZItudu9e7eQlZVlPj558mRh9erVDomPPTdWampqQnl5OYxGo3mfUqmE0WjE3r17JV0jLS0NNTU1uHLlCkwmE3bu3InY2FhHhSwbe+SqoaEBdXV1AID6+np89dVX6Nu3r0PilZM9clVQUIDKykqcO3cOc+fOxfjx4zFz5kxHhSwre+Srurra/L117do17Ny5EzExMQ6JV072yJUgCMjNzcUTTzyBV155xVGhys4eueqqpOQuPT0dR48exfnz51FfX4///Oc/GDZsmEPi4ShWK12+fBnNzc0ICgqy2B8UFIQTJ05IuoaHhwdmz56NQYMGQRAEDB06FE8//bQjwpWVPXJVXV2NZ599FoA4u2X8+PFIS0uze6xys0euuhJ75OuHH37Aa6+9Zh5IPGnSJMTHxzsiXFnZI1e7d+/GunXrkJCQYB5n8c9//tPt8mWv/4dGoxGHDx9GQ0MDwsPDsX79egwYMMDe4boUKbnz8PDAvHnzkJGRAZPJhDfeeMNhsxNZ3MgkMzOTM1ok6NmzJw4fPix3GA+c3NxcuUNweenp6Th06JDcYTwQfvnLX8JkMskdxgNj27Ztcofgsp555hk888wzDn8f3payUmBgIFQq1X0DD6urqxEcHCxTVK6JuZKOubIO8yUdcyUdc2U7V8sdixsrqdVqpKSkoLi42LzPZDKhuLjY7bsdrcVcScdcWYf5ko65ko65sp2r5Y63pVpRX1+P06dPm1+fPXsWhw4dQrdu3RAREYG8vDzk5OQgNTUV6enpmD9/PhoaGjB27FgZo5YHcyUdc2Ud5ks65ko65sp2D1TuHDIH6wFXUlIiALhvy8nJMbdZuHChEBERIajVaiE9PV3Yt2+ffAHLiLmSjrmyDvMlHXMlHXNluwcpd1xbioiIiNwKx9wQERGRW2FxQ0RERG6FxQ0RERG5FRY3RERE5FZY3BAREZFbYXFDREREboXFDREREbkVFjdERETkVljcEJHZoEGDsGbNGvPrqqoqPPXUU/D19YW/v798gXXg3LlzUCgUXWKV7+3bt0OhUODq1asAgC+//BJJSUlctZvoHixuiAgA8Nlnn6G6uhq//vWvzfs++ugjXLx4EYcOHcJ3331n1/dr+SXd2lZVVWXX97KH3Nxcc3yenp6Ijo7GG2+8gcbGRqfG8dhjj+HixYvQ6/UAgOHDh8PT0xOrV692ahxErowLZxIRAODjjz/G2LFjoVTe/ZvnzJkzSElJQe/evW2+blNTE9RqdZvHT548CZ1OZ7GvR48eNl3L0YYPH44VK1bg9u3bKC8vR05ODhQKBT744AOnxaBWqxEcHGyxLzc3Fx9//DFeeeUVp8VB5MrYc0PkZoYMGYKJEydi4sSJ0Ov1CAwMxFtvvYX2lpG7dOkSvvrqK4wcOdK8LyoqCp988glWrVoFhUKB3NxcAEBFRQVGjRoFrVYLnU6HF198EdXV1ebzZs2ahaSkJCxbtgzR0dHw8vJqN94ePXogODjYYmspsHJzc5GVlYX33nsPoaGhiImJAQCUlpYiOTkZXl5eSE1NxcGDB++77o4dO5Ceng6NRoOQkBBMmzYNd+7cMR/fsGED4uPj4e3tje7du8NoNKKhoaHdWDUaDYKDg2EwGJCVlQWj0YiioiLzcZPJhIKCAkRHR8Pb2xuJiYnYsGGD+XhLb1VxcTFSU1Ph4+ODxx57DCdPngQg3l5TKpUoKyuzeN/58+cjMjISJpPpvttSADBy5EiUlZXhzJkz7cZP1FWwuCFyQytXroSHhwdKS0uxYMECfPjhh1i2bFmb7b/++mv4+PggNjbWvO+bb77B8OHD8eKLL+LixYtYsGABTCYTRo0ahdraWuzYsQNFRUX4/vvv8dJLL1lc7/Tp0/jkk0/w6aefdnocTHFxMU6ePImioiJ88cUXqK+vx9NPP424uDiUl5dj1qxZmDJlisU558+fx4gRI5CWlobDhw9jyZIlWL58Od59910AwMWLFzFmzBi8+uqrOH78OLZv347Ro0e3WwD+3NGjR7Fnzx6LnqSCggKsWrUKf/nLX3Ds2DG8/vrr+O1vf4sdO3ZYnDt9+nTMmzcPZWVl8PDwwKuvvgpALCiNRiNWrFhh0X7FihXIzc216FW7V0REBIKCgrBr1y7J8RO5NVnWIicihxk8eLAQGxsrmEwm876pU6cKsbGxbZ7z0UcfCT179rxv/6hRo4ScnBzz661btwoqlUqoqKgw7zt27JgAQCgtLRUEQRDy8/MFT09Poaampt04S0pKBACCr6+vxRYXF2duk5OTIwQFBQm3bt0y7/vrX/8qdO/eXbh586Z535IlSwQAwsGDBwVBEIQ333xTiImJschBYWGhoNVqhebmZqG8vFwAIJw7d67dGO+Vk5MjqFQqwdfXV9BoNAIAQalUChs2bBAEQRAaGxsFHx8fYc+ePRbnjRs3ThgzZozFZ962bZv5+ObNmwUA5s+zbt06ISAgQGhsbBQEQRDKy8sFhUIhnD171uIaV65csXif5ORkYdasWZI/D5E7Y88NkRvq378/FAqF+fWAAQNw6tQpNDc3t9r+5s2bHd4+AoDjx4/DYDDAYDCY98XFxcHf3x/Hjx8374uMjMRDDz0kKdZdu3bh0KFD5m3Lli0Wx+Pj4y16R44fP46EhASLeAcMGHBfnAMGDLDIweOPP476+nr8+OOPSExMxJNPPon4+Hi88MILWLp0Ka5cuWKOR6vVmrd7B+pmZGTg0KFD2L9/P3JycjB27Fg899xzAMTeqhs3buCpp56yOH/VqlX33S5KSEgwfx0SEgIAqKmpAQBkZWVBpVJh48aNAIB//OMfyMjIQFRUVLt59Pb2xo0bN9ptQ9RVcEAxESEwMND8y90efH19JbeNjo5ud5q5NdeSSqVSoaioCHv27MHWrVuxcOFCTJ8+Hfv370dqaqrFrbSgoCCLWHr16gUA+Pvf/47ExEQsX74c48aNQ319PQBg8+bNCAsLs3g/jUZj8drT09P8dUsB1jKVW61WIzs7GytWrMDo0aOxZs0aLFiwoMPPVFtbK7mgJHJ37LkhckP79++3eL1v3z707t0bKpWq1fbJycmoqqrqsMCJjY1FZWUlKisrzfu+/fZbXL16FXFxcZ0PXILY2FgcOXLEYgr2vn377muzd+9eizE0u3fvhp+fH8LDwwGIRcXjjz+Ot99+GwcPHoRarcbGjRvh7e2NXr16mTc/P79W41AqlXjzzTcxY8YM3Lx5E3FxcdBoNKioqLA4v1evXhY9XVL87ne/w7Zt27B48WLcuXMHo0ePbrd9Y2Mjzpw5g+TkZKveh8hdsbghckMVFRXIy8vDyZMnsXbtWixcuBCTJ09us31ycjICAwOxe/fudq9rNBoRHx+Pl19+GQcOHEBpaSmys7MxePBgpKam2hRrTU0NqqqqLLbbt2+32f43v/kNFAoFxo8fj2+//RZbtmzB3LlzLdr88Y9/RGVlJSZNmoQTJ05g06ZNyM/PR15eHpRKJfbv34/Zs2ejrKwMFRUV+PTTT3Hp0iWLAdVSvPDCC1CpVCgsLISfnx+mTJmC119/HStXrsSZM2dw4MABLFy4ECtXrrTqurGxsejfvz+mTp2KMWPGwNvbu932+/btg0ajue/2HFFXxeKGyA1lZ2fj5s2bSE9Px4QJEzB58mS89tprbbZXqVQYO3Zshw+CUygU2LRpEwICAjBo0CAYjUb07NkT69atsznWmJgYhISEWGzl5eVtttdqtfj888/xv//9D8nJyZg+ffp9z5kJCwvDli1bUFpaisTERPz+97/HuHHjMGPGDACATqfDzp07MWLECPTp0wczZszAvHnzkJmZaVXsHh4emDhxIubMmYOGhga88847eOutt1BQUIDY2FgMHz4cmzdvRnR0tNV5GTduHJqamswzqdqzdu1avPzyy/Dx8bH6fYjckUIQrJj7SEQub8iQIUhKSsL8+fOtOq+qqgp9+/bFgQMHEBkZ6ZjgSLJ33nkH69evx5EjR9ptd/nyZcTExKCsrMymIorIHbHnhogAAMHBwVi+fDkqKirkDqVLq6+vx9GjR7Fo0SJMmjSpw/bnzp3D4sWLWdgQ3YOzpYjILCsrS+4QuryJEydi7dq1yMrKknRLKjU11ebxTkTuireliIiIyK3wthQRERG5FRY3RERE5FZY3BAREZFbYXFDREREboXFDREREbkVFjdERETkVljcEBERkVthcUNERERuhcUNERERuZX/B7XdehqCcmQ1AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "l = [\"Simple contagion\", \"Threshold contagion, tau=2\", \"Threshold contagion, tau=3\"]\n", + "\n", + "for i in range(sps.shape[0]):\n", + " ps_mean = np.mean(sps, axis=2)[i]\n", + " ps_std = np.std(sps, axis=2)[i]\n", + " plt.semilogx(p, ps_mean, label=l[i])\n", + " plt.fill_between(p, ps_mean - ps_std, ps_mean + ps_std, alpha=0.4)\n", + "plt.legend()\n", + "plt.xlabel(\"p (for Erdos-Renyi)\")\n", + "plt.ylabel(\"PS\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nx.draw(nx.watts_strogatz_graph(50, 6, 1e-6))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "hyper", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/run_dynamical_inference.ipynb b/run_dynamical_inference.ipynb index 0a86f06..1d9c95e 100644 --- a/run_dynamical_inference.ipynb +++ b/run_dynamical_inference.ipynb @@ -30,13 +30,20 @@ "metadata": {}, "outputs": [], "source": [ - "G = nx.karate_club_graph()\n", - "G = nx.fast_gnp_random_graph(50, 20.0 / 49)\n", - "\n", - "A = nx.adjacency_matrix(G, weight=None).todense()\n", + "A = erdos_renyi(50, 0.2)\n", + "# A = zkc()\n", "n = A.shape[0]" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.spy(A)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -48,11 +55,11 @@ "x0 = np.zeros(n)\n", "x0[list(random.sample(range(n), int(rho0 * n)))] = 1\n", "\n", - "alpha = 0\n", "gamma = 0.1\n", "b = 0.04\n", "\n", "contagion_function = lambda nu, b: 1 - (1 - b) ** nu\n", + "contagion_function = lambda nu, b: b * (nu >= 2)\n", "c = contagion_function(np.arange(n), b)\n", "\n", "x = contagion_process(A, gamma, c, x0, tmin=0, tmax=1000, random_seed=None)" @@ -80,10 +87,10 @@ "rho0 = beta(p_rho[0], p_rho[1]).rvs()\n", "print(rho0)\n", "\n", - "A0 = nx.adjacency_matrix(nx.fast_gnp_random_graph(n, rho0))\n", + "A0 = erdos_renyi(n, rho0)\n", "\n", "samples, l = infer_adjacency_matrix(\n", - " x, A0, p_rho, p_c, nsamples=100, burn_in=10000, skip=1000, return_likelihood=True\n", + " x, A0, p_rho, p_c, nsamples=100, burn_in=100, skip=150, return_likelihood=True\n", ")" ] }, @@ -115,57 +122,8 @@ "metadata": {}, "outputs": [], "source": [ - "posterior_similarity(A, samples)" + "samplewise_posterior_similarity(samples, A)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "samples = 1000\n", - "\n", - "gamma = np.zeros(samples)\n", - "c_samples = np.zeros((samples, n))\n", - "\n", - "p_rho = np.array([1, 1])\n", - "p_gamma = np.array([1, 1])\n", - "p_c = np.ones((2, n))\n", - "\n", - "for i in range(1000):\n", - " g, b = infer_dynamics(x, A, p_gamma, p_c)\n", - " gamma[i] = g\n", - " c_samples[i] = b" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure()\n", - "plt.subplot(211)\n", - "plt.title(r\"$\\gamma$\")\n", - "plt.hist(gamma, bins=100)\n", - "\n", - "plt.subplot(212)\n", - "plt.title(r\"$\\mathbf{c}$\")\n", - "plt.plot(np.mean(c_samples, axis=0))\n", - "plt.xlabel(r\"$\\nu$\")\n", - "plt.plot(c)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/sbm.py b/sbm.py new file mode 100644 index 0000000..13adae0 --- /dev/null +++ b/sbm.py @@ -0,0 +1,120 @@ +import json +import multiprocessing as mp +import os + +import numpy as np + +from lcs import * + + +def target_ipn(n, k, epsilon, gamma, c, mode, rho0, tmax, realizations): + x0 = np.zeros(n) + x0[random.sample(range(n), int(round(rho0 * n)))] = 1 + ipn = 0 + for _ in range(realizations): + A = sbm(n, k, epsilon) + x = contagion_process(A, gamma, c, x0, tmin=0, tmax=tmax) + ipn += infections_per_node(x, mode) / realizations + return ipn + + +def single_inference( + fname, gamma, c, b, rho0, A, tmax, p_c, p_rho, nsamples, burn_in, skip +): + n = np.size(A, axis=0) + x0 = np.zeros(n) + x0[random.sample(range(n), int(round(rho0 * n)))] = 1 + + x = contagion_process(A, gamma, c, x0, tmin=0, tmax=tmax) + p = beta(p_rho[0], p_rho[1]).rvs() + A0 = erdos_renyi(n, p) + samples = infer_adjacency_matrix( + x, A0, p_rho, p_c, nsamples=nsamples, burn_in=burn_in, skip=skip + ) + + # json dict + data = {} + data["gamma"] = gamma + data["c"] = c.tolist() + data["b"] = b + data["p-rho"] = p_rho.tolist() + data["p-c"] = p_c.tolist() + data["x"] = x.tolist() + data["A"] = A.tolist() + data["samples"] = samples.tolist() + + datastring = json.dumps(data) + + with open(fname, "w") as output_file: + output_file.write(datastring) + + +data_dir = "Data/sbm" +os.makedirs(data_dir, exist_ok=True) + +for f in os.listdir(data_dir): + os.remove(os.path.join(data_dir, f)) + +n = 50 +k = 6 + +n_processes = len(os.sched_getaffinity(0)) +realizations = 10 +epsilon = np.linspace(0.0, 1.0, 33) + +# MCMC parameters +burn_in = 100000 +nsamples = 100 +skip = 1500 +p_c = np.ones((2, n)) +p_rho = np.array([1, 1]) + +# contagion functions and parameters +cf1 = lambda nu, beta: 1 - (1 - beta) ** nu # simple contagion +cf2 = lambda nu, beta: beta * (nu >= 2) # complex contagion, tau=2 +cf3 = lambda nu, beta: beta * (nu >= 3) # complex contagion, tau=3 + +cfs = [cf1, cf2, cf3] + +rho0 = 1.0 +gamma = 0.1 +b = 0.04 +mode = "max" + +tmax = 1000 + + +arglist = [] +for e in epsilon: + c = cfs[0](np.arange(n), b) + ipn = target_ipn(n, k, e, gamma, c, mode, rho0, tmax, 1000) + for i, cf in enumerate(cfs): + if i != 0: + A = sbm(n, k, e) + bscaled = fit_ipn(0.5, ipn, cf, gamma, A, rho0, tmax, mode) + else: + bscaled = b + c = cf(np.arange(n), bscaled) + print((e, i), flush=True) + + for r in range(realizations): + A = sbm(n, k, e) + arglist.append( + ( + f"{data_dir}/{e}_{i}_{r}", + gamma, + c, + bscaled, + rho0, + A, + tmax, + p_c, + p_rho, + nsamples, + burn_in, + skip, + ) + ) + +with mp.Pool(processes=n_processes) as pool: + pool.starmap(single_inference, arglist) diff --git a/tests/test_contagion.py b/tests/test_contagion.py index 5a658d5..fb68a63 100644 --- a/tests/test_contagion.py +++ b/tests/test_contagion.py @@ -8,6 +8,7 @@ def test_contagion_process(A4): A4, 0.1, np.zeros(n), np.ones(n), tmin=0, tmax=5, dt=1, random_seed=None ) assert infections_per_node(x) == 0 + assert x.shape == (5, n) x = contagion_process( A4, 1, np.zeros(n), np.ones(n), tmin=0, tmax=5, dt=1, random_seed=None diff --git a/tests/test_generative.py b/tests/test_generative.py new file mode 100644 index 0000000..5391dde --- /dev/null +++ b/tests/test_generative.py @@ -0,0 +1,14 @@ +from lcs import * + + +def test_erdos_renyi(): + A = erdos_renyi(10, 0) + assert A.shape == (10, 10) + assert np.all(A == np.zeros((10, 10))) + + A = erdos_renyi(10, 1) + + assert np.all(A == np.ones((10, 10)) - np.eye(10)) + + A = erdos_renyi(10, 0.3, seed=0) + assert A.sum() == 16 diff --git a/tests/test_inference.py b/tests/test_inference.py index 0429be5..d9143f1 100644 --- a/tests/test_inference.py +++ b/tests/test_inference.py @@ -14,6 +14,9 @@ def test_infer_adjacency_matrix(x4, A4): ) assert samples.shape == (5, 10, 10) + mean_diag = np.diag(samples.mean(axis=0)) + assert np.all(mean_diag == np.zeros(10)) + def test_count_all_infection_events(x4, A4): assert np.array_equal( diff --git a/tests/test_utilities.py b/tests/test_utilities.py index 59de1d3..11b2f71 100644 --- a/tests/test_utilities.py +++ b/tests/test_utilities.py @@ -2,15 +2,15 @@ def test_posterior_similarity(A1, A2, A3, samples1): - assert np.isclose(posterior_similarity(A1, samples1), 0.857142857142857) - assert np.isclose(posterior_similarity(A2, samples1), 0.916666666666666) - assert np.isclose(posterior_similarity(A3, samples1), 0.888888888888888) + assert np.isclose(posterior_similarity(samples1, A1), 0.857142857142857) + assert np.isclose(posterior_similarity(samples1, A2), 0.916666666666666) + assert np.isclose(posterior_similarity(samples1, A3), 0.888888888888888) def test_samplewise_posterior_similarity(A1, A2, A3, samples1): - assert np.isclose(samplewise_posterior_similarity(A1, samples1), 0.869047619047619) - assert np.isclose(samplewise_posterior_similarity(A2, samples1), 0.915343915343915) - assert np.isclose(samplewise_posterior_similarity(A3, samples1), 0.879629629629629) + assert np.isclose(samplewise_posterior_similarity(samples1, A1), 0.869047619047619) + assert np.isclose(samplewise_posterior_similarity(samples1, A2), 0.915343915343915) + assert np.isclose(samplewise_posterior_similarity(samples1, A3), 0.879629629629629) def test_hamming_distance(A1, A2, A3): diff --git a/tests/unit_test.py b/tests/unit_test.py index ae296c9..d7ae5e3 100644 --- a/tests/unit_test.py +++ b/tests/unit_test.py @@ -24,8 +24,7 @@ """ Generate Paramters for Test """ -G = nx.karate_club_graph() -A = nx.adjacency_matrix(G).todense() +A = zkc() A = np.array(A, dtype=float) n = np.size(A, axis=0) x0 = np.zeros(n) diff --git a/watts-strogatz.py b/watts-strogatz.py new file mode 100644 index 0000000..85b5c18 --- /dev/null +++ b/watts-strogatz.py @@ -0,0 +1,120 @@ +import json +import multiprocessing as mp +import os + +import numpy as np + +from lcs import * + + +def target_ipn(n, k, p, gamma, c, mode, rho0, tmax, realizations): + x0 = np.zeros(n) + x0[random.sample(range(n), int(round(rho0 * n)))] = 1 + ipn = 0 + for _ in range(realizations): + A = watts_strogatz(n, k, p) + x = contagion_process(A, gamma, c, x0, tmin=0, tmax=tmax) + ipn += infections_per_node(x, mode) / realizations + return ipn + + +def single_inference( + fname, gamma, c, b, rho0, A, tmax, p_c, p_rho, nsamples, burn_in, skip +): + n = np.size(A, axis=0) + x0 = np.zeros(n) + x0[random.sample(range(n), int(round(rho0 * n)))] = 1 + + x = contagion_process(A, gamma, c, x0, tmin=0, tmax=tmax) + p = beta(p_rho[0], p_rho[1]).rvs() + A0 = erdos_renyi(n, p) + samples = infer_adjacency_matrix( + x, A0, p_rho, p_c, nsamples=nsamples, burn_in=burn_in, skip=skip + ) + + # json dict + data = {} + data["gamma"] = gamma + data["c"] = c.tolist() + data["b"] = b + data["p-rho"] = p_rho.tolist() + data["p-c"] = p_c.tolist() + data["x"] = x.tolist() + data["A"] = A.tolist() + data["samples"] = samples.tolist() + + datastring = json.dumps(data) + + with open(fname, "w") as output_file: + output_file.write(datastring) + + +data_dir = "Data/watts-strogatz" +os.makedirs(data_dir, exist_ok=True) + +for f in os.listdir(data_dir): + os.remove(os.path.join(data_dir, f)) + +n = 50 +k = 6 + +n_processes = len(os.sched_getaffinity(0)) +realizations = 10 +probabilities = np.logspace(-6, 0, 49) + +# MCMC parameters +burn_in = 100000 +nsamples = 100 +skip = 1500 +p_c = np.ones((2, n)) +p_rho = np.array([1, 1]) + +# contagion functions and parameters +cf1 = lambda nu, beta: 1 - (1 - beta) ** nu # simple contagion +cf2 = lambda nu, beta: beta * (nu >= 2) # complex contagion, tau=2 +cf3 = lambda nu, beta: beta * (nu >= 3) # complex contagion, tau=3 + +cfs = [cf1, cf2, cf3] + +rho0 = 1.0 +gamma = 0.1 +b = 0.04 +mode = "max" + +tmax = 1000 + + +arglist = [] +for p in probabilities: + c = cfs[0](np.arange(n), b) + ipn = target_ipn(n, k, p, gamma, c, mode, rho0, tmax, 1000) + for i, cf in enumerate(cfs): + if i != 0: + A = watts_strogatz(n, k, p) + bscaled = fit_ipn(0.5, ipn, cf, gamma, A, rho0, tmax, mode) + else: + bscaled = b + c = cf(np.arange(n), bscaled) + print((p, i), flush=True) + + for r in range(realizations): + A = watts_strogatz(n, k, p) + arglist.append( + ( + f"{data_dir}/{p}_{i}_{r}", + gamma, + c, + bscaled, + rho0, + A, + tmax, + p_c, + p_rho, + nsamples, + burn_in, + skip, + ) + ) + +with mp.Pool(processes=n_processes) as pool: + pool.starmap(single_inference, arglist)