\n",
" {\n",
" let req = new XMLHttpRequest();\n",
" req.addEventListener(\"load\", function() {\n",
" if (this.status != 200 && this.response != 'OK') {\n",
" let p = document.getElementById('02d58720-ec5e-41b0-a1e9-5a1ec87f6d57').parentNode;\n",
" p.innerHTML +=\n",
" 'The nengo_gui.jupyter notebook server ' +\n",
" 'extension was not loaded. Please activate it ' +\n",
" 'with the following command:' +\n",
" 'jupyter serverextension enable ' +\n",
" 'nengo_gui.jupyter';\n",
" p.classList.add('output_stderr');\n",
" }\n",
" });\n",
" req.open('GET', './nengo/check', true);\n",
" req.send();\n",
" }\n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vdom.v1+json": {
"attributes": {
"id": "a5cc6dda-5da9-47b8-89e0-49517d0e8c0b"
},
"children": [
{
"attributes": {
"allowfullscreen": "allowfullscreen",
"class": "cell",
"frameborder": "0",
"height": "600",
"src": "./nengo/53543/?token=6e5f3656270d8453a857ec688ed8abd71a7c8cc51cfc42c6",
"style": {
"border": "1px solid #eee",
"boxSizing": "border-box"
},
"width": "100%"
},
"tagName": "iframe"
}
],
"tagName": "div"
},
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nengo_gui.ipython import IPythonViz\n",
"IPythonViz(model, \"configs/action_selection.py.cfg\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
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" \n",
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" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nengo_gui.ipython import IPythonViz\n",
"IPythonViz(model, \"configs/action_selection2.py.cfg\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nengo_gui.ipython import IPythonViz\n",
"IPythonViz(model, \"configs/bg_simple1.py.cfg\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Much less memory, but it's still there\n",
"- And slower to respond to changes\n",
"- Note that this speed is dependent on $e$, $i$, and the time constant of the neurotransmitter used\n",
"- Can be hard to find good values\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- And this gets harder to balance as the number of actions increases\n",
" - Also hard to balance for a wide range of $Q$ values\n",
" - (Does it work for $Q$=[0.9, 0.9, 0.95, 0.9] and $Q$=[0.2, 0.2, 0.25, 0.2]?)\n",
"\n",
"- But this is still a pretty standard approach\n",
" - Nice and easy to get working for special cases\n",
" - Don't really need the NEF (if you're willing to assume non-realistic non-spiking neurons)\n",
" - (Although really, if you're not looking for biological realism, why not just compute the max function?)\n",
" \n",
"- Example: [OReilly, R.C. (2006). Biologically Based Computational Models of High-Level Cognition. Science, 314, 91-94.](http://grey.colorado.edu/mediawiki/sites/CompCogNeuro/images/a/a5/OReilly06.pdf)\n",
" - [Leabra](http://grey.colorado.edu/CompCogNeuro/index.php/CCNBook/Main)\n",
"- They tend to use a \"kWTA\" (k-Winners Take All) approach in their models\n",
" - Set up inhibition so that only $k$ neurons will be active\n",
" - But since that's complex to do, just [do the math instead of doing the inhibition](http://grey.colorado.edu/CompCogNeuro/index.php/CCNBook/Networks#kWTA_Approximation_for_Inhibition)\n",
" - We think that doing it their way means that the dynamics of the model will be wrong (i.e. all the effects we saw above are being ignored).\n",
" \n",
"- Any other options?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Biology\n",
"\n",
"- Let's look at the biology\n",
"- Where is this action selection in the brain?\n",
"- General consensus: the basal ganglia\n",
"\n",
"![](\"files/lecture_selection/basal_ganglia.jpg\")
\n",
"\n",
"- Pretty much all of cortex connects in to this area (via the striatum)\n",
"- Output goes to the thalamus, the central routing system of the brain\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Disorders of this area of the brain cause problems controlling actions:\n",
" - Parkinson's disease\n",
" - Neurons in the substantia nigra die off\n",
" - Extremely difficult to trigger actions to start\n",
" - Usually physical actions; as disease progresses and more of the SNc is gone, can get cognitive effects too\n",
" - Huntington's disease\n",
" - Neurons in the striatum die off\n",
" - Actions are triggered inappropriately (disinhibition)\n",
" - Small uncontrollable movements\n",
" - Trouble sequencing cognitive actions too\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Also heavily implicated in reinforcement learning\n",
" - The dopamine levels seem to map onto reward prediction error\n",
" - High levels when get an unexpected reward, low levels when didn't get a reward that was expected\n",
" \n",
"![](\"files/lecture_selection/dopamine.png\")
\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Connectivity diagram:\n",
"\n",
"![](\"files/lecture_selection/basal_ganglia2.gif\")
\n",
"\n",
"\n",
"- Old terminology:\n",
" - \"direct\" pathway: cortex -> striatum -> GPi -> thalamus\n",
" - \"indirect\" pathway: cortex -> striatum -> GPe -> STN -> GPi -> thalamus\n",
"- Then they found:\n",
" - \"hyperdirect\" pathway: cortex -> STN -> GPi -> thalamus\n",
" - and lots of other connections"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Activity in the GPi (output)\n",
" - generally always active\n",
" - neurons stop firing when corresponding action is chosen\n",
" - representing [1, 1, 0, 1] instead of [0, 0, 1, 0]\n",
"\n",
"- Leabra approach\n",
" - Each action has two groups of neurons in the striatum representing $Q(s, a_i)$ and $1-Q(s, a_i)$ (\"go\" and \"no go\")\n",
" - Mutual inhibition causes only one of the \"go\" and one of the \"no go\" groups to fire\n",
" - GPi neuron get connections from \"go\" neurons, with value multiplied by -1 (direct pathway)\n",
" - GPi also gets connections from \"no go\" neurons, but multiplied by -1 (striatum->GPe), then -1 again (GPe->STN), then +1 (STN->GPi)\n",
" - Result in GPi is close to [1, 1, 0, 1] form\n",
"- Seems to match onto the biology okay\n",
" - But why the weird double-inverting thing? Why not skip the GPe and STN entirely?\n",
" - And why split into \"go\" and \"no-go\"? Just the direct pathway on its own would be fine\n",
" - Maybe it's useful for some aspect of the learning...\n",
" - What about all those other connections?\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### An alternate model of the Basal Ganglia\n",
"\n",
"- Maybe the weird structure of the basal ganglia is an attempt to do action selection without doing mutual inhibition\n",
"- Needs to select from a large number of actions\n",
"- Needs to do so quickly, and without the memory effects\n",
"\n",
"- [Gurney, Prescott, and Redgrave, 2001](http://neuroinformatics.usc.edu/mediawiki/images/3/37/Gurney_etal_01_A_computational_model_of_action_selection_in_the_basal_ganglia_-_II.pdf)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Let's start with a very simple version\n",
"\n",
"![](\"files/lecture_selection/gpr1.png\")
\n",
"\n",
"- Sort of like an \"unrolled\" version of one step of mutual inhibition\n",
"- Note that both A and B have surround inhibition and local excitation that is 'flipped' (in slightly different ways) on the way to the output\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Unfortunately this doesn't easily map onto the basal ganglia because of the diffuse inhibition needed from cortex to what might be the striatum (the first layer). Instead, we can get similar functionality using something like the following\n",
"- Notice the importance of the hyperdirect pathway (from cortex to STN).\n",
"\n",
"![](\"files/lecture_selection/gpr2.png\")
\n",
"\n",
"- But that's only going to work for very specific $Q$ values. (Here, the winning option is the sum of the losing ones)\n",
"- Need to dynamically adjust the amount of +ve and -ve weighting\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Here the GPe is adjusting the weighting by monitoring STN & D2 activity.\n",
"- Notice that the GPe gets the same inputs as GPi, but projects back to STN, to 'regulate' the action selection.\n",
"\n",
"![](\"files/lecture_selection/gpr3.png\")
\n",
"\n",
"- This turns out to work surprisingly well\n",
"- But extremely hard to analyze its behaviour\n",
"- They showed that it qualitatively matches pretty well\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- So what happens if we convert this into realistic spiking neurons?\n",
"- Use the same approach where one \"neuron\" in their model is a pool of neurons in the NEF\n",
"- The \"neuron model\" they use was rectified linear\n",
" - That becomes the function the decoders are computing\n",
"- Neurotransmitter time constants are all known\n",
"- $Q$ values are between 0 and 1\n",
"- Firing rates max out around 50-100Hz\n",
"- Encoders are all positive and thresholds are chosen for efficiency"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: pylab import has clobbered these variables: ['negative']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n"
]
}
],
"source": [
"%pylab inline\n",
"mm=1\n",
"mp=1\n",
"me=1\n",
"mg=1\n",
"\n",
"#connection strengths from original model\n",
"ws=1\n",
"wt=1\n",
"wm=1\n",
"wg=1\n",
"wp=0.9\n",
"we=0.3\n",
"\n",
"#neuron lower thresholds for various populations\n",
"e=0.2 \n",
"ep=-0.25\n",
"ee=-0.2\n",
"eg=-0.2\n",
"\n",
"le=0.2\n",
"lg=0.2\n",
"\n",
"D = 10\n",
"tau_ampa=0.002\n",
"tau_gaba=0.008\n",
"N = 50\n",
"radius = 1.5\n",
"\n",
"import nengo\n",
"from nengo.dists import Uniform\n",
"\n",
"model = nengo.Network('Basal Ganglia', seed=4)\n",
"\n",
"with model:\n",
" stim = nengo.Node([0]*D)\n",
"\n",
" StrD1 = nengo.networks.EnsembleArray(N, n_ensembles=D, intercepts=Uniform(e,1), \n",
" encoders=Uniform(1,1), radius=radius)\n",
" StrD2 = nengo.networks.EnsembleArray(N, n_ensembles=D, intercepts=Uniform(e,1), \n",
" encoders=Uniform(1,1), radius=radius)\n",
" STN = nengo.networks.EnsembleArray(N, n_ensembles=D, intercepts=Uniform(ep,1), \n",
" encoders=Uniform(1,1), radius=radius)\n",
" GPi = nengo.networks.EnsembleArray(N, n_ensembles=D, intercepts=Uniform(eg,1), \n",
" encoders=Uniform(1,1), radius=radius)\n",
" GPe = nengo.networks.EnsembleArray(N, n_ensembles=D, intercepts=Uniform(ee,1), \n",
" encoders=Uniform(1,1), radius=radius)\n",
"\n",
" nengo.Connection(stim, StrD1.input, transform=ws*(1+lg), synapse=tau_ampa)\n",
" nengo.Connection(stim, StrD2.input, transform=ws*(1-le), synapse=tau_ampa)\n",
" nengo.Connection(stim, STN.input, transform=wt, synapse=tau_ampa)\n",
" \n",
" def func_str(x): #relu-like function\n",
" if x[0]\n",
" \n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nengo_gui.ipython import IPythonViz\n",
"IPythonViz(model, \"configs/bg_good2.py.cfg\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Notice that we are also flipping the output from [1, 1, 0, 1] to [0, 0, 1, 0]\n",
" - Mostly for our convenience, but we can also add some mutual inhibition there\n",
"\n",
"- Works pretty well\n",
"- Scales up to many actions\n",
"- Selects quickly\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Gets behavioural match to empirical data, including timing predictions (!)\n",
" - Also shows interesting oscillations not seen in the original GPR model\n",
" - But these are seen in the real basal ganglia\n",
"\n",
"![](\"files/lecture_selection/gpr-latency.png\")
\n",
"\n",
"- [Dynamic Behaviour of a Spiking Model of Action Selection in the Basal Ganglia](http://compneuro.uwaterloo.ca/files/publications/stewart.2010.pdf)\n",
"\n",
"- Let's make sure this works with our original system\n",
"- To make it easy to use the basal ganglia, there is a special network constructor\n",
" - Since this is a major component of the SPA, it's also in that module"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"//anaconda/envs/python3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['e', 'positive', 'select']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n",
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
]
}
],
"source": [
"%pylab inline\n",
"import nengo\n",
"from nengo.dists import Uniform\n",
"\n",
"model = nengo.Network(label='Selection')\n",
"\n",
"D=4\n",
"\n",
"with model:\n",
" stim = nengo.Node([0,0])\n",
" \n",
" s = nengo.Ensemble(200, dimensions=2)\n",
" Qs = nengo.networks.EnsembleArray(50, n_ensembles=D)\n",
"\n",
" nengo.Connection(stim, s)\n",
" nengo.Connection(s, Qs.input, transform=[[1,0],[-1,0],[0,1],[0,-1]])\n",
" \n",
" Action = nengo.networks.EnsembleArray(50, n_ensembles=D, intercepts=Uniform(0.2,1), \n",
" encoders=Uniform(1,1))\n",
" bias = nengo.Node([1]*D)\n",
" nengo.Connection(bias, Action.input)\n",
" nengo.Connection(Action.output, Action.input, transform=(np.eye(D)-1), synapse=0.008) \n",
"\n",
" basal_ganglia = nengo.networks.BasalGanglia(dimensions=D) \n",
" \n",
" nengo.Connection(Qs.output, basal_ganglia.input, synapse=None)\n",
" nengo.Connection(basal_ganglia.output, Action.input)\n"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nengo_gui.ipython import IPythonViz\n",
"IPythonViz(model, \"configs/bg_good1.py.cfg\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- This system seems to work well\n",
"- Still not perfect\n",
"- Matches biology nicely, because of how we implemented it\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Some more details on the basal ganglia implementation\n",
" - all those parameters come from here\n",
"\n",
"![](\"files/lecture_selection/gpr-diagram.png\")
\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- In the original model, each action has a single \"neuron\" in each area that responds like this:\n",
"\n",
"$$\n",
"y = \\begin{cases}\n",
" 0 &\\mbox{if } x < \\epsilon \\\\ \n",
" m(x- \\epsilon) &\\mbox{otherwise} \n",
" \\end{cases}\n",
"$$\n",
"\n",
"- These need to get turned into groups of neurons\n",
" - What is the best way to do this?\n",
"\n",
"![](\"files/lecture_selection/gpr-tuning.png\")
\n",
"\n",
"- encoders are all +1\n",
"- intercepts are chosen to be $> \\epsilon$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Action Execution\n",
"\n",
"- Now that we can select an action, how do we perform it?\n",
"- Depends on what the action is\n",
"- Let's start with simple actions\n",
" - Move in a given direction\n",
" - Remember a specific vector\n",
" - Send a particular value as input into a particular cognitive system\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Example:\n",
" - State $s$ is 2-dimensional\n",
" - Four actions (A, B, C, D)\n",
" - Do action A if $s$ is near [1,0], B if near [-1,0], C if near [0,1], D if near [0,-1]\n",
" - $Q(s, a_A)=s \\cdot [1,0]$\n",
" - $Q(s, a_B)=s \\cdot [-1,0]$\n",
" - $Q(s, a_C)=s \\cdot [0,1]$\n",
" - $Q(s, a_D)=s \\cdot [0,-1]$\n",
" - To do Action A, set $m=[1,0]$\n",
" - To do Action B, set $m=[-1,0]$\n",
" - To do Action C, set $m=[0,1]$\n",
" - To do Action D, set $m=[0,-1]$"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"%pylab inline\n",
"import nengo\n",
"from nengo.dists import Uniform\n",
"\n",
"model = nengo.Network(label='Selection')\n",
"\n",
"D=4\n",
"\n",
"with model:\n",
" stim = nengo.Node([0,0])\n",
" \n",
" s = nengo.Ensemble(200, dimensions=2)\n",
" Qs = nengo.networks.EnsembleArray(50, n_ensembles=4)\n",
"\n",
" nengo.Connection(stim, s)\n",
" nengo.Connection(s, Qs.input, transform=[[1,0],[-1,0],[0,1],[0,-1]])\n",
" \n",
" Action = nengo.networks.EnsembleArray(50, n_ensembles=D, intercepts=Uniform(0.2,1), \n",
" encoders=Uniform(1,1))\n",
" bias = nengo.Node([1]*D)\n",
" nengo.Connection(bias, Action.input)\n",
" nengo.Connection(Action.output, Action.input, transform=(np.eye(D)-1), synapse=0.008) \n",
"\n",
" basal_ganglia = nengo.networks.BasalGanglia(dimensions=D) \n",
" \n",
" nengo.Connection(Qs.output, basal_ganglia.input, synapse=None)\n",
" nengo.Connection(basal_ganglia.output, Action.input)\n",
" \n",
" motor = nengo.Ensemble(100, dimensions=2)\n",
" nengo.Connection(Action.output[0], motor, transform=[[1],[0]])\n",
" nengo.Connection(Action.output[1], motor, transform=[[-1],[0]])\n",
" nengo.Connection(Action.output[2], motor, transform=[[0],[1]])\n",
" nengo.Connection(Action.output[3], motor, transform=[[0],[-1]])\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nengo_gui.ipython import IPythonViz\n",
"IPythonViz(model, \"configs/bg_good3.py.cfg\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- What about more complex actions?\n",
"- Consider a simple creature that goes where it's told, or runs away if it's scared\n",
" - Action 1: set $m$ to the direction it's told to do\n",
" - Action 2: set $m$ to the direction we started from\n",
"- Need to pass information from one group of neurons to another\n",
" - But only do this when the action is chosen\n",
" - How?\n",
"- Well, let's use a function\n",
" - $m = a \\times d$\n",
" - where $a$ is the action selection (0 for not selected, 1 for selected)\n",
"- Let's try that with the creature"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"//anaconda/envs/python3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['negative']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n",
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
]
}
],
"source": [
"%pylab inline\n",
"import nengo\n",
"from nengo.dists import Uniform\n",
"\n",
"model = nengo.Network('Creature')\n",
"\n",
"with model:\n",
" stim = nengo.Node([0,0], label='stim')\n",
" command = nengo.Ensemble(100, dimensions=2, label='command')\n",
" motor = nengo.Ensemble(100, dimensions=2, label='motor')\n",
" position = nengo.Ensemble(1000, dimensions=2, label='position')\n",
" scared_direction = nengo.Ensemble(100, dimensions=2, label='scared direction')\n",
"\n",
" def negative(x):\n",
" return -x[0], -x[1]\n",
"\n",
" nengo.Connection(position, scared_direction, function=negative)\n",
" nengo.Connection(position, position, synapse=.05)\n",
" \n",
" def rescale(x):\n",
" return x[0]*0.1, x[1]*0.1\n",
" nengo.Connection(motor, position, function=rescale)\n",
" nengo.Connection(stim, command)\n",
" \n",
" D=4\n",
" Q_input = nengo.Node([0,0,0,0], label='select')\n",
" Qs = nengo.networks.EnsembleArray(50, n_ensembles=4)\n",
" nengo.Connection(Q_input, Qs.input)\n",
" Action = nengo.networks.EnsembleArray(50, n_ensembles=D, intercepts=Uniform(0.2,1), \n",
" encoders=Uniform(1,1))\n",
" bias = nengo.Node([1]*D)\n",
" nengo.Connection(bias, Action.input)\n",
" nengo.Connection(Action.output, Action.input, transform=(np.eye(D)-1), synapse=0.008) \n",
"\n",
" basal_ganglia = nengo.networks.BasalGanglia(dimensions=D) \n",
" \n",
" nengo.Connection(Qs.output, basal_ganglia.input, synapse=None)\n",
" nengo.Connection(basal_ganglia.output, Action.input)\n",
"\n",
" do_command = nengo.Ensemble(300, dimensions=3, label='do command')\n",
"\n",
" nengo.Connection(command, do_command[0:2])\n",
" nengo.Connection(Action.output[0], do_command[2])\n",
" \n",
" def apply_command(x):\n",
" return x[2]*x[0], x[2]*x[1]\n",
" nengo.Connection(do_command, motor, function=apply_command)\n",
" \n",
" do_scared = nengo.Ensemble(300, dimensions=3, label='do scared')\n",
"\n",
" nengo.Connection(scared_direction, do_scared[0:2])\n",
" nengo.Connection(Action.output[1], do_scared[2])\n",
" nengo.Connection(do_scared, motor, function=apply_command)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nengo_gui.ipython import IPythonViz\n",
"IPythonViz(model, \"configs/bg_creature.py.cfg\")\n",
"#first dimensions activates do_command, i.e. go in the indicated direciton\n",
"#second dimension activates do_scared, i.e. return 'home' (0,0)\n",
"#creature tracks the position it goes to (by integrating)\n",
"#creature inverts direction to position via scared direction/do_scared and puts that into motor"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- There's also another way to do this\n",
"- A special case for forcing a function to go to zero when a particular group of neurons is active\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"//anaconda/envs/python3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['negative']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n",
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
]
}
],
"source": [
"%pylab inline\n",
"import nengo\n",
"from nengo.dists import Uniform\n",
"\n",
"model = nengo.Network('Creature')\n",
"\n",
"with model:\n",
" stim = nengo.Node([0,0], label='stim')\n",
" command = nengo.Ensemble(100, dimensions=2, label='command')\n",
" motor = nengo.Ensemble(100, dimensions=2, label='motor')\n",
" position = nengo.Ensemble(1000, dimensions=2, label='position')\n",
" scared_direction = nengo.Ensemble(100, dimensions=2, label='scared direction')\n",
"\n",
" def negative(x):\n",
" return -x[0], -x[1]\n",
"\n",
" nengo.Connection(position, scared_direction, function=negative)\n",
" nengo.Connection(position, position, synapse=.05)\n",
" \n",
" def rescale(x):\n",
" return x[0]*0.1, x[1]*0.1\n",
" nengo.Connection(motor, position, function=rescale)\n",
" nengo.Connection(stim, command)\n",
" \n",
" D=4\n",
" Q_input = nengo.Node([0,0,0,0], label='select')\n",
" Qs = nengo.networks.EnsembleArray(50, n_ensembles=4)\n",
" nengo.Connection(Q_input, Qs.input)\n",
" Action = nengo.networks.EnsembleArray(50, n_ensembles=D, intercepts=Uniform(0.2,1), \n",
" encoders=Uniform(1,1))\n",
" bias = nengo.Node([1]*D)\n",
" nengo.Connection(bias, Action.input)\n",
" nengo.Connection(Action.output, Action.input, transform=(np.eye(D)-1), synapse=0.008) \n",
"\n",
" basal_ganglia = nengo.networks.BasalGanglia(dimensions=D) \n",
" \n",
" nengo.Connection(Qs.output, basal_ganglia.input, synapse=None)\n",
" nengo.Connection(basal_ganglia.output, Action.input)\n",
"\n",
" do_command = nengo.Ensemble(300, dimensions=2, label='do command')\n",
" nengo.Connection(command, do_command)\n",
" nengo.Connection(Action.output[1], do_command.neurons, transform=-np.ones([300,1]))\n",
" nengo.Connection(do_command, motor)\n",
" \n",
" do_scared = nengo.Ensemble(300, dimensions=2, label='do scared')\n",
" nengo.Connection(scared_direction, do_scared)\n",
" nengo.Connection(Action.output[0], do_scared.neurons, transform=-np.ones([300,1]))\n",
" nengo.Connection(do_scared, motor) "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nengo_gui.ipython import IPythonViz\n",
"IPythonViz(model, \"configs/bg_creature2.py.cfg\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- This is a situation where it makes sense to ignore the NEF!\n",
" - All we want to do is shut down the neural activity\n",
" - So just do a very inhibitory connection"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## The Cortex-Basal Ganglia-Thalamus loop\n",
"\n",
"- We now have everything we need for a model of one of the primary structures in the mammalian brain\n",
" - Basal ganglia: action selection\n",
" - Thalamus: action execution\n",
" - Cortex: everything else\n",
" \n",
"![](\"lecture_selection/ctx-bg-thal.png\")
\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- We build systems in cortex that give some input-output functionality\n",
" - We set up the basal ganglia and thalamus to make use of that functionality appropriately\n",
" \n",
"- Example\n",
" - Cortex stores some state (integrator)\n",
" - Add some state transition rules\n",
" - If in state A, go to state B\n",
" - If in state B, go to state C\n",
" - If in state C, go to state D\n",
" - ...\n",
" - For now, let's just have states A, B, C, D, etc be some randomly chosen vectors \n",
" - $Q(s, a_i) = s \\cdot a_i$\n",
" - The effect of each action is to input the corresponding vector into the integrator\n",
" - This is the basic loop of the SPA, so we can use that module"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: pylab import has clobbered these variables: ['negative']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n"
]
}
],
"source": [
"%pylab inline\n",
"import nengo\n",
"from nengo import spa\n",
"\n",
"D = 16\n",
"\n",
"def start(t):\n",
" if t < 0.05:\n",
" return 'A'\n",
" else:\n",
" return '0'\n",
" \n",
"model = spa.SPA(label='Sequence_Module', seed=5)\n",
"\n",
"with model:\n",
" model.cortex = spa.Buffer(dimensions=D, label='cortex')\n",
" model.input = spa.Input(cortex=start, label='input') \n",
"\n",
" actions = spa.Actions(\n",
" 'dot(cortex, A) --> cortex = B',\n",
" 'dot(cortex, B) --> cortex = C',\n",
" 'dot(cortex, C) --> cortex = D',\n",
" 'dot(cortex, D) --> cortex = E',\n",
" 'dot(cortex, E) --> cortex = A'\n",
" )\n",
" model.bg = spa.BasalGanglia(actions=actions)\n",
" model.thal = spa.Thalamus(model.bg)\n",
"\n",
" cortex = nengo.Probe(model.cortex.state.output, synapse=0.01)\n",
" actions = nengo.Probe(model.thal.actions.output, synapse=0.01)\n",
" utility = nengo.Probe(model.bg.input, synapse=0.01)\n",
" \n",
"sim = nengo.Simulator(model)\n",
"sim.run(0.5)\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from nengo_gui.ipython import IPythonViz\n",
"IPythonViz(model, \"configs/bg_alphabet.py.cfg\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
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bt7IGYu84bzSs2xB9W/U1y3j16tRDvTr1sHDYQrx38j3M7D8TSk0qTlVHRATg7S2qIj3x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\n8iR2eyxJZ5MMx5YkCd8hvoTPD8dtVxoOVk6oZsfQsiU8OJCDbV0V3d/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EFyeIWlb5yjf0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\nDqvhv2Ki8CzeGvUWJ9vA6rK472KcTz2PYlExIJFQAemVK7kLOZHDMLTk9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FIpblfQioq1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O8q73s8/K9XqrY5/v6vi4+GDF/SuwYewG/Hb8NwR9E4Rlh5bVe1r6GyL4TolJR76nAg8nD6wY\n8ynOvxmHPlMUjJ38AF5rsRpb3orBntYHcPb9syhOrP5L/eRJy1o9rke26HJSHVdX+Z195gzQqZPs\nRnrXXXJghWrPT/W8fX8tt7a5FXsn78X3D3yP347/hjYL22Dm9plIyE6oNv2ZkhLLW76rcnCQkV1i\nojwADz4o+yx+/70829aATITSlFI4BdbzAgBALzc3bOrZExt79sTuvDy0CQ/HS/HxOFaHh4ZOnapf\nly0HTwd0/LwjBp4aCI2XBof7H8ax+44ha0NWnccGj8usX/BdoXlz4OuvZV9LjUZ+aYwZI587rFN8\naeU7NgDQt2VfbB6/GWvGrMHWM1vR9su2eHXrqxYN95ZRVgY/S4JvQH6zjh4t+4K/9Rbw6acyKJ87\nF0hLMyuL7E3ZaH5/c8v2X0WQiwvWdO+OXb17I1anQ7sDB/DsyZM4WFBgVsBX3zoMyNv/rf7bCoMS\nBsF3jC9OPX0Kh3ofQuqSVBjzzQt+FFKQUpCC1u6tLS7HfffJ2/0hIXKwpY4dZS+M1NqGhbZyXVUJ\nFSb2noiEqQl4qtdTmPLXFPRa1gtfR3xd6wPEFrV8X2nkSNkKPneuvCJp3x6YM8fsYV/r8lB7TVo6\nOmL5TTchun9/FJlM6BoZiceOH8eO3FyzR56qaBysy/Wpa3dX9FjXAz239ERBRAEOBB5A/IvxKIwq\ntLhrVH0aNtzcgA8+kL9LmzZyuo777pMXhxb3Dqpny3dVAwMG4t+n/sUvj/yCTfGbEPhlPbtBWDo7\nT2NcUMMMl/N7/0LPjl1NGy5epIIqU9YVlhbSwtDvqd2c26jzpAH02k3L6C+XnbSr1yFKmneOdAmX\npmOaPZvovfeqzf6G82fcn5VTxzak0lKiX38luvNOIk9PoiefJFq/nqigoDxB9+5ER482SFlOZp6k\nN7a9QX6f+1G/5f3ok72fUFxmXOWUvi3376dzxcXW33FZGdHGjUSjR8sZEceMIfrlF6K8vMuSFUYX\n0oGgA9bfPxGdKy6m2YmJ1DosjLpFRND7iYkUVVBwzwcV9QAAIABJREFUzVncZs4k+ugj65XBWGSk\ntB/S6PCth2mfzz6KeyaOsv7OIqPeWOtnH/ntEZvMaJqXR7RsGdHAgXJ26ylTiP75x4xp6ocPJ9q6\n1erlqSopN4lmbZ9FAQsCqMeSHvTh7g/pSNoRMim1zyr6cEwMrc3IqDWd2Q4dInrmGSIvL6I77iBa\ntIjozJlqk5ZmltIe9z1kMlh/9tP0khKam5REHcLDqdOBAzTrzBk6mJ9Pxhrq8W23Ee3cWe1bFlNM\nCuX8m0OxY2Jpj/seOnb/MUr/XzqVZtRcadIK0sj/c3+rluPQIaIXXpB/kqFDib76iii5ugkS//xT\nTv9qIybFRDsTd9K4dePI/RN3GvXTKFp5ZCVdLLp4Vdrxx4/T6vR06xYgOproxRflgRgyhOjLL2Xd\nrKFOxD4aS+k/WrcMeWVltCglhXodPEit9u+nqfHxtCsnh0qvMa326tVEjz9ev/2WpJbQ2TlnKbxd\nOB3ofIAS302k/IP5pBjNn52z66KudPSCdb6DdTqiH3+UM2P6+BA9/bT86tObO4GswUCk0dhsOvJT\nWafqNcOloAYcfL06QogRAL6EbIVfSUSfVZPmawAjAegATCKi6Bryoup+n8WBv2DNs17Qep3Ggc6d\n0ddkwt1+fri1fXv0c3eHh0aDMzmJWLxjPf48ugktThOGRN+P28/3gNpZC49bfbE9rTm6jfPA4687\nW3z783rxdcTXiM+Ox6JRi+xWhrQ02VXvjz9k19JevYCdRzxw8Lck3BzshYrhtUNDQxEcHGyzchgV\nI/Yk78G6E+uwKX4TFFJwW7vh+NN7Io71CEDn5kEW9S02S2am7DD3+++yI2fXrrJ1PDgY56OCoE8h\n3LTMdk8IK0SIKCjAusxMbMrORq7RiGBPT9zl6Yk7PD1xk7Nz5eyeDz0k+/M9+qj1y1GcVIysP7KQ\n+UcmiqKL0Kx/M3jd6QXPOz3RrF8zqF0v7yvWbXE3vN7ydTz78LPWL0y5kydlD4v16+XgH4MHy7mf\n7rhDDql5WY+kLl1kRTZ3GI16UEhB2PkwrDm+Bv+c+QfZxdkY1n4YhncYjsGtB6OLTxdoVJc/PHbb\nkSOY26ED7vA0/4FHs5SWyslP1q0D/v1XHpThw2UXq4EDgXbtcHFdJi6suoBef/Wy7r6rICIcKSrC\nusxM/LJtGwp69sQdHh6408sLQz080N3VFQ4qFfz95bPQVSf5tCZjvhHZm7ORuS4TuTtz4RToBM+7\nPOF1pxfcB7tD6y/vPkSkRGDqlqk4+F/rz9xYXCz/FL//Lh/S9PCoPKXgjjuANuu/gTh1Elhs+8md\nCkoL8Ff8X/g97nf8m/gv2ni0QXBgMO5sfyeGtBmCCWcuYHqbNrjX29v6Oy8tlQdi7Vpg2zaEEiF4\n1Ch563XIEKBdOxAB+/32Y8DRAXAMsGx429qc1OmwLjMT67OycKq4GLe6u+MuLy8Ee3riZldXOJX3\ng33vPfmoxZw59d8nEaEwshAX11xEztYcGNIM8LzTE17DvOAxxAMu3Vygcrj6O63MVIZmnzRD3qw8\nOGnqf8e1quRkeS7dsEHO8dSv36Xz6eDBstX8KmlpMmF6ulXLUpUQAkRkUUBo1+BbCKECEA9gGIA0\nAJEAxhLRySppRgJ4mYjuE0IMAvAVEQ2uIb9qg+/fPTdh04vJWDXpbuj37MGe06exnQgR7dohumNH\nBBgM6E+Ebs2a4SY/P7Tw9UDC2WNYf2APUmJPok2qBj1PD8KArK5wNjpCdBFw6+kGv75+8O7lDad2\nTnBs5QihvjGC8jf+eQN+rn6YOWSmvYsCQH5hRPyTj1vGBODOfoU4FiPQooXsxpedHYIpU0IQFCRv\nrVb7T2olRITE3ESsPrMfCwuawTPmVeQW56KXfy/0btEbvfx7oaNXR3Tw6oA2Hm2uCnLqpbT00gNu\nu3bh2J6RaOF1EH63GuSoKd27y9v9HTrIp/5tcAGZUlKCXXl52JmXh335+UgrLUUPV1f0cXPD+oVu\nmP2sE4Z3c0agkxO015oYox6MhUbk78tH3q485IXmQRerg2NbR7j1doNbbzc4Bjnizp134kHXB/HJ\nvE9sUoYr5eTIOQL27JFLTIysi716ATf3IkwPcUPCngto271ZnUbSsIZz+eewI3EHdpzdgci0SKQW\npKKnf0/0bdEX3Xy7Iah5EJ6/6IK/et6Mbm62ecYDgHyQ78QJGfDs3CmfbdDrccrlHbgEOaPNk46y\nw3VQkLyNbKMGkJCQELzw1lsILa/He/PzkVxSgs6OLji+wQ0Lprihk7MzOjo7o52TExxtVI8Vo4Ki\nw0XI3ZWLvJ15KIwshMpZBbc+bjgfcB77HffjzXFvwrmjMxz8HGzSIKQo8k8SGiqXffuAt/NnwrmV\nN+IemIWePWU9bt9e9jW20aEAIBs5otKjsCtpF0KTQhGeEo6ingsxoCgUQ71bortfd3Tw6oAOXh3g\n7+pv3eNBhJCXX0ZIt27Ajh3yPKvTobDjCJxIHI9B8zLkgejYUR4IGz0YlltWht3l9XJPXh5OFRej\nk7Mz+ri54egfbhjW1RlP3+OE9s7OcLViGUrTS5G7Ixe5/+aiMKIQJcklcO3uCrd+bnDt7grnIGe4\nBLngrMtZjF47GqenWWcYx5oUFMgHh3fvlufTI0dkb9NeveTSo0f5V11+FNxeniT7V9lIUw6+BwOY\nTUQjy9dnQTbjf1YlzTIAu4jot/L1OADBRJRRTX5XBd+lxUbsdd+Dc4uS8fTzky//QFYWjMeO4eSp\nUziUnY04IsQ7O+OUry8SW7WCR2kpWpeUoJXJCM+yEjibCiCyC6BKNsAlWcD9vDNaZPuiRZ4v3HSu\nKPYtRlnLMqgCVND6a+HawhWuLVzh1tIN7i3d0cy3GbQeWqibqaFyVDXZFvTH1j6Gh7s+jLE9xtq7\nKJfExsoOt3FxMJlkv7GoKGDx4hD4+YXg9GnZd9zDQ54fW7UCWraUr61aAT4+gJeXnP624tXd3bLv\n+PWZmfguPR2be/VCTnEOjl44iugL0Yi5GIPE3EQk5iYiQ5eB1u6t0d6zPVo1awV/V3/4ufrB380f\n/q7+8HHxgbujOzycPODh6AFHjfktK0qZgv0++zFoux+052LkyefECeDsWdl3XFHk2aldO/m4ub+/\nfCil4tXLSx4od3egWTOLv0wKjEYcKyrCoYIiTF+kQ/DYEpw1FCO1tBQttVp0cHZGS60W/hWLgwP8\ntVo0d3CAh0YDD7UaHhoNnFSW/68oZQr0J/Uoii5CUXQRMk9k4szRM9iRuQPPej4Lp/ZOcGzrCK2/\ntnJx8HOA1k8LjacGag81NO4aqN3UECrr/L+WlMiHNY8dA05HZOPNbztiQKc8JCXJw92+/aU/jZ+f\nHAHIz08u3t7yz+LuLp+JsPYppKC0AFHpUTiSfgQns04iIScBoa3fgDZyIgJc3BHQLACtmrWqfPV3\n84eXkxe8nL0ue61Lfa1RRgYi+h5H94ePwa0wWv5TJyTIA9i69aV/5IpXX19Zd6sunp6yQ76ZQkJC\nEBISctk2ncmEXyKK8MHPRbhvmg6JxcVILCnBuZIS+Gm16OjkhFaOjpfVYX+tFj4ODnAvr8MeGg2c\n61GPiQil50pRGFWI7Ru3Q0lU0LW4K4rPFEMpUeDcwRlOgU7QttDCwd/hsrqs8dRA4yEXtbsaKo3l\nUXLxg2ORcNP92OL9BGJj5enk7Fl5gRkYKOtuy5byVFL1tNK8+aVTirs7YOkjBFWPR6uwffjIKw+p\nmVE4kXkCZ/POIjE3EfoyPdp7tkc7z3Zo4dYC/q7+8HcrP7+6+sPb2bvy3Oru6G5WI8hV9eLiRZx7\n5xhKYrPQ+aat8sslMVFOg1txICrOrVWXinNrxVKPA1GqKDiu0yGqqAhvrihC17uKkeVYgqSSEnhq\nNOjg5ISAauqlb/n5taJuuqnVdaqXxiIjdEd1KDxcCH2cHvoEPYoTilFyoQT5Hvlo27ktHAMc4djK\nEdpWWmhbaKHx0sDBywEaL03lonayzgVCWZl8HuPYMbnExsopBjqd3opXTF/gjV7/oF27S6eIinOp\nr6+sl82ayTrp5lb3r7qmHHw/AuBeInqufP1JAAOJaFqVNJsAfEJEYeXr/wKYSURHqsmPvnjpx8u2\nlWUZEbgrECOPdoN7ixbmFay0FKakJFxMSkJKVhZSi4qQWlqKFEXBRQA5KhVytVrkODsj19kZOe7u\nIEWDdimlaJ1qQssLCrxyFHjkA+55QLNCFdwK1HAqVsOxVAPHUjUECRgcjTA4GWFwNKLM0QjFwQRF\nrYA0clE0BEVDIAcCaQByKF/XKIC6/ItXRYAKECoAovxVBQi1rBhCRTJoKE9DKgJUAqioLkL+LETF\nNgEIkq8qUb6pSt0SwKH0SAxufQtauPldth0ACJeywRWvV9XQmt4z80RwWarzKUBCPPDcc5dtX/XL\nQkwa9xoAQCF51ZyfL18rlvw8QK+XLej6YvlaXCz/qZ2cAK0D4KAFHDTy+UcHbflrlUWjkS0/ajWQ\n2jwfKggMSg+AWo3Ll/I0pC5DEWUgX0mHzpSDYsqFjnKhN+VCp+SihApQouhgID1KTDoIATiqXOGk\ncoODcIRGaKFRaaERWjhUvJZv883yws17O2Dde+FQCTXUQgMVyl+FGm76Uvhl5cM3Ox8e+UXwKNCh\nWWER3AsK4ZZfCGe9Hk7FxXDS66EtKUGZoyNKnZ1R6uyMMq0jjA4OMGq1MDo4wOSghdFBC6PWASat\n/FlRa0AqFRS1GqRSQ29Q4VC0Go8+Lg+CQaNBuosbUlybIdPJFVmOTsjWOiFL64QcjRb5GgcUqTUo\nVGlQpNZAgUAzxQhXxQQnxQRHUuBICrSKAkciaMvXHUmBFgQHUqAGoAZBBUBD8lUNQpbuIjKL0qEO\ny8JDD0yEUzrgmCGgyQPUueVLnoA6FxB6QKWTr6IUIGeAXOQCLUBa+QqH8lctVW4TDgLlhYBQA1BX\n+T9VA6h41RUCR4+AJj4JUgnoSwXyCmUd1ekAXbGAXgfoioEiHVBcIm9ylJYCZSYBrRbQOgGOWkDj\nIKDRABoHQK2RdVJdvk1dsV0ty6BSyYf/hABU6vJ1Uf6qkvVUqAAhCH94n8WrmZ2ho2wUmrKhU7JQ\naMpGoZIFnSkPpVSIYqUIJaZCFCuFKFGKoBJqaFXOcBCOclHJV41KW/mzfNVCJdSVi1qooYYaKpUa\nTsVa3PK/Tlj/dQzUag3UQi6uegM8cvPgkZsPj9xceOTlwyMnF65FRXDW6eCs05e/6uCo18Oo1cLg\n6ASjo1bWX235q6MWZVonGLVamLSOMDlosSzxNJ7t2h2kUkNRly8qNVIvqpFXpEbw3WqQRtbjMrUG\nF1xckOLkiotaWYezNY7IdtAiW+uIPI0WReV1uEitgUkIuJpMcFOMcFKq1NnKV4IjLv3sAKqoQrIu\nE0EDwqnME2jp4oe+/j2hFgLaIsAxneB0UUCTC2hyAXWugDpPQJUr67BKB4jyukxagFwv1ePL6nJ5\nPYajuFS3q9bjqEPAoAEQAS3kdo38nigDUKATyC8SKCour7d6oEgP6PSX6m1JqUBJqczL0VHWX422\nor4KqMvPpRoHXPpZI6DWXKq3qvI6/G/7s5iQ0ANalQrq8jqsVgEmUYxCXEAhXYAeudArudAredAp\nudCZclFChTAoepQoOpSQDg5CC0eVKxxVLpfOreXnU7XaARqhRczBCAwcPAwalbb8fKrCwM1BOH53\nCs73y6usu05lCvyz8uCbnQ/3fB08CorQrKAQHvmFcMsvkOdWvR6OxcVwKi6GolKhxMUFpc4ulefU\nylcHLYxaR3luddDCqJXnVkWtAqnUoPLzq6JWY9sONcY9qYaDsxomtQqZLm4479IMGU4usl46OCLb\nwRFZDo7I1WihU6tRpHZAoVoDg1DBVTGiman6ellZN8tftaDyc+vl9TMl4yx88t0wyOFmOGULaLMB\nbaasj5oiQFUkoCoCVIWAqkh+gSsV51JHucCxvC46AaQlwLG8Tl52LgWguXxdXCrIpeVcMkx5hbg4\n6D45Goxefs/r9fJ8qtMJ6ErkBD8lBsBgkOdKraOA1hFwKP/uVzsAGrWsg+rydXk+FfhyVSAH3+Xv\n2bcDO2OMMcYYuyFYGnxbsdOpRVIBVB0brnX5tivTtKklDQDLDwJjjDHGGGMNwd7jfEcC6CSECBRC\naAGMBbDxijQbATwFVPYRz6uuvzdjjDHGGGONnV1bvonIJIR4GcA/uDTUYJwQ4nn5Nq0gor+FEKOE\nEKchhxqcfK08GWOMMcYYa6zsPs43Y4wxxhhjNwp7dzupMyHECCHESSFEvBDizRrSfC2ESBBCRAsh\nejd0GVnDq61eCCFuEkKECSFKhBCv26OMrOGZUS/GCyGOli/7hBA97VFO1rDMqBejy+tElBDioBBi\niD3KyRqWOfFFeboBQogyIcTDDVk+Zh9mnC+GCiHyhBBHypd3a82zKbV8W3tSHnZ9MLNe+AAIBPAg\ngFwi+sIeZWUNx8x6MRhAHBHll8+2G8Lni+ubmfXChYj05T/3BLCGiLrao7ysYZhTL6qk2w6gGMD3\nRPRHQ5eVNRwzzxdDAUwnotHm5tvUWr4HAkggomQiKgPwK4AHrkjzAIAfAYCIIgB4CCH8G7aYrIHV\nWi+IKIuIDgMw2qOAzC7MqRcHiCi/fPUAgIAGLiNreObUC32VVTcASgOWj9mHOfEFAEwFsA7AxYYs\nHLMbc+tFnUbba2rBdwCA81XWU3D1l+WVaVKrScOuL+bUC3bjqWu9eBbAFpuWiDUGZtULIcSD5TMq\nbwLwdAOVjdlPrfVCCNEKwINEtBR1DLZYk2Xu98gt5V2d/xJCdKstU3uP880YY3YnhLgTciSl2+xd\nFtY4ENF6AOuFELcB+AjA3XYuErO/LwFU7fPLATgDgMMA2hKRvrzr83oAna/1gabW8m3VSXnYdcOc\nesFuPGbVCyFELwArAIwmotwGKhuznzqdL4hoH4AOQghvWxeM2ZU59aI/gF+FEGcBPApgsRDC7H6+\nrEmqtV4QUVFFVzUi2gLAobbzRVMLvnlSHlYdc+pFVdxacWOotV4IIdoC+B3ABCI6Y4cysoZnTr3o\nWOXnvgC0RJTTsMVkDazWekFEHcqX9pD9vqcQ0bW+a1jTZ875wr/KzwMhBzO55vmiSXU74Ul5WHXM\nqRfl/xyHADQDoAghXgHQjYiK7FdyZkvm1AsA7wHwBrBECCEAlBHRQPuVmtmamfXiESHEUwAMkKNa\nPGa/ErOGYGa9uOwjDV5I1uDMrBePCiFeBFAGeb54vLZ8m9RQg4wxxhhjjDVlTa3bCWOMMcYYY00W\nB9+MMcYYY4w1EA6+GWOMMcYYayAcfDPGGGOMMdZAOPhmjDHGGGOsgXDwzRhjjDHGWAPh4Jsxxhhj\njLEGwsE3Y4wxxhhjDcTuwbcQYqUQIkMIcewaab4WQiQIIaKFEL0bsnyMMcYYY4xZi92DbwA/ALi3\npjeFECMBdCSiIADPA1jWUAVjjDHGGGPMmuwefBPRPgC510jyAIAfy9NGAPAQQvg3RNkYY4wxxhiz\nJrsH32YIAHC+ynpq+TbGGGOMMcaalKYQfDPGGGOMMXZd0Ni7AGZIBdCmynrr8m1XEUJQg5SIMcYY\nY4zd0IhIWPK5xhJ8i/KlOhsBvATgNyHEYAB5RJRRU0ZEHH+zS0JCQhASEmLvYrBGhusFqw7XC1Yd\nrhesOkJYFHcDaATBtxDiZwDBAJoLIc4BmA1AC4CIaAUR/S2EGCWEOA1AB2Cy/UrLGGOMMcaY5ewe\nfBPReDPSvNwQZWGMMcYYu9G0a9cOycnJ9i5GoxcYGIikpKR652P34JsxWwoODrZ3EVgjxPWCVYfr\nBavOjVAvkpOTuduuGerT1eSyfK6ngy2EoOvp92GMMcYYszUhBAffZqh6nMp/tiga56EGGWOMMcYY\nayAcfDPGGGOMMZsaM2ZMrdt3796NX3/9tU75hoaGYvHixfUq25dffonHHnuscn3q1KkwGo31yvNa\nuM83Y4wxxhi7TF26N9fUY+XUqVOYPXs2OnfuDJ1OBwB45ZVXoNFoYDKZMGPGDJw4cQIffPABnnnm\nGfz4449Yvnw5CgoKMG3aNPj6+mLo0KHo2rUrQkJC4OzsjPvvvx+9evXCE088gdGjR8NgMODAgQPw\n8vJCt27dsGrVKhiNRtxyyy246aabsHbtWkyaNAnLli3DV199VW05IyMjERQUhPPnz6NNmza49957\n8eeff9Z4wVBf3PLNGGOMMcYuQ2T+UpNvv/0W8+bNw+zZs2EwGHD8+HF4e3tjwYIFaN68OfLy8tC9\ne3e8//77CAgIQHZ2NjQaDX766SdMmDABn3/+Of7zn/9g2bJl+Pjjj7FixQqsXr0aQgh0794dM2fO\nxO23345Ro0Zh/Pjx+OKLL+Dt7Q0/Pz9ER0ejf//+8PT0xLRp0/Dpp59WW8aIiAj069cP48aNw8qV\nKwEAffr0wa5du2xxWAFwy7dNFRQAmzcD584Bb7wBaPhoM8YYY+wGIYSAg4MD1Go11Gr1Ve8T0WUj\niKhUqmq3V1WRxt3dvXK94iHIsrIyvPLKK/Dw8KhMn56eDjc3NxQXF8PZ2fmq/H744Qfo9XokJCTg\n4MGDCAkJgZeXFwoLCy38rWvH4aCVREYCn38O5OcDo0YBMTHAH38At94K5OXJAHzx4rrdxmGMMcYY\na6qeeeYZfPTRR+jQoUNla/WyZcswc+ZMlJSUoEePHujWrRtmzpyJadOmQVPeSvnEE0/g1Vdfxfbt\n23H77bfj+eefx7vvvgtXV1eMGzfusuC8c+fOWLhwIdzc3PDmm2/i5ZdfRosWLdCuXTsEBARU5j9j\nxgysXLkSc+fOxdtvvw0A0Ov1yMnJwZo1awAAixYtwtatWxEQEIDOnTvb7LjwUIP1lJQEPPssEB8P\nvP464O8P7NgB9OwJPPII0Lq1DMjvvBMwGICQEODRRxu0iIwxxhhjNWosQw0uXLgQo0ePRseOHW2S\nv06nw8KFC/Huu+9eM93cuXPx6KOPXhWAW2uoQQ6+6yEmBhgxAnj1Vbk4ONScVlGAXbuAxx8HwsNl\nN5SCAhmUP/UU0K5dgxWbMcYYY6xSYwm+dTodYmJiMHjwYLuWY+vWrRgxYsRV26+bcb6FECOEECeF\nEPFCiDered9dCLFRCBEthIgRQkyyQzGvYjAA48cDH34IzJhx7cAbAFQqYNgwYMoUoHdvwMMDmDUL\nyMwE+vUDTp5smHIzxhhjjDVGrq6udg+8AVQbeFuTXVu+hRAqAPEAhgFIAxAJYCwRnayS5i0A7kT0\nlhDCB8ApAP5EdNUAjA3Z8v3RR7IFe/PmuvXjLi4GVqyQQXhFwL5kCfC//wH79/NDmYwxxhhrWI2l\n5buxuy66nQghBgOYTUQjy9dnASAi+qxKmlkAWhPRy0KI9gC2EVG1veAbKvg+dQoYMgQ4cgRo27b+\n+SkKcP/9sjX92WeBY8eA0aOBgQP5AU3GGGOM2RYH3+a5XrqdBAA4X2U9pXxbVYsAdBNCpAE4CuCV\nBirbVZYulX22J04E3n/fOoE3ILukbNgA3HEH8MMPQFkZ8NhjwBdfWCd/xhhjjDF7aqwzXE6ePBkv\nvvginnnmGfzwww8AeIZLALgXQBQR3SWE6AhguxCiFxEVVZc4JCSk8ufg4GAEBwdbpRCJicB77wHT\npwPu7sBLL1kl20oajcy/wpQpwIABsp94795yW34+cPEiEBRk3X0zxhhjjFUl5pjfqEuzq281bwoz\nXAohsGDBAjg5OeGZZ57B5MmTrznDZdU402JEZLcFwGAAW6uszwLw5hVpNgMYUmV9B4D+NeRHtlBS\nQnTXXUSffGKT7Gv0889EbdsSxccT5ecTDRhA5ONDdP58w5aDMcYYY9cvW8VP06dPp+TkZDIajTRs\n2DCKjY2lkJAQIiL64IMPKDY2lsaMGVOZ/oEHHiAioiVLltC///5buf2NN96g5ORkIiIaM2YMJScn\n03PPPUdERKGhobR48WIiIpowYQLNmTOH5syZQ2+88QYREc2dO5eGDx9Oer2+2jJOmjSJXn75ZXr4\n4Yfp559/JiKilJQUevHFF69KW/U4lf9sUfxr75bvSACdhBCBANIBjAUw7oo0yQCGA9gvhPAH0BlA\nYkMV0GAAHnoI8PSUrd4Nadw4QK+Xo6GoVHJ0ldGjgbFjgb//li3wjDHGGGONUVOY4RIAPvvsM7i4\nuGDs2LEYN27c9T3DJRGZhBAvA/gHsv/5SiKKE0I8L9+mFQA+ArBKCHGs/GMziSinocr455+yu8fG\njfYZieSZZ+TDmIoiJ/AhAtLSgD59gC1bABtOwMQYY4wxZrHGPsMlIC8Qpk+fDpVKhcDAQADAmTNn\neIZLc9litJNRo2SL85NPWjXbevv8cxl879hhuxFRsvXZmP7PdPyV8Bd0Bh28nb3Rw68HxvUYhwk3\nT4BK2Pt5XcYYY4zVV2MZ7YRnuGyCrB18p6cD3boBKSmAq6vVsrUKo1F2Rxk7FujbFwgOBnJygDVr\ngBMngHfeqf9oLNO2TEOWPgufDv8UzZ2bI0ufhci0SLy/6328d8d7GNfzyh5CjDHGGGtqGkvwfaPM\ncMnB9zUsWCAD2ZUrrZalVR06BMyZA+TmAgkJcojCBx8EfHyAn36SreJduliW99ncs+j/bX/EvRQH\nP1e/y977N/FfvPT3Szg+5Tg0Kns/NsAYY4yx+mgswXdjZ63gmyOna1i7Vk4f31j17w9s2iR/TkgA\nvLxk4A0AJpNsBX///brnezrnNB5b+xheG/zaVYE3AAxrPwz+rv5YfXQ1JveZXI/fgDHGGGPsxsKd\ndmuQkgKcPi27czQFQUGXAm8AuPVW2TJeV6kFqbjt+9swufdkvHP7O9WmEULgi3u/wKwds5BemG5h\niRljjDHGbjwcfNfgjz/kKCMODvYuiWX696/M98SVAAAgAElEQVR78K2QgskbJmPKgCmYOmhqjcP8\nAED/Vv3xbJ9n8cJfL/CtKsYYY4xdU2Od4fLcuXN4+umnMXXqVDz33HO4ePEiz3BpL2vWALNm2bsU\nlmvbVvYBT0sDWrUy7zOrolchvzQfb9/+du2JAbw/9H30/7Y/for5CU/2amTDwTDGGGPMcnUZSq2G\nRrimMMPl7NmzsWDBAnh7ewMAjEbjNWe4tAZu+a5GeLjsdnLPPfYuieWEqFvrd2FpId7d+S4WjVxk\n9kOUjhpH/O/B/+H1ba8jrTCtHqVljDHGWKNCZP5Sg2+//Rbz5s3D7NmzYTAYcPz4cXh7e2PBggVo\n3rw58vLy0L17d7z//vsICAhAdnY2NBoNfvrpJ0yYMAGff/45/vOf/2DZsmX4+OOPsWLFCqxevbpy\nzPCZM2fi9ttvx6hRozB+/Hh88cUX8Pb2hp+fH6Kjo9G/f394enpi2rRp+PTTT6stY3FxcWXgDQAa\njQZ9+vTBrl27rH5IK/dhs5ybsDlzgLfeArRae5ekfvr1k8H36NG1p/1s/2e4u+PdGBAwoE776Nuy\nL17o/wKe3/w8No7deM2uKowxxhi7cTSFGS5dXFyQk5NTGYCbTKbre4bLxujIEeD4cWDDBnuXpP76\n9QO++672dNn6bCw9tBRHnjti0X7eveNdDPh2AH6N/ZXH/maMMcYYgKYxw+Xs2bMxffp0NGvWDAaD\nAR9++CEuXLjAM1yayxrjfD/3HBAYKCepaepOnwbuvhs4e/ba6d7b+R4ydBlYcf8Ki/f1V/xf+Hjv\nxwh7JsziPBhjjDHW8BrLON88w2UDEUKMAPAlZP/zlUT0WTVpggEsBOAAIJOI7qwhr3oF3wUFMvA+\ncQJo2dLibBoNkwlwcwOysmqeofNC0QX0WNIDB/97EB28Oli8L6NiRJuFbRA6MRQ3+dxkcT6MMcYY\na1iNJfjmGS4bgBBCBSAewDAAaQAiAYwlopNV0ngACANwDxGlCiF8iCirhvzqFXwvXSpnhVy3zuIs\nGp2bbwa+/152QanOxPUT0cK1BT67+6prnjqb8c8MOKgdMHfY3HrnxRhjjLGG0ViC78bOWsG3vUc7\nGQgggYiSiagMwK8AHrgizXgAvxNRKgDUFHjXFxGwfDnwwgu2yN1+unUD4uKqf+9w2mHsSNyB94a+\nZ5V9Pd3naayMWoksvU3+RIwxxhhjTZ69g+8AAOerrKeUb6uqMwBvIcQuIUSkEGKCLQoSEQHodMBd\nd9kid/vp1k12o6nOisMr8NKAl+CmdbPKvrr6dsX4HuPx2rbXrJIfY4wxxtj1pimMdqIB0BfAXQBc\nAYQLIcKJ6HR1iUNCQip/Dg4ORrCZ88MvXy4ftlTZ+3LEyrp1A1avvnq7vkyPtSfWIubFGKvu76O7\nPkKPpT0QmhSK4HbBVs2bMcYYY03TmDFjsHbt2mtu3717N9LT0zF27Fiz8w0NDcXx48fx0ksvWVSu\nyZMnw9nZGQ7lU5rPnz8fMTExiIyMxPPPP39V+qpxpqXsHXynAmhbZb11+baqUgBkEVEJgBIhxB4A\nNwOoNfg2V2mpnE6+hvHXm7SuXavvdvJH3B8Y1HoQAtyvvNFQP65aV3x454d4d+e72Dt5L4/7zRhj\njDVBIjTU7LRUQ0NnU5jhUgiB+fPnw8XFBf/++y+WLl2KadOmYeHChdcMvufMmWP28bmSvYPvSACd\nhBCBANIBjAVw5UDRGwB8I4RQA3AEMAjAF9YsxJ49QPfugL+/NXNtHDp1As6dA0pKACenS9s3ntqI\nsd3Nv7Ksi3E9xmHu3rnYenorRgaNtMk+GGOMMWY7NQXUdVExw2VAQADuvffeyhkuZ8+ejQ8//PCy\nGS4BXDXD5bBhwwAAM2bMwMcff4y2bdvisccew/z58ytnuNy9eze8vb0xfvx4PPXUU+jUqRMAIDo6\nGk888QS2b9+OadOmYePGjdX/nlUeNO3fvz/Wr18PAHByckJmZiZ8fX3rfRyuZNfgm4hMQoiXAfyD\nS0MNxgkhnpdv0woiOimE2AbgGAATgBVEVEMvZsv89Rdw333WzLHx0Gpl6/fRo8CgQZe2R6RG4OO7\nPrbJPtUqNT4Z9gle2/Ya7mp/Fxw1jjbZD2OMMcYar6Yww2VVBw8eRJcuXQAAXl5eKCgouP6CbwAg\noq0Abrpi2/Ir1ucDmG+b/QObN19fwwteaeBA+UBpRfB9oegCigxF6OTdyWb7HH3TaHwX9R3mh83H\nO3dcBzMWMcYYY6xOmsIMl0IIvPHGG9BoNJVdUAAgNTUVbdu2vfqXsgK7T7JjTZaM852QANx5J3D+\nPHC9dk/+/ntg507g//5Prm84uQHLDi/Dlie22HS/SXlJ6Lu8LxKmJqC5S3Ob7osxxhhjlmks43w3\nlhkudTodZsyYgSVLlly2/XoZ59vu9u0Dhg69fgNvQLZ4Hzx4aT0iNQIDWw20+X7bebbDI10fwdcR\nX9t8X4wxxhhr2p577jlkZmbaLH9XV9daA28ASEtLw4wZM2xWjhs++A4PB+w8i6nNdekCXLgA5OTI\n9YjUCAxqPejaH7KSmUNmYsmhJSgsLbRephs3Ao89Bjz9NHD2rPXyZYwxxpjduLq62n1qeQAICgpC\n+/btbZb/DR98Hzhw/QffarWcXv7gQTm+9+G0wxgU0DDBd1DzIAzvMBzLDi2zToaFhcDkycCIEUBg\nIDBgAHD4sHXyZowxxhizMbs/cGlPhYVAYiJw8832Lont3XKLbOXPCViPwa0HN2gf7Lduewv3/t+9\neHngy3B2uPaTxrVatUpOQ/r003K9Z0/ggQeAhQtl36GDB2U/onvuAcoHzGeMMcYYayxu6JbvgweB\n3r3lcHzXuzvuAHbvBlZFr8Kk3pMadN+9/HthQKsBWBm1sn4ZmUzAV18Br756advDDwOffy6fJv3u\nOzmY+ccfywHOr+chbBhjjLEmZMyYMbVu3717N3799dc65RsaGorFixdbXK7Jkyfjueeew5QpU7B2\n7VpkZGRUjjtuKzd0y3dY2PXf5aTCrbcCB0+mwCntEDaM3dDg+58TPAcjfxqJp25+Cu6O7pZlsnYt\n4Osrf5mqxo2TS4UPPpB/3AcfBDp2BPr0sbzgjDHG2A0oVISanTaYgqvd3lRmuPzyyy/h4uJSuS0t\nLQ1Go7Fy6ENru6GD7z//BObbZPTwxsfdHfAYvgS3+oyvf9cPC/Rp2Qcjg0Zi7t65+HT4p3XPQFGA\nDz8EFiwwb2iaW2+VreSPPw5s2wbY8MEJxhhj7HpTU0BdF01lhstXX30VGo0GDz30EO6++260b98e\nUVFRGDBgQL2PQXVu2OA7Ph5IT5fdg28ERYYi5Hf8Fu0uHLBbGT6880P0WNIDs4fOrvsFwJo1gJsb\ncO+95n9m3Djg4kU51uKGDbLjO2OMMcYaRFOZ4fLKlu+K2S1t5YYNvn/7DRgzRo4EciNYFb0Kvb2G\n4sQ22wxcb47W7q3Rr1U/bDm9BQ93fdj8DxYXA7NmyYct6zog+yuvAG3aABMmAEePAq6udfs8Y4wx\nxizSVGa4rGj5HjRoECZOnIgzZ87g/vvvt9lxsfsMl0KIEQC+hHz4cyURfVZDugEAwgA8TkR/1JDG\n7Bkuu3cHVqwAhgyxrNxNze0/3I7nu76D10ePQEaG/SYVWn5oOUKTQ/HLI7+Y/6GPPwaOHAF+/93y\nHU+YIB/GXLHi+p5RiTHGGKsjnuHycpMmTcKqVauu2m6tGS7NCr6FED2JKMaSHdSSrwpAPIBhANIA\nRAIYS0Qnq0m3HUAxgO/rG3zHxgKjRgFJSYDqBhjvJb8kH60XtkbG9Ito38YZhw8DrVvbpywXdRfR\n+ZvOSJ+ebl7Xk7IyWdjdu+VsQZbKz5dDvowdC7z1luX5MMYYY9eZxhJ863Q6xMTE2HWinczMTKSk\npKBPNYM1NPT08kuEEAeFEFOEEB61JzfbQAAJRJRMRGUAfgXwQDXppgJYB+CiNXb666/yObwbIfAG\ngJ1nd+LWNrfCReuMvn1lI7K9+Ln6YWDAQKw/ud68D2zbJkcsqU/gDQAeHsCWLcCiRcCePfXLqxaZ\nBgOyy8psug/GGGPsetMYZrj09fWtNvC2JrPCTyK6HcATANoAOCyE+FkIcbcV9h8A4HyV9ZTybZWE\nEK0APEhESwHUu78A0aXg+0ax7cw23NtRPqjYty8QFWXf8vy373+x/PBy8xL/73/AxInW2XGrVsDi\nxcB//wuUlFgnz3LHiorwaGwsekdGIigiAh0OHMB/jh1DmaJYdT/XQkQoTS0FKfZtvbhwAVi2TE5g\nxRhjrPELDAyEEIKXWpbAwECrHO869fkWQqgBPAjgawAFkMHw2zV1AzEjv0cA3EtEz5WvPwlgIBFN\nq5JmDYD5RHRQCPEDgM1EVG3nXyEEzZ49u3I9ODgYwcHBl6WJjgYeeQQ4ffrG6PpLROjwdQdsHrcZ\n3f264/ffgR9/lIN/2IvBZEDbhW0ROikUXXyu0aKdlSVbvZOSAC8v6xXg8cdl/29LHuC8wm8XLyKx\nuBgLU1LwXmAgBru7o7ebGwDggdhY9HZzw9wOHaxQ6GsrjCpEzKgYmIpM8B7pja6ru0Ll2PC3dkpL\n5QhCzZrJ51tvvlnOgdS7d4MXhdVBYnExzpWU4CYXF7R0dLR3cRhjrNEJDQ1FaGho5fqcOXNs3ue7\nF4DJAO6D7Hu9koiOlLdKhxORRZcCQojBAEKIaET5+iwAVPWhSyFERfuZAOADQAfgOSK6asBGc/p8\nf/GFDLyXLLGkxE3P4bTDeGzdYzg99TSEEEhMlMHR+fO1f9aW3t7xNnQGHb4aefWA95U+/FAG3ivr\nOTPmlXQ6YPhwOU39xx9bnM2q9HR8lJyM+3188IiPD27z9Lzs/YsGA/ofPoxOzs54PzAQwda8gKii\n5FwJom6LQsf5HdF8dHPEjY9D/t58OLZ1hGNrR7R6sRWaj2huk31XZTIBkybJw/v774DBIC/03nkH\nWL/+6rmRmP3pTCZMiIvD/vx8dHZxweniYvzStStu9/SE2o6tE/pTemi8NND6NY7ph//6C/jpJ3lx\n+dRTwIgRAF+jMHZjq0+fbxBRrQuA3QAmAHCu5r0J5uRRQ75qAKcBBALQAogG0PUa6X8A8PA13qfa\n3H8/0a+/1prsujHjnxn01r9vVa4rCpGHB1Fmph0LRUQp+Snk9akXZeuzq0+g1xP5+xOdOGGbAmRm\nErVpQ/T33xZ9/JRORz779tHxoqJrptMbjfTzhQvks28fheflWbSvazm38Bzt9d5L5786X7lNURQq\nSSmh/IP5dOH/LlBY2zBKmJ5AiqJYff8VkpKIHn6YaPhwoisPydatRL6+RMeO2Wz317Tr7C7q+FVH\n6vxNZ1oauZQ2n9pMQ38YSq9tfY0yijJsu/PiYqLTp2tPFxlJtHAh0ZIlRPHxti1TFUtTUuje6GjS\nGY1ERPRXVha1DQsjx9BQGhMbS7G11G9byNyQSXub76V9fvvo4u8XG3z/VZWVEc2ZQ9S6NdH338vl\nttuI3Nzkn+u6VlJCtHgxUWGhvUvCWKNUHnNaFv+alQh4tZptr1i60yvyGQHgFIAEALPKtz0P2bp9\nZdrv6xN8G41Enp5E6el1PMJNlKIoFLgwkKLToy/bfsstRLt326lQVUz8cyJ9tPuj6t+cN4/oP/+x\nbQF27yby8SF6+22i7BouAmrw7MmTNOfsWbPT/5WVRc337qUVqalWC4KztmRRWJsw0p/VXzOdIddA\nhwcfplNTTtkkAN+wgcjbm+iNN2SsWZ2ff5YBzKZN8v/QhtcBl9mXvI985vnQplObKCIlgvos60Nt\nvmhDv8T8Qv/d+F8a/N1g0huuffwspihE48cTOTrKIKamX3r+fKJWrYimTCF6+ml5pdIAAbiiKNQ9\nIoJ25ORc9V5+WRnNS06m1mFhlFlaavOyVCiKLaJ9Pvso/2A+5R/Mp73N91Lx+RoqlZUZDETR0UTv\nvkvUpQtRu3ZEXbsS3X03UWrq5Wnj42Wdt8H1dOOQnk7Up49sAJkxw96lYaxRaojg+0g126Is3amt\nltqC7yNHiG66ycyjeh0IPx9ON31z01UB1zPPEC1daqdCVRGbEUt+n/tRjv6KL/+ICBmAnDlj+0JE\nRxM98ADRK6+Y/ZELpaXkuXcvXaxjUBJbVERdIiJoUUpKXUt5FX2inva33E85u64OnKpTll9Gkb0j\nL2shtwa9nigwkGjHjtrTbtxI1LMnkRDyVaezalGukpiTSC3mt6AtCVsqtxlNRiozlRGRDD7HrhtL\nI/5vBOUW51q/AMuXE/XqRXT0KFH37jKIuTIA//xzGemdO3dp25Il8gBlZVm/TFXszMmhrhER17wg\nm3n6NHWPiKBuERE0+PBhWn5lFGpFiqJQVHAUnf/mUh1NfDeRYh+Ptdk+K7z2GpGDA1HHjkTTpxMd\nOEB0/DjR9u01XzM9+STR3Lm2KY+iKLTyyErq8FUHunXlrfT61tfpcNrhq9KVlJVYf+cGA9HttxO9\n9ZYMwn187HfbioiOFBTQ/9LTKdtgsFsZWPVy9DkUlR5l72LYjc2CbwDjAGwCkAtgY5VlF4Adlu7U\nVkttwffChUTPP1+nY9ukvbDphWpblhcsIJo61Q4FqsaUzVPouY3PXdpgMsnmpjVrGq4QycmyGcvM\n26tvnzlDz588adGu4su7q0Tk51v0eSIi/Wk9hbUJo5TFdQvi9Wf0tM93H52dc5ZKL1inNXPOHKJH\nH63758aNI5o2zSpFqFZcZhx1+roTfX3g62umMxgNNPXvqdTp604UkxFjvQKcO0fUvDlRXJxcz84m\nGjRIRncTJhCFhBC9+qrs+nT+igsiRSGaNYuofXuiH36wSSt4lsFAHcPD6deMa3e7MZhM9MuFC3S4\noIB25uRQu/Bw+i4tzerlURSFkj5KosjekWQqM1VuN+qMFNE1ghKmJ5D+rJ4y1mTQiadOkCHHeoHY\nunVEHToQ5dbx+is2lsjPzzZ3UpdGLqUeS3rQvuR9tDNxJ80JnUO+83zpxMVL3fBiM2LJ5WMX+jXG\nyv0oP/yQ6N575bmYiOjHH2Vd/ugjolrqizWZFIU+OHuW/Pbto9HHjpH33r20JiODtmZn0xPHj1O3\niAhakZpKxoa6jcYus/3Mdmq1oBV5fepFRy8ctXdxiNLSiP78U7Ysjh9PNHq07CdWlU5n1UY9Wwbf\ngQCCAYQDGFpl6QtAY+lObbXUFnw/+STRypV1OrZNlt6gJ+/PvOlc3rmr3tuyRfbNbQxyi3Op5fyW\nFH4+XG5Yu5Zo4MCG65dQ4aGHzLodcFqvp+Z799K5mvpXmOH3ixcpMCyMsixsyYm+J5qSP0u26LNF\nx4soblIcHex1kBRj/Y5xUpL8Tk5Kqvtns7OJAgLMazGvq7jMOPKd50srj5j/z/5j9I/kM8/HOoGM\nosiHS0JCLt9uMhHFxBB99x3RO+8Qvfce0bUu4n7/XV6l+PrKOzPffy8jvnoyKQrdHR1NM8zpi36F\nUzodtdi/n/64aL2+2Iqi0OkZpymiWwSVpFzdkmvINtDRkUcprE0YRQ+PpqMjj1L8y9a5IDGZZI+f\nsDDLPv/ee5fHqVYpk2KiLou6UOjZ0Mu2/xD1A3X4qgPtTd5LBqOBBn07iF7b+hr5f+5PG09utM7O\n9frquz0lJBBNnCj7bc6ZIwOYbdtknSwosM6+qzCYTPRobCwNOXyYUktknThcUEBtw8Lo5oMHaXFK\nCv2bk0ODDh2iB2NiKp9ZuN4dPEg0aZI8LSxeLP8s9rj2iM+KJ595PrQjcQf9fOxnav9le/r+yPe2\nf4amrEz+syYlEa1eTfTCC/K7e+xY2YB2331EkyfLQG/tWqKWLeXV9b59MgBs1kwuW7bUvi8z2Lzb\nSVNZagu++/aVtxNvBD8d+4nuWX1Pte8lJckvnMbih6gfaPB3g0kxmWQ/ww0bGr4QO3bIrgG1nMlG\nHT1K85ItC3yrmp6QQA9YcCs3e3s2Heh0gEwGy7/tFUWhI3ccoZSl9ev+MmaM/B621N9/yy4r1uw3\na1JMdOvKW2lRxKI6f/ZI2hFq/2V7WhC2wPICKIrstzBggHxgzRoyMmSeTz1F1KKF7Bdej2/cpSkp\nNOjQIYtbDA8VFJDvvn0UWY+7NxUUo0Jxz8TRoYGHyJBl3sWoIdtA+/z2UcGh+gd9+/YR9ehh+ecN\nBqJ+/Yh++aXeRam07fQ26rmkZ7XdgX469hO1XdiWVHNUNPL/RpJJMVFESgT5zvOldcfXVXapstjy\n5fLCsSYpKTLAadOG6K67iIYNkw8RWaEuVPXWmTN0T3Q0lVxxVaMoymXHpcRkovHHj9PQI0eo2MYB\nuFFnpPhX4il9lX0eGsvOlof900/lNc/EifJ7fPjwut+1qY8cfQ7dvPRmWnxwceW230/8To+ueZQ8\nPvGgISuH0Gf7PqO4zDjr7vj0aaL+/Yk6d5a3nO67j+jrr2WQvXKlrJtX2rdPPmzUuzfRJ5/Ig7hn\nj/x8QkK9i2TLlu995a+FkON6VyyFAAos3amtlmsF3yYTkYuL1c8RjVL4+XBqtaAVbT61udr3TSYi\nV9eG/Ye9FpNior7L+9L21XNkp3xrNiOZS1Fkd5ddu2pMciA/nwLDwshghfKVmkzUNiysTt1PygrL\nKKJbBGWsqX/rQkFUAe1tvpdSv021qAU8Olqe+PX1fFZxypTqR0ix1DcR39CQlUPIpFj2N0rKTSLv\nz7wta8EpKZH92nr1qvMDvGbT6Yhuvll2BbBAakmJWaP01GZ1ejr1joykMgv/F85+cJZOzzhNMY/E\nUNSwKCorrFvQmLEmg/YH7Cf9mfpVwNdeI5o9u15Z0ObNsou+NVog9QY9Dfp2EH17+Nsa05gU01X1\ne0/SHuq9rDf5zPOhRRGLyGiyIBA1GuX5d+dO8z9jMhG9/LJsuDh4kCgqSuZTD7tzc6nV/v2UYeYz\nNSZFocdjY2nk0aO0IyeHfkhLo41WGs6rrLCMMjdmUsJrCRTeIZxiH42lfX77KHVZKp15+wwVxdl+\nNKDYWNktKjDw6q56RqPc1rNnwzz8m16YTr2W9qLXtr5W7cVhSVkJbUnYQi9ufpECFgTQsP8No5OZ\nlnXRvMp99xG9/751/tGWLJF1tp4j+XDLtxnBd2KivAC63v0Y/SP5zvOlTac2XTNd//6W32q1hfVx\n6+mPu1rKfoX2sngx0SOP1Pj2IzEx9NWV/XPr4Zvz52m0ma3fiqJQzEMxFPd0nNVGLMmPzKcjtx2h\nw4MPU/rqdEpZmmJ2ID5unByQpr6MRtmCM2xYvb+zKSk3iZp/1rzeLS4v/fUSvb719bp9SK8nGjpU\n3gK19RX+4cOy5caCbiPT4uPpdSu0+CiKQndFRVHI2bN1ro8lqSW012svxU+Np1MvnCJTiWUBfMrS\nFApvH15tVxVzKIoMaI7Ws7uqosjroY317PmhKAo99OtDNHbdWIsvHmMyYuiW726hZzc8W/fzxOrV\nREOG1D24URTZYt6mjVwee+zSXZ/i4jrnN/TIEfq/Cxfq9JlSk4neS0ykwYcP08MxMeRnhaFdc3bk\nUFjrMIoaFkVJHydRXngeKYpC2f9k05GhRyh+ajztbb6Xzn54lsoKyyj/QH697kjWZMQI+bW4dy9R\nddcjiiIbMUaNko26lnQDNEdSbhIFfR1EH4R+YFbdKjOV0cLwheT3uV/9H8qMi5PnvPq29lRQFKL/\n/ld2QQkKIjp0yKJsbNny7X2txdKd2mq5VvC9ebPsm3e9MpqM9Ob2N6n9l+0pNqP2fqFPPUX0bc2N\nKw2uRF9IF10FpR+14xVBQQGRl9flI0+QbFlZk5FBPvv2UZEVb23qjUZquX8/rTejlSbjtwz5MJqF\ngUpNFJNCKUtSKOahGIrsE0mJsxNr/cyZM7Kvt7ViTKORKDi4fiNHKIpCI/5vRM1DV9ZBakEq+c7z\npd9ifzN353LEnHHjGu6uzfLlsjWhDoF0WkkJee3dS+lW6g6TXFxM3SMi6P5jx2hYVBT9YmbAdGbW\nGYqfap0+28mfJlNElwgqPFr3FqzwcPm9a41r2Y0bZV71GcHnp2M/Ud/lfes9gklBSQF1W9yNlkUu\nM/9DBoN8GLgurd7VKS6WDRhdusjgxtFR/l+Y2S88LC+P2oWHW3xHpcKfFy9Su/Dwq7qtmENRFEr+\nPJn2t9xP2duufQerOLmYjo46SqGaUArvGE4RXSIodUUqFSdbZ3jMNWtkvartJoDBQPT44/KZ7pYt\nZWOjNZUaS6nX0l40f//8On927fG11PL/2bvu8KiKt3tCAEF6CCT0FqRJL0qRakMUEAUUUVFAmqiA\nImABlA7SexFQpArSe0nIbnrvvffedze7e8/3x5CQwCbZJLuL/p7vPM8+yd47d2Z27ty577zlvFta\n8KTPScbnxFeNWeqLL6pvonoSkkSmpIhBbtZMcIxWgj6YNK7wHQkg4tHfJz8RVW3UWJ/yhO9Nm4SJ\n8X8ROcocvnPyHY44OoKp+fqZ23bsEPP5X4OrVxnStTm3OT7jzBULF4pguEfI02j4qpcX+7m66uRD\nri7ccnJoJZPxXDksAtpCLZ1snJh+x0iuDI+gTFRS3krOjLvl/85vviGXLjVs2zExYv3z8Kja9ce9\njrP3vt4s1BiGBcMr0YutfmvFX2x/qdiE7+UlVKgm5MMmKfjBR4/WW3qcERjIRQbQepdEjlrNnbGx\nPJ2czDYODtwS83SAd0loFBraN7WvtrtIScQfjKesmazSNJoffiiYnwyFjz4iR40S1NgXL1bu2jxV\nHltvbU37aHuD9CU4LZhNNzZlcFpwxYUjIsghQ0SWLENAkkQMzcqVYqc+bZqIEtQDb/v4GISKlSRf\n9/Li8SpQ0SQeS6RzD2cqYvQToCVJoiZPQ0mSmHY1jf4f+VNmKWP67eqt13v3CkHa2bly1+3aRXbv\nXn1LYkmstl3NcX+Nq7LV1TbSlkOODO79fcAAACAASURBVKHVZiu23tqaCnUlNidyuXioDBjk/RT8\n/IT71AsvVCpW5//dTvQQvmfM+Hdpeg2F1PxU9tzbk19c/oIqjf4vfxcX4Sf2r8GHH9J/1QIOODjA\nqJkYK0RQkDBvKRTUShJHeHjws8BAo9JZeeXm0lou58kyNIcJRxPoOcY0XKppN9Lo2MGRmjzdK3du\nrggqN4Zp88gREbymrmTMWGJuIpttaqaTB7k6iMuO44ijI/jJP5+UX/Cnn0RApKmhVgsN47VrFRY9\nn5LCjo6OzKns4FYCMQoFuzs7c3Fo2dlU026k0X2oYe8TKTSQ8lZyxu2OY65PxVrw2Fhh5DKkn2xG\nhlDw7N4ttJWVITNacXcFp56barjOkNzptJODDw8uf/OoVgufmTVrjGe1SU4WaZUrGOyHmZls5+Bg\nsMDJq2lp7O/qWqn3iVappUNbB2bJqjcxMh9mUtZMxpQLVRMYIyLEOltVDfbLL1d+A1gWHkQ+YPPN\nzRmbbRiXy3F/jSsVrFku0tMF5eo//xik7QoxcSL5yy96FZUkyaia766P/vbT9alqo8b6lCd8Dxok\nNlD/S8gvzOfLh1/m93e+r7TAqlKJoEsjsERVHrm5ZKNGVCclsNe+XjzudfzZ9uf118kNG3glNZV9\nXF2pNcFmwC8vj81lMt7XoV33eduHSacq5wNZHQRMD2DYUt3+xPv2ifXJGJAkoTmsjDayyE92+d3l\nRulTfmE+229vz9tht8su1KPHswuguHhRBHjqmKMbo6P5oosLLezt2dTennITRGRlFBayj6trmRrw\n4HnBVabJrAg5Hjn0ftNbCD1/ly307N4tbtk33xilGyRFEPHevfqV9U7ypuUmSybkGJY/vYj557D7\n4bILbdsmAi6Mvca99x65v2w3GEmS+JKbG/80IGm6VpLYydGR/1RCYxqzLYbe4wzDWZ3jnkO5tZyx\n22MpaSs3vtOnV8/L4s8/RWbW6iI0PZRWm614N/xu9St7BKdYJ7bZ2qZiZWFGhqCoM7SZtTxERwuf\nygoshIlKJSf4+BhV+D746O8DHZ/7VW30iTbeBBAEIATA9zrOTwPg/egjA9CznLp0DpQkkfXri3v5\nv4SNso2ccGpClTXFQ4eSdw33TFUdf/4pokUo6N6abWrGuGzDmB6rhKgosm1bDr9yhSeNkFCkLNzN\nyKCVTEa/EkwUGoWGDxs8ZGG66bK7KaIVtLewpya/tAYqIUEw3Tk6Gq/tkBCx9umj8YnPieeKuyvY\ndXfXypkxK4krwVfYfnv7p3iXSQpLScuWz4ahhyyTpeeXyEj2cHama3Y2k1Qqk1qTohUKtpDLKXtC\n2JckifJWcqMzRGQ+zKS8pZzqrKe1/EVxW/fvV97CUhm4u4tnpSIyBYVawX4H+vGg20Gj9MM13pXW\nW6yZrdQRoBEXJx62KiYMqxRu3BB0b2U8J8cTE9nfCIqOh5mZtJbLuT4qqsJnQJWqosxSxlzf6jFg\nlER+SD7dh7rT61UvvYMx798Xc6c6MTVKpZjnAQEVly0LGQUZ7LKrS+ViB/TEG3++UfGc//xzwR5l\nakv4pk0iQFBHu2qtlmujotjU3p7Lw8NNkl6+jj7HKt04UANA2KNkPrUAeBVp20uUeRlAIz4W1J3K\nqU/nWEZHC9+p/yVoJS07bO9Ap9iqE5d/++2zJRcpxhtvkH/9Vfz1F9tfOOLoiOpz1lYDTr6+bPfP\nP1R36kS6upqs3ZNJSWwuk/H6o9Ti6bfS6T7E8Gb6iuD9ljcTjz/WQqnVQpv388/Gb3v9enLs2LLP\nayUtN8o2sunGppx9eTZD0gyfAfJJnPA+wQ7bO/C1P14rnc1t7VpywQKjt18u9uwp5a+boFSysb09\nk0ztg14CRxMSOMqztKtUjlsOnTo7mWQjEDwvmF6veT1FYTh79tO5j4yFadMqbmvWpVmccm6KUcfk\n4wsf6w5EnjJFBJmZAlqtSFm/detTpzIKC2ktlxuEN14XYhWK4mQ8WeXsuIK+CGLIV4ZfSyStRK/X\nvRjxs37B7FZWhkk+tm5d1bIPF+Gj8x9x4XXjpMKWRcvYfnv7smN0UlNFQidj+nmXhcJCQf5/vLQF\nPlet5jhvb47x9GToo8hqUwjfHvocq3TjQrC+UeL7Ml3a7xLnGwOILee8zrG8cUNY1v6XcD3kOvsd\n6FetRfv8+WKF87NDRITQvpSgENJoNRx9fDTX269/Zt0qphW8cIG0tBQcTibCvYwM2jg58S1vb/os\nDGLkr5Ema7sIKf+kFPvmajQimOzNN42rLSxCYSFpY0PeuaP7/AG3A+yzvw/DMwyXJlivfmkKudt5\nN1tsafGYB7x//+ozRFQXRY74j+iyfomM5BxTaDPLQaFWyw6OjqW0335T/Ew2l7VqLQM/C6TXq17F\na2RSkmnf5xERYunw99d93i/Zjy22tGCO0ri+f27xbmy7rW1p329HR7J9e8NRt+mDsDAxICUiCP3z\n8tjbxcUg9JflQanVcn5wMDs7ORULTiWRdj2N8lZyFmYax8KoTFBSbi1n2o204mNPvrs1GhH3qmN/\nUiXk5wtCpKq42yblJrHxhsbMUhjPVW308dFlJzRbt05kqnxW8PF56uGdERjID/z9S+X5MKbbiTWA\n/gACn/D3HgkgqKqNlqj/vSLXlkffpwPYWU75b0uW13Fe5zj+9psgsfhfgSRJHH18dPm+fHqg6GX0\nTDPzfv+9ThqawNRAWm22qjbtVlUQmp9fmlbw7FkRlGRC14JCrZbfhoTynPUDRjgbl+VEF7SFWjq9\n4MTg/cl84w1BqmHK9/SZM8Ld78khT8tPMwxvbDWw7M4yvn3ybUFLZWlpmh1JRfjnH7JFC6qdnNhK\nLqdXNZNHGAJHEhLYz9WV+RoNsxyyKG8lLzOQ1xiQNBJderoUB7399JPIRm1KHD4s/Mt1URCuuLuC\n39761iT9GHhwYOmka19++WzMnleuCFqjW7eYrVazpVzOQ/HxJnOL2hEby54uLiwo8dJTRCsos5Ix\n0864Weey5FmUWcro/5E/5a3ktKtjx4x7j31ht20TxgFDvmb++EN4+6hUIjwkUM8UCOseruPMSzMN\n1xEdCE0PZZutbZ52P5EkEWRpQouzTvz+u3Dpy83l5dRUdnB0ZO4Ta70xhe9PH/l35wK4X+JzCcCk\nqjZaon69hW8AowD4A2hSTn06x3DmTBEo9r+CM35n2GtfL4O4ZXTpIpjSngmKHNOCddNhvfHnG88k\n+PLLkBD+WNLpWJJExK4hc0jrgfRb6bzYXcYZ+q6Yhm7fKYeXzWVc83muyeVLrVZkrd65s/Tx7+98\nzzlX5pi2M09ApVGx/fb2jP75a7G4/Ftw/DgvTJrEob//TqalVVzeyJAkidMDAjjZ15fug92ZcNR0\n8RNFSL+VTscOjgz8OozDGmeWtdQYDZJEvv02efAp+UJixx0dDc7QUxaOeBzhyGMjRfIejUY4FZt6\nMIpgb082a8ZFV69yponXNkmSOM3fn295e9MrN5eFmYV07uHMmN/Kp8g0FDJtMxm7I5YF4QVMOplE\n136ulLQS09OFAdjQt0SSRGLI4cNFaErTpuTJk+Vfo9Ko2G5bO7rFVy3xTGUQmh5Ky02WDEgp4Zwe\nECCSNT1L1rMizJhBzfTp7OzkxNs6shZXR/iugfJhCeAqgFUArj36/yiAb0heqOBafRAPoG2J760f\nHSsFMzOzXgAOAhhPMrO8CletWlX8sbW1BQAEBADduxugt/8CKNQKLLm9BHvf2ouaNWpWu77hw4GH\nDw3Qsarg/HmgZ0/ghRd0nv7qpa+w3Wl70cbKJNBIEs6mpOAza+vHB83MgHXrgJUrAUkyWV8SDyWi\nx7y2uJGeDs/cXJO1W4S9dxrA7kUbjLzsjfgt0Sa9DzVqAMePA6tXA/7+4phKo8JRr6NYPHixyfqh\nC7XNa2Nu/7koOH8aePfdZ9qXUvjkE+z96SfMz8oC3n4bUCqfaXfMzMxwuEsX1Lycg6y8Qlh/bF3x\nRQaGxesWaL2oNfyCzbBM4Y+2dUw7JmZmwOefA6dPlz7uHO+MWjVqoa91X5P04+NeH0OtVWODbAMg\nkwHW1mWuu0bHsGHwvXkTJzQarL9/36RNm5mZ4VCXLhjWqBFe9faGw+wANB7ZGK0XtTZJ+41HNEbr\nr1qjbse6aP5Bc5iZmyH5ZDK2bwcmTjT8LTEzAw4dAho2BORy4MED4KuvgODgsq/Z6rgV3Zt1R/+W\n/Q3bGR2wsbDBz8N/xtxrcxGbHSveMdevA2+9JTr/rLFnDy5LEppkZuLVJk1ga2tbSsasFsqTzAGs\n1PHZAcFO8kFVJf4S9ZvjccBlbYiAy25PlGkLIBTAy3rU99TORJIExageSQT/E9jvul+YvA2EP/+s\nXlBGtTBsGPn332We1kpa9t3fl6d9T5usS/cyMthfl7mrSPt94YJJ+qGMU/Jho4dUZ6l5NCGBnRwd\nGaswHqPHk8jKEs9NdDSpiFXQta+rSAVuhPTJ5WHjRpF+nhQWn1HHRpm0/bKQGh/GnNpgepphuG8N\ngeD8fDaXyajUaISD/h49uXSNCK1Ky7vtZPxgr2kCLXVBkkROg3tfRNNtgJtOFhRjQqEQ7n1FxEmS\nJPG1P17jdsftJu1HXHYcW/3Winff7MKclStM2nZJSJLE4R4e3OPtLYI7DJnpqBK47BHPKw0fMDPD\ndOvqk8h2zqZ9Mxm7NFYw3EQhLLt3i0yYutxbojKj2HRjU5PG02i0Gk47P42Wmyw5/+p84eN46ZLJ\n2q8IQ2Qynnv7bZ0p6GHqJDsQ6eWrHXD5qK43AQQ/ErCXPTo2B8AXj/4/BCAdgAcATwAu5dT11ODE\nxwsXs/8FaLQadt7ZmXZRdgarMzpaeH6Y/L3o4yPsYBVkorCNtGW7be2MSiVXEnOCgrgxugwe4r//\nFquWKXi/p/oxfMXjBXBLTAy7ODmV8lU0Jk6fLh2Mq85W03usN91fdqcy3nR++EXJUFQqcszxMSbd\niJWLv/+md58W3OpgoOgoA+DrkBAuL3qDOziIgLpn7I8ety+OXq97sbeLC78OCTFqkp+y4OgoZDyN\nRmLw/GC6v+xucgH8k0/ERpIkLwRcYPc93Q2WjbUyyMpLZ1bjupy/43WTt12E08nJ7OvqKhKXRUeT\nbduWYrsyFUKXhHLPDGdODwh4ponddgyN4Tlrd5P1QasVSXiO6/Do/OziZ/zxnokYcJ5AliKLXda1\npLpe3Yo5Ok0EeVYWOzo6UnP+vIhefSL7qsmFb9EmPKt6rbE+uoTvmzfJkSP1HOl/MdRaNZffXc5B\nhwYZ/CHt2JH0NHX82vTpemeSGvfXOKPx4JaEUqtlM5mM4WVFFmo05IAB5OLFRhXA0++k07G941M8\n2+/5+nJlVVOeVRLTppEHDpQ+Jmklhn0bRu+3vE36shoyhPzrUhIbrW9ksk1YhZg1i+Erv2LnnZ2F\nH+0zRrZazSb29qWtI8OHP9NgF41CQ4fWDsx2zmaiUslPAwLYSi7niaQkk86fzz9/LPhKkhDA3V4y\nrQbcy0vQ3f6wJo2tt7bm/YhnxJBz/z61ffuw7ba2BktlXxlIksReLi7FVKokSW9voSErI8OvMaDO\nUdPewp4ZYXns6eLC/fHxJmu7JPz8yOaWEh17uDD1sunM83K5cKsu+aoLSw9j041NmVHw7BKieGxe\nwgfd6jJPZdxcAPpioq8vdxcJ3GvXCnarEtHT1RG+K/L51gkzM7NRAMr1vf63wM0NGDDgWfeiekjO\nS8ao46PgmuCKC1MuwMzAvlDvvgtcMIQHv77w8QFu3wa+/lqv4vMHzsdhz8NG7hRwKjkZ/erXR8e6\ndXUXMDcX/ZbJgDVrjNIHSSUhdEEobHbawPx581LnttnYYHd8PA4nJKBAqzVK+wCgVgM3bgi34ZIw\nq2GGDms7QBmtROrfqUZr/0lMngzsvnsRYzuPRZ2adUzWbpkggZs30eHD+ahbqy7uR5rWb1UXfk9M\nxBsWFmhdp8T47NoF/PorsHgxsGwZcOuWSWMWko4moV7vemg4qCGsn3sOx7p1w9kePfBbbCyGe3kh\nxgQ+6UFBwKVLwIwZ4ruZmRk67+6MBgMawPsNb2iyNUbvAwD07g24uRFbgudgtNVkjOowyiTtPoWz\nZ1Fj6gdYO3otFlxfgLzCPJM2fyczExKANy0sHh/s1Qv47DNg4ULAiOtaSSQdTUKTMU3QpFM9nO7e\nHT9GRiJXY5q5UAQSWLQI+OEnM3Ra0x5RP0cVKRGNjiFDgIEDge3bHx9babsSXw76Ek3qNjFJH3Sh\n7x1f+L/ZDz/e//GZ9aEIIQUFkGdnP47/Wr5cBA9Omwakp1e/gfIkcwC+AHye+MQBcMETyXD+DR/o\n0Hy/+64wof9XkaPMYbtt7fjz/Z+NpmGTywUdlskwcSK5XX9/R41Ww9ZbW5dObmJgFGlkbumIaH4K\ncXFCU+NueKaCqLVR9HnHp8zz9pmZfNvHh63kcp40kqbo3j1y4MCyz2fJsyi3llOVbJokLnFxZM3P\nXuMJj3Mmaa9CXL8uqCcliQfdDnLgwYG6MwiaCBpJYgdHRzrpSlISESEsNStXij6/845JXFEkjUQn\nGydm2j9N36aRJH4fFsax3sa1oGi1Iqxk1y4d/ZMkhnwZQs9RnibTwnskeLDRqnacNfcZWW/y8sS6\nFR5OSZI44+IMjj813qSWmze9vXlUV9bgvDxhoh48WKiDjQhJI9GxgyOzHB5zWH/g71+2u6GRcPmy\nYLIrLBTz0f1ld70S8RgKRdmEU1LIu+F32XZbW+aqnqG7R0IC2bgx01Jj2GJLC4OmtK8Kpvr58ZfI\nyNIHlUqRqatJE3LiRKNSDbZ74tMWQL2qNmbsjy7hu00bwe3/X8VOp51878x7Rm1DqxUmUZPk5Sgo\nIOvXFxF9lcDP93/mF5e/MFKnyLPJyezh7Kz/i/iPP4o5QA0FrVpLmaWM+SE6SIGfgGNWFlvJ5Trp\nj6qLr78mf/21/DJhS8PoM8HHJIJLekE6zX9swMN//DtMkRw9Wtx/iqDguVfmcvDhwc8sI+uFlBQO\n1mcjWFgossnOn2/0PqVeTKXbQLcy54dKq2VXZ2f+Y8SMN6dPi01kWbzJklaic3dnpt8yDY/+d7e/\n48KLy9m4MZnxLCz7a9eSU6cWf1VpVBz2+zBusN9gkuaTVSo2eviQ+WXFrWi1YqdkaSlIr42ExD8T\n6f5y6efFJzeXVjIZg3URshsBGo2g+b1+/fExVbKKzl2dGfZtGDUFpontWbiQ/HyWmp13dubloMsm\nabNM/PADOWMGSfJexD1abbZiVGbUM+mKc3Y2W8rlj3N9PInERPLUqWfj8/1v/DwpfCcniyjzfwNd\nZFWglbTstKMTZdEyo7e1cCG5whQB8LdvCyfeSiItP42Wmyzpn1JGurhqwCMnh81kMrpUJr2xJAmO\n5wkTyL17SQ+Pavcj0y6Trn31TyxwOz2dreRyplUQtFoZSJKI0/MpW/lOktQqtXTt52oSftzzAefZ\nZ/ObHD3a6E1VDGdnslUrEQH6CJIkccTRETzmeeyZdOkVDw+eSU7Wr3BOjhBujEitIEkS3Qe7M/l0\n+X26l5HBdg4OZQtj1eqDcM+siDQh6WQS3YcYP9hNK2nZdltb+iT5cPZsco6pqeozMsR9DymdPj06\nK5rNNjXjqgereDP0plG7sC8ujh+Wle6zJKKiRMbWGMOvLapUFWVWMmY7P73W74uLo6VMxle9vLgo\nNJTaR3Mio7CQN9PTDTpHzp4VQY9PVqlKUtFvsh8d2jkwekM080PyjTo3MzPJFsOvs+P6ckydpsCR\nIyLwtoT1YbN8M/sf6M+CQhNmd3uEV728eECPOIDqCN9V8vn+r8DdHejf/99BF1kVXAu5hiZ1m2BI\nmyFGb+vrr4EDB4CcHCM3dOcO8Nprlb6s6fNNsWLYCnxz8xuotWqDdSdPo8Fkf3/s6twZAxs21P9C\nMzNg926gWTMRWPDGG8DWrdXqS9rFNFhOsNS7/GsWFpjSvDnmBAcXbT6rDT8/8ffFF8svV+O5Gnjx\nwouI3RyLLLssg7RdFh5GP8R7A0bAxwewtzdqU+UjOhp47z3gt9+A2rWLD5uZmWHVyFX49eGv0Eim\n9Rt1z81FlFKJSZZ6zpsGDYQD9L59RutTxq0MaLI0aPZ+s3LLjW7SBIMbNcLa6GiD98HWFsjLezpu\n4Uk0n9Ic6jQ1sh4Ydw47xjqiQe0G6GnVE5s3C/f7a9eM2mRpHD4MjB0LdO5c6nDbRm1xYeoFFKgL\nMOPSDNwJv2O0LpxJTcWUZuXPCQBAu3bAvHnAihUGbZ8kQueFwmqaFRoOenqtn9uqFVz79cPSNm3g\nkpODZRER8MrNxQgvL8wICsJEPz8kqlQG6IdIG7FixdOySW2r2uhxtgd6nOkBRZgCXqO84NrdFdFr\no5F+LR1ahWF94hs3Bnp/8geSb32KxESDVq0/vLyA778XskHbx2lflgxegk4WnTD32lyDvd/06k5u\nLgLz8zHD2sh5Caoqtf8bP3hC8/3LLyJ7+X8V4/4ax6OeR03W3rRp5AZjWyD79BEZzqoAlUbFsSfG\ncsiRIQbz/54XHMxPAgIqLlgRoqOFG0pF/hplQJKEH2KuV+XcWBQaDXu6uPB4YmKV2n0Sa9aQX32l\nf/m0a2l0bO9Ida7xXC767u9LeYycN28K5d3hw6QRvG3KR04O2b17ubEKY46P4bDfh/GM3xmT+RF/\nHBDATZX1VQ0PF86eRjCxS5JEt4FuTD6jnyY+Tqlkc5mMs4OCeDcjgxkGsOJIEjlihFCm6YPEPxLp\nMaL6lqvy8N3t7/jz/Z+Lv9vZCVc/I3rdPIYkCa5FB4dyi9lG2tJqsxXjsuPKLVcVJCiVbGxvT4W+\nVo6cHLJdu3LzQFQWUeuj6DbIjRpFxX1IUak43MODNk5OXBMVRaVWy58iIthMJuP9avoM3bgheOf1\nSSMvSRKzZFkM/SaUHiM8KG8lZ+olw7GiZCmy2Gh9I85dnFapdd9gUKlELMrRozpP56pyOfjwYM6/\nOt9ka+r0gABu0HNNxf+7negWvv/LwZbRWdG02GjB/ELT+KCRpKurWKONhrg4smHDCrm9y4NW0nK7\n43a2/K0lJ5yawMTcqgudfyUlsYOjIzMN5baRmCh4G6vwwshyzKJjR8cqLTAOWVls7+hI9aPV/Hpa\nGj8PDOT6qKhi06k+iI4mrax05hIoF4EzAuk7yZeqJMMHYGYpslhvbT2qNKJumUx4+jRoQL72mlEs\n009DksjJk8lZs8otplAreD7gPHvv682xJ8YanRYxQalkE3v7qgmsU6eSS5cavE8ZdzPo3M2Zklb/\neZdWWMifIiI4wsODzWUyXq5mRrTLl8U+Sd+4Uq1aS8dOjkz5O4WF6YWM2RrD+APxVMQY7v712teL\nDjGlhd+lS0XsudFx756Q9vRYC1Y+WMnxp8YbXNBZHxXFWZUNKnJxEbvtkydF4LCDg/hbhb7lBeZR\nZimjMq56OQoup6bSxsmJKn0k5zLwyitVpzXPtM+kzFJGRZRh5uZel71878x7TEoSMYRGjnV9Gr//\nLmJoyrmnWYosDjo0iF9d/8roAniKSsXGlVhT/1/4LkP47tSJDAzUawz/dfjh3g/88tqXJm1TkoQ2\nJjTUSA3Mn08uWmSQqpRqJVfcXcGWv7Us1tRkK7M55dwUzr48u0LtjV9eHi1lMvoYmszf0VFIsJGR\nYpPh7a3Xy8J7nDfj9lRd4/SKhwf/Skri1pgYtpDLuSs2lgPc3LhVT+m0sFD4IBbxIVcG6hw1Q74O\nob2FPcOXhTN2ZywTjuhgNKgCrodc15nVMi9PxCi88ooJyDtu3iS7dhWpCvVAoaaQ755+l9/d/q7U\n8ficeCbkiHGpLsOEJEmcHRTE+cHBVasgOVnM03v3xO/avVv8X82Xm/dYb8Yfqjpnsiwri+0cHDjR\n15eBeZUPsM3NJV94gbx6tXLXpd9Kp1NnJ9rVsWPA9AAGfBJAWTMZk8/p6UtfDuJz4tlkQ5OnAnKV\nSqHsuGtsUof33tNN+aIDSrWSPfb04Bm/MwZrXpIk2jg50bGSQfYkyTt3yFdfFQlOBg4UbC1ffy3m\naXS03vPVb6ofo9ZFVb59HXjL21tvzeiTePhQ6Geqs2ZFrYui5yhPatXVW0O0kpZddnWhbaQtSZHX\noVkzcsqUSvMhVA2SRL74oogDqwCZikwOODiArX5rRZudNlz1YBVvhd0yOCf5gfh4Tq3EDuT/hW8d\nwndODvn88yKq+L+Gi4EXabXZimHppqdp+ewz8R42OMLChKm7mlqtJ/Gr3a8ccXQENVoNv7v9HSed\nmcQp56Zw6e3ytXqzgoK4Nsowi/FT2LhR5GavW1cwu5w6VW7xHLccylvJ9TKHloUrqak0f/CAQ9zd\ni5MEhRcU0FIm4/LwcD7MzCxXa7BmDfn66/qZQsuCIkbB4AXBDJwZSMf2jky5IGzqWpWWcfvi6DHM\ngwVhlQueWXh9IVfbrtZ5Tqslx4wht2ypep8rhCSJrKaVNKGl5KXQeos1HWIcqJW0XHh9IS02WtBi\nowXrra3Humvq0jHWscrdWhsVxV4uLtWz2ty4IXbbzZqRY8cKt6nRo6vsC5Hnl0eZlaxa85gkCzQa\nboqOpqVMxpfd3dlSLucrHh7s7uzMZjIZvwwJYaGOiSpJ5Pvvi6Q6VdlDSJJEdc5jqSjbOZtOLzjR\ne5x3tbK6HvU8yslnJ+s8d+oUOWiQEUkBQkOF9rgSSgbHWEdab7FmWn5axYX1gG1mJrtXhkmqPGRk\nkL16iaDnhg3F7mX3bqFB7dJFp9SY65tLmZXMYK5xIfn5bOvgwA/8/SnPymKhVkutJFXoUhMXJzxp\nzlRzX6NVa+n9pjcDPw8sd0wrEs6vhVxj3/19S9WhUAgdWdeuQoYyKm7e1NsiQwrLYlh6GN3i3fjV\n9a848thINtnQhD/e+9FgGvExC2ykMgAAIABJREFUnp48X4n17/+Fbx3Ct1wukhH+1xCeEc6mG5vS\nNV5/1gtD4swZctw4I1S8YAH5o+HT1mq0Go49MZa99vVi041NmZibSO8kb7bd1rbMBzJbrWajhw+Z\noDRimnStlszOJh88IDt0EGouHZAkiZ5jPBm3u3p+lpIk0TYz8yk3E4+cHP4YEcHuzs7s5eJSOgPi\nI/j4iPezIWlus52zKbOUMXZ7LD1e8aDnaE8GLwim5xj9eZVzlDm02GjBmKyytfdubuKFVp1NQ7m4\ndk34MFRhF3/K9xR77+vNPS57+NKhl5ian0qtpGWWIosXAi6w/fb2zFQ8zYNdEWwzM9lSLjfM/FUo\nhHWGFL9xxQqhmqskh7wmT0OX3i7VnsclkV5YyLsZGYwoKOCd9HS6ZmczrKCAg9zcdLK77NwplKN6\nGij0glalZcTKCMpbypnydwq1yspPtMlnJ/Ow+2Hd9WuFy+uxY9XtaRmYO7dK6+43N77h+2ffp1qr\n5vmA8/zw7w8569KsSvPZKzQa9nZx4RFd3N5VRUaGYB2SJGFpfP11sm9fcuhQ8uDTmZBDvgxhxErD\n8mfnaTRcExXFHs7ONH/wgDUePOBztra8W4Y/eHq6WEaqYlnUBXWumu4vu9Nvil+pDSNJ5vnn0aGd\nAx+YPaDnaE9mOepWY5cXU/bxx0aOl0tIELRa589Xq5q47Dj23NuTv3v8Xu0uFVFhFlRirf9PC98A\n3gQQBCAEwPdllNkJIBSAF4A+5dRVPCj79gkmuP8app2fVqamzxRITxf+tIZ8gVGhENRRRtI0S5LE\ny0GXeSX4SvH3bru7PeVjWYT98fF819fXKH3RiXfeIb/7TucOP/VyKp27OlNbaNxEF5IkcWN0NLs4\nOTGqxM3NzRUKo+PHDd9mjlsOfd/zZcTKCEpaiVq1lq79XZnwu3gRK2IU5QriO512lqkxLIl+/YQS\nxeAoLBTa4IsXq3S5JEkcdWwUn/v1OfokPc3dOOfKHM6+PLtSdeaq1ezg6MiraYbRSurEzz+LZCen\nTglhZt++x8JOGQicGcjAGeVr4gyFs8nJHP4EtaevrzCsPcGkZzCk30ynxwgPOrR1KPYF1xRomB+c\nT0kjNOa6nuGEnAQ23tC4XC1y0ebXy8vAnU5OFo68VUjGlV+Yz9f/fJ299/Vm221teczzGGddmsVe\n+3oxPCOc6QXpXG27miOPjaRbvO4gEUmSOCcoiJP9/EwTLHflivCdKwGtUuROKIgwHl2dJEnUSBKv\nPPIHf1J4KygQj9O33xq2XY1Cw8DPA+n+snuxtakwo5BOnZ2YcDiBWqWW8Yfi6dDagb6TfKnJf9yv\njIIMNlzfkDlK3erthATxPFXVq61CDB1KrjaMnOOT5EPLTZa8HnK94sJlQCtJnBkYyI8rSb7wnxW+\nAdQAEAaRwKfWI+G66xNlxgK49uj/lwA4lVNf8aDMnUvu2FGpcXzm8Ez0pNVmq2ebZYqCKaAiftxK\n4fRp4bdnQqy2Xc25V+bqPDfMw6PaQV2VQnKyMJUuWlRKgypJEl1edGHqFdP1ZWtMDC3s7bk0LIyR\nBQX84gvTblJzPHMoayZj4h+JfGD+gFHrdW/IspXZbLetnV4c9/v3i+Bqg2PnTjFvqyE4hGeE85Sv\nbrejTEUmrbdY0znOWe/6VkdGcpo+XMnVgVZLLlsmnD9nzRJ+HDY2IjqwiAs/Lk5YdWJjWZhRyIeN\nHrIw03B88+WhUKtlS7mcPrm51GrFe7xRI+F5YGxEb4qmcw9ner3uRbvn7ejQ1oF29exoW9tWZMt8\nItB0+d3lXHBtQYX1/vmn0Iwa1Bj388/kF1VPTFaoKeQm2abi+BlJkrjDaQebbWpG6y3WnHVpFtfb\nr+cLu15gnupp//wtMTHs4ezMLBNkVCUpHKlbtBAbxUdIOZ9Cz5Gepmmf5GQ/P84LDi612fjpJ/Ho\nGGP/IWkl+k3xo/c4b0aujqS8pZzhy0pz+GsUGgZMD6DPeJ/iDeIxz2OceLr8aN8dO8SmweC3z89P\n+PAb0Fx5O+w2O+/szLEnxjIgpXICtFKr5fSAAL7i4cGcSv7Y/7Lw/TKAGyW+L3tS+w1gP4CpJb4H\nArAqo77iQRk8mLS11XME/yVZeMaeGMudTjufdTe4d6+gHTQY3nij6uHdVURSbhKbb27+lFYm6ZFp\nSW/KK0MhPV340w4fTn75JfnBB8yZ+hMd2zuYjEKpCFEKBb8JDWWTh/asNyjL5LR94cvCaVvblsmn\nk+nQxoEhX4Uw/U56sfZGkiROPju5zM3Tk8jJEe9cJycDdlKjES8IfTJHVgN/eP3B7nu6M72g4puQ\nqlKxqb19sU+/SaFUCo2GlZVwrLawEMmy2rZl3OYQ+k01LU3C2qgofujvTzs7/WnbDAFJkhi3J47J\np5OpyXukbcwspFappdsgN8YfeBxsmpKXwqYbm+oVuyNJ5Pjx5MqVBupoUSp5I6gufZJ86Jn4WKD9\n9J9POez3YaWEnp2xsWzj4MAYg5pQ9cDp08KMcOAASdL7LW8mHjMMDas+yFKr+aKLC1dHRlKl1XKO\nWzjND7tyiJMnV0dGlp0xsRrQ5GsYuTqSYd+FMdtJt1uQtlBL30m+tG9qz6AvgvjJxk/4l0/57+Si\nmJrvvjPw8/XTT+TixQasUEClUXGb4zZabrLkwusL9VpTs9RqDnZ353u+vlW6N/9l4fs9AAdLfJ8O\nYOcTZa4AGFLi+10A/cqoj3uXn+HmsVe5uY4jw3clUJNbKEhVN216OqtbaqpwbmrQQKQcMzl58GPY\nRtqyw/YOxZRqzxLJyUKTZBAa4JgY8aJ+BgLDn95/0manDb+58Q0nnp7IT//5lDujwzjF5HxKj6DR\niJfC9u3kiRMMbb2O4UP0JCM2Aj47kMa61+Q6/cCNCa1Kyzx/oSkriChg5KpIug9258NGDxm3J44X\nAy6yx54elaLq++MPkdHQYO+2q1dFoKWRIUkSl9xawpcOvcQsRdkUA9lqNSf4+HCe0ezAeiIgQMzf\nIsvRokV0a3ySadeN6AajA7lqNa3lck5ensN168SxQq2WEc9iY1LUJ99c2lvY02OYByM2RHD9q+t5\ns+lNJhxO0GuDHRtLNm8uXlfVxq5dRjIHPQ2NVsOdTjvZdGNTfvj3h9wUEcwXnJwY+azuRUgI2akT\n8+evpazJA2pyjBjbowOxCgWHuruzib09mx7z5Lxd2byelsYJPj6c6OtbKfpXQ0MRo6D/T/68WO8i\nY6/F6ixT0nqTkCAUma+9RuqR8LFiSJKgI3LW39pXWaTmp3L+1flstqkZdzrtZKFGt0VOpdXyVS8v\nzg0OrvI9+X/hu4TwPaHRR3zD6iP26/kBd1iuYnCnnSJ0d+ZMIQTu3StGLS1N+HN+9ZWICF+wQJQ7\ne1YsXOPHCzaAkoiIEAEC586JQI/du8k33xT1uLhU6eaRgt6p7/6+POF9osp1GBqvvlr9qGySgkbD\n5LmUBSRJ4lm/s9wi38LTvqe54NoC1r20g3OczpT5QCrVplmoJY1EuZU981oMfjwnTQiNRlBxfm4f\noV/KZxMgPySfrv1cub/Hfl48eJExW2LoM8GHkb9EVsgbLUniUZwwwUBR+uPH65+lpZqQJIkLri1g\n/wP9dVJkKjQa9nB25hQvJ4ZmmoLYXH8UBGZTVvMStbv3mbztbdGxrLnXg/6hGmokiVP9/FjHzq7M\noDdTQKPQMO16Gk++c5Kb39zM9AfpdOnpwvDlQvEjSRKTzyUz+XQyValPK1puXdJwWd1gXlyTVfV5\nrFaLYDbHqrPpVAXZymx+fm0Ra969Qo+sym3GCjWFjMyMZEhaSKm1uax1ukLExzOk01aGNf7umbx/\nJEnitms57NxVW+xKpNJqOdzDg+sNHfskSZUKGPjN4Td+++u3lDWXMW5fHPODRNxCpl0mPUZ40O55\nO/pO9C2ObVCrhXt28+aCKrFacHYWBAQm2ID4JvvytT9eY9fdXXX6g38ZEsK3fXyoedQXhxgH3o+4\nX26dDx484MqVK4s//2Xh+2UAN0t818ftJKgitxONVsPXLm1k/70n+LD2TaqiH61i4eFkmzbC7N+j\nx9NJJg4cEM5ZM2aIIKPWrYXP47lz5NtviwiECROERqFfP/LTT8W5U6eEie/KlXJvXFlYdHMRJ56e\naHL3g/Jw4YKIwq+Wuakos5pB/QGqjmy1ms/b3ueg30fxs4uflTrnFOtEm502NF9tznlX5+lNsxWc\nFszLQZfLZeTQhfiD8XQf7C7mZMeO5KFDJGmyOXDggODIzlVr2EourxoHrxHwIOQB50+cT7dBbgxe\nEMykv5LoPsSd7oPd6fO2D+Ut5XQb4MbUy6nUqsTkzLTNpOdoT+ZGKjlrFvlaLwWDf41mtlN2pZK9\nFOP8efE8V4FnuqqQJIm/2P7Cxhsac8zxMZxxcQYPux9mliKL66Oi+KLdRTbe0Jidd3ZmSp4p0iLq\nh9jtsQyc5ERaWlITGMBD7ofYY08Pdt7ZmbfCbhmlzSKO9DN/a9l0uz+HurtzqLs7R3t68lZ6Opva\n23NWUBDDnpHmNVuZTctNlgxOE1YKVYqKTp2dGPCJ8Lt16elCn/E+dGjjUIp6szC9kC4vuvB2Dw+e\nrefMdq20fPBA6HzUarEW//23EISeNOKWwunT5LBhxv2ROqDWavlxQAC73dzN0cdHP8WMkqnI5F8+\nf/Gg20E6xAh3O4VawTlX5rDOmjpss7UN229vz0brG7HfgX5ss7UNa6yuUSVXTHWWmrZNbPnqipeY\nZt2QUjVZNSoLd3fBhvhkIHhofj6b2tsz3VCJ3UgR7ACUm323CJIkscuuLrSPtmfG3QwGfBJAhzYO\ntH3Ols49nBm/P56qFBWj1kZR3kLOpJNJ1ORpmO2azbvD/PiXuROdR3pTmVCxkkqdo2aWLItxe+IY\nviyc6iy1oFLTk3PeEJAkiVeDr7Lzzs78/OLnxdbUNQF2bCuXMbOwkHmqPE46M4nttrVj221t+e2t\nb/XmD/8vC9/mJQIuaz8KuOz2RJm3SgRcvqxvwGVafhqfO7eKW961Y8jyEj53QUEi8O3SpYp3X2lp\nImhlxAghHJXnh+HiIraGv/9eqV3dtZBrbLO1jV7+SaZEEb1xtVy1b98WJPr/kk3FmeRkvuntzVxV\nLrvt7sZ9rkJbp5W07H+gPw+5H2JafhrnXZ3HJhua8Fe78lPFXwy8yKYbm/KNP99g041NOePiDPba\n14vvnn6XS24t4bI7y7jywUpeDLxI/xR/xmXHUZIkFoQVUGYpK3a9oL8/aWnJzNgwvrDrBTZa34hz\nrsx5KilHVSBJEjfKNpZKz5uZKVx3iwgjTiUl8Xk7O/ZzdWV/V1e+5+vLK6YMSC2B0cdH84hHaY2z\nRqFhyoUUplxIYUFYAZPPJdPtJTfaN7an3/t+lDWTMWhWEB07OtJvih9v1rHnFssAOnZ1ptxazvAf\nwsvkni4IL2DgjEA693Bm/jlHsdlu0eLx4JgYEfHZnL/tGr8/e4DjT43nCweHse79W+x0cDgjMiK4\n4u4KNt/cnEOPDGVousiGpdaqufjmYg44OICdd3bmW3+9VcqFJSEngad9T5e7oUzLT+MW+RbOvTK3\nTFq8TbJN7LO/D7fItxQnC/Ic48mI0xHU7trFMJumHLJvIB9GPeRZv7Nsv7293hl60wvSK3S5807y\n5ri/xrH2r7X57c3v2amLktdvabk9Npb/pKQUx3HEKZX8OSKCbRwcnonrw7qH6zjtfOmgGUWsgjHb\nYhizLYaaAtHP+P3xxQK4pJXo/ZY3Q78JpSRJ9HrDi7fGh7JLGzVbtCB79dDyjdFaDhgg4mDHjBHL\naq53LpNOJbEg/NHvzMkRWu9bxtn4lIXIggIOdHPjKE9PpqmUnHtlLrvs6sK1D9dy5qWZ7LGnB+uv\nq8/xp8Zz5qWZtNlpw447OtJ6izXfP/t+qfmamJtIlzgXhmeEMzgtmJabLOmd5F18Xq1Vl7k2JuQk\n8ELABW6bvo2r+q7iAbcD/GRZF+Y1ep68/7RWUytpucdlDxffrJoP8o3QG9zmuI0haSHFfXJ2Jpta\narn7RKROZcoXQUH8OiSEGkninrg4TvL15fLw8KopXmJihI/7lStCo9y2LTlvXplRkg8iH7D7nu6l\n2pIkSWewdMb9DHoM96BtTVs6dnJkzNYYbvsmj5+ZR/JvMzlvHFOUqqMIBZEFdO3rSrvn7eg2yI1B\ns4Po/aY3g99/KJSfxqT4LQO5qlxOPTeV7be355AL37DG7YtsvOdlzrs6j3339+Wn/3xKlUbFlLwU\nfnzhYzbe0Jg/3vuxwvWrOsK3mbj+2cHMzOxNADsgmE+OkNxgZmY259GPOviozG4ISsJ8AJ+R9Cij\nLpb8Pb85bsfvAfWwaXkX9Pq+A1ovbg0zMzPj/RhfX+Djj4EmTYAlS4C33gJq1CizeGJuIvod7Icz\n75/B8HbDjdevKsLODvjkE8DHB2jUqAoVjBkDfPqpqORfgA8DAjCqcWN80bIlgtKCMO7kOIxsNxKN\n6jSCPFYOp5lOxfMjPicer594HZ/1+QyjO4zGdqftuBV+C4NaDUI3y25ILUjFtZBruP7RdQxoOQBB\naUG4GnIVQ9sMRWRWJBJyE6DWqlGgLoBzvDPicuKQWpCKfqH9MO/MPPhM9cFnmz9Dm0ZtIFGCYu4s\n2EXb4ebX4/Dj8B/x6cVPUadmHZybfA41a9Ss8LeFZYThfMB5LBmypLh8fmE+Ft9aDKd4J9SpWQev\nd3wdX/f6BRMmmGHQIGDbtsfXF2i18M3PBwAEFxRgSXg4bvXqhX4NGlR9wMPDAWtroF69x8fS04F/\n/gHu3QP8/IC6dYF33wW++w73Yuww99pcBM7zR007e+DWLWDpUsDSUmf1qiQV0q+mo163emg0tBHS\nrqZBm6tFwyGNsGh9Hfj7A//sKkDSmggUBBWgz4M+qN2sdvH1WXZZ8J/ij1YLWsG8hgrxKz1hOZSo\nPbwnWi3vBvN65lX/7VWAJAFjxwJaLRAWBkz8SI3Tr92COvE6vMetQOuGrUESoRmhOON3BtdCr+H+\np/ex6OYiRGVHYfXI1WhSpwm2O22HX6ofZvWdhRthN3Ar/Bb6t+gPvxQ/nJh0Aq92fBUA4JbghnP+\n5xCXG4drIdcwvst4DGo1CNudtmPTa5swqdsk+Cb7Yq39WgxtMxRr7NfgwNsHcDn4Mv4J+gcft/8Y\n73z2DiYtnoSmTS1w+vdc9B83C7U2bAIAfPD3B8hQZKBfi34IzQhFrRq1MKHLBHzY88NSv1ulUaH3\n/t6wsbDBxQ8u6pzvf3j/gSW3l2D1yNUYazMWE/ctQWiuJ05+ug0Tu07UOZ474+JwMCEB7gMG4Lly\n1uGnUFgIJCeLeVe3ru4yycmAXA7k5gLx8UDbtsDzzyM+wguDVXtxZ44cXSy7VNhUwoEERK+JRq3m\ntWBe3xy97/ZGjVo1oIxVInxJODJuZuD5rs8jO1ABdS1zDHXti1rWtTG6pwqresahnnsqGg5qiKyH\nWWg1vxXaJWyApDJDzvR1qNm4Jhq+1BCAeNQCA4HJk/UbgthY4PZtYNIk8TorgmN2Nvo1aFBqPL1y\nczHO1xdL2rTBotbiHUsSdyPu4nLwZXSy6IQR7Uagq2VX1K0lxlOihIDUANStWRcdm3Qs97180vck\nFt5YiA96fICIrAg4xDqgRf0WuP3xbbRt1La4XF5hHnru64neDXpj/rfz0f1Od7Tu3xop+SmY820X\nnD0L1Hoog3sTJR5EPUDHJh2x2WEzzGCGtII0bHh1AyZ1m6TfAAGwjbLFlHNTMLbzWNhG2SKtIA2r\nhv+KHVvqInfAKkjmBfio50fY89YemNd4vJbEq1R429cXkQoFutWrh0WtW+OHyEgceuEFjCw52BVB\nqQRGjgTGjwdWrABUKiAuDliwAHjuOeDIEYAU66i/P2BhgRn17uClge9iXvv3gWbN9GqGZKn7k5MD\neC6LQeihVMR+1xdvdsqFZqkvzMwAbZc6yAzPh9OndbBoVR9Y1X0OAKA+dxMOHxUi/hdg6Devo12d\nOvr/zipAqQRq1y4tfgUHA5ttHfBX53xc6dUDVtp03Iu8B+v61pjaY2qp3xiXE4clt5fgn8B/YGNh\ng91v7cboDqOhUCtwwucERrQfgU5NOqGmeU2QrJJQ+cyFb0PiSeFbK2kx+OgIZDZYiSMrG6Dd9BZo\n90M743ZCowFOnBCSTefOwNq1YjHv2fOposvvLkeBugA7xu4wbp+qgQULxMP255+VvNDFRaz0YWFA\nrVpG6VtloJIkWDs4IGjQIFjVFgJYtjIbWxy2ICU/BV8O+hI9rUrfo6isKIw7OQ5mMMPErhMxo88M\neCd5Izg9GI3rNMabNm+iY5OOevchxy0HXmO9YHHEAucanMN25+3obdUbIekhaJyvhfNvOagrc0bt\nXn1QqC3EhNMT0KpBKxx651CZLyelRomNso3Y5bILrRu2xuDWg7F33F7EZMfg1T9fxUutXsKet/ZA\nqVHilUOvIcGvM6Zbbsfe9W3K2xfiTEoKfoyMhNeAAahnrqcQmpUFLF8O9OgBtG8PfPSRkCQHDgSG\nDQPMzYE9e4DRo4WU2asXoFAAP/8MKpX44K0CfF1jMIacsBNzxsoKaNECOHZMSKaVEKAkCZgzR7xz\nxo8H0jdFoL+UCesTvTDy7VpQp6vh0t0F3U50g8VrFsDChUgKbIvC16Yi1yMXOU456H2vN+p2EsJC\necJBTAwwb57Ya06fDjRvrnc3AYjlwddXDE1ICGBrC2RmEi+c9UON7Nr4uX5nfP1V6d8uUcI7p97B\nnfA7GNZ2GC59cAkNnmtQfG6DbAMC0wLRs3lPzB84H/Vr18fdiLv49OKn8J/vj7zCPAw8NBAz+85E\nqwatMLnHZFg+LzY58hg53j/3PuYNmIe9rnsxs+9MXAu9htUjV2NC1wkAgExFJg79cAgtbFvgLdlb\nCEgNQB+zFmjwyhhg61bgvfeQpczCOf9zSM5Pho2FDTSSBkvvLMWxiccwpsMY1DCrATMzM6x5uAZO\ncU7QSBp0bNIRe8ftLf6dKo0Ki24twt2Iuzg/5Tx6WvWESgV06gSs/N0OP3hPxs3pN9GvRb+nxpUk\nJvr5oV+DBljZvn3FN8LVVazXd+8CDRsCNWsCu3YBEyaUGHgJuHpVTK7+/YHGjYGWLYHISGgK8vEw\n+iEGZtZFg5nzgBEjgMGDgeefL7fZ9JvpMKtphsYjG6NGzdL3WZOtQb5fPp5r9xzSLqQhalUUJJUE\nTb1acFE0wrzAzmjYqha2LFWgz+6raEQNVM+1Qv2+DZDvl48BHgOw73wdrF8v5LGffgK++KL8Yfj7\nb7Hu9+8PODoCU6cCy5YB955LxLyQEIxp0gQXevRAXXNzJKlUGOjhgS2dOmFqZSd+JRCRGYG/fP5C\nT6ueGNZ2GE74nMAm+SbM7jcbw9oOQ8sGLbHPbR+yVdn4xeMXFAQXoMfZHsXXH/U8ivAtKzBbVoDh\nc+vgzRcnIig9CDP7zsT0XtPhEOuAyecmY+vrWzGo1SAk5CbgQZQtVBolYnJiEJoeCi216GvdFxIl\nuCa4IiorChemXMCYjmMAANFZ0Rix6wNkZZnBfulBtG/cDhPPTIRFXQucePcEnqv5XHF/SCJMoUCH\nOnVQs0YNHE9KwvGkJNzv2FHMuwrmDAoKgBkzhHB95kzptVGlEjfsjz/E+vvqq0CvXsgM9YHy8j9o\nPuptmF+7LgT3334T6zAgFiJzc/GpACThPCEQ2XczoS4Ettbuig+21MFFtRemWVvhTi81rGvXxka1\nGvj+e2SHhuLjaXvwxS5zfLuvBta+0gXv6Sn8a7Xld0mlAr77DkhMBLp3F/qeH38E1OZajF+Wg/c/\n1mCwZImXXjKD9EMA0t3q4e+J7dCrF3D5sthozp4NdOjwdN2F2kLcjbiL2Vdm46VWLyEuJw51a9VF\nUFoQUvNTwVWssvD9TN1ODP1BCbeTIngmerLh8Rn88J4XnWycGLtTd4SvwaFUChN2mzaktbUI+nBw\nKI4GkySJnXZ0onuCcanMqov8fOG2fe1aJS/q1Ys8rNt8bSrkqtWcGRhI28xM/hgRwaFGpo0rD+pc\nNZ1snJh85nFmvlxVLq+FXGN4xiMHzm3bRNRgifN99vfhftf9xcdsI2256sEqrrFbw8Puh9lxR0dO\nOjOJMVkxzFZms8/+Pnzn5Dt8YdcL3Oa4rfg6T0+yqZWC72xfziYbmvDjCx+XMuXqwkf+/vw2TLhs\npahU/CokhCmqR64BkiQYQW7fFpyeW7aQLVuKOd+jh2AQcnQUWXyuXhXuW199JThen4RWy7Afv6Sy\nphml4a8Ik7kkiWvbtBFEzmZmZL165OXLj6+TJPLXXwUntQ6TrVYrEluMG0c6Okq89EYoz5g7csXI\nVMonBDJo4SM+3kduP0UMHvkaDbf96slLrezY65KcrR0cuDM2lm45OdwXF8edsbFUPwqGyMwUVHdL\nlpDTpwuWoI8/fsy6kqpS8UJKylNcx8nJIqxk1CiyTh2y09ACzl+tYEKi+B1bYmI4wNWN9+y07NRJ\nhJU8iYRELWMTK8dUM+fKHI48NpI2O2247uG6Msud9j3NH+79wDvhd3SeVyWrKLeWM0v2RKyAm5sY\nyz/+0HmdXZQdn1/7PM1Xm7PZpma02WnDxmstGZkRxWxlNrvs6sLjXiLjU2JuIgcdGsR3T79byi3h\nyBGR1JAkT/qcZPvt7fnGn2/oDFiPVShoKZPx7ydSRocVFPBaWhrzi27UunVind67V2RQJAWP+Qsv\niODbBQvIt94Sjry9eul0YVhjt0YkhZLJxJwcPFjM2ZdfFgE0o0aJ56US7gWhJV0dJYn5R+9QnZrH\nexkZbHfYn28vyOOn76TTtuE7vNhlIkeutqPFhQcc6enJBysC+XCED5s3F0wqoaGi+7/8QjpkZvOL\noKBSTEdpaSLcycbmMX8tA/KAAAAgAElEQVRAYiL5ww9kgw/jaflAzoC8PL7+0J+tTnty6Af5bHzK\njfUWRHDQIPEYFUGpJI8eFaxyy5eLV5+h4ZHgwcU3F3PUsVHstrsbBx4cyCjbKMqay57ySZYkiYfc\nDjLs7aEs7Ntb3L8nAnOvBV/lmONj2GF7B/bcNYD1J33HcRt/5WH3I5THyCmPkXOH0w7ucNzNa56u\nT7lJZWSIqR8YWGIc1EpOOjOJY46PKTOhDUkW5uSw0/nzvDpihHBf3b1b9zzx9SXXrhWZ0aZPLz8u\nJTKSzMqiUqvlHD8Xtri8hWt3fU1u2EBmZTF33z4enjKFaw8cYMyFC3R/6SWmvfJK+cGbYWHkwoXi\nmWjXjorvNjM/OJ8uvmrW3u3JV06FsrCQjFEoaHH7NlM7dKC0eTNn+/tzdlAQozdH066nE62vyFn7\nspydj/nzQlRGmWwjN26QtWqJXFFLv5eYlVW6XG6uWAsmTRIEEYsXk6++LnGiLIj17R6yzkF3Wlxz\notXSSE7ZmkYrmYy37dW0sBB1fvqp8EK2sCjtYvtkd7KV2TzgdoD7XPdRkiRqJS0lSfpvu50YEk9q\nvovQ79DLiO26HicbdEP98ZFo/0t7tJjRwnQdy84WZiEXF2GmtLWFBxMw+dxkhC0MM64rjAFw5Qqw\n9DvC2weoXftxXyW1BEWoAvW61yt9wezZYmd+4gTwDH/bwtBQ+OfnI1KhhFns8+j7sDPO7y7DjGxk\nxGyOQa5rbiltzFMoLBTqpm7dgFWrgO7dEZgaiOHHhuPg2wdxK/wWboXfwrQXp6FAXYCwzDAsfnkx\nRnUYVVyFUqPEb7JdMIMZVoz8FgCQlgYMGgRs2ABMmSK0lgfcD2Cn8070suqFz/t+jrE2Y4s1p0VI\nLSxET1dXTLOywrX0dDSvXRv5Wi3u29igcZFauXlzYe3p0AH4+mugXz9hKomIAPr00Xt8Jp6eiLGt\nRmLOK9+UPuHkBERGCvu3pyfwzjvAvn3CqnTsGHDnDtCgAdC7N+zXr8f62FhIJH6xssLAtWthNnEi\nMGYMSGJrXBy8j4RhyDkJ9TLM8OVuc+TVN8MQvxD8mqfGc6/PQ+/ewLL4UEQqlZj9Vw00fVCA/JOd\n8bVjAvJb5GFQ83rI1KoRk1+I3Fwg774FZtbogO3rzWFmJhT5Y8cCrVsDSekSPD/xgmULCTE1ClDL\nzAz1zc2BAnOkxJjDoq452liaQ91IhWR1IWqYmcG6dm30r18ftzIz8bBPH3SoWxdeXkJ5NWaM0PIA\nwP794ntWltBUDh6s3zhnKbOwzXEbRrYfiZHtR5a79uS45CB6XTQUwQrUbFITrRa0QvNpzZHrkouw\nb8LQeGRjdFyvw/Lj6wu8/z5Qvz7w/fdi0pVAXmEe6tSsg6TcZKzelIUjG7tg2dKaWLcO8Evxw6jj\no7D5tc3Y5bILY23G4tdRvxb308tLTIUjR4BRj6b9+YDzUGlV+ObmN7jy4RW81PqlUu255+Zikp8f\nxjVtijUdOuC5GjXQ29UVTWvVQmJhIRwSEtB68WLA2VmozUpCqQQOHRIa7w4dgK5dgRdeeOonpxWk\noevurnCa5QQbC5vHJ/LzhUa9fn0gKAjYuFE855MmCQtQy5aIOXgQ28aNQz0bG6zpKMYzSaXC0ogI\nnExOxswWLbDdxgZ1Dx4EVqxAZvPm6PXbb3g7IwdHm7ZCi9wUvJmXi7tdeyBpdxsMV1mj55cpOM4w\n7HjXDPlLuwKzCnE+NRVR+YXIetAYSS8mo1umJYJapEBbQ0ITv2Yw29QF0943x7p1ortF2BsfjzUh\nsVB93QtThj6Pvy8Q7Xf/X3vnGR5VtTXg90x6bwRSqaEX6SCCIoodUPkE9drQe0VBrwW7WLCLXiui\nIvaGYkFUEKVDaCEJIYFQQhoppNdJMnV9PzZSExIQEtD9Pg8PmZk9Z/acWWeftVfdSWKrAsab2/FM\nl3asXGHw2GNq6fr3v5Uxtq4Ozj1XWS7nzoX4eIiMbFDcTpjqpGrqMuvw7u5N0oVJxLweQ+j4Bqyq\nIkp43nsPdu9Wpv0ePSApCS66CPr1o+6lNzh7UjcuvRR+/VU5cKdPV2/Py4Prr1e38lmzYNKkg7e4\nhx+G0lIlLoficDq489c72ZS7iXC/cFxNrlzX6zqu7329GuB0wvjxrOzalevHjCHJz4/Qm29WJ++F\nF1TYXlERO6ZOpX1cHJ5XXgmXXkrVqFH4urgcuDYsTifuhnHYNb2zeCdXbvqVPTZXzg30J9m1M7/1\n6UNHLy/OTkigM9B6+3a+bdOGKA8PckW4ZsUK/jd8OP6XXMKesjIWb9hAeEYGZRER5C5fjmevXlwT\nGkpGRASejz+ObdIkpnXpQs+d2Vz09BbWWoby71HpfBiYxxejLicAb5wI42L7cOk5rphe2E7KDhMx\n4z1IKqhmUfcakoc4Ca30wSPYgVew8EyPtgxzC2LoWa68NtfGm/7bWV9ZgdNh0NPbB89NobiF2Eiy\nV2BvaybA1+A/4eFMjojgjZwcEqqr+bFnT/bucOPm+ywUPpxEaKCJp9q348rQUBIS1Br9p7MmJUWt\np9OmqTX1vfdgxAiYM0c5YBtif4iVDjtpSPl+Y8MbLC7ex9bgsazz7UH+xdvpPKtzwxfoqeTZZ+Hj\nj1k9oBWrJwxl+jVvNf8cmoA4BEeNAxdvF8w7a1h49g7cg1wZG9cd91B3LLkWtk3chjnZjN8APwJH\nBdL6mtZ41+yEyy9X/vNDV/CTwLZtSif94APl7T0Wd82u4L3wbVy/dBAbl7oRE6Ni17//XimizYmj\nzsHGjhvps7gPvmc1ck5qalTI0qxZSsF85RU+8Ujl3Q0fEVDTn1eveJo+Xer/8pWVMGUK/PSTitP8\n8EO175s2Td0knn768PEWu4WvU77mm23fEJsdy/kdzufR4Y8yNGqoGlBbS/zOnfyRl0d7k4mJ557L\nvX/8QVxBAb8nJOD7xhvg6YmIYBU5vrjaQ9hSsI0LvryMvXen4u3WiLt18WLlKs3OVm7TZ58lEbh3\n8WJygoN5tH9/7FVVvJqSQrmvL4EVFfR0cyOqe3fW7drFW2+/jd9FF7Erey+Dl63HEdWZeVHBPHn1\n9QQmtsFmNuE8r5Berw/kzn+50vmLFJK2QOKQThQWG8iOKsIC7SwNc6X7cC/sV++l0ruONf36YXM6\nKbTZWJJbwRPr8zHaWIio82Xvf3rw3HOwM8vBH7EOiqsd/G+2g6gYB9UOB34uLgwLCMBAhfvEVVUx\nvV07gg4J18rMhDVrVBzjihXw1VcqnGbiRLj1VhUJMWRIA+fsCESEtP+mYS+3EzQ6iNYTW2PyOPy3\nc9Q6iOsdR9R/owgcFYglx8KeB/ZQl1mHext3oqdFE357+FFhEgew22H5chV6tGzZQdf2Icyerf59\n9x2MH682EJdeCi99mkhKj6tp67iA28M+wG43+P13FbNps8Ezz6jQiSP3DQt3LuSGH26gf3h/rvM9\nmxFbSmm9r4qQoEjKHn6YJ0pL+baoiO5ubkT7+PBlz57MXLiQr0pLWdenD979jw5daSpPrXiKAnMB\n713x3rEHiigl/7ffYNkyatPT6fHBB1y9fDm/jBjBpMRElnfqRFx4ODe4uvJ43748kJvL8vx87po/\nnxF33830sjL6VFTw9jff4Pz6a7bPns2Ciy7CzWTijoC2LFigbjP7PM1MGLaF9pl21r8axK1hYUR4\neLCgqJhWeQE4Y1sREumgTYTwtsdO9rnUMqNzO65u1epg7ovFwllxcazv3x+PYm8efVTtsQcOEvZa\nLIfF7lqt6mu9+y60a6eWMNf94fvPPKPWpRtvVDK8Zw+Ulam1vKlRbYdiK7GRPTOb6sRqzMlmPKI9\nqEqoI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JJqP/pI3TzCwkReeeVgUuctt4g8/fTB91ZXi9x3nxTG/iHtXm93IMn4uM+vwyFZ\ntbXicDgkNjv2QHOYMV+NkY8TPz6hY54uOJ1OSZmQIqm3pDY++BR8dtoDabKx5ya57ao6CfOzyf9d\n6ZDZs1VtgX9FFMgiz7U64fJPjmX5BmX97taqO5cPmMaa8nK+Kiykj92TV4vCCDw3kNLfStl99246\nz+pM6wknv2zS9OXTic+PZ8HEBYeVHaqXpCRl2kxPV1v5U4Q4hcxnMsl9OxffPr54dfGi86zOmNwa\n9gLk5UH/IU58Po0nIsiVbWYznepsvH7PU1zX9kd87s8CXzs1+wz++4iV4T0CiBweQPbL2XgM92Nb\nShk77vBjfR/ItcOivr1xVrhx663Kom2zKcN/+7OsPLgxh7Q2xYzu4sN6SxkG4OPigo+LC6MCA5mb\nn08vHx9mde7M0IAACgvBL8SBp8l03ImsIirGy2JRnvLkZBVi/P77yrjm7a1CWux2cM5Koya2jNbX\ntKZsaZkKzRkTQvXWaipjK7GV2Ai6MIiiH4rou7ofZX4+fPMNvPiiyu8ZNUpZ4RMTlfXdx0dZFL/5\nRrlKly9XeV1ffw3RrWpVfHPXrsrSt2mTOsg336gQiuP9kqAC3D7/XGVHbd+u4hg2b4ZRo9hw11WM\nTXiAx0Y8xprsNXQJ7sJD5zxEkFfjNWhrHA7+t3cvb+fmEuDqyoVBQVwcFMS8wkK8XVz4sGvXA7/L\n5J8n42py5Z3LT8zq09I4ahzkz80n67ksuszpQuiVpy6HxLzdjHuEO26BBz1hJYtK2HHrDto/3Z7I\nOyIxbzez5bwtDEweCA7Y3H8zEZMjKPi8gHbT22HyMbHn/j0E/zCQ869y59ZbVYjHoWEq5eUqRGnI\nEOXpSk9XRuW5c5WXadEiJbNt95dXFlEVzT78UHmee/VSeWwlJar817RpKtdi4MD9y1hVlYofeekl\n5XJasACefFKF3DXE3r0qfurQ6/mpp1Q8wHffqWvhT7k+xjVfa6vlhh9vwOqwcmnMpYxoO+Kw8qKP\npafzyb59eJpMnOXryxfdux8osykibDObmV9UxHdFRRTabDhEGO1tY+3Km9g2JZlAz0YSUU4TKtZX\nkHJVCh4RHohNsBZY6Ty7M63/78TveeZUM6k3pmJyN+E/1B+Th4nSJaWYt5vxjPbEXmWnx7welP1e\nRvHCYqx5VjzbeVKXXYfJ3YTfYD/K0yzs2GXQ1aeWkJvDeWVfOxb+4cpNN6kQgI8/VqIzcKBaj51O\nFa40caIKX+ne/eh5vfqqMkqPGnW4M6xRMjOVRTswUF0I27ertfLxx9Xj//5X3aPff/9g5u+hJCWp\nhX3RIiXbWVnK+l1PslJyQTLj5o3Dz8OPEK8QyuvKqbBUUGmpxMfNhzsG3sEjwx+pd5oiws+7fubZ\n1c+q0ncIF3e6mO9TvyfznsyjkujPNBw1Djb320zHFzoeyNFzWp1sn7idwPMDiZwaieFy/IZnp9VJ\n5tOZWPOtdHq902HrqrXAStp9adSm19JnUR/cgt0OJLZv3Kg8MxMmwPAeFjwjPLXlWxqxfIuI5Fbm\nStirYbI8XZWJqrHbpcfGjfJpfv6BMVVJVRIbGSt739x7YHfudDqlIq5C6vKVlcJWbpPy2HJxWJtu\nFSioLpCgl4Iksyyzye+Ryy5TJYdOEU6HU3b8Z4fEnxMvdTlNt8BYHQ65aOV28Xg5Wa69zinh5++S\nHV6d5IVhP0vF4R2F5dnH7fJ66HZJmbRDKjZViMXilDnX7JXZnTbKRwGxMu5iq3h7K8vFc88pq8SG\nDao8XO/eIk897ZTZmXnyUV6e5NbVicPplE0VFVK536rUUImiv4rDIXJkx/U5c0S8vETGeebLAu/1\nYi2pv0VwcbHIjHGlco/vHrn87Dpp3VoZ5q6+WmTXrqPH5+SI5KmGgfLll6pKW3r6EYPy85WF5Ysv\nlHn8gQdUGbTdu1U3M8uxuwOKiCpz1r69qt0UGSny0EOqNtbQoSILFhw0Z4rIppxNcukXl8qsjbNk\n0oJJ4vmcp4TODJXBHwyWuxfdLXG5ccf8KIfTKUlVVfJqdrZckJgoF27ZIjWHuA7e2vCWdH6rs5TV\nnjpPU3NREVch66LXyZaLtkjWzCxJn54uW8dslfLY8sbffAh1eXViKbIc9dzWK7ZKbFisrPZdLdtv\n3i5VyVWyddxWWcJXvFIAABm0SURBVN9pvZSuOLxcWtpDabJ58GbZ1GuTZDyTISIi5h1mWdtqrcQN\niJOyNep85+SI3HSTqoQ3YoTI0qXKUtipk7J0H3lZlZWJTJ9+7M7QaWnKupiefriX6Cg2bVK1vdq0\nUTIYEqKeM5uVfE+dKvLyy6pd9sUXK3m94YYDZVoPlIYsKDjGh9SPxW6RB5Y8ILf9dJuEzgyVVZmr\nDrzmdDoluapK4ioqGlxXqi3Vcu/ie+XnzI2ytTRHOrzRQX7Z+ctxz6OlMe8yS86sHHHanVIZXymx\nEbGS82793sLGcDqdkjgyUTKeyZCylWWSMSND0p9Il7I1ZeKwOqRiQ4WYdx/uCavLrZPSFaXisDqk\nOqVaCr8vlIqNFTLnrjLp3t4mgwaJTJly0JrtcIisXq3+F1FL3oYNTVv2TgrZ2aqMakSEkr3p05Ub\ntTFKSkTi4g53s9aDw+mQNVlrZOmepRKXGye7S3bLvqp9klyQLK1faX3AO3gk05ZMk57v9JTvt38v\nDqdDFu1aJM+vfl6yy4/9eWcS5bHlEhsWe8BDk/ZAmmwZvUUSzk2QjT02St5HeeJ0NKwHlPxeIvHD\n4mXzkM2ye9puKfqpSDadtUm2jtkqO6fslPUd1ktFnFJcSv4okbWha2X3/bvFVtV4h2m05VvRmOUb\n4Pc9vzPpp0kk3J5AG982JFVXMzopiZ2DBx+oLlCbWcu2q7dRs6MGry5eIOCocmAvt+Osc4IB7q3d\nCRgRQLdPuzXJwjptyTQsDguzLpvV9C+UlAQXX6yCna+6SmUmHSvbqBGsRVZqd9fif7Y/COyasgtz\nipk+i/vg6td4F8Vqu503c3P5taSEYFdXXvDsya/fCPcvHInHFaOVNeoInE5VefCXX9RX2bZNJU3d\ncrMQ89Mu3GqsdPiiFz4+RktWJTwMe6Wduow6DA8Dn24HyyhaC6ykPpaFeVkJjzh68egcXy69VFnr\nlyxRxpHMTJXYc/31cPfd6nG3bipZ56QzZ46qgeVwKHP8uHHK2lJRoWILMzNV55ahQ9VJX7ZMvWfk\nSGV1dHVVP5BhNBpP6hQnBdUFpJelszprNa9teI0VN6+gV+v6Y0cPJbUoleUZy/H38CevKo+VWStJ\nKUxhzaQ1tA9sf1JORUvjtDgp/KaQ6qRqTB4mPNp6kPl0Jv5D/Am+JBifnj74DfY7KnZRRKjaXEXO\nazmULilFHELQBUHEvB6Do9pBypUphE4Mpf0T7RG7sOuOXRT9UES7x9sRdX/UUcdz1Dgo+KoAtxA3\nQsaEHKhG4rQ7MVyMo9aqujqVhDxjhuoVM2WKqt5wyvnhB+Xa6dVLWb/vvluZxjt1UkkdublKpnv3\nVrXeHnxQBfBed50yZd58s7I+/gWWpi/luu+vI/bWWLqEHF0+sKSmhDnxc5g6eCr+Hv7EZscyddFU\nIv0j2ZS7CU9XTyYPmMz0c6f/pXmcDtTuqSXpoiRajWtFdWI1TquT8FvDCbowiNq0WmzFNlwCXHD1\nc8VR68Cro9eBBN19n+0j5/Uc+sf1b7j6zXHw7rvK4zJzZotWqq2fzZtVFa9u3ZrtI7/c+iWPLnuU\nK7tdyb1D76VDYAdSi1NJKUzh/iX3k3RHEiHeIY0f6Axmz4N7qMuqo9XVrUh/MJ0BiQNwC3GjfEU5\nGU9kgED47eF4d/XGxc8Fn54+GIbBvs/2kfF4Bp1e64R7uDslC0soW1pG9EPRtJ7YGsMwKJxfyO4p\nu/Hs5EldRh095/ck8NymebF0tZP9NEX5Bnhi+ROsz1nPkhuW4GJy4fadOwl2deWlI1yf9mo7NTtq\ncFQ5CDwvUCnhtQ5cfFxw1jpJuiCJwPMD6fhCR6wFVgq+KsCy14J3V28CLwjEO0Yl7zjFSdirYWz4\n9wY6BnVEnIJhauLvlZ2tygAkJKi/fX1Vpsidd6qbVHGx8gOPGqXKKdSTGOO0O8l+KZuc13JwDXbF\n5GbC8DBw8XGhz29NU7xB1c3eXVPDpPBwxrdqpZKPXnhBxUj8/vsxOxCmpqpIB39/VeLWMJTrJ2FY\nAuH/DifyjlNQAPY4sRZZ2T5xO1VxVXh28MRWasPV3xWTpwl7hR1biY3W17am4wsdWbHZjRtuUPui\n2bOVPtunj0o8u+KKJnfu/eukpqpEM3d3lXr/zjvq70suUSEpt96qMpEKCpR7f381kb/K50mfM2PV\nDP648Q86BNXTGgxVc3zKr1P4dfevjOkyhlp7LWE+YQyOHMxlnS87412ijWErt1HycwnlK8sxp5ip\nSa0h5IoQgi4IwrzdTHVSNeYkMyYfE1H/jSL8tnAMV4OcN3LIej4L10BX2j7Wlqi7og4cU0QQh5wU\nJee0Yu5c1c79yvpbxQOq5NEvv6gYrTvvPFjH7i/w/ub3eWPjGwyJHEJ2RTauJleu73091/S4hut/\nuJ6MsgxKakvwcvXCIQ6ePPdJbul7CxtzN1JeV84lMZf85TmcLljyLaTdk0bwJcG4tXKj4IsCyleU\n49XFC49ID+wVdhxVDkxeJszJZnz7+xI+KZzdd+2mzx998Ov7976eWwoRYU32Gv7Y8wfvx79Pu8B2\nFFQX4O/hz1uXvsWoDqNaeoqnHEetCj+xl9g5a8VZ+PY6WLJXnELBVwWULCyhLrsOW4ENk6cJvyF+\nlC4qpe+Kvvj09DnG0cFabKV2Vy0e0R54Rjc9zPeMVL4NwwgCvgHaAZnABBGpOGJMFPAZ0AZwAh+I\nSIOFsZuqfNuddkZ8PILb+9/OpH6TyLVY6BMXx3+jomjn4UErNzeuaELVAmuxlcRzEvHt50vZ0jJa\njWmFd09vzClmSn8rxX+QP1H3RbHTayfTlk7j13N/Jf/jfEp+KWFgwkA82x5nLHddnQrKrKtTGf2p\nqUoZ79NHBSf/WQrjENOV0+ok6cIkTJ4mun7QVdVDjatC7IJvf19cvJqWRbyhooKrt20jZdAggt3c\nVBzmrFkqEC8+XqWqnwDmVDOJIxLpt6pfoxfIycKSZ6Hw20LMW814tPUg4vYIDBeD5DHJBJ4fSPsZ\n7XHxdEGcQlVCFYZh4BLggnuYO66+B2/48fHqdN99d/1hfy1CZqbS/H1O/bl8e+PbPL/meZ4b9Rxj\nuoyhwFxATmUOFruF/Op8Pkj4gK4hXflw7If4uDfPb3s6Yyuxse+zfVRtrsKnlw++Z/nie5Yv7hHu\nR1mkHXUOTB7Hn7egOT5EhA8SPsDAoGNQR8rqyvhkyyesylpF15CurLttHalFqbi7uNMlpAsuppYr\nAXs64bQ4yZ+bT+YzmXR+qzOtJ5661vKag2zO20x2RTZXdrsSk/E324A3Qs3uGsQqjeoJIkLlxkrM\nW8349vPFf9DJMTjVx5mqfL8MlIjITMMwHgaCROSRI8aEAWEissUwDF8gHhgnIjsaOGaTlG+AjTkb\nGf/teHbetRMfdx8Sqqr4oqCAIpuN30tLWdKnD339Gt/J12bUkv1iNm0fbntY7VRHnYPct3MpXlBM\nYVoh4hTadGlD0OggxKZczX1+63PMm2tVfBWZT2ciDiFsUhihV4c2nFwgoizQU6aoMINhwyAmhr2r\n21Aaa6HP7/2bbm0HlTEFEBJCqc3GwPh4XunUifGhoSrRaepUVffvzjshJubYx2qEfV/sI+OxDPqt\n6YdnO8/9X0ew5lmxV9kPC/04EWp21lDwZQGVGyupy6rDVmgjZEwIAecEYN5mpuDzAhCIuCOCDi90\n0ArPcbB+73qeX/M86/auI8Ivgkj/SLzdvAn0DGRsl7Fc2e1KfT41Zxy5lbl4uHrQyrv5S0eeSYiI\nvr41/1jOVOV7B3CeiBTsV7JXisgxA6kMw1gAvC0iyxp4vcnKN8C1311L//D+PHTOQ4c9Pycvj88L\nCljdt+9JWVgGfTCIV0a/wsj2IwEVBrJlxBacVidtbmxD4LmBlPxcgq3YRtDoIFqNbYWIkDA0gVZj\nW+HZyZOc13Jw8Xeh85udcQlwwTPKE3EKFWsrKF1ciqPWQdgtYfh1QnWvyM3FkpTL5s130M/rIbxH\nd1NdWKKiYNUqFUdZUaEU9vPOO7xAvogqxbFlC1UTJnDVv/5F36wsXk1MVMW1V6+GefNU2vlJImdW\nDtnPZ9PptU54d/Mm47EMqjYrC33vX3sTMKzhGtmF3xTi09vnqDb3TquTjCcy2PfRPtrc1IagUUF4\nRHuoeLBDNjHWIismT1OTw280Go1Go9H8szlTle9SEQlu6HE949sDK4FeIlLdwJjjUr435W7iuu+v\nY/fduw9z4ThEGJaQwE1hYUytr1dsE8mpzOH51c/zw44fyLkvBzeXg+VsxCGUrSij4PMCKtZUEDIm\nBM8OnmS/mE2vH3phybWQ9UIWAxMGYpgMxKFKAhZ8VoC9yo5HlAe2IhturdxoNa4VmCDv3Tx6LehF\nwNkB2MptJJ2fROg1obS7N1SFh3z9tQoXGTxYdfcTUXHjsbFqUldfrbpe7NwJ//sfCQsWcHNKCkOS\nk3mvpATXkBAVY/7EEyctdvhQyteo5AlLjoWwm8No+2hbSn4uYc+DexgYPxDXgKOV4/JV5aSMT8Fw\nNej9U2/8h6h5WYutbL9mOy6+LnSd2xX3NieeqKrRaDQajUZzKKet8m0Yxh+oeO0DTwECTAc+OUL5\nLhGRelN294ecrASeFZGfjvF58tQhFTdGjhzJyJEjG5yfiND3/b7876L/cWHHCw97bVdNDeckJvJ7\nnz70a0L4SX1c9uVltAtox/RzpxPp3zQlvmRRCSlXpeDq70qPb3sQdP7RtZXFoSzebq3d8Ol+0Npb\nvLCYXZN34RroinWflTY3tSHmjZj6rfd79qi44LAwpYSnpsLXXyPz5/NdZCRfPPEEm9zdeaFDB24J\nC2tR1+KeB/dQOL+Q7p91PywLuS6njsRhiXR5rwviFHZO2knndztjL7WT+XQmbW5qQ8fnO55QHVCN\nRqPRaDSaP1m5ciUrV6488HjGjBmnp/J9zA82jFRg5CFhJytE5Kgy+YZhuAK/AItF5M1Gjnlclm+A\nWZtmsSxjGT9O/PGo1+YXFnLnrl0qzhm4oU0bciwWXsrOpqOXF2/GxNC2gQY4yzOW85+f/0PqVJWs\nczzYK+24+LmckMLrtDsxJ5lxj3Q/qjX0sTA7HKytqODtnBxyLRbuiYpifGgofiehosDJoGRRCTsm\n7aDjix0JHBlIXUYdu+7YRfjkcNo+oDp+lK8pZ9fkXXj38Cb6/uhjhqpoNBqNRqPRnCinreX7mB+s\nEi5LReTlhhIu94/7DCgWkfubcMzjVr6rLFUMmTuEuwbfxZRBUwCosdXw886fGdN1DMUOE/MKCzEB\nb+Xm4mEy8U7nzvxeWkqS2czvfY5OmnSKk8EfDObBYQ8ysdfE45pPS7CqvJybUlNp5+nJ5SEh3BcV\nhfsxyga2FNVbq9l15y4sORY8oj0IuzGMiMkRLT0tjUaj0Wg0/zDOVOU7GPgWiAayUKUGyw3DCEeV\nFLzCMIxzgNVAMipcRYDHROS3Bo553Mo3QHpZOud8dA6ltaX4uKkwjiCvIIa3Hc4n4z45oFxbnU4M\nwM1kwu50Miwxketat+a+I8rszUuZx2vrX2PDvzec9uWAdtfUcHZCAp91785lIX/vQv0ajUaj0Wg0\nJ4MzUvk+FZyo8g1gc9hwiIMqSxU2p40AjwCGzB3Cg8Me5Oa+N9f7nj21tZy/ZQuPt2vH5IgIRIT4\n/HgmzJ/AR+M+OlDd5HRFRLh461YuDg5m2gnW6dZoNBqNRqP5p6GV7/38FeW7PjbkbGDidxNJuzvt\nsEolh7KntpZRW7Zwa4gny9fdS25lLpMHTObBcx48afM4FdyflsYH+fl08/ZmXb9+uJ2GYSYajUaj\n0Wg0pyN/RfnWGtcxGBo1lM7Bnfli6xcNjunk5cXsSA+eydhNYMxkvrklnoSgyxm4eTPT0tLIrK1t\nxhk3jU2VlXxdWMjuwYPZ1L+/Vrw1Go1Go9FomgmtdTXCk+c9yYxVMyitLa339bTSNG7/bgyzwk2k\ne3Tm4q3JDPX3542YGEyGwYD4eCZu20aexXLgPeU2W3NN/yicIvx3925e7NCBMA8P3Z1Mo9FoNBqN\nphnRYSdNYNqSaSQXJrP4X4txMbkceD6vKo/hHw3nkeGPcPuA26m227GIEOJ2MESlym7nlb17eS8v\nj9djYiiwWnlozx6mRUczo317PF1c6vvIU8Zn+/bxTm4u6/v3x6QVb41Go9FoNJrjRsd87+dUKd92\np53Rn49mdMfRPDbiMWpttdiddkZ8PIIJPSfw2IjHGj1GfFUVN6emYhVhXo8evJidTYrZzJsxMVwQ\nFIRLMyjCVXY7XTdt4sdevRhyCjpUajQajUaj0fwT0Mr3fk6V8g2wt2IvA+YMoF94P5alLyPYK5ir\nul3Fe1e81+TQDZvTiVUEn/3W7vmFhbyYnU1abS0G0N/Pj7EhIZwfGEjfE+yqeSxeyMpim9nMlz16\nnPRjazQajUaj0fxT0Mr3fk6l8g2waPcidhTv4PYBt7OndA89W/fE1fTXO0CW7I8BX1NezpKyMn4q\nLuaeqCgeio4+aTHZNQ4HHTdsYFnfvvT08Wn8DRqNRqPRaDSaetHK935OtfLdXORaLIxJTiazro7L\ngoN5p0sXAv5im/c39u5lVUUFP/bqdZJmqdFoNBqNRvPPRCvf+/m7KN+gGuAU2WzMyMxkcWkpk8LC\n2FRVRVZdHXdFRjIyMBABglxdae3ufsxjba6s5LLkZFZoq7dGo9FoNBrNX0Yr3/v5Oynfh7K2vJz5\nRUV09/YmxsuLOfn5xFVV4QJYREgcMACLCAEuLvgeYSHPqK3l3C1beCsmhqtCQ1vmC2g0Go1Go9H8\njTgjlW/DMIKAb4B2QCYwQUQqGhhrAjYDOSIy9hjH/Fsq38fiwT17WFRSQo7FQmt3d+Z06cKwgAA8\nTCZy6uo4d8sWpkVHMzUysqWn2iKsXLmSkSNHtvQ0NKcZWi409aHlQlMfWi409XGmdrh8BFgqIl2B\n5cCjxxh7D7C9WWZ1hvF8hw78q00btg0axDPt23NvWhqtY2N5MSuLC5KSmBoZ+Y9VvEEtmhrNkWi5\n0NSHlgtNfWi50JxsWlL5Hgd8uv/vT4Er6xtkGEYUcBkwt5nmdUbhbjLxWLt2RHl6cl2bNiQNGkT8\ngAEsLSvj5rAwpkVHt/QUNRqNRqPRaDT7+et18k6c1iJSACAi+wzDaN3AuNeBB4GAZpvZGU6MtzfL\n+vZt6WloNBqNRqPRaI7glMZ8G4bxB9Dm0KcAAaYDn4hI8CFjS0Qk5Ij3Xw5cKiJ3GYYxEpgmImOO\n8Xn/rIBvjUaj0Wg0Gk2LcKIx36fU8i0ioxt6zTCMAsMw2ohIgWEYYUBhPcPOAcYahnEZ4AX4GYbx\nmYjc1MDnnfoe7RqNRqPRaDQazQnSktVOXgZKReRlwzAeBoJE5JFjjD8PZflusNqJRqPRaDQajUZz\nOtOSCZcvA6MNw9gJXAC8BGAYRrhhGL+04Lw0Go1Go9FoNJpTwt+qyY5Go9FoNBqNRnM605KW7xPC\nMIxLDMPYYRjGrv3hKvWNecswjN2GYWwxDEOX/fgH0JhcGIbR1TCMdYZh1BmGcX9LzFHT/DRBLq43\nDCNp/7+1hmH0bol5apqXJsjF2P0ykWgYxibDMM5piXlqmpem6Bf7xw0yDMNmGMbVzTk/TcvQhPXi\nPMMwyg3DSNj/b3qjxzyTLN/7O13uQoWp5AFxwLUisuOQMZcCd4nI5YZhDAHeFJGhLTJhTbPQRLlo\nheqmeiVQJiKvtcRcNc1HE+ViKJAqIhWGYVwCPK3Xi783TZQLbxGp2f93b+BbEeneEvPVNA9NkYtD\nxv0B1AIficgPzT1XTfPRxPXiuHMSzzTL92Bgt4hkiYgNmIdq1nMo44DPAERkIxBgGEYbNH9nGpUL\nESkWkXjA3hIT1LQITZGLDSJSsf/hBuCf2w72n0NT5KLmkIe+gLMZ56dpGZqiXwDcDXxH/RXaNH8/\nmioXx1Vt70xTviOBvYc8zuHom+WRY3LrGaP5e9EUudD88zheufg3sPiUzkhzOtAkuTAM40rDMFKB\nn4Fbm2lumpajUbkwDCMCuFJE3uU4lS3NGUtT7yNn7w91/tUwjB6NHbQlO1xqNBrNaYFhGOcDk4Dh\nLT0XzemBiCwAFhiGMRx4Dmiwb4XmH8MbwKExv1oB1wDEA21FpGZ/6PMCoMux3nCmWb5zgbaHPI7a\n/9yRY6IbGaP5e9EUudD882iSXBiG0QeYA4wVkbJmmpum5Tiu9UJE1gIdDcMIbmiM5m9BU+RiIDDP\nMIwM4P+AdwzD0L1H/t40KhciUv1nqJqILAbcGlsvzjTlOw6IMQyjnWEY7sC1wMIjxiwEboIDyVTl\nIlLQvNPUNDNNkYtD0daKfwaNyoVhGG2B74EbRWRPC8xR0/w0RS46HfJ3f8BdREqbd5qaZqZRuRCR\njvv/dUDFfU8RkWPdazRnPk1ZL9oc8vdgVDGTY64XZ1TYiYg4DMO4C/gdtXH4UERSDcOYrF6WOSKy\nyDCMywzDSAPMKFey5m9MU+Ri/8WxGfADnIZh3AP0EJHqlpu55lTSFLkAngCCgdmGYRiATUQGt9ys\nNaeaJsrFeMMwbgKsqKoWE1puxprmoIlycdhbmn2SmmaniXLxf4Zh3AnYUOvFxMaOe0aVGtRoNBqN\nRqPRaM5kzrSwE41Go9FoNBqN5oxFK98ajUaj0Wg0Gk0zoZVvjUaj0Wg0Go2mmdDKt0aj0Wg0Go1G\n00xo5Vuj0Wg0Go1Go2kmtPKt0Wg0Go1Go9E0E1r51mg0Go1Go9Fomon/B7133GzHBIgmAAAAAElF\nTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = figure(figsize=(12,8))\n",
"p1 = fig.add_subplot(3,1,1)\n",
"\n",
"p1.plot(sim.trange(), model.similarity(sim.data, cortex))\n",
"p1.legend(model.get_output_vocab('cortex').keys, fontsize='x-small')\n",
"p1.set_ylabel('State')\n",
"\n",
"p2 = fig.add_subplot(3,1,2)\n",
"p2.plot(sim.trange(), sim.data[actions])\n",
"p2_legend_txt = [a.effect for a in model.bg.actions.actions]\n",
"p2.legend(p2_legend_txt, fontsize='x-small')\n",
"p2.set_ylabel('Action')\n",
"\n",
"p3 = fig.add_subplot(3,1,3)\n",
"p3.plot(sim.trange(), sim.data[utility])\n",
"p3_legend_txt = [a.condition for a in model.bg.actions.actions]\n",
"p3.legend(p3_legend_txt, fontsize='x-small')\n",
"p3.set_ylabel('Utility')\n",
"\n",
"fig.subplots_adjust(hspace=0.2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Behavioural Evidence\n",
"\n",
"- Is there any evidence that this is the way it works in brains?\n",
" - Consistent with anatomy/connectivity\n",
"- What about behavioural evidence?\n",
"\n",
"- A few sources of support\n",
"- Timing data\n",
" - How long does it take to do an action?\n",
" - There are lots of existing computational (non-neural) cognitive models that have something like this action selection loop\n",
" - Usually all-symbolic\n",
" - A set of IF-THEN rules\n",
" - e.g. [ACT-R](http://act-r.psy.cmu.edu/)\n",
" - Used to model mental arithmetic, driving a car, using a GUI, air-traffic control, staffing a battleship, etc etc\n",
" - Best fit across all these situations is to set the loop time to 50ms\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- How long does this model take?\n",
" - Notice that all the timing is based on neural properties, not the algorithm\n",
" - Dominated by the longer neurotransmitter time constants in the basal ganglia\n",
"\n",
"![](\"files/lecture_selection/timing-simple.png\")
\n",
"Simple actions\n",
"![](\"files/lecture_selection/timing-complex.png\")
\n",
"Complex actions (routing)\n",
"\n",
"- This is in the right ballpark\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- But what about this distinction between the two types of actions?\n",
" - Not a distinction made in the literature\n",
" - But once we start looking for it, there is evidence\n",
" - [Resolves an outstanding weirdness where some actions seem to take twice as long as others](http://www.cogsci.northwestern.edu/cogsci2004/papers/paper425.pdf)\n",
" - Starting to be lots of citations for 40ms for simple tasks \n",
" - [Task artifacts and strategic adaptation in the change signal task](http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2014/01/2013-moore_gunzelmann-CSR.pdf)\n",
"- This is a nice example of the usefulness of making neural models!\n",
" - This distinction wasn't obvious from computational implementations"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### More complex tasks\n",
"\n",
"- Lots of complex tasks can be modelled this way\n",
" - Some basic cognitive components (cortex)\n",
" - action selection system (basal ganglia and thalamus)\n",
"- The tricky part is figuring out the actions\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Example: the Tower of Hanoi task\n",
" - 3 pegs\n",
" - N disks of different sizes on the pegs\n",
" - move from one configuration to another\n",
" - can only move one disk at a time\n",
" - no larger disk can be on a smaller disk\n",
" \n",
"![](\"files/lecture_selection/hanoi.png\")
\n",
"\n",
"- can we build rules to do this?"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import YouTubeVideo\n",
"YouTubeVideo('sUvHCs5y0o8', width=640, height=390, loop=1, autoplay=0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- How do people do this task?\n",
" - Studied extensively by cognitive scientists\n",
" - Simon (1975):\n",
" 1. Find the largest disk not in its goal position and make the goal to get it in that position. This is the initial “goal move” for purposes of the next two steps. If all disks are in their goal positions, the problem is solved\n",
" 2. If there are any disks blocking the goal move, find the largest blocking disk (either on top of the disk to be moved or at the destination peg) and make the new goal move to move this blocking disk to the other peg (i.e., the peg that is neither the source nor destination of this disk). The previous goal move is stored as the parent goal of the new goal move. Repeat this step with the new goal move.\n",
" 3. If there are no disks blocking the goal move perform the goal move and (a) If the goal move had a parent goal retrieve that parent goal, make it the goal move, and go back to step 2. (b) If the goal had no parent goal, go back to step 1."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- What do the actions look like?\n",
"- State:\n",
" - `goal`: what disk am I trying to move (D0, D1, D2)\n",
" - `focus`: what disk am I looking at (D0, D1, D2)\n",
" - `goal_peg`: where is the disk I am trying to move (A, B, C)\n",
" - `focus_peg`: where is the disk I am looking at (A, B, C)\n",
" - `target_peg`: where am I trying to move a disk to (A, B, C)\n",
" - `goal_final`: what is the overall final desired location of the disk I'm trying to move (A, B, C)\n",
"\n",
"- Note: we're not yet modelling all the sensory and memory stuff (e.g. loading) here, so we manually set things like `goal_final`.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Action effects: when an action is selected, it could do the following\n",
" - set `focus`\n",
" - set `goal`\n",
" - set `goal_peg`\n",
" - actually try to move a disk to a given location by setting `move` and `move_peg`\n",
" - Note: we're also not modelling the full motor system, so we fake this too, i.e. the peg moves when we want it to\n",
" \n",
"- Is this sufficient to implement the algorithm described above?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- What do the action rules look like?\n",
" - if `focus`=NONE then `focus`=D2, `goal`=D2, `goal_peg`=`goal_final`\n",
" - the antecedent is implemented as $Q$=`focus` $\\cdot$ NONE \n",
" - if `focus`=D2 and `goal`=D2 and `goal_peg`!=`target_peg` then `focus`=D1\n",
" - the antecedent is implemented as $Q$=`focus` $\\cdot$ D2 + `goal` $\\cdot$ D2 - `goal_peg` $\\cdot$ `target_peg`\n",
" - if `focus`=D2 and `goal`=D2 and `goal_peg`==`target_peg` then `focus`=D1, `goal`=D1, `goal_peg`=`goal_final`\n",
" - if `focus`=D1 and `goal`=D1 and `goal_peg`!=`target_peg` then `focus`=D0\n",
" - if `focus`=D1 and `goal`=D1 and `goal_peg`==`target_peg` then `focus`=D0, `goal`=D0, `goal_peg`=`goal_final`\n",
" - if `focus`=D0 and `goal_peg`==`target_peg` then `focus`=NONE\n",
" - if `focus`=D0 and `goal`=D0 and `goal_peg`!=`target_peg` then `focus`=NONE, `move`=D0, `move_peg`=`target_peg`\n",
" - if `focus`!=`goal` and `focus_peg`==`goal_peg` and `target_peg!=focus_peg` then `goal`=`focus`, `goal_peg`=A+B+C-`target_peg`-`focus_peg`\n",
" - trying to move something, but smaller disk is on top of this one\n",
" - if `focus`!=`goal` and `focus_peg`!=`goal_peg` and `target_peg==focus_peg` then `goal`=`focus`, `goal_peg`=A+B+C-`target_peg`-`goal_peg`\n",
" - trying to move something, but smaller disk is on top of target peg\n",
" - if `focus`=D0 and `goal`!=D0 and `target_peg`!=`focus_peg` and `target_peg`!=`goal_peg` and `focus_peg`!=`goal_peg` then `move`=`goal`, `move_peg`=`target_peg`\n",
" - move the disk, since there's nothing in the way\n",
" - if `focus`=D1 and `goal`!=D1 and `target_peg`!=`focus_peg` and `target_peg`!=`goal_peg` and `focus_peg`!=`goal_peg` then `focus`=D0\n",
" - check the next disk\n",
" \n",
" \n",
" \n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Sufficient to solve any version of the problem\n",
"- Is it what people do?\n",
"- How can we tell?\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Do science\n",
" - What predictions does the theory make\n",
" - Errors?\n",
" - Reaction times?\n",
" - Neural activity?\n",
" - fMRI?\n",
"\n",
"- Timing:\n",
"\n",
"![](\"files/lecture_selection/hanoi-timing.png\")
\n",
"\n",
"- The model doesn't quite capture some aspects of the human data\n",
"- Much longer pauses in some situations than there should be\n",
"- At those stages, it is recomputing plans that it has made previously\n",
" - People probably remember those, and don't restart from scratch\n",
" - Need to add that into the model\n",
"- [Neural Cognitive Modelling: A Biologically Constrained Spiking Neuron Model of the Tower of Hanoi Task](http://compneuro.uwaterloo.ca/files/publications/stewart.2011a.pdf)"
]
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