tag:blogger.com,1999:blog-1157737960184961252024-02-06T23:55:59.160-08:00Miss CacheRendering, animation, gamesmihuhttp://www.blogger.com/profile/04739819757498405391noreply@blogger.comBlogger8125tag:blogger.com,1999:blog-115773796018496125.post-25635664497790492722016-11-17T13:28:00.001-08:002016-11-17T14:58:15.204-08:00Normal vectors? Normal covectors? Normal pseudevectors?<br />
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Normal to a parametrized surface, if defined as dp/du x dp/dv (cross product), is a <b>pseudovector</b>. However this is not how we usually treat normal vectors, because if a surface is mirrored (across itself for example), we usually want the normal vector to be reflected as well and this doesn't happen if we were to stick to the definition precisely.<br />
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But otherwise, normal "vector" obviously does not transform like a regular vector. Wikipedia page for a pseudevector (<a href="https://en.wikipedia.org/wiki/Pseudovector">https://en.wikipedia.org/wiki/Pseudovector</a>) says that if a regular vector is transformed by v' = Rv, then a pseudovector transforms by v' = det(R)*(Rv). But this considers only rotations - proper and improper. E.g., the reflection mentioned above is an improper rotation, and it is exactly the situation where we don't want to think of a normal as a pseudovector, and I suppose that any improper rotation in CG represents a similar situation (under proper rotations, vectors and pseudevectors behave the same way). So the above formula for transforming normals (the way pseudovectors transform) seems to be useless in CG.<br />
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So what isn't useless for transforming normals? The formula that is well known in graphics: the inverse transpose of a regular transformation. Reflection transformation doesn't change under inverse transpose because it is orthonormal (we remember a useful mnemonic from CG - if there is no nonuniform scaling in the transformation, it is equal to its inverse transpose). Otherwise, the inverse transpose technique takes care of preserving the correct normal in other kinds of transformations, and this is precisely how <b>covectors</b> transform.<br />
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So, what is a normal (the mathematical one, defined through the cross product)? It has to be a pseudovector. But it also seems to be a covector, since a covector transformation rule is what turns out to be correct for transforming normals in CG. Apparently, the distinction between vectors and pseudovectors is orthogonal to the distinction between covariant and contravariant vectors. The terminology is a little confusing, but the actual possible combinations are: contravariant vector, contravariant pseudovector, covariant vector, covariant pseudovector. The normal to a surface seems to be of the last type.<br />
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What is most confusing to me is that whenever I read a more advanced and supposedly mathematically correct description of transforming normals in CG, they mention that normals are pseudovectors (which is mildly useful, because as described above it apparently boils down to flipping or not flipping the sign when the transformation changes the handedness), but they almost never mention that they are covectors as well, which is where the inverse-transform formula comes from (I actually only found out about this when learning basic tensor analysis and noticing that the transformation for covectors has this specific form).mihuhttp://www.blogger.com/profile/04739819757498405391noreply@blogger.com0tag:blogger.com,1999:blog-115773796018496125.post-2068573654034659542015-01-19T04:13:00.000-08:002015-01-21T06:09:13.989-08:00Modelling with distance functions - SHAPEFor a couple of years I've been fascinated with the rendering techniques used by the guys on the demoscene. I've dabbled a bit with sphere tracing before, but never had enough patience or skill to actually finish a demo.<br />
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Since I started making graphics apps for Android, I wanted to try out the idea of generating animated images with distance functions on a mobile device - but in 2D, instead of 3D (of course mobiles are too slow for a full sphere tracing just yet).<br />
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Here is the final app (working as a live wallpaper) - check out the screenshots, and get the free version if you happen to possess an Android device:<br />
<a href="https://play.google.com/store/apps/details?id=pl.madscientist.shape.free">https://play.google.com/store/apps/details?id=pl.madscientist.shape.free </a><br />
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For anyone that did any sphere tracing, the basic idea should be obvious - a scene is defined by a distance function, and since it's in 2D, no tracing is necessary - just check the value at the pixel, and if it's negative, you're inside.<br />
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Check out this ShaderToy entry - a single shader doing all I'm going to describe below:<br />
<a href="https://www.shadertoy.com/view/Mtl3WH">https://www.shadertoy.com/view/Mtl3WH</a><br />
The way I do it on mobile is somewhat different - not in a single fullscreen shader - and I'll explain it a bit later.<br />
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The fist step in generating a shape is a domain (coordinate space) transformation: first rotation and then one of the various kinds of bends and stretches. The shape will be generated in this deformed domain.<br />
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A starting shape is generated by a CSG operation between two of the
basic shapes (disc, square, line etc.). The shapes and the CSG op are chosen randomly.<br />
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At all times two such 'compound shapes' are active and temporally transformed (blended) one to another. After the transformation to the second shape is finished, another (third) compound shape is randomized, and the process continues, blending second to third, and so on.<br />
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This way, at some point in time, I may obtain something like this. Through all these transformations I probably lose a distance property of the function, but it's still good enough to procedurally antialias the edges, which makes for a much smoother look.<br />
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<img alt="" src="data:image/png;base64,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" /><br />
<br />
<br />
Another step is to replicate the domain near the origin:<br />
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<img alt="" 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" /><br />
<br />
You can notice that every second shape is mirrored left to right or top to bottom (in a chessboard pattern). This makes for a kaleidoscope-like animation and assures the image continuity whenever the shape transformations cause it to cross the edges of the small domain area that is being replicated (it's really obvious, but it's hard explain in words).<br />
<br />
A final step in pattern generation is a use of some cool-looking 2D coordinate system (actually, in terms of the code, it is the first step, but conceptually it's easier to think of it as the last one). My favourite one is a log-polar coordinate system that makes for a tunnel-like look.<br />
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<img alt="" 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" /><br />
<br />
One problem is that the shape is minified in some areas and magnified in others. This causes poor antialiasing and too much blurring, respectively. I don't know how to fix this easily - this "procedural antialiasing" is done by making a smoothstep transition between a shape color and a background color across the fixed distance range, e.g. (-0.1, 0.1). I could probably make this interval dependent on a coordinate differential, but it wouldn't always work, because 1) the function loses it's distance property often anyway, and 2) because the implementation is different on mobile.<br />
<br />
Implicit function also allows for a procedural fun with colors - I have as much as four colors assigned to different ranges of the implicit function values:<br />
- shape interior color<br />
- second shape interior color, across some range deeper inside the shape<br />
- shape border color - across a very narrow range around zero - between the first interior color and a glow color<br />
- glow color, which is strongest near the border and fades to the background color away from the border<br />
<br />
Glow color is very cool and can be used to make an impression of either a light glow or a shadow.<br />
<br />
Combining all this, you get images like this one (this is in a regular polar coordinate system):<br />
<br />
<img alt="" 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rMMxzxN9G61+VP8AwUi/bb1L4L/FRrrS7qRVL7JY1b7wzwa8h8Df8Fm9StZFW4upFYddzdK+so8J4mrRVWn1PpKfDlepSVWHU/btZ1b+JT9DStIqjrX5V+Bv+Cy0N2E82+X05avUvDf/AAVh07U4l3XkfP8AtCuOrwzjae8TlqZDio7o+/J76O2XczhVFcL8QfjbZ+EW+aZVRsgNnoa+S9a/4KPWN/aFo7xDuHZ6+ef2gP2301mxnVLr7wJXDdDWuD4erzmlNF4bJasppSR9MftM/tbQx6RdLHcj7pHX2NFflF8Vv2tLvxHbyxm4dpACjfN1or7rB8MqFOzR9bh8h5YWaPd/2zf+CKPib4HxQXXhuKXUJJS00vlgsqL2Ar5l0z4AfF7Srv7Na+F9YlZTtykZwa/oU039oHT5tKWHULaO6+XBEibs1Tm+N2g6eSbHw/Yh+zfZ1r21m9Hl97U/oHEeGeMqVLQg0vwPw/8AA37AH7R3xSdY7PwtqUCP/HNlVFe7fDn/AIN8fjF4z2zeK/E2neH4G5YNJucfrX6aap+0PrVyrLarDaJ0AQYwK5TVvHGra0zG41C4fPo2BXLUzqC0pxPXwHhC3Z4iSX4nzN4B/wCDf34Q+BvLm8aeONQ1yeM5eKFtqn2r3j4Tf8E6f2ctFmm0fw74JtrjUJ7aRYb28/eMj7SFYZ75/lVuR8sGkdmJ/vHrWr4M1w+G/FNleKSvlSjd9K5XnFWU0tkfWR8M8uoUJOOsrO3a5+RXwBlvP2WP2z/EHhS7LQtpeqyRANxld5I/Q1+7P7PfjePxv8OrG4WTeyxjJz7CvyP/AOC7HwZb4Kftj+H/AIkabH5el+MoVeWRRhfOXrX2T/wS1/aJi8VeC7WzmnDExheW+leTxpgPa0o4mCP4945ymVKq3bVM+4Qc0U2NxIu4d+lOr8v1PzMKKKKACg9KKKAM/wASaUut6HdWr4YSRkYPevwx/wCCrPw7uPg38dLHxJao0c2l38d2jDggo4b+lfu8RkV+bX/Bb74C/wBu+CrjUood3yFsha+q4RxnscaovZn0fDOMdHFpdz6V8ReLYfij8NfA3jK1ZZIfEWjQTFgeN+wZ/XNeb+OfhXpfxAkje+RmaMYU56V8jfsSf8FlfBvwH/Zj8L/Dn4jeC/FWrT+GRJDHqFpt2+WWO0AHngfzr6H8H/8ABWD9lX4hFY5vEHiLwrM/G3ULFgin3Ir73HZPOpNyirpn9x8H+JWBwmEpU6lRxqRW6INY/ZK06cH7LeSQt2B5Fchr/wCy3rWmlmtZo7lV6Dua+nPA/iL4Z/GSFZPBPxQ8K6yZBlIWu1jkPthiDmtrXfg/r+hQ+a9m1xBjIkgYSKR+FeBiMjS1lCx+xZT4qupZUcSpeUj4V1r4fa14eci6sZlC9WC5FdF+z74GtfGvjlodQt/tFrbxGR42yAT2zX05fackxaGeBd38SyR/4is/QvBFjourST2NqsdxdARkIPvc8V5kcp5aqknp2PssRx7Ovgp0pR5ZNaST/E4L9rL4xW/7GP7OemeJPD/hex13xJ4m8QweG9C0d5Gt4LiZ0JLt5QEj7TtG1WXJYfMK6eL4N+LPDfjfw5r/AMTPEul67rXhXdqOmaFomiJY6PouoSxeS9wS0s093PCjTRRySMoQyyOqgkGuL+J994Z+In7YVjr3ibxR4c0L4afsuwGFbnV9Ris7PVvF1yFkkRWcjzRZxhC6plt+xcHJrH8R/wDBWn9nfw4Lvy/Enir4gatM+2GDQfD1wEvJi3CpLN5eS5OAdpzngHivuY4ScKMaeHgua2rP5ZnxBhMXmFbF5tXboxlaELv3rPdpb/M9++NHx4Pw7+Dniz4j+L9Ulm8O+E7F7t7E3Tj7ZNwsFsFz/wAtZGC59n6AZHmH7GHhjXtF+AP/AAn3jKb7V8Svjo6eJtfuCm02dhgrp9hGP4IY4gGCDgZHpXOeL/gLpPj7wZpjfHa68UeLtcvCNV1HwuNauovDuj3JJf7JBY2hjDLbLIsHmSM7O0btu+bFbHjL9r3RzFHa2em3w+x2qWNrGsIgitoo0CRoqsc4VQAM88V5+Kx9DD0XS57ye7/Q+x4f4TzTOMyp5g6HLQirwjpo3s2tv1O08P8AxC0vxNqV1aw3CfaLWVomUnqQccV3Wna/puu+E77wr4r06HXfCurRmG6s513AKeCR6EevavhG3127stVa8t7iSG4aQuXU9STmvZvhV+04s5jsdcxHI2Atz2/GvncDnFp3bs+h+x8T+HTqYZxivaQtquq9D5v/AGtf2GfF3/BNnxsvxK+GM974j+E91N5rmHLz6Jk52Sgc7R2avu//AIJ1f8FF9D/aM8HWdjeXsK6gqBRufBJ963fBfjj+y7OREW31XR9QjKXdnMBJb3UbDBVl6cg18V/thf8ABPLVv2ctbufjR+zuLy48P27m613wnES1xpX8TywDq8XfaOQPavoMdg8Pm1LllpU6Pufw74l+FFTDqWIw6vDv1XqfrorgrxyPWnN0r41/4Jp/8FLdF/ac8IWul6jeRw6xCoQrI2GJHGDmvsgNla/L8dgquFqujVVmj+YsVhamHqOnUWp83f8ABUrx4PA/7LGsSbgpkjYfXivmL/gj34LbwV/wT0vtekXbefEjxPcXZP8AE8EH7tPwzmvUP+C4ekeINd/Zqkh0Wxnul2MZXT7q/WsX9gDxr4d+J3/BPX4cx+EbhpI/AlodG16zkULcWF9uLO0ij+Fycq3cV+gcNx5MpnKOrb/A/e/AvD0JZlCVVpat69+hzP7W3ifbHp+lq33szSD6dM0fsr/DJsN4guoiWk/dWasOT6sPr0FUf2qvAdxFqseuRlprWRRG4/55H/Cui/Zs+N66hHa6PcMtrqNpg2kigKrgcjj+8K8aPK8d+++R/o7WlWjw0ll+u/P5dX/wfI5r9r671P8Aal+Omh/syeEJHaxgmg1f4j30B+TcCHg00sP4UGJJB/eKD+E191/8Mx+F/wDhnr/hW/2CH+wfsP2Py9vGduN/1zzXwl+0bpOpfsa/HGP9pLwFA6+HdWu0g+JWi24JjtZJGVV1RFHSKQ/LL2VyrdGOPvD/AIaX8K/8M/8A/CyP7Qg/4R37F9s83dx93OzPrniunil4jmo+wv7PTlt/N5+Z/l54sSzd55L68tb6W28rfofCX7IN3qn7LXx01z9mTxdM62E80+r/AA4vpz8m4kvPpoY/wuMyRj+8HAHzCtD9qj4YkBfEFrEysv7q8VRyPRj9OlZn7OWlal+2X8cZP2kvHkEh8O6TdvB8NdFnB2XUkbFW1R1PWKM/LF2ZwzdFGen/AGlPjcunx3Wj2rLc6jdgtdyMAwQHrx6mvSzmMfq0Xifjsr27/wCZ/Yn0dZ56sNS9rHS6t/h638u3mZf7JHibcmoaWzfdxNGD79cVn/8ABYTwUfGv/BPSx16Nd158OPFFvdg90guMxt+uKd+yp4DuZNVk1x2khtY1McY/56n/AArtP2//ABr4c+GH/BPT4jR+Lrho4fHdoNG0GzjUPcahfBgyFFP8KEZZuwquGZVE43XdfI+n8dKOEnh8QlJdH/291R7P/wAEtvHg8c/sr6RJu3mONc+3FfSY4FfD/wDwQ80jxBoX7NUcGtWE9opRTE7/AHW+lfbxbA5496+DzqmoY2pGO12f5jZpBRxU4x2uDOFXngepr5C/4KK/8FF9D/Zz8HXljZ3kLagyFSVbkfSqf/BSv/gpZov7MnhC60vTryObWJlKYjbLA9MCvlT9j7/gnlq37RuuW3xm/aIW8t/D9w4u9C8JysVuNV/iSSdeqRd9p5PtXv8AD/DvtV9bxekFt5n1XB/B2KzXERUYN36HD/slfsM+Lv8AgpN42b4lfE6e98O/Ce1n81DMSk+t4OdkQPO092r9GNR1/TdC8J2XhXwrp8Og+FdJjENrZwLtBUcZPqT1z1qn4z8c/wBqWkayLb6To+nx+XaWcIEdvaxKMBVUccAV4H8Vf2m1hMljoIEkikq1x2H0r6XMs2hCHs4e7BbLuf3d4b+FX1ZR5Ic1Tq+kT07xD8RNL8M6jbWs1xH9oupViVc9CTiuW/bP8L65rPwBXx94Nm+y/Er4Fu/ibQLgJuN5p+NuoafIP44ZIizFDwcH1r50uddur3VFvJ55JrhXDh2PQg5r6A8GfteaQsL215pt8TeWr2N1G0P2iK5ikQpIrKpzhlJHHPNeTledRjW5p6L80fpnHXhvXxGWexwyc52vftJbNdUj1r4L/Hk/ET4M+E/iR4R1OaDw74ssY7xLIXTkWcxys9uVz/yylUrn3TrnJ42X4NeLPEnjjxHr3w08SaXoWteKNuoanoWt6It9o+tahFEYUuAVlhmtJ5kWGKSSNmDmKN2UkZrz/wAI/AXSfAPgrUm+BN14p8I65ZE6rp3hc61dS+HtYuQQ/wBknsbsyBVuVjaDzI2RkaRGB+XFU/DP/BWj9nXxELXzPEnir4f6tA+2aDXvD9wUtJQ3KvND5gUo3BO0YxyODX09OMpy9rhXzQ6o/DcZWpYWEcDny9jiUly1FdXaur3Wt+9+/Y6X9k74x2/7Zn7Oep+JPEHhew0HxL4Z8RTeGte0dHa4t7eaNAQ6+YDIm4lhtZmwUPzGvLv2gPA9r4K8cLDp9t9nt7iISJGuWGe+P8K9E+F994Z+HX7Yd9r3hnxR4d174a/tRQLAbrSNRivbPSvF9sGkjRmQnyxeRmQoHw3mb0wMLn0rXfA9jrGrxzX1qslxagxgOPu885/KvIz7LYSadNKN7P8AzP0Lwl4yrUKco4qo6kqbcXruvsvsfKOifD3WvEL/AOi2EzK38TLgV2ug/st63qW03U0Vsrc4719E2OmpDtht4V3dFWNOf0rqtC+D/iDXYfMWzNvb4yZLhhGoH414+HyWL0+I/Sc08Sq0E2nGmvPf8T560f8AZK0+AL9qu5ZvUDgV3Pgb4V6X8P5JHsY2VpFwxz1rtPHHiP4Z/Bq3aTxt8UPCui+WMvCt2skn0wuTmvGvGH/BWD9lX4eu0cPiDxF4rnTtYWLFWPsTXtYfIZrWELH5lnHizhZJwxOJcl2W36HvPh3xXB8L/hr448Z3TLHD4d0WeYOTgb9hx+pFflj/AMEpvh3cfGX4533iO6RpJtUv5Lt2PJJdy39a9N/bc/4LKeDfjx+zF4o+HXw58F+KtJm8TCOGTULrbs8sMNwI68j+Ve2f8EQfgL/Yfgq31KWHb+7DZ2135pL6jlklLdn8o+LHFVLH1JV6D92ySP0V8N6UmiaHbWq4CxxquBWgOlAGBRX47J3dz+bm7u4UUUUhBRRRQAE1wn7QnjePwR8Ob64eTazRnBz7Gu5kfYCx/hGea+IP+CpP7REPhXwVdWcM+1lRgcN7Gu/LcK8RiI013OzA4d1qygj8zvj9LeftTftoeH/ClqWmbU9VjiYLzhd4JP5Cv1U+LX/BOn9nHW5IdH8ReCbW31CG2jWa9s/3bO4UBmOO+cn8a+FP+CE/wZb41ftkeIPiRqUfmaX4Nhd4pGGV85un9K/Qvxprp8SeKb68LbvNlIU+oFfseMrvCUoUqZ/XnhjwjQx1GbxMfdS/E+Z/H3/Bv98IPHIebwX441DQ5nOUinfeorw74i/8G+Pxi8GB5fCnifTvEEC8qFk2sf1r7zjfDFo5CrD+6cVraV441bRCpt7+4Tb0y2Qa54Z1NfHG59tmHhLg6mtCVvU/IHxz+wD+0d8LnaO88LalPGn8cOWU15vqXwA+L2rXn2e58MavEznGXiOP5V++WlftEa1bKFulivE6Heuf51qw/G7QdQIN94fsS/dvs6/4V2083w76WPjcV4T4unK8FdeR+SX7GP8AwRR8TfHCG4uvEkcunyRFZovMBVZF7g/lRX69aj+0Dp8OltDp9tFa/KQBGu3FFKecRT91ndhfDet7P36ep492qOa6jtk3SOqKPU15N8SP2iz4X1i50+GHM1uxRjjrXlvij4za14jLZmMSNngGvha2ZUqbstWf1hlvBuNxUVUlaMXrc+iPEXxY0fw4jebdRkgdF5zXnfiX9q+OMtHp9vu9GNeI3FzJdtumd5GPUlq0/CfgTUfGV2I7G2Zl/ikI+VR9a82eZVqj5aasfZYfg3LcJD2uLlzW76I67Tfjxq2seLLN7qXbb+aMqp4NfSNhdLfWMcyHIkUMDXxff2jaXqU0O757eQrn3FfTvwE8WDxL4Li3Nulg+UgmujLcTKU3Cb1PI42yejTw9PE4WKUVpp57Gj/wUg/Z/H7Yf/BP/U4beNZvEHg3N9anGXAUfMBX50/8E0f2lZvh94nj0+6maGSCTy3VmxtIOCDX64fBfxHHpHiCSwvNsmn6ohgmQjKsG4x+Rr8i/wDgqN+ypqX7Cn7Wt1rOlwyL4X8QzG7tpVHyIWOcV9/h4xxmEdGe5/Cnitwz7PFSqRXuy1P2q+CHxrsfHPh23PnRlyo5zXo6TrIPlO7ivxU/ZP8A+CgR0Kzt45rzaFAyS3Svvv8AZw/bhs/G0KyXFym1/lQFu1fmWacP18PNtLQ/mHH5PVoybS0PrPNFc7oXxM0rXIFdLqNdw/vVsR6zazD5Z42/4FXzrpyWjR4vK+qLVFQ/b4f+e0X/AH1R/aEA/wCW0f8A30KVmSTGuG+N3wJ0n47aFHp2rLutVOWXb9+u1S4WX7rK30bpUgOaqnUlCSnF2aLjKUHzR0Z8j+Lv+CSHgPXkfy7a3Xd6oK8X+Jn/AAQz0PVI5DYwxZbkYFfpARmm7cetevR4gx1LWNRno0s4xdPabPxF+LH/AARc8ReAZpLrRWvLWWIlkeB2jYfQiuR8HftIftNfsJamq6f4m1TVNLt2y1jqZNxE4HbnpX7xXumQalE0c0McisMHcM14j+0P+xroPxR0S4aOziExU9FFfSZfxrVTUcSro+jy3jLE0pJVGfNP7FH/AAV18Bftq6nH4U+IGk23gzxkwCx3CsFt7h+nHpzX0bq3gdfhrr901zfTWnl27Np91DbC4ZJmBCSBSQpKZ3jccZC544r8iP23f2ItY+DvjoapoMM0F3YTC4SWMEeUVII6V9p/8Epf+CnUn7W1rN8Jfia0MXjC2h2aVdFdgugowF924/lX2cqdHFU1icNutT+h+C+PpVofVMVNuE9N9dTqPhL+xl8HfgaWu9O8D2fijWmkknl13xlJ/bV9JLIxeSRVcCCIsxJPlxjJJyT1r1OD4rw2VusVu3hq1ihOUSDTraMQ46FdqjaQeQRyMVHr+jSaRqV1Y3C4aFjGwI618h/FvRZPDPxB1C3VpEjL71w3GDXzWYZtiadpSbfzP6e4P8P8lxzdGlTjGyTTcVK6+Z6z8MPHzfEn45ateK26ztbUwWwbn5A33v8AgRyfpivW9b+AsPjPTopJtJs9Se5g+0iOFka6WPcy7zGPn27lYZwQcHmvn39kYf8AFa6h/wBeo/nWN+3f8KR8X/25f2fNDj1vVvDt5qvhC+gttS0y7e2urKVb92R1dCDgFuR3GazymnTxFGU6+2rb3OPxe4hnwpbE4RaQ5FbVacr7ddD0Dxl+yRp87yHTbi402dc/uZgWUH+Yry3xh8DvEXg7c01mbu3X/lrB84/EV6N4l8M/te/spblvm0D4+eFbYf6vWIfL1RUH927iAk3YHVw/0rb/AGfv2zfA/wC0j49t/BMmjeLvhv8AEK6jmePQNftDcWt35UbSSCC7iGCAiscSKhxUyyXDYmHPhZJry3+48Pgv6R2ExrjSqT1elpa/c1r955N8MPjXqXw5vFhZnuLHPzwufmX6f4V9OfCj4sLqcEOsaHdbWT5XU9PdGHcV49+1T4b0vS9Ds7yCzhhvri42GSMbSVxnmtL9kmXd4Kvhwdtxg/lXn4KdWhX9g3ofsfEWHwWZ5V/akafK2+WS0s+jueDft/8Awh0D4A/8FAPhXrvgO1k8LS/EyyfVNZsrKTZa+eswQvGmPk3ckgcZr9bfAgb/AIQvS9zMzfZUyzHJPyivy3/4KrN5X7X/AOzTcHOxtCnjz7rdDP8AOv1I8DNv8GaWR/z6p/6CK240vJUJPdo/y68VcPCjm86dNWSkxvjnwTY/EHwvd6TqUKTWt3GUYMM4461+Snjm01n/AII9fttt4qhtbi8+GPi5/sPibT4wfLmtXPEyjp5kRO4H6jvX7CmvDf29f2W9O/ad+B2qabNbxvfQwM0LbcnpXi8O5w8FiLT+CWjR8dw3ndXLsXGrB2VzgfH/AIQ0zXtJj+yXEOqeHdftUvNNvIzujuLeQZRwfofzr488Y+GLv4a+MZrXcyS20nmQSA4yOoNdT/wSg+MGoLZ+J/2cPFkzDX/BPm6r4QmmPzXFmGzNaA99mdyj0J9K9G/ad+Hn9v8AhhdUt4/9M08ZfA5ZO4/Cvps+y+z9pT9U/I/078GePIY/DxVR6StGS6X6P5m58BPi3Z/EvwxcWWq2trqAlt30/VtNnG6LUbaRSsisvdXUkexwa+cx+yj41HxW/wCGaf7Q1j/hRf2n/hN/7c3Hf/wj33PsO/8A5+ftH+jY65+fGKb4F8V3ngbX4dXs1crbMFmH8LKeMN9e2a+sP+Fy2/8AwqT7d9r/AOJLj7Vn+Lf02fn29eaMnzWLpNVldx1V+66nH4seE+FzHHUK8I6c69fOP+Rzvx6+Ldn8M/DNvY6Ta2unrDbJp+k6bAMQ6daxrtjVV7KqgD3PNfOvg3wxd/EnxhDabnklupN88h7DPJqPx34svfHOvzateLIq3LEQgjCqo6AfTvXuP7MXw9/4R/w2dUuI/wDTNQGUBHKp2H4141SpPG4nX4Ufr2FweH4ZyW1JL2jVtOjtt6RPVfAHhLS9B0h/tdzDpfh3QLV7zUryT5Ut7eMZdyfoD+NfFPgaz1r/AILCfttr4qmtbiz+GHhFzY+GdPkBEcNqh/1zDp5khG4n6DtXef8ABV/4wag1l4Z/Zv8ACcjf2/428rVfF80J+a3sycw2hPbfjcw9AK+2P2C/2W9P/Zi+B2l6bDbol9NArTNtweg4r6LM8YsswXLD+JPbyR/nj42eIE8TiHg6EtE/vfVnrHgXwTY/D7wvZ6Tp0McNraRhFCDGcDrTvHe7/hCtV2syt9lfBBwRwa11GBWR45bZ4L1Ru32V/wD0GvzOMnKom+/6n8xxblNN9z8kv+Cf/wAItA+P3/BQD4qa548s5PFUvwzsk1TRrO9k3WonacoHkQj59vUA8A19j/Fb4srpkE2sa5dbmb5UUdPZFHYdq+Xf+CVB879r/wDaXuF3bI9BgTPubo4/lXqn7W7bfBVj0+a44/Kv1XOq8qVCHLsop2P9BvAHJ8NicNTUo2cpWbW9rXPMfif8atT+I140Ks1vY5+SFDy/1qHwd8DvEXjLa0Nm1rbt/wAtZ/kH4CvQv2V/Del6pol5eXFnDNfW1xtEkg3ELjPFb/7QP7Zvgf8AZu8eXHgqPRfF3xI+IVrHC8nh/QLQ29raeaiyR+fdyjABRlOI1c4NfO4HKZ4z99Ud+vkf0Fxh4kYDhWnLDRiqajpd63fkuvzKHg39kjT4GjOp3FxqEzf8solKqT/M16zo3wGh8F6dJJFpNnprW0H2kpMyLdNHuVS4jPzldzKN2AORzXjXhnwx+17+1YFWxbQfgD4VueNmjw+ZqjRn+9dygybsHqgT6VxP7CPwp/4VB+3J+0Focmt6t4ivNK8H2UFzqep3b3N1eytfxs7s7knGV4HYYr2sLgcEnKnTmpSir2X6s/nX/iPk88zanl2FbkpSSbb0tfoloeg/FDx63w3+Omk3bNts7m1EFyAcYUtw3/ATz9M17RP8Vob6BorhvDV1DNy6T6fbSLLnqWDKdxJ5JPXNfOH7XI/4rbT/APr1P86434SaNJ4l+IOn25aR41fewLZ4FeJHMqtCvKlDq1bWx/SeI4LwGa5XRxuJSvCDveKd+vX7j234s/sZfB3457bvUPA9l4X1pZI54te8Gyf2LexyRtvjkKoDBKysAR5kZwQMEda980jwQPiT4htTbX0135luh1C7mtxbtJMoAeUqCVUvjcdpxkn6DktB0WTWdStrC3XLTMI0AHSvCf8Agq1/wU5k/ZJtYvhL8MWjm8YXEO3VboLvFqHGCvsea+twsa2M9yo7pH88cS18t4cvisJDkqSTVls13a/I1/22P+Cu3gH9ivU5PCnw/wBJtvGXjJQUknZt1vbv7nviviDxj+0h+01+3bqbDUPE2qaXpdw3y2OmE28KKe3HWqf7EX7EesfGLx02qa9DNNd6hMbh5ZAT5pY5P86/YD9nj9jbQfhdolv5lnEZgo4K1nm3EGHy5eyoq8j+XOKOPMROq3Obb9T8ufhP/wAEXPEXj+eO61pry7lmIZ3ndnY/Umvpz4Zf8EMtD0uONr6GH5cfeFfoxZaZBp0YSGGONRwABipgPavh8TxZjqr0lZH5piOJMZVejsfJPhH/AIJIeA9BRfMtrdtvHCCvfvgj8CNJ+BWhyafpK7bZjlVx9yu6AxRXh4jMMRWXLVk2jya2Nr1VapJsKKje4SL7zKv1NN/tCH/nrH/30K4zlJqKh/tCDH+ui/76qOTWbWH71xEv/AqdmOzLROKZJMsa5Y7R71ga78TNJ0OBne6jO0Z+9Xzb+0h+3DZeCoWkguU2x/K4D9q68NgqtaVoI6KGFqVXaKPavjd8bLHwL4euD50YcKe9fiz/AMFLv2lp/iD4mk0+1maaSeTy0RTncScACvQv2r/+CgR160uI4bzcGBA2t1rzn/glz+ynqX7df7W1prWqQyN4X8PTC7uZWHyOVOcZr9M4ZyF4b99W3P0Phjh+aqptan6Nf8E4PgAv7Hn/AAT/ANMhuI1h8QeMv9NuiR85DD5Qa6K/uhY2MszHaI1LEmu4+NPiSLVvEEdhZhU0/SkEEKKMKoXjH4CvFPj34sXw14KmCttkm+UDNTm2L5pyn0R/d/h9w68PhaOFS96bVzyHUfjxq2keLbxrWXfb+acKT0rrfDP7VscjLHqFvt9WArxOwtW1TUoocnzLiQKCexJrR8WeBNQ8G3nl31uyq33ZAPkYfWvjaeMrxvOL0P6UxXD+VVOXD1YpTtp0eh9P+HPizo/iNF8u6jUsOjHGK6OG6juY90bq6+oOa+Kre5ktG3Qu0ZHTBrqfDHxm1rw4y7Z2ljXsTXdSzZbVEfMY/wAP2vewk/kz6w6CivIvhv8AtFnxTrFrp80P7+4YIuf50V61GtCrHmifC47KsRg6nsq8bM8v+PFr9m+KeqZ43MGArk4IXvJlihRpZHOFVRkmuksdE1v4zeL7i6WNpZbqQtJKR8kQ7D8Bivefhd8C9N8BWyySILu/bq7LnB9q+dp4WeIquUfhvufrGL4go5Rg4UKr5qiilZd7LfyPOvhb+zLcar5d5rW6CE4Ih/ib617hoXhmz8M2C29nbxwx4xgDrU/xa8eeC/2X/AX/AAlPxQ16Hw7pTDdaWC4k1HVGHRIYfvHPTPQe1L8OfjToP7T37PfhD4i+F9Nk0fStYa6tvskrb5bdopmULIR/GVAJ9Ca+poZQ6NF1rWX4s/C808Q6eZZnHL5VOaTvonorfmz5O+LWmf2P8RNSi24DSlwPY10v7OPjX+wfEpspG2xXA+XnjNT/ALU2if2f47huVX5bqL9RXnGn6hJpV/DcRnDQsGr5KcnQxLa6M/oDDU45lk0YS+1Ffej7VinaJ1kjJDIQykdvSun+NvwD8M/t8fAKXwr4gWFdUtVxazsPmRvr6V5n8LfF8fi7wvbzKwLquGGeldl4e1i80TVonsWk+0SMFVEBJc9hX2eAxbpyU47M/m3izhuOMoyw1fSUb6s/KP8Aaj/4JE/FL9l1dW1bT4Zr3QbBxsaMEu/sP89q8n+HH7WGvfCi4W1vFurWSD5SkgK4/Cv6Ah8X4ke60XxVaQ3yqVSWNk3IDgEj8P515n42/Zi/Z1+IF7Jeap4NtJZ5Dltq4z+lfSvH4atHlrH885l4a4ub/cw5k+qV0/Q/LXwx/wAFZtQ0GFR9qf5R/erdH/BZ7WI1/dzTN9Ca/QpP2Mv2Y9LfdF8NrO4YcgOM1r6Z8IfgX4ZTbpvwj8N8cBpYA38686dHKr3cTyafg9j6j1pH5tn/AILOeLJ3228OoSN22Rs2f0qOb/grv8SGgkmj0nXjDGpd5Ps0m1VAySTjAA65r9RLHWPB+hDGl/DnwfZhemLCI4/8drX03VdF+LFpfeEta0HQbXSPEVpLp8v2ezjjK71IzkDiojTypyUVT38jsqeCuIp0pVZxXupvz0PFf+CT/wC2jN8fdDk/ta8868vDuwW+76AfSvuMNmvwj/Z8vdY/4J1/tx698O9WaSCPS9QK2rscLNAzZjYexUiv26+Fnjq3+Ifg2z1K3kV/NjG7B74r4PirK/quJ54L3ZbH858S5a8JiWkrI6QHIooHSivlT5sKRhmlooA8v+PH7N+j/Fvw5dQyWsfnyKedudxr8ZP22fgDrv7Fnx10vx14fWW0vNBvku43jGMhTkj6EcV+9RGa+XP+Clv7Ldr8avg9qE8VurXUcRJIXnvX1HDedTwmIUJv3XofQZDm08LXSb0Ytt8S9P8A2ivg34N+J2jsjWnizT45LgKciK4UYkU+4bNfOn7Wvhv7LrFnqUa/LMpjc+/auR/4Ik/FSceHviV8AdalYX2hynxB4fWQ8lM4mjX6cNxXuX7QnhX/AISX4dXW1B51mfNHHPHUV9VxDg17zjs9Uf6PeC3FCrUqFST1i+V+j2PLv2S5dvj68TP37M4/OtL9qs/2d+3j+ynqR4Wax1ayzjut5G3/ALP+tc5+zBf/AGb4qQqflE0DrzXrX7Tf7Mlr+0/p/gW5s/HOp/D7xZ8PZ76bS9Si0pdQt5ftPlErIu9HG0xDBU5+Y1x8PyjKhKnJ2upL7zs+kVkeJzLDTw+Ei5Sai7L0aPvAja/1/WvgH462scX/AAW4+HOxFRIvCmszKqjADf2fLzj8ao+Hfid+17+zRGrTad4X+PHh235abw7d41IIOubSYLNnHZN/WvP/AA5+1vov7Uf/AAVb+E+sw6Xq/hrXodF1rR9V0TVLd7a8spv7NuCuUcBtrbTg47fnGSZHisFjPaSs42eqd1sfwdwzwvmOU59QWOpuK5lq/U7D9sGfNnocP94u/wClTfsg3edK1aD+7Kr/AIYqj+1637/Qf+ucn9Kp/si6n5PibUrUnmSEMPfBrzOa2Yf12P8AU6FDm4U09f8AyY5T/gsBEdO8b/sy+IP4I59Q05mPqJI2A/Imv02+D2pDVvhfoVwrbhJZpz68V+dP/BY/w5Jqv7EHg3xPCpeTwF41iaVh1SK5Qpn6bgv6V9ofsBfEmH4k/s16DcxyLI0MCxtg9OBXpcWwc8HRq9tD/MLxpwbpZxUk/wCZ/ie2010V1wwyG4I9adSMeK/PT8VPyU/4Kx+CtQ/Y5/au8IfGjwvGbe80HUY719gwJ4t2JI29QyZBFfZHjKfS/G+nafr2jlJtB8ZadFq1mQcqI5kDFf8AgJOPrXI/8Fsfh3b+L/2X7q6kUeZbowBxntXN/wDBMTVJfiz/AME2PhQ1xdQRzaTPe6MZXkB2xxSMVU5P38EAKSM8c96/UsqqPF5Ur6uLsf1H4E8RSo1vZTfutO/y1TNf4e/CnRPht4S1661S4s7PR7SCbUtXv7tcwWdqgLEvnsF4A6liAOTXmH/C4fgWPgt/wnP/AAsDRv8AhW32nf5G+Tzvtezd9n+xbftXm7fm8rZjHz58v5qmvNTtf25PidefDjxouo/Dvwv4Wv1uF+GWolrDxN8RzGdyXd5IwEbWKkZW3s2mY5yzrgNXvI8PaOPH39pf8IH4T+3f2V/ZG3+w4N/2Hbt+x52eZ9m/6ZZ2/jXZPL8LQjGNZO/l27H7/T4xz7NcTWxGWTjGmlb33q5L7Vr6NLa5x/xB+FWi/EjwloV1pdxZ3mj3kMOpaRf2i4gvLVwGBXHYrwR1DAg8iu78HT6X4H07UNe1grDoPg3TpdWuyeF8uFCwX/gRGK+e7PUrX9hz4n2Pw48FrqPxE8L+Kb9rlvhjpxa/8S/DkyHc91ZyIDGtixOWt7xoWGMq7ZLV2X/BT3VJPhP/AME2viu1rdQSTavNZaKJlkA3pLIpZRg/fwCCuTj9a1p5XCOJjKHwy/Q8/FceV62R1qNd2rUU00tVd6Np7anhP/BJzwTqH7Y37V3i/wCNHihDcXevajLep5gyIItxEUY9AqBRj2r9ao0CKABgLwB6V8X/APBE/wCHdv4Q/ZgtbqNR5lwignbjtX2kvFfnnE2MdfHz7R0R/BPEOMliMbOcu4VzPxh1JdI+FuuzsdoSzc59OK6avEf2/fiRD8Nv2a9euJJBG00DIoJ68V5GDpudeMF1aPLwsHOrGK7nwh/wR/iOo+N/2mvEH8Ek2n6are5kkYj8gK9F/a/utmkaTD/emaTH4VzP/BHDw5Jpf7EHjLxNMpWTx741laJj/HFbIFyPbcWH4Vc/a61Lz/E+m2oP+rhLEema/SOJZcseXskj/TD6POBcMPQb7yf3KxqfsfTf6JrkOfusj/oa3PgVaxz/APBbf4j7o1kWXwno0rKy5G7+z4ucfh1rm/2QW/f69/uR/wBa5fxF+1xov7Ln/BVz4sa1NpereJdem0XRdH0rRNKtnuby9mGm25bCIC21dwycd/y48tp1KuWVadNXbjZL5nyn0qMPKpTdKkrylJWXnyo/UxRlh+lfmx+yp/xMv28P2rNSHKw2Ok2Wfd7uRv8A2StvxH8T/wBr79pePdDpvhj4D+HbjlZvEN3nUih9LSENLnHZ9lbn7Mf7M1r+zDp3jm6vPHOp/EHxZ8Qp7GbVNSl0pdPt4/s3mkLGu93bcZTksc/KKeS5TUwCqVMRJXlGySd2fz14TcBZvQzejj69FqCad/xPNv2tZd/j60XPK2gz+Jq7+yV4b+06zealIvywqI0Pv3rn/wBp++F18U5lVuI4EXj869k/Z78L/wDCNfDq13Ltmuz5pwOTnoK8mjT9pjXLsf6R5livqvDVKn1mkvluz0e8+Jmn/s6fBrxl8TtYaNbTwnp8j24Y8S3DDEaj1JYivy3/AGJ/gBrn7afx11Tx14hSW8vNdvnu5Hk5xubIA9gOK+j/APgtt8U7j/hH/ht8AdFkY32uSjxB4gWM87M4hjb6nLc19cf8E0v2XLX4K/B7T55bdVupYgRleRwK+szTHf2bl6Ufjmf5y+MPFntcdONN6R0XyPVPgR+zho/wl8O2sUdrH58ajB242mvT1GKAMUtfk9WrKpLnnufzjUqSnLmkFFFFZkBTWbinHpXN/FLx1bfDzwbealcSKgjjJXJ74qoRcpKKKjFt2R8e/wDBWD9tCb4B6HH/AGTeCG9tDu4bG71Br4Ri/wCCu3xJW3SZ9J17yZFDpJ9mkKupGQQccg+tVv2g73Wf+CiX7cWhfDvSWkmi1TUAt0ynKwwK2ZGPttBr9YtS1bRfhNaWPhPRdB0G60jw9ZxWEXn2cchYIoGckd/61+s5flODwmDh9aheTP3jgXw7nm9HkhFXSvdn5Tj/AILNeLLd8XEOoxH/AKaRutP/AOHz2sSj95NMPqSK/T6/1nwfro26p8OfB94DySbCMZ/8drD1L4Q/AvxKu3UvhH4by3BMUAXH5V0xo5S/sW+R9lW8FMZD4aafpY/MHxN/wVm1DXoGX7S+W/2q8T+I/wC1jr3xYuGtbRbq6kuDtCRgsTX7Jv8AsZfsx6q26b4bWdux5OwYro/BP7MX7Ovw+vY7vS/BtpFNCcpuXOP0ruoPLqWtOxz0vCvMKMv4X4H5Q/suf8Eifil+1Euk6rfwzWWg37/Ozgh0Hof896/XX4I/APwz+wP8A4/Cvh9YW1S6TF1Og+Z2+vpXa/8AC34mktdG8K2sNirEpFEq7UJxkD8cYrzHxDq95rerSyXxk+0RsVZHByh7is8dmnNDlpn6hwlwBGhXVTE6NWdnv/wxSluGlLSyE7nyzEmvmr9o7xs2veJFsonzFbjnnvXuHxT8Xx+EfCtxMzfvGXCjNfJ+o6jJq2oTXEhJaViea+FzavaPs113P6s4BynmqvGTWkdEbfwl0w6v8RNNixkLKH/KvqnXPDVn4lsGtryCOePG3BHSvAv2WNF/tDx5Jcsvy2sX6mvob4jfGrQf2YP2e/F/xE8T6bJrGlaO1rbG0ifZLcNLMqlUP98KSRn0rpyPCurDkX2meH4o5/DAV/rE3ZUktvNng3xS/ZmuNJEl5ou6eEZLQ/xL9K8nnhe0naGZGjlU4KsMEV9ofCbx74L/AGoPAX/CVfC/XofEWkqu67sGwmo6W3dJovvDHTPQ+prjfih8C9N8fW8kkafZL5ejqMZPuKyzDJZU5e6rPsdnB/iZRxdKPtZqcH9pbr1R4b8CLb7V8UdN2jO1i1FVr7RNa+DPi+3unjaGW1k3xyAfJKO4/EcUVz4DFUqEHCtdO59FxHk+LzStDE4FxlDlte/mz6v+GvwvFssWl6HY7mx8xUcAdyx7Dvk14v8AtU/8FLPDf7NevTeCfhZY2vxM+Kz/ALmS6X95pOhSHjkrnzZB/dHAxya5X/grF8c/H/hz4k+BfhD4V1aHwN8P/H+kpdzarZbhfao5YpLBJN1VVOOFxkHmvob9gj/gln4H/Z48L2GrTQW+salcIJvOYbgSec57+tfUYqvhcqoqpV96TWi6H8G+J3i9iadSeEo3i7tNv4mfKHwy/wCCanxE/aLOsfFj4zaxqHiDXvsktxbrdN8sbbTtSNOiICRwBjArqf8AgjX4teb4E/Fr4YXTMb7wFr661ZRnqLa4+WTH0Zf1r9QLvSobjSpLPy1WGSMxbVGAARivyj+E8bfshf8ABaGHSbzFr4f+KNtc+Hrnd8qPJIC0B+vmKoH+9XLkOdVcxqVaNd7rRH5J4d8WV1nkMRXfVP5dT1r9q/w+174WtdQVfmtZMNx0BrwC3tpb+ZYYY2kkk+VVUZJNfYPxg8HnUPDOs6TMp82EOuPRlz/hXiP7JuiLdePtS86HzGtbYKoK5IYvjj34rwcfhObEqPf9D/UXhXPo0MlqVd/Z6peTtY7j9n74a6n4N0jz9RmaPzuVtwPu/Wrn7Q37RrfAv4m/Df4baHtuPiX8UtZsbIhV3f8ACMaVPOqS3Un92aVBIsQPICu/8INaf7Un7SWl/sW+FtPM1h/wlPxV8UOtt4P8G2/7ye8uJDtjmnUZKxAkHBwWxgeo840f4L/8Kg/ap/Zjt/FepJ4n+KXj3xlrXinxprRwy3V9Z6WIobaE9BbWv2p44wOCUZv4sD7bJ8rjRip1Pkv1P5j8QuOauY1Z0MI1q1zyWyu0uVevfyPoTxbfrqHinUrj+Ge6kYfTccfpis2W6jtQd8kaL7muZ+OWqXmjeBb68spGS4t3Lk+2TmvmvU/iXrWs582+mUHspr5jGZgqM7Nan7hw1wrUzDDKVOajGNl+B9Taj8QNI0pMzX0K+o3VzerftGeHNNyFmaVh02jrXzHPeTXDkyzSSZ67mzVnQvDd94ju1hsbWa4djj5F+UfjXmzzarJ2hE+2p8BYGjHnxNR269F957df/tZ2Ak2wWcjc4yTgCvV/DOvrq+l2moW7YLAOpH8Jr5L8d/DbUfh/FZ/2jGI2u1LBR/D7GvVP2XfiKJYn0W4k+ZeYi3cVtg8dVdX2dY8viDhnBrALF5d7yW+t7rb8Dm/+C137Mk3xg+D+g/HLwvblvEngtVtddjhX55rcdJOOTtPf0rof+CRP7cdv4t8L2mj6hdAttCYZu9fSPw812yhmvNH1iGO60HXoWs76BxlWRxg1+Uv7UnwL8Qf8EqP2vW+w+c3gfXbg3mj3a58vy2OfLJ7Fen0FfZ18LHM8E6MvijsfwP4t8E+yrSqU17stV5eR+8dtcrd26yRsrIwyCKlr4x/Ys/4KHaT4+8M2ltfXSb9gGS1fXXh/xfYeKIA1ncRy5XOA3SvyXGYGrhpuFRH8w4rCVKE3GaNSikU5pa4zlCqus6VFremz2syq0U6FGBHqKtUEZoWgH41/tn+HNS/4Jz/8FAvCPxW0uGRNPstRH29UGFntJDtmU+uVJP4V97fEfTbHUb1rrTpFuNF8QWqahYyjlZYJl3KR+dR/8FTP2Vrf9or9n/Uilusl9YwsVwvzEV8TfsZf8FOfC/wS+Beg/C/42aV4y0+68HyTWWmeI7OzFzA1mXzEkq/f+TJGQDxiv1TKcR/aWXqH24aep/TXgxxzRwNR08VK0dn69GdL4t+FfiT4ea/cXVnb3SQJIxint+SFJ/Sm6N+0H4q0Bwr33nqnWO4jyf6GvqD4YeL/AAT+0Tpf2r4b+OvDHjaNly1rb3QivU9mgfD5/CsD4ifAHSdZmeHVNLksbz+8EMcgP8jXj4jJ69B3g3E/u3JfEbKs0go4qEKnS6tf5p6nm3hf9sELNH/aemtGV5M1q3zA+vPP5V6p4W+N/hb4keJdH1a7j0TXtY0FzJps+p26nUNOZlKHypjiRcqzAgMVIJBBrwvx7+zDq3h1JJ9NddQtl52/8tAP61k/Bv4Uw/EHxTdWOoyXli1rGJMR4V85xj5gf0rHD47G0qipvqerm3C3DOY4SWKhtHXvb5PX7mj0T9pnwxP4q17wtY2ilpLyZ7dT/d6Ek/Qc/hV7wN8Apvhx46h1TT74XtjgpJFINsmD6Hoasaj4g+Hv7PXjjQdE8VeLtUm1iaw/tey0hLK81XUHsiWRpIktrd8Z2Oo3MACOcdao+C9Z/aobw5p2t+ItO/Z41+38QKuof2Nqct9ouq+HFmAl+wvLBHtc24YRElXYFOSx5r2MPkvtJPEVfdejV2fmOaeJlPBQhlWBvWhFSU+RXtd9V/wdNT0r4n/BmP8Aag/Zu+Ivwxcqt14o0ln05m/gvIf3kRH1ZQPxr53/AOCGX7VEmkC8+HfiKRrTVNOmazkt5TiRJEJUjB9DXt3wK+NHirxJ8UfiDpviDwN4V8Nj4dTW2nDU9A8S3epwz6tLGsxtFM0EXMMLBpePlZ0TqTj5n/4KLeEZv2Tv24vCnx98PwrH4Q+IzxwaoYY9qWGpRgBw+OB5gG4Z6nNepisD7fCTwVT4kro/mfxXyulneE/tfCRkov3XdW1XzZ+uGcd6ca5T4L/Eyz+Lnw10vXLGZZoryBWYqc4bHNdXX4/UpyhNwlumfyZOLjJxe6PJ/wBtfwNF49/Zv8SWkkayGO1Z1yM44r4R/wCCNWoyQfsd/FDQ5Cxj0Hx8BCCeFEsAYgfiv61+knxctRffDHXoW6PYy/8AoJr84/8AglDafYv2ffjBJztvPiUYh7+VZIf/AGevveEqr+p14PZWP23wNlKWdwpra57V8YPhd4X/AGnPAr+DfGBmaKxcXGk6tbtt1Twpd4zFeWsv3kKsASgO1wCDzgjhT4H+Of8Awg/2X/hoa1/4Wx/Z/wBh+2/2LB/YPkY8vzdnlfaft2Pn+3Zzu+Tytn7yuP8AiR8WtR8G/G3WLrTpV2ogtSj5KkheCR7E5/CusHgLVx8Fv7XNzc/8JR5n9rfat37zOP8AV59NnG3pXdh88qxTpwV+XX7j+5s38K8DUqQxuIn7P20VH3Xa7l1aXbq/M774P/C3wv8AsxeBF8G+DzIsV85uNW1a4O7VPFd5jMt5dy/ecsxJCE7UBA65J8O/4LK6jJP+x18MdDjz5evePj5wB4byoCwB/wC+qsfDf4s6h4x+Nuj3eoyLh0NsEXIUZXkge5GaT/gq/afbf2fPg/IclbP4liI+3m2Tn/2Su3KMweJruq/P8j4nxY4VjkHDn1OkktIvT1a1fV6K7Pub9ijwND4B/Zv8N2ccax77VXbAxnIr1gVzvwktFsfhloMK/dSxi/8AQa6KvyzFTc60pPuz/OXES5qspebG5r8uf+C5v7U8mriz+Hfh2RrvVNRmWyjt4iS8krkKAAPUmv0d+NHxLs/hH8NdU1y+mWGKzgYqWOMtjivy4/4J0+EJv2sv24vFXx98QQrJ4R+HUkkGlmaMMl/qUgIQJng+WCWOOhIr6ng/L1UrvFVPhh+Z9hwNkdTMswjCCb1SPrH4Y/BiP9l79m/4dfDJNrXXhbSVfUmX+K9m/eTE/RmI/CvPPHXwBm+I3jqbUtRvxZ2eAkcUY3SYHr2FdV8d/jR4r8N/FL4f6b4f8C+FfEY+Is1zpx1PX/Et3pkMGrRI0wtCYoJcGaFS0XHzMjp1Azx/jPWv2p18O6jrXh3T/wBnnw/B4fVtQ/sbTJb7WtV8SJCDL9gSWePahuApiBCo2X4ZTg19hict+ty56slaWq1P7oyPjRcPUfqeCozlOkuWTUVp1fV2v6Fb9mbwzceFfEHiqxulxJaTJbs394DJB/FcH8a9G8UfG/wr8N/EmsataR6HoOsa84k1K40y3UahqLKoRfNmGZGwqqACwUAAACuQ07X/AIe/tC+Ode0Xwr4u1SHWILE6vfaQ9leaTqCWYKoskqXNuhO3einaxBJ4z1ryX4yfCmH4e+KbWw06S8vmvIjJiQBnznH8IH614FZ4nL6fs4Ky/PU/WspjknF2NWMr1FKS6JbNRV9Xonp2v5nbeKf2wN8sn9l6a0m7kTXTck+uBz+dcPrX7QnirxA5C332dW6Jbx4P9TWx4D/Zh1bxGkdxqci6fbNzsHMpH8hXtHw6+AGk6PKkWl6W99eH+IoZJCa4KdLGYjWTsj67FY7hzKY8tKCnJdd1829PuPAfCPws8SfEPX7e6vLe4eF5FMs1wcEqK+uPhvpthp16t1qEi22i+HrV7++lPCxwwruYn8qxfif4u8E/s7aX9q+I/jrwv4KjVcra3FyJb1/ZYEy+fwr47/bN/wCCnPhb42fA3Xvhb8E9L8Z6jdeMJYbLU/EV5Zi2gFmGzKkS/f8AnwBkgcZr6PK8klSlzz0W7bPw7xE8WMFVw0qcJxUopqMY7K/W5j/sX+HdS/4KL/8ABQHxd8VtUhkbT73UT/Z6PysFpGdkKj0woB/Gv2K0bSodE0y3tIFCxW6BFAHpXzP/AMEs/wBla3/Z1/Z/00yW6x317CpbK/NjFfUQGK+M4lzL63i3y/DHRH+dHEWZvG4yVR7XYUUUV86eCFFNLYrN8Q+MNP8ADEJa8uI4sDOCeTVRi5OyGotuyL9zcra27SSMFRRkk+lfnL/wV2/bjt/CXhe70fT7pQ20phW716j+2n/wUO0nwD4au7axuo92wjKtX5zfst/ArxB/wVW/a+X7d5yeB9CuBeaxdtny/LU58sHuW6fjX3PC+QynU+s11aMdT7Phfh+pia8ZSXXQ+qP+CKP7Mc3wg+D2vfHLxRblfEnjRWtdCSYfPDbnrIM8jcf0r6D8S68ukaZdX9y+doaRiT949a674h65ZTzWej6PAlroOgQrZ2MCDCqiDHT8K+cf2oviMIoU0W3f5nOZSvYV7OdZgm3PotEf6E+F/BroUaWFS96esvJdi1p/7WmnmTbPZyLg4yp4NdJpX7Rfh3VMBrgwsf7w6V8/eBfhvqPxAjvP7OjWRrQBip/i+lZuueG77w5dNDf2s1vIpx868H8a+VjmGJjHna09D99qcI5PVqOhCTU1ur6/cfW+neP9I1VMw30DZ961IrqK5Hyujr14NfFdvezQSAxTSR47q2K2tO+JetaKB5V9MVHZjmuiGcP7cTzcT4dta0Kv3o+zfCd8NN8U6Zc/ww3UbH6bhn9M15d+zx+0Y3x0+JvxI+GuubYPiZ8LtZvrIBht/wCEn0qCcpFdJ/eniQxrKByQyP8AxHGx8DtUu9a8DWF5eyM9xO+/J9M8V5nrHwW/4W9+1V+03b+E9RTwz8UvAPjLRfFPgvWhhVtb280sxTW0x6G2uvsqRyKeAXVv4cH7DKYwr0Jqpto/Q/nHj+visszTD1MLrNOUWv5krO3+R1P7QPw21LxhpHn6dMZPJ5aAj7w9jXzpc20unTNDNG0ckZ2srDBBr6p/Zb/aU0r9tLwvqDQ2H/CLfFTwu7WvjDwbcDy7izuIztkmgU4LRMVJxyVzg+p8o/az0NbXx5pvkw+W13bFWULglw5H5818vnmWSotzf/D+h+5eF/HVHMqUcNTty2b7OLSu0zrP2UPD32Pwtdag4+a6k2r7gVwP/BZTxY8PwJ+EvwwtWYX3jzX21m8iHU21v8sefqzfpX0B8H/B7af4Z0bSYVIkmCLj1ZsV8x/FiNv2vf8AgtFNpNni68P/AAttrbw9b7fmRJIwGnI9zIzA/wC7Xu5NBYehKu/sx/E/BPH7iRRw1TX45N/KOiKXxO/4Jp/ET9nM6P8AFj4M6xqHh/XvskdxcLaN8kjbQXSROjoSDwQRg16n+yv/AMFLvDX7SWvw+CfinY2vwy+KyfuYrtj5Wka8444LY8qQn+E8HPBr9FrTSobfSo7Py1aGOMRbWGQQBjpXyJ+3x/wSx8DftE+F77VoYYNH1K3Qzeco2gEc5zXi5bxVGo/YY9XT2fVH8gcI+JGNyrEJqTtf+ky78Svhctwsml65YrG2PlLjOR2ZT3H0or5t/wCCTnxy8f8AiL4k+OfhD4q1aHxz8P8AwDpL3kOrXu5r7S33BIoI5urKxzw2cAcUV7eKyymp3ummrq5/bPCfiPWxmAVWmpx7pbX8tToP+Cs/w0k+If7Ctn4wsUZtd+EOtxagjIPmFlMQkoJ64BKn8DX1J/wTR+Ocfxx/Zh0S6MwkuLWFY35zxisfw14d034i6XrXg/WAsmj+NtMn0i6Vvu4lQqrfgSD+FfFf/BJz9o5v2NfEHxE8B+NLj7M3gW4uLWdJTtyYnKqfx+U/jXPnGFeNyxcivKDP5v8AHbhuVPGfWKUfi1Xr1R+sfiDxFZ+FtLkvdQuobW1hGXd2wBX4+f8ABXr9oTwr8c/jN4cj+G/9oap8QtJ1CGbTU06EzyTTo4ZdoXnqBWx8R/2mPi9/wVi+Kt34L+FML6b4XsX26nrs7GKw02LPJZ+7Y6KMk19Nfsxfsr+AP2F/Dxt/BluuueNLtNuq+Mb+MSXly38SwZz5UfX7uCe+axyPJP7OaxeKl73SK/U+Q8PfDXMMwxEa0bxt16I9I8ZPf61Y6Jq+sadJo+ta7pUF1qumylfNsbpox5sbAE4+bNeW/s3+B18L/tAatBIu6Ge6s3XP8SNISR+lS/Fb452XgcyeZI+oalMeI9+5iT6nrVC7+IeqfBXwPa+JrjT/AO3PHniW+g0nwxoMbbG1TVZMm2tc9ooxumnkP3I0bkErnT3cTi4+zWqd/Q/ujlrZHw5UeMn7kocqb0cmrWsvw89DgP8AgnT+z0g8K3v7RvjjUpPFPxQ+JEk02jTXc/nf8I7p7zOFaMH/AJbMEYAgDy1UY6km3+2JrTfCX9vv9jHWpo5/LuNS1OwkjCFnH2iaGLheuWEo/BR6V6p8LPhhJ8G/hb4P8Ex3i6kvhfSrfS5rwRmNbuRE+eVE6qrSFioPO3Gea5XR7pPip8Wr34sa7+/s/h7car4O8B2hUNLaGG5e11PVJPW4uLiOSCBOkUEe7G+Tcv0EcYvbTrS+GKsj8hxHDM45bhcrwy/e1pKbvvoru77K/wCJ0H7UHizTfA2iazazSR3TXk0tvaojf63k/N/uj1r5f8NeE9S8V3a2+n2slzJ0JA+UfjXtqfBzUvit4qbWfEsn2W1b5beyU58uMdAT69zjvXsHgf4WxWGjyNp9tZ6XpNipa4v7h1gtoFHVnkbAH518XUwdTGVrtWXRdT+mMFxJhOHMu9nzKVTeTbtFNK3z87dTxTwF+ytHF5dxrs/mt18iM/KPrXuHgD4QTS2X/El0jy7WEcyhNq/n3PsK8A+MP/BUT4f/AA51ubw78LdCuvjV40jYxGeINDoNm/I5cDdNg/3Rj3rwn4//ABX/AGoPAEvg/wCOHxC168h0/wAOa7Be2fhjTY/smmQwIcyL5K43ZQldz5PNe/hcno0GoVJKLey6s/AeMPHajKuqEJ+0k3btFei6n1L+034RbXvAn2pF3Tae28+u3vXzx4f16bw5rFvfW7MskTBuuMjuK+2fHMOl+N9Lh1XSZEuPDvjCwi1XTpFOVeCdA4GfbOPbFfFvi7w/J4W8T3lhKCpt5Soz3Xt+lfMZ1h5Uq3P2P6V8N84o4/L3Q3i1deklr9zPqz4feMoPGvhmC8jZSxUbx3Brqvil8GPCn7b/AMErv4c+NljWZVLaVqBH7y0l7EN169RXyn8Cfie3gbX1hmY/Yrg4IJ4U19OafqC3cMNxBJ1G9HU9K9bKcxlZVI7rc/P+PuDaUnLCVl7ktYvsflX8bf2Qfjb/AME8PHclu+m6jq+ixuzWt9Zo0iSRr0JxnHFevfsb/wDBWG68Kzxw6tdsshfbIJDjbjsa/UbSPiNqFp4Nt28R2NrqWi3T/ZxHcRh3mRgfmwf4eCM98H0r5b+PX/BID9nr9oXVJNV0fUdR8B6pOSz/AGXBjLHknbX09b6njY8mIVmz+ROIvC6tJSdCPPG7V1r8j2/4Q/8ABSjwf44s4xdXtvHIwGTvH+NezeH/ANoDwn4kjVrfWrTJ7GQf41+YWqf8ECNc0mUv4S+PWl+X/wAs0vrdlP4lTWeP+CQf7TPhRv8AiSfFDwLqgX7oOoPCT+Yr5rEcG4abvRq2Py3GeF+Pi3ywkvkz9dLLxHYaiube8t5c/wB2UGritu+7z9Oa/Ia2/ZJ/bj+HTbrGHQdcVOn2PXomLfgxFJ4m/at/bA/ZR8N3GueOPh74gsfD+m7Td6llJ7a3UsF3MyMcDJFeVV4JxS/hTTPn8TwHmVLVxf3M/Xa6tI9RtZIZo1khlXa6sMq49DXk/wAU/wBh34b/ABatZI9R8O2avIMb44wP0qn+w/8AtVab+1N8HNP1aC5Sa+WJftChhknHXH1r2jHNfKy9vhKrgm4yR8jL2uHqOCdmj82fj5/wQT0eW/bWfAOqXeiarCd8MtrM0EqN2wykGvNbf9pb9qT9hGVNJ8fabH8YvBdsdhj1iM/2hDGP+ed0o35A6b91frjtrI8X+BNJ8d6ZJaapY295FICpDoDxX0OB4uxVL3K9px8z6XJ+NMxwE1KE3ofGX7NP7Wvwr/bViNv4H1ibQfFyLm48Ja8whvlPcQv92Yf7vPtXV3/hGPRPEUlxPZfY9SjBhlym1iPQ/lXhv/BQf/gj5aX4k8X+ATPper2L/aYZbNjHNA45BVl5B9xVf/gn/wD8FCrj4wamvwg+OV3Z6b8SNFKQ6Jrl4/kf8JAgOPs8rEY83HGT97PrX2FD6rmFL2+Edmt4n9SeH/jJ9bSw2Ne+l+vz7r1PTvB/ha+8Q/8ABQrx54tsbWO8tvhv8O9G8ElyuVjvr521CdSThRstygbP/PZR3Ndp4i8Yab8BdZ8P33iSw1vXJNbleXStL0TTZtWvNRaCIzSbY7dHJjVVG52+UeavPIFcV8SP2LfhHrnxH8Val4q+Fuma1rfiPUptQ1VtX1bULlZbiQjcyRidYkGAoGxBhVAHArE8B/sw/B79nTWNZ8beCPCLeG9Uk0qWwlS31i5urGOBnjlkMcMzuY5GMKLlWxtyMc12Vq+FbUpt3itFbsfa5XleexhKjQpxSryvKaldpSfRabLtuR/saaH4g+Hn7EvhebxfpusaX4s1u81rxV4gtdUhkhvDe3N/MxaRZPmBMaR4z2IPeu7n07wj+0j8G9Y8IeJoW1XwX4uhEdyEP76wlHMdzCeqyxtyPXBFeeatrHjjXPAVrq2k3VjqllqNmGmtGtwJ48jDAFcZxz7/AFryv4X/ABS1D4U64yhZGs2bbc2r5GPoOzV8/iM5lHFKtayet/usfsGT+GtOtkUstlNVJQVrdXq73WlnfZncf8E8vjP4h/YV/aGvv2fviderNBIBceHNa6W+s2bE+VPGTxz0ZeqsCD0r9KFbcqkc55618D/HH4MaH+3H8ErPTrfUodL8SaLIdQ8G+Ijw2jX3BMExHP2aYgJIv8Jw4Hy4PqH/AATK/bE1D43eD9S8C+OLWTRfid4AmbTNa024OJg8fBYf3geCGHDKQRkGvL4iy+NaH9o4ZaP4l28/mf58+LXh7ieH8fKfK/Zt7/5+fc+kfiIu/wABa0P+nGb/ANANfnV/wTEg+yfsm+K7g4H274l61KfpHb2iD+Zr9GvGkfneDtWX+9ZTD/xw1+dH/BPx/wCyv2Jb6T7ufGfiec/USwr/AOyU+F5Ww2I/7dPY+j7T5+IlFdv0ZxvhzQ/+FlfG7yG+aO61B5ps9PLRiT+YAH417h/w0Lb/APDUf/CnfscP2f8A4Rv+1ftm35jq+37V9hz6f2X++x1zxXE/scaBay6xq+ualItvp9qmJ7hukEKjzp3J/wBmNc/hUH/Cs9X/AOHff/DRf2GX/hOv+E3/AOFu+Rj979g83y/sXrt+wfusdNtd2USpUaPta3/Lx8q+Z/W30gPED+xsfgcFSfwyjf0Vr/fe3yOM8RaGfhp8bvJX5Y7bUEmix08t2BH5A4/Cuv8A+CnkK3f7JvhSfr9g+JmjSj2Elvdof1Aq1+2R4ftYta0fXNMkW40+6jAguF6TwsPOgcH/AGo2B/GoP+CgUi6r+xLYy4zt8Z+GJx+Msy/+z1rktN0cXUpPz/Jn0HjJi45jwZDHxd7w1+TX57/M/RL4drs8BaKP+nGH/wBAFa7NtDE8BeetZvguLyPB2kr/AHbKEf8Ajgr5q/4KZ/tiah8EfB2m+BfA9rJrXxO+IEw0zRtOtjmYvJwG/wBkDkljwqgk4Ar4XD4SpisT7GktW/6Z/l3hsJUxOIVCkruTsfP3/BQ34z+If26v2hrH9n74Y3qwwRgz+I9a62+jWakebcSEccdFXqzEAda9fttO8I/s3fBjR/B/hmH+yfBfhGEx2wc/vr+XrJczHq0srZY/UCuR+BvwY0P9hr4JXunXGpQ6p4k1qQah4y8RjltYvuSIIT1+zQklI1/iO5zy2B4r8UvijqHxW11V2yLZK222tUBOfr6tX3eKxVLA4dYOhrbfzZ/oZ4FeDrwuHji8UrO1230Vunm/wPXf2zND8QfEP9iXxRN4P03WNU8WaHeaL4p8P2ulwyTXgvba/hYNGseWyI3kzjsCe1ep+HPGOn/HnWfEF94csNb0KXQ5Ul1XS9b02bSbzTnmiE6b47hEIjZWO11+U+W3PBFeR6TrHjjRPAd1q+rXVjpdlp9mWhtFtwZ5ABhQS2cZ49/pV3x7+zB8Hv2itX0Xxt448JP4k1SPSotPiWfWLi1sZIFeSWMyRQuhkkUzOuWbG3A28Zr0MHjaNWkqNdNW1T6nucQcM5lgcfPMsrlGpztQcfs6Lfm6vbubni/wtfeHv+ChXgPxZfWqWlr8Sfh3rPggSBcLJfWLrqEKg8q2+3D7cdfJYdhXWWXhGPWfEcdxb2f2zUpAIo9qbmAz0H51yXw2/Yu+EWhfEfwrqXhX4W6Xout+HdSh1DS20fVtQtljuIydrNGZ2icYLKd6HKsR0NeY/wDBQD/goVcfCDVG+D/wOurPUviRrReLW9cs38//AIR9CcfZ4mAx5uOCR93616FSjTxbjyPSK1bPjKeeVeF6OIq46Ki6j5oxjK++jXdI9Q/aW/a1+Ff7FUQt/HGsTa/4udd0HhLQWWa9Y9hO/wB2Ef73PtXzlP8AtLftSft4SNpPgPS4vg74KuDsWPSIz/aE0Z/56XTDfnH9zaK9G/4J8/8ABH200/y/GHj4z6pq9832maW8YyTTueSWZuSfc1+inhHwLpPgTTY7PSrG3s4YwAAiAZFfO5hxNh8G3RwUVKS3bP5s4y8XMwzCo6dOWnZaI/PD4Cf8EE9Iiv11nx9qd3reqTEPNLdTNPM7d8sxJr7J+Fn7Dvw4+EtrHHpvh2zZowBvkjFeu7aQjmvjcZnWMxLvUm/RaH5His2xWId6k2R2tpFp1rHBDGsUMS7URRhVHoKkZvLHzfqcV4v+3D+1Vpv7LPwcv9WuLlYb5om+zjcM5x1/Ovzt8L/tV/tg/tXeG7fXPA3w91++8PaluNpqOUgtrhQxXcrOwyMg11ZXw7isdD2tOyj3Z2ZVw/i8wXNQV0frde+I7DTRm4vLeH/ekFcz4h/aA8J+Go2NxrVplewkH+Nflzc/sj/tx/EZt19DoOiLJ/z+a/CrL9QpNV2/4JBftM+K2/4nnxQ8DaWH+8o1B5iPyFfQUeCH/wAvaqPqsP4bZjPeL+4+5/i9/wAFJ/B/gezkFre28jqDg7xX53ftk/8ABWC68UzSQ6Tds0gbbGIzndntXdaV/wAECtc1hxJ4s+PWl+X1kSyt2Y/m1e0/AT/gkB+z3+z3qkeq6xqOoePdUtyGT7VgRhhznA9697A8P5dg3zzlzNH2GT+FeN51+6k35o/P74J/shfG3/god48jt003UNJ0WR1a5vr1GjSOM9SM9eM1+snws+DPhT9h/wCCNp8OfBCo0zKG1XUAP3l3L3Jbr1r0DV/iLqF34PuW8OWNtp2i2jfZilvGEeJFA+bA/h5Az2yPWvL9R1BbWGW4uJMADezsetdeOzC8PZUlaJ/QfA3h9RwVT29e0pRdrdn5/wCRjfEHxjB4J8MXF5Iy7lUhB3Jr5L8Qa5N4j1i4vrhi0kzFuTnA7V2Px1+J7eOfEDQws32K3OAAeCRXK+EtAk8UeJrPT4huNxKqnHZe9fn+OxPt6ns4bI/sfhXKI5bg3iq/xNXfkux9Afsx+E20HwL9qkTbNqDb/wDgPYV6h49+EM0dkBrekiS1mXiUpuX/AL6HQ/WneBotL8D6XNq2ryJb+HfB+nyarqMjHaqQQJvP54x75r4H+AHxW/ag8fy+MPjh8PdevJtP8R67Pe3nhjUU+16XNA5zGvktkLhFC7kweK+sweBpfVb1pcq0Svs2fzD4heKFPJ80jVlrztt62aXSx9C+Pf2Vo5DJcaHcCJuogkPB+hrx7xN4T1LwpeNb6hayW0g4BI+U++a9U+D3/BUP4f8AxF1yHw78U9Duvgr40dhELiYNNoN4/HSQjdDk8fNke9e8+OvhZDqGkRtqFrZ6ppN6oa3v7dhPbzqejJIvBH415+P4fa9+H3rVH6RwV4z4fHQjHnU12ekl/mcn+zB4s03xxoWj2sLx2rWc0dvdI7f6rkfN/un1rzT9jnW2+LX7fn7Z2tQRzeXb6jplhHGUKsTbzTR8r1yoiP4MfWtZ/g1qXwq8VLrXhuT7Var8txZMceZGeoB9uo961dXuo/hV8WrH4saD+4s/iFcaV4N8d2gULLdma5S10zVI/Se3uJI4J0ziWCTd9+PcfUyWs/ZTw1VWk1Zedj4fxOyyLxmHznBT5qMJ80k943et+uj/AA+ZxH/BRb9npf8AhFbH9o3wPqUnhb4ofDeSGXWZrSfyf+Eh09JkDNJj/lsocAk58xWOegI9J/aP8EDxR+0BpMEa4hgu7x2PoiyAgfrWv8VPhjJ8ZPhb4w8ESXa6aPFOlXGlw3hQyLaSOnySuvVlWRVLAclc45rn7X4iap8afA914lt9P/sPx54bvp9J8TaDId7aXqseDc2ue8Ug2zQSD78bqc5DAVj5SxOCXNryPX0MeF6NHJeJZqlJR+sQbj25ndNfJNM9b8HPf6LY61q+j6fJrGtaDpU91pWmxFfNvrpYz5UahiAfmxXwP/wSF/aE8K/Az4zeJI/iR/aGm/EPVtQmm1JNRhMEkNxJIWfcG56k+1fVnwp+Odl43MeyZ9P1OE8x79rBh6HrUv7T37K3gD9ufw+LfxpbjQ/GlomzSfGNhGI7y2YfdWfGPOjzj73I7EVOHqYfE4aWFm7KXVHxnjB4dZlmSck3eK+H9U+tz7R8P+IbLxTpcd7p91FdWswyjxtuBrwj/gpd8dI/gf8Asxa1dCVY7i6iaNOcHGK+C/hv+0z8Xv8Agk58VbTwX8VoZNS8MXz40zXIGMthqUWeCr9mx1U4Iqf/AIKyftGt+2V4g+HfgLwXP9pbx1Pb20CRNuwZXCsT/u/MT9K+cw/C9almFOE9YXvfyR/GkOFsVh8yjhq0Xa56T/wSY+Gknw8/YWvPGF8jLrvxe1uXUXZx85soCUiAPXBYOfxor6L8TeHdN+HOmaL4P0cKmj+CdMg0e1CjC4iQKzfi2SaK+jzCvz121stPuP8AQLgnJ/qGUUqKWrV38zjPhD4vPiDwhpuoRyfvolXkHoy//XFeS/ta/wDBKab9qz9stfiFHrqeFfhr4s0i3u/Fk8Dbbi4u4m8swQj+/IFBLdvyqf8AZQ8YiKa60aV/vfvIgT+de7an4ikh0OOG4uWWwtcuqFvlUnknFYZRmXJSc+6/E9jxG4FpY7GrC1doyuvOL6INB07w58Ivh3Z+DfA+j2/hfwbpa4jtoeHuSOskz9Xc9ST614z8ZP2io9LM2m6GyyTY2vMOkZ9veuf+Nvx+k16aTTNJkaO1XKyTIcF/pXkx5O71PJ9a8LMs2nUm1F/P/I/UuCfD3D4OhGdaHLFbR/VnqH7O3gWTx/4xm1jUmae301g/znJllP3fwHX64rU/bPsZ/BP7cX7GnjBbqZvDra9PpMlpId0MF7JIgklA/wCejxsgz1xEgHSqf7J/iJrHxTd6ex/d3ke4DPG4V3X7Z/h9fEPgD4F3zBd3hn42+HiGPZbiTYR+JCn8K9zhWUVrbV3v6n5b49Ua04O792Dg0ltb/hz0n4yfHbwx+zb4I8XfEjx1NH/Yuhb4baDH7/VLsnbDbQgEZYgAE/wrknHWvn74Cazr1/8A8E9bHxLrumTaLrHiTUda8TC1eJovKS71ie5TaG52neSp7qQa2pvhxpf7c37VV9428RqupfA34K30+k+GNOY5t/GeviQ/bLxh0eGOQCMHoViQDq2fRP2o/GreI/h9rEuoXEUcs0Hk28IwiRqCMIijooxwK9DMuShhJUftPVnyPBMcTmvEFLMUv3FNqEV32TdvkaHwh8b2/ibRNK1aSGO5jBU3ELjhsfeBr5J/b20XxV8W/wDgodD8NPih461qD4Xa9BDqfhGysVFrpk1o/GySNNokkjYFGLZPA9a9O/ZU8d/2fqM+iXEn7u4HmQ57N3Fbn/BRz4D3n7QX7Jv9s6DCzeOvg7O3iDS2Qfvrqx4+1QA9eAA4H+z71y5FivaU+SLs5K1+zNvHXhPELD1Pqza5LtWe8X/kfT37Mf7APw1/Zu0C1/sHRbO4n2KwuZEDbuM5Fbv7ZHwCs/2h/wBn7W/Ds1ukkjW7tbrt6Ng8CvPv+CXf7V9p+05+zRpd010smoabAsc4ZvmAA6n6Vu/tSf8ABRT4b/sr6TNJrWsW9xeRKSLeKQHJ9DXwdSnj3j+V3lUi/M/zzlTxn1uzvKaf6nyJ/wAEsviNeap8G/FPwW192XxR8Grx59MWU/vLnR5pDlRnk+VISPYOKvftX+B/Ku7fXIF+RwIpyB37E15X8GNR+Jv7Uv7cvh/45fCn4dzeGPDthcmHWNY1qf7BpOsWMnyzw5cbpCyZI2BsMFNfYnxh8GaX4mTV9LsZ1utPmLfZ5MdPTr6HjNfeZzhXWoqc1aclqvNH9/eAvFWLo0YQxcWuTq07cr3XyPFv2fvgrpPiDw3a65fq91JJIypAfuKVYr+PSvUPjD8U9H/Zs0vR7S60uXxF478UuLTwh4FsyFvteuG4VpAP9TaqeXkbAChj2486v/iN4n/Zn/Yp8d+IPDK2aeLvCtm4097u3SaOGWS/t4TKVf5fljmZgTwOvauk/Zv/AGOZP2a9fm8VeMvEV58QPjh4wi8rxB4kupvOGnRM4D2NpydqAjY7jG4IQoCkink+DoU8Oq9T7u7PpPEPiTNMbnM8owTfVubekYXe3nZFP9ju/wDG+sfDT4lXXxE1uHXvGEPxP1LTNQmt+LS3a1tLaIQWy9Et4yHVAAPlGepJPcXfjPS9PmaG4vreGSM4ZWbBBrzf9kPX2tfjt+1h8Ob6Vf7a0H4iT+MbaInmS0vPvso7qu6M5/2s965f9q/Q7ew8XWd1FMglu4sSRA4bjocf1pcQVJUZuol0Rp4QYWjmWDhg5zaaclfd3Tvr957JN8TvD8I+bVLX/vuoj8X/AA1GedWtl/4FXyQy7m6bu2O5rY0P4c654jI+xaVdSD+8U2r+Zr5eObVZaRhf7z9yrcB4ClG9au162R9RQfGjw8H/AHeuQqf+uuP6113hvxhZ+I9LuLTUiuveGNctnstQtJn8yG5t5Bhhg5Ge4PavlOb9mvxFa6FcahcR28a28Zk8oHc7YrT/AGefjC3hHUV0nUJP9AuXwhbpC2f5V3YfMqsKi9ouXsfN5twRl+KwNR4KoqttJKy28vM828PSa5/wRx/bMt9Da5uL34U+MpDd+GtRY5jeBm5t3PTzI84IPUAGv1q+G/xD074n+EbPWdLmjntbqMOCpztz2r5W+LHwb8K/tafBW/8Ahv4yZV0vUG+0aTqiDM2hXgH7ueM9cZwGHQjIr5a/Y2/aq8a/8E4f2gLv4NfFrfGtu4FleMT9n1G2P+rniburDHTocg13Z9lSzCl9bw6/eR3Xc/zt8VfDutluKdajG8Xs+6/zR+tlG3NZHgrxtpvj7QYdR0u5iubWZQwKNnGfWtfOK/N5RcXZn4Q007MjmgW4iaORVeNhhlI4Ir4R/wCCk/8AwSh0f43+HrzxJ4XgNn4gt/8ASQ8J2yh1+YMpGDwRnI54r7yprIGHI3A8EHvXZgMwrYOqqtF2f5nTg8ZVw1RVaTsz85f2Av2q9Y/aH8Fal8N/iC7L8XvhlB/rpfll8SaWh2ib/aliOA+OqkH1rG/aY8M3nhTxE2oWMs0On64pS4jjciNnxyCOnIrsf+Cn37IuufC3xlo3x++E8S2njDwXci+lhRf3d2nSSKRR96ORNysPRjW7da74d/bC/Z80zxh4ZzHo3iy3a4ggcgy6RfIcT2cno8UmV91Knoa++xU6eY4RYqjo+q7M/vb6PfiZTruGExTu46a9n+qeq+Zm/skeI5tR+Gd5ZWunQ6xqmnw3UdlZSymFbucxtJbxeYASm+Xam7Bxuzg9DyfgLxb8Of28/B6ap4ZmuNH8TfYUv7nSL+NYtWsoWAPmsg+W6t+cefFlR0cRtlRmfsqeKbjwN8U5dPl3W88v3FJwY54juH8jXcfs/wD7J3g/4yeI/if8MtTF54f8RfDvxI3ifwL4i0iU2eqaFp+qD7ZB9nlXDeXFJLJEVzgmNge4rmwtbDvAN4iN+VpN9Utr/I+v8Z+Lsx4JzqnnOCu6NT4kttdbr53XyPOfDOs65+zZ4z+z6pDI2mXR+cL80cy/89Iz/eHp1Ndh+0X4b1NdT0X9oD4Zhrrx94FtVOr2lqMt4v0OMbnQqPvXVsgZkzy8QdOSiCulutK8SW3j25+FHxc0/TdQ8QSaXNrGjeJdNjSCz8T2kEsUUrvAMfZb2PzkdhGPLdQ7BY8bWyv2QvEB8D/EjVNDuJGkg0a/WdEk5/drJtfH1GMj3q8H/s81TTUqVRaea/r8j0c1xWS+JfC9TH0Y+/Fe9Zbva/qnv3R9m/BT4xaP+0r8DdK8WeH50uNN8SWHmREHhSy4K/gTXwp+x3o01j8BviF8PblWs/E3gnxjrEWp20g+aKPUD59pMPWNwkqg/wB6Fh6V7Z/wR6VvB3wH8YfD2Zv9I+GnjPVNBCnqIo7h/KP0KAEexrD/AGzfCMf7Ov7aHgv4owqtv4Y+JijwF4ubGIoppmDadeP2Hl3SxAsekckvrXn5XGGGx1fL+ktvVar7z+I/DHO1w3xYoVPh5uXXp0/U8s+Dfhx/if8AAf4gfDe2uBpPiDWtL1DSI5ycBZbmB4UZvRN/7tu+xmxg4Ne9n9p3RR/wSd/4TT+xI/L/AOEV/sT+wNnzf2hj7F9h29d/n/JjOa8F+ICyfBT9pCDUdslva6odtwpGNu4hJQf91gGq0be8/wCGnf8AhXW3/ikv+Eg/4XZsx+7/ANT5HlemP7c/fbemztjmuqjhPrNOOHnp7KV3/h6/kj+l/pEcDzznHZfm2Fu1WlFPyvuvk7/gU/jH4bk+GPwJ8A/Da6uBq2vaLpen6RLOpzultYEhcr6rvzGv+wq5JOTUn7YujzX/AMB/h98PLVWvPE3jXxlpEOmW0Q+aWPTz593MfSNA8Sk/3plHrTfh8knxr/aPuNQ2vcWulnbbLjO7adkQ+rMS1epfsY+EI/2iv2z/ABp8UZlW48L/AAzQ+AvCJ6xyywuW1G8TsfMumlAYdY44vStYYxQVbMp9NF5vZfr9x7fjlnFHhjg+hkcdZOK37u35JL7z6c+NXxg0f9mr4F6r4s1+dLfTfDlhvkJPDMq4VfxIr4h/Zz8N6odT1v8AaA+Jata+PvHVqx0m0uhg+ENDkGUQA/cublNrPjlIiicF3Fev/wDBYRW8Y/Afwf8AD2Fv33xM8Z6XoJUHkwyXCeafoEJJ+leYfte6+3jb4k6XolvK6W+sX7TyJGcYjaTamfoM4HtXn5NFYbAyxcV7820vTt82fiP0a+BcPnGYyx+KWlPXyVk2/wAjz/xNrOuftJeMvs+lwyLpdqf3e/5Y4V/vyH+8euOtdF4+8W/Dn9g3we+qeJprnVvE32F7+20iwjWbVr2FQSZQhO21t+MefLhT0QSNhT2lppPiS68eW3wp+EVhpun+II9Lg1jWPEupRpPZ+GLSeWWOJ0tzn7VeyeS7qJB5aAoxWTO0Z/x//ZP8H/BvxF8Mfhjpf2zxB4i+IniVfFHjnxFq8pvNU13T9LH2yf7RKxLeXLJHHCFztBdQOwr0MJRoUsTGOIfNVlrZdFvd9j9m8RPHXlxkOFuHI2d1F+Wurfd/gWP2tfEc2m/DSzsrrT4dH1TUIrWK9sopTMLScRq9xF5hAL7JdybsDO3OF6Dgf2Z/DN54s8RLqF9JNNp+iIEt43clFfHAA6cCm/tWeKrjxx8VIdPjLXFxF99Qc755TuP48jmvUrfXvDv7Hv7Pmp+LvEymTR/CdsLieBCBLq985xBZx+ryyYXvhQx6CsIUni8e3FaL7j90xWYUuHuEYfWX78o3be9rK79WlZebOI/b+/ap1j9nfwXpvw3+HsjN8XviZB/rovml8N6W52mb/Zll5CegBPpXV/8ABNj/AIJRaP8ABDw7Z+I/FEBvNfuP9JZ5jukZz8xZieTknPPPNZ3/AATA/ZF134p+MdY+P3xYjW78YeNLk30ULr8lpH0jhjB+7HGgVVHoor9AVQKOBjsBWPEOeci+oYR+6t33fU/zN8ROOMRnGPqOL92/4dhsNutvEscahEUYVVGABTwuKWjNfDn5eFYPxI+IenfDDwjeazqk8cFtaxlyWbG7A6VN418bab4B0KbUtUuYra2hUsS7YzgV+Vf7ZP7VXjb/AIKO/H+1+DPwlEki3DkX14pP2fTrYH95PK3ZVGevU4Ar2Mnyerj63LFe6t2evk+T1sfXVKmtDE8Qvrn/AAWQ/bNuNDW6uLH4U+DJBd+JdRUkRrArZW3Q9PMkxgAdASa+4vEnjKz8O6Xb2mm7dB8L6HbJZafaQv5cNtboMKMDAzjknvXO/Cj4N+Ff2SvgrY/DjwYytpent9o1bVHGJtdvD/rJ5D3GeFHQAAV4R+0N8YW8Xai2k6fJ/oNq2JCv/LVs/wAq+8zLGUsNRVCh8MdF5s/0H8GfC36vTj7SNpSSu39mP+bPap/jP4fL/vNchY/9ds0xPi94afgatat9XrwWH9mzxFd6Fb6hbx28i3EYk8ottdc1zWu/DnXPDhP23SbqNe7CMsPzFfPSzHExV3D8z+g8Pwjk1WXJSxOq6aH1LF8TvD8w+XVLX/vurdp4z0vUJkht763lkkOFVXySa+N1XacbdrDseor1f9lDQ4L7xbeXcsymW0ixHET83PU49Penh8ynVqKFkLOODMNgsJPFKq3yray/Q7n9sO/8b6N8M/hrdfDvWodC8YTfE/TdM0+a5+a0uGurS5iMFyvR7eQlFcEH5TnqBjrvg98UtH/aT0vWLS10uTw3478LObTxh4FvW3Xug3A4Zowf9daseUlXIKsp788D+11rzXPx4/ZP+HNjMv8AbWvfESHxhcxDrFaWf3GYdg22XBP93Patz9o79jiT9pTxBD4s8H+Irz4ffHDwfF5Xh/xJazeSNRiV8R2N3yNyEnYjnO0OAQVAFfdRo0pUKdGto5LRn8q1Mwx9HNsZmGXPmVOUVKHdWs2vNNHI/tBfBTSdA8N3WuWKtayRyKrwj7jFmA/DrVf9lHwN5t1c65Mvyx/uoMjv3IrSsPiP4m/aZ/Yo8CeIPEy2beLvFVmg1B7W3SGOaWO/uIfNCr8vzRwqxI4PXvXr3wd8GaX4ZXR9Lvp1ttPhKi4kx94Dr09TxntXyU8tjDHWW0fuvc/oLB8ZVMRwtGtNtyqa23aiknb79DwH/gqb8R7zTPg14X+C3h9mPij4yXcc+prGf3lto8MgwpA5HmyADnqFNfb37G3wCs/2eP2f9D8Ow26xyLbo1wu37zYHBr82/jPqHxM/Za/bl8QfHH4rfDubxP4cv7oQaPq+iz/2hpOj2MfywQ5Qboyq4J3hcsWNfoD+y3/wUV+G/wC1RpMEmi6xbW95KoJt5ZAOfQVXFlHEqjCjRi3Tirtrq/kf5zeK2YZhmGYzq1YNRv1T+RP+07+wF8Nf2kdBuhr2i2dvPsZjcxoF28Zya/Pf9gnRPFXwk/4KHzfDT4X+Otan+F2hQTap4usr0LdaZFaJxsjjfIjkkchFK4PJ64r7k/4KiftYWf7Mf7NOqXS3Sx6hqUDRwBW+Ygg8j614J/wTj+A95+z5+yYda16Ep46+MU66/qjOP31rY8/ZYD3GVJcj/a9q04VniIYWdetJuGyT2udfhHkuNzHMYxjOSh+nU9N+L/ja38N6Hq2qxxR20bFjBCgwBn7orxH49axr1h/wT1vvEuhaZNrWr+G9R0XxM1qkTS+alprEFy+4LzsGwFj2UGpv2rPHn27UbfRbeTMdv+8nx0LdhXrH7LnjaTw38PdHk0+4ikmggMVxCcOkiknKOp6qe4NGCx0Vj7vaJ/f/ABLwzUnwo6UNJ1Xo+yS0v6nSfBz47eF/2kvBPhH4keBZo/7F17ZDcQAfv9Luwds1tMCThgCQD/EuCM9T4P8AsYWM/jX9uP8AbK8YNczL4dXXoNJjtIyFhnvY5HEcpH/PREVxkc4lYHrWjF8ONL/YZ/aqsfGvhxV0z4GfGq+g0nxPpynFv4M18yA2d4o6LBJIxjJ6Ksrg9Fxu/sX+Hk8PeAPjpfJjd4l+N3iEkj+JbeTYB+DEn8a+qlCNOjUnDWMkrfkfz9h8TiMZmGCweJvGtRk1LvZap/dc89/aJ8CyeAPGEWs6azQ2+pMX+Q4MUo+9+B6/XNdV8HP2iY9UMOm644SbAWOYjAk+vvXOftY+IWvvFVpp6k+XZpuP1NeU/wBP0r82rYh0MQ/Zbdj+2MvyeGZ5PSjjPjtpLql0Pt3XtO8O/F34d3fg3x1o9v4o8G6ouJLabl7YnpLC/VHHUEHtXgf7JP8AwSnm/ZU/bKb4hSa4vin4a+FNHuLvwnPO264t7qVhGLeYf34wxIbv+dVfgl+0BLoEsemaxI0lqfljmY8p9a+h9L8RST6E8Nvcs1jdbZGQN8jHqDivrMszpypcq/4KP57418L6Kx8MRVilKLTulpJX/M534veLj4e8HalqEj/v5lY5J5LNn+poryz9rLxjvltdFjfO395KPX0FFfM47HzjV5YPY/dOE+GsPPARq4iOstV6dDyzwJ4kk8JeLLK+RtqxyAOB0Kk812fxq+OU/jGY2GnsY7GM4dlP+s9a5X4j+BJ/AniCa3lX92zEo3YijwV8OtS8c3arbQt5RPMhHGK4YyrQToRPpq1DLq0oZpVaslo3sYMEDXMyxxo0kjdAB1r0HwB+zzqXilkmu1a3gPPTGa9Z+G/wH03wfbrJcR+fdHHJ5xXfRQrBGFRenAAHWvSw2V21q/cfIZ1x49aWAWn8z/Q5TwR8INL8FGJ4Y900f8eOc1zX7c/wz8UfGf8AZWh8L+DWaHxJfePfDpsJ14+xv5kwNwT2WNcyE9ghr2nSvAOsa2A0FnMV9SuM1t6boOseDdN1O2WyZrjUI1WN9ufK+8GYeh2sy5/2jX0mXx+rzU1HRH4lxdWWbYWphalVOcrddV7yb+44XTPDGk/CvwBoXg3wvCY/DfgrT00vTU6NcBAd87eryybnY9y1fL3xu1zWNX8XSjU1aOND+4jB+QD/ABr6smtZLSXZLG0bL1BFcR8XfhRb+PNIdlULeRglGxXl5pTqYhN313PvOBsVg8onCkoLkSST7ef+Z8w6Jq83h/V7e+gO2W3cOCPavsz4P/Epbu103XbcLNGy4nib5lkUja6EdwQSCK+NNe0G48PajJaXSNHLGxHI6j1r079mD4i/2JrD6LdPi3vDuiyeFbuK8fK8VKjW5Xpr+J+icc5LSzLAe3guayd/OLPM/Gf7Mfxo/Zj/AGtvF3gP4D6Le6h4X+JUC67ot/u8qx0W0lc+ck0v3YxFICoB5IIwK9e+Bf8AwTP8CfCHWofEnxM1BfjP8RM+cReBv7B0uTriOE8zEH+J+P8AZr3+98fXuneEv7Pm1BoNNhJcpu2gg84J9M5OOnNeD/FP9pxLVpLHQBufO1rk9B9K+wxmc06Sc4pRk931Z/LPCfgjh62YTruHPd6dIpdLv9D1z4ofG220e1STWdQjWOBdltZxAJHEo6LHGvCgdMYrxtPjH4g+MviyPR/D6tptnJ/r7gDLpF3b29vevHdU1W616+e4vJ5LiZzks7ZzXrX7Jfii3tNVvtLeNEuLjEiSfxOBxj8P618v/aE8TXUW7L8T+kpcIYbJMtlXpwU5xWit7sfNLy8zU+J50/x38ZfDv7MdrJNYzeNtDk13xTeSqfPbSYv34sbdmGPtN2YRvmw3kxcgFz8nu13NqF14k1bVI4Zb5rdri7eKyOPNkHmSbUfkLvI2g84znnFeO/tS6BJp/wC0R+yz8VLIeTqXh/xxD4H1W4H8djqGUjDn0BaRRn+/Wd+0D44+IHxh+IGufs//AAbuLTRdQhtEPxI8cTnNv4VtpQf+JdGw/wCXloyC4X5xv2jaQxH3SwkZwpKnpFK7P5UqcQVsLisfUxfvV5SUYJbtNO1vJb+o/wCDFsNP8Ht8VpIrHXPiZ8drCw1zU7+GMW9vFDNaxz2unW46pbwRlA7MTJPJGXcnChYrL9mi78UazJqfinVmuLq6bc0dv0H+zk9AOmBWn8VPhxZ/BP8AZq8M+G/D2qXmqQfD+ysdPt9QuIhDLc+T8nmFQSFGCBj0Fd1+zz8aY9dsrXVUXzLi3HlXkKna/Iwdp6q3dWHIOK+ex0qeIxjhVd107WP2HhWhjMn4bhi8vp8s2rT/AJuayere1+47w5+zrpXgrQn1R9Ls9J023G6XVNWlS1t4x6mWYhf1rzvxz+358BfhjctZN4/bxhqUZ2nT/Bemyaq2fTzgFgB/4Ga+f/iB+zTofh/9u618O/tCeI/GXjzwj40dr/wZ4k1TWJbiG4hLc20qOSiTREhWChcjDAYNfpf8Hv2IvhT8G9Ot/wDhG/COjqgQGOZollLDHBB6e9XmGKwWWNU5Qcm1ddF95/LvG/jznOHxMsO6bjJO2ur+9/ofNn7Of7VHhD9rS38U2Og6H4k8L+IPCPl3N3o3iBovtl5p8oULeIsfyhQ/ysvJXcpJ5rxz4/fDNvAvixri3jP9n6gTLEQOI36lf617X/wUm/Zd8QfC7x7ov7QnwltI18YeDSf7S09ExDq9kRtmt5FHWN0ypHY4I5Ap1vrPhb9r34Faf4q8NM58P+I1fyopObjRr1OJrOYfwyRNx/tKVYcGsMTCjmOEWJw6s1uuz/4J+seAfjA8ZP2WNnefXzX+a39Dhf2e/jgCIdC1ib5vu20zH73+yTXpn7RX7PPg39t74VQ+D/GzNY6hpuZPDviWFQbrRZj0Unq0J7qTXytrfhW+8OeJZNMmjkW+hl8tFHVyT8pH17V9G/A+98UJoq2viCwaOONcRTMw3kdgV61yZRmNalLkd7r+rM/cvEbgzLcdhvaprlnra+uv2o/qfKvhf9pD40/8ElviLH4W+JNjdXnh1nxp+uQbpdO1KLs6P0BI6q2COlfoh+yl/wAFHvAv7TdkqWmoW9vebVyrSABiap2+o2useE49B8XweG9S8I67eRaXFa+Ightry6lYLHbwh+XlY4AVMnvwBmvlP40/8EkvC+ueKZvFHwM8ZN8K/EEj+ZJoGol20lpB1Eci5aPJ7HIFe1jsnwWYR57ezm+vRn8I8X+Ds1WqSwDU+Xfl3V9ro/T+KZZkVkYMrDII5BFPzX5e+Df2h/2uP2KbZIfG3wx1bxt4btRzqfhxhrNsUH8X7nc69P4lFe5/Af8A4LffB/4p3i6brV9N4V1hTtltr5DEUbuGVsEV8XjuGcdh/eUeaPdan4ZmXCuY4OTVWm/uPsbUtLt9Z02ezuoUntrpDHLG4yrqeCK/OLxN4N/4djftW32n6hKYPgX8Yr1S10/+o8LawflhuyeiRvkRS9BtKsfuV+hHgn4k+H/iRp6XWg6xp2rQSDcGt51fj6daxf2h/gJ4f/aX+Eur+D/EtnHdabq0DRfMuTExGAw9xWOTZlPAV7VE+WWkl+vyK4Z4gxOSZhDFUW04tXR8M/tM/Dy8+GXjyHxLb2zQTWlyq3yY/wBXKp4P0YDGeh49a7CLVfEFr8SPDvxa+GTaHrOuafosmg6toGoXP2RPEGnmf7RFHFc52RXUMjz7RMBGwlwXTb83B6N8Rrr9l7R4fhT+0BDqtjY6K/8AZegeO7m1e40fWdOUAW0V9KuXhmiX92JmUoUVN7KVLHQ8W/ATWPhzcQ+IPAuoNNa3kQuoFtrlLiG8hPKvDIpMc8Z7EE57GvqqlH2LlWpLnpTWv9f00f6H4TNeHfErh6nlmNrKNRLTX7k3urPZ6q2jO6/aI/aR8J/Gu08AfEbTftmh6/8ACXxVDpvjLQNYgNrqvh/TtVjbTbp54W58uP7SkyyDKN5asCQRnz3xAJPhj+1ivnq0MeqEQzKexcbGH4SKKx/EuteEv2otBbQPHkM2g+Io7ObS7TX7P91c2sMoKyW5ZuHt2yd1tNmMkkqY5MSLtftMeDdXsvAHg/WtW1C01TXrO2is9Sv7XcEu5woU3G1vmQyOnmFTnaZsAsAGOFSnQWDUsPK6hK9nuk90R4Q8B5hwbmeI4dx3vUMQn7OXTVbeuzR6x+yLqo+HX/BQ/wCJuhM3l2fxK8Oab4zs17PcRKbG7x7+ZbM5/wCugPevev2x/gDa/tO/sz+L/BNwv7zV7B/sjg4aG4UFonU9iGA596+Qtd+IqeHfEPwP+MUbYh8L67/YGvOP4dO1cJbs7n+7HfQ2y46A3RNfoSDhvl+oNeRn8pU8VTx1N/Ek/mrH8TeMGRVck4rrwty3lzL7z80fG/ie4+P/AOyh4O8cXyY10Q+RrakYaK/gkazvgR2zcwtJj+66nvXTn4j2v/DM/wDb3lJ/wkH2T+x/tX/LTbvx5f03fvMevNX9T8AJ4L+Nnx++GaoIrLULq28b6Mn/AEy1KEwXQUf3Y7mztz7Ncn1qNf2ZboeG/sOJv7H8r+0tpHH2ny/Iz/ub/m+vtXuYiFSVR18PtUjf7z+7/DfijAZzwngKmYP3qc0/uSuvVs47wP4ouP2f/wBlHxh44sU3a80Jg0NQMtJfzutlYgDvi4mWTH91GPavuH9jj4BWv7MX7M3g/wAE26/vNHsI/tch5aa4YBpXY9yWJyfavlPS/h/H40+NnwB+GZRZrLT7q58b6yn/AEz02Fbe1DD+7Jc3dwfdrYelffDHcTuPuTmvEz6p7KhRwce3M/XofyL9JbiuWa8UToQfuU/1/wCBY+O/2utS/wCFif8ABQ/4Z6CpEln8NPDmp+M7xeyXEqixtM+/mXKuP+uZ9K8j8PrJ8Tv2si0CmaPSyYYVHcxjywPxkY/lWvoPxGTxD4g+OHxilbdF4n13+wNBc87tO0gPbqyH+7JfTXK46E2oPYVlfsz+DtXvfh/4w1rSdQtNL168tZbPTb+63GOznK4FxhfmYxu/mBR94w4JUHcPX9hyrD4Lsk3+Z/RvgTk8si4Gr5rKPv1ItRXVuXRef+Z6B+zv+0f4U+Cln4++I2pC+17X/i14qm07wboGjwG61XxBpulRrp1q9vCvPlyC2eZpDhFEjMSADijNq3iC5+JPiL4tfE06Ho2u6hoseg6RoGn3X2tPD+niY3EsctznZNdTSJBuEIMaiLAd9xx5n4b1rwl+y7oK6B4Dhn17xFLZw6Vea/eYlubqGJVWO3BXhIFwNttDiMEAt5kmZG6Dwl8BNY+I083iHx1qDQ2tnGbmdbi5S3gs4RyzzSMRHBGO5JAA6mtJSprEzlhVz1JaX6JbWX9XPN4J8EaGWYp8V8V1eSpKTmo9e+36vREf7Mvw8vPiZ49m8S3Nu9xPd3LLYx/35WPJ+ig4z0HPpVfw34O/4edftW2Wn6fM1x8C/g5esVuk/wBT4p1cfLNdg9HjXBii6jaGYffqPWfiRdftRaNN8Kf2f4dUvrHWmGl6/wCO7W1e30fRtObIuYrGV8PNNKv7szKoQKz7GYsGH3l+zx8BPD/7NPwl0jwf4as47XTdJgWIFV2mVgMFj7nFY4zFRyzC+yg/30t/JdX5M/MfpAeL9PNK39nZZP3FppsktEvl+evY67TNMt9F02GztIUgtbVBHFGgwqKOgq1urB8bfErw/wDDfTnute1jT9JgjGS1xOqHH0618n/Hj/gt98H/AIV3jabot9N4q1hjsit7FDK0jdgqrkt+FfH4bL8ViX+6g5H8o4fA4jEStSi5H2VLMsSFnZVVRkknAAr55/as/wCCj3gX9mOyZLvULe4vCrYVZAQrCvkjxl+0N+1x+2rbtD4J+GWreCfDd0ONU8RsNGtgh/i/fFXbr/Cpo+C//BJLwtofiqHxR8c/GTfFTxBG/mR6BpxddJWQ9PNkOGkx6DANfW5fwio/vMfJRXa+p+icN+GOaZhNc1N29DyfxV+0d8af+CtHxDk8LfDWxurPw6rkahrc+6LT9Ni7u8nQkDOFXJJ4r7E/Z2/Z68HfsRfCqbwf4JZr7UNSxJ4i8SzKBdazMOqg9VhHZQa76bUrXRvCUug+EIPDmneEdCvJdMltfDoQW1ndRNtkt5gnKSqeCr4PfkHNeTfHG88UNoj2vh+waSKRcSzI67wPQCvoMViIYWl7DDQ5Y/iz+sPDDwvwOFlCrKcZSezuuVer7nH/ALQfxx2ibQ9Hmy33bmZT93/ZBri/gD8M28d+LFuLiMnT7AiSViOHfqF/rXK6H4VvvEniWPS4Y5GvppfLZT1Qg4Yn6d6+mZ9Y8K/shfAnUPFXiZn/AOEf8OKhlijOLjWb18+TZwj+KSVhj/ZUMx4FfL4PD1cdiVKW39f0z+o+Js3wPC2TyhTklJq7l1t3+eyKn7Rf7VHhH9kq38L2OvaH4k8UeIPF3m3Fpo3h9oheWmnxAhrx1f5Spf5FXgttYg8VieBf2/vgL8TbhbJfHzeD9SkO0af4002TSWz/AHfOw0B/77FdH/wTZ/Zc8QfFHx5rX7QnxatI38YeMsf2dp7pmHSLIDbDbxqekcaYUDuck8k19F/GH9iP4U/GPTp/+Ek8I6OyFSXmWJYyoxySRxxXp4vPMDQrfVnByt9pPr1P88sx8esxoZrUlh9aV9Fo7eR4p4k/Z10rxtoMeqLpdnq2m3K7otU0mVLm3kHqssRK/rXmF9+zPeeF9Yj1PwvqzW91btlY7jof9nPcH0NeFfD/APZp0PxB+3fdeHf2e/EXjLwH4R8Fut/4z8SaXrEtvDbwhuLaJEIR5pSCihg2BkkYFfYX7Q3xnj0OxutWdfLuLgeTZRMdz8DA3HqzcZYnknNdOOwOFjSVfVX11VmvuP6e8LfEXO+IKfLGm1GWmuqd+nK7/f0PN/jNbDUPBq/FaOGx0P4mfAmwv9c02/liFxbzQwWsk9zp1wOr288YcIykSQSSB0Iywb3Cym1C08SaTqkkUlg90YLtIr07vJkby5Nrvxu2E7WOBnGeM1478LPhzZ/Gv9mrxN4b8RareaXB8QLK+sLjULeITS2vnHZ5gUnBGARj0NZv7P8A45+IHwe8faH+z/8AGS4s9a1CW1c/Dfxxbn/RvFVtEBnT5GP/AC8rGCUDfOdm07iVJ78GpYnBxkneUdUutjzOIp0ck4kq05U+WjWXLKSXu892v8l6DfhedP8AAvxl8Rfsx3cs9/P4L0OLXfC13Gp88aTKPPNjcMox9qtDKdk2B50XJAcfPTk+Mmv/AAa8WPo+vg6pZx/6i4xtd4uze/v710H7LOgyX/7Q37U3xSvF83UPEHjmbwNpVweqWOn4SQIfQlI1OP7lcx+1r4ot7rU7HS0jRp7UGR3/AIkz2H1/pXDxHy0pKpTdpK1/Nn1PgxKvjqDweLgp0XKSj3jFdU/Lb0Pavhb8b7fWbWR9Gv42juE2XFnMA8cqnqrxtwwPTBFePfHX/gmh4E+LutTeJvhnqA+DHxEyZh9jDf2DqcvXEkI/1JJ/iTj/AGa8b0vVbrRL9bizneCZDkMrYxXtPws/acW6Mdj4gG2T7qXI6H61w5dnzj7s3v32Z9Rx14N4XHU5TUOdd18S/wAzxrwb+zJ8aP2nf2tfCPgP48aLead4W+GsDa7rV8W82x1q0ikHkpDKPlkEshCkDkAHIr7K+MPxIW1tdS1y4VYY1XEESjasagbUQDsAABitSy8e3mo+E/7Ph1Bp9MmIYJu3AAc4B646HHtXzd+0/wDEUa3q8ei2r7re0O6XH8TV3ZpmUFQSpLlXbzPhfCvwxp5di5UIa3erta0VrZ+bPMNb1ibxBrNxe3B3TXDlzmuo+CWt6xpPi+IaWGkjkb99GT8hH+NczoOg3HiLUo7S1RmlkYDgdBX058I/hRb+A9IVmUNeSAF2xXymCw86tXnWlup/SXFGbYXBYP6u0pOSso+Xc6zU/C+k/FT4fa94M8UQ7/DfjbT30vUk6tAHxsnT0eJ9rqexWvNf2Gfhn4o+DH7K83hfxkzTeJLHx74iN/O3/L4/mwgXAPdZVxID3Dg16rDayXcuyJGkdhjAFdfqWg6x4y07TbdrFluNPjZZH24837oVj6ttVVz/ALIr7ejiJvDyob32P5izPK8PHN6GaXUXG6l6NWT+Wx4/43+D+meNjK80eJ3539814p4+/Z61PwszTWqtcQDnp0r6x1bwHq+iqWnsptvqFJrFkhWeMrIvXgg9q8XFZfCb95WZ+l5HxdicLb2M1OHa9z4nnga3maORWRl4IYV6R8FvjnP4OmGn6gxksZDhXY/6v/61epfEf4Dab4vt2lgjEN0OhAxmvA/Gvw51PwNdMl1CxjB4kA4xXhyw9fCS547H6dh80y7PcP8AV6ukuz3v5Fbx34jk8W+LL2+kbcsjkJ6BQTiirPw58DXHjnxFHBEp8tSN7dhRWVPDVa37yK3PSr5xgcv5cLOVrJaH0x49+Gtl49ih+0KN0bhiSO1amg+GrTw1YrDawpGqjrjrXTeJfBF94Y8RNp0kMjyMcR7RnzB2Iq9451LwX+y74Fk8W/E7WbbS7SFDJFaM/wC9nI6AL3r7GjgZTq+7HU/nHMuKqGFwUZVqt6e6V9/kN8E/DHVPGs2YYfJt/wCKZ+FArm/jt+2h8Dv2JbGT+39ctdc16Mf8edq4lbd6EjNfAf7Yv/BaPx7+0zc3HhP4S2c3hfwqxMX2iNcT3K+ue2a8R+Fn7A3if4qX51LWftt9dXB3PLOS7MT9a96ng8NhY89d6n898VeKlWq5U8O+WB9XePf+Dke8OtGPwv4SW20yM4Vmj+Zx/wDqrqfA3/ByDocuhWY17w5u1Fpis+E+4leIQ/8ABMC4j08Zszn/AHK4fUP+CbF89xeMtm4AfaMD8a2jm2BlomfmcONKjnze0P1G/Z//AG1/hD+3rYNb6JqVrpfiLHy20rhWY+1XPGHgu88Daq1veRsvPyvj5XFfi94w/ZY8YfAbXI9b8Pyahpt9ZN5kcsJKsCOe1fe//BO//gsNpvxhtrX4Y/G0x6freBBp+tS/KsrdAHJ71jicDSxEfaYd6n6twX4mOnJUMU7wf4HqXxo+DUHjrTWuLdNt9GNwIH3q+cbq1vPCurhWV7e6tZARng5HpX3d478A3Hgm8Vty3FhcAPb3MR3RyKehyK8f+M3wVtvHdi1xaosN/Hkgj+Ovicwy6TfNFWkj+vuDeMaapxo1pc1GWz3tfv5HiXjz4y6t47to7eSZoLWNApjU43kDkmsTw94R1LxNOsdjaTTbuNwX5R+Ne0eAP2XrWxMc+ryCaTqY/wCEV6tonhqz0ZFhsbVI+MAImWNctPLqtV89Zn0GL4wwOAg6GXQvbrsv+CeHeEf2VLy9EcmqTCFDyUXrXp/hD4LaL4Nnjmt4T9piOVlPXNer6B8G9c1y386SFbG0UZaa5by1UeuTVDXtY+Fnw+uPJ8SfE/wzZ3IODEl4rMD+Ga9zC5M1rCHzPyvPfEqm24YrEJLtE8t/at8AeLPir+y7rmkeA4VufGmn67oGt6ErY2pd2+qW4WRs8BFDlmJ4CqT2ro/hv8N9P+AHwqsfBWi3M2qLaSPea1q8pzc+JtUkYtc30zHli8hcqCflXAr0/wAEy+BfEtpdXfhH4iaHcRtEba4dbxVKo+CepHWqvib4K6toFt9ptfK1Ozx/rbZhIMfhXrVViKeH9hbTqfneXVMmxWcTzP2icpJKKfRrRv1PkX9oP4m69fX0mk3Fq2n6fnG0jmYe5rkvhZ8RLj4b+KI7qPc1rIQlxGD99f8AEV9NeOPh7p/jrT5La8h+fGFbHzIa+bfiZ8H9Q+HF6zOr3FixwkyjgfWvisdRr06vtk7/AKH9OcNZnluLwf8AZsoqLa1XSXmn3PoH4qfCjwv+2T8CZ/B+uXX2Wzv3F9oWsxrun8O6io/dXKf7JPyyL/EpPcA15r+zv/wVTl/Yr8La98M/j5b3Fn408DSpaWwizJ/a0TD91LCf443UBlYZ4OOuQMH4CfGVvAmprpuoSM2l3D8N/wA8GPf6GvpK88S6Pb3Om+Ir3TfDM2s6Ta/ZdP168sYZr6yty27y4pnBKqG5GORXuUcThcdhvZYtbfevmfzJ4weB6zLGRdLRt7918uvc81uf29/2jP2mbB2+GXwVk8PeG7xSo1zxk6aVZyRt/EBPh5AR/cRqyv2QP2UPFX7OnxO8TeJPFHxA8K3Wm+Mo8634U8L6NPJYT3Kg+VdJPM8YjnTJG9IyGUlSCMY2fiN+17Ym8kaKS616+z/rZHJT/vo/0ryfxP8AtF+JvEu5UuV0+Bv4IBhvzPNRHNMFg4unhIJX36tnTwP9GqOBnHE3lGSs735fw1bPXPj/APDqy8SWEeoW89rZ6ppzCSCWeRYzOoOdrZPXjipPi18ZfGfhHT/h7pvw88E+GfE/ib4gX1zYJfeJNTez0zRGitFu1aZFwZN0SXT43ZxbNgNnFcB+zh4f03xv4hmvtYvJb/VLNg0NvcfOoHd+fvHp7Cus8RXN14e/4KIeD7LxdI2ofD3xdodwfhr5O6GHRvEMEP8ApaXKAlZbyS3adY5pMhEmxEEy9dmRxjVm61lte3ex9P4pVquX4SllPtJ35kudaKKla6vvZ9PPaxy37R/7PmueGPg7rvxK8Za9ffFb4wfDGaw8U6fc20TWWk6Hb2Oo21zc2emWQwsaNbrIZJnBkcL2Uc+7eKfFVjq3h6LxR4bZdY0LXrSPWtKeFx/pdrMvmLtPTcMlSD0ZSDiuH/ax+PVj+yp+zP4j8T3finQ/D2qapaNaaRZ6gFnudVV5EjnFtak/vmEBnC7x5QkC7yVypq6H4QsP2cvhf4Z+Gfw7dF0Wzjll0e61i8e4a4Ex815c4xuk3iQRoI41DfKnWvRx1Zzwiq1tHfQ+O4WwEMPxBPCYCSlBQXtLvXmV9euu9+xN4b/bB0bTZ90c2saTOpwTGrLtP/ATWh4z+JHw1/aHsha+NNM8D+NY2G0f2/pcMlwn+7MQJVPurCvPU/ZC8QeJb+S4uNVtprq4cvKYbV3yx68DH8q0o/2B9fkXd514f+4VLXzdHFZlF+7E/as0yHgzEQ5MTVV+t1f843KK/sE/DuzvP7R+GPijx98JdSU74zoWsf2tpgPvbXDF9vsswxXV2f7S/wAff2N9J/tfxpeeG/jF8NdLdW1XV9Hgmtda0e1JwbmezcEmNMje8byBBy2FBIxof2HfGGjN5lnfajbMvddPnj/lUPhr4u+JPgF4xhsfE1xNd2bc2uoR/NgEYPUfMvJDKc8E9Rwd62KVVKOYUdO+mnzR+QcSeA/C+d0qn9j1IyqWulG6f3Pf0TPufw/r3g39pr4VWuoWh0rxV4V16AOm9FmhlVh0IOcEZ5HWvmbxr/wTx8Tfs63F5rX7Pur29vpVxK11qHw81yR5dB1FzyzW5B32c55xJEQScbg4+WuB8I6T4g/ZG8UTePPgjp58QeBdYY3niP4c2TbtgPzPeaKnPI5ZrMcjBMQYfux9nfs+ftF+EP2ovhvZ+KfBmr2+q6bdKNwU4mtX7xyp1RwcjB9K8TEUsXlU/a4aXNSl816SR/EudZLn3BeZuN5QcXo1ezsfDskXhn9qLUNQ0hdN1HwH8UNEhaTUvC+sKF1CBF+9LGVAW9tR/wA94hlAR5iR5AOT4Z0XxXNo+teC9Ut/tlvHBmNGf99AM/JNCW4kQOBlc5Hb0P3B+0z+yL4R/ak0e0GtR3Wm+INHkFzoviLTJTbarotwv3JYJlwykHtnBBx0Jr5nttd8TfCL4qaX4G+LENpH4mvpTD4V8YWkK2+m+MCelu4ACWuonA/dfLHOR+7CSbY378HWw2Plz0vcn1j3Xl3P6o8K/pEPH0I5NnVvaackn/MnpZ7q4lv+z9Z638Ide8IXsstr4c8bac9oXI3HS55FGJU/2op1inUHBDRjsRX0D+wN8adQ+M/7NulNr8YtfGHhWWXw14ltc5a11GzYwzD6MVDA9wwIr5t8OfC25g+K3j7XG8WeJbfxRDqUFnYzXRS9sG0qRDfw2tzafIbmJJr28gDvKtxH9lBimiDOrdT+zx48m+Gf7TekeILqGHTdH+ML3PhbxBbwTtLaWvibTJri2tLlGYBgt5b2U8XzDIa2iDEvIC3oZ1lcZ4FxpO7hqvK61X3a/I+E8bsHXz7BrOJ0nGrTcru9+aKdm7907Xvve9zp/wBrbww+j/t0/CnWLeNd3jjw3r3hG4bO0FoUi1WHP42TAE/3j7142NDtRL/wuY61rnk/2x/wkf8AZ32pv7O/4Rz7J/Zn2zyMf63+yf8AT85z/DjdzX0t+31oW3wh8PfFELNDceDfHuj3BmU4aK3u5/7OuMHtmK8OfYV4L/YWuN4q/wCEN/4RWP8A4V5/aH9tf2luHl+Ts83+yfJ248r+0vnxu2fZv3G3bxW/DWIUsDG71V079t/1PP8ACTFTxWRLD6v2dRqNuk5JcrfkrO56R+yT4XfWf26fitrFxGu7wP4b0HwjbsCCA0yS6rNg/W9UZ/2R7V6H+3x8ab/4Mfs26qdAQXHjHxVNF4a8NWu7ButRvGEMQ+i7ixPYKSazv2BdCL+EfiH4omLS3HjLx7rNwJm5aW3tJ/7Ot8nviKzGPY15L+0R49m+Jn7Tmr+ILaGHUdH+Dr23hbw9bzTNFaXXiXU5re1u7l2UFitnb3sEXyjJa5lClXQlfB+r/Xs6cfswtf0jbT5s/LcRg58Q8X1oy1jzycn2jFtv8jJuP2frPQ/hH4f8H2ckl14b8E6cloHA2HVZ41OZW/2pZ2lnZRyWlJ6A15v4m0bxZDo2i+C9Lt/slvJATIiv++nGfnlmI4jjZycLnJ7+g7bxJ8Lrqb4q+AdeHizxLN4ok1OezvprUpZWC6VEgv5rW3tMObaJ5rKzgLJK1xJ9qJlnlARV0brW/E3xd+KmqeB/hPDZyeJrGUQeKfGF1ALjTfB7DrboCCl1qIyf3XMcBP7wNJujT3swwVN/vnOyfxPZWv8A1Y/tnB+JWH4ay+bzCmoUsPZQi2m2+XTRacyvd9tEcbHH4Z/Zd1HT9IbTdR8efFDWolk0zwvo6BtQnRvuyyEgrZWp/wCe8oywB8tJMED1PwV/wTw8TftF3NnrX7QWrW9xpdvKt3YfDzQ3aLQdOccq1wc7ryccZklLEHO0IOK92/Zm/ZF8JfsuaNdrosN1qXiHWJDc614h1OU3Oq61cN9+WeZssxPpnGBjoBW1+0L+0X4R/Zc+G954q8Z6vb6TptqpCBzmW6ftHEnV3JwMD1r5itnMm/quWxcU9L/af+R/H3iF4yZ5xXi5RUnGm3ZRXX7iz4h17wb+zL8KbrULs6T4W8K6FAXfYqwwxKo6ADGScV8bXv7THx9/bI0r+1vBd54b+Dvw11Z2bStX1eCa61rWLXOFuYLNACI352PI8YcfMuVwTR8XaT4g/a48UQ+Ovjdp7eH/AALo7C88O/Di9O3cB80d5rS+p4ZbM88jzQo/dHP8S/FzxJ8fvGMtj4ZuJrOzXm51B/lyOg7fKvACqOwHQcD0MLl9LBJTxS9pWey3t/m/wP0jwl8Aa2bU/wC088XJTSvrpZd2/P5tlM/sDfDu8vf7S+J3inx98WtSzvkOvax/ZWmE+1tbMH2+zTHPevQPB3xJ+Gv7PNobXwXpngfwVGBtP/CP6XDHcP8A70wBlY+7Ma5GX9hzxhrTeZeX2pXDNyS2nzyfzqOT9gjXo03edecf9QuWu6pjswkrQhZdtvyP6lyTgHgPLkoqtC67Rv8Ai7/kdB4l/bB0bUpy0k2satMTgGQM24/8CNd54W8VWOj+HpPFHiRl0fQ/D9nJrWqyTMP9EtYV8xtx6bjgKB3ZlArxFv2QfEHhu/iuLfVLeG6gcPEZrV0ww9jn+VdZrfhCx/aM+GPib4Z/ERkbRbyOGXV7rR7x7drcQt5qy5xjdHsMnluHjYL8yHipy+pVlXSxatqenxdg8DRyuc+H2pNRd23a3Tta366HGfs3/s+a54m+D2hfErwbr198J/jB8TJr/wAU6hc3MTXuk65b32o3NzbWep2RysiLbtGY5kAkQPjlTx3fwm+MvjPxfp/xC074h+CfDPhfxN8P7+1sHvfDepvd6ZrbS2jXTNEjZMe2J7V8bsgXK5CninfsofHqx/aq/Zo8OeJrXxTofiHVNLtBaavZ6eFguNKVHdIBc2oP7ljCIA2wCIyFthC4UZfhq5uvEX/BQ/xlZeEZG074e+EtDtz8SvP3TQ6z4hnhxaJbISFivI7dYFkmjwGSHEofCV9TWc6vtKdRLTZs/B8q+rZesDjcJOUlUajOEX8Ttq7d72b8i98Afh3ZeGtPfUbi4tbzVNSYyTywSLIYATnYvPXnmsL9sD9k/wAVftF/E/w14i8L/EDwpa6b4Nj3aJ4U8T6NcR6fBcsB5t088LyCSeQgDe8YCqAoAGc4P7R+gad4I8QQ32j3kmn6neMXmtrf5VI/v8fdP6Gue8MftGeJvDZVXuV1CBT92cZb8+tfIYfNY4OTpOK/M/oLiTw4fF2CWIq1G4y+z8Plbqml06Hqlt+3v+0Z+zRYq3xM+CsniDw3aKFOueDXTVbOOMfxEQEtGuP76LiuW/aH/wCCqM37avhfQfhn8A7e4vPGfjmV7S5Evyf2REo/eyzn+CNFJZmOOmOuAet+HH7XunrexvLLd6Dff89Y5CEP/Ah/WvR7LxJo9zd6l4istN8Mw6xq1r9l1DXrOxhhvry2Db/LlmQAupbk55OK6qFLKalRV1CzWtk9H6o/mHOPo0vB46E43Ub6p/o9mcp8LfhT4X/Y3+BMHg/Q7r7VZ6e5v9e1mUbZ/EWosP3ty/8Asg/LGv8ACoHfJr51+KXxFuPiR4oku5Ny2sZKW8efur/ia6L4+fGVvHmpHTbCQrpds/JH/Ldh3+g7VjfDP4Qah8SL1WVHt7JTh5mGM/SvHzTHVMZU5If1/wABH9mcD8K4PhvLo1qyUbLS/Rf5s6X9n34m69ZXsOkwWrahp6nG1QMwj617p8Sfhvp3x/8AhVfeCtauJdLS6kS90XV4uLnwzqkbB7a+hYcqUkVNwB+ZcioPA/w90/wLp8dtZQ/PgAtjLOa9N8MfBXVtftPtN15WmWeP9bcsIwR+NezlVOvRtZ3a/qx+dce47Kcw9p7aKhCXV7t9/U8X/ZT8A+LPhX+y5oekePIVtvGmoa74g1zXUGNrXdxqlxukXHBR9gZSOCrA1a8X/BbRfGU0k08P+kSHLSg8k17R42m8D+GrS1u/F3xE0K3jWIW1u7XiksiZIHBPSuf0HWPhX8Qbg2/hv4neG7y5zgQvdqrE/jivTxmArYmbqSjo+h8HwxxZluT4eng6OItKKtdX11ufNHi79lS8sg8mlziZRkhG6ivMvEHhHUvDNw0d9ZzQ7TjcV+U/jX3pr3wb1zRLfzo4V1C1YZWa2bzFYeuRXE654as9aVob21jk7EOmGFfN4jJop6e6z9ryPxInNLnkqse63Pl3wH8ZdW8B20lvFK01rIhURsc7CRwRXO21teeKdY2orz3V05Jxycn1r27x/wDsvWt8ZJ9IkEMn3vL/AITW/wDBr4KW/gWwW4u0Wa/kGSTzsNcH1HESkqc37qPrJcUZXRozxmFX7yXS1ncX4MfBmHwLpq3F0oa+kG4kj7teseEPBd5441VbezjZuQHfHyqKseBPh/c+Nr0tuW30+3Be4uZSFjjUdck8V8m/8FEP+Cw+m/B62uvhj8ETHqGuHMGoa1FhliboQhHU19bluVuaSjpFH878dcfwwblUqy5qsunY+nv2gf21vhD+wVYLb63qVtqniL+K2icMyn3r5f8AHH/ByBocOhXn9g+HNuorMFgJT78f+eK+CfCH7LHjH4865JrfiCW/1K+vG8ySWYliSee9eoWH/BNi+W4s2azbG/acivoPrGBw65Gz+Xc148r16rnOpue/+Av+Dki8XWhH4n8Irc6bIcMUj+ZRmvrv4EftofA39tqxj/sDXLbQ9ekAzZ3TiNt3pk4r8/Zf+CYFxJp5/wBDP/fFeL/FL9gbxN8LL8alo326xubc7klhJRlP4Vn9dwGIfI2rkZP4hYjDVlKnUP2P8b/DLVPBU2ZofOtzysycqRXGa/4atPEti0N3CkisPTpXwj+x1/wWj8e/szXNv4T+LVnN4o8KqRF9okXNxbL657//AFq/SHwPqfgr9qLwLH4t+GOtWuqWkyiSW0Vx5sB7gr2rzsblTguanrE/oXhLxJw+McY1nyT7nm/gH4a2PgSOb7OvzSOWBx0BNFd34Y8E33ifxEunRwyLIrYkBGNg7k0V5lHDVOW1OOh95mXEGGVbmxdX3mr79D47+E3/AAcB2Phj9lZD4m0P+1PixoYGm2iFcrdfIcTN9MD86+SdWs/it/wUh+Kv9veMr29u45pcw2oLeRbr2Cj2HevoFv8AglJNqP7SE0f2Nvsynefl45av0Z/Zp/Yr0P4PaHb+ZawmZFHG0V2ZnxRh8PTvh/ikfwfnXG9SdCMOa+h8v/sgf8ErLLw7Z29xfWiblAJJWvtfwV+zhoPg+yjijtY/kHYV39taR2UQjiRURRgADpU3SvzfG5tiMTPmmz8zxeZVsRK8mYLfDnS3i2/ZY/8Avmslvgdorhs28fzkn7tdpRXAq01s2caqzWzPEPil+xpofjbTZo1t4dzqRjbX5tft0/8ABK+60d7jUNKtpI3jJdGj4II6EYr9kmrM8TeErHxbp7217bpJG4wSV6V6+WZ9icJO6d0elgc4r4afMmfj1+wH/wAFV9a/Zpv4PhX8cI7jVPBcjC3s9WlBebTDnAyT1Wv0K1/wZDNpFprnh28h17w5qqiSzvLVhIrg9BxXhX7ev/BKvTviNpV1faXaxmQgthU5FfE3wR/at+Of/BL3U9U8L6XC2veH9QVo7TT9QUyRWc5+7ImemDziv0jDYzCZrT5ou0z+iOAfFGeE9yb5o9Uz9S9X8K6X8NvCz+IvHmv6b4Q0GFd7S3kwR3A/ujqfwr5J+On/AAXU8F/D66m0X4H+D5/F+pITGNZ1GMrb7um5V6t+NfN2k/s3fGz/AIKLeOl134i6zqmqLO4dLZ2KW8Ck9FToBX3Z+zF/wR78O/Duyt5dStofMUAkFQSazxWZZdlys/ekZ8XeMGJrtwhLlj2R8DeOfiN+0/8Atv3zSa/4m1mz02c/LZWG63gjU9gFrKj/AOCUfizVIPtF59uuJ2+YtI7MxP1NfuB4N/Z28L+CbSOO102H5BjOwV06+DdLWPaLGELjH3K+ercdVub91GyPxvFcaYmrK6/E/n3v/wDgnz8QvB2oS22m3er2cbKHdYZnUMR0zg10/wALfjf+0p+xPrUd9o2vatqVjGf3thqBae3nX+6Qa/dC6+EmgXly0kmnwlmGD8grmfGX7KfhHxlZvDPp0S7hjOwVrR46ntVjdGuG42r05Jnxv+zB/wAFXvhX+19d2+g+No4/hZ8QpMRr9pO3Tr+T0VzwpJ7Ma9u8e/DWfRI2tdVtEuLW4GY5Vw8MynoVYcV4b+19/wAEU9H8f2VxeaFFGtxguuwYYH2r53+F/wC018fP+CZl7/wjvinTLn4k/DVG2PpuoMzXFpHnk28x5Uj0PFe1RxGAzJXovll2Z+88DeM0qXLRxMnKPrqvRnuXxZ/Zqm08yX2ggzQ5LNbZ+ZfpXC2HhLxV41jitY7XULiG3+RFkyI0/Ovpj9nz9oL4a/tp6E198MfECtq0Kbr3wvqREGqWZ7hUP+sUeq56V1nirQ7H4W6EdU8Za94c8D6YoLGfWL6O2LDvtQncx+grx6/D8va8qTXof11lfi/hXgVWnOE7bOW69V1PmjQf2TtavwrXl1a2YbqF+dq6/R/2SdFtfmvLy6uWHXB2LWP8QP8Agq5+z18PLqS00W88ZfFLVIztEegaabezZv8ArtLjI9wDXBXH/BVz4peNJTH8Of2d9F02Fv8AVXOu3U19MfchQq11U+G4QV6ll6s+Jzz6QlCm2lXf/bqSX3vU+gfC/wAEfD/hS+jurC1lW5i+7IrnNTfHH4fa98UfhDcaf4TjsX8deGtVsPFvhE30gihGpWVwknls7YCpLB5sbHIypr5tvP2wf23dVTzrPSfBejw9RFbeHEIH4sSahsf+Cm/7VHw7n8vxl8MvBfjK3kXYqnTns5A397dH/LFelg8HSozUqNWOnQ/Lc68YsszmlPC4xN8ytzNptdmvQ+kPh18CNG+HnjC48Z642n+PPi5qSqdV8X3lqskdoVzttdKgcFLS0iB2R7RvYKGJyTXA/tTfFF/EA+x3mm6ol9HOJ4tRnODvHGQcnPHHUY4rjfDf/BY/RZLlIfib8DfFfhHccSal4cuBeRxHuTBIFJHsGzXunw0+Knwr/a60pofAvjTw74vkZcvo1wfsWrReoNrNtckeqZFY5ngcXXi7SuvLVH3fh1xpwthK0HQS5lvd2k33cut+z0OD+D/7SEOq6TceG/FjeZa6pAbKaRpHjju4zxtdkZWRh1EiMrKeQVPNcDrP7Ivg79n/AMSz654s8O+Jvil8I5iZrm+i1m8k8QeDUP8AHcRpIBe2a/8APxGodFH71RjzG9A+Jf7H7Wl1MdHZrG4jJJsbwFcewJ5H41z/AIG+LHiz9nvVYbbVLa+FnC48rLlZIP8AaikHH4Z/LNeHhsdXw8fq+Kvyvqm9PmfeceeGmS8ZYZ18qqcla10k7P5a6r8OzPoLwF/wTK/Zv+L3gG213wVb31xpuqwb7LVtH8S3gaMsPldGWX5XU4ODyCK8/wDi34Z1z4IQ/wDCL/GxjrXha6kEGkfEpYxHBITwker7Ri0uegF3xDJ1k8s7pH5VfD/wsmvLzXdD8T+L/h62tP5+p6Z4S186LZX8/eZ7VYnEcpydxh8tWPJBYljVvr/4Zi0mhm8ZfFzUoZkKSRzeMtWkSZT1VlDKjA+hGK0o/u5NVMQp030le/39H6aH83cJ+EviRkGaPFZbJyUXbVvVL8irfWPiv9lPxF8m690GR1dc5Cc8rz/yzcjBDD5W4IzXSaFbw+OfHE3xE+FPiK38B/FCUiTVYbiMnSfE/qupWyAne3/P5Cpfu6y9s74Y/Er4Y/CHwPceGdJh8UXPh9hi3sNRuJb6DTwSSVgEuTHGcn92D5YJJCg81U8GeD/DPiHx1DqHh221qaxZ8SQiGaE2bH+OKZTjg9UY9DwcgA4xqwpT9nh5qdN7xZ/T2e8M0uJsnlT4rwTpVkviSTTfrpZ+enmfVPwB/b60fx34rt/BPxA0ub4Z/Epo96aRqUqNaauoOPO0+7UmK6iJ6FGJHQgMCo9Z+M/wY8N/H34c6l4T8XaTb6voerR7JoJV5Q/wujdVdTyGHIIFfHfxD1vwp411u++GfiDwn4i8dLY2sOrTWjaPHdQwo5KC4jkWdZIJUdWQyIYpEKg5Cspa98G/iN4++Bs91aeEdQ17xfoOjiP7Z4G8e3ltDrmnW8mfJm0/VIpJYJYXKsiLdPt3Qyo9wkkbIM8bwzNP6xg/de9m/wAn/mfwPxZ4S1cJiJVchq+2im7JXUk107N+Sd9CHxH8OfEPwO+IGm+HvGuq6rJfXyR6X4S+ItnKY5ddRGdodL1WNt1vNer5knlG4jkS5DyBDHcMDNmt4CuIPhdruj+MfFtvf6h4i1S41RdQsbEWY0iVriOaKeCPccNBcxx3W0kkOzhpJRl2+mvC/wAY/h1+1/4b1PwTqlpIl9qFqV1Twj4jtTZamsR/5aCJuZIwcFbiBnTIDK+cGvnL9pT4E698Mby3s7+e88RWDSFdO1GVv32uQhCTaXDD7upRoCVfgXKoH5ljcDqw+dV503QxCtUXdb2/rVfcfoXhLxVg8xxq4f4nTp1WnFN6KfMrOMk7Lm6J/ez2r4heLbr9o3/gn94yuLyGO18TWWiXaanbRcrZ6tYDzW2esZlhSWNv4o5ImH3hXhx+LE3/AAub+y9zf2d/a32ff/Bs3/ac49PL7+tdZ+xh46GheOm8O6pci90fxpZR2pumwEvgFMdpcnPRzHvs5sksXSwUDGTXkX/CGReX/Zf2xv7a+3/2Z94faN/9h+T5nX732j5cZ3b+OtcWDi6MpU4aK6l8nuvw+Z9p4Y5FDhLOM7yjGawhBVab7xXNZr02fZqx9RfD3xbc/s5f8E//AAbcWcMV14mvtEtI9MtpThbvVr8eau/0jEszyyN/DHHKx+6a8IXwFcT/AAv0LR/BviyCx1Dw3qtvqjahfWAvP7XlFxJNLPPHuGWnuZJLraCCzqgWSIYdex/bP8dLr3jtfDul3AstH8F2T2oul5Sxygju7kYyC4j22cOMMHa/UjGDXN/s1fArX/ifdXFnYzX3h3T1kC6jqMTfvdChKgi0t2P3tRkQgtJyLVXLYEsiA6ZbW+qp157ybk2/wX4/e/I+a4D4cy3IOEsTxVxBPlniPhitJON7pLreb106WfVXl8OfDjxB8cviBqXh/wAFatqkd7YpJpfi34i3sxkm0JHaN5tL0qNQtvDetsj81reONLYJGHMlyp8n68+DXwY8NfAD4c6b4T8I6Tb6RoelR+XDBEvLn+J3bq7seSx5JNcX4p+MXw6/ZC8N6X4K0uzka+sLULpfhHw7aNeam8Q/5aGJf9WhOS1xOyJklmfOTXz78ZPiN4++ONxb2ni7UNe8H6DrAk+x+BvAV5bTa5qNumPOm1DVJZIoIYULKjravt3TRIlw8jqhwrQx2cTS+Gmtr/n5v8D8Ux1HPOMsW69Kk4UE9N1FX8/tSfXds9c+Pv7fWj+BPFVx4J+H+mS/Ez4lLHvfSNNlVbPSFPHnahdsRFaxA93YE9ACxCn5z1uCHwL45h+InxW8RW/jz4oREyaVFbow0nwx6Lpls4B3r/z+TBZO6LF36L4d674V8F63Y/DLw/4T8ReBVvrWbVobNdGjtYZVQhDcSSNO0k8ruyoJXMsjkk5KqzL5/wCM/CHhvw/46mv/ABFb61DYq2I4fJmnN4w6tLMxxz2RT06nOQPQlglltO1CNpP7Ut/lvY/pzwZ8HuHsPW+tY2bxFWO6inv2Sdvy0IbHT/Fn7V3iP5t1noMbF225K8ctz/y0cDJLH5V5Jx3774S+Gdc+OEP/AAjPwTb+xfCtvIYdW+JTRCSCMjh49I3DF3c9R9r5gj6xeYcOnKfE74k/DH4veB7fwzq0Xie10BQBc2GnXEtjBqAByFnERBkjGP8AVk+XkAlSRmq1lf8AwzNtDBD4x+LmmwwqEijh8ZarGkKjoqqWZFA7ADApUfq8Ic8KqdV9Wr29EfpHidl/Hub4b+zMgwv1fDrTrd/ct33vc9y8ff8ABMv9m/4P/D+517xrBf2+m6TBvvdW1jxLeFpNo+Z3ZpPmdjk4HJJPWvmDRv2RfBvx/wDE0GueE/Dvib4XfCSLE9tfS6zex+IPGSA8PbxvIRZWbf8APxIpd1P7tTnevbnQPhZDe2eu654n8YfEJtFfz9M03xZ4gOtWdhP2mS1aJBJKMDaZjIq4yFDYYY/jr4s+LP2hdTmtdJtr77JM2ZSGJkn9WlkPH4Z/Oiji3hPflWdWb2Svb7v6R+U+G/0d84liXi+JazjCL1Tk0vne33L52N74wftIQaXpNv4b8Jt5VrpkAsoZFkaWOzjAxtRnZndu5kZmYk5JY81D+y18UpPDwNnZ6bqr30k7Ty6hAcneeMk8YwOOtWPhp+yA13dwtrDNfXD4K2NmC272JHJ/Cu5+JfxU+Ff7IumLD468aeHfCMirmPR7Y/bdWm9haw7nBPq+0UsJhcdia/tpbn9JZ9nvC+S5X/ZtO3Lpdt2vby3a8thnxH+A+j/EPxhb+NNDbT/Afxc01WOleL7O1WOO7JxutdVgQBLu0lAKPuBdQ24EkCrXwO+H2vfC/wCEVvp3iuOxj8deJNVv/Fvi42MgmhOp31w8nlq4yGSKDyo0OThR714L4i/4LH6Kly8Pwz+Bvirxbt4j1HxHcC0jlPYiCMMQPYtn6VgX3/BTf9qj4iT+V4N+GXgvwbbxjYyjTnvJC397dJ/LFfV+zquj7OtUS9Xr6H8x4njXhjBZisdhIu6vpoo3f2kujPpHxN8EPD/iq9kutQtZWuJeshc7q5TV/wBkrRboFrK8urVm6Z+da8ds/wBsH9tzS08670nwXrEPUxXPhxAD7ZQg1ctv+CrnxR8GSiP4jfs76LqUK8S3OhXU1jN9QGDLXkzyXDVX7s4yfqfY5X9IbDwtCM5RXk019x0/iD9k3WrEM1ndWt6vo3ytXI33hLxV4JjktZLXUIIbj5HSI5jcfhxXonw//wCCrn7PXxCuY7XXLzxl8LdUkIXy9e003Fmrf9dot2B7kCvoDwrodj8UtAXVPBuveG/HGmMA4n0e+juio7bkB3KfqK87EcNyp+9FNemqP1vJPG3B41KMpwn5SXK/8j51+E37NMuomK+11TDDwyW/dh719EeAvhvNrMa2uk2qQWtuuZJW/dwwqOpZjxXKftBftB/DX9izRFvvifr6rq0qbrPwvprCfVL09gUH+rU+rYr4w+J/7TXx8/4KaXn/AAjnhbTbr4b/AA1dtiabp7Mtxdx54M8w5Yn0HFehgcnp0Ie0re7Hu9z838QfGijTcvf55LZL4V/mfRn7T/8AwVe+Fv7IF3caD4Jjj+KnxCjzG32f5tP0+T0ZxwxB7Ke1fDvxS+N/7Sv7bGsyX2sa9q2m2Mh/dWGnloLeBf7oA/nX27+yD/wRT0fwBZ295rsUbXGA7bxks1fZng39lLwj4Os0hg0+FtoxnYKxxXFmEwv7rCRv5n8g8S+JuJx1ZylJv8j8L7D/AIJ8fELxhqEVrqV3q93Gql0WaZ3Ck9cZNdDL/wAEo/FelwfaLP7dbzryGjdlYH6iv3WtfhJoFncrLHp8IZRtHyCtBvB2llNv2GErjH3RXlz46xLd4o+NnxfiW7o/C3wP8Rv2n/2Ib5ZNB8Tazeadbnmyv91xbuo7Yavp/wCBf/BdLwZ8Q7qDRPjf4Pn8IanIRGdZ06MtbluBuZeq+tfob4x/Z38L+NbV4rrTYfnGM7BXyb+05/wR78O/EOyuJtMtoRKwJwFAIr0sJxhhq/uYyNvM+pyPxIxOEqJqbjbzPW9H8K6X8SPC6eIfAevad4u0KZd6y2cyu6A9mXqD9afoPgyGHSLvXPEV5DoPhzS0Ml5eXTCNUA6jmvy01b9m/wCNn/BOnx02vfDvWdV01YWLPbIxa3nUHoydCKZ8bf2rvjp/wVB1PS/C+qwt4f8AD+nqsd3p+nqY4rycffkfHXJ5xXt08HhKv7+nP3Op+50PGWpPL5c1uf8AmPRP2/P+Cq+tftLX8/wr+B8dxpfgyNvs95q0QKT6nzg4PZam/YW/4JX3WsPb6hq1tJI8hDu0nJJ7kk19HfsFf8ErNO+HOlWt9qlrH5gAbDJya+8vDPhGx8J6fHb2NukUcYwMLivms74rUV9Xwei7n878ScZV8XVbUrt7s8k+Fv7Gmh+CdNhja3h3KoGNtd2PgdoqhQLeP5TkfLXZLTq+DqYytN3lJnwc8VVk7uRgr8OdLWLb9lT8q5nxt+zhoPjCzkikto/nGORXolHWpjiKkXdMiNacXdM/OP8Aa/8A+CVlj4isri4sbRMsCRha+BdJsviv/wAE4PiqNe8G3t7aRwy7prUlvJuF7hh05Ff0IXNpHeQtHKgdW6givCv2lv2K9C+MOhXBjtYVmZTxtFfYZLxZVoNU6+sT6nJ+KK2Gmudnw18Wv+DgOx8T/srN/wAIzof9mfFjXAdNu0C4FsNozMv1yfyorih/wSkm079pCGP7E32ZjvAC8cNRX23+sGBppW66n3mL42lNxlKfRH7BReE7GHWWv1t4xcSAAtitELTqK/FHJvc/FnJvcKKKKQgooooAKKKKAI5YFmRldVZW6gjrXjfxg/Yp8KfFnxBY39zZwrJbzeY/y/e4r2ijFbUa9Sk+am7M0p1p03zQdjnfA3wu0b4eadHb6bZQwrGMbgoya6ICiis5ScneRMpOTuwoooqSQoPNFFADSue1cl8TPgf4b+LOlS2utaZb3CyDG4oN38q6+g1VOcoPmg7MuFSUXzRPzT/an/4IkbNcHiv4Z6ld6Hrli/n2s1lK0E8TjkYK81hfC3/ghhrnxU1tPEXxY8Uat4h1OdhJLJqV29xISef4icfhX6j54pBya+ijxRmEaPsoz+fU9uPEWOVH2MZux88/B/8A4Ji/Cz4R28Yt9Dt7qWMY3Ogr2nQ/hd4e8NwrHY6NYW6r02xCugorxa2Mr1nzVJt/M8mriqtR3nJsqro1oq7fstvj08sVS1DwHourMDc6XZTFeQWiFa9Fc6lJbMx5mtjh/En7N3gfxZbtHfeG9NmVuD+6Ar51+Ov/AARU+EPxgc3VnZTeHtUU74bqzOx4m7EMuCMeor7CoNdmHzLFUHelNr5nTQx2Iou9ObR+e9x+yt+1Z+y7arb+E/G2lfGDwtaj91onjCM3c0aD+GO5ytxHxwMSEe1Yl3+2xoei50n4xfDPx18KLpvkluXsm8Q6E3qfNiUXMY+sL4zy3Wv0g71U1rQ7HxBZtDqFna3lu3DJcRLIpH/AhXvUuK6sly4ump+ez/yP0fh3xcz/AChr2dRyS6P/ACPzd1P4j/sy2W28l+Jfw9lS5HmJFZX91dyEf9cII3kQ/wCyyKQeoFVYvjf+z1K23TU8S+IG6D+y/A2tTKfo0sEY/Wvvfwt8Afhjo2pXE2j+FfB63TSFpWgtYWYP37Gu6s9HtdMj229pb2yjoIoVUD8hVVM8wUfhw/3tf5H6RivpQcUSXLTdvm/8z824vHXwxv1za/Cr40Xa9mh8BSKD/wB9XKmtHw78S/Afgi7N1a+CfjZ4fZvvtL4JvfLP+8sMkuf++TX6OeYw/iagSNj7zVn/AKxUU9MPFfNnly+knxdJWqTUovo7tH5y3H7QHwrj8TXuvaH420fwX4s1IqdQXxDpGp+H9O8R7eFj1P7TaRoZACRHeRlriEkH9/GGt5Nq/hv/AI9Hw7J4a8c/DqO60HUBeiTwhdJr149uRie0W+8xcQy4QuDaKjmKPfG4XB++L/SLfWIGjurO3uo24KywrIp/AiuD8b/st/CvxhH53iL4feAr3yzu8680a13J77yoK/UGvSo8XQdr0nfve/y1SOPDeNlabaxWFTjJptQk4ptdbJb+h8j/ALRHiVbC+htbqxsGsobj7RbDU4ZlitZMkb42CGS3m6bZYZQV6kjArqPAX7TmrJ4QfS/F2l3Xi/wfqG2GVbm5W/ZVzuHlahFlmZSoKC6USb9rG6TaK9A1vwX+zv4SlOm2njSy8P3CvhdN0bxrcPtb+6tis8kZ/wBzyiPY1wPinwJ8H9K1K4mh8UeLo7qYHH2rw+unJIcdFfyLLzs8cGRsnA74OUsRDEPmlGVul4tW9Hr+aP1aPHXCfF2Gjh8flFb2q2qU4OUk+jUo8sl81JeRzXjTwZHpSx+IvCGsS6xolvdC5SXcFvNMmcgYmVeFcvtxLGGikkUMuGcvGv8AaSj9q3/hM/slr5P2L/hIPKw3l/bPO3eTtzu3fbv3WP73zbdvFVNJt/Dem6q39ma54m+2M0VusQ0GBSYnd1dFiW+cSqVCDawwwYjLZIjQ2tr/AG9j/R/7cN39h+web8v9m53f6jdt2Y/e5xjz/wBx5u3566VG61/4PzPUxNSrJclecptU5U+aacKjpyteMrpXkraSSV7t2u23c8FeDY9WWTxF4v1iXR9DnujctJuDXupTITxCrYDOHzmVwsUbsWbLKHk6Lx/+05qzeD00vwjpd14P8H6fmGJba4WwZlzubzdQmwys5YlxaqZN+5hdPuNclq1r4b1LVl/tLXPE32xWlt2hbQYGYRI6IiNEb5BEqqZAFUYUKACuAJOv8K+Bfg/qt/bzyeKPF0t1DwRa6AuorHx0Z/s975OOeBIuDkdsDGpGN05Ju2ySul+afzRnjpZPKcMw4iw1fFQpJKnThSk6MEl2VlJ95OVtElFJHK/s8eJl1C+mtbexsFspp/PuBpkMzR3MmceZIxQSXE396WWQluCCc1HYQX3wGbxFJ4l8cfDuS617UTfGXxfdJoN4kCjEFo195jZhiy5QC0ZEMsmyJA2B7Zofgv8AZ38WSjTbvxnY+IJ2fDabrXjW4Tc391rFp44xj+55QA9BXqPgj9lv4V+EE87w78PvANl5h3efZaNa7nz33hSW+uauHEEcPFwnCUr73Vvzuz4fijxyy+VWKy3L50VT+FOXLbz5Ukj4ot/2gPhW/iWy17XPG2j+NPFmmlv7OXw9pGp+INO8ObuGj0z7NayIJCABJeSFbiYgn9xGVt42+IfiX4D8bXi3d14J+NniFl+40Xgm+EY/3Vlkix/3yK/RSw0m30eBY7W1t7WNeiwwrGo/ACrHmsD95vzrixHFEKj96jdeb/ySR8jhfH/PMFf6hTjTb1bV3Jvu3vc/NKbx38MbAf6T8KvjRaL3abwFIwH/AHzcsapyfG/9nmM7dSTxNoLd/wC1PA2tQhfq0UMg/Wv058xifvNVa80i11KLbcWdvcKeolhVwfzFYR4hw32sOvk/+Az0qP0mOLoO853+bPzW0v4j/sy3ha8j+Jfw+hjtwZHjvL+6s5AB/wBMJ40kc/7KoxJ6A1oWv7a+h61jSvg78MfHXxWvF+SK6Sxbw/oI9D5sqm4kH0hTOOG6V9weKvgD8MdZ1KCbWPCvg97pZA0TT2kKtu7dhzXaaLoVjoFmsOn2drZwLwqW8SooH4Cun/WLC0481HD6vu9PwJzr6R/EmPoKk3Z97t/nc+Bbb9lf9qz9qK1aDxZ420r4PeFbofvdE8HxtaSyIf4ZLklriTjrlwD6V6N8Cv8Agip8Ifg/ILq7spvEGqMd011eHe8rdyWOSSfU19gCnCvLxXEmOr6c3Kuy0PxnNOK80x9RzxNVtvzOH8N/s2+B/CdssVj4b02FV6fuga6DT/AWi6SWNtpdlCW67YhWxRXiyrVJO8m/vPnpVJvdsqto1qy4+y2+P+uYrJ1z4XeH/EsTJfaNp9wrf3oRXQUVMaklqmJSa2Z86/GD/gmL8LPi5byCfQ7e1lkGNyIO9fIHxT/4IYa58LNafxB8J/FGreH9ThJkik027e3kB6/wkZ+hr9Saaetexg+IMdhn7k3bs9T1MHnWMwzvTmz81f2V/wDgiPu1z/hK/iZqN1rmuXz+fdTXsrTzTOeTktzX378NPgf4b+E2lR2ui6Xb26xgAMFG4/pXWluKcDXPmGb4rGSvWl8uhjjszxGKlzVZMaFx2/KnDgUUV5p54UUUUAFBoooA53xz8LtG+IenPb6lZxTK4xkqM15z8H/2KfCnwm8QX1/b2cLSXExlT5enFe0UYreGKqwi4Rk7M2jiKkY8kXoRxQLCiqqqqr0AHSpKKKwMQooooAKKKKACmladRQBmzeFLGfWVv2t4/tEa7Q2KK0qKPUbk3uV01SF7xrcSR+coyVzyKn3V+WUf/BVyOw/aQmh+2L9lbCY3fL9419+fAv8Aad0P4v6JDJDdRLPIoJG7rXrY7J8Rhoqc1oz0cXllbDxUpLSx6lRTVkEi7lbcvrTs15J5oUUUUAFFIxxVbU9Vt9HtWmuZlijUZJNC1dkBZJwM1wXxD/aM8M/DjV7Oz1C+hWa6l8vG8fKa+ef21v8AgpTonwa0S6t7G6jE6gjcCM5r81tK1T47f8FIfHmqX3w80q/vrPQVa7e4ZjHCzKOI1boXPQCvq8n4XrYtc9T3Yn02T8N4jGu9tD93dA8T2PiezW4sbmK4jYZBVs1fBzX4hfAP/gp749/Ze8X/APCNePtP1TRb+zfy5YL2No2444zwR7iv0Y/Z6/4KdeEfi1YQC4vIY5pAM4aubMuG8XhHe1490cuYZBicLKzV0fU1FYOgfEjR/EtuslrfwSK3TDVrf2jDt3efHj6ivn3GS0aPFcZLdFignFZ0vinT4JijXkIZRkjcOBWP4h+MXh/w1btLdalbqqjJ+aqjTnLZDVOT2R1BNZXijxnpng3T3utTvbe0hjGSXbFfJP7U/wDwVu8I/BnTLiOwuIZblFOCGBwa+JdJ8eftC/8ABVzxVcWvgm1m0bwjG5F5r+os0Fjapnkhurkf3Vr6LK+F8Vivfn7se7Posp4YxeOmowi9T7A/a9/4LR+DvgjDNp+gyLqeqHKRBDvZn6AAD1rnf2d/+C8fgvxw8dh4oi/svUFISVZB5bK3Q5B5Bqf9mf8A4J+fCn9jWaHVI7f/AIWZ8Rl+aTxFrUQktrOTv9mgOVXBzhjk12fxi+Bnwp/aUhb/AIWJ8M/Dms3jj/kKWMZ0/UEP97zYsZI/2s19TLIcphH2E7t/zLuft+E8B8ZWwKquNpdm7M+hPh1+1r4B+J9tHJpfiGxYyDO15ADXoVpqtvfx7oLiCZT3Rwa/LnxP/wAEcfCdtdtefCv4y+LvA9wfmj0/XrcX1snoolQq2PqDWVH+yh+2j8Gst4Z8QeD/AIgWMf3G0/WxFI4/65zbT+FeZW4NhLXC1k/JnwWbeEub4OWsH80z9YQ2fSmS3sMB/eTRJnpuYCvyW1L4v/txeGkNvdfCPxbMw432wSdT+Ksaq2PgD9ur45XW4eE5vC8MY8zzta1GOzRu2Bkk5rnhwRi2/elFHz1PgPMpS5eX8GfrLqXjLSdHiL3Wp2Nuq9S8wGK8e+NX/BR34R/Amwlm1rxVYs8IJ8qFwzGvhHTv+CZnx78cyK/xQ+N/h3wlYtzNaaP5mqXpB6hQNqA/U4+teq/Cz9gP4H/AOdNTTw3cePvEEOGOueN5xfbGH8UdrxAnPIyrEetdUOE8NS1xNa/kj7bIPBPN8fJcyaj3asjT/wCHsHxG/aTkmg+A3wf1vxFY5KHxBqYFlpMPu1zMVi49ASfasjUvgd8YfjR/pHxc+O11ptpJ80vh/wCHkGxVH919QuFCj0+SBh6N0re+Kv7X9raL9jtppNYltl2RRRsIrO2A4wqqAoA9FArzXS7jxv8AtD3bL9qaz0pTh3TMduo9ABy59s/lWka2Bw0vZ4KknLu/eZ/T3CX0ZMuwlFYzNmlFaty0XyW7/A0P+GTvgjpN15Wm6/8AEDQNQiOJLqx+IlwLqV/78gmWSIse+2NR7Ct7TfhK+hD/AIp/9pH41aWi/dS51jS9SQfnbRk/nWB4z8MfBj4SasfDXizxpZab4mt41kuoGNzNNZ7uV88QRSJbsRyElKuVwcYOawTp/wADL19tr8XPD8bNwqtfxqfydFNdkp49q86UX6pf5Hs4jhLwqqzdGrVhGUdHe3T/ALePVIk+Ienf6n9qnxXs7fafDGlzH8/PX+VPtr/xzq9wtrJ+1D40vJZP+WWnaFpVtK305m/PFcJ4A/Z68F/FfTtQvPC/jyz1yz0uTybmezVZIYpP+ee/G0yDqUBLAckAVb8AXui/DHxn/Zuj+Ikmjzm9ubiKCO3XH8CtjfI56AA4HXPGDyyq1ISUa1GMb+S/I6cP4L+H+Loyr5XH21uyaj82rnSeLvh2tteWul638RPjj8QPEWqQm4sdAn8aLpq3kQOGuJjYWsDWlkhzvuXYjgrGs8xSF8Hxb4B8FfC620XUNd+EEmoLqurQaNBeSai3jK2huZQ7KS19KbrbhGIItVc4wqEkA9uvwxk0f4k+LfHmieLoPDNj4gsreTXUn0O2Wxfyck3t1LLMqGQ7iomfZsQKqkfMzZ/wr1LXPjj4mkufhvY23xC1JUazHjbU9Ln0LwjoFs2N8NlGZJbm8eRl3SmCRRP8g+0LHFFEn0HNhqVPnbSilq/h1+Vl+Z+T5phMm4fcqmJoww7T91WhJcq62d5tvpt8rB8VvD2qaDBNYeH5JbHQrFS0tvp94mk2sKDOXkaNBFFGBycuSexGKm+Dv7POufEDSmvrNdF0Pw+UM11rpt2is2iUEvJ9suN012oGHzEqQOFP+kIa+hPAn7Ivh3wJCviLx/rTeOdV0kG9a81lI7XRtH2DJlt7FcW8OwDiaTzJ8DJlNeL/ALTX7U158XbeFNJaSx8LXUiyaYtyhQ6ooyw1S7U8rbKFL29uwzIV85wQI1X5F4xV6rlh72W8nsvTq36teaNsr8WM84rxcOHODsPGlD7VWUU+WPWXKvdXldN9jnfF3iXQ/CltJofgy1mmhcfZrjWLqMi4vAU3MkaHJhhKjPl/NIyFRISrMY0/4R2Q/tGDwP5cH2j+x/7J2iRP+P7zPOxnO3Z9r+TZ08r5Nu7iul/Y88Cx/EX4m6bqF1HJHoPhm3XV/wDSATIYzI5gklPUzXNxHLcEgnbHZbGG2cGvNz4xsRrH/CZbpf7Y/tT+2PtGT5m3+yf7R6Z+99p/eddueMY4raMk3yRfRfO+x7Ustp1MzxmTYCbqzwlBzqVJO8pVntd/3UttlflWx0XhHxLoXiq0j0PxpbywxoPs1vq9tExuLMBdypIgwZoQvPl/LIqAiMhVBkn+MX7PWu/D/SEvrqPR9b8P7BNa64Lcy2YiYApJ9st9s1opGWzKrwIGH+kOTW/+2F4FX4d/E7UdQtY5JNB8S27at+4BEixiRDPLEeomtriSK4DEjdHe7FG23Jpv7Mf7Ut38I7aZdVaS98K2sjSamlspk/stThjqlog5a2YMHuLdRmIt5yKAZFbNzcoqdHW6vbv6ef3nLTzPOcPkVLjPg+SqUrfvsPL3kmvicPtRae6i1dNS6u3O/Cnw/quv28Nh4gkmvtBvlDw2+oXiarazxnGHjaRDDLGeow4I7k5rmfCXgHwX8UbbW9Q0H4QNp66Tq0+jT3kepN4OtpbmIIWIexkF1jDqSTas4zhkBBA+sPHf7Ivh3x3E3iL4f603gbVtWAvheaMkV1o2sbxuElxYtm3m3g586Py5yDkSivnX4qajrnwN8TxXXxIsbX4e6iyLZnxtpml3Gu+EdftVzthvYxJFc2bxs2+IzyOIPnAuGjllib1MnzDDVYukn73RSf6df60Pnf8AiLGScVVIfXKcaNTaUXGDv/hlJaNdpfeVvCHw7W5vLnS9E+Ivxw+H/iLS4RcX2g2/jRdSW0iJwtxCb+1nN3ZOfuXEbAchJFgmDwpcub/xxpFybWP9qHxpaypx5WoaHpV1Kv15h/lV3/hWUms/Efwl481rxdb+J7Hw/Z3EmhrBodsbGMzYIvbWWKdkEg2hfOTeXjLKxPysvA+P77Rfif4y/s3V/EaQw7ibK5t4oHt2z/Cz43xuDwQTg8HvgdGZTpU2nCMW30aW/q7M/ReFfCjIc3UpZjglGK2lHVuPdqN439H5nXTRfEPUeJv2qvFe0/8APt4Y0uE/n9ob+VZGpfCV9eDf2/8AtI/GrU0b7yW2r6XpqH8raQj8657x/wDs9eDPhRp2nXnijx7Z6HZ6pJ5FtPeKsUMsv/PPzMFRIRyEJDEcgYrEWw+Bdi226+Lvh+Rl4KpfRsR/wFEY1y0/re8cPFedl/kepU8OPCrBT5ataKa6NWf3No3j+yf8EdXuvL1LXviBr+oSnEd1ffES4N1E/wDfjEKxxBh23RsPY1u6Z8EPjD8GALn4RfHa61KzTmPw/wDEOAOrD+6moW6lT6fPAg9W61g+C/DXwY+LerL4a8J+NLPUfEtxG0lrApuYZrzbyfIFxFGlww6lIizAc4ABNZ+qTeNv2d7xV+0teaWzYR3Bkt2HoQeUb2z+dGIxmIpK2MpJwfkmv0O2n4TcCcS4d0cmnFyWi2Tfo7v7j0Q/8FYPiN+zXJFD8efg/rXh6yJCDX9MAvtJm91uYS0XPoSD7V9DfBb/AIKN/CT462Mc2i+KrFXlGfLmcKwrwD4Vftf2t2ptLmaTRpbpfLlikIls7kHghg2VIPowrE+KX7AfwP8Aj3cPqb+G7jwF4gmy413wROLHex/iktcGB+euFUn1rmeVZTjFzU7035ar7uh+D8ZfRqxeBm3hb+j0+7o/kz7603xnpOsRLJa6lY3Ct0KTA1fivYZ/uTRv67WBr8otR/4Jm/HvwNIz/C/43+HfFtivMNprHmaXfADorA7kz7g4rJvvh/8At1fA25LN4Tm8UQyDzPO0bUY7xF/2Tgg5rmlwTVetGrFn4fjvDPNsLJxnFq3dH67F8VDd6tbWEe6e4ghUd3cCvyZ034v/ALcXiVPs9r8I/FsLHjfchIF/NmFW5P2UP20PjN83ibxB4P8Ah/YyffbUNbEsqD/rnDuP4VEOCcV/y8nFL1OTC+Hua1pcsYP7mfoz8Rv2tfAXwvtnk1TxBYqYxnakgJr5B/aI/wCC8fgvwO8mn+F4v7U1BiUiWMeYzt0GAK858Nf8EcvCc92l38VPjJ4u8cXH3pNP0G3FjbP/ALPmyFnx9AK+gPg78DPhV+zVEv8Awrv4Z+HNFvEwP7UvozqGoOf73my5wT/s4r06HDeW4fWvNzfZbH6TkPgXmWIaliIuK/vaGP8Ashf8FovB3xvggsNekXTNU+5IHO1lfoQQfQ19o+F/GemeMtPS60y8t7qGQAgo2a+Ev2mP+Cfnwp/bKuJdUkhX4ZfEVvmj8RaNEI7a8k7faYBhWycZYYP1r5b1Xx7+0J/wSk8VW9t42tZta8Is4Fnr+ns09jcpnglh9w/7LVljeFKGIj7TAS1/lZ87xd4V47K5tqLt96+R+0mTTgcivjP9lf8A4K3+EvjNplvHf3EMNy6gH5gOa+ovDvxj8P8Aia2WS11KBlYZGWr4nFZfiMPPkqxaPyfEYOtRlyzidVRWdF4p0+eURreQlmGQNw5qz/aMOM+fFjr1rk5X1OblZYoJrB174kaP4at2kur+CNV6/NXzv+0N/wAFOvCPwlsJxb3kMk0YOCW710YfA168uWlFs3o4WrVfLTjc+lte8T2Phmya4vrmG3jUZyzYrkfh5+0X4Z+JGrXlnYX0LTWsvl43g7j/AJNfj/8AHv8A4KeePf2ovGH/AAjXgHT9U1m+vH8qKGyjaQ8nHOOAPc1yGq6p8dv+Cb3j3S774h6Xf2Nnr6rdrcKxeFWYcxs3QOOhFfYYfgmvKi5VHaXRH1eH4OxM8O6rP3sDZHtS18d/sU/8FKNF+Muh2tvfXUf2hgFyTzmvrrTNWg1e0Wa2lWWNxkEGvj8Xg6uGm6dVWZ8picLUoT5KiLVFIppa5TnCiijNABTS1DSBF3M20V5f8dP2ndD+EGiTyTXUbTRqcfN0rSjRlUlywWppTpynLlielPqkMd4tuZI/OYbgueSKK/LmX/gq5Hf/ALSEMIvF+zLlMbvl+8KK9upw3jI203XY9SpkuIjbTdHkPwn/AOCEHjr4rfsut43utWbTfiJqTC/03TZmx5lvtJ2N/tE4rx34V/tO/ED9jH4kN4Z8ZWl/pN7YymNknyqsBxlT0Ir9gvE/xU1DWfFSX1tM1qlr8tukfCog4Ax+HSsH9oH9nr4Zft++D20fx5psFj4gVNtrq8KBZo27Enqfxr9R+v0MVejWWnQ/rLiDwqqxwUalNcztquxx/wCyl/wUx0nx3p1vDeXUe5gB8zdK+svDXxc0fxNbJJBdRYcZ+9X4dftR/wDBOH4w/wDBPrX5NU0dbrxN4QVi8N/aAuUQf3gKk+A//BTzVPDkaWt9czRyRfKyuSGU+9fMZpwbzP2mGeh/O+ccH1KU3yqzP3bbxJZKm77RH+dUP+FiaWAxNwnynHUV+VSf8FSfN08H7Y2cf3q4PUP+Cnk0M94v2zo+R8/4V4dPhPEy3PBhw3Xe5+svxA/aT0HwXYSSSXEe5FJ+9X58ft3f8FWo9Ms7qy027UNyo2NXxv8AGX9v3XvH0hsNNluLq4uTsjjiBZnJ46Cvo7/gnd/wR71H4pXFv8Tvji01loMLC4tNIlJEl2eo3D0r6bK+E6WH/fYln1nD/Bc69ZRs22ee/sZf8E/vHX/BSbxh/wAJh45uLzw58MrSTzJrmU7ZL8ZzsjB7H1r9ONEudB+CPgiy8G/DfTYfD/h3S8BfIXbJcuv8bn+Inrk1N4w8bw3unW+h6Jaw6R4d01BDbWkC7ECjpwK8t+KvxYs/hzpTMzrJdvxHGDXbmGaLl5afuxR/YnAPhpTwvJzw5qstlbY7T4xeA/hx+1r4b/sb4qeEbHVsrsi1WCMR3kB7EOOePfNfFXxq/wCCGPjP4ezTa98BfGy+JNNX94mk30vlXcY5O1T0b0r6J8A/tE6T4oCRXTfY7hsAhjgE16Tomvy2ci3Gn3jRt95Xies8HnTcbO0ondxZ4T4WtJqdN0p+mjPy8i/a8+Nn7I+sf2X438O+IdHktjtLTwP5ZA7hsYxXeab/AMFo9QbT8NdOGxjG7pX6YXfxOh8WaX9g8W6HpPiixYbWS9tkkOPYkV5f4q/YN/Ze+Jt99r1H4dxaVcsdzGzYxoT16c12cuWV3zVI2Z+F5t4N4unNuEFJd0fnZ4n/AOCvGvXusPJbzzMrR4O0ng1h6R+0R8bv2u9ej0PwVoWu6rdXR2gxxusa+7McACv1U8B/sgfs6/DLTLu10T4dWOpJIonna4TzGRVwN1devxK0/wAIac1j4N8P6X4ZtWG1ja26o5H1Az+daXy6ir043aKyrwhxNWolKCiut+h8Z/s2/wDBFXRfAtza+LP2htcfxTrmRND4Ysp82yN1Amb+L3AOK+sNc8aqvh+HS9NtbHw74d09Nltp1lGILeFB7DrXP+LfGVvolvNf6pd9izNI2WavnH4s/Hq88cTSWdizWunqe3DSD3rxM0zx2s9OyR/SvAPhTQpNexjdrebWi9Dufix+0rDo/m2Oh7Zrj7rT/wAKfSvOtE+Pnirw5IrPdNdRyHcFnThh7Gm/Bf4TT/EvXN8oaLS7dv3z/wB8/wB0V9OT/AC38V+Eoo/7BS6sYoz5YjQGUIOC4A+baDxuHGa8GjTxWKvVi7dj9dzLHZFkXLgqsFNv4m2r37evkjx7Qv2vEAVdS0plPdoXz+ldjoX7Snhe/YH7ZJZyHtIpXH41xvjL9kcBpJNDvDG2cfZ7gZx7bv8AGvMPEvwo1/wmzfbdNm8tT/rY18xD+I/rUyxGMo/Grm9HKeG8yV8PPkb6X/R6fcfWWh/Gm3vnSOw8SSMzkBUS5bJ+gzVb41fGjWvhT8INa8UWll/wlWuJPZ6NoGkXtw4h1bVL65S2t4G5Bx87sSDwEJ7V4j+yrJocHiG4F023XG+W2SQAKy99v+16jr6V1nim+uPib/wUQ8F+EtVMnh/wT8KtGm8aaG1ywU+PNbkRYhPbgZ8yOyWQkxj95GUZnVdwx9Hks3iP3lTZa2ufinidQpZP/sWDi+eo1GMnG1r7tNfg+rO++FXxD0n47afqEugRXVj4g0Vzb+I/Cd9/yG/C1ypIeGeLhnjBGUnQFJFKtkZryf8AaZ8N69Y6a19d66rab5gjSzCGJmY9sZOfxrW/bE/Z40n46fDLVfEWk6LrifF7wzZoNC8TeGruSy1yCJJEDoTGym6WKFpZFibLkRhI2BKitjxV4j0743aDoXjrwncW/wARPDADf2fMLti900YEZaTeu43CBQHSQK4YsSORRmmFp1cP7ajfXp2L4Fz7HYPN/wCzsx5W4JOMtLzXTSWz7u+h578G/wBnE+IoP7Y8QtHp2k20ZumSeRbdFhUZaWWRiFjiAySzEADkkCpNP/al8O/HHxJ/wifw/wDiV4T+GngixPk6n45ur+G31K9A4aHRrdiGiU8j7ZIoc9YggxI3Rw/ti29jbXVjd6DfQxzjy7q1lWKRHwfuujjBAx0IrOvPj58PNVGbv4e+G7ps9ZvDWmSH9Yq87A4jAYek4xlaXe1/wZ6viVwvxxxLF0cJJUab7au3k1p8+p9CfBz4s/su/sj/AAul0vw342+Htjptsj3d9PJqsNxeahIAWeaZyS8srckk5yTXmPxH+Kus/tT6ZJq2qR3fwx+DewT2+nRKbDxD4vgP3ZbqVMSWNlIMFY4ytxMpBzEpV24KD48fD3SZ45bH4aeFoJ0YOjxeGdMidWByCCsWQQe47ipJNP8AE37VPi7zrxb3T9DEu5Ufc81zIT155dyT17Z4zXNRWGpTdSg3UqPZvZefZ/M/HOEfox/2bjnmHE9W9OOrv1fp1fl95lav4y1X4wfZ/CfgrS4dD8KaXELeG3tIltbaCLJ42phY077AMsSSdxJraj0rw5+znrumaHDo998QPiprUay6Z4b04qLtkb7s8zMClja9/OkDOw+4knONVPEuqap49k+EfwJsdNu/F2mnyfEPiaaJbnSPBHZwc5S71Af3GzHE2N4dgYx9X/sr/sf+F/2U/Dt0umm71nxNrUhutc8R6nKbnU9auW5eWaZsscnPGarGVaWCftcV+8rPZdF5vsZ+Kn0gMNlFB5DwrFQS0urfjbf02PL/AIZf8E/b74nahY+IvjzqFn4ovLOUXOneDNODReF9BkH3W8oktdzr086dnbOcbVwo+lNe13R/hp4OutQ1C40/Q9B0S2Ms0zlYLezhQck9AqgDoPwrC+Ofx98I/s2/D+68T+M9atdF0m0X78rfvJ27JGnV3JwAq85r5G8W3vif9snxNY658SdLl8PeALKZbnw/8Pbofv8AVJF+aO91lO6j7yWJ6nBmAUGJ/Hw+Hxea1Pa15Wprr09IrufylkuR55xlmVuaU3J3lJ3aiupc+KHx+b9qWbTNQ15ZdB+Ft2y3XhjwvLE0msfER1YeXez2aBppLFW2tFaqjeccSz7YFCS497420Px/8OvEHiLxJofiLw6vhu8uoNXttSiU3bNEUdtgViJJZFeGNNpKmSTbG8mA5PDPx40PUvit49VbrVbzxdd6nDplhpdlYSnUtZ0+0QwNLE0gSJ7X7cNUdrhpUtoldN8itIoO18IfBV18WP2g/C3gfUI7drXwjcXXxF8ZxwTfaIVu7rUbi60fTGlwA5VpjdSADBNraMuVwW+4rYXDYPCSSjaMUnvv/wAF7X8z+isvzDC8FZRKvl0laKley1lPaLlJ+eqitkj1a98OX37Pf7BfjrVdVhjtPFms6Je6rqiQnItr2e3EMFqh7rboLe2Q9WEIbqxrwMfDC4/4XZ/q1/sj+1923b/yyx9jx6Y28/T2r6P/AOCgGvrF8LPCfh8nc3jPxxoWmOnd4I72O7uOO4ENtJn29q8OHjDxYPiT5n/En/4V7/a3/CNedj/Tv7T27vMztx5P2/8A0D7+7z+2Oa8HI8LPFUZYiW7k391tDy/AXPp4ahj8xxd5SxU1Bvyak5N+SvqevWXhy+/aF/YJ8C6rpUUd54s0fRLLVdMjmOBc3sFuYZ7Rz2W4T7RbOeqict1UV4fZeONF8AfDrw/4i8N6H4i8RL4mvLW30i20yJRdq0pdl3hmAjljVZo33EKJE2yPHkuPoP8A4J/68s3ws8WeH1O1vBnjjXdLRO6QPeyXdvx2BhuY8e1eLfF7wVdfCb9oPxR4H0+O2W18XXFr8RfBkdxMbeFru11G2u9Y01JcEIWaEXMYIwDdXbMQuSpksoxx1TB1ejbV/wAV+p8f4Z8XYvJswxnDvO4xqSly9bTi3bTrzLS3ew34YfH9v2WZdU1DQRLrvwstGa68T+Foo2j1j4duzHzL2CzcLNHYsdzS2rIvkndLBugYpF9laFrmj/Erwfbahp9zp+uaDrdsJIZoys1teQuMg9wwIPQ18O+Jvjvoem/FbwGrXWrWPi201ObTNQ0u9sJBqWj6feRiBZZWj3xJam+Olus6SvbSqj7JHaNgLnhG98TfsbeJb3XPhtpc3iLwBfTNc+Ifh7aj9/pUjcyXujJ/dPLPYjocmEFSIk3zvIY1rVaC5am9ujt59/Pr6mnH3hq8W62bZHaTg/fjFNXur8yW61umuj8j0L4m/wDBPy++Gd/feJPgPqNn4XvLuVrrUfBmohpfC+uyHlj5SkNaTt2mgKNnGdy5U+FyaV4b/aO1zU9DuNHvvh/8VdFjaXU/DWolWu1RfvTwuoVL617+dGFdR99E4z91/Az4++Ev2kvh9aeJ/ButWmtaTdj78R+eBu8cidUcEEFTzkVzH7VP7H3hf9q3w7arqRu9G8TaLILrQvEmlym21PRLleUlhlXDDB7Z9a8LC5w1/suYptbX+0j5nw68Ys84SxcYKblTT1i3t958Q6P4z1T4PG48K+NdKg1zwnqkRt5re7iW6tp4sjja4KyIOuw8qQCNpAr074cfFPWf2WdNj1bSY7z4nfB3aZrjTpQdQ8QeEIRy0trK2Zb6yjGS0Uha4hUE5lUM64DeJdU0vx7H8I/jtY6ba+LtTbyfD3iaGJbbSfG/ZAMYS11A90G2OU52BGPlnGi0/wATfsreLfOs1vNQ0NpcsibkmtpAeDxykikde+Oxr250/Y01Co/aUJbPqvNPc/sbMMu4W8Vcp+sYflp4q26srv8AR+Wz9WfQ3xj+LH7Lv7W3wui0vxJ43+Ht9pt0iXdlcR6rDbXmnyEBkmhcEPFIvBBGCCK+ar/9qTw78DfEf/CJ/ED4leE/iZ4Jvf3Gm+OLW/huNSsVPCw6zbqS0qjgfbI1LDGZQ4zItmf48/D3Vbh5b74aeFriZ2LO8vhnTJXZickktFkkn1qey+Pnw80kf6J8PfDdq3XMPhnTIz+kQpZfUwOGvH2rlB/ZaX+e58Dwr9HrjPh7FrE5fiLK+1rp+plfGT9nH/hH7f8Atjw60epaTdRLdKkEizo0TDKywyKSskTDkMpII5BIrT/Zm8N69f6ct/aa8F03zDG9mUMrBh2wSMfh61qz/tjW99Ba2NnoN7MluPKtbWJYo0QE/dREGADnoBW14U8Raf8ABLQdd8deLbi2+Hnhhgv9oTfa2VrZpAYwYtg3C4cNhEiDOWAIHBrHD4XD1MUnhW7Psf01m2dZrgcilHOYxU4q6k2tbdNdV5St5O51XxU+Iek/AnTtPl16G7v/ABBrci2/hzwlYH/id+KrpiAkMEX3kjJILzuAkaBmycVJ8FPjTrfxW+D+jeKLyy/4RbXHuL3Rtf0iyncw6TqljcvbXEC5JO35EYEnkOD3rzn9jv8AZ40n4F/DDSvEmraLrsnxf8TWjjXfE3iW6kvdcuIXkcIgMjMbVJYVikaJcOokKSMSGFS+Gb64+Gf/AAUP8aeE9LL+IPBPxW0aHxprrWzBv+ED1tI2iM84OPLjvVjBEZ/eSF1ZA205+t9hR5J0afxR1u/yP55WcZj9Yw2Z49L2VVuKgk3ZO1pbau/loj0rWvjVb2TPHf8AiORWQ4ZHuWyPqM1x2uftJeF7Dcftkl5IP+ealifxrzj9qp9Dm8QW4tWLa4vy3CRrlQvbf/teg6461wvhr4V+IPFbj7HplwY2/wCWsi+Wg/E18ZiMwrqo6cFf8T+lsl4SyqeEhi8TJ079HaP/AA6PUNe/a6jwy6bpTMezTPj9K4fXPj74q8SOWS6NrHEdxWBflUe5ruPBv7I6l45NbvDIzH/j3t8/+hdfyr2O3+ANv4U8JSoNBS1sZox5gkQCUo3Acg/NtJ43EYzRTw2NrK8nZFYjPeGsslGFGmpyb3b39L7v5HlHwl/aVh1ho7HXNsNx91Zx9x/rXuuieNFbw/NpWpWtj4i8O3ybLjTr2MTW8yn2OcV8g/Gj4Sz/AAy1vfGrS6ZcMfJcfwf7JPtWl8J/j3eeBpo7S+drrT2Pf70Y9qMJmdWhU5KujXUnPuCsFmuE+s4BKUJLWL/G3Zoj/aS/4IqaL47urnxb+zzrj+FtcyZpvDF7Mfszt1Ihb+H2BOOlfJOsftE/G79kTXZND8a6FrulXVq20mSJmjb3VhkEV+o3hLxlBrdvDfaXd84DBo2wy13h+JOn+LtOWx8ZeH9L8SWqDapurdXdR9SM/lX2VHMqNePLiYp+Z/J/Fng/GpUlPCL/ALde6PyY8L/8FeNestZjknmmVFj2jcT1rs9S/wCC0eoLYbVumLYxgN1r9BfHn7IH7OvxN0y0tdb+HdjpqRKZ4Gt08pnVsjP41zXhX9g39l74Y332vTfh5Dqtyhypu2MiA/Tiqlhcql7zR+Z/8QjxlSooxo38+h+ckv7Xfxs/a41gaX4I8O+INYkuTtVoIH8sZ7lsYxXuHwU/4IY+M/iHcQ698efGy+G9NbEj6TYy+bdyDrtZui+nrX31Z/E6HwlpX2Dwloek+GLFRtVLK2SM49yBXL63r8t3I1xqF48jHlnlkqf7Rw9BcuFgl5n6Hw94NQptSxbt5Lcf8HvAfw5/ZL8N/wBj/CrwnY6Qdu2XVZoxJeTnpkueefbFbGuXOg/G7wTe+DfiPpsPiDw5qoIbzl3SWzn+ND/CR1yK8b8f/tFaT4W3w2rfa7heAFOQDWx8K/ixZ/EbSVYOkd2g+eMnvXkf2tKVa/PqftcvDmjRyxxeHtTel+vqfCf7Zv8AwT+8df8ABNnxiPGHge5vPEXwzupPMhuYiWlsBnOyQDsB3r3z9hH/AIKtx6nZ2tnqV2u7hSHavsLwf43hs9OuND1u0h1fw7qSGG5tJ13oVPB4NfAf/BRD/gj3qPwuuLj4nfA5przQZmNxd6RESZLQ9TtHpXs1qOGzOnyVVaR/LPH3hnLCyc4Lmg9mj9QvAH7SeheNLCOSO4j3OAfvCur/AOFiaXhf9JT5jjrX4B/Br9v3XvAEn2HUpbi1uLY7HjlBVlI46Gva7D/gp7PNPZp9s/jyfnr47E8GVoSfJsfhGI4Xqwl7p+z6+JLJk3faI/zrG8TfFzRvDFs8k91HhBn71flq/wDwVIMVgT9sbPb568I+PH/BT3VPESNa2N1NJJL8oVCSzGsMPwhiakrPYxo8M15z5Wfof+1Z/wAFMNJ8CabcQ2d1HuUEfK3WvzL+Kv7T/wAQP2zviQvhnwbZ3+rXl9L5apBllUHAyT2FdD+y3/wTh+MP/BQbX49U1hbvwz4PZt819dgoXQ/3Aa/UL9n/APZ5+GX7APg5dG8B6bDf+IGTbdavMgaaRu5B6ivtMFlOEyyHNPWR+s8H+HNbFVEqcL+Z+f3xY/4IP+OvhT+y4vji11Y6n8RNNY3+pabC2fLt9oOxf9oHJor9IvDPxU1DRfFD31zM10l1lbhH+ZXU8EY/Giu2lnFNr30frGY+FdaM4qiuZWX3nA+KfGlj4Riia7lC+Y4TBOOtaGn6nDqdus1vIkiHkEGvlf4ufEibx54glIdhbwthFBqT4d/GXUvA86p5jT2ucFGOcCvglmkFVcXsf2FLgSs8EqsX+83aZ9teEfi5daTYPp2qQQ6tpMw2yW86h1ZfcGvD/wBpH/gkB8Ff2vUm1Tw0yeDfEUuWPkHbGzfStbwH8WdN8b2ilJljmwMoTXWW901uwkhkZG6hlODX0eDzadNJxd0fi/E3AOFxUnCvDkqeh+dvxC/4N/vjB4S1podK1CPVNOZsJMnPGe+K6bwJ/wAG6XjrX9BtLrVdbWzuppitxEWxhPWv0X0P41a9oaKguGkUcDdWrH8TPEXimx1C+t5GH9lxK8ig9UJIJ/DGfoDXtxzpSXurU/L63hcqMuacly9793oeGfsn/wDBJL4V/sRsviDxB5HibxFD80QmAkWNvoa9T8f/ABHuvHl5tZvJs4uIYE+VVH0H8qxtS1m61u4826laV255PSuS+JHxEtfAWjSTyOvnYOxc9TXiY7MpVdZOyP1HhPgihgpKFGPNUe3l6FX4q/FS1+Hmju29WumGEQV8xeI/EV74x1hri4Zpppnwi9hnoBT/ABj4vufGWsSXl07NuY7V/uiu2/Zu+HX/AAlnif8AtC4jzZ2ByM9Gf/61fHVq0sXVVOOx/R2X5Zh8hwMsVW1nbV+fZHH+LPAGqeCvKa8t5FjlUSJIAcc849jVrwj8WNb8HTK1rdPJEv8Ayzc7hivrLX/BUfiHw3uvrHzNPmYxq7J+7yB2Pavn/wCKX7Nd3oJkvNF3XVrncYR9+Me1bYjL6tF89H/gnBk/F2BzNfVswSTvbun5eTOl8IftWWt2Ej1S3+zt0Lr0r0fQPiFpPiVVNreRSM/3Uz8xr4/mja2maOVGjZTgq3BX6ivWv2UfBv8AaOu3OrSt8lp+7jXP8R6nFVg8dXlUVKWpjxHwrl2Hws8ZSbjbZLVN9j1T9ov43eIP2a/2bfE3jLwrYxap4igvtH0rTbGRNy6hLc6lbo1tjr+9j8xOORvroPCvi/R/iz8N9B8beE5mn8M+Krb7TZGQfvLN8kS2kw/hmhfdGwPdc159+1d4iZPiB+zN8O4f3t945+Jlh4jvLcfeFhpjiXeR6b+c/wCwa5z4zal4s/Yn8d+Ivi94N8OyeMfg54snW7+Ivg21bEuh35JD6vZD/lmsnDPgbQ5fdgEEfd08HCeGhSekndr/ACP5SxHEmIwmc4jGwXNQg4wml00+L5PQ5/8AaH0rxQmrtNqW6TTc/unhBEYHv/8AXri/Angu68eeJYdPtV++cySY4jXua95+OXjvRfiF+zzpfiHwnNfXmieNLa0vdMN3B5Vx5czZCsuThvlI4rf/AGbvgk3h/TYbf5F1LUF825mYfLboBkk/7KjJNfFVsrlLGOm7+f8Akf05lvG9Gnw4sbGyv8NtNLJ8zXS1/vLF9q3hL9lz4Kan4r8TTNY+E/CsQafysfaNUuG4itYR/FNK3AHYZJwAa8R+C/7E3xL/AOCi2p6p8a/F/irXvhxr1zsh8H2+j3stqPD9ihJjt4yhBxggs38bEk+g4HxN+0x8PP24f2z9L0nxb4otfC/wk+GN20ejaRe74rnXbwHbJfzxkAqXIwgblU7ZJr9Vvhn8QPB/iXQLSDwtq2j3VhbxhII7WZSEUDgYzXpZxjq2W0o0MKmpPeVtF5I/zs8X/EjHZjjnTwzkoRd766vq/mfD+qeB/wBsP4Afu76Hwf8AHjQ7fgNqtt9j1YIPS6t9jFveRXqx+zf+15pP7SfxU1TwNe/Dzxp4I8Q6HZtfavI13aatpOmRjgebOGjkQu3yohQsx6A4JHoX/BTb9srWvAaaR8JfhnGdU+KPxAnGn2MELfNbBxy7H+FVXLMx4CqTXJ/Db4W+Hf2KfgU3h2zvF1KaOQ6j4j1wj994l1Rhh5WJ5Ma/ciU8Koz1Y11ZfjKtbCPE4+K120s35s+m8G8dxVnGIVONZqkrX6+lr9X0+8p/Ha48N+FtNWabS7K81S4cJY7V8qUvnhiV5AHHem/GjwT8TL6x+F2s/D/SPCnja78D6zdavqnh3W9WOnz6o7WBs4Wt5DhRIqXN4QS33miOGxXg/jD4gXni3xg2sTtmZZQ8CEZWIKcqAPwH1r6F+CnibxN4t0pbrWre1it2GY3CFZJPfFefleaRjiHKMbo/srjjgmrVyanQq1eV3u3fVNWaUU+itrbc5L4w/tZw+Nv2e/iFo/huPVvhr8WtZS08KL4Q8QoLbVtKn1O+gsRPEAAtxb+XLKVniyFI5CnGfVD8NtP+Dfw103wD4JihsNG8I2a6TppI4LL/AK25cfxSSybpGPUluuBXSJ8ItO+N2reG9S8TeGbDxEvgHUYdX0fU71vJm0SeKQSKY7jIIjLKu6JiUbAyMgGvE/2kf+Ckvwg/Zs8Q3Hh/TV1L4ueOo3xLp2hyhNPt5D/DJc8g4PULk19NUpuvRVPDxsrtu+n4n4VRx1LI8fPGZ1WjOcYxjBqzbS7ro+/cr2/7JEd7dNNqWuXNxPM2+QpGNzk9eua7HRv2LNH07TGv7rT7w2MI3SXmoT/ZbVB6s7FUA+prx7wX8Y/2vP2tZ1XwXo3hX4N6DPwsum6Yt1fKp7m4uA53c9VVa9K8Kf8ABE5vihqcOrfG74j+MfiNqGQ5h1LU5riJT6BGbao9lAFfO4ijlWE/jTTl2Wp53EP0oZ0ounhpN221svuj/mYPiP8AaZ/Z/wDgnftYw+JtF8R61Gdo0vwbp76/ds393zI/3Cn/AHpRitDSNQ+Nf7XNiNG+H/gPWvg/4T1Vhb33jDXL9DrqWh4kFpBGvl2zuuV8wPI6gnaVPI+vPgt+xr8M/wBn6wih8LeENHsGhAAlNuryH3yR/Kux+JPxG0j4ReBdS8Ra5eRWGk6TA080jkKAFGcD3PpXlz4jgmqeApa9G9X8l3PwDibx14gzhSowlyqWmm/+f4nOfs7fs5+D/wBkv4S2fhfwnYW+l6Tp8YMsxwr3D/xSyN3JOTknvXj3xp/4KS2P/CXXngf4O6K3xQ8eWy/6U1vKItF0AHjzb27P7uJBzxnLYwoY8V4MmseMv+ChWht408eeKte8G/B3V7iSTw94T0VxY3viCxQ7Vu7u6+9DbyENtCL5kijcGRSrFdT+M+h/DXw3b+B/hb4f03TdPt5D5FlpdsVtUkPBkI5eeY95ZSzN1LGtI5RRoP2+Yy5pvXlXfzf6I+h8N/AHNuIqkcbj7xpPVt9u7fQs6xpdv8OPF1v8Qvip4k/4WR8UUJk0x1jMeleHG/u6XasPlYZx9rlHm8ZRYckNk+E/it4m1y51jxdcW8cNhZwsY55SfLtwDnZCvG+Vm2jeTgH8jnX/AMPtL+Hnhubxx8W9bNjp7Oyx2pJlub+VVLmGJF+aWQKCxjjwEUFnZEBYbX7Qnjxbz4M+ELK30eTw/LrlpBqEumu4aa1jZRLHFJtAUSDfEGVRhXRgC2Nx6q0q8qX1ia5IR+GKP7Q4TybhnKK0eF8mjGpVa/eSW0Yrf59Nd/Q9D0/4taDa+HvEPizUVhNh4d0j7T4h1WGJftF9DaJLcmHzPvSbN8gRc7RJNwNzMT7B/wAE7Phbqng34BHxP4nhWPxt8Tr1/Fev46QyzgeTbL/0zggEcSDsqAV8xeKvhx/a3hL4R/B6FT5nxM8RwvrCgf8AML00x6heZ9N1wbBPdd46V+isMSwRxxxqqRxqFRRwFAGABXDxBi6kcLCjJ+9LV+i2X6n8Y/SOz6i82WS4DSlSbbt1k+rPl/8Aa/8AE7337afwV0W32yf8IrpviDxjdRknAH2UabATjp+9vlwa8N/4SPw7/wAIt/wpP+0G/wCEn+3f8Id9m2N5n9jeX9r/ALR37dn2j+xv327O77V8+Mc11PirxxH4y/am+Onj8ssmn+ELWy8B6XIDwzW6G/1BR/21k04fVSKT/he+k7ftf2Gw8vy8fbfsyeZ9t+y/38Z8z7P+535zs/d9OK97La1PB4WnQqb8t/Ru7/U/RvCLgfHVuF6NfDaOpKXNpvCXLdertoelfsfeJ3sf20vjVo1xti/4SrTdA8Y2sY6EfZDpsxHr+9sWya6j/gon8LtU8Z/AL/hJ/DEKyeNvhjex+K9Az/y2ltwfOtm/6ZzwGSJx3VyK8N8KeOI/Bn7U3wL+IAYR6f4vtb3wHqkueA1wgv8AT1P0lj1EfVgK+6polmikjkVXjkUq6nkMD1H5V8xnNWWHzCGMh9pKX3aP8vxP538TcDWyPi6vye61LmX33Pie++LOg3Wg+HfFunLD/Z/iLSPtPh3VZox9osYbtIrkQ+Z96PeUjDqDtLwDI3KpHmfiz4r+JtEutH8XW9usljeQr5k8RIjuATnZMvPlyo24bgcH9KveFvhx/ZfhP4t/B+ZW874Z+I5n0dcf8wvUTJqFnj123H2+P2XYO2Kh/Z78erY/BjxfZ3GjyeIJNDtJ9Qi01HCzXUYUyyRR7gVMh2ShVIwzuoJXO4e7i3OpXjShKykrxfrrY/v7gHOMvxPC64k9kptJe1jbdWs5addPw7l7RdLt/iP4suPiD8K/Ev8Awrf4pSfvNSdozJpPiNv7uqWqj5mPT7XEPN7uJsAL7f8ABf8A4KS2K+LrPwP8ZNFb4X+Prlf9Fa4lEui6+o482xu1zHKh9M5XOGCnivmrT/h/pfxE8OQ+OvhLrbXtgrqHtgTFc2ErKHEMiN80Um0hhG+Q6kMjOhVjvab8ZtD+Jfhy48DfFLQdN1LT7hwZrLVLUtavKOBIBw8Ew7SxFWXswrDEU6Vd+wzKPLLZSW/+T/rU/N+PPAvI+K8O844Xmry1aW69UvzPtj9oj9nTwf8AtafCW98L+LNPt9U0nUIyYphhnt3x8ssbdiDg8GvjXV9R+Nn7I1j/AGL8QPAes/GDwnpLG3sfGGh36DXWtBxGt5BIuy5dF+XzC8bsoG4sfmOe2seMv+Cemhr408B+Kte8ZfB3SZ438Q+E9acX174fsXO1ru0uvvTW8bFdwdfMjUhizgMw+/vht8R9H+LvgTTvEWh3cV9pOrQLPDIjBgQR0PuPSuCUsVk9krVKMvu/4DP5IqS4j4CzB003F/gz4B8N/tM/s+/Gq/Wxn8TaL4d1qQ7TpfjHT30C7Vv7vmSfuGP+7Mc12utfsW6PqGmrqFpp94tjMN0d5p84urVx6q6llI+hr6X+NP7Gnwz/AGgbCSHxT4R0e/aUEGYQKsn5gV8o+LP+CJzfDDU5tW+CPxH8YfDnUMl1h03VJraJj6FVbaw9mBrso5plWJ/jJwf3r79z9h4b+k9meHtDFX+Ta/4H4GLc/skR2V0s2m65cQTxMHjLxjcpHTkc16gfhrp3xi+GmpeAvG0dvfaN4ws20nUiF+VWbiK5QH7skUmyRT2K9ea+f/Gvxi/a8/ZKlZfGejeFfjJoUPDS6lpa2t8yjuLi3CHdx1ZWrt/2b/8AgpH8H/2lPENv4f1JdS+EfjqR8RadrkwfT7mQc7Y7ngDPYNivcwWXwg/bYKSkt9H+h+tf8RmyzijDfVMfPdNapO113X+Wm5U+D37WcXgn9nv4f6P4kj1b4mfFrRUu/CjeD/D0f2nV9Vn0u+nsTPKCCtvb+XFEWnlwGJwAxzjovgx4K+JljZfFDWfiBpHhPwTdeONYttY0vw9omqnUJ9LdbAWczXMgypkZLazOQ2dwlOFzivVm+EWnfBDVvEup+GfDNh4dXx9qM2r6xqlk3nTa3PLIZGMlxkkxhmO2JSEUE4BJJrzP41+JfE3hLSmutFt7WW2UZlfYWki98V05hjqdKM1GG+/c7uDeFsZi62GrV8Qv3ekIprl7e8+raZV+Bc/hnxZprTRaXZ2eqW77L7cnmyh88uC3JBOe9V/2kf2vNK/Zs+Kul+B7P4e+NPG/iLXbNb3SJFu7TSNJ1SM8Hypy0kjlG+V1CBlOMgZBPhvg74gXnhHxgusQNiZpS86AYWQMcsMfifpX0R8Svhf4d/bW+BS+HLy7XTZpJRqPhzXAP33hvVFGElUjkRtwkqjhlOeqivGyPFUaj5KiV/61P0Dxc4dzrC4V4jKarTtdddbaxu/wfVaHP6V4I/bD/aAPl2MPg74D6HPwW0q2+2asUPrdXG9g3vGqVw/xp/Yl+Jf/AATp1PS/jZ4Q8Va98R9etd8XjC21i8luh4gsnIMlvIXJOMAlW/gbBHTB+gv+CZP7ZOtePE1f4S/EyM6X8Uvh/MdOvYJm+a5CDh1J++rLhlYcMpBr6W+JnxA8HeG/D11D4q1bR7WwnjKTx3Uy/OpHIxXDis+zLDYz2U4qyfwpbo/znzLi7P45ipYuo3OD217nyrZav4R/aj+Cmm+KvDMzX3hPxVEWg8z/AI+NLuF4ltZl/hmibII7jBGQRXy/478F3XgLxLNp90v3CTHJ2kXsRVPwz+0z8PP2Hf20NU0fwn4otfFHwj+J12ses6RZ75bnQrwnEd/BGASxQnDhclk7ZAr6k/aR+CjeINNmtyEbUrBfNtZk+7cIRkEHurDBruzrLlVpLEUla6vZ7+jP9APA7xQWOw8aOKdnopJ/hJfr9543+zxpPil9WWbTt0Om5/evKCYyPb/61fQnivxhovwm+G+u+NvFs7W/hfwpbfar1ox+8vXyBFawj+KaZysaqO756VwPwN8d6L8Pf2edV8ReK5r6z0XwXa3d7qZtYfNuPKibJVVyMt8wHNcz8GtT8Wfts+O/Dvxe8Y+HZPB/wd8Jzm8+HXg66OZdcvwQE1e9H/LRY/vpn5S4TbkAk9WSZfFUVWqP3V/VjXxO4uqyzOWWYOC9vK6Vlsr25m+vdHon7Onxu1/9pP8AZt8M+M/FVjHpfiSe+1jStTsI12rp8ttqM6LbAdf3UZROecKK0dd+IOkeGkb7VeQqydU3fMK439lHxC0nxA/aZ+Hc37q+8DfEu+8R2cB+8dP1JzIXA7rvwc/7Yrjf2rvBn9n63bavCw8u8+SRc/xdjijPXLDzdSC7My8J40c1wtLC4mbTjeLa6tP9Ub3i/wDattbUPHpduZ26B26V5R4u+LGt+MpWa6unjiP8CHbxXOwRNPOsccbSMxwFXkn8K9U+Fn7Nd3rzR3mtbrW1zkQ/xyfWvk1UxOJlZbfgf0J/Z+T5LT9rNK/nq36HB+FPh/qnjUytZ28jRwqZHkI+U4GfxNVfDviK98G6wtxbs0M0LfOvrjsa+xtA8Fx+HfDebGxMenxkRtIqfJkj17mvnP8AaS+HH/CJ+J/7Rt0xZ6gcnjhWrbEZfKhTVRbnn5LxbRzTFTwlSKUHovl0fmex/Cv4qWnxD0dW3Kt0ow6V6j4B+I914EvNqt51lNxLA/zKw78GvhXwf4uuvBmsx3lsxUqw3KP4hX1J8N/iJa+PdGjnjkXzsfMmehr0suzDn0btJHxfGXCEKF3y81GXTsUf2sP+CSXwr/bcZvEHh7yPDXiKb5phCAiyN+FfI/jv/g3S8daDoN3daTra3t3DOFt4gwOU9TX3ppms3WiXHm2szxMOeD1ruJPiZ4h8K2On3txKx/tSJpI1PZQQAfxzn6EV9fh84ly2l0P5vzrw1w8q16El72y6n5W/Dz/g39+MHi7WRBqmoR6Vp6th5n44z2zX2h+zd/wSA+Cv7IccGqeJWTxl4ihAYCf5o1b2Fe2658ate1yMqblolbrtNcpcXL3DNJNI0j9SznJqMRnbatTR0ZP4WUKU/aYp/JanXeLvi3datYJp+lww6TpMI2x28ChFA9MCuJ1DU4dMt2muJFjVeSxNc347+LOm+CrNmknV5ucIK+f/AIi/GfVPHUzqJGgtegRTjIr5fG5oou83eR+88McEzqRUKEPZ0+/9bn0l4V8aWPiyKVrWRW8tyuM+hor5p+EnxIn8C+IUyzNbyEb1JNFY4fMKc4XqaM9fNuEMXQxHLhlzR7mD4W0KTxN4js7GNSzXEgDY7DIzXS/Fz4PXfw8vvOjRpdPkPyv12e1dT+yn4NF9q9xq0q/u7ceXHnua9017w9Drek+TeW/mW9wCAWXg9jj6Vw4TL/a0OaW/Q+lzzi6eCzSNKnrCOkl3b1+9Hxrp2p3Gj3SzW0zQuv8AdOM16r8Pf2mrjTtlvqw3p93fWH8ZPghceALxryzVptNkJOQMmP6158Rn864Yzq4afLsfTyw+XZ1hlVaUk+vVH2B4a+IGm+KoozbXClmHC5rmP2wP2gtY/ZW/Zp/4TrRLVr2603xnocM1mo/5CFs7Ti4t/wDtpEXX6kHtXnP7KukS6j4zmumZvs9lGTjPG416F+2VqUGm/DH4OafcKki+JvjZ4bhEbjIdYZGdhjuMHB+tfZ8P1XWnGc13/I/m3xcwdPLMFWoUJ6+7Z9nzJ/oekXtzY63omn614eu0v9A8R2MWqaNdg5W4tpV3xn6jOCOoINfKfxrv9Ym8YTQ6qrRiMnyl/hYeor1H4VeLNN/Za/aPvP2fdeuF0/wL4ukm1/4S6tcPiGyMszm60SRzwPLmLCMZ4BT++K6b9p3wFHZ+CdW/tSz8nUdNQsu9cMp6de4rDPsuai3B+7uu3oel4U8ZU51Y0sTH96rQkuqe115PqfLtjYyapeQ20K7pp2CKPc19g/BT4YNpOk6XoVnGv2q5YCQgfxHkk+wrwv8AZd8CjXfEkmrTRn7PYjEeRwXr0P8Abi/aGuP2UP2R9W1rTG2+MvHkp8MeGEX/AFkTyLie5H/XOMnBHdhXBkGA9pJSl1/I+q8XuL4YDDTV9Kav6yey+W5418aP+CkHxTn/AGrfEy/BmysfE/wv+GFumh3+j3cHmWviGRXJubgMPmVxISquvZehBr2n9mv9s/4Y/tgSrp+g3zeC/HgGLnwhr0iwzs46i2mOFmHoBhvauy/4JG/sb2XwC/Zptp9Ss0m1LX4vMuDMm5nDcnOfU079sH/gj18Pf2lVk1DTrdfD+uKS8U9uPL2v1BDDkHPvXoVuJMFLESw1WPurRSR/nrlfjDjsvzKpU5rwk9U9U9TB+K37Pmn+JLiWHULGTS9UjyN+zY2fcd68r0Hwn4k/Z58XLeLDJqGizELdeRlsx/3iv94f/WrkPhn8dvjt+zL+1B4T+A+sanonxrs9cvFs7a11WRv7R0i3HLy/bEG5UjjBYh93C4r6w+KWq6T4Rn1a4s3kk0uz3BDId28D+Yz0zRjMtpJRxNOWj1TP7K8PPFOefYV4eNNyg1Zxeq125Xun2PGviZbWvhD9rn4e/tJX15PqvhXwzoEvhrV0X508MpOjw2+rIijJtg0wS4CgtHnzACNwX3KS7vdKv9c0m3WHT5L6G4sXE6edBGzLJGqScMWRSRuGDlc4ycV4H498P+JfjP8AsH/Ey38J6Lca14i1nTpGtNMto9z3SLf2zSRKowTmFJMgcnnvXon7Of7YfhX9snQzr3h+Kbw94w0OUL4s8MXimO+0e4aTDSgNy8BdiFfquVVucV6tOVSvhaeIX2fyPk8XhsFlWe4vJ56Ksrq/82qaT+V1pucV8FrNtT+AXh3wvZ2Y0/xh8G7HTvDniDw/qBHm6de29msauxGQ8FyEaeC4XKOr/wB5WA0/CX7VlnY6s0GpR33h7U7Z9jsGPysPccisf9k7w+viP9q79rb4pX0LfbrzxifAlgrcrFDa4Mxx0P8AqosHqOvU1zv7WMmnjxhZpDCi33lZuJBxuH8II/rXmZ8nhqntaT13aZ9z4S1I51gI4DHU/djeMZLdKNtWtn29T2nxpD4D/aY0nyPGnhnwX8Qrd1wsmp2Mcl5GP9i4TbMn4NXkWrf8Ey/hzbXZvPh940+J3wlvc7litL/+2NOQ+0cxEoHsJDXkVpfz6bL5ltNJbydmRyv8q67w/wDH7xR4fVVXUPtUYH3Jl3152H4mmly1P80fScReAuWY27jCLv5cr/DRnrP7M37Js37PHjXxR468UeOLf4l/ELxBAukafrC2clsNI00KvmgJISVmmbCsykjYuAeTXn/7SPxPbxV4h/sm1kzp+nnD4PEknf8AKrlz+1fqF/oF1ZyafHHdTRFEmjbhSe+Kw/gd8JpviRr/ANqvEb+z7d90jH/lq2c4rPMMyljeWlR/Doj1OA+AcJwjhp1a8eWMNtU22+v6I2vgD8Dz4muY9Y1WFlsYzmGJh/rT6n2r3r4hePPCH7NXwkufHnxA1D+yfC1j+5tbaP8A4+tXnx8sFun8RPqOBU2r+IvC/wAEfhZq3jjxjdLpPgvwrEGnIPz3cn8FvEP4pHOAAPWvjH4S/DPx1/wWI/aSj8eeMrWbS/Auiv5Xh/Q+Ra6Zag/L8vQyNwWbqTx0Fexl+CoYTDuvXdor8WfiPjF4uPC80Yy9+2i/lX+Zk+J/G3x2/wCCt/ib+z7Rb7wJ8LVfbZaBp7tEskWeGuHGDK5xzngelfZn7Fv/AAR/8Gfs5xpe6law6hfEB8Ou7a3fmvqP4Q/BnQ/gx4Wt9L0Wzht44UCl1XBautxmvl824oxGKvTo+7Dsj+D874rxmY1JSnJ2f4lXStHtdEs0t7O3ht7eMYVI1CqBVrbiijNfLNtu7Pl7t6kcsiW8LSSMqRxqWZicBQOpNfnX+0f8RG/4KW/tP3Hw4sbi5T4LfDV0vvGt1byFP7Ykz+501GB+9OwIPdYw7dcZ7z/gqt+25qPgfSLH4V/DmNtW+Inji4TSrK2tjmTzJSFA46DnJPQAEnpWZ4E+GWjfsO/s4W3hK0uo9QuNJ33+u6mPva7rEuPOmJ6lFbEcYPREHqa+14fy2NCl/aNda/ZX6n7f4N+HtTPczhVqxvTTXz2/P/M5z9qT4jTeIdVtfCumrbwveGKKSC2QRxW0YwkVuijhUUBQFHRVArWtdJuPhv4o8O+Avh34V07xF8RPE2nzaiJNRuPs+n6VZwyLE17eOp86RWlLqkERQuY3+dMDd57+zrotz8R/jDNql0GuJbfNwc8hpHO1B+GePpXrX7Pvxx8F/CTxx8bPjh441q10vQbXVU8AeHnkbdNd22l5ilW2QcyGa9M8gC5yZBXRRqylCpjeXmltFb6vbT8T+uvHrievwlkVHKMoly1ZWT5e7XbyVkuxm/tI/so2HgPR/Buh+J9auvHnxM+L2uwaFquuXUawrpugW4a/1S0063UCOztza28qYjALl/nLMxJ5T4iTyfFj9q63t5sSR2brNOAPlG3Mz/8AjzAV3d74v8TfGj4uw/Fjx5pKeBNF8PaNeab4W8OXuTqlvHdtF52oX/O23kaFGiSDBkCzsX2EBTzP7Kmif8LE+MOuav8AM0epXi2cMhHQSSc4+i4rSpCs4UqFd3nJ3l5eXy0PI+j7k+OyrJMbn2apqrUT1lvrovwuz0D9mTSv+Fk/8FJPFmpMvmWfwm8HWPh+A9VS+vyb65I9G2TQxnv+5A7V9KftH/Gmw/Z1+BPirxtqUiR2vh3Tpbv5j95wp2L+LYrwD/gkfIPHXw++JnxHKgt8RvHWp6lA+PvWqTNHbgewiCAewrM/4KD+J4/j5+0P8PfgdC3n6Layf8Jv42ReV/s2yZWjtn9ri4aCHHpMT2NeXjMP9czj2P2Y2T8lFan8YZ1GtxDxXUhDV1J2+VzxW+0i++Dn7GvhrRdW3J4o8Tltb17dxJ9uvpDe3Ct7xq0UH0hFb3/CqF/4ZN2/u/7Sz/ankZHm/wB7dj02fL9eKp/G++m+M/7QVno/medHauXumA+UFj5kx/LA+tWv7evv+GvP7T3Sf8IV5n/CocZ/c/2r9m/tXzMdN/2r/QM9c8e9exGnHGV6s5bW5V69Ef6HZhnmG4JyTKcuT5XOpBteWyMOx0q++Mv7G/iTRdI3N4o8Lldb0DbzJ9usHF7bqnvIqywe4mNffn7N/wAabD9or4EeFfG2myLLa+ItOiu+D9xyo3r+DZr4V+CN9N8F/wBoO80fzPJjuHD2rHplT5kJ/LI+tesf8E+PE8fwD/aG+IXwNmb7PotzIPG/glGOF/s29Zmltk9re4WeHHpCD3FeXjsO8TlvN9qk/wAOv3afefz79K7hJfXKefYZe7UV212ev+Y39pzSv+Fb/wDBSTwnqSr5dn8WfB194fnPRWvrDF9bH/e2QzRjv++I715V8O5pPhP+1dcQQ/u471zLAD905xMn/jykfhXtX/BW9x4E+H3wz+Iyrz8OfHWmalO+Pu2rzLHcA+xiLA+xryj9qrRx8O/jFoerfMsem3jWc0gH8McnH/ju6tqNRzy+jiFvB2+Sf+TPpvos5x9cynE5LU1TUkl8rr8Ua/7Nv7KGn+PNI8ZaH4Y1q68B/Ez4Q67PoWla5axrMupaBcBb/S7TUbdgY7y3FrcRpiQEoY/kKsoIiu9JufiP4n8ReAviJ4V07w78RPDOnw6iZNOuftGn6rZzSNCt7Zux86NFlCK8EpcoZI/nfJ26ll4x8TfBj4uz/FjwHpKePNF8Q6NZ6b4q8OWQP9q3Edo0vk6hY87biRYXWN4MCQrApQOSVEv7Qnxw8GfFzxv8E/jd4I1q11XQbrVX8AeIXjO2a1ttUxFEtyh5jMN6IJCG6GM16T9tLFOlUXNSqfC97Sa2+/ofluV5tn3BHGP1WTlDDTqaJ7Wb0t23OQ/Zb+I8/h7Vrnwrqa28z2ZlijguUEkNzGcpLbup4ZGBYFT1ViKh/Zy+Irf8E0f2oLb4b31xcP8ABX4lvJfeCru4kLjR5M/vtOdj/FAxAHdoyjetcz+0Votz8OvjDDqlqpt5rjFwMDG2VDtcfj3+pr0/x58MNF/bi/ZvufCd3dR6fcats1DQdTI+bQtYiB8iYHqFZsxyAdUc+grHA+zrQnl2J26eXZo/pDx08PaHEOUQzbDR96Ub6d7ar79V5n3XHKtxCskbLJHIAysDkMD0IqQrmviP/glT+27qPjfR774V/EZG0n4ieB7h9Kvba4bEgkjO0jnqOMgjggg96+3Aa+Hx+BqYSvKhU3X4o/zXx2CqYWs6FVaoq6rpFrrVm1veW8VxbyDDJIoZTXxz+2l/wSA8GftGRPeabaw6ffAF8Iu3c3bmvtDrSbaMHjq+Gnz0JWZOFxlXDy56Tsz8ZPC/jf47f8Ej/E/9n3a33jv4WNJtvdA1BzKscWeWt3OTE46jHBxyK+3fh7488H/tK/CS28d/D/UP7W8L3x8q6tpB/pWkT/xQTp/CR2J4NfSPxe+DWh/GfwtcaXrNnDcRzKVDsuWXIr8qPiz8M/HX/BHf9pGTx54NtptU8C6y/leIND5NrqdqTz8vQSLklW6gjHQ1+h5XnVLM4+wxFo1Oj7n7x4a+KmIwGIVGvL3Xa6fXzXZnpvx/+CB8NXEmsaVCzWMhzNEo/wBSfUe1Vf2bvie3hbxAuk3Um3T9QbCbukcn9M19FaT4i8L/ABw+Fuk+N/B10ureC/FURaBjzJaSdJLeUfwyIeCD6V8y/HH4TTfDfXvtVmjf2fcPuiYf8sWznH+FeNmGBnhK3tYaW3X9dD/RbhPiXC8Q5csFXlzcy9yX+fmj1D9pj9k2b9ofxp4Y8deF/HFt8NPiF4fgbSNQ1hrOS5Gr6aVPlApGQWnhbKqzEDY2CeBXK6T/AMEyvhzdXf2v4geNPid8Wr7O5oru/wD7H01z/wBcoSZSPYyCqtt+1fqFhoFtaR6fHJcwxhHmdvvEd8VzWv8A7QHinxApU6h9ljP8MChK9GXEkIxXLv3tr95+cr6PuFxOYTx2IpK8n1enqku+59G+C4vAn7M2keT4L8NeC/h7BGuGl02xjjvJB/t3D7pn/FjXIeLf2rLO+1dYNNjvvEOpXLbEZmPzMfc8mvnS8v5tTl8y5mkuJD1Z3Lfzr1D9k6TTv+EvvEmhRr7yswSMMlR3A/xrzv7Yr4mooN2v31P0Sn4d5ZkWCniY0+ZxW0Vyr5ve3z1On+NFodM+APiLwveWf9oeMPjJY6j4c8P+H9OI87Ub64s2iZ1J4SC2DrPPcPhI1T+8yg+2xXN5q1/oWk3Cw6g9hDbWSC3TybeRlWONkj4UqjMDtGBhccA5rw39rTw+vhv9qz9kn4pWMP8Ap1n4xHgS/Vfuyw3W4xE9h/rZckcnr1Fd1+0X+2H4V/Y10P8At7X4ZvEHjDXJSvhPwxZq0l9rNwsuElKqcrArgBn6ttZVyc19hHCP2NKlS15tWfztW4giszx2YZglF0koR+d7W63frscL8NLe18X/ALXfxC/aSsbufSfC3ibw/F4Z0lD8qeJ1gRIbjVnQjP2YtCUtwwDSY8zAG0Nh674S8SftDeLvtjQy6bo0J2W3ngriP+8F/vHr+laXgLw94k+DH7B/wzt/Fmi3GieItG06N7zTLmPbJao1/ctHEynOMQyJweRx3r3D4W6tpPi+fSLm7Z49LvNoYxnaYwf5AHrXlZp/tOJ9hN6Rtp3R95wBFZJkSzbDU7zq3bbV2nZNpLvZ2RyPwo/Z60/w7cxQ2NjJqmpyYw+ze2fb0rJ/aU/bN+GP7H07afr18/jTx5jFt4Q0CVZp1fHAuZhlYV9QctjtXiXxL+O3x2/ab/ah8WfAfR9U0T4KWeh3rWdza6VI39o6vbnlJftjgMySRkMAm3hq+of2Pv8Agj18Pf2alj1DUbdfEGuNh5p7geZufuSx5Jz6mniauByuPLW96VtIo/APETx6rurOjh7qW13v8ui+R8xfBX/gpB8Urb9q3wyvxmsbHwx8L/ifbtodho9pB5dr4ekZwba4LH5ncyDazt2boMV9LfGv4Ytqulapod5GFurYkRnH8Q5BHsfWpf8Agrn+xtY/H39mm5n02zSHUvD8XmW5gTayBeRjHpXE/sO/tDXH7WH7I2la1qb7vGfgOQeGPE6t/rJHjXEFwf8ArpGBknupq6eMp5pgvbRVnHRryJ8D/ESrWxboV5+9J8yu9pf8HY+b7+wk0u/mt5l2ywMUYehFdZ8FL/WIPF8MOlK0gkYeaP4VHqTXQ/tR+A/7D8Rx6tBH+4vxh8Do4/xr1X9mLwDHe+B9JOl2XnajqaAtsG5nOfXsPevlMPgZvE8i0sf3pnXE2HWSxxU4p86trsnbW/odrZXVjomi6hrfiK6XT9A8N2Muqazdnhbe2hXfIfqcYA6kkAV53+x/+0FrH7VP7NH/AAnWt2rWV1qXjPXIYbNhzp9tG0At7f8A7ZxBF+oJrJ+KvizTf2pf2j7P9n3QbhdR8C+EZINf+LWrW75hvTFMptdEjccHzJwokAPI3/3DWn+xrqVvqPwv+Mmn26JGvhn42eJITGgwEWaRXUY7DAwB7V+gfVVSwkk/idm/JdD+RlxHLH8R0JU/4MXKMe0pcur+9qx1XiX4g6b4Wjka5uFDKPu5rx34hftNXGo77fSv3aHjf0qn+1Zo8um+MoboFvs94nTtuFeXYxXwWOx1VTdNaH9Y8LcL4GeHhjKnvt62eyJ9S1O41i6aa5meaQ92PSuu+EfwevPiHfedIrR6fG3zOeN/tVv4OfA668fXa3V4rQ6apzkjaZPp7V9I6D4dh0PSPs9nb+Xb24CsVXgdhn61OAy+VV+0qbfmacTcWU8FD6pg2uba/Rf8E+PfFWhyeGPEl5YyBla3cgZ7jJxRXp37Vvg37Dq9vq0Kfu7geW+OgNFcGIpezqODPqclzBY3BQxHVrX1W56p8FPCP/CMeBbG1VMTzKGYY5LGud/aW/4Kb+DP2V/2n9J+D/izSWuvCdvpEEmu6zbLvm0K/ncsjkDrGEI3DqK9k8APp+gXdxrWqMsOj+F7KXVb12OFSKFC5z25xivgn/gn/wDs7t/wUg+KHxW8eeLIWkTxxc3FxG0gz5SliYVH+6oUV+gYL2OFwssRW+FaH8JeM3HFTAVIThK0+bnf6H3R4g8LWmo6Ba6lY3lj4g8M61EJbHUrRxLb3cZ6EMOM+1fO/wAY/wBnWTSJZtR0NGlt/vSQDqv0ryfTJvjB/wAEcPG91ZR6fP43+EN/MWvvD9wxZIlJ5lt258tx144Pevsj4P8AxL8F/tV/DdfF3wx1ZNa0xRm/0xyBqOjP3jmi6jH97ofeuXGZXRxNP21B80e63R9V4Y+MlPEcq51GfVP4Zf8ABPCf2W/HUPh7xFc6NeKsP9pHMTkYYOo+6fqOnuKuftu62vjL9sf9jj4bWokkhk8Rt4punVSELpMqjB6EoInJx04z1Fdb8UPgDb+J5zqGjsthqcLeYCvCswOQfrTPE3gzWfjb4S8O3mmw2dj8WPhlqq694VkvEBtzfgbXtZP+nW9jURsw5RxG/BQ5nIan1ar7Krs9Ez6/xawazrLv7QwD1TUpw66dvlt93U7L9pn9lPQ/2zvhr4g8A6/J/Zeo29xLqnhrWMhJtFvww2uCSD5cgCq4HbDcYyPJfhX4w8aeP/8Agn5p8fjy+n1TxjosuqeGb2eVleSVrLU5bVQWH3yPL27jknbmvYvBXxSt/i34R8N+NNMjuU0/xVp0GrQpdMPPtUkjB8pyOGZGBjJHB25HBrhfAum/8Iv4l8UfCrVt1nqF1qGreNvCt2VwniDTLy/lvJwgPH2qxuZnimi6mLypQNhYj0anPVwtTDW1R8bgoYbA57gs7TtGqkm+jdk439TsPgX8OjoPh3S9JXZHcXTL5rthQGbqT9K+R/26vi7Yyf8ABT7R7X4oabr3hf4f/D2BNO8MQ3lk0cOqc7prxGPyt5j9CCTtUV9D6L8dG8OeJZNC8UQ/ZLu3IC3Cf6qVezfjXqOraho/xU8Ct4f8WaRo/jrwndDDWGpxC4jXPeNj80bDsVIxXFlmKw8ISovqrXW6PR8VeDM1zqhpPl5ry8pX7Pbboe3fAn9oDwT8X/Ctm3hTV7G5t44lVIVcBkAGOlSftK/GO0+BHwZ1vxFdyLF9jt3Me4/x4OK/PLxh/wAEutW+HmuSeI/2a/H1xod6rmY+D/EV0VjPU7be5P5AOPxrxz9qb9pr45fHrRvDvwC8ceEde8LeOtc1iDTbc3UDCO8SVgjOkgGyRVALZUnjPSvJ/wBTubERnQmpQb17o/h/NvDbMcuxkaVaL5brf17ntn/BLnwle+Lbbx9+0T4iV5NZ8eXU2g+F3k+9DYo/+kzr6b2AQHuENdP+1d46+z2ltocLHdNiSfB/h7Cva9S8N6P8G/B2i+D9FWOLw/4B0uLSbbbwH8pf3kh93fcxPqa+OfiB4ok8Y+Mb6+kYsJZCE9lHAro4ixit7KntsvRH+iHgbwZDBYWm5R+FKT/xPZfJHtn7MXxV03R/Ctlpa37WOqWsjujbih+Zyww1dV8ZPgVH8XvHWn/EXwhe6f4O+Onh9D/ZeutGE0/xNGQA2naog4khlX5N55XOf4Rj5++D3w6m8feJY1w32aBsu/pj3r6n03Tk0zT4reMNsjXaB1pZLjq8IKXRaep6fiZwplmKruDd5yvK/WL7p779Dy/9iDxbqfjH4WfFS81zw5eeEfEN18W9YvdU0a6H73Tp5re2lMJP8Sq0jhX6MuG712GufCLQvEuoSXd9aLNNIfmYmvavD3gDWPHuhRR3VnFaq0yTS6kyhZJkSMovmt/EVXADHnaAOgGPI/jj+2Z+zv8Asvz/AGTxN40fVdTT71rpo85gfTivbxODnjanPGOjtuflmScQ4bhnBLC1q3vxbV4vdXuc9N+zn4VnH/Hlt9w1Qt+zN4Vbn7PIv0c15h4j/wCC6vwP0+Ro9B+GfjTxBt4V3ZYVf8+a5m6/4LppfP8A8U/+zpJNn7pvdQYg/wDfK1n/AKu/zKJtV8c8NT0Vap9//BPdF/Zo8Jq3/HvI3sZDXoXwz+HVrapHpdiI7PTrVGmuZpDtS2hUbnkc+gAzmvj8/wDBXb45eJ22+HfgL4J07d91p0nuCP5Csr4jfGL9sb9rXwBqnhG60vQPDfh7xBELa+j0fSfs88kJYFkEuSwBAwcdRRTyvDUZc1ScUup8znXj1h6tCVPnlJ9OZ6J97C/F3xlqX/BW/wDaz03wb4TWaL4MfD67MVgq5C6xOp2yXkn97OCEB6L9a/VL4FfBXSfgT8P7HQ9Jt4oUt4wrsq4LECvH/wDgm3+xlpv7KfwWsYfsqR6pdRKZWKYZeORX0lnBr4niTOfrdb2VL+HHReZ/FvFXENbNMXKpN3V/vY4DFGaM1V1TVbfRrR7i6mjt4kBJZzgAV8ytdEfLJNuxYZsD2r5P/wCCg3/BSzw1+yz4FvobO+guNaMbIArZMZ+lcX/wUK/4Kz+G/gX4cvNJ0G+iutUkUxAxtuO48DFfP37CH7A2ofHHxKvx0/aA02ZrO+mjn8LeGr2RozdvuBFzcR9fLHBCnr1PFfa5Bw26tsVjNILZdz7vhLg3E5piIrl0v/Vzrv8AgnZ8Bdet3v8A9oj4lQzJ448axSW/hCwugfM0jT5OJL0qeVklHyr3C5PerP7VPjttS1iHQbdi8VmPOudvOWxxn6da9+8UfFbw54v8d61azePvh0dX0W4ks76w/wCEms7ebS3iJQwvDJIrx7CMYKiuHGo/Dfx3L4g8MeHvGPgPXvE39nveX1lo2pRahcxQFkiaWSSIsnEkkYI3FvmHGM19Dm2HxGIg404uMVtp0P728L8RkXDcaNH2kZzbWia+J6L7lt5mb+x14fvtH8DX2s6WLEauy3N3p/22TyrczwxN9nEjYJCGYKDgE7c4BPB434QfAHwb+xX4Z0DUvEWtTeOfHGg23k6fql3Hti0tzkyf2basSIXd2ZmuZMzMWJBiB2Da/wCEh8TeCPh5p+j6H4cvvOs7QfaL6dNsaHGWKq2M855P615P4Y8M618YvFzL5k11NMc3F1KdwiX1J/oK8eGYzw1COGoL3n18z9KxXAeAz7N5Z9m8k6dO/Krp2V9HZXs2tNfuOpudd8Q/tNeMFs1LWmlwv5jqpJjgXP33P8Tnt712fx38cXnwE8IeHfhv8OYQ3xU+Iym10CJF3tolmx8ufV5h6j5khB+9Jl+RERXQeLfFnhH9iH4B3XizW4XurO1kFrp2nxnbeeKNTcfu7WPvgnl3GQiBj1wD03/BMj9l3xBJqWsfHL4oeXefEjx6RMoKbY9KtQNsVvCv8EUaYRVHQDPUk1tHly/DvHYjWb283/kup+D+PXi5RwGD/sfKbRVrJLp5vzf4Hvn7LXwD0/8AZK/Zs0DwXprNNB4dsf3ju2TJJjc5z9c18X/speIpfFngv4o/GDVm+2eI/HXie/spmA/49dP0o+Xb20fopkklkb1Pl/3c1+hXjGTyPCGrPn7tlMf/ABw1+c/7A0Y1b9iG+j6k+MPFEJ9yZom/9mrz+HZSnTxOIl8Ttr6vU/nfwAo0sRxQquJXM9/nZk3wW8QW/wAOfhv44+LGrQ/2lJotjeaq9suWaZbdGlMR9A8gRSeirkngGvZz+ybqH/Dq/wD4R/8AtKH/AITr7H/wnn9sb12/2/5v9o/at3T/AF3Gc4xXjn7Idxp/iGx8ReE9ZVZNJ1WCS3vIj/y0triNra4H/fDn8TWkfj1r/wDw6x/4Vj9q/wCLof8ACQf8KZ6/vPtn2j7N5/8Au+R+8z/d56VvCnVq4Wh9X3U1f9H6bn7D9KbCZl/b2F9m/c5koq2z0t9ysZPxp8Q23xH+G/gf4r6VB/ZsmtWNnqq2rfK0K3CLMIh67JC6g9CvI4Iqx+1d4im8J+C/hd8X9Jb7H4i8CeJ7Cygb/n60/VT5c9q/+yskcUi+h8z++TTP2vLrT/D9n4e8J6Kvl6TpNvHbWcQ/5Z21vGtvbj/vhB+Iqp+3zF/ZH7ENjF0P/CYeF4R9RNK3/steng+WePqwgvdad130Z+s+I+BlLw7pLMVzVFB6vycf1v8Aefb37UvwC039rX9mvX/BepM8MHiKx+R0bBjkI3Ic+xxXx78CPHF58e/B/iL4b/EaHb8VPhyi2mvxOuxtbs1Ijt9XhH94/Ik2PuyYbgSgD798Hy/aPCGkv13WUJ/8cFfJH/BTf9l3xAmo6P8AHH4X+XZ/EjwGTMwCbo9VtSMS28yj78UiZRlPUH1ANfN5Dj4QnLA4j4JPTyfT5PZn8M+GHHVfhrNo1YO0G1dHg1prviL9mTxi1mS11pcreYisSEmUH76H+Fx3x3H41q/GD4A+Df20vC+val4d1qbwL441628q/wBUtI8xam4wYzqVqpAmdHVWW5jxMpUEmUDae+8JeLPCP7bvwDtfFmiQSWtlcyG11HT5DuvPC+pqP3lpJ3wDyjnh4yp65A8D8UeGta+DXi1V8ya1mhOYLqE4WRfUH+YNetKpiMsq2WsOx/pHHLcg8QsrjOqo+2aun389Oq6/qj3D9sTw9f6x4GsNa1MWJ1hUt7vUfsUvm24nmiX7SI2wCUExcDIBwBkA8Vxn7Kfj1tN1ibQblisd6POtt3GHxzj69a2/+Ei8TeN/h5faPrnhu9868syLe/gTfG7YyhZVzjnHI/Suqe/+G/gSbw94Z8Q+MfAeg+Jv7Pju7Gy1nUotPuZoA7xCaOSQqnMkcgA3BvlPGKI0KmJxXt6C82rHTWzbCZLkSyrM5p2fLGXMraK6d+979up5f/wUU+AuvXMlh+0R8NYZm8b+C4o4PGFjaqfM1ewj4jvVUctJEBtfuVwf4a+iP+CfX/BSzwz+1N4FsYby+t7fWtiowZuZD0o8L/Fbw34Q8c6LaweP/h0NW1q4SzsbAeJrO4m1VpWCCFIY3Z5N5OMBT1r5J/bv/YG1D4HeJW+On7P+myrZ2M0k/inw1ZSNIbV9xLXNvH18s8kqOnUV7mMyqOY0FTrrlqLZvS5/FfihwHhMbUnj8rlGS3fK78rfp0bP1mDginbs18M/8E9v+Cs/hv46eHLPSdevo7XVIwIiZGCsGHGDX25pmq2+s2qT2k0dxC4yGQ5GK/L8dgK+FqOnWVj+YcZg6uGm6dVWLRGa4v47fBbSfjr8P77Q9Wt4pkuIiqMy5KE12mcU3PzVywqShJTjo1sc8Zyg+aO6Px7+EfjHUv8Agkf+1nqXg3xYs83wY+IN0Ir9WyV0e4J2x3kfpjID46jntX2r8Tfhza3KyaXfeVfabdIs9tOh3JcQsMpIp9CDnIre/wCCkf7Gem/tWfBW+h+ypJqtrCxiITLPwcCvgv4c/GL9sb9kn4f6X4RtdL0HxJ4d8PxG2sY9Y0n7TPHCGJVDLkMQAcDPQV+o4HMKGZ4Ze2ko1Fo79T+l/CvxY/sulyYpu3a9mn3R9Jv+zR4TY5+zyKPaQ0J+zP4V6/Z5G+rk14CP+Cu3xy8MPt8RfAXwRqG3hnt0nt2P8xVq2/4Lpx2LY8Qfs6SQ4+8bLUGwP++lrePD9OWsOVn79Q8fsPPerU+898h/Z08Kwj/jy3fVq0tD+EGheG9QjurG0WGaI5Vga8X8Of8ABdX4Hag6x698M/GmgZ4Z0ZZlX8ua92+B37ZX7O37UM32Xwz40bSdSk4W11IeSxPpzRLIZw96MV8j1aPi/g8X7k687Puzgf24PFmpeEPhZ8K7zQ/Dd54u8QWvxb0e80vRrUfvdRnht7mUQg/whmjQM/RVyx6V1fwb+BUfwg8c6h8RfGF7p/jH46eIEH9qa6qBtP8ADMWCF07S0ORHDEvybx8zYzn5jn2/xD4A1jwDoUkVtZxXKrM88WoqoaSJHjCN5Z/hLLkFhztJHQmvNdS05NT0+W3kzskXaQDjFdFTGVKVBYeKs+rPLwHDeDzDM6maV5KUG1yx3WitzNd+yPL/ANp34qadrHhW80ttQa+1S6kR2bcZOjhjlqzf2UfHX2izudCmY7of3kGf7vcD6V518Yfh1N4B8SyLy1vM2Uc98+9Zfw+8USeDvGNjfxllWKQB/dTwa+HljKixfPU0tp8j+oMPw7hZ5E8PhXzJ+8n/AHrLbsuljs/+CovhG+8I23gH9ojw6rR6z4DuodA8UPH96exdv9Gnf12MShPYOK/QT9mn4yWnx3+DOi+IrSRZPtlupk2no+BmvDtM8OaP8Y/B2s+DtbWOXw/4+0qXSLrfysfmr+7kHuj7WB7EV8Afss/tNfHL4C6N4i+AXgbwjr3inx5oWsT6bcG1gYx2aRMUV3kPyRqwIbLEcY619LmeVyzTCwlT+OOj9D/N/wAaeBakczVTCw+N3X6/ifrZ8dvj/wCCfhB4WvG8V6vY21vJEyvA0gLOCMYxX5c/sKfF2xT/AIKfaxa/C7Tde8UfD/4hW76d4ohs7JpIdL53Q3jsPkXy36kkHax613vg/wD4Jdat8Q9cj8RftKePLjXL1m84eD/DtzuiU9dtxcjjrwQg/GvpvSdQ0f4V+Bk0HwnpGkeBfCdquFsNMiFuj47yMPmdj3LEmtMry+hlUZc0+aUlqlsdHhz4SZtCvDGOTp7av8LLc5/46fDv+3fDeraS2ySe1LeU64YMV6EfWvJ/in4x8aeAP+CfmoR+A76bS/GOtS6X4ZsriJlSSJ73U4rVgHP3CRJt3DBG7Oa6jW/jo/iPxImh+F4RdXdwSrXD/wCqiXu34Cl8dab/AMJR4k8LfCrS1a81C11DSfG3iq8Vcx+H9Ms7+K7gEgHH2q+uYUihi7RebKw2BSVlahUxft6W0d+x/VXHEq+F4ceW4p2nVa5F9pt6bdL7nY/sz/sp6H+xf8NvD/gHQJP7U1G4uItU8S6zw0utX5Y7nOCT5cYLBAe3zc5yfI/2ItbXwb+2R+2N8NboSRwx+JF8U2zspKB3mZTk9AXEqEZ5POOhr2Txn8UoPhH4S8SeNNTjupNP8K6dPq0yWrD7RcpHGT5UZPCs7FYwTwN2TwK4Xw14M1n4JeEvEV5qUNpf/Ff4maq2v+KpLJcW635UrHax4/5dbKNvLVjy7mRzkuMen9cSoVa1X7WiPgv9WZPM8BluCsvZLmk+yb3fr+SbOJ/ak8cxeIPEdvotmqynTj+9cDLF2/hH0HX3NTfBz9nWTV3h1HXEaG3+9HARy/1rs/hd8ArfwxcDUNYkF/qkzF2LfMoY8k+5rvfjB8S/Bf7Knw4bxd8TtWTRdNZc2GlphtR1l+yQRfeOf73QetfMYXK6mJrc9Ravp/mfumf8c4PJMuWFoVOWEVrPq29+U2dA8MWuneH7vUL68sfD/hjRojLfalduIbe0jHUljxn2ryf9mn/gpt4L/ap/ae1b4P8AhPSWtfCdzpE8mh6zcrsm12/gcM7gHpGUB2jqea+ddTn+MH/BZDxza2UlhceCfhDYTBrHQLdiqTKDxLcN/wAtHx68DtUH/BQD9nhv+Cb3xR+FPjzwnCY08D3NvO7RjHnKGzMp/wB5Swr6bDzwdGssG5XnL8D+NOIvFyWMzanQw8rQUk7d7Pdn1p8bfCX/AAlPgO/tWTM0KllHcEUV6F8QH0/X7y31nS3WbRfFFlFqtk4OVeKZA4x+dFfP4zAQdRqe60P6+4Y4urUsDGVB+7O0vvSPAf8AgqL8U5Pg/wD8E+NW0yymaHXvitqcPh20C/ee2BD3BHttG3/gVe8f8EovgInwR/Zd0lXhEdxfRK7cdsV8d/8ABZTRPE+gfGH4U+MLzw/fav8ABvwdpyoL/T2E0cd9I+6Zp1HMXRQCwwcV9tfsRftx/D/9oP4faXZ6HfW1rNbwrGtu0g7Ct+KqdaGAp0qSvHdtH+fvjJmOJxmYzk4vlv8Agj2j4jfDHRfin4el0vXLGG8tZlKkOoJX6V+Qv/BRH9ntf+Ccnxq0vxd8I/FGqeH/ABjqFykVpa6eeLlnfAikT7rqxIBUg5r9l7q6S1tZJnOI41Lk+wGa/J4S/wDDan/BaLw7a3YF14d+Hxn8R3yt80arbDMYbtzLsGK8jgt13imlJ8iV2uh8DwTTxNbHxpUZNK62PszxZLqEVhoUevR2I8UR6VbvrslpD5MLXjRhpdqZIADEivOf2ffGq+Lf2g9SIbFva3VlEnoAJGBP610Xxb8ZmLTNc1qaT95N5kmT6nOK8N/ZH1uRfiFq0jSbZp7dZg3+0JM/1r0sZil9chbu2f6acP8AD8lw9X5m24wUU33drlz/AIJnfHfQfip+zlJ8MZo10r4mfCHzdP1DTJyyzX1jFO6i6hBxu27trrjjah75Lf20Rf8AxD/b5/Yw0+xvJrG/XUdSuJrq2+SQQpLFvXj+Fo45FYHgqzA5BrrP2q/2U3/aI1vT/iR8Obi38GftCeEWW70rUYQI7bxOI+Ta3I+6WcDAY8HOGyOnN6X8VdG/aI/aY/ZJ8cWti+ga5pXifxJ4Z8R6BcrtuPDmqvp0cz2bg87d0MrxE/eR1PXIH2WHjCdT6xS2aaa8z+b80lisNglk2PXvwnFwl0cbpWT8tDtP2qfhnp3jKx1O8tFWzm0uWR7Zj0MQY/uyfT09K+e/A/xR1jwJcK1jcMYepgkOUxX0f8excS/D7UobVHkuLhim1evJOa+XLvw/faZxNazR44zsNfmua3hX56ejP7W4DjCvljw+KalFOyUtdLa767n0B4A/aX0nxGY4dQU6fdHqWPyE+xr2rQPilcm3sjJ9i1qGwfz7FrqNZns5MEB4nPKNg9R618EuMtg5H4Yre8I/EzWfBVwrWN5IEXrG5LJVYXOp037/AN6MOIPDPCYyP7hL/DJXXyfQ9/8A2kPGLeHvAM0av/pGoN5fXk56mvmjTdOm1bUIbWFS0spCgV03xR+LN18ToLH7TGsLWoIYJ91j613H7L/w5+23ja1cx7o4ziEEfrWNaX1vEpQ2PVy2j/YGUSqVlaeunnskenfCfwBD4D8LQwhcTuu6Ru+cV6PeeIfDH7Pvwt1D4jePrqOx0PTELwxyHDXT9lUdyaf8OfB6+Ktb/wBKdLfTLFDcXkz8LHGoycn6V+Yv/BRT9qzVv+CkH7Tkfgvwm0y/DvwncG0tIo/uXjqdrSt65I4r7bKsDC3PPSMT+V/E7jmWCpyipfvJ6t9kRftNf8Fc/jJ+2H4sn03wHJdeE/C+54YIbUYeaM8fN9RWJ+yv/wAEydb+L2pi+1qG5uLmaQtM82WZiTknJr7i/YZ/4JnWHh3QLO81C1RfkDfMlfcXgb4T6P4AhxY2scbbcEheteXm3F0ad6OF+8/jHPOMKtWbjF3fc+Rvgp/wSF8L+HLSKTUbSDdgZBUZr3vwl+wv8PfC0a7dHt5GUd0FeyAYp1fC4jN8XWd5zf3nw9bMsRVd5SZyui/Bjwv4eUC10Wxjx0/djiugtNMtrAfubeCID+4gFWqM1wSqTlu2cjqSe7GF1iG5mAUDkk9K5bxr8b/Cvw+tnm1XW7C2CDODKC1eV/8ABRP9pi2/Zy+AOq3jTLHeXMDLH82CBivzr/Zf/YI8V/tw/Cex+JnxN+Kmt+FfCviG6n/svSbK0Mt9fW8bbRJvY7VRuQDgnivo8l4deNpuvUlywXXufWcL8I4rOqnJh02+yPrr9pD/AILZ/Db4PW00OmXUepXi5C4YHJ+lfMU/xk/ai/4KYTt/whPhu88L+DZiRJr+sk6fYRp6qzYaTjsgOa+iPgz+xT8C/wBmy4juvC3w9tdd1uHBGs+KZP7RuQw/iVG+RfwWu5+I3xrVLZW1zWI44YhiOAMFjUDsqDgflX1eHweWYFc0I88u72P6J4U8AaspxeKVvLd/ceP/ALNf/BNn4Z/sw63D4m8T3jfGD4jRt5i3+oRkaTpsvrDC33yD0Z8/SvbfEvjC48Vaz5moXXn3kgJVAfuL0wB2Ue1eCePP2rmkje30G22g8C4k7fQVz/wY+LsOh+NbzUvEl9cN9oh8tZCjSYOc8AA4FcmIzyNaooX0+5I/qzh/wlWVYGVWjSs0trXnL7tjuv8AhWHg/wAWft2/E7wlrfgPwZ4o/wCE68I6N8RLQ6zo1rftHcJ/xLr9UMqFk8wpDIdpHzIx/irrtO/Zi8J674m0D/hA7HQvg/4g0tZ7S31Pw7oFgi3q3EYjeC5imgdJ4y4hbLZZWiyGByK5/UvB3gv4/wDxm0Pxtp/izxt4V8V6dof/AAi8Oq6HdxwxfYmkZ23w3FtIkmGkLMMjIUDjGaz/AAD8Xf2gPEvhzTbG6/ZstbHWNH22GseINf8AG8Gh2Wp3kX7ue4tIhEX8mSRGkQqrrtYbSRg19PTrOuo1KE00kk09j8TxOW0sqnVwmbYZxlOUnTlGN5JX02s9NC5+yb8Rtc/aB/Yp8H6/4mure68V3y6rpOtzw2kNqovLe9mhKGOFVRSsfljAUZGDznNdt4F8GeHPg58PtU1XU7630Pwr4ZtWv9b1WbgRRKOeT952Pyqo5JIrD+APw++IGheP/ic3ibTPAOgeHfGWpp4n0vTdD8TNq72mruqx3oXdbwny7naJiMfLKpxw5x4H/wAFTfH198Zf2gfCP7MvhiSb+y9Ikg1fxa8Rx9vu3AaKFsdVjU5wf4jXNUwVKeJdV25Ur/8AANq3HVbJ+F+Sq5KpqvevrbaTvvo9PM6T9k7wPq3/AAVL/ani+KvinTZ9L+Gfg0tbeD9Bl+7awZz5rjoZ5SAzt9F6KK/Tm3gjtYI4o1WOONQqqo4UDsK4T9mn4L2PwI+D+k6DY26wi3gUybVxlsV6BX5rnmaSxmJcvsrRLyP4d4gzmtmWMnia0m7vqY/xDbZ4D1o/9OM3/oBr86/+CZMovf2RvEsPX7D8SdchPt5kNpIP61+hnxWuBafDXXZG4C2Mv/oJr84/+CU139s/Z5+LUIPFj8TJJB7CWyj/APiK97hON8NiPkfq3gLU5OIIvvp+DMT4f67/AMK6+N6mQ7YBeyWcw6DY7Ec/Tg/hXq//AAzpfD9s/wD4WduX/hE/7I/tPyd3H/CUeT/Zfm7f+wf++3f89OeteY/FX4fX+s/GrWLLTbWS4mk/0oKpxxtz3469PUmvSP8Ahc1x/wAM/wDnfvjq27+zfs20+Z9pxszt65284rTK8W8N7WElprb/AIB/odx/wrhuIFgMQ7OUXBtdtNL+T1+48t+IGu/8LE+N7GM74Tex2kI7bEYDj68n8a6v/gptILL9kjw3D0+3fEnQ4R7iOG7kP9K574U/D+/0f40aPZ6layW8sf8ApRVueNuR0468H0IrS/4KtXf2P9nj4Swk4F98TY5D7+VZSf8AxddnDsZSqznLd3/I+R8fq9Glw8sNQd4xgkmttX/wD9JPh427wHop/wCnGH/0AVqTwJdQSRSqrxyKVZWHDA9jWH8KbkXfw20KRTlWsYsf98iugr87q6VH6s/yxqaTZ+YP7WPgbVv+CWv7U03xV8Labcap8MvGRW38YaDD926gz/rUHQTxZLI31Xoxr3Tx34N8O/GL4e6Xqml31vrnhbxNarf6JqsHKyxMOOf4XX7rKeQQa+iv2l/gxYfHf4PatoF9AkwuIGMW5c4bFfm7/wAEsvH198Gf2gfF37MvieSY6Xq0k+reEnlOfsN2gLSwrk8LIvOB/EK/RslxSzLBujV1qQ/FH9J+CXiPXy/ERwlab5b/AHdmv1PYv2sviLrf7Pv7FPjDXvDN1bWviyxTStJ0Sea1hula8uL2GEII5lZGLR+YMEHAyeMZrpNT/Zi8J6F4m17/AITyx0L4weINUWCzuNT8R6BYOtmtvGY0gt4oYESCMOZnO3DM0uSxOBVX4/8Aw9+IGu+P/hi3hnTPAPiDw74N1J/E+qabrniZtIe71hFaOyDbbeYmO23GYAj5pWGeEGeZ8e/Fz9oDw34d1KxtP2bLS+1jWN1jo/iDQPG8GuWOmXkwMcFzdxGIP5KSOsjllRdqncQMmvoqFGaoRhRaT6u+vofsuaZlhama18VmVOc6eiguVuLaWsu2vQcfhj4P8Jft2/DHwlofgPwZ4X/4QXwjrPxEuzo2jWtiz3D/APEusFcxIGfyy80o3E/M6n+GvYPDnjC48K635mn3fk3kYy6f31PGGXup968s07wb4L+AHxl1zxtqHizxt4q8VajoX/CLzatrt3HNF9iDq67Ibe1jSPLRhlGTtBI5zmvOPjP8XYdc8a2epeG7+4X7PD5bSBGjyc56EDI614+bZpThy8kr8unmfovh3wRjMSq6xNH2aqttaLlUbaJ20R137Sf/AATZ+Gf7Tuty+JvDF43we+I0reY1/p8ZOk6lL6zQr9wk9WTH0rx+3+Mn7Uf/AATQmT/hN/Dd54o8HQnCa9oxN/YSIO7MuWj+jgYr1LwF+1a0SJb69a5C8faIu/uRXt/w6+NayWpfQ9YSSKVdskBYMjg9mRuD+VFPNMNi4ezxEVJfifHcdeAdKq3JU+R/fF/NbHNfs4f8FtPhv8YLaGHU7qLTbxgA2Wxz34r6u8E/G/wr8QbZJtK1uwuQwyAJQGr47+M/7FPwL/aSnkuvFPw9t9C1yXJOs+FpP7OuS395kX9234rXy3+1B+wT4r/Ye+E998Tfhj8VNb8VeFfD11ANU0m9tDFfWMEjbTJvU7WVTgE4B5rhrcK4LEa4SfK+zP5h4m8Gcwy+Mqyj7q6rVf8AAP2RDrIoZWDKRkEHrUF3pdtqC/vreCQf7aA5rwn/AIJ2ftL237RnwB0q8WZZLu2gVZPmyTxX0BXwOIozoVXSluj8TrUpUajpvdHK618GPC/iAH7VotjJ6/uxXB+Lf2F/h74qRt2j28bN6IK9moop4utD4JNfMccTVj8Mn958P/Gv/gkL4X8RWkr6faW+7BwAoya/PP8Aao/4Jk638INT+3aLDc29zDJuheHKspByDkV+9TDNcz45+E+j+P4dt9axyNtwCV6V9DlvFOLw8lzy5kezgOIcTQkuZ3R+K37Mv/BXP4yfse+LINN8dvc+LPC+Ugniuhl4Yxx8tfp3aeIPC/7QPwt074jeAbqO90PU0DzRxnLWz91Yeorwv9ub/gmbYeItAvLvT7VW+Rm4Svj3/gnZ+1Zq3/BOD9puTwV4saZvh34suBaXcUhOyzkY4WVc9ME1+gYXGYbNaPuaTR+7eHviFUw+Ii3K8Xuj7y+K/gGHx34VuISo85QWjbvmvlHU9Pm0q/mtZlKzQkqRX3t8RvCCeFdc/wBGdbjTb9BcWcyHKSRsMjB+hr5m/ag+G/2K7XWraPbHIcS4r47N8G7OaWq3P7+8PeJIS5aHNeFTWPqdz+zh4xbxD4ChRn/0jT28vryMdDXrWv8AxSuVgvWjNlosN8/n3rWsawveSYALyuOXbgcn0r43+FvxZuvhjDffZo1ma6UBQ/3UI71Q8XfE3WfGszNfXkjIx4jQ7VFRRzhUqCir36m2ZeHKx2ZyrzjHkTum1dq+9ke2+Pv2l9J8OtJDp6/2hdDoyn5Afc14p44+KWseO7hnvrhlh7QxnCAVzyjDYG4/hVy28PX2qYWC1mkz3CmvMr42rWdunZH3WV8N5flsVJJc3eX6dj6Y/ZX+GeneDdP029vFjvJtUlje5YdFiLD92D6evrXCfsXfb/h9+31+2dp1/eTX1+2o6bcQ3VwQ0hieWXYvP8KxyRqoHAVVAwBXo3wEFxD8PtNhuleO4t22bW68EYrz/VPiro37O/7TH7W3ji6sn1/XNV8UeGvDPhzQLcbrjxHqqadJMlmgHO3dNE8pH3URj1wD99kVO+GnTgtWl+J/JPitiVHOsPisXO6jOTfay7L8CH/gpj8eNA+Ff7OUfwxhjXVfiZ8XvK0/T9MtyzTWNjLOim6mAzt3bdqLjnc57Ej0X9oHxqvhT9oLTSXzDdXV7E/PUGRQD+lYv7Kn7KTfs767qHxI+I1xbeM/2hPFzNd6rqMwElt4YEnP2W2XoGQHBYcDGBx14n9rnWWPxB0l1k3S29u0zN33GTP9K5M+r0qVBUqW8Xq/M93wryvG5jmtXH4+NlWi1GPaNnb8WfS3hOXUJNO12PQUsT4ok0q4fQpLuHzoUvFjLR7kzggkAc1+fv8AwTt/Z7X/AIKOfGrVPF3xc8Uap4g8Y6fcvHd2uoHi2ZHw0UafdRVIICgDFfbnwj8ZtJpmh61C53w+XJkeoxmvlkyf8MWf8FpPEVnaL9l8OfEEweI7JR8sbLcjMgXtxLvGK3p1Kk8vqqg7SSvdbn4x49ZXiqMVXpSa0aau7XXkfqJ8Ofhlonwr8PQ6XotjDZ2sKhfkUAtj1NeA/wDBV34CJ8bf2XdWVYRLcWMTOvHbFfTdrdJdWscynMcih1PsRmvnr9t39uP4f/s+fD7VLPXL62uri4haNrZJB6V+a5bLEPFwqU7ylc/jrAyrPExnTu5Jo+cP+CXfxUf4wf8ABPrSdMvZmm174U6pL4duw33ktiS9uT7bTt/4DRXmn/BGrQvE3iD4wfFbxhZ+H73R/g34x05k/tDUGEMcl9G+6FoFPMnVgSowM0V+pZjhnKrzrS6V1fZn96+HfEEv7HhTxEJXjps9j668GePV/suRbb7LqWj6jGY7qyuVE1tdIeCroeDkV8t/tDf8Eu7vSPEVx8Rv2Zb3+xdZhJudQ8DzzmOOYjljZuTjP+wfwNUfBfxK1r4P69NZybzHbyGOa3c9Men86+iPhr8XdP8AG0Ed1p90be8jIYqDteM+1ePludaeyn84s/SvELwmw+MpOc4pxe00r79zxL9nX/gsNfaj4S13wF8QLK80DxhY2ktt5F7GYZ45lU/KQ3POMfjWd/wRt8Nyah4R+N3xbutxu/FGqR+G9PkYcmJD5kuD9So/Cvd/2l/2Xvhz+3HoyQ+PLNtH8VWybNO8XaZGI722b+HzsY81PZvzFaXwK/Z2T9jz9lXwf8N11Kz1q4sri81C7v7YYjvJJZmKyY6jKBeOx4r1qNHB4ejVq4XRy6dj+c+FfDGtk/EcPax9xu91s7anB/tUeIv7O8FRWKt815IMj2FeAaNrd34bv47qymkgmj53Keo9DXpf7VuufbPGdrZq3y2seT9TXl9tatfXMcMYJaRgoxXwOYVHLEO3TQ/0J4TwcKWUx9otJXb/AK9D6X+B3xhm8e6eq3EUlveW5/1yD5WI7g9jWH+0v+z1eeLfj38PPjd4QjaPxR4O8R6Xe+NNMgGF8Q6fBIYv7QCjrc28M0yuerROT/Bz1nwb8ER+EvC0KlV86RcscV3ehteJrNvJp/mLdQtuQqOn/wBb1FfXZXiqtCzlrfc/AOPMhwGaOpCklHld4vs1+jDxTpYsvEOoWrAMtvcyR898MRWNe+G7HUVKzWsLD/dFeu2fwavPHGr3mp6ht00XDCSRTwoJAzj2zz+NZmuaB8OPDMrQ6h460ezmXhle6UYrb+z6lRtxjoeRHi7BYSEYValpJK9u9u6PE9X+Bnh/WF+azVC3UrxXL6r+yfpdwS1tcSQ+g9K+gFtvhvqLbbP4leG2foAbxOfzNWofhZpupru0/wAX+Grz0C3qZP61z1clk/ipns4LxOowdqWLt6/8E+UNQ/ZKvIZP3N7HIuckHvXtfg7w3H4Y8P2tjCg3RqF4HLGvR2+AuuSDNu+n3Y/6ZXAbNWvDnwtuvA+oSa34mt1s9F0WF765ldvkCou48/hWeHyl05+7Fq505x4gQxmGcq1eMlBN2TV2z5W/4K9ftSy/sq/sw2Hw58O3Hl+OviUuLpo2/eWlmev03dK4v/gj9+wlHZaba63qVqMkCQsw61846br+qf8ABTD/AIKD6340uFkm0n7b9m0uLkpDbRttQD6gZ/Gv2r+Bnwytfhb4AsdOt41jZY13YHtWnFOZLB4ZYOju9z+BvEjiqpjMVN31k/wOq03TYdJsY7eCNUjjAAAFWaB0or8veruz8h8wooooAKhvruOwtZJpWCxxKWYnsBUxOK+ff+CgP7R9r8D/AIN6i3nrHcyxMPvcjit8Nh5VqqpR3Zth6Mqs1Tj1Pz6/4KZfEfVf24/2wPCfwf8ADczSf25qaWcmw5EMW7Mjn2VAT+Ffc/i6w0vwTZaf4b0WNYfD/g6wj0uxjUbVCQrgtj3OT9a+Mv8Agit8OZ/G3xC+Jn7Qmtx+YunhtC8PtKM5nk/1si59FwuR619G/HbxU3hr4e31wW/f3WY1Pu3Wv1XHRjgsJDCw+yrv1P7k8CeE+SCrW1k1FfO12eO+Pf2jNe1vU7q001vs9qsjIhiXc7AHFcna+BvE3ja63/YNSupH/wCWkikD8zW9+zTpwv8A4p2/mLvWOF3ORxmvbPj7+0Z4K/ZW8N+G77xTB4s1W+8WzXUOj6R4d05Li5uzb+X5hZndUjX94vJz9OK+XwOBqY/VyfU/rHiri7AcI07ezjFJK8nfd/8ADHk/hv8AZQ1zUGV9RurewiPUA+Y4H8q9a+HX7H+lWvkzfYW1BncIlxfOI4XfrtXOAW4PyjJ615/4e+Nn7RX7Rkoj+Ffwd0X4cabMQF1zxZKdWv0H99YsJbofqr4rldI+CfjH4Y/8FTPg7Y/ET4ha78QvFLaXrOsXE95OfsdiV024CRW8C4jjALZO1R2/H2cLleBjV9lKac9XZa7fgfzvm30kHmGMjl2XycnNpae6lr5a/id/8fvFLfDzxB4TurNI0hs55JDGg2h48BWXH+6Tit3Q/wBoOz+IHjSHS9MhubosvzXNyThFHoDyfQVxP7X2fP0H/rnJ/Ss/9kjTPtHi6/uiP9TCFGexJrxvrFSOLdKL0bR/REMnwlXIY4+srzjGVn5uXU+hNc+KVj+zt8IPHHxM1OOOS38D6RLeQRt0luWBSFPqXK18n/8ABFr9nXUvjR8SdW+LfjCS4vtZ1i7e/mupmLM7u2cZPYdB9K63/gsF4qk8PfsB6P4et2KTfELxlbWjgdXht1MjD6bgtfW//BOH4Vw/C/8AZm0SGONUkuIVdj68V7Wd4p4XK1GG8/yP8+PHbPqksdLDQeitFfqe+U4nFFI3SvzE/ms80/a98Xp4L/Z48TXjuF/0R0HPqK+A/wDgjdBJffso/FvV3/1Oq+P4/Kz3KW+D/MV9Df8ABZrx7H4Q/ZZvYTM0clwrEBWxnivP/wDglNolt8PP+Cafw5fULFGXxFqd/q9wik7rkNIVSVuRkgKPlJAOMe9fpHCtFQy6pVf2mkfvHgjhZf2nGvBXabdl1stjuPFOr+F/g74V1z4g+Lr6HQ/DekxK2oajIPml2g7LeEdZJnPCIuSSc9ATXkQ/aisz4H/4TL/hSPxZ/wCEy/s3+2/+EQ/siT7R9gxn+0ftvl+R9n8vjOPtG7915X/LSotW0yH4cfHbSNQ/aM1JfGkOpavs+Gvi+dBH4L0SY/dtJ9MQBNPv+m2efz0fBIkTBNfU32Hxn/wme3defb87/M3cebs+9u+7nb/F/wCPYr3pYWhQjFOHPfW62P6Up55mmZ1606WIWHUU48j+L1d/wtoed+FtW8L/ABh8KaF8QvCN9DrnhrVYi+n6jGPnj3Ab7ecdY5ozw6NggjPIIJ8B/wCCyMEll+yh8JdYTiHSfH8nnexe3wv8jW5pOmQ/Eb476tqP7Omor4Lh0zV9nxK8X28Yk8F63MPvWkGmOCmoX/XdPB9njTIJkfINdX/wVa0K2+IX/BNP4jNp+noq+HdSsNXt0JO62CyBXlXk7SQx+UEgZx710UMLTpYlcunN06o8HPs4xea8O1qNeN3TsudfDKz6X16300PrX9kPxfH40/Z58M3iOG/0REPPoK9MBr49/wCCMvj2Pxf+yzYwiZpZLdVJDNnHFfYQOa/H8zo+xxVSn2bP4dzCl7PETj5saemPzr8n/wDgtL+zrqXwX+JWk/FvwfJcWOtaPdpfw3MTFWR0bOMjseQfrX6xV4D/AMFHvhXD8UP2ZtaheNXkt4WdT6cV2cP46WFxsJLZuz+Z1ZLjZ4bFwqRfU4vQ/inY/tE/CDwP8TNMjjit/HGkR3c8a9IrlRsmT8HDVwOu/tB2fgHxpNpepw3Nqyj5bq3Jw6n1A5HfPavO/wDgj94rk8Q/sB6x4duGLTfD3xlc2iA9UhuFEij6bi1N/a30z7P4v0+6x/roNrY7kGvtc+cqE3On3T+8/wBNPCF0M7wVGli9bxa87rb8O51PwA8UN8QvEHiy6vI43hvJkkETjcqR4KqP++QK6v4i/sf6Xd+dP9hewKOUa5sXEkCP/dbbkK3P3TgjivO/2QgftGve8cf9a5nV/gn4x+J3/BU34xWPw7+IWu/D3xSul6NrNvPZzn7HfFtOtw8VxA2Y5ASuRuU9/Xjly6jQr4R1cU9Fre3mV4rccVeDMasThrqndJpO1lyq3r8zQ8S/soa5pxZtOurfUIV6Any3I/lXHXXgXxN4JuvM+walaSJz5kakj81r0vxF8bP2iv2c5DH8VPg7ovxH02I4bXPCcp0m/Yf32iw0Dn6Kld98Af2i/BX7VPhrxJfeFYfFmlX/AIRltYtX0jxFpyW9zaG48zyyro7JIv7tuRjtxzUTyKnOLq4eaa7p/puXwn9IfBZs44e6nKWlmtX81p+B5D4B/aM17Q9TtbTUm+0WzSKjmVSrIDxmvqLwjYaX43s9Q8N61Gs3h/xlYSaVfRsNymOZcA/UEgj3r5O/aW08af8AFO48v5VkhRxgcV7j8CvFLeJPh9Y3Ab9/b/IxzyCvSsMrxU6dd05O/Lsfd8cZLhsblUMXSpqMaqtJLa7WjPln/gmd8RtV/Yc/bA8WfB/xJK0f9h6lJZR7zgTRbsxuPYoVP41+wFjdx39rHNE26OVQykdwa/Jf/gtP8OJ/A/xC+Gn7QmiRtGuoBdC8QNGPuzxf6qRseq5XPtX3Z/wT+/aPtfjh8HNObz1kuYogOvJ4p8Y5em446ktJLX1P8u/ELIJ4DHzTWzaZ9BUUA5or4M/OQooooAq6lpsOrWMlvPGrxyAggivy5/4LAfsJR3um3WtabajIBkDKOnev1QPSuL+OXwytfin4AvtOnjWRmjO3Iz2r1smzKeDxMakdr6noZbjpYWsprY+F/wDgkL+1NJ+1T+zDqHw48RXHmeOvhqmLV5W/eXlmPu9eu3pXrHjHw3H4n8P3VjNGN0ilQD2NfnLqevap/wAE0f8AgoPonjS3WSLSftv2bVIuQk1tI21wfoDn8K/WrxJ8LrrxvqEeteGYFvdE1qBL62mV/kKOu4c1+oZlh44iEcRSV1Lc/uDwl4yhLDexrT5eXWLbPkWw/ZLvJpD517HGucgL2rpNK/ZQ0q2UNdXEk3qK+jU+A2toB9ofT7Rf+ms4XFMn+Fum6Yu7UPGHhmy9Q16mR+teDTyJJ6Uz9zxfik7WlikvSx47pHwM8P6Oq7LMOy92Ga6Kz8N2OnqBFawoP90V2z23w3059t58SvDat0IW7Q/1rU0Pw/8ADfxNKsOn+O9HvJm4VUuVOTXfDKakVpCx8xiPETA1ZfvMQ5fecf4Y0wX/AIh0+1VQomuY04HTLAV4v+zP+z3eeEvj38Q/jf4vjaXxP4x8R6pe+C9MmGV8PafPIIv7QKnpc3EMMKoeqxID/Hx9VXfwbvPA+sWepadt1IW7GSNR8ykgHGT6Z5rz3X2vH1m4k1DzGu5WLOW7/T29q3hUqYWlKCVnI8nEYHCZ5jaOJclKnSTdu8n3XZW+Z5n8cfjBN4C09lt4ZJ724P8ArnHyqT3J7mvm3WdbuvEl/JdXkz3E0nJZj09vpX1F8ZPBEfi3wrMNo86NcqcV8sXNq1hdSQyDDxsVNfGZs6imuZ6H9JcAxwn1Z+yjaa3fW3+R9B/sreIv7R8Ey2LH57OQ4+hrzP8A4LJeG5NP8I/BH4t2uRd+F9Uk8N6hIo6ROfMiyfqGH41s/sp639i8Z3NozfLdR5H1Fe4fHT9nVP2xP2VvGHw3bUrPRbi9uLPULO/uuY7KSKZd0mOpwm7gdTxX0nDmITSU9mmmfhnjlw/7ejXpwjd3Ul89GeD/ALRX/BYW+07wjofgL4f2V3r/AIwvrSK38ixjM08kzKPlUDnjOPwql+z1/wAEu7vV/EUHxG/aavP7a1iYi60/wPDP5kcJJyDeMDjj+4PTmvc/2aP2Xvhz+w5o0kPgOxbWPFV0mzUfF+poJL24b+IRA8RJ6Bf1qf4lfFzT/BVvJdahdGe8ky20tueQ+9daqYTL4NYVK/WR+J+HvgoozjWxcLyeqj1fr2Oq8ZePFOlxi5+y6bpGnxiO1srdBDb2qDgKiDgACivk3xr8Stb+MGvQ2ce/y7iQRw26Hrk9/wCdFfOVcyq1ZuVOLku5/XmB4HwWDoRpYqqqbtpFdEJ8e7r7X8VNTbjKsFP5VzGkatdaBfLc2czwTIQQVPWvYPir8ANQ13xHealbtlrhy+3HSvLte8B6r4edluLWTA/iA4rysVRqQqOdnuffZHmGCrYKnQU02opNfI9e+Fv7TUOoKllrm2OXgCYfdb617BaanHqlpHJDMs0KjKEHIAr4oI2nDDa3pXVeA/i7q3gSTbDM01q3DRO3b2rswuaOPuVdu587nnA1Or+/wPuvt0+XYb8ZdVOsfEnUpN2Qj7B+Fa37P/g1vEvi1Z5F/c2vPtmuK1e//tXV7i45/wBIkLYPXk19Ifs7eEF0Dweszria4+bp2rnwdP22I5ntuern+M/s7KI0o6NpRX3anoUcWyNY1HbaoFd5rfjjwz+yR8G7nxt4tmjhwuYI2IDSHHSs74P+FV8U+K0aX5bWz/eyueigc8/lX5df8Fkv2zb79rT9pBvAXhu4k/4Rfw2/2bbE3yzOPpX6BlOD5n7WeyP5A8SOJ/qNFYWm9XqzZ/bB/wCC8Hi/4y2GseHfCNs1jptw4EF1F8rJj9cV8n6R8O/Hvxw1NrzUNU1S6luDuJMr4Ofxr6A/Zf8A2A5fFVvA81qzK4Gfkr7/AP2YP2B7bwzbpDdWiDyehK9RWmZcRUMKrU9z+WM34qd7yldn5haB/wAE6fF2sxq8c+pBjzxK4/rW/a/8E3/iVYDNrqmuw4/uXMi/1r9x/DHwC0LQbdF+yxFlHZRXRQ/D/SIBhbG3/FBXyc+OK99EfIy4wrp+6fhTYfsa/Hrw82dP8YeLrfb02X0vH61d1P4G/tOX/h+80m48deLrrTr6Jre4t5rl3jmjYYZSD2Ir9zx4K0r/AJ8Lf/vgUHwVpX/Pjbf98VP+vWI/lRS40xa/4c/P/wD4I5fsbSfCrRjdatY/Z7y0O1w68gj/ABr9ElGB7VW0/RLXSS32a3ig8z72wY3VbHAr5LMcfPGVnWn1Pl8djJYmq6suoDgUUE4pobNcBxjqCaZLMIV3Myqo5ya80+Nf7SWifC3RbiSS8h85VOPm6VpTpTqPlgrmlOnKb5Ym18V/jJpfww8P3F1c3EYeNT8u7oa/GX/gpV+1dqv7S/xHtfB/h9pLq81i8Szgij+YszHA4qz/AMFAf+ChGo+LfEc1hot1JM11J5IhRs7yeBgV7R/wSB/4Jp6v4V8Un44fFi0ktLiwUzaLp90vzM5GVkOa/UOHOH44SP1vEb9D9Q4P4Tq1q8Xa7dj6p+HXwYs/2UP2ZfAvwvsAizaPZrdamy9ZruT5nY+4JI/CvDf2uPEm+5sdLRs7cyuM9+1fQXivxBJ4k1661Cckm4cvz2Havj/42eIl8Q/Ei/k3fJE3lrz0xXm59inUTb+0/wAD/SLwoyCOGlSpW/hxu/VnT/smwhviBdSEf6u0JHtkitT9rRP7U/bg/ZR0tvmWO31e+IPq11En/slUv2Rx/wAVrqB/6df/AGasn9uz4xeHfgR+3H+z34p8S3ItdJ0Dwjf3cvdpSb9wqIO7MVwPr7VtkMZPCzUFd8srH519J6c3gqkYdeVfgz9SFjWJQiKqqvRVGAK+Bfj3J/xu6+HKsfveEdZRfr/Z8tZ2o/8ABQD9or9rZJF+Dvwpu/DPh2bhfEvic/2ZZKp/jDygM/riNWNP+A/7ImpfDr45ab8U/iZ8UNQ+I3xA0m3uYLOy0q2Fto9gJ4nhkUyygyzDa56LGMjNY5DktbBVfrOKajo1a+up/G3hzwLnU8zpYyNF8sWr/wDD7GX+2BD+40Obsu9T+QNTfsg2u2x1afH3pFj/AEqb9rZ7eTwxp48+H7RDcZ8sNltuMZx1qx+yRDt8G3zd5LjP5AVwqP8At+nY/wBNnWa4VSenvW/8mueX/wDBYedtR1n9mnw31juL7UNTdB3O+NAfyzX6Y/BPS10X4TaBbquFSzTj04r8yv8Agq0PtH7XH7M9qT8keh3EuPdrof4V+pHgRfL8FaWv/Tqn/oIrq4wlalQh5H+XvjBWc86qf4pGxSMcUpOK8t/a5/aC0/8AZz+Deq65eTpHMsLeSpPJOOtfC0aUqs1Thuz8np05VJKEd2fnn/wWq+J998ffjH4X+Efhfdd6v4iv4dMhijOSGdwpJ9gMk+wr621Twtp3wl8I+HfBOlMraP4F0mHSY2HSRo0HmP8Ai2T+NfJf/BLP4dX3xa+Kni79pzxZC0kcMk2jeC4Zh/rZ2+Wa7XPZF+UEdyfSvZ/2kPiM3hTwjJDHIft+p5XOfmwfvNX6piFHAYKGF7K79T+5PAPgmqoxrOPvS0X6v5G7Y3/hb9oX4XeJPC+u2a6v4V1+GTTtWsmxuGM7JkJ+7Ihw6MOhA7ZryH/hlj4UnwH/AMIj/wAJB46/tP7H/Yv/AAm//CRS/wBvbNm37H9o/wBR9m28fY/L8nH/AE1/eV5/4NXWNWvho2k3Fwjao4WVY2KhwOctjsK+lh8DLT/hTf8AYn2dv7Pz5f2nZz9o+9nPr/TivNy/NsVOnalstT+g+KvD3IaGM9rjrSlU91d1H+Z92n8PXfUmvdQ8Lfs8/C/w34X0Gzj0jwr4fhj07SbIY3HON8zkfekkbLux6kn2rstM8Lab8XPCPiLwVqzKNI8d6TNpMrHpG0iHy3/BsGvjHxkusaRfnRtVuLiRtLYrEsjEhQfTPY9a+gv2bviM3irwjHbySsNQ0whc5+YgfdaowubSniuae6/pnVn3h7QwuQqjQadNpp2WiT+F/wCfmeK/8EVvihffAL4x+KPhH4o3WmreHb+bTJopDtJZHKgj2IwR7EV+rq9K/KT/AIKl/Dm++EnxV8I/tOeE4Wjjmlh0bxrDCv8AqZ14hu2x2dflJPcD1r9Cv2Rv2gtP/aM+DWl65ZzpLM0KiYA8g461wcYYC1VY2n8M9/U/zK8QeHq2WZjOM11sepVynxs0pda+E2vW7LuV7N+Pwrq1ORWP47TzPBOqqehtXz+VfIUZWqJ+a/M+Do6TXqfl3/wR4nbTtZ/aX8N5+S3vtP1NFz0+eRCfyxXoX7X9nu0/SLjHKyNGT+Ga85/4JTf6P+1x+0xahvkk0O3lxjut0Rn9a9U/a3j3+DLFscrcZz9RX6bxFrSUv7qP9I/o64h+wo/4n+MTL/Y+hzBrk397YoP4Gui+AcmP+C3XxGVW+74R0ZHx6/2fFWT+yTJbw+GdQXzoftE1xnyy2G2gYzjrVn48/si6l8RvjnqXxT+GXxQ1D4c+P9XgtoLyy1a2Fzo98IIlhjUSxASwjag+8sgyc1yZXRjVwE8PzJOSsr7bmH0jshx+cKpQwNNyal+Fkj9CGjWZdjqrK3BVhkGvzZ/ZNH9kftv/ALV2lr8qyW+kXwX3W6mT/wBnra07/goB+0V+yTHGvxi+FN34l8Ow8N4l8MH+07JlH8ZeIFk6ZxIqn2rgv2E/jJ4e+O/7cf7Qninw1crdaTr/AIRsLuI9GjIv1DI47MpOD9KWR5PicDUq+2XuuLs1sfyV4aZNjss4moxxUHH3kvxND9rOAR/EG1cf8tLQZ/Amtb9kfxJ5VzfaW7YDASoM9+9Z/wC1zz430/8A69f61yPwU8Qjw98R7CTdhJG8tvfNeHKp7PG8z7n+qNHCfW+GlT68t18j6w+I/wAGLP8Aav8A2ZPHXwvvgrTaxZtdaWzf8sruP5kK++Rj8a/OX/gmt+1bqn7NPxGuvB/iB5LW80e7aznik+UqynBr9KvCviKTw3r1pqELEG3cNx/EO9fI/wDwV/8A+CamreK/FX/C8PhPaSXc98BNrWn2q/MrgZaQYr77Bxp4vDSwlX5H8L+MHCnt5/W6cbqW+nU/Sj4UfGPS/ifoFvdW1xG0kij5d3U12gNfh1/wT/8A+Cg+o+E/EcNhrV1JC1pJ5Pku2NpHByK/Xf4KftJaJ8UtFhkjvIfOZQT8w5r81zrI6uCqWt7p/IeaZRVwk2nsenUUyOZZl3KysvqO9Oya8E8cWmsuR+HNOBooA/Ov/gsb+xtJ8U9GF1pNibi8ujtVUXqT/hXxjpnwN/acsNAtNKt/HXi+106xhW3t7eG5dUhjUYVQB2Ar919Q0S11Yr9pt4pvL+7vGcVW/wCEK0v/AJ8bb/vivrMt4sr4OgqCV7H0+X8T4jCUfZQPwjv/ANjb49eIW/4mHjDxdc7uu++l5/Wsu5/4JwfErUB/peqa7Nn/AJ6XMjf1r98P+EK0r/nwt/8AvgVDN8PtIuB81jB+Ciu7/XnE/wAp1/65Yl7/AJn8++vf8E6vF2jRlpJ9SJ68yv8A4157q/w78e/A7UlvNP1TVLWW3O4EStgY9s1/RR4n+AOha9bsv2WJWYd1FfK37T/7A9r4lt2htbVP3x5IXoK9TAcae0laqjuwfFkpS/eHxj+x9/wXf8XfBmx0fw94utmvtNt3InupfmZ8/qBX6eaJ448M/tb/AAbtfG3hGaOb5MzRoQWjPoa/JL9qD9gOXwrbzvDasqxg4+Stf/gjb+2bffsl/tHr4D8SXEn/AAi/iR/swWVvlhc/WvrOahjqTdM/XOEOMqlDExnGWmh+jUsO+No391YGvmL9oHwa3hnxYbiNf3N1z+NfZXxh8Jjwv4skaH5rW8HmxMOjA88fnXiH7RXhBdf8HNMi5mtzu4Havhs0wrcHF7o/t7gbPIxxNOtF+5UsvvPDvg3qp0f4j6bJnaHfYeeua+rrvVI9LtJJZphDEw+cscAgV8Y6RfHStYt7n/n3kDY+h5rovHvxd1bx3JslmaG1XhYlbt7mvHwWOVCm09z9E4m4ZqZnjKc4tKNvefz0t5np3xT/AGnIdPWSz0PbJLypmP3V+leI6tq91r961zeTyTTSHJLGqoBfhRubsBWzoPgLVvEDqtvayYb+IiuWtiKtd6/cj3MuynA5VT92yfWT3Zr/AAFuPsvxT0xuMliPzFFd18K/gBqOg+IrPUrhsG3cPtPGaK93K6c6dNqWmp+a8Y4zCYrGqdKSaSt+J7nj5ap3+g2mqRFZ4Y5N3Bytet6J+zjcapp6zSTGLcMnJwBTb79mfUEUm1uopvRcg17LwdVq9j8vjxRl8Z8vtbP5nzn4p/Z70XX1YxxCFz02ivN/Ev7LGoWLM9lIJlXoDX1frHwc8QaLu3WTSKP7ornLzTbiwLLNbzQkdQyEV51bLacvjjY+0ynjbE07ewrKS7N3/wCCfJekfCvVLXxZZ2t5avHG0oBbHBr6m0iwXS9MhhjHyxqB+NOltobplLRqzLyCRzWp4Z0dte8QWdouf30qg/TvU4XBqk2o9TTiHiSrmEIzrKygn6GF+2f8d4v2Nf2C/EniLcIta8QRmyshnDZcYyO/Svy0/wCCe/7P958XPHR1bUEa4mvZzNK7c7mY5Jr3r/gv78a2+I37QvhH4T6XKXsfDMKy3Uanjzm9fpX0l/wSm/Zuj0Dwra3k0KjCK3I69K+mzbFLAYDTdn8E+JXETrYmpUv10Ppr9nv9nXT/AAT4dtmkt0DBRxtr1+2sI7RQIkVQBjgU+CFYIlRQAqjAAqSvx2tWlVk5SPwGpVlOXNIKKKKxMwooooADXN/ET4paR8LrCO61i4+zwSHAdugPvXSHpX56/wDBaz49f8Ir8PJtOhm2N5ZAwcYNehleBeLxEaPc7Mvwv1iuqXc+lvEv/BQPwB4eRi2pQvt/2xXkvxE/4LB+C/DEcn2e4t2K/wC0K+E/2Q/+CR/jX9r74DaL8Q9a+KkPhXSdeMhhtZ4GeYqrEAg5wQRXu3hf/ggz8G9AZZPFnxS8VeI3HLRWirCjevPWv0KHCGAov99UP1jK/CvF4mKnThKSfWxyvxx/4LmJPHNFp0w+bIUKa+ar/wCNPxr/AG39e+w+DfDOvar9qbaJEhdYlz6seK/SH4b/ALCH7M/wOaOTRfhuuvXkONtzrEhnyfXB4r16D4uTeH9MFj4d0vSvDdmF2rHY2yx4H4AV6VCnluE1pRuz9MyXwXxSac4qK8z45/YW/wCCJMfwn8S2vjz46alaXWoW5E9vo8b7/LfqNx9RX2t4r8e2/wAQ7u7td2o2+m2No8lnbWUQleXylLFFjyNzFVO1QQSQAOSBXE6trF1q0xmvLma4buZGyBVHSPEsMWsIbO5T7XZssw2NyhB4P51niczdaST+HsftmR8BUstw8nQd6ltJW0T6Gf8ACHx34R/aT0SS6+Hvi7w740t1B863srryr639pbWXbNGc8YK9e5q1d/swQxvLJN4Oug3JkZrVvl9TmvBvG37Nvwl0T9tK78G+K/BOmXOg/HW0k8U+CdThlk06+0TXIzjUtNhu4GSREmOJo0YlFkO0KfMq7f8A/BN1pLBn8H/tIfHbwbdWDrNZWur6r/aVnA6sCgJDRsyqwGQVPA5B5retluEbTcrX1V/8zy8t434ipwmqdFTcHyy5W4tPzV+vQ0Pgp4Km+Hnxr1rTWy0X2bzbdz/HEW+X8un4V7J4gvfDZ1PR9c1Dw74Vk1/w/ZGwstZ1Cyjubu0h8xpNsRkBEfzOxyoyfXpXkdt8fPCPjNtNtfGnirRfhh8UooWsdZ0bUrhdKe3vUfZOLd7lVhmt3kQywvG5BjkTnqKj+If7K2sPJLNN4iuLqTyxMqXa7/MQjcpUqdpBHII4INeFKjicBFxpRdtddtGfq9HH5LxXOnUx1aPMlG8Wm2mla7XT5nW/Eb9rHS1nZrjUrrXLtegVyyj2BPA/CvIvGX7Tuu+Ig0NiqaXbtx8nzSEfWvORZyfajCsbNKHKhQuTnpXqnwr/AGa7rXzHeazut7TOfJ/ikHvXgfXMViZcsdD9Xp8P5HklFVayTttfr6RWhx/g7wFrXxT1rMfnTAn97cykkL6819M/CP4VL4N0uLStNWa8url8kDku3Tj0FdB8Ovhu108OmaHZpGoGGYDCoB1Zj2r5q/bb/wCCk6/D/Vbr4R/AOZNc8d3mbTWfFUI8yHSc/K0VsehkHTf0HbmvoMpyZ355P1Z+O+JHilQwtBwdoxXwwW77NnI/8FRvGek63+3z8E/D+l6la6trXgnSns9cgsyZv7OlecOqOwGN23tnjviv1Z8BS+d4J0lv71rH/wCgivhD/gl5/wAEu7X4X2aeMPF8cmo69ft9pkmuiZJJZG+YsxPJJJ61+gkUCwRKkaqsajCqBgAV4XFWY0cRVjRoaqGl+5/m/wAb59HNMwlXiurb+ZV17Xrbw1pFxfXkyw21shd3JxgAV+Sv7WfxH8Rf8FU/2xtP+D/g+6ktfDtrIZ9c1FD+602xjP72Rj0yRwo7kivqf/gtR8UvFHwx/Zunm0CZYo5kYTDB3H8a4j/gnL8GdB+AP7EXhnWtDmbU9e+Llmuta9rUibZpfmIFqvXbHGcjHc816PCuX06VF5jU1a0S8z6fwt4P/tfHxu+v4Lc9Y1ZfD/w48H6Z4d8O28emeDvBtmtjp0I4xGg5c+rOQWJ7k18i/FDx1N8RvGE92u5oVbyrdB/dzgY9zXpH7UfxLeFl8P2rMgIDTtjG4dgKP2dfgWzywa5qkO49bS2K5LHsxHr6CubMK08XiPZR+Z/p1wll2E4eytY6qraWgvLv8+vY6z9mP4HSaBBHdXSxJql+u9nlYLHZwgZZmPRQBksT0AryH/h4NqR/aH/4TTdc/wDDMf2r/hAvtPlnbv3eZ/b2Mf8APxx/178da6z9sLx/rHxP8a237Nfw6mdvFHihU/4TvVLZ8jQtNbB/s9XH3ZphzKRyqfL1Y19bf8MIeDf+GP8A/hUX2GD+x/sXk7tg/wBdtx5n5/pXrVMZh8phClJXct12j/mz+LPGTxiq1s4hDDTvyTTbXl28l0Pnv9p34HSa9DJdWqxSapYrvV4mDR3sJGVZSOGBGCpHUGvCvhh46m+HPjCG7bcsRby50P8Ad6H8q9F/Y9+IGsfDHxrc/s1/EaZl8UeF1f8A4QPVLlsDXdNXJ/s9mPWaFRmMk5ZPl6qMv/aK+BbRTza5pcO05zd2wXBU92A7e4rhzbL/AGc1iqDunqvNH9W+E3iJguIMqWExDXLJW32b6enVfce56VH4f+I3g7VPDviK2TVPB/jKyax1GE85jccOPRkOGB7EV8b/ALJvxG8Rf8ErP2xtQ+D/AIxupLrw7dSCfQ9Rc/utTsZD+7kU9MgfKw7MDXq37LfxMkmd/D10zSYBeB8ZwO6mtn/go38G9B+P37EfibWtcmbTde+Edm2taDrUabpovmANq3TdHIcDHY4NevltaljcO8LV2l+DPxjxz8OYzp1Kr+KC1fePRn33oOvW3iTSLe+s5VmtrlBIjKc5Bqn49k8rwTqzH+G1f/0E18j/APBFf4p+J/id+zdDNr8yzRQoohGDkfjX2dLAs8TLIqujAhlI4Ir84xuG+q4qVJu/Kz+AsVQeHrum/ss/JH/gl14z0jQ/2+/jZ4f1TUrXSta8baUlnocF4TD/AGjKk5dkRiMbtvbPPavqL4t/ClfGWly6TqUc1ndWrkgHgow/pXEf8FQv+CXlr8UbR/GHhCOTTtdsX+0xy2xMcsMi/MGUjkEEZzXB/sSf8FJl8f6ra/CP4+TLofjuzxaaN4qmHlw6tjCrFcnoJD03dD35r9Nozo5rhVKi/eSs0f1j4N+J2HwMI4ab5dU0+z218jmvGXgPWvhZrWZPOiVW/dXMRIDenIrovB37T2u+HgsN8E1S3Xj5ztkA+tfTHxE+G7WbzaXrlmkisMKSNyOp6Mp7186/FT9my68PNJeaLuuLXJPk9WjHtXyuIwdfDS5qLdux/deU8S5ZnVGNLMIxu9pdH6PdM9P+HH7WWlvMrW+pXWh3TdQzlVPsSOD+NdnoN74bbVNY1zTvDvhWLX/EFmLC91nT7KO2vLuHzEk2ymMASfMinLDI9etfFJtG+1iFo2WTcFKsMH0r3b4dfsrayJopYvEVxayeWZjHaLs8tANzEljtAA5JPAAzXTgMyxcm4RV+9jxOKuCchw/Li601BrWN1fbXda2I/jR4Jm+IXxs0bTVUrD9l824fPCRBufz6fjXrlp+zBE8kTw+D7hmwDGy2rc+hzXld18fPCPgt9StfBfinRfif8UJYVsdF0bTrhdVkuL1n2QC4e2VoYbaORxLM8jgCON+ScA49h/wTdZbBW8YftIfHbxldX7tNe2mkar/ZtnPIzEvgs0jKrMTgBRgHgDivcwuU00pVcU+Vt7M/L8+8QcbKdHA5HH20YRs3GVo3be7v/wAMkexfF7x34R/Zr0OO6+IXi3w74Mt2GIba9uvNv7nsBFaxbppDnjAX8RXqnhTx7b/D28tbXdqFxpt9ZpJd217GIni81QwRowTtYKw3KSSCSOoNfHfgn9m34T63+2ja+DvCfgnTbXQfgXaR+KPG2qTSyaje63rknGm6bNdzs8jpDzNIikI0gwVHl19A6v4ljl1hmvLlPtl4zTNvb5nJPJ/OuzGU6WFUfZX5t7s+a4fxGOz+db68l7KN4pRV7y6u/l+Z82ft0/8ABEeP4q+I7nx58C9Ss7XULkme50eSTZ5j9TtPqa+RrD40/Gv9h/X/AOz/ABl4Z17SvsrbDI8LtE2PRhxX6s6TrN1pMwms7ia3bsY2610s3xdm8Qaa1l4i0vSvElmww0d9brJkfiDXRHNKVaHs8TG6PjOIPCFV254WSfkz4W+B3/Bc1YIoY9RmHy4BDGvp/wCHf/BYPwX4njj+03FupbA+8BWb8SP2Ef2Z/jkZJNa+G66DeTfeudHkMGD64HFeOeKP+CDPwb15mk8J/FLxV4dc8rFdosyJ+PWvNrZFlVfWL5T8hzTwcx1Nt+yfyPs/w1/wUE8AeIVUrqUMe7p84r1H4d/FPSPijYSXWj3AuYIzguD8pPtX40/tef8ABI7xr+yF8Btb+Iei/FSHxVpOgGMzW0EDJMFZgCc5wABmvrD/AIIpfHr/AISr4eQafNNvYxgHJzk187nXC9PC4b6xRlzH5fxFwjUyyLdS6fZn6FUUA8UV8SfEhRRRQAEZqG6sI7xSJUVuMcipqKAPGf2hP2ddP8beHbgx26FmU/w1+LP/AAUI/Z/vPhJ47GrafG1vNZziaJ142spyDX9BE8KzxsjKGVhgg18B/wDBVr9m2LX/AApdXkMIO5GbgdOtfX8L5tKhiFTlsz6bh7MpUa6i3od/+xf8dov2yv2C/DviIsJNb8PoLK+AOWygxz+AqfVrJdU0uaB13LIhBFfE/wDwQC+NTfDr9obxf8J9UlK2PiaFpbWNzwJl9PrX3d4m0dtA8QXlm2QYZWAz6V9rnOHSmprZn9z+FmdPEYH2LesNUfIer/CrVLnxbeW1nas8aykBscCuo8M/ssahfOr30giVuoFfQEVrDbMxWNVZjkkL1q5Z6bcX+BBBNMW6BVzXytPKqSd3qf0HiuPMZ7JQhaKta5534V/Z90XQQpkhE7DruHFdpp2h2mlRqsEEcYUYGB0rs9G+DniDWiNtm0a+rCulsv2Z9QZQbq6ih9RkCvWo4CSVqcD4HMuLqMpXxWIu/U8yxlaK9P1v9nGfTLBpo5vN2qTwc0Vo8LVjpY8+nxBgai5oVFY/Ov8AbZ/4LrXGv2MFr4Fc28mWgnjjboR3r5N03/gqD8eDfG4sfEF1GrEkJkkfSuC+G/7Ml94jSOdYWZlw5GM7q+ufgT+w2uvWUMhtM+YOcr0PevqcTmOGwy1sfxjmHEk0+eUzlfAH/BdD9oDwEyfb1tdXhTqs0fJFe7fD7/g47sr4LD47+G6sOjzWq811Wmf8EybXVbLD2K5YddnSsXxT/wAEhob6JjHZLk99leQ+JMBN2mjkw3HlSjL3ajPXvAP/AAVl/Zi+MhjW51K78L3kvG25TCqfrXunwo+JXwra6k8ReH/Hug6zDY20s4gFyvmkqpYYB5J46V+avjr/AII3XShvJs5B34U15R4h/wCCW3jLwfcNJpcmpWjr90wyOhH5GtKeMyyrJSjJJn2GF8VsS6Toyq3i1ZmZ4N1G6/a7/by8TeKrjdcJf6q5jY84jDEL+gr9yv2Y/h/H4D+GllEse1mjHb2Ffl3/AMEvP2SNT8A/FU2usWsizrLvDuv+sGeTX7BaRZrp+nQwqMCNAK+N4yzBVqqpwd4o/FuLMw9vWtF6blkCloor4c+PCiiigAoPSig0AU9a1BdL0q4uHOFjQmvw9/4LAfFS4+KPxWg0G1kMk1/dpbIoPUswUfzr9ev2r/iNH4F+F9+/mBGaJu/sa/GP4F+G5P2sP+Cpng3S5FNxaWuqC9uP4gEjO7n8hX33BODvWdeS2PtuDMC6uJUrH6rR+Bofg58Cfh14Ht1WNdB0SBZFHHzlQW/U1yHiLxrpfhUqt9cxwsw3ANxkV6D8atXXVviNflD+7tz5SD0UcV8mftV6mLzxhb2+eIY8nBruzbGOEpVfM/0k8O+HoV6FDBS0XLd2PRtY/aX8O6ZkRzNcMvQJXIa9+1u0hZbCxPP3Wc15p4T+FmteNdMN1ptqtxCrFWIb7p+la8f7Pfix/wDmHqPq9fPyxmLqK8Y6eSP16nw/w/hpuFaonJbpy/Qh8Q/HTxF4iLBro28bfwx8Vufs3eNP7N+IEgv7zbHeQ7N80mAWB45P1qnb/sz+Kp2+aG3jz/ekrUsv2StcusfaLyyh9cfNWVKnivaKo4t2OrG4rIXhJ4SNSEVJWutTtP24/gz4i/aE+AXhM+AbvTYfiN8OfFcPiTQHvLsWsciBP3sazMNgJZYyA5VTjkiu0sfju3xV8b6fomueDPE3w38aa5aS3MOn6hawtZ6vLAqPd/YbyCWaGcpvL7CVk8oE7TtOON8Dfs/al4RkHl+KtShX/nlAfkP4NkfpR4k8PW/7RPhDXPAt5r194c8R6Lfx6honiCyfy7zRL6Ld9nv4yuDwHkikVcbo5ZBwSCPscPmHtaccPio8vRO5/OedcIywOKrZxklb2krJyjZpSV/PZ+avqd1+0h8EbP8AaU/Z38VfCnxLGkMus2zNpN7cvuGnagg3W0i54Vd2UZh/DIQenHL/ALHnxO1D4v8A7K+hxeILS40/x38Lm/4Qfxhp067Z7O5tRthkYf3ZIQpDdCQcZ61haB+zb8f/AA+Imm/bH/tiaHhrfVvBsepWkvqpEwYke+Miorj4SfHnwx+0xpvxHs9c+CfjA31jFoni2z0xrrw5J4r05OI/Ohn3wfa4B/qpldCAApBXAHsexpui6DqJ9vJn5v8AX8ZSzKnm0cHOm7WmrXTT6q2zOw8LfBbSfDmt3WoNCss9xM0q7hwmTkAV6d4Q8Dt4ijmvLmeHTdHsUMl3ezNsihQDJJJ44FamifChtX8dQ6ekqyWUwE6y71ZhGcEqxUld4zg4OMjg4r8+P+CxX7e+o/Eb4hf8KH+G941n4b09hFrNxaNhr6TuhYdvavNy7K4yk76RW59lxt4hfVMKqsZc05LS/Ren6Fn9uL/gpfqnx31u6+Df7P7XFj4ddzaaz4lhG241T+FkiYfdj68jrX0H/wAExf8Agl7pfwc0C117XLVZb6QeZ+8GWZjzk/41zX/BKL9gTT/CejWur31mpZVD5Zea/R20tI7O2SKNQkaDAAHAFfO8ScQP/csJpFbn8P8AGHF2JzCvJSk3fdiwW6W0KxxqEjUYVQOgqQ9KKRvu18Gfn58wf8FY/AH/AAnP7Kuqrs3tEjY49q+e/wDgkp43Pj3/AIJv2OmyNvvPh34hu9KkBOSkMjeZH9B8xr7c/aw8NL4q+BGvWrLu3W7EDHsa/Nf/AIIoeIzoXx0+OnwtnbausWSa1ZRE9ZYHKtgeu1v0r9I4RmquAq0O2p+3eDWafVcyhJ7c34M9H/a28MeXeafqiL8simGQ+p7VufsrfFSS+sRo80xW804iW1fPzFQf6Gur+OHhU+Kvh9eQhd00I8xPYrXzD4c1+68I69b31qzR3Fq+4e/qDXk4ibw2K9otnuf6dZPhqec5G8HL4obfp9+x6V8cby4/YN/bB0f49eH7fb4A+Jl6mneMrSNfk0rV2H+v/wBmO5Ubs9BIrD+IV+iH/C2NCPwv/wCEv+3Qf2H9j+3faN427NuevTPavlPw9d+Gv2g/hNqmg+IrX+0vCvi6yOn6xaZ+dAeRIh/hljfDqexUV8jf8Iv8Zzrf/DG/9pXH2f7f/aP/AAlW0/Z/+EY27vt27p935Nuf9b8te1jsqjmqp14O0lZS813P88fF7wtr4bOIzw0Hyzla3Z31R6r8Db24/bx/bA1j48+ILfd4B+Gd6+neDLSRfk1XVlH+u/2o7ZTuz0Lso/hNdR+1T8VXsbE6RDMTfaiTLdOD8wUnn8Sa7zxFeeGv2fPhNpeg+HbX+zvCnhGyXT9HtM/PJjkyOf4pZHy7N3LGvlHxHrt14s1y4vrpmkubp8n29APpXLnWOjTgsLQ2Ssv8/mf194F+G9PK8HGrVj8Nm/N9F8t35nq/7JHhjffahqjL8saiGM+/epf+Ctfjk+Af+Cb19psbbLz4ieIbTSYwDgyQxnzJPw+UV6F8D/Ch8LfD2yhK7Zph5je5PNfN/wDwWv8AEJ1347/Av4WwtuXR7J9avYgeks7gLkeu0frXqcO4dU1zP7KbPk/HbiBSwtZp/E7L0ifaX/BJ3wCPAv7KmlJs2tNGufyr6gHIrzf9lDw0vhX4EaDaqu3bbqSMewr0dfu1+a5hV9piZz7tn+buOqOdecn3Yye3W5haORVeNhhlI6ivgb/gp3/wS80v4yaDda9odqsN8n7z92PmVhzkf419+1Fd2kd5bNFIoaNxggjgiqy/MKuDqqrSdv1HgsZUwtVVKbPyF/Ye/wCCmOqfAfWrb4NftANcX3hxHFro3iSbLXGl/wAKpKx+9H0GTyK+6fF/gZvD0cN5b3EWpaPfIJLS9gYPFMhGQQRxyK+c/wDgq7+wJp/izRrrVrGzTcVL5Vec14z/AMEdP299R+HHxC/4UP8AEm8a88M6ixi0e4u2y1jJ2QMf4T6V+sYWpRzXD+2hpNLU/qHw08S6tHloVnzQe67eh9SeKfgtpPiXXLXUFiWK4t5llbaP9Zg55ql+2J8TdR+D/wCyvrkPh+1uNQ8dfFBv+EG8H6dAu6e8ubobJpFH92OEsS3QEjNe3618KH0nxzNp7SiOxhHntLvVSYxyFUsQu9sYGSBk8kDmvm21+Enx68T/ALSupfEa+1z4J+EGsrGXRPCVnqb3XiOTwpp7n955MMHlwG6nH+tmZ2JBKgBODhluDp06rqVLRt36s/c+NuI8RjMBDA4Tmqc63Sb5YvV/NnpX7OHwSs/2av2ePC/wp8NxrNLo1srate20m0ajqDjdcyHGAy7sIrH+GMAcHmve/HZvhV421DRdC8G+JviR400S0iuZtP0+1hSz0iWdXe0+3Xc8sMNuXCB9gLyeUQdo3DPBa9+zb8f9fEjQ/tkf2PNL9230rwbHptpF6KBDtIHv1PerXhzQLf8AZ28I6H4Fs9evvEfiPWr+TUNb8QXr+Zea5fS7ftF/JuyeQkcMatnbHEgOSDnfFSo0v9pqTU3fRLY8zIcNmWOismweHlh6Sj705K7aS1+b/AT9hv4MeIv2evgD4s/4T6602b4jfEbxXN4k197O7F1HGhT91G0yjYzBmkJCFlGeprg/2kPGf9peP4/sN4Gjs4QgaGTIDE88j6V33jn9n7UvF8pMnirUplP/ACynPyD8FwP0rib79krXLUH7PeWU3pn5a+XzjEYrFNvksfuvh5kuS5DSjF4jmsno093u22c34d+OniLw6VVbk3Ea9pOa7rQv2tnTaL+x46FkNcrcfszeKoG+WC3k/wB2Sqr/ALPfixD/AMg8N9HzXkU5Yynsn9x+h4qjw7ivelKF+6dj2PR/2lvDuphVeZoGbqGFdZ4d8a6X4qZlsbiOYqMkLzgV8teK/hZrXgvTvtWpWot4WYKuW6n6V2f7KeqfY/F9xbluJo8jNduHzCq6qp1FufO5rwrgFgp4zAzclHzuvPU+n5PA8Pxj+BPxE8D3CrIuvaLOsaN/fCEr+oFfmb/wR/8AircfC74rT6DdSGOawu3tnUnoUYqf5V+o3wW1ddJ+I1gX/wBXOfKceoNfkv8AHXw3J+yd/wAFTfGWlxg29pdaqb2DsCkjbuPzNfZ0YLEYCdB9D+IfGrKE63tUvij+J+8mi6guq6VBcIflkQHrVwdK8r/ZQ+I8fjr4YWL+ZuZYl7+wr1QdK/Hq9N05uDP4/rU3CbiwooorEzCiiigAIrzf9pzwBH47+Gl7E0YZljPbPY16RVXV7NdQ06aFl3CRCK0o1HCakjSnNwmpI/n48Z6jd/sift4+GfFVvut0sNWQyMvy/uy4Dfoa/ZP4rfEr4Vrcx+IvEHj3QdGhvraOdoDcr5oLKGOR1HXpX54/8FQv2SNT8ffFT7No9rI1w0m8ui/6sZ4NeGeH/wDglv4y8YXCyanJqN47feM0juT+Zr9noY7CV8LB4iSP3rhXjeplVD2lKVpSR+hnj7/grL+zH8Gy622qXnii7iJG22TKsfrXiXxC/wCDjuysg0PgT4cKo6JNdDmvN/Av/BG25YL51nIe/K16x4V/4JDQ2MS+ZZL+KVEs6yyj8NmVmHibiK79+rc+efiB/wAFz/2gPHrN9gW20iFuAsKdK8t1H/gqD8eFvhcX2v3UiqclckA195ap/wAEybXSrIrHYr8o67OteE/Hb9htdBsp5FtMbATwvU9q2w/EWFqy5YpHh0+LZVp/G/vO2/Ym/wCC69xoFjcWvjpjcP8ALBBG7dSe9FfCHxH/AGY77w5HJOYWVmy44xtFFexGWHqLmuj6XD8TVowtGR+rP7Mn7H8baXavJbY+Ufw+1fV/ws/Z5s/B42+SgjbDDK9DUX7O/wDyALf/AHRXr8X+q/CvxHMcwrTm02fhOOxtWc2mytZ6Fb2SKqRr8vtVgWkY/gX8qkoryOZnm3ZVl0i2m+9BE31WqN54E0m+H72xhb1+Wtimv0q4yl0Y4ya2OZs/hLomm6tDfW9nHBcQnKsq4rph1oXoacOlTKcpP3mEpSk/eCiiipJCiiigArN17xDbaBYPNNIo2j1rSPWvHfj3/wAgO7+h/nWlGmpzSZrRhzSsz5A/4Ka/tSxp4evrWG4G7YwGD9a8U/4N6vhm3i/9oHx58SryPdBodm8EEhHG9s5wfx/SvNf+CifS6+rf1r6g/wCDeb/kz/x9/wBfbfyr9lyPDww+Abpn7d4f4Gm8TSj3Z9GateG/1a6uG5aWRnz9TXyb8eL/APtD4k3hzkR/LX1Qfut/u18kfFr/AJKDqX+/XxmcTfs0/M/0i8O6UY4qSXSNl+B6p+yHebtF1KHP3JAwH1r1P4t/FL4d/s8+C9F1z4heNoPCtr4glmgsQ9jNcee8WN6jywRkAg4ODg147+yB01j6LXJ/8Fnv+Tfvgb/2N1//AOkor2OHqMa0IxqbWZ+beNWbYnKXXxeEdpKS6eR2mr/8FSf2a9GB8nxl4y19h0TS/DM3zH2Mm0Vzt7/wVp8B6gxh8HfB/wCLHi64ziM3skOnQse2SN7Y+gr53+Cv/ISX8f6V9tfAP/kWbv8A69Zf/QDXqYupQwy92mn6tn8j5l4tZ/ayqW9NCT9k79p9f2wfgDrHiWTwjp/g3UfDviqXQrixtbp7oqi26SIXkbGWJZugA4/GvLfjRqV18NfjkmrWJKSMiSkdFkXoyn2NR/8ABJb/AJIf8dP+yiRf+ktWv2uf+Rysf+vX+teTxJGMbTgrbM/rrwFzCrj8HTeLfN7SMlK+t9jWP7XE17exxW2kxr5jBQZHzjP0r2bS52v9MhmkC7pEDHaMDkV8aaD/AMhmz/66r/OvsnRP+QHa/wDXJf5V5eW4ipVb9o77H6Nxnk+EwHso4WNr3vruaX7QXx7tf2K/2L9e8fBLf+17pGt9PEi5zIwxX5M/sL/C7Wfj38cbrxVqFut1catetdzgDozEk4r7o/4Lvf8AKOzwj/2FB/SvIf8Agjx/x82P+8tfZ4qs6GWOcFrY/gLxSzGtLF1m3s2j9VPgd4Oi8F/D6xtY4fKYRjcMV2Q6VBYf8eUf+6KnHSvxepNym2+5/NU5NybYUN0ooPSoJOe+KVuLrwFqkbcq1uwx+FfjZ+zt4wX4Bf8ABZvwldbvJs9eu5tIuOcKyzqV5/HFfsx8Rf8AkS9S/wCuLfyr8Mfi9/yk0+H3/YzW/wD6HX33Av8AGnHuj7rgWrKGMTj5H6mfELw//Y3i7U9Pdf3ayuAD6E8fpXxl8V/C7eEPHF9b7SI2cyR/Q19z/Hj/AJKnffUfyNfJH7WH/I9W/wDuCpzymlfyZ/qZ4X46o5wg9pwTfyMT4I/FCX4aeJ447rzF02+wZAwwEz0cV9WHx2q+D/m+x+T5Pl/b9i+d9l3eZ5HmdfK3/PtzjNfNnxv/AOSf+H/+uQ/lXXr/AMmvf9un9azy7ETpqVNPZX/4B38UZZhcy9hjqkEpSnZ9dU7KXqeb/G74ozfErxNJHb7202xz5YQZD44Lmsr4T+Fm8YeOLG3Clo1cSSY6YHrXW/A3/kQPEX/XI079lD/ke7r/AHGrzIx9pWhKf2j7GWIWDwGJw+HjyqirJ97rf1Pp74e+H/7Z8XaXp8a5jaVQQOwH/wCqvzo/aK8YL8fv+Czfi273+dZaDdw6TbD+FUhULx+Oa/TH4Df8lT0//eP8hX5N/CL/AJSafED/ALGaf/0Ov0DBaYOrNb2P4R8cMVUUYU+lm/vP3I+F1str4B0uNeFWBePTiugAwKxfh3/yJem/9cFrar8eq/Gz+KZu8mwobpRQelZknG/HPwdD41+H99ayRecxjO0Yr8If25/hdrHwE+ONr4q0+3W1uNJvVuoAR1ZTkZr+gm+/485P9w1+QH/BYn/j6vv99/619twbipwxPs1sz6rhXFTp11GJ9ufs+fHu1/bT/Yu0Hx7st/7YtUFvqAjXGJFGD+dZ+qXDWOlzTR7d8aFhu6HFeIf8EIv+Udvi7/sKtXteuf8AIEuP+ubV9Rm8VGs7dj+9vDPFVMRl0VV1tK3yPHf+Gt5rO9kiudJjYRuVJjfGcfWsP4M6ndfEn44Nqt6d7qjygdoh0VR7CvN9f/5DF5/10b+demfskf8AI46h/wBev9a+Ko4irWqRjUelz+oMfk+DwGW1cThYcspQt99j079rD9qBf2PfgFo/iaLwjp/jLUfEPiqLQrexurp7UlGt3kcpIudrAqvUEc15TZf8FafAenkReMfg/wDFjwjcDiQ2UkOowqfYtsbH4Un/AAVp/wCSI/A3/socv/pLXqfx9/5Fq1/69Y//AEAV9xUrUqNOlB01LmW5/nrx5x5m+VZ9VoYSo1HtfQ4/R/8AgqR+zTrP+u8ZeMdBcnBTVPDM3yn3Me4V618JPil8PP2hvBmta58PfG0Hiq18PywwX2yxmt/s7y52KfMAGSATgZ4Ffn38bf8AkIv+H9a94/4Ixf8AJv3xz/7G+x/9Ja66mFoSoynGNml3PW4N8Rc5zHMqOExE1yyavojuv2u73ZommQ5/1khYj1xXnPwGvv7P+JFnz/rPlrtv2weujf7rV538J/8Akoenf79fnmKk/ri+R/eGQ0o/6vOPdSPsDSbs6fq1rcLwYZFbP418O/8ABwr8M28I/tA+BPiVZx7YNcs0t55AON6Yxn8v1r7YH3E+n9K+ff8Ag4Y/5M+8A/8AX4tff5HJ+0cOjP5D8W8LCWBhUe+qNX/gmT+1NG/h6xtZrgE7FBBb6V+iGheILfXrFJoXU7hzzX4df8E7etr/AMB/pX66/AP/AJAtr9BXwfFGCp067lHufxHn2FhTrNxPYaKB1or5M+bCiiigApD1paD0oA5e8+Euiajq017cWcc9xMcszLmtCz8CaTYj91Ywrjp8tazdBRHWntJtbmntJNbkEWk28P3YI1+gqU2cePuKPwqSiouzO7KN5olveoyvGvze1eYfFP8AZ5s/F42+SpjX5jgdTXro6U2b/VmtaVecJXizSnWlGV4n56/tNfsfxppV08drn5T0XpxRX1V+0R/yAbj/AHT/ACNFfXYHNKzpbn0eFzGr7NH/2Q==" /><br />
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Initially I also had transitions between various coordinate systems, which looked pretty cool, but I dumped them because of 1) a performance issue on mobile, and 2) transitions between some pairs of systems made the image discontinuous during the transition, and it looked really bad. You can still see some transitions (but performed in a different way) in the ShaderToy prototype.<br />
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Now, I said it's done a bit differently on mobile devices, but this post is getting long, so I guess the explanation will have to wait for a next post.mihuhttp://www.blogger.com/profile/04739819757498405391noreply@blogger.com0tag:blogger.com,1999:blog-115773796018496125.post-75723948168393565782014-05-18T17:33:00.002-07:002014-05-18T18:10:37.853-07:00Waterfloo and ramblings about fluid simulationAs it is usually the case, a strong resolution to update the blog regularly clashed with the dull reality and lost in shame. As did a plan to write and publish a game in six months.<br />
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Well, it's been only fourteen months since I announced that plan. I was doing various things during that time, but only recently I managed to finish something and it's not a game. It's another entertainment app / live wallpaper for Android - Waterfloo. The previous one, Magic Fluids, was actually quite successful (over million downloads of the free version, and a reasonable amount of purchases of the paid version too), so it seemed promising to further explore the visually pleasing side of fluid simulation.<br />
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As it happens, fluid simulation was a topic of my Masters thesis. Back when I knew nothing about fluids (I don't know much now, either), I stared in awe at the videos on <a href="http://physbam.stanford.edu/~fedkiw/">Ron Fedkiw's academic webpage</a> and decided I want to at least touch this subject. I was very glad to find out that fluid simulation is a favourite hobby-topic of my advisor. We decided that I'll be exploring simulating liquids using level-set techniques.<br />
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Fluid simulation is actually a beautiful subject, not only for it's visual attractiveness. The physical derivations are mostly analysis and they lead to partial differential equations. Then, when you try to solve them on a computer, it turns out that many other areas of mathematics are useful - mostly linear algebra (because usually the slowest part of the simulation is solving a big system of linear equations), but there are also approaches based on variational calculus or Fourier transform. Lots of math, especially if you want to understand it well! Coders usually have rather limited understanding of the math behind the problems they solve. It really bothers me and I am planning on teaching myself math on a deeper level. If something comes out of it, I'll probably write about it in the future.<br />
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Magic Fluids was actually a very simple Jos-Stam-like simulation, so not much learning was needed. My new app on the other hand uses more advanced techniques. A good starting point for learning them is <a href="http://www.amazon.com/Simulation-Computer-Graphics-Robert-Bridson/dp/1568813260">Fluid Simulation for Computer Graphics</a>. I actually haven't read the whole book, only the first few chapters covered most of what I needed. However, this isn't a book that you can simply hold next to the keyboard and write code. I doesn't hold your hand and you need to fill out lots of details by yourself. I had to read many research papers and look inside lots of source code to finally get something working. One problem is the lack of readable, complete, contained and approachable code samples. There are a few small and clean codes for 2D simulations, but none of them covers the level set method. Mostly they are implementations of particle methods, optionally with a generation of a level set function from the particles (note that when I say particles, I don't mean SPH, these are all Eulerian techniques).<br />
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You could argue that going from a particle-based liquid simulation to level set-based one shouldn't be that hard, but unfortunately there are lots of small things that are really difficult to get right - mostly boundary conditions. Unlike some other problems in graphics, where wrong boundary condition gives you incorrect behaviour visible only at the boundary, in fluid simulation anything can happen: liquid leaking through a solid wall and disappearing from the screen in the blink of an eye is just one (and common) example.<br />
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I have found only one complete level set simulation with source code: <a href="https://code.google.com/p/levelset2d/">https://code.google.com/p/levelset2d/</a>. But I only found it after I already had most of the work done. :)<br />
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Eulerian liquid simulation is surprisingly hard to get right for one more reason. A simulation constitutes of a couple of procedures, running sequentially, each solving a different part of the differential equation (or doing something else, like redistancing, which AFAIR is still a PDE, but not the main one that governs the fluid motion). Unless you're a mathematician (and a very good one) or you've been working on the subject for years, you probably understand each of the algorithms on the <span class="short_text" id="result_box" lang="en"><span class="hps">intuitive </span></span>level only (which sometimes is enough, but often it isn't).<br />
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The problem is, trying to isolate one of the algorithms and visualizing the effects is usually hopeless, because the results are meaningless to an untrained eye. Only when running eveything together, you can (more or less) tell if you got it right. But then again, isolating procedures one by one is usually necessary to pin down a bug. I blame myself for not taking time to write some additional code or more complex visualization tools that would allow to run an algorithm solving a single part of the equation and compare the results with data obtained elsewhere (e.g. math software). If you're about to take on doing fluid simulation, I strongly advise this.<br />
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Back to the matters at hand: somehow I managed to complete both my thesis and Waterfloo. It uses a pure level set simulation, without any particle-based enhancements (something to explore in the future). It looks pretty great with some simply rendering on top (rather ordinary 2D version of refraction). Entire simulation is done on CPU and all time-consuming (and easily parallelizable :)) procedures are multithreaded.<br />
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Besides pouring water, in Waterfloo you can put some paint (dye) into the water. Paint densities need to be simulated in a grid that is four times bigger in both directions than the velocity and level set function grid (so 16 times more cells), otherwise the paint is too blocky. The velocity field is linearly interpolated when advecting paint values. <br />
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While paint and velocities are advected in the simplest manner possible, level set function advection is more complex. Spatial interpolation is cubic and instead of simple Euler (semi-Lagrangian) time integration, second-order Runge-Kutta is used. As is turns out, both are really important to the quality of the simulation.<br />
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I also experimented with cubic interpolation while rendering the distance field in a shader, but full version that takes 16 samples was really slow on some devices. I know there is an optimization described in GPU Gems that allows to do it using just four samples, but I didn't do that. I did something else though that gave similar effect, but I think I'll write it up another time. Eventually, since the optimized version was still too slow on some devices, I decided to stick with linear interpolation. Full 16-tap cubic interpolation is used when taking screenshots from the simulation though, because speed isn't important then.<br />
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I thought about at least leaving cubic interpolation as an option (I have some speed/quality tradeoff options in the app after all), but I decided against it for the following reason: even when the GPU is a little underutilised and giving it some more work wouldn't lower the FPS, you may want to keep it that way to keep latency on as low level as possible. Even without adding cubic interpolation on top, I can already see with a naked eye an increase in latency in Waterfloo compared to Magic Fluids (which was rather light on GPU, unless a lot of big particles were on the screen). Responsiveness is critically important for this kind of apps - it may look great, but only when it <i>feels</i> great, it leaves a good impression. <br />
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Now for some links and screenshots: <br />
<br />
<a href="https://play.google.com/store/apps/details?id=pl.madscientist.waterfloo">https://play.google.com/store/apps/details?id=pl.madscientist.waterfloo</a><br />
<a href="https://play.google.com/store/apps/details?id=pl.madscientist.waterfloo.demo">https://play.google.com/store/apps/details?id=pl.madscientist.waterfloo.demo</a><br />
<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg8Smh56Q-Amnw59EfFIx0KcJungqVj8eTKVdM3MPez9prIivv121_7cUmNYoXZvPde2QjuXgJeIDuo5G5FTMOpxpECMEohO5emox44L5INldqA0kjwSfvNs0jUrAwWfA-URrYrVS17UV0B/s1600/7-1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg8Smh56Q-Amnw59EfFIx0KcJungqVj8eTKVdM3MPez9prIivv121_7cUmNYoXZvPde2QjuXgJeIDuo5G5FTMOpxpECMEohO5emox44L5INldqA0kjwSfvNs0jUrAwWfA-URrYrVS17UV0B/s1600/7-1.png" height="184" width="320" /></a></div>
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<br />
I don't know what I'll be doing in months to come. Hopefully both apps will keep me independent while I'm figuring it out. :) I have some ideas for Magic Fluids 2 and I still really want to make and publish a game by myself, but I don't fool myself anymore that I could manage to do that in less than a year. My NIH syndrome will make sure of that!mihuhttp://www.blogger.com/profile/04739819757498405391noreply@blogger.com0tag:blogger.com,1999:blog-115773796018496125.post-22128469021695848872013-04-04T08:17:00.000-07:002013-04-12T15:33:16.008-07:00My app on Google Play!I've been rather busy lately finishing my first production that was supposed to go online instead of being forgotten in the abyss of my hard drive.<br />
<br />
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<a href="http://gamedev.students.wmi.amu.edu.pl/magicfluids/screenshots/s4.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="188" src="http://gamedev.students.wmi.amu.edu.pl/magicfluids/screenshots/s4.jpg" width="320" /></a></div>
<br />
<br />
<a href="https://play.google.com/store/apps/details?id=com.magicfluids">Magic Fluids</a> is an Android application with an option to use it as animated wallpaper. It draws animations of fluid based on Navier-Stokes equations and adds particles on top of that. It's been well received by Android users and after three weeks on Google Play, paid version is close to 3000 downloads and free version has almost 40000.<br />
<br />
The goal was to use graphics programming skills and make something small, while learning Android development and Google's store intricacies. Since I'm currently in the process of completing my Master's degree, it still took 5 months to finish...<br />
<br />
Technically, it's written in Android native code almost exclusively with thin Java layer (UI controls and input). It makes use of multithreading so capabilities of new multicore devices are exploited nicely.<br />
<br />
Of course, with Android device market fragmentation, it couldn't go without problems. There are issues on less common devices and a few people wrote in comments that the application restarts or hangs their device. I had to exclude some older phones that apparently didn't like the way I handled live wallpaper mode (using OpenGL in Android live wallpapers is quite tricky and AFAIK there is no commonly accepted way of doing this - see e.g. <a href="http://www.learnopengles.com/how-to-use-opengl-es-2-in-an-android-live-wallpaper/">here</a>).<br />
<br />
Now, obviously this wasn't as fun as writing a game. I am hoping for a steady stream of revenue for at least a few months (here where I live $1000/month is actually more than enough for a comfortable living if you don't have any debts and don't expect to make savings) and I'm going to spend this time writing a game that I want to release on both Android and Apple devices (and maybe Ouya?). It's really difficult not to go overboard with scope and features, but I'll try my best not to spend more than 6-8 months on it (which in game producers language means one year at best - that would be fine too). I might post something here when I actually have something to show.mihuhttp://www.blogger.com/profile/04739819757498405391noreply@blogger.com1tag:blogger.com,1999:blog-115773796018496125.post-87727663356072990162013-01-04T16:08:00.002-08:002014-04-17T14:30:20.286-07:00Android: running native code on multiple CPU coresRecently I've been trying to speed up some C++ code running on my dual-core Android device. The problem was that two threads that I used were not necessarily running on two cores in parallel (some insight <a href="http://grokbase.com/t/gg/android-ndk/12a8vdge4t/4-pthreads-same-performance-as-1-pthread-other-cores-not-utilized-tegra-3">here</a>). I'm going to briefly describe my situation and how I managed to fix the problem.<br />
<br />
My device is Ainol Aurora II tablet. I don't know if methods described here will work with yours. It probably depends on CPU model and/or OS version. Maybe you won't even have such problems in the first place.<br />
<br />
<h3>
Problem </h3>
Application is realtime and needs high, consistent framerate, so I need predictable threads behaviour. If for 90% of the time app runs faster thanks to multithreading, but for remaining 10% entire computation is squeezed into one core and framerate drops by one third, then this kind of multithreading is useless for me.<br />
<br />
One thread is the default thread that is called through JNI from <a href="http://developer.android.com/reference/android/opengl/GLSurfaceView.Renderer.html#onDrawFrame%28javax.microedition.khronos.opengles.GL10%29">onDrawFrame</a> method. Other threads (in case of my dual-core device only one, but possibly more) are spawned as worker threads when application is brought to foreground. Such thread spends part of it's time waiting on a condition variable (I'll write CV from now on) for signals from main thread. Signal tells it that it should start working. When finished, it signals back through another CV that work is completed and starts waiting for another piece of work.<br />
<br />
When work pieces are reasonably big (taking 5-10 milliseconds), two threads seem to run on separate cores for most of the time. Not ALL time (every few seconds there is an increase in frame processing time indicating that one core is idle and another is overloaded), but it might be acceptable.<br />
<br />
In general, it seems that when thread spends significant (~50%) part of it's time on a CV and doesn't do huge chunks of work, but rather multiple small chunks separated by waiting on a CV, scheduler doesn't bother assigning it to separate core for all (or at least most of) the time.<br />
<br />
Unfortunately, work pieces that I have happen to be small - more like half of a millisecond. The most important part of processing is iterative algorithm and after each iteration all threads need to be synchronized (wait for each other). When they wait using CVs, problem described above happens.<br />
<br />
<h3>
Solution 1 - busy waiting</h3>
First solution that seems to work most of the time is very simple. Instead of waiting on condition variables between subsequent chunks of work, I made threads do the following:<br />
<br />
<pre style="background: white; color: black; font-family: Consolas; font-size: 13;"><span style="color: blue;">while</span>(<span style="color: blue;">true</span>)
{
pthread_mutex_lock(&t.MutexSignal);
<span style="color: blue;">if</span>(t.WorkToDo)
<span style="color: blue;">break</span>;
pthread_mutex_unlock(&t.MutexSignal);
}</pre>
<br />
(I'm not sure locks are necessary here, but if I wanted to avoid them I would probably need to dive deep into CPU memory models to make sure everything is ordered properly.)<br />
I think it's usually advised to use CVs for this kind of stuff, but well... now it works. I'm very lame in concurrency and so rather hesitant to draw conclusions, but it seems like in this case thread is perceived by the scheduler as working all the time (opposite to situation when thread waits on CV for another piece of work), so it considers it appropriate to assign it separate core.<br />
<br />
<h3>
Solution 2 - thread affinity</h3>
OK, but busy-waiting is not always that good I suppose. Additionaly, this solution may not be very general. Another solution is to assign threads to separate cores manually. Given that apparently sched_setaffinity() is still not working correctly on Android, I successfully used code given in <a href="http://stackoverflow.com/questions/7467848/is-it-possible-to-set-affinity-with-sched-setaffinity-in-android">this stackoverflow topic</a> to set thread affinities.<br />
<br />
Seems like fast and easy solution, but there is a tricky part here. Initially, I only called this code when threads were spawned. It did not work out very well and I was close to dumping this idea, but after closer examination I noticed that it was slightly better than before (second core was used a little more often).<br />
I then modified affinity setting code to run EVERY FRAME. Apparently it's important, because from then on both cores started to be used consistently all the time.<br />
<br />
<h3>
Update </h3>
I'm working on a new application and it turns out setting thread affinity doesn't always work. I got my hands on Nexus 7 with quad-core CPU. Two of the four cores are powered off most of the time and the syscall that is supposed to set the affinity returns with an error claiming that there are only two cores available! (Well it doesn't say exactly that, but you can infer that from the error code and the circumstances.) I have a procedure that takes ~17ms (so most of the frame time) and even though the execution is split between four threads with their affinities updated every frame, only two cores are actually used (I test it with <a href="https://play.google.com/store/apps/details?id=com.iattilagy.usemon">Usemon</a>). This still gives a nice speedup: ~9ms.<br />
<br />
Using busy waiting as described above powers up all of the cores and the execution time drops down to ~5ms. Not the perfect solution, since all cores' usage is now 100%.<br />
<br />
I am going to multithread a couple more functions and see if running more code in four threads will convince the scheduler to give the app more power. My last app uses the affinity setting solution and it runs on four Nexus cores very well, probably because things are executed on multiple threads basically all the time (which also results in almost 100% CPU usage, but at least it's time well spent :) ).<br />
<br />
Otherwise, I might just make the app user choose in runtime whether he wants to use all available cores or save the battery.mihuhttp://www.blogger.com/profile/04739819757498405391noreply@blogger.com2tag:blogger.com,1999:blog-115773796018496125.post-6282348057515440462012-08-26T05:36:00.001-07:002012-12-20T15:50:00.216-08:00Lighting transparent surfaces with deferred shading. Part IIAs promised <a href="http://miss-cache.blogspot.com/2012/08/lighting-transparent-surfaces-with.html">last time</a>, today I'm going to describe a technique for rendering transparent objects in deferred shading pipeline. I hope it will provide an interesing read. If you don't care about my motivations and how this little project started, feel free to skip this section.<br />
<br />
Roughly two months ago I decided to add some particle effects like smoke to my engine ("engine" is a slight exaggeration, but let's stick with it). Engine currently supports deferred scheme, so I had to make up my mind whether I want particles to be lit (Yes! was an immediate answer. Unlit particles are more like 1912 than 2012), and if so, how do I do that. I described few possibilites in my last post, but all of them seemed just wrong. They were either problematic performance-wise (multipass), messy to handle and interoperate with rest of the engine (forward), or rather special-cased (deep G-buffer). Also, it's not immediately obvious how to apply e.g. multipass or deep G-buffer to particles. In case of smoke you could probably do something like <a href="http://www2.imm.dtu.dk/visiondag/VD06/graphical/smoke.pdf">this</a> (accumulating all effect's particles' normals and alphas into offscreen buffer and illuminate the result as a single object). <br />
<br />
What I wanted was a versatile method, even if it would mean sacrificing some quality. I also wanted to avoid keeping all shadow maps in memory.<br />
<br />
(Actually, Creative Assembly method that I also mentioned last time fullfils my requirements pretty well. I've been lucky that they hadn't publicly described their method when I was starting this, because I would probably stick with it if I knew it. :) )<br />
<br />
There is another, maybe even more important reason for this endeavour. Currently, I am probably somewhere in the middle between novice and intermediate graphics programmer. Most of the work that I've done before was rather implementing well-known and well-described algorithms and only sometimes adding small things here and there from myself. I wanted to make a step into the "intermediate" direction and use the basic skills that I've been acquiring during past ~two years to "invent" something on my own. I like to think of this work as my first "grown-up" project as a serious graphics coder.<br />
<br />
<h3>
Assumptions</h3>
<ul>
<li>low-frequency lighting will suffice for smoke</li>
<li>small number of particle systems to render at one instant of time (~10)</li>
<li>I am aiming at DX 11 level hardware, so modern GPU features can be used </li>
</ul>
<h3>
</h3>
<h3>
Idea</h3>
Since I am still a baby in graphics world, lots of stuff that's conceptually easy is still unobvious to me. I'm getting better with this but as with everything it takes a lot of time to get used to some patterns and ways of thinking. After that time, suddenly all dots start to connect and ideas relate to each other very naturally. Before I reach that state, apart from doing project and implementation work, I need to read a lot to absorb correct ways of thinking. Idea for the method came when I was reading Aras' <a href="http://aras-p.info/blog/2012/03/02/2012-theory-for-forward-rendering/">2012 Theory for Forward Rendering</a>. From this article:<br />
<blockquote class="tr_bq">
All the deferred lighting/shading approaches are essentially caching
schemes. We cache some amount of surface information, in screen space,
in order to avoid fetching or computing the same information over and
over again, while applying lights one by one in traditional forward
rendering.<br />
Now, the “cache in screenspace” leads to disadvantages like “it’s
really hard to do transparencies” - since with transparencies you do not
have one point in space mapping to one pixel on screen anymore.<b> There’s
no reason why caching should be done in screen space however; lighting
could also just as well be computed in texture space (like some skin
rendering techniques, but they do it for a different reason), world
space (voxels?), etc.</b></blockquote>
Idea of storing light in world space is not difficult in itself and it was not the first time I read about it, but due to yet imperfect thinking patterns I needed such stimulus to acknowledge that it is one possible solution to the problem and may be worth exploring.<br />
<br />
(Side note: I just realized that if I were smarter and from the two examples he mentioned (texture space and world space) followed the former instead of the latter, maybe I would come up with <a href="http://www.creative-assembly.com/develop-2012-the-light-fantastic/">Creative Assembly method</a>, since that's exactly what they're doing - storing lighting in texture space.)<br />
<br />
(Side side note: I was obviously kidding. I have a long way to reach their level.) <br />
<br />
I decided to try sampling and storing lighting in uniform 3D grid. Of course grid covering entire scene was out of the question. But given an assumption of small number of objects that require special treatment, maybe computing small grid for every such object (i.e. covering it's bounding box) would not be unfeasible?<br />
<br />
Even with small number of small grids, they still need to be rather sparse. That, and a simple fact that grid points don't lay on object surface, but rather represent lighting environments in their small neighbourhoods, means that light needs to be captured into some kind of light probes instead of surface-orientation-dependent values. First I thought of spherical harmonics, but eventually pursued a simpler approach.<br />
<br />
I started with projecting light onto six world-space directions corresponding to coordinate axes: X+, X-, Y+, Y-, Z+, Z-. If you do a dot product of light direction vector with (1, 0, 0) vector, the result will tell you how much light has in common with positive X axis (very informally speaking). Do that for all axes, and...<br />
<br />
For example, let's take L = (1, 2, 3) light direction vector. (What? It's not normalized? Oh, well. Just assume that it's length represents light intensity. It needs to be incorporated anyway). For six axes we obtain:<br />
<br />
dot((1, 2, 3), X+) = 1<br />
dot((1, 2, 3), X-) = -1<br />
dot((1, 2, 3), Y+) = 2<br />
dot((1, 2, 3), Y-) = -2<br />
dot((1, 2, 3), Z+) = 3<br />
dot((1, 2, 3), Z-) = -3<br />
<br />
These might be light contributions for six axes. But some of them are negative and it's hard to imagine that light might be "decontributed". Everything less than zero is just a no-light, so we need to clamp negative values to zero.<br />
<br />
So, how could we use it? Well, if we compute those six values for all grid points, then while rendering an object, for every pixel we could just pick the closest grid point and use the values to evaluate lighting. How? Simply by taking dot product of all axes with pixel's normal vector, multypling results by values corresponding to each axis and summing them. Let's take it one step at a time:<br />
<br />
Assume normal N = (0.8, 0.535, 0.267) (roughly normalized)<br />
<br />
dot((0.8, 0.535, 0.267), X+) = 0.8<br />
dot((0.8, 0.535, 0.267), X-) = -0.8<br />
dot((0.8, 0.535, 0.267), Y+) = 0.535<br />
dot((0.8, 0.535, 0.267), Y-) = -0.535<br />
dot((0.8, 0.535, 0.267), Z+) = 0.267<br />
dot((0.8, 0.535, 0.267), Z-) = -0.267<br />
<br />
Once again, this tells us how much normal vector has in common with each of the axes. Again, as you are going to see in a second, negative values don't make sense in here neither, so just clamp them to zero.<br />
<br />
Now, as said before, multiply the results with corresponding light-strength-per-axis values and sum them (I only write out non-zero terms):<br />
<br />
dot(N, X+) * 1 + dot(N, Y+) * 2 + dot(N, Z+) * 3 = 0.8 * 1 + 0.535 * 2 + 0.267 * 3 = 2.671<br />
<br />
That's final light intensity on the pixel. Let's do a sanity check. Is it a reasonable value? Well, initial intensity was sqrt(3*3 + 2*2 + 1*1) = 3.74. Since both N and L point into the same octant, but they also differ quite substantially, the value might be OK. I think you can now observe how this procedure behaves for other values. If you take a normal that points away from light direction, only non-zero terms of dot(N, AXIS) sequence will happen in axes, for which dot(L, AXIS) was negative (i.e. zero after clamping). So final lighting, as expected, will be zero. It is also easy to observe that if you take N = L, you will get full light intensity.<br />
<br />
However, if you take normal perpendicular to light vector, final light intensity might not necessarily be zero. L had a non-zero dot product with an X+ axis. It is easy to find a vector perpendicular to L which at the same time also has a non-zero dot product with X+ axis, which means pixel with such normal vector will also receive some contribution from the light. This is were we see it is only an approximation. It's not that bad though, since accidentally we obtained something similar to "half-Lambert" lighting model.<br />
<br />
There are few more things we need to consider to make the idea usable:<br />
<ul>
<li>Colors - up until know we only had light intensity. We want to store colors, which means that for every axis we will store R, G, B components of light color. To obtain these three values, we will simply multiple light color with dot product of axis and light direction. We also need to incorporate intensity (affected by power of the light, attenuation etc.). As mentioned before we actually did it already - exemplary light direction was not normalized. So we can just premultiply light intensity into light direction or we can use normalized light direction for dot product and then multiply it with intensity and color</li>
<li>Interpolation - for simplification I described pixel lighting process using closest set of axes (closest sample in 3D grid). Fortunately, process of evaluating light from axes is linear, which means we can safely interpolate between values across neighbouring samples and have smooth results</li>
<li>Storing multiple lights - as with usual light accumulation, it is correct to simply add values obtained for each light. So light evaluation will give six values and each will be added to corresponding axis accumulator value</li>
</ul>
Since we already know how to store multiple lights, I'd like to go back for a second to accuracy of this solution.<br />
Representation of lighting using finite set of axes will be more accurate when light direction will be closely aligned with one of the axes. The further light direction is from any of axes, the less accurate it will be. Consider two lights with exactly opposite directions (1, 0, 0) and (-1, 0, 0), both with intensity equal to 1. Six axes values will then be 1, -1, 0, 0, 0, 0. So we had lights from two directions along X axis and now we have non-zero light values only on X+ and X- axes. Reasonable.<br />
Now, let's rotate those lights a bit. Their directions are now: (0.577, 0.577, 0.577) and (-0.577, -0.577, -0.577). After storing them into six axes, all axis values are 0.577. We have lost all directional information about lighting. That's how it (not) works.<br />
<br />
<h3>
Implementation</h3>
The most natural storage method for uniform 3D grid is 3D texture and that's what I am using. Since we have six axes, each with R, G, B color components, I started with six R8G8B8A8 textures with alpha component unused (I don't care about HDR for now, so 8 bits per component is enough). After light textures have been filled with values, it is straightforward to write a pixel shader that samples them with interpolation, extracts six axes values, dot's them with pixel normal vector and sums to obtain final lighting. It is the light texture creation process ("baking" lights) where problems arise.<br />
<br />
Obviously, I wanted to harness GPU power for this process, but how? It would be great, if I could write to entire 3D texture in one go. Straightforward pixel shader would only allow me to write to one texture slice at a time (or maybe a few: as many as there are MRTs - though I'm not sure if it could work this way - but even with a single slice I still need to access six render targets (one for each axis) at the same time anyway). Or maybe I could map slices onto a 2D texture, one next to another, and write to that in pixel shader, but I think I'd need to copy slices into a 3D texture again. Or I could use geometry shader to choose a slice to write to. All these looked overcomplicated.<br />
<br />
If there wasn't a better way I would surely get more accurate information about above options, but fortunately there was (or at least I think it is better!) - compute shader (CS), which can be easily made to run in a 3D grid which makes it straightfoward to map threads to points in 3D space and 3D texture cells.<br />
<br />
<h3>
Fresh meat!</h3>
So what I was going to do was to get a subset of space - a subset that will accurately cover the transparent object I want to illuminate - and calculate lighting in a uniform 3D grid of positions inside this subset of space. Size of the subset along each of axes helps determine how many threads along each axis need to be dispatched.<br />
<br />
Since I hadn't used a CS before, to be safe and not lose to much time on debugging in environment I'm not familiar with, I started with a CPU implementation. Once I had that working, I moved on to CS. Then, some issues came out. But let's start with looking at an <a href="http://pastebin.com/ztvq87SV">example spot light shader</a>. It is a simplified version of what I currently use.<br />
<br />
Let's take a look at more important parts:<br />
<ul>
<li>There are five texture variables declared that constitute what I called before a "light texture". Why not six? I said before that alpha channel of texture is not used so first obvious optimization was to squeeze sixth axis into free alpha channels. </li>
<li>Shader calculates light influence from a single spot light without shadows at some point in space. Since I want to have light texture for
some part of space only, I need to tell the shader what that part of
space is. I do it by providing it with front-bottom-left corner of the
cuboid and it's scale along each of axes. It is enough data to transform
thread index into sample position. That's what gLightVolPos and
gLightVolScale are for. Actually, single scale value should be
enough, since I said it is uniform grid, but I lied a little. It's not
exactly uniform. The reason is this: compute shaders are called in groups, and groups
have a specific size marked in shader code (in above example: 8x8x8). In
single CS dispatch I can only run groups of one given size, so when I
pick a group size, I need to decide how many groups to squeeze into
considered area so there is enough density of sample points. What I do,
is I simply assume that my chosen group size is enough to cover some
experimentally found space region (let's say: 50x50x50 in world space
units). If area that I want to cover is of size 150x100x50, then it is
clear how many groups in each axis to use: (3, 2, 1) = (150, 100, 50) / (50, 50, 50). However, if it's for example 170x130x50, numbers of groups along X and Y axes won't be whole. I round them to the nearest bigger whole number, but then parts of the additional groups will be wasted (they will cover space outside considered region). So I squeeze them along problematic axes to fit the region and effectively there may be different density of threads along each axis - thus, three individual scale values are needed</li>
<li>Code that calculates lighting in sample point is straightforward.
However, there is some magic going on when accessing textures. Compute shaders can read from and write to (among others)
so called Unordered Access Views (UAVs), which can be used as views
("aliases") for ordinary textures. As said before, I need to accumulate
lighting into textures. To perform a "+=" operation on an UAV entry, I
need both read and write access at the same time. You can have
read-only textures pretty much without restricions in CS,
but it turns out there is a special requirement for textures that need write access. They have to be single-component. So
while I create textures as R8G8B8A8, in CS I use UAV with R32_UINT
format. It means I can only access texture value as 32-bit integer, so I
have to convert it to float4 on reading, add light influence, and
convert the sum back to int before storing. More info on <a href="http://lunaproject.googlecode.com/svn/trunk/dxsdk/Include/D3DX_DXGIFormatConvert.inl">lunaproject source code comments</a>.</li>
<li>Addition of vector of 0.5f in:<br /><pre style="background: white; color: black; font-family: Consolas; font-size: 13;"><span style="color: blue;">float3</span> samplePos = gLightVolPos + (<span style="color: blue;">float3</span>(DispatchThreadID) +
<span style="color: blue;">float3</span>(<span style="color: navy;">0.5f</span>, <span style="color: navy;">0.5f</span>, <span style="color: navy;">0.5f</span>)) * gLightVolScale;</pre>
is needed to obtain sample positions in grid cell centers instead of corners</li>
</ul>
I have similar shaders for directional light and point lights.<br />
<br />
As for scheduling light volume baking in rendering process: since I currently store all my shadow maps in memory anyway, I bake light textures just before using them (drawing transparent object). But if I wanted to reuse shadow map memory, light shader for a given light could be applied immediately after rendering light's shadow map - the same way it can be done in regular deferred rendering in order to avoid storing shadow maps. In this setting, however, you need to store multiple light textures for all active transparent objects. Light textures are quite low resolution and hopefully you have less transparent objects than lights, so it may be a win anyway.<br />
<br />
Useful optimization described below that is based on storing all light textures in one big texture can be used with this setting and has similar storage requirements.<br />
<br />
<h3>
Optimizations</h3>
<h4>
</h4>
<h4>
Smaller set of axes</h4>
Six axes seems to be pretty good tradeoff between quality and performance, but there's nothing stoping you from using different set of axes. I tried using four axes:<br />
<pre style="background: white; color: black; font-family: Consolas; font-size: 13;"> <span style="color: blue;">const</span> <span style="color: blue;">float3</span> axis0 = <span style="color: blue;">float3</span>(<span style="color: navy;">0</span>, <span style="color: navy;">1</span>, <span style="color: navy;">0</span>);
<span style="color: blue;">const</span> <span style="color: blue;">float3</span> axis1 = <span style="color: blue;">float3</span>(<span style="color: navy;">0</span>, -<span style="color: navy;">0.341987</span>, <span style="color: navy;">0.939705</span>);
<span style="color: blue;">const</span> <span style="color: blue;">float3</span> axis2 = <span style="color: blue;">float3</span>( <span style="color: navy;">0.813808</span>, -<span style="color: navy;">0.341987</span>, -<span style="color: navy;">0.469852</span>);
<span style="color: blue;">const</span> <span style="color: blue;">float3</span> axis3 = <span style="color: blue;">float3</span>(-<span style="color: navy;">0.813808</span>, -<span style="color: navy;">0.341987</span>, -<span style="color: navy;">0.469852</span>);</pre>
One is straight up. Second is obtained by rotating previous one around X axis by ~100 degrees. Last two are created from the second one by rotating it 120 and 240 degrees around Y axis. Initially I wanted to have a symmetrical set of axes (it would be similar to set of vectors pointing to orthocenters of tetrahedron faces) but I decided that I don't care that much about lighting coming from below and adjusted second, third and fourth axes to be more horizontal.<br />
<br />
Difference in quality was clearly visible, though I think it can still be useful for low-quality settings. As for performance, I had varying results, differing between applications runs - sometimes four axes method was ~25% faster, and sometimes only marginally faster. <br />
<br />
I also thoroughly explored using sets of axes that don't cover entire sphere, but only some part of it pointing toward the viewer. I tried both:<br />
<ul>
<li>using small set of axes (three) that rotate with the camera movement and always face the viewer</li>
<li>using bigger set (six or eight) stationary axes and choosing on a per-frame basis only three to five that were facing the camera. Light was stored only in this reduced set of axes.</li>
</ul>
However, both approaches gave poor results, because with both rotating and switching axes, ugly flickering of lighting arose.<br />
<br />
A slight performance disadvantage of using any axes other than coordinate axes is that you actually have to perform the dot products described in the beginning. With X, Y, Z axes dot products amount to simply taking x, y or z component of considered vector, possibly with minus sign. <br />
<br />
<h4>
Batching lights</h4>
Baking multiple lights can obviously be packed into single shader dispatch. Currently I bake all point lights in single call, but use separate calls for spot lights, since they cast shadows. The problem with batching lights that cast shadows is that shader needs to access array of texture variables in loop that processes all lights. Such accesses unfortunately can not be done with dynamic array index - it has to be known at compile time. Because of that, loop that processes lights has to be unrolled, which is very inconvenient when you process less lights than a predefined maximum. I'm not sure if using actual texture arrays (not just arrays of texture variables) wouldn't solve this problem.<br />
<br />
And again, when batching lights that cast shadows you can't reuse shadow maps.<br />
<br />
<h4>
Packing light textures</h4>
If you have a lot of transparent objects, having to bake all lights separately to all objects' textures can become prohibitive in terms of performance. One method to deal with it, is to pack all light textures into one big texture and do single light baking pass on this. It is in some ways problematic but definitely doable.<br />
<br />
Now, instead of determining how many thread groups to run along each axis to cover object's bounding box, you have to pack those bounding boxes one next to other and find out how many threads to run to cover all consecutive regions. For simplicity, I use 3D texture extended along X axis and allocate space for objects along this axis only. Now, the number of thread groups to run along X is sum of widths of all boxes. The number of thread groups to run along Y and Z is maximums of boxes' heights and depths, which is not optimal when regions have substantially varying sizes. Better solution would be to pack those boxes into big box of smallest possible volume, but this would probably mean some NP-complete packing along all three axes (I'm just guessing, I'm not really algorithm guy. Maybe it's only N^3 or something like that :)). Approximate solutions would surely be possible, but for now I assume transparent objects of similar sizes so there is no big waste in simply putting them one next to another along single axis.<br />
<br />
There are few more little obstacles:<br />
<ul>
<li>As seen before, shader needs region position and scale to determine position of sampling point. Now, there are no single values of position and scale, but instead they differ, depending on which region (which object) given thread group deals with. Array of positions and scales needs to be supplied, and it needs to be indexed by SV_GroupIndex.x. I use dynamic 1D textures for this.</li>
<li>In single object method, thread indices started from (0, 0, 0). Now, only the part of them that covers first packed region start with (0, 0, 0). Indices for subsequent regions start from (NumberOfThreadsForObjectsThusFar, 0, 0). But thread indices are needed to calculate sample positions across the region and they are assumed to start from (0, 0, 0) for a bottom-left-front region corner:<br /><pre style="background: white; color: black; font-family: Consolas; font-size: 13;"><span style="color: blue;">float3</span> samplePos = gLightVolPos + (<span style="color: blue;">float3</span>(DispatchThreadID) +
<span style="color: blue;"> float3</span>(<span style="color: navy;">0.5f</span>, <span style="color: navy;">0.5f</span>, <span style="color: navy;">0.5f</span>)) * gLightVolScale;
</pre>
Correction is needed for all thread groups but first, so another 1D texture is used that stores offsets along X axis needed to obtain correct thread indices</li>
<li>Slight correction has to be done to object rendering shader to account for that now it needs to access only part of light texture</li>
</ul>
Disadvantage of this optimization is that CPU per-object light culling is now impossible (though you could still do a "per-all-transparent-objects" light culling and only send to shader those that affect any of objects. Since we assumed there is a moderate amount of transparent objects in a scene, there should still be a lot of lights that don't affect any of them).<br />
<br />
<h4>
Varying light baking resolution</h4>
Quite obvious idea to control both performance and quality is to make baking resolution depend on how far an object is to the viewer. I haven't done it yet though. I suspect it could suffer from light shimmering though.<br />
<br />
<h3>
Quality</h3>
Below you can see comparison between forward per-pixel method and 3D light texture method. Forward per-vertex method is much, much worse so I don't include it in comparison (even though instead of direct per-vertex lighting I use method of storing lighting in HL2 basis per-vertex and sample it per-pixel - as decribed in <a href="http://www.bitsquid.se/presentations/practical-particle-lighting.pdf">Practical particle lighting</a> - so I have a rough approximation of per-pixel lighting).<br />
<br />
In presented scenes light texture of size 16x24x16 was used.<br />
<br />
First setting shows smoke lit by two lights - green point light very close to particles and red spot light in front of smoke casting shadow of object.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEihWeE869uH7RFuQEasuJHvePEWls9n6fsJplgfg0AXVhJc2MEbxuIiVseQBcz2jtHMiMIxCjfxkn63mBU9NW-nXojsdEv0XAqHSdt7dUuYxG7O6yhp8eM_y_vT3Xvtc76oJtBQ4REVYVHa/s1600/particle-quality-test-forward.png" style="margin-left: auto; margin-right: auto;"><img border="0" height="233" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEihWeE869uH7RFuQEasuJHvePEWls9n6fsJplgfg0AXVhJc2MEbxuIiVseQBcz2jtHMiMIxCjfxkn63mBU9NW-nXojsdEv0XAqHSdt7dUuYxG7O6yhp8eM_y_vT3Xvtc76oJtBQ4REVYVHa/s400/particle-quality-test-forward.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Forward rendering method</td></tr>
</tbody></table>
<br />
<div style="text-align: left;">
</div>
<div style="text-align: left;">
</div>
<br />
<div style="text-align: left;">
</div>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhJG9eFItAdH-cLA49ewY4p9_t5tYDjfnVMa7WVFXF4WOJsBhqCbiXlqMFIbag7R-zw5r-Dcq7b4y1NpAXRkm-goDTjSrblveUqGTcGIekIYOS1SDYX1yXdomPpSawUFmBs4vWpzjFmutyx/s1600/particle-quality-test-cs.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="230" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhJG9eFItAdH-cLA49ewY4p9_t5tYDjfnVMa7WVFXF4WOJsBhqCbiXlqMFIbag7R-zw5r-Dcq7b4y1NpAXRkm-goDTjSrblveUqGTcGIekIYOS1SDYX1yXdomPpSawUFmBs4vWpzjFmutyx/s400/particle-quality-test-cs.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">3D light texture method</td></tr>
</tbody></table>
<br />
Next screenshots show smoke partially lit by single directional light.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjibtW9pdenP9t1S0PJcnZQi2FAbVQRYaHT4yJo-r_oXhn6A-5aPoBNlROCZg0foeanIkwbs6XEsjSbMbGcihqOZjuef3SxxIoI30qIIGSob8ZA7uRCc74HLvCOtoVImXW-qB1OE3zvjnCY/s1600/particle-quality-test2-forward.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="232" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjibtW9pdenP9t1S0PJcnZQi2FAbVQRYaHT4yJo-r_oXhn6A-5aPoBNlROCZg0foeanIkwbs6XEsjSbMbGcihqOZjuef3SxxIoI30qIIGSob8ZA7uRCc74HLvCOtoVImXW-qB1OE3zvjnCY/s400/particle-quality-test2-forward.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Forward rendering method</td></tr>
</tbody></table>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhdv35N5DbB0ZMlQTwVQjm3zvRAlwH3KkJ1t9UiEGjXZKQTK0XvsleIKQW8XuJmz25WUCUG3soXCkiuUQZ65tAYZ1WWt3_3hlSr0h-c0CVv04fRdspj2oUZjf9Ml3xySplZmMQ-YoMw4VTA/s1600/particle-quality-test2-cs.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="232" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhdv35N5DbB0ZMlQTwVQjm3zvRAlwH3KkJ1t9UiEGjXZKQTK0XvsleIKQW8XuJmz25WUCUG3soXCkiuUQZ65tAYZ1WWt3_3hlSr0h-c0CVv04fRdspj2oUZjf9Ml3xySplZmMQ-YoMw4VTA/s400/particle-quality-test2-cs.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">3D light texture method</td></tr>
</tbody></table>
<br />
<h3>
Performance</h3>
Since this method has been developed as an experiment and not for particular game, it's hard to test it in a real, "game-like" scenario. I have left some modifications and optimizations for later, when I actually want to use it, so I have specific needs and frame time limit to fit in. Also, I spent much more time on optimizing light texture method than forward rendering method. Thus, following comparison should only be considered as an approximation of how methods could perform in game. Below I describe exact circumstances and simplifications concerning both algorithms, that may be very different in actual game.<br />
<br />
Both methods:<br />
<ul>
<li>The same set of lights - one directional light with CSM, multiple point lights (without shadows), multiple spot lights (with and without shadows)</li>
<li>No CPU light culling at all </li>
</ul>
<br />
Forward rendering method:<br />
<ul>
<li>Everything is done in single pass with heavy pixel shader. </li>
<li>There are bounds on maximum number of point lights and spot lights without shadows (128 for both), since their data is stored in arrays in constant buffers. They are processed in loops</li>
<li>There is harder restriction on number of spot lights with shadows - 16. That's because loop that processes them needs to be unrolled for the reason mentioned in <b>Batching lights</b> section</li>
</ul>
Light texture method:<br />
<ul>
<li>Here multiple passes are not so expensive, so I have one pass over light texture for directional light and one for every spot light. All point lights however are accumulated in a single pass.</li>
<li>Six coordinate axes are used</li>
<li>Method for packing regions into one big light texture described in <b>Packing light textures</b> section is used</li>
<li>Compute shader is dispatched with 6x3x2 = 36 groups of size 8x8x8. Each particle system gets 16x24x16 part of the light texture</li>
</ul>
Test scene contains three particle systems moderately close to the viewer. All are lit be a directional light. Additionaly, each one is lit by a couple of point lights and one spot light casting shadows.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg9ow2iPUxJH3VnWqrBmRNYnehFAGA-aKwy6zETWkqzjNBI-wW3LURlQxz0VUc9OU7C6xmHZokDQcrb6DcxZuCDPAZZihzVPa_ggkjwcew9KF4kEZzNIg5MilMckZifGcGO8bOgvFrwPzfx/s1600/perftest-cs.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="232" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg9ow2iPUxJH3VnWqrBmRNYnehFAGA-aKwy6zETWkqzjNBI-wW3LURlQxz0VUc9OU7C6xmHZokDQcrb6DcxZuCDPAZZihzVPa_ggkjwcew9KF4kEZzNIg5MilMckZifGcGO8bOgvFrwPzfx/s400/perftest-cs.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Performance test scene with light texture method</td></tr>
</tbody></table>
Results on Radeon HD 5670:<br />
<br />
Forward rendering method: 9.47ms<br />
Light texture method: 1.347ms (light baking: 0.311ms, rendering: 1.016ms)<br />
<br />
As you can see, performance difference is huge. What is promising in light texture method, is that with increasing amount of lights, only light baking time will go up. Rendering time in both methods mostly depends on how close an object is to the viewer, which determines how many pixels need to be processed.<br />
<br />
<h3>
Other considerations</h3>
Something I haven't done and what could really make the above comparison favour forward rendering method is to calculate forward lighting "per-domain" - use tesselation on particles with density dependent on particle's distance from camera and do a per-vertex lighting on those tesselated vertices - described in <a href="http://www.bitsquid.se/presentations/practical-particle-lighting.pdf">Practical particle lighting</a> with great quality and performance results. Such method would still have the same disadvantages like having to store all shadow maps in memory (I feel like I'm writing about this issue for the hundredth time since previous post).<br />
<br />
I have yet to find out how to tackle the problem of amount of lighting
that object receives. I haven't noticed it before I started messing
around with smaller sets of axes. Suddenly, objects became darker. After
a while I realized it is reasonable - you store lighting by projecting
it on a set of axes and when rendering you just sum influence from all
of them. If you use less axes, there will on average be less lighting in
a single light probe. With six axes, amount of lighting is comparable
to that with forward rendering method, but it's very possible that it's
not exact too. I haven't yet wrapped my head around it completely, but I
think maybe simple normalization factors dependent on the number of
axes and their "density" might solve the problem. I have to do more
testing and thinking. I welcome any insights from you.<br />
<br />
<h3>
Final thoughts</h3>
Due to my lack of experience in big game projects, I can't tell if this or similar method could possibly be used in some real-world setting. Nevertheless, I am satisfied with results - method meets the requirements. While sacrificing some quality, it allows having particle systems' lighting consistent with opaque objects and it does it fast enough.<br />
<br />
Also, it made me learn a lot and gave a topic to start my blog with. :)<br />
<br />
Since it is incorporated into engine, I don't have any small sample with described method in action to post so you could easily compile and run it yourself, however I'd be happy to answer questions and post more code if someone's interested. <br />
<br />mihuhttp://www.blogger.com/profile/04739819757498405391noreply@blogger.com0tag:blogger.com,1999:blog-115773796018496125.post-45589896024516055252012-08-09T11:16:00.004-07:002012-11-06T05:53:03.895-08:00Lighting transparent surfaces with deferred shading. Part IAs the reader hopefully knows (if not, there are multiple good explanations of deferred shading and it's not hard to find them - just read them all), rendering transparent surfaces in deferred shading (DS) pipeline is kind of problematic. Well, the main point of DS is to enable optimization of lighting process by evaluating lights only on visible surfaces. The way it is traditionally realized (using depth buffering - at every pixel only the closest surface is considered) doesn't really get along with transparent objects. Transparency causes multiple surfaces affect a single pixel outcome, so we have to cheat.<br />
<br />
Developers have come up with many workarounds to this problem. First I will briefly describe those that I am aware of. Next (in <a href="http://miss-cache.blogspot.com/2012/08/lighting-transparent-surfaces-with_26.html">part II</a>), I will explain in more detail the method that may be useful in specific situations (small amount of transparent objects that may get away with low-frequency lighting) which is based on compute shaders. I haven't seen anyone mention this method before, however it is quite specific and closely related to other techniques (it can be described as a well-known technique used in a slightly unconventional way), so I consider it to be a very humble contribution. :)<br />
I will also present comparison of said method with forward rendering technique.<br />
<br />
<h2>
<span style="font-size: small;">Forward rendering</span></h2>
The simplest way to light transparent surfaces with deferred shading is not to. :) Instead, use traditional forward pipeline for this. It means having to deal with all forward-related goodies:<br />
<ul>
<li>necessity to store multiple shadows maps in GPU memory (instead of render-and-forget DS way)</li>
<li>CPU code to find closest/most important lights for a given object</li>
<li>possibly multi-pass rendering</li>
</ul>
Lots of fun, in a word. Opportunity to avoid all that mess was an important reason for developers to turn to deferred shading in the first place.<br />
<br />
<h2>
<span style="font-size: small;">Multipass deferred shading</span></h2>
I've just made up that name, but this one is very simple. DS works as long as you need to light only a single surface at a given pixel. So if you render only one object at a time, you should be OK.<br />
<br />
First render opaque objects as usual. Then, for all transparent objects:<br />
<ol>
<li>render object to G-buffer and mark affected pixels in stencil buffer</li>
<li>accumulate lighting into a separate buffer</li>
<li>blend result into final buffer (the one that opaque objects were rendered to)</li>
</ol>
You may need to adjust it here and there to get the blending right, but basic idea stands. More accurate description can be found here: <a href="http://www.john-chapman.net/content.php?id=13">Deferred Rendering, Transparency & Alpha Blending</a>.<br />
<br />
The good news is that we reuse the deferred shading pipeline. But notice that step 2 involves going through all the lights to process a G-buffer that contains single object! So in terms of big Oh notation we're back to Oh(number of objects * number of lights) instead of Oh(number of objects + number of lights). Oh-oh. That's bad news. More bad news:<br />
<ul>
<li>as with forward rendering, you can't reuse shadow map memory (you need to keep them since they are being used over and over)</li>
<li>later stages that use G-buffer (like SSAO) might not like that you overwrote all useful data with that window glass on the foreground </li>
</ul>
<h2>
<span style="font-size: small;"> </span></h2>
<h2>
<span style="font-size: small;">Deep G-buffer </span></h2>
Again, simple idea. Have a G-buffer with multiple layers. If you create four layers, you can have correct rendering of as much as three transparent surfaces in front of opaque surface. Unfortunataly, memory consumption is huge. On the other hand, processing overhead doesn't need to be very high, as <a href="http://www.humus.name/index.php?page=3D&ID=75">Humus describes</a>.<br />
<br />
<h2>
<span style="font-size: small;">Smart deep G-buffer</span></h2>
I haven't seen anyone mention this one and I haven't tried it yet, but I think it might not be completely unfeasible.<br />
<br />
In deep G-buffer method, on average a lot of memory is wasted. Usually, you're not looking at opaque surfaces through exactly three (or whatever number of G-buffer layers) transparent objects covering entire screen. Big part of view will only be covered by opaque stuff, and at those places additional layers of G-buffer will be wasted. On the other hand, in rare cases, you might need more than 3-4 transparency layers in some small area of screen and you can't have them, while the rest of G-buffer in unused! Thus, it would be great if we could use memory in a smarter way.<br />
<br />
There's been recently some hype on order-independent transparency using linked lists in compute shaders. The idea is to use a GPU memory buffer (RWStructuredBuffer in DirectX) to render transparent objects into per-pixel linked lists, storing color, alpha, depth and possibly blending type for each transparent pixel drawn. Then, resolve pass sorts and blends those lists and final pixel colors are obtained.<br />
<br />
Now, since you already store so many data per transparent sample, you could add few more attributes (normals, texture samples) and use those linked lists as deep G-buffer with dynamic amount of memory per pixel! So, geometry stage of transparent objects would fill linked lists. Then, in light shader, lists would be traversed, samples shaded, and light accumulation of each sample updated accordingly. Light accumulation would probably need to be stored along the rest of the sample data in G-buffer. <strike>This means that light shader cannot be pixel shader, but a compute shader (pixel shaders can't write to RWStructured buffer I believe) which unfortunately excludes optimization based on rendering light geometries to minimize amount of pixels processed.</strike> Apparently pixel shaders can write to arbitrary buffers accessed as UAVs after all.<br />
<br />
I realize that my "thought implementation" probably missed some important issues that might make this idea more inconvenient or even impossible. I'd be glad to hear about such issues in comments.<br />
<br />
<h2>
<span style="font-size: small;">Creative Assembly method</span></h2>
I don't really know how to call this one, because it would have to sound something like "Unfolding transparent objects into textures for lightmaps generation". This method has been <a href="http://www.creative-assembly.com/develop-2012-the-light-fantastic/">described very recently by Creative Assembly guys</a>. I have only seen slides and it's possible that I misunderstood some details, but general idea is clear, though might be hard to explain in words. Open the slides now, look at the pictures and you'll know everything.<br />
<br />
Each transparent object surface is "unfolded" into low resolution texture that stores object-space positions of corresponding surface points. These positions basically become sampling points in the lighting process. So, to perform the lighting, all such maps are packed into one big map, and deferred shading is just run on it. Light is accumulated into light probes. When rendering transparent object they sample proper area in lightmap, intepolate between light probes and evaluate lighting. The outcome is probably rather low-frequency lighting.<br />
<br />
This idea is pretty decent and made me a little embarassed, since the method I've been working on is similar in some ways (light probes, low-frequency lighting) but apparently not as smart as theirs.<br />
<br />
<br />
As I said before, in <a href="http://miss-cache.blogspot.com/2012/08/lighting-transparent-surfaces-with_26.html">part II</a> I am going to describe in more detail another method that is based on compute shaders and compare it to forward rendering method.mihuhttp://www.blogger.com/profile/04739819757498405391noreply@blogger.com0tag:blogger.com,1999:blog-115773796018496125.post-83064673418893442252012-08-08T12:04:00.002-07:002012-08-08T12:04:41.609-07:00Hello!Miss Cache blog will concern computer graphics and game development from the programmer's viewpoint. I'm going to use it as a record of interesting results of my pet (or non-pet) projects. I hope to write about things that are not-so-obvious. Often, for the sake of beginners, I will try to provide detailed explanations of stuff that in my opinion is not so well covered in the rest of the Internet. As time goes by and my experience increases, blog will hopefully become a place where more advanced developers will find something on their level.<br />
<br />
Topics that I hope to cover include:<br />
- real-time rendering<br />
- physically-based animation<br />
- design of graphics and physics engines<br />
- low-level optimizations<br />
- games that catch my attention <br />
- thoughts on computer science education <br />
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Does anyone read welcome posts anyway?mihuhttp://www.blogger.com/profile/04739819757498405391noreply@blogger.com0