From mboxrd@z Thu Jan 1 00:00:00 1970 Path: news.gmane.org!not-for-mail From: Chong Yidong Newsgroups: gmane.emacs.devel Subject: Subpixel averaging Date: Fri, 28 Mar 2008 16:21:19 -0400 Message-ID: <87lk42ppkw.fsf@stupidchicken.com> NNTP-Posting-Host: lo.gmane.org Mime-Version: 1.0 Content-Type: multipart/mixed; boundary="=-=-=" X-Trace: ger.gmane.org 1206735896 19483 80.91.229.12 (28 Mar 2008 20:24:56 GMT) X-Complaints-To: usenet@ger.gmane.org NNTP-Posting-Date: Fri, 28 Mar 2008 20:24:56 +0000 (UTC) To: emacs-devel@gnu.org Original-X-From: emacs-devel-bounces+ged-emacs-devel=m.gmane.org@gnu.org Fri Mar 28 21:25:27 2008 Return-path: Envelope-to: ged-emacs-devel@m.gmane.org Original-Received: from lists.gnu.org ([199.232.76.165]) by lo.gmane.org with esmtp (Exim 4.50) id 1JfL8Z-0006Be-FB for ged-emacs-devel@m.gmane.org; Fri, 28 Mar 2008 21:25:21 +0100 Original-Received: from localhost ([127.0.0.1] helo=lists.gnu.org) by lists.gnu.org with esmtp (Exim 4.43) id 1JfL7w-0006Mh-Vz for ged-emacs-devel@m.gmane.org; Fri, 28 Mar 2008 16:24:41 -0400 Original-Received: from mailman by lists.gnu.org with tmda-scanned (Exim 4.43) id 1JfL7s-0006MN-E3 for emacs-devel@gnu.org; Fri, 28 Mar 2008 16:24:36 -0400 Original-Received: from exim by lists.gnu.org with spam-scanned (Exim 4.43) id 1JfL7q-0006LG-Iz for emacs-devel@gnu.org; Fri, 28 Mar 2008 16:24:36 -0400 Original-Received: from [199.232.76.173] (helo=monty-python.gnu.org) by lists.gnu.org with esmtp (Exim 4.43) id 1JfL7q-0006LB-FX for emacs-devel@gnu.org; Fri, 28 Mar 2008 16:24:34 -0400 Original-Received: from cyd.mit.edu ([18.115.2.24]) by monty-python.gnu.org with esmtp (Exim 4.60) (envelope-from ) id 1JfL7p-0004Pl-Vm for emacs-devel@gnu.org; Fri, 28 Mar 2008 16:24:34 -0400 Original-Received: by cyd.mit.edu (Postfix, from userid 1000) id 8D0C34E3F7; Fri, 28 Mar 2008 16:21:19 -0400 (EDT) X-detected-kernel: by monty-python.gnu.org: Linux 2.6 (newer, 2) X-BeenThere: emacs-devel@gnu.org X-Mailman-Version: 2.1.5 Precedence: list List-Id: "Emacs development discussions." List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Original-Sender: emacs-devel-bounces+ged-emacs-devel=m.gmane.org@gnu.org Errors-To: emacs-devel-bounces+ged-emacs-devel=m.gmane.org@gnu.org Xref: news.gmane.org gmane.emacs.devel:93711 Archived-At: --=-=-= Although Emacs now performs anti-aliasing, it doesn't seem to perform subpixel averaging. I, for one, find the anti-aliased text in Emacs blurry and very uncomfortable to read, especially when using bright-on-dark color schemes. As a result, I'm forced to use the old fonts. The attached screenshot shows the difference between gedit (top) and Emacs 23 (bottom). --=-=-= Content-Type: image/png Content-Disposition: attachment; filename=emacs-aa.png Content-Transfer-Encoding: base64 iVBORw0KGgoAAAANSUhEUgAAAUIAAAFKCAIAAADnnMGmAAAAAXNSR0IArs4c6QAAAAlwSFlzAAAN 1wAADYkBZhDrogAAAAd0SU1FB9gDHBQIKmeb7mcAAAAZdEVYdENvbW1lbnQAQ3JlYXRlZCB3aXRo IEdJTVBXgQ4XAAAgAElEQVR42uyddXgVx9fHz+51jbsnQDyBBAvuTtDi7m4VoIWWIkUK9EeLFPcg pbi7k5AAASIECBIhLtd1d94/bhICyQ0JhEjf+TzLw93J7s6ZM/PdMzNrBEIIivG/pbMBg8HUKpgf CXjSnF+xUzCYWinj/y2dbRCwQpoPAHJZHk1R5TwEyWAIRWbYlRhMdcrYoGG5JNeQpFLKpfl54bev lPMQTVu0J0kGjy/E3sRgqrlTbRgkq1WK/Nzs8NtX+gyZbGlj/8n9szPeHQvb1LRFewDg8gTYoRhM 1cMwZUrGTV8gl+YZ1jUqxc2rZ8upYQDgC0V29k7XLhx3da/HYnOxQzGYaovGH81XW9rYf5RSBuZW NqUeBIPBVG2nulCBBEF8niYJggAsYwymGEuW/w4AC+d/X/XRmCgaLFfwUASOxpjKZemK1QvmfVd8 FQDKTqmBlKoLg+UEQTAYDB6PZ21l6efj5efrUxRHv0jGXxKNsYwxlcI//x43MzNt0qghAEilsojI qLy8/G/69TbWMmt4wyvDvB9/mENTVE5e3qPHT0+eOf8kNm5A395MJvOLo/H7YFxB1xB4bIypHDq0 b3M/8sGWHbsAYPP23X6+Xu3btSlqXcZk/NuqtQDQo1vnO/ciJPkScwvzLh3aOzs7AsDb5JTrN25l ZmUhBM6ODo0bBbu7uRr2vR/18PLV6wRBCPi8uh512rVrhSj6z01baJqeNmm8UCgAALlcvv7vbSRJ zpg8gcPlREY9jIp+LJVIhQJBcFD9po0bFgW/+1EPIqMeyeRyc3OzkCaNPiljhBBBkpYWFh3btWEx mXfD79+6G96mZXPDn+5FRD6KfiKTyYQiYYPAwGZNGxVl9PhJTNTD6OycHD6f16J50xLRuFDHqIKd agJ3qjGVF75QYceQIA0tEn1SxgaSk1PHjBz2+s3bo8dPnT5/cfL40QBw4tRZuVzer0+ou6tLWnpG eESkm6uLYXudTjd+9EhTU3Fs/LOz5y9RNNW9aydfH6/HT2KexsY1bdwQAJ7GxtM0HeDny+Fywu9H Xbtxy9/Pp/PI4RGRkddu3CIIokmjYACIevDo8tUbnnXrjBoxBADOXbxcHhkX/a4f6H83/H5cXHzr Fs0A4G74/Ru37jRt3LBl85Abt+7cuHWbIFBIk8YA8OBh9MUr1+p4uA/s34cgidt3wwtkrNdrCvvG ZEGKVl3BaEwUHQSD+RIuXblqamIyavigTVt2jR4+KDLq0aUrV/uEdvuorRZRPCWkaTBJ0G4uDgAg kUgMf9JqtQwGg89jU5TWztayT6+uRbs0bhgIAACUt2eds+cvvUxM1Os1Der7Pn4SExMT1zDIHwBi YmMBoH6gr16vefgoGgCaNGpAEFSDQN9bd8IfPIwObuAHAJEPHgJA61ZN2SwSAFq3aPr8+ctSDS7V ch6XBQBSucKQ+PDxYwAIqu8HQAUH+d+Pevgw+kmj4EAAiIh8AABtW4VwOAwAaNe62cfRmCQLonGF bsY0jI1pGkdjTCXQu2fXot8CPq9Nq2bFm2jZ0Vgo4COEDJEcoYIY3qJZo5u3w/eGHSFJwsrKsnXz pobOdmrqu5t3I7OysnU6nWFLlVqDELI0N3d2dEhKSc3IyEKAsrJznZ0cLS3MEEJSmRwAtu3cX5Sj VCYz7CuRygBAKBAaVsUiYYWiseHIIgHfkCiXKQCAx+MihHhcHgDIZHLDn6QyGQCIRKKi3Y12qmm6 QMbz5v9UqgUrli/7QMa4U42pbL6dOal4o+JxOSq1RqVWczkcAFCr1UUN3dh8kmG1QaC/v69PdnZO Usq7W3fCT5+/PHn8SAA4dfaSXKHsE9rV1cWJptG6DVuLlN+gvl9SSmps/DOECMNqoThFEql08vgR fD7/o1xMxMJ8iUwqk5qIxQCQL5FVSMbRj2MAwMuzriFRIBDI5XKFUiXg8xRKJQCIhMLiNkglUhNT cYEG349GDAUonOKiacqwGLPg4w2I9wfBYL4Gnp51AeBeeJRGo9VotHcjHgCAt2fdIu190JKLrZ44 fSErK8fK0tzJwR4AGAyGIZ1GCADYbJZOp791J7z4Lu5uLiZiUXzCy/iEFyZisbursyE9qL4fANy4 Ha7WqDUa7evXb/89dtrwp/qB/gBw4+Y9lUqtUqlv3rr7kUnFKfoTpdfn5ORevXE76uFjF2eHJo2C DBv4etcFgKiH0Tqd/sHDaADw9/My/Cm4gT8AXL15R6FQqlTq6zfvGr39g6Zpw4+lSxYZkTH94dAY 3/6B+bq0ad6Ex+EkvHgZ/TQOAEzFwmZNGjZuWP+DhvdRI0QIAPx9vG7evZeengkA9nY2rZo3NaR3 69Tu2o27/xw9JRAIGgfXL74LARDo73vzTjgANAoKJArTGwT6MRmM6Kdxm7buZTJIezvboAYBBX8K 8KVp6lF0zN/b9piaiBoHN3iR+LoUk4rxx19bGAySy+VamJt2bt/a26tukY5CGgfTNB2f8PLhoycC Pr9Zk4aNgwtKWj/Al8lkPnocs2Xnfj6PG9I4mPhjyaxx0xdkpL42HJfLE5w9HjZ+9pLczJRyOpcv FO/d/Hu33kPUKgVuahhM1VM4NoYPonF2xjtA5Z3iys3KMOyIAEdjDKYaZVwY93U6TZfQgcfCNrXu GGpqbvHJ/fNzc25cOtkldKBOp8FTXBhMdcq4qPuu1ai5PEG33kPOHg8r5yG69R5C05RWo8bexGBq RDQGAJVSLhCa9Og7rJyHoGlapZRjV2IwNUjGACCX5WPXYDC1ScanDm/DjsBgareMew4Yp9WoAL++ A4OpnZDYBRjMf2RsXAiOxhhMbZcxVjEGU9tljFWMweBONQaDqQbIj1Vc0UVyqbfnoHOSz9oX1ZIc /zNLtbsO193XWcgPVYzKu0gv9vYadE6KCh+HQKgKl4Ici9lQ+ctXPXj1LVVfWTXNgP/kUr5oLL3U x97N+v0y+Dx0PBZ/oIvoyyL5J3Jxs7YffF5aYrMiRIU2SC/18Spty08tLRtMv5yHPkqkMs6O7zz1 uKpdwcG/8nmUynu044ehIT51rO3drJ2CWgz6cdPVZDX9dbL74soqUUFG6uirGVBaU6nn2bTXiAV7 7qTrakHk/Ny2WvbRyjk2RiBuuud+WDHdog9/VMqgumQupR65ZI6facMc34g1R5PajHEu5gXdy7A/ 77vP/d2KrIqZAn3S7tFjd3j88MeZv+o7iui8t9E3z+77e114w9/biL5Gfl9aWcdSXwEAyC71abxz 4vuaQlVmQImmgvSq/LQX0Rf2/zmq7cV5p7aP9WDX7GFsJerl/dE+9/YP2aU+3kPOyz5M1KacXjQ0 yN3d2sEvZOzmSAldaUVXPNs1rbO7g7u1e+tBa+7l0MVsSL3Ux2viHWn4CC93a4cSJpVJl2975mzb +VhZLCNZ5PoweuiMZqbyYgUsWS71kwXN2i+J1QAA6NPO776fjwAANLGrQpotqYAFqsQLz91mzRvQ 1NWMy2TyrTya9Zu+8VChhkv3J614dWHxoFYuDu7WDnUD+i49n04VeMOz6y9LJzTzdLd2GnJeBkj9 5t8FA+u7uVu7tBj8Z1SeYW8kebBlZhsfd2sHd9eWkzdGy9EXV83umT09Hd2tHYPbzwyLU6Cy04tO YKnnfuvX1M/awd3aL3TqzqfSz2wsBJNn5hTQdtzK/UdGpq9adNXQNoxaVdInH7XkolXZpT5e3Zeu mdU+sK61g7tzyOjfbyU93je3S5CntYO7S8vJW2IKD1pqNcku9fHqvmzNh66WlWyrleOHD8fGRgEE xlKKfqiifx/9Q2zI/248SY47Opu/b9yCW3l0hd61VDIXA4r7y8b/ltl3/4OY13dXhcQceiYvtr2o w9H4zc3ETXbHJ2ak7O8srEB+XN9RU23Pr7mURRUk6N+dXn+zzuThdVifKBfbo2tDza37mTqEtMmn V61afy2LQkifEXFX3ahjRSxwb+/xYsWPG49HJGYp9R/+zYg/pdemTDxmPmnnw8TnqfEXV/jc/Hbu hSwKIQRIkXDwVYu/bsWlJe3vLFQ8+G3k/Lhm/7v+5O2jLf3k1+MVCCFA8vi9sfWXX3yS8uLu/j4Z a2fveqH9jLdiFTlHfu/XMcveddsV9fR11MbQ1N+HLI6UlZVeuKM8cvHc0w7zjye8evbizPz6L049 VXxhU+F5DxjgEHfhmbKM3I34BIw0bHn8nmifRScfprwMPzxY+uegLiOv1l14PCrlZfihbzJXz9rz UltGsy/N1ZwSbbWS/FDuaCyNGOntYeNoWEoLeqr4/acYY5dOaOkoYIvr9p07yfbe8ThlBc8qH+RS mJEy7sAl3vglo5va8Pg2jSctHedWWR9gZdr3nt3i2fpDiToAANA8370ltfOsjraMT5aL59PTJ+tK rAzp3pz89xX1dMeFdArJnlzJ9u/hVREDnMfs2jnD7vGG6X3869az9e3Yf96e+7lUWf4Utdt1cdPU 1u7mHAZT5NplxiT32AsJKgAA4HvO/nlIkBWHBABV/L5z7DG/TWrtLOCaefeb/22I4XWrAt95i4c3 dRCweDYhI0Z7Z0a++ZLnxJVxh64Kxy0d09SGz7dpPGnpCMGVI7FK4+nFgyiTkrx7m5Sl4Ts3Hbds frMv/so9y8xJrM6S08ZzN+YTYwh85v02poWzkMW1ajx0VCDffebiUc2cClY908Nfq8ts9uVx9Zf7 QRox0tuj3F+LETfZHVFs1Cq7vPnjYV7uq9Tnu9t5rXxvYtMK9xA+yqUgr/xkhXk7a5ZhjW3jacO+ X0k6JsRNJo1ijP4rYsS6FkLJ3c1HhKP+bcAvR7kIcWA39zdXn+W7XD9OjF0x6NqWM296Bl5669Yr sGKDWoZZg+GLNg9fBEBrsl9Gnlj/87DRcPHfEa5G/Unl3N0899c9F2KztIXpMoOfGabOZoVnIH1e ssKinU2JgSIptBEXbEOwuQxaS32J/yhJitK8vTW7qGoslQ/yKQAwll4Iv8HPm6csXb1qyMoX2SZB Y5b879eeDqwvq0tdXpKEay1klGGVEZ8Y7aqK7EwKfcXiMpkiW/H7VQalpcpu9uVx9Zf7Qdxkd0QY 86NOtZEBECrscpdIgcIfTFMXe5+2B45OcWWW55DlyqXAmyaOgtxnGdoeJiwA0GU8z9B+mDUgggCa /qwHtJgug2f4dl53NrVhs8t/Pgqe8YszAyFUzBgj5QJucHfLrRcvHbjI7rm7fUfR3zP2Xsl5atnp W9PPfV83wbao23z0whnH2h57rhzuYsyfiodLZuxn/7AlvHM9axGHzDzSvc2xIm+8dzjD1FmQE5eu 6WHCNuphYw4vf00xxA78nGcZBblo0xOy+fYmJEJgJJ0qypE0DR6++sDw1bQq6dKyvvNWd2+7tpng S5qKKv7QP2l+c+pxEVIZyZ0o1ScEi6RUGhoJCQCgJOky+sPWhcp0nbFqkhnZ/uO2Wjl+qLwnnHg+ I3vr/p636Wpivlavyoi5sHb24tuV8lIQvveg9orti/ZEZqnVWQ+2/rL19UedE1Jgycp9lqr6LPWQ Fm2m9s7avuHI1q3SXjNbmBLlLBfTunkbet+SU4J+3ZwFLqF92GG/HqLaNrWp0Nfw5LdnDvlu7dGI 51lKHaWTpkT/s/bv507N3bnG80V6tZ7k8EUiPkOdGrnz59WPFKVXx5Cu2h0/br6ZotTkPft35drw r/GCFr73wLbyrT/vjMhUqzMjtyzcLW/b14dvPP19we/9NGfD+Wc5atrwavTPf0SWUktSYq7v/HFY /z223y1oa0EYz71Un3BdQqwTtuyLytDoFamR2xf+8VTxNZv9R221kvxAQnlv4wLjKYYfHP9vd/2v Sdzi3o2c3fybTNiX23xgfUEFL4RJI0b51LF1KlqGnpchAH7jHzd/b3p4YJCfa9M5t7y+8RJ+mDXf b9xgwd+d/O2cDdtXcOHWGzvFOmzev3aTBntyShbQWLmYzm2bCeUug7vYs4Dh2LWfm1TQqo0Dq0JZ C4K/neib8e8vvRsFOLl61+s4c5ekx6YdozxYxvMVBP24uM2zBZ3qunnV6bzwjlsvbwGUVkfcoHnb l3remt4ywCVo4jFBKy9+qfUIX3ANGgEImi7cMs/61IhgP9fgScds5+xd2FBUVnrhjgL/YSHZm0c0 d3ULaPXL29AVPzT+3KbiULdx+/HrrjFCt1/eOsaDVaZVpfmE5Tb2j2l2x8YG1vHy6PzrQ7+BvqX7 09hqGc2+tO0/aqtf7gdDj8LwnmrDawN0+LV4GEwt5KMnnEoP6XbO9T5KSUt6XummlMzl6+VVdo5f NdMaZcAXGlxdRpbhtxrruq/qh3KN5KrGL1Xv/Wqv71rX4GqIwf9/hFpOP+DXBmAw/z861RgMptbI GKsYg6n9MsY6xmBqu4xT3r7AHsFgareMG7XqgT2CwdRuGZf/0+QYDKbmgL8agcFgGWMwGCxjDAaD ZYzBYBljMBgsYwwGU70wK+MgSoKUEyABQkqAGggKEAMBF5AJTdsD8LGX/5+g0tIRr+Qv0jQ6vb6u DbeZl1jAZtSyIuQ/4IoDCZJZLbkr1So+l/d1ZRz//Pnte+EfJfJ4ai5HIxYrREIFj6dmsXQsFken 09R19+Px7EgiDyFXRFtVSiHPXrh48szZkumjhw9rGNSAwWD8lyQx+oRHycT13R4JWOIaa/PNBCkQ jF4hTgihuzHvLjzI6BtiX4t8Lks/9+7xtHod4yopvFWY1LRUa0srE5HJV5Tx7Xvh3buGenh48HgF JwyE5Ahl0nSiXvcKoVRAWao3fwMAbdU7PeOupUU9HseVILQUwQVUCR9BOHnm7KBBodbWlsUTMzOz d+7dBwBNGjX8L8mYIMn1Q+/xGCYKKjdPnwQAy46M1erlNVnGbzJk/VrWzcxT6ijU2Mf+0MWntcjh uW+2ytIOoep9qgCBRCrV6fSW5hZfsVPt4eFBUZRcbnhlmB6hLECJuuSubKc5AEna5CNmPiqC4ObG Eg72jfPynpCkjsMmSJJLU/UBKiFaWltbxsc/KZ7i7R1gbsbduXefQcwladOq5aD+/WqjkinQpWii U7WPYxVn2pl+W1mHTXwW3ahVj69xx55KpaUQodLqSZLU6Cml8mu9E6qSi4DojPifae0r/467o453 rt5Kt7d3yMzMTM9It7G2IQjiq8iYx+MZNIwAIaQAlEtABgBok9cCgNjzPkFwAcDMJy8vzswUADS3 SIfvaVpIEPkIWVRKOaXSD95cGBFxr1PnTkbjw5vk6zdv1VIZ1zp0Wi0QRMTTZAaBOrfwUWu1Nd9m RKtToyfxReZuTdeSDG6120MQhLW1dW5ubkpaqr2NPYNBVr6MC/vShtfj6gDl61JGCetcYbJakCS7 mCmm5r4IAHTaZ7IX3kz7lQSRV1kyVihUAPDuXU5urqxQyfGlbmluLurUqW14+KNaEHgp6vzlq2kZ 6e/fvGxeymZh/xxhI1NDZTvY23Vq145B1pRrDQghuUyuUmu0ahVJkFqNTqVQIoTKH1Kqwe3a7OSo Edau7e29hxH6x0AGMViiN3e7sPguPJNGZs4jSJboa9tA07REJlWpVTqdliAJAFBplObm5jKpNPld kqOdI5PJrHwZF/t6jAZAyXRcIn/Z3sxHV/qcwQtvttMARCuByK3kkUyu7PDBI2VvM2BQf4RqxxPU 8c+f29jY9us3gCAIDoerVqvuHSjlk26jRoyhVVwLCwsAuH7jWsKLlz6e9arX8rfvUrNycw1+7hHM yUp61LweANCS9MeDWptGxTw1nHSszM1d7B1qlM+18sSkqKHuwbMsnFqCNgpAA9Tb4NBTeq1Eq0rP T7mZeLOVmctIyzozAb7WmShPkp+blysWiU1NTZlMJkkydDotopFKrRCKhEwWKyk12d7WnsvhVLaM abro01UkoQfQA4BGvYDDXf7ReVevjy0cQlMEVN7XFYspMy8vx9hmZmYWhlNdrVByVlZO/QbBer1u +CEbQwqXJWAQrKIXORAEySRY004EGlZ39kt1c3VPSIitdhknvn7Tv8/AT1bZv8cP1ygZqyVPkyIH erVcIjRz1OXvNDRjgCcABBACDsPUtm5L6zrd3zxa9+7xNDv/dV/jElRmdpZOr3N0dEKAdHqdXqNG CAEBBBAIgUqt4nJ4trZ2Ke9S7O3sy74QVWHjDB+LAxohYCJgADBYTgOVr1ayvb4nGB90m6UJfixH HwAmQTBoJK4sDdM0TdMFjTs6OtrYlm3btq9Fo8o8SZ6pqRkA0ctn1o23e3/ouZ3PMs3Xp6DC099r 9b25/f6mKfp/Z6YHW/TWaDUisShfkl+NNt+7fz8m/pmllSUAZGVnlDHYszC3kkgkW3fv8fHybN6k SU1wOFvozhXVy357nMPrrZFcobUvS25Dsus4+U5Livkn89nPNj6/Va4BObk5Or3ewsJCppDqdO87 swQAEEAQBJPB5LC5crmcxWJzWJUdjQHRCAzfY2TQJJcEHqKFAECQpQhVpeLweAKE+AiZVZaGKT1d 1Ljr169f9i4kWQtuU0MISaRSPl+g02lDvb59mxf797UfprVfJ6MyeGTBJcQc3SsnVvDOa0sceP7d 6sxSq9UmYpP8fEm1jQKeJUjkyn59++8P25eWlvYu3eikMUmQGrUuJTll5IhRd+7ejX+W4O3lWe0+ JxkCp8b/vHs0/s3j/a6BM7T52yjNE5awK1MYCnQurUumdSl61V1Nzhon32+fXllk5jyWLfSorNz1 lD5fKrG3t5cpZDRF8bg8BoPBYDBJklRr1CqVkslgCPii3NwcitI7Ozh+cn6h4p1qQIg2fKqMA7SY JkwJ0hwACIKFkCovjg8Apl6ZJMMKAORyIY9riZAFQCXMbxEEQRAEjd73k8sTjWvyFIsBuUIhEpmQ JAEEqFWqcYHrl97pcuzBn0Ma/ySl0gGAR5rWF/Q/eX8rpYJRDf9H0RRF6UmS5PF4cqVCyBdUscFS qTQyOnralJmRD8ObNQ+JiY8ue/vs3MxmzUPSMt717/vN31s2ONjbicXVf/WbIFgODXakxX73Mmqr R/A4QnaIJewcc30Fg8UTmjmKzG1NbH/R5m3Xy49Z2ovzkrZXYkCWSCRisViv1xMAJmJTlUqlUCm0 Wp1Or3V0cKL0OgFfmJOTTQLhYOtQngZc8U71+y+PMwDsSDKHoKWk/czcWAIAOM7+BKHLf2YNAFyX qXkJbyzMbWiqPkLsSnI9odfri2T8yWhcO3rU+RIHBwedTkfTNMlgqFTK2cH/LL7Xzt7sYCfPsWna uAbC/rfij796Fzsv+IJOpyNJEiHQ6fXWNrYSiaTqZfzoaUz3rj0QolUqZdtWHSnq0x9XZTAYN+9c JQiie7eej548at28WY1wPUHa+a3Nev7b83t/1W0ymSBFenWuQ/2jWsWr1Jfr8zMeOnq20En/YbPy pJkPK3NkrlWLRGKdXicQiHJzc3U6rYWZBceU/Tr5LQDwecLMzCwOh21jaV3OA1ZMxjKZjKYoVPCF R4QQl6acEENFIpqw/ZYkcvR6BYCOtPdDtIBGJnl5JsjFHyGbSnSBXq83qFitVk2cPLHsjQ8dOgUA k2bMWrxwgbWVZc2Ucb5EYm1lo9Np8/JybWztaYrSyDXT/cNWR/ZxNff3tOhw7fWuu7EXZvofovWI ySFZbLZMJtVoNJaWFvn5Uge7qr7h8c3bpC5deuRL8hBCGo2azxeUHTEQQkqVEiGUL8l1sHc8depE TZExAABY1fsx941V/O01ns2mAwBb4MYReQmt270N75WTdEEkzmOxQadKq8QcdTodk8mkAem0Wq1W 4+zgXDT6o2gqKzNTyBdYVORGrs+64FQwU22Y7bKkaDaTZBOkgCAsCFAAQSFEIprHYru9eImCAnwq peR7ww5SFPXnnzsKzYAZs2Z279opuKG/VitXqbU0TdGGfzRN6SmaonQ6LZfHV8h1/x49w2IyoaYi keS7uHqo1WqFSpGc8sbBzsnU1Dw3VzjBb/tfl8aHBk88FbVzrNdmGxM3gUCo02mzc7L1lF6tVlmY WyYmJlS9wTKZjMFgyORSC3OL8Mg7NP3pyxAkSVqYmxOaiwyTgTKZrKZVgbnreAbL/NnNZRSlLozT XI7YW6/+F8SgUQNH6F6ZE2xsjk6vZzNZGo2GyWQWn8FJT0szNzM3FVfsturPkDGNCr7jXfSfUEsF AOSRZB5J5CKgaEpE0WKewE8qe1tZJY+MerhgwXQWk0UAUIimaRrRSKvV5uam6ylKp9PTFE0bprFp mqIoiqJ1Wi2DwcqXSCiKZjJr7oMT79IzWrQ0ycvPvX3rVqPGjRNexPH5AjNTi2beXZFgzaa70yY0 WdfIroNMLsvMTFeolAihJ4+ftGzZ0tLCKi09s+oN1uq0FEWp1EoWm2Vra1uBkqZIhRaUVlcT7+4y cejHYJmmPCro3yFKpc6LElgDACilLK5pSCXmxePyNGo1x8SEyWJptTqapg1KJgnSysJKJBRW9IDl lXHRGZcuHo8RAoRooAGRCJlTlCkgVxoQQoBoBMCpzFEMg6AoWqXKpyg9QkDRNKIRTdOG84pOq6Uo ikaIomlE0RRFUTSt0WiFNK1QKEUiAYNRc6OxQqEQCoVJKW8aBDUwNzNPz0jTaDS5uTkIgI/qfOt9 DklQrORpsRtvwMvbk88XmplZKOSKqjeYpmmKphCgqMgHUomMJEmSJBkMkiQMv8iClKJfJEmSJJfH dXZtQdFUeaJ3lRWEpmlkuPCCEMu0pX1QmFarJ0iU/ngUm5MuNgeVAiT5HPeAcZV5yhCJ3yS/5QsE bBbbRGySkpbqaOdAEISdrS2PwzP4p2icUplTXPqiaQyahvfNCSGg38v5A3UjhUJu8FSlXPXh8/kI IbVagxBNURRdEHoRjRBFUVqNVk8ZwjFF04imaIqiNBoNMrdITUlzd3Vjsr4oGq9a/QcA/PDd7HKm V4xmF+0AACAASURBVKyDQyCaphsEFjyh5e5Wt7z6Vyqq5eYWiqIpmgIAW2u7J48f9+ndX6aQqdXK 0iMPjy/ii4+f+HfcuAl6pKVoiqKqX8Z6vb6guVBUUVMCAGDVU6n10jcLQffQvo5Gq4bU13xrr2VM lnkl5k6SpI2VdXZ2tpWVtVAkoigqPTPd2tKazeQUOccgXsPVmeI/vkjGWo0GABQKOV0Uigv714Zx suH2qsIx8/t7rTQaDYfD+XIls9lsnU6v1+vVaiUAUdh7pmlE6ylap9FSNEKoYHBs6FRrtRoEIFco TcRmWo2Ozfr82XKEUEnFrlr9x8rf136hhgHA0d7xjz//YJAfVJJAICiK1YZVPp+flZVV9MNwirQy t6h6DdA0RVOUlbnNq8TE+g2CGjdqkifJMXZCIQjC3NQi8fXLxNcvGgY1pimKpqlqFDBCiKIoiqL0 ej1F0XpKX9B5K7zhj0BKRfq/9erTsnxIe8s1d58rsA6t9JvDBXwBRVHp6WkWlpYmpqapqSlanY7B YECRGw3CRUAQ6JORubwy1un1BV6gDRG4SLaFuoYiZRv+VHBSUapULBar7HNJuaIxl3fq1JXevbrk 5z9HCNGUoU+NDKLVanUURaOCm7wQRVE0Tel0FE1BXl5+HRd3jVYjhM+/MDP3+zkAsPL3tRqNZv7c 7wBg+crV//tzww/fzTb86Uuwt7ZesWa9iVggkSoIgmCzmGw2k8lgsplMNpvp7e354sVLb2+vyKhH QUGBFy9dB4CgBgERUQ9lEtmcaROrIxpTS5Yu5vN57nXcB/QdzOFwbKzsyhoQEUT/vt8c+nf/nxvW qRSqag/FxQaFhiaDaJrWU3qaRiRJMEg+i1/35eNXJFNs5beJZ1r/K31pVCQUsVjs9Mw0giBJkmAw yMIJJwAAAgABEASBEHxSOuWVMYMs6JQyKxjT0Icd/c9m+JCBf2/Z9ujRU76ASRsib+H5g6KBphkA JAEIASJJBASTpBGDgU6cvMBhc52dnVhM1hcaYJDrqtV/7N6zHwDy8vMrRcMAIJPJC4ZMYgFJEAwG ySAZBEkwSJIgCYqm3T3c1Wq1n5+3RqPx8/VECNQaDQDQBKmpjocBRw8bmpOdE/7ogYuzS/TTh0zy 0wMWPU25OLtmZmR1bNnawtKiGjVMEASDQSJgMAEIgiIIgiRJBs1g0cyiLbh+B5EunSNwZTAYTCaL yWR8jZuICILgcjguji5arZbBZAICRKCibAjDfZlEQVSunE41h8N2tLXbvXdnhQy1tbJmsdiV4gJL S8s+vUI379hZ0R27dWrl7u4qEFTCK8GKlGzoXVeKhgGAx+PWcXd7+ep1qX9NSjU6F+3i7OTkWA3P GzjY23M4nPj9YWNGjufxuWUOz5Eh+gGASqnas3tf3x49LS2qU8aGoSmbJBGTWXCF0jDVUhQGCYIk SZLhaZig+6p3ARrEyeFwCp79LebK8gyJKyxjNpvdrm1ruVwuk8lVKpVWo6VoSqfTURRNUXpDz6To zMHhcAQCgUgkEomEAgG/Uqa42CxWgL/fut9XosKOR/EfhoclSPL9ScuQSFEUk8ng8/mVdXN1kXQr S8MA4OToMHvKuLy8fJ1OhxDS6/UFY7XCO+YMxTEUjSRJBoPBZDLZbJapiYmVlVW1KMHC3NzLs96c 72ZptbpyNyGWr4+Phbk51AwIgmAwGAwGA1isarfkC08WzPLnxGGzOebm1VgNBU6vbipRwEUdDUvL GnqHWRntYeKY0YCpGeD3VGMwWMYYDAbLGIOp0UgvhtYbcE5afQbo05/euJOrxzLG/Idb+f+PcMuz tGITXyJj6cVQGye73rtf66q48mjJ432zQ5s62DhZ2NQNDP1hZ7SU/k/UCZX/cPt3gxp7ulrYOFnY B4YMmLfxarIa1Tx92jhZFCw+TYb9fiWjtIgg7nTy+eGu4hqp81KzrhZ79OlPL5+4XbCciox8lCbT V3mnWujeg7ll6raXmiosuCZh84BvNkh6rLn9/OW755fX95T/OWDYX880tV7EuqRdI0Zto3qtO/8o NfV18uN/Vw+we7rxj3vymmeqSci+F8k5GW9TnuyfzD80burxNAownwnLOvibqaFDpvboP7iBkyYp LkVHElUrY4ZNr5W/uOyevT6utO8AaFNO/TywvrOThY1n41Gb7ufToH78Y5PWi2M0AAD6d+d2RuQj AABNzPLGTRaVK0eUc2nJFvXUvZsmNHcz4XBMXFqO/9+haZqNS69kIwDpxVCPDvMXDmvg5GRh491i 8t5YBTJqjOEEXKfzkt+ntvJ0srBxcmo2fkO0vNqCn+rF+QSPOT8NCnEz4zKZfKs6zfvP3HRkbVsR ANCKxHO/fhPiaONkYePm22vxuTQ9lfZP74BBB4sJiEr7p3fgkMPpVOmF/Ro9OpsGwxfN8og9G6c0 OL/jwsVjmng4WdgNOJdaGNxKdbL0YmjdsXck94bVdbKwqWF9b+OtAinid0xu72LjZOHcbMDvd3IL fY8U8bumd61r62RhG9B2+r73ra6cII08KyU/X860dOGrs5FQxABVzvM7kVdP3L585sHjF1Jd4QEp aVrM1XuXT9y+ein2VVZRMlDStNgbEZdP3L58Ljr2jYKq4NiYYdVh2Qqfw9P/jFZ+ZLr68arh38W0 +PNu/Lvnp74T7Bnz0618Tp3ujTQ372fpAXTJp1euWHc1mwbQZ0Tc0TQu38c1lC/OxNgO6uVa7FlH tntof/vYCwmGe3LlCfvimm+MfJ7yePeAzFVDl0QpjBljsFcetzcmaOXV+LQ3UQf7ZayesfOFrppa D69OxzoJy+etPxaRmK36MLpJr04af9R86r7HSW8yXl793e/67O8v5Fl3md3q9YYDrwrvutS9PLAl qePMbrY6o4X9KhBFb9sFxbODia033nuZlXa4a/H3sZd0Mq/TyRfbmxdE9TL73tVC6a1CcX/JmN8y +x98/Dz5/h/NYg7EF3SU5Pd+Gbkktcee6ITk6K29UlYMXhT5GV0oWifPe/tCwTHnMknFy3sv5TY+ HUZ26/eNtyD5+Ss5EACgl76ISNK61u82pluvzraaJKXeELj1kufhbzVOAd1Gd+vT3Yl+9uyNwvAK 7fJPcZHmrRf8EXJ+2sooWfG2oorde5wxfsWkVo5Cjkm9fj9Osbv7b6yS79vLN+vyUynSvT5xOJF6 sv1cGoVkTy5n+4eW72UglCxdJXQy/eDuFKapo0iZWTCm4LlN/21ciC2PZ9t40m+jeRcOxyiNGQMA AAL/+b+NCnEQsnk2zUaN88kIf11dt+izXMbu2zfT7tFfk3t4u7paerbp8/2uCMMJX9xh79Wt09t4 WHAYTJFb11lTPWLPPVOLmk7tT+3fWtBk5A82HWSNnhwsLKOwlT1Joc58ErZ43Uvvbt6GW1r5XnN+ HRpszfm49dQcJ5eTUg1Wxu2/wJvw27gQWx7ftsnk5RPdDN+EUcYduCKYsHx8iC2fb9tk8vLR/MuH YsrvcF3mgyPbrl46cffGldhkjnuThib6jHfp4NggxEFMq+QqnmMdTnYyxeMSlDQji+kQ1NSer1Uq kLm7J5/BIAkASpKZw3QMamrP1ykVyNy9DpGeQnM5ULG3fxCikPlrOvecs+TO8RUBRcblJqYm7Gzp vvz9Zs2kFCEO7OH++vKzfNdrR4nxq4de3XTqTWiDC2/ce9cv39c0GEIbnjw5Xw/FlKzPT5HxrUVM AD0A28rLpuAeOraNp6UyMk8PAKUaU3BAW1HBHWAEi8tAumqcLWOYBY1csmPkEgBak/3y/vF1Pw4e jq4cH+3GorLvbPz+550XYrI0hfbLKODUGzrFOXTdpayQPhbZlzbe8pm2wI0JcuOFrSwk94bVdSqY HWk3avOGPvYGFzLNXMyYpdZazXGywQqSoPUfGUHrEVE4Ki3VYH1+stK8Q2Hr4th62XIiDLEwRWnR 0YZdlG6ljMov/0QVyzq49zcOLFonS35575ZcpVYQ+QqN7N2VbcVuprfRsvhc0Kppjq2IUMvlehoR HD6LzKYRANKpKa6tuFi6PlnL5nNVmoq9E4MQNPjuz149Jiy+sr9DoXFmrg5+7f85Nc3tw0PxG/Ww 3Hzhwv4L7N77OnYWb5i8+0rOU8suP5iVL/7z6nXzSVtz8s2YGXUK+9XaVyf/feczw5MLIAfQZj3L 0IWasgBAm/E8h+9gygQgjBhTYy+HkBzLei3H/jrn3+ZHnqtGu+keLp66lzNvV2RXTxsxh8w43LnF EQAAhl2P6U1X/XbkdedOZ/5O6bqyrSVp3POVO8UVVXpPuEK3ABMEVM+nO1jmHoKMqCRNT9P3gzP1 2wdpAncLFoCxURXT1ImfW6x1JaQbzqlME0d+TnyGNtSUDQCa9GfZfHvTCvkeaeRZWVrEtPZ2j4kK z2vmzOOY2XUcVE9UXBQaOcHmMrQKjY6iEQDQGqmGMjwJzeIyNEp1YbpaqmPwWSQTPuO6Mc9v8sah r+f/dDGTKlgf3Ve74bv1l1/ma3TKjKfnVk//+ZYMgGnTsh21Z9FxwYAeLgLXXv3Z+xeGUe2b2ZSz 2KRFpwVjWX8On7z1zmuJViNJur111sA/GZMWtLM0mKx6vX7hzvuZanVm1NaFOxUdB/rxjRtTo5Dd nPbNrNVH7iVkKnSUTpry8NCq9QnOLT14AEin1pEcgUjMZ6hTI7b/tPxhwdiLMG0++RvV3rW7Nhzg jZ7gxyvL8zUNhsCKlROfqkLF55aKLvkY+1050xC+Iwewd0/7Zf/9ZImOpnXSt/cPzp92gDdwqH8Z n1Lhew/uqNha0LoiN/+0+bVhYpfvM6idfMuP28Iz1OqM+3/P3yFr943v5zw4hwi2ja0g/bXEwtGe ehVxPznpdXb6m/TEyMfXzr1+l6UlxdaWurcP4mV6mtbmvEtIVNMEAQAME2tL3ZuoWJmOpjU57xJe 6a3qiBl6+KzbPzieE9ZOVp54UdDCuAHfh60PiVnUPcDe0TNozO6clkMbCAGA5dKupUjuOrSbAwuY Tt0HukmFrds6lf9hZa7P1CP/TBaemN28nod9vbaTj/OmHQ6b6V346Uqh51DPmxOD6zr4D9tv9u2+ nxsKyzKmJiFs/P0Uv/R/fupZ38vW3t2t9dQdkt5b94yrwwIQNFrwW7v4eW1cHd1c2s2/5d7XR/je 66MmWh/59ZLf9FAnZtmer2HwAycMEW5sW8/StupnqrkB3x3cO1C1d1IHd0cXK0ffFpMOqgdtO/qt b5lfQBU2/XnHXNMD/fzrOgTPuOk9yLvAq8Jmv+780ebE0IC6DgFjjtrNPfBrk8/84CLBMXOyUD1P 5nu19DLPeXn/7N2rZ6KevNZbeVqam7IIpkmdJq6shIdXT9299VAichOwmCQBAEyTOk1dOYnR107d vRWRxQ1oEOTB0igBgPhjyaxx0xdoNTV8LqLEpYKG2yZH1byZTwzGqHR5IkuhJjtLW/SWHra5iVAv y1OQfFM+j8cgAVEajSJfpdYVdKFF5jwui0B6nUpN8lgqw75F6UBTaqlcJqcQMLF7MZiqAKlkWR8E S6TNzc8FAKAUOZKSLzhFOrU0Q13Uf5EbSTeMQbF7MZjaTu2MxuJOJ593wpWHweBojMFgGWMwGCxj DAaDZYzBYLCMMRgsYwwGUxP44IJT4rNo7BEMpnbL2MOr/v341F0XY5IzcrBrMJUFg0HaW5qN6uTX 2NsBe+Ory/jpq8y1R6NM3II86lgRJO5vYyoHRNMqSdbao1E/DWb5u1tjh3xdGe+59HjpuI5BXi6U Xk8jupoeEsX8B2VMUSaxb0y3ngr/fWJH7JCvPDZOzfHzcNDrtAjR2DWYyoIgCRIx6zqYJabiwdrX l3Fz3jM2i6XVqgCHYUwlK5lkktCc9wy74qvLuODrjLgvjfkaXWuEvuq3grGMCzB8BBirGPNVZAyI xPOmVSJjw7uI3+s4PDwiKvqhsZ179ejp5OiInYgpZzQmSRyNq6JTTX7UqY6KfvjDd/OLb0NRFADo dDoWi7Xmj1W9evRwcvivKFl6rqvfpqkxJ3vgdwN9HR0XNDDM147GH8TiQi5fO198VcQzTctMbdwo 5NvZP6z5Y1Xof0nJBd0/zCc4eepMxP3IJo0bhfbsXjzx8pWrHdq3K56Io3EVQJYcGwNC7xcAAGgQ 0Ki+f8NA/4YBfsEBvkFu7m55eXkCviAjM2PY0JEnT5/+YJfii+RsN+fQMxIjq2UkVtfyUfEN5plY iAoWO+eADgN/2HYrTVMjrC17Kdux78v1Oc5/mfiqU6fOJ06ePnmyoPZPnjx94uTpPn363r5zr9Rd EMJj46qJxgQBCKES0ejRk8jiq37e9YUC8dHj/yiVSoVCYZi9+GiX7UN63W/+7c/f0ABAq1Ou7V2+ 6k7DDX/ZGDZGJYIfqhEza6h080yaH3h6vLsY6VV57xIenNu9enDTswsvhU2oy67ZlVumY8VdzuRn gfRcd//Nn+H82Ng4X1+/AQMGHD58uGfPbgBw4tSZcePGvXnzJicnB5XeoUEknqmugmhccAMmKrYA AECAb5C/T5C/TwN/n/q+3oE0ohs0CHJxdgsMaNClU/ePd0EACMbs2DCEeWr64F/vSyIXDp5zgjlw y46RBZ83KLFxQaLkXHenNr8un9DM1UpsamXbcNRfD2QIASBdyqlfega4iE2txO7tJ2yJllIAiFa8 OP1z7yAbUyuxqZ1n14Vn3umpdwd7evYNe0cVHZl6d7Cn1zcH0yhQJ5/4sY+vjZXY1CVo6F8RebRh AySP2zaupb2pldgmuO/yWzmUcfMQweSaOwd2nLj22Kmx73778WI2VXCEHZPauZpZic08W07aHSNH BUdWvfrn+57eNlZiywb9VkfkUQCSc91dep+RFB62aFVyrrtTm0XLJ7Wsayc2tbIJHLj82tvonTPb eTmITa1sGo7e9FiODLuUWopS/SY51915xC3JncHOVmLTYpkaLV3FluFDBx8+fDgzMzM0NHTKtNlT ps0eMmRIcnLy5cuXhw8dXOouCCF8h2/VdKoJKE3HT2IfPo17+DTu0dO46Nj4xwghHpdnZWXF4XBM TU2NSZMggCiaOyOKDzlRKacKww957K6nwatvvcp+9+TIN2krp259rkWgCP959nHHXy4lpaekXF0U nHDssRKB9NL40f9YTD8Ul56Wm3R7jf+1mbPP5lp3ndPm1V/7XmgLjqx5sW/jmy6ze9hqo1cMnPO0 5YbIV1lvLvwg2DFy7vV8hADkEb8MXZI54Ej82/Tov1o83RcnL8O8ooXnO2SoY8yZeBUCkN1dMHhR aq+wuDfpcTv7pCwdsDBcDghAEfXrgO9iWm6MeJWRsHeg/HKcsuShPij4jod+Sy8lZKfFHB8h/aNP iyGXPH+9EJ+dFnN8cPryKdtfahGAykgpSvMbt8uZt3tamjQ/8DZTmnesu7gMEX+Ojlu3aj5z+uTr 168rlcquXbt27do1MzPzwoULM6dPbt2qORjRMR4bV4mMCbK0oTH4edf39Qr08Qrw8QzwrudvqH1z Mwt7OwdnZ+eSw0mEYMeYKfs0PdaF/dLIpNHisNU9NQcmjN3zyiCvkuOmokRBwIIVY0MchCyubbPR E30z7r1SAQIWj6mXpL5+k6nhObeY8PuiFgJAok77b+6a0baOOZvBELp3+3aGx9Mz8SpxyPSB1N7N ETJACJAsakMYe+zUhgJlzK6jjAmrprZ2FLLFnv0XzLS7/c9TBSBFzN7zgkkrJoTY8Hg2IVNWTnXn lmle4cI0cxGrM2R6QIqY/ReFk1ZOCrHh82xCpqwcx79w4IkCkDJm9yn2+NUz2zgLuWa+3/z8YzNB aYd6X3D/hasntXIWsTjWTUaMr8+vO2fFhBZOBaveabdfqgAZK4UxvyEj3v5U6cq5+Pn6Bgb4Jycn M5lMDoeTnJwcGODv5+tbxi4knqmumpnqj64bG4iJN/ocsoODQ6mTu2P2HR8DANLzAIjg2LeZur7N VADpBZKgDR+Seg+tQwRBGPJlCG3EZEEwZ3MZtJYCBPzgxbtmLlq+pP/ShCyTRuNX/r2styMbqOxb 6+cs2Hb+aWbhxwebSynE8Rw+1bnrHxcymvWzzLrw1w3fGQvdmCDPSUx5tiPEcdn78UNzCYUA8pIU 5h1tWIYcObZetpy7JcqCSvpEl/smn2sjYiLQ5ycrLToVO4KVMiJPjwBy3yosO9iyPtzReAxkiOxM CgvO4jJZIlvx+1UGpaUAgc5YKYz4rTTLSxs/o8+bm09LT3/85OmAAQNkMhkABAYGHj58OC09zc7W 1khONI7GVSFjgiQRACox3+Fdz/8TcynGpkhEnU+/6vz+70xzD2H6/bfqnibFvnD3Juqd0MOCiZCu YKK4YGNUtEqaNRq77ujYdbTq7fmfe8xZFtphYwsi8pdJOzk/7X3YzctGzCEzDnZsehgQQqRtz5kh K5YeTuzS5cyG5G6r21kSCDFNXR39Oxw9O92N9YHVShMnQU5curbg23npz9I1xQyAj8woSlLFhoWl Bczz5CKkMnHkZ8elawq+spcWn813MGMgRJi6CLJj0grSi6Z4WCSl0tAIEQBASdKkFLyfUyyl4B+u soyUQmpke0AEAYhGZc1flVK6cnHz1p19YYcGDBiQmJj49OlTNpvl6ek1YMCAnxctGzZkYKuWzUvN C4+Nq6ZTTZR6Xo5//tTYUrHceH6jB7J3TV6wLyJJoqVpreRtRNjcyft5g4YHlPGFO9ntuVP/OBuX raZoIArPCUhf+PFBUp0SvnXesgdFHx9sMXWAcveaHX+G8cdMMnw5j+c/pq92/ew/L73M1+iU6U/O rJry000ZAN93aCf5lp+2RWSq1Zn3/56/8ZW6LPMptST5yZWt3/cL3WE3d3FHSwKA7zOkg/zveVvC M9TqjIhNc7dJOwz05QPw/Ib30G79/q/ryQp1btzhJSvuygG4rs1t4jftikjX6BQp4VvmrnyiqJD3 jJTCGAyhFSsnLkVV6ZcATp05V6ThBw8e/DT/+++/nfXgwYPExMQBAwbsCzt06sy5Us8YeKa6Oq8b e9fzN7a8r6JyLZyA7w/vH6TcM6GNi529uZ1nyIQw9eAdx7/z5ZZ62dawKggc2SJrw8AGtrYeTea/ 7r12YVM+An6jn5d3iP++hZOtk2PrH2659/MRFu7L9hwz2fqfXy74zejtxCjIN3DuwQ0hT3/u4m1j 615/1M7cVsOCBAiQoOkve+aZhfXxdrUNnHrDZ4i3sLSySO4OcbM1tbC1cPBtMXLVFbLv3lv7Jniw ACFAwmaL9/xkc2yQj6utz4gjdvMOL24iQggQr+HC/Su8rk9q6GHrPeJfYTtvHgKWx8T1c+z+Gepl 7+TQ+seogGF+RdmVWvCPV42Vwsj2vICJw4QbWrmZWfY9W/LKsORcDwtbU7dRtwtKV9o2RpZTp88V aXj61Il2NtZ2NtbTp04sUvKp0+fwdeOq5IMvKh7YuXH8rAVK+fvPPG3eseOH7+Z/dBdXcTq07bJq 9fKJY8ZgV/7/4ebtu/vDDgHA0CEDW7Vo9sn0gqGIQnr0wK7Bo6dgB371sfH78VJFxsb4qaj/V7Rq HtKqeUjJqjeWXjSBgsfGVTZTXcotWYXT0WVMd2IZYz55qsfXjatIxqTh/r0i7G1tV61eXvYh7G1t sYox5dExHhtXiYwJAtAHVxF7dOlczirCrsR8OhrjmeoqGxvjcS7mKwkZj42rKhrj0IrB0bi2Udp1 YwzmK7U23MCqRMb4ZIn5qjLGDezrd6o5HI5CmoedgvkaCESmHA7n8ulD2BVfV8YAIBCbYSVjvhK9 hkzCTqgKGQMAPl9iMLVexgCwbNNh7BoMpraAZw4xGCzjmgA/cMHmb4P5/2/M4Acu2Lt5/97N+/eW mV0NccuX+62yCvJfPQ6OxrUS5eOlwycOnbgxXol9gfmkjEueLWrLCR6DwTLGYDC1COZnql/g1mXs yL6N7HigTr1/esv2Ky91jsOWj9GsW/ZPsq5oM5ZT6LKZ7E3zj7zWGYn2fw1DSeBTh5d0/dxLl87t 3Kj4w6tXXrGc+0eHc7PXPFAWbla0Sgrqdug/pGtwPUsOKJJuHtm752qSCgCAZd9iWGhoizomhDYr 5vim7adeKOlSTWc5l2HnW3aJcinp0surpAGA4Dp1GDXqm+aOAir3ycUHgjJuUvqMfIsX/CM/GKEC 9hTt0KLP6G9ae5oh2as7+zYevJ2hh2r0W4Xqtwz/GD1OQbF5Lu0mj6v75O8dV1K1qMyGXrPsqdRo zPcdPiWUeWP51Omjp/7vMqvz7OGefH36/Timj4ewWMshRB6ejLhHqboymhFK/2fxpN/uiNq0hsM/ TVwabt6ulQvLqBQcus/4vh26sWHRuDHTxv9y5LVDY1fDmye5bl183oX9PHPE2Lmrbgp6jmtrZ+wE pTNup760chEAhLF0rtfQqX14t1ZMmzZ65pZo++bOXOMl/Yx8K0qF7DHAcWnjmXdyxbejx85dE2U3 dFJLK7Ia/VYZ9Vv2cQxm+/T+foJLxJ/bL39CMzXNns+WMb/enM2GSdHN+/du3r95ijcfgOPQ3Ed2 7uCtRKlWK3199eBVuU8TZ7Y2OeKVsL4TjxQ2+n79zjkBQpLnUl/4MiJVW8bxdVkxSXJZ8otMVVbM G7k89WUe10pozCNsx47t+Fc3hV1/mavS6ZQZCRd3H4k3vMtSm3Jy//WEXA2lzX929UqSuI610e8r GbeTXWq5AIymO7UKUJ4/ePuVRKeVvL4SdimtjBPWZ+RbUSpmDwAAqBP2bjsf/U6h1Upf3riRZuVj z6k+v1VO/ZZ5HGBYNR2xaLL91TW77mR9yjs1zZ7P71Qrn68t3ovjBy74owOQPHO28pGkoPell6TL OZ4CBqheRyZb+DmI9SF1WGzU2FUkD7BIufe6zHfGAq2nEYCORoYfekSyGEaNFdvxJFeyS+v1XfE8 0wAAIABJREFU0cpsRUEnGum1NMks4/xk1E5G6eUCMJpuwVFGS9+nS/XeUJn5VhBGxewBIERenUcN aRfkZlLQCFECk6g+v1VS/ZZ1HJ7HoPbSWJ1b8wDze9ezqU+Io4bZU8mdalqVq+XbiAtOAUwTG6E2 X0EBqF5H5DgEBjZ2STt/Kt2lWaCfc86DRNVnZID0NMkpbFCkwEJgMFMvTVOJ3S2Z8IUYs5MyUi6j 6cpsDd9GzChKFzErNV9jfnjvJYLFKCa7itrD9hg0pR1x7e/vJ04bPnzi8Om7E9VEdfqtwvVb8Xai erV15Zb/rTpN9h3X85MdnppmTyXLWJN6J17UdWBzdzGTbeLSdmB7Ufz9JC0AUiZE6lv29sm7dvPy 9fyAPiG6yETFZ3T2dVnPJA6d2nqYski2mUeHwV0KCqhNuXxN03byoFYeZlwGk2dZp92wPt7czzhL GLFTa6RcxtI1Kbee8DoPbO4qYrJNXNsN6mTPqtx8jfihoJOb9Uxq16aRLbeoDitqD8ngkJRCKlNo aLZFnfZDe3pwqtVvFa1f7We0E71aT+vSb67fltxsch/vsmcgapo9lT1TrYzds/ns+OE/bhjCA03a g7Pr9sQrEQDQkoRHUh7z5pO8HLibMahNVIKU/ozD6zMubb9Qd8qsDf1ZSPr62okrL339DPpOOb1u rab/4BlLJpqzQJF8+8iee1qACivZiJ3IWLmMpauf7d94fPTonzYO5VO5Ty7eSVK7VGa+Rv1Q4KXL O274zVi0fTQAPF87cc0DZQXtUSce3BM9ZeSv22YxkCzp1umbiQE+1eq3Ctbv57cTWvbk8Dr7aTNH NPhty8N8o220ptljfHRU/HXzl08f6jVk0omwv/GjERhMLQLf/oHBYBljMBgsYwwGg2WMwWAZYzAY LGMMBoNljMFgsIwxGCxjDAaDZYzBYLCMMRgMljEGg2WMwWCwjDEYDJYxBoPBMsZgsIwxGAyWMQaD wTLGYDBYxhgMljEGg8EyxmAwWMYYDAbLGIPBMsZgMFjGGAwGyxiDwWAZYzBYxhgMBssYg8FgGWMw GCxjDAbLGIPBYBljMJiqhll85d27lE2rFwDA6F7NsGswmFom4z1b1gDApDm/Yo9gMLVSxtv+WmoQ sEKaDwByWR5NUeXtlDMYQpEZ9iMGU80ynjTnV7kk17CuUsql+Xnht6+Uc/+mLdqTJIPHF2JXYjDV 3KlGCAGAWqXIz80Ov32lz5DJljb2n9w5O+PdsbBNTVu0BwAuT4C9icFUC4QsP1splxhWFLL8y+eO lVPDBt69fXHm6J4OXfsIRKbYmxhMtUVjQyguwtLG/qOUMjC3sikezzEYTDV1qgsVSBDE52mSIAjA MsZgakY0Jgp/VFSTBI7GGEyNkPGXRGMsYwymZkTj98G4gpok8NgYg6kGEEKRUQ8/jMaFOkYV7FQT uFONwVQH4fejrt24xQQAvV5T2DcueFJCr1VXMBoTRQfBYDBVxsNH0R93qkmyIBpX6GZMw9iYpnE0 xmCqGqlM/rGMizrVNF0g43nzfyp15xXLl30gY9ypxmCqA7FIJJFKS5/iKpKxMT7eAE9xYTDVQVB9 v2s375Z++wdN04YfS5csMiJj+sOhMb79A4OpBhoE+jEZjNJv/yj/2LhoRxyNMZhqwc/XiwnFLi8Z onF2xjtA5ZVxblaGYUcEWMYYTPVAyPKz01ISDStsNockGedPHmrdMdTU3OKTO+fn5ty4dLJL6ECa prRafMEJg6lGGSe/LFrn8gQkyTh7PKyc+3frPYSmKbVKgV2JwVSnjN8lvSieJBCakGR535hJ07Si 8HFlDAZTLZTyvLFclo/9gsHUJhmfOrwNewGDqfWdaq1GBfj1HRhMrQV/NQKD+U+MjQvB0RiDqe0y xirGYGq7jLGKMRjcqcZgMNUD+YGKK7pILvX2HHRO8ln7olqS439noXNvrujm42plP+icpPjv2mA8 rvcyF7KYilF5F+nF3l6DzklR4eMQCFXhUpBjMRuqc6khZpRn0b3e++tJlzXhqakHuvCK/RbXBuOr o6XVokZVjmgsvdTH3s36/TL4PHQ8Fn+giwi+KJJ/Ihc3a/vB56UlNitCVGiD9FIfr9K2LF+AkjwO +7Z3c2d7N2t776De83ZHS+lP2lk8uyIzqvgE/JG7HAKbDV56LFGNythFlXw/07FzY2vWR7+rZClR uUbq19hSWS2tnM2sKuuxZOuteKMqz9gYgbjpnvthxXSLPvxRKYPqkrmUeuSSOX6+DZqELYMG7nOY 8/vN3UF2kHH/8O+zBo2UHjsw3YtTlp01Yh6hmLsQpcx6ceXv72eN2eR9cZYX22hxFVqSY3gy9YPf VcGx1FcAALJLfRrvnPi+llEFyls5bi9nM6vCeqwMAz7r9g/ZpT7eQ87LPkzUppxeNDTI3d3awS9k 7OZICV1pBVU82zWts7uDu7V760Fr7uXQxWxIvdTHa+IdafgIL3drhxImfeK4uZd/26aesnPj+BBX MYcjdm45bs2BqZpNv13LQQCyS308u/y0aFSwm7u1Q0CrafvjFAhkJbIr5gqkeLZ7Zk9PR3drx+D2 M8PiFKjATq/uy9bMbOPjbu3g7tpy8sZoOQIA0Kee+61fUz9rB3drv9CpO59KP9thBINv7dVjynjv rEdJmhK1U4qj3K0/KkWpdSe71Mez6y9LJzTzdLd2GnJeVnkGg3F3GU8vpJLNeO8lr+5L18xqH1jX 2sHdOWT077eSHu+b2yXI09rB3aXl5C0xBjtoxasLiwe1cnFwt3aoG9B36fn0gifzkfrNvwsG1ndz t3ZpMfjPqDz6C0x9X4OlHaG0lllsbGwUQGAspeiHKvr30T/EhvzvxpPkuKOz+fvGLbiVR6OKUDIX A4r7y8b/ltl3/4OY13dXhcQceiYvtr2ow9H4zc3ETXbHJ2ak7O8srEiGiudnYmwG9nRlvU9iufXo axd38ZkSIQRI/nx/XMj6ezFvHmz7JnPNsGUP5MKS2RWZLb/365hl77rtinr6OmpjaOrvQxZHygx2 yuP3xtZffvFJyou7+/tkrJ2964UWIXnk4rmnHeYfT3j17MWZ+fVfnHqq+Gx36ZVZCec2b3/h3Nyd W9KTJR2V+H/tnXl8FEX6/58+585M7vsOuUi4AgRYQDkFRUBEcUER0UVU8GJZ1K83rqv7VdYDRUQE 0VXRVRRWEFlRAbkSEq6QALmABHLOPZmZPqp+f3QSrmTITKIL31+9X3lBd6fSVf1UfaqeOrq6/qKn 6CzvALuOf145/O2dx86d/ucNVDcTfEmyOzNX52ZU/rAn7XbxdWfpuoPZz28sqinf+8Uf7W/dMeHu 7b2e+aawpnzv+tsaXnt0XbmAsf2nB+/fEDJ/TVHFidrSH17J3rFoydZGGWPsOvDy3U8eG/bGz4dP Fb9/q/PnUlcXk9phenw/bAcls2utsX3f3VmpkXHKz8zv7ZcFcJf+cxNz70vzRsTp+KBe05bMj9rz zbEWP2vEi2JJjYyb+b0DoOXYZ9s0f1p6z5BIjTZy8PyX7ktW91Arjxz1HkOciblo/s0Ua3A3OJUa VpP00NJ7hkRqNJGD7l86W7PtXyU+nqjl2Prt+vtemjskUquNHDz/pdm6H9vC63o/8eJdQ2J1nCZy 6Ox7shoKqj0AFKthZdvZU6cbvdqEIff99clh+oDNlZ7Ub+KclfLcV2alsv7bwUfeaTMee3bmgHAV DT2R4K6Yy4cZW12PHrTbBcUMAHTZT7w8d3iCnlOHD541p6825ZEX5wyLbz3NqNtb5QEwjF77w4qH rksJUTGsIWnCw/NTSrYedwO4Sz/Zws99ef51CTp1cNatTy4aqu+JpHZ2h8tKZtdk3F6L11TU13w6 IeiyAJK5svbEq6MzFdPE5D9TXFfvt7dzUSwV9TWfTjAAyNYzrpCMCE4JwkdmRPI9NddmiFA7aqwX bVckWWsdmgi9Im0uvD1eLiI9zHVJ2IuRbTUtIZkRfHs6w1rOtoan9ZFBrZUFxasZJMgAoO3/7MoH Q3/++8zhfeP63fbUploRAjbXydNHtn3yEL/myc8qJf/t4CPvGFNCcFs11/0Ed8VcPszYU8nosJgB AG2INrZlE6dmWUNU0PlTRhZkAAC5efe7990wJD4uNTIuNar/n/fVNToQgGQ54wrNvqRodphUx39u uaT68EFnD3tZyeyKU40v/237ldYDxpQYk/3szrK6M+WtP6c/8c/F7SgWjDGmjXE6c1m9oJwJ9Sfq BbgkPEUBQv77d+q0iVl16zdVec9f8lZu+vps1th0FcYYg9h4YbzNutgguoPoWpNBB8Vqm8vqW2/m rTvepI0x0h08V9spbcq767XPthwpP7LvlfStz7y23xmwuSjelDxm7t29Tu0ob8EYUxwtu72tSZRs dQ4El1v4/HHneXdxuehugrtkriubsQft5ut6x6euoqUP/5O/9/29JSU1p8vPFr7SX9dqwwRd87E6 7yVlt4Ok6sd83Wrnf7YbufPEdPywl5fMHnrDSZN991TxvSdWbK+wCpK7/ujWZY+9uMvZE3fWZt0x xrX6+XUFjR5P44FVz62quuS7NLQujDOX1br9HuyjQ8Y+eQ+3fO5Dq/dU2wXBfubXDxfPXM7Oe/L6 UMUq7uoVrfEWffD8OteYW3trO49OmzVjlHPVs2v2NXg8DQXvP/ORc9S0bG3nsTv3/M/j73xf1uxB GCiAbu0PjEVb9U9rPyoL7ROrAlAnDo04/v4nhfVeyVVbsPqZfxxx9UTe9WSCOzfXFc3Ys8nw29KS R6JVWoNBy3hqC9Y8+1qxq9WGMycKHz61ckdNi9dS9tWry/Y6eyKpnd3hspJJd21KDjq/ohyochet fSP/2ItTByUk5+bP+8T8hxn9dH5Ootn3zclOi4pv/5n1vQMDaAc/tXKx6YsZA3KShjy+M/O2TP3F UWtz7vuj7r3xudEJSng/ftRZ89Z/9if9vxeP7J2d0PuGBRvVD366ZmGmqjWAPv2PGbseHJKTNGDO Z8GPfPTUAH3H0Snp0Q155v0nIjbNzstJypu/Ierxj58ZaOjYegCAQZd759CmlbP/kJTcZ+Rzpya/ 8pfBgZurV3zOpMW/Zj638v4sHgOXfO8/FkRvuLdvWmbqDS8U5czorYOO8rH92EfeXRC++wmGrpjr SmbsSbtdWMw6yaZLc23AUy9eX/b0+F7JmWk3PPNr8pSsVtuqBzyx+qWMnQtH9EkccP8G3chMbZeT enl67Fd62MtK5vltA0SvBwjnB/3/M23o2nl7Prl4gpFAuBpL5oVvOJFXIy6dl8fkWxiEa6Fkkt0/ CIRrnvNOteBxE3MQCNcixKkmEP4PyZiomEC49mVMdEwgXOsyrjl1kpiDQLgWOT/EFRIRR8xBIFzb rbG5oYaYg0C4FiHzxgQCkTGBQCAyJhAIRMYEApExgUC4xmF/szu3ULSTAhtQdgo8QMmAGQxqwEaE YgC0xPT/B/j3iXc3nlguyl5//5BjVJPTF0xKf7D7aXBbD6iD+lJ0T5bk/UXF8bEx0ZGRnQUoKSvd f6D4nlkz/4/IuPTEiV179l5yUaPxqFXeoCCXQe/SaDwcJ3KcShS9vVJyNJpomrJgnIRReNdj2bz1 h43fbb78+j133TlwQH+GYXrQIoIgvLrsjabm5iuGDDYG/eXxx7VazbWiuhOVHazwSUtKpekAnbJN J95Zd+cZLW/0u44XbHd/ktB9GTvqtpw9tCB93LEebJDsDsehI0eqqqvumH4bRh1svyaK4qGjR3me rzl7Li4m2t/7Hzh4sOjQYd9hoiOjJk0Y//vJeNeevTdNnJyamqrRtJZmjJ0YNyBUIYmVGNcCbnRX vwcAKHxqXf3usNB0jSqJogSZUgPu6iv5G7/bfMcdkyMiwi682NDQtObjTwAgf9DAHizrGzdvHpI/ ZMzYsT7CKJ/c+M+2rRs2bZo14/ZrpvWkoFdqOk3RMkKyLAHAqdPVCCM60L6VIHu0vHHb9i2XXB83 euLtd0z/4vN/dfaHWt4oyN3dpsJcvcpxbn2PvxF+sqLiuuEjG5oayquq0pIS8WURHDp6dOTw63Q6 /ZHDBwOQcdGhw0sWP+U7zKv/+3J3W+P9u34YOX6ap8XZfuD7FqmpqbIsO51KMAnjRsAV4pmJfPzj AKeFM/8KznZTlNpcQsXGDLZYDtO0qOIpmlYjuR9AVxvSiIiw0tKL6rCsrD4hweo1H3+iiPlyrh85 4o7pt/pljpra2kNHSp5/7nmMAXdSQNo3PRs1eszSl148fbomIcHvBXANDQ1fbvjW5W4Zlp8/fOiQ DsMcKSn59t/f6bTayZNuSk1O7pEyijEIkiBKgtvjDtIHXavePEb1pc8ioTJ33EeF39zQs/euqT17 y9Rh2XTO2o8/TE1KuuRdA6fLVXX6zHXXjW1pcdU3NgQcS/HBAopmGIamaYZhGFmWGYahKZpm6LSU 9B5wqkeOn3bJgW80Go2iYQwYYxdgMwX1ACCcWQYAQRn7KUoNAMHZFsuxYBMAeHfSsYsR0lOUFeNQ P7wd+0V7w+3bt2f8DZ06HtXVZ37esdNfGX/86edz597LMKwkSZ1IGDBGyr8sw82+6+5PPv/sqb8s 9tfuP/78S1RMjF6v/3XvPqvVOmnihEtdr+KDGzdvGTJkiCiI/9rw7ZLHH70K1cQz6hbBNm70xMt/ 5aMpVpxqnlEHKmFP7cH5WkNI8pBlNKPu8YdytbTISNbp9f379N9/oCg/bwDG5/dqLjhQNGH8RKCo FpejxR24Q8GwbHHxQYqmaYqiKRphRFM0RVNDhw7rmb5xe/N7xXb40jYKALAI2CrWzNGn/chyw2n6 /M69FGUK6Y0BQBTKHCez2JhXKcril4xdLjcAnD3bbDY72pRc2mHIkBDD+PGj9u4t9m+wxO2pq68P NpmqqisuzLn2B5RlGSHU9i8CwKGhEbXn6kRJ4lj/uicOpytYpbbZbLm5uYcPHwGAC5WsaDg/P9/h sOu0eo4LpO+DMW5obPZ6vUD58jtrzp6jKQoAAFNqtSo8LJRSTrvAzekPzf4k/pIhLoqm35y1wyrV 1AqHSlzfjTYtevWrB72i6/IhrgAeShaazhTOjkgaE5N1JyUdAnoAwxmqd0/gtIka46DghNk0193N 0zxer+B1i4Kmf/8BH6z5oHd2lk7d+k2vxuZmGUFMdExzU70syxQV+EQPy7L5+fkMzdAM3S5jhmFo mukZGfvrVMOFux9jL0ALG7fUWT4mOLvjncAdJ7P4+NsxagHKHEiPyOzwXdMDwO13TMf+95kwxhRF MQyNkLxz169VVVU+AsdEx/Tv31cZHKL8f4qbJtzw7qoPBg8abLPZcnNyjhw9r+QDRcUbt3w/ePBg h92uNxj2798/Y9otARjK6WpRqVTRMdEAQNM0Qqjs5LHLg8XHxSGEeZ4HAHNzs6ulRa/TdTGKSekP JsmjPF5p+PCRFEWpVGqPxz3zs7DLQy6fcgi51aGhoQDw8y8/mYL02el+u46Cs+J04ayUvEdD40eA UAjgBflU3uRNkmAT3HXWmh0VO0YGJ94dlvZIQHnSitFgcDocSJbCI2OvH3ndjl27bhw3TqnW9+zb P2nS1JYWp9fjbjJbYqIiA45FkqTi4sLfsDX216kGAIxQ+6doaEoCkADA63lapf7bJVW7JJW0daFl CpC/Mms/tlg6HUkODg4FAIQQDmj0Q6kOq6qqZtx+G0VRCCFJkiRJkmVZakWWJPHX3Xv69Mlh6ACL S2xM9H1zZn+wdl1e3kCrzZqdlX3kWAkAREdGfvPd5kEDB9msVr0haO/evVMn3ZSdlRnI+JMgGIKC MEbHT5a1PRp9cW5QQEFFVbly0is1Q6PVtjid0GUZA0BjY3O//nmSJN61vrVMqzkdQ3HtXUqKolmK W/BtX+V0za21yUkpx4+XZGf4J2OP7cjpghmZI5bqg+NE6xqljAEcBqCA0qkYU1SvERFpN1UXv3n2 0ILo3DcDnoLK6Z1dUlo6KC/P6bBlpGft3be39lxdTFREVfWpxIQkg15fc6ZKo9EeOHho1B+GXL2t cQBOtfLhLkAYA4uBAWC4+Bktla/ymYsp5iK32X48h4vLBmApikE4yC8NI4QQai0cBw8e7CzkqFFj uuNTtc++UBSlONJbvt/qdrsBQK1WDckfgpCMEAKAbrpVyYmJipL79etvtVkyMzIPHS3Zs79gYN5A m92q1xkK9u+fevNNef37BXZ/URQ5jgOgQkNCbXZbQnwiTTOyfH4GxSt4EuOTAOMztWd0Wj3GiGVZ QfTvayoWm8VkCgagpmQ/+supj/9y82otZ7JKNbitjq7y7Fly63tIRm98tzAvdKpX8BqCDFab1e9+ uD5FbUhvOvWNSjPVa/sRCeUd5B2fFt97wemjXzaUPRuZ/XJgdktPTT16rNRstgRjpNUZxowe++P2 /0ybfPOBQ4dn/fHO5qZ6tVpTVnY8LMSkOBcBypjhCgsLaZqmKIqiKYwwRVM0RQ8fPkIJsOqjdReG 9z0F1TNONWCEQfkOHINoNQ0ajPQAQNFBHXVBVRqNDmMtxsF+aViWUHvh6NevXxfV2B3kVqSLT2VF DLIs4+5Nd7Qp+aPc3L4WqyU9PZ0Cymaz6vT6wgOF026eFLCGAUAUJYZhMMYhwWEer/fsudrYmDgZ yXRb1SPJEo/RubpzPMcHm0JkGbEsK4qSX3WrzW7XanWiKEzOXHTKUvLeT39ZMOZNh1yvoVsnk5vF yngub81PS2M1uTemPerxeIxBRqvV5nf1yujiB395tvhP1Yf+mdT3YcH6gew9zOknsvrJgMxIPIPE Gsm929v8enzvRUd+fD444V5enxqA3SiKGjVi+NYff7pxwgS71Rwfl6DVaLf/snPY0KGyJEqSZLPb y04enzb55u5kPcuyw/4wjKFpmmZomkFIpmmGoen2cnvJpJTvKagecqoBY4QBMAYVoCBEmSg6BAAo isPYbTmmBQBTZgPNhAOA06nXqMMwDgUI7bplKYpC+Lyf3JXWuOtDNZ31XhTFXjdypCAIkiQDYIQU XSutMVKa5W4r+e73Vq/p26dPXd05hHBkRGRxcdGtkyfnDejfnQqIZdl2hyIyPOrM2dNNTY3h4ZEI yUo1p1XrmpobEULRkTFtX8Oi2ic/utb9dhkMRpqmgAKP231f3+Uv/Tphw4G3Zg7+H7tcBwAa2tRP N33j/lWyG+YMfENGsixLNE1rNBpni0uv1fkpMC62/4fnSv5cXrgqNe8+yrGe099w9OdXGE6jD44z hEQZo54TLKsl54awmCDL6dUBN8ghwcGD8vpt27594rjxsiznZOcWFB9ITk4zN56TJHnHrl1jR13H c1x38p1hmf379lMUTbf1y0aMGKEoWTktKT3S9SmoHnKqz3+ajAGIpulmCtnpmEfMJRQAqBJyKUq0 lkUAgDrxIcvx6tCQSCT3w5j3q46UJKldxldsjbsDQigpMfFfX33tI0x4eHj3W2OFpmYzxzCo9W5Y lCSVSn2uvr479xRFUa1WKwMEFEUhJMdExp45e4rn+eDgEFEUtWqd1WZtcbfERscjhClKWdOCVCpe lKQuythitcXGxoqiiBCiGcbtbnks78sX94yOCf58fMa954Rj/fXTd5Z+U3m25Im8raIo0jSNMYiS FBEZZbPZ/JWxMg4enbOs8cTLJ/a83Sv/AYo2SB5zbL+vBVdlbflya31RXMZw0f4lz1nsDUXdMWBa corL2fLLzh3jxo4tO1l2+/TbCwv3JSclbftx2+CBA8K74U63OdXsiBEjKJphaPr8vDFNt8u4uLi4 64NePeBUOxwOJMvKEBdgjLEayfGYcdMYUVGLaKpZklwAIh2Tg5EOYaPFYsSJuRhHBtA8KqrxeNz3 P3C/78Dr128CgPkPP/riM09HhIf55SjKspyfP3jgwIFKN/iSeaYLLsjdb40LDhR98+/veqX1slgt QUFGjLHDYU9MTCwoKr5kFsq/8S1R4nkeYySKokqlFgSMsRQdEXO2rlat1qhUarvdarNZoyJiAGOg KIqiZVlCCHEcL4qSWqXqSixWmy0iPFIUBYvFHBkVg2TZ6/QuzP30tYJbkkJyM0LH/lS1dnfJ1kdy 1yMJsyqa43mHw+71esPCQq1We2x0TGBPF57+lLk6vHTX6xnDFgIAr0tWGTL1EaNP7Z3SfHqrIcjC 8SC6z3Uza/rm5ri97u+2bDZbrG63p/BA4cnyk1mZ6alJSd2vu2Nj4n0HGDp0aNcHvXrIqW5TMFb8 XhwmI56leYrWUVQoBS6gZIxpjDQcn3yyHA/ok931B/74089lWX7rrQ/b4oKHH33kponj8wbmCoLT 7RFaxYYQQkiWZCTLoiioNVqXU/zq6+/8ndFVtPrr7t21tbU+goWGhKampnSzNd5/4MCGjf9OTUmx 2qx6nb6srExGqFdamt3uSIiP31d4IGAli6Ko0WplGcmy7Ha71Wo1x3GiKESGR9WePRMaGt7U1BgR FsnzPMsyCGFBEDDGsox4Fd/icnUxFpvNmpiU6vF4XG7XmZrq2Oh4kynEbNbPy1n99rY/Tc67f1Ph mnszV0Yak3U6vSgKTc1Nkix5PO7QkLCKiuPdMV1I0p8YLqRsx1/ltkWdFK1WBWVJnq8gCLweUOlT Aruz62R52VPPmvcXAEVhWVKHh6W9/eb6Lz/TaLR4+0818x46p1JRCJkG9Et/6YWgnN7+3n9wXt4V 11rSNL1nz55utcYBONWKenHrBLLyn16Q+wBYaNpCU2YMMpINMgrS6HLsjlP+tVeFRU8/vZBjOQpA xgghhBEWBMFsrpNkWRQlJCOkDGO39lyRKAgMw1ltNllGLOvfCD7GGCG5trZWmXACAEkURUmSZURR GGMQRVEQhJ9+/iUpKbF95DxQDW9KSEiy2W1arb6isuLWqVPCQkPe/3BNQnyiw+GIi4ssfkzbAAAP gUlEQVTbW1AQmJK9Xi/LsqIoms1mk8nkcjlpmuF5zmg0UTR1ru5sVGS0XmeQZdnrFZQRO7vdHhIS wvO819vVN5bO1tUPH2G0WM27du4cNHjw8ZPHtFpdsCl0WNZErHt9xe4F8/LfHBQ91uF0NDTUudwt GOPDhw6PGDEiLDT8XF1DNxs0Y+ytDGeqKW71y7Ds9lgKdREAAC12Tm0aGogX09xcMHV6dlqvAePG 0TQNNH2ytLRm4aOmxY+hgoKMguLsKVMohDBAU2Nj8e0zB2/brImN9a+Rz+nd92Lxr/po3ZLFTx0r O0LTTGBTUN1yqttdSnRhe6z0sQABpjEOkWUT4CSkLGNEGEDlr2UphpJl5HZbZVnCGGSEMMIIIaXy EAVBlmWEsYwQlls9Xa9X0CPkcrUYDDqGCaA1li8cIdv03XderwAAPM+NHjVKkiRZRm0j1QHOTp84 WfHVNxvj4uLsdptWq6uqqpo2eVK/3FwMeO7su1Z/tC42Js7pdERHxezeV2DQ668bMdzPDojMsqzb 7Q4KMnIc7/V6ZBmJrZNJVHRkDMbQvoRWmeLV6/UMw3AcJ0lyVxsul0uv15+uqe4/oH9IcEhd/Tmv 12s2N2MALU5blLUF23CJ7cgFq4MgMytDq9UHB4e6nK4A7KZU1liZFcGYM42IGfCpIEgUjesOzeFV dUEh4HaBzapK6XNfAPev/Puy9JQU3XXX/bRiheByUSw76eabT371FVN9il/z8YDnn9+2bp3r1ClO rc7IzMxOzyh/8eXcle/0yIjMFaegfiunWmqfhERtH7RX7AvovJwvUjd2uZxKZnR9Qkir1WKMPR4v xq290rYmGcuyLHgFSVaaYxkhjGQky7LX68UhobU151KSklnO79b4wslVAGAYFkAAAIZh2pZ/SACg xBhYnv2ya2d0dLTD7tDqNGfOnBo/ZkxifHxDYwPGWKfRTp869YuvN0RHRztdzqioqO2/7PBXxkAB xthoNLbbsOtD3F2vmDCFEUL9+7a+YZaS3Kur+m9x+Vv9SZLUms2y3F4EAAC4dLdHslc/A2JRTJpX 8EBtlTYi868sFxJAvjRv/zm5d+9dy5cP37tTFRG+JSoBI0RR1MgHH9j33gfeoiJvXd31JcU0wM6B w0Zef33Jjl09NbB6xSmo38qpFrxeAHC5nKi9KW7zr5V+ctu7BLjtdQLc7vKpVKouKpnneVGUJEny eFoAqDbvGSGMJBmJXkFGiiN83qkWBC8GcLpajEHBglfkOb7rGm5vjZUxXgCYOGGCJIntSJKkNGtK axxYnpmMpsamCo5X1dTUjho5PCEurqm5WZZkDJimaK1GM2nCDRs3b4mIiHC73SHBfpdIjUZdVVV1 yYxbu2OizIQzDMswtCAI7QfK0IOK7+pUSlxM3D/e+sclS9l0bYvAXC6XcqrVahsbG9sPlHo8PCTU r4pVWUUny0iSpbbhxVZXiMItrrqv0vshhxXOnVKHpCzRRUxuzz7/WntZoiiK4XnJ4ZDsdpVWKwiC ymQSRIEPC/V4PJxW6zhRro+PpWkaKAohuadkfMUpqN/KqRbbXgPCSGmB22XbpusLXgkCjNsXb7S4 3RzHKbPBV26N1ZpNm36cOmWC1XoCY4xkxadu1ZsgiLKMcOsiL+XtBVkUZSSDxWJNS0zxCl496Pxt jSMjI9d/8eUlM14XngYFGdtXlTU3myMjI/zKs/FjRouSWN/QcPONE2Kjoj1eryAIXq+AkMxxHMY4 PCxsxrRpBw8fxhhNvulGf8uEmldVVFSybOurWlSrr0bRNE3RlEGvd7la9Hqd1WozGo02WxMAGI1G q9UqiVJKcldHYmMiIl55fbkxSGezuyiK4jmW51mWYXmW5Xk2Kyvj5MnyrKzMgsLiAQP6/rDtZwAY 0L/PvsIih83x+IL7/cmU8+v2McZK7kuyhBCmaYqhtZy2V/mhSpoNCs9ZoTH1C/i7gkF9cm2Njbnp 6bvHTWRV6t59+9bV15vGjeF5VeiNE+sKi3Kzsg7PnSe2uPpm925patL6vzI84Cmo38qpZto63yzH +5VijBB0eXnGXTNnvPf+B8XFR7Q6Fiktb1slISNAiAGglVlPmsZAsTTCDIO/3bhVxasTEuI51o9p esVZS03plZKcptQMSqFpH4Rvu9b6nyxLGOPG5iZ/ZWw0Bt12yy0ej8creAWvwPG8Rq2WFbMAsCzL cmxIcHBaWopOpwtgpYHUtvKMZVloWz/TegAUxqDVamUZGQwGhJDBoMcArV0JCro+i+ZwtFbxxiAd TVHK+AxFUwxNUzQlI5SSmuLxeHJysrxeb07vDIzB4/UCAKJoryB0tX9AUQxDY2BYAIqSlcqIQQyH 2PYQ6pzPsVin0iUxDMOyHMsygS3+Cbp3zuG59w/s02f86DGyLJ89d66suirxpec4jk1d9Mje4aPT YmNHDhrEcJzNai08dCj4ry84HE6DQf87TEH9Vk61SsXHRUV/9PEav6KMCo/gOL7rVg4LC7tlyuSV H67x99luHD8yJSVJp+tqt1CjUUeER7y49IXO2+mOWqToaJYJZBU+x7Ecp9djneIxKs6FUkZpmqZp JuCyqNSwOq3W1dLS4W89nk5flNWoNWq1uusWS0tJLq/s+FWw07WdjkUnJsTHx/kxwEvTNE/TmGVR 21T+ha++tBqNyVB6kt1ZvRfev5/zb0uL/neZWFCAWZbrkxP+zlvB8XEajQY0mtwNX5x44aWyX3/F Xi8dFqr986OhQ/K7v4tTV6agNBpfmXL+G04BgDEWRNHpdDocTrfbLXgFGcmiKMoyUpopZXmQ0hSo VCqdTmcwGAwGvUGvZxg/yqjSL2rfduPCA8WtbXUZKar9oizLLMtotVq/FlebLRahy61Ea0NB00BR Yd1e1tOzCKIoeL2iJClNK0ZIKfPtdruks6CMjlIUxXM8z3Oqri3/aGpqamhsslisoihijJXlq22D yefziGrz5xmGYVmW5zmT0RgeHh4dHQVXJaIoiaKAMWY5TsXzHdqWpmmVStWz+8AFTLdkTCAQrgbI PtUEApExgUAgMiYQCETGBAKRMYFAuMY5P+FZUXaQmINAuBY5P+GU2z+/wxBhMXHETAQCcaoJBAKR MYFA+P9Exn978fkbxo3976Zhzp2zHrp/3u8T14q33vhgxTsb1n+m63yD+N8zPb+13Xoqf++4bfrT S/5y9ZS37qfnyjKOCA/fsP6zuLadSoYNyf/sozVAuAp44OFHl/zPM8QOBOJUEwjXPN36TLnRGPTg vHm5Ob0lSdqzb//qNWsFUVz17vK3333v8NGj7cFuvnFi3z59Xnrl1Q5vkj9o4N13zpJlmeO4Xbv3 3HjD+KKDB1974y3Fafl5566t2/6jOB4J8fF/X/YP5a+GDB58+/Rp0ZGRHq930+YtX3/zbavvEBb2 t6UvpCYnV1RWvvz31xzOTt+19JHODp+rs+cFgNiYmIUPzE9MiD915kxTU5Pb5/cyA4jXhx06xK/0 AMD4MaNvvunG8LAwQRD27N+/eu06H695/T528yt/AygnCiqVasnjj9mdjrfeWeH7LeurLT092Rov nD8fY3TPvPkLHlvUKzVlxvRbAeBYaVlG+kV7MmWk9zpWWurjPjzPP77kycamprCw0HvnPzgoL89o 9PV5p97ZWY8uePDT9V/MnDN3wWOLLtyJdvCggcvefPveBx5SqdU3TvD1AWsf6ezwuXxcX/TIwqrq 6ln33PvBh2sH5eX5NloA8fqLX+kBAI/Xu+yt5XfMnrNw0Z/T03rdOnXKf9duPZK/vu8DABqN5tkn n6hvaHhz+bu+NXO1pSdwGb+y9IV1q1etW71qwQPz22uO/v36fv3tRq/Xa7fbt/ywbeiQfAAoPX48 PS0NAJ5+Ysmdf7wDANLTeh0rLfNx84aGRlEU6+rqa2vPuj0eq80WYgr22XqM2bV7T+GBIoyxy+Xa V1DY/qufd+xsbGpyOBwHiorjfO482lk6O3uuzq5HhIcnJyVt2LgJIVReWVlYdIWPKvsbr7/4mx4A 2LHr16rqagCw2ey79+5NSkz879qtR/LX9310et2Lzz5ttlhWrv7wipv7XW3p6YaMX1v22OIljy1e snbdx8oVvU5H07TF0vppPLPFEmQwKLV1eq80lUplMhpzsrODTSajMai8stLHzWUkg7KzLELKAc34 SlhoSEh9Q8c7Szjbtkq/4oeIOktnZ8/V2XXlX7PFolxvNpuv2Br7Fa+/+JseAOjXt8/S5555/523 31/+9uSbbvL9VfTfwW49kr++75OZnl5f39A3Nyck+MrfA7za0hO4jK02W7PZ3Gw2t6fb6XIhhIKD TcppSHCw3eEAgDM1NTTNjB11fVFxsc1uHzYk/0R5uSRJfiWLAgoApAtMc+EWrc1mc2REBHSPztLZ 2XN1dt3msCuFVblu0Ot7Nl4fdgBQ9jGECz9b7G96tFrtU4v//P0P2+5f8PC8BQs3bd7sezu638Fu /uZvAOWk+OCh1954c/+BoscfWXjF/WGutvT0ZN/Y6/UePHz4lsk38zwfFBQ0cfy4vfsLAABjXHb8 +LSpUwqLiw8UFU2bOqW0LMDPfJw9V5edmQEAGrV68MDzfadt27eP+MOwgXkDKIrSaDRd6f5dTmfp 7Oy5Orve2NhUWVU9csRwADAZjVf8DKK/8fqwAwDYbDan09UnN6f9ir/p4TmOZdnKqmqMsV6vG/GH Yf91u/mbvwGUE6Xz+cGatSaj8Y7bpvu+/9WWnh4e4lq+YiXDMGtXrXznH8vKKyo///JfrX5XWRnL MOUVlYVFxSHBwcfKygK7/9fffBsbE/v2stcee3jBhXXB0ZJjb727YtaMGZ+u/XDFm28kxAe46ruz dHb2XJ1df/3Nt4bmD379lb/9+bFHyssrejzezuygZP/K1avnzb3ns4/WzJ41M4D0WG22tR9/8uxT T7z8wvOPLlhQdvzEf91u/uZvwOXE4/G8/ubbUybd1Ccnx8f9r7b0dOC9klcjCIRrHbL8g0AgMiYQ CETGBAKByJhAIDImEAhExgQCgciYQCAQGRMIRMYEAoHImEAgEBkTCAQiYwKByJhAIBAZEwgEImMC gUBkTCAQGRMIBCJjAoFAZEwgEIiMCQQiYwKBQGRMIBCIjAkEApExgUBkTCAQiIwJBAKRMYFAIDIm EIiMCQQCkTGBQCAyJhAIRMYEApExgUAgMiYQCETGBAKByJhAIDImEAhExgQCgciYQCAQGRMIRMYE AoHImEAgEBkTCAQiYwKByJhAIBAZEwgEImMCgUBkTCD8/8P/Axnv+MuYA70LAAAAAElFTkSuQmCC --=-=-= I don't know how easy it is to add subpixel averaging. Could someone who has been working on this part of the code enlighten me? Even if you are too busy to work on this yourself, I'd appreciate it if you could point me to the right starting place for adding this feature. --=-=-=--