From ambite@ISI.EDU Tue May 6 03:47:48 2003 From: ambite@ISI.EDU (Jose-Luis Ambite) Date: Mon, 05 May 2003 19:47:48 -0700 Subject: [KP] Ram Nevatia Seminar Friday 2003.5.16 Message-ID: <200305060247.h462lmA18863@altamira.isi.edu> Hi, Yigal Arens asked me to encourage you to attend Ram Nevatia's AI Seminar on Friday 2003.5.16. Ram's methods for deducing that an event is taking place from (probably) recognizing elements of it may be used for cognitive networking applications. You'll see the ISI-wide announcement shortly, but we wanted to give you advance notice. Also, if you want to meet with Ram Nevatia Friday afternoon, please send a message to Erika Barragán-Nuñez (erikabn@isi.edu) that will coordinate his schedule. Best, Jose Luis -- Jose Luis Ambite, Ph.D. Tel: (310) 448-8472 USC/Information Sciences Institute Fax: (310) 822-0751 4676 Admiralty Way ambite@isi.edu Marina del Rey, CA 90292, USA http://www.isi.edu/~ambite/ ---------------------------------------------------------------------- AI Seminar: Ram Nevatia Friday, 2003.5.16 Title: Event Recognition in Video Streams Abstract: It is important to recognize events of interest in dynamic environments for many applications. Video cameras provide a common sensor to view common physical environments. This talk will describe recent work at the computer vision laboratory of USC on automatic recognition of events from video streams. The task is made complicated by the ambiguities inherent in imaging sensors and by the allowed variations, in duration and in style, for the same event. The methods we have deveoped are based on stochastic finite state automata, similar to the hidden Markov model machines used in speech recognition, and based on rigorous Bayesian reasoning. We have also developed an early version of an "event representation language" to simplify the task of specifying desired events for recogntion. This talk will focus on the event recognition methods and touch on the image analysis aspects only lightly. The methods used for higher-level, complex event recognition are relatively independent of sensor modality and should apply to outputs of inferences from a variety of sensors as well. Bio: R. Nevatia recived his Ph.D. from Stanford University. He has been on USC faculty since 1975 and currently is Professor of Computer Science and Electrical Engineering. He is also director of the Institute for Robotics and Intelligent Systems on campus. He teaches courses in computer vision and in artificial intelligence. He has worked on many aspects of computer vision including early processing, object description and recognition and more recently on event recognition. From peyman@MIT.EDU Thu May 15 20:31:02 2003 From: peyman@MIT.EDU (Peyman Faratin) Date: Thu, 15 May 2003 15:31:02 -0400 Subject: [KP] KP distribution list Message-ID: Hi, can you please put me and Steve Bauer peyman@mit.edu bauer@MIT.EDU on the distribution list of the KP? Thank you Peyman Faratin -- ______________________________________________________________________________ Peyman Faratin: MIT, Laboratory for Computer Science, 200 Technology Square, NE43-534, Cambridge, MA, 02139 Tel: +1 (617) 258-0458, Fax: +1 (617) 253-2673 http://ana.lcs.mit.edu/peyman/ email: peyman@mit.edu ______________________________________________________________________________ From chrisramming@yahoo.com Fri May 16 10:56:30 2003 From: chrisramming@yahoo.com (J. Christopher Ramming) Date: Fri, 16 May 2003 02:56:30 -0700 (PDT) Subject: [KP] experimental design for the knowledge plane Message-ID: <20030516095630.54787.qmail@web41511.mail.yahoo.com> Folks, Given that experimental design and metrics are crucial aspects of defining the Knowledge Plane program, I would like to invite some brainstorming on how best to do approach these topics. Momentarily setting aside the difficult question of how to assess progress in learning and reasoning, my goal in this mail is to generate discussion about how to conduct experiments in the open Internet (a realistic and meaningful environment) instead of a purely artificial environment such as a testbed. Of course, the problem with real-world experiments is in accounting for control groups and in controlling the independent variables. Yet there have been very interesting and worthwhile experiments conducted on the open Internet revolving around ideas like BGP beacons [1] (maybe there are some examples around honeynets [2] as well). I would like to invite comment on experimental design over the open Internet for a variant of the "Why?" program (detection, diagnosis, perhaps repair and prevention). The goal would be to exercise performance (in the machine learning sense) as well as learning and reasoning in distributed, nonstationary environments. Some research projects [3] have performed experiments via fault injection in cooperation with large ISPs, but there is a lot of overhead in establishing those relationships, and possibly some restrictions on the kinds of experiments that can be performed. An extrapolation for brainstorming purposes, but presumably within reach for a DARPA program, would be to consider whether and how our research communities could benefit from a "Distributed EMbedded Internet TESTbed" (DEMITEST?) for cognitive networking comprising a set of independent and artificial (e.g. completely controlled) autonomous systems scattered across the open Internet, and which could be used to conduct a wide variety of experiments involving fault injection, reconfiguration, etc to exercise the capabilities we're interested in developing. What would DEMITEST look like? What classes of experiment could it support? Might it be the basis for developing an ongoing challenge for the emerging area of cognitive networking in the vein of Robocup [5]? And what would that challenge problem look like? Thanks, Chris [1] http://www.psg.com/~zmao/BGPBeacon.html [2] http://project.honeynet.org/ [3] http://www.acm.org/sigcomm/sigcomm2000/conf/paper/sigcomm2000-5-2.pdf [4] http://www.robocup.org/ From chrisramming@yahoo.com Mon May 19 23:45:52 2003 From: chrisramming@yahoo.com (J. Christopher Ramming) Date: Mon, 19 May 2003 15:45:52 -0700 (PDT) Subject: [KP] clarifications re robocup of C.N. Message-ID: <20030519224552.75958.qmail@web41501.mail.yahoo.com> Folks, A few clarifications after today's discussions: 1. I did not mean to restrict thinking *solely* to adversarial challenges (e.g. where one team is injecting faults) although that's a neat idea. A challenge/game where several teams are competing to solve the same puzzle or problem would also be OK. For instance several teams might be trying to diagnose an injected fault (possibly following a configuration change to test reasoning in nonstationary environments); that might enable comparisons of learning vs. nonlearning approaches, or of distributed learning vs. localized learning, etc. 2. I did not mean to imply that the challenge needs to be of "real-world" difficulty or relevance, although that would be neat too. For a research program like cognitive networking, it might be OK to develop a challenge problem which is seemingly simple or irrelevant, but which nonetheless if solved is evidence that something powerful is behind the solution. The quantum computing challenge involving the factoring of 15 into 3 and 5 is not a problem of real-world consequence, but it does demonstrate some ability to perform arbitrary quantum computing that might inspire industry and academia. IBM also has a good history of inspiring challenges: deep blue, arranging atoms into the IBM logo, hosting the first olympic web site, et. All of these have implications beyond the demonstration, and serve I think as good models. 3. As noted, there are indeed risks when injecting certain kinds of faults in something like the DEMI testbed. It would of course be necessary to consider those risks and how to guard against them. 4. Also as noted, I am not arguing yet for or against building a testbed (as opposed to borrowing and adapting one as appropriate), but I do want to understand what one would be used for and what its characteristics would need to be. Thx, Chris From bsabata@iet.com Tue May 20 18:07:29 2003 From: bsabata@iet.com (Bikash Sabata) Date: Tue, 20 May 2003 10:07:29 -0700 Subject: [KP] clarifications re robocup of C.N. In-Reply-To: <20030519224552.75958.qmail@web41501.mail.yahoo.com> References: <20030519224552.75958.qmail@web41501.mail.yahoo.com> Message-ID: <3ECA60D1.5020907@iet.com> This is a multi-part message in MIME format. --------------070205000203030805010102 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Chris, This was the first email on the topic I saw on the list... To give everyone the context; at IET we are involved in evaluations of a few AI based programs at DARPA. The approach we have found to be most effective are the challenge problems. If you look at the slide attached with this email; it describes our laboratory for doing the evaluation for the Evidence Extraction and Link Discovery program. Briefly the program tries to discover links between sequence of events across different information sources to detect a high level intention. The application is in detecting terrorist activities. Some of the ideas in this program are related in spirit to the "why" problem -- the key difference being in the KP there is no deliberate attempt by anyone to fool the system. IET is the evaluator for the program and we have an approach based on creating Challenge Problems where the parameters of the CPs are controlled. We create synthetic datasets with controlled "complexity" and evaluate the different systems. 1. The testbed starts with the modeling of the world (the box titled "Agent/World System Specification"). The specific implementation is based on IET's extensions (with probabilities) to CyC to represent and model knowledge. The models are generative and therefore can be used in simulations to create scenarios. Note that an important aspect of the scenarios is that we specify high level plans (of terrorist and non-terrorist activities). The tasks associated with the high level objective are represented using Hierarchical task networks (HTNs). The particular instantiation of the detailed scenario is sampled from these models. 2. The entities are composed in a principled manner to describe a particular scenario with a set complexity. The synthetic world is then populated with instances of the agents which derive behaviors from the descriptions in the entity models. 3. The simulation generates the actual behaviors of the agents. The behavior is moderated with the relationship models between entities of this world. 4. The data is extracted from the simulated world to create the evidence dataset. 5. An additional noise process corrupts the evidence. The corrupted evidence is reported to the system that discovers the links. This is the system that is being evaluated. 6. At this point we can compare the different systems based on what was discovered. What was missed and what was incorrect. Note that the system has the same model of the world that the simulation used -- therefore not knowing is not an excuse. 7. The evaluation however, goes one extra step. We really do not care about the actual accuracy measures. The reason is that the impact of what happens as a result of the discovery is more important than the discovery itself. In other words there is different cost associated with the different links. Further it is the combination of discovered links that is more important than the individual links. All this is very difficult to capture quantitatively. Therefore, our approach has been to directly try to evaluate the impact of the decisions taken as a result of the discovery. Missed detections will lead to bad things happening (i.e., the terrorists will succeed) while false detections will lead to actions that will have social/economic impact (e.g., a false alarm can cause a major airport to shutdown which has a huge impact on the local and the national economy). To compute the impact of the decisions, the system creates a policy of what to do if different types of links are discovered. The actions are then fed back into the synthetic world simulation. The new situation is then evaluated using some economic and social metrics. These kinds of challenge problems may be of relevance to KP. On another note ... since you mention robocup in the subject line; there is another competition -- Trading Agents Competition ( http://ai.eecs.umich.edu/people/wellman/pubs/iaai02ext.pdf ). Briefly it is a competition with agents that participate in 5 different auctions to obtain a bundle of goods for the user at the best value. There are some very interesting lessons learned. For me the two most significant ones are 1. Learning based method does not necessarily does the best. Simple strategies (open loop -- with no feedback) work great many times. 2. Although this was framed as an intelligent multiagent problem, no one in the competition tried to model and learn the behavior of their adversaries. Bikash J. Christopher Ramming wrote: > Folks, > > A few clarifications after today's discussions: > > 1. I did not mean to restrict thinking *solely* to > adversarial challenges (e.g. where one team is > injecting faults) although that's a neat idea. > A challenge/game where several teams are > competing to solve the same puzzle or problem > would also be OK. For instance several teams might be > trying to diagnose an injected fault (possibly following a > configuration change to test > reasoning in nonstationary environments); > that might enable comparisons of learning > vs. nonlearning approaches, or of distributed > learning vs. localized learning, etc. > > 2. I did not mean to imply that the challenge > needs to be of "real-world" difficulty or > relevance, although that would be neat too. > For a research program like cognitive networking, > it might be OK to develop a challenge problem > which is seemingly simple or irrelevant, but > which nonetheless if solved is evidence that > something powerful is behind the solution. > The quantum computing challenge involving the > factoring of 15 into 3 and 5 is not a problem > of real-world consequence, but it does demonstrate > some ability to perform arbitrary quantum computing > that might inspire industry and academia. IBM > also has a good history of inspiring challenges: > deep blue, arranging atoms into the IBM logo, > hosting the first olympic web > site, et. All of these have implications beyond the > demonstration, and serve I think as good models. > > 3. As noted, there are indeed risks when injecting > certain kinds of faults in something like the > DEMI testbed. It would of course be necessary > to consider those risks and how to guard against > them. > > 4. Also as noted, I am not arguing yet for or against > building a testbed (as opposed to borrowing and adapting > one as appropriate), but I do want to understand what one would > be used for and what its characteristics > would need to be. > > Thx, > > Chris > _______________________________________________ > Know-plane mailing list > Know-plane@mailman.isi.edu > http://mailman.isi.edu/mailman/listinfo/know-plane > --------------070205000203030805010102 Content-Type: application/pdf; name="IET Evaluations.pdf" Content-Transfer-Encoding: base64 Content-Disposition: inline; filename="IET Evaluations.pdf" JVBERi0xLjMNJeLjz9MNCjYgMCBvYmoNPDwgDS9MaW5lYXJpemVkIDEgDS9PIDggDS9IIFsg MTEzMyAyMjIgXSANL0wgNjAwMTYgDS9FIDU4MTk3IA0vTiAxIA0vVCA1OTc3OSANPj4gDWVu ZG9iag0gICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICB4cmVmDTYgMzUgDTAwMDAwMDAwMTYgMDAwMDAgbg0KMDAwMDAwMTA0NCAw MDAwMCBuDQowMDAwMDAxMzU1IDAwMDAwIG4NCjAwMDAwMDE1NjIgMDAwMDAgbg0KMDAwMDAw MTc1NiAwMDAwMCBuDQowMDAwMDAxOTcwIDAwMDAwIG4NCjAwMDAwMDIxNTAgMDAwMDAgbg0K MDAwMDAwMjM1NSAwMDAwMCBuDQowMDAwMDAyNzc4IDAwMDAwIG4NCjAwMDAwMDI4MTcgMDAw MDAgbg0KMDAwMDAwMjgzOCAwMDAwMCBuDQowMDAwMDAzNjY4IDAwMDAwIG4NCjAwMDAwMDM2 ODkgMDAwMDAgbg0KMDAwMDAwNDI4MSAwMDAwMCBuDQowMDAwMDA0MzAyIDAwMDAwIG4NCjAw MDAwMDQ5MDMgMDAwMDAgbg0KMDAwMDAwNDkyNCAwMDAwMCBuDQowMDAwMDA1NTIxIDAwMDAw IG4NCjAwMDAwMDU1NDIgMDAwMDAgbg0KMDAwMDAwNjE5NiAwMDAwMCBuDQowMDAwMDA2NjUw IDAwMDAwIG4NCjAwMDAwMDY4ODQgMDAwMDAgbg0KMDAwMDAwNjkwNSAwMDAwMCBuDQowMDAw MDA3NzcyIDAwMDAwIG4NCjAwMDAwMDc3OTMgMDAwMDAgbg0KMDAwMDAwODgwOSAwMDAwMCBu DQowMDAwMDA4ODMwIDAwMDAwIG4NCjAwMDAwMDk4MTMgMDAwMDAgbg0KMDAwMDAxMjQ5MCAw MDAwMCBuDQowMDAwMDE5Nzc4IDAwMDAwIG4NCjAwMDAwMzM3ODYgMDAwMDAgbg0KMDAwMDA1 MDYwMiAwMDAwMCBuDQowMDAwMDU4MDg5IDAwMDAwIG4NCjAwMDAwMDExMzMgMDAwMDAgbg0K MDAwMDAwMTMzNCAwMDAwMCBuDQp0cmFpbGVyDTw8DS9TaXplIDQxDS9JbmZvIDQgMCBSIA0v Um9vdCA3IDAgUiANL1ByZXYgNTk3NzAgDS9JRFs8ODU4NTExMDhhYTYwNDgwODM4ZDcwNzYz ZDcxMTNmYzY+PDUzOWEyOGFlYjJhY2M2NDU1ZDIxNWRmY2M5ZjA2NGNlPl0NPj4Nc3RhcnR4 cmVmDTANJSVFT0YNICAgICANNyAwIG9iag08PCANL1R5cGUgL0NhdGFsb2cgDS9QYWdlcyAz IDAgUiANL01ldGFkYXRhIDUgMCBSIA0vUGFnZUxhYmVscyAyIDAgUiANPj4gDWVuZG9iag0z OSAwIG9iag08PCAvUyAzNiAvTCAxMjMgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA0 MCAwIFIgPj4gDXN0cmVhbQ0KSIliYGBQZGBgyWUAgrtvGLABDigtAMTyUMzAIMrAz1jCtIWN g/ODOENKAycDA1sQA4PgDQYGjwYGRgWgmZ/YjrCtYGDIPMDA+LGBgb1Fs+GxdJaFcbpXg0wD 1EhGBoZHkUCaCYg9AAIMACSXE5kNZW5kc3RyZWFtDWVuZG9iag00MCAwIG9iag0xMTAgDWVu ZG9iag04IDAgb2JqDTw8IA0vVHlwZSAvUGFnZSANL1BhcmVudCAzIDAgUiANL1Jlc291cmNl cyA5IDAgUiANL0NvbnRlbnRzIFsgMTYgMCBSIDE4IDAgUiAyMCAwIFIgMjIgMCBSIDI0IDAg UiAyOCAwIFIgMzAgMCBSIDMyIDAgUiBdIA0vUm90YXRlIDkwIA0vTWVkaWFCb3ggWyAwIDAg NjEyIDc5MiBdIA0vQ3JvcEJveCBbIDM2IDM2IDU3NiA3NTYgXSANPj4gDWVuZG9iag05IDAg b2JqDTw8IA0vUHJvY1NldCBbIC9QREYgL1RleHQgL0ltYWdlQyBdIA0vRm9udCA8PCAvVFQy IDExIDAgUiAvVFQ0IDEzIDAgUiAvVFQ2IDI1IDAgUiA+PiANL1hPYmplY3QgPDwgL0ltMSAz NyAwIFIgPj4gDS9FeHRHU3RhdGUgPDwgL0dTMSAzOCAwIFIgPj4gDS9Db2xvclNwYWNlIDw8 IC9DczYgMTQgMCBSID4+IA0+PiANZW5kb2JqDTEwIDAgb2JqDTw8IA0vVHlwZSAvRm9udERl c2NyaXB0b3IgDS9Bc2NlbnQgOTA1IA0vQ2FwSGVpZ2h0IDcxOCANL0Rlc2NlbnQgLTIxMSAN L0ZsYWdzIDMyIA0vRm9udEJCb3ggWyAtNjI4IC0zNzYgMjAzNCAxMDEwIF0gDS9Gb250TmFt ZSAvRUZOSkdKK0FyaWFsLEJvbGQgDS9JdGFsaWNBbmdsZSAwIA0vU3RlbVYgMTQ0IA0vRm9u dEZpbGUyIDM1IDAgUiANPj4gDWVuZG9iag0xMSAwIG9iag08PCANL1R5cGUgL0ZvbnQgDS9T dWJ0eXBlIC9UcnVlVHlwZSANL0ZpcnN0Q2hhciA1NCANL0xhc3RDaGFyIDU0IA0vV2lkdGhz IFsgNTU2IF0gDS9FbmNvZGluZyAvV2luQW5zaUVuY29kaW5nIA0vQmFzZUZvbnQgL0VGTkdM TytBcmlhbCANL0ZvbnREZXNjcmlwdG9yIDEyIDAgUiANPj4gDWVuZG9iag0xMiAwIG9iag08 PCANL1R5cGUgL0ZvbnREZXNjcmlwdG9yIA0vQXNjZW50IDkwNSANL0NhcEhlaWdodCAwIA0v RGVzY2VudCAtMjExIA0vRmxhZ3MgMzIgDS9Gb250QkJveCBbIC02NjUgLTMyNSAyMDI4IDEw MDYgXSANL0ZvbnROYW1lIC9FRk5HTE8rQXJpYWwgDS9JdGFsaWNBbmdsZSAwIA0vU3RlbVYg MCANL0ZvbnRGaWxlMiAzNCAwIFIgDT4+IA1lbmRvYmoNMTMgMCBvYmoNPDwgDS9UeXBlIC9G b250IA0vU3VidHlwZSAvVHJ1ZVR5cGUgDS9GaXJzdENoYXIgMzIgDS9MYXN0Q2hhciAxMjEg DS9XaWR0aHMgWyAyNzggMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAw IDAgMCAwIDAgMCAzMzMgMCAwIDAgMCANMCAwIDAgMCAwIDcyMiA2NjcgNjExIDc3OCAwIDI3 OCAwIDAgNjExIDAgMCA3NzggNjY3IDAgMCAwIDYxMSA3MjIgDTAgMCAwIDAgMCAwIDAgMCAw IDAgMCA1NTYgNjExIDU1NiAwIDU1NiAzMzMgMCAwIDI3OCAwIDAgMjc4IDg4OSANNjExIDYx MSA2MTEgMCAzODkgNTU2IDMzMyA2MTEgNTU2IDAgMCA1NTYgXSANL0VuY29kaW5nIC9XaW5B bnNpRW5jb2RpbmcgDS9CYXNlRm9udCAvRUZOSkdKK0FyaWFsLEJvbGQgDS9Gb250RGVzY3Jp cHRvciAxMCAwIFIgDT4+IA1lbmRvYmoNMTQgMCBvYmoNWyANL0lDQ0Jhc2VkIDMzIDAgUiAN XQ1lbmRvYmoNMTUgMCBvYmoNNzUyIA1lbmRvYmoNMTYgMCBvYmoNPDwgL0ZpbHRlciAvRmxh dGVEZWNvZGUgL0xlbmd0aCAxNSAwIFIgPj4gDXN0cmVhbQ0KSImUlVlP20AQgN/3V8xjghSz u96zj6UUgdQDxVUfEA9VGloQCSVBRfz7zuxh7zrupUiDJ85888344JEJuAXWGrDagFYcFlZy 2K3Z5yPYsuOTvYHVHkT47Ff4zdlSwLf9VMUNu2SPf+Gl6kfGQegWFto30gEHbxurAMtWG3Z8 vhHw5gFpl70Ab5RqMXKuMGpvKpnQU3DetA5si7+SsAm5JAe4Z4j3Lh26RuQf3bPvdKqswrQv Mo1y/aEZim7Y644dd53EnXQ3OIqHhcchtMEKsAoFJXQbPEEfEuXQrSg8s5mZd3dUrHKx4OS2 iH8ipFUgnWykIooYKDg+zk8oOtLEu5qdn3bwcffwY3e7fvqye5kjSMxewduHHZzNca2zh5/r 3Xaz3j7Bp/18gfTZGj5s71/m190FO+1Y3qoRMkVqprhqpATDfaNoMSF3mLvG00par8N5aRuX c2Ewd00blnQUL97JEg4bLE/e4+QXONcdnTXwjGuAd3B1zeHrQesxevl7Od8IV8twNy03SJkU +6l70H9NHRqVYJljBQ6gCMbLTLkyGewCWMmJRpNgWuMYPC5cDnVapJiFBoEohAeGh0clCtB5 aSfsSpuemm1K6riwsLEuxWwzdI82RoVc5/VomsrQ1yO70qanZpuSWlUNKk7GEERaQcJWYP+w FnzonQm5xyIlPBGsVPF6U24xbQ+uUoYGjzG0ripMWpFidCl6RxWNGd5xKrR2NBWphHt6rFa6 9NhoU2NHdYWNsilmm759tOEu5K1LOkYGjJ6wK216bLYpsaO6wkb7FLPN0B7rpKfdUh5uGOJE GyMP7UqbHpttKmxdV9gYm2KwqbpHGxExJi4HX8RxKqsO7Uqdnht0xty6bjnZGB+00DjPzVOF +3NjK1LMAw2cfxuo7FOBdYoZPIAI7OhRwDRx8N+xCZypNpPYvKgKOyosLlx4Q7j83inbRxsp Sx1Db9WgYw71Kp2MjTo1ti4rZOJbIr57fgkwAFNj7bcNZW5kc3RyZWFtDWVuZG9iag0xNyAw IG9iag01MTQgDWVuZG9iag0xOCAwIG9iag08PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVu Z3RoIDE3IDAgUiA+PiANc3RyZWFtDQpIiYyVPU4EMQxG+znF1hRRJnH+anqaOQISFdBwf4kk thMna9A0Fpbwe/aXXTjAZePcI53BJHh8HWCLsaP/PHwpJsAjOTA51h7OaIroP14Oa6yFhzU5 O6rX69vxBN4Gr03V1LmrpNpGnFA2qeadsP/KNXdLmerP+/dmQnMz1T6iKpuIHG0TefPg9ps3 7jYn1wlUeZ2pb+ukrq99iLROi7JiolPWW/ZhMO8jwfvgpaozJjpUgBeoySzqkyreJEH3bpIi CY6JKoMnCMEnXuBxwyrunKBoVCxGtWL3wUsVYwtAJovJqAcuZk+VDxqcO/dIi6SGQpWpE9Ow sb1Y608gLCDIuyeNiuWYJHYduxRt6vu2njURBwD+1waqfM3E4DUFP10uEibgp2eEJjUL+KSK YAm6F5MUSTAkqgyeIATXiXSCKcSxuKDNikfl4gOs3H3wUs0ti9oPlUf1yO5PtafKJ00QggO+ teVsHGbjFI/k+kKVuZOD3LZQi4oXxC/SeBKhWbCBKmMn5tYLCMuCPakydmCQ2v8F1a85LlvT b1hfc1M0KpcfVnD3uUsTtxhqO0V4wHiNv8QuUeWDJojAvU2ZDir9nvEYUrNguQ4sYygn3D45 wmZA7rNloWJh5oTcCX+RSGr9I4V1cAencUP7ofWeueA6J4DiUcH8qBK8Df4KMADAvN/kDWVu ZHN0cmVhbQ1lbmRvYmoNMTkgMCBvYmoNNTIzIA1lbmRvYmoNMjAgMCBvYmoNPDwgL0ZpbHRl ciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxOSAwIFIgPj4gDXN0cmVhbQ0KSImM0z1y3DAMBeB+ T6E6hYbiD0jV6d3oCJlJ5aTJ/WdC6gEgSNHjLQwb9up7IgG/rld0tDu/5SPuRNuf2qf9RLt9 vsJJ7dfZhz0X7usPrS+x9r9/vNzuXNzcXg7P9d+vvwMDtH6+tSkKC9UvUqzqCldRuwO3vU7t o7xe/UODiJ45gytV3O6wi0uJ8oIRx6ZFjHXvLzXVgNkOWfsgSP1DQ/ROTIYx80lcxe0O3HD3 jBxAfFyELNXr58dDnR+8lrncclDAVeskvgz2XPU4zABNGPAhTIDqFylWLYWrqN2BGzHgww+u zsLEDKxUYTvDLGbsxPFYjPBMGVgURRV5Y6AmwZKZuArKCETXmrpaQOo/Z0N8+/YMWaqyJarO T13P0HBn9gwshE72y8zAVU8iDth2AbUv4jishE7W5liXTq7idghwe6HaZ57lrehgbcqgJq6i doVVj97efJgTBvHgqqII78zSJFg0Za6CdqWpcS8I8Z6VfKOpLEKWriyIdecHr0VyuhvNKeX+ ePHf5MaTK59HlE5GEtLfRKZFxEAmrpaMxPdzYlnkkjMBXWQM5sEVpmH41rEsYWJpEWPZkLkK 2523pmljBjdwVVcduNRev94dOwnHPugZs2SxJCM7PXctczN2RoMIaxG/CfaJq5ynO6M7sYuU gfVcZ1bU+5JOvSUsXJjZ7fO/AAMANsnhSw1lbmRzdHJlYW0NZW5kb2JqDTIxIDAgb2JqDTUx OSANZW5kb2JqDTIyIDAgb2JqDTw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjEg MCBSID4+IA1zdHJlYW0NCkiJjJY9rhsxDIR7n2LrFAuJokhtnT7NHiFAqpc0uT8Q/VBaUmHy XHiAsc1vJFJr+fXjyyucIeARTo5Tf3//9cKAJx8c4bzK8bNbwuHh+HilK58ZDgY4oUxfuk9Y veEOmdQHY7DFUiM5KZoaSHTjztUidZsmty6LIboxLvb++m3H7nW3l9u6UC2jBLV1tIL/51Lt 8tC5nYcjXOieQHNjm85fOYY7ZFEXxVDzhIwekZOhoQVEN2x+r/cqRVO5iE7qgxnYhol1OYKB 0jleioudE9XYre52c8UD2SD6LDiKbvupHOF2G9HuJ3sxmktJdHEnR2MDWCx6MRqbs6jFBniv +zrGYKPoxC7OwLa6anEttz0g4bzIyXG5c6yKu9fdXrBYLjo4PlP+VzAm0W1DXOyOiCz4mbMK 0uCURSf4ARlw3kABnCANBhbdwBnfnYEKMuCpGxjnmWmFsYLmiiN1MBYnyAXP4WrwXnj70cMn m1zwk+RIotuWksXGYrkMTo7mhiK6ceN2agJZEDk5mttfGzPQm+1XGYqZLxJd3MVZXArXc14q h6CcCZwcF7zGqsB74f1cyv0WY3WNPQtYz3LtbfuB/Zi/tBR4XFzNF+i+PxkYcusQBRrdbJ66 74baMaf6Hotl7BbGd7lvtiKFze3+b76M8jLYOP4ANJ/H5/3ixtolXvaPAAMABbHd+A1lbmRz dHJlYW0NZW5kb2JqDTIzIDAgb2JqDTU3NiANZW5kb2JqDTI0IDAgb2JqDTw8IC9GaWx0ZXIg L0ZsYXRlRGVjb2RlIC9MZW5ndGggMjMgMCBSID4+IA1zdHJlYW0NCkiJdFYxst0gDOx9ilen YAwIgev0v3l3SBXVuX4ASSBs0+zMjndBCCT5gHA5xA+e4Ap+/h4QT3eWzq8iPFWaXAhCC3YO LPculM6z0NQofwvOd+JyEA6h8uw8CM+lc9kquKvSoktHdzb51VyDZ3+6C4Y8e19xLJZ9cLHM zbKPGngPpnGEGWn24KI5SOOI86CNXyMN2ScNvWepcd6Ns9h4QuGevyN/LzU/jeZur2dMsPIT jbymBMEs11KGZruWUjDhVN5To8HX60A0h6u8r6+Hb7dtk1M5oEle5RFmbisNJvN/fh31+Cd8 Tpfrsozf3z+Hb1H8+6hBro6Yl361fYHv8GNBweZfdaRxzCexcNK4xwv6buJadexD6Lzn1/hq 2IzqmzrSvI0XbHzgBdU3daT5H5VgfCkLss/q2BeZx1ucOQqqb+pI75/L0JiKoppERPqCtI6N 50JB9hgZu2KZ3WDaSq0ORrVNHen7HV3F+AKDuqaKtApGa7IuRfZZHWk1jRZnfNELqm/qSKuy 8Qw3XxJkn9WRVnfjeDtdvATVN3UkTeLVtmxnZaSt5jXK5XSLjo57499mc9HRY0AYn0fB7lt0 dB8k26diZfSYN9uHuejoPpe2NWBltI6vbbFNETtKea3QtbIXHT1m47aTLDp6zNBt51p07MPX hrc2Siujx4Te9u1FR89Jbqh8xbdp8Axm6ujxQ2B8vgiyz+qGb4wvm7MkaHyiG3G2KXjfD5Kg iVN0enodnsaFl+BwiYr+CzAASZnXTQ1lbmRzdHJlYW0NZW5kb2JqDTI1IDAgb2JqDTw8IA0v VHlwZSAvRm9udCANL1N1YnR5cGUgL1RydWVUeXBlIA0vRmlyc3RDaGFyIDMyIA0vTGFzdENo YXIgMTIyIA0vV2lkdGhzIFsgMjI4IDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAyMjgg MCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgDTAgMCA1OTIgNTkyIDU5MiA1OTIgNTQ3 IDUwMSA2MzggNTkyIDIyOCAwIDAgNTAxIDAgMCA2MzggNTQ3IDAgNTkyIA01NDcgNTAxIDAg MCA3NzQgMCAwIDAgMCAwIDAgMCAwIDAgNDU2IDUwMSA0NTYgNTAxIDQ1NiAyNzMgNTAxIDUw MSANMjI4IDAgMCAyMjggNzI5IDUwMSA1MDEgNTAxIDAgMzE5IDQ1NiAyNzMgNTAxIDQ1NiAw IDQ1NiA0NTYgNDEwIA1dIA0vRW5jb2RpbmcgL1dpbkFuc2lFbmNvZGluZyANL0Jhc2VGb250 IC9FRk5KS0grQXJpYWxOYXJyb3ctQm9sZCANL0ZvbnREZXNjcmlwdG9yIDI2IDAgUiANPj4g DWVuZG9iag0yNiAwIG9iag08PCANL1R5cGUgL0ZvbnREZXNjcmlwdG9yIA0vQXNjZW50IDkz NSANL0NhcEhlaWdodCA3MTggDS9EZXNjZW50IC0yMTEgDS9GbGFncyAzMiANL0ZvbnRCQm94 IFsgLTEzNyAtMzA3IDEwMDAgMTEwOSBdIA0vRm9udE5hbWUgL0VGTkpLSCtBcmlhbE5hcnJv dy1Cb2xkIA0vSXRhbGljQW5nbGUgMCANL1N0ZW1WIDExOCANL1hIZWlnaHQgNTE1IA0vRm9u dEZpbGUyIDM2IDAgUiANPj4gDWVuZG9iag0yNyAwIG9iag03ODkgDWVuZG9iag0yOCAwIG9i ag08PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDI3IDAgUiA+PiANc3RyZWFtDQpI iXSVTY/TMBCG7/kVcwSkpvb4M8f94AACDjQS56p0oSht0X6wEr+eGY+d1GlQtdO+m/cZ22N7 0liDbYgQNLYmwtBsGtUqZUHRf0vc3H1pat8xaZ9kTcUxFqq4hLFI0rSINYUSCjO5hNIxaW9r ynQ5Fm7yMafbyHoGOZ2jQKNJCJdEa3wN+ZBjgSafcCjazzgqmcTCTT7mOGfSXY11EgS6MAnj SLl2vq4u5lio0SaUiknPdrlTPseCTT7isOOpsnb1yjpdYuIqn3Ba8vjZeDrmWLjJx1xsQ5KB V1dJeqpi6+zF40rLc+WXR61mW/mYC3xkllZZV6fyCaf9UlVnm1H5mPM84cVNvNj4yiWUiUun pT5klzahFC6ezfpMVz7mnOzo9V2oL1DlE87461tXX9XJJETajev7fdkVKhdTlh8t9ZK6A1U+ 4dIezhvXrN1d2oSyfrFL+i7Hgk0+4Qyn0bM9C9blWLDRJlTqE5obYYWhy7Fgky9zvFY6snUl Q7p1ody6yjdyXnVlvNu+Wfe9Bw39Q6NAp7Oxki8FFqmm4LmkHvojGejTms7D0+7UrHhQAndp dNri/rV58+ke7s7H3+fT/vT8tv/VvO/z3DR9mDKdoXWBNpZb63HU1CAor+kCtTONvkULViEf hax21LQczztraguWUtvRbSmRL4mKkmGIFW3o9KfrUbROnTpnMtpBHoR/EiUzYDE0Mjf+neec 4aIk9a55eMdlvS4m3W/kGTna+/8U043V7FI1b35QGeF2/3P753B+hK/77XD4u30+nE9cW90G ruyKZqFoW/v7hNJgjK7h2/lx+A6bw/FlGJG0HTFQifnqRBnb8MwsIK3PAwYu30o7y93ocd88 TBeO/qRBavgI9GqEV4jwGUoCTTV1CNTLaQ6cgN4wlGCzWA7DdSYXp1muBpZioBytm93zy3aA D/dPsB5r8rR4yJCbYgQdqaXSGRulam0E1Pyy0cHzAUA0rcGidg06z++KrIcGvU5Ly27v0tpy oqJ4FEJFcgaFCRXtFXeYnAh96p95mKx2/wQYADcx64gNZW5kc3RyZWFtDWVuZG9iag0yOSAw IG9iag05MzggDWVuZG9iag0zMCAwIG9iag08PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVu Z3RoIDI5IDAgUiA+PiANc3RyZWFtDQpIiZRV227jNhB911fwMVnAXHJ4kfjYNkEvWKBFI2Af ij54vXLjQrID2Zsi/fqeGYqy49i7KAyYOBpyeObMhRWR044URa9dVH1FttF1gWS9Tg2Q0dHP KERtSK2q9bvq+7Yyygpe5MUoclEHUjZZ3TSqHbADP+1SVPvVFqj9p7q5f9587rar7rb9u1rA GGs40GSIVHvHX4yxXrWr6ubHbtuNy8Nmt+W997gQNq8sfuzOeq9rUs475jbM2CUOwPpGB4DA /G1w2sSCVpVtovZUcF8xY3guuxMcxdnRhPI1OJuxhyQmytkJNzrm2OHJE2k4ytdkgJOZRYY4 KAynnZl7cVKQXHFVbhujJjALRtMFuUVKsiLlueiE7+eqm6z6D7tx/PL0SvVUO3X6n9XH5aRi irpBvDVTWNQOEY1dtZ7NqLFiLcaHq8G4RkVnebkcjHXC8PfuaTceus/Mr313Nc5vUbe4KjQz dbn+lHsIrO7EvVi/QV6Tv8Y9CcFfP+278Tlzx+eEAjvPw/8JxSVpzODRcZkrs8IafI5l2sCL K7Gy3X8lGm+DtlzDgbv5a6nIdWOlr7/7q9se1Hv1cTf2Eh7i8j5c6G5RQiJ1cvLhZX/oBvXw 1K02681q7vj3bcvd3q5BgBqhmBeDuuJIeVaFo+BHdsaVK0yTh879hzv1Wzeud+OwhKjq/nnZ f5GbFF8FP3XDEmjT8PS5q44dkcfWh+WnHYbRbnyZEmI5DSDB/0Gu9slxFmqfSl2xx7rhj5KM aUOojbR3TgbsieZkIOZYYj5LS+BxgJayNQ+pK2khqZ2fXp52h8duv/k3V9oCM4beZoLFmZJo A0f5x037ON6iZW+65WGPZJ5AlRP8893+9s/2l+M4Lv8e/6ICZhgkDCkxTaiDCkcVIECuK0y3 kGTIDdU07aLxvPSVJ3ti71kwm7RF8TodZb+gIMOxL8dd7tNezHQBY0EH5P08Ls/xqb/aCyY/ YXsBC5u+ehS+fAANO8ynIWQvkU4WDsOhIpA5DF3w59muFqILNGETv6ycVNZkemaITNag2DFH iybOSRlEKSIEiyzO7nxTM8eYJ9EgFSfYTDFM9lBnvJbni7I71piSlGuGvUAebICWXmPRzCHL 5N/ikPhN7Gd3R4w39YRPwSGVhzizKfgRHpvMr0RkZr7iodhPIoqJOVpH/HwPBRN0y3cAgxNj O0F6A53LAlj0KhfRa/jKFxJNzmd92P4GTkw4Gn4opDAkvuE/AQYA/VgC/Q1lbmRzdHJlYW0N ZW5kb2JqDTMxIDAgb2JqDTkwNSANZW5kb2JqDTMyIDAgb2JqDTw8IC9GaWx0ZXIgL0ZsYXRl RGVjb2RlIC9MZW5ndGggMzEgMCBSID4+IA1zdHJlYW0NCkiJfFbJjts4EL3rK3icBDBDFouL jskkAfo2QPwDDY2NOJDswHYmM3+fWkjK7R4HDch6XdurjdTg02gzmhCiTWjmwWcQDKPi/QDZ GQBvvUG0EczG2TGZ844E3jow4LNNYJYBUhQMjvHc5RAUkyt0FovxOdmc2ELxGDjwOFpIL9+R 7WdWW9/DyCTGwIYdVo/z8HUA5yyQgnheyBUKbPpVmhTuB86fQiGARVZXGIlzYAXG5Q7TSyT1 gitOXAMtH8W9h6wdUtXOtzgLm9WbYiXDyfiUOHzySUtc6SRSkJJyt1Y554NYOERvGJS1YYgj V6k3DDEIbg1r8rVhzjqHxtPfZToOOAKXLRSvtW04C0mkogKBYD2lSHUuHU1DDGh9aXgmLDSb NgJ3sTpqSMOQrWKkTnquWsfkglqhnqKjf5oaRgFZKguFlJ8wVFS5VycdSYhp2L8dPmwHZ6iu NHUb/XEmepnCUKT824U06M8GKi/XZ8Plgmi2kxQORrP9Ofzx6d/d9ON6+Gdn3pm/TvNh+u/N 9tvgbfYRybUFRy3fflRrr9Zi+PnHcboeTkdW/7QdPEUaSZWfsfYjSBG94x/MPDsbX2iGkNu9 bwo+OM448zCs4i8PUgx2JIvC9XiVIVN0Xtg9Ha+7898HYUiZPR2n5/O0Oz83xtTY4P3rBMOa 4MfDeSf2l5qijlt7Ij05aKR5JW4xMxMisknSf0ohAicWXaHnInqxN5t71WQ8yiwd+wQuHaNn Z7PgcIdpGAPeAqqS+k5UpDuEoGeK0ngBeWa6HwfrGnyVFDgsG0GlJbjTwBv5650MDvnACtHR vi8dYpTNdD3HzOxpbBuYBtYZG5wH9sBbV3Uj8pg0PxVJELJUiGPk3Zkb5m1TpchbRqsERoNU QJZCoUIyVH5NV7lXNwpqjIcbGSBa5uf5QH2wkNgXsuhCTqfjaTlM5v3lsrtclt3xyvPH9x4t Pw2sH0tcBzY93EiaxhzM7ZOD8tFLA58gS5Fk6zYeJB3ZyqrAg0rJ17Xs8v9fS6C28L3j5Up9 kCj0RNPLRJ+W78+TJMmHRymPjh1+00Prnflymg7Ps/nzdLn+djsha9fo8pdLhb8ccsdzx73n UM/rO0z6shKsz9VJaJN8RFT3Cul+z1HloHsMqepX+y4vivctQgj0jUCfClJXOt+CnIIBEqvz 9498ktTw61oEuJGzu18CDADnV+Q/DWVuZHN0cmVhbQ1lbmRvYmoNMzMgMCBvYmoNPDwgL04g MyAvQWx0ZXJuYXRlIC9EZXZpY2VSR0IgL0xlbmd0aCAyNTc1IC9GaWx0ZXIgL0ZsYXRlRGVj b2RlID4+IA1zdHJlYW0NCkiJnJZ5VFN3Fsd/b8mekJWww2MNW4CwBpA1bGGRHQRRCEkIARJC SNgFQUQFFEVEhKqVMtZtdEZPRZ0urmOtDtZ96tID9TDq6Di0FteOnRc4R51OZ6bT7x/v9zn3 d+/v3d+9953zAKAnpaq11TALAI3WoM9KjMUWFRRipAkAAwogAhEAMnmtLi07IQfgksZLsFrc CfyLnl4HkGm9IkzKwDDw/4kt1+kNAEAZOAcolLVynDtxrqo36Ez2GZx5pZUmhlET6/EEcbY0 sWqeved85jnaxAqNVoGzKWedQqMw8WmcV9cZlTgjqTh31amV9ThfxdmlyqhR4/zcFKtRymoB QOkmu0EpL8fZD2e6PidLgvMCAMh01Ttc+g4blA0G06Uk1bpGvVpVbsDc5R6YKDRUjCUp66uU BoMwQyavlOkVmKRao5NpGwGYv/OcOKbaYniRg0WhwcFCfx/RO4X6r5u/UKbeztOTzLmeQfwL b20/51c9CoB4Fq/N+re20i0AjK8EwPLmW5vL+wAw8b4dvvjOffimeSk3GHRhvr719fU+aqXc x1TQN/qfDr9A77zPx3Tcm/JgccoymbHKgJnqJq+uqjbqsVqdTK7EhD8d4l8d+PN5eGcpy5R6 pRaPyMOnTK1V4e3WKtQGdbUWU2v/UxN/ZdhPND/XuLhjrwGv2AewLvIA8rcLAOXSAFK0Dd+B 3vQtlZIHMvA13+He/NzPCfr3U+E+06NWrZqLk2TlYHKjvm5+z/RZAgKgAibgAStgD5yBOxAC fxACwkE0iAfJIB3kgAKwFMhBOdAAPagHLaAddIEesB5sAsNgOxgDu8F+cBCMg4/BCfBHcB58 Ca6BW2ASTIOHYAY8Ba8gCCJBDIgLWUEOkCvkBflDYigSiodSoSyoACqBVJAWMkIt0AqoB+qH hqEd0G7o99BR6AR0DroEfQVNQQ+g76CXMALTYR5sB7vBvrAYjoFT4Bx4CayCa+AmuBNeBw/B o/A++DB8Aj4PX4Mn4YfwLAIQGsJHHBEhIkYkSDpSiJQheqQV6UYGkVFkP3IMOYtcQSaRR8gL lIhyUQwVouFoEpqLytEatBXtRYfRXehh9DR6BZ1CZ9DXBAbBluBFCCNICYsIKkI9oYswSNhJ +IhwhnCNME14SiQS+UQBMYSYRCwgVhCbib3ErcQDxOPES8S7xFkSiWRF8iJFkNJJMpKB1EXa QtpH+ox0mTRNek6mkR3I/uQEciFZS+4gD5L3kD8lXybfI7+isCiulDBKOkVBaaT0UcYoxygX KdOUV1Q2VUCNoOZQK6jt1CHqfuoZ6m3qExqN5kQLpWXS1LTltCHa72if06ZoL+gcuiddQi+i G+nr6B/Sj9O/oj9hMBhujGhGIcPAWMfYzTjF+Jrx3Ixr5mMmNVOYtZmNmB02u2z2mElhujJj mEuZTcxB5iHmReYjFoXlxpKwZKxW1gjrKOsGa5bNZYvY6WwNu5e9h32OfZ9D4rhx4jkKTifn A84pzl0uwnXmSrhy7gruGPcMd5pH5Al4Ul4Fr4f3W94Eb8acYx5onmfeYD5i/on5JB/hu/Gl /Cp+H/8g/zr/pYWdRYyF0mKNxX6LyxbPLG0soy2Vlt2WByyvWb60wqzirSqtNliNW92xRq09 rTOt6623WZ+xfmTDswm3kdt02xy0uWkL23raZtk2235ge8F21s7eLtFOZ7fF7pTdI3u+fbR9 hf2A/af2Dxy4DpEOaocBh88c/oqZYzFYFTaEncZmHG0dkxyNjjscJxxfOQmccp06nA443XGm Ooudy5wHnE86z7g4uKS5tLjsdbnpSnEVu5a7bnY96/rMTeCW77bKbdztvsBSIBU0CfYKbrsz 3KPca9xH3a96ED3EHpUeWz2+9IQ9gzzLPUc8L3rBXsFeaq+tXpe8Cd6h3lrvUe8bQrowRlgn 3Cuc8uH7pPp0+Iz7PPZ18S303eB71ve1X5Bfld+Y3y0RR5Qs6hAdE33n7+kv9x/xvxrACEgI aAs4EvBtoFegMnBb4J+DuEFpQauCTgb9IzgkWB+8P/hBiEtISch7ITfEPHGGuFf8eSghNDa0 LfTj0BdhwWGGsINhfw8XhleG7wm/v0CwQLlgbMHdCKcIWcSOiMlILLIk8v3IySjHKFnUaNQ3 0c7Riuid0fdiPGIqYvbFPI71i9XHfhT7TBImWSY5HofEJcZ1x03Ec+Jz44fjv05wSlAl7E2Y SQxKbE48nkRISknakHRDaieVS3dLZ5JDkpcln06hp2SnDKd8k+qZqk89lganJadtTLu90HWh duF4OkiXpm9Mv5MhyKjJ+EMmMTMjcyTzL1mirJass9nc7OLsPdlPc2Jz+nJu5brnGnNP5jHz ivJ25z3Lj8vvz59c5Lto2aLzBdYF6oIjhaTCvMKdhbOL4xdvWjxdFFTUVXR9iWBJw5JzS62X Vi39pJhZLCs+VEIoyS/ZU/KDLF02KpstlZa+Vzojl8g3yx8qohUDigfKCGW/8l5ZRFl/2X1V hGqj6kF5VPlg+SO1RD2s/rYiqWJ7xbPK9MoPK3+syq86oCFrSjRHtRxtpfZ0tX11Q/UlnZeu SzdZE1azqWZGn6LfWQvVLqk9YuDhP1MXjO7Glcapusi6kbrn9Xn1hxrYDdqGC42ejWsa7zUl NP2mGW2WN59scWxpb5laFrNsRyvUWtp6ss25rbNtenni8l3t1PbK9j91+HX0d3y/In/FsU67 zuWdd1cmrtzbZdal77qxKnzV9tXoavXqiTUBa7ased2t6P6ix69nsOeHXnnvF2tFa4fW/riu bN1EX3DftvXE9dr11zdEbdjVz+5v6r+7MW3j4QFsoHvg+03Fm84NBg5u30zdbNw8OZT6TwCk AVv+mLiZJJmQmfyaaJrVm0Kbr5wcnImc951kndKeQJ6unx2fi5/6oGmg2KFHobaiJqKWowaj dqPmpFakx6U4pammGqaLpv2nbqfgqFKoxKk3qamqHKqPqwKrdavprFys0K1ErbiuLa6hrxav i7AAsHWw6rFgsdayS7LCszizrrQltJy1E7WKtgG2ebbwt2i34LhZuNG5SrnCuju6tbsuu6e8 IbybvRW9j74KvoS+/796v/XAcMDswWfB48JfwtvDWMPUxFHEzsVLxcjGRsbDx0HHv8g9yLzJ Osm5yjjKt8s2y7bMNcy1zTXNtc42zrbPN8+40DnQutE80b7SP9LB00TTxtRJ1MvVTtXR1lXW 2Ndc1+DYZNjo2WzZ8dp22vvbgNwF3IrdEN2W3hzeot8p36/gNuC94UThzOJT4tvjY+Pr5HPk /OWE5g3mlucf56noMui86Ubp0Opb6uXrcOv77IbtEe2c7ijutO9A78zwWPDl8XLx//KM8xnz p/Q09ML1UPXe9m32+/eK+Bn4qPk4+cf6V/rn+3f8B/yY/Sn9uv5L/tz/bf//AgwA94Tz+wpl bmRzdHJlYW0NZW5kb2JqDTM0IDAgb2JqDTw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5n dGggNzE5OCAvTGVuZ3RoMSAxODA5MiA+PiANc3RyZWFtDQpIiVxVC3hNVxb+19773Bt5CQ15 oM51JFN5CDHqlYmQ3NAhSELdGCo3D0lU5Ho2aVOPqsRcj6oPU49qFRW0emLChGFGW33MaMTo U6fjVR30ozLzfapTdc+se6lhzvrOPWuvvZ7/XntdEIAwLILEuLH5KalFda7eQHZPlo4prnR7 YirERCDTBCiteP5cfb3n1Hze+wqw9ZnmKauM2X05BLCH8npS2YyaaQtqPtgOJCex/rnyUnfJ 8dqriv3NZZtHy1nQsbLDbg5Yzuue5ZVzq//62pwMXq8AIl+dUVXsRru6N4AU9he5vdJd7Wl3 kdaxPfuAPtNdWbrp04RugJP38YOnas5czpufrKv+fc/sUk+e79gVoAfHD7a0Q4jhN1bbiRgV j2jAusTvZf/XV2Fd9u/7v+Jbtm6++wINeJMq8Cb+jHeoja3ewkE04UNEIQubUYu1qIcNk1jy W+QxaSxfSzFWE1KwlfPZihbWnYgFOITOFG1dwUIslR+z1VJGugeGYRyqsJJGW/MwGWfVEgzA aMyEhxZZLmuVtcbajh04KD+0biMEsShmarG+076wvkIyW6zDBpylNe32I4OjLGLNlzEbG+UU RVaZ9SNn4MBTnINCDlroqEhk76W4RNFUKzPZyzbLtI6xVldMQTk24hD1pxHCoU22cqwWdOYY 1ex1A/bhAFMzjuBLCtXarO1WG2KQhMe4niacoKPSd3uxbygjpjFKvTCId6rwJ3yAk2TQ26JK C9VStQztaesTRKIvJnC2O9nyn3RTLGBaKN9X2dZwhDMuL/rRxns4T7GUQmPpcdFLVIktcjaC OGJfphJUMN4vsfczlEgHRKholdvUHnXL1s13zgrnE4nHJryMtymMK9VpDj1Hn9HXIlNMFZvE BblW7VKn7G6u+glUYiX24CZ1pIGUS7+hcqqlenqRNlALnaTLYpgYL54U12W5nCWPqOFM+WqO WqLVacttl30u3zHf33w3rVSrDrncD4s5+3XYwpUdRCtOM53FBdIohMKZdHLQBHqGaQGtpNeo gXZRE0c5SRfoCv2bbtAtASab6CIcogeTIWaLp8RasVm0Mp0UV8V/ZJTsIRNlf5kmC2QVZ1Uv VzPtl+dVrGpVFuOcqq3XXtEatD3aO1qbLdT+XBCCPvpp2+2E22d88C3zrfft8zVZ59GJzzCW UeiONM7ezTSdz3s9d9xb+JhCGbtYSqB0Gs3ITKXpNIuqGcnnaSPtCOS+lw4zSp/Tdc45THQN 5Nxb9BfDxVimJ0SpmCVWizWiSXwmfpR2GSLby04yQY6QU2SpnCtr5Hppyo/kP+QF+b38iclS waq76qHiVaIaoaaqeWqLuqQuaZO149o3tmBbpa3O1mz7l/1Re7p9nD3XPsX+gv2A/ZOgQu7O d7Eff8B9D52Ti6VT7scq0U/FiBPiBPfzVJTIHMGdKhpomXiWmkRPrdo2RAyhMWhT8Yz1++IV 8b0YInNoFOVjuuh7x5stUvG0Qpp6F9fUYa7tBHuutoXSAnHdFop9BDGIY74n+6hEeRxfyrNk V1vxdxVMUXRN7JTjuAuOqHTNBYfcjL1yFj2L/cLJ0+lW0Aru4zG0m+fCeEqlH6QFKcZwFw2Q X2MJnhRf4Brf42X4HZWoMqxCP6rFJbzOt6KXNtOWYOtEfxEVyiseoiYItYurG0Q9SWqReJ6m yI226+I05qFVBeOMfIOzbxV7ZY5q0/KonG/As6jDLGsxajSXOkVlkPQ44tQ5nm61MlU5+LuQ p8pknmkH+HYf4jkwTOawJJo7ZzT3xQSeEBuZXuI5obiDKviOT+QpdgJNtvGiGWVaOPHUAdRx Xx4mWa9jg1WGmdYaJPM8qLdq2WMDvsELaKClvmfgwcN8c87QaC1btGrZVrLwitMiX6x/8HwZ 7TiKxrdMe5GNdO2P8KrPkY+h1grrU+7uR3jCbkARfo2LXOV3HGGkPIp+vjGi0cqWHq73LHKt nVZ3Cka5NQNjcRg77Brc9sSMzAnjh2UMTf9V2pDBgwYO6P/Lfql9+6T0Tk5KTOj1yC/i43oa PRx694e7de0SGxMd1blT5EMdO0S0Dw8LDQluF2S3aUoKQpLTyC7UzfhCU8UbI0cm+9eGmwXu +wSFps6i7Ad1TL0woKY/qJnBmtP+TzPjjmbGPU2K0NOQlpykOw3dbMky9GaalOtifmWWUaCb 1wJ8ToBfHeDDmHc42EB3Rpdn6SYV6k4ze36511mYxe4aQ4IzjczSYP4jbwwOYTaEOTPK8DRS VDoFGBHlHNwoEBTGSZmxRpbTjDGy/BmYMs7pLjHH5bqcWV0cjoLkJJMyi40iE8Zws31iQAWZ gTCmLdO0B8LoFf5qsFxvTDrqXdEcgaLCxNASo8Q92WVKd4E/RodEjptlRj19Mfp/S3beMdNV f/9uF+l1Rlfo/qXXW6+br+a67t91+H8LCtgH24q47EJvNodewSCOytc5mlha4DJpKYfU/ZX4 q7pTX6nh9EsKp+tmO2O4Ue6dXshHE+s1kVfj2Bcbm3HQOodYp+4d7zIc5tAuRoE7q2tjJLx5 Nb+PydBjHtxJTmqM6HAH2Mbw9neZ0LD7mdJ7ewEuoO7nRuXdQ5b8GRmPcUOYerHOmbgMrmmg /6d0ILzFA1mNnwJiK7OET6TCbJdZ6I0Y7Jf77U0tLsLQvTfAHWBcu/qgxH1XYouLuAE/6++T e63G+z/zZmKimZDgbxF7Jp8p55geWPdPTprfLAzDE6Hzh+HDOMbWXTA4heF3OPwHvLw5A0W8 MBfluu6sdRR12YeMlP9yX/XBUVVX/Lz37nu7IJSFuFTIIAkBBSEQyPCVQgkEIpCChmSTzUpL +KhFIpVKtbSjskz4CEvSsbYwEZAmKZQ0ocMGY00YWwMzmmJHmDoNtpV++JGZajqtOuiMYPL6 O/fdt2wejKG2/aeZ/eX3zrlf55577rn3Ti6L6+Vc0uGWBENcEnVLEs3LMxDJrcRX2WDcf0fi NywwMmXJxpy4NvIzir/ulBcUZRQURsJpS2LlyrcFxf0kp3xOokx9xVPywkaqrr70VEOWIihX JyqzEB4SFxPws2RQb2jz+RGVUqOl5ccD5Uud/2WD09NvslGb/T63knStmTIznjO5v/ylfnI/ 84bEDBiMY7CgOBKLDe5XhlBzBlymCBFPxeH0NLwIQtiZE/BrszvmMMpS47lwWR5XQPw5KiX2 q5iqvsvwx9GZOSUfiS4Wy89Iy4+Vx9a22dF1GWmBjFi7flY/G9uypNwNnDb79L7UeH51GXy1 UcvBptBpUUuGVlXYkqtVFUXC7QG8A6qKw6d0Tc8rX1TWMh5l4fY0olyp1RNaltJYogINszyl +2VRajueI1FZKqRCyuvbNJI6v6vTaH2b7ugCUoc/zid5xeHkSJHbryyTjy28kr7ct5LyAnTl ZF92IEfGY9KfOdFSKr5TKMT11+lrYisFgWW+MfQds4TC2h6K6E30GMMYQ7niBD2Muk2QF4JP c1vUDwF/AeYBJcBopVsBrAWKWEbddm6LPrZwP5K3UsQ/lh4yS+xejHfA7KT7gSP4bhBvU6M1 lzZDPop2L+JhNpvroM0Bq4lqoT+M8vXQHQGHIdfjezXaZanvQb4avMvAgAX9JPSzT833TuMM zRJb7TcxlzL0uRzYjTHuBecDBaiTAl4E7NE6qUrrtBtQDqZKjL+H9cBixUvRzy6UL0C78ZAr 8T0adljgYUA6MFE/QXP1W+kF8DTMv9SZN9BJG3nOiTnBfmXT9XBsLEgGxvwlkKHPtbvBg5Js 86LSg2VGNkXBFUAqUKi/SpvFV0iDv542u8lg+InYT38G5osNtBKyBjuLzFY6yDKwQmKr3SsO U51xmeag7HvWAcxjA/yNW67+MU3T/06Z1gTajvhajP53AEfQ599kPGygYow/FZwtumUM7Qaq MdY/XT+xbyDvwLquwlif+jmGm6gIuBvrEgUeZHsw/jT2Oa+7VtI3F3XfQZ3VDOi/KIG5c0xy G26PviaoOGy4xtSAOjXw61/BAgiyDS5knCmg7GX0MwqwgDHAVKAbaAAqgBzgeWAixiaMa8h4 RcxwbMr4QGyYnfAhbJMx68zhiFxPZ8/Uq754nHTrBFUopHOfvF84ZmFLi9s37ymOGZdlfFdw 3Gsf8Dw5phKMvSd66G62Qe5BxJbLvO9gM++HA3qIqsAHEceVHLNsn8vsF4416RPsCcXzkuaa JfcI2CDKULFe6bLriwRvpKPos9xah5xSR0vFt3HP/gGtE+/TYmMSTTWzoMN8UDeu99AqP+7g WMt7ID/t4VqGr0vbZHZgns3wZxc9A59+S3Tp40SXZprN9rsmaefMZv0J+X0de6F1OGXMjOSy f1f/eaBfNJuRM5vt98wu28Z8nuI94evRsoA0l6E/BUSBu/yTtVp/hdbmC1HAIroMPCRyKcfM pdmiA+sTRJ7HXoA+ZL5JLxo1tFd02X/QohTVu2i3L0hr8VYaxmPpF6mSwf2DtyTFUb+Y88aS y268eplzvoqpsWAL+++8wjsKHwMfIY5+ojljzOb8LM8H5GhgtxOv9pVEfJ6jY+B9bnx64rTC E59DvHHpZXm2IL+7+xR27HXnz/mRcxznSM5znGfc+l5Oah/TmxDHnIdfpYja1+MUlsPGt9Te Rx7GepfatpVvH7da7UZjhN1ozcD37wHTPo55b0ucqWG7T52nk9yz1NHTLe45ambTZpXPjsp8 8yH9SJ6jJdK+QdZJ2m5exbojB0p769QehD9hd4Uoh88PUjXmMcrYg/0IPbCafSLXgug2Phf4 TDT2w898FtVQpfEG7gvcNpuGy/NiAZXC9nNShzOVmXVmKTVYPTRDhJBrO2gDrxXPg+3htfc/ QkP9QeSJLpoufoY6QRqMenXSB7l0XMYFt63A7Qe+8K0nH2J2Jepwf/WyTS6NUP44Kn0h2+Mu wvHFvkCfVpBWyftED/3YDFEp9lC9L0r1Vgh7LkiN6OMY2oXYFrQbLc/r/XQf9lcVclMVcg7J +I/YV41mzGcb8jpgROGjZrrNjMKHFXLui4WTY/fw/jGa6A6OEWs/8jDfJ/ZTTEymJVYF1UBX YyJPYtx90O3E/s3C3t2L9mNV3iaMvRd6bruA7zJ8R+D94sulFCsq7wEkbeB7CsY33qV6YzlV IY4X+vfDD7soE+eFhti7HZjuQMpPKFQ7kLqAw1q6EaDHpT6bXtObjFsQt3yGtosd9IAooRnG dBolhlOm+C326id0yBhGa8QrdEi0UTXLIoUmGriRG624W7L+At3Lev01yLUUEfPQvoq+KdbQ VqMFsfc7Gizux1qjnfl9xMl4tP8Q/Spob1PEKMHe2o3vT+wTXE+O0WqXMsRSypTtkiBtdeGx WS+A35ZjTWEvf/ezF7Ym7HRtvIF9cp7cL9pxHXGI5hHZl4AJDvcV6jXUDNTpf6Q8YwV9V2tE gjlM+Vo3cFjh57RUcgtQiDN+pvYYMFXMpOeBHfieAv4VcNKRcXebSW8Au9D3GfCz/C5g6Ito FjN0R4Ba4DduWTJ4rBvpk2GmUn/5OYoytMt2L8NbH36ehfFmifnwJ4BYfJJhbaeI71Gs353Q 344+PTLGmSGeo00D2TMQtAuUJX3oIDd5ju56gEfeBC4lcRqzOhv+I/s+D7C+24GvSv/+g4Iq hr6gXaRx4BJwifEIbWNAzoRc5vpTu4xYYzTSD6U+sX6OHrFCuMfN9+q9snddB5L1Z+lYMtw4 SMTDU7STIRagPuCV/edoJ8N6CWUvXS+L4wMgQncZB6VNJGPMI1v34MwE9PGwdbRsU81IyBew lwGuK9sPpRqG3LuA3koPMBLlM5G/gSS/zmK/YkxZ7q6Puy7e9YF9ueI8EMFZcZ6ywEXghS4n 4lvli34xX+jEe0LmXNLtqXNtT1zbGxf4rLlxn/9PwN55BegEXv5fj8VZhnNEgPPEJdxDFuAe 2YX7yX1USdSLXPLpNOCnyEPF4Nehw+ndNwkYiu/h0H0D/AzR1Y/w/TD0XQ5sXaRSnbpXjoLu F6qtX/VX5LS/+muiK4ioKyed9lebgE34/gB4HN9/Ap8B16L+e2i3E3zWKe9dA/lR4AXIPZAf BML4fhIcBE8BUoARaH+AwfeR696h/3W+8fvjZhl3lvWwcyz4NPgx7xviptldzwHY+9Zw138g NtVb4np2/IA301u498WT3z6f9cZxGevZlwwRsntxpxzC92i+y/L9Wd4fFcv3m7zHYlyiW12G Pf9ivPpjm7ju+HvvHN8lYPyjwQ6N4zvHiSkxkGACDiSNz8EZUIsmkMDiLCEBEo1BJdgcQOo2 OKQylbIC6iQ2mLYwNE1dK9SLzeglICVdtnbNuoFaxqQWWugirf0jDVRVGSrU+7yzgaExaXf+ fD/f9/1+7r137969ey7k+1e+d+b7V/CvwM9bC8z+rEe/enm/htrtsXLBQ6aBLCAQGbYaaAF6 gCPAIGAl9nxkB7APGAVumBlV8KRfWqwaoEMmZbY9EzaLm3LFrm6zmPlmMsdr1uY4vjonW56T LarNhRc25Xju/By7KsMa5yJbeCzmFtzkIsDITljK/kDslBKZnBRmEx1ggjUfUQVXpiIYHhwV LIQKTKCkj8jZMYGmbc5wrIhl2TRxYa59xqZyGTaVmeUMD8aeYh+T14BRQGAf47zOruPDfg1r hgM2CgwCo8AFYBqwsms4P8L5IfuQ2NlVUg1EgR5gEBgFpgGRXYV1sCt8BTIt96MAY1dgHewD 3NYHsHZsQyl7n72Prr2XjiwLD5tOqDrvyJV5x1Oad1zusMHeTd+eJxvsHxklJJ+M1bBLRAcY GruEyi8RBWgFeoGdgBXeZXiXiQYcBU4COoC/N7AOQGETwDvAZVIDqEArILGLaTRjsAvpYJMc c7O/sreIB4P6F/Ynk99hb5r8Z/ZHk98G+8AT7M20TyaxGcgTXOMAO8DVyBewNzIVLjkbc7JR DI8MWw1EgRagBzgCWNkoK0/3yS5Uco5MYHGVWZp8avJvyCmJqNtkNbgCc0zhJrj8SXgwg8pg kKnBY8dR5CZ4+CV43ASf+zE8boLP7ofHTfCZ3fC4CfZtg8dNsLMHHjfBlnZ4MAb75esVc+VI y3aqxOxsD0ZpD0ZpD0ZpD7GwPfwkty28bz9PV1VhxE6ooXlVsjZCtfNUW0e1U1Trp9pequ2n WgPVNlItRDUv1XxUU6l2jtZhKDSqnnmouEwtodoE1U5TLUW1INUqqVZBNYVGVIP506sXm9Rs UibG3yvwk41hO/rox4j6Ma39eO1HYS8AWbOkQqSU58RzfJzLM1XRXHnh8vCO2Co2jgvH8RjG yUeABQ9oHNNoHJWMowI7bBToAcaAaSALWKEuR8ePmNYOWw1EgR5gHzANWM3uTAOM7Mh38TWz Y9X5TrfwEhvHWY7Tz/xqmcPrCDlWCUe81O6jLb6sj0WIG38KiMspOQ1qO3vL9q9bNlIYK2SH 2RFShgdxNM9H0rfLZIP+LB08J8dm058SnwWzji4jQVoJrsP+kpeXEK/EuZZ42avgcNq7AZfZ 08H58gidxa86K9/2Tsqfeg0G9xPvOfnvimGhaflviLx6Vr7kPSi/XW1IiJwPGhQ0opjSYW+d fHrClO5H4kRa3svprPxD70p5u9dM9OcSG1MoqXZ5XbBTXoX64t7NsppCnWflqHej3JBTLeHX nJVr0IVQzq1CZ+d5zUYDPrPC9RGDblXni8fEDrFFXCqGxfmiX5TFMrFULJZckkOaJc2UiiRJ skoWiUlEKjay19QQtsGk2OrgxD96+Kdj+g7GLYy5rlGJkaeI/piQYIm2JprQx7aQxGZF/7It YNCitZ16QaCJ6q4ESbQ36XWhhCFm1+mRUEIXW7/VMUTp4SSiOnveoKS9w6BZHjpQqrtWdAwT Sp0HXizl/MSBF5NJUuLeHS2Juhqdy74Rf4TpzdvQg6PkIb9MP5Zo69BfKUvqYe5ky5IJ/Sdt SlfHMP2c3miOD9ObnJIdw0Ij/bx5HY8LjfFkMmHQDaaOKPQmdJgxN02d5CMK1xFF8uV0J3K6 SlwPXQUn6AoLSaWpqywsNHUWynVDqYrm+FBFhanxKCRlalIe5T81E5XQVFaaGrdGJkzNhFvj Gr3RlHi9kPi8poQ+TrymxEsfNyUbHkiq85KD9yUHzZYE+kDjzWls1+5pbNegCf2/R39TKEQz 9cktXc39gebeQHM/0Ksf2r21RNc2K8rQliRPKLoQ7N28ZSvnTf16MtAf17cE4spQfdcj0l08 XR+ID5Gu5vaOoS61P56uV+ubA5viyczK1trIQ20dvN9WbesjKmvlldXytlZGHpGO8PRK3laE txXhba1UV5ptEXOOt3YMSaQpuaIrxxk2owjztbfUn2xyO3Y2mpO33l+yt3TEwv+Zzggl9ZmB Jt0G8NSC2IIYT+Gd4qlZCNvzqZK99f7SEfpyPuVA2BloIqGBXaldpKT5O/HcL4UDoYFdfMBz NpT6Xwdyzbq6KZ4aICShV7Ul9Ojazo4hUUS0l9+SvvxebMaMZiM7lgsuRHA5DwrCfSGPNfBY YWFe+N/Pf1eeV/C3QGPnMlT10QGSSgq6L9HOsBS0d+Jeuzo7RrBd4p+HVBI3mKIhmrpXh9lt kvMJv997GNiV9/LjMJDn3FW4JHVvOO4ffJT4OoX1qgAnPi4iaTrD6KRVNNhx9TFSYJkUSJFo maRkjmQtmGTCebaIFNLjdCEpCTm+bLjb8LTji4Y1dxtIFL7jDsyiGr/T76yEwapI7ijC2B21 gHxFFMsY2iJrsv+0zC4Yw0enikypC7bPoXFRnR2fE1c6Xe3KdqFP7JO2ufqUAWmX94D0I+9l 6ZLbKSo2m3X9XCy9Z7gT4GvwDO75zYTIE3OVgOLnCecTPtXWamM2W3Epfa8Ho2iwrWphga+0 oNxXbKMGrVNd5HeVKYfqD9TWOLBsOxzMYWRvvD5zpnW94+j8ohF88HzYWLijnh7PDs8+j8Xj 5jmPmzfnMVhFJvTWIT4CoVD31BSo+7uhqS+mSHQqOnW3e9LpWlbdPbWohtbh4CNNu0k3FYNz g4Fy0WoVly5dHHbNLrZaA+XE6Yig5KbFbvfi8NIltZBYha8yJfNXb98QW7+Zxc5/+8zdPRef u/715C8OfnL66t1Iy+Gnv/frU99/9hVL26xtNWtqGj+7sqX361vvvjC1lyboD+hv33j59/9m j/xZGoaiKH7yp+EJUYs4VF0Fix3cMkihdmgLUlpwEEREothQyFRwcfILCA4OnRwcXbKJH8KP oCAUHNxdRIn3JQ8HpYgFweGc8Lvv5L6bB+/k7WHveuf2cpgkyBX/PdYjIYQQQgghhBBCCCGE fAc2LGjNw9HOWhQ8TKiFST/8f3JRzqqb5ROkaV7TkckLktdPUuiZaQe+VMuc7MuTe09cSSfv TkmnhGXjbcygarwj/U3jXfEHxnviTxrNTqvdrdQH/TAe59FAEx200EYXFdQxQB8hYmzhCBGO xYXSGzf1277crFCWUsMhCnKTItawLdeelgwdedeRnsuO0uHmEecrevachPSpr3HWRNhAgEDp Y+6U7wxNqvZTdH+2+7w/W31RSyqbvhqtrOr15vTCek3eo+K60v9A55yd/CHAAAxDsKIKZW5k c3RyZWFtDWVuZG9iag0zNSAwIG9iag08PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3Ro IDEzOTE3IC9MZW5ndGgxIDI3MDg0ID4+IA1zdHJlYW0NCkiJfFUNeIxXFn7PvfebiUgiCPml 32RIrSQ0aREEkcyErIoQJfHTnZGEBJG0Uku2bSrY7A7dUqntLrUopX66X0hLKaJdfVA/zUNV rbZZpX4qz6bUssp8e2aoZZ9ne+/zJeeee+457/kdEIBgvASJnBG5PZMnnc6rBEoczM0uKHWX n3r3b2OAySkArSiYVaH/c1PtYb47A1j7TS6fUhpwcWc3ICAC0D6fMn3O5Dkh3/8DeLwL0Lm8 uMhd+El0+0dYXy2/6V3MjHYftT3ABlv43KW4tGJ26UtjjwIhYUDYqullBW7UrrsJjGT5sLWl 7tnlQTaq4fesD/oMd2mR62BdKjDlB8bTs7xsZgXj5jXlhO++/Nmi8sG2QubE1gOhZdpOxPi/ 9YhRcYgBzHM/fd4S85zvzvdfXGZtne5+99ZWbMbn1I10bKNbCMdNiqQkZEHhBlv8K+7gNYRh NJZRO3RBRzyFLFIsE49FtNycZV7CALyKNeZ2qjY38v0r+Bg3GcFXitAH2Sz/FIpwSZ5Hvvln BKAGrdEfo6gj3DjJ+zpjWIpa7KHnzZtsNQzVrC8VgzHY3GfeRncsUou1U63exRLsIotZYJag M2LhEfHmSfNrxCEfb2IzY4qnBjUUNkzDArxOkfJjpl7DWngpSEyUGdpetpSFMZiBX8ODjThE 7ShHO6W1mL8xL8CC9ujGmEpwiXrRcLFOBZkDzdMYj/dxgP317QY1Xq3XxnsHmW+YH6IDtlMg fUD7tGTtD3fmmqvNdxDEeJI4ItlsZxLmYR8O4ntcFVVmFYYily3vp06kUxxH/KSIFC+KF+Vx 9GBvJzLa5/AXGJyRndiF3Rybv6MJ5ymMoumXNImW0FURJArFMblc1ssTitTbHG87unKMKrAO 7+EwjuAYaaz/McqhqVRGf6Q3qEkY4oq4oQLUPPWjuqPFeZu8P5rZ5nVEIApPohJVHNs3sQ31 OIrPcBXX8C8KpRQqptVkUBNdEa1ErBghysUysU5skdlyidyneql0NU0dUae132oLrW6r9/Zb 3qXeLd5Gc7vZyLUTwvrjkMkRnctVsQ57cZy1f4EvcdZXP6y/P42jp9nKTPod1dIW2k+NdJm9 hH/Hiv7CwVbLxLMcp2qxVNSy9WO8PxWnxZfiO3FdajJW9pbPyNXSkDvkp/JbFariVA+VpEao ccrkzCRrQ7RcbYO2SftQa7GkWgot5ZaL1mrr/IDDd7rf+coLb7HX8G7j2g3gSqrkSKzEGq77 es7BIY7oUUbchB84C1Fko0cZd1/KpGE0nMbSBCqiaqqhV+l1Wk5r6B32gH0QVsYeLwaLXOEW RWK+qBEvi3reO8VBcVKcEs2MPFzaZbxMkllynBwvZ7APFfJFOZ8ju0RulMfkcXlBXpTNnLVw 1Vk9pyrVn9R6Va8atSe1Ut5rtL1ag9ao3dZuW4QlyhJj6WmZatlgOWu1WHtbc6y/t56wXgso pxjqzsh1PLBEJPdgZ7FRhKkqamZGJ1Jow57Hcx5yuSuuYZD0cl5CfPeMrYOIVO19Ly1pyuD3 FbQLvWg/qixC8lRVTdhKZ0ST+kgMwGfkoki1Xs7QDgkbNvE0Wiw+ELsoHfUiVYwRKyToPG3A ea732ailaTQTm6iZ+tEL1IeqcEJ0lLk0H6nmGqGoFWVRCxgB5qpCPI2fXdSXp/Ul70oVrJ7n +bQDyzijm/E1vY1bpJlXeLpJnkZunjKLuN4XwDf1JnKfVXE/RvIEmW45hnqy8MTvYxmoKtGC f+OStpMrKp0n6QVviVqpvjH7mIncYdxl2MB9V4wh3DHnuUp289l3msCdHsizJJm7OgfjUIgX eOotMQ1zhTnPnGOW4RN+e4sS6Bat4o7YwS9ScYD3K/iCFnIfDvl5P//f8haiAZcpgrpSMvdD szZLW6xt1Oq1PdoRSxJHez6Wc0Wf5WoOZA8K0IjLuEEBnJtIJOAJxpvC2PMwXeTL3cigKJRz z3bjOZ5+z5OZrKWao7eC+3k390YLz4kJ2INTJCicPSpg+wGsZxjH+Vcs/RZncB5tY04hT+3u +I79DqEUUcH20ljTMp5aDYzpDL7laJt+XAk8Fxw0hnXdwFgUsoXeyKE6ZJrv8aTKhkMe5nh3 oVCkUyyt5Xcu7tAQdEJf7RsSSPBmmymiRO7m3xiT+av41ysaA+gZRtGG/biDDjQCvbyjGMNx IG3w6LRBAwek9u/XN6VPryceT056rGePxIT47r/o9mhc1y72WJv+SOdOMdFRkRHhHTuEtW/X NrRNSHBQ68BWAVaLpqQgJDjtmS7diHMZKs4+dGii72x3M8P9AMNl6MzKfFjG0F1+Mf1hyTSW nPw/kml3JdPuS1KonorUxATdadeNIw67voPGjcxj+mWHPV83mv30cD+92E8HM22z8QPdGVHs 0A1y6U4jc1axx+lysLq61oEZ9oyiwMQE1AW2ZrI1U0a4vbyOwgeSnxDhzn51AgHBDMqIsjuc RqTd4UNgyK5Od6GRMzLP6Yi22fITEwzKKLBPMmBPN9rE+0WQ4TdjWDIMq9+MXuLzBgv1uoQG z6IdoZjkig8qtBe6J+QZ0p3vs9E2nu06jPDKcxH/PbLydhl5NQ/eRkuPM6JE9x09nhrdaBiZ 9+Ctzfc3P5918FvRNdPlyWTTiziIw3J1tiYW5OcZtIBN6j5PfF7d9a/I7vRxXFN1o5U93V7s meri1ER5DIyaY9saFZX2vtmEKKfuGZ1ntxmDou35bkdMXRg8o+Zsi0zTIx++SUyoC217N7B1 IW3uEUHBDxJF9+/8lF/cRw0bdT+y5ENkz+KCMPQC/T/cV31wVNUVP++9+95bItVtaVJIRli6 TRpIaAD5DFYWMonY1JhAEjaB0SV8KFALNUNGOkNIGStxQ1q+CSRQqlU0wbpB/lgMbZfaKYJN dUZjHeo4FkoLBDp1hGoTyOvv3PfeslmhVDv9pzv55dx77j33nnvu7557HzwJ+rGmafxv6TQK L56GbvhVKbCKLMGOLI8MKQiFvfmsZ/uInun1+8JXCAzwX7o4WLPI0RiZ3ivEReZJnGpod8uR nJzI2LFMEbMAewof75H1yeNy66LqFP9qrw8C4aNSxHZRVX4ewj96NG9wUzRANahEGsqCdt1H NRmHKJCXUxVRQ9wSc1tSK7ilwW2Jm4f8YPJh4kd9asSTFf+7w5s2rPCR/IiS9m+al9rtxfP8 xWXVQV9hOOTEtrh8UM1unxZvc0qRYQVBLUN1SmqGJltByoXxzlwJDo2ITPwZktRLIhpIKRWK ryjiDc2x/1eljB59U5uo6Ukwilp/Zysprps5XkbycwbXZwyqD/JuaFiDvyJLLS6vDodTBrUV IQGFw0V+X1E4FF4UtRpq/D6vP3xEPaAeCK8uDLkbGrVeacqIFG2qwiIeUfJBVpVmd/qVxrLO gNI4rzp4xIsvlcby4CFVUQtCs6s6v4a24BE8RQJSq8a1XPNxjYoVEP2Q6pFNGUcCRA2yVUiF rC+OKiR1Hlen0OKoauu8UoffOOK9N+8ZKKECL/X1DZR5CyUbEn56tuGo1OkO2imqvU6rRS19 CSgy76Qq/ThVK3+lhWhbCRRod+Ib6yBVoP8a1Gsht6nTrWvoXwk8DdwF3A9kAQuA+Q7mAbNg cwJoxxgP8ThSnqEVZjd9E3MRsBNYBGzXK2kH2nYZ06mG9ZhrE8bwo7wb+r1GO21BuQXtVdxX SravpG+hPRflbXqlZZnNZEJHKF+DPg3zb2WfIbMwf62otS6hPBZj34f2jZAVkOWOv8Nl+Qzb yLXyGp/iMuJTD/0WYC7QBCxAfNh+POxGod6M8m3wawjkUOB2gY9W9Lkbb8UI5DjMX+Csm+S6 sY74muC/9OnG4JjOSgR84nWdB7qBNxN8S0bzINTiVXGX3D9e8xeAGWo3zUZcBnhd+lnrY4aH 6F2sqwvQ8R6d4CGrHX7O1A9TC+oTgbslakkRbbRKu4w9OEzfN3bST6EndQLwD8pUL1K6kUlT Eb8gxp8PLMWYr0o+LGEfrIuQo8RZSsdYIWAF5j7hxoljg/oc7GsQfa+ijEctPQEsRwxagMfY P8yfxzHHvn+sVA68gL4fYJ5iBuYcJYG12/tKa2D/PYylyHnsfbAlgPYViOnPgV8Bx9gHF5Jn DuRY7aSp7dZHkMOAdKAb2MJ8A0LAfu6D+VPQP0XyFZxhbjI/mBv6ccnVeey7vQZ5FpqcM/Mo 7BcAI4Bs4yAtdJCNvhyfGuYsnxd3bOYW89qVktMrmffKBV4ncypBbtdjVMY+yHnBLVfyucO4 a1niu4R92qP10GbmLPPNlRwX5hqfRz4TjixNWGuuc0ZyYT9Sch1cdKUbi7h8g/ZgzEpjC3ja SyXiFJXgJVyir4XcivUdgQ7rEfii0HLoAU+MxmAvH4Dt7iTZwjB7lBWY68eiA7Hoob0yrj3q V0WPousd1nmdlBN6h1ovy5+SyVBidhtLRmLbZ9V/Hqjv6B20DOULeo9lYT1b+UyYvcp4wOdK 6A8BDcBYT47S4lmpRM0K8uKT7zKwSgQoXw/QVBGjmSKVAohTJvQVxr0y727G+MeVXmrGfj1p ppJfO4/ciLnUd3A/ADw+5P0JPBrEuWQuudLla7JkznDehdQhR+DcvQJ0Aacc/Ak4DT5+V55f 3A2cn+X9gBwNNNt8tS7F+XmC2iB/5PIziadjk/hpJvMyWfLdwvld3i04p/Cj2V0/50fOcZwj Oc/x3ef2T5YJ9juQO/4g83A3VTvnegwwHsjDGEedPNKlRa3LOKPnjLesLnOm1aWdtLqM3dZz 5krrNeOw1YZ1j4nfqTE7l/F5cu9SjhPfi+49qmfRMief7ZF9Mb+8RytlHiBjLc7fCqrBuL/j e5XPodaGc4d4YrwN4nn6jjhNm+H7HdpLtl7MoxLOiaIOZeiR07n9Nm2zbJ8rPqI6MQbl5yFb 6YuGSXXGr9nG6pa6M3Yb6/Rq2gXe5Ymn6Gd6JwV5r3gd6mTrJO89zny6p4H2mgQOn6Y9og9r jmGNx6VslXxi25etPl6fOYO+omtYH/cB2EbfSz4nHjtlLGIyRjskhxELHtN4W743SH8X/X9C 6zwptMfzdeSnK5RuIpfIuTppvicg4y7kff0hzkcvOFZBjfqXrX9K/h+0LK0PZ6gX54uBp52e SiP0XmrFWWqU8bFlE58frZdSmSNYX7l8T/SC48/SY0YHbTJi4F0P7oIe7Fsv1rKSpqG8RXRY /ehbiDGI54a+TL5P+J4KWG/yeTFjNNwMYH70YR/k+w/zamfh7zZqRC6Z5emlZwwfv2sUBdwb CUywIevrgXpgkw2p89pSGY0x1kn9UnpNbddU8JvbT4gXcPZaaZZ2gFLEMrwfLtAGNY82aiXg 3SXcGRrsUBe5lK1domLtE3n/bNRTaKrsl4Z7/ByViirYx2iJOERLNAvl4cAO8BF2epSq9cV4 Zz2IcRyoU2AzhEqNJpTzrIPcT87xiZXGEGtporRLgPTVBfv8dILPOxDbH4AP7C/Kif6yr3E/ HR9v5J9cJ48LO9nnjzSLyHoPyLTlQJnaTB3AfvUU3uExqld24rHSRkXKWaDNwYs0R8pOoIyK RL3SCJQCQtTTPshxkBeAHqANOAr8TUymH2LsY5Av83cBQ/0lchck2p8FfgG877Ylgue6kT4R 4i80qK5PpPUMNRdvwlz6dP99NEk8jjw8HvEEtDoqZRi30yrTQ6vU09BzTkqq69m0S6yikbfy 51ZQ3qDxMoY2AolrdPcDMu0/wHsJ0scS52sc38//rY+fFdjf9cDDMv776RuSQ+cQf5OGKEfp QeUD8K+Nvs1w6iEZz304984+Qd8o9Un7B65M0eZSIFmP8gaGW0/e11vVMe5LiXB54MKciLcI IN5HfyC5jvvgSYbBHMuV9XUMtx6f92Yop0mIUxEkSY4l1Q0vrWGoq1FvIeb5o4x4vRzvqnKb nwzEdjkDMSQGdA8zEDtioO8TjIS4BjmumJNtyd0fl+fJ+8N+id+g35/xZi6n9GQZ57eTLwZx vszme7zOueRsUp/rZ+L62cBZudmY/0/A2TkJHAd++7+ei7MM5wgv54m38N6I4K36DL4xX6dm omuNRP3HiK4+hDw0AfJF6CpQzoL8EBgO3XJI3Eb9YNlVsHHgbaAb2C8y6HHnXTkC9ULb9tpz zniZtj3b9eG10z/Ftu/fCLSi/HsALOt/FXI75BX0j8CuCrIeug2Qk/7FevnGtnnUcfz+OH6c LI7dNEm9puk9bWo7cebF81LcUil5nCWdmEHx2qxy6Kp57SJNIK2WmrTQbklaFKlJ1SyAhhhD i0EimihbnpxpZ5OUepRJ09CoAU1kSAi/KC8QnboXExLSpvC9s7tWY7BNkOp733ue5/e5+z13 18d3uE5Ce3H9O1z3Qgz1L0J/g5DnB9jGfNAN/kXouNqPfMI59P/r/+H88VkdOX4NOqT3nMj3 42eIz+y35vNT/ONnjVvz/2l+6yzxb14dB+z53lS64+zzX884txzz+c+q3oduOmbWP8Se0tD7 aOxl9Z5b7R+rrvfbb+v9JNV7yqpjPFUe9WrvrPav8B/Cp2uuIZ9j5MvI64DK65dkmP+AeCgl Yr3Iv5/zNkWtPH8+59kYteJe/hx+AZ4jjNj8K6QIMXKUf5tMQgzhCRm+L1pQlVxdQ9SL+HPE hKYgTrIoqb62IBV/LrexRTX/LenZoLlTMtJTqeS8vmgy3sS/QSgf5U+RdiL4BHwr/Ai8DX6Y P0HcOk8r5/FGp9BfH8L7eDPpxOM49oBR+ADfTFp12LhsqPQzLjtC0Xgdf4D7dIiHu0kP3MUN GRXmCreQqcXP5mrvUvmdld7m6GU+zQ3ShKgpRG0Snsu8jnRD6k2Gc7Xu6Hy8ng/jNYcxLAI5 UrKgS4s/JdEQ+hvkW0gLnn0dx95m+F6+VTaL4gp26yrsO6oV9NcrXfcry7kbosV4Le/FU5vP YcTndG/zucCuKIkHeAeJQAyDOonaJGpePovaLKZpFlMzi6mZRRaz+L4RPoMnM4jp5idJhp8g 89AC6g402SwxggVd2dERLfC7uQ8j4V3B2FHc3ZyrbVCZ+WTjRh3my9U3RPsu82NkCGJIfiy3 yRc9usJD+lXuyflaFZCRtfUYuk2VuQDYoubgMt/Ct+qRaNMjYMcFrimOsIJQ9iYrqdFhf2Bv q/ll13Ct/DdVf6vqv634epGVcujFyrPfKy/Ht7C/orHH2J/JAmqMrbCrJALgTyyvsmDvsALp g6/h+gl4AX4//Bdy2xsiz/I5GHJ/Qbpb1Muyq7Kru1oR/mplU2u10tgSjfvZr9hrZAua+CN8 B/w1ViTb4VfgPniRjZE34BfZTrIH/vOq/5qtqjXNXmWXyC54TjaoFGxpKFuSTmWvSFK5SnaL VfYKu0A2I/RlGdiMuy/lAjuEZwXtUfYTNibbRGO8jv2Ipuj7CMqSNeWkkf1YxlQj83LVFAU2 z+YtX8zyW2FrkUf8kXBkkZt+M2zGzEUz7mVzpAaDh/+w7BzKGDEZVg9kQfNsRjpidvxDvJN6 L0amUGZ1LY0yo2sEpfejp+/pWh+bJkMQQxsT0CQ0BZ0mDpQnoVPQ09Az+s4YNA6dwOcjAyID IgMio4kMiAyIDIiMJjK693FIEWkQaRBpEGlNpEGkQaRBpDWh8k2DSGsiCSIJIgkiqYkkiCSI JIikJpIgkiCSmrBAWCAsEJYmLBAWCAuEpQkLhAXC0kQERAREBEREExEQERAREBFNREBEQEQ0 YYIwQZggTE2YIEwQJghTEyYIE4SpCS8ILwgvCK8mvCC8ILwgvJrw6vkZhxRRBlEGUQZR1kQZ RBlEGURZE2UQZRBldmKZl+KvAykBKQEpaaQEpASkBKSkkRKQEpBS9dXH9GAwLJsJaBKaghRb BFsEWwRb1GxRL69xSLE2CBuEDcLWhA3CBmGDsDVhg7BB2JrIgsiCyILIaiILIgsiCyKriaxe uOOQIj7/ovzcU8NO05QLP65sinZqnyQ3tE+QNe3PkGXtT5NF7afIGe0nSUz7CRLQjva0jxHh olLEPPEWfAKGoMego9ACtARdgQxduwb9BVpnO63tDo8xZCwYS8YVo2bJKBvM4xxyLjiXnFec NUvOspOZ8Vbm1t9RfFrIs7qcRHkTwo8Iyj5d62M96LcH39md+NfDeqwN75o3Q/RaiF4J0aUQ fTZE47XsQerQXzqTxBgSpymrPtAr1qBYINiLL9PcpRubhAx8QeTpasU6rS74DWgZWoTOQDEo CoUhPyT0vRDiU9b2apOrUBDaBpmqC9LSgl194waXVWBuuph73U1qVT/BDnArMhiB5WVwCPaq DB4W8Vp6iQTVNohexMxdgC9JcR2PX67Yz6RYgb0kRQ/skAzeCzsog2+JuJs+QoRDocNV34/3 Vr5PigMIe1iKTliXDAZUdAgd+fG0k6bIdbi/Su2o9NQuxR7Ydil2q2gXCaqJp04S1unVQMp5 DgndLNCUg1p3iXfFd8UN4H/HwGJ5vGPmHbBr/jw9YNWJ1fCLCI4LGa9T8fh9WK66rfyiWPTP iBfQFvVfEs+Le8VcOO/C7fPIe0Z3IcUZM88uWBvFlIiIsfB1cUw8JB4X+8QhP+5L8ahYVWmS EZpiFy6JJBr8Et7CL8WD/rxOca/4prBEUOw2V9X4kl2VdmPhVTUCJFrp/R6Mb8ifV2v8kVie brBCxnvGvHHQ6Df2GO3GdmOr0WY0uRpdXleDq95V53K5nC6Hi7mIqym/Xra6CJZtk9OrzOlQ pUPXvUyVKNSxj1EXIw8ReyNPsMT+fpqwi0dI4rBp/2N/e57WPfxVu6a9n9qNCZIY7rd3dSXy xvo+O9aVsI3kwdQypXMjuGuzs3lKhlN5uq5uTbfajQ/gIZk+31oglN49fX5khPhajvf5+hp7 N+zeO/AJRbpadt3+891ZbbO/l9ifsn/aNmJHVWW9bSRhn95vPpoqMA9zDw4UWIOykVTBkWGe wX3qviMzMIKw6zoMq7kBYSSoDGGufmKqMHxP+lUY5qgSFwCOuG3KEFfnJgEdF6hz6zgHVXHL a+bgwLJp6hgcY9d0zJqf3BGDFQN2YDkQ0FHtJk2pKJpqN3VinbohIRASFjqEYl+nGxJUd2Z3 3w7xV0N2fhSyU/fF6e0YUYlp6rgV09SBmK7/8W+0v4vm7hufuDo42j6Ybh8chdL2ueNP+uyp w6a5PDGuHpg2D6QPH3lS+eOj9nj76IA90T5gLv+L7WqNjeK6wvfOzJ3Hvjy7632ON571smuX xXjXb5cNOwYbTC1TExLipd1iaHhFabCxjaM2NY4CBKdEcUvAtCmKGzU0Iil+BbCdoDigJqVS HwpVCYkQVhsg/WEJtci4tXfcc9c2BcU7e+fO3DNzzvnOOfe7c6OXFhFfouJooGoAXap+vGHg kra9ajCqRasDW6sSQ/FYQ+VDtrru22qILaIsRpU1UFvxykXElVQcp7Yqqa1KaiuuxdO2qnfT uq9vGBDRqsTq7871Q4zRADXcqPgTq5xy00pa0CMr/O4OZZRD+G1kDCf6TYFV/WZoVJRfmV9J RTDPqMgCwxnzInfHCr8yit+eF8kwbA2sQguhRfSh2v6SDbX9/o2bG2ip9GtbF89ZC/2lxW5U vbsK/nDfmm5wPPgkaln017rYr62trYWe2sItCNX2L91Y21+6ATwRBDDVWJWAseULYyybHhuQ pOrh2TEQhsEJ3ErN0aswDkMENQPsugSml+8VGLpVaB3y+gr3XIAVfD802Mcx7YMF0fQuon0o J0j3L61DBSVzPexPaT/o9ReChaEyeJX2wbles+bDRXewO7+7rDfYm99bxsPouVMwmH2KLqWD BadY1BpuWQgEXLYmINjgFrX3q8EsX9pwL70IhxPhFpyO19eDjReCfj+wLfNaW9LqWxcSMjfe guYenhOG2xZeapt/JS1sS7+SNgjUCxRM4ICvKQGteo/BOi8MM3HNjgins8ggcDpGHpEnOsN+ gENIwv3YjdxheTKWiq2X78bqUjEUh2t5Bk7RiN/qtwbhBESPZlR2bEYjaBqp3BjYQpeB7v/O hdK2lmsKW455vpwzSH0sw/AhrJIIYUif+Md3qP4kVRqbRPGJ+EQ0Yge9GNpl7NFvYw9rpv3M v+mZan4NUGwlo0iGpWu/VpRH8gxrXdu57Say1FXhqnEmnLucpMJVqryk/JwcN5JsaxCWH7st mCGLntw+AQs0v5KxGMAf0eydfqz6I37Gb7WpSJUjMiMPMz8ZUqMb3WEAnaSo6+Rk82S4uW4i DT+eRo+SzThp9xe6nE6bI1Pg6RHwY2tRYdlKpqQ4FMoNBV5jfOcbXxhuzC/bUffitl+nruC8 68+X1WyJxZ7ZuPIsGc0KXdRv/+nsi73fr12azV2cKbHYNv3u9OlzO2wWuloeQ4i7A0iNqFt7 VCScIAZ5WzbBEdIHwSMSywUZzBikoBGJAl/LMjUGZMRGr2qOmDUza+YkFasoAjGjiEwPIlov JyHqdXdjd2P3MVkrCpLNtG4RmR0b9FWQ4dnOQW+6G7BXQA0l4CGWyDFAX2T1O/zz7RgXn/kn M55S2SIyOqW/f09vvgfe94D3B8B7Ce3V4uA9T4KCKkbED8UbIlcgdsOXg4jmIEjgfxw+ihn+ MRbqlPGqxoiRMT7sv2Ex/5PUfeq8jTq/mH897ERqBfNU6pfUt7emUj+lkd02e5tcIFeQij7T 1pQ/UvvIJmGfuM90UDxgOug6oEi8i1dsLpuSZ81z53nzHhFrjN/hHpc2G5/mfsT90N3qPWc5 J39i/li+Kt+WLWwWryJKFtneimzQDpCwMyufl2yaxVZsq/22Hds1h7vYPozztKXO/AwWPlJU zxYYzrVtYrJVlQXIOZEcJseT22vAGYZsQ8TAGqA+h/wdb8yhhuqjoCl4+e5EsxUAA/bU3XDy y3B8AnIXSzWHYzBMSxMnk7jEb+W5QM4SKEZbWWmRyrlIKBTI4R2yraiwtKyEjTMdSf2Ns7f0 0++OjbzyKbbiomX659nvdF68+dUHyfdXM8q91PDmro/wzis38VNb1t38Q9kzP578lz6tT68r HgWcRyCU70GGWbQnXTNDhcXFhE6uQDDda/FMVzEiGqknnWSckGzSSJrIHcJ1EgxZZpHIsNeA lfrROGLH0B3E0Gz/Be449CwXXQC+d5544pBXDLdQiZBe6xGcR0b/uwb8OAmV9hY5A9T2qOat F6hujg0SJHLEKzDsg0XER0ceLCKd6q1LzaumWv2OkziPGSdnptfdo2yTgEq5DZWSgRT0pvZE D+kRT5hOWDgRCxYxQ3Dnup+T2m1Cu/U5xyGuS+wyHbIctHVlHnYcdh12H/KaBJuYKXgdNm+m 1+3wCvZ8s+TJF1hnbp8BI4NsUOfyrKkRn+Zr9DX5On29Pl713fExPjm3F+EMoLkIhJgWQ1bH pfvFkOakZJqT0qwJKW9GSXtxWWkpzTayysivIpxJkw10BIlPrC787c6uIVyFD+od+gV9RO/A 0VsDA/+4fv78OPPX8RNNg+Fv6s/qv9BP6nvwq3jXf/TZ2dmZqWkaB8pFU5BrGod2LciTkcwR N7uW4J3kb4SxWYNmiwUpMp3NGUh0fo1nndm+yDw+4pMzHsxJ1sNUe59p56f1/9kW8qMC3Toy ecq1AQ8D0Cg2YNpj+Atseazj9Lae9U9f/ujNvn2rv1dT0ktGnf7rfS8N77Y6Ule5i3rj8m2V 9bvMBqiZZYBnBPAIwE6BAinCRUi91CR1St2SwGPCBDmWEZAouVxebj/BZBjnawZeUHEE7ad+ w62VtdQzTUwn081wjEdMvTuHonZDwwCjlSfS7JSKwal6e9WX83hi6UKDMiuh3IRv6HXcK/p6 7uLU1PRK8OooVNsS8MqDXtbKBVGQBNklOqW14lpJeFLaJB+Xe6wnHK87fyOfd1513OQneaPZ ZILlTQjaJZNRNf/Zgi0QUy1HU+qVRoVtUjoVRlUiSq8ypnAKhhmmeiKeMQ/roaH3RtseXuX2 TibnZttEPAZLcnrC2f3WTAh7OtgwQ2QLE8ihy1vJUZxntL/6fEenF+dFXvjszKfXOjJ9ZHTm 1oXyzT/YefwMG57R9anPjye2vv5ExyStonWzX3HLuZUoAJvOZm2X4BWziM/p/ZZSk7Uu+IV8 wyqVetZ4ngzt8OwMHQr9zHPUe8o7onzi/b1i4nmzw8l7nLn8NxwJTztziDnFn+U/5k0fFl+T Gd+Swqh1mXmJFv4f2dUC3MR1Rd/b/0orabWSVquvLRBaKyIYrLWNYxEt9Sd8YwPGgwDV7rS4 eNrBOCkDaYfgSR0ooemEhPCLU0hKC20zE+IEsEkpDG2hnbYDQyZpJ22GfmiMQzzQGQZIY697 38rQdirN7tW+fav39tx7zz13lpE0p1XAKRQzepLjSSrZHCNcNNvtMebFMIrJseOxz2JMLDYT Z5EJoyTDKLQyYUa9+YQZkeGkhY0EiMITDC+5HDNJHMM928Jt28KMmTDDNP3O+JyUkBYrXIUy 6ZBElUl4UsKS6VYNKdxiYKMT/Pq92RjjbDrREcRXg7gl2BHsCdLBULZ7/n2OWzp2u3esSBg+ U7q6ZnsBuA4CZwLM7WLmml3kMm9xVEPbqsHKGO4tjJUuhlFy8typSMxoS34lSRUzhSI8AbWA dsulkOstkgzSa2qyVZBAtF8NJsCDOseBK6sNQhm1JZLAJLcCfvA2DNVU43WTmSuXfja0mI7M sD5xyjy94EjxyJn2V1781ZLWnsVt+Is1nyRrVzUuacrKTupvsw7uKew8ZQ1999kl0dqQ0Nw8 +J3Vzy+OziiPLmuqt64oVZqeq2+vStUm1wHkOyAa9ticEkWvDiNl8p45x1lXG3ksQintXLuj XW3XCtG7PFfN1LvqfdWRJmaxa7GvKbKHPyA6JDcQPQoT7c7yfuILn9PpQY5gQghvjOO4nKbo lGcIp00Jb0R9sF4oli/h3ZtbOjaR+/hx4JoS04yReAeW6YXK2bDKdHZxXY4utUvrjrLFAira 9QagU4BUATA94IOEeMCrO3DomcHzljUxvOYtUzEWPlX8dv9X121nT0/c2mONWJ9Zt6wP1xQG qId+2LLx0E9PvvYqUSAr4d3zkAkh9Fdz2SpPQQHZ6ulWutWt2lOhfdQ+6YJ8QfuD/IE2yo0K o77RwD3ON9c3N7BIWaQ2awWpW+IfUWrVWo3ezG727GC3e3aGjilH1WHlpCq67QiNGMSeUPyG O+siI6G4YVuP13CdxgxyAGaK14lMmIpMmIeyL0Ccnsag6OFWeZDHZBQnUKWL/HAlWoBewhE+ 4Q+FV5WgJKrkTnHpWOb2WIbIEVAjGaJKMmBLtA2YTgkPElU1tSwJOlKeIBSZOdYN95dburdu +1prVwD7M7d/P2rdwOrY+X9Qn1ataNv9kzMDa3oqf34epzCDeTzjKGGR+YCdDtj5URT/YBjJ EDfNzroD4kHXXvkYe9TxrviuaygsCH68gHqMa3a0xI+5TnInwxcdv5Y+cPxRusffdbminmjA hIwJmG6v4QmcDVwK0AEbnXjetu4gWOp5U/K4lVZ3p5tyawomzV8oYuCsQgTf27Fyw7bT0iWb ebhktahtTQ/QC9RwBM0KhToUhTScjFPRSLQmnTxK4MpACdTKeEe8J34ozsQ9CcF0eQwhFJti h0xJ+QHIt0mxJw2vXzMr/HnNjHvgBJSkEe4ibV4hP2EreAU2ATMUshmYpExRF7GD96cC7di9 ov0AghtKHdn0YJCY42+Ljkfty/mJvN2uFq4RRinay7tNQMlNFnWT5d0mgGX3owUo15kMSLQc dEJ29YDswcTl5VAwiM8RnbBriY/EBM8FqX9hrWb0TevGs93Y/94YVrgJk37mS19YrdNb2tfm chgvrzz42ondH2EBZ6yL1pmtuxbgr39zW0PDkySPNAiIj0GfqWjIrKph8ENMuVzuLTB9Gisw ZzUqoHopv6J63T4Pkt0+jGTKLwoeJ+5wTkKLQRzh4LDXo+JJFavkMi7D/96Cv+Z8foeYzQst QqtACxVypbfDS3mHMGO63L4U5e9Ah9VzKqWSmBAlQw0FtwxT3ajkM6CYcaj340UQAKFrSAOS If0JHHk41VV54DPFy74sYeBsVZC3sySQDUwHupmuDdQd2LTlyVTDo/Oqr1yxRgaYVOv2/hXJ X8p1yxZ/NH6KXkhy4SXY7Bu2BufR5mEkEtXtdeRNsVWk+sTj4jnxsnhTZMvETnGbeBgGWJrj EcvQHoRNW2vTqAg8yrEczzgoHvKMBK6YSBpMSMjnSm/zQIHn7Vewmyx5qrA8kfHBdjEcL+GQ NYJDzEnMWOOfL2JSn39INM2DHa6wuwQzTfYHPQHVxx5nz7GX2Zul1mAbexgGWNgMDdREpzC6 vxMUYv5vJ1NrZ0vrTnUCTyPE7Qdm0HH9MErD00VYC6JPCnCqZNCGYGjG9EaqSWjSGqdL5XRl eoXYme5LH0of4Y7yP5JOcCek4+nL6b+k3ShdmW6FG2fTV9Nc2gxHjTxc99k3WT7B8OEYCZdB B5+wo4bhZa9Xj0SjKR2UPeeRU4rXXF3d6cU9AM4Q1Wx6wpFULApjPVHcGcVRGHtnBggpDDVq ECHdJh8xT6xZA/vWYapuzocjB0dSN3TzkXlGpX5Jv6rTHr1M79NppJfrs/VJndFDFX/P3RcT mdIHFN2YPJGDLiEDoXint0iMDR44j3xJ3cOQ1MhuG/ETGRKOOONLBIhOCNpqIagGAGJDJ7HJ 2T9T938+jeld57r2zm5+fe2m1yti1khMX1a/fpY1Es/XzF//sDXCpHb/uG3lyraOtY37JwpU x/dn5Rbs2mtRVPMrq2c29x+YGAef7QYlXgCfqeiQqfG+oG+1sF5ghhgM3pIbhUbPqMxyBOKY l3e7OMnphMaJwikVmeVJ402EJ+FPwhpBTZ2WNF7QDmvURu2WRt3UsOZwpiQ3wdflkmxChkcO S/gWKLRQcCqmAK8ppB4nCjhzxx6wkSIY5Uh3BbD0FhP//fJeG6Q4FWAK1khyWd3Cb2Qg+Nld 7xUPtpRR8TfWzW3tH7TKmNTAOw3r+79FcnU51K2D8KYuqPr7zAXX8Yhw13c3wFykrkMLFWJD IlWQ233takHbR+3n9gv7pCHxfepP7J/F9yVoQ7nrLvmo8Fvqd9wvhAsSu0nYyfULtNeOQmeQ QORneH8dH+6MbIxQEXcC/U+ZLokdu1m0hU4vKB2xW+5SutRujcFE6GBoIBV4LRTwg9BJpmb4 /yNzlj83MfBPbFi/+fRF6+5zuHzvhg0vv7xhw17q3+xXfWwT5xl/3rvz2ee788f57LMdk8Tn xHHsQFxiJzik9RVCCoSQ8JGSdHOT8TkGawkVjFbTSFkHtLDSTeOjqC1RNakZm0YgtEu6TWIb Gt2madXGHy2aNtTRrZuGmlaMtRKx97xnAxkT6/bP9o9f5/d++F5f7vnd8z7P79EPEv6Z/IX3 P8j/5KnC6EujoyMvjI5Sew/kt3JH0V4X6rvjxpx5nsUeRkmxGTnjSVW0s0vkJZ72io8rBKr1 +pWi2rtu/bjChudnpq7ziaLL6bip69z1Docz6nLRY2OIdyq7rqtt+CJdV/5F2xHq2mixTLXd ZqWo7nhqNHV3anNJ3LnDZIbVBwjf9N3PTRImf2Oy71A3vmLfsxvX7tm7btN+fLU96/O/y0/n r+ff7uid/jM7Of7tF8dfefkEOuQ+ALbFtH3UiB21EMFBVlk2WnZY2Ealz/FZxzaFswtOqUpi DkkFiclK3RIjTTBfMOqtVvRvluHtMRBcQhKLUk4I7lZOKMyAsls5pbypcIoLooQ17WeYYTKC Qjjgzk6SENyUt7fc+Xou0FVMP8gEendmbpGKIegc01Z1jqVXPNR32j53HvIQNn36ViLi3WSE evTCLe2D/WseuHf+ykYuenRLe/pvc+4/mf8AbUyiP7vQxjjzY+Mc7+YjtjrNrUWOKcfUo3WH 44JV7VAZ5fvypONC+N3IR/J1na+Xe+UN8mHxqPKKPilZ748YNe3RTfr66D5ln7pX/3KN0BJd xHeIS+VuZ0d4gW7Va+qiLVI6nNbTkXSNlbdb3ELYL9dJuq5HrDW60fCYtEt93Luzfkd8v/ep +HHv4fhZ/WxEHiaHtIP+5+Pfio818FrYZ4QjKZ8RqkpV+cjvMdU32cI9tYdqmVrDPytVG6RF naFh1O1pIMkG0thAGirDSRdxNaH8LUVmc8QtxbwkyJiXErsmKOU3MNqaFVwpgiSG6Arj8FUo FmhGmieEJz4S1ZvDHeHVpF9bTzZr14mdaAwXDOtMzCNLTCw4wBGuIyb2BEmww2NFrYB/bkXL 3ERuqGIS9MIvxmNxLFGLoz5RuDxeWUPXl8eraorrQNBcGxU42SKTZr1DPyZ/Qz+vX9T5sC7J HBekdryKahaaqK4d12ZnSUn4mWu9NkVHYxbmPiBJYpAewg2SYTJFWCAuXA2iKKc7PT7cSYjR BRwZ4KY4hprgM/DWvibNwPtqBt5UM9ItKc1IzMGuth47vK9Tq9IGtEc1TusNGhi9nUHSEywE mZLxQ7TsNduVBF1eS5SyG9WilIzixf6ikhrClsuZcram8DNDEJWsM4Yd8vDX1+SMpEoZOj0j ZZChv5wWM6ZkJfh7jIeeWp9Z9KYx1dWh06Emo9nPUpSpXiz4OEyFKtYu0SQJKo+s+3xLrepd kv/Op7506d1LF2P5v7sH+h5NVoei5Ef9fdfef3uaNCZW9sZCjdVe1d1534PPP/ODZw/cc9+C Kl+k0hvauLRz79d/PYanqKrwHvM1y4uYE35p1FdDNYnY652tjqWOfqc14AU/6/OCpnhUoimM SvysYLVbJT+l2wnaiDamsYM4nNNYDaXpGSyiaJEBXt5KQ6dDEoVGeyNAIxnAKEHFa8zPRjWl 15tVT6inVHZQHVafU99Up1QLqC61Wk2qHJZ3u0ZuionOsRaME/MxTkyCWjg3r7+obK/l2lzX TGWL4RUjLm69gjLC3VRStjmCMlY1OdUoaVGk1B1JN6Vr3cwT58S6UN1S/9ovLnsiIwpPPkmC XPRyfvWeRKjiUrxpxaJ7DpNfXf7NN/NPIz9fxSiziouiPnjB0Na4N7mPWFiBD/BtTJu7k+l0 /4mxOqmpbk70gd2romhH5R71eoEGSIfPVAlFef9vVIJguyUPbGTKRmz/LA9maoNiirlDHeTC ad40M43SwDS7uZlO2eWtP9y85eQyEqhamV28PU4CJ3rXPnzyCDOS91/eML97xxVyDmUy2imi DnoI7RRJheG1xIKNKSvteNrZaMdOFN4ax9EU6NXB1tRxjvCsaLPZJdFLvIzCBoWgXYfZ4gVR wrM9ZfiwPrWDRVQhINZCXExBq7gPhGJIOmsnsmTeSxS0FEdAIDzYIZttw9eYoLIwU2EoItg5 0S4IDEN4nAsZmf7CH4qlRLlKTsqGzMmaFnTZs/ZuO2ufYJKGyDEZkcty3RzLvc4kUaANG04p DaQaQwhLAtJ59K0Ada6Ev+tqDjNVLrB80Yb2P5prU59ScapkCD6CebQTOVpFmkc9TMIerbml ucUTJuR7+dWk7o1WjXe4fk7CeWRv+p1XF/lmz2Yqi5w68iu4lciph6TOKjEL8dCI6JecKZtP dqastONpZ/Hhdwy1rAp5xYKBk0UH72LAw3MehmNZQsvBQUwHE+QUkuKUGx0xqPYmvYNedgrJ N+NlNEVHQwlVprzICZdhDX8gtZulqbrOEBhzxRCGrhSSASPUnKqGJNUv6vnSeUt0TQewp9xM m6QkEkPbu1zXrqByyzUWmUFe3KZmR37cGavDhfIGSvzkOsdceFxb8bie4VzwegEr2cLUadZF 5mHrN4OjpfCe4ZDdWY/LE8BO8Wct6C3juKDjGVwX79XvCXuQZquDjeh1ddSlWxwkkf+IRPJP L6xduGZ3z4rlgQXptQ8HkHgH8+ENZjK39l7d/Vv5sX4ota3/B/z0PwMZQ5H6COo0O568nhLe ug1L6jb4NgCrdAdeBrDt+e9gfw5APAggvVGE4yKAcwZcO2fgHQDlCIBnGEDdBuDdWoQPnzOA 3wdfA6h4CSD04W1UbiujjDLKKKOMMsooo4wyyiijjDLKKKOMMm4CGCBAmwosnZEggodPbOxd r8y6NauL1cehAaARYG4TrlvmzdjWvqgDFi8BWAbQ3bNi5SrofXBNXz/Apz/5f/9PGgf7sQ+B C02VoBpa4QFYgk/bA72wDjbCZtgGO+HxQgF30avt5tVuWA2fgQ14dStsp1cLf7jbp8T73dvd Kb7ZbPgcpLTXjT0pPbkbP8U5jzOdvllOwG90aCrNGXCgHcU5i9+vK805nH/lH4Oy2YDs5a5u fl7uXtqORZmJOTpO+TkphAUYXBncGPwYvIAB5sWgzeAIDIhMYKDkAJOCE0M+kE5hCAIGUTpD KZCdCJQlrJ4aKkAhwqrB8InBBuhbVmAICAATZigwiPyAccgM5AM9zzgBKMPBAmSBeDCaIY1J CKgdDtCjwR4IGByAqaCSA2TMGQ5RFiZobDA9S1c9M70unt/mK4ckB1j1osc24Cyy9eSyrl+/ /vwVcOUQBXJB8QM2GQB+6gnmCmVuZHN0cmVhbQ1lbmRvYmoNMzYgMCBvYmoNPDwgL0ZpbHRl ciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxNjcyNSAvTGVuZ3RoMSAyNTMxMiA+PiANc3RyZWFt DQpIidxWC1QTVxq+kxeQKI8m6K5FvUJRwBAnQFBArSEEjMtDk4BIrXUSBjKaZMLMAFJUIFoF X9VW8UlFbX27Phof69m69OjxgULxVdFqdbVutb5bfIPuHVifrbvn7Dm7Z8/OnMnc+9/v/+93 /0f+ARgAwBeUAyEYm2QypHSaELoWSa4DoAjLMPWLGh/hnAVAoALJsqwOwsVc/m4eAB9wAGCz rEUcnFowrBtatwMg2p3nyneMOOfvD0C3MDTfl28vyevdB1sCQIwNgNAfbCSRu/vwqFAALPwe sTYk8CW9YwDogfYD79gc3ASPcYMHgJ7IhjTVTluJW6ZH0QDkpAIgDnMQE1xek4R5SL8S4aGT cJAD5jfJACAg4pPvolkOnQNdBP8DXQzpyq3ejYbhowGQdUIDrP3m30AhR2/+XPxLhrsVXhKf iGlDp93vjHkJat3yB7hb3iLAMLUv3kni3bEiEIsBPlYi7SvBRJi7vwAT1Rrx4bjyJUnQyh7l QWBg+50BLIAFNLADEnDoGczfOHzVnsh/394o+k733FsNm27lwLfXl9a6/d7F3YI69IQLFPIq z9EZV9bs+0pzYOnsyvqe9aaseXjn51wxEaJU8Zm6J95dIswUSeVdskiGMlH5TmhmClkOppNc Mc2MV3fFA3mATO77DKCEBqdVpVbiER0LIS80KQcJTRzhcFHOfGgimSLKSkIjTXPqGDyqA903 PQOmGrSJhlSDeRTU6nT64WZ9khKGWcPj+sNX98B7dO0c1x/XqKPw/ji6ctA0Th0Vrf7H9H// ABXLX/Y5JgbCitnI71WCigpwXAVv2yYqI1UVQVsl29bIdgZ0HnnG1Fx46VB0xLYT93zei/nl 6twnPp2avns7508NP96r3FpTNz302qRsf3bchMMFgW37s++Fb8geUy1qi7QEZFcE1RfMPxmc 3e/kEYV4auzu+es9acOu3kwI3pS1eHKvZfZpdcNSFo7zrI492eoTedwTt1QgREn9WkoIEa/4 gGUfiQcfu1r+uPTkupaNJa3i1gWDCkLW9Q07P0tOVj1RTsc+zlliqQ9YU96yc49i59GsxeO9 Lfr9K784oykTB3/PRIqmiddM9OnyqUJ3+36XtG+95iz1t2c/kWoW1lctPy9yLYuYRMz5+oqs YMnaA3mWxEEL5gdHLQqumvEo1/udu8ceofxtQE+sIBB8FbDkjO5Gr8fJ2VOr6pMr54beVIz9 /0vijeo+eGiH4R7/nMazk8reeNJ/i+Iz/0h/5Z8A3I9f8JJ7G5wcyThJDq+o+VVKz0RRmM6n 9AbihmfT7Lkpc896AsZQZ6VllrkSdUPj08p5yacM8fOvnpC8W7Np5YSc6w9brfqMXTInfmtl 7IZIn/N36D4bOo8YK9ZklDWaM5p2KhObZU2zd415uqO86VK1pyzYkOhvP75oC5a1au83quXx LWVrs1efCiYvz9owYdmfT6ck2t6LnNS2XYAJfyOhHWMfL/7gc+rL46WuvpaQHklwxOaQwAOc 4KHh5z7dRm+cVqDx7nvv4+8vbK++MnPNHy6xB4f61Gw5M/NM4Cf1wss+oVmSH9M/T/ni6Mjk EwOy7vZq2Ns7ITI0qnHpxb8MSfmp2ZFSdLkOX+VX3ljWnDC59uGCCHXfwEcHFTfObbmaqXUl Ryon426f1ejxqxUKMIHAvySv2jllS9MO7C1nTZ2HLHiZsQAlNPEbXn9zhKJxdUfAI55nhI52 OEjGShF2aKLzuGKCIeHwQoudYm0kw0Kdtj0lB+Ax6lgcf56S/DQqWhOnicvB3dj7/3ES6mQ8 qUNpUHFxsaoIKbJIUWWlHf1QB6ZZiqOZkn664SZ+D5pxqaClBBrJPJWSz2tVqjmJz+VY9WB8 YIcdTRKVT3FoQ0MS1NkJloXRMBKmUVaGZhGFFzyyCDuVS3AU7YRFUWoZ7sPrS+SCTJNajgfw E2+5dCTB2lDpcbRT7Y/7drjCy0jmOmhnrroHHsRLhIrAF+Z1iCPNtJt9ti57wzpyMHy9itxY Z4Dk3gI3hgHP3GO91+b+7Vrg3qeOUm2G9CEdUdCo+p1pdVTshRO2v2raDG81V7eS35gUcI/o 0Id3D7kc868f+XJzBL4kKnvijnXjQ/MX110s/kl8+dal6vubZL9f/ceBU10XH9CjMybRfkb9 jMBT5NkEKL40aIV9YbyvLFR+o9dhOCfuQ8sU8aGQbq3Gmo01qdWnBqZnD3KX3vTRZG231SXq VyaoVz1uXvA484By7aq94RmNLZ/eFvYsvRMYv+7B+uFTxA7L7ZnyygGnLwX5sl9LhuwO23ut 4ZOCA3vytq0wB38ry5/4YHpJ1cY86foRj9qYXq3T3t/fMsz3ejYRkta0NT73gvyzMQc/cqR2 2TzICxXyKrf4HO4Wn26PTne5SIADXMYP/UQioUBci1dU8jNMVFGOTy73L63+4aiuzbbolwFH nAk/y9wrrP+FQnKLBeizE8N78UxEGPZU1BVX4PyX34svuy5CgVc5QNFGEKlIgiPykiG4WxT7 EkbKq7pFIUjcsza8vI+N41xsfL9+/6IwVriFuyrcQo/ZRrHQSjIclUdZCY6EVHvB8MlGsnzV MGQeyZBOK6mEhDMXUhwLC1kEYyHLMZSVs5dI2ULLONLKQY5WQs5GwhdOeG6Xr5fhDGHl+IaI WhNHOkgnB8MQk3AposnyALUKR5sUEZSdsNh5Jq9ae3EASHDx0jcdNIFnrY90IDMIB9EOkQxZ UEiyHDvkVRzNSBH0GfDVmCphlCYuGoWRQB1SW0QiQRpd6OQIxCqLIouVKIQwLgaPiZZmmrQI 5yphqHwbxzdJdVxc7GvmINTa7dDII1j0R8SinkzmqqBObzRrDenSkVqjUZtuNuhNMMlg0qVq DWn6JKhNT3qpD6ca0gyoDaukPDrdkJ4SD81D9TDTpIcZyWhoMLWbMyQbdFqzHqKpyWw06Myp o6ApM3GYXmeG5gxeRZqlN/6d+SoPi+LI4q+qukcOReRSPGIrK6ggzgARFCGOOMgozMAMGNaT +0g4lEu8yICIoiu4KFFR4oW4l1Gjn+HQoEZ0dY3iiVfisdGJbkRhNaiovW8GEcKu37d/7bdd M193Vb9X9Xvv/d6raiV+eKm6yCvVKiFYI/cLVfopUA8nCFKoQhG2YQmlVhuG6wnysNAAtQax mHWA1HZYICiDggOVbzErwoM1Cq1W6LQKnaDyCwybZJilc9QMcQcpNH4B2O2wUq0R/JWhKoO6 Pz7LhWA5YvQLC5RrhOAwTbBaq3AxLvKxMjBQUKlDzSYqjE4KVBgV/NQqrSIkDMEr5YEuqKJS hiqnvdXpAKtGqzTCJHmQfLJC6ypoFQozg52G/cIwxyQFSgVq0dN+qZj7KRiy1LjuXIxPTMey EBsjpKSmGGgVlxgbo21PBHkGZkZUJiaQWWw26hvJnRWZlBkrpCdEIg9SUjOEqFghOhVfxRgn iUwXIqOjM9PaMzAuNS3ZmDNmWe3bDUogUw0IlHJXsx1jdB7/TZp3jCelxqe6xifGSXP3GCqJ wOXukuqkOol5REEAKXiuID0IwYHhEhOsKjyPFdRu4HvnRydJo95JUuk0qa1dt3ooxcMK6e/T MeiUbvRsYudO/K6mCEmJkVGuQlIG5sKvT5dgvKR2XSrdAM5EKsFqh79u5x7DSa00cPuCsOsZ M1cNO1YpPEmq2r/If9EXWxbWzJME2FrFnpk14nmId+G8fU9tvLKvF39prhuzZlbA+nrwMtPW TfAUV1o7JcNkj9aAQNe0lhMXc15PSh1afL5ky9/XPdKLcOpYU9rAa5tZysGj0Yvcsid5f7Fs ZVt+gedwV32ll6dvzat/5jnI8jgvrMEeaLo083+wf/yHw2BPiUm7UyjPw9bcY1L7d14yZbKu GwuHZ4zOnrms27YjHdypyMmsOMt1h6ymfDWfUYcLs6eHFFjUSeO6iPeUhUunbXXVuYAc0iAR IiEJBFDhPQ1bKszH3kS8J0EMPsXhUwpkbPuNbqiBY28pltxx1DFyLCMtMzZjwdzY0d0OOlwe gRDPfnNzW0ltZGVtTa9z0m0V5y+p62coi8eM45vPbXL8m+8FcHW89YD/dHBK3vYRTYVTP760 UFpUt/iIr2lF+TerJ2TLLX44fm1WS/mRzxe0lQ05HFL44JbDTVuXIc+so58k3roXvjgtrnTd tSTv8zV1X8umWuTRTTWXi0qeqA6VXxK8i336TdBHNSSEceUFOU6mbm7Zpa2ttUtuTCnTxlv/ zkUy6+w/bq+ynddKBzUn63J8wq62qrLrN3kfnNh8P3epXdN+YeDUth9/+cueeTK2Y/d3R/O3 LVgreTHg6upB/SX9yxICXLbSYKvMEHLcRbdwfTMfNL4g4ojJ4+3Nqd+t6r/YwUXq/8GrbXmk Ac96ZzojJJHlkcM4VG2gXm7V//1XLbWFw33Krvs9GtLmH55feNp/xZphTTYR3ej7W2m/ruw1 f9fpQZC8797wst7GD5IP3bzc3Dzc3b2m/xt5d/WUfVZZPq7tpOnonakVLKo7qXJ1XuxC3+qC /Qfyb1wpPLDlrn/ACm3zgWkTa1uaAuJXqTI2hlRvbNidkl3vaJnk3MvxZFrW5ATHkLLIpidZ NbftZ5zNfa2/PKr83iWbvSU65yk/L6naF1U5JfzR2RcvH+746Fm/nLGqK3fY/nXPZsT9oWV2 j+EPb01Md6vT3Cg6+qDPmHNtCQpnPAcyB3oIGABfxrtj4VvbfqfmEEetTHhqLpFQShnlcGw5 dLmC1Co1ZpXQRtv7PbLIHgFgy61GQ4+vAXvjvxLsOUewBxD1Hf83SaLe8O5NtNhE9WITfxSs aKP4lD8CPcUruA7eu64kvueiPtTH+L4R3nO1y8FDfHz4PhlQgxsYZvgMnoOe2EM2LAIKfeFn 8EO/bAa1uAdagMBLuCt+Dx/CffEkVpZ7YjFKKWAevEbdAbAFbuPYYaw+d1DSGm6CC/jC76Ec KmAXNMD3cBdMoT94o24hnIH78ILw4nHUtUPvDIARMAUy4WuohSvwIwJfCebwAfb18AieECum FL+CQSgzE+ZAFmyECurMNGAFq2Af7IdTOL+eUGIvzhQTxIviVbAFB/AEL1BCLMyFUmw74SDU oeQ5XOEaotFDM7Enk8kskkGqmANzZTpRBzMQ3XrYANWI8TK0wmtiQUYSZzKTzCUbSBVdCINh OIxCOxMhHXTYlqOVB+EEzteKtB5IykgVuUcV9CUzY4PZBraR1XCEm8OtRn/xGFk/1A0BDdbj T9HiRZCLrQh2wJewF2rgG2iGNqzvU0kyEWk9s2F9WQR7LJaJe8VrGIVe0BucEIEzjIYx2Lxg AtoYDtE4XwJ8grbOh8WQg3Muw1YKm4z+/zPObfDtITiOSE+jZY1wA312B+PwC66HGxPhiQ3p hx5xIp5EietHk3hSRErIbtJITdEaFUtm+ayOnWDn2SOuLzeWG8/9xBPeRzJSUvxG/+ax6CYe EGvEJ2gngx4Y7UEwBLE6gyv4Y1PCdPTuHIhHv2VhW4iMy0eMy2EFrIESRLkLo3MaLsIlxHYT fkDWtSC6VhAJEBPSB7G1t4GI0Y24I87xREXmk3VkF6km9eQCaaKW1Io6URn1oGqqpVE0msbT tYyy3mwoRtidebEIzpEL52K45dxe7hBaALwl78tr+Ar+W8koST48gKfw069TBLMiCvKMj3NM HLhqMo7m4J6rhm2wmZSSAjIbblOBbAAJ8uoY/Aktmc1CXu17LSEryCiiIQ1kNfGkA+gMyCGE WZBebCk7yhXBZNYLlpFPqAWpoQrWyHZSa3KKDmc2UMvCyBJyllrxPvy3tB49NAwjcp1LgJEs Aqayx6yEeWEUYrjxGBkZ5oI5HQv+pAWZ9UdkfgOnJw9IM7LNjjqhN2+SClIBKmqNXL1NQmk4 lZKl2I5hRlvCSfgcmZIHf2WWABO8P/IdN9bL08PdTSYd7TrKxXnkv5iv+tiojiM+7/POGOwD bDAclHd5nEmxLUNSgkNdOLgPfyVgEwN31KXPxlBD2sSEJM7ZoXZURahnImgqIUD5w5ESFDWR skdR5ESoctU/oqpCgVpu1OYfpDaiH5BaCFCaJrz+Zu89c3bU/l3rfju7M7Ozs7Ozs8/ffHBN dXS1/UDEWvWNlSvCy5dVLV1SWbF40cJQedmC+aXzSoIB09Dx9Um1STvlWKLaEXq13dxcx2O7 G4zuIoYjLLBSs3WE5Ug1a7ZmDJoH52jGCpqxGU0lZDVSY12tlbQtcTlhW+PK3o40+q8k7Iwl bsr+47KvV8vBAgwiEcywklV9CUsojpUUqef7ckknAXv50nlxO35gXl0t5eeVoluKnkjZ/Xkl tVmRHTWV3JRXKbgAXolWO5EULXaCXRBaNNndK9o70slEOBLJ1NUKJb7f7hFkbxPlNVKF4nIZ YcZFQC5jHeLt0KiVr53InRgPUY9TM7/X7u3uSgutO8NrLKwRTXZCNA3+paqudlw535kWJfFx hTrT71OrO5JvGUkkMrzaonj6eLF6WMslqw5ZPMzljltirCNdLI1wm8nAaF1t2850BF7byRMW b2NnWu4ARpWqejjJPN5mYcMH7CRznMOWKLG32X25ww4Oa3lO0M5s5MLy1tj77jVqTVq5zrQd EVvCdqY7sSJfQbmd2V+2xKyW2ZK62nxoYSHS+bJyrzN/QXHnwIxM9qQ69+C1H2qFPbJbkCLC 2m/Bk7Qt1GgDNwcaKLe/AWr4yyiI6CHEz8mFNvFBGNGQbeXuEBLBvnljNqfb45jR0B3iLqfL TMpB7vdFTY1Yu5YzJRDH0cKzzXK8oa72edFm94cs0YaQUXsakzKb6hHySIRPeXQ8Rj0YiJGO dGFsUU/4AsXqazJCdVgy4Usqd7FkxJfMTHdspPNFPBFElSJYPfMrDy1ZnOzbJJQl/0N8oCDH 9Ulaed2I5trT1d250XC1kzuRwdGkcBVzuZRtpXJOrnvcHemxrZCdy7e15fqTjr+lcXdiNCxi JzJ9CoIqHi5EQyyOp7Wwmin01LCGXtsTdlvH3nSDd2hCj+LX0msnew8hhUZ6DuO88Os+wYkW yYVE690I66nR0BX714pQFguqCAmlUbqtCFosFBx8i9CWNkBYx/sMvHhvO1HwSbxN1wOu3HnR n/pzZRLfKFzdJwtQ6+m0tpK6tPNUDmwypqhFv0Styklqg2wPsFF9hZZqU5SEvoPxBtCsMune g34T8DKwFtgCrALagGZPlgQ2YM4bwChsbGc7wOO6SnvMg/QQ1iJgEGgHsrpDQ5C9aISpA+Mh rHUUNlaifwz8l4wBGuA+5K3QHQRlf4fQ/w7kq9F/AX0y15MBquiOew/8RVh/gH0GXYX1v6+d d2+jb8N2I+RPgaZA2d9HwK/gPvCCt9eL4B/hPuLzA/AHgDjwDNCG+DwN+YOYtwxj9rcEfgVB 5wGlWjOtgM565WP6GWgU62/09k1y37xnf0/wn336L0ixf8WAT/DRvQl8DPyxyLe5OFoMzG3Q Hqbd3hmVAuvVx9imjNcz+pT7OcM8SXns6yyg67203jzpfgA/v2VcpDMYPwQ0MjBf0V9DHG5T LWSD5ml6HXxS1wM/olVqL1WaUdqI+DXDPuMJ2BzjfIBeJ9adBl2lf0rLYWsHsAdrvwnqx4k4 PuB9G2eL/HK/Qp8Q2x8CuxGHY8D32Ef4UM974rNXlt27jn4Ua20GOEf4jJbx/gtnSw7md8GW ItcpnEWBOjL/nvJjC3zOPviQueaBbcG+qta7d0DLgUrgLc47zjlgB/AT1sH6QeiXcM5y3nB+ co5wfuiX3K8wTrDvhT3gbuA8vHuTwXy+lxWAZWSpzUNED8r4dHDe8p2ZsY38krnt0yDiTZz7 yqu8T5n/92lWfw9y9gF75/yaobh7sNvLFP8TZiV9B7RwLwd8KuPC+YY7yffCo51Fe13t3ZPV WhSU48c56VMvFj7VbtGwjPc+xCSIGvMFsJK26Mdoi/oZrdEvoE/uP/V9dFo9R1sD79IgQrED /7qenUPPMAJTymHjQ5rQJrHPy3QWMW3Sp9QH9CnFMN52/6bfVCaMt9Ufc//rtBiI9/8HDsKf vxtTrqtPofKjtgf+UQzFYr6PYI1yJvikMh7YhRpJdBt4Wo/BTgw1aQI6lQBRFPxdyCuuE1mj jx7l94DfCbODWrQbyLcpelX9A/IZQP8CaH9RHs3Oubm55FE/X+dSWc9Vea90XcUdnnR/D1wF rgN/BW4VKO0DDvH7wDWac5DrtPYWPVfIV8TEz89f0TDoc35+fi1P7+enLWvd3LycQ/l94Rrv 31P48az+Zxm7RlkjUee4TnKt4/fP159Li+YfQe34t7zzl2mvd6/5jkeBJGy87tUR1GL3uKyH 59w7xi33jlYJetC9ai5x/2UMu5ex79TMu/qeV8twn/z3VMYEb6T/luqPUqdXz5g/qE3L2KyW byfeUGM7pYxt1C7fF+YFvTuIeHJN0K7Sbo4xfJ+v6QW+NkpbuCbyWTAfvCP8Lqo3pDyul6Hu 9sHOVdAPqUy/RY4h57h/Yh7rMGUe+298RGu4Fui/wNuBs+J9sD989oE6Wmp+gW+DS1SHecN6 BfY8jr1cknSY4yDnvuN+KW19hliEKQrZkATPeY3CXjyGimMh32eOBWwaZ+Taiv47qX/QPEPD pkB9ukgV5ih44wX/zN/IN6RE3oUyegzxGlIv0RG9Rp71IzreWC2Bb4yg9xY3I/9qMP6EhrW7 6PPe70Kf6z6/+RtRC5EX2F+Cvykw74j2EWIzQEeNn8LGb2mZ8Qn8DSL/x6jeOIezmHS/LNRt fPNgbfC3cX573zN8ny6aXVRh3GIf4E+z/HYr5XWhK/3FmzgVaKeXDZPWIO/KAH5H1xS+m9xP t7r0rtpFArgGaHjpu/DF+az6XbQhfE3EgBFAozG01wCVLLWV1gH9wAgwAVwBTHB2YN6I2oHW QTsGXAE0jNrBm0A7Daiwu5PaARWrNMNiM3qE1h+NAKeAMcCEZjMsNMP+bMkEMA0EMa8J85rg VxNsN2FHTZA2Ya6DdgQ4BYx5EgNrNc2ao8/MuAJcA6alXjtattA/x4qJWSmslII0BWkK0hQk KS7JaC1groYJ2ynYTsF2Ssbk/sxTgAAmZiyE5lhplxJfd8zTLbYYkPq+LlvXYX8b4m6hdQAe jQECmAbMrVWQxSGLQxaHLI45PodH1yQnpOTJAtYp+VipZmXXZWPZ/qze/4HyH8arPziKq46/ 7/643c0Gbi+5rJeE3N5xBRJSkgNS25hrswl3UbyhRHI0uRx3vQTTEDsKaTjHBnuTP6T8mvZi mcEJrQ2K/LCtk82mwCVBE52WERwFwUodBoIjWlEzZBSw1ibx+y6R1ik67t3b7/d9P5/3/b7v 29033xchPRDRVYZ093TjeaCnkZFwR6mcSoC0QE6LxNQkKvSXNWJVrC6r18pV9loPWQ3ruPWC 9bp1yipIVtCgHKqBq+yHARiD8zABt2AWEOE1vpyv5hHhB/gx/jw/wd/iZ3lEWI0tZ6tZRNgB dow9z06wt9hZVpCIrMgu2StzVkETyoVqAQPKh2RDHpcvyNflKVnoFwaEMeG8MCHcEmYFQU8z Ln0ISEpJuVLelJ6qT8VT21I9qd5UVjw1lWLmrOOpC6nr2BVc57znxs+x+7h9/Cg3ynOFXCEf 5II8V8VV8a9zr/Pceq1fY6yapjHrnf1OxurUnIxkdVo1RtyaD9X5ej6Wr0o+s9UB1Q7dwRCH 4sB1c5B8JOT15jHVeXoeQ/KUPGarvdfOVNt1O4PnRjuS7FiUisZTFs14anT2Nr4RKpwxLwta Gs7on7rcIKgb4w5tY9zmUEldHVYSOTZRH4WrqEnwhpksReJxMxlHccxMXtNqZPguaceyS4Pv QIj7PjmGDg9CyPyWqg5D35yShpiZVHFIxEyuQNFkJp+nI58gSb4CR9ZDiHmWhHFkEAdMuHDk Wgjp0tki9cNksfaPyGkagLwPIVh2arn6++Qa7UayJgtOo/EChKQNsI6o5Dx6uWq2qz8fRt5B 8zPqz9IQGrrtUc9R+ZNF6tk0ddqbp45iGsPzTk8heY1Zpp5A8M03C9QjLWm+wtS+FxnNwIfR itPop+Fk7L8KoZw3EFHJtzHcRjOkvkIHvqup30RK8auYj0p6EaKDU+h7q1mh7vnRvUnuQtOA +ajag5NkT5vPq88hJnwNfau4K4csFeY1dRualjyZ8fRl6snUOpI1SmaNj5JERh4hzcVByoB+ 0oiONQibiWPaD1FtZLLJegQCQ40Dhbj0mpkY02oUKMKK4sdkDJFFOOSL5NOoFaJWRspQKxhq vOFBdv6Jxstu9YPmYereVP/emIYFp0rUSwmv9svuNJ3LLxqHF9+i2EgiDfJJLd14TTvanOaF oWPqy0jP1bOXqy/hZHYj8KXu4exWOK3b1E3ooU6u4+vEjRJevWkg+iqh96bQ+7bQGxYeEBeL LtEpLhILRIeoinYxR1TEhWK2mCWKokXkREbEbbpkDF/FSmyfw/ZTbBwYuWyQCTbUQtAY30yC rS7jboMnDVlfaDZ4Ty0YOUESDNUaj5QGcR/ZYDxcGjSE+kjTIMCLYYPZjfMINWHetL+z0MhZ 0zRMAEp2vlBI5ezOF8Jh6HIQtfSTl4PeIFj/7Aiuv5MIpcEGVHszqqPIOBBsaDJeKwobq6gy WxQOGlsbXJuahnF7uhrwD8M1KsJNw6wbJgIbqJ11+8NIO5ahkXa4hjSSpAJpWB+1Uxpp5/5K abj6c7wIDkdeCxXIE/aQSIYXEfZkeHwF5Q1ebA/4B9vbM5xlG8jFDOfisg0f4+BLiWP9g5FI hrVkN4QyEwst2Y0sEjQeyXjq7kZOojvDgb2kO+OpG/ZmJv/ZjyjN85Q79yh3MpT4R5TGOQpz /N8U5jhSYBtm97eGJrPOXRfY58f5se/RXkumZybb6wJbPIG4/3/TWiL/D22EXMSs55nkPo95 7oL/itz/aquFodiVvh2BNvTrCbRhixv7vrrFYfS0ulyDfVco4DLYpfHWzVuobGkzrnja/Eaf x+8ajO24D7yDwjGPf5DsCISaBnfobX4zpscCnhZ/eCja2br/P2LtuRertfM+zjqps1YaK7r/ PvB+CkdprP001n4aK6pHM7Eg0EE/t/qmQZHUhtdsmpNDjJyFX0+80B2uVZVtj2U+pSq3I1k4 whE4TuTSsJHtqTUWYKPQipoVNRTC4xKFFqLZOg85klXuwhE4Pg8paLbhpzz/CLD6CGxJwx8D Wwx9X9xwefyGhRpuzhsWo4FQw5/nDGn4k8dPYl2xrsz1CWX7dmyJrkQCuzG8fbyVlma07Yh3 Qdf2LkrFToKKRBdVtt/7YQX6ICEwiMUhi7XUukGeS4PXJBZhFLwIArxzgmVJloVPw8qTLMt8 XhI4qgJZKz7xtKP0ceW2b92073Hlrm+dMu0j1T68T9PbSu9qm9u2xG1zPwg7Z16D4pl3efJP 8hB3CAPOzsy+B+/wlzBqhZ7PYpxXGNbOMCwDwOKx8jDzDM9+gwxDNuGUO5PK7Un0XO3bxZeV 7nrurZVeAVYDA8t/MHMpn//LB3jIwUoaT00n+RHMIov8To9XkSqeedSySqgWvVkRS0j4tXDT 8gdBOms5I/zGwpZYlgqMiNGkLJbjJEEC6CACOhIsEsd18BY7z1skgWGbdTErS8YJchwvWgRG z34y28hmhRFmAbEw2bpdcsENHY9XFSREesg4mcIKOs3IujUuQYUUknqkcWlK4iW0nXBxN3he VnzKZEH+dLQT/wUOVJVpx91oJkGEfLbKcl/BtC+ncj7bXWUOKgTF59ulvIXJQ2c0SqIlwLpx F3bnysCdnHm/Y8a1eWYGfgWw+MhhWDczxI98uJdxTV+nT7Yb16YD18ZGikgx6dNr+WyQFh5g D3B9S48yZ5feXCpI0kIQnbk5oj23ubjcCU6nrSinGQjj0poVmyjy+TlEs+Uf4fkHkttkkNOM V3do7V5bvY0hNsXmsrFzwmvjbMPwEilR7j7TGZ2cjk5+vbLcMWnLqazEhllO+2jLqSyPoqhk ecxrpZfEorFoZ657lRPy7BZh7m4pgVzb6lUPPwYPVZSBh0V9Xu2G37596Cux4NPPHuxYO3P3 X2RXe1BU5xX/Hve5d/dyd9ldnsoubFeoCBsQEEflqgHFZGRNghrMDcSYUo2NoIOP0SKZGp9J 3RiNUGyho6Qm0ZAxGhEykdhMp9P+oWlNTZv4aMNoErvBIorF7KXnu0BqpzPsd+/eZe53zvn9 fud3PrN2oD2SPquseFHvy3/f/0TxzOcO8d3z31+36u2sYNe29svJLu535nHeVlk+e5E7dsbc mLasbL6RAaeE8MgNrhZ4mIre0AMtthYvmU9KhVLbfJWbRgqEAhvNJEEhaKNejzcpKYVq3XgH cuJK3eZusNMSXAE07MJ7TkfQNUCeoi58QrclN3By9UTs7NDA00+jDh1Xs38j+fqEhMYKKSyR ammrtFdqkzqls9J56arUL43A0HAG70ITtLujVRuMAiVig0afAWsfoG4YDHacIHAZ6ZOCTq2o MD8vwZsgQjnSBafmzc8rpFtuFo2gnpv7jr752r0/7Hxmyq2EXcZLR4/8rOZlkrLs7penPsHP 4g0Xu3Yv/6hsw9avzDvmzW8OgHY2jfHDjpLRTn3ON85hJ1lCK51HvbRYKufKJZrJZUrFHE12 MErEqVWKXXIg1e5uzBXqhH6Bxgm5wl6hU+DC8J0IjB3Jaq3PHrKH7RTZNbtv/BKyc3bGj5Qx frCFkeN7ZoymigyLDC6Pm6g4w894MIsw7CcFMzbhnqNvRx5bvK3nNXPawPsNK+dmT6l+OH8f 3/3ICbPvzx+aN448zp37riD7yd5fHv5gtdMBHQdthhwdkKOCdutTeUm2KTJiiqaEw8Inooi4 KkzkKiSFHJjDMhKayQFdqVGaFBpS2hQCjH9InxB2YLnWh0IoDGAjDfnGLyEwBZaWfTQtw6w3 BvtYXoBitCQKdB+XgPUZoz20SI9/7LOZbomtJ0/FjpB2vvue2TNk1g9B3FtAwCchbhmt1hMh bqEqF85xZDxgJGkKFruwqntyyXnST2gcSSNryF7SSc4SgXSRPN31PyETDd7KQrWNIVBv9K1l 3YcFaUT/L7ItdFqsGw+ZMouq417sVVbNRaCbU6AbH/qVnldqn+edl0yLvEXJpRIN2gukAjv1 er3JQalZ+yz+6/h/x4s+VAXdNtXj7P/ahV1doCC/p0GlqQ0CtF5foqvDeQBaTS0JUZ3W0Qjt pTztIlN1F0oPpZPExgpb2HbQRjUbtjGZ+CF2qHKM9RBwByhpLqsz0wu7/55GBi7wO5liAkAe VyA/jxsTjEdLYIopKiBvEaXCvLLvhHmr7dcYv3cJ0x/cSomsaO398ZPN81pmkJShWJexvRMb /xzAS9/6/GJR/cZ/DZlDuzfMnd49xqsZFq/26NM5WaI2haNVss2GBJ4nmIiSpCCJl2rDYp3Y JlJdrBH3ip0il2t9PyteFUdEQewiTj0VEaX2v3Qapdc4cmPkMurXMnu1LgAa+IW1jOZsYTcG H3PffGcGrJsH8Be3b5sBvjt2lsweLiMvxrYyDNfB8hxETtGjegFESpFUwa/h3+UpobV4PA4d dm9CETjIWA9qUB3iH4yJs2KCzUcdbHTrdQN893AZ7PEisPc8/w54YrZugy1AXxqP+S5SqNto 7YPvEeA90W/rGYpj4XteHCBP8e/cLx9i/r4AGPcZME6FHvWqvqyM4AV8uVzmoIVkOl8s0yDJ 4gsd1OvMkoMOmhSflOBNet3LHUzEiQ0cbQbSNShyWyr2NOpiGDBoErk2ETM06uC+XewVL4jX xFuiDFC49Li4xtH0aQVESBDjHHQsY22UlZ615qgBf2MkM4x4zVWY7wMHFAW/b1Iw3s3I5Zwa BKYtaB0+Ae2/9NhBM2beP3hsuPfnez84vX9/N7mIBfz4G+Z6c78ZOX/61Hm8Lha7j7mRwbuw I/PseEDHgRLRGj0ngf+9i8TZHVoc9F47SlRBTjCCSOFk7G4sseLvFzlNDEFyFFJIAeWojlr8 oPCtOicx5VgjGmtTFmaxB+nDUoLi5yUwD1bxZJxEHrDd7X+b+/yejSe/aNu+45lZ+tPNfLcn /Upn26UlsUvcObMm+Oz88hXpsBNTBYwgyIP8qE33vU6xHJeolStVSovWknRdg3lDw9IKCTtk RdXAJ5fp2YrDrSiOZBnL3qpcN0Zu7HYjPzQ5FUlqj8+BHY6MFMtwmMEk63HI7xmnkG+sp6Uz +7QEwrpZfXS8r0GuxcYONWcy/1PtYwxzSA7MHAZmU4eV7fjYkRDvpzkEksajSQcnBSfjzXjk 46OrO5oX/mjpPvPqu9UPP/pE3smOqmnTQumHP+S7K36789ifUqa9dMz8By45vsQfO0QXTlha XlapwdyIZo5c51TgbSZeqE/I8q+07dK2+35ha/GdyPw8TSrl8fNpeB7CaX4/K8IcWXHLsiKD PSUqxcoj8gJlmWwoe+Q9SqtyXD6uXJb75aiirZR3yq3ym8pfla/k+4pYpaxSiKz40zjs6cbZ yEsmnE5qGALSs5EkW89BWMM6DmOOLTW4CUdwO+7FF/A1DAPsIngUwR0wPX6J72AJRpVUvYA2 TJIrfoittwZUb8cmzy4P8XiEjgDuWIzWo+2MVVklgepATaAp0B7gSwM4wIgXTO1AKvapIXWO GlZr1Dq1SY2oveo11cYehuGWUweytEGmpGi9sQXwSTSiWmyLdXW6EizTvAM/95UY0Tt98GCt Uc/QUwE9awBG9UZ8kQs0Zo0EOdDcZwFi49OQxw2D0UQCs0OGb2Y088oLjdlVx1sjlT/pfeXi t+JtbdXy5atCoeOnDy++OWyW4cup5o11lXllUwpyX3ivdmPv8ut/cax/+rGZM7KyHipoOLPt rPkd65cB6GVXrRPGbH2KgAmpokREkk8ukXWZ8p+KtejTEMHMf0OkidwivM8yYUpyIZXBWJ+l tqilMbDWAmaueJfZSnvMQ9y5e/fuz2K7wFFMyGWzGP6j/pFGNSFNCdOwUK1EaERoUy7QC8JV xQ61F+IUnepChdJEm4S94z9kCb9RyMT/cF3tQVFdZ/y87vvu4+7j7rLqhQWElDoCAqItypVE LEjKqsSNko1QiXFNlV3GWrGmovURNBY0E5T4zqStY5xqrFbSjlOtTqd/ZNROGmM68dHGR9KJ 1XRIpw9Z+527mibdmXvv4S6zc873/b7fA40B7vAqe9Ee9orQr1xFV9i7wkVFV5AKOUZQNFXQ G9FMNl14Qkmy54TNaAvbIKxXdgjGB+iS8gm6pbBn2SxhGWsX2AV2WnhHPaezBWpM71TbdPaU flD9p07Dynz1sv6xzkIoTEMilaksKlrKzVWQr+DFKQCjiMQH1xF9cN1eqeUpoiQlZSUggwMD gezWVBh6lQGndVMWoJQhSkiSDz9GVNQoVmHThAkSk2XH7jS7FwCm+t1MUmRVEJmuIUKxLcbE NmAGJoqyRin2yrZMCuVKOSWfk5k8RHJtT0CzNVKoVWop7ZzGwM/lvpWHS50kdieRBhBy55Bw HNrkydl7AoU5QQIA+Wt4fDWTffWBJk2aBKwS9t7I8kr2k+5CcEVxxSgcHQWxNaphXJj587+O f4ij7749nPkwk7n3NwhrBr17H0T/3/XsW/8ZAgatefAxXcqmggiNx6Pteg8STCGk5vRF+/L7 CvoKXx674bFLglYoVoqHwjfNm6HPzc9DUlBs8M3z0UsS9kSbo33Ra1G2IHo3+iBKc6OJKAGK GW3X2pFYhJBIXWRjZG/kSESIRCzdpcZKrpfcK6H1JdgowSVxQ8NraxH2oFxUiqjiQVjOBZYC gA7haXb1Auuu9cCiFgTFOJM8ZXh8pPXLcUaUz7r2cvPfo/fr+/Wj+kX9ui7rQ6Ta9gfWwa9c wFvyisuKSXF7Cnj+bfwZKuUGIDF8J83JexhIHLzASFdiuCvtVPNG7R1wq7U1hkPmwAdZHkik cTEffSBwGjBDBcWQFvKLOB1UT6yudNgAQ5KUgBNMh9Lx2D8UHXlj+2t1xLRuWN/c1LTm9bqV 6/fX5jw+ZeoMbI1LvBCNNlSXt40lfxl/YOvsrb/KDG3e1LS8vv7NH8/fGMsdm/d0dcGkzAVf eHR+/sQpcx5/ppu7k2XQrTMwtV40Bq20Z6/QNkoDEm1lre5F0iLtBfcKSRwVFyWC4T/UYFzX DA/1htublcVKSqHKMcBwq3cPLkP4kedi2UebY75EEDc/srh0p2vuZPUtG5rucHzW3nGMCHdN vBY+w0ugCMVBf8DCDkdyH7LsdtWahtfe+9MrM7dXeCrbqpq+u7QllgJTeK+xMfPXzGeZv2eu PPP0blLS2z9z3+ETB3ZzNqqHc60GFIbQz217g+tVFxlw443uXu+glyYD3d7uABXdopn0niDH NcGTA45vPz4Kg+rBoCNXaTwoudamHBRQfQgk8OtK3KOV4lpwRO3oQKleqxO9vcwVc7W5Uq4e V7/rqOui655LQSD3HC363RwMPyu2d4KYcZiEHZgkHPcyzA0M9/xw4yAZdjDhpEcsFTlC4Jj8 EInmkyov6IVJUx/lrH9q8eL4tBbfR/WZX7x3/pNTg2fIpxN+1v/mqd3z0qWZHvzEDWzgMa/z vk6H8+982Nfn7boOablEBlXcrfaKvSptoS2uDpoUk2qHq1uURsU1ycPTFXRY0MIgBe5WvMfb DJaZoGNGe6eMZX4Eix8Bogs00vj/HsLWowVVHMTEcDYcMirwl9tId75f1d+0/YM/7mpYU3V7 ZLC8dXJTindy9/z5uAC7QeiDjY3Eez/Vv3XGGz85eWAvP8dEOEcH9NFEFkb2DE8e3uweIAMi TZJu0ks2ihtU4fviCnWVe6fInhcXqUvctCfUY5EQ7NayrZTVY120xJjVBsvr1j1L8MLoD+GI nWd4fM2+Ph+FBuVCj3zByF38ANq79jTCRzlfENNuiAXbgieD1BfEwTjTOiN4TARH4rpk+SC3 IbIVXUGgWhi14wOlodoQCbWXGTGjzUgZPUa/cdS4Z8jIsA1iACZO+L6oZK5TySwUgB2MCihm V4Jf4B5ucC4v5RMzwm9AzWnHradxiNe3uMjwVk+smBAyDSnKC+znbCGJIdJ1c/xvt5xJrn5x ycFfL1mFITUtfbJzAu2Y3jChAuPZpft3rR2AQqv7N23em3kn74eb8fHVL06rWwH7D0Cx28Dt mWinvayQ4STrZr2MTmaNbB6jQdMgAZ9puP0e5HX7gQxIQFa0uAc4Chses88k5pAXif6Aui+M PWEMeofls4Z3o9vfSjyB3EBp4DeBawEh8Duz+X9U0Yf2AUOAyai5D2etGa4ZTk9IP7T26Zra xMiECg98HHyhhL8ia5ZCzoSMwRXBAoiJBYGe6h/M/86z1qSK8nGHD9/qYUWxbesaC895J82e eeX+SdrApyEzi+4AFOWj8eiQnRz0D4y55KEvuV/y73TTpL/bvdJPxdE5eS1m0t1hLjdFtcg1 Lh6QcsJi0SqXCpyA12aTCJQKnUYX0XXEFPhDRlfbWIoRFrc8Xwup4dYcVWnNaY6kIiRyzMBD +OBJtKWgvdPAxhdSMQyOMRtknCEaSTgUAJ4x7QwU58eH8Swk8PZWT8VT8KPGO5NVVWEE+Ivx uCob2LITthDL4pS6ptJoYeW65m0Lu94fnLWh8nLLkV19h1vPL31yzor0nNlL64u/UZ7ja1my a+7cHw1gbq+Dc+Ov3p9x83yyacdCsv3Q7r0HfrrP4dCV4Bu3AYeY6Ht2xe/FyyIpFqvFekK9 IJ2qpgWCJnyHdHD6XhWXqs3qArVPPaJeUK+pd1VZHSJuOxREgVazLIhzg2VBkgc3OxgLsiCX hRBUI93lVIJjvYY3Hkym9xGlYCPrjc0g9Lmy2OFFWOEF1Ru+vfhly7yVU9Ayb+FtUIKbseZl zzUeG9lGys+W1/WdG/kUtg3dAonDbbCk4H3nv4XpEHnMrhQFUWJqp3JUIVQEIyxgIrXiMmaz GOth/UzIY2WMd5XxBX/LGN+u/Gi73GL5uMfiQP3H+fIyP+gXhav79u3b7JeY/Zfsqo1t6jrD 55x77rlfvva1fW0nIf5MbKekjQ3BhaRZc0sIn4viLQmBhjRmWlegSHFSdV0pFFYKIYoQrqBD QwkdK4SxrgIxKIHtB6uirut+JCOIr2UKmsKySaVUU1atg5i9106g2uTce+65zvU973ue932e J/vg/moauX8Lcvh4BYbxBLAoh+RO/gxPgDZxvgg4sxpS0GrpIwbNkSade13+PQvilbk38Jf+ sxx+9yWE+E2A6DC6aDxXKoTVRUJCXSY0C5PCpDotTKsyFahKXK2K4m/lBYyYZrcXFhWVhuVY NBnNRM9EqU1rcyShS5nNyWMoRQWFbfOSRRmArjmPhUtK28J4ACEUjoeNcDKcCfNxGFLhNFxe DrPA7P1UeDTMwhdxDYqYO9ldowHCa8Am1cydzRBq8lsLmwvf5/jOWenKb/CsDjJ3eW6Tc9tt Ne+9dLVqX2PzG9HqPWvW/7huasnShhemnIXfrlwbmaKRd1qam1tamluOHp9ZTzre23zwWpaQ 5R8uXLpsd//MA8jTayaGc/zxlmGE9YROCvXX9V79p/op/aLOvtaxzYEtMmt1WFXmEGw2xYLb YlAWCMdQI+qAHnUajaAJdA+JyAS0W2kz5SHxW+IWEoCTATNqeYznu2YKzCIGgqiFUE3lBy28 HQIOQlxmr87FZc9Hb+q/d6aKVjxV/UJk6lrVgdYX+xLE9/7W6jVvX8j6aQTYcfOJfsByHTDh JxCJigrRZmPxoPiRSH7PX+eJgiUwD5xnG7VvU6igKKPzsL5xpbBOSAlpISOcEUaFLwURwUCE IaIZqnVjOqfOuE7IkNmXimDl3Tk+Au1aC9t3N0860HQXOYBqHOBIWUkIfbPb1G3/dMeVL3f8 8fWPH6LP31zfuv3Nda07SKgfo13ZC+PHs//eg8swd+zE4M+PDg7C+ruzW7lhWL+GitFeI3GY 4R8Cb/cwTmBtXJv6MvcDYO83OBOqRYBYfZui2DSrnVixZ2OjuAkMEQd2aIlRolltbZqJy7iG A1pcM7SkRvNDSktrGY1p5m5486Izj8VHshPgZ3bZfIOdBeCc7LSXfDO+7utPZprevX7j3frX lk5NvtrUuLUz2ZyGDWlOZSez97NfZP9St27mH9zFc78cOHvq2IDZMTuh6byfi/ENwwd1zJMk n+LTPDfhAJz5HUSWID4Z+g5oyvXGk4IgS0jCoipNSESSQI0A8Now5gaSJEPIbYIDJE4MkiSU mDHZ8wiDeGpAM8RMg1E5K8PslQvNoLrAWATnsJVjSgAbXjt1bXFrVePKlVWV8VUBGjm8pT7x r4oVl76GNZcBrtbCmqP4C+MzhhQLr/bIex17intLR4uvesd8V/xjgbGgulpuUBosDWqdd7mv zr80UB+UFJWPJnyr1OXeZXCrHm6tiO5T3vbu9u3y7w7sDt5QzOdHc8+7w7YqJRGo97X4un3d /hO+874/KDcVpVguVootxarudfo0vzPgDG6QNygbLBvUZm+TL+lvCjQFHUfkfqXf0q8e8h70 ZfwHAweD4/K4Mm4ZVx+/wDHgxr2F+BkbliWv3z9EbMaHkqJLkvJnCStKr0Iski6VSFuk/dKg dE4al8YVab60SloPPkbxeynWgaw4hB04gLm38Dk8jLmP8RVQhJhzubgOD/a02pMc5lrDil2w PYEVr+SnIavq2u4iMVetq9O1E5htRCf4FUwCOI5TOI0p2AVdD7FrCP8EDaKP4A3IWsY2hopG UAjHQ6lQOpQJnQnxIbO7WDqtt63kphU3WrHVrMwyQLEpE7ugJrebTaWgvSs/anDTPOwOT5VZ tXfbYQYmE/4AIw6TouCb9q6u7h5rRbl1hzas5WDf1Y26oTC6upyL82CPRqKlUXCeT5uteNZ9 eNzmx6VD0UfKpnz9Td91OJdkJxtSv/31B2dLb3p7WlYGg7/6Xf2ysUunruFY+UmjskTXbWuW tRw6dHbPoQW7FkRLPAXxpxsadh359CSgrPDh30kRP4AK0H6jYrP1R1Yy31ptXW193koLdOTh XDpy2x1ODELTiT2cbJEEt+4RtlkUzxA+b5RDn3YecBLnENKZJPdhUzoStNfDtbntuvMTZA/Y 46Czk3bebhZKIRTKNNDNTM2D9prphSgnKLXJnLgCGn0sKzGISIgWVJbHjBXyYC9JVCYW24kw IFjLAzV68sV1WxzWLVuAbW5nm/vcZfNuzW9urD6LR26PHc/2QmyvQAX10QhwzC7jW/NZNfuM 3WL0VYanBawJGFM3km3gHly6LksMY2dbzI01N465G90doJ1Pu0fcE+57btE9RzBiUiR+MS6S AJwMmFHxfwhmxqSXvFaaJZiuHMMkZnUhBJkzX8/mLsn5eH/L2n11U66VldXf8//tvbUbnx88 Sn6WLfhT17Pf2XkDXwaxApFIObaMgHiPGGGGJLEaJcS9qIfsY73icXQS/0L4QLRIFEtwiBQ5 xaGHd40qnucpZRwhlDMvGRUpY2lB1AWgHUpIGvwFxkhgjGAmIrlWzsjEJmNxCAeMmHBUAh9R 1cg6WCfbySbYPfaQCbWskY3AhAZYnBksyfrYaXaZCWwIDxhPSTYKT9AO2kl30gl6jz6kQi1t pCMweSTb+uhpehlkEDxxlhxFvwHfgfFOJGlfTc4UmpmE9jk7FMxMzhoPSGkPX1Hes2O4p6LA HJBZYD1Wbdg8hofxkiWoAtJtanbcHsSVTs9iJ7irTdlj/zweWzD4eXaQRmb+ev488eVzKkNO D0NOFXTV2F+OS4Vn8CKB2tSX8fcFDphaIGEcoPOFmJwAEVot1MqX6Yg8SidkS4p2ykRWGJEE hXGQb54TMZCKJIkclyaSTojEM0VJIx78G894niNMhI6HUmpazagcf4f1SXdSwJ0HxIx4WhwR mV+sFTtEzg/+DLTABcTdIceUO6ACdiKL9lXOnJgNZ/puO4rlBdv/ZSRfPCbg4D8rJRych4Nw VrCMq7NjuOjIf9mvutioiih85szc3717792h2NpikFALssAm1m4tbMiltf91u5aWokBqE1BJ mnTZAG14ACMvxJqUBAgPJsbIj4lPNqg0MSQ8kKCmKMYHJaIkgBgfJKaivLh3PbO7mBiIVBN9 6nzZmXNn5s7c/WbO39vMDS+zlvD7qfdxKfLwCqsNrfwXrDM8oxiJUDb2GjEi4adgS4DdfgbP oXiSvBw24fOIK7Q1WjumtR69RW7EIW2znpEH8Rgekaf0D/VPtc9iN/GOXKAhSAnIY8iR8azQ KoTQkCFnWZDEh8RptAJL2oJzCiqcj9g+OowoezVY6u21Pa+CaTwmGToT0SgCfzMYEiwjJkt3 SExj1ekMrTaNxtSjkKh+OL+V9K66qhjZ3VDBhFRmN3+TrEpJDxVJJplZaqtKwnmTbk/T6pJf VvFuDatnhkvBRoIp29sY+e3zmVvhxbXLnhkbbAkqkvHmgRq6PS7O/o4Xsi8/FbsS3bSLGFtE d6iOGPPhTPBIxLYd+sdC2I5D/oU0SzAHIujawiLt2PeBZ0yqSI+dDGzL1D1wX3HRVY9JnwyR ZFnJhOMxy7Z905hg4N8NolQ0laEQ6mdf833ZSxsb5oSKEA+oyCN1O5XYKZV38UkkN5MqZkE7 S8ZVVbcpoY0pYpruXhj3fFFtKHlpYMlG3dAalixkRrJ+ycJFrD1Tt7w5vIiXwtO7X6hk279h n2QTnOGPF8KVbxh3KO+AtXPAtjlglu1HxF14idfwt3heJIroL2JGixPe04/qHysYM/fCPG55 1qT1i/1O5KHIlCOdDUXsjy6Ijke/Ji/W5Z7wvvJb/Xdj/YRv5R45O495zGMe/z3ITiKZalUq VP5CLraafjo8sPC/Psp7JtQ+Vrds+eMr4isBEgBP1ENDshFgzZ/jT7e2tXd0dnX3APRmnu3b AAMbBzc9B5u3bH3w5v9HETBOdRX5TQ4mkBuHTuiBDPTDIOyALOyBvYUCzVAj62mkF/poZBhG IAfjhULh+v1Q5vr+hf/NWKmY8GJ5BQ6VAGVZkFxZlnWSVquTFBb11EJrWUZw6atLMqcZY2VZ kHyyLOskf9nalu7q7oivz+0YHkkP53KjY6uaR0e2/bNu2rYN0tAF3dABcaInR5sratJUUw4J o/QBfbAdXoLd1Kv65vLGKmimdgTmtv6/na2YxMMwCyk6a42Y8+mEA6J5gN0q6ohiGgHWXfvu 8pCX+hUWm0Xqjy+ePqvaqUP9+fCH/OtGwbxKjxE1V5U/BBgAYM92LAplbmRzdHJlYW0NZW5k b2JqDTM3IDAgb2JqDTw8IC9UeXBlIC9YT2JqZWN0IC9TdWJ0eXBlIC9JbWFnZSAvV2lkdGgg NDAzIC9IZWlnaHQgMTU2IC9CaXRzUGVyQ29tcG9uZW50IDggDS9Db2xvclNwYWNlIDE0IDAg UiAvTGVuZ3RoIDczMjAgL0ZpbHRlciAvRENURGVjb2RlID4+IA1zdHJlYW0NCv/Y/+4ADkFk b2JlAGSAAAAAAf/bAIQADAgICAkIDAkJDBELCgsRFQ8MDA8VGBMTFRMTGBcSFBQUFBIXFxsc HhwbFyQkJyckJDUzMzM1Ozs7Ozs7Ozs7OwENCwsNDg0QDg4QFA4PDhQUEBEREBQdFBQVFBQd JRoXFxcXGiUgIx4eHiMgKCglJSgoMjIwMjI7Ozs7Ozs7Ozs7/8AAEQgAnAGTAwEiAAIRAQMR Af/EAT8AAAEFAQEBAQEBAAAAAAAAAAMAAQIEBQYHCAkKCwEAAQUBAQEBAQEAAAAAAAAAAQAC AwQFBgcICQoLEAABBAEDAgQCBQcGCAUDDDMBAAIRAwQhEjEFQVFhEyJxgTIGFJGhsUIjJBVS wWIzNHKC0UMHJZJT8OHxY3M1FqKygyZEk1RkRcKjdDYX0lXiZfKzhMPTdePzRieUpIW0lcTU 5PSltcXV5fVWZnaGlqa2xtbm9jdHV2d3h5ent8fX5/cRAAICAQIEBAMEBQYHBwYFNQEAAhED ITESBEFRYXEiEwUygZEUobFCI8FS0fAzJGLhcoKSQ1MVY3M08SUGFqKygwcmNcLSRJNUoxdk RVU2dGXi8rOEw9N14/NGlKSFtJXE1OT0pbXF1eX1VmZ2hpamtsbW5vYnN0dXZ3eHl6e3x//a AAwDAQACEQMRAD8A9USSSSUpJJJJSkkkklKSSSSUpJJJJSkkkklKSSSSUpJJJJSkkkklKSSS SUpJJJJSkkkklKSSSSUpJJJJSkkkklKSSSSUpJJJJSkkkklKSSLk0oKXSUS4TE/6lCrysey1 9TLGvsq/nGNILmzxuaCSEVJ0kCnLoussZVY2x1J2WtaZ2uOuqajMxsg2DHuZd6LjXZ6bg/a8 ctdtJg+SSmwkoB+p8lLcJhJS6SSSSlJJJJKUkkkkpSSSSSlJJJJKUkkmlJS6SaZ448Ujwkpd JR3axOqkkpSSSSSlJJk0jx45SUySQ32116veGA8Fxj8qb7Vi/wCmZ/nBJSVJB+1Y3+mZ/nBL 7Vjf6Zn+cElJkkH7Vjf6Zn+cEjlYwBJtYANSdw7JKTJKI5J8/wAicmBKSl0kwIITykpSSSSS lJJJJKUkklKSlJJKM/d4pKZJJJJKUkkkkpSYkBOeFlZ/X+l4GXVh5d7arrhLd3AEwC5wBAnz SFkqAb5saX+mHDfEhvdY7ut5dPXh03NoFONeIxMprtwe8a7XTt2ntHjHYhUOpfVa6zPHW+jZ jq876QFzjZU9p/N3e50eWo+C2cct6lieh1DHNVzSBbQ7s8atdW5vPEgg6eKNAJ2QjFy8brhy 2WWWYmazZZUSXCq1g3Me0HQNcJEeMeKs0dPro6plZ1Y2nMrpbYI5dT6gDnHxh4HyUszPwum4 5yM29tdTIabHnUu5ja3848wAuRzv8Z+Kx5bgYb7miR6lrhWNP5Ia8x8YTowlLYWkAl6/Fwm4 +RmWjU5louMeArrqj/oSgYuEzpWHk2NaX2WW35duwQ577HOe1sDvEN+S4mv/ABo9SD5fhUuZ PALgf847vyLb6R/jD6VnWNqy2nAuJ03ndWSf5cNI+bQPNE4cg1I0TwkOh0zEZ9X+kZGZ1Gwv veX5OY8ajeddlbfAEwPEnzTfVnM69m02ZnVGV00XGcSoAi3YToXumIgiNJPPgtO7Gxcz032A XVscLGM0LC4fzb47x+b279gRk9br611Qnp3Sz9moOmVnukf1q6gNpOn5w07eKaPFb11dPC63 0vNvtx8XKrvuoJba1h1EeHj8pWhIXJYXRfq19UaRnZVgNzBDcm73P10ilg/77J8Sj/V/67dP 61nWYddbqLBrRvIJsaBJPtmCPDX4pGPbZRD08pJgnCYhSSSSKlJJJJKUkkkkpSxfriXN+qvV nNJBGJbBBj8wraWL9dP/ABJ9X/8AClv/AFBSU/Op1Oup5lNqkkkuer/xYPcPrv05odAd64cA eYotK96Xgn+LD/xc9N/6/wD+29q97SWqTHjlOuc+u31oZ9W+jPyRDsy6a8Os6y8/nOHg0a/g kpq/XL6+9P8Aq037O0DJ6k8SzHmAwHh1p1gfCSfLleT9Z+vH1m6w4nJzrKqzxj0uNVYB/kti fmSsbJyrcm6zIyLHW33O3WWO1Jd/K/1/uQeEk0pznOcXOJcTqSdSfvTf69k4VjG6fnZhP2TG tyC3n0WOfH+buSU1tPD8iWnh+RaH/N/r/wD5W5f/AGxZ/wCRS/5v9e/8rcv/ALYs/wDIpKc/ T/WEgYOmh8Vof83+vf8Albl/9sWf+RS/5v8AXf8Ayuyh5mmwD8WpKt+lAANANBoFl/Wv/wAS /WP/AAjk/wDnp60/ArN+s1dlv1b6rVU02WWYWQ1jGgucXGp4AaBySip+b5+Sb/XhaH/N/r3/ AJW5f/bFn/kUv+b/AF7/AMrcv/tiz/yKCg58pa/6haH/ADf68Nf2bl/9sWf+RCpWMfW51bwW vYS1zXCCCORCSWEpa+SSfv8A6/7UlLf68JfctD/m/wBejTpuXHE+hYQfntTf83+vf+VuX/2x Z/5FJTQ+77l73/iwn/mP03/r/wD5/tXig+r/AF7/AMrcv/tiz/yK9u/xcUX431N6dTk1PouZ 62+uxpa4TfaR7SB2KSHp0kkkkKSSTEpKR33MopfbYdtdbS5zvAASeFzmR0j6q/WV7stj2232 AA3U2EPAbo32Hc0R/VW31TCrz8K3DfY+ptzdrn16O2nmCQfgudZ/i+6fU8Oqyr6yI2kFoOnm APwTo0OtFcG7gdC6l0k7cDNORiz/AEXKB08dlrZLf835LS6jn4/TsGzMyTtrqbucBrJ7Nbxq eBwoYPTsrDAY7Ntyahy24Ncf84NBXK/4zMp7m4fTmmGvL7rW+Meyv5fSPyToR4pgWobvG9a6 znddzHZOSfbxVUJ2MaToAD5d/Ht4VG4rnamf9yu0Ys6/66q9Xh6DRX4iMRQZdnH+x+SE7Hc0 QBI8Piui+xacKvdhx2hOu1W6H1J+tWRgZVXTM6wvwrnbKXO/wbydBuMe1xPy7RqF6JmfbjUG 4PpCw6epbuhvi7a0Dd8JA8+y8bycVomRA8P9fivWvq5m29R6Bi5LnRfZXte/mXsmtztZ7tVL mMYiRIbFZMAahyrfqLRnXnK6zm3Zt8RAhjA2Z2BvugeQKE7N+ov1aduobS7JZuLfSHr2hw0c PUO7afIuCvdR+qlnU5bl9UyXsd/ghsaz/Ma0NP8ArKzj/iz6Q6TZlZBJ1kemOP7BTI8JHqkf sWh6fpPU8bqmDVnYpJquEgHlsaEOgu1V5Yn1f6XgdGpf0/Eyn37nG3Za9jnNmA7aGBsCVtBR yFHTZBC6SSSCFJJJJKUkkkkpSxfrn/4lOr/+FLf+pK2li/XP/wASnV//AApb/wBSUlPzp3SS 7pJLnqv8WH/i56b/ANf/APbe1e9rwT/Fh/4uem/9f/8Abe1e99kkFZeDf4yOunrH1lvbW7di 4P6tRB0JaZsd839/ABe2dazx03pGbnmD9lostAPBLGktHzK+anvc5xe8lz3ElziZJn/ekoLJ fikmlJLu/UzoLfrB1/G6e+RjkmzIcOfSYNzhp4nSfNe/4WDiYGOzFw6W0UVgBjGAACO+kary f/EvS09azrzq5mMGNP8AWewu/wCpC9gSQVQU6ZJJS6Y+CSZx2iTwEVMoTEJpnupIIWASTpJK YkGQvmz6wf8AL3Uv/Dd//nx6+lF81/WH/l/qX/hu/wD8+PSSGgmBg6c+KSQSS/UYaApJhwE6 S1ZIABOkkpSSSSSlJinTHhJTx/1t6H1frHUsduNW37LVXHqOcA0Oc6XaansOEunfU/qdAHqd SdQf3aC/8u6v/qUb62dezen3UVYVjWusa5zztDiP3DLvb4qvgV/WzqYabciyik67z+jJnuAx rT+QKUcXDuAFw2ejwenHE1dlZGSf+Gs3fdwuM+v9Lz1rHsj2nHa0Hza+zd+BC6/E6bRhAXZF 9l9rf8Nc8mPvMLM+ufTzlYVOZUNzsZ0ujWWO5484QxkCYUN3j8TGmNNFr42CSOEDAr4nx/Fd J0/GDiNFanKha8ly3dOMcKjlYW0HRdrZgsDCYWF1CgNB0hNhktALx2XjxK7z6lVlv1cxw4mH GyOxAD3Dy7hcnkY9l9zaaWzY8gMZyTu9oGvxXe4uNjYPT6MGwt2NYKvcY3kD3eGp5TeYncYh Ejo0c/6t3ZO59HVcyh7jx6jnV/5g2/lXL9T+oXXHlzq8lmWB9EPc5rz/AJ4d/wBUuj6j9Xs2 Db0nqF1LuRS6x3pmPBw1C5nO659cekPDMq1zRwDYxjmmPB4n/XwUeO+hH1UEf1X6L17pH1lx rMrEsZTZurteBvZD2u5exzm/SAXpo4XnPTf8YHV7OoY2LfVRYy61lb3tDg8B7g0nR0L0VpkA /NNyiQI4hSJWySSSUa1SSSSSlJJJJKUsX65/+JTq/wD4Ut/6kraWL9c//Ep1f/wpb/1JSU/O ndJLukkueq/xYf8Ai56b/wBf/wDbe1e9rwT/ABYf+Lnpv/X/AP23tXvaSC8t/jLvNH1L6iW6 GwVV/wCfawO/BeBr3b/Gp/4i8z+vT/58YvCUlBSSSSSWdd1tc+m9zJ0JaSJ79lP7Xlf6az/O KjXVbZPpsc6NTtBdE6dlL7Jlf6Gz/NKSFvtWV/prP84/3pfasr/TWf5x/vT/AGTJ/wBFZ/ml L7Jk/wCis/zSkq1fasv/AE1n+cf70vteVI/TWc/vFL7Jk/6Gz/NKQxMqR+hsOvG0pKt+ngAB pponUQZ15nhSSQpJJJJSivmr6w/8v9S/8N3f+fHr6VK+avrD/wAv9S/8N3f+fHpKDnpJJJLn 6lHASSHASSWqSSSSUpJJJJS0pnHX+J801jg1pc7QAST8F5f1/wCtWV1PMsqxbnV4Lfa2thID xGr3bdpO7wPYqTHjMzQXCNvX5eT9Xen5lmZk2DK6h2mLLGx9FrWgBrPLhUrfrdl5NgpwKQwO O1hd73k+QEAH71yfTMHIz720Y9fqPdr5AHSXO0gefy00C7rCwemfV7G+0ZL9+UR9M8knTbW3 t4f6wpJwjDS+KSapN07plxLcvqjzfdyxj9Wt+RmCtJl2PkixjYsrA2vdEsMyNvmsFvUMvrGQ Men9FSdXNB1DR3e7+C2nOpwMdlLBqBDGDvPJ0UJH2oLzWf049Myxt/o9xPp94j80/BaPT8lr APJafVMQ5eA9hEWAb2d4c0TGvjwuOxs4hokwY/2KfGeOPCd0jV7GzPaayNOFhdRva6Y5VQ9R 01cn6cw9Q6hXQ4TWPfb30brH9rhEQ4ASqqdT6v8ASdpHUMge90+g09mn8/XjcNFpW2YPUDbh XN3uYYspf7XAdnN/vCfOzDg+nYWj0J22kctH5pHkqXW8A5lDczCJOTUJYWabmjWPj3/BQfMb JQdXJ6iOufV5xvxrnX4M8WDc1s9nTq34jlKj679NyWHH6tj7GHRzwBZWR4lp1Hy3IvTvrXU7 9W6rAa/2euR7TI/wjf5XH8As/wCsf1RO05/Rx6tbhvfjtIJjma+xHkpYiO0xR6SGyQ2K/qn0 DPzMbqXRspobRdXbZSw+ow7Xtee+5hgHn7l2jRp/r2XmX1C6e/J679rIPp4bCXEaAvd7GtM+ Rc4L00KPLpKr4kSZJJJKNapJJJJSkkkklKWL9c//ABKdX/8AClv/AFJW0sX65/8AiU6v/wCF Lf8AqSkp+dO6SXdJJc9V/iw/8XPTf+v/APtvave14J/iw/8AFz03/r//ALb2r3tJBeW/xk0e v9SuogCXMbXYP7Ftbj/0V4H/AAX0x1fBb1DpWXgnQZVFlI+L2loXzVbW6t7q3gtew7XNPII0 MpKDBJJJJL6Z/iUsH23qdepJrqd9znz/ANUvWV4v/idyhT9ZcihxgZGK4MHi5jq3f9TuXs8p IXSSSSQpMRIg8J0klLQnSSSUpJJJJSl81fWH/l/qX/hu7/z49fSq+avrD/y/1L/w3d/58eko OekkkkufqUcBJIcBJJapJJJJSkkkklOZ9YsTLzejZeJhnbffWWMkxM/SEjiRovOel/VDrWXm fZsih+HWwzZdY2GgHX2ancfhovV4jsuV+v12dV0hv2Tc2t9gGRYwx7IIa2R2c4qTHOQ0HVdA nZqW9c6R0HHOF0cNvvOt2QTLQ7+U784+Q0WOzIzeoZQda512Ra4AdzJnQDssOl408Ae3l8Z/ iu36BhUdIwD1jqPtscP0NZ+mN3kYhzvDsOfKeQGMXvIriKdjHZR0Pp4NsOyHiSP3nDs3yEoP S3WZ+ab7juaz3HwE/Rb9y53L6rfn5RvtJAJhjBwxo8OOfgunxNnSukC60Rc8btp/eI9rfkFH KNR1+aS0usy5j7H1tMurjd8TOi8x6nYcTqmVjgbW12vawH93cSPwXbdByXWZNrHGd7d7j3mR /euE+vRsx/rNkmIbaK3sjuCxrJ/zmlHAKyUeyYjWljneJXUfUNvq2ZeQeWBtbCPOd/8A1LV5 19rJ+a9K/wAXFbx0O2541uyHlp8WhrGflBUvMioDxXTFB37BV1DGvo4Ic5h8nNPtP4Bc70rr LunZH2TLMUbi1wP+DcND8tOEum9X+z55fc6Ksh0WE9iTO7/XxQfrfh+hltymia8kbXR++z3R 8wJ+KgjDURO0tlgC31s6M1jf2piDdRbreG8Df/hBHZxifP5rC6T9ZOodKtayo+vjuPvxnEQT zDedp15+Gi1/q99Y6qv8m9RcH4lw2Mtf9Fs/mvOnsIPPb4ao2B9Un4n1mqc4ep0+ppvpedYL S0MY6I9wcZnggJ98MTCeoGydt3rMTHprDrq6BRZkkW3ACHF5EHdHdWwEzRp/r3UlW6rDupJJ JJSkkkklKSSSSUpYv10/8SfV/wDwpb/1BW0sX66f+JPq/wD4Ut/6gpKfnTukkOUklz1X+LD/ AMXPTf8Ar/8A7b2r3teCf4sP/Fz03/r/AP7b2r3tJBWIXhf+M3oJ6V9Y7cmtkYnUf09RHG8/ zzdP5Z3fAr3U8LA+t/1Zo+sfR7cJ5bXe0+pi3O/MsH738k8GPikgPzz2SVnOwcnAyrMPLqdT kUuLbK3cgj4c+Pw+9Vx/rykudDoHV7ei9XxepVCTjvDntH57D7Xt+bSV9B9J6zgdYw68zp9w upfzB9zSNSx7ddrvJfNg0U6ci/HcX0WOpdEbmOLTB+EJIp+oEl8y/tXqn/cy/wD7cf8A3pft Xqn/AHMv/wC3X/3pKp+m0iV8yftXqv8A3Lv/AO3H/wB6cdV6oCD9svBH/Cv/AL0lU/TMynUQ I8gNIHgpJIUkkkkpS+avrD/y/wBS/wDDd3/nx6+lV81fWH/l/qX/AIbu/wDPj0lBz0kkklz9 SjgJJDgJJLVJJJJKUkkkkpS5767YuTlfV/Jbjalu217Yncxh3O/AcLoUOxoLSCJB5H+9GJog pD5h9Vul0OY7rXU/Z07FMt3f4R4PtbB5AMfE6eKj1brt/Vsv13iKm+2ir91p7Ge57+PHYKr9 ZOsuyM09PoaKOn9Pe6mihvBLT6bnu8Zj/XVZ1dnnqB3V2EL9ct2WtLeo+rmJ9u6jXWQTWz32 nxazifidPnK0vrB1ZuTmmmp0043t04LwYd/r5Kt0+5vQ/q47MOmb1IluOOC2sTDvkDu89FhM vOhJ+Z802I4pmX6MdEVZe3+qbd5vvI1G1jXfHV3/AFIQ/rX0PoPUHV39UyW4NzRsruL2MLmj Xb+kndEzoj/VZ9WP0mp9joOXc8MB8R7Y1/4tcj/jPrtHWsW4z6Tsfa09tzXvLufJwUMQZZSA eFA1kWdP1S+qIsHrdfqewfmsspYTPmXO/IvQOmYuJh4NGPggDGY0CvbqCDru05leF6xEEjjw 5Xr/ANULDj/VTDtynGK6XWOcRHsDnPBg/wAhP5mEoiNyMvoqYqnmOqA0Z19AEbLHtYD3Ey38 q2MW4df+r12E4+pmYzQayeXbRLDr4/RKy/row09ZsP8Ap62WCPL2f99WX0jrLum9SryZmuS2 9vixx9ydw8WMSG4UBYc+97pdOrgde3h2/HyXoX1DysrJ6LOQ71G1WuqoJ52MaAP7lxv1xwmY fU/tFB3YucPXod2l3ucAR5/hC7X6ggf82cVwMlxsJP8A1xw/ghnMTjEhuVS2ejCdJJVlikkk kEKSSSSUpJJJJSljfXFrnfVbqzWguccS2ANT9ArZTEJKflyO3fw5iPgmX1JEJQkl8G/xYtcf rv05wEhvrFxHgaLR5dyvelGNZUkkKTRonSSU8t9bfqN076y1b3xj57BFWU1sn+q/jcP9e68n 6z/i9+tPSXu34b8qgcX402gjxLWw4fNq+gCEoKKX5etqtqfttY6t/wC64QRHkYUF9SR8k8IK t+W0y+pIShJVvy2nAJIA1JOgj+5fUcJFoIgiQeyKGIMqaaE6ClJJJJKWJXzb9Y2OZ9YOpNcC 0/arjDhyDY4jmOy+kiJ80gPkil+XE7Gue8MaNznGGgckntovqKEiJ/igq1hH+vgpJoH8U6SF JJJJKUkkkkpSi/hSTOGiSQ+D59dlOfkstB9Wu1weDpruVvoWFZ1PqVGE2QHu3WvA+ixv0na+ Q+9ei/WH6i9O6zknLFj8XIfpY5gDmu7S5h7+aH0j6hYnTqsppybX2ZbPSdYwBjmsJ3Oa36X0 oiVb+8R9sCvUycYqnjuv9ab1DqLvR0w8YehjNA9oYzuP6x18hCpMu0A76+fJ816Kz/F99WmN DfQc4gcusdP4ELJ6z9Q6KDTd051hYbq2XVPMw2x7Wbmu2g+2fFKOeHDw0gSRdcz7em43R8Sk mu7HrZk2NPZ5iCf7Qd8itv0elfXDpAfdTNjNzJJIdVaWtPteJ0+ifPuOy4X6zdS+2dcy7AZY x5qr10iuGf8AVCUPp31i6p0ytzMK/wBJrzuLS1r9zuPzpS9kmIkDUk8LvdH/AMWuVVnss6u/ HuxAHbqqnvBJI/qs0nnyWt9b+uYmDhO6Phw24tax1bBArqge3SI0gBZfWvrX1fGwOl2Y+Vtt yqPUuIbWdzpidWu/BYT8L6x9ayXZbsa++26CbPT9NrgBtaJ0ZoAEypSlc5aRQNdy6/1nv+29 K6T1MEl1jHU2vPd7P/Mty5R9umhgx+Rdxi/VLrOT9XXdLytmPazJF9DnO3ANIhzfbPjx5pYv +K/H5zc57/5NLGsiP5T/AFE/HmhAEWkEBwsXIHWvqzd09wH2zpX6xiEnV1Optr8fa3X7vArr P8W2SLegGnvj3PZxGjosn4e5X+lfU3ofSrm5GNU45DQWi57iSAeRAIafuWviYOHhsLMSiuhh MltTGsBPj7YUOTICCANza2UrbCSSSiWKSSSSUpJJJJSkkkklKSSSSUpJJJJSkkkklKSSSSUp JJJJSkkkklKSSSSUpJJJJSkkkklKSSSSUpJJJJSkkkklKSSSSUpJJJJSkkkklKSSSSUpJJJJ SkK9thqsFUCwtOwu43RpKKkkp4DF/wAV7Z3Zue5+7VzamAanX6by78i2sT6g/V3GA3UuvcCD ute7Uj+S0tH4LpUk85Jncp4i1sfAxMZrW0UsqbWNrA1oEN8NIRw0eH+pUoSTbRa0Jwkkm0pS SSSKlJJJJKUkkkkpSSSSSlJJJJKUkkkkpSSSSSlJJJJKUkkkkpSSSSSlJJJJKUkkkkpSSSSS lJJJJKUkkkkpSSSSSlJJJJKUkkkkpSSSSSlJJJJKUkkkkpSSSSSlJJJJKUkkkkpSSSSSlJJJ JKUkkkkpSSSSSlJJJJKUkkkkp//ZCmVuZHN0cmVhbQ1lbmRvYmoNMzggMCBvYmoNPDwgDS9U eXBlIC9FeHRHU3RhdGUgDS9TQSBmYWxzZSANL1NNIDAuMDIgDS9UUjIgL0RlZmF1bHQgDT4+ IA1lbmRvYmoNMSAwIG9iag08PCANL1MgL0QgDT4+IA1lbmRvYmoNMiAwIG9iag08PCANL051 bXMgWyAwIDEgMCBSIF0gDT4+IA1lbmRvYmoNMyAwIG9iag08PCANL1R5cGUgL1BhZ2VzIA0v S2lkcyBbIDggMCBSIF0gDS9Db3VudCAxIA0+PiANZW5kb2JqDTQgMCBvYmoNPDwgDS9DcmVh dGlvbkRhdGUgKEQ6MjAwMzA1MjAwMDAyMjMtMDcnMDAnKQ0vTW9kRGF0ZSAoRDoyMDAzMDUy MDAwMDIyMy0wNycwMCcpDS9Qcm9kdWNlciAoQWNyb2JhdCBEaXN0aWxsZXIgNS4wIFwoV2lu ZG93c1wpKQ0vQXV0aG9yIChCaWthc2ggU2FiYXRhKQ0vQ3JlYXRvciAoUFNjcmlwdDUuZGxs IFZlcnNpb24gNS4yKQ0vVGl0bGUgKE1pY3Jvc29mdCBQb3dlclBvaW50IC0gSUVUIEV2YWx1 YXRpb25zLnBwdCkNPj4gDWVuZG9iag01IDAgb2JqDTw8IC9UeXBlIC9NZXRhZGF0YSAvU3Vi dHlwZSAvWE1MIC9MZW5ndGggMTEyOCA+PiANc3RyZWFtDQo8P3hwYWNrZXQgYmVnaW49Jycg aWQ9J1c1TTBNcENlaGlIenJlU3pOVGN6a2M5ZCcgYnl0ZXM9JzExMjcnPz48cmRmOlJERiB4 bWxuczpyZGY9J2h0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMn IHhtbG5zOmlYPSdodHRwOi8vbnMuYWRvYmUuY29tL2lYLzEuMC8nPjxyZGY6RGVzY3JpcHRp b24gYWJvdXQ9JycgeG1sbnM9J2h0dHA6Ly9ucy5hZG9iZS5jb20vcGRmLzEuMy8nIHhtbG5z OnBkZj0naHR0cDovL25zLmFkb2JlLmNvbS9wZGYvMS4zLycgcGRmOkNyZWF0aW9uRGF0ZT0n MjAwMy0wNS0yMFQwNzowMjoyM1onIHBkZjpNb2REYXRlPScyMDAzLTA1LTIwVDA3OjAyOjIz WicgcGRmOlByb2R1Y2VyPSdBY3JvYmF0IERpc3RpbGxlciA1LjAgKFdpbmRvd3MpJyBwZGY6 QXV0aG9yPSdCaWthc2ggU2FiYXRhJyBwZGY6Q3JlYXRvcj0nUFNjcmlwdDUuZGxsIFZlcnNp b24gNS4yJyBwZGY6VGl0bGU9J01pY3Jvc29mdCBQb3dlclBvaW50IC0gSUVUIEV2YWx1YXRp b25zLnBwdCcvPgo8cmRmOkRlc2NyaXB0aW9uIGFib3V0PScnIHhtbG5zPSdodHRwOi8vbnMu YWRvYmUuY29tL3hhcC8xLjAvJyB4bWxuczp4YXA9J2h0dHA6Ly9ucy5hZG9iZS5jb20veGFw LzEuMC8nIHhhcDpDcmVhdGVEYXRlPScyMDAzLTA1LTIwVDA3OjAyOjIzWicgeGFwOk1vZGlm eURhdGU9JzIwMDMtMDUtMjBUMDc6MDI6MjNaJyB4YXA6QXV0aG9yPSdCaWthc2ggU2FiYXRh JyB4YXA6TWV0YWRhdGFEYXRlPScyMDAzLTA1LTIwVDA3OjAyOjIzWic+PHhhcDpUaXRsZT48 cmRmOkFsdD48cmRmOmxpIHhtbDpsYW5nPSd4LWRlZmF1bHQnPk1pY3Jvc29mdCBQb3dlclBv aW50IC0gSUVUIEV2YWx1YXRpb25zLnBwdDwvcmRmOmxpPjwvcmRmOkFsdD48L3hhcDpUaXRs ZT48L3JkZjpEZXNjcmlwdGlvbj4KPHJkZjpEZXNjcmlwdGlvbiBhYm91dD0nJyB4bWxucz0n aHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8nIHhtbG5zOmRjPSdodHRwOi8vcHVy bC5vcmcvZGMvZWxlbWVudHMvMS4xLycgZGM6Y3JlYXRvcj0nQmlrYXNoIFNhYmF0YScgZGM6 dGl0bGU9J01pY3Jvc29mdCBQb3dlclBvaW50IC0gSUVUIEV2YWx1YXRpb25zLnBwdCcvPgo8 L3JkZjpSREY+PD94cGFja2V0IGVuZD0ncic/PgplbmRzdHJlYW0NZW5kb2JqDXhyZWYNMCA2 IA0wMDAwMDAwMDAwIDY1NTM1IGYNCjAwMDAwNTgxNjcgMDAwMDAgbg0KMDAwMDA1ODE5NyAw MDAwMCBuDQowMDAwMDU4MjM5IDAwMDAwIG4NCjAwMDAwNTgzMDMgMDAwMDAgbg0KMDAwMDA1 ODU1OSAwMDAwMCBuDQp0cmFpbGVyDTw8DS9TaXplIDYNL0lEWzw4NTg1MTEwOGFhNjA0ODA4 MzhkNzA3NjNkNzExM2ZjNj48NTM5YTI4YWViMmFjYzY0NTVkMjE1ZGZjYzlmMDY0Y2U+XQ0+ Pg1zdGFydHhyZWYNMTczDSUlRU9GDQ== --------------070205000203030805010102-- From tgd@cs.orst.edu Tue May 20 18:21:07 2003 From: tgd@cs.orst.edu (Thomas G. Dietterich) Date: Tue, 20 May 2003 10:21:07 -0700 Subject: [KP] clarifications re robocup of C.N. In-Reply-To: <3ECA60D1.5020907@iet.com> (message from Bikash Sabata on Tue, 20 May 2003 10:07:29 -0700) References: <20030519224552.75958.qmail@web41501.mail.yahoo.com> <3ECA60D1.5020907@iet.com> Message-ID: <41-Tue20May2003102107-0700-tgd@cs.orst.edu> I would like to speak against challenge problems. I believe they overly focus the research and become a barrier to addressing the fundamental problems that must be solved to achieve DARPA's goals. They mostly serve to exclude research groups that have important ideas which don't exactly match the challenge problems. We absolutely need quantitative metrics for evaluation, but we need to formulate them in a sufficiently general way that they promote good research rather than impeding it and wasting resources. --Tom -- Thomas G. Dietterich Voice: 541-737-5559 School of Electrical Engineering FAX: 541-737-3014 and Computer Science URL: http://www.cs.orst.edu/~tgd Dearborn Hall 102, Oregon State University, Corvallis, OR 97331-3102 From weinstein@draper.com Wed May 28 18:19:15 2003 From: weinstein@draper.com (Bill Weinstein) Date: Wed, 28 May 2003 13:19:15 -0400 Subject: [KP] KP Mailing list Message-ID: Please add me to the KP mailing list. weinstein@draper.com Thanks. -WWW -- Bill Weinstein Charles Stark Draper Laboratory, Inc. 555 Technology Square MS 3F Cambridge, MA 02139 Ph (617) 258-2227 FAX (617) 258-1799 From braden@ISI.EDU Wed May 28 18:31:23 2003 From: braden@ISI.EDU (Bob Braden) Date: Wed, 28 May 2003 10:31:23 -0700 (PDT) Subject: [KP] KP Mailing list Message-ID: <200305281731.KAA07903@gra.isi.edu> *> From know-plane-admin@ISI.EDU Wed May 28 10:20:59 2003 *> From: Bill Weinstein *> X-Sender: www1485@mail.draper.com *> To: know-plane@ISI.EDU *> MIME-version: 1.0 *> Content-transfer-encoding: 7BIT *> Subject: [KP] KP Mailing list *> X-BeenThere: know-plane@mailman.isi.edu *> X-Mailman-Version: 2.0.5 *> List-Help: *> List-Archive: *> Date: Wed, 28 May 2003 13:19:15 -0400 *> X-AntiVirus: scanned by AMaViS 0.2.1 *> *> Please add me to the KP mailing list. *> *> weinstein@draper.com *> *> Thanks. *> *> -WWW Bill, You can subscribe yourself. There is a link at the bottom of the www.isi.edu/know-plane web page. Bob Braden *> -- *> Bill Weinstein *> Charles Stark Draper Laboratory, Inc. *> 555 Technology Square MS 3F *> Cambridge, MA 02139 *> Ph (617) 258-2227 *> FAX (617) 258-1799 *> _______________________________________________ *> Know-plane mailing list *> Know-plane@mailman.isi.edu *> http://mailman.isi.edu/mailman/listinfo/know-plane *> From education-software02@yahoo.com Thu May 29 03:29:52 2003 From: education-software02@yahoo.com (Grelirith) Date: Thu, 29 May 2003 02:29:52 GMT Subject: [KP] FW: Education Discounts on Adobe, Microsoft, & Macromedia Message-ID: <200305291301.h4TD0xi19105@tnt.isi.edu> Adobe Photoshop at 56% OFF, Office XP Standard at 71% OFF, Macromedia Studio MX at 76% OFF, Adobe Design Collection at 62% OFF Microsoft Visual Studio.NET at 91% OFF, FREE SHIPPING THROUGH MAY 31, 2003 WITH BELOW CODE. FREE SHIPPING CODE (Ground Only): EXT522 YOU MUST PLACE ORDER BY TELEPHONE AND YOU MUST TELL OUR OPERATOR THAT YOU HAVE A FREE SHIPPING CODE. Dear Students, Teachers, Faculty, Staff and Schools: COMPUTER PRODUCTS FOR EDUCATION is pleased to offer to you the best prices on ACADEMIC EDITION SOFTWARE from MICROSOFT, ADOBE, MACROMEDIA, COREL, and others - AT UP TO 91% OFF STANDARD COMMERCIAL RETAIL PRICES. If you are a Qualified Education Buyer (defined below) you can purchase software products from CPE at HUGE DISCOUNTS during our MAY SALE! SALE PRICES VALID THROUGH MAY 31, 2003. Qualified Education Buyers include K-12 and HIGHER EDUCATION STUDENTS, TEACHERS, FACULTY, STAFF, and SCHOOLS. Call 800-679-7007 to order any of these products. For our website address, please send a blank email here: Mailto: monony878@gmx.net?subject=MORE%20INFO You will receive a response with our web address. ---------------------- Education Standard You ADOBE (Windows & Mac): Price Retail Save! ---------------------- --------- ------ ----- Acrobat 5.0* $57.95 $249 77% Acrobat 6.0 Standard* $89.95 $299 70% Acrobat 6.0 Professional* $104.95 $449 77% After Effects 5.5 Production Bndl $359.95 $1699 79% GoLive 6.0/LiveMotion 2.0 $84.95 $399 79% Illustrator 10.0 $89.95 $399 77% InDesign 2.0 $179.95 $699 74% PageMaker 7.0 $269.95 $499 46% PageMaker 7.0 Upgrade $89.95 - - Photoshop 7.0 $279.95 $609 54% Photoshop 7.0 Upgrade $149.95 - - Premiere 6.5 $219.95 $549 60% Premiere 6.5 Upgrade $149.95 - *******Adobe Collections********** Design Collection 6.0 $379.95 $999 62% (InDesign 2/Photoshop 7/Illustrator 10/Acrobat 5) Digital Video Collection 8.0 $479.95 $1199 60% (Premiere 6.5/AfterEffects 5.5/Photoshop 7/Illustr 10) Publishing Collection 12.0 $479.95 $999 52% (PageMaker 7/Photoshop 7/Illustrator 10/Acrobat 5) Web Collection 6.0 $379.95 $999 62% (Photoshop 7/Illustrator 10/GoLive 6/Acrobat 5) *Acrobat 6.0 available May 26th. Purchase of Acrobat 5.0 prior to May 26th qualifies for free upgrade through Adobe for the price of shipping only. Call 800-679-7007 to order any of the products below. ---------------------- Education Commercial You MACROMEDIA (Windows & Mac): Price Retail Save! ---------------------- --------- ------ ----- Authorware 6.5 E-Doc $349.95 $2699 87% Director MX $479.95 $1199 60% Dreamweaver MX $98.95 $299 67% eLearning Studio $489.95 $2999 84% (Authorware 6/FlashMX/DreamweaverMX) Fireworks MX $98.95 $199 50% Flash MX $98.95 $399 75% FreeHand 10 $98.95 $399 75% STUDIO MX 1.1 $189.95 $799 76% (Dreamweaver MX/Fireworks MX/Flash MX/Freehand 10/ColdFusion MX) Call 800-679-7007 to order any of the products below. --------------------------- Education Standard You Microsoft: Price Retail Save! --------------------------- --------- ------ ----- Office XP Standard $144.95 $479 71% Office XP Professional $192.95 $579 67% Office 2001 Macintosh $199.95 $499 60% Office Mac v.X for Mac OS X $209.95 $459 53% FrontPage 2002 $79.95 $169 53% Publisher 2002 $79.95 $129 38% Visio Standard 2002 $69.95 $199 65% Visio Professional 2002 $159.95 $499 69% Visual Basic.Net Standard $59.95 $109 45% Visual C++.Net Standard $59.95 $109 45% Visual C#(sharp).Net Standard $59.95 $109 45% Visual Studio.Net Professional $92.95 $1079 91% Windows XP Professional Upg* $ 89.95 $299 68% Windows 2000 Professional Upg* $129.95 $319 59% * Windows XP/2000 Pro Upgrade will install on a blank hard drive. Call 800-679-7007 to order any of the products below. --------------------------- Education Standard You Corel: Price Retail Save! --------------------------- --------- ------ ----- Corel WordPerfect Office 11 $98.95 $299 67% Corel Draw 11.0 $142.95 $549 72% Corel Designer 9 $98.95 $469 79% Corel Painter 8.0 $97.95 $299 67% Call 800-679-7007 to order any of the products below. For our website address, please send a blank email here: Mailto: monony878@gmx.net?subject=MORE%20INFO You will receive a response with our web address. PURCHASE ORDERS MAY BE FAXED TO: 800-679-6996 REFERENCE THE FREE SHIPPING CODE ON YOUR PURCHASE ORDER FOR FREE SHIPPING. ---------- LICENSING: ---------- For school purchases of five to ten (5-10) or more units, depending on the product, please call 800-679-7007 for even deeper discounts on license packs. ---------- For hundreds of other software products available from CPE at similar discounts, call us at 800-679-7007. Academic Edition software is exactly the same as the Full-Retail versions* except that it has been deeply discounted for Qualified Education Buyers. No verification is required for purchases of Microsoft Office XP Standard. For all other products, purchasers must provide fax-verification of status as being a current faculty, staff, or student. After placing your order, you simply fax to CPE either: (a) a copy of a current picture School I.D. Card or, (b) a current paycheck stub with an alternative picture I.D. (drivers license, etc.). Schools may purchase by faxing a valid school purchase order. For more details, call us for our website address. All software sold by CPE is authentic original software from the manufacturer. THESE ARE NOT PIRATED COPIES. ALL SOFTWARE COMES IN ORIGINAL MANUFACTURER'S BOXES AND INCLUDES A VALID LICENSE. CPE is an Authorized Education Reseller for Microsoft, Adobe, Corel, Symantec and many other major software manufacturers. CPE is a national software distributor committed to providing the lowest prices possible to the Education community with the best customer service!! All prices and availability are subject to change without notice. *Some Academic Edition boxes may not include supplemental materials, such as extra fonts, image libraries, or third-party(OEM) products, which are included in the Full-Retail versions. However, the core-programs themselves are exactly the same. ___________________ We hope you find this message valuable. If you do not wish to receive any more special offers and updates, please send an email to: Mailto: monony878@gmx.net?subject=REMOVE ___________________ THANK YOU! From braden@ISI.EDU Thu May 29 18:30:36 2003 From: braden@ISI.EDU (Bob Braden) Date: Thu, 29 May 2003 10:30:36 -0700 (PDT) Subject: [KP] Paper by Tom Dietterich Message-ID: <200305291730.KAA08330@gra.isi.edu> There is a new paper on "Learning and Reasoning" by Tom Dietterich on the www.isi.edu/know-plane web page. Bob Braden From eitan@pnphome.com Thu May 29 19:46:08 2003 From: eitan@pnphome.com (Eitan M Fenson) Date: Thu, 29 May 2003 11:46:08 -0700 Subject: [KP] Web100 Message-ID: <002401c32612$9175d030$1006000a@delft> I have looked at thw Web100 site that John W. pointed us at during Tuesday's call and I think it has useful tools for data collection, paticularly from the end-user perspective. Over the next few months we plan to adapt some of those tools (among others) to use in an application to be deployed in a pilot program for WiFi hostpots, and begin collecting end-user data in a systematic way. Thanks, John. Eitan M. Fenson PnP Networks 1525 Siesta Drive Los Altos, CA 94024 eitan@pnphome.com 650-964-7210 cell 650-302-8760