CFP: Perceptual and Statistical Audition (Martin Heckmann )

Subject: CFP: Perceptual and Statistical Audition From: Martin Heckmann <Martin.Heckmann@xxxxxxxx> Date: Wed, 21 Jan 2009 18:11:25 +0100 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY> --=_mixed 005E6D93C1257545_= Content-Type: multipart/alternative; boundary="=_alternative 005E6D93C1257545_=" --=_alternative 005E6D93C1257545_= Content-Type: text/plain; charset="US-ASCII" Dear AUDITORY list, this is a call for papers for a special issue of Speech Communication on Perceptual and Statistical Audition Aims and Scope Current trends in audio analysis are strongly founded in statistical principles, or on approaches that are influenced by empirically derived, or perceptually motivated rules of auditory perception. These approaches are perceived as orthogonal, but new ideas that draw upon both perceptual and statistical principles can often result in superior performance. The relationship of these two approaches however, has not been thoroughly explored and is still a developing field of research. In this special issue we invite researchers to submit papers on original and previously unpublished work on both approaches, and especially on hybrid techniques that combine perceptual and statistical principles, as applied to speech, music and audio analysis. Recent advances in neurosciences have emphasized the important role of spectro-temporal modulations in human perception. We encourage submission of original and previously unpublished work on techniques that exploit the information in spectro-temporal modulations, particularly within a statistical framework. Papers describing relevant research and new concepts are solicited on, but not limited to, the following topics: - Analysis of audio including speech and music - Audio classification - Speech recognition - Signal separation - Multi-channel analysis - Computational Auditory Scene Analysis (CASA) - Spectro-temporal modulation methods - Perceptual aspects of statistical algorithms, such as Independent Component Analysis and Non-negative Matrix Factorization. - Hybrid methods that use CASA-like cues in a statistical framework Guest Editors Martin Heckmann Bhiksha Raj Paris Smaragdis Honda Research Institute Europe Carnegie Mellon University Adobe Advanced Technology Labs 63073 Offenbach a. M., Germany Pittsburgh, PA 15217 Newton, MA 02446 martin.heckmann@xxxxxxxx bhiksha@xxxxxxxx paris@xxxxxxxx Deadline Papers due 29th June, 2009 Submission Guidelines Authors should consult the "Guide for Authors", available online, at http://www.elsevier.com/locate/specom for information about the preparation of their manuscripts. Authors, please submit your paper via http://ees.elsevier.com/specom, choosing "Perceptual and Statistical Audition" as the Article Type. If you are a first time user of the system, please register yourself as an author. Best Bhiksha, Paris, and Martin --=_alternative 005E6D93C1257545_= Content-Type: text/html; charset="US-ASCII" Dear AUDITORY list, this is a call for papers for a special issue of Speech Communication on Perceptual and Statistical Audition Aims and Scope Current trends in audio analysis are strongly founded in statistical principles, or on approaches that are influenced by empirically derived, or perceptually motivated rules of auditory perception. These approaches are perceived as orthogonal, but new ideas that draw upon both perceptual and statistical principles can often result in superior performance. The relationship of these two approaches however, has not been thoroughly explored and is still a developing field of research. In this special issue we invite researchers to submit papers on original and previously unpublished work on both approaches, and especially on hybrid techniques that combine perceptual and statistical principles, as applied to speech, music and audio analysis.  Recent advances in neurosciences have emphasized the important role of spectro-temporal modulations in human perception. We encourage submission of original and previously unpublished work on techniques that exploit the information in spectro-temporal modulations, particularly within a statistical framework. Papers describing relevant research and new concepts are solicited on, but not limited to, the following topics:  - Analysis of audio including speech and music  - Audio classification  - Speech recognition  - Signal separation  - Multi-channel analysis  - Computational Auditory Scene Analysis  (CASA)  - Spectro-temporal modulation methods  - Perceptual aspects of statistical algorithms, such as Independent Component Analysis and Non-negative Matrix Factorization.  - Hybrid methods that use CASA-like cues in a statistical framework Guest Editors Martin Heckmann                        Bhiksha Raj                        Paris Smaragdis Honda Research Institute Europe        Carnegie Mellon University        Adobe Advanced Technology Labs 63073 Offenbach a. M., Germany                Pittsburgh, PA 15217                Newton, MA 02446         martin.heckmann@xxxxxxxx                 bhiksha@xxxxxxxx                paris@xxxxxxxx Deadline Papers due 29th June, 2009 Submission Guidelines Authors should consult the "Guide for Authors", available online, at http://www.elsevier.com/locate/specom for information about the preparation of their manuscripts. Authors, please submit your paper via http://ees.elsevier.com/specom, choosing "Perceptual and Statistical Audition" as the Article Type. If you are a first time user of the system, please register yourself as an author.   Best Bhiksha, Paris, and Martin --=_alternative 005E6D93C1257545_=-- --=_mixed 005E6D93C1257545_= Content-Type: application/octet-stream; name="CFP-SpeechCom-Auditory.pdf" Content-Disposition: attachment; filename="CFP-SpeechCom-Auditory.pdf" Content-Transfer-Encoding: base64 JVBERi0xLjQKJcfsj6IKNSAwIG9iago8PC9MZW5ndGggNiAwIFIvRmlsdGVyIC9GbGF0ZURlY29k ZT4+CnN0cmVhbQp4nMU9y3Ylt417fcU9s7o3x6ouvkmvxnE8tnNix7E7J4tkFmqpW+pYrSvbUsed 38j8y/zegGSRAFgoSXbcnmTh6lssEgTxBgh9t5snpXdz/n97OH9zUv69+/rT5eH7y5PvTuJk8v/K D/T5/M3ut89Pnn0ddspN86zU7vmrk3lKKc4xlhFqZ2Y3eeV2Qakp6LB7/uZkvzs8/3v9TE06JPqZ Wz7TOkyzVjufwpR0zJ/9df/x4VRPIWmr92cHpSc7u7i/PpyqKRqtNH3cHeYpBgsjXx1O0xSiU3F/ PAAMNka7//6gJxfSTAd+dTi1k01hLrMvA28Pp3FKag5+/7IvWb6Oyuv9D4dTANzPZlYwU1/oG5zq tkzltc3fL5Oek69eA8wwbUxq2NPyY4VPRZj187qAdoGtS5/v+xS4Gu4Q90+QsjvoEP/7+e8fO8cY JucjPRByjh5wFL1wjiq5SWu788FOKtRj/KYAbJ1zJiO9P39Sn62Pvj2X35djNzrtP8twxxSSg8UB EDjCmN/nH71W+z8eTs3kTYL3XxwiEJyP+UlNXs/W7f9cZ1LO7b/EmT4fZ8o/fnTwk/Iz4Or54TRM eg6zgZENlWShLw92AiqPGaSOx23CTgkow+28gwMIqmLk2I/tphyRmnGy+BB2vZ9m73fewH+VqZNR lFYqAN4Dms2ErK3P5Nd+xNe3+aM5xZh02N+VGYIxygBFkRdnh1M3GZfgJK4LKgp+kcDI+5v8nZpn r+P+gjzjYEoGdMXKfM5wOIBP/KSNShEIvkHNB3SAcIMM4DKBCYVT21wf5cNTFj66ByqYZxdCBrcd +OtOGncZf3MI8A1Z6Uh2Vnes4IWlh7dJCdFMJpmdVzOIIlfP7qODipPTQJ+vkUPfgDQAEkjLxn1M CxZN8GlBV/7x5mAmwJu1sIP2tAglE7tQAvmYEbTQ27Hu1SufSSA/2ugrXWgVUt9I2gGYsJ7tG1Gz bVQIIl6B0HDJTLPzTVZ7kPtA7VkmTVEZ7TINKgubrY96StGFIqngjEAIZQxmpgAY6snCaaRUJWt0 MNr7/Dtg1FtjHp3ioqxrfZaWAAsI41jQAXIsqJQRjCvc9LFthCmEuMx6319f4PxsAuBgQHn+Ws1p Ui6Rr3HyMziXYLUtEh4/foeTVlCVH+DDnxl8MJtJvmMz6IaJpBEUuv+GPq/IN8d+QDf96bI8zVY3 SD3QaABIVabaykMNjlfLsZpEprofJs1TXRRiAroClszE8/wCCGWB2JcDozj0DQXJZtl909+2ddfb wkPzlv7IJmKf4dZeI2rO+zGddXRe42sCwi2hbWBcUAeKToT4PO8Tkde3HZvXCCueIAL6QWbdGIGI +uKzW5CdEYtnKb5emMJYJ5B3yjZKf083xOljLvK9Iea8o/mqfjx7Q46ygA577AB5y07papjSmSzM C3XMIJpTpw6wThyc0azzu1NrHYitsMvzpJSlah5Tp1AhNpi9TQBL+XEuHA0QT6BBO0/pWDVU4brg MgW3767pkErE2tllurxzeqbtN3x7QQVJXTbuX/RP3rHXxQQwRHq9yXLXOe3JObwWJd5rSrqUXkUB M0gbAcSLvh7uZWu5t2Rsg/wCqeCDfPbwmaeYPyKJfZ9Np3m2xfbsA247RawACFlhgW6KVplEiAxx RMQayp2zSk/gSPhkGj0xaXZNxcK7/mGHKi0H4n0kku2OfkVO523nZjwFNnakkmEt3O09IovBixMw jdYmQBBfUXnQMIxQ3ROJ3FdiR8zgPg4yIc1gGb3r4rgtT6QIyjB6hGtuuX0YqV6tdBNY5PupSsM8 Adne80JW1oABdzXQUpYuP2TLSjuV2a3TEpcujVTy4jFkg5tLF4T2tssFYtIcCeU1tjjvH1+htKHH CDsBd6TaOCrNACFlYjI7fkRG3nZWfil9Q1HeIALcagMuGWDsbT3CFRNnOyQjdobJwYQ8ewRcRh5l 6WaZN4lz1cHEsZf96djf3hAMDnKsEgbXgw2AF0h49zJB9d1QEwdR9g948tHMDLeEs6lwLDLFqMnq JlLOBq40iq7H4LgSNrgF6MXAdZmHzgjIQMTAiAhyNFRu3A7orWYAPIFes3SZF8PAQZxeMUm1fHNL 8EEYvRmBaBs8yukE4rWpBZgni54JGvii760P1FtmIZoZgtFXrMkmIJbPwNsR1Gyxt3E2Jp8HcVKp JM8VXLNVblEMEGNlsEuacEHpQeBcjMZkuKmC9g7TDeA4WtSfOirml1B2X0YyJSJys2QK4ffMK1ur K/SkyCmPzp6O+V9GctEaJS4jpIlvBQ5fmzLNbesoIENwN9tzFSWIenbUkfm3N+Wk7OxF2l2OxGrd pAp42NF3sbJoOWcQJTGAlgM3w+pkmkIxdtQNGXKiB2qYZ2HI68NPcJSo7u1rIcZRNLCvblfnFMOm abK8HoSkYEfgsuvNaiqwUJYfBeo8EwQSWp4EZdTnaTCgOhVxL/FdwdGsQ6IGMMrul12ivBVMFqSk D7rjnJxD16is4pOmPtgZ8cGKtoZzHb0oQHtUcfCiiDu0snWriNlVivSzXrQGOkZKDMKEjPrsDKQk eH9d58zZyiDsNwYRMlNfVqkXHTEmCDG/668bkMWvakfzoyAcmOReywEWS2GGUfNuthXS4ls1OLYj OW3EIyEJY7gZdD2YDE1OVIgKZzQgLyRmeitItWvO+cSQaLJJWdVIj6lFIhwuSXioQUCiQ69lZ4Yt zVR629ixz8vER3tNj2zNjfjb2agPAmfqtvJUvk2qhT9PwUMG0Zw9gzA51TyDz0vIJw70fqr0DDMa zjtMWKLJQSVG+cw2OyR0U2GmJjqC2+ZRqbkocxjNkT6puGhf6X610hI/LF93gaW1GqQvzr4ihzzZ 286Vo3M5BLsCm27DhOjS7Kcd6Ep5N2qk1voScCSQHSkd4tksn6AHTZyPNwureNizJqzSVNPdeDh1 URRJo3baioYwyBfQBjO/673+msZflnmY3kbeJcTyqEdGlhjlYY0vCLBIMUbc5tsVHCwMgB4eEvJa EQSGGykWfYsqkx4miXqLPgAGNq96PAK1OBVe3BUIWegPvkA1VaztxgHxCgAl365dL8vjIYsfQF4z by4796LWFWMNt0RHEmXYotpHgRIwqE1ZrXsaq/h1VnViYGFUoMOmUUqg9ryVVjynYY7B0OUx/DGr QXh9Fdbg4fP2Hrf8bqAfTtmEikmCY5lkiXkbnRExZESSLMsINIx/v+tQ3BPUyAHDxVP+2bEJPPgj FX/N3VlkYtTrFM4KsIyMEAE2LQQQvf15AcQHwwp+I9j2U8IKSDuYhHsksBAEP4uG77sBVyYj8mcU JZVWuPH+q4YVPug2PUlijFGwRmqr11TOEFt8wMwYahDzKit1PbxHSKRA6Usix9ZsNoisZUZM0BCb CZdh1H5OaXwdhN9QkjqoyRS6ahxwT+hSZn3m6SLGq2iZJ+tiEy246EqfN1FIPP2FbokEw61mGznM NYje11w2qmez/7rk/+cYaLxdYmEEiRyniDGyfSnXQgSjtM5iQBqSlkqjeOomARkiCa37Qc6XYBCe AmKJmeuS74O7Zykuk7NOasG38RQjinv7DwUQRslRSaGmN5rkQP9dSj5SpdDDVSO9e6NtUvt/0oAW Tyk4Lgw2kgNMKbSxREShgiEWCg28gRqZ56T5/JKAHy3xthgPQiy+i5ib75zK024NgjZnshsmy1r0 MAXGk++wkeT3p+U3wPuguHGyBUHGUlUqVQkw5qc8t+Bh8WRMCS812XEUBNY6U1lhWpkQXSuOUot7 LKLu4P4Imvft/ZUEDuJCErnk641kBk88E6L2DLtbJSQ8bxmiA74dIrrL+n85qDAB13uBuuhvRDph IuLYnZB7eszNxUUlckm8lbKjUEoMuXyox82dFBph7zYG2rkoNJixQYMMg3KsA1bWdctCFFvQ0IDc Kx6YzwPSVuSeWQ+X1dx+mjuLXnk3DHn91iq810C9FSGpB+eiJdE2pgOOI9Xy8q+xZIN4tQuAo1db Y9lDKpR7JWs7ayyJkmIm3IlfVv8HZkKVICI23FjJuaK+1+IJbMg32R1q35Nz/m4IF/BACguflBWd 7CeLKZJOg7axUhYOP1Zaw7jN9Wib0tgfBoMwiiJtXToNnqKWnKtXxWmvRStC1KeyrInC/lbhjlUN 2WIcKR+mObot0bxKg6o0mB3UNB8KIxSrm2BRVKoWS11rsFkvAuZULum8kz2XB+yGzMqEaTfKp0ik HuXdUZAK9xKnoSDZcgDR1iBYrpEm56u7BVv1FA4paEg2suWLYoCECJ1r2VxCZI91UiiLmj7DrNtW vPNKCuuxUCVmMWoY1UzaB5Lsau9XjviGybEq0Nr48KmldCzdkktQMBchWVZogEiFJrKkzEdiFXju EznzmoFIanJzLBkIb+LCRV/VknOnA5KZ4hzVzpsQB0tBAa/kInya4hLrk84lMmOoe7ESc0zjXmLB WFmS1U4hnKK1/XYUyayq+q6WNJlhbimHUDxa52nMUM4hYGCTxv7q7J4ElahtII18tNyoASyG1Kh3 iB5t9xNR29x1gx1c7sYz3YHPyyTANM0OS6F3Ag7yylGSaRuBDvbzk2ofFzSt9lxDMBl+4HmygRdU sy8CZYgbVMMZpzkSLAkDrzekxZuDBtrX3P94cE/NsSh+oV1Hp7L1zSvYyFAmKbvJkk/LrLIJDgjA h8FQf3UQKhGvDzDG8RQJvsUExFZhNDJtUzzMt8apMMDGTPBCGsCthgidD2vQIKqU8JaTmukFE62m 2WRhB+dvarp1/7/9shmOA6DmpJbA+X65jsauqpRadwytf9RZ7wmXMrhTutzJ4MqkCtGW6hZrp6SC AXqfRBCax1EtkiqClmRtI85l6DdWkGa4JKu1wO7aPGN6gRi+Yw5oyNw/Gu9sA9FII058t9THyxlu tn6DdvLNSRO1A+opDth7oZ4nHeBQBnIu6TjUZ0hQK5e5nhhh8E2TZdvSpAxba20zlr0OT0GjXez2 XwaN3xCrZRV9CXHPEnprjw+pVCyPxk9ws5ejgU3ub7GC94ol8SQL/cJw9TSE/ZJ0RxEmRTjIps4k IqN83Q06KYkm+hJP8l9oMAewpOyvj6V84ddG44zshgmBSK9dLp85LTcHlJ0D4dErQT/c8H2OtTki xlf5iXpKP1flBKWfyrK/JG6X25wscCflC+7Rf90Iwo+MJtbgPRCnW3nkH3UjhibKxsjfaLbk14TG 3wnSpXFdirR8WrxqyiK3zb6jtgZP2Ky9SiwhkFQejYpn6OZFc6Y5Jxj+tu+m/Mc96VYXB+OQbgMh +tuhik5TikOfoEZ/TfnPciLC3bdjj6gvoZ8kl5RJBMrCPk15bFw9aHbbYzGfxwIpD5E8D2MOFgPa RC8llAxltINtVeNHYf5/0Fc0LCFGIBg/tTAduhB3siX7uCyQihBIodcjJMaST21KJBieh3ikdHf7 9NcOhEQfxH2So1E9X9HEjUjEWNQnXuLb8mxJ1APDWEM5V1IxUi5ZJYoMKxSi9lu956tz5TdeoiLp ZQxbtM193uvQSJZfrG0Wi4hYlEZnt3+m5d0sMYw1Qm1nH5ftBrPSfTnEjjEY6Rq9dFdGXGOlLDav RGH8lfkJm0RcvTDEBEHf4IZlRH9Z4IhhXVCqwO44rcHIfHbARtsXCJDRLgXu3XJO3vax9PJ2U8df FHkNsDnZIt0KS/4oXQ34ryqhLBzhup4xbFQWbeUZ/ylw6RP9iYL+qeryXOyyLaqjN+/Dpf2s091y oXdVwuiCcBfy31NU3G8WKVYoERFLE5kYzKCM9cFZwNIIJ2Fq5SfnUiAW5Ggrgf5cbAw/WmwEJd8K OcZdL49CGX5PRZNULddgW8XPV0GVxs5406jbjU9MikjJ37uNAnYp1LtVV9lglJMjDW6Un9vJEV5G +203+n5Cbx3tYm5zUzvSfJor7qfgY66Qbf1xagek3D0Hm+vc1eZghcR7m51PsHsOdtchTXruek+w Y3mtVXEwYL82KbPMrg2a2w911Gng2zlTdQX/i+ylqlkFT+Qf4qexG6nEWF+bbNe3PusU/1IQft+i RYum77YXnLStLZWCS7Ch/W+7ryheekFu6SQ7dgPhnPp1U79Icn9fMj5LRx9YDDbWzc5EqZNUwwhU bxRVft/kaLkNNWW80KeUHiLGyhKBwQTA+rYA4G859J4B/8PJ89/8lZwCqf8fi01ywS62IEB12LEi Zutejnw62t88rNYqWrIoMdzaaq/FW9Vb13pW1/DHskc83U+wbcP9qGl7nr/Q4lDDq0ERZqAj7A8M FhKikI6MmmTj4TG6kGBspkfYuEo73J0bgkQ1I9jm+vOQgajc0bJ3LvZamt6dwWTvBThwvP+zKji+ Y0biUjVYkmwGVqIBiYtBIeenF5LhTAS69PVbgXE3bjgPVyk5Up5XBOvgydqPXH5geckR/TnVdlnH 5tZsvH4KqNyC3feHg86NL5MnVbQvhvqL3OatpeDb+fBCFo+sazpEc38KnYmNdCn2j10EvGqXCUnq iMaahmMisd5+TyTby2t+1kQETKyovT1i4HTqJ/KBOPJT6SIgkSsPq4sa5IqmkJWzabKmC+1iiTPF dTfIFslgYdnglcmAPcxWZf01/9pMtK8Gs6/l5hvpqz6R6xNppERFTrsyMiBJKVK64myOsbjFuzKz bPawHYvdu1hrpQYdHp8I/Nwn0h1OS5765D7/qLVLlXRStgIX2v/k+cmfTqppsnSGVU/rDPsUI2d2 k7apGjkLAaXmLmaSJhdaWIGXWGUyoZFDi+fWcR8sS3zMxmkM9Z8HVxqKRubPDDHroUdgnRHOtLgR ALg3NPDI9CdhTtpwxlrbj8AnUPA29/1MYcr/nYMDUtvZ3fcvT179rN69D52QBr/TzZof0W4oTXov tJGLBsGa5isjt6OUWxmW3KOUPMd6js56UkfdrcHpgC1T1s7K/UhnWY6Iuu3+4Oa5o0clnUAT9nOD IVMy8X2dm1EFDIq9Ggt4L2dlQpqcTvysHsgpjheP8bTwZOiln3WQ+4XkSJPq+o3riaH2OS5IMOCp wYf9RLwuvWjf14lYZ8HI1esTeZIbq6KaSlfmjNnfHWoP4xCI84pNYJfmtc6ppUVv8V5ry1qjVKnu IW1eNSxQGvE92SlVHszSRHsN9wJEHsnH+OfQmnEMeWRD6AKto3U3ghZxTyDuqBpLjcky4GCcWQWY 61h0vvWnteDV+dxHOU5AFBXy7K8bk3sPX9FZHuhya32pwGT7r3rWg1bY/76Hm+5HpYA6SKkgmFbG BrKruQu3mey0XKOKpOrlKXRj0qSM7V23WwDjHmnkRY9lvMlNkY1RM6OWpdeyK30plp7BS9Qj9xTG WAc2H97h608Ln0cX4TRI0IWETTCW8rKvdI3Lv+7dom9yiTyguronCyQVupnkuJ7Sp1hp+Mr41nGZ p481qdwDk0AKXY6O2FJJq5zKZWtM+wjl5qw4Ac1d0kCTzCUVs9z0gPYPhOQeq3ggk4qRWX7BoIy0 +//IQa15BgX5ac8n442KVVQYaxZbAxGnNbh1j/TJIqDRCqvxjtV4JULMaHVFjqAzo7ktREI4tLg4 ExHIG9pmiekqEutE74uoo+utUEJfWMoSkVmFKx29xUS50zm0QOzbae+HQyd1o+/TiM+tTMADrCxF Wl0yg53Q3W2n4Q/piGeb/8BSVfSX8ImYzqu2QjQSx9sBYb5p49I58WzJChgoRZ7EOzXPVmGhMdW/ Ua/XunKizmtTqUVlOsdVq9D2gkJEzZ3mOIDvDsZtAM3g4eO0M9Xc+TeNdRsmZxIlgsHc/EWdEjCl vEqc5F6NAUN+5SnrWWNytke+sTTKJ34vQwoG39XCal4dvtGDpy2NX0tNN5nAK5Nb8jWGV1lTTiZi 2jrYVlwq8n/YMn9KV0ChVJ+svtHqr71+9H4bW0w8QnoePMNfL8KWGIy1uSUqXoXAG/nrGxC8DcYy z91KfJS2P6z5MOuql+PQYGzx8CA1L2SpeNXNjnUXIG4ts74b5LCn3g5PEmJShbFINpI1QaKNmONd V41gFhZMqxQs6TEudW7irTbXO0GQxR5o0udS3yhDC4LKYb2/eIUHMMGhz38cJwSuB1cmDPsHqVf5 sMsUokSeyRYF7YzZG6MRF1jMU2woQVoq6QX9FzQNd4wmKS+Hebb27I1i7cx4MxByi71ORLWWzpol gpExA4g5fDJ79R7DXUA8IDMCP0Qecl1IVUpNELlIHjdq+TGtT29fLBT67OvspCCYOXaQYv+TCP/q IuorzJ2xQ1vfzaDiv8e67/rx3tcfwU2roYQ6Pe8Nkf/2grKGvL8hX12U1KkPY091Az43uH9DT3Va AXpH06ooIclf00CpxpKQpMvR+nrrGQqOsVgQnGJvLZfMPQVx0VPAQzJi/OseQy1PniE742b/P+3K VD9NRnNgiM1q7opJks5bt602OqXwQuhsDq8vAq/qkJA01hcueYFjy80l1UqEpOaErB1Qa0GMeeSh bSeIK5KTE20g8Q4RcfS4nzl29xVv6ZLtS7nU4UZ+Y/utDh+0hEWo/pb6FEqSld4eKzUCKV+g6PTx Shj4uP/egMClx6sFQyKf9pDJNzhMRrGYbRI7HCAGxDZBIoJYf+q2+roJZS9/N4qawFKvlh2aZpS4 ehb4vksuevyZHLWe2x8WMWxjFP3sCmYyGZ2DsYd1GutS4lmT12JRDr5mkajRUhzSTsxH5eeVO/DR JM133Q30s/Uw7e7NiVIBVGr/4br9AP5cmhT8u33Sf7g6+cvu5uS7nQ1xSjXE6m3Mt3h3dazOYwOM PX+TVZne/e4Ii//pROUQXNbSIArjBB6cczm/BIvnfGmOvYN2BXOgKPffEKUOA/L8ofxgc+oERClM kv9aR51kTstipi32f+lC1rhlbmRzdHJlYW0KZW5kb2JqCjYgMCBvYmoKNjY3NQplbmRvYmoKNCAw IG9iago8PC9UeXBlL1BhZ2UvTWVkaWFCb3ggWzAgMCA2MTIgNzkyXQovUm90YXRlIDAvUGFyZW50 IDMgMCBSCi9SZXNvdXJjZXM8PC9Qcm9jU2V0Wy9QREYgL0ltYWdlQiAvSW1hZ2VDIC9UZXh0XQov WE9iamVjdCAxNCAwIFIKL0ZvbnQgMTUgMCBSCj4+Ci9Db250ZW50cyA1IDAgUgo+PgplbmRvYmoK MyAwIG9iago8PCAvVHlwZSAvUGFnZXMgL0tpZHMgWwo0IDAgUgpdIC9Db3VudCAxCi9Sb3RhdGUg MD4+CmVuZG9iagoxIDAgb2JqCjw8L1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDMgMCBSCi9NZXRhZGF0 YSAyMCAwIFIKPj4KZW5kb2JqCjE0IDAgb2JqCjw8L1IxMwoxMyAwIFIvUjEyCjEyIDAgUj4+CmVu ZG9iagoxMyAwIG9iago8PC9TdWJ0eXBlL0ltYWdlCi9JbWFnZU1hc2sgdHJ1ZQovV2lkdGggMTI0 MAovSGVpZ2h0IDg0NAovQml0c1BlckNvbXBvbmVudCAxCi9GaWx0ZXIvQ0NJVFRGYXhEZWNvZGUK L0RlY29kZVBhcm1zPDwvSyAtMQovQ29sdW1ucyAxMjQwPj4vTGVuZ3RoIDM1MjI+PnN0cmVhbQr/ loAipwnQOnBOg4ToHThOg6IMQ4JMOgg3CTdJg6BA3CTDoIMOk8JMHQQbpMOCTdAgbpMOEEDDpPCT B0EG6TDgkDdArpBg4QQbpMHCQPSboJg4JBugm6QYcJN0CYOEgbpN0EDDgldAmHSBuEm6Cg6QNwkw 6CBuCTeCg6QNwSYOggelcJBg6CDcEnwQMHSbgkwdBB6TcIIMHSbgkweCB6TDgkDDoK6TcIIGHSbg kDB4K4SDcJBh0E3SYOEg3QTcEgYdJuggw4SbwTdQYOCCboIySgdvQIGGHBKG3QSYMHBIJt4JB3SR CIgw4JIIG26CVsMHpAreCVBtugkEGwYOCQS28F2DB0qduCSsNugkg2GHr24JLhugkE2w4JU23gtw YOku3BKmw3QSTbD17cEvBuglbYcEkrbwXhg6Tu3BJJsGHQR3YXtvR2OC6W3BJB+GDoJB7bdJQk2D cIEn8OkiCR9uEkCBtJsGHQSTdO3Wn8G4Jb+wdAkE2rbdJJh1YYOEqf26CX94SQTq2GDoF6TbdL/B uCr+w6BU9OG4SStXYOl/bdBf4MOEu/boEk2k2G6/7BwS31boEl7g3CXdNsOguvbpPrYYcJLe7cIF 64N6/eDgq/t0C9KwYcJbfsOgX+D169g6Xq23CBfVhh0v9uv+G4QL9th0l6duCBeuDdf9g6X+G4QJ Xptg663twk/4PQL/YOl64bggX1sOv+3S/cG4QL6bYOv6g3CX99BersHS/bBuCX+wdAvvbpfWG4QX q7Dpbfg3CT/sOgvq26C7XDcF/2DoF/hulde3CC7Vth0v8G4S/2DpXSVhugv3YcEv9nHeC0vG6X7b DggvXBh0qf3177BwQV6uG6C17YcF9cG8Ff90vrYOCC9XBuk/qwevfDwQXpO3QT/bBwVP8G8F9tvS 9LDBwQJv9utfsHS/weEE+lsOlpbgw4JX93glv2HS68NwQTp7t0t9WU4Ol/jcIK9dh0v2wbgl/t4V /sOk9LhuCX+w8E/4MOl19wgvXDDhJ+sMPS728ECv8MHS/tuCTaq7B4Wvg9L124QXW52nEDDhI7Wd tPcINh6TV/BBuXUEC/9Bgx0k3+wm3BLtL0wYPC/9EEh3ST/0CBtuEFvroJsGHCXf4SdvX/02DBwQ Sf+k30F3XpNhuCW/2k4YPC/3Sbbpf+lsOCCTbX0mww6X/+3r/0mwYcEC/9bult/02GHBLv9LDeF/ 623S/+mwYcEFvrpbDpbaXq23r/0sMOCC/7226C9L0sMOlt6Te7DhBf61b0v/XBg4QXek/t0mnraS bBg6X//wS/tpbboLb8asGDpNa6u3CBd9dg9fXXg4QXFdOG6Caf2wwdL/24S+124QTTXVhhwk1rTt 0CHpcGDpf9wk/TYN0E+uw6X+3BLtbBugn6dhwk/ThugVJLsHS/tuEvXBugm1p2HS9cNwS/2DoJu+ 24Sbk41gw6Bfr3S1vtulf+DDhBPvTt0vVcG6X/bBwgm/p26V+uGDggT/7dL/3pFcSd1+GDhBJ66T bdJNd/B6C//YOEtNLSw3S79XYOCBXt/t0v/wel3/bBwgu0tJwbpL/e5H6W9enFwgtNfwbpd+lsOC BF1r/yndR//G6VyTv62HCBaTfq2Ddf/+DhL/qtugS0332DdLV/3YcEC/r8G6/ffsHS0vX24QLSb9 cN13/9h0v9/DcIFq2ulhh0un/w3BAv9e2/7/7B0vb/w3BArW912HX//Yev/3B4IF/v7B0tJtL7Bu CKcF//vj//2Dpe2vWG4IFpXX2wdJad9cG//f1wQLSbrq2Dpe2lfg3BAvr1ztAVv9qu9gmw6Xt/6Y bggRkaB9JvtcEDYOkZJYaP/fTBvV/9LQTbgggXVv6tEIdsOkg+v30EGw3BAr//0nYPohHM///pwb pIEJ4TSdfbQTbcECSb0m/XSYYYdKr///b6Qf/f92DggST+366Thukk31fe6VsOCKHX/10vt4hU+n //k0AgG6S303/02EGw4II7WAz0399+k2VgMwN6O1UMv///yqBpsOlf+//fbDhBIGrfX/0G03hIEQ 1wpPq3962S0FyGGF0dqQKiX1/9YIERAeoIFIWCxyhy3fr+//IcwGEFSQIRE9EcOlek2l/uiKKMJQ gQUW6fX/0rBW0FVIGGv//+gmwSpIE+q1/X8IN1CCSDaT+3/pBtINoFCQSbSv26f6bCCsEtQmD9fv /0nWCBKD03/r+9NggVJBN7qv/rYQQbqECQTDSvptf6bSsIFSSb9V//9sLS3//v10rSpBJtJ9JtL/ bCCDaBVSYYS1r39W0mwl8P/9f/6BbSftf/+1sFt1aX0//ToJurYSbSb1v/SYYSYYQKwYVh+p39+/ 97YSg3VryT09//bSbW2kHS6/b/0mGEmGEChhglBhJtL1vv9iEG0raTH//X/sGgVgwSfr1r/2rBYN pNNrdoK//YQMGlYMEmnS3ptb9JqQXWgUGzvUGA1/9b/hpkM5IuCQfr++vxBtKwcEdjeY9fV/9IHB KDdCLdfTf+rp4OC/f//auHZHIqpd6/+IJwwx19X/0kDsNv/vv8E4N6XdP/8HYP///1hu18Jfd9Ag 7Dbr1bp36QdvXf1/wTg39///B2G6X0+76pw3/faX1ggdvS/719Owfv1ul9IHDdpft97qE8G/++70 knT19L/8EHQfX/33p09L0r93pA4Jt9trr3qE6B6X17/oOCf/fr9J0G2ltpN09+Fgg7/rX3Va6T/1 /ggoIH/X/XoKm6Tdr/tpJQQO13pvtu1CCpvXp+/pJQXS7/68EqB/6T122kgoJsNL0r74PSoHq//9 pBAoJtpfX/6VNtK/17gwkEFh2k7aTe72Ektuv//IFyKWDhpf/8GEgQLbaXq3tuiLYpLDbBL0v7oM JBTUB4ZjFf+vhwlbrv/3aSsMNO9P/QZDZIlChtrbSvfUMqCFktK2wv/vUQZpGShgwYX+v0IIFDZX obbaT7+lYMXjuvSBAob+vvSsPr1/g3tXuuCBW7p67pKDB/3+gRH9+vvhRsNtpunvSg3+tqoIFb96 /Shg9a/ftt9+qggVg9p1u9KDf+/hbfS/6BQw32+9JK2/76ggVg//kY+koN3frT4St++9OkgoMN0/ 0r0rfX3+CBWD/r90obbT90tUrD9tfbwgoNvr/6Vvp916CUMHp/7aoK22t/vpWD9//BAoPrT/elbu Sd2t7pIKGDbS3r21CVvq1310rD///BAoNuk9r70lYbYS326bSCCgw/61WtLfS//pWDfd1vboIKDd rek+6hK///fUMG/rVfggUN2gm729tJKwbhLf/UIKG//0r0rD/f+tKDDbSf20m6CBW7//bSCWHr3/ 8FBt6vrb6BbD0n/SbVKDbaX7e3QQK3//+tg3/9LtKG266V9tIECsG2rf3TYQSUG/r63wQK7pXdf/ YO0utvdJKG3Sb11bwQKwdrv29qsHf//6t2k3+raCBAoYNtL0m9tJK26X694IFYd/1/1BttL90nSS sNuv7VhqCCgw2wu/7DBJLbaX7/oKGDbSv+m0glDcGk7a2rBgkgrDZB/V9dNl0aBgEFBtitNfiKSt sLjfDCQVgw7tXtBKGwwvfdKwwwv8GCCBQYYMFr5VQYStg03t0ynSCwx/xDJbMlBv/FKw26fQUG/t Qlb/dKwf+EChu61SsG7t0goN9eCVv/SsP3wgUN2tpBKwdenBQb98FttrdJQw7p1CBW3+krD/1gw2 6vCBWH74Sg3/UFb/wWwft6UNt1qCBWD9dKDf+lb6boIKGG36pW128ECsG/9QbvbSStt06QILD/0o N/6CsN6uggoN2r6Vt/wQUMH/pW+rVKGDtXwgob/0rfTegUMNtXSCVturSQVg/8EoN7fpWG2ldBAo benVK3/rDB63ggUN++lYO0m0kFB/8JW7rVKwba2kECht6dJKwevoEXUG+3cFG23VpIKGG6TdIKG/ rCWDvvWDfWoIFbbSbSSw/+Cg3feC2DdW0kFBttJtIEFf36gwf+tt6bSBAoYO0m1St39QVh/4JQ22 k6SCthwkw0gSsHf9Qb/0CsN0m0gSg2w0m0kt/4IKwd+qUGw4SYaSCsHaTpBKDb96Vv9JArDbSbSC UNsMJBhpIKwb+oJQbtK6StuEEw1QKGGw0g2EEErcNW0krBuCUGqCg22gmygOErDYMEgwYJIFBsMb GC7YVJQwYNNBAobhBpK2wmlgwwYTCBArYMhBwQZBjdShgwwhDBBArYilYaUNhBW0rBoFBsErDSg2 EFbSsGlDYIKwaUG0C2FhhoKDYILBpQbgrYJQYaVuFgwSg2grBglBtLcFDDBKG0rYIKDDStoFDBhK 2grDShsJWGgVhgsG0rDQKDDCV0FDBpWwlYNAoNglYaChsJW0rBoFBsErDSg2EFbSwaCg2ErBpQbQ K2CVhpQ2EFYaUG0CsMJQw0FbSsMEoYaCsMEraUG0CsGEoNpWDBBQ9W0FYMEoNpWGgUNhK2gVgwlB tbDQKDDBK6CwYSg2lYNAoNglbSsNBQ2ErBoFBsJWGlDaCtglYNBQbCVg0ugUGwSsGlBsIKwaUHQV sJWDCChuFYYJQ2gUMMJYawbQKwwlBhhBXqxUMFag9goasFag9goNYK8GrBQa9goNWCg1asKGrBQa tWUOFGrCg1asFDVqDCtQwVq8GCtQwV7UGCtQfBQ1DBWoNWFahgrUNR8AEAEKZW5kc3RyZWFtCmVu ZG9iagoxMiAwIG9iago8PC9TdWJ0eXBlL0ltYWdlCi9Db2xvclNwYWNlL0RldmljZVJHQgovV2lk dGggMTQ3Ci9IZWlnaHQgMjAwCi9CaXRzUGVyQ29tcG9uZW50IDgKL0ZpbHRlci9EQ1REZWNvZGUv TGVuZ3RoIDQ3MTA+PnN0cmVhbQr/2P/uAA5BZG9iZQBkAAAAAAH/2wBDAA4KCw0LCQ4NDA0QDw4R FiQXFhQUFiwgIRokNC43NjMuMjI6QVNGOj1OPjIySGJJTlZYXV5dOEVmbWVabFNbXVn/2wBDAQ8Q EBYTFioXFypZOzI7WVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZWVlZ WVlZWVn/wAARCADIAJMDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL /8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2Jy ggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWG h4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo 6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQD BAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRom JygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaX mJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6 /9oADAMBAAIRAxEAPwCCkyPUfnQ3Q1UuJpEkIU4H0rhSufWTmoK7LeR6j86M+9UPtEvZv0FXYySo J6kCm1YmFRTeg7I9aMgd6a0SOcsgJ9SKQQxjGEUYORgUjTUfn3oyPWoxBEDkIufpS+THu3bBuHeg XvDwwPQg/jS0xIkQ5VQp9qdSGr9QooooGLSE4GSeKKGUMpDDI9KAE3L/AHh+dG9f7w/Om+TEOiL+ VBhiPVF/KmT7xJRRRSKAfWilHSigQ1uhqvOIg/znJYgcdqsHoajZMknLDPof8+lNEzV0Ut6xzBtx wGznHUVoKQwDDoRxUZiGOr8e9SKMDHPAxzTbuRTg4t3HUUlLUmwUlLT0id1LAfKDgsTgZ+poBuwy kq6NPZbb7RNLHHEThTnduP4Zqq0bCTZgluwHOfTFOxKnF7DaSry6XcuxRQhlAyYww3D6+lRGyn3t HhGkU4KK6ls/TNFmJVIPqV6SlIIJBBBHUHtSUixaKKKACiiigBRRQKKBCUlLSUDFooooAKACzBVB JPAA70VtaLbpDby38zBAuRGzDOD647+lUldmdSoqcbjIdJiihklvZDujUMYU6jPQE+ppA13KY0b7 PaJ0RXQA4PoCCa04wi6akscj26SsWdiN0r9cY9zVW2uIotRjihiAZm+Yn5mHqWb19h09a0skcSqS ldvW39f1uaklvGJN1y8flDCqrqB+uec+lKumWqOHjUoyghSD0B9P1/OsaK72GS9lPmup2Ix/ib+6 o7AdzTrjVJhcuC+Rbou4AYDMGXd/Miq5kY+xqXsmXb+3gto0AuJ7WIn/AJZDjPqSBn86rX6SyRwT 2l4si7dhLEAMw788A1B9suLe4lt0lZ9p3Q7jnepGdp/Dp7/WnrMraXJKJmSPzVIKAblJ4Ibpnt9a m6ZpGEo2b1/4I2VFubWWW/idLqHG5kABZT0OOh/Csq4tmhCuGEkT/dkXof8AA+1bthOjMILghVmB VWT/AFcmfT+6fy+lMi03ZLLbqWktZDtYMPmib+Fsf1pONzWFX2baf9en9eRz9JT5Y2ileNxhkJU/ UUysju3FooooAUdKKTP0ooAKKKKACiiigCS3ge4nSGMZZzgV0l1axW0cX2lj9jtkGE/56Pz2/wA9 aqeG7cnz7gAFlGxM9M9/6UuuzyJd5fjZxCvvjlz/ACFaxVo3OCrN1K3s10DW7x0+zbYvKleLOSeU B7D0PvUOlYtr+GDA82QHzD/dGDhf8alu9qWlneyncUgVUVudz+p9h1qvpCLuluZSSzbkj9S5BJP5 fzofxBFJUmv6v/wBhaOXUYYY+ba26f7QXlj+OKpRsXE7HksmT+YqW0Gy1u5vRBGPqx/wBqKH7k// AFz/AKioZ0xVrrtYmuGL2trODhkBiJHqvI/Q/pV2B4xpd1OUV0kZA6dMHnOPT1FZ0J32k8XpiQfg cH9D+lXbCHzdHvQDhiy7fcjnFNbkVElHXuvzIIZDZXCMP3tu5DgH+LB6+xFdDFdr/azwThRJn906 /wAQ/un6VzljtnItXIG85jY/wt/9fp+VT3T79XuI92wmU7G/usOhpxdkZ1aanKz3sJr8Ji1SQ7cL IAw9+Of1rOrpNbie40iG5kQrLHgsPTPBH54rm6U1ZmuGnzU15aCUtFJUHQOHSigdKKYhKKKKQwoo ooA6nw8Vj0p3Y4G9mJ9MAf4VjayfP1ESR5ZZ0RkB9CKuWcu3wxd+zFfzwP606wjSW0trx8H7GHDD 1xyv862eqSPOj7lSVR97EOtxiK1sUVg/lBo2I/vDGaahEN/p9r2j27/95uT+mKXT4/t9o8bnLRzr K2f7p4b/ABqiLgyamLg95Q/61LfU1hF2cH0v+JJOht9OWI/eedifovH8yarw/wCrn/65/wDsy1oe IiBqAjXhUXP4kkms+H/Vz/7g/wDQlqXo7GtN80FJ9QtT/pCKej5Q/jx/Wr0cjW2iK68OboEfgP8A EVmAkEEdRWtrYWKK2hQ8HfMf+BHihbNhU1lGPf8AQhs40GspjiJW80eygbh+lV7w+ddmVAcT/OB9 eo/PIqeMFNPkuTjlPIBz3zz/AOO0/SohPLEz/dt33uT2Xr/MfrT30Fflbm+mh0F4pfT7iA/Nsh5b Ofmx0P6H8a4zrXRabO81pqtw/wB1wWA/4CeP5Vzo4FObvZmeFi4OUX/WgUtFJWZ2CiigDiimAUUU UgCiiigDc0aH7XpN7bdyQR9e36ik0mDfpt3FIxjMz+UoP94DOKr6Hd/ZZpv7rKGP0B5/TJrR11k+ wLJb8EXG5iOzYPP8q1VrXPPqcyqOHRtGboykXU6O/loYmRye2SAP1NZzo0bsjDDKcH6itu68qXSZ 76Ph59iuvowPP58VSuU897a6HSYhX9nHB/MYNS1ob053k5P0+aJvEUZS/Rz1kjBP1HFZ0X+rm/3B /wChLWvr7edubvBMY/wKgj+RrIi/1Ux/2R/6EKJbjoO9JXGRoZJFRRlmIA+pq9rT7tSkUfdjAjH4 D/8AXRoqA36yv9yFTI34D/GlYCXUJrmVf3cYEjD1JHA/E0JaDlL956L8x14ixaZDCM+ZG4aQehYE gfkBRbqLfSp3YkSXCkIP9kHk0WcLX0Vy8r7VEiSSueww2akttl9eSyyqyWkUe3A/hXsPqafmZt2T i+mr/wAvvLQQ2nhZzj5puT+JA/lXPV0WsXTGwaEgJjaCo7EnIH4AD8652lPsPC3acn1YtJS0VB1C jpRSAUUAFFFFABRRRQA+GUwzJIoyVOcHuPSul0+4hmspbWVS6KhZeOXTt+I6fgK5erum332OdC4L Rhs8HlfXH9RVxdmc+Ipc8brc2be3gtNLuC7efZyMGDL12nAz9RVK1jW0u/slw263mIeKQdM9j/Q1 s2kdndRzNAN0E330z8ue/HY1RitBG5hXNxbxvuCHiSE56jPUfzrRraxwxqfEpf1/wfzKN4d99qUJ /i+cfVef5ZrOj/495j/uj9a6DUrdm1BZkiIkQjgdJk7j69Rj0rJFjKJJbUA7jKigkdsMc/lUSTud VGpHk+4kgRoNHkZQTLduIkA6kDr/AIU6+jZpo7C3UtJw0mO7Yxj6AfzroYrCOOWFuogTZGvp6n61 Uu4LezVyZfK89iXYcyPk/dX0FU4aHPHEJz03/r8l+JQkswunra2rht0mJ5ei/KPX0GaswzWtvp37 mMuqSDys8ec/r9M/yqSawV3jgLC3sUXcY92C56nPsKxtSvknlItwViUbVJ4wvoB2H8/0ofulQXtd L+f9f1oQXk5lYJu3bSWZv7znqfp2H0qrRS1k9T0YxUVZBSUUtIYCigdKKACiiigAooooAKKKKAJ7 O8mspvMhbHqp6N9a3Rq8F4IyTFBMh6Sg4P0YdK5uiqUmjCpQhN36nfRSLKgIZGOOdhyKCIxICQoc 8DOMmuDilkgffC7Rt6qcUsk8skokeV2kHRixyPoe1ae1ON4B30kd9msm9udOsZfMZBLcHpj5mH4n pXLzTzXBBnleXHTec4qPAHak6nYqngbP3pFu5vXm3quVVvvEtuZ/qfT26VUpaKzbuehGKirISilp KQwopaSgBw6UUDpRQAlFFFABWcdRd2c29s8sanBcGtAjIIPesqE3WnK0Zg86IHIZetXFJnNXnKLV nZdWlcsw3xnt5JY4GYo2AmeT/nNQpqcsgJSzdgDg7Wzj9KtWU0E0ZaBAnPzLgAg1m2F7HbJKjq7M XJG0ZqklroYTqyXJeejvrZF9L4S2zSwxM7qcGPuKhj1KaVd0dm7L0yG/+tTtOik33FxIhj805Cnr 3qrp120NtsFvLINxOVHFPlWtkJ1Z+7zSte/Tt8upcjvpJIpWFs2+NgpTPP8AKo49SmmXdHZu69Mh v/rVbgRAhkWMoZfmYHOc1l6bdtBbFBbyyfMTlRx2pJJ3sipznBxUp736f8A0bS6Nz5m6IxlDggnN QyalukKWsLTkdSOlJdkR6dLLGhjeXBYHrzVmxiWK0iVB1UMT6k0rJalqVSTVPm6Xb/IqNqrRnbLb Ojk8Ang1Jc37wT+X9mZgxwp3Y3fTj3q5JFHKAJEV8dNwzis/VmKT2bBSxDkhR1PIojyt2sKr7WnB yc+3RFme5kitkl+zsxIyy5+7xRZ3TXSMzQmNexJzu61VuL2V7eVTZzKCpG4g4HH0qzpn/IPh+h/m aHG0dhwquda0ZaW7f8At0lLSVmdg4UUDpRQISiiigYyUkROV6hSR+tZdvqjRxYu0kLHlWC9a1ZHW ONnY4VRk1D58g+aSApHgktuB2jGeRVx21Rz1U+ZOMrfK/wB5Ts2aFbm8ljZVkYEIBz1/+vTdHmAM kRVwzMXBxxirwuHBUvCURzgHPQnpkdqal4GhkcoQyHG3OcnoP14qm276GMYRi42ltfp82SXMywQl 2VmA4woyay9PvUtrby5I5S24nhavpdlnhDR7RKu4HdnHpQ14QZtsW5Yu+7Gecf0NJaKzRVRqUlOM rW8vmQwXxMdxNIJDGHwi7eQDVbTr1LW3MckcpO4nha0Jbp4/MzFnywCcN1zQ13jJRNy+WZM7scU/ kQ000+fVX6d2QW7NfxXSSFgjHCZGCKjhu5LFBBdxvheFdeQRV37Q7HbHFuYAFvmwBntSSXRjEReM jf1GeVHrSv0sVypJSUtV1t5ldb6a6lVbSIhP4nkHAqHUbgfbIAEc+S+WOOvQ8VeNxIJ/LEBPPXPb OM4/GkS6Z1XbFmRicLu7A4JJ+tNaO9hSXNFxc9fTt2GXtyosd21z5yEKMdMjvTdJlD2oi2sGjHOR xyTUj3nlrlo2BDBXGeme/vUscxkldNo2qAQwOc5pfZtY0VnVUubytb+uxLS0lLWZ1CjpRQOlFMQl FFFIYyVBLG6N0YEcVF5UrqUmkRkIIO1cFqlaNSSTnJGOtNaJADwT7A1SZnKN3exH5ErbFlkVkQg8 LgtjpmmLaOCrF13KWOAODySPyJNShBkYRxzjk0m0FTmN8+mad2ZuEXq1+ZELSTYimRPlj2Ahecjk H9BSi0bbIm9QrIFHHII7n8zUjAHrE/Jz1oI+8fLf86LsXs4dvzGPbyukm50Dvt6A4AFDWp3ysjge YhXBHAJ6mnqozjy3weOTSbRj/Vv+dF2HJF9Pz/rqAhmjO6N0ywAYMpxkDGRTHtGldjM4f5Nq4yv5 4NSbQAQIn/OgrkKfLfg+tF2Nwi1Zr8xIoZVlRnkRtqbDgHJ96bHbSxqpWRPMXd/CcEE5xT8DAHlP +PvRsGP9W/PbNFw5Y/02MNtISGMimQuGY4446AU+CAQSSENlWxtH90en60qIG4ZWGBjk9acII/T9 aV+g4wV+ZIkooAwAKKk3HDpRQOlFAhKKKKBhRRRQAUUlLQAlLSUtACUUtFABSUtFACUUtFACUtJS 0AFFFFACiigdKKYhKKKKQwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAFFFFFMR//9kK ZW5kc3RyZWFtCmVuZG9iagoxNSAwIG9iago8PC9SMTAKMTAgMCBSL1IxMQoxMSAwIFIvUjcKNyAw IFIvUjgKOCAwIFIvUjkKOSAwIFI+PgplbmRvYmoKMTAgMCBvYmoKPDwvQmFzZUZvbnQvU3ltYm9s L1R5cGUvRm9udAovU3VidHlwZS9UeXBlMT4+CmVuZG9iagoxMSAwIG9iago8PC9CYXNlRm9udC9U aW1lcy1Cb2xkL1R5cGUvRm9udAovRW5jb2RpbmcgMTYgMCBSL1N1YnR5cGUvVHlwZTE+PgplbmRv YmoKMTYgMCBvYmoKPDwvVHlwZS9FbmNvZGluZy9EaWZmZXJlbmNlc1sKMzIvLm5vdGRlZgoxNDcv cXVvdGVkYmxsZWZ0L3F1b3RlZGJscmlnaHRdPj4KZW5kb2JqCjcgMCBvYmoKPDwvQmFzZUZvbnQv SGVsdmV0aWNhL1R5cGUvRm9udAovRW5jb2RpbmcgMTcgMCBSL1N1YnR5cGUvVHlwZTE+PgplbmRv YmoKMTcgMCBvYmoKPDwvVHlwZS9FbmNvZGluZy9EaWZmZXJlbmNlc1sKMzIvLm5vdGRlZl0+Pgpl bmRvYmoKOCAwIG9iago8PC9CYXNlRm9udC9IZWx2ZXRpY2EtQm9sZC9UeXBlL0ZvbnQKL0VuY29k aW5nIDE4IDAgUi9TdWJ0eXBlL1R5cGUxPj4KZW5kb2JqCjE4IDAgb2JqCjw8L1R5cGUvRW5jb2Rp bmcvRGlmZmVyZW5jZXNbCjMyLy5ub3RkZWZdPj4KZW5kb2JqCjkgMCBvYmoKPDwvQmFzZUZvbnQv VGltZXMtUm9tYW4vVHlwZS9Gb250Ci9FbmNvZGluZyAxOSAwIFIvU3VidHlwZS9UeXBlMT4+CmVu ZG9iagoxOSAwIG9iago8PC9UeXBlL0VuY29kaW5nL0RpZmZlcmVuY2VzWwozMi8ubm90ZGVmXT4+ CmVuZG9iagoyMCAwIG9iago8PC9UeXBlL01ldGFkYXRhCi9TdWJ0eXBlL1hNTC9MZW5ndGggMTM3 MT4+c3RyZWFtCjw/eHBhY2tldCBiZWdpbj0n77u/JyBpZD0nVzVNME1wQ2VoaUh6cmVTek5UY3pr YzlkJz8+Cjw/YWRvYmUteGFwLWZpbHRlcnMgZXNjPSJDUkxGIj8+Cjx4OnhtcG1ldGEgeG1sbnM6 eD0nYWRvYmU6bnM6bWV0YS8nIHg6eG1wdGs9J1hNUCB0b29sa2l0IDIuOS4xLTEzLCBmcmFtZXdv cmsgMS42Jz4KPHJkZjpSREYgeG1sbnM6cmRmPSdodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIy LXJkZi1zeW50YXgtbnMjJyB4bWxuczppWD0naHR0cDovL25zLmFkb2JlLmNvbS9pWC8xLjAvJz4K PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9Jzk3YzI3ZDI4LWU5NDMtMTFkZC0wMDAwLTkyNDc1 ZjlmNDlkOScgeG1sbnM6cGRmPSdodHRwOi8vbnMuYWRvYmUuY29tL3BkZi8xLjMvJyBwZGY6UHJv ZHVjZXI9J0dQTCBHaG9zdHNjcmlwdCA4LjYxJy8+CjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0 PSc5N2MyN2QyOC1lOTQzLTExZGQtMDAwMC05MjQ3NWY5ZjQ5ZDknIHhtbG5zOnhhcD0naHR0cDov L25zLmFkb2JlLmNvbS94YXAvMS4wLycgeGFwOk1vZGlmeURhdGU9JzIwMDktMDEtMjBUMTI6NDY6 NDQrMDE6MDAnIHhhcDpDcmVhdGVEYXRlPScyMDA5LTAxLTIwVDEyOjQ2OjQ0KzAxOjAwJz48eGFw OkNyZWF0b3JUb29sPlBERkNyZWF0b3IgVmVyc2lvbiAwLjkuNTwveGFwOkNyZWF0b3JUb29sPjwv cmRmOkRlc2NyaXB0aW9uPgo8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0nOTdjMjdkMjgtZTk0 My0xMWRkLTAwMDAtOTI0NzVmOWY0OWQ5JyB4bWxuczp4YXBNTT0naHR0cDovL25zLmFkb2JlLmNv bS94YXAvMS4wL21tLycgeGFwTU06RG9jdW1lbnRJRD0nOTdjMjdkMjgtZTk0My0xMWRkLTAwMDAt OTI0NzVmOWY0OWQ5Jy8+CjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSc5N2MyN2QyOC1lOTQz LTExZGQtMDAwMC05MjQ3NWY5ZjQ5ZDknIHhtbG5zOmRjPSdodHRwOi8vcHVybC5vcmcvZGMvZWxl bWVudHMvMS4xLycgZGM6Zm9ybWF0PSdhcHBsaWNhdGlvbi9wZGYnPjxkYzp0aXRsZT48cmRmOkFs dD48cmRmOmxpIHhtbDpsYW5nPSd4LWRlZmF1bHQnPkNGUC1BdWRpdG9yeS1maW48L3JkZjpsaT48 L3JkZjpBbHQ+PC9kYzp0aXRsZT48ZGM6Y3JlYXRvcj48cmRmOlNlcT48cmRmOmxpPm1oZWNrbWFu bjwvcmRmOmxpPjwvcmRmOlNlcT48L2RjOmNyZWF0b3I+PC9yZGY6RGVzY3JpcHRpb24+CjwvcmRm OlJERj4KPC94OnhtcG1ldGE+CiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAg ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKPD94cGFj a2V0IGVuZD0ndyc/PgplbmRzdHJlYW0KZW5kb2JqCjIgMCBvYmoKPDwvUHJvZHVjZXIoR1BMIEdo b3N0c2NyaXB0IDguNjEpCi9DcmVhdGlvbkRhdGUoRDoyMDA5MDEyMDEyNDY0NCswMScwMCcpCi9N b2REYXRlKEQ6MjAwOTAxMjAxMjQ2NDQrMDEnMDAnKQovVGl0bGUoQ0ZQLUF1ZGl0b3J5LWZpbikK L0NyZWF0b3IoUERGQ3JlYXRvciBWZXJzaW9uIDAuOS41KQovQXV0aG9yKG1oZWNrbWFubikKL0tl eXdvcmRzKCkKL1N1YmplY3QoKT4+ZW5kb2JqCnhyZWYKMCAyMQowMDAwMDAwMDAwIDY1NTM1IGYg CjAwMDAwMDcwMjIgMDAwMDAgbiAKMDAwMDAxNzg3MiAwMDAwMCBuIAowMDAwMDA2OTU0IDAwMDAw IG4gCjAwMDAwMDY3ODAgMDAwMDAgbiAKMDAwMDAwMDAxNSAwMDAwMCBuIAowMDAwMDA2NzYwIDAw MDAwIG4gCjAwMDAwMTU5OTcgMDAwMDAgbiAKMDAwMDAxNjEzNyAwMDAwMCBuIAowMDAwMDE2Mjgy IDAwMDAwIG4gCjAwMDAwMTU3NjIgMDAwMDAgbiAKMDAwMDAxNTgyNCAwMDAwMCBuIAowMDAwMDEw ODM1IDAwMDAwIG4gCjAwMDAwMDcxMzAgMDAwMDAgbiAKMDAwMDAwNzA4NyAwMDAwMCBuIAowMDAw MDE1NjkyIDAwMDAwIG4gCjAwMDAwMTU5MDYgMDAwMDAgbiAKMDAwMDAxNjA3NyAwMDAwMCBuIAow MDAwMDE2MjIyIDAwMDAwIG4gCjAwMDAwMTYzNjQgMDAwMDAgbiAKMDAwMDAxNjQyNCAwMDAwMCBu IAp0cmFpbGVyCjw8IC9TaXplIDIxIC9Sb290IDEgMCBSIC9JbmZvIDIgMCBSCi9JRCBbPENEOUQ5 RDY3REJBOTE3QUQzNTYzNzY2Mzk5MTY1NzY4PjxDRDlEOUQ2N0RCQTkxN0FEMzU2Mzc2NjM5OTE2 NTc2OD5dCj4+CnN0YXJ0eHJlZgoxODA5NwolJUVPRgo= --=_mixed 005E6D93C1257545_=--

This message came from the mail archive
http://www.auditory.org/postings/2009/
maintained by:

DAn Ellis <dpwe@ee.columbia.edu>
Electrical Engineering Dept., Columbia University