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28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015

作者： Imberman, Susan P. Kress, Michael E. McCloskey, Dan P. Dept. Psychology College of Staten Island 2800 Victory Blvd Staten IslandNY10314 United States CSI/IBR Center for Developmental Neuroscience College of Staten Island 2800 Victory Blvd Staten IslandNY10314 United States Vice President of Technology Systems College of Staten Island 2800 Victory Blvd Staten IslandNY10314 United States Dept. Computer Science College of Staten Island 2800 Victory Blvd Staten IslandNY10314 United States Doctoral Program Computer Science the Graduate Center CUNY United States

ISBN: (纸本)9781577357308

Many Stat. and Data Mining Techniques Have Been Used to Analyze the Deluge of Data Generated by Comp. Sensing Devices. Behav. Psychologists Traditionally Have Relied on low-tech Methodologies for Observing Anim. Behav. in the Wild and the Lab.. These Methods Are Time Intensive and Laborious. When the Observed Anim. Is A Colony Anim., with Many Individuals to Observe, Traditional Methods Fail We Inject RFID Passive Transponders under the Skin of Our Stud. Anim., the Naked Mole-rat . RFID Readers Are Placed Throughout the Housing Environ., Allowing Us to Track Anim. Movements As They Move Through These Areas, with Sub-second Resolution for Long Periods of Time. This Methodol. Generates Huge Amounts of Data Requiring Big Data Analyt. Techniques. in This Paper, We Investigate Equivalence Metrics, Specifically the Pearson Correlation Coefficient and Hamming Distance, to Analyze Behav. Changes in the Social Netwk. Struct. of A Naked Mole Rat Colony. Our Results Showed That A Pearson Correlation Was Sufficient to Detect Equip. Error and Hamming Distance Could Detect Changes in Colony Behav.. Copyright 2015, Assoc. for the Adv. of Artif. Intell. . All Rights Reserved.

关键词： Hamming distance

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Dark Energy Survey Year 1 results: Methodology and projections for joint analysis of galaxy clustering, galaxy lensing, and CMB lensing two-point functions

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Physical Review D 2019年第2期99卷 023508-023508页

作者： E. J. Baxter Y. Omori C. Chang T. Giannantonio D. Kirk E. Krause J. Blazek L. Bleem A. Choi T. M. Crawford S. Dodelson T. F. Eifler O. Friedrich D. Gruen G. P. Holder B. Jain M. Jarvis N. MacCrann A. Nicola S. Pandey J. Prat C. L. Reichardt S. Samuroff C. Sánchez L. F. Secco E. Sheldon M. A. Troxel J. Zuntz T. M. C. Abbott F. B. Abdalla J. Annis S. Avila K. Bechtol B. A. Benson E. Bertin D. Brooks E. Buckley-Geer D. L. Burke A. Carnero Rosell M. Carrasco Kind J. Carretero F. J. Castander R. Cawthon C. E. Cunha C. B. D’Andrea L. N. da Costa C. Davis J. De Vicente D. L. DePoy H. T. Diehl P. Doel J. Estrada A. E. Evrard B. Flaugher P. Fosalba J. Frieman J. García-Bellido E. Gaztanaga D. W. Gerdes R. A. Gruendl J. Gschwend G. Gutierrez W. G. Hartley D. Hollowood B. Hoyle D. J. James S. Kent K. Kuehn N. Kuropatkin O. Lahav M. Lima M. A. G. Maia M. March J. L. Marshall P. Melchior F. Menanteau R. Miquel A. A. Plazas A. Roodman E. S. Rykoff E. Sanchez R. Schindler M. Schubnell I. Sevilla-Noarbe M. Smith R. C. Smith M. Soares-Santos F. Sobreira E. Suchyta M. E. C. Swanson G. Tarle A. R. Walker W. L. K. Wu J. Weller Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 Dept. of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 Department of Physics and McGill Space Institute McGill University Montreal Quebec H3A 2T8 Canada Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians Universität München Scheinerstr. 1 81679 München Germany Department of Physics & Astronomy University College London Gower Street London WC1E 6BT United Kingdom Department of Astronomy/Steward Observatory 933 North Cherry Avenue Tucson Arizona 85721-0065 USA Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. Pasadena California 91109 USA Center for Cosmology and Astro-Particle Physics The Ohio State University Columbus Ohio 43210 USA Institute of Physics Laboratory of Astrophysics École Polytechnique Fédérale de Lausanne (EPFL) Observatoire de Sauverny 1290 Versoix Switzerland Department of Physics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Scottish Universities Physics Alliance Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill Edinburgh EH9 3HJ United Kingdom Center for Cosmology and AstroParticle Physics The Ohio State University 191 West Woodruff Avenue Columbus Ohio 43210 USA Department of Astronomy and Astrophysics University of Chicago Chicago Illinois 60637 USA Observatories of the Carnegie Institution of Washington 813 Santa Ba

Optical imaging surveys measure both the galaxy density and the gravitational lensing-induced shear fields across the sky. Recently, the Dark Energy Survey (DES) Collaboration used a joint fit to two-point correlations between these observables to place tight constraints on cosmology (T. M. C. Abbott et al. (Dark Energy Survey Collaboration), Phys. Rev. D 98, 043526 (2018)). In this work, we develop the methodology to extend the DES Year 1 joint probes analysis to include cross-correlations of the optical survey observables with gravitational lensing of the cosmic microwave background as measured by the South Pole Telescope (SPT) and Planck. Using simulated analyses, we show how the resulting set of five two-point functions increases the robustness of the cosmological constraints to systematic errors in galaxy lensing shear calibration. Additionally, we show that contamination of the SPT+Planck cosmic microwave background lensing map by the thermal Sunyaev-Zel’dovich effect is a potentially large source of systematic error for two-point function analyses but show that it can be reduced to acceptable levels in our analysis by masking clusters of galaxies and imposing angular scale cuts on the two-point functions. The methodology developed here will be applied to the analysis of data from the DES, the SPT, and Planck in a companion work.

关键词： Cosmic microwave background Cosmological parameters Dark energy Evolution of the Universe Large scale structure of the Universe

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Dark Energy Survey year 1 results: Joint analysis of galaxy clustering, galaxy lensing, and CMB lensing two-point functions

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Physical Review D 2019年第2期100卷 023541-023541页

作者： T. M. C. Abbott F. B. Abdalla A. Alarcon S. Allam J. Annis S. Avila K. Aylor M. Banerji N. Banik E. J. Baxter K. Bechtol M. R. Becker B. A. Benson G. M. Bernstein E. Bertin F. Bianchini J. Blazek L. E. Bleem S. L. Bridle D. Brooks E. Buckley-Geer D. L. Burke J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero F. J. Castander R. Cawthon C. Chang C. L. Chang H-M. Cho A. Choi R. Chown T. M. Crawford A. T. Crites M. Crocce C. E. Cunha C. B. D’Andrea L. N. da Costa C. Davis T. de Haan J. DeRose S. Desai J. De Vicente H. T. Diehl J. P. Dietrich M. A. Dobbs S. Dodelson P. Doel A. Drlica-Wagner T. F. Eifler J. Elvin-Poole W. B. Everett B. Flaugher P. Fosalba O. Friedrich J. Frieman J. García-Bellido M. Gatti E. Gaztanaga E. M. George D. W. Gerdes T. Giannantonio D. Gruen R. A. Gruendl J. Gschwend G. Gutierrez N. W. Halverson N. L. Harrington W. G. Hartley G. P. Holder D. L. Hollowood W. L. Holzapfel K. Honscheid Z. Hou B. Hoyle J. D. Hrubes D. Huterer B. Jain D. J. James M. Jarvis T. Jeltema M. W. G. Johnson M. D. Johnson S. Kent D. Kirk L. Knox N. Kokron E. Krause K. Kuehn O. Lahav A. T. Lee E. M. Leitch T. S. Li M. Lima H. Lin D. Luong-Van N. MacCrann M. A. G. Maia A. Manzotti D. P. Marrone J. L. Marshall P. Martini J. J. McMahon F. Menanteau S. S. Meyer R. Miquel L. M. Mocanu J. J. Mohr J. Muir T. Natoli A. Nicola B. Nord Y. Omori S. Padin S. Pandey A. A. Plazas A. Porredon J. Prat C. Pryke M. M. Rau C. L. Reichardt R. P. Rollins A. K. Romer A. Roodman A. J. Ross E. Rozo J. E. Ruhl E. S. Rykoff S. Samuroff C. Sánchez E. Sanchez J. T. Sayre V. Scarpine K. K. Schaffer L. F. Secco S. Serrano I. Sevilla-Noarbe E. Sheldon E. Shirokoff G. Simard M. Smith M. Soares-Santos F. Sobreira Z. Staniszewski A. A. Stark K. T. Story E. Suchyta M. E. C. Swanson G. Tarle D. Thomas M. A. Troxel D. L. Tucker K. Vanderlinde J. D. Vieira P. Vielzeuf V. Vikram A. R. Walker R. H. Wechsler J. Weller R. Williamson W. L. K. Wu B. Yanny O. Zahn Y. Zhang J. Zuntz Cerro Tololo Inter-American Observatory National Optical Astronomy Observatory Casilla 603 La Serena Chile Department of Physics & Astronomy University College London Gower Street London WC1E 6BT United Kingdom Department of Physics and Electronics Rhodes University P.O. Box 94 Grahamstown 6140 South Africa Institut d’Estudis Espacials de Catalunya (IEEC) 08193 Barcelona Spain Institute of Space Sciences (ICE CSIC) Campus UAB Carrer de Can Magrans s/n 08193 Barcelona Spain Fermi National Accelerator Laboratory P.O. Box 500 Batavia Illinois 60510 USA Institute of Cosmology & Gravitation University of Portsmouth Portsmouth PO1 3FX United Kingdom Department of Physics University of California Davis California 95616 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA LSST 933 North Cherry Avenue Tucson Arizona 85721 USA Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Department of Astronomy and Astrophysics University of Chicago Chicago Illinois 60637 USA CNRS UMR 7095 Institut d’Astrophysique de Paris F-75014 Paris France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris F-75014 Paris France School of Physics University of Melbourne Parkville VIC 3010 Australia Center for Cosmology and Astro-Particle Physics The Ohio State University Columbus Ohio 43210 USA Institute of Physics Laboratory of Astrophysics École Polytechnique Fédérale de Lausanne (EPFL) Observatoire de Sauverny 1290 Versoix Switzerland High Energy Physics Division Argonne National Laboratory Argonne Illinois 60439 USA Jodrell Bank Center for Astrophysics School of Physic

We perform a joint analysis of the auto and cross-correlations between three cosmic fields: the galaxy density field, the galaxy weak lensing shear field, and the cosmic microwave background (CMB) weak lensing convergence field. These three fields are measured using roughly 1300 sq. deg. of overlapping optical imaging data from first year observations of the Dark Energy Survey (DES) and millimeter-wave observations of the CMB from both the South Pole Telescope Sunyaev-Zel’dovich survey and Planck. We present cosmological constraints from the joint analysis of the two-point correlation functions between galaxy density and galaxy shear with CMB lensing. We test for consistency between these measurements and the DES-only two-point function measurements, finding no evidence for inconsistency in the context of flat ΛCDM cosmological models. Performing a joint analysis of five of the possible correlation functions between these fields (excluding only the CMB lensing autospectrum) yields S8≡σ8Ωm/0.3=0.782−0.025+0.019 and Ωm=0.260−0.019+0.029. We test for consistency between these five correlation function measurements and the Planck-only measurement of the CMB lensing autospectrum, again finding no evidence for inconsistency in the context of flat ΛCDM models. Combining constraints from all six two-point functions yields S8=0.776−0.021+0.014 and Ωm=0.271−0.016+0.022. These results provide a powerful test and confirmation of the results from the first year DES joint-probes analysis.

关键词： Cosmic microwave background Cosmological parameters Evolution of the Universe Large scale structure of the Universe

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Dark energy survey year 1 results: Methodology and projections for joint analysis of galaxy Clustering, galaxy lensing, and CMB lensing two-point functions

arXiv

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arXiv 2018年

作者： Baxter, E.J. Omori, Y. Chang, C. Giannantonio, T. Kirk, D. Krause, E. Blazek, J. Bleem, L. Choi, A. Crawford, T.M. Dodelson, S. Gruen, D. Holder, G.P. Jain, B. Jarvis, M. MacCrann, N. Nicola, A. Pandey, S. Prat, J. Samuroff, S. Sánchez, C. Sheldon, E. Troxel, M.A. Zuntz, J. Abbott, T.M.C. Abdalla, F.B. Annis, J. Avila, S. Bechtol, K. Bertin, E. Brooks, D. Buckley-Geer, E. Burke, D.L. Rosell, A. Carnero Kind, M. Carrasco Carretero, J. Castander, F.J. Cawthon, R. Cunha, C.E. D'Andrea, C.B. Da Costa, L.N. Davis, C. De Vicente, J. DePoy, D.L. Diehl, H.T. Doel, P. Eifler, T.F. Estrada, J. Evrard, A.E. Flaugher, B. Fosalba, P. Frieman, J. García-Bellido, J. Gaztanaga, E. Gerdes, D.W. Gruendl, R.A. Gschwend, J. Gutierrez, G. Hartley, W.G. Hollowood, D. Hoyle, B. James, D.J. Kent, S. Kuehn, K. Kuropatkin, N. Lahav, O. Lima, M. Maia, M.A.G. March, M. Marshall, J.L. Melchior, P. Menanteau, F. Miquel, R. Plazas, A.A. Roodman, A. Rykoff, E.S. Sanchez, E. Schindler, R. Schubnell, M. Sevilla-Noarbe, I. Smith, M. Smith, R.C. Soares-Santos, M. Sobreira, F. Suchyta, E. Swanson, M.E.C. Tarle, G. Walker, A.R. Weller, J. Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall StanfordCA94305 United States Dept. of Physics Stanford University 382 Via Pueblo Mall StanfordCA94305 United States Department of Physics McGill Space Institute McGill University MontrealQCH3A 2T8 Canada Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Universitäts-Sternwarte Fakultät für Physik Ludwig-Maximilians Universität München Scheinerstr. 1 München81679 Germany Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Department of Astronomy/Steward Observatory 933 North Cherry Avenue TucsonAZ85721-0065 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. PasadenaCA91109 United States Center for Cosmology and Astro-Particle Physics Ohio State University ColumbusOH43210 United States Observatoire de Sauverny Versoix1290 Switzerland Department of Physics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States Argonne National Laboratory 9700 South Cass Avenue LemontIL60439 United States Scottish Universities Physics Alliance Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Center for Cosmology and AstroParticle Physics Ohio State University 191 West Woodruff Avenue ColumbusOH43210 United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Observatories of the Carnegie Institution of Washington 813 Santa Barbara St. PasadenaCA91101 United States Kavli Institute for Particle

Optical imaging surveys measure both the galaxy density and the gravitational lensing-induced shear fields across the sky. Recently, the Dark Energy Survey (DES) collaboration used a joint fit to two-point correlations between these observables to place tight constraints on cosmology (DES Collaboration et al. 2018). In this work, we develop the methodology to extend the DES year one joint probes analysis to include cross-correlations of the optical survey observables with gravitational lensing of the cosmic microwave background (CMB) as measured by the South Pole Telescope (SPT) and Planck. Using simulated analyses, we show how the resulting set of five two-point functions increases the robustness of the cosmological constraints to systematic errors in galaxy lensing shear calibration. Additionally, we show that contamination of the SPT+Planck CMB lensing map by the thermal Sunyaev-Zel'dovich effect is a potentially large source of systematic error for two-point function analyses, but show that it can be reduced to acceptable levels in our analysis by masking clusters of galaxies and imposing angular scale cuts on the two-point functions. The methodology developed here will be applied to the analysis of data from the DES, the SPT, and Planck in a companion work. Copyright © 2018, The Authors. All rights reserved.

关键词： Galaxies

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A latency hiding framework for enhanced ubiquitous access to big data in a constrained digital ecosystem: Application to digital medical archives 12

A latency hiding framework for enhanced ubiquitous access to...

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International Conference on Management of Emergent Digital EcoSystems, MEDES 2012

作者： Mequanint, Dessalegn Brunie, Lionel Libsie, Mulugeta Coquil, David IT Doctoral Program Addis Ababa University Ethiopia France Dept. of Computer Science Addis Ababa University Ethiopia German-French Doctoral College- MDPS University of Passau Germany

ISBN: (纸本)9781450317559

This paper presents our latency hiding framework for access to big data in a constrained digital ecosystem with application to digital medical archives. Aiming to enhance ubiquitous access of big data such as patient-oriented access of medical archives, we apply complex/multi-context prefetching to reduce latency thereby improving response time. We propose a formal model for prefetch requests rate and network workload or stress bound that takes into account a diverse set of constraints a digital ecosystem could be in. In addition to that, components of our latency hiding framework such as a generic multi-context functional architecture, use case model, medical database model with emphasis on API (abstracted patient information) and a high-level system architecture have been designed. The development of a complex or multi-context prefetch algorithm that uses a patient's chief complaints, slackness sensitivity, popular content tag, user specified contexts and constraints is underway. A prototype system will also be developed to validate the proposed solutions. Moreover, input and output metrics will be developed to gauge the efficiency and effectiveness of the prefetch algorithm under development. Copyright © 2012 ACM.

关键词： Complex networks

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Using frequent pattern mining to identify behaviors in a naked mole rat colony

Using frequent pattern mining to identify behaviors in a nak...

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25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25

作者： Imberman, Susan P. Kress, Michael E. McCloskey, Dan P. Dept. Psychology Staten Island NY 10314 United States CSI/IBR Center for Developmental Neuroscience Staten Island NY 10314 United States Technology Systems Staten Island NY 10314 United States Dept. Computer Science College of Staten Island 2800 Victory Blvd Staten Island NY 10314 United States Doctoral Program Computer Science the Graduate Center at CUNY United States

ISBN: (纸本)9781577355588

Animal behavior analysis has, in the past, taken a very low tech approach, with direct observer surveillance and automated video surveillance as the norm. These methods are insufficient when one wants to study interactions between large numbers of animals in their housing environment. In this paper we use a housing environment that has been equipped with a system of RFID sensors. RFID transponders were implanted into the study animal, the naked mole rat. The resulting data was analyzed using principal component analysis and frequent pattern mining. Results showed that these methods can identify time periods of high behavioral activity from that of low aclivity, along with which groups of animals interacted with one another. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Rats

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PeerWise: Exploring conflicting efficacy studies 11

PeerWise: Exploring conflicting efficacy studies

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7th International Computing Education Research Workshop, ICER 2011

作者： Denny, Paul Hanks, Brian Simon, Beth Bagley, Spencer Dept. of Computer Science University of Auckland Auckland New Zealand BFH Educational Consulting Seattle WA 98119 United States Computer Science and Engineering Dept. Univ. of California San Diego San Diego CA 92093 United States Math and Science Education Doctoral Program UCSD SDSU San Diego CA 92023 United States

ISBN: (纸本)9781450308298

PeerWise (PW) is an online tool that allows students in a course to collaborate and learn by creating, sharing, answering and discussing multiple-choice questions (MCQs). Previous studies of PW at the introductory level have shown that students in computing courses like it, and report statistically significant learning gains in courses taught by the investigators at different institutions. However, we recently conducted three quasi-experimental studies of PW use in upper-division computing courses in the U.S. and failed to replicate these positive results. In this paper we consider various factors that may impact the effectiveness of PW, including instructor engagement, usage requirements and subject-matter issues. We also report several positive results from other STEM courses at the same institution, discuss methodological issues pertaining to our recent studies and propose approaches for further investigation. © 2011 ACM.

关键词： Students

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Informing daylighting design with the Lightsolve approach: Why and how

Informing daylighting design with the Lightsolve approach: W...

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25th International Conference on Passive and Low Energy Architecture: Towards Zero Energy Building, PLEA 2008

作者： Andersen, Marilyne Kleindienst, Siân Yi, Lu Lee, Jaime Bodart, Magali Cutler, Barbara Building Technology Program Dept. of Architecture Massachusetts Institute of Technology United States Post-doctoral Researcher FNRS Architecture et Climat Université Catholique de Louvain Belgium Computer Science Department Rensselaer Polytechnic Institute United States

ISBN: (纸本)9781905254347

To efficiently and appropriately integrate daylighting strategies in their projects, building designers need reliable methods to address issues such as daily and seasonal variations or the balance between sufficient illumination with visual and thermal comfort aspects. This integration must also happen early in the design process to have a significant impact on energy savings and ultimate building performance. This paper proposes to address this need by fulfilling three major objectives: support the design process using a goal-oriented approach based on iterative design improvement suggestions;provide climate-based annual metrics in a visual and synthesized form;and relate quantitative and qualitative performance criteria thanks to a novel interface for browsing daylighting analysis data in various forms. A methodology to achieve these objectives is described here as the Lightsolve approach.

关键词： Daylighting

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