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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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Dynamic timeout power policy for network interfaces

Dynamic timeout power policy for network interfaces

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IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Bruna M. J. C. Vilar Fabiano De M Silva Saulo O. D. Luiz J. J. Silva Hyggo Almeida Angelo Perkusich Post-Graduate Program in Electrical Engineering - PPgEE COPELE Embedded Systems and Pervasive Computing Laboratory Universidade Federal de Campina Grande Campina Grande PB BR Center of Electrical Engineering and Informatics Federal University of Campina Grande Campina Grande Brazil Embedded Systems and Pervasive Computing Laboratory Federal University of Campina Grande

Network interfaces are present in a variety of battery powered computer systems, such as notebooks, ultrabooks, tablets and mobile phones. Considering that the battery life is still a constraint, there are opportunities for power management by reducing the power consumption of idle network interfaces. In this work, a dynamic timeout power policy for network interfaces is presented, incorporating workload estimation and a problem optimization at runtime, thus providing power savings and suitable performance.

关键词： Heuristic algorithms Network interfaces Power demand Optimization Mobile handsets Internet Stochastic processes

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Characterising acute and chronic care needs: insights from the Global Burden of Disease Study 2019

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Nature communications 2025年第1期16卷 4235-4235页

作者： Cecilia Anza-Ramirez J. Jaime Miranda Benedetta Armocida Jorge César Correia Harriette Gillian Christine Van Spall David Beran Amirali Aali Kalkidan Hassen Abate Semagn Mekonnen Abate Cristiana Abbafati Mohsen Abbasi-Kangevari Zeinab Abbasi-Kangevari Zahra Aryan Sina Azadnajafabad Mohammadreza Azangou-Khyavy Seyyed-Hadi Ghamari Mohammad Keykhaei Javad Khanali Sara Momtazmanesh Fateme Montazeri Shayan Rahmani Mohammad-Mahdi Rashidi Nazila Rezaei Negar Rezaei Sahar Saeedi Moghaddam Hossein Hassanian-Moghaddam Ali-Asghar Kolahi Ali Nikoobar Hedayat Abbastabar Ahmed M. Abdel-Azeem Michael Abdelmasseh Juan Sanabria Sherief Abd-Elsalam Ahmed Abdelwahab Gholamreza Abdoli Mohammad Abdollahi Meriem Abdoun Deldar Morad Abdulah Abu Yousuf Md Abdullah Ame Mehadi Abdurehman Getachew Abebe Aidin Abedi Chirag P. Doshi Vida Abedi Hassan Abidi Richard Gyan Aboagye Hassan Abolhassani Nima Rezaei Lucas Guimarães Abreu Michael R. M. Abrigo Yonas Derso Abtew Hiwa Abubaker Ali Eman Abu-Gharbieh Hiba Jawdat Barqawi Rabih Halwani Maha Mohamed Saber-Ayad Iman M. Talaat Ahmed Abu-Zaid Manfred Mario Kokou Accrombessi Juan Manuel Acuna Denberu Eshetie Adane Tigist Demssew Adane Isaac Yeboah Addo Giovanni Addolorato Oluwafemi Atanda Adeagbo Victor Adekanmbi Miracle Ayomikun Adesina Isaac Iyinoluwa Olufadewa Olatunji O. Adetokunboh Daniel Adedayo Adeyinka Qorinah Estiningtyas Sakilah Adnani Aanuoluwapo Adeyimika Afolabi Olayinka Stephen Ilesanmi Muhammad Sohail Afzal Saira Afzal Gina Agarwal Prerna Agarwal Pradyumna Agasthi Anurag Agrawal Marcela Agudelo-Botero Adolfo Martinez-Valle Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Sohail Ahmad Tauseef Ahmad Ali Ahmadi Siamak Sabour Keivan Ahmadi Sanjay Basu Sepideh Ahmadi Ali Ahmed Shaun Wen Huey Lee Ayman Ahmed Haroon Ahmed Jivan Qasim Ahmed Gahin Abdulraheem Tayib Yahya Yahya Luai A. Ahmed Iffat Elbarazi Syed Mahboob Shah Janardhana P. Aithala Marjan Ajami Budi Aji Hossein Akbarialiabad Yibeltal Akelew Meisam Akhlaghdoust Elham Jamshidi Addis Aklilu Azene Tesfaye Melat Woldemariam Maxwell Ak CRONICAS Center of Excellence in Chronic Diseases Universidad Peruana Cayetano Heredia Lima Peru School of Medicine Universidad Peruana Cayetano Heredia Lima Peru Sydney School of Public Health Faculty of Medicine and Health University of Sydney Sydney NSW Australia Faculty of Epidemiology and Population Health London School of Hygiene & Tropical Medicine London UK Division of Tropical and Humanitarian Medicine University of Geneva Geneva Switzerland Department of Cardiovascular Endocrine-metabolic Diseases and Aging Istituto Superiore di Sanità Rome Italy Division of Endocrinology Diabetology Nutrition and Therapeutic Patient Education Department of Medicine Geneva University Hospitals Geneva Switzerland Department of Medicine and Department of Health Research Evidence and Impact McMaster University Hamilton ON Canada Research Institute of St. Joe’s Hamilton ON Canada Population Health Research Institute Hamilton Canada Geneva University Hospitals Geneva Switzerland Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran Department of Nutrition and Dietetics Jimma University Jimma Ethiopia Anesthesiology Department Dilla University Addis Ababa Ethiopia Department of Juridical and Economic Studies La Sapienza University Rome Italy Non-Communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Brigham and Women’s Hospital Harvard University Boston MA USA Students’ Scientific Research Center (SSRC) Tehran University of Medical Sciences Tehran Iran School of Medicine Tehran University of Medical Sciences Tehran Iran School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Institute Tehran University of Medical Sciences Tehran Iran Chapter of Addiction Medicine University of Sydney Sydney NSW Australia Advanced Diagnostic and Intervent

Chronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly. © 2025. The Author(s).

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Two-level feature selection for naive bayes with kernel density estimation in question classification based on Bloom's cognitive levels

Two-level feature selection for naive bayes with kernel dens...

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2013 5th International Conference on Information Technology and Electrical Engineering, ICITEE 2013

作者： Supriyanto, Catur Yusof, Norazah Nurhadiono, Bowo Sukardi Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia Faculty of Computing Universiti Teknologi Malaysia Skudai Malaysia Program of Informatics Engineering College of Computing and Informatics Adhi Guna Palu Indonesia

ISBN: (纸本)9781479904259

This paper proposes a two-level feature selection to improves Naïve Bayes with kernel density estimation. The performance of the proposed feature selection is evaluated on question item set based on Bloom's cognitive levels. This two-level feature selection contains of filter and wrapper based feature selection. This paper uses chi square and information gain as the filter based feature selection and forward feature selection and backward feature elimination as the wrapper based feature selection. The result shows that the two-level feature selection improves the Naïve Bayes with kernel density estimation. The combination of chi square and backward feature elimination give more optimal quality than the other combination. © 2013 IEEE.

关键词： Feature Selection

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Discovering frequent itemsets on uncertain data: A systematic review

Discovering frequent itemsets on uncertain data: A systemati...

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9th International Conference on International Conference on Machine Learning and Data Mining, MLDM 2013

作者： De Carvalho, Juliano Varella Ruiz, Duncan Dubugras Computing Science Graduate Program - Faculty of Informatics Pontifical Catholic University of RS - PUCRS Porto Alegre RS Brazil

ISBN: (纸本)9783642397110

In this paper, we describe the development of a systematic review about the topic "Discovering Frequent Itemsets on Uncertain Data". To the best of our knowledge, this work seems to be the first systematic review addressing the topic. We show the whole process executed and its findings. Initially we define a rigorous protocol to lead the process. In the first phase, we create a systematic mapping of the area. In addition, from the complete reading of each article, a panorama of this area is presented. We reveal the search engines that most publicize this topic and which publishing types, authors and research institutions are involved in these papers. Moreover we identify the algorithms and the classes of these algorithms most compared over the years, how are made these comparisons, as well as their availabilities. Therefore this systematic review becomes a rich material for understanding this knowledge area. © 2013 Springer-Verlag.

关键词： Search engines

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Environmental feedback drives multiple behaviors from the same neural circuit 12

Environmental feedback drives multiple behaviors from the sa...

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12th European Conference on the Synthesis and Simulation of Living Systems: Advances in Artificial Life, ECAL 2013

作者： Williams, Paul L. Beer, Randall D. Cognitive Science Program Indiana University BloomingtonIN47406 United States School of Informatics and Computing Indiana University BloomingtonIN47406 United States

ISBN: (纸本)9780262317092

The ability of a single neural circuit to produce qualitatively distinct behaviors is typically attributed to some adaptive mechanism in the circuit itself. However, neural circuits are also embedded in particular bodies and environments, and feedback through the sensorimotor loop may also serve to drive behavioral differentiation. Here we explore the ability of a single neural circuit to produce qualitatively different behaviors based on changing patterns of environmental feedback. Agents equipped with two sets of effectors and controlled by fixed neural circuits are evolved to catch circles under three different motor conditions. In one condition, the agent must coordinate both sets of effectors, while in each of the other conditions one set of effectors is lesioned and the agent must rely on the other set alone to accomplish the task. A detailed behavioral analysis of the best evolved agent is reported, providing numerous insights into its evolved behavioral mechanism. The agent is found to produce significantly different motor outputs in each of the three conditions, to rely on continuous environmental feedback for successful behavior, and to switch flexibly between different behavioral conditions. © 2013 Proceedings of the 12th European Conference on the Synthesis and Simulation of Living Systems: Advances in Artificial Life, ECAL 2013. All rights reserved.

关键词： Feedback

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Code compression using Multi-Level Dictionary

Code compression using Multi-Level Dictionary

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2013 IEEE 4th Latin American Symposium on Circuits and Systems, LASCAS 2013

作者： Dias, Wanderson Roger Azevedo Moreno, Edward David Federal University of Amazonas - UFAM Institute of Computing - IComp Post-Graduate Program in Informatics - PPGI Manaus Brazil Federal University of Sergipe - UFS Center for Exact Sciences and Technology - CCET Department of Computing - DComp Aracaju Brazil

ISBN: (纸本)9781424494859

This paper presents a new code compression method in which we merge the Huffman algorithm with the formation of patterns of blocks and three dictionaries. We seek two consecutive blocks in two levels, one of them into compressed code and the other related to uncompressed code. We have used simulation with SimpleScalar and programs from MiBench. Our proposal has achieved a compression rate of 32.6% and 31.9%, respectively, for ARM and MIPS processor. © 2013 IEEE.

关键词： Codes (symbols)

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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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A Web Portal for Bariatric Patients - Effective Relationships Management

A Web Portal for Bariatric Patients - Effective Relationship...

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Hawaii International Conference on System Sciences

作者： Sonya HY Hsu Informatics Program School of Computing and Informatics Ray P. Authement College of Sciences University of Louisiana Lafayette

ISBN: (纸本)9781467359337

Drawn upon patient relationship management, this project focuses on increasing effectiveness of weight loss surgery by managing the relationship between patients and physicians through a Web portal. Data collection from bariatric patients and some discussion among physicians and advisory teams provide some basis for Web portal content. Iterations were and will be made based on the collected data from users. Bariatric patients prefer getting information from physicians and online sources;however, they do not use online WLS resources or websites. The content they would like to access is medical information about WLS, then nutrition and exercise. The supports they prefer to obtain are from family, friends and coworkers. This may lead to unsuccessful support group meetings. Suggested either trust or information overload issue needs to be dealt with.

关键词： WLS Bariatric Patients WLS gene websites Otolaryngologists Patients Bariatric Surgery Data Collection Methods

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