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IEEE International Electron Devices Meeting

作者： Joanna M. Nassar Galo A. Torres Sevilla Seneca J. Velling Marlon D. Cordero Muhammad Mustafa Hussain Integrated Nanotechnology Lab and Integrated Disruptive Electronic Applications (IDEA) Lab Computer Electrical Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Material & Nanoscience Program Faculty of Science University of Waterloo Waterloo ON N2L-3G1 Canada

ISBN: (纸本)9781509039036

We report CMOS technology enabled fabrication and system level integration of flexible bulk silicon (100) based multi-sensors platform which can simultaneously sense pressure, temperature, strain and humidity under various physical deformations. We also show an advanced wearable version for body vital monitoring which can enable advanced healthcare for IoT applications.

关键词： CMOS-compatible Large-Scale Flexible Silicon

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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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Information-theoretic segmentation of natural language 3

Information-theoretic segmentation of natural language

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3rd International Workshop on Artificial Intelligence and Cognition, AIC 2015

作者： Griffiths, Sascha S. McGinity, Mariano Mora Forth, Jamie Purver, Matthew Wiggins, Geraint A. Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London 10 Godward Square LondonE1 4FZ United Kingdom

We present computational experiments on language segmentation using a general information-theoretic cognitive model. We present a method which uses the statistical regularities of language to segment a continuous stream of symbols into "meaningful units" at a range of levels. Given a string of symbols-in the present approach, textual representations of phonemes-we attempt to find the syllables such as grea and sy (in the word greasy);words such as in, greasy, wash, and water;and phrases such as in greasy wash water. The approach is entirely information-theoretic, and requires no knowledge of the units themselves;it is thus assumed to require only general cognitive abilities, and has previously been applied to music. We tested our approach on two spoken language corpora, and we discuss our results in the context of learning as a statistical processes.

关键词： Information theory

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Author Correction: Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets

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Nature communications 2021年第1期12卷 7260页

作者： Merel C Postema Daan van Rooij Evdokia Anagnostou Celso Arango Guillaume Auzias Marlene Behrmann Geraldo Busatto Filho Sara Calderoni Rosa Calvo Eileen Daly Christine Deruelle Adriana Di Martino Ilan Dinstein Fabio Luis S Duran Sarah Durston Christine Ecker Stefan Ehrlich Damien Fair Jennifer Fedor Xin Feng Jackie Fitzgerald Dorothea L Floris Christine M Freitag Louise Gallagher David C Glahn Ilaria Gori Shlomi Haar Liesbeth Hoekstra Neda Jahanshad Maria Jalbrzikowski Joost Janssen Joseph A King Xiang Zhen Kong Luisa Lazaro Jason P Lerch Beatriz Luna Mauricio M Martinho Jane McGrath Sarah E Medland Filippo Muratori Clodagh M Murphy Declan G M Murphy Kirsten O'Hearn Bob Oranje Mara Parellada Olga Puig Alessandra Retico Pedro Rosa Katya Rubia Devon Shook Margot J Taylor Michela Tosetti Gregory L Wallace Fengfeng Zhou Paul M Thompson Simon E Fisher Jan K Buitelaar Clyde Francks Department of Language & Genetics Max Planck Institute for Psycholinguistics Nijmegen The Netherlands. Department of Cognitive Neuroscience Donders Institute for Brain Cognition and Behaviour Donders Centre for Cognitive Neuroimaging Radboud University Medical Centre Nijmegen The Netherlands. Bloorview Research Institute Holland Bloorview Kids Rehabilitation Hospital and Department of Pediatrics University of Toronto Toronto ON USA. Child and Adolescent Psychiatry Department Gregorio Marañón General University Hospital School of Medicine Universidad Complutense IiSGM CIBERSAM Madrid Spain. Institut de Neurosciences de la Timone UMR 7289 Aix Marseille Université CNRS Marseille France. Department of Psychology Carnegie Mellon University Pittsburgh PA USA. Laboratory of Psychiatric Neuroimaging (LIM-21) Departamento e Instituto de Psiquiatria Hospital das Clinicas HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. IRCCS Stella Maris Foundation viale del Tirreno 331 56128 Pisa Italy. Department of Clinical and Experimental Medicine University of Pisa Pisa Italy. Department of Child and Adolescent Psychiatry and Psychology Hospital Clinic Psychiatry Unit Department of Medicine 2017SGR881 University of Barcelona IDIBAPS CIBERSAM Barcelona Spain. Department of Forensic and Neurodevelopmental Sciences Institute of Psychiatry Psychology & Neuroscience King's College London London UK. Institute for Pediatric Neuroscience NYU Child Study Center New York NY USA. Department of Psychology Ben-Gurion University of the Negev Beer Sheva Israel. Brain Center Rudolf Magnus Department of Psychiatry University Medical Center Utrecht Utrecht The Netherlands. Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Goethe University Frankfurt am Main Frankfurt Germany. Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University o

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Exploiting temporal and spatial variation for WiFi interference avoidance in ZigBee networks

Exploiting temporal and spatial variation for WiFi interfere...

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作者： Shi, Gaotao Xu, Ruitao Shu, Yantai Luo, Jun School of Computer Science and Technology Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300072 China School of Computer Science and Technology Tianjin University Tianjin300072 China School of Computer Engineering Nanyang Technological University Singapore639798 Singapore

Specifically, ZigBee networks share the 2.4 GHz ISM band with WiFi but have much lower transmission power. Therefore, the ZigBee networks inevitably suffer the interference from WiFi networks. In this paper, we focus on utilising the temporal and spatial feature of WiFi interference to adjust Zigbee channel over the whole networks. We experimentally examine the spatiotemporal variation of interference and then present a novel interference assessing method which jointly considers the intensity and density of WiFi interference in order to better characterise the relation between interference and link quality. Further considering the interference locality, we propose MuZi (Multi-channel ZigBee) as an interference avoiding approach for ZigBee networks. It aims to determine a better working channel for each node while taking network connectivity into consideration. Our experiments show that, under the existence of WiFi interference, MuZi can achieve 3.3 times throughput than the traditional single-channel method. Copyright © 2015 Inderscience Enterprises Ltd.

关键词： Internet of things

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From brain volumes to subgroup classification in genetic mutation carriers for frontotemporal dementia: A cluster analysis in the GENFI study

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Alzheimer's & Dementia 2021年第S5期17卷

作者： Martina Bocchetta Emily G. Todd Jennifer M. Nicholas Carolin Heller Imogen J. Swift Georgia Peakman David M. Cash Rhian S. Convery Lucy L. Russell David L. Thomas Juan Eugenio Iglesias John C. van Swieten Lize C. Jiskoot Harro Seelaar Barbara Borroni Daniela Galimberti Raquel Sanchez-Valle Robert Laforce Jr. Fermin Moreno Matthis Synofzik Caroline Graff Mario Masellis Maria Carmela Tartaglia James B. Rowe Rik Vandenberghe Elizabeth Finger Fabrizio Tagliavini Alexandre Mendonca Isabel Santana Christopher Butler Simon Ducharme Alexander Gerhard Adrian Danek Johannes Levin Markus Otto Sandro Sorbi Isabelle Le Ber Florence Pasquier Jonathan D. Rohrer Genetic Frontotemporal dementia Initiative (GENFI) Dementia Research Centre UCL Queen Square Institute of Neurology University College London London United Kingdom Department of Medical Statistics London School of Hygiene and Tropical Medicine London United Kingdom Dementia Research Centre Queen Square Institute of Neurology University College London London United Kingdom Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Dementia Research Centre Department of Neurodegenerative Disease UCL Institute of Neurology Queen Square London United Kingdom Neuroradiological Academic Unit UCL Queen Square Institute of Neurology UCL London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Charlestown MA USA Alzheimer Center Erasmus Medical Center Rotterdam Netherlands Erasmus MC Rotterdam Netherlands Erasmus Medical Center Rotterdam Netherlands Centre for Neurodegenerative Disorders Neurology Unit Department of Clinical and Experimental Sciences University of Brescia Brescia Italy University of Milan Milan Italy Department of Biomedical Surgical and Dental Sciences University of Milan Milan Italy Neurology Department Hospital Clínic. Institut d'Investigacions Biomediques Barcelona Spain Clinique Interdisciplinaire de Mémoire CHU de Québec/Université Laval/Hôpital de l’Enfant-Jésus Quebec City QC Canada Hospital Universitario Donostia San Sebastian Spain Centre for Neurology and Hertie-Institute for Clinical Brain Research Hoppe-Seyler-Str Tuebingen Germany Karolinska Institutet Department NVS Division of Neurogeriatrics Karolinska Institutet Stockholm Sweden Unit for Hereditary Dementia Theme Aging Karolinska University Hospital-Solna Stockholm Sweden LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute Toronto ON Canada T

Background Genetic frontotemporal dementia (FTD) is highly heterogeneous, with carriers of mutations in the same gene manifesting different phenotypes. Using in vivo MR images from the Genetic FTD Initiative (GENFI), we aimed to identify subgroups within the same genetic group whose brains were affected differently. Method Cortical and subcortical volumes of interest were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans for 479 carriers (198 GRN , 202 C9orf72 , and 79 MAPT mutation carriers). W-scores for 85 volumes of interest were computed from a linear regression model carried out on 298 non-carrier cognitively normal controls adjusting for the effect of age, sex, total intracranial volume and scanner type. Cluster analyses with the Ward agglomerating method were performed on all w-scores while considering the three genetic groups independently. The identified clusters were then compared for age, estimated years from onset, global and sum of boxes scores of the CDR® plus NACC FTLD (at baseline and after one year), neurofilament light chain (NfL) levels in the plasma and w-scores in brain regions typically showing early atrophy (Kruskal-Wallis test). Result We identified three clusters among the GRN mutation carriers and four in the MAPT and C9orf72 groups, which were all significantly different for the variables reported in the Table (p-value<0.003). For all three genetic groups, one cluster was formed by patients with a clinical diagnosis of FTD, with more extensive atrophy and increased disease severity. For the remaining clusters, there seemed to be an association with disease severity for MAPT and GRN mutation carriers but not so for C9orf72 expansion carriers where clinical scores were not clearly associated with a specific cluster. Conclusion By only looking at regional brain volumes, we were able to detect different clusters within carriers of mutations in the same gene, with C9orf72 expansion carriers being the most he

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A Smart Pool Search Matheuristic for Solving a Multi-objective Microgrid Storage Planning Problem

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Energy Procedia 2016年 103卷 292-297页

作者： Vitor N. Coelho Igor M. Coelho Bruno N. Coelho Marcone J.F. Souza Haroldo Gambini N. Mladenović Frederico G. Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Instituto de Pesquisa e Desenvolvimento de Tecnologias Ouro Preto Brazil Department of Computer Science State University of Rio de Janeiro Rio de Janeiro Brazil Department of Computer Science Universidade Federal de Ouro Preto Ouro Preto Brazil Mathematical Institute Serbian Academy of Science and Arts Serbia LAMIH Université de Valenciennes et du Hainaut Cambrésis Valenciennes France Department of Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil

In this paper, a multi-objective power dispatching problem that uses Plug-in Electric Vehicle (PEV) as storage units is considered. The problem involves several PEVs and a microgrid community, composed of small houses, residential areas, and different Renewable Energy Resources. Three different objectives are considered: microgrid total costs; usage of PEV batteries and maximum grid peak load. In order to find sets of non-dominated solutions, a matheuristic black box solves several Mixed Integer Linear programming (MILP) subproblems. We improve a previously developed MILP model and design a new multi-objective matheuristic including new problem initialization mechanisms.

关键词： Microgrids Power dispatching Energy storage management Matheuristic Mixed-Integer Linear programming Multi-objetive optimization

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Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble

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Journal of computer and Communications 2016年第4期4卷 20-35页

作者： Tipawan Silwattananusarn Wanida Kanarkard Kulthida Tuamsuk Department of Library and Information Science Faculty of Humanities and Social Sciences Prince of Songkla University Hat Yai Thailand Department of Computer Engineering Faculty of Engineering Khon Kaen University Khon Kaen Thailand Information and Communication Management Program Faculty of Humanities and Social Sciences Khon Kaen University Khon Kaen Thailand

In this paper ensemble learning based feature selection and classifier ensemble model is proposed to improve classification accuracy. The hypothesis is that good feature sets contain features that are highly correlated with the class from ensemble feature selection to SVM ensembles which can be achieved on the performance of classification accuracy. The proposed approach consists of two phases: (i) to select feature sets that are likely to be the support vectors by applying ensemble based feature selection methods;and (ii) to construct an SVM ensemble using the selected features. The proposed approach was evaluated by experiments on Cardiotocography dataset. Four feature selection techniques were used: (i) Correlation-based, (ii) Consistency-based, (iii) ReliefF and (iv) Information Gain. Experimental results showed that using the ensemble of Information Gain feature selection and Correlation-based feature selection with SVM ensembles achieved higher classification accuracy than both single SVM classifier and ensemble feature selection with SVM classifier.

关键词： Classification Feature Selection Support Vector Machines Ensemble Learning Classification Accuracy

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Detecting clinical progression from abnormal regional brain volumes at baseline in genetic frontotemporal dementia: A GENFI study

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Alzheimer's & Dementia 2021年第S1期17卷

作者： Martina Bocchetta Emily G Todd Jennifer M Nicholas Georgia Peakman David M Cash Rhian S Convery Lucy L Russell David L Thomas Juan Eugenio Iglesias John C van Swieten Lize C. Jiskoot Harro Seelaar Barbara Borroni Daniela Galimberti Raquel Sanchez-Valle Robert Laforce Jr. Fermin Moreno Matthis Synofzik Caroline Graff Mario Masellis Maria Carmela Tartaglia James B Rowe Rik Vandenberghe Elizabeth Finger Fabrizio Tagliavini Alexandre Mendonca Isabel Santana Christopher Butler Simon Ducharme Alexander Gerhard Adrian Danek Johannes Levin Markus Otto Sandro Sorbi Isabelle Le Ber Florence Pasquier Jonathan D Rohrer Genetic Frontotemporal dementia Initiative (GENFI) Dementia Research Centre UCL Queen Square Institute of Neurology University College London London United Kingdom Department of Medical Statistics London School of Hygiene and Tropical Medicine London United Kingdom Dementia Research Centre Department of Neurodegenerative Disease UCL Institute of Neurology Queen Square London United Kingdom Dementia Research Centre Queen Square Institute of Neurology University College London London United Kingdom Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Neuroradiological Academic Unit UCL Queen Square Institute of Neurology UCL London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Charlestown MA USA Alzheimer Center Erasmus Medical Center Rotterdam Netherlands Erasmus MC Rotterdam Netherlands Erasmus Medical Center Rotterdam Netherlands Centre for Neurodegenerative Disorders Neurology Unit Department of Clinical and Experimental Sciences University of Brescia Brescia Italy University of Milan Milan Italy Department of Biomedical Surgical and Dental Sciences University of Milan Milan Italy Neurology Department Hospital Clínic Institut d'Investigacions Biomediques Barcelona Spain Clinique Interdisciplinaire de Mémoire CHU de Québec/Université Laval/Hôpital de l’Enfant-Jésus Quebec City QC Canada Hospital Universitario Donostia San Sebastian Spain Centre for Neurology and Hertie-Institute for Clinical Brain Research Hoppe-Seyler-Str Tuebingen Germany Karolinska Institutet Department NVS Division of Neurogeriatrics Karolinska Institutet Stockholm Sweden Unit for Hereditary Dementia Theme Aging Karolinska University Hospital-Solna Stockholm Sweden LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute Toronto ON Canada T

Background Genetic frontotemporal dementia is highly heterogeneous, with different progression patterns seen between individuals. Using in vivo MR images from the Genetic FTD Initiative (GENFI), we aimed to identify clinical progression in genetic mutation carriers from their brain volumes at baseline. Method Cortical and subcortical volumes of interest (VOIs) were generated using automated parcellation methods on volumetric 3T T1-weighted MRI scans for 480 carriers (198 GRN , 202 C9orf72 , 80 MAPT ). W-scores for 79 VOIs were computed from a linear regression model carried out on 298 non-carrier cognitively normal controls adjusting for the effect of age, sex, total intracranial volume and scanner type. Carriers were divided into three disease stages based on their global CDR® plus NACC FTLD score (CDR-GS): asymptomatic (0), possibly or mildly symptomatic (0.5) and fully symptomatic (1 or more). Cut-off points for each VOI were derived from Youden indices estimated with ROC curves to distinguish between CDR-GS=0 and CDR-GS≥1 within each gene. CDR-GS=0.5 carriers (30 GRN , 32 C9orf72 , 13 MAPT ) were classified as ‘normal’ or ‘abnormal’ based on these cut-off points. We compared the CDR® plus NACC FTLD sum-of-boxes scores (CDR-SOB) at one year follow-up in these two groups. Result Compared to those with normal baseline volumes, C9orf72 expansion carriers at CDR-GS=0.5 showed significantly higher CDR-SOB scores at follow-up if they had abnormal volumes in the total frontal (+5 points), orbitofrontal (+3), dorsolateral prefrontal (+6), or anterior cingulate (+4) cortices, the basal-paralaminar amygdala region (+2), CA1 region of the hippocampus (+4), total hippocampus (+6), cerebellar lobule VIIIb (+4), or lateral ventricles (+8). GRN mutation carriers showed significantly higher CDR-SOB scores if their volumes were abnormal in the frontal (+10), parietal (+14), insula (+8), orbitofrontal (+12), or medial parietal (+7) cortices, or the total hippocampus (+10). MAPT mu

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Fabrication and characterization of InGaAsP/Ag luminescent hyperbolic metamaterials

Fabrication and characterization of InGaAsP/Ag luminescent h...

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Microelectronics Technology and Devices (SBMicro)

作者： J. S. T. Smalley F. Vallini S. Montoya S. Shahin B. Kanté Z. Liu Y. Fainman L. Ferrari C. T. Riley E. E. Fullerton Department of Electrical & Computer Engineering University of California San Diego La Jolla USA Material Science and Engineering Program University of California San Diego La Jolla USA Department of NanoEngineering University of California San Diego La Jolla USA

We fabricate and characterize a luminescent hyperbolic metamaterial consisting of InGaAsP/Ag multilayers. For the first time, one of the metamaterial constituents is a gain media. The hyperbolic dispersion is confirme... 详细信息

关键词： Metamaterials Anisotropic magnetoresistance Optical polarization Optical device fabrication Optical pumping Optimized production technology

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