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Corrigendum to “Initial prototype testing of Ray's Night Out: A new mobile app targeting risky drinking in young people” [Comput. Hum. Behav.] 54 (2016) 207–214

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computers in Human Behavior 2016年 64卷 939-939页

作者： Nina Pocuca Leanne Hides Oksana Zelenko Lake-Hui Quek Stoyan Stoyanov Kristen Tulloch Daniel Johnson Dian Tjondronegoro David J. Kavanagh Institute of Health and Biomedical Innovation (IHBI) School of Psychology and Counselling Faculty of Health Queensland University of Technology (QUT) Brisbane Australia The Young and Well Cooperative Research Centre (Young and Well CRC) Melbourne Australia School of Design Creative Industries Faculty Queensland University of Technology (QUT) Brisbane Australia Electrical Engineering and Computer Science School Science and Engineering Faculty Queensland University of Technology (QUT) Brisbane Australia Information Systems School Science and Engineering Faculty Queensland University of Technology (QUT) Brisbane Australia

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Publisher Correction: Brain charts for the human lifespan

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Nature 2022年第7931期610卷 E6页

作者： R A I Bethlehem J Seidlitz S R White J W Vogel K M Anderson C Adamson S Adler G S Alexopoulos E Anagnostou A Areces-Gonzalez D E Astle B Auyeung M Ayub J Bae G Ball S Baron-Cohen R Beare S A Bedford V Benegal F Beyer J Blangero M Blesa Cábez J P Boardman M Borzage J F Bosch-Bayard N Bourke V D Calhoun M M Chakravarty C Chen C Chertavian G Chetelat Y S Chong J H Cole A Corvin M Costantino E Courchesne F Crivello V L Cropley J Crosbie N Crossley M Delarue R Delorme S Desrivieres G A Devenyi M A Di Biase R Dolan K A Donald G Donohoe K Dunlop A D Edwards J T Elison C T Ellis J A Elman L Eyler D A Fair E Feczko P C Fletcher P Fonagy C E Franz L Galan-Garcia A Gholipour J Giedd J H Gilmore D C Glahn I M Goodyer P E Grant N A Groenewold F M Gunning R E Gur R C Gur C F Hammill O Hansson T Hedden A Heinz R N Henson K Heuer J Hoare B Holla A J Holmes R Holt H Huang K Im J Ipser C R Jack A P Jackowski T Jia K A Johnson P B Jones D T Jones R S Kahn H Karlsson L Karlsson R Kawashima E A Kelley S Kern K W Kim M G Kitzbichler W S Kremen F Lalonde B Landeau S Lee J Lerch J D Lewis J Li W Liao C Liston M V Lombardo J Lv C Lynch T T Mallard M Marcelis R D Markello S R Mathias B Mazoyer P McGuire M J Meaney A Mechelli N Medic B Misic S E Morgan D Mothersill J Nigg M Q W Ong C Ortinau R Ossenkoppele M Ouyang L Palaniyappan L Paly P M Pan C Pantelis M M Park T Paus Z Pausova D Paz-Linares A Pichet Binette K Pierce X Qian J Qiu A Qiu A Raznahan T Rittman A Rodrigue C K Rollins R Romero-Garcia L Ronan M D Rosenberg D H Rowitch G A Salum T D Satterthwaite H L Schaare R J Schachar A P Schultz G Schumann M Schöll D Sharp R T Shinohara I Skoog C D Smyser R A Sperling D J Stein A Stolicyn J Suckling G Sullivan Y Taki B Thyreau R Toro N Traut K A Tsvetanov N B Turk-Browne J J Tuulari C Tzourio É Vachon-Presseau M J Valdes-Sosa P A Valdes-Sosa S L Valk T van Amelsvoort S N Vandekar L Vasung L W Victoria S Villeneuve A Villringer P E Vértes K Wagstyl Y S Wang S K Warfield V Warrier E Westman M L Westwater H C Whalley A V Witte N Yang B Yeo H Y Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Brain Mapping Unit Department of Psychiatry University of Cambridge Cambridge UK. rb643@medschl.cam.ac.uk. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Child and Adolescent Psychiatry and Behavioral Science The Children's Hospital of Philadelphia Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Lifespan Brain Institute The Children's Hospital of Philadelphia and Penn Medicine Philadelphia PA USA. jakob.seidlitz@pennmedicine.upenn.edu. Department of Psychiatry University of Cambridge Cambridge UK. MRC Biostatistics Unit University of Cambridge Cambridge UK. Department of Psychiatry University of Pennsylvania Philadelphia PA USA. Lifespan Informatics & Neuroimaging Center University of Pennsylvania Philadelphia PA USA. Department of Psychology Yale University New Haven CT USA. Developmental Imaging Murdoch Children's Research Institute Melbourne Victoria Australia. Department of Medicine Monash University Melbourne Victoria Australia. UCL Great Ormond Street Institute for Child Health London UK. Weill Cornell Institute of Geriatric Psychiatry Department of Psychiatry Weill Cornell Medicine New York USA. Department of Pediatrics University of Toronto Toronto Canada. Holland Bloorview Kids Rehabilitation Hospital Toronto Canada. The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformation University of Electronic Science and Technology of China Chengdu China. University of Pinar del Río "Hermanos Saiz Montes de Oca" Pinar del Río Cuba. MRC Cognition and Brain Sciences Unit University of Cambridge Cambridge UK. Autism Research Centre Department of Psychiatry University of Cambridge Cambridge UK. Department of Psychology School of Philosophy Psychology and Language Sciences University of Edinburgh Edinburgh UK. Queen's

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The Impact of Mobility Patterns on the Efficiency of Data Forwarding in MANETs

The Impact of Mobility Patterns on the Efficiency of Data Fo...

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IEEE International Conference on Communications

作者： Thomas D. Lagkas Argyro Lamproudi Panagiotis Sarigiannidis Charalabos Skianis Computer Science Department The University of Sheffield International Faculty CITY College Department of Signals and Systems Chalmers University of Technology Department of Informatics and Telecommunications Engineering University of Western Macedonia Department of Information and Communication Systems at the University of the Aegean in Samos

ISBN: (纸本)9781467364300

One of the most challenging requirements in cutting-edge Mobile Ad hoc Networks is the need for adaptive and efficient routing. Networks capable of adapting their behavior based on current conditions are often characterized as self-organizing networks, which are lately considered very promising for future applications. This work examines the impact of the different mobility properties on the performance of self-organizing networks. For that purpose, a simulator was developed to model different mobility patterns and study the way they affect the effectiveness of the well-known AODV routing protocol. Particularly, this paper focuses on the effect of the different mobility schemes on network topology and consequently to the overall network performance. The results reveal the tight correlations between node mobility characteristics and network metrics.

关键词： AODV MANETs Mobility patterns Mobile routing Self-organizing networks WMNs

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An optimal health service discovery for ubiquitous health service provisioning 3

An optimal health service discovery for ubiquitous health se...

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3rd IASTED International Conference on Health Informatics, AfricaHI 2014

作者： Olaifa, Moses O. Ojo, Sunday O. Zuva, Tranos Department of Computer Systems Engineering Faculty of Information and Communication Technology Tshwane University of Technology Pretoria South Africa

ISBN: (纸本)9780889869738

Locating appropriate service and communicating it to the requester at the point of need within the shortest possible time is a key issue in ubiquitous health service provisioning. Failure to provide the health service at the exact time needed can result in erroneous diagnosis and treatment. Hence, there is a need for real time service discovery of requested services for prompt healthcare service provisioning. This paper proposes a peer-to-peer health service discovery solution that discovers and maintains a set of shortest service path to existing healthcare service types within a distributed health service network. We introduce an optimal service path cache (OSPC) for maintaining the shortest service paths provided by learning automata mapped to the super-nodes. The proposed solution was compared with existing methods and showed an improved performance in service discovery.

关键词： information services

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Learning multiple linear mappings for efficient single image super-resolution

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IEEE Transactions on Image Processing 2015年第3期24卷 846-861页

作者： Zhang, Kaibing Tao, Dacheng Gao, Xinbo Li, Xuelong Xiong, Zenggang State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi'an710071 China School of Computer and Information Science Hubei Engineering University Xiaogan432000 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia State Key Laboratory of Transient Optics and Photonics Chinese Academy of Sciences Xi'an710119 China

Example learning-based superresolution (SR) algorithms show promise for restoring a high-resolution (HR) image from a single low-resolution (LR) input. The most popular approaches, however, are either time-or space-intensive, which limits their practical applications in many resource-limited settings. In this paper, we propose a novel computationally efficient single image SR method that learns multiple linear mappings (MLM) to directly transform LR feature subspaces into HR subspaces. In particular, we first partition the large nonlinear feature space of LR images into a cluster of linear subspaces. Multiple LR subdictionaries are then learned, followed by inferring the corresponding HR subdictionaries based on the assumption that the LR-HR features share the same representation coefficients. We establish MLM from the input LR features to the desired HR outputs in order to achieve fast yet stable SR recovery. Furthermore, in order to suppress displeasing artifacts generated by the MLM-based method, we apply a fast nonlocal means algorithm to construct a simple yet effective similarity-based regularization term for SR enhancement. Experimental results indicate that our approach is both quantitatively and qualitatively superior to other application-oriented SR methods, while maintaining relatively low time and space complexity. © 1992-2012 IEEE.

关键词： Mathematical transformations

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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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Experimental Evaluation of a Distributed Sensor Network Applying Source Node Grouping

Experimental Evaluation of a Distributed Sensor Network Appl...

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International Symposium on Computing and Networking (CANDAR)

作者： Yohsuke Ishikawa Hiraku Okada Katsuhiro Naito Kentaro Kobayashi Masaaki Katayama Graduate School of Electrical Engineering and Computer Science Nagoya University Nagoya JAPAN Institute of Materials and Systems for Sustainability Nagoya University Nagoya JAPAN Faculty of Information Science Aichi Institute of Technology Toyota-shi JAPAN

In conventional sensor networks, information sensed by distributed sensor nodes is collected to a sink node, and applications are executed based on this information. However, when actuator nodes in a field execute the applications, the sink node does not have to collect all the sensing information, but each actuator node collects the information required for its application. We aim to collect the required information to each actuator node, and generate a group whose members are source sensor nodes involved by each actuator node. We propose a protocol to efficiently collect sensing information of grouped source nodes. Furthermore, we experimentally demonstrate that actuator nodes can collect the information and that the proposed protocol can decrease the number of packets transmitted in the network.

关键词： Relays Actuators Sensors Protocols Wireless communication Wireless sensor networks Microcomputers

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Robust sliding mode control of general time-varying delay stochastic systems with structural uncertainties

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Control Theory and technology 2014年第4期12卷 357-367页

作者： Sheng-Guo WANG Libin BAI Mingzhi CHEN Department of Engineering Technology and Department of Software and Information Systems (Graduate Faculty) University of North Carolina at Charlotte (UNC Charlotte) Department of Software and Information Systems UNC Charlotte NC 28223-0001 U.S.A. College of Mathematics and Computer Science Fuzhou University

This paper presents a new robust sliding mode control （SMC） method with well-developed theoretical proof for general uncertain time-varying delay stochastic systems with structural uncertainties and the Brownian noise （Wiener process）. The key features of the proposed method are to apply singular value decomposition （SVD） to all structural uncertainties and to introduce adjustable parameters for control design along with the SMC method. It leads to a less-conservative condition for robust stability and a new robust controller for the general uncertain stochastic systems via linear matrix inequality （LMI） forms. The system states are able to reach the SMC switching surface as guaranteed in probability 1. Furthermore, it is theoretically proved that the proposed method with the SVD and adjustable parameters is less conservatism than the method without the SVD. The paper is mainly to provide all strict theoretical proofs for the method and results.

关键词： Robust control Time-varying delay systems Stochastic systems Lyapunov methods Sliding mode control Time-varying systems

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Microscopic image segmentation using quantum inspired evolutionary algorithm

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Journal of Advanced Microscopy Research 2015年第3期10卷 164-173页

作者： Choudhury, Alokeparna Samanta, Sourav Dey, Nilanjan Ashour, Amira S. Balas-Timar, Dana Gospodinov, Mitko Gospodinova, Evgeniya Department of Computer Science and Engineering University Institute of Technology Burdwan West Bengal713104 India Department of Information Technology Techno India College of Technology Kolkata700156 India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University 31527 Egypt Aurel Vlaicu University of Arad Arad310130 Romania Institute of Systems Engineering and Robotics Bulgarian Academy of Sciences Sofia1113 Bulgaria

Image segmentation has became an important research area for various medical imaging fields. The automated complex analytical process makes it a huge time consuming procedure. The intention to microscopic images segmentation is to extract the cellular, nuclear or tissue components. This is considered a challenging process due to the large variations of features of these components (size, shape, orientation or texture). In this paper, a novel approach to deal with rats' microscopic hippocampus images segmentation based on the hybrid evolutionary strategy (ES) is proposed. The proposed work used the quantum-inspired evolutionary algorithm (QIEA) to establish the optimal level for the multilevel thresholding. The QIEA depends on both quantum and evolutionary computing. An intensive works for automatic hippocampus segmentation is performed on a database of 179 images. The acquired results have demonstrated that the proposed algorithm provides superior segmentation with eight levels (L = 8), that offer the highest correlation between the original and the corresponding segmented image. © Copyright 2015 American Scientific Publishers All rights reserved.

关键词： Evolutionary algorithms

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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