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Author Correction: National identity predicts public health support during a global pandemic

Nature communications 2022年第1期13卷 1949页

作者： Jay J Van Bavel Aleksandra Cichocka Valerio Capraro Hallgeir Sjåstad John B Nezlek Tomislav Pavlović Mark Alfano Michele J Gelfand Flavio Azevedo Michèle D Birtel Aleksandra Cislak Patricia L Lockwood Robert Malcolm Ross Koen Abts Elena Agadullina John Jamir Benzon Aruta Sahba Nomvula Besharati Alexander Bor Becky L Choma Charles David Crabtree William A Cunningham Koustav De Waqas Ejaz Christian T Elbaek Andrej Findor Daniel Flichtentrei Renata Franc Biljana Gjoneska June Gruber Estrella Gualda Yusaku Horiuchi Toan Luu Duc Huynh Agustin Ibanez Mostak Ahamed Imran Jacob Israelashvili Katarzyna Jasko Jaroslaw Kantorowicz Elena Kantorowicz-Reznichenko André Krouwel Michael Laakasuo Claus Lamm Caroline Leygue Ming-Jen Lin Mohammad Sabbir Mansoor Antoine Marie Lewend Mayiwar Honorata Mazepus Cillian McHugh John Paul Minda Panagiotis Mitkidis Andreas Olsson Tobias Otterbring Dominic J Packer Anat Perry Michael Bang Petersen Arathy Puthillam Julián C Riaño-Moreno Tobias Rothmund Hernando Santamaría-García Petra C Schmid Drozdstoy Stoyanov Shruti Tewari Bojan Todosijević Manos Tsakiris Hans H Tung Radu G Umbreș Edmunds Vanags Madalina Vlasceanu Andrew Vonasch Meltem Yucel Yucheng Zhang Mohcine Abad Eli Adler Narin Akrawi Hamza Alaoui Mdarhri Hanane Amara David M Amodio Benedict G Antazo Matthew Apps F Ceren Ay Mouhamadou Hady Ba Sergio Barbosa Brock Bastian Anton Berg Maria P Bernal-Zárate Michael Bernstein Michał Białek Ennio Bilancini Natalia Bogatyreva Leonardo Boncinelli Jonathan E Booth Sylvie Borau Ondrej Buchel C Daryl Cameron Chrissie F Carvalho Tatiana Celadin Chiara Cerami Hom Nath Chalise Xiaojun Cheng Luca Cian Kate Cockcroft Jane Conway Mateo Andres Córdoba-Delgado Chiara Crespi Marie Crouzevialle Jo Cutler Marzena Cypryańska Justyna Dabrowska Michael A Daniels Victoria H Davis Pamala N Dayley Sylvain Delouvee Ognjan Denkovski Guillaume Dezecache Nathan A Dhaliwal Alelie B Diato Roberto Di Paolo Marianna Drosinou Uwe Dulleck Jānis Ekmanis Arhan S Ertan Tom W Etienne Hapsa Hossain Farhana Fahima Farkhari Har Department of Psychology and Neural Science New York University New York NY USA. jay.vanbavel@nyu.edu. School of Psychology University of Kent Canterbury England. Department of Economics Middlesex University London London England. Department of Strategy and Management Norwegian School of Economics Bergen Norway. SWPS University of Social Sciences and Humanities Poznań Poland. Department of Psychological Sciences College of William and Mary Williamsburg VA USA. Institute of Social Sciences Ivo Pilar Zagreb Croatia. Department of Philosophy Macquarie University Sydney NSW Australia. Stanford Graduate School of Business Stanford University Stanford CA USA. Institute of Communication Science Friedrich-Schiller University Jena Jena Germany. flavio.azevedo@uni-jena.de. School of Human Sciences Institute for Lifecourse Development University of Greenwich London England. Department of Experimental Psychology University of Oxford Oxford England. Center for Human Brain Health School of Psychology University of Birmingham Birmingham England. Department of Psychology Macquarie University Sydney NSW Australia. KU Leuven Leuven Belgium. National Research University Higher School of Economics (HSE) Moscow Russia. De La Salle University Manila Philippines. Department of Psychology University of the Witwatersrand Johannesburg South Africa. Department of Political Science Aarhus University Aarhus Denmark. X University Toronto ON Canada. Department of Government Dartmouth College Hanover NH USA. Department of Psychology University of Toronto Toronto ON Canada. Gatton College of Business and Economics University of Kentucky Lexington KY USA. Department of Mass Communication National University of Science and Technology (NUST) Islamabad Pakistan. Department of Management Aarhus University Aarhus Denmark. Faculty of Social and Economic Sciences Comenius University Bratislava Slovakia. IntraMed Buenos Aires Argentina. Macedonian Academy of Sciences and

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Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

arXiv

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arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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Complete Inference of Causal Relations between Dynamical Systems

arXiv

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arXiv 2018年

作者： Benko, Zsigmond Zlatniczki, Ádám Stippinger, Marcell Fabó, Dániel Sólyom, András Erőss, Loránd Telcs, András Somogyvári, Zoltán Department of Computational Sciences Institute for Particle and Nuclear Physics HUN-REN Wigner Research Centre for Physics Konkoly-Thege Miklós út 29-33. BudapestH-1121 Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Magyar tudósok körútja 2. BudapestH-1111 Hungary Epilepsy Unit Department of Neurology National Institute of Mental Health Neurology and Neurosurgery Amerikai út 57. BudapestH-1145 Hungary Department of Functional Neurosurgery National Institute of Mental Health Neurology and Neurosurgery Amerikai út 57. BudapestH-1145 Hungary Faculty of Information Technology and Bionics Péter Pázmány Catholic University Práter u. 50/A BudapestH-1083 Hungary Department of Quantitative Methods Faculty of Business and Economics University of Pannonia Egyetem u. 10. VeszprémH-8200 Hungary Axoncord LLC Dunakeszi utca 23. Fsz. 1. ajtó BudapestH-1048 Hungary

From ancient philosophers to modern economists, biologists, and other researchers, there has been a continuous effort to unveil causal relations. The most formidable challenge lies in deducing the nature of the causal relationship: whether it is unidirectional, bidirectional, or merely apparent — implied by an unobserved common cause. While modern technology equips us with tools to collect data from intricate systems such as the planet’s ecosystem or the human brain, comprehending their functioning requires the identification and differentiation of causal relationships among the components, all without external interventions. In this context, we introduce a novel method capable of distinguishing and assigning probabilities to the presence of all potential causal relations between two or more time series within dynamical systems. The efficacy of this method is verified using synthetic datasets and applied to EEG (electroencephalographic) data recorded from epileptic patients. Given the universal applicability of our method, it holds promise for diverse scientific fields. Copyright © 2018, The Authors. All rights reserved.

关键词： Time series

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Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study

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The Lancet. Planetary health 2021年第7期5卷 e415-e425页

作者： Qi Zhao Yuming Guo Tingting Ye Antonio Gasparrini Shilu Tong Ala Overcenco Aleš Urban Alexandra Schneider Alireza Entezari Ana Maria Vicedo-Cabrera Antonella Zanobetti Antonis Analitis Ariana Zeka Aurelio Tobias Baltazar Nunes Barrak Alahmad Ben Armstrong Bertil Forsberg Shih-Chun Pan Carmen Íñiguez Caroline Ameling César De la Cruz Valencia Christofer Åström Danny Houthuijs Do Van Dung Dominic Royé Ene Indermitte Eric Lavigne Fatemeh Mayvaneh Fiorella Acquaotta Francesca de'Donato Francesco Di Ruscio Francesco Sera Gabriel Carrasco-Escobar Haidong Kan Hans Orru Ho Kim Iulian-Horia Holobaca Jan Kyselý Joana Madureira Joel Schwartz Jouni J K Jaakkola Klea Katsouyanni Magali Hurtado Diaz Martina S Ragettli Masahiro Hashizume Mathilde Pascal Micheline de Sousa Zanotti Stagliorio Coélho Nicolás Valdés Ortega Niilo Ryti Noah Scovronick Paola Michelozzi Patricia Matus Correa Patrick Goodman Paulo Hilario Nascimento Saldiva Rosana Abrutzky Samuel Osorio Shilpa Rao Simona Fratianni Tran Ngoc Dang Valentina Colistro Veronika Huber Whanhee Lee Xerxes Seposo Yasushi Honda Yue Leon Guo Michelle L Bell Shanshan Li Department of Epidemiology School of Public Health Cheeloo College of Medicine Shandong University Jinan China Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia. Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia. Electronic address: yuming.guo@monash.edu. Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia. Department of Public Health Environments and Society London School of Hygiene and Tropical Medicine London UK Centre for Statistical Methodology London School of Hygiene and Tropical Medicine London UK Centre on Climate Change and Planetary Health London School of Hygiene and Tropical Medicine London UK. Shanghai Children's Medical Centre Shanghai Jiao Tong University Shanghai China School of Public Health Institute of Environment and Population Health Anhui Medical University Hefei China Center for Global Health Nanjing Medical University Nanjing China School of Public Health and Social Work Queensland University of Technology Brisbane QLD Australia. Laboratory of Management in Science and Public Health National Agency for Public Health of the Ministry of Health Chisinau Moldova. Institute of Atmospheric Physics Czech Academy of Sciences Prague Czech Republic Faculty of Environmental Sciences Czech University of Life Sciences Prague Czech Republic. Institute of Epidemiology Helmholtz Zentrum München-German Research Center for Environmental Health Neuherberg Germany. Faculty of Geography and Environmental Sciences Hakim Sabzevari University Sabzevar Iran. Institute of Social and Preventive Medicine University of Bern Bern Switzerland Oeschger Center for Climate Change Res

BACKGROUND:Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. methods:In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature-mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature-mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. FINDINGS:Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967-5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58-11·07) of all deaths (8·52% [6·19-10·47] were cold-related and 0·91% [0·56-1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60-87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000-03 to 2016-19, the global cold-related excess death ratio changed by -0·51 percentage points (95% eCI -0·61 to -0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13-0·31), leading to a

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Atomic bound state and scattering properties of effective momentum-dependent potentials

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Physical Review E 2016年第4期94卷 043205-043205页

作者： Gautham Dharuman John Verboncoeur Andrew Christlieb Michael S. Murillo Department of Electrical and Computer Engineering Michigan State University East Lansing Michigan 48824 USA Department of Computational Mathematics Science and Engineering Michigan State University East Lansing Michigan 48824 USA Department of Mathematics Michigan State University East Lansing Michigan 48824 USA New Mexico Consortium Los Alamos New Mexico 87544 USA Computational Physics and Methods Group Los Alamos National Laboratory Los Alamos New Mexico 87544 USA

Effective classical dynamics provide a potentially powerful avenue for modeling large-scale dynamical quantum systems. We have examined the accuracy of a Hamiltonian-based approach that employs effective momentum-dependent potentials (MDPs) within a molecular-dynamics framework through studies of atomic ground states, excited states, ionization energies, and scattering properties of continuum states. Working exclusively with the Kirschbaum-Wilets (KW) formulation with empirical MDPs [C. L. Kirschbaum and L. Wilets, Phys. Rev. A 21, 834 (1980)], optimization leads to very accurate ground-state energies for several elements (e.g., N, F, Ne, Al, S, Ar, and Ca) relative to Hartree-Fock values. The KW MDP parameters obtained are found to be correlated, thereby revealing some degree of transferability in the empirically determined parameters. We have studied excited-state orbits of electron-ion pair to analyze the consequences of the MDP on the classical Coulomb catastrophe. From the optimized ground-state energies, we find that the experimental first- and second-ionization energies are fairly well predicted. Finally, electron-ion scattering was examined by comparing the predicted momentum transfer cross section to a semiclassical phase-shift calculation; optimizing the MDP parameters for the scattering process yielded rather poor results, suggesting a limitation of the use of the KW MDPs for plasmas.

关键词： Molecular dynamics

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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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πη photoproduction on the nucleon

Acta Physica Polonica B, Proceedings Supplement

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Acta Physica Polonica B, Proceedings Supplement 2015年第2期8卷 419-424页

作者： Bibrzycki, Lukasz Kamiński, Robert Department of Computer Science and Computational Methods Pedagogical University of Cracow Podchorazych 2 Kraków30-084 Poland Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences Radzikowskiego 152 Kraków31-342 Poland

We calculate the amplitude of the πη photoproduction on the proton and the associated cross sections. The primary objective of the model is to describe the photoproduction of the isovector resonances in the πη channel which is important in the context of elucidating the nature of the scalar a0(980) meson. The model can be also applied to description of the photoproduction of tensor isovector resonance a2(1320).

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2015 Brainhack Proceedings Abstracts

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GIGAscience 2016年第1期5卷 1-26页

作者： [Anonymous] Computational Neuroimaging Lab Center for Biomedical Imaging and Neuromodulation Nathan Kline Institute for Psychiatric Research Orangeburg USA Center for the Developing Brain Child Mind Institute New York USA Centre de Recherche Institut Universitaire de Gériatrie de Montréal Montréal Canada Department of computer science and operational research University of Montreal Montreal Canada Max Planck Research Group for Neuroanatomy & Connectivity Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany SRI International Menlo Park USA Department of Psychiatry and Behavioral Sciences Stanford University Stanford USA Functional Imaging Unit Center for Diagnostic Radiology Unviersity Medicine Greifswald Greifswald Germany Université de Montréal Montréal Canada University of Massachusetts Medical School Worcester USA University of California Berkeley USA Institut Pasteur Paris France University of California San Diego La Jolla USA University of Washington Seattle USA Poldrack Lab Department of Psychology Stanford University Stanford USA Montréal Neurological Institute McGill University and Institute of Pscychology Montréal Canada Douglas Mental Health Institute Montréal Canada Otto-von-Guericke University Magdeburg Germany Montreal Neurological Institute McGill University Montreal Canada University of Hawaii John A. Burns School of Medicine Honolulu USA Oregon Health & Science University Portland USA University of Greifswald Greifswald Germany Duke Institute for Brain Sciences Durham USA Department of Psychology and Neuroscience University of Colorado Boulder USA Institute of Cognitive Neuroscience University College London London United Kingdom Section on Functional Imaging Methods Laboratory of Brain and Cognition National Institute of Mental Health Bethedsa USA City University of New York-Hunter College New York USA Monash Institute of Cognitive and Clinical Neurosciences Monash University Melbourne Australia McGill Centre fo

I1 Introduction to the 2015 Brainhack Proceedings R. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jörg P. Pfannmöller A1 Distributed collaboration: the case for the enhancement of Brainspell’s interface AmanPreet Badhwar, David Kennedy, Jean-Baptiste Poline, Roberto Toro A2 Advancing open science through NiData Ben Cipollini, Ariel Rokem A3 Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC Daniel Clark, Krzysztof J. Gorgolewski, R. Cameron Craddock A4 Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI R. Cameron Craddock, Daniel J. Clark A5 LORIS: DICOM anonymizer Samir Das, Cécile Madjar, Ayan Sengupta, Zia Mohades A6 Automatic extraction of academic collaborations in neuroimaging Sebastien Dery A7 NiftyView: a zero-footprint web application for viewing DICOM and NIfTI files Weiran Deng A8 Human Connectome Project Minimal Preprocessing Pipelines to Nipype Eric Earl, Damion V. Demeter, Kate Mills, Glad Mihai, Luka Ruzic, Nick Ketz, Andrew Reineberg, Marianne C. Reddan, Anne-Lise Goddings, Javier Gonzalez-Castillo, Krzysztof J. Gorgolewski A9 Generating music with resting-state fMRI data Caroline Froehlich, Gil Dekel, Daniel S. Margulies, R. Cameron Craddock A10 Highly comparable time-series analysis in Nitime Ben D. Fulcher A11 Nipype interfaces in CBRAIN Tristan Glatard, Samir Das, Reza Adalat, Natacha Beck, Rémi Bernard, Najmeh Khalili-Mahani, Pierre Rioux, Marc-Étienne Rousseau, Alan C. Evans A12 DueCredit: automated collection of citations for software, methods, and data Yaroslav O. Halchenko, Matteo Visconti di Oleggio Castello A13 Open source low-cost device to register dog’s heart rate and tail movement Raúl Hernández-Pérez, Edgar A. Morales, Laura V. Cuaya A14 Calculating the Laterality Index Using FSL for Stroke Neuroimaging Data Kaori L. Ito, Sook-Lei Liew A15 Wrapping FreeSurfer 6 for use in high-performance computing environments Hans J. Johns

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Scale-space line curvature estimation for straight line and circle detection

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Pattern Recognition and Image Analysis 2012年第2期22卷 360-370页

作者： Semeikina, E.V. Yurin, D.V. Krylov, A.S. Chung, Kuo-Liang Huang, Yong-Huai Laboratory of Mathematical Methods of Image Processing Faculty of Computational Mathematics and Cybernetic Moscow State University Moscow 119991 Russia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Road Taipei 10672 Taiwan Institute of Computer and Communication Engineering and Department of Electronic Engineering Jinwen University of Science and Technology No. 99 An-Chung Road Hsin-Tien Taipei 23154 Taiwan

A scale-space algorithm for estimating the local curvature of lines (edges or isolines) is presented. Two variants of edge curvature estimation based on differential invariants are suggested and compared. The first variant uses the edge curvature formula derived in the paper and the second is based on image preprocessing, which allows one to use the isoline curvature formula for edge curvature estimation. An analysis of scale selection needed to reach a desired accuracy is presented. Also, noise influence analysis has been performed. The application of curvature estimation to detection of straight lines and circles is suggested and implemented. Curvature information usage in parametric curve detection speeds up search algorithms and makes the results more stable. © 2012 Pleiades Publishing, Ltd.

关键词： Curve fitting

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Reactive Systems: Modelling, Specification and Verification, Luca Aceto, Anna Ingólfsdóttir, Kim Guldstrand Larsen, Jiri Srba, Cambridge University Press (2007), ISBN: 9780521875462

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The Journal of Logic and Algebraic Programming 2008年第1期78卷 52-52页

作者： Jos Baeten Ana Sokolova Formal Methods Group Division of Computer Science Department of Mathematics and Computer Science Eindhoven University of Technology The Netherlands Computational Systems Group Department of Computer Sciences University of Salzburg Austria

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