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

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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Open Data from the Third Observing Run of LIGO, Virgo, KAGRA, and GEO

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Astrophysical Journal, Supplement Series 2023年第2期267卷 29-29页

作者： Abbott, R. Abe, H. Acernese, F. Ackley, K. Adhicary, S. Adhikari, N. Adhikari, R.X. Adkins, V.K. Adya, V.B. Affeldt, C. Agarwal, D. Agathos, M. Aguiar, O.D. Aiello, L. Ain, A. Ajith, P. Akutsu, T. Albanesi, S. Alfaidi, R.A. Al-Jodah, A. Alléné, C. Allocca, A. Almualla, M. Altin, P.A. Amato, A. Amez-Droz, L. Amorosi, A. Anand, S. Ananyeva, A. Andersen, R. Anderson, S.B. Anderson, W.G. Andia, M. Ando, M. Andrade, T. Andres, N. Andrés-Carcasona, M. Andrić, T. Ansoldi, S. Antelis, J.M. Antier, S. Aoumi, M. Apostolatos, T. Appavuravther, E.Z. Appert, S. Apple, S.K. Arai, K. Araya, A. Araya, M.C. Areeda, J.S. Arène, M. Aritomi, N. Arnaud, N. Arogeti, M. Aronson, S.M. Arun, K.G. Asada, H. Ashton, G. Aso, Y. Assiduo, M. Assis de Souza Melo, S. Aston, S.M. Astone, P. Aubin, F. AultONeal, K. Babak, S. Badalyan, A. Badaracco, F. Badger, C. Bae, S. Bagnasco, S. Bai, Y. Baier, J.G. Baiotti, L. Baird, J. Bajpai, R. Baka, T. Ball, M. Ballardin, G. Ballmer, S.W. Baltus, G. Banagiri, S. Banerjee, B. Bankar, D. Baral, P. Barayoga, J.C. Barber, J. Barish, B.C. Barker, D. Barneo, P. Barone, F. Barr, B. Barsotti, L. Barsuglia, M. Barta, D. Barthelmy, S.D. Barton, M.A. Bartos, I. Basak, S. Basalaev, A. Bassiri, R. Basti, A. Bawaj, M. Bayley, J.C. Baylor, A.C. Bazzan, M. Bécsy, B. Bedakihale, V.M. Beirnaert, F. Bejger, M. Bell, A.S. Benedetto, V. Beniwal, D. Benoit, W. Bentley, J.D. Ben Yaala, M. Bera, S. Berbel, M. Bergamin, F. Berger, B.K. Bernuzzi, S. Beroiz, M. Berry, C.P.L. Bersanetti, D. Bertolini, A. Betzwieser, J. Beveridge, D. Bevins, N. Bhandare, R. Bhandari, A.V. Bhardwaj, U. Bhatt, R. Bhattacharjee, D. Bhaumik, S. Bianchi, A. Bilenko, I.A. Bilicki, M. Billingsley, G. Bini, S. Birnholtz, O. Biscans, S. Bischi, M. Biscoveanu, S. Bisht, A. Biswas, B. Bitossi, M. Bizouard, M.-A. Blackburn, J.K. Blair, C.D. Blair, D.G. Blair, R.M. Bobba, F. Bode, N. Boër, M. Bogaert, G. Boileau, G. Boldrini, M. Bolingbroke, G.N. Bonavena, L.D. Bondarescu, R. Bondu, F. Bonilla, E. Bonilla, G.S. Bonnand, R. Booker, P. Bork, R. Boschi, V. Bose, N. LIGO Laboratory California Institute of Technology Pasadena 91125 CA United States Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8551 Japan Dipartimento di Farmacia Università di Salerno Salerno Fisciano I-84084 Italy INFN Sezione di Napoli Napoli I-80126 Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 VIC Australia The Pennsylvania State University University Park 16802 PA United States University of Wisconsin-Milwaukee Milwaukee 53201 WI United States Louisiana State University Baton Rouge 70803 LA United States OzGrav Australian National University Canberra 0200 ACT Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Hannover D-30167 Germany Leibniz Universität Hannover Hannover D-30167 Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Instituto Nacional de Pesquisas Espaciais São Paulo São José dos Campos 12227-010 Brazil Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa Pisa I-56127 Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Dipartimento di Fisica Università degli Studi di Torino Torino I-10125 Italy INFN Sezione di Torino Torino I-10125 Italy SUPA University of Glasgow Glasgow G12 8QQ United Kingdom OzGrav University of Western Australia Crawley 6009 WA Australia Univ. Savoie Mont Blanc CNRS Laboratoire d'Annecy de Physique des Particules - IN2P3 Annecy F-74000 France Università di Napoli “Federico II” Napoli I-80126 Italy University of Minnesota Minneapolis 55455 MN United States M

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://***. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages. © 2023. The Author(s). Published by the American Astronomical Society.

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Astro2020 APC white paper project: The simons observatory

arXiv

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arXiv 2019年

作者： Abitbol, Maximilian H. Adachi, Shunsuke Ade, Peter Aguirre, James Ahmed, Zeeshan Aiola, Simone Ali, Aamir Alonso, David Alvarez, Marcelo A. Arnold, Kam Ashton, Peter Atkins, Zachary Austermann, Jason Awan, Humna Baccigalupi, Carlo Baildon, Taylor Lizancos, Anton Baleato Barron, Darcy Battaglia, Nick Battye, Richard Baxter, Eric Bazarko, Andrew Beall, James A. Bean, Rachel Beck, Dominic Beckman, Shawn Beringue, Benjamin Bhandarkar, Tanay Bhimani, Sanah Bianchini, Federico Boada, Steven Boettger, David Bolliet, Boris Richard Bond, J. Borrill, Julian Brown, Michael L. Bruno, Sarah Marie Bryan, Sean Calabrese, Erminia Calafut, Victoria Calisse, Paolo Carron, Julien Carl, Fred M. Cayuso, Juan Challinor, Anthony Chesmore, Grace Chinone, Yuji Chluba, Jens Cho, Hsiao-Mei Sherry Choi, Steve Clark, Susan Clarke, Philip Contaldi, Carlo Coppi, Gabriele Cothard, Nicholas F. Coughlin, Kevin Coulton, Will Crichton, Devin Crowley, Kevin D. Crowley, Kevin T. Cukierman, Ari D'Ewart, John M. Dünner, Rolando De Haan, Tijmen Devlin, Mark Dicker, Simon Dober, Bradley Duell, Cody J. Duff, Shannon Duivenvoorden, Adri Dunkley, Jo Bouhargani, Hamza El Errard, Josquin Fabbian, Giulio Feeney, Stephen Fergusson, James Ferraro, Simone Fluxà, Pedro Freese, Katherine Frisch, Josef C. Frolov, Andrei Fuller, George Galitzki, Nicholas Gallardo, Patricio A. Ghersi, Jose Tomas Galvez Gao, Jiansong Gawiser, Eric Gerbino, Martina Gluscevic, Vera Goeckner-Wald, Neil Golec, Joseph Gordon, Sam Gralla, Megan Green, Daniel Grigorian, Arpi Groh, John Groppi, Chris Guan, Yilun Gudmundsson, Jon E. Halpern, Mark Han, Dongwon Hargrave, Peter Harrington, Kathleen Hasegawa, Masaya Hasselfield, Matthew Hattori, Makoto Haynes, Victor Hazumi, Masashi Healy, Erin Henderson, Shawn W. Hensley, Brandon Hervias-Caimapo, Carlos Hill, Charles A. Colin Hill, J. Hilton, Gene Hilton, Matt Hincks, Adam D. Hinshaw, Gary Hložek, Renée Ho, Shirley Ho, Shuay-Pwu Patty Hoang, Thuong D. Hoh, Jonathan Hotinli, Selim C. Huang, Zhiqi Hubmayr, Johannes Huffenberger, Kevin Hughes, John P University of Oxford Denys Wilkinson Building Keble Road OxfordOX1 3RH United Kingdom Department of Physics Faculty of Science Kyoto University Kyoto606 Japan School of Physics and Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States Kavli Institute for Particle Astrophysics and Cosmology Menlo ParkCA94025 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics University of California BerkeleyCA94720 United States Computational Cosmology Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics University of California San Diego CA92093 United States Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States University of Tokyo Kashiwa277- 8583 Japan Joseph Henry Laboratories of Physics Jadwin Hall Princeton University PrincetonNJ08544 United States NIST Quantum Sensors Group 325 Broadway Mailcode 687.08 BoulderCO80305 United States Department of Physics and Astronomy Rutgers State University of New Jersey PiscatawayNJ08854-8019 United States Via Bonomea 265 Trieste34136 Italy INFN Sezione di Trieste Padriciano 99 Trieste34149 Italy Department of Physics University of Michigan Ann Arbor48103 United States Kavli Institute for Cosmology Cambridge Madingley Road CambridgeCB3 0HA United Kingdom Institute of Astronomy Madingley Road CambridgeCB3 0HA United Kingdom Department of Physics and Astronomy University of New Mexico AlbuquerqueNM87131 United States Department of Astronomy Cornell University IthacaNY14853 United States Jodrell Bank Centre for Astrophysics School of Physics and Astronomy University of Manchester Manchester United Kingdom AstroParticule e

The Simons Observatory (SO) is a ground-based cosmic microwave background (CMB) experiment sited on Cerro Toco in the Atacama Desert in Chile that promises to provide breakthrough discoveries in fundamental physics, cosmology, and astrophysics. Supported by the Simons Foundation, the Heising-Simons Foundation, and with contributions from collaborating institutions, SO will see first light in 2021 and start a five year survey in 2022. SO has 287 collaborators from 12 countries and 53 institutions, including 85 students and 90 postdocs. The SO experiment in its currently funded form ('SO-Nominal') consists of three 0.4 m Small Aperture Telescopes (SATs) and one 6 m Large Aperture Telescope (LAT). Optimized for minimizing systematic errors in polarization measurements at large angular scales, the SATs will perform a deep, degree-scale survey of 10% of the sky to search for the signature of primordial gravitational waves. The LAT will survey 40% of the sky with arc-minute resolution. These observations will measure (or limit) the sum of neutrino masses, search for light relics, measure the early behavior of Dark Energy, and refine our understanding of the intergalactic medium, clusters and the role of feedback in galaxy formation. With up to ten times the sensitivity and five times the angular resolution of the Planck satellite, and roughly an order of magnitude increase in mapping speed over currently operating ("Stage 3") experiments, SO will measure the CMB temperature and polarization fluctuations to exquisite precision in six frequency bands from 27 to 280 GHz. SO will rapidly advance CMB science while informing the design of future observatories such as CMB-S4. Construction of SO-Nominal is fully funded, and operations and data analysis are funded for part of the planned five-year observations. We will seek federal funding to complete the observations and analysis of SO-Nominal, at the $25M level. The SO has a low risk and cost efficient upgrade path - the 6 m LAT

关键词： Observatories

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Analytical Solution for Time-dependent Flow of a Third Grade Fluid Induced Due to Impulsive Motion of a Flat Porous Plate

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Acta Mathematicae Applicatae Sinica 2015年第3期31卷 757-766页

作者： Taha Aziz F.M.Mahomed Azeem Shahzad Asim Aziz School of Computational and Applied Mathematics DST-NRF Centre of Excellence in Mathematical and Statistical Sciences Differential Equations Continuum Mechanics and Applications University of the Witwatersrand School of Natural Sciences National University of Sciences and Technology Department of Mathematics Quaid-i-Azam University Department of Basic Sciences and Humanities National University of Sciences and Technology College of Electrical and Mechanical Engineering

The aim of the present communication is to discuss the analytical solution for the unsteady flow of a third grade fluid which occupies the space y 〉 0 over an infinite porous plate. The flow is generated due to the motion of the plate in its own plane with an impulsive velocity V（t）. Translational symmetries in variables t and y are utilized to reduce the governing non-linear partial differential equation into an ordinary differential equation. The reduced problem is then solved using homotopy analysis method（HAM）. Graphs representing the solution are plotted and discussed and proper conclusions are drawn.

关键词： third grade fluid Porous plate translational symmetries Homotopy analysis method（HAM）

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Spectral representations of one-homogeneous functionals 5

Spectral representations of one-homogeneous functionals

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5th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2015

作者： Burger, Martin Eckardt, Lina Gilboa, Guy Moeller, Michael Institute for Computational and Applied Mathematics University of Münster Münster Germany Electrical Engineering Department Technion IIT Haifa Israel Department of Mathematics Technische Universität München Munich Germany

ISBN: (纸本)9783319184609

This paper discusses a generalization of spectral representations related to convex one-homogeneous regularization functionals, e.g. total variation or 1-norms. Those functionals serve as a substitute for a Hilbert space structure (and the related norm) in classical linear spectral transforms, e.g. Fourier and wavelet analysis. We discuss three meaningful definitions of spectral representations by scale space and variational methods and prove that (nonlinear) eigenfunctions of the regularization functionals are indeed atoms in the spectral representation. Moreover, we verify further useful properties related to orthogonality of the decomposition and the Parseval identity. The spectral transform is motivated by total variation and further developed to higher order variants. Moreover, we show that the approach can recover Fourier analysis as a special case using an appropriate 1-type functional and discuss a coupled sparsity example. © Springer International Publishing Switzerland 2015.

关键词： Fourier analysis

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Preface

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Lecture Notes in computational Science and engineering 2017年 117卷 v-vii页

作者： Sakurai, Tetsuya Zhang, Shao-Liang Imamura, Toshiyuki Yamamoto, Yusaku Kuramashi, Yoshinobu Hoshi, Takeo University of Tsukuba Department of Computer Science TsukubaIbaraki Japan Nagoya University Department of Electrical Engineering and Computer Science NagoyaAichi Japan RIKEN Advanced Institute for Computational Science KobeHyogo Japan University of Electro-Communications Graduate School of Informatics and Engineering Department of Communication Engineering and Informatics Tokyo Japan University of Tsukuba Department of Particle Physics TsukubaIbaraki Japan Tottori University Department of Applied Mathematics and Physics Tottori Japan

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On normalization of inconsistency indicators in pairwise comparisons

arXiv

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arXiv 2017年

作者： Koczkodaj, W.W. Magnot, J.-P. Mazurek, J. Peters, J.F. Rakhshani, H. Soltys, M. Strzalka, D. Szybowski, J. Tozzi, A. Computer Science Laurentian University SudburyONP3E 2C6 Canada Lycée Jeanne D'arc rue de grande Bretagne Clermont-Ferrand63000 Department of Informatics and Mathematics School of Business Administration in Karvina Silesian University in Opava Univ. Square 1934/3 73340 Czech Republic Computational Intelligence Laboratory University of Manitoba WinnipegMBR3T 5V6 Canada Department of Mathematics Faculty of Arts and Sciences Adiyaman University Adiyaman02040 Turkey Department of Computer Science Faculty of Mathematics University of Sistan and Baluchestan Zahedan Iran California State University Channel Islands Bell Tower West 2265 One University Drive CamarilloCA93012 United States Faculty of Electrical and Computer Engineering Rzeszów University of Technology Al. Powstá nców Warszawy Rzeszów35-959 Poland AGH University of Science and Technology Faculty of Applied Mathematics al. Mickiewicza 30 Kraków30-059 Poland Center for Nonlinear Science University of North Texas DentonTX76203 United States

In this study, we provide mathematical and practice-driven justification for using [0, 1] normalization of inconsistency indicators in pairwise comparisons. The need for normalization, as well as problems with the lack of normalization, are presented. A new type of paradox of infinity is described. Copyright © 2017, The Authors. All rights reserved.

关键词： Decision making

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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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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Photocatalysts: Enhanced Solar‐to‐Hydrogen Generation with Broadband Epsilon‐Near‐Zero Nanostructured Photocatalysts (Adv. Mater. 27/2017)

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Advanced Materials 2017年第27期29卷

作者： Yi Tian Francisco Pelayo García de Arquer Cao‐Thang Dinh Gael Favraud Marcella Bonifazi Jun Li Min Liu Xixiang Zhang Xueli Zheng Md. Golam Kibria Sjoerd Hoogland David Sinton Edward H. Sargent Andrea Fratalocchi PRIMALIGHT Faculty of Electrical Engineering Applied Mathematics and Computational Science King Abdullah University of Science and Technology (KAUST) Thuwal 23955‐6900 Saudi Arabia Department of Electrical and Computer Engineering University of Toronto 35 St. George Street Toronto Ontario M5S 1A4 Canada Division of Physical Science and Engineering King Abdullah University of Science and Technology Thuwal 239955 Saudi Arabia Department of Mechanical and Industrial Engineering University of Toronto 5 Kings College Road Toronto Ontario M5S 3G8 Canada

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