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arXiv 2022年

作者： Seskir, Zeki C. Migdal, Piotr Weidner, Carrie Anupam, Aditya Case, Nicky Davis, Noah Decaroli, Chiara Ercan, İlke Foti, Caterina Gora, Pawel Jankiewicz, Klementyna La Cour, Brian R. Malo, Jorge Yago Maniscalco, Sabrina Naeemi, Azad Nita, Laurentiu Parvin, Nassim Scafirimuto, Fabio Sherson, Jacob F. Surer, Elif Wootton, James Yeh, Lia Zabello, Olga Chiofalo, Marilù Karlsruhe Institute of Technology ITAS Karlstraße 11 Karlsruhe76133 Germany Quantum Flytrap ul. Ceglowska 29 Warsaw01-809 Poland Quantum Engineering Technology Laboratories H.H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Georgia Institute of Technology School of Literature Media and Communication 686 Cherry St NW AtlantaGA30313 United States The University of Texas at Austin Center for Quantum Research Applied Research Laboratories 10000 Burnet Rd AustinTX78758 United States National Quantum Computing Center Oxfordshire DidcotOX11 0GD United Kingdom TU Delft Department of Microelectronics Mekelweg 4 Delft2628 CD Netherlands QPlayLearn Aalto University InstituteQ - The Finnish Quantum Institute Department of Applied Physics AaltoFI-00076 11000 Finland Quantum AI Foundation Sanocka 9/103 Warsaw02-110 Poland University of Pisa Department of Physics Largo Bruno Pontecorvo 3 PisaI-56126 Italy University of Helsinki InstituteQ - The Finnish Quantum Institute Department of Physics Helsinki Finland Georgia Institute of Technology School of Electrical and Computer Engineering 777 Atlantic Drive NW AtlantaGA30332 United States Quarks Interactive Strada Bailor 93a Miercurea-Ciuc Harghita4500 Romania IBM Quantum IBM Research - Zurich Säumerstrasse 4 Rüschlikon8803 Switzerland ScienceAtHome Fuglesangs Allé 4 Aarhus8210 Denmark Aarhus University Institute for Physics and Astronomy Ny Munkegade 120 Aarhus8000 Denmark Middle East Technical University Graduate School of Informatics Department of Modeling and Simulation Üniversiteler Mah. Dumlupınar Blv. No:1 Ankara06800 Turkey University of Oxford Department of Computer Science Wolfson Building Parks Road OxfordOX1 3QD United Kingdom IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States Offenburg University of Applied Sciences Department of Business and Industrial Eng

In this article, we provide an extensive overview of a wide range of quantum games and interactive tools that have been employed by the community in recent years. The paper presents selected tools, as described by their developers. The list includes Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta, QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, scienceAtHome, and The Virtual Quantum Optics Laboratory. Additionally, we present events for quantum game development: hackathons, game jams, and semester projects. Furthermore, we discuss the Quantum Technologies Education for Everyone (QUTE4E) pilot project, which illustrates an effective integration of these interactive tools with quantum outreach and education activities. Finally, we aim at providing guidelines for incorporating quantum games and interactive tools in pedagogic materials to make quantum technologies more accessible for a wider population. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

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Quantum simulations in integrated photonics

Quantum simulations in integrated photonics

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Quantum Information and Measurement, QIM 2019

作者： Sparrow, Chris Paesani, Stefano Maraviglia, Nicola Santagati, Raffaele Vigliar, Caterina Neville, Alex Harrold, Chris Russell, Nicholas J. O'Brien, Jeremy Tew, David Matsuda, Nobuyuki Hashimoto, Toshikazu Martín-López, Enrique Carolan, Jacques Joglekar, Yogesh Laing, Anthony Quantum Engineering and. Technology Labs School of Physics Department of Electrical and Electronic Engineering University of Bristol BS8 1UB United Kingdom NTT Basic Research Laboratories NTT Corporation 3-1 Morinosato-Wakamiya Atsugi Kanagawa243-0198 Japan Nokia Research Centre Broers Building 21 J.J. Thomson Avenue CambridgeCB3 0FA United Kingdom Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States IndianapolisIN46202 United States

ISBN: (纸本)9781943580569

关键词： Simulation platform

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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2022 Roadmap on Neuromorphic Computing and engineering

arXiv

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arXiv 2021年

作者： Christensen, Dennis Valbjørn Dittmann, Regina Linares-Barranco, Bernabé Sebastian, Abu Le Gallo, Manuel Redaelli, Andrea Slesazeck, Stefan Mikolajick, Thomas Spiga, Sabina Menzel, Stephan Valov, Ilia Milano, Gianluca Ricciardi, Carlo Liang, Shi-Jun Miao, Feng Lanza, Mario Quill, Tyler J. Keene, Scott T. Salleo, Alberto Grollier, Julie Marković, Danijela Mizrahi, Alice Yao, Peng Yang, J. Joshua Indiveri, Giacomo Strachan, John Paul Datta, Suman Vianello, Elisa Valentian, Alexandre Feldmann, Johannes Li, Xuan Pernice, Wolfram H.P. Bhaskaran, Harish Furber, Steve Neftci, Emre Scherr, Franz Maass, Wolfgang Ramaswamy, Srikanth Tapson, Jonathan Panda, Priyadarshini Kim, Youngeun Tanaka, Gouhei Thorpe, Simon Bartolozzi, Chiara Cleland, Thomas A. Posch, Christoph Liu, Shih-Chii Panuccio, Gabriella Mahmud, Mufti Mazumder, Arnab Neelim Hosseini, Morteza Mohsenin, Tinoosh Donati, Elisa Tolu, Silvia Galeazzi, Roberto Christensen, Martin Ejsing Holm, Sune Ielmini, Daniele Pryds, N. Department of Energy Conversion and Storage Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Peter Gruenberg Institute 7 Forschungszentrum Juelich GmbH Juelich52425 Germany JARA-FIT RWTH Aachen University Aachen52056 Germany CSIC Universidad de Sevilla Seville41092 Spain IBM Research Zurich Switzerland STMicroelectronics Agrate Italy Dresden01187 Germany Institute of Semiconductors and Microsystems TU Dresden Dresden Germany CNR-IMM Unit of Agrate Brianza via C. Olivetti 2 MB Agrate Brianza Italy Juelich Germany Torino Italy Department of Applied Science and Technology Politecnico di Torino Torino Italy National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing China Thuwal23955-6900 Saudi Arabia Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Unité Mixte de Physique CNRS Thales Université Paris-Saclay Palaiseau91767 France Electrical and Computer Engineering Department University of Southern California Los AngelesCA United States Institute of Neuroinformatics University of Zurich ETH Zurich Switzerland Hewlett Packard Laboratories Hewlett Packard Enterprise San JoseCA United States Department of Electrical Engineering University of Notre Dame Notre DameIN United States CEA LETI Université Grenoble Alpes Grenoble France CEA LIST Université Grenoble Alpes Grenoble France Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom Institute of Physics University of Münster Heisenbergstr. 11 Heisenbergstr Münster48149 Germany Center for Soft Nanoscience University of Münster Münster48149 Germany The University of Manchester United Kingdom Department of Cognitive Sciences University of California Irvine IrvineCA United States Institute of Theoretica

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community. © 2021,

关键词： Data transfer

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Roadmap on spatiotemporal light fields

arXiv

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arXiv 2022年

作者： Shen, Yijie Zhan, Qiwen Wright, Logan G. Christodoulides, Demetrios N. Wise, Frank W. Willner, Alan E. Zhao, Zhe Zou, Kai-Heng Liao, Chen-Ting Hernández-García, Carlos Murnane, Margaret Porras, Miguel A. Chong, Andy Wan, Chenhao Bliokh, Konstantin Y. Yessenov, Murat Abouraddy, Ayman F. Wong, Liang Jie Go, Michael Kumar, Suraj Guo, Cheng Fan, Shanhui Papasimakis, Nikitas Zheludev, Nikolay I. Chen, Lu Zhu, Wenqi Agrawal, Amit Jolly, Spencer W. Dorrer, Christophe Alonso, Benjamín Lopez-Quintas, Ignacio López-Ripa, Miguel Sola, Íñigo J. Fang, Yiqi Gong, Qihuang Liu, Yunquan Huang, Junyi Zhang, Hongliang Ruan, Zhichao Mounaix, Mickael Fontaine, Nicolas K. Carpenter, Joel Dorrah, Ahmed H. Capasso, Federico Forbes, Andrew Optoelectronics Research Centre Centre for Photonic Metamaterials University of Southampton SouthamptonSO17 1BJ United Kingdom School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Zhangjiang Laboratory 100 Haike Road Shanghai201204 China Physics & Informatics Laboratories NTT Research Inc. 940 Stewart Drive SunnyvaleCA94085 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States College of Optics & Photonics-CREOL University of Central Florida OrlandoFL32816 United States Department of Electrical Engineering University of Southern California Los AngelesCA90089 United States JILA Department of Physics University of Colorado and NIST BoulderCO80309 United States Grupo de Investigación en Aplicaciones del Láser y Fotónica Departamento de Física Aplicada Universidad de Salamanca Salamanca37008 Spain Grupo de Sistemas Complejos ETSIME Universidad Politécnica de Madrid Rios Rosas 21 Madrid28003 Spain Physics Department Pusan National Umiversity Geumjeong-gu Busan46241 Korea Republic of School of Optical and Electronic Information Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan430074 China Theoretical Quantum Physics Laboratory RIKEN Cluster for Pioneering Research Saitama Wako-shi351-0198 Japan School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Department of Applied Physics Stanford University StanfordCA94305 United States Ginzton Laboratory Department of Electrical Engineering Stanford University StanfordCA94305 United States Centre for Disruptive Photonic Technologies School of Physical and Mathematical Sciences The Photonics Institute Nanyang Technological University Singapore637378 Singapore National Institute of Standards and Technology GaithersburgMD20899 United States Maryla

Spatiotemporal sculpturing of light pulse with ultimately sophisticated structures represents the holy grail of the human everlasting pursue of ultra-fast information transmission and processing as well as ultra-intense energy concentration and extraction. It also holds the key to unlock new extraordinary fundamental physical effects. Traditionally, spatiotemporal light pulses are always treated as spatiotemporally separable wave packet as solution of the Maxwell's equations. In the past decade, however, more generalized forms of spatiotemporally nonseparable solution started to emerge with growing importance for their striking physical effects. This roadmap intends to highlight the recent advances in the creation and control of increasingly complex spatiotemporally sculptured pulses, from spatiotemporally separable to complex nonseparable states, with diverse geometric and topological structures, presenting a bird's eye viewpoint on the zoology of spatiotemporal light fields and the outlook of future trends and open challenges. © 2022, CC0.

关键词： Maxwell equations

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Genetic algorithm optimized support vector machine in noma-based satellite networkswith imperfect csi

arXiv

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arXiv 2020年

作者： Yan, Xiaojuan An, Kang Wang, Cheng-Xiang Zhu, Wei-Ping Li, Yusheng Feng, Zhiqiang National Mobile Commun. Research Lab School of Info. Science and Eng. Southeast University Nanjing210096 China College of Mechanical Naval Architecture and Ocean Engineering Beibu Gulf University Qinzhou535011 China Sixty-third Research Institute National University of Defense Technology Nanjing210007 China Purple Mountain Laboratories Nanjing211111 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada

With the help of a power-domain non-orthogonal multiple access (NOMA) scheme, satellite networks can simultaneously serve multiple users within limited time/spectrum resource block. However, the existence of channel estimation errors inevitably degrade the judgment on users' channel state information (CSI) accuracy, thus affecting the user pairing processing and suppressing the superiority of the NOMA scheme. Inspired by the advantages ofmachine learning (ML) algorithms, we propose an improved support vector machine (SVM) scheme to reduce the inappropriate user pairing risks and enhance the performance of NOMA based satellite networks with imperfect CSI. Particularly, a genetic algorithm (GA) is employed to optimize the regularization and kernel parameters of the SVM, which effectively improves the classification accuracy of the proposed scheme. Simulations are provided to demonstrate that the performance of the proposed method is better than that with random user paring strategy, especially in the scenario with a large number of users. Copyright © 2020, The Authors. All rights reserved.

关键词： Genetic algorithms

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Spectral and energy efficiency of IRS-assisted MISO communication with hardware impairments

arXiv

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arXiv 2020年

作者： Zhou, Shaoqing Xu, Wei Wang, Kezhi Di Renzo, Marco Alouini, Mohamed-Slim National Mobile Communications Research Laboratory Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Department of Computer and Information Sciences Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes Gif-sur-Yvette France Division of Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this letter, we analyze the spectral and energy efficiency of an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) downlink system with hardware impairments. An extended error vector magnitude (EEVM) model is utilized to characterize the impact of radio-frequency (RF) impairments at the access point (AP) and phase noise is considered for the imperfect IRS. We show that the spectral efficiency is limited due to the hardware impairments even when the numbers of AP antennas and IRS elements grow infinitely large, which is in contrast with the conventional case with ideal hardware. Moreover, the performance degradation at high SNR is shown to be mainly affected by the AP hardware impairments rather than the phase noise of IRS. We further obtain the optimal transmit power in closed form for energy efficiency maximization. Simulation results are provided to verify these results. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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2022 Review of Data-Driven Plasma science

arXiv

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arXiv 2022年

作者： Anirudh, Rushil Archibald, Rick Asif, M. Salman Becker, Markus M. Benkadda, Sadruddin Bremer, Peer-Timo Budé, Rick H.S. Chang, C.S. Chen, Lei Churchill, R.M. Citrin, Jonathan Gaffney, Jim A. Gainaru, Ana Gekelman, Walter Gibbs, Tom Hamaguchi, Satoshi Hill, Christian Humbird, Kelli Jalas, Sören Kawaguchi, Satoru Kim, Gon-Ho Kirchen, Manuel Klasky, Scott Kline, John L. Krushelnick, Karl Kustowski, Bogdan Lapenta, Giovanni Li, Wenting Ma, Tammy Mason, Nigel J. Mesbah, Ali Michoski, Craig Munson, Todd Murakami, Izumi Najm, Habib N. Erik J. Olofsson, K. Park, Seolhye Peterson, J. Luc Probst, Michael Pugmire, David Sammuli, Brian Sawlani, Kapil Scheinker, Alexander Schissel, David P. Shalloo, Rob J. Shinagawa, Jun Seong, Jaegu Spears, Brian K. Tennyson, Jonathan Thiagarajan, Jayaraman Ticoş, Catalin M. Trieschmann, Jan van Dijk, Jan Van Essen, Brian Ventzek, Peter Wang, Haimin Wang, Jason T.L. Wang, Zhehui Wende, Kristian Xu, Xueqiao Yamada, Hiroshi Yokoyama, Tatsuya Zhang, Xinhua Lawrence Livermore National Laboratory LivermoreCA94550 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States University of California Riverside RiversideCA92521 United States Felix-Hausdorff-Str. 2 Greifswald17489 Germany Aix Marseille University CNRS PIIM UMR 7345 Marseille France Department of Applied Physics Eindhoven University of Technology Netherlands Princeton Plasma Physics Laboratory Princeton NJ08540 United States Institute of Ion Physics and Applied Physics University of Innsbruck Technikerstrasse 25 Innsbruck6020 Austria DIFFER - Dutch Institute for Fundamental Energy Research Eindhoven Netherlands Science and Technology of Nuclear Fusion Group Eindhoven University of Technology Eindhoven Netherlands Department of Physics and Astronomy University of California Los AngelesCA90095 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95051 United States Center for Atomic and Molecular Technologies Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka Japan Department of Nuclear Sciences and Applications International Atomic Energy Agency Wagramer Strasse 5 PO Box 100 Vienna1400 Austria Center for Free-Electron Laser Science Department of Physics Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Division of Information and Electronic Engineering Graduate School of Engineering Muroran Institute of Technology Hokkaido Muroran050-8585 Japan Seoul National University Seoul151-741 Korea Republic of Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Los Alamos National Laboratory Los AlamosNM87545 United States Center for Ultrafast Optical Science University of Michigan Ann ArborMI48109-2099 United States Department of Mathematics KULeuven University of Leuven Belgium Department of Physical Sciences The University of Kent CanterburyCT2 7NH United Kingdom Department of Chemical and Biomolecular Engineering University of California Berk

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma-processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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