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The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions

The Journal of Data Education 2025年第1期65卷

作者： Keng-Boon Ooi Garry Wei-Han Tan Mostafa Al-Emran Mohammed A. Al-Sharafi Alexandru Capatina Amrita Chakraborty Yogesh K. Dwivedi Tzu-Ling Huang Arpan Kumar Kar Voon-Hsien Lee Xiu-Ming Loh Adrian Micu Patrick Mikalef Emmanuel Mogaji Neeraj Pandey Ramakrishnan Raman Nripendra P. Rana Prianka Sarker Anshuman Sharma Ching-I Teng Samuel Fosso Wamba Lai-Wan Wong a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiab FORE School of Management New Delhi Indiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysia a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysiad College of Business Administration Adamson University Manila Philippines e Faculty of Engineering & IT The British University in Dubai Dubai UAEf Department of Computer Techniques Engineering Dijlah University College Baghdad Iraq g Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Selangor Malaysia h Department of Business Administration “Dunarea de Jos” University of Galati Galati Romania i Tech Talk New Delhi India j Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Swansea UKk Department of Management Symbiosis International (Deemed University) Pune India l Department of Information Management National Central University Taoyuan Taiwan m Department of Management Studies Indian Institute of Technology Delhi New Delhi India n Faculty of Business and Finance Universiti Tunku Abdul Rahman Kampar Malaysia o Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norwayp Department of Technology Management SINTEF Digital Trondheim Norway q Keele Business School Keele University Keele UK r Indian Institute of Management Mumbai Mumbai India k Department of Management Symbiosis International (Deemed University) Pune India s College of Business and Economics Qatar University Doha Qatar t Business School Manchester Metropolitan University Manchester UK u Department of Marketing College of Business Administration Ajman university Ajman UAE v Graduate Institute of Management Chang Gung University Taoyuan Taiw

ABSTRACTIn a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

关键词： Generative artificial intelligence machine learning large language model ChatGPT Bard

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Quantum Games and Interactive Tools for Quantum Technologies Outreach and Education

arXiv

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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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Multiwavelength study of GRB 201216C using the sub-TeV emission detected by MAGIC 38

Multiwavelength study of GRB 201216C using the sub-TeV emiss...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Fukami, Satoshi Berti, Alessio Loporchio, Serena Nava, Lara Suda, Yusuke Noda, Koji Asano, Katsuaki Bošnjak, Željka Longo, Francesco Jordana-Mitjans, Nuria Melandri, Andrea Mundell, Carole Shrestha, Manisha Steele, Iain Abe, H. Abe, S. Abhir, J. Acciari, V.A. Agudo, I. Aniello, T. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Arcaro, C. Artero, M. Asano, K. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernardos, M. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bonnoli, G. Bošnjak, Ž. Burelli, I. Busetto, G. Campoy-Ordaz, A. Carosi, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Ceribella, G. Chai, Y. Chilingarian, A. Cifuentes, A. Cikota, S. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D’Amico, G. D’Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Del Popolo, A. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Emery, G. Escudero, J. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giesbrecht Paiva, J.G. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Grau, R. Green, D. Green, J.G. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera, J. Hrupec, D. Hütten, M. Imazawa, R. Inada, T. Iotov, R. Ishio, K. Jiménez Martínez, I. Jormanainen, J. Kerszberg, D. Kluge, G.W. Kobayashi, Y. Kouch, P.M. Kubo, H. Kushida, J. Láinez Lezáun, M. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Lyard, E. Machado de Oliveira Fraga, B. Majumdar, P. Makariev, M. Maneva, G. Mang, N. Manganaro, M. Mangano, S. Mannheim, K. Mariotti, M. Martínez, M. Martínez-Chicharro, M. Mas-Aguilar, A. Mazin, D. Menchiari, S. Mender, S. Institute for Particle Physics and Astrophysics ETH Zürich Otto-Stern-Weg 5 Zürich8093 Switzerland Institute for Cosmic Ray Research The University of Tokyo Kashiwanoha 5-1-5 Kashiwa Japan Max Planck Institut for Physics Föhringer Ring 6 Munich Germany INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica Università e del Politecnico di Bari BariI-70125 Italy RomeI-00136 Italy Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia INFN Sezione di Trieste via Valerio 2 Trieste Italy Department of Physics University of Bath Claverton Down BathBA2 7AY United Kingdom INAF-Osservatorio Astronomico di Brera Via E. Bianchi 46 LC MerateI-23807 Italy European Space Agency European Space Astronomy Centre Villanueva de la Canñada Madrid28692 Spain Astrophysics Research Institute Liverpool John Moores University Liverpool Science Park IC2 146 Brownlow HillL3 5RF United Kingdom Steward Observatory University of Arizona 933 North Cherry Avenue TucsonAZ85721-0065 United States The University of Tokyo Chiba Kashiwa277-8582 Japan ETH Zürich ZürichCH-8093 Switzerland Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38200 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Università di Udine INFN Trieste UdineI-33100 Italy Max-Planck-Institut für Physik MünchenD-80805 Germany Università di Padova INFN PadovaI-35131 Italy Bellaterra BarcelonaE-08193 Spain Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain URCA RJ Rio de Janeiro22290-180 Brazil University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Pola

Recently, several gamma-ray bursts (GRBs) have been detected in the very-high-energy (VHE) gamma-ray energy range by ground-based gamma-ray experiments such as MAGIC, H.E.S.S., and LHASSO. For some GRBs, the VHE emission is consistent with synchrotron self-Compton (SSC) emission from high-energy electrons accelerated in the forward shock of the relativistic jet. However, more statistics are needed to further constrain the emission models. GRB 201216C is a long bright GRB detected in a broad energy range from radio to VHE. The redshift is estimated to be 1.1, making the GRB the most distant source detected in the VHE energy range. MAGIC started the observation 56 seconds after the GRB was triggered by the Swift-BAT telescope. We performed a detailed analysis and detected the signal with about 6 sigma in the first 20 minutes. MAGIC continued the observation for 2.2 hours on the same night and 4 hours on the next night. No signal was detected later than 40 minutes after the GRB trigger. We have performed modelling of the multi-wavelength emission using the MAGIC data. We analysed simultaneous optical data from Liverpool Telescope with MAGIC and included the results in the modelling. The sub-TeV emission is consistent with the single-zone SSC model in the forward shock. In this presentation, we show the final results of the MAGIC data analysis of GRB 201216C and discuss the emission mechanism of the multi-wavelength data. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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Lithium-Ion Batteries: Operando Magnetometry Probing the Charge Storage Mechanism of CoO Lithium-Ion Batteries (Adv. Mater. 12/2021)

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Advanced Materials 2021年第12期33卷

作者： Hongsen Li Zhengqiang Hu Qingtao Xia Hao Zhang Zhaohui Li Huaizhi Wang Xiangkun Li Fengkai Zuo Fengling Zhang Xiaoxiong Wang Wanneng Ye Qinghao Li Yunze Long Qiang Li Shishen Yan Xiaosong Liu Xiaogang Zhang Guihua Yu Guo-Xing Miao College of Physics Center for Marine Observation and Communications Qingdao University Qingdao 266071 China School of Physics State Key Laboratory of Crystal Materials Shandong University Jinan 250100 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences Shanghai 200050 China College of Material Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Materials Science and Engineering Program and Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA Department of Electrical and Computer Engineering & Institute for Quantum Computing University of Waterloo Waterloo ON N2L 3G1 Canada

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Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv

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arXiv 2022年

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michálek, Jan Matula, Petr Keřkovský, Miloš Kopřivová, Tereza Dostál, Marek Vybíhal, Václav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcão, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C.A. Sprenger, Flávia Menotti, David Lucio, Diego R. LaMontagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Derrick, Murcia Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, Tos University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany Intel Corporation Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Department of Neuropathology Heidelberg University Hospital Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Department of Radiology & Biomedical Imaging University of California San Francisco San FranciscoCA United States Symbiosis Center for Medical Image Analysis Symbiosis International University Maharashtra Pune India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Karnataka Bangalore India Department of Radiology School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Neuroradiology Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Leeds Teaching Hospitals Trust Department of Radiology Leeds United Kingdom Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Catalan Institute of Oncology Badalona Spain Consorci MAR Parc de Salut de Barcelona Catalonia Spain Josep Trueta University Hospital Girona Spain

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

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Very-high-energy gamma-ray emission from GRB 201216C detected by MAGIC 37

Very-high-energy gamma-ray emission from GRB 201216C detecte...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Fukami, Satoshi Berti, Alessio Loporchio, Serena Suda, Yusuke Nava, Lara Noda, Koji Bošnjak, Željka Asano, Katsuaki Longo, Francesco Acciari, V.A. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Artero, M. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Becerra González, J. Bednarek, W. Bellizzi, L. Bernardini, E. Bernardos, M. Besenrieder, J. Bhattacharyya, W. Bigongiari, C. Biland, A. Blanch, O. Bökenkamp, H. Bonnoli, G. Busetto, G. Carosi, R. Ceribella, G. Cerruti, M. Chai, Y. Chilingarian, A. Cikota, S. Colak, S.M. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D'Amico, G. D'Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Do Souto Espiñeira, E. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Fallah Ramazani, V. Fattorini, A. Fonseca, M.V. Font, L. Fruck, C. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Green, J.G. Green, D. Hadasch, D. Hahn, A. Heckmann, L. Herrera, J. Hoang, J. Hrupec, D. Hütten, M. Inada, T. Ishio, K. Iwamura, Y. Jiménez Martínez, I. Jormanainen, J. Jouvin, L. Karjalainen, M. Kerszberg, D. Kobayashi, Y. Kubo, H. Kushida, J. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. López-Coto, R. López-Moya, M. López-Oramas, A. Machado de Oliveira Fraga, B. Maggio, C. Majumdar, P. Makariev, M. Mallamaci, M. Maneva, G. Manganaro, M. Mannheim, K. Maraschi, L. Mariotti, M. Martínez, M. Mazin, D. Menchiari, S. Mender, S. Mićanović, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. Molina, E. Moralejo, A. Morcuende, D. Moreno, V. Moretti, E. Nakamori, T. Neustroev, V. Nigro, C. Nilsson, K. Nishijima, K. Nozaki, S. Ohtani, Y. Oka, T. Otero-Santos, J. Paiano, S. Palatiello, M. Paneque, D. Paoletti, R. Paredes, J.M. Pavletić, L. Peñil, P. Persic, M. Pihet, M. Prada Moroni, P.G. Prandini, E. Institute for Cosmic Ray Research The University of Tokyo Kashiwanoha 5-1-5 Kashiwa Japan Max Planck Institut for Physics Föhringer Ring 6 Munich Germany INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica Università e del Politecnico di Bari BariI-70125 Italy Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Street number Merate Italy Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia University of Trieste Department of Physics via Valerio 2 Trieste Italy INFN Sezione di Trieste via Valerio 2 Trieste Italy Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38200 Spain Università di Udine INFN Trieste UdineI-33100 Italy RomeI-00136 Italy ETH Zürich ZürichCH-8093 Switzerland Bellaterra BarcelonaE-08193 Spain The University of Tokyo Chiba Kashiwa277-8582 Japan Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain URCA RJ Rio de Janeiro22290-180 Brazil Università di Padova INFN PadovaI-35131 Italy University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Poland Università di Siena INFN Pisa SienaI-53100 Italy ZeuthenD-15738 Germany Max-Planck-Institut für Physik MünchenD-80805 Germany Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Università di Pisa INFN Pisa PisaI-56126 Italy Universitat de Barcelona ICCUB IEEC-UB BarcelonaE-08028 Spain Armenian MAGIC Group A. Alikhanyan National Science Laboratory Yerevan0036 Armenia Centro de Investigaciones Energéticas Medioambientales y Tecnológicas MadridE-28040 Spain INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino TorinoI-10125 Italy Croatian MAGIC Group University o

Gamma-ray bursts (GRBs) are the most energetic phenomena in the Universe. Many aspects of GRB physics are still under debate, such as the origin of their gamma-ray emission above the GeV energy range. In 2019, MAGIC detected TeV gamma rays from the long GRB 190114C, whose emission can be well explained by synchrotron-self Compton emission by relativistic electrons. However, it is still unclear whether such a process is common in GRBs, given the reduced number of GRBs detected until now at the very high energies (VHE). GRB 201216C is a long GRB and is the second one detected by MAGIC in this energy range. After receiving the alert provided by Swift-BAT, MAGIC automatically slewed to the GRB position, starting observations 56 seconds after the GRB onset. In the offline analyses of the collected data, we confirmed the detection of gamma-ray emission with a significance above 5 sigma. Following measurements from optical facilities, the redshift of this GRB was estimated to be z = 1.1. This makes GRB 201216C the most distant object ever detected by ground-based gamma-ray telescopes. In this contribution we will show the analysis results of the MAGIC data, also in comparison with past detected GRBs in the same energy range. Finally, accounting for available multi-wavelength observations, we will comment on the possible origin of the VHE emission detected by MAGIC. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Germanium compounds

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Search for Gamma-Ray Spectral Lines from Dark Matter Annihilation up to 100 TeV toward the Galactic Center with MAGIC

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Physical Review Letters 2023年第6期130卷 061002-061002页

作者： H. Abe S. Abe V. A. Acciari T. Aniello S. Ansoldi L. A. Antonelli A. Arbet Engels C. Arcaro M. Artero K. Asano D. Baack A. Babić A. Baquero U. Barres de Almeida J. A. Barrio I. Batković J. Baxter J. Becerra González W. Bednarek E. Bernardini M. Bernardos A. Berti J. Besenrieder W. Bhattacharyya C. Bigongiari A. Biland O. Blanch G. Bonnoli Ž. Bošnjak I. Burelli G. Busetto R. Carosi M. Carretero-Castrillo G. Ceribella Y. Chai A. Chilingarian S. Cikota E. Colombo J. L. Contreras J. Cortina S. Covino G. D’Amico V. D’Elia P. Da Vela F. Dazzi A. De Angelis B. De Lotto A. Del Popolo M. Delfino J. Delgado C. Delgado Mendez D. Depaoli F. Di Pierro L. Di Venere E. Do Souto Espiñeira D. Dominis Prester A. Donini D. Dorner M. Doro D. Elsaesser G. Emery V. Fallah Ramazani L. Fariña A. Fattorini L. Font C. Fruck S. Fukami Y. Fukazawa R. J. García López M. Garczarczyk S. Gasparyan M. Gaug J. G. Giesbrecht Paiva N. Giglietto F. Giordano P. Gliwny N. Godinović J. G. Green D. Green D. Hadasch A. Hahn T. Hassan L. Heckmann J. Herrera D. Hrupec M. Hütten R. Imazawa T. Inada R. Iotov K. Ishio I. Jiménez Martínez J. Jormanainen D. Kerszberg Y. Kobayashi H. Kubo J. Kushida A. Lamastra D. Lelas F. Leone E. Lindfors L. Linhoff S. Lombardi F. Longo R. López-Coto M. López-Moya A. López-Oramas S. Loporchio A. Lorini E. Lyard B. Machado de Oliveira Fraga P. Majumdar M. Makariev G. Maneva N. Mang M. Manganaro S. Mangano K. Mannheim M. Mariotti M. Martínez A. Mas Aguilar D. Mazin S. Menchiari S. Mender S. Mićanović D. Miceli T. Miener J. M. Miranda R. Mirzoyan E. Molina H. A. Mondal A. Moralejo D. Morcuende V. Moreno T. Nakamori C. Nanci L. Nava V. Neustroev M. Nievas Rosillo C. Nigro K. Nilsson K. Nishijima T. Njoh Ekoume K. Noda S. Nozaki Y. Ohtani T. Oka J. Otero-Santos S. Paiano M. Palatiello D. Paneque R. Paoletti J. M. Paredes L. Pavletić M. Persic M. Pihet F. Podobnik P. G. Prada Moroni E. Prandini G. Principe C. Priyadarshi I. Puljak W. Rhode M. Ribó J. Rico C. Righi A. Rugliancich N. Sahakyan T. Saito S. Sakurai K. Satalecka F. G. Sat Japanese MAGIC Group: Institute for Cosmic Ray Research (ICRR) The University of Tokyo Kashiwa 277-8582 Chiba Japan Instituto de Astrofísica de Canarias and Departamento de Astrofísica Universidad de La Laguna E-38200 La Laguna Tenerife Spain National Institute for Astrophysics (INAF) I-00136 Rome Italy Università di Udine and INFN Trieste I-33100 Udine Italy Max-Planck-Institut für Physik D-80805 München Germany Università di Padova and INFN I-35131 Padova Italy Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology (BIST) E-08193 Bellaterra (Barcelona) Spain Technische Universität Dortmund D-44221 Dortmund Germany Croatian MAGIC Group: University of Zagreb Faculty of Electrical Engineering and Computing (FER) 10000 Zagreb Croatia IPARCOS Institute and EMFTEL Department Universidad Complutense de Madrid E-28040 Madrid Spain Centro Brasileiro de Pesquisas Físicas (CBPF) 22290-180 URCA Rio de Janeiro (RJ) Brazil University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics 90-236 Lodz Poland Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n 18008 Granada Spain Deutsches Elektronen-Synchrotron (DESY) D-15738 Zeuthen Germany ETH Zürich CH-8093 Zürich Switzerland Università di Pisa and INFN Pisa I-56126 Pisa Italy Universitat de Barcelona ICCUB IEEC-UB E-08028 Barcelona Spain Armenian MAGIC Group: A. Alikhanyan National Science Laboratory 0036 Yerevan Armenia Centro de Investigaciones Energéticas Medioambientales y Tecnológicas E-28040 Madrid Spain Department for Physics and Technology University of Bergen Norway INFN MAGIC Group: INFN Sezione di Catania and Dipartimento di Fisica e Astronomia University of Catania I-95123 Catania Italy INFN MAGIC Group: INFN Sezione di Torino and Università degli Studi di Torino I-10125 Torino Italy INFN MAGIC Group: INFN Sezione di Bari and Dipartimento Interateneo di Fisica dell’Università e del Politecnico di Bari I-70125 Bari Italy

Linelike features in TeV γ rays constitute a “smoking gun” for TeV-scale particle dark matter and new physics. Probing the Galactic Center region with ground-based Cherenkov telescopes enables the search for TeV spectral features in immediate association with a dense dark matter reservoir at a sensitivity out of reach for satellite γ-ray detectors, and direct detection and collider experiments. We report on 223 hours of observations of the Galactic Center region with the MAGIC stereoscopic telescope system reaching γ-ray energies up to 100 TeV. We improved the sensitivity to spectral lines at high energies using large-zenith-angle observations and a novel background modeling method within a maximum-likelihood analysis in the energy domain. No linelike spectral feature is found in our analysis. Therefore, we constrain the cross section for dark matter annihilation into two photons to ⟨σv⟩≲5×10−28 cm3 s−1 at 1 TeV and ⟨σv⟩≲1×10−25 cm3 s−1 at 100 TeV, achieving the best limits to date for a dark matter mass above 20 TeV and a cuspy dark matter profile at the Galactic Center. Finally, we use the derived limits for both cuspy and cored dark matter profiles to constrain supersymmetric wino models.

关键词： Dark matter Gamma ray astronomy Particle dark matter Milky Way Cosmic ray & astroparticle detectors Cosmic rays & astroparticles Electromagnetic radiation astronomy Telescopes

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