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arXiv 2017年

作者： Tan, Qingyang Gao, Lin Lai, Yu-Kun Yang, Jie Xia, Shihong Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences School of Computer Science & Informatics Cardiff University

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale deformations, and may not always be able to identify important deformation components. In this paper we propose a novel mesh-based autoencoder architecture that is able to cope with meshes with irregular topology. We introduce sparse regularization in this framework, which along with convolutional operations, helps localize deformations. Our framework is capable of extracting localized deformation components from mesh data sets with large-scale deformations and is robust to noise. It also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. Copyright © 2017, The Authors. All rights reserved.

关键词： Mesh generation

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A survey of automatic facial micro-expression analysis: Databases, methods and challenges

arXiv

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arXiv 2018年

作者： Oh, Yee-Hui See, John Le Ngo, Anh Cat Phan, Raphael C.-W. Baskaran, Vishnu Monn Multimedia University Faculty of Engineering Cyberjaya Selangor63100 Malaysia Multimedia University Faculty of Computing and Informatics Cyberjaya Selangor63100 Malaysia University of Nottingham School of Psychology University Park NottinghamNG7 2RD United Kingdom Multimedia University Research Institute for Digital Security Cyberjaya Selangor63100 Malaysia Monash University Malaysia School of Information Technology Sunway Selangor47500 Malaysia

Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems. Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial microexpressions possible today, in contrast to decades ago when it was primarily the domain of psychiatrists where analysis was largely manual. Indeed, although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems. In this survey, we present a comprehensive review of state-of-the-art databases and methods for micro-expressions spotting and recognition. Individual stages involved in the automation of these tasks are also described and reviewed at length. In addition, we also deliberate on the challenges and future directions in this growing field of automatic facial micro-expression analysis. Copyright © 2018, The Authors. All rights reserved.

关键词： Surveys

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Variational autoencoders for deforming 3d mesh models

arXiv

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arXiv 2017年

作者： Tan, Qingyang Gao, Lin Lai, Yu-Kun Xia, Shihong Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences School of Computer Science and Informatics Cardiff University

3D geometric contents are becoming increasingly popular. In this paper, we study the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D meshes are flexible to represent 3D animation sequences as well as collections of objects of the same category, allowing diverse shapes with large-scale non-linear deformations. We propose a novel framework which we call mesh variational autoencoders (mesh VAE), to explore the probabilistic latent space of 3D surfaces. The framework is easy to train, and requires very few training examples. We also propose an extended model which allows flexibly adjusting the significance of different latent variables by altering the prior distribution. Extensive experiments demonstrate that our general framework is able to learn a reasonable representation for a collection of deformable shapes, and produce competitive results for a variety of applications, including shape generation, shape interpolation, shape space embedding and shape exploration, outperforming state-ofthe-art methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Overview of health behavior change interventions to promote physical-activity-related adherence in patients with heart disease

Overview of health behavior change interventions to promote ...

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Joint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107

作者： Livitckaia, Kristina Koutkias, V. Maglaveras, N. Kouidi, E. van Gils, M. Chouvarda, I. Lab of Computing Medical Informatics & Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Greece Institute of Applied Biosciences Centre for Research and Technology Hellas Thermi Thessaloniki Greece Laboratory of Sports Medicine School of Physical Education and Sports Sciences Aristotle University of Thessaloniki Greece VTT Technical Research Centre of Finland Ltd Tampere Finland

ISBN: (纸本)9789811051210

The ongoing increase in the incidence of cardiovascular disease is often associated with unhealthy lifestyle choices, while healthy lifestyle is one of the most important medical recommendations for patients with heart disease. Despite the importance of making healthy decisions daily, patient adherence to such changes is typically poor with the lowest level reported on physical activity regimen. To facilitate patient health behavior change towards a healthy lifestyle, use of health behavior interventions seems to be the most preferred tool in cardiac care routine. However, the question about use and development of successful interventions is remaining of relevance due the increasing rates of patient dropouts in rehabilitation programs and hospital readmissions. Researchers from different domains, including eHealth, are working on enabling intervention optimization. Thus, to analyze the design components that can be further used in patient adherence intervention development from the Health behavior informatics perspective, this paper provides an overview of interven-tions’ designs in the domain of physical-activity-related adherence in patients with heart disease. The analysis of the design approaches lead to the conclusion that the central elements for intervention design are the target patient population with its specific characteristics, and chosen type of physical-activity-related behavior. Additionally, we have found that study design and its quality should be considered when analyzing a specific intervention or intervention program effect. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Cardiology

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Comparison of color model for flower recognition

Comparison of color model for flower recognition

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International Conference on Information technology, Information Systems and Electrical Engineering (ICITISEE)

作者： P. Rosyani M. Taufik A. A. Waskita D. H. Apriyanti Department of Informatics Universitas Pamulang Pamulang Tangerang Selatan Banten Indonesia Graduate Program Eresha School of Information Technology Tangerang Selatan Banten Indonesia Computing lab. Kawasan Puspiptek Serpong Tangerang Selatan 15310 Indonesia Purwodadi Botanic Garden - LIPI Jl. Raya Surabaya - Malang Km. 65 Purwodadi Pasuruan - Jawa Timur 67163 Indonesia

ISBN: (纸本)9781538670835;9781538670828

Comparison to RGB, HSV, LAB and YCbCr color model in a flower recognition has been conducted for 12 species from two families. Flower images were obtained from the ImageCLEF 2017 dataset. After segmenting the flower from its background using the k-means clustering method, the statistical parameters are extracted from each color model. These parameters including the minimum and maximum pixel value, and also mean and standard deviation. Then, the classifying result of SVM shows that the HSV color model performs the worst amongst the other investigated color model, which is around 30% using different kernel. While the other color model has the accuracy around 70% using linear and polynomial kernel. This result in line with the surface plot of their statistical characteristics.

关键词： Image color analysis Kernel Image segmentation Support vector machines Solid modeling Testing Data models

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Toward a more scientific science

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Science 2018年第6408期361卷 1194-1197页

作者： Wible, Brad Azoulay, Pierre Graff-Zivin, Joshua Uzzi, Brian Wang, Dashun Williams, Heidi Evans, James A. Zhe Jin, Ginger Feng Lu, Susan Jones, Benjamin F. Börner, Katy Lakhani, Karim R. Boudreau, Kevin J. Guinan, Eva C. Massachusetts Institute of Technology (MIT) Cambridge MA United States National Bureau of Economic Research (NBER) Cambridge MA United States University of California San Diego San Diego CA United States Kellogg School of Management Northwestern University Evanston IL United States McCormick School of Engineering Northwestern University Evanston IL United States Northwestern Institute on Complex Systems (NICO) Northwestern University Evanston IL United States University of Chicago Chicago IL United States University of Maryland College Park MD United States Purdue University West Lafayette IN United States School of Informatics Computing and Engineering Indiana University Bloomington IN United States Alexander von Humboldt Fellow Technische Universität Dresden Dresden 01062 Germany Harvard Business School Boston MA United States Laboratory for Innovation Science Harvard University Cambridge MA United States Northeastern University Boston MA United States Dana Farber Cancer Institute Boston MA United States Harvard Medical School Boston MA United States

Climb atop shoulders and wait for funerals. That, suggested Newton and then Planck, is how science advances (more or less). We’ve come far since then, but many notions about how people and practices, policies, and resources influence the course of science are still more rooted in traditions and intuitions than in evidence. We can and must do better, lest we resign ourselves to “intuition-based policy” when making decisions and investments aimed at driving scientific progress. Science invited experts to highlight key aspects of the scientific enterprise that are steadily yielding to empirical investigation—and to explain how Newton and Planck got it right (and Einstein got it wrong). 2017 © The Authors, some rights reserved.

关键词： EMPIRICAL research

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Maintaining reading experience continuity across e-book revisions

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Research and Practice in technology Enhanced Learning 2018年第1期13卷 1页

作者： Yang, Christopher Flanagan, Brendan Akcapinar, Gokhan Ogata, Hiroaki Graduate School of Informatics Kyoto University 36-1 Yoshida-Honmachi Sakyo-ku Kyoto 606-8501 Japan Academic Center for Computing and Media Studies Kyoto University Yoshida-Nihonmatsu Sakyo-ku Kyoto 606-8501 Japan Department of Computer Education and Instructional Technology Hacettepe University Ankara 06800 Turkey

E-book reader supports users to create digital learning footprints in many forms like highlighting sentences or taking memos. Nowadays, it also allows an instructor to update their e-books in the e-book reader. However, e-book users often face problems when trying to find learning footprints they made in a new version e-book. Thus, users’ reading experience continuity across e-book revisions is hard to be maintained and seems to become a shortcoming within the e-book system. In this paper, in order to maintain users’ reading experience continuity, we deal with the transfer of learning footprints such as a marker, memo, and bookmark across e-book revisions on an e-book reader in a coursework scenario. We first give introduction and related works to demonstrate how researchers dedicated on the problem mentioned in this paper and page similarity comparison. Then, we compare three page similarity comparison methods using similarity computing models to compute page pairwise similarity in image level, text level, and image & text level. In the analysis, for each level, we analyze the performance of transferring learning footprint across e-book revisions and also the optimal threshold for similar page determination. After that, we give the analysis results to show the performances of three methods in image level, text level, and image & text level, and then, the error analysis is presented to specify the error types that occur in the results. We then propose page image & text similarity comparison as the optimal method to automatically transfer learning footprints across e-book revisions based on the analysis results and error analysis among three compared methods. Finally, the discussion and conclusions are shown in the end of this paper. © 2018, The Author(s).

关键词： E-book reader Image similarity processing Learning footprint Page similarity comparison Text similarity processing

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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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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