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Computer Science Society (SCCC) International Conference Chilean FLAGGED

作者： Gustavo Rostirolla Rodrigo da Rosa Righi Cristiano André da Costa Jorge Luis Victória Barbosa Applied Computing Graduate Program - Unisinos Av. Unisinos 950 - São Leopoldo RS Brazil

ISBN: (纸本)9781509033409

As a result of rural and suburban migration to the cities, urban life has become a significant challenge for citizens and mayors, imposing a huge concern in the sustainable use of resources like energy, water, transportation and housing. Smart cities are the biggest bet to address these challenges efficiently through a real-time monitoring approach which aims to assist in intelligent planning and sustainable urban development. However, to accomplish this monitoring efficiently and to enable the sustainable use of resources, it is necessary an integration between citizens, city devices and the platform where the data is stored and processed. In this context, we are proposing the 3LES model (3 Levels of Energy Saving for Smart Cities), a model that combines data from citizens, city devices and nodes from the processing plataform to offer a multilevel energy saving proposal. 3LES must act transparently without affecting the quality of the services already offered in the city. This paper presents the components of management and monitoring of power consumption at various levels, as well as a model that monitors the energy consumption of elastic cloud applications. The results are promising, with a model that allows the estimation of the energy consumption of elastic applications based on CPU and memory traces with an average and median precision of 97.15% and 97.72%. In addition, we obtained a reduction of more than 90% in the energy spent in public lighting in the city of Rome when analyzing the citizens' location.

关键词： Portable document format IEEE Xplore

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Overview of KAGRA : Data transfer and management

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Progress of Theoretical and Experimental Physics 2023年第10期2023卷

作者： Akutsu, T Ando, M Arai, K Arai, Y Araki, S Araya, A Aritomi, N Asada, H Aso, Y Bae, S Bae, Y Baiotti, L Bajpai, R Barton, M A Cannon, K Cao, Z Capocasa, E Chan, M Chen, C Chen, K Chen, Y Chiang, C-Y Chu, H Chu, Y-K Eguchi, S Enomoto, Y Flaminio, R Fujii, Y Fujikawa, Y Fukunaga, M Fukushima, M Gao, D Ge, G Ha, S Hagiwara, A Haino, S Han, W-B Hasegawa, K Hattori, K Hayakawa, H Hayama, K Himemoto, Y Hiranuma, Y Hirata, N Hirose, E Hong, Z Hsieh, B Huang, G-Z Huang, H-Y Huang, P Huang, Y-C Huang, Y Hui, D C Y Ide, S Ikenoue, B Imam, S Inayoshi, K Inoue, Y Ioka, K Ito, K Itoh, Y Izumi, K Jeon, C Jin, H-B Jung, K Jung, P Kaihotsu, K Kajita, T Kakizaki, M Kamiizumi, M Kanda, N Kang, G Kawaguchi, K Kawai, N Kawasaki, T Kim, C Kim, J Kim, J C Kim, W S Kim, Y-M Kimura, N Kita, N Kitazawa, H Kobayashi, Y Kojima, Y Kokeyama, K Komori, K Kong, A K H Kotake, K Kozakai, C Kozu, R Kumar, R Kume, J Kuo, C Kuo, H-S Kuromiya, Y Kuroyanagi, S Kusayanagi, K Kwak, K Lee, H K Lee, H W Lee, R Leonardi, M Li, K L Lin, L C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H L Liu, G C Luo, L-W Majorana, E Marchio, M Michimura, Y Mio, N Miyakawa, O Miyamoto, A Miyazaki, Y Miyo, K Miyoki, S Mori, Y Morisaki, S Moriwaki, Y Nagano, K Nagano, S Nakamura, K Nakano, H Nakano, M Nakashima, R Nakayama, Y Narikawa, T Naticchioni, L Negishi, R Quynh, L Nguyen Ni, W-T Nishizawa, A Nozaki, S Obuchi, Y Ogaki, W Oh, J J Oh, K Oh, S H Ohashi, M Ohashi, T Ohishi, N Ohkawa, M Ohta, H Okutani, Y Okutomi, K Oohara, K Ooi, C Oshino, S Otabe, S Pan, K Pang, H Parisi, A Park, J Arellano, F E Peña Pinto, I Sago, N Saito, S Saito, Y Sakai, K Sakai, Y Sakuno, Y Sasaki, Y Sato, S Sato, T Sawada, T Sekiguchi, T Sekiguchi, Y Shao, L Shibagaki, S Shimizu, R Shimoda, T Shimode, K Shinkai, H Shishido, T Shoda, A Somiya, K Son, E J Sotani, H Sugimoto, R Suresh, J Suzuki, T Tagoshi, H Takahashi, H Takahashi, R Takamori, A Takano, S Takeda, H Takeda, M Tanaka, H Tanaka, K Tanaka, T Tanioka, S Martin, E N Tapia San Telada, S Tomaru, T Tomigami, Y Tomura, T Travasso, F Trozzo, L Tsang, T Ts Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research (ICRR) KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Hirosaki University 3 Bunkyo-cho Hirosaki City Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information (KISTI) 245 Daehak-ro Yuseong-gu Daejeon 34141 Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University No. 19 Xinjiekou Street Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Rd. Danshui Dist. New Taipei City 25137 Taiwan Department of Physics and Institute of Astronomy Nation

KAGRA is a newly built gravitational wave observatory, a laser interferometer with a 3 km arm length, located in Kamioka, Gifu prefecture, Japan. In this article, we describe the KAGRA data management system, i.e., recording of data, transfer from the KAGRA experiment site to computing resources, as well as data distribution to tier sites, including international sites in Taiwan and Korea. The amount of KAGRA data exceeded 1.0 PiB and increased by about 1.5 TB per day during operation in 2020. Our system has succeeded in data management, and has achieved performance that can withstand observations after 2023, that is, a transfer rate of 20 MB s-1or more and file storage of sufficient capacity for petabyte class. We also discuss the sharing of data between the global gravitational-wave detector network with other experiments, namely LIGO and Virgo. The latency, which consists of calculation of calibrated strain data and transfer time within the global network, is very important from the view of multi-messenger astronomy using gravitational waves. Real-time calbrated data delivered from the KAGRA detector site and other detectors to our computing system arrive with about 4–15 seconds of latency. These latencies are sufficiently short compared to the time taken for gravitational wave event search computations. We also established a high-latency exchange of offline calibrated data that was aggregated with a better accuracy compared with real-time data.

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Population of Merging Compact Binaries Inferred Using Gravitational Waves through GWTC-3

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Physical Review X 2023年第1期13卷 011048-011048页

作者： R. Abbott T. D. Abbott F. Acernese K. Ackley C. Adams N. Adhikari R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain P. Ajith T. Akutsu P. F. de Alarcón S. Akcay S. Albanesi A. Allocca P. A. Altin A. Amato C. Anand S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando T. Andrade N. Andres T. Andrić S. V. Angelova S. Ansoldi J. M. Antelis S. Antier F. Antonini S. Appert Koji Arai Koya Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud M. Arogeti S. M. Aronson K. G. Arun H. Asada Y. Asali G. Ashton Y. Aso M. Assiduo S. M. Aston P. Astone F. Aubin C. Austin S. Babak F. Badaracco M. K. M. Bader C. Badger S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer A. Balsamo G. Baltus S. Banagiri D. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi C. P. L. Berry D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge R. Bhandare U. Bhardwaj D. Bhattacharjee S. Bhaumik I. A. Bilenko G. Billingsley S. Bini R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini L. D. Bonavena F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. Brandt J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic C. Buy R. L. Byer L. Cadonati LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia LIGO Livingston Observatory Livingston Louisiana 70754 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Universitat de les Illes Balears IAC3–IEEC E-07122 Palma de Mallorca Spain University College Dublin Dublin 4 Ireland INFN Sezione di Torino I-10125 Torino Italy Università di Napoli “Federico II” Complesso Universitario di Monte S. Angelo I-80126 Napoli

We report on the population properties of compact binary mergers inferred from gravitational-wave observations of these systems during the first three LIGO-Virgo observing runs. The Gravitational-Wave Transient Catalog 3 (GWTC-3) contains signals consistent with three classes of binary mergers: binary black hole, binary neutron star, and neutron star–black hole mergers. We infer the binary neutron star merger rate to be between 10 and 1700 Gpc−3 yr−1 and the neutron star–black hole merger rate to be between 7.8 and 140 Gpc−3 yr−1, assuming a constant rate density in the comoving frame and taking the union of 90% credible intervals for methods used in this work. We infer the binary black hole merger rate, allowing for evolution with redshift, to be between 17.9 and 44 Gpc−3 yr−1 at a fiducial redshift (z=0.2). The rate of binary black hole mergers is observed to increase with redshift at a rate proportional to (1+z)κ with κ=2.9−1.8+1.7 for z≲1. Using both binary neutron star and neutron star–black hole binaries, we obtain a broad, relatively flat neutron star mass distribution extending from 1.2−0.2+0.1 to 2.0−0.3+0.3M⊙. We confidently determine that the merger rate as a function of mass sharply declines after the expected maximum neutron star mass, but cannot yet confirm or rule out the existence of a lower mass gap between neutron stars and black holes. We also find the binary black hole mass distribution has localized over- and underdensities relative to a power-law distribution, with peaks emerging at chirp masses of 8.3−0.5+0.3 and 27.9−1.8+1.9M⊙. While we continue to find that the mass distribution of a binary’s more massive component strongly decreases as a function of primary mass, we observe no evidence of a strongly suppressed merger rate above approximately 60M⊙, which would indicate the presence of a upper mass gap. Observed black hole spins are small, with half of spin magnitudes below χi≈0.25. While the majority of spins are preferentially aligned wi

关键词： Gravitational wave sources Gravitational waves Gravitational wave detection

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Management of intellectual capital in a system of management accounting information 6

Management of intellectual capital in a system of management...

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6th International Conference on Computers Communications and Control, ICCCC 2016

作者： Piontkewicz, Regiane Freitas, Maria Do Carmo D. Kemczinski, Avanilde Martin, Claudia Duran San Department of Industrial Engineering Federal University of Paraná Curitiba Brazil Department of Science and Information Management Faculty of Information Management Federal University of Paraná Curitiba Brazil Graduate Program in Applied Computing Computer Science Department Santa Catarina State University Joinville Brazil Department of Industrial Engineering Faculty of Engineering University of Santiago de Chile Santiago Chile

ISBN: (纸本)9781509017355

The recognition of Intellectual Capital as a source of competitive advantage and differentiation element for organizations requires an application of new management strategies that include this feature. Considering the importance of the Accounting Information System (AIS) in organizations, the research has the main objective to investigate the contributions of accounting as Management Information System in management of Intellectual Capital in an organization. Therefore, it was adopted a single case study as research strategy, applied in a Brazilian industry of consumption goods, on the market for over 85 years. The results obtained in the documentary analysis, questionnaires, interviews and literature show that the Accounting Management Information System (AMIS) contributes to the management of Intellectual Capital. In the investigated organization, AMIS is widely used and shows similarities with the specifications found in the literature, possessing the ability of adaptation to receive indicators or variables that enable the management of Intellectual Capital. It was found that the incorporation of the variables of Intellectual Capital in AMIS could be accomplished by adopting the method proposed by López-Ruiz and Nevado-Pena, that uses both traditional financial indicators and indicators of Intellectual Capital, not conflicting with the traditional AMIS, but complementing it. Inserting variables of Intellectual Capital in AMIS also allows uniformity of understanding on the subject by all company managers. © 2016 IEEE.

关键词： Competition

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Proton acceleration in thermonuclear nova explosions revealed by gamma rays (Apr, 10.1038/s41550-022-01640-z, 2022)

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NATURE ASTRONOMY 2022年第6期6卷 760-760页

作者： Acciari, V. A. Ansoldi, S. Antonelli, L. A. Engels, A. Arbet Artero, M. Asano, K. Baack, D. Babic, A. Baquero, A. de Almeida, U. Barres Barrio, J. A. Batkovic, I. Gonzalez, J. Becerra Bednarek, W. Bellizzi, L. Bernardini, E. Bernardos, M. Berti, A. Besenrieder, J. Bhattacharyya, W. Bigongiari, C. Biland, A. Blanch, O. Bokenkamp, H. Bonnoli, G. Bosnjak, Z. 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. Del Popolo, A. Delfino, M. Delgado, J. Mendez, C. Delgado Depaoli, D. Di Pierro, F. Di Venere, L. Espineira, E. Do Souto Prester, D. Dominis Donini, A. Dorner, D. Doro, M. Elsaesser, D. Ramazani, V. Fallah Alonso, L. Farina Fattorini, A. Fonseca, M. V. Font, L. Fruck, C. Fukami, S. Fukazawa, Y. Lopez, R. J. Garcia Garczarczyk, M. Gasparyan, S. Gaug, M. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, J. G. Green, D. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera, J. Hoang, J. Hrupec, D. Hutten, M. Inada, T. Ishio, K. Iwamura, Y. Martinez, I. Jimenez Jormanainen, J. Jouvin, L. Kerszberg, D. Kobayashi, Y. Kubo, H. Kushida, J. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. Lopez-Coto, R. Lopez-Moya, M. Lopez-Oramas, A. Loporchio, S. de Oliveira Fraga, B. Machado Maggio, C. Majumdar, P. Makariev, M. Mallamaci, M. Maneva, G. Manganaro, M. Mannheim, K. Maraschi, L. Mariotti, M. Martinez, M. Aguilar, A. Mas Mazin, D. Menchiari, S. Mender, S. Micanovic, S. Miceli, D. Miener, T. Miranda, J. M. Mirzoyan, R. Molina, E. Moralejo, A. Morcuende, D. Moreno, V. Moretti, E. Nakamori, T. Nava, L. Neustroev, V. Rosillo, M. Nievas Nigro, C. Nilsson, K. Nishijima, K. Noda, K. Nozaki, S. Ohtani, Y. Oka, T. Otero-Santos, J. Paiano, S. Palatiello, M. Paneque, D. Paoletti, R. Paredes, J. M. Pavletic, L. Penil, P. Persic, M. Pihet, M. Moroni, P. G. Prada Prandini, E. Priyadarshi, C. Puljak, I. Rhode, W. Ribo, M. Rico, Universidad de La Laguna Instituto de Astrofísica de Canarias and Departamento de Astrofísica La Laguna Spain (GRID:grid.10041.34) (ISNI:***) Università di Udine and INFN Trieste Udine Italy (GRID:grid.5390.f) (ISNI:0000 0001 2113 062X) International Center for Relativistic Astrophysics (ICRA) Rome Italy (GRID:grid.5390.f) National Institute for Astrophysics (INAF) Rome Italy (GRID:grid.4293.c) (ISNI:0000 0004 1792 8585) Max-Planck-Institut für Physik Munich Germany (GRID:grid.435824.c) (ISNI:0000 0001 2375 0603) The Barcelona Institute of Science and Technology (BIST) Institut de Física d’Altes Energies (IFAE) Barcelona Spain (GRID:grid.473715.3) (ISNI:0000 0004 6475 7299) Japanese MAGIC Group: Institute for Cosmic Ray Research (ICRR) The University of Tokyo Kashiwa Japan (GRID:grid.26999.3d) (ISNI:0000 0001 2151 536X) Technische Universität Dortmund Dortmund Germany (GRID:grid.5675.1) (ISNI:0000 0001 0416 9637) Croatian MAGIC Group: University of Zagreb Faculty of Electrical Engineering and Computing (FER) Zagreb Croatia (GRID:grid.4808.4) (ISNI:0000 0001 0657 4636) Universidad Complutense de Madrid IPARCOS Institute and EMFTEL Department Madrid Spain (GRID:grid.4795.f) (ISNI:0000 0001 2157 7667) Centro Brasileiro de Pesquisas Físicas (CBPF) Rio de Janeiro Brazil (GRID:grid.418228.5) (ISNI:0000 0004 0643 8134) Università di Padova and INFN Padua Italy (GRID:grid.5608.b) (ISNI:0000 0004 1757 3470) University of Lodz Department of Astrophysics Faculty of Physics and Applied Informatics Lodz Poland (GRID:grid.10789.37) (ISNI:0000 0000 9730 2769) Università di Siena and INFN Pisa Siena Italy (GRID:grid.9024.f) (ISNI:0000 0004 1757 4641) Deutsches Elektronen-Synchrotron (DESY) Zeuthen Germany (GRID:grid.7683.a) (ISNI:0000 0004 0492 0453) ETH Zürich Zurich Switzerland (GRID:grid.5801.c) (ISNI:0000 0001 2156 2780) Instituto de Astrofísica de Andalucía-CSIC Granada Spain (GRID:grid.450285.e) (ISNI:0000 0004 1793 7043) Università di Pisa and INFN Pisa Pisa Italy (GRID:grid.5395.

Classical novae are cataclysmic binary star systems in which the matter of a companion star is accreted on a white dwarf1,2. Accumulation of hydrogen in a layer eventually causes a thermonuclear explosion on the surface of the white dwarf3, brightening the white dwarf to ~105 solar luminosities and triggering ejection of the accumulated matter. Novae provide the extreme conditions required to accelerate particles, electrons or protons, to high energies. Here we present the detection of gamma rays by the MAGIC telescopes from the 2021 outburst of RS Ophiuchi, a recurrent nova with a red giant companion, which allowed us to accurately characterize the emission from a nova in the 60 GeV to 250 GeV energy range. The theoretical interpretation of the combined Fermi LAT and MAGIC data suggests that protons are accelerated to hundreds of gigaelectronvolts in the nova shock. Such protons should create bubbles of enhanced cosmic ray density, of the order of 10 pc, from the recurrent *** of the 2021 outburst of the nova RS Oph in very-high-energy gamma rays by the MAGIC telescopes is reported. Investigation of the gamma-ray emission provides evidence for acceleration of protons within the nova shock, which then propagate outwards to create bubbles of enhanced cosmic ray density.

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Publisher Correction: Deep Machine Learning Techniques for the Detection and Classification of Sperm Whale Bioacoustics

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Scientific reports 2019年第1期9卷 18481页

作者： Peter C Bermant Michael M Bronstein Robert J Wood Shane Gero David F Gruber Radcliffe Institute for Advanced Study Harvard University Cambridge MA USA. Department of Computing Imperial College London UK. Twitter 20 Air Street London W1B 5DL United Kingdom. Wyss Institute for Biologically Inspired Engineering Harvard University Cambridge MA USA. Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA USA. Department of Zoophysiology Institute for Bioscience Aarhus University C.F. Møllers Allé 3 Aarhus 8000 Denmark. Radcliffe Institute for Advanced Study Harvard University Cambridge MA USA. David.Gruber@baruch.cuny.edu. Department of Natural Sciences Baruch College and The Graduate Center PhD Program in Biology City University of New York New York NY USA. David.Gruber@baruch.cuny.edu.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Cloud elasticity for HPC applications: Observing energy, performance and cost 41

Cloud elasticity for HPC applications: Observing energy, per...

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41st Latin American computing Conference, CLEI 2015

作者： Rodrigues, Vinicius Facco Rostirolla, Gustavo Da Rosa Righi, Rodrigo André Da Costa, Cristiano Barbosa, Jorge Luis Victória Applied Computing Graduate Program - Unisinos Av. Unisinos 950 Sâo Leopoldo RS Brazil

ISBN: (纸本)9781467391436

Elasticity is one of the most known capabilities related to cloud computing, being largely deployed using thresholds. In this way, limits are used to drive resource mangement actions, leading to the following problem statements: How can cloud users set the threshold values to enable elasticity in their cloud applications? And what is the impact of the application's load pattern in the elasticity? This article answers these questions for iterative high performance computing applications, showing the impact of both thresholds and load patterns on application performance and resource consumption. To accomplish this, we developed a reactive and PaaS-based elasticity model called AutoElastic and employed it over a private cloud to execute a numerical integration application. Here, we are presenting an analysis of best practices and possible optimizations regarding the elasticity and HPC pair. Considering the results, we observed that the upper threshold influences the application time more than the lower one. © 2015 IEEE.

关键词： Elasticity

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Vulcanus: A recommender system for accessibility based on trails 41

Vulcanus: A recommender system for accessibility based on tr...

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41st Latin American computing Conference, CLEI 2015

作者： Cardoso, Ismael Gomes Mota, Bruno Barbosa, Jorge Luis Victória Da Rosa Righi, Rodrigo Applied Computing Graduate Program - Unisinos Av. Unisinos 950 Sâo Leopoldo RS Brazil

ISBN: (纸本)9781467391436

The use of recommender systems is widespread in several everyday systems. People are now exposed to different offerings, based on their interests, in order to anticipate decisions. However, in a different way recommender systems for accessibility domain require further studies and researches. In order to provide resources for people with special needs, we developed the Vulcanus, a recommender system designed for people with disabilities, offering access to resources according user's needs. The recommender system approaches concepts from ubiquitous computing, such as user profiles, context awareness, trails management, and similarity analysis. Vulcanus uses two different approaches: resources patterns and categories patterns;where different systems can use it, for example as a Web Service. Vulcanus was evaluated in different scenarios. These scenarios used more than 800 thousands of trails that simulate wheelchair users using resources along six months. The obtained results show that both approaches are able to provide relevant recommendations, providing resources that in fact can support users' needs. © 2015 IEEE.

关键词： Recommender systems

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