检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Bunne, Charlotte Roohani, Yusuf Rosen, Yanay Gupta, Ankit Zhang, Xikun Roed, Marcel Alexandrov, Theo AlQuraishi, Mohammed Brennan, Patricia Burkhardt, Daniel B. Califano, Andrea Cool, Jonah Dernburg, Abby F. Ewing, Kirsty Fox, Emily B. Haury, Matthias Herr, Amy E. Horvitz, Eric Hsu, Patrick D. Jain, Viren Johnson, Gregory R. Kalil, Thomas Kelley, David R. Kelley, Shana O. Kreshuk, Anna Mitchison, Tim Otte, Stephani Shendure, Jay Sofroniew, Nicholas J. Theis, Fabian Theodoris, Christina V. Upadhyayula, Srigokul Valer, Marc Wang, Bo Xing, Eric Yeung-Levy, Serena Zitnik, Marinka Karaletsos, Theofanis Regev, Aviv Lundberg, Emma Leskovec, Jure Quake, Stephen R. Department of Computer Science Stanford University StanfordCA United States Genentech South San FranciscoCA United States Chan Zuckerberg Initiative Redwood CityCA United States School of Computer and Communication Sciences School of Life Sciences EPFL Lausanne Switzerland Arc Institute Palo AltoCA United States KTH Royal Institute of Technology Science for Life Laboratory Department of Protein Science Stockholm Sweden Department of Bioengineering Stanford University StanfordCA United States Department of Pharmacology University of California San DiegoCA United States Department of Bioengineering University of California San DiegoCA United States Department of Systems Biology Columbia University New YorkNY United States Cellarity SomervilleMA United States Vagelos College of Physicians and Surgeons Columbia University Irving Medical Center New YorkNY United States Chan Zuckerberg Biohub New YorkNY United States Department of Molecular and Cell Biology University of California Berkeley BerkeleyCA United States Department of Statistics Stanford University StanfordCA United States Chan Zuckerberg Biohub San Francisco CA United States Chan Zuckerberg Institute for Advanced Biological Imaging Redwood CityCA United States Department of Bioengineering University of California BerkeleyCA United States Microsoft Research RedmondWA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Google Research Mountain ViewCA United States NewLimit San FranciscoCA United States Schmidt city United States Calico Life Sciences LLC San FranciscoCA United States Chan Zuckerberg Biohub Chicago IL United States Northwestern University EvanstonIL United States Cell Biology and Biophysics Unit European Molecular Biology Laboratory Heidelberg Germany Department of Systems Biology Harvard Medical School BostonMA United States Department of Genome Sciences University of Washington SeattleWA Unit

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach. © 2024, CC BY.

关键词： Virtual environments

来源：评论

学校读者我要写书评

暂无评论

INTRODUCTION

引用

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

关键词：

来源：评论

学校读者我要写书评

暂无评论

Generalized word shift graphs: A method for visualizing and explaining pairwise comparisons between texts

arXiv

引用

arXiv 2020年

作者： Gallagher, Ryan J. Frank, Morgan R. Mitchell, Lewis Schwartz, Aaron J. Reagan, Andrew J. Danforth, Christopher M. Dodds, Peter Sheridan Network Science Institute Northeastern University BostonMA02115 United States Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15260 United States Connection Science Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Human-Centered Artifical Intelligence Stanford University StanfordCA94305 United States School of Mathematical Sciences The University of Adelaide SA5005 Australia Computational Story Lab Vermont Complex Systems Center Vermont Advanced Computing Core The University of Vermont BurlingtonVT05401 United States Gund Institute for Environment & Rubenstein School of Environment and Natural Resources The University of Vermont BurlingtonVT05401 United States Department of Ecology and Evolutionary Biology University of Colorado at Boulder BoulderCO80309 United States MassMutual Data Science AmherstMA01002 United States MassMutual Center of Excellence for Complex Systems and Data Science Department of Mathematics and Statistics The University of Vermont BurlingtonVT05401 United States

A common task in computational text analyses is to quantify how two corpora differ according to a measurement like word frequency, sentiment, or information content. However, collapsing the texts' rich stories into a single number is often conceptually perilous, and it is difficult to confidently interpret interesting or unexpected textual patterns without looming concerns about data artifacts or measurement validity. To better capture fine-grained differences between texts, we introduce generalized word shift graphs, visualizations which yield a meaningful and interpretable summary of how individual words contribute to the variation between two texts for any measure that can be formulated as a weighted average. We show that this framework naturally encompasses many of the most commonly used approaches for comparing texts, including relative frequencies, dictionary scores, and entropy-based measures like the Kullback-Leibler and Jensen-Shannon divergences. Through several case studies, we demonstrate how generalized word shift graphs can be flexibly applied across domains for diagnostic investigation, hypothesis generation, and substantive interpretation. By providing a detailed lens into textual shifts between corpora, generalized word shift graphs help computational social scientists, digital humanists, and other text analysis practitioners fashion more robust scientific narratives. Copyright © 2020, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

THE ARCTIC

引用

Bulletin of the American Meteorological Society 2024年第8期105卷 S277-S330页

作者： Druckenmiller, M.L. Thoman, R.L. Moon, T.A. National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder Colorado International Arctic Research Center University of Alaska Fairbanks Fairbanks Alaska National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Colorado Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff Arizona Biospherical Instruments Inc. San Diego California Geophysical Institute University of Alaska Fairbanks Fairbanks Alaska Plant Soils and Climate Department Utah State University Logan Utah Norwegian Institute for Nature Research Trondheim Norway FRAM – High North Research Centre for Climate and the Environment Tromsø Norway Geological Survey of Denmark and Greenland (GEUS) Copenhagen Denmark NOAA/NWS Alaska Region Anchorage Alaska Alaska Native Tribal Health Consortium Anchorage Alaska Geological Survey of Canada Natural Resources Canada Ottawa Canada NOAA Chemical Sciences Laboratory Boulder Colorado Arctic Geophysics Department University Centre in Svalbard Longyearbyen Norway Geology Department University Centre in Svalbard Longyearben Norway Centre National de Recherches Météorologiques Météo-France/CNRS Toulouse France Climate Research Division Environment and Climate Change Canada Toronto Canada Norwegian Polar Institute Fram Centre Tromsø Norway Danish Meteorological Institute Copenhagen Denmark Department of Physics University of Toronto Toronto Canada Department of Environmental Sciences University of Virginia Charlottesville Virginia Department of Geographical Sciences University of Maryland College Park Maryland University of Liège Liège Belgium Environment and Climate Change Canada Toronto Canada Northern Rocky Mountain Science Center U.S. Geological Survey Bozeman Montana Arctic C

来源：评论

学校读者我要写书评

暂无评论

A fine-grained perspective on the robustness of global cargo ship transportation networks

引用

Journal of Geographical sciences 2018年第7期28卷 881-899页

作者：彭澎程诗奋陈金海廖梦迪吴琳刘希亮陆锋 State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS University of Chinese Academy of Sciences Navigation Aids Technology Research Center of Jimei University National & Local Joint Engineering Research Center for Marine Navigation Aids Services College of Geomatics Shandong University of Science and Technology Institute of Computing Technology CAS Fujian Collaborative Innovation Center for Big Data Applications in Governments Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation ***： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current resear

关键词： complex network fine-grained cargo ship transportation network robustness automatic identification system

来源：评论

学校读者我要写书评

暂无评论

NL-Augmenter A Framework for Task-Sensitive natural Language Augmentation

arXiv

引用

arXiv 2021年

作者： Dhole, Kaustubh D. Gangal, Varun Gehrmann, Sebastian Gupta, Aadesh Li, Zhenhao Mahamood, Saad Mahendiran, Abinaya Mille, Simon Shrivastava, Ashish Tan, Samson Wu, Tongshuang Sohl-Dickstein, Jascha Choi, Jinho D. Hovy, Eduard Dušek, Ondřej Ruder, Sebastian Anand, Sajant Aneja, Nagender Banjade, Rabin Barthe, Lisa Behnke, Hanna Berlot-Attwell, Ian Boyle, Connor Brun, Caroline Sobrevilla Cabezudo, Marco Antonio Cahyawijaya, Samuel Chapuis, Emile Che, Wanxiang Choudhary, Mukund Clauss, Christian Colombo, Pierre Cornell, Filip Dagan, Gautier Das, Mayukh Dixit, Tanay Dopierre, Thomas Dray, Paul-Alexis Dubey, Suchitra Ekeinhor, Tatiana Giovanni, Marco Di Goyal, Tanya Gupta, Rishabh Hamla, Louanes Han, Sang Harel-Canada, Fabrice Honoré, Antoine Jindal, Ishan Joniak, Przemyslaw K. Kleyko, Denis Kovatchev, Venelin Krishna, Kalpesh Kumar, Ashutosh Langer, Stefan Lee, Seungjae Ryan Levinson, Corey James Liang, Hualou Liang, Kaizhao Liu, Zhexiong Lukyanenko, Andrey Marivate, Vukosi de Melo, Gerard Meoni, Simon Meyer, Maxime Mir, Afnan Moosavi, Nafise Sadat Muennighoff, Niklas Hon Mun, Timothy Sum Murray, Kenton Namysl, Marcin Obedkova, Maria Oli, Priti Pasricha, Nivranshu Pfister, Jan Plant, Richard Prabhu, Vinay Pais, Vasile Qin, Libo Raji, Shahab Rajpoot, Pawan Kumar Raunak, Vikas Rinberg, Roy Roberts, Nicholas Rodriguez, Juan Diego Roux, Claude Vasconcellos, P.H.S. Sai, Ananya B. Schmidt, Robin M. Scialom, Thomas Sefara, Tshephisho Shamsi, Saqib N. Shen, Xudong Shi, Yiwen Shi, Haoyue Shvets, Anna Siegel, Nick Sileo, Damien Simon, Jamie Singh, Chandan Sitelew, Roman Soni, Priyank Sorensen, Taylor Soto, William Srivastava, Aman Aditya Srivatsa, K.V. Sun, Tony Mukund Varma, T. Tabassum, A. Tan, Fiona Anting Teehan, Ryan Tiwari, Mo Tolkiehn, Marie Wang, Athena Wang, Zijian Wang, Zijie J. Wang, Gloria Wei, Fuxuan Wilie, Bryan Winata, Genta Indra Wu, Xinyi Wydmanski, Witold Xie, Tianbao Yaseen, Usama Yee, Michael A. Zhang, Jing Zhang, Yue ACKO Agara Amelia R&D New York United States Applied Research Laboratories The University of Texas at Austin United States Bloomberg Brigham Young University United States Carnegie Mellon University United States Center for Data and Computing in Natural Sciences Universität Hamburg Germany Charles River Analytics Charles University Prague Czech Republic Columbia University United States Council for Scientific and Industrial Research DeepMind United Kingdom Department of Computer Science University of Pretoria South Africa Drexel University United States Eberhard Karls University of Tübingen Germany Edinburgh Napier University United Kingdom Emory University United States Fablab by Inetum in Paris France Fraunhofer IAIS Germany Georgia Tech United States Google Brain United States Google Research United States Harbin Institute of Technology China Hasso Plattner Institute University of Potsdam Germany Hong Kong University of Science and Technology Hong Kong IBM Research IIT Delhi India IIT Madras India Illinois Mathematics and Science Academy United States Imperial College London United Kingdom Independent Indian Institute of Science Bangalore India Institut Teknologi Bandung Indonesia Institute of Data Science National University of Singapore Singapore International Institute of Information Technology Hyderabad India Jagiellonian University Poland Jean Monnet University France Johns Hopkins United States KTH Royal Institute of Technology Sweden KU Leuven Belgium MTS AI France Microsoft RedmondWA United States Mphasis NEXT Labs National University of Ireland Galway Ireland National University of Science and Technology Pakistan National University of Singapore Singapore Naver Labs Europe France Peking University China Politecnico di Milano University of Bologna Italy Polytechnic Institute of Paris France Pompeu Fabra University Spain Pontifical Catholic University of Minas Gerais Brazil Princeton University United States Rakuten India India Research Institu

data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its tranformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robutstness analysis results are available publicly on the NL-Augmenter repository (https://***/GEM-benchmark/NL-Augmenter). © 2021, CC BY.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

引用

arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

The first fermi-LAT solar flare catalog

arXiv

引用

arXiv 2021年

作者： Ajello, M. Baldini, L. Bastieri, D. Bellazzini, R. Berretta, A. Bissaldi, E. Blandford, R.D. Bonino, R. Bruel, P. Buson, S. Cameron, R.A. Caputo, R. Cavazzuti, E. Cheung, C.C. Chiaro, G. Costantin, D. Cutini, S. D’Ammando, F. de Palma, F. Desiante, R. Di Lalla, N. Di Venere, L. Fana Dirirsa, F. Fegan, S.J. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giordano, F. Giroletti, M. Green, D. Guiriec, S. Hays, E. Hewitt, J.W. Horan, D. Jóhannesson, G. Kovacevic, M. Kuss, M. Larsson, S. Latronico, L. Li, J. Longo, F. Lovellette, M.N. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. Mereu, I. Michelson, P.F. Mizuno, T. Monzani, M.E. Morselli, A. Moskalenko, I.V. Negro, M. Omodei, N. Orienti, M. Orlando, E. Paneque, D. Pei, Z. Persic, M. Pesce-Rollins, Melissa Petrosian, V. Piron, F. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Tak, D. Troja, E. Valverde, J. Wood, K. Zaharijas, G. Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Dipartimento di Fisica Università degli Studi di Perugia PerugiaI-06123 Italy Dipartimento di Fisica"M. Merlin" dell’Università e del Politecnico di Bari via Amendola 173 BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France NASA Goddard Space Flight Center GreenbeltMD20771 United States Italian Space Agency Via del Politecnico snc Roma00133 Italy Space Science Division Naval Research Laboratory WashingtonDC20375-5352 United States INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 MilanoI-20133 Italy University of Padua Department of Statistical Science Via 8 Febbraio 2 Padova35122 Italy Istituto Nazionale di Fisica Nucleare Sezione di Perugia PerugiaI-06123 Italy INAF Istituto di Radioastronomia BolognaI-40129 Italy Department of Physics University of Johannesburg PO Box 524 Auckland Park2006 South Africa Department of Physical Sciences Hiroshima University Higashi-HiroshimaHiroshima739-8526 Japan Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Centre for Astroparticle Ph

We present the first Fermi-Large Area Telescope (LAT) solar flare catalog covering the 24th solar cycle. This catalog contains 45 Fermi-LAT solar flares (FLSFs) with emission in the γ-ray energy band (30 MeV - 10 GeV) detected with a significance ≥5σ over the years 2010-2018. A subsample containing 37 of these flares exhibit delayed emission beyond the prompt-impulsive hard X-ray phase with 21 flares showing delayed emission lasting more than two hours. No prompt-impulsive emission is detected in four of these flares. We also present in this catalog the observations of GeV emission from 3 flares originating from Active Regions located behind the limb (BTL) of the visible solar disk. We report the light curves, spectra, best proton index and localization (when possible) for all the FLSFs. The γ-ray spectra is consistent with the decay of pions produced by >300 MeV protons. This work contains the largest sample of high-energy γ-ray flares ever reported and provides the unique opportunity to perform population studies on the different phases of the flare and thus allowing to open a new window in solar physics. Copyright © 2021, The Authors. All rights reserved.

关键词： Germanium alloys

来源：评论

学校读者我要写书评

暂无评论

LiteBIRD science Goals and Forecasts: Primordial Magnetic Fields

arXiv

引用

arXiv 2024年

作者： Paoletti, D. Rubino-Martin, J. Shiraishi, M. Molinari, D. Chluba, J. Finelli, F. Baccigalupi, C. Errard, J. Gruppuso, A. Lonappan, A.I. Tartari, A. Allys, E. Anand, A. Aumont, J. Ballardini, M. Banday, A.J. Barreiro, R.B. Bartolo, N. Bersanelli, M. Bortolami, M. Brinckmann, T. Calabrese, E. Campeti, P. Carones, A. Casas, F.J. Cheung, K. Clermont, L. Columbro, F. Conenna, G. Coppolecchia, A. Cuttaia, F. D’Alessandro, G. de Bernardis, P. Torre, S. Della Diego-Palazuelos, P. Eriksen, H.K. Fuskeland, U. Galloni, G. Galloway, M. Gerbino, M. Gervasi, M. Ghigna, T. Giardiello, S. Gimeno-Amo, C. Gjerløw, E. Grupp, F. Hazumi, M. Henrot-Versillé, S. Hergt, L.T. Hivon, E. Ichiki, K. Ishino, H. Kohri, K. Komatsu, E. Krachmalnicoff, N. Lamagna, L. Lattanzi, M. Lembo, M. Levrier, F. López-Caniego, M. Luzzi, G. Martínez-González, E. Masi, S. Matarrese, S. Micheli, S. Migliaccio, M. Monelli, M. Montier, L. Morgante, G. Mousset, L. Nagata, R. Namikawa, T. Natoli, P. Novelli, A. Obata, I. Occhiuzzi, A. Odagiri, K. Pagano, L. Paiella, A. Pascual-Cisneros, G. Piacentini, F. Piccirilli, G. Remazeilles, M. Ritacco, A. Ruiz-Granda, M. Sakurai, Y. Scott, D. Stever, S.L. Sullivan, R.M. Takase, Y. Tassis, K. Terenzi, L. Tristram, M. Vacher, L. van Tent, B. Vielva, P. Wehus, I.K. Weymann-Despres, G. Zannoni, M. Zhou, Y. INAF - OAS Bologna via Piero Gobetti 93/3 Bologna40129 Italy INFN Sezione di Bologna Viale C. Berti Pichat 6/2 Bologna40127 Italy Instituto de Astrofísica de Canarias La Laguna Canary Islands TenerifeE-38200 Spain Tenerife La LagunaE-38206 Spain Suwa University of Science Nagano Chino391-0292 Japan High Performance Computing Department CINECA via Magnanelli 2/3 Casalecchio di Reno40033 Italy Jodrell Bank Centre for Astrophysics Alan Turing Building Department of Physics and Astronomy School of Natural Sciences The University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Via Bonomea 265 Trieste34136 Italy INFN Sezione di Trieste via Valerio 2 Trieste34127 Italy IFPU Via Beirut 2 Grignano Trieste34151 Italy Université de Paris CNRS Astroparticule et Cosmologie ParisF-75013 France Dipartimento di Fisica Università di Roma Tor Vergata Via della Ricerca Scientifica 1 Roma00133 Italy INFN Sezione di Pisa Largo Bruno Pontecorvo 3 Pisa56127 Italy Dipartimento di Fisica Università di Pisa Largo B. Pontecorvo 3 Pisa56127 Italy Laboratoire de Physique de l’École Normale Supérieure ENS Université PSL CNRS Sorbonne Université Université de Paris Paris75005 France IRAP Université de Toulouse CNRS CNES UPS Toulouse France Dipartimento di Fisica e Scienze della Terra Università di Ferrara Via Saragat 1 Ferrara44122 Italy INFN Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Avenida los Castros SN Santander39005 Spain Dipartimento di Fisica e Astronomia "G. Galilei" Universita‘degli Studi di Padova via Marzolo 8 PadovaI-35131 Italy INFN Sezione di Padova via Marzolo 8 PadovaI-35131 Italy INAF Osservatorio Astronomico di Padova Vicolo dell’Osservatorio 5 PadovaI-35122 Italy Dipartimento di Fisica Universita’ degli Studi di Milano Via Celoria 16 Milano20133 Italy INFN Sezione di Milano Via Celoria 16 Milano20133 Italy School of Physics and Astronomy Cardiff University CardiffCF24 3AA United

We present detailed forecasts for the constraints on the characteristics of primordial magnetic fields (PMFs) generated prior to recombination that will be obtained with the LiteBIRD satellite. The constraints are driven by some of the main physical effects of PMFs on the CMB anisotropies: the gravitational effects of magnetically-induced perturbations;the effects on the thermal and ionization history of the Universe;the Faraday rotation imprint on the CMB polarization spectra;and the non-Gaussianities induced in polarization anisotropies. LiteBIRD represents a sensitive probe for PMFs. We explore different levels of complexity, for LiteBIRD data and PMF configurations, accounting for possible degeneracies with primordial gravitational waves from inflation. By exploiting all the physical effects, LiteBIRD will be able to improve the current limit on PMFs at intermediate and large scales coming from Planck. In particular, thanks to its accurate B-mode polarization measurement, LiteBIRD will improve the constraints on infrared configurations for the gravitational effect, giving Bn1BMpc=−2.9 marg1 Mpc p(B2)marg n1BMpc=−2.9 © 2024, CC BY.

关键词： Magnetic fields

来源：评论

学校读者我要写书评

暂无评论

Correction: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

引用

Molecular psychiatry 2024年第4期29卷 1216页

作者： Bo-Gyeom Kim Gakyung Kim Yoshinari Abe Pino Alonso Stephanie Ameis Alan Anticevic Paul D Arnold Srinivas Balachander Nerisa Banaj Nuria Bargalló Marcelo C Batistuzzo Francesco Benedetti Sara Bertolín Jan Carl Beucke Irene Bollettini Silvia Brem Brian P Brennan Jan K Buitelaar Rosa Calvo Miguel Castelo-Branco Yuqi Cheng Ritu Bhusal Chhatkuli Valentina Ciullo Ana Coelho Beatriz Couto Sara Dallaspezia Benjamin A Ely Sónia Ferreira Martine Fontaine Jean-Paul Fouche Rachael Grazioplene Patricia Gruner Kristen Hagen Bjarne Hansen Gregory L Hanna Yoshiyuki Hirano Marcelo Q Höxter Morgan Hough Hao Hu Chaim Huyser Toshikazu Ikuta Neda Jahanshad Anthony James Fern Jaspers-Fayer Selina Kasprzak Norbert Kathmann Christian Kaufmann Minah Kim Kathrin Koch Gerd Kvale Jun Soo Kwon Luisa Lazaro Junhee Lee Christine Lochner Jin Lu Daniela Rodriguez Manrique Ignacio Martínez-Zalacaín Yoshitada Masuda Koji Matsumoto Maria Paula Maziero Jose M Menchón Luciano Minuzzi Pedro Silva Moreira Pedro Morgado Janardhanan C Narayanaswamy Jin Narumoto Ana E Ortiz Junko Ota Jose C Pariente Chris Perriello Maria Picó-Pérez Christopher Pittenger Sara Poletti Eva Real Y C Janardhan Reddy Daan van Rooij Yuki Sakai João Ricardo Sato Cinto Segalas Roseli G Shavitt Zonglin Shen Eiji Shimizu Venkataram Shivakumar Noam Soreni Carles Soriano-Mas Nuno Sousa Mafalda Machado Sousa Gianfranco Spalletta Emily R Stern S Evelyn Stewart Philip R Szeszko Rajat Thomas Sophia I Thomopoulos Daniela Vecchio Ganesan Venkatasubramanian Chris Vriend Susanne Walitza Zhen Wang Anri Watanabe Lidewij Wolters Jian Xu Kei Yamada Je-Yeon Yun Mojtaba Zarei Qing Zhao Xi Zhu Paul M Thompson Willem B Bruin Guido A van Wingen Federica Piras Fabrizio Piras Dan J Stein Odile A van den Heuvel Helen Blair Simpson Rachel Marsh Jiook Cha Department of Psychology College of Social Sciences Seoul National University Seoul Republic of Korea. Department of Brain and Cognitive Sciences College of Natural Sciences Seoul National University Seoul Republic of Korea. Graduate School of Medical Science Kyoto Prefectural University of Medicine Department of Psychiatry Kyoto City Japan. Bellvitge Biomedical Research Insitute-IDIBELL Bellvitge University Hospital Barcelona Spain. CIBER of Mental Health (CIBERSAM) Carlos III Health Institute Madrid Spain. Department of Clinical Sciences University of Barcelona Barcelona Spain. The Margaret and Wallace McCain Centre for Child Youth & Family Mental Health and Campbell Family Mental Health Research Institute Centre for Addiction and Mental Health Toronto ON Canada. Department of Psychiatry University of Toronto Toronto ON Canada. Program in Neurosciences and Mental Health The Hospital for Sick Children Toronto ON Canada. Department of Psychiatry Yale University School of Medicine New Haven CT 06510 USA. The Mathison Centre for Mental Health Research & Education Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Calgary AB Canada. Departments of Psychiatry and Medical Genetics Cumming School of Medicine University of Calgary Calgary AB Canada. OCD clinic Department of Psychiatry National Institute of Mental Health And Neurosciences (NIMHANS) Bangalore India. Laboratory of Neuropsychiatry Department of Clinical and Neuroscience and Neurorehabilitation IRCCS Santa Lucia Foundation Rome Italy. Center of Image Diagnostic Hospital Clínic de Barcelona Barcelona Spain. Magnetic Resonance Image Core Facility Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Barcelona Spain. Departamento e Instituto de Psiquiatria do Hospital das Clinicas IPQ HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. Department of Methods and Techniques in Psychology Pontifical Catholic University São Paulo SP

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：