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GRB 221009A: the B.O.A.T Burst that Shines in Gamma Rays

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arXiv 2024年

作者： Axelsson, M. Ajello, M. Arimoto, M. Baldini, L. Ballet, J. Baring, M.G. Bartolini, C. Bastieri, D. Becerra Gonzalez, J. Bellazzini, R. Berenji, B. Bissaldi, E. Blandford, R.D. Bonino, R. Bruel, P. Buson, S. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Cheung, C.C. Chiaro, G. Cibrario, N. Ciprini, S. Cozzolongo, G. Cristarella Orestano, P. Crnogorcevic, M. Cuoco, A. Cutini, S. Ammando, F.D. De Gaetano, S. di Lalla, N. Dinesh, A. Di Tria, R. Di Venere, L. Domínguez, A. Fegan, S.J. Ferrara, E.C. Fiori, A. Franckowiak, A. Fukazawa, Y. Funk, S. Fusco, P. Galanti, G. Gargano, F. Gasbarra, C. Germani, S. Giacchino, F. Giglietto, N. Giliberti, M. Gill, R. Giordano, F. Giroletti, M. Granot, J. Green, D. Grenier, I.A. Guiriec, S. Gustafsson, M. Hashizume, M. Hays, E. Hewitt, J.W. Horan, D. Kayanoki, T. Kuss, M. Laviron, A. Li, J. Liodakis, I. Longo, F. Loparco, F. Lorusso, L. Lott, B. Lovellette, M.N. Lubrano, P. Maldera, S. Malyshev, D. Manfreda, A. Martí-Devesa, G. Martinelli, R. Martinez castellanos, I. Mazziotta, M.N. McEnery, J.E. Mereu, I. Meyer, M. Michelson, P.F. Mirabal, N. Mitthumsiri, W. Mizuno, T. Monti-Guarnieri, P. Monzani, M.E. Morishita, T. Morselli, A. Moskalenko, I.V. Negro, M. Niwa, R. Omodei, N. Orienti, M. Orlando, E. Paneque, D. Panzarini, G. Persic, M. Pesce-Rollins, M. Petrosian, V. Pillera, R. Piron, F. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Ryde, F. Sánchez-Conde, M. Saz Parkinson, P.M. Serini, D. Sgrò, C. Sharma, V. Siskind, E.J. Spandre, G. Spinelli, P. Suson, D.J. Tajima, H. Tak, D. Thayer, J.B. Torres, D.F. Valverde, J. Zaharijas, G. Lesage, S. Briggs, M.S. Burns, E. Bala, S. Bhat, P.N. Cleveland, W.H. Dalessi, S. de Barra, C. Gibby, M. Giles, M.M. Hamburg, R. Hristov, B.A. Hui, C.M. Kocevski, D. Mailyan, B. Malacaria, C. McBreen, S. Poolakkil, S. Roberts, O.J. Scotton, L. Veres, P. von Kienlin, A. Wilson-Hodge, C.A. Wood, J. Department of Physics KTH Royal Institute of Technology AlbaNova StockholmSE-106 91 Sweden The Oskar Klein Centre for Cosmoparticle Physics AlbaNova StockholmSE-106 91 Sweden Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Faculty of Mathematics and Physics Institute of Science and Engineering Kanazawa University Kakuma Ishikawa Kanazawa920-1192 Japan Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-YvetteF-91191 Cedex France Rice University Department of Physics and Astronomy MS-108 P. O. Box 1892 HoustonTX77251 United States Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy Università degli studi di Trento via Calepina 14 Trento38122 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova Via F. Marzolo 8 PadovaI-35131 Italy Center for Space Studies and Activities "G. Colombo" University of Padova Via Venezia 15 PadovaI-35131 Italy Instituto de Astrofísica de Canarias Universidad de La Laguna Dpto. Astrofísica La Laguna Tenerife 38200 Spain Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy California State University Los Angeles Department of Physics and Astronomy Los AngelesCA90032 United States Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari via Amendola 173 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 CNRS IN2P3 Éc

We present a complete analysis of Fermi Large Area Telescope (LAT) data of GRB 221009A, the brightest Gamma-Ray Burst (GRB) ever detected. The burst emission above 30 MeV detected by the LAT preceded by 1 s the low-en... 详细信息

关键词： Gamma rays

The Fermi-LAT Light Curve Repository

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arXiv 2023年

作者： Abdollahi, S. Ajello, M. Baldini, L. Ballet, J. Bastieri, D. Gonzalez, J. Becerra Bellazzini, R. Berretta, A. Bissaldi, E. Bonino, R. Brill, A. Bruel, P. Burns, E. Buson, S. Cameron, R.A. Caputo, R. Caraveo, P.A. Cibrario, N. Ciprini, S. Orestano, P. Cristarella Crnogorcevic, M. Cutini, S. D’Ammando, F. De Gaetano, S. Digel, S.W. Di Lalla, N. Di Venere, L. Domínguez, A. Ramazani, V. Fallah Fegan, S.J. Ferrara, E.C. Fiori, A. Fleischhack, H. Franckowiak, A. Fukazawa, Y. Fusco, P. Gammaldi, V. Gargano, F. Garrappa, S. Gasbarra, C. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Guiriec, S. Gustafsson, M. Hays, E. Horan, D. Hou, X. Jóhannesson, G. Kerr, M. Kocevski, D. Kuss, M. Latronico, L. Li, J. Liodakis, I. Longo, F. Loparco, F. Lorusso, L. Lott, B. Lovellette, M.N. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. Mereu, I. Meyer, M. Michelson, P.F. Mizuno, T. Monzani, M.E. Morselli, A. Moskalenko, I.V. Negro, M. Omodei, N. Orlando, E. Ormes, J.F. Paneque, D. Panzarini, G. Perkins, J.S. Persic, M. Pesce-Rollins, M. Pillera, R. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Sánchez-Conde, M. Parkinson, P.M. Saz Scargle, Jeff Scotton, L. Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Suson, D.J. Tajima, H. Thompson, D.J. Torres, D.F. Valverde, J. Venters, T. Wadiasingh, Z. Wagner, S. Wood, K. IRAP Université de Toulouse CNRS UPS CNES ToulouseF-31028 France 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 Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-YvetteF-91191 Cedex France Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova Via F. Marzolo 8 PadovaI-35131 Italy Center for Space Studies and Activities "G. Colombo" University of Padova Via Venezia 15 PadovaI-35131 Italy Instituto de Astrofísica de Canarias Observatorio del Teide C/Via Lactea s/n Tenerife La LagunaE-38205 Spain 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 Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy NASA Postdoctoral Program Fellow United States NASA Goddard Space Flight Center GreenbeltMD20771 United States Laboratoire Leprince-Ringuet CNRS IN2P3 École polytechnique Institut Polytechnique de Paris Palaiseau91120 France Department of physics and Astronomy Louisiana State University Baton RougeLA70803 United States Institut für Theoretische Physik and Astrophysik Universität Würzburg WürzburgD-97074 Germany 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 INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassi

The Fermi Large Area Telescope (LAT) light curve repository (LCR) is a publicly available, continually updated library of gamma-ray light curves of variable Fermi-LAT sources generated over multiple timescales. The Fermi-LAT LCR aims to provide publication-quality light curves binned on timescales of 3 days, 7 days, and 30 days for 1525 sources deemed variable in the source catalog of the first 10 years of Fermi-LAT observations. The repository consists of light curves generated through full likelihood analyses that model the sources and the surrounding region, providing fluxes and photon indices for each time bin. The LCR is intended as a resource for the time-domain and multi-messenger communities by allowing users to quickly search LAT data to identify correlated variability and flaring emission episodes from gamma-ray sources. We describe the sample selection and analysis employed by the LCR and provide an overview of the associated data access portal. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

Hierarchical taxonomy-aware and attentional graph capsule RCNNs for large-scale multi-label text classification

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arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Wang, Senzhang He, Lifang Li, Bo Wang, Lihong Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 State Key Laboratory of Software Development Environment Beihang University Beijing100083 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Biostatistics and Epidemiology University of Pennsylvania PhiladelphiaPA19104 United States National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics, but how to consider them both coherently is less studied. In addition, most existing methods treat output labels as independent medoids, but ignore the hierarchical relations among them, leading to useful semantic information loss. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding words-matrix representation which preserves both the non-consecutive, long-distance and local sequential semantics. Then the words-matrix is input to the proposed attentional graph capsule recurrent CNNs for more effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

Learning Visual Features from Snapshots for Web Search

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arXiv 2017年

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Xu, Jun Pang, Liang Cheng, Xueqi University of Chinese Academy of Sciences CAS Key Lab of Network Data Science and Technology Institute of Computing Technology

When applying learning to rank algorithms to Web search, a large number of features are usually designed to capture the relevance signals. Most of these features are computed based on the extracted textual elements, link analysis, and user logs. However, Web pages are not solely linked texts, but have structured layout organizing a large variety of elements in different styles. Such layout itself can convey useful visual information, indicating the relevance of a Web page. For example, the query-independent layout (i.e., raw page layout) can help identify the page quality, while the query-dependent layout (i.e., page rendered with matched query words) can further tell rich structural information (e.g., size, position and proximity) of the matching signals. However, such visual information of layout has been seldom utilized in Web search in the past. In this work, we propose to learn rich visual features automatically from the layout of Web pages (i.e., Web page snapshots) for relevance ranking. Both query-independent and query-dependent snapshots are considered as the new inputs. We then propose a novel visual perception model inspired by human’s visual search behaviors on page viewing to extract the visual features. This model can be learned end-to-end together with traditional human-crafted features. We also show that such visual features can be efficiently acquired in the online setting with an extended inverted indexing scheme. Experiments on benchmark collections demonstrate that learning visual features from Web page snapshots can significantly improve the performance of relevance ranking in ad-hoc Web retrieval tasks. Copyright © 2017, The Authors. All rights reserved.

关键词： Websites

Breast Cancer: IL1R2 Blockade Suppresses Breast Tumorigenesis and Progression by Impairing USP15-Dependent BMI1 Stability (Adv. Sci. 1/2020)

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Advanced science 2020年第1期7卷 2070002-2070002页

作者： Lixing Zhang Jiankun Qiang Xiaoli Yang Dong Wang Adeel ur Rehman Xueyan He Weilong Chen Dandan Sheng Lei Zhou Yi-zhou Jiang Tao Li Ying Du Jing Feng Xin Hu Jian Zhang Xi-chun Hu Zhi-ming Shao Suling Liu Fudan University Shanghai Cancer Center and Institutes of Biomedical Sciences Shanghai Medical College Key Laboratory of Breast Cancer in Shanghai Innovation Center for Cell Signaling Network Cancer Institute Fudan University Shanghai 200032 China Department of Oncology Department of Breast Surgery Shanghai Medical College Fudan University Shanghai 200032 China School of Life Science The CAS Key Laboratory of Innate Immunity and Chronic Disease University of Science and Technology of China Hefei Anhui 230027 China State Key Laboratory of Proteomics Institute of Basic Medical Sciences National Center of Biomedical Analysis Beijing 100850 China Department of Laboratory Medicine and Central Laboratory Southern Medical University Affiliated Fengxian Hospital Shanghai 201499 China Department of Medical Oncology Shanghai Medical College Fudan University Shanghai 200032 China

In article https://***/10.1002/advs.201901728 , Suling Liu and co‐workers report that the “decoy receptor” IL‐1 receptor 2 (IL1R2) is highly expressed on the most tumorigenic intermediate‐state breast tumor initiation cells (BTICs), which fall in between the epithelial‐like state and mesenchymal‐like state. IL‐1β in the tumor microenvironment activates the IL1R2 signaling pathway and then increases intermediate‐state BTIC self‐renewal and tumor progression, which can be specifically blocked by IL1R2 neutralizing antibody.

关键词： BMI1 breast cancer IL1R2 neutralizing antibody tumor initiating cells USP15

Parkes transient events: I. database of single pulses, initial results and missing frbs

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arXiv 2020年

作者： Zhang, S.-B. Hobbs, G. Russell, C.J. Toomey, L. Dai, S. Dempsey, J. Manchester, R.N. Johnston, S. Staveley-Smith, L. Wu, X.-F. Li, D. Yang, Y.-Y. Wang, S.-Q. Qiu, H. Luo, R. Wang, C. Zhang, C. Zhang, L. Mandow, R. Purple Mountain Observatory Chinese Academy of Sciences Nanjing210008 China University of Chinese Academy of Sciences Beijing100049 China CSIRO Astronomy and Space Science PO Box 76 EppingNSW1710 Australia International Centre for Radio Astronomy Research University of Western Australia CrawleyWA6009 Australia CSIRO Scientific Computing Services Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia CSIRO Information Management & Technology Box 225 DicksonACT2602 Australia National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China FAST Collaboration CAS Key Laboratory of FAST NAOC Chinese Academy of Sciences Beijing100101 China School of Physics and Electronic Science Guizhou Education University Guiyang550018 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Urumqi Xinjiang830011 China Sydney Institute for Astronomy School of Physics University of Sydney SydneyNSW2006 Australia CSIRO Data61 SydneyNSW2015 Australia Centre for Astrophysics and Supercomputing Swinburne University of Technology Mail H30 PO Box 218 HawthornVIC3122 Australia

A large number of observations from the Parkes 64 m-diameter radio telescope, recorded with high time resolution, are publicly available. We have re-processed all of the observations obtained during the first four years (from 1997 to 2001) of the Parkes Multibeam receiver system in order to identify transient events and have built a database that records the 568,736,756 pulse candidates generated during this search. We have discovered a new fast radio burst (FRB), FRB 010305, with a dispersion measure of 350±5 cm−3 pc and explored why so few FRBs have been discovered in data prior to 2001. After accounting for the dispersion smearing across the channel bandwidth and the sky regions surveyed, the number of FRBs is found to be consistent with model predictions. We also present five single pulse candidates from unknown sources, but with Galactic dispersion measures. We extract a diverse range of sources from the database, which can be used, for example, as a training set of data for new software being developed to search for FRBs in the presence of radio frequency interference. Copyright © 2020, The Authors. All rights reserved.

关键词： database systems

Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems

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arXiv 2018年

作者： Liang, Junjie Hu, Jinlong Dong, Shoubin Honavar, Vasant Artificial Intelligence Research Laboratory Center for Big Data Analytics and Discovery Informatics College of Information Sciences and Technology Pennsylvania State University University ParkPA16802 United States Guangdong Key Laboratory of Communication & Computer Network School of Computer Science and Engineering South China University of Technology Guangzhou510006 China

We propose Top-N-Rank, a novel family of list wise Learning-to-Rank models for reliably recommending the N top-ranked items. The proposed models optimize a variant of the widely used discounted cumulative gain (DCG) objective function which differs from DCG in two important aspects: (i) It limits the evaluation of DCG only on the top N items in the ranked lists, thereby eliminating the impact of low-ranked items on the learned ranking function;and (ii) it incorporates weights that allow the model to leverage multiple types of implicit feedback with differing levels of reliability or trustworthiness. Because the resulting objective function is non-smooth and hence challenging to optimize, we consider two smooth approximations of the objective function, using the traditional sigmoid function and the rectified linear unit (ReLU). We propose a family of learning-to-rank algorithms (Top-N-Rank) that work with any smooth objective function. Then, a more efficient variant, ***, is introduced, which effectively exploits the properties of ReLU function to reduce the computational complexity of Top-N-Rank from quadratic to linear in the average number of items rated by users. The results of our experiments using two widely used benchmarks, namely, the MovieLens data set and the Amazon Video Games data set demonstrate that: (i) The "top-N truncation" of the objective function substantially improves the ranking quality of the top N recommendations;(ii) using the ReLU for smoothing the objective function yields significant improvement in both ranking quality as well as runtime as compared to using the sigmoid;and (iii) *** substantially outperforms the well-performing list-wise ranking methods in terms of ranking quality. Copyright © 2018, The Authors. All rights reserved.

关键词： Benchmarking

A deep investigation of deep IR models

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arXiv 2017年

作者： Pang, Liang Lan, Yanyan Guo, Jiafeng Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

The effective of information retrieval (IR) systems have become more important than ever. Deep IR models have gained increasing attention for its ability to automatically learning features from raw text;thus, many deep IR models have been proposed recently. However, the learning process of these deep IR models resemble a black box. Therefore, it is necessary to identify the difference between automatically learned features by deep IR models and hand-crafted features used in traditional learning to rank approaches. Furthermore, it is valuable to investigate the differences between these deep IR models. This paper aims to conduct a deep investigation on deep IR models. Specifically, we conduct an extensive empirical study on two different datasets, including Robust and LETOR4.0. We first compared the automatically learned features and handcrafted features on the respects of query term coverage, document length, embeddings and robustness. It reveals a number of disadvantages compared with hand-crafted features. Therefore, we establish guidelines for improving existing deep IR models. Furthermore, we compare two different categories of deep IR models, i.e. representation-focused models and interaction-focused models. It is shown that two types of deep IR models focus on different categories of words, including topic-related words and query-related words. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

Spherical paragraph model

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arXiv 2017年

作者： Zhang, Ruqing Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Representing texts as fixed-length vectors is central to many language processing tasks. Most traditional methods build text representations based on the simple Bag-of-Words (BoW) representation, which loses the rich semantic relations between words. Recent advances in natural language processing have shown that semantically meaningful representations of words can be efficiently acquired by distributed models, making it possible to build text representations based on a better foundation called the Bag-of-Word-Embedding (BoWE) representation. However, existing text representation methods using BoWE often lack sound probabilistic foundations or cannot well capture the semantic relatedness encoded in word vectors. To address these problems, we introduce the Spherical Paragraph Model (SPM), a probabilistic generative model based on BoWE, for text representation. SPM has good probabilistic interpretability and can fully leverage the rich semantics of words, the word co-occurrence information as well as the corpus-wide information to help the representation learning of texts. Experimental results on topical classification and sentiment analysis demonstrate that SPM can achieve new state-of-the-art performances on several benchmark datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Sentiment analysis