检索结果-内蒙古大学图书馆

Global tracking of marine megafauna space use reveals how to achieve conservation targets

science (New York, N.Y.) 2025年第6751期388卷 1086-1097页

作者： Ana M M Sequeira Jorge P Rodríguez Sarah A Marley Hannah J Calich Mirjam van der Mheen Michelle VanCompernolle Lucy M Arrowsmith Lauren R Peel Nuno Queiroz Marisa Vedor Ivo da Costa Gonzalo Mucientes Ana Couto Nicolas E Humphries Sara Abalo-Morla Francisco J Abascal Debra L Abercrombie Katya Abrantes F Alberto Abreu-Grobois André S Afonso Pedro Afonso Heidi Ahonen Susanne Åkesson Joanna Alfaro-Shigueto Russel D Andrews Frédéric Angelier Marina Antonopoulou Javier A Arata Gonzalo Araujo Randall Arauz José Manuel Arcos Igor Arregui Haritz Arrizabalaga Marie Auger-Méthé Steffen Bach Fred Bailleul Robin W Baird George H Balazs Susan G Barco Adam Barnett Warren Baverstock Alastair M M Baylis Annalea Beard Juan Bécares Eduardo J Belda Ian Bell Ashley Bennison Scott R Benson Diego Bernal Michael L Berumen Sandra Bessudo Natalia P A Bezerra Antonin V Blaison Gabriela S Blanco Barbara A Block Mark Bolton Mark E Bond Ramón Bonfil Camrin D Braun Annette C Broderick Michael de L Brooke Annabelle M L Brooks Edward J Brooks Ignacio M Bruno Jennifer M Burns Michael E Byrne Steven E Campana Hamish A Campbell Richard A Campbell Aaron Carlisle Ruth H Carmichael Gemma Carroll Paolo Casale Filipe R Ceia Demian D Chapman Taylor K Chapple Jean-Benoit Charrassin Andre Chiaradia John Chisholm Christopher R Clarke Thomas A Clay Christophe Cleguer Elizabeth Clingham Eric E G Clua Jesse E M Cochran Rochelle Constantine Robert W Cooper Estelle Crochelet Michelle Cronin Eduardo Cuevas Kayla P DaCosta Laurent Dagorn Ryan Daly Randall W Davis P J Nico de Bruyn Carlos Delgado-Trejo Thomas Dellinger Solène Derville Stella Diamant Andrew DiMatteo Kara L Dodge Philip D Doherty Michael C Double Alistair D M Dove Thomas K Doyle Michael J Drew Lindsay L Dubbs Clinton A J Duffy Peter H Dutton Ewan W J Edwards Luke D Einoder Mark V Erdmann Eduardo Espinoza Nicole Esteban Ana Isabel Fagundes Chris Feare Steven H Ferguson Luciana C Ferreira Francesco Ferretti John Filmalter Brittany Finucci G Chris Fischer Richard J Fitzpatrick Jorge Fontes Angela Formia Division of Ecology and Evolution Research School of Biology The Australian National University Canberra (Australian Capital Territory) Australia. UWA Oceans Institute and the School of Biological Sciences The University of Western Australia Perth (Western Australia) Australia. Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC) CSIC-UIB Palma de Mallorca Spain. Instituto Mediterráneo de Estudios Avanzados (IMEDEA) CSIC-UIB Esporles Illes Balears Spain. C.A. Illes Balears Universidad Nacional de Educación a Distancia (UNED) Palma Spain. Delegación Territorial en Illes Balears Agencia Estatal de Meteorología (AEMET) Muelle de Poniente s/n Palma Spain. Scotland's Rural College (SRUC) Craibstone Estate Bucksburn Aberdeen UK. Oceans Graduate School and the UWA Oceans Institute The University of Western Australia Perth (Western Australia) Australia. School of Engineering and the UWA Oceans Institute The University of Western Australia Perth (Western Australia) Australia. Department of Biodiversity Conservation and Attractions Biodiversity and Conservation Science Kensington (Western Australia) Australia. Save Our Seas Foundation Geneva Switzerland. The Manta Trust Catemwood House Norwood Lane Corscombe Dorset UK. Centro de Investigação em Biodiversidade e Recursos Genéticos (CIBIO) InBIO Laboratório Associado Campus de Vairão Universidade do Porto Vairão Portugal. BIOPOLIS Program in Genomics Biodiversity and Land Planning Campus de Vairao Vairão Portugal. Instituto de Investigaciones Marinas Consejo Superior de Investigaciones Científicas (IIM-CSIC) Vigo Pontevedra Spain. School of Biological Sciences University of Aberdeen Aberdeen UK. Biomathematics and statistics Scotland The kings building Edinburgh Scotland. Marine Biological Association (MBA) The Laboratory Citadel Hill Plymouth UK. Instituto de Investigación para la Gestión Integrada de Zonas Costeras Universitat Politècnica de València Gandia València Spain. Instituto Español de O

The recent Kunming-Montreal Global Biodiversity Framework (GBF) sets ambitious goals but no clear pathway for how zero loss of important biodiversity areas and halting human-induced extinction of threatened species will be achieved. We assembled a multi-taxa tracking dataset (11 million geopositions from 15,845 tracked individuals across 121 species) to provide a global assessment of space use of highly mobile marine megafauna, showing that 63% of the area that they cover is used 80% of the time as important migratory corridors or residence areas. The GBF 30% threshold (Target 3) will be insufficient for marine megafauna's effective conservation, leaving important areas exposed to major anthropogenic threats. Coupling area protection with mitigation strategies (e.g., fishing regulation, wildlife-traffic separation) will be essential to reach international goals and conserve biodiversity.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Match-SRNN: Modeling the recursive matching structure with spatial RNN 25

Match-SRNN: Modeling the recursive matching structure with s...

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

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

Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i.e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position. Based on this idea, we propose a novel deep architecture, namely Match-SRNN, to model the recursive matching structure. Firstly, a tensor is constructed to capture the word level interactions. Then a spatial RNN is applied to integrate the local interactions recursively, with importance determined by four types of gates. Finally, the matching score is calculated based on the global interaction. We show that, after degenerated to the exact matching scenario, Match-SRNN can approximate the dynamic programming process of longest common subsequence. Thus, there exists a clear interpretation for Match-SRNN. Our experiments on two semantic matching tasks showed the effectiveness of Match-SRNN, and its ability of visualizing the learned matching structure.

关键词： Dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Predict anchor links across social networks via an embedding approach 25

Predict anchor links across social networks via an embedding...

引用

25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Man, Tong Shen, Huawei Liu, Shenghua Jin, Xiaolong Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Predicting anchor links across social networks has important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation. One challenging problem is: whether and to what extent we can address the anchor link prediction problem, if only structural information of networks is available. Most existing methods, unsupervised or supervised, directly work on networks themselves rather than on their intrinsic structural regularities, and thus their effectiveness is sensitive to the high dimension and sparsity of networks. To offer a robust method, we propose a novel supervised model, called PALE, which employs network embedding with awareness of observed anchor links as supervised information to capture the major and specific structural regularities and further learns a stable cross-network mapping for predicting anchor links. Through extensive experiments on two realistic datasets, we demonstrate that PALE significantly outperforms the state-of-the-art methods.

关键词： network embeddings

来源：评论

学校读者我要写书评

暂无评论

The brightest GRB ever detected: GRB 221009A as a highly luminous event at z = 0.151

arXiv

引用

arXiv 2023年

作者： Malesani, D.B. Levan, A.J. Izzo, L. de Ugarte Postigo, A. Ghirlanda, G. Heintz, K.E. Kann, D.A. Lamb, G.P. Palmerio, J. Salafia, O.S. Salvaterra, R. Tanvir, N.R. Fernández, J.F. Agüí Campana, S. Chrimes, A.A. D’Avanzo, P. D’Elia, V. Della Valle, M. De Pasquale, M. Fynbo, J.P.U. Gaspari, N. Gompertz, B.P. Hartmann, D.H. Hjorth, J. Jakobsson, P. Palazzi, E. Pian, E. Pugliese, G. Ravasio, M.E. Rossi, A. Saccardi, A. Schady, P. Schneider, B. Sollerman, J. Starling, R.L.C. Thöne, C.C. van der Horst, A.J. Vergani, S.D. Watson, D. Wiersema, K. Xu, D. Zafar, T. Zheng, S.Y. Department of Astrophysics IMAPP Radboud University Nijmegen6525 AJ Netherlands Denmark Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen N2200 Denmark Department of Physics University of Warwick CoventryCV4 7AL United Kingdom DARK Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen N2200 Denmark Artemis Université Côte d’Azur Observatoire de la Côte d’Azur CNRS NiceF-06304 France INAF Osservatorio Astronomico di Brera via E. Bianchi 46 LC MerateI-23807 Italy INFN - Sezione di Milano-Bicocca piazza della Scienza 3 MI MilanoI-20146 Italy Hessian Research Cluster ELEMENTS Giersch Science Center Goethe University Frankfurt Campus Riedberg Max-von-Laue-Strasse 12 Frankfurt am Main60438 Germany Astrophysics Research Institute Liverpool John Moores University 146 Brownlow Hill LiverpoolL3 5RF United Kingdom GEPI Observatoire de Paris Université PSL CNRS 5 Place Jules Janssen Meudon92190 France INAF Istituto di Astrofisica Spaziale e Fisica cosmica Via Alfonso Corti 12 MI MilanoI-20133 Italy School of Physics and Astronomy University of Leicester University Road LeicesterLE1 7RH United Kingdom Instituto de Astrofísica de Andalucía - CSIC Glorieta de la Astronomía s/n Granada18008 Spain Keplerlaan 1 Noordwijk2201 AZ Netherlands ASI - Italian Space Agency Space Science Data Centre Via del Politecnico snc Rome00133 Italy INAF Osservatorio Astronomico di Capodimonte Salita Moiariello 16 Napoli80131 Italy Department of Mathematics Informatics Physics and Earth Sciences University of Messina via F. D’Alcontres 31 Papardo Messina98166 Italy School of Physics and Astronomy Institute for Gravitational Wave Astronomy University of Birmingham BirminghamB15 2TT United Kingdom Department of Physics and Astronomy Kinard Lab of Physics Clemson University ClemsonSC29634 United States Centre for Astrophysics and Cosmology Science Institute University of Iceland Dunhagi 5 Reykjavík107 Icelan

Context. The extreme luminosity of gamma-ray bursts (GRBs) makes them powerful beacons, thus effective probes of the distant Universe. The most luminous bursts are typically detected at moderate and high redshift, where the volume for seeing such rare events is maximized and the star-formation activity is greater than at z = 0. For distant events, not all observations are feasible, such as those at TeV energies. Aims. Here we present a spectroscopic redshift measurement for the exceptional GRB 221009A, the brightest GRB observed to date, with emission extending well into the TeV regime. Methods. We used the X-shooter spectrograph at the ESO Very Large Telescope (VLT) to obtain simultaneous optical to near-infrared (NIR) spectroscopy of the burst afterglow 0.5 days after the explosion. Results. The spectra exhibit both absorption and emission lines from material in a host galaxy at zGRB = 0.15095 ± 0.00005. Thus, GRB 221009A was a relatively nearby burst with a luminosity distance of dL = 745 Mpc. Its host galaxy properties (star-formation rate and metallicity) are consistent with those of long GRB hosts at low redshift. This redshift measurement yields information on the energy of the burst. The inferred isotropic energy release, Eiso > 5 × 1054 erg, lies at the high end of the distribution, making GRB 221009A one of the nearest and also most energetic GRBs observed to date. We estimate that such a combination (nearby as well as intrinsically bright) occurs between once every few decades and once per millennium. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

来源：评论

学校读者我要写书评

暂无评论

The Fourth Catalog of Active Galactic Nuclei Detected by the Fermi Large Area Telescope - data Release 3

arXiv

引用

arXiv 2022年

作者： Ajello, Marco Baldini, Luca Ballet, Jean Bastieri, Denis Gonzalez, Joseff Becerra Bellazzini, Ronaldo Berretta, Alessandra Bissaldi, Elisabetta Bonino, Raffaella Brill, Ari Bruel, Philippe Buson, Sara Caputo, Regina Caraveo, Patrizia A. Cheung, C.C. Chiaro, Graziano Cibrario, Nicolo Ciprini, Stefano Crnogorcevic, Milena Cutini, Sara D’Ammando, Filippo De Gaetano, Salvatore Di Lalla, Niccolo Di Venere, Leonardo Domínguez, Alberto Ramazani, Vandad Fallah Ferrara, Elizabeth C. Fiori, Alessio Fukazawa, Yasushi Funk, Stefan Fusco, Piergiorgio Gammaldi, Viviana Gargano, Fabio Garrappa, Simone Gasparrini, Dario Giglietto, Nico Giordano, Francesco Giroletti, Marcello Green, David Grenier, Isabelle A. Guiriec, Sylvain Horan, Deirdre Hou, Xian Kayanoki, Taishu Kuss, Michael Larsson, Stefan Latronico, Luca Lewis, Tiffany Li, Jian Liodakis, Ioannis Longo, Francesco Loparco, Francesco Lott, Benoit Lovellette, Michael N. Lubrano, Pasquale Madejski, Grzegorz M. Maldera, Simone Manfreda, Alberto Martí-Devesa, Guillem Mazziotta, Mario Nicola Mereu, Isabella Michelson, Peter F. Mirabal, Nestor Mitthumsiri, Warit Mizuno, Tsunefumi Monzani, Maria Elena Morselli, Aldo Moskalenko, Igor V. Negro, Michela Ojha, Roopesh Orienti, Monica Orlando, Elena Ormes, Jonathan F. Pei, Zhiyuan Peña-Herazo, Harold A. Persic, Massimo Pesce-Rollins, Melissa Petrosian, Vahe Pillera, Roberta Poon, Helen Porter, Troy A. Principe, Giacomo Rainò, Silvia Rando, Riccardo Rani, Bindu Razzano, Massimiliano Razzaque, Soebur Reimer, Anita Reimer, Olaf Scotton, Lorenzo Serini, Davide Sgrò, Carmelo Siskind, Eric J. Spandre, Gloria Spinelli, Paolo Suson, Dan J. Tajima, Hiro Torres, Diego F. Valverde, Janeth Yassin, Hend Zaharijas, Gabrijela 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 AIM CEA CNRS Université Paris-Saclay Université de Paris Gif-sur-YvetteF-91191 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 La Laguna TenerifeE-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" 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 United States NASA Goddard Space Flight Center GreenbeltMD20771 United States Laboratoire Leprince-Ringuet École polytechnique CNRS IN2P3 PalaiseauF-91128 France Institut für Theoretische Physik and Astrophysik Universität Würzburg WürzburgD-97074 Germany INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 MilanoI-20133 Italy Space Science Division Naval Research Laboratory WashingtonDC20375-5352 United States Istituto Nazionale di Fisica Nucleare Sezione di Roma "Tor Vergata" RomaI-00133 Italy Space Science Data Center - Agenzia Spaziale Italiana Via del Politecnico snc RomaI-00133 Italy Department of Astronomy University of Maryland College ParkMD20742 United States Istituto Nazionale di Fisica Nucleare Sezio

An incremental version of the fourth catalog of active galactic nuclei (AGNs) detected by the Fermi-Large Area Telescope is presented. This version (4LAC-DR3) derives from the third data release of the 4FGL catalog based on 12 years of E>50 MeV gamma-ray data, where the spectral parameters, spectral energy distributions (SEDs), yearly light curves, and associations have been updated for all sources. The new reported AGNs include 587 blazar candidates and four radio galaxies. We describe the properties of the new sample and outline changes affecting the previously published one. We also introduce two new parameters in this release, namely the peak energy of the SED high-energy component and the corresponding flux. These parameters allow an assessment of the Compton dominance, the ratio of the Inverse-Compton to the synchrotron peak luminosities, without relying on X-ray data. © 2022, CC BY.

关键词： Active Galactic Nuclei

来源：评论

学校读者我要写书评

暂无评论

1299P PD-1 blockade plus chemotherapy followed by concurrent hypofractionated radiotherapy with simultaneous integrated boost as induction therapy for patients with borderline resectable and locally advanced pancreatic cancer: A phase II clinical trial

引用

Annals of Oncology 2022年 33卷 S1138-S1138页

作者： J. Du C. Lu W. Kong Y. Zhu Q. Xin H. Cheng G. Li M. Tang J. Chen Q. Li A. Li Q. Gu Q. Xiaoping S. Bao L. Wang Y. Qiu B. Liu Clinical Cancer Institute - Oncology Dept. Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Nanjing China Clinical Cancer Institute - Oncology Dept. Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine Nanjing China Department of Hepatopancreatobiliary Surgery Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Nanjing China Imaging Department Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Nanjing China Pathology Department Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Nanjing China Nuclear Medicine Department Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Nanjing China State Key Lab of Novel Software Technology & National Institute of Healthcare Data Science Nanjing University Nanjing China Clinical Cancer Institute - Oncology Dept. Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Nanjing Jiangsu Province China Digestive Department Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Nanjing China

来源：评论

学校读者我要写书评

暂无评论

Utilizing Dynamic Properties of Sharing Bits and Registers to Estimate User Cardinalities over Time

arXiv

引用

arXiv 2018年

作者： Wang, Pinghui Jia, Peng Zhang, Xiangliang Tao, Jing Guan, Xiaohong Towsley, Don MOE Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University China Shenzhen Research Institute of Xi’an Jiaotong University Shenzhen China King Abdullah University of Science and Technology Thuwal Saudi Arabia Zhejiang Research Institute of Xi’an Jiaotong University Hangzhou China Department of Automation and NLIST Lab Tsinghua University Beijing China School of Computer Science University of Massachusetts Amherst MA United States

—Online monitoring user cardinalities (or degrees) in graph streams is fundamental for many applications. For example in a bipartite graph representing user-website visiting activities, user cardinalities (the number of distinct visited websites) are monitored to report network anomalies. These real-world graph streams may contain user-item duplicates and have a huge number of distinct user-item pairs, therefore, it is infeasible to exactly compute user cardinalities when memory and computation resources are limited. Existing methods are designed to approximately estimate user cardinalities, whose accuracy highly depends on parameters that are not easy to set. Moreover, these methods cannot provide anytime-available estimation, as the user cardinalities are computed at the end of the data stream. Real-time applications such as anomaly detection require that user cardinalities are estimated on the fly. To address these problems, we develop novel bit and register sharing algorithms, which use a bit array and a register array to build a compact sketch of all users’ connected items respectively. Compared with previous bit and register sharing methods, our algorithms exploit the dynamic properties of the bit and register arrays (e.g., the fraction of zero bits in the bit array at each time) to significantly improve the estimation accuracy, and have low time complexity (O(1)) to update the estimations each time they observe a new user-item pair. In addition, our algorithms are simple and easy to use, without requirements to tune any parameter. We evaluate the performance of our methods on real-world datasets. The experimental results demonstrate that our methods are several times more accurate and faster than state-of-the-art methods using the same amount of memory. Copyright © 2018, The Authors. All rights reserved.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

引用

NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

来源：评论

学校读者我要写书评

暂无评论

A Margin-based MLE for Crowdsourced Partial Ranking

arXiv

引用

arXiv 2018年

作者： Xu, Qianqian Xiong, Jiechao Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Cas Beijing100190 Tencent Ai Lab Shenzhen518057 School of Mathematical Sciences Peking University Beijing100871 China DeepWise Ai Lab Beijing100085 Inst. of Info. Engin. Cas Beijing100093 China University of Chinese Academy of Sciences Beijing100049 China Key Lab of Big Data Mining and Knowledge Management Cas Beijing100190 Department of Mathematics Hong Kong University of Science and Technology Hong Kong Hong Kong

A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order. However, in real-world scenarios, some items are intrinsically ambiguous in comparisons, which may very well be an inherent uncertainty of the data. In this case, the conventional total order ranking can not capture such uncertainty with mere global ranking or utility scores. In this paper, we are specifically interested in the recent surge in crowdsourcing applications to predict partial but more accurate (i.e., making less incorrect statements) orders rather than complete ones. To do so, we propose a novel framework to learn some probabilistic models of partial orders as a margin-based Maximum Likelihood Estimate (MLE) *** prove that the induced MLE is a joint convex optimization problem with respect to all the parameters, including the global ranking scores and margin parameter. Moreover, three kinds of generalized linear models are studied, including the basic uniform model, Bradley-Terry model, and Thurstone-Mosteller model, equipped with some theoretical analysis on FDR and Power control for the proposed methods. The validity of these models are supported by experiments with both simulated and real-world datasets, which shows that the proposed models exhibit improvements compared with traditional state-ofthe-art algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Emergency Events Classification Based on Minsheng Hotline Unbalanced Short-Text

Emergency Events Classification Based on Minsheng Hotline Un...

引用

IEEE International Conference on Big Knowledge (ICBK)

作者： Li-Yuan Geng Wei Jin Han-Bing Qu Research Center for Big Data Beijing Institute of New Technology Applications Beijing China Key Lab. of Artificial Intell. & Big Data Anal. Beijing Academy of Science and Technology Beijing CN

ISBN: (纸本)9781538631218

In this work, a combined strategy is proposed to solve the unbalance problem in the classification of the short-text data sets. The improved K-means sampling method and category guide words are used to improve the classification accuracy of unbalanced data, and then VSM(vector space method) is used to express text. Finally, Naive Bayesian classifiers are used to classify the unbalanced short-text. Experiments show that this method is effective and feasible in the classification of small class events in unbalanced short-text data. The method can improve the small class classification accuracy and provide the decision basis for the government to respond quickly and precisely to emergencies.

关键词： Training Classification algorithms Feature extraction Government Text categorization Clustering algorithms Vocabulary

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：