检索结果-内蒙古大学图书馆

Updated search for spectral lines from Galactic dark matter interactions with pass 8 data from the Fermi Large Area Telescope

引用

Physical Review D 2015年第12期91卷 122002-122002页

作者： M. Ackermann M. Ajello A. Albert B. Anderson W. B. Atwood L. Baldini G. Barbiellini D. Bastieri R. Bellazzini E. Bissaldi R. D. Blandford E. D. Bloom R. Bonino E. Bottacini T. J. Brandt J. Bregeon P. Bruel R. Buehler S. Buson G. A. Caliandro R. A. Cameron R. Caputo M. Caragiulo P. A. Caraveo C. Cecchi E. Charles A. Chekhtman J. Chiang G. Chiaro S. Ciprini R. Claus J. Cohen-Tanugi J. Conrad A. Cuoco S. Cutini F. D’Ammando A. de Angelis F. de Palma R. Desiante S. W. Digel L. Di Venere P. S. Drell A. Drlica-Wagner C. Favuzzi S. J. Fegan A. Franckowiak Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini N. Giglietto F. Giordano M. Giroletti G. Godfrey G. A. Gomez-Vargas I. A. Grenier J. E. Grove S. Guiriec M. Gustafsson J. W. Hewitt A. B. Hill D. Horan G. Jóhannesson R. P. Johnson M. Kuss S. Larsson L. Latronico J. Li L. Li F. Longo F. Loparco M. N. Lovellette P. Lubrano D. Malyshev M. Mayer M. N. Mazziotta J. E. McEnery P. F. Michelson T. Mizuno A. A. Moiseev M. E. Monzani A. Morselli S. Murgia E. Nuss T. Ohsugi M. Orienti E. Orlando J. F. Ormes D. Paneque M. Pesce-Rollins F. Piron G. Pivato S. Rainò R. Rando M. Razzano A. Reimer T. Reposeur S. Ritz M. Sánchez-Conde A. Schulz C. Sgrò E. J. Siskind F. Spada G. Spandre P. Spinelli H. Tajima H. Takahashi J. B. Thayer L. Tibaldo D. F. Torres G. Tosti E. Troja G. Vianello M. Werner B. L. Winer K. S. Wood M. Wood G. Zaharijas S. Zimmer Deutsches Elektronen Synchrotron DESY D-15738 Zeuthen Germany Department of Physics and Astronomy Kinard Lab of Physics Clemson University Clemson South Carolina 29634-0978 USA W.W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford California 94305 USA Department of Physics Stockholm University AlbaNova SE-106 91 Stockholm Sweden The Oskar Klein Centre for Cosmoparticle Physics AlbaNova SE-106 91 Stockholm Sweden Department of Physics and Department of Astronomy and Astrophysics Santa Cruz Institute for Particle Physics University of California at Santa Cruz Santa Cruz California 95064 USA Università di Pisa and Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica e Astronomia “G. Galilei ” Università di Padova I-35131 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari 70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Torino I-10125 Torino Italy Dipartimento di Fisica Generale “Amadeo Avogadro ” Università degli Studi di Torino I-10125 Torino Italy NASA Goddard Space Flight Center Greenbelt Maryland 20771 USA Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 Montpellier France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 Route de Saclay F91128 Palaiseau France Consorzio Interuniversitario per la Fisica Spaziale (CIFS) I-10133 Torino Italy INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica I-20133 Milano Italy Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy Dipartime

Dark matter in the Milky Way may annihilate directly into γ rays, producing a monoenergetic spectral line. Therefore, detecting such a signature would be strong evidence for dark matter annihilation or decay. We search for spectral lines in the Fermi Large Area Telescope observations of the Milky Way halo in the energy range 200 MeV–500 GeV using analysis methods from our most recent line searches. The main improvements relative to previous works are our use of 5.8 years of data reprocessed with the Pass 8 event-level analysis and the additional data resulting from the modified observing strategy designed to increase exposure of the Galactic center region. We search in five sky regions selected to optimize sensitivity to different theoretically motivated dark matter scenarios and find no significant detections. In addition to presenting the results from our search for lines, we also investigate the previously reported tentative detection of a line at 133 GeV using the new Pass 8 data.

关键词： SPECTRAL lines DARK matter (Astronomy) ATOMIC spectra FERMI energy ELECTRONIC structure MILKY Way

来源：评论

学校读者我要写书评

暂无评论

Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

引用

作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for data Center Management and Cloud computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

来源：评论

学校读者我要写书评

暂无评论

Reports of the AAAI 2010 conference workshops

Reports of the AAAI 2010 conference workshops

引用

作者： Aha, David W. Boddy, Mark Bulitko, Vadim D'Avila Garcez, Artur S. Doshi, Prashant Edelkamp, Stefan Geib, Christopher Gmytrasiewicz, Piotr Goldman, Robert P. Halevy, Alon Hitzler, Pascal Isbell, Charles Josyula, Darsana Kaelbling, Leslie Pack Kersting, Kristian Kunda, Maithilee Lamb, Luis C. Marthi, Bhaskara McGreggor, Keith Mihalkova, Lilyana Nastase, Vivi Natarajan, Sriraam Provan, Gregory Raja, Anita Ram, Ashwin Riedl, Mark Russell, Stuart Sabharwal, Ashish Smaus, Jan-Georg Sukthankar, Gita Tuyls, Karl Van Der Meyden, Ron Adaptive Systems Section Naval Research Laboratory Washington DC United States Adventium Labs. Minneapolis MN United States Department of Computer Science University of Alberta Canada City University London United Kingdom University of Georgia United States University of Bremen Germany School of Informatics University of Edinburgh United Kingdom University of Illinois Chicago United States Smart Information Flow Technologies LLC Minneapolis MN United States Structured Data Group Google Research United States Department of Computer Science Wright State University Dayton OH United States School of Interactive Computing Georgia Institute of Technology Atlanta GA United States Department of Computer Science Bowie State University United States Department of Computer Science and Engineering Massachusetts Institute of Technology United States Statistical Relational Mining Group Fraunhofer IAIS Germany Department of Computer Science Federal University of Rio Grande Do sul Brazil Willow Garage Inc. CA United States Computer Science Department University of Maryland College Park United States Heidelberg Institute for Theoretical Studies Heidelberg Germany Department of Computer Science University of Wisconsin Madison United States University College Cork Ireland Cork Complex Systems Lab. Ireland Department of Software and Information Systems University of North Carolina Charlotte United States Department of Computer Science and Engineering University of California Berkeley United States Department of Computer Science Cornell University United States Institute for Computational Sustainability United States University of Freiburg Germany School of Electrical Engineering and Computer Science University of Central Florida United States Maastricht University Netherlands University of New South Wales NICTA Australia

The AAAI-10 workshop program was held on July 11-12, 2010, at the Westin Peachtree Plaza in Atlanta, Georgia. The workshop program included 13 workshops covering a wide range of topics in artificial intelligence. There were several presentations on particular formalisms for combining the levels of planning from the perspectives of classical planning, motion planning, POMDPs, search-based planning, and temporal logic-based constraints. Henry Lieberman from Massachusetts Institute of Technology Media laboratory presented work developing a platform for knowledge acquisition and usage from expert users, built upon experience with eliciting information from nonexperts in a collaborative manner. The objective of the AAAI Workshop on Goal-Directed Autonomy (GDA) was to encourage discussion and novel contributions on intelligent agents that can self-select their goals. Jeremy Baxter from QinetiQ focused on user interaction, while Nick Hawes described a goal-directed reasoning framework for controlling a mobile robot.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Asymmetric group key agreement

Asymmetric group key agreement

引用

28th Annual International Conference on the Theory and Applications of Cryptographic Techniques, EUROCRYPT 2009

作者： Qianhong, Wu Yi, Mu Susilo, Willy Bo, Qin Domingo-Ferrer, Josep Universitat Rovira i Virgili Dept. of Comp. Eng. and Maths UNESCO Department of Data Privacy Tarragona Catalonia Spain Key Lab. of Aerospace Information Security and Trusted Computing School of Computer Wuhan University China Centre for Computer and Information Security Research School of Computer Science and Software Engineering University of Wollongong Australia Dept. of Maths School of Science Xi'an University of Technology China

ISBN: (纸本)3642010008

A group key agreement (GKA) protocol allows a set of users to establish a common secret via open networks. Observing that a major goal of GKAs for most applications is to establish a confiDential channel among group members, we revisit the group key agreement definition and distinguish the conventional (symmetric) group key agreement from asymmetric group key agreement (ASGKA) protocols. Instead of a common secret key, only a shared encryption key is negotiated in an ASGKA protocol. This encryption key is accessible to attackers and corresponds to different decryption keys, each of which is only computable by one group member. We propose a generic construction of one-round ASGKAs based on a new primitive referred to as aggregatable signaturebased broadcast (ASBB), in which the public key can be simultaneously used to verify signatures and encrypt messages while any signature can be used to decrypt ciphertexts under this public key. Using bilinear pairings, we realize an efficient ASBB scheme equippe d with useful properties. Following the generic construction, we instantiate a one-round ASGKA protocol tightly reduced to the decision Bilinear Diffie-Hellman Exponentiation (BDHE) assumption in the standard model. © International Association for Cryptologic research 2009.

关键词： Public key cryptography

来源：评论

学校读者我要写书评

暂无评论

A Multi-layers Neural Network Model based on Characteristics of Ganglions' Receptive Fields in Retina and an Algorithm for Watchfulness-keeping

A Multi-layers Neural Network Model based on Characteristics...

引用

The Third Asian Conference on Vision (ACV2004)

作者：危辉陆汝钤 Department of Computer Science Lab of Algorithm for Cognitive Model Brain Science Research Center Fudan University Shanghai 200433 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100080 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100080 China

Machine vision is an active branch of Artificial Intelligence. An important problem in this area is the balance among efficiency, accuracy and huge computing. The visual system of human can keep watchfulness to the perimeter of visual field while at same time their central attention is focused to the center of visual field for fine information processing. This mechanism of computing resource assignment could ease the demand for huge and complex hardware structure. Therefore designing computer model based on biological visual

关键词： A Multi-layers Neural Network Model based on Characteristics of Ganglions Receptive Fields in Retina and an Algorithm for Watchfulness-keeping Model

来源：评论

学校读者我要写书评

暂无评论

Message from the Program Co-Chairs

Message from the Program Co-Chairs

引用

Proceedings - Graphics Interface 2003

作者： Moller, Torsten Ware, Colin Graphics Usability/Visualisation Lab School of Computing Science Simon Fraser University Canada Data Visualization Research Lab Center for Coastal and Ocean Mapping University of New Hampshire United States

No abstract available

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reasoning using inheritance from a mixture of knowledge and beliefs 2nd

Reasoning using inheritance from a mixture of knowledge and ...

引用

2nd International Conference on Knowledge Based Computer Systems, KBCS 1989

作者： Ballim, Afzal Candelaria de Ram, Sylvia Fass, Dan Institut Dalle Molle pour les Etudes Semantiques & Cognitive Route des Acacias 54 Geneva1227 Switzerland Computing Research Lab New Mexico State University Las CrucesNM88003 United States Centre for Systems Science Simon Fraser University BumabyBCV5A 1S6 Canada

ISBN: (纸本)9783540528500

Certain inadequacies of homogeneous inheritance systems have caused an interest in heterogeneous inheritance systems. Heterogeneous representations allow for mixing of ‘known’ relations (inherited through ‘strict’ links) and what is ‘believed’ (inherits through ‘defeasible’ links). However, few well-founded systems have been proposed and heterogeneous systems have been considered to be not yet well understood. This paper presents a theory and implementation of a heterogeneous inheritance system. The principles of the system are that (i) rules of composition allow paths to be considered as single links (effective relationships), and (ii) rules of comparison allow selection of those effective relationships which state the most definite, specific information. These rules are enumerated and discussed, then an implementation of the theory is shown. An example of the operation of the system is explained in detail. Related recent work is noted. © Springer-Verlag Berlin Heidelberg 1990.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Active Learning for Name Entity Recognition with External Knowledge

引用

ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Ying Ma Yu Zhang Arun Kumar Sangaiah Ming Yan Guoqi Li Tian Wang Harbin Institute of Technology Xiamen University of Technology China Xiamen University of Technology China National Yunlin University of Science and Technology China Institute of High Performance Computing Agency for Science Technology and Research Singapore Institute of Automation China Academy of Sciences China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College (UIC) China

Named Entity Recognition (NER) is an important task in knowledge extraction, which targets extracting structural information from unstructured text. To fully employ the prior-knowledge of the pre-trained language models, some research works formulate the NER task into the machine reading comprehension form (MRC-form) to enhance their model generalization capability of commonsense knowledge. However, this transformation still faces the data-hungry issue with limited training data for the specific NER tasks. To address the low-resource issue in NER, we introduce a method named active multi-task-based NER (AMT-NER), which is a two-stage multi-task active learning training model. Specifically, A multi-task learning module is first introduced into AMT-NER to improve its representation capability in low-resource NER tasks. Then, a two-stage training strategy is proposed to optimize AMT-NER multi-task learning. An associated task of Natural Language Inference (NLI) is also employed to enhance its commonsense knowledge further. More importantly, AMT-NER introduces an active learning module, uncertainty selective, to actively filter training data to help the NER model learn efficiently. Besides, we also find different external supportive data under different pipelines improves model performance differently in the NER tasks. Extensive experiments are performed to show the superiority of our method, which also proves our findings that the introduction of external knowledge is significant and effective in the MRC-form NER tasks.

关键词： Named Entity Recognition Machine Reading Comprehension Multi-task Learning Active Learning

来源：评论

学校读者我要写书评

暂无评论

AMSPM: Adaptive Model Selection and Partition Mechanism for Edge Intelligence-driven 5G Smart City with Dynamic computing Resources

引用

ACM Transactions on Sensor Networks 1000年

作者： Xin Niu Xuejiao Cao Chen Yu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-latency, and high-reliable intelligent services, but also enable intelligent maintenance and management of smart city. However, the constantly changing available computing resources of end devices and edge servers cannot continuously guarantee the performance of intelligent inference. In order to guarantee the sustainability of intelligent services in smart city, we propose the Adaptive Model Selection and Partition Mechanism (AMSPM) in 5G smart city where EI provides services, which mainly consists of Adaptive Model Selection (AMS) and Adaptive Model Partition (AMP). In AMSPM, the model selection and partition of deep neural network (DNN) are formulated as an optimization problem. Firstly, we propose a recursive-based algorithm named AMS based on the computing resources of edge devices to derive an appropriate DNN model that satisfies the latency demand of intelligent services. Then, we adaptively partition the selected DNN model according to the computing resources of edge devices. The experimental results demonstrate that, when compared with state-of-the-art model selection and partition mechanisms, AMSPM not only reduces latency but also enhances computing resource utilization.

关键词： Edge intelligence changing available computing resources adaptive model selection and partition smart city sustainable intelligent services

来源：评论

学校读者我要写书评

暂无评论

Cheetah: Accelerating Dynamic Graph Mining with Grouping Updates

引用

ACM Transactions on Architecture and Code Optimization 1000年

作者： Yi Zhang Xiaomeng Yi Yu Huang Jingrui Yuan Chuangyi Gui Dan Chen Long Zheng Jianhui Yue Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Software Engineering Huazhong University of Science and Technology Wuhan China Eastern Institute of Technology Ningbo China National University of Singapore Singapore Singapore Michigan Technological University Houghton United States School of Computer Science and Engieering UNSW Sydney Kensington Australia

Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods use fine-grained incremental computation to avoid full re-mining after each update, which improves speed but often overlooks potential gains from examining inter-update interactions holistically, thus missing out on overall efficiency *** this paper, we introduce Cheetah, a dynamic graph mining system that processes updates in a coarse-grained manner by leveraging exploration domains. These domains exploit the community structure of real-world graphs to uncover data reuse opportunities typically missed by existing approaches. Exploration domains, which encapsulate extensive portions of the graph relevant to updates, allow multiple updates to explore the same regions efficiently. Cheetah dynamically constructs these domains using a management module that identifies and maintains areas of redundancy as the graph changes. By grouping updates within these domains and employing a neighbor-centric expansion strategy, Cheetah minimizes redundant data accesses. Our evaluation of Cheetah across five real-world datasets shows it outperforms current leading systems by an average factor of 2.63 ×.

关键词： Graph mining system dynamic graph incremental computing

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：