检索结果-内蒙古大学图书馆

28th International Conference on Software engineering and Knowledge engineering, SEKE 2016

作者： Huang, Ruozi Long, Yonghao Chen, Xiangping School of Data and Computer Science Sun Yat-san University Guangzhou China School of Data and Computer Science National Engineering Research Center of Digital Life Sun Yat-san University Guangzhou China Institute of Advanced Technology Sun Yat-san University Guangzhou China Research Institute of Sun Yat-sen University Shenzhen China

ISBN: (纸本)189170639X

UI is an important part of software product. Considering the complexity of web UI, generating the web page from a mockup proposes requirements for rich experience of developer. Extracting visible elements and their relationship, selecting proper tags, generating source code are time-consuming and error-prone task. In this paper, we propose a method to automate the transforming of the mockup to the web page. Our approach starts from the mockup designed by the art designers, and extracts the elements based on the color features of the edges. Then a bottom-up tag generating method based on the Random Forest is proposed to select the tags for elements. Finally the web page is generated by the definition of the elements. The generating tags can achieve an average accuracy of more than 84%, which can meet the basic requirements of the developers.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Robust frequent directions with application in online learning

arXiv

引用

arXiv 2017年

作者： Luo, Luo Chen, Cheng Zhang, Zhihua Li, Wu-Jun Zhang, Tong Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University 5 Yiheyuan Road Beijing100871 China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University 163 Xianlin Avenue Nanjing210023 China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Heuristic methods

来源：评论

学校读者我要写书评

暂无评论

DESI Strong Lens Foundry I: HST Observations and Modeling with GIGA-Lens

arXiv

引用

arXiv 2025年

作者： Huang, X. Baltasar, S. Ratier-Werbin, N. Storfer, C. Sheu, W. Agarwal, S. Tamargo-Arizmendi, M. Schlegel, D.J. Aguilar, J. Ahlen, S. Aldering, G. Banka, S. BenZvi, S. Bianchi, D. Bolton, A. Brooks, D. Cikota, A. Claybaugh, T. de la Macorra, A. Dey, A. Doel, P. Edelstein, J. Filipp, A. Forero-Romero, J.E. Gaztañaga, E. Gontcho, S.A. Gontcho Gu, A. Gutierrez, G. Honscheid, K. Jullo, E. Juneau, S. Kehoe, R. Kirkby, D. Kisner, T. Kremin, A. Kwon, K.J. Lambert, A. Landriau, M. Lang, D. Le Guillou, L. Liu, J. Meisner, A. Miquel, R. Moustakas, J. Myers, A.D. Perlmutter, S. Pérez-Ràfols, I. Prada, F. Rossi, G. Rubin, D. Sanchez, E. Schubnell, M. Shu, Y. Silver, E. Sprayberry, D. Suzuki, N. Tarlé, G. Weaver, B.A. Zou, H. Department of Physics & Astronomy University of San Francisco San FranciscoCA94117 United States Physics Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Physics Complutense University of Madrid Madrid28040 Spain Department of Mathematics Complutense University of Madrid Madrid28040 Spain Institute for Astronomy University of Hawai’i HonoluluHI96822-1897 United States Department of Physics & Astronomy University of California Los Angeles Los AngelesCA90095 United States University of Chicago Department of Astronomy ChicagoIL60615 United States Department of Physics & Astronomy University of Pittsburgh PittsburghPA15260 United States Physics Dept. Boston University 590 Commonwealth Avenue BostonMA02215 United States Department of Electrical Engineering & Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Physics & Astronomy University of Rochester 206 Bausch and Lomb Hall P.O. Box 270171 RochesterNY14627-0171 United States Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 MilanoI-20133 Italy NSF’s National Optical-Infrared Astronomy Research Laboratory TucsonAZ85719 United States Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Gemini Observatory NSF’s NOIRLab Casilla 603 La Serena Chile Instituto de Física Universidad Nacional Autónoma de México Circuito de la Investigación Científica Ciudad Universitaria Cd. de MéxicoC. P. 04510 Mexico Space Sciences Laboratory University of California Berkeley 7 Gauss Way BerkeleyCA94720 United States Université de Montréal Physics Department 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Ciela – Montreal Institute for Astrophysical Data Analysis and Machine Learning 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Technical University Munich TUM School of Natural Sciences

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered ∼ 3500 new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a subset (51) of our candidates using the Hubble Space Telescope (HST). All of them were confirmed to be strong lenses. We also briefly describe spectroscopic follow-up observations by DESI and Keck NIRES programs. From this very rich dataset, a number of studies will be carried out, including evaluating the quality of the ResNet search candidates and lens modeling. In this paper, we present our initial effort in these directions. In particular, as a demonstration, we present the lens model for DESI-165.4754-06.0423, with imaging data from HST, and lens and source redshifts from DESI and Keck NIRES, respectively. In this effort, we have applied a fully forward-modeling Bayesian approach (GIGA-Lens), using multiple GPUs, for the first time in both regards, to a strong lens with HST data, or any high resolution imaging. © 2025, CC BY.

关键词： Dark energy

来源：评论

学校读者我要写书评

暂无评论

Updated measurement of decay-time-dependent CP asymmetries in D0→K+K− and D0→π+π− decays

引用

Physical Review D 2020年第1期101卷 012005-012005页

作者： R. Aaij C. Abellán Beteta T. Ackernley B. Adeva M. Adinolfi H. Afsharnia C. A. aidala S. aiola Z. Ajaltouni S. Akar P. Albicocco J. Albrecht F. Alessio M. Alexander A. Alfonso Albero G. Alkhazov P. Alvarez Cartelle A. A. Alves, Jr. S. Amato Y. Amhis L. An L. Anderlini G. Andreassi M. Andreotti F. Archilli P. d’Argent J. Arnau Romeu A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann J. J. Back S. Baker V. Balagura W. Baldini A. Baranov R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov G. Bassi V. Batozskaya B. Batsukh A. Battig V. Battista A. Bay M. Becker F. Bedeschi I. Bediaga A. Beiter L. J. Bel V. Belavin S. Belin N. Beliy V. Bellee K. Belous I. Belyaev E. Ben-Haim G. Bencivenni S. Benson S. Beranek A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein E. Bertholet A. Bertolin C. Betancourt F. Betti M. O. Bettler M. van Beuzekom Ia. Bezshyiko S. Bhasin J. Bhom M. S. Bieker S. Bifani P. Billoir A. Bizzeti M. Bjørn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska V. Bocci O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk T. J. V. Bowcock C. Bozzi S. Braun A. Brea Rodriguez M. Brodski J. Brodzicka A. Brossa Gonzalo D. Brundu E. Buchanan A. Buonaura C. Burr A. Bursche J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calero Diaz S. Cali R. Calladine M. Calvi M. Calvo Gomez A. Camboni P. Campana D. H. Campora Perez L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini P. Carniti K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero R. Cenci J. Cerasoli M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis M. Chefdeville V. Chekalina C. Chen S. Chen A. Chernov S.-G. Chitic V. Chobanova M. Chrzaszcz A. Chubykin P. Ciambrone M. F. Cicala X. Cid Vidal G. Ciezarek F. Cindolo P. E. L. Clarke M. Clemencic H. V. Cliff J. Closier J. L. Cobbledick V. Coco J. A. B. Coelho J. Cogan E. Cogneras L. Cojocariu P. Collins T. Colombo A. Comerma-Montells A. Contu N. Cooke G. Coombs Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H. H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France University of Michigan Ann Arbor Michigan USA (associated with Syracuse University Syracuse New York USA) INFN Sezione di Milano Milano Italy University of Cincinnati Cincinnati Ohio USA INFN Laboratori Nazionali di Frascati Frascati Italy Fakultät Physik Technische Universität Dortmund Dortmund Germany European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom ICCUB Universitat de Barcelona Barcelona Spain Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Imperial College London London United Kingdom Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil LAL Université Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France INFN Sezione di Firenze Firenze Italy Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland INFN Sezione di Ferrara Ferrara Italy Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany Aix Marseille Université CNRS/IN2P3 CPPM Marseille France Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI) Protvino Russia Syracuse University Syracuse New York USA Yandex School of Data Analysis Moscow Russia INFN Sezione di Cagliari Monserrato Italy Department of Physics University of Warwick Coventry United Kingdom Department of Physics and Astronomy University of Manchester Manchester United Kingdom INFN Sezione di Genova Genova Italy INFN Sezione

A search for decay-time-dependent charge-parity (CP) asymmetry in D0→K+K− and D0→π+π− decays is performed at the LHCb experiment using proton-proton collision data recorded at a center-of-mass energy of 13 TeV, and corresponding to an integrated luminosity of 5.4 fb−1. The D0 mesons are required to originate from semileptonic decays of b hadrons, such that the charge of the muon identifies the flavor of the neutral D meson at production. The asymmetries in the effective decay widths of D0 and D¯0 mesons are determined to be AΓ(K+K−)=(−4.3±3.6±0.5)×10−4 and AΓ(π+π−)=(2.2±7.0±0.8)×10−4, where the uncertainties are statistical and systematic, respectively. The results are consistent with CP symmetry and, when combined with previous LHCb results, yield AΓ(K+K−)=(−4.4±2.3±0.6)×10−4 and AΓ(π+π−)=(2.5±4.3±0.7)×10−4.

关键词： Particle decays Particle interactions

来源：评论

学校读者我要写书评

暂无评论

YZU-NLP team at SemEval-2016 Task 4: Ordinal Sentiment classification using a recurrent convolutional network 10

YZU-NLP team at SemEval-2016 Task 4: Ordinal Sentiment class...

引用

10th International Workshop on Semantic Evaluation, SemEval 2016

作者： He, Yunchao Yu, Liang-Chih Yang, Chin-Sheng Lai, K. Robert Liu, Weiyi Department of Information Management Yuan Ze University Taiwan Department of Computer Science and Engineering Yuan Ze University Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taiwan School of Information Science and Engineering Yunnan University Yunnan China

ISBN: (纸本)9781941643952

Sentiment analysis of tweets has attracted considerable attention recently for potential use in commercial and public sector applications. Typical sentiment analysis classifies the sentiment of sentences into several discrete classes (e.g., positive and negative). The aim of Task 4 subtask C of SemEval-2016 is to classify the sentiment of tweets into an ordinal five-point scale. In this paper, we present a system that uses word embeddings and recurrent convolutional networks to complete the competition task. The word embeddings provide a continuous vector representation of words for the recurrent convolutional network to use in building sentence vectors for multipoint classification. The proposed method ranked second among eleven teams in terms of micro-averaged MAE (mean absolute error) and eighth for macro-averaged MAE. © 2016 Association for Computational Linguistics.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Global contrast based salient region boundary sampling for action recognition 22nd

Global contrast based salient region boundary sampling for a...

引用

22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Xu, Zengmin Hu, Ruimin Chen, Jun Chen, Huafeng Li, Hongyang National Engineering Research Center for Multimedia Software School of Computer Wuhan University Wuhan China Collaborative Innovation Center of Geospatial Technology Wuhan China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783319276700

Although the excellent representation ability of improved Dense Trajectory (iDT) based features for action video had been proved on several action datasets, the performance of action recognition still suffers from large camera motion of videos. In this paper, we improve the iDT method by advancing a novel salient region boundary based dense sampling strategy, which reduces the number of trajectories while preserves the discriminative power. We first implement the iDT sampling based on motion boundary image, then introduce a global contrast based salient object segmentation method in interest points sampling step of action recognition. To overcome the flaws of global color contrast-based salient region sampling, we apply morphological gradient to generate a more robust mask for sampling dense points, as motion boundaries are much clearer. To evaluate the proposed method, we conduct extensive experiments on two benchmarks including HMDB51 and UCF 50. The results show that our sampling strategy can improve the performance of action recognition with minor computational cost of mask production. In particular, on the HMDB51 dataset, the improvement over the original iDT result is 3%. Meanwhile, any other dense features of action recognition can achieve more competitive performance by utilizing our sampling strategy and Fisher vector encoding method simply. © Springer International Publishing Switzerland 2016.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Search for CP violation and observation of P violation in Λb0→pπ−π+π− decays

引用

Physical Review D 2020年第5期102卷 051101(R)-051101(R)页

作者： R. Aaij C. Abellán Beteta T. Ackernley B. Adeva M. Adinolfi H. Afsharnia C. A. aidala S. aiola Z. Ajaltouni S. Akar P. Albicocco J. Albrecht F. Alessio M. Alexander A. Alfonso Albero G. Alkhazov P. Alvarez Cartelle A. A. Alves, Jr. S. Amato Y. Amhis L. An L. Anderlini G. Andreassi M. Andreotti F. Archilli J. Arnau Romeu A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann J. J. Back S. Baker V. Balagura W. Baldini A. Baranov R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov G. Bassi V. Batozskaya B. Batsukh A. Battig V. Battista A. Bay M. Becker F. Bedeschi I. Bediaga A. Beiter L. J. Bel V. Belavin S. Belin N. Beliy V. Bellee K. Belous I. Belyaev G. Bencivenni E. Ben-Haim S. Benson S. Beranek A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein E. Bertholet A. Bertolin C. Betancourt F. Betti M. O. Bettler Ia. Bezshyiko S. Bhasin J. Bhom M. S. Bieker S. Bifani P. Billoir A. Birnkraut A. Bizzeti M. Bjørn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska V. Bocci O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk T. J. V. Bowcock C. Bozzi S. Braun A. Brea Rodriguez M. Brodski J. Brodzicka A. Brossa Gonzalo D. Brundu E. Buchanan A. Büchler-Germann A. Buonaura C. Burr A. Bursche J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese S. Cali R. Calladine M. Calvi M. Calvo Gomez A. Camboni P. Campana D. H. Campora Perez L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini P. Carniti K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero R. Cenci J. Cerasoli M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis M. Chefdeville V. Chekalina C. Chen S. Chen A. Chernov S.-G. Chitic V. Chobanova M. Chrzaszcz A. Chubykin P. Ciambrone M. F. Cicala X. Cid Vidal G. Ciezarek F. Cindolo P. E. L. Clarke M. Clemencic H. V. Cliff J. Closier J. L. Cobbledick V. Coco J. A. B. Coelho J. Cogan E. Cogneras L. Cojocariu P. Collins T. Colombo A. Comerma-Montells A. Contu N. Cooke G. Coombs S. Coquere Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France University of Michigan Ann Arbor USA (associated with Syracuse University Syracuse New York USA) INFN Sezione di Milano Milano Italy University of Cincinnati Cincinnati Ohio USA INFN Laboratori Nazionali di Frascati Frascati Italy Fakultät Physik Technische Universität Dortmund Dortmund Germany European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom ICCUB Universitat de Barcelona Barcelona Spain Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Imperial College London London United Kingdom Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France INFN Sezione di Firenze Firenze Italy Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland INFN Sezione di Ferrara Ferrara Italy Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI) Protvino Russia Protvino Russia Syracuse University Syracuse New York USA Yandex School of Data Analysis Moscow Russia INFN Sezione di Cagliari Monserrato Italy Department of Physics University of Warwick Coventry United Kingdom Department of Physics and Astronomy University of Manchester Manchester United Kingdom INFN Sezione di Genova Genova Italy National University of Science

A search for CP violation in the Λb0→pπ−π+π− decay is performed using LHCb data corresponding to an integrated luminosity of 6.6 fb−1 collected in pp collisions at center-of-mass energies of 7, 8 and 13 TeV. The analysis uses both triple product asymmetries and the unbinned energy test method. The highest significances of CP asymmetry are 2.9 standard deviations from triple product asymmetries and 3.0 standard deviations for the energy test method. Once the global p-value is considered, all results are consistent with no CP violation. Parity violation is observed at a significance of 5.5 standard deviations for the triple product asymmetry method and 5.3 standard deviations for the energy test method. The reported deviations are given in regions of phase space.

关键词： Particle decays Bottom baryons Bottom quark Hadron colliders Heavy baryons CP symmetry CP violation P-symmetry

来源：评论

学校读者我要写书评

暂无评论

Building Chinese affective resources in valence-arousal dimensions 15

Building Chinese affective resources in valence-arousal dime...

引用

15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016

作者： Yu, Liang-Chih Lee, Lung-Hao Hao, Shuai Wang, Jin He, Yunchao Hu, Jun Lai, K. Robert Zhang, Xuejie Department of Information Management Yuan Ze University Taiwan Department of Computer Science and Engineering Yuan Ze University Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taiwan Information Technology Center National Taiwan Normal University Taiwan School of Software Nanchang University Jiangxi China School of Information Science and Engineering Yunnan University Yunnan China

ISBN: (纸本)9781941643914

An increasing amount of research has recently focused on representing affective states as continuous numerical values on multiple dimensions, such as the valence-arousal (VA) space. Compared to the categorical approach that represents affective states as several classes (e.g., positive and negative), the dimensional approach can provide more fine-grained sentiment analysis. However, affective resources with valence-arousal ratings are still very rare, especially for the Chinese language. Therefore, this study builds 1) an affective lexicon called Chinese valence-arousal words (CVAW) containing 1,653 words, and 2) an affective corpus called Chinese valence-arousal text (CVAT) containing 2,009 sentences extracted from web texts. To improve the annotation quality, a corpus cleanup procedure is used to remove outlier ratings and improper texts. Experiments using CVAW words to predict the VA ratings of the CVAT corpus show results comparable to those obtained using English affective resources. ©2016 Association for Computational Linguistics.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Tunable magnetization relaxation of Fe2Cr1−xCoxSi half-metallic Heusler alloys by band structure engineering

引用

Physical Review Materials 2017年第6期1卷 064401-064401页

作者： Shikun He Yifan Liu Yuhong Zheng Qing Qin Zhenchao Wen Qingyun Wu Yi Yang Yupu Wang YuanPing Feng Kie Leong Teo Christos Panagopoulos Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Data Storage Institute Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way 08-01 Innovis Singapore 138634 Department of Electrical and Computer Engineering National University of Singapore Singapore 117582 Department of Materials Science and Engineering National University of Singapore Singapore 117574 Center for Spintronics Research Network (CSRN) Tohoku University Sendai 980-8577 Japan Department of Physics National University of Singapore Singapore 117542

We report a systematic investigation on the magnetization relaxation properties of iron-based half-metallic Heusler alloy Fe2Cr1−xCoxSi (FCCS) thin films using broadband angular-resolved ferromagnetic resonance. Band structure engineering through Co doping (x) demonstrated by first-principles calculations is shown to tune the intrinsic magnetic damping over an order of magnitude, namely 1×10−2–8×10−4. Notably, the intrinsic damping constants for samples with high Co concentration are among the lowest reported for Heusler alloys and even comparable to magnetic insulator yttrium iron garnet. Furthermore, a significant reduction of both isotropic and anisotropic contributions of extrinsic damping of the FCCS alloys was found in the FCCS films with x=0.5–0.75, which is of particular importance for applications. These results demonstrate a practical recipe to tailor functional magnetization for Heusler alloy-based spintronics at room temperature.

关键词： Ferromagnetism Spin relaxation Spin transfer torque

来源：评论

学校读者我要写书评

暂无评论

A Self-Adapting Approach to Topic Boundary Detection in Video Subtitles 6

A Self-Adapting Approach to Topic Boundary Detection in Vide...

引用

6th International Conference on Digital Home, ICDH 2016

作者： Qi, Xin Zhao, Baoquan Lin, Shujin Luo, Xiaonan School of Data and Computer Science Sun Yat-sen university GuangZhou510006 China National Engineering Research Center of Digital Life GuangZhou510006 China School of Communication and Design Sun Yat-sen University Guangzhou510006 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781509044009

Our goal is to automatically partitioning video subtitles into coherent segments which reflect subtopic structure of the video subtitles. Our approach enlists self-adapting text segmentation to adapt both long and short video subtitles. We evaluate our algorithm by comparing the automatically detected boundaries with the ground truth results by human judgements, and computing precision. © 2016 IEEE.

关键词： Image segmentation

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：