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8th SIGHAN Workshop on Chinese Language Processing, SIGHAN 2015

作者： Zhang, Shuiyuan Xiong, Jinhua Hou, Jianpeng Zhang, Qiao Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology China Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781941643570

Increased interest in China from foreigners has led to a corresponding interest in the study of Chinese. However, the learning of Chinese by non-native speakers will encounter many difficulties, Chinese spelling check techniques for Chinese as a Foreign Language(CFL) learners is highly desirable. This paper presents our work on the SIGHAN-2015 Chinese Spelling Check task. The task focuses on spelling checking on Chinese essays written by CFL learners. We propose a unified framework called HANSpeller++ based on our previous HANSpeller for Chinese spelling correction. The framework consists of candidate generating,candidates re-ranking and final global decision making. Experiments show good performance on the test data of the task. © 2015 Proceedings of the 8th SIGHAN Workshop on Chinese Language Processing, SIGHAN 2015 - co-located with 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, ACL IJCNLP 2015. All rights reserved.

关键词： Decision making

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Big data Research in Italy： A Perspective

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Engineering 2016年第2期2卷 163-170页

作者： Sonia Bergamaschi Emanuele Carlini Michelangelo Ceci Barbara Furletti Fosca Giannotti Donato Malerba Mario Mezzanzanica Anna Monreale Gabriella Pasi Dino Pedreschi Raffele Perego Salvatore Ruggieri Department of Engneering "Enzo Ferrari " University of Modena and Reggio Emilia Modena 41125 Italy High Performance Computing Laboratory Institute of Information Science and Technologies of the Italian National Research Council (ISTI-CNR) Pisa 56124 Italy Department of Computer Science University ofBari Aldo Moro Bari 70125 Italy Knowledge Discovery and Data Mining Laboratory ISTI-CNR Pisa 56127 Italy Big Data Laboratory National Interuniversity Consortium for Informatics Rome 00185 Italy Department of Statistics and Quantitative Methods University of Milano-Bicocca Milan 20126 Italy Department of Computer Science Systems and Communications University of Milano-Bicocca Milan 20126 Italy Department of Computer 5denee University of Pisa Pisa 56127 Italy

The aim of this article is to synthetically describe the research projects that a selection of Italian univer- sities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.

关键词： Big data Smart cities EnergyJob offersPrivacy

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Undoped Layered Perovskite Oxynitride Li2LaTa2O6N for Photocatalytic CO2Reduction with Visible Light

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Angewandte Chemie 2018年第27期130卷

作者： Takayoshi Oshima Tom Ichibha Ken Sinkou Qin Kanemichi Muraoka Junie Jhon M. Vequizo Keisuke Hibino Ryo Kuriki Shunsuke Yamashita Kenta Hongo Tomoki Uchiyama Kotaro Fujii Daling Lu Ryo Maezono Akira Yamakata Hideki Kato Koji Kimoto Masatomo Yashima Yoshiharu Uchimoto Masato Kakihana Osamu Ishitani Hiroshi Kageyama Kazuhiko Maeda Department of Chemistry School of Science Tokyo Institute of Technology 2-12-1-NE-2 Ookayama Meguro-ku Tokyo 152-8550 Japan Japan Society for the Promotion of Science Kojimachi Business Center Building 5-3-1 Kojimachi Chiyoda-ku Tokyo 102-0083 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Graduate School of Engineering Toyota Technological Institute 2-12-1 Hisakata Tempaku Nagoya 468-8511 Japan Research Center for Advanced Measurement and Characterization National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa 923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0047 Japan PRESTO (Japan) Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama 322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama 351-0198 Japan Graduate school of Human and Environmental Studies Kyoto University Yoshida-nihonmatsu-cho Sakyo-ku Kyoto 606-8501 Japan Suzukakedai Materials Analysis Division Technical Department Tokyo Institute of Technology 4259 Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan Institute of Multidisciplinary Research for Advanced Materials Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto 615-8510 Japan

Oxynitrides are promising visible‐light‐responsive photocatalysts, but their structures are almost confined with three‐dimensional (3D) structures such as perovskites. A phase‐pure Li 2 LaTa 2 O 6 N with a layered perovskite structure was successfully prepared by thermal ammonolysis of a lithium‐rich oxide precursor. Li 2 LaTa 2 O 6 N exhibited high crystallinity and visible‐light absorption up to 500 nm. As opposed to well‐known 3D oxynitride perovskites, Li 2 LaTa 2 O 6 N supported by a binuclear Ru II complex was capable of stably and selectively converting CO 2 into formate under visible light ( λ >400 nm). Transient absorption spectroscopy indicated that, as compared to 3D oxynitrides, Li 2 LaTa 2 O 6 N possesses a lower density of mid‐gap states that work as recombination centers of photogenerated electron/hole pairs, but a higher density of reactive electrons, which is responsible for the higher photocatalytic performance of this layered oxynitride.

关键词： CO2-Reduktion Oxynitride Perowskite Photokatalyse Sichtbares Licht

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A decade of Gamma-Ray Bursts observed by Fermi-LAT:

arXiv

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arXiv 2019年

作者： Ajello, M. Arimoto, M. Axelsson, M. Baldini, L. Barbiellini, G. Bastieri, D. Bellazzini, R. Bhat, P.N. Bissaldi, E. Blandford, R.D. Bonino, R. Bonnell, J. Bottacini, E. Bregeon, J. Bruel, P. Buehler, R. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Chen, S. Cheung, C.C. Chiaro, G. Ciprini, S. Costantin, D. Crnogorcevic, M. Cutini, S. Dainotti, M. D’Ammando, F. de la Torre Luque, P. de Palma, F. Desai, A. Desiante, R. Di Lalla, N. Di Venere, L. Fana Dirirsa, F. Fegan, S.J. Franckowiak, A. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Grove, J.E. Guiriec, S. Hays, E. Hewitt, J.W. Horan, D. Jóhannesson, G. Kocevski, D. Kuss, M. Latronico, L. Li, J. Longo, F. Loparco, F. Lovellette, M.N. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. Mereu, I. Meyer, M. Michelson, P.F. Mirabal, N. Mitthumsiri, W. Mizuno, T. Monzani, M.E. Moretti, E. Morselli, A. Moskalenko, I.V. Negro, M. Nuss, E. Ohno, M. Omodei, N. Orienti, M. Orlando, E. Palatiello, M. Paliya, V.S. Paneque, D. Persic, M. Pesce-Rollins, M. Petrosian, V. Piron, F. Poolakkil, S. Poon, H. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Reposeur, T. Ryde, F. Serini, D. Sgrò, C. Siskind, E.J. Sonbas, E. Spandre, G. Spinelli, P. Suson, D.J. Tajima, H. Takahashi, M. Tak, D. Thayer, J.B. Torres, D.F. Troja, E. Valverde, J. Veres, P. Vianello, G. von Kienlin, A. Wood, K. Yassine, M. Zhu, S. Zimmer, S. 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 Kanazawa Ishikawa920-1192 Department of Physics and Oskar Klein Center for Cosmoparticle Physics Stockholm University Stockholm106 91 Sweden Department of Physics KTH Royal Institute of Technology AlbaNova StockholmSE-106 91 Sweden Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy University of Alabama in Huntsville HuntsvilleAL35899 United States Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy NASA Goddard Space Flight Center GreenbeltMD20771 United States Department of Astronomy University of Maryland College ParkMD20742 United States Department of Physics and Astronomy University of Padova Vicolo Osservatorio 3 PadovaI-35122 Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 MontpellierF-34095 France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France

The Large Area Telescope (LAT) aboard the Fermi spacecraft routinely observes high-energy emission from gamma-ray bursts (GRBs). Here we present the second catalog of LAT-detected GRBs, covering the first 10 years of operations, from 2008 August 4 to 2018 August 4. A total of 186 GRBs are found;of these, 91 show emission in the range 30–100 MeV (17 of which are seen only in this band) and 169 are detected above 100 MeV. Most of these sources were discovered by other instruments (Fermi/GBM, Swift/BAT, AGILE, INTEGRAL) or reported by the Interplanetary Network (IPN);the LAT has independently triggered on 4 GRBs. This catalog presents the results for all 186 GRBs. We study onset, duration and temporal properties of each GRB, as well as spectral characteristics in the 100 MeV–100 GeV energy range. Particular attention is given to the photons with highest energy. Compared with the first LAT GRB catalog, our rate of detection is significantly improved. The results generally confirm the main findings of the first catalog: the LAT primarily detects the brightest GBM bursts, and the high-energy emission shows delayed onset as well as longer duration. However, in this work we find delays exceeding 1 ks, and several GRBs with durations over 10 ks. Furthermore, the larger number of LAT detections shows that these GRBs cover not only the high-fluence range of GBM-detected GRBs, but also samples lower fluences. In addition, the greater number of detected GRBs with redshift estimates allows us to study their properties in both the observer and rest frames. Comparison of the observational results with theoretical predictions reveals that no model is currently able to explain all results, highlighting the role of LAT observations in driving theoretical models. Copyright © 2019, The Authors. All rights reserved.

关键词： Stars

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Stable quantum memories with limited measurement

arXiv

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arXiv 2017年

作者： Daniel Freeman, C. Sarovar, Mohan Herdman, C.M. Whaley, K.B. Berkeley Quantum Information & Computation Center University of California BerkeleyCA94720 United States Department of Physics University of California BerkeleyCA94720 United States Extreme-scale Data Science and Analytics Sandia National Laboratories LivermoreCA94550 United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics & Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Chemistry University of California BerkeleyCA94720 United States

We demonstrate the existence of a finite temperature threshold for a 1D stabilizer code under an error correcting protocol that requires only a fraction of the syndrome measurements. Below the threshold temperature, encoded states have exponentially long lifetimes, as demonstrated by numerical and analytical arguments. We sketch how this algorithm generalizes to higher dimensional stabilizer codes with string-like excitations, like the toric code. Copyright © 2017, The Authors. All rights reserved.

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Convex Matrix Completion: A trace-ball optimization perspective

Convex Matrix Completion: A trace-ball optimization perspect...

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SIAM International Conference on data Mining 2015, SDM 2015

作者： Zeng, Guangxiang Luo, Ping Chen, Enhong Xiong, Hui Zhu, Hengshu Liu, Qi University of Science and Technology of China China Institute of Computing Technology CAS China Rutgers University United States Baidu Research-Big Data Lab. United States

ISBN: (纸本)9781510811522

The problem of Matrix Completion (MC) refers to the process of adding entries for unknown or missing values in a matrix. In this paper, we study the convex matrix completion problem in the form of trace norm bounding. Specifically, we propose a robust solution for this problem based on trace-ball optimization, which can creatively change the original trace norm constraint into the problem of low-rank matrix factorization. Therefore, by searching in a ball space defined by the new trace constraint, the rank of new matrix can be self-determined such that the local minimum for matrix factorization is the global minimum for the original matrix completion task. Meanwhile, we define a free parameter γ to control the model complexity of our approach in terms of how well it fits the training data. Particularly, we identify a value of γb, which is the minimal value of the trace norm, in a way such that the model can exactly fit the known entries in the matrix. Furthermore, we also empirically reveal an important property of our approach: that is, a variable η∗ generated by γ is always stable with the increase of the amount of training data. This can help to speed up the tuning of optimal parameters for large matrices. Finally, extensive experiments on several real-world datasets clearly validate the effectiveness of the proposed approach. Copyright © SIAM.

关键词： Matrix algebra

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Collective biobjective optimization algorithm for parallel test paper generation 24

Collective biobjective optimization algorithm for parallel t...

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24th International Joint Conference on Artificial Intelligence, IJCAI 2015

作者： Nguyen, Minh Luan Hui, Siu Cheung Fong, Alvis C.M. Data Analytics Department Institute for Infocomm Research Singapore Singapore School of Computer Engineering Nanyang Technological University Singapore Singapore School of Computing Science University of Glasgow United Kingdom

ISBN: (纸本)9781577357384

Parallel Test Paper Generation (k-TPG) is a biobjective distributed resource allocation problem, which aims to generate multiple similarly optimal test papers automatically according to multiple userspecified criteria. Generating high-quality parallel test papers is challenging due to its NP-hardness in maximizing the collective objective functions. In this paper, we propose a Collective Biobjective Optimization (CBO) algorithm for solving k-TPG. CBO is a multi-step greedy-based approximation algorithm, which exploits the submodular property for biobjective optimization of k-TPG. Experiment results have shown that CBO has drastically outperformed the current techniques in terms of paper quality and runtime efficiency.

关键词： Approximation algorithms

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Correction: X-chromosome-linked miR548am-5p is a key regulator of sex disparity in the susceptibility to mitochondria-mediated apoptosis

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Cell death & disease 2019年第11期10卷 828页

作者： Paola Matarrese Paolo Tieri Simona Anticoli Barbara Ascione Maria Conte Claudio Franceschi Walter Malorni Stefano Salvioli Anna Ruggieri Center for Gender Specific Medicine Istituto Superiore di Sanità viale Regina Elena 299 Rome Italy. CNR National Research Council IAC Institute for Applied Computing Via dei Taurini 19 Rome Italy. Data Science Program La Sapienza University of Rome Rome Italy. Department of Experimental Diagnostic and Specialty Medicine (DIMES) University of Bologna Bologna Italy. Interdepartmental Centre "L. Galvani" (CIG) University of Bologna via San Giacomo 12 40126 Bologna Italy. IRCCS Istituto delle Scienze Neurologiche di Bologna Via Altura 3 40139 Bologna Italy. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia. School of Mathematical Physical and Natural Sciences and Faculty of Medicine University of Tor Vergata Rome Italy. Department of Experimental Diagnostic and Specialty Medicine (DIMES) University of Bologna Bologna Italy. stefano.salvioli@unibo.it. Interdepartmental Centre "L. Galvani" (CIG) University of Bologna via San Giacomo 12 40126 Bologna Italy. stefano.salvioli@unibo.it. Center for Gender Specific Medicine Istituto Superiore di Sanità viale Regina Elena 299 Rome Italy. anna.ruggieri@iss.it.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Context-adaptive matrix factorization for multi-context recommendation 15

Context-adaptive matrix factorization for multi-context reco...

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Man, Tong Shen, Huawei Huang, Junming Cheng, Xueqi CAS Key Lab. of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China CompleX Lab. Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781450337946

data sparsity is a long-standing challenge for recommender systems based on collaborative filtering. A promising solution for this problem is multi-context recommendation, i.e., leveraging users' explicit or implicit feedback from multiple contexts. In multi-context recommendation, various types of interactions between entities (users and items) are combined to alleviate data sparsity of a single context in a collective manner. Two issues are crucial for multi-context recommendation: (1) How to differentiate context-specific factors from entity-intrinsic factors shared across contexts? (2) How to capture the salient phenomenon that some entities are insensitive to contexts while others are remarkably context-dependent? Previous methods either do not consider context-specific factors, or assume that a context imposes equal influence on different entities, limiting their capability of combating data sparsity problem by taking full advantage of multiple contexts. In this paper, we propose a context-adaptive matrix factorization method for multi-context recommendation by simultaneously modeling context-specific factors and entity-intrinsic factors in a unified model. We learn an entity-intrinsic latent factor for every entity, and a context-specific latent factor for every entity in each context. Meanwhile, using a context-entity mixture parameter matrix we explicitly model the extent to which each context imposes influence on each entity. Experiments on two real scenarios demonstrate that our method consistently outperforms previous multi-context recommendation methods on all different sparsity levels. Such a consistent performance promotion forms the unique superiority of our method, enabling it to be a reliable model for multi-context recommendation. © 2015 ACM.

关键词： Collaborative filtering

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A Search for Cosmic-ray Proton Anisotropy with the Fermi Large Area Telescope

arXiv

引用

arXiv 2019年

作者： Ajello, M. Baldini, L. Barbiellini, G. Bastieri, D. Bechtol, K. Bellazzini, R. Bissaldi, E. Blandford, R.D. Bonino, R. Bottacini, E. Brandt, T.J. Bruel, P. Buson, S. Cameron, R.A. Caputo, R. Cavazzuti, E. Chen, S. Chiaro, G. Ciprini, S. Cohen-Tanugi, J. Costantin, D. Cuoco, A. Cutini, S. D’Ammando, F. de la Torre Luque, P. de Palma, F. Desai, A. Digel, S.W. Lalla, N. Di Venere, L. Di Domínguez, A. Fegan, S.J. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Guiriec, S. Hayashi, K. Hays, E. Hewitt, J.W. Horan, D. Jóhannesson, G. Kuss, M. Latronico, L. Li, J. Liodakis, I. Longo, F. Loparco, F. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. Meehan, M. Mereu, I. Meyer, M. Michelson, P.F. Mirabal, N. Mitthumsiri, W. Mizuno, T. Morselli, A. Negro, M. Nuss, E. Omodei, N. Orienti, M. Orlando, E. Paliya, V.S. Paneque, D. Persic, M. Pesce-Rollins, M. Piron, F. Porter, T.A. Principe, G. Rainò, S. Rando, R. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Suson, D.J. Tajima, H. Thayer, J.B. Torres, D.F. Troja, E. Vandenbroucke, J. Yassine, M. Zimmer, S. Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Department of Physics University of Wisconsin-Madison MadisonWI53706 United States Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy Department of Physics and Astronomy University of Padova Vicolo Osservatorio 3 PadovaI-35122 Italy 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 NASA Goddard Space Flight Center GreenbeltMD20771 United States Italian Space Agency Via del Politecnico snc Roma00133 Italy INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica Milano via E. Bassini 15 MilanoI-20133 Italy 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 Labo

The Fermi Large Area Telescope (LAT) has amassed a large data set of primary cosmic-ray protons throughout its mission. The LAT’s wide field of view and full-sky survey capabilities make it an excellent instrument for studying cosmic-ray anisotropy. As a space-based survey instrument, the LAT is sensitive to anisotropy in both right ascension and declination, while ground-based observations only measure the anisotropy in right ascension. We present the results of the first ever proton anisotropy search using Fermi LAT. The data set uses eight years of data and consists of approximately 179 million protons above 78 GeV, enabling it to probe dipole anisotropy below an amplitude of 10−3, resulting in the most stringent limits on the declination dependence of the dipole to date. We measure a dipole amplitude δ = 3.9 ±1.5 ×10−4 with a p-value of 0.01 (pre-trials) for protons with a minimum energy of 78 GeV. We discuss various systematic effects that could give rise to a dipole excess and calculate upper limits on the dipole amplitude as a function of minimum energy. The 95% CL upper limit on the dipole amplitude is δUL = 1.3 × 10−3 for protons with a minimum energy of 78 GeV and δUL = 1.2 × 10−3 for protons with a minimum energy of 251 GeV. Copyright © 2019, The Authors. All rights reserved.

关键词： Anisotropy

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