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Undoped Layered Perovskite Oxynitride Li2LaTa2O6N for Photocatalytic CO2Reduction with Visible Light

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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Bright γ-ray flares observed in GRB131108A

arXiv

引用

arXiv 2019年

作者： Ajello, M. Arimoto, M. Asano, K. Axelsson, M. Baldini, L. Barbiellini, G. Bastieri, D. Bellazzini, R. Berretta, A. Bissaldi, E. Blandford, R.D. Bonino, R. Bottacini, E. Bregeon, J. Bruel, P. Buehler, R. Buson, S. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Chen, S. Chiaro, G. Ciprini, S. Costantin, D. Cutini, S. D'Ammando, F. de la Torre Luque, P. de Palma, F. Di Lalla, N. Di Venere, L. Fana Dirirsa, F. Fegan, S.J. Franckowiak, A. Fukazawa, Y. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Grondin, M.-H. Guiriec, S. Hays, E. Horan, D. Jóhannesson, G. Kocevski, D. Kovac'evic, M. Kuss, M. Larsson, S. Latronico, L. Li, J. Liodakis, I. Longo, F. Loparco, F. Lovellette, M.N. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. McEnery, J.E. Mereu, I. Michelson, P.F. 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. Pei, Z. Persic, M. Pesce-Rollins, M. Petrosian, V. Piron, F. Poon, H. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Reimer, A. Reimer, O. Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Tajima, H. Takagi, K. Tak, D. Torres, D.F. Valverde, J. Wood, K. Yamazaki, R. Yassine, M. Zhu, S. Lucas Uhm, Z. Zhang, Bing Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Faculty of Mathematics and Physics Institute of Science and Engineering Kanazawa University Kakuma Ishikawa Kanazawa920-1192 Japan Institute for Cosmic-Ray Research University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Department of Physics Stockholm University AlbaNova StockholmSE-106 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 Dipartimento di Fisica Università degli Studi di Perugia PerugiaI-06123 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 Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 MontpellierF-34095 France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France Deutsches Elektronen Synchrotron DESY ZeuthenD-15738 Germany Insti

GRB 131108A is a bright long Gamma-Ray Burst (GRB) detected by the Large Area Telescope and the Gamma-ray Burst Monitor on board the Fermi Gamma-ray Space Telescope. Dedicated temporal and spectral analyses reveal three γ-ray flares dominating above 100 MeV, which are not directly related to the prompt emission in the GBM band (10 keV-10 MeV). The high-energy light curve of GRB 131108A (100 MeV - 10 GeV) shows an unusual evolution: a steep decay, followed by three flares with an underlying emission, and then a long-lasting decay phase. The detailed analysis of the γ-ray flares finds that the three flares are 6-20 times brighter than the underlying emission and are similar to each other. The fluence of each flare, (1.6 ∼ 2.0) × 10−6 erg cm−2, is comparable to that of emission during the steep decay phase, 1.7 × 10−6 erg cm−2. The total fluence from three γ-ray flares is 5.3 × 10−6 erg cm−2. The three γ-ray flares show properties similar to the usual X-ray flares that are sharp flux increases, occurring in ∼ 50% of afterglows, in some cases well after the prompt emission. Also, the temporal and spectral indices during the early steep decay phase and the decaying phase of each flare show the consistency with a relation of the curvature effect (Equation presented), which is the first observational evidence of the high-latitude emission in the GeV energy band. Copyright © 2019, The Authors. All rights reserved.

关键词： Gamma rays

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A new evolving clustering algorithm for online data streams 10

A new evolving clustering algorithm for online data streams

引用

10th IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2016

作者： Bezerra, Clauber Gomes Costa, Bruno Sielly Jales Guedes, Luiz Affonso Angelov, Plamen Parvanov Campus EaD Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Campus Natal - Zona Norte Federal Institute of Rio Grande do Norte - IFRN Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande do Norte - UFRN Brazil Lancaster University Data Science Group School of Computing and Communications LancasterLA1 4WA United Kingdom Department of Excellence Carlos III University Madrid Spain

ISBN: (纸本)9781509025831

In this paper, we propose a new approach to fuzzy data clustering. We present a new algorithm, called TEDA-Cloud, based on the recently introduced TEDA approach to outlier detection. TEDA-Cloud is a statistical method based on the concepts of typicality and eccentricity able to group similar data observations. Instead of the traditional concept of clusters, the data is grouped in the form of granular unities called data clouds, which are structures with no pre-defined shape or set boundaries. TEDA-Cloud is a fully autonomous and self-evolving algorithm that can be used for data clustering of online data streams and applications that require real-time response. Since it is fully autonomous, TEDA-Cloud is able to "start from scratch" (from an empty knowledge basis), create, update and merge data clouds, in a fully autonomous manner, without requiring any user-defined parameters (e.g. number of clusters, size, radius) or previous training. Moreover, TEDA-Cloud, unlike most of the traditional statistical approaches, does not rely on a specific data distribution or on the assumption of independence of data samples. The results, obtained from multiple data sets that are very well known in literature, are very encouraging. ©2016 IEEE.

关键词： Clustering algorithms

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Convolutional neural networks for transient candidate vetting in large-scale surveys

arXiv

引用

arXiv 2017年

作者： Gieseke, Fabian Bloemen, Steven van den Bogaard, Cas Heskes, Tom Kindler, Jonas Scalzo, Richard A. Ribeiro, Valério A.R.M. van Roestel, Jan Groot, Paul J. Yuan, Fang Möller, Anais Tucker, Brad E. Institute for Computing and Information Sciences Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands Department of Computer Science University of Copenhagen Sigurdsgade 41 Copenhagen2200 Denmark Department of Astrophysics IMAPP Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands NOVA Optical InfraRed Instrumentation Group Oude Hoogeveensedijk 4 PD Dwingeloo7991 Netherlands Institute of Cognitive Science University of Osnabrück Wachsbleiche 27 Osnabrück49090 Germany Research School of Astronomy and Astrophysics Australian National University CanberraACT2611 Australia Australia Centre for Translational Data Science University of Sydney DarlingtonNSW2008 Australia CIDMA Departamento de Física Universidade de Aveiro Campus de Santiago Aveiro3810-193 Portugal Instituto de Telecomunicações Campus de Santiago Aveiro3810-193 Portugal Department of Physics and Astronomy Botswana International University of Science and Technology Private Bay 16 Palapye Botswana

Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an approach based on convolutional neural networks to discriminate between true astrophysical sources and artefacts in reference-subtracted optical images. We show that relatively simple networks are already competitive with state-of-the-art systems and that their quality can further be improved via slightly deeper networks and additional preprocessing steps – eventually yielding models outperforming state-of-the-art systems. In particular, our best model correctly classifies about 97.3% of all "real"and 99.7% of all"bogus"instances on a test set containing 1,942"bogus"and 227 "real" instances in total. Furthermore, the networks considered in this work can also successfully classify these objects at hand without relying on difference images, which might pave the way for future detection pipelines not containing image subtraction steps at all. Copyright © 2017, The Authors. All rights reserved.

关键词： Surveys

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Human mobility: Models and applications

arXiv

引用

arXiv 2017年

作者： Barbosa-Filho, Hugo Barthelemy, Marc Ghoshal, Gourab James, Charlotte R. Lenormand, Maxime Louail, Thomas Menezes, Ronaldo Ramasco, José J. Simini, Filippo Tomasini, Marcello Department of Physics & Astronomy Goergen Institute of Data Science University of Rochester RochesterNY United States Institut de Physique Théorique CEA CNRS-URA 2306 Gif-sur-YvetteF-91191 France Centre d'Etudes et de Mathématique Sociales EHESS Paris75006 France Department of Engineering Mathematics University of Bristol United Kingdom Irstea UMR TETIS 500 rue JF Breton Montpellier34093 France Campus UIB Palma de Mallorca07122 Spain CNRS UMR 8504 Géographie-cités 13 rue du four ParisF-75006 France BioComplex Laboratory School of Computing Florida Institute of Technology Melbourne United States

Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between shortrange and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications. Copyright © 2017, The Authors. All rights reserved.

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Collective biobjective optimization algorithm for parallel test paper generation 24

Collective biobjective optimization algorithm for parallel t...

引用

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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Fermi and Swift Observations of Grb 190114C: Tracing The Evolution of High-Energy Emission From Prompt to Afterglow

arXiv

引用

arXiv 2019年

作者： Ajello, M. Arimoto, M. Axelsson, M. Baldini, L. Barbiellini, G. Bastieri, D. Bellazzini, R. Berretta, A. Bissaldi, E. Blandford, R.D. Bonino, R. Bottacini, E. Bregeon, J. Bruel, P. Buehler, R. Burns, E. Buson, S. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Chen, S. Chiaro, G. Ciprini, S. Cohen-Tanugi, J. Costantin, D. Cutini, S. D’Ammando, F. DeKlotz, M. de la Torre Luque, P. de Palma, F. Desai, A. Lalla, N. Di Venere, L. Di Dirirsa, F. Fana Fegan, S.J. Franckowiak, A. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Gill, R. Giordano, F. Giroletti, M. Granot, J. Green, D. Grenier, I.A. Grondin, M.-H. Guiriec, S. Hays, E. Horan, D. Jóhannesson, G. Kocevski, D. Kovacevic, M. Kuss, M. Larsson, S. Latronico, L. Lemoine-Goumard, M. Li, J. Liodakis, I. Longo, F. Loparco, F. Lovellette, M.N. Lubrano, P. Maldera, S. Malyshev, D. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. McEnery, J.E. Mereu, I. Meyer, M. Michelson, P.F. Mitthumsiri, W. Mizuno, T. Monzani, M.E. Moretti, E. Morselli, A. Moskalenko, I.V. Negro, M. Nuss, E. Omodei, N. Orienti, M. Orlando, E. Palatiello, M. Paliya, V.S. Paneque, D. Pei, Z. Persic, M. Pesce-Rollins, M. Petrosian, V. Piron, F. Poon, H. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Rani, B. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Ryde, F. Parkinson, P.M. Saz Serini, D. Sgrò, C. Siskind, E.J. Spandre, G. Spinelli, P. Tajima, H. Takagi, K. Takahashi, M.N. Tak, D. Thayer, J.B. Thompson, D.J. Torres, D.F. Troja, E. Valverde, J. van Klaveren, B. Wood, K. Yassine, M. Zaharijas, G. Bhat, P.N. Briggs, M.S. Cleveland, W. Giles, M. Goldstein, A. Hui, M. Malacaria, C. Preece, R. Roberts, O. Veres, P. von Kienlin, A. Cenko, S.B. O’Brien, P. Beardmore, A.P. Lien, A. Osborne, J.P. Tohuvavohu, A. D’Elia, V. Da, A. Perri, M. Gropp, J. Klingler, N. Capalbi, M. Tagliaferri, G. Stamatikos, M. 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 Stockholm University AlbaNova StockholmSE-106 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 Dipartimento di Fisica Università degli Studi di Perugia PerugiaI-06123 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 Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 MontpellierF-34095 France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France Deutsches Elektronen Synchrotron DESY ZeuthenD-15738 Germany NASA Goddard Space Flight Center GreenbeltMD20771 United States NASA Postdoctoral Program Fellow United States In

We report on the observations of gamma-ray burst (GRB) 190114C by the Fermi Gamma-ray Space Telescope and the Neil Gehrels Swift Observatory. The prompt gamma-ray emission was detected by the Fermi Gamma-ray Burst Monitor (GBM), the Fermi Large Area Telescope (LAT), and the Swift Burst Alert Telescope (BAT) and the long-lived afterglow emission was subsequently observed by the GBM, LAT, Swift X-ray Telescope (XRT), and Swift UV Optical Telescope (UVOT). The early-time observations reveal multiple emission components that evolve independently, with a delayed power-law component that exhibits significant spectral attenuation above 40 MeV in the first few seconds of the burst. This power-law component transitions to a harder spectrum that is consistent with the afterglow emission observed by the XRT at later times. This afterglow component is clearly identifiable in the GBM and BAT light curves as a slowly fading emission component on which the rest of the prompt emission is superimposed. As a result, we are able to observe the transition from internal shock to external shock dominated emission. We find that the temporal and spectral evolution of the broadband afterglow emission can be well modeled as synchrotron emission from a forward shock propagating into a wind-like circumstellar environment. We estimate the initial bulk Lorentz factor using the observed high-energy spectral cutoff. Considering the onset of the afterglow component, we constrain the deceleration radius at which this forward shock begins to radiate in order to estimate the maximum synchrotron energy as a function of time. We find that even in the LAT energy range, there exist high-energy photons that are in tension with the theoretical maximum energy that can be achieved through synchrotron emission from a shock. These violations of the maximum synchrotron energy are further compounded by the detection of very high energy (VHE) emission above 300 GeV by MAGIC concurrent with our observations. We con

关键词： Stars

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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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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Multivariate Spearman's p for Aggregating Ranks Using Copulas

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Journal of Machine Learning Research 2016年 17卷 1-30页

作者： Bedo, Justin Ong, Cheng Soon Walter and Eliza Hall Institute 1G Royal Parade ParkvilleVIC3052 Australia Department of Computing and Information Systems University of Melbourne VIC3010 Australia Data61 CSIRO 7 London Circuit CanberraACT2601 Australia Research School of Computer Science Australian National University Department of Electrical and Electronic Engineering VIC3010 Australia

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks and generalise Spearman's to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's p, which measures correlation between a set of ranked lists. Multivariate Spearman's p is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best (top-k) or worst (bottom-k) elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's κ. We discuss an optimistic and pessimistic imputation of missing values, which respectively maximise and minimise correlation, and show its effect on aggregating university rankings. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008. © 2016 Justin Bed}o and Cheng Soon Ong.

关键词： Benchmarking

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