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arXiv 2020年

作者： Desprez, Guillaume Paltani, S. Coupon, J. Almosallam, I. Alvarez-Ayllon, A. Amaro, V. Brescia, M. Brodwin, M. Cavuoti, S. de Vicente-Albendea, J. Fotopoulou, S. Hatfield, P.W. Hartley, W.G. Ilbert, O. Jarvis, M.J. Longo, G. Saha, R. Speagle, J.S. Tramacere, A. Castellano, M. Dubath, F. Galametz, A. Kuemmel, M. Laigle, C. Merlin, E. Mohr, J.J. Salvato, M. Rau, M.M. Andreon, S. Auricchio, N. Baccigalupi, C. Balaguera-Antolínez, A. Baldi, M. Bardelli, S. Bender, R. Biviano, A. Bodendorf, C. Bonino, D. Bozzo, E. Branchini, E. Brinchmann, J. Burigana, C. Cabanac, R. Camera, S. Capobianco, V. Cappi, A. Carbone, C. Carretero, J. Carvalho, C.S. Casas, R. Casas, S. Castander, F.J. Castignani, G. Cimatti, A. Cledassou, R. Colodro-Conde, C. Congedo, G. Conselice, C.J. Conversi, L. Copin, Y. Corcione, L. Courtois, H.M. Cuby, J.-G. da Silva, A. de la Torre, S. Degaudenzi, H. Di Ferdinando, D. Douspis, M. Duncan, C.A.J. Dupac, X. Ealet, A. Fabbian, G. Fabricius, M. Farrens, S. Ferreira, P.G. Finelli, F. Fosalba, P. Fourmanoit, N. Frailis, M. Franceschi, E. Fumana, M. Galeotta, S. Garilli, B. Gillard, W. Gillis, B. Giocoli, C. Gozaliasl, G. Graciá-Carpio, J. Grupp, F. Guzzo, L. Hailey, M. Haugan, S.V.H. Holmes, W. Hormuth, F. Humphrey, A. Jahnke, K. Keihanen, E. Kermiche, S. Kilbinger, M. Kirkpatrick, C.C. Kitching, T.D. Kohley, R. Kubik, B. Kunz, M. Kurki-Suonio, H. Ligori, S. Lilje, P.B. Lloro, I. Maino, D. Maiorano, E. Marggraf, O. Markovic, K. Martinet, N. Marulli, F. Massey, R. Maturi, M. Mauri, N. Maurogordato, S. Medinaceli, E. Mei, S. Meneghetti, M. Benton Metcalf, R. Meylan, G. Moresco, M. Moscardini, L. Munari, E. Niemi, S. Padilla, C. Pasian, F. Patrizii, L. Pettorino, V. Pires, S. Polenta, G. Poncet, M. Popa, L. Potter, D. Pozzetti, L. Raison, F. Renzi, A. Rhodes, J. Riccio, G. Rossetti, E. Saglia, R. Sapone, D. Schneider, P. Scottez, V. Secroun, A. Serrano, S. Sirignano, C. Sirri, G. Stanco, L. Stern, D. Department of Astronomy University of Geneva ch. d’Écogia 16 VersoixCH-1290 Switzerland Saudi Information Technology Company Riyadh12382 Saudi Arabia King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia Information Engineering Parks Road OxfordOX1 3PJ United Kingdom School of Physics and Astronomy Sun Yat-sen University Guangzhou Zhuhai Campus 519082 China INAF-Osservatorio Astronomico di Capodimonte Via Moiariello 16 NapoliI-80131 Italy Department of Physics and Astronomy University of Missouri 5110 Rockhill Road Kansas CityMO64110 United States Department of Physics "E. Pancini" University Federico II Via Cinthia 6 NapoliI-80126 Italy INFN section of Naples Via Cinthia 6 NapoliI-80126 Italy Avenida Complutense 40 Madrid28040 Spain School of Physics HH Wills Physics Laboratory University of Bristol Tyndall Avenue BristolBS8 1TL United Kingdom Department of Physics Oxford University Keble Road OxfordOX1 3RH United Kingdom Aix-Marseille Univ CNRS CNES LAM Marseille France Center for Astrophysics Harvard & Smithsonian 60 Garden St. CambridgeMA02138 United States INAF-Osservatorio Astronomico di Roma Via Frascati 33 Monteporzio CatoneI-00078 Italy Universitäts-Sternwarte München Fakultät für Physik Ludwig-Maximilians-Universität München Scheinerstrasse 1 München81679 Germany Sorbonne Universités UPMC Univ Paris 6 et CNRS UMR 7095 Institut d’Astrophysique de Paris 98 bis bd Arago Paris75014 France Max Planck Institute for Extraterrestrial Physics Giessenbachstr. 1 GarchingD-85748 Germany McWilliams Center for Cosmology Department of Physics Carnegie Mellon University PittsburghPA15213 United States INAF-Osservatorio Astronomico di Brera Via Brera 28 MilanoI-20122 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 BolognaI-40129 Italy SISSA International School for Advanced Studies Via Bonomea 265 Trieste TSI-34136 Italy INFN Sezione di Trieste Via

Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2–2.6 redshift range that the Euclid mission will probe. We design a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data are divided into two samples: one calibration sample for which photometry and redshifts are provided to the participants;and the validation sample, containing only the photometry, to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates sources they consider unfit for use in cosmological analyses. The performance of each method is assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, sources for which the photo-z deviates by more than 0.15(1 + z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. We find that no machine-learning method provides good results in the regions of galaxy color-space that are sparsely populated by spectroscopic-redshifts, e.g

关键词： Surveys

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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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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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Large-eddy simulation of a three-dimensional hypersonic shock wave/turbulent boundary layer interaction of a single-fin 53

Large-eddy simulation of a three-dimensional hypersonic shoc...

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53rd AIAA Aerospace Sciences Meeting, 2015

作者： Fang, Jian Lu, Lipeng Yao, Yufeng Zheltovodov, Alexander A. National Key Laboratory of Science and Technology on Aero-Engine Aero-Thermodynamics School of Energy and Power Engineering Beihang University Beijing100191 China Faculty of Environment and Technology Department of Engineering Design and Mathematics University of the West of England BristolBS16 1QY United Kingdom Khristianovich Institute of Theoretical and Applied Mechanics Siberian Branch of Russian Academy of Science Novosibirsk630090 Russia

ISBN: (数字)9781624103438

ISBN: (纸本)9781624103438

The three-dimensional (3D) shock wave/turbulent boundary layer interaction (SWTBLI) of a hypersonic flow passing a single-fin placed on a flat-plate at Mach 5 and Re=3.7 × 107/m is conducted by the large-eddy simulation (LES) approach. A newly developed in-house LES code is used that applies the seventh-order low-dissipation monotonicity-preserving scheme to solve the inviscid Euler fluxes and the dynamic Smagorinsky subgrid model to take account of the subgrid stress and heat flux. The performed LES has demonstrated very good agreement with experimental data in the mean flowfield structure, surface pressure as well as surface flow pattern. The shock system, flow separation structure, and turbulence characteristics are all investigated by analyzing the obtained LES data. The turbulence in this 3D SWTBLI problem is found to be dominated by wall turbulence, large-scale free shear turbulence, as well as the corner vortex in different regions. In the reverse flow region, the streamwise elongated coherent structures are regenerated beneath the main separation vortex, almost immediately after the flow reattachment location. © 2015 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Shock waves

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First search for K+ → π+νν¯ using the decay-in-flight technique

arXiv

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arXiv 2018年

作者： Gil, E. Cortina Minucci, E. Padolski, S. Petrov, P. Velghe, B. Georgiev, G. Kozhuharov, V. Litov, L. Numao, T. Bryman, D. Fu, J. Husek, T. Kampf, K. Zamkovsky, M. Aliberti, R. Khoriauli, G. Kunze, J. Lomidz, D. Marchevski, R. Peruzzo, L. Vormstein, M. Wanke, R. Dalpiaz, P. Fiorini, M. Gamberini, E. Neri, I. Norton, A. Petrucci, F. Wahl, H. Ramusino, A. Cotta Gianoli, A. Iacopini, E. Latino, G. Lenti, M. Bizzeti, A. Bucci, F. Volpe, R. Antonelli, A. Lamanna, G. Lanfranchi, G. Mannocchi, G. Martellotti, S. Moulson, M. Raggi, M. Spadaro, T. Ambrosino, F. Capussela, T. Corvino, M. Filippo, D. Di Massarotti, P. Mirra, M. Napolitano, M. Saracino, G. Anzivino, G. Brizioli, F. Imbergamo, E. Lollini, R. Santoni, C. Barbanera, M. Cenci, P. Checcucci, B. Duk, V. Lubrano, P. Lupi, M. Pepe, M. Piccini, M. Costantini, F. Lella, L. Di Doble, N. Giorgi, M. Giudici, S. Pedreschi, E. Sozzi, M. Cerri, C. Fantechi, R. Piandani, R. Pinzino, J. Pontisso, L. Spinella, F. Mannelli, I. D’Agostini, G. Biagioni, A. Leonardi, E. Lonardo, A. Valente, P. Vicini, P. Ammendola, R. Bonaiuto, V. Federici, L. Fucci, A. Salamon, A. Sargeni, F. Arcidiacono, R. Bloch-Devaux, B. Boretto, M. Menichetti, E. Migliore, E. Soldi, D. Biino, C. Filippi, A. Marchetto, F. Engelfried, J. Estrada-Tristan, N. Bragadireanu, A.M. Ghinescu, S.A. Hutanu, O.E. Enik, T. Falaleev, V. Kekelidze, V. Korotkova, A. Madigozhin, D. Misheva, M. Molokanova, N. Movchan, S. Polenkevich, I. Potrebenikov, Yu. Shkarovskiy, S. Zinchenko, A. Fedotov, S. Gushchin, E. Khotyantsev, A. Kleimenova, A. Kudenko, Y. Kurochka, V. Medvedeva, M. Mefodev, A. Shaikhiev, A. Kholodenko, S. Kurshetsov, V. Obraztsov, V. Ostankov, A. Semenov, V. Sugonyaev, V. Yushchenko, O. Bician, L. Blazek, T. Cerny, V. Koval, M. Kucerova, Z. Ceccucci, A. Danielsson, H. Simone, N. De Duval, F. Döbrich, B. Gatignon, L. Guida, R. Hahn, F. Jenninger, B. Laycock, P. Miotto, G. Lehmann Lichard, P. Mapelli, A. Massri, K. Noy, M. Université Catholique de Louvain Louvain-La-Neuve Belgium Faculty of Physics University of Sofia Sofia Bulgaria TRIUMF VancouverBC Canada University of British Columbia VancouverBC Canada Charles University Prague Czech Republic Institut für Physik PRISMA Cluster of excellence Universität Mainz Mainz Germany Dipartimento di Fisica e Scienze della Terra dell’Università e INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Ferrara Ferrara Italy Dipartimento di Fisica e Astronomia dell’Università e INFN Sezione di Firenze Sesto Fiorentino Italy INFN Sezione di Firenze Sesto Fiorentino Italy Laboratori Nazionali di Frascati Frascati Italy Dipartimento di Fisica " Ettore Pancini" e INFN Sezione di Napoli Napoli Italy Dipartimento di Fisica e Geologia dell’Università e INFN Sezione di Perugia Perugia Italy INFN Sezione di Perugia Perugia Italy Dipartimento di Fisica dell’Università e INFN Sezione di Pisa Pisa Italy INFN Sezione di Pisa Pisa Italy Scuola Normale Superiore e INFN Sezione di Pisa Pisa Italy Dipartimento di Fisica Sapienza Università di Roma e INFN Sezione di Roma I Roma Italy INFN Sezione di Roma I Roma Italy INFN Sezione di Roma Tor Vergata Roma Italy Dipartimento di Fisica dell’Università e INFN Sezione di Torino Torino Italy INFN Sezione di Torino Torino Italy Instituto de Física Universidad Autónoma de San Luis Potosí San Luis Potosí Mexico Horia Hulubei national Institute of Physics and Nuclear Engineering Bucharest-Magurele Romania Joint Institute for Nuclear Research Dubna Russia Institute for Nuclear Research of the Russian Academy of Sciences Moscow Russia Institute for High Energy Physics State Research Center of Russian Federation Protvino Russia Faculty of Mathematics Physics and Informatics Comenius University Bratislava Slovakia CERN European Organization for Nuclear Research Geneva Switzerland University of Birmingham Birmingham United Kingdom University of Bristol Bristol United Kingdom University o

The NA62 experiment at the CERN SPS reports the first search for K+ → π+νν¯ using the decay-in-flight technique, based on a sample of 1.21 ×1011 K+ decays collected in 2016. The single event sensitivity is 3.15 × 10−10, corresponding to 0.267 Standard Model events. One signal candidate is observed while the expected background is 0.152 events. This leads to an upper limit of 14 × 10−10 on the K+ → π+νν¯ branching ratio at 95% CL. Copyright © 2018, The Authors. All rights reserved.

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DIRECT NUMERICAL SIMULATION OF SUPERSONIC TURBULENT FLOW IN AN EXPANSION-COMPRESSION CORNER AT MACH=2.9

DIRECT NUMERICAL SIMULATION OF SUPERSONIC TURBULENT FLOW IN ...

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第十七届全国流体力学数值方法研讨会

作者： Jian Fang Yufeng Yao Lipeng Lu Alexander A.Zheltovodov National Key Laboratory of Science and Technology on Aero-Engine Aero-Thermodynamics School of Energy and Power Engineering Beihang UniversityBeijing 100191 China Faculty of Environment and Technology Department of Engineering Design and Mathematics University of the West of England Bristol BS16 1QY United Kingdom Khristianovich Institute of Theoretical and Applied Mechanics Siberian Branch of Russian Academy of Science Novosibirsk 630090 Russia

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Implementation and test of human-operated and human-like adaptive impedance controls on Baxter robot

Implementation and test of human-operated and human-like ada...

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15th Annual Conference on Advances in Autonomous robotics Systems, TAROS 2014

作者： Liang, Peidong Yang, Chenguang Wang, Ning Li, Zhijun Li, Ruifeng Burdet, Etienne Centre for Robotics and Neural Systems School of Computing and Mathematics Plymouth University United Kingdom MOE Key Lab. of Autonomous System and Network Control College of Automation Science and Engineering South China University of Technology China State Key Laboratory of Robotics and System Harbin Institute of Technology China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Bioengineering Imperial College of Science Technology and Medicine London United Kingdom

ISBN: (纸本)9783319104003

This paper presents an improved method to teleoperate impedance of a robot based on surface electromyography (EMG) and test it experimentally. Based on a linear mapping between EMG amplitude and stiffness, an incremental stiffness extraction method is developed, which uses instantaneous amplitude identified from EMG in a high frequency band, compensating for non-linear residual error in the linear mapping and preventing muscle fatigue from affecting the control. Experiments on one joint of the Baxter robot are carried out to test the approach in a disturbance attenuation task, and to compare it with automatic human-like impedance adaptation. The experimental results demonstrate that the new human operated impedance method is successful at attenuating disturbance, and results similarly to as automatic disturbance attenuation, thus demonstrating its efficiency. © 2014 Springer International Publishing.

关键词： Stiffness

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Search for heavy neutral lepton production in K+ decays

arXiv

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arXiv 2017年

作者： Cortina Gil, E. Minucci, E. Padolski, S. Petrov, P. Velghe, B. Georgiev, G. Kozhuharov, V. Litov, L. Numao, T. Bryman, D. Fu, J. Husek, T. Kampf, K. Zamkovsky, M. Aliberti, R. Khoriauli, G. Kunze, J. Lomidze, D. Marchevski, R. Peruzzo, L. Vormstein, M. Wanke, R. Dalpiaz, P. Fiorini, M. Gamberini, E. Neri, I. Norton, A. Petrucci, F. Wahl, H. Cotta Ramusino, A. Gianoli, A. Iacopini, E. Latino, G. Lenti, M. Bizzeti, A. Bucci, F. Volpe, R. Antonelli, A. Gonnella, F. Lamanna, G. Lanfranchi, G. Mannocchi, G. Martellotti, S. Moulson, M. Raggi, M. Spadaro, T. Ambrosino, F. Capussela, T. Corvino, M. Filippo, D. Massarotti, P. Mirra, M. Napolitano, M. Saracino, G. Anzivino, G. Brizioli, F. Imbergamo, E. Lollini, R. Santoni, C. Barbanera, M. Cenci, P. Checcucci, B. Duk, V. Lubrano, P. Lupi, M. Pepe, M. Piccini, M. Costantini, F. Lella, L. Doble, N. Giorgi, M. Giudici, S. Pedreschi, E. Sozzi, M. Cerri, C. Fantechi, R. Piandani, R. Pinzino, J. Pontisso, L. Spinella, F. Mannelli, I. D’Agostini, G. Biagioni, A. Leonardi, E. Lonardo, A. Valente, P. Vicini, P. Ammendola, R. Bonaiuto, V. de Simone, N. Federici, L. Fucci, A. Salamon, A. Sargeni, F. Arcidiacono, R. Bloch-Devaux, B. Boretto, M. Iacobuzio, L. Menichetti, E. Migliore, E. Soldi, D. Biino, C. Filippi, A. Marchetto, F. Engelfried, J. Estrada-Tristan, N. Bragadireanu, A.M. Ghinescu, S.A. Hutanu, O.E. Enik, T. Falaleev, V. Kekelidze, V. Korotkova, A. Madigozhin, D. Misheva, M. Molokanova, N. Movchan, S. Polenkevich, I. Potrebenikov Shkarovskiy, S. Zinchenko, A. Fedotov, S. Gushchin, E. Khotyantsev, A. Kleimenova, A. Kudenko, Y. Kurochka, V. Medvedeva, M. Mefodev, A. Shaikhiev, A. Kholodenko, S. Kurshetsov, V. Obraztsov, V. Ostankov, A. Semenov, V. Sugonyaev, V. Yushchenko, O. Bician, L. Blazek, T. Cerny, V. Koval, M. Kucerova, Z. Ceccucci, A. Danielsson, H. Duval, F. Döbrich, B. Gatignon, L. Guida, R. Hahn, F. Jenninger, B. Laycock, P. Lehmann Miotto, G. Lichard, P. Mapelli, A. Université Catholique de Louvain Louvain-La-Neuve Belgium Faculty of Physics University of Sofia Sofia Bulgaria TRIUMF VancouverBC Canada University of British Columbia VancouverBC Canada Charles University Prague Czech Republic Institut für Physik PRISMA Cluster of excellence Universität Mainz Mainz Germany Dipartimento di Fisica e Scienze della Terra dell’Università e INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Ferrara Ferrara Italy Dipartimento di Fisica e Astronomia dell’Università e INFN Sezione di Firenze Sesto Fiorentino Italy INFN Sezione di Firenze Sesto Fiorentino Italy Laboratori Nazionali di Frascati Frascati Italy Dipartimento di Fisica "Ettore Pancini" e INFN Sezione di Napoli Napoli Italy Dipartimento di Fisica e Geologia dell’Università e INFN Sezione di Perugia Perugia Italy INFN Sezione di Perugia Perugia Italy Dipartimento di Fisica dell’Università e INFN Sezione di Pisa Pisa Italy INFN Sezione di Pisa Pisa Italy Scuola Normale Superiore e INFN Sezione di Pisa Pisa Italy Dipartimento di Fisica Sapienza Università di Roma e INFN Sezione di Roma I Roma Italy INFN Sezione di Roma I Roma Italy INFN Sezione di Roma Tor Vergata Roma Italy Dipartimento di Fisica dell’Università e INFN Sezione di Torino Torino Italy INFN Sezione di Torino Torino Italy Instituto de Física Universidad Autónoma de San Luis Potosí San Luis Potosí Mexico Horia Hulubei national Institute of Physics and Nuclear Engineering Bucharest-Magurele Romania Joint Institute for Nuclear Research Dubna Russia Institute for Nuclear Research of the Russian Academy of Sciences Moscow Russia Institute for High Energy Physics State Research Center of Russian Federation Protvino Russia Faculty of Mathematics Physics and Informatics Comenius University Bratislava Slovakia CERN European Organization for Nuclear Research Geneva Switzerland University of Birmingham Birmingham United Kingdom University of Bristol Bristol United Kingdom University of

A search for heavy neutral lepton production in K+ decays using a data sample collected with a minimum bias trigger by the NA62 experiment at CERN in 2015 is reported. Upper limits at the 10−7 to 10−6 level are established on the elements of the extended neutrino mixing matrix |Ue4|2 and |Uµ4|2 for heavy neutral lepton mass in the ranges 170–448 MeV/c2 and 250–373 MeV/c2, respectively. This improves on the previous limits from HNL production searches over the whole mass range considered for |Ue4|2, and above 300 MeV/c2 for |Uµ4|2. Copyright © 2017, The Authors. All rights reserved.

关键词： Elementary particles

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Boson Sampling from a Gaussian State

引用

Physical Review Letters 2014年第10期113卷 100502-100502页

作者： A. P. Lund A. Laing S. Rahimi-Keshari T. Rudolph J. L. O’Brien T. C. Ralph Centre for Quantum Computation and Communication Technology School of Mathematics and Physics University of Queensland Brisbane Queensland 4072 Australia Centre for Quantum Photonics H. H. Wills Physics Laboratory and Department of Electrical and Electronic Engineering University of Bristol Bristol BS8 1UB United Kingdom Optics Section Blackett Laboratory Imperial College London London SW7 2AZ United Kingdom

We pose a randomized boson-sampling problem. Strong evidence exists that such a problem becomes intractable on a classical computer as a function of the number of bosons. We describe a quantum optical processor that can solve this problem efficiently based on a Gaussian input state, a linear optical network, and nonadaptive photon counting measurements. All the elements required to build such a processor currently exist. The demonstration of such a device would provide empirical evidence that quantum computers can, indeed, outperform classical computers and could lead to applications.

关键词： BOSONS GAUSSIAN processes PHOTONS QUANTUM theory QUANTUM computers

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