检索结果-内蒙古大学图书馆

Measurement of differential tt¯ production cross sections using top quarks at large transverse momenta in pp collisions at s=13 TeV

引用

Physical Review D 2021年第5期103卷 052008-052008页

作者： A. M. Sirunyan A. Tumasyan W. Adam F. Ambrogi T. Bergauer M. Dragicevic J. Erö A. Escalante Del Valle R. Frühwirth M. Jeitler N. Krammer L. Lechner D. Liko T. Madlener I. Mikulec N. Rad J. Schieck R. Schöfbeck M. Spanring S. Templ W. Waltenberger C.-E. Wulz M. Zarucki V. Chekhovsky A. Litomin V. Makarenko J. Suarez Gonzalez M. R. Darwish E. A. De Wolf D. Di Croce X. Janssen T. Kello A. Lelek M. Pieters H. Rejeb Sfar H. Van Haevermaet P. Van Mechelen S. Van Putte N. Van Remortel F. Blekman E. S. Bols S. S. Chhibra J. D’Hondt J. De Clercq D. Lontkovskyi S. Lowette I. Marchesini S. Moortgat Q. Python S. Tavernier W. Van Doninck P. Van Mulders D. Beghin B. Bilin B. Clerbaux G. De Lentdecker H. Delannoy B. Dorney L. Favart A. Grebenyuk A. K. Kalsi I. Makarenko L. Moureaux L. Pétré A. Popov N. Postiau E. Starling L. Thomas C. Vander Velde P. Vanlaer D. Vannerom L. Wezenbeek T. Cornelis D. Dobur I. Khvastunov M. Niedziela C. Roskas K. Skovpen M. Tytgat W. Verbeke B. Vermassen M. Vit G. Bruno F. Bury C. Caputo P. David C. Delaere M. Delcourt I. S. Donertas A. Giammanco V. Lemaitre J. Prisciandaro A. Saggio A. Taliercio M. Teklishyn P. Vischia S. Wuyckens J. Zobec G. A. Alves G. Correia Silva C. Hensel A. Moraes W. L. Aldá Júnior E. Belchior Batista Das Chagas W. Carvalho J. Chinellato E. Coelho E. M. Da Costa G. G. Da Silveira D. De Jesus Damiao S. Fonseca De Souza H. Malbouisson J. Martins D. Matos Figueiredo M. Medina Jaime M. Melo De Almeida C. Mora Herrera L. Mundim H. Nogima P. Rebello Teles L. J. Sanchez Rosas A. Santoro S. M. Silva Do Amaral A. Sznajder M. Thiel E. J. Tonelli Manganote F. Torres Da Silva De Araujo A. Vilela Pereira C. A. Bernardes L. Calligaris T. R. Fernandez Perez Tomei E. M. Gregores D. S. Lemos P. G. Mercadante S. F. Novaes Sandra S. Padula A. Aleksandrov G. Antchev I. Atanasov R. Hadjiiska P. Iaydjiev M. Misheva M. Rodozov M. Shopova G. Sultanov M. Bonchev A. Dimitrov T. Ivanov L. Litov B. Pavlov P. Petkov A. Petrov W. Fang Q. Guo H. Wang L. Yuan M. Ahmad Z. Hu Y. Wang E. Chapon G. M. Chen H. Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Wien Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual Paulista São Paulo Brazil Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China University of Illinois at Chicago (UIC) Chicago Illinois USA Department of Physics Tsinghua University Beijing China University of Virginia Charlottesville Virginia USA Institute of High Energy Physics Beijing China Massachusetts Institute of Technology Cambridge Massachusetts USA State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China The University of Kansas Lawrence Kansas USA Sun Yat-Sen University Guangzhou China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE)—Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physic

A measurement is reported of differential top quark pair (tt¯) production cross sections, where top quarks are produced at large transverse momenta. The data collected with the CMS detector at the LHC are from pp collisions at a center-of-mass energy of 13 TeV corresponding to an integrated luminosity of 35.9 fb−1. The measurement uses events where at least one top quark decays as t→Wb→qq¯′b and is reconstructed as a large-radius jet with transverse momentum in excess of 400 GeV. The second top quark is required to decay either in a similar way or leptonically, as inferred from a reconstructed electron or muon, a bottom quark jet, and missing transverse momentum due to the undetected neutrino. The cross section is extracted as a function of kinematic variables of individual top quarks or of the tt¯ system. The results are presented at the particle level, within a region of phase space close to that of the experimental acceptance, and at the parton level and are compared to various theoretical models. In both decay channels, the observed absolute cross sections are significantly lower than the predictions from theory, while the normalized differential measurements are well described.

关键词： Top quark Hadron colliders

来源：评论

学校读者我要写书评

暂无评论

The breakthrough listen search for intelligent life: Public data, formats, reduction and archiving

arXiv

引用

arXiv 2019年

作者： Lebofsky, Matthew Croft, Steve Siemion, Andrew P.V. Price, Danny C. Enriquez, J. Emilio Isaacson, Howard MacMahon, David H.E. Anderson, David Brzycki, Bryan Cobb, Jeff Czech, Daniel DeBoer, David DeMarines, Julia Drew, Jamie Foster, Griffin Gajjar, Vishal Gizani, Nectaria Hellbourg, Greg Korpela, Eric J. Lacki, Brian Sheikh, Sofia Werthimer, Dan Worden, Pete Yu, Alex Zhang, Yunfan Gerry Department of Astronomy University of California Berkeley BerkeleyCA94720 United States SETI Institute Mountain ViewCA United States Department of Astrophysics IMAPP Radboud University Nijmegen Netherlands University of Malta Institute of Space Sciences and Astronomy Centre for Astrophysics & Supercomputing Swinburne University of Technology HawthornVIC3122 Australia University of Southern Queensland ToowoombaQLD4350 Australia Space Sciences Laboratory University of California Berkeley BerkeleyCA94720 United States Breakthrough Initiatives Astronomy Department University of Oxford Keble Rd OxfordOX13RH United Kingdom Hellenic Open University School of Science & Technology Parodos Aristotelous Perivola Patron Greece International Centre for Radio Astronomy Research Curtin University Australia Breakthrough Listen Department of Astronomy University of California Berkeley BerkeleyCA94720 United States Department of Astronomy and Astrophysics Pennsylvania State University University ParkPA16802 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States

Breakthrough Listen is the most comprehensive and sensitive search for extraterrestrial intelligence (SETI) to date, employing a collection of international observational facilities including both radio and optical telescopes. During the first three years of the Listen program, thousands of targets have been observed with the Green Bank Telescope (GBT), Parkes Telescope and Automated Planet Finder. At GBT and Parkes, observations have been performed ranging from 700 MHz to 26 GHz, with raw data volumes averaging over 1 PB / day. A pseudo-real time software spectroscopy suite is used to produce multi-resolution spectrograms amounting to approximately 400 GB h−1 GHz−1 beam−1. For certain targets, raw baseband voltage data is also preserved. Observations with the Automated Planet Finder produce both 2-dimensional and 1-dimensional high resolution (R ∼ 105) echelle spectral data. Although the primary purpose of Listen data acquisition is for SETI, a range of secondary science has also been performed with these data, including studies of fast radio bursts. Other current and potential research topics include spectral line studies, searches for certain kinds of dark matter, probes of interstellar scattering, pulsar searches, radio transient searches and investigations of stellar activity. Listen data are also being used in the development of algorithms, including machine learning approaches to modulation scheme classification and outlier detection, that have wide applicability not just for astronomical research but for a broad range of science and engineering. In this paper, we describe the hardware and software pipeline used for collection, reduction, archival, and public dissemination of Listen data. We describe the data formats and tools, and present Breakthrough Listen Data Release 1.0 (BLDR 1.0), a defined set of publicly-available raw and reduced data totalling 1 PB. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Erratum to: Search for dark matter produced in association with a leptonically decaying Z boson in proton–proton collisions at s = 13 Te

引用

The European Physical Journal C 2021年第4期81卷 1-16页

作者： A. M. Sirunyan A. Tumasyan W. Adam T. Bergauer M. Dragicevic J. Erö A. Escalante Del Valle R. Frühwirth M. Jeitler N. Krammer L. Lechner D. Liko T. Madlener I. Mikulec F. M. Pitters N. Rad J. Schieck R. Schöfbeck M. Spanring S. Templ W. Waltenberger C.-E. Wulz M. Zarucki V. Chekhovsky A. Litomin V. Makarenko J. Suarez Gonzalez M. R. Darwish E. A. De Wolf D. Di Croce X. Janssen T. Kello A. Lelek M. Pieters H. Rejeb Sfar H. Van Haevermaet P. Van Mechelen S. Van Putte N. Van Remortel F. Blekman E. S. Bols S. S. Chhibra J. D’Hondt J. De Clercq D. Lontkovskyi S. Lowette I. Marchesini S. Moortgat A. Morton Q. Python S. Tavernier W. Van Doninck P. Van Mulders D. Beghin B. Bilin B. Clerbaux G. De Lentdecker B. Dorney L. Favart A. Grebenyuk A. K. Kalsi I. Makarenko L. Moureaux L. Pétré A. Popov N. Postiau E. Starling L. Thomas C. Vander Velde P. Vanlaer D. Vannerom L. Wezenbeek T. Cornelis D. Dobur M. Gruchala I. Khvastunov M. Niedziela C. Roskas K. Skovpen M. Tytgat W. Verbeke B. Vermassen M. Vit G. Bruno F. Bury C. Caputo P. David C. Delaere M. Delcourt I. S. Donertas A. Giammanco V. Lemaitre K. Mondal J. Prisciandaro A. Taliercio M. Teklishyn P. Vischia S. Wuyckens J. Zobec G. A. Alves C. Hensel A. Moraes W. L. Aldá Júnior E. Belchior Batista Das Chagas H. BRANDAO MALBOUISSON W. Carvalho J. Chinellato E. Coelho E. M. Da Costa G. G. Da Silveira D. De Jesus Damiao S. Fonseca De Souza J. Martins D. Matos Figueiredo M. Medina Jaime C. Mora Herrera L. Mundim H. Nogima P. Rebello Teles L. J. Sanchez Rosas A. Santoro S. M. Silva Do Amaral A. Sznajder M. Thiel F. Torres Da Silva De Araujo A. Vilela Pereira C. A. Bernardes L. Calligaris T. R. Fernandez Perez Tomei E. M. Gregores D. S. Lemos P. G. Mercadante S. F. Novaes Sandra S. Padula A. Aleksandrov G. Antchev I. Atanasov R. Hadjiiska P. Iaydjiev M. Misheva M. Rodozov M. Shopova G. Sultanov M. Bonchev A. Dimitrov T. Ivanov L. Litov B. Pavlov P. Petkov A. Petrov W. Fang Q. Guo H. Wang L. Yuan M. Ahmad Z. Hu Y. Wang E. Chapon G. M. Chen H. S. Chen M. Chen A. Kapoor D. Leggat H. Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Vienna University of Technology Vienna Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Department of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Université Libre de Bruxelles Bruxelles Belgium Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Federal de Pelotas Pelotas Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ‘K

A Correction to this paper has been published: https://***/10.1140/epjc/s10052-020-08739-5

关键词：

来源：评论

学校读者我要写书评

暂无评论

A hybridizable discontinuous Galerkin method for computing nonlocal electromagnetic effects in three-dimensional metallic nanostructures

arXiv

引用

arXiv 2017年

作者： Vidal-Codina, F. Nguyen, N.C. Oh, S.-H. Peraire, J. Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

The interaction of light with metallic nanostructures produces a collective excitation of electrons at the metal surface, also known as surface plasmons. These collective excitations lead to resonances that enable the confinement of light in deep-subwavelength regions, thereby leading to large near-field enhancements. The simulation of plasmon resonances presents notable challenges. From the modeling perspective, the realistic behavior of conduction-band electrons in metallic nanostructures is not captured by Maxwell's equations, thus requiring additional modeling. From the simulation perspective, the disparity in length scales stemming from the extreme field localization demands efficient and accurate numerical methods. In this paper, we develop the hybridizable discontinuous Galerkin (HDG) method to solve Maxwell's equations augmented with the hydrodynamic model for the conduction-band electrons in noble metals. This method enables the efficient simulation of plasmonic nanostructures while accounting for the nonlocal interactions between electrons and the incident light. We introduce a novel postprocessing scheme to recover superconvergent solutions and demonstrate the convergence of the proposed HDG method for the simulation of a 2D gold nanowire and a 3D periodic annular nanogap structure. The results of the hydrodynamic model are compared to those of a simplified local response model, showing that differences between them can be significant at the nanoscale. Copyright © 2017, The Authors. All rights reserved.

关键词： Galerkin methods

来源：评论

学校读者我要写书评

暂无评论

PSSE Redux: Convex relaxation, decentralized, robust, and dynamic approaches

arXiv

引用

arXiv 2017年

作者： Kekatos, Vassilis Wang, Gang Zhu, Hao Giannakis, Georgios B. Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States

来源：评论

学校读者我要写书评

暂无评论

Spatially controlled electrostatic doping in graphene p-i-n junction for hybrid silicon photodiode

arXiv

引用

arXiv 2018年

作者： Li, T.T. Mao, D. Petrone, N.W. Grassi, R. Hu, H. Ding, Y. Huang, Z. Lo, G.-Q. Hone, J.C. Low, T. Wong, C.W. Gu, T. Department of Electrical and Computer Engineering University of Delaware NewarkDE19711 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States Department of Electrical Engineering University of Minnesota MinneapolisMN55455 United States DTU Fotonik Technical University of Denmark Kgs. LyngbyDK-2800 Denmark Hewlett-Packard Laboratories 1501 Page Mill Rd. Palo AltoCA94304 United States Institute of Microelectronics Singapore Science Park II 11 Science Park Road Singapore117685 Singapore Mesoscopic Optics and Quantum Electronics Laboratory University of California Los AngelesCA90095

Sufficiently large depletion region for photocarrier generation and separation is a key factor for two-dimensional material optoelectronic devices, but few device configurations has been explored for a deterministic control of a space charge region area in graphene with convincing scalability. Here we investigate a graphene-silicon p-i-n photodiode defined in a foundry processed planar photonic crystal waveguide structure, achieving visible - near-infrared, zero-bias and ultrafast photodetection. Graphene is electrically contacting to the wide intrinsic region of silicon and extended to the p an n doped region, functioning as the primary photocarrier conducting channel for electronic gain. Graphene significantly improves the device speed through ultrafast out-of-plane interfacial carrier transfer and the following in-plane built-in electric field assisted carrier collection. More than 50 dB converted signal-to-noise ratio at 40 GHz has been demonstrated under zero bias voltage, with quantum efficiency could be further amplified by hot carrier gain on graphene-i Si interface and avalanche process on graphene-doped Si interface. With the device architecture fully defined by nanomanufactured substrate, this study is the first demonstration of post-fabrication-free two-dimensional material active silicon photonic devices. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphene

来源：评论

学校读者我要写书评

暂无评论

Solving almost all systems of random quadratic equations

arXiv

引用

arXiv 2017年

作者： Wang, Gang Giannakis, Georgios B. Saad, Yousef Chen, Jie Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

This paper deals with finding an n-dimensional solution x to a system of quadratic equations of the form yi = jhai;xij2 for 1 ≤ i ≤ m, which is also known as phase retrieval and is NP-hard in general. We put forth a novel procedure for minimizing the amplitude-based least-squares empirical loss, that starts with a weighted maximal correlation initialization obtainable with a few power or Lanczos iterations, followed by successive refinements based upon a sequence of iteratively reweighted (generalized) gradient iterations. The two (both the initialization and gradient flow) stages distinguish themselves from prior contributions by the inclusion of a fresh (re)weighting regularization technique. The overall algorithm is conceptually simple, numerically scalable, and easy-to-implement. For certain random measurement models, the novel procedure is shown capable of finding the true solution x in time proportional to reading the data f(ai;yi)g1≤i≤m. This holds with high probability and without extra assumption on the signal x to be recovered, provided that the number m of equations is some constant c > 0 times the number n of unknowns in the signal vector, namely, m > cn. Empirically, the upshots of this contribution are: I) (almost) 100% perfect signal recovery in the high-dimensional (say e.g., n ≥2;000) regime given only an information-theoretic limit number of noiseless equations, namely, m = 2n - 1 in the real-valued Gaussian case;and, ii) (nearly) optimal statistical accuracy in the presence of additive noise of bounded support. Finally, substantial numerical tests using both synthetic data and real images corroborate markedly improved signal recovery performance and computational efficiency of our novel procedure relative to state-of-the-art approaches. Copyright © 2017, The Authors. All rights reserved.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Neuromatch academy: Teaching computational neuroscience with global accessibility

arXiv

引用

arXiv 2020年

作者： van Viegen, Tara Akrami, Athena Bonnen, Kate DeWitt, Eric Hyafil, Alexandre Ledmyr, Helena Lindsay, Grace W. Mineault, Patrick Murray, John D. Pitkow, Xaq Puce, Aina Sedigh-Sarvestani, Madineh Stringer, Carsen Achakulvisut, Titipat Alikarami, Elnaz Atay, Melvin Selim Batty, Eleanor Erlich, Jeffrey C. Galbraith, Byron V. Guo, Yueqi Juavinett, Ashley L. Krause, Matthew R. Li, Songting Pachitariu, Marius Straley, Elizabeth Valeriani, Davide Vaughan, Emma Vaziri-Pashkam, Maryam Waskom, Michael L. Blohm, Gunnar Kording, Konrad Schrater, Paul Wyble, Brad Escola, Sean Peters, Megan A.K. Princeton Neuroscience Institute Princeton University PrincetonNJ United States Sainsbury Wellcome Centre University College London London United Kingdom Center for Neural Science New York University New York CityNY United States Champalimaud Research Champalimaud Foundation Lisbon Portugal Centre de Recerca Matemàtica Bellaterra Universitat Autònoma de Barcelona Barcelona Spain International Neuroinformatics Coordinating Facility Sweden Gatsby Computational Neuroscience Unit Sainsbury Wellcome Centre University College London London United Kingdom Department of Psychiatry Yale University New HavenCT United States Baylor College of Medicine Dept. of Neuroscience HoustonTX United States Baylor College of Medicine Center for Neuroscience and Artificial Intelligence HoustonTX United States Rice University Dept of Electrical and Computer Engineering HoustonTX United States Psychological & Brain Sciences Indiana University BloomingtonIN United States Max Planck Florida Institute for Neuroscience JupiterFL United States HHMI Janelia Research Campus AshburnVA United States University of Pennsylvania Dept of Neuroscience and Bioengineering PhiladelphiaPA United States Faculty of dentistry McGill University MontrealQB Canada Middle East Technical University Ankara Turkey Department of Neurobiology Harvard University CambridgeMA United States New York University Shanghai Shanghai China Talla Inc BostonMA United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD United States Division of Biological Sciences UC San Diego San diegoCA United States Montreal Neurological Institute McGill University MontrealQB Canada School of Mathematical Sciences MOE-LSC Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China Department of Otolaryngology - Head & Neck Surgery Massachusetts Eye and Ear BostonMA United States Department of Otolaryngology - Head & Neck Surgery Harvard Medical School BostonM

Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility. © 2020, CC BY.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

引用

arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Giant Perpendicular Exchange Bias in a Subnanometer Inverted (Co/Pt)n/Co/IrMn Structure

引用

Physical Review Applied 2017年第5期7卷 054005-054005页

作者： Jiafeng Feng H. F. Liu H. X. Wei X.-G. Zhang Yong Ren Xinxi Li Yan Wang J. P. Wang X. F. Han Beijing National Laboratory of Condensed Matter Physics Institute of Physics University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing 100190 China Department of Physics and Quantum Theory Project University of Florida Gainesville Florida 32611 USA Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics Mianyang 621999 China The Center for Micromagnetics and Information Technologies (MINT) and Department of Electrical and Computer Engineering University of Minnesota 200 Union St SE Minneapolis Minnesota 55455 USA

We report a giant perpendicular exchange bias Hexper exhibited in the R−H curves of an inverted (Co/Pt)n/Co/IrMn multilayer and an inverted (Co/Pt)n/Co/Cu/(Co/Pt)4/Co/IrMn spin-valve structure, in which the Pt layers are only 1/4 nm in thickness and thinner than the Co layers. Hexper shows an unusual linear scaling with the repetition n+1 of the (Co/Pt)n/Co free layer, reaching 792 Oe for n+1=9. In contrast, the in-plane exchange bias Hexin is almost constant. This effect originates from Co/IrMn interfacial magnetic coupling in the inverted (Co/Pt)4/Co/IrMn reference layer and is magnified by a near-perpendicular exchange coupling between the interface Co layer and the multilayer (Co/Pt)n (n=4 for the spin-valve structures) in the reference layer. The latter also leads to a linear scaling of the exchange bias with the number of repetitions of Co/Pt in the reference layer.

关键词： Exchange bias Spintronics Magnetic multilayers Spin valves Transition metal alloys

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：