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arXiv 2018年

作者： Liu, Si Sun, Yao Zhu, Defa Ren, Guanghui Chen, Yu Feng, Jiashi Han, Jizhong Institute of Information Engineering Chinese Academy of Sciences *** Department of Ece National University of Singapore Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science and Technology Collab. Innov. Ctr. of Atmosph. Environ. and Equip. Technol. Nanjing Univ. of Info. Sci. and Technol. Nanjing China

Human parsing has been extensively studied recently (Yamaguchi et al. 2012;Xia et al. 2017) due to its wide applications in many important scenarios. Mainstream fashion parsing models (i.e., parsers) focus on parsing the high-resolution and clean images. However, directly applying the parsers trained on benchmarks of high-quality samples to a particular application scenario in the wild, e.g., a canteen, airport or workplace, often gives non-satisfactory performance due to domain shift. In this paper, we explore a new and challenging cross-domain human parsing problem: Taking the benchmark dataset with extensive pixel-wise labeling as the source domain, how to obtain a satisfactory parser on a new target domain without requiring any additional manual labeling? To this end, we propose a novel and efficient crossdomain human parsing model to bridge the cross-domain differences in terms of visual appearance and environment conditions and fully exploit commonalities across domains. Our proposed model explicitly learns a feature compensation network, which is specialized for mitigating the cross-domain differences. A discriminative feature adversarial network is introduced to supervise the feature compensation to effectively reduces the discrepancy between feature distributions of two domains. Besides, our proposed model also introduces a structured label adversarial network to guide the parsing results of the target domain to follow the high-order relationships of the structured labels shared across domains. The proposed framework is end-to-end trainable, practical and scalable in real applications. Extensive experiments are conducted where LIP dataset is the source domain and 4 different datasets including surveillance videos, movies and runway shows without any annotations, are evaluated as target domains. The results consistently confirm data efficiency and performance advantages of the proposed method for the challenging cross-domain human parsing problem. Copyright

关键词： Security systems

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Author Correction: The Genome Sequences of 90 Mushrooms

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Scientific reports 2020年第1期10卷 8460页

作者： Huiying Li Surui Wu Xiao Ma Wei Chen Jing Zhang Shengchang Duan Yun Gao Ling Kui Wenli Huang Peng Wu Ruoyu Shi Yifan Li Yuanzhong Wang Jieqing Li Xiang Guo Xiaoli Luo Qiang Li Chuan Xiong Honggao Liu Mingying Gui Jun Sheng Yang Dong Kunming University of Science and Technology Kunming 650500 Yunnan China. Kunming Edible Fungi Institute of All China Federation of Supply and Marketing Cooperatives Kunming 650032 Yunnan China. Yunnan Plateau Characteristic Agricultural Industry Research Institute Kunming 650201 Yunnan China. Key Laboratory of Puer Tea Science Ministry of Education Yunnan Agricultural University Kunming 650201 Yunnan China. College of Biological Big Data Yunnan Agriculture University Kunming 650201 Yunnan China. Nowbio Biotechnology Company Kunming 650201 Yunnan China. State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming 650223 Yunnan China. Kunming College of Life Science University of Chinese Academy of Sciences Kunming 650204 Yunnan China. Biotechnology and Nuclear Technology Research Institute Sichuan Academy of Agricultural Sciences Chengdu 610061 Sichuan China. College of Agronomy and Biotechnology Yunnan Agricultural University Kunming 650201 Yunnan China. Kunming Edible Fungi Institute of All China Federation of Supply and Marketing Cooperatives Kunming 650032 Yunnan China. guimingying@***. Yunnan Plateau Characteristic Agricultural Industry Research Institute Kunming 650201 Yunnan China. shengj@***. Key Laboratory of Puer Tea Science Ministry of Education Yunnan Agricultural University Kunming 650201 Yunnan China. shengj@***. College of Biological Big Data Yunnan Agriculture University Kunming 650201 Yunnan China. loyalyang@***. State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan Yunnan Agricultural University Kunming 650201 Yunnan China. loyalyang@***. Key Laboratory for Agro-biodiversity and Pest Control of Ministry of Education Yunnan Agricultural University Kunming 650201 Yunnan China. loyalyang@***.

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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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

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Nature 2024年第8003期627卷 347-357页

作者： Ken Suzuki Konstantinos Hatzikotoulas Lorraine Southam Henry J Taylor Xianyong Yin Kim M Lorenz Ravi Mandla Alicia Huerta-Chagoya Giorgio E M Melloni Stavroula Kanoni Nigel W Rayner Ozvan Bocher Ana Luiza Arruda Kyuto Sonehara Shinichi Namba Simon S K Lee Michael H Preuss Lauren E Petty Philip Schroeder Brett Vanderwerff Mart Kals Fiona Bragg Kuang Lin Xiuqing Guo Weihua Zhang Jie Yao Young Jin Kim Mariaelisa Graff Fumihiko Takeuchi Jana Nano Amel Lamri Masahiro Nakatochi Sanghoon Moon Robert A Scott James P Cook Jung-Jin Lee Ian Pan Daniel Taliun Esteban J Parra Jin-Fang Chai Lawrence F Bielak Yasuharu Tabara Yang Hai Gudmar Thorleifsson Niels Grarup Tamar Sofer Matthias Wuttke Chloé Sarnowski Christian Gieger Darryl Nousome Stella Trompet Soo-Heon Kwak Jirong Long Meng Sun Lin Tong Wei-Min Chen Suraj S Nongmaithem Raymond Noordam Victor J Y Lim Claudia H T Tam Yoonjung Yoonie Joo Chien-Hsiun Chen Laura M Raffield Bram Peter Prins Aude Nicolas Lisa R Yanek Guanjie Chen Jennifer A Brody Edmond Kabagambe Ping An Anny H Xiang Hyeok Sun Choi Brian E Cade Jingyi Tan K Alaine Broadaway Alice Williamson Zoha Kamali Jinrui Cui Manonanthini Thangam Linda S Adair Adebowale Adeyemo Carlos A Aguilar-Salinas Tarunveer S Ahluwalia Sonia S Anand Alain Bertoni Jette Bork-Jensen Ivan Brandslund Thomas A Buchanan Charles F Burant Adam S Butterworth Mickaël Canouil Juliana C N Chan Li-Ching Chang Miao-Li Chee Ji Chen Shyh-Huei Chen Yuan-Tsong Chen Zhengming Chen Lee-Ming Chuang Mary Cushman John Danesh Swapan K Das H Janaka de Silva George Dedoussis Latchezar Dimitrov Ayo P Doumatey Shufa Du Qing Duan Kai-Uwe Eckardt Leslie S Emery Daniel S Evans Michele K Evans Krista Fischer James S Floyd Ian Ford Oscar H Franco Timothy M Frayling Barry I Freedman Pauline Genter Hertzel C Gerstein Vilmantas Giedraitis Clicerio González-Villalpando Maria Elena González-Villalpando Penny Gordon-Larsen Myron Gross Lindsay A Guare Sophie Hackinger Liisa Hakaste Sohee Han Andrew T Hattersley Christian Herder Momoko Horikoshi Annie-Green Howard Willa Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester UK. Department of Diabetes and Metabolic Diseases Graduate School of Medicine University of Tokyo Tokyo Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. konstantinos.hatzikotoulas@helmholtz-munich.de. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD USA. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Heart and Lung Research Institute University of Cambridge Cambridge UK. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor MI USA. Corporal Michael J. Crescenz VA Medical Center Philadelphia PA USA. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Genetics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. TIMI Study Group Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London L

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

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Observation of the Mass Difference between Neutral Charm-Meson Eigenstates

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Physical Review Letters 2021年第11期127卷 111801-111801页

作者： R. Aaij C. Abellán Beteta T. Ackernley B. Adeva M. Adinolfi H. Afsharnia C. A. Aidala S. Aiola Z. Ajaltouni S. Akar J. Albrecht F. Alessio M. Alexander A. Alfonso Albero Z. Aliouche G. Alkhazov P. Alvarez Cartelle S. Amato Y. Amhis L. An L. Anderlini A. Andreianov M. Andreotti F. Archilli A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann M. Bachmayer J. J. Back P. Baladron Rodriguez V. Balagura W. Baldini J. Baptista Leite R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov J. M. Basels G. Bassi B. Batsukh A. Battig A. Bay M. Becker F. Bedeschi I. Bediaga A. Beiter V. Belavin S. Belin V. Bellee K. Belous I. Belov I. Belyaev G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein C. Bertella A. Bertolin C. Betancourt F. Betti Ia. Bezshyiko S. Bhasin J. Bhom L. Bian M. S. Bieker S. Bifani P. Billoir M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti M. Bjørn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska J. A. Boelhauve O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk S. A. Bouchiba T. J. V. Bowcock A. Boyer C. Bozzi M. J. Bradley S. Braun A. Brea Rodriguez M. Brodski J. Brodzicka A. Brossa Gonzalo D. Brundu A. Buonaura C. Burr A. Bursche A. Butkevich J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calefice L. Calero Diaz S. Cali R. Calladine M. Calvi M. Calvo Gomez P. Camargo Magalhaes P. Campana A. F. Campoverde Quezada S. Capelli L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini I. Carli P. Carniti L. Carus K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero S. Celani J. Cerasoli A. J. Chadwick M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis C. A. Chavez Barajas M. Chefdeville C. Chen S. Chen A. Chernov V. Chobanova S. Cholak M. Chrzaszcz A. Chubykin V. Chulikov P. Ciambrone M. F. Cicala X. Cid Vidal G. Ciezarek P. E. L. Clarke M. Clemencic H. V. Cliff J. Closier J. L. Cobbledick V. Coco J. A. B. Coelho J. Cogan E. Cogneras Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France University of Michigan Ann Arbor MI USA (associated with Syracuse University Syracuse New York USA) INFN Sezione di Milano Milano Italy University of Cincinnati Cincinnati Ohio USA Fakultät Physik Technische Universität Dortmund Dortmund Germany European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom ICCUB Universitat de Barcelona Barcelona Spain Department of Physics and Astronomy University of Manchester Manchester United Kingdom Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Cavendish Laboratory University of Cambridge Cambridge United Kingdom Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France INFN Sezione di Firenze Firenze Italy INFN Sezione di Ferrara Ferrara Italy Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI) Protvino Russia Protvino Russia Syracuse University Syracuse New York USA Yandex School of Data Analysis Moscow Russia Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Department of Physics University of Warwick Coventry United Kingdom Centro Brasileiro de Pesq

A measurement of mixing and CP violation in neutral charm mesons is performed using data reconstructed in proton-proton collisions collected by the LHCb experiment from 2016 to 2018, corresponding to an integrated luminosity of 5.4 fb−1. A total of 30.6 million D0→KS0π+π− decays are analyzed using a method optimized for the measurement of the mass difference between neutral charm-meson eigenstates. Allowing for CP violation in mixing and in the interference between mixing and decay, the mass and decay-width differences are measured to be xCP=[3.97±0.46(stat)±0.29(syst)]×10−3 and yCP=[4.59±1.20(stat)±0.85(syst)]×10−3, respectively. The CP-violating parameters are measured as Δx=[−0.27±0.18(stat)±0.01(syst)]×10−3 and Δy=[0.20±0.36(stat)±0.13(syst)]×10−3. This is the first observation of a nonzero mass difference in the D0 meson system, with a significance exceeding seven standard deviations. The data are consistent with CP symmetry and improve existing constraints on the associated parameters.

关键词： Electroweak interaction Hadron mixing Particle mixing & oscillations Quark mixing Charm quark Mesons CP symmetry Discrete symmetries

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Network failure–aware redundant virtual machine placement in a cloud data center

Network failure–aware redundant virtual machine placement i...

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作者： Zhou, Ao Wang, Shangguang Hsu, Ching-Hsien Kim, Myung Ho Wong, Kok-Seng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Mathematics and Big Data Foshan University Foshan China Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan School of Software Soongsil University Seoul Korea Republic of

Cloud has become a very popular infrastructure for many smart city applications. A growing number of smart city applications from all over the world are deployed on the clouds. However, node failure events from the cloud data center have negative impact on the performance of smart city applications. Survivable virtual machine placement has been proposed by the researchers to enhance the service reliability. Because of the ignorance of switch failure, current survivable virtual machine placement approaches cannot achieve the best effect. In this paper, we study to enhance the service reliability by designing a novel network failure–aware redundant virtual machine placement approach in a cloud data center. Firstly, we formulate the network failure–aware redundant virtual machine placement problem as an integer nonlinear programming problem and prove that the problem is NP-hard. Secondly, we propose a heuristic algorithm to solve the problem. Finally, extensive simulation results show the effectiveness of our algorithm. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Reliability

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Two-phase locality-sensitive hashing for privacy-preserving distributed service recommendation 1

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9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Qi, Lianyong Dou, Wanchun Zhang, Xuyun School of Information Science and Engineering Qufu Normal University Jining China Chinese Academy of Education Big Data Qufu Normal University Jining China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand

ISBN: (数字)9783319694719

ISBN: (纸本)9783319694702

With the ever-increasing volume of services registered in various web communities, it becomes a challenging task to find the web services that a target user is really interested in from the massive candidates. In this situation, Collaborative Filtering (i.e., CF) technique is introduced to alleviate the heavy burden on the service selection decisions of target users. However, present CF-based recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios where data are multi-sourced. Furthermore, distributed service recommendation calls for the collaborations among multiple involved parties, during which the private information of users may be exposed. In view of these challenges, we propose a novel privacy-preserving distributed service recommendation approach based on two-phase Locality-Sensitive Hashing (LSH), named SerRectwo-LSH, in this paper. Concretely, in SerRectwo-LSH, we first look for the "similar friends" of a target user through a privacy-preserving two-phase LSH process;afterwards, we determine the services preferred by the "similar friends" of the target user, and then recommend them to the target user. Finally, through a set of experiments conducted on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation. © 2017, Springer International Publishing AG.

关键词： Efficiency

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Observation of Λ0b → D+pπ-π- and Λ0b → D∗+pπ-π- decays

arXiv

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arXiv 2021年

作者： Aaij, R. Abellán Beteta, C. Ackernley, T. Adeva, B. Adinolfi, M. Afsharnia, H. Aidala, C.A. Aiola, S. Ajaltouni, Z. Akar, S. Albrecht, J. Alessio, F. Alexander, M. Alfonso Albero, A. Aliouche, Z. Alkhazov, G. Alvarez Cartelle, P. Amato, S. Amhis, Y. An, L. Anderlini, L. Andreianov, A. Andreotti, M. Archilli, F. Artamonov, A. Artuso, M. Arzymatov, K. Aslanides, E. Atzeni, M. Audurier, B. Bachmann, S. Bachmayer, M. Back, J.J. Baker, S. Baladron Rodriguez, P. Balagura, V. Baldini, W. Baptista Leite, J. Barlow, R.J. Barsuk, S. Barter, W. Bartolini, M. Baryshnikov, F. Basels, J.M. Bassi, G. Batsukh, B. Battig, A. Bay, A. Becker, M. Bedeschi, F. Bediaga, I. Beiter, A. Belavin, V. Belin, S. Bellee, V. Belous, K. Belov, I. Belyaev, I. Bencivenni, G. Ben-Haim, E. Berezhnoy, A. Bernet, R. Berninghoff, D. Bernstein, H.C. Bertella, C. Bertolin, A. Betancourt, C. Betti, F. Bezshyiko, Ia. Bhasin, S. Bhom, J. Bian, L. Bieker, M.S. Bifani, S. Billoir, P. Birch, M. Bishop, F.C.R. Bitadze, A. Bizzeti, A. Bjørn, M. Blago, M.P. Blake, T. Blanc, F. Blusk, S. Bobulska, D. Boelhauve, J.A. Boente Garcia, O. Boettcher, T. Boldyrev, A. Bondar, A. Bondar, N. Borghi, S. Borisyak, M. Borsato, M. Borsuk, J.T. Bouchiba, S.A. Bowcock, T.J.V. Boyer, A. Bozzi, C. Bradley, M.J. Braun, S. Brea Rodriguez, A. Brodski, M. Brodzicka, J. Brossa Gonzalo, A. Brundu, D. Buonaura, A. Burr, C. Bursche, A. Butkevich, A. Butter, J.S. Buytaert, J. Byczynski, W. Cadeddu, S. Cai, H. Calabrese, R. Calefice, L. Calero Diaz, L. Cali, S. Calladine, R. Calvi, M. Calvo Gomez, M. Camargo Magalhaes, P. Camboni, A. Campana, P. Campoverde Quezada, A.F. Capelli, S. Capriotti, L. Carbone, A. Carboni, G. Cardinale, R. Cardini, A. Carli, I. Carniti, P. Carus, L. Carvalho Akiba, K. Casais Vidal, A. Casse, G. Cattaneo, M. Cavallero, G. Celani, S. Cerasoli, J. Chadwick, A.J. Chapman, M.G. Charles, M. Charpentier, Ph. Chatzikonstantinidis, G. Chavez Barajas, C.A. Chefdeville, M. Chen, C. Rio de Janeiro Brazil Rio de Janeiro Brazil Center for High Energy Physics Tsinghua University Beijing China Beijing China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Particle Physics Central China Normal University Hubei Wuhan China Univ. Savoie Mont Blanc CNRS IN2P3-LAPP Annecy France Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano Milano Italy INFN Sezione di Milano-Bicocca Milano Italy INFN Sezione di Cagliari Monserrato Italy Universita degli Studi di Padova Universita e INFN Padova Padova Italy INFN Sezione di Pisa Pisa Italy INFN Sezione di Roma La Sapienza Roma Italy INFN Sezione di Roma Tor Vergata Roma Italy Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Nikhef National Institute for Subatomic Physics VU University Amsterdam Amsterdam Netherlands AGH - University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Henryk Niewodniczanski Institute of Nuclear Physics Polish

The multihadron decays Λ0b → D+pπ-π- and Λ0b → D∗+pπ-π- are observed in data corresponding to an integrated luminosity of 3 fb-1, collected in proton-proton collisions at centre-of-mass energies of 7 and 8 TeV by the LHCb detector. Using the decay Λ0b → Λ+c π+π-π- as a normalisation channel, the ratio of branching fractions is measured to be B(Λ0b → D+pπ-π-) B(Λ0b → Λ+c π+π-π-) × BB (D(Λ++c→→KpK-π-+ππ++ ) ) = (5.35 ± 0.21 ± 0.16) % , where the first uncertainty is statistical and the second systematic. The ratio of branching fractions for the Λ0b → D∗+pπ-π- and Λ0b → D+pπ-π- decays is found to be BB(Λ(Λ0b0b→→DD∗++ppππ--ππ-- ) ) ×(B(D∗+ → D+π0) + B(D∗+ → D+γ)) = (61.3 ± 4.3 ± 4.0) % . © 2021, CC BY.

关键词： Tellurium compounds

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Search for the doubly heavy baryons (Equation presented) and (Equation presented) decaying to (Equation presented) and (Equation presented)

arXiv

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arXiv 2021年

作者： Aaij, R. Abellán Beteta, C. Ackernley, T. Adeva, B. Adinolfi, M. Afsharnia, H. Aidala, C.A. Aiola, S. Ajaltouni, Z. Akar, S. Albrecht, J. Alessio, F. Alexander, M. Alfonso Albero, A. Aliouche, Z. Alkhazov, G. Alvarez Cartelle, P. Amato, S. Amhis, Y. An, L. Anderlini, L. Andreianov, A. Andreotti, M. Archilli, F. Artamonov, A. Artuso, M. Arzymatov, K. Aslanides, E. Atzeni, M. Audurier, B. Bachmann, S. Bachmayer, M. Back, J.J. Baladron Rodriguez, P. Balagura, V. Baldini, W. Baptista Leite, J. Barlow, R.J. Barsuk, S. Barter, W. Bartolini, M. Baryshnikov, F. Basels, J.M. Bassi, G. Batsukh, B. Battig, A. Bay, A. Becker, M. Bedeschi, F. Bediaga, I. Beiter, A. Belavin, V. Belin, S. Bellee, V. Belous, K. Belov, I. Belyaev, I. Bencivenni, G. Ben-Haim, E. Berezhnoy, A. Bernet, R. Berninghoff, D. Bernstein, H.C. Bertella, C. Bertolin, A. Betancourt, C. Betti, F. Bezshyiko, Ia. Bhasin, S. Bhom, J. Bian, L. Bieker, M.S. Bifani, S. Billoir, P. Birch, M. Bishop, F.C.R. Bitadze, A. Bizzeti, A. Bjørn, M. Blago, M.P. Blake, T. Blanc, F. Blusk, S. Bobulska, D. Boelhauve, J.A. Boente Garcia, O. Boettcher, T. Boldyrev, A. Bondar, A. Bondar, N. Borghi, S. Borisyak, M. Borsato, M. Borsuk, J.T. Bouchiba, S.A. Bowcock, T.J.V. Boyer, A. Bozzi, C. Bradley, M.J. Braun, S. Brea Rodriguez, A. Brodski, M. Brodzicka, J. Brossa Gonzalo, A. Brundu, D. Buonaura, A. Burr, C. Bursche, A. Butkevich, A. Butter, J.S. Buytaert, J. Byczynski, W. Cadeddu, S. Cai, H. Calabrese, R. Calefice, L. Calero Diaz, L. Cali, S. Calladine, R. Calvi, M. Calvo Gomez, M. Camargo Magalhaes, P. Camboni, A. Campana, P. Campoverde Quezada, A.F. Capelli, S. Capriotti, L. Carbone, A. Carboni, G. Cardinale, R. Cardini, A. Carli, I. Carniti, P. Carus, L. Carvalho Akiba, K. Casais Vidal, A. Casse, G. Cattaneo, M. Cavallero, G. Celani, S. Cerasoli, J. Chadwick, A.J. Chapman, M.G. Charles, M. Charpentier, Ph. Chatzikonstantinidis, G. Chavez Barajas, C.A. Chefdeville, M. Chen, C. Chen, S. Chernov, A. Chobanova, V. Rio de Janeiro Brazil Rio de Janeiro Brazil Center for High Energy Physics Tsinghua University Beijing China Beijing China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Particle Physics Central China Normal University Hubei Wuhan China Univ. Savoie Mont Blanc CNRS IN2P3 LAPP Annecy France Université Clermont Auvergne CNRS IN2P3 LPC Clermont-Ferrand France Aix Marseille Univ CNRS IN2P3 CPPM Marseille France Université Paris-Saclay CNRS IN2P3 IJCLab Orsay France Laboratoire Leprince-Ringuet CNRS IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano Milano Italy INFN Sezione di Milano-Bicocca Milano Italy INFN Sezione di Cagliari Monserrato Italy Universita degli Studi di Padova Universita e INFN Padova Padova Italy INFN Sezione di Pisa Pisa Italy INFN Sezione di Roma La Sapienza Roma Italy INFN Sezione di Roma Tor Vergata Roma Italy Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Nikhef National Institute for Subatomic Physics VU University Amsterdam Amsterdam Netherlands AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Henryk Niewodniczanski Institute of Nuclear Physics

The first search for the doubly heavy (Equation presented) baryon and a search for the (Equation presented) baryon are performed using pp collision data collected via the LHCb experiment from 2016 to 2018 at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 5.2 fb−1. The baryons are reconstructed via their decays to (Equation presented) and (Equation presented). No significant excess is found for invariant masses between 6700 and 7300 MeV/c2, in a rapidity range from 2.0 to 4.5 and a transverse momentum range from 2 to 20 GeV/c. Upper limits are set on the ratio of the (Equation presented) and (Equation presented) production cross-section times the branching fraction to (Equation presented) relative to that of the (Equation presented) baryon, for different lifetime hypotheses, at 95% confidence level. The upper limits range from 0.5 × 10−4 to 2.5 × 10−4 for the (Equation presented) decay, and from 1.4 × 10−3 to 6.9 × 10−3 for the (Equation presented) decay, depending on the considered mass and lifetime of the (Equation presented) baryon. © 2021, CC BY.

关键词： Hadrons

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A Comparison of CPU and GPU implementations for the LHCb Experiment Run 3 Trigger

arXiv

引用

arXiv 2021年

作者： Aaij, R. Adinolfi, M. Aiola, S. Akar, S. Albrecht, J. Alexander, M. Amato, S. Amhis, Y. Archilli, F. Bala, M. Bassi, G. Bian, L. Blago, M.P. Boettcher, T. Boldyrev, A. Borghi, S. Brea Rodriguez, A. Calefice, L. Calvo Gomez, M. Cámpora Pérez, D.H. Cardini, A. Cattaneo, M. Chobanova, V. Ciezarek, G. Cid Vidal, X. Cobbledick, J.L. Coelho, J.A.B. Colombo, T. Contu, A. Couturier, B. Craik, D.C. Currie, R. d’Argent, P. de Cian, M. Derkach, D. Dordei, F. Dorigo, M. Dufour, L. Durante, P. Dziurda, A. Dzyuba, A. Easo, S. Esen, S. Fernandez Declara, P. Filippov, S. Fitzpatrick, C. Frank, M. Gandini, P. Gligorov, Vladimir Vava Golobardes, E. Graziani, G. Grillo, L. Günther, P.A. Hansmann-Menzemer, S. Hennequin, A.M. Henry, L. Hill, D. Hollitt, S.E. Hu, J. Hulsbergen, W. Hunter, R.J. Hushchyn, M. Jashal, B.K. Jones, C.R. Klaver, S. Klimaszewski, K. Kopecna, R. Krzemien, W. Kucharczyk, M. Lane, R. Lazzari, F. Le Gac, R. Li, P. Lopes, J.H. Lucio Martinez, M. Lupato, A. Lupton, O. Lyu, X. Machefert, F. Madejczyk, O. Malde, S. Marchand, J.F. Mariani, S. Marin Benito, C. Martinez Santos, D. Martinez Vidal, F. Matev, R. Mazurek, M. Mitreska, B. Mitzel, D.S. Morello, M.J. Mu, H. Muzzetto, P. Naik, P. Needham, M. Neri, N. Neufeld, N. Nolte, N.S. O’Hanlon, D. Oyanguren, A. Pepe Altarelli, M. Petrucci, S. Petruzzo, M. Pica, L. Pisani, F. Piucci, A. Polci, F. Poluektov, A. Polycarpo, E. Prouve, C. Punzi, G. Quagliani, R. Rabadan Trejo, R.I. Ramos Pernas, M. Rangel, M.S. Ratnikov, F. Raven, G. Reiss, F. Renaudin, V. Robbe, P. Ryzhikov, A. Santimaria, M. Saur, M. Schiller, M. Schwemmer, R. Sciascia, B. Solomin, A. Suljik, F. Skidmore, N. Sokoloff, M.D. Spradlin, P. Stahl, M. Stahl, S. Stevens, H. Sun, L. Szabelski, A. Szumlak, T. Szymanski, M. Tou, D.Y. Tuci, G. Usachov, A. Valls Canudas, N. Vazquez Gomez, R. Vecchi, S. Vesterinen, M. Vilasis-Cardona, X. Vom Bruch, D. Wang, Z. Wojton, T. Whitehead, M. Williams, M. Witek, M. Xie, Y. Xu, A. Yin, H. Zdybal, M. Zenaiev, O. Zhang, D. Zhang, L. Zhu, X. Rio de Janeiro Brazil Center for High Energy Physics Tsinghua University Beijing China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Particle Physics Central China Normal University Hubei Wuhan China Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS IN2P3-LAPP Annecy France Aix Marseille Univ CNRS IN2P3 CPPM Marseille France Université Paris-Saclay CNRS IN2P3 IJCLab Orsay France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS IN2P3 Paris France Fakultät Physik Technische Universität Dortmund Dortmund Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Milano Milano Italy INFN Sezione di Cagliari Monserrato Italy INFN Sezione di Pisa Pisa Italy Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Nikhef National Institute for Subatomic Physics VU University Amsterdam Amsterdam Netherlands AGH - University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences Kraków Poland Warsaw Poland Gatchina Russia Moscow Russia ICCUB Universitat de Barcelona Barcelona Spain Universidade de Santiago de Compostela Santiago de Compostela Spain Instituto de Fisica Corpuscular Centro Mixto Universidad de Valencia - CSIC Valencia Spain Geneva Switzerland Lausanne Switzerland Physik-Institut Universität Zürich Zürich Switzerland H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Cavendish Laboratory University of Cambridge Cambridge United Kingdom Department of Physics University of Warwick Coventry United Kingdom STFC Rutherford Appleton Laboratory Didcot Uni

The LHCb experiment at CERN is undergoing an upgrade in preparation for the Run 3 data taking period of the LHC. As part of this upgrade the trigger is moving to a fully software implementation operating at the LHC bunch crossing rate. We present an evaluation of a CPU-based and a GPU-based implementation of the first stage of the High Level Trigger. After a detailed comparison both options are found to be viable. This document summarizes the performance and implementation details of these options, the outcome of which has led to the choice of the GPU-based implementation as the baseline. © 2021, CC BY.

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Corrigendum to: The TianQin project: current progress on science and technology

引用

Progress of Theoretical and Experimental Physics 2021年第5期2021卷

作者： Mei, Jianwei Bai, Yan-Zheng Bao, Jiahui Barausse, Enrico Cai, Lin Canuto, Enrico Cao, Bin Chen, Wei-Ming Chen, Yu Ding, Yan-Wei Duan, Hui-Zong Fan, Huimin Feng, Wen-Fan Fu, Honglin Gao, Qing Gao, TianQuan Gong, Yungui Gou, Xingyu Gu, Chao-Zheng Gu, De-Feng He, Zi-Qi Hendry, Martin Hong, Wei Hu, Xin-Chun Hu, Yi-Ming Hu, Yuexin Huang, Shun-Jia Huang, Xiang-Qing Jiang, Qinghua Jiang, Yuan-Ze Jiang, Yun Jiang, Zhen Jin, Hong-Ming Korol, Valeriya Li, Hong-Yin Li, Ming Li, Pengcheng Li, Rongwang Li, Yuqiang Li, Zhu Li, Zhulian Li, Zhu-Xi Liang, Yu-Rong Liang, Zheng-Cheng Liao, Fang-Jie Liu, Qi Liu, Shuai Liu, Yan-Chong Liu, Li Liu, Pei-Bo Liu, Xuhui Liu, Yuan Lu, Xiong-Fei Lu, Yang Lu, Ze-Huang Luo, Yan Luo, Zhi-Cai Milyukov, Vadim Ming, Min Pi, Xiaoyu Qin, Chenggang Qu, Shao-Bo Sesana, Alberto Shao, Chenggang Shi, Changfu Su, Wei Tan, Ding-Yin Tan, Yujie Tan, Zhuangbin Tu, Liang-Cheng Wang, Bin Wang, Cheng-Rui Wang, Fengbin Wang, Guan-Fang Wang, Haitian Wang, Jian Wang, Lijiao Wang, Panpan Wang, Xudong Wang, Yan Wang, Yi-Fan Wei, Ran Wu, Shu-Chao Xiao, Chun-Yu Xu, Xiao-Shi Xue, Chao Yang, Fang-Chao Yang, Liang Yang, Ming-Lin Yang, Shan-Qing Ye, Bobing Yeh, Hsien-Chi Yu, Shenghua Zhai, Dongsheng Zhang, Caishi Zhang, Haitao Zhang, Jian-dong Zhang, Jie Zhang, Lihua Zhang, Xin Zhang, Xuefeng Zhou, Hao Zhou, Ming-Yue Zhou, Ze-Bing Zhu, Dong-Dong Zi, Tie-Guang Luo, Jun TianQin Research Center for Gravitational Physics & School of Physics and Astronomy Sun Yat-sen University (Zhuhai Campus) Zhuhai 519082 P.R. China Centre for Gravitational Experiments School of Physics MOE Key Laboratory of Fundamental Physical Quantities Measurement & Hubei Key Laboratory of Gravitation and Quantum Physics PGMF Huazhong University of Science and Technology Wuhan 430074 P.R. China SISSA Via Bonomea 265 34136 Trieste Italy and INFN Sezione di Trieste & IFPU – Institute for Fundamental Physics of the Universe Via Beirut 2 34014 Trieste Italy Former Faculty Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy Yunnan Observatories Chinese Academy of Sciences Kunming 650011 China School of Physical Science and Technology Southwest University Chongqing 400715 China Beijing Institute of Control Engineering Beijing 100094 P.R. China SUPA School of Physics and Astronomy University of Glasgow Glasgow G12 8QQ UK DFH Satellite Co. Ltd. Beijing 100094 P.R. China National Astronomical Observatories Chinese Academy of Sciences Beijing 100012 China School of Astronomy and Space Science University of Chinese Academy of Sciences Beijing 100049 China School of Physics and Astronomy University of Birmingham Birmingham B15 2TT United Kingdom Center for High Energy Physics & Department of Physics and State Key Laboratory of Nuclear Physics and Technology Peking University No. 5 Yiheyuan Rd Beijing 100871 P.R. China Lomonosov Moscow State University Sternberg Astronomical Institute Moscow 119992 Russia Dipartimento di Fisica “G. Occhialini” Universitá degli Studi Milano Bicocca Piazza della Scienza 3 I-20126 Milano Italy School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai 200240 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing 210023 & School of Astronomy and Space Science University of Science and Technology of China Hefei Anhui 230026 P.R. China Max-Planck-Institut für Gravitationsphysik (Al

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