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Constraints on the KS0→μ+μ− Branching Fraction

Physical Review Letters 2020年第23期125卷 231801-231801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 P. Albicocco J. Albrecht F. Alessio M. Alexander A. Alfonso Albero G. Alkhazov P. Alvarez Cartelle A. A. Alves, Jr. S. Amato Y. Amhis L. An L. Anderlini G. Andreassi M. Andreotti F. Archilli J. Arnau Romeu A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann J. J. Back S. Baker V. Balagura W. Baldini A. Baranov R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov G. Bassi V. Batozskaya B. Batsukh A. Battig A. Bay M. Becker F. Bedeschi I. Bediaga A. Beiter L. J. Bel V. Belavin S. Belin N. Beliy V. Bellee K. Belous I. Belyaev G. Bencivenni E. Ben-Haim S. Benson S. Beranek A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein C. Bertella E. Bertholet A. Bertolin C. Betancourt F. Betti M. O. Bettler Ia. Bezshyiko S. Bhasin J. Bhom M. S. Bieker S. Bifani P. Billoir A. Bizzeti M. Bjørn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska V. Bocci O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk T. J. V. Bowcock C. Bozzi M. J. Bradley S. Braun A. Brea Rodriguez M. Brodski J. Brodzicka A. Brossa Gonzalo D. Brundu E. Buchanan A. Büchler-Germann A. Buonaura C. Burr A. Bursche J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calero Diaz S. Cali R. Calladine M. Calvi M. Calvo Gomez A. Camboni P. Campana D. H. Campora Perez L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini P. Carniti K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero S. Celani R. Cenci J. Cerasoli M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis M. Chefdeville V. Chekalina C. Chen S. Chen A. Chernov S.-G. Chitic V. Chobanova M. Chrzaszcz A. Chubykin P. Ciambrone M. F. Cicala X. Cid Vidal G. Ciezarek F. Cindolo P. E. L. Clarke M. Clemencic H. V. Cliff J. Closier J. L. Cobbledick V. Coco J. A. B. Coelho J. Cogan E. Cogneras L. Cojocariu P. Collins T. Colombo A. Comerma-Montells A. Contu N. 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 USA (associated with Syracuse University Syracuse New York USA) INFN Sezione di Milano Milano Italy Los Alamos National Laboratory (LANL) Los Alamos USA INFN Laboratori Nazionali di Frascati Frascati Italy 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 Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Imperial College London London 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 Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland INFN Sezione di Ferrara Ferrara Italy Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France 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 INFN Sezione di Cagliari Monserrato Italy Department of Physics University of Warwick Coventry United Kingdom Department of Physics and Astronomy University of Manchester Manchester United Kingdom INFN Sezione di Genova Genova Italy INFN Sezione di Pisa Pi

A search for the decay KS0→μ+μ− is performed using proton-proton collision data, corresponding to an integrated luminosity of 5.6 fb−1 and collected with the LHCb experiment during 2016, 2017, and 2018 at a center-of-mass energy of 13 TeV. The observed signal yield is consistent with zero, yielding an upper limit of B(KS0→μ+μ−)<2.2×10−10 at 90% C.L.. The limit reduces to B(KS0→μ+μ−)<2.1×10−10 at 90% C.L. once combined with the result from data taken in 2011 and 2012.

关键词： Branching fraction Electroweak interaction Flavor changing neutral currents Leptonic, semileptonic & radiative decays Rare decays Kaons Strange quark

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Topic modeling for social media content: A practical approach

Topic modeling for social media content: A practical approac...

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International Conference on computer and Information sciences (ICCOINS)

作者： Vala Ali Rohani Shahid Shayaa Ghazaleh Babanejaddehaki Department of Data Analytics Berkshire Media Kuala Lumpur Malaysia Faculty of Computer Science and Information Technology Univerity Purtra Malaysia Kuala Lumpur Malaysia

Perceiving the discussed topic in social media brings a great amount of value to different fields, such as marketing, security, education, and management. Topic modeling provides a powerful method for projecting text documents into topic space. In this paper, we explore an unsupervised topic modeling approach that incorporates LDA algorithm toward discovering the topics in social media content. Empirical experiments on social media datasets with 90,527 records reveal that this approach is quite effective for detecting the topic facets and extracting their dynamics over time. By analyzing the studied dataset, five main topics were discovered accurately by the presented algorithm according to the domain experts' comments. The presented model is quite general and can be applied in a wide variety of domains to automatically mining topics from the social media channels.

关键词： Social network services Computational modeling Probabilistic logic data models Resource management Hidden Markov models computers

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C/2014 UN271(Bernardinelli-Bernstein): The nearly spherical cow of comets

arXiv

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arXiv 2021年

作者： Bernardinelli, Pedro H. Bernstein, Gary M. Montet, Benjamin T. Weryk, Robert Wainscoat, Richard Aguena, M. Allam, S. Andrade-Oliveira, F. Annis, J. Avila, S. Bertin, E. Brooks, D. Burke, D.L. Rosell, A. Carnero Kind, M. Carrasco Carretero, J. Cawthon, R. Conselice, C. Costanzi, M. Da Costa, L.N. Pereira, M.E.S. De Vicente, J. Diehl, H.T. Everett, S. Ferrero, I. Flaugher, B. Frieman, J. García-Bellido, J. Gaztanaga, E. Gerdes, D.W. Gruen, D. Gruendl, R.A. Gschwend, J. Gutierrez, G. Hinton, S.R. Hollowood, D.L. Honscheid, K. James, D.J. Kuehn, K. Kuropatkin, N. Lahav, O. Maia, M.A.G. Marshall, J.L. Menanteau, F. Miquel, R. Morgan, R. Ogando, R.L.C. Paz-Chinchón, F. Pieres, A. Plazas Malagón, A.A. Rodriguez-Monroy, M. Romer, A.K. Roodman, A. Sanchez, E. Schubnell, M. Serrano, S. Sevilla-Noarbe, I. Smith, M. Soares-Santos, M. Suchyta, E. Swanson, M.E.C. Tarle, G. To, C. Troxel, M.A. Varga, T.N. Walker, A.R. Zhang, Y. Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States School of Physics University of New South Wales SydneyNSW2052 Australia UNSW Data Science Hub University of New South Wales SydneyNSW2052 Australia Dept. of Physics & Astronomy University of Western Ontario 1151 Richmond Street LondonONN6A 3K7 Canada Institute for Astronomy University of Hawaii 2680 Woodlawn Drive HonoluluHI96822 United States Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Instituto de Física Teórica Universidade Estadual Paulista São Paulo Brazil Instituto de Fisica Teorica CSIC Universidad Autonoma de Madrid Madrid28049 Spain CNRS UMR 7095 Institut d'Astrophysique de Paris ParisF-75014 France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d'Astrophysique de Paris ParisF-75014 France Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Kavli Institute for Particle Astrophysics & Cosmology Stanford University P. O. Box 2450 StanfordCA94305 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Center for Astrophysical Surveys National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States The Barcelona Institute of Science and Technology Campus UAB Bellaterra Barcelona08193 Spain Physics Department University of Wisconsin-Madison 2320 Chamberlin Hall 1150 University Avenue MadisonWI53706-1390 United States Jodrell Bank Center for Astrophysics School of Physics and Astronomy University of Manchester Oxford Road ManchesterM13 9PL United Kingdom University of Nottingham School of Physics and Astronomy NottinghamNG7 2RD U

C/2014 UN271(Bernardinelli-Bernstein) is a comet incoming from the Oort cloud which is remarkable in having the brightest (and presumably largest) nucleus of any well-measured comet, and having been discovered at heliocentric distance rh≈29 au farther than any Oort-cloud member. We describe the properties that can be inferred from images recorded until the first reports of activity in June 2021. The orbit has i=95°, with perihelion of 10.97 au to be reached in 2031, and previous aphelion at 40,400±260 au. Backwards integration of the orbit under a standard Galactic tidal model and known stellar encounters suggests this is a pristine new comet, with a perihelion of q≈18 au on its previous perihelion passage 3.5 Myr ago. The photometric data show an unresolved nucleus with absolute magnitude Hr=8.0, colors that are typical of comet nuclei or Damocloids, and no secular trend as it traversed the range 34-23 au. For r-band geometric albedo pr, this implies a diameter of 150 (pr/0.04)-0.5km. There is strong evidence of brightness fluctuations at ±0.2 mag level, but no rotation period can be discerned. A coma consistent with a "stationary" 1/ρ surface-brightness distribution grew in scattering cross-section at an exponential rate from Afρ ≈ 1 m to ≈ 150 m as the comet approached from 28 to 20 au. The activity is consistent with a simple model of sublimation of a surface species in radiative equilibrium with the Sun. The inferred enthalpy of sublimation matches those of CO2and NH3. More-volatile species - N2, CH4, and CO - must be far less abundant on the sublimating surfaces. © 2021, CC BY-NC-ND.

关键词： Comets

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Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

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AIP Conference Proceedings 2017年第1期1891卷

作者： Siti Rohaidah Ahmad Nurhafizah Moziyana Mohd Yusop Azuraliza Abu Bakar Mohd Ridzwan Yaakub 1Department of Computer Science Faculty of Defence Science & Technology Universiti Pertahanan Nasional Malaysia 57000 Kuala Lumpur Malaysia 2Data Mining and Optimization Research Group Centre for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia 46000 Bangi Selangor Malaysia

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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A power-distance based handover triggering algorithm for LTE-R using WINNERII-D2a channel model

A power-distance based handover triggering algorithm for LTE...

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Asia-Pacific Conference on Communications

作者： Ehab Ahmed Ibrahim Ehab F. Badran M. R. M. Rizk Computer Networks and Data Center Arab Academy for science and Technology Alexandria Egypt Electronics and Engineering Department Arab Academy for Science and Technology Alexandria Egypt Electrical Engineering Department Faculty of Engineering Alexandria Egypt

ISBN: (纸本)9781509006779

Railway systems cannot be apart from the rapid evolution of wireless communication systems. The Long Term Evolution communication system for Railway (LTE-R) is believed to be the normal evolution for current Global System for Mobile Communications Railways (GSM-R). One of the essential targets of LTE is to provide seamless and fast handover from one cell to another to achieve a strict delay requirement while, at the same time, keeping network management as simple as possible. Hence, the decision to trigger a handover is very important in the design of handover process. For handover to be successful, it requires to choose handover parameters correctly and optimize their setting. The LTE-R system uses two parameters to decide handover triggering: Hysteresis and Time-to-Trigger (TTT). The handover triggering is highly depended on the train speed, which means that as the train speed changed, the handover triggering time is correspondingly varied. To determine a more accurate handover triggering point is a critical issue. Too late handover triggering caused by train speed change will lead to handover failure. In this paper, we propose a new handover triggering algorithm called Power-Distance Algorithm that assets to avoid this problem. It eliminates the dependency on the train speed, also, reduces the system processing power. This Algorithm depends on the distance between the two base stations and power received from them. At a certain (predefined) point, the handover triggering must occur. This triggering point is the same for all values of train speed i.e., it doesn't change as the train speed changes. The change occurs only when the Hysteresis value changes.

关键词： Handover Long Term Evolution Mobile communication Rail transportation Hysteresis 3GPP

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Computational analysis of the structural properties of economic and financial networks

arXiv

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arXiv 2017年

作者： Emmert-Streib, Frank Musa, Aliyu Baltakys, Kestutis Kanniainen, Juho Tripathi, Shailesh Yli-Harja, Olli Jodlbauer, Herbert Dehmer, Matthias Predictive Medicine and Data Analytics Lab Department of Signal Processing Tampere University of Technology Finland Institute of Biosciences and Medical Technology Tampere Finland Industrial and Information Management Tampere University of Technology Tampere Finland Institute for Systems Biology Seattle United States Institute for Intelligent Production Faculty for Management University of Applied Sciences Upper Austria Steyr Campus Austria Department for Biomedical Computer Science and Mechatronics UMIT - The Health and Lifesciences University Hall in Tyrol Austria College of Computer and Control Engineering Nankai University Tianjin300350 China

In recent years, methods from network science are gaining rapidly interest in economics and finance. A reason for this is that in a globalized world the interconnectedness among economic and financial entities are crucial to understand and networks provide a natural framework for representing and studying such systems. In this paper, we are surveying the use of networks and network-based methods for studying economy related questions. We start with a brief overview of graph theory and basic definitions. Then we discuss descriptive network measures and network complexity measures for quantifying structural properties of economic networks. Finally, we discuss different network and tree structures as relevant for applications. Copyright © 2017, The Authors. All rights reserved.

关键词： Finance

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Measurement of CP-Averaged Observables in the B0→K*0μ+μ− Decay

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Physical Review Letters 2020年第1期125卷 011802-011802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 P. Albicocco J. Albrecht F. Alessio M. Alexander A. Alfonso Albero G. Alkhazov P. Alvarez Cartelle A. A. Alves, Jr. S. Amato Y. Amhis L. An L. Anderlini G. Andreassi M. Andreotti F. Archilli A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann J. J. Back S. Baker V. Balagura W. Baldini A. Baranov R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov J. M. Basels G. Bassi V. Batozskaya B. Batsukh A. Battig A. Bay M. Becker F. Bedeschi I. Bediaga A. Beiter V. Belavin S. Belin V. Bellee K. Belous I. Belyaev G. Bencivenni E. Ben-Haim S. Benson A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein C. Bertella E. Bertholet A. Bertolin C. Betancourt F. Betti M. O. Bettler Ia. Bezshyiko S. Bhasin J. Bhom M. S. Bieker S. Bifani P. Billoir A. Bizzeti M. Bjørn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska V. Bocci O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk T. J. V. Bowcock C. Bozzi M. J. Bradley S. Braun A. Brea Rodriguez M. Brodski J. Brodzicka A. Brossa Gonzalo D. Brundu E. Buchanan A. Büchler-Germann A. Buonaura C. Burr A. Bursche A. Butkevich J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calero Diaz S. Cali R. Calladine M. Calvi M. Calvo Gomez P. Camargo Magalhaes A. Camboni P. Campana D. H. Campora Perez A. F. Campoverde Quezada L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini I. Carli P. Carniti K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero S. Celani R. Cenci J. Cerasoli M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis M. Chefdeville V. Chekalina C. Chen S. Chen A. Chernov S.-G. Chitic V. Chobanova S. Cholak M. Chrzaszcz A. Chubykin V. Chulikov P. Ciambrone M. F. Cicala X. Cid Vidal G. Ciezarek F. Cindolo P. E. L. Clarke M. Clemencic H. V. Cliff J. Closier J. L. Cobbledick V. Coco J. A. B. Coelho J. Cogan E. Cogneras L. Cojoca 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 Michigan USA (associated Syracuse University Syracuse New York USA) INFN Sezione di Milano Milano Italy Los Alamos National Laboratory (LANL) Los Alamos USA INFN Laboratori Nazionali di Frascati Frascati Italy 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 Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Imperial College London London United Kingdom Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Université Paris-Saclay CNRS/IN2P3 IJCLab 91405 Orsay France Orsay France INFN Sezione di Firenze Firenze Italy Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland 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 Syracuse University Syracuse New York USA Yandex School of Data Analysis Moscow Russia Aix Marseille Universite CNRS/IN2P3 CPPM Marseille France Department of Physics University of Warwick Coventry United Kingdom Department of Physics and Astronomy University of Manchester Manchester United Kingdom INFN Sezione di Genova Genova Italy National University of Science and Technology “MISIS”

An angular analysis of the B0→K*0(→K+π−)μ+μ− decay is presented using a dataset corresponding to an integrated luminosity of 4.7 fb−1 of pp collision data collected with the LHCb experiment. The full set of CP-averaged observables are determined in bins of the invariant mass squared of the dimuon system. Contamination from decays with the K+π− system in an S-wave configuration is taken into account. The tension seen between the previous LHCb results and the standard model predictions persists with the new data. The precise value of the significance of this tension depends on the choice of theory nuisance parameters.

关键词： Flavor changing neutral currents Leptonic, semileptonic & radiative decays Bottom mesons Kaons Muons Hadron colliders

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Improving artificial bee colony algorithm with historical archive 11th

Improving artificial bee colony algorithm with historical ar...

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Zhou, Yalan Wang, Jiahai Gao, Shangce Yang, Xing Yin, Jian College of Information Guangdong University of Finance & Economics Guangzhou510320 China Department of Computer Science Sun Yat-sen University Guangzhou510006 China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Faculty of Engineering University of Toyama Toyama-shi930-8555 Japan

ISBN: (纸本)9789811036132

In this study, an artificial bee colony with historical archive (HAABC) is proposed to help ABC escape from stagnation situation. The proposed framework keeps track of the search history and stores excellent successful solutions into an archive. Once stagnation is detected in scout bees phase of ABC, a new individual is generated by utilizing the historical archive. Experimental results on 28 benchmark functions show that the proposed framework significantly improves the performance of basic ABC and five state-of-the-art ABC algorithms. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Benchmarking

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An ecosystem for linked humanities data 1

An ecosystem for linked humanities data

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1st Workshop on Humanities in the Semantic Web, WHiSe 2016

作者： Hoekstra, Rinke Meronõ-Penũela, Albert Dentler, Kathrin Rijpma, Auke Zijdeman, Richard Zandhuis, Ivo Department of Computer Science Vrije Universiteit Amsterdam Netherlands International Institute of Social History KNAW Amsterdam Netherlands Ivo Zandhuis Research and Consultancy Haarlem Netherlands Data Archiving and Networked Services KNAW The Hague Netherlands Faculty of Law University of Amsterdam Netherlands University of Stirling Stirling United Kingdom Utrecht University Utrecht Netherlands

The main promise of the digital humanities is the ability to perform scholar studies at a much broader scale, and in a much more reusable fashion. The key enabler for such studies is the availability of sufficiently well described data. For the field of socio-economic history, data usually comes in a tabular form. Existing efforts to curate and publish datasets take a top-down approach and are focused on large collections. This paper presents QBer and the underlying structured data hub, which address the long tail of research data by catering for the needs of individual scholars. QBer allows researchers to publish their (small) datasets, link them to existing vocabularies and other datasets, and thereby contribute to a growing collection of interlinked datasets. We present QBer, and evaluate our first results by showing how our system facilitates two use cases in socio-economic history.

关键词： Linked data

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