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Author Correction: The power of genetic diversity in genome-wide association studies of lipids

Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

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Characterizing Multi-media Retrieval Applications

Characterizing Multi-media Retrieval Applications

引用

International Conference on Parallel Processing (ICPP)

作者： Yunping Lu Xin Wang Weihua Zhang Yi Li Wenyun Zhao Shanghai Key Laboratory of Data Science Fudan University Shanghai China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi China Software School Fudan University Shanghai China Parallel Processing Institute Fudan University Shanghai China

Multimedia data, especially image and video data, have become one of the most overwhelming data types on the Internet recently. Considering the user experience and real application requirements, multimedia data always demand a real-time processing speed. As a result, the huge amount of such data make retrieving useful information from them not only data-intensive, but also computation-intensive, which poses significant challenges to current system and architecture designs. Unfortunately, most prior studies focus only on text based retrieval systems or traditional multimedia processing applications. As far as we know, there is no systematic study on analyzing the characteristics of multimedia retrieval applications and how they might impact system and architecture designs. In this paper, we make the first attempt to construct a multimedia retrieval benchmark suite (called MMR Bench) to evaluate the corresponding system and architecture designs. To embody diverse multimedia retrieval applications, we collect eight state-of-the-art multimedia retrieval algorithms which cover the whole retrieval stages, including feature extraction, feature matching, and spatial verification. To satisfy diverse evaluation purposes, we implement multiple versions for each algorithm, including sequential version, pthread version for multi-core evaluation and data-parallel (i.e., Map-reduce) version for data-center evaluation. Moreover, MMR Bench provides flexible interfaces through retrieval stages, as well as a tool to adjust parameters and regenerating different scales of reasonable input. With such a flexible design, the algorithms in MMR Bench may be not only suitable for individual kernel-level evaluation, but also capable to be integrated into a complete infrastructure for system-level evaluation. Based on MMR Bench, we further analyze the inherent architectural characteristics, such as input size sensitivity and workload balance, which provides some insights into system and archite

关键词： Multimedia communication Feature extraction Benchmark testing Streaming media Algorithm design and analysis Computer architecture

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Cover Picture: Brownmillerite-type Ca2FeCoO5as a Practicable Oxygen Evolution Reaction Catalyst (ChemSusChem 14/2017)

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ChemSusChem 2017年第14期10卷 2840-2840页

作者： Dr. Etsushi Tsuji Dr. Teruki Motohashi Hiroyuki Noda Dr. Damian Kowalski Dr. Yoshitaka Aoki Dr. Hajime Tanida Junji Niikura Dr. Yukinori Koyama Dr. Masahiro Mori Dr. Hajime Arai Dr. Tsutomu Ioroi Dr. Naoko Fujiwara Dr. Yoshiharu Uchimoto Dr. Zempachi Ogumi Dr. Hiroki Habazaki Division of Applied Chemistry Faculty of Engineering Hokkaido University Sapporo 060-8628 Japan Department of Chemistry and Biotechnology Tottori University Tottori 680-8550 Japan Department of Materials and Life Chemistry Kanagawa University Yokohama 221-8686 Japan Graduate School of Chemical Sciences and Engineering Hokkaido University Sapporo 060-8628 Japan Office of Society-Academia Collaboration for Innovation Kyoto University Kyoto 611-0011 Japan Present address: Battery Analysis Laboratory Device Analysis Department NISSAN ARC LTD. Kanagawa 237-0061 Japan Present address: Research and Services Division of Materials Data and Integrated System (MaDIS) Center for Materials Research by Information Integration (CMI2) National Institute for Materials Science (NIMS) Ibaraki 305-0047 Japan Present address: Materials Science Research Group Research Institute of Electrochemical Energy Department of Energy and Environment National Institute of Advanced Industrial Science and Technology (AIST) Osaka 563-8577 Japan Present address: School of Materials and Chemical Technology Tokyo Institute of Technology Yokohama 226-8502 Japan National Institute of Advanced Industrial Science and Technology Osaka 563-8577 Japan Graduate School of Human and Environmental Studies Kyoto University Kyoto 606-8501 Japan

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Extensions to quantile regression forests for very high-dimensional data

Extensions to quantile regression forests for very high-dime...

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18th Pacific-Asia Conference on Advances in Knowledge Discovery and data Mining, PAKDD 2014

作者： Tung, Nguyen Thanh Huang, Joshua Zhexue Khan, Imran Li, Mark Junjie Williams, Graham Shenzhen Key Laboratory of High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9783319066042

This paper describes new extensions to the state-of-the-art regression random forests Quantile Regression Forests (QRF) for applications to high-dimensional data with thousands of features. We propose a new subspace sampling method that randomly samples a subset of features from two separate feature sets, one containing important features and the other one containing less important features. The two feature sets partition the input data based on the importance measures of features. The partition is generated by using feature permutation to produce raw importance feature scores first and then applying p-value assessment to separate important features from the less important ones. The new subspace sampling method enables to generate trees from bagged sample data with smaller regression errors. For point regression, we choose the prediction value of Y from the range between two quantiles Q0.05 and Q0.95 instead of the conditional mean used in regression random forests. Our experiment results have shown that random forests with these extensions outperformed regression random forests and quantile regression forests in reduction of root mean square residuals. © 2014 Springer International Publishing.

关键词： data mining

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Dense matter with eXTP

arXiv

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arXiv 2018年

作者： Watts, Anna L. Yu, Wenfei Poutanen, Juri Zhang, Shu Bhattacharyya, Sudip Bogdanov, Slavko Ji, Long Patruno, Alessandro Riley, Thomas E. Bakala, Pavel Baykal, Altan Bernardini, Federico Bombaci, Ignazio Brown, Edward Cavecchi, Yuri Chakrabarty, Deepto Chenevez, Jérôme Degenaar, Nathalie Santo, Melania Del Salvo, Tiziana Di Doroshenko, Victor Falanga, Maurizio Ferdman, Robert D. Feroci, Marco Gambino, Angelo F. Ge, MingYu Greif, Svenja K. Guillot, Sebastien Gungor, Can Hartmann, Dieter H. Hebeler, Kai Heger, Alexander Homan, Jeroen Iaria, Rosario in’t Zand, Jean Kargaltsev, Oleg Kurkela, Aleksi Lai, Xiaoyu Li, Ang Li, XiangDong Li, Zhaosheng Linares, Manuel Lu, FangJun Mahmoodifar, Simin Méndez, Mariano Miller, M. Coleman Morsink, Sharon Nättilä, Joonas Possenti, Andrea Prescod-Weinstein, Chanda Qu, JinLu Riggio, Alessandro Salmi, Tuomo Sanna, Andrea Santangelo, Andrea Schatz, Hendrik Schwenk, Achim Song, LiMing Šrámková, Eva Stappers, Benjamin Stiele, Holger Strohmayer, Tod Tews, Ingo Tolos, Laura Török, Gabriel Tsang, David Urbanec, Martin Vacchi, Andrea Xu, RenXin Xu, Ypeng Zane, Silvia Zhang, Guobao Zhang, ShuangNan Zhang, Wenda Zheng, ShiJie Zhou, Xia Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Shanghai Astronomical Observatory Shanghai200030 China Tuorla Observatory Department of Physics and Astronomy University of Turku Väisäläntie 20 PiikkiöFIN-21500 Finland Nordita KTH Royal Institute of Technology Stockholm University Roslagstullsbacken 23 StockholmSE-10691 Sweden Institute of High Energy Physics CAS Beijing100049 China Tata Institute of Fundamental Research Mumbai400005 India Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Institut für Astronomie und Astrophysik Tübingen Universität Tübingen Sand 1 TübingenD-72076 Germany Leiden Observatory Leiden University PO Box 9513 Leiden2300 RA Netherlands Research Center for Computational Physics and Data Processing Silesian University in Opava Bezrucovo nam. 13 OpavaCZ-74601 Czech Republic Physics Department Middle East Technical University Ankara06531 Turkey INAF Osservatorio Astronomico di Roma Via Frascati 33 Monteporzio CatoneI-00078 Italy New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Dipartimento di Fisica Enrico Fermi University of Pisa PisaI-56127 Italy INFN Italian National Institute for Nuclear Physics Pisa Section Pisa56127 Italy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Mathematical Sciences and STAG Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom MIT Kavli Institute for Astrophysics and Space Research CambridgeMA02139 United States DTU Space Technical University of Denmark Elektrovej 327-328 Kgs Lyngby2800 Denmark INAF/IASF Palermo via Ugo La Malfa 153 PalermoI-90146 Italy Universita degli Studi di Palermo Dipartimento di Fisica e Chimica via Archira 36 Palermo90123 Italy

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry (eXTP) mission for determining the nature of dense matter;neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of science, the eXTP mission is expected to be launched in the mid 2020s. Copyright © 2018, The Authors. All rights reserved.

关键词： Pulsars

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Unbiased feature selection in learning random forests for high-dimensional data

Scientific World Journal

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Scientific World Journal 2015年第1期2015卷 471371页

作者： Nguyen, Thanh-Tung Huang, Joshua Zhexue Nguyen, Thuy Thi Shenzhen Key Laboratory of High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China University of Chinese Academy of Sciences Beijing 100049 China School of Computer Science and Engineering Water Resources University Hanoi 10000 Viet Nam College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Faculty of Information Technology Vietnam National University of Agriculture Hanoi 10000 Viet Nam

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures. © 2015 Thanh-Tung Nguyen et al.

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Parallelized Race Detection Based on GPU Architecture

Parallelized Race Detection Based on GPU Architecture

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10th Annual Conference of advanced Computer Architecture, ACA 2014

作者： Dai, Zhuofang Zhang, Zheng Wang, Haojun Li, Yi Zhang, Weihua Software School Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Parallel Processing Institute Fudan University Shanghai 201203 China State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou 450001 China

ISBN: (纸本)9783662444900

In order to harness abundant hardware resources, parallel programming has become a necessity in multicore era. However, parallel programs are prone to concurrency bugs, especially data races. Even worse, current software tools always suffer from both large runtime overheads and poor scalability, while most of hardware supports for race detection are not available in parallel programming. Therefore, it has been a challenge that how to introduce a practical and fast race detection tools. Nowadays, GPUs with massive parallel computation resources have become one of the most popular hardware platforms. Hence, the prevalence of GPU architectures has opened an opportunity of accelerating data race detection. In this paper, we first have a deeply analysis on data race detection algorithms like happens-before and observe that these algorithms have very good computation and data parallelism. Based on the observation, we propose Grace, a software approach that leverages massive parallelism computation units of GPU architectures to accelerate data race detection. Grace deploys detection, the most computation intensive workload, on GPU to fully utilize the computation resource in GPU. Moreover, Grace leverages coarse-grained pipeline parallelism and data parallelism through exploiting the computation resource in multi-core CPUs to further improve performance. Experimental results show that Grace is fast and scalable. It achieves over 80x speedup compared to the sequential version even under a 128-thread configuration. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Graphics processing unit

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Design of a high-performance full-system instrumentation

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Dongbei Daxue Xuebao/Journal of Northeastern University 2014年 35卷 129-134页

作者： Min, Qing-Hao Zhang, Zheng Wang, Hao-Jun Zhang, Wei-Hua Software School Fudan University Shanghai201203 China Shanghai Key Laboratory of Data Science Fudan University Shanghai201203 China Parallel Processing Institute Fudan University Shanghai201203 China State Key Laboratory of Mathematic Engineering and Advanced Computing PLA Information Engineering University Zhengzhou450001 China

Instrumentation is an important mechanism to collect run-time information of applications for further analysis like bottleneck analysis, hotspot analysis and even bug detection and revision. Currently there are lots of analyzers using instrumentation to collect their source data. However, most of these tools lack of full-system support and thus become inadequate for analyzing multi-core programs as they cannot provide full-system information like scheduling, system calls and inter-process communication, etc. Meanwhile, full-system instrumentation needs to collect OS and hardware information, resulting to performance degradation. In this paper, a high-performance full-system instrumentation method was designed for collecting full-system information as well as application information. The proposed method was implemented inside the QEMU full-system emulator, and 40 MIPS performance on average was achieved after optimization, which was appropriated for normal program analysis. ©, 2014, Northeastern University. All right reserved.

关键词： Full-system High-performance Instrumentation Multi-core

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Gauge and Higgs bosons

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Chinese Physics C 2016年第10期40卷 601-632页

作者： C.Patrignani K.Agashe G.Aielli C.Amsler M.Antonelli D.M.Asner H.Baer Sw.Banerjee R.M.Barnett T.Basaglia C.W.Bauer J.J.Beatty V.I.Belousov J.Beringer S.Bethke H.Bichsel O.Biebel E.Blucher G.Brooijmans O.Buchmueller V.Burkert M.A.Bychkov R.N.Cahn M.Carena A.Ceccucci A.Cerri D.Chakraborty M.-C.Chen R.S.Chivukula K.Copic G.Cowan O.Dahl G.D'Ambrosio T.Damour D.de Florian A.de Gouvea T.DeGrand P.de Jong G.Dissertori B.A.Dobrescu M.D'Onofrio M.Doser M.Drees H.K.Dreiner D.A.Dwyer P.Eerola S.Eidelman J.Ellis J.Erler V.V.Ezhela W.Fetscher B.D.Fields B.Foster A.Freitas H.Gallagher L.Garren H.-J.Gerber G.Gerbier T.Gershon T.Gherghetta A.A.Godizov M.Goodman C.Grab A.V.Gritsan C.Grojean D.E.Groom M.Grnewald A.Gurtu T.Gutsche H.E.Haber K.Hagiwara C.Hanhart S.Hashimoto Y.Hayato K.G.Hayes A.Hebecker B.Heltsley J.J.Hernandez-Rey K.Hikasa J.Hisano A.Hcker J.Holder A.Holtkamp J.Huston T.Hyodo K.Irwin J.D.Jackson K.F.Johnson M.Kado M.Karliner U.F.Katz S.R.Klein E.Klempt R.V.Kowalewski F.Krauss M.Kreps B.Krusche Yu.V.Kuyanov Y.Kwon O.Lahav J.Laiho P.Langacker A.Liddle Z.Ligeti C.-J.Lin C.Lippmann T.M.Liss L.Littenberg K.S.Lugovsky S.B.Lugovsky A.Lusiani Y.Makida F.Maltoni T.Mannel A.V.Manohar W.J.Marciano A.D.Martin A.Masoni J.Matthews U.-G.Meiβner D.Milstead R.E.Mitchell P.Molaro K.Mnig F.Moortgat M.J.Mortonson H.Murayama K.Nakamura M.Narain P.Nason S.Navas M.Neubert P.Nevski Y.Nir K.A.Olive S.Pagan Griso J.Parsons J.A.Peacock M.Pennington S.T.Petcov V.A.Petrov A.Piepke A.Pomarol A.Quadt S.Raby J.Rademacker G.Raffelt B.N.Ratcliff P.Richardson A.Ringwald S.Roesler S.Rolli A.Romaniouk L.J.Rosenberg J.L.Rosner G.Rybka R.A.Ryutin C.T.Sachrajda Y.Sakai G.P.Salam S.Sarkar F.Sauli O.Schneider K.Scholberg A.J.Schwartz D.Scott V.Sharma S.R.Sharpe T.Shutt M.Silari T.Sjstrand P.Skands T.Skwarnicki J.G.Smith G.F.Smoot S.Spanier H.Spieler C.Spiering A.Stahl S.L.Stone Y.Sumino T.Sumiyoshi M.J.Syphers F.Takahashi M.Tanabashi K.Terashi J.Terning R.S.Thorne L.Tiator M.Titov N.P.Tkachenko N.A.Trnqvist D.Tovey G.Valencia R.Van de Water N.Varelas G Particle Data Group Universita di Bologna and INFN Dip.Scienze per la Qualita della Vita University of Maryland Department of Physics College Park Universit degli Studi di Roma "Tor Vergata" Albert Einstein Center for Fundamental Physics Universitt Bern Stefan Meyer Institute for Subatomic Physics Austrian Academy of Sciences Lab.Nazionali di Frascati dell'INFN Pacific Northwest National Laboratory Department of Physics and Astronomy University of Oklahoma University of Louisville Physics Division Lawrence Berkeley National Laboratory CERN European Organization for Nuclear Research Department of Physics The Ohio State University COMPAS Group Institute for High Energy Physics Max-Planck-Institute of Physics Department of Physics University of Washington Ludwig-Maximilians-Universitt Fakultt fr Physik Enrico Fermi Institute and Department of Physics University of Chicago Department of Physics Columbia University High Energy Physics Group Blackett Laboratory Imperial College Jefferson Lab Department of Physics University of Virginia Fermi National Accelerator Laboratory Kavli Institute for Cosmological Physics University of Chicago Department of Physics and Astronomy University of Sussex Department of Physics Northern Illinois University Department of Physics and Astronomy University of California Michigan State University Dept.of Physics and Astronomy Department of Physics Royal Holloway University of London INFN-Sezione di Napoli Complesso Universitario Monte Sant'Angelo Institut des Hautes Etudes Scientifiques International Center for Advanced Studies(ICAS) UNSAM Department of Physics and Astronomy Northwestern University Department of Physics University of Colorado at Boulder Nikhef P.O.Box 41882 1009 DB Amsterdam and University of Amsterdam Institute for Particle Physics ETH Zurich University of Liverpool Department of Physics Oliver Lodge Lab Physikalisches Institut Universitt Bonn Helsinki Institute of Physics University of Helsinki Department of Physics Budker Institute of Nuclear Physics SB RAS Novosib

The Review summarizes much of particle physics and *** data from previous editions,plus 3,062 new measurements from 721 papers,we list,evaluate,and average measured properties of gauge bosons and the recently discovered Higgs boson,leptons,quarks,mesons,and *** summarize searches for hypothetical particles such as supersymmetric particles,heavy bosons,axions,dark photons,*** the particle properties and search limits are listed in Summary *** also give numerous tables,figures,formulae,and reviews of topics such as Higgs Boson Physics,Supersymmetry,Grand Unified Theories,Neutrino Mixing,Dark Energy,Dark Matter,Cosmology,Particle Detectors,Colliders,Probability and *** the 117 reviews are many that are new or heavily revised,including new reviews on Pentaquarks and Inflation.

关键词： Gauge and Higgs bosons Rev

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Quarks

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Chinese Physics C 2016年第10期40卷 653-681页

关键词： Quarks QCD

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