Oklahoma State University–Stillwater
Education•Stillwater, Oklahoma, United States•
About: Oklahoma State University–Stillwater is a(n) education organization based out in Stillwater, Oklahoma, United States. It is known for research contribution in the topic(s): Population & Large Hadron Collider. The organization has 18267 authors who have published 36743 publication(s) receiving 1107500 citation(s). The organization is also known as: Oklahoma State University & OKState.
Papers published on a yearly basis
Claude Amsler1, Michael Doser2, Mario Antonelli, D. M. Asner3 +173 more•Institutions (86)
01 Jul 1996-Physics Letters B
TL;DR: This biennial Review summarizes much of particle physics, using data from previous editions.
Abstract: This biennial Review summarizes much of particle physics. Using data from previous editions., plus 2778 new measurements from 645 papers, we list, evaluate, and average measured properties of gauge bosons, leptons, quarks, mesons, and baryons. We also summarize searches for hypothetical particles such as Higgs bosons, heavy neutrinos, and supersymmetric particles. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as the Standard Model, particle detectors., probability, and statistics. Among the 108 reviews are many that are new or heavily revised including those on CKM quark-mixing matrix, V-ud & V-us, V-cb & V-ub, top quark, muon anomalous magnetic moment, extra dimensions, particle detectors, cosmic background radiation, dark matter, cosmological parameters, and big bang cosmology.
Georges Aad1, T. Abajyan2, Brad Abbott3, Jalal Abdallah4 +2964 more•Institutions (200)
17 Sep 2012-Physics Letters B
TL;DR: In this article, a search for the Standard Model Higgs boson in proton-proton collisions with the ATLAS detector at the LHC is presented, which has a significance of 5.9 standard deviations, corresponding to a background fluctuation probability of 1.7×10−9.
Abstract: A search for the Standard Model Higgs boson in proton–proton collisions with the ATLAS detector at the LHC is presented. The datasets used correspond to integrated luminosities of approximately 4.8 fb−1 collected at View the MathML source in 2011 and 5.8 fb−1 at View the MathML source in 2012. Individual searches in the channels H→ZZ(⁎)→4l, H→γγ and H→WW(⁎)→eνμν in the 8 TeV data are combined with previously published results of searches for H→ZZ(⁎), WW(⁎), View the MathML source and τ+τ− in the 7 TeV data and results from improved analyses of the H→ZZ(⁎)→4l and H→γγ channels in the 7 TeV data. Clear evidence for the production of a neutral boson with a measured mass of View the MathML source is presented. This observation, which has a significance of 5.9 standard deviations, corresponding to a background fluctuation probability of 1.7×10−9, is compatible with the production and decay of the Standard Model Higgs boson.
29 Dec 1995
TL;DR: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.
Abstract: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks. In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm, the text also covers Bayesian regularization and early stopping, which ensure the generalization ability of trained networks. Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks. A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction, along with five chapters presenting detailed real-world case studies. Detailed examples and numerous solved problems. Slides and comprehensive demonstration software can be downloaded from hagan.okstate.edu/nnd.html.
Tohoku University1, University of Zurich2, Lawrence Berkeley National Laboratory3, Stanford University4, College of William & Mary5, University of Urbino6, CERN7, Budker Institute of Nuclear Physics8, University of California, Irvine9, Cornell University10, Argonne National Laboratory11, ETH Zurich12, Tata Institute of Fundamental Research13, Hillsdale College14, Spanish National Research Council15, Ohio State University16, University of Notre Dame17, Kent State University18, University of California, San Diego19, University of California, Berkeley20, University of Minnesota21, University of Alabama22, University of Helsinki23, Los Alamos National Laboratory24, California Institute of Technology25, George Washington University26, Syracuse University27, Lawrence Livermore National Laboratory28, Oklahoma State University–Stillwater29, University of Washington30, Max Planck Society31, Boston University32, University of California, Los Angeles33, Royal Holloway, University of London34, Université Paris-Saclay35, University of Pennsylvania36, University of Illinois at Urbana–Champaign37, University of Bristol38, University of Tokyo39, University of Delaware40, Carnegie Mellon University41, University of California, Santa Cruz42, Karlsruhe Institute of Technology43, Heidelberg University44, Florida State University45, University of Mainz46, University of Edinburgh47, Brookhaven National Laboratory48, Durham University49, University of Lausanne50, Massachusetts Institute of Technology51, University of Southampton52, Nagoya University53, University of Oxford54, Northwestern University55, University of British Columbia56, Columbia University57, Lund University58, University of Sheffield59, University of California, Santa Barbara60, Iowa State University61, University of Alberta62, University of Cambridge63
01 Aug 1994-Physical Review D
TL;DR: This biennial Review summarizes much of Particle Physics using data from previous editions, plus 2205 new measurements from 667 papers, and features expanded coverage of CP violation in B mesons and of neutrino oscillations.
Abstract: This biennial Review summarizes much of Particle Physics. Using data from previous editions, plus 2205 new measurements from 667 papers, we list, evaluate, and average measured properties of gauge bosons, leptons, quarks, mesons, and baryons. We also summarize searches for hypothetical particles such as Higgs bosons, heavy neutrinos, and supersymmetric particles. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as the Standard Model, particle detectors, probability, and statistics. This edition features expanded coverage of CP violation in B mesons and of neutrino oscillations. For the first time we cover searches for evidence of extra dimensions (both in the particle listings and in a new review). Another new review is on Grand Unified Theories. A booklet is available containing the Summary Tables and abbreviated versions of some of the other sections of this full Review. All tables, listings, and reviews (and errata) are also available on the Particle Data Group website: http://pdg.lbl.gov.
Broad Institute1, Commonwealth Scientific and Industrial Research Organisation2, Massachusetts Institute of Technology3, Hebrew University of Jerusalem4, Science for Life Laboratory5, Pittsburgh Supercomputing Center6, Oklahoma State University–Stillwater7, Griffith University8, University of Wisconsin-Madison9, Dresden University of Technology10, California Institute for Quantitative Biosciences11, Flanders Institute for Biotechnology12, Parco Tecnologico Padano13, United States Department of Agriculture14, Purdue University15, Indiana University16
01 Aug 2013-Nature Protocols
TL;DR: This protocol provides a workflow for genome-independent transcriptome analysis leveraging the Trinity platform and presents Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes.
Abstract: De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.
Showing all 18267 results
|Gerald I. Shulman||164||579||109520|
|James M. Tiedje||150||688||102287|
|Robert J. Sternberg||149||1066||89193|
|Luis M. Liz-Marzán||132||616||61684|
|Nicholas A. Kotov||123||574||55210|
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