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Global analysis of charge exchange meson production at high energies

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Physical Review D 2018年第3期98卷 034020-034020页

作者： J. Nys A. N. Hiller Blin V. Mathieu C. Fernández-Ramírez A. Jackura A. Pilloni J. Ryckebusch A. P. Szczepaniak G. Fox Department of Physics and Astronomy Ghent University Ghent 9000 Belgium Center for Exploration of Energy and Matter Indiana University Bloomington Indiana 47403 USA Physics Department Indiana University Bloomington Indiana 47405 USA Institut für Kernphysik & PRISMA Cluster of Excellence Johannes Gutenberg Universität D-55099 Mainz Germany Theory Center Thomas Jefferson National Accelerator Facility 12000 Jefferson Avenue Newport News Virginia 23606 USA Instituto de Ciencias Nucleares Universidad Nacional Autónoma de México Ciudad de México 04510 Mexico School of Informatics and Computing Indiana University Bloomington Indiana 47405 USA

Many experiments that are conducted to study the hadron spectrum rely on peripheral resonance production. Hereby, the rapidity gap allows the process to be viewed as an independent fragmentation of the beam and the target, with the beam fragmentation dominated by production and decays of meson resonances. We test this separation by determining the kinematic regimes that are dominated by factorizable contributions, indicating the most favorable regions to perform this kind of experiments. In doing so, we use a Regge model to analyze the available world data of charge exchange meson production with beam momentum above 5 GeV in the laboratory frame that are not dominated by either pion or Pomeron exchanges. We determine the Regge residues and point out the kinematic regimes which are dominated by factorizable contributions.

关键词： Diffractive production Fragmentation into hadrons Hadron-hadron interactions

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THE ARCTIC

Bulletin of the American Meteorological Society 2024年第8期105卷 S277-S330页

作者： Druckenmiller, M.L. Thoman, R.L. Moon, T.A. National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder Colorado International Arctic Research Center University of Alaska Fairbanks Fairbanks Alaska National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Colorado Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff Arizona Biospherical Instruments Inc. San Diego California Geophysical Institute University of Alaska Fairbanks Fairbanks Alaska Plant Soils and Climate Department Utah State University Logan Utah Norwegian Institute for Nature Research Trondheim Norway FRAM – High North Research Centre for Climate and the Environment Tromsø Norway Geological Survey of Denmark and Greenland (GEUS) Copenhagen Denmark NOAA/NWS Alaska Region Anchorage Alaska Alaska Native Tribal Health Consortium Anchorage Alaska Geological Survey of Canada Natural Resources Canada Ottawa Canada NOAA Chemical Sciences Laboratory Boulder Colorado Arctic Geophysics Department University Centre in Svalbard Longyearbyen Norway Geology Department University Centre in Svalbard Longyearben Norway Centre National de Recherches Météorologiques Météo-France/CNRS Toulouse France Climate Research Division Environment and Climate Change Canada Toronto Canada Norwegian Polar Institute Fram Centre Tromsø Norway Danish Meteorological Institute Copenhagen Denmark Department of Physics University of Toronto Toronto Canada Department of Environmental Sciences University of Virginia Charlottesville Virginia Department of Geographical Sciences University of Maryland College Park Maryland University of Liège Liège Belgium Environment and Climate Change Canada Toronto Canada Northern Rocky Mountain Science Center U.S. Geological Survey Bozeman Montana Arctic C

Learning from learning machines: A new generation of AI technology to meet the needs of science

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

Structure of pion photoproduction amplitudes

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arXiv 2018年

作者： Mathieu, V. Nys, J. Fernández-Ramírez, C. Hiller Blin, A.N. Jackura, A. Pilloni, A. Szczepaniak, A.P. Fox, G. Theory Center Thomas Jefferson National Accelerator Facility 12000 Jefferson Avenue Newport NewsVA23606 United States Department of Physics and Astronomy Ghent University Belgium Instituto de Ciencias Nucleares Universidad Nacional Autónoma de México Ciudad de México04510 Mexico Institut für Kernphysik & PRISMA Cluster of Excellence Johannes Gutenberg Universität MainzD-55099 Germany Center for Exploration of Energy and Matter Indiana University BloomingtonIN47403 United States Physics Department Indiana University BloomingtonIN47405 United States School of Informatics and Computing Indiana University BloomingtonIN47405 United States

We derive and apply the finite energy sum rules to pion photoproduction. We evaluate the low energy part of the sum rules using several state-of-the-art models. We show how the differences in the low energy side of the sum rules might originate from different quantum number assignments of baryon resonances. We interpret the observed features in the low energy side of the sum rules with the expectation from Regge theory. Finally, we present a model, in terms of a Regge-pole expansion, that matches the sum rules and the high-energy observables. Copyright © 2018, The Authors. All rights reserved.

关键词： Hadrons

Global analysis of charge exchange meson production at high energies

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arXiv 2018年

作者： Nys, J. Hiller Blin, A.N. Mathieu, V. Fernández-Ramírez, C. Jackura, A. Pilloni, A. Ryckebusch, J. Szczepaniak, A.P. Fox, G. Department of Physics and Astronomy Ghent University Belgium Center for Exploration of Energy and Matter Indiana University BloomingtonIN47403 United States Physics Department Indiana University BloomingtonIN47405 United States Institut für Kernphysik & PRISMA Cluster of Excellence Johannes Gutenberg Universität MainzD-55099 Germany Theory Center Thomas Jefferson National Accelerator Facility 12000 Jefferson Avenue Newport NewsVA23606 United States Instituto de Ciencias Nucleares Universidad Nacional Autónoma de México Ciudad de México04510 Mexico School of Informatics and Computing Indiana University BloomingtonIN47405 United States

Many experiments that are conducted to study the hadron spectrum rely on peripheral resonance production. Hereby, the rapidity gap allows the process to be viewed as an independent fragmentation of the beam and the target, with the beam fragmentation dominated by production and decays of meson resonances. We test this separation by determining the kinematic regimes that are dominated by factorizable contributions, indicating the most favorable regions to perform this kind of experiments. In doing so, we use a Regge model to analyze the available world data of charge exchange meson production with beam momentum above 5 GeV in the laboratory frame, that are not dominated by either pion or Pomeron exchanges. We determine the Regge residues and point out the kinematic regimes which are dominated by factorizable contributions. Copyright © 2018, The Authors. All rights reserved.

关键词： Charge transfer

energy modeling and data structure framework for Sustainable Human-Building Ecosystems （SHBE）- a review

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Frontiers in energy 2018年第2期12卷 314-332页

作者： Suraj TALELE Caleb TRAYLOR Laura ARPAN Cali CURLEY Chien-Fei CHEN Julia DAY Richard FEIOCK Mirsad HADZIKADIC William J. TOLONE Stan INGMAN Dale YEATTS Omer T. KARAGUZEL Khee Poh LAM Carol MENASSA Svetlana PEVNITSKAYA Thomas SPIEGELHALTER Wei YAN Yimin ZHU Yong X. TAO Department of Mechanical and Energy Engineering University of North Texas Denton Texas 76203 USA College of Communication & Information Florida State University Tallahassee FL 32306 USA School of Public and Environmental Affairs Indiana University-Purdue University Indianapolis Indianapolis 1N 46202 USA Department of Sociology University of Tennessee Knoxville Knoxville TN 37996 USA Human Ecology Department of Construction Management Washington State University Pullman WA 99164 USA Reubin O'D. Askew School of Public Administration and Policy Florida State University Tallahassee FL 32306 USA College of Computing and Informatics University of North Carolina Charlotte Charlotte NC 28223 USA Department of Gerontology University of North Texas Denton TX 76203 USA Department of Gerontology and Department of Sociology University of North Texas Denton TX 76203 USA CMU School of Architecture Carnegie Mellon University Pittsburgh PA 15213 USA School of Design & Environment National University of Singapore Singapore 117566 Singapore Department of Civil and Environmental Engineering University of Michigan Ann Arbor MI 48109 USA Department of Economics Florida State University Tallahassee FL 32306 USA Department of Architecture Florida Intemational University Miami FL 33199 USA College of Architecture Texas A & M University College Station TX 77843 USA Bert S. Turner Department of Construction Management Louisiana State University Baton Rogue LA 70803 USA College of Engineering and Computing Nova Southeastern University Davie FL 33314 USA

This paper contributes an inclusive review of scientific studies in the field of sustainable human building ecosystems （SHBEs）. Reducing energy consumption by making buildings more energy efficient has been touted as an easily attainable approach to promoting carbon-neutral energy societies. Yet, despite significant progress in research and technology development, for new buildings, as energy codes are getting more stringent, more and more technologies, e.g., LED lighting, VRF systems, smart plugs, occupancy-based controls, are used. Nevertheless, the adoption of energy efficient measures in buildings is still limited in the larger context of the developing countries and middle income/low-income population. The objective of Sustainable Human Building Ecosystem Research Coordination Network （SHBE-RCN） is to expand synergistic investigative podium in order to subdue barriers in engineering, architectural design, social and economic perspectives that hinder wider application, adoption and subsequent performance of sustainable building solutions by recognizing the essential role of human behaviors within building-scale ecosystems. Expected long-term outcomes of SHBE-RCN are collaborative ideas for transformative technologies, designs and methods of adoption for future design, construction and operation of sustainable buildings.

关键词： sustainability building energy modeling（BEM） occupant behaviors （OB） sustainable ecosystems System for the Observation of Populous Heterogeneous Information （SOPHI）

Regge phenomenology of the N∗ and Δ∗ poles

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arXiv 2018年

作者： Silva-Castro, J.A. Fernández-Ramírez, C. Albaladejo, M. Danilkin, I.V. Jackura, A. Mathieu, V. Nys, J. Pilloni, A. Szczepaniak, A.P. Fox, G. Instituto de Ciencias Nucleares Universidad Nacional Autónoma de México Ciudad de México04510 Mexico Theory Center Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Departamento de Física Universidad de Murcia MurciaE-30071 Spain Institut für Kernphysik PRISMA Cluster of Excellence Johannes Gutenberg Universität MainzD-55099 Germany Center for Exploration of Energy and Matter Indiana University BloomingtonIN47403 United States Physics Department Indiana University BloomingtonIN47405 United States Department of Physics and Astronomy Ghent University Ghent9000 Belgium I-38123 Italy School of Informatics and Computing Indiana University BloomingtonIN47405 United States

We use Regge phenomenology to study the structure of the poles of the N∗ and Δ∗ spectrum. We employ the available pole extractions from partial wave analysis of meson scattering and photoproduction data. We assess the importance of the imaginary part of the poles (widths) to obtain a consistent determination of the parameters of the Regge trajectory. We compare the several pole extractions and show how Regge phenomenology can be used to gain insight in the internal structure of baryons. We find that the majority of the states in the parent Regge trajectories are compatible with a mostly compact three-quark state picture. Copyright © 2018, The Authors. All rights reserved.

关键词： Poles

Efficient memory compression in deep neural networks using coarse-grain sparsification for speech applications

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Efficient memory compression in deep neural networks using c...

IEEE International Conference on Computer-Aided Design

作者： Deepak Kadetotad Sairam Arunachalam Chaitali Chakrabarti Jae-sun Seo Computer and Energy Engineering School of Electrical School of Computing Informatics Decision Systems Engineering Arizona State University Tempe AZ

ISBN: (纸本)9781509034215

Recent breakthroughs in deep neural networks have led to the proliferation of its use in image and speech applications. Conventional deep neural networks (DNNs) are fully-connected multi-layer networks with hundreds or thousands of neurons in each layer. Such a network requires a very large weight memory to store the connectivity between neurons. In this paper, we propose a hardware-centric methodology to design low power neural networks with significantly smaller memory footprint and computation resource requirements. We achieve this by judiciously dropping connections in large blocks of weights. The corresponding technique, termed coarse-grain sparsification (CGS), introduces hardware-aware sparsity during the DNN training, which leads to efficient weight memory compression and significant computation reduction during classification without losing accuracy. We apply the proposed approach to DNN design for keyword detection and speech recognition. When the two DNNs are trained with 75% of the weights dropped and classified with 5-6 bit weight precision, the weight memory requirement is reduced by 95% compared to their fully-connected counterparts with double precision, while maintaining similar performance in keyword detection accuracy, word error rate, and sentence error rate. To validate this technique in real hardware, a time-multiplexed architecture using a shared multiply and accumulate (MAC) engine was implemented in 65nm and 40nm low power (LP) CMOS. In 40nm at 0.6 V, the keyword detection network consumes 36μW and the speech recognition network consumes 552μW, making this technique highly suitable for mobile and wearable devices.

关键词： Speech recognition Training Memory management Hidden Markov models Biological neural networks Hardware Neurons

Erratum to: Search for exclusive Higgs and Z boson decays to ϕγ and ργ with the ATLAS detector

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Journal of High energy Physics 2023年第12期2023卷 1-21页

作者： Aaboud, M. Aad, G. Abbott, B. Abdinov, O. Abeloos, B. Abidi, S. H. AbouZeid, O. S. Abraham, N. L. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B. S. Adachi, S. Adamczyk, L. Adelman, J. Adersberger, M. Adye, T. Affolder, A. A. Afik, Y. Agatonovic-Jovin, T. Agheorghiesei, C. Aguilar-Saavedra, J. A. Ahlen, S. P. Ahmadov, F. Aielli, G. Akatsuka, S. Akerstedt, H. Åkesson, T. P. A. Akilli, E. Akimov, A. V. Alberghi, G. L. Albert, J. Albicocco, P. Alconada Verzini, M. J. Alderweireldt, S. C. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Ali, B. Aliev, M. Alimonti, G. Alison, J. Alkire, S. P. Allbrooke, B. M. M. Allen, B. W. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Alshehri, A. A. Alstaty, M. I. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M. G. Amadio, B. T. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amoroso, S. Amundsen, G. Anastopoulos, C. Ancu, L. S. Andari, N. Andeen, T. Anders, C. F. Anders, J. K. Anderson, K. J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Angerami, A. Anisenkov, A. V. Anjos, N. Annovi, A. Antel, C. Antonelli, M. Antonov, A. Antrim, D. J. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J. P. Araujo Ferraz, V. Arce, A. T. H. Ardell, R. E. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A. J. Armitage, L. J. Arnaez, O. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Artz, S. Asai, S. Asbah, N. Ashkenazi, A. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N. B. Augsten, K. Avolio, G. Axen, B. Ayoub, M. K. Azuelos, G. Baas, A. E. Baca, M. J. Bachacou, H. Bachas, K. Backes, M. Bagnaia, P. Bahmani, M. Bahrasemani, H. Baines, J. T. Bajic, M. Baker, O. K. Bakker, P. J. Baldin, E. M. Balek, P. Balli, F. Balunas, W. K. Banas, E. Bandyopadhyay, A. Banerjee, Sw. Bannoura, A. A. E. Barak, L. Barberio, E. L. Barberis, D. Barbero, M. Barillari, T. Barisits, M.-S. Barkeloo, J. T. Barklow, T. Barlow, N. Barnes, S. L. Barnett, B. M. Barnett, R. M. Barno Faculté des Sciences Université Mohamed Premier and LPTPM Oujda Morocco CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France Department of Physics University of Toronto Toronto Canada Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz United States of America Department of Physics and Astronomy University of Sussex Brighton United Kingdom Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene United States of America Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom International Center for Elementary Particle Physics and Department of Physics The University of Tokyo Tokyo Japan AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics Northern Illinois University DeKalb United States of America Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Institute of Physics University of Belgrade Belgrade Serbia Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Laboratório de Instrumentação e Física Experimental de Partículas – LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos Universidad de Granada Granada Portugal Department of Physics Boston University Boston United States of America Joi