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arXiv 2024年

作者： Afzal, F. Akondi, C.S. Albrecht, M. Amaryan, M. Arrigo, S. Arroyave, V. Asaturyan, A. Austregesilo, A. Baldwin, Z. Barbosa, F. Barlow, J. Barriga, E. Barsotti, R. Barton, D. Baturin, V. Berdnikov, V.V. Black, T. Boeglin, W. Boer, M. Briscoe, W.J. Britton, T. Cao, S. Chudakov, E. Chung, G. Cole, P.L. Cortes, O. Crede, V. Dalton, M.M. Darulis, D. Deur, A. Dobbs, S. Dolgolenko, A. Dugger, M. Dzhygadlo, R. Ebersole, D. Edo, M. Egiyan, H. Erbora, T. Eugenio, P. Fabrizi, A. Fanelli, C. Fang, S. Fitches, J. Foda, A.M. Furletov, S. Gan, L. Gao, H. Gardner, A. Gasparian, A. Glazier, D.I. Gleason, C. Goryachev, V.S. Grube, B. Guo, J. Guo, L. Hernandez, J. Hernandez, K. Hoffman, N.D. Hornidge, D. Hou, G. Hurck, P. Hurley, A. Imoehl, W. Ireland, D.G. Ito, M.M. Jaegle, I. Jarvis, N.S. Jeske, T. Jing, M. Jones, R.T. Kakoyan, V. Kalicy, G. Khachatryan, V. Kourkoumelis, C. LaDuke, A. Larin, I. Lawrence, D. Lersch, D.I. Li, H. Liu, B. Livingston, K. Lolos, G.J. Lorenti, L. Lyubovitskij, V. Ma, R. Mack, D. Mahmood, A. Marukyan, H. Matveev, V. McCaughan, M. McCracken, M. Meyer, C.A. Miskimen, R. Mitchell, R.E. Mizutani, K. Neelamana, V. Ng, L. Nissen, E. Orešić, S. Ostrovidov, A.I. Papandreou, Z. Paudel, C. Pedroni, R. Pentchev, L. Peters, K.J. Prather, E. Rakshit, S. Reinhold, J. Remington, A. Ritchie, B.G. Ritman, J. Rodriguez, G. Romanov, D. Saldana, K. Salgado, C. Schadmand, S. Schertz, A.M. Scheuer, K. Schick, A. Schmidt, A. Schumacher, R.A. Schwiening, J. Septian, N. Sharp, P. Shen, X. Shepherd, M.R. Sikes, J. Smith, A. Smith, E.S. Sober, D.I. Somov, A. Somov, S. Stevens, J.R. Strakovsky, I.I. Sumner, B. Suresh, K. Tarasov, V.V. Taylor, S. Teymurazyan, A. Thiel, A. Viducic, T. Whitlatch, T. Wickramaarachchi, N. Wunderlich, Y. Yu, B. Zarling, J. Zhang, Z. Zhou, X. Zihlmann, B. Polytechnic Sciences and Mathematics School of Applied Sciences and Arts Arizona State University TempeAZ85287 United States Department of Physics National and Kapodistrian University of Athens Athens15771 Greece Ruhr-Universität-Bochum Institut für Experimentalphysik BochumD-44801 Germany Helmholtz-Institut für Strahlen- und Kernphysik Universität Bonn BonnD-53115 Germany Department of Physics Carnegie Mellon University PittsburghPA15213 United States Department of Physics The Catholic University of America WashingtonDC20064 United States Department of Physics University of Connecticut StorrsCT06269 United States Department of Physics Duke University DurhamNC27708 United States Department of Physics Florida International University MiamiFL33199 United States Department of Physics Florida State University TallahasseeFL32306 United States Department of Physics The George Washington University WashingtonDC20052 United States School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom GSI Helmholtzzentrum für Schwerionenforschung GmbH DarmstadtD-64291 Germany Institute of High Energy Physics Beijing100049 China Department of Physics Indiana University BloomingtonIN47405 United States National Research Centre Kurchatov Institute Moscow123182 Russia Department of Physics Lamar University BeaumontTX77710 United States Department of Physics University of Massachusetts AmherstMA01003 United States National Research Nuclear University Moscow Engineering Physics Institute Moscow115409 Russia Department of Physics Mount Allison University SackvilleNBE4L 1E6 Canada Department of Physics Norfolk State University NorfolkVA23504 United States Department of Physics North Carolina A&T State University GreensboroNC27411 United States Department of Physics and Physical Oceanography University of North Carolina at Wilmington Wilmington28403 United States Department of Physics Old Dominion University Norfo

We measure the spin-density matrix elements (SDMEs) of the ∆++(1232) in the photoproduction reaction γp → π−∆++(1232) with the GlueX experiment in Hall D at Jefferson Lab. The measurement uses a linearly-polarized photon beam with energies from 8.2 to 8.8 GeV and the statistical precision of the SDMEs exceeds the previous measurement by three orders of magnitude for the momentum transfer squared region below 1.4 GeV2. The data are sensitive to the previously undetermined relative sign between couplings in existing Regge-exchange models. Linear combinations of the extracted SDMEs allow for a decomposition into natural and unnatural-exchange amplitudes. We find that the unnatural exchange plays an important role in the low momentum transfer region. Copyright © 2024, The Authors. All rights reserved.

关键词： Momentum transfer

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Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events

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Computing and Software for Big Science 2022年第1期6卷 1-35页

作者： Aad, G. Abbott, B. Abbott, D.C. Abud, A. Abed Abeling, K. Abhayasinghe, D.K. Abidi, S.H. AbouZeid, O.S. Abraham, N.L. Abramowicz, H. Abreu, H. Abulaiti, Y. Hoffman, A. C. Abusleme Acharya, B.S. Achkar, B. Adam, L. Bourdarios, C. Adam Adamczyk, L. Adamek, L. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akilli, E. Akimov, A.V. Khoury, K. Al Alberghi, G.L. Albert, J. Verzini, M. J. Alconada Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Camelia, E. Alunno Estevez, M. Alvarez Alviggi, M.G. Coutinho, Y. Amaral Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Santos, S. P. Amor Dos Amoroso, S. Amrouche, C.S. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Andrei, V. Anelli, C.R. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Pozo, J. A. Aparisi Aparo, M.A. Bella, L. Aperio Aranzabal, N. Ferraz, V. Araujo Arcangeletti, C. Arce, A.T.H. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Tame, Z. P. Arrubarrena Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Austrup, V.A. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Backman, F. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Gupta, D. Bakshi Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Balli, F. Balunas, W.K. Balz, J. Banas, E. Bandieramonte, M. Bandyopadhyay, A. Barak, L. Barbe, W.M. Ba Department of Physics University of Adelaide Adelaide Australia Physics Department SUNY Albany Albany NY United States Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Istanbul Aydin University Application and Research Center for Advanced Studies Istanbul Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Univ. Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory and University of California Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Ei

The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy. © 2022, The Author(s).

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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A predictive surrogate model for heat transfer of an impinging jet on a concave surface

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International Journal of Heat and Mass Transfer 2025年 251卷

作者： Sajad Salavatidezfouli Saeed Rakhsha Armin Sheidani Giovanni Stabile Gianluigi Rozza Mathematics Area MathLab International School for Advanced Studies (SISSA) Trieste Italy Department of Electrical and Computer Engineering - Signal Processing and Machine Learning Aarhus University Aarhus Denmark Department of Mechanical Engineering Semnan University Semnan Iran Department of Applied Physics Eindhoven University of Technology The Netherlands Department of Pure and Applied Sciences Informatics and Mathematics Section University of Urbino Carlo Bo Urbino Italy The Biorobotics Institute Sant’Anna School of Advanced Studies Pisa Italy

This paper aims to comprehensively investigate the efficacy of model order reduction and deep learning techniques in predicting heat transfer of pulsatile impinging jets on a concave surface. We introduce two predictive approaches: one employing a Fast Fourier Transform-augmented Artificial Neural Network for predicting the area-averaged Nusselt number under constant-frequency jet scenarios, and another comparing the performance of LSTM and Transformer neural networks for random-frequency jets. Results indicate that the Transformer significantly outperforms the LSTM, achieving higher accuracy and robustness in predicting Nusselt numbers — covering up to 50% of the cycle with precision, whereas the LSTM covers only 20% with greater error margins. Additionally, the integration of Proper Orthogonal Decomposition with Transformer networks yields a novel strategy for predicting local Nusselt number distributions. This method significantly reduces computational complexity while maintaining high accuracy, with a maximum prediction error of 5%. These findings demonstrate the efficacy of advanced deep learning techniques for temporal prediction of the Nusselt number on complex surfaces, suggesting further applicability in broader fluid dynamics and heat transfer analyses.

关键词： Concave surface Deep learning Heat transfer Model order reduction Predictive surrogate model

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Measurement of spin-density matrix elements in ρ(770) production with a linearly polarized photon beam at Eγ=8.2–8.8 GeV

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Physical Review C 2023年第5期108卷 055204-055204页

作者： S. Adhikari F. Afzal C. S. Akondi M. Albrecht M. Amaryan V. Arroyave A. Asaturyan A. Austregesilo Z. Baldwin F. Barbosa J. Barlow E. Barriga R. Barsotti T. D. Beattie V. V. Berdnikov T. Black W. Boeglin W. J. Briscoe T. Britton W. K. Brooks D. Byer E. Chudakov P. L. Cole O. Cortes V. Crede M. M. Dalton D. Darulis A. Deur S. Dobbs A. Dolgolenko R. Dotel M. Dugger R. Dzhygadlo D. Ebersole M. Edo H. Egiyan T. Erbora P. Eugenio A. Fabrizi C. Fanelli S. Fang S. Fegan J. Fitches A. M. Foda S. Furletov L. Gan H. Gao A. Gardner A. Gasparian C. Gleason K. Goetzen V. S. Goryachev B. Grube J. Guo L. Guo T. J. Hague H. Hakobyan J. Hernandez N. D. Hoffman D. Hornidge G. Hou G. M. Huber P. Hurck A. Hurley W. Imoehl D. G. Ireland M. M. Ito I. Jaegle N. S. Jarvis T. Jeske R. T. Jones V. Kakoyan G. Kalicy V. Khachatryan M. Khatchatryan C. Kourkoumelis A. LaDuke I. Larin D. Lawrence D. I. Lersch H. Li B. Liu K. Livingston G. J. Lolos L. Lorenti V. Lyubovitskij R. Ma D. Mack A. Mahmood H. Marukyan V. Matveev M. McCaughan M. McCracken C. A. Meyer R. Miskimen R. E. Mitchell K. Mizutani V. Neelamana L. Ng E. Nissen S. Orešić A. I. Ostrovidov Z. Papandreou C. Paudel R. Pedroni L. Pentchev K. J. Peters E. Prather S. Rakshit J. Reinhold A. Remington B. G. Ritchie J. Ritman G. Rodriguez D. Romanov K. Saldana C. Salgado S. Schadmand A. M. Schertz K. Scheuer A. Schick A. Schmidt R. A. Schumacher J. Schwiening P. Sharp X. Shen M. R. Shepherd A. Smith E. S. Smith D. I. Sober A. Somov S. Somov J. R. Stevens I. I. Strakovsky B. Sumner K. Suresh V. V. Tarasov S. Taylor A. Teymurazyan A. Thiel T. Viducic T. Whitlatch N. Wickramaarachchi M. Williams Y. Wunderlich B. Yu J. Zarling Z. Zhang Z. Zhao J. Zhou X. Zhou B. Zihlmann Department of Physics Old Dominion University Norfolk Virginia 23529 USA Helmholtz-Institut für Strahlen- und Kernphysik Universität Bonn D-53115 Bonn Germany Department of Physics Florida State University Tallahassee Florida 32306 USA Thomas Jefferson National Accelerator Facility Newport News Virginia 23606 USA Department of Physics Florida International University Miami Florida 33199 USA Department of Physics and Physical Oceanography University of North Carolina at Wilmington Wilmington North Carolina 28403 USA A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) 0036 Yerevan Armenia Department of Physics Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Department of Physics Indiana University Bloomington Indiana 47405 USA Department of Physics University of Regina Regina Saskatchewan S4S 0A2 Canada Department of Physics The Catholic University of America Washington D.C. 20064 USA Department of Physics The George Washington University Washington D.C. 20052 USA Departamento de Física Universidad Técnica Federico Santa María Casilla 110-V Valparaíso Chile Department of Physics Duke University Durham North Carolina 27708 USA Department of Physics Lamar University Beaumont Texas 77710 USA School of Physics and Astronomy University of Glasgow Glasgow G12 8QQ United Kingdom National Research Centre Kurchatov Institute Moscow 123182 Russia Polytechnic Sciences and Mathematics School of Applied Sciences and Arts College of Integrative Sciences and Arts Arizona State University Tempe Arizona 85287 USA GSI Helmholtzzentrum für Schwerionenforschung GmbH D-64291 Darmstadt Germany Department of Physics University of Connecticut Storrs Connecticut 06269 USA Department of Physics University of Massachusetts Amherst Massachusetts 01003 USA Department of Physics William & Mary Williamsburg Virginia 23185 USA Institute of High Energy Physics Beijing 100049 People's Republic of China Department of Physics North Caro

The GlueX experiment at Jefferson Lab studies photoproduction of mesons using linearly polarized 8.5GeV photons impinging on a hydrogen target which is contained within a detector with near-complete coverage for charged and neutral particles. We present measurements of spin-density matrix elements for the photoproduction of the vector meson ρ(770). The statistical precision achieved exceeds that of previous experiments for polarized photoproduction in this energy range by orders of magnitude. We confirm a high degree of s-channel helicity conservation at small squared four-momentum transfer t and are able to extract the t dependence of natural- and unnatural-parity exchange contributions to the production process in detail. We confirm the dominance of natural-parity exchange over the full t range. We also find that helicity amplitudes in which the helicity of the incident photon and the photoproduced ρ(770) differ by two units are negligible for −t<0.5GeV2/c2.

关键词： Hadron production Photonuclear reactions Polarization phenomena Strong interaction Vector mesons

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AtlFast3: The Next Generation of Fast Simulation in ATLAS

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Computing and Software for Big Science 2022年第1期6卷 1-54页

作者： Aad, G. Abbott, B. Abbott, D.C. Abud, A. Abed Abeling, K. Abhayasinghe, D.K. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Hoffman, A. C. Abusleme Acharya, B.S. Achkar, B. Adam, L. Bourdarios, C. Adam Adamczyk, L. Adamek, L. Addepalli, S.V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A.A. Afik, Y. Agapopoulou, C. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J.A. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ai, X. Aielli, G. Aizenberg, I. Akatsuka, S. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Khoury, K. Al Alberghi, G.L. Albert, J. Albicocco, P. Verzini, M. J. Alconada Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Allaire, C. Allbrooke, B.M.M. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Camelia, E. Alunno Estevez, M. Alvarez Alviggi, M.G. Coutinho, Y. Amaral Ambler, A. Ambroz, L. Amelung, C. Amidei, D. Santos, S. P. Amor Dos Amoroso, S. Amos, K.R. Amrouche, C.S. Ananiev, V. Anastopoulos, C. Andari, N. Andeen, T. Anders, J.K. Andrean, S.Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A.V. Annovi, A. Antel, C. Anthony, M.T. Antipov, E. Antonelli, M. Antrim, D.J.A. Anulli, F. Aoki, M. Pozo, J. A. Aparisi Aparo, M.A. Bella, L. Aperio Aranzabal, N. Ferraz, V. Araujo Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Armstrong, A. Arnaez, O. Arnold, H. Tame, Z. P. Arrubarrena Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. Asquith, L. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Austrup, V.A. Avner, G. Avolio, G. Ayoub, M.K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahrasemani, H. Bailey, A.J. Bailey, V.R. Baines, J.T. Bakalis, C. Baker, O.K. Bakker, P.J. Bakos, E. Gupta, D. Bakshi Balaji, S. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Bal Department of Physics University of Adelaide Adelaide Australia Department of Physics University of Alberta Edmonton AB Canada Department of Physics Ankara University Ankara Turkey Division of Physics TOBB University of Economics and Technology Ankara Turkey LAPP Univ. Savoie Mont Blanc CNRS/IN2P3 Annecy France High Energy Physics Division Argonne National Laboratory Argonne IL United States Department of Physics University of Arizona Tucson AZ United States Department of Physics University of Texas at Arlington Arlington TX United States Physics Department National and Kapodistrian University of Athens Athens Greece Physics Department National Technical University of Athens Zografou Greece Department of Physics University of Texas at Austin Austin TX United States Bahcesehir University Faculty of Engineering and Natural Sciences Istanbul Turkey Istanbul Bilgi University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institute of High Energy Physics Chinese Academy of Sciences Beijing China Physics Department Tsinghua University Beijing China Department of Physics Nanjing University Nanjing China University of Chinese Academy of Science (UCAS) Beijing China Institute of Physics University of Belgrade Belgrade Serbia Department for Physics and Technology University of Bergen Bergen Norway Physics Division Lawrence Berkeley National Laboratory and University of California Berkeley CA United States Institut für Physik Humboldt Universität zu Berlin Berlin Germany Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland School of Physics and Astronomy University of Birmingham Birmingham United Kingdom Facultad de Ciencias y Cen

The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes. © 2022, Springer Nature Switzerland AG.

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

关键词： Decision making

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JUNO physics Prospects 37

JUNO Physics Prospects

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37th International Cosmic Ray Conference, ICRC 2021

作者： de André, João Pedro Athayde Marcondes Abusleme, Angel Adam, Thomas Ahmad, Shakeel Ahmed, Rizwan Aiello, Sebastiano Akram, Muhammad An, Fengpeng An, Qi Andronico, Giuseppe Anfimov, Nikolay Antonelli, Vito Antoshkina, Tatiana Asavapibhop, Burin Auguste, Didier Babic, Andrej Balashov, Nikita Baldini, Wander Barresi, Andrea Basilico, Davide Baussan, Eric Bellato, Marco Bergnoli, Antonio Birkenfeld, Thilo Blin, Sylvie Blum, David Blyth, Simon Bolshakova, Anastasia Bongrand, Mathieu Bordereau, Clément Breton, Dominique Brigatti, Augusto Brugnera, Riccardo Bruno, Riccardo Budano, Antonio Buscemi, Mario Busto, Jose Butorov, Ilya Cabrera, Anatael Cai, Hao Cai, Xiao Cai, Yanke Cai, Zhiyan Callegari, Riccardo Cammi, Antonio Campeny, Agustin Cao, Chuanya Cao, Guofu Cao, Jun Caruso, Rossella Cerna, Cédric Chang, Jinfan Chang, Yun Chen, Pingping Chen, Po-An Chen, Shaomin Chen, Xurong Chen, Yi-Wen Chen, Yixue Chen, Yu Chen, Zhang Cheng, Jie Cheng, Yaping Chetverikov, Alexey Chiesa, Davide Chimenti, Pietro Chukanov, Artem Claverie, Gérard Clementi, Catia Clerbaux, Barbara Di Lorenzo, Selma Conforti Corti, Daniele Dal Corso, Flavio Dalager, Olivia De La Taille, Christophe Deng, Jiawei Deng, Zhi Deng, Ziyan Depnering, Wilfried Diaz, Marco Ding, Xuefeng Ding, Yayun Dirgantara, Bayu Dmitrievsky, Sergey Dohnal, Tadeas Dolzhikov, Dmitry Donchenko, Georgy Dong, Jianmeng Doroshkevich, Evgeny Dracos, Marcos Druillole, Frédéric Du, Ran Du, Shuxian Dusini, Stefano Dvorak, Martin Enqvist, Timo Enzmann, Heike Fabbri, Andrea Fajt, Lukas Fan, Donghua Fan, Lei Fang, Jian Fang, Wenxing Fargetta, Marco Fedoseev, Dmitry Fekete, Vladko Feng, Li-Cheng Feng, Qichun Ford, Richard Fournier, Amélie Gan, Haonan Gao, Feng Garfagnini, Alberto Gavrikov, Arsenii Giammarchi, Marco Giaz, Agnese Giudice, Nunzio Gonchar, Maxim Gong, Guanghua Gong, Hui Gornushkin, Yuri Göttel, Alexandre Grassi, Marco Grewing, Christian Gromov, Vasily Gu, Minghao Gu, Xiaofei Gu, Yu Guan, Mengyun Guardone, Nunzio Gul, Maria Guo, Cong Guo, Jingyuan Guo, Wanlei Guo, Xinheng Guo, Yuh IPHC Université de Strasbourg CNRS IN2P3 StrasbourgF-67037 France Yerevan Physics Institute Yerevan Armenia Université Libre de Bruxelles Brussels Belgium Universidade Estadual de Londrina Londrina Brazil Pontificia Universidade Catolica Do Rio de Janeiro Brazil Pontificia Universidad Católica de Chile Santiago Chile Universidad Tecnica Federico Santa Maria Valparaiso Chile Beijing Institute of Spacecraft Environment Engineering Beijing China Beijing Normal University Beijing China China Institute of Atomic Energy Beijing China Institute of High Energy Physics Beijing China North China Electric Power University Beijing China School of Physics Peking University Beijing China Tsinghua University Beijing China University of Chinese Academy of Sciences Beijing China Jilin University Changchun China College of Electronic Science and Engineering National University of Defense Technology Changsha China Chongqing University Chongqing China Dongguan University of Technology Dongguan China Jinan University Guangzhou China Sun Yat-Sen University Guangzhou China Harbin Institute of Technology Harbin China University of Science and Technology of China Hefei China The Radiochemistry and Nuclear Chemistry Group University of South China Hengyang China Wuyi University Jiangmen China Shandong University Jinan China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao China Institute of Modern Physics Chinese Academy of Sciences Lanzhou China Nanjing University Nanjing China Guangxi University Nanning China East China University of Science and Technology Shanghai China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai China Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological Sciences Shijiazhuang China Nankai University Tianjin China Wuhan University Wuhan China Xi’an Jia

JUNO is a multi-purpose underground neutrino observatory being constructed in the south of China. The main detector, with a 20 kton liquid scintillator target instrumented with about 18k 20" PMT and about 26k 3" PMT, will be strategically located 53 km from the Taishan and Yangjiang Nuclear Power Plants. Using reactor antineutrinos, JUNO will be able to measure several neutrino oscillation parameters with sub-percent precision as well as to determine the neutrino mass ordering to ∼3 σ over 6 years of operation. Furthermore, JUNO will have a broad physics program, ranging from studying neutrinos from other sources, such as solar and supernova neutrinos, to searching for BSM physics such as proton decay. This talk will give an overview on the JUNO’s broad physics potential. © Copyright owned by the author(s).

关键词： Nuclear power plants

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Radio Resource Management in 5G networks

Radio Resource Management in 5G networks

引用

Engineering and Telecommunication (En&T), International Conference

作者： Alexey Chernov Vladislav Matyukhin Michael Somov Egor Barashov Julia Chernova Victor Mishin MIPT-School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Moscow Russian Federation Moscow State University Moscow Russian Federation V.A. Trapeznikov Institute of Control Sciences Moscow State University Moscow Russian Federation Faculty of Mechanics and Mathematics Moscow State University Moscow Russian Federation

ISBN: (纸本)9781728188300

In this work, we study the resource allocation problem for downlink in5G New Radio (NR) mobile networks in the case of orthogonal frequency-division multiple access (OFDMA). One of the key features of 5G mobile networks is the usage of antenna arrays that allows them to form beams in a specific direction and send data to several users at the same transmission time interval (TTI) via the same carrier or resource block (RB). Antenna arrays usually contain up to 64 single antennas. We modeled the process of the data transmission from the cell to the user device or user equipment (UE), formulated mathematical abstraction of the resource allocation problem, and proposed a lightweight algorithm that uses a proportional fair metric.

关键词： OFDM Transmitting antennas Directive antennas Mobile antennas Throughput New Radio Resource management

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Self-isolation or borders closing: What prevents the spread of the epidemic better?

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Physical Review E 2020年第1期102卷 010401(R)-010401(R)页

作者： O. Valba V. Avetisov A. Gorsky S. Nechaev Department of Applied Mathematics National Research University Higher School of Economics 101000 Moscow Russia Federal Research Center of Chemical Physics RAS 119991 Moscow Russia Institute of Information Transmission Problems RAS 127051 Moscow Russia Moscow Institute of Physics and Technology Dolgoprudny 141700 Russia Interdisciplinary Scientific Center Poncelet CNRS UMI 2615 119002 Moscow Russia P.N. Lebedev Physical Institute RAS 119991 Moscow Russia

Pandemic propagation of COVID-19 motivated us to discuss the impact of the human network clustering on epidemic spreading. Today, there are two clustering mechanisms which prevent of uncontrolled disease propagation in a connected network: an “internal” clustering, which mimics self-isolation (SI) in local naturally arranged communities, and an “external” clustering, which looks like a sharp frontiers closing (FC) between cities and countries, and which does not care about the natural connections of network agents. SI networks are “evolutionarily grown” under the condition of maximization of small cliques in the entire network, while FC networks are instantly created. Running the standard SIR model on clustered SI and FC networks, we demonstrate that the evolutionary grown clustered network prevents the spread of an epidemic better than the instantly clustered network with similar parameters. We find that SI networks have the scale-free property for the degree distribution P(k)∼kη, with a small critical exponent −2<η<−1. We argue that the scale-free behavior emerges as a result of the randomness in the initial degree distributions.

关键词： Clustering Diseases Evolving networks

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