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Deep Generative Models for Fast Photon Shower Simulation in ATLAS

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Computing and Software for Big science 2024年第1期8卷 1-40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. 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. 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 Appelt, C. Aranzabal, N. Ferraz, V. Araujo Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A.J. Arnaez, O. Arnold, H. Tame, Z. P. Arrubarrena Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Asimakopoulou, E.M. 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. Bahmani, M. 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. Balli, F. Baltes, L.M. Balunas, W.K. Balz, J. Banas 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 Université Savoie Mont Blanc CNRS/IN2P3 Annecy France APC Université Paris Cité CNRS/IN2P3 Paris 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 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 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 Department of Physics Bogazici University Istanbul Turkey Department of Physics Engineering Gaziantep University Gaziantep Turkey Department of Physics Istanbul University Istanbul Turkey Istinye University Sariyer Istanbul

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques. © The Author(s) 2024.

关键词：

Search for pair production of squarks or gluinos decaying via sleptons or weak bosons in final states with two same-sign or three leptons with the ATLAS detector

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Journal of High Energy Physics 2024年第2期2024卷 107页

作者： Aad, G. Abbott, B. Abeling, K. Abicht, N.J. Abidi, S.H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Abusleme Hoffman, A.C. Acharya, B.S. Adam Bourdarios, C. Adamczyk, L. Adamek, L. Addepalli, S.V. Addison, M.J. Adelman, J. Adiguzel, A. Adye, T. Affolder, A.A. Afik, Y. Agaras, M.N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Ahmad, A. Ahmadov, F. Ahmed, W.S. Ahuja, S. Ai, X. Aielli, G. Ait Tamlihat, M. Aitbenchikh, B. Aizenberg, I. Akbiyik, M. Åkesson, T.P.A. Akimov, A.V. Akiyama, D. Akolkar, N.N. Al Khoury, K. Alberghi, G.L. Albert, J. Albicocco, P. Albouy, G.L. Alderweireldt, S. Aleksa, M. Aleksandrov, I.N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Algren, M. Alhroob, M. Ali, B. Ali, H.M.J. Ali, S. Alibocus, S.W. Aliev, M. Alimonti, G. Alkakhi, W. Allaire, C. Allbrooke, B.M.M. Allen, J.F. Allendes Flores, C.A. Allport, P.P. Aloisio, A. Alonso, F. Alpigiani, C. Alvarez Estevez, M. Alvarez Fernandez, A. Alves Cardoso, M. Alviggi, M.G. Aly, M. Amaral Coutinho, Y. Ambler, A. Amelung, C. Amerl, M. Ames, C.G. Amidei, D. Amor Dos Santos, S.P. Amos, K.R. Ananiev, V. Anastopoulos, C. 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. Aoki, T. Aparisi Pozo, J.A. Aparo, M.A. Aperio Bella, L. Appelt, C. Apyan, A. Aranzabal, N. Arcangeletti, C. Arce, A.T.H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Arnaez, O. Arnold, H. Arrubarrena Tame, Z.P. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N.A. Assamagan, K. Astalos, R. Atashi, S. Atkin, R.J. Atkinson, M. Atlay, N.B. Atmani, H. Atmasiddha, P.A. Augsten, K. Auricchio, S. Auriol, A.D. Austrup, V.A. Avolio, G. Axiotis, K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahmani, M. Bailey, A.J. Bailey, V.R. Baines, J.T. Baines, L. Bakalis, C. Baker, O.K. Bakos, E. Bakshi Gupta, D. Balasubramanian, R. Baldin, E.M. Balek, P. Ballabene, E. Balli, F. Baltes, L.M. Balunas, W CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman OK United States II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Physics Department Brookhaven National Laboratory Upton NY United States Faculté des sciences Université Mohammed V Rabat Morocco 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 Department of Physics New York University New York NY United States Departamento de Física Pontificia Universidad Católica de Chile Santiago Chile INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow Poland Department of Physics University of Toronto Toronto ON Canada Department of Physics Brandeis University Waltham MA United States School of Physics and Astronomy University of Manchester Manchester United Kingdom Department of Physics Northern Illinois University DeKalb IL United States Department of Physics Istanbul University Istanbul Turkey Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz CA United States CERN Geneva Switzerland Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain INFN Sezione di Pavia Pavia Italy Dipartimento di Fisica Università di Pavia Pavia Italy Institut für Physik Universität Mainz Mainz Germany Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Institute of Physics Azerbaijan Acade

A search for pair production of squarks or gluinos decaying via sleptons or weak bosons is reported. The search targets a final state with exactly two leptons with same-sign electric charge or at least three leptons without any charge requirement. The analysed data set corresponds to an integrated luminosity of 139 fb⁻¹ of proton-proton collisions collected at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Multiple signal regions are defined, targeting several SUSY simplified models yielding the desired final states. A single control region is used to constrain the normalisation of the WZ + jets background. No significant excess of events over the Standard Model expectation is observed. The results are interpreted in the context of several supersymmetric models featuring R-parity conservation or R-parity violation, yielding exclusion limits surpassing those from previous searches. In models considering gluino (squark) pair production, gluino (squark) masses up to 2.2 (1.7) TeV are excluded at 95% confidence level. © The Author(s) 2024.

关键词： Beyond Standard Model Hadron-Hadron Scattering Lepton Production Supersymmetry

VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

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arXiv 2020年

作者： Sekuboyina, Anjany Kumar Husseini, Malek E. Bayat, Amirhossein Löffler, Maximilian Liebl, Hans Li, Hongwei Tetteh, Giles Kukačka, Jan Payer, Christian Štern, Darko Urschler, Martin Chen, Maodong Cheng, Dalong Lessmann, Nikolas Hu, Yujin Wang, Tianfu Yang, Dong Xu, Daguang Ambellan, Felix Amiranashvili, Tamaz Ehlke, Moritz Lamecker, Hans Lehnert, Sebastian Lirio, Marilia de Olaguer, Nicolás Pérez Ramm, Heiko Sahu, Manish Tack, Alexander Zachow, Stefan Jiang, Tao Ma, Xinjun Angerman, Christoph Wang, Xin Brown, Kevin Kirszenberg, Alexandre Puybareau, Élodie Chen, Di Bai, Yiwei Rapazzo, Brandon H. Yeah, Timyoas Zhang, Amber Xu, Shangliang Hou, Feng He, Zhiqiang Zeng, Chan Xiangshang, Zheng Liming, Xu Netherton, Tucker J. Mumme, Raymond P. Court, Laurence E. Huang, Zixun He, Chenhang Wang, Li-Wen Ling, Sai Ho Huynh, Lê Duy Boutry, Nicolas Jakubicek, Roman Chmelik, Jiri Mulay, Supriti Sivaprakasam, Mohanasankar Paetzold, Johannes C. Shit, Suprosanna Ezhov, Ivan Wiestler, Benedikt Glocker, Ben Valentinitsch, Alexander Rempfler, Markus Menze, Björn H. Kirschke, Jan S. Department of Informatics Technical University of Munich Germany Munich School of BioEngineering Technical University of Munich Germany Department of Neuroradiology Klinikum Rechts der Isar Germany Department for Quantitative Biomedicine University of Zurich Switzerland Friedrich Miescher Institute for Biomedical Engineering Switzerland Institute of Biological and Medical Imaging Helmholtz Zentrum München Germany Department of Computing Imperial College London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Austria Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria School of Computer Science The University of Auckland New Zealand Computer Vision Group iFLYTEK Research South China China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Shenzhen Research Institute of Big Data China School of Biomedical Engineering Health Science Center Shenzhen University China NVIDIA Corporation United States Zuse Institute Berlin Germany 1000shapes GmbH Berlin Germany Damo Academy Alibaba Group China Department of Mathematics University of Innsbruck Austria Department of Electronic Engineering Fudan University China New York University United States France Deep Reasoning AI Inc United States Technical University of Munich Germany East China Normal University China Chinese Academy of Sciences China Lenovo Group China Ping An Technologies China College of Computer Science and Technology Zhejiang University China Real Doctor AI Research Centre Zhejiang University China The University of Texas MD Anderson Cancer Center United States Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Department of Computing The Hong Kong Polytechnic University China The School of Biomedical Engineering University of Technology Sydney Australia Department of Biomedical Engineering Brno University of Technology Czech Republic India

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://***/nqjyw/, https://***/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://***/anjany/verse. © 2020, CC BY-NC-ND.

关键词： computerized tomography

Measurement of the inclusive cross section in final states with at least one lepton and additional jets with 302 pb−1 of pp collisions at = 5.02 TeV

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Journal of High Energy Physics 2025年第4期2025卷 1-44页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J. W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Hussain, P. S. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Waltenberger, W. Wulz, C.-E. Janssen, T. Van Laer, T. Van Mechelen, P. Breugelmans, N. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Heyen, F. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G. P. Van Putte, S. Vannerom, D. Bilin, B. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Shahzad, M. A. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Gokbulut, G. Hong, Y. Knolle, J. Lambrecht, L. Marckx, D. Mota Amarilo, K. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. De Favereau De Jeneret, J. Delaere, C. Donertas, I. S. Giammanco, A. Guzel, A. O. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Tran, T. T. Wertz, S. Alves, G. A. Alves Gallo Pereira, M. Coelho, E. Correia Silva, G. Hensel, C. Menezes De Oliveira, T. Mora Herrera, C. Moraes, A. Rebello Teles, P. Soeiro, M. Vilela Pereira, A. Aldá Júnior, W. L. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E. M. Da Silveira, G. G. De Jesus Damiao, D. Fonseca De Souza, S. Gomes De Souza, R. Laux Kuhn, T. Macedo, M. Martins, J. Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Sznajder, A. Thiel, M. Bernardes, C. A. Calligaris, L. Fernandez Perez Tomei, T. R. Gregores, E. M. Maietto Silverio, I. Mercadante, P. G. Novaes, S. F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Laroze, D. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liang, Z. Liu, J. Chen, G. M. Chen, H. S. Chen, M. Iemmi, F. Jiang, C. H. Kapoor Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium Université Libre de Bruxelles Bruxelles Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China China Center of Advanced Science and Technology Beijing China China Spallation Neutron Source Guangdong China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Guangdong Provincial Key Laboratory of Nuclear Science and Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Nanjing Normal University Nanjing China Henan Normal University Xinxiang China The University of Iowa Iowa City USA Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineeri

A measurement of the top quark pair ( $$ \textrm{t}\overline{\textrm{t}} $$ ) production cross section in proton-proton collisions at a centre-of-mass energy of 5.02 TeV is presented. The data were collected at the LHC in autumn 2017, in dedicated runs with low-energy and low-intensity conditions with respect to the default configuration, and correspond to an integrated luminosity of 302 pb−1. The measurement is performed using events with one electron or muon, and multiple jets, at least one of them being identified as originating from a b quark (b tagged). Events are classified based on the number of all reconstructed jets and of b-tagged jets. Multivariate analysis techniques are used to enhance the separation between the signal and backgrounds. The measured cross section is $$ 62.5\pm 1.6{\left(\textrm{stat}\right)}_{-2.5}^{+2.6}\left(\textrm{syst}\right)\pm 1.2\left(\textrm{lumi}\right) $$ pb. A combination with the result in the dilepton channel based on the same data set yields a value of 62.3 ± 1.5 (stat) ± 2.4 (syst) ± 1.2 (lumi) pb, to be compared with the standard model prediction of $$ {69.5}_{-3.7}^{+3.5} $$ pb at next-to-next-to-leading order in perturbative quantum chromodynamics.

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Parametric verification: An introduction

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arXiv 2019年

作者： Andr, Etienne Knapik, Michal Lime, Didier Penczek, Wojciech Petrucci, Laure LIPN CNRS UMR 7030 Universite Paris 13 Villetaneuse France Institute of Computer Science PAS Warsaw Poland Ecole Centrale de Nantes LS2N CNRS UMR Nantes6004 France University of Natural Sciences and Humanities II Siedlce Poland

This paper constitutes a short introduction to parametric verification of concurrent systems. It originates from two 1-day tutorial sessions held at the Petri nets conferences in Torun (2016) and Zaragoza (2017). A video of the presentation is available at ***/playlist?list=PL9SOLKoGjbeqNcdQVqFpUz7HYqD1fbFIg, consisting of 14 short sequences. The paper presents not only the basic formal concepts tackled in the video version, but also an extensive literature to provide the reader with further references covering the area. We first introduce motivation behind parametric verification in general, and then focus on different models and approaches, for verifying several kinds of systems. They include Parametric Timed Automata, for modelling real-time systems, where the timing constraints are not necessarily known a priori. Similarly, Parametric Interval Markov Chains allow for modelling systems where probabilities of events occurrences are intervals with parametric bounds. Parametric Petri Nets allow for compact representation of systems, and cope with different types of parameters. Finally, Action Synthesis aims at enabling or disabling actions in a concurrent system to guarantee some of its properties. Some tools implementing these approaches were used during hands-on sessions at the tutorial. The corresponding practicals are freely available on the Web. Copyright © 2019, The Authors. All rights reserved.

关键词： Petri nets

CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis

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arXiv 2022年

作者： Prazuch, Wojciech Suwalska, Aleksandra Socha, Marek Tobiasz, Joanna Foszner, Pawel Jaroszewicz, Jerzy Gruszczynska, Katarzyna Sliwinska, Magdalena Walecki, Jerzy Popiela, Tadeusz Przybylski, Grzegorz Cieszanowski, Andrzej Nowak, Mateusz Pawlowska, Malgorzata Flisiak, Robert Simon, Krzysztof Zapolska, Gabriela Gizycka, Barbara Szurowska, Edyta Marczyk, Michal Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice Poland Department of Infectious Diseases and Hepatology Medical University of Silesia Katowice Poland Department of Diagnostic Imaging Voivodship Specialist Hospital Wroclaw Poland Department of Diagnostic Radiology Central Clinical Hospital of The Ministry of Internal Affairs and Administration Warsaw Poland Department of Radiology Jagiellonian University Medical College Krakow Poland Department of Lung Diseases Cancer and Tuberculosis Kujawsko-Pomorskie Pulmonology Center Bydgoszcz Poland Department of Imaging Diagnostics National Institute of Oncology Warsaw Poland Department of Radiology Silesian Hospital Cieszyn Poland Department of Infectious Diseases and Hepatology Collegium Medicum in Bydgoszcz Nicolaus Copernicus University Torun Poland Department of Infectious Diseases and Hepatology Medical University of Bialystok Bialystok Poland Department of Infectious Diseases and Hepatology Wroclaw Medical University Wroclaw Poland Department of Radiology Czerniakowski Hospital Warsaw Poland PDepartment of Imaging Diagnostics MEGREZ Hospital Tychy Poland 2nd Department of Radiology Medical University of Gdansk Poland Yale Cancer Center Yale School of Medicine New HavenCT United States

Background: Due to the large accumulation of patients requiring hospitalization, the COVID-19 pandemic disease caused a high overload of health systems, even in developed countries. Deep learning techniques based on medical imaging data can help in the faster detection of COVID-19 cases and monitoring of disease progression. However, regardless of the numerous proposed solutions using lung X-rays, none of them is a product that can be used in the clinic. Methods: Five different datasets (clinical POLCOVID and AIforCOVID databases, a public collection of images called COVIDx set, pneumonia patients from NIH, and artificially generated data) were used to construct a representative dataset of 23 799 CXRs for model training, 1 050 images as a hold-out test set and 44 247 CXRs as independent test set (BIMCV database). A U-Net-based model was developed to identify a clinically relevant region of the CXR that contains the lungs and each class (normal, pneumonia, and COVID-19) was divided into 3 subtypes using a 2D Gaussian mixture model. A decision tree was used to aggregate predictions from the InceptionV3 convolutional network based on processed CXRs and a dense 7-layer neural network on radiomic features determined independently for the upper, middle, and lower lung fragments. Results: The lung segmentation model gave the Sorensen-Dice coefficient of 94.86% in the validation dataset, and 93.36% in the testing dataset. In 5-fold cross-validation, the accuracy for all classes ranged from 91% to 93%, keeping slightly higher specificity than sensitivity. Also, NPV was higher than PPV for each class to guarantee a low rate of false negative cases. In the hold-out test set, the balanced accuracy ranged between 68% and 100%. The highest performance was obtained for the subtypes N1, P1, and C1, while images assigned to subtypes N3, P3, or C3 have been wrongly predicted among themselves. A similar performance was obtained on the independent dataset for normal and COVID-19 class

关键词： COVID-19

Facial expression recognition based on feature fusion and sparse representation 2017

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Facial expression recognition based on feature fusion and sp...

9th International Conference on Machine Learning and Computing, ICMLC 2017

作者： Fu, Xiao-Feng Fu, Ke-Bo Zhang, Yu Institute of Graphics and Image School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781450348171

Aiming at the limitation of single feature classification, this paper proposes a facial expression recognition algorithm based on feature fusion and sparse representation residual fusion. The proposed method is composed of three components. Firstly, a feature fusion method based on local binary quantization (LBP) and local phase quantization (LPQ) is proposed. The LBP/LPQ features are an expression feature with image space and frequency domain. Second, Curvelet transform is used to extract the global features of facial expression, which is a very effective multi-scale, band-pass and direction analysis tool. Finally, the LBP/LPQ features and the Curvelet feature are classified by the sparse representation based classification (SRC) with stronger discrimination and robustness respectively. According to the sparse residuals of LBP/LPQ+SRC and Curvelet+SRC, a classification method of residual fusion is proposed. Experiments on JAFFE database and Cohn-Kanade database show that the proposed method effectively improves the ability of facial expression recognition and achieves a high recognition rate. © 2017 ACM.

关键词： Face recognition

STROBE-X Mission Overview

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arXiv 2024年

作者： Ray, Paul S. Roming, Peter W.A. Argan, Andrea Arzoumanian, Zaven Ballantyne, David R. Bogdanov, Slavko Bonvicini, Valter Brandt, Terri J. Bursa, Michal Cackett, Edward M. Chakrabarty, Deepto Christophersen, Marc Coderre, Kathleen M. De Geronimo, Gianluigi Del Monte, Ettore DeRosa, Alessandra Dietz, Harley R. Evangelista, Yuri Feroci, Marco Ford, Jeremy J. Froning, Cynthia Fryer, Christopher L. Gendreau, Keith C. Goldstein, Adam Gonzalez, Anthony H. Hartmann, Dieter Hernanz, Margarita Hutcheson, Anthony Zand, Jean in't Jenke, Peter Kennea, Jamie Lloyd-Ronning, Nicole M. Maccarone, Thomas J. Maes, Dominic Markwardt, Craig B. Michalska, Malgorzata Okajima, Takashi Patruno, Alessandro Persyn, Steven C. Phillips, Mark L. Prescod-Weinstein, Chanda Redfern, Jillian A. Remillard, Ronald A. Santangelo, Andrea Schwendeman, Carl L. Sleator, Clio Steiner, James Strohmayer, Tod E. Svoboda, Jiri Tenzer, Christoph Thompson, Steven P. Warwick, Richard W. Watts, Anna L. Wilson-Hodge, Colleen A. Wu, Xin Wulf, Eric A. Zampa, Gianluigi Space Science Division U.S. Naval Research Laboratory WashingtonDC20375 United States Southwest Research Institute San AntonioTX78238 United States NASA’s Goddard Space Flight Center GreenbeltMD20771 United States Columbia Astrophysics Laboratory Columbia University New YorkNY10027 United States Trieste TS Italy SRON Netherlands Institute for Space Research Niels Bohrweg 4 CA Leiden2333 Netherlands Astronomical Institute of the Czech Academy of Sciences Boční II 1401 Prague14100 Czech Republic Department of Physics & Astronomy Wayne State University DetroitMI48201 United States Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Department of Nuclear Engineering and Radiological Sciences Univeristy of Michigan Ann ArborMI United States INAF - Istituto di Astrofisica e Planetologia Spaziali Roma00133 Italy Los Alamos National Laboratory Los AlamosNM87545 United States Universities Space Research Association HuntsvilleAL35805 United States Department of Astronomy University of Florida GainesvilleFL32611 United States IEEC Campus UAB Carrer de Can Magrans s/n Bellaterra Barcelona08193 Spain University of Alabama in Huntsville HuntsvilleAL35805 United States Department of Astronomy and Astrophysics The Pennsylvania State University University ParkPA16802 United States Department of Physics & Astronomy Texas Tech University LubbockTX79409 United States Space Research Center Polish Academy of Sciences Warszawa Poland Department of Physics and Astronomy University of New Hampshire DurhamNH03824 United States Harvard-Smithsonian Center for Astrophysics CambridgeMA02138 United States Eberhard Karls Universität Tübingen Germany Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam1090GE Netherlands Astrophysics Branch NASA’s Marshall Space Flight Center HuntsvilleAL35812 United States Department of Nuclear and Particle Physics Univ

We give an overview of the science objectives and mission design of the Spectroscopic Time-Resolving Observatory for Broadband Energy X-rays (STROBE-X) observatory, which has been proposed as a NASA probe-class (∼$1.... 详细信息

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Measurement of the and tH production rates in the H → decay channel using proton-proton collision data at = 13 TeV

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Journal of High Energy Physics 2025年第2期2025卷 1-75页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J. W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Hussain, P. S. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M. R. Janssen, T. Van Mechelen, P. Breugelmans, N. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Heyen, F. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G. P. Van Putte, S. Vannerom, D. Bilin, B. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Shahzad, M. A. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Gokbulut, G. Hong, Y. Knolle, J. Lambrecht, L. Marckx, D. Mota Amarilo, K. Samalan, A. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. De Favereau De Jeneret, J. Delaere, C. Donertas, I. S. Giammanco, A. Guzel, A. O. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Tran, T. T. Wertz, S. Alves, G. A. Alves Gallo Pereira, M. Coelho, E. Correia Silva, G. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. Vilela Pereira, A. Aldá Júnior, W. L. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E. M. Da Silveira, G. G. De Jesus Damiao, D. Fonseca De Souza, S. Gomes De Souza, R. Macedo, M. Martins, J. Mora Herrera, C. Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Sznajder, A. Thiel, M. Bernardes, C. A. Calligaris, L. Fernandez Perez Tomei, T. R. Gregores, E. M. Maietto Silverio, I. Mercadante, P. G. Novaes, S. F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liang, Z. Liu, J. Yi, K. Chen, G. M. Chen, H. S. Chen, M. Iemmi, F. Jiang, C. H. Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium Université Libre de Bruxelles Bruxelles Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Estadual Paulista Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Nanjing China The University of Iowa Iowa City U.S.A. Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China China Center of Advanced Science and Technology Beijing China China Spallation Neutron Source Guangdong China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Guangdong Provincial Key Laboratory of Nuclear Science and Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Henan Normal University Xinxiang China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Hangzhou

An analysis of the production of a Higgs boson (H) in association with a top quark-antiquark pair ( $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ ) or a single top quark (tH) is presented. The Higgs boson decay into a bottom quark-antiquark pair (H → $$ \textrm{b}\overline{\textrm{b}} $$ ) is targeted, and three different final states of the top quark decays are considered, defined by the number of leptons (electrons or muons) in the event. The analysis utilises proton-proton collision data collected at the CERN LHC with the CMS experiment at $$ \sqrt{s} $$ = 13 TeV in 2016–2018, which correspond to an integrated luminosity of 138 fb−1. The observed $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ production rate relative to the standard model expectation is 0.33 ± 0.26 = 0.33 ± 0.17(stat) ± 0.21(syst). Additionally, the $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ production rate is determined in intervals of Higgs boson transverse momentum. An upper limit at 95% confidence level is set on the tH production rate of 14.6 times the standard model prediction, with an expectation of $$ {19.3}_{-6.0}^{+9.2} $$ . Finally, constraints are derived on the strength and structure of the coupling between the Higgs boson and the top quark from simultaneous extraction of the $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ and tH production rates, and the results are combined with those obtained in other Higgs boson decay channels.

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