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arXiv 2023年

作者： Shendure, Jay Ahituv, Nadav Kircher, Martin Agarwal, Vikram Blair, Andrew Chalkiadakis, Theofilos Chardon, Florence M. Dash, Pyaree M. Deng, Chengyu Hamazaki, Nobuhiko Keukeleire, Pia Kubo, Connor Lalanne, Jean-Benoît Maass, Thorben Martin, Beth McDiarmid, Troy Nobuhara, Mai Page, Nicholas F. Regalado, Sam Schubach, Max Sims, Jasmine Ushiki, Aki Zhao, Jingjing Starita, Lea M. Fowler, Douglas M. Best, Sabrina M. Boyle, Gabe Camp, Nathan Casadei, Silvia Da, Estelle Y. Dawood, Moez Dawson, Samantha C. Fayer, Shawn Hamm, Audrey James, Richard G. Jarvik, Gail P. McEwen, Abbye E. Moore, Nick Muffley, Lara A. Pendyala, Sriram Popp, Nicholas A. Post, Mason Rubin, Alan F. Shendure, Jay Smith, Nahum T. Rubin, Alan F. Stone, Jeremy Tejura, Malvika Wang, Ziyu R. Wheelock, Melinda K. Woo, Ivan Zapp, Brendan D. Engreitz, Jesse M. Quertermous, Thomas Amgalan, Dulguun Aradhana, Aradhana Arana, Sophia M. Bassik, Michael C. Bauman, Julia R. Bhattacharya, Asmita Cai, Xiangmeng Shawn Chen, Ziwei Conley, Stephanie Deshpande, Salil Doughty, Benjamin R. Du, Peter P. Gifford, Casey Greenleaf, William J. Gschwind, Andreas R. Guo, Katherine Isobe, Sarasa Jagoda, Evelyn Jain, Nimit Jones, Hank Kang, Helen Y. Kim, Samuel H. Kim, YeEun Klemm, Sandy Kundaje, Anshul Kundu, Soumya Lago-Docampo, Mauro Lee-Yow, Yannick C. Levin-Konigsberg, Roni Li, Daniel Y. Lindenhofer, Dominik Rosa Ma, X. Marinov, Georgi K. Martyn, Gabriella E. Metzl-Raz, Eyal Monteiro, Joao P. Montgomery, Michael T. Mualim, Kristy S. Munger, Chad Munson, Glen Nguyen, Tri C. Nguyen, Trieu Palmisano, Brian T. Pampari, Anusri Park, Chong Y. Rabinovitch, Marlene Ramste, Markus Ray, Judhajeet Roy, Kevin R. Rubio, Oriane M. Schaepe, Julia M. Schnitzler, Gavin Schreiber, Jacob Sharma, Disha Sheth, Maya U. Shi, Huitong Singh, Vasundhara Sinha, Riya Steinmetz, Lars M. Tan, Jason Tan, Anthony Tycko, Josh Valbuena, Raeline C. Pour Amiri, Valeh Valiollah van Kooten, Mariëlle J.F.M. Vaughan-Jackson, Alun Venida, Anthony Weldy, Chad S. Yao, David Zeng, Tony Zhou, Ronghao Vidal, Marc Calderw Department of Genome Sciences University of Washington SeattleWA United States Department of Bioengineering and Therapeutic Sciences Institute for Human Genetics University of California San Francisco San FranciscoCA United States Institute of Human Genetics University Medical Center Schleswig-Holstein University of Lübeck Lübeck23562 Germany Exploratory Diagnostic Sciences Berlin Institute of Health Charité-Universitätsmedizin Berlin Berlin10117 Germany Brotman Baty Institute for Precision Medicine SeattleWA United States Center of immunotherapy and Immunity Seattle Children's Research Institute SeattleWA United States Bioinformatics Division WEHI ParkvilleVIC Australia Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Laboratory Medicine and Pathology University of Washington SeattleWA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Basic Science and Engineering Initiative Stanford Children’s Health Betty Irene Moore Children’s Heart Center StanfordCA United States The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard CambridgeMA United States Division of Cardiovascular Medicine School of Medicine Stanford University United States Department of Computer Science Stanford University School of Medicine StanfordCA United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Department of Pediatrics Stanford University School of Medicine StanfordCA United States Institute for Stem Cell Biology and Regenerative Medicine Stanford University School of Medicine StanfordCA United States Division of Pediatric Cardiology Cardiovascular Institute Stanford University School of Medicine Stanford University United States Genome Biology Unit Heidelberg Germany Department of Biology Stanford University StanfordCA United States Stanford Genome Techn

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations. © 2023, CC BY.

关键词： Genome

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DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

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Genomics, Proteomics & Bioinformatics 2020年第2期18卷 104-119页

作者： Tiansheng Zhu Yi Zhu Yue Xuan Huanhuan Gao Xue Cai Sander R.Piersma Thang V.Pham Tim Schelfhorst Richard R.G.D.Haas Irene V.Bijnsdorp Rui Sun Liang Yue Guan Ruan Qiushi Zhang Mo Hu Yue Zhou Winan J.Van Houdt Tessa Y.S.Le Large Jacqueline Cloos Anna Wojtuszkiewicz Danijela Koppers-Lalic Franziska Bottger Chantal Scheepbouwer Ruud H.Brakenhoff Geert J.L.H.van Leenders Jan N.M.Ijzermans John W.M.Martens Renske D.M.Steenbergen Nicole C.Grieken Sathiyamoorthy Selvarajan Sangeeta Mantoo Sze S.Lee Serene J.Y.Yeow Syed M.F.Alkaff Nan Xiang Yaoting Sun Xiao Yi Shaozheng Dai Wei Liu Tian Lu Zhicheng Wu Xiao Liang Man Wang Yingkuan Shao Xi Zheng Kailun Xu Qin Yang Yifan Meng Cong Lu Jiang Zhu Jin'e Zheng Bo Wang Sai Lou Yibei Dai Chao Xu Chenhuan Yu Huazhong Ying Tony K.Lim Jianmin Wu Xiaofei Gao Zhongzhi Luan Xiaodong Teng Peng Wu Shi'ang Huang Zhihua Tao Narayanan G.Iyer Shuigeng Zhou Wenguang Shao Henry Lam Ding Ma Jiafu Ji Oi L.Kon Shu Zheng Ruedi Aebersold Connie R.Jimenez Tiannan Guo Zhejiang Provincial Laboratory of Life Sciences and Biomedicine Westlake UniversityHangzhou 310024China Key Laboratory of Structural Biology of Zhejiang Province School of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzhou 310024China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Thermo Fisher Scientific(BREMEN)GmbH Bremen 28195Germany OncoProteomics Laboratory Department of Medical OncologyVU University Medical CenterVU UniversityAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamUrologyCancer Center AmsterdamAmsterdam 1011The Netherlands Thermo Fisher Scientific Shanghai 201206China The Netherlands Cancer Institute Surgical OncologyAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamSurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPediatric Oncology/HematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamHematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamMedical OncologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamNeurosurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPathologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamOtolaryngology/Head and Neck SurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Erasmus MC University Medical Center PathologyRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center SurgeryRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center Medical OncologyRotterdam 1016LVThe Netherlands Department of Anatomical Pathology Singapore General HospitalSingapore 169608Singapore Division of Medical Scie

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been *** optimal performance,they require the fragment ion spectra of targeted peptides as prior *** this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue *** build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on *** then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer *** extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)***,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was *** a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control *** expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by *** data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker *** is freely accessible at https://***/page/***?id=IPX0001400000.

关键词： Data-independent acquisition Parallel reaction monitoring Spectral library Prostate cancer Diffuse large B cell lymphoma

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Fast online reinforcement learning control using state-space dimensionality reduction

arXiv

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arXiv 2019年

作者： Sadamoto, Tomonori Chakrabortty, Aranya Imura, Jun-Ichi Department of Mechanical and Intelligent Systems Engineering Graduate School of Informatics and Engineering University of Electro Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States Department of Systems and Control Engineering Graduate School of Engineering Tokyo Institute of Technology Oh-Okayama Meguro Tokyo2-12-1 Japan

Reinforcement Learning (RL) is an effective way of designing model-free linear quadratic regulator (LQR) controller for linear time-invariant (LTI) networks with unknown state-space models. However, when the network size is large, conventional RL can result in unacceptably long learning time. In this paper we resolve this problem by developing an alternative approach for RL-based LQR that combines dimensionality reduction with RL theory. The approach is to construct a compressed state vector by projecting the measured state through a projective matrix. This matrix is constructed from the state measurements, and can be viewed as an empirical controllability gramian that captures the level of redundancy in the controllability of the open-loop network model. Next, a RL-controller is learned using the reduced-dimensional state instead of the original state such that the resultant cost is close to the optimal LQR cost. Numerical benefits as well as the cyber-physical implementation benefits of the proposed approach are verified using illustrative examples including an example of wide-area control of the IEEE benchmark power system. Copyright © 2019, The Authors. All rights reserved.

关键词： Controllers

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Preface

Smart Innovation, Systems and Technologies

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Smart Innovation, Systems and Technologies 2020年 180卷 ix-xi页

作者： Kountchev, Roumen Patnaik, Srikanta Shi, Junsheng Favorskaya, Margarita N. Department of Radio Communications and Video Technologies Technical University of Sofia Sofia Bulgaria Department of Computer Science and Engineering SOA University BhubaneswarOdisha India Department of Electro-Optical Engineering Yunnan Normal University Yunnan China Informatics and Computer Techniques Reshetnev Siberian State University of Science and Technology Krasnoyarsk Krai Russia

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Search for vector-like leptons coupling to first- and second-generation Standard Model leptons in pp collisions at = 13 TeV with the ATLAS detector

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Journal of High Energy Physics 2025年第5期2025卷 1-61页

作者： Aad, G. Aakvaag, E. Abbott, B. Abdelhameed, S. Abeling, K. Abicht, N. J. Abidi, S. H. Aboelela, M. Aboulhorma, A. Abramowicz, H. Abulaiti, Y. Acharya, B. S. Ackermann, A. Adam Bourdarios, C. Adamczyk, L. Addepalli, S. V. Addison, M. J. Adelman, J. Adiguzel, A. Adye, T. Affolder, A. A. Afik, Y. Agaras, M. N. Aggarwal, A. Agheorghiesei, C. Ahmadov, F. Ahuja, S. Ai, X. Aielli, G. Aikot, A. Ait Tamlihat, M. Aitbenchikh, B. Akbiyik, M. Åkesson, T. P. A. Akimov, A. V. Akiyama, D. Akolkar, N. N. Aktas, S. Alberghi, G. L. Albert, J. Albicocco, P. Albouy, G. L. Alderweireldt, S. Alegria, Z. L. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexopoulos, T. 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. S. Allen, J. F. Allendes Flores, C. A. Allport, P. P. Aloisio, A. Alonso, F. Alpigiani, C. Alsolami, Z. M. K. 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. Amini, B. Amirie, K. Amirkhanov, A. Amor Dos Santos, S. P. Amos, K. R. Amperiadou, D. An, S. Ananiev, V. Anastopoulos, C. Andeen, T. Anders, J. K. Anderson, A. C. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A. V. Annovi, A. Antel, C. Antipov, E. Antonelli, M. Anulli, F. Aoki, M. Aoki, T. Aparo, M. A. Aperio Bella, L. Appelt, C. Apyan, A. Arbiol Val, S. J. Arcangeletti, C. Arce, A. T. H. Arguin, J-F. Argyropoulos, S. Arling, J.-H. Arnaez, O. Arnold, H. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N. A. Ashby Pickering, R. A. Aslam, A. M. Assamagan, K. Astalos, R. Astrand, K. S. V. Atashi, S. Atkin, R. J. Atmani, H. Atmasiddha, P. A. Augsten, K. Auriol, A. D. Austrup, V. A. Avolio, G. Axiotis, K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Bachmann, E. Badea, A. Baer, T. M. Bagnaia, P. Bahmani, M. Bahner, D. Bai, K. Baines, J. T. Baines, L. Baker, O. K. Bakos, E. Bakshi Gupta, D. Balabram Filho, L. E. Balakrishnan, V. Balasubramanian, R. Baldin, E. M. Balek CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Department for Physics and Technology University of Bergen Bergen Norway Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America New York University Abu Dhabi Abu Dhabi United Arab Emirates II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Physics Department Brookhaven National Laboratory Upton United States of America Physics Department Southern Methodist University Dallas United States of America 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 New York University New York United States of America INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom Kirchhoff-Institut für Physik Ruprecht-Karls-Universität Heidelberg Heidelberg Germany LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow Poland SLAC National Accelerator Laboratory Stanford United States of America School of Physics and Astronomy University of Manchester Manchester United Kingdom Department of Physics Northern Illinois University DeKalb United States of America Department of Physics Istanbul University Istanbul Türkiye Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz United States of America Enrico Fermi Institute University of Chicago Chicago United States of America Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain Institut für Physik Universität Mainz Mainz Germany Department of Physics Alexand

A search for pair production of vector-like leptons coupling to first- and second-generation Standard Model leptons is presented. The search is based on a dataset of proton-proton collisions at $$ \sqrt{s} $$ = 13 TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 140 fb−1. Events are categorised depending on the flavour and multiplicity of leptons (electrons or muons), as well as on the scores of a deep neural network targeting particular signal topologies according to the decay modes of the vector-like leptons. In each of the signal regions, the scalar sum of the transverse momentum of the leptons and the missing transverse momentum is analysed. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No significant excess above the Standard Model background expectation is observed and limits are set at 95% confidence level on the production cross-sections of vector-like electrons and muons as a function of the vector-like lepton mass, separately for SU(2) doublet and singlet scenarios. The resulting mass lower limits are 1220 GeV (1270 GeV) and 320 GeV (400 GeV) for vector-like electrons (muons) in the doublet and singlet scenarios, respectively.

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Building an international consortium for tracking coronavirus health status (vol 78, pg 931, 2020)

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NATURE MEDICINE 2020年第8期26卷 1309-1309页

作者： Segal, Eran Zhang, Feng Lin, Xihong King, Gary Shalem, Ophir Shilo, Smadar Allen, William E. Alquaddoomi, Faisal Altae-Tran, Han Anders, Simon Balicer, Ran Bauman, Tal Bonilla, Ximena Booman, Gisel Chan, Andrew T. Cohen, Ori Coletti, Silvano Davidson, Natalie Dor, Yuval Drew, David A. Elemento, Olivier Evans, Georgina Ewels, Phil Gale, Joshua Gavrieli, Amir Geiger, Benjamin Grad, Yonatan H. Greene, Casey S. Hajirasouliha, Iman Jerala, Roman Kahles, Andre Kallioniemi, Olli Keshet, Ayya Kocarev, Ljupco Landua, Gregory Meir, Tomer Muller, Aline Nguyen, Long H. Oresic, Matej Ovchinnikova, Svetlana Peterson, Hedi Prodanova, Jana Rajagopal, Jay Raetsch, Gunnar Rossman, Hagai Rung, Johan Sboner, Andrea Sigaras, Alexandros Spector, Tim Steinherz, Ron Stevens, Irene Vilo, Jaak Wilmes, Paul Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel Howard Hughes Medical Institute Chevy Chase Maryland USA Broad Institute of MIT and Harvard Cambridge MA USA Departments of Biostatistics and Statistics Harvard T.H. Chan School of Public Health Harvard University Cambridge MA USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Pediatric Diabetes Unit Ruth Rappaport Children’s Hospital Rambam Healthcare Campus Haifa Israel Harvard Society of Fellows Harvard University Cambridge MA USA ETH Zurich NEXUS Personalized Health Technologies Zurich Switzerland Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Center for Molecular Biology (ZMBH) University of Heidelberg Heidelberg Germany Clalit Research Institute Clalit Health Services Ramat Gan Israel Mapping and Geo-Information Engineering Civil and Environmental Engineering Faculty The Technion Haifa Israel ETH Zurich Department for Computer Science Zurich University Hospital Zurich Medical Informatics Zurich and SIB Swiss Institute of Bioinformatics Zurich Switzerland Regen Network Mar del Plata Argentina Massachusetts General Hospital (MGH) Boston MA USA Chelonia Applied Science Allschwil Switzerland IMRIC Developmental Biology and Cancer Research School of Medicine The Hebrew University Jerusalem Israel Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Quantitative Social Science Harvard University Cambridge MA USA Science for Life Laboratory (SciLifeLab) Department of Biochemistry and Biophysics Stockholm University Stockholm Sweden http://*** Canada Department of Immunology Weizmann Institute of Scienc

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Computational platforms and environments Databases Infectious diseases

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2020年 1064卷 xiii-xiv页

作者： Jain, Lakhmi C. Piuri, Vincenzo Virvou, Maria Balas, Valentina E. Faculty of Engineering and IT University of Technology Sydney BroadwayNSW Australia Department of Computer Science University of Milan Milan Italy Department of Informatics University of Piraeus Piraeus Greece Department of Automatics and Applied Software University of Arad Arad Romania

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Search for supersymmetry in final states with missing transverse momentum and charm-tagged jets using 139 fb−1 of proton-proton collisions at = 13 TeV with the ATLAS detector

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Journal of High Energy Physics 2025年第2期2025卷 1-53页

作者： Aad, G. Aakvaag, E. Abbott, B. Abdelhameed, S. Abeling, K. Abicht, N. J. Abidi, S. H. Aboelela, M. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Acharya, B. S. Ackermann, A. Adam Bourdarios, C. Adamczyk, 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. Ahmadov, F. Ahmed, W. S. Ahuja, S. Ai, X. Aielli, G. Aikot, A. Ait Tamlihat, M. Aitbenchikh, B. Akbiyik, M. Åkesson, T. P. A. Akimov, A. V. Akiyama, D. Akolkar, N. N. Aktas, S. Al Khoury, K. Alberghi, G. L. Albert, J. Albicocco, P. Albouy, G. L. Alderweireldt, S. Alegria, Z. L. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexopoulos, T. 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. Alsolami, Z. M. K. 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. Amirie, K. J. Amor Dos Santos, S. P. Amos, K. R. An, S. Ananiev, V. Anastopoulos, C. Andeen, T. Anders, J. K. Anderson, A. C. Andrean, S. Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A. V. Annovi, A. Antel, C. Antipov, E. Antonelli, M. Anulli, F. Aoki, M. Aoki, T. Aparo, M. A. Aperio Bella, L. Appelt, C. Apyan, A. Arbiol Val, S. J. Arcangeletti, C. Arce, A. T. H. Arena, E. Arguin, J-F. Argyropoulos, S. Arling, J.-H. Arnaez, O. Arnold, H. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N. A. Ashby Pickering, R. A. Assamagan, K. Astalos, R. Astrand, K. S. V. Atashi, S. Atkin, R. J. Atkinson, M. 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. Baer, T. M. Bagnaia, P. Bahmani, M. Bahner, D. Bai, K. Baines, J. T. Baines, L. Baker, O. K. Bakos, E. Bakshi Gupta, D. Balabram Filho, L. E CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Department for Physics and Technology University of Bergen Bergen Norway Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America New York University Abu Dhabi Abu Dhabi United Arab Emirates II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Physics Department Brookhaven National Laboratory Upton United States of America Physics Department Southern Methodist University Dallas United States of America 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 United States of America INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom Kirchhoff-Institut für Physik Ruprecht-Karls-Universität Heidelberg Heidelberg Germany 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 Brandeis University Waltham United States of America School of Physics and Astronomy University of Manchester Manchester United Kingdom Department of Physics Northern Illinois University DeKalb United States of America Department of Physics Istanbul University Istanbul Türkiye Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz United States of America Enrico Fermi Institute University of Chicago Chicago United States of America Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain I

The paper presents a search for supersymmetric particles produced in proton-proton collisions at $$ \sqrt{s} $$ = 13 TeV and decaying into final states with missing transverse momentum and jets originating from charm quarks. The data were taken with the ATLAS detector at the Large Hadron Collider at CERN from 2015 to 2018 and correspond to an integrated luminosity of 139 fb−1. No significant excess of events over the expected Standard Model background expectation is observed in optimized signal regions, and limits are set on the production cross-sections of the supersymmetric particles. Pair production of charm squarks or top squarks, each decaying into a charm quark and the lightest supersymmetric particle $$ {\overset{\sim }{\chi}}_1^0 $$ , is excluded at 95% confidence level for squarks with masses up to 900 GeV for scenarios where the mass of $$ {\overset{\sim }{\chi}}_1^0 $$ is below 50 GeV. Additionally, the production of leptoquarks with masses up to 900 GeV is excluded for the scenario where up-type leptoquarks decay into a charm quark and a neutrino. Model-independent limits on cross-sections and event yields for processes beyond the Standard Model are also reported.

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Measurements of WH and ZH production with Higgs boson decays into bottom quarks and direct constraints on the charm Yukawa coupling in 13 TeV pp collisions with the ATLAS detector

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Journal of High Energy Physics 2025年第4期2025卷 1-71页

作者： Aad, G. Aakvaag, E. Abbott, B. Abdelhameed, S. Abeling, K. Abicht, N. J. Abidi, S. H. Aboelela, M. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Acharya, B. S. Ackermann, A. Adam Bourdarios, C. Adamczyk, 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. Ahmadov, F. Ahmed, W. S. Ahuja, S. Ai, X. Aielli, G. Aikot, A. Ait Tamlihat, M. Aitbenchikh, B. Akbiyik, M. Åkesson, T. P. A. Akimov, A. V. Akiyama, D. Akolkar, N. N. Aktas, S. Al Khoury, K. Alberghi, G. L. Albert, J. Albicocco, P. Albouy, G. L. Alderweireldt, S. Alegria, Z. L. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexopoulos, T. 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. S. Allen, J. F. Allendes Flores, C. A. Allport, P. P. Aloisio, A. Alonso, F. Alpigiani, C. Alsolami, Z. M. K. 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. Amini, B. Amirie, K. Amor Dos Santos, S. P. Amos, K. R. Amperiadou, D. An, S. Ananiev, V. Anastopoulos, C. Andeen, T. Anders, J. K. Anderson, A. C. Andrean, S. Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A. V. Annovi, A. Antel, C. Antipov, E. Antonelli, M. Anulli, F. Aoki, M. Aoki, T. Aparo, M. A. Aperio Bella, L. Appelt, C. Apyan, A. Arbiol Val, S. J. Arcangeletti, C. Arce, A. T. H. Arguin, J-F. Argyropoulos, S. Arling, J.-H. Arnaez, O. Arnold, H. Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N. A. Ashby Pickering, R. A. Assamagan, K. Astalos, R. Astrand, K. S. V. Atashi, S. Atkin, R. J. Atkinson, M. 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. Bachmann, E. Backman, F. Badea, A. Baer, T. M. Bagnaia, P. Bahmani, M. Bahner, D. Bai, K. Baines, J. T. Baines, L. Baker, O. K. Bakos, E. CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Department for Physics and Technology University of Bergen Bergen Norway Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America New York University Abu Dhabi Abu Dhabi United Arab Emirates II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Physics Department Brookhaven National Laboratory Upton United States of America Physics Department Southern Methodist University Dallas United States of America 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 United States of America INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom Kirchhoff-Institut für Physik Ruprecht-Karls-Universität Heidelberg Heidelberg Germany 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 Brandeis University Waltham United States of America School of Physics and Astronomy University of Manchester Manchester United Kingdom Department of Physics Northern Illinois University DeKalb United States of America Department of Physics Istanbul University Istanbul Türkiye Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz United States of America Enrico Fermi Institute University of Chicago Chicago United States of America Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain I

A study of the Higgs boson decaying into bottom quarks (H → $$ b\overline{b} $$ ) and charm quarks (H → $$ c\overline{c} $$ ) is performed, in the associated production channel of the Higgs boson with a W or Z boson, using 140 fb−1 of proton-proton collision data at $$ \sqrt{s} $$ = 13 TeV collected by the ATLAS detector. The individual production of WH and ZH with H → $$ b\overline{b} $$ is established with observed (expected) significances of 5.3 (5.5) and 4.9 (5.6) standard deviations, respectively. Differential cross-section measurements of the gauge boson transverse momentum within the simplified template cross-section framework are performed in a total of 13 kinematical fiducial regions. The search for the H → $$ c\overline{c} $$ decay yields an observed (expected) upper limit at 95% confidence level of 11.5 (10.6) times the Standard Model prediction. The results are also used to set constraints on the charm coupling modifier, resulting in |κc| < 4.2 at 95% confidence level. Combining the H → $$ b\overline{b} $$ and H → $$ c\overline{c} $$ measurements constrains the absolute value of the ratio of Higgs-charm and Higgs-bottom coupling modifiers (|κc/κb|) to be less than 3.6 at 95% confidence level.

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Dynamic laplace: Efficient centrality measure for weighted or unweighted evolving networks

arXiv

引用

arXiv 2018年

作者： Cordeiro, Mário Sarmento, Rui Portocarrero Brazdil, Pavel Gama, João Doctoral Program in Informatics Engineering Faculty of Engineering University of Porto INESC TEC - Institute for Systems and Computer Engineering Technology and Science

With its origin in sociology, Social Network Analysis (SNA), quickly emerged and spread to other areas of research, including anthropology, biology, information science, organizational studies, political science, and computer science. Being it's objective the investigation of social structures through the use of networks and graph theory, Social Network Analysis is, nowadays, an important research area in several domains. Social Network Analysis cope with different problems namely network metrics, models, visualization and information spreading, each one with several approaches, methods and algorithms. One of the critical areas of Social Network Analysis involves the calculation of different centrality measures (i.e.: the most important vertices within a graph). Today, the challenge is how to do this fast and efficiently, as many increasingly larger datasets are available. Recently, the need to apply such centrality algorithms to non static networks (i.e.: networks that evolve over time) is also a new challenge. Incremental and dynamic versions of centrality measures are starting to emerge (betweenness, closeness, etc). Our contribution is the proposal of two incremental versions of the Laplacian Centrality measure, that can be applied not only to large graphs but also to, weighted or unweighted, dynamically changing networks. The experimental evaluation was performed with several tests in different types of evolving networks, incremental or fully dynamic. Results have shown that our incremental versions of the algorithm can calculate node centralities in large networks, faster and efficiently than the corresponding batch version in both incremental and full dynamic network setups. Copyright © 2018, The Authors. All rights reserved.

关键词： Laplace transforms

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