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Measurement of the inclusive cross sections for W and Z boson production in proton-proton collisions at = 5.02 and 13 TeV

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Journal of High Energy Physics 2025年第4期2025卷 1-52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. Bols, E. S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G. P. Van Putte, S. Vannerom, D. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Hohov, D. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Mota Amarilo, K. Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I. S. Giammanco, A. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Tran, T. T. Wertz, S. Alves, G. A. Coelho, E. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. Aldá Júnior, W. L. Alves Gallo Pereira, M. 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. Martins, J. Mora Herrera, C. Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C. A. Calligaris, L. Fernandez Perez Tomei, T. R. Gregores, E. M. 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. Liu, J. Yi, K. Chen, G. M. Chen, H. S. Chen, M. Iemmi, F. Jiang, C. H. Kapoor, A. Liao, H. Liu, Z.-A. Sharma, R. Song, J. N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos, A. Ban, Y. Levin, A. 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 PaulistaUniversidade 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 Iowa USA 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 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 China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical

Measurements of fiducial and total inclusive cross sections for W and Z boson production are presented in proton-proton collisions at $$ \sqrt{s} $$ = 5.02 and 13 TeV. Electron and muon decay modes (ℓ = e or μ) are studied in the data collected with the CMS detector in 2017, in dedicated runs with reduced instantaneous luminosity. The data sets correspond to integrated luminosities of 298 ± 6 pb−1 at 5.02 TeV and 206 ± 5 pb−1 at 13 TeV. Measured values of the products of the total inclusive cross sections and the branching fractions at 5.02 TeV are σ(pp → W + X) $$ \mathcal{B} $$ (W → ℓν) = 7300 ± 10 (stat) ± 60 (syst) ± 140 (lumi) pb, and σ(pp → Z+X) $$ \mathcal{B} $$ (Z → ℓ+ℓ−) = 669 ± 2 (stat) ± 6 (syst) ± 13 (lumi) pb for the dilepton invariant mass in the range of 60–120 GeV. The corresponding results at 13 TeV are 20480 ± 10 (stat) ± 170 (syst) ± 470 (lumi) pb and 1952 ± 4 (stat) ± 18 (syst) ± 45 (lumi) pb. The measured values agree with cross section calculations at next-to-next-to-leading-order in perturbative quantum chromodynamics. Fiducial and total inclusive cross sections, ratios of cross sections of W+ and W− production as well as inclusive W and Z boson production, and ratios of these measurements at 5.02 and 13 TeV are reported.

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Semantic Segmentation With Labeling Uncertainty and Class Imbalance

Research Square

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Research Square 2021年

作者： Bressan, Patrik Olã Junior, José Marcato Martins, José Augusto Correa de Melo, Maximilian Jaderson Gonçalves, Diogo Nunes Freitas, Daniel Matte Ramos, Ana Paula Marques Osco, Lucas Prado de Andrade Silva, Jonathan Luo, Zhipeng Li, Jonathan Garcia, Raymundo Cordero Gonçalves, Wesley Nunes Faculty of Computer Science Federal University of Mato Grosso do Sul Av. Costa e Silva Campo Grande79070-900 Brazil Federal Institute of Mato Grosso do Sul Jardim79240-000 Brazil Faculty of Engineering Architecture and Urbanism and Geography Federal University of Mato Grosso do Sul Av. Costa e Silva Campo Grande79070-900 Brazil Rodovia Raposo Tavares km 572-Limoeiro São Paulo Presidente Prudente19067-175 Brazil Rodovia Raposo Tavares km 572-Limoeiro São Paulo Presidente Prudente19067-175 Brazil School of Informatics Xiamen University XiamenFJ 361005 China WaterlooONN2L 3G1 Canada

Recently, methods based on Convolutional Neural Networks (CNN) achieved impressive success in semantic segmentation tasks. However, challenges such as the class imbalance and the uncertainty in the pixel-labeling process are not completely addressed. As such, we present a new approach that calculates a weight for each pixel considering its class and uncertainty during the labeling process. The pixel-wise weights are used during training to increase or decrease the importance of the pixels. Experimental results using different datasets like aerial, terrestrial, and ultrasound images show that the proposed approach leads to significant improvements in three challenging segmentation tasks in comparison to baseline methods. It was also proved to be more invariant to noise. The approach presented here may be used within a wide range of semantic segmentation methods to improve their robustness. © 2021, CC BY.

关键词： Semantic Segmentation

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DLPlib： A Library for Deep Learning Processor

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Journal of computer science & Technology 2017年第2期32卷 286-296页

作者： Hui-Ying Lan Lin-Yang Wu Xiao Zhang Jin-Hua Tao Xun-Yu Chen Bing-Rui Wang Yu-Qing Wang Qi Guo Yun-Ji Chen State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Microprocessor Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Department of Computer Science University of Science and Technology of China Hefei 230026 China

Recently, deep learning processors have become one of the most promising solutions of accelerating deep learning algorithms. Currently, the only method of programming the deep learning processors is through writing assembly instructions by bare hands, which costs a lot of programming efforts and causes very low efficiency. One solution is to integrate the deep learning processors as a new back-end into one prevalent high-level deep learning framework （e.g., TPU （tensor processing unit） is integrated into Tensorflow directly）. However, this will obstruct other frameworks to profit from the programming interface, The alternative approach is to design a framework-independent low-level library for deep learning processors （e.g., the deep learning library for GPU, cuDNN）. In this fashion, the library could be conveniently invoked in high-level programming frameworks and provides more generality. In order to allow more deep learning frameworks to gain benefits from this environment, we envision it as a low-level library which could be easily embedded into current high-level frameworks and provide high performance. Three major issues of designing such a library are discussed. The first one is the design of data structures. Data structures should be as few as possible while being able to support all possible operations. This will allow us to optimize the data structures easier without compromising the generality. The second one is the selection of operations, which should provide a rather wide range of operations to support various types of networks with high efficiency. The third is the design of the API, which should provide a flexible and user-friendly programming model and should be easy to be embedded into existing deep learning frameworks. Considering all the above issues, we propose DLPIib, a tensor-filter based library designed specific for deep learning processors. It contains two major data structures, tensor and filter, and a set of operators including basic neur

关键词： deep learning processor API library DLPlib

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Improving seismic performance of welded joints in CFST Truss towers: Effects of concrete fill and ring stiffener configurations

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Structures 2025年 77卷

作者： Cao, Yan Zandi, Y. Mohammed, Khidhair Jasim Sadighi Agdas, A. Ali, H. Elhosiny Khadimallah, Mohamed Amine Sadaghdes, Z. Asllzadhe, Hamid School of Computer Science and Engineering Xi'an Technological University Xi'an710021 China School of Mechatronic Engineering Xi'an Technological University Xi'an710021 China Department of Civil Engineering Tabriz Branch Islamic Azad University Tabriz Iran Air Conditioning and Refrigeration Techniques Engineering Department College of Engineering and Technologies Al-Mustaqbal University Babylon Hilla51001 Iraq Ghateh Gostar Novin Company Tabriz51579 Iran Department of Physics Faculty of Science King Khalid University P.O. Box 9004 Abha61413 Saudi Arabia Department of Civil Engineering College of Engineering in Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam Department of Biomaterials Saveetha Dental College and Hospital Saveetha Institute of Medical and Technical Sciences Chennai600077 India

This study presents a novel approach to enhance the longevity and seismic resilience of ‎ Concrete-Filled Steel Tubular (CFST) frame towers commonly used in ‎onshore wind turbines. Fatigue-related failures at welded joints pose significant challenges to the durability of these towers. This research investigates the impact of optimized ring stiffener ‎configurations and concrete infill on reducing stress concentrations and improving ‎fatigue resistance under simulated seismic loading. The study utilizes with samples including CFST-gusset plate joints and non-concrete-filled joints, with different configurations of concrete infill and ring stiffeners. ‎These samples were evaluated under static loading to assess the influence of stiffener width, thickness, and spacing on Stress Concentration Factors (SCFs). Subsequently, cyclic loading tests were conducted to observe crack initiation and progression. The results show that ‎optimized configurations of both concrete fill and ring stiffeners significantly reduce ‎SCFs, improve stress distribution, and delay crack initiation, enhancing the seismic ‎fatigue performance of CFST joints. Moreover, the study integrates Artificial Intelligence ‎‎(AI) techniques, specifically Support Vector Machines (SVM), to predict the fatigue ‎behavior of these joints under different loading conditions. The SVM model was trained on experimental data and demonstrated high predictive accuracy in forecasting the fatigue life of CFST joints, achieving an R² value of 0.95, an RMSE of 0.11, and an MAE of 0.08. These results validate the AI model's ‎ability to optimize design parameters and predict fatigue life, contributing to more ‎efficient and reliable CFST joint design. This integration of AI enhances design ‎optimization by providing precise predictions based on complex variables like stiffener ‎geometry and concrete filling. Overall, the findings suggest that incorporating ‎optimized stiffener configurations, concrete infill, and AI-based desi

关键词： Stress concentration

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Correction to: High-speed tracking based on multi-CF filters and attention mechanism

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Signal, Image and Video Processing 2020年第3期14卷 637-637页

作者： Fan, Songyuan Wang, Rui Wu, Zhihao Rho, Seungmin Liu, Shaohui Xiong, Jiaxin Fu, Sen Jiang, Feng School of Computer Science Harbin Institute of Technology Harbin China School of Architecture Harbin Institute of Technology Harbin China College of Software and Convergence Technology Sejong University Seoul Republic of Korea Beijing Institute of Technology Beijing China Shanghai Radio Equipment Research Institute Shanghai China

Unfortunately, the Acknowledgement section has been published as blinded version.

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Effect of lattice mismatch strain grading on the electromechanical behavior of functionally Graded Quantum Dots

Effect of lattice mismatch strain grading on the electromech...

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作者： Bishay, Peter L. Sampat, Bhavin Sladek, Jan Pan, Ernian Sladek, Vladimir College of Engineering and Computer Science California State University NorthridgeCA91330 United States Institute of Construction and Architecture Slovak Academy of Sciences Bratislava84503 Slovakia Department of Civil Engineering University of Akron AkronOH44325-3905 United States

ISBN: (纸本)9783035712193

A fully coupled thermo-electro-mechanical models of cylindrical and truncated conical GaN/AlN Functionally Graded Quantum Dot (FGQD) systems with and without WL are analyzed in this study to determine the effect of lattice mismatch strain grading on the electromechanical behavior of the FGQD system. This has a technological and fundamental importance because the production methodology adopted for manufacturing QDs enables the composition of the QD material to be graded in the growth direction, so the material properties as well as the induced mismatch strain between the QD and the carrier matrix are accordingly graded. The power law is used to describe the grading function. Based on the obtained results, grading of material properties and lattice mismatch strain have significant effect on the distribution of the electromechanical quantities inside the QD and can be used as another tuning parameter in the design of QD systems. © 2018 Trans Tech Publications, Switzerland.

关键词： Finite element method

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Tslearn, a machine learning toolkit for time series data

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 4686-4691页

作者： Romain Tavenard Johann Faouzi Gilles Vandewiele Felix Divo Guillaume Androz Chester Holtz Marie Payne Roman Yurchak Marc Rußwurm Kushal Kolar Eli Woods Université de Rennes CNRS LETG-Rennes IRISA-Obelix Rennes France Aramis Lab INRIA Paris Paris Brain Institute Paris France IDLab Ghent University imec Ghent Belgium Technische Universität Darmstadt Darmstadt Germany Icentia Inc. Québec Canada Department of Computer Science and Engineering University of California San Diego La Jolla CA McGill University Montreal Québec Canada Symerio Paris France Technical University of Munich Chair of Remote Sensing Technology Munich Germany Sars International Centre for Marine Molecular Biology University of Bergen Norway Eaze Technologies Inc. San Francisco CA

tslearn is a general-purpose Python machine learning library for time series that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression. It follows scikit-learn's Application Programming Interface for transformers and estimators, allowing the use of standard pipelines and model selection tools on top of tslearn objects. It is distributed under the BSD-2-Clause license, and its source code is available at https://***/tslearn-team/tslearn.

关键词： time series clustering classification pre-processing data mining

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A quixotic view of spatial bias in modelling the distribution of species and their diversity

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NPJ Biodiversity 2023年第1期2卷 10页

作者： Rocchini, Duccio Tordoni, Enrico Marchetto, Elisa Marcantonio, Matteo Barbosa, A. Márcia Bazzichetto, Manuele Beierkuhnlein, Carl Castelnuovo, Elisa Gatti, Roberto Cazzolla Chiarucci, Alessandro Chieffallo, Ludovico Da Re, Daniele Di Musciano, Michele Foody, Giles M. Gabor, Lukas Garzon-Lopez, Carol X. Guisan, Antoine Hattab, Tarek Hortal, Joaquin Kunin, William E. Jordán, Ferenc Lenoir, Jonathan Mirri, Silvia Moudrý, Vítězslav Naimi, Babak Nowosad, Jakub Sabatini, Francesco Maria Schweiger, Andreas H. Šímová, Petra Tessarolo, Geiziane Zannini, Piero Malavasi, Marco BIOME Lab Department of Biological Geological and Environmental Sciences Alma Mater Studiorum University of Bologna via Irnerio 42 Bologna 40126 Italy Czech University of Life Sciences Prague Faculty of Environmental Sciences Department of Spatial Sciences Kamýcka 129 Praha - Suchdol 16500 Czech Republic Department of Botany Institute of Ecology and Earth Science University of Tartu J. Liivi 2 Tartu 50409 Estonia Evolutionary Ecology and Genetics Group Earth and Life Institute UCLouvain Louvain-la-Neuve 1348 Belgium CICGE (Centro de Investigação em Ciências Geo-Espaciais) Universidade do Porto Porto Portugal Biogeography BayCEER University of Bayreuth Universitaetsstraße 30 Bayreuth 95440 Germany Georges Lemaître Center for Earth and Climate Research Earth and Life Institute UCLouvain Louvain-la-Neuve Belgium Department of Life Health and Environmental Sciences University of L’Aquila Piazzale Salvatore Tommasi 1 L’Aquila 67100 Italy School of Geography University of Nottingham Nottingham United Kingdom Dept of Ecology and Evolutionary Biology Yale University New Haven CT United States Center for Biodiversity and Global Change Yale University New Haven CT United States Knowledge Infrastructures Campus Fryslan University of Groningen Leeuwarden Netherlands Department of Ecology and Evolution University of Lausanne Lausanne 1015 Switzerland Institute of Earth Surface Dynamics University of Lausanne Lausanne 1015 Switzerland MARBEC Univ Montpellier CNRS Ifremer IRD Sète France Department of Biogeography and Global Change Museo Nacional de Ciencias Naturales (MNCN-CSIC) Madrid Spain University of Leeds Leeds United Kingdom University of Parma Parma Italy UMR CNRS 7058 “Ecologie et Dynamique des Systèmes Anthropisés” (EDYSAN) Université de Picardie Jules Verne 1 Rue des Louvels Amiens 80000 France Department of Computer Science and Engineering Alma Mater Studiorum University of Bologna via Irnerio 42 Bologna 40126 Italy Rui Nabeiro

Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias;this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias;(ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses;and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling. © The Author(s) 2023.

关键词： Biodiversity Biogeography Community ecology Ecological modelling

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

arXiv

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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

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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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