检索结果-内蒙古大学图书馆

Research Square 2019年

作者： Castillo-Secilla, Daniel Galvez, Juan Manuel Ortuno, Francisco Manuel Herrera, Luis Javier Rojas, Ignacio Department of Computer Architecture and Technology University of Granada Periodista Rafael Gómez Montero 2 Granada18014 Spain Hospital Universitario Virgen del Rocío Avenida Manuel Siurot s/n Sevilla41013 Spain

Background: The number of gene expression analyses has grown exponentially over the last years. The main triggers of this increase are the reduction in the sequencing cost per sample and the technological advances, specially in the computing scope. Those analyses generally involve a number of steps. Firstly, a raw samples alignment and a quality analysis are needed. After that, a Differentially Expressed Genes (DEGs) extraction and a subsequent gene enrichment can be performed. The development of intelligent predictive tools results essential in bioinformatics given that there exists a real need of assistance for decision-making systems towards precision medicine. Therefore, KnowSeq incorporates novel steps of feature selection and classifier design in the traditional RNA-seq pipeline. No tool exists in the research community that achieves this complete RNA-seq analysis, encapsulating all those steps in one single tool. Results: In order to show the functionalities provided by the general pipeline designed for the KnowSeq package, an application to a real problem is presented. Concretely, an analysis of a breast cancer set of patients collected from the controlled repository GDC portal is performed, keeping paired samples between tumour and control. As results show, KnowSeq achieves extracting more relevant biological knowledge related with breast cancer from the RNA raw data acquisition. KnowSeq is available through Bioconductor. Conclusions: KnowSeq R/bioc package is born with the purpose of providing an integrative tool, containing the necessary steps to address complex RNA-seq analyses in a modular and flexible way. In this paper a breast study case is addressed with KnowSeq, obtaining outstanding results and demonstrating the validity of KnowSeq to carry out gene expression analyses. © 2019, CC BY.

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

引用

arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

The CMS Statistical Analysis and Combination Tool: Combine

引用

Computing and Software for Big Science 2024年第1期8卷 1-45页

作者： Zhokin, A. Zhizhin, I. Zarubin, A. Yuldashev, B.S. Voytishin, N. Vorotnikov, G. Vorobyev, A. Volkov, P. Uzunian, A. Uvarov, L. Toropin, A. Tlisova, I. Teryaev, O. Terkulov, A. Tcherniaev, E. Sulimov, V. Sosnov, D. Smirnov, V. Slabospitskii, S. Skovpen, Y. Shulha, S. Shmatov, S. Shalaev, V. Savrin, V. Savina, M. Radchenko, O. Popov, V. Polikarpov, S. Perfilov, M. Perelygin, V. Palichik, V. Oreshkin, V. Obraztsov, S. Nikitenko, A. Murzin, V. Matveev, V. Malakhov, A. Makarenko, V. Lychkovskaya, N. Levchenko, P. Lanev, A. Krasnikov, N. Kozyrev, A. Korenkov, V. Konstantinov, D. Kodolova, O. Klyukhin, V. Kirsanov, M. Kirpichnikov, D. Kirakosyan, M. Kim, V. Karneyeu, A. Karjavine, V. Kachanov, V. Ivanov, Y. Gribushin, A. Gorbunov, I. Golutvin, I. Golubev, N. Golovtcov, V. Gninenko, S. Gavrilov, V. Gavrilov, G. Dudko, L. Dubinin, M. Druzhkin, D. Dimova, T. Dermenev, A. Chistov, R. Chekhovsky, V. Chadeeva, M. Bunichev, V. Budkouski, D. Borshch, V. Boos, E. Blinov, V. Babaev, A. Azarkin, M. Aushev, T. Andreev, Yu. Andreev, V. Alexakhin, V. Afanasiev, S. Warden, A. Vetens, W. Tsoi, H.F. Teague, D. Smith, W.H. Sharma, V. Shang, V. Savin, A. Pinna, D. Pétré, L. Parida, G. Mondal, S. Mohammadi, A. Mallampalli, A. Sreekala, J. Madhusudanan Loveless, R. Lanaro, A. Koraka, C.K. Herve, A. Herndon, M. He, H. Galloni, C. Everaerts, P. De Bruyn, I. Dasu, S. Bose, T. Black, K. Banerjee, S. Aravind, A. Karchin, P.E. Bhattacharya, S. Neu, C. Ledovskoy, A. Hirosky, R. Hakala, J. Cox, B. Cardwell, B. Viinikainen, J. Velkovska, J. Tuo, S. Sheldon, P. Romeo, F. Melo, A. Elayavalli, R. Kunnawalkam Johns, W. Gurrola, A. Greene, S. Chen, Y. Appelt, E. Volobouev, I. Peltola, T. Mankel, A. Lee, S.W. Lamichhane, K. Kazhykarim, Y. Hussain, A. Hegde, V. Gogate, N. Damgov, J. Akchurin, N. Safonov, A. Rathjens, D. Overton, D. Mueller, R. Luo, S. Kim, H. Kamon, T. Huang, T. Gilmore, J. Eusebi, R. Bouhali, O. Akhter, T. Ahmad, M. Aebi, D. Spanier, S. Nibigira, E. Lee, L. Karunarathna, N. Kanuganti, A.R. Holmes, T. Higginbotham, S. Fiorendi, S. Delannoy Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent 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 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 Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing 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 Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai China Zhejiang University Hangzhou Zhejiang China Universidad de Los Andes Bogota Venezuela Universidad de Antioquia Medellin Colombia Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture University of Split Split Croatia Faculty of Science University of Split Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Universidad San Francisco de Quito Quito Ecuador Egyptian Network of High Energy Physics Academy of Scientific Research and Technology of the Arab Republic of Egypt Cairo Egypt Center for High

This paper describes the Combine software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to run Combine and reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details of Combine. However, the online documentation referenced within this paper provides an up-to-date and complete user guide. © CERN, for the benefit of the CMS Collaboration 2024.

关键词： CMS Combine Higgs Higgs boson Statistics

来源：评论

学校读者我要写书评

暂无评论

A sliding window method for detecting corners of openings from terrestrial LiDAr data 13

A sliding window method for detecting corners of openings fr...

引用

13th 3D GeoInfo Conference 2018

作者： Li, Jiaqiang Xiong, Biao Biljecki, Filip Schrotter, Gerhard ETH Zurich Future Cities Laboratory Singapore-ETH Centre Singapore School of Computer Science and Technology Wuhan University of Technology China Dipper 3D Netherlands Department of Architecture National University of Singapore Singapore City of Zurich Switzerland

Architectural building models (LoD3) consist of detailed wall and roof structures including openings, such as doors and windows. Openings are usually identified through corner and edge detection, based on terrestrial LiDAR point clouds. However, singular boundary points are mostly detected by analysing their neighbourhoods within a small search area, which is highly sensitive to noise. In this paper, we present a global-wide sliding window method on a projected façade to reduce the influence of noise. We formulate the gradient of point density for the sliding window to inspect the change of façade elements. With derived symmetry information from statistical analysis, border lines of the changes are extracted and intersected generating corner points of openings. We demonstrate the performance of the proposed approach on the static and mobile terrestrial LiDAR data with inhomogeneous point density. The algorithm detects the corners of repetitive and neatly arranged openings and also recovers angular points within slightly missing data areas. In the future we will extend the algorithm to detect disordered openings and assist to façade modelling, semantic labelling and procedural modelling. © Authors 2018. CC BY 4.0 License.

关键词： Edge detection

来源：评论

学校读者我要写书评

暂无评论

Correction to: Gyral-sulcal contrast in intrinsic functional brain networks across task performances

引用

Brain imaging and behavior 2021年第4期15卷 2235页

作者： Lin Zhao Tuo Zhang Lei Guo Tianming Liu Xi Jiang School of Automation Northwestern Polytechnical University Xi'an China. Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia Athens GA USA. School of Automation Northwestern Polytechnical University Xi'an China. tuozhang@***. School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China. xijiang@***.

A Correction to this paper has been published: https://***/10.1007/s11682-020-00426-z

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring vestibulo-ocular adaptation in a closed-loop neuro-robotic experiment using STDP. A simulation study

arXiv

引用

arXiv 2020年

作者： Naveros, Francisco Garrido, Jesús A. Arleo, Angelo Ros, Eduardo Luque, Niceto R. Department of Computer Architecture and Technology University of Granada Granada18014 Spain Institut de la Vision Institut National de la Santé et de la Recherche Médicale U968 and Centre National de la Recherche Scientifique ParisUMR_7210 France UMR_S 968 Sorbonne Universités Université Pierre et Marie Curie Paris 06 Paris France

Studying and understanding the computational primitives of our neural system requires for a diverse and complementary set of techniques. In this work, we use the Neuro-robotic Platform (NRP) to evaluate the vestibulo ocular cerebellar adaptation (Vestibulo-ocular reflex, VOR) mediated by two STDP mechanisms located at the cerebellar molecular layer and the vestibular nuclei respectively. This simulation study adopts an experimental setup (rotatory VOR) widely used by neuroscientists to better understand the contribution of certain specific cerebellar properties (i.e. distributed STDP, neural properties, coding cerebellar topology, etc.) to r-VOR adaptation. The work proposes and describes an embodiment solution for which we endow a simulated humanoid robot (iCub) with a spiking cerebellar model by means of the NRP, and we face the humanoid to an r-VOR task. The results validate the adaptive capabilities of the spiking cerebellar model (with STDP) in a perception-action closed-loop (r-VOR) causing the simulated iCub robot to mimic a human behavior. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

Communication lower bound in convolution accelerators

arXiv

引用

arXiv 2019年

作者： Chen, Xiaoming Han, Yinhe Wang, Yu Center for Intelligent Computing Systems State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Electronic Engineering Tsinghua University Beijing China

In current convolutional neural network (CNN) accelerators, communication (i.e., memory access) dominates the energy consumption. This work provides comprehensive analysis and methodologies to minimize the communication for CNN accelerators. For the off-chip communication, we derive the theoretical lower bound for any convolutional layer and propose a dataflow to reach the lower bound. This fundamental problem has never been solved by prior studies. The on-chip communication is minimized based on an elaborate workload and storage mapping *** in addition design a communication-optimal CNN accelerator architecture. Evaluations based on the 65nm technology demonstrate that the proposed architecture nearly reaches the theoretical minimum communication in a threelevel memory hierarchy and it is computation dominant. The gap between the energy efficiency of our accelerator and the theoretical best value is only 37-87%. Copyright © 2019, The Authors. All rights reserved.

关键词： Acceleration

来源：评论

学校读者我要写书评

暂无评论

Comparing the Design Neurocognition of Mechanical Engineers and Architects: A Study of the Effect of Designer’s Domain

引用

Proceedings of the Design Society: International Conference on Engineering Design 2019年第1期1卷 1853-1862页

作者： Sonia Liliana da Silva Vieira John S. Gero Jessica Delmoral Valentin Gattol Carlos Fernandes António A. Fernandes INEGI Institute of Science and Innovation in Mechanical and Industrial Engineering Porto Portugal Department of Computer Science and School of Architecture University of North Carolina at Charlotte United States AIT Austrian Institute of Technology Vienna Austria FMUP Faculty of Medicine of the University of Porto Portugal FEUP Faculty of Engineering University of Porto Portugal

New tools from neuroscience allow design researchers to explore design neurocognition. By taking the advantage of EEG's temporal resolution we give up spatial resolution to focus on the performance of time-related design tasks. This paper presents results from an experiment using EEG to measure brain activation to study mechanical engineers and architects to compare their design neurocognition. In this study, we adopted and extended the tasks described in a previous fMRI study of design neurocognition reported in the literature. The block experiment consists of a sequence of 3 tasks: problem solving, basic design and open design using a physical interface. The block is preceded by a familiarizing pre-task using the physical interface and then extended to a fourth task using free-hand sketching. Brainwaves were collected from both mechanical engineers and architects. Results comparing 36 mechanical engineers and architects while designing were produced. These results indicate design cognition differences between the two domains in task-related power between the problem-solving task and the design tasks, in temporal resolution and transformed power.

关键词： Design cognition Research methodologies and methods Human behaviour in design Design domains Design neuroscience

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Measurement of the τ lepton polarization in Z boson decays in proton-proton collisions at (Formula presented.)

引用

Journal of High Energy Physics 2024年第1期2024卷 101页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J.W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Escalante Del Valle, A. Hussain, P.S. Jeitler, M. Krammer, N. 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. El Faham, H. Lowette, S. Makarenko, I. Müller, D. Sahasransu, A.R. Tavernier, S. Tytgat, M. Van Putte, S. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Khalilzadeh, A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. Wezenbeek, L. Benecke, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I.S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. 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. Martins, J. Mora Herrera, C. Mota Amarilo, K. Mundim, L. Nogima, H. Santoro, A. Silva Do Amaral, S.M. 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. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. Hu, Z. Yi, K. Chen, G.M. Chen, H.S. Chen, M. Iemmi, F. Jiang, C.H. Kapoor, A. Liao, H. Liu, Z.-A. Monti, F. Sharma, R. Song, J.N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos, A. Ban, Y. Lev Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Vienna Austria Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent 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 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 Casilla 7 D Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing 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 Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) — Fudan University Shanghai China Zhejiang University Zhejiang Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Academy of Scientific Research and Technology of the Arab Republic of Egypt Egyptian Network of High Energy Physics Cairo Egypt Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt National Institute of Chemical Physics and Biophysics Tallinn Estonia Department of Physics

The polarization of τ leptons is measured using leptonic and hadronic τ lepton decays in Z → τ+ τ− events in proton-proton collisions at (Formula presented.) recorded by CMS at the CERN LHC with an integrated lum... 详细信息

The polarization of τ leptons is measured using leptonic and hadronic τ lepton decays in Z → τ⁺ τ⁻ events in proton-proton collisions at (Formula presented.) recorded by CMS at the CERN LHC with an integrated luminosity of 36.3 fb⁻¹. The measured τ⁻ lepton polarization at the Z boson mass pole is P_τ(Z) = −0.144 ± 0.006 (stat) ± 0.014 (syst) = −0.144 ± 0.015, in good agreement with the measurement of the τ lepton asymmetry parameter of A_τ = 0.1439 ± 0.0043 = − P_τ(Z) at LEP. The τ lepton polarization depends on the ratio of the vector to axial-vector couplings of the τ leptons in the neutral current expression, and thus on the effective weak mixing angle sin² θWeff , independently of the Z boson production mechanism. The obtained value sin² θWeff = 0.2319 ± 0.0008(stat) ± 0.0018(syst) = 0.2319 ± 0.0019 is in good agreement with measurements at e⁺ e⁻ colliders. [Figure not available: see fulltext.]. © 2024, The Author(s).

关键词： Hadron-Hadron Scattering Tau Physics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：