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Cervical single cell of squamous intraepithelial lesion classification using shape features and extreme learning machine

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Journal of Physics: Conference Series 2021年第1期1816卷

作者： R Riries Winarno C N Asiah A Y Putri A B Suksmono I S Sitanggang N A Setiawan A S Rahaju E H Kusumastuti Departement of Physics Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department of Computer Science IPB University Bogor Indonesia Electrical and Information Engineering Universitas Gadjah Mada Jogjakarta Department of Anatomical Pathology Faculty of Medicine Universitas Airlangga Dr. Soetomo General Academic Hospital Universitas Airlangga Hospital

Cervical cancer is an abnormal growth of cells found on the cervix. In general, cervical cancer is identified early by doing a pap smear test. However, this examination is still manually performed by doctors and the results are still subjective. Therefore, this study aims to determine the classification of Squamous Intraepithelial Lesion automatically from cervical single cells. The classification of those Squamous Intraepithelial Lesion includes normal cervical cells, Low-Grade Squamous Intraepithelial Lesion (LSIL), and High-Grade Squamous Intraepithelial Lesion (HSIL). We used Extreme Learning Machine (ELM) as a classifier and tried to compare the ELM's performances with Backpropagation Neural Network method. We used 225 data and 3 classes include normal, LSIL, and HSIL. The classification was carried out by manual cropping and segmentation as the image pre-processing and the feature extraction was based on shape features consisting of Circularity, Semi Major and Minor Axis Length, Equivalent Diameter, Average Radius, and Compactness. This study concluded that Squamous Intraepithelial Lesion classification by using ELM had better performances than Backpropagation Neural Network. The highest accuracy result of 96.67% was obtained in Backpropagation training, while the highest accuracy in ELM's training was 100% when both methods were tried by using 225 data.

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From word to sense embeddings: A survey on vector representations of meaning

arXiv

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arXiv 2018年

作者： Camacho-Collados, Jose Pilehvar, Mohammad Taher School of Computer Science and Informatics Cardiff University United Kingdom School of Computer Engineering Iran University of Science and Technology Tehran Iran

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality. Copyright © 2018, The Authors. All rights reserved.

关键词： Surveys

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Sub-percent Precision Measurement of Neutrino Oscillation Parameters with JUNO

arXiv

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arXiv 2022年

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

JUNO is a multi-purpose neutrino observatory under construction in the south of China. This publication presents new sensitivity estimates for the measurement of the ∆m231, ∆m221, sin2 θ12, and sin2 θ13 oscillation parameters using reactor antineutrinos, which is one of the primary physics goals of the experiment. The sensitivities are obtained using the best knowledge available to date on the location and overburden of the experimental site, the nuclear reactors in the surrounding area and beyond, the detector response uncertainties, and the reactor antineutrino spectral shape constraints expected from the TAO satellite detector. It is found that the ∆m231, ∆m221, and sin2 θ12 oscillation parameters will be determined to better than 0.5% precision in six years of data collection, which represents approximately an order of magnitude improvement over existing constraints. © 2022, CC BY.

关键词： Nuclear reactors

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Model Independent Approach of the JUNO 8B Solar Neutrino Program

arXiv

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arXiv 2022年

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

The physics potential of detecting 8B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model independent manner by using three distinct channels of the charged-current (CC), neutral-current (NC) and elastic scattering (ES) interactions. Due to the largest-ever mass of 13C nuclei in the liquid-scintillator detectors and the expected low background level, 8B solar neutrinos would be observable in the CC and NC interactions on 13C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC and ES channels to guarantee the 8B solar neutrino observation. From the sensitivity studies performed in this work, we show that JUNO, with ten years of data, can reach the 1σ precision levels of 5%, 8% and 20% for the 8B neutrino flux, sin2 θ12, and ∆m221, respectively. It would be unique and helpful to probe the details of both solar physics and neutrino physics. In addition, when combined with SNO, the world-best precision of 3% is expected for the 8B neutrino flux measurement. © 2022, CC BY.

关键词： Neutrons

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Software-Defined Metasurface Paradigm: Concept, Challenges, Prospects

Software-Defined Metasurface Paradigm: Concept, Challenges, ...

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International Congress on Advanced Electromagnetic Materials in Microwaves and Optics (METAMATERIALS)

作者： A. Pitilakis A.C. Tasolamprou C. Liaskos F. Liu O. Tsilipakos X. Wang M.S. Mirmoosa K. Kossifos J. Georgiou A. Pitsilides N.V. Kantartzis S. Ioannidis E.N. Economou M. Kafesaki S.A. Tretyakov C.M. Soukoulis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Foundation for Research and Technology Hellas Crete Greece Department of Electronics and Nanoengineering Aalto University Espoo Finland Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus Department of Materials Science and Technology University of Crete Crete Greece Ames Laboratory and Department of Physics and Astronomy Iowa State University Ames Iowa USA

HyperSurfaces (HSFs) are devices whose electromagnetic (EM) behavior is software- driven, i.e., it can be defined programmatically. The key components of this emerging technology are the metasurfaces, artificial layered materials whose EM properties depend on their internal subwavelength structuring. HSFs merge metasurfaces with a network of miniaturized custom electronic controllers, the nanonetwork, in an integrated scalable hardware platform. The nanonetwork receives external programmatic commands expressing the desired end-functionality and appropriately alters the metasurface configuration thus yielding the respective EM behavior for the HSF. In this work, we will present all the components of the HSF paradigm, as well as highlight the underlying challenges and future prospects.

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Prospects for Detecting the Diffuse Supernova Neutrino Background with JUNO

arXiv

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arXiv 2022年

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

We present the detection potential for the diffuse supernova neutrino background (DSNB) at the Jiangmen Underground Neutrino Observatory (JUNO), using the inverse-beta-decay (IBD) detection channel on free protons. We employ the latest information on the DSNB flux predictions, and investigate in detail the background and its reduction for the DSNB search at JUNO. The atmospheric neutrino induced neutral current (NC) background turns out to be the most critical background, whose uncertainty is carefully evaluated from both the spread of model predictions and an envisaged in situ measurement. We also make a careful study on the background suppression with the pulse shape discrimination (PSD) and triple coincidence (TC) cuts. With latest DSNB signal predictions, more realistic background evaluation and PSD efficiency optimization, and additional TC cut, JUNO can reach the significance of 3σ for 3 years of data taking, and achieve better than 5σ after 10 years for a reference DSNB model. In the pessimistic scenario of non-observation, JUNO would strongly improve the limits and exclude a significant region of the model parameter space. © 2022, CC BY.

关键词： Forecasting

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JUNO Sensitivity on Proton Decay p → ν¯K+ Searches

arXiv

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arXiv 2022年

作者： Abusleme, Angel Adam, Thomas Ahmad, Shakeel Ahmed, Rizwan Aiello, Sebastiano Akram, Muhammad An, Fengpeng An, Qi Andronico, Giuseppe Anfimov, Nikolay Antonelli, Vito Antoshkina, Tatiana Asavapibhop, Burin de André, João Pedro Athayde Marcondes Auguste, Didier Balashov, Nikita Baldini, Wander Barresi, Andrea Basilico, Davide Baussan, Eric Bellato, Marco Bergnoli, Antonio Birkenfeld, Thilo Blin, Sylvie Blum, David Blyth, Simon Bolshakova, Anastasia Bongrand, Mathieu Bordereau, Clément Breton, Dominique Brigatti, Augusto Brugnera, Riccardo Bruno, Riccardo Budano, Antonio Buscemi, Mario Busto, Jose Butorov, Ilya Cabrera, Anatael Caccianiga, Barbara Cai, Hao Cai, Xiao Cai, Yanke Cai, Zhiyan Callegari, Riccardo Cammi, Antonio Campeny, Agustin Cao, Chuanya Cao, Guofu Cao, Jun Caruso, Rossella Cerna, Cédric Chang, Jinfan Chang, Yun Chen, Pingping Chen, Po-An Chen, Shaomin Chen, Xurong Chen, Yi-Wen Chen, Yixue Chen, Yu Chen, Zhang Cheng, Jie Cheng, Yaping Chetverikov, Alexey Chiesa, Davide Chimenti, Pietro Chukanov, Artem Claverie, Gérard Clementi, Catia Clerbaux, Barbara Di Lorenzo, Selma Conforti Corti, Daniele Corso, Flavio Dal Dalager, Olivia De La Taille, Christophe Deng, Zhi Deng, Ziyan Depnering, Wilfried Diaz, Marco Ding, Xuefeng Ding, Yayun Dirgantara, Bayu Dmitrievsky, Sergey Dohnal, Tadeas Dolzhikov, Dmitry Donchenko, Georgy Dong, Jianmeng Doroshkevich, Evgeny Dracos, Marcos Druillole, Frédéric Du, Ran Du, Shuxian Dusini, Stefano Dvorak, Martin Enqvist, Timo Enzmann, Heike Fabbri, Andrea Fahrendholz, Ulrike Fan, Donghua Fan, Lei Fang, Jian Fang, Wenxing Fargetta, Marco Fedoseev, Dmitry Feng, Li-Cheng Feng, Qichun Ford, Richard Fournier, Amélie Gan, Haonan Gao, Feng Garfagnini, Alberto Gavrikov, Arsenii Giammarchi, Marco Giaz, Agnese Giudice, Nunzio Gonchar, Maxim Gong, Guanghua Gong, Hui Gornushkin, Yuri Göttel, Alexandre Grassi, Marco Grewing, Christian Gromov, Vasily Gu, Minghao Gu, Xiaofei Gu, Yu Guan, Mengyun Guardone, Nunzio Gul, Maria Guo, Cong Guo, Jingyuan Guo, Wanlei Guo, Xinheng Guo, Yuhang Hackspache Pontificia Universidad Católica de Chile Santiago Chile IPHC Université de Strasbourg CNRS IN2P3 StrasbourgF-67037 France Pakistan Institute of Nuclear Science and Technology Islamabad Pakistan INFN Catania and Dipartimento di Fisica e Astronomia Università di Catania Catania Italy East China University of Science and Technology Shanghai China University of Science and Technology of China Hefei China Joint Institute for Nuclear Research Dubna Russia INFN Sezione di Milano Dipartimento di Fisica Università di Milano Milano Italy Department of Physics Faculty of Science Chulalongkorn University Bangkok Thailand IJCLab Université Paris-Saclay CNRS IN2P3 Orsay91405 France Department of Physics and Earth Science University of Ferrara INFN Sezione di Ferrara Ferrara Italy INFN Milano Bicocca University of Milano Bicocca Milano Italy INFN Sezione di Padova Padova Italy III. Physikalisches Institut B RWTH Aachen University Aachen Germany Eberhard Karls Universität Tübingen Physikalisches Institut Tübingen Germany Institute of High Energy Physics Beijing China SUBATECH Université de Nantes IMT Atlantique CNRS-IN2P3 Nantes France Univ. Bordeaux CNRS LP2I UMR 5797 GradignanF-33170 France Department of Physics National Taiwan University Taipei Taiwan Dipartimento di Fisica e Astronomia Università di Padova INFN Sezione di Padova Padova Italy University of Roma Tre INFN Sezione Roma Tre Roma Italy Aix Marseille Univ CNRS IN2P3 CPPM Marseille France Wuhan University Wuhan China INFN Milano Bicocca Politecnico of Milano Milano Italy National United University Miao-Li Taiwan Dongguan University of Technology Dongguan China Tsinghua University Beijing China Institute of Modern Physics Chinese Academy of Sciences Lanzhou China Institute of Physics National Yang Ming Chiao Tung University Hsinchu Taiwan North China Electric Power University Beijing China Sun Yat-Sen University Guangzhou China Beijing Institute of Spacecraft Environment

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in p → ν¯K+ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via p → ν¯K+ is 36.9% ± 4.9% with a background level of 0.2 ± 0.05(syst) ± 0.2(stat) events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is 9.6 × 1033 years, which is competitive with the current best limits on the proton lifetime in this channel and complementary using different detection technologies. © 2022, CC BY-NC-SA.

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Test of lepton flavour universality in W-boson decays into electrons and τ-leptons using 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 1-49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. 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. J. 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. 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. 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 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

A measurement of the ratio of the branching fractions, Rτ/e = B(W → τν)/B(W → eν), is performed using a sample of W bosons originating from top-quark decays to final states containing τ-leptons or electrons. This measurement uses pp collisions at $$ \sqrt{s} $$ = 13 TeV, collected by the ATLAS experiment at the Large Hadron Collider during Run 2, corresponding to an integrated luminosity of 140 fb−1. The W → τντ (with τ → eνeντ) and W → eνe decays are distinguished using the differences in the impact parameter distributions and transverse momentum spectra of the electrons. The measured ratio of branching fractions Rτ/e = 0.975 ± 0.012 (stat.) ± 0.020 (syst.), is consistent with the Standard Model assumption of lepton flavour universality in W-boson decays.

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Underwater photogrammetry in very shallow waters: Main challenges and caustics effect removal

Underwater photogrammetry in very shallow waters: Main chall...

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2018 ISPRS TC II Mid-term Symposium "Towards Photogrammetry 2020"

作者： Agrafiotis, P. Skarlatos, D. Forbes, T. Poullis, C. Skamantzari, M. Georgopoulos, A. Cyprus University of Technology Civil Engineering and Geomatics Dept. Lab of Photogrammetric Vision 2-8 Saripolou str. Limassol3036 Cyprus Immersive and Creative Technologies Lab. Department of Computer Science and Software Engineering Concordia University QuebecH3G 1M8 Canada National Technical University of Athens School of Rural and Surveying Engineering Lab. of Photogrammetry Zografou Campus 9 Heroon Polytechniou str. Zografou Athens15780 Greece

In this paper, main challenges of underwater photogrammetry in shallow waters are described and analysed. The very short camera to object distance in such cases, as well as buoyancy issues, wave effects and turbidity of the waters are challenges to be resolved. Additionally, the major challenge of all, caustics, is addressed by a new approach for caustics removal (Forbes et al., 2018) which is applied in order to investigate its performance in terms of SfM-MVS and 3D reconstruction results. In the proposed approach the complex problem of removing caustics effects is addressed by classifying and then removing them from the images. We propose and test a novel solution based on two small and easily trainable Convolutional Neural Networks (CNNs). Real ground truth for caustics is not easily available. We show how a small set of synthetic data can be used to train the network and later transfer the le arning to real data with robustness to intra-class variation. The proposed solution results in caustic-free images which can be further used for other tasks as may be needed. © Authors 2018.

关键词： Underwater photogrammetry

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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