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JUNO sensitivity on proton decay p→νK^(+)searches

Chinese Physics C 2023年第11期47卷 7-22页

作者： Angel Abusleme Thomas Adam Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Muhammad Akram 安丰鹏安琪 Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Burin Asavapibhop João Pedro Athayde Marcondes de André Didier Auguste Nikita Balashov Wander Baldini Andrea Barresi Davide Basilico Eric Baussan Marco Bellato Antonio Bergnoli Thilo Birkenfeld Sylvie Blin David Blum Simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Mario Buscemi Jose Busto Ilya Butorov Anatael Cabrera Barbara Caccianiga 蔡浩蔡啸蔡严克蔡志岩 Riccardo Callegari Antonio Cammi Agustin Campeny 曹传亚曹国富曹俊 Rossella Caruso Cédric Cerna 常劲帆 Yun Chang 陈平平 Po-An Chen 陈少敏陈旭荣 Yi-Wen Chen 陈义学陈羽陈长程捷程雅苹 Alexey Chetverikov Davide Chiesa Pietro Chimenti Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux Selma Conforti Di Lorenzo Daniele Corti Flavio Dal Corso Olivia Dalager Christophe De La Taille 邓智邓子艳 Wilfried Depnering Marco Diaz Xuefeng Ding 丁雅韵 Bayu Dirgantara Sergey Dmitrievsky Tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko 董建蒙 Evgeny Doroshkevich Marcos Dracos Frédéric Druillole 杜然杜书先 Stefano Dusini Martin Dvorak Timo Enqvist Heike Enzmann Andrea Fabbri Ulrike Fahrendholz 范东华樊磊方建方文兴 Marco Fargetta Dmitry Fedoseev Li-Cheng Feng 冯启春 Richard Ford Amélie Fournier 甘浩男 Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Agnese Giaz Nunzio Giudice Maxim Gonchar 龚光华宫辉 Yuri Gornushkin Alexandre Göttel Marco Grassi Christian Grewing Vasily Gromov 顾旻皓谷肖飞古宇关梦云 Nunzio Guardone Maria Gul 郭聪郭竞渊郭万磊郭新恒郭宇航 Paul Hackspacher Caren Hagner 韩然 Yang Han Muhammad Sohaib Hassan 何苗何伟 Tobias Heinz Patrick Hellmuth 衡月昆 Rafael Herrera 贺远强侯少静 Yee Hsiung Bei-Zhen Hu 胡航胡健润胡俊胡守扬胡涛胡宇翔胡焯钧黄春豪黄桂鸿黄翰雄黄文昊黄鑫黄性涛黄永波惠加琪霍雷霍文驹 Cédric Huss Safeer Hussain Ara Ioannisian Roberto Isocrate Beatrice Jelmini Kuo-Lun Jen Ignacio Jeria 季筱璐吉星曌贾慧慧贾俊基蹇司玉蒋荻蒋炜江晓山金如意荆小平 Cécile Jollet Jari Joutsenvaara Sirichok Jungthawan Leonidas Kalousis Philipp Ka Pontificia Universidad Católica de Chile SantiagoChile IPHC Universitéde StrasbourgCNRS/IN2P3F-67037 StrasbourgFrance Pakistan Institute of Nuclear Science and Technology IslamabadPakistan INFN Catania and Dipartimento di Fisica e Astronomia dell Universitàdi Catania CataniaItaly East China University of Science and Technology ShanghaiChina University of Science and Technology of China HefeiChina Joint Institute for Nuclear Research DubnaRussia INFN Sezione di Milano and Dipartimento di Fisica dell Universitàdi Milano MilanoItaly Department of Physics Faculty of ScienceChulalongkorn UniversityBangkokThailand IJCLab UniversitéParis-SaclayCNRS/IN2P391405 OrsayFrance Department of Physics and Earth Science University of Ferrara and INFN Sezione di FerraraFerraraItaly INFN Milano Bicocca and University of Milano Bicocca MilanoItaly INFN Sezione di Padova PadovaItaly III.Physikalisches Institut B RWTH Aachen UniversityAachenGermany Eberhard Karls Universität Tübingen Physikalisches InstitutTübingenGermany Institute of High Energy Physics BeijingChina SUBATECH Universitéde NantesIMT AtlantiqueCNRS-IN2P3NantesFrance Univ.Bordeaux CNRSLP2IUMR 5797F-33170 GradignanFrance Department of Physics National Taiwan UniversityTaipei Dipartimento di Fisica e Astronomia dell’Universitàdi Padova and INFN Sezione di Padova PadovaItaly University of Roma Tre and INFN Sezione Roma Tre RomaItaly Aix Marseille Univ CNRS/IN2P3CPPMMarseilleFrance Wuhan University WuhanChina INFN Milano Bicocca and Politecnico of Milano MilanoItaly National United University Miao-Li Dongguan University of Technology DongguanChina Tsinghua University BeijingChina Institute of Modern Physics Chinese Academy of SciencesLanzhouChina Institute of Physics National Yang Ming Chiao Tung UniversityHsinchu North China Electric Power University BeijingChina Sun Yat-Sen University GuangzhouChina Beijing Institute of Spacecraft Environment Engineering BeijingChina Universidade Estadual de Londrina LondrinaBrazil INFN Sez

The Jiangmen Underground Neutrino Observatory(JUNO)is a large liquid scintillator detector designed to explore many topics in fundamental *** this study,the potential of searching for proton decay in the p→νK^(+)mode with JUNO is *** kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for ***,the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed *** on these advantages,the detection efficiency for the proton decay via p→νK^(+)is 36.9%±4.9%with a background level of 0.2±0.05(syst)±0.2(stat)events after 10 years of data *** estimated sensitivity based on 200 kton-years of exposure is 9.6×1033 years,which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies.

关键词： proton decay grand unified theories JUNO liquid scintillator detector

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Control of Nonlinear Constrained Ultra-Supercritical Boiler–Turbine Units Using Offset-Free Output-Feedback Stable MPC

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IFAC-PapersOnLine 2018年第28期51卷 155-160页

作者： Chen, Chen Pan, Lei Shen, Jiong Lee, Kwang Y. Zhang, Fan Sun, Li Wu, Xiao Zhang, Junli Xue, Wenchao Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education Southeast University Nanjing210096 China Department of Electrical and Computer Engineering Baylor University One Bear Place #97356 WacoTX76798-7356 United States State Key Lab of Power System Department of Energy and Power Engineering Tsinghua University Beijing100084 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

It is extremely challenging to control ultra-supercritical boiler–turbine unit on account of its nonlinearity, coupling among multi-variables, hard input constraints, and unknown disturbances. An offset-free output-feedback stable model predictive control (MPC) (OFOF-SMPC) for nonlinear constrained ultra-supercritical boiler-turbine units is investigated. It is shown that the OFOF-SMPC is composed of a disturbance and state observer, a steady-state target calculator as well as a stable MPC, which can remove unknown disturbances from output channels and ensure the stability of the closed-loop system with input constraints. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2018

关键词： Feedback

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Reinforcement learning meets minority game: Toward optimal resource allocation

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Physical Review E 2019年第3期99卷 032302-032302页

作者： Si-Ping Zhang Jia-Qi Dong Li Liu Zi-Gang Huang Liang Huang Ying-Cheng Lai The Key Laboratory of Biomedical Information Engineering of Ministry of Education The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou 730000 China Institute of Theoretical Physics Key Laboratory of Theoretical Physics Chinese Academy of Sciences P.O. Box 2735 Beijing 100190 China School of Software Engineering Chongqing University Chongqing 400044 People's Republic of China School of Electrical Computer and Energy Engineering Department of Physics Arizona State University Tempe Arizona 85287 USA

The main point of this paper is to provide an affirmative answer through exploiting reinforcement learning (RL) in artificial intelligence (AI) for eliminating herding without any external control in complex resource allocation systems. In particular, we demonstrate that when agents are empowered with RL (e.g., the popular Q-learning algorithm in AI) in that they get familiar with the unknown game environment gradually and attempt to deliver the optimal actions to maximize the payoff, herding can effectively be eliminated. Furthermore, computations reveal the striking phenomenon that, regardless of the initial state, the system evolves persistently and relentlessly toward the optimal state in which all resources are used efficiently. However, the evolution process is not without interruptions: there are large fluctuations that occur but only intermittently in time. The statistical distribution of the time between two successive fluctuating events is found to depend on the parity of the evolution, i.e., whether the number of time steps in between is odd or even. We develop a physical analysis and derive mean-field equations to gain an understanding of these phenomena. Since AI is becoming increasingly widespread, we expect our RL empowered minority game system to have broad applications.

关键词： Collective dynamics Competition Self-organized systems Mean field theory

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Convolutional sparse coding for compressed sensing ct reconstruction

arXiv

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arXiv 2019年

作者： Bao, Peng Xia, Wenjun Yang, Kang Chen, Weiyan Chen, Mianyi Xi, Yan Niu, Shanzhou Zhou, Jiliu Zhang, He Sun, Huaiqiang Wang, Zhangyang Zhang, Yi College of Computer Science Sichuan University Chengdu610065 China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China School of Electronics Engineering and Computer Science Peking University China Shanghai First-imaging Information Technology co. LTD School of Mathematics and Computer Science Gannan Normal University Ganzhou341000 China State University of New Jersey RutgersNJ08854 United States Department of Radiology West China Hospital of Sichuan University Chengdu610041 China Department of Computer Science and Engineering Texas AandM University United States

Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels in overlapped patches. In addition, the features learned by these methods always contain shifted versions of the same features. In recent years, convolutional sparse coding (CSC) has been developed to address these problems. In this paper, inspired by several successful applications of CSC in the field of signal processing, we explore the potential of CSC in sparse-view CT reconstruction. By directly working on the whole image, without the necessity of dividing the image into overlapped patches in DL-based methods, the proposed methods can maintain more details and avoid artifacts caused by patch aggregation. With predetermined filters, an alternating scheme is developed to optimize the objective function. Extensive experiments with simulated and real CT data were performed to validate the effectiveness of the proposed methods. Qualitative and quantitative results demonstrate that the proposed methods achieve better performance than several existing state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Conformal mesh parameterization using discrete Calabi flow

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computer AIDED GEOMETRIC DESIGN 2018年 63卷 96-108页

作者： Zhao, Hui Li, Xuan Ge, Huabin Lei, Na Zhang, Min Wang, Xiaoling Gu, Xianfeng [a]Tsinghua University China [b]Beijing Advanced Innovation Center for Imaging Technology Capital Normal University China [c]Department of Mathematics Beijing Jiaotong University China [d]DUT-RU International School of Information and Software Engineering Dalian University of Technology China [e]Computer Science Department State University of New York at Stony Brook USA [f]Brigham and Women's Hospital Harvard Medical School USA [g]Center of Mathematical Sciences and Applications Harvard University USA [h]Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China [i]School of Mechanical Engineering University of Science and Technology Beijing China

In this paper, we introduce discrete Calabi flow to the graphics research community and present a novel conformal mesh parameterization algorithm. Calabi energy has a succinct and explicit format. Its corresponding flow is conformal and convergent under certain conditions. Our method is based on the Calabi energy and Calabi flow with solid theoretical and mathematical base. We demonstrate our approach on dozens of models and compare it with other related flow based methods, such as the well-known Ricci flow and conformal equivalence of triangle meshes (CETM). Our experiments show that the performance of our algorithm is comparably the same with other methods. The discrete Calabi flow in our method provides another perspective on conformal flow and conformal parameterization. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Calabi flow Mesh parameterization Conformal mapping

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Protecting privacy of users in brain-computer interface applications

arXiv

引用

arXiv 2019年

作者： Agarwal, Anisha Dowsley, Rafael McKinney, Nicholas D. Wu, Dongrui Lin, Chin-Teng De Cock, Martine Nascimento, Anderson C.A. School of Engineering and Technology University of Washington TacomaWA98402 United States Department of Computer Science Bar-Ilan University Israel School of Engineering and Technology University of Washington TacomaWA98402 United States Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China School of Software University of Technology Sydney Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e. such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Integrating LDA into the weighted average method for semantic friend recommendation 6th

Integrating LDA into the weighted average method for semanti...

引用

6th CCF Academic Conference on Big Data, CCF Big Data 2018

作者： Gong, Jibing Chen, Shuai Gao, Xiaoxia Song, Yanqing Wang, Shuli School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao066004 China The Department of Applied Mathematics Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9789811329210

Friend recommendation is a fundamental service for both social networks and practical applications. The majority of existing friend-recommendation methods utilize user profiles, social relationships, or static post content data, but rarely consider the semantic intentions and dynamic behaviors of users. In this paper, we propose FRec++, a friend recommendation method based on semantic and dynamic information. In FRec++, the first plus stands for new friend recommendation using a semantic model based on LDA while the next plus stands for the use of dynamic user attributes (e.g., behaviors or positions). More specifically, we first use the LDA method to generate semantic topics for user interests, and then compute the topic similarities between the target user and candidate friends. Next, we calculate the similarities of dynamic behaviors (i.e., forwarding, making comments, liking, and replying) and investigate the static attributes of users to measure the relevance of preferences. Finally, the weighted average method is used to integrate the above factors. We conducted experiments on the Weibo dataset and the results show that FRec++ outperforms several existing methods. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Semantics

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Optimal verification of two-qubit pure states

arXiv

引用

arXiv 2019年

作者： Wang, Kun Hayashi, Masahito Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Graduate School of Mathematics Nagoya University Nagoya464-8602 Japan Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117542 Singapore

In a recent work [Phys. Rev. Lett. 120, 170502 (2018)], Pallister et al. proposed an optimal strategy to verify non-maximally entangled two-qubit pure states under the constraint that the accessible measurements being locally projective and non-adaptive. Their nice result leads naturally to the question: What is the optimal strategy among general LOCC measurements? In this Letter, we answer this problem completely for two-qubit pure states. To be specific, we give the optimal strategy for each of the following available classes of measurements: (i) local operations and one-way classical communication (one-way LOCC) measurements;(ii) local operations and two-way classical communication (two-way LOCC) measurements;and (iii) separable measurements. Surprisingly, our results reveal that for the two-qubit pure state verification problem, two-way LOCC measurements remarkably outperforms one-way LOCC measurements and has the same power as the separable measurements. Copyright © 2019, The Authors. All rights reserved.

关键词： Optimal systems

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Roadmap for Optical Tweezers

arXiv

引用

arXiv 2022年

作者： Volpe, Giovanni Maragò, Onofrio M. Rubinzstein-Dunlop, Halina Pesce, Giuseppe Stilgoe, Alexander B. Volpe, Giorgio Tkachenko, Georgiy Truong, Viet Giang Chormaic, Síle Nic Kalantarifard, Fatemeh Elahi, Parviz Käll, Mikael Callegari, Agnese Marqués, Manuel I. Neves, Antonio A.R. Moreira, Wendel L. Fontes, Adriana Cesar, Carlos L. Saija, Rosalba Saidi, Abir Beck, Paul Eismann, Jörg S. Banzer, Peter Fernandes, Thales F.D. Pedaci, Francesco Bowen, Warwick P. Vaippully, Rahul Lokesh, Muruga Roy, Basudev Thalhammer, Gregor Ritsch-Marte, Monika García, Laura Pérez Arzola, Alejandro V. Castillo, Isaac Pérez Argun, Aykut Muenker, Till M. Vos, Bart E. Betz, Timo Cristiani, Ilaria Minzioni, Paolo Reece, Peter J. Wang, Fan McGloin, David Ndukaife, Justus C. Quidant, Romain Roberts, Reece P. Laplane, Cyril Volz, Thomas Gordon, Reuven Hanstorp, Dag Marmolejo, Javier Tello Bruce, Graham D. Dholakia, Kishan Li, Tongcang Brzobohatý, Oto Simpson, Stephen H. Zemánek, Pavel Ritort, Felix Roichman, Yael Bobkova, Valeriia Wittkowski, Raphael Denz, Cornelia Kumar, G.V. Pavan Foti, Antonino Donato, Maria Grazia Gucciardi, Pietro G. Gardini, L. Bianchi, G. Kashchuk, A. Capitanio, M. Paterson, Lynn Jones, P.H. Berg-Sørensen, Kirstine Barooji, Younes F. Oddershede, Lene B. Pouladian, Pegah Preece, Daryl Adiels, Caroline Beck De Luca, Anna Chiara Magazzù, A. Ciriza, D. Bronte Iatì, M.A. Swartzlander, Grover A. Department of Physics University of Gothenburg Gothenburg41296 Sweden CNR IPCF Istituto per i Processi Chimico-Fisici Messina Italy ARC CoE for Engineered Quantum Systems School of Mathematics and Physics The University of Queensland Australia Department of Chemistry University College London 20 Gordon Street LondonWC1H 0AJ United Kingdom Dipartimento di Fisica Università degli studi di Napoli Federico II Italy Okinawa Institute of Science and Technology Graduate University Japan Department of Health Technology Technical University of Denmark Lyngby Denmark Physics Department Boğaziçi University Istanbul Turkey Chalmers University of Technology Gothenburg Sweden Departamento de Física de Materiales IFIMAC Instituto Nicolás Cabrera Universidad Autónoma de Madrid Spain Santo André São Paulo Brazil Tratamento de dados geofísicos Tecnologia e processamento de geologia e geofísica Exploração Petrobras - Petróleo Brasileiro S.A. Rio de Janeiro Rio de Janeiro Brazil Pernambuco Recife Brazil Ceará Fortaleza Brazil Campinas São Paulo Brazil Dipartimento di Scienze Matematiche e Informatiche Scienze Fisiche e Scienze della Terra Università di Messina Italy Institute of Optics Information and Photonics University Erlangen-Nuremberg Staudtstr. 7/B2 ErlangenD-91058 Germany Max Planck Institute for the Science of Light Staudtstr. 2 ErlangenD-91058 Germany Institute of Physics University of Graz NAWI Graz Universitätsplatz 5 Graz8010 Austria Centre de Biologie Structurale CNRS INSERM Univ.Montpellier France Indian Institute of Technology Madras India Institute for Biomedical Physics Medical University of Innsbruck Austria Instituto de Física Universidad Nacional Autónoma de México Ciudad de MéxicoC. P. 04510 Mexico Departamento de Física Universidad Autónoma Metropolitana-Iztapalapa San Rafael Atlixco 186 Ciudad de México09340 Mexico Third Institute of Physics - Biophysics Georg August University Göttingen Germany Department of Electrical

Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nanoparticle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical tweezers

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AI-skin: Skin disease recognition based on self-learning and wide data collection through a closed loop framework

arXiv

引用

arXiv 2019年

作者： Chen, Min Zhou, Ping Wu, Di Hu, Long Hassan, Mohammad Mehedi Alamri, Atif School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Chair of Pervasive and Mobile Computing Software Engineering Department CCIS King Saud University Riyadh11543 Saudi Arabia

There are a lot of hidden dangers in the change of human skin conditions, such as the sunburn caused by long-time exposure to ultraviolet radiation, which not only has aesthetic impact causing psychological depression and lack of selfconfidence, but also may even be life-threatening due to skin canceration. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. However, the excessive dependence on the image sample database is unable to provide individualized diagnosis service for different population groups. To overcome this problem, a medical AI framework based on data width evolution and self-learning is put forward in this paper to provide skin disease medical service meeting the requirement of real time, extendibility and individualization. First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed. Next, a data set filter algorithm based on information entropy is given, to lighten the load of edge node and meanwhile improve the learning ability of remote cloud analysis model. In addition, the framework provides an external algorithm load module, which can be compatible with the application requirements according to the model selected. Three kinds of deep learning model, i.e. LeNet-5, AlexNet and VGG16, are loaded and compared, which have verified the universality of the algorithm load module. The experiment platform for the proposed real-time, individualized and extensible skin disease recognition system is built. And the system's computation and communication delay under the interaction scenario between tester and remote data center are analyzed. It is demonstrated that the system we put forward is reliable and effective. Copyright © 2019, The Authors. All rights reserved.

关键词： Data acquisition

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