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arXiv 2019年

作者： Hu, Guilei Xiao, Yang Cao, Zhiguo Meng, Lubin Fang, Zhiwen Zhou, Joey Tianyi Yuan, Junsong National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University China School of Energy and Mechanicalelectronic Engineering Hunan University of Humanities Science and Technology China Institute of High Performance Computing A∗STAR Singapore Computer Science and Engineering Department of University at Buffalo State University of New York United States

Effective and real-time eyeblink detection is of widerange applications, such as deception detection, drive fatigue detection, face anti-spoofing. Despite previous efforts, most of existing focus on addressing the eyeblink detection problem under constrained indoor conditions with relative consistent subject and environment setup. Nevertheless, towards practical applications, eyeblink detection in the wild is highly preferred, and of greater challenges. In this paper, we shed the light to this research topic. A labelled eyeblink in the wild dataset (i.e., HUST-LEBW) of 673 eyeblink video samples (i.e., 381 positives, and 292 negatives) is first established. These samples are captured from the unconstrained movies, with the dramatic variation on face attribute, head pose, illumination condition, imaging configuration, etc. Then, we formulate eyeblink detection task as a binary spatial-temporal pattern recognition problem. After locating and tracking human eyes using SeetaFace engine and KCF (Kernelized Correlation Filters) tracker respectively, a modified LSTM model able to capture the multi-scale temporal information is proposed to verify eyeblink. A feature extraction approach that reveals the appearance and motion characteristics simultaneously is also proposed. The experiments on HUSTLEBW reveal the superiority and efficiency of our approach. The comparisons with the existing state-of-the-art methods validate the advantages of our manner for eyeblink detection in the wild. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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Understanding co-run performance on CPU-GPU integrated processors： observations, insights, directions

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Frontiers of computer science 2017年第1期11卷 130-146页

作者： Qi ZHU Bo WU Xipeng SHEN Kai SHEN Li SHEN Zhiying WANG National Key Laboratory of High Performance Computing National University of Defense Technology Changsha 410073 China Department of Computer Science North Carolina State University Raleigh NC 27695 USA Department of Electrical Engineering & Computer Science Colorado School of Mines Golden CO 80401 USA Department of Computer Science University of Rochester Rochester NY 14627 USA

Recent years have witnessed a processor develop- ment trend that integrates central processing unit （CPU） and graphic processing unit （GPU） into a single chip. The inte- gration helps to save some host-device data copying that a discrete GPU usually requires, but also introduces deep re- source sharing and possible interference between CPU and GPU. This work investigates the performance implications of independently co-running CPU and GPU programs on these platforms. First, we perform a comprehensive measurement that covers a wide variety of factors, including processor ar- chitectures, operating systems, benchmarks, timing mecha- nisms, inputs, and power management schemes. These mea- surements reveal a number of surprising observations. We an- alyze these observations and produce a list of novel insights, including the important roles of operating system （OS） con- text switching and power management in determining the program performance, and the subtle effect of CPU-GPU data copying. Finally, we confirm those insights through case studies, and point out some promising directions to mitigate anomalous performance degradation on integrated heteroge- neous processors.

关键词： performance analysis GPGPU co-run degrada-tion fused processor program transformation

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Blockchain and smart contract for digital certificate

Blockchain and smart contract for digital certificate

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International Conference on Applied System Innovation (ICASI)

作者： Jiin-Chiou Cheng Narn-Yih Lee Chien Chi Yi-Hua Chen Department of Computer Science and Information Engineering Southern Taiwan University of Science and Technology Tainan Taiwan National Applied Research Laboratories Natinal Center for High-Performance Computing

According to the Taiwan Ministry of Education statistics, about one million graduates each year, some of them will go to countries, high schools or tertiary institutions to continue to attend, and some will be ready to enter the workplace employment. During the course of study, the students' all kinds of excellent performance certificates, score transcripts, diplomas, etc., will become an important reference for admitting new schools or new works. As schools make various awards or diplomas, only the names of the schools and the students are input. Due to the lack of effective anti-forge mechanism, events that cause the graduation certificate to be forged often get noticed. In order to solve the problem of counterfeiting certificates, the digital certificate system based on blockchain technology would be proposed. By the unmodifiable property of blockchain, the digital certificate with anti-counterfeit and verifiability could be made. The procedure of issuing the digital certificate in this system is as follows. First, generate the electronic file of a paper certificate accompanying other related data into the database, meanwhile calculate the electronic file for its hash value. Finally, store the hash value into the block in the chain system. The system will create a related QR-code and inquiry string code to affix to the paper certificate. It will provide the demand unit to verify the authenticity of the paper certificate through mobile phone scanning or website inquiries. Through the unmodifiable properties of the blockchain, the system not only enhances the credibility of various paper-based certificates, but also electronically reduces the loss risks of various types of certificates.

关键词： Contracts Companies Conferences Technological innovation Distributed databases Certification

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Experimental Verification and Analysis of Dynamic Loop Scheduling in Scientific Applications

Experimental Verification and Analysis of Dynamic Loop Sched...

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International Symposium on Parallel and Distributed computing

作者： Ali Mohammed Ahmed Eleliemy Florina M. Ciorba Franziska Kasielke Ioana Banicescu Department of Mathematics and Computer Science University of Basel Switzerland Center for Information Services and High Performance Computing Technische Universität Dresden Germany Department of Computer Science and Engineering Mississippi State University USA

Scientific applications are often irregular and characterized by large computationally-intensive parallel loops. Dynamic loop scheduling (DLS) techniques improve the performance of computationally-intensive scientific applications via load balancing of their execution on high-performance computing (HPC) systems. Identifying the most suitable choices of data distribution strategies, system sizes, and DLS techniques which improve the performance of a given application, requires intensive assessment and a large number of exploratory native experiments (using real applications on real systems), which may not always be feasible or practical due to associated time and costs. In such cases, simulative experiments are more appropriate for studying the performance of applications. This motivates the question of 'How realistic are the simulations of executions of scientific applications using DLS on HPC platforms?' In the present work, a methodology is devised to answer this question. It involves the experimental verification and analysis of the performance of DLS in scientific applications. The proposed methodology is employed for a computer vision application executing using four DLS techniques on two different HPC platforms, both via native and simulative experiments. The evaluation and analysis of the native and simulative results indicate that the accuracy of the simulative experiments is strongly influenced by the approach used to extract the computational effort of the application (FLOP-or time-based), the choice of application model representation into simulation (data or task parallel) and the available HPC subsystem models in the simulator (multi-core CPUs, memory hierarchy and network topology). The minimum and the maximum percent errors achieved between the native and the simulative experiments are 0.95% and 8.03%, respectively.

关键词： Dynamic scheduling Analytical models Task analysis Computational modeling Power capacitors Processor scheduling

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Laser-Damage Attack Against Optical Attenuators in Quantum Key Distribution

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Physical Review Applied 2020年第3期13卷 034017-034017页

作者： Anqi Huang Ruoping Li Vladimir Egorov Serguei Tchouragoulov Krtin Kumar Vadim Makarov Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 People’s Republic of China Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Faculty of Photonics and Optical Information ITMO University 199034 Kadetskaya line 3b St. Petersburg Russia QGLex Incorporated 105 Schneider Road Suite 111 Ottawa Ontario K2K 1Y3 Canada Russian Quantum Center Skolkovo Moscow 121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia

Many quantum key distribution systems employ a laser followed by an optical attenuator to prepare weak coherent states in the source. Their mean photon number must be precalibrated to guarantee the security of key distribution. Here we experimentally show that this calibration can be broken with a high-power laser attack. We test four fiber-optic attenuator types used in quantum key distribution systems, and find that two of them exhibit a permanent decrease in attenuation after laser damage. This results in higher mean photon numbers in the prepared states and may allow an eavesdropper to compromise the key.

关键词： Light-matter interaction Photon statistics Photonics Quantum communication Quantum cryptography Quantum optics Devices Fiber lasers

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Energy Load Forecasting using Deep Learning Approach-LSTM and GRU in Spark Cluster

Energy Load Forecasting using Deep Learning Approach-LSTM an...

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International Conference on Emerging Applications of Information Technology (EAIT)

作者： Sumit Kumar Lasani Hussain Sekhar Banarjee Motahar Reza B. Tech CSE 3rd Year Students High Performance Computing Lab School of Computer Science and Engineering National Institute of Science & Technology Berhampur India

Electric load forecasting has a significant role in power grids in order to facilitate the decision making process of energy generation & consumption. Long term forecasting is not feasible as there might be an uncertainty in the prediction because of irregular increase in the demand for power with the growing population and dependency on electric power. Since the behaviour of electric load time series is very much non-linear and seasonal, Neural Networks are best suited model for learning the Non-Linear behaviour within the data and for forecasting purpose. This paper deals with the Recurrent Neural Networks based Models: Long-Short-Term-Memory (LSTM) and Gated-Recurrent-Unit (GRU) to deal with this challenge. Observations have been made based on the distributed implementation of various configurations of LSTM-RNN & GRU-RNN on spark clusters for hyper parameter tuning purpose and deploying best suited configuration with least RMSE value using apache memos resource management.

关键词： Load modeling Predictive models Logic gates Computational modeling Load forecasting Forecasting Sparks

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Experimental verification and analysis of dynamic loop scheduling in scientific applications

arXiv

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arXiv 2018年

作者： Mohammed, Ali Eleliemy, Ahmed Ciorba, Florina M. Kasielke, Franziska Banicescu, Ioana Department of Mathematics and Computer Science University of Basel Switzerland Center for Information Services and High Performance Computing Technische Universität Dresden Germany Department of Computer Science and Engineering Mississippi State University United States

Scientific applications are often irregular and characterized by large computationally-intensive parallel loops. Dynamic loop scheduling (DLS) techniques can be used to improve the performance of computationally-intensive scientific applications via load balancing of their execution on high-performance computing (HPC) systems. Identifying the most suitable choices of data distribution strategies, system sizes, and DLS techniques which improve the performance of a given application, requires intensive assessment and a large number of exploratory native experiments (using real applications on real systems), which may not always be feasible or practical due to associated time and costs. In such cases, to avoid the execution of a large volume of exploratory native experiments, simulative experiments which are faster and less costly are more appropriate for studying the performance of applications for the purpose of optimizing it. This motivates the question of ‘How realistic are the simulations of executions of scientific applications using DLS on HPC platforms?’ In the present work, a methodology is devised to answer this question It involves the experimental verification and analysis of the performance of DLS in scientific applications. The proposed methodology is employed for a computer vision application executing using four DLS techniques on two different HPC platforms, both via native and simulative experiments. Moreover, the evaluation and the analysis of the native and simulative results indicate that the accuracy of the simulative experiments is strongly influenced by the values obtained by the chosen approach used to extract the computational effort of the application (FLOP- or time-based), the choice of application model representation into simulation (data or task parallel), and the choice of HPC subsystem models available in the simulator (multi-core CPUs, memory hierarchy, and network topology). Further insights into the native performance on two HPC platf

关键词： Scheduling

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Noise subtraction from KAGRA O3GK data using Independent Component Analysis

arXiv

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arXiv 2022年

作者： Abe, H. Akutsu, T. Ando, M. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Bajpai, R. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, D. Chen, K. Chen, Y. Chiang, C.-Y. Chu, Y.-K. Eguchi, S. Eisenmann, M. Enomoto, Y. Flaminio, R. Fong, H.K. Fujii, Y. Fujikawa, Y. Fujimoto, Y. Fukunaga, I. Gao, D. Ge, G.-G. Ha, S. Hadiputrawan, I.P.W. Haino, S. Han, W.-B. Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hirata, N. Hirose, C. Ho, T.-C. Hsieh, B.-H. Hsieh, H.-F. Hsiung, C. Huang, H.-Y. Huang, P. Huang, Y.-C. Huang, Y.-J. Hui, D.C.Y. Ide, S. Inayoshi, K. Inoue, Y. Ito, K. Itoh, Y. Jeon, C. Jin, H.-B. Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanda, N. Kato, T. Kawaguchi, K. Kim, C. Kim, J. Kim, J.C. Kim, Y.-M. Kimura, N. Kiyota, T. Kobayashi, Y. Kohri, K. Kokeyama, K. Kong, A.K.H. Koyama, N. Kozakai, C. Kume, J. Kuromiya, Y. Kuroyanagi, S. Kwak, K. Lee, E. Lee, H.W. Lee, R. Leonardi, M. Li, K.L. Li, P. Lin, L.C.-C. Lin, C.-Y. Lin, E.T. Lin, F.-K. Lin, F.-L. Lin, H.L. Liu, G.C. Luo, L.-W. Ma'arif, M. Majorana, E. Michimura, Y. Mio, N. Miyakawa, O. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Morisue, N. Moriwaki, Y. Nagano, K. Nakamura, K. Nakano, H. Nakano, M. Nakayama, Y. Narikawa, T. Naticchioni, L. Nguyen Quynh, L. Ni, W.-T. Nishimoto, T. Nishizawa, A. Nozaki, S. Obayashi, Y. Ogaki, W. Oh, J.J. Oh, K. Ohashi, M. Ohashi, T. Ohkawa, M. Ohta, H. Okutani, Y. Oohara, K. Oshino, S. Otabe, S. Pan, K.-C. Parisi, A. Park, J. Peña Arellano, F.E. Saha, S. Saito, Y. Sakai, K. Sawada, T. Sekiguchi, Y. Shao, L. Shikano, Y. Shimizu, H. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Song, I. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takano, S. Takeda, H. Takeda, M. Tanaka, K. Tanaka, T. Tanaka, T. Tanioka, S. Taruya, A. Graduate School of Science Tokyo Institute of Technology Meguro-ku Tokyo152-8551 Japan Mitaka City Tokyo181-8588 Japan Mitaka City Tokyo181-8588 Japan Department of Physics The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan Earthquake Research Institute The University of Tokyo Bunkyo-ku Tokyo113-0032 Japan Department of Mathematics and Physics Gravitational Wave Science Project Hirosaki University Aomori Hirosaki City036-8561 Japan Kamioka-cho Hida City Gifu506-1205 Japan Mitaka City Tokyo181-8588 Japan Yuseong-gu Daejeon34141 Korea Republic of National Institute for Mathematical Sciences Yuseong-gu Daejeon34047 Korea Republic of Ibaraki Tsukuba City305-0801 Japan Department of Astronomy Beijing Normal University Beijing100875 China Department of Applied Physics Fukuoka University Jonan Fukuoka Fukuoka City814-0180 Japan Department of Physics Tamkang University Danshui Dist. New Taipei City25137 Taiwan Department of Physics Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Department of Physics Center for High Energy and High Field Physics National Central University Zhongli District Taoyuan City32001 Taiwan Department of Physics National Tsing Hua University Hsinchu30013 Taiwan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Université Savoie Mont Blanc CNRS IN2P3 AnnecyF-74941 France Department of Astronomy The University of Tokyo Mitaka City Tokyo181-8588 Japan Faculty of Engineering Niigata University Nishi-ku Niigata Niigata City950-2181 Japan Department of Physics Graduate School of Science Osaka City University Sumiyoshi-ku Osaka City Osaka558-8585 Japan Chinese Academy of Sciences Xiao Hong Shan Wuhan430071 China Ulju-gun Ulsan44919 Korea Republic of Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China KAGRA Observatory The University of Tok

In April 2020, KAGRA conducted its first science observation in combination with the GEO 600 detector (O3GK) for two weeks. According to the noise budget estimation, suspension control noise in the low frequency band and acoustic noise in the middle frequency band are identified as the dominant contribution. In this study, we show that such noise can be reduced in offline data analysis by utilizing a method called Independent Component Analysis (ICA). Here the ICA model is extended from the one studied in iKAGRA data analysis by incorporating frequency dependence while linearity and stationarity of the couplings are still assumed. By using optimal witness sensors, those two dominant contributions are mitigated in the real observational data. We also analyze the stability of the transfer functions for whole two weeks data in order to investigate how the current subtraction method can be practically used in gravitational wave search. Copyright © 2022, The Authors. All rights reserved.

关键词： Data handling

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Transfer Learning for Object Detection using State-of-the-Art Deep Neural Networks

Transfer Learning for Object Detection using State-of-the-Ar...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： J. Talukdar S. Gupta P. S. Rajpura R. S. Hegde Dept. of Electronics & Communication Engineering Nirma University Ahmedabad Dept. of Computer Engineering & Information Science BITS Pilani Hyderabad Campus High Performance Computing Lab Indian Institute of Technology Gandhinagar

Transfer learning through the use of synthetic images and pretrained convolutional neural networks offers a promising approach to improve the object detection performance of deep neural networks. In this paper, we explore different strategies to generate synthetic datasets and subsequently improve them to achieve better object detection accuracy (mAP) when trained with state-of-the-art deep neural networks, focusing on detection of packed food products in a refrigerator. We develop novel techniques like dynamic stacking, pseudo random placement, variable object pose, distractor noise etc. which not only aid in diversifying the synthetic data but also help in improving the overall object detection mAP by more than 40%. The synthetic images, generated using Blender-Python API, are clustered in a variety of configurations to cater to the diversity of real scenes. These datasets are then utilized to train TensorFlow implementations of state-of-the-art deep neural networks like Faster-RCNN, R-FCN, and SSD and their performance is tested on real scenes. The object detection performance of various deep CNN architectures is also studied, with Faster-RCNN proving to be the most suitable choice, achieving the highest mAP of 70.67.

关键词： Object detection Neural networks Feature extraction Solid modeling Pipelines computer architecture Task analysis

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Erratum: Towards a muon collider

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The European Physical Journal C 2024年第1期84卷 1-4页

作者： Accettura, Carlotta Adams, Dean Agarwal, Rohit Ahdida, Claudia Aimè, Chiara Amapane, Nicola Amorim, David Andreetto, Paolo Anulli, Fabio Appleby, Robert Apresyan, Artur Apyan, Aram Arsenyev, Sergey Asadi, Pouya Mahmoud, Mohammed Attia Azatov, Aleksandr Back, John Balconi, Lorenzo Bandiera, Laura Barlow, Roger Bartosik, Nazar Barzi, Emanuela Batsch, Fabian Bauce, Matteo Berg, J. Scott Bersani, Andrea Bertarelli, Alessandro Bertolin, Alessandro Black, Kevin Boattini, Fulvio Bogacz, Alex Bonesini, Maurizio Bordini, Bernardo Bottaro, Salvatore Bottura, Luca Braghieri, Alessandro Breschi, Marco Bruhwiler, Natalie Buffat, Xavier Buonincontri, Laura Burrows, Philip N. Burt, Graeme Buttazzo, Dario Caiffi, Barbara Calviani, Marco Calzaferri, Simone Calzolari, Daniele Capdevilla, Rodolfo Carli, Christian Casaburo, Fausto Casarsa, Massimo Castelli, Luca Catanesi, Maria Gabriella Cavallucci, Lorenzo Cavoto, Gianluca Celiberto, Francesco Giovanni Celona, Luigi Cerri, Alessandro Cesarini, Gianmario Cesarotti, Cari Chachamis, Grigorios Chance, Antoine Chen, Siyu Chien, Yang-Ting Chiesa, Mauro Colaleo, Anna Collamati, Francesco Collazuol, Gianmaria Costa, Marco Craig, Nathaniel Curatolo, Camilla Curtin, David Da Molin, Giacomo Dam, Magnus Damerau, Heiko Dasu, Sridhara de Blas, Jorge De Curtis, Stefania De Matteis, Ernesto De Rosa, Stefania Delahaye, Jean-Pierre Denisov, Dmitri Denizli, Haluk Densham, Christopher Dermisek, Radovan Di Luzio, Luca Di Meco, Elisa Di Micco, Biagio Dienes, Keith Diociaiuti, Eleonora Dorigo, Tommaso Dudarev, Alexey Edgecock, Robert Errico, Filippo Fabbrichesi, Marco Farinon, Stefania Ferrari, Anna Somoza, Jose Antonio Ferreira Filthaut, Frank Fiorina, Davide Fol, Elena Forslund, Matthew Franceschini, Roberto Ximenes, Rui Franqueira Gabrielli, Emidio Gallinaro, Michele Garosi, Francesco Giambastiani, Luca Gianelle, Alessio Gilardoni, Simone Giove, Dario Augusto Giraldin, Carlo Glioti, Alfredo Greco, Mario Greljo, Admir Groeber, Ramona Grojean, Christophe Grudiev, Alexej Gu, Jiayin Han, Chengcheng Han, T Organisation Européenne pour la Recherche Nucléaire (CERN) Geneva Switzerland STFC Rutherford Appleton Laboratory (RAL) Oxford UK Computer Science Department University of California (UC) Berkeley USA Dipartimento di Fisica Università di Pavia Pavia Italy INFN Sezione di Pavia Pavia Italy INFN Sezione di Torino Turin Italy Dipartimento di Fisica Università di Torino Turin Italy INFN Sezione di Padova Padua Italy INFN Sezione di Roma Rome Italy School of Physics and Astronomy University of Manchester Manchester UK Fermi National Accelerator Laboratory Batavia USA Department of Physics Brandeis University Waltham USA IRFU CEA University Paris-Saclay Gif-sur-Yvette France Center for Theoretical Physics Massachusetts Institute of Technology Cambridge USA Center for High Energy Physics (CHEP-FU) Fayoum University El-Fayoum Egypt SISSA International School for Advanced Studies Trieste Italy INFN Sezione di Trieste Trieste Italy Department of Physics University of Warwick Coventry UK Dipartimento di Fisica Università di Milano Milan Italy INFN Laboratori Acceleratori e Superconduttività Applicata (LASA) Milan Italy INFN Sezione di Ferrara Ferrara Italy School of Computing and Engineering The University of Huddersfield Huddersfield UK Ohio State University Columbus USA Brookhaven National Laboratory Upton USA INFN Sezione di Genova Genoa Italy University of Wisconsin Wisconsin USA Center for Advanced Studies of Accelerators Jefferson Lab Newport News USA INFN Sezione di Milano Bicocca Milan Italy Dipartimento di Fisica Università di Milano Bicocca Milan Italy Scuola Normale Superiore Pisa Italy INFN Sezione di Pisa Pisa Italy Dipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione Università di Bologna Bologna Italy INFN Sezione di Bologna Bologna Italy Department of Physics University of California (UC) Berkeley USA Dipartimento di Fisica e Astronomia Università di Padova Padoa Italy John Adams Institute University of O

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