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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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In-silico structural and functional analysis of nonsynonymous single nucleotide polymorphisms in human FOLH1 gene

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In silico pharmacology 2025年第1期13卷 32页

作者： Abtin Tondar Muhammad Irfan Sergio Sánchez-Herrero Hafsa Athar Aleena Haqqi Asim Kumar Bepari Laura Calvet Liñán David Hervás Marin Department of Computer Science Multimedia and Telecommunication Interuniversity Doctoral Program in Bioinformatics Universitat Oberta de Catalunya Barcelona (UOC) Spain. Stanford Deep Data Research Center Stanford University Stanford USA. Atta-Ur-Rahman School of Applied Biosciences (ASAB) National University of Sciences and Technology (NUST) Islamabad Punjab Pakistan. School of Medical Laboratory Technology Minhaj University Lahore (MUL) Lahore Punjab Pakistan. Department of Pharmaceutical Sciences North South University (NSU) Dhaka Bangladesh. Telecommunications and Systems Engineering Department Universitat Autònoma de Barcelona (UAB) Sabadell Spain. Department of Applied Statistics and Operational Research and Quality Alcoy Universitat Politècnica de València (UPV) Alcoy Spain.

Non-synonymous single nucleotide polymorphisms (nsSNPs), also known as missense SNPs, can seriously affect an individual’s vulnerability to numerous diseases, including cancer. In this study, we conducted a comprehensive in-silico analysis to examine the structural and functional implications of nsSNPs within the Folate Hydrolase 1(FOLH1) gene, which encodes the Prostate-Specific Membrane Antigen (PSMA). A total of 504 SNPs were retrieved, and after filtering, 15 pathogenic nsSNPs were identified using five different in-silico tools. Three of these SNPs—R255H (rs375565491), R255C (rs201789325), and G168E (rs267602926)—were consistently predicted to be pathogenic across all in-silico tools. MutPred2 was used to predict the structural and functional consequences of the identified mutations. The analysis revealed multiple alterations in the PSMA protein, including changes in helical conformations, glycosylation patterns, transmembrane properties, and solvent accessibility. Furthermore, I-Mutant 2.0 analysis demonstrated a decrease in protein stability for most nsSNPs, except for rs267602926 (G168E), which was predicted to increase stability. Conservation analysis using ConSurf revealed varying degrees of amino acid conservation, with R255H and R255C identified as highly conserved residues, indicating their potential functional and structural significance. Additionally, post-translational modification (PTM) analysis indicated that while phosphorylation and methylation sites remained unchanged, specific glycosylation sites were lost in two pathogenic mutant variants (R255H and R255C), potentially affecting PSMA function and adversely impacting prostate cancer. Our findings highlight the importance of in silico studies to investigate the structural and functional impacts of FOLH1 nsSNPs on the PSMA protein. Such in silico studies can deepen our understanding of the roles of nsSNPs in prostate cancer onset, progression, and drug resistance.

关键词： FOLH1 In-silico structural and functional analyses PSMA PTM nsSNPs

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Supernova model discrimination with Hyper-Kamiokande

arXiv

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arXiv 2021年

作者： Abe, K. Adrich, P. Aihara, H. Akutsu, R. Alekseev, I. Ali, A. Ameli, F. Anghel, I. Anthony, L.H.V. Antonova, M. Araya, A. Asaoka, Y. Ashida, Y. Aushev, V. Ballester, F. Bandac, I. Barbi, M. Barker, G.J. Barr, G. Batkiewicz-Kwasniak, M. Bellato, M. Berardi, V. Bergevin, M. Bernard, L. Bernardini, E. Berns, L. Bhadra, S. Bian, J. Blanchet, A. Blaszczyk, F.D.M. Blondel, A. Boiano, A. Bolognesi, S. Bonavera, L. Booth, N. Borjabad, S. Boschi, T. Bose, D. Boyd, S.B. Bozza, C. Bravar, A. Bravo-Berguño, D. Bronner, C. Brown, L. Bubak, A. Buchowicz, A. Avanzini, M. Buizza Cafagna, F.S. Calabria, N.F. Calvo-Mozota, J.M. Cao, S. Cartwright, S.L. Carroll, A. Catanesi, M.G. Cebriàn, S. Chabera, M. Chakraborty, S. Checchia, C. Choi, J.H. Choubey, S. Cicerchia, M. Coleman, J. Collazuol, G. Cook, L. Cowan, G. Cuen-Rochin, S. Danilov, M. Lopez, G. Daz de la Fuente, E. de Perio, P. de Rosa, G. Dealtry, T. Densham, C.J. Dergacheva, A. Deshmukh, N. Devi, M.M. Di Lodovico, F. Di Meo, P. Di Palma, I. Doyle, T.A. Drakopoulou, E. Drapier, O. Dumarchez, J. Dunne, P. Dziewiecki, M. Eklund, L. El Hedri, S. Ellis, J. Emery, S. Esmaili, A. Esteve, R. Evangelisti, A. Feely, M. Fedotov, S. Feng, J. Fernandez, P. Fernández-Martinez, E. Ferrario, P. Ferrazzi, B. Feusels, T. Finch, A. Finley, C. Fiorentini, A. Fiorillo, G. Fitton, M. Frankiewicz, K. Friend, M. Fujii, Y. Fukuda, Y. Galinski, G. Gao, J. Garde, C. Garfagnini, A. Garode, S. Gialanella, L. Giganti, C. Gomez-Cadenas, J.J. Gonin, M. González-Nuevo, J. Gorin, A. Gornea, R. Gousy-Leblanc, V. Gramegna, F. Grassi, M. Grella, G. Guigue, M. Gumplinger, P. Hadley, D.R. Harada, M. Hartfiel, B. Hartz, M. Hassani, S. Hastings, N.C. Hayato, Y. Hernando-Morata, J.A. Herrero, V. Hill, J. Hiraide, K. Hirota, S. Holin, A. Horiuchi, S. Hoshina, K. Hultqvist, K. Iacob, F. Ichikawa, A.K. Ibnsalih, W. Idrissi Iijima, T. Ikeda, M. Inomoto, M. Inoue, K. Insler, J. Ioannisian, A. Ishida, T. Ishidoshiro, K. AGH University of Science and Technology Faculty of Computer Science Electronics and Telecommunications Krakow Poland Boston University Department of Physics BostonMA United States University of British Columbia Department of Physics and Astronomy VancouverBC Canada British Columbia Institute of Technology Physics Department BurnabyBC Canada Università della Campania L. Vanvitelli INFN Sezione di Napoli Napoli Italy Carleton University Department of Physics OttawaON Canada University of California Davis Department of Physics DavisCA United States University of California Irvine Department of Physics and Astronomy IrvineCA United States California State University Department of Physics CarsonCA United States Charles University IPNP FMF Prague Czech Republic Chonnam National University Department of Physics Gwangju Korea Republic of Dongshin University Laboratory for High Energy Physics Naju Korea Republic of Donostia International Physics Center Ikerbasque Foundation Basque Country Spain Ecole Polytechnique IN2P3-CNRS Laboratoire Leprince-Ringuet Palaiseau France University of Edinburgh School of Physics and Astronomy Edinburgh United Kingdom ETH Zurich Institute for Particle and Astroparticle Physics Zurich Switzerland University of Geneva Section de Physique DPNC Geneva Switzerland GIST College Gwangju Institute of Science and Technology Gwangju Korea Republic of University of Glasgow School of Physics and Astronomy Glasgow United Kingdom Universidad de Guadalajara CUCEI Departamento de Fisica Jal Guadalajara Mexico Universidad de Guadalajara CUCEA IT.Ph.D. program Jal Guadalajara Mexico H. Niewodniczá nski Institute of Nuclear Physics PAN Cracow Poland University of Hawaii Department of Physics and Astronomy HonoluluHI United States Imperial College London Department of Physics London United Kingdom Indian Institute of Technology Guwahati Guwahati India Indian Institute of Technology Jodhpur Department of Physics Raj

Core-collapse supernovae are among the most magnificent events in the observable universe. They produce many of the chemical elements necessary for life to exist and their remnants-neutron stars and black holes-are interesting astrophysical objects in their own right. However, despite millennia of observations and almost a century of astrophysical study, the explosion mechanism of core-collapse supernovae is not yet well understood. Hyper-Kamiokande is a next-generation neutrino detector that will be able to observe the neutrino flux from the next galactic core-collapse supernova in unprecedented detail. We focus on the first 500 ms of the neutrino burst, corresponding to the accretion phase, and use a newly-developed, high-precision supernova event generator to simulate Hyper-Kamiokande's response to five different supernova models. We show that Hyper-Kamiokande will be able to distinguish between these models with high accuracy for a supernova at a distance of up to 100 kpc. Once the next galactic supernova happens, this ability will be a powerful tool for guiding simulations towards a precise reproduction of the explosion mechanism observed in nature. Copyright © 2021, The Authors. All rights reserved.

关键词： Supernovae

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The Hyper-Kamiokande experiment - snowmass LOI

arXiv

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arXiv 2020年

作者： Abe, K. Adrich, P. Aihara, H. Akutsu, R. Alekseev, I. Ali, A. Ameli, F. Anthony, L.H.V. Araya, A. Asaoka, Y. Aushev, V. Bandac, I. Barbi, M. Barr, G. Batkiewicz-Kwasniak, M. Bellato, M. Berardi, V. Bernard, L. Bernardini, E. Berns, L. Bhadra, S. Bian, J. Blanchet, A. Blondel, A. Boiano, A. Bolognesi, S. Bonavera, L. Borjabad, S. Boschi, T. Bose, D. Boyd, S.B. Bozza, C. Bravar, A. Bronner, C. Bubak, A. Buchowicz, A. Avanzini, M. Buizza Cafagna, F.S. Calabria, N.F. Calvo-Mozota, J.M. Cao, S. Catanesi, M.G. Chakraborty, S. Choi, J.H. Choubey, S. Cicerchia, M. Coleman, J. Collazuol, G. Cuen-Rochin, S. Danilov, M. Fuente, E. De la de Perio, P. de Rosa, G. Dealtry, T. Densham, C.J. Dergacheva, A. Deshmukh, N. Devi, M.M. Di Lodovico, F. Di Meo, P. Di Palma, I. Doyle, T.A. Drakopoulou, E. Drapier, O. Dumarchez, J. Eklund, L. El Hedri, S. Ellis, J. Emery, S. Esmaili, A. Fedotov, S. Feng, J. Fernández-Martinez, E. Ferrario, P. Ferrazzi, B. Finch, A. Finley, C. Fiorillo, G. Fitton, M. Friend, M. Fujii, Y. Fukuda, Y. Galinski, G. Gao, J. Garde, C. Garfagnini, A. Garode, S. Gialanella, L. Giganti, C. Gomez-Cadenas, J.J. Gonin, M. González-Nuevo, J. Gorin, A. Gornea, R. Gramegna, F. Grassi, M. Grella, G. Guigue, M. Hadley, D.R. Harada, M. Hartz, M. Hassani, S. Hastings, N.C. Hayato, Y. Hiraide, K. Hoshina, K. Hultqvist, K. Iacob, F. Ichikawa, A.K. Ibnsalih, W. Idrissi Ikeda, M. Inomoto, M. Ioannisian, A. Ishida, T. Ishidoshiro, K. Ishino, H. Ishitsuka, M. Ito, H. Ito, S. Itow, Y. Iwamoto, K. Izumi, N. Izumiyama, S. Jakkapu, M. Jamieson, B. Jang, J.S. Jo, H.S. Jonsson, P. Joo, K.K. Kajita, T. Kakuno, H. Kameda, J. Kano, Y. Karlen, D. Kataoka, Y. Kato, A. Katori, T. Kazarian, N. Khabibullin, M. Khotjantsev, A. Kikawa, T. Kim, J.Y. Kim, S.B. King, S. Kinoshita, T. Kisiel, J. Klekotko, A. Kobayashi, T. Koerich, L. Kolev, N. Konaka, A. Kormos, L.L. Koshio, Y. Kotsar, Y. Kouzakov, K.A. Kowalik, K.L. AGH University of Science and Technology Faculty of Computer Science Electronics and Telecommunications Krakow Poland British Columbia Institute of Technology Physics Department BurnabyBC Canada Università della Campania"L. Vanvitelli" INFN Sezione di Napoli Napoli Italy Carleton University Department of Physics OttawaON Canada University of California Irvine Department of Physics and Astronomy IrvineCA United States Charles University IPNP FMF Prague Czech Republic Chonnam National University Department of Physics Gwangju Korea Republic of Dongshin University Laboratory for High Energy Physics Naju Korea Republic of Donostia International Physics Center Ikerbasque Foundation Basque Country Spain Ecole Polytechnique IN2P3-CNRS Laboratoire Leprince-Ringuet Palaiseau France University of Edinburgh School of Physics and Astronomy Edinburgh United Kingdom ETH Zurich Institute for Particle and Astroparticle Physics Zurich Switzerland University of Geneva Section de Physique DPNC Geneva Switzerland GIST College Gwangju Institute of Science and Technology Gwangju Korea Republic of University of Glasgow School of Physics and Astronomy Glasgow United Kingdom Universidad de Guadalajara CUCEA IT.Ph.D. program GuadalajaraJal Mexico Universidad de Guadalajara CUCEI Departamento de Fisica GuadalajaraJal Mexico H. Niewodniczá nski Institute of Nuclear Physics PAN Cracow Poland Imperial College London Department of Physics London United Kingdom Indian Institute of Technology Guwahati Guwahati India Indian Institute of Technology Jodhpur Department of Physics Karwar Rajasthan India Indian Institute of Technology Kharagpur Department of Physics Kharagpur India INFN Gruppo Collegato di Salerno Fisciano Italy INFN Laboratori Nazionali di Legnaro LegnaroPD Italy INFN Sezione di Bari Università e Politecnico di Bari Bari Italy INFN Sezione di Napoli Università Federico II di Napoli Dipartimento di Fisica Napoli Italy INFN Sezione di Napoli Nap

Hyper-Kamiokande is the next generation underground water Cherenkov detector that builds on the highly successful Super-Kamiokande experiment. The detector which has an 8.4 times larger effective volume than its predecessor will be located along the T2K neutrino beamline and utilize an upgraded J-PARC beam with 2.6 times beam power. Hyper-K’s low energy threshold combined with the very large fiducial volume make the detector unique, that is expected to acquire an unprecedented exposure of 3.8 Mton·year over a period of 20 years of operation. Hyper-Kamiokande combines an extremely diverse science program including nucleon decays, long-baseline neutrino oscillations, atmospheric neutrinos, and neutrinos from astrophysical origins. The scientific scope of this program is highly complementary to liquid-argon detectors for example in sensitivity to nucleon decay channels or supernova detection modes. Hyper-Kamiokande construction has started in early 2020 and the experiment is expected to start operations in 2027. The Hyper-Kamiokande collaboration is presently being formed amongst groups from 19 countries including the United States, whose community has a long history of making significant contributions to the neutrino physics program in Japan. US physicists have played leading roles in the Kamiokande, Super-Kamiokande, EGADS, K2K, and T2K programs. Copyright © 2020, The Authors. All rights reserved.

关键词： Atomic physics

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A reconfigurable ray-tracing Multi-Processor SoC with hardware replication-aware instruction set extension

A reconfigurable ray-tracing Multi-Processor SoC with hardwa...

引用

13th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2013

作者： Nery, Alexandre S. Nedjah, Nadia França, Felipe M.G. Jozwiak, Lech Corporaal, Henk LAM - Computer Architecture and Microeletronics Laboratory Systems Engineering and Computer Science Program COPPE Federal University of Rio de Janeiro Brazil Department of Electronics Engineering and Telecommunications Faculty of Engineering State University of Rio de Janeiro Brazil Department of Electrical Engineering - Electronic Systems Eindhoven University of Technology Netherlands

ISBN: (纸本)9783319038582

Application code and processor parallelization, together with instruction set customization, are the most common and effective ways to enhance the performance and efficiency of application-specific processors (ASIPs). Both the effective code parallelization and data/task parallelism exploitation, as well as effective instruction set customization, enable an ASIP to achieve a significant performance improvement using limited extra hardware resources. However, a naive parallelization or instruction set customization may not result in the required performance improvement, leading to a waste of computing and energy resources. Therefore, when performing parallelization or custom instruction selection, complex tradeoffs between processing speed, circuit area and power consumption must be closely observed. In this paper, we propose and discuss an efficient ASIP-based Multi-Processor System-on-a-Chip (MPSoC) design for ray-tracing, exploiting application parallelism and hardware replication-aware instruction set customization. Without hardware sharing among the custom instructions units, the proposed parallel ray-tracer MPSoC design with custom instructions achieves 77% speed up in comparison to a single microprocessor design with the default instruction set. However, with the replication-aware instruction set customization, the speed up increases to 81%. © Springer International Publishing Switzerland 2013.

关键词： Integrated circuit design

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An efficient parallel architecture for ray-tracing

An efficient parallel architecture for ray-tracing

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作者： Nery, Alexandre S. Nedjah, Nadia França, Felipe M.G. LAM - Computer Architecture and Microeletronics Laboratory Systems Engineering and Computer Science Program COPPE Universidade Federal Do Rio de Janeiro Rio de Janeiro RJ Brazil Department of Electronics Engineering and Telecommunications Faculty of Engineering Universidade Do Estado Do Rio de Janeiro Rio de Janeiro RJ Brazil

Real time rendering of three-dimensional scenes in high photorealistic details is a hard task, such as in the ray tracing rendering algorithm. In general, the performance achieved by a sequential software-based implementation of ray tracing is far from satisfactory. However, parallel implementations of ray tracing have been enabling reasonable real time performance, as the algorithm is embarrassingly parallel. Thus, a custom parallel design in hardware is likely to achieve an even higher performance. In this paper, we propose a hardware parallel architecture capable of dealing with the main desirable features of ray tracing, such as shadows and reflection effects, imposing low area cost and a promising rendering performance. Such architecture, called GridRT, is based on the Uniform Grid acceleration structure and is intended to deliver massive parallelism through parallel ray-triangle intersection tests as well as parallel processing of many rays. A hardware implementation of the proposed architecture is presented, together with some performance results and resources requirements. The rendering is reduced by 80% using a grid configuration of eight processing elements. © Springer science+Business Media, LLC 2011.

关键词： Field programmable gate arrays (FPGA)

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A parallel Ray Tracing architecture suitable for application-specific hardware and GPGPU implementations

A parallel Ray Tracing architecture suitable for application...

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2011 14th Euromicro Conference on Digital System Design: Architectures, Methods and Tools, DSD 2011

作者： Nery, Alexandre S. Nedjah, Nadia Franca, Felipe M.G. Jozwiak, Lech LAM - Computer Architecture and Microelectronics Laboratory Systems Engineering and Computer Science Program COPPE Universidade Federal Rio de Janeiro Brazil Department of Electronics Engineering and Telecommunications Faculty of Engineering Universidade do Estado Rio de Janeiro Brazil Department of Electrical Engineering - Electronic Systems Eindhoven University of Technology Netherlands

ISBN: (纸本)9780769544946

The Ray Tracing rendering algorithm can produce high-fidelity images of 3-D scenes, including shadow effects, as well as reflections and transparencies. This is currently done at a processing speed of at most 30 frames per second. Therefore, actual implementations of the algorithm are not yet suitable for interactive real-time rendering, which is required in games and virtual reality based applications. Fortunately, the algorithm allows for massive parallelization of its computations. In this paper, we present a parallel architecture for ray tracing based on a uniform spatial subdivision of the scene and exploiting an embedded computation of ray-triangle intersections. This approach allows for a significant acceleration of intersection computations, as well as, a reduction of the total number of the required intersections checks. Furthermore, it allows for these checks to be performed in parallel and in advance for each ray. In this paper we discuss and analyze an ASIP-based implementation using FPGAs and a GPGPU-based parallel implementation of the proposed architecture. The performance of both implementations are reported and compared." © 2011 IEEE.

关键词： Ray tracing

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