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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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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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DeepWarp: DNN-based nonlinear deformation

arXiv

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arXiv 2018年

作者： Luo, Ran Shao, Tianjia Wang, Huamin Xu, Weiwei Zhou, Kun Yang, Yin Department of Electrical and Computer Engineering University of New Mexico NM87131 United States The School of Computing University of Leeds LS29JT United Kingdom Department of Computer Science and Engineering Ohio State University OH43210 United States State Key Lab of CAD&CG Zhejiang University China

DeepWarp is an efficient and highly re-usable deep neural network (DNN) based nonlinear deformable simulation framework. Unlike other deep learning applications such as image recognition, where different inputs have a uniform and consistent format (e.g. an array of all the pixels in an image), the input for deformable simulation is quite variable, high-dimensional, and parametrization-unfriendly. Consequently, even though DNN is known for its rich expressivity of nonlinear functions, directly using DNN to reconstruct the force-displacement relation for general deformable simulation is nearly impossible. DeepWarp obviates this difficulty by partially restoring the force-displacement relation via warping the nodal displacement simulated using a simplistic constitutive model – the linear elasticity. In other words, DeepWarp yields an incremental displacement fix based on a simplified (therefore incorrect) simulation result other than returning the unknown displacement directly. We contrive a compact yet effective feature vector including geodesic, potential and digression to sort training pairs of per-node linear and nonlinear displacement. DeepWarp is robust under different model shapes and tessellations. With the assistance of deformation substructuring, one DNN training is able to handle a wide range of 3D models of various geometries including most examples shown in the paper. Thanks to the linear elasticity and its constant system matrix, the underlying simulator only needs to perform one pre-factorized matrix solve at each time step, and DeepWarp is able to simulate large models in real time. Copyright © 2018, The Authors. All rights reserved.

关键词： Elasticity

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OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model 11

OMAM at SemEval-2017 Task 4: Evaluation of English State-of-...

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11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

作者： Baly, Ramy Badaro, Gilbert Hamdi, Ali Moukalled, Rawan Aoun, Rita El-Khoury, Georges El-Sallab, Ahmad Hajj, Hazem Habash, Nizar Shaban, Khaled Bashir El-Hajj, Wassim Department of Electrical and Computer Engineering American University of Beirut Lebanon Faculty of Computing Universiti Teknologi Malaysia Malaysia Computer Engineering Department Cairo University Egypt Computational Approaches to Modeling Language Lab New York University Abu Dhabi United Arab Emirates Computer Science and Engineering Department Qatar University Qatar Department of Computer Science American University of Beirut Lebanon

ISBN: (纸本)9781945626555

While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to Twitter data that comes with additional sources of noise including dialects, misspellings, grammatical mistakes, code switching and the use of non-textual objects to express sentiments. This paper describes the "OMAM" systems that we developed as part of SemEval-2017 task 4. We evaluate English state-of-the-art methods on Arabic tweets for subtask A. As for the remaining subtasks, we introduce a topic-based approach that accounts for topic specificities by predicting topics or domains of upcoming tweets, and then using this information to predict their sentiment. Results indicate that applying the English state-of-the-art method to Arabic has achieved solid results without significant enhancements. Furthermore, the topic-based method ranked 1st in subtasks C and E, and 2nd in subtask D. © 2017 Association for Computational Linguistics

关键词： Sentiment analysis

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In-Silico Ear Model Based on Episcopic Images for Percutaneous Auricular Vagus Nerve Stimulation

In-Silico Ear Model Based on Episcopic Images for Percutaneo...

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EMF-Med World Conference on Biomedical Applications of Electromagnetic Fields

作者： Babak Dabiri Razlighi Stefan Kampusch Stefan H. Geyer Van Hoang Le Florian Thurk Simon Brenner Jozsef Constantin Szeles Wolfgang J. Weninger Eugenijus Kaniusas Institute of Electrodynamics Microwave and Circuit Engineering Vienna University of Technology Division of Anatomy MIC Medical University of Vienna Institute of Visual Computing & Human-Centered Technology Computer Vision Lab Vienna University of Technology Department for Vascular Surgery University Clinic for Surgery Medical University of Vienna

ISBN: (纸本)9781538659229

Auricular branch of vagus nerve stimulation is continuously evolving as a minimal-invasive treatment option in patients suffering from chronic pain. For optimization of stimulation mechanisms, we conducted insilico modelling of the electrode-tissue-nerve interface by a thorough literature review of the anatomical structures combined with nerve and vessel structures from episcopic images of a human ear. Four nerves and two vessels in the ear were implemented in a 3D CAD model for future optimization of stimulation regions and patterns. Mean diameters for nerves were 30 μm, and for vessels 100 μm. Mean distance between nerves and vessels was about 100 μm.

关键词： Auricular Vagus Nerve Stimulation In-Silico Modelling Episcopic Imaging

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Balson: Bayesian least squares optimization with nonnegative L1-norm constraint

arXiv

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arXiv 2018年

作者： Xie, Jiyang Ma, Zhanyu Zhang, Guoqiang Xue, Jing-Hao Chien, Jen-Tzung Lin, Zhiqing Guo, Jun Pattern Recognition and Intelligent Systems Lab Beijing University of Posts and Telecommunications China School of Computing and Communications University of Technology Sydney Australia Department of Statistical Science University College London United Kingdom Department of Electrical and Computer Engineering National Chiao Tung University Taiwan

A Bayesian approach termed BAyesian Least Squares Optimization with Nonnegative L1-norm constraint (BALSON) is proposed. The error distribution of data fitting is described by Gaussian likelihood. The parameter distribution is assumed to be a Dirichlet distribution. With the Bayes rule, searching for the optimal parameters is equivalent to finding the mode of the posterior distribution. In order to explicitly characterize the nonnegative L1-norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution. We estimate the statistics of the approximating Dirichlet posterior distribution by sampling methods. Four sampling methods have been introduced. With the estimated posterior distributions, the original parameters can be effectively reconstructed in polynomial fitting problems, and the BALSON framework is found to perform better than conventional methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Bayesian networks

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Greatly extended depth of field for microscopic imaging

Greatly extended depth of field for microscopic imaging

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Frontiers in Optics, FiO 2017

作者： Li, Zengzhuo Li, Guoqiang Visual and Biomedical Optics Lab The Ohio State University ColumbusOH43212 United States Department of Ophthalmology and Visual Science The Ohio State University ColumbusOH43212 United States Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43212 United States

Based on wavefront-coding technique, a custom transmissive phase plate can extend the depth of field (DoF) of a microscope by more than 13 times with diffraction-limited resolution in comparison with the conventional ... 详细信息

ISBN: (纸本)9781943580330

关键词： Extended depth of field Liquid crystal modulators Modulation transfer function Phase plates Point spread function Retina

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BALSON: BAYESIAN LEAST SQUARES OPTIMIZATION WITH NONNEGATIVE L1-NORM CONSTRAINT

BALSON: BAYESIAN LEAST SQUARES OPTIMIZATION WITH NONNEGATIVE...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Jiyang Xie Zhanyu Ma Guoqiang Zhang Jing-Hao Xue Jen-Tzung Chien Zhiqing Lin Jun Guo Pattern Recognition and Intelligent Systems Lab. Beijing University of Posts and Telecommunications China School of Computing and Communications University of Technology Sydney Australia Department of Statistical Science University College London United Kingdom Department of Electrical and Computer Engineering National Chiao Tung University Taiwan

A Bayesian approach termed the BAyesian Least Squares Optimization with Nonnegative L 1 -norm constraint (BALSON) is proposed. The error distribution of data fitting is described by Gaussian likelihood. The parameter distribution is assumed to be a Dirichlet distribution. With the Bayes rule, searching for the optimal parameters is equivalent to finding the mode of the posterior distribution. In order to explicitly characterize the nonnegative L 1 -norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution. We estimate the moments of the approximated Dirichlet posterior distribution by sampling methods. Four sampling methods have been introduced and implemented. With the estimated posterior distributions, the original parameters can be effectively reconstructed in polynomial fitting problems, and the BALSON framework is found to perform better than conventional methods.

关键词： Bayes methods Optimization Monte Carlo methods Sampling methods Linear programming Laplace equations Data models

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Generation and detection of coherent longitudinal acoustic waves in ultrathin 1T'-MoTe2

arXiv

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arXiv 2019年

作者： Rivas, Nicolas Zhong, Shazhou Dekker, Tina Cheng, Meixin Gicala, Patrick Chen, Fangchu Luo, Xuan Sun, Yuping Petruk, Ariel A. Pichugin, Kostyantyn Tsen, Adam W. Sciaini, Germán Ultrafast Electron Imaging Lab Department of Chemistry Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L3G1 Canada Institute for Quantum Computing Department of Physics and Astronomy Department of Electrical and Computer Engineering Department of Chemistry University of Waterloo WaterlooONN2L3G1 Canada Key Laboratory of Materials Physics Institute of Solid-State Physics Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China High Magnetic Field Laboratory Chinese Academy of Sciences Hefei230031 Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China

Layered transition metal dichalcogenides have attracted substantial attention owing to their versatile functionalities and compatibility with current nanofabrication technologies. Thus, noninvasive means to determine the mechanical properties of nanometer (nm) thick specimens are of increasing importance. Here, we report on the detection of coherent longitudinal acoustic phonon modes generated by impulsive femtosecond (fs) optical excitation. Broadband fs-transient absorption experiments in 1T-f-MoTe2 flakes as a function of thickness (7 nm . 30 nm) yield a longitudinal sound speed of vl = (2990± 90) m s-1. In addition, temperature dependent measurements unveil a linear decrease of the normalized Young-fs modulus El/El,295Kwith a slope of δ(***,%' ))/δT = (.2.0± 0.1) 10-3K-1and no noticeable change caused by the Td-1T-f structural phase transition or variations in film thickness. Copyright © 2019, The Authors. All rights reserved.

关键词： Elastic moduli

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Memory attention networks for skeleton-based action recognition

arXiv

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arXiv 2018年

作者： Xie, Chunyu Li, Ce Zhang, Baochang Chen, Chen Han, Jungong Zou, Changqing Liu, Jianzhuang School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Computer Science and Technology China University of Mining & Technology Beijing China Department of Electrical and Computer Engineering University of North Carolina at Charlotte School of Computing & Communications Lancaster University LA1 4YW United Kingdom University of Maryland Institute for Advanced Computer Studies College ParkMD20742 United States Noah's Ark Lab Huawei Technologies Co. Ltd. China

Skeleton-based action recognition task is entangled with complex spatio-temporal variations of skeleton joints, and remains challenging for Recurrent Neural Networks (RNNs). In this work, we propose a temporal-then-spatial recalibration scheme to alleviate such complex variations, resulting in an end-to-end Memory Attention Networks (MANs) which consist of a Temporal Attention Recalibration Module (TARM) and a Spatio-Temporal Convolution Module (STCM). Specifically, the TARM is deployed in a residual learning module that employs a novel attention learning network to recalibrate the temporal attention of frames in a skeleton sequence. The STCM treats the attention calibrated skeleton joint sequences as images and leverages the Convolution Neural Networks (CNNs) to further model the spatial and temporal information of skeleton data. These two modules (TARM and STCM) seamlessly form a single network architecture that can be trained in an end-to-end fashion. MANs significantly boost the performance of skeleton-based action recognition and achieve the best results on four challenging benchmark datasets: NTU RGB+D, HDM05, SYSU-3D and UT-Kinect. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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