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Soft computing 2024年第SUPPL 1期28卷 99页

作者： Alexandra A de Souza Danilo Candido de Almeida Thiago S Barcelos Rodrigo Campos Bortoletto Roberto Munoz Helio Waldman Miguel Angelo Goes Leandro A Silva Laboratory of Applied Computing - LABCOM3 Federal Institute of Education Science and Technology of São Paulo São Paulo Brazil. Nephrology Division - Department of Medicine Federal University of São Paulo São Paulo Brazil. Escuela de Ingeniería Informática Universidad de Valparaíso Valparaíso Chile. Department of Communications FEEC Unicamp Campinas SP Brazil. Laboratory of Big Data and Applied Analytical Methods - Big MAAp Mackenzie Presbiterian University São Paulo Brazil.

[This retracts the article DOI: 10.1007/s00500-021-05810-5.].

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Multi‑lingual character handwriting framework based on an integrated deep learning based sequence‑to‑sequence attention model

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TechRxiv

TechRxiv 2021年

作者： Rabhi, Besma Elbaati, Abdelkarim Boubaker, Houcine Hamdi, Yahia Hussain, Amir Alimi, Adel M. BP 1173 Sfax3038 Tunisia Higher Institute of Applied Sciences and Technology of Mahdia REGIM-Laboratory: Research Groups in Intelligent Machines LR11ES48 University of Monastir Mahdia5121 Tunisia School of Computing Edinburgh Napier University Edinburgh United Kingdom Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multilingual handwriting. Our framework is based on an integrated sequence-to-sequence attention model. The proposed system involves extracting a hidden representation from an image using a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BGRU), and decoding the encoded vectors to generate dynamic information using a BGRU with temporal attention. We validate our framework using an online recognition system applied to a benchmark Latin, Arabic and Indian On/Off dual-handwriting character database. The performance of the proposed multi-lingual system is demonstrated through a low error rate of point coordinates and high accuracy system rate. © 2021, CC BY.

关键词： Convolutional neural networks

Resonant Soft X-Ray Scattering on LaPt2Si2

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arXiv 2022年

作者： Mukkattukavil, Deepak John Hellsvik, Johan Ghosh, Anirudha Chatzigeorgiou, Evanthia Nocerino, Elisabetta Wang, Qisi von Arx, Karin Huang, Shih-Wen Ekholm, Victor Hossain, Zakir Thamizhavel, Arumugum Chang, Johan Månsson, Martin Nordström, Lars Såthe, Conny Agåker, Marcus Rubensson, Jan-Erik Sassa, Yasmine Department of Physics and Astronomy Uppsala University Box 516 UppsalaSE-751 20 Sweden PDC Center for High Performance Computing KTH Royal Institute of Technology StockholmSE-106 91 Sweden Nordita KTH Royal Institute of Technology Stockholm University Hannes Alfvéns väg 12 StockholmSE-106 91 Sweden Department of Applied Physics KTH Royal Institute of Technology StockholmSE-106 91 Sweden Physik-Institut Universität Zürich Winterthurerstrasse 190 ZürichCH-8057 Switzerland Chalmers University of Technology GöteborgSE-412 96 Sweden MAX IV Laboratory Lund University LundSE-221 00 Sweden Department of Physics Indian Institute of Technology Kanpur208016 India DCMPMS Tata Institute of Fundamental Research Mumbai400005 India Department of Physics Chalmers University of Technology GöteborgSE-412 96 Sweden

X-ray absorption (XAS) and Resonant Inelastic X-ray Scattering (RIXS) spectra of LaPt2Si2 single crystal at the Si L and La N edges are presented. The data are interpreted in terms of density functional theory, showing that the Si spectra can be described in terms of Si s and d local partial density of states (LPDOS), and the La spectra are due to quasi-atomic local 4f excitations. Calculations show that Pt d-LPDOS dominates the occupied states, and a sharp localized La f state is found in the unoccupied states, in line with the observations. Copyright © 2022, The Authors. All rights reserved.

关键词： Single crystals

Solving inverse stochastic problems from discrete particle observations using the fokker-planck equation and physics-informed neural networks

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arXiv 2020年

作者： Chen, Xiaoli Yang, Liu Duan, Jinqiao Karniadakis, George Em Center for Mathematical Sciences Huazhong University of Science and Technology Wuhan430074 China Division of Applied Mathematics Brown University ProvidenceRI02912 United States Department of Applied Mathematics College of Computing Illinois Institute of Technology ChicagoIL60616 United States Pacific Northwest National Laboratory RichlandWA99354 United States

The Fokker-Planck (FP) equation governing the evolution of the probability density function (PDF) is applicable to many disciplines but it requires specification of the coefficients for each case, which can be functions of space-time and not just constants, hence requiring the development of a data-driven modeling approach. When the data available is directly on the PDF, then there exist methods for inverse problems that can be employed to infer the coefficients and thus determine the FP equation and subsequently obtain its solution. Herein, we address a more realistic scenario, where only sparse data are given on the particles’ positions at a few time instants, which are not sufficient to accurately construct directly the PDF even at those times from existing methods, e.g., kernel estimation algorithms. To this end, we develop a general framework based on physics-informed neural networks (PINNs) that introduces a new loss function using the Kullback-Leibler divergence to connect the stochastic samples with the FP equation, to simultaneously learn the equation and infer the multi-dimensional PDF at all times. In particular, we consider two types of inverse problems, type I where the FP equation is known but the initial PDF is unknown, and type II in which, in addition to unknown initial PDF, the drift and diffusion terms are also unknown. In both cases, we investigate problems with either Brownian or Lévy noise or a combination of both. We demonstrate the new PINN framework in detail in the one-dimensional case (1D) but we also provide results for up to 5D demonstrating that we can infer both the FP equation and dynamics simultaneously at all times with high accuracy using only very few discrete observations of the particles. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

CONVERGENCE ANALYSIS FOR MINIMUM ACTION METHODS COUPLED WITH A FINITE DIFFERENCE METHOD

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arXiv 2021年

作者： Hong, Jialin Jin, Diancong Sheng, Derui Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

The minimum action method (MAM) is an effective approach to numerically solving minimums and minimizers of Freidlin–Wentzell (F-W) action functionals, which is used to study the most probable transition path and probability of the occurrence of transitions for stochastic differential equations (SDEs) with small noise. In this paper, we focus on MAMs based on a finite difference method, and present the convergence analysis of minimums and minimizers of the discrete F-W action functional. The main result shows that the convergence orders of the minimum of the discrete F-W action functional in the cases of multiplicative noises and additive noises are 1/2 and 1, respectively. Our main result also reveals the convergence of the stochastic θ-method for SDEs with small noise in terms of large deviations. Copyright © 2021, The Authors. All rights reserved.

关键词： Finite difference method

First-step experiment for sensitivity improvement of DECIGO: Sensitivity optimization for simulated quantum noise by completing the square

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Physical Review D 2023年第2期107卷 022007-022007页

作者： Tomohiro Ishikawa Yuki Kawasaki Kenji Tsuji Rika Yamada Izumi Watanabe Bin Wu Shoki Iwaguchi Ryuma Shimizu Kurumi Umemura Koji Nagano Yutaro Enomoto Kentaro Komori Yuta Michimura Akira Furusawa Seiji Kawamura Department of Physics Nagoya University Nagoya Aichi 464-8602 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Sagamihara Kanagawa 252-5210 Japan Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Physics University of Tokyo Bunkyo Tokyo 113-0033 Japan LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Research Center for the Early Universe (RESCEU) Graduate School of Science University of Tokyo Bunkyo Tokyo 113-0033 Japan Center for Quantum Computing RIKEN 2-1 Hirosawa Wako Saitama 351-0198 Japan The Kobayashi-Maskawa Institute for the Origin of Particles and the Universe Nagoya University Nagoya Aichi 464-8602 Japan

Decihertz Interferometer Gravitational Wave Observatory (DECIGO) is a future mission for a space-borne laser interferometer. DECIGO has 1000-km-long arm cavities mainly to detect the primordial gravitational waves (PGWs) at lower frequencies around 0.1 Hz. Observations in the electromagnetic spectrum have lowered the bounds on the upper limit of PGWs energy density (Ωgw∼10−15→10−16). As a result, DECIGO’s target sensitivity, which is mainly limited by quantum noise, needs further improvement. To maximize the feasibility of detection while constrained by DECIGO’s large diffraction loss, a quantum locking technique with an optical spring was theoretically proposed to improve the signal-to-noise ratio of the PGWs. In this paper, we experimentally verify one key element used in the theory: sensitivity optimization by completing the square of multiple detector outputs. This experiment is operated on a simplified tabletop optical setup with classical noise simulating quantum noise. We succeed in getting the best of the sensitivities with two different laser powers by the square completion method.

关键词： Gravitational wave detection Gravitational wave detectors

DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models

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arXiv 2021年

作者： Lu, Denghui Jiang, Wanrun Chen, Yixiao Zhang, Linfeng Jia, Weile Wang, Han Chen, Mohan HEDPS CAPT College of Engineering Peking University Beijing100871 China Songshan Lake Materials Laboratory Guangdong Dongguan523808 China Institute of Physics Chinese Academy of Sciences Beijing100190 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Beijing Institute of Big Data Research Beijing100871 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China

Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via deep neural networks to predict the energy and atomic forces, resulting in lower running efficiency as compared to the typical empirical force fields. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning based PES model. This scheme, we call DP Compress, is an efficient post-processing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DP-based molecular dynamics simulations by an order of magnitude faster, and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available online. Copyright © 2021, The Authors. All rights reserved.

关键词： Molecular dynamics

SYNAPSE: An international roadmap to large brain imaging

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Physics Reports 2023年第1期999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： Brain mapping Phase contrast Radiology Supercomputing Synchrotron X-rays

Hybrid Spin and Anomalous Spin-Momentum Locking in Surface Elastic Waves

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arXiv 2023年

作者： Yang, Chenwen Zhang, Danmei Zhao, Jinfeng Gao, Wenting Yuan, Weitao Long, Yang Pan, Yongdong Chen, Hong Nori, Franco Bliokh, Konstantin Y. Zhong, Zheng Ren, Jie Center for Phononics and Thermal Energy Science China-EU Joint Lab on Nanophononics Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China School of Aerospace Engineering and Applied Mechanics Tongji University Shanghai200092 China School of Mechanics and Aerospace Engineering Southwest Jiaotong University Sichuan Chengdu610031 China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research Riken Saitama Wako-shi351-0198 Japan Center for Quantum Computing Riken Saitama Wako-shi351-0198 Japan Physics Department University of Michigan Ann ArborMI48109-1040 United States Centre of Excellence ENSEMBLE3 Sp. Z O.o. Warsaw01-919 Poland Donostia-San Sebastián20018 Spain

Transverse spin of surface waves is a universal phenomenon which has recently attracted significant attention in optics and acoustics. It appears in gravity water waves, surface plasmon-polaritons, surface acoustic waves, and exhibits remarkable intrinsic spin-momentum locking, which has found useful applications for efficient spin-direction couplers. Here we demonstrate, both theoretically and experimentally, that the transverse spin of surface elastic (Rayleigh) waves has an anomalous sign near the surface, opposite to that in the case of electromagnetic, sound, or water surface waves. This anomalous sign appears due to the hybrid (neither transverse nor longitudinal) nature of elastic surface waves. Furthermore, we show that this sign anomaly can be employed for the selective spin-controlled excitation of symmetric and antisymmetric Lamb modes propagating in opposite directions in an elastic plate. Our results pave the way for spin-controlled manipulation of elastic waves and can be important for a variety of areas, from phononic spin-based devices to seismic waves. Copyright © 2023, The Authors. All rights reserved.

关键词： Surface waves

Observation of a giant Goos-Hänchen shift for matter waves

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arXiv 2024年

作者： McKay, S. de Haan, V.O. Leiner, J. Parnell, S.R. Dalgliesh, R.M. Boeni, P. Bannenberg, L.J. Le Thien, Q. Baxter, D.V. Ortiz, G. Pynn, R. Department of Physics Indiana University BloomingtonIN47405 United States Center for Exploration of Energy and Matter Indiana University Bloomington47408 United States BonPhysics Research and Investigations BV Laan van Heemstede 38 Puttershoek 3297AJ Netherlands Neutron Sciences Directorate Oak Ridge National Laboratory Oak RidgeTN37830 United States Faculty of Applied Sciences Delft University of Technology Mekelweg 15 Delft2629 JB Netherlands ISIS Rutherford Appleton Laboratory Oxfordshire ChiltonOX11 0QX United Kingdom Physics Department E21 Technical University of Munich GarchingD-85748 Germany SwissNeutronics AG Brühlstrasse 28 KlingnauCH-5313 Switzerland Quantum Science and Engineering Center Indiana University BloomingtonIN47408 United States Institute for Advanced Study PrincetonNJ08540 United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada

The Goos-Hänchen (GH) shift describes a phenomenon in which a specularly-reflected beam is laterally translated along the reflecting surface such that the incident and reflected rays no longer intersect at the surface. Using a neutron spin-echo technique and a specially-designed magnetic multilayer mirror, we have measured the relative phase between the reflected up and down neutron spin states in total reflection. The relative GH shift calculated from this phase shows a strong resonant enhancement at a particular incident neutron wavevector, which is due to a waveguiding effect in one of the magnetic layers. Calculations based on the observed phase difference between the neutron states indicate a propagation distance along the waveguide layer of 0.65 mm for the spin-down state, which we identify with the magnitude of the giant GH shift. The existence of a physical GH shift is confirmed by the observation of neutron absorption in the waveguide layer. We propose ways in which our experimental method may be exploited for neutron quantum-enhanced sensing of thin magnetic layers. Copyright © 2024, The Authors. All rights reserved.

关键词： Neutrons