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FAST ALGEBRAIC MULTIGRID FOR BLOCK-STRUCTURED DENSE AND TOEPLITZ-LIKE-PLUS-CROSS systems ARISING FROM NONLOCAL DIFFUSION PROBLEMS

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arXiv 2022年

作者： Chen, Minghua Cao, Rongjun Serra-Capizzano, Stefano School of Mathematics and Statistics Gansu Key Laboratory of Applied Mathematics and Complex Systems Lanzhou University Lanzhou730000 China Department of Science and High Technology University of Insubria Via Valleggio 11 Como22100 Italy Department of Information Technology Division of Scientific Computing Uppsala University - ITC L ägerhyddsv. 2 hus 2 P.O. Box 337 UppsalaSE-751 05 Sweden

Algebraic multigrid (AMG) is one of the most efficient iterative methods for solving large sparse system of equations. However, how to build/check restriction and prolongation operators in practical of AMG methods for nonsymmetric sparse systems is still an interesting open question [Brezina, Manteuffel, McCormick, Runge, and Sanders, SIAM J. Sci. Comput. (2010);Manteuffel and Southworth, SIAM J. Sci. Comput. (2019)]. This paper deals with the block-structured dense and Toeplitz-like-plus-Cross systems, including nonsymmetric indefinite, symmetric positive definite (SPD), arising from nonlocal diffusion problem and peridynamic problem. The simple (traditional) restriction operator and prolongation operator are employed in order to handle such block-structured dense and Toeplitz-like-plus-Cross systems, which is convenient and efficient when employing a fast AMG. We focus our efforts on providing the detailed proof of the convergence of the two-grid method for such SPD situations. The numerical experiments are performed in order to verify the convergence with a computational cost of only O(NlogN) arithmetic operations, by using few fast Fourier transforms, where N is the number of the grid points. To the best of our knowledge, this is the first contribution regarding Toeplitz-like-plus-Cross linear systems solved by means of a fast AMG. Copyright © 2022, The Authors. All rights reserved.

关键词： Fast Fourier transforms

ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification

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arXiv 2021年

作者： Wang, Jinping Tan, Xiaojun Lai, Jianhuang Li, Jun Xiang, Canqun School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing School of Computer Science and Engineering Sun Yatsen University Ministry of Education Guangzhou510006 China Zhuhai519082 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China

Previous studies have shown the great potential of capsule networks for the spatial contextual feature extraction from hyperspectral images (HSIs). However, the sampling locations of the convolutional kernels of capsules are fixed and cannot be adaptively changed according to the inconsistent semantic information of HSIs. Based on this observation, this paper proposes an adaptive spatial pattern capsule network (ASPCNet) architecture by developing an adaptive spatial pattern (ASP) unit, that can rotate the sampling location of convolutional kernels on the basis of an enlarged receptive field. Note that this unit can learn more discriminative representations of HSIs with fewer parameters. Specifically, two cascaded ASP-based convolution operations (ASPConvs) are applied to input images to learn relatively high-level semantic features, transmitting hierarchical structures among capsules more accurately than the use of the most fundamental features. Furthermore, the semantic features are fed into ASP-based conv-capsule operations (ASPCaps) to explore the shapes of objects among the capsules in an adaptive manner, further exploring the potential of capsule networks. Finally, the class labels of image patches centered on test samples can be determined according to the fully connected capsule layer. Experiments on three public datasets demonstrate that ASPCNet can yield competitive performance with higher accuracies than state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Spectroscopy

Observation of a single protein by ultrafast X-ray diffraction

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Light(Science & Applications) 2024年第1期13卷 80-90页

作者： Tomas Ekeberg Dameli Assalauova Johan Bielecki Rebecca Boll Benedikt J.Daurer Lutz A.Eichacker Linda E.Franken Davide E.Galli Luca Gelisio Lars Gumprecht Laura H.Gunn Janos Hajdu Robert Hartmann Dirk Hasse Alexandr Ignatenko Jayanath Koliyadu Olena Kulyk Ruslan Kurta Markus Kuster Wolfgang Lugmayr Jannik Lübke Adrian P.Mancuso Tommaso Mazza Carl Nettelblad Yevheniy Ovcharenko Daniel E.Rivas Max Rose Amit K.Samanta Philipp Schmidt Egor Sobolev Nicusor Timneanu Sergey Usenko Daniel Westphal Tamme Wollweber Lena Worbs Paul Lourdu Xavier Hazem Yousef Kartik Ayyer Henry N.Chapman Jonas A.Sellberg Carolin Seuring Ivan A.Vartanyants Jochen Küpper Michael Meyer Filipe R.N.C.Maia Laboratory of Molecular Biophysics Department of Cell and Molecular BiologyUppsala UniversityHusargatan 3(Box 596)SE-75124 UppsalaSweden Deutsches Electronen-Synchrotron DESY Notkestrasse 8522607 HamburgGermany European XFEL Holzkoppel 422869 SchenefeldGermany Diamond Light Source Harwell Science&Innovation CampusDidcot OX110DEUK University of Stavanger Centre Organelle ResearchRichard-Johnsensgate 44021 StavangerNorway Leibniz Institute for Experimental Virology(HPI) Centre for Structural Systems BiologyNotkestraße 8522607 HamburgGermany Dipartimento di Fisica“Aldo Pontremoli” Universitàdegli Studi di Milanovia Celoria 1620133 MilanoItaly Center for Free-Electron Laser Science DESY22607 HamburgGermany Plant Biology Section School of Integrative Plant ScienceCornell UniversityIthacaNY 14853USA PNSensor GmbH Otto-Hahn-Ring 6D-81739 MünchenGermany Biomedical and X-Ray Physics Department of Applied PhysicsAlbaNova University CenterKTH Royal Institute of TechnologySE-10691 StockholmSweden ELI Beamlines/IoP Institute of Physics AS CR v.v.i.Na Slovance 218221 Prague 8Czech Republic Multi-User CryoEM Facility Centre for Structural Systems BiologyNotkestr.8522607 HamburgGermany University Medical Center Hamburg-Eppendorf(UKE) Martinistrasse 5220246 HamburgGermany The Hamburg Center for Ultrafast Imaging Universität HamburgLuruper Chaussee 14922761 HamburgGermany Department of Physics Universität HamburgLuruper Chaussee 14922761 HamburgGermany Department of Chemistry and Physics La Trobe Institute for Molecular ScienceLa Trobe UniversityMelbourneVIC 3086Australia Division of Scientific Computing Science for Life LaboratoryDepartment of Information TechnologyUppsala UniversityBox 337SE-75105 UppsalaSweden European Molecular Biology Laboratory c/o DESYNotkestrasse 8522607 HamburgGermany Department of Physics and Astronomy Uppsala UniversityBox 516SE-75120 UppsalaSweden Max Planck Institute for the Structure and Dynamics of Matter Luruper Chaussee 14922761 HamburgGe

The idea of using ultrashort X-ray pulses to obtain images of single proteins frozen in time has fascinated and inspired *** was one of the arguments for building X-ray free-electron *** to theory,the extremely intense pulses provide sufficient signal to dispense with using crystals as an amplifier,and the ultrashort pulse duration permits capturing the diffraction data before the sample inevitably *** was first demonstrated on biological samples a decade ago on the giant *** then,a large collaboration has been pushing the limit of the smallest sample that can be *** ability to capture snapshots on the timescale of atomic vibrations,while keeping the sample at room temperature,may allow probing the entire conformational phase space of *** we show the first observation of an X-ray diffraction pattern from a single protein,that of Escherichia coli GroEL which at 14 nm in diameter is the smallest biological sample ever imaged by X-rays,and demonstrate that the concept of diffraction before destruction extends to single *** the pattern,it is possible to determine the approximate orientation of the *** experiment demonstrates the feasibility of ultrafast imaging of single proteins,opening the way to single-molecule time-resolved studies on the femtosecond timescale.

关键词： smallest intense ultrafast

A RNNs-based algorithm for decentralized-partial-consensus constrained optimization

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arXiv 2021年

作者： Xia, Zicong Liu, Yang Qiu, Jianlong Ruan, Qihua Cao, Jinde College of Mathematics and Information Science Zhejiang Normal University Jinhua321004 China School of Automation and Electrical Engineering Key Laboratory of Complex Systems Intelligent Computing in Universities of Shandong Linyi University Linyi276005 China School of Mathematics and Finance Putian University Putian351100 China Jiangsu Provincial Key Laboratory of Networked Collective Intelligence The School of Mathematics Southeast University Nanjing210096 China

This technical note proposes the decentralized-partial-consensus optimization with inequality constraints, and a continuous-time algorithm based on multiple interconnected recurrent neural networks (RNNs) is derived to solve the obtained optimization problems. First, the partial-consensus matrix originating from Laplacian matrix is constructed to tackle the partial-consensus constraints. In addition, using the nonsmooth analysis and Lyapunov-based technique, the convergence property about the designed algorithm is further guaranteed. Finally, the effectiveness of the obtained results is shown while several examples are presented. Copyright © 2021, The Authors. All rights reserved.

关键词： Matrix algebra

Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

Predicting extreme events from data using deep machine learning: When and where

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Physical Review Research 2022年第2期4卷 023028-023028页

作者： Junjie Jiang Zi-Gang Huang Celso Grebogi Ying-Cheng Lai The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi'an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Xi'an Shaanxi 710049 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University Tempe Arizona 85287 USA

We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.

关键词： Machine learning Spatiotemporal chaos Artificial neural networks Extreme event statistics

Discovery of terahertz-frequency orbitally-coupled magnons in a kagome ferromagnet

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arXiv 2024年

作者： Che, Mengqian Chen, Weizhao Wang, Maoyuan Bartram, F. Michael Liu, Liangyang Dong, Xuebin Liu, Jinjin Li, Yidian Lin, Hao Wang, Zhiwei Liu, Enke Yao, Yugui Yuan, Zhe Zhang, Guang-Ming Yang, Luyi State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Center for Advanced Quantum Studies Department of Physics Beijing Normal University Beijing100875 China Department of Physics Xiamen University Xiamen361005 China Beijing National Center for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Fudan University Shanghai200433 China Frontier Science Center for Quantum Information Beijing100084 China

In ferromagnetic materials, magnons - quanta of spin waves - typically resonate in the gigahertz range. Beyond conventional magnons, while theoretical studies have predicted magnons associated with orbital magnetic moments, their direct observation has remained challenging. Here, we present the discovery of two distinct terahertz orbitally-coupled magnon resonances in the topological kagome ferromagnet Co3Sn2S2. Using time-resolved Kerr rotation spectroscopy, we pinpoint two magnon resonances at 0.61 and 0.49 THz at 6 K, surpassing all previously reported magnon resonances in ferromagnets due to strong magnetocrystalline anisotropy. These dual modes originate from the strong coupling of localized spin and orbital magnetic moments. These findings unveil a novel category of magnons stemming from orbital magnetic moments, and position Co3Sn2S2 as a promising candidate for high-speed terahertz spintronic applications. © 2024, CC BY.

关键词： Magnetocrystalline anisotropy

A RIS-Based Vehicle DOA Estimation Method With Integrated Sensing and Communication System

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arXiv 2022年

作者： Chen, Zhimin Chen, Peng Guo, Ziyu Zhang, Yudong Wang, Xianbin The School of Electronic and Information Shanghai Dianji University Shanghai201306 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China The School of Information Science and Technology Fudan University Shanghai200433 China The State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai200433 China The School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada

With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain information on the vehicle’s direction of arrival (DOA) using reconfigurable intelligent surfaces (RIS). A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS. Moreover, different from the existing estimation methods, we also consider the interference signals introduced by wireless communication in the ISAC system. Then, we propose a novel atomic norm-based method to remove the interference signals and reconstruct the sparse signal. Additionally, a novel Hankel-based multiple signal classification (MUSIC) method is formulated to obtain the DOA information after the interference removal. To reduce the interference signals more efficiently and improve the performance of the sparse reconstruction, we optimize the measurement matrix to improve the signal-to-interference-plus-noise ratio (SINR). Finally, the theoretical Cram’er-Rao lower bound (CRLB) is derived for the ISAC system on the vehicle DOA estimation. Simulation results show that the proposed method can achieve better performance in the DOA estimation, and the corresponding CRLB with different distributions of the sensing nodes are shown. The code for the proposed method is available online https://***/chenpengseu/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Vehicles

Boundary Control for Exponential Synchronization of Reaction-Diffusion Neural Networks Based on Coupled PDE-ODEs

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IFAC-PapersOnLine 2020年第2期53卷 3415-3420页

作者： Chengdong Yang Zhenxing Li Xiangyong Chen Ancai Zhang Jianlong Qiu School of Information Science and Technology Linyi UniversityLinyi 276005 P.R. China and Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong (LinyiUniversity) Linyi 276005 P.R. China School of Automation and Electrical Engineering Linyi UniversityLinyi 276005 P. R. China and Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong (Linyi University) Linyi 276005 P.R. China

This paper studies synchronization and exponential synchronization of reaction-diffusion neural networks based on semi-linear coupled partial differential equations-ordinary differential equations. Two kinds of boundary control methods are studied, one collocated boundary measurement based form and the other distributed measurement based form. Sufficient conditions for existence of the two proposed boundary controllers for synchronization and exponential synchronization are respectively obtained in terms of LMIs. Two examples are given to show the effectiveness of the proposed boundary control.

关键词： Reaction-diffusion neural networks Boundary control Synchronization