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A natural deep Ritz method for essential boundary value problems

Journal of Computational Physics 2025年 537卷

作者： Haijun Yu Shuo Zhang State Key Laboratory of Mathematical Sciences (SKLMS) and State Key Laboratory of Scientific and Engineering Computing (LSEC) Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China

Deep neural network approaches show promise in solving partial differential equations. However, unlike traditional numerical methods, they face challenges in enforcing essential boundary conditions. The widely adopted penalty-type methods, for example, offer a straightforward implementation but introduces additional complexity due to the need for hyper-parameter tuning; moreover, the use of a large penalty parameter can lead to artificial extra stiffness, complicating the optimization process. In this paper, we propose a novel, intrinsic approach to impose essential boundary conditions through a framework inspired by intrinsic structures. We demonstrate the effectiveness of this approach using the deep Ritz method applied to Poisson problems, with the potential for extension to more general equations and other deep learning techniques. Numerical results are provided to substantiate the efficiency and robustness of the proposed method.

关键词： Deep neural network Essential boundary value problem Deep Ritz method Penalty free Interfacial value problem

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Network Topology Overlapping Group Detection Based on h-Core Pruning

Network Topology Overlapping Group Detection Based on h-Core...

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Qi Zhang Yan Liu Yuheng Cai Zhong Yuan Network Security Research Institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9781665447300

The network group detection has a large number of application researches and has achieved certain results in network security, IP entity location and load balancing. However, due to the large scale of the network topology graph and the large number of terminal nodes, most of the existing overlapping group detection algorithms are relatively complex, and the data size that can be processed is limited, and cannot be directly applied to the network topology. This paper proposes a network topology overlapping group detection algorithm based on h-core pruning. This algorithm decomposes the network into smaller strong connected components, reduces the interference of noisy nodes to overlapping group discovery, and more efficiently detect the largest k-plex group on the target network. A large number of experiments show that the method in this paper is effective. Compared with the full-enum algorithm, our method increases the network size that can be handled by the largest k-plex group by two orders of magnitude, and can detect overlapping groups in the network topology more efficiently.

关键词： Network topology Signal processing algorithms Interference Signal processing Load management Topology Noise measurement

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Elastic Full Waveform Inversion Based on the Trust Region Strategy

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American Journal of Computational Mathematics 2021年第4期11卷 241-266页

作者： Wensheng Zhang Yijun Li State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy Sciences Beijing China

In this paper, we investigate the elastic wave full-waveform inversion (FWI) based on the trust region method. The FWI is an optimization problem of minimizing the misfit between the observed data and simulated data. Usually, the line search method is used to update the model parameters iteratively. The line search method generates a search direction first and then finds a suitable step length along the direction. In the trust region method, it defines a trial step length within a certain neighborhood of the current iterate point and then solves a trust region subproblem. The theoretical methods for the trust region FWI with the Newton type method are described. The algorithms for the truncated Newton method with the line search strategy and for the Gauss-Newton method with the trust region strategy are presented. Numerical computations of FWI for the Marmousi model by the L-BFGS method, the Gauss-Newton method and the truncated Newton method are completed. The comparisons between the line search strategy and the trust region strategy are given and show that the trust region method is more efficient than the line search method and both the Gauss-Newton and truncated Newton methods are more accurate than the L-BFGS method.

关键词： Elastic Wave Equations Full-Waveform Inversion Trust Region Strate-gy Line Search Strategy Newton-Type Method Time Domain

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Variable Rate Syndrome-Trellis Codes for Steganography on Bursty Channels 19th

Variable Rate Syndrome-Trellis Codes for Steganography on Bu...

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19th International Workshop on Digital Forensics and Watermarking, IWDW 2020

作者： Feng, Bingwen Liu, Zhiquan Wei, Kaimin Lu, Wei Lin, Yuchun College of Information Science and Technology Jinan University Guangzhou510632 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen518060 China School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Guangzhou Institute of Science and Technology Guangzhou510006 China

ISBN: (纸本)9783030694487

This paper presents a type of variable rate syndrome-trellis codes (VR-STC) for bursty channels. It can embed message bits with two different embedding rates. In the embedding, a cover vector is sliced into segments, and the embedding rates for each segment depend on the local channel distribution. The parities of stego segments are exploited to indicate the selected embedding rates according to a mapping function between parities and embedding rates. The core of the VR-STC is the parity-aware encoder, which can simultaneously output two candidate stego segments with different embedding rates and opposite parities, either of which can be used to constitute the final stego vector. A Viterbi algorithm is also suggested to find the closed stego segments. Besides, the mapping function between parities and embedding rates is designed by minimizing the embedding costs on a down sampled version of the cover vector. It can further improve the undetectability of the VR-STC. Experimental results on artificial signals and binary images suggest that the proposed VR-STC can provide high success rate of embedding and reduce the embedding cost on bursty channels. © 2021, Springer Nature Switzerland AG.

关键词： Steganography

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A Novel Method for Detection of ECG with Deep Learning

A Novel Method for Detection of ECG with Deep Learning

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International Conference on Computer and Communications (ICCC)

作者： Haiyan Wang Yanjie Zhou Bing Zhou Zongmin Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Simulation Experiment Centre Zhengzhou University of Aeronautics Collaborative Innovation Centre for Internet Healthcare Zhengzhou University Zhengzhou China School of Management Engineering Zhengzhou University Zhengzhou China Collaborative Innovation Centre for Internet Healthcare Zhengzhou University Zhengzhou China School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665409513

With the rapid development of machine learning and deep learning, ECG intelligent detection models lean more heavily on the labeled data. However, the development of ECG annotation cannot satisfy the development of an intelligent detection model because of its high complexity and heavy workload. Developing a model with low dependence on labeled data becomes the core for solving the ECG intelligent detection. To overcome this limitation, this paper proposes a deep active learning (DAL) model for ECG intelligent detection, which combines high-performance deep learning (DL) with active learning (AL) that is less dependent on labeled data. Experimental results show that the proposed method can achieve high performance with the support of a small amount of labeled data. The proposed method could be adopted for data annotation, which achieves a high-quality data annotation and avoids useless annotation.

关键词： Deep learning Machine learning algorithms Annotations Electrocardiography Rhythm Data models Complexity theory

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Research and Design of Square Kilometer Array Astronomical Data Management Model Based on Fabric 13

Research and Design of Square Kilometer Array Astronomical D...

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2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020

作者： Fu, Jinhua Xu, Jie Zhang, Shulin Zhang, Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou University of Light Industry Zhengzhou China Zhengzhou University of Light Industry Zhengzhou China Qinghai University Department of Computer Technology and Applications Xining China

ISBN: (纸本)9781728176475

At present, how to safely share data among different participants has become a huge challenge. The reliable traceability of SKA data is a powerful technical tool for controlling its security and sharing. In order to solve the problem of high management cost and low data credibility of traditional centralized big data management system, SKA data management and credible traceability model design based on Hyperledger Fabric were studied. In this paper, the overall framework and architecture of SKA data management are studied and designed, and the key technologies such as big data storage scheme, big data transmission, big data processing, blockchain metadata storage, alliance chain member allocation, traceability query, contribution mechanism and data sharing are discussed. Distributed architecture, Hyperledger Fabric alliance chain technology, database technology, Golang network programming, Rust asynchronous programming, Java Web and *** are comprehensively adopted. The functions of SKA data transmission, processing, storage, traceability query and data sharing are designed. The functions of transmission, processing, storage, management, sharing and credible traceability of SKA data management system are realized, which provides a reference for the design and implementation of SKA data management system based on blockchain. © 2020 IEEE.

关键词： Metadata

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Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

Enhancing reliability and efficiency for real-time robust ad...

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作者： Zhang, Yi Luo, Xiangyang Zhu, Xiaodong Li, Zhenyu Bors, Adrian G. State Key Laboratory of Mathematical Engineering and Advanced Computing 62# Science Rd Zhengzhou China Department of Computer Science University of York YorkYO10 5GH United Kingdom

The development of multimedia and deep learning technology bring new challenges to steganography and steganalysis techniques. Meanwhile, robust steganography, as a class of new techniques aiming to solve the problem of covert communication under lossy channels, has become a new research hotspot in the field of information hiding. To improve the communication reliability and efficiency for current real-time robust steganography methods, a concatenated code, composed of Syndrome–Trellis codes (STC) and cyclic redundancy check (CRC) codes, is proposed in this paper. The enhanced robust adaptive steganography framework proposed is this paper is characterized by a strong error detection capability, high coding efficiency, and low embedding costs. On this basis, three adaptive steganographic methods resisting JPEG compression and detection are proposed. Then, the fault tolerance of the proposed steganography methods is analyzed using the residual model of JPEG compression, thus obtaining the appropriate coding parameters. Experimental results show that the proposed methods have a significantly stronger robustness against compression, and are more difficult to be detected by statistical based steganalytic methods. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Steganography

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Measurement-Based Non-Stationary Markov Tapped Delay Line Channel Model for 5G-Railways

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Zhang, Xuejian He, Ruisi Yang, Mi Ding, Jianwen Chen, Ruifeng Gao, Shuaiqi Qi, Ziyi Zhang, Zhengyu Ai, Bo Zhong, Zhangdui Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Henan High Speed Railway Operation and Maintenance Engineering Research Center Zhengzhou451460 China China Academy of Railway Sciences Corporation Ltd. Institute of Computing Technology Beijing100081 China

5 G for Railways (5G-R) is globally recognized as a promising next-generation railway communication system designed to meet increasing demands. Channel modeling serves as foundation for communication system design, with tapped delay line (TDL) models widely utilized in system simulations due to their simplicity and practicality and serves as a crucial component of various standards like 3GPP. However, existing TDL models applicable to 5G-R systems are limited. Most fail to capture non-stationarity, a critical characteristic of railway communications, while others are unsuitable for the specific frequency bands and bandwidths of 5G-R. In this paper, a channel measurement campaign for 5G-R dedicated network is carried out, resulting in a measurement-based 5-tap TDL model utilizing a first-order two-state Markov chain to represent channel non-stationarity. key model parameters, including number of taps, statistical distribution of amplitude, phase and Doppler shift, and state transition probability matrix, are extracted. The correlation between tap amplitudes is also obtained. Finally, the accuracy of model is validated through comparisons with measurement data and 3GPP model. These findings are expected to offer valuable insights for design and link-level simulation of 5G-R systems. © 2025 IEEE.

关键词： Markov chains

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

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arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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On the Impact of Flaky Tests in Automated Program Repair

On the Impact of Flaky Tests in Automated Program Repair

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Yihao Qin Shangwen Wang Kui Liu Xiaoguang Mao Tegawendé F. Bissyandé National University of Defense Technology Changsha China Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China University of Luxembourg Luxembourg

ISBN: (纸本)9781728196305;9781728196312

The literature of Automated Program Repair is largely dominated by approaches that leverage test suites not only to expose bugs but also to validate the generated patches. Unfortunately, beyond the widely-discussed concern that test suites are an imperfect oracle because they can be incomplete, they can include tests that are flaky. A flaky test is one that can be passed or failed by a program in a non-deterministic way. Such tests are generally carefully removed from the repair benchmarks. In practice, however, flaky tests are available test suite of software repositories. To the best of our knowledge, no study has discussed this threat to validity for evaluation of program repair. In this work, we highlight this threat and further investigate the impact of flaky tests by reverting their removal from the Defects4J benchmark. Our study aims to characterize the impact of flaky tests for localizing bugs and the eventual influence on the repair performance. Among other insights, we find that (1) although flaky tests are few (≈0.3%) of total tests, they affect experiments related to a large proportion (98.9%) of Defects4J real-world faults; (2) most flaky tests (98%) actually provide deterministic results under specific environment configurations (with the jdk version influencing the results); (3) flaky tests drastically hinder the effectiveness of spectrum-based fault localization (e.g., the rankings of 90 bugs drop down while none of the bugs obtains better location results compared with results achieved without flaky tests); and (4) the repairability of APR tools is greatly affected by the presence of flaky tests (e.g., 10 state of the art APR tools can now fix significantly fewer bugs than when the benchmark is manually curated to remove flaky tests). Given that the detection of flaky tests is still nascent, we call for the program repair community to relax the artificial assumption that the test suite is free from flaky tests. One direction that we propose is to co

关键词： Location awareness Degradation Conferences Computer bugs Maintenance engineering Tools Benchmark testing

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