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On greedy randomized coordinate updating iteration methods for solving symmetric eigenvalue problems

Applied Numerical Mathematics 2025年 216卷 76-97页

作者： Zhong-Zhi Bai State Key Laboratory of Mathematical Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 PR China State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences P.O. Box 2719 Beijing 100190 PR China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 PR China

In order to compute the smallest eigenvalue and its corresponding eigenvector of a large-scale, real, and symmetric matrix, we propose a class of greedy randomized coordinate updating iteration methods based on the principle that the indices of larger entries in absolute value of the current residual are selected with a higher probability and, with respect to this index set, the next iterate is updated from the current iterate along with the selected coordinate such that the corresponding entry of the residual is annihilated, resulting in fast convergence rates of the proposed iteration methods. Under appropriate conditions, we prove the convergence of both sequences of the Rayleigh-quotients and the acute angles between the iterates and the eigenvector in terms of the expectation. By numerical experiments, we show that this class of greedy randomized coordinate updating iteration methods are advantageous over the parameterized power method and the coordinate descent method in both iteration counts and computing times. Moreover, with theoretical analysis and computational performance, we confirm that the convergence property of this class of iteration methods can be improved significantly by suitably choosing the arbitrary nonnegative parameter involved in the greedy probability criterion.

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An Integrated Knowledge Distillation Framework for Battery SOH Estimation

An Integrated Knowledge Distillation Framework for Battery S...

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Smart Grid and Green Energy (ICSGGE), International Conference on

作者： Yang Li Chaojie Li Borui Zhang Renyou Xie Huadong Mo Jian Huang School of Electrical Engineering and Telecommunication University of New South Wales Sydney Australia School of Systems and Computing University of New South Wales Sydney Australia School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

State of health (SOH) estimation plays an important role in Battery Management systems (BMS), especially in electric vehicles (EVs). However, deploying deep learning-based SOH estimation models in EVs is challenging due to their large computational demands and model sizes. Moreover, battery datasets often contain environmental noise, which affects the accuracy of predictions. We propose an integrated knowledge distillation (KD) framework for model compression. Under this framework, the size of the model is compressed by knowledge distillation. This framework uses the Wasserstein distance to solve the extreme value problems during the knowledge distillation process. Additionally, we refine Wasserstein distance with a Gradient Penalty (G-P) technique. This method controls the gradients of the model outputs within a certain range, which can solve the gradient explosion or disappearance problems. The experimental results indicate the validity of our framework.

关键词： Deep learning Electric potential Accuracy Computational modeling Working environment noise Estimation Predictive models Electric vehicles Stability analysis Smart grids

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CESFusion: Cross-Frequency Enhanced Spatial-Spectral Fusion Network for Hyperspectral and Multispectral Image Fusion

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Haozheng Yang, Yanhong Li, Chaoyang Lu, Yanjie Zhang, Guodao Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China Engineering Research Center of Learning- Based Intelligent System Ministry of Education Key Laboratory of Computer Vision and Systems School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Institute of Intelligent Media Computing Hangzhou Dianzi University Hangzhou310018 China

The fusion of hyperspectral and multispectral images involves integrating high spectral resolution hyperspectral image (HSI) and high spatial resolution multispectral image (MSI) to generate a HSI with high spatial and spectral resolution (HR-HSI). Existing HSI-MSI fusion methods primarily focus on information fusion within the spatial domain;however, few solutions have explored the employment of frequency analysis to enhance spatial resolution, limiting their capability for global perception. In this paper, we propose an efficient and novel paradigm for HSI-MSI fusion through the cross-frequency enhanced spatial-spectral fusion network, named CESFusion, exploring the complementary fusion of information between the spatial and frequency domains. Specifically, we first present the cross-frequency domain fusion module (CFFM) to perform global analysis through the Fourier transform and effectively integrate and enhance the frequency domain information from both HSI and MSI. Subsequently, we propose the spectral modeling module (SpeMM) based on state space model (SMM) to capture long-range spectral dependencies with linear complexity, and integrate it with the spatial residual block-based module (SRM) for joint spatial-spectral feature extraction. Finally, to enable sufficient interaction between the spatial and frequency domains, we adopt the cross-domain interaction module (CDIM), capturing and integrating complementary information from both domains. Moreover, a frequency-based loss function is purposely designed to further improve the restoration of global information. Extensive experiments conducted on both synthetic and real datasets demonstrate the superiority of our CESFusion, as evidenced by both quantitative and qualitative evaluation results. © 2025 IEEE.

关键词： Image fusion

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Self-Support Prototype-Aware For Few-Shot Semantic Segmentation

Self-Support Prototype-Aware For Few-Shot Semantic Segmentat...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiaxiang Fang Shiqiang Ma Shengfeng He Fei Guo School of Computer Science and Engineering Central South University Changsha China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computing and Information Systems Singapore Management University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, significant progress has been made in prototype-based learning methods for few-shot semantic segmentation. However, prototype features originating from the support images are interfered with by intra-class diversity and thus cannot be aligned with the query foreground, resulting in poor segmentation accuracy. Therefore, we propose a novel self-support prototype-aware (SSPA) network to obtain highly confident query foreground pixel points and their corresponding query features. We design Cycle Consistency Collection module and Self-Support Collection module to address the interference of invalid support prototypes. Experimental results demonstrate that our SSPA significantly improves the quality of prototypes and achieves state-of-the-art segmentation results on multiple datasets. In particular, SSPA achieves mIoU scores of 69.7% and 76.4% for 1-shot and 5-shot segmentation, respectively, on PASCAL-5 i .

关键词： Learning systems Accuracy Semantic segmentation Semantics Prototypes Interference Signal processing Acoustics Speech processing

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Unknown-Aware Bilateral Dependency Optimization for Defending Against Model Inversion Attacks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Peng, Xiong Liu, Feng Wang, Nannan Lan, Long Liu, Tongliang Cheung, Yiu-Ming Han, Bo Hong Kong Baptist University Department of Computer Science Hong Kong The University of Melbourne School of Computing and Information Systems Australia Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China National University of Defense Technology State Key Laboratory of High Performance Computing School of Computer Hunan Changsha410000 China The University of Sydney Sydney AI Centre School of Computer Science Faculty of Engineering Australia

By abusing access to a well-trained classifier, model inversion (MI) attacks pose a significant threat as they can recover the original training data, leading to privacy leakage. Previous studies mitigated MI attacks by imposing regularization to reduce the dependency between input features and outputs during classifier training, a strategy known as unilateral dependency optimization. However, this strategy contradicts the objective of minimizing the supervised classification loss, which inherently seeks to maximize the dependency between input features and outputs. Consequently, there is a trade-off between improving the model's robustness against MI attacks and maintaining its classification performance. To address this issue, we propose the bilateral dependency optimization strategy (BiDO), a dual-objective approach that minimizes the dependency between input features and latent representations, while simultaneously maximizing the dependency between latent representations and labels. BiDO is remarkable for its privacy-preserving capabilities. However, models trained with BiDO exhibit diminished capabilities in out-of-distribution (OOD) detection compared to models trained with standard classification supervision. Given the open-world nature of deep learning systems, this limitation could lead to significant security risks, as encountering OOD inputs—whose label spaces do not overlap with the in-distribution (ID) data used during training-is inevitable. To address this, we leverage readily available auxiliary OOD data to enhance the OOD detection performance of models trained with BiDO. This leads to the introduction of an upgraded framework, unknown-aware BiDO (BiDO+), which mitigates both privacy and security concerns. As a highlight, with comparable model utility, BiDO-HSIC+ reduces the FPR95 by 55.02% and enhances the AUCROC by 9.52% compared to BiDO-HSIC, while also providing superior MI robustness. © 1979-2012 IEEE.

关键词： Differential privacy

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Phase-bias-blind computational ghost imaging with optical phased array

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Optics Express 2025年第10期33卷 21132-21141页

作者： Sota Hirata Naoki Matsuda Kento Komatsu Takuo Tanemura Ryoichi Horisaki Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan

We propose a method for computational ghost imaging (CGI) with an optical phased array (OPA), wherein phase biases on the phase shifters are intrinsically unknown. In our method, an object is illuminated with a number of light fields from the OPA using random phase shifts, and the light from the object is captured by a single-pixel detector. The object and the phase biases are simultaneously estimated from the measured intensity signals. We numerically demonstrate the proposed method. Furthermore, we present complex amplitude field acquisition in OPA-based CGI with intensity measurements performed without reference light by simply adding an iris to the detector.

关键词： Computational imaging Imaging systems Phase shift Phased arrays Scattering media Spatial light modulators

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SIGFinger: A Subtle and Interactive GNN Fingerprinting Scheme via Spatial Structure Inference Perturbation

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Jia, Ju Li, Renjie Wu, Cong Ma, Siqi Wang, Lina Deng, Robert H. Southeast University School of Cyber Science and Engineering Nanjing210096 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore University of New South Wales School of Engineering and Information Technology CanberraACT2612 Australia Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Singapore Management University School of Information Systems 188065 Singapore

There have been significant improvements in intellectual property (IP) protection for deep learning models trained on Euclidean data. However, the complex and irregular graph-structured data in non-Euclidean space poses a huge challenge to the IP protection of graph neural networks (GNNs). To address this issue, we propose a subtle and interactive GNN fingerprinting scheme through spatial structure inference perturbation, which captures the stable coordination patterns of fingerprint to guarantee the reliability of copyright verification. Specifically, the data augmentation based on adaptive graph diffusion is first exploited to generate more samples, which enables the exploration of fingerprint information from coarse to fine. Subsequently, the graph-structured data are manipulated by multi-constrained spectral clustering to analyze intrinsic and extrinsic structure correlations in a causal inference manner. Ultimately, the cycle-consistent statistical optimization is performed to determine the copyright of GNN models from both intra-graph and inter-graph perspectives. Extensive experiments show that our proposed scheme can effectively verify the IP of GNN models on various challenging graph-structured datasets. Furthermore, we reveal that the space causality inference can facilitate the acquisition of inherent structural information, which improves the quality and robustness of the fingerprint under model modification operations and other model stealing attacks. © 2004-2012 IEEE.

关键词： causal inference GNN fingerprinting IP protection quality and robustness stable structure knowledge

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Secure and Reliable 6G Communications with BCH-Based GLDPC and Kyber

Secure and Reliable 6G Communications with BCH-Based GLDPC a...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Linh Nguyen Cheol-Hong Min Hanho Lee Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University USA Department of Electrical. and Computer Engineering University of St. Thomas USA Department of Electrical and Computer Engineering Inha University Republic of Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Channel coding in sixth-generation (6G) networks must attain exceptionally low bit error rate (BER), typically in the range of 10– 6 to 10– 9 , to ensure the requisite level of reliability. While fifth-generation (5G) New Radio (NR) coding techniques have made substantial progress, the inherent limitations of the low error floor impede the attainment of such stringent BER targets. Compared to the low-density parity check (LDPC) codes employed in 5G communications, generalized low-density parity check (GLDPC) codes offer a more advantageous tradeoff between computational complexity and error correction performance in 6G networks. GLDPC codes enhance traditional LDPC structures by incorporating more complex local decoding units within their check nodes. When coupled with cryptographic techniques, GLDPC codes can establish a robust information security framework. In this paper, we propose a new security-based GLDPC that uses a number of BCH matrices for the GLDPC decoding process combined with the Kyber algorithm (BCH-based KyGLDPC) before transmitting data to increase reli-ability and solve the security problem against quantum computer attacks. The system's performance is assessed using both image data and data encrypted prior to transmission in a white additive Gaussian noise environment. The results indicate that the system provides strong error correction and data security.

关键词： 6G mobile communication Quantum computing Computer network reliability System performance Bit error rate Parity check codes Error correction codes Decoding Reliability Channel coding

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An Adaptive Proximal Inexact Gradient Framework and Its Application to Per-Antenna Constrained Joint Beamforming and Compression Design

arXiv

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arXiv 2025年

作者： Fan, Xilai Jiang, Bo Liu, Ya-Feng 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 Beijing100190 China Ministry of Education Key Laboratory of NSLSCS School of Mathematical Sciences Nanjing Normal University Nanjing210023 China

In this paper, we propose an adaptive proximal inexact gradient (APIG) framework for solving a class of nonsmooth composite optimization problems involving function and gradient errors. Unlike existing inexact proximal gradient methods, the proposed framework introduces a new line search condition that jointly adapts to function and gradient errors, enabling adaptive stepsize selection while maintaining theoretical guarantees. Specifically, we prove that the proposed framework achieves an ϵ-stationary point within O(ϵ−2) iterations for nonconvex objectives and an ϵ-optimal solution within O(ϵ−1) iterations for convex cases, matching the best-known complexity in this context. We then custom-apply the APIG framework to an important signal processing problem: the joint beamforming and compression problem (JBCP) with per-antenna power constraints (PAPCs) in cooperative cellular networks. This customized application requires careful exploitation of the problem’s special structure such as the tightness of the semidefinite relaxation (SDR) and the differentiability of the dual. Numerical experiments demonstrate the superior performance of our custom-application over state-of-the-art benchmarks for the JBCP. © 2025, CC BY.

关键词： Beamforming

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Interval-Shared Information Integration and False-Negative Association Reduction in Multi-Source MiRNA-Disease Association Prediction

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Cui, Qinghang Guo, Honglie Cai, Yueyi Fei, Yu Wang, Shunfang Yunnan University School of Information Science and Engineering Yunnan Kunming650500 China Yunnan University of Finance and Economics School of Statistics and Mathematics Yunnan Kunming650221 China Key Lab of Intelligent Systems and Computing of Yunnan Province Yunnan Kunming650500 China

Numerous studies have demonstrated that microRNAs (miRNAs) play crucial roles in the development and progression of various diseases, making the identification of miRNA-disease associations (MDAs) essential for understanding human disease etiology. While several computational models have been developed to predict MDAs, challenges persist—particularly the limited consideration of information interactions among multi-source similarities and the presence of "false-negative" associations in the original topology. To address these issues, we propose ISFNMDA, a model designed to infer potential MDAs by leveraging multi-view collaborative learning for feature extraction and optimizing association topology through graph structure momentum contrastive learning. Specifically, multi-source similarities of miRNAs and diseases are mapped into a unified feature space via encoders. The Pearson correlation coefficient is employed to derive pairwise constraints between nodes, facilitating information interactions and constructing interval-shared information constraints. Subsequently, an inference graph learner models the representations to generate an inferred graph topology. By maximizing mutual information between the inferred topology and the original "false-negative" associations through momentum contrastive learning, the model effectively reduces spurious correlations. The final comprehensive representations and optimized graph structure are then used to predict potential MDAs. Experimental results demonstrate that ISFNMDA outperforms existing methods, and case studies further validate its predictive capability. © 2013 IEEE.

关键词： Contrastive Learning

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