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Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels

IEEE Transactions on Signal Processing 2025年

作者： Wang, Zhe Zhang, Jiayi Bjornson, Emil Niyato, Dusit Ai, Bo Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China KTH Royal Institute of Technology Department of Computer Science Stockholm114 28 Sweden Nanyang Technological University College of Computing & Data Science 639798 Singapore

In this paper, we explore the low-complexity optimal bilinear equalizer (OBE) combining scheme design for cell-free massive multiple-input multiple-output networks with spatially correlated Rician fading *** provide a spectral efficiency (SE) performance analysis framework for both the centralized and distributed processing schemes with bilinear equalizer (BE)-structure combining schemes applied. The BE-structured combining is a set of schemes that are constructed by the multiplications of channel statistics-based BE matrices and instantaneous channel estimates. Notably, we derive closed-form achievable SE expressions for centralized and distributed BE-structured combining schemes. We propose one centralized and two distributed OBE schemes: Centralized OBE (C-OBE), Distributed OBE based on Global channel statistics (DG-OBE), and Distributed OBE based on Local channel statistics (DL-OBE), which maximize their respective SE expressions. OBE matrices in these schemes are tailored based on varying levels of channel statistics. Notably, we obtain new and insightful closed-form results for the C-OBE, DG-OBE, and DL-OBE combining schemes. Numerical results demonstrate that the proposed OBE schemes can achieve excellent SE, even in scenarios with severe pilot contamination. © 2025 IEEE. All rights reserved.

关键词： Fading channels

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A Four-Pronged Defense Against Byzantine Attacks in Federated Learning

arXiv

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arXiv 2023年

作者： Wan, Wei Hu, Shengshan Li, Minghui Lu, Jianrong Zhang, Longling Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Federated learning (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious participants could independently or collusively upload well-crafted updates to deteriorate the performance of the global model. However, existing defenses could only mitigate part of Byzantine attacks, without providing an all-sided shield for FL. It is difficult to simply combine them as they rely on totally contradictory assumptions. In this paper, we propose FPD, a four-pronged defense against both non-colluding and colluding Byzantine attacks. Our main idea is to utilize absolute similarity to filter updates rather than relative similarity used in existingI works. To this end, we first propose a reliable client selection strategy to prevent the majority of threats in the bud. Then we design a simple but effective score-based detection method to mitigate colluding attacks. Third, we construct an enhanced spectral-based outlier detector to accurately discard abnormal updates when the training data is not independent and identically distributed (non-IID). Finally, we design update denoising to rectify the direction of the slightly noisy but harmful updates. The four sequentially combined modules can effectively reconcile the contradiction in addressing non-colluding and colluding Byzantine attacks. Extensive experiments over three benchmark image classification datasets against four state-of-the-art Byzantine attacks demonstrate that FPD drastically outperforms existing defenses in IID and non-IID scenarios (with 30% improvement on model accuracy). © 2023, CC BY.

关键词： Classification (of information)

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Self-consistent quasiparticle-vibration coupling approach for nuclear giant resonances with Skyrme interactions

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Physical Review C 2024年第6期110卷 064317-064317页

作者： Z. Z. Li (李征征) Y. F. Niu (牛一斐) G. Colò State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing 100871 China School of Nuclear Science and Technology Lanzhou University Lanzhou 730000 China Frontiers Science Center for Rare isotope Lanzhou University Lanzhou 730000 China Dipartimento di Fisica Università degli Studi di Milano via Celoria 16 20133 Milano Italy INFN sezione di Milano via Celoria 16 20133 Milano Italy

We present a self-consistent quasiparticle random-phase approximation (QRPA) plus quasiparticle-vibration coupling (QPVC) approach, based on the Skyrme Hartree-Fock-Bogoliubov (HFB) model within the spherical assumption. Isovector giant dipole resonances (IVGDRs) and isoscalar giant quadrupole resonances (ISGQRs) are studied by taking Ca, Sn, and Pb isotopes as examples. We demonstrate that the QRPA+QPVC approach is very powerful for the description of giant resonances and, in particular, describes their widths well. By using the experimental measurements of the ISGQR in Sn120 and Pb208, we discuss the extraction of an empirical value for the nucleon isoscalar effective mass m*/m.

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Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

arXiv

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arXiv 2023年

作者： Zhang, Yechao Hu, Shengshan Zhang, Leo Yu Shi, Junyu Li, Minghui Liu, Xiaogeng Wan, Wei Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Adversarial examples for deep neural networks (DNNs) have been shown to be transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical use. In this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient similarity. Building on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifest in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other,

关键词： Deep neural networks

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Lightweight Method for Interactive 3D Medical Image Segmentation with Multi-Round Result Fusion

arXiv

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arXiv 2024年

作者： Shen, Bingzhi Chang, Lufan Chen, Siqi Guo, Shuxiang Liu, Hao Aerospace Center Hospital School of Life Science The Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China Yizhun Medical AI Co. Ltd Beijing100081 China Shenzhen Key Laboratory of Ubiquitous Data Enabling Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China

In medical imaging, precise annotation of lesions or organs is often required. However, 3D volumetric images typically consist of hundreds or thousands of slices, making the annotation process extremely time-consuming and laborious. Recently, the Segment Anything Model (SAM) has drawn widespread attention due to its remarkable zero-shot generalization capabilities in interactive segmentation. While researchers have explored adapting SAM for medical applications, such as using SAM adapters or constructing 3D SAM models, a key question remains: CAN TRADITIONAL CNN NETWORKS ACHIEVE THE SAME STRONG ZERO-SHOT GENERALIZATION IN THIS TASK? In this paper, we propose the Lightweight Interactive Network for 3D Medical Image Segmentation (LIM-Net), a novel approach demonstrating the potential of compact CNN-based models. Built upon a 2D CNN backbone, LIM-Net initiates segmentation by generating a 2D prompt mask from user hints. This mask is then propagated through the 3D sequence via the Memory Module. To refine and stabilize results during interaction, the Multi-Round Result Fusion (MRF) Module selects and merges optimal masks from multiple rounds. Our extensive experiments across multiple datasets and modalities demonstrate LIM-Net’s competitive performance. It exhibits stronger generalization to unseen data compared to SAM-based models, with competitive accuracy while requiring fewer interactions. Notably, LIM-Net’s lightweight design offers significant advantages in deployment and inference efficiency, with low GPU memory consumption suitable for resource-constrained environments. These promising results demonstrate LIM-Net can serve as a strong baseline, complementing and contrasting with popular SAM models to further boost effective interactive medical image segmentation. The code will be released at https://***/goodtime-123/LIM-Net. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Achieving high hetero-deformation induced(HDI)strengthening and hardening in brass by dual heterostructures

引用

Journal of Materials science & technology 2022年第3期98卷 244-247页

作者： X.T.Fang Z.K.Li Y.F.Wang M.Ruiz X.L.Ma H.Y.Wang Y.Zhu R.Schoell C.Zheng D.Kaoumi Y.T.Zhu Department of Materials Science and Engineering North Carolina State UniversityRaleighNC 27695USA Key Laboratory for Anisotropy and Texture of Materials of Ministry of Education Northeastern UniversityShenyang 110819China School of Aeronautics and Astronautics Sichuan UniversityChengdu 610065China Department of Materials Science and Engineering Texas A and M UniversityCollege StationTX 77840USA Department of Mechanical and Aerospace Engineering North Carolina State UniversityRaleighNC 27695USA Department of Nuclear Engineering North Carolina State UniversityRaleighNC 27695USA Science and Technology on Thermostructural Composite Materials Laboratory Northwestern Polytechnical UniversityXi’an 710072China

Heterostructured materials,defined as materials that contain multiple zones with dramatically different flow stresses,have the potential to push the envelope of the strength-ductility of metals and alloys beyond what can be obtained conventionally[1–3].A prominent example is the heterogeneous lamella Ti that is as strong as its ultrafine-grained Ti,while as ductile as the coarsegrained Ti[4].

关键词： grain strengthening deformation

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Zibo Miao Yu Pan Qing Gao Guangdong Key Laboratory of Intelligent Morphing Mechanisms School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique.

关键词： Perturbation methods Design methodology Quantum optics Robustness Feedback control Closed loop systems Tuning Cybernetics

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Dbfl: Dynamic Byzantine-Robust Privacy Preserving Federated Learning in Heterogeneous data Scenario

SSRN

引用

SSRN 2024年

作者： Chen, Xiaoli Tian, Youliang Wang, Shuai Yang, Kedi Zhao, Wei Xiong, Jinbo State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guizhou University Guizhou Guiyang550025 China College of Mathematics and Statistics Guizhou University Guizhou Guiyang550025 China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350117 China

Privacy Preserving Federated Learning (PPFL) protects the clients' local data privacy by securely uploading encrypted gradients to the server. However, in real-world scenarios, heterogeneous client data distribution makes identifying poisoning gradients challenging. And during local iterations, models moving in different directions constantly shift the boundary between benign and malicious gradients. To address these challenges, we design a Dynamic Byzantine-robust defense strategy (DBFL) based on two-trapdoor homomorphic encryption, achieving detection of encrypted model poisoning attacks. Specifically, we first propose a secure Manhattan Distance method, which accurately measures the difference between each element in two encrypted gradients, thus enabling precise detection of poisoning attacks in heterogeneous data scenarios without exposing any privacy. Furthermore, we design a Byzantine-tolerant aggregation mechanism based on dynamic threshold, where the dynamic threshold changes according to the continuously changing boundary between poisoning and benign gradients in heterogeneous data scenarios. Even when malicious clients controlled by Byzantine attackers constitute 70\% of the DBFL, our method accurately excludes poisoned gradients. Security analysis demonstrates that DBFL achieves IND-CPA security. Extensive evaluation on two benchmark datasets shows that DBFL outperforms existing defense strategies. In particular, DBFL achieves a 15\%-40\% accuracy improvement in the non-IID setting compared to existing solutions. © 2024, The Authors. All rights reserved.

关键词： Network security

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Feature Aggregation Network for Memory-Based Industrial Anomaly Detection

Feature Aggregation Network for Memory-Based Industrial Anom...

引用

IEEE International Conference on data science in Cyberspace (DSC)

作者： Min Li Delong Han Rui Shao Jinghui He Faculty of Data Science City University of Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation Network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet.

关键词： Training Runtime Shape Memory management Training data Production Feature extraction Robustness Anomaly detection Testing

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Resolution of a possible misinterpretation of the nuclear excitation mode along the yrast line: An investigation on the evolution of rotation and vibration

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Physical Review C 2022年第1期105卷 014315-014315页

作者： Hai-Yan Meng Hua-Lei Wang Min-Liang Liu Key Laboratory of High Precision Nuclear Spectroscopy Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China and School of Nuclear Science and Technology University of Chinese Academy of Sciences Beijing 100049 China

An empirical approach, called E-GOS (E-gamma over spin) curves, which is used for discerning the evolution between vibration and rotation in nuclei as a function of spin, is discussed. Considering the possible mechanisms and the results of total-Routhian-surface calculations, we find that the decrease in an E-GOS plot may be misinterpreted as the onset of vibration behavior along the yrast line. The insufficiency of the vibration-like criterion of the E-GOS curves to determine the transition from rotation to vibration is emphasized. It is pointed out that the crossing between the ground-state and S bands, especially with a strong interaction, will be responsible for such vibration-like evolution of nuclear excitation modes.

关键词： Collective levels nuclear structure & decays 150 ≤ A ≤ 189 190 ≤ A ≤ 219 90 ≤ A ≤ 149 A ≥ 220 Collective models

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