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PCA: Semi-Supervised Segmentation with Patch Confidence Adversarial Training

IEEE Transactions on Network science and engineering 2025年第4期12卷 2473-2486页

作者： Xu, Zhenghua Yang, Runhe Xu, Zihang Zhang, Shuo Yang, Yuchen Liu, Weipeng Xu, Weichao Chen, Junyang Lukasiewicz, Thomas Leung, Victor C. M. Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The Johns Hopkins University Department of Applied Mathematics and Statistic Baltimore United States Hebei University of Technology School of Artificial Intelligence Tianjin China Hebei Provincial Hospital of Traditional Chinese Medicine Digestive Disease Center Shijiazhuang China Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of Oxford Department of Computer Science University of Oxford United Kingdom Department of Computer Science United Kingdom Vienna University of Technology institute of Logic and Computation nstitute of Logic and Computation Vienna Austria

Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. © 2025 IEEE. All rights reserved.

关键词： Adversarial machine learning

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Endogenous Security Techniques for Safeguarding Next-Generation Spectrum Usage at the Physical Layer

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IEEE Network 2025年

作者： Li, Guyue Zhou, Yajie Sun, Yingjie Wen, Shiping Wang, Shuai Hu, Aiqun Wen, Guanghui Southeast University School of Cyber Science and Engineering Nanjing210096 China Purple Mountain Laboratories Nanjing210096 China University of Technology Sydney Australian AI Institute Faculty of Engineering and Information Technology NSW2007 Australia Beihang University School of Computer Science and Engineering Beijing100191 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University School of Mathematics Department of Systems Science Nanjing211189 China

The rapid increase in the data transmission rates for next generation communication systems has intensified the demand for spectrum, straining limited resources under traditional management. This has spurred a move toward dynamic spectrum sharing and exploration of higher frequency bands. Although spectrum sharing enables other users to access the underutilized whitespace spectrum, it also raises additional security concerns. The possibility of attacks at higher carrier frequencies has not yet been well characterized. To address these challenges, endogenous properties of wireless communication systems can be explored as new opportunities for security design. The purpose of this tutorial is to provide a comprehensive overview of the state-of-the-art wireless endogenous security techniques that guarantee wireless security and privacy for spectrum usage at the physical layer, along with the principles, solutions, and future research trends. © 1986-2012 IEEE.

关键词： next-generation spectrum usage physical layer security spectrum sharing wireless endogenous security

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A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps

arXiv

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arXiv 2023年

作者： Chang, Tyler H. Elias, Jakob R. Wild, Stefan M. Chaudhuri, Santanu Libera, Joseph A. Mathematics and Computer Science Division Argonne National Laboratory United States Applied Materials Division Argonne National Laboratory United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory United States Department of Civil Materials and Environmental Engineering University of Illinois Chicago United States

In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balancing performance trade-offs and constraints. For these reasons, we present an active learning process based on multiobjective black-box optimization with continuously updated machine learning models. This workflow is built on open-source technologies for real-time data streaming and modular multiobjective optimization software development. We demonstrate a proof of concept for this workflow through the autonomous operation of a continuous-flow chemistry laboratory, which identifies ideal manufacturing conditions for the electrolyte 2,2,2-trifluoroethyl methyl carbonate. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Visualizing dynamics of charges and strings in (2 + 1)D lattice gauge theories

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Nature 2025年第8067期642卷 315-320页

作者： Cochran, T.A. Jobst, B. Rosenberg, E. Lensky, Y.D. Gyawali, G. Eassa, N. Will, M. Szasz, A. Abanin, D. Acharya, R. Aghababaie Beni, L. Andersen, T.I. Ansmann, M. Arute, F. Arya, K. Asfaw, A. Atalaya, J. Babbush, R. Ballard, B. Bardin, J.C. Bengtsson, A. Bilmes, A. Bourassa, A. Bovaird, J. Broughton, M. Browne, D.A. Buchea, B. Buckley, B.B. Burger, T. Burkett, B. Bushnell, N. Cabrera, A. Campero, J. Chang, H.-S. Chen, Z. Chiaro, B. Claes, J. Cleland, A.Y. Cogan, J. Collins, R. Conner, P. Courtney, W. Crook, A.L. Curtin, B. Das, S. Demura, S. De Lorenzo, L. Di Paolo, A. Donohoe, P. Drozdov, I. Dunsworth, A. Eickbusch, A. Elbag, A. Moshe Elzouka, M. Erickson, C. Ferreira, V.S. Burgos, L. Flores Forati, E. Fowler, A.G. Foxen, B. Ganjam, S. Gasca, R. Genois, É. Giang, W. Gilboa, D. Gosula, R. Grajales Dau, A. Graumann, D. Greene, A. Gross, J.A. Habegger, S. Hansen, M. Harrigan, M.P. Harrington, S.D. Heu, P. Higgott, O. Hilton, J. Huang, H.-Y. Huff, A. Huggins, W. Jeffrey, E. Jiang, Z. Jones, C. Joshi, C. Juhas, P. Kafri, D. Kang, H. Karamlou, A.H. Kechedzhi, K. Khaire, T. Khattar, T. Khezri, M. Kim, S. Klimov, P. Kobrin, B. Korotkov, A. Kostritsa, F. Kreikebaum, J. Kurilovich, V. Landhuis, D. Lange-Dei, T. Langley, B. Lau, K.-M. Ledford, J. Lee, K. Lester, B. Le Guevel, L. Li, W. Lill, A.T. Livingston, W. Locharla, A. Lundahl, D. Lunt, A. Madhuk, S. Maloney, A. Mandrà, S. Martin, L. Martin, O. Maxfield, C. McClean, J. McEwen, M. Meeks, S. Megrant, A. Miao, K. Molavi, R. Molina, S. Montazeri, S. Movassagh, R. Neill, C. Newman, M. Nguyen, A. Nguyen, M. Ni, C.-H. Ottosson, K. Pizzuto, A. Potter, R. Pritchard, O. Quintana, C. Ramachandran, G. Reagor, M. Rhodes, D. Roberts, G. Sankaragomathi, K. Satzinger, K. Schurkus, H. Shearn, M. Shorter, A. Shutty, N. Shvarts, V. Sivak, V. Small, S. Smith, W.C. Springer, S. Sterling, G. Suchard, J. Sztein, A. Thor, D. Torunbalci, M. Vaishnav, A. Vargas, J. Vdovichev, S. Vidal, G. Vollgraff Heidweiller, C. Waltman, S. Wang, S.X. Ware, B. White, T. Wong, K. Woo, B.W.K. Xing, C. Yao, Z. J Google Research Mountain View CA United States Department of Physics Princeton University Princeton NJ United States Department of Physics TUM School of Natural Sciences Technical University of Munich Garching Germany Munich Center for Quantum Science and Technology (MCQST) Munich Germany Department of Physics Cornell University Ithaca NY United States Laboratory of Atomic and Solid State Physics Cornell University Ithaca NY United States Department of Physics and Astronomy Purdue University West Lafayette IN United States Department of Electrical and Computer Engineering University of Massachusetts Amherst MA United States Department of Physics University of Connecticut Storrs CT United States Department of Electrical and Computer Engineering University of California Riverside Riverside CA United States School of Physics and Astronomy University of Nottingham Nottingham United Kingdom Centre for the Mathematics and Theoretical Physics of Quantum Non-Equilibrium Systems (CQNE) University of Nottingham Nottingham United Kingdom

Lattice gauge theories (LGTs)1, 2, 3–4 can be used to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials5...

Lattice gauge theories (LGTs)^{1, 2, 3–4} can be used to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials^{5, 6–7}. Studying dynamical properties of emergent phases can be challenging, as it requires solving many-body problems that are generally beyond perturbative limits^{8, 9–10}. Here we investigate the dynamics of local excitations in a Z2 LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit that prepares low-energy states that have a large overlap with the ground state;then we create charge excitations with local gates and simulate their quantum dynamics by means of a discretized time evolution. As the electric field coupling constant is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the electric field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT, from which we uncover two distinct regimes inside the confining phase: for weak confinement, the string fluctuates strongly in the transverse direction, whereas for strong confinement, transverse fluctuations are effectively frozen^11,12. We also demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a new set of techniques for investigating emergent excitations and string dynamics. © The Author(s) 2025.

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Bidirectionally Guided Large Language Models for Consumer-Centric Personalized Recommendation

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IEEE Transactions on Consumer Electronics 2025年

作者： Yu, Linfang Xiao, Peng Xu, Liqun Kumari, Saru Alenazi, Mohammed J.F. Industrial Research Institute Chengdu610213 China University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Chengdu University of Technology School of Computer and Network Security Chengdu China Industrial Research Institute Chengdu610213 China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut250004 India Department of Computer Engineering Riyadh11451 Saudi Arabia

With the increasing abundance of consumer electronics, consumers are facing the challenge of information overload, making personalized recommendation systems (RSs) crucial for enhancing consumer experience and promoting sales. Recently, LLM-driven RSs have attracted widespread attention. Nevertheless, the application of LLMs in recommendation still encounters challenges: how to effectively integrate user/item content (CT) and collaborative filtering (CF) information;how to effectively process heterogeneous token sequences;and how to efficiently generate recommendations. Therefore, we propose BGLLM4Rec, a bidirectionally guided LLM for recommendation. Specifically, a bidirectionally guided pre-training strategy is proposed to extend user/item ID tokens into the vocabulary of the pre-trained LLM, and leverage both CF LLM and CT LLM to learn their embedding representations. Meanwhile, a heterogeneous-homogeneous (Het-Hom) prompting strategy is proposed to decompose the input sequence into heterogeneous prompt and homogeneous main text parts, performing language modeling solely on the latter to enhance modeling effectiveness and stability. Finally, a recommendation-specific masked prompting fine-tuning strategy is employed to further refine the pre-trained CF LLM, enabling the efficient generation of multiple recommended items without hallucinations. Extensive experiments conducted on three public datasets have demonstrated that BGLLM4Rec exhibits exceptional performance in recommendation tasks. © 1975-2011 IEEE.

关键词： Collaborative filtering

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Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

arXiv

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arXiv 2023年

作者： Wang, Zhaorui Liu, Ya-Feng Wang, Ziyue Liu, Liang Pan, Haoyuan Cui, Shuguang The Future Network of Intelligence Institute The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The 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 The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices K. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of K is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for K and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of K are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of K and the design of the ADC quantizer;2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol. © 2023, CC BY.

关键词： Stochastic systems

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STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

arXiv

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arXiv 2022年

作者： Ji, Jiahao Wang, Jingyuan Jiang, Zhe Jiang, Jiawei Zhang, Hu State Key Laboratory of Software Development Environment School of Computer Science & Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer & Information Science & Engineering The University of Florida United States MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science & Engineering Beihang University Beijing China

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio- Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Differential equations

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Optimal vaccination and treatment policies with constrained inequalities to study limited vaccination resources for a multistrain reaction–diffusion SEIR model of COVID-19

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Partial Differential Equations in Applied mathematics 2024年 10卷

作者： El Alami Laaroussi, Adil El Bhih, Amine Rachik, Mostafa Applied Mathematics and Computer Science Team Higher Normal School Tetuan Abdelmalek Essaadi University Tétouan Morocco Laboratory of Analysis Modeling and Simulation Department of Mathematics and Computer Science Faculty of Sciences Ben M'Sik Hassan II University Casablanca BP 7955 Morocco Multidisciplinary Research and Innovation Laboratory (LPRI) Moroccan School of Engineering Sciences (EMSI) Casablanca Morocco

The availability of an effective vaccine is a major challenge in COVID-19 vaccination campaigns. In this article, we propose a comprehensive strategy incorporating distributed controls via vaccination and treatment while considering the limitations of available resources expressed as inequalities constrained. Our approach utilizes a spatiotemporal model that includes multiple strains of the virus and considers spatial heterogeneity, providing a more accurate representation of disease transmission. By using a generalized two-strain ODE model, we effectively capture the evolution of COVID-19, including various strains and variants. To validate our model, we establish the positivity and boundedness of the system's solution. Through the exploration of vaccination and treatment controls. Our study aims to reduce the spread of the disease between strains and identify optimal values that minimize the number of infected individuals and associated costs. This work provides useful insights into effective strategies for controlling the COVID-19 epidemic, even with limited vaccination resources. Furthermore, we demonstrate the existence of controls and describe their characterizations in terms of state and adjoint functions. The penalty method is used to treat the inequalities constrained. More importantly, our numerical simulation shows that adequate vaccination resources and simultaneous treatment can effectively reduce and even eliminate the spread of COVID-19. © 2024 The Author(s)

关键词： Forward-Backward Sweep numerical method Limited vaccine supply Optimal control with inequalities constraints SEIR spatiotemporal and multi-strain model

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PIG: Prompt Images Guidance for Night-Time Scene Parsing

arXiv

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arXiv 2024年

作者： Xie, Zhifeng Qiu, Rui Wang, Sen Tan, Xin Xie, Yuan Ma, Lizhuang Department of Film and Television Engineering Shanghai University Shanghai200072 China Shanghai Key Laboratory of Computer Software Testing and Evaluating China Hanghai Engineering Research Center of Motion Picture Special Effects Shanghai200072 China School of Computer Science and Technology China Normal University Shanghai200062 China Chongqing Institute of East China Normal University Chongqing401120 China School of Computer Science and Technology East China Normal University Shanghai200062 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Night-time scene parsing aims to extract pixel-level semantic information in night images, aiding downstream tasks in understanding scene object distribution. Due to limited labeled night image datasets, unsupervised domain adaptation (UDA) has become the predominant method for studying night scenes. UDA typically relies on paired day-night image pairs to guide adaptation, but this approach hampers dataset construction and restricts generalization across night scenes in different datasets. Moreover, UDA, focusing on network architecture and training strategies, faces difficulties in handling classes with few domain similarities. In this paper, we leverage Prompt Images Guidance (PIG) to enhance UDA with supplementary night knowledge. We propose a Night-Focused Network (NFNet) to learn night-specific features from both target domain images and prompt images. To generate high-quality pseudo-labels, we propose Pseudo-label Fusion via Domain Similarity Guidance (FDSG). Classes with fewer domain similarities are predicted by NFNet, which excels in parsing night features, while classes with more domain similarities are predicted by UDA, which has rich labeled semantics. Additionally, we propose two data augmentation strategies: the Prompt Mixture Strategy (PMS) and the Alternate Mask Strategy (AMS), aimed at mitigating the overfitting of the NFNet to a few prompt images. We conduct extensive experiments on four night-time datasets: NightCity, NightCity+, Dark Zurich, and ACDC. The results indicate that utilizing PIG can enhance the parsing accuracy of UDA. The code is available at https://***/qiurui4shu/PIG. Copyright © 2024, The Authors. All rights reserved.

关键词： Network architecture

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

SpreadFGL: Edge-Client Collaborative Federated Graph Learnin...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Luying Zhong Yueyang Pi Zheyi Chen Zhengxin Yu Wang Miao Xing Chen Geyong Min College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster UK School of Engineering Computing and Mathematics University of Plymouth UK Department of Computer Science University of Exeter UK

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.

关键词： Training Accuracy Collaboration Benchmark testing Imputation Generators Topology

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