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Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging

IEEE Transactions on Medical Imaging 2025年

作者： Li, Yuanhao Chen, Badong Hu, Zhongxu Suzuki, Keita Bai, Wenjun Koike, Yasuharu Yamashita, Okito RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Advanced Telecommunications Research Institute International Department of Computational Brain Imaging Kyoto619-0237 Japan Xi’an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an710049 China Huazhong University of Science and Technology School of Mechanical Science and Engineering Wuhan430074 China Institute of Science Tokyo Institute of Integrated Research Yokohama226-8501 Japan

Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood function for Bayesian inference. However, the electromagnetic measurements of brain activity are usually affected by miscellaneous artifacts, leading to a potentially non-Gaussian distribution for the observation noise. Hence the conventional Gaussian likelihood model is a suboptimal choice for the real-world source imaging task. In this study, we aim to solve this problem by proposing a new likelihood model which is robust with respect to non-Gaussian noises. Motivated by the robust maximum correntropy criterion, we propose a new improper distribution model concerning the noise assumption. This new noise distribution is leveraged to structure a robust likelihood function and integrated with hierarchical prior distributions to estimate source activities by variational inference. In particular, the score matching is adopted to determine the hyperparameters for the improper likelihood model. A comprehensive performance evaluation is performed to compare the proposed noise assumption to the conventional Gaussian model. Simulation results show that, the proposed method can realize more precise source reconstruction by designing known ground-truth. The realworld dataset also demonstrates the superiority of our new method with the visual perception task. This study provides a new backbone for Bayesian source imaging, which would facilitate its application using real-world noisy brain signal. © 1982-2012 IEEE.

关键词： Gaussian noise (electronic)

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RL-Based USV Path Planning under the Marine Multimodal Features Considerations

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IEEE Internet of Things Journal 2025年

作者： Lin, Quanbao Gou, Huaxing Tian, Peidong Zuo, Tian-Yu Zhang, Hanzhong Wang, Xin Sun, Poly Z. H. Shanghai Jiao Tong University School of Aeronautics and Astronautics Shanghai200240 China Southeast University School of Computer Science and Engineering Nanjing211189 China Shanghai University School of Microelectronics Shanghai200444 China Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Guangdong Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Shenzhen518055 China East China Normal University School of Psychology and Cognitive Science Shanghai200062 China Shanghai Jiao Tong University Department of Industrial Engineering Shanghai200240 China

Path planning is an important step in ensuring the safety of Unmanned Surface Vehicle (USV) navigation and executing missions quickly and efficiently. However, current USV path planning methods lack comprehensive consideration of electronic nautical charts and meteorological data, resulting in planned paths being unable to fully utilize marine environmental conditions, which may easily lead to collisions and long navigation times. Based on the above considerations, our study designs a USV path planning system that comprehensively considers the multimodal information from electronic nautical charts and meteorological data. The system consists of three parts: (1) image processing module, (2) meteorological analysis module, and (3) path planning module. In detail, the image processing module obtains the geographical feature information from the electronic chart and constructs a static obstacle environment. The meteorological analysis module obtains the meteorological feature information from meteorological data and constructs a dynamic meteorological vector field environment. The path planning module introduces a designed double deep Q-Network (DQN) structure, a multivariate weighted Dueling network, and a priority sampling mechanism to enhance the DQN algorithm for promising performance in USV path planning. Extensive experiments illustrate the superior performance of the proposed fusion DQN algorithm. Furthermore, the feasibility of the entire path planning system is confirmed. © 2014 IEEE.

关键词： Unmanned surface vehicles

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TVEG: Model Selection of the Time-Varying Exponential Family Distributions Graphical Models

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IEEE Transactions on Network Science and Engineering 2025年

作者： Liu, Juan Mei, Guofeng Xia, Yuanqing Wu, Xiaoqun Lu, Jinhu Beijing Forestry University School of Technology Beijing100107 China Italy Zhongyuan University of Technology Henan 451191 China Wuhan University School of Mathematics and Statistics Wuhan430072 China Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence School of Automation Science and Electrical Engineering Beijing100191 China

The undirected graphical model, a popular class of statistical model, offers a way to describe and explain the relationships among a set of variables. However, it remains a challenge to choose a certain graphical model to explain the relationships of variables adequately, especially when the relationships of variables are rewiring over time. This paper proposes the Time-Varying Exponential Family Distributions Graphical (TVEG) models, with time-varying structures and exponential family node-wise conditional distributions. TVEG models extend the scope of available graph models and can be applied to time-varying and exponential family distribution observation data in reality. We propose the Temporally Smoothed L1regularized exponential family graphical estimator (TSLEG), an estimator to infer the structure of TVEG from observations. We derive sufficient conditions for the TSLEG to recover the block partition and sparse pattern with high probability. We derive a message-passing optimization method to solve the TSLEG for time-varying Ising, Gaussian, exponential, and Poisson graphs based on the ADMM. The synthetic network simulations corroborate the theoretical analysis. Analysing of real data of stocks and the US Senate by the time-varying exponential model and Poisson model indicates the effectiveness and practicality of TVEG models. © 2025 IEEE. All rights reserved.

关键词： ADMM exponential family distributions Graphical model pseudo maximum likelihood estimation

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Weakly supervised action anticipation without object annotations

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Frontiers of Computer Science 2023年第2期17卷 101-110页

作者： Yi ZHONG Jia-Hui PAN Haoxin LI Wei-Shi ZHENG School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of EducationGuangzhou 510006China

Anticipating future actions without observing any partial videos of future actions plays an important role in action prediction and is also a challenging *** obtain abundant information for action anticipation,some methods integrate multimodal contexts,including scene object ***,extensively labelling each frame in video datasets requires considerable *** this paper,we develop a weakly supervised method that integrates global motion and local finegrained features from current action videos to predict next action label without the need for specific scene context ***,we extract diverse types of local features with weakly supervised learning,including object appearance and human pose representations without ground ***,we construct a graph convolutional network for exploiting the inherent relationships of humans and objects under present *** evaluate the proposed model on two datasets,the MPII-Cooking dataset and the EPIC-Kitchens dataset,and we demonstrate the generalizability and effectiveness of our approach for action anticipation.

关键词： action anticipation weakly supervised learning relation modelling graph convolutional network

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Development of yellow-light TiO2 integrated photonics

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Optics Letters 2025年第11期50卷 3489-3492页

作者： Zequn Chen Yiheng Tang Maoliang Wei Xiaojing Li Boshu Sun Yingchun Wu Sishuo Zou Ji Huang Ke Si Wei Gong Hongtao Lin Lan Li Institute of Advanced Technology Westlake Institute for Advanced Study Hangzhou 310024 China Zhejiang Key Laboratory of 3D Micro/Nano Fabrication and Characterization School of Engineering Westlake University Hangzhou 310030 China The State Key Lab of Brain-Machine Intelligence College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China MOE Frontier Science Center for Brain Science & Brain-Machine Integration Zhejiang University Hangzhou 310027 China Nanhu Brain-Computer Interface Institute Hangzhou 311100 China College of Optical Science and Engineering Zhejiang University Hangzhou 310027 China Westlake Institute for Optoelectronics Fuyang Hangzhou Zhejiang 311421 China

Visible-light photonic integrated circuits (PICs) are rapidly emerging as a crucial technology to overcome the scaling challenges in quantum information processing and biosensing. Titanium dioxide (TiO 2 ), with its high transparency in the visible range, is regarded as a promising material for facilitating the development of high-performance visible-light integrated systems. In this paper, we introduce a series of passive TiO 2 integrated devices, which include waveguides, multimode interferometers (MMIs), and thermo-optic switches (TOSs), monolithically integrated for the first time to our knowledge at a working wavelength of 589 nm. A propagation loss of 5.46 ± 0.17 dB/cm demonstrates a significant improvement over previous reports on yellow-light TiO 2 waveguides. This study marks the first fabrication of a TOS operating at 589 nm, featuring a TiO 2 core and polymer cladding. Notably, it achieves a power consumption (P π ) of 8.24 mW, which represents a threefold reduction compared to silicon nitride-based TOSs operating in the visible spectrum. These results offer new insight into the design and fabrication of visible waveguide-integrated devices, paving the way for future emerging applications, such as quantum information, sensors, and biomedical science.

关键词： Biosensors Effective refractive index Information processing Photonics Quantum information Titanium dioxide

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Edge Graph intelligence: Reciprocally Empowering Edge Networks With Graph intelligence

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IEEE Communications Surveys and Tutorials 2025年

作者： Zeng, Liekang Ye, Shengyuan Chen, Xu Zhang, Xiaoxi Ren, Ju Tang, Jian Yang, Yang Shen, Xuemin Sherman Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Guangzhou511453 China Ministry of Education School of Computer Science and Engineering Sun Yat-sen University The Key Laboratory of Machine Intelligence and Advanced Computing Guangzhou510275 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Midea Group Shanghai201702 China Guangzhou511453 China Peng Cheng Laboratory Shenzhen518055 China Terminus Group Beijing100027 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services. Meanwhile, Artificial intelligence (AI) frontiers have extrapolated to the graph domain and promoted Graph intelligence (GI). Given the inherent relation between graphs and networks, the interdiscipline of graph learning and edge networks, i.e., Edge GI or EGI, has revealed a novel interplay between them - GI aids in optimizing edge networks, while edge networks facilitate GI model deployment. Driven by this delicate closed-loop, EGI is recognized as a promising solution to fully unleash the potential of edge computing power and is garnering growing attention. Nevertheless, research on EGI remains nascent, and there is a soaring demand within both the communications and AI communities for a dedicated venue to share recent advancements. To this end, this paper promotes the concept of EGI, explores its scope and core principles, and conducts a comprehensive survey concerning recent research efforts on this emerging field. Specifically, this paper introduces and discusses: 1) fundamentals of edge computing and graph learning, 2) emerging techniques centering on the closed loop between graph intelligence and edge networks, and 3) open challenges and research opportunities of future EGI. By bridging the gap across communication, networking, and graph learning areas, we believe that this survey can garner increased attention, foster meaningful discussions, and inspire further research ideas in EGI. © 1998-2012 IEEE.

关键词： Contrastive Learning

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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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Imputing DNA Methylation by Transferred Learning Based Neural Network

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Journal of Computer Science & Technology 2022年第2期37卷 320-329页

作者： Xin-Feng Wang Xiang Zhou Jia-Hua Rao Zhu-Jin Zhang Yue-Dong Yang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510000China Key Laboratory of Machine Intelligence and Advanced Computing of Ministry of Education(Sun Yat-sen University)Guangzhou 510000 China

DNA methylation is one important epigenetic type to play a vital role in many diseases including *** the development of the high-throughput sequencing technology,there is much progress to disclose the relations of DNA methylation with ***,the analyses of DNA methylation data are challenging due to the missing values caused by the limitations of current *** many methods have been developed to impute the missing values,these methods are mostly based on the correlations between individual samples,and thus are limited for the abnormal samples in *** this study,we present a novel transfer learning based neural network to impute missing DNA methylation data,namely the TDimpute-DNAmeth *** method learns common relations between DNA methylation from pan-cancer samples,and then fine-tunes the learned relations over each specific cancer type for imputing the missing *** on 16 cancer datasets,our method was shown to outperform other commonly-used *** analyses indicated that DNA methylation is related to cancer survival and thus can be used as a biomarker of cancer prognosis.

关键词： neural network transfer learning DNA methylation data imputation survival analysis

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RobustPFL: Robust Personalized Federated Learning

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Chen, Guorong Wang, Wei Wu, Yufang Li, Chao Xu, Guangquan Ji, Shouling Li, Tao Shen, Meng Han, Yufei Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China Beijing Jiaotong University 3 Shangyuancun Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin300350 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China ITAI Haihe Lab Tianjin China Nankai University College of Computer Science Tianjin300350 China Beijing Institute of Technology School of Cyberspace Security Beijing100081 China Peng Cheng Laboratory Cyberspace Security Research Center Shenzhen518066 China INRIA Bretagne Rennes35042 France

Conventional federated learning (FL) coordinated by a central server focuses on training a global model and protecting the privacy of clients' training data by storing it locally. However, the statistical heterogeneity hinders the global model from adapting to the non-IID distributions among clients. Moreover, untrusted and unreliable central servers and malicious clients may compromise model integrity and availability, thus degrading the robustness of FL. To address these challenges, we present RobustPFL, a decentralized personalized federated learning (PFL) approach that combines α-based Layer-position Normalized Similarity (α-LNS) and local collaborative training to improve personalized performance while utilizing a blockchain-based committee mechanism to coordinate the aggregation process, thereby achieving high personalized accuracy and robustness. Extensive experiments show that our RobustPFL approach outperforms multiple algorithms, including Local training, FedAvg, FedReptile, Per-FedAvg, FedBN, and SPFL, on MNIST, CIFAR10, EMNIST, and N-BaIoT datasets in four non-IID settings. We also evaluate RobustPFL's effectiveness against attacks - poisoning attacks and free-riding attacks. Particularly, for three prevalent poisoning attacks (backdoor, label flipping, and model poisoning attacks), we compare non-defensive (FedAvg) and defensive (Krum, trimmed mean, Bulyan, FedBN, FLAME, and FangTrmean) methods with our proposed RobustPFL. The results show that our approach achieves significant defensive effects. © 2004-2012 IEEE.

关键词： Federated learning

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Characterization of Exact One-Query Quantum Algorithms for Partial Boolean Functions

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Journal of Computer Science & Technology 2023年第6期38卷 1423-1430页

作者：叶泽坤李绿周 Institute of Quantum Computing and Computer Theory School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhou 510006China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen UniversityGuangzhou 510006China

The query model(or black-box model)has attracted much attention from the communities of both classical and quantum ***,quantum advantages are revealed by presenting a quantum algorithm that has a better query complexity than its classical *** the history of quantum algorithms,the Deutsch algorithm and the Deutsch-Jozsa algorithm play a fundamental role and both are exact one-query quantum *** leads us to con-sider the problem:what functions can be computed by exact one-query quantum algorithms?This problem has been ad-dressed in the literature for total Boolean functions and symmetric partial Boolean functions,but is still open for general partial Boolean ***,in this paper,we continue to characterize the computational power of exact one-query quantum algorithms for general partial Boolean ***,we present several necessary and sufficient conditions for a partial Boolean function to be computed by exact one-query quantum ***,inspired by these conditions,we discover some new representative functions that can be computed by exact one-query quantum algorithms but have an essential difference from the already known ***,it is worth pointing out that before our work,the known func-tions that can be computed by exact one-query quantum algorithms are all symmetric functions and the quantum algo-rithm used is essentially the Deutsch-Jozsa algorithm,whereas the functions discovered in this paper are generally asym-metric and new algorithms to compute these functions are ***,this expands the class of functions that can be computed by exact one-query quantum algorithms.

关键词： quantum computing quantum query complexity quantum algorithm

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