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arXiv 2025年

作者： Lyu, Kedi Chen, Haipeng Liu, Zhenguang Yin, Yifang Lin, Yukang Jiao, Yingying College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China A*STAR Singapore Tsinghua Shenzhen International Graduate School Nanshan District Shenzhen China

Grasping the intricacies of human motion, which involve perceiving spatio-temporal dependence and multi-scale effects, is essential for predicting human motion. While humans inherently possess the requisite skills to navigate this issue, it proves to be markedly more challenging for machines to emulate. To bridge the gap, we propose the Human-like Vision and Inference System (HVIS) for human motion prediction, which is designed to emulate human observation and forecast future movements. HVIS comprises two components: the human-like vision encode (HVE) module and the human-like motion inference (HMI) module. The HVE module mimics and refines the human visual process, incorporating a retina-analog component that captures spatiotemporal information separately to avoid unnecessary crosstalk. Additionally, a visual cortex-analogy component is designed to hierarchically extract and treat complex motion features, focusing on both global and local features of human poses. The HMI is employed to simulate the multi-stage learning model of the human brain. The spontaneous learning network simulates the neuronal fracture generation process for the adversarial generation of future motions. Subsequently, the deliberate learning network is optimized for hard-to-train joints to prevent misleading learning. Experimental results demonstrate that our method achieves new state-of-the-art performance, significantly outperforming existing methods by 19.8% on Human3.6M, 15.7% on CMU Mocap, and 11.1% on G3D. © 2025, CC BY.

关键词： Motion estimation

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SSBM: A Signed Stochastic Block Model for Multiple Structure Discovery in Large-Scale Exploratory Signed Networks

arXiv

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arXiv 2023年

作者： Li, Yang Yang, Bo Zhao, Xuehua Yang, Zhejian Chen, Hechang Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China Aviation University of Air Force Changchun130062 China School of Digital Media Shenzhen Institute of Information Technology Shenzhen518055 China School of Artificial Intelligence Jilin University Changchun130012 China

Signed network structure discovery has received extensive attention and has become a research focus in the field of network science. However, most of the existing studies are focused on the networks with a single structure, e.g., community or bipartite, while ignoring multiple structures, e.g., the coexistence of community and bipartite structures. Furthermore, existing studies were faced with challenge regarding large-scale signed networks due to their high time complexity, especially when determining the number of clusters in the observed network without any prior knowledge. In view of this, we propose a mathematically principled method for signed network multiple structure discovery named the Signed Stochastic Block Model (SSBM). The SSBM can capture the multiple structures contained in signed networks, e.g., community, bipartite, and coexistence of them, by adopting a probabilistic model. Moreover, by integrating the minimum message length (MML) criterion and component-wise EM (CEM) algorithm, a scalable learning algorithm that has the ability of model selection is proposed to handle large-scale signed networks. By comparing state-of-the-art methods on synthetic and realworld signed networks, extensive experimental results demonstrate the effectiveness and efficiency of SSBM in discovering large-scale exploratory signed networks with multiple structures. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic models

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When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions

arXiv

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arXiv 2023年

作者： Zhang, Chunxu Zhang, Zijian Long, Guodong Yan, Peng Zhou, Tianyi Yang, Bo College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China College of Computer Science and Technology Jilin University City University of Hong Kong China Australian Artificial Intelligence Institute FEIT University of Technology Sydney Sydney Australia Computer Science and UMIACS University of Maryland Maryland United States

Federated recommendation systems usually trains a global model on the server without direct access to users’ private data on their own devices. However, this separation of the recommendation model and users’ private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings. This paper introduces a novel method called Item-aligned Federated Aggregation (IFedRec) to address this challenge. It is the first research work in federated recommendation to specifically study the cold-start scenario. The proposed method learns two sets of item representations by leveraging item attributes and interaction records simultaneously. Additionally, an item representation alignment mechanism is designed to align two item representations and learn the meta attribute network at the server within a federated learning framework. Experiments on four benchmark datasets demonstrate IFedRec’s superior performance for cold-start scenarios. Furthermore, we also verify IFedRec owns good robustness when the system faces limited client participation and noise injection, which brings promising practical application potential in privacy-protection enhanced federated recommendation systems. The implementation code is available1 Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems

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A Multiple Energy Conversion Channels Fusion Grid-Connected Inverter for Large-Scale PV Active Power Injection Preventing Power Loss Caused by Insufficient MPP PV Voltage

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IEEE Transactions on Power Electronics 2025年

作者： Pang, Ying Yang, Hao Ji, Yanju Zhang, Xinhao Zhu, Yongji Li, Gang Xiang, Zeng Gong, Cheng Qiu, Shilin Zhou, Haigen Wong, Man-Chung Lin, Jun Jilin University State Key Laboratory of Deep Earth Exploration and Imaging College of Instrumentation and Electrical Engineering Changchun130026 China Jilin University College of Instrumentation and Electrical Engineering Changchun130021 China Jilin University Department of Laboratory Animals College of Animal Sciences Changchun China Tongda Electromagnetic Energy Co. Ltd. Changsha410000 China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI Macao999078 China University of Macau Institute of Microelectronics Macao999078 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology Macao999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Macao999078 China

In the context of the increasing global demand for renewable energy and the rapid development of large-scale photovoltaic (PV) power generation, efficient grid connection of PV energy has become crucial. However, existing gridconnected inverters (GCI) face issues such as high-power losses issue of inductive GCI (IGCI) or limited operating ranges issue of capacitive GCI (CGCI). This paper introduces a multiple energy conversion channels fusion GCI (MC-FGCI) for largescale PV power injection. First, MC-FGCI is proposed based on a new topology circuit configuration. Second, an equivalent circuit model of IGCI and CGCI is introduced to calculate the critical point for channel switching. The IGCI and CGCI efficient operating boundaries are determined based on the critical point. Third, based on the efficient operating boundaries, a finite state machine (FSM) based method is proposed to choose the optimal VSI for high-efficiency grid connection. Finally, experimental results show that compared to previous multiple parallel IGCI (MP-IGCI), the proposed MC-FGCI saves around 11% power loss by eliminating the lossy DC/DC link. Compared to MP-CGCI, the proposed MC-FGCI expands the active power injection range under low maximum power point (MPP) voltage. © 1986-2012 IEEE.

关键词： Equivalent circuits

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Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification 25

Towards Multi-resolution Spatiotemporal Graph Learning for M...

引用

34th ACM Web Conference, WWW 2025

作者： Fan, Wei Fei, Jingru Guo, Dingyu Yi, Kun Song, Xiaozhuang Xiang, Haolong Ye, Hangting Li, Min University of Oxford Oxford United Kingdom Beijing Institute of Technology Beijing China University of Chicago Chicago United States State Information Center of China North China Institute of Computing Technology Beijing China The Chinese University of Hong Kong Shenzhen Shenzhen China Nanjing University of Information Science and Technology Nanjing China Jilin University Changchun China School of Computer Science and Engineering Central South University Changsha China Xiangjiang Laboratory Central South University Changsha China Furong Laboratory Central South University Changsha China

ISBN: (纸本)9798400712746

Medical time series has been playing a vital role in real-world healthcare systems as valuable information in monitoring health conditions of patients. Traditional methods towards medical time series classification rely on handcrafted feature extraction and statistical methods;with the recent advancement of artificial intelligence, the machine learning and deep learning methods have become more popular. However, existing methods often fail to fully model the complex spatial dynamics under different scales, which ignore the dynamic multi-resolution spatial and temporal joint inter-dependencies. Moreover, they are less likely to consider the special baseline wander problem as well as the multi-view characteristics of medical time series, which largely hinders their prediction performance. To address these limitations, we propose a Multi-resolution Spatiotemporal Graph Learning framework, MedGNN, for medical time series classification. Specifically, we first propose to construct multi-resolution adaptive graph structures to learn dynamic multi-scale embeddings. Then, to address the baseline wander problem, we propose Difference Attention Networks to operate self-attention mechanisms on the finite difference for temporal modeling. Moreover, to learn the multi-view characteristics, we utilize the Frequency Convolution Networks to capture complementary information of medical time series from the frequency domain. In addition, we introduce the Multi-resolution Graph Transformer architecture to model the dynamic dependencies and fuse the information from different resolutions. Finally, we have conducted extensive experiments on multiple medical real-world datasets that demonstrate the superior performance of our method. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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MIMO-aided Irregular Repetition Schemes for Mission Critical Communications

MIMO-aided Irregular Repetition Schemes for Mission Critical...

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IEEE Conference on Vehicular Technology (VTC)

作者： Linlin Zhao Shaodan Ma Guanghua Yang Xuefen Chi Wanting Yang Department of Communications Engineering Jilin University Changchun China Yibin Institute of Jilin University Yibin China Department of Electrical and Computer Engineering University of Macau Macau SAR China State Key Laboratory of Internet of Things for Smart City University of Macau Macau SAR China School of Intelligent Systems Science and Engineering Jinan University Zhuhai China

Mission critical communications require ultra-high reliability guarantees. It is a natural idea to combine massive multiple-input multiple-output (MIMO) and repetition transmission to further improve the reliability. In this paper, we combine the irregular repetition scheme with the massive MIMO with grant-free access. By introducing random transmission patterns for replicas, the collision problem of grant-free access is mitigated. Considering finite block-length coding, as well as sporadic and periodic arrivals of mission critical traffic, the reliability is thoroughly analyzed and derived in closed form. The theoretical analysis is finally verified through computer simulations. It is observed that the reliability of periodic traffic is superior to the sporadic one in large MIMO systems.

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GPFedRec: Graph-Guided Personalization for Federated Recommendation

arXiv

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arXiv 2023年

作者： Zhang, Chunxu Long, Guodong Zhou, Tianyi Zhang, Zijian Yan, Peng Yang, Bo College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Australian Artificial Intelligence Institute FEIT University of Technology Sydney Sydney Australia UMIACS University of Maryland Maryland United States College of Computer Science and Technology Jilin University City University of Hong Kong China

The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner. Using user-relation graphs to enhance federated recommendations is a promising topic. However, it is still an open challenge to construct the user-relation graph while preserving data locality-based privacy protection in federated settings. Inspired by a simple motivation, similar users share a similar vision (embeddings) to the same item set, this paper proposes a novel Graph-guided Personalization for Federated Recommendation (GPFedRec). The proposed method constructs a user-relation graph from user-specific personalized item embeddings at the server without accessing the users' interaction records. The personalized item embedding is locally fine-tuned on each device, and then a user-relation graph will be constructed by measuring the similarity among client-specific item embeddings. Without accessing users' historical interactions, we embody the data locality-based privacy protection of vanilla federated learning. Furthermore, a graph-guided aggregation mechanism is designed to leverage the user-relation graph and federated optimization framework simultaneously. Extensive experiments on five benchmark datasets demonstrate GPFedRec's superior performance. The in-depth study validates that GPFedRec can generally improve existing federated recommendation methods as a plugin while keeping user privacy safe. Code is available to ease reproducibility. Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems

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Degradation and dechlorination of trichloroacetic acid induced by an in situ 222 nm KrCl* excimer radiation

引用

Chemosphere 2023年第1期331卷 138753页

作者： Gan, Jiaming Zhu, Ting Zhang, Yizhan Li, Dailin Li, Ting Zhao, Min Zhao, ZengXia Wang, Lei School of Environmental Science & Engineering Xiamen University of Technology Xiamen361024 China School of Electrical Engineering and Automation Xiamen University of Technology Xiamen361024 China International Joint Research Laboratory of Nano-Micro Architecture Chemistry Laboratory of Theoretical and Computational Chemistry Institute of Theoretical Chemistry Jilin University Changchun130023 China

Since the coronavirus disease 2019 (COVID-19) pandemic epidemic, the excessive usage of chlorinated disinfectants raised the substantial risks of disinfection by-products (DBPs) exposure. While several technologies may remove the typical carcinogenic DBPs, trichloroacetic acid (TCAA), their application for continuous treatment is limited due to their complexity and expensive or hazardous inputs. In this study, degradation and dechlorination of TCAA induced by an in situ 222 nm KrCl* excimer radiation as well as role of oxygen in the reaction pathway were investigated. Quantum chemical calculation methods were used to help predict the reaction mechanism. Experimental results showed that UV irradiance increased with increasing input power and decreased when the input power exceeded 60 W. Decomposition and dechlorination were simultaneously achieved, where around 78% of TCAA (0.62 mM) can be eliminated and 78% dechlorination within 200 min. Dissolved oxygen showed little effect on the TCAA degradation but greatly boosted the dechlorination as it can additionally generate hydroxyl radical (•OH) in the reaction process. Computational results showed that under 222 nm irradiation, TCAA was excited from S0 to S1 state and then decayed by internal crossing process to T1 state, and a reaction without potential energy barrier followed, resulting in the breaking of C–Cl bond and finally returning to S0 state. Subsequent C–Cl bond cleavage occurred by a barrierless •OH insertion and HCl elimination (27.9 kcal/mol). Finally, the •OH attacked (14.6 kcal/mol) the intermediate byproducts, leading to complete dechlorination and decomposition. The KrCl* excimer radiation has obvious advantages in terms of energy efficiency compared to other competitive methods. These results provide insight into the mechanisms of TCAA dechlorination and decomposition under KrCl* excimer radiation, as well as important information for guiding research toward direct and indirect photolysis of halogenate

关键词： Chlorine compounds

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Generative AI for Advanced UAV Networking

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IEEE Network 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Du, Hongyang Kang, Jiawen Wu, Jing Sun, Sumei Zhang, Ping Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong999077 Hong Kong Guangdong University of Technology School of Automation Guangzhou510006 China Agency for Science Technology and Research Institute for Infocomm Research Singapore639798 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions. © 2024 IEEE.

关键词： Aircraft communication

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Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing

arXiv

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arXiv 2024年

作者： Zhao, Yong Zhu, Zhengqiu Gao, Chen Wang, En Huang, Jincai Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Hunan Province Changsha410073 China BNRist Tsinghua University Beijing100084 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Spatial Crowdsourcing (SC) is gaining traction in both academia and industry, with tasks on SC platforms becoming increasingly complex and requiring collaboration among workers with diverse skills. Recent research works address complex tasks by dividing them into subtasks with dependencies and assigning them to suitable workers. However, the dependencies among subtasks and their heterogeneous skill requirements, as well as the need for efficient utilization of workers’ limited work time in the multi-task allocation mode, pose challenges in achieving an optimal task allocation scheme. Therefore, this paper formally investigates the problem of Dependency-aware Multi-task Allocation (DMA) and presents a well-designed framework to solve it, known as Heterogeneous Graph Reinforcement Learning-based Task Allocation (HGRL-TA). To address the challenges associated with representing and embedding diverse problem instances to ensure robust generalization, we propose a multi-relation graph model and a Compound-path-based Heterogeneous Graph Attention Network (CHANet) for effectively representing and capturing intricate relations among tasks and workers, as well as providing embedding of problem state. The task allocation decision is determined sequentially by a policy network, which undergoes simultaneous training with CHANet using the proximal policy optimization algorithm. Extensive experiment results demonstrate the effectiveness and generality of the proposed HGRL-TA in solving the DMA problem, leading to average profits that is 21.78% higher than those achieved using the metaheuristic methods. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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