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Service decoupling for open and intelligent service-based RAN

Frontiers of Information technology & Electronic Engineering 2025年第2期26卷 230-245页

作者： Chunjing YUAN Tong LEI Ze XUE Lin TIAN Shuyuan ZHANG Na LI Zhou TONG Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Hubei Intelligent Cloud Network Operation Center China TelecomWuhan 430024China Nanjing Institute of InforSuperBahn Nanjing 210008China Future Research Lab China Mobile Research InstituteBeijing 100053China

Task diversity is one of the biggest challenges for future sixth-generation(6G)*** the task as the center and driving the dynamic 6G radio access network(RAN)with artificial intelligence(AI)are necessary to accurately meet the personalized demands of ***,AI can only configure the parameters of a monolithic RAN and cannot schedule the *** development trend of 6G RANs is to enhance dynamic capability and scheduling *** this paper,we propose a service-based RAN architecture that can deploy decoupled RAN functions and customize networks according to *** analysis shows that the interactive relationship between RAN control plane(CP)functions is complex and needs to be decoupled according to the principles of high cohesion and low *** on the graph theory rather than expert experience,we design a RAN decoupling *** functional connection and interaction of the CP are represented by constructing an undirected weighted graph,followed by achieving decoupling of the CP through a minimum spanning *** an integration decoupling scheme of a RAN-CN(core network)is introduced considering the duplicate and redundant functions of the RAN and *** granularity of decoupling in a service-based RAN is determined by analyzing the flexibility of decoupling,complexity of signaling,and processing *** find that it is more appropriate to decouple the RAN CP into four *** integration decoupling of the RAN-CN resolves the technical bottleneck of low serial efficiency in the Ng interface,supporting AI-based global service scheduling.

关键词： Service decoupling Open and intelligent Service-based radio access network(RAN) Graph theory Full-service 6G network

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Task Prioritization in Multiagent Environments: A Novel Approach Using Nash Q-Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Kexin Hu, Yu Wang, Jielei Li, Lin Zhang, Shuyuan Luo, Guangchun Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

—With the rapid proliferation of smartphones and various terminal devices, edge computing has gained increasing importance in overcoming computational limitations and enhancing service quality through task offloading. However, most existing approaches handle tasks on a first-come, first-served basis, neglecting task prioritization, which is critical in real-world scenarios where tasks have different levels of urgency and resource requirements. This paper addresses the challenge of effective task prioritization in multi-agent task offloading by proposing a Nash Q-learning-based strategy. Our methodology involves optimizing task allocation decisions using Nash Q-learning to integrate Nash equilibrium principles, thereby enhancing adaptability and dynamic decision-making capabilities within a multi-agent environment. The proposed algorithm is evaluated through extensive simulations, comparing total delay, resource utilization, and task success rates across various task offloading strategies. The results demonstrate that our Nash Q-learning approach outperforms conventional techniques by dramatically lowering overall latency, optimizing resource allocation, and maintaining high task success rates. This work demonstrates the efficacy of Nash Q-learning for dynamically setting priorities for tasks in multi-agent edge computing environments, providing a robust framework for effective resource management and collaborative task completion. And also highlight the potential of Nash Q-learning in advancing intelligent task management in edge computing, suggesting its applicability in real-world, resource-constrained environments. © 2025 IEEE. All rights reserved,

关键词： Computation offloading

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Domain-aware Node Representation Learning for Graph Out-of-Distribution Generalization

Domain-aware Node Representation Learning for Graph Out-of-D...

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

作者： Yi Qiao Yang Liu Qing He Xiang Ao Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciencs Beijing China Institute of Intelligent Computing Technology Suzhou CAS

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Neural Networks (GNNs) have demonstrated impressive success across diverse fields when data satisfies in-distribution (ID) assumption. Nevertheless, GNN performance significantly declines in cases of distribution shifts between training and testing graph data. This degradation primarily stems from spurious correlations between irrelevant domain information and target labels in out-of-distribution (OOD) scenarios. Thus, maximizing the utilization of domain information becomes imperative. In light of this, we propose a novel approach named Domain-aware Node Representation Learning (DNRL), comprehensively incorporates domain information to bolster generalization capability. Specifically, DNRL selectively interpolates nodes with the same label but different domains, extending training data into unseen domains and alleviating the effects caused by domain-related spurious correlations. Futhermore, by introducing a domain-aware contrastive learning strategy, our method implicitly decouples domain information from node information to learn domain-independent node representations. Extensive experiments on graph out-of-distribution benchmarks demonstrate that DNRL can achieve effective OOD generalization performance across diverse domains.

关键词： Representation learning Training Degradation Interpolation Correlation Training data Signal processing Graph neural networks Data models Speech processing

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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

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Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators 31

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Yang, Siling He, Shuibing Wang, Wenjiong Yin, Yanlong Wu, Tong Chen, Weijian Zhang, Xuechen Sun, Xian-He Feng, Dan The State Key Laboratory of Blockchain and Data Security Zhejiang University China Zhejiang Lab China Institute of Blockchain and Data Security China Zhejiang Key Laboratory of Big Data Intelligent Computing China Washington State University Vancouver United States Illinois Institute of Technology United States Huazhong University of Science and Technology China Wuhan National Laboratory for Optoelectronics China

ISBN: (纸本)9798331506476

Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capability. However, existing accelerators can be severely underutilized even with pipelines, due to the oversight of the skewed execution times of various GCN stages and the ignorance of skewed degrees of graph vertices. In this work, we propose GOPIM, a GCN-oriented pipeline optimization for PIM accelerators to expedite GCN training. First, GOPIM proposes an ML-based scheme that allocates crossbar resources to the most needed stages to streamline the overall pipeline. Second, GOPIM utilizes a selective vertex updating technique that evenly distributes vertices on crossbars by interleaved mapping. These techniques collectively reduce the overall execution time without losing much accuracy. We also provide a practical architecture design for GOPIM. Our experimental results show that, GoPIM achieves up to 191 × speedup and 16.1 × energy saving, compared to the state-of-the-art work. © 2025 IEEE.

关键词： Graph neural networks

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Visual Prompt Flexible-Modal Face Anti-Spoofing

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IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 2597-2606页

作者： Yu, Zitong Cai, Rizhao Cui, Yawen Liu, Ajian Chen, Changsheng Great Bay University School of Computing and Information Technology Dongguan523000 China Nanyang Technological University ROSE Lab School of EEE 639798 Singapore Hong Kong Polytechnic University Kowloon Hong Kong Chinese Academy of Sciences University of Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Beijing100190 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security College of Electronics and Information Engineering Shenzhen518060 China

Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, multimodal face data collected from the real world is often imperfect due to missing modalities from various imaging sensors. Recently, flexible-modal FAS (Yu et al. 2023) has attracted more attention, which aims to develop a unified multimodal FAS model using complete multimodal face data but is insensitive to test-time missing modalities. In this paper, we tackle one main challenge in flexible-modal FAS, i.e., when missing modality occurs either during training or testing in real-world situations. Inspired by the recent success of the prompt learning in language models, we propose Visual Prompt flexible-modal FAS (VP-FAS), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to downstream flexible-modal FAS task. Specifically, both vanilla visual prompts and residual contextual prompts are plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 4% learnable parameters compared to training the entire model. Furthermore, missing-modality regularization is proposed to force models to learn consistent multimodal feature embeddings when missing partial modalities. Extensive experiments conducted on two multimodal FAS benchmark datasets demonstrate the effectiveness of our VP-FAS framework that improves the performance under various missing-modality cases while alleviating the requirement of heavy model re-training. © 2004-2012 IEEE.

关键词： Alignment

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A Flexible Knowledge Graph Error Detection Framework Combined with Semantic Information 12th

A Flexible Knowledge Graph Error Detection Framework Combine...

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12th CCF Conference on BigData, BigData 2024

作者： Zhao, Yangwu Liu, Yang Ao, Xiang He, Qing Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China

ISBN: (纸本)9789819610235

Knowledge graphs (KGs) are extensively utilized in numerous applications, including question-answering systems and recommender systems. However, knowledge graphs are often constructed through web crawling or crowdsourcing, leading to errors in the data. The task of knowledge graph error detection aims to identify inaccurate triplets in KGs and has received substantial attention in recent years. However, the majority of current error detection methods overlook the semantic information of the triplets, which can be vital for accurate error detection. In this paper, we introduce a Flexible Knowledge Graph Error Detection Framework that integrates Semantic Information (FKED), which combines both structural and semantic information to detect errors within the knowledge graph. FKED first extracts the structural information of KGs using a graph embedding model. Next, FKED employs a pre-trained language model (PLM) to extract semantic information from the triplets. Then the structural and semantic information are combined to detect errors within the knowledge graph. FKED can be flexibly added to other structure-based error detection models, enhancing their capabilities in downstream tasks of knowledge graphs. We assess FKED using two benchmark datasets: FB15k-237 and WN18RR. Experimental results indicate that our method is both superior and effective, substantially improving the performance of the base models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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New Offloading Method of computing Task Based on Gray Wolf Hunting Optimization Mechanism for the IOV

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IEEE Transactions on Network and Service Management 2025年

作者： Zhang, Jie Zhang, De-Gan Qiao, Meng Hong-Lin, E. Zhang, Ting Zhang, Ping University of Macau Key Lab of Internet of Things for Smart City Faculty of Science and Technology 999078 China Tianjin University of Technology Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin300384 China Tianjin University of Sport School of Sports Economics and Management Tianjin301617 China

Task offloading, as an effective solution, provides low latency and sufficient computing resources for mobile users in the network. However, how to reasonably offload to reduce system overhead is a challenging issue today. This article takes user terminals, edge servers, and idle vehicles with resources as the network structure, and is inspired by the highly social nature of the gray wolf pack. It proposes a new offloading method of edge computing task based on hunting optimizing mechanism of gray wolf for the Internet of Vehicle (IOV). Firstly, an adaptive weight factor is proposed to balance the weight ratio of delay and energy consumption in the system cost under the constraints of delay and energy consumption. With delay and computing resources of vehicles and servers as constraints, a multi constraint minimization system cost problem is proposed. Secondly, the hunting process of the gray Wolf optimization algorithm is used to find the optimal solution of the unloading scheme, The Levy flight strategy was added to enhance the global search ability of the algorithm, and a dynamic weight strategy was introduced to improve the convergence performance of the algorithm. Finally, the improved gray Wolf optimization algorithm was used to solve the optimal unloading plan and minimum *** simulation results show that compared with traditional gray Wolf optimization algorithm offloading schemes, the proposed scheme in this paper requires lower system costs. © 2004-2012 IEEE.

关键词： Mobile edge computing

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Bridge Frame and Event: Common Spatiotemporal Fusion for High-Dynamic Scene Optical Flow

arXiv

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arXiv 2025年

作者： Zhou, Hanyu Wang, Haonan Liu, Haoyue Duan, Yuxing Chang, Yi Yan, Luxin National Key Lab of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China School of Computing National University of Singapore Singapore

High-dynamic scene optical flow is a challenging task, which suffers spatial blur and temporal discontinuous motion due to large displacement in frame imaging, thus deteriorating the spatiotemporal feature of optical flow. Typically, existing methods mainly introduce event camera to directly fuse the spatiotemporal features between the two modalities. However, this direct fusion is ineffective, since there exists a large gap due to the heterogeneous data representation between frame and event modalities. To address this issue, we explore a common-latent space as an intermediate bridge to mitigate the modality gap. In this work, we propose a novel common spatiotemporal fusion between frame and event modalities for high-dynamic scene optical flow, including visual boundary localization and motion correlation fusion. Specifically, in visual boundary localization, we figure out that frame and event share the similar spatiotemporal gradients, whose similarity distribution is consistent with the extracted boundary distribution. This motivates us to design the common spatiotemporal gradient to constrain the reference boundary localization. In motion correlation fusion, we discover that the frame-based motion possesses spatially dense but temporally discontinuous correlation, while the event-based motion has spatially sparse but temporally continuous correlation. This inspires us to use the reference boundary to guide the complementary motion knowledge fusion between the two modalities. Moreover, common spatiotemporal fusion can not only relieve the cross-modal feature discrepancy, but also make the fusion process interpretable for dense and continuous optical flow. Extensive experiments have been performed to verify the superiority of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Optical flows

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Deep Time Series Anomaly Detection with Local Temporal Pattern Learning

Deep Time Series Anomaly Detection with Local Temporal Patte...

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

作者： Yizhou Li Yijie Wang Hongzuo Xu Xiaohui Zhou National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab (IGDL) Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniques rely on manual- or neural-based transformations when designing proxy tasks, overlooking the intrinsic temporal patterns of time series. This paper proposes a local temporal pattern learning-based time series anomaly detection (LTPAD). LTPAD first generates sub-sequences. Pairwise sub-sequences naturally manifest proximity relationships along the time axis, and such correlations can be used to construct supervision and train neural networks to facilitate the learning of temporal patterns. Time intervals between two sub-sequences serve as labels for sub-sequence pairs. By classifying these labeled data pairs, our model captures the local temporal patterns of time series, thereby modeling the temporal pattern-aware "normality". Abnormal scores of testing data are acquired by evaluating their conformity to these learned patterns shared in training data. Extensive experiments show that LTPAD significantly outperforms state-of-the-art competitors.

关键词： Time series analysis Semantics Neural networks Training data Manuals Signal processing Data models Speech processing Anomaly detection Testing

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