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arXiv 2025年

作者： Zhang, Qiquan Wickramasinghe, Buddhi Ambikairajah, Eliathamby Sethu, Vidhyasaharan Li, Haizhou School of Electrical Engineering and Telecommunications The University of New South Wales Sydney2052 Australia School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen51872 China

Hand-crafted features, such as Mel-filterbanks, have traditionally been the choice for many audio processing applications. Recently, there has been a growing interest in learnable front-ends that extract representations directly from the raw audio waveform. However, both hand-crafted filterbanks and current learnable front-ends lead to fixed computation graphs at inference time, failing to dynamically adapt to varying acoustic environments, a key feature of human auditory systems. To this end, we explore the question of whether audio front-ends should be adaptive by comparing the Ada-FE front-end (a recently developed adaptive front-end that employs a neural adaptive feedback controller to dynamically adjust the Q-factors of its spectral decomposition filters) to established learnable front-ends. Specifically, we systematically investigate learnable front-ends and Ada-FE across two commonly used back-end backbones and a wide range of audio benchmarks including speech, sound event, and music. The comprehensive results show that our Ada-FE outperforms advanced learnable front-ends, and more importantly, it exhibits impressive stability or robustness on test samples over various training epochs. Copyright © 2025, The Authors. All rights reserved.

关键词： Gabor filters

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Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-agent Reinforcement Learning Framework

arXiv

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arXiv 2023年

作者： Guo, Yingya Tang, Qi Ma, Yulong Tian, Han Chen, Kai The College of Computer and Data Science Fuzhou University China The Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China The Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fujian 350003 China The Department of Computer Science & Engineering Hong Kong University of Science & Technology Hong Kong

Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decision-making problems. To coordinate the multiple agents for achieving a global optimization goal, we construct an interactive environment for training the routing agents that own partial link utilization observations. To optimize credit assignment of multi-agent, we introduce the difference reward assignment mechanism for encouraging agents to take better action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen. Copyright © 2023, The Authors. All rights reserved.

关键词： Global optimization

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Semantic Pre-Extraction for Energy-Efficient AoI Minimization in UAV-Assisted Wireless Networks

Semantic Pre-Extraction for Energy-Efficient AoI Minimizatio...

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IEEE Conference on Wireless Communications and Networking

作者： Yusi Long Gary C.F. Lee Lanhua Li Shimin Gong Sumei Sun Dusit Niyato School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China Institute for Infocomm Research (IR) A*STAR Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic communication network. The energy-limited ground users (GUs) provide semantic services to periodically generated raw data and a UAV relays the extracted semantic information to a base station (BS). Semantic extraction enhances data responsiveness and reduces the age-of-information (AoI) by transmitting only the most essential information. However, more complex semantic extraction increases energy consumption, making it easier for the GUs to deplete their energy. Therefore, we introduce a novel energy-efficient AoI (EAoI) metric to capture both information freshness and energy consumption of the GUs. We formulate a time-averaged EAoI minimization problem by jointly optimizing the GUs' scheduling, pre-extraction strategy, semantic control, computing resource allocation, and the UAV's trajectory. We further propose a semantic-aware joint pre-extraction and trajectory planning (Sem-JPT) algorithm to decompose the complex optimization problem into three subproblems, which are solved by a series of approximation methods. Simulation results demonstrate that semantic communication can reduce the overall EAoI by more than 18% compared with conventional bit-based communication. Moreover, the proposed Sem-JPT algorithm can maintain information freshness and prolong the GUs' lifetimes, outperforming existing baselines.

关键词： Energy consumption Trajectory planning Processor scheduling Wireless networks Semantic communication Minimization Information age Energy efficiency Trajectory data mining

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Adaptive-Propagating Heterophilous Graph Convolutional Network

SSRN

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SSRN 2024年

作者： Huang, Yang Pi, Yueyang Shi, Yiqing Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China College of Photonic and Electronic Engineering Fujian Normal University Fuzhou350007 China

Graph convolutional network have significant advantages in tasks dealing with graph-structured data, but most existing approaches based on graph convolutional network usually potentially assume that nodes belonging to the same class in a graph tend to form edges, yet inter-class edges exist in many real graph-structured data. Due to the propagation mechanism of graph convolutional network, it is usually difficult to avoid the aggregation of interference information of nodes from different classes, leading to the inclusion of noise and irrelevant information in the results, thus affecting the performance of the model. We propose a new framework to address this issue, which can extend graph convolutional network to heterophilous graph-structured data. The proposed method has two main parts: Firstly, from the perspective of feature space, the heterophily of the graph-structured data is modeled so that the model can adaptively change the information propagation process according to the homophily of the edges, and mitigate the influence of inter-class information on the model. Secondly, the implicit node interaction information is captured through the feature space, which is then fused with the original interaction information to aggregate sufficient intra-class information. Extensive experiments on real-world datasets demonstrate the competitiveness of our approach among current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Graphic methods

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A Low-Complexity Expectation Propagation Detector for MIMO-OTFS

A Low-Complexity Expectation Propagation Detector for MIMO-O...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Xinhua Zheng Zhihao Chen Xiang Chen Xijun Wang Xiao Huang School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China School of Microelectronics Science and Technology Sun Yat-sen University Zhuhai China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Orthogonal time frequency space (OTFS) modu-lation has been demonstrated to achieve reliable communication over time-varying wireless channels in high-speed dynamic environments. In this paper, the expectation propagation (EP) algorithm is employed for signal detection in multiple-input multiple-output (MIMO)-OTFS systems, aiming to enhance de-tection reliability compared to linear minimum mean square error and message passing approaches. By exploiting the sparsity of the time-domain matrix, the computational complexity of matrix inversion in the iterative process of the EP algorithm is reduced. A time-domain EP detection algorithm with log-linear order complexity is proposed. Numerical experiments validate that the proposed receiver achieves a bit error rate performance comparable to traditional EP receivers while maintaining lower computational complexity.

关键词： Wireless communication Time-frequency analysis Simulation Receivers Detectors Sparse matrices Reliability Time-domain analysis Computational complexity Signal detection

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A GA-Based Energy Aware Virtual Machine Placement Algorithm for Cloud data Centers

A GA-Based Energy Aware Virtual Machine Placement Algorithm ...

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Xiaodong Wu School of Mathmatics and Computer Science Quanzhou Normal University Quanzhou China Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Quanzhou China

ISBN: (纸本)9781665496407

Virtual machine placement (VMP) is among the important techniques to improve the energy efficiency of green cloud data centers. To address this issue, bionic optimization algorithms have been widely used. One of the most effective methods is to use the genetic algorithm (GA) to find the best or near-optimal solution for the VMP problem. In this paper, a GGA-based algorithm is proposed to solve the energy-aware VMP problem. The algorithm regards the VMP problem as BPP and strives to reduce the number of active hosts in order to reduce the system energy consumption. Further, since it is important to find the solution of VMP in a short time especially in the online scenario, several optimization strategies are developed and applied in the proposed algorithm to improve the convergence speed of the algorithm and reduce the execution time of the algorithm. The experimental results demonstrate that compared with the traditional genetic algorithm, our method can effectively decrease the number of iterations and improve the speed of seeking optimal solutions.

关键词： data centers Cloud computing Energy consumption Heuristic algorithms Simulation Programming Virtual machining

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Lyapunov-guided Deep Reinforcement Learning for Semantic-aware AoI Minimization in UAV-assisted Wireless Networks

arXiv

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arXiv 2024年

作者： Long, Yusi Gong, Shimin Sun, Sumei Lee, Gary Li, Lanhua Niyato, Dusit The School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen Campus Shenzhen518000 China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology China A*STAR Singapore The College of Computing and Data Science Nanyang Technological University Singapore

This paper investigates an unmanned aerial vehicle (UAV)-assisted semantic network where the ground users (GUs) periodically capture and upload the sensing information to a base station (BS) via UAVs’ relaying. Both the GUs and the UAVs can extract semantic information from large-size raw data and transmit it to the BS for recovery. Smaller-size semantic information reduces latency and improves information freshness, while larger-size semantic information enables more accurate data reconstruction at the BS, preserving the value of original information. We introduce a novel semantic-aware age-of-information (SAoI) metric to capture both information freshness and semantic importance, and then formulate a time-averaged SAoI minimization problem by jointly optimizing the UAV-GU association, the semantic extraction, and the UAVs’ trajectories. We decouple the original problem into a series of subproblems via the Lyapunov framework and then use hierarchical deep reinforcement learning (DRL) to solve each subproblem. Specifically, the UAV-GU association is determined by DRL, followed by the optimization module updating the semantic extraction strategy and UAVs’ deployment. Simulation results show that the hierarchical structure improves learning efficiency. Moreover, it achieves low AoI through semantic extraction while ensuring minimal loss of original information, outperforming the existing baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Liang, Hanzhe Zhou, Jie Chen, Xuanxin Dai, Tao Wang, Jinbao Gao, Can College of Computer Science and Software Engineering Shenzhen University China Shenzhen Audencia Financial Technology Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Faculty of Education Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preprocessing as a dual-objective semantic isolator: (1) mitigating cross-semantic interference to the greatest extent feasible and (2) confining anomaly judgements to aligned semantic spaces wherever viable, thereby establishing intra-semantic comparability. Any preprocessing approach achieves this goal through a two-stage process of Semantic-Division and Spatial-Constraints stage. Through systematic deconstruction, we theoretically and experimentally subsume existing preprocessing methods under this theorem via tripartite evidence: qualitative analyses, quantitative studies, and mathematical proofs. Guided by the Fence Theorem, we implement Patch3D, consisting of Patch-Cutting and Patch-Matching modules, to segment semantic spaces and consolidate similar ones while independently modeling normal features within each space. Experiments on Anomaly-ShapeNet and Real3D-AD with different settings demonstrate that progressively finer-grained semantic alignment in preprocessing directly enhances point-level AD accuracy, providing inverse validation of the theorem’s causal logic. Copyright © 2025, The Authors. All rights reserved.

关键词： Fences

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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A discovery of two slow pulsars with FAST:“Ronin”from the globular cluster M15

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Science China(Physics,Mechanics & Astronomy) 2024年第6期67卷 166-180页

作者： Dengke Zhou Pei Wang Di Li Jianhua Fang Chenchen Miao Paulo CCFreire Lei Zhang Dandan Zhang Huaxi Chen Yi Feng Yifan Xiao Jintao Xie Xu Zhang Chenwu Jin Han Wang Yinan Ke Xuerong Guo Rushuang Zhao Chenhui Niu Weiwei Zhu Mengyao Xue Yabiao Wang Jiafu Wu Zhenye Gan Zhongyi Sun Chengjie Wang Jie Zhang Junshuo Zhang Jinhuang Cao Wanjin Lu Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311121China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China University of Chinese Academy of Sciences Beijing 100049China New Cornerstone Science Laboratory Shenzhen 518054China Max Planck Institut f¨ur Radioastronomie Bonn 53121Germany Centre for Astrophysics and Supercomputing Swinburne University of TechnologyHawthorn 3122Australia School of Mathematical Sciences School of Physics and Electronic SciencesGuizhou Normal UniversityGuiyang 550001China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang 550001China School of Physics and Electronic Engineering Qilu Normal UniversityJinan 250200China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001China College of Physical Science and Technology Central China Normal UniversityWuhan 430079China Tencent Youtu Lab Shanghai 201103China

Globular clusters harbor numerous millisecond pulsars,but long-period pulsars(P 100 ms)are rarely *** this study,we employed a fast folding algorithm to analyze observational data from multiple globular clusters obtained by the Five-hundredmeter Aperture Spherical radio Telescope(FAST),aiming to detect the existence of long-period *** estimated the impact of the median filtering algorithm in eliminating red noise on the minimum detectable flux density(S_(min))of ***,we successfully discovered two isolated long-period pulsars in M15 with periods approximately equal to 1.928451 and3.960716 s,*** the P-˙P diagram,both pulsars are positioned below the spin-up line,suggesting a possible history of partial recycling in X-ray binary systems disrupted by dynamical encounters later *** to timing results,these two pulsars exhibit remarkably strong magnetic *** the magnetic fields were weakened during the accretion process,then a short duration of accretion might explain the strong magnetic fields of these pulsars.

关键词： pulsar globular cluster FAST

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