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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

IEEE Transactions on network and Service Management 2025年

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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A Chinese multimodal depression dataset with personality labeling for older adults with underlying medical conditions

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IEEE Transactions on Affective Computing 2025年

作者： Zhao, Yuliang Zhang, Huawei Li, Jian Song, Siyang Lian, Chao Liu, Yinghao Wang, Yulin Fu, Changzeng School of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Northeastern University SSTC NEU Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Qinhuangdao066004 China Osaka University Japan University of Cambridge AFAR Lab Department of Computer Science and Technology CambridgeCB3 0FT United Kingdom First Hospital of Qinhuangdao Qinhuangdao066004 China

Elderly individuals often suffer from underlying medical conditions, resulting in a significant decline in quality of life and a heightened susceptibility to depression. Presently, AI screening tools based on behavioral indicators offer an objective and effective approach to diagnosing depression. However, current AI depression screening tools are primarily tailored to adolescents and adults, exhibiting shortcomings in their applicability and accuracy for elderly individuals with underlying medical conditions. To address the above issues, firstly, this paper constructs a depression dataset for elderly people with underlying diseases by using semi-structured interviews. Secondly, based on cognitive science insights, it is recognized that personality factors significantly influence behavioral expressions and also determine the attitudes of elderly individuals toward current life circumstances/health issues. Therefore, besides annotating depression severity, the Big Five-10 personality scale was utilized to annotate participant personalities. Finally, a late fusion-based multi-task learning framework was proposed, and the effects of introducing gait information and personality annotation on the performance of depression assessment were investigated. The experimental findings affirm the importance of integrating gait information and personality assessment in improving depression detection effectiveness. This study provides valuable foundational resources, as well as beneficial references and insights, for the research on depression in the elderly. © 2010-2012 IEEE.

关键词： Depression Multimodal dataset Older adults Personality Underlying diseases

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Graph Representation Learning for Microarchitecture Design Space Exploration 23

Graph Representation Learning for Microarchitecture Design S...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Xiaoling Yi Jialin Lu Xiankui Xiong Dong Xu Li Shang Fan Yang State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China and School of Microelectronics Fudan University Shanghai China ZTE Corporation China and State Key Laboratory of Mobile Network and Mobile Multimedia Technology China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China and China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798350323481

Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic microarchitecture search framework based on graph embeddings. GRL-DSE uses graph representation learning to build a compact and continuous embedding space. Multi-objective Bayesian optimization using an ensemble surrogate model conducts microarchitecture design space exploration in the graph embedding space to efficiently and holistically optimize performance-power-area (PPA) objectives. Experimental studies on RISC-V BOOM show that GRL-DSE outperforms previous techniques by 74.59% on Pareto front quality and outperforms manual designs in terms of PPA.

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Multi-Modal Multi-Kernel Graph Learning for Autism Prediction and Biomarker Discovery

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks Bioinformatics

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Rotation-Adaptive Point Cloud Domain Generalization via Intricate Orientation Learning

arXiv

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arXiv 2025年

作者： Liu, Bangzhen Zheng, Chenxi Xu, Xuemiao Xu, Cheng Zhang, Huaidong He, Shengfeng South China University of Technology Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology The State Key Laboratory of Subtropical Building and Urban Science The Ministry of Education Key Laboratory of Big Data and Intelligent Robot The Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China Singapore Management University Singapore

The vulnerability of 3D point cloud analysis to unpredictable rotations poses an open yet challenging problem: orientation-aware 3D domain generalization. Cross-domain robustness and adaptability of 3D representations are crucial but not easily achieved through rotation augmentation. Motivated by the inherent advantages of intricate orientations in enhancing generalizability, we propose an innovative rotation-adaptive domain generalization framework for 3D point cloud analysis. Our approach aims to alleviate orientational shifts by leveraging intricate samples in an iterative learning process. Specifically, we identify the most challenging rotation for each point cloud and construct an intricate orientation set by optimizing intricate orientations. Subsequently, we employ an orientation-aware contrastive learning framework that incorporates an orientation consistency loss and a margin separation loss, enabling effective learning of categorically discriminative and generalizable features with rotation consistency. Extensive experiments and ablations conducted on 3D cross-domain benchmarks firmly establish the state-of-the-art performance of our proposed approach in the context of orientation-aware 3D domain generalization. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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LIPT: Latency-aware Image Processing Transformer

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IEEE Transactions on Image Processing 2025年 34卷 3056-3069页

作者： Qiao, Junbo Li, Wei Xie, Haizhen Chen, Hanting Hu, Jie Lin, Shaohui Han, Jungong East China Normal University School of Computer Science and Technology Shanghai200062 China Huawei Technologies Ltd Huawei Noah’s Ark Lab Shanghai China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science China Tsinghua University Department of Automation Beijing100084 China

Transformer is leading a trend in the field of image processing. While existing lightweight image processing transformers have achieved notable success, they primarily focus on reducing FLOPs (floating-point operations) or the number of parameters, rather than on practical inference acceleration. In this paper, we present a latency-aware image processing transformer, termed LIPT. We devise the low-latency proportion LIPT block that substitutes memory-intensive operators with the combination of self-attention and convolutions to achieve practical speedup. Specifically, we propose a novel non-volatile sparse masking self-attention (NVSM-SA) that utilizes a pre-computing sparse mask to capture contextual information from a larger window with no extra computation overload. Besides, a high-frequency reparameterization module (HRM) is proposed to make LIPT block reparameterization friendly, enhancing the model’s ability to reconstruct fine details. Extensive experiments on multiple image processing tasks (e.g., image super-resolution (SR), JPEG artifact reduction, and image denoising) demonstrate the superiority of LIPT on both latency and PSNR. LIPT achieves real-time GPU inference with state-of-the-art performance on multiple image SR benchmarks. © 1992-2012 IEEE.

关键词： Photointerpretation

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Quantum-based Variational Approach for Solving Graph Isomorphism Problems

arXiv

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arXiv 2025年

作者： Wang, Yukun Shen, Yingtong Zhang, Zhichao Wan, Linchun Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Beijing102249 China State Key Lab of Processors Institute of Computing Technology CAS Beijing100190 China School of Mathematics and Physics University of Science and Technology Beijing Beijing100083 China School of Computer and Information Science Southwest University Chongqing400715 China

The graph isomorphism problem remains a fundamental challenge in computer science, driving the search for efficient decision algorithms. Due to its ambiguous computational complexity, heuristic approaches such as simulated annealing are frequently used to explore the solution space selectively. These methods often achieve high probabilities of identifying solutions quickly, avoiding the exhaustive enumeration required by exact algorithms. However, traditional simulated annealing usually struggles with low sampling efficiency and reduced solution-finding probability in complex or large graph problems. In this study, we integrate the principles of quantum technology to address the graph isomorphism problem. By mapping the solution space to a quantum many-body system, we developed a parameterized model for variational simulated annealing. This approach emphasizes the regions of the solution space that are most likely to contain the optimal solution, thereby enhancing the search accuracy. Artificial neural networks were utilized to parameterize the quantum many-body system, leveraging their capacity for efficient function approximation to perform accurate sampling in the intricate energy landscapes of large graph problems. Copyright © 2025, The Authors. All rights reserved.

关键词： Heuristic methods

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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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DAPO-QAOA: An algorithm for solving combinatorial optimization problems by dynamically constructing phase operators

arXiv

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arXiv 2025年

作者： Wang, Yukun Li, ZeYang Wan, Linchun Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum Beijing102249 China State Key Lab of Processors Institute of Computing Technology CAS Beijing100190 China School of Computer and Information Science Southwest University Chongqing400715 China

The Quantum Approximate Optimization Algorithm (QAOA) is a well-known hybrid quantum-classical algorithm for combinatorial optimization problems. Improving QAOA involves enhancing its approximation ratio while addressing practical constraints of Noisy Intermediate Scale Quantum (NISQ) devices, such as minimizing the number of two-qubit gates and reducing circuit depth. Although existing research has optimized designs for phase and mixer operators to improve performance, challenges remain, particularly concerning the excessive use of two-qubit gates in the construction of phase operators. To address these issues, we introduce a Dynamic Adaptive Phase Operator (DAPO) algorithm, which dynamically constructs phase operators based on the output of previous layers and neighborhood search approach, optimizing the problem Hamiltonian more efficiently. By using solutions generated by QAOA itself to simplify the problem Hamiltonian at each layer, the algorithm captures the problem’s structural properties more effectively, progressively steering the solution closer to the optimal target. Experimental results on MaxCut and NAE3SAT problems show that DAPO achieves higher approximation ratios and significantly reduces two-qubit RZZ gates, especially in dense graphs. Compared to vanilla QAOA, DAPO uses only 66% of RZZ gates at the same depth while delivering better results, demonstrating its potential for efficient combinatorial optimization in the NISQ era. © 2025, CC BY.

关键词： Hamiltonians

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