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34th ACM Web Conference, WWW 2025

作者： Li, Yishuo Zhang, Qi Lu, Wenpeng Peng, Xueping Zhang, Weiyu Si, Jiasheng Gong, Yongshun Hu, Liang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Computer Science and Technology Tongji University Shanghai China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Shandong University Jinan China

ISBN: (纸本)9798400712746

Medication recommendation aims to suggest personalized drug combinations to patients based on their longitudinal medical histories stored in electronic health record (EHR) datasets. Patients’ Dynamic Treatment Regimes (DTRs) determine how patients’ drug combinations change along with the evolution of disease treatment. DTRs are effective for comprehending disease-treatment dynamics and for recommending a timely and personalized combination of medications for patients. However, existing medication recommender systems (MRSs) overlook the multiple treatment pathways generated by the intervention of DTRs and can only recommend a single treatment paradigm, ignoring the fact that patients may be at different treatment stages and thus require different treatment regime. Such disregard leads to a significant limitation in recommending personalized medication combinations tailored to different treatment stages, yielding greatly compromised accuracy and applicability of MRSs. Moreover, existing methods often overlook the time interval information over patients’ successive visits, which is critical to indicate patients’ treatment evolution. To address these significant gaps, we propose a Time-aware Medication Recommendation Framework via Intervention of Dynamic Treatment Regimes, called MR-DTR. To explicitly illustrate the intervention processes of DTRs on similar patients, we employ a co-guided graph to connect various patient sequences. In addition, to fully utilize the time interval information, we design a time-aware guidance mechanism dedicated to the co-guided graph to efficiently learn medication representation using the patient’s guidance information. We also introduce relative time intervals in the encoder to act as positional information. Extensive experiments on two real-world datasets demonstrate that MR-DTR surpasses state-of-the-art models in terms of recommendation performance. © 2025 Copyright held by the owner/author(s).

关键词： Electronic health record

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A Survey on Anomaly Detection with Few-Shot Learning 8th

A Survey on Anomaly Detection with Few-Shot Learning

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Junyang Wang, Changbo Hong, Yifan Mi, Rui Zhang, Liang-Jie Wu, Yirui Wang, Huan Zhou, Yue College of Computer Science and Software Engineering Shenzhen University Shenzhen China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Informatics Huazhong Agricultural University Wuhan China Hohai University Nanjing China Department of Obstetrics and Gynecology Tianyou Hospital Affiliated to Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031779534

The primary objective of anomaly detection is to identify abnormal or unusual patterns within a dataset, where the number of normal samples typically exceeds that of abnormal samples. Due to the scarcity of labeled abnormal samples, traditional methods face challenges when dealing with anomaly detection. To overcome these limitations, few-shot learning has emerged as a promising solution. By leveraging a limited number of labeled anomaly samples, few-shot learning enables the construction of models that enhance anomaly detection performance and generalization. This paper provides a comprehensive investigation of anomaly detection, covering its definition, fundamental principles, methods, and challenges. Furthermore, it introduces few-shot learning as a solution and explores its principles, applications, and technical categorization, including meta-learning, transfer learning, generative models, prototypical learning, and siamese networks. The paper explores the utilization of few-shot learning in anomaly detection across diverse data types This paper delves into significance across different domains. Additionally, it addresses the challenges faced by few-shot learning in the field of anomaly detection and proposes future directions for development. This comprehensive analysis aims to provide profound insights and guidance for prospective research and application in anomaly detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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ConZone: A Zoned Flash Storage Emulator for Consumer Devices

ConZone: A Zoned Flash Storage Emulator for Consumer Devices

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Design, Automation and Test in Europe Conference and Exhibition

作者： Dingcui Yu Jialin Liu Yumiao Zhao Wentong Li Ziang Huang Zonghuan Yan Mengyang Ma Liang Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China College of Computer Science and Technology East China Normal University Shanghai China Software Engineering Institute East China Normal University Shanghai China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Considering the potential benefits to lifespan and performance, zoned flash storage is expected to be incorporated into the next generation of consumer devices. However, due to the limited volatile cache and heterogeneous flash cells of consumer-grade flash storage, adopting a zone abstraction requires additional internal hardware design to maximize its benefits. To understand and efficiently improve the hardware design on consumer-grade zoned flash storage, we present ConZone—the first emulator tailored to the characteristics of consumer-grade zoned flash storage. Users can explore the internal architecture and management strategies of consumer-grade zoned flash storage and integrate the optimization with software. We validate the accuracy of ConZone by realizing a hardware architecture for consumer-grade zoned flash storage and comparing it with the state-of-the-art. We also make a case study for read performance research with ConZone to explore the design of mapping mechanisms and cache management strategies.

关键词： Performance evaluation Accuracy Europe computer architecture Hardware software Planning Optimization Next generation networking

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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PVBF: A Framework for Mitigating Parameter Variation Imbalance in Online Continual Learning

arXiv

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arXiv 2025年

作者： Tao, Zelin Deng, Hao Liu, Mingqing Zhang, Lijun Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai201804 China College of Electronic and Information Engineering Tongji University Shanghai201804 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai201804 China

Online continual learning (OCL), which enables AI systems to adaptively learn from non-stationary data streams, is commonly achieved using experience replay (ER)-based methods that retain knowledge by replaying stored past during training. However, these methods face challenges of prediction bias, stemming from deviations in parameter update directions during task transitions. This paper identifies parameter variation imbalance as a critical factor contributing to prediction bias in ER-based OCL. Specifically, using the proposed parameter variation evaluation method, we highlight two types of imbalance: correlation-induced imbalance, where certain parameters are disproportionately updated across tasks, and layer-wise imbalance, where output layer parameters update faster than those in preceding layers. To mitigate the above imbalances, we propose the Parameter Variation Balancing Framework (PVBF), which incorporates: 1) a novel method to compute parameter correlations with previous tasks based on parameter variations, 2) an encourage-and-consolidate (E&C) method utilizing parameter correlations to perform gradient adjustments across all parameters during training, 3) a dual-layer copy weights with reinit (D-CWR) strategy to slowly update output layer parameters for frequently occuring sample categories. Experiments on short and long task sequences demonstrate that PVBF significantly reduces prediction bias and improves OCL performance, achieving up to 47% higher accuracy compared to existing ER-based methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Low-shot Video Object Segmentation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Yan, Kun Wei, Fangyun Dai, Shuyu Wu, Minghui Wang, Ping Xu, Chang Peking University School of Computer Science Beijing100871 China University of Sydney School of Computer Science DarlingtonNSW2008 Australia Peking University School of Software and Microelectronics Beijing100871 China Shanghai Artificial Intelligence Laboratory and Mininglamp Technology China Peking University National Engineering Research Center for Software Engineering Beijing100871 China Peking University School of Software and Microelectronics China China

Prior research in video object segmentation (VOS) predominantly relies on videos with dense annotations. However, obtaining pixel-level annotations is both costly and time-intensive. In this work, we highlight the potential of effectively training a VOS model using remarkably sparse video annotations - specifically, as few as one or two labeled frames per training video, yet maintaining near equivalent performance levels. We introduce this innovative training methodology as low-shot video object segmentation, abbreviated as low-shot VOS. Central to this method is the generation of reliable pseudo labels for unlabeled frames during the training phase, which are then used in tandem with labeled frames to optimize the model. Notably, our strategy is extremely simple and can be incorporated into the vast majority of current VOS models. For the first time, we propose a universal method for training VOS models on one-shot and two-shot VOS datasets. In the two-shot configuration, utilizing just 7.3% and 2.9% of labeled data from the YouTube-VOS and DAVIS benchmarks respectively, our model delivers results on par with those trained on completely labeled datasets. It is also worth noting that in the one-shot setting, a minor performance decrement is observed in comparison to models trained on fully annotated datasets. Code and models are available at https://***/yk-pku/Low-shot-VOS. © 1979-2012 IEEE.

关键词： Video analysis

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Global Feature Enhancing and Fusion Framework for Strain Gauge Status Recognition 25

Global Feature Enhancing and Fusion Framework for Strain Gau...

引用

Companion Proceedings of the ACM on Web Conference 2025

作者： Xu Zhang Peng Wang Chen Wang Zhe Xu Xiaohua Nie Wei Wang Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shang Hai China Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shanghai China National Engineering Research Center for Big Data Software Tsinghua University Beijing China School of Software Tsinghua University Beijing China National Key Laboratory of Strength and Structural Integrity Aircraft Strength Research Institute of China Xi'an Shaanxi China

ISBN: (纸本)9798400713316

Strain Gauge Status (SGS) recognition is crucial in the field of intelligent manufacturing based on the Internet of Things, as accurate identification helps timely detection of failed mechanical components, avoiding accidents. The loading and unloading sequences generated by strain gauges can be identified through time series classification (TSC) algorithms. Recently, deep learning models, e.g., convolutional neural networks (CNNs) have shown remarkable success in the TSC task, as they can extract discriminative local features from the subsequences to identify the time series. However, we observe only the local features may not be sufficient for expressing the time series, especially when the local sub-sequences between different time series are very similar, e.g., SGS data of aircraft wings in static strength experiments. Nevertheless, CNNs suffer from the limitation in extracting global features due to the nature of convolution operations. For extracting global features to more comprehensively represent the SGS time series, we propose two insights: (i) Constructing global features through feature engineering. (ii) Learning high-order relationships between local features to capture global features. To realize and utilize them, we propose a hypergraph-based global feature learning and fusion framework, which learns and fuses global features for semantic consistency to enhance the representation of SGS time series, thereby improving recognition accuracy. Our method designs are validated on industrial SGS and public UCR datasets, showing better generalization for unseen data in SGS recognition.

关键词： deep learning models

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Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks

arXiv

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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Broadband high efficiency polarization grating with continuously varying periodicity based on liquid crystal polymer

引用

Journal of Molecular Liquids 2025年 426卷

作者： Zhao, Yadi Wang, Jianguo Chen, Yi Wang, Fajia Wang, Jundong Zhu, Xiangyu Hong, Ruijin Tao, Chunxian Zhang, Dawei Lian, Yafei Zhao, Yuan-an Research and Development Center of Thin Film Optics Shanghai Institute of Optics and Fine Mechanics No.390 Qinghe Road Jiading District Shanghai201800 China Key Laboratory of Materials for High Power Laser Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Liquid crystal polarization gratings (LCPGs) exhibit unique optical characteristics, making them widely applicable in various optical fields such as optical field manipulation, beam deflection, and laser radar. Consequently, improving their achromatic performance holds significant importance. In this work, we demonstrate a broadband, high diffraction efficiency, and varying periodicity liquid crystal polarization grating fabricated through the photoalignment technique. A spherical wave is employed for pattern exposure within the interference optical path, with the surface anchoring periodicity varying from 2.56um to 12.04um. Subsequently, a double-layer twisted structure is introduced to construct a high diffraction efficiency PG, which attains an average diffraction efficiency exceeding 98 % within the 520–700 nm spectral range. The structural and diffraction properties of the grating are thoroughly investigated. The manufacturing process for this grating structure is both straightforward and efficient. This work has significant implications for the application and advancement of LCPGs in achromatic performance and contributes substantially to the fields of optical field manipulation and beam deflection. © 2025

关键词： Diffraction efficiency

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Practical Strategies to Improve Test Efficiency

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Tsinghua Science and technology 2007年第S1期12卷 250-254页

作者：丁志刚汪泓澄凌良合 Shanghai Development Center of Software Technology Shanghai Key Laboratory of Computer Software Evaluating and Testing

This paper introduces strategies to detect software bugs in earlier life cycle stage in order to improve test efficiency. Static analysis tool is one of the effective methods to reveal software bugs during software development. Three popular static analysis tools are introduced, two of which, PolySpace and Splint, are compared with each other by analyzing a set of test cases generatedd by the authors. PolySpace can reveal 60% bugs with 100% R/W ratio (ratio of real bugs and total warnings), while Splint reveal 73.3% bugs with 44% R/W ratio. And they are good at finding different categories of bugs. Two strategies are concluded to improve test efficiency, under the guideline that static analysis tools should be used in finding different categories of bugs according to their features. The first one aims at finding bugs as many as possible, while the second concentrates to reduce the average time on bug revelation. Experimental data shows the first strategy can find 100% bugs with 60% R/W ratio, the second one find 80% bugs with 66.7% R/W ratio. Experiment results prove that these two strategies can improve the test efficiency in both fault coverage and testing time.

关键词： static analysis PolySpace splint software bug

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