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20th European Conference on Computer Systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Chen, Yanqing Wang, Li Wang, Jingzhi Liu, Songyue He, Keqiang Wang, Jian Wang, Xiaoliang Dou, Wanchun Chen, Guihai Tian, Chen State Key Laboratory for Novel Software Technology Nanjing University China School of Electronic Science and Engineering Nanjing University China Shanghai Jiao Tong University China

ISBN: (纸本)9798400711961

Cloud providers require high-throughput traffic to test the effectiveness of congestion control (CC) configurations (i.e., CC algorithm selection and their parameter settings) in networks. A network tester capable of evaluating CC configurations needs to fulfill the following requirements: (R1) Capable of generating traffic with CC behaviors. (R2) Ability to customize CC algorithms. (R3) High throughput CC traffic generation. However, existing network testers fail to meet these requirements simultaneously. The paper presents Marlin, a novel high-throughput network tester designed for CC evaluation. Marlin leverages a high-throughput, low-programmability device to amplify the traffic generated by a low-throughput, high-programmability device. The low-throughput device is responsible for complex computational tasks, such as running CC and flow scheduling algorithms, and communicates with the high-throughput device at a high frequency using small packets to instruct it to generate high-throughput traffic with CC behaviors. This hybrid approach allows for customizable, high-throughput CC testing. Our experiments demonstrate that Marlin can accurately emulate CC behaviors and replicate real-world scenarios. Marlin can generate 1.2 Tbps of CC traffic using a single programmable switch pipeline and one 100 Gbps port of an FPGA NIC, supporting up to 65,536 concurrent flows. © 2025 Copyright held by the owner/author(s).

关键词： Scheduling algorithms

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Investigation of the Dynamic Response of Soil/Steel Composite Structures of Vacuum Explosion Containment Vessels

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Journal of Pressure Vessel Technology 2025年第3期147卷 031401页

作者： Zhou, Dezheng Li, Xiaojie Wang, Xiaohong Liu, Zhenfeng Yan, Honghao College of Science Heilongjiang University of Science and Technology Harbin150022 China School of Mechanics and Aerospace Engineering State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Dalian116024 China School of Mechanics and Aerospace Engineering Dalian University of Technology Dalian116024 China

Vacuum explosive welding technology has better performance, lower production cost, more environmentally friendly and other advantages. In order to meet the industrial demand for production in the area of metal composite plate, it is urgent to develop a super-large vacuum explosion containment vessel which can be used for explosive welding. Since soil is generally regarded as an effective energy-absorbing material, covering a certain thickness of soil above the steel plate can tremendously dissipate the energy of shock waves. Therefore, the dynamic response of the soil/steel composite structure on the explosion containment vessel under different explosive quantities and vacuum degrees was analyzed by experiments and numerical simulations. The dynamic strain curve collected by the experiment consists of an abruptly rising phase and a gradually decreasing vibration recovery phase. Comparing the peak strains, it was found that near-vacuum environment can significantly weaken the dynamic strain of soil/steel composite structures. Comparing the variation of the vibration amplitude of the composite structure with the cycles, it was found that reducing the amount of explosive and the vacuum degree inside the explosion containment vessel can effectively weaken the peak strain of the cover, and can also reduce the vibration amplitude of the composite structure. The dynamic response of soil/steel composite structure can be analyzed in four phases: Abrupt Increasing, Followed Impulse, Inertia Hysteresis, Static Pressure Stabilization. Copyright © 2025 by ASME.

关键词： Dynamic response

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Prediction of New Functional Fluorooxoborates with Expected Anionic Framework:A Case of CaB_(3)O_(5)F

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Chinese Physics Letters 2025年第3期42卷 85-92页

作者： Ke-Wang Zhang Wen-Qi Jin Ke Li Abudukadi Tudi Lin-Lin Liu Cong-Wei Xie Yu Xie Research Center for Crystal Materials CAS Key Laboratory of Functional Materials and Devices for Special Environmental ConditionsXinjiang Key Laboratory of Functional Crystal MaterialsXinjiang Technical Institute of Physics&ChemistryCASUrumqi 830011China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Materials Simulation Methods&Software of Ministry of Education International Center of Future ScienceKey Laboratory of Physics and Technology for Advanced Batteries(Ministry of Education)College of PhysicsJilin UniversityChangchun 130012China

The prediction of new fluorooxoborates as ultraviolet(UV)/deep ultraviolet(DUV)opto-electronic functional materials from a largely unexplored chemical space is a challenging *** has been suggested that the anionic frameworks formed by B–O and B–O–F units significantly determine the physical properties of ***,the rational design of anionic frameworks could facilitate the materials discovery ***,we propose that a candidate anionic framework can be efficiently derived from an existing one by slightly altering its oxygen *** this idea,we hypothesized the existence of a 1D[B_(3)O_(5)F]_(∞)chain from the wellknown 2D[B_(6)O_(9)F_(2_)]_(∞)***,seven CaB_(3)O_(5)F structures with the expected anionic framework were successfully ***-principles calculations show that all these structures have potential in the UV/DUV birefringent or nonlinear optical(NLO)material field,indicating that the 1D[B_(3)O_(5)F]_(∞)chain is indeed a promising anionic framework for achieving UV/DUV birefringent and NLO performance.

关键词： process borate anionic

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A theoretical and experimental study of deformation mechanism dictated by disclination-dislocation coupling in Mg alloys at different temperatures

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材料科学技术（英文版） 2025年第5期208卷 176-188页

作者： Chunfeng Du Yipeng Gao Yizhen Li Quan Li Min Zha Cheng Wang Hailong Jia Hui-Yuan Wang Key Laboratory of Automobile Materials of Ministry of Education&School of Materials Science and Engineering Nanling CampusJilin UniversityChangchun 130025China Key Laboratory of Automobile Materials of Ministry of Education&School of Materials Science and Engineering Nanling CampusJilin UniversityChangchun 130025China International Center of Future Science Jilin UniversityChangchun 130012China International Center of Future Science Jilin UniversityChangchun 130012China State Key Lab of Superhard Materials and Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchun 130012China Key Laboratory of Automobile Materials of Ministry of Education&School of Materials Science and Engineering Nanling CampusJilin UniversityChangchun 130025China International Center of Future Science Jilin UniversityChangchun 130012China State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Material Science and EngineeringHebei University of TechnologyTianjin 300130China

Dislocations and disclinations are fundamental topological defects within crystals,which determine the mechanical properties of metals and *** their important roles in multiple physical mecha-nisms,e.g.,dynamic recovery and grain boundary mediated plasticity,the intrinsic coupling and corre-lation between disclinations and dislocations,and their impacts on the deformation behavior of metallic materials still remain obscure,partially due to the lack of a theoretical tool to capture the rotational na-ture of *** using a Lie-algebra-based theoretical framework,we obtain a general equation to quantify the intrinsic coupling of disclinations and *** quasi in-situ electron backscat-ter diffraction characterizations and disclination/dislocation density analyses in Mg alloys,the generation,coevolution and reactions of disclinations and dislocations during dynamic recovery and superplastic de-formation have been quantitatively *** has been demonstrated that the obtained governing equa-tion can capture multiple physical processes associated with mechanical deformation of metals,e.g.,grain rotation and grain boundary migration,at both room temperature and high *** establishing the disclination-dislocation coupling equation within a Lie algebra description,our work provides new insights for exploring the coevolution and reaction of disclinations/dislocations,with profound implica-tions for elucidating the microstructure-property relationship and underlying deformation mechanisms in metallic materials.

关键词： Magnesium alloys Dislocations Grain boundaries Plastic deformation Grain rotation Disclination-dislocation coupling

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Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 1-14页

作者： Tan, Mingkui Chen, Guohao Wu, Jiaxiang Zhang, Yifan Chen, Yaofo Zhao, Peilin Niu, Shuaicheng South China University of Technology School of Software Engineering China Pazhou Laboratory Guangzhou China XVERSE China National University of Singapore School of Computing Singapore Tencent AI Lab China Nanyang Technological University College of Computing and Data Science Singapore

Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important when the test environment changes frequently. Although some recent attempts have been made to handle this task, we still face two key challenges: 1) prior methods have to perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications;2) while existing TTA solutions can significantly improve the test performance on out-of-distribution data, they often suffer from severe performance degradation on in-distribution data after TTA (known as catastrophic forgetting). To this end, we have proposed an Efficient Anti-Forgetting Test-Time Adaptation (EATA) method which develops an active sample selection criterion to identify reliable and non-redundant samples for test-time entropy minimization. To alleviate forgetting, EATA introduces a Fisher regularizer estimated from test samples to constrain important model parameters from drastic changes. However, in EATA, the adopted entropy loss consistently assigns higher confidence to predictions even when the samples are underlying uncertain, leading to overconfident predictions that underestimate the data uncertainty. To tackle this, we further propose EATA with Calibration (EATA-C) to separately exploit the reducible model uncertainty and the inherent data uncertainty for calibrated TTA. Specifically, we compare the divergence between predictions from the full network and its sub-networks to measure the reducible model uncertainty, on which we propose a test-time uncertainty reduction strategy with divergence minimization loss to encourage consistent predictions instead of overconfident ones. To further re-calibrate predicting confidence on different samples, we utilize the disagreement among predicted labels as an indicator of the data uncertainty. Based on this, we devise a min-max entropy

关键词： Semantic Segmentation

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Federated Deep Reinforcement Learning for Multimodal Content Caching in Edge-Cloud Networks

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 2188-2201页

作者： Feng, Weijia Zuo, Xinyu Zhang, Ruojia Zhu, Yichen Wang, Chenyang Guo, Jia Sun, Chuan Tianjin Normal University School of Computer and Information Engineering Tianjin300387 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518132 China Shenzhen University Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen518132 China Chongqing Normal University School of Computer and Information Science Chongqing401333 China

Edge caching presents a promising avenue for mitigating backbone network congestion by strategically caching frequently accessed content at the network periphery. As most current edge caching solutions are designed for single-modal content requests, they cannot deal with the increasing volume of multi-modal content requests. In this study, we investigate the issue of multimodal content caching in edge-cloud networks. Firstly, we establish a heterogeneous edge-cloud network adept at caching multimodal content proximate to end-users to facilitate expeditious content delivery. By leveraging latent representations of multimodal content, we identify distinct user request modalities for multimodal content. Subsequently, we formulate caching replacement operations as a Markov Decision Process (MDP) aimed at minimizing user-content access latency. Moreover, we propose a decentralized multimodal content caching framework at the network edge based on federated deep reinforcement learning. This framework affords distributed decision-making and learning capabilities, thereby alleviating the strain on centralized resources and augmenting caching efficacy. To demonstrate the efficacy of our proposed framework, we conduct comprehensive experiments utilizing the Noah-Wukong dataset. Experimental results provide evidence that our framework reduces average latency by up to 10% compared to traditional methods, highlighting its proficiency in enhancing cache performance in edge-cloud networks. © 2013 IEEE.

关键词： Federated learning

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EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Duan, Chiming Jia, Tong Yang, Yong Liu, Guiyang Liu, Jinbu Zhang, Huxing Zhou, Qi Li, Ying Huang, Gang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China Alibaba Cloud Computing Company Hangzhou China

ISBN: (纸本)9798350368741

Logs record essential information about system operations and serve as a critical source for anomaly detection, which has generated growing research interest. Utilizing large language models (LLMs) within a retrieval-augmented generation (RAG) framework for log-based anomaly detection is an effective approach due to its strong generalization capabilities and efficient few-shot performance. However, the effectiveness of this method hinges on the quality of the knowledge source, which can be impacted by noise and changes within the software systems. Facing these problems, in this paper, we propose a novel log-based anomaly detection method named EagerLog, employing active learning to choose the logs for humans to label, thereby adding them to the knowledge source, thus enhancing the knowledge source and maintaining its quality. Our experiments on three open datasets (BGL, Thunderbird, Zookeeper) and one industrial dataset demonstrate that EagerLog can achieve 93.65% F1 score with approximately 10 labeled log sequences, surpassing existing methods by 15.32%. © 2025 IEEE.

关键词： Active Learning Anomaly Detection Log Analysis Retrieval Augmented Generation

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Learned Video Compression With Refined Adaptive Flow Pyramid And Coordinate-Aware Attention

Learned Video Compression With Refined Adaptive Flow Pyramid...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Liu, Wenting Li, Xin Wang, Yiming College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China

ISBN: (纸本)9798350368741

In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression efficiency, they still have limitations. SpyNet often loses details and fails to fully utilize the feature extraction capabilities of deep networks. Furthermore, bilinear interpolation inherently attenuates high-frequency information, leading to frame blurring and distortion. In this paper, we propose a novel video compression algorithm. To overcome the limitations of SpyNet, we propose a refined adaptive flow pyramid network. This network uses a multi-scale feature pyramid to capture more details. Moreover, we use an iterative cost volume refinement engine that improves the feature representation of the network and iteratively improve the accuracy of motion estimation. In addition, to overcome the limitation of bilinear interpolation, we propose a coordinate-aware attention module, which captures more high-frequency information to improve the accuracy of motion compensation. Experimental results show that our method outperforms VTM-13.2 (LDP) in terms of PSNR. © 2025 IEEE.

关键词： coordinate network cross attention flow pyramid learned video compression

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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