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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Zhuohui Duan Hao Feng Haikun Liu Xiaofei Liao Hai Jin Bangyu Li National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133458

The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of Value region, especially for garbage collection (GC) operation that is used to reduce the redundant space occupation. In response, many efforts have been made to optimize the GC mechanism for KV separation. However, our analysis indicates that such solution based on trade-offs between CPU and I/O overheads cannot simultaneously satisfy the three requirements of KV separated systems in terms of throughput, tail latency, and space usage. This limitation hinders their real-world *** this paper, we introduce AegonKV, a "three-birds-one-stone" solution that comprehensively enhances the throughput, tail latency, and space usage of KV separated systems. AegonKV first proposes a SmartSSD-based GC offloading mechanism to enable asynchronous GC operations without competing with LSM read/write for bandwidth or CPU. AegonKV leverages offload-friendly data structures and hardware/ software execution logic to address the challenges of GC offloading. Experiments demonstrate that AegonKV achieves the largest throughput improvement of 1.28-3.3 times, a significant reduction of 37%-66% in tail latency, and 15%-85% in space overhead compared to existing KV separated systems.

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Compressed Domain Prior-Guided Video Super-Resolution for Cloud Gaming Content

arXiv

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arXiv 2025年

作者： Wang, Qizhe Yin, Qian Huang, Zhimeng Jiang, Weijia Su, Yi Ma, Siwei Zhang, Jiaqi National Engineering Research Center of Visual Technology School of Computer Science Peking University China Migu Interactive Entertainment Co. Ltd. China

Cloud gaming is an advanced form of Internet service that necessitates local terminals to decode within limited resources and time latency. Super-Resolution (SR) techniques are often employed on these terminals as an efficient way to reduce the required bit-rate bandwidth for cloud gaming. However, insufficient attention has been paid to SR of compressed game video content. Most SR networks amplify block artifacts and ringing effects in decoded frames while ignoring edge details of game content, leading to unsatisfactory reconstruction results. In this paper, we propose a novel lightweight network called Coding Prior-Guided Super-Resolution (CPGSR) to address the SR challenges in compressed game video content. First, we design a Compressed Domain Guided Block (CDGB) to extract features of different depths from coding priors, which are subsequently integrated with features from the U-net backbone. Then, a series of re-parameterization blocks are utilized for reconstruction. Ultimately, inspired by the quantization in video coding, we propose a partitioned focal frequency loss to effectively guide the model’s focus on preserving high-frequency information. Extensive experiments demonstrate the advancement of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Network coding

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research on air interface interference recognition in mobile communication networks based on LSTM autoencoder 5

Research on air interface interference recognition in mobile...

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5th International Conference on Signal Processing and computer Science, SPCS 2024

作者： Sun, Hongwei Hu, Mingwei Wang, Baoji Chen, Yue Fu, Jingwen Huang, Jia Jiangsu Automation Research Institute Lianyungang222006 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100094 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Sankuai Online Technology Co. Ltd. Beijing100102 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Due to the diverse nature of interference signals in mobile communication frequency bands, feature extraction is challenging, and the rapid and automated identification of behaviors such as illegal frequency usage and malicious interference are difficult. Moreover, evaluating the precise impact of interference signals on communication networks is challenging. In response to this problem, this paper proposes an air interface interference recognition technology for communication links based on base station measurement reporting data. It constructs an interference recognition model to achieve automatic interference identification and evaluates the accuracy of interference recognition. On the base station side, this paper utilizes channel measurement data such as signal strength, signal quality, and signal-to-noise ratio exported by the base station. It uses a Long Short-Term Memory (LSTM) autoencoder model to learn normal signal patterns for interference discrimination and evaluates the impact of interference on communication networks. This method achieves precise identification of fixed-frequency interference on the base station side and evaluates the impact of interference on communication networks. Experimental results show that the base station-side approach achieves an F1 score of 0.99 in identifying fixed-frequency interference, outperforming One-Class Support Vector Machine (OCSVM), Principal Component Analysis (PCA), and Isolation Forest (IForest) methods under similar conditions. © 2025 SPIE.

关键词： Base stations

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OUTLIER SYNTHESIS VIA HAMILTONIAN MONTE CARLO FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

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arXiv 2025年

作者： Li, Hengzhuang Zhang, Teng National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS. Our code is available at: https://***/Fir-lat/HamOS_OOD. © 2025, CC BY.

关键词： Markov chains

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A 41.32Gb/s 1.8 pJ/bit Collaborative Baud-Rate CDR With Background Eye-Climbing Algorithm and Low-Power Global Clock Distribution

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IEEE Journal of Solid-State Circuits 2025年

作者： Kim, Jihee Park, Jia Shin, Jiwon Kim, Hanseok Kim, Kahyun Shin, Haengbeom Park, Ha-Jung Choi, Woo-Seok Georgia Institute of Technology Department of Electrical and Computer Engineering AtlantaGA30332 United States Inter University Semiconductor Research Center Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Samsung Electronics Hwaseong18448 Korea Republic of

This article presents design techniques for an energy-efficient multi-lane receiver (RX) with baud-rate clock and data recovery (CDR), which is essential for high-throughput low-latency communication in high-performance computing systems. The proposed low-power global clock distribution not only significantly reduces power consumption across multi-lane RXs but is capable of compensating for the frequency offset without any phase interpolators (PIs). To this end, a fractional divider (FDIV) controlled by CDR is placed close to the global phase locked loop. Moreover, in order to address the suboptimal lock point of conventional baud-rate phase detectors, the proposed CDR employs a background eye-climbing algorithm (ECA), which optimizes the sampling phase and maximizes the vertical eye margin (VEM). Fabricated in a 28 nm CMOS process, the proposed 43.32Gb/s RX shows a low integrated fractional spur of -40.4 dBc at a 2500 ppm frequency offset. Furthermore, it improves bit-error-rate (BER) performance by increasing the VEM by 26 mV. The entire RX achieves the energy efficiency of 1.8 pJ/bit with the aggregate data rate of 128 Gb/s. © 1966-2012 IEEE.

关键词： Phase locked loops

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Applying Importance Sampling to MCTS for Mahjong

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IEEE Transactions on Games 2025年

作者： Tang, Shih-Chieh Chen, Jr-Chang Wu, I-Chen National Taipei University Department of Computer Science and Information Engineering New Taipei City23741 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30050 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

Mahjong is a four-player, stochastic, imperfect-information game. In this paper, we utilize importance sampling within Monte Carlo Tree Search (abbr. MCTS) to enhance the playing strength of our Mahjong program, MeowCaTS. First, we propose a tree structure called the merging solitary-tile model, which facilitates the application of importance sampling. This model also reduces the branching factor of the search tree. Second, we apply importance sampling to MCTS and introduce the calculation of importance weights during the backpropagation stage. Finally, we design a multi-depth transposition table to accumulate simulation results of similar positions in MCTS, further enhancing the strength of MeowCaTS. In the experiments, the performance of the proposed methods was analyzed, and the results showed a significant improvement. Notably, MeowCaTS won the first place in computer Olympiad 2023. © 2018 IEEE.

关键词： Stochastic systems

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HeMDAP: Heterogeneous Graph Self-Supervised Learning for MiRNA-Disease Association Prediction

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 769-780页

作者： Yunjie Ma Fei Wang Qiyu Feng Zuocheng Wang Luyu Xie School of Computer Science and Information Engineering Hefei University of Technology Hefei China Institute of Dataspace Hefei Comprehensive National Science Center Hefei China Cancer Research Center First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei China

MiRNA-disease associations (MDAs) are particularly insightful for revealing the complex pathology of human diseases. Traditional experimental methods for MDA studies are costly, time-consuming, and low-throughput. Thus, many machine learning and deep learning-based methods have been proposed to predict MDAs. However, due to the limitations of supervised learning, these methods often fall short in predictive performance. To address this issue, we propose a novel MDA prediction method, HeMDAP, which is based on graph contrastive learning using meta-path view and network structure view of heterogeneous graph. The main innovation of HeMDAP lies in designing two complementary graph representations, network structure view and meta-path view, as well as introducing self-supervised contrastive learning and supervised contrastive learning to effectively optimize node embeddings. Additionally, we utilize knowledge-aware enhancement to further improve embedding quality. This multi-view learning and multi-task training strategy can more comprehensively capture the complex relationships among miRNAs, genes, and diseases. Experimental results on public datasets show that HeMDAP outperforms all existing methods in terms of prediction accuracy. In five-fold cross-validation, HeMDAP achieved an AUC of 94.92% and an AUPR of 95.07%. These results demonstrate the effectiveness and superiority of our proposed method in the MDA prediction task.

关键词： Diseases Feature extraction Contrastive learning Semantics Vectors Databases Predictive models Attention mechanisms Accuracy Supervised learning

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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A Transformer-Based Spatio-Temporal Graph Neural Network for Anomaly Detection on Dynamic Graphs 12th

A Transformer-Based Spatio-Temporal Graph Neural Network for...

引用

12th CCF Conference on BigData, BigData 2024

作者： Gao, Yuanjun Zhu, Quntao Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819610235

Dynamic graphs have emerged as a pivotal data structure underpinning real-world network applications. Against this backdrop, detecting anomalies in dynamic graphs has become particularly important, serving as a foundation for multiple downstream tasks. However, existing research has been limited by only considering the simplistic spatial features of the target edge and being confined to local spatial structures. This confinement imposes significant limitations on model performance. Furthermore, current models have not adequately addressed the issue of missing node attributes, relying solely on one-hot encoding to represent nodes, which restricts the expressive capacity of spatial features. In response to these challenges, we propose an end-to-end transformer-based spatio-temporal graph neural network model called TransSTGNN for detecting anomalous edges on dynamic graphs. This model innovatively integrates the graph convolution network with the transformer model to extract multi-dimensional spatial features of the target edge. Simultaneously, TransSTGNN incorporates a novel node enhancement module that reinforces the representation capability of spatial features by encoding the spatial attributes of nodes, effectively tackling the problem of missing node attributes. Extensive experiments confirm that TransSTGNN surpasses the state-of-the-art methods in anomaly detection on six benchmark datasets and effectively resolves the challenges mentioned above. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

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Hydrogen peroxide-enhanced magnetic resonance imaging: A novel approach for diagnosing anorectal-fistula

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World Journal of Radiology 2025年第3期17卷 5-8页

作者： Riya Karmakar Devansh Gupta Arvind Mukundan Hsiang-Chen Wang Department of Mechanical Engineering National Chung Cheng UniversityChiayi 62102Taiwan Department of Computer Science&Engineering Thapar Institute of Engineering&TechnologyPatiala 147001PunjabIndia Department of Mechanical Engineering Center for Innovative Research on Aging SocietyNational Chung Cheng UniversityChiayi 621Taiwan

In this editorial,a commentary on the article by Chang et al has been provided,the course of treatment of anorectal fistulas,especially complex and recurring ones,require accurate diagnostic procedures for determining ideal surgical *** ways of imaging sometimes fall short,offering insufficient insights in aggravated *** this editorial,a novel application of hydrogen peroxide-enhanced magnetic resonance imaging(HP-MRI)that promises significant improvements in the imaging of anorectal *** is based on a retrospective investigation of 60 patients,contrasts the new HP-MRI with conventional diagnostic techniques such as physical examination,trans-perineal ultrasonography and poor spatial resolution *** findings demonstrate HP-MRI's incredible diagnostic performance,with sensitivity and specificity rates of 96.08%and 90.91%,respectively,and unparalleled interobserver agreement(Kappa values ranging from 0.80 to 0.89).It has been a significant advancement for assessment of anorectal fistulas providing a better roadmap for surgical planning,lowering recurrence rates as well as reduced personal and financial burden on patients by reducing the need for repeated treatment and extended hospital *** remaining funds can be utilized for treatment of other medical *** HP-MRI provides us a healthier&more efficient society by improvising patients well-being&optimized healthcare infrastructure.

关键词： Anorectal fistulas Magnetic resonance imaging Hydrogen peroxide Diagnostic imaging Fistula tract visualization Diagnostic accuracy Minimally invasive imaging Gadolinium contrast agent Retrospective analysis Perianal fistulas

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