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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Confident Information Coverage Reliability Evaluation for Sensor Networks of Openly Deployed ICP Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 565-574页

作者： Chen, Suning Yi, Yuanyuan Yi, Lingzhi Liu, Shenghao Deng, Xianjun Xia, Yunzhi Fan, Xiaoxuan Yang, Laurence T. Park, Jong Hyuk Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security The School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan430074 China Zhongnan University of Economics and Law School of Information and Safety Engineering Wuhan430073 China Seoul National University of Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of

Industrial Cyber-Physical (ICP) systems are integration of computation and physical processes to help achieve operational excellence. As sensors and actuators compose the openly deployed ICP systems and are often susceptible to interference, it puts forward higher requirements on reliability. Reliable coverage ensures sensors acquiring information and therefore coverage reliability of sensor network is studied from various perspectives. However, most existing reliability evaluation methods ignore the belief degree of coverage which influences the reliability to an extent. To evaluate the coverage reliability from the perspective of belief degree, we firstly define Confident Information Coverage Belief Reliability (CICBR) based on the Confident Information Coverage (CIC) model and D-S evidence theory, which comprehensively considers the coverage rate, interference from the environment, and belief degree of the result. Next, to evaluate the belief degree more reasonably, an Evidence-discount-based basic probability assignment generating method is proposed based on D-S theory to describe the interference in the belief degree. Moreover, a belief evaluation algorithm is proposed to calculate the belief degree of the coverage. Finally, an evidence-Discount-based Confident Information Coverage Belief Reliability Evaluation (D-CICBRE) algorithm is proposed to evaluate CICBR. The simulation result indicates that the proposed method can obtain higher and more reasonable reliability. Therefore, the proposed D-CICBRE algorithm can be used to evaluate the reliability of ICP systems more accurately and reasonably. © 2023 IEEE.

关键词： Sensor networks

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Distributed Channel Estimation and Optimization for 6D Movable Antenna: Unveiling Directional Sparsity

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IEEE Journal on Selected Topics in Signal Processing 2025年第2期19卷 349-365页

作者： Shao, Xiaodan Zhang, Rui Jiang, Qijun Park, Jihong Quek, Tony Q. S. Schober, Robert Friedrich-Alexander-University Erlangen-Nurnberg Institute for Digital Communications Erlangen91054 Germany Shenzhen Research Institute of Big Data The Chinese University of HongKong School of Science and Engineering Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore The Chinese University of HongKong School of Science and Engineering Shenzhen518172 China Singapore University of Technology and Design 487372 Singapore

Six-dimensional movable antenna (6DMA) is an innovative and transformative technology to improve wireless network capacity by adjusting the 3D positions and rotations of antennas/antenna surfaces based on the channel spatial distribution. To achieve optimal antenna positions and rotations, acquiring statistical channel state information (CSI) is essential for 6DMA systems. However, existing works assume that a central processing unit (CPU) jointly processes the signals of all 6DMA surfaces. This incurs prohibitively high processing cost and latency for channel estimation due to the vast numbers of 6DMA candidate positions/rotations and antenna elements. Therefore, we propose a distributed 6DMA processing architecture to reduce the processing complexity of the CPU by equipping each 6DMA surface with a local processing unit (LPU). Furthermore, we unveil for the first time the directional sparsity property of the 6DMA channels with respect to distributed users, where each user has significant channel gains only for a (small) subset of 6DMA position-rotation pairs. Based on this property, we propose a practical three-stage protocol for the 6DMA system and corresponding algorithms to conduct statistical CSI acquisition for all 6DMA candidate positions/rotations, 6DMA position/rotation optimization based on statistical CSI, and instantaneous CSI estimation for user data transmission with optimized 6DMA positions/rotations. Simulation results show that the proposed channel estimation algorithms achieve higher accuracy than benchmark schemes, while requiring lower pilot overhead. Moreover, the proposed 6DMA system with statistical CSI-based position/rotation optimization achieves a higher ergodic sum rate than fixed-position and fluid antenna systems, even if the latter have perfect instantaneous CSI. © 2025 IEEE.

关键词： Channel estimation

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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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Unveiling the planar deformation mechanisms for improved formability in pre-twinned AZ31 Mg alloy sheet at warm temperature

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Journal of Magnesium and Alloys 2023年第12期11卷 4659-4678页

作者： Xiaohuan Pan Lifei Wang Pengbin Lu Hua Zhang Guangsheng Huang Liuwei Zheng Bin Xing Weili Cheng Hongxia Wang Wei Liang Kwang Seon Shin Shanxi Key Laboratory of Advanced Magnesium-Based Materials College of Materials Science and EngineeringTaiyuan University of TechnologyTaiyuan 030024China National Engineering Laboratory for Industrial Big-data Application Technology Chongqing Innovation Center of Industrial Big-Data Co.Ltd.Chongqing 400707China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute for Advanced Studies in Precision Materials Yantai UniversityYantai 264005China National Engineering Research Center for Magnesium Alloys College of Materials Science&EngineeringChongqing UniversityChongqing 400044China Magnesium Technology Innovation Center School of Materials Science and EngineeringSeoul National University1 Gwanak-roGwanak-guSeoul 08826South Korea

To investigate the role of pre-twins in Mg alloy sheets during warm planar deformation,the stretch forming is conducted at 200℃.Results suggest the formability of the pre-twinned AZ31 Mg alloy sheet is enhanced to 11.30 *** mechanisms for the improved formability and the deformation behaviors during the planar stretch forming are systematically investigated based on the planar stress *** Schmid factor for deformation mechanisms are calculated,the results reveal that planar stress states extremely affect the Schmid factor for{10-12}*** detwinning is activated and the prismatic slip is enhanced in the pre-twinned sheet,especially under the planar extension stress state in the outer ***,the thickness-direction strain is accommodated *** dynamic recrystallization(DRX)type is continuous DRX(CDRX)regardless of the planar stress ***,the CDRX degree is greater under the planar extension stress *** twin lattices deviate from the perfect{10-12}twinning relation due to the planar compression stress state and the *** basal texture is weakened when the planar stress state tends to change the texture components.

关键词： AZ31 Mg alloy sheet Planar stretch forming Planar stress states Continuous dynamic recrystallization {10-12}tensile twinning

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DivLog: Log Parsing with Prompt Enhanced In-Context Learning 24

DivLog: Log Parsing with Prompt Enhanced In-Context Learning

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Xu, Junjielong Yang, Ruichun Huo, Yintong Zhang, Chengyu He, Pinjia The Chinese University of Hong Kong Dept. of Computer Science & Engineering Hong Kong Eth Zurich Switzerland School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China

ISBN: (纸本)9798400702174

Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing super-vised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose Di-vLog, an effective log parsing framework based on the in-context learning (ICL) ability of large language models (LLMs). Specifically, before log parsing, DivLog samples a small amount of offline logs as candidates by maximizing their diversity. Then, during log parsing, DivLog selects five appropriate labeled candidates as examples for each target log and constructs them into a prompt. By mining the semantics of examples in the prompt, DivLog generates a target log template in a training-free manner. In addition, we design a straightforward yet effective prompt format to extract the output and enhance the quality of the generated log templates. We conducted experiments on 16 widely-used public datasets. The results show that DivLog achieves (1) 98.1% Parsing Accuracy, (2) 92.1% Precision Template Accuracy, and (3) 92.9% Recall Template Accuracy on average, exhibiting state-of-the-art performance. © 2024 ACM.

关键词： Semantics

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A Few-shot Classification Algorithm Based on Two-stream Frequency-Domain Information Networks 9

A Few-shot Classification Algorithm Based on Two-stream Freq...

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Hu, Siquan Jia, Jun Shi, Zhiguo Zhao, Jun University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Beijing Big Data Center Beijing100101 China

ISBN: (纸本)9798350383706

In the domain of few-shot learning, where the scarcity of training data poses a significant challenge, this paper introduces an innovative approach. We present a few-shot classification algorithm that utilizes the Two-Stream Frequency Domain Information Network. This algorithm delves deep into the efficacy of training different frequency components of images in the context of few-shot classification. It integrates filtered and enhanced image frequency domain information as supplementary data to be adaptively fused with the original image data, enhancing and augmenting the available information. Experimental results establish that the few-shot classification model, employing the Two-Stream Frequency Domain Information Network, outperforms leading algorithms in the field of few-shot learning. This enhancement is evidenced by an accuracy improvement of 1.06% and 0.93% on the miniImageNet and tiredImageNet datasets, respectively, when compared to the Meta-Baseline, illustrating the efficacy of this specialized approach. © 2023 IEEE.

关键词： Frequency domain analysis

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Multi-topography dataset for wind turbine detection from remote sensing image 4

Multi-topography dataset for wind turbine detection from rem...

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4th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2023

作者： Mi, Hao Zhao, Jinghao Fang, Sheng Tian, Yajun College of Computer Science and Engineering Shandong University of Science and Technology Qingdao Shandong China Extended Energy Big Data and Strategy Research Center Qibebt Chinese Academy of Sciences Qingdao Shandong China

ISBN: (纸本)9781510672741

Owing to the global energy conservation and emission reduction, the wind power industry is developing rapidly in China because of abundant wind energy resources. Wind power has become the third largest power source and remain growing rapidly in China. Efficient and accurate measurement of the location of wind turbine is important for assessing local wind power development. Remote sensing and deep learning technologies offer technical possibilities for wind turbine identification. However, until now there is few publicly available land remote sensing wind power turbine object detection dataset. Aiming at this question, this paper designs a land remote sensing wind turbine dataset, which includes 4459 individual wind turbines in various environmental backgrounds, including sandy land, woodland, grassland, snowy land, and wasteland. In addition, the dataset is composed of YOLO and VOC markup formats to meet the experimental needs of researchers. This paper also evaluates the dataset under the various mainstream object detection frameworks and reach 92% in mAP. The dataset can be obtained from Zenodo website https://***/10.5281/zenodo.7808269. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Remote sensing

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Application of U-Net and Optimized Clustering in Medical Image Segmentation:A Review

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computer Modeling in engineering & sciences 2023年第9期136卷 2173-2219页

作者： Jiaqi Shao Shuwen Chen Jin Zhou Huisheng Zhu Ziyi Wang Mackenzie Brown School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200China School of Data Science Perdana UniversitySerdangSelangor43400Malaysia

As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so *** with natural images,medical images have a variety of ***,the emphasis of information which is conveyed by images of different modes is quite *** it is time-consuming and inefficient to manually segment medical images only by professional and experienced ***,large quantities of automated medical image segmentation methods have been ***,until now,researchers have not developed a universal method for all types of medical image *** paper reviews the literature on segmentation techniques that have produced major breakthroughs in recent *** the large quantities of medical image segmentation methods,this paper mainly discusses two categories of medical image segmentation *** is the improved strategies based on traditional clustering *** other is the research progress of the improved image segmentation network structure model based on *** power of technology proves that the performance of the deep learning-based method is significantly better than that of the traditional *** paper discussed both advantages and disadvantages of different algorithms and detailed how these methods can be used for the segmentation of lesions or other organs and tissues,as well as possible technical trends for future work.

关键词： Medical image segmentation clustering algorithm U-Net

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