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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Pengming Feng Kaihan Wang Jian Guan Guangjun He Shichao Jin State Key Laboratory of Space-Ground Integrated Information Technology Space Star Technology CO. Ltd. Beijing China Group of Intelligent Signal Processing (GISP) College of Computer Science and Technology Harbin Engineering University Harbin China

Though deep learning methods have achieved the state-of-the-art performance for hyperspectral image (HSI) classification, they often highly rely on large amount of samples for training, and introduce few-shot challenge due to the lack of labeled samples. In this paper, a self-supervised method is presented for HSI classification in the few-shot scenario, where masked autoencoder is employed to reconstruct the masked bands in spectral domain for model pretraining with limited labeled sample, namely Spectral-MAE. The proposed method not only avoids the overfitting via the pretraining, but also provides the model’s ability for effective feature extraction while avoiding the high spatial redundancy. Experiments conducted verify the effectiveness of the proposed method for HSI classification in few-shot situation as compared with other methods.

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SCESP: An Edge Server Placement Method Based on Spectral Clustering in Mobile Edge computing 8th

SCESP: An Edge Server Placement Method Based on Spectral Cl...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Wang, Lijuan Guo, Yingya Yao, Jiangyuan Zhou, Siyu College of Computer and Data Science Fuzhou University Fuzhou350000 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350000 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350003 China School of Computer Science and Cyberspace Security Hainan University Haikou570228 China Tandon School of Engineering New York University New York10012 United States

ISBN: (纸本)9783031067600

With the rapid development of Internet of Things (IoT) and 5G, mobile edge computing is gaining popularity for its low computation latency, bandwidth costs and energy consumption. In mobile edge computing, the placement of edge servers is one of the most significant problems and attracts worldwide attention. However, two major problems of edge server placement: high access delay and unbalanced workload of edge servers, have not been completely solved yet. To better solve these two problems, this paper proposes a new Spectral-Clustering-based Edge Server Placement (SCESP) algorithm, which can effectively reduce the access delay and make the workload of each edge server more balanced. In the evaluation, we use the Shanghai Telecom’s base station dataset to test the performance of SCESP and extensive experiments demonstrate the superior performance of SCESP in reducing the access delay and balancing the edge server workload. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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Existence of Solutions for the Continuous Algebraic Riccati Equation via Polynomial Optimization

arXiv

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arXiv 2024年

作者： Zhang, Juan Zhao, Wenjie Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Mathematics and Statistics Hunan First Normal University Hunan Changsha410205 China School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

This paper studies the solution existence of the continuous-time algebraic Riccati equation (CARE). We formulate the CARE as two constrained polynomial optimization problems, and then use Lasserre's hierarchy of semi-definite relaxations to solve them. Compared to the existing work, our approaches can obtain the exact positive semi-definite solution of the CARE when the coefficient matrices are symmetric. Moreover, our methods can detect the nonexistence of the positive semi-definite solution for the CARE. Numerical examples show that our approaches are effective compared to the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Riccati equations

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A posteriori error estimation for an interior penalty virtual element method of Kirchhoff plates

arXiv

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arXiv 2024年

作者： Feng, Fang Hu, Yuming Yu, Yue Zhao, Jikun School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Mathematics and Statistics Zhengzhou University Zhengzhou450001 China

In this paper, we develop a residual-type a posteriori error estimation for an interior penalty virtual element method (IPVEM) for the Kirchhoff plate bending problem. Building on the work in [18], we adopt a modified discrete variational formulation that incorporates the H1-elliptic projector in the jump and average terms. This allows us to simplify the numerical implementation by including the H1-elliptic projector in the computable error estimators. We derive the reliability and efficiency of the a posteriori error bound by constructing an enriching operator and establishing some related error estimates that align with C0-continuous interior penalty finite element methods. As observed in the a priori analysis, the interior penalty virtual elements exhibit similar behaviors to C0continuous elements despite its discontinuity. This observation extends to the a posteriori estimate since we do not need to account for the jumps of the function itself in the discrete scheme and the error estimators. As an outcome of the error estimator, an adaptive VEM is introduced by means of the mesh refinement strategy with the one-hanging-node rule. Numerical results from several benchmark tests confirm the robustness of the proposed error estimators and show the efficiency of the resulting adaptive VEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Discrete element methods

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Direct Contrastive Learning for Incomplete Multi-view Clustering

Direct Contrastive Learning for Incomplete Multi-view Cluste...

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Chinese Control and Decision Conference, CCDC

作者： Kaiwu Zhang Yaoying Wang Shengxia Gao Shiqiang Du Xiangzhen He Key Laboratory of Linguistic and Cultural Computing of Ministry of Education Northwest Minzu University Lanzhou China Chinese National Information Technology Research Institute Northwest Minzu University Lanzhou China College of Mathematics and Computer Science Northwest Minzu University Lanzhou China Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Province Northwest Minzu University Lanzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Incomplete multi-view clustering (IMVC) is a emerging research topic. Although several attempts to solve IMVC have been proposed, they suffered from the following problems: (1) Most existing methods usually solve the dimensional collapse problem by adding additional projection heads, whereby the latent features are only effective in low-dimensional subspaces. However this approach will increase the complexity of the model. (2) Most existing methods typically use consistent learning and reconstruction learning on the same feature, which can result in irrelevant private information affecting the learning of the common representation. In this paper, a novel incomplete multi-view clustering framework based on direct contrastive learning is proposed to solve the above problems. Specifically, our method prevents dimensionality collapse without adding additional projection heads by utilizing sub-vectors of learned feature vectors for consistency learning. Furthermore, this framework reduces the adverse effects of inconsistent and useless private information by performing reconstruction loss and contrastive loss on the feature vector and its sub-vectors respectively. To handle incomplete data, a cross-view prediction mechanism is adopted to recover missing information. Extensive experimental results show the effectiveness of the proposed method and obtain significant performance improvements on public datasets.

关键词： Contrastive learning Vectors Complexity theory

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Predefined-time consensus control for MASs with limited communication range and data injection attacks

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Expert Systems with Applications 2025年 294卷

作者： Haijiao Yang An Liu Shuping He Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Electrical Engineering and Automation Anhui University Hefei 230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei 230601 China

In this paper, the consensus control problem of multi-agent systems under data injection attacks and limited communication range is studied. To avoid the impact of data injection attacks, a security estimation strategy based on historical data is proposed, which can effectively parse the valid neighbor states. At the same time, due to the discrete communication between agents, a state holder is designed to make the discrete information continuous. In addition, due to the limited communication range, a topological weight design scheme is designed based on distance with a penalty factor. On this basis, combining adaptive and backstepping techniques, a predefined-time convergent and connectivity-preserving safe control scheme is constructed. This scheme not only solves the data injection attacks but also ensures that the system achieves consensus within the predefined time without any agent exceeding the communication range. Finally, rigorous theoretical analysis and comparative simulation results are provided to demonstrate the effectiveness and superiority of the proposed control method.

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Research on patent recommendation method based on graph neural network

Research on patent recommendation method based on graph neur...

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International Conference on Computer Supported Cooperative Work in Design

作者： Enchao Zhang Weidong Liu Shi Liu Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation.

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Pan-omics-based characterization and prediction of highly multidrugadapted strains from an outbreak fungal species complex

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The Innovation 2024年第5期5卷 101-109,100页

作者： Xin Fan Lei Chen Min Chen Na Zhang Hong Chang Mingjie He Zhenghao Shen Lanyue Zhang Hao Ding Yuyan Xie Yemei Huang Weixin Ke Meng Xiao Xuelei Zang Heping Xu Wenxia Fang Shaojie Li Cunwei Cao Yingchun Xu Shiguang Shan Wenjuan Wu Changbin Chen Xinying Xue Linqi Wang Department of Infectious Diseases and Clinical Microbiology Beijing Institute of Respiratory Medicine and Beijing Chao-Yang HospitalCapital Medical UniversityBeijing 100020China Beijing Research Center for Respiratory Infectious Diseases Beijing 100020China State Key Laboratory of Mycology Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Department of Dermatology Shanghai Key Laboratory of Molecular Medical MycologyChangzheng HospitalShanghai 200003China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Department of Respiratory and Critical Care Emergency and Critical Care Medical CenterBeijing Shijitan HospitalCapital Medical UniversityBeijing 100038China Department of Laboratory Medicine State Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalChinese Academy of Medical SciencesBeijing 100730China Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases Beijing 100730China Department of Clinical Laboratory First Affiliated Hospital of Xiamen UniversityXiamen 361003China Institute of Biological Science and Technology Guangxi Academy of SciencesNanning 530007China Department of Dermatology The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021China Guangxi Key Laboratory of Mycosis Prevention and Treatment Nanning 530021China Department of Laboratory Medicine Shanghai East HospitalTongji University School of MedicineShanghai 200120China The Unit of Pathogenic Fungal Infection&Host Immunity Shanghai Institute of Immunity and InfectionChinese Academy of SciencesShanghai 200031China Nanjing Advanced Academy of Life and Health Nanjing 211135China Department of Respiratory and Critical Care Shandong Second Medical UniversityWeifang 261035China

Strains from the Cryptococcus gattii species complex(CGSC)have caused the Pacific Northwest cryptococcosis outbreak,the largest cluster of lifethreatening fungal infections in otherwise healthy human hosts known to *** this study,we utilized a pan-phenome-based method to assess the fitness outcomes of CGSC strains under 31 stress conditions,providing a comprehensive overview of 2,821 phenotype-strain associations within this pathogenic *** clustering analysis revealed a strong correlation between distinct types of stress phenotypes in a subset of CGSC strains,suggesting that shared determinants coordinate their adaptations to various ***,a specific group of strains,including the outbreak isolates,exhibited a remarkable ability to adapt to all three of the most commonly used antifungal drugs for treating cryptococcosis(amphotericin B,5-fluorocytosine,and fluconazole).By integrating pan-genomic and pan-transcriptomic analyses,we identified previously unrecognized genes that play crucial roles in conferring multidrug resistance in an outbreak strain with high multidrug *** these genes,we identified biomarkers that enable the accurate prediction of highly multidrug-adapted CGSC strains,achieving maximum accuracy and area under the curve(AUC)of 0.79 and 0.86,respectively,using machine learning ***,we developed a pan-omic approach to identify cryptococcal multidrug resistance determinants and predict highly multidrug-adapted CGSC strains that may pose significant clinical concern.

关键词： multidrug adapted prediction

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Large Model-Based Data Augmentation for Imbalanced Text Classification

Large Model-Based Data Augmentation for Imbalanced Text Clas...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Dawei Zhang Rongxin Mi Peiyao Zhou Dawei Jin Manman Zhang Tianhang Song School of Computer Science Jiangsu University of Science and Technology Zhenjiang Jiangsu China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

This study focuses on the application of large models to deal with imbalanced data problems in text classification. In view of the central position of text in web data and the negative impact of class imbalance on classifier performance, researchers have explored the method of using large models to generate high-quality minority class samples to enhance model performance. This paper reviews the technical progress of machine learning, deep learning, and large language models and their applications in text classification tasks. Although large models perform well in complex tasks due to their excellent language understanding ability, traditional machine learning and deep learning methods are popular in text classification scenarios that require fast response due to their simple structure and higher computational efficiency. This study proposes a data augmentation technique inspired by SMOTE, which uses a large language model combined with a simple prompt engineering strategy to generate high-quality minority samples. The experimental results show that the proposed method significantly improves the macro average precision, recall and F1 score on multiple text classification models, and effectively alleviates the challenge of class imbalance.

关键词： Deep learning Seminars Computational modeling Large language models Text categorization Data augmentation Data models

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

arXiv

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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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