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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Li, Tong-Jun Zhang, Run-Ze School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang316022 China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province China

ISBN: (纸本)9798350303780

Formal concept analysis is a key tool in data analysis and knowledge representation and a specific method for granular computing. Object granules constitute the basic granular knowledge in a concept lattice. Three-way decision is a reasonable approach to extracting decision rules. This study employs the principle of three-way decision to divide all the object granules of a formal context into three parts: positive, negative and boundary regions, then two groups of certainty decision rules are extracted from the positive region and the negative region, and one group of possibility decision rules is induced from the boundary region. The notion of attribute reduction keeping the performance of the decision rules unchanged are proposed. An approach for computing attribute reducts is put forward by utilizing the discernibility matrix and discernibility function. Some examples are taken to explain or show the relevant conclusions and prove the feasibility of the reduction method. © 2023 IEEE.

关键词： Formal concept analysis

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Vision Transformer Based Hash Coding for Efficient Image and Audio Retrieval with Global and Local Equilibrium Distance Constraints

Vision Transformer Based Hash Coding for Efficient Image and...

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

作者： Liu, Ye Pan, Yan Yin, Jian School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Lizhi Inc. Artificial Intelligence and Big Data Department Guangzhou Beijing China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665410205

With the application of efficient retrieval in information systems and retrieval augmented generation with vector database for large language models, hash coding algorithms have made progress in recent years. The rise of transformer technology in the field of deep learning has brought the possibility to further improve the effect of hash coding algorithms. We introduce the vision transformer framework to both images and audios, and propose a novel approach for the tasks of multi-label image retrieval and audio event retrieval. In the proposed hash coding model, global and local equilibrium distance constraints are integrated, so that the hash codes for images can be better obtained through the global hash centers and local similar samples. In order to realize end-to-end training and hash code generation for audios, we adopt the adapter of mel spectrogram, thus the proposed approach can be simply converted and applied to audio hash coding. Comparative experiments verify that better results can be achieved on multiple image and audio datasets. © 2024 IEEE.

关键词： Image coding

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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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Multi-Label Learning with Label-Specific Feature Selection via Local Positive and Negative Correlation 5

Multi-Label Learning with Label-Specific Feature Selection v...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Xia, Shuxin School of Information Engineering Zhe Jiang Ocean University Zhoushan316022 China Zhejiang Province School of Information Engineering Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application Zhoushan316022 China

ISBN: (纸本)9798350385557

High dimension data increasingly emerged in many application areas. Feature selection methods are widely studied to solve the issues, playing a crucial role in eliminating redundant or irrelevant feature. Previous feature selection algorithms seldom consider the fact that each label might be determined by its specific features, which has its own characteristics. Meanwhile, they often neglect mutual exclusion among labels, named 'negative label correlations' and assume labels share global correlation in exploring label correlation. To address the above problem, we propose a novel algorithm called 'Multi-label learning with label-specific feature selection via local positive and negative correlation' (LLCPN-MIFS). Multiple datasets manifest that the effectiveness of the proposed method. © 2024 IEEE.

关键词： Feature Selection

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CHDbase:A Comprehensive knowledgebase for Congenital Heart Disease-related Genes and Clinical Manifestations

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Genomics, Proteomics & Bioinformatics 2023年第1期21卷 216-227页

作者： Wei-Zhen Zhou Wenke Li Huayan Shen Ruby W.Wang Wen Chen Yujing Zhang Qingyi Zeng Hao Wang Meng Yuan Ziyi Zeng Jinhui Cui Chuan-Yun Li Fred Y.Ye Zhou Zhou State Key Laboratory of Cardiovascular Disease Beijing Key Laboratory for Molecular Diagnostics of Cardiovascular DiseasesCenter of Laboratory MedicineFuwai HospitalNational Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100037China International Joint Informatics Laboratory&Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information ManagementNanjing UniversityNanjing 210023China Institute of Molecular Medicine Peking UniversityBeijing 100871China

Congenital heart disease(CHD)is one of the most common causes of major birth defects,with a prevalence of 1%.Although an increasing number of studies have reported the etiology of CHD,the findings scattered throughout the literature are difficult to retrieve and utilize in research and clinical *** therefore developed CHDbase,an evidence-based knowledgebase of CHD-related genes and clinical manifestations manually curated from 1114 publications,linking 1124 susceptibility genes and 3591 variations to more than 300 CHD types and related *** such as the information of each publication and the selected population and samples,the strategy of studies,and the major findings of studies were integrated with each item of the research *** also integrated functional annotations through parsing50 databases/tools to facilitate the interpretation of these genes and variations in disease *** further prioritized the significance of these CHD-related genes with a gene interaction network approach and extracted a core CHD sub-network with 163 *** clear genetic landscape of CHD enables the phenotype classification based on the shared genetic ***,CHDbase provides a comprehensive and freely available resource to study CHD susceptibilities,supporting a wide range of users in the scientific and medical *** is accessible at http://***.

关键词： Congenital heart disease Congenital heart defect database Genetics Classification

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Dual Branch PnP Based Network for Monocular 6D Pose Estimation

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Intelligent Automation & Soft Computing 2023年第6期36卷 3243-3256页

作者： Jia-Yu Liang Hong-Bo Zhang Qing Lei Ji-Xiang Du Tian-Liang Lin Department of Computer Science and Technology Huaqiao UniversityXiamen361000China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao UniversityXiamen361000China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao UniversityXiamen361000China College of Mechanical Engineering and Automation Xiamen361000China

Monocular 6D pose estimation is a functional task in the field of com-puter vision and *** recent years,2D-3D correspondence-based methods have achieved improved performance in multiview and depth data-based ***,for monocular 6D pose estimation,these methods are affected by the prediction results of the 2D-3D correspondences and the robustness of the per-spective-n-point(PnP)*** is still a difference in the distance from the expected estimation *** obtain a more effective feature representation result,edge enhancement is proposed to increase the shape information of the object by analyzing the influence of inaccurate 2D-3D matching on 6D pose regression and comparing the effectiveness of the intermediate ***,although the transformation matrix is composed of rotation and translation matrices from 3D model points to 2D pixel points,the two variables are essentially different and the same network cannot be used for both variables in the regression ***,to improve the effectiveness of the PnP algo-rithm,this paper designs a dual-branch PnP network to predict rotation and trans-lation ***,the proposed method is verified on the public LM,LM-O and YCB-Video *** ADD(S)values of the proposed method are 94.2 and 62.84 on the LM and LM-O datasets,*** AUC of ADD(-S)value on YCB-Video is *** experimental results show that the performance of the proposed method is superior to that of similar methods.

关键词： 6D pose monocular RGB edge enhancement dual-branch PnP 2D-3D correspondence

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A multi-target UAV fault diagnosis method based on feature discriminant dimension reduction under small samples 4

A multi-target UAV fault diagnosis method based on feature d...

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： An, Xue Zhang, Yizong Li, Shaobo Xiong, Xuan Guizhou University School of Mechanical Engineering Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China Guizhou University College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350354560

In recent years, deep learning has provided many efficient and accurate solutions to UAV fault diagnosis, but most of the current deep learning can only deal with a single task, whereas the UAV system is a complex coupled system, which needs to perform several different fault position diagnostics (FPD) and fault degree diagnostics (FDD) at the same time. Therefore, this paper proposed a multi-target UAV fault diagnosis method based on feature discriminant dimensionality reduction (MFD-FDDR) under small samples. Firstly, We connect specific features of different tasks with the same spatial definition through the channel dimension. Then, we perform feature discriminant dimensionality reduction (FDDR) for each task feature, and the output characteristics satisfy the input size of the following layer of LSTM. Finally, we validate the effectiveness of our proposed method with extensive experiments on real UAV flight data. © 2024 IEEE.

关键词： discriminant dimensionality reduction fault diagnosis multi-task learning small samples

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The default mode network is affected in the early stage of simian immunodeficiency virus infection:a longitudinal study

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Neural Regeneration Research 2023年第7期18卷 1542-1547页

作者： Zhen-Chao Tang Jiao-Jiao Liu Xue-Tong Ding Dan Liu Hong-Wei Qiao Xiao-Jie Huang Hui Zhang Jie Tian Hong-Jun Li Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingChina Key Laboratory of Big Data-Based Precision Medicine(Beihang University) Ministry of Industry and Information Technology of the People’s Republic of ChinaBeijingChina Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina Beijing Youan Hospital Capital Medical UniversityBeijingChina Renji Hospital Shanghai Jiao Tong University School of MedicineShanghaiChina Institute of Laboratory Animal Sciences Chinese Academy of Medical SciencesBeijingChina

Acquired immune deficiency syndrome infection can lead to cognitive dysfunction represented by changes in the default mode *** recent studies have been cross-sectional and thus have not revealed dynamic changes in the default mode network following acquired immune deficiency syndrome infection and antiretroviral ***,when brain imaging data at only one time point are analyzed,determining the duration at which the default mode network is the most effective following antiretroviral therapy after the occurrence of acquired immune deficiency ***,because infection times and other factors are often uncertain,longitudinal studies cannot be conducted directly in the ***,in this study,we performed a longitudinal study on the dynamic changes in the default mode network over time in a rhesus monkey model of simian immunodeficiency virus *** found marked changes in default mode network connectivity in 11 pairs of regions of interest at baseline and 10 days and 4 weeks after virus *** interactions between treatment and time were observed in the default mode network connectivity of regions of interest pairs area 31/V6.R and area 8/frontal eye field(FEF).L,area 8/FEF.L and caudal temporal parietal occipital area(TPOC).R,and area 31/V6.R and *** administered 4 weeks after infection not only interrupted the progress of simian immunodeficiency virus infection but also preserved brain function to a large *** findings suggest that the default mode network is affected in the early stage of simian immunodeficiency virus infection and that it may serve as a potential biomarker for early changes in brain function and an objective indicator for making early clinical intervention decisions.

关键词： acquired immune deficiency syndrome analysis of variance antiretroviral therapy default mode network functional magnetic resonance imaging human immunodeficiency virus longitudinal study rhesus monkeys simian immunodeficiency virus SIV-mac239

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Customized scheduling for shared bus with deadlines

Customized scheduling for shared bus with deadlines

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作者： Jin, Yong Xu, Jia Xu, Lijie Liu, Linfeng Xiao, Fu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science & Engineering Changshu Institute of Technology Jiangsu Changshu China

Public transportation system is one of the most effective ways to conserve energy and reduce carbon emissions. However, the traditional public transportation system does not provide customized service and cannot guarantee the arrival time to destination. To address these issues, we formulate the minimum shared bus scheduling problem to minimize the number of shared buses such that all orders can be completed under constraints of deadlines and capacity of shared bus. We propose the approximation algorithms, S-MBSA for the shared bus with strong endurance and E-MBSA for the large-scale order scenario, to solve the minimum shared bus scheduling problem. We further formulate the constrained maximum revenue shared bus scheduling problem to maximize the revenue under the limited number of shared buses, and propose an approximation algorithm, CMRBSA, to find the shared bus route schedules. Through the extensive simulations, we demonstrate the significant superiority of S-MBSA and E-MBSA in terms of number of shared buses. Furthermore, CMRBSA outperforms the benchmark algorithms significantly in terms of revenue. © 2022 John Wiley & Sons Ltd.

关键词： Scheduling

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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

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Control Theory and Technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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