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Towards practical data alignment in production federated learning

Frontiers of Computer Science 2025年第1期19卷 167-169页

作者： Yexuan SHI Wei YU Yuanyuan ZHANG Chunbo XUE Yuxiang ZENG Zimu ZHOU Manxue GUO Lun XIN Wenjing NIE State Key Laboratory of Complex&Critical Software Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Zhongguancun Pan Connected Mobile Communication Technology Innovation and Application Research Institute Beijing 100088China China Mobile Research Institute Beijing 100053China School of Data Science City University of Hong KongHong Kong 999077China

1 Introduction Federated learning has emerged as a promising par-adigm for collaborative model training that facilitates cooperation among multiple parties while ensuring data privacy[1].Successful alignment of data across parties is crucial for effective federated learning[2].This alignment involves harmonizing heterogeneous data from different parties to identify shared data for joint model *** set intersection(PSI)is a technique that allows the alignment of common entities between parties without revealing additional ***,efficiently performing data alignment with PSI in federated learning[3],especially when dealing with highly unbalanced data,remains challenging due to the low efficiency.

关键词： alignment dealing parties

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Microstructural and corrosion characteristics of coatings on SiC/2009 aluminum matrix composites fabricated via plasma electrolytic oxidation: Variation with Zr4+ concentration

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Ceramics International 2025年

作者： Li, Zhanpeng Ma, Guofeng Wang, Ziyao Wang, Jiahao Sun, Yuan Kou, Ronghui Ren, Shengtao Shenyang Key Laboratory of Micro-arc Oxidation Technology and Application Shenyang University Shenyang110044 China School of Mechanical Engineering Shenyang University Shenyang110044 China College of Environment Shenyang University Shenyang110044 China Shi-changxu Innovation Center for Advanced Materials Institute of Metal Research Chinese Academy of Sciences Liaoning 110016 China

Plasma electrolytic oxidation was utilized to prepare ZrO2-Al2O3-SiO2 composite coatings on the surface of SiC/2009 aluminum matrix composites. The effect of Zr4+ concentration on the microstructure of the coatings was investigated by SEM, AFM, EDS, XRD, and XPS techniques. The electrochemical corrosion behavior, long-term exposure corrosion behavior, and cavitation corrosion behavior were explored in a 3.5 wt% NaCl corrosion solution. The results showed that the surface quality of the coatings was improved by the addition of K2ZrF6. The thickness of the coatings gradually increased with the increasing amount of K2ZrF6 added. At an addition level of 6 g/L, the coatings possessed the smallest porosity and pore size, and were the most uniform. The successful participation of Zr4+ in the reaction was confirmed. The coatings were mainly composed of α-Al2O3, γ-Al2O3 and Mullite. When K2ZrF6 was added at 6 g/L, the coatings demonstrated the highest corrosion resistance and improved durability upon long-term exposure. The formation mechanism of the composite coatings and their corrosion behavior are also discussed in detail. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Composite coatings

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Research on Fe removal from 3104 aluminum alloy by gravity sedimentation with excessive Mn addition

Research on Fe removal from 3104 aluminum alloy by gravity s...

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Chinese Materials Conference 2024, CMC 2024

作者： Yang, Shuyu Han, Yi Feng, Dan Xu, Dongwei Zhu, Ranran Cao, Yiheng Feng, Qianwei Zhao, Pizhi Chinalco Materials Application Research Institute Co. Ltd. Suzhou Branch Suzhou China Chinalco Materials Application Research Institute Co. Ltd. Beijing China Chongqing National Innovation Institute of Light Alloys CO. LTD. Chongqing China Chinalco Advanced Manufacturing Co. Ltd. Chongqing China Key Laboratory of Electromagnetic Processing of Materials Ministry of Education Northeastern University Shenyang China

In this study, the effect of the excessive addition of Mn in 3104 aluminum alloy on the removal of Fe impurities was systematically studied by using the thermodynamic calculation software JMatPro and experimental means. Using direct reading spectrum analysis, optical metallographic microscope observation, scanning electron microscope (SEM) imaging, and energy dispersive spectrometer (EDS) composition analysis, the evolution of chemical composition in the alloy and the morphological changes of the Fe-rich phase were deeply discussed. The results show that the introduction of excessive Mn promotes the formation of a dense primary Al6(MnFe) phase in molten aluminum, and the precipitation of this phase gradually increases with the decrease in temperature and settles in molten aluminum. In the purification area of ingot, the average content of Fe decreased from 0.79 wt.% to 0.45 wt.%, and the actual Fe removal rate reached 43%. The primary Al6(FeMn) phases with long needles and tetragonal blocks are mainly enriched at the bottom of the ingot, and the overlapping phenomenon of the primary Al6(FeMn) phases with long needles is the key factor to causes the large area of Fe-rich phase deposition area at the bottom of the ingot. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Needles

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Single-cell technologies: current and near future

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Science China(Life Sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

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Single-cell omics: experimental workflow, data analyses and applications

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Science China(Life Sciences) 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University(The Second People’s Hospital of Wuhu City)Wuhu 241000China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 201210China Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Beijing Institutes of Life Science Chinese Academy of SciencesBeijing 100101China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal U

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular *** exploration of how the genomes orchestrate the formation and maintenance of each cell,and control the cellular phenotypes of various organismsis,is both captivating and *** the inception of the first single-cell RNA technology,technologies related to single-cell sequencing have experienced rapid advancements in recent *** technologies have expanded horizontally to include single-cell genome,epigenome,proteome,and metabolome,while vertically,they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR ***-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases,including ***,we comprehensively summarize recent advances in single-cell omics technologies,with a specific focus on the methodology *** overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Few-shot image recognition based on multi-scale features prototypical network

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High Technology Letters 2024年第3期30卷 280-289页

作者： LIU Jiatong DUAN Yong School of Information Science Engineering Shenyang University of TechnologyShenyang 110870P.R.China Shenyang Key Laboratory of Advanced Computing and Application Innovation Shenyang 110870P.R.China

In order to improve the model􀆳s capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is *** metric learning algo-rithm is employed to extract image features and project them into a feature space,thus evaluating the similarity between samples based on their relative distances within the metric *** sufficiently extract feature information from limited sample data and mitigate the impact of constrained data vol-ume,a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature ***,the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the *** loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric *** evaluations are conducted on small-sample datasets mini-ImageNet and *** method in this paper can achieve higher classification ***,in the 5-way 1-shot experiment,classification accuracy reaches 50.13%and 66.79%respectively on these two ***,in the 5-way 5-shot ex-periment,accuracy of 66.79%and 85.91%are observed,respectively.

关键词： few-shot learning multi-scale feature prototypical network channel attention label-smoothing

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A Lane Detection Method for Advancing Sustainable Energy Internet

A Lane Detection Method for Advancing Sustainable Energy Int...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Xiting Peng Yang Yang Xiaoyu Zhang Xiaoling Zhang School of Information Science and Engineering Shenyang University of Technology Liaoning Liaohe Laboratory Shenyang Key Laboratory of Advanced Computing and Application Innovation Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China School of Artificial Intelligence Shenyang University of Technology Shenyang Key Laboratory of Industrial Intelligent Chip and Network System Innovation Application Shenyang China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Artificial Intelligence (AI) technology plays a key role in driving the development of a green and smart Energy Internet. As one of the application areas of the Energy Internet, lane detection in intelligent transportation systems can provide accurate real-time traffic data, thus supporting intelligent scheduling and resource optimization, reducing congestion, energy consumption, and emissions, and improving overall energy efficiency. Therefore, deep learning-based lane detection methods can further enhance the intelligence level of the Energy Internet and promote its efficient operation. However, current methods often rely on Convolutional Neural Networks (CNNs), which excel at local feature extraction but struggle to capture global information in complex conditions. In varying light, shadows, and obstacles, CNNs struggle to integrate information, leading to inaccuracies, particularly in dense traffic or complex road structures, where diverse local features result in identification errors and hinder lane continuity. This reduces decision-making efficiency and increases energy consumption. Consequently, we present LFKNet, a lane detection framework designed for the fusion of the Lane Focus Mechanism (LFM) and Kolmogorov-Arnold Network (KAN) to enhance accuracy and robustness. It consists of two key components: the Lane Focus Mechanism , which utilizes the geometrical topology of lanes for improved global fea ture extraction in complex scenarios, and the Kolmogorov-Arnold Network, which employs a learnable nonlinear activation function to optimize performance on complex and nonlinear features. The experiments demonstrate that LFKNet achieves 97.00% accuracy with a 97.76% F1-score on the Tusimple dataset. It also achieves a 79.43% F1-score on the CULane dataset. Overall, LFKNet’s performance validates its effectiveness in lane detection, enabling accurate traffic monitoring and analysis while providing reliable data support for the Energy Internet.

关键词： Energy consumption Accuracy Lane detection Decision making Energy Internet System integration Feature extraction Robustness Topology Artificial intelligence

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A New Predefined Time Stability Theorem and its application in Multi-Modal Memristive Neural Network Synchronization

A New Predefined Time Stability Theorem and its Application ...

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2023 IEEE International Conference on Memristive computing and applications, ICMCA 2023

作者： Zhao, Hui Zhou, Lei Wang, Qingjie Liu, Aidi Peng, Jingliang School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China College of Information Science and Engineering Northeastern University Shenyang China Collaborative Innovation Center of Memristive Computing Application Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350316230

This article proposes a new predefined-time (PDT) stability theorem, which offers more evident advantages compared to previous predefined-time stability. Firstly, based on the limitations of existing PDT stability, this theorem relaxed the limitations of sufficient conditions, which fill the gap in the research on PDT stability. Secondly, the PDT synchronization of multi-modal memristive neural networks (MM-MNN) is analyzed by the usability of the new judgment theorem. A matching PDT controller is designed and sufficient synchronization control conditions is obtained by utilizing generalized Lyapunov functional method, set-valued mapping, inequality analysis technique and differential inclusion theory. Meanwhile, the topological characteristics of multi-modal neuron and its impact on network synchronization behavior are presented. Finally, we provide some confirmatory numerical simulations. © 2023 IEEE.

关键词： Topology

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Analysis of Factors Influencing Driver Lane-Changing Intentions Based on a Naturalistic Trajectory Data Set on Highways 24

Analysis of Factors Influencing Driver Lane-Changing Intenti...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Guangchen Lu, Guangquan Wang, Jinghua Liu, Miaomiao School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Accurately analyzing and predicting driver lane-changing intentions is of paramount importance, as it significantly enhances the safety of self-driving vehicles in their decision-making processes, holding great promise for the future of intelligent transportation systems. This study extracts fundamental lane-changing characteristic parameters from the Next Generation Simulation (NGSIM) trajectory data set and employs Structural Equation Modeling (SEM) to construct a model that elucidates the factors influencing drivers’ lane-changing intentions. Furthermore, it provides insight into how lane-changing intentions are influenced by lane-changing behavior and explores the intricate connection between intention and associated risks. The results emphasize the substantial negative impact of the longitudinal speed difference between the self-vehicle and the preceding vehicle on lane-changing intentions. Additionally, the consideration of lane-changing risk plays a pivotal role in drivers’ decisions to engage in lane-changing behavior. These findings have significant implications for a deeper comprehension of lane-changing intentions, guiding lane-changing decisions, and enhancing overall traffic safety. © ASCE.

关键词： Autonomous vehicles

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Self-Supervised Rotation Learning for 3D Segmentation on Nasopharyngeal Carcinoma MRI Images

Self-Supervised Rotation Learning for 3D Segmentation on Nas...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Changtai Jiang, Ruohui Yin, Shihua Yang, Jinzhu Ban, Xiaojuan University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering School of Intelligence Science and Technology Beijing China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing China Second Affiliated Hospital of Guangxi Medical University Nanning China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9798350337488

Segmenting the tumors of nasopharyngeal carcinoma (NPC) in Magnetic Resonance Imaging (MRI) images is critical for its diagnosis and treatment. In medical image segmentation, it is vital to exploit the rich information contained in 3D sliced images. However, the precise identification and location of lesion regions often require plenty of labels. To alleviate this situation, in this article, we propose 3DRotNPC, a framework based on a tailored Self-Supervised Learning (SSL) strategy, to accurately segment tumor regions for NPC under the circumstance of label limitation. To learn the rich 3D spatial and geometric nature of MRI images in a Self-Supervised way, the proxy task of randomly selecting and rotating images in consecutively sliced data is designed. After the SSL pre-training stage, the learned parameters of the Convolutional Neural Networks (CNNs) based model are transferred to adapt the downstream segmentation task. We verify the capability of 3DRotNPC on the NPC tumor dataset which is collected and curated from the clinical treatment in the representative hospital. Extensive experiments demonstrate that our approach delivers considerable gains in downstream 3D voxel segmentation, especially with a 10% to 50% number of labels. © 2023 IEEE.

关键词： 3D tumor segmentation MRI image nasopharyngeal carcinoma Self-Supervised Learning

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