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28th International Conference on Database Systems for advanced Applications, DASFAA 2023

作者： Zhang, Kaining Tong, Yongxin Shi, Yexuan Zeng, Yuxiang Xu, Yi Chen, Lei Zhou, Zimu Xu, Ke Lv, Weifeng Zheng, Zhiming State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Computer Science and Engineering and Institute of Artificial Intelligence Beihang University Beijing China Department of Computer Science and Engineering HKUST Hong Kong Guangzhou China City University of Hong Kong Hong Kong

ISBN: (纸本)9783031306365

Approximate nearest neighbor query is a fundamental spatial query widely applied in many real-world applications. In the big data era, there is an increasing demand to scale these queries over a spatial data federation, which consists of multiple data owners, each holding a private, disjoint partition of the entire spatial dataset. However, it is non-trivial to enable approximate k-nearest neighbor query over a spatial data federation. This is because stringent security constraints are often imposed to protect the sensitive, privately owned data partitions, whereas naively extending prior secure query processing solutions leads to high inefficiency (e.g., 100 s per query). In this paper, we propose two novel algorithms for efficient and secure approximate k-nearest neighbor query over a spatial data federation. We theoretically analyze their communication cost and time complexity, and further prove their security guarantees and approximation bounds. Extensive experiments show that our algorithms outperform the state-of-the-art solutions with respect to the query efficiency and often yield a higher accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nearest neighbor search

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation 15th

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Malicious Traffic Detection Model Refactor Method Based on Adaptive Sample Generation

Malicious Traffic Detection Model Refactor Method Based on A...

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IEEE advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Liang Jiao Jinlong Fei Min Zhao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Tianjin University of Science and Technology School of Electronic Information and automation Tianjin China

ISBN: (纸本)9781665479691

As machine learning models are widely used in malicious traffic detection, model security has become an important research topic. Aiming at the problems that the model refactor attack methods need a large amount of prior knowledge of the target model, rely on a large amount of real data, and have a narrow scope of application, a model refactor method based on adaptive sample generation was proposed. By means of an adaptive sample generation algorithm based on graph retrieval, a small number of real samples are used to quickly generate generated samples uniformly distributed in the input domain. The boundary of the refactored model is corrected according to the decision of the target model, the refactored model is iteratively trained by combining the network traffic feature selection method and the model hyperparameter optimization method, and the model refactor attack is finally realized. The refactor attack experiment against five malicious traffic detection machine learning models shows that the refactor rate and accuracy of the refactored model increase gradually with the increase of iteration rounds, and the refactor rate is more than 90%, using only the target model output label.

关键词： Training Adaptation models Fuses Optimization methods Machine learning Telecommunication traffic Feature extraction

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Solid-state stepwise temperature-programmable synthesis of bioinspired Fe-N-C oxygen reduction electrocatalyst featuring Fe-N_(5)configuration

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Nano Research 2025年第3期18卷 654-665页

作者： Wei Sang Somboon Chaemchuen Longyang Zhang Zechen Wang Xingchuan Li Cocoro Andrew Nagasaka Mo Xiong Naoki Ogiwara Cheng Chen Zhao Wang Jian Zhang Francis Verpoort Shichun Mu Zongkui Kou Tingting Wang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China Department of Basic Science School of Arts and SciencesThe University of TokyoKomabaMeguro-kuTokyo 153-8902Japan MOE Key Laboratory for Non-Equilibrium Synthesis and Modulation of Condensed Matter School of PhysicsXi’an Jiao tong UniversityXi’an 710049China Sanya Science and Education Innovation Park of Wuhan University of Technology Sanya 572000China Department of Chemical Engineering Faculty of EngineeringMahidol UniversityNakhon Pathom 73170Thailand School of Mathematical&Physical Sciences Wuhan Textile UniversityWuhan 430200China Joint Institute of Chemical Research(FFMiEN) Peoples Friendship University of Russia(RUDN University)6 Miklukho-Maklaya Str.117198 MoscowRussia National Research Tomsk Polytechnic University Lenin Avenue 30634050 TomskRussia Hubei Key Laboratory of Ferro &Piezo-electric Materials and DevicesFaculty of Physics and Electronic SciencesHubei UniversityWuhan 430062China School of Chemistry and Chemical Engineering Hainan UniversityHaikou 57022China

The bioinspired Fe-N-C features an asymmetric Fe-N_(5)configuration to produce active metal-oxygen intermediates by introducing axial N ligand into a symmetric Fe-N_(4) structure,enabling highly active oxygen reduction reaction(ORR).However,the artificial creation of active Fe-N_(5)configuration with a direct,facile and green method has been rarely developed yet,as current techniques involve complex processes and costly ***,we advance a novel solid-state stepwise temperature-programmable(SST)route to directly produce bioinspired Fe-N_(5)-*** then demonstrate that such a Fe-N_(5)-C exhibits a quite higher half-wave potential(0.92 V)with 22-fold faster ORR kinetics(15.6 mA·cm^(−2)@0.85 V)over that of the commercial Pt/C ***,we perform density functional theory(DFT)to find that the Fe is discharged with an extra 0.1 e^(−)through the axially coordinate N ligand,which significantly enhances the ability to activate O_(2) and enables an easier desorption of the crucial intermediate ^(*)OH on the Fe-N_(5)configuration over the conventional Fe-N_(4) structure.

关键词： single atom catalysts Fe-N_(5)coordination solvent-free synthesis oxygen reduction reaction(ORR) metal-air battery

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Adaptive Datum Points Location Optimization Method for Flexible Workpieces based on Centroid Characteristics

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Procedia CIRP 2024年 129卷 61-66页

作者： Jia Luo Yanlong Cao Fan Liu Junnan Zhi Jiangxin Yang Tukun Li Hongsheng Fu Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province College of Mechanical Engineering Zhejiang University Hangzhou 310027 China State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou 310027 China School of Computing and Engineering University of Huddersfield Huddersfield HD1 3DH U.K.

This paper proposes a method for adapting the layout of flexible workpieces of various shapes, in order to reduce the difficulty of location design for personalized shapes. Different from the previous location design that relied on the experiences of engineers, this method establishes an optimization model that determines the number of datum points and the optimal solution based on the centroid characteristics of the workpieces. The optimization index of this model is to control the minimum deformation of the overall flexible workpieces and the minimum variance of the deformation at the maximum deformation point. Taking the characteristics of the workpieces as a priori constraints for location determination, this model obtains the optimal solution combined with finite element analysis. Finally, the effectiveness of the method is verified through a numerical example.

关键词： adaptive location optimization flexible workpieces finite element analysis

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Multikernel:Operating system solution to generalized functional safety

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Security and Safety 2023年第2期2卷 30-43页

作者： Yijing Song Huasheng Dai Jinhu Jiang Weihua Zhang School of Computer Science Fudan UniversityShanghai 200433China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Institute of Big Data Fudan UniversityShanghai 200433China Parallel Processing Institute Fudan UniversityShanghai 200433China

With the trend of digitalization,intelligence,and networking sweeping the world,functional safety and cyber security are increasingly intertwined and overlapped,evolving into the issue of generalized functional *** system reliability technology and network defense technology cannot provide quantifiable design implementation theories and *** the cornerstone of software systems,operating systems in particular are in need of efficient safety *** DHR architecture is a mature and comprehensive solution,and it is necessary to implement an OS-level DHR architecture,for which the multi-kernel operating system is a good *** multi-kernel operating system takes the kernel as the processing scenario element and constructs redundancy,heterogeneity,and dynamism on the kernel,so it has the generalized robustness of the DHR *** article analyzes the significance and requirements of OS-level DHR architecture,and systematically explains how the multi-kernel operating system responds to the requirements of OS-level DHR architecture by analyzing the technical routes of multi-kernel operating systems and develops an operating system solution idea for the generalized functionally safety.

关键词： Metaverse New infrastructure Security base Highly trusted network Trusted identiry Trusted circulation of data

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Practical software diversification tool chain for dissimilar redundancy architecture 9

Practical software diversification tool chain for dissimilar...

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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Zhang, Gaofei Zhang, Zheng Wang, Jiang Ji, Xinsheng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Engineering University Zhengzhou China

With the rapid development of the information society, there are corresponding tool solutions for various daily problems. Aiming at the complex generation process and inconvenient operation of heterogeneous components in a security defense system constructed with dissimilar redundancy, in this paper, we propose a practical basic software application isomerization tool chain, which integrates a series of key technologies of heterogeneous structures. At the same time, corresponding automated tests are provided for users to conduct comprehensive evaluation. Experimental evaluation shows that the tool chain will be able to provide heterogeneous component support for security defense technologies based on dissimilarity redundancy construction and improve the security of software systems. © 2020 IEEE.

关键词： Application programs

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MXenes@Te as a composite material for high-performance aluminum batteries

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Science China Materials 2022年第1期65卷 85-94页

作者： Xiaogeng Huo Xiaoxu Wang Jianling Li Bao Zhang Yu Zhang Te Qin Feiyu Kang State Key Laboratory of Advanced Metallurgy School of Metallurgical and Ecological EngineeringUniversity of Science and Technology BeijingBeijing 100083China Beijing computing center Beijing Academy of Science and TechnologyBeijing 100094China Laboratory of Advanced Materials School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China

The emerging two-dimensional(2D)materials,MXenes,play an important role in various fields of energy storage and exhibit excellent electrochemical ***,we prepared few-layered MXenes(F-Ti_(3)C_(2)T_(x))and loaded Te on the surface of F-Ti_(3)C_(2)T_(x) by using a simple hightemperature evaporation *** addition,the electrochemical performance of the aluminum battery with F-Ti_(3)C_(2)T_(x) as support material was *** initial charge/discharge specific capacities are 987/1096mAh g^(-1)at 0.2Ag^(-1).An obvious discharge voltage plateau of about 1.3V appears at various current *** specific capacity is about 258mAh g^(-1)with MXenes@Te as the active material in the aluminum battery,which benefits from the excellent electronic conductivity of the MXenes and their 2D layered *** functional theory calculations were carried out to explore the ***_(3)C_(2)O_(2)@Te is more inclined to adsorb[AlCl_(4)]^(-) than Ti_(3)C_(2)O_(2).Furthermore,the valence change behavior of element Te was studied by using thermodynamic calculation(FactSage 7.1).X-ray photoelectron spectroscopy results show that when the battery is fully charged to 2.4V element Te and Ti ions(Ti^(3+),Ti^(2+))are oxidized to Te^(4+)and Ti^(4+).In contrast to the charging process,the high-valence Te^(4+)and Ti^(4+)are reduced again during *** Te is reduced to lower-valence Te^(2-)when the discharge voltage is lower than 0.6 V,and a higher charge voltage(2.56 V)is required for Te to be oxidized to Te^(6+).

关键词： MXenes@Te aluminum batteries specific capacity density functional theory

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Angle-resolved magneto-chiral anisotropy in a non-centrosymmetric atomic layer superlattice

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Science Bulletin 2025年第9期70卷 1406-1409页

作者： Cheng, Long Bao, Mingrui Zhang, Xue Zhang, Jingxian Yang, Qun Li, Qiang Cao, Hui Qiu, Dawei Li, Hui Cheng, Guanglei Zhou, Hua Zuo, Jian-Min Zhou, Xiaodong Shen, Jian Zhu, Zhifeng Wang, Wenbo Zhai, Xiaofang School of Physical Science and Technology ShanghaiTech University Shanghai201210 China CAS Key Laboratory of Microscale Magnetic Resonance and School of Physical Sciences University of Science and Technology of China Hefei230026 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China X-ray Science Division Advanced Photon Source Argonne National Laboratory LemontIL60439 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Department of Materials Science and Engineering and Materials Research Laboratory University of Illinois Urbana–Champaign UrbanaIL61801 United States ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China

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Neural Manifold Learning Based on 40 nm Dual-Mode PCM Compute-in-Memory Chip with Hardware Adaptive Drift Compensation

Neural Manifold Learning Based on 40 nm Dual-Mode PCM Comput...

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2024 IEEE International Electron Devices Meeting, IEDM 2024

作者： Yan, Longhao Li, Yuqi Li, Xi Pan, Zelun Wang, Zeyu Wang, Xile Wang, Bowen Zhan, Zhe Tang, Xiyuan Tao, Yaoyu Ge, Woo-Ping Song, Zhitang Huang, Ru Yang, Yuchao Beijing Advanced Innovation Center for Integrated Circuits School of Integrate Circuits Peking University Beijing China Beijing China Center for Brain Inspired Chips Institute for Artificial Intelligence Peking University Beijing China Guangdong Provincial Key Laboratory of In-Memory Computing Chips School of Electronic and Computer Engineering Peking University Shenzhen China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China

ISBN: (纸本)9798350365429

Neural manifold learning (NML), as a significant research topic in the field of neuroscience, plays a vital role in the realization of intelligent brain-computer interface (BCI) at the edge. However, traditional edge computing platforms fall short in meeting the demands for the huge computing power needed to process massive neural data. Here, we design a 288Kb dual-mode phase change memory (PCM) compute-in-memory chip in 40nm technology and utilize its two cores for two fundamental kernels in NML, vector matrix multiplication (VMM) and true random number generation, and develop a PCM-based NML system for the first time. In particular, in order to mitigate the performance degradation caused by PCM conductance drift, we propose a novel adaptive-PCM-drift-compensation scheme that significantly improves the accuracy of VMM results. Compared with CPU platforms running the same tasks, the resulting chip improves the computing energy efficiency and throughput by 476× and 264×, respectively. © 2024 IEEE.

关键词： Random number generation

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