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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Binocular active stereo matching based on multi-scale random forest 13

Binocular active stereo matching based on multi-scale random...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Liu, Jiatong Pu, Mingqi Wang, Ghutian Chen, Dongyue Jia, Tang College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University China Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9798350315196

With the rapid development of 3D vision technology, the existing passive binocular cameras can no longer meet the practical needs of depth perception. Therefore, this paper proposes a binocular active stereo matching method based on multi-scale random forest. Firstly, a binocular active vision system consists of a near-infrared random speckle projector and a binocular camera is constructed, and the system was calibrated using Zhang's calibration method. Secondly, gamma image enhancement and image difference method are used for processing to reduce the impact of ambient light on the measurement results. Next, extract the points of interest and use the multi-scale random forest algorithm to match the window where the points of interest are located to generate a sparse structured light anchor disparity map globally. Finally, using the Census transforms as the matching cost, iterate continuously, refine the disparity and get a dense disparity map. The experimental results show that this system can achieve good depth perception accuracy and robustness under complex indoor lighting conditions. © 2023 IEEE.

关键词： Binoculars

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PID-YOLOX: An X-Ray Prohibited Items Detector Based on YOLOX 13

PID-YOLOX: An X-Ray Prohibited Items Detector Based on YOLOX

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Li, Mingyuan Ma, Bowen Wang, Hao Li, Yingbo Chen, Dongyue Jia, Tong College of Information Science and Engineering Northeastern-University Shenyang China Foshan Graduate School of Innovation Northeastern University China Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9798350315196

X-ray prohibited items detection is an effective and crucial measure in various security inspection scenarios. However, the overlapping phenomenon in X-ray images exacerbates the foreground-background class imbalance, and the imaging principle of X-ray images results in missing texture features. To address these challenges, we propose an end-to-end X-ray Prohibited Items Detector (PID-YOLOX) based on YOLOX-Tiny, which offers fast detection speed and high accuracy. Specifically, we introduce the Generalized Label Assignment (GLA) scheme to tackle the foreground-background class imbalance problem, and the Multi-Cardinality Attention (MCA) mechanism to alleviate the issue of missing texture features. Our experimental results show that PID-YOLOX achieves 54.9% average precision (AP) on the PIXray dataset, surpassing YOLOX-Tiny by 2.2% AP. Furthermore, extensive experiments demonstrate that PID-YOLOX is superior to the state-of-the-art methods, indicating its potential applications in the prohibited items detection field. © 2023 IEEE.

关键词： Textures

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Understanding Large Language Model Supply Chain: Structure, Domain, and Vulnerabilities

arXiv

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arXiv 2025年

作者： Hu, Yanzhe Wang, Shenao Nie, Tianyuan Zhao, Yanjie Wang, Haoyu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Large Language Models (LLMs) have revolutionized artificial intelligence (AI), driving breakthroughs in natural language understanding, text generation, and autonomous systems. However, the rapid growth of LLMs presents significant challenges in the security and reliability of the Large Language Model Supply Chain (LLMSC), a complex network of open-source components, libraries, and tools essential for LLM development and deployment. Despite its critical importance, the LLMSC remains underexplored, particularly regarding its structural characteristics, domain composition, and security vulnerabilities. To address this gap, we conduct the first empirical study of the LLMSC, analyzing a curated dataset of open-source packages from PyPI and NPM across 14 functional domains. We construct a directed dependency graph comprising 15,725 nodes, 10,402 edges, and 180 unique vulnerabilities to investigate the structural characteristics of the LLMSC and analyze how security risks propagate through its dependency network. Our findings reveal that the LLMSC exhibits a "locally dense, globally sparse" topology, with 79.7% of dependency trees containing fewer than 5 nodes, while a few large trees dominate the ecosystem, accounting for 77.66% of all nodes. The graph is characterized by high-degree hubs, with the top 5 most connected nodes averaging 1,282 dependents each. Security analysis shows that critical vulnerabilities propagate to an average of 142.1 nodes at the second layer of dependency trees and peak at 237.8 affected nodes at the third layer. Notably, cascading risks are concentrated in critical hub nodes such as transformers, which directly or indirectly affect over 1,300 downstream packages. These findings provide quantitative insights into the structural and security dynamics of the LLMSC and emphasize the need for targeted mitigation strategies to enhance ecosystem resilience. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

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Characterizing and Detecting Gas-Inefficient Patterns in Smart Contracts

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Journal of Computer Science & Technology 2022年第1期37卷 67-82页

作者： Que-Ping Kong Zi-Yan Wang Yuan Huang Xiang-Ping Chen Xiao-Cong Zhou Zi-Bin Zheng Gang Huang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China School of Software Engineering Sun Yat-sen UniversityZhuhai 519082China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China Peking University Shenzhen Graduate School Shenzhen 518000China

Ethereum blockchain is a new internetware with tens of millions of smart contracts running on *** from general programs,smart contracts are decentralized,tamper-resistant and permanently ***,to avoid resource abuse,Ethereum charges users for deploying and invoking smart contracts according to the size of contract and the operations executed by *** is necessary to optimize smart contracts to save ***,since developers are not familiar with the operating environment of smart contracts(i.e.,Ethereum virtual machine)or do not pay attention to resource consumption during development,there are many optimization opportunities for smart *** fill this gap,this paper defines six gas-inefficient patterns from more than 25,000 posts and proposes an optimization approach at the source code level to let users know clearly where the contract is *** evaluate the prevalence and economic benefits of gas-inefficient patterns,this paper conducts an empirical study on more than 160,000 real smart *** promising experimental results demonstrate that 52.75%of contracts contain at least one gas-inefficient pattern proposed in this *** these patterns are removed from the contract,at least 0.30 can be saved per contract.

关键词： smart contract anti-pattern detection optimization empirical study

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Self-feedback LSTM regression model for real-time particle source apportionment

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Journal of Environmental Sciences 2022年第4期34卷 10-20页

作者： Wei Wang Weiman Xu Shuai Deng Yimeng Chai Ruoyu Ma Guoliang Shi Bo Xu Mei Li Yue Li Trusted AI System Laboratory College of Computer ScienceNankai UniversityTianjin 300350China KLMDASR Tianjin Key Laboratory of Network and Data Security TechnologyTianjin 300350China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and EngineeringNankai UniversityTianjin 300071China Institute of Mass Spectrometry and Atmospheric Environment Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution Jinan UniversityGuangzhou 510632China Guangdong-Hongkong-Macao Joint Laboratory of Collaborative Innovation for Environmental Quality Guangzhou 510632China

Atmospheric particulate matter pollution has attracted much wider attention *** recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments *** demands are summarized,in this paper,as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances,such as the existence of secondary source and similar *** this study,we firstly analyze the possible and potential restraints in single particle source apportionment,then propose a novel three-step self-feedback long short-term memory(SF-LSTM)network for approximating the source *** proposed deep learning neural network includes three modules,as generation,scoring and refining,and regeneration *** from the scoring modules,SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment,meanwhile,the regeneration module calculates the source contribution in a non-linear *** results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators(residual sum of squares,stability,sparsity,negativity)for the ***,in short time-resolution analyzing,SF-LSTM provides better results under the restraint of stability.

关键词： Time series Regression Self-feedback LSTM network Particle source apportionment

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National Science Review 2025年第5期12卷 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

关键词： pretrained model diabetes continuous glucose monitoring glucose dynamics

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A new gene regulatory network reconstruction method based on single cell sequencing and transfer entropy 2

A new gene regulatory network reconstruction method based on...

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2nd International Conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2023

作者： Zhu, Zhengtong Gao, Jing Liu, Zhenyu College of Computer and Information Engineering The Inner Mongolia Agricultural University Huhhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry huhhot010018 China

ISBN: (纸本)9781510664968

In systems biology, gene regulatory networks can well reveal complex biological systems and dynamic biological processes. Traditional gene regulatory network reconstruction methods ignore dynamic biological processes. A gene regulatory network reconstruction method based on transfer entropy and single-cell dynamic time series data is proposed to solve this problem. And from a theoretical point of view, the possibility and rationality of transfer entropy in the application of big data and causal reasoning are proved in detail. Firstly, the method construct pseudo-time series dynamic gene expression data based on trajectory inference. Secondly, The transfer entropy is used to calculate the directional transfer information between paired genes, and screen the major gene regulatory relationships. Finally, remove the indirect gene regulation relationship according to the data processing inequality, and construct the gene regulation network. This method takes the single cell sequencing data of early mouse embryo blood development as an example, and selects TENET and DynGENIE3 algorithms as comparisons, which proves the feasibility and effectiveness of this method from the theoretical and experimental perspectives. The experimental results not only identify the key cell development regulatory factors and regulatory relationships, but also consume less time. It can not only provide important reference value and assumptions for biological disturbance experiment, but also reduce the time cost and research cost of biological experiment. © 2023 SPIE.

关键词： Gene expression

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Stabilization of switched systems with unstable modes via mode-partition-dependent ADT

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Journal of the Franklin Institute 2023年第2期360卷 1308-1326页

作者： Jin, Cui-Li Wang, Qing-Guo Wang, Rui Wu, Di Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian116024 China Institute of Artificial Intelligence and Future Networks Beijing Normal University at ZhuhaiGuangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Key Laboratory of Advanced Technology for Aerospace Vehicles Liaoning Province and School of Aeronautics and Astronautics Dalian University of Technology Dalian116024 China

This paper concerns the stability of switched systems orchestrating between unstable modes. First, a mode partition is applied to select stabilizing switching from the switching between modes from different subsets. Second, a piecewise Lyapunov-like function is applied to decline at stabilizing switching times. Then, the stability criteria build on mode-partition-dependent average dwell time by collecting enough stabilizing switching behavior to stabilize the destabilizing switching behavior and unstable modes. Finally, the simulation verification shows the feasibility and effectiveness of the method. © 2022 The Franklin Institute

关键词： Stability criteria

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Edge Detectors Based on Pauta Criterion with Application to Hybrid Compact-WENO Finite Difference Scheme

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Advances in Applied Mathematics and Mechanics 2023年第6期15卷 1379-1406页

作者： Chunhua Zhang Zhen Gao Sa Ye Peng Li School of Mathematical Sciences Ocean University of ChinaQingdaoShandong 266100China Institute of Agricultural Information Chinese Academy of Agricultural SciencesKey Laboratory of Agricultural Big DataMinistry of Agriculture and Rural AffairsBeijing 100081China Department of Engineering Mechanics Shijiazhuang Tiedao UniversityShijiazhuangHebei 050043China

In the last two decades,many edge detection methods have been developed and widely used in image processing for edge detection and the hybrid compactWENO finite difference(hybrid)schemes for solving the system of hyperbolic conservation laws with solutions containing both discontinuous and complex fine-scale ***,many edge detection methods include the problem-dependent parameters such as the high order multi-resolution(MR)analysis(Harten,JCP,49(1983)).Therefore,we combined the Tukey’s boxplot method with MR analysis(Gao et al.,JSC,73(2017))to overcome this problem in a *** the Tukey’s boxplot method needs to sort the data at the beginning of Runge-Kutta time integration method,which is relatively time-consuming and *** this study,we employ the PauTa criterion and remove the problem-dependent parameters in the MR ***,two new edge detection approaches,which are based on second-order central difference scheme and Ren’s idea(Ren et al.,JCP,192(2003)),are also *** accuracy,efficiency and robustness of the hybrid scheme with the new edge detectors are verified by numerous classical one-and two-dimensional examples in the image processing and compressible Euler equations with discontinuous solutions.

关键词： Edge detection Pauta criterion multi-resolution hybrid

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