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International Conference on Mechatronics (ICM)

作者： Chuanqi Hu Jin Wang Yikai Chen Yonghang Chen Zhi Zheng Jun Meng Guodong Lu Polytechnic Institute Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China School of Mechanical Engineering Zhejiang University Hangzhou China National Elite Institute of Engineering Chongqing University Chongqing China College of Electrical Engineering Zhejiang University Hangzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This paper presents a trajectory tracking algorithm designed for autonomous quadrotors to navigate complex dy-namic environments that is resilient to collision disturbances. Since it combines Model Predictive Contouring Control (MPCC) with the capabilities of neural networks, our method is named MPCC Integrated with Neural Augmentation (MINA). By equip-ping the quadrotor with PVDF piezoelectric sensors, we achieve real-time perception of collision forces and utilize S4D-Conv Adaptive Net Architecture (SANA) for state computation, a network that integrates the advantages of S4D and convolutional kernels to enhance the accuracy of state predictions. We incorporate the predicted results into the MPCC framework to optimize the thrust output of the quadrotor for the next time step, ensuring precise trajectory tracking. Experimental validation demonstrates the reliability and effectiveness of our algorithm, providing an innovative solution for autonomous flight and trajectory tracking of quadrotors in complex dynamic environments.

关键词： Trajectory tracking Navigation Heuristic algorithms Neural networks Reliability theory Predictive models Prediction algorithms Real-time systems Sensors Quadrotors

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A fine-tuned large language model based molecular dynamics agent for code generation to obtain material thermodynamic parameters

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Scientific Reports 2025年第1期15卷 1-11页

作者： Shi, Zhuofan Xin, Chunxiao Huo, Tong Jiang, Yuntao Wu, Bowen Chen, Xingyue Qin, Wei Ma, Xinjian Huang, Gang Wang, Zhenyu Jing, Xiang School of Software and Microelectronics Peking University Beijing 102600 China National Key Laboratory of Data Space Technology and System Beijing 100195 China Advanced Institute of Big Data Beijing 100195 China Institute of Information Engineering Chinese Academy of Sciences Beijing 100085 China

In the field of materials science, addressing the complex relationship between the material structure and properties has increasingly involved leveraging the text generation capabilities of AI-generated content (AIGC) models for tasks that include literature mining and data analysis. However, theoretical calculations and code development remain labor-intensive challenges. This paper proposes a novel approach based on text-to-code generation, utilizing large language models to automate the implementation of simulation programs in materials science. The effectiveness of automated code generation and review is validated with thermodynamics simulations based on the LAMMPS software as a foundation. This study introduces Molecular Dynamics Agent (MDAgent), a framework designed to guide large models in automatically generating, executing, and refining simulation code. In addition, a thermodynamic simulation code dataset for LAMMPS was constructed to fine-tune the language model. Expert evaluation scores demonstrate that MDAgent significantly improves the code generation and review capabilities. The proposed approach reduces the average task time by 42.22%, as compared to traditional models, thus highlighting its potential applications in the field of materials science. © The Author(s) 2025.

关键词： Agent LLM Materials science

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Hadamard Attention Recurrent Transformer: A Strong Baseline for Stereo Matching Transformer

arXiv

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arXiv 2025年

作者： Chen, Ziyang Zhang, Yongjun Li, Wenting Wang, Bingshu Wu, Yabo Zhao, Yong Chen, C.L. Philip College of Computer Science The State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Information Engineering Guizhou University of Commerce Guiyang550021 China School of Software Northwestern Polytechnical University Xi’an710129 China Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University Shenzhen518055 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

In light of the advancements in transformer technology, extant research posits the construction of stereo transformers as a potential solution to the binocular stereo matching challenge. However, constrained by the low-rank bottleneck and quadratic complexity of attention mechanisms, stereo transformers still fail to demonstrate sufficient nonlinear expressiveness within a reasonable inference time. The lack of focus on key homonymous points renders the representations of such methods vulnerable to challenging conditions, including reflections and weak textures. Furthermore, a slow computing speed is not conducive to the application. To overcome these difficulties, we present the Hadamard Attention Recurrent Stereo Transformer (HART) that incorporates the following components: 1) For faster inference, we present a Hadamard product paradigm for the attention mechanism, achieving linear computational complexity. 2) We designed a Dense Attention Kernel (DAK) to amplify the differences between relevant and irrelevant feature responses. This allows HART to focus on important details. DAK also converts zero elements to non-zero elements to mitigate the reduced expressiveness caused by the low-rank bottleneck. 3) To compensate for the spatial and channel interaction missing in the Hadamard product, we propose MKOI to capture both global and local information through the interleaving of large and small kernel convolutions. Experimental results demonstrate the effectiveness of our HART. In reflective area, HART ranked 1st on the KITTI 2012 benchmark among all published methods at the time of submission. Code is available at https://***/ ZYangChen/HART. Copyright © 2025, The Authors. All rights reserved.

关键词： Hadamard transforms

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ATHENA: An In-vehicle CAN Intrusion Detection Framework Based on Physical Characteristics of Vehicle Systems

arXiv

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arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： Network intrusion

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Leapfrog Polymorphic Neural Ordinary Differential Equation

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IEEE Signal Processing Letters 2025年 32卷 2139-2143页

作者： Zhang, Xiao Wei, Wei Zhang, Zhen Zhang, Lei Northwestern Polytechnical University School of Software Xi'an710072 China Northwestern Polytechnical University Shaanxi Provincial Key Laboratory of Speech and Image Information Processing School of Computer Science Xi'an710072 China Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Xi'an710072 China The University of Adelaide school of computer science AdelaideSA5005 Australia

Neural Ordinary Differential Equations (NODEs) revolutionize the way we view residual networks as solvers for initial value problems (IVPs), with layer depth serving as the time step. In this study, we propose a more efficient extension of NODEs called Leap-Frog Polymorphic Neural ODEs (LF-NODEs). LF-NODEs introduce the leap-frog updating scheme, breaking free from the specific structure of time-evolving mixtures of multiple dynamical systems. In each time step (corresponding to each residual network layer), LF-NODEs integrate the features of multiple dynamical systems (residual network layers) by employing time-variant weights from different dynamical systems. Our LF-NODEs not only overcome the limitations in the representation power of individual residual networks but also provide a more flexible and effective structure for residual networks based on multiple dynamical systems. The efficiency of the leap-frog updating scheme is theoretically derived and demonstrated. Furthermore, we expand the model by incorporating a multi-scale approach (LF-MSNODEs) to achieve even more accurate model representations. Empirical results showcase the improvements provided by our proposed methods in 2D concentric spheres task, irregularly sampled time series prediction, and classification tasks. Additionally, the results provide empirical evidence that the learned feature space enhances system efficiency with fewer parameters and function evaluations compared to the baseline methods. © 1994-2012 IEEE.

关键词： Ordinary differential equations

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Rethinking the Bias of Foundation Model under Long-tailed Distribution

arXiv

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arXiv 2025年

作者： Chen, Jiahao Qin, Bin Li, Jiangmeng Chen, Hao Su, Bing Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China Electrical and Computer Engineering Carnegie Mellon University United States

Long-tailed learning has garnered increasing attention due to its practical significance. Among the various approaches, the fine-tuning paradigm has gained considerable interest with the advent of foundation models. However, most existing methods primarily focus on leveraging knowledge from these models, overlooking the inherent biases introduced by the imbalanced training data they rely on. In this paper, we examine how such imbalances from pre-training affect long-tailed downstream tasks. Specifically, we find the imbalance biases inherited in foundation models on downstream task as parameter imbalance and data imbalance. During fine-tuning, we observe that parameter imbalance plays a more critical role, while data imbalance can be mitigated using existing re-balancing strategies. Moreover, we find that parameter imbalance cannot be effectively addressed by current re-balancing techniques, such as adjusting the logits, during training, unlike data imbalance. To tackle both imbalances simultaneously, we build our method on causal learning and view the incomplete semantic factor as the confounder, which brings spurious correlations between input samples and labels. To resolve the negative effects of this, we propose a novel backdoor adjustment method that learns the true causal effect between input samples and labels, rather than merely fitting the correlations in the data. Notably, we achieve an average performance increase of about 1.67% on each dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Digital elevation model

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Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks

arXiv

引用

arXiv 2025年

作者： Cui, Shuang Li, Yi Li, Jiangmeng Tang, Xiongxin Su, Bing Xu, Fanjiang Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China China Department of Computer Science & Engineering the Hong Kong University of Science and Technology Hong Kong

Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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TVEG: Model Selection of the Time-Varying Exponential Family Distributions Graphical Models

引用

IEEE Transactions on Network Science and engineering 2025年

作者： Liu, Juan Mei, Guofeng Xia, Yuanqing Wu, Xiaoqun Lu, Jinhu Beijing Forestry University School of Technology Beijing100107 China Italy Zhongyuan University of Technology Henan 451191 China Wuhan University School of Mathematics and Statistics Wuhan430072 China Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence School of Automation Science and Electrical Engineering Beijing100191 China

The undirected graphical model, a popular class of statistical model, offers a way to describe and explain the relationships among a set of variables. However, it remains a challenge to choose a certain graphical model to explain the relationships of variables adequately, especially when the relationships of variables are rewiring over time. This paper proposes the Time-Varying Exponential Family Distributions Graphical (TVEG) models, with time-varying structures and exponential family node-wise conditional distributions. TVEG models extend the scope of available graph models and can be applied to time-varying and exponential family distribution observation data in reality. We propose the Temporally Smoothed L1regularized exponential family graphical estimator (TSLEG), an estimator to infer the structure of TVEG from observations. We derive sufficient conditions for the TSLEG to recover the block partition and sparse pattern with high probability. We derive a message-passing optimization method to solve the TSLEG for time-varying Ising, Gaussian, exponential, and Poisson graphs based on the ADMM. The synthetic network simulations corroborate the theoretical analysis. Analysing of real data of stocks and the US Senate by the time-varying exponential model and Poisson model indicates the effectiveness and practicality of TVEG models. © 2025 IEEE. All rights reserved.

关键词： ADMM exponential family distributions Graphical model pseudo maximum likelihood estimation

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Fed-GAN: Federated Generative Adversarial Network with Privacy-Preserving for Cross-Device Scenarios

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Han, Song Ding, Hongxin Zhao, Shuai Ren, Siqi Zeng, Shengke Xue, Mengqi Wang, Ruili Hangzhou City University School of Computer and Computing Science Hangzhou China Zhejiang Gongshang University School of Computer Science and Technology China Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology Hangzhou China Xihua University School of Computer and Software Engineering Chengdu China Massey University School of Mathematical and Computational Science Auckland New Zealand

Huge amounts of data from various sources are substantial to dependable distributed machine learning, especially for trustworthy federated learning (FL). However, existing FL methods are difficult to collect enough data for training the global model more accurately, especially in cross-device scenarios. In this paper, we propose a new federated generative adversarial network empowered by differential privacy and knowledge transfer named Fed-GAN, which can be used to address the problem of data shortage and prevent generator leakage from resource-constrained devices, as well as generate high-quality synthetic data while ensuring strict DP guarantees. Different from other generative model methods, our Fed-GAN framework can achieve efficient and secure generative model training and limited permission for resource-constrained devices to prevent them from leaking or misusing the generator. In addition, we propose a pHash-KT method for our Fed-GAN framework, which selects potentially high-quality data through the knowledge of each client for improving the utility of synthetic data. Our FedGAN framework satisfies (Formula presented)-DP, and also has high resistance when number of adversaries is 10%-70% of the total number of clients. Extensive experiments demonstrate that our Fed-GAN framework not only generates high-quality synthetic data, but also provides strict DP guarantees, compared with other generative model methods. © 2004-2012 IEEE.

关键词： Generative adversarial networks

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Optimized Dynamic Watermarking for Audio DNNs with Adaptive Embedding and Boundary Sampling

Optimized Dynamic Watermarking for Audio DNNs with Adaptive ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hao Fei Hewang Nie Siqi Sun Songfeng Lu Ting Luo Ling Qian Dunbo Cai Zhiguo Huang Runqing Zhang 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 Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China China Mobile (Suzhou) Software Technology Company Limited Suzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The intensified concerns arising from the widespread adoption of deep learning have led to increased scrutiny of intellectual property protection in DNN models. Existing audio watermarking techniques, predominantly based on traditional signal processing methods, struggle to balance robustness, imperceptibility, and defense resistance in the face of evolving adversarial attacks. These limitations underscore the urgent need for more effective watermarking solutions in the audio domain. In this paper, we propose a dynamic audio watermarking framework that introduces an optimization-based approach to attach robust and adaptable triggers at arbitrary positions within audio signals, and innovatively integrates boundary sample selection driven by forgetting events and an adaptive watermark trigger embedding technique based on the SNR. Comprehensive experimental results reveal that our scheme preserves high model performance while maintaining remarkable stealthiness and robustness, offering a secure and reliable solution for safeguarding intellectual property in the audio domain and advancing the field of DNN watermarking.

关键词： Resistance Deep learning Adaptation models Watermarking Intellectual property Signal processing Robustness Speech processing Protection Signal to noise ratio

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