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Distributed time-varying formation control with uncertainties based on an event-triggered mechanism

science China(Information sciences) 2021年第3期64卷 167-176页

作者： Xiaoduo LI Yumeng BAI Xiwang DONG Qingdong LI Zhang REN Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical EngineeringBeihang University Department of Electrical and Computer Engineering University of Wisconsin-Madison Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the distributed time-varying formation(TVF) problems for general linear multi-agent systems(MASs) subject to matched bounded uncertainties based on an adaptive event-triggered mechanism. A TVF protocol was designed with an event-triggered mechanism by introducing adaptive weights into the formation control protocol and triggering condition, and the large chattering phenomenon was avoided by the σ-modification adaptive law. According to the Lyapunov stability theory, proof has been established that the MASs in the presence of uncertainties can realize the expected formation that satisfies the given feasible condition. Finally, an example is provided to verify the effectiveness of the proposed algorithm.

关键词： formation control multi-agent systems event-triggered adaptive control

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Find the Bad Apples: An efficient method for perfect key recovery under imperfect SCA oracles – A case study of Kyber

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded systems 2022年第1期2023卷 89-112页

作者： Shen, Muyan Cheng, Chi Zhang, Xiaohan Guo, Qian Jiang, Tao Hubei Key Laboratory of Intelligent Geo-Information Processing School of Computer Science China University of Geosciences Wuhan China State Key Laboratory of Integrated Services Networks Xidian University Xian China Lund University Lund Sweden Research Center of 6G Mobile Communications School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

Side-channel resilience is a crucial feature when assessing whether a post-quantum cryptographic proposal is sufficiently mature to be deployed. In this paper, we propose a generic and efficient adaptive approach to improve the sample complexity (i.e., the required number of traces) of plaintext-checking (PC) oracle-based side-channel attacks (SCAs), a major class of key recovery chosen-ciphertext SCAs on lattice-based key encapsulation mechanisms (KEMs). This new approach is preferable when the constructed PC oracle is imperfect, which is common in practice, and its basic idea is to design new detection codes that can determine erroneous positions in the initially recovered secret key. These secret entries are further corrected with a small number of additional traces. This work benefits from the generality of PC oracle and thus is applicable to various schemes and implementations. Our main target is Kyber since it has been selected by NIST as the KEM algorithm for standardization. We instantiated the proposed generic attack on Kyber512 and then conducted extensive computer simulations against Kyber512 and FireSaber. We further mounted an electromagnetic (EM) attack against an optimized implementation of Kyber512 in the pqm4 library running on an STM32F407G board with an ARM Cortex-M4 microcontroller. These simulations and real-world experiments demonstrate that the newly proposed attack could greatly improve the state-of-the-art in terms of the required number of traces. For instance, the new attack requires only 41% of the EM traces needed in a majority-voting attack in our experiments, where the raw oracle accuracy is fixed. © 2022, Ruhr-University of Bochum. All rights reserved.

关键词： Standardization

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VT-SGN:Spiking Graph Neural Network for Neuromorphic Visual-Tactile Fusion

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IEEE Transactions on Haptics 2024年 PP卷 PP-PP页

作者： Wu, Peiliang Zhang, Haozhe Li, Yao Chen, Wenbai Gao, Guowei Yanshan University School of Information Science and Engineering Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao066004 China Beijing Information Science and Technology University School of Automation Beijing100192 China

Current issues with neuromorphic visual-tactile perception include limited training network representation and inadequate cross-modal fusion. To address these two issues, we proposed a dual network called visual-tactile spiking graph neural network (VT-SGN) that combines graph neural networks and spiking neural networks to jointly utilize the neuromorphic visual and tactile source data. First, the neuromorphic visual-tactile data were expanded spatiotemporally to create a taxel-based tactile graph in the spatial domain, enabling the complete exploitation of the irregular spatial structure properties of tactile information. Subsequently, a method for converting images into graph structures was proposed, allowing the vision to be trained alongside graph neural networks and extracting graph-level features from the vision for fusion with tactile data. Finally, the data were expanded into the time domain using a spiking neural network to train the model and propagate it backwards. This framework effectively utilizes the structural differences between sample instances in the spatial dimension to improve the representational power of spiking neurons, while preserving the biodynamic mechanism of the spiking neural network. Additionally, it effectively solves the morphological variance between the two perceptions and further uses complementary data between visual and tactile. To demonstrate that our approach can improve the learning of neuromorphic perceptual information, we conducted comprehensive comparative experiments on three datasets to validate the benefits of the proposed VT-SGN framework by comparing it with state-of-the-art studies. © 2008-2011 IEEE.

关键词： Graph neural networks

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Short mid-infrared watt-level all-fiber nonlinear pulse compressor above 100-MHz pulse repetition rate

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High Power Laser science and Engineering 2023年第1期11卷 107-113页

作者： Jingcheng Shang Chao Mei Shengzhi Zhao Yizhou Liu Kejian Yang Chun Wang Tao Li Tianli Feng School of Information Science and Engineering Shandong Provincial Key Laboratory of Laser Technology and ApplicationShandong UniversityQingdaoChina Key Laboratory of Laser&Infrared System(Shandong University) Ministry of EducationQingdaoChina Institute of Novel Semiconductors State Key Laboratory of Crystal MaterialsShandong UniversityJinanChina Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services SCCEUniversity of Science and Technology BeijingBeijingChina

We firstly report a 2-μm all-fiber nonlinear pulse compressor based on two pieces of normal dispersion fiber(NDF),which enables a high-power scaling ability of watt-level and a high pulse compression ratio of *** the NDFbased all-fiber nonlinear pulse compressor,the 450-fs laser pulses with a repetition rate of 101.4 MHz are compressed to 35.1 fs,corresponding to a 5.2 optical oscillation cycle at the 2-μm wavelength *** output average power reaches 1.28 W,which is believed to be the highest value never achieved from the previous 2-μm all-fiber nonlinear pulse compressors with a high pulse repetition rate above 100 *** dynamic evolution of the ultrafast pulse inside the all-fiber nonlinear pulse compressor is numerically analyzed,matching well with the experimental results.

关键词： few-optical-cycle pulses high pulse repetition rate nonlinear pulse compression

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Using Experience Classification for Training Non-Markovian Tasks

SSRN

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SSRN 2023年

作者： Miao, Ruixuan Lu, Xu Tian, Cong Yu, Bin Cui, Jin Duan, Zhenhua Institute of Computing Theory and Technology State Key Laboratory of Integrated Services Networks Xidian University China School of Computer Science Xi’an Shiyou University China

Unlike the standard Reinforcement Learning (RL) model, many real-world tasks are non-Markovian, whose rewards are predicated on state history rather than solely on the current state. Solving a non-Markovian task, frequently applied in practical applications such as autonomous driving, financial trading, and medical diagnosis, can be quite challenging. We propose a novel RL approach to achieve non-Markovian rewards expressed in temporal logic LTLf (Linear Temporal Logic over Finite Traces). To this end, an encoding of linear complexity from LTLf into MDPs (Markov Decision Processes) is introduced to take advantage of advanced RL algorithms. Then, a prioritized experience replay technique based on the automata structure (semantics equivalent to LTLf specification) is utilized to improve the training process. We empirically evaluate several benchmark problems augmented with non-Markovian tasks to demonstrate the feasibility and effectiveness of our approach. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Joint DNN Partition and Thread Allocation Optimization for Energy-Harvesting MEC systems

Joint DNN Partition and Thread Allocation Optimization for E...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Yizhou Shi Liying Li Yue Zeng Peijin Cong Junlong Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Deep neural networks (DNNs) have demonstrated exceptional performance, leading to diverse applications across various mobile devices (MDs). Considering factors like portability and environmental sustainability, an increasing number of MDs are adopting energy harvesting (EH) techniques for power supply. However, the computational intensity of DNNs presents significant challenges for their deployment on these resource-constrained devices. Existing approaches often employ DNN partition or offloading to mitigate the time and energy consumption associated with running DNNs on MDs. Nonetheless, existing methods frequently fall short in accurately modeling the execution time of DNNs, and do not consider to use thread allocation for further latency and energy consumption optimization. To solve these problems, we propose a dynamic DNN partition and thread allocation method to optimize the latency and energy consumption of running DNNs on EH-enabled MDs. Specifically, we first investigate the relationship between DNN inference latency and allocated threads and establish an accurate DNN latency prediction model. Based on the prediction model, a DRL-based DNN partition (DDP) algorithm is designed to find the optimal partitions for DNNs. A thread allocation (TA) algorithm is proposed to reduce the inference latency. Experimental results from our test-bed platform demonstrate that compared to four benchmarking methods, our scheme can reduce DNN inference latency and energy consumption by up to 37.3% and 38.5%.

关键词： Energy consumption Instruction sets Heuristic algorithms Artificial neural networks Predictive models Prediction algorithms Inference algorithms Partitioning algorithms Resource management Optimization

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Unleashing the Power of Visual Foundation Models for Generalizable Semantic Segmentation 39

Unleashing the Power of Visual Foundation Models for General...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Tang, PeiYuan Zhang, Xiaodong Yang, Chunze Yuan, Haoran Sun, Jun Shan, Danfeng Yang, Zijiang James School of Computer Science and Technology Xi’an Jiaotong University China School of Computer Science and Technology Xidian University China Shaanxi Key Laboratory of Network and System Security Xidian University China Synkrotron Inc. United States Singapore Management University Singapore University of Science and Technology of China China

ISBN: (纸本)157735897X

Deep learning models often suffer from performance degradation in unseen domains, posing a risk for safety-critical applications such as autonomous driving. To tackle this problem, recent studies have leveraged pre-trained Visual Foundation Models (VFMs) to enhance generalization. However, exsiting works mainly focus on designing intricate networks for VFMs, neglecting their inherent strong generalization potential. Moreover, these methods typically perform inference on low-resolution images. The loss of detail hinders accurate predictions in unseen domains, especially for small objects. In this paper, we argue that simply fine-tuning VFMs and leveraging high-resolution images unleash the power of VFMs for generalizable semantic segmentation. Therefore, we design a VFM-based segmentation network (VFMNet) that adapts VFMs to this task with minimal fine-tuning, preserving their generalizable knowledge. Then, to fully utilize high-resolution images, we train a Mask-guided Refinement Network (MGRNet) to refine VFMNet’s predictions combining detailed image features. Furthermore, we adopt a two-stage coarse-to-fine inference approach. MGRNet is used to refine the low-confidence regions predicted by VFMNet to obtain fine-grained results. Extensive experiments demonstrate the effectiveness of our method, outperforming state-of-the-art methods by 3.3% on the average mIoU in synthetic-to-real domain generalization. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction

arXiv

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arXiv 2022年

作者： Wang, Zixiao Guo, Yuluo Zhao, Jin Zhang, Yu Yu, Hui Liao, Xiaofei Wang, Biao Yu, Ting National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China

In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model. This model generalizes graph diffusion in different feature spaces, and uses the inception module to avoid the large amount of computations caused by complex network structures. We evaluate GIDN model on Open Graph Benchmark(OGB) datasets, reached an 11% higher performance than AGDN on ogbl-collab dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Complex networks

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Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit

arXiv

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arXiv 2023年

作者： Wan, Yao He, Yang Bi, Zhangqian Zhang, Jianguo Zhang, Hongyu Sui, Yulei Xu, Guandong Jin, Hai Yu, Philip S. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Simon Fraser University Vancouver Canada School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Salesforce Research United States Chongqing University China University of New South Wales Australia University of Technology Sydney Australia University of Illinois at Chicago Chicago United States

Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineering, machine learning, data mining, natural language processing, and programming languages. In this paper, we conduct a comprehensive literature review on deep learning for code intelligence, from the aspects of code representation learning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models. In particular, we inspect the existing code intelligence models under the basis of code representation learning, and provide a comprehensive overview to enhance comprehension of the present state of code intelligence. Furthermore, we publicly release the source code and data resources to provide the community with a ready-to-use benchmark, which can facilitate the evaluation and comparison of existing and future code intelligence models (https://***). At last, we also point out several challenging and promising directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： computer programming languages

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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