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Industrial Automation, Robotics and Control engineering (IARCE), International Conference on

作者： Mingyi Sun Weigang Cui Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China School of Engineering Medicine Beihang University Beijing China Department of Automation Science and Electrical Engineering State Key Laboratory of Virtual Reality Technology and Systems The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Electroencephalography (EEG) has emerged as a crucial cornerstone within the realm of brain-computer interface (BCI) applications, with its significance notably pronounced in the field of fatigue detection. However, the inherent limitations of EEG acquisition equipment during real driving scenarios have contributed to the constrained robustness of existing models. Moreover, most of recent methods failed to extract robust multi-domain features, leading to a suboptimal performance. To address these challenges, we propose a novel channel-augmented multi-domain graph convolutional network (CA-MDGCNet). Specifically, the initial EEG signals are enriched by incorporating supplementary virtual EEG channels, distinguished as learnable parameters within the network architecture. Then, differential entropy features are extracted from the augmented EEG signals. Following this, a multi-domain graph convolutional network is designed to encode high-level EEG features by means of convolutions in diverse paths, which is beneficial to integrating the characteristics extracted from multiple domains. Finally, the classification block derives the detection outcome from the refined feature maps. To substantiate the potency of the proposed method, the validation was conducted on the publicly accessible SEED-VIG. The proposed CA-MDGCNet not only demonstrates more promising performance compared to state-of-the-art approaches but also underscores the potential viability of our method for the realm of fatigue driving detection.

关键词： Graph convolutional networks Convolution Service robots Neural activity Feature extraction Fatigue Brain modeling Electroencephalography Entropy Robustness

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Sea-urchin-like ReS_(2) nanosheets with charge edge-collection effect as a novel cocatalyst for high-efficiency photocatalytic H_(2) evolution

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Chinese Chemical Letters 2022年第2期33卷 943-947页

作者： Bo Lin Bowen Ma Jiangang Chen Yao Zhou Jiadong Zhou Xiaoqing Yan Chao Xue Xiao Luo Qing Liu Jinyong Wang Renji Bian Guidong Yang Fucai Liu School of Optoelectronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Key Lab of Advanced Optoelectronic Quantum Architecture and Measurement(Ministry of Education) Beijing Key Lab of Nanophotonics&Ultraﬁne Optoelectronic Systemsand School of PhysicsBeijing Institute of TechnologyBeijing 100081China XJTU-Oxford International Joint Laboratory for Catalysis School of Chemical Engineering and TechnologyXi’an Jiaotong UniversityXi’an 710049China State Centre for International Cooperation on Designer Low-carbon and Environmental Materials(CDLCEM) School of Materials Science and EngineeringZhengzhou UniversityZhengzhou 450001China

The recombination of charge carriers arriving from the random charge movement in semiconductor pho-tocatalysts greatly limits the practical application of solar-driven H_(2)*** design of photo-catalytic systems with spatially oriented charge-transfer is a promising route to achieve high charge-separation efficiency for ***,novel sea-urchin-like Re S_(2)nanosheet/TiO_(2)nanoparticle heterojunctions(SURTHs)are *** unique sea-urchin-like structure endows the ReS_(2)cocat-alyst with an unusual charge edge-collection effect,which leads to a significant acceleration of charge separation and transfer,as evidenced by the well-designed selective photodeposition of Pt quantum dots in *** markedly improved charge transfer capacity contributes to a high photocatalytic H_(2)evo-lution rate of 3.71 mmol h^(−1)g^(−1)for SURTHs(an apparent quantum efficiency(AQE)of 16.09%),up to 231.9 times by contrast with that of P25 TiO_(2).This work would provide a new platform for designing the high-efficiency cocatalyst/photocatalyst system with excellent charge transfer capacity.

关键词： Sea-urchin-like ReS_(2)nanosheets Charge edge-collection effect 2D planar edges/tips Charge transfer Photocatalytic H_(2)evolution

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Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness

arXiv

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arXiv 2024年

作者： Mao, Xiaojun Wang, Hengfang Wang, Zhonglei Yang, Shu School of Mathematical Sciences Ministry of Education Key Laboratory of Scientific and Engineering Computing Shanghai Jiao Tong University China School of Mathematics and Statistics Fujian Provincial Key Laboratory of Statistics and Artificial Intelligence Fujian Normal University China Wang Yanan Institute for Studies in Economics School of Economics Xiamen University China Department of Statistics North Carolina State University United States

Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey sampling, with entries following different canonical exponential distributions and subject to heterogeneous missingness. To tackle this challenging task, we propose a two-stage procedure: in the first stage, we model the entry-wise missing mechanism by logistic regression, and in the second stage, we complete the target parameter matrix by maximizing a weighted log-likelihood with a low-rank constraint. We propose a fast and scalable estimation algorithm that achieves sublinear convergence, and the upper bound for the estimation error of the proposed method is rigorously derived. Experimental results support our theoretical claims, and the proposed estimator shows its merits compared to other existing methods. The proposed method is applied to analyze the National Health and Nutrition Examination Survey data. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Malware Detection Method based on Control Flow Analysis 2019

Malware Detection Method based on Control Flow Analysis

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Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City

作者： Peng Shan Qingbao Li Ping Zhang Yanyang Gu State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450376631

With the rapid development of computer technology and the iteration of malware, numerous computer malware detection methods have been proposed, among which malware detection based on machine learning has become an important research direction. Opcode sequences are often used as an important feature of machine learning models for training and testing. However, opcode sequences extracted orderly from raw disassembly text may not adequately reflect the behavior of executables. In order to solve this problem, this paper proposes a new malware detection method based on the control flow of executables. This method analyzes the control flow of executables and extracts execution traces from by the unit of function. This paper uses the execution traces to represent the behavior of the executables and present the execution traces by opcode sequences. In order to improve the training efficiency and reduce the feature dimensionality, this paper propose an x86 intermediate code. Then we convert the opcode sequences into the corresponding intermediate code sequences and vectorize them using Vector Space Model (VSM) for training and testing. This paper implements Naïve Bayes Classifier, Support Vector Machines and Random Forest for classification. The experiment result shows that the Random Forest algorithm has the best performance, with an accuracy of 90.8% and the model establishment time is 22s. Compared with the traditional methods based on disassembly text, the accuracy is improved by 1.4%, compared with the methods using x86 opcode, the efficiency is increased by 62%.

关键词： Control Flow Analysis Machine Learning Intermediate Code Malware Detection

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Weakly-supervised Audio Temporal Forgery Localization via Progressive Audio-language Co-learning Network

arXiv

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arXiv 2025年

作者： Wu, Junyan Xu, Wenbo Lu, Wei Luo, Xiangyang Yang, Rui Guo, Shize MoE Key Laboratory of Information Technology Sun Yat-sen University Guangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Alibaba Group Hangzhou China

Audio temporal forgery localization (ATFL) aims to find the precise forgery regions of the partial spoof audio that is purposefully modified. Existing ATFL methods rely on training efficient networks using fine-grained annotations, which are obtained costly and challenging in real-world scenarios. To meet this challenge, in this paper, we propose a progressive audio-language co-learning network (LOCO) that adopts co-learning and self-supervision manners to prompt localization performance under weak supervision scenarios. Specifically, an audio-language co-learning module is first designed to capture forgery consensus features by aligning semantics from temporal and global perspectives. In this module, forgery-aware prompts are constructed by using utterance-level annotations together with learnable prompts, which can incorporate semantic priors into temporal content features dynamically. In addition, a forgery localization module is applied to produce forgery proposals based on fused forgery-class activation sequences. Finally, a progressive refinement strategy is introduced to generate pseudo frame-level labels and leverage supervised semantic contrastive learning to amplify the semantic distinction between real and fake content, thereby continuously optimizing forgery-aware features. Extensive experiments show that the proposed LOCO 1 achieves SOTA performance on three public benchmarks. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Complexity of Tensor Product Functions in Representing Antisymmetry

arXiv

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arXiv 2025年

作者： Wang, Yuyang Hu, Yukuan Liu, Xin State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China CERMICS Ecole des Ponts IP Paris Marne-la-Vallée77455 France

Tensor product function (TPF) approximations have been widely adopted in solving high-dimensional problems, such as partial differential equations and eigenvalue problems, achieving desirable accuracy with computational overhead that scales linearly with problem dimensions. However, recent studies have underscored the extraordinarily high computational cost of TPFs on quantum many-body problems, even for systems with as few as three particles. A key distinction in these problems is the antisymmetry requirement on the unknown functions. In the present work, we rigorously establish that the minimum number of involved terms for a class of TPFs to be exactly antisymmetric increases exponentially fast with the problem dimension. This class encompasses both traditionally discretized TPFs and the recent ones parameterized by neural networks. Our proof exploits the link between the antisymmetric TPFs in this class and the corresponding antisymmetric tensors and focuses on the Canonical Polyadic rank of the latter. As a result, our findings uncover a fundamental incompatibility between antisymmetry and low-rank TPFs in high-dimensional contexts and offer new insights for further developments. Copyright © 2025, The Authors. All rights reserved.

关键词： Tensors

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Malware detection method based on control flow analysis 7

Malware detection method based on control flow analysis

引用

7th International Conference on Information Technology: IoT and Smart City, ICIT 2019

作者： Shan, Peng Li, Qingbao Zhang, Ping Gu, Yanyang State Key Laboratory Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450376631

关键词： Malware

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LinNet: linear network for efficient point cloud representation learning 24

LinNet: linear network for efficient point cloud representat...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Hao Deng Kunlei Jing Shengmei Cheng Cheng Liu Jiawei Ru Jiang Bo Lin Wang State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University and Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality School of Software Engineering Xi'an Jiaotong University and Department of Computing The Hong Kong Polytechnic University

ISBN: (纸本)9798331314385

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of down-sampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet.

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zibo Miao Yu Pan Qing Gao Guangdong Key Laboratory of Intelligent Morphing Mechanisms School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique.

关键词： Perturbation methods Design methodology Quantum optics Robustness Feedback control Closed loop systems Tuning Cybernetics

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New Insights into the Microstructure and Tribological Behaviors of the Boronized Layer on Inconel 718 Alloy Surface

SSRN

引用

SSRN 2024年

作者： Wu, Zhenjiang Chen, Shuqun Shao, Mengxue Liu, Yue Li, Yangzhong Peng, Jian Li, Hongyi Wang, Jinshu Key Laboratory of Advanced Functional Materials Education Ministry of China College of Materials and Engineering Beijing University of Technology Beijing100124 China Beijing University of Technology China High Performance Computing Department National Supercomputing Center in Shenzhen Guangdong Shenzhen518055 China State Key Lab of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China

Boronizing of nickel based superalloys is of great interests in aerospace industry due to the excellent wear resistance, however, little information has been known on its phase formation and load-dependent wear mechanism. In this work, high quality boronized layer was developed on the Inconel 718 alloy by powder pack-boriding process at 850 °C for 6 h under argon atmosphere. The microstructure characteristics and load-dependent tribological behaviors of the boronized layers were systematically investigate. It is found that the main boronized layer on Inconel 718 alloy consists of a ~11.3 um thick compound layer with nanocrystalline (Ni,Fe)23B6 and (Cr,Fe)B grains, and a ~10.9 um thick diffusion layer with dendritic Cr2B precipitation within the matrix. The phase evolution of the boronizing layer are explainable by thermodynamic modeling of Ni-Cr-B system and the phase stability for metastable Ni-B compounds is confirmed by DFT simulation. Due to the good interfacial bonding strength and high microhardness value, the boronized layer displays excellent wear resistance against Si3N4 ceramic balls at room temperature. In particular, the wear rates of the specimens under applied loads of 10-20 N are higher than those in the 50-100 N loads case. The latter is mainly facilitated by the formation of lubricating boron oxide tribolayer on friction ball surface. We believe the findings in present work will contribute to a deeper understanding of the boronizing treatment in nickel-based superalloys. © 2024, The Authors. All rights reserved.

关键词： Wear resistance

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