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arXiv 2022年

作者： Li, Luxi Zou, Qin Zhang, Fan Yu, Hongkai Chen, Long Song, Chengfang Huang, Xianfeng Wang, Xiaoguang Li, Qingquan Department of Computer Science Technology United International College of Beijing Normal University HongKong Baptist University Zhuhai China Machine Vision and Robotics Laboratory School of Computer Science Wuhan University Wuhan China State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan China Department of Electrical Engineering and Computer Science Cleveland State University OH United States Institute of Automation Chinese Academy of Sciences Beijing China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan China Guangming Laboratory Shenzhen University Shenzhen China

Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in restoration or completion of some pre-defined objects, e.g., human face, fabric texture, and printed texts, etc., however, are not suitable for repairing murals with varying subjects and large damaged areas. Moreover, due to discrete colors in paints, mural inpainting may suffer from apparent color bias. To this end, in this paper, we propose a line drawing guided progressive mural inpainting method. It divides the inpainting process into two steps: structure reconstruction and color correction, implemented by a structure reconstruction network (SRN) and a color correction network (CCN), respectively. In structure reconstruction, SRN utilizes the line drawing as an assistant to achieve large-scale content authenticity and structural stability. In color correction, CCN operates a local color adjustment for missing pixels which reduces the negative effects of color bias and edge jumping. The proposed approach is evaluated against the current state-of-the-art image inpainting methods. Qualitative and quantitative results demonstrate the superiority of the proposed method in mural image inpainting. The codes and data are available at https://***/qinnzou/mural-image-inpainting . © 2022, CC BY-NC-ND.

关键词： Restoration

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Robust and digital hyper-polarization protocol of nuclear spins via magic sequential sequence

arXiv

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arXiv 2025年

作者： Li, Haiyang Liao, Hao Wang, Ping Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Hyper-polarization of nuclear spins is crucial for advancing nuclear magnetic resonance (NMR) and quantum information technologies, as nuclear spins typically exhibit extremely low polarization at room temperature due to their small gyro-magnetic ratios. A promising approach to achieving high nuclear spin polarization is transferring the polarization of electron to nuclear spin. The nitrogen-vacancy (NV) center in diamond has emerged as a highly effective medium for this purpose, and various hyper-polarization protocols have been developed. Among these, the pulsed polarization (PulsePol) method has been extensively studied due to its robustness against static energy shifts of the electron spin. In this study, we introduce a sequential polarization protocol and identify a series of magic and digital sequences for hyper-polarizing nuclear spins. Notably, we demonstrate that some of these magic sequences exhibit significantly greater robustness compared to the PulsePol protocol in the presence of finite half π pulse duration of the protocol. This enhanced robustness positions our protocol as a more suitable candidate for hyper-polarizing nuclear spins species with large gyromagnetic ratios and also ensures better compatibility with high-efficiency readout techniques at high magnetic fields. Additionally, the generality of our protocol allows for its direct application to other solid-state quantum systems beyond the NV center. © 2025, CC BY.

关键词： Chemical shift

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Optimization of a global seventh-order dissipative compact finite-difference scheme by a genetic algorithm

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Applied Mathematics and Mechanics(English Edition) 2018年第11期39卷 1679-1690页

作者： Yu LIN Yaming CHEN Chuanfu XU Xiaogang DENG College of Computer National University of Defense Technology College of Aerospace Science and Engineering National University of Defense Technology State Key Laboratory of High Performance Computing National University of Defense Technology

A global seventh-order dissipative compact finite-difference scheme is optimized in terms of time stability. The dissipative parameters appearing in the boundary closures are assumed to be different, resulting in an optimization problem with several parameters determined by applying a generic algorithm. The optimized schemes are analyzed carefully from the aspects of the eigenvalue distribution, the ε-pseudospectra, the short time behavior, and the Fourier analysis. Numerical experiments for the Euler equations are used to show the effectiveness of the final recommended scheme.

关键词： high-order dissipative compact finite-difference scheme genetic algorithm time stable

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET

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Procedia computer science 2021年 194卷 45-59页

作者： Muhammad Haleem Junejo Ab Al-Hadi Ab Rahman Riaz Ahmed Shaikh Kamaludin Mohamad Yusof Dileep Kumar Imran Memon Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science & Engineering Zhejiang University Hangzhou china Department of computer science bahria university Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET.

关键词： VANET Trust Model Machine Learning

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Robust laser self-mixing displacement demodulation viacontinuous wavelet–Hilbert transform for high-precision accelerometercalibration

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Applied Optics 2025年第16期64卷 4559-4567页

作者： Zhikun Chang Song Song Dongbai Xue Chunling He Zhoumiao Shi Jing Yu Ming Kong Yuying Xie Xiao Deng Tongbao Li Xinbin Cheng Institute of Precision Optical Engineering Tongji University Shanghai 200092 China National Metrology and Testing Center for Integrated Circuit Measurement and Inspection Equipment Industry (Shanghai) Tongji University Shanghai 200092 China MOE Key Laboratory of Advanced Micro-Structured Materials Tongji University Shanghai 200092 China Shanghai Frontiers Science Center of Digital Optics Tongji University Shanghai 200092 China Shanghai Professional Technical Service Platform for Full-Spectrum and High-Performance Optical Thin Film Devices and Applications Tongji University Shanghai 200092 China School of Physics Science and Engineering Tongji University Shanghai 200092 China School of Computer Science and Technology Zhejiang University of Water Resources and Electric Power Hangzhou 310018 China Shenzhen Institute of Technology Innovation NIM Shenzhen 518107 China College of Metrology and Testing Engineering China Jiliang University Hangzhou 310018 China

The laser self-mixing grating interferometer based on Cr atomlithography gratings hasbeen applied to the primarycalibration of accelerometers due to its compact structure, low cost,high accuracy, and directon-site traceability. However, the high line density of Cr gratings(4700 l/mm) introduces dense outliers in interferometricsignals, complicating displacement demodulation via conventionalderivative-based methods and causing frequent phase jumps. To addressthis challenge, we propose a hybrid algorithm integrating thecontinuous wavelet transform and the Hilbert transform, which enablesrobust displacement demodulation under high-noise *** validation on a commercial MEMS accelerometerdemonstrated exceptional accuracy: 100 consecutive data segments weresuccessfully demodulated with a displacement fitting goodness of $R^2 = 0.9964$ ( $\sigma_{R_2} =0.0027$ ), and the derived sensitivitydeviated by only 0.1% from the nominal value. This algorithmestablishes a paradigm for the high-accuracy dynamic calibration ofinertial sensors in field applications.

关键词： Laser interferometry Lithography Phase shift Refractive index Systems design Wavelet transforms

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A neural kernel framework for relation extraction

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Expert Systems with Applications 2025年 286卷

作者： Wang, Kai Chen, Yanping Yang, Weizhe Huang, Ruizhang Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University China State Key Laboratory of Public Big Data Guizhou University China

Relation extraction (RE) poses significant challenges due to the complexity of identifying semantic relationships between overlapping entity pairs within sentences. Traditional kernel-based methods effectively leverage instance-level similarities but heavily depend on manually designed kernel functions, requiring substantial domain expertise and limiting their flexibility. On the other hand, neural network approaches excel at automatically learning abstract feature representations but typically neglect critical instance-specific information, potentially missing valuable relational details during inference. These limitations motivate the exploration of a hybrid approach that effectively integrates the strengths of traditional kernels with the representational power of neural networks. In this paper, we propose a neural kernel framework designed to bridge this critical gap. The proposed framework employs neural kernels, whose parameters are initialized based on task-specific objectives and optimized through neural training procedures, enabling adaptive learning of similarity measures without relying on handcrafted kernel functions. By maintaining instance-level details through annotated training examples, neural kernels create discriminative, flexible decision boundaries tailored specifically to the relation extraction task. We further develop three complementary neural kernel components, instance kernel, description kernel, and cluster kernel, to show the advantages of kernel substitution. Extensive experiments on the ACE 2005, SemEval 2010, and CoNLL 2004 datasets demonstrate that our neural kernel framework significantly outperforms existing state-of-the-art methods, achieving F1-scores of 88.11 %, 91.08 %, and 98.63 %, respectively. © 2025 Elsevier Ltd

关键词： Natural language processing systems

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Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Distributed Adaptive Fault-Tolerant Output Regulation of Heterogeneous Multi-Agent Systems With Coupling Uncertainties and Actuator Faults

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1098-1106页

作者： Chao Deng Weinan Gao Weiwei Che the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the Department of Electrical and Computer Engineering Allen E.Paulson College of Engineering and ComputingGeorgia Southern UniversityStatesboroGA 30460 USA the State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027 the Institute of Complexity Science Qingdao UniversityQingdao 266071China

In this paper, we consider the distributed adaptive fault-tolerant output regulation problem for heterogeneous multiagent systems with matched system uncertainties and mismatched coupling uncertainties among subsystems under the influence of actuator faults. First, distributed finite-time observers are proposed for all subsystems to observe the state of the exosystem. Then, a novel fault-tolerant controller is designed to compensate for the influence of matched system uncertainties and actuator faults. By using the linear matrix inequality technique, a sufficient condition is provided to guarantee the solvability of the considered problem in the presence of mismatched coupling uncertainties. Moreover, it is shown that the system in closed-loop with the developed controller can achieve output regulation by using the Lyapunov stability theory and cyclic-small-gain ***, a numerical example is given to illustrate the effectiveness of the obtained result.

关键词： Adaptive control cyclic-small-gain fault-tolerant control multi-agent systems(MASs)

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Tuning Ru–O Coordination for Switching Redox Centers in Acidic Oxygen Evolution Electrocatalysis

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Angewandte Chemie 2025年

作者： Yajing Mu Jinchang Fan Tianyi Gao Lina Wang Lei Zhang Xiaoxin Zou Weitao Zheng Yong-Wei Zhang Zhi Gen Yu Xiaoqiang Cui School of Materials Science and Engineering Key Laboratory of Automobile Materials of MOE Jilin University Changchun 130012 China State Key Laboratory of Inorganic Synthesis and Preparative Chemistry College of Chemistry Jilin University Changchun 130012 China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Republic of Singapore

Avoiding lattice oxygen involvement (oxygen redox) while promoting the coupling of adjacent adsorbed oxygen (metal redox) during the acidic oxygen evolution reaction (OER) is essential for gaining high activity and robust stability in RuO 2 -based catalysts but remains elusive. Here, we present a precise strategy to selectively activate the metal redox process while suppressing the undesired oxygen redox pathway by fine-tuning the Ru–O coordination number in amorphous RuO x . The optimized catalyst exhibits outstanding acidic OER performance, achieving a low overpotential of 215 mV at 10 mA cm −2 and maintaining stability for 300 h with a negligible degradation rate of 100 µV h −1 . X-ray absorption measurements and multiple operando spectra reveal that only Ru 2 –O 11 moieties can selectively activate the metal redox process, whereas Ru 2 –O 9 and Ru 2 –O 8 moieties either trigger both redox pathways or bypass them. Theoretical calculations reveal that Ru 2 –O 11 moiety reduces crystal field splitting energy at active Ru sites, disables lattice oxygen activation, and lowers the energy barrier for oxygen coupling. The strategy developed in this work offers new avenues for switching redox centers and refining OER mechanisms to enhance catalytic performance and long-term stability.

关键词： Amorphous structures Coordination number Electrocatalysis Oxygen evolution reaction Ruthenium oxides

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Randomised benchmarking for characterizing and forecasting correlated processes

arXiv

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arXiv 2023年

作者： Zhang, Xinfang Wu, Zhihao White, Gregory A.L. Xiang, Zhongcheng Hu, Shun Peng, Zhihui Liu, Yong Zheng, Dongning Fu, Xiang Huang, Anqi Poletti, Dario Modi, Kavan Wu, Junjie Deng, Mingtang Guo, Chu Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Physics and Astronomy Monash University VIC3800 Australia Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute of Physics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Quantum for NSW Sydney2000 Australia

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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