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An Improved Compressive Sensing-Based Model for Efficient Analysis of Monostatic Electromagnetic Scattering Problems

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 388-391页

作者： Qi, Qi Fan, Yunuo Cao, Xinyuan Liu, Yi Kong, Meng Huang, Zhixiang Wu, Xianliang Hefei Normal University Anhui Province Key Laboratory of Simulation Calculation and Design for Electronic Information System Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

To further accelerate the analysis of monostatic electromagnetic (EM) scattering problems of objects, an improved compressive sensing (CS)-based model is proposed. First, an orthogonal subspace spanned by the wide-angle induced currents is identified by means of the wide-angle incident sources. Then, the sparse transform of the induced currents is replaced by the subspace projection. In addition, an optimal number of measurements is determined, only slightly larger than the dimension of the subspace. Finally, the recovery algorithm is simplified to the least-squares method to solve the resulting overdetermined system. Compared with the traditional CS-based model, the proposed model significantly improves the efficiency of the recovery step and optimizes both the requirement for and the way of determining the number of measurements. © 2023 IEEE.

关键词： Least squares approximations

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HyFit: Hybrid Fine-Tuning With Diverse Sampling for Abstractive Summarization

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IEEE Transactions on Big Data 2025年第3期11卷 1054-1065页

作者： Zhao, Shu Cheng, Yuanfang Zhang, Yanping Chen, Jie Duan, Zhen Sun, Yang Wang, Xinyuan Anhui University Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing Hefei230601 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230601 China

Abstractive summarization has made significant progress in recent years, which aims to generate a concise and coherent summary that contains the most important facts from the source document. Current fine-tuning approaches based on pre-training models typically rely on autoregressive and maximum likelihood estimation, which may result in inconsistent historical distributions generated during the training and inference stages, i.e., exposure bias problem. To alleviate this problem, we propose a hybrid fine-tuning model(HyFit), which combines contrastive learning and reinforcement learning in a diverse sampling space. Firstly, we introduce reparameterization and probability-based sampling methods to generate a set of summary candidates called candidates bank, which improves the diversity and quality of the decoding sampling space and incorporates the potential for uncertainty. Secondly, hybrid fine-tuning with sampled candidates bank, upweighting confident summaries and downweighting unconfident ones. Experiments demonstrate that HyFit significantly outperforms the state-of-the-art models on SAMSum and DialogSum. HyFit also shows good performance on low-resource summarization, on DialogSum dataset, using only approximate 8% of the examples exceed the performance of the base model trained on all examples. © 2015 IEEE.

关键词： Reinforcement learning

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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Confidence-Guided Feature Alignment for Cloth-Changing Person Re-identification 27th

Confidence-Guided Feature Alignment for Cloth-Changing Pers...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Sirong Zheng, Huicheng School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783031781094

Much progress has been made in the field of person re-identification, but changes in clothing have hindered the practical application of long-term person re-identification. Cloth-changing person re-identification (CC-ReID) aims to address this problem, with the main challenge being the extraction of discriminative features unrelated to clothing. Existing methods, which mainly focus on introducing clothing-irrelevant cues such as key points, contours, and 3D shapes, require additional modules for feature extraction, resulting in increased complexity and potential inaccuracies due to dependence on the performance of external models. Few studies directly use the original RGB images to make the model constantly focus on clothing-independent information. In this paper, we propose a Confidence-Guided Feature Alignment Network (CGFA) for CC-ReID. Specifically, we design a confidence module that automatically learns to make confidence adjustments to fine-grained information, prompting the model to mine clothing-independent discriminative features without introducing other modal cues. By transferring knowledge, we encourage the model to learn discriminative identity features that are independent of clothing bias. As a result, the confidence module can be removed during the inference phase. The proposed simple but efficient method uses only RGB modality without additional cues, and can serve as a powerful baseline for CC-ReID to drive future research. Extensive experiments on the CC-ReID datasets demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature extraction

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iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detection 27th

iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detec...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jiang, Jian-Jian Wu, Xiao-Ming Chen, Zibo Wei, Yi-Lin Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031781124

For 6-DoF grasp detection, we aim at introducing a new interactive 2D-3D framework which filters out irrelevant information and makes both modalities collaborate effectively to generate robust grasps and accelerate inference speed greatly. This cannot be accomplished by existing works on learning to grasp that merely utilize 3D point clouds or leverage both 2D textures and 3D point clouds. Our framework is called iGrasp, a novel three-step design between 2D textures and 3D point clouds, where the interaction modelling enhances both modalities. Concretely, we propose the 2D-to-3D interaction to leverage objectness masks generated from 2D textures to filter out target-irrelevant information in 3D point clouds. Then, we introduce the 3D-to-2D interaction to leverage structural priors from 3D point cloud features with cross-attention and cylinder grouping to refine 2D texture features. Finally, we combine the refined 2D texture features and 3D point cloud features for generating high-quality 6-DoF grasp poses. Our experiments on the large-scale real-world dataset, namely GraspNet-1Billion, demonstrate that iGrasp surpasses state-of-the-art methods by 4.66/3.53 mAP on RealSense/Kinect and reduces the inference time by 28%. Real-world experiments further verify the effectiveness of iGrasp. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Truthful Mechanism for Resource Allocation and Pricing in Vehicle-Assisted Mobile Edge computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8171-8186页

作者： Liu, Xi Liu, Jun Li, Weidong Qujing Normal University Key Laboratory of Intelligent Sensor and System Design School of Information Engineering Qujing655001 China Qujing Normal University Engineering Research Center of Intelligent System and Advanced Materials Yunnan Province Universities Qujing655001 China Yunnan College of Business Management School of Education Kunming650000 China Yunnan University School of Mathematics and Statistics Kunming650504 China

Mobile edge computing (MEC) provides stable and powerful computing services, and vehicle computing provides rich sensing services. Therefore, we develop vehicle-assisted mobile edge computing (VAMEC), integrating MEC and vehicle computing to maximize their advantages and broaden their applications. We address the resource allocation and pricing problem in VAMEC, in which the vehicle, as a resource provider, collaborates with the MEC servers to provide services to users. To enable MEC servers and vehicles to work in cooperation, we propose a mixed auction model that consists of two-step auctions: the first one-way auction and the second double auction. We consider three types of users based on their characteristics, where users participate in different auctions depending on the types. Users and vehicles are self-interested and may submit untrue values to harm the system's profits. Therefore, we propose the overall truthfulness for the mixed auction model, that is users or vehicles that cannot obtain higher utility by declaring untrue values regardless of which auction they participate in. To the best of our knowledge, this is the first study proposing the overall truthfulness and showing how to design the mechanism that achieves it. We then propose an overall truthfulness mechanism and show it achieves individual rationality, budget balance, and consumer sovereignty. Simulation results indicate that the proposed mechanism can increase social welfare by at least 24%, compared with other mechanisms. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Amplified emission and lasing enabled by high-Q quasi-bound states in asymmetric dielectric metasurfaces via eigenfield perturbation

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Optics Express 2025年第10期33卷 21551-21565页

作者： Xu, Ke Fei, Yuhang Sun, Yingying Feng, Jian Deng, Xuesong Fang, Ming Huang, Zhixiang The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Anhui Hefei 230039 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China

Symmetry-protected bound states in the continuum represent a distinct class of nonradiative modes in continuous media. By introducing asymmetric parameters into a symmetric metasurface, the inherent symmetry can be broken, transforming BICs—originally decoupled from external radiation channels—into high-Q quasi-BICs with controlled radiation capabilities. However, manufacturing constraints pose a significant challenge in fabricating nanostructures with ultra-small asymmetric parameters, thereby limiting the achievable Q-factor. In this work, we propose an asymmetric perforated metasurface structure that supports BICs. When the perforations are shifted along the x or y axis to a specific position, the linewidth vanishes, and simulations confirm the emergence of a BIC in the system, with a quality factor reaching up to 10⁹. Furthermore, we demonstrate that selective eigenmode perturbation can effectively transform BICs into practical quasi-BICs. Notably, for the same structure, perturbing the weak-field region yields higher Q-factors, offering a flexible approach to optimizing performance. This approach enables precise Q-factor control and enhanced radiation properties. Furthermore, we design a laser cavity structure based on these quasi-BICs, achieving amplified emission and lasing with excellent agreement between simulations and theoretical predictions. These findings expand the BIC library and offer new methods for Q-factor manipulation, paving the way for high-performance optoelectronic devices such as nanolasers, biosensors, and nonlinear harmonic generators. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Destructive interference Light matter interactions Material properties Nanostructures Optical amplifiers Photonic crystal cavities

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MBF-DTI: A Fused Multi-dimensional Biochemical Feature-Based Drug Target Prediction Method Based on Heterogeneous Graph Attention Networks 10th

MBF-DTI: A Fused Multi-dimensional Biochemical Feature-Based...

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10th China Health Information Processing Conference, CHIP 2024

作者： Zhao, Haixue Yao, Kui Liu, Yunjiong Che, Chao Tang, Lin Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116622 China School of Software Engineering Dalian University Dalian116622 China

ISBN: (纸本)9789819637515

Drug-target interaction prediction help reduce the cost and time of drug development. However, existing research often overlooks the complexity of biological interactions. To address this issue, this paper proposes a DTI prediction model that integrates multi-dimensional biochemical features. Specifically, the model utilizes a heterogeneous graph attention network to capture the topological relationships among biological entities from diverse data, providing a deep understanding of the interactions among drugs, proteins, diseases, and side effects. A molecular attention Transformer network and a CBiNet module are used to extract the key structural features of drugs and targets. By automatically optimizing the weight distribution between drugs and targets, the model enhances the information transfer during network training, significantly improving model performance. Experimental results on real-world datasets demonstrate that the proposed model outperforms the current state-of-the-art methods in the field. Among the top 50 novel COVID-19 therapeutic drugs predicted by our model, 30 have been supported by clinical trials or scientific literature, further demonstrating the effectiveness of our proposed method in drug repurposing. Experimental data and supplementary materials are available online at: https://***/Eadog/MBF-DTI. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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A Tetrahedral Spectral Element Method for Thermo-Mechanical Analysis of Electronic Devices

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Feng, Naixing Zhang, Shuai Huang, Zhixiang Liu, Qi Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Hefei China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou310018 China

For the coupled thermo-mechanical simulation of complex electronic devices, we propose a Tetrahedral Spectral Element Method (TSEM), which truly combines the high accuracy of spectral methods with the strong geometric adaptability of Finite Element Methods (FEMs). The TSEM employs a collapse transformation method and successfully converts the orthogonal Gauss-Lobatto-Legendre (GLL) basis functions from a hexahedron to a tetrahedral reference element. This ensures numerical accuracy and stability for arbitrary high-order interpolation. We also generate non-equidistant GLL spectral interpolation nodes for tetrahedral elements through Warp&Blend transformation, enabling direct nodal numerical integration calculation through Vandermonde matrices. This approach replaces the classical Gaussian quadrature in the FEM, not only significantly enhancing the computational efficiency of element matrix but also avoiding potential numerical dissipation and dispersion associated with Gaussian integration, thus achieving rapid convergence. In the TSEM thermo-mechanical coupling, we further construct a separate solver based on the implicit Newmark-β method, which realizes unconditional stability for temporal integration for arbitrary time step increment. Moreover, it supports independent inversion of system matrices for temperature and stress fields, without increasing computational resource consumption due to the coupling of multi-physics equations. By quantitatively comparing with the COMSOL Multiphysics software, several typical case studies are used to demonstrate the significant advantages of the TSEM in numerical accuracy, efficiency, and stability. © 2025 IEEE. All rights reserved.

关键词： Orthogonal functions

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A Nonconformal SETI-DP Method for Transient Thermal Simulation

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Feng, Naixing Li, Hongyang Huang, Zhixiang Liu, Qi Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education The Information Materials and Intelligent Sensing Laboratory of Anhui Province The Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Hefei China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou310018 China

This paper proposes a novel nonconformal meshbased dual-primal spectral element tearing and interconnecting (SETI-DP) method designed for the efficient transient simulation of complex large-scale heat conduction problems utilizing a domain decomposition method (DDM). This method allows for the division of complex, large-scale problems into smaller subdomain problems. By applying Robin-type transmission conditions at the subdomain interfaces, rapid convergence of the global interface iterative solution is achieved for arbitrary non-conformal subdomain problems. This non-conformal DDM approach supports independent meshing of subdomains, eliminating the need to generate a globally continuous mesh for large-scale problems, thereby significantly enhancing meshing efficiency. In addition, this non-conformal meshing strategy enables high-contrast spatial discretization and mixed-order spectral interpolation between different subdomains. This significantly increases meshing flexibility, minimizes the number of degrees of freedom (DoF) in multi-scale problems, and reduces computational costs. In addition, by developing a subdomain-coupled time iteration scheme, the global corner problem of residual transfer is explicitly given, which greatly improves the versatility and flexibility of the algorithm. Finally, to demonstrate the accuracy, efficiency, and robustness of our algorithm, we conduct benchmark tests on several representative examples. © 2011-2012 IEEE.

关键词： Domain decomposition methods

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