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Enhancing Graph Structure Learning via Motif-Driven Hypergraph Construction

IEEE Transactions on Network Science and Engineering 2025年第3期12卷 2333-2344页

作者： Zhao, Jia-Le Zhang, Xian-Jie Ding, Xiao Zhang, Xingyi Zhang, Hai-Feng Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China

Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on pairwise graphs often limits their capacity to capture latent higher-order topological semantic information. Thus, it is crucial to find a way to extract the latent higher-order information of graphs without missing the lower-order information of the original graph. To address this issue, we here develop a method to construct hypergraph based on motifs, and then a novel neural network framework, named MD-HGNN, is proposed for enhanced graph learning. Specifically, we first utilize motifs of the original graph to construct the hypergraph and eliminate nested structures within the hypergraph to prevent information redundancy. Subsequently, GNNs and hypergraph neural networks (HGNNs) are employed separately to extract the lower-order and higher-order topological semantic information of the graph. Finally, the lower-order and higher-order information are integrated to obtain an embedded representation of graph. Extensive experimental results demonstrate that MD-HGNN preserves the original lower-order graph structure information while effectively extracting higher-order features. Moreover, its performance and robustness are validated across different downstream tasks. © 2013 IEEE.

关键词： Graph neural networks

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Dual Averaging for Distributed Unbalanced Optimization with Delayed Information

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IEEE Transactions on signal and Information processing over Networks 2025年 11卷 366-377页

作者： Huang, Qing Fan, Yuan Cheng, Songsong Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui Hefei230601 China

In this paper, we study a category of distributed constrained optimization problems where each agent has access to local information, communicates with its neighbors, and cooperatively minimizes the aggregated cost functions over time-varying unbalanced graphs. To address the considered problems, we propose a distributed dual averaging algorithm based on a row-stochastic weighted matrix (DDAR), which improves the robustness of network topology compared to conventional push-sum algorithms. Moreover, we develop a modified version of DDAR with delayed information (DDARD), which considers the delays of both network communication and gradient calculation, enhancing the algorithm's flexibility in communication and iteration. Our analysis demonstrates that the DDAR and DDARD achieve the optimal value at rates of O(N/(1-λ)√T) and O(τ2N/(1-λ}})√T}), respectively. Finally, the theoretical results are confirmed by simulation on a logistic regression problem. © 2015 IEEE.

关键词： Cost functions

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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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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Determining the Covering Radius of All Generalized Zetterberg Codes in Odd Characteristic

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IEEE Transactions on Information Theory 2025年第5期71卷 3602-3613页

作者： Shi, Minjia Li, Shitao Helleseth, Tor Ozbudak, Ferruh Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Hefei230601 China Anhui University School of Mathematical Sciences Anhui Hefei230601 China University of Bergen Department of Informatics Bergen5007 Norway Sabancı University Faculty of Engineering and Natural Sciences İstanbul34956 Turkey

For an integer s ≥ 1, let Cs(q0) be the generalized Zetterberg code of length q0s + 1 over the finite field Fq0 of odd characteristic. Recently, Shi et al. determined the covering radius of Cs(q0) for (Formula presented) (mod 8), and left the remaining case as an open problem. In this paper, we develop a general technique involving arithmetic of finite fields and algebraic curves over finite fields to determine the covering radius of all generalized Zetterberg codes for q0s ≡ 7 (mod 8), which therefore solves this open problem. We also introduce the concept of twisted half generalized Zetterberg codes of length (Formula presented) , and show the same results hold for them. As a result, we obtain some quasi-perfect codes. © 1963-2012 IEEE.

关键词： Turbo codes

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Simplicial complexes graph convolution networks with higher-order features learning for limited samples diagnosis

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Control Engineering Practice 2025年 162卷

作者： Zhang, Xian-Jie Zhang, Hai-Feng Zhong, Kai Zhang, Xiao-Ming The Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education School of Mathematical Science Anhui University Hefei 230601 Anhui China The Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei 230601 Anhui China

With the advancement of industrial automation, there is an increasing focus on research concerning limited fault samples. Although meta-learning and other methods can address this issue, they often necessitate the incorporation of additional data and are unable to directly diagnose faults using only unlab.led data along with a small amount of lab.led data. In response, this article proposes the use of simplicial complexes graph convolutional networks for fault diagnosis, which simultaneously account for both higher-order and lower-order topological structures among samples. This approach effectively addresses the challenge of limited samples by extracting relevant information from unlab.led data without the need to introduce new knowledge. Initially, simplices of varying dimensions are employed within a constructed simple graph to represent different relationships among samples. Subsequently, the simplicial complexes convolutional network is introduced to capture the higher-order information, while the graph convolutional network is utilized to obtain the lower-order information. The combined feature information is then input into a classifier for fault diagnosis. Finally, experiments conducted on two datasets characterized by small sample sizes or imbalanced samples demonstrate the method's commendable diagnostic performance, as well as its robustness and practicality. © 2025 Elsevier Ltd

关键词： Industrial research

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Accuracy verification of sulfur dioxide data based on ultraviolet hyperspectral solar radiometer observations 3

Accuracy verification of sulfur dioxide data based on ultrav...

引用

3rd International Conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Zeng, Hao Wang, Shun Xun, Lina The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9781510690219

One of the primary air pollutants that has a detrimental effect on both the environment and human health is sulfur dioxide. The ecosystem and climate greatly depend on the high-precision monitoring of sulfur dioxide. In this study, SO2 ground-based data in Xi'an and Hangzhou were calculated using the UV continuous spectral inversion method, and the accuracy was confirmed using OMI satellite data that had undergone bilinear spatial interpolation. Although there are still some discrepancies in the comparative analysis results, the results indicate a good correlation between the ground-based measurement results of the UV hyperspectral solar radiometer and OMI data products;the correlation numbers are 0.938 and 0.941, respectively, and the relative errors are roughly 10%. The ultraviolet hyperspectral solar radiometer can complement this study's findings with accuracy and is a valuable tool for environmental monitoring. © 2025 SPIE.

关键词： accuracy verification correlation sulfur dioxide distribution ultraviolet hyperspectral solar radiometer

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A Novel Construction of Two-Dimensional Z-Complementary Array Code Sets Based on Extended Boolean Functions

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7821-7834页

作者： Tian, Liying Song, Xiaoshi Li, Yubo Zhang, Haijun Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Two-dimensional (2-D) array sets with good 2-D correlation properties have received considerable attention in wireless communication systems. This paper focuses on 2-D Z-complementary array code sets (ZCACSs), which have ideal 2-D correlation properties within a rectangular zone around the origin. By developing a theory linking 2-D extended Boolean functions with 2-D arrays, we establish an efficient construction of 2-D ZCACSs. In contrast to previous work, the proposed construction is capable of producing 2-D ZCACSs with new flexible parameters. Constructions of three-dimensional (3-D) and higher-dimensional ZCACSs are further investigated. We remark that 2-D and higher-dimensional complete complementary codes (CCCs) can also be generated from the proposed constructions. © 1967-2012 IEEE.

关键词： Codes (symbols)

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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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