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4th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2023

作者： Luo, Zhijun Zhu, Zhibin School of Mathematics and Finance Hunan University of Humanities Science and Technology Loudi China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350337181

Magnetic resonance imaging is an essential tool for clinical diagnosis, but acquisition time and reconstruction errors have always been bottleneck issues limiting the development of this field. In this paper, we use SCAD nonconvex function to construct a new MRI reconstruction nonconvex model, which can overcome the shortcomings of the traditional convex model. In model solving, considering the symmetry ADMM method improves the image reconstruction performance while increasing the reconstruction speed. The experiment shows that the method proposed in this paper has significant improvements over traditional TV methods. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Dynamical relaxation of a long-range Kitaev chain

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Physical Review B 2024年第9期109卷 094309-094309页

作者： Yu-Huan Huang Yin-Tao Zou Chengxiang Ding School of Microelectronics and Data Science Anhui University of Technology Maanshan 243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan 243002 China

We study the universal real-time relaxation behaviors of a long-range Kitaev chain following a quench, and the results are compared to a short-range quantum XY chain. Our research includes both noncritical and critical quenches. In the case of noncritical quench, i.e., neither the initial state nor the postquench Hamiltonian is at a critical point of the equilibrium phase transition, a quench to the commensurate phase or incommensurate phase gives a scaling of t−3/2 or t−1/2, respectively, which is the same as the counterpart of the short-range quantum XY model. However, for a quench to the boundary line between the commensurate and incommensurate phases, the scaling law t−μ may be different from the t−3/4 law of the counterpart of the short-range quantum XY model. More interestingly, the decaying exponent μ may depend on the choice of the parameters of the postquench Hamiltonian because of the different asymptotic behaviors of the energy spectrum. Furthermore, in certain cases, the scaling behavior may be outside the range of predictions made by the stationary phase approximation, because an inflection point emerges in the energy spectrum. For the critical quench, i.e., the initial state or the postquench Hamiltonian is at a critical point of equilibrium phase transition, the aforementioned scaling law t−μ may be changed because of the gap-closing property of the energy spectrum of the critical point.

关键词： Quantum quench Spin relaxation Exact solutions for many-body systems Integrable systems

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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Pediatric Vascular Anomalies Recognition with a Swin Transformer Framework 9

Pediatric Vascular Anomalies Recognition with a Swin Transfo...

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9th International Conference on Image, Vision and computing, ICIVC 2024

作者： Zhang, Xieyang Zhang, Zhao Chen, Siyu Lang, Xun He, Bingbing Zhang, Yufeng School of Information Science and Engineering Yunnan University Kunming China Department of Dermatology Kunming Children’s Hospital Kunming Children’s Hospital Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350385991

Vascular anomalies primarily manifest in the head and neck area, impacting roughly one in every 22 children. Early and accurate diagnosis of these anomalies holds the potential to significantly improve physical functioning impairments in children. While computer-aided diagnosis (CAD) system can enhance diagnostic accuracy and address the scarcity of medical resources, existing systems for vascular anomalies identification are limited and mainly focus on binary classification tasks like infantile hemangiomas. Motivated by the above problem, this work develops a classification framework using clinical imaging data from Kunming Children’s Hospital, targeting six common vascular anomalies. Specifically, Swin Transformer was chosen as the backbone network for classification due to its ability to capture long-range dependencies of lesions. In addition, Modified RandAugment was employed to enrich the diversity of lesion data and enhance the robustness of the constructed Swin Transformer. Our experimental results demonstrate that the proposed framework can achieve good overall performance and excel in certain disease recognition tasks. This achievement may promote the development of high-quality CAD systems for diagnosing vascular anomalies in children. © 2024 IEEE.

关键词： Pediatrics

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CausalDiff: causality-inspired disentanglement via diffusion model for adversarial defense 24

CausalDiff: causality-inspired disentanglement via diffusion...

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

作者： Mingkun Zhang Keping Bi Wei Chen Quanrun Chen Jiafeng Guo Xueqi Cheng CAS Key Laboratory of AI Safety Institute of Computing Technology CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology CAS School of Statistics University of International Business and Economics

ISBN: (纸本)9798331314385

Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated by subtle manipulations, since we make judgments only based on essential factors. Inspired by this observation, we attempt to model label generation with essential label-causative factors and incorporate label-non-causative factors to assist data generation. For an adversarial example, we aim to discriminate the perturbations as non-causative factors and make predictions only based on the label-causative factors. Concretely, we propose a casual diffusion model (CausalDiff) that adapts diffusion models for conditional data generation and disentangles the two types of casual factors by learning towards a novel casual information bottleneck objective. Empirically, CausalDiff has significantly outperformed state-of-the-art defense methods on various unseen attacks, achieving an average robustness of 86.39% (+4.01%) on CIFAR-10, 56.25% (+3.13%) on CIFAR-100, and 82.62% (+4.93%) on GTSRB (German Traffic Sign Recognition Benchmark). The code is available at https://***/CAS-AISafetyBasicResearchGroup/CausalDiff.

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Adversarial Learning data Augmentation for Graph Contrastive Learning in Recommendation 28th

Adversarial Learning Data Augmentation for Graph Contrastiv...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Huang, Junjie Cao, Qi Xie, Ruobing Zhang, Shaoliang Xia, Feng Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China WeChat Tencent Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031306716

Recently, Graph Neural Networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance. Most GCL methods consist of data augmentation and contrastive loss (e.g., InfoNCE). GCL methods construct the contrastive pairs by hand-crafted graph augmentations and maximize the agreement between different views of the same node compared to that of other nodes, which is known as the InfoMax principle. However, improper data augmentation will hinder the performance of GCL. InfoMin principle, that the good set of views shares minimal information and gives guidelines to design better data augmentation. In this paper, we first propose a new data augmentation (i.e., edge-operating including edge-adding and edge-dropping). Then, guided by InfoMin principle, we propose a novel theoretical guiding contrastive learning framework, named Learnable data Augmentation for Graph Contrastive Learning (LDA-GCL). Our methods include data augmentation learning and graph contrastive learning, which follow the InfoMin and InfoMax principles, respectively. In implementation, our methods optimize the adversarial loss function to learn data augmentation and effective representations of users and items. Extensive experiments on four public benchmark datasets demonstrate the effectiveness of LDA-GCL. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph neural networks

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The Mean Time to Absorption on Horizontal Partitioned Sierpinski Gasket Networks

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Analysis in Theory and Applications 2024年第1期40卷 1-21页

作者： Zhizhuo Zhang Bo Wu Zuguo Yu School of Mathematics Southeast UniversityNanjingJianagsu 211189China School of Applied Mathematics Nanjing University of Finance and EconomicsNanjingJiangsu 210023China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan UniversityXiangtanHunan 411105China

The random walk is one of the most basic dynamic properties of complex networks,which has gradually become a research hotspot in recent years due to its many applications in actual *** important characteristic of the random walk is the mean time to absorption,which plays an extremely important role in the study of topology,dynamics and practical application of complex *** the mean time to absorption on the regular iterative self-similar network models is an important way to explore the inﬂuence of self-similarity on the properties of random walks on the *** existing literatures have proved that even local self-similar structures can greatly affect the properties of random walks on the global network,but they have failed to prove whether these effects are related to the scale of these self-similar *** this article,we construct and study a class of Horizontal Par-titioned Sierpinski Gasket network model based on the classic Sierpinski gasket net-work,which is composed of local self-similar structures,and the scale of these structures will be controlled by the partition coefﬁcient ***,the analytical expressions and approximate expressions of the mean time to absorption on the network model are obtained,which prove that the size of the self-similar structure in the network will directly restrict the inﬂuence of the self-similar structure on the properties of random walks on the ***,we also analyzed the mean time to absorption of different absorption nodes on the network toﬁnd the location of the node with the highest absorption efﬁciency.

关键词： Mean time to absorption self-similar network Sierpinski Gasket

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Quasi-Developable B-Spline Surface Design with Control Rulings

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Journal of Computer science & Technology 2022年第5期37卷 1221-1238页

作者： Zi-Xuan Hu Peng-Bo Bo Cai-Ming Zhang School of Computer Science and Technology Harbin Institute of TechnologyWeihai 264209China Shandong Co-Innovation Center of Future Intelligent Computing Yantai 264005China Shandong Province Key Laboratory of Digital Media Technology Shandong University of Finance and EconomicsJinan 250061China

We propose a method for generating a ruled B-spline surface fitting to a sequence of pre-defined ruling lines and the generated surface is required to be ***,the terminal ruling lines are treated as hard *** from existing methods that compute a quasi-developable surface from two boundary curves and cannot achieve explicit ruling control,our method controls ruling lines in an intuitive way and serves as an effective tool for computing quasi-developable surfaces from freely-designed *** treat this problem from the point of view of numerical optimization and solve for surfaces meeting the distance error tolerance allowed in *** performance and the efficacy of the proposed method are demonstrated by the experiments on a variety of models including an application of the method for path planning in 5-axis computer numerical control(CNC)flank milling.

关键词： developable surface surface modeling B-spline surface numerical optimization

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A Multimodal Knowledge Representation Method for Fake News Detection

A Multimodal Knowledge Representation Method for Fake News D...

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Computer, Control and Robotics (ICCCR), International Conference on

作者： Fanhao Zeng Jiaxin Yao Yijie Xu Yanhua Liu Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Engineering Research Center of Big Data Intelligence Ministry of Education (Fuzhou University) Fuzhou China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

To address the key challenge encountered in fake news detection, i.e., multimodal data is difficult to be effectively semantically represented due to its intrinsic heterogeneity, this paper proposes a multimodal knowledge representation method for fake news detection. First, visual feature extraction is performed for fake news image data, the relevant images are sliced into multiple blocks, and then visual modal features are obtained by linear projection layer mapping. This simplifies the feature extraction process and reduces the computational cost, which helps to improve the fake news recognition performance. Second, to meet the actual fake news detection needs, a long text representation method based on topic words is investigated for the text data in fake news. Finally, the multimodal representation of the same fake news data is optimized by establishing a connection between two different modalities, visual and text, and inputting it into a BiLSTM-Attention based network to achieve the fusion of multimodal features. The experiment selects the same fake news data of EANN model and uses four classical classification methods to verify the effect of knowledge representation and compare it with the fusion model ViLT which is not optimized for long text. The experiment proves that the accuracy rate of fake news detection using the multimodal representation proposed in this paper is improved by 7.4% compared to the EANN model, and by 9.3% compared to the ViLT representation.

关键词： Digital control Visualization Accuracy Knowledge representation Machine learning Feature extraction data models

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Electronic structure and optical properties of Tb3+ host-sensitized NaTb(MoO4)2 narrow-band green-emitting phosphor

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Applied Physics A 2025年第7期131卷 1-8页

作者： Zou, Wenqian Zhang, Mengyu Liu, Xichen Ding, Shoujun J. Zou, Yong School of Microelectronics and Data Science Anhui University of Technology Maanshan PR China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan PR China

Developing highly efficient and stable inorganic narrow-band green-emitting phosphor is crucial for phosphor converted backlighting devices. In this study, trivalent Tb3+ ions self-actived NaTb(MoO4)2 (NTM) narrow-band green-emitting phosphor was prepared by a conventional high-temperature solid-state reaction method. The electronic structure, luminescence properties and luminescence thermal stability of NTM were studied through experiments and theoretical calculations. The structure of NTM was determined to be a tetragonal phase with the space group of I41/a (88). Besides, the NTM was confirmed with direct bandgap characteristics with bandgap width of 3.1231 eV. Upon 266 nm and 355 nm ultraviolet (UV) excitation, the narrow-band green-emitting properties of the phosphor were investigated. Importantly, the phosphor was confirmed with well luminescence thermal stability and the thermal activation energy (Ea) was fitted to be 0.32 eV. The work demonstrates that trivalent Tb3+ ions self-actived NTM is promising as high luminescence stable narrow-band green-emitting phosphor for backlighting display applications.

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