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The Adjacency Codes of the First Yellow Graphs

Journal of Systems Science & Complexity 2023年第4期36卷 1757-1768页

作者： SHI Minjia LI Shitao KIM Jon-Lark SOLE Patrick Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationSchool of Mathematical SciencesAnhui UniversityHefei 230601China State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijing 100093China School of Mathematical Sciences Anhui UniversityHefei 230026China Department of Math Sogang UniversitySeoulSouth Korea Aix Marseille Univ CNRSCentrale MarseilleI2MMarseilleFrance

The authors study the binary codes spanned by the adjacency matrices of the strongly regular graphs(SRGs)on at most two hundred vertices whose existence is *** authors show that in length less than one hundred they cannot be cyclic,except for the exceptions of the SRGs of parameters(85,42,20,21)and(96,60,38,36).In particular,the adjacency code of a(85,42,20,21)is the zero-sum *** the range[100,200]the authors find 29 SRGs that could possibly have a cyclic adjacency code.

关键词： Cyclic codes strongly regular graphs adjacency codes self-orthogonal codes

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MixerSR: A New Feature Extraction Paradigm for Single Image Super-Resolution 23

MixerSR: A New Feature Extraction Paradigm for Single Image ...

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15th International Conference on Digital Image processing, ICDIP 2023

作者： Ding, Hui Chen, Weifeng He, Zhifen Li, Bo Liu, Bin Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Jiangxi Science and Technology Infrastructure Center and Jiangxi Computing Center Jiangxi Computing Center Nanchang China

ISBN: (纸本)9798400708237

Single-image super-resolution (SR) tasks have achieved fancy success in recent years by leveraging deep convolution neural network (CNN). Although CNNs obtain powerful representation capabilities of reconstructing a high-resolution (HR) image from its corresponding low-resolution (LR) observation, it is hard to apply these accomplishments to real world for its large number of parameters that bring about huge computing resource cost. To solve this problem, many researchers turn their directions to lightening the network by delicate designs while keeping the performance in the high level. In this paper, we propose a new method to further reduce the parameters by using the new feature extraction paradigm called Mixer which only contains Multi-Layer Perceptron (MLP). Compared to convolutional operations, Mixer operation has the same representation capabilities with fewer parameters. In this paper, three typical lightweight networks, where we replace the convolutional operation with Mixer, are used to show the excellent ability. Our experimental results demonstrate that Mixer can help these lightweight networks further reduce the number of parameters by up to 38% while keep their performance in the same level. © 2023 ACM.

关键词： Feature extraction

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A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic Algorithm in Smart Grid

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1325-1335页

作者： Chentao Xu Xing He the Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China

A fully distributed microgrid system model is presented in this *** the user side,two types of load and plug-in electric vehicles are considered to schedule energy for more *** charging and discharging states of the electric vehicles are represented by the zero-one variables with more *** solve the nonconvex optimization problem of the users,a novel neurodynamic algorithm which combines the neural network algorithm with the differential evolution algorithm is designed and its convergence speed is faster.A distributed algorithm with a new approach to deal with the inequality constraints is used to solve the convex optimization problem of the generators which can protect their *** results and comparative experiments show that the model and algorithms are effective.

关键词： Differential evolution algorithm distributed algorithm electric vehicle neural network zero-one variable.

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Research on Weld Detection Based on Weighted Feature Fusion Network 23

Research on Weld Detection Based on Weighted Feature Fusion ...

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15th International Conference on Digital Image processing, ICDIP 2023

作者： Wang, Mengxue Ma, Xing Mu, Chunyang Hou, Bing College of Electronic and Information Engineering North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing North Minzu University Yinchuan China

ISBN: (纸本)9798400708237

Weld seam grinding is a crucial process for welding cast products. Not only are the polished welds more attractive and durable, but they also have superior stress effects. However, the workplace is harsh and can significantly impact health. The first task for automated weld grinding is weld detection. This paper, a pyramid feature fusion network (YOLOv5-BiCA) learning model with embedded coordinate attention as a weighting condition that fully utilizes the shallow and deep network information. An online data augmentation strategy employing Mosaic+Mixup is proposed to address the problem of insufficient sample size. We use Focal Loss to enhance the classification function and increase the positive influence of positive samples on the loss function during the training process to address the problem of an imbalance between positive and negative sample. Our proposed YOLOv5-BiCA performs better in weld detection, mAP improves by 3.5%, and recall improves by 7.97%, according to experimental results based on homegrown datasets. © 2023 ACM.

关键词： Welds

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Surrogate-Assisted Multiobjective Gene Selection for Cell Classification From Large-Scale Single-Cell RNA Sequencing Data

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 601-615页

作者： Lin, Jianqing He, Cheng Jiang, Hanjing Huang, Yabing Jin, Yaochu Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20 000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data. © 1997-2012 IEEE.

关键词： Gene expression

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Unveiling High-speed Follow-up Consultation for Chronic Disease Treatment: A Pediatric Hospital Case in China

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Proceedings of the ACM on Human-Computer Interaction 2022年第CSCW2期6卷 1-29页

作者： Fu, Jiaojiao Zhou, Yangfan Wang, Xin School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing 220 Handan Rd. Shanghai200433 China

In developing countries suffering from a severe shortage of physicians, the follow-up clinical consultation of long-term chronic care becomes a high-speed collaborative work, which has to be conducted in a few minutes. Although much existing work studies how time factors affect physicians' workflows and requirements for information systems, high-speed chronic care is yet to be well investigated. This work bridges the gap by presenting a case of a pediatric hospital in China. We focus on the processes of follow-up consultations, the factors enabling physicians to complete consultation in several minutes, as well as the challenges faced by physicians and patients. Through observations and interviews, we find that physicians conduct multiple tasks (information acquisition, patient-provider communication, and medical data documentation) simultaneously to reduce the consultation duration. Adopting an information summary alternative is the key to fast information acquisition. Templates and references in EMR contribute to rapid documentation and prescription. However, multitasking brings physicians a heavy cognitive load. It also severely compresses the duration of patient-provider communication. As a result, some of the patients' needs, especially emotional ones, are neglected. Based on these findings, we discuss the characteristics and requirements of high-speed chronic care and accordingly propose design suggestions. © 2022 ACM.

关键词： Hospitals

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Monotonicity and nonmonotonicity in L3-valued propositional logic

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Frontiers of Computer Science 2022年第4期16卷 33-43页

作者： Wei Li Yuefei Sui State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing100049China

A sequent is a pair (Γ, Δ), which is true under an assignment if either some formula in Γ is false, or some formula in Δ is true. In L_(3)-valued propositional logic, a multisequent is a triple Δ∣Θ∣Γ, which is true under an assignment if either some formula in Δ has truth-value t, or some formula in Θ has truth-value m, or some formula in Γ has truth-value f. There is a sound, complete and monotonic Gentzen deduction system G for sequents. Dually, there is a sound, complete and nonmonotonic Gentzen deduction system G′ for co-sequents Δ: Θ: Γ. By taking different quantifiers some or every, there are 8 kinds of definitions of validity of multisequent Δ∣Θ∣Γ and 8 kinds of definitions of validity of co-multisequent Δ: Θ: Γ, and correspondingly there are 8 sound and complete Gentzen deduction systems for sequents and 8 sound and complete Gentzen deduction systems for co-sequents. Correspondingly their monotonicity is discussed.

关键词： sequent mulisequent gentzen deduction system monotonicity nonmonotonicity

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Dual-view Label and Feature Supervision Network for Semi-supervised Medical Image Segmentation 24

Dual-view Label and Feature Supervision Network for Semi-sup...

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7th International Conference on Signal processing and Machine Learning, SPML 2024

作者： Zhou, Zhen Li, Zuoyong Wang, Zhengyuan Teng, Shenghua Wang, Tao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798400717192

Semi-supervised learning has garnered significant attention, particularly in medical image segmentation, owing to its capacity to leverage a large number of unlabeled data and a limited amount of labeled data to improve performance. However, most existing semi-supervised segmentation methods exhibit shortcomings in supervising unlabeled data, both in the label space due to potential noise in pseudo-labels, and the feature space due to indistinct class boundaries. To address the issues above, we propose Dual-view Label and Feature Supervision Network, termed DLFS-Net, to enhance label and feature supervision for unlabeled data. Our approach is based on a dual-view learning strategy that incorporates two modules: the Confidence Fusion Supervision Module (CFS) and the Dual-view Prototype Learning Module (DPL). Specifically, the CFS module generates weight matrices to integrate complementary information from two model outputs, producing more accurate pseudo-labels in the label space. The DPL module reduces intra-class variations in the feature space from two different views through prototype learning to generate clear class boundaries. Experiments on the LA and Pancreas-CT datasets demonstrate that our framework show solid gains (e.g.,1.76% Dice and 2.37% Jaccard improvement on Pancreas-CT dataset with 20% labeled data) compared with the state-of-the-art methods. © 2024 ACM.

关键词： Self-supervised learning

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Video Group Activity recognition Based On Multidimensional Spatio-Temporal Relationships

Video Group Activity Recognition Based On Multidimensional S...

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International Conference on Signal processing Proceedings (ICSP)

作者： Yanshan Li Hailin Zong Suixuan He Miaomiao Wei Institute of Intelligent Information Processing Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Spatio-temporal relationships play an important role in group activity recognition. The spatio-temporal relationships are complex and varied, containing relationships between individuals, between individuals and subgroups, and between subgroups. However, there is no unified method for measuring these multidimensional spatio-temporal relationships. Therefore, this paper proposes a set of metric methods for multidimensional spatio-temporal relationships based on geometric algebra, including the metric method based on positional features and the metric method based on appearance features. And this paper proposed a deep network for video group activity recognition based on multidimensional spatio-temporal relationships. The experimental results show that the proposed algorithm can effectively improve the accuracy of group activity recognition.

关键词： Measurement Accuracy Algebra Signal processing algorithms Activity recognition Signal processing Spatiotemporal phenomena

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Element-Wise Layer Normalization for Continuous Signal Representation 23

Element-Wise Layer Normalization for Continuous Signal Repre...

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15th International Conference on Digital Image processing, ICDIP 2023

作者： Chen, Weifeng Ding, Hui He, Zhifen Li, Bo Liu, Bin Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Jiangxi Science and Technology Infrastructure Center Jiangxi Computing Center Jiangxi Computing Center Nanchang China

ISBN: (纸本)9798400708237

Implicit neural representation (INR), sometimes also referred to coordinate-based representation or fitting, has gained the state-of-the-art performance in numerous research fields including computer vision and computer graphics due to the powerful continuous representation ability. Recent researches indicate that Fourier embedding is critical for INR to fit realistic images with high-frequency details. However, is Fourier embedding all we need for high-frequency coordinate (image) fitting? In this paper, we revisit the problem of coordinate fitting from a novel perspective of distribution mapping. Fourier embedding, as a preprocessing step of coordinate fitting, essentially performs the operation of mapping the uniform coordinate distribution to a normal distribution, and makes the learning of mapping function between two similar smooth distribution become easier. However, the number of discrete Fourier basis function affects the fitting performance dramatically and cannot be determined automatically. Based on the above analysis, a simple yet efficient INR coordinate fitting method is proposed in this paper, which demonstrates that the Fourier embedding is not the only way to improve INR. The proposed method only adds an element-wise layer normalization (ELN) module to the vanilla multi-layer perception (MLP) with ReLU activation. Experimental results on public database demonstrate that the proposed method outperforms the state-of-the-art methods using Fourier embedding. © 2023 ACM.

关键词： Mapping

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