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Adaptive symbiotic graph convolutional network

Neurocomputing 2025年 637卷

作者： Zhou, Lin Xiao, Yuzhi Ye, Zhonglin Zhao, Haixing Liu, Zhen School of Computer Qinghai Normal University Qing Hai Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qing Hai Xining810008 China Key Laboratory of Tibetan Information Processing Ministry of Education Qing Hai Xining810008 China Qinghai Provincial Key Laboratory of Tibetan Information Processing and Machine Translation Qing Hai Xining810008 China of Engineering Nagasaki Institute of Applied Science Kyushu Nagasaki851-0193 Japan

Within the intrinsic fabric of graph and network data, the latent reciprocity between network nodes forms a profound symbiotic relationship. Traditional graph neural networks struggle to adaptively parse and integrate multi-source features when learning these symbiotic relationships. To address this challenge, we propose a novel Adaptive Symbiotic Graph Convolutional Network (ASGCN) for semi-supervised node classification tasks. First, The initial contribution of this investigation is the introduction of a multi-scale feature convolution module, which enables the extraction of hierarchical features at varying scales and the construction of k-nearest neighbor graphs. This facilitates the deepening of the symbiotic features of nodes. Second, a symbiotic co-convolution module is put forth as a means of reinforcing the profound interdependence inherent to symbiotic relationships. Finally, an adaptive dynamic feature selection mechanism is introduced to flexibly respond to data characteristics, effectively identifying and fusing the most influential features in the processing information flow. Experimental results demonstrate that ASGCN exhibits significant advantages in deeply analyzing and integrating the intrinsic attributes of nodes with graph structural relationships, thereby improving performance in node classification tasks. © 2025 Elsevier B.V.

关键词： Convolutional neural networks

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IsoVISoR: Towards 3D Mesoscale Brain Mapping of Large Mammals at Isotropic Sub-micron Resolution

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Neuroscience Bulletin 2025年第2期41卷 344-348页

作者： Chao-Yu Yang Yan Shen Xiaoyang Qi Lufeng Ding Yanyang Xiao Qingyuan Zhu Hao Wang Cheng Xu Pak-Ming Lau Pengcheng Zhou Fang Xu Guo-Qiang Bi Division of Life Sciences and Medicine CAS Key Laboratory of Brain Function and DiseaseUniversity of Science and Technology of ChinaHefei230026China Interdisciplinary Center for Brain Information Shenzhen-Hong Kong Institute of Brain ScienceShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Center for Integrative Imaging Hefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei230027China National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application MoE Key Laboratory of Brain-Inspired Intelligent Perception and CognitionSchool of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei230026China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefei230088China Faculty of Life and Health Sciences Shenzhen University of Advanced TechnologyShenzhen518107China CAS Key Laboratory of Brain Connectome and Manipulation The Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China

Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing ***,large brain samples pose major challenges to this approach.

关键词： brain mapping three dimensional fluorescence microscopy mesoscale brain mapping lightsheet based methods block face methods high throughput volumetric imaging tissue clearing techniques

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

引用

IEEE International Symposium on Biomedical Imaging

作者： Mingyue Zhao Xiaolan Qiu S.Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo.

关键词： Codes Atmospheric modeling Semantic segmentation Pulmonary diseases Biological system modeling Extraterrestrial measurements Biomedical imaging

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DSA image fusion based on dynamic fuzzy logic and curvelet entropy

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Journal of Multimedia 2009年第3期4卷 129-136页

作者： Zhang, Guangming Cui, Zhiming Li, Fanzhang Wu, Jian The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China

The curvelet transform as a multiscale transform has directional parameters occurs at all scales, locations, and orientations. It is superior to wavelet transform in image processing domain. This paper analyzes the characters of DSA medical image, and proposes a novel approach for DSA medical image fusion, which is using curvelet information entropy and dynamic fuzzy logic. Firstly, the image was decomposed by curvelet transform to obtain the different level information. Then the entropy from different level of DSA medical image was calculated, and a membership function based on dynamic fuzzy logic was constructed to adjust the weight for image subbands coefficients via entropy. At last an inverse curvelet transform was applied to reconstruct the image to synthesize one DSA medical image which could contain more integrated accurate detail information of blood vessels than any one of the individual source images. By compare, the efficiency of our method is better than weighted average, laplacian pyramid and traditional wavelet transform method. © 2009 ACADEMY PUBLISHER.

关键词： Image fusion

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Active transfer learning of matching query results across multiple sources

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Frontiers of Computer Science 2015年第4期9卷 595-607页

作者： Jie XIN Zhiming CUI Pengpeng ZHAO Tianxu HE The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China

Entity resolution （ER） is the problem of identi- fying and grouping different manifestations of the same real world object. Algorithmic approaches have been developed where most tasks offer superior performance under super- vised learning. However, the prohibitive cost of labeling training data is still a huge obstacle for detecting duplicate query records from online sources. Furthermore, the unique combinations of noisy data with missing elements make ER tasks more challenging. To address this, transfer learning has been adopted to adaptively share learned common structures of similarity scoring problems between multiple sources. Al- though such techniques reduce the labeling cost so that it is linear with respect to the number of sources, its random sam- piing strategy is not successful enough to handle the ordinary sample imbalance problem. In this paper, we present a novel multi-source active transfer learning framework to jointly select fewer data instances from all sources to train classi- fiers with constant precision/recall. The intuition behind our approach is to actively label the most informative samples while adaptively transferring collective knowledge between sources. In this way, the classifiers that are learned can be both label-economical and flexible even for imbalanced or quality diverse sources. We compare our method with the state-of-the-art approaches on real-word datasets. Our exper- imental results demonstrate that our active transfer learning algorithm can achieve impressive performance with far fewerlabeled samples for record matching with numerous and var- ied sources.

关键词： entity resolution active learning transfer learn-ing convex optimization

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A level set method for color image segmentation based on bayesian classifier

A level set method for color image segmentation based on bay...

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International Conference on Computer Science and Software Engineering, CSSE 2008

作者： Xu, Jing Wu, Jian Ye, Feng Cui, Zhi-Ming The Institute of Intelligent Information Processing and Application Soochow University

ISBN: (纸本)9780769533360

Traditional level-set methods use gray level images as its main study objects. The basic idea is coupling image data and speed of the curve deformation, stopping the curve evolution on the edge of target, which requires a speed function to control the evolvement. However, traditional methods only set gray-scale gradient information as the stopping power of contour lines to define the velocity function, which will be distort applied to color images. This paper re-designed a speed function based on color gradient function to color images to replace the traditional method of gray-scale gradient to stop the movement of the contour line, and combining regional color statistic characteristics, making the approach is robust against the noise caused by the color gradual change and weakening of the target edge. Experiment shows that accuracy and robust increase significantly in the improved algorithm in color image processing, superior to the original algorithm. ©2008 IEEE.

关键词： Color

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Examining the Fourier Spectrum of Speech Signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

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IEEE Transactions on Affective Computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

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Deep Web sources focused crawling

Deep Web sources focused crawling

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2007 International Conference on Enterprise information Systems and Web Technologies, EISWT 2007

作者： Zhao, Pengpeng Lin, Chao Gao, Ling Cui, Zhiming Institute of Intelligent Information Processing and Application Soochow University China

ISBN: (纸本)9781615677245

Searching engines work well for finding crawlable pages, however there are a lot of pages on internet that are generated dynamically by the back-end database and the traditional searching engines can't reach these pages, which are called Deep Web. The Deep Web has clearly rendered large-scale integration a real necessity and a real challenge. Deep Web sources discovery is one of the critical steps toward the large-scale integration. This paper proposes a focused crawling algorithm of Deep Web sources avoiding to download a large number of irrelevant pages. Experimental results indicate that this approach is very effective.

关键词： Web crawler

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A deep neural network for tactile perception in open scenes

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iScience 2025年第5期28.0卷 112330页

作者： Fang, Huirong Yang, Qianhui Liu, Kunhong Huang, Xiangyi Xie, Yu School of Electronic Information Zhangzhou Institute of Technology Zhangzhou 363000 China Intelligent Monitoring of the Fujian Provincial Higher Education Application Technology Engineering Center Zhangzhou 363000 China Digital Media Technology Department Film School of Xiamen University Xiamen 361102 China Key laboratory of Digital Protection and Intelligent Processing of lntangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen 361102 China Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Xiamen 361102 China

Tactile perception is important for the robots to understand their working environment. While in real-world applications, robots usually must face unexpected changes in external conditions, such as the re-installation of the robot end effector or the change of the installation location. Consequently, the collected tactile material data tend to vary to a certain extent, which brings great difficulties to the tactile perception. To handle this problem, different from the former studies of tactile perception in enclosed environments, this study focuses on the tactile material recognition task using robot electronic skin in open scenes. We construct a cross-batch tactile dataset to simulate open scenes and propose the multi-receptive field attention enhancement network (MRFE) to handle tactile material recognition. Compared with other machine learning algorithms, experiments show that the proposed method overcomes the problem of data drift caused by changes in posture, contact force, sliding velocities, exploratory motions, and assembly conditions. © 2025 The Author(s)

关键词： Kinesiology Robotics

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Adaptive detection of moving vehicle based on on-line clustering

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Journal of Computers 2011年第10期6卷 2045-2052页

作者： Wu, Jian Xia, Jie Chen, Jian-Ming Cui, Zhi-Ming The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China

Detection of moving vehicles plays a very important role in intelligent Transport. Aiming at the deficiency of moving vehicle detection, we proposed the adaptive detection method of moving vehicles based on the online clustering. First extracts background adaptively using a new online clustering algorithm which does not need to set any parameters when extract the background image. Then adaptively select the background updating rate according to the road disturbance of background and illumination changes after background building is completed. Finally, realize correct and complete moving object segmentation through foreground detection using background difference. Experimental results show that the proposed method is able to detect moving target accurately in the transport video, it not only has good self-adaptability and real-time but also insensitive to the light changes and background interference. © 2011 ACADEMY PUBLISHER.

关键词： Clustering algorithms

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