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A prediction-correction based iterative convolution-thresholding method for topology optimization of heat transfer problems

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arXiv 2024年

作者： Chen, Huangxin Dong, Piaopiao Wang, Dong Wang, Xiao-Ping School of Mathematical Sciences Fujian Provincial Key Laboratory on Mathematical Modeling and High Performance Scientific Computing Xiamen University Fujian361005 China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong518172 China

In this paper, we propose an iterative convolution-thresholding method (ICTM) based on prediction-correction for solving the topology optimization problem in steady-state heat transfer equations. The problem is formulated as a constrained minimization problem of the complementary energy, incorporating a perimeter/surface-area regularization term, while satisfying a steady-state heat transfer equation. The decision variables of the optimization problem represent the domains of different materials and are represented by indicator functions. The perimeter/surface-area term of the domain is approximated using Gaussian kernel convolution with indicator functions. In each iteration, the indicator function is updated using a prediction-correction approach. The prediction step is based on the variation of the objective functional by imposing the constraints, while the correction step ensures the monotonically decreasing behavior of the objective functional. Numerical results demonstrate the efficiency and robustness of our proposed method, particularly when compared to classical approaches based on the ICTM. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

A Prediction-Correction Based Iterative Convolution-Thresholding Method for Topology Optimization of Heat Transfer Problems

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SSRN

SSRN 2024年

关键词： Convolution

Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs

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arXiv 2023年

作者： Chen, Zhaoliang Wu, Zhihao Zhong, Luying Plant, Claudia Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science University of Vienna Vienna1090 Austria ds:UniVie Vienna1090 Austria

Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks. One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings. Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices. The proposed model first builds different orders of adjacency matrices from manually designed node connections. After that, an intact multi-order adjacency matrix is attached from the automatic fusion of various orders of adjacency matrices. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we utilize a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN gains superior semi-supervised classification performance compared with state-of-the-art competitors. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning

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arXiv 2022年

作者： Chen, Zhaoliang Fu, Lele Yao, Jie Guo, Wenzhong Plant, Claudia Wang, Shiping College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science University of Vienna Vienna1090 Austria Research Network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms. However, given multi-view data, there is limited work for learning discriminative node relationships and graph information simultaneously via graph convolutional network that has drawn the attention from considerable researchers in recent years. Most of existing methods only consider the weighted sum of adjacency matrices, yet a joint neural network of both feature and graph fusion is still under-explored. To cope with these issues, this paper proposes a joint deep learning framework called Learnable Graph Convolutional Network and Feature Fusion (LGCN-FF), consisting of two stages: feature fusion network and learnable graph convolutional network. The former aims to learn an underlying feature representation from heterogeneous views, while the latter explores a more discriminative graph fusion via learnable weights and a parametric activation function dubbed Differentiable Shrinkage Activation (DSA) function. The proposed LGCN-FF is validated to be superior to various state-of-the-art methods in multi-view semi-supervised classification. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

Transformer-Based Hierarchical Dynamic Decoders for Salient Object Detection

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SSRN

SSRN 2023年

作者： Zheng, Qingping Zheng, Ling Deng, Jiankang Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northewestern Polytechnical University Shaanxi Xi’an710129 China Fuzhou Institute of Data Technology Fujian350200 China Department of Computing Imperial College London LondonSW7 2AZ United Kingdom

HighlightsTransformer-Based Hierarchical Dynamic Decoders for Salient Object DetectionQingping Zheng, Ling Zheng, Jiankang Deng, Ying Li, Changjing Shang, Qiang Shen• T-HDDNet employs dynamic upsampling and fusion for data-driven salient object detection.• T-HDDNet uses a spatially sensitive loss to learn salient object structures, yielding a high-resolution map.• Our method exceeds benchmarks at 22.19G MACs and 27M Params, reducing costs compared to the top model. © 2023, The Authors. All rights reserved.

关键词： Object detection

ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks

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arXiv 2024年

作者： Chen, Zhaoliang Wu, Zhihao Sadikaj, Ylli Plant, Claudia Dai, Hong-Ning Wang, Shiping Cheung, Yiu-Ming Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Department of Computer Science Hong Kong Baptist University Hong Kong Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science and with the research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention in the literature. Among the existing GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges. Consequently, the effectiveness of message passing is weakened. In this paper, we propose a novel adversarial edgedropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on eight graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

Gaf-Net: Graph Attention Fusion Network for Multi-View Semi-Supervised Classification

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SSRN

SSRN 2023年

作者： Song, Na Du, Shide Wu, Zhihao Zhong, Luying Wang, Shiping School of Computer Science and Technology Hainan University Hainan570228 China School of Mechanical Electrical and Information Engineering Putian University Putian351100 China College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

Multi-view semi-supervised classification is a typical task to classify data using a small amount of supervised information, which has attracted a lot of attention from researchers in past years. In practice, existing methods focus more on extracting spatial or spectral features using graph convolutional neural networks without considering the diversity and variability of graph structures and the contributions of different views. To address this challenge, a network framework termed graph attention fusion network is proposed, which is comprised of two phases: view-specific feature embedding and graph embedding fusion. In the former feature extraction stage, view-specific feature embedding module can flexibly focus on the neighborhood calculation operation to learn a weight for each neighboring node. In the latter feature fusion stage, the graph embedding fusion module is performed by complementarity and consistency to fuse these embeddings for semi-supervised classification tasks. We conduct extensive experiments in terms of semi-supervised classification on real-world datasets to validate the superiority of the proposed method over various existing state-ofthe-art methods. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

An automatic biomedical ontology meta-matching technique

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Journal of Network Intelligence

Journal of Network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous Information. All rights reserved.

关键词： Semantics

SurgicalGaussian: Deformable 3D Gaussians for High-Fidelity Surgical Scene Reconstruction

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arXiv 2024年

作者： Xie, Weixing Yao, Junfeng Cao, Xianpeng Lin, Qiqin Tang, Zerui Dong, Xiao Guo, Xiaohu Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China Department of Computer Science The University of Texas at Dallas Dallas United States

Dynamic reconstruction of deformable tissues in endoscopic video is a key technology for robot-assisted surgery. Recent reconstruction methods based on neural radiance fields (NeRFs) have achieved remarkable results in the reconstruction of surgical scenes. However, based on implicit representation, NeRFs struggle to capture the intricate details of objects in the scene and cannot achieve real-time rendering. In addition, restricted single view perception and occluded instruments also propose special challenges in surgical scene reconstruction. To address these issues, we develop SurgicalGaussian, a deformable 3D Gaussian Splatting method to model dynamic surgical scenes. Our approach models the spatio-temporal features of soft tissues at each time stamp via a forward-mapping deformation MLP and regularization to constrain local 3D Gaussians to comply with consistent movement. With the depth initialization strategy and tool mask-guided training, our method can remove surgical instruments and reconstruct high-fidelity surgical scenes. Through experiments on various surgical videos, our network outperforms existing method on many aspects, including rendering quality, rendering speed and GPU usage. The project page can be found at https://***. © 2024, CC BY-SA.

关键词： Tissue