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Few-Shot Knowledge Graph Completion with Structure-Aware Graph Attention Network 认领 引用
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作者 YANG Rongtai SHAO Yubin +2 位作者 DU Qingzhi ZHANG Feng QI Yuting 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期1024-1033,I0019,共10页
Few-shot knowledge graph completion refers to inferring missing entity using limited instances.A key challenge lies in entity representation,which is complicated by diverse neighbor attributes.Although the entity'... Few-shot knowledge graph completion refers to inferring missing entity using limited instances.A key challenge lies in entity representation,which is complicated by diverse neighbor attributes.Although the entity's neighborhood topology holds potential to address this,its significance is overlooked in current research.In this paper,we propose a structure-aware graph attention network for few-shot knowledge graph completion.Firstly,to enhance entity representations,we design a structure-aware graph attention encoder to capture the graph's structural features of nodes,generating embedding for entity pairs.Secondly,a semantic prototype matching network is employed to compute the prediction score.Experiments on the NELL-One and Wiki-One datasets show that our proposed model outperforms the best baseline models by 0.021,0.026,0.039,0.032 and 0.016,0.064,0.043,0.040 in terms of MRR,Hits@10,Hits@5,and Hits@1 metrics,respectively.This demonstrates that our model can effectively leverage neighborhood topological information to improve the accuracy of knowledge completion,and achieve a better generalization. 展开更多
关键词 knowledge graph completion neighborhood topology structure-aware graph attention entity representations semantic prototype
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Structure-Aware Diffusion Image Outpainting for Echocardiographic Field-of-View Extension 认领 引用
2
作者 Ruijia He Yixin Hu +3 位作者 Jinze Liu Qixin Zhang Duo Peng Yanyan Chen 《Computers, Materials & Continua》 SCIE EI 2026年第9期1698-1718,共21页
Transthoracic Echocardiography(TTE)often suffers from a limited field of view(FoV),which may obscure peripheral cardiac structures and hinder comprehensive visual assessment.Although FoV extension can be formulated as... Transthoracic Echocardiography(TTE)often suffers from a limited field of view(FoV),which may obscure peripheral cardiac structures and hinder comprehensive visual assessment.Although FoV extension can be formulated as outpainting,public studies on echocardiographic FoV outpainting remain scarce and are mainly represented by cGAN-based reconstruction methods such as echoGAN.However,in noisy ultrasound images with weak boundaries,the challenge is not only realistic texture synthesis,but also structural continuity across the observed-generated boundary.To address this issue,we propose a structure-aware diffusion framework for echocardiographic FoV outpainting.To the best of our knowledge,this is the first work to introduce diffusion models into this task,moving beyond the existing cGAN-based formulation.Our framework combines a diffusion baseline,a Structural Cue Encoder(SCE)for structure-sensitive conditioning,and Structure-aware Diffusion Learning(SDL)for structural regularization during denoising.Experiments show clear and consistent improvements over cGAN-based reconstruction,producing more coherent structures,smoother transitions,and better FoV extension quality. 展开更多
关键词 Field-of-view extension diffusion model image generation outpainting structure-aware learning transthoracic echocardiography
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Structure-aware fusion network for 3D scene understanding 认领 引用 被引量：2
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作者 Haibin YAN Yating LV Venice Erin LIONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第5期194-203,共10页
In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complem... In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complementary data can improve the discriminative ability of the model.Fusion is a very challenging task since 2D and 3D data are essentially different and show different formats.The existing methods first extract 2D multi-view image features and then aggregate them into sparse 3D point clouds and achieve superior performance.However,the existing methods ignore the structural relations between pixels and point clouds and directly fuse the two modals of data without adaptation.To address this,we propose a structural deep metric learning method on pixels and points to explore the relations and further utilize them to adaptively map the images and point clouds into a common canonical space for prediction.Extensive experiments on the widely used ScanNetV2 and S3DIS datasets verify the performance of the proposed SAFNet. 展开更多
关键词 3D point clouds Data fusion Structure-aware 3D scene understanding Deep metric learning
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Guided Structure-Aware Review Summarization 认领 引用 被引量：3
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作者 金锋 黄民烈 朱小燕 《Journal of Computer Science & Technology》 SCIE EI CSCD 2011年第4期676-684,共9页
Although the goal of traditional text summarization is to generate summaries with diverse information, most of those applications have no explicit definition of the information structure. Thus, it is difficult to gene... Although the goal of traditional text summarization is to generate summaries with diverse information, most of those applications have no explicit definition of the information structure. Thus, it is difficult to generate truly structure-aware summaries because the information structure to guide summarization is unclear. In this paper, we present a novel framework to generate guided summaries for product reviews. The guided summary has an explicitly defined structure which comes from the important aspects of products. The proposed framework attempts to maximize expected aspect satisfaction during summary generation. The importance of an aspect to a generated summary is modeled using Labeled Latent Dirichlet Allocation. Empirical experimental results on consumer reviews of cars show the effectiveness of our method. 展开更多
关键词 structure-aware summarization review mining topic model importance is modeled
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Distributed quantum circuit partitioning and teleportation optimization based on a multi-dimensional evaluation strategy 认领 引用
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作者 Le Zhang Zhijin Guan +3 位作者 Shuo Qin Zheng Luo Fei Ding Xueyun Cheng 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期345-357,共13页
Distributed quantum computing has emerged as a key approach to extending current quantum computing capabilities,with its performance largely determined by the cost of qubit transmissions across physical nodes.To minim... Distributed quantum computing has emerged as a key approach to extending current quantum computing capabilities,with its performance largely determined by the cost of qubit transmissions across physical nodes.To minimize such crosspartition transmission costs,this paper proposes a distributed quantum circuit partitioning and teleportation optimization method based on a multidimensional evaluation strategy.First,a scoring function is designed using interaction strength and temporal fragmentation,guiding the partitioning process to balance structural compactness with temporal continuity.Building on this,we employ multiple starting points and parameter search strategies to progressively construct candidate partition schemes.Subsequently,a transmission-cost optimization method based on teleportation group partitioning is introduced to evaluate candidates more accurately,taking into account gate timing,interfering operations,and communication resource conflicts,thereby yielding a more realistic estimate of teleportation counts.Simulation results on several benchmark quantum circuits demonstrate that the proposed method consistently generates superior partitions under challenging conditions,such as high interaction density and significant gate interleaving.In some cases,teleportation counts are reduced by up to 64.7%,with an overall average improvement of 8%,verifying the adaptability and effectiveness of the method in optimizing communication across different interaction structures. 展开更多
关键词 distributed quantum computing structure-aware scoring merged teleportation qubit partitioning
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Learning accurate template matching with differentiable coarseto-fine correspondence refinement 认领 引用 被引量：2
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作者 Zhirui Gao Renjiao Yi +3 位作者 Zheng Qin Yunfan Ye Chenyang Zhu Kai Xu 《Computational Visual Media》 SCIE EI CSCD 2024年第2期309-330,共22页
Template matching is a fundamental task in computer vision and has been studied for decades.It plays an essential role in manufacturing industry for estimating the poses of different parts,facilitating downstream task... Template matching is a fundamental task in computer vision and has been studied for decades.It plays an essential role in manufacturing industry for estimating the poses of different parts,facilitating downstream tasks such as robotic grasping.Existing methods fail when the template and source images have different modalities,cluttered backgrounds,or weak textures.They also rarely consider geometric transformations via homographies,which commonly exist even for planar industrial parts.To tackle the challenges,we propose an accurate template matching method based on differentiable coarse-tofine correspondence refinement.We use an edge-aware module to overcome the domain gap between the mask template and the grayscale image,allowing robust matching.An initial warp is estimated using coarse correspondences based on novel structure-aware information provided by transformers.This initial alignment is passed to a refinement network using references and aligned images to obtain sub-pixel level correspondences which are used to give the final geometric transformation.Extensive evaluation shows that our method to be significantly better than state-of-the-art methods and baselines,providing good generalization ability and visually plausible results even on unseen real data. 展开更多
关键词 template matching differentiable homography structure-awareness transformers
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